=start]","def test_get_occurrences_monthly_mid_month(self):\n print()\n print(\"Get occurrences of a monthly expense between:\")\n expense = BudgetExpense.objects.get(id = 100)\n start_date = expense.start_date\n end_date = start_date + timedelta(days = 40)\n print(start_date.strftime(\"%B %d, %y\")+\" and \"+end_date.strftime('%B %d, %y'))\n print(\"======================================\")\n result = get_anticipated_transaction_occurences(anticipated_transaction= expense, start_date = start_date, end_date = end_date)\n result_dates = []\n for current_expense in result.keys():\n print(current_expense)\n print(\"========================\")\n result_dates.extend(result.get(current_expense))\n for current_date in result_dates:\n print(\"Date: \"+current_date.strftime(\"%B %d, %y %T\"))\n print(\"======================\")\n print()\n date_1 = start_date\n days_in_month = monthrange(start_date.year, start_date.month)[1]\n date_2 = start_date + timedelta(days = days_in_month)\n \n self.assertEquals([date_1, date_2], result_dates)","def calc_monthly_cash(self):\n # shortcut to self\n s = self\n\n # Start the DataFrames, base and w/ heat pump\n # Each starts with just an index column with the month\n # Make shortcut variables as well.\n s.df_mo_dol_base = dfb = s.df_mo_en_base[[]].copy()\n s.df_mo_dol_hp = dfh = s.df_mo_en_base[[]].copy()\n\n # Determine the base electric use by month. Approach is different \n # if there is electric heat.\n is_electric_heat = (s.exist_heat_fuel_id == constants.ELECTRIC_ID)\n if not is_electric_heat:\n # Fuel-based space heat.\n # The User supplied a January and a May kWh usage value that should\n # be used for the base case (no heat pump) total electricity use.\n # But, need to come up with a kWh value for every month. Do that by\n # adjusting the kWh pattern available for this city.\n #\n # Determine the multiplier to adjust to the pattern to the actual.\n pat_use = np.array(s.city.avg_elec_usage)\n mult = (s.elec_use_jan - s.elec_use_may) / (pat_use[0] - pat_use[4])\n pat_use = mult * pat_use\n pat_use += s.elec_use_jan - pat_use[0]\n\n # The electricity use in the base case\n dfb['elec_kwh'] = pat_use\n\n # rough estimate of a base demand: not super critical, as the demand rate \n # structure does not have blocks. Assume a load factor of 0.4\n dfb['elec_kw'] = dfb.elec_kwh / (DAYS_IN_MONTH * 24.0) / 0.4\n\n else:\n # Electric Heat Case\n # No Jan and May values are provided. Instead we have possibly some\n # DHW, clothes drying, and cooking. Plus, we have base lights/other appliances.\n # And finally we have the Elecric heat making up the base electric usage.\n\n # First, DHW, Clothes Drying and Cooking. Assume flat use through year.\n # This is a numpy array because DAYS_IN_MONTH is an array.\n elec_kwh = s.fuel_other_uses / 8760.0 * DAYS_IN_MONTH * 24.0\n\n # Now lights and other misc. appliances. Some monthly variation, given\n # by LIGHTS_OTHER_PAT.\n elec_kwh += s.lights_other_elec / 8760.0 * LIGHTS_OTHER_PAT * DAYS_IN_MONTH * 24.0\n\n # For the peak demand of those two categories of use, just assume 40% load factor.\n elec_kw = elec_kwh / (DAYS_IN_MONTH * 24.0) / 0.4\n\n # Now add in space heating kWh and kW\n elec_kwh += s.df_mo_en_base.total_kwh.values\n elec_kw += s.df_mo_en_base.total_kw.values\n\n # store results\n dfb['elec_kwh'] = elec_kwh\n dfb['elec_kw'] = elec_kw\n\n # Make an object to calculate electric utility costs\n elec_cost_calc = ElecCostCalc(s.utility, sales_tax=s.sales_tax, pce_limit=s.pce_limit)\n # cost function that will be applied to each row of the cost DataFrame\n cost_func = lambda r: elec_cost_calc.monthly_cost(r.elec_kwh, r.elec_kw)\n\n dfb['elec_dol'] = dfb.apply(cost_func, axis=1)\n\n if not is_electric_heat:\n # Now fuel use by month. Remember that the home heat model only looked at\n # space heating, so we need to add in the fuel use from the other end uses\n # that use this fuel.\n dfb['secondary_fuel_units'] = s.df_mo_en_base.secondary_fuel_units + \\\n s.fuel_other_uses / 12.0\n dfb['secondary_fuel_dol'] = dfb.secondary_fuel_units * s.exist_unit_fuel_cost * (1. + s.sales_tax)\n else:\n # Electric Heat, so no secondary fuel\n dfb['secondary_fuel_units'] = 0.0\n dfb['secondary_fuel_dol'] = 0.0\n\n # Total Electric + space heat\n dfb['total_dol'] = dfb.elec_dol + dfb.secondary_fuel_dol\n\n # Now with the heat pump\n # determine extra kWh used in the heat pump scenario. Note, this will\n # be negative numbers if the base case used electric heat.\n extra_kwh = (s.df_mo_en_hp.total_kwh - s.df_mo_en_base.total_kwh).values\n dfh['elec_kwh'] = dfb['elec_kwh'] + extra_kwh\n extra_kw = (s.df_mo_en_hp.total_kw - s.df_mo_en_base.total_kw).values\n dfh['elec_kw'] = dfb['elec_kw'] + extra_kw\n dfh['elec_dol'] = dfh.apply(cost_func, axis=1)\n\n # Now fuel, including other end uses using the heating fuel\n if not is_electric_heat:\n dfh['secondary_fuel_units'] = s.df_mo_en_hp.secondary_fuel_units + \\\n s.fuel_other_uses / 12.0\n dfh['secondary_fuel_dol'] = dfh.secondary_fuel_units * s.exist_unit_fuel_cost * (1. + s.sales_tax)\n else:\n # Electric Heat, so no secondary fuel\n dfh['secondary_fuel_units'] = 0.0\n dfh['secondary_fuel_dol'] = 0.0\n\n # Total Electric + space heat\n dfh['total_dol'] = dfh.elec_dol + dfh.secondary_fuel_dol","def monthly(self):\r\n return RecordsMonthly(self)","def test_04_dataverse_counts_by_month_all(self):\n print (self.test_04_dataverse_counts_by_month_all.__doc__)\n\n stats_maker = StatsMakerDataverses()\n\n r = stats_maker.get_dataverse_counts_by_month()\n\n # check number of months\n self.assertEqual(len(r.result_data['records']), 16)\n\n # check 1st month\n first_month = {'count': 39,\n 'month_name': 'April',\n 'month_name_short': 'Apr',\n 'month_num': 4,\n 'running_total': 39,\n 'year_num': 2015,\n 'yyyy_mm': '2015-04'}\n self.assertEqual(dict(r.result_data['records'][0]), first_month)\n\n # check last month\n last_month = {'count': 10,\n 'month_name': 'July',\n 'month_name_short': 'Jul',\n 'month_num': 7,\n 'running_total': 356,\n 'year_num': 2016,\n 'yyyy_mm': '2016-07'}\n self.assertEqual(dict(r.result_data['records'][-1]), last_month)","def test_02_dataverse_counts_by_month_published(self):\n print (self.test_02_dataverse_counts_by_month_published.__doc__)\n\n kwargs=dict(selected_year=2016)\n stats_maker = StatsMakerDataverses(**kwargs)\n\n r = stats_maker.get_dataverse_counts_by_month_published()\n\n # check number of months\n self.assertEqual(len(r.result_data['records']), 7)\n\n # check 1st month\n first_month = {'count': 5,\n 'month_name': 'January',\n 'month_name_short': 'Jan',\n 'month_num': 1,\n 'running_total': 131,\n 'year_num': 2016,\n 'yyyy_mm': '2016-01'}\n self.assertEqual(dict(r.result_data['records'][0]), first_month)\n\n # check last month\n last_month = {'count': 4,\n 'month_name': 'July',\n 'month_name_short': 'Jul',\n 'month_num': 7,\n 'running_total': 187,\n 'year_num': 2016,\n 'yyyy_mm': '2016-07'}\n self.assertEqual(dict(r.result_data['records'][-1]), last_month)","def contract_data_chart(request):\n current_date = datetime.today()\n months = [i for i in range(1, 13)]\n data = {\n 'series': [],\n 'labels': settings.CHART_MONTHS_LABELS,\n }\n\n contract_count = []\n for month in months:\n contract_count.append(\n Contract.objects.filter(\n start_date__month=month,\n start_date__year=current_date.year,\n ).values('id').count()\n )\n data['series'].append({\n \"name\": _(\"Contract\"),\n \"data\": contract_count,\n })\n\n return JsonResponse(data)","def monthly_fee(self):\n total_salary = self.base_salary\n if self.is_salary_allowances is True:\n fixed_allowances = self.summarize( self.fixed_allowances )\n non_fixed_allowances = self.summarize( self.non_fixed_allowances )\n total_salary = total_salary + non_fixed_allowances + fixed_allowances\n #end if\n\n company_old_age_insurance = 0\n individual_old_age_insurance = 0\n if self.old_age_insurance_status is True:\n company_old_age_insurance = \\\n self._company_old_age_insurance(total_salary)\n\n individual_old_age_insurance = \\\n self._individual_old_age_insurance(total_salary)\n #end if\n\n company_pension_insurance = 0\n individual_pension_insurance = 0\n if self.pension_insurance_status is True:\n company_pension_insurance = \\\n self._company_pension_insurance(total_salary)\n\n individual_pension_insurance = \\\n self._individual_pension_insurance(total_salary)\n #end if\n\n company_health_insurance = 0\n individual_health_insurance = 0\n if self.health_insurance_status is True:\n company_health_insurance = \\\n self._company_health_insurance(total_salary)\n\n individual_health_insurance = \\\n self._individual_health_insurance(total_salary)\n #end if\n\n death_insurance = 0\n if self.death_insurance_status is True:\n death_insurance = self._death_insurance(total_salary)\n #end if\n\n accident_insurance = 0\n if self.accident_insurance_status is True:\n accident_insurance = \\\n self._accident_insurance(total_salary, \\\n self.industry_risk_rate)\n #end if\n\n monthly = {\n \"old_age_insurance\" : {\n \"company\" : company_old_age_insurance,\n \"individual\" : individual_old_age_insurance,\n },\n \"pension_insurance\" : {\n \"company\" : company_pension_insurance,\n \"individual\" : individual_pension_insurance,\n },\n \"health_insurance\" : {\n \"company\" : company_health_insurance,\n \"individual\" : individual_health_insurance,\n },\n \"death_insurance\" : death_insurance,\n \"accident_insurance\" : accident_insurance\n }\n return monthly","def month_summary(phenology_df, out_csv=None):\n month_list = ['Jul','Aug','Sep','Oct','Nov','Dec','Jan','Feb','Mar','Apr',\n 'May','Jun']\n species_list = phenology_df['species'].unique().tolist()\n\n count_list = []\n for species in species_list:\n count_dict = {'species':species}\n for month in month_list:\n month_check = phenology_df['monthStart'] == month\n species_check = phenology_df['species'] == species\n occurrence_count = phenology_df[month_check & species_check]['numSpecimen'].sum()\n count_dict[month] = occurrence_count\n count_list.append(count_dict)\n\n month_df = pd.DataFrame(count_list)\n month_df = month_df.set_index('species')\n month_df = month_df[month_list]\n\n return month_df","def monthlyhourlyplot(metdat, catinfo, category=None, basecolor='span'):\n\n if category is None:\n print('not sure what to plot...')\n pass\n\n months = utils.monthnames()\n colors = utils.get_colors(len(catinfo['columns'][category]), basecolor=basecolor, reverse=True)\n colnames, vertlocs, ind = utils.get_vertical_locations(catinfo['columns'][category], reverse=True)\n \n plotdat = metdat[colnames].groupby([metdat.index.month.rename('month'), metdat.index.hour.rename('hour')]).mean()\n \n fig, ax = plt.subplots(4,3, figsize=(9,11), sharex=True, sharey=True)\n for iax in range(len(months)):\n for catitem in range(len(colnames)):\n ax.flatten()[iax].plot(plotdat[colnames[catitem]].xs(iax+1), color=colors[catitem])\n ax.flatten()[iax].set_title(months[iax], fontsize=12)\n \n \n fig.text(0.5,0.2, 'Time of Day [hour]', ha='center', va='center')\n leg = fig.legend([str(v) + ' m' for v in vertlocs], loc = 'upper center', bbox_to_anchor = (0,-0.825,1,1), bbox_transform = plt.gcf().transFigure, frameon=False, ncol=2)\n fig.tight_layout()\n fig.subplots_adjust(bottom=0.25)\n fig.text(0,0.6125, catinfo['labels'][category], ha='center', va='center', rotation='vertical')\n\n return fig, ax","def count_risk_categories_by_month(data, month, year):\n return count_risk_categories(\n filter_inspection_type(\n filter_month(data, month, year), 'Routine - Unscheduled'))","def create_spend_chart(category_list:list) -> str:\n\n header = 'Percentage spent by category\\n'\n table = create_table(category_list)\n divider = ' ' + '-'*len(category_list*3) + '-'\n footer = create_footer(category_list)\n full_chart = header + table + divider + footer\n\n return full_chart[:-1]","def sea_ice_statistics_monthly(configfile):\n _sea_ice_statistics_monthly(configfile)","def mortgage_detail_summary(self):\n monthly = self.calculate_loan()\n report_dic = {'Year': [], 'Balance': [], 'Payments': []}\n years = []\n balance = []\n payments = []\n # loop through the year to get the balance and add to list\n for x in range(1, self.year + 1):\n mon = x * 12\n rem = self.get_balance(mon)\n years.append(x)\n balance.append(float(rem))\n payments.append(float(monthly * mon))\n # add to the dictionary\n report_dic['Year'] = years\n report_dic['Balance'] = balance\n report_dic['Payments'] = payments\n\n return report_dic","def _get_month_inv_per_cus_cate1(self):\n inv_cus_cate1_month = self._inv_cus_sku_month.reset_index()\n inv_cus_cate1_month['first_cate_id'] = self._sku_info_encoded.first_cate_id.values\n inv_cus_cate1_month_index = inv_cus_cate1_month[['customer_code', 'first_cate_id']]\n inv_cus_cate1_month = inv_cus_cate1_month.groupby(\n ['customer_code', 'first_cate_id'])[self._inv_cus_sku_month.columns].sum()\n inv_cus_cate1_month = inv_cus_cate1_month.reindex(inv_cus_cate1_month_index)\n return inv_cus_cate1_month","def _get_month_inv_per_cus_sku(self):\n tmp = self._inv.copy()\n tmp['order_month'] = tmp.order_date.astype(str).apply(lambda x: x[:7])\n tmp = tmp.groupby(['customer_code', 'item_code', 'order_month'])[['inv_qty']].sum()\n tmp['inv_qty'] = tmp.inv_qty.apply(lambda x: x if x > 0 else 0)\n inv_cus_sku_month = tmp.unstack(level=-1).fillna(0)\n inv_cus_sku_month.columns = pd.date_range(start='2017-04-30', periods=len(inv_cus_sku_month.columns), freq='M')\n return inv_cus_sku_month","def _get_month_inv_per_sku(self):\n inv_sku_month = self._inv_cus_sku_month.groupby(['item_code'])[self._inv_cus_sku_month.columns].sum()\n inv_sku_month = inv_sku_month.reindex(self._index.get_level_values(1))\n return inv_sku_month","def sort_and_aggregate_periods(budget_model):\n # Calculate total number of months.\n periods = budget_model[\"periods\"] \n total_number_of_months = len(periods) \n budget_model[\"total_number_of_months\"] = total_number_of_months\n\n # Get the reference to the total revenue in the budget model.\n total_revenue = budget_model[\"total_revenue\"]\n\n # Initialize variables used to calculate greatest increase in revenue.\n greatest_increase_revenue = 0\n greatest_increase_name = \"\"\n\n # Initialize variables used to calculate greatest decrease in revenue.\n greatest_decrease_revenue = 0\n greatest_decrease_name = \"\"\n\n # Retrieve sort keys for budget model and sort them into chronological order.\n period_keys = periods.keys()\n period_key_list = list(period_keys)\n period_key_list.sort()\n\n # Initialize previous revenue.\n # There is no revenue change for the first period.\n previous_revenue = periods[period_key_list[0]][\"revenue\"]\n total_revenue_change = 0\n\n # Calculate aggregations by processing periods in chronological order.\n for period_key in period_key_list:\n period = periods[period_key]\n total_revenue = total_revenue + period[\"revenue\"]\n\n budget_model[\"total_revenue\"] = total_revenue\n\n revenue = period[\"revenue\"]\n revenue_change = revenue - previous_revenue\n total_revenue_change = total_revenue_change + revenue_change\n \n if revenue_change > greatest_increase_revenue:\n greatest_increase_revenue = revenue_change\n greatest_increase_name = period[\"name\"]\n\n if revenue_change < greatest_decrease_revenue:\n greatest_decrease_revenue = revenue_change\n greatest_decrease_name = period[\"name\"]\n\n previous_revenue = revenue\n\n # Write aggregations to the budget model.\n budget_model[\"greatest_increase\"] = {\"name\": greatest_increase_name, \"revenue\": greatest_increase_revenue}\n budget_model[\"greatest_decrease\"] = {\"name\": greatest_decrease_name, \"revenue\": greatest_decrease_revenue}\n budget_model[\"average_revenue_change\"] = round(total_revenue_change / total_number_of_months, 0)","def create_monthly_statistics(fromDate, toDate, siteId):\n logger = logging.getLogger(__name__)\n logger.error(\"create monthly stat from {} to {}\".format(fromDate, toDate))\n fromDate = fromDate.replace(hour=0, minute=0, second=0, day=1)\n if (toDate.month < 12):\n toDate = toDate.replace(month=toDate.month + 1, day=1, hour=0, minute=0, second=0)\n else:\n toDate = toDate.replace(year=toDate.year + 1, month=1, day=1, hour=0, minute=0, second=0)\n f = fromDate\n while f < toDate:\n rawDataSet = DailyStatistics.objects.filter(year=f.year, month=f.month).filter(siteId=siteId)\n if rawDataSet.count():\n d, created = MonthlyStatistics.objects.update_or_create(siteId=siteId, month=f.month, year=f.year)\n d.dataAvailable = rawDataSet.count()\n tempmins = []\n tempmaxs = []\n tempavgs = []\n precipitation = decimal.Decimal(0.0)\n for j in rawDataSet:\n if j.tempMin is not None:\n tempmins.append(j.tempMin)\n if j.tempMax is not None:\n tempmaxs.append(j.tempMax)\n if j.tempAvg is not None:\n tempavgs.append(j.tempAvg)\n if j.precipitation is not None:\n precipitation = precipitation + j.precipitation\n d.dataAvailable = rawDataSet.count()\n if len(tempmins) > 0:\n d.tempMin = min(tempmins)\n d.tempMinAvg = sum(tempmins) / len(tempmins)\n if len(tempmaxs) > 0:\n d.tempMax = max(tempmaxs)\n d.tempMaxAvg = sum(tempmaxs) / len(tempmaxs)\n if len(tempavgs) > 0:\n d.tempAvg = sum(tempavgs) / len(tempavgs)\n\n\n start_of_current_month = datetime.datetime(\n year=f.year, month=f.month, day=1, hour=0, minute=0)\n start_of_next_month = start_of_current_month + relativedelta(months=1)\n rawDataSet = RawData.objects\\\n .filter(createdDate__range=(start_of_current_month, start_of_next_month))\\\n .filter(siteId=siteId)\n temps = []\n rhs = []\n winds = []\n for j in rawDataSet:\n if j.temperature is not None:\n temps.append(j.temperature)\n if j.humidity is not None:\n rhs.append(j.humidity)\n if j.windDir is not None:\n winds.append(j.windDir)\n tempDistribution = Climate.calculate_temperature_distribution(temps)\n d.tempDistribution = ''.join(str(e) + ',' for e in tempDistribution)[:-1]\n rhDistribution = Climate.calculate_rh_distribution(rhs)\n d.rhDistribution = ''.join(str(e) + ',' for e in rhDistribution)[:-1]\n windDistribution = Climate.calculate_wind_distribution(winds)\n d.windDistribution = ''.join(str(e) + ',' for e in windDistribution)[:-1]\n d.precipitation = precipitation\n d.summerDays = Climate.get_nr_summer_days(tempmaxs)\n d.frostDays = Climate.get_nr_frost_days(tempmins)\n d.winterDays = Climate.get_nr_winter_days(tempmaxs)\n d.coldDays = Climate.get_nr_cold_days(tempmins)\n d.warmNights = Climate.get_nr_warm_nights(tempmins)\n d.warmDays = Climate.get_nr_warm_days(tempmaxs)\n d.hotDays = Climate.get_nr_hot_days(tempmaxs)\n\n manualDataSet = RawManualData.objects.filter(siteId=siteId).filter(year=f.year).filter(month=f.month)\n significants = {}\n for day in manualDataSet:\n significants = Climate.count_significants(significants, day.weatherCode)\n d.significants = significants\n d.save()\n\n if (f.month == 12):\n f = f.replace(year=f.year + 1, month=1)\n else:\n f = f.replace(month=f.month + 1)","def interactive_annual_monthly_bar_report_with_buttons(year: int):\n condition_dict = {'key': 'category', 'operation': '!=', 'value': 16}\n average_monthly_amount = Spending().get_average_monthly_spending_amount(condition_dict=condition_dict)\n current_annual_monthly_amount = Spending().get_monthly_total_spending_of_a_year(year, include_doctor=False)\n\n df_average_monthly = pd.DataFrame(average_monthly_amount, columns=['avg', 'mon'])\n df_current_annual_monthly = pd.DataFrame(current_annual_monthly_amount, columns=['sum', 'mon'])\n\n fig = go.Figure()\n\n fig.add_trace(\n go.Bar(\n x=df_current_annual_monthly['mon'],\n y=df_current_annual_monthly['sum'],\n name=f'{year} monthly spending'\n )\n )\n\n fig.add_trace(\n go.Scatter(\n x=df_average_monthly['mon'],\n y=df_average_monthly['avg'],\n name='Average Monthly Spending',\n visible=True,\n line=dict(color=\"#33CFA5\", dash=\"dash\")\n )\n )\n\n fig.update_layout(\n updatemenus=[\n dict(\n type='buttons',\n direction='right',\n active=1,\n x=0.57,\n y=1.2,\n buttons=list([\n dict(\n label='Current Year',\n method='update',\n args=[\n {'visible': [True, False]},\n {'title': f'{year} monthly spending', \"annotations\": []}\n ]\n ),\n dict(\n label='Average',\n method='update',\n args=[\n {'visible': [True, True]},\n {'title': 'Compare with Average monthly', \"annotations\": []}\n ]\n ),\n ]),\n )\n ]\n )\n\n fig.update_layout(\n {\n 'width': 960,\n 'height': 550\n },\n overwrite=True\n )\n\n aPlot = plotly.offline.plot(fig,\n config={\"displayModeBar\": False},\n show_link=False,\n include_plotlyjs=False,\n output_type='div')\n\n return aPlot","def aggregate_monthly(organization, account, from_date=None):\n customers = []\n receivables = []\n new_customers = []\n new_receivables = []\n churn_customers = []\n churn_receivables = []\n queryset = None\n # We want to be able to compare *last* to *from_date* and not get django\n # warnings because timezones are not specified.\n dates = month_periods(13, from_date)\n first_date = dates[0]\n seam_date = dates[1]\n for last_date in dates[2:]:\n churn_query = RawQuery(\n\"\"\"SELECT COUNT(DISTINCT(prev.dest_organization_id)), SUM(prev.amount)\n FROM saas_transaction prev\n LEFT OUTER JOIN (\n SELECT distinct(dest_organization_id)\n FROM saas_transaction\n WHERE created_at >= '%(seam_date)s'\n AND created_at < '%(last_date)s'\n AND orig_organization_id = '%(organization_id)s'\n AND orig_account = '%(account)s') curr\n ON prev.dest_organization_id = curr.dest_organization_id\n WHERE prev.created_at >= '%(first_date)s'\n AND prev.created_at < '%(seam_date)s'\n AND prev.orig_organization_id = '%(organization_id)s'\n AND prev.orig_account = '%(account)s'\n AND curr.dest_organization_id IS NULL\"\"\" % {\n \"first_date\": first_date,\n \"seam_date\": seam_date,\n \"last_date\": last_date,\n \"organization_id\": organization.id,\n \"account\": account }, 'default')\n churn_customer, churn_receivable = iter(churn_query).next()\n query_result = Transaction.objects.filter(\n orig_organization=organization,\n orig_account=account,\n created_at__gte=seam_date,\n created_at__lt=last_date).aggregate(\n Count('dest_organization', distinct=True),\n Sum('amount'))\n customer = query_result['dest_organization__count']\n receivable = query_result['amount__sum']\n new_query = RawQuery(\n\"\"\"SELECT count(distinct(curr.dest_organization_id)), SUM(curr.amount)\n FROM saas_transaction curr\n LEFT OUTER JOIN (\n SELECT distinct(dest_organization_id)\n FROM saas_transaction\n WHERE created_at >= '%(first_date)s'\n AND created_at < '%(seam_date)s'\n AND orig_organization_id = '%(organization_id)s'\n AND orig_account = '%(account)s') prev\n ON curr.dest_organization_id = prev.dest_organization_id\n WHERE curr.created_at >= '%(seam_date)s'\n AND curr.created_at < '%(last_date)s'\n AND curr.orig_organization_id = '%(organization_id)s'\n AND curr.orig_account = '%(account)s'\n AND prev.dest_organization_id IS NULL\"\"\" % {\n \"first_date\": first_date,\n \"seam_date\": seam_date,\n \"last_date\": last_date,\n \"organization_id\": organization.id,\n \"account\": account }, 'default')\n new_customer, new_receivable = iter(new_query).next()\n period = last_date\n churn_customers += [ (period, churn_customer) ]\n churn_receivables += [ (period, - int(churn_receivable or 0)) ]\n customers += [ (period, customer) ]\n receivables += [ (period, int(receivable or 0)) ]\n new_customers += [ (period, new_customer) ]\n new_receivables += [ (period, int(new_receivable or 0)) ]\n first_date = seam_date\n seam_date = last_date\n return ((churn_customers, customers, new_customers),\n (churn_receivables, receivables, new_receivables))","def _get_month_inv_per_cate1(self):\n inv_cate1_month = self._inv_cus_sku_month.reset_index()\n inv_cate1_month['first_cate_id'] = self._sku_info_encoded.first_cate_id.values\n inv_cate1_month_index = inv_cate1_month['first_cate_id']\n inv_cate1_month = inv_cate1_month.groupby(['first_cate_id'])[self._inv_cus_sku_month.columns].sum()\n inv_cate1_month = inv_cate1_month.reindex(inv_cate1_month_index)\n return inv_cate1_month","def sum_monthly_budget(self):\n\n my_sumbud = 0\n \n month_bud = self.data['Monthly Budget']\n for i in month_bud:\n my_sumbud = my_sumbud + float(i)\n\n return my_sumbud","def SumCostByBetweenDatesPerCategory(startDate, endDate, category):\n\n logs.logger.debug(\n \"Start to adds all amount of Cost objects between two payment date and on the category\")\n try:\n searchedCostByBetweenDatesFromDB = GetAllCostByDateOfPaymentBandFromDB(\n startDate, endDate)\n sumTotal = 0\n for item in searchedCostByBetweenDatesFromDB:\n if item.category.value == category:\n sumTotal += item.amount\n logs.logger.info(\n \"Between two payment date and on the category adds all amount of Cost objects.\")\n return sumTotal\n except Exception as e:\n logs.logger.error(e, exc_info=True)","def normalized_monthly_hist_by_stability(metdat, catinfo, vertloc=80):\n\n months = utils.monthnames()\n hours = np.arange(24)\n stabcol, _, _= utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc)\n stabconds = utils.get_stabconds()\n colors = utils.get_colors(5,basecolor='span')\n\n temp = metdat[stabcol].dropna()\n plotdata = temp.groupby([temp.index.month.rename('month'), temp.index.hour.rename('hour')]).value_counts(normalize=True)\n plotdata.index.names = ['month','hour','stabclass']\n temp = plotdata.reorder_levels(['month','stabclass','hour'])\n\n indexvals = [np.arange(1,13),stabconds, np.arange(24)]\n indx = pd.MultiIndex.from_product(indexvals, names=['month','stabclass','hour'])\n temp = temp.reindex(index=indx).fillna(0.0)\n\n fig,ax = plt.subplots(4,3, figsize=(9,10), sharex=True, sharey=True)\n for ii,month in enumerate(months):\n newbottom = np.zeros(24)\n\n for jj,cond in enumerate(stabconds):\n \n pdat = temp.loc[ii+1,cond]\n \n ax.flatten()[ii].bar(hours, pdat, color=colors[jj],bottom=newbottom)\n \n newbottom += pdat\n \n # fig.legend(stabconds, loc=8, bbox_to_anchor=(0, -0.1), edgecolor='w')\n fig.text(-0.02,0.58, 'Probability [%]', ha='center', va='center', rotation='vertical')\n leg = fig.legend(stabconds, loc=9, bbox_to_anchor=(0.55, 0.125), frameon=False)\n \n fig.tight_layout()\n fig.subplots_adjust(bottom=0.21)\n fig.text(0.5, 0.165, 'Time of Day [Hour]', ha='center', va='center')\n\n return fig, ax","def amortization_schedule(\n cls, mortgage_term: int, mortgage_amount: float, interest_rate: float\n ) -> AmortizationSchedule:\n monthly_data = []\n annual_data = []\n\n payment = cls.payment_calc(\n years=mortgage_term, interest=interest_rate, mortgage_amount=mortgage_amount\n )\n\n # total number of payments\n total_nr_payments = mortgage_term * 12\n\n # used to calculate the yearly (payments)\n year = 1\n\n monthly_rate = interest_rate / 100 / 12\n\n payment_monthly_date = date.today() # starts from today\n payment_yearly_date = date.today() # starts from today\n\n beginning_balance_per_month = mortgage_amount\n beginning_balance_per_year = mortgage_amount\n\n # interest rates\n interest_rate_per_year = 0\n principal_amount_per_year = 0\n\n # interest rate amount per year\n total_interest_rate_amount = 0\n\n for _ in range(total_nr_payments):\n interest = round(beginning_balance_per_month * monthly_rate, 2)\n principal_amount_per_month = round(payment - interest, 2)\n remaining_balance_per_month = round(\n beginning_balance_per_month - principal_amount_per_month, 2\n )\n\n interest_rate_per_year = interest_rate_per_year + interest\n principal_amount_per_year = (\n principal_amount_per_year + principal_amount_per_month\n )\n\n if year == 12:\n remaining_balance_per_year = (\n beginning_balance_per_year - principal_amount_per_year\n )\n\n annual_payment_payload = cls._construct_annual_payments(\n year=payment_yearly_date,\n interest_amount=interest_rate_per_year,\n principal_amount=principal_amount_per_year,\n beginning_balance=beginning_balance_per_year,\n ending_balance=remaining_balance_per_year,\n )\n\n annual_data.append(annual_payment_payload)\n\n beginning_balance_per_year = remaining_balance_per_year\n payment_yearly_date = payment_yearly_date + relativedelta(years=+1)\n\n # interest rate amount per year\n total_interest_rate_amount = (\n total_interest_rate_amount + interest_rate_per_year\n )\n\n # reset the data\n interest_rate_per_year = 0\n principal_amount_per_year = 0\n year = 0\n\n monthly_payments = cls._construct_monthly_payments(\n month=payment_monthly_date,\n beginning_balance=beginning_balance_per_month,\n interest_amount=interest_rate,\n principal_amount=principal_amount_per_month,\n ending_balance=remaining_balance_per_month,\n )\n\n monthly_data.append(monthly_payments)\n\n beginning_balance_per_month = remaining_balance_per_month\n payment_monthly_date = payment_monthly_date + relativedelta(months=+1)\n\n year += 1\n\n amortization_schedule = {\n \"monthly\": monthly_data,\n \"annual\": annual_data,\n \"monthly_mortgage_payment\": payment,\n \"total_interest_rate_amount\": total_interest_rate_amount,\n }\n\n return AmortizationSchedule(**amortization_schedule)","def get(self, request, unit_id):\n start_date = get_start_date(request)\n end_date = get_end_date(request)\n months = {}\n while(start_date <= end_date):\n first_of_month = start_date.replace(day=1)\n last_of_month = start_date.replace(\n day=1) + relativedelta(months=1) - relativedelta(days=1)\n readings_sum = MeterReading.objects.filter(\n date__gte=first_of_month,\n date__lte=last_of_month,\n unit=unit_id\n ).aggregate(Sum('usage'))\n months[start_date.strftime(\n \"%Y-%m-%d\")] = round(readings_sum['usage__sum']*.06, 0)\n start_date += relativedelta(months=1)\n return JsonResponse(months, safe=False)","def get_monthly_countries_data(country, month, region):\n m_cases = []\n m_deaths = []\n m_rec = []\n actual_dates = []\n\n # start date / actual date\n today_date = datetime.today().strftime('%m-%d-%Y')\n today_day = today_date.split(\"-\")[1]\n\n # monthly data\n if month == 3:\n today_day = '32' # grab all March (it's done)\n if month == 4:\n today_day = '31' # grab all April (it's done)\n for day in range(1, int(float(today_day))):\n date_object = datetime(day=day,\n month=month,\n year=2020).strftime('%d-%m-%Y')\n date = (date_object.split(\"-\")[0], date_object.split(\"-\")[1])\n month_cases, month_deaths, month_rec, exp_dates = \\\n _get_daily_countries_data(date, country, region)\n m_cases.append(month_cases)\n m_deaths.append(month_deaths)\n m_rec.append(month_rec)\n actual_dates.append(exp_dates)\n\n return m_cases, m_deaths, m_rec, actual_dates","def get_stats(cls, contract_month_start_day=1):\n # I could do this in SQL with date_trunc, but eventually this'll need\n # to be contract-month, so like the 7th-7th or something, which AFAIK\n # can't be done in SQL (and certainly not in Django). So just do this\n # by hand. There are only a few hundred reports/month right now, so this\n # should be OK.\n stats = {}\n\n reports = cls.objects.filter(days_until_triage__isnull=False)\n for report in reports:\n first_day, last_day = dates.contract_month(report.created_at, contract_month_start_day)\n if first_day not in stats:\n stats[first_day] = {\n 'count': 0,\n 'triaged_accurately': 0,\n 'false_negatives': 0,\n 'triaged_within_one_day': 0,\n 'last_day': last_day,\n\n }\n\n stats[first_day]['count'] += 1\n stats[first_day]['triaged_accurately'] += report.is_accurate\n stats[first_day]['false_negatives'] += report.is_false_negative\n if report.days_until_triage <= 1:\n stats[first_day]['triaged_within_one_day'] += 1\n\n stats[\"totals\"] = {\n key: sum(month_stats[key] for month_stats in stats.values()) if stats else 0\n for key in ('count', 'triaged_accurately', 'false_negatives', 'triaged_within_one_day')\n }\n\n return stats","def per_month(start: datetime, end: datetime, n: int = 1):\n curr = start.replace(day=1, hour=0, minute=0, second=0, microsecond=0)\n while curr < end:\n curr_end = add_month(curr, n)\n yield curr, curr_end\n curr = curr_end","def month_overview(items, month_long):\n events = []\n for item in items:\n dt = datetime.strptime(item['Date'], '%m/%d/%Y')\n if filters.month_l_filter(dt.month) == month_long:\n events.append(item)\n return events","def month(self):\n return self._months","def monthly_returns(df, stats): \n retbymonth = {}\n tradingmonths = list()\n years = [2012,2013,2014,2015,2016]\n months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct',\n 'Nov','Dec'] \n i = 0\n for y in years:\n for m, month in enumerate(months):\n if m == 11:\n retbymonth[i] = \\\n pd.DataFrame(df.loc[(df.index > dt(y,(m+1),1)) & \n (df.index < dt((y+1),1,1))]['Returns'])\n else:\n retbymonth[i] = \\\n pd.DataFrame(df.loc[(df.index > dt(y,(m+1),1)) & \n (df.index < dt(y,(m+2),1))]['Returns'])\n #Dropping non-trading months\n if retbymonth[i].empty == True:\n del retbymonth[i]\n else:\n tradingmonths.append('%s%s' %(month, y))\n i = i + 1\n #Renaming keys\n for i, key in enumerate(retbymonth.keys()):\n retbymonth[i] = retbymonth.pop(key) \n \n #Monthly returns\n monthlyret = {}\n for m in xrange(len(tradingmonths)):\n monthlyret[m] = retbymonth[m].sum()\n monthlyret = pd.DataFrame(monthlyret).T \n monthlyret['CumRet'] = monthlyret['Returns'].cumsum()\n monthlyret['Months'] = tradingmonths\n stats['Values']['months'] = len(monthlyret)\n stats['Values']['monthly_sharpe'] = (monthlyret['Returns'].mean() /\n monthlyret['Returns'].std())\n stats['Values']['yearly_sharpe'] = (stats['Values']['monthly_sharpe'] *\n np.sqrt(12))\n \n return monthlyret, stats","def get_month_course_metrics(site, course_id, month_for, **_kwargs):\n # TODO: handle invalid \"month_for\" exception\n # month, year = [int(val) for val in month_for.split('/')]\n # start_date = datetime.date(year=year, month=month, day=1)\n # end_date = datetime.date(year=year, month=month, day=days_in_month(start_date))\n\n first_day, last_day = first_last_days_for_month(month_for)\n params_dict = dict(site=site,\n course_id=course_id,\n start_date=first_day,\n end_date=last_day)\n\n active_users = get_mau_from_site_course(site=site,\n course_id=course_id,\n year=first_day.year,\n month=first_day.month)\n course_enrollments = get_course_enrolled_users_for_time_period(**params_dict)\n num_learners_completed = get_course_num_learners_completed_for_time_period(**params_dict)\n avg_days_to_complete = get_course_average_days_to_complete_for_time_period(**params_dict)\n avg_progress = get_course_average_progress_for_time_period(**params_dict)\n\n return dict(\n course_id=course_id,\n month_for=month_for,\n active_users=active_users.count(),\n course_enrollments=course_enrollments,\n num_learners_completed=num_learners_completed,\n avg_days_to_complete=avg_days_to_complete,\n avg_progress=avg_progress,\n )","def track_monthly(self, **kwargs):\n return self.track(periods=[self.PERIOD_MONTHLY], **kwargs)","def month(m):\n\t\tx = db.cquery(\"month\",m)\n\t\tprint \"Total:\", x[0] #@BUG when zero sometimes displays \"1\"\n\t\tf = raw_input(\"[L]ist [N]ew overview or[B]ack to home \").lower()\n\t\tif f == \"l\":\n\t\t\tui.mont1(m)\n\t\t\tfor i in x[1]:\n\t\t\t\tprint ui.statsid(),i[0], i[1],\" \",ui.statstimein(), i[2], ui.statstimeout(),i[3]\n\t\t\traw_input(\"[Enter] to go back to search\")\n\t\t\thome_stats()\n\t\telif f == \"n\": home_stats()\n\t\telif f == \"b\": home()\n\t\telse:\n\t\t\traw_input(\"I didnt get that... Press [Enter] to go back to stats...\")\n\t\t\thome_stats()","def add_expense_view(request):\n if request.method == \"POST\":\n form = ExpenseForm(request, request.POST)\n if form.is_valid():\n expense = form.save(commit=False)\n expense.user_id = request.user.id\n expense.category_id = form.cleaned_data[\"category\"]\n expense.save()\n return redirect(\"add_expense\")\n else:\n form = ExpenseForm(request)\n latest_10 = Expense.objects\\\n .by_user(request.user.id)\\\n .order_by(\"-date\", \"-id\")\\\n .only(\"amount\", \"date\", \"description\")[:10]\n\n latest_10_total = latest_10.aggregate(Sum(\"amount\"))\n this_last_month = Expense.objects.total_of_this_and_last_month(request.user.id)\n last_3_months = Expense.objects.last_n_months_expense(3, request.user.id)\\\n .aggregate(Sum(\"amount\"))\n this_last_year = Expense.objects.total_of_this_and_last_year(request.user.id)\n\n return render(request, \"tracker/add.html\", {\n \"form\": form, \"latest_10\": latest_10, \"3_months\": last_3_months[\"amount__sum\"],\n \"this_month\": this_last_month.this, \"last_month\": this_last_month.last,\n \"this_year\": this_last_year.this, \"last_year\": this_last_year.last,\n \"latest_10_sum\": latest_10_total[\"amount__sum\"]\n })","def month(self):\n return 0","def month(self):\n return 0","def get(self, request, unit_id):\n start_date = get_start_date(request)\n end_date = get_end_date(request)\n months = {}\n while(start_date <= end_date):\n first_of_month = start_date.replace(day=1)\n last_of_month = start_date.replace(\n day=1) + relativedelta(months=1) - relativedelta(days=1)\n readings_sum = MeterReading.objects.filter(\n date__gte=first_of_month,\n date__lte=last_of_month,\n unit=unit_id,\n reading_type='GAS'\n ).aggregate(Sum('usage'))\n months[start_date.strftime(\n \"%Y-%m-%d\")] = round(readings_sum['usage__sum']*.06, 0)\n start_date += relativedelta(months=1)\n return JsonResponse(months, safe=False)","def TopMonth(results, search_term):\n top_months = Counter([row[0].strftime(\"%B %Y\") for row in results])\n (month, count) = top_months.most_common()[0]\n if search_term == 'total':\n search_term = 'checkins'\n text = 'The most popular month for %s was %s with %s.' % (search_term,\n month, count)\n return text","def new_month(self, month: int, year: int, bill: Bill) -> None:\n self.bill = bill\n self.bill.set_rates(\"TERM\", TERM_MINS_COST)\n self.bill.add_fixed_cost(TERM_MONTHLY_FEE)\n if not ((self.end.month < month and self.end.year <= year) or\n self.end.year < year):\n # refresh included minutes and SMSs\n self.bill.add_free_minutes((-1) * self.bill.free_min)\n if self.start.month == month and self.start.year == year:\n # if first month, add term deposit to bill.\n self.bill.add_fixed_cost(TERM_DEPOSIT)\n else:\n self._carried_term = True","def getMonthlyDataForTaxons(self, month, year):\n return self.callMonthlyProc(\"monthly_by_type\", month, year)","def _get_sum_detail(self, cr, uid, ids=None, number_month=None,\n context=None):\n fy_obj = self.pool.get('account.fiscalyear')\n period_obj = self.pool.get('account.period')\n context = context and dict(context) or {}\n cx = context.copy()\n res = 0.0\n\n if not cx.get('fiscalyear'):\n cx['fiscalyear'] = fy_obj.find(cr, uid)\n\n fy_id = cx['fiscalyear']\n\n brw = self.browse(cr, uid, ids)\n\n if brw.acc_val == 'init':\n if cx.get('whole_fy', False):\n cx['periods'] = period_obj.search(cr, uid, [\n ('fiscalyear_id', '=', fy_id), ('special', '=', True)])\n else:\n period_from = period_obj.search(cr, uid, [\n ('fiscalyear_id', '=', fy_id), ('special', '=', True)])\n # Case when the period_from is the first non-special period\n # of the fiscalyear\n if period_obj.browse(cr, uid, cx['period_from']).date_start ==\\\n fy_obj.browse(cr, uid, fy_id).date_start:\n cx['period_to'] = period_from[0]\n else:\n cx['period_to'] = period_obj.previous(\n cr, uid, cx['period_from'])\n cx['period_from'] = period_from[0]\n elif brw.acc_val == 'var':\n # it is going to be the one sent by the previous cx\n if cx.get('whole_fy', False):\n cx['periods'] = period_obj.search(cr, uid, [\n ('fiscalyear_id', '=', fy_id), ('special', '=', False)])\n else:\n # it is going to be from the fiscalyear's beginning\n if cx.get('whole_fy', False):\n cx['periods'] = period_obj.search(cr, uid, [\n ('fiscalyear_id', '=', fy_id)])\n else:\n period_from = period_obj.search(cr, uid, [\n ('fiscalyear_id', '=', fy_id), ('special', '=', True)])\n cx['period_from'] = period_from[0]\n cx['periods'] = \\\n period_obj.build_ctx_periods(cr, uid, cx['period_from'],\n cx['period_to'])\n\n if brw.type == 'detail':\n # Si es de tipo detail\n # If we have to only take into account a set of Journals\n cx['journal_ids'] = [aj_brw.id for aj_brw in brw.journal_ids]\n cx['analytic'] = [an.id for an in brw.analytic_ids]\n cx['ifrs_tax'] = [tx.id for tx in brw.tax_code_ids]\n cx['ifrs_partner'] = [p_brw.id for p_brw in brw.partner_ids]\n cx['ifrs_query'] = self._get_ifrs_query(cr, uid, brw, context)\n\n # NOTE: This feature is not yet been implemented\n # cx['partner_detail'] = cx.get('partner_detail')\n\n # Refreshing record with new context\n brw = self.browse(cr, uid, ids, context=cx)\n\n for aa in brw.cons_ids:\n # Se hace la sumatoria de la columna balance, credito o debito.\n # Dependiendo de lo que se escoja en el wizard\n if brw.value == 'debit':\n res += aa.debit\n elif brw.value == 'credit':\n res += aa.credit\n else:\n res += aa.balance\n return res","def total_tbr(cc): # pragma: no cover\n cc.execute(\"\"\"SELECT DISTINCT DATE_FORMAT(git_commit.timestamp, '%Y-%m')\n FROM git_commit\"\"\")\n months = cc.fetchall()\n results = []\n for month in months:\n month = month[0]\n cc.execute(\"\"\"SELECT COUNT(DISTINCT git_commit_hash)\n FROM commit_people\n INNER JOIN git_commit\n ON commit_people.git_commit_hash = git_commit.hash\n WHERE commit_people.type = 'tbr'\n AND YEAR(git_commit.timestamp) = %s\n AND MONTH(git_commit.timestamp) = %s\"\"\" % (month[:4], month[5:]))\n result = cc.fetchone()\n results.append([month, int(result[0])])\n return results","def _get_month_order_per_cate2(self):\n order_cate2_month = self._order_sku_month.reset_index()\n order_cate2_month['second_cate_id'] = self._sku_info.second_cate_id.values\n order_cate2_month_index = order_cate2_month['second_cate_id']\n order_cate2_month = order_cate2_month.groupby(['second_cate_id'])[self._order_sku_month.columns].sum()\n order_cate2_month = order_cate2_month.reindex(order_cate2_month_index)\n return order_cate2_month","def calc_schedule_monthly(self):\n schedule = self.schedule\n \n schedule['year'] = schedule['Date'].dt.year\n schedule['month'] = schedule['Date'].dt.month\n schedule['day'] = schedule['Date'].dt.day\n schedule['end_day'] = schedule[['year', 'month']].apply(lambda x: calendar.monthrange(x.year, x.month)[1],\n axis=1)\n\n\n schedule_mon = schedule\\\n .groupby(['year', 'month'])\\\n .agg({'Payment': 'sum',\n 'Interest': 'sum',\n 'Additional_Payment': 'sum'\n }\n )\n\n schedule_mon.index.names = ['year', 'month']\n schedule_mon.reset_index(inplace=True)\n \n month_end_balances = schedule.loc[schedule['day'] == schedule['end_day'], \n ['year', 'month', 'End Balance']]\n\n \n schedule_mon = schedule_mon.merge(month_end_balances, how='inner', on=['year', 'month'])\n \n schedule_mon['day'] = 1 # start of month\n schedule_mon['Month Date'] = pd.to_datetime(schedule_mon[['year', 'month', 'day']])\n\n # Monthly principal amounts \n schedule_mon['Principal'] = schedule_mon['Payment'] - schedule_mon['Interest']\n \n schedule_mon.drop(['day', 'month', 'year'], axis=1, inplace=True)\n \n return schedule_mon","def display_categories(\n sortby: str = \"market_cap_desc\",\n limit: int = 15,\n export: str = \"\",\n sheet_name: Optional[str] = None,\n pie: bool = False,\n) -> None:\n fig = OpenBBFigure()\n\n df = gecko.get_top_crypto_categories(sortby)\n df_data = df\n if not df.empty:\n if pie or fig.is_image_export(export):\n df_data[f\"% relative to top {limit}\"] = (\n df_data[\"Market Cap\"] / df_data[\"Market Cap\"].sum()\n ) * 100\n stables_to_display = df_data[df_data[f\"% relative to top {limit}\"] >= 1]\n other_stables = df_data[df_data[f\"% relative to top {limit}\"] < 1]\n values_list = list(stables_to_display[f\"% relative to top {limit}\"].values)\n values_list.append(other_stables[f\"% relative to top {limit}\"].sum())\n labels_list = list(stables_to_display[\"Name\"].values)\n labels_list.append(\"Others\")\n\n fig = plot_pie_chart(\n labels=labels_list,\n values=values_list,\n title=f\"Market Cap distribution of top {limit} crypto categories\",\n )\n if not fig.is_image_export(export):\n fig.show()\n\n df = df.applymap(lambda x: lambda_long_number_format_with_type_check(x))\n print_rich_table(\n df,\n headers=list(df.columns),\n floatfmt=\".2f\",\n show_index=False,\n export=bool(export),\n limit=limit,\n )\n\n export_data(\n export,\n os.path.dirname(os.path.abspath(__file__)),\n \"cgcategories\",\n df_data,\n sheet_name,\n fig,\n )\n else:\n console.print(\"\\nUnable to retrieve data from CoinGecko.\\n\")","def monthly_usage(self, ending_date, ending_meter_reading):\r\n\t\tyyyy= int(self.initial_date[:4])\r\n\t\tmm= int(self.initial_date[4:6])\r\n\t\tdd= int(self.initial_date[6:])\r\n\t\tstarting_date_formatted = datetime.date(yyyy,mm,dd)\r\n\r\n\t\tyyyy= int(ending_date[:4])\r\n\t\tmm= int(ending_date[4:6])\r\n\t\tdd= int(ending_date[6:])\r\n\t\tending_date_formatted = datetime.date(yyyy,mm,dd)\r\n\r\n\r\n\t\tnumber_of_days = (ending_date_formatted - starting_date_formatted).days\r\n\t\tunits_consumed = self.total_units_consumed - ending_meter_reading\r\n\t\taverage_daily_usage_calculation = units_consumed/ number_of_days\r\n\t\taverage_daily_usage= format(average_daily_usage_calculation, \".2f\")\r\n\r\n\t\tstart= starting_date_formatted.strftime(\"%d,%B\")\r\n\t\tend= ending_date_formatted.strftime(\"%d,%B\")\r\n\t\ttoday = datetime.date.today().strftime(\"%d,%B,%Y\")\r\n\t\tprint(f\"\"\"\r\n-------------------------------------------\r\nEnergy Usage Report as at {today}.\r\n-------------------------------------------\r\nUsage report for days between {start}\r\nand {end}.\r\n\t\r\nAverage daily Usage = {average_daily_usage} kWh\r\nTotal units consumed = {format(units_consumed,\".2f\")} kWh\r\nTotal amount spent = KES{format(self.total_amount_spent, \",\")}\r\n\r\n-------------------------------------------\r\n\"\"\")","def testMonthlyPayrollTotals(self):\n self._setupMonthlyTotals()\n totals = self.rows[-1]\n\n work_total = Decimal('110.00')\n self.assertEquals(totals['work_total'], work_total)\n\n self.assertEquals(len(totals['billable']), 1 + 1)\n for entry in totals['billable']:\n self.assertEquals(entry['hours'], Decimal('90.00'))\n self.assertEquals(entry['percent'],\n Decimal('90.00') / work_total * 100)\n\n self.assertEquals(len(totals['nonbillable']), 1 + 1)\n for entry in totals['nonbillable']:\n self.assertEquals(entry['hours'], Decimal('20.00'))\n self.assertEquals(entry['percent'],\n Decimal('20.00') / work_total * 100)\n\n self.assertEquals(len(totals['leave']), 2 + 1)\n sick_index = self.labels['leave'].index(self.sick.name)\n vacation_index = self.labels['leave'].index(self.vacation.name)\n self.assertEquals(totals['leave'][sick_index]['hours'],\n Decimal('80.00'))\n self.assertEquals(totals['leave'][sick_index]['percent'],\n Decimal('80.00') / Decimal('120.00') * 100)\n self.assertEquals(totals['leave'][vacation_index]['hours'],\n Decimal('40.00'))\n self.assertEquals(totals['leave'][vacation_index]['percent'],\n Decimal('40.00') / Decimal('120.00') * 100)\n self.assertEquals(totals['leave'][-1]['hours'], Decimal('120.00'))\n self.assertEquals(totals['leave'][-1]['percent'], Decimal('100.00'))\n\n self.assertEquals(totals['grand_total'], Decimal('230.00'))","def deciles_chart(\n df,\n filename,\n period_column=None,\n column=None,\n title=\"\",\n ylabel=\"\",\n time_window=\"\",\n):\n\n max_practices = df.groupby(period_column).agg(\n {\"practice\": \"nunique\"}\n ).max().values[0]\n\n\n # remove any practices with value of 0 each month\n df = df.loc[df[\"value\"]>0, :]\n\n # monthly number of practices with column > 0\n practice_numbers = df.groupby(period_column).agg(\n {\"practice\": \"nunique\"}\n )\n \n practice_numbers = practice_numbers.apply(lambda x: round(x / 5) * 5)\n df = compute_deciles(df, period_column, column, has_outer_percentiles=False)\n \n # calculate monthyl proportion of practices with non zero values\n\n sns.set_style(\"whitegrid\", {\"grid.color\": \".9\"})\n\n fig, ax = plt.subplots(1, 1, figsize=(15, 8))\n\n linestyles = {\n \"decile\": {\n \"line\": \"b--\",\n \"linewidth\": 1,\n \"label\": \"Decile\",\n },\n \"median\": {\n \"line\": \"b-\",\n \"linewidth\": 1.5,\n \"label\": \"Median\",\n },\n \"percentile\": {\n \"line\": \"b:\",\n \"linewidth\": 0.8,\n \"label\": \"1st-9th, 91st-99th percentile\",\n },\n }\n label_seen = []\n for percentile in range(1, 100): # plot each decile line\n data = df[df[\"percentile\"] == percentile]\n add_label = False\n\n if percentile == 50:\n style = linestyles[\"median\"]\n add_label = True\n\n else:\n style = linestyles[\"decile\"]\n if \"decile\" not in label_seen:\n label_seen.append(\"decile\")\n add_label = True\n if add_label:\n label = style[\"label\"]\n else:\n label = \"_nolegend_\"\n\n ax.plot(\n data[period_column],\n data[column],\n style[\"line\"],\n linewidth=style[\"linewidth\"],\n label=label,\n )\n ax.set_ylabel(ylabel, size=15, alpha=0.6)\n if title:\n ax.set_title(title, size=14, wrap=True)\n # set ymax across all subplots as largest value across dataset\n\n ax.set_ylim(\n [0, 100 if df[column].isnull().values.all() else df[column].max() * 1.05]\n )\n ax.tick_params(labelsize=12)\n ax.set_xlim(\n [df[period_column].min(), df[period_column].max()]\n ) # set x axis range as full date range\n\n plt.setp(ax.xaxis.get_majorticklabels(), rotation=90)\n ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter(\"%B %Y\"))\n\n plt.xticks(sorted(df[period_column].unique()), rotation=90)\n\n plt.vlines(\n x=[pd.to_datetime(\"2020-03-01\")],\n ymin=0,\n ymax=100,\n colors=\"orange\",\n ls=\"--\",\n label=\"National Lockdown\",\n )\n\n if not time_window == \"\":\n plt.vlines(\n x=[pd.to_datetime(time_window)],\n ymin=0,\n ymax=100,\n colors=\"green\",\n ls=\"--\",\n label=\"Date of expected impact\",\n )\n\n ax.legend(\n bbox_to_anchor=(1.1, 0.8), # arbitrary location in axes\n # specified as (x0, y0, w, h)\n loc=CENTER_LEFT, # which part of the bounding box should\n # be placed at bbox_to_anchor\n ncol=1, # number of columns in the legend\n fontsize=20,\n borderaxespad=0.0,\n ) # padding between the axes and legend\n # specified in font-size units\n\n\n # plot proportions opn second y axis\n ax2 = ax.twinx()\n ax2.bar(practice_numbers.index, practice_numbers.practice, color=\"gray\", label=\"Proportion of practices with non-zero values\", width=20, alpha=0.3)\n # st y lim to 0- (proportions max rounded up to nearest 10)\n ax2.set_ylim(0, max_practices + 10 - (max_practices % 10))\n\n ax2.set_ylabel(\"Number of practices included\", size=15, alpha=0.6)\n\n plt.tight_layout()\n plt.savefig(filename)\n plt.clf()","def lookup_monthly(self, **kwargs):\n return self.lookup(period=self.PERIOD_MONTHLY, **kwargs)","def _get_month_order_per_sku(self):\n order_sku_month = self._order.copy()\n order_sku_month['order_month'] = order_sku_month.order_date.astype(str).apply(lambda x: x[:7])\n order_sku_month = order_sku_month.groupby(['item_code', 'order_month'])[['ord_qty']].sum()\n order_sku_month['ord_qty'] = order_sku_month.ord_qty.apply(lambda x: 0 if x < 0 else x)\n order_sku_month = order_sku_month.unstack(level=-1).fillna(0.0)\n order_sku_month.columns = pd.date_range(start='2015-09-30', periods=len(order_sku_month.columns), freq='M')\n return order_sku_month","def best_session_monthly(df):\n df = convert_to_datetime(df)\n today = datetime.date.today()\n this_month_start = today - timedelta(days=30)\n last_month_start = today - timedelta(days=60)\n this_month_card_ids = []\n lastmonth_card_ids = []\n for index, row in df.iterrows():\n try:\n if str(row['session_start'].date()) >= str(this_month_start):\n this_month_card_ids.append(row['id'])\n elif str(last_month_start) <= str(row['session_start'].date()) < str(this_month_start):\n lastmonth_card_ids.append(row['id'])\n except IndexError:\n this_month_card_ids = []\n lastmonth_card_ids = []\n thismonth = df[df['id'].isin(this_month_card_ids)]\n lastmonth = df[df['id'].isin(lastmonth_card_ids)]\n thismonth_best_session = best_session_length(thismonth)\n lastmonth_best_session = best_session_length(lastmonth)\n\n if thismonth_best_session > lastmonth_best_session:\n color_code = \"09B109\"\n arrow = \"\\u2191\"\n elif thismonth_best_session < lastmonth_best_session:\n color_code = \"CE2929\"\n arrow = \"\\u2193\"\n else:\n color_code = \"000000\"\n arrow = \"\\u003D\"\n\n if lastmonth_best_session > 0:\n difference = abs((thismonth_best_session - lastmonth_best_session) / lastmonth_best_session) * 100\n else:\n # if last month has no sessions, the difference is up 100%\n # if both best_sessions are zero, this will display '0 100% =' in black\n difference = 100\n result = make_results_dict(thismonth_best_session, difference, color_code, arrow)\n result['best_session_monthly'] = result.pop('metric')\n return result","def get(self, request, unit_id):\n start_date = get_start_date(request)\n end_date = get_end_date(request)\n months = {}\n while(start_date <= end_date):\n first_of_month = start_date.replace(day=1)\n last_of_month = start_date.replace(\n day=1) + relativedelta(months=1) - relativedelta(days=1)\n readings_sum = MeterReading.objects.filter(\n date__gte=first_of_month,\n date__lte=last_of_month,\n unit=unit_id,\n reading_type='ELECTRICITY'\n ).aggregate(Sum('usage'))\n months[start_date.strftime(\n \"%Y-%m-%d\")] = round(readings_sum['usage__sum']*.06, 0)\n start_date += relativedelta(months=1)\n return JsonResponse(months, safe=False)","def monthly_rose_fig(metdat, catinfo, category=None, vertloc=80, bins=6, nsector=36, ylim=None, noleg=False):\n \n # set up data\n dircol, _, _= utils.get_vertical_locations(catinfo['columns']['direction'], location=vertloc)\n varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc)\n plotdat = metdat.groupby(metdat.index.month)\n winddir = plotdat[dircol]\n var = plotdat[varcol]\n\n months = utils.monthnames()\n # wind speed bins to use in wind roses\n # get var divisions set up\n if isinstance(bins, int):\n nbins = bins\n else:\n nbins = len(bins)\n\n # set up plotting colors\n colors = utils.get_colors(nbins-1, basecolor='span')\n colors += ['#3A4246'] # add something dark to the end.\n colors = tuple(colors[0:nbins])\n\n fig = plt.figure(figsize=(9,13))\n\n for iax,month in enumerate(months):\n\n ax = fig.add_subplot(4,3,iax+1, projection=\"windrose\")\n\n ax.bar(winddir.get_group(iax+1), var.get_group(iax+1),\n bins=bins, nsector=36, colors=colors,\n linewidth=0.35,\n normed=True)\n\n # Set the tick labels font\n for label in (ax.get_xticklabels() + ax.get_yticklabels()):\n label.set_fontname('Arial')\n label.set_fontsize(12)\n ax.set_title(month,fontsize=12,y=1.15)\n\n if iax == 10:\n leg = plt.legend(loc=8, ncol=2, bbox_to_anchor = (0.5,-0.65), frameon=False)\n leg.set_title(catinfo['labels'][category])\n fig.text(0.5, -0.085, r'$z={}$ m'.format(vertloc), ha='center', va='center')\n\n axes = fig.get_children()[1:]\n # adjust plot for specified max frequency\n if ylim is None:\n ylim = 0.0\n for iax,month in enumerate(months):\n ylim = np.max([ylim, axes[iax].get_ylim()[-1]])\n \n for iax,month in enumerate(months):\n axes[iax].set_ylim(0,ylim)\n axes[iax].set_yticks(np.linspace(0.0,ylim,4))\n # print(axes[iax].get_yticks())\n axes[iax].set_yticklabels([str(np.round(x,decimals=1)) for x in axes[iax].get_yticks()])\n\n fig.tight_layout() \n\n return fig, axes, leg","def scrape(startyear, startmonth, endyear, endmonth):\n year = startyear\n month = startmonth\n while (not (year == endyear and month == endmonth)):\n ys = \"{}\".format(year)\n ms = \"{:02d}\".format(month)\n gather_all_profiles(ys,ms) \n if month == 12:\n year += 1\n month = 0\n month += 1","def get_currency_exchange_monthly(self, from_symbol, to_symbol, outputsize='compact'):\n _FUNCTION_KEY = 'FX_MONTHLY'\n return _FUNCTION_KEY, \"Time Series FX (Monthly)\", 'Meta Data'","def view_account(request, id):\n account = get_object_or_404(Account, pk=id, user=request.user)\n months, n = [], now()\n for o in reversed(range(0, -12, -1)):\n y, m = adjust_month(n.year, n.month, o)\n months.append({\n 'url': reverse('ledger:view_month', kwargs={\n 'year': y,\n 'month': m,\n }),\n 'year': y,\n 'month': m,\n 'amount': str(Transaction.month(y, m, account=account).sum()),\n })\n return render(request, 'ledger/pages/view_account.html', {\n 'title': account,\n 'account': account,\n 'months': dumps(months),\n })","def get_monthly_history_metric(func, site, date_for, months_back,\n include_current_in_history=True): # pylint: disable=unused-argument\n date_for = as_date(date_for)\n history = []\n\n for month in previous_months_iterator(month_for=date_for, months_back=months_back,):\n period = period_str(month)\n value = func(\n site=site,\n start_date=datetime.date(month[0], month[1], 1),\n end_date=datetime.date(month[0], month[1], month[2]),\n )\n history.append(dict(period=period, value=value,))\n\n if history:\n # use the last entry\n current_month = history[-1]['value']\n else:\n # This should work for float too since '0 == 0.0' resolves to True\n current_month = 0\n return dict(\n current_month=current_month,\n history=history,)","def __calculate_monthly_interest(self):\n return self.__percentage_interest / 12","def current_monthly_data(self):\n return self._current_monthly_data","def get_monthly_amount(sub):\n price = min(60, sub.price_paid) / Decimal('1.2') * Decimal('0.7')\n nb_month = max([(sub.date_over - sub.date_created).days//30, 1])\n return (price / nb_month).quantize(Decimal('1.00'))","def calculate_total_fees_collected(year=None, month=None):\n total = 0\n fees = load_fees()\n for fee in fees:\n if year and month:\n date = datetime.datetime.fromtimestamp(fee[\"created\"])\n if date.year == year and date.month == month:\n total += fee[\"amount\"]\n else:\n total += fee[\"amount\"]\n return total","def get_budgets(budg_path, exp_path, dates=None):\n exp_budg = data_help.read_jsonFile(budg_path)\n exp_data = data_help.read_jsonFile(exp_path)\n if dates == None:\n dates = [util.get_current_month()]\n for date in dates:\n exp_budg_keys = exp_budg.keys()\n if date not in exp_budg_keys: # check for current month to find exp categories\n print(\n f\"I have detected some data with for the month {date} that has no budget set.\")\n print(\n \"Please set the budget for this month.. or delete the data and run the program again.\")\n if len(exp_budg) != 0:\n user_in = util.get_user_input_for_chars(\n \"Would you like to the whole thing (w) or create new (n)? \", ['w', 'n'])\n\n if user_in == 'w':\n key = util.select_dict_key_using_integer(\n exp_budg, \"Please select a budget to copy: \", print_children=True, print_vals=False, \n print_child_vals=True)\n exp_budg[date] = exp_budg[key]\n elif user_in == 'n':\n exp_budg[date] = declare_new_budget(date, exp_data)\n else:\n exp_budg[date] = declare_new_budget(date, exp_data)\n\n print(f\"Your budget is now saved for {date}.\")\n\n else:\n print(f\"Your monthly budget for {date} is: \")\n\n util.print_simple_dict(exp_budg[date], print_vals=True)\n\n data_help.write_to_jsonFile(budg_path, exp_budg)\n return","def add_month(cab_data):\n return cab_data.assign(month=lambda x: x.time.dt.month)"],"string":"[\n \"def SumCostByMonthPerCategory(year, numberOfMonth, category):\\n\\n logs.logger.debug(\\n \\\"Start to adds all amount of Cost objects based on the month of payment date and on the category.\\\")\\n try:\\n num_days = calendar.monthrange(year, numberOfMonth)[1]\\n searchedCostByMonthFromDB = GetAllCostByDateOfPaymentBandFromDB(\\n date(year, numberOfMonth, 1), date(year, numberOfMonth, num_days))\\n sumTotal = 0\\n for item in searchedCostByMonthFromDB:\\n if item.category.value == category:\\n sumTotal += item.amount\\n logs.logger.info(\\n \\\"Based on the month of payment date and on the category adds all amount of Cost objects.\\\")\\n return sumTotal\\n except Exception as e:\\n logs.logger.error(e, exc_info=True)\",\n \"def monthly_sales(self):\\n last_thirty_days = timezone.now() - timedelta(days=30)\\n items = self.item_set.filter(status=\\\"sold\\\", updated_at__gte=last_thirty_days)\\n total_sales = 0\\n for item in items:\\n total_sales += item.price\\n return total_sales\",\n \"def view_month(request, year, month):\\n year, month = int(year), int(month)\\n transactions = Transaction.month(\\n year,\\n month,\\n user=request.user,\\n account__include_in_balance=True,\\n )\\n start = make_aware(datetime(year, month, 1))\\n balance = Transaction.objects.filter(\\n user=request.user,\\n account__include_in_balance=True,\\n date__lt=start,\\n ).sum()\\n return render(request, 'ledger/pages/view_month.html', {\\n 'title': start.strftime('%B %Y'),\\n 'transactions': transactions,\\n 'balance': balance,\\n 'prev_month': adjust_month(year, month, -1),\\n 'next_month': adjust_month(year, month, 1),\\n })\",\n \"def monthly_benefit(self):\\n \\\"\\\"\\\"Calculate weekly benefit of this company from this day\\\"\\\"\\\"\\n total_purchase_price = 0\\n total_selling_price = 0\\n last_thirty_days = timezone.now() - timedelta(days=30)\\n items = self.item_set.filter(status=\\\"sold\\\", updated_at__gte=last_thirty_days)\\n for item in items:\\n total_purchase_price += item.price\\n total_selling_price += item.selling_price\\n benefit = total_selling_price - total_purchase_price\\n return benefit\",\n \"def create_spend_chart(categories):\\n graph = \\\"Percentage spent by category\\\\n\\\"\\n total_spendings = 0\\n spendings = {}\\n for category in categories:\\n spendings[category.name] = 0\\n for x in category.ledger:\\n if x['amount'] < 0: #the withdraws are the ones with negative values\\n spendings[category.name] += x['amount']\\n spendings[category.name] = abs(spendings[category.name])\\n for amount in spendings:\\n total_spendings += spendings[amount]\\n for amount in spendings:\\n spendings[amount] = round_down(spendings[amount] / total_spendings * 100) #getting the percentage rounded down\\n\\n for i in range(100, -10, -10):\\n \\\"\\\"\\\"getting the main part of the graph\\\"\\\"\\\"\\n graph += str(i).rjust(3) + '| '\\n for category in categories:\\n if spendings[category.name] >= i:\\n graph += 'o '\\n else:\\n graph += ' '\\n graph += '\\\\n'\\n graph += ' ' + '-' * (1 + len(categories) * 3) + '\\\\n'\\n\\n maxlen = 0\\n for category in categories:\\n if len(category.name) > maxlen:\\n maxlen = len(category.name) # max string length between category names\\n for i in range(maxlen):\\n \\\"\\\"\\\"getting the labels for the x-axis\\\"\\\"\\\"\\n graph += ' '\\n for category in categories:\\n if len(category.name) > i:\\n graph += category.name[i] + ' '\\n else:\\n graph += ' '\\n graph += '\\\\n '\\n return graph[0:-1]\",\n \"def summary(ctx, month, year):\\n daze = ctx.obj['daze']\\n if not year:\\n year = date.today().year # defaults to this year\\n if month:\\n first = date(year, month, 1)\\n last = max([day for day in cal.itermonthdates(year, month) if day.month == month])\\n s = daze.summarize(firstdate=first, lastdate=last)\\n else:\\n s = daze.summarize()\\n # summarize values: placeDict, number of days, first date, last date\\n for (p, v) in s[0].items():\\n click.echo(\\\"%s: %d\\\" % (p, v))\\n\\n missing_days = ((s[3] - s[2]) - timedelta(days=s[1]-1)).days\\n\\n click.echo(\\\"\\\\nBetween %s and %s:\\\" % (s[2].isoformat(), s[3].isoformat()))\\n click.secho(\\\"%d total dates\\\" % s[1], bg='green', nl=(missing_days == 0))\\n if missing_days > 0:\\n click.secho(\\\"with %d missing days.\\\" % missing_days, bg='red')\",\n \"def monthly_profile(metdat, catinfo, category=None, basecolor='cycle'):\\n\\n if category is None:\\n print('not sure what to plot...')\\n pass\\n\\n months = utils.monthnames()\\n colors = utils.get_colors(len(months), basecolor=basecolor)\\n colnames, vertlocs, ind = utils.get_vertical_locations(catinfo['columns'][category])\\n \\n plotdat = metdat[colnames].groupby(metdat.index.month).mean()\\n\\n fig, ax = plt.subplots(figsize=(3.5,5), sharex=True, sharey=True)\\n for iax in range(len(months)):\\n ax.plot(plotdat.xs(iax+1), vertlocs, color=colors[iax])\\n\\n leg = ax.legend(months, loc=7, bbox_to_anchor=(1.75, 0.5), edgecolor='w')\\n ax.set_ylabel('Probe Height [m]')\\n ax.set_xlabel(catinfo['labels'][category])\\n\\n fig.tight_layout()\\n \\n return fig, ax\",\n \"def news_for_month(self):\\n\\n raise NotImplementedError\",\n \"async def fetch_monthly_data(self):\\n self._logger.info(\\\"Fetching monthly data\\\")\\n await self._client.select_customer(self.account_id, self.customer_id)\\n headers = {\\\"Content-Type\\\": \\\"application/json\\\"}\\n res = await self._client.http_request(MONTHLY_DATA_URL, \\\"get\\\", headers=headers)\\n text_res = await res.text()\\n # We can not use res.json() because the response header are not application/json\\n json_res = json.loads(text_res)\\n if not json_res.get('results'):\\n return\\n\\n for month_data in json_res.get('results', []):\\n month = month_data['courant']['dateDebutMois'][:-3]\\n self._current_monthly_data[month] = {}\\n if 'compare' in month_data:\\n self._compare_monthly_data[month] = {}\\n\\n for key, raw_key in MONTHLY_MAP:\\n self._current_monthly_data[month][key] = month_data['courant'][raw_key]\\n if 'compare' in month_data:\\n self._compare_monthly_data[month][key] = month_data['compare'][raw_key]\",\n \"def depart_arrive_stats_by_month(flights):\\n\\n return ...\",\n \"def _setupMonthlyTotals(self):\\n self.billable_project = factories.BillableProject()\\n self.nonbillable_project = factories.NonbillableProject()\\n self.all_logs(self.user, self.billable_project,\\n self.nonbillable_project)\\n self.all_logs(self.user2, self.billable_project,\\n self.nonbillable_project)\\n self.login_user(self.superuser)\\n self.response = self.client.get(self.url, self.args)\\n self.rows = self.response.context['monthly_totals']\\n self.labels = self.response.context['labels']\",\n \"def get_transactions_sum_data(month_objects, amount_type):\\n if (amount_type == 'expenses'):\\n chart_data = [\\n result['amount__sum'].quantize(D('0.01')).copy_abs()\\n if result['amount__sum'] != None\\n else 0\\n for result in \\n [month.transactions.filter(amount__lte=0).aggregate(Sum('amount')) for month in month_objects]\\n ]\\n elif (amount_type == 'incomes'):\\n chart_data = [\\n result['amount__sum'].quantize(D('0.01')).copy_abs() \\n if result['amount__sum'] != None\\n else 0\\n for result in \\n [month.transactions.filter(amount__gt=0).aggregate(Sum('amount')) for month in month_objects]\\n ]\\n \\n return chart_data\",\n \"def monthly_stability_profiles(metdat, catinfo, category=None, vertloc=80, basecolor='span'):\\n\\n if category is None:\\n print('not sure what to plot...')\\n pass\\n\\n stab, stabloc, ind = utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc)\\n\\n plotdat = metdat.groupby([metdat.index.month, stab])\\n colors = utils.get_colors(5,basecolor='span')\\n months = utils.monthnames()\\n stabconds = utils.get_stabconds()\\n\\n # extract vertical locations of data from variable names\\n _, vertlocs, ind = utils.get_vertical_locations(catinfo['columns'][category]) \\n\\n fig, ax = plt.subplots(4,3, figsize=(8,13), sharex=True, sharey=True)\\n for iax, month in enumerate(months):\\n \\n for ii, cond in enumerate(stabconds):\\n\\n pdat = plotdat[catinfo['columns'][category]].get_group((iax+1, cond)).mean()\\n ax.flatten()[iax].plot(pdat[ind], vertlocs, color=colors[ii])\\n\\n ax.flatten()[iax].set_title(month)\\n\\n fig.text(0,0.58, 'Probe Height [m]', ha='center', va='center', fontsize=14, rotation='vertical')\\n leg = fig.legend(stabconds, loc=9, bbox_to_anchor=(0.55, 0.12), frameon=False)\\n\\n fig.tight_layout()\\n fig.subplots_adjust(bottom=0.175)\\n fig.text(0.525,0.135, catinfo['labels'][category], ha='center', va='center', fontsize=14)\\n\\n return fig, ax\",\n \"def monthly_stacked_hist_by_stability(metdat, catinfo, category, vertloc=80):\\n\\n stabconds = utils.get_stabconds()\\n stabcol, _, _= utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc)\\n varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc)\\n colors = utils.get_colors(len(stabconds), basecolor='span')\\n months = utils.monthnames()\\n \\n plotdat = metdat.groupby([metdat.index.month, stabcol])\\n plotdat = plotdat[varcol]\\n\\n fig, ax = plt.subplots(4,3, figsize=(9,10), sharex=True, sharey=True)\\n\\n for iax, month in enumerate(months):\\n temp = pd.DataFrame({cond: plotdat.get_group((iax+1,cond)) for cond in stabconds})\\n temp.plot.hist(ax=ax.flatten()[iax],\\n stacked=True,\\n color=colors,\\n bins=35,\\n edgecolor='k',\\n legend=False,\\n # weights = np.ones(temp.dropna().shape) / np.prod(temp.shape), \\n density=True)\\n ax.flatten()[iax].set_title(month)\\n ax.flatten()[iax].set_ylabel('') \\n \\n # fig.legend(stabconds, loc=8, bbox_to_anchor=(0, -0.1), edgecolor='w')\\n fig.text(0,0.58, 'Frequency', ha='center', va='center', fontsize=14, rotation='vertical')\\n leg = fig.legend(stabconds, loc=9, bbox_to_anchor=(0.55, 0.15), frameon=False)\\n fig.tight_layout()\\n fig.subplots_adjust(bottom=0.21)\\n fig.text(0.5, 0.16, catinfo['labels'][category], ha='center', va='center', fontsize=14)\\n\\n return fig, ax#, leg\",\n \"def query_monthly_hits(cls,N=10):\\n bmkey = 'BookMonthlyHits'\\n bks = from_cache(bmkey)\\n if not bks:\\n dt = datetime.utcnow() - timedelta(days=30)\\n daus = SuiDau.all().filter('date >=',dt).order('-visits').fetch(N)\\n bids = [d.book for d in daus]\\n bks = map(lambda e:str(e.key().id()),SuiBook.get_by_id(bids))\\n to_cache(bmkey,bks)\\n return bks\",\n \"def test_monthly_report(self):\\n self.register_user()\\n result = self.login_user()\\n access_token = json.loads(result.data.decode())['access_token']\\n res = self.client().post('/expenses/', headers=dict(Authorization=\\\"Bearer \\\" + access_token),\\n data=self.expense)\\n self.assertEqual(res.status_code, 201)\\n rv = self.client().post('/expenses/', headers=dict(Authorization=\\\"Bearer \\\" + access_token), data=\\n {'name': 'soda', 'amount': 200, 'date_of_expense': '10-01-2021'})\\n self.assertEqual(rv.status_code, 201)\\n fetch = self.client().get('/expenses?name=soda', headers=dict(Authorization=\\\"Bearer \\\" + access_token))\\n result = json.loads(fetch.data)\\n\\n consolidated_total = 212.23\\n res = self.client().get('/monthly_report?month=01-2021', headers=dict(Authorization=\\\"Bearer \\\" + access_token))\\n self.assertEqual(res.status_code, 200)\\n results = json.loads(res.data)\\n self.assertEqual(results['consolidated_total'], consolidated_total)\",\n \"def compute_monthly_breakdown_stats(cc): # pragma: no cover\\n monthly_breakdown_stats = {}\\n monthly_breakdown_stats['timeframe'] = 'monthly_breakdown'\\n tot_commits = total_commits(cc)\\n monthly_breakdown_stats['total_commits'] = tot_commits\\n tot_suspicious = total_suspicious(cc)\\n monthly_breakdown_stats['total_suspicious'] = tot_suspicious\\n monthly_breakdown_stats['suspicious_to_total_ratio'] = ratio_calculator(\\n tot_suspicious, tot_commits)\\n tot_tbr = total_tbr(cc)\\n monthly_breakdown_stats['total_tbr'] = tot_tbr\\n tot_tbr_no_lgtm = tbr_no_lgtm(cc)\\n monthly_breakdown_stats['tbr_no_lgtm'] = tot_tbr_no_lgtm\\n monthly_breakdown_stats['no_tbr_lgtm_to_total_ratio'] = ratio_calculator(\\n tot_tbr_no_lgtm, tot_tbr)\\n tot_blank_tbr = blank_tbr(cc)\\n monthly_breakdown_stats['blank_tbrs'] = tot_blank_tbr\\n monthly_breakdown_stats['blank_tbr_to_total_ratio'] = ratio_calculator(\\n tot_blank_tbr, tot_tbr)\\n tot_no_review_url = no_review_url(cc)\\n monthly_breakdown_stats['no_review_url'] = tot_no_review_url\\n monthly_breakdown_stats['no_review_url_to_total_ratio'] = ratio_calculator(\\n tot_no_review_url, tot_commits)\\n return monthly_breakdown_stats\",\n \"def month():\\n \\n # get month entered by user - if no month entered default to current month\\n month = request.args.get(\\\"month\\\", datetime.now().strftime(\\\"%Y-%m\\\"))\\n \\n # get budget data for month as a dictionary\\n data = budget_data(month)\\n \\n return json.dumps(data)\",\n \"def collapse_monthly(self, **kwargs):\\n return self.collapse(periods=[self.PERIOD_MONTHLY], **kwargs)\",\n \"def test_03_dataverse_counts_by_month_unpublished(self):\\n print (self.test_03_dataverse_counts_by_month_unpublished.__doc__)\\n\\n stats_maker = StatsMakerDataverses()\\n\\n r = stats_maker.get_dataverse_counts_by_month_unpublished()\\n\\n # check number of months\\n self.assertEqual(len(r.result_data['records']), 16)\\n\\n # check 1st month\\n first_month = {'count': 13,\\n 'month_name': 'April',\\n 'month_name_short': 'Apr',\\n 'month_num': 4,\\n 'running_total': 13,\\n 'year_num': 2015,\\n 'yyyy_mm': '2015-04'}\\n self.assertEqual(dict(r.result_data['records'][0]), first_month)\\n\\n # check last month\\n last_month = {'count': 6,\\n 'month_name': 'July',\\n 'month_name_short': 'Jul',\\n 'month_num': 7,\\n 'running_total': 169,\\n 'year_num': 2016,\\n 'yyyy_mm': '2016-07'}\\n self.assertEqual(dict(r.result_data['records'][-1]), last_month)\",\n \"def _construct_monthly_climate(self, cl, now, end):\\n # how many months should we aggregate\\n if self.simulation:\\n if self.md.duration_unit=='month':\\n months = range(self.timestep_length)\\n else:\\n months = range(12 * self.timestep_length)\\n else:\\n # use the first year for steady state computation\\n months = range(12)\\n rain = 0.0\\n temp = 0.0\\n maxtemp = 0.0\\n mintemp = 0.0\\n maxind = len(self.md.monthly_climate) - 1\\n for m in months:\\n if self.curr_month_ind > maxind:\\n self.curr_month_ind = 0\\n mtemp = self.md.monthly_climate[self.curr_month_ind].temperature\\n temp += mtemp\\n if mtemp < mintemp:\\n mintemp = mtemp\\n if mtemp > maxtemp:\\n maxtemp = mtemp\\n # monthly rain converted into yearly rain\\n rain += 12 * self.md.monthly_climate[self.curr_month_ind].rainfall\\n self.curr_month_ind += 1\\n cl['rain'] = rain / len(months)\\n cl['temp'] = temp / len(months)\\n cl['amplitude'] = (maxtemp - mintemp) / 2.0\\n return cl\",\n \"def create_spend_chart(categories: list):\\n\\n BAR = \\\"o\\\"\\n TITLE = \\\"Percentage spent by category\\\"\\n \\n # Sum up the total withdrawn amount\\n withdrawals = {}\\n total_amount_withdrawn = 0\\n for category in categories:\\n amount = category.get_withdrawals()\\n withdrawals[category.name] = {\\\"amount\\\" : amount, \\\"percentage\\\" : 0}\\n total_amount_withdrawn += amount\\n \\n # Calculate the percentages\\n for category_name in withdrawals:\\n percentage = withdrawals[category_name][\\\"amount\\\"]/total_amount_withdrawn*100\\n # Why use floor() instead of int():\\n # https://stackoverflow.com/a/31195540\\n percentage = int(floor(percentage/10.)*10)\\n withdrawals[category_name][\\\"percentage\\\"] = percentage\\n\\n # Make the bars\\n percentages_lines = []\\n for percentage in range(100, -10, -10):\\n percentages_line = \\\"{:3}|\\\".format(percentage)\\n for category_name in withdrawals:\\n if withdrawals[category_name][\\\"percentage\\\"] >= percentage:\\n percentages_line += \\\" \\\" + BAR + \\\" \\\"\\n else:\\n percentages_line += \\\" \\\"\\n percentages_lines.append(percentages_line + \\\" \\\")\\n\\n # Make the horizontal line\\n horizontal_line = \\\" {}\\\".format(\\\"---\\\"*len(categories) + \\\"-\\\")\\n \\n # Make the names\\n bar_names_lines = []\\n # find the length of the longest name\\n max_name_len = max([len(name) for name in withdrawals])\\n for line_num in range(max_name_len):\\n bar_names_line = \\\" \\\"\\n for category_name in withdrawals:\\n if line_num < len(category_name):\\n bar_names_line += \\\" \\\" + category_name[line_num] + \\\" \\\"\\n else:\\n bar_names_line += \\\" \\\"\\n bar_names_lines.append(bar_names_line + \\\" \\\")\\n\\n chart_lines = [TITLE] + percentages_lines + [horizontal_line] + bar_names_lines\\n\\n chart_lines = \\\"\\\\n\\\".join(chart_lines)\\n\\n return chart_lines\",\n \"def monthly_hist(metdat, catinfo, category, vertloc=80, basecolor='blue'):\\n \\n colors = utils.get_nrelcolors()\\n color = colors[basecolor][0]\\n months = utils.monthnames()\\n \\n # set up data\\n varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc)\\n\\n temp = metdat.groupby(metdat.index.month)\\n temp = temp[varcol]\\n\\n binwidth = (metdat[varcol].dropna().max() - metdat[varcol].dropna().min())/35\\n bins = np.arange(metdat[varcol].dropna().min(),metdat[varcol].dropna().max(), binwidth)\\n\\n fig, ax = plt.subplots(4,3, figsize=(9,9), sharex=True, sharey=True)\\n \\n for im,month in enumerate(months):\\n data = temp.get_group(im+1).dropna()\\n ax.flatten()[im].hist(data, \\n bins=bins, \\n color=color, \\n edgecolor='k',\\n weights=np.ones(len(data))/len(data)*100)\\n ax.flatten()[im].set_title(month, fontsize=12)\\n \\n fig.tight_layout()\\n fig.text(0,0.5,'Frequency [%]',rotation='vertical', ha='center', va='center')\\n fig.text(0.5,0,catinfo['labels'][category], ha='center', va='center')\\n \\n return fig, ax\",\n \"def SumCostByMonth(year, numberOfMonth):\\n\\n logs.logger.debug(\\n \\\"Start to adds all amount of Cost objects based on a defined month.\\\")\\n try:\\n num_days = calendar.monthrange(year, numberOfMonth)[1]\\n searchedCostByMonthFromDB = GetAllCostByDateOfPaymentBandFromDB(\\n date(year, numberOfMonth, 1), date(year, numberOfMonth, num_days))\\n sumTotal = 0\\n for item in searchedCostByMonthFromDB:\\n sumTotal += item.amount\\n logs.logger.info(\\n \\\"Based on a defined month adds all amount of Cost objects.\\\")\\n return sumTotal\\n except Exception as e:\\n logs.logger.error(e, exc_info=True)\",\n \"def all_monthly_stats(cc, filename): # pragma: no cover\\n output = {}\\n stats_monthly_breakdown = compute_monthly_breakdown_stats(cc)\\n stats_by_time = compute_stats_by_time(cc)\\n stats_by_time.append(stats_monthly_breakdown)\\n for d in stats_by_time:\\n label = d['timeframe']\\n output[label] = d\\n with open(filename, 'w') as f:\\n json.dump(output, f)\",\n \"def get_spend_by_campaign_this_month(self, account_id):\\n try:\\n account = DependentAccount.objects.get(id=account_id)\\n except DependentAccount.DoesNotExist:\\n return\\n\\n client = get_client()\\n client.client_customer_id = account.dependent_account_id\\n\\n report_downloader = client.GetReportDownloader(version=settings.API_VERSION)\\n\\n campaign_report_selector = {\\n 'fields': ['Cost', 'CampaignId', 'CampaignStatus', 'CampaignName', 'Labels', 'Impressions'],\\n 'predicates': [\\n {\\n 'field': 'Cost',\\n 'operator': 'GREATER_THAN',\\n 'values': '0'\\n }\\n ]\\n }\\n\\n campaign_report_query = {\\n 'reportName': 'CAMPAIGN_PERFORMANCE_REPORT',\\n 'dateRangeType': 'THIS_MONTH',\\n 'reportType': 'CAMPAIGN_PERFORMANCE_REPORT',\\n 'downloadFormat': 'CSV',\\n 'selector': campaign_report_selector\\n }\\n\\n in_use_ids = []\\n\\n campaign_report = Reporting.parse_report_csv_new(report_downloader.DownloadReportAsString(campaign_report_query))\\n for campaign_row in campaign_report:\\n print(campaign_row)\\n campaign_id = campaign_row['campaign_id']\\n in_use_ids.append(campaign_row['campaign_id'])\\n campaign, created = Campaign.objects.get_or_create(campaign_id=campaign_id, account=account)\\n # Update campaign name\\n campaign.campaign_name = campaign_row['campaign']\\n # This is the cost for this month\\n cost = int(campaign_row['cost']) / 1000000\\n campaign.campaign_cost = cost\\n campaign.save()\\n print('Campaign: ' + str(campaign) + ' now has a spend this month of $' + str(campaign.campaign_cost))\\n\\n today = datetime.datetime.today()\\n\\n if today.day != 1:\\n yesterday = datetime.datetime.now() - datetime.timedelta(1)\\n first_day_of_month = datetime.datetime(yesterday.year, yesterday.month, 1)\\n\\n campaign_report_selector = {\\n 'fields': ['Cost', 'CampaignId', 'CampaignStatus', 'CampaignName', 'Labels', 'Impressions'],\\n 'predicates': [\\n {\\n 'field': 'Cost',\\n 'operator': 'GREATER_THAN',\\n 'values': '0'\\n }\\n ]\\n }\\n\\n campaign_report_query = {\\n 'reportName': 'CAMPAIGN_PERFORMANCE_REPORT',\\n 'dateRangeType': 'CUSTOM_DATE',\\n 'reportType': 'CAMPAIGN_PERFORMANCE_REPORT',\\n 'downloadFormat': 'CSV',\\n 'selector': campaign_report_selector\\n }\\n\\n start_date = first_day_of_month\\n end_date = yesterday\\n\\n campaign_report_selector['dateRange'] = {\\n 'min': start_date.strftime('%Y%m%d'),\\n 'max': end_date.strftime('%Y%m%d')\\n }\\n\\n campaign_yest_report = Reporting.parse_report_csv_new(\\n report_downloader.DownloadReportAsString(campaign_report_query))\\n for campaign_row in campaign_yest_report:\\n campaign_id = campaign_row['campaign_id']\\n campaign, created = Campaign.objects.get_or_create(campaign_id=campaign_id, account=account)\\n campaign.campaign_name = campaign_row['campaign']\\n # This is the cost for this month until yesterday\\n spend_until_yesterday = int(campaign_row['cost']) / 1000000\\n campaign.spend_until_yesterday = spend_until_yesterday\\n campaign.save()\\n print(\\n 'Campaign: ' + str(campaign) + ' has spend until yesterday of $' + str(campaign.spend_until_yesterday))\\n\\n return 'get_spend_by_campaign_this_month'\",\n \"def monthly_viewed(df):\\n df = convert_to_datetime(df)\\n today = datetime.date.today()\\n this_month_start = today - timedelta(days=30)\\n last_month_start = today - timedelta(days=60)\\n month_per_min = []\\n lastmonth_per_min = []\\n thismonth_viewed = []\\n lastmonth_viewed = []\\n for index, row in df.iterrows():\\n if row['session_start'].date() >= this_month_start:\\n per_min = get_cards_per_min(row)\\n month_per_min.append(per_min)\\n thismonth_viewed.append(row['total_looked_at'])\\n if last_month_start <= row['session_start'].date() < this_month_start:\\n per_min = get_cards_per_min(row)\\n lastmonth_per_min.append(per_min)\\n lastmonth_viewed.append(row['total_looked_at'])\\n month_viewed_result = total_viewed(thismonth_viewed, lastmonth_viewed)\\n month_viewed_result['total_viewed_monthly'] = month_viewed_result.pop('total_viewed')\\n return month_viewed_result\",\n \"def _get_month_inv_per_cus_cate2(self):\\n inv_cus_cate2_month = self._inv_cus_sku_month.reset_index()\\n inv_cus_cate2_month['second_cate_id'] = self._sku_info_encoded.second_cate_id.values\\n inv_cus_cate2_month_index = inv_cus_cate2_month[['customer_code', 'second_cate_id']]\\n inv_cus_cate2_month = inv_cus_cate2_month.groupby(\\n ['customer_code', 'second_cate_id'])[self._inv_cus_sku_month.columns].sum()\\n inv_cus_cate2_month = inv_cus_cate2_month.reindex(inv_cus_cate2_month_index)\\n return inv_cus_cate2_month\",\n \"def organization_monthly_revenue_customers(organization, from_date=None):\\n account = 'Income'\\n customers, incomes = aggregate_monthly(organization, account, from_date)\\n churned_custs, total_custs, new_custs = customers\\n churned_income, total_income, new_income = incomes\\n net_new_custs = []\\n cust_churn_percent = []\\n last_nb_total_custs = 0\\n for index in range(0, 12):\\n period, nb_total_custs = total_custs[index]\\n period, nb_new_custs = new_custs[index]\\n period, nb_churned_custs = churned_custs[index]\\n net_new_custs += [ (period, nb_new_custs - nb_churned_custs) ]\\n if last_nb_total_custs:\\n cust_churn_percent += [ (\\n period, nb_churned_custs * 100.0 / last_nb_total_custs) ]\\n else:\\n cust_churn_percent += [ (period, 0) ]\\n last_nb_total_custs = nb_total_custs\\n table = [ { \\\"key\\\": \\\"Total %s\\\" % account,\\n \\\"values\\\": total_income\\n },\\n { \\\"key\\\": \\\"%s from new Customers\\\" % account,\\n \\\"values\\\": new_income\\n },\\n { \\\"key\\\": \\\"%s from churned Customers\\\" % account,\\n \\\"values\\\": churned_income\\n },\\n { \\\"key\\\": \\\"Total # of Customers\\\",\\n \\\"values\\\": total_custs\\n },\\n { \\\"key\\\": \\\"# of new Customers\\\",\\n \\\"values\\\": new_custs\\n },\\n { \\\"key\\\": \\\"# of churned Customers\\\",\\n \\\"values\\\": churned_custs\\n },\\n { \\\"key\\\": \\\"Net New Customers\\\",\\n \\\"values\\\": net_new_custs\\n },\\n { \\\"key\\\": \\\"% Customer Churn\\\",\\n \\\"values\\\": cust_churn_percent\\n },\\n ]\\n return table\",\n \"def month_report_handle(fans_type):\\n\\tstart = datetime.date.today() - timedelta(days=datetime.date.today().day - 1)\\n\\ttoday = datetime.date.today()\\n\\t#import pdb;pdb.set_trace()\\n\\t#fans_list = [\\\"wwwttshow\\\", \\\"ttshowpet\\\", \\\"draw.fans\\\", \\\"TTShowMusic\\\", \\\"GoodNews.FANS\\\"]\\n\\tfans_pages = FansPage.objects.filter(fans_type=fans_type, date__gte=start, date__lte=today).order_by(\\\"date\\\")\\n\\n\\tstart = fans_pages[0]\\n\\tlast = fans_pages[len(fans_pages) - 1]\\n\\n\\ttalk_about_is = (last.talk_about_is - start.talk_about_is)\\n\\ttotal_like_count = (last.total_like_count - start.total_like_count)\\n\\ttotal_fans = (last.total_fans - start.total_fans)\\n\\treturn {\\\"talk_about_is\\\":talk_about_is, \\\"total_like_count\\\":total_like_count, \\\"total_fans\\\":total_fans, \\\"start\\\":start.date, \\\"last\\\":last.date}\",\n \"def _get_month_inv_per_cate2(self):\\n inv_cate2_month = self._inv_cus_sku_month.reset_index()\\n inv_cate2_month['second_cate_id'] = self._sku_info_encoded.second_cate_id.values\\n inv_cate2_month_index = inv_cate2_month['second_cate_id']\\n inv_cate2_month = inv_cate2_month.groupby(['second_cate_id'])[self._inv_cus_sku_month.columns].sum()\\n inv_cate2_month = inv_cate2_month.reindex(inv_cate2_month_index)\\n return inv_cate2_month\",\n \"def create_monthly_miles_chart(monthly_sum_df, s3_resource_bucket):\\n month_plot_df = monthly_sum_df.tail(12).reset_index()\\n\\n fig, ax = plt.subplots(1, 1, figsize=(8, 5))\\n plt.xticks(rotation=45)\\n title = ax.set_title('Miles Per Month (past year)', pad=20)\\n title.set_weight('bold')\\n title.set_size(16)\\n ax.set_ylabel('Miles')\\n ax.bar(month_plot_df['yr_month'], month_plot_df['miles_sum'])\\n ax.plot(month_plot_df['yr_month'], month_plot_df['3mo_rolling'], color='green')\\n ax.legend(['3 month rolling avg'])\\n ax.spines['right'].set_visible(False)\\n ax.spines['top'].set_visible(False)\\n for i in range(len(month_plot_df['yr_month'])):\\n plt.text(i,\\n month_plot_df['miles_sum'][i]+0.8,\\n round(month_plot_df['miles_sum'][i]), ha='center')\\n fig.savefig('monthly_miles.png')\\n s3_resource_bucket.upload_file('monthly_miles.png', 'monthly_miles.png',\\n ExtraArgs={'ContentType': 'image/png'})\\n # remove local file\\n os.remove('monthly_miles.png')\",\n \"def climatology_monthly(da, climatology_slice=None, time_dim='time'):\\n if climatology_slice is None:\\n clim = da.groupby(time_dim+'.month').mean()\\n else:\\n clim = da.sel({time_dim: climatology_slice}).groupby(time_dim+'.month').mean()\\n return clim\",\n \"def last_n_months(self, month_num=1):\\n return [\\n timezone.now() - datetime.timedelta(days=month_num*30),\\n timezone.now()\\n ]\",\n \"def get_monthly(self,start=_first_ym):\\n return .01*self.Xm.loc[self.Xm.index>=start]\",\n \"def test_get_occurrences_monthly_mid_month(self):\\n print()\\n print(\\\"Get occurrences of a monthly expense between:\\\")\\n expense = BudgetExpense.objects.get(id = 100)\\n start_date = expense.start_date\\n end_date = start_date + timedelta(days = 40)\\n print(start_date.strftime(\\\"%B %d, %y\\\")+\\\" and \\\"+end_date.strftime('%B %d, %y'))\\n print(\\\"======================================\\\")\\n result = get_anticipated_transaction_occurences(anticipated_transaction= expense, start_date = start_date, end_date = end_date)\\n result_dates = []\\n for current_expense in result.keys():\\n print(current_expense)\\n print(\\\"========================\\\")\\n result_dates.extend(result.get(current_expense))\\n for current_date in result_dates:\\n print(\\\"Date: \\\"+current_date.strftime(\\\"%B %d, %y %T\\\"))\\n print(\\\"======================\\\")\\n print()\\n date_1 = start_date\\n days_in_month = monthrange(start_date.year, start_date.month)[1]\\n date_2 = start_date + timedelta(days = days_in_month)\\n \\n self.assertEquals([date_1, date_2], result_dates)\",\n \"def calc_monthly_cash(self):\\n # shortcut to self\\n s = self\\n\\n # Start the DataFrames, base and w/ heat pump\\n # Each starts with just an index column with the month\\n # Make shortcut variables as well.\\n s.df_mo_dol_base = dfb = s.df_mo_en_base[[]].copy()\\n s.df_mo_dol_hp = dfh = s.df_mo_en_base[[]].copy()\\n\\n # Determine the base electric use by month. Approach is different \\n # if there is electric heat.\\n is_electric_heat = (s.exist_heat_fuel_id == constants.ELECTRIC_ID)\\n if not is_electric_heat:\\n # Fuel-based space heat.\\n # The User supplied a January and a May kWh usage value that should\\n # be used for the base case (no heat pump) total electricity use.\\n # But, need to come up with a kWh value for every month. Do that by\\n # adjusting the kWh pattern available for this city.\\n #\\n # Determine the multiplier to adjust to the pattern to the actual.\\n pat_use = np.array(s.city.avg_elec_usage)\\n mult = (s.elec_use_jan - s.elec_use_may) / (pat_use[0] - pat_use[4])\\n pat_use = mult * pat_use\\n pat_use += s.elec_use_jan - pat_use[0]\\n\\n # The electricity use in the base case\\n dfb['elec_kwh'] = pat_use\\n\\n # rough estimate of a base demand: not super critical, as the demand rate \\n # structure does not have blocks. Assume a load factor of 0.4\\n dfb['elec_kw'] = dfb.elec_kwh / (DAYS_IN_MONTH * 24.0) / 0.4\\n\\n else:\\n # Electric Heat Case\\n # No Jan and May values are provided. Instead we have possibly some\\n # DHW, clothes drying, and cooking. Plus, we have base lights/other appliances.\\n # And finally we have the Elecric heat making up the base electric usage.\\n\\n # First, DHW, Clothes Drying and Cooking. Assume flat use through year.\\n # This is a numpy array because DAYS_IN_MONTH is an array.\\n elec_kwh = s.fuel_other_uses / 8760.0 * DAYS_IN_MONTH * 24.0\\n\\n # Now lights and other misc. appliances. Some monthly variation, given\\n # by LIGHTS_OTHER_PAT.\\n elec_kwh += s.lights_other_elec / 8760.0 * LIGHTS_OTHER_PAT * DAYS_IN_MONTH * 24.0\\n\\n # For the peak demand of those two categories of use, just assume 40% load factor.\\n elec_kw = elec_kwh / (DAYS_IN_MONTH * 24.0) / 0.4\\n\\n # Now add in space heating kWh and kW\\n elec_kwh += s.df_mo_en_base.total_kwh.values\\n elec_kw += s.df_mo_en_base.total_kw.values\\n\\n # store results\\n dfb['elec_kwh'] = elec_kwh\\n dfb['elec_kw'] = elec_kw\\n\\n # Make an object to calculate electric utility costs\\n elec_cost_calc = ElecCostCalc(s.utility, sales_tax=s.sales_tax, pce_limit=s.pce_limit)\\n # cost function that will be applied to each row of the cost DataFrame\\n cost_func = lambda r: elec_cost_calc.monthly_cost(r.elec_kwh, r.elec_kw)\\n\\n dfb['elec_dol'] = dfb.apply(cost_func, axis=1)\\n\\n if not is_electric_heat:\\n # Now fuel use by month. Remember that the home heat model only looked at\\n # space heating, so we need to add in the fuel use from the other end uses\\n # that use this fuel.\\n dfb['secondary_fuel_units'] = s.df_mo_en_base.secondary_fuel_units + \\\\\\n s.fuel_other_uses / 12.0\\n dfb['secondary_fuel_dol'] = dfb.secondary_fuel_units * s.exist_unit_fuel_cost * (1. + s.sales_tax)\\n else:\\n # Electric Heat, so no secondary fuel\\n dfb['secondary_fuel_units'] = 0.0\\n dfb['secondary_fuel_dol'] = 0.0\\n\\n # Total Electric + space heat\\n dfb['total_dol'] = dfb.elec_dol + dfb.secondary_fuel_dol\\n\\n # Now with the heat pump\\n # determine extra kWh used in the heat pump scenario. Note, this will\\n # be negative numbers if the base case used electric heat.\\n extra_kwh = (s.df_mo_en_hp.total_kwh - s.df_mo_en_base.total_kwh).values\\n dfh['elec_kwh'] = dfb['elec_kwh'] + extra_kwh\\n extra_kw = (s.df_mo_en_hp.total_kw - s.df_mo_en_base.total_kw).values\\n dfh['elec_kw'] = dfb['elec_kw'] + extra_kw\\n dfh['elec_dol'] = dfh.apply(cost_func, axis=1)\\n\\n # Now fuel, including other end uses using the heating fuel\\n if not is_electric_heat:\\n dfh['secondary_fuel_units'] = s.df_mo_en_hp.secondary_fuel_units + \\\\\\n s.fuel_other_uses / 12.0\\n dfh['secondary_fuel_dol'] = dfh.secondary_fuel_units * s.exist_unit_fuel_cost * (1. + s.sales_tax)\\n else:\\n # Electric Heat, so no secondary fuel\\n dfh['secondary_fuel_units'] = 0.0\\n dfh['secondary_fuel_dol'] = 0.0\\n\\n # Total Electric + space heat\\n dfh['total_dol'] = dfh.elec_dol + dfh.secondary_fuel_dol\",\n \"def monthly(self):\\r\\n return RecordsMonthly(self)\",\n \"def test_04_dataverse_counts_by_month_all(self):\\n print (self.test_04_dataverse_counts_by_month_all.__doc__)\\n\\n stats_maker = StatsMakerDataverses()\\n\\n r = stats_maker.get_dataverse_counts_by_month()\\n\\n # check number of months\\n self.assertEqual(len(r.result_data['records']), 16)\\n\\n # check 1st month\\n first_month = {'count': 39,\\n 'month_name': 'April',\\n 'month_name_short': 'Apr',\\n 'month_num': 4,\\n 'running_total': 39,\\n 'year_num': 2015,\\n 'yyyy_mm': '2015-04'}\\n self.assertEqual(dict(r.result_data['records'][0]), first_month)\\n\\n # check last month\\n last_month = {'count': 10,\\n 'month_name': 'July',\\n 'month_name_short': 'Jul',\\n 'month_num': 7,\\n 'running_total': 356,\\n 'year_num': 2016,\\n 'yyyy_mm': '2016-07'}\\n self.assertEqual(dict(r.result_data['records'][-1]), last_month)\",\n \"def test_02_dataverse_counts_by_month_published(self):\\n print (self.test_02_dataverse_counts_by_month_published.__doc__)\\n\\n kwargs=dict(selected_year=2016)\\n stats_maker = StatsMakerDataverses(**kwargs)\\n\\n r = stats_maker.get_dataverse_counts_by_month_published()\\n\\n # check number of months\\n self.assertEqual(len(r.result_data['records']), 7)\\n\\n # check 1st month\\n first_month = {'count': 5,\\n 'month_name': 'January',\\n 'month_name_short': 'Jan',\\n 'month_num': 1,\\n 'running_total': 131,\\n 'year_num': 2016,\\n 'yyyy_mm': '2016-01'}\\n self.assertEqual(dict(r.result_data['records'][0]), first_month)\\n\\n # check last month\\n last_month = {'count': 4,\\n 'month_name': 'July',\\n 'month_name_short': 'Jul',\\n 'month_num': 7,\\n 'running_total': 187,\\n 'year_num': 2016,\\n 'yyyy_mm': '2016-07'}\\n self.assertEqual(dict(r.result_data['records'][-1]), last_month)\",\n \"def contract_data_chart(request):\\n current_date = datetime.today()\\n months = [i for i in range(1, 13)]\\n data = {\\n 'series': [],\\n 'labels': settings.CHART_MONTHS_LABELS,\\n }\\n\\n contract_count = []\\n for month in months:\\n contract_count.append(\\n Contract.objects.filter(\\n start_date__month=month,\\n start_date__year=current_date.year,\\n ).values('id').count()\\n )\\n data['series'].append({\\n \\\"name\\\": _(\\\"Contract\\\"),\\n \\\"data\\\": contract_count,\\n })\\n\\n return JsonResponse(data)\",\n \"def monthly_fee(self):\\n total_salary = self.base_salary\\n if self.is_salary_allowances is True:\\n fixed_allowances = self.summarize( self.fixed_allowances )\\n non_fixed_allowances = self.summarize( self.non_fixed_allowances )\\n total_salary = total_salary + non_fixed_allowances + fixed_allowances\\n #end if\\n\\n company_old_age_insurance = 0\\n individual_old_age_insurance = 0\\n if self.old_age_insurance_status is True:\\n company_old_age_insurance = \\\\\\n self._company_old_age_insurance(total_salary)\\n\\n individual_old_age_insurance = \\\\\\n self._individual_old_age_insurance(total_salary)\\n #end if\\n\\n company_pension_insurance = 0\\n individual_pension_insurance = 0\\n if self.pension_insurance_status is True:\\n company_pension_insurance = \\\\\\n self._company_pension_insurance(total_salary)\\n\\n individual_pension_insurance = \\\\\\n self._individual_pension_insurance(total_salary)\\n #end if\\n\\n company_health_insurance = 0\\n individual_health_insurance = 0\\n if self.health_insurance_status is True:\\n company_health_insurance = \\\\\\n self._company_health_insurance(total_salary)\\n\\n individual_health_insurance = \\\\\\n self._individual_health_insurance(total_salary)\\n #end if\\n\\n death_insurance = 0\\n if self.death_insurance_status is True:\\n death_insurance = self._death_insurance(total_salary)\\n #end if\\n\\n accident_insurance = 0\\n if self.accident_insurance_status is True:\\n accident_insurance = \\\\\\n self._accident_insurance(total_salary, \\\\\\n self.industry_risk_rate)\\n #end if\\n\\n monthly = {\\n \\\"old_age_insurance\\\" : {\\n \\\"company\\\" : company_old_age_insurance,\\n \\\"individual\\\" : individual_old_age_insurance,\\n },\\n \\\"pension_insurance\\\" : {\\n \\\"company\\\" : company_pension_insurance,\\n \\\"individual\\\" : individual_pension_insurance,\\n },\\n \\\"health_insurance\\\" : {\\n \\\"company\\\" : company_health_insurance,\\n \\\"individual\\\" : individual_health_insurance,\\n },\\n \\\"death_insurance\\\" : death_insurance,\\n \\\"accident_insurance\\\" : accident_insurance\\n }\\n return monthly\",\n \"def month_summary(phenology_df, out_csv=None):\\n month_list = ['Jul','Aug','Sep','Oct','Nov','Dec','Jan','Feb','Mar','Apr',\\n 'May','Jun']\\n species_list = phenology_df['species'].unique().tolist()\\n\\n count_list = []\\n for species in species_list:\\n count_dict = {'species':species}\\n for month in month_list:\\n month_check = phenology_df['monthStart'] == month\\n species_check = phenology_df['species'] == species\\n occurrence_count = phenology_df[month_check & species_check]['numSpecimen'].sum()\\n count_dict[month] = occurrence_count\\n count_list.append(count_dict)\\n\\n month_df = pd.DataFrame(count_list)\\n month_df = month_df.set_index('species')\\n month_df = month_df[month_list]\\n\\n return month_df\",\n \"def monthlyhourlyplot(metdat, catinfo, category=None, basecolor='span'):\\n\\n if category is None:\\n print('not sure what to plot...')\\n pass\\n\\n months = utils.monthnames()\\n colors = utils.get_colors(len(catinfo['columns'][category]), basecolor=basecolor, reverse=True)\\n colnames, vertlocs, ind = utils.get_vertical_locations(catinfo['columns'][category], reverse=True)\\n \\n plotdat = metdat[colnames].groupby([metdat.index.month.rename('month'), metdat.index.hour.rename('hour')]).mean()\\n \\n fig, ax = plt.subplots(4,3, figsize=(9,11), sharex=True, sharey=True)\\n for iax in range(len(months)):\\n for catitem in range(len(colnames)):\\n ax.flatten()[iax].plot(plotdat[colnames[catitem]].xs(iax+1), color=colors[catitem])\\n ax.flatten()[iax].set_title(months[iax], fontsize=12)\\n \\n \\n fig.text(0.5,0.2, 'Time of Day [hour]', ha='center', va='center')\\n leg = fig.legend([str(v) + ' m' for v in vertlocs], loc = 'upper center', bbox_to_anchor = (0,-0.825,1,1), bbox_transform = plt.gcf().transFigure, frameon=False, ncol=2)\\n fig.tight_layout()\\n fig.subplots_adjust(bottom=0.25)\\n fig.text(0,0.6125, catinfo['labels'][category], ha='center', va='center', rotation='vertical')\\n\\n return fig, ax\",\n \"def count_risk_categories_by_month(data, month, year):\\n return count_risk_categories(\\n filter_inspection_type(\\n filter_month(data, month, year), 'Routine - Unscheduled'))\",\n \"def create_spend_chart(category_list:list) -> str:\\n\\n header = 'Percentage spent by category\\\\n'\\n table = create_table(category_list)\\n divider = ' ' + '-'*len(category_list*3) + '-'\\n footer = create_footer(category_list)\\n full_chart = header + table + divider + footer\\n\\n return full_chart[:-1]\",\n \"def sea_ice_statistics_monthly(configfile):\\n _sea_ice_statistics_monthly(configfile)\",\n \"def mortgage_detail_summary(self):\\n monthly = self.calculate_loan()\\n report_dic = {'Year': [], 'Balance': [], 'Payments': []}\\n years = []\\n balance = []\\n payments = []\\n # loop through the year to get the balance and add to list\\n for x in range(1, self.year + 1):\\n mon = x * 12\\n rem = self.get_balance(mon)\\n years.append(x)\\n balance.append(float(rem))\\n payments.append(float(monthly * mon))\\n # add to the dictionary\\n report_dic['Year'] = years\\n report_dic['Balance'] = balance\\n report_dic['Payments'] = payments\\n\\n return report_dic\",\n \"def _get_month_inv_per_cus_cate1(self):\\n inv_cus_cate1_month = self._inv_cus_sku_month.reset_index()\\n inv_cus_cate1_month['first_cate_id'] = self._sku_info_encoded.first_cate_id.values\\n inv_cus_cate1_month_index = inv_cus_cate1_month[['customer_code', 'first_cate_id']]\\n inv_cus_cate1_month = inv_cus_cate1_month.groupby(\\n ['customer_code', 'first_cate_id'])[self._inv_cus_sku_month.columns].sum()\\n inv_cus_cate1_month = inv_cus_cate1_month.reindex(inv_cus_cate1_month_index)\\n return inv_cus_cate1_month\",\n \"def _get_month_inv_per_cus_sku(self):\\n tmp = self._inv.copy()\\n tmp['order_month'] = tmp.order_date.astype(str).apply(lambda x: x[:7])\\n tmp = tmp.groupby(['customer_code', 'item_code', 'order_month'])[['inv_qty']].sum()\\n tmp['inv_qty'] = tmp.inv_qty.apply(lambda x: x if x > 0 else 0)\\n inv_cus_sku_month = tmp.unstack(level=-1).fillna(0)\\n inv_cus_sku_month.columns = pd.date_range(start='2017-04-30', periods=len(inv_cus_sku_month.columns), freq='M')\\n return inv_cus_sku_month\",\n \"def _get_month_inv_per_sku(self):\\n inv_sku_month = self._inv_cus_sku_month.groupby(['item_code'])[self._inv_cus_sku_month.columns].sum()\\n inv_sku_month = inv_sku_month.reindex(self._index.get_level_values(1))\\n return inv_sku_month\",\n \"def sort_and_aggregate_periods(budget_model):\\n # Calculate total number of months.\\n periods = budget_model[\\\"periods\\\"] \\n total_number_of_months = len(periods) \\n budget_model[\\\"total_number_of_months\\\"] = total_number_of_months\\n\\n # Get the reference to the total revenue in the budget model.\\n total_revenue = budget_model[\\\"total_revenue\\\"]\\n\\n # Initialize variables used to calculate greatest increase in revenue.\\n greatest_increase_revenue = 0\\n greatest_increase_name = \\\"\\\"\\n\\n # Initialize variables used to calculate greatest decrease in revenue.\\n greatest_decrease_revenue = 0\\n greatest_decrease_name = \\\"\\\"\\n\\n # Retrieve sort keys for budget model and sort them into chronological order.\\n period_keys = periods.keys()\\n period_key_list = list(period_keys)\\n period_key_list.sort()\\n\\n # Initialize previous revenue.\\n # There is no revenue change for the first period.\\n previous_revenue = periods[period_key_list[0]][\\\"revenue\\\"]\\n total_revenue_change = 0\\n\\n # Calculate aggregations by processing periods in chronological order.\\n for period_key in period_key_list:\\n period = periods[period_key]\\n total_revenue = total_revenue + period[\\\"revenue\\\"]\\n\\n budget_model[\\\"total_revenue\\\"] = total_revenue\\n\\n revenue = period[\\\"revenue\\\"]\\n revenue_change = revenue - previous_revenue\\n total_revenue_change = total_revenue_change + revenue_change\\n \\n if revenue_change > greatest_increase_revenue:\\n greatest_increase_revenue = revenue_change\\n greatest_increase_name = period[\\\"name\\\"]\\n\\n if revenue_change < greatest_decrease_revenue:\\n greatest_decrease_revenue = revenue_change\\n greatest_decrease_name = period[\\\"name\\\"]\\n\\n previous_revenue = revenue\\n\\n # Write aggregations to the budget model.\\n budget_model[\\\"greatest_increase\\\"] = {\\\"name\\\": greatest_increase_name, \\\"revenue\\\": greatest_increase_revenue}\\n budget_model[\\\"greatest_decrease\\\"] = {\\\"name\\\": greatest_decrease_name, \\\"revenue\\\": greatest_decrease_revenue}\\n budget_model[\\\"average_revenue_change\\\"] = round(total_revenue_change / total_number_of_months, 0)\",\n \"def create_monthly_statistics(fromDate, toDate, siteId):\\n logger = logging.getLogger(__name__)\\n logger.error(\\\"create monthly stat from {} to {}\\\".format(fromDate, toDate))\\n fromDate = fromDate.replace(hour=0, minute=0, second=0, day=1)\\n if (toDate.month < 12):\\n toDate = toDate.replace(month=toDate.month + 1, day=1, hour=0, minute=0, second=0)\\n else:\\n toDate = toDate.replace(year=toDate.year + 1, month=1, day=1, hour=0, minute=0, second=0)\\n f = fromDate\\n while f < toDate:\\n rawDataSet = DailyStatistics.objects.filter(year=f.year, month=f.month).filter(siteId=siteId)\\n if rawDataSet.count():\\n d, created = MonthlyStatistics.objects.update_or_create(siteId=siteId, month=f.month, year=f.year)\\n d.dataAvailable = rawDataSet.count()\\n tempmins = []\\n tempmaxs = []\\n tempavgs = []\\n precipitation = decimal.Decimal(0.0)\\n for j in rawDataSet:\\n if j.tempMin is not None:\\n tempmins.append(j.tempMin)\\n if j.tempMax is not None:\\n tempmaxs.append(j.tempMax)\\n if j.tempAvg is not None:\\n tempavgs.append(j.tempAvg)\\n if j.precipitation is not None:\\n precipitation = precipitation + j.precipitation\\n d.dataAvailable = rawDataSet.count()\\n if len(tempmins) > 0:\\n d.tempMin = min(tempmins)\\n d.tempMinAvg = sum(tempmins) / len(tempmins)\\n if len(tempmaxs) > 0:\\n d.tempMax = max(tempmaxs)\\n d.tempMaxAvg = sum(tempmaxs) / len(tempmaxs)\\n if len(tempavgs) > 0:\\n d.tempAvg = sum(tempavgs) / len(tempavgs)\\n\\n\\n start_of_current_month = datetime.datetime(\\n year=f.year, month=f.month, day=1, hour=0, minute=0)\\n start_of_next_month = start_of_current_month + relativedelta(months=1)\\n rawDataSet = RawData.objects\\\\\\n .filter(createdDate__range=(start_of_current_month, start_of_next_month))\\\\\\n .filter(siteId=siteId)\\n temps = []\\n rhs = []\\n winds = []\\n for j in rawDataSet:\\n if j.temperature is not None:\\n temps.append(j.temperature)\\n if j.humidity is not None:\\n rhs.append(j.humidity)\\n if j.windDir is not None:\\n winds.append(j.windDir)\\n tempDistribution = Climate.calculate_temperature_distribution(temps)\\n d.tempDistribution = ''.join(str(e) + ',' for e in tempDistribution)[:-1]\\n rhDistribution = Climate.calculate_rh_distribution(rhs)\\n d.rhDistribution = ''.join(str(e) + ',' for e in rhDistribution)[:-1]\\n windDistribution = Climate.calculate_wind_distribution(winds)\\n d.windDistribution = ''.join(str(e) + ',' for e in windDistribution)[:-1]\\n d.precipitation = precipitation\\n d.summerDays = Climate.get_nr_summer_days(tempmaxs)\\n d.frostDays = Climate.get_nr_frost_days(tempmins)\\n d.winterDays = Climate.get_nr_winter_days(tempmaxs)\\n d.coldDays = Climate.get_nr_cold_days(tempmins)\\n d.warmNights = Climate.get_nr_warm_nights(tempmins)\\n d.warmDays = Climate.get_nr_warm_days(tempmaxs)\\n d.hotDays = Climate.get_nr_hot_days(tempmaxs)\\n\\n manualDataSet = RawManualData.objects.filter(siteId=siteId).filter(year=f.year).filter(month=f.month)\\n significants = {}\\n for day in manualDataSet:\\n significants = Climate.count_significants(significants, day.weatherCode)\\n d.significants = significants\\n d.save()\\n\\n if (f.month == 12):\\n f = f.replace(year=f.year + 1, month=1)\\n else:\\n f = f.replace(month=f.month + 1)\",\n \"def interactive_annual_monthly_bar_report_with_buttons(year: int):\\n condition_dict = {'key': 'category', 'operation': '!=', 'value': 16}\\n average_monthly_amount = Spending().get_average_monthly_spending_amount(condition_dict=condition_dict)\\n current_annual_monthly_amount = Spending().get_monthly_total_spending_of_a_year(year, include_doctor=False)\\n\\n df_average_monthly = pd.DataFrame(average_monthly_amount, columns=['avg', 'mon'])\\n df_current_annual_monthly = pd.DataFrame(current_annual_monthly_amount, columns=['sum', 'mon'])\\n\\n fig = go.Figure()\\n\\n fig.add_trace(\\n go.Bar(\\n x=df_current_annual_monthly['mon'],\\n y=df_current_annual_monthly['sum'],\\n name=f'{year} monthly spending'\\n )\\n )\\n\\n fig.add_trace(\\n go.Scatter(\\n x=df_average_monthly['mon'],\\n y=df_average_monthly['avg'],\\n name='Average Monthly Spending',\\n visible=True,\\n line=dict(color=\\\"#33CFA5\\\", dash=\\\"dash\\\")\\n )\\n )\\n\\n fig.update_layout(\\n updatemenus=[\\n dict(\\n type='buttons',\\n direction='right',\\n active=1,\\n x=0.57,\\n y=1.2,\\n buttons=list([\\n dict(\\n label='Current Year',\\n method='update',\\n args=[\\n {'visible': [True, False]},\\n {'title': f'{year} monthly spending', \\\"annotations\\\": []}\\n ]\\n ),\\n dict(\\n label='Average',\\n method='update',\\n args=[\\n {'visible': [True, True]},\\n {'title': 'Compare with Average monthly', \\\"annotations\\\": []}\\n ]\\n ),\\n ]),\\n )\\n ]\\n )\\n\\n fig.update_layout(\\n {\\n 'width': 960,\\n 'height': 550\\n },\\n overwrite=True\\n )\\n\\n aPlot = plotly.offline.plot(fig,\\n config={\\\"displayModeBar\\\": False},\\n show_link=False,\\n include_plotlyjs=False,\\n output_type='div')\\n\\n return aPlot\",\n \"def aggregate_monthly(organization, account, from_date=None):\\n customers = []\\n receivables = []\\n new_customers = []\\n new_receivables = []\\n churn_customers = []\\n churn_receivables = []\\n queryset = None\\n # We want to be able to compare *last* to *from_date* and not get django\\n # warnings because timezones are not specified.\\n dates = month_periods(13, from_date)\\n first_date = dates[0]\\n seam_date = dates[1]\\n for last_date in dates[2:]:\\n churn_query = RawQuery(\\n\\\"\\\"\\\"SELECT COUNT(DISTINCT(prev.dest_organization_id)), SUM(prev.amount)\\n FROM saas_transaction prev\\n LEFT OUTER JOIN (\\n SELECT distinct(dest_organization_id)\\n FROM saas_transaction\\n WHERE created_at >= '%(seam_date)s'\\n AND created_at < '%(last_date)s'\\n AND orig_organization_id = '%(organization_id)s'\\n AND orig_account = '%(account)s') curr\\n ON prev.dest_organization_id = curr.dest_organization_id\\n WHERE prev.created_at >= '%(first_date)s'\\n AND prev.created_at < '%(seam_date)s'\\n AND prev.orig_organization_id = '%(organization_id)s'\\n AND prev.orig_account = '%(account)s'\\n AND curr.dest_organization_id IS NULL\\\"\\\"\\\" % {\\n \\\"first_date\\\": first_date,\\n \\\"seam_date\\\": seam_date,\\n \\\"last_date\\\": last_date,\\n \\\"organization_id\\\": organization.id,\\n \\\"account\\\": account }, 'default')\\n churn_customer, churn_receivable = iter(churn_query).next()\\n query_result = Transaction.objects.filter(\\n orig_organization=organization,\\n orig_account=account,\\n created_at__gte=seam_date,\\n created_at__lt=last_date).aggregate(\\n Count('dest_organization', distinct=True),\\n Sum('amount'))\\n customer = query_result['dest_organization__count']\\n receivable = query_result['amount__sum']\\n new_query = RawQuery(\\n\\\"\\\"\\\"SELECT count(distinct(curr.dest_organization_id)), SUM(curr.amount)\\n FROM saas_transaction curr\\n LEFT OUTER JOIN (\\n SELECT distinct(dest_organization_id)\\n FROM saas_transaction\\n WHERE created_at >= '%(first_date)s'\\n AND created_at < '%(seam_date)s'\\n AND orig_organization_id = '%(organization_id)s'\\n AND orig_account = '%(account)s') prev\\n ON curr.dest_organization_id = prev.dest_organization_id\\n WHERE curr.created_at >= '%(seam_date)s'\\n AND curr.created_at < '%(last_date)s'\\n AND curr.orig_organization_id = '%(organization_id)s'\\n AND curr.orig_account = '%(account)s'\\n AND prev.dest_organization_id IS NULL\\\"\\\"\\\" % {\\n \\\"first_date\\\": first_date,\\n \\\"seam_date\\\": seam_date,\\n \\\"last_date\\\": last_date,\\n \\\"organization_id\\\": organization.id,\\n \\\"account\\\": account }, 'default')\\n new_customer, new_receivable = iter(new_query).next()\\n period = last_date\\n churn_customers += [ (period, churn_customer) ]\\n churn_receivables += [ (period, - int(churn_receivable or 0)) ]\\n customers += [ (period, customer) ]\\n receivables += [ (period, int(receivable or 0)) ]\\n new_customers += [ (period, new_customer) ]\\n new_receivables += [ (period, int(new_receivable or 0)) ]\\n first_date = seam_date\\n seam_date = last_date\\n return ((churn_customers, customers, new_customers),\\n (churn_receivables, receivables, new_receivables))\",\n \"def _get_month_inv_per_cate1(self):\\n inv_cate1_month = self._inv_cus_sku_month.reset_index()\\n inv_cate1_month['first_cate_id'] = self._sku_info_encoded.first_cate_id.values\\n inv_cate1_month_index = inv_cate1_month['first_cate_id']\\n inv_cate1_month = inv_cate1_month.groupby(['first_cate_id'])[self._inv_cus_sku_month.columns].sum()\\n inv_cate1_month = inv_cate1_month.reindex(inv_cate1_month_index)\\n return inv_cate1_month\",\n \"def sum_monthly_budget(self):\\n\\n my_sumbud = 0\\n \\n month_bud = self.data['Monthly Budget']\\n for i in month_bud:\\n my_sumbud = my_sumbud + float(i)\\n\\n return my_sumbud\",\n \"def SumCostByBetweenDatesPerCategory(startDate, endDate, category):\\n\\n logs.logger.debug(\\n \\\"Start to adds all amount of Cost objects between two payment date and on the category\\\")\\n try:\\n searchedCostByBetweenDatesFromDB = GetAllCostByDateOfPaymentBandFromDB(\\n startDate, endDate)\\n sumTotal = 0\\n for item in searchedCostByBetweenDatesFromDB:\\n if item.category.value == category:\\n sumTotal += item.amount\\n logs.logger.info(\\n \\\"Between two payment date and on the category adds all amount of Cost objects.\\\")\\n return sumTotal\\n except Exception as e:\\n logs.logger.error(e, exc_info=True)\",\n \"def normalized_monthly_hist_by_stability(metdat, catinfo, vertloc=80):\\n\\n months = utils.monthnames()\\n hours = np.arange(24)\\n stabcol, _, _= utils.get_vertical_locations(catinfo['columns']['stability flag'], location=vertloc)\\n stabconds = utils.get_stabconds()\\n colors = utils.get_colors(5,basecolor='span')\\n\\n temp = metdat[stabcol].dropna()\\n plotdata = temp.groupby([temp.index.month.rename('month'), temp.index.hour.rename('hour')]).value_counts(normalize=True)\\n plotdata.index.names = ['month','hour','stabclass']\\n temp = plotdata.reorder_levels(['month','stabclass','hour'])\\n\\n indexvals = [np.arange(1,13),stabconds, np.arange(24)]\\n indx = pd.MultiIndex.from_product(indexvals, names=['month','stabclass','hour'])\\n temp = temp.reindex(index=indx).fillna(0.0)\\n\\n fig,ax = plt.subplots(4,3, figsize=(9,10), sharex=True, sharey=True)\\n for ii,month in enumerate(months):\\n newbottom = np.zeros(24)\\n\\n for jj,cond in enumerate(stabconds):\\n \\n pdat = temp.loc[ii+1,cond]\\n \\n ax.flatten()[ii].bar(hours, pdat, color=colors[jj],bottom=newbottom)\\n \\n newbottom += pdat\\n \\n # fig.legend(stabconds, loc=8, bbox_to_anchor=(0, -0.1), edgecolor='w')\\n fig.text(-0.02,0.58, 'Probability [%]', ha='center', va='center', rotation='vertical')\\n leg = fig.legend(stabconds, loc=9, bbox_to_anchor=(0.55, 0.125), frameon=False)\\n \\n fig.tight_layout()\\n fig.subplots_adjust(bottom=0.21)\\n fig.text(0.5, 0.165, 'Time of Day [Hour]', ha='center', va='center')\\n\\n return fig, ax\",\n \"def amortization_schedule(\\n cls, mortgage_term: int, mortgage_amount: float, interest_rate: float\\n ) -> AmortizationSchedule:\\n monthly_data = []\\n annual_data = []\\n\\n payment = cls.payment_calc(\\n years=mortgage_term, interest=interest_rate, mortgage_amount=mortgage_amount\\n )\\n\\n # total number of payments\\n total_nr_payments = mortgage_term * 12\\n\\n # used to calculate the yearly (payments)\\n year = 1\\n\\n monthly_rate = interest_rate / 100 / 12\\n\\n payment_monthly_date = date.today() # starts from today\\n payment_yearly_date = date.today() # starts from today\\n\\n beginning_balance_per_month = mortgage_amount\\n beginning_balance_per_year = mortgage_amount\\n\\n # interest rates\\n interest_rate_per_year = 0\\n principal_amount_per_year = 0\\n\\n # interest rate amount per year\\n total_interest_rate_amount = 0\\n\\n for _ in range(total_nr_payments):\\n interest = round(beginning_balance_per_month * monthly_rate, 2)\\n principal_amount_per_month = round(payment - interest, 2)\\n remaining_balance_per_month = round(\\n beginning_balance_per_month - principal_amount_per_month, 2\\n )\\n\\n interest_rate_per_year = interest_rate_per_year + interest\\n principal_amount_per_year = (\\n principal_amount_per_year + principal_amount_per_month\\n )\\n\\n if year == 12:\\n remaining_balance_per_year = (\\n beginning_balance_per_year - principal_amount_per_year\\n )\\n\\n annual_payment_payload = cls._construct_annual_payments(\\n year=payment_yearly_date,\\n interest_amount=interest_rate_per_year,\\n principal_amount=principal_amount_per_year,\\n beginning_balance=beginning_balance_per_year,\\n ending_balance=remaining_balance_per_year,\\n )\\n\\n annual_data.append(annual_payment_payload)\\n\\n beginning_balance_per_year = remaining_balance_per_year\\n payment_yearly_date = payment_yearly_date + relativedelta(years=+1)\\n\\n # interest rate amount per year\\n total_interest_rate_amount = (\\n total_interest_rate_amount + interest_rate_per_year\\n )\\n\\n # reset the data\\n interest_rate_per_year = 0\\n principal_amount_per_year = 0\\n year = 0\\n\\n monthly_payments = cls._construct_monthly_payments(\\n month=payment_monthly_date,\\n beginning_balance=beginning_balance_per_month,\\n interest_amount=interest_rate,\\n principal_amount=principal_amount_per_month,\\n ending_balance=remaining_balance_per_month,\\n )\\n\\n monthly_data.append(monthly_payments)\\n\\n beginning_balance_per_month = remaining_balance_per_month\\n payment_monthly_date = payment_monthly_date + relativedelta(months=+1)\\n\\n year += 1\\n\\n amortization_schedule = {\\n \\\"monthly\\\": monthly_data,\\n \\\"annual\\\": annual_data,\\n \\\"monthly_mortgage_payment\\\": payment,\\n \\\"total_interest_rate_amount\\\": total_interest_rate_amount,\\n }\\n\\n return AmortizationSchedule(**amortization_schedule)\",\n \"def get(self, request, unit_id):\\n start_date = get_start_date(request)\\n end_date = get_end_date(request)\\n months = {}\\n while(start_date <= end_date):\\n first_of_month = start_date.replace(day=1)\\n last_of_month = start_date.replace(\\n day=1) + relativedelta(months=1) - relativedelta(days=1)\\n readings_sum = MeterReading.objects.filter(\\n date__gte=first_of_month,\\n date__lte=last_of_month,\\n unit=unit_id\\n ).aggregate(Sum('usage'))\\n months[start_date.strftime(\\n \\\"%Y-%m-%d\\\")] = round(readings_sum['usage__sum']*.06, 0)\\n start_date += relativedelta(months=1)\\n return JsonResponse(months, safe=False)\",\n \"def get_monthly_countries_data(country, month, region):\\n m_cases = []\\n m_deaths = []\\n m_rec = []\\n actual_dates = []\\n\\n # start date / actual date\\n today_date = datetime.today().strftime('%m-%d-%Y')\\n today_day = today_date.split(\\\"-\\\")[1]\\n\\n # monthly data\\n if month == 3:\\n today_day = '32' # grab all March (it's done)\\n if month == 4:\\n today_day = '31' # grab all April (it's done)\\n for day in range(1, int(float(today_day))):\\n date_object = datetime(day=day,\\n month=month,\\n year=2020).strftime('%d-%m-%Y')\\n date = (date_object.split(\\\"-\\\")[0], date_object.split(\\\"-\\\")[1])\\n month_cases, month_deaths, month_rec, exp_dates = \\\\\\n _get_daily_countries_data(date, country, region)\\n m_cases.append(month_cases)\\n m_deaths.append(month_deaths)\\n m_rec.append(month_rec)\\n actual_dates.append(exp_dates)\\n\\n return m_cases, m_deaths, m_rec, actual_dates\",\n \"def get_stats(cls, contract_month_start_day=1):\\n # I could do this in SQL with date_trunc, but eventually this'll need\\n # to be contract-month, so like the 7th-7th or something, which AFAIK\\n # can't be done in SQL (and certainly not in Django). So just do this\\n # by hand. There are only a few hundred reports/month right now, so this\\n # should be OK.\\n stats = {}\\n\\n reports = cls.objects.filter(days_until_triage__isnull=False)\\n for report in reports:\\n first_day, last_day = dates.contract_month(report.created_at, contract_month_start_day)\\n if first_day not in stats:\\n stats[first_day] = {\\n 'count': 0,\\n 'triaged_accurately': 0,\\n 'false_negatives': 0,\\n 'triaged_within_one_day': 0,\\n 'last_day': last_day,\\n\\n }\\n\\n stats[first_day]['count'] += 1\\n stats[first_day]['triaged_accurately'] += report.is_accurate\\n stats[first_day]['false_negatives'] += report.is_false_negative\\n if report.days_until_triage <= 1:\\n stats[first_day]['triaged_within_one_day'] += 1\\n\\n stats[\\\"totals\\\"] = {\\n key: sum(month_stats[key] for month_stats in stats.values()) if stats else 0\\n for key in ('count', 'triaged_accurately', 'false_negatives', 'triaged_within_one_day')\\n }\\n\\n return stats\",\n \"def per_month(start: datetime, end: datetime, n: int = 1):\\n curr = start.replace(day=1, hour=0, minute=0, second=0, microsecond=0)\\n while curr < end:\\n curr_end = add_month(curr, n)\\n yield curr, curr_end\\n curr = curr_end\",\n \"def month_overview(items, month_long):\\n events = []\\n for item in items:\\n dt = datetime.strptime(item['Date'], '%m/%d/%Y')\\n if filters.month_l_filter(dt.month) == month_long:\\n events.append(item)\\n return events\",\n \"def month(self):\\n return self._months\",\n \"def monthly_returns(df, stats): \\n retbymonth = {}\\n tradingmonths = list()\\n years = [2012,2013,2014,2015,2016]\\n months = ['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct',\\n 'Nov','Dec'] \\n i = 0\\n for y in years:\\n for m, month in enumerate(months):\\n if m == 11:\\n retbymonth[i] = \\\\\\n pd.DataFrame(df.loc[(df.index > dt(y,(m+1),1)) & \\n (df.index < dt((y+1),1,1))]['Returns'])\\n else:\\n retbymonth[i] = \\\\\\n pd.DataFrame(df.loc[(df.index > dt(y,(m+1),1)) & \\n (df.index < dt(y,(m+2),1))]['Returns'])\\n #Dropping non-trading months\\n if retbymonth[i].empty == True:\\n del retbymonth[i]\\n else:\\n tradingmonths.append('%s%s' %(month, y))\\n i = i + 1\\n #Renaming keys\\n for i, key in enumerate(retbymonth.keys()):\\n retbymonth[i] = retbymonth.pop(key) \\n \\n #Monthly returns\\n monthlyret = {}\\n for m in xrange(len(tradingmonths)):\\n monthlyret[m] = retbymonth[m].sum()\\n monthlyret = pd.DataFrame(monthlyret).T \\n monthlyret['CumRet'] = monthlyret['Returns'].cumsum()\\n monthlyret['Months'] = tradingmonths\\n stats['Values']['months'] = len(monthlyret)\\n stats['Values']['monthly_sharpe'] = (monthlyret['Returns'].mean() /\\n monthlyret['Returns'].std())\\n stats['Values']['yearly_sharpe'] = (stats['Values']['monthly_sharpe'] *\\n np.sqrt(12))\\n \\n return monthlyret, stats\",\n \"def get_month_course_metrics(site, course_id, month_for, **_kwargs):\\n # TODO: handle invalid \\\"month_for\\\" exception\\n # month, year = [int(val) for val in month_for.split('/')]\\n # start_date = datetime.date(year=year, month=month, day=1)\\n # end_date = datetime.date(year=year, month=month, day=days_in_month(start_date))\\n\\n first_day, last_day = first_last_days_for_month(month_for)\\n params_dict = dict(site=site,\\n course_id=course_id,\\n start_date=first_day,\\n end_date=last_day)\\n\\n active_users = get_mau_from_site_course(site=site,\\n course_id=course_id,\\n year=first_day.year,\\n month=first_day.month)\\n course_enrollments = get_course_enrolled_users_for_time_period(**params_dict)\\n num_learners_completed = get_course_num_learners_completed_for_time_period(**params_dict)\\n avg_days_to_complete = get_course_average_days_to_complete_for_time_period(**params_dict)\\n avg_progress = get_course_average_progress_for_time_period(**params_dict)\\n\\n return dict(\\n course_id=course_id,\\n month_for=month_for,\\n active_users=active_users.count(),\\n course_enrollments=course_enrollments,\\n num_learners_completed=num_learners_completed,\\n avg_days_to_complete=avg_days_to_complete,\\n avg_progress=avg_progress,\\n )\",\n \"def track_monthly(self, **kwargs):\\n return self.track(periods=[self.PERIOD_MONTHLY], **kwargs)\",\n \"def month(m):\\n\\t\\tx = db.cquery(\\\"month\\\",m)\\n\\t\\tprint \\\"Total:\\\", x[0] #@BUG when zero sometimes displays \\\"1\\\"\\n\\t\\tf = raw_input(\\\"[L]ist [N]ew overview or[B]ack to home \\\").lower()\\n\\t\\tif f == \\\"l\\\":\\n\\t\\t\\tui.mont1(m)\\n\\t\\t\\tfor i in x[1]:\\n\\t\\t\\t\\tprint ui.statsid(),i[0], i[1],\\\" \\\",ui.statstimein(), i[2], ui.statstimeout(),i[3]\\n\\t\\t\\traw_input(\\\"[Enter] to go back to search\\\")\\n\\t\\t\\thome_stats()\\n\\t\\telif f == \\\"n\\\": home_stats()\\n\\t\\telif f == \\\"b\\\": home()\\n\\t\\telse:\\n\\t\\t\\traw_input(\\\"I didnt get that... Press [Enter] to go back to stats...\\\")\\n\\t\\t\\thome_stats()\",\n \"def add_expense_view(request):\\n if request.method == \\\"POST\\\":\\n form = ExpenseForm(request, request.POST)\\n if form.is_valid():\\n expense = form.save(commit=False)\\n expense.user_id = request.user.id\\n expense.category_id = form.cleaned_data[\\\"category\\\"]\\n expense.save()\\n return redirect(\\\"add_expense\\\")\\n else:\\n form = ExpenseForm(request)\\n latest_10 = Expense.objects\\\\\\n .by_user(request.user.id)\\\\\\n .order_by(\\\"-date\\\", \\\"-id\\\")\\\\\\n .only(\\\"amount\\\", \\\"date\\\", \\\"description\\\")[:10]\\n\\n latest_10_total = latest_10.aggregate(Sum(\\\"amount\\\"))\\n this_last_month = Expense.objects.total_of_this_and_last_month(request.user.id)\\n last_3_months = Expense.objects.last_n_months_expense(3, request.user.id)\\\\\\n .aggregate(Sum(\\\"amount\\\"))\\n this_last_year = Expense.objects.total_of_this_and_last_year(request.user.id)\\n\\n return render(request, \\\"tracker/add.html\\\", {\\n \\\"form\\\": form, \\\"latest_10\\\": latest_10, \\\"3_months\\\": last_3_months[\\\"amount__sum\\\"],\\n \\\"this_month\\\": this_last_month.this, \\\"last_month\\\": this_last_month.last,\\n \\\"this_year\\\": this_last_year.this, \\\"last_year\\\": this_last_year.last,\\n \\\"latest_10_sum\\\": latest_10_total[\\\"amount__sum\\\"]\\n })\",\n \"def month(self):\\n return 0\",\n \"def month(self):\\n return 0\",\n \"def get(self, request, unit_id):\\n start_date = get_start_date(request)\\n end_date = get_end_date(request)\\n months = {}\\n while(start_date <= end_date):\\n first_of_month = start_date.replace(day=1)\\n last_of_month = start_date.replace(\\n day=1) + relativedelta(months=1) - relativedelta(days=1)\\n readings_sum = MeterReading.objects.filter(\\n date__gte=first_of_month,\\n date__lte=last_of_month,\\n unit=unit_id,\\n reading_type='GAS'\\n ).aggregate(Sum('usage'))\\n months[start_date.strftime(\\n \\\"%Y-%m-%d\\\")] = round(readings_sum['usage__sum']*.06, 0)\\n start_date += relativedelta(months=1)\\n return JsonResponse(months, safe=False)\",\n \"def TopMonth(results, search_term):\\n top_months = Counter([row[0].strftime(\\\"%B %Y\\\") for row in results])\\n (month, count) = top_months.most_common()[0]\\n if search_term == 'total':\\n search_term = 'checkins'\\n text = 'The most popular month for %s was %s with %s.' % (search_term,\\n month, count)\\n return text\",\n \"def new_month(self, month: int, year: int, bill: Bill) -> None:\\n self.bill = bill\\n self.bill.set_rates(\\\"TERM\\\", TERM_MINS_COST)\\n self.bill.add_fixed_cost(TERM_MONTHLY_FEE)\\n if not ((self.end.month < month and self.end.year <= year) or\\n self.end.year < year):\\n # refresh included minutes and SMSs\\n self.bill.add_free_minutes((-1) * self.bill.free_min)\\n if self.start.month == month and self.start.year == year:\\n # if first month, add term deposit to bill.\\n self.bill.add_fixed_cost(TERM_DEPOSIT)\\n else:\\n self._carried_term = True\",\n \"def getMonthlyDataForTaxons(self, month, year):\\n return self.callMonthlyProc(\\\"monthly_by_type\\\", month, year)\",\n \"def _get_sum_detail(self, cr, uid, ids=None, number_month=None,\\n context=None):\\n fy_obj = self.pool.get('account.fiscalyear')\\n period_obj = self.pool.get('account.period')\\n context = context and dict(context) or {}\\n cx = context.copy()\\n res = 0.0\\n\\n if not cx.get('fiscalyear'):\\n cx['fiscalyear'] = fy_obj.find(cr, uid)\\n\\n fy_id = cx['fiscalyear']\\n\\n brw = self.browse(cr, uid, ids)\\n\\n if brw.acc_val == 'init':\\n if cx.get('whole_fy', False):\\n cx['periods'] = period_obj.search(cr, uid, [\\n ('fiscalyear_id', '=', fy_id), ('special', '=', True)])\\n else:\\n period_from = period_obj.search(cr, uid, [\\n ('fiscalyear_id', '=', fy_id), ('special', '=', True)])\\n # Case when the period_from is the first non-special period\\n # of the fiscalyear\\n if period_obj.browse(cr, uid, cx['period_from']).date_start ==\\\\\\n fy_obj.browse(cr, uid, fy_id).date_start:\\n cx['period_to'] = period_from[0]\\n else:\\n cx['period_to'] = period_obj.previous(\\n cr, uid, cx['period_from'])\\n cx['period_from'] = period_from[0]\\n elif brw.acc_val == 'var':\\n # it is going to be the one sent by the previous cx\\n if cx.get('whole_fy', False):\\n cx['periods'] = period_obj.search(cr, uid, [\\n ('fiscalyear_id', '=', fy_id), ('special', '=', False)])\\n else:\\n # it is going to be from the fiscalyear's beginning\\n if cx.get('whole_fy', False):\\n cx['periods'] = period_obj.search(cr, uid, [\\n ('fiscalyear_id', '=', fy_id)])\\n else:\\n period_from = period_obj.search(cr, uid, [\\n ('fiscalyear_id', '=', fy_id), ('special', '=', True)])\\n cx['period_from'] = period_from[0]\\n cx['periods'] = \\\\\\n period_obj.build_ctx_periods(cr, uid, cx['period_from'],\\n cx['period_to'])\\n\\n if brw.type == 'detail':\\n # Si es de tipo detail\\n # If we have to only take into account a set of Journals\\n cx['journal_ids'] = [aj_brw.id for aj_brw in brw.journal_ids]\\n cx['analytic'] = [an.id for an in brw.analytic_ids]\\n cx['ifrs_tax'] = [tx.id for tx in brw.tax_code_ids]\\n cx['ifrs_partner'] = [p_brw.id for p_brw in brw.partner_ids]\\n cx['ifrs_query'] = self._get_ifrs_query(cr, uid, brw, context)\\n\\n # NOTE: This feature is not yet been implemented\\n # cx['partner_detail'] = cx.get('partner_detail')\\n\\n # Refreshing record with new context\\n brw = self.browse(cr, uid, ids, context=cx)\\n\\n for aa in brw.cons_ids:\\n # Se hace la sumatoria de la columna balance, credito o debito.\\n # Dependiendo de lo que se escoja en el wizard\\n if brw.value == 'debit':\\n res += aa.debit\\n elif brw.value == 'credit':\\n res += aa.credit\\n else:\\n res += aa.balance\\n return res\",\n \"def total_tbr(cc): # pragma: no cover\\n cc.execute(\\\"\\\"\\\"SELECT DISTINCT DATE_FORMAT(git_commit.timestamp, '%Y-%m')\\n FROM git_commit\\\"\\\"\\\")\\n months = cc.fetchall()\\n results = []\\n for month in months:\\n month = month[0]\\n cc.execute(\\\"\\\"\\\"SELECT COUNT(DISTINCT git_commit_hash)\\n FROM commit_people\\n INNER JOIN git_commit\\n ON commit_people.git_commit_hash = git_commit.hash\\n WHERE commit_people.type = 'tbr'\\n AND YEAR(git_commit.timestamp) = %s\\n AND MONTH(git_commit.timestamp) = %s\\\"\\\"\\\" % (month[:4], month[5:]))\\n result = cc.fetchone()\\n results.append([month, int(result[0])])\\n return results\",\n \"def _get_month_order_per_cate2(self):\\n order_cate2_month = self._order_sku_month.reset_index()\\n order_cate2_month['second_cate_id'] = self._sku_info.second_cate_id.values\\n order_cate2_month_index = order_cate2_month['second_cate_id']\\n order_cate2_month = order_cate2_month.groupby(['second_cate_id'])[self._order_sku_month.columns].sum()\\n order_cate2_month = order_cate2_month.reindex(order_cate2_month_index)\\n return order_cate2_month\",\n \"def calc_schedule_monthly(self):\\n schedule = self.schedule\\n \\n schedule['year'] = schedule['Date'].dt.year\\n schedule['month'] = schedule['Date'].dt.month\\n schedule['day'] = schedule['Date'].dt.day\\n schedule['end_day'] = schedule[['year', 'month']].apply(lambda x: calendar.monthrange(x.year, x.month)[1],\\n axis=1)\\n\\n\\n schedule_mon = schedule\\\\\\n .groupby(['year', 'month'])\\\\\\n .agg({'Payment': 'sum',\\n 'Interest': 'sum',\\n 'Additional_Payment': 'sum'\\n }\\n )\\n\\n schedule_mon.index.names = ['year', 'month']\\n schedule_mon.reset_index(inplace=True)\\n \\n month_end_balances = schedule.loc[schedule['day'] == schedule['end_day'], \\n ['year', 'month', 'End Balance']]\\n\\n \\n schedule_mon = schedule_mon.merge(month_end_balances, how='inner', on=['year', 'month'])\\n \\n schedule_mon['day'] = 1 # start of month\\n schedule_mon['Month Date'] = pd.to_datetime(schedule_mon[['year', 'month', 'day']])\\n\\n # Monthly principal amounts \\n schedule_mon['Principal'] = schedule_mon['Payment'] - schedule_mon['Interest']\\n \\n schedule_mon.drop(['day', 'month', 'year'], axis=1, inplace=True)\\n \\n return schedule_mon\",\n \"def display_categories(\\n sortby: str = \\\"market_cap_desc\\\",\\n limit: int = 15,\\n export: str = \\\"\\\",\\n sheet_name: Optional[str] = None,\\n pie: bool = False,\\n) -> None:\\n fig = OpenBBFigure()\\n\\n df = gecko.get_top_crypto_categories(sortby)\\n df_data = df\\n if not df.empty:\\n if pie or fig.is_image_export(export):\\n df_data[f\\\"% relative to top {limit}\\\"] = (\\n df_data[\\\"Market Cap\\\"] / df_data[\\\"Market Cap\\\"].sum()\\n ) * 100\\n stables_to_display = df_data[df_data[f\\\"% relative to top {limit}\\\"] >= 1]\\n other_stables = df_data[df_data[f\\\"% relative to top {limit}\\\"] < 1]\\n values_list = list(stables_to_display[f\\\"% relative to top {limit}\\\"].values)\\n values_list.append(other_stables[f\\\"% relative to top {limit}\\\"].sum())\\n labels_list = list(stables_to_display[\\\"Name\\\"].values)\\n labels_list.append(\\\"Others\\\")\\n\\n fig = plot_pie_chart(\\n labels=labels_list,\\n values=values_list,\\n title=f\\\"Market Cap distribution of top {limit} crypto categories\\\",\\n )\\n if not fig.is_image_export(export):\\n fig.show()\\n\\n df = df.applymap(lambda x: lambda_long_number_format_with_type_check(x))\\n print_rich_table(\\n df,\\n headers=list(df.columns),\\n floatfmt=\\\".2f\\\",\\n show_index=False,\\n export=bool(export),\\n limit=limit,\\n )\\n\\n export_data(\\n export,\\n os.path.dirname(os.path.abspath(__file__)),\\n \\\"cgcategories\\\",\\n df_data,\\n sheet_name,\\n fig,\\n )\\n else:\\n console.print(\\\"\\\\nUnable to retrieve data from CoinGecko.\\\\n\\\")\",\n \"def monthly_usage(self, ending_date, ending_meter_reading):\\r\\n\\t\\tyyyy= int(self.initial_date[:4])\\r\\n\\t\\tmm= int(self.initial_date[4:6])\\r\\n\\t\\tdd= int(self.initial_date[6:])\\r\\n\\t\\tstarting_date_formatted = datetime.date(yyyy,mm,dd)\\r\\n\\r\\n\\t\\tyyyy= int(ending_date[:4])\\r\\n\\t\\tmm= int(ending_date[4:6])\\r\\n\\t\\tdd= int(ending_date[6:])\\r\\n\\t\\tending_date_formatted = datetime.date(yyyy,mm,dd)\\r\\n\\r\\n\\r\\n\\t\\tnumber_of_days = (ending_date_formatted - starting_date_formatted).days\\r\\n\\t\\tunits_consumed = self.total_units_consumed - ending_meter_reading\\r\\n\\t\\taverage_daily_usage_calculation = units_consumed/ number_of_days\\r\\n\\t\\taverage_daily_usage= format(average_daily_usage_calculation, \\\".2f\\\")\\r\\n\\r\\n\\t\\tstart= starting_date_formatted.strftime(\\\"%d,%B\\\")\\r\\n\\t\\tend= ending_date_formatted.strftime(\\\"%d,%B\\\")\\r\\n\\t\\ttoday = datetime.date.today().strftime(\\\"%d,%B,%Y\\\")\\r\\n\\t\\tprint(f\\\"\\\"\\\"\\r\\n-------------------------------------------\\r\\nEnergy Usage Report as at {today}.\\r\\n-------------------------------------------\\r\\nUsage report for days between {start}\\r\\nand {end}.\\r\\n\\t\\r\\nAverage daily Usage = {average_daily_usage} kWh\\r\\nTotal units consumed = {format(units_consumed,\\\".2f\\\")} kWh\\r\\nTotal amount spent = KES{format(self.total_amount_spent, \\\",\\\")}\\r\\n\\r\\n-------------------------------------------\\r\\n\\\"\\\"\\\")\",\n \"def testMonthlyPayrollTotals(self):\\n self._setupMonthlyTotals()\\n totals = self.rows[-1]\\n\\n work_total = Decimal('110.00')\\n self.assertEquals(totals['work_total'], work_total)\\n\\n self.assertEquals(len(totals['billable']), 1 + 1)\\n for entry in totals['billable']:\\n self.assertEquals(entry['hours'], Decimal('90.00'))\\n self.assertEquals(entry['percent'],\\n Decimal('90.00') / work_total * 100)\\n\\n self.assertEquals(len(totals['nonbillable']), 1 + 1)\\n for entry in totals['nonbillable']:\\n self.assertEquals(entry['hours'], Decimal('20.00'))\\n self.assertEquals(entry['percent'],\\n Decimal('20.00') / work_total * 100)\\n\\n self.assertEquals(len(totals['leave']), 2 + 1)\\n sick_index = self.labels['leave'].index(self.sick.name)\\n vacation_index = self.labels['leave'].index(self.vacation.name)\\n self.assertEquals(totals['leave'][sick_index]['hours'],\\n Decimal('80.00'))\\n self.assertEquals(totals['leave'][sick_index]['percent'],\\n Decimal('80.00') / Decimal('120.00') * 100)\\n self.assertEquals(totals['leave'][vacation_index]['hours'],\\n Decimal('40.00'))\\n self.assertEquals(totals['leave'][vacation_index]['percent'],\\n Decimal('40.00') / Decimal('120.00') * 100)\\n self.assertEquals(totals['leave'][-1]['hours'], Decimal('120.00'))\\n self.assertEquals(totals['leave'][-1]['percent'], Decimal('100.00'))\\n\\n self.assertEquals(totals['grand_total'], Decimal('230.00'))\",\n \"def deciles_chart(\\n df,\\n filename,\\n period_column=None,\\n column=None,\\n title=\\\"\\\",\\n ylabel=\\\"\\\",\\n time_window=\\\"\\\",\\n):\\n\\n max_practices = df.groupby(period_column).agg(\\n {\\\"practice\\\": \\\"nunique\\\"}\\n ).max().values[0]\\n\\n\\n # remove any practices with value of 0 each month\\n df = df.loc[df[\\\"value\\\"]>0, :]\\n\\n # monthly number of practices with column > 0\\n practice_numbers = df.groupby(period_column).agg(\\n {\\\"practice\\\": \\\"nunique\\\"}\\n )\\n \\n practice_numbers = practice_numbers.apply(lambda x: round(x / 5) * 5)\\n df = compute_deciles(df, period_column, column, has_outer_percentiles=False)\\n \\n # calculate monthyl proportion of practices with non zero values\\n\\n sns.set_style(\\\"whitegrid\\\", {\\\"grid.color\\\": \\\".9\\\"})\\n\\n fig, ax = plt.subplots(1, 1, figsize=(15, 8))\\n\\n linestyles = {\\n \\\"decile\\\": {\\n \\\"line\\\": \\\"b--\\\",\\n \\\"linewidth\\\": 1,\\n \\\"label\\\": \\\"Decile\\\",\\n },\\n \\\"median\\\": {\\n \\\"line\\\": \\\"b-\\\",\\n \\\"linewidth\\\": 1.5,\\n \\\"label\\\": \\\"Median\\\",\\n },\\n \\\"percentile\\\": {\\n \\\"line\\\": \\\"b:\\\",\\n \\\"linewidth\\\": 0.8,\\n \\\"label\\\": \\\"1st-9th, 91st-99th percentile\\\",\\n },\\n }\\n label_seen = []\\n for percentile in range(1, 100): # plot each decile line\\n data = df[df[\\\"percentile\\\"] == percentile]\\n add_label = False\\n\\n if percentile == 50:\\n style = linestyles[\\\"median\\\"]\\n add_label = True\\n\\n else:\\n style = linestyles[\\\"decile\\\"]\\n if \\\"decile\\\" not in label_seen:\\n label_seen.append(\\\"decile\\\")\\n add_label = True\\n if add_label:\\n label = style[\\\"label\\\"]\\n else:\\n label = \\\"_nolegend_\\\"\\n\\n ax.plot(\\n data[period_column],\\n data[column],\\n style[\\\"line\\\"],\\n linewidth=style[\\\"linewidth\\\"],\\n label=label,\\n )\\n ax.set_ylabel(ylabel, size=15, alpha=0.6)\\n if title:\\n ax.set_title(title, size=14, wrap=True)\\n # set ymax across all subplots as largest value across dataset\\n\\n ax.set_ylim(\\n [0, 100 if df[column].isnull().values.all() else df[column].max() * 1.05]\\n )\\n ax.tick_params(labelsize=12)\\n ax.set_xlim(\\n [df[period_column].min(), df[period_column].max()]\\n ) # set x axis range as full date range\\n\\n plt.setp(ax.xaxis.get_majorticklabels(), rotation=90)\\n ax.xaxis.set_major_formatter(matplotlib.dates.DateFormatter(\\\"%B %Y\\\"))\\n\\n plt.xticks(sorted(df[period_column].unique()), rotation=90)\\n\\n plt.vlines(\\n x=[pd.to_datetime(\\\"2020-03-01\\\")],\\n ymin=0,\\n ymax=100,\\n colors=\\\"orange\\\",\\n ls=\\\"--\\\",\\n label=\\\"National Lockdown\\\",\\n )\\n\\n if not time_window == \\\"\\\":\\n plt.vlines(\\n x=[pd.to_datetime(time_window)],\\n ymin=0,\\n ymax=100,\\n colors=\\\"green\\\",\\n ls=\\\"--\\\",\\n label=\\\"Date of expected impact\\\",\\n )\\n\\n ax.legend(\\n bbox_to_anchor=(1.1, 0.8), # arbitrary location in axes\\n # specified as (x0, y0, w, h)\\n loc=CENTER_LEFT, # which part of the bounding box should\\n # be placed at bbox_to_anchor\\n ncol=1, # number of columns in the legend\\n fontsize=20,\\n borderaxespad=0.0,\\n ) # padding between the axes and legend\\n # specified in font-size units\\n\\n\\n # plot proportions opn second y axis\\n ax2 = ax.twinx()\\n ax2.bar(practice_numbers.index, practice_numbers.practice, color=\\\"gray\\\", label=\\\"Proportion of practices with non-zero values\\\", width=20, alpha=0.3)\\n # st y lim to 0- (proportions max rounded up to nearest 10)\\n ax2.set_ylim(0, max_practices + 10 - (max_practices % 10))\\n\\n ax2.set_ylabel(\\\"Number of practices included\\\", size=15, alpha=0.6)\\n\\n plt.tight_layout()\\n plt.savefig(filename)\\n plt.clf()\",\n \"def lookup_monthly(self, **kwargs):\\n return self.lookup(period=self.PERIOD_MONTHLY, **kwargs)\",\n \"def _get_month_order_per_sku(self):\\n order_sku_month = self._order.copy()\\n order_sku_month['order_month'] = order_sku_month.order_date.astype(str).apply(lambda x: x[:7])\\n order_sku_month = order_sku_month.groupby(['item_code', 'order_month'])[['ord_qty']].sum()\\n order_sku_month['ord_qty'] = order_sku_month.ord_qty.apply(lambda x: 0 if x < 0 else x)\\n order_sku_month = order_sku_month.unstack(level=-1).fillna(0.0)\\n order_sku_month.columns = pd.date_range(start='2015-09-30', periods=len(order_sku_month.columns), freq='M')\\n return order_sku_month\",\n \"def best_session_monthly(df):\\n df = convert_to_datetime(df)\\n today = datetime.date.today()\\n this_month_start = today - timedelta(days=30)\\n last_month_start = today - timedelta(days=60)\\n this_month_card_ids = []\\n lastmonth_card_ids = []\\n for index, row in df.iterrows():\\n try:\\n if str(row['session_start'].date()) >= str(this_month_start):\\n this_month_card_ids.append(row['id'])\\n elif str(last_month_start) <= str(row['session_start'].date()) < str(this_month_start):\\n lastmonth_card_ids.append(row['id'])\\n except IndexError:\\n this_month_card_ids = []\\n lastmonth_card_ids = []\\n thismonth = df[df['id'].isin(this_month_card_ids)]\\n lastmonth = df[df['id'].isin(lastmonth_card_ids)]\\n thismonth_best_session = best_session_length(thismonth)\\n lastmonth_best_session = best_session_length(lastmonth)\\n\\n if thismonth_best_session > lastmonth_best_session:\\n color_code = \\\"09B109\\\"\\n arrow = \\\"\\\\u2191\\\"\\n elif thismonth_best_session < lastmonth_best_session:\\n color_code = \\\"CE2929\\\"\\n arrow = \\\"\\\\u2193\\\"\\n else:\\n color_code = \\\"000000\\\"\\n arrow = \\\"\\\\u003D\\\"\\n\\n if lastmonth_best_session > 0:\\n difference = abs((thismonth_best_session - lastmonth_best_session) / lastmonth_best_session) * 100\\n else:\\n # if last month has no sessions, the difference is up 100%\\n # if both best_sessions are zero, this will display '0 100% =' in black\\n difference = 100\\n result = make_results_dict(thismonth_best_session, difference, color_code, arrow)\\n result['best_session_monthly'] = result.pop('metric')\\n return result\",\n \"def get(self, request, unit_id):\\n start_date = get_start_date(request)\\n end_date = get_end_date(request)\\n months = {}\\n while(start_date <= end_date):\\n first_of_month = start_date.replace(day=1)\\n last_of_month = start_date.replace(\\n day=1) + relativedelta(months=1) - relativedelta(days=1)\\n readings_sum = MeterReading.objects.filter(\\n date__gte=first_of_month,\\n date__lte=last_of_month,\\n unit=unit_id,\\n reading_type='ELECTRICITY'\\n ).aggregate(Sum('usage'))\\n months[start_date.strftime(\\n \\\"%Y-%m-%d\\\")] = round(readings_sum['usage__sum']*.06, 0)\\n start_date += relativedelta(months=1)\\n return JsonResponse(months, safe=False)\",\n \"def monthly_rose_fig(metdat, catinfo, category=None, vertloc=80, bins=6, nsector=36, ylim=None, noleg=False):\\n \\n # set up data\\n dircol, _, _= utils.get_vertical_locations(catinfo['columns']['direction'], location=vertloc)\\n varcol, vertloc, _= utils.get_vertical_locations(catinfo['columns'][category], location=vertloc)\\n plotdat = metdat.groupby(metdat.index.month)\\n winddir = plotdat[dircol]\\n var = plotdat[varcol]\\n\\n months = utils.monthnames()\\n # wind speed bins to use in wind roses\\n # get var divisions set up\\n if isinstance(bins, int):\\n nbins = bins\\n else:\\n nbins = len(bins)\\n\\n # set up plotting colors\\n colors = utils.get_colors(nbins-1, basecolor='span')\\n colors += ['#3A4246'] # add something dark to the end.\\n colors = tuple(colors[0:nbins])\\n\\n fig = plt.figure(figsize=(9,13))\\n\\n for iax,month in enumerate(months):\\n\\n ax = fig.add_subplot(4,3,iax+1, projection=\\\"windrose\\\")\\n\\n ax.bar(winddir.get_group(iax+1), var.get_group(iax+1),\\n bins=bins, nsector=36, colors=colors,\\n linewidth=0.35,\\n normed=True)\\n\\n # Set the tick labels font\\n for label in (ax.get_xticklabels() + ax.get_yticklabels()):\\n label.set_fontname('Arial')\\n label.set_fontsize(12)\\n ax.set_title(month,fontsize=12,y=1.15)\\n\\n if iax == 10:\\n leg = plt.legend(loc=8, ncol=2, bbox_to_anchor = (0.5,-0.65), frameon=False)\\n leg.set_title(catinfo['labels'][category])\\n fig.text(0.5, -0.085, r'$z={}$ m'.format(vertloc), ha='center', va='center')\\n\\n axes = fig.get_children()[1:]\\n # adjust plot for specified max frequency\\n if ylim is None:\\n ylim = 0.0\\n for iax,month in enumerate(months):\\n ylim = np.max([ylim, axes[iax].get_ylim()[-1]])\\n \\n for iax,month in enumerate(months):\\n axes[iax].set_ylim(0,ylim)\\n axes[iax].set_yticks(np.linspace(0.0,ylim,4))\\n # print(axes[iax].get_yticks())\\n axes[iax].set_yticklabels([str(np.round(x,decimals=1)) for x in axes[iax].get_yticks()])\\n\\n fig.tight_layout() \\n\\n return fig, axes, leg\",\n \"def scrape(startyear, startmonth, endyear, endmonth):\\n year = startyear\\n month = startmonth\\n while (not (year == endyear and month == endmonth)):\\n ys = \\\"{}\\\".format(year)\\n ms = \\\"{:02d}\\\".format(month)\\n gather_all_profiles(ys,ms) \\n if month == 12:\\n year += 1\\n month = 0\\n month += 1\",\n \"def get_currency_exchange_monthly(self, from_symbol, to_symbol, outputsize='compact'):\\n _FUNCTION_KEY = 'FX_MONTHLY'\\n return _FUNCTION_KEY, \\\"Time Series FX (Monthly)\\\", 'Meta Data'\",\n \"def view_account(request, id):\\n account = get_object_or_404(Account, pk=id, user=request.user)\\n months, n = [], now()\\n for o in reversed(range(0, -12, -1)):\\n y, m = adjust_month(n.year, n.month, o)\\n months.append({\\n 'url': reverse('ledger:view_month', kwargs={\\n 'year': y,\\n 'month': m,\\n }),\\n 'year': y,\\n 'month': m,\\n 'amount': str(Transaction.month(y, m, account=account).sum()),\\n })\\n return render(request, 'ledger/pages/view_account.html', {\\n 'title': account,\\n 'account': account,\\n 'months': dumps(months),\\n })\",\n \"def get_monthly_history_metric(func, site, date_for, months_back,\\n include_current_in_history=True): # pylint: disable=unused-argument\\n date_for = as_date(date_for)\\n history = []\\n\\n for month in previous_months_iterator(month_for=date_for, months_back=months_back,):\\n period = period_str(month)\\n value = func(\\n site=site,\\n start_date=datetime.date(month[0], month[1], 1),\\n end_date=datetime.date(month[0], month[1], month[2]),\\n )\\n history.append(dict(period=period, value=value,))\\n\\n if history:\\n # use the last entry\\n current_month = history[-1]['value']\\n else:\\n # This should work for float too since '0 == 0.0' resolves to True\\n current_month = 0\\n return dict(\\n current_month=current_month,\\n history=history,)\",\n \"def __calculate_monthly_interest(self):\\n return self.__percentage_interest / 12\",\n \"def current_monthly_data(self):\\n return self._current_monthly_data\",\n \"def get_monthly_amount(sub):\\n price = min(60, sub.price_paid) / Decimal('1.2') * Decimal('0.7')\\n nb_month = max([(sub.date_over - sub.date_created).days//30, 1])\\n return (price / nb_month).quantize(Decimal('1.00'))\",\n \"def calculate_total_fees_collected(year=None, month=None):\\n total = 0\\n fees = load_fees()\\n for fee in fees:\\n if year and month:\\n date = datetime.datetime.fromtimestamp(fee[\\\"created\\\"])\\n if date.year == year and date.month == month:\\n total += fee[\\\"amount\\\"]\\n else:\\n total += fee[\\\"amount\\\"]\\n return total\",\n \"def get_budgets(budg_path, exp_path, dates=None):\\n exp_budg = data_help.read_jsonFile(budg_path)\\n exp_data = data_help.read_jsonFile(exp_path)\\n if dates == None:\\n dates = [util.get_current_month()]\\n for date in dates:\\n exp_budg_keys = exp_budg.keys()\\n if date not in exp_budg_keys: # check for current month to find exp categories\\n print(\\n f\\\"I have detected some data with for the month {date} that has no budget set.\\\")\\n print(\\n \\\"Please set the budget for this month.. or delete the data and run the program again.\\\")\\n if len(exp_budg) != 0:\\n user_in = util.get_user_input_for_chars(\\n \\\"Would you like to the whole thing (w) or create new (n)? \\\", ['w', 'n'])\\n\\n if user_in == 'w':\\n key = util.select_dict_key_using_integer(\\n exp_budg, \\\"Please select a budget to copy: \\\", print_children=True, print_vals=False, \\n print_child_vals=True)\\n exp_budg[date] = exp_budg[key]\\n elif user_in == 'n':\\n exp_budg[date] = declare_new_budget(date, exp_data)\\n else:\\n exp_budg[date] = declare_new_budget(date, exp_data)\\n\\n print(f\\\"Your budget is now saved for {date}.\\\")\\n\\n else:\\n print(f\\\"Your monthly budget for {date} is: \\\")\\n\\n util.print_simple_dict(exp_budg[date], print_vals=True)\\n\\n data_help.write_to_jsonFile(budg_path, exp_budg)\\n return\",\n \"def add_month(cab_data):\\n return cab_data.assign(month=lambda x: x.time.dt.month)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6153713","0.58537513","0.58471185","0.5838708","0.56815994","0.5626167","0.5604199","0.5594761","0.55863005","0.5585975","0.55839974","0.55758244","0.55617726","0.55524075","0.55136883","0.55043674","0.54847926","0.54786766","0.5466748","0.54640615","0.5458293","0.54563385","0.54373395","0.5436501","0.53874975","0.5380341","0.5372382","0.5372254","0.5358576","0.53444314","0.5341109","0.53020287","0.52788836","0.5275202","0.5273152","0.52571356","0.5249144","0.5236534","0.5235945","0.52335167","0.52292085","0.5228563","0.5222203","0.52136475","0.52049804","0.5204536","0.5200366","0.5195364","0.51909363","0.51721334","0.5165026","0.5161816","0.5161334","0.51385075","0.5131762","0.51245654","0.51075363","0.51055926","0.50903356","0.50821495","0.50642776","0.5058599","0.5057797","0.5034588","0.5029297","0.5024028","0.5007628","0.49992222","0.49940845","0.4993942","0.49897918","0.49847603","0.49847603","0.49796358","0.49781346","0.49670273","0.4964765","0.4946403","0.4942368","0.4924673","0.49071246","0.4897674","0.4893762","0.48873302","0.48855856","0.4884757","0.48804364","0.4877578","0.4865963","0.48587304","0.48566014","0.48538876","0.48529506","0.48509505","0.4850203","0.4850065","0.4844761","0.48401338","0.4838139","0.48348737"],"string":"[\n \"0.6153713\",\n \"0.58537513\",\n \"0.58471185\",\n \"0.5838708\",\n \"0.56815994\",\n \"0.5626167\",\n \"0.5604199\",\n \"0.5594761\",\n \"0.55863005\",\n \"0.5585975\",\n \"0.55839974\",\n \"0.55758244\",\n \"0.55617726\",\n \"0.55524075\",\n \"0.55136883\",\n \"0.55043674\",\n \"0.54847926\",\n \"0.54786766\",\n \"0.5466748\",\n \"0.54640615\",\n \"0.5458293\",\n \"0.54563385\",\n \"0.54373395\",\n \"0.5436501\",\n \"0.53874975\",\n \"0.5380341\",\n \"0.5372382\",\n \"0.5372254\",\n \"0.5358576\",\n \"0.53444314\",\n \"0.5341109\",\n \"0.53020287\",\n \"0.52788836\",\n \"0.5275202\",\n \"0.5273152\",\n \"0.52571356\",\n \"0.5249144\",\n \"0.5236534\",\n \"0.5235945\",\n \"0.52335167\",\n \"0.52292085\",\n \"0.5228563\",\n \"0.5222203\",\n \"0.52136475\",\n \"0.52049804\",\n \"0.5204536\",\n \"0.5200366\",\n \"0.5195364\",\n \"0.51909363\",\n \"0.51721334\",\n \"0.5165026\",\n \"0.5161816\",\n \"0.5161334\",\n \"0.51385075\",\n \"0.5131762\",\n \"0.51245654\",\n \"0.51075363\",\n \"0.51055926\",\n \"0.50903356\",\n \"0.50821495\",\n \"0.50642776\",\n \"0.5058599\",\n \"0.5057797\",\n \"0.5034588\",\n \"0.5029297\",\n \"0.5024028\",\n \"0.5007628\",\n \"0.49992222\",\n \"0.49940845\",\n \"0.4993942\",\n \"0.49897918\",\n \"0.49847603\",\n \"0.49847603\",\n \"0.49796358\",\n \"0.49781346\",\n \"0.49670273\",\n \"0.4964765\",\n \"0.4946403\",\n \"0.4942368\",\n \"0.4924673\",\n \"0.49071246\",\n \"0.4897674\",\n \"0.4893762\",\n \"0.48873302\",\n \"0.48855856\",\n \"0.4884757\",\n \"0.48804364\",\n \"0.4877578\",\n \"0.4865963\",\n \"0.48587304\",\n \"0.48566014\",\n \"0.48538876\",\n \"0.48529506\",\n \"0.48509505\",\n \"0.4850203\",\n \"0.4850065\",\n \"0.4844761\",\n \"0.48401338\",\n \"0.4838139\",\n \"0.48348737\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69854707"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":86683,"cells":{"query":{"kind":"string","value":"Ensure findlinks option endup being a list of strings."},"document":{"kind":"string","value":"def _fixup_find_links(find_links):\n if isinstance(find_links, str):\n return find_links.split()\n assert isinstance(find_links, (tuple, list))\n return find_links"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def produce_links_search(self, value_list:list) -> list:\n return [\n [self.produce_link_google(f) for f in value_list],\n [self.produce_link_qwant(f) for f in value_list],\n [self.produce_link_bing(f) for f in value_list],\n [self.produce_link_duckduckgo(f) for f in value_list],\n [self.produce_link_yahoo(f) for f in value_list]\n ]","def collectLinks(self, output):\n pass","def urls(self) -> list[str]:\r\n ...","def links(self):\n\t\treturn self.list_of_links","def test_should_pass_when_list_of_url_are_not_empty(self):\n self.ebooksPage = EbooksPage(self.driver)\n list_of_links = self.ebooksPage.get_all_links()\n\n assert (isinstance(list_of_links, list) and (len(list_of_links) > 0 ))","def in_collections(self):\n links = []\n for link in self.link:\n if link.rel == PARENT_LINK_REL and link.href:\n links.append(link)\n return links","def getMyLinks(self, link_list, plant):\n my_links = []\n for links in link_list:\n if plant in links:\n my_links.append(links)\n return my_links","def gen_links(text):\n return []","def _validate_links(cls, links, relationship: Optional[str] = None):\n errors = []\n for name in links:\n qual_name = cls._qualname(name, relationship)\n if qual_name in cls.__links_factories__:\n if not isinstance(links[name], Mapping):\n errors.append(f\" You must provide an arguments dictionary for '{qual_name}' link.\")\n continue\n provided_link = links.get(name)\n if provided_link is None:\n errors.append(f\" Nothing provided for building '{qual_name}' link.\")\n elif not isinstance(links[name], str):\n errors.append(f\" Provided '{qual_name}' link is not a string.\")\n if errors:\n raise ValueError(\"\\n\" + \"\\n\".join(errors))","def check_for_list(check):","def legacy_check(link = None):\n if type(link) == type(\"\"):\n # If we are given a string, we split it ourselves\n return link.split(\"/\")\n else:\n return link","def run_search(self, links):\n for s in links:\n self._run_command(\" s \\\"{}\\\" \\n\".format(s))","def fix_links():\n pass","def get_link_list_from_request(request):\n\tquery = request.POST.get('link_list','')\n #Clear all whitespaces from textarea\n whitespace = re.compile(r'\\s+')\n\turls = query.split('\\n')\n\tlinks = list()\n\tfor url in urls:\n\t\turl = whitespace.sub('', url)\n \tif url and url.lower().find('rapidshare') is not -1:\n \tlinks.append(url)\n\n\treturn links","def _validate_command(self):\n if not isinstance(self.command, list):\n raise securesystemslib.exceptions.FormatError(\n \"Invalid Link: field `command` must be of type list, got: {}\"\n .format(type(self.command)))","def test_get_pci_link_list(self):\n pass","def list(self):\n\t\treturn self.link_words","def _build_links(links):\n for link in links:\n link['href'] = link['href'].replace('servers', 'instances')\n return links","def get_urls(self):\n urls = []\n params = ['<{}>'.format(x) for x in self.args]\n args_length = len(self.args) - len(self.defaults)\n for i in range(len(self.defaults) + 1):\n index = -i if i > args_length else None\n urls.append(self.get_url(params[:index]))\n return urls","def link_to_list(link):\n \"*** YOUR CODE HERE ***\"\n if link is Link.empty:\n return []\n else:\n return [link.first] + link_to_list(link.rest)","def getExpandedLinks():","def check_page_links():\n\tprint(\"\\nChecking page's link\")\n\treturn [check_link_is_valid(link) for link in get_page_links()]","def get_links(self) -> List[str]:\n return self.__links","def test_get_link_incorrect_type_list(self):\n with pytest.raises(Exception) as typeError:\n parse_topo.get_link(kytos_link=[], oxp_url=helper._oxp_url)\n assert str(typeError.typename) == \"TypeError\"","def serialize_redirect_uris_field(self, obj, **kwargs):\n return list(filter(len, obj.redirect_uris.split()))","def get_links(value):\n\ttry:\n\t\ttry:\n\t\t\tfrom BeautifulSoup import BeautifulSoup\n\t\texcept ImportError:\n\t\t\tfrom beautifulsoup import BeautifulSoup\n\t\tsoup = BeautifulSoup(value)\n\t\treturn soup.findAll('a')\n\texcept ImportError:\n\t\tif settings.DEBUG:\n\t\t\traise template.TemplateSyntaxError, \"Error in 'get_links' filter: BeautifulSoup isn't installed.\"\n\treturn value","def __url_list(self, page):\n url_list = []\n for tag_a in page.find_all('a'):\n href = str(tag_a.get('href'))\n if self.__verify(href):\n url = parse.quote(self.__add_main_site(href), '/:#')\n url_list.append(url)\n return url_list","def get_or_make_links(self, link_names):\n link_names = link_names if isinstance(link_names, (list, tuple)) else [link_names]\n urls = []\n for name in link_names:\n try:\n url = reverse(name)\n except NoReverseMatch as e:\n print(e)\n url = None\n # if name == 'password_reset':\n # path('test-password/', views.PasswordChangeView.as_view(template_name='update.html'), name=name)\n # else:\n # pass\n urls.append(url)\n # print(urls)\n return urls","def auto_link_usernames_or_lists(self, options = {}):\r\n return self.auto_link_entities(self.extractor.extract_mentions_or_lists_with_indices(), options)","def get_links(html, outformat):\n if outformat == FORMAT_BIBTEX:\n refre = re.compile(r'<a href=\"(/scholar\\.bib\\?[^\"]*)')\n elif outformat == FORMAT_ENDNOTE:\n refre = re.compile(r'<a href=\"(/scholar\\.enw\\?[^\"]*)\"')\n elif outformat == FORMAT_REFMAN:\n refre = re.compile(r'<a href=\"(/scholar\\.ris\\?[^\"]*)\"')\n elif outformat == FORMAT_WENXIANWANG:\n refre = re.compile(r'<a href=\"(/scholar\\.ral\\?[^\"]*)\"')\n reflist = refre.findall(html)\n # escape html entities\n reflist = [re.sub('&(%s);' % '|'.join(name2codepoint), lambda m:\n chr(name2codepoint[m.group(1)]), s) for s in reflist]\n return reflist","def run_check_links(db,wfcollection,nrmlist,elimit,verbose):\n\n for nrmcol in nrmlist:\n errs=db._check_links(xref_key=nrmcol,\n collection=wfcollection,\n error_limit=elimit)\n broken=errs[0]\n undef=errs[1]\n if verbose:\n if len(broken)==0:\n print('check_links found no broken links with normalized key=',nrmcol)\n else:\n print('check_link found the following docs in ',wfcollection,\n ' with broken links to ',nrmcol)\n print_bad_wf_docs(db[wfcollection],broken)\n if len(undef)==0:\n print('check_links found no undefined linking key to normalized key=',\n nrmcol)\n else:\n print('check_link found the following docs in ',wfcollection,\n ' with undefined link keys to ',nrmcol)\n print_bad_wf_docs(db[wfcollection],undef)\n else:\n if len(broken)==0:\n print('check_links found no broken links with normalized key=',nrmcol)\n else:\n print('normalization test on normalized key=',nrmcol,' found problems')\n print('Found broken links in ',len(broken),\n 'documents checked')\n print('Note error count limit=',elimit)\n print('If the count is the same it means all data probably contain missing cross referencing ids')\n print('Run in verbose mode to find out more information you will need to fix the problem')","def old_list_links(self, link_list, dd):\n link_names = []\n for link in link_list:\n if \"subgroup\" in link:\n sublinks = list(link[\"subgroup\"])\n for sublink in sublinks:\n link_names.append(sublink[\"name\"])\n else:\n link_names.append(link[\"name\"])\n return link_names","def get_urls():\r\n return []","def build_links(self):\n super().build_links()\n if len(self.links) > 1:\n raise MapParserError(\"may have at most one link connecting to it.\", self)","def test_urllist_command(self):\n # TODO: Write tests for when there is no superuser.\n # This seemed to not work when using this command on PythonAnywhere the first time\n pass","def links(self) -> str:\n return pulumi.get(self, \"links\")","def filter_substitution_image_links(links):\n return [link for link in links if '{' not in link]","def linked_entity_guids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"linked_entity_guids\")","def linked_entity_guids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"linked_entity_guids\")","def linked_entity_guids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"linked_entity_guids\")","def linked_entity_guids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"linked_entity_guids\")","def linked_entity_guids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"linked_entity_guids\")","async def _find_links(self, res: aiohttp.ClientResponse) -> Iterator[str]:\n\n content = await res.text()\n soup = BeautifulSoup(content, 'html.parser')\n links = [self._format(res.url, a) for a in soup.find_all('a')]\n return filter(lambda l: l is not None, links)","def get_links(self):\n msg = self.get_message()\n return msg.split()","def cleanLinks(self, links: list) -> list:\n returnLinks = dict()\n\n for link in links:\n linkBase = SpiderFootHelpers.urlBaseUrl(link)\n linkFQDN = self.sf.urlFQDN(link)\n\n # Skip external sites (typical behaviour..)\n if not self.getTarget().matches(linkFQDN):\n # self.debug('Ignoring external site: ' + link)\n continue\n\n # Optionally skip sub-domain sites\n if self.opts['nosubs'] and not \\\n self.getTarget().matches(linkFQDN, includeChildren=False):\n # self.debug(\"Ignoring subdomain: \" + link)\n continue\n\n # Skip parent domain sites\n if not self.getTarget().matches(linkFQDN, includeParents=False):\n # self.debug(\"Ignoring parent domain: \" + link)\n continue\n\n # Optionally skip user directories\n if self.opts['filterusers'] and '/~' in link:\n # self.debug(\"Ignoring user folder: \" + link)\n continue\n\n # If we are respecting robots.txt, filter those out too\n if linkBase in self.robotsRules and self.opts['robotsonly']:\n if list(filter(lambda blocked: type(blocked).lower(blocked) in link.lower() or blocked == '*', self.robotsRules[linkBase])):\n # self.debug(\"Ignoring page found in robots.txt: \" + link)\n continue\n\n # All tests passed, add link to be spidered\n self.debug(f\"Adding URL for spidering: {link}\")\n returnLinks[link] = links[link]\n\n return list(returnLinks.keys())","def __setup_recursion(folder_found, links_titles):\n urls = []\n if folder_found:\n for element_x, _, element_z in links_titles:\n if element_z == 'folder':\n # fill urls with sub-links to recursively call crawl function on them\n urls.append(element_x)\n return urls","def parse_urls(start_urls: Union[str, List[str]]) -> List[str]:\n if not (\n (type(start_urls) == str)\n or ((type(start_urls) == list) and (all([type(x) == str for x in start_urls])))\n ):\n error_message = (\n \"Argument 'start_urls' need to be of type list or (comma-separated) string.\"\n )\n logger.error(error_message)\n raise ValueError(error_message)\n\n return start_urls.split(\",\") if type(start_urls) == str else start_urls # type: ignore","def check_links(self,df,node,dd):\n errors = []\n links = self.list_links(node, dd)\n if \"core_metadata_collections\" in links:\n links.remove(\"core_metadata_collections\")\n if \"core_metadata_collections.submitter_id\" in links:\n links.remove(\"core_metadata_collections.submitter_id\")\n for link in links:\n link_col = \"{}.submitter_id\".format(link)\n if link_col not in df:\n error = \"'{}' link header not found in '{}' TSV.\".format(link_col,node)\n print(error) # this is not necessarily an error, as some links may be optional, but must have at least 1 link\n errors.append(error)\n return errors","def links(self):\n return self.container['links']","def getURLs():","def limitDomain(absoluteLinks, domain):\n domainRegex = re.compile(\"%s/\" % domain)\n return set(filter(domainRegex.search, absoluteLinks))","def _construct_links_of_interest(self):\n if self.opts.selected_links:\n links, num_links = self.parse_selected_links(\n self.opts.selected_links)\n elif (self.opts.selected_terms or\n self.opts.selected_terms_with_all):\n if self.opts.selected_terms:\n selected_terms_file_name = self.opts.selected_terms\n else:\n selected_terms_file_name = \\\n self.opts.selected_terms_with_all\n logger.info(\"Parsing selected terms file %s.\" %\n selected_terms_file_name)\n selected_terms_file = open(\n selected_terms_file_name, 'rb')\n num_selected_terms = convutils.count_lines(\n selected_terms_file)\n selected_terms = \\\n parsers.parse_selected_terms_file(\n selected_terms_file)\n if self.opts.selected_terms:\n num_links = self._calc_num_links_selected_terms(\n num_selected_terms)\n links = self.make_selected_terms_links(\n selected_terms)\n else:\n num_links = self._calc_num_links_selected_with_all(\n num_selected_terms)\n links = self.make_selected_terms_links_with_all(\n selected_terms, self.annotations_dict)\n else:\n links, num_links = self.make_all_possible_links(\n self.annotations_dict)\n\n self.links, self.num_links = links, num_links","def _parse_links(self, item, start, links_list):\n result_list = []\n target_str_1 = start.strftime(\"%m-%d-%Y\").replace(\" 0\", \" \")\n target_str_2 = start.strftime(\"%m-%d-%y\").replace(\" 0\", \" \")\n for item in links_list:\n if item[\"date\"] in target_str_1 or item[\"date\"] in target_str_2:\n new_dict = {}\n new_dict[\"href\"] = item[\"href\"]\n new_dict[\"title\"] = item[\"title\"]\n result_list.append(new_dict)\n return result_list","def links(self, links):\n self._links = links","def auto_link_urls(self, options = {}):\r\n return self.auto_link_entities(self.extractor.extract_urls_with_indices({'extract_url_without_protocol': False}), options)","def get_docs_urls(self):\n docs_urls = []\n link_labels = []\n for tag in self.post_div.find_all(\"a\"):\n url = tag[\"href\"]\n if url.startswith(\"https://docs.google.com\") or \\\n url.startswith(\"https://drive.google.com\"):\n docs_urls += [url]\n link_labels += [tag.text]\n return docs_urls, link_labels","def get_links(self):\r\n return self.links","def _go_list(self, *args):\n return subprocess.check_output((\"go\", \"list\") + self.tag_args + args).strip().split(\"\\n\")","def getLinks():\n curDIR = os.path.dirname(os.path.realpath(__file__))\n inputfile_path = os.path.join(curDIR, 'sitemaplinks.txt')\n links=[]\n with open(inputfile_path,'r') as outputfile:\n for line in outputfile:\n links.append(line.split(',')[0])\n return links","def list_of_links_to_check(main_link):\n # TODO: try else-here, when didn't finds links, then restart\n list_of_links = []\n driver.get(main_link)\n sleep(1)\n temp_list = driver.find_elements_by_class_name(\"table-main__tt\")\n for block in temp_list: \n elements = block.find_elements_by_tag_name(\"a\")\n for el in elements:\n if \"soccer\" in el.get_attribute(\"href\"):\n list_of_links.append(el.get_attribute(\"href\"))\n return list_of_links","def find_usefull_links(links, classmodel, class_count_vect):\n\n import re\n final_links = []\n seclinks = links\n for link in links:\n fulllink = link\n if link == None:\n continue\n else:\n link = link.replace('://', ' ')\n link = link.replace('@', ' ')\n link = link.replace('#', ' ')\n link = link.replace('/', ' ')\n link = link.replace('-', ' ')\n link = link.replace('.', ' ')\n link = link.replace('https', '')\n link = link.replace('http', '')\n link = link.replace('www', '')\n link = link.replace('&', ' ')\n link = link.replace('=', ' ')\n linkpd = pd.Series(link.strip())\n link_feature = class_count_vect.transform(linkpd)\n result = classmodel.predict(link_feature)\n\n result = result.tolist()\n result = str(result)\n if result == '[1]':\n final_links.append(fulllink)\n final_links = list(dict.fromkeys(final_links))\n \n if len(final_links) == 0 or len(final_links) < 5:\n for linksec in seclinks:\n linkwords = ['cabinet', 'gover', 'goverment', 'composition', 'ministers', 'minister',\n 'president', 'composicao', 'parliament', 'person', 'who', 'mini', 'compo',\n 'governor', 'secretariat', 'secretary']\n for w in linkwords:\n if re.search(w, linksec):\n final_links.append(linksec)\n else:\n continue\n final_links = list(dict.fromkeys(final_links))\n return (final_links)","def find_links(obj):\n if isinstance(obj, dict):\n for key, value in obj.iteritems():\n for url in find_links(value):\n yield url\n elif isinstance(obj, list):\n for item in obj:\n for url in find_links(item):\n yield url\n else:\n try:\n if is_link(str(obj)):\n yield obj\n except Exception:\n pass","def getLinks(content):\n soup = BeautifulSoup(content, 'lxml')\n links = set([link.get('href') for link in soup.find_all('a')])\n return links","def external_search_engines_links(search, deep=0, debug=0, links=[]):\n s = Subseek()\n for search_engine in SEARCH_ENGINES:\n for subtitle_search_engine in SUBTITLE_SEARCH_ENGINES:\n if debug == 1:\n print \"Searching '%s' in '%s'\" % (search,\n search_engine['name'])\n links_aux = s.get_links(search_engine, search, deep,\n subtitle_search_engine[\"name\"])\n if not links_aux or len(links_aux) == 0:\n if debug == 1:\n print \"No match found in '%s'\" % search_engine['name']\n else:\n if debug == 1:\n print \"%s matches found in '%s'\" % (len(links_aux),\n search_engine['name'])\n links = links_aux + links\n\n return links","def iter_links(self):","def is_valid_listings(link):\n if link.has_attr(\"href\") and link.attrs[\"href\"].startswith(LISTING_PREFIX):\n return True\n return False","def get_urls(argument_urls: Optional[str] = None) -> List[str]:\n if argument_urls:\n raw_urls = argument_urls\n elif 'amqp_url' in config:\n raw_urls = config['amqp_url']\n elif 'AMQP_SERVERS' in environ:\n raw_urls = environ['AMQP_SERVERS']\n else:\n raise ValueError('AMQP server url is not configured')\n\n return [process_url(url) for url in raw_urls.split(',')]","def _parse_links(self, item) -> list:\n # TODO This would be a \"nice to have\" but is not necessary right now.\n return [{\"href\": \"\", \"title\": \"\"}]","def get_non_docs_urls(self):\n non_docs_urls = []\n link_labels = []\n for tag in self.post_div.find_all(\"a\"):\n url = tag[\"href\"]\n if tag.text not in link_label_blacklist and \\\n not (url.startswith(\"https://docs.google.com\") or \\\n url.startswith(\"https://drive.google.com\")):\n non_docs_urls += [url]\n link_labels += [tag.text.strip()]\n return non_docs_urls, link_labels","def get_all_links(file1):\n links = []\n #Measurements.logger.critical(\"Value of is_from_file is %s\", str(Measurements.is_from_file))\n if Measurements.is_from_file is True:\n with open(file1) as f:\n #if the file is valid:, store it\n for line in f:\n if line:\n links.append(line)\n else:\n links = file1 # file1 is alrealy a list of data\n # do processing here\n return links","def _is_list(arg):\n return isinstance(arg, collections.Sequence) and not _is_string(arg)","def get_listings(soup):\n listings = []\n for link in soup.find_all(\"a\"):\n if is_valid_listings(link):\n listings.append(link.attrs[\"href\"])\n return listings","def run_linkfinder(self, text, urljoin_fnct):\n\n urls = set()\n # store the text in a separate file for Linkfinder\n tmp_filename_in = \"linkfinder_tmp_%d.in\" % random.randint(0, 2**32)\n with open(tmp_filename_in, \"w\") as f:\n f.write(text)\n\n # Run Linkfinder as subprocess and remove the input file thereafter\n linkfinder_out = \"\"\n try:\n linkfinder_out = subprocess.check_output([\"python3 LinkFinder/linkfinder.py -i \" +\n tmp_filename_in + \" -o cli 2>/dev/null\"], shell=True)\n linkfinder_out = linkfinder_out.decode()\n except subprocess.CalledProcessError:\n pass\n os.remove(tmp_filename_in)\n\n # process Linkfinder's output\n for line in linkfinder_out.split(\"\\n\"):\n if not line:\n continue\n line = line.strip()\n line = self.to_absolute_url(line, urljoin_fnct)\n # if configured, check if the discovered URL is valid and exists\n if self.config[\"check_linkfinder\"].lower() == \"true\":\n try:\n timeout = float(self.config[\"linkfinder_check_timeout\"])\n if str(requests.head(line, timeout=timeout).status_code) != \"404\":\n urls.add(line)\n except:\n pass\n else:\n urls.add(line)\n\n return urls","def test_link_in_list(self):\n self.assertEquals(\"* [name](name)\\n* name2\\n* name3\",\n trans(\" * [name]\\n * name2\\n * name3\"))","def get_links(self, response, find_in_text=None, find_in_href=None, exclude_from_href=None): \n \n if not (find_in_text or find_in_href):\n logging.error(f\"Supply one or both of find_in_text and find_in_href to search for links in {response.url}\")\n return None\n\n hrefs = []\n if find_in_text and find_in_href:\n hrefs = response.xpath(f'//a[contains(text(), \"{find_in_text}\")][contains(@href, \"{find_in_href}\")]/@href').getall()\n elif find_in_text:\n hrefs = response.xpath(f'//a[contains(text(), \"{find_in_text}\")]/@href').getall()\n elif find_in_href:\n hrefs = response.xpath(f'//a[contains(@href, \"{find_in_href}\")]/@href').getall()\n \n if len(hrefs) == 0:\n logging.error(f\"No links found with text containing {find_in_text} and/or href containing {find_in_href} in {response.url}. Please inspect the HTML element(s) you are hoping to find to check if find_in_href and find_in_text need to be updated.\")\n return hrefs\n\n # Remove duplicates, exclude undesired links, and sort\n hrefs = list(set(hrefs))\n if exclude_from_href:\n hrefs = [x for x in hrefs if exclude_from_href not in x]\n hrefs.sort()\n\n logging.info(f\"Found links: {hrefs}\")\n\n return hrefs","def _get_links(self):\n with open(self.source, \"r\", encoding=\"utf-8\") as link_doc:\n return link_doc.readlines()","def test_list(self):\n parser = parse_args(['-g', '10', '-s', 'bubble', '-l'])\n self.assertTrue(parser.list)\n self.assertEqual(True, parser.list)\n\n parser = parse_args(['-g', '10', '-s', 'bubble'])\n self.assertEqual(False, parser.list)","def to_list(a, args):\n if args.filter(follow=a).exists():\n exists = True\n else:\n exists = False\n return exists","def test_splits_urls_for_nouns(self):\r\n test_value = \"http://google.com/drives/autonomous/cars\"\r\n self.assertEqual(\r\n set([u'cars', u'autonomous']),\r\n suggest_tags(test_value))","def _filter_return_url_from_list(self, paths, har=None):\r\n if not har:\r\n har = self.har\r\n \r\n matches = []\r\n for entry in har[\"log\"][\"entries\"]:\r\n for path in paths:\r\n if path in entry[\"request\"][\"url\"]:\r\n temp = entry[\"request\"][\"url\"].encode('ascii', 'ignore')\r\n matches.append(temp)\r\n return matches","def get_urls(links):\n\n temp_list=[]\n url_list=[]\n temp_list2=[]\n #Open the file where the url's are saved and copy the tuple values into an empty list\n z=open('dbdocs.txt','r')\n for line in z:\n temp_list.append(line)\n #print temp_list\n for x in temp_list:\n index=x.find(',')\n if index==-1:\n y=x.split(\" \",1)\n key=int(y[0])\n val=str(x[1]).replace('\\n','')\n url_list.append((key,val))\n else:\n #find the tab seperator between the key and the url, and\n #split them, in order to put in a list\n key=x[0:index-1]\n #print key\n value=str(x[index+3:len(x)-1])\n #print value\n temp_list2.append((int(key),value))\n #Find the url's of the links where the word was found\n for k in links:\n for i,j in temp_list2:\n #print j\n if i==k:\n url_list.append((i,j))\n break\n #print len(url_list)\n #print len(links)\n z.close()\n return url_list","def reformat(array):\n global searched_domain\n response = []\n for tag in array:\n link = tag.get(\"href\", None)\n if link is not None:\n p = parse.urlparse(link)\n if re.match(searched_netloc, p.netloc):\n if p.scheme == \"\":\n link = parse.ParseResult(\"http\", *p[1:]).geturl()\n response.append(link)\n return response","def get_anchor_types(self) -> list:\n pass","def test_get_variant_links(variant_obj):\n # GIVEN a variant object without links\n assert \"thousandg_link\" not in variant_obj\n # WHEN fetching the variant links\n links = get_variant_links(variant_obj)\n # THEN check that links are returned\n assert \"thousandg_link\" in links","def link_to_set(page):\n #s = set()\n links = Measurements.get_all_links(page)\n s = set(links)\n return s","def getLinks(self, url, tag = \"a\", attr = \"href\"): \n try: \n response = open(self.filename(url)).read() #read from the file\n except IOError:\n raise IOError\n parsed_url = urlparse(url)\n domain = parsed_url[0] + '://' + parsed_url[1]\n \n try:\n soup = BeautifulSoup.BeautifulSoup(response)\n l = soup.findAll(tag, href = True)\n except Exception:\n raise Exception\n links = []\n \n for tag in l:\n link = str(tag[attr]) #convert the link to a string\n purl = urlparse(link)\n if purl[1] == '': #if the link is relative make it absolute\n link = domain+link\n #check if the extension is that of a document \n if splitext(link)[1] in self._invalidExt: \n self.docs_list.append(link)\n \n #append only the html link\n links.append(link)\n \n \n \n return list(set(links)) #returns only distinct links","def extract_links(self, source: str) -> List[str]:\n return self.links_compiled_exp.findall(source)","def convert_link(link):\n\n ###############\n # My Solution #\n ###############\n\n if link == Link.empty:\n return []\n else: \n return [link.first] + convert_link(link.rest)","def target_list_option(s):\n return _convert(s, (list, tuple))","def list_urls(self, prefix: str = \"\", etl_name: str = None) -> Iterable[str]:","def linkify(obj, link_text=''):\n try:\n lst = []\n # if obj is not a list, convert it into a list\n if not getattr(obj, '__iter__', False):\n obj = [obj]\n for item in obj:\n if hasattr(item, 'child'):\n item = item.child\n if link_text == '':\n l_text = unicode(item)\n else:\n try:\n link_text = link_text.encode('ascii')\n l_text = getattr(item, link_text, link_text)\n except UnicodeEncodeError:\n l_text = link_text\n if not (isinstance(item, Content) and\n isinstance(l_text, SafeText)):\n l_text = filter.force_escape(l_text)\n format_args = (item.get_absolute_url(), l_text)\n lst.append(mark_safe('<a href=\\'%s\\'>%s</a>' % format_args))\n\n # nonlists obj's should be returned as nonlists\n return lst[0] if len(lst) == 1 else lst\n except:\n return ''","def simplelist(inline):\n return paramfinder.findall(inline)","def subtitle_search_engines_links(search, deep=0, debug=0, links=[]):\n s = Subseek()\n for subtitle_search_engine in SUBTITLE_SEARCH_ENGINES:\n if debug == 1:\n print \"Searching '%s' in '%s'\" % (search,\n subtitle_search_engine['name'])\n links_aux = s.get_links(subtitle_search_engine, search, deep)\n if not links_aux or len(links_aux) == 0:\n if debug == 1:\n print \"No match found in '%s'\" % subtitle_search_engine['name']\n else:\n if debug == 1:\n print \"%s matches found in '%s'\" % (len(links_aux),\n subtitle_search_engine['name'])\n\n links = links_aux + links\n\n return links","def mergeURLS(inputs):\n urls = set()\n for i in inputs:\n # Re-raise any exceptions\n try:\n urls = urls.union(i.urls())\n except:\n raise\n return urls","def get_links(self, response, domain, port, folder):\n\t\t# find link in tags: a, link, form, button\n\t\t# call to all function in file get_link\n\t\t# for method in get_link:\n\t\tlinks = get_link(response, domain, port, folder)\n\t\tlinks = filter(None, links.getResults())\n\t\treturn links","def find_links(url, html):\n\tif html is None:\n\t\treturn set()\n\turl = urlparse.urlparse(url)\n\tlinks = url_regex.findall(html)\n\tlink_set = set()\n\tfor link in links:\n\t\tif link is None:\n\t\t\tcontinue\n\t\ttry:\n\t\t\tlink = str(link)\n\t\t\tif link.startswith(\"/\"):\n\t\t\t\tlink_set.add('http://' + url.netloc + link)\n\t\t\telif link.startswith(\"http\") or link.startswith(\"https\"):\n\t\t\t\tif link.find(url.netloc):\n\t\t\t\t\tlink_set.add(link)\n\t\t\telif link.startswith(\"#\"):\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tlink_set.add(urlparse.urljoin(url.geturl(), link))\n\t\texcept:\n\t\t\tpass\n\n\treturn link_set","def get_links(self):\n return (link for link in self.links)","def links(self):\n return self._links","def links(self):\n return self._links","def links(self):\n return self._links"],"string":"[\n \"def produce_links_search(self, value_list:list) -> list:\\n return [\\n [self.produce_link_google(f) for f in value_list],\\n [self.produce_link_qwant(f) for f in value_list],\\n [self.produce_link_bing(f) for f in value_list],\\n [self.produce_link_duckduckgo(f) for f in value_list],\\n [self.produce_link_yahoo(f) for f in value_list]\\n ]\",\n \"def collectLinks(self, output):\\n pass\",\n \"def urls(self) -> list[str]:\\r\\n ...\",\n \"def links(self):\\n\\t\\treturn self.list_of_links\",\n \"def test_should_pass_when_list_of_url_are_not_empty(self):\\n self.ebooksPage = EbooksPage(self.driver)\\n list_of_links = self.ebooksPage.get_all_links()\\n\\n assert (isinstance(list_of_links, list) and (len(list_of_links) > 0 ))\",\n \"def in_collections(self):\\n links = []\\n for link in self.link:\\n if link.rel == PARENT_LINK_REL and link.href:\\n links.append(link)\\n return links\",\n \"def getMyLinks(self, link_list, plant):\\n my_links = []\\n for links in link_list:\\n if plant in links:\\n my_links.append(links)\\n return my_links\",\n \"def gen_links(text):\\n return []\",\n \"def _validate_links(cls, links, relationship: Optional[str] = None):\\n errors = []\\n for name in links:\\n qual_name = cls._qualname(name, relationship)\\n if qual_name in cls.__links_factories__:\\n if not isinstance(links[name], Mapping):\\n errors.append(f\\\" You must provide an arguments dictionary for '{qual_name}' link.\\\")\\n continue\\n provided_link = links.get(name)\\n if provided_link is None:\\n errors.append(f\\\" Nothing provided for building '{qual_name}' link.\\\")\\n elif not isinstance(links[name], str):\\n errors.append(f\\\" Provided '{qual_name}' link is not a string.\\\")\\n if errors:\\n raise ValueError(\\\"\\\\n\\\" + \\\"\\\\n\\\".join(errors))\",\n \"def check_for_list(check):\",\n \"def legacy_check(link = None):\\n if type(link) == type(\\\"\\\"):\\n # If we are given a string, we split it ourselves\\n return link.split(\\\"/\\\")\\n else:\\n return link\",\n \"def run_search(self, links):\\n for s in links:\\n self._run_command(\\\" s \\\\\\\"{}\\\\\\\" \\\\n\\\".format(s))\",\n \"def fix_links():\\n pass\",\n \"def get_link_list_from_request(request):\\n\\tquery = request.POST.get('link_list','')\\n #Clear all whitespaces from textarea\\n whitespace = re.compile(r'\\\\s+')\\n\\turls = query.split('\\\\n')\\n\\tlinks = list()\\n\\tfor url in urls:\\n\\t\\turl = whitespace.sub('', url)\\n \\tif url and url.lower().find('rapidshare') is not -1:\\n \\tlinks.append(url)\\n\\n\\treturn links\",\n \"def _validate_command(self):\\n if not isinstance(self.command, list):\\n raise securesystemslib.exceptions.FormatError(\\n \\\"Invalid Link: field `command` must be of type list, got: {}\\\"\\n .format(type(self.command)))\",\n \"def test_get_pci_link_list(self):\\n pass\",\n \"def list(self):\\n\\t\\treturn self.link_words\",\n \"def _build_links(links):\\n for link in links:\\n link['href'] = link['href'].replace('servers', 'instances')\\n return links\",\n \"def get_urls(self):\\n urls = []\\n params = ['<{}>'.format(x) for x in self.args]\\n args_length = len(self.args) - len(self.defaults)\\n for i in range(len(self.defaults) + 1):\\n index = -i if i > args_length else None\\n urls.append(self.get_url(params[:index]))\\n return urls\",\n \"def link_to_list(link):\\n \\\"*** YOUR CODE HERE ***\\\"\\n if link is Link.empty:\\n return []\\n else:\\n return [link.first] + link_to_list(link.rest)\",\n \"def getExpandedLinks():\",\n \"def check_page_links():\\n\\tprint(\\\"\\\\nChecking page's link\\\")\\n\\treturn [check_link_is_valid(link) for link in get_page_links()]\",\n \"def get_links(self) -> List[str]:\\n return self.__links\",\n \"def test_get_link_incorrect_type_list(self):\\n with pytest.raises(Exception) as typeError:\\n parse_topo.get_link(kytos_link=[], oxp_url=helper._oxp_url)\\n assert str(typeError.typename) == \\\"TypeError\\\"\",\n \"def serialize_redirect_uris_field(self, obj, **kwargs):\\n return list(filter(len, obj.redirect_uris.split()))\",\n \"def get_links(value):\\n\\ttry:\\n\\t\\ttry:\\n\\t\\t\\tfrom BeautifulSoup import BeautifulSoup\\n\\t\\texcept ImportError:\\n\\t\\t\\tfrom beautifulsoup import BeautifulSoup\\n\\t\\tsoup = BeautifulSoup(value)\\n\\t\\treturn soup.findAll('a')\\n\\texcept ImportError:\\n\\t\\tif settings.DEBUG:\\n\\t\\t\\traise template.TemplateSyntaxError, \\\"Error in 'get_links' filter: BeautifulSoup isn't installed.\\\"\\n\\treturn value\",\n \"def __url_list(self, page):\\n url_list = []\\n for tag_a in page.find_all('a'):\\n href = str(tag_a.get('href'))\\n if self.__verify(href):\\n url = parse.quote(self.__add_main_site(href), '/:#')\\n url_list.append(url)\\n return url_list\",\n \"def get_or_make_links(self, link_names):\\n link_names = link_names if isinstance(link_names, (list, tuple)) else [link_names]\\n urls = []\\n for name in link_names:\\n try:\\n url = reverse(name)\\n except NoReverseMatch as e:\\n print(e)\\n url = None\\n # if name == 'password_reset':\\n # path('test-password/', views.PasswordChangeView.as_view(template_name='update.html'), name=name)\\n # else:\\n # pass\\n urls.append(url)\\n # print(urls)\\n return urls\",\n \"def auto_link_usernames_or_lists(self, options = {}):\\r\\n return self.auto_link_entities(self.extractor.extract_mentions_or_lists_with_indices(), options)\",\n \"def get_links(html, outformat):\\n if outformat == FORMAT_BIBTEX:\\n refre = re.compile(r'<a href=\\\"(/scholar\\\\.bib\\\\?[^\\\"]*)')\\n elif outformat == FORMAT_ENDNOTE:\\n refre = re.compile(r'<a href=\\\"(/scholar\\\\.enw\\\\?[^\\\"]*)\\\"')\\n elif outformat == FORMAT_REFMAN:\\n refre = re.compile(r'<a href=\\\"(/scholar\\\\.ris\\\\?[^\\\"]*)\\\"')\\n elif outformat == FORMAT_WENXIANWANG:\\n refre = re.compile(r'<a href=\\\"(/scholar\\\\.ral\\\\?[^\\\"]*)\\\"')\\n reflist = refre.findall(html)\\n # escape html entities\\n reflist = [re.sub('&(%s);' % '|'.join(name2codepoint), lambda m:\\n chr(name2codepoint[m.group(1)]), s) for s in reflist]\\n return reflist\",\n \"def run_check_links(db,wfcollection,nrmlist,elimit,verbose):\\n\\n for nrmcol in nrmlist:\\n errs=db._check_links(xref_key=nrmcol,\\n collection=wfcollection,\\n error_limit=elimit)\\n broken=errs[0]\\n undef=errs[1]\\n if verbose:\\n if len(broken)==0:\\n print('check_links found no broken links with normalized key=',nrmcol)\\n else:\\n print('check_link found the following docs in ',wfcollection,\\n ' with broken links to ',nrmcol)\\n print_bad_wf_docs(db[wfcollection],broken)\\n if len(undef)==0:\\n print('check_links found no undefined linking key to normalized key=',\\n nrmcol)\\n else:\\n print('check_link found the following docs in ',wfcollection,\\n ' with undefined link keys to ',nrmcol)\\n print_bad_wf_docs(db[wfcollection],undef)\\n else:\\n if len(broken)==0:\\n print('check_links found no broken links with normalized key=',nrmcol)\\n else:\\n print('normalization test on normalized key=',nrmcol,' found problems')\\n print('Found broken links in ',len(broken),\\n 'documents checked')\\n print('Note error count limit=',elimit)\\n print('If the count is the same it means all data probably contain missing cross referencing ids')\\n print('Run in verbose mode to find out more information you will need to fix the problem')\",\n \"def old_list_links(self, link_list, dd):\\n link_names = []\\n for link in link_list:\\n if \\\"subgroup\\\" in link:\\n sublinks = list(link[\\\"subgroup\\\"])\\n for sublink in sublinks:\\n link_names.append(sublink[\\\"name\\\"])\\n else:\\n link_names.append(link[\\\"name\\\"])\\n return link_names\",\n \"def get_urls():\\r\\n return []\",\n \"def build_links(self):\\n super().build_links()\\n if len(self.links) > 1:\\n raise MapParserError(\\\"may have at most one link connecting to it.\\\", self)\",\n \"def test_urllist_command(self):\\n # TODO: Write tests for when there is no superuser.\\n # This seemed to not work when using this command on PythonAnywhere the first time\\n pass\",\n \"def links(self) -> str:\\n return pulumi.get(self, \\\"links\\\")\",\n \"def filter_substitution_image_links(links):\\n return [link for link in links if '{' not in link]\",\n \"def linked_entity_guids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"linked_entity_guids\\\")\",\n \"def linked_entity_guids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"linked_entity_guids\\\")\",\n \"def linked_entity_guids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"linked_entity_guids\\\")\",\n \"def linked_entity_guids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"linked_entity_guids\\\")\",\n \"def linked_entity_guids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"linked_entity_guids\\\")\",\n \"async def _find_links(self, res: aiohttp.ClientResponse) -> Iterator[str]:\\n\\n content = await res.text()\\n soup = BeautifulSoup(content, 'html.parser')\\n links = [self._format(res.url, a) for a in soup.find_all('a')]\\n return filter(lambda l: l is not None, links)\",\n \"def get_links(self):\\n msg = self.get_message()\\n return msg.split()\",\n \"def cleanLinks(self, links: list) -> list:\\n returnLinks = dict()\\n\\n for link in links:\\n linkBase = SpiderFootHelpers.urlBaseUrl(link)\\n linkFQDN = self.sf.urlFQDN(link)\\n\\n # Skip external sites (typical behaviour..)\\n if not self.getTarget().matches(linkFQDN):\\n # self.debug('Ignoring external site: ' + link)\\n continue\\n\\n # Optionally skip sub-domain sites\\n if self.opts['nosubs'] and not \\\\\\n self.getTarget().matches(linkFQDN, includeChildren=False):\\n # self.debug(\\\"Ignoring subdomain: \\\" + link)\\n continue\\n\\n # Skip parent domain sites\\n if not self.getTarget().matches(linkFQDN, includeParents=False):\\n # self.debug(\\\"Ignoring parent domain: \\\" + link)\\n continue\\n\\n # Optionally skip user directories\\n if self.opts['filterusers'] and '/~' in link:\\n # self.debug(\\\"Ignoring user folder: \\\" + link)\\n continue\\n\\n # If we are respecting robots.txt, filter those out too\\n if linkBase in self.robotsRules and self.opts['robotsonly']:\\n if list(filter(lambda blocked: type(blocked).lower(blocked) in link.lower() or blocked == '*', self.robotsRules[linkBase])):\\n # self.debug(\\\"Ignoring page found in robots.txt: \\\" + link)\\n continue\\n\\n # All tests passed, add link to be spidered\\n self.debug(f\\\"Adding URL for spidering: {link}\\\")\\n returnLinks[link] = links[link]\\n\\n return list(returnLinks.keys())\",\n \"def __setup_recursion(folder_found, links_titles):\\n urls = []\\n if folder_found:\\n for element_x, _, element_z in links_titles:\\n if element_z == 'folder':\\n # fill urls with sub-links to recursively call crawl function on them\\n urls.append(element_x)\\n return urls\",\n \"def parse_urls(start_urls: Union[str, List[str]]) -> List[str]:\\n if not (\\n (type(start_urls) == str)\\n or ((type(start_urls) == list) and (all([type(x) == str for x in start_urls])))\\n ):\\n error_message = (\\n \\\"Argument 'start_urls' need to be of type list or (comma-separated) string.\\\"\\n )\\n logger.error(error_message)\\n raise ValueError(error_message)\\n\\n return start_urls.split(\\\",\\\") if type(start_urls) == str else start_urls # type: ignore\",\n \"def check_links(self,df,node,dd):\\n errors = []\\n links = self.list_links(node, dd)\\n if \\\"core_metadata_collections\\\" in links:\\n links.remove(\\\"core_metadata_collections\\\")\\n if \\\"core_metadata_collections.submitter_id\\\" in links:\\n links.remove(\\\"core_metadata_collections.submitter_id\\\")\\n for link in links:\\n link_col = \\\"{}.submitter_id\\\".format(link)\\n if link_col not in df:\\n error = \\\"'{}' link header not found in '{}' TSV.\\\".format(link_col,node)\\n print(error) # this is not necessarily an error, as some links may be optional, but must have at least 1 link\\n errors.append(error)\\n return errors\",\n \"def links(self):\\n return self.container['links']\",\n \"def getURLs():\",\n \"def limitDomain(absoluteLinks, domain):\\n domainRegex = re.compile(\\\"%s/\\\" % domain)\\n return set(filter(domainRegex.search, absoluteLinks))\",\n \"def _construct_links_of_interest(self):\\n if self.opts.selected_links:\\n links, num_links = self.parse_selected_links(\\n self.opts.selected_links)\\n elif (self.opts.selected_terms or\\n self.opts.selected_terms_with_all):\\n if self.opts.selected_terms:\\n selected_terms_file_name = self.opts.selected_terms\\n else:\\n selected_terms_file_name = \\\\\\n self.opts.selected_terms_with_all\\n logger.info(\\\"Parsing selected terms file %s.\\\" %\\n selected_terms_file_name)\\n selected_terms_file = open(\\n selected_terms_file_name, 'rb')\\n num_selected_terms = convutils.count_lines(\\n selected_terms_file)\\n selected_terms = \\\\\\n parsers.parse_selected_terms_file(\\n selected_terms_file)\\n if self.opts.selected_terms:\\n num_links = self._calc_num_links_selected_terms(\\n num_selected_terms)\\n links = self.make_selected_terms_links(\\n selected_terms)\\n else:\\n num_links = self._calc_num_links_selected_with_all(\\n num_selected_terms)\\n links = self.make_selected_terms_links_with_all(\\n selected_terms, self.annotations_dict)\\n else:\\n links, num_links = self.make_all_possible_links(\\n self.annotations_dict)\\n\\n self.links, self.num_links = links, num_links\",\n \"def _parse_links(self, item, start, links_list):\\n result_list = []\\n target_str_1 = start.strftime(\\\"%m-%d-%Y\\\").replace(\\\" 0\\\", \\\" \\\")\\n target_str_2 = start.strftime(\\\"%m-%d-%y\\\").replace(\\\" 0\\\", \\\" \\\")\\n for item in links_list:\\n if item[\\\"date\\\"] in target_str_1 or item[\\\"date\\\"] in target_str_2:\\n new_dict = {}\\n new_dict[\\\"href\\\"] = item[\\\"href\\\"]\\n new_dict[\\\"title\\\"] = item[\\\"title\\\"]\\n result_list.append(new_dict)\\n return result_list\",\n \"def links(self, links):\\n self._links = links\",\n \"def auto_link_urls(self, options = {}):\\r\\n return self.auto_link_entities(self.extractor.extract_urls_with_indices({'extract_url_without_protocol': False}), options)\",\n \"def get_docs_urls(self):\\n docs_urls = []\\n link_labels = []\\n for tag in self.post_div.find_all(\\\"a\\\"):\\n url = tag[\\\"href\\\"]\\n if url.startswith(\\\"https://docs.google.com\\\") or \\\\\\n url.startswith(\\\"https://drive.google.com\\\"):\\n docs_urls += [url]\\n link_labels += [tag.text]\\n return docs_urls, link_labels\",\n \"def get_links(self):\\r\\n return self.links\",\n \"def _go_list(self, *args):\\n return subprocess.check_output((\\\"go\\\", \\\"list\\\") + self.tag_args + args).strip().split(\\\"\\\\n\\\")\",\n \"def getLinks():\\n curDIR = os.path.dirname(os.path.realpath(__file__))\\n inputfile_path = os.path.join(curDIR, 'sitemaplinks.txt')\\n links=[]\\n with open(inputfile_path,'r') as outputfile:\\n for line in outputfile:\\n links.append(line.split(',')[0])\\n return links\",\n \"def list_of_links_to_check(main_link):\\n # TODO: try else-here, when didn't finds links, then restart\\n list_of_links = []\\n driver.get(main_link)\\n sleep(1)\\n temp_list = driver.find_elements_by_class_name(\\\"table-main__tt\\\")\\n for block in temp_list: \\n elements = block.find_elements_by_tag_name(\\\"a\\\")\\n for el in elements:\\n if \\\"soccer\\\" in el.get_attribute(\\\"href\\\"):\\n list_of_links.append(el.get_attribute(\\\"href\\\"))\\n return list_of_links\",\n \"def find_usefull_links(links, classmodel, class_count_vect):\\n\\n import re\\n final_links = []\\n seclinks = links\\n for link in links:\\n fulllink = link\\n if link == None:\\n continue\\n else:\\n link = link.replace('://', ' ')\\n link = link.replace('@', ' ')\\n link = link.replace('#', ' ')\\n link = link.replace('/', ' ')\\n link = link.replace('-', ' ')\\n link = link.replace('.', ' ')\\n link = link.replace('https', '')\\n link = link.replace('http', '')\\n link = link.replace('www', '')\\n link = link.replace('&', ' ')\\n link = link.replace('=', ' ')\\n linkpd = pd.Series(link.strip())\\n link_feature = class_count_vect.transform(linkpd)\\n result = classmodel.predict(link_feature)\\n\\n result = result.tolist()\\n result = str(result)\\n if result == '[1]':\\n final_links.append(fulllink)\\n final_links = list(dict.fromkeys(final_links))\\n \\n if len(final_links) == 0 or len(final_links) < 5:\\n for linksec in seclinks:\\n linkwords = ['cabinet', 'gover', 'goverment', 'composition', 'ministers', 'minister',\\n 'president', 'composicao', 'parliament', 'person', 'who', 'mini', 'compo',\\n 'governor', 'secretariat', 'secretary']\\n for w in linkwords:\\n if re.search(w, linksec):\\n final_links.append(linksec)\\n else:\\n continue\\n final_links = list(dict.fromkeys(final_links))\\n return (final_links)\",\n \"def find_links(obj):\\n if isinstance(obj, dict):\\n for key, value in obj.iteritems():\\n for url in find_links(value):\\n yield url\\n elif isinstance(obj, list):\\n for item in obj:\\n for url in find_links(item):\\n yield url\\n else:\\n try:\\n if is_link(str(obj)):\\n yield obj\\n except Exception:\\n pass\",\n \"def getLinks(content):\\n soup = BeautifulSoup(content, 'lxml')\\n links = set([link.get('href') for link in soup.find_all('a')])\\n return links\",\n \"def external_search_engines_links(search, deep=0, debug=0, links=[]):\\n s = Subseek()\\n for search_engine in SEARCH_ENGINES:\\n for subtitle_search_engine in SUBTITLE_SEARCH_ENGINES:\\n if debug == 1:\\n print \\\"Searching '%s' in '%s'\\\" % (search,\\n search_engine['name'])\\n links_aux = s.get_links(search_engine, search, deep,\\n subtitle_search_engine[\\\"name\\\"])\\n if not links_aux or len(links_aux) == 0:\\n if debug == 1:\\n print \\\"No match found in '%s'\\\" % search_engine['name']\\n else:\\n if debug == 1:\\n print \\\"%s matches found in '%s'\\\" % (len(links_aux),\\n search_engine['name'])\\n links = links_aux + links\\n\\n return links\",\n \"def iter_links(self):\",\n \"def is_valid_listings(link):\\n if link.has_attr(\\\"href\\\") and link.attrs[\\\"href\\\"].startswith(LISTING_PREFIX):\\n return True\\n return False\",\n \"def get_urls(argument_urls: Optional[str] = None) -> List[str]:\\n if argument_urls:\\n raw_urls = argument_urls\\n elif 'amqp_url' in config:\\n raw_urls = config['amqp_url']\\n elif 'AMQP_SERVERS' in environ:\\n raw_urls = environ['AMQP_SERVERS']\\n else:\\n raise ValueError('AMQP server url is not configured')\\n\\n return [process_url(url) for url in raw_urls.split(',')]\",\n \"def _parse_links(self, item) -> list:\\n # TODO This would be a \\\"nice to have\\\" but is not necessary right now.\\n return [{\\\"href\\\": \\\"\\\", \\\"title\\\": \\\"\\\"}]\",\n \"def get_non_docs_urls(self):\\n non_docs_urls = []\\n link_labels = []\\n for tag in self.post_div.find_all(\\\"a\\\"):\\n url = tag[\\\"href\\\"]\\n if tag.text not in link_label_blacklist and \\\\\\n not (url.startswith(\\\"https://docs.google.com\\\") or \\\\\\n url.startswith(\\\"https://drive.google.com\\\")):\\n non_docs_urls += [url]\\n link_labels += [tag.text.strip()]\\n return non_docs_urls, link_labels\",\n \"def get_all_links(file1):\\n links = []\\n #Measurements.logger.critical(\\\"Value of is_from_file is %s\\\", str(Measurements.is_from_file))\\n if Measurements.is_from_file is True:\\n with open(file1) as f:\\n #if the file is valid:, store it\\n for line in f:\\n if line:\\n links.append(line)\\n else:\\n links = file1 # file1 is alrealy a list of data\\n # do processing here\\n return links\",\n \"def _is_list(arg):\\n return isinstance(arg, collections.Sequence) and not _is_string(arg)\",\n \"def get_listings(soup):\\n listings = []\\n for link in soup.find_all(\\\"a\\\"):\\n if is_valid_listings(link):\\n listings.append(link.attrs[\\\"href\\\"])\\n return listings\",\n \"def run_linkfinder(self, text, urljoin_fnct):\\n\\n urls = set()\\n # store the text in a separate file for Linkfinder\\n tmp_filename_in = \\\"linkfinder_tmp_%d.in\\\" % random.randint(0, 2**32)\\n with open(tmp_filename_in, \\\"w\\\") as f:\\n f.write(text)\\n\\n # Run Linkfinder as subprocess and remove the input file thereafter\\n linkfinder_out = \\\"\\\"\\n try:\\n linkfinder_out = subprocess.check_output([\\\"python3 LinkFinder/linkfinder.py -i \\\" +\\n tmp_filename_in + \\\" -o cli 2>/dev/null\\\"], shell=True)\\n linkfinder_out = linkfinder_out.decode()\\n except subprocess.CalledProcessError:\\n pass\\n os.remove(tmp_filename_in)\\n\\n # process Linkfinder's output\\n for line in linkfinder_out.split(\\\"\\\\n\\\"):\\n if not line:\\n continue\\n line = line.strip()\\n line = self.to_absolute_url(line, urljoin_fnct)\\n # if configured, check if the discovered URL is valid and exists\\n if self.config[\\\"check_linkfinder\\\"].lower() == \\\"true\\\":\\n try:\\n timeout = float(self.config[\\\"linkfinder_check_timeout\\\"])\\n if str(requests.head(line, timeout=timeout).status_code) != \\\"404\\\":\\n urls.add(line)\\n except:\\n pass\\n else:\\n urls.add(line)\\n\\n return urls\",\n \"def test_link_in_list(self):\\n self.assertEquals(\\\"* [name](name)\\\\n* name2\\\\n* name3\\\",\\n trans(\\\" * [name]\\\\n * name2\\\\n * name3\\\"))\",\n \"def get_links(self, response, find_in_text=None, find_in_href=None, exclude_from_href=None): \\n \\n if not (find_in_text or find_in_href):\\n logging.error(f\\\"Supply one or both of find_in_text and find_in_href to search for links in {response.url}\\\")\\n return None\\n\\n hrefs = []\\n if find_in_text and find_in_href:\\n hrefs = response.xpath(f'//a[contains(text(), \\\"{find_in_text}\\\")][contains(@href, \\\"{find_in_href}\\\")]/@href').getall()\\n elif find_in_text:\\n hrefs = response.xpath(f'//a[contains(text(), \\\"{find_in_text}\\\")]/@href').getall()\\n elif find_in_href:\\n hrefs = response.xpath(f'//a[contains(@href, \\\"{find_in_href}\\\")]/@href').getall()\\n \\n if len(hrefs) == 0:\\n logging.error(f\\\"No links found with text containing {find_in_text} and/or href containing {find_in_href} in {response.url}. Please inspect the HTML element(s) you are hoping to find to check if find_in_href and find_in_text need to be updated.\\\")\\n return hrefs\\n\\n # Remove duplicates, exclude undesired links, and sort\\n hrefs = list(set(hrefs))\\n if exclude_from_href:\\n hrefs = [x for x in hrefs if exclude_from_href not in x]\\n hrefs.sort()\\n\\n logging.info(f\\\"Found links: {hrefs}\\\")\\n\\n return hrefs\",\n \"def _get_links(self):\\n with open(self.source, \\\"r\\\", encoding=\\\"utf-8\\\") as link_doc:\\n return link_doc.readlines()\",\n \"def test_list(self):\\n parser = parse_args(['-g', '10', '-s', 'bubble', '-l'])\\n self.assertTrue(parser.list)\\n self.assertEqual(True, parser.list)\\n\\n parser = parse_args(['-g', '10', '-s', 'bubble'])\\n self.assertEqual(False, parser.list)\",\n \"def to_list(a, args):\\n if args.filter(follow=a).exists():\\n exists = True\\n else:\\n exists = False\\n return exists\",\n \"def test_splits_urls_for_nouns(self):\\r\\n test_value = \\\"http://google.com/drives/autonomous/cars\\\"\\r\\n self.assertEqual(\\r\\n set([u'cars', u'autonomous']),\\r\\n suggest_tags(test_value))\",\n \"def _filter_return_url_from_list(self, paths, har=None):\\r\\n if not har:\\r\\n har = self.har\\r\\n \\r\\n matches = []\\r\\n for entry in har[\\\"log\\\"][\\\"entries\\\"]:\\r\\n for path in paths:\\r\\n if path in entry[\\\"request\\\"][\\\"url\\\"]:\\r\\n temp = entry[\\\"request\\\"][\\\"url\\\"].encode('ascii', 'ignore')\\r\\n matches.append(temp)\\r\\n return matches\",\n \"def get_urls(links):\\n\\n temp_list=[]\\n url_list=[]\\n temp_list2=[]\\n #Open the file where the url's are saved and copy the tuple values into an empty list\\n z=open('dbdocs.txt','r')\\n for line in z:\\n temp_list.append(line)\\n #print temp_list\\n for x in temp_list:\\n index=x.find(',')\\n if index==-1:\\n y=x.split(\\\" \\\",1)\\n key=int(y[0])\\n val=str(x[1]).replace('\\\\n','')\\n url_list.append((key,val))\\n else:\\n #find the tab seperator between the key and the url, and\\n #split them, in order to put in a list\\n key=x[0:index-1]\\n #print key\\n value=str(x[index+3:len(x)-1])\\n #print value\\n temp_list2.append((int(key),value))\\n #Find the url's of the links where the word was found\\n for k in links:\\n for i,j in temp_list2:\\n #print j\\n if i==k:\\n url_list.append((i,j))\\n break\\n #print len(url_list)\\n #print len(links)\\n z.close()\\n return url_list\",\n \"def reformat(array):\\n global searched_domain\\n response = []\\n for tag in array:\\n link = tag.get(\\\"href\\\", None)\\n if link is not None:\\n p = parse.urlparse(link)\\n if re.match(searched_netloc, p.netloc):\\n if p.scheme == \\\"\\\":\\n link = parse.ParseResult(\\\"http\\\", *p[1:]).geturl()\\n response.append(link)\\n return response\",\n \"def get_anchor_types(self) -> list:\\n pass\",\n \"def test_get_variant_links(variant_obj):\\n # GIVEN a variant object without links\\n assert \\\"thousandg_link\\\" not in variant_obj\\n # WHEN fetching the variant links\\n links = get_variant_links(variant_obj)\\n # THEN check that links are returned\\n assert \\\"thousandg_link\\\" in links\",\n \"def link_to_set(page):\\n #s = set()\\n links = Measurements.get_all_links(page)\\n s = set(links)\\n return s\",\n \"def getLinks(self, url, tag = \\\"a\\\", attr = \\\"href\\\"): \\n try: \\n response = open(self.filename(url)).read() #read from the file\\n except IOError:\\n raise IOError\\n parsed_url = urlparse(url)\\n domain = parsed_url[0] + '://' + parsed_url[1]\\n \\n try:\\n soup = BeautifulSoup.BeautifulSoup(response)\\n l = soup.findAll(tag, href = True)\\n except Exception:\\n raise Exception\\n links = []\\n \\n for tag in l:\\n link = str(tag[attr]) #convert the link to a string\\n purl = urlparse(link)\\n if purl[1] == '': #if the link is relative make it absolute\\n link = domain+link\\n #check if the extension is that of a document \\n if splitext(link)[1] in self._invalidExt: \\n self.docs_list.append(link)\\n \\n #append only the html link\\n links.append(link)\\n \\n \\n \\n return list(set(links)) #returns only distinct links\",\n \"def extract_links(self, source: str) -> List[str]:\\n return self.links_compiled_exp.findall(source)\",\n \"def convert_link(link):\\n\\n ###############\\n # My Solution #\\n ###############\\n\\n if link == Link.empty:\\n return []\\n else: \\n return [link.first] + convert_link(link.rest)\",\n \"def target_list_option(s):\\n return _convert(s, (list, tuple))\",\n \"def list_urls(self, prefix: str = \\\"\\\", etl_name: str = None) -> Iterable[str]:\",\n \"def linkify(obj, link_text=''):\\n try:\\n lst = []\\n # if obj is not a list, convert it into a list\\n if not getattr(obj, '__iter__', False):\\n obj = [obj]\\n for item in obj:\\n if hasattr(item, 'child'):\\n item = item.child\\n if link_text == '':\\n l_text = unicode(item)\\n else:\\n try:\\n link_text = link_text.encode('ascii')\\n l_text = getattr(item, link_text, link_text)\\n except UnicodeEncodeError:\\n l_text = link_text\\n if not (isinstance(item, Content) and\\n isinstance(l_text, SafeText)):\\n l_text = filter.force_escape(l_text)\\n format_args = (item.get_absolute_url(), l_text)\\n lst.append(mark_safe('<a href=\\\\'%s\\\\'>%s</a>' % format_args))\\n\\n # nonlists obj's should be returned as nonlists\\n return lst[0] if len(lst) == 1 else lst\\n except:\\n return ''\",\n \"def simplelist(inline):\\n return paramfinder.findall(inline)\",\n \"def subtitle_search_engines_links(search, deep=0, debug=0, links=[]):\\n s = Subseek()\\n for subtitle_search_engine in SUBTITLE_SEARCH_ENGINES:\\n if debug == 1:\\n print \\\"Searching '%s' in '%s'\\\" % (search,\\n subtitle_search_engine['name'])\\n links_aux = s.get_links(subtitle_search_engine, search, deep)\\n if not links_aux or len(links_aux) == 0:\\n if debug == 1:\\n print \\\"No match found in '%s'\\\" % subtitle_search_engine['name']\\n else:\\n if debug == 1:\\n print \\\"%s matches found in '%s'\\\" % (len(links_aux),\\n subtitle_search_engine['name'])\\n\\n links = links_aux + links\\n\\n return links\",\n \"def mergeURLS(inputs):\\n urls = set()\\n for i in inputs:\\n # Re-raise any exceptions\\n try:\\n urls = urls.union(i.urls())\\n except:\\n raise\\n return urls\",\n \"def get_links(self, response, domain, port, folder):\\n\\t\\t# find link in tags: a, link, form, button\\n\\t\\t# call to all function in file get_link\\n\\t\\t# for method in get_link:\\n\\t\\tlinks = get_link(response, domain, port, folder)\\n\\t\\tlinks = filter(None, links.getResults())\\n\\t\\treturn links\",\n \"def find_links(url, html):\\n\\tif html is None:\\n\\t\\treturn set()\\n\\turl = urlparse.urlparse(url)\\n\\tlinks = url_regex.findall(html)\\n\\tlink_set = set()\\n\\tfor link in links:\\n\\t\\tif link is None:\\n\\t\\t\\tcontinue\\n\\t\\ttry:\\n\\t\\t\\tlink = str(link)\\n\\t\\t\\tif link.startswith(\\\"/\\\"):\\n\\t\\t\\t\\tlink_set.add('http://' + url.netloc + link)\\n\\t\\t\\telif link.startswith(\\\"http\\\") or link.startswith(\\\"https\\\"):\\n\\t\\t\\t\\tif link.find(url.netloc):\\n\\t\\t\\t\\t\\tlink_set.add(link)\\n\\t\\t\\telif link.startswith(\\\"#\\\"):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\telse:\\n\\t\\t\\t\\tlink_set.add(urlparse.urljoin(url.geturl(), link))\\n\\t\\texcept:\\n\\t\\t\\tpass\\n\\n\\treturn link_set\",\n \"def get_links(self):\\n return (link for link in self.links)\",\n \"def links(self):\\n return self._links\",\n \"def links(self):\\n return self._links\",\n \"def links(self):\\n return self._links\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59844595","0.59079087","0.58242726","0.5810565","0.5720067","0.56380796","0.56306934","0.5554415","0.55288565","0.55027115","0.54880166","0.54853743","0.54810125","0.54488546","0.54431033","0.5396246","0.5343557","0.5296428","0.5295228","0.5291945","0.52896595","0.5288885","0.52870846","0.5265327","0.5238194","0.52121127","0.5193789","0.51839083","0.51826715","0.5178424","0.51704013","0.516714","0.51549596","0.51490223","0.51374286","0.5125407","0.5123189","0.511953","0.511953","0.511953","0.511953","0.511953","0.51150626","0.5114392","0.51117927","0.511026","0.5107452","0.5084548","0.50773233","0.50754","0.50628173","0.5059689","0.5051773","0.50388503","0.5018736","0.50176114","0.50154614","0.5012493","0.5011008","0.5002898","0.50006837","0.49963507","0.4988551","0.49863186","0.4973184","0.49707347","0.4969258","0.49639657","0.4962387","0.49554914","0.49457294","0.49449897","0.49383983","0.49324572","0.49314672","0.493122","0.4927813","0.4927539","0.49272066","0.4925244","0.4922278","0.48980123","0.4881105","0.48779786","0.48747313","0.48742804","0.4873288","0.48711377","0.48490077","0.4846464","0.4834255","0.48329297","0.4825128","0.48220435","0.4816929","0.4812772","0.48118234","0.4809133","0.4809133","0.4809133"],"string":"[\n \"0.59844595\",\n \"0.59079087\",\n \"0.58242726\",\n \"0.5810565\",\n \"0.5720067\",\n \"0.56380796\",\n \"0.56306934\",\n \"0.5554415\",\n \"0.55288565\",\n \"0.55027115\",\n \"0.54880166\",\n \"0.54853743\",\n \"0.54810125\",\n \"0.54488546\",\n \"0.54431033\",\n \"0.5396246\",\n \"0.5343557\",\n \"0.5296428\",\n \"0.5295228\",\n \"0.5291945\",\n \"0.52896595\",\n \"0.5288885\",\n \"0.52870846\",\n \"0.5265327\",\n \"0.5238194\",\n \"0.52121127\",\n \"0.5193789\",\n \"0.51839083\",\n \"0.51826715\",\n \"0.5178424\",\n \"0.51704013\",\n \"0.516714\",\n \"0.51549596\",\n \"0.51490223\",\n \"0.51374286\",\n \"0.5125407\",\n \"0.5123189\",\n \"0.511953\",\n \"0.511953\",\n \"0.511953\",\n \"0.511953\",\n \"0.511953\",\n \"0.51150626\",\n \"0.5114392\",\n \"0.51117927\",\n \"0.511026\",\n \"0.5107452\",\n \"0.5084548\",\n \"0.50773233\",\n \"0.50754\",\n \"0.50628173\",\n \"0.5059689\",\n \"0.5051773\",\n \"0.50388503\",\n \"0.5018736\",\n \"0.50176114\",\n \"0.50154614\",\n \"0.5012493\",\n \"0.5011008\",\n \"0.5002898\",\n \"0.50006837\",\n \"0.49963507\",\n \"0.4988551\",\n \"0.49863186\",\n \"0.4973184\",\n \"0.49707347\",\n \"0.4969258\",\n \"0.49639657\",\n \"0.4962387\",\n \"0.49554914\",\n \"0.49457294\",\n \"0.49449897\",\n \"0.49383983\",\n \"0.49324572\",\n \"0.49314672\",\n \"0.493122\",\n \"0.4927813\",\n \"0.4927539\",\n \"0.49272066\",\n \"0.4925244\",\n \"0.4922278\",\n \"0.48980123\",\n \"0.4881105\",\n \"0.48779786\",\n \"0.48747313\",\n \"0.48742804\",\n \"0.4873288\",\n \"0.48711377\",\n \"0.48490077\",\n \"0.4846464\",\n \"0.4834255\",\n \"0.48329297\",\n \"0.4825128\",\n \"0.48220435\",\n \"0.4816929\",\n \"0.4812772\",\n \"0.48118234\",\n \"0.4809133\",\n \"0.4809133\",\n \"0.4809133\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71301365"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":86684,"cells":{"query":{"kind":"string","value":"Fetch an egg needed for building. Use pip/wheel to fetch/build a wheel."},"document":{"kind":"string","value":"def fetch_build_egg(dist, req):\n _DeprecatedInstaller.emit()\n _warn_wheel_not_available(dist)\n return _fetch_build_egg_no_warn(dist, req)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def build_essential(self):\n self.install_package(\"build-essential\")","def build():\n local('python' + python_version + ' setup.py bdist_egg')","def ensure_wheel():\n wheels = sorted(DIST.glob(\"*.whl\"))\n if not wheels:\n subprocess.check_call([\"pyproject-build\", \".\", \"--wheel\", \"--no-isolation\"], cwd=ROOT)\n wheels = sorted(DIST.glob(\"*.whl\"))\n return wheels[-1]","def cli(ctx, wheels_config, image, output, uid, gid, fetch_srcs, wheel):\n wheel_config_manager = WheelConfigManager.open(ctx.config, wheels_config)\n cachemgr = CacheManager(ctx.config.cache_path)\n wheel_config = wheel_config_manager.get_wheel_config(wheel)\n # `image` is an image_name until here\n try:\n image = wheel_config.get_image(image)\n except KeyError:\n warn(\"Warning: Image '%s' is not in '%s' imageset\", image, wheel_config.imageset.name)\n # we could do imageset autodetection here but that seems like a waste of time, this command isn't supposed to be\n # user facing like `wheel`. Just use whatever image is instructed.\n wheel_config.set_imageset(imageset=ctx.config.make_imageset('_ephemeral_', [image]), force=True)\n image = wheel_config.get_image(image)\n ectx = LocalExecutionContext(image)\n forge = ForgeWheel(wheel_config, cachemgr, ectx.run_context, image=image)\n if fetch_srcs:\n forge.cache_sources()\n forge.bdist_wheel(output=output, uid=uid, gid=gid)","def build_wheel( # noqa:C901\n req=None, # type: Optional[TInstallRequirement]\n reqset=None, # type: Optional[Union[TReqSet, Iterable[TInstallRequirement]]]\n output_dir=None, # type: Optional[str]\n preparer=None, # type: Optional[TPreparer]\n wheel_cache=None, # type: Optional[TWheelCache]\n build_options=None, # type: Optional[List[str]]\n global_options=None, # type: Optional[List[str]]\n check_binary_allowed=None, # type: Optional[Callable[TInstallRequirement, bool]]\n no_clean=False, # type: bool\n session=None, # type: Optional[TSession]\n finder=None, # type: Optional[TFinder]\n install_command=None, # type: Optional[TCommand]\n req_tracker=None, # type: Optional[TReqTracker]\n build_dir=None, # type: Optional[str]\n src_dir=None, # type: Optional[str]\n download_dir=None, # type: Optional[str]\n wheel_download_dir=None, # type: Optional[str]\n cache_dir=None, # type: Optional[str]\n use_user_site=False, # type: bool\n use_pep517=None, # type: Optional[bool]\n verify=False, # type: bool\n editable=False, # type: bool\n format_control_provider=None, # type: Optional[TShimmedFunc]\n wheel_cache_provider=None, # type: Optional[TShimmedFunc]\n preparer_provider=None, # type: Optional[TShimmedFunc]\n wheel_builder_provider=None, # type: Optional[TShimmedFunc]\n build_one_provider=None, # type: Optional[TShimmedFunc]\n build_one_inside_env_provider=None, # type: Optional[TShimmedFunc]\n build_many_provider=None, # type: Optional[TShimmedFunc]\n install_command_provider=None, # type: Optional[TShimmedFunc]\n finder_provider=None, # type: Optional[TShimmedFunc]\n reqset_provider=None, # type: Optional[TShimmedFunc]\n):\n # type: (...) -> Generator[Union[str, Tuple[List[TInstallRequirement], ...]], None, None]\n wheel_cache_provider = resolve_possible_shim(wheel_cache_provider)\n preparer_provider = resolve_possible_shim(preparer_provider)\n wheel_builder_provider = resolve_possible_shim(wheel_builder_provider)\n build_one_provider = resolve_possible_shim(build_one_provider)\n build_one_inside_env_provider = resolve_possible_shim(build_one_inside_env_provider)\n build_many_provider = resolve_possible_shim(build_many_provider)\n install_cmd_provider = resolve_possible_shim(install_command_provider)\n format_control_provider = resolve_possible_shim(format_control_provider)\n finder_provider = resolve_possible_shim(finder_provider) or get_package_finder\n reqset_provider = resolve_possible_shim(reqset_provider)\n global_options = [] if global_options is None else global_options\n build_options = [] if build_options is None else build_options\n options = None\n kwarg_map = {\n \"cache_dir\": cache_dir,\n \"src_dir\": src_dir,\n \"download_dir\": download_dir,\n \"wheel_download_dir\": wheel_download_dir,\n \"build_dir\": build_dir,\n \"use_user_site\": use_user_site,\n }\n if not req and not reqset:\n raise TypeError(\"Must provide either a requirement or requirement set to build\")\n with contextlib.ExitStack() as ctx:\n kwargs = kwarg_map.copy()\n if wheel_cache is None and (reqset is not None or output_dir is None):\n if install_command is None:\n assert isinstance(install_cmd_provider, (type, functools.partial))\n install_command = install_cmd_provider()\n kwargs, options = populate_options(install_command, options, **kwarg_map)\n format_control = getattr(options, \"format_control\", None)\n if not format_control:\n format_control = format_control_provider(None, None) # type: ignore\n wheel_cache = ctx.enter_context(\n wheel_cache_provider(options.cache_dir, format_control)\n )\n if req and not reqset and not output_dir:\n output_dir = get_ireq_output_path(wheel_cache, req)\n if not reqset and build_one_provider:\n build_one_kwargs = {\n \"req\": req,\n \"output_dir\": output_dir,\n \"verify\": verify,\n \"editable\": editable,\n \"build_options\": build_options,\n \"global_options\": global_options,\n }\n yield call_function_with_correct_args(build_one_provider, **build_one_kwargs)\n elif build_many_provider:\n yield build_many_provider(\n reqset, wheel_cache, build_options, global_options, check_binary_allowed\n )\n else:\n builder_args, builder_kwargs = get_allowed_args(wheel_builder_provider)\n if \"requirement_set\" in builder_args and not reqset:\n reqset = reqset_provider()\n if session is None and finder is None:\n session = get_session(install_cmd=install_command, options=options)\n finder = finder_provider(\n install_command, options=options, session=session\n )\n if preparer is None:\n preparer_kwargs = {\n \"build_dir\": kwargs[\"build_dir\"],\n \"src_dir\": kwargs[\"src_dir\"],\n \"download_dir\": kwargs[\"download_dir\"],\n \"wheel_download_dir\": kwargs[\"wheel_download_dir\"],\n \"finder\": finder,\n \"session\": session\n if session\n else get_session(install_cmd=install_command, options=options),\n \"install_cmd\": install_command,\n \"options\": options,\n \"use_user_site\": use_user_site,\n \"req_tracker\": req_tracker,\n }\n preparer = ctx.enter_context(preparer_provider(**preparer_kwargs))\n check_bin = check_binary_allowed if check_binary_allowed else lambda x: True\n builder_kwargs = {\n \"requirement_set\": reqset,\n \"finder\": finder,\n \"preparer\": preparer,\n \"wheel_cache\": wheel_cache,\n \"no_clean\": no_clean,\n \"build_options\": build_options,\n \"global_options\": global_options,\n \"check_binary_allowed\": check_bin,\n }\n builder = call_function_with_correct_args(\n wheel_builder_provider, **builder_kwargs\n )\n if req and not reqset:\n if not output_dir:\n output_dir = get_ireq_output_path(wheel_cache, req)\n if use_pep517 is not None:\n req.use_pep517 = use_pep517\n yield builder._build_one(req, output_dir)\n else:\n yield builder.build(reqset)","def fetch_pkgbuild(self):\n\n package_dir = os.path.join(Package.cache_dir, self.pkgbase)\n\n # check if repo has ever been fetched\n if os.path.isdir(package_dir):\n if run([\"git\", \"fetch\"], cwd=package_dir).returncode != 0:\n logging.error(\"git fetch failed in directory {}\".format(package_dir))\n raise ConnectionProblem(\"git fetch failed in directory {}\".format(package_dir))\n\n head = run(\n [\"git\", \"rev-parse\", \"HEAD\"], stdout=PIPE, universal_newlines=True, cwd=package_dir\n ).stdout.strip()\n u = run(\n [\"git\", \"rev-parse\", \"@{u}\"], stdout=PIPE, universal_newlines=True, cwd=package_dir\n ).stdout.strip()\n\n # if new sources available\n if head != u:\n reset_return = run(\n [\"git\", \"reset\", \"--hard\", \"HEAD\"],\n stdout=DEVNULL, stderr=PIPE, cwd=package_dir, universal_newlines=True\n )\n if reset_return.returncode != 0:\n print(reset_return.stderr)\n logging.error(\"git reset failed in directory {}\".format(package_dir))\n raise InvalidInput(\"git reset failed in directory {}\".format(package_dir))\n\n pull_return = run(\n [\"git\", \"pull\"],\n stdout=DEVNULL, stderr=PIPE, cwd=package_dir, universal_newlines=True\n )\n if pull_return.returncode != 0:\n print(pull_return.stderr)\n logging.error(\"git pull failed in directory {}\".format(package_dir))\n raise ConnectionProblem(\"git pull failed in directory {}\".format(package_dir))\n\n # repo has never been fetched\n else:\n # create package dir\n try:\n os.makedirs(package_dir, mode=0o700, exist_ok=True)\n except OSError:\n logging.error(\"Creating package dir {} failed\".format(package_dir))\n raise InvalidInput(\"Creating package dir {} failed\".format(package_dir))\n\n # clone repo\n if run(\n [\"git\", \"clone\", \"{}/{}.git\".format(AurVars.aur_domain, self.pkgbase)],\n cwd=Package.cache_dir\n ).returncode != 0:\n logging.error(\"Cloning repo of {} failed in directory {}\".format(self.name, package_dir))\n raise ConnectionProblem(\"Cloning repo of {} failed in directory {}\".format(self.name, package_dir))","def cabal_get(name, unpack_to, version=None):\n pkg = name\n if version is not None:\n pkg += \"==\"\n pkg += version\n\n return run([\"cabal\", \"get\", pkg, \"-d\", unpack_to])","def download_fabric_factory():\n local('hg clone http://bitbucket.org/yml/fabric_factory/')","def _find_wheel(ctx):\n\n wheel = ctx.path.ant_glob(\"dist/*-\" + VERSION + \"-*.whl\")\n\n if not len(wheel) == 1:\n ctx.fatal(\"No wheel found (or version mismatch)\")\n else:\n wheel = wheel[0]\n Logs.info(\"Wheel %s\", wheel)\n return wheel","def build_wheel_cache():\n pkg_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\n pyvers = ['2.6', '2.7', '3.3', '3.4', '3.5']\n old_python_path = os.environ['PYTHONPATH']\n for pyver in pyvers:\n pycmd = which('python{0}'.format(pyver))\n if not pycmd:\n print('Python {0} not found'.format(pyver))\n continue\n pipcmd = which('pip{0}'.format(pyver))\n if not pipcmd:\n print('pip {0} not found'.format(pyver))\n continue\n os.environ['PYTHONPATH'] = ''\n lines = load_requirements(pkg_dir, pyver)\n for line in lines:\n if 'numpy' in line:\n numpy_line = line\n break\n else:\n raise RuntimeError('Numpy dependency could not be found')\n for line in lines:\n print(\n _pcolor(\n 'Building {0} wheel cache for Python {1}'.format(\n line,\n pyver\n ),\n 'cyan'\n )\n )\n if 'scipy' in line:\n # Install numpy before scipy otherwise pip throws an exception\n pobj = subprocess.Popen(\n [\n 'pip{0}'.format(pyver),\n 'install',\n '--upgrade',\n '--force-reinstall',\n numpy_line.strip()\n ],\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT\n )\n stdout, _ = pobj.communicate()\n print(stdout)\n pobj = subprocess.Popen(\n ['pip{0}'.format(pyver), 'wheel', line],\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT\n )\n stdout, _ = pobj.communicate()\n print(stdout)\n os.environ['PYTHONPATH'] = old_python_path","def build():\n click.secho('Creating package ...')\n pkg = utils.create_package()\n click.secho('Package created: {}'.format(pkg), fg='green')\n click.secho('Creating wheel...')\n wheel_path = utils.create_wheel()\n click.secho('Wheel created in {}'.format(wheel_path), fg='green')","def _fetch_full():\n resource(\n target=data_path(\"eeg\", \"eeg_full.tar\"),\n url=\"https://kdd.ics.uci.edu/databases/eeg/eeg_full.tar\",\n )\n dependency(\n target=data_path(\"eeg\", \"full\"),\n source=data_path(\"eeg\", \"eeg_full.tar\"),\n commands=[\n \"mkdir full\",\n \"tar xf eeg_full.tar -C full\",\n \"ls full | grep gz$ | xargs -I {} tar xzf full/{} -C full\",\n \"ls full | grep gz$ | xargs -I {} rm full/{}\",\n \"find full | grep gz$ | xargs gunzip\",\n ],\n )","def get_setup_requirements() -> str:\n return \"\"\"\n setuptools\n wheel\n\"\"\"","def fetch(opts):\n\n assert opts\n cache_dir = opts.cache_dir\n name = opts.name\n revision = opts.revision\n\n if not GIT.exists():\n err('unable to fetch package; git is not installed')\n return None\n\n git_dir = '--git-dir=' + cache_dir\n\n # check if we have the target revision cached; if so, package is ready\n if os.path.isdir(cache_dir) and not opts.ignore_cache:\n erv = revision_exists(git_dir, revision)\n if erv in REVISION_EXISTS:\n # ensure configuration is properly synchronized\n if not _sync_git_configuration(opts):\n return None\n\n # if no explicit ignore-cache request and if the revision is a\n # branch, force ignore-cache on and allow fetching to proceed\n if opts.ignore_cache is None and erv == GitExistsType.EXISTS_BRANCH:\n opts.ignore_cache = True\n # return cache dir if not verifying or verification succeeds\n elif not opts._git_verify_revision or _verify_revision(\n git_dir, revision, quiet=True):\n return cache_dir\n\n note('fetching {}...', name)\n sys.stdout.flush()\n\n # validate any cache directory (if one exists)\n has_cache, bad_validation = _validate_cache(cache_dir)\n if bad_validation:\n return None\n\n # if we have no cache for this repository, build one\n if not has_cache:\n if not ensure_dir_exists(cache_dir):\n return None\n\n if not _create_bare_git_repo(cache_dir):\n return None\n\n # ensure configuration is properly synchronized\n if not _sync_git_configuration(opts):\n return None\n\n # fetch sources for this repository\n if not _fetch_srcs(opts, cache_dir, revision, refspecs=opts._git_refspecs):\n return None\n\n # verify revision (if configured to check it)\n if opts._git_verify_revision:\n if not _verify_revision(git_dir, revision):\n err('''\\\nfailed to validate git revision\n\nPackage has been configured to require the verification of the GPG signature\nfor the target revision. The verification has failed. Ensure that the revision\nis signed and that the package's public key has been registered in the system.\n\n Package: {}\n Revision: {}''', name, revision)\n return None\n\n # fetch submodules (if configured to do so)\n if opts._git_submodules:\n if not _fetch_submodules(opts, cache_dir, revision):\n return None\n\n return cache_dir","def get_python_package_info(name):\n command = [\"python\", \"setup.py\", \"--name\"]\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\n assert proc.stdout.readline().strip().decode(\"utf-8\") == name\n\n command = [\"python\", \"setup.py\", \"--version\"]\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\n release_version = proc.stdout.readline().strip().decode(\"utf-8\")\n\n command = [\"python\", \"setup.py\", \"--url\"]\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\n github_url = proc.stdout.readline().strip().decode(\"utf-8\")\n\n github_repo = urlparse(github_url)\n assert github_repo.netloc == \"github.com\", \"specified repo is not on GitHub\"\n return (release_version, github_repo)","def install(self, egg, dir_path):","def _fetch_submodule(opts, name, cache_dir, revision, site):\n git_dir = '--git-dir=' + cache_dir\n\n # check if we have the target revision cached; if so, submodule is ready\n if os.path.isdir(cache_dir) and not opts.ignore_cache:\n if not revision:\n return _sync_git_origin(cache_dir, site)\n\n if revision_exists(git_dir, revision) in REVISION_EXISTS:\n return _sync_git_origin(cache_dir, site)\n\n log('processing submodule (package: {}) {}...', opts.name, name)\n sys.stdout.flush()\n\n # validate any cache directory (if one exists)\n has_cache, bad_validation = _validate_cache(cache_dir)\n if bad_validation:\n return None\n\n # if we have no cache for this repository, build one\n if not has_cache:\n if not ensure_dir_exists(cache_dir):\n return False\n\n if not _create_bare_git_repo(cache_dir):\n return False\n\n # ensure configuration is properly synchronized\n if not _sync_git_origin(cache_dir, site):\n return False\n\n # fetch sources for this submodule\n desc = 'submodule ({}): {}'.format(opts.name, name)\n return _fetch_srcs(opts, cache_dir, revision, desc=desc)","def install():\n build()\n sh(\"%s setup.py develop\" % PYTHON)","def install():\n return InstallGit()","def PythonEgg(glob, name=None):\r\n # TODO(John Sirois): Rationalize with globs handling in ParseContext\r\n eggs = fsglob(ParseContext.path(glob))\r\n\r\n requirements = set()\r\n for egg in eggs:\r\n if os.path.isdir(egg):\r\n metadata = PathMetadata(egg, os.path.join(egg, 'EGG-INFO'))\r\n else:\r\n metadata = EggMetadata(zipimporter(egg))\r\n dist = Distribution.from_filename(egg, metadata=metadata)\r\n requirements.add(dist.as_requirement())\r\n\r\n if len(requirements) > 1:\r\n raise ValueError('Got multiple egg versions! => %s' % requirements)\r\n\r\n return PythonRequirement(str(requirements.pop()), name=name)","def install_deps_temp(self):\n if self.distribution.install_requires:\n self.distribution.fetch_build_eggs(\n self.distribution.install_requires)\n if self.distribution.tests_require:\n self.distribution.fetch_build_eggs(self.distribution.tests_require)","def test_load_one_pip(self, build_image_for_jupyterlab):\n\n key = \"pip>munit1\"\n lb, username = build_image_for_jupyterlab[0], build_image_for_jupyterlab[5]\n\n loader = PackageLatestVersionLoader([key], lb, username)\n promise1 = loader.load(key)\n assert isinstance(promise1, Promise)\n\n pkg = promise1.get()\n assert pkg == '0.12.4'","def build():\n os.makedirs(DIST_DIR, exist_ok=True)\n\n if \"WHEEL\" in os.environ:\n whl = build_wheel()\n else:\n click.echo(\"Not building wheels.\")\n\n if \"WHEEL\" in os.environ and \"DOCKER\" in os.environ:\n # Docker image requires wheels\n build_docker_image(whl)\n else:\n click.echo(\"Not building Docker image.\")\n\n if \"PYINSTALLER\" in os.environ:\n build_pyinstaller()\n else:\n click.echo(\"Not building PyInstaller packages.\")","def dist(c, wheel=False):\n commands = \"sdist\" if not wheel else \"sdist bdist_wheel\"\n c.run(f\"python {SETUP_FILE} {commands}\")","def build(extension):\n result = call([PYTHON, 'setup.py', '-q', 'build'], cwd=join(DEPS, extension))\n if result > 0:\n raise OSError(\"Could not build %s\" % extension)","def get_comrec_build(pkg_dir, build_cmd=build_py):\n class MyBuildPy(build_cmd):\n ''' Subclass to write commit data into installation tree '''\n def run(self):\n build_cmd.run(self)\n import subprocess\n proc = subprocess.Popen('git rev-parse --short HEAD',\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True)\n repo_commit, _ = proc.communicate()\n # Fix for python 3\n repo_commit = str(repo_commit)\n # We write the installation commit even if it's empty\n cfg_parser = ConfigParser()\n cfg_parser.read(pjoin(pkg_dir, 'COMMIT_INFO.txt'))\n cfg_parser.set('commit hash', 'install_hash', repo_commit)\n out_pth = pjoin(self.build_lib, pkg_dir, 'COMMIT_INFO.txt')\n cfg_parser.write(open(out_pth, 'wt'))\n return MyBuildPy","def main(repo):\n print(subprocess.call(['make', 'setup']))\n with Docker('doppins') as docker:\n print(docker.run('git clone {repo} cloned'.format(repo=repo)).out)","def bootstrap(): # pragma: no cover, exercised via test_bootstrap() functional test\n pspec = PackageSpec(CFG, \"%s==%s\" % (PICKLEY, __version__))\n grand_parent = runez.parent_folder(runez.parent_folder(__file__))\n if grand_parent and grand_parent.endswith(\".whl\"):\n # We are indeed running from pex\n setup_audit_log()\n python = CFG.find_python(\"/usr/bin/python3\") # Prefer system py3, for stability\n if not python or python.problem:\n python = pspec.python\n\n LOG.debug(\"Bootstrapping pickley %s with %s (re-installing as venv instead of pex package)\" % (pspec.version, python))\n target = pspec.install_path\n venv = PythonVenv(target, python, pspec.index)\n venv.pip_install(\"wheel\")\n with runez.TempFolder():\n venv.run_python(\"-mwheel\", \"pack\", grand_parent)\n names = os.listdir(\".\")\n assert len(names) == 1\n venv.pip_install(names[0])\n\n delivery = DeliveryMethod.delivery_method_by_name(pspec.settings.delivery)\n return delivery.install(pspec, venv, {PICKLEY: \"bootstrapped\"})\n\n else:\n manifest = pspec.get_manifest()\n if not manifest:\n # We're not running from pex, but we need to re-install pickley with latest version, so it gets a manifest etc\n return perform_install(pspec, is_upgrade=False, quiet=False)","def command_package_build(*args):\n\n if len(args) == 0:\n print 'No package name specified'\n return 1\n packname = args[0]\n package_load_config(packname)\n\n builddir = package_path(packname, packageconfig.build_dir_name)\n depsdir = os.path.join(builddir, 'dependencies')\n buildcmakedir = os.path.join(builddir, 'build', 'cmake')\n installdir = install_dir(packname)\n\n try:\n # If nothing is there yet, do checkout and build\n # dependencies. Otherwise, just make sure our repo is fully\n # up-to-date.\n if not os.path.exists(package_path(packname, packageconfig.build_dir_name, '.git')):\n subprocess.check_call(['git', 'clone', packageconfig.repository, package_path(packname, packageconfig.build_dir_name)])\n else:\n subprocess.check_call(['git', 'fetch', 'origin'], cwd=builddir)\n\n # Make sure we're on the requested branch\n subprocess.check_call(['git', 'checkout', packageconfig.version], cwd=builddir)\n\n # We always need to make sure deps are up to date, either for\n # a fresh checkout or because we may have switched\n # branches/versions. This includes making sure the submodules\n # are up to date.\n subprocess.check_call(['make', 'update-dependencies'], cwd=builddir)\n subprocess.check_call(['make'] + packageconfig.dependencies_targets, cwd=depsdir)\n\n # We need to select whether to use cmake with tools (ccache),\n # or just bare cmake. Using ccache in some setups can be\n # counterproductive, and if we add support for things like\n # distcc/icecream, we'll probably want to filter in some other\n # conditions. These are heuristics for choosing whether or not\n # to use ccache.\n cmake_cmd = './cmake_with_tools.sh'\n if 'nfs' in subprocess.Popen(['mount'], stdout=subprocess.PIPE).communicate()[0].split():\n cmake_cmd = 'cmake'\n\n # Normal build process, making sure we clean out any previous config\n subprocess.check_call(['rm', '-f', 'CMakeCache.txt'], cwd=buildcmakedir)\n subprocess.check_call([cmake_cmd,\n '-DCMAKE_INSTALL_PREFIX='+installdir,\n '-DCMAKE_BUILD_TYPE='+packageconfig.build_type]\n + packageconfig.additional_cmake_args + ['.'],\n cwd=buildcmakedir)\n subprocess.check_call(['make'] + packageconfig.additional_make_args, cwd=buildcmakedir)\n subprocess.check_call(['make', 'install'] + packageconfig.additional_make_args, cwd=buildcmakedir)\n except subprocess.CalledProcessError:\n return 1\n\n return 0","def download_and_install(self):\n if self.is_installed_from_bin:\n try:\n self.installer.install_from_rpm_py_package()\n return\n except RpmPyPackageNotFoundError as exc:\n Log.warn('RPM Py Package not found. reason: {0}'.format(exc))\n\n # Download and install from the source.\n top_dir_name = self.downloader.download_and_expand()\n rpm_py_dir = os.path.join(top_dir_name, 'python')\n\n setup_py_in_found = False\n with Cmd.pushd(rpm_py_dir):\n if self.installer.setup_py.exists_in_path():\n setup_py_in_found = True\n self.installer.run()\n\n if not setup_py_in_found:\n self.installer.install_from_rpm_py_package()","def fetch(\n name: str,\n tag: Optional[str] = None,\n secret: Optional[str] = None,\n ) -> HubExecutor:\n\n with ImportExtensions(required=True):\n import requests\n\n pull_url = get_hubble_url() + f'/{name}/?'\n path_params = {}\n if secret:\n path_params['secret'] = secret\n if tag:\n path_params['tag'] = tag\n\n request_headers = HubIO._get_request_header()\n\n pull_url += urlencode(path_params)\n resp = requests.get(pull_url, headers=request_headers)\n if resp.status_code != 200:\n if resp.text:\n raise Exception(resp.text)\n resp.raise_for_status()\n\n resp = resp.json()\n\n result = HubExecutor(\n resp['id'],\n resp.get('alias', None),\n resp['tag'],\n resp['visibility'],\n resp['image'],\n resp['package']['download'],\n resp['package']['md5'],\n )\n\n return result","def download_dependency_github(name, repo, tag, temp_path, build_path, config):\n wp = os.getcwd()\n os.chdir(temp_path)\n # Clone into the repo, pull the specified tag\n clone_cmd = f\"git clone https://github.com/{repo}.git\"\n tag_cmd = f\"git checkout master && git fetch && git fetch --tags && git checkout {tag}\"\n os.system(clone_cmd)\n os.chdir(name)\n os.system(tag_cmd)\n os.chdir(wp)\n # Move the contents of GameData into the build directory\n shutil.copytree(os.path.join(temp_path, name, \"GameData\", name), os.path.join(build_path, \"GameData\", name))","def get_requires_for_build_wheel(config_settings=None):\n info = read_flit_config(pyproj_toml)\n # If we can get version & description from pyproject.toml (PEP 621), or\n # by parsing the module (_via_ast), we don't need any extra\n # dependencies. If not, we'll need to try importing it, so report any\n # runtime dependencies as build dependencies.\n want_summary = 'description' in info.dynamic_metadata\n want_version = 'version' in info.dynamic_metadata\n\n module = Module(info.module, Path.cwd())\n docstring, version = get_docstring_and_version_via_ast(module)\n\n if (want_summary and not docstring) or (want_version and not version):\n return info.metadata.get('requires_dist', [])\n else:\n return []","def pull(self) -> None:\n cached_zip_filepath = None\n try:\n scheme, name, tag, secret = parse_hub_uri(self.args.uri)\n\n executor = HubIO.fetch(name, tag=tag, secret=secret)\n\n if not tag:\n tag = executor.tag\n\n uuid = executor.uuid\n image_name = executor.image_name\n archive_url = executor.archive_url\n md5sum = executor.md5sum\n\n if scheme == 'jinahub+docker':\n # pull the Docker image\n with TimeContext(f'pulling {image_name}', self.logger):\n image = self._client.images.pull(image_name)\n if isinstance(image, list):\n image = image[0]\n image_tag = image.tags[0] if image.tags else ''\n self.logger.success(\n f'🎉 pulled {image_tag} ({image.short_id}) uncompressed size: {get_readable_size(image.attrs[\"Size\"])}'\n )\n return\n if exist_local(uuid, tag):\n self.logger.debug(\n f'The executor `{self.args.uri}` has already been downloaded.'\n )\n return\n # download the package\n with TimeContext(f'downloading {self.args.uri}', self.logger):\n cache_dir = Path(\n os.environ.get(\n 'JINA_HUB_CACHE_DIR', Path.home().joinpath('.cache', 'jina')\n )\n )\n cache_dir.mkdir(parents=True, exist_ok=True)\n cached_zip_filename = f'{uuid}-{md5sum}.zip'\n cached_zip_filepath = download_with_resume(\n archive_url,\n cache_dir,\n cached_zip_filename,\n md5sum=md5sum,\n )\n\n with TimeContext(f'unpacking {self.args.uri}', self.logger):\n try:\n install_local(\n cached_zip_filepath,\n uuid,\n tag,\n install_deps=self.args.install_requirements,\n )\n except Exception as ex:\n raise HubDownloadError(str(ex))\n\n except Exception as e:\n self.logger.error(\n f'Error when pulling the executor `{self.args.uri}`: {e!r}'\n )\n finally:\n # delete downloaded zip package if existed\n if cached_zip_filepath is not None:\n cached_zip_filepath.unlink()","def main():\n setuptools.setup(**setup_params)","def install_egg(self, egg_name):\n if not os.path.exists(self.egg_directory):\n os.makedirs(self.egg_directory)\n self.requirement_set.add_requirement(\n InstallRequirement.from_line(egg_name, None)\n )\n try:\n self.requirement_set.prepare_files(self.finder)\n self.requirement_set.install([\"--prefix=\" + self.egg_directory], [])\n except DistributionNotFound:\n self.requirement_set.requirements._keys.remove(egg_name)\n raise PipException()","def build_gem_download_url(purl):\n purl_data = PackageURL.from_string(purl)\n\n name = purl_data.name\n version = purl_data.version\n\n if not (name and version):\n return\n\n return \"https://rubygems.org/downloads/{name}-{version}.gem\".format(\n name=name, version=version\n )","def _install():\n download_file='http://www.ipol.im/pub/art/2015/136/inpaint_8.tgz'\n tools.download_and_extract(download_file) \n this_file_path=os.path.dirname(__file__)\n subprocess.call(' mkdir build; cd build; cmake ..; make', shell=True,cwd=exec_folder)","def task_build(argv):\n pytaskmaster.generator(\"setup.py.in\", \"setup.py\", config)\n pytaskmaster.generator(\"pytaskmaster/version.py.in\", \"pytaskmaster/version.py\", config)\n shell(\"python setup.py bdist_wheel\")\n if \"--sign\" in argv:\n for file in os.listdir(\"dist\"):\n asc_file = \"dist/\" + file + \".asc\"\n if file.endswith(\".whl\") and not os.path.isfile(asc_file):\n shell(\"gpg --detach-sign -a dist/{}\".format(file))","def install_package(buildout, specification, working_set):\n\n new_ws = zc.buildout.easy_install.install(\n specs = [specification],\n dest = buildout['eggs-directory'],\n links = buildout.get('find-links', '').split(),\n index = buildout.get('index', None),\n path = [buildout['develop-eggs-directory']],\n working_set = working_set,\n newest = bool_option(buildout, 'newest', 'true'),\n )\n\n merge_working_sets(working_set, new_ws)\n\n return working_set","def main():\n # Packaging tools expects either README.txt, README, or README.rst.\n # Convert the README markdown file to ReStructured text.\n try:\n run(\"pandoc README.md -f markdown -t rst -o README.rst\".split(\" \"), check=True)\n except CalledProcessError:\n print(\"pandoc does not appear to be installed. Get it from http://pandoc.org/\")\n exit(1)\n\n copy_metadata()\n\n for item in (\"sdist\", \"bdist_wheel\"):\n run([\"python\", \"setup.py\", item], check=True)","def test_require_setuptools():\n\n from fabtools.require.python import setuptools\n\n setuptools()\n\n assert run('easy_install --version', warn_only=True).succeeded","def fetch_executable_from_jenkins():\n\n base_job_url = os.environ.get('JENKINS_JOB_URL')\n if not base_job_url:\n error('Jenkins job URL for the builder is not specified.')\n\n build_json = json.loads(requests.get('%s/api/json'\n % base_job_url).text)\n last_build = build_json['lastCompletedBuild']['number']\n print 'Last build ID: %d' % last_build\n\n job_url = '%s/%d' % (base_job_url, last_build)\n last_build_json = json.loads(requests.get('%s/api/json'\n % job_url).text)\n if not last_build_json['artifacts']:\n error('No artifacts found!')\n\n artifacts_deb = [artifact for artifact in\n last_build_json['artifacts'] if '.dmg'\n in artifact['fileName']]\n artifact_url = '%s/artifact/%s' % (job_url,\n artifacts_deb[0]['relativePath'])\n file_name = artifacts_deb[0]['fileName']\n print 'Tribler installer url: %s' % artifact_url\n\n # Download the file\n file_path = os.path.join(os.environ.get('WORKSPACE'), file_name)\n download_response = requests.get(artifact_url, stream=True)\n download_response.raise_for_status()\n\n with open(file_path, 'wb') as handle:\n for block in download_response.iter_content(1024):\n handle.write(block)\n\n return file_path","def _obtain(self, requirement, source=None):\n # Look at zc.buildout source for comments on this logic\n index = self._index\n if index.obtain(requirement) is None:\n return None\n dists = [dist for dist in index[requirement.project_name] if (\n dist in requirement and (\n dist.location not in self._site_packages or\n self.allow_site_package_egg(dist.project_name))\n and (\n (not source) or\n (dist.precedence == pkg_resources.SOURCE_DIST))\n )\n ]\n\n # Here we pick between 'dev' or 'final' versions.\n # We want to use different logic depending on if this is a third-party\n # or in-house package:\n # In-house-packages: we usually want the latest dev version as keeping\n # on head revisions is sensible to stop code\n # going stale\n # Third-party: we usually want the latest final version to protect\n # ourselves from OSS developers exposing their\n # latest untested code to the intenet at large.\n # To overridde this logic the packager needs to specify an explicit\n # version pin in install_requires or similar for third-party packages,\n # or use the prefer-final setup flag for in-house packages.\n\n # If we prefer final dists, filter for final and use the\n # result if it is non empty.\n\n if manage.is_inhouse_package(requirement.project_name):\n if self._prefer_final:\n log.debug(' in-house package, prefer-final')\n version_comparitor = easy_install._final_version\n else:\n log.debug(' in-house package, prefer-dev')\n version_comparitor = self.is_dev_version\n else:\n log.debug(' third-party package, always prefer-final')\n version_comparitor = easy_install._final_version\n\n filtered_dists = [dist for dist in dists\n if version_comparitor(dist.parsed_version)]\n if filtered_dists:\n log.debug(' filtered to {0}'.format(filtered_dists))\n dists = filtered_dists\n\n # The rest of this logic is as-is from buildout\n best = []\n bestv = ()\n for dist in dists:\n distv = dist.parsed_version\n if distv > bestv:\n best = [dist]\n bestv = distv\n elif distv == bestv:\n best.append(dist)\n\n log.debug(' best picks are {0}'.format(best))\n\n if not best:\n return None\n if len(best) == 1:\n return best[0]\n if self._download_cache:\n for dist in best:\n if (easy_install.realpath(os.path.dirname(dist.location)) ==\n self._download_cache):\n return dist\n best.sort()\n return best[-1]","def install():\n remote_egg_path = os.path.join(remote_egg_dir, get_egg_name())\n sudo('easy_install -U %s' % remote_egg_path)\n sudo('rm %s' % remote_egg_path)","def fetch_the_data():\n subprocess.run([\"wget\", \"https://storage.googleapis.com/recipe-box/recipes_raw.zip\"])\n subprocess.run([\"unzip\", \"recipes_raw.zip\", \"-d\", RECIPES_DIRPATH])\n subprocess.run([\"rm\", \"recipes_raw.zip\"])","def install():\n return {\n \"actions\": [TaskCreator.get_pip() + \" install --upgrade dist/*.whl\"],\n \"verbosity\": 2,\n \"setup\": [\"make_distribution\"],\n }","def get_buildrequire_pkgs_from_build(build_id, session, config):\n print(\"Retriewing build metadata from: \", config.koji_host)\n build = session.getBuild(build_id)\n if not build:\n raise Exception(\"Build with id '{id}' has not been found.\".format(id=build_id))\n\n print(\"Build with the ID\", build_id, \"found.\")\n tags = session.listTags(build[\"build_id\"])\n\n build_tag = [t[\"name\"] for t in tags if t[\"name\"].endswith(\"-build\")]\n if not build_tag:\n raise Exception(\"Build with id '{id}' is not tagged in a 'build' tag.\".format(id=build_id))\n\n tag_data = session.listTaggedRPMS(build_tag[0], latest=True, inherit=True)\n\n print(\"Found the build tag '\", build_tag[0], \"' associated with the build.\")\n tagged_rpms = tag_data[0]\n tagged_pkgs = tag_data[1]\n pkgs = []\n archs = [config.arch, \"noarch\"]\n print(\"Gathering packages and rpms tagged in '\", build_tag[0],\"'.\")\n for pkg in tagged_pkgs:\n pkg_md = {\n \"package\": pkg,\n \"rpms\": [],\n }\n\n for rpm in tagged_rpms:\n if pkg[\"build_id\"] == rpm[\"build_id\"] and rpm[\"arch\"] in archs:\n pkg_md[\"rpms\"].append(rpm)\n tagged_rpms.remove(rpm)\n\n if pkg_md[\"rpms\"]:\n pkgs.append(pkg_md)\n print(\"Gathering done.\")\n return pkgs","def package():\n call([sys.executable, \"setup.py\", \"clean\", \"--all\", \"bdist_egg\"], cwd=\"src\")\n call([sys.executable, \"setup.py\", \"clean\", \"--all\", \"bdist_wheel\"], cwd=\"src\")","def setup(ctx):\r\n ctx.run('pip3 install -r requirements.txt')","def _build(registry, image_name, git_sha):\n\n image = f\"{registry}/{image_name}\"\n\n # If this image is not in our registry, nothing we can do.\n if registry != config.DOCKER_REGISTRY:\n return \"\"\n\n # If image:{git_sha} already exists, then return.\n logger.info(\n f\"Pulling git_sha tag {image}:{git_sha} to check if it already exists\"\n )\n if (\n not args.dry_run\n and docker_silent.pull(f\"{image}:{git_sha}\").returncode == 0\n ):\n # better to find the hash-{hash} of this image, and return hash\n # but currently, it is not easy to find all tags of the same image digest through registry API.\n # so we return image digest instead.\n digest = get_image_digest(f\"{image}:{git_sha}\")\n logger.info(\n f\"git_sha tag {image}:{git_sha} already exists, digest: %s\", digest\n )\n if not digest:\n raise Exception(\"Failed to get digest for existing image\")\n _tag_to_extra_tags(args, image, git_sha)\n return digest\n\n # Enter build context directory if it is specified\n build_context = enter_build_context(image_name)\n\n # Parse .dockerignore in build context\n dockerignore_files_set = parse_dockerignore(build_context)\n\n # Check if the dockerfile exists\n dockerfile_path = locate_dockerfile(image_name)\n if not os.path.isfile(dockerfile_path):\n logger.error(\n \"%s not exists or is not a file, so %s cannot get build\",\n dockerfile_path,\n image_name,\n )\n raise Exception(\"Building cannot continue\")\n\n dockerfile = Dockerfile(dockerfile_path, build_arg=build_arg)\n\n hasher = sha256()\n\n # Build parents, and calc parents hash\n for parent_image in dockerfile.parent_images:\n (\n parent_image_registry,\n parent_image_name,\n parent_image_tag,\n ) = parse_docker_image_identity(parent_image)\n parent_digest = _build(\n parent_image_registry, parent_image_name, parent_image_tag\n )\n if parent_digest is None:\n raise Exception(f\"Failed to get parent_digest for {image}\")\n hasher.update(parent_digest.encode())\n hash_logger.info(\n \"parent: %s, digest: (%s, %s), hash: %s\",\n parent_image,\n parent_digest,\n parent_digest.encode(),\n hasher.hexdigest(),\n )\n\n # Calc current image files hash\n\n def update_file_hash(f):\n if not os.path.isfile(f):\n return\n if f in dockerignore_files_set:\n hash_logger.debug(\"ignore: %s\", f)\n return\n with open(f, \"rb\") as open_file:\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\n while len(buf) > 0:\n hasher.update(buf)\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\n hash_logger.info(\"update: %s, hash: %s\", f, hasher.hexdigest())\n\n srcs = [dockerfile_path] + dockerfile.copied_srcs + dockerfile.added_srcs\n # TODO: if the src is a url, download it and hash it (even crane didn't do that)\n for src in srcs:\n for f in sorted(glob.glob(src)):\n # We match every file in a directory recursively\n if os.path.isdir(f):\n for sub_f in sorted(glob.glob(f\"{f}/**\", recursive=True)):\n update_file_hash(sub_f)\n else:\n update_file_hash(f)\n\n files_hash = hasher.hexdigest()\n hash_logger.info(\"image: %s, hash: %s\", image, files_hash)\n\n hash_tag = config.FILES_HASH_TAG_PATTERN.format(files_hash=files_hash)\n # FIXME(harry): hack, remove this\n old_hash_image = f\"docker-registry.example.com:5000/{image_name}:{hash_tag}\"\n\n logger.info(\n f\"Pulling files_hash tag {image}:{hash_tag} to check if it already exists\"\n )\n # If image:hash-{hash} already exists,\n # then content didn't change, return.\n # We just need to tag it to latest code version.\n if (\n not args.dry_run\n and docker_silent.pull(f\"{image}:{hash_tag}\").returncode == 0\n ):\n logger.info(\n f\"files_hash tag {image}:{hash_tag} already exists, \"\n \"it means content didn't change, we can just tag the old image to new git_sha version tag\"\n )\n # FIXME(harry): hack, remove this\n elif not args.dry_run and docker_silent.pull(old_hash_image).returncode == 0:\n logger.info(f\"NOTE: files_hash tag {old_hash_image} already exists!\")\n # tag and push this hash image\n if docker.tag(old_hash_image, f\"{image}:{hash_tag}\").returncode != 0:\n logger.error(\"Failed to tag old hash image\")\n return\n if docker.push(f\"{image}:{hash_tag}\").returncode != 0:\n logger.error(\"Failed to push hash_tag image\")\n return\n # If image:hash-{hash} not exists, then build it from Dockerfile.\n else:\n logger.info(\n f\"files_hash tag {image}:{hash_tag} dosen't exists, \"\n \"it means content may changed, gonna build it from Dockerfile\"\n )\n if build_with_raw_command(args, image, dockerfile_path, hash_tag) != 0:\n logger.error(f\"Failed to build {image}:{hash_tag}\")\n return\n if docker.push(f\"{image}:{hash_tag}\").returncode != 0:\n logger.error(f\"Failed to push image\")\n return\n logger.info(f\"image files_hash tag {image}:{hash_tag} is pushed\")\n\n # tag and push this final image\n if docker.tag(f\"{image}:{hash_tag}\", f\"{image}:{git_sha}\").returncode != 0:\n logger.error(\"Failed to tag image\")\n return\n _tag_to_extra_tags(args, image, git_sha)\n if docker.push(f\"{image}:{git_sha}\").returncode != 0:\n logger.error(\"Failed to push image\")\n return\n digest = get_image_digest(f\"{image}:{git_sha}\")\n if not digest:\n logger.error(\"Failed to get digest for image\")\n return\n logger.info(f\"image {image}:{git_sha} is pushed, digest: {digest}\")\n\n return digest","def develop():\n# Install package in development mode\n sh('python setup.py develop')","def build_base():\n with lcd(env.local_path):\n put('./requirements.txt', '/srv/build/requirements.txt')\n\n with cd('/srv/build'):\n run('docker build -t {base_image_name} .'.format(\n base_image_name=env.base_image_name,\n ))","def required():\n pip = path(\"bin/pip\")\n if not pip.exists():\n sh('%s install -E tg2env -r normal-reqs.txt --extra-index-url=http://www.turbogears.org/2.0/downloads/current/index' % pip)\n call_pavement('pavement.py', 'develop')","def install_deps():\n default = open('requirements.txt', 'r').readlines()\n new_pkgs = []\n links = []\n for resource in default:\n if 'git+https' in resource:\n pkg = resource.split('#')[-1]\n links.append(resource.strip())\n new_pkgs.append(pkg.replace('egg=', '').rstrip())\n else:\n new_pkgs.append(resource.strip())\n return new_pkgs, links","def build_wheel(wheel_directory, config_settings=None, metadata_directory=None):\n info = make_wheel_in(pyproj_toml, Path(wheel_directory))\n return info.file.name","def dist_build(c):\n c.run(\"python setup.py sdist\")\n c.run(\"python setup.py bdist_wheel\")\n c.run(\"twine check dist/*\")","def upload_wheels():\n build()\n sh(\"%s -m twine upload dist/*.whl\" % PYTHON)","def download_dependency(name, info, temp_path, build_path, config):\n if info[\"location\"] == \"s3\":\n logger.info(f\"Collecting {name} {info['version']} from S3\")\n download_dependency_s3(name, info[\"version\"], temp_path, build_path, config)\n\n if info[\"location\"] == \"github\":\n logger.info(f\"Collecting {name} at tag {info['tag']} from repository {info['repository']}\")\n download_dependency_github(name, info['repository'], info[\"tag\"], temp_path, build_path, config)\n\n if info[\"location\"] == \"url\":\n logger.info(f\"Collecting {name} at from {info['url']}\")\n download_dependency_url(name, info['url'], temp_path, build_path, config, zip=info.get(\"zip\", False))","def _download_model(download_url):\n\n pip_args = [\"--no-cache-dir\"]\n cmd = [sys.executable, \"-m\", \"pip\", \"install\"] + pip_args + [download_url]\n\n return subprocess.call(cmd, env=os.environ.copy())","def install_git():\n git_version = sudo(GET_GIT_VERSION)\n if git_version != GIT_VERSION:\n git_file = 'git-%s' % GIT_VERSION\n sudo('apt-get -y -qq install build-essential')\n sudo('apt-get -y -qq install git-core')\n sudo('apt-get -y -qq install libcurl4-gnutls-dev')\n sudo('apt-get -y -qq install libexpat1-dev')\n sudo('apt-get -y -qq install gettext')\n sudo('apt-get -y -qq install libz-dev')\n sudo('apt-get -y -qq install libssl-dev')\n sudo('wget --quiet http://git-core.googlecode.com/files/%s.tar.gz' % git_file)\n sudo('tar -xzf %s.tar.gz' % git_file)\n with cd(git_file):\n sudo('make --silent prefix=/usr/local all > /dev/null')\n sudo('make --silent prefix=/usr/local install > /dev/null')","def fetch_package(self, package_name):\n\t\t\t\n\t\t\tpackage_root_url = urlparse.urljoin(self.packages_root_url,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tpackage_name + \"/\")\n\t\t\t\n\t\t\tpackage_info_url = urlparse.urljoin(package_root_url, \"info\")\n\t\t\tpackage_archive_url = urlparse.urljoin(package_root_url, \"archive\")\n\t\t\t\n\t\t\tlogger.debug(\"Get: {0}\".format(package_info_url))\n\t\t\ttry:\n\t\t\t\tinfo = json.loads(urllib2.urlopen(package_info_url).read())\n\t\t\t\treturn ups.package.Package(self, package_root_url, info)\n\t\t\texcept urllib2.HTTPError as e:\n\t\t\t\traise RepositoryError(e)\n\t\t\texcept ValueError as e:\n\t\t\t\traise RepositoryError(\"Unable to parse info file: {0}\".format(e))","def build_a_bear():\n if os.path.isdir(c.BEAR_PREFIX):\n logging.debug(\"skipping Bear installation\")\n return\n\n # download\n if not os.path.isfile(c.BEAR_ARCHIVE):\n curl = get_cmd_or_die(\"curl\")\n curl['-s', c.BEAR_URL, '-o', c.BEAR_ARCHIVE] & pb.TEE\n\n # remove any existing build dir since we don't know if\n # bear was built for the current host environment.\n if os.path.isdir(c.BEAR_SRC):\n shutil.rmtree(c.BEAR_SRC, ignore_errors=True)\n\n # unpack\n tar = get_cmd_or_die(\"tar\")\n with pb.local.cwd(c.DEPS_DIR):\n tar['xf', c.BEAR_ARCHIVE] & pb.TEE\n\n # cmake\n bear_build_dir = os.path.join(c.BEAR_SRC, \"build\")\n bear_install_prefix = \"-DCMAKE_INSTALL_PREFIX=\" + c.BEAR_PREFIX\n ensure_dir(bear_build_dir)\n with pb.local.cwd(bear_build_dir):\n cmake = get_cmd_or_die(\"cmake\")\n cmake[\"..\", bear_install_prefix] & pb.TEE\n make = get_cmd_or_die(\"make\")\n make[\"install\"] & pb.TEE","def setup(app):\n wheel = ensure_wheel()\n subprocess.check_call([\n \"jupyter\", \"lite\", \"build\", f\"--LiteBuildConfig.federated_extensions={wheel}\",\n ], cwd=DEMO)","def dist(context):\n context.run(\"python setup.py sdist\")\n context.run(\"python setup.py bdist_wheel\")","def setuptools_command_bdist_egg_call_command_hotfix(self, cmdname, **kw):\n if cmdname != 'install_data':\n for dirname in INSTALL_DIRECTORY_ATTRS:\n kw.setdefault(dirname, self.bdist_dir)\n kw.setdefault('skip_build', self.skip_build)\n kw.setdefault('dry_run', self.dry_run)\n cmd = self.reinitialize_command(cmdname, **kw)\n self.run_command(cmdname)\n return cmd","def git_pull(where=None):\n config = get_config(where)\n with settings(host_string=config['host_string']), cd(config['installation_dir']):\n run('git pull')\n run('git submodule update')\n collectstatic(where)\n restart(where)","def deps(ctx):\n header(deps.__doc__)\n with ctx.cd(ROOT):\n ctx.run(\n \"pip install -r requirements/develop.pip -r requirements/doc.pip\", pty=True\n )","def core():\n\n from fabtools.require.deb import package as require_deb_package\n from fabtools.require.rpm import package as require_rpm_package\n\n family = distrib_family()\n\n # Check if sudo command exists\n if not files.exists('/usr/bin/sudo'):\n raise Exception(\"Please install the sudo package and execute adduser %s sudo\" % env.user)\n\n\n if not files.exists('/usr/bin/docker'):\n if family == 'debian':\n require_deb_package('curl')\n elif family == 'redhat':\n require_rpm_package('curl')\n else:\n raise UnsupportedFamily(supported=['debian', 'redhat'])\n\n # Download docker installation\n run_as_root('curl -sSL https://get.docker.com/ | sh')","def _get_egg_path(self):\n try:\n _dist = get_distribution('janitoo_nut')\n return _dist.__file__\n except AttributeError:\n return 'src-nut/config'","def fetch_exe_from_jenkins():\n base_job_url = os.environ.get(\"JENKINS_JOB_URL\")\n if not base_job_url:\n print \"Jenkins job URL for the builder is not specified.\"\n sys.exit(-1)\n\n build_json = json.loads(requests.get(\"%s/api/json\" % base_job_url).text)\n last_build = build_json['lastCompletedBuild']['number']\n print \"Last build ID: %d\" % last_build\n\n job_url = '%s/%d' % (base_job_url, last_build)\n last_build_json = json.loads(requests.get(\"%s/api/json\" % job_url).text)\n if len(last_build_json['artifacts']) == 0:\n error(\"No artifacts found!\")\n\n artifact_url = \"%s/artifact/%s\" % (job_url, last_build_json['artifacts'][0]['relativePath'])\n file_name = last_build_json['artifacts'][0]['fileName']\n print \"Tribler installer url: %s\" % artifact_url\n\n # Download the file\n file_path = os.path.join(os.environ.get('WORKSPACE'), file_name)\n download_response = requests.get(artifact_url, stream=True)\n download_response.raise_for_status()\n\n with open(file_path, 'wb') as handle:\n for block in download_response.iter_content(1024):\n handle.write(block)\n\n return file_path","def get_built_wheel_path(link):\n sdist_path = download_file(\n link.url, filename=link.filename, check=link.check_download,\n )\n container = os.path.dirname(sdist_path)\n\n unpacked_dir = tempfile.mkdtemp()\n atexit.register(shutil.rmtree, unpacked_dir, ignore_errors=True)\n unpack_file(sdist_path, unpacked_dir, None, PipLink(link))\n\n wheel_content_dir = tempfile.mkdtemp()\n atexit.register(shutil.rmtree, wheel_content_dir, ignore_errors=True)\n proc = subprocess.Popen(\n [sys.executable, 'setup.py', 'bdist_wheel', '-d', container],\n cwd=unpacked_dir, stdout=subprocess.PIPE, stderr=subprocess.PIPE,\n )\n stdout, stderr = proc.communicate()\n if proc.returncode != 0:\n warnings.warn('failed to build wheel\\n{}'.format(stderr))\n return None\n for fn in os.listdir(container):\n if fn != os.path.basename(sdist_path):\n return os.path.join(container, fn)\n warnings.warn('failed to find built wheel')\n return None","def download_and_install(self, version=\"latest\", os_name=None, bitness=None, show_progress_bar=True):\n filename_with_path = self.download(version,\n os_name=os_name,\n bitness=bitness,\n show_progress_bar=show_progress_bar)\n filename = os.path.split(filename_with_path)[1]\n if filename.lower().endswith(\".tar.gz\"):\n extract_dir = os.path.join(self.get_download_path(version), filename[:-7])\n elif filename.lower().endswith(\".zip\"):\n extract_dir = os.path.join(self.get_download_path(version), filename[:-4])\n else:\n error_message = \"Unknown archive format: {0}\".format(filename)\n logger.error(error_message)\n raise RuntimeError(error_message)\n if not os.path.isdir(extract_dir):\n os.makedirs(extract_dir)\n logger.debug(\"Created directory: {0}\".format(extract_dir))\n if filename.lower().endswith(\".tar.gz\"):\n with tarfile.open(os.path.join(self.get_download_path(version), filename), mode=\"r:*\") as tar:\n tar.extractall(extract_dir)\n logger.debug(\"Extracted files: {0}\".format(\", \".join(tar.getnames())))\n elif filename.lower().endswith(\".zip\"):\n with zipfile.ZipFile(os.path.join(self.get_download_path(version), filename), mode=\"r\") as driver_zipfile:\n driver_zipfile.extractall(extract_dir)\n driver_filename = self.get_driver_filename(os_name=os_name)\n for root, dirs, files in os.walk(extract_dir):\n for curr_file in files:\n if curr_file == driver_filename:\n actual_driver_filename = os.path.join(root, curr_file)\n break\n if os_name is None:\n os_name = platform.system()\n if os_name in ['Darwin', 'Linux']:\n symlink_src = actual_driver_filename\n symlink_target = os.path.join(self.link_path, driver_filename)\n if os.path.islink(symlink_target):\n if os.path.samefile(symlink_src, symlink_target):\n logger.info(\"Symlink already exists: {0} -> {1}\".format(symlink_target, symlink_src))\n return tuple([symlink_src, symlink_target])\n else:\n logger.warning(\"Symlink {0} already exists and will be overwritten.\".format(symlink_target))\n os.unlink(symlink_target)\n os.symlink(symlink_src, symlink_target)\n logger.info(\"Created symlink: {0} -> {1}\".format(symlink_target, symlink_src))\n st = os.stat(symlink_src)\n os.chmod(symlink_src, st.st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)\n return tuple([symlink_src, symlink_target])\n elif os_name == \"Windows\":\n src_file = actual_driver_filename\n dest_file = os.path.join(self.link_path, driver_filename)\n if os.path.isfile(dest_file):\n logger.info(\"File {0} already exists and will be overwritten.\".format(dest_file))\n shutil.copy2(src_file, dest_file)\n return tuple([src_file, dest_file])","def _fetch_package_requests(source, headers, auth):\n import requests\n dest = build_temp_package_filepath()\n response = requests.get(source, stream=True, headers=headers, auth=auth)\n response.raise_for_status()\n with open(dest, 'wb') as handle:\n for block in response.iter_content(1024):\n handle.write(block)\n package = tarfile.open(dest)\n return package","def wheels():\n with lcd(env.local_path):\n put('./requirements.txt', '/srv/build/wheel_requirements.txt')\n put('./etc/base_image/image_requirements.txt',\n '/srv/build/requirements.txt')\n\n with cd('/srv/build/wheelhouse'):\n run('rm -rf *.whl')\n\n compose(cmd='-f service.yml -p %s run --rm wheel-factory' %\n env.project_name, path='/srv/build')","def InstallPrereqs():\n #Collect the source for Cython and put in _deps/cython-master\n import urllib,zipfile\n print('getting cython sources')\n urllib.urlretrieve('https://github.com/cython/cython/archive/master.zip', filename = 'master.zip')\n with zipfile.ZipFile('master.zip', 'r') as myzip:\n myzip.extractall(path='_deps')\n os.remove('master.zip')\n for python_install in PYTHONVERSIONS:\n for cwd in ['_deps/cython-master']:\n print(subprocess.check_output([python_install, 'setup.py', 'install'], cwd = cwd))","def fetch_package(source, method=None, headers=None, auth=None):\n #if method not in ('requests', 'curl_cli'):\n # raise Exception('Fetch package method \"{}\" not found'.format(method))\n if not method:\n method = detect_fetch_method()\n print('Using fetch method \"{}\"'.format(method))\n print('Source {}'.format(source))\n fetch_method = '_fetch_package_{}'.format(method)\n package = eval(fetch_method)(source, headers, auth)\n return package","def resolve(config, interpreter, logger=print):\r\n\r\n setuptools_requirement = failsafe_parse(\r\n 'setuptools==%s' % config.get('python-setup', 'setuptools_version', default='2.2'))\r\n wheel_requirement = failsafe_parse(\r\n 'wheel==%s' % config.get('python-setup', 'wheel_version', default='0.22.0'))\r\n\r\n interpreter = resolve_interpreter(config, interpreter, setuptools_requirement, logger=logger)\r\n if interpreter:\r\n return resolve_interpreter(config, interpreter, wheel_requirement, logger=logger)","def fetch_repo(data):\n repo = Repository.objects.get(**data)\n\n # create a temporary directory\n tmp_dir = util.tmp_dir('github')\n\n # log\n log.info(\"Fetching repo %s to %s\", repo.full_name, tmp_dir)\n\n # clone the repository to the directory\n git.Repo.clone_from(repo.git_url, tmp_dir)\n\n # add the repo path to the database\n repo.local_path = tmp_dir\n repo.save()\n\n # tell workers the repo is available\n publish('github.repo_available', data)","def build_orc8r():\n subprocess.check_call('./build.py -a', shell=True, cwd=orc8_docker_path)","def __download(self):\n\n # Use the default repository if set to True\n if self.repository is True:\n self.repository = self.__default_repository\n\n if not self.repository and not self.url:\n tarball = 'ucx-{}.tar.gz'.format(self.__version)\n self.url = '{0}/v{1}/{2}'.format(self.__baseurl, self.__version,\n tarball)","def elim_bootstrap_fetch(tree):\n\n boot = tree.find('.//target[@name=\"boot\"]')\n for child in boot.findall(\"./exec\"):\n boot.remove(child)\n echo = boot.find(\"./echo\")\n echo.attrib[\"message\"] = \"Not fetching bootstrap libraries in the Fedora build\"","async def get(name, version, results, progress):\n\n buf = BytesIO()\n\n client = httpx.AsyncClient()\n\n async with client.stream(\n \"GET\", f\"https://pydocs.github.io/pkg/{name}-{version}.zip\"\n ) as response:\n total = int(response.headers[\"Content-Length\"])\n\n download_task = progress.add_task(f\"Download {name} {version}\", total=total)\n async for chunk in response.aiter_bytes():\n buf.write(chunk)\n progress.update(download_task, completed=response.num_bytes_downloaded)\n\n if response.status_code != 200:\n results[(name, version)] = None\n else:\n buf.seek(0)\n results[(name, version)] = buf.read()","async def pull(ctx, pip=None):\n dev = ctx.message.author\n if bot.botdev_role in dev.roles or bot.owner_role in dev.roles:\n await ctx.send(\"`Pulling changes...`\")\n call([\"git\", \"stash\", \"save\"])\n call([\"git\", \"pull\"])\n call([\"git\", \"stash\", \"clear\"])\n pip_text = \"\"\n if pip == \"-p\" or pip == \"--pip\" or pip == \"-Syu\":\n await ctx.send(\"`Updating python dependencies...`\")\n call([\"python3.6\", \"-m\", \"pip\", \"install\", \"--user\", \"--upgrade\", \"-r\",\n \"requirements.txt\"])\n pip_text = \" and updated python dependencies\"\n await ctx.send(\"Pulled changes{}! Restarting...\".format(pip_text))\n call([\"python3.6\", \"SchmuckBot.py\"])\n else:\n if \"pacman\" in ctx.message.content:\n await ctx.send(\"`{} is not in the sudoers file. This incident will be reported.`\".format(ctx.message.author.display_name))\n else:\n await ctx.send(\"Only bot devs and / or owners can use this command\")","def mixin_hub_pull_options_parser(parser):\n\n gp = add_arg_group(parser, title='Pull')\n gp.add_argument(\n '--install-requirements',\n action='store_true',\n default=False,\n help='If set, install `requirements.txt` in the Hub Executor bundle to local',\n ),\n gp.add_argument(\n '--force',\n action='store_true',\n default=False,\n help='If set, always pull the latest Hub Executor bundle even it exists on local',\n )","def setup_prereqs():\n # Run the contrib download script -- easier that way\n gmp = os.path.join(flag_gcc_subdir, \"gmp\")\n if not os.path.exists(gmp):\n dochdir(flag_gcc_subdir)\n docmd(\"./contrib/download_prerequisites\")\n # Hack -- fix up gmp dir\n patch_gmp_configure()\n dochdir(\"..\")","def build(ctx):\n ctx.run(\"vsce package\", replace_env=False)","def GetOrBuild(self, image):\n full_image = self.GetFullRegistryTag(image)\n # Log in to the registry to see if image exists\n self.Login()\n if not FLAGS.force_container_build:\n # manifest inspect inpspects the registry's copy\n inspect_cmd = ['docker', 'manifest', 'inspect', full_image]\n _, _, retcode = vm_util.IssueCommand(\n inspect_cmd, raise_on_failure=False)\n if retcode == 0:\n return full_image\n self._Build(image)\n return full_image","def download_and_extract(self, package_name):\n self.download(package_name)\n self.extract(package_name)","def cli(ctx, name, owner, changeset_revision):\n return ctx.gi.repositories.get_repository_revision_install_info(name, owner, changeset_revision)","def official(ctx, build_number):\n # TODO: Cache API calls to be nice too kashike and the gang\n minecraft_version = ctx.parent.minecraft_version\n known_builds = minecraft_version.known_paper_builds\n if not known_builds:\n raise ClickException()\n if build_number is None:\n build_number = max(known_builds)\n if build_number not in known_builds:\n print(f\"Known builds for {minecraft_version}:\", file=sys.stderr)\n print_wrapped(', '.join(map(str, known_builds)))\n raise ClickException(f\"Build {build_number} is not a valid build for {minecraft_version}\")\n latest_build = max(known_builds)\n if build_number != latest_build:\n click.echo(f\"The latest build for {minecraft_version} is {latest_build}.\")\n click.confirm(f\"Are you sure you want to use {build_number} instead?\", abort=True)\n jar = OfficialPaperJar(minecraft_version, build_number)\n try:\n jar.validate_cache()\n except CacheInvalidationException as e:\n e.print(\"Paper jar\")\n print()\n print(f\"Downloading Paper {build_number}....\")\n jar.update()\n assert jar.resolved_path.exists()\n return jar","def install_git(self):\n self.host.sudo('apt-get install git')","async def check_and_install_dependencies(module):\n try:\n async with pip_lock:\n kwargs = pip_kwargs(None)\n from fhempy import lib\n\n initfile = inspect.getfile(lib)\n fhempy_root = os.path.dirname(initfile)\n with open(fhempy_root + \"/\" + module + \"/manifest.json\", \"r\") as f:\n manifest = json.load(f)\n\n if \"requirements\" in manifest:\n for req in manifest[\"requirements\"]:\n if is_installed(req) == False:\n inst_tries = 0\n while inst_tries < 3:\n with concurrent.futures.ThreadPoolExecutor() as pool:\n ret = (\n await asyncio.get_event_loop().run_in_executor(\n pool,\n functools.partial(\n install_package, req, **kwargs\n ),\n )\n )\n if ret:\n break\n inst_tries += 1\n except FileNotFoundError:\n pass\n\n return","def build_env_wheels() -> Iterable[Path]:\n return []","def curl(self):\n self.summarize_operation(\"Installing Curl\")\n self.install_package(\"curl\")","def update_requirements():\n\n require('code_root', provided_by=env.environments)\n requirements = os.path.join(env.code_root, 'requirements')\n sdists = os.path.join(requirements, 'sdists')\n base_cmd = ['pip install']\n base_cmd += ['-q -E %(virtualenv_root)s' % env]\n base_cmd += ['--no-index --find-links=file://%s' % sdists]\n # install GDAL by hand, before anything else that might depend on it\n cmd = base_cmd + ['--no-install \"GDAL==1.6.1\"']\n sudo(' '.join(cmd), user=env.deploy_user)\n # this directory won't exist if GDAL was already installed\n if files.exists('%(virtualenv_root)s/build/GDAL' % env):\n sudo('rm -f %(virtualenv_root)s/build/GDAL/setup.cfg' % env, user=env.deploy_user)\n with cd('%(virtualenv_root)s/build/GDAL' % env):\n sudo('%(virtualenv_root)s/bin/python setup.py build_ext '\n '--gdal-config=gdal-config '\n '--library-dirs=/usr/lib '\n '--libraries=gdal1.6.0 '\n '--include-dirs=/usr/include/gdal '\n 'install' % env, user=env.deploy_user)\n # force reinstallation of OpenBlock every time\n with settings(warn_only=True):\n sudo('pip uninstall -y -E %(virtualenv_root)s ebpub ebdata obadmin' % env)\n for file_name in ['ebpub.txt', 'ebdata.txt', 'obadmin.txt', 'openrural.txt']:\n apps = os.path.join(requirements, file_name)\n cmd = base_cmd + ['--requirement %s' % apps]\n sudo(' '.join(cmd), user=env.deploy_user)","def download(self, tag):\n pkg = PACKAGES.get(tag, None)\n if not pkg:\n print \"Could not find package {}\".format(tag)\n sys.exit(1)\n\n pth = self._ensure_relative_directory('data/{}'.format(tag))\n pkg['download'](pth)","def get_module(module_type, spec, get_full_path, module_set_name=\"default\", required=True):\n try:\n upstream = spec.installed_upstream\n except spack.repo.UnknownPackageError:\n upstream, record = spack.store.db.query_by_spec_hash(spec.dag_hash())\n if upstream:\n module = spack.modules.common.upstream_module_index.upstream_module(spec, module_type)\n if not module:\n return None\n\n if get_full_path:\n return module.path\n else:\n return module.use_name\n else:\n writer = spack.modules.module_types[module_type](spec, module_set_name)\n if not os.path.isfile(writer.layout.filename):\n if not writer.conf.excluded:\n err_msg = \"No module available for package {0} at {1}\".format(\n spec, writer.layout.filename\n )\n raise ModuleNotFoundError(err_msg)\n elif required:\n tty.debug(\"The module configuration has excluded {0}: \" \"omitting it\".format(spec))\n else:\n return None\n\n if get_full_path:\n return writer.layout.filename\n else:\n return writer.layout.use_name","def get_egg_name():\n global eggname\n if not eggname:\n version = local('git describe --abbrev=4', capture=True)\n if version:\n version = '%s-%s' % (version, datetime.datetime.today().strftime('%Y%m%d'))\n eggname = APP_NAME + '-%s-py%s.egg' % (version.replace('-', '_'), python_version)\n return eggname","def build(request):\n \n def get(name):\n return request.GET.get(name)\n def get_arr(name):\n val = get(name)\n if val:\n return val.split(\",\")\n else:\n return []\n\n require = get_arr(\"require\")\n exclude = get_arr(\"exclude\")\n excludeLibs = get_arr(\"excludeLibs\")\n requireLibs = get_arr(\"requireLibs\")\n download = get(\"download\")\n reset = get(\"reset\")\n client = get(\"client\")\n compression = get(\"compression\")\n\n dpdr = None\n global depender\n if settings.DEPENDER_DEBUG:\n dpdr = make_depender()\n else:\n dpdr = depender\n if reset == \"true\":\n depender = dpdr\n \n if compression is None:\n compression = dpdr.default_compression\n if settings.DEPENDER_DEBUG:\n compression = \"none\"\n\n if client == \"true\" and require.count(\"Depender.Client\") == 0:\n require.append(\"Depender.Client\")\n \n includes = dpdr.get_dependencies(require, exclude, requireLibs, excludeLibs)\n output = \"//No files included for build\"\n if len(includes) > 0:\n \n libraries_and_copyrights = dict()\n for i in includes:\n libraries_and_copyrights[i.library] = i.copyright\n\n output = \"\"\n for lib, copy in libraries_and_copyrights.iteritems():\n if len(copy) > 0:\n output += copy + \"\\n\"\n\n output += \"\\n//Contents: \"\n output += \", \".join([ i.name for i in includes ])\n output += \"\\n\\n\"\n \n location = request.META[\"SERVER_PROTOCOL\"].split(\"/\")[0].lower() + \"://\" + request.META[\"HTTP_HOST\"] + request.path\n args = request.META[\"QUERY_STRING\"]\n if args.find(\"download=\") >= 0:\n clean = []\n for arg in args.split(\"&\"):\n if arg.find(\"download=\") == -1:\n clean.append(arg)\n args = '&'.join(clean)\n\n output += \"//This lib: \" + location + '?' + args\n output += \"\\n\\n\"\n\n for i in includes:\n output += i.compressed_content[compression] + \"\\n\\n\"\n\n if client == \"true\":\n output += dpdr.get_client_js(includes, location)\n\n response = HttpResponse(output, content_type=\"application/x-javascript\")\n \n if (download == \"true\"):\n response['Content-Disposition'] = 'attachment; filename=built.js'\n return response"],"string":"[\n \"def build_essential(self):\\n self.install_package(\\\"build-essential\\\")\",\n \"def build():\\n local('python' + python_version + ' setup.py bdist_egg')\",\n \"def ensure_wheel():\\n wheels = sorted(DIST.glob(\\\"*.whl\\\"))\\n if not wheels:\\n subprocess.check_call([\\\"pyproject-build\\\", \\\".\\\", \\\"--wheel\\\", \\\"--no-isolation\\\"], cwd=ROOT)\\n wheels = sorted(DIST.glob(\\\"*.whl\\\"))\\n return wheels[-1]\",\n \"def cli(ctx, wheels_config, image, output, uid, gid, fetch_srcs, wheel):\\n wheel_config_manager = WheelConfigManager.open(ctx.config, wheels_config)\\n cachemgr = CacheManager(ctx.config.cache_path)\\n wheel_config = wheel_config_manager.get_wheel_config(wheel)\\n # `image` is an image_name until here\\n try:\\n image = wheel_config.get_image(image)\\n except KeyError:\\n warn(\\\"Warning: Image '%s' is not in '%s' imageset\\\", image, wheel_config.imageset.name)\\n # we could do imageset autodetection here but that seems like a waste of time, this command isn't supposed to be\\n # user facing like `wheel`. Just use whatever image is instructed.\\n wheel_config.set_imageset(imageset=ctx.config.make_imageset('_ephemeral_', [image]), force=True)\\n image = wheel_config.get_image(image)\\n ectx = LocalExecutionContext(image)\\n forge = ForgeWheel(wheel_config, cachemgr, ectx.run_context, image=image)\\n if fetch_srcs:\\n forge.cache_sources()\\n forge.bdist_wheel(output=output, uid=uid, gid=gid)\",\n \"def build_wheel( # noqa:C901\\n req=None, # type: Optional[TInstallRequirement]\\n reqset=None, # type: Optional[Union[TReqSet, Iterable[TInstallRequirement]]]\\n output_dir=None, # type: Optional[str]\\n preparer=None, # type: Optional[TPreparer]\\n wheel_cache=None, # type: Optional[TWheelCache]\\n build_options=None, # type: Optional[List[str]]\\n global_options=None, # type: Optional[List[str]]\\n check_binary_allowed=None, # type: Optional[Callable[TInstallRequirement, bool]]\\n no_clean=False, # type: bool\\n session=None, # type: Optional[TSession]\\n finder=None, # type: Optional[TFinder]\\n install_command=None, # type: Optional[TCommand]\\n req_tracker=None, # type: Optional[TReqTracker]\\n build_dir=None, # type: Optional[str]\\n src_dir=None, # type: Optional[str]\\n download_dir=None, # type: Optional[str]\\n wheel_download_dir=None, # type: Optional[str]\\n cache_dir=None, # type: Optional[str]\\n use_user_site=False, # type: bool\\n use_pep517=None, # type: Optional[bool]\\n verify=False, # type: bool\\n editable=False, # type: bool\\n format_control_provider=None, # type: Optional[TShimmedFunc]\\n wheel_cache_provider=None, # type: Optional[TShimmedFunc]\\n preparer_provider=None, # type: Optional[TShimmedFunc]\\n wheel_builder_provider=None, # type: Optional[TShimmedFunc]\\n build_one_provider=None, # type: Optional[TShimmedFunc]\\n build_one_inside_env_provider=None, # type: Optional[TShimmedFunc]\\n build_many_provider=None, # type: Optional[TShimmedFunc]\\n install_command_provider=None, # type: Optional[TShimmedFunc]\\n finder_provider=None, # type: Optional[TShimmedFunc]\\n reqset_provider=None, # type: Optional[TShimmedFunc]\\n):\\n # type: (...) -> Generator[Union[str, Tuple[List[TInstallRequirement], ...]], None, None]\\n wheel_cache_provider = resolve_possible_shim(wheel_cache_provider)\\n preparer_provider = resolve_possible_shim(preparer_provider)\\n wheel_builder_provider = resolve_possible_shim(wheel_builder_provider)\\n build_one_provider = resolve_possible_shim(build_one_provider)\\n build_one_inside_env_provider = resolve_possible_shim(build_one_inside_env_provider)\\n build_many_provider = resolve_possible_shim(build_many_provider)\\n install_cmd_provider = resolve_possible_shim(install_command_provider)\\n format_control_provider = resolve_possible_shim(format_control_provider)\\n finder_provider = resolve_possible_shim(finder_provider) or get_package_finder\\n reqset_provider = resolve_possible_shim(reqset_provider)\\n global_options = [] if global_options is None else global_options\\n build_options = [] if build_options is None else build_options\\n options = None\\n kwarg_map = {\\n \\\"cache_dir\\\": cache_dir,\\n \\\"src_dir\\\": src_dir,\\n \\\"download_dir\\\": download_dir,\\n \\\"wheel_download_dir\\\": wheel_download_dir,\\n \\\"build_dir\\\": build_dir,\\n \\\"use_user_site\\\": use_user_site,\\n }\\n if not req and not reqset:\\n raise TypeError(\\\"Must provide either a requirement or requirement set to build\\\")\\n with contextlib.ExitStack() as ctx:\\n kwargs = kwarg_map.copy()\\n if wheel_cache is None and (reqset is not None or output_dir is None):\\n if install_command is None:\\n assert isinstance(install_cmd_provider, (type, functools.partial))\\n install_command = install_cmd_provider()\\n kwargs, options = populate_options(install_command, options, **kwarg_map)\\n format_control = getattr(options, \\\"format_control\\\", None)\\n if not format_control:\\n format_control = format_control_provider(None, None) # type: ignore\\n wheel_cache = ctx.enter_context(\\n wheel_cache_provider(options.cache_dir, format_control)\\n )\\n if req and not reqset and not output_dir:\\n output_dir = get_ireq_output_path(wheel_cache, req)\\n if not reqset and build_one_provider:\\n build_one_kwargs = {\\n \\\"req\\\": req,\\n \\\"output_dir\\\": output_dir,\\n \\\"verify\\\": verify,\\n \\\"editable\\\": editable,\\n \\\"build_options\\\": build_options,\\n \\\"global_options\\\": global_options,\\n }\\n yield call_function_with_correct_args(build_one_provider, **build_one_kwargs)\\n elif build_many_provider:\\n yield build_many_provider(\\n reqset, wheel_cache, build_options, global_options, check_binary_allowed\\n )\\n else:\\n builder_args, builder_kwargs = get_allowed_args(wheel_builder_provider)\\n if \\\"requirement_set\\\" in builder_args and not reqset:\\n reqset = reqset_provider()\\n if session is None and finder is None:\\n session = get_session(install_cmd=install_command, options=options)\\n finder = finder_provider(\\n install_command, options=options, session=session\\n )\\n if preparer is None:\\n preparer_kwargs = {\\n \\\"build_dir\\\": kwargs[\\\"build_dir\\\"],\\n \\\"src_dir\\\": kwargs[\\\"src_dir\\\"],\\n \\\"download_dir\\\": kwargs[\\\"download_dir\\\"],\\n \\\"wheel_download_dir\\\": kwargs[\\\"wheel_download_dir\\\"],\\n \\\"finder\\\": finder,\\n \\\"session\\\": session\\n if session\\n else get_session(install_cmd=install_command, options=options),\\n \\\"install_cmd\\\": install_command,\\n \\\"options\\\": options,\\n \\\"use_user_site\\\": use_user_site,\\n \\\"req_tracker\\\": req_tracker,\\n }\\n preparer = ctx.enter_context(preparer_provider(**preparer_kwargs))\\n check_bin = check_binary_allowed if check_binary_allowed else lambda x: True\\n builder_kwargs = {\\n \\\"requirement_set\\\": reqset,\\n \\\"finder\\\": finder,\\n \\\"preparer\\\": preparer,\\n \\\"wheel_cache\\\": wheel_cache,\\n \\\"no_clean\\\": no_clean,\\n \\\"build_options\\\": build_options,\\n \\\"global_options\\\": global_options,\\n \\\"check_binary_allowed\\\": check_bin,\\n }\\n builder = call_function_with_correct_args(\\n wheel_builder_provider, **builder_kwargs\\n )\\n if req and not reqset:\\n if not output_dir:\\n output_dir = get_ireq_output_path(wheel_cache, req)\\n if use_pep517 is not None:\\n req.use_pep517 = use_pep517\\n yield builder._build_one(req, output_dir)\\n else:\\n yield builder.build(reqset)\",\n \"def fetch_pkgbuild(self):\\n\\n package_dir = os.path.join(Package.cache_dir, self.pkgbase)\\n\\n # check if repo has ever been fetched\\n if os.path.isdir(package_dir):\\n if run([\\\"git\\\", \\\"fetch\\\"], cwd=package_dir).returncode != 0:\\n logging.error(\\\"git fetch failed in directory {}\\\".format(package_dir))\\n raise ConnectionProblem(\\\"git fetch failed in directory {}\\\".format(package_dir))\\n\\n head = run(\\n [\\\"git\\\", \\\"rev-parse\\\", \\\"HEAD\\\"], stdout=PIPE, universal_newlines=True, cwd=package_dir\\n ).stdout.strip()\\n u = run(\\n [\\\"git\\\", \\\"rev-parse\\\", \\\"@{u}\\\"], stdout=PIPE, universal_newlines=True, cwd=package_dir\\n ).stdout.strip()\\n\\n # if new sources available\\n if head != u:\\n reset_return = run(\\n [\\\"git\\\", \\\"reset\\\", \\\"--hard\\\", \\\"HEAD\\\"],\\n stdout=DEVNULL, stderr=PIPE, cwd=package_dir, universal_newlines=True\\n )\\n if reset_return.returncode != 0:\\n print(reset_return.stderr)\\n logging.error(\\\"git reset failed in directory {}\\\".format(package_dir))\\n raise InvalidInput(\\\"git reset failed in directory {}\\\".format(package_dir))\\n\\n pull_return = run(\\n [\\\"git\\\", \\\"pull\\\"],\\n stdout=DEVNULL, stderr=PIPE, cwd=package_dir, universal_newlines=True\\n )\\n if pull_return.returncode != 0:\\n print(pull_return.stderr)\\n logging.error(\\\"git pull failed in directory {}\\\".format(package_dir))\\n raise ConnectionProblem(\\\"git pull failed in directory {}\\\".format(package_dir))\\n\\n # repo has never been fetched\\n else:\\n # create package dir\\n try:\\n os.makedirs(package_dir, mode=0o700, exist_ok=True)\\n except OSError:\\n logging.error(\\\"Creating package dir {} failed\\\".format(package_dir))\\n raise InvalidInput(\\\"Creating package dir {} failed\\\".format(package_dir))\\n\\n # clone repo\\n if run(\\n [\\\"git\\\", \\\"clone\\\", \\\"{}/{}.git\\\".format(AurVars.aur_domain, self.pkgbase)],\\n cwd=Package.cache_dir\\n ).returncode != 0:\\n logging.error(\\\"Cloning repo of {} failed in directory {}\\\".format(self.name, package_dir))\\n raise ConnectionProblem(\\\"Cloning repo of {} failed in directory {}\\\".format(self.name, package_dir))\",\n \"def cabal_get(name, unpack_to, version=None):\\n pkg = name\\n if version is not None:\\n pkg += \\\"==\\\"\\n pkg += version\\n\\n return run([\\\"cabal\\\", \\\"get\\\", pkg, \\\"-d\\\", unpack_to])\",\n \"def download_fabric_factory():\\n local('hg clone http://bitbucket.org/yml/fabric_factory/')\",\n \"def _find_wheel(ctx):\\n\\n wheel = ctx.path.ant_glob(\\\"dist/*-\\\" + VERSION + \\\"-*.whl\\\")\\n\\n if not len(wheel) == 1:\\n ctx.fatal(\\\"No wheel found (or version mismatch)\\\")\\n else:\\n wheel = wheel[0]\\n Logs.info(\\\"Wheel %s\\\", wheel)\\n return wheel\",\n \"def build_wheel_cache():\\n pkg_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\\n pyvers = ['2.6', '2.7', '3.3', '3.4', '3.5']\\n old_python_path = os.environ['PYTHONPATH']\\n for pyver in pyvers:\\n pycmd = which('python{0}'.format(pyver))\\n if not pycmd:\\n print('Python {0} not found'.format(pyver))\\n continue\\n pipcmd = which('pip{0}'.format(pyver))\\n if not pipcmd:\\n print('pip {0} not found'.format(pyver))\\n continue\\n os.environ['PYTHONPATH'] = ''\\n lines = load_requirements(pkg_dir, pyver)\\n for line in lines:\\n if 'numpy' in line:\\n numpy_line = line\\n break\\n else:\\n raise RuntimeError('Numpy dependency could not be found')\\n for line in lines:\\n print(\\n _pcolor(\\n 'Building {0} wheel cache for Python {1}'.format(\\n line,\\n pyver\\n ),\\n 'cyan'\\n )\\n )\\n if 'scipy' in line:\\n # Install numpy before scipy otherwise pip throws an exception\\n pobj = subprocess.Popen(\\n [\\n 'pip{0}'.format(pyver),\\n 'install',\\n '--upgrade',\\n '--force-reinstall',\\n numpy_line.strip()\\n ],\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT\\n )\\n stdout, _ = pobj.communicate()\\n print(stdout)\\n pobj = subprocess.Popen(\\n ['pip{0}'.format(pyver), 'wheel', line],\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT\\n )\\n stdout, _ = pobj.communicate()\\n print(stdout)\\n os.environ['PYTHONPATH'] = old_python_path\",\n \"def build():\\n click.secho('Creating package ...')\\n pkg = utils.create_package()\\n click.secho('Package created: {}'.format(pkg), fg='green')\\n click.secho('Creating wheel...')\\n wheel_path = utils.create_wheel()\\n click.secho('Wheel created in {}'.format(wheel_path), fg='green')\",\n \"def _fetch_full():\\n resource(\\n target=data_path(\\\"eeg\\\", \\\"eeg_full.tar\\\"),\\n url=\\\"https://kdd.ics.uci.edu/databases/eeg/eeg_full.tar\\\",\\n )\\n dependency(\\n target=data_path(\\\"eeg\\\", \\\"full\\\"),\\n source=data_path(\\\"eeg\\\", \\\"eeg_full.tar\\\"),\\n commands=[\\n \\\"mkdir full\\\",\\n \\\"tar xf eeg_full.tar -C full\\\",\\n \\\"ls full | grep gz$ | xargs -I {} tar xzf full/{} -C full\\\",\\n \\\"ls full | grep gz$ | xargs -I {} rm full/{}\\\",\\n \\\"find full | grep gz$ | xargs gunzip\\\",\\n ],\\n )\",\n \"def get_setup_requirements() -> str:\\n return \\\"\\\"\\\"\\n setuptools\\n wheel\\n\\\"\\\"\\\"\",\n \"def fetch(opts):\\n\\n assert opts\\n cache_dir = opts.cache_dir\\n name = opts.name\\n revision = opts.revision\\n\\n if not GIT.exists():\\n err('unable to fetch package; git is not installed')\\n return None\\n\\n git_dir = '--git-dir=' + cache_dir\\n\\n # check if we have the target revision cached; if so, package is ready\\n if os.path.isdir(cache_dir) and not opts.ignore_cache:\\n erv = revision_exists(git_dir, revision)\\n if erv in REVISION_EXISTS:\\n # ensure configuration is properly synchronized\\n if not _sync_git_configuration(opts):\\n return None\\n\\n # if no explicit ignore-cache request and if the revision is a\\n # branch, force ignore-cache on and allow fetching to proceed\\n if opts.ignore_cache is None and erv == GitExistsType.EXISTS_BRANCH:\\n opts.ignore_cache = True\\n # return cache dir if not verifying or verification succeeds\\n elif not opts._git_verify_revision or _verify_revision(\\n git_dir, revision, quiet=True):\\n return cache_dir\\n\\n note('fetching {}...', name)\\n sys.stdout.flush()\\n\\n # validate any cache directory (if one exists)\\n has_cache, bad_validation = _validate_cache(cache_dir)\\n if bad_validation:\\n return None\\n\\n # if we have no cache for this repository, build one\\n if not has_cache:\\n if not ensure_dir_exists(cache_dir):\\n return None\\n\\n if not _create_bare_git_repo(cache_dir):\\n return None\\n\\n # ensure configuration is properly synchronized\\n if not _sync_git_configuration(opts):\\n return None\\n\\n # fetch sources for this repository\\n if not _fetch_srcs(opts, cache_dir, revision, refspecs=opts._git_refspecs):\\n return None\\n\\n # verify revision (if configured to check it)\\n if opts._git_verify_revision:\\n if not _verify_revision(git_dir, revision):\\n err('''\\\\\\nfailed to validate git revision\\n\\nPackage has been configured to require the verification of the GPG signature\\nfor the target revision. The verification has failed. Ensure that the revision\\nis signed and that the package's public key has been registered in the system.\\n\\n Package: {}\\n Revision: {}''', name, revision)\\n return None\\n\\n # fetch submodules (if configured to do so)\\n if opts._git_submodules:\\n if not _fetch_submodules(opts, cache_dir, revision):\\n return None\\n\\n return cache_dir\",\n \"def get_python_package_info(name):\\n command = [\\\"python\\\", \\\"setup.py\\\", \\\"--name\\\"]\\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\\n assert proc.stdout.readline().strip().decode(\\\"utf-8\\\") == name\\n\\n command = [\\\"python\\\", \\\"setup.py\\\", \\\"--version\\\"]\\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\\n release_version = proc.stdout.readline().strip().decode(\\\"utf-8\\\")\\n\\n command = [\\\"python\\\", \\\"setup.py\\\", \\\"--url\\\"]\\n with subprocess.Popen(command, stdout=subprocess.PIPE) as proc:\\n github_url = proc.stdout.readline().strip().decode(\\\"utf-8\\\")\\n\\n github_repo = urlparse(github_url)\\n assert github_repo.netloc == \\\"github.com\\\", \\\"specified repo is not on GitHub\\\"\\n return (release_version, github_repo)\",\n \"def install(self, egg, dir_path):\",\n \"def _fetch_submodule(opts, name, cache_dir, revision, site):\\n git_dir = '--git-dir=' + cache_dir\\n\\n # check if we have the target revision cached; if so, submodule is ready\\n if os.path.isdir(cache_dir) and not opts.ignore_cache:\\n if not revision:\\n return _sync_git_origin(cache_dir, site)\\n\\n if revision_exists(git_dir, revision) in REVISION_EXISTS:\\n return _sync_git_origin(cache_dir, site)\\n\\n log('processing submodule (package: {}) {}...', opts.name, name)\\n sys.stdout.flush()\\n\\n # validate any cache directory (if one exists)\\n has_cache, bad_validation = _validate_cache(cache_dir)\\n if bad_validation:\\n return None\\n\\n # if we have no cache for this repository, build one\\n if not has_cache:\\n if not ensure_dir_exists(cache_dir):\\n return False\\n\\n if not _create_bare_git_repo(cache_dir):\\n return False\\n\\n # ensure configuration is properly synchronized\\n if not _sync_git_origin(cache_dir, site):\\n return False\\n\\n # fetch sources for this submodule\\n desc = 'submodule ({}): {}'.format(opts.name, name)\\n return _fetch_srcs(opts, cache_dir, revision, desc=desc)\",\n \"def install():\\n build()\\n sh(\\\"%s setup.py develop\\\" % PYTHON)\",\n \"def install():\\n return InstallGit()\",\n \"def PythonEgg(glob, name=None):\\r\\n # TODO(John Sirois): Rationalize with globs handling in ParseContext\\r\\n eggs = fsglob(ParseContext.path(glob))\\r\\n\\r\\n requirements = set()\\r\\n for egg in eggs:\\r\\n if os.path.isdir(egg):\\r\\n metadata = PathMetadata(egg, os.path.join(egg, 'EGG-INFO'))\\r\\n else:\\r\\n metadata = EggMetadata(zipimporter(egg))\\r\\n dist = Distribution.from_filename(egg, metadata=metadata)\\r\\n requirements.add(dist.as_requirement())\\r\\n\\r\\n if len(requirements) > 1:\\r\\n raise ValueError('Got multiple egg versions! => %s' % requirements)\\r\\n\\r\\n return PythonRequirement(str(requirements.pop()), name=name)\",\n \"def install_deps_temp(self):\\n if self.distribution.install_requires:\\n self.distribution.fetch_build_eggs(\\n self.distribution.install_requires)\\n if self.distribution.tests_require:\\n self.distribution.fetch_build_eggs(self.distribution.tests_require)\",\n \"def test_load_one_pip(self, build_image_for_jupyterlab):\\n\\n key = \\\"pip>munit1\\\"\\n lb, username = build_image_for_jupyterlab[0], build_image_for_jupyterlab[5]\\n\\n loader = PackageLatestVersionLoader([key], lb, username)\\n promise1 = loader.load(key)\\n assert isinstance(promise1, Promise)\\n\\n pkg = promise1.get()\\n assert pkg == '0.12.4'\",\n \"def build():\\n os.makedirs(DIST_DIR, exist_ok=True)\\n\\n if \\\"WHEEL\\\" in os.environ:\\n whl = build_wheel()\\n else:\\n click.echo(\\\"Not building wheels.\\\")\\n\\n if \\\"WHEEL\\\" in os.environ and \\\"DOCKER\\\" in os.environ:\\n # Docker image requires wheels\\n build_docker_image(whl)\\n else:\\n click.echo(\\\"Not building Docker image.\\\")\\n\\n if \\\"PYINSTALLER\\\" in os.environ:\\n build_pyinstaller()\\n else:\\n click.echo(\\\"Not building PyInstaller packages.\\\")\",\n \"def dist(c, wheel=False):\\n commands = \\\"sdist\\\" if not wheel else \\\"sdist bdist_wheel\\\"\\n c.run(f\\\"python {SETUP_FILE} {commands}\\\")\",\n \"def build(extension):\\n result = call([PYTHON, 'setup.py', '-q', 'build'], cwd=join(DEPS, extension))\\n if result > 0:\\n raise OSError(\\\"Could not build %s\\\" % extension)\",\n \"def get_comrec_build(pkg_dir, build_cmd=build_py):\\n class MyBuildPy(build_cmd):\\n ''' Subclass to write commit data into installation tree '''\\n def run(self):\\n build_cmd.run(self)\\n import subprocess\\n proc = subprocess.Popen('git rev-parse --short HEAD',\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE,\\n shell=True)\\n repo_commit, _ = proc.communicate()\\n # Fix for python 3\\n repo_commit = str(repo_commit)\\n # We write the installation commit even if it's empty\\n cfg_parser = ConfigParser()\\n cfg_parser.read(pjoin(pkg_dir, 'COMMIT_INFO.txt'))\\n cfg_parser.set('commit hash', 'install_hash', repo_commit)\\n out_pth = pjoin(self.build_lib, pkg_dir, 'COMMIT_INFO.txt')\\n cfg_parser.write(open(out_pth, 'wt'))\\n return MyBuildPy\",\n \"def main(repo):\\n print(subprocess.call(['make', 'setup']))\\n with Docker('doppins') as docker:\\n print(docker.run('git clone {repo} cloned'.format(repo=repo)).out)\",\n \"def bootstrap(): # pragma: no cover, exercised via test_bootstrap() functional test\\n pspec = PackageSpec(CFG, \\\"%s==%s\\\" % (PICKLEY, __version__))\\n grand_parent = runez.parent_folder(runez.parent_folder(__file__))\\n if grand_parent and grand_parent.endswith(\\\".whl\\\"):\\n # We are indeed running from pex\\n setup_audit_log()\\n python = CFG.find_python(\\\"/usr/bin/python3\\\") # Prefer system py3, for stability\\n if not python or python.problem:\\n python = pspec.python\\n\\n LOG.debug(\\\"Bootstrapping pickley %s with %s (re-installing as venv instead of pex package)\\\" % (pspec.version, python))\\n target = pspec.install_path\\n venv = PythonVenv(target, python, pspec.index)\\n venv.pip_install(\\\"wheel\\\")\\n with runez.TempFolder():\\n venv.run_python(\\\"-mwheel\\\", \\\"pack\\\", grand_parent)\\n names = os.listdir(\\\".\\\")\\n assert len(names) == 1\\n venv.pip_install(names[0])\\n\\n delivery = DeliveryMethod.delivery_method_by_name(pspec.settings.delivery)\\n return delivery.install(pspec, venv, {PICKLEY: \\\"bootstrapped\\\"})\\n\\n else:\\n manifest = pspec.get_manifest()\\n if not manifest:\\n # We're not running from pex, but we need to re-install pickley with latest version, so it gets a manifest etc\\n return perform_install(pspec, is_upgrade=False, quiet=False)\",\n \"def command_package_build(*args):\\n\\n if len(args) == 0:\\n print 'No package name specified'\\n return 1\\n packname = args[0]\\n package_load_config(packname)\\n\\n builddir = package_path(packname, packageconfig.build_dir_name)\\n depsdir = os.path.join(builddir, 'dependencies')\\n buildcmakedir = os.path.join(builddir, 'build', 'cmake')\\n installdir = install_dir(packname)\\n\\n try:\\n # If nothing is there yet, do checkout and build\\n # dependencies. Otherwise, just make sure our repo is fully\\n # up-to-date.\\n if not os.path.exists(package_path(packname, packageconfig.build_dir_name, '.git')):\\n subprocess.check_call(['git', 'clone', packageconfig.repository, package_path(packname, packageconfig.build_dir_name)])\\n else:\\n subprocess.check_call(['git', 'fetch', 'origin'], cwd=builddir)\\n\\n # Make sure we're on the requested branch\\n subprocess.check_call(['git', 'checkout', packageconfig.version], cwd=builddir)\\n\\n # We always need to make sure deps are up to date, either for\\n # a fresh checkout or because we may have switched\\n # branches/versions. This includes making sure the submodules\\n # are up to date.\\n subprocess.check_call(['make', 'update-dependencies'], cwd=builddir)\\n subprocess.check_call(['make'] + packageconfig.dependencies_targets, cwd=depsdir)\\n\\n # We need to select whether to use cmake with tools (ccache),\\n # or just bare cmake. Using ccache in some setups can be\\n # counterproductive, and if we add support for things like\\n # distcc/icecream, we'll probably want to filter in some other\\n # conditions. These are heuristics for choosing whether or not\\n # to use ccache.\\n cmake_cmd = './cmake_with_tools.sh'\\n if 'nfs' in subprocess.Popen(['mount'], stdout=subprocess.PIPE).communicate()[0].split():\\n cmake_cmd = 'cmake'\\n\\n # Normal build process, making sure we clean out any previous config\\n subprocess.check_call(['rm', '-f', 'CMakeCache.txt'], cwd=buildcmakedir)\\n subprocess.check_call([cmake_cmd,\\n '-DCMAKE_INSTALL_PREFIX='+installdir,\\n '-DCMAKE_BUILD_TYPE='+packageconfig.build_type]\\n + packageconfig.additional_cmake_args + ['.'],\\n cwd=buildcmakedir)\\n subprocess.check_call(['make'] + packageconfig.additional_make_args, cwd=buildcmakedir)\\n subprocess.check_call(['make', 'install'] + packageconfig.additional_make_args, cwd=buildcmakedir)\\n except subprocess.CalledProcessError:\\n return 1\\n\\n return 0\",\n \"def download_and_install(self):\\n if self.is_installed_from_bin:\\n try:\\n self.installer.install_from_rpm_py_package()\\n return\\n except RpmPyPackageNotFoundError as exc:\\n Log.warn('RPM Py Package not found. reason: {0}'.format(exc))\\n\\n # Download and install from the source.\\n top_dir_name = self.downloader.download_and_expand()\\n rpm_py_dir = os.path.join(top_dir_name, 'python')\\n\\n setup_py_in_found = False\\n with Cmd.pushd(rpm_py_dir):\\n if self.installer.setup_py.exists_in_path():\\n setup_py_in_found = True\\n self.installer.run()\\n\\n if not setup_py_in_found:\\n self.installer.install_from_rpm_py_package()\",\n \"def fetch(\\n name: str,\\n tag: Optional[str] = None,\\n secret: Optional[str] = None,\\n ) -> HubExecutor:\\n\\n with ImportExtensions(required=True):\\n import requests\\n\\n pull_url = get_hubble_url() + f'/{name}/?'\\n path_params = {}\\n if secret:\\n path_params['secret'] = secret\\n if tag:\\n path_params['tag'] = tag\\n\\n request_headers = HubIO._get_request_header()\\n\\n pull_url += urlencode(path_params)\\n resp = requests.get(pull_url, headers=request_headers)\\n if resp.status_code != 200:\\n if resp.text:\\n raise Exception(resp.text)\\n resp.raise_for_status()\\n\\n resp = resp.json()\\n\\n result = HubExecutor(\\n resp['id'],\\n resp.get('alias', None),\\n resp['tag'],\\n resp['visibility'],\\n resp['image'],\\n resp['package']['download'],\\n resp['package']['md5'],\\n )\\n\\n return result\",\n \"def download_dependency_github(name, repo, tag, temp_path, build_path, config):\\n wp = os.getcwd()\\n os.chdir(temp_path)\\n # Clone into the repo, pull the specified tag\\n clone_cmd = f\\\"git clone https://github.com/{repo}.git\\\"\\n tag_cmd = f\\\"git checkout master && git fetch && git fetch --tags && git checkout {tag}\\\"\\n os.system(clone_cmd)\\n os.chdir(name)\\n os.system(tag_cmd)\\n os.chdir(wp)\\n # Move the contents of GameData into the build directory\\n shutil.copytree(os.path.join(temp_path, name, \\\"GameData\\\", name), os.path.join(build_path, \\\"GameData\\\", name))\",\n \"def get_requires_for_build_wheel(config_settings=None):\\n info = read_flit_config(pyproj_toml)\\n # If we can get version & description from pyproject.toml (PEP 621), or\\n # by parsing the module (_via_ast), we don't need any extra\\n # dependencies. If not, we'll need to try importing it, so report any\\n # runtime dependencies as build dependencies.\\n want_summary = 'description' in info.dynamic_metadata\\n want_version = 'version' in info.dynamic_metadata\\n\\n module = Module(info.module, Path.cwd())\\n docstring, version = get_docstring_and_version_via_ast(module)\\n\\n if (want_summary and not docstring) or (want_version and not version):\\n return info.metadata.get('requires_dist', [])\\n else:\\n return []\",\n \"def pull(self) -> None:\\n cached_zip_filepath = None\\n try:\\n scheme, name, tag, secret = parse_hub_uri(self.args.uri)\\n\\n executor = HubIO.fetch(name, tag=tag, secret=secret)\\n\\n if not tag:\\n tag = executor.tag\\n\\n uuid = executor.uuid\\n image_name = executor.image_name\\n archive_url = executor.archive_url\\n md5sum = executor.md5sum\\n\\n if scheme == 'jinahub+docker':\\n # pull the Docker image\\n with TimeContext(f'pulling {image_name}', self.logger):\\n image = self._client.images.pull(image_name)\\n if isinstance(image, list):\\n image = image[0]\\n image_tag = image.tags[0] if image.tags else ''\\n self.logger.success(\\n f'🎉 pulled {image_tag} ({image.short_id}) uncompressed size: {get_readable_size(image.attrs[\\\"Size\\\"])}'\\n )\\n return\\n if exist_local(uuid, tag):\\n self.logger.debug(\\n f'The executor `{self.args.uri}` has already been downloaded.'\\n )\\n return\\n # download the package\\n with TimeContext(f'downloading {self.args.uri}', self.logger):\\n cache_dir = Path(\\n os.environ.get(\\n 'JINA_HUB_CACHE_DIR', Path.home().joinpath('.cache', 'jina')\\n )\\n )\\n cache_dir.mkdir(parents=True, exist_ok=True)\\n cached_zip_filename = f'{uuid}-{md5sum}.zip'\\n cached_zip_filepath = download_with_resume(\\n archive_url,\\n cache_dir,\\n cached_zip_filename,\\n md5sum=md5sum,\\n )\\n\\n with TimeContext(f'unpacking {self.args.uri}', self.logger):\\n try:\\n install_local(\\n cached_zip_filepath,\\n uuid,\\n tag,\\n install_deps=self.args.install_requirements,\\n )\\n except Exception as ex:\\n raise HubDownloadError(str(ex))\\n\\n except Exception as e:\\n self.logger.error(\\n f'Error when pulling the executor `{self.args.uri}`: {e!r}'\\n )\\n finally:\\n # delete downloaded zip package if existed\\n if cached_zip_filepath is not None:\\n cached_zip_filepath.unlink()\",\n \"def main():\\n setuptools.setup(**setup_params)\",\n \"def install_egg(self, egg_name):\\n if not os.path.exists(self.egg_directory):\\n os.makedirs(self.egg_directory)\\n self.requirement_set.add_requirement(\\n InstallRequirement.from_line(egg_name, None)\\n )\\n try:\\n self.requirement_set.prepare_files(self.finder)\\n self.requirement_set.install([\\\"--prefix=\\\" + self.egg_directory], [])\\n except DistributionNotFound:\\n self.requirement_set.requirements._keys.remove(egg_name)\\n raise PipException()\",\n \"def build_gem_download_url(purl):\\n purl_data = PackageURL.from_string(purl)\\n\\n name = purl_data.name\\n version = purl_data.version\\n\\n if not (name and version):\\n return\\n\\n return \\\"https://rubygems.org/downloads/{name}-{version}.gem\\\".format(\\n name=name, version=version\\n )\",\n \"def _install():\\n download_file='http://www.ipol.im/pub/art/2015/136/inpaint_8.tgz'\\n tools.download_and_extract(download_file) \\n this_file_path=os.path.dirname(__file__)\\n subprocess.call(' mkdir build; cd build; cmake ..; make', shell=True,cwd=exec_folder)\",\n \"def task_build(argv):\\n pytaskmaster.generator(\\\"setup.py.in\\\", \\\"setup.py\\\", config)\\n pytaskmaster.generator(\\\"pytaskmaster/version.py.in\\\", \\\"pytaskmaster/version.py\\\", config)\\n shell(\\\"python setup.py bdist_wheel\\\")\\n if \\\"--sign\\\" in argv:\\n for file in os.listdir(\\\"dist\\\"):\\n asc_file = \\\"dist/\\\" + file + \\\".asc\\\"\\n if file.endswith(\\\".whl\\\") and not os.path.isfile(asc_file):\\n shell(\\\"gpg --detach-sign -a dist/{}\\\".format(file))\",\n \"def install_package(buildout, specification, working_set):\\n\\n new_ws = zc.buildout.easy_install.install(\\n specs = [specification],\\n dest = buildout['eggs-directory'],\\n links = buildout.get('find-links', '').split(),\\n index = buildout.get('index', None),\\n path = [buildout['develop-eggs-directory']],\\n working_set = working_set,\\n newest = bool_option(buildout, 'newest', 'true'),\\n )\\n\\n merge_working_sets(working_set, new_ws)\\n\\n return working_set\",\n \"def main():\\n # Packaging tools expects either README.txt, README, or README.rst.\\n # Convert the README markdown file to ReStructured text.\\n try:\\n run(\\\"pandoc README.md -f markdown -t rst -o README.rst\\\".split(\\\" \\\"), check=True)\\n except CalledProcessError:\\n print(\\\"pandoc does not appear to be installed. Get it from http://pandoc.org/\\\")\\n exit(1)\\n\\n copy_metadata()\\n\\n for item in (\\\"sdist\\\", \\\"bdist_wheel\\\"):\\n run([\\\"python\\\", \\\"setup.py\\\", item], check=True)\",\n \"def test_require_setuptools():\\n\\n from fabtools.require.python import setuptools\\n\\n setuptools()\\n\\n assert run('easy_install --version', warn_only=True).succeeded\",\n \"def fetch_executable_from_jenkins():\\n\\n base_job_url = os.environ.get('JENKINS_JOB_URL')\\n if not base_job_url:\\n error('Jenkins job URL for the builder is not specified.')\\n\\n build_json = json.loads(requests.get('%s/api/json'\\n % base_job_url).text)\\n last_build = build_json['lastCompletedBuild']['number']\\n print 'Last build ID: %d' % last_build\\n\\n job_url = '%s/%d' % (base_job_url, last_build)\\n last_build_json = json.loads(requests.get('%s/api/json'\\n % job_url).text)\\n if not last_build_json['artifacts']:\\n error('No artifacts found!')\\n\\n artifacts_deb = [artifact for artifact in\\n last_build_json['artifacts'] if '.dmg'\\n in artifact['fileName']]\\n artifact_url = '%s/artifact/%s' % (job_url,\\n artifacts_deb[0]['relativePath'])\\n file_name = artifacts_deb[0]['fileName']\\n print 'Tribler installer url: %s' % artifact_url\\n\\n # Download the file\\n file_path = os.path.join(os.environ.get('WORKSPACE'), file_name)\\n download_response = requests.get(artifact_url, stream=True)\\n download_response.raise_for_status()\\n\\n with open(file_path, 'wb') as handle:\\n for block in download_response.iter_content(1024):\\n handle.write(block)\\n\\n return file_path\",\n \"def _obtain(self, requirement, source=None):\\n # Look at zc.buildout source for comments on this logic\\n index = self._index\\n if index.obtain(requirement) is None:\\n return None\\n dists = [dist for dist in index[requirement.project_name] if (\\n dist in requirement and (\\n dist.location not in self._site_packages or\\n self.allow_site_package_egg(dist.project_name))\\n and (\\n (not source) or\\n (dist.precedence == pkg_resources.SOURCE_DIST))\\n )\\n ]\\n\\n # Here we pick between 'dev' or 'final' versions.\\n # We want to use different logic depending on if this is a third-party\\n # or in-house package:\\n # In-house-packages: we usually want the latest dev version as keeping\\n # on head revisions is sensible to stop code\\n # going stale\\n # Third-party: we usually want the latest final version to protect\\n # ourselves from OSS developers exposing their\\n # latest untested code to the intenet at large.\\n # To overridde this logic the packager needs to specify an explicit\\n # version pin in install_requires or similar for third-party packages,\\n # or use the prefer-final setup flag for in-house packages.\\n\\n # If we prefer final dists, filter for final and use the\\n # result if it is non empty.\\n\\n if manage.is_inhouse_package(requirement.project_name):\\n if self._prefer_final:\\n log.debug(' in-house package, prefer-final')\\n version_comparitor = easy_install._final_version\\n else:\\n log.debug(' in-house package, prefer-dev')\\n version_comparitor = self.is_dev_version\\n else:\\n log.debug(' third-party package, always prefer-final')\\n version_comparitor = easy_install._final_version\\n\\n filtered_dists = [dist for dist in dists\\n if version_comparitor(dist.parsed_version)]\\n if filtered_dists:\\n log.debug(' filtered to {0}'.format(filtered_dists))\\n dists = filtered_dists\\n\\n # The rest of this logic is as-is from buildout\\n best = []\\n bestv = ()\\n for dist in dists:\\n distv = dist.parsed_version\\n if distv > bestv:\\n best = [dist]\\n bestv = distv\\n elif distv == bestv:\\n best.append(dist)\\n\\n log.debug(' best picks are {0}'.format(best))\\n\\n if not best:\\n return None\\n if len(best) == 1:\\n return best[0]\\n if self._download_cache:\\n for dist in best:\\n if (easy_install.realpath(os.path.dirname(dist.location)) ==\\n self._download_cache):\\n return dist\\n best.sort()\\n return best[-1]\",\n \"def install():\\n remote_egg_path = os.path.join(remote_egg_dir, get_egg_name())\\n sudo('easy_install -U %s' % remote_egg_path)\\n sudo('rm %s' % remote_egg_path)\",\n \"def fetch_the_data():\\n subprocess.run([\\\"wget\\\", \\\"https://storage.googleapis.com/recipe-box/recipes_raw.zip\\\"])\\n subprocess.run([\\\"unzip\\\", \\\"recipes_raw.zip\\\", \\\"-d\\\", RECIPES_DIRPATH])\\n subprocess.run([\\\"rm\\\", \\\"recipes_raw.zip\\\"])\",\n \"def install():\\n return {\\n \\\"actions\\\": [TaskCreator.get_pip() + \\\" install --upgrade dist/*.whl\\\"],\\n \\\"verbosity\\\": 2,\\n \\\"setup\\\": [\\\"make_distribution\\\"],\\n }\",\n \"def get_buildrequire_pkgs_from_build(build_id, session, config):\\n print(\\\"Retriewing build metadata from: \\\", config.koji_host)\\n build = session.getBuild(build_id)\\n if not build:\\n raise Exception(\\\"Build with id '{id}' has not been found.\\\".format(id=build_id))\\n\\n print(\\\"Build with the ID\\\", build_id, \\\"found.\\\")\\n tags = session.listTags(build[\\\"build_id\\\"])\\n\\n build_tag = [t[\\\"name\\\"] for t in tags if t[\\\"name\\\"].endswith(\\\"-build\\\")]\\n if not build_tag:\\n raise Exception(\\\"Build with id '{id}' is not tagged in a 'build' tag.\\\".format(id=build_id))\\n\\n tag_data = session.listTaggedRPMS(build_tag[0], latest=True, inherit=True)\\n\\n print(\\\"Found the build tag '\\\", build_tag[0], \\\"' associated with the build.\\\")\\n tagged_rpms = tag_data[0]\\n tagged_pkgs = tag_data[1]\\n pkgs = []\\n archs = [config.arch, \\\"noarch\\\"]\\n print(\\\"Gathering packages and rpms tagged in '\\\", build_tag[0],\\\"'.\\\")\\n for pkg in tagged_pkgs:\\n pkg_md = {\\n \\\"package\\\": pkg,\\n \\\"rpms\\\": [],\\n }\\n\\n for rpm in tagged_rpms:\\n if pkg[\\\"build_id\\\"] == rpm[\\\"build_id\\\"] and rpm[\\\"arch\\\"] in archs:\\n pkg_md[\\\"rpms\\\"].append(rpm)\\n tagged_rpms.remove(rpm)\\n\\n if pkg_md[\\\"rpms\\\"]:\\n pkgs.append(pkg_md)\\n print(\\\"Gathering done.\\\")\\n return pkgs\",\n \"def package():\\n call([sys.executable, \\\"setup.py\\\", \\\"clean\\\", \\\"--all\\\", \\\"bdist_egg\\\"], cwd=\\\"src\\\")\\n call([sys.executable, \\\"setup.py\\\", \\\"clean\\\", \\\"--all\\\", \\\"bdist_wheel\\\"], cwd=\\\"src\\\")\",\n \"def setup(ctx):\\r\\n ctx.run('pip3 install -r requirements.txt')\",\n \"def _build(registry, image_name, git_sha):\\n\\n image = f\\\"{registry}/{image_name}\\\"\\n\\n # If this image is not in our registry, nothing we can do.\\n if registry != config.DOCKER_REGISTRY:\\n return \\\"\\\"\\n\\n # If image:{git_sha} already exists, then return.\\n logger.info(\\n f\\\"Pulling git_sha tag {image}:{git_sha} to check if it already exists\\\"\\n )\\n if (\\n not args.dry_run\\n and docker_silent.pull(f\\\"{image}:{git_sha}\\\").returncode == 0\\n ):\\n # better to find the hash-{hash} of this image, and return hash\\n # but currently, it is not easy to find all tags of the same image digest through registry API.\\n # so we return image digest instead.\\n digest = get_image_digest(f\\\"{image}:{git_sha}\\\")\\n logger.info(\\n f\\\"git_sha tag {image}:{git_sha} already exists, digest: %s\\\", digest\\n )\\n if not digest:\\n raise Exception(\\\"Failed to get digest for existing image\\\")\\n _tag_to_extra_tags(args, image, git_sha)\\n return digest\\n\\n # Enter build context directory if it is specified\\n build_context = enter_build_context(image_name)\\n\\n # Parse .dockerignore in build context\\n dockerignore_files_set = parse_dockerignore(build_context)\\n\\n # Check if the dockerfile exists\\n dockerfile_path = locate_dockerfile(image_name)\\n if not os.path.isfile(dockerfile_path):\\n logger.error(\\n \\\"%s not exists or is not a file, so %s cannot get build\\\",\\n dockerfile_path,\\n image_name,\\n )\\n raise Exception(\\\"Building cannot continue\\\")\\n\\n dockerfile = Dockerfile(dockerfile_path, build_arg=build_arg)\\n\\n hasher = sha256()\\n\\n # Build parents, and calc parents hash\\n for parent_image in dockerfile.parent_images:\\n (\\n parent_image_registry,\\n parent_image_name,\\n parent_image_tag,\\n ) = parse_docker_image_identity(parent_image)\\n parent_digest = _build(\\n parent_image_registry, parent_image_name, parent_image_tag\\n )\\n if parent_digest is None:\\n raise Exception(f\\\"Failed to get parent_digest for {image}\\\")\\n hasher.update(parent_digest.encode())\\n hash_logger.info(\\n \\\"parent: %s, digest: (%s, %s), hash: %s\\\",\\n parent_image,\\n parent_digest,\\n parent_digest.encode(),\\n hasher.hexdigest(),\\n )\\n\\n # Calc current image files hash\\n\\n def update_file_hash(f):\\n if not os.path.isfile(f):\\n return\\n if f in dockerignore_files_set:\\n hash_logger.debug(\\\"ignore: %s\\\", f)\\n return\\n with open(f, \\\"rb\\\") as open_file:\\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\\n while len(buf) > 0:\\n hasher.update(buf)\\n buf = open_file.read(config.READ_FILE_BLOCKSIZE)\\n hash_logger.info(\\\"update: %s, hash: %s\\\", f, hasher.hexdigest())\\n\\n srcs = [dockerfile_path] + dockerfile.copied_srcs + dockerfile.added_srcs\\n # TODO: if the src is a url, download it and hash it (even crane didn't do that)\\n for src in srcs:\\n for f in sorted(glob.glob(src)):\\n # We match every file in a directory recursively\\n if os.path.isdir(f):\\n for sub_f in sorted(glob.glob(f\\\"{f}/**\\\", recursive=True)):\\n update_file_hash(sub_f)\\n else:\\n update_file_hash(f)\\n\\n files_hash = hasher.hexdigest()\\n hash_logger.info(\\\"image: %s, hash: %s\\\", image, files_hash)\\n\\n hash_tag = config.FILES_HASH_TAG_PATTERN.format(files_hash=files_hash)\\n # FIXME(harry): hack, remove this\\n old_hash_image = f\\\"docker-registry.example.com:5000/{image_name}:{hash_tag}\\\"\\n\\n logger.info(\\n f\\\"Pulling files_hash tag {image}:{hash_tag} to check if it already exists\\\"\\n )\\n # If image:hash-{hash} already exists,\\n # then content didn't change, return.\\n # We just need to tag it to latest code version.\\n if (\\n not args.dry_run\\n and docker_silent.pull(f\\\"{image}:{hash_tag}\\\").returncode == 0\\n ):\\n logger.info(\\n f\\\"files_hash tag {image}:{hash_tag} already exists, \\\"\\n \\\"it means content didn't change, we can just tag the old image to new git_sha version tag\\\"\\n )\\n # FIXME(harry): hack, remove this\\n elif not args.dry_run and docker_silent.pull(old_hash_image).returncode == 0:\\n logger.info(f\\\"NOTE: files_hash tag {old_hash_image} already exists!\\\")\\n # tag and push this hash image\\n if docker.tag(old_hash_image, f\\\"{image}:{hash_tag}\\\").returncode != 0:\\n logger.error(\\\"Failed to tag old hash image\\\")\\n return\\n if docker.push(f\\\"{image}:{hash_tag}\\\").returncode != 0:\\n logger.error(\\\"Failed to push hash_tag image\\\")\\n return\\n # If image:hash-{hash} not exists, then build it from Dockerfile.\\n else:\\n logger.info(\\n f\\\"files_hash tag {image}:{hash_tag} dosen't exists, \\\"\\n \\\"it means content may changed, gonna build it from Dockerfile\\\"\\n )\\n if build_with_raw_command(args, image, dockerfile_path, hash_tag) != 0:\\n logger.error(f\\\"Failed to build {image}:{hash_tag}\\\")\\n return\\n if docker.push(f\\\"{image}:{hash_tag}\\\").returncode != 0:\\n logger.error(f\\\"Failed to push image\\\")\\n return\\n logger.info(f\\\"image files_hash tag {image}:{hash_tag} is pushed\\\")\\n\\n # tag and push this final image\\n if docker.tag(f\\\"{image}:{hash_tag}\\\", f\\\"{image}:{git_sha}\\\").returncode != 0:\\n logger.error(\\\"Failed to tag image\\\")\\n return\\n _tag_to_extra_tags(args, image, git_sha)\\n if docker.push(f\\\"{image}:{git_sha}\\\").returncode != 0:\\n logger.error(\\\"Failed to push image\\\")\\n return\\n digest = get_image_digest(f\\\"{image}:{git_sha}\\\")\\n if not digest:\\n logger.error(\\\"Failed to get digest for image\\\")\\n return\\n logger.info(f\\\"image {image}:{git_sha} is pushed, digest: {digest}\\\")\\n\\n return digest\",\n \"def develop():\\n# Install package in development mode\\n sh('python setup.py develop')\",\n \"def build_base():\\n with lcd(env.local_path):\\n put('./requirements.txt', '/srv/build/requirements.txt')\\n\\n with cd('/srv/build'):\\n run('docker build -t {base_image_name} .'.format(\\n base_image_name=env.base_image_name,\\n ))\",\n \"def required():\\n pip = path(\\\"bin/pip\\\")\\n if not pip.exists():\\n sh('%s install -E tg2env -r normal-reqs.txt --extra-index-url=http://www.turbogears.org/2.0/downloads/current/index' % pip)\\n call_pavement('pavement.py', 'develop')\",\n \"def install_deps():\\n default = open('requirements.txt', 'r').readlines()\\n new_pkgs = []\\n links = []\\n for resource in default:\\n if 'git+https' in resource:\\n pkg = resource.split('#')[-1]\\n links.append(resource.strip())\\n new_pkgs.append(pkg.replace('egg=', '').rstrip())\\n else:\\n new_pkgs.append(resource.strip())\\n return new_pkgs, links\",\n \"def build_wheel(wheel_directory, config_settings=None, metadata_directory=None):\\n info = make_wheel_in(pyproj_toml, Path(wheel_directory))\\n return info.file.name\",\n \"def dist_build(c):\\n c.run(\\\"python setup.py sdist\\\")\\n c.run(\\\"python setup.py bdist_wheel\\\")\\n c.run(\\\"twine check dist/*\\\")\",\n \"def upload_wheels():\\n build()\\n sh(\\\"%s -m twine upload dist/*.whl\\\" % PYTHON)\",\n \"def download_dependency(name, info, temp_path, build_path, config):\\n if info[\\\"location\\\"] == \\\"s3\\\":\\n logger.info(f\\\"Collecting {name} {info['version']} from S3\\\")\\n download_dependency_s3(name, info[\\\"version\\\"], temp_path, build_path, config)\\n\\n if info[\\\"location\\\"] == \\\"github\\\":\\n logger.info(f\\\"Collecting {name} at tag {info['tag']} from repository {info['repository']}\\\")\\n download_dependency_github(name, info['repository'], info[\\\"tag\\\"], temp_path, build_path, config)\\n\\n if info[\\\"location\\\"] == \\\"url\\\":\\n logger.info(f\\\"Collecting {name} at from {info['url']}\\\")\\n download_dependency_url(name, info['url'], temp_path, build_path, config, zip=info.get(\\\"zip\\\", False))\",\n \"def _download_model(download_url):\\n\\n pip_args = [\\\"--no-cache-dir\\\"]\\n cmd = [sys.executable, \\\"-m\\\", \\\"pip\\\", \\\"install\\\"] + pip_args + [download_url]\\n\\n return subprocess.call(cmd, env=os.environ.copy())\",\n \"def install_git():\\n git_version = sudo(GET_GIT_VERSION)\\n if git_version != GIT_VERSION:\\n git_file = 'git-%s' % GIT_VERSION\\n sudo('apt-get -y -qq install build-essential')\\n sudo('apt-get -y -qq install git-core')\\n sudo('apt-get -y -qq install libcurl4-gnutls-dev')\\n sudo('apt-get -y -qq install libexpat1-dev')\\n sudo('apt-get -y -qq install gettext')\\n sudo('apt-get -y -qq install libz-dev')\\n sudo('apt-get -y -qq install libssl-dev')\\n sudo('wget --quiet http://git-core.googlecode.com/files/%s.tar.gz' % git_file)\\n sudo('tar -xzf %s.tar.gz' % git_file)\\n with cd(git_file):\\n sudo('make --silent prefix=/usr/local all > /dev/null')\\n sudo('make --silent prefix=/usr/local install > /dev/null')\",\n \"def fetch_package(self, package_name):\\n\\t\\t\\t\\n\\t\\t\\tpackage_root_url = urlparse.urljoin(self.packages_root_url,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tpackage_name + \\\"/\\\")\\n\\t\\t\\t\\n\\t\\t\\tpackage_info_url = urlparse.urljoin(package_root_url, \\\"info\\\")\\n\\t\\t\\tpackage_archive_url = urlparse.urljoin(package_root_url, \\\"archive\\\")\\n\\t\\t\\t\\n\\t\\t\\tlogger.debug(\\\"Get: {0}\\\".format(package_info_url))\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tinfo = json.loads(urllib2.urlopen(package_info_url).read())\\n\\t\\t\\t\\treturn ups.package.Package(self, package_root_url, info)\\n\\t\\t\\texcept urllib2.HTTPError as e:\\n\\t\\t\\t\\traise RepositoryError(e)\\n\\t\\t\\texcept ValueError as e:\\n\\t\\t\\t\\traise RepositoryError(\\\"Unable to parse info file: {0}\\\".format(e))\",\n \"def build_a_bear():\\n if os.path.isdir(c.BEAR_PREFIX):\\n logging.debug(\\\"skipping Bear installation\\\")\\n return\\n\\n # download\\n if not os.path.isfile(c.BEAR_ARCHIVE):\\n curl = get_cmd_or_die(\\\"curl\\\")\\n curl['-s', c.BEAR_URL, '-o', c.BEAR_ARCHIVE] & pb.TEE\\n\\n # remove any existing build dir since we don't know if\\n # bear was built for the current host environment.\\n if os.path.isdir(c.BEAR_SRC):\\n shutil.rmtree(c.BEAR_SRC, ignore_errors=True)\\n\\n # unpack\\n tar = get_cmd_or_die(\\\"tar\\\")\\n with pb.local.cwd(c.DEPS_DIR):\\n tar['xf', c.BEAR_ARCHIVE] & pb.TEE\\n\\n # cmake\\n bear_build_dir = os.path.join(c.BEAR_SRC, \\\"build\\\")\\n bear_install_prefix = \\\"-DCMAKE_INSTALL_PREFIX=\\\" + c.BEAR_PREFIX\\n ensure_dir(bear_build_dir)\\n with pb.local.cwd(bear_build_dir):\\n cmake = get_cmd_or_die(\\\"cmake\\\")\\n cmake[\\\"..\\\", bear_install_prefix] & pb.TEE\\n make = get_cmd_or_die(\\\"make\\\")\\n make[\\\"install\\\"] & pb.TEE\",\n \"def setup(app):\\n wheel = ensure_wheel()\\n subprocess.check_call([\\n \\\"jupyter\\\", \\\"lite\\\", \\\"build\\\", f\\\"--LiteBuildConfig.federated_extensions={wheel}\\\",\\n ], cwd=DEMO)\",\n \"def dist(context):\\n context.run(\\\"python setup.py sdist\\\")\\n context.run(\\\"python setup.py bdist_wheel\\\")\",\n \"def setuptools_command_bdist_egg_call_command_hotfix(self, cmdname, **kw):\\n if cmdname != 'install_data':\\n for dirname in INSTALL_DIRECTORY_ATTRS:\\n kw.setdefault(dirname, self.bdist_dir)\\n kw.setdefault('skip_build', self.skip_build)\\n kw.setdefault('dry_run', self.dry_run)\\n cmd = self.reinitialize_command(cmdname, **kw)\\n self.run_command(cmdname)\\n return cmd\",\n \"def git_pull(where=None):\\n config = get_config(where)\\n with settings(host_string=config['host_string']), cd(config['installation_dir']):\\n run('git pull')\\n run('git submodule update')\\n collectstatic(where)\\n restart(where)\",\n \"def deps(ctx):\\n header(deps.__doc__)\\n with ctx.cd(ROOT):\\n ctx.run(\\n \\\"pip install -r requirements/develop.pip -r requirements/doc.pip\\\", pty=True\\n )\",\n \"def core():\\n\\n from fabtools.require.deb import package as require_deb_package\\n from fabtools.require.rpm import package as require_rpm_package\\n\\n family = distrib_family()\\n\\n # Check if sudo command exists\\n if not files.exists('/usr/bin/sudo'):\\n raise Exception(\\\"Please install the sudo package and execute adduser %s sudo\\\" % env.user)\\n\\n\\n if not files.exists('/usr/bin/docker'):\\n if family == 'debian':\\n require_deb_package('curl')\\n elif family == 'redhat':\\n require_rpm_package('curl')\\n else:\\n raise UnsupportedFamily(supported=['debian', 'redhat'])\\n\\n # Download docker installation\\n run_as_root('curl -sSL https://get.docker.com/ | sh')\",\n \"def _get_egg_path(self):\\n try:\\n _dist = get_distribution('janitoo_nut')\\n return _dist.__file__\\n except AttributeError:\\n return 'src-nut/config'\",\n \"def fetch_exe_from_jenkins():\\n base_job_url = os.environ.get(\\\"JENKINS_JOB_URL\\\")\\n if not base_job_url:\\n print \\\"Jenkins job URL for the builder is not specified.\\\"\\n sys.exit(-1)\\n\\n build_json = json.loads(requests.get(\\\"%s/api/json\\\" % base_job_url).text)\\n last_build = build_json['lastCompletedBuild']['number']\\n print \\\"Last build ID: %d\\\" % last_build\\n\\n job_url = '%s/%d' % (base_job_url, last_build)\\n last_build_json = json.loads(requests.get(\\\"%s/api/json\\\" % job_url).text)\\n if len(last_build_json['artifacts']) == 0:\\n error(\\\"No artifacts found!\\\")\\n\\n artifact_url = \\\"%s/artifact/%s\\\" % (job_url, last_build_json['artifacts'][0]['relativePath'])\\n file_name = last_build_json['artifacts'][0]['fileName']\\n print \\\"Tribler installer url: %s\\\" % artifact_url\\n\\n # Download the file\\n file_path = os.path.join(os.environ.get('WORKSPACE'), file_name)\\n download_response = requests.get(artifact_url, stream=True)\\n download_response.raise_for_status()\\n\\n with open(file_path, 'wb') as handle:\\n for block in download_response.iter_content(1024):\\n handle.write(block)\\n\\n return file_path\",\n \"def get_built_wheel_path(link):\\n sdist_path = download_file(\\n link.url, filename=link.filename, check=link.check_download,\\n )\\n container = os.path.dirname(sdist_path)\\n\\n unpacked_dir = tempfile.mkdtemp()\\n atexit.register(shutil.rmtree, unpacked_dir, ignore_errors=True)\\n unpack_file(sdist_path, unpacked_dir, None, PipLink(link))\\n\\n wheel_content_dir = tempfile.mkdtemp()\\n atexit.register(shutil.rmtree, wheel_content_dir, ignore_errors=True)\\n proc = subprocess.Popen(\\n [sys.executable, 'setup.py', 'bdist_wheel', '-d', container],\\n cwd=unpacked_dir, stdout=subprocess.PIPE, stderr=subprocess.PIPE,\\n )\\n stdout, stderr = proc.communicate()\\n if proc.returncode != 0:\\n warnings.warn('failed to build wheel\\\\n{}'.format(stderr))\\n return None\\n for fn in os.listdir(container):\\n if fn != os.path.basename(sdist_path):\\n return os.path.join(container, fn)\\n warnings.warn('failed to find built wheel')\\n return None\",\n \"def download_and_install(self, version=\\\"latest\\\", os_name=None, bitness=None, show_progress_bar=True):\\n filename_with_path = self.download(version,\\n os_name=os_name,\\n bitness=bitness,\\n show_progress_bar=show_progress_bar)\\n filename = os.path.split(filename_with_path)[1]\\n if filename.lower().endswith(\\\".tar.gz\\\"):\\n extract_dir = os.path.join(self.get_download_path(version), filename[:-7])\\n elif filename.lower().endswith(\\\".zip\\\"):\\n extract_dir = os.path.join(self.get_download_path(version), filename[:-4])\\n else:\\n error_message = \\\"Unknown archive format: {0}\\\".format(filename)\\n logger.error(error_message)\\n raise RuntimeError(error_message)\\n if not os.path.isdir(extract_dir):\\n os.makedirs(extract_dir)\\n logger.debug(\\\"Created directory: {0}\\\".format(extract_dir))\\n if filename.lower().endswith(\\\".tar.gz\\\"):\\n with tarfile.open(os.path.join(self.get_download_path(version), filename), mode=\\\"r:*\\\") as tar:\\n tar.extractall(extract_dir)\\n logger.debug(\\\"Extracted files: {0}\\\".format(\\\", \\\".join(tar.getnames())))\\n elif filename.lower().endswith(\\\".zip\\\"):\\n with zipfile.ZipFile(os.path.join(self.get_download_path(version), filename), mode=\\\"r\\\") as driver_zipfile:\\n driver_zipfile.extractall(extract_dir)\\n driver_filename = self.get_driver_filename(os_name=os_name)\\n for root, dirs, files in os.walk(extract_dir):\\n for curr_file in files:\\n if curr_file == driver_filename:\\n actual_driver_filename = os.path.join(root, curr_file)\\n break\\n if os_name is None:\\n os_name = platform.system()\\n if os_name in ['Darwin', 'Linux']:\\n symlink_src = actual_driver_filename\\n symlink_target = os.path.join(self.link_path, driver_filename)\\n if os.path.islink(symlink_target):\\n if os.path.samefile(symlink_src, symlink_target):\\n logger.info(\\\"Symlink already exists: {0} -> {1}\\\".format(symlink_target, symlink_src))\\n return tuple([symlink_src, symlink_target])\\n else:\\n logger.warning(\\\"Symlink {0} already exists and will be overwritten.\\\".format(symlink_target))\\n os.unlink(symlink_target)\\n os.symlink(symlink_src, symlink_target)\\n logger.info(\\\"Created symlink: {0} -> {1}\\\".format(symlink_target, symlink_src))\\n st = os.stat(symlink_src)\\n os.chmod(symlink_src, st.st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)\\n return tuple([symlink_src, symlink_target])\\n elif os_name == \\\"Windows\\\":\\n src_file = actual_driver_filename\\n dest_file = os.path.join(self.link_path, driver_filename)\\n if os.path.isfile(dest_file):\\n logger.info(\\\"File {0} already exists and will be overwritten.\\\".format(dest_file))\\n shutil.copy2(src_file, dest_file)\\n return tuple([src_file, dest_file])\",\n \"def _fetch_package_requests(source, headers, auth):\\n import requests\\n dest = build_temp_package_filepath()\\n response = requests.get(source, stream=True, headers=headers, auth=auth)\\n response.raise_for_status()\\n with open(dest, 'wb') as handle:\\n for block in response.iter_content(1024):\\n handle.write(block)\\n package = tarfile.open(dest)\\n return package\",\n \"def wheels():\\n with lcd(env.local_path):\\n put('./requirements.txt', '/srv/build/wheel_requirements.txt')\\n put('./etc/base_image/image_requirements.txt',\\n '/srv/build/requirements.txt')\\n\\n with cd('/srv/build/wheelhouse'):\\n run('rm -rf *.whl')\\n\\n compose(cmd='-f service.yml -p %s run --rm wheel-factory' %\\n env.project_name, path='/srv/build')\",\n \"def InstallPrereqs():\\n #Collect the source for Cython and put in _deps/cython-master\\n import urllib,zipfile\\n print('getting cython sources')\\n urllib.urlretrieve('https://github.com/cython/cython/archive/master.zip', filename = 'master.zip')\\n with zipfile.ZipFile('master.zip', 'r') as myzip:\\n myzip.extractall(path='_deps')\\n os.remove('master.zip')\\n for python_install in PYTHONVERSIONS:\\n for cwd in ['_deps/cython-master']:\\n print(subprocess.check_output([python_install, 'setup.py', 'install'], cwd = cwd))\",\n \"def fetch_package(source, method=None, headers=None, auth=None):\\n #if method not in ('requests', 'curl_cli'):\\n # raise Exception('Fetch package method \\\"{}\\\" not found'.format(method))\\n if not method:\\n method = detect_fetch_method()\\n print('Using fetch method \\\"{}\\\"'.format(method))\\n print('Source {}'.format(source))\\n fetch_method = '_fetch_package_{}'.format(method)\\n package = eval(fetch_method)(source, headers, auth)\\n return package\",\n \"def resolve(config, interpreter, logger=print):\\r\\n\\r\\n setuptools_requirement = failsafe_parse(\\r\\n 'setuptools==%s' % config.get('python-setup', 'setuptools_version', default='2.2'))\\r\\n wheel_requirement = failsafe_parse(\\r\\n 'wheel==%s' % config.get('python-setup', 'wheel_version', default='0.22.0'))\\r\\n\\r\\n interpreter = resolve_interpreter(config, interpreter, setuptools_requirement, logger=logger)\\r\\n if interpreter:\\r\\n return resolve_interpreter(config, interpreter, wheel_requirement, logger=logger)\",\n \"def fetch_repo(data):\\n repo = Repository.objects.get(**data)\\n\\n # create a temporary directory\\n tmp_dir = util.tmp_dir('github')\\n\\n # log\\n log.info(\\\"Fetching repo %s to %s\\\", repo.full_name, tmp_dir)\\n\\n # clone the repository to the directory\\n git.Repo.clone_from(repo.git_url, tmp_dir)\\n\\n # add the repo path to the database\\n repo.local_path = tmp_dir\\n repo.save()\\n\\n # tell workers the repo is available\\n publish('github.repo_available', data)\",\n \"def build_orc8r():\\n subprocess.check_call('./build.py -a', shell=True, cwd=orc8_docker_path)\",\n \"def __download(self):\\n\\n # Use the default repository if set to True\\n if self.repository is True:\\n self.repository = self.__default_repository\\n\\n if not self.repository and not self.url:\\n tarball = 'ucx-{}.tar.gz'.format(self.__version)\\n self.url = '{0}/v{1}/{2}'.format(self.__baseurl, self.__version,\\n tarball)\",\n \"def elim_bootstrap_fetch(tree):\\n\\n boot = tree.find('.//target[@name=\\\"boot\\\"]')\\n for child in boot.findall(\\\"./exec\\\"):\\n boot.remove(child)\\n echo = boot.find(\\\"./echo\\\")\\n echo.attrib[\\\"message\\\"] = \\\"Not fetching bootstrap libraries in the Fedora build\\\"\",\n \"async def get(name, version, results, progress):\\n\\n buf = BytesIO()\\n\\n client = httpx.AsyncClient()\\n\\n async with client.stream(\\n \\\"GET\\\", f\\\"https://pydocs.github.io/pkg/{name}-{version}.zip\\\"\\n ) as response:\\n total = int(response.headers[\\\"Content-Length\\\"])\\n\\n download_task = progress.add_task(f\\\"Download {name} {version}\\\", total=total)\\n async for chunk in response.aiter_bytes():\\n buf.write(chunk)\\n progress.update(download_task, completed=response.num_bytes_downloaded)\\n\\n if response.status_code != 200:\\n results[(name, version)] = None\\n else:\\n buf.seek(0)\\n results[(name, version)] = buf.read()\",\n \"async def pull(ctx, pip=None):\\n dev = ctx.message.author\\n if bot.botdev_role in dev.roles or bot.owner_role in dev.roles:\\n await ctx.send(\\\"`Pulling changes...`\\\")\\n call([\\\"git\\\", \\\"stash\\\", \\\"save\\\"])\\n call([\\\"git\\\", \\\"pull\\\"])\\n call([\\\"git\\\", \\\"stash\\\", \\\"clear\\\"])\\n pip_text = \\\"\\\"\\n if pip == \\\"-p\\\" or pip == \\\"--pip\\\" or pip == \\\"-Syu\\\":\\n await ctx.send(\\\"`Updating python dependencies...`\\\")\\n call([\\\"python3.6\\\", \\\"-m\\\", \\\"pip\\\", \\\"install\\\", \\\"--user\\\", \\\"--upgrade\\\", \\\"-r\\\",\\n \\\"requirements.txt\\\"])\\n pip_text = \\\" and updated python dependencies\\\"\\n await ctx.send(\\\"Pulled changes{}! Restarting...\\\".format(pip_text))\\n call([\\\"python3.6\\\", \\\"SchmuckBot.py\\\"])\\n else:\\n if \\\"pacman\\\" in ctx.message.content:\\n await ctx.send(\\\"`{} is not in the sudoers file. This incident will be reported.`\\\".format(ctx.message.author.display_name))\\n else:\\n await ctx.send(\\\"Only bot devs and / or owners can use this command\\\")\",\n \"def mixin_hub_pull_options_parser(parser):\\n\\n gp = add_arg_group(parser, title='Pull')\\n gp.add_argument(\\n '--install-requirements',\\n action='store_true',\\n default=False,\\n help='If set, install `requirements.txt` in the Hub Executor bundle to local',\\n ),\\n gp.add_argument(\\n '--force',\\n action='store_true',\\n default=False,\\n help='If set, always pull the latest Hub Executor bundle even it exists on local',\\n )\",\n \"def setup_prereqs():\\n # Run the contrib download script -- easier that way\\n gmp = os.path.join(flag_gcc_subdir, \\\"gmp\\\")\\n if not os.path.exists(gmp):\\n dochdir(flag_gcc_subdir)\\n docmd(\\\"./contrib/download_prerequisites\\\")\\n # Hack -- fix up gmp dir\\n patch_gmp_configure()\\n dochdir(\\\"..\\\")\",\n \"def build(ctx):\\n ctx.run(\\\"vsce package\\\", replace_env=False)\",\n \"def GetOrBuild(self, image):\\n full_image = self.GetFullRegistryTag(image)\\n # Log in to the registry to see if image exists\\n self.Login()\\n if not FLAGS.force_container_build:\\n # manifest inspect inpspects the registry's copy\\n inspect_cmd = ['docker', 'manifest', 'inspect', full_image]\\n _, _, retcode = vm_util.IssueCommand(\\n inspect_cmd, raise_on_failure=False)\\n if retcode == 0:\\n return full_image\\n self._Build(image)\\n return full_image\",\n \"def download_and_extract(self, package_name):\\n self.download(package_name)\\n self.extract(package_name)\",\n \"def cli(ctx, name, owner, changeset_revision):\\n return ctx.gi.repositories.get_repository_revision_install_info(name, owner, changeset_revision)\",\n \"def official(ctx, build_number):\\n # TODO: Cache API calls to be nice too kashike and the gang\\n minecraft_version = ctx.parent.minecraft_version\\n known_builds = minecraft_version.known_paper_builds\\n if not known_builds:\\n raise ClickException()\\n if build_number is None:\\n build_number = max(known_builds)\\n if build_number not in known_builds:\\n print(f\\\"Known builds for {minecraft_version}:\\\", file=sys.stderr)\\n print_wrapped(', '.join(map(str, known_builds)))\\n raise ClickException(f\\\"Build {build_number} is not a valid build for {minecraft_version}\\\")\\n latest_build = max(known_builds)\\n if build_number != latest_build:\\n click.echo(f\\\"The latest build for {minecraft_version} is {latest_build}.\\\")\\n click.confirm(f\\\"Are you sure you want to use {build_number} instead?\\\", abort=True)\\n jar = OfficialPaperJar(minecraft_version, build_number)\\n try:\\n jar.validate_cache()\\n except CacheInvalidationException as e:\\n e.print(\\\"Paper jar\\\")\\n print()\\n print(f\\\"Downloading Paper {build_number}....\\\")\\n jar.update()\\n assert jar.resolved_path.exists()\\n return jar\",\n \"def install_git(self):\\n self.host.sudo('apt-get install git')\",\n \"async def check_and_install_dependencies(module):\\n try:\\n async with pip_lock:\\n kwargs = pip_kwargs(None)\\n from fhempy import lib\\n\\n initfile = inspect.getfile(lib)\\n fhempy_root = os.path.dirname(initfile)\\n with open(fhempy_root + \\\"/\\\" + module + \\\"/manifest.json\\\", \\\"r\\\") as f:\\n manifest = json.load(f)\\n\\n if \\\"requirements\\\" in manifest:\\n for req in manifest[\\\"requirements\\\"]:\\n if is_installed(req) == False:\\n inst_tries = 0\\n while inst_tries < 3:\\n with concurrent.futures.ThreadPoolExecutor() as pool:\\n ret = (\\n await asyncio.get_event_loop().run_in_executor(\\n pool,\\n functools.partial(\\n install_package, req, **kwargs\\n ),\\n )\\n )\\n if ret:\\n break\\n inst_tries += 1\\n except FileNotFoundError:\\n pass\\n\\n return\",\n \"def build_env_wheels() -> Iterable[Path]:\\n return []\",\n \"def curl(self):\\n self.summarize_operation(\\\"Installing Curl\\\")\\n self.install_package(\\\"curl\\\")\",\n \"def update_requirements():\\n\\n require('code_root', provided_by=env.environments)\\n requirements = os.path.join(env.code_root, 'requirements')\\n sdists = os.path.join(requirements, 'sdists')\\n base_cmd = ['pip install']\\n base_cmd += ['-q -E %(virtualenv_root)s' % env]\\n base_cmd += ['--no-index --find-links=file://%s' % sdists]\\n # install GDAL by hand, before anything else that might depend on it\\n cmd = base_cmd + ['--no-install \\\"GDAL==1.6.1\\\"']\\n sudo(' '.join(cmd), user=env.deploy_user)\\n # this directory won't exist if GDAL was already installed\\n if files.exists('%(virtualenv_root)s/build/GDAL' % env):\\n sudo('rm -f %(virtualenv_root)s/build/GDAL/setup.cfg' % env, user=env.deploy_user)\\n with cd('%(virtualenv_root)s/build/GDAL' % env):\\n sudo('%(virtualenv_root)s/bin/python setup.py build_ext '\\n '--gdal-config=gdal-config '\\n '--library-dirs=/usr/lib '\\n '--libraries=gdal1.6.0 '\\n '--include-dirs=/usr/include/gdal '\\n 'install' % env, user=env.deploy_user)\\n # force reinstallation of OpenBlock every time\\n with settings(warn_only=True):\\n sudo('pip uninstall -y -E %(virtualenv_root)s ebpub ebdata obadmin' % env)\\n for file_name in ['ebpub.txt', 'ebdata.txt', 'obadmin.txt', 'openrural.txt']:\\n apps = os.path.join(requirements, file_name)\\n cmd = base_cmd + ['--requirement %s' % apps]\\n sudo(' '.join(cmd), user=env.deploy_user)\",\n \"def download(self, tag):\\n pkg = PACKAGES.get(tag, None)\\n if not pkg:\\n print \\\"Could not find package {}\\\".format(tag)\\n sys.exit(1)\\n\\n pth = self._ensure_relative_directory('data/{}'.format(tag))\\n pkg['download'](pth)\",\n \"def get_module(module_type, spec, get_full_path, module_set_name=\\\"default\\\", required=True):\\n try:\\n upstream = spec.installed_upstream\\n except spack.repo.UnknownPackageError:\\n upstream, record = spack.store.db.query_by_spec_hash(spec.dag_hash())\\n if upstream:\\n module = spack.modules.common.upstream_module_index.upstream_module(spec, module_type)\\n if not module:\\n return None\\n\\n if get_full_path:\\n return module.path\\n else:\\n return module.use_name\\n else:\\n writer = spack.modules.module_types[module_type](spec, module_set_name)\\n if not os.path.isfile(writer.layout.filename):\\n if not writer.conf.excluded:\\n err_msg = \\\"No module available for package {0} at {1}\\\".format(\\n spec, writer.layout.filename\\n )\\n raise ModuleNotFoundError(err_msg)\\n elif required:\\n tty.debug(\\\"The module configuration has excluded {0}: \\\" \\\"omitting it\\\".format(spec))\\n else:\\n return None\\n\\n if get_full_path:\\n return writer.layout.filename\\n else:\\n return writer.layout.use_name\",\n \"def get_egg_name():\\n global eggname\\n if not eggname:\\n version = local('git describe --abbrev=4', capture=True)\\n if version:\\n version = '%s-%s' % (version, datetime.datetime.today().strftime('%Y%m%d'))\\n eggname = APP_NAME + '-%s-py%s.egg' % (version.replace('-', '_'), python_version)\\n return eggname\",\n \"def build(request):\\n \\n def get(name):\\n return request.GET.get(name)\\n def get_arr(name):\\n val = get(name)\\n if val:\\n return val.split(\\\",\\\")\\n else:\\n return []\\n\\n require = get_arr(\\\"require\\\")\\n exclude = get_arr(\\\"exclude\\\")\\n excludeLibs = get_arr(\\\"excludeLibs\\\")\\n requireLibs = get_arr(\\\"requireLibs\\\")\\n download = get(\\\"download\\\")\\n reset = get(\\\"reset\\\")\\n client = get(\\\"client\\\")\\n compression = get(\\\"compression\\\")\\n\\n dpdr = None\\n global depender\\n if settings.DEPENDER_DEBUG:\\n dpdr = make_depender()\\n else:\\n dpdr = depender\\n if reset == \\\"true\\\":\\n depender = dpdr\\n \\n if compression is None:\\n compression = dpdr.default_compression\\n if settings.DEPENDER_DEBUG:\\n compression = \\\"none\\\"\\n\\n if client == \\\"true\\\" and require.count(\\\"Depender.Client\\\") == 0:\\n require.append(\\\"Depender.Client\\\")\\n \\n includes = dpdr.get_dependencies(require, exclude, requireLibs, excludeLibs)\\n output = \\\"//No files included for build\\\"\\n if len(includes) > 0:\\n \\n libraries_and_copyrights = dict()\\n for i in includes:\\n libraries_and_copyrights[i.library] = i.copyright\\n\\n output = \\\"\\\"\\n for lib, copy in libraries_and_copyrights.iteritems():\\n if len(copy) > 0:\\n output += copy + \\\"\\\\n\\\"\\n\\n output += \\\"\\\\n//Contents: \\\"\\n output += \\\", \\\".join([ i.name for i in includes ])\\n output += \\\"\\\\n\\\\n\\\"\\n \\n location = request.META[\\\"SERVER_PROTOCOL\\\"].split(\\\"/\\\")[0].lower() + \\\"://\\\" + request.META[\\\"HTTP_HOST\\\"] + request.path\\n args = request.META[\\\"QUERY_STRING\\\"]\\n if args.find(\\\"download=\\\") >= 0:\\n clean = []\\n for arg in args.split(\\\"&\\\"):\\n if arg.find(\\\"download=\\\") == -1:\\n clean.append(arg)\\n args = '&'.join(clean)\\n\\n output += \\\"//This lib: \\\" + location + '?' + args\\n output += \\\"\\\\n\\\\n\\\"\\n\\n for i in includes:\\n output += i.compressed_content[compression] + \\\"\\\\n\\\\n\\\"\\n\\n if client == \\\"true\\\":\\n output += dpdr.get_client_js(includes, location)\\n\\n response = HttpResponse(output, content_type=\\\"application/x-javascript\\\")\\n \\n if (download == \\\"true\\\"):\\n response['Content-Disposition'] = 'attachment; filename=built.js'\\n return response\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6031584","0.59851366","0.5983711","0.5879789","0.58144295","0.57931286","0.5760505","0.5605846","0.5510201","0.54663193","0.5444306","0.54275984","0.5427478","0.5391566","0.5365108","0.53289986","0.53218055","0.5299065","0.5275417","0.5265924","0.52530026","0.52498347","0.5241606","0.5241001","0.5190366","0.51511323","0.51510626","0.515032","0.5146777","0.5130175","0.5122196","0.51144916","0.51079464","0.5096734","0.5071871","0.50700784","0.5067386","0.5054486","0.5048544","0.50284475","0.5017738","0.5006308","0.50034004","0.49965054","0.49794525","0.49634427","0.49625945","0.49567276","0.49523127","0.49468127","0.4933672","0.49333388","0.49314347","0.49212638","0.4920079","0.49197528","0.49187908","0.4918098","0.4871003","0.4856389","0.48474738","0.4845157","0.48398688","0.48343518","0.48014084","0.48004052","0.47995904","0.47963208","0.47944054","0.4790554","0.47790566","0.47777325","0.47766826","0.47695833","0.47655576","0.47613648","0.4759312","0.47571248","0.47529498","0.47463673","0.47441885","0.47133908","0.47078186","0.47040108","0.46990135","0.46989918","0.46865475","0.46791473","0.46790817","0.4676135","0.4670962","0.46643537","0.46635377","0.46594617","0.46524101","0.46519318","0.4651321","0.46450204","0.46341252","0.46318117"],"string":"[\n \"0.6031584\",\n \"0.59851366\",\n \"0.5983711\",\n \"0.5879789\",\n \"0.58144295\",\n \"0.57931286\",\n \"0.5760505\",\n \"0.5605846\",\n \"0.5510201\",\n \"0.54663193\",\n \"0.5444306\",\n \"0.54275984\",\n \"0.5427478\",\n \"0.5391566\",\n \"0.5365108\",\n \"0.53289986\",\n \"0.53218055\",\n \"0.5299065\",\n \"0.5275417\",\n \"0.5265924\",\n \"0.52530026\",\n \"0.52498347\",\n \"0.5241606\",\n \"0.5241001\",\n \"0.5190366\",\n \"0.51511323\",\n \"0.51510626\",\n \"0.515032\",\n \"0.5146777\",\n \"0.5130175\",\n \"0.5122196\",\n \"0.51144916\",\n \"0.51079464\",\n \"0.5096734\",\n \"0.5071871\",\n \"0.50700784\",\n \"0.5067386\",\n \"0.5054486\",\n \"0.5048544\",\n \"0.50284475\",\n \"0.5017738\",\n \"0.5006308\",\n \"0.50034004\",\n \"0.49965054\",\n \"0.49794525\",\n \"0.49634427\",\n \"0.49625945\",\n \"0.49567276\",\n \"0.49523127\",\n \"0.49468127\",\n \"0.4933672\",\n \"0.49333388\",\n \"0.49314347\",\n \"0.49212638\",\n \"0.4920079\",\n \"0.49197528\",\n \"0.49187908\",\n \"0.4918098\",\n \"0.4871003\",\n \"0.4856389\",\n \"0.48474738\",\n \"0.4845157\",\n \"0.48398688\",\n \"0.48343518\",\n \"0.48014084\",\n \"0.48004052\",\n \"0.47995904\",\n \"0.47963208\",\n \"0.47944054\",\n \"0.4790554\",\n \"0.47790566\",\n \"0.47777325\",\n \"0.47766826\",\n \"0.47695833\",\n \"0.47655576\",\n \"0.47613648\",\n \"0.4759312\",\n \"0.47571248\",\n \"0.47529498\",\n \"0.47463673\",\n \"0.47441885\",\n \"0.47133908\",\n \"0.47078186\",\n \"0.47040108\",\n \"0.46990135\",\n \"0.46989918\",\n \"0.46865475\",\n \"0.46791473\",\n \"0.46790817\",\n \"0.4676135\",\n \"0.4670962\",\n \"0.46643537\",\n \"0.46635377\",\n \"0.46594617\",\n \"0.46524101\",\n \"0.46519318\",\n \"0.4651321\",\n \"0.46450204\",\n \"0.46341252\",\n \"0.46318117\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7449293"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":86685,"cells":{"query":{"kind":"string","value":"Return a new requirement without the environment marker to avoid calling pip with something like `babel; extra == \"i18n\"`, which would always be ignored."},"document":{"kind":"string","value":"def strip_marker(req):\n import pkg_resources # Delay import to avoid unnecessary side-effects\n\n # create a copy to avoid mutating the input\n req = pkg_resources.Requirement.parse(str(req))\n req.marker = None\n return req"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def avoid_pip_isolation(env: Mapping[str, str]) -> dict[str, str]:\n new_env = {k: v for k, v in env.items() if k != \"PYTHONNOUSERSITE\"}\n if \"PYTHONPATH\" not in new_env:\n return new_env\n\n new_env[\"PYTHONPATH\"] = os.pathsep.join(\n [\n path\n for path in new_env[\"PYTHONPATH\"].split(os.pathsep)\n if \"pip-build-env-\" not in path\n ]\n )\n return new_env","def test_environment_marker_extras(self, data):\n reqset = self.basic_reqset()\n req = InstallRequirement.from_editable(\n data.packages.join(\"LocalEnvironMarker\"))\n reqset.add_requirement(req)\n finder = PackageFinder([data.find_links], [], session=PipSession())\n reqset.prepare_files(finder)\n # This is hacky but does test both case in py2 and py3\n if sys.version_info[:2] in ((2, 7), (3, 4)):\n assert reqset.has_requirement('simple')\n else:\n assert not reqset.has_requirement('simple')","def no_prereq_install():\n return str2bool(os.environ.get('NO_PREREQ_INSTALL', 'False'))","def required():\n pip = path(\"bin/pip\")\n if not pip.exists():\n sh('%s install -E tg2env -r normal-reqs.txt --extra-index-url=http://www.turbogears.org/2.0/downloads/current/index' % pip)\n call_pavement('pavement.py', 'develop')","def require_pip(version_spec, warn=False, extra_msg=None, prereleases=None):\n valid_specifiers = ('===', '==', '<=', '>=', '!=', '~=', '<', '>')\n for spec in valid_specifiers:\n if version_spec.startswith(spec):\n break\n else:\n if version_spec[0].isdigit():\n version_spec = f'~={version_spec}'\n else:\n raise InvalidSpecifier(\n f\"Invalid version specifier: '{version_spec}'\"\n )\n\n version_constraint = SpecifierSet(version_spec, prereleases=prereleases)\n pip_version = metadata.version('pip')\n if pip_version not in version_constraint:\n msg = (\n \"The version of pip installed in the environment \"\n f\"(v{pip_version}) does not satisfy the constraint \"\n f\"'{version_constraint}'\"\n )\n if extra_msg is not None:\n msg = f\"{msg}.\\n{extra_msg}\"\n\n if warn:\n warnings.warn(msg, category=RuntimeWarning)\n else:\n raise DavosError(msg)","def freeze():\n dependencies = sh('pip freeze', capture=True).split(os.linesep)\n\n with open('requirements.txt', 'w') as file:\n for dep in dependencies:\n if not dep.startswith('bones-testing'):\n file.write(dep+'\\n')","def unsatisfied_requirements(buildout, package, working_set):\n\n # read all lines from \"requirements.txt\"\n specs = [k.strip() for k in package_readlines(package, 'requirements.txt')]\n\n # discard empty lines and comments\n specs = [k for k in specs if k and k[0] not in ('#', '-')]\n\n # do not consider packages which are already installed, with a reasonable\n # version matching the user specification, either on the current working\n # set, the installed eggs or the system paths\n newest = bool_option(buildout, 'newest', 'true')\n\n left_over = []\n for k in specs:\n if requirement_is_satisfied(k, working_set, newest):\n dist = working_set.require(k)[0]\n logger.info(\"taking requirement `%s' (%s) from `%s'\", dist.key,\n dist.version, dist.location)\n else:\n left_over.append(k)\n specs = left_over\n\n return left_over","def get_setup_requirements() -> str:\n return \"\"\"\n setuptools\n wheel\n\"\"\"","def from_ireq(cls, ireq):\n # type: (pip.req.InstallRequirement) -> HashableInstallRequirement\n return cls(\n req=ireq.req,\n comes_from=ireq.comes_from,\n source_dir=ireq.source_dir,\n editable=ireq.editable,\n link=ireq.link,\n as_egg=ireq.as_egg,\n update=ireq.update,\n pycompile=ireq.pycompile,\n markers=ireq.markers,\n isolated=ireq.isolated,\n options=ireq.options,\n wheel_cache=ireq._wheel_cache,\n constraint=ireq.constraint)","def pip_lock_file() -> None:\n with open(\"requirements.freeze.all\", \"w\") as ff:\n subprocess.check_call([sys.executable, \"-m\", \"pip\", \"freeze\"], stdout=ff)\n with open(\"requirements.freeze.tmp\", \"w\") as ff:\n subprocess.check_call([\"grep\", \"inmanta\", \"requirements.freeze.all\"], stdout=ff)\n # pip freeze can produce lines with @ that refer to folders outside the container\n # see also https://github.com/pypa/pip/issues/8174\n # also ignore inmanta-dev-dependencies as this is pinned in the requirements.dev.txt\n with open(\"requirements.freeze\", \"w\") as ff:\n subprocess.check_call(\n [\n \"grep\",\n \"-v\",\n \"-e\",\n \"@\",\n \"-e\",\n \"inmanta-dev-dependencies\",\n \"-e\",\n \"inmanta-module-\",\n \"requirements.freeze.tmp\",\n ],\n stdout=ff,\n )\n yield","def no_require():\n from twill import commands\n \n l = commands.browser._post_load_hooks\n l = [ fn for fn in l if fn != _require_post_load_hook ]\n commands.browser._post_load_hooks = l\n\n global _requirements\n _requirements = []","def complete_env() -> Python:\n return Python([\n 'click==0.0.1',\n 'googleapis-common-protos==0.0.1',\n 'numpy==0.0.1',\n 'pandas==0.0.1',\n 'Pillow==0.0.1',\n 'requests==0.0.1',\n 'scikit-learn==0.0.1',\n 'torch==0.0.1',\n 'urllib3==0.0.1',\n 'PyYAML==0.0.1',\n ]) # `verta` and `cloudpickle` included by default","def __init__(self, requirement):\r\n self.pip_requirement = requirement\r\n self.setuptools_requirement = requirement.req\r\n # In pip-accel 0.10.4 and earlier the list of requirements returned by\r\n # unpack_source_dists() contained tuples in the following format.\r\n self.old_interface = (self.name, self.version, self.source_directory)","def test_global_dep(monkeypatch):\n stdin = io.StringIO(\"y\\n\")\n buildspec = deepcopy(BS_BASE)\n buildspec[\"build_toml\"][\"deps\"] = {\n \"build\": [\n \"cmake\"\n ]\n }\n with base_test_invocator(monkeypatch, stdin=stdin, buildspec=buildspec) \\\n as (_, stdout, stderr):\n assert stderr.getvalue() == \"\"\n assert \"cmake\" in stdout.getvalue()","def filter_depend( self, atoms ):\n\n def dep_string_reduce(dep_string,enabled_useflags):\n dest = []\n tokens = iter(dep_string.split())\n useflags = enabled_useflags.split()\n\n for token in tokens:\n if token[-1] == \"?\":\n if token.startswith(\"!\"):\n skip = token[1:-1] in useflags\n else:\n skip = token[:-1] not in useflags\n if skip:\n level = 0\n while 1:\n token = next(tokens)\n if token == \"(\":\n level+=1\n if token == \")\":\n level-=1\n if level < 1:\n break\n continue\n elif token == \"(\" or token == \")\":\n continue\n else:\n dest.append(token)\n\n return \" \".join(dest)\n\n # gjl does not use use flags\n try:\n use = os.environ[\"USE\"]\n atoms = dep_string_reduce(atoms, use)\n except KeyError:\n pass\n return atoms","def resolve_specifier(specifier, # type: str\n prereleases=False, # type: bool\n resolve_versions=True, # type: bool\n *args # type: str # noqa: C812\n ):\n # type: (...) -> HashableInstallRequirement\n ireq = HashableInstallRequirement.from_line(specifier)\n pip_options, session = build_pip_session(*args)\n repository = PyPiRepository(pip_options, session)\n if (ireq.editable or\n piptools.utils.is_pinned_requirement(ireq) or\n not resolve_versions):\n return ireq\n else:\n return HashableInstallRequirement.from_ireq(\n repository.find_best_match(ireq, prereleases=prereleases))","def parse_requirement(req_text):\n req_text = req_text.strip()\n if not req_text:\n return None\n if req_text[0] == \"#\":\n return None\n return pkg_resources.Requirement.parse(req_text)","def test_starting_with_python(self):\n self.assertEqual({\"python-python-xyz\": None},\n pr.sanitize_requirements([\"python-xyz\"]))","def requirements(context):\n pip_compile = \"pip-compile --annotate --quiet\"\n\n command = (\n f\"{pip_compile} requirements/base.in \"\n f\"&& {pip_compile} requirements/local.in \"\n f\"&& {pip_compile} requirements/production.in\"\n )\n command = f\"run --rm django bash -c '{command}'\"\n run_command(context, get_local_user(), False, None, None, command)","def requires(*requirements, **kwargs):\n if '/.tox/' in sys.executable:\n venv = os.path.dirname(os.path.dirname(sys.executable))\n elif env.virtual_env: # pragma: no cover\n venv = env.chut_virtualenv = env.virtual_env\n else: # pragma: no cover\n venv = os.path.expanduser(kwargs.get('venv', '~/.chut/venv'))\n if not env.pip_download_cache: # pragma: no cover\n env.pip_download_cache = os.path.expanduser('~/.chut/cache')\n sh.mkdir('-p', env.pip_download_cache)\n bin_dir = os.path.join(venv, 'bin')\n if bin_dir not in env.path: # pragma: no cover\n env.path = [bin_dir] + env.path\n requirements = list(requirements)\n if 'chut' not in requirements:\n requirements.insert(0, 'chut')\n if not test.d(venv): # pragma: no cover\n import urllib\n url = 'https://raw.github.com/pypa/virtualenv/master/virtualenv.py'\n urllib.urlretrieve(url, '/tmp/_virtualenv.py')\n sh[sys.executable]('-S /tmp/_virtualenv.py', venv) > 1\n sh.rm('/tmp/_virtualenv*', shell=True)\n info('Installing %s...' % ', '.join(requirements))\n sh.pip('install -qM', *requirements) > 1\n elif env.chut_virtualenv:\n upgrade = '--upgrade' in sys.argv\n if (env.chut_upgrade or upgrade): # pragma: no cover\n installed = ''\n else:\n installed = str(sh.pip('freeze')).lower()\n requirements = [r for r in requirements if r.lower() not in installed]\n if requirements: # pragma: no cover\n info('Updating %s...' % ', '.join(requirements))\n sh.pip('install -qM --upgrade', *requirements) > 1\n executable = os.path.join(bin_dir, 'python')\n if not env.chut_virtualenv: # pragma: no cover\n env.chut_virtualenv = venv\n os.execve(executable, [executable] + sys.argv, env)","def _require_environment():\n require('environment', 'host', provided_by=ENVS.keys())","def freeze():\n proc = subprocess.run(['pip', 'freeze'], stdout=subprocess.PIPE)\n with open('requirements.txt', 'wb') as fout:\n fout.write(proc.stdout)","def install_requires():\n skip_install_requires = environ.get('SKIP_INSTALL_REQUIRES')\n if not skip_install_requires:\n with open('requirements.pip') as r:\n return r.readlines()\n return []","def resolve( # noqa:C901\n ireq, # type: TInstallRequirement\n reqset_provider=None, # type: Optional[TShimmedFunc]\n req_tracker_provider=None, # type: Optional[TShimmedFunc]\n install_cmd_provider=None, # type: Optional[TShimmedFunc]\n install_command=None, # type: Optional[TCommand]\n finder_provider=None, # type: Optional[TShimmedFunc]\n resolver_provider=None, # type: Optional[TShimmedFunc]\n wheel_cache_provider=None, # type: Optional[TShimmedFunc]\n format_control_provider=None, # type: Optional[TShimmedFunc]\n make_preparer_provider=None, # type: Optional[TShimmedFunc]\n tempdir_manager_provider=None, # type: Optional[TShimmedFunc]\n options=None, # type: Optional[Values]\n session=None, # type: Optional[TSession]\n resolver=None, # type: Optional[TResolver]\n finder=None, # type: Optional[TFinder]\n upgrade_strategy=\"to-satisfy-only\", # type: str\n force_reinstall=None, # type: Optional[bool]\n ignore_dependencies=None, # type: Optional[bool]\n ignore_requires_python=None, # type: Optional[bool]\n ignore_installed=True, # type: bool\n use_user_site=False, # type: bool\n isolated=None, # type: Optional[bool]\n build_dir=None, # type: Optional[str]\n source_dir=None, # type: Optional[str]\n download_dir=None, # type: Optional[str]\n cache_dir=None, # type: Optional[str]\n wheel_download_dir=None, # type: Optional[str]\n wheel_cache=None, # type: Optional[TWheelCache]\n require_hashes=None, # type: bool\n check_supported_wheels=True, # type: bool\n):\n # (...) -> Set[TInstallRequirement]\n reqset_provider = resolve_possible_shim(reqset_provider)\n finder_provider = resolve_possible_shim(finder_provider)\n resolver_provider = resolve_possible_shim(resolver_provider)\n wheel_cache_provider = resolve_possible_shim(wheel_cache_provider)\n format_control_provider = resolve_possible_shim(format_control_provider)\n make_preparer_provider = resolve_possible_shim(make_preparer_provider)\n req_tracker_provider = resolve_possible_shim(req_tracker_provider)\n install_cmd_provider = resolve_possible_shim(install_cmd_provider)\n tempdir_manager_provider = resolve_possible_shim(tempdir_manager_provider)\n if install_command is None:\n assert isinstance(install_cmd_provider, (type, functools.partial))\n install_command = install_cmd_provider()\n kwarg_map = {\n \"upgrade_strategy\": upgrade_strategy,\n \"force_reinstall\": force_reinstall,\n \"ignore_dependencies\": ignore_dependencies,\n \"ignore_requires_python\": ignore_requires_python,\n \"ignore_installed\": ignore_installed,\n \"use_user_site\": use_user_site,\n \"isolated\": isolated,\n \"build_dir\": build_dir,\n \"src_dir\": source_dir,\n \"download_dir\": download_dir,\n \"require_hashes\": require_hashes,\n \"cache_dir\": cache_dir,\n }\n kwargs, options = populate_options(install_command, options, **kwarg_map)\n with contextlib.ExitStack() as ctx:\n ctx.enter_context(tempdir_manager_provider())\n kwargs = ctx.enter_context(\n ensure_resolution_dirs(wheel_download_dir=wheel_download_dir, **kwargs)\n )\n wheel_download_dir = kwargs.pop(\"wheel_download_dir\")\n if session is None:\n session = get_session(install_cmd=install_command, options=options)\n if finder is None:\n finder = finder_provider(\n install_command, options=options, session=session\n ) # type: ignore\n format_control = getattr(options, \"format_control\", None)\n if not format_control:\n format_control = format_control_provider(None, None) # type: ignore\n wheel_cache = ctx.enter_context(\n wheel_cache_provider(kwargs[\"cache_dir\"], format_control)\n ) # type: ignore\n ireq.is_direct = True # type: ignore\n build_location_kwargs = {\n \"build_dir\": kwargs[\"build_dir\"],\n \"autodelete\": True,\n \"parallel_builds\": False,\n }\n call_function_with_correct_args(ireq.build_location, **build_location_kwargs)\n if reqset_provider is None:\n raise TypeError(\n \"cannot resolve without a requirement set provider... failed!\"\n )\n reqset = reqset_provider(\n install_command,\n options=options,\n session=session,\n wheel_download_dir=wheel_download_dir,\n **kwargs,\n ) # type: ignore\n\n preparer_args = {\n \"build_dir\": kwargs[\"build_dir\"],\n \"src_dir\": kwargs[\"src_dir\"],\n \"download_dir\": kwargs[\"download_dir\"],\n \"wheel_download_dir\": wheel_download_dir,\n \"build_isolation\": kwargs[\"isolated\"],\n \"install_cmd\": install_command,\n \"options\": options,\n \"finder\": finder,\n \"session\": session,\n \"use_user_site\": use_user_site,\n \"require_hashes\": require_hashes,\n }\n if isinstance(req_tracker_provider, (types.FunctionType, functools.partial)):\n preparer_args[\"req_tracker\"] = ctx.enter_context(req_tracker_provider())\n resolver_keys = [\n \"upgrade_strategy\",\n \"force_reinstall\",\n \"ignore_dependencies\",\n \"ignore_installed\",\n \"use_user_site\",\n \"isolated\",\n \"use_user_site\",\n ]\n resolver_args = {key: kwargs[key] for key in resolver_keys if key in kwargs}\n if resolver_provider is None:\n raise TypeError(\"Cannot resolve without a resolver provider... failed!\")\n preparer = ctx.enter_context(make_preparer_provider(**preparer_args))\n resolver = resolver_provider(\n finder=finder,\n preparer=preparer,\n session=session,\n options=options,\n install_cmd=install_command,\n wheel_cache=wheel_cache,\n **resolver_args,\n ) # type: ignore\n resolver.require_hashes = kwargs.get(\"require_hashes\", False) # type: ignore\n _, required_resolver_args = get_method_args(resolver.resolve)\n resolver_args = []\n if \"requirement_set\" in required_resolver_args.args:\n if hasattr(reqset, \"add_requirement\"):\n reqset.add_requirement(ireq)\n else: # Pip >= 22.1.0\n resolver._add_requirement_to_set(reqset, ireq)\n resolver_args.append(reqset)\n elif \"root_reqs\" in required_resolver_args.args:\n resolver_args.append([ireq])\n if \"check_supported_wheels\" in required_resolver_args.args:\n resolver_args.append(check_supported_wheels)\n if getattr(reqset, \"prepare_files\", None):\n if hasattr(reqset, \"add_requirement\"):\n reqset.add_requirement(ireq)\n else: # Pip >= 22.1.0\n resolver._add_requirement_to_set(reqset, ireq)\n results = reqset.prepare_files(finder)\n result = reqset.requirements\n reqset.cleanup_files()\n return result\n if make_preparer_provider is None:\n raise TypeError(\"Cannot create requirement preparer, cannot resolve!\")\n result_reqset = resolver.resolve(*resolver_args) # type: ignore\n if result_reqset is None:\n result_reqset = reqset\n results = result_reqset.requirements\n cleanup_fn = getattr(reqset, \"cleanup_files\", None)\n if cleanup_fn is not None:\n cleanup_fn()\n return results","def make_package_deprecated(tmp_path, pyproject_toml):\n return make_package_base(\n tmp_path, pyproject_toml, setup_func=setup_maker_deprecated, include_js=True\n )","def js_prerelease(command, strict=False):\n class DecoratedCommand(command):\n def run(self):\n jsdeps = self.distribution.get_command_obj('jsdeps') # noqa\n self.distribution.run_command('jsdeps')\n command.run(self)\n update_package_data(self.distribution)\n return DecoratedCommand","def _remove_build(dep):\n m = re.match(r\"([^=]+=[^=]+)=([^=]+)$\", dep, re.IGNORECASE)\n return m.group(1) if m else dep","def filter_working_set_soft(working_set, requirements):\n\n unmet_requirements = []\n\n retval = pkg_resources.WorkingSet([])\n\n for req in requirements:\n try:\n dists = working_set.require(req)\n for dist in dists: retval.add(dist)\n except:\n unmet_requirements.append(req)\n\n return retval, unmet_requirements","def prod_server():\n sh(\"bin/pip freeze -r requirements.txt production/requirements.txt\")","def install_strict_dependencies():\n strict_requirements_file = pkg_resources.resource_filename(\n \"starfish\", \"REQUIREMENTS-STRICT.txt\")\n subprocess.check_call([\n sys.executable, \"-m\", \"pip\", \"install\", \"-r\", strict_requirements_file\n ])","def install_requires():\n return reqs('requirements.txt')","def install_requirements_without_virtualenv(requirements_json):\n\n @task\n def requirements_json_install():\n with cd('/home/vagrant'):\n put(StringIO(requirements_json), '/home/vagrant/requirements.json')\n run('cappa install --no-venv -r /home/vagrant/requirements.json')\n\n return requirements_json_install","def __init__(self, package, version=''):\n PackageRequirement.__init__(self, 'cargo', package, version)","def get_default_pip_requirements(include_cloudpickle=False):\n _check_soft_dependencies(\"mlflow\", severity=\"error\")\n from mlflow.utils.requirements_utils import _get_pinned_requirement\n\n pip_deps = [_get_pinned_requirement(\"sktime\")]\n if include_cloudpickle:\n pip_deps += [_get_pinned_requirement(\"cloudpickle\")]\n\n return pip_deps","def install_requires():\n return reqs(\"requirements.txt\")","def get_pip():\n return 'pip'","def pip_install_req_file(req_file):\n pip_cmd = 'pip install -q --disable-pip-version-check --exists-action w'\n sh(f\"{pip_cmd} -r {req_file}\")","def require(dep, install=None):\n\n def wrapper(fn):\n assert isfunction(fn)\n\n @wraps(fn)\n def ask_install(*args, **kwargs):\n name = fn.__qualname__.replace('.__init__', '')\n ins = install or f'pip install \"{dep}\"'\n raise ImportError(\n f'{name} requires {dep}, please install it by `{ins}`.')\n\n if satisfy_requirement(dep):\n fn._verify_require = getattr(fn, '_verify_require', lambda: None)\n return fn\n\n ask_install._verify_require = ask_install\n return ask_install\n\n return wrapper","def test_build_system_flags_not_implemented(self, temp_env):\n s = spack.spec.Spec(\"mpileaks cppflags=-g\").concretized()\n s.package.flag_handler = build_system_flags\n try:\n spack.build_environment.setup_package(s.package, False)\n assert False\n except NotImplementedError:\n assert True","def fetch_build_egg(dist, req):\n _DeprecatedInstaller.emit()\n _warn_wheel_not_available(dist)\n return _fetch_build_egg_no_warn(dist, req)","def get_install_requirements(provider_package_id: str, version_suffix: str) -> str:\n\n def apply_version_suffix(install_clause: str) -> str:\n if install_clause.startswith(\"apache-airflow\") and \">=\" in install_clause and version_suffix != \"\":\n # This is workaround for `pip` way of handling `--pre` installation switch. It apparently does\n # not modify the meaning of `install_requires` to include also pre-releases, so we need to\n # modify our internal provider and airflow package version references to include all pre-releases\n # including all development releases. When you specify dependency as >= X.Y.Z, and you\n # have packages X.Y.Zdev0 or X.Y.Zrc1 in a local file, such package is not considered\n # as fulfilling the requirement even if `--pre` switch is used.\n return install_clause + \".dev0\"\n return install_clause\n\n install_requires = [\n apply_version_suffix(clause) for clause in ALL_DEPENDENCIES[provider_package_id][DEPS]\n ]\n return \"\".join(f\"\\n {ir}\" for ir in install_requires)","def update_ireq_name(install_requirement, package_name):\n # type: (pip.req.InstallRequirement, str) -> pip.req.InstallRequirement\n requirement = install_requirement.req\n if hasattr(requirement, 'project_name'):\n requirement.project_name = package_name\n else:\n requirement.name = package_name\n return install_requirement","def requirements_installer(language: str, requirements: List[str]) -> str:\n _, installer = LANGUAGES[language]\n return installer.format(package=' '.join(requirements))","def get_extras_require() -> Dict[str, List[str]]:\n extras = {\n \"testing\": [\n \"pytest==6.1.2\",\n \"pytest-cov==2.10.1\",\n ],\n \"linting\": [\n \"pylint==2.6.0\",\n \"flake8==3.8.4\",\n \"black>=20.8b1\",\n \"darglint==1.5.5\",\n \"mypy==0.790\",\n # \"data-science-types>=0.2.20\", # pandas, numpy, matplotlib\n ],\n }\n extras[\"all\"] = [item for group in extras.values() for item in group]\n return extras","def requires_dependency(name):\n if name == 'Fermi ST':\n name = 'pyLikelihood'\n\n try:\n __import__(name)\n skip_it = False\n except ImportError:\n skip_it = True\n\n reason = 'Missing dependency: {}'.format(name)\n return pytest.mark.skipif(skip_it, reason=reason)","def python_requirements(self):\n try:\n dist = self.requirement.pip_requirement.get_dist()\n extras = self.requirement.pip_requirement.extras\n requirements = list(dist.requires(extras))\n except Exception:\n logger.warning(\"Failed to determine installation requirements of %s \"\n \"using pkg-resources, falling back to old implementation.\",\n self, exc_info=True)\n requirements = self.python_requirements_fallback\n logger.debug(\"Python requirements of %s: %r\", self, requirements)\n return requirements","def _get_installation_args(self, install_optional, production_only, force, frozen_lockfile):\n raise NotImplementedError","def test_lock_handle_eggs(PipenvInstance):\n with PipenvInstance() as p:\n with open(p.pipfile_path, 'w') as f:\n f.write(\"\"\"\n[packages]\nRandomWords = \"*\"\n \"\"\")\n c = p.pipenv('lock --verbose')\n assert c.return_code == 0\n assert 'randomwords' in p.lockfile['default']\n assert p.lockfile['default']['randomwords']['version'] == '==0.2.1'","def merge_requirements(self, reqs=None):\n openerp_dir = getattr(self, 'openerp_dir', None)\n openerp_project_name = 'openerp'\n if openerp_dir is not None: # happens in unit tests\n openerp_project_name = self.develop(openerp_dir)\n self.requirements.append(openerp_project_name)\n\n if self.with_gunicorn:\n self.requirements.extend(('psutil', 'gunicorn'))\n\n if self.with_devtools:\n self.requirements.extend(devtools.requirements)\n\n BaseRecipe.merge_requirements(self, reqs=reqs)\n return openerp_project_name","def _get_pip_versioned_package_string(\n library_name: str, version_string: str\n) -> str:\n return '%s==%s' % (library_name, version_string)","def test_vcs_entry_supersedes_non_vcs(pipenv_instance_pypi):\n with pipenv_instance_pypi(chdir=True) as p:\n jinja2_uri = p._pipfile.get_fixture_path(\"git/jinja2\").as_uri()\n with open(p.pipfile_path, \"w\") as f:\n f.write(\n \"\"\"\n[[source]]\nurl = \"https://pypi.org/simple\"\nverify_ssl = true\nname = \"pypi\"\n\n[packages]\nFlask = \"*\"\nJinja2 = {{ref = \"2.11.0\", git = \"{0}\"}}\n \"\"\".format(jinja2_uri).strip()\n )\n c = p.pipenv(\"install\")\n assert c.returncode == 0\n installed_packages = [\"Flask\", \"Jinja2\"]\n assert all([k in p.pipfile[\"packages\"] for k in installed_packages])\n assert all([k.lower() in p.lockfile[\"default\"] for k in installed_packages])\n assert all([k in p.lockfile[\"default\"][\"jinja2\"] for k in [\"ref\", \"git\"]]), str(p.lockfile[\"default\"])\n assert p.lockfile[\"default\"][\"jinja2\"].get(\"ref\") is not None\n assert (\n p.lockfile[\"default\"][\"jinja2\"][\"git\"]\n == jinja2_uri\n )","def pip_requirements():\n\n require(\n \"virtualenv_path\",\n \"requirements_path\",\n \"http_proxy\",\n \"https_proxy\",\n \"sudo_user\",\n )\n cmd = \"pip install --quiet --requirement %s\" % env.requirements_path\n\n # append packages url if specified\n if env.get(\"packages_url\") is not None:\n cmd += \" -f %s\" % env.get(\"packages_url\")\n\n with context_managers.proxy(env.http_proxy, env.https_proxy):\n with context_managers.virtualenv(env.virtualenv_path):\n sudo(cmd, user=env.sudo_user)","def setPkgRequired(self, *args):\n return _libsbml.SBMLDocument_setPkgRequired(self, *args)","def get_version_comparitor(self, requirement):\n if manage.is_inhouse_package(requirement.project_name):\n if self._prefer_final:\n log.debug(' in-house package, prefer-final')\n return easy_install._final_version\n else:\n log.debug(' in-house package, prefer-dev')\n return self.is_dev_version\n else:\n log.debug(' third-party package, always prefer-final')\n return easy_install._final_version","def test_skip_python3_requires(self):\n self.assertEqual(\n {\"python-ovs\": \"2.5.0\"},\n pr.sanitize_requirements(\n [\"ovs>=2.5.0;python_version=='2.7' # Apache-2.0\",\n \"ovs>=2.6.0.dev1;python_version>='3.4' # Apache-2.0\"])\n )","def is_requirement(line):\n # Remove whitespace at the start/end of the line\n line = line.strip()\n\n # Skip blank lines, comments, and editable installs\n return not (\n line == '' or\n line.startswith('-r') or\n line.startswith('#') or\n line.startswith('-e') or\n line.startswith('git+')\n)","def get_install_requires() -> List[str]:\n return [\n \n ]","def install_requirements():\n run_commands('pip install -r ./requirements/dev.txt')","def get_requirements(req):\n\n install_requires = []\n with open(req) as f:\n for line in f:\n if not line.startswith(\"#\"):\n install_requires.append(line.strip())\n return install_requires","def _remove_version_spec(req: str) -> str:\n components = re.split(r'>=|~=|<=|==', req)\n return components[0]","def pur(**options):\n\n if not options['requirement']:\n options['requirement'] = 'requirements.txt'\n\n format_list_arg(options, 'skip')\n format_list_arg(options, 'only')\n format_list_arg(options, 'minor')\n format_list_arg(options, 'patch')\n format_list_arg(options, 'pre')\n\n options['echo'] = True\n\n global PUR_GLOBAL_UPDATED\n PUR_GLOBAL_UPDATED = 0\n\n update_requirements(\n input_file=options['requirement'],\n output_file=options['output'],\n force=options['force'],\n interactive=options['interactive'],\n skip=options['skip'],\n only=options['only'],\n minor=options['minor'],\n patch=options['patch'],\n pre=options['pre'],\n dry_run=options['dry_run'],\n no_recursive=options['no_recursive'],\n echo=options['echo'],\n index_urls=options['index_url'],\n verify=options['verify'],\n )\n\n if not options['dry_run']:\n _echo('All requirements up-to-date.')\n\n if options['nonzero_exit_code']:\n if PUR_GLOBAL_UPDATED > 0:\n raise ExitCodeException(11)\n raise ExitCodeException(10)","def test_with_ending_client(self):\n self.assertEqual({\"python-xyzclient\": None},\n pr.sanitize_requirements([\"python-xyzclient\"]))\n self.assertEqual({\"python-xyz-client\": None},\n pr.sanitize_requirements([\"python-xyz-client\"]))\n self.assertEqual({\"python-xyzclient\": None},\n pr.sanitize_requirements([\"xyzclient\"]))","def _patch_pip(obuffer, updates, **options):\n\n seen = []\n\n def patched_parse_requirements(*args, **kwargs):\n if not options['no_recursive']:\n filename = args[0]\n if filename not in seen:\n if os.path.isfile(filename):\n seen.append(filename)\n buf = StringIO()\n _internal_update_requirements(\n buf, updates,\n input_file=filename,\n output_file=options['output_file'],\n force=options['force'],\n interactive=options['interactive'],\n skip=options['skip'],\n only=options['only'],\n minor=options['minor'],\n patch=options['patch'],\n pre=options['pre'],\n dry_run=options['dry_run'],\n no_recursive=options['no_recursive'],\n echo=options['echo'],\n index_urls=options['index_urls'],\n verify=options['verify'],\n )\n if not options['dry_run']:\n if options['output_file']:\n obuffer.write(buf.getvalue())\n else:\n with open(filename, 'w') as output:\n output.write(buf.getvalue())\n buf.close()\n return []\n req_file.parse_requirements = patched_parse_requirements","def require(name):","def get_install_requires():\n requirements = []\n for line in open('requirements.txt').readlines():\n # skip to next iteration if comment or empty line\n if line.startswith('#') or line == '' or line.startswith('http') or line.startswith('git'):\n continue\n # add line to requirements\n requirements.append(line)\n return requirements","def is_requirement(line):\n # Remove whitespace at the start/end of the line\n line = line.strip()\n\n # Skip blank lines, comments, and editable installs\n return not (\n line == '' or\n line.startswith('-r') or\n line.startswith('#') or\n line.startswith('-e') or\n line.startswith('git+')\n )","def update_requirements():\n\n with virtualenv(VIRTUALENV_PATH):\n cmd = ['pip install']\n cmd += ['--requirement %s' % os.path.join(CODE_DIR,'requirements.txt')]\n run(' '.join(cmd))","def update_requirements():\n\n check_prompt = (\n not env.prompt or\n console.confirm(\n \"Update virtualenv requirements based on requirements.txt file?\",\n default=True,\n )\n )\n\n if check_prompt:\n with cd(\"%s\" % env.repo_path):\n with prefix(\"source %s/bin/activate\" % env.env_path):\n run(\n \"pip install\"\n \" --requirement %s/requirements.txt\" % env.repo_path\n )","def require(what):\n global _requirements\n from twill import commands\n\n #\n # install the post-load hook function.\n #\n\n if _require_post_load_hook not in commands.browser._post_load_hooks:\n if DEBUG:\n print('INSTALLING POST-LOAD HOOK', file=commands.OUT)\n commands.browser._post_load_hooks.append(_require_post_load_hook)\n\n #\n # add the requirement.\n #\n\n if what not in _requirements:\n if DEBUG:\n print('Adding requirement', what, file=commands.OUT)\n _requirements.append(what)","def install_deps():\n default = open('requirements.txt', 'r').readlines()\n new_pkgs = []\n links = []\n for resource in default:\n if 'git+https' in resource:\n pkg = resource.split('#')[-1]\n links.append(resource.strip())\n new_pkgs.append(pkg.replace('egg=', '').rstrip())\n else:\n new_pkgs.append(resource.strip())\n return new_pkgs, links","def filter_working_set_hard(working_set, requirements):\n\n retval = pkg_resources.WorkingSet([])\n\n for req in requirements:\n dists = working_set.require(req)\n for dist in dists: retval.add(dist)\n\n return retval","def substitute_crypto_to_req(req):\n crypto_backend = get_crypto_req()\n\n if crypto_backend is None:\n return req\n\n def is_not_crypto(r):\n CRYPTO_LIBS = PYCRYPTO_DIST, 'cryptography'\n return not any(r.lower().startswith(c) for c in CRYPTO_LIBS)\n\n return [r for r in req if is_not_crypto(r)] + [crypto_backend]","def install(cmd, reqs, add_to_global=False, prefer_final=True,\n force_upgrade=False, use_existing=False):\n # Remove anything we're upgrading\n if force_upgrade:\n uninstall_eggs(reqs)\n\n # Create installer class configured to install into wherever the command\n # class was setup for\n installer = Installer(dest=cmd.install_dir, index=cmd.index_url,\n prefer_final=prefer_final)\n\n # Now apply our runtime additions to its working environment. This includes\n # adding eggs for the egg cache, and removing eggs that we're forcing to be\n # upgraded.\n\n installer.update_search_path()\n\n # This is a bit nasty - we have to monkey-patch the filter for final\n # versions so that we can also filter for dev versions as well.\n\n # Set prefer_final to True, always, so it enables the filter\n easy_install.prefer_final(True)\n\n # This returns a WorkingSet of the packages we just installed.\n ws = None\n if use_existing:\n # NOTE here we pass in the existing stuff - this will prefer installed\n # packages over ones on the server, eg an installed release\n # version won't get trumped by a dev version on the server\n ws = pkg_resources.WorkingSet(pkg_resources.working_set.entries)\n\n # We must remove the package we're installing from the 'baseline' ws.\n # This way we won't get any weird requirement conflicts with new\n # versions of the package we're trying to set up\n if cmd.distribution.metadata.name:\n dist = dependency.get_dist(cmd.distribution.metadata.name)\n if dist:\n dependency.remove_from_ws(ws, dist)\n\n # There's a chance that there were packages in setup_requires that were\n # also in install_requires. Because these are classed as 'already\n # installed' by the installer, they won't have been added to the workingset\n # of packages to set-up in the next step.\n\n # Here we ensure that they're added in along with any of their\n # own dependencies if they are also part of the package install_requires.\n # FIXME: this won't pick up non-direct dependencies.\n # Eg: setup_requires = numpy,\n # install_requires = something that has numpy as a dependency\n def also_required(dist):\n for req in pkg_resources.parse_requirements(reqs):\n if dist in req:\n return True\n return False\n\n setup_dists = [i for i in pkg_resources.working_set.resolve(\n get_setup_requires(cmd.distribution))\n if also_required(i)]\n\n if setup_dists:\n log.debug(\"setup_requires distributions to be set-up:\")\n [log.debug(\" %r\" % i) for i in setup_dists]\n\n # Now run the installer\n try:\n to_setup = installer.install(reqs, working_set=ws,\n use_existing=use_existing)\n except easy_install.MissingDistribution, e:\n log.error(e)\n # TODO: call missing distro hook here\n sys.exit(1)\n\n # Add any of the setup_requires dists to be set-up.\n to_setup = set(to_setup + setup_dists)\n if to_setup:\n log.debug('Packages to set-up:')\n for i in to_setup:\n log.debug(' %r' % i)\n\n # Now we selectively run setuptool's post-install steps.\n # Luckily, the buildout installer didnt strip off any of the useful\n # metadata about the console scripts.\n for dist in to_setup:\n if dist.location.startswith(manage.get_site_packages()):\n fix_permissions(dist)\n cmd.process_distribution(None, dist, deps=False)\n # Add the distributions to the global registry if we asked for it.\n # This makes the distro importable, and classed as 'already\n # installed' by the dependency resolution algorithm.\n if add_to_global:\n pkg_resources.working_set.add(dist)\n else:\n log.debug('Nothing to set-up.')\n return to_setup","def install(cmd, reqs, add_to_global=False, prefer_final=True,\n force_upgrade=False, use_existing=False):\n # Remove anything we're upgrading\n if force_upgrade:\n uninstall_eggs(reqs)\n\n # Create installer class configured to install into wherever the command\n # class was setup for\n installer = Installer(dest=cmd.install_dir, index=cmd.index_url,\n prefer_final=prefer_final)\n\n # Now apply our runtime additions to its working environment. This includes\n # adding eggs for the egg cache, and removing eggs that we're forcing to be\n # upgraded.\n\n installer.update_search_path()\n\n # This is a bit nasty - we have to monkey-patch the filter for final\n # versions so that we can also filter for dev versions as well.\n\n # Set prefer_final to True, always, so it enables the filter\n easy_install.prefer_final(True)\n\n # This returns a WorkingSet of the packages we just installed.\n ws = None\n if use_existing:\n # NOTE here we pass in the existing stuff - this will prefer installed\n # packages over ones on the server, eg an installed release\n # version won't get trumped by a dev version on the server\n ws = pkg_resources.WorkingSet(pkg_resources.working_set.entries)\n\n # We must remove the package we're installing from the 'baseline' ws.\n # This way we won't get any weird requirement conflicts with new\n # versions of the package we're trying to set up\n if cmd.distribution.metadata.name:\n dist = dependency.get_dist(cmd.distribution.metadata.name)\n if dist:\n dependency.remove_from_ws(ws, dist)\n\n # There's a chance that there were packages in setup_requires that were\n # also in install_requires. Because these are classed as 'already\n # installed' by the installer, they won't have been added to the workingset\n # of packages to set-up in the next step.\n\n # Here we ensure that they're added in along with any of their\n # own dependencies if they are also part of the package install_requires.\n # FIXME: this won't pick up non-direct dependencies.\n # Eg: setup_requires = numpy,\n # install_requires = something that has numpy as a dependency\n def also_required(dist):\n for req in pkg_resources.parse_requirements(reqs):\n if dist in req:\n return True\n return False\n\n setup_dists = [i for i in pkg_resources.working_set.resolve(\n get_setup_requires(cmd.distribution))\n if also_required(i)]\n\n if setup_dists:\n log.debug(\"setup_requires distributions to be set-up:\")\n [log.debug(\" %r\" % i) for i in setup_dists]\n\n # Now run the installer\n try:\n to_setup = installer.install(reqs, working_set=ws,\n use_existing=use_existing)\n except easy_install.MissingDistribution, e:\n log.error(e)\n # TODO: call missing distro hook here\n sys.exit(1)\n\n # Add any of the setup_requires dists to be set-up.\n to_setup = set(to_setup + setup_dists)\n if to_setup:\n log.debug('Packages to set-up:')\n for i in to_setup:\n log.debug(' %r' % i)\n\n # Now we selectively run setuptool's post-install steps.\n # Luckily, the buildout installer didnt strip off any of the useful\n # metadata about the console scripts.\n for dist in to_setup:\n if dist.location.startswith(manage.get_site_packages()):\n fix_permissions(dist)\n cmd.process_distribution(None, dist, deps=False)\n # Add the distributions to the global registry if we asked for it.\n # This makes the distro importable, and classed as 'already\n # installed' by the dependency resolution algorithm.\n if add_to_global:\n pkg_resources.working_set.add(dist)\n else:\n log.debug('Nothing to set-up.')\n return to_setup","def main(requirements_file, skip_requirements_file, pipfile, skip_pipfile):\n # type: (str, bool, str, bool) -> None\n pipfile_path = path.Path(pipfile)\n pf = load_pipfile(pipfile_path)\n\n if not skip_requirements_file:\n requirements_file_path = path.Path(requirements_file)\n update_requirements(requirements_file_path, pf)\n\n if not skip_pipfile:\n dump_pipfile(pipfile_path, pf)","def update_requirements():\n with cd(REMOTE_REPO_DIR):\n cmd = ['npm install']\n # cmd += ['--requirement %s' % os.path.join(CODE_DIR,'requirements.txt')]\n run(' '.join(cmd))","def check_venv():\n if not args.novenv:\n assert not sys.prefix == sys.base_prefix, 'Please use pip-upgrade in a virtualenv. If you would like to surpass this use pip-upgrade --novenv'","def test_upgrade_and_dependency_not_removed_caused_required_by_another_item(self):\n assert self.DEPENDENCY_PUBLIC_ID in self.load_config().protocols\n # do not add dependencies for the package\n\n with self.with_oef_installed(), self.with_config_update(), patch(\n \"aea.cli.add._add_item_deps\"\n ):\n result = self.runner.invoke(\n cli,\n [\n \"-v\",\n \"DEBUG\",\n \"upgrade\",\n *self.LOCAL,\n self.ITEM_TYPE,\n f\"{self.ITEM_PUBLIC_ID.author}/{self.ITEM_PUBLIC_ID.name}:latest\",\n ],\n catch_exceptions=True,\n )\n assert result.exit_code == 0\n assert self.DEPENDENCY_PUBLIC_ID in self.load_config().protocols","def test_url_preserved_line_req(self):\n url = 'git+http://foo.com@ref#egg=foo'\n req = InstallRequirement.from_line(url)\n assert req.link.url == url","def require(require_pattern, broken_in=None):\n tagging_decorator = attr('required')\n if IGNORE_REQUIRE:\n return tagging_decorator\n require_pattern = str(require_pattern)\n git_branch = ''\n git_branch = cassandra_git_branch()\n\n if git_branch:\n git_branch = git_branch.lower()\n run_on_branch_patterns = (require_pattern, 'cassandra-{b}'.format(b=require_pattern))\n # always run the test if the git branch name matches\n if any(re.match(p, git_branch, re.IGNORECASE) for p in run_on_branch_patterns):\n return tagging_decorator\n # if skipping a buggy/flapping test, use since\n elif broken_in:\n def tag_and_skip_after_version(decorated):\n return since('0', broken_in)(tagging_decorator(decorated))\n return tag_and_skip_after_version\n # otherwise, skip with a message\n else:\n def tag_and_skip(decorated):\n return unittest.skip('require ' + str(require_pattern))(tagging_decorator(decorated))\n return tag_and_skip\n else:\n return tagging_decorator","def test_skip_windows_requires(self):\n self.assertEqual(\n {\"python-true\": \"1\"},\n pr.sanitize_requirements(\n [\"true>=1\",\n \"wmi;sys_platform=='win32'\"]))","def test_generate_requirements(mocker):\n mocked_function = mocker.patch(\"os.system\")\n generate_requirements()\n mocked_function.assert_called_once_with(\n \"poetry export -f requirements.txt > requirements.txt\"\n )","def generate_requirements(output_path=None):\n from django.conf import settings\n reqs = set()\n \n for app in settings.INSTALLED_APPS:\n if app in mapping.keys():\n reqs |= set(mapping[app])\n if output_path is None:\n print \"--extra-index-url=http://opensource.washingtontimes.com/pypi/simple/\"\n for item in reqs:\n print item\n else:\n try:\n out_file = open(output_path, 'w')\n out_file.write(\"--extra-index-url=http://opensource.washingtontimes.com/pypi/simple/\\n\")\n for item in reqs:\n out_file.write(\"%s\\n\" % item)\n finally:\n out_file.close()","def construct_pkg_dep(terms):\n return SlottedPackageDependency(terms[0], terms[1] if terms[1] else AnySlot())","def requirement_missing(script):\n if \"requires\" in script:\n if script[\"requires\"] is None:\n return False\n for package in script[\"requires\"].split():\n try:\n pkg_resources.working_set.require(package)\n except Exception:\n return True\n return False","def name_from_req(req):\n if hasattr(req, \"project_name\"):\n # from pkg_resources, such as installed dists for pip-sync\n return req.project_name\n else:\n # from packaging, such as install requirements from requirements.txt\n return req.name","def install_deps_temp(self):\n if self.distribution.install_requires:\n self.distribution.fetch_build_eggs(\n self.distribution.install_requires)\n if self.distribution.tests_require:\n self.distribution.fetch_build_eggs(self.distribution.tests_require)","def test_url_preserved_editable_req(self):\n url = 'git+http://foo.com@ref#egg=foo'\n req = InstallRequirement.from_editable(url)\n assert req.link.url == url","def get_requirements(package):\n requirements: list = requires(package)\n requires_dict = defaultdict(dict)\n for requirement in requirements:\n req = Requirement(requirement)\n package_name, package_marker = req.name, req.marker\n if package_marker and \"extra ==\" in str(package_marker):\n group = str(package_marker).split(\"extra == \")[1].strip('\"').strip(\"'\").strip()\n else:\n group = \"required\"\n # De-duplicate (the same package could appear more than once in the extra == 'all' group)\n if package_name in requires_dict[group]:\n continue\n requires_dict[group][package_name] = req\n return requires_dict","def log_installed_python_prereqs():\n sh(\"pip freeze > {}\".format(Env.GEN_LOG_DIR + \"/pip_freeze.log\"))","def doesnt_require(self, requirements, **overrides):\n return self.where(\n requires=tuple(\n set(self.route.get(\"requires\", ())).difference(\n requirements if type(requirements) in (list, tuple) else (requirements,)\n )\n )\n )","def test_use_pep517(shared_data: TestData, source: str, expected: bool) -> None:\n src = shared_data.src.joinpath(source)\n req = InstallRequirement(None, None)\n req.source_dir = os.fspath(src) # make req believe it has been unpacked\n req.load_pyproject_toml()\n assert req.use_pep517 is expected","def getPkgRequired(self, *args):\n return _libsbml.SBMLDocument_getPkgRequired(self, *args)","def install_requirements():\n run('source %(env_path)s/bin/activate; pip install -U -r %(repo_path)s/requirements.txt' % env)","def before_packages(manager):\n if manager not in b.packages:\n return\n if 'apt' == manager:\n s.add('export APT_LISTBUGS_FRONTEND=\"none\"')\n s.add('export APT_LISTCHANGES_FRONTEND=\"none\"')\n s.add('export DEBIAN_FRONTEND=\"noninteractive\"')\n s.add('apt-get -q update')\n elif 'yum' == manager:\n s.add('yum makecache')","def remove_requirement(self, name: str):\n assert name in self._required_processes\n del self._required_processes[name]\n\n if name in self._required_actions:\n del self._required_actions[name]","def __init__(self, package, version='', repo=''):\n PackageRequirement.__init__(self, 'conda', package, version, repo)","def get_requirements():\n name = 'pypeit/requirements.txt'\n\n requirements_file = os.path.join(os.path.dirname(__file__), name)\n install_requires = [line.strip().replace('==', '>=') for line in open(requirements_file)\n if not line.strip().startswith('#') and line.strip() != '']\n return install_requires","def get_crypto_req():\n crypto_backend = os.environ.get('ANSIBLE_CRYPTO_BACKEND', '').strip()\n\n if crypto_backend == PYCRYPTO_DIST:\n # Attempt to set version requirements\n return '%s >= 2.6' % PYCRYPTO_DIST\n\n return crypto_backend or None","def tidy_requirements(requirement_file):\n outdata = []\n with open(requirement_file) as dependencies:\n for line in dependencies:\n line = line.strip()\n if line and not line.startswith('#') and line not in outdata:\n outdata.append(line)\n return outdata"],"string":"[\n \"def avoid_pip_isolation(env: Mapping[str, str]) -> dict[str, str]:\\n new_env = {k: v for k, v in env.items() if k != \\\"PYTHONNOUSERSITE\\\"}\\n if \\\"PYTHONPATH\\\" not in new_env:\\n return new_env\\n\\n new_env[\\\"PYTHONPATH\\\"] = os.pathsep.join(\\n [\\n path\\n for path in new_env[\\\"PYTHONPATH\\\"].split(os.pathsep)\\n if \\\"pip-build-env-\\\" not in path\\n ]\\n )\\n return new_env\",\n \"def test_environment_marker_extras(self, data):\\n reqset = self.basic_reqset()\\n req = InstallRequirement.from_editable(\\n data.packages.join(\\\"LocalEnvironMarker\\\"))\\n reqset.add_requirement(req)\\n finder = PackageFinder([data.find_links], [], session=PipSession())\\n reqset.prepare_files(finder)\\n # This is hacky but does test both case in py2 and py3\\n if sys.version_info[:2] in ((2, 7), (3, 4)):\\n assert reqset.has_requirement('simple')\\n else:\\n assert not reqset.has_requirement('simple')\",\n \"def no_prereq_install():\\n return str2bool(os.environ.get('NO_PREREQ_INSTALL', 'False'))\",\n \"def required():\\n pip = path(\\\"bin/pip\\\")\\n if not pip.exists():\\n sh('%s install -E tg2env -r normal-reqs.txt --extra-index-url=http://www.turbogears.org/2.0/downloads/current/index' % pip)\\n call_pavement('pavement.py', 'develop')\",\n \"def require_pip(version_spec, warn=False, extra_msg=None, prereleases=None):\\n valid_specifiers = ('===', '==', '<=', '>=', '!=', '~=', '<', '>')\\n for spec in valid_specifiers:\\n if version_spec.startswith(spec):\\n break\\n else:\\n if version_spec[0].isdigit():\\n version_spec = f'~={version_spec}'\\n else:\\n raise InvalidSpecifier(\\n f\\\"Invalid version specifier: '{version_spec}'\\\"\\n )\\n\\n version_constraint = SpecifierSet(version_spec, prereleases=prereleases)\\n pip_version = metadata.version('pip')\\n if pip_version not in version_constraint:\\n msg = (\\n \\\"The version of pip installed in the environment \\\"\\n f\\\"(v{pip_version}) does not satisfy the constraint \\\"\\n f\\\"'{version_constraint}'\\\"\\n )\\n if extra_msg is not None:\\n msg = f\\\"{msg}.\\\\n{extra_msg}\\\"\\n\\n if warn:\\n warnings.warn(msg, category=RuntimeWarning)\\n else:\\n raise DavosError(msg)\",\n \"def freeze():\\n dependencies = sh('pip freeze', capture=True).split(os.linesep)\\n\\n with open('requirements.txt', 'w') as file:\\n for dep in dependencies:\\n if not dep.startswith('bones-testing'):\\n file.write(dep+'\\\\n')\",\n \"def unsatisfied_requirements(buildout, package, working_set):\\n\\n # read all lines from \\\"requirements.txt\\\"\\n specs = [k.strip() for k in package_readlines(package, 'requirements.txt')]\\n\\n # discard empty lines and comments\\n specs = [k for k in specs if k and k[0] not in ('#', '-')]\\n\\n # do not consider packages which are already installed, with a reasonable\\n # version matching the user specification, either on the current working\\n # set, the installed eggs or the system paths\\n newest = bool_option(buildout, 'newest', 'true')\\n\\n left_over = []\\n for k in specs:\\n if requirement_is_satisfied(k, working_set, newest):\\n dist = working_set.require(k)[0]\\n logger.info(\\\"taking requirement `%s' (%s) from `%s'\\\", dist.key,\\n dist.version, dist.location)\\n else:\\n left_over.append(k)\\n specs = left_over\\n\\n return left_over\",\n \"def get_setup_requirements() -> str:\\n return \\\"\\\"\\\"\\n setuptools\\n wheel\\n\\\"\\\"\\\"\",\n \"def from_ireq(cls, ireq):\\n # type: (pip.req.InstallRequirement) -> HashableInstallRequirement\\n return cls(\\n req=ireq.req,\\n comes_from=ireq.comes_from,\\n source_dir=ireq.source_dir,\\n editable=ireq.editable,\\n link=ireq.link,\\n as_egg=ireq.as_egg,\\n update=ireq.update,\\n pycompile=ireq.pycompile,\\n markers=ireq.markers,\\n isolated=ireq.isolated,\\n options=ireq.options,\\n wheel_cache=ireq._wheel_cache,\\n constraint=ireq.constraint)\",\n \"def pip_lock_file() -> None:\\n with open(\\\"requirements.freeze.all\\\", \\\"w\\\") as ff:\\n subprocess.check_call([sys.executable, \\\"-m\\\", \\\"pip\\\", \\\"freeze\\\"], stdout=ff)\\n with open(\\\"requirements.freeze.tmp\\\", \\\"w\\\") as ff:\\n subprocess.check_call([\\\"grep\\\", \\\"inmanta\\\", \\\"requirements.freeze.all\\\"], stdout=ff)\\n # pip freeze can produce lines with @ that refer to folders outside the container\\n # see also https://github.com/pypa/pip/issues/8174\\n # also ignore inmanta-dev-dependencies as this is pinned in the requirements.dev.txt\\n with open(\\\"requirements.freeze\\\", \\\"w\\\") as ff:\\n subprocess.check_call(\\n [\\n \\\"grep\\\",\\n \\\"-v\\\",\\n \\\"-e\\\",\\n \\\"@\\\",\\n \\\"-e\\\",\\n \\\"inmanta-dev-dependencies\\\",\\n \\\"-e\\\",\\n \\\"inmanta-module-\\\",\\n \\\"requirements.freeze.tmp\\\",\\n ],\\n stdout=ff,\\n )\\n yield\",\n \"def no_require():\\n from twill import commands\\n \\n l = commands.browser._post_load_hooks\\n l = [ fn for fn in l if fn != _require_post_load_hook ]\\n commands.browser._post_load_hooks = l\\n\\n global _requirements\\n _requirements = []\",\n \"def complete_env() -> Python:\\n return Python([\\n 'click==0.0.1',\\n 'googleapis-common-protos==0.0.1',\\n 'numpy==0.0.1',\\n 'pandas==0.0.1',\\n 'Pillow==0.0.1',\\n 'requests==0.0.1',\\n 'scikit-learn==0.0.1',\\n 'torch==0.0.1',\\n 'urllib3==0.0.1',\\n 'PyYAML==0.0.1',\\n ]) # `verta` and `cloudpickle` included by default\",\n \"def __init__(self, requirement):\\r\\n self.pip_requirement = requirement\\r\\n self.setuptools_requirement = requirement.req\\r\\n # In pip-accel 0.10.4 and earlier the list of requirements returned by\\r\\n # unpack_source_dists() contained tuples in the following format.\\r\\n self.old_interface = (self.name, self.version, self.source_directory)\",\n \"def test_global_dep(monkeypatch):\\n stdin = io.StringIO(\\\"y\\\\n\\\")\\n buildspec = deepcopy(BS_BASE)\\n buildspec[\\\"build_toml\\\"][\\\"deps\\\"] = {\\n \\\"build\\\": [\\n \\\"cmake\\\"\\n ]\\n }\\n with base_test_invocator(monkeypatch, stdin=stdin, buildspec=buildspec) \\\\\\n as (_, stdout, stderr):\\n assert stderr.getvalue() == \\\"\\\"\\n assert \\\"cmake\\\" in stdout.getvalue()\",\n \"def filter_depend( self, atoms ):\\n\\n def dep_string_reduce(dep_string,enabled_useflags):\\n dest = []\\n tokens = iter(dep_string.split())\\n useflags = enabled_useflags.split()\\n\\n for token in tokens:\\n if token[-1] == \\\"?\\\":\\n if token.startswith(\\\"!\\\"):\\n skip = token[1:-1] in useflags\\n else:\\n skip = token[:-1] not in useflags\\n if skip:\\n level = 0\\n while 1:\\n token = next(tokens)\\n if token == \\\"(\\\":\\n level+=1\\n if token == \\\")\\\":\\n level-=1\\n if level < 1:\\n break\\n continue\\n elif token == \\\"(\\\" or token == \\\")\\\":\\n continue\\n else:\\n dest.append(token)\\n\\n return \\\" \\\".join(dest)\\n\\n # gjl does not use use flags\\n try:\\n use = os.environ[\\\"USE\\\"]\\n atoms = dep_string_reduce(atoms, use)\\n except KeyError:\\n pass\\n return atoms\",\n \"def resolve_specifier(specifier, # type: str\\n prereleases=False, # type: bool\\n resolve_versions=True, # type: bool\\n *args # type: str # noqa: C812\\n ):\\n # type: (...) -> HashableInstallRequirement\\n ireq = HashableInstallRequirement.from_line(specifier)\\n pip_options, session = build_pip_session(*args)\\n repository = PyPiRepository(pip_options, session)\\n if (ireq.editable or\\n piptools.utils.is_pinned_requirement(ireq) or\\n not resolve_versions):\\n return ireq\\n else:\\n return HashableInstallRequirement.from_ireq(\\n repository.find_best_match(ireq, prereleases=prereleases))\",\n \"def parse_requirement(req_text):\\n req_text = req_text.strip()\\n if not req_text:\\n return None\\n if req_text[0] == \\\"#\\\":\\n return None\\n return pkg_resources.Requirement.parse(req_text)\",\n \"def test_starting_with_python(self):\\n self.assertEqual({\\\"python-python-xyz\\\": None},\\n pr.sanitize_requirements([\\\"python-xyz\\\"]))\",\n \"def requirements(context):\\n pip_compile = \\\"pip-compile --annotate --quiet\\\"\\n\\n command = (\\n f\\\"{pip_compile} requirements/base.in \\\"\\n f\\\"&& {pip_compile} requirements/local.in \\\"\\n f\\\"&& {pip_compile} requirements/production.in\\\"\\n )\\n command = f\\\"run --rm django bash -c '{command}'\\\"\\n run_command(context, get_local_user(), False, None, None, command)\",\n \"def requires(*requirements, **kwargs):\\n if '/.tox/' in sys.executable:\\n venv = os.path.dirname(os.path.dirname(sys.executable))\\n elif env.virtual_env: # pragma: no cover\\n venv = env.chut_virtualenv = env.virtual_env\\n else: # pragma: no cover\\n venv = os.path.expanduser(kwargs.get('venv', '~/.chut/venv'))\\n if not env.pip_download_cache: # pragma: no cover\\n env.pip_download_cache = os.path.expanduser('~/.chut/cache')\\n sh.mkdir('-p', env.pip_download_cache)\\n bin_dir = os.path.join(venv, 'bin')\\n if bin_dir not in env.path: # pragma: no cover\\n env.path = [bin_dir] + env.path\\n requirements = list(requirements)\\n if 'chut' not in requirements:\\n requirements.insert(0, 'chut')\\n if not test.d(venv): # pragma: no cover\\n import urllib\\n url = 'https://raw.github.com/pypa/virtualenv/master/virtualenv.py'\\n urllib.urlretrieve(url, '/tmp/_virtualenv.py')\\n sh[sys.executable]('-S /tmp/_virtualenv.py', venv) > 1\\n sh.rm('/tmp/_virtualenv*', shell=True)\\n info('Installing %s...' % ', '.join(requirements))\\n sh.pip('install -qM', *requirements) > 1\\n elif env.chut_virtualenv:\\n upgrade = '--upgrade' in sys.argv\\n if (env.chut_upgrade or upgrade): # pragma: no cover\\n installed = ''\\n else:\\n installed = str(sh.pip('freeze')).lower()\\n requirements = [r for r in requirements if r.lower() not in installed]\\n if requirements: # pragma: no cover\\n info('Updating %s...' % ', '.join(requirements))\\n sh.pip('install -qM --upgrade', *requirements) > 1\\n executable = os.path.join(bin_dir, 'python')\\n if not env.chut_virtualenv: # pragma: no cover\\n env.chut_virtualenv = venv\\n os.execve(executable, [executable] + sys.argv, env)\",\n \"def _require_environment():\\n require('environment', 'host', provided_by=ENVS.keys())\",\n \"def freeze():\\n proc = subprocess.run(['pip', 'freeze'], stdout=subprocess.PIPE)\\n with open('requirements.txt', 'wb') as fout:\\n fout.write(proc.stdout)\",\n \"def install_requires():\\n skip_install_requires = environ.get('SKIP_INSTALL_REQUIRES')\\n if not skip_install_requires:\\n with open('requirements.pip') as r:\\n return r.readlines()\\n return []\",\n \"def resolve( # noqa:C901\\n ireq, # type: TInstallRequirement\\n reqset_provider=None, # type: Optional[TShimmedFunc]\\n req_tracker_provider=None, # type: Optional[TShimmedFunc]\\n install_cmd_provider=None, # type: Optional[TShimmedFunc]\\n install_command=None, # type: Optional[TCommand]\\n finder_provider=None, # type: Optional[TShimmedFunc]\\n resolver_provider=None, # type: Optional[TShimmedFunc]\\n wheel_cache_provider=None, # type: Optional[TShimmedFunc]\\n format_control_provider=None, # type: Optional[TShimmedFunc]\\n make_preparer_provider=None, # type: Optional[TShimmedFunc]\\n tempdir_manager_provider=None, # type: Optional[TShimmedFunc]\\n options=None, # type: Optional[Values]\\n session=None, # type: Optional[TSession]\\n resolver=None, # type: Optional[TResolver]\\n finder=None, # type: Optional[TFinder]\\n upgrade_strategy=\\\"to-satisfy-only\\\", # type: str\\n force_reinstall=None, # type: Optional[bool]\\n ignore_dependencies=None, # type: Optional[bool]\\n ignore_requires_python=None, # type: Optional[bool]\\n ignore_installed=True, # type: bool\\n use_user_site=False, # type: bool\\n isolated=None, # type: Optional[bool]\\n build_dir=None, # type: Optional[str]\\n source_dir=None, # type: Optional[str]\\n download_dir=None, # type: Optional[str]\\n cache_dir=None, # type: Optional[str]\\n wheel_download_dir=None, # type: Optional[str]\\n wheel_cache=None, # type: Optional[TWheelCache]\\n require_hashes=None, # type: bool\\n check_supported_wheels=True, # type: bool\\n):\\n # (...) -> Set[TInstallRequirement]\\n reqset_provider = resolve_possible_shim(reqset_provider)\\n finder_provider = resolve_possible_shim(finder_provider)\\n resolver_provider = resolve_possible_shim(resolver_provider)\\n wheel_cache_provider = resolve_possible_shim(wheel_cache_provider)\\n format_control_provider = resolve_possible_shim(format_control_provider)\\n make_preparer_provider = resolve_possible_shim(make_preparer_provider)\\n req_tracker_provider = resolve_possible_shim(req_tracker_provider)\\n install_cmd_provider = resolve_possible_shim(install_cmd_provider)\\n tempdir_manager_provider = resolve_possible_shim(tempdir_manager_provider)\\n if install_command is None:\\n assert isinstance(install_cmd_provider, (type, functools.partial))\\n install_command = install_cmd_provider()\\n kwarg_map = {\\n \\\"upgrade_strategy\\\": upgrade_strategy,\\n \\\"force_reinstall\\\": force_reinstall,\\n \\\"ignore_dependencies\\\": ignore_dependencies,\\n \\\"ignore_requires_python\\\": ignore_requires_python,\\n \\\"ignore_installed\\\": ignore_installed,\\n \\\"use_user_site\\\": use_user_site,\\n \\\"isolated\\\": isolated,\\n \\\"build_dir\\\": build_dir,\\n \\\"src_dir\\\": source_dir,\\n \\\"download_dir\\\": download_dir,\\n \\\"require_hashes\\\": require_hashes,\\n \\\"cache_dir\\\": cache_dir,\\n }\\n kwargs, options = populate_options(install_command, options, **kwarg_map)\\n with contextlib.ExitStack() as ctx:\\n ctx.enter_context(tempdir_manager_provider())\\n kwargs = ctx.enter_context(\\n ensure_resolution_dirs(wheel_download_dir=wheel_download_dir, **kwargs)\\n )\\n wheel_download_dir = kwargs.pop(\\\"wheel_download_dir\\\")\\n if session is None:\\n session = get_session(install_cmd=install_command, options=options)\\n if finder is None:\\n finder = finder_provider(\\n install_command, options=options, session=session\\n ) # type: ignore\\n format_control = getattr(options, \\\"format_control\\\", None)\\n if not format_control:\\n format_control = format_control_provider(None, None) # type: ignore\\n wheel_cache = ctx.enter_context(\\n wheel_cache_provider(kwargs[\\\"cache_dir\\\"], format_control)\\n ) # type: ignore\\n ireq.is_direct = True # type: ignore\\n build_location_kwargs = {\\n \\\"build_dir\\\": kwargs[\\\"build_dir\\\"],\\n \\\"autodelete\\\": True,\\n \\\"parallel_builds\\\": False,\\n }\\n call_function_with_correct_args(ireq.build_location, **build_location_kwargs)\\n if reqset_provider is None:\\n raise TypeError(\\n \\\"cannot resolve without a requirement set provider... failed!\\\"\\n )\\n reqset = reqset_provider(\\n install_command,\\n options=options,\\n session=session,\\n wheel_download_dir=wheel_download_dir,\\n **kwargs,\\n ) # type: ignore\\n\\n preparer_args = {\\n \\\"build_dir\\\": kwargs[\\\"build_dir\\\"],\\n \\\"src_dir\\\": kwargs[\\\"src_dir\\\"],\\n \\\"download_dir\\\": kwargs[\\\"download_dir\\\"],\\n \\\"wheel_download_dir\\\": wheel_download_dir,\\n \\\"build_isolation\\\": kwargs[\\\"isolated\\\"],\\n \\\"install_cmd\\\": install_command,\\n \\\"options\\\": options,\\n \\\"finder\\\": finder,\\n \\\"session\\\": session,\\n \\\"use_user_site\\\": use_user_site,\\n \\\"require_hashes\\\": require_hashes,\\n }\\n if isinstance(req_tracker_provider, (types.FunctionType, functools.partial)):\\n preparer_args[\\\"req_tracker\\\"] = ctx.enter_context(req_tracker_provider())\\n resolver_keys = [\\n \\\"upgrade_strategy\\\",\\n \\\"force_reinstall\\\",\\n \\\"ignore_dependencies\\\",\\n \\\"ignore_installed\\\",\\n \\\"use_user_site\\\",\\n \\\"isolated\\\",\\n \\\"use_user_site\\\",\\n ]\\n resolver_args = {key: kwargs[key] for key in resolver_keys if key in kwargs}\\n if resolver_provider is None:\\n raise TypeError(\\\"Cannot resolve without a resolver provider... failed!\\\")\\n preparer = ctx.enter_context(make_preparer_provider(**preparer_args))\\n resolver = resolver_provider(\\n finder=finder,\\n preparer=preparer,\\n session=session,\\n options=options,\\n install_cmd=install_command,\\n wheel_cache=wheel_cache,\\n **resolver_args,\\n ) # type: ignore\\n resolver.require_hashes = kwargs.get(\\\"require_hashes\\\", False) # type: ignore\\n _, required_resolver_args = get_method_args(resolver.resolve)\\n resolver_args = []\\n if \\\"requirement_set\\\" in required_resolver_args.args:\\n if hasattr(reqset, \\\"add_requirement\\\"):\\n reqset.add_requirement(ireq)\\n else: # Pip >= 22.1.0\\n resolver._add_requirement_to_set(reqset, ireq)\\n resolver_args.append(reqset)\\n elif \\\"root_reqs\\\" in required_resolver_args.args:\\n resolver_args.append([ireq])\\n if \\\"check_supported_wheels\\\" in required_resolver_args.args:\\n resolver_args.append(check_supported_wheels)\\n if getattr(reqset, \\\"prepare_files\\\", None):\\n if hasattr(reqset, \\\"add_requirement\\\"):\\n reqset.add_requirement(ireq)\\n else: # Pip >= 22.1.0\\n resolver._add_requirement_to_set(reqset, ireq)\\n results = reqset.prepare_files(finder)\\n result = reqset.requirements\\n reqset.cleanup_files()\\n return result\\n if make_preparer_provider is None:\\n raise TypeError(\\\"Cannot create requirement preparer, cannot resolve!\\\")\\n result_reqset = resolver.resolve(*resolver_args) # type: ignore\\n if result_reqset is None:\\n result_reqset = reqset\\n results = result_reqset.requirements\\n cleanup_fn = getattr(reqset, \\\"cleanup_files\\\", None)\\n if cleanup_fn is not None:\\n cleanup_fn()\\n return results\",\n \"def make_package_deprecated(tmp_path, pyproject_toml):\\n return make_package_base(\\n tmp_path, pyproject_toml, setup_func=setup_maker_deprecated, include_js=True\\n )\",\n \"def js_prerelease(command, strict=False):\\n class DecoratedCommand(command):\\n def run(self):\\n jsdeps = self.distribution.get_command_obj('jsdeps') # noqa\\n self.distribution.run_command('jsdeps')\\n command.run(self)\\n update_package_data(self.distribution)\\n return DecoratedCommand\",\n \"def _remove_build(dep):\\n m = re.match(r\\\"([^=]+=[^=]+)=([^=]+)$\\\", dep, re.IGNORECASE)\\n return m.group(1) if m else dep\",\n \"def filter_working_set_soft(working_set, requirements):\\n\\n unmet_requirements = []\\n\\n retval = pkg_resources.WorkingSet([])\\n\\n for req in requirements:\\n try:\\n dists = working_set.require(req)\\n for dist in dists: retval.add(dist)\\n except:\\n unmet_requirements.append(req)\\n\\n return retval, unmet_requirements\",\n \"def prod_server():\\n sh(\\\"bin/pip freeze -r requirements.txt production/requirements.txt\\\")\",\n \"def install_strict_dependencies():\\n strict_requirements_file = pkg_resources.resource_filename(\\n \\\"starfish\\\", \\\"REQUIREMENTS-STRICT.txt\\\")\\n subprocess.check_call([\\n sys.executable, \\\"-m\\\", \\\"pip\\\", \\\"install\\\", \\\"-r\\\", strict_requirements_file\\n ])\",\n \"def install_requires():\\n return reqs('requirements.txt')\",\n \"def install_requirements_without_virtualenv(requirements_json):\\n\\n @task\\n def requirements_json_install():\\n with cd('/home/vagrant'):\\n put(StringIO(requirements_json), '/home/vagrant/requirements.json')\\n run('cappa install --no-venv -r /home/vagrant/requirements.json')\\n\\n return requirements_json_install\",\n \"def __init__(self, package, version=''):\\n PackageRequirement.__init__(self, 'cargo', package, version)\",\n \"def get_default_pip_requirements(include_cloudpickle=False):\\n _check_soft_dependencies(\\\"mlflow\\\", severity=\\\"error\\\")\\n from mlflow.utils.requirements_utils import _get_pinned_requirement\\n\\n pip_deps = [_get_pinned_requirement(\\\"sktime\\\")]\\n if include_cloudpickle:\\n pip_deps += [_get_pinned_requirement(\\\"cloudpickle\\\")]\\n\\n return pip_deps\",\n \"def install_requires():\\n return reqs(\\\"requirements.txt\\\")\",\n \"def get_pip():\\n return 'pip'\",\n \"def pip_install_req_file(req_file):\\n pip_cmd = 'pip install -q --disable-pip-version-check --exists-action w'\\n sh(f\\\"{pip_cmd} -r {req_file}\\\")\",\n \"def require(dep, install=None):\\n\\n def wrapper(fn):\\n assert isfunction(fn)\\n\\n @wraps(fn)\\n def ask_install(*args, **kwargs):\\n name = fn.__qualname__.replace('.__init__', '')\\n ins = install or f'pip install \\\"{dep}\\\"'\\n raise ImportError(\\n f'{name} requires {dep}, please install it by `{ins}`.')\\n\\n if satisfy_requirement(dep):\\n fn._verify_require = getattr(fn, '_verify_require', lambda: None)\\n return fn\\n\\n ask_install._verify_require = ask_install\\n return ask_install\\n\\n return wrapper\",\n \"def test_build_system_flags_not_implemented(self, temp_env):\\n s = spack.spec.Spec(\\\"mpileaks cppflags=-g\\\").concretized()\\n s.package.flag_handler = build_system_flags\\n try:\\n spack.build_environment.setup_package(s.package, False)\\n assert False\\n except NotImplementedError:\\n assert True\",\n \"def fetch_build_egg(dist, req):\\n _DeprecatedInstaller.emit()\\n _warn_wheel_not_available(dist)\\n return _fetch_build_egg_no_warn(dist, req)\",\n \"def get_install_requirements(provider_package_id: str, version_suffix: str) -> str:\\n\\n def apply_version_suffix(install_clause: str) -> str:\\n if install_clause.startswith(\\\"apache-airflow\\\") and \\\">=\\\" in install_clause and version_suffix != \\\"\\\":\\n # This is workaround for `pip` way of handling `--pre` installation switch. It apparently does\\n # not modify the meaning of `install_requires` to include also pre-releases, so we need to\\n # modify our internal provider and airflow package version references to include all pre-releases\\n # including all development releases. When you specify dependency as >= X.Y.Z, and you\\n # have packages X.Y.Zdev0 or X.Y.Zrc1 in a local file, such package is not considered\\n # as fulfilling the requirement even if `--pre` switch is used.\\n return install_clause + \\\".dev0\\\"\\n return install_clause\\n\\n install_requires = [\\n apply_version_suffix(clause) for clause in ALL_DEPENDENCIES[provider_package_id][DEPS]\\n ]\\n return \\\"\\\".join(f\\\"\\\\n {ir}\\\" for ir in install_requires)\",\n \"def update_ireq_name(install_requirement, package_name):\\n # type: (pip.req.InstallRequirement, str) -> pip.req.InstallRequirement\\n requirement = install_requirement.req\\n if hasattr(requirement, 'project_name'):\\n requirement.project_name = package_name\\n else:\\n requirement.name = package_name\\n return install_requirement\",\n \"def requirements_installer(language: str, requirements: List[str]) -> str:\\n _, installer = LANGUAGES[language]\\n return installer.format(package=' '.join(requirements))\",\n \"def get_extras_require() -> Dict[str, List[str]]:\\n extras = {\\n \\\"testing\\\": [\\n \\\"pytest==6.1.2\\\",\\n \\\"pytest-cov==2.10.1\\\",\\n ],\\n \\\"linting\\\": [\\n \\\"pylint==2.6.0\\\",\\n \\\"flake8==3.8.4\\\",\\n \\\"black>=20.8b1\\\",\\n \\\"darglint==1.5.5\\\",\\n \\\"mypy==0.790\\\",\\n # \\\"data-science-types>=0.2.20\\\", # pandas, numpy, matplotlib\\n ],\\n }\\n extras[\\\"all\\\"] = [item for group in extras.values() for item in group]\\n return extras\",\n \"def requires_dependency(name):\\n if name == 'Fermi ST':\\n name = 'pyLikelihood'\\n\\n try:\\n __import__(name)\\n skip_it = False\\n except ImportError:\\n skip_it = True\\n\\n reason = 'Missing dependency: {}'.format(name)\\n return pytest.mark.skipif(skip_it, reason=reason)\",\n \"def python_requirements(self):\\n try:\\n dist = self.requirement.pip_requirement.get_dist()\\n extras = self.requirement.pip_requirement.extras\\n requirements = list(dist.requires(extras))\\n except Exception:\\n logger.warning(\\\"Failed to determine installation requirements of %s \\\"\\n \\\"using pkg-resources, falling back to old implementation.\\\",\\n self, exc_info=True)\\n requirements = self.python_requirements_fallback\\n logger.debug(\\\"Python requirements of %s: %r\\\", self, requirements)\\n return requirements\",\n \"def _get_installation_args(self, install_optional, production_only, force, frozen_lockfile):\\n raise NotImplementedError\",\n \"def test_lock_handle_eggs(PipenvInstance):\\n with PipenvInstance() as p:\\n with open(p.pipfile_path, 'w') as f:\\n f.write(\\\"\\\"\\\"\\n[packages]\\nRandomWords = \\\"*\\\"\\n \\\"\\\"\\\")\\n c = p.pipenv('lock --verbose')\\n assert c.return_code == 0\\n assert 'randomwords' in p.lockfile['default']\\n assert p.lockfile['default']['randomwords']['version'] == '==0.2.1'\",\n \"def merge_requirements(self, reqs=None):\\n openerp_dir = getattr(self, 'openerp_dir', None)\\n openerp_project_name = 'openerp'\\n if openerp_dir is not None: # happens in unit tests\\n openerp_project_name = self.develop(openerp_dir)\\n self.requirements.append(openerp_project_name)\\n\\n if self.with_gunicorn:\\n self.requirements.extend(('psutil', 'gunicorn'))\\n\\n if self.with_devtools:\\n self.requirements.extend(devtools.requirements)\\n\\n BaseRecipe.merge_requirements(self, reqs=reqs)\\n return openerp_project_name\",\n \"def _get_pip_versioned_package_string(\\n library_name: str, version_string: str\\n) -> str:\\n return '%s==%s' % (library_name, version_string)\",\n \"def test_vcs_entry_supersedes_non_vcs(pipenv_instance_pypi):\\n with pipenv_instance_pypi(chdir=True) as p:\\n jinja2_uri = p._pipfile.get_fixture_path(\\\"git/jinja2\\\").as_uri()\\n with open(p.pipfile_path, \\\"w\\\") as f:\\n f.write(\\n \\\"\\\"\\\"\\n[[source]]\\nurl = \\\"https://pypi.org/simple\\\"\\nverify_ssl = true\\nname = \\\"pypi\\\"\\n\\n[packages]\\nFlask = \\\"*\\\"\\nJinja2 = {{ref = \\\"2.11.0\\\", git = \\\"{0}\\\"}}\\n \\\"\\\"\\\".format(jinja2_uri).strip()\\n )\\n c = p.pipenv(\\\"install\\\")\\n assert c.returncode == 0\\n installed_packages = [\\\"Flask\\\", \\\"Jinja2\\\"]\\n assert all([k in p.pipfile[\\\"packages\\\"] for k in installed_packages])\\n assert all([k.lower() in p.lockfile[\\\"default\\\"] for k in installed_packages])\\n assert all([k in p.lockfile[\\\"default\\\"][\\\"jinja2\\\"] for k in [\\\"ref\\\", \\\"git\\\"]]), str(p.lockfile[\\\"default\\\"])\\n assert p.lockfile[\\\"default\\\"][\\\"jinja2\\\"].get(\\\"ref\\\") is not None\\n assert (\\n p.lockfile[\\\"default\\\"][\\\"jinja2\\\"][\\\"git\\\"]\\n == jinja2_uri\\n )\",\n \"def pip_requirements():\\n\\n require(\\n \\\"virtualenv_path\\\",\\n \\\"requirements_path\\\",\\n \\\"http_proxy\\\",\\n \\\"https_proxy\\\",\\n \\\"sudo_user\\\",\\n )\\n cmd = \\\"pip install --quiet --requirement %s\\\" % env.requirements_path\\n\\n # append packages url if specified\\n if env.get(\\\"packages_url\\\") is not None:\\n cmd += \\\" -f %s\\\" % env.get(\\\"packages_url\\\")\\n\\n with context_managers.proxy(env.http_proxy, env.https_proxy):\\n with context_managers.virtualenv(env.virtualenv_path):\\n sudo(cmd, user=env.sudo_user)\",\n \"def setPkgRequired(self, *args):\\n return _libsbml.SBMLDocument_setPkgRequired(self, *args)\",\n \"def get_version_comparitor(self, requirement):\\n if manage.is_inhouse_package(requirement.project_name):\\n if self._prefer_final:\\n log.debug(' in-house package, prefer-final')\\n return easy_install._final_version\\n else:\\n log.debug(' in-house package, prefer-dev')\\n return self.is_dev_version\\n else:\\n log.debug(' third-party package, always prefer-final')\\n return easy_install._final_version\",\n \"def test_skip_python3_requires(self):\\n self.assertEqual(\\n {\\\"python-ovs\\\": \\\"2.5.0\\\"},\\n pr.sanitize_requirements(\\n [\\\"ovs>=2.5.0;python_version=='2.7' # Apache-2.0\\\",\\n \\\"ovs>=2.6.0.dev1;python_version>='3.4' # Apache-2.0\\\"])\\n )\",\n \"def is_requirement(line):\\n # Remove whitespace at the start/end of the line\\n line = line.strip()\\n\\n # Skip blank lines, comments, and editable installs\\n return not (\\n line == '' or\\n line.startswith('-r') or\\n line.startswith('#') or\\n line.startswith('-e') or\\n line.startswith('git+')\\n)\",\n \"def get_install_requires() -> List[str]:\\n return [\\n \\n ]\",\n \"def install_requirements():\\n run_commands('pip install -r ./requirements/dev.txt')\",\n \"def get_requirements(req):\\n\\n install_requires = []\\n with open(req) as f:\\n for line in f:\\n if not line.startswith(\\\"#\\\"):\\n install_requires.append(line.strip())\\n return install_requires\",\n \"def _remove_version_spec(req: str) -> str:\\n components = re.split(r'>=|~=|<=|==', req)\\n return components[0]\",\n \"def pur(**options):\\n\\n if not options['requirement']:\\n options['requirement'] = 'requirements.txt'\\n\\n format_list_arg(options, 'skip')\\n format_list_arg(options, 'only')\\n format_list_arg(options, 'minor')\\n format_list_arg(options, 'patch')\\n format_list_arg(options, 'pre')\\n\\n options['echo'] = True\\n\\n global PUR_GLOBAL_UPDATED\\n PUR_GLOBAL_UPDATED = 0\\n\\n update_requirements(\\n input_file=options['requirement'],\\n output_file=options['output'],\\n force=options['force'],\\n interactive=options['interactive'],\\n skip=options['skip'],\\n only=options['only'],\\n minor=options['minor'],\\n patch=options['patch'],\\n pre=options['pre'],\\n dry_run=options['dry_run'],\\n no_recursive=options['no_recursive'],\\n echo=options['echo'],\\n index_urls=options['index_url'],\\n verify=options['verify'],\\n )\\n\\n if not options['dry_run']:\\n _echo('All requirements up-to-date.')\\n\\n if options['nonzero_exit_code']:\\n if PUR_GLOBAL_UPDATED > 0:\\n raise ExitCodeException(11)\\n raise ExitCodeException(10)\",\n \"def test_with_ending_client(self):\\n self.assertEqual({\\\"python-xyzclient\\\": None},\\n pr.sanitize_requirements([\\\"python-xyzclient\\\"]))\\n self.assertEqual({\\\"python-xyz-client\\\": None},\\n pr.sanitize_requirements([\\\"python-xyz-client\\\"]))\\n self.assertEqual({\\\"python-xyzclient\\\": None},\\n pr.sanitize_requirements([\\\"xyzclient\\\"]))\",\n \"def _patch_pip(obuffer, updates, **options):\\n\\n seen = []\\n\\n def patched_parse_requirements(*args, **kwargs):\\n if not options['no_recursive']:\\n filename = args[0]\\n if filename not in seen:\\n if os.path.isfile(filename):\\n seen.append(filename)\\n buf = StringIO()\\n _internal_update_requirements(\\n buf, updates,\\n input_file=filename,\\n output_file=options['output_file'],\\n force=options['force'],\\n interactive=options['interactive'],\\n skip=options['skip'],\\n only=options['only'],\\n minor=options['minor'],\\n patch=options['patch'],\\n pre=options['pre'],\\n dry_run=options['dry_run'],\\n no_recursive=options['no_recursive'],\\n echo=options['echo'],\\n index_urls=options['index_urls'],\\n verify=options['verify'],\\n )\\n if not options['dry_run']:\\n if options['output_file']:\\n obuffer.write(buf.getvalue())\\n else:\\n with open(filename, 'w') as output:\\n output.write(buf.getvalue())\\n buf.close()\\n return []\\n req_file.parse_requirements = patched_parse_requirements\",\n \"def require(name):\",\n \"def get_install_requires():\\n requirements = []\\n for line in open('requirements.txt').readlines():\\n # skip to next iteration if comment or empty line\\n if line.startswith('#') or line == '' or line.startswith('http') or line.startswith('git'):\\n continue\\n # add line to requirements\\n requirements.append(line)\\n return requirements\",\n \"def is_requirement(line):\\n # Remove whitespace at the start/end of the line\\n line = line.strip()\\n\\n # Skip blank lines, comments, and editable installs\\n return not (\\n line == '' or\\n line.startswith('-r') or\\n line.startswith('#') or\\n line.startswith('-e') or\\n line.startswith('git+')\\n )\",\n \"def update_requirements():\\n\\n with virtualenv(VIRTUALENV_PATH):\\n cmd = ['pip install']\\n cmd += ['--requirement %s' % os.path.join(CODE_DIR,'requirements.txt')]\\n run(' '.join(cmd))\",\n \"def update_requirements():\\n\\n check_prompt = (\\n not env.prompt or\\n console.confirm(\\n \\\"Update virtualenv requirements based on requirements.txt file?\\\",\\n default=True,\\n )\\n )\\n\\n if check_prompt:\\n with cd(\\\"%s\\\" % env.repo_path):\\n with prefix(\\\"source %s/bin/activate\\\" % env.env_path):\\n run(\\n \\\"pip install\\\"\\n \\\" --requirement %s/requirements.txt\\\" % env.repo_path\\n )\",\n \"def require(what):\\n global _requirements\\n from twill import commands\\n\\n #\\n # install the post-load hook function.\\n #\\n\\n if _require_post_load_hook not in commands.browser._post_load_hooks:\\n if DEBUG:\\n print('INSTALLING POST-LOAD HOOK', file=commands.OUT)\\n commands.browser._post_load_hooks.append(_require_post_load_hook)\\n\\n #\\n # add the requirement.\\n #\\n\\n if what not in _requirements:\\n if DEBUG:\\n print('Adding requirement', what, file=commands.OUT)\\n _requirements.append(what)\",\n \"def install_deps():\\n default = open('requirements.txt', 'r').readlines()\\n new_pkgs = []\\n links = []\\n for resource in default:\\n if 'git+https' in resource:\\n pkg = resource.split('#')[-1]\\n links.append(resource.strip())\\n new_pkgs.append(pkg.replace('egg=', '').rstrip())\\n else:\\n new_pkgs.append(resource.strip())\\n return new_pkgs, links\",\n \"def filter_working_set_hard(working_set, requirements):\\n\\n retval = pkg_resources.WorkingSet([])\\n\\n for req in requirements:\\n dists = working_set.require(req)\\n for dist in dists: retval.add(dist)\\n\\n return retval\",\n \"def substitute_crypto_to_req(req):\\n crypto_backend = get_crypto_req()\\n\\n if crypto_backend is None:\\n return req\\n\\n def is_not_crypto(r):\\n CRYPTO_LIBS = PYCRYPTO_DIST, 'cryptography'\\n return not any(r.lower().startswith(c) for c in CRYPTO_LIBS)\\n\\n return [r for r in req if is_not_crypto(r)] + [crypto_backend]\",\n \"def install(cmd, reqs, add_to_global=False, prefer_final=True,\\n force_upgrade=False, use_existing=False):\\n # Remove anything we're upgrading\\n if force_upgrade:\\n uninstall_eggs(reqs)\\n\\n # Create installer class configured to install into wherever the command\\n # class was setup for\\n installer = Installer(dest=cmd.install_dir, index=cmd.index_url,\\n prefer_final=prefer_final)\\n\\n # Now apply our runtime additions to its working environment. This includes\\n # adding eggs for the egg cache, and removing eggs that we're forcing to be\\n # upgraded.\\n\\n installer.update_search_path()\\n\\n # This is a bit nasty - we have to monkey-patch the filter for final\\n # versions so that we can also filter for dev versions as well.\\n\\n # Set prefer_final to True, always, so it enables the filter\\n easy_install.prefer_final(True)\\n\\n # This returns a WorkingSet of the packages we just installed.\\n ws = None\\n if use_existing:\\n # NOTE here we pass in the existing stuff - this will prefer installed\\n # packages over ones on the server, eg an installed release\\n # version won't get trumped by a dev version on the server\\n ws = pkg_resources.WorkingSet(pkg_resources.working_set.entries)\\n\\n # We must remove the package we're installing from the 'baseline' ws.\\n # This way we won't get any weird requirement conflicts with new\\n # versions of the package we're trying to set up\\n if cmd.distribution.metadata.name:\\n dist = dependency.get_dist(cmd.distribution.metadata.name)\\n if dist:\\n dependency.remove_from_ws(ws, dist)\\n\\n # There's a chance that there were packages in setup_requires that were\\n # also in install_requires. Because these are classed as 'already\\n # installed' by the installer, they won't have been added to the workingset\\n # of packages to set-up in the next step.\\n\\n # Here we ensure that they're added in along with any of their\\n # own dependencies if they are also part of the package install_requires.\\n # FIXME: this won't pick up non-direct dependencies.\\n # Eg: setup_requires = numpy,\\n # install_requires = something that has numpy as a dependency\\n def also_required(dist):\\n for req in pkg_resources.parse_requirements(reqs):\\n if dist in req:\\n return True\\n return False\\n\\n setup_dists = [i for i in pkg_resources.working_set.resolve(\\n get_setup_requires(cmd.distribution))\\n if also_required(i)]\\n\\n if setup_dists:\\n log.debug(\\\"setup_requires distributions to be set-up:\\\")\\n [log.debug(\\\" %r\\\" % i) for i in setup_dists]\\n\\n # Now run the installer\\n try:\\n to_setup = installer.install(reqs, working_set=ws,\\n use_existing=use_existing)\\n except easy_install.MissingDistribution, e:\\n log.error(e)\\n # TODO: call missing distro hook here\\n sys.exit(1)\\n\\n # Add any of the setup_requires dists to be set-up.\\n to_setup = set(to_setup + setup_dists)\\n if to_setup:\\n log.debug('Packages to set-up:')\\n for i in to_setup:\\n log.debug(' %r' % i)\\n\\n # Now we selectively run setuptool's post-install steps.\\n # Luckily, the buildout installer didnt strip off any of the useful\\n # metadata about the console scripts.\\n for dist in to_setup:\\n if dist.location.startswith(manage.get_site_packages()):\\n fix_permissions(dist)\\n cmd.process_distribution(None, dist, deps=False)\\n # Add the distributions to the global registry if we asked for it.\\n # This makes the distro importable, and classed as 'already\\n # installed' by the dependency resolution algorithm.\\n if add_to_global:\\n pkg_resources.working_set.add(dist)\\n else:\\n log.debug('Nothing to set-up.')\\n return to_setup\",\n \"def install(cmd, reqs, add_to_global=False, prefer_final=True,\\n force_upgrade=False, use_existing=False):\\n # Remove anything we're upgrading\\n if force_upgrade:\\n uninstall_eggs(reqs)\\n\\n # Create installer class configured to install into wherever the command\\n # class was setup for\\n installer = Installer(dest=cmd.install_dir, index=cmd.index_url,\\n prefer_final=prefer_final)\\n\\n # Now apply our runtime additions to its working environment. This includes\\n # adding eggs for the egg cache, and removing eggs that we're forcing to be\\n # upgraded.\\n\\n installer.update_search_path()\\n\\n # This is a bit nasty - we have to monkey-patch the filter for final\\n # versions so that we can also filter for dev versions as well.\\n\\n # Set prefer_final to True, always, so it enables the filter\\n easy_install.prefer_final(True)\\n\\n # This returns a WorkingSet of the packages we just installed.\\n ws = None\\n if use_existing:\\n # NOTE here we pass in the existing stuff - this will prefer installed\\n # packages over ones on the server, eg an installed release\\n # version won't get trumped by a dev version on the server\\n ws = pkg_resources.WorkingSet(pkg_resources.working_set.entries)\\n\\n # We must remove the package we're installing from the 'baseline' ws.\\n # This way we won't get any weird requirement conflicts with new\\n # versions of the package we're trying to set up\\n if cmd.distribution.metadata.name:\\n dist = dependency.get_dist(cmd.distribution.metadata.name)\\n if dist:\\n dependency.remove_from_ws(ws, dist)\\n\\n # There's a chance that there were packages in setup_requires that were\\n # also in install_requires. Because these are classed as 'already\\n # installed' by the installer, they won't have been added to the workingset\\n # of packages to set-up in the next step.\\n\\n # Here we ensure that they're added in along with any of their\\n # own dependencies if they are also part of the package install_requires.\\n # FIXME: this won't pick up non-direct dependencies.\\n # Eg: setup_requires = numpy,\\n # install_requires = something that has numpy as a dependency\\n def also_required(dist):\\n for req in pkg_resources.parse_requirements(reqs):\\n if dist in req:\\n return True\\n return False\\n\\n setup_dists = [i for i in pkg_resources.working_set.resolve(\\n get_setup_requires(cmd.distribution))\\n if also_required(i)]\\n\\n if setup_dists:\\n log.debug(\\\"setup_requires distributions to be set-up:\\\")\\n [log.debug(\\\" %r\\\" % i) for i in setup_dists]\\n\\n # Now run the installer\\n try:\\n to_setup = installer.install(reqs, working_set=ws,\\n use_existing=use_existing)\\n except easy_install.MissingDistribution, e:\\n log.error(e)\\n # TODO: call missing distro hook here\\n sys.exit(1)\\n\\n # Add any of the setup_requires dists to be set-up.\\n to_setup = set(to_setup + setup_dists)\\n if to_setup:\\n log.debug('Packages to set-up:')\\n for i in to_setup:\\n log.debug(' %r' % i)\\n\\n # Now we selectively run setuptool's post-install steps.\\n # Luckily, the buildout installer didnt strip off any of the useful\\n # metadata about the console scripts.\\n for dist in to_setup:\\n if dist.location.startswith(manage.get_site_packages()):\\n fix_permissions(dist)\\n cmd.process_distribution(None, dist, deps=False)\\n # Add the distributions to the global registry if we asked for it.\\n # This makes the distro importable, and classed as 'already\\n # installed' by the dependency resolution algorithm.\\n if add_to_global:\\n pkg_resources.working_set.add(dist)\\n else:\\n log.debug('Nothing to set-up.')\\n return to_setup\",\n \"def main(requirements_file, skip_requirements_file, pipfile, skip_pipfile):\\n # type: (str, bool, str, bool) -> None\\n pipfile_path = path.Path(pipfile)\\n pf = load_pipfile(pipfile_path)\\n\\n if not skip_requirements_file:\\n requirements_file_path = path.Path(requirements_file)\\n update_requirements(requirements_file_path, pf)\\n\\n if not skip_pipfile:\\n dump_pipfile(pipfile_path, pf)\",\n \"def update_requirements():\\n with cd(REMOTE_REPO_DIR):\\n cmd = ['npm install']\\n # cmd += ['--requirement %s' % os.path.join(CODE_DIR,'requirements.txt')]\\n run(' '.join(cmd))\",\n \"def check_venv():\\n if not args.novenv:\\n assert not sys.prefix == sys.base_prefix, 'Please use pip-upgrade in a virtualenv. If you would like to surpass this use pip-upgrade --novenv'\",\n \"def test_upgrade_and_dependency_not_removed_caused_required_by_another_item(self):\\n assert self.DEPENDENCY_PUBLIC_ID in self.load_config().protocols\\n # do not add dependencies for the package\\n\\n with self.with_oef_installed(), self.with_config_update(), patch(\\n \\\"aea.cli.add._add_item_deps\\\"\\n ):\\n result = self.runner.invoke(\\n cli,\\n [\\n \\\"-v\\\",\\n \\\"DEBUG\\\",\\n \\\"upgrade\\\",\\n *self.LOCAL,\\n self.ITEM_TYPE,\\n f\\\"{self.ITEM_PUBLIC_ID.author}/{self.ITEM_PUBLIC_ID.name}:latest\\\",\\n ],\\n catch_exceptions=True,\\n )\\n assert result.exit_code == 0\\n assert self.DEPENDENCY_PUBLIC_ID in self.load_config().protocols\",\n \"def test_url_preserved_line_req(self):\\n url = 'git+http://foo.com@ref#egg=foo'\\n req = InstallRequirement.from_line(url)\\n assert req.link.url == url\",\n \"def require(require_pattern, broken_in=None):\\n tagging_decorator = attr('required')\\n if IGNORE_REQUIRE:\\n return tagging_decorator\\n require_pattern = str(require_pattern)\\n git_branch = ''\\n git_branch = cassandra_git_branch()\\n\\n if git_branch:\\n git_branch = git_branch.lower()\\n run_on_branch_patterns = (require_pattern, 'cassandra-{b}'.format(b=require_pattern))\\n # always run the test if the git branch name matches\\n if any(re.match(p, git_branch, re.IGNORECASE) for p in run_on_branch_patterns):\\n return tagging_decorator\\n # if skipping a buggy/flapping test, use since\\n elif broken_in:\\n def tag_and_skip_after_version(decorated):\\n return since('0', broken_in)(tagging_decorator(decorated))\\n return tag_and_skip_after_version\\n # otherwise, skip with a message\\n else:\\n def tag_and_skip(decorated):\\n return unittest.skip('require ' + str(require_pattern))(tagging_decorator(decorated))\\n return tag_and_skip\\n else:\\n return tagging_decorator\",\n \"def test_skip_windows_requires(self):\\n self.assertEqual(\\n {\\\"python-true\\\": \\\"1\\\"},\\n pr.sanitize_requirements(\\n [\\\"true>=1\\\",\\n \\\"wmi;sys_platform=='win32'\\\"]))\",\n \"def test_generate_requirements(mocker):\\n mocked_function = mocker.patch(\\\"os.system\\\")\\n generate_requirements()\\n mocked_function.assert_called_once_with(\\n \\\"poetry export -f requirements.txt > requirements.txt\\\"\\n )\",\n \"def generate_requirements(output_path=None):\\n from django.conf import settings\\n reqs = set()\\n \\n for app in settings.INSTALLED_APPS:\\n if app in mapping.keys():\\n reqs |= set(mapping[app])\\n if output_path is None:\\n print \\\"--extra-index-url=http://opensource.washingtontimes.com/pypi/simple/\\\"\\n for item in reqs:\\n print item\\n else:\\n try:\\n out_file = open(output_path, 'w')\\n out_file.write(\\\"--extra-index-url=http://opensource.washingtontimes.com/pypi/simple/\\\\n\\\")\\n for item in reqs:\\n out_file.write(\\\"%s\\\\n\\\" % item)\\n finally:\\n out_file.close()\",\n \"def construct_pkg_dep(terms):\\n return SlottedPackageDependency(terms[0], terms[1] if terms[1] else AnySlot())\",\n \"def requirement_missing(script):\\n if \\\"requires\\\" in script:\\n if script[\\\"requires\\\"] is None:\\n return False\\n for package in script[\\\"requires\\\"].split():\\n try:\\n pkg_resources.working_set.require(package)\\n except Exception:\\n return True\\n return False\",\n \"def name_from_req(req):\\n if hasattr(req, \\\"project_name\\\"):\\n # from pkg_resources, such as installed dists for pip-sync\\n return req.project_name\\n else:\\n # from packaging, such as install requirements from requirements.txt\\n return req.name\",\n \"def install_deps_temp(self):\\n if self.distribution.install_requires:\\n self.distribution.fetch_build_eggs(\\n self.distribution.install_requires)\\n if self.distribution.tests_require:\\n self.distribution.fetch_build_eggs(self.distribution.tests_require)\",\n \"def test_url_preserved_editable_req(self):\\n url = 'git+http://foo.com@ref#egg=foo'\\n req = InstallRequirement.from_editable(url)\\n assert req.link.url == url\",\n \"def get_requirements(package):\\n requirements: list = requires(package)\\n requires_dict = defaultdict(dict)\\n for requirement in requirements:\\n req = Requirement(requirement)\\n package_name, package_marker = req.name, req.marker\\n if package_marker and \\\"extra ==\\\" in str(package_marker):\\n group = str(package_marker).split(\\\"extra == \\\")[1].strip('\\\"').strip(\\\"'\\\").strip()\\n else:\\n group = \\\"required\\\"\\n # De-duplicate (the same package could appear more than once in the extra == 'all' group)\\n if package_name in requires_dict[group]:\\n continue\\n requires_dict[group][package_name] = req\\n return requires_dict\",\n \"def log_installed_python_prereqs():\\n sh(\\\"pip freeze > {}\\\".format(Env.GEN_LOG_DIR + \\\"/pip_freeze.log\\\"))\",\n \"def doesnt_require(self, requirements, **overrides):\\n return self.where(\\n requires=tuple(\\n set(self.route.get(\\\"requires\\\", ())).difference(\\n requirements if type(requirements) in (list, tuple) else (requirements,)\\n )\\n )\\n )\",\n \"def test_use_pep517(shared_data: TestData, source: str, expected: bool) -> None:\\n src = shared_data.src.joinpath(source)\\n req = InstallRequirement(None, None)\\n req.source_dir = os.fspath(src) # make req believe it has been unpacked\\n req.load_pyproject_toml()\\n assert req.use_pep517 is expected\",\n \"def getPkgRequired(self, *args):\\n return _libsbml.SBMLDocument_getPkgRequired(self, *args)\",\n \"def install_requirements():\\n run('source %(env_path)s/bin/activate; pip install -U -r %(repo_path)s/requirements.txt' % env)\",\n \"def before_packages(manager):\\n if manager not in b.packages:\\n return\\n if 'apt' == manager:\\n s.add('export APT_LISTBUGS_FRONTEND=\\\"none\\\"')\\n s.add('export APT_LISTCHANGES_FRONTEND=\\\"none\\\"')\\n s.add('export DEBIAN_FRONTEND=\\\"noninteractive\\\"')\\n s.add('apt-get -q update')\\n elif 'yum' == manager:\\n s.add('yum makecache')\",\n \"def remove_requirement(self, name: str):\\n assert name in self._required_processes\\n del self._required_processes[name]\\n\\n if name in self._required_actions:\\n del self._required_actions[name]\",\n \"def __init__(self, package, version='', repo=''):\\n PackageRequirement.__init__(self, 'conda', package, version, repo)\",\n \"def get_requirements():\\n name = 'pypeit/requirements.txt'\\n\\n requirements_file = os.path.join(os.path.dirname(__file__), name)\\n install_requires = [line.strip().replace('==', '>=') for line in open(requirements_file)\\n if not line.strip().startswith('#') and line.strip() != '']\\n return install_requires\",\n \"def get_crypto_req():\\n crypto_backend = os.environ.get('ANSIBLE_CRYPTO_BACKEND', '').strip()\\n\\n if crypto_backend == PYCRYPTO_DIST:\\n # Attempt to set version requirements\\n return '%s >= 2.6' % PYCRYPTO_DIST\\n\\n return crypto_backend or None\",\n \"def tidy_requirements(requirement_file):\\n outdata = []\\n with open(requirement_file) as dependencies:\\n for line in dependencies:\\n line = line.strip()\\n if line and not line.startswith('#') and line not in outdata:\\n outdata.append(line)\\n return outdata\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6552357","0.5994064","0.589006","0.5876299","0.57701266","0.5718214","0.56937885","0.5686367","0.5629351","0.55940837","0.5577624","0.549431","0.54820436","0.5460692","0.54408735","0.5440247","0.5432639","0.5432031","0.54307127","0.54157686","0.53946084","0.5383564","0.53676444","0.5360177","0.5344118","0.534408","0.53437465","0.5332154","0.5324484","0.5315772","0.53097725","0.52944726","0.52930874","0.5273374","0.5265368","0.5262838","0.5261478","0.5256397","0.5255641","0.5250536","0.5248521","0.5236511","0.5234571","0.5217088","0.5214382","0.51935124","0.5187225","0.5183102","0.51780736","0.5177052","0.51747066","0.51734775","0.5162329","0.51620924","0.51568305","0.5143642","0.5127428","0.5106015","0.5094855","0.50927675","0.5089368","0.50827956","0.5078885","0.5078652","0.5076579","0.50710565","0.5069884","0.50642294","0.505529","0.50428396","0.50403893","0.50393146","0.50300187","0.50300187","0.5028359","0.5026751","0.5024026","0.50198257","0.5019759","0.501259","0.50092465","0.50068754","0.49926558","0.49869895","0.49698305","0.4969103","0.4962539","0.495996","0.49565393","0.4954033","0.49428135","0.49150667","0.49146053","0.49101847","0.49094382","0.49004555","0.49000433","0.48978564","0.4896103","0.48952997"],"string":"[\n \"0.6552357\",\n \"0.5994064\",\n \"0.589006\",\n \"0.5876299\",\n \"0.57701266\",\n \"0.5718214\",\n \"0.56937885\",\n \"0.5686367\",\n \"0.5629351\",\n \"0.55940837\",\n \"0.5577624\",\n \"0.549431\",\n \"0.54820436\",\n \"0.5460692\",\n \"0.54408735\",\n \"0.5440247\",\n \"0.5432639\",\n \"0.5432031\",\n \"0.54307127\",\n \"0.54157686\",\n \"0.53946084\",\n \"0.5383564\",\n \"0.53676444\",\n \"0.5360177\",\n \"0.5344118\",\n \"0.534408\",\n \"0.53437465\",\n \"0.5332154\",\n \"0.5324484\",\n \"0.5315772\",\n \"0.53097725\",\n \"0.52944726\",\n \"0.52930874\",\n \"0.5273374\",\n \"0.5265368\",\n \"0.5262838\",\n \"0.5261478\",\n \"0.5256397\",\n \"0.5255641\",\n \"0.5250536\",\n \"0.5248521\",\n \"0.5236511\",\n \"0.5234571\",\n \"0.5217088\",\n \"0.5214382\",\n \"0.51935124\",\n \"0.5187225\",\n \"0.5183102\",\n \"0.51780736\",\n \"0.5177052\",\n \"0.51747066\",\n \"0.51734775\",\n \"0.5162329\",\n \"0.51620924\",\n \"0.51568305\",\n \"0.5143642\",\n \"0.5127428\",\n \"0.5106015\",\n \"0.5094855\",\n \"0.50927675\",\n \"0.5089368\",\n \"0.50827956\",\n \"0.5078885\",\n \"0.5078652\",\n \"0.5076579\",\n \"0.50710565\",\n \"0.5069884\",\n \"0.50642294\",\n \"0.505529\",\n \"0.50428396\",\n \"0.50403893\",\n \"0.50393146\",\n \"0.50300187\",\n \"0.50300187\",\n \"0.5028359\",\n \"0.5026751\",\n \"0.5024026\",\n \"0.50198257\",\n \"0.5019759\",\n \"0.501259\",\n \"0.50092465\",\n \"0.50068754\",\n \"0.49926558\",\n \"0.49869895\",\n \"0.49698305\",\n \"0.4969103\",\n \"0.4962539\",\n \"0.495996\",\n \"0.49565393\",\n \"0.4954033\",\n \"0.49428135\",\n \"0.49150667\",\n \"0.49146053\",\n \"0.49101847\",\n \"0.49094382\",\n \"0.49004555\",\n \"0.49000433\",\n \"0.48978564\",\n \"0.4896103\",\n \"0.48952997\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75084823"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":86686,"cells":{"query":{"kind":"string","value":"Test Case for setup of Logistic Regression"},"document":{"kind":"string","value":"def test_setup_log_reg_classifier(self):\n \n\n model ,vec, x_testing=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\"text\", method=\"count\")\n \n model2 ,vec_tfidf, x_testing2=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\"text\", method=\"tfidf\")\n \n \n \"\"\" Test correct data types for countVectorizer\"\"\" \n \n self.assertIsInstance(vec,\n sklearn.feature_extraction.text.CountVectorizer)\n \n self.assertIsInstance(x_testing, scipy.sparse.csr.csr_matrix)\n \n self.assertIsInstance(model, sklearn.linear_model.LogisticRegression)\n \n \"\"\" Test correct data types TfidfVectorizer\"\"\" \n \n self.assertIsInstance(vec_tfidf,\n sklearn.feature_extraction.text.TfidfVectorizer)\n \n self.assertIsInstance(x_testing2, scipy.sparse.csr.csr_matrix)\n \n self.assertIsInstance(model2, sklearn.linear_model.LogisticRegression)\n \n \n \"\"\" Test correct behaviour for wrong method\"\"\" \n \n self.assertTrue(setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\"text\", method=\"ijfsiohf\"),\n 1)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_logistic_regression_c_parameter(params, X_train, X_test, y_train, y_test):","def test_train_logist(x_train_variable, y_train_dep):\n # Ensure the function works\n try:\n lrc = cls.train_logistic(x_train_variable, y_train_dep)\n logging.info(\"Successful Logistic Model\")\n except Exception as err:\n logging.error(\"Errors in Fitting the Logistic Regression\")\n raise err\n return lrc","def train_logistic_regression(train_x, train_y):\n\n logistic_regression_model = LogisticRegression(penalty='l2', C=1.0)\n logistic_regression_model.fit(train_x, train_y)\n return logistic_regression_model","def LogisticRegression_sklearn(X_train, X_test, y_train, y_test):\n\n\tlog_reg = LogisticRegression()\n\tlog_reg.fit(X_train, y_train.ravel())\n\tyPred =log_reg.predict(X_test)\n\n\t#Printing metrics of the logistic regression model\n\tprint('Accuracy:', metrics.accuracy_score(y_test, yPred))\n\tprint('Precision:', metrics.precision_score(y_test, yPred))\n\tprint('Recall', metrics.recall_score(y_test, yPred))\n\n\t#confusion matrix\n\n\tconfusionMatrix = matrix.confusion_matrix(y_test, yPred)\n\tsb.heatmap(pd.DataFrame(confusionMatrix), annot= True, fmt='g')\n\tplt.title('Confustion matrix with default value 1')\n\tplt.ylabel('True values')\n\tplt.xlabel('Predicted values')\n\tplt.show()","def test_class_logistic(\n y_train_dep,\n y_test_dep,\n y_train_preds_lr,\n y_test_preds_lr):\n # Ensure the function works\n try:\n cls.classification_logistic_results(\n y_train_dep, y_test_dep, y_train_preds_lr, y_test_preds_lr\n )\n logging.info(\n \"Successfully Plotting Classification Results using logistic regression\"\n )\n except Exception as err:\n logging.error(\"Errors in plotting logistic classification results\")\n raise err\n # Ensure the output exists\n try:\n assert os.path.isfile(\"images/results/assessment_logistic.png\")\n except AssertionError as err:\n logging.error(\"Errors in plotting logistic classification file\")\n raise err","def main():\r\n x = [\r\n [ 1,1 ], [ 0,0 ], [ 1,0 ], [ 0,0 ], [ 0,0 ], [ 0,0 ],\r\n [ 0,0 ], [ 0,0 ], [ 1,1 ], [ 0,0 ], [ 0,0 ], [ 1,0 ],\r\n [ 1,0 ], [ 0,0 ], [ 1,1 ], [ 0,0 ], [ 1,0 ], [ 0,0 ]\r\n ]\r\n\r\n # Encoding of the correct classes for the training material\r\n y = [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0]\r\n b = BinaryLogisticRegression(x, y)\r\n b.fit()\r\n b.print_result()","def test_logistic_regression(x, y, tune):\n # Perform classification without tuning\n lrc = LogisticRegression()\n pipeline = create_pipeline(lrc)\n return accuracy(pipeline, x, y)","def __init__(self, reg_penalty='l2', reg_inv=1.0, k_fold=5, random_state=0):\n print(\"Initialize model Logistic Regression\")\n self.reg_penalty = reg_penalty\n self.reg_inv = reg_inv\n self.k_fold = k_fold\n self.random_state = random_state\n self.model = sklearn.linear_model.LogisticRegression(penalty=self.reg_penalty,\n C=self.reg_inv,\n max_iter=1000, \n random_state=self.random_state)","def run_logistic_regression(training, testing, feature_cols, outcome_col):\n if 'intercept' not in training.columns:\n training['intercept'] = 1\n if 'intercept' not in testing.columns:\n testing['intercept'] = 1\n intercept_feature_cols = feature_cols + ['intercept']\n logit = sm.Logit(training[outcome_col], training[intercept_feature_cols])\n fitted_logit_model = logit.fit()\n logit_diagnostics = get_diagnostics(testing[outcome_col], testing[intercept_feature_cols], fitted_logit_model, model_type = 'logit')\n predicted_logit_probs = fitted_logit_model.predict(testing[intercept_feature_cols])\n\n return fitted_logit_model, logit_diagnostics, predicted_logit_probs","def train_logistic_regression(X_train_input, y_train_input, C=1):\r\n from sklearn.linear_model import LogisticRegression\r\n logr_clf = LogisticRegression(C=C)\r\n logr_clf.fit(X_train_input, y_train_input)\r\n return logr_clf","def run_logistic(X_train, X_test, y_train, y_test, C=1, penalty = 'l2', solver = 'lbfgs'):\n \n logreg = LogisticRegression(fit_intercept=True, C=C, penalty = penalty, solver = solver)\n logreg.fit(X_train, y_train)\n get_scores(logreg, X_train, X_test, y_train, y_test)","def log_reg(x_train, y_train):\n\n log_reg_classifier = LogisticRegression(max_iter=1000, solver='lbfgs')\n log_reg_classifier.fit(x_train, y_train)\n return log_reg_classifier\n\n # log_reg_classifier.fit(x_train, y_train)","def trainRegressionModel(X,y):\n # # instantiate a logistic regression model, and fit with X and y\n # model = LogisticRegression()\n # model = model.fit(X, y)\n # # check the accuracy on the training set\n # print(model.score(X, y))\n #X['intercept'] = 1.0\n #del X['isCapitalized']\n #del X['isNN']\n #del X['isNNP']\n #del X['isJJ']\n #del X['isUpper']\n #del X['isPrecedingIN']\n logit = sm.Logit(y, X)\n result = logit.fit()\n print(result.summary())\n print(result.conf_int())\n model = LogisticRegression()\n model = model.fit(X, y)\n print(model.score(X, y))\n print(y.mean())\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n model2 = LogisticRegression()\n model2.fit(X_train, y_train)\n # predict class labels for the test set\n predicted = model.predict(X_test)\n print(predicted)\n for i in predicted:\n if i==1:\n print(\"Test:\"+str(i))\n print(max(predicted))\n #generate class probabilities\n probs = model2.predict_proba(X_test)\n print(probs)\n # generate evaluation metrics\n print(\"Accuracy: \"+str(metrics.accuracy_score(y_test, predicted)))\n print(\"AUC: \"+str(metrics.roc_auc_score(y_test, probs[:, 1])))\n print(metrics.confusion_matrix(y_test, predicted))\n print(metrics.classification_report(y_test, predicted))\n\n from sklearn.cross_validation import cross_val_score\n # evaluate the model using 10-fold cross-validation\n scores = cross_val_score(LogisticRegression(), X, y, scoring='accuracy', cv=10)\n print(scores)\n print(scores.mean())","def create_logistic_regression():\n\n pause_data = shuffle(pd.read_csv(sys.argv[1]))\n pause_data = pause_data.replace([np.inf, -np.inf], np.nan).dropna()\n # X = pause_data.drop([HAS_DEMENTIA, TRANSCRIPT_ID], axis=1)\n X = pause_data[MEMORY_FEATURES]\n y = pause_data[HAS_DEMENTIA]\n split_tracker = []\n rskf = RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=36851234)\n # n_repeats 10 too\n for train_index, test_index in rskf.split(X, y):\n X_train, X_test = X.iloc[list(train_index)], X.iloc[list(test_index)]\n y_train, y_test = y.iloc[list(train_index)], y.iloc[list(test_index)]\n logmodel = LogisticRegression()\n logmodel.fit(X_train, y_train)\n predictions = logmodel.predict(X_test)\n split_tracker.append({\n TRAIN: train_index,\n TEST: test_index,\n PREDICTIONS: predictions,\n Y_TEST: y_test\n })\n accuracy = []\n f1 = []\n auc = []\n print(\"Predictions\", split_tracker[0])\n for predictions in split_tracker:\n # print(classification_report(predictions[Y_TEST], predictions[PREDICTIONS]))\n accuracy.append(accuracy_score(predictions[Y_TEST], predictions[PREDICTIONS]))\n f1.append(f1_score(predictions[Y_TEST], predictions[PREDICTIONS]))\n auc.append(roc_auc_score(predictions[Y_TEST], predictions[PREDICTIONS]))\n print(accuracy)\n accuracy = np.array(accuracy)\n f1 = np.array(f1)\n auc = np.array(auc)\n print(len(accuracy))\n print('mean accuracy: ', accuracy.mean())\n print('mean f1 score: ', f1.mean())\n print('mean auc: ', auc.mean())","def create_regression(f_train, f_test, l_train, l_test):\n model = LogisticRegression()\n\n # train it on training data\n model.fit(f_train, l_train)\n\n # gather the model's predictions for train\n train_predictions = model.predict(f_train)\n\n # gather the model's predictions for test\n test_predictions = model.predict(f_test)\n\n # calculate accuaracy of train\n print('Reg Train Accuracy: ', accuracy_score(l_train, train_predictions))\n\n # calculate accuracy of test\n print('Reg Test Accuracy: ', accuracy_score(l_test, test_predictions))\n\n return model","def fit_logistic_regression():\n\n logger.debug(\"Running the fit_logistic_regression function now\")\n\n #Loading the configuration\n with open(os.path.join(\"config\",\"config.yml\"), \"r\") as f:\n config = yaml.safe_load(f)\n\n #Loading and pre processing the data\n logger.debug(\"Loading and pre processing the data\")\n train_df = load_data(config[\"load_data\"][\"train_file\"])\n train_df = pre_process_data(train_df, resample = True, resample_count = 500000)\n\n #Defining Pipeline\n pipeline = Pipeline([\n ('tfidf', TfidfVectorizer(analyzer='word', token_pattern=r'[A-Za-z0-9@-]+')),\n ('model', LogisticRegression(random_state=12345, verbose = 1, solver = 'saga')),\n ])\n\n #Defining parameters to vary\n parameters = {\n 'tfidf__max_df': (0.25, 0.5, 0.75),\n 'tfidf__max_features': (None, 5000, 10000, 50000),\n 'tfidf__ngram_range': ((1, 1), (1, 2)),\n 'model__C': (0.01, 1, 100)\n }\n\n scoring_list = [\"accuracy\", \"f1\", \"precision\", \"recall\", \"roc_auc\"]\n \n #Performing 5fold CV to determine best hyperparameters\n model = GridSearchCV(pipeline, parameters, cv=5,\n n_jobs=-1, verbose=1, scoring=scoring_list, refit='f1',)\n\n t0 = datetime.datetime.now()\n\n model.fit(train_df[\"Review\"].tolist(), train_df[\"Ratings\"].to_numpy())\n \n logger.info(\"Grid Search performed in {}\".format(str(datetime.datetime.now()-t0)))\n\n #Saving results\n res_df = pd.DataFrame(model.cv_results_)\n res_df.to_csv(os.path.join(config[\"summary_stats\"][\"save_location\"], \"LogisticRegressionResults.csv\"))\n \n #Saving the model\n pickle.dump(model, open(os.path.join(config[\"models\"][\"save_location\"], \"LogisticRegression.pkl\"),'wb'))\n\n return","def __init__(self):\n super().__init__()\n import sklearn\n import sklearn.linear_model\n self.model = sklearn.linear_model.LogisticRegression","def train_logisticRegression(data: np.array, labels: np.array)->None:\n\n n_examples = np.size(data, 0)\n n_features = np.size(data, 1)\n n_categories = np.size(labels, 1)\n\n data = np.hstack((np.ones((n_examples, 1)), data))\n\n print(data[0:5, :])\n\n X_train, X_test, y_train, y_test, idx_test = split_data(data, labels, 0.7)\n\n convergence_goal = 1e-3\n learning_rate = 0.01\n\n theta = np.random.uniform(size=((n_features+1, n_categories)))\n\n for i in range(n_categories):\n\n cost_var = 1\n\n previous_cost = 1e6\n iterations = 0\n cost_to_plot = []\n\n while cost_var > convergence_goal:\n iterations += 1\n cost, grad = costFunction(X_train, y_train[:, i], theta[:, i])\n theta[:, i] = update_theta(theta[:, i], grad, learning_rate)\n cost_var = previous_cost - cost\n previous_cost = cost\n if iterations == 1: cost_var = 1\n cost_to_plot.append(cost)\n # print(cost)\n\n plt.plot(range(iterations), cost_to_plot, 'g-', label = 'cost')\n plt.xlabel('iterations')\n plt.ylabel('cost')\n # plt.show()\n\n predictions = lrPredict(theta, X_test)\n\n print(predictions[0:5, :])\n print(y_test[0:5, :])\n\n accuracy = np.mean([p == l for p, l in zip(predictions, y_test)])\n print(\"Accuracy = {}\".format(accuracy))\n\n pass","def train_model(self):\r\n alpha, accuracy_rate = self.select_model()\r\n # Initialize logistic regression with alpha(learning rate)\r\n lg = logisticregression(C=alpha)\r\n # Train the model.\r\n lg.fit(self.training_data, self.target_data)\r\n # Save the trained model as .pkl file.\r\n joblib.dump(value=lg, filename=self.intention_id+'.pkl', compress=1)\r\n print \"Estimated Parameters of Logistic Regression\"\r\n # Estimated parameters of logistic regression.\r\n print lg.get_params()","def test_machine_learning():","def __init__(self):\n self.label = \"Logistic regression\"\n self.description = \"This tool is a useful complement to Weights-of-Evidence Calculate Response tool as Logistic Regression does not make the assumption of conditional independence of the evidence with regards to the training sites. Using the evidence and assocaited weights tables, this tool creates the outputs the response and standard deviation rasters. The calculations are based on the Gen_Class attribute in the weights table and the type of evidence. Please note that the Logistic Regression tool accepts a maximum of 6,000 unique conditions or it fails. Also note that there is an upper limit of 100,000 unit cells per class in each evidence raster layer. If a class in an evidence raster goes above this, the script contains a function to increase the unit cell size to ensure an upper limit of 100,000. These issues are unable to be fixed due to a hard coded limitation in the Logistic Regression executable sdmlr.exe.\"\n self.canRunInBackground = False\n self.category = \"Weights of Evidence\"","def logistic(weights, data, targets, hyperparameters):\n\n # TODO: Finish this function\n\n return f, df, y","def fit_and_predict_LR(X_train, Y_train, X_test):\n\n # Import the package\n from sklearn.linear_model import LogisticRegression\n\n #referenced to sklearn documentation \n \n # fit the model... \n clf = LogisticRegression().fit(X_train, Y_train) \n # make predictions \n predicted_LR = clf.predict(X_test)\n return predicted_LR\n ### END SOLUTION ### ","def test_predictor():","def train_logistic_regression(train_exs: List[SentimentExample], feat_extractor: FeatureExtractor) -> LogisticRegressionClassifier:\n lr = LogisticRegressionClassifier(feat_extractor.corpus_length, feat_extractor)\n alpha = 1e0\n # beta = 1e-4\n for epoch in range(8):\n loss = 0.\n acc = 0\n indices = np.arange(len(train_exs))\n np.random.shuffle(indices)\n for i in indices:\n feat = feat_extractor.feats[i]\n sentimentExample = train_exs[i]\n y = sentimentExample.label\n z = 1 / (1 + np.exp(-feat.dot(np.expand_dims(lr.w, axis=1))))[0, 0]\n loss += -y * np.log(z) - (1 - y) * np.log(1 - z) \\\n # + beta * np.expand_dims(lr.w, axis=0).dot(np.expand_dims(lr.w, axis=1))[0, 0]\n predict = int(feat.dot(np.expand_dims(lr.w, axis=1))[0, 0] > 0)\n acc += (predict == y)\n grad = (z - y) * feat.toarray()[0] # + 2 * beta * lr.w\n lr.w = lr.w - alpha * grad\n print(\"epoch {:d}, loss: {:f}, accuracy: {:f}\".format(epoch, loss / len(train_exs), acc / len(train_exs)))\n\n for i in indices:\n feat = feat_extractor.feats[i]\n sentimentExample = train_exs[i]\n y = sentimentExample.label\n z = 1 / (1 + np.exp(-feat.dot(np.expand_dims(lr.w, axis=1))))[0, 0]\n loss += -y * np.log(z) - (1 - y) * np.log(1 - z)\n print(\"training loss: {:f}\".format(loss / len(train_exs)))\n\n return lr","def __init__(self, seed = None):\n self.data_dir = pkg_resources.resource_filename('logistic_control_variate', 'data/')\n self.generate_data(seed)\n # Holds logistic regression object for this example\n self.lr = None","def train(references, corpus):\n sim_vectors, classes = mod.model(references, corpus, labeled=True)\n pred = lm.LogisticRegression()\n pred.fit(sum(sim_vectors, []), classes)\n return pred","def test_logistic():\n r=np.random.normal(size=20)\n assert np.isclose( ilogistic(logistic(r)),r ).all()","def run_logistic_regression(file_path):\n\n df_train = pd.read_csv(f'{file_path}/without_anom.csv')\n features_list = ['Direction', 'Speed']\n df_train = df_train[features_list]\n\n scalar = MaxAbsScaler()\n\n X_train = scalar.fit_transform(df_train)\n\n logistic_model = LogisticRegression()\n\n # multi_model = MultiOutputRegressor(LogisticRegression())\n #\n # multi_model.fit(X_train, X_train)\n # multi_predict = multi_model.predict(X_train)\n\n logistic_model.fit(X_train, X_train)\n predict = logistic_model.predict(X_train)","def __init__(self, train, validation=None, initial_weight=None,\n loss_function_name='logistic',\n calculate_weight='gradient',\n regularizer=None, regularizer_p=None):\n # Initialize the super class with given data.\n # Transform the y into {0,1}\n y, tx = train\n y[np.where(y < 0)] = 0\n train = (y, tx)\n if validation:\n val_y, val_tx = validation\n val_y[np.where(val_y < 0)] = 0\n validation = (val_y, val_tx)\n super(LogisticRegression, self).__init__(train, validation,\n initial_weight=initial_weight,\n loss_function_name=loss_function_name,\n cal_weight=calculate_weight,\n regularizer=regularizer,\n regularizer_p=regularizer_p)\n # Set predicted label\n self.pred_label = [-1, 1]","def train(self):\n feature = Feature(trained=False)\n classifier = LogisticRegression(\n penalty='l2',\n max_iter=100,\n solver='liblinear',\n random_state=self.RAND_SEED)\n\n true_labels = []\n predicted_labels = []\n\n for subj in self.subjects:\n print(subj)\n # preprocess training and testing set\n self.dataset_gen(subject=subj, valid=False)\n\n # train and predict\n pipeline_steps = [('vectorized', feature.vector)]\n if self.istfidf:\n pipeline_steps.append(('tf-idf', feature.tfidftransform))\n if self.islda == 'small':\n pipeline_steps.append(('lda', feature.ldatransform_small))\n elif self.islda == 'large':\n pipeline_steps.append(('lda', feature.ldatransform_large))\n else:\n pass\n if self.isnorm:\n pipeline_steps.append(('scalar', StandardScaler(with_mean=False)))\n pipeline_steps.append(('clf', classifier))\n model = Pipeline(steps=pipeline_steps)\n\n model.fit(self.X_train, self.y_train)\n\n predicted = model.predict(self.X_test)\n # hamming\n predicted_labels.append(predicted)\n true_labels.append(self.y_test)\n\n true_matrix, pred_matrix = np.array(true_labels, int).T, np.array(predicted_labels, int).T\n true_matrix[true_matrix == -1] = 0\n pred_matrix[pred_matrix == -1] = 0\n\n evaluation = Evaluation(self.subjects)\n evaluation.model_evaluate(true_matrix=true_matrix, pred_matrix=pred_matrix, model_name=self.modelname)","def build_logistic_regr():\n logistic_pipeline = None\n\n logistic_pipeline = Pipeline([\n ('vect', CountVectorizer()),\n ('clf', LogisticRegression()), \n ])\n \n return logistic_pipeline","def _fit(self, _X, _y):\n\n self.model = linear_model.LogisticRegression(penalty=self.penalty, random_state=self.seed,\n solver='saga', n_jobs=self.n_jobs)\n self.model.fit(_X, _y)","def test_training(self):\n\t\tpass","def __init__(self, estimator, **kwargs):\n super(LogisticRegression, self).__init__(\n estimator, **kwargs)\n\n self.estimator = estimator","def __init__(self, **kwargs):\n super(LogisticRegression, self).__init__()\n self.C = kwargs.pop(\"C\", 100)\n self.clf = _LogisticRegression(C=self.C, **kwargs)","def testLogisticRegression_MatrixData(self):\n cont_features = [\n tf.contrib.layers.real_valued_column('feature', dimension=4)]\n\n classifier = tf.contrib.learn.DNNClassifier(\n feature_columns=cont_features,\n hidden_units=[3, 3],\n config=tf.contrib.learn.RunConfig(tf_random_seed=1))\n\n classifier.fit(input_fn=_iris_input_logistic_fn, steps=100)\n scores = classifier.evaluate(input_fn=_iris_input_logistic_fn, steps=1)\n self.assertGreater(scores['accuracy'], 0.9)\n self.assertLess(scores['loss'], 0.3)","def __init__(self, estimator = LogisticRegression()): \n\t self.estimator = estimator","def testLogisticRegression_TensorData(self):\n language_column = tf.contrib.layers.sparse_column_with_hash_bucket(\n 'language', hash_bucket_size=20)\n feature_columns = [\n tf.contrib.layers.embedding_column(language_column, dimension=1),\n tf.contrib.layers.real_valued_column('age')\n ]\n\n classifier = tf.contrib.learn.DNNClassifier(\n n_classes=2,\n feature_columns=feature_columns,\n hidden_units=[3, 3],\n config=tf.contrib.learn.RunConfig(tf_random_seed=1))\n\n classifier.fit(input_fn=_input_fn, steps=100)\n\n scores = classifier.evaluate(input_fn=_input_fn, steps=1)\n self.assertGreater(scores['accuracy'], 0.9)\n self.assertLess(scores['loss'], 0.3)\n predict_input_fn = functools.partial(_input_fn, num_epochs=1)\n predictions = list(\n classifier.predict(input_fn=predict_input_fn, as_iterable=True))\n self.assertListEqual(predictions, [1, 0, 0])","def spark_LogisticRegression(*args, **kwargs): \n return LogisticRegression(*args, **kwargs)","def analysis(houses:pd.DataFrame) -> None:\n \n \"\"\"\n #Me just trying to fit the data without any outside influences\n f= f'SELLER_HOUSE ~ SQFT_PER + PRICE + C(LOCATION)' \n result= smf.logit(formula= str(f), data= houses).fit()\n print(result.summary2())\n y= ['SELLER_HOUSE']\n x= ['SQFT_PER', 'PRICE', 'LOC_699 - Not Defined', 'LOC_AA - Airport Area', 'LOC_CG - Columbus Grove',\n 'LOC_CV - Cypress Village', 'LOC_EASTW - Eastwood', 'LOC_EC - El Camino Real', 'LOC_GP - Great Park',\n 'LOC_IRSP - Irvine Spectrum', 'LOC_LGA - Laguna Altura', 'LOC_NK - Northpark', 'LOC_NW - Northwood', \n 'LOC_OC - Oak Creek', 'LOC_OH - Orchard Hills', 'LOC_OT - Orangetree', 'LOC_PS - Portola Springs', \n 'LOC_QH - Quail Hill', 'LOC_SH - Shady Canyon', 'LOC_SJ - Rancho San Joaquin', 'LOC_STG - Stonegate', \n 'LOC_Stonegate', 'LOC_TR - Turtle Rock', 'LOC_TRG - Turtle Ridge', 'LOC_UP - University Park',\n 'LOC_UT - University Town Center', 'LOC_WB - Woodbridge', 'LOC_WD - Woodbury', \n 'LOC_WI - West Irvine', 'LOC_WN - Walnut (Irvine)', 'LOC_WP - Westpark']\n x_train, x_test, y_train, y_test= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\n logreg = LogisticRegression()\n logreg.fit(x_train, y_train.values.ravel())\n y_pred= logreg.predict(x_test)\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test, y_test), 3))\n # This model is really bad\n \n \"\"\"\n \n \"\"\n houses= houses.drop(['DAYS_ON_MARKET', 'ADDRESS', 'LOCATION',\n 'STATUS', 'PROPERTY_TYPE', 'ZIP_CODE'], axis= 1)\n columns= houses.columns.values.tolist()\n y= ['SELLER_HOUSE']\n x= [i for i in columns if i not in y]\n \n # Over Sampling Using SMOTE \n x_train, _, y_train, _= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\n x_columns= x_train.columns\n \n os= SMOTE(random_state= 0)\n os_x, os_y= os.fit_sample(x_train, y_train)\n os_x= pd.DataFrame(data= os_x, columns= x_columns)\n os_y= pd.DataFrame(data= os_y, columns= y)\n \n \n #Recursive Feature Elimination\n logreg= LogisticRegression(max_iter= 600)\n rfe= RFE(logreg, 20)\n rfe= rfe.fit(os_x, os_y.values.ravel())\n \n lst= [i for count, i in enumerate(x) if rfe.support_[count] == True]\n X= os_x[lst]\n Y= os_y['SELLER_HOUSE']\n \n \n #logit_model= sm.Logit(Y, X)\n #result= logit_model.fit()\n #print(result.summary2()) # Model choosen by RCE\n \n #These are features have a p-value less than 0.05\n final_x= ['BATHS', 'ZIP_92602.0', 'ZIP_92618.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\n #final_x= ['ZIP_92602.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\n X2= os_x[final_x]\n \n logit_model2= sm.Logit(Y, X2)\n result2= logit_model2.fit()\n print(result2.summary2()) # Final Model\n \n x_train2, x_test2, y_train2, y_test2= train_test_split(X2, Y, test_size= 0.3, random_state= 500)\n logreg = LogisticRegression()\n logreg.fit(x_train2, y_train2)\n \n y_pred= logreg.predict(x_test2)\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test2, y_test2), 2))\n \n conf_matrix= confusion_matrix(y_test2, y_pred)\n print(conf_matrix)\n # So 22+61 correct predictions and 13+44 wrong predictions\n \n logit_roc_auc = roc_auc_score(y_test2, logreg.predict(x_test2))\n fpr, tpr, _ = roc_curve(y_test2, logreg.predict_proba(x_test2)[:,1])\n plt.figure()\n plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n plt.plot([0, 1], [0, 1],'r--')\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.05])\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n plt.title('Receiver operating characteristic')\n plt.legend(loc=\"lower right\")\n plt.show()\n \"\"","def LogisticRegression_self_test(X_train, X_test, y_train, y_test, learning_rates, epochs, iteration):\n\n\t# scoping number of training samples\n\n\tn_inputs = X_train.shape[0]\n\tn_features = X_train.shape[1]\n\n\t\n\n\teta_ = 1e-12\n\tbeta_opt = np.random.randn(X_train.shape[1], 2)\n\tcalc_beta_GD, norm = GradientDescent(X_train, beta_opt, y_train, iteration, eta_)\n\tprob_GD, predict_GD= Probability_GD(X_test, calc_beta_GD) #defining values to be between 0 and 1\n\t#yPred_GD = (predict_GD >= 0.5).astype(int) # converting to just 0 or 1\n\n\t#Define Logistic regression\n\tclf = LogisticRegression(solver='lbfgs', max_iter=1e5)\n\tclf = clf.fit(X_train, np.ravel(y_train))\n\tpred_sklearn = clf.predict(X_test)\n\tprob_sklearn = clf.predict_proba(X_test)\n\t#print(prob_sklearn)\n\n\t#for eta in np.logspace(np.log10(1e-6), np.log10(1e0), 7):\n\taccuracy = np.zeros(len(learning_rates))\n\tauc_score = np.zeros(len(learning_rates))\n\n\tfor i, eta in enumerate(learning_rates):\n\t\tbeta_SGD = stochastic_gradient_descent(X_train, beta_opt, y_train, eta, epochs, iteration)\n\t\tprob_SGD, predict_SGD= Probability(X_test, beta_SGD) #defining values to be between 0 and 1\n\t\t\n\t\t\n\t\taccuracy[i] = metrics.accuracy_score(y_test, predict_SGD)\n\t\tauc_score[i] = metrics.roc_auc_score(y_test, predict_SGD)\n\t\tdifference = y_test - predict_SGD\n\n\t\t\n\n\t\tif i> 0 and auc_score[i] > auc_score[i-1]:\n\t\t\tbest_pred_SGD= predict_SGD\n\t\t\tbest_prob_SGD = prob_SGD\n\t\n\n\t\tprint('Accuracy {}, learning rate= {}, iterations = {}'.format(accuracy[i], eta, iteration))\n\t\n\t\tprint('Auc score: {}'.format(auc_score[i]))\n\n\n\t\t\"\"\"\n\t\tplt.plot(yPred, label='predict')\n\t\tplt.plot(optimal_beta, label ='optimal beta')\n\t\tplt.plot(y_test, label='test')\n\t\tplt.show()\n\t\t\"\"\"\n\n\tsns.set()\n\tsns.heatmap(pd.DataFrame(accuracy), annot= True, fmt='.4g')\n\tplt.title('Grid-search for logistic regression')\n\tplt.ylabel('Learning rate: $\\\\eta$')\n\tplt.xlabel('Regularization Term: $\\\\lambda$')\n\t#plt.xticks(ticks=np.arange(len(learning_rates)) + 0.5, labels=learning_rates)\n\t#plt.yticks(ticks=np.arange(len(lambda_values)) + 0.5, labels=lambda_values)\n\tb, t = plt.ylim() # discover the values for bottom and top\n\tb += 0.5 # Add 0.5 to the bottom\n\tt -= 0.5 # Subtract 0.5 from the top\n\tplt.ylim(b, t) # update the ylim(bottom, top) values\n\t#plt.savefig('accuracy_logreg.png')\n\tplt.show()\n\n\tsns.heatmap(pd.DataFrame(auc_score), annot= True, fmt='.4g')\n\tplt.title('Grid-search for logistic regression')\n\tplt.ylabel('Learning rate: $\\\\eta$')\n\tplt.xlabel('Regularization Term: $\\\\lambda$')\n\t#plt.xticks(ticks=np.arange(len(learning_rates)) + 0.5, labels=learning_rates)\n\t#plt.yticks(ticks=np.arange(len(lambda_values)) + 0.5, labels=lambda_values)\n\tb, t = plt.ylim() # discover the values for bottom and top\n\tb += 0.5 # Add 0.5 to the bottom\n\tt -= 0.5 # Subtract 0.5 from the top\n\tplt.ylim(b, t) # update the ylim(bottom, top) values\n\t#plt.savefig('auc_score_logreg.png')\n\tplt.show()\n\n\t#plot confusion matrix\n\tConfusion_Matrix(y_test, predict_GD)\n\t#Confusion_Matrix(y_test, best_pred_SGD)\n\t#Confusion_Matrix(y_test, pred_sklearn)\n\n\t#diff = np.concatenate((1- predict, predict), axis=1)\n\n\tdiff_sklearn = np.concatenate((1- prob_sklearn, prob_sklearn), axis=1)\n\tdiff_GD = np.concatenate((1- prob_GD, prob_GD), axis=1)\n\tdiff_SGD = np.concatenate((1- best_prob_SGD, best_prob_SGD), axis=1)\n\n\t#plot roc curves\n\tplot_roc(y_test, prob_sklearn)\n\tplot_roc(y_test, diff_SGD)\n\tplot_roc(y_test, prob_GD)\n\tplt.show()\n\n\t#plot cumulative gain curves\n\tplot_cumulative_gain(y_test, prob_sklearn)\n\tax = plot_cumulative_gain(y_test, diff_SGD)\n\tplot_cumulative_gain(y_test, prob_GD)\n\t#plt.show()\n\n\n\n\t\"\"\"\n\t#plot roc curves\n\tplot_roc(y_test, diff_sklearn, plot_micro=False, plot_macro= False)\n\tplot_roc(y_test, diff_GD, plot_micro=False, plot_macro= False)\n\tplot_roc(y_test, diff_SGD, plot_micro=False, plot_macro= False)\n\tplt.show()\n\n\t#plot cumulative gain curves\n\tplot_cumulative_gain(y_test, diff_sklearn)\n\tplot_cumulative_gain(y_test, diff_GD)\n\tplot_cumulative_gain(y_test, diff_SGD)\n\tplt.show()\t\n\n\t\"\"\"\n\n\tmodel_curve = auc_score\n\tarea_baseline = 0.5\n\tarea_ratio = (model_curve - area_baseline)/(area_baseline)\n\tprint('Area Ratio:',area_ratio)\n\n\n\treturn accuracy, learning_rates","def stability_logistic(x, y, **kwargs):\n rlr = RandomizedLogisticRegression(n_jobs=kwargs.get('n_jobs', 4))\n if 'param' in kwargs:\n rlr.set_params(**kwargs['param'])\n rlr.fit(x, y)\n return rlr.get_support()","def build_logistic_regr():\n logistic_pipeline = None\n ##### Write code here #######\n logistic_pipeline = Pipeline([\n ('vect', CountVectorizer()),\n ('clf', LogisticRegression())\n ])\n ##### End of your work ######\n return logistic_pipeline","def test_01_train(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-train-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n if os.path.exists(log_file):\n os.remove(log_file)\n \n ## update the log\n country = 'india'\n date_range = ('2017-11-29', '2019-05-24')\n metric = {'rmse':0.5}\n runtime = \"00:00:01\"\n model_version = 0.1\n model_version_note = \"test model\"\n \n update_train_log(country, date_range, metric, runtime,\n model_version, model_version_note, test=True, prefix=LOG_PREFIX)\n\n self.assertTrue(os.path.exists(log_file))","def _fit(self):\n\n\t\tclf = LogisticRegression()\n\t\tclf.fit(inputs, labels)\n\n\t\treturn clf","def testLogRegres(weights, dev_x, dev_y):\n predict_y = []\n numSamples, numFeatures = np.shape(dev_x)\n for i in range(numSamples):\n if sigmoid(dev_x[i, :] * weights) > 0.5:\n label = 1\n else:\n label = 0\n predict_y.append(label)\n print('Congratulations, testing complete!')\n return predict_y","def test_02_train(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-train-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n \n ## update the log\n country = 'india'\n date_range = ('2017-11-29', '2019-05-24')\n metric = {'rmse':0.5}\n runtime = \"00:00:01\"\n model_version = 0.1\n model_version_note = \"test model\"\n \n update_train_log(country, date_range, metric, runtime,\n model_version, model_version_note ,test=True, prefix=LOG_PREFIX)\n\n df = pd.read_csv(log_file)\n logged_metric = [literal_eval(i) for i in df['metric'].copy()][-1]\n self.assertEqual(metric,logged_metric)","def __init__(self, classific_method=\"LogisticRegression\"):\n\t\tself.classific_method = classific_method","def logistic(weights, data, targets, hyperparameters):\n y = logistic_predict(weights, data)\n\n #####################################################################\n # TODO: #\n # Given weights and data, return the averaged loss over all data #\n # points, gradient of parameters, and the probabilities given by #\n # logistic regression. #\n #####################################################################\n f = None\n df = None\n\n f = evaluate(targets, y)[0]\n\n N = len(data)\n M = len(weights) - 1 \n temp = np.ones([N, M + 1])\n temp[: N, : M] = np.array(data)\n\n\n df = np.zeros([M+1, 1])\n\n df[:, 0] = np.array([[np.mean([(y.flatten()[i] - targets.flatten()[i]) * temp[i][j] for i in range(0, N)]) for j in range(0, M + 1)],])\n\n # df = np.matrix([[np.mean([(y[i] - targets[i]) * temp[i][j] for i in range(0, N)]) for j in range(0, M + 1)],])\n\n #####################################################################\n # END OF YOUR CODE #\n #####################################################################\n return f, df, y","def logistic_predict(weights, data):\n\n # TODO: Finish this function\n\n return y","def logistic_reg(training_data):\r\n \r\n \"\"\" Setting guesses for minimum and maximum values of regularization parameter then\r\n find the value of parameter that minimizes error on cross validation data. If\r\n local minimum is found the return this model. If not, extend minimum or maximum \r\n appropriately and repeat \"\"\"\r\n from sklearn.linear_model import LogisticRegression\r\n C_min = 1.0e-5\r\n C_max = 1.0e5\r\n regularization_flag = 1 # To set 1 until local minimum is found\r\n regularization_param = 0\r\n \r\n# while regularization_flag != 0:\r\n# regularization_param, regularization_flag = set_reg_param(training_data, cv_data, alpha_min, alpha_max)\r\n# if regularization_flag == -1:\r\n# \"\"\" The local minimum is at point less than alpha_min \"\"\"\r\n# alpha_min = alpha_min * 0.3\r\n# if regularization_flag == 1:\r\n# \"\"\" The local minimum is at point greater then alpha_max \"\"\"\r\n# alpha_max = alpha_max * 3\r\n \r\n lr = LogisticRegression (C=C_max, random_state=0)\r\n lr.fit(training_data.X, training_data.y)\r\n return lr, C_max","def logreg(mode, vectorizer, training_dir, test_dir):\n # 1. load the training dataset\n logging.basicConfig(level=logging.INFO)\n logging.info(\"loading training dataset\")\n x_train, y_train_age, y_train_gender, y_train_occ, _ = \\\n load_dataset(training_dir, mode, vectorizer)\n\n # 2. train models\n logging.info(\"fitting model age\")\n age_model = LogisticRegression(multi_class='multinomial', solver=\"newton-cg\")\n age_model.fit(x_train, y_train_age)\n logging.info(\"fitting model gender\")\n gender_model = LogisticRegression(multi_class='multinomial', solver=\"newton-cg\")\n gender_model.fit(x_train, y_train_gender)\n logging.info(\"fitting model acc\")\n occ_model = LogisticRegression(multi_class='multinomial', solver=\"newton-cg\")\n occ_model.fit(x_train, y_train_occ)\n\n # 3. load the test dataset\n logging.info(\"loading test dataset ...\")\n x_test, y_test_age, y_test_gender, y_test_occ, cid = \\\n load_dataset(test_dir, mode, vectorizer)\n\n # 4. Predict and Evaluate\n logging.info(\"predicting\")\n age_pred = age_model.predict(x_test)\n gender_pred = gender_model.predict(x_test)\n occ_pred = occ_model.predict(x_test)\n output_labels = [{\"id\": i, \"birthyear\": int(a), \"gender\": inv_g_dict[g], \"occupation\": inv_o_dict[o]}\n for i, a, g, o in zip(cid, age_pred, gender_pred, occ_pred)]\n\n open(\"labels.ndjson\", \"w\").writelines(\n [json.dumps(x) + \"\\n\" for x in output_labels]\n )","def log_reg(x, y, s):\n usx = np.array(x)\n usy = np.array(y)\n\n # split data into train and validation set\n x_train, x_test, y_train, y_test = train_test_split(usx, usy, test_size=s)\n cls_log = LogisticRegression()\n cls_log.fit(x_train, y_train)\n y_predict = cls_log.predict(x_test)\n\n # select only the probabilities of being fraud\n y_pred_prob = cls_log.predict_proba(x_test)[:, 1]\n return y_predict, y_test, y_pred_prob","def set_initial_params(model: LogisticRegression):\n n_classes = 15 # threat types\n n_features = 33 # Number of features in dataset\n model.classes_ = np.array([i for i in range(15)])\n\n model.coef_ = np.zeros((n_classes, n_features))\n if model.fit_intercept:\n model.intercept_ = np.zeros((n_classes,))","def run_regression(train_embeds, train_labels, test_embeds, test_labels):\n np.random.seed(1)\n from sklearn.linear_model import SGDClassifier\n from sklearn.dummy import DummyClassifier\n from sklearn.metrics import f1_score\n dummy = DummyClassifier()\n dummy.fit(train_embeds, train_labels)\n log = SGDClassifier(loss=\"log\", n_jobs=10, tol=1e-3)\n log.fit(train_embeds, train_labels)\n print(\"F1 score:\", f1_score(test_labels, log.predict(test_embeds), average=\"micro\"))\n print(\"Random baseline f1 score:\", f1_score(test_labels, dummy.predict(test_embeds), average=\"micro\"))","def test_intent_classifier_train(self):\n pass","def sklearn_model(train_data):\n X, y = train_data\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\")\n model = LogisticRegression(\n multi_class=\"multinomial\", solver=\"lbfgs\", max_iter=1000\n )\n model.fit(X, y)\n return model","def train_logistic_regression(x_train, y_train, learning_rate, fit_intercept=False, max_iter=500):\r\n if fit_intercept:\r\n intercept = np.ones(x_train.shape[0], 1)\r\n x_train = np.hstack((intercept, x_train)) # hstacks merges 2 arrays column wise\r\n weights = np.zeros(x_train.shape[1])\r\n for iteration in range(max_iter):\r\n weights = update_weights(x_train, y_train, weights, learning_rate)\r\n # printing cost for every 100 iterations\r\n if iteration % 100 == 0:\r\n print(calculate_cost(x_train, y_train, weights))\r\n return weights","def linear_regression(X, Y, Xs_test, Ys_test):\n\n ## YOUR CODE HERE\n #################\n return 0","def main():\n logging.basicConfig(filename=\"regression_model.log\", level=logging.INFO)\n\n # Define seed for random processes\n SEED = 42\n\n # File path\n filename = \"../data/movies_data.csv\"\n\n # Name of the title column\n title = \"Titre\"\n\n # Name of the target variable\n target = \"Rentabilité (%)\"\n\n # List of feature names\n features = [\n \"Popularité genre\",\n \"Popularité thème\",\n \"Rareté émotion\",\n \"Référence\",\n \"Budget (M$)\",\n ]\n from sklearn.linear_model import LinearRegression\n\n # Create a Linear Regression model\n model = LinearRegression()\n\n # Create a RegressionModel object\n regressor = RegressionModel(\n model, features, target, title, feature_to_encode=\"Genre\"\n )\n\n # Load the data\n regressor.load_data(filename)\n\n # Preprocess the data\n regressor.preprocess()\n\n # Train the model and calculate the mean MSE\n metrics = regressor.train_and_evaluate(seed=SEED, test_size=0.1)\n\n logging.info(f\"Metrics: {metrics}\")\n print(f\"Metrics: {metrics}\")\n\n # Make predictions and save them to a file\n regressor.make_predictions(metrics=metrics)","def train_logistic_regression(train_exs: List[SentimentExample],\n feat_extractor: FeatureExtractor) -> LogisticRegressionClassifier:\n indexer = feat_extractor.get_indexer()\n weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\n learning_rate = 0.1\n for i in range(15):\n for ex in train_exs:\n features_of_str = feat_extractor.extract_features(ex.words, False)\n expo = math.exp(np.dot(weights, features_of_str))\n possibility = expo / (1 + expo)\n gradient_of_w = np.dot(ex.label - possibility, features_of_str)\n weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\n return LogisticRegressionClassifier(weights, feat_extractor)\n\n # Methods for plotting average training loss\n\n # x = np.arange(0, 14)\n # # learning_rate = 1\n # indexer = feat_extractor.get_indexer()\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\n # avrg_losses = np.zeros(14)\n # for i in range(15):\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\n # weights = np.add(weights, gradient_of_w)\n # loss = 0\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\n # avrg_losses[i - 1] = loss / train_exs.__len__()\n # plt.plot(x, avrg_losses)\n #\n # # learning_rate = 0.01\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\n # learning_rate = 0.01\n # avrg_losses = np.zeros(14)\n # for i in range(15):\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\n # weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\n # loss = 0\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\n # avrg_losses[i - 1] = loss / train_exs.__len__()\n # plt.plot(x, avrg_losses)\n #\n # # learning_rate = 0.1\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\n # learning_rate = 0.1\n # avrg_losses = np.zeros(14)\n # for i in range(15):\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\n # weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\n # loss = 0\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\n # avrg_losses[i - 1] = loss / train_exs.__len__()\n # plt.plot(x, avrg_losses)\n # plt.xlabel('Epochs')\n # plt.ylabel('Average Training Loss')\n # plt.legend(['step size 1', 'step size 0.01', 'step size 0.1'], loc='upper left')\n # plt.show()\n # return LogisticRegressionClassifier(weights, feat_extractor)","def TrainingAlgorithm():\n urlFile = 'C:\\\\Users\\\\pokef\\\\Documents\\\\CybersecurityProg\\\\badandgoodurls.csv' # Replace this with data file path\n urlCSV = pd.read_csv(urlFile, ',',error_bad_lines=False)\n urlDF = pd.DataFrame(urlCSV)\n \n urlData = np.array(urlDF)\n \n labels = [d[1] for d in urlData]\n corpus = [d[0] for d in urlData]\n vectorizer = TfidfVectorizer(tokenizer=getTokens) # get a vector for each URL but use our tokenizer\n X = vectorizer.fit_transform(corpus)\n X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)\n \n logisticReg = LogisticRegression()\n logisticReg.fit(X_train, y_train)\n print(logisticReg.score(X_test, y_test)) # print accuracy score\n return vectorizer, logisticReg","def model_performance_comparison(self, yvar, prev_group, C_FP, C_FN):\n trn_bl_df = self.reweigh()\n # sample_weights = self.reweigh(bl_df=trn_bl_df)\n\n s_weights = trn_bl_df.instance_weights\n print(s_weights)\n\n trainset = trn_bl_df.convert_to_dataframe()[0]\n testset = trainset\n\n X = trainset.loc[:, trainset.columns != yvar]\n y = trainset[yvar]\n #X_test = testset.loc[:, trainset.columns != yvar]\n #y_test = testset[yvar]\n\n X_test = X\n y_test = y\n\n clf_ww = sklearn.linear_model.LogisticRegression(random_state=999).fit(X, y, sample_weight=s_weights)\n clf_wow = sklearn.linear_model.LogisticRegression(random_state=999).fit(X, y)\n\n output_probabilities_to_csv(model=clf_ww, x_test=X_test, path='probs_ww_withprvgroup.csv',priv_group_col=trainset[prev_group], actuals=y_test)\n output_probabilities_to_csv(model=clf_wow, x_test=X_test, path='probs_wow_withprvgroup.csv', priv_group_col=trainset[prev_group], actuals=y_test)\n\n print(\"------------------------------------------\")\n print(\"Accuracy of Vanila Logistic Model\")\n print(\"------------------------------------------\")\n print(\"Without Weights : \", round(clf_wow.score(X_test, y_test), 3))\n print(\"With Weights : \", round(clf_ww.score(X_test, y_test), 3))\n\n X_test_age1 = testset.loc[:, trainset.columns != yvar][testset[prev_group] == 1.0]\n y_test_age1 = testset[yvar][testset[prev_group] == 1.0]\n X_test_age0 = testset.loc[:, trainset.columns != yvar][testset[prev_group] == 0.0]\n y_test_age0 = testset[yvar][testset[prev_group] == 0.0]\n\n wow = round(abs(clf_wow.score(X_test_age0, y_test_age0) - clf_wow.score(X_test_age1, y_test_age1)), 3)\n ww = round(abs(clf_ww.score(X_test_age0, y_test_age0) - clf_ww.score(X_test_age1, y_test_age1)), 3)\n\n #output_probabilities_to_csv(model=clf_ww, x_test=X_test_age0, path='probs_unpriv_ww.csv')\n #output_probabilities_to_csv(model=clf_ww, x_test=X_test_age1, path='probs_priv_ww.csv')\n #output_probabilities_to_csv(model=clf_wow, x_test=X_test_age0, path='probs_unpriv_wow.csv')\n #output_probabilities_to_csv(model=clf_wow, x_test=X_test_age1, path='probs_priv_wow.csv')\n\n print(\"\")\n print(\"\")\n print(\"--------------------------------------------------------------\")\n print(\"Difference in accuracy between privileged and unprivileged\")\n print(\"--------------------------------------------------------------\")\n print(\"without weights : \", wow)\n print(\"with weights : \", ww)\n\n Ypredclf = clf_ww.predict(X_test)\n Ypredclf2 = clf_wow.predict(X_test)\n withw = confusion_matrix(y_test, Ypredclf)\n without = confusion_matrix(y_test, Ypredclf2)\n print(\"\")\n print(\"\")\n print(\"--------------------------------------------------------------\")\n print(\"Confusion Matrix\")\n print(\"--------------------------------------------------------------\")\n print(\"without weights\")\n print(without)\n print(\"\")\n print(\"\")\n print(\"with weights\")\n print(withw)\n\n a, b, c, d = without.ravel() #(tn, fp, fn, tp)\n a1, b1, c1, d1 = withw.ravel() #(tn, fp, fn, tp)\n\n withweights = b1 * C_FP + c1 * C_FN\n withoutweights = b * C_FP + c * C_FN\n\n print(\"\")\n print(\"\")\n print(\"cost with weights: \", withweights)\n print(\"cost without weights: \", withoutweights)\n print(\"Has cost decreased after reweighing?\", withweights < withoutweights)\n\n print('')\n print('SUMMARY TABLE')\n\n cost = fr.CostingFairness(input_dataframe=self.data,\n label_names=['credit'],\n protected_attribute_names=['Age_previliged'],\n trained_model=clf_ww)\n\n metrics_table = self.generate_pre_train_metrics_table(model_without_weights=clf_wow,\n model_with_weights=clf_ww,\n test_set=testset,\n target=yvar,\n privileged=prev_group,\n false_positive_cost=C_FP,\n false_negative_cost=C_FN)\n priv_diff_table = generate_privileged_diff(metrics_table)\n delta_table = generate_delta_table(metrics_table)\n costs_table = cost.return_cost_fairness_accuracy_optimised()\n\n # pdf = PDF()\n # pdf.add_page()\n # pdf.write_table_to_pdf(metrics_table)\n # pdf.write_table_to_pdf(priv_diff_table)\n # pdf.write_table_to_pdf(delta_table)\n # pdf.output('TEST01.pdf', 'F')\n\n print(\"\")\n print(\"What we see is interesting, after re-weighing the bias of the model has decreased significantly by {}%, \"\n \"with a very slight decrease in accuracy as shown earlier\".format(round((wow - ww) * 100)))\n\n return metrics_table, priv_diff_table, delta_table, costs_table","def __init__(self, log=True, normalize=False):\r\n self.model = LinearRegression(normalize=normalize)\r\n self.log = log","def fit_test(self):","def do_stats_model(x, y):\n Xx = sm.add_constant(x)\n sm_logit = sm.Logit(y, Xx)\n result = sm_logit.fit()\n print result.summary()\n result.pred_table()\n # linear model\n print \"linear regression model:\\n\"\n sm_linear = sm.OLS(y, Xx)\n result = sm_linear.fit()\n print result.summary()","def getInputSpecification(cls):\n specs = super(LogisticRegression, cls).getInputSpecification()\n specs.description = r\"\"\"The \\xmlNode{LogisticRegression} is\n a logit, MaxEnt classifier.\n In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme\n if the ``multi_class'' option is set to ``ovr'', and uses the cross-entropy loss if the\n ``multi_class'' option is set to ``multinomial''. (Currently the ``multinomial'' option\n is supported only by the ``lbfgs'', ``sag'', ``saga'' and ``newton-cg'' solvers.)\n This class implements regularized logistic regression using the ``liblinear'' library, ``newton-cg'',\n ``sag'', ``saga'' and ``lbfgs'' solvers. Regularization is applied by default. It can handle both dense and sparse input.\n The ``newton-cg'', ``sag'', and ``lbfgs'' solvers support only L2 regularization with primal formulation,\n or no regularization. The ``liblinear'' solver supports both L1 and L2 regularization, with a dual formulation\n only for the L2 penalty. The Elastic-Net regularization is only supported by the ``saga'' solver.\n \\zNormalizationPerformed{LogisticRegression}\n \"\"\"\n specs.addSub(InputData.parameterInputFactory(\"penalty\", contentType=InputTypes.makeEnumType(\"penalty\", \"penaltyType\",['l1','l2', 'elasticnet', 'none']),\n descr=r\"\"\"Used to specify the norm used in the penalization. The newton-cg, sag and lbfgs solvers\n support only l2 penalties. elasticnet is only supported by the saga solver. If none (\n not supported by the liblinear solver), no regularization is applied.\"\"\", default='l2'))\n specs.addSub(InputData.parameterInputFactory(\"dual\", contentType=InputTypes.BoolType,\n descr=r\"\"\"Select the algorithm to either solve the dual or primal optimization problem.\n Prefer dual=False when $n_samples > n_features$.\"\"\", default=True))\n specs.addSub(InputData.parameterInputFactory('C', contentType=InputTypes.FloatType,\n descr=r\"\"\"Regularization parameter. The strength of the regularization is inversely\n proportional to C.Must be strictly positive.\"\"\", default=1.0))\n specs.addSub(InputData.parameterInputFactory(\"tol\", contentType=InputTypes.FloatType,\n descr=r\"\"\"Tolerance for stopping criterion\"\"\", default=1e-4))\n specs.addSub(InputData.parameterInputFactory(\"fit_intercept\", contentType=InputTypes.BoolType,\n descr=r\"\"\"Whether to calculate the intercept for this model. Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.\"\"\", default=True))\n specs.addSub(InputData.parameterInputFactory(\"intercept_scaling\", contentType=InputTypes.FloatType,\n descr=r\"\"\"When fit_intercept is True, instance vector x becomes $[x, intercept_scaling]$,\n i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended\n to the instance vector. The intercept becomes $intercept_scaling * synthetic_feature_weight$\n \\nb the synthetic feature weight is subject to $l1/l2$ regularization as all other features.\n To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept)\n $intercept_scaling$ has to be increased.\"\"\", default=1.))\n specs.addSub(InputData.parameterInputFactory(\"solver\", contentType=InputTypes.makeEnumType(\"solver\", \"solverType\",['newton-cg','lbfgs', 'liblinear','sag','saga']),\n descr=r\"\"\"Algorithm to use in the optimization problem.\n \\begin{itemize}\n \\item For small datasets, ``liblinear'' is a good choice, whereas ``sag'' and ``saga'' are faster for large ones.\n \\item For multiclass problems, only ``newton-cg'', ``sag'', ``saga'' and ``lbfgs'' handle multinomial loss; `\n `liblinear'' is limited to one-versus-rest schemes.\n \\item ``newton-cg'', ``lbfgs'', ``sag'' and ``saga'' handle L2 or no penalty\n \\item ``liblinear'' and ``saga'' also handle L1 penalty\n \\item ``saga'' also supports ``elasticnet'' penalty\n \\item ``liblinear'' does not support setting penalty=``none''\n \\end{itemize}\"\"\", default='lbfgs'))\n specs.addSub(InputData.parameterInputFactory(\"max_iter\", contentType=InputTypes.IntegerType,\n descr=r\"\"\"Hard limit on iterations within solver.``-1'' for no limit\"\"\", default=100))\n specs.addSub(InputData.parameterInputFactory(\"multi_class\", contentType=InputTypes.makeEnumType(\"multiClass\", \"multiClassType\",['auto','ovr', 'multinomial']),\n descr=r\"\"\"If the option chosen is ``ovr'', then a binary problem is fit for each label. For ``multinomial''\n the loss minimised is the multinomial loss fit across the entire probability distribution, even when the\n data is binary. ``multinomial' is unavailable when solver=``liblinear''. ``auto'' selects ``ovr'' if the data is\n binary, or if solver=``liblinear'', and otherwise selects ``multinomial''.\"\"\", default='auto'))\n specs.addSub(InputData.parameterInputFactory(\"l1_ratio\", contentType=InputTypes.FloatType,\n descr=r\"\"\"The Elastic-Net mixing parameter, with $0 <= l1_ratio <= 1$. Only used if penalty=``elasticnet''.\n Setting $l1_ratio=0$ is equivalent to using penalty=``l2'', while setting $l1_ratio=1$ is equivalent to using\n $penalty=``l1''$. For $0 < l1_ratio <1$, the penalty is a combination of L1 and L2.\"\"\", default=0.5))\n specs.addSub(InputData.parameterInputFactory(\"class_weight\", contentType=InputTypes.makeEnumType(\"classWeight\", \"classWeightType\",['balanced']),\n descr=r\"\"\"If not given, all classes are supposed to have weight one.\n The “balanced” mode uses the values of y to automatically adjust weights\n inversely proportional to class frequencies in the input data\"\"\", default=None))\n specs.addSub(InputData.parameterInputFactory(\"random_state\", contentType=InputTypes.IntegerType,\n descr=r\"\"\"Used when solver == ‘sag’, ‘saga’ or ‘liblinear’ to shuffle the data.\"\"\",\n default=None))\n return specs","def utest_SGD_Test():\n model_fname = \"../work/model\"\n # test binary classification.\n if False:\n #test_fname = \"../work/train.bz2\"\n test_fname = \"../work/rcv1_test.binary.bz2\"\n if True:\n test_fname = \"../work/iris_multi.train\"\n test_logreg(model_fname,test_fname,prob=True,acc=True)\n pass","def logistic_fit(self, penalty: str = 'l2', c: float = 1.0):\r\n self.LogisticModel = LogisticRegression(solver='liblinear', penalty=penalty, C=c).fit(self.x, self.y)","def train(self, trainfile):\r\n sentences_emb,labels=self.read_data(trainfile)\r\n logReg = LogisticRegression(penalty=\"l2\",C = 10, multi_class='auto',solver='newton-cg')\r\n logReg.fit(sentences_emb,labels)\r\n self.clf=logReg","def execute(self, parameters, messages):\n try:\n importlib.reload (arcsdm.logisticregression)\n except :\n reload(arcsdm.logisticregression);\n arcsdm.logisticregression.Execute(self, parameters, messages)\n return\n \n #execute_tool(arcsdm.logisticregression.Execute, self, parameters, messages)","def test_1():\n clf1 = mord.OrdinalRidge(alpha=0.)\n clf1.fit(X, y)\n\n clf2 = mord.LogisticAT(alpha=0.)\n clf2.fit(X, y)\n\n # the score is - absolute error, 0 is perfect\n # assert clf1.score(X, y) < clf2.score(X, y)\n\n clf3 = mord.LogisticSE(alpha=0.)\n clf3.fit(X, y)\n pred3 = clf3.predict(X)\n pred2 = clf2.predict(X)\n\n # check that it predicts better than the surrogate\n # for other loss\n assert np.abs(pred2 - y).mean() <= np.abs(pred3 - y).mean()\n # # the score is - absolute error, 0 is perfect\n # assert_almost_equal(clf.score(X, y), 0., places=2)\n #\n # clf = mord.LogisticIT(alpha=0.)\n # clf.fit(X, y)\n # # the score is classification error, 1 is perfect\n # assert_almost_equal(clf.score(X, y), 1., places=2)\n\n # test on sparse matrices\n X_sparse = sparse.csr_matrix(X)\n clf4 = mord.LogisticAT(alpha=0.)\n clf4.fit(X_sparse, y)\n pred4 = clf4.predict(X_sparse)\n assert metrics.mean_absolute_error(y, pred4) < 1.","def test_xy(self):\n x = np.array([[1,3], [2,8], [1,3]])\n y = np.array([1,1,-1])\n lro = LogisticRegressionOptimiser(x,y)\n expected = np.array([[1,3], [2,8], [-1,-3]])\n for i in 0,1,2:\n for j in 0,1:\n self.assertEqual(lro.xy[i][j], expected[i][j])","def logistic(self, data, weights, biases):\n\n state_weight_prods = np.dot(data, weights)\n print(-state_weight_prods - biases)\n activations = 1.0 / (1 + np.exp(-state_weight_prods - biases))\n plt.plot(state_weight_prods, activations)\n plt.show()\n return activations","def train(self):\n df = self.df\n self.scaler = MinMaxScaler()\n self.scaler.fit(df)\n df[df.columns] = self.scaler.transform(df)\n\n\n X_train, y_train = get_X_y(df, self.n_days, self.length , self.style)\n X_train = np.array(X_train)\n X_train.shape = (X_train.shape[0], X_train.shape[2])\n\n self.clf = LogisticRegression().fit(X_train, y_train)\n\n #es = EarlyStopping(monitor = 'accuracy',mode = 'min' , verbose = 1, patience = 100, restore_best_weights = True)","def learn(self, Xtrain, ytrain):","def test_train(self):\n print \"x=\",self.trainer.train()","def test_sklearn_log_loss(self):\n X, y = datasets.load_breast_cancer(return_X_y=True)\n\n star = sb.BoostingClassifier(\n loss=sb.losses.LogLoss(),\n init_estimator=ScikitLearnLogOdds(),\n base_estimator=tree.DecisionTreeRegressor(max_depth=3, random_state=42),\n base_estimator_is_tree=True,\n n_estimators=30,\n learning_rate=0.1,\n )\n star = star.fit(X, y)\n\n scikit = ensemble.GradientBoostingClassifier(\n loss='deviance',\n max_depth=3,\n n_estimators=30,\n learning_rate=0.1,\n random_state=42\n )\n scikit = scikit.fit(X, y)\n\n for y1, y2 in zip(star.iter_predict_proba(X), scikit.staged_predict_proba(X)):\n np.testing.assert_allclose(y1, y2, rtol=1e-5)","def on_train_end(self, logs={}):\n LOSSES.append(self.losses)\n print(self.j)\n (x_test, y_test) = get_test_data(self.j)\n y_pred = model.predict(x_test)\n y_pred = y_pred.squeeze()\n y_pred[y_pred < 0.5] = 0\n y_pred[y_pred >= 0.5] = 1\n print(y_pred)\n\n y_test = y_test.squeeze()\n print(y_test)\n confmat = confusion_matrix(y_test,y_pred)\n print(confmat)\n calc_TSS(confmat,2)","def test_test_regression_model(self):\n model = RegressionTestModel()\n example = {'x_1': 3, 'x_2': 2}\n pred = list(model.predict([example]))[0]\n self.assertEqual(pred['score'], 20)","def logistic(weights, data, targets, hyperparameters):\n \n t = np.transpose(np.repeat(np.reshape(weights[:-1], (len(weights)-1, 1)), len(data), axis = 1))\n f_e = data * t\n z_sums = np.sum(f_e, axis=1)\n y = sigmoid(z_sums +weights[-1])\n f = np.sum(np.log(1 + np.exp(-z_sums - weights[-1])) + (1 - np.transpose(targets)) * (z_sums + weights[-1]))\n df = np.sum(data * np.transpose(((-np.exp(-z_sums - weights[-1]) / (1 + np.exp(-z_sums - weights[-1]))) + (1 - np.transpose(targets)))), axis = 0)\n df = np.append(df, np.sum(np.transpose(((-np.exp(-z_sums - weights[-1]) / (1 + np.exp(-z_sums - weights[-1]))) + (1 - np.transpose(targets)))), axis = 0))\n df = np.reshape(df, ((len(df), 1)))\n\n return f, df, np.reshape(y, (len(y), 1))","def test_draw():\n b_time = datetime.datetime.now()\n print('Begining reading data')\n DATA_TRAIN_PATH = get_filepath('train')\n y, tX, ids = load_csv_data(DATA_TRAIN_PATH)\n print(\"Finish loading in {s} seconds\".\n format(s=(datetime.datetime.now() - b_time).total_seconds()))\n\n # data, _, _ = standardize(tX)\n\n # test_bias(y)\n # nb_pc = 5\n # print(\"test the PCA with {} elements\".format(nb_pc))\n # pcs, pc_data = pca_transform(data, nb_pc, concatenate=False)\n #\n # print(\"get interactions\")\n # interaction = interactions(data, range(0, 10))\n # interaction, _, _ = standardize(interaction)\n # print(\"select first 10 data entry with pc data\")\n # data = np.c_[data[:, 0:10], pc_data]\n # data = np.c_[data, interaction]\n # # Begin the least square sgd\n # e_time = datetime.datetime.now()\n # print(\"Finish data reading in {s} seconds\".\n # format(s=(e_time - b_time).total_seconds()))\n data, _, _ = compose_complex_features_further(tX, intercept=True,\n interaction=True,\n log=True,\n sqrt=True,\n power=True,\n pca=True)\n train, valid = draw_balanced_subsample(y, data, trainsize=6000)\n # t_data, _, _ = compose_complex_features_further(test_x, intercept=True,\n # interaction=True,\n # log=True,\n # sqrt=True,\n # power=True,\n # pca=True)\n\n\n logistic = LogisticRegression(train=train, validation=valid, regularizer='Lasso', regularizer_p=0.5)\n result = logistic.train(lr=0.01, decay=0.5, early_stop=400, max_iters=2000)\n print(result)","def linear_regresstion_action(X_train=\"not defined\", X_test=\"not defined\", y_train=\"not defined\", y_test=\"not defined\",\r\n input_data=\"not defined\"):\r\n if \"not defined\" in [X_train, X_test, y_train, y_test]:\r\n X_train, X_test, y_train, y_test = splitting.splitting_data()\r\n\r\n if input_data == \"not defined\":\r\n raise ValueError(\"please provide input data\")\r\n\r\n linreg = LinearRegression()\r\n grid = {\r\n \"normalize\": [\"True\", \"False\"],\r\n }\r\n\r\n model = RandomizedSearchCV(linreg, grid, random_state=1007486)\r\n model.fit(X_train, y_train)\r\n y_pred = model.predict(X_test)\r\n predicted_units = model.predict(input_data)\r\n\r\n # assert score > 0.6, \"fuck this model is too bad!!!\"\r\n return y_pred, predicted_units","def run_model(Xp_train=None,y_train=None,Xp_test=None,y_test=None,mt='bl', params={}):\n models = { \n \"bl\": lambda : None,\n \"lr\": LinearRegression,\n \"dtr\": DecisionTreeRegressor,\n \"gbr\": GradientBoostingRegressor\n }\n\n # Select Model, returns None for baseline\n model = models[mt](**params)\n\n # Fit Model and make predictions\n if mt != 'bl': # If model not baseline\n # Dummy encoding , (each category of n levels or attributes is converted into n-1 dichotomous variables)\n X_train = pd.get_dummies(Xp_train, columns=['type', 'duration','location'],drop_first=True)\n X_test = pd.get_dummies(Xp_test, columns=['type', 'duration','location'],drop_first=True)\n model.fit(X_train,y_train)\n # Make Predictions\n y_pred = model.predict(X_test)\n #print X_test.shape\n y_predtr = model.predict(X_train) \n else: # Compute baseline\n y_pred = np.median(y_train).repeat(len(y_test))\n y_predtr = np.median(y_train).repeat(len(y_train))\n \n \n\n # Report metrics\n print(\"Model name: %s\" % (model.__class__.__name__ if model else \"Price Median Baseline\"))\n if mt != 'bl':\n print(\"hyper-parameters: \" + \", \".join( \"{0}: {1}\".format(k,v) for (k,v) in params.items() ) )\n print(\"Mean absolute error training set: %.2f\" % mean_absolute_error(y_train, y_predtr)) \n print(\"Mean absolute error testing set: %.2f \\n\" % mean_absolute_error(y_test, y_pred))","def setUp(self):\n\n self.ach_model = FundingSources.get_ach_model()\n self.verify = self.get_basic_verification()","def test_04_predict(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-predict-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n\n ## update the log\n y_pred = [0]\n y_proba = [0.6,0.4]\n runtime = \"00:00:02\"\n model_version = 0.1\n country = \"india\"\n target_date = '2018-01-05'\n\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\n model_version, test=True, prefix=LOG_PREFIX)\n\n df = pd.read_csv(log_file)\n logged_y_pred = [literal_eval(i) for i in df['y_pred'].copy()][-1]\n self.assertEqual(y_pred,logged_y_pred)","def test_intent_classifier_add_training_samples(self):\n pass","def logistic_pen(weights, data, targets, hyperparameters):\n\n # TODO: Finish this function\n\n return f, df, y","def logistic_regression(X, Y):\n m, n = X.shape\n theta = np.zeros(n)\n learning_rate = 10\n\n i = 0\n while True:\n i += 1\n prev_theta = theta\n grad = calc_grad(X, Y, theta)\n theta = theta - learning_rate * grad\n if i % 10000 == 0:\n print('Finished %d iterations' % i)\n # plot decision boundary for the ith iteration listed in i_lst\n i_lst = [1, 2, 3, 10, 100, 200, 500, 1000, 10000, 30370, 40000, 50000]\n if i in i_lst:\n save_path = \"output/p01_b_a\" + str(i) + \".png\"\n plot(X, Y, theta, save_path)\n if np.linalg.norm(prev_theta - theta) < 1e-15:\n print('Converged in %d iterations' % i)\n break\n return","def train(self, X, y, reg=1e-4, lr=0.1, epochs=5, batch_size=16):\n self.classes = np.sort(np.unique(y))\n self.models = []\n ### YOUR CODE HERE 5-10 lines\n for classs in range(len(self.classes)):\n model = LogisticClassifierTwoClass()\n z = np.array([1 if label == classs else 0 for label in y])\n model.train(X, z, reg=reg, lr=lr, epochs=epochs, batch_size=batch_size)\n self.models.append(model)\n ### END CODE\n assert len(self.models) == len(self.classes)","def _train(self, log_prob):\n raise NotImplementedError","def logistic_predict(self, x: np.array) -> np.array:\r\n if self.LogisticModel is None:\r\n print('Logistic Model not trained, please run logistic_fit first!')\r\n return None\r\n else:\r\n return self.LogisticModel.predict(x)","def train_classifier(X, y, Cs=10):\n cls = LogisticRegressionCV(Cs=Cs, random_state=0, solver='lbfgs', max_iter=10000)\n cls.fit(X, y)\n return cls","def fit(self, x, y):\n # Note Logistic Regression Runtime\n start_time = time.time()\n\n # Converting Pandas DataFrame to Numpy arrays\n if not type(x).__module__ == np.__name__:\n x = x.to_numpy()\n if not type(y).__module__ == np.__name__:\n y = y.to_numpy()\n\n # Insert a column of 1 in the feature vector X for the bias term in the weights\n x = np.insert(x,0,1,axis=1)\n \n # Verify dimension of input\n if len(x) != len(y):\n print(\"The number of input features vector must be to be the same as the number of target variables\")\n else:\n losses = self.gradient_descent(x,y)\n\n # Note end time\n end_time = time.time()\n\n # Log runtime\n print(\"Logistic Regression training time: {0:.2f}s\".format(end_time - start_time))\n \n return losses","def test_train_model_attributes_good():\n\tdf = pd.read_csv(\"test/sample_features.csv\")\n\n\ty_train = df['price']\n\tX_train = df.loc[:, df.columns != 'price']\n\n\tparams = {'n_estimators': 5, 'random_state': 2}\n\trf_test = train(X_train, y_train, params)\n\n\t# test model attributes\n\tassert rf_test.get_params()['n_estimators'] == 5\n\tassert rf_test.get_params()['random_state'] == 2","def logistic_regression_model_by_features(xTrain, yTrain, features, iter_step, resolution, initial_w0, step, max_iters):\r\n\r\n model = lgm.LogisticRegressionModel(initial_w0=initial_w0,\r\n initial_weights=[0.0] * len(features))\r\n\r\n # Extend xTrains and xTest with 1 at [0]\r\n xTrain = [[1] + x for x in xTrain]\r\n\r\n for i, iters in enumerate([iter_step] * resolution):\r\n fit_tic = time.time()\r\n model.fit(xTrain, yTrain, iterations=iters, step=step)\r\n fit_toc = time.time() - fit_tic\r\n iter_cnt = iter_step * (i + 1)\r\n print(\"Took {} sec. Fitted data for {} iterations\".format(fit_toc, iter_cnt))\r\n\r\n return model","def test_training(self):\n warnings.filterwarnings('ignore')\n example_args = example_args_parser()\n example_args.unittest = True\n # prepare data\n example_args.stage = 'prepare'\n example_wrapper(example_args)\n # train goalDNN model\n example_args.stage = 'train'\n example_args.model = 'goalDNN'\n example_wrapper(example_args)\n # train cVAE model\n example_args.model = 'cVAE'\n example_wrapper(example_args)\n # train gcVAE model\n example_args.model = 'gcVAE'\n example_wrapper(example_args)\n # cVAE harmonization\n example_args.stage = 'predict'\n example_args.model = 'cVAE'\n example_wrapper(example_args)\n # gcVAE harmonization\n example_args.model = 'gcVAE'\n example_wrapper(example_args)\n # goalDNN prediction\n example_args.model = 'goalDNN'\n example_wrapper(example_args)\n # XGBoost\n example_args.stage = 'train'\n example_args.model = 'XGBoost'\n example_wrapper(example_args)\n # compare with reference results\n check_args = check_results_args_parser()\n check_args.unittest = True\n check_reference_results(check_args)","def test_Bernoulli_NB_estimators():","def test(self, X, y):\n\t\tself.test_X = X\n\t\tself.test_y = y\n\n\t\tclassifier = self.classifier.fit(self.X, self.y)\n\t\ty_pred = classifier.predict(X) \t\t\t# class prediction\n\t\ty_prob = classifier.predict_proba(X)\t# probability of each class\n\t\tself.test_metrics = ModelMetrics(classifier, y, y_pred, y_prob, 'holdout')"],"string":"[\n \"def test_logistic_regression_c_parameter(params, X_train, X_test, y_train, y_test):\",\n \"def test_train_logist(x_train_variable, y_train_dep):\\n # Ensure the function works\\n try:\\n lrc = cls.train_logistic(x_train_variable, y_train_dep)\\n logging.info(\\\"Successful Logistic Model\\\")\\n except Exception as err:\\n logging.error(\\\"Errors in Fitting the Logistic Regression\\\")\\n raise err\\n return lrc\",\n \"def train_logistic_regression(train_x, train_y):\\n\\n logistic_regression_model = LogisticRegression(penalty='l2', C=1.0)\\n logistic_regression_model.fit(train_x, train_y)\\n return logistic_regression_model\",\n \"def LogisticRegression_sklearn(X_train, X_test, y_train, y_test):\\n\\n\\tlog_reg = LogisticRegression()\\n\\tlog_reg.fit(X_train, y_train.ravel())\\n\\tyPred =log_reg.predict(X_test)\\n\\n\\t#Printing metrics of the logistic regression model\\n\\tprint('Accuracy:', metrics.accuracy_score(y_test, yPred))\\n\\tprint('Precision:', metrics.precision_score(y_test, yPred))\\n\\tprint('Recall', metrics.recall_score(y_test, yPred))\\n\\n\\t#confusion matrix\\n\\n\\tconfusionMatrix = matrix.confusion_matrix(y_test, yPred)\\n\\tsb.heatmap(pd.DataFrame(confusionMatrix), annot= True, fmt='g')\\n\\tplt.title('Confustion matrix with default value 1')\\n\\tplt.ylabel('True values')\\n\\tplt.xlabel('Predicted values')\\n\\tplt.show()\",\n \"def test_class_logistic(\\n y_train_dep,\\n y_test_dep,\\n y_train_preds_lr,\\n y_test_preds_lr):\\n # Ensure the function works\\n try:\\n cls.classification_logistic_results(\\n y_train_dep, y_test_dep, y_train_preds_lr, y_test_preds_lr\\n )\\n logging.info(\\n \\\"Successfully Plotting Classification Results using logistic regression\\\"\\n )\\n except Exception as err:\\n logging.error(\\\"Errors in plotting logistic classification results\\\")\\n raise err\\n # Ensure the output exists\\n try:\\n assert os.path.isfile(\\\"images/results/assessment_logistic.png\\\")\\n except AssertionError as err:\\n logging.error(\\\"Errors in plotting logistic classification file\\\")\\n raise err\",\n \"def main():\\r\\n x = [\\r\\n [ 1,1 ], [ 0,0 ], [ 1,0 ], [ 0,0 ], [ 0,0 ], [ 0,0 ],\\r\\n [ 0,0 ], [ 0,0 ], [ 1,1 ], [ 0,0 ], [ 0,0 ], [ 1,0 ],\\r\\n [ 1,0 ], [ 0,0 ], [ 1,1 ], [ 0,0 ], [ 1,0 ], [ 0,0 ]\\r\\n ]\\r\\n\\r\\n # Encoding of the correct classes for the training material\\r\\n y = [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0]\\r\\n b = BinaryLogisticRegression(x, y)\\r\\n b.fit()\\r\\n b.print_result()\",\n \"def test_logistic_regression(x, y, tune):\\n # Perform classification without tuning\\n lrc = LogisticRegression()\\n pipeline = create_pipeline(lrc)\\n return accuracy(pipeline, x, y)\",\n \"def __init__(self, reg_penalty='l2', reg_inv=1.0, k_fold=5, random_state=0):\\n print(\\\"Initialize model Logistic Regression\\\")\\n self.reg_penalty = reg_penalty\\n self.reg_inv = reg_inv\\n self.k_fold = k_fold\\n self.random_state = random_state\\n self.model = sklearn.linear_model.LogisticRegression(penalty=self.reg_penalty,\\n C=self.reg_inv,\\n max_iter=1000, \\n random_state=self.random_state)\",\n \"def run_logistic_regression(training, testing, feature_cols, outcome_col):\\n if 'intercept' not in training.columns:\\n training['intercept'] = 1\\n if 'intercept' not in testing.columns:\\n testing['intercept'] = 1\\n intercept_feature_cols = feature_cols + ['intercept']\\n logit = sm.Logit(training[outcome_col], training[intercept_feature_cols])\\n fitted_logit_model = logit.fit()\\n logit_diagnostics = get_diagnostics(testing[outcome_col], testing[intercept_feature_cols], fitted_logit_model, model_type = 'logit')\\n predicted_logit_probs = fitted_logit_model.predict(testing[intercept_feature_cols])\\n\\n return fitted_logit_model, logit_diagnostics, predicted_logit_probs\",\n \"def train_logistic_regression(X_train_input, y_train_input, C=1):\\r\\n from sklearn.linear_model import LogisticRegression\\r\\n logr_clf = LogisticRegression(C=C)\\r\\n logr_clf.fit(X_train_input, y_train_input)\\r\\n return logr_clf\",\n \"def run_logistic(X_train, X_test, y_train, y_test, C=1, penalty = 'l2', solver = 'lbfgs'):\\n \\n logreg = LogisticRegression(fit_intercept=True, C=C, penalty = penalty, solver = solver)\\n logreg.fit(X_train, y_train)\\n get_scores(logreg, X_train, X_test, y_train, y_test)\",\n \"def log_reg(x_train, y_train):\\n\\n log_reg_classifier = LogisticRegression(max_iter=1000, solver='lbfgs')\\n log_reg_classifier.fit(x_train, y_train)\\n return log_reg_classifier\\n\\n # log_reg_classifier.fit(x_train, y_train)\",\n \"def trainRegressionModel(X,y):\\n # # instantiate a logistic regression model, and fit with X and y\\n # model = LogisticRegression()\\n # model = model.fit(X, y)\\n # # check the accuracy on the training set\\n # print(model.score(X, y))\\n #X['intercept'] = 1.0\\n #del X['isCapitalized']\\n #del X['isNN']\\n #del X['isNNP']\\n #del X['isJJ']\\n #del X['isUpper']\\n #del X['isPrecedingIN']\\n logit = sm.Logit(y, X)\\n result = logit.fit()\\n print(result.summary())\\n print(result.conf_int())\\n model = LogisticRegression()\\n model = model.fit(X, y)\\n print(model.score(X, y))\\n print(y.mean())\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\\n model2 = LogisticRegression()\\n model2.fit(X_train, y_train)\\n # predict class labels for the test set\\n predicted = model.predict(X_test)\\n print(predicted)\\n for i in predicted:\\n if i==1:\\n print(\\\"Test:\\\"+str(i))\\n print(max(predicted))\\n #generate class probabilities\\n probs = model2.predict_proba(X_test)\\n print(probs)\\n # generate evaluation metrics\\n print(\\\"Accuracy: \\\"+str(metrics.accuracy_score(y_test, predicted)))\\n print(\\\"AUC: \\\"+str(metrics.roc_auc_score(y_test, probs[:, 1])))\\n print(metrics.confusion_matrix(y_test, predicted))\\n print(metrics.classification_report(y_test, predicted))\\n\\n from sklearn.cross_validation import cross_val_score\\n # evaluate the model using 10-fold cross-validation\\n scores = cross_val_score(LogisticRegression(), X, y, scoring='accuracy', cv=10)\\n print(scores)\\n print(scores.mean())\",\n \"def create_logistic_regression():\\n\\n pause_data = shuffle(pd.read_csv(sys.argv[1]))\\n pause_data = pause_data.replace([np.inf, -np.inf], np.nan).dropna()\\n # X = pause_data.drop([HAS_DEMENTIA, TRANSCRIPT_ID], axis=1)\\n X = pause_data[MEMORY_FEATURES]\\n y = pause_data[HAS_DEMENTIA]\\n split_tracker = []\\n rskf = RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=36851234)\\n # n_repeats 10 too\\n for train_index, test_index in rskf.split(X, y):\\n X_train, X_test = X.iloc[list(train_index)], X.iloc[list(test_index)]\\n y_train, y_test = y.iloc[list(train_index)], y.iloc[list(test_index)]\\n logmodel = LogisticRegression()\\n logmodel.fit(X_train, y_train)\\n predictions = logmodel.predict(X_test)\\n split_tracker.append({\\n TRAIN: train_index,\\n TEST: test_index,\\n PREDICTIONS: predictions,\\n Y_TEST: y_test\\n })\\n accuracy = []\\n f1 = []\\n auc = []\\n print(\\\"Predictions\\\", split_tracker[0])\\n for predictions in split_tracker:\\n # print(classification_report(predictions[Y_TEST], predictions[PREDICTIONS]))\\n accuracy.append(accuracy_score(predictions[Y_TEST], predictions[PREDICTIONS]))\\n f1.append(f1_score(predictions[Y_TEST], predictions[PREDICTIONS]))\\n auc.append(roc_auc_score(predictions[Y_TEST], predictions[PREDICTIONS]))\\n print(accuracy)\\n accuracy = np.array(accuracy)\\n f1 = np.array(f1)\\n auc = np.array(auc)\\n print(len(accuracy))\\n print('mean accuracy: ', accuracy.mean())\\n print('mean f1 score: ', f1.mean())\\n print('mean auc: ', auc.mean())\",\n \"def create_regression(f_train, f_test, l_train, l_test):\\n model = LogisticRegression()\\n\\n # train it on training data\\n model.fit(f_train, l_train)\\n\\n # gather the model's predictions for train\\n train_predictions = model.predict(f_train)\\n\\n # gather the model's predictions for test\\n test_predictions = model.predict(f_test)\\n\\n # calculate accuaracy of train\\n print('Reg Train Accuracy: ', accuracy_score(l_train, train_predictions))\\n\\n # calculate accuracy of test\\n print('Reg Test Accuracy: ', accuracy_score(l_test, test_predictions))\\n\\n return model\",\n \"def fit_logistic_regression():\\n\\n logger.debug(\\\"Running the fit_logistic_regression function now\\\")\\n\\n #Loading the configuration\\n with open(os.path.join(\\\"config\\\",\\\"config.yml\\\"), \\\"r\\\") as f:\\n config = yaml.safe_load(f)\\n\\n #Loading and pre processing the data\\n logger.debug(\\\"Loading and pre processing the data\\\")\\n train_df = load_data(config[\\\"load_data\\\"][\\\"train_file\\\"])\\n train_df = pre_process_data(train_df, resample = True, resample_count = 500000)\\n\\n #Defining Pipeline\\n pipeline = Pipeline([\\n ('tfidf', TfidfVectorizer(analyzer='word', token_pattern=r'[A-Za-z0-9@-]+')),\\n ('model', LogisticRegression(random_state=12345, verbose = 1, solver = 'saga')),\\n ])\\n\\n #Defining parameters to vary\\n parameters = {\\n 'tfidf__max_df': (0.25, 0.5, 0.75),\\n 'tfidf__max_features': (None, 5000, 10000, 50000),\\n 'tfidf__ngram_range': ((1, 1), (1, 2)),\\n 'model__C': (0.01, 1, 100)\\n }\\n\\n scoring_list = [\\\"accuracy\\\", \\\"f1\\\", \\\"precision\\\", \\\"recall\\\", \\\"roc_auc\\\"]\\n \\n #Performing 5fold CV to determine best hyperparameters\\n model = GridSearchCV(pipeline, parameters, cv=5,\\n n_jobs=-1, verbose=1, scoring=scoring_list, refit='f1',)\\n\\n t0 = datetime.datetime.now()\\n\\n model.fit(train_df[\\\"Review\\\"].tolist(), train_df[\\\"Ratings\\\"].to_numpy())\\n \\n logger.info(\\\"Grid Search performed in {}\\\".format(str(datetime.datetime.now()-t0)))\\n\\n #Saving results\\n res_df = pd.DataFrame(model.cv_results_)\\n res_df.to_csv(os.path.join(config[\\\"summary_stats\\\"][\\\"save_location\\\"], \\\"LogisticRegressionResults.csv\\\"))\\n \\n #Saving the model\\n pickle.dump(model, open(os.path.join(config[\\\"models\\\"][\\\"save_location\\\"], \\\"LogisticRegression.pkl\\\"),'wb'))\\n\\n return\",\n \"def __init__(self):\\n super().__init__()\\n import sklearn\\n import sklearn.linear_model\\n self.model = sklearn.linear_model.LogisticRegression\",\n \"def train_logisticRegression(data: np.array, labels: np.array)->None:\\n\\n n_examples = np.size(data, 0)\\n n_features = np.size(data, 1)\\n n_categories = np.size(labels, 1)\\n\\n data = np.hstack((np.ones((n_examples, 1)), data))\\n\\n print(data[0:5, :])\\n\\n X_train, X_test, y_train, y_test, idx_test = split_data(data, labels, 0.7)\\n\\n convergence_goal = 1e-3\\n learning_rate = 0.01\\n\\n theta = np.random.uniform(size=((n_features+1, n_categories)))\\n\\n for i in range(n_categories):\\n\\n cost_var = 1\\n\\n previous_cost = 1e6\\n iterations = 0\\n cost_to_plot = []\\n\\n while cost_var > convergence_goal:\\n iterations += 1\\n cost, grad = costFunction(X_train, y_train[:, i], theta[:, i])\\n theta[:, i] = update_theta(theta[:, i], grad, learning_rate)\\n cost_var = previous_cost - cost\\n previous_cost = cost\\n if iterations == 1: cost_var = 1\\n cost_to_plot.append(cost)\\n # print(cost)\\n\\n plt.plot(range(iterations), cost_to_plot, 'g-', label = 'cost')\\n plt.xlabel('iterations')\\n plt.ylabel('cost')\\n # plt.show()\\n\\n predictions = lrPredict(theta, X_test)\\n\\n print(predictions[0:5, :])\\n print(y_test[0:5, :])\\n\\n accuracy = np.mean([p == l for p, l in zip(predictions, y_test)])\\n print(\\\"Accuracy = {}\\\".format(accuracy))\\n\\n pass\",\n \"def train_model(self):\\r\\n alpha, accuracy_rate = self.select_model()\\r\\n # Initialize logistic regression with alpha(learning rate)\\r\\n lg = logisticregression(C=alpha)\\r\\n # Train the model.\\r\\n lg.fit(self.training_data, self.target_data)\\r\\n # Save the trained model as .pkl file.\\r\\n joblib.dump(value=lg, filename=self.intention_id+'.pkl', compress=1)\\r\\n print \\\"Estimated Parameters of Logistic Regression\\\"\\r\\n # Estimated parameters of logistic regression.\\r\\n print lg.get_params()\",\n \"def test_machine_learning():\",\n \"def __init__(self):\\n self.label = \\\"Logistic regression\\\"\\n self.description = \\\"This tool is a useful complement to Weights-of-Evidence Calculate Response tool as Logistic Regression does not make the assumption of conditional independence of the evidence with regards to the training sites. Using the evidence and assocaited weights tables, this tool creates the outputs the response and standard deviation rasters. The calculations are based on the Gen_Class attribute in the weights table and the type of evidence. Please note that the Logistic Regression tool accepts a maximum of 6,000 unique conditions or it fails. Also note that there is an upper limit of 100,000 unit cells per class in each evidence raster layer. If a class in an evidence raster goes above this, the script contains a function to increase the unit cell size to ensure an upper limit of 100,000. These issues are unable to be fixed due to a hard coded limitation in the Logistic Regression executable sdmlr.exe.\\\"\\n self.canRunInBackground = False\\n self.category = \\\"Weights of Evidence\\\"\",\n \"def logistic(weights, data, targets, hyperparameters):\\n\\n # TODO: Finish this function\\n\\n return f, df, y\",\n \"def fit_and_predict_LR(X_train, Y_train, X_test):\\n\\n # Import the package\\n from sklearn.linear_model import LogisticRegression\\n\\n #referenced to sklearn documentation \\n \\n # fit the model... \\n clf = LogisticRegression().fit(X_train, Y_train) \\n # make predictions \\n predicted_LR = clf.predict(X_test)\\n return predicted_LR\\n ### END SOLUTION ### \",\n \"def test_predictor():\",\n \"def train_logistic_regression(train_exs: List[SentimentExample], feat_extractor: FeatureExtractor) -> LogisticRegressionClassifier:\\n lr = LogisticRegressionClassifier(feat_extractor.corpus_length, feat_extractor)\\n alpha = 1e0\\n # beta = 1e-4\\n for epoch in range(8):\\n loss = 0.\\n acc = 0\\n indices = np.arange(len(train_exs))\\n np.random.shuffle(indices)\\n for i in indices:\\n feat = feat_extractor.feats[i]\\n sentimentExample = train_exs[i]\\n y = sentimentExample.label\\n z = 1 / (1 + np.exp(-feat.dot(np.expand_dims(lr.w, axis=1))))[0, 0]\\n loss += -y * np.log(z) - (1 - y) * np.log(1 - z) \\\\\\n # + beta * np.expand_dims(lr.w, axis=0).dot(np.expand_dims(lr.w, axis=1))[0, 0]\\n predict = int(feat.dot(np.expand_dims(lr.w, axis=1))[0, 0] > 0)\\n acc += (predict == y)\\n grad = (z - y) * feat.toarray()[0] # + 2 * beta * lr.w\\n lr.w = lr.w - alpha * grad\\n print(\\\"epoch {:d}, loss: {:f}, accuracy: {:f}\\\".format(epoch, loss / len(train_exs), acc / len(train_exs)))\\n\\n for i in indices:\\n feat = feat_extractor.feats[i]\\n sentimentExample = train_exs[i]\\n y = sentimentExample.label\\n z = 1 / (1 + np.exp(-feat.dot(np.expand_dims(lr.w, axis=1))))[0, 0]\\n loss += -y * np.log(z) - (1 - y) * np.log(1 - z)\\n print(\\\"training loss: {:f}\\\".format(loss / len(train_exs)))\\n\\n return lr\",\n \"def __init__(self, seed = None):\\n self.data_dir = pkg_resources.resource_filename('logistic_control_variate', 'data/')\\n self.generate_data(seed)\\n # Holds logistic regression object for this example\\n self.lr = None\",\n \"def train(references, corpus):\\n sim_vectors, classes = mod.model(references, corpus, labeled=True)\\n pred = lm.LogisticRegression()\\n pred.fit(sum(sim_vectors, []), classes)\\n return pred\",\n \"def test_logistic():\\n r=np.random.normal(size=20)\\n assert np.isclose( ilogistic(logistic(r)),r ).all()\",\n \"def run_logistic_regression(file_path):\\n\\n df_train = pd.read_csv(f'{file_path}/without_anom.csv')\\n features_list = ['Direction', 'Speed']\\n df_train = df_train[features_list]\\n\\n scalar = MaxAbsScaler()\\n\\n X_train = scalar.fit_transform(df_train)\\n\\n logistic_model = LogisticRegression()\\n\\n # multi_model = MultiOutputRegressor(LogisticRegression())\\n #\\n # multi_model.fit(X_train, X_train)\\n # multi_predict = multi_model.predict(X_train)\\n\\n logistic_model.fit(X_train, X_train)\\n predict = logistic_model.predict(X_train)\",\n \"def __init__(self, train, validation=None, initial_weight=None,\\n loss_function_name='logistic',\\n calculate_weight='gradient',\\n regularizer=None, regularizer_p=None):\\n # Initialize the super class with given data.\\n # Transform the y into {0,1}\\n y, tx = train\\n y[np.where(y < 0)] = 0\\n train = (y, tx)\\n if validation:\\n val_y, val_tx = validation\\n val_y[np.where(val_y < 0)] = 0\\n validation = (val_y, val_tx)\\n super(LogisticRegression, self).__init__(train, validation,\\n initial_weight=initial_weight,\\n loss_function_name=loss_function_name,\\n cal_weight=calculate_weight,\\n regularizer=regularizer,\\n regularizer_p=regularizer_p)\\n # Set predicted label\\n self.pred_label = [-1, 1]\",\n \"def train(self):\\n feature = Feature(trained=False)\\n classifier = LogisticRegression(\\n penalty='l2',\\n max_iter=100,\\n solver='liblinear',\\n random_state=self.RAND_SEED)\\n\\n true_labels = []\\n predicted_labels = []\\n\\n for subj in self.subjects:\\n print(subj)\\n # preprocess training and testing set\\n self.dataset_gen(subject=subj, valid=False)\\n\\n # train and predict\\n pipeline_steps = [('vectorized', feature.vector)]\\n if self.istfidf:\\n pipeline_steps.append(('tf-idf', feature.tfidftransform))\\n if self.islda == 'small':\\n pipeline_steps.append(('lda', feature.ldatransform_small))\\n elif self.islda == 'large':\\n pipeline_steps.append(('lda', feature.ldatransform_large))\\n else:\\n pass\\n if self.isnorm:\\n pipeline_steps.append(('scalar', StandardScaler(with_mean=False)))\\n pipeline_steps.append(('clf', classifier))\\n model = Pipeline(steps=pipeline_steps)\\n\\n model.fit(self.X_train, self.y_train)\\n\\n predicted = model.predict(self.X_test)\\n # hamming\\n predicted_labels.append(predicted)\\n true_labels.append(self.y_test)\\n\\n true_matrix, pred_matrix = np.array(true_labels, int).T, np.array(predicted_labels, int).T\\n true_matrix[true_matrix == -1] = 0\\n pred_matrix[pred_matrix == -1] = 0\\n\\n evaluation = Evaluation(self.subjects)\\n evaluation.model_evaluate(true_matrix=true_matrix, pred_matrix=pred_matrix, model_name=self.modelname)\",\n \"def build_logistic_regr():\\n logistic_pipeline = None\\n\\n logistic_pipeline = Pipeline([\\n ('vect', CountVectorizer()),\\n ('clf', LogisticRegression()), \\n ])\\n \\n return logistic_pipeline\",\n \"def _fit(self, _X, _y):\\n\\n self.model = linear_model.LogisticRegression(penalty=self.penalty, random_state=self.seed,\\n solver='saga', n_jobs=self.n_jobs)\\n self.model.fit(_X, _y)\",\n \"def test_training(self):\\n\\t\\tpass\",\n \"def __init__(self, estimator, **kwargs):\\n super(LogisticRegression, self).__init__(\\n estimator, **kwargs)\\n\\n self.estimator = estimator\",\n \"def __init__(self, **kwargs):\\n super(LogisticRegression, self).__init__()\\n self.C = kwargs.pop(\\\"C\\\", 100)\\n self.clf = _LogisticRegression(C=self.C, **kwargs)\",\n \"def testLogisticRegression_MatrixData(self):\\n cont_features = [\\n tf.contrib.layers.real_valued_column('feature', dimension=4)]\\n\\n classifier = tf.contrib.learn.DNNClassifier(\\n feature_columns=cont_features,\\n hidden_units=[3, 3],\\n config=tf.contrib.learn.RunConfig(tf_random_seed=1))\\n\\n classifier.fit(input_fn=_iris_input_logistic_fn, steps=100)\\n scores = classifier.evaluate(input_fn=_iris_input_logistic_fn, steps=1)\\n self.assertGreater(scores['accuracy'], 0.9)\\n self.assertLess(scores['loss'], 0.3)\",\n \"def __init__(self, estimator = LogisticRegression()): \\n\\t self.estimator = estimator\",\n \"def testLogisticRegression_TensorData(self):\\n language_column = tf.contrib.layers.sparse_column_with_hash_bucket(\\n 'language', hash_bucket_size=20)\\n feature_columns = [\\n tf.contrib.layers.embedding_column(language_column, dimension=1),\\n tf.contrib.layers.real_valued_column('age')\\n ]\\n\\n classifier = tf.contrib.learn.DNNClassifier(\\n n_classes=2,\\n feature_columns=feature_columns,\\n hidden_units=[3, 3],\\n config=tf.contrib.learn.RunConfig(tf_random_seed=1))\\n\\n classifier.fit(input_fn=_input_fn, steps=100)\\n\\n scores = classifier.evaluate(input_fn=_input_fn, steps=1)\\n self.assertGreater(scores['accuracy'], 0.9)\\n self.assertLess(scores['loss'], 0.3)\\n predict_input_fn = functools.partial(_input_fn, num_epochs=1)\\n predictions = list(\\n classifier.predict(input_fn=predict_input_fn, as_iterable=True))\\n self.assertListEqual(predictions, [1, 0, 0])\",\n \"def spark_LogisticRegression(*args, **kwargs): \\n return LogisticRegression(*args, **kwargs)\",\n \"def analysis(houses:pd.DataFrame) -> None:\\n \\n \\\"\\\"\\\"\\n #Me just trying to fit the data without any outside influences\\n f= f'SELLER_HOUSE ~ SQFT_PER + PRICE + C(LOCATION)' \\n result= smf.logit(formula= str(f), data= houses).fit()\\n print(result.summary2())\\n y= ['SELLER_HOUSE']\\n x= ['SQFT_PER', 'PRICE', 'LOC_699 - Not Defined', 'LOC_AA - Airport Area', 'LOC_CG - Columbus Grove',\\n 'LOC_CV - Cypress Village', 'LOC_EASTW - Eastwood', 'LOC_EC - El Camino Real', 'LOC_GP - Great Park',\\n 'LOC_IRSP - Irvine Spectrum', 'LOC_LGA - Laguna Altura', 'LOC_NK - Northpark', 'LOC_NW - Northwood', \\n 'LOC_OC - Oak Creek', 'LOC_OH - Orchard Hills', 'LOC_OT - Orangetree', 'LOC_PS - Portola Springs', \\n 'LOC_QH - Quail Hill', 'LOC_SH - Shady Canyon', 'LOC_SJ - Rancho San Joaquin', 'LOC_STG - Stonegate', \\n 'LOC_Stonegate', 'LOC_TR - Turtle Rock', 'LOC_TRG - Turtle Ridge', 'LOC_UP - University Park',\\n 'LOC_UT - University Town Center', 'LOC_WB - Woodbridge', 'LOC_WD - Woodbury', \\n 'LOC_WI - West Irvine', 'LOC_WN - Walnut (Irvine)', 'LOC_WP - Westpark']\\n x_train, x_test, y_train, y_test= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\\n logreg = LogisticRegression()\\n logreg.fit(x_train, y_train.values.ravel())\\n y_pred= logreg.predict(x_test)\\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test, y_test), 3))\\n # This model is really bad\\n \\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\n houses= houses.drop(['DAYS_ON_MARKET', 'ADDRESS', 'LOCATION',\\n 'STATUS', 'PROPERTY_TYPE', 'ZIP_CODE'], axis= 1)\\n columns= houses.columns.values.tolist()\\n y= ['SELLER_HOUSE']\\n x= [i for i in columns if i not in y]\\n \\n # Over Sampling Using SMOTE \\n x_train, _, y_train, _= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\\n x_columns= x_train.columns\\n \\n os= SMOTE(random_state= 0)\\n os_x, os_y= os.fit_sample(x_train, y_train)\\n os_x= pd.DataFrame(data= os_x, columns= x_columns)\\n os_y= pd.DataFrame(data= os_y, columns= y)\\n \\n \\n #Recursive Feature Elimination\\n logreg= LogisticRegression(max_iter= 600)\\n rfe= RFE(logreg, 20)\\n rfe= rfe.fit(os_x, os_y.values.ravel())\\n \\n lst= [i for count, i in enumerate(x) if rfe.support_[count] == True]\\n X= os_x[lst]\\n Y= os_y['SELLER_HOUSE']\\n \\n \\n #logit_model= sm.Logit(Y, X)\\n #result= logit_model.fit()\\n #print(result.summary2()) # Model choosen by RCE\\n \\n #These are features have a p-value less than 0.05\\n final_x= ['BATHS', 'ZIP_92602.0', 'ZIP_92618.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\\n #final_x= ['ZIP_92602.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\\n X2= os_x[final_x]\\n \\n logit_model2= sm.Logit(Y, X2)\\n result2= logit_model2.fit()\\n print(result2.summary2()) # Final Model\\n \\n x_train2, x_test2, y_train2, y_test2= train_test_split(X2, Y, test_size= 0.3, random_state= 500)\\n logreg = LogisticRegression()\\n logreg.fit(x_train2, y_train2)\\n \\n y_pred= logreg.predict(x_test2)\\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test2, y_test2), 2))\\n \\n conf_matrix= confusion_matrix(y_test2, y_pred)\\n print(conf_matrix)\\n # So 22+61 correct predictions and 13+44 wrong predictions\\n \\n logit_roc_auc = roc_auc_score(y_test2, logreg.predict(x_test2))\\n fpr, tpr, _ = roc_curve(y_test2, logreg.predict_proba(x_test2)[:,1])\\n plt.figure()\\n plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\\n plt.plot([0, 1], [0, 1],'r--')\\n plt.xlim([0.0, 1.0])\\n plt.ylim([0.0, 1.05])\\n plt.xlabel('False Positive Rate')\\n plt.ylabel('True Positive Rate')\\n plt.title('Receiver operating characteristic')\\n plt.legend(loc=\\\"lower right\\\")\\n plt.show()\\n \\\"\\\"\",\n \"def LogisticRegression_self_test(X_train, X_test, y_train, y_test, learning_rates, epochs, iteration):\\n\\n\\t# scoping number of training samples\\n\\n\\tn_inputs = X_train.shape[0]\\n\\tn_features = X_train.shape[1]\\n\\n\\t\\n\\n\\teta_ = 1e-12\\n\\tbeta_opt = np.random.randn(X_train.shape[1], 2)\\n\\tcalc_beta_GD, norm = GradientDescent(X_train, beta_opt, y_train, iteration, eta_)\\n\\tprob_GD, predict_GD= Probability_GD(X_test, calc_beta_GD) #defining values to be between 0 and 1\\n\\t#yPred_GD = (predict_GD >= 0.5).astype(int) # converting to just 0 or 1\\n\\n\\t#Define Logistic regression\\n\\tclf = LogisticRegression(solver='lbfgs', max_iter=1e5)\\n\\tclf = clf.fit(X_train, np.ravel(y_train))\\n\\tpred_sklearn = clf.predict(X_test)\\n\\tprob_sklearn = clf.predict_proba(X_test)\\n\\t#print(prob_sklearn)\\n\\n\\t#for eta in np.logspace(np.log10(1e-6), np.log10(1e0), 7):\\n\\taccuracy = np.zeros(len(learning_rates))\\n\\tauc_score = np.zeros(len(learning_rates))\\n\\n\\tfor i, eta in enumerate(learning_rates):\\n\\t\\tbeta_SGD = stochastic_gradient_descent(X_train, beta_opt, y_train, eta, epochs, iteration)\\n\\t\\tprob_SGD, predict_SGD= Probability(X_test, beta_SGD) #defining values to be between 0 and 1\\n\\t\\t\\n\\t\\t\\n\\t\\taccuracy[i] = metrics.accuracy_score(y_test, predict_SGD)\\n\\t\\tauc_score[i] = metrics.roc_auc_score(y_test, predict_SGD)\\n\\t\\tdifference = y_test - predict_SGD\\n\\n\\t\\t\\n\\n\\t\\tif i> 0 and auc_score[i] > auc_score[i-1]:\\n\\t\\t\\tbest_pred_SGD= predict_SGD\\n\\t\\t\\tbest_prob_SGD = prob_SGD\\n\\t\\n\\n\\t\\tprint('Accuracy {}, learning rate= {}, iterations = {}'.format(accuracy[i], eta, iteration))\\n\\t\\n\\t\\tprint('Auc score: {}'.format(auc_score[i]))\\n\\n\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tplt.plot(yPred, label='predict')\\n\\t\\tplt.plot(optimal_beta, label ='optimal beta')\\n\\t\\tplt.plot(y_test, label='test')\\n\\t\\tplt.show()\\n\\t\\t\\\"\\\"\\\"\\n\\n\\tsns.set()\\n\\tsns.heatmap(pd.DataFrame(accuracy), annot= True, fmt='.4g')\\n\\tplt.title('Grid-search for logistic regression')\\n\\tplt.ylabel('Learning rate: $\\\\\\\\eta$')\\n\\tplt.xlabel('Regularization Term: $\\\\\\\\lambda$')\\n\\t#plt.xticks(ticks=np.arange(len(learning_rates)) + 0.5, labels=learning_rates)\\n\\t#plt.yticks(ticks=np.arange(len(lambda_values)) + 0.5, labels=lambda_values)\\n\\tb, t = plt.ylim() # discover the values for bottom and top\\n\\tb += 0.5 # Add 0.5 to the bottom\\n\\tt -= 0.5 # Subtract 0.5 from the top\\n\\tplt.ylim(b, t) # update the ylim(bottom, top) values\\n\\t#plt.savefig('accuracy_logreg.png')\\n\\tplt.show()\\n\\n\\tsns.heatmap(pd.DataFrame(auc_score), annot= True, fmt='.4g')\\n\\tplt.title('Grid-search for logistic regression')\\n\\tplt.ylabel('Learning rate: $\\\\\\\\eta$')\\n\\tplt.xlabel('Regularization Term: $\\\\\\\\lambda$')\\n\\t#plt.xticks(ticks=np.arange(len(learning_rates)) + 0.5, labels=learning_rates)\\n\\t#plt.yticks(ticks=np.arange(len(lambda_values)) + 0.5, labels=lambda_values)\\n\\tb, t = plt.ylim() # discover the values for bottom and top\\n\\tb += 0.5 # Add 0.5 to the bottom\\n\\tt -= 0.5 # Subtract 0.5 from the top\\n\\tplt.ylim(b, t) # update the ylim(bottom, top) values\\n\\t#plt.savefig('auc_score_logreg.png')\\n\\tplt.show()\\n\\n\\t#plot confusion matrix\\n\\tConfusion_Matrix(y_test, predict_GD)\\n\\t#Confusion_Matrix(y_test, best_pred_SGD)\\n\\t#Confusion_Matrix(y_test, pred_sklearn)\\n\\n\\t#diff = np.concatenate((1- predict, predict), axis=1)\\n\\n\\tdiff_sklearn = np.concatenate((1- prob_sklearn, prob_sklearn), axis=1)\\n\\tdiff_GD = np.concatenate((1- prob_GD, prob_GD), axis=1)\\n\\tdiff_SGD = np.concatenate((1- best_prob_SGD, best_prob_SGD), axis=1)\\n\\n\\t#plot roc curves\\n\\tplot_roc(y_test, prob_sklearn)\\n\\tplot_roc(y_test, diff_SGD)\\n\\tplot_roc(y_test, prob_GD)\\n\\tplt.show()\\n\\n\\t#plot cumulative gain curves\\n\\tplot_cumulative_gain(y_test, prob_sklearn)\\n\\tax = plot_cumulative_gain(y_test, diff_SGD)\\n\\tplot_cumulative_gain(y_test, prob_GD)\\n\\t#plt.show()\\n\\n\\n\\n\\t\\\"\\\"\\\"\\n\\t#plot roc curves\\n\\tplot_roc(y_test, diff_sklearn, plot_micro=False, plot_macro= False)\\n\\tplot_roc(y_test, diff_GD, plot_micro=False, plot_macro= False)\\n\\tplot_roc(y_test, diff_SGD, plot_micro=False, plot_macro= False)\\n\\tplt.show()\\n\\n\\t#plot cumulative gain curves\\n\\tplot_cumulative_gain(y_test, diff_sklearn)\\n\\tplot_cumulative_gain(y_test, diff_GD)\\n\\tplot_cumulative_gain(y_test, diff_SGD)\\n\\tplt.show()\\t\\n\\n\\t\\\"\\\"\\\"\\n\\n\\tmodel_curve = auc_score\\n\\tarea_baseline = 0.5\\n\\tarea_ratio = (model_curve - area_baseline)/(area_baseline)\\n\\tprint('Area Ratio:',area_ratio)\\n\\n\\n\\treturn accuracy, learning_rates\",\n \"def stability_logistic(x, y, **kwargs):\\n rlr = RandomizedLogisticRegression(n_jobs=kwargs.get('n_jobs', 4))\\n if 'param' in kwargs:\\n rlr.set_params(**kwargs['param'])\\n rlr.fit(x, y)\\n return rlr.get_support()\",\n \"def build_logistic_regr():\\n logistic_pipeline = None\\n ##### Write code here #######\\n logistic_pipeline = Pipeline([\\n ('vect', CountVectorizer()),\\n ('clf', LogisticRegression())\\n ])\\n ##### End of your work ######\\n return logistic_pipeline\",\n \"def test_01_train(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-train-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n if os.path.exists(log_file):\\n os.remove(log_file)\\n \\n ## update the log\\n country = 'india'\\n date_range = ('2017-11-29', '2019-05-24')\\n metric = {'rmse':0.5}\\n runtime = \\\"00:00:01\\\"\\n model_version = 0.1\\n model_version_note = \\\"test model\\\"\\n \\n update_train_log(country, date_range, metric, runtime,\\n model_version, model_version_note, test=True, prefix=LOG_PREFIX)\\n\\n self.assertTrue(os.path.exists(log_file))\",\n \"def _fit(self):\\n\\n\\t\\tclf = LogisticRegression()\\n\\t\\tclf.fit(inputs, labels)\\n\\n\\t\\treturn clf\",\n \"def testLogRegres(weights, dev_x, dev_y):\\n predict_y = []\\n numSamples, numFeatures = np.shape(dev_x)\\n for i in range(numSamples):\\n if sigmoid(dev_x[i, :] * weights) > 0.5:\\n label = 1\\n else:\\n label = 0\\n predict_y.append(label)\\n print('Congratulations, testing complete!')\\n return predict_y\",\n \"def test_02_train(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-train-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n \\n ## update the log\\n country = 'india'\\n date_range = ('2017-11-29', '2019-05-24')\\n metric = {'rmse':0.5}\\n runtime = \\\"00:00:01\\\"\\n model_version = 0.1\\n model_version_note = \\\"test model\\\"\\n \\n update_train_log(country, date_range, metric, runtime,\\n model_version, model_version_note ,test=True, prefix=LOG_PREFIX)\\n\\n df = pd.read_csv(log_file)\\n logged_metric = [literal_eval(i) for i in df['metric'].copy()][-1]\\n self.assertEqual(metric,logged_metric)\",\n \"def __init__(self, classific_method=\\\"LogisticRegression\\\"):\\n\\t\\tself.classific_method = classific_method\",\n \"def logistic(weights, data, targets, hyperparameters):\\n y = logistic_predict(weights, data)\\n\\n #####################################################################\\n # TODO: #\\n # Given weights and data, return the averaged loss over all data #\\n # points, gradient of parameters, and the probabilities given by #\\n # logistic regression. #\\n #####################################################################\\n f = None\\n df = None\\n\\n f = evaluate(targets, y)[0]\\n\\n N = len(data)\\n M = len(weights) - 1 \\n temp = np.ones([N, M + 1])\\n temp[: N, : M] = np.array(data)\\n\\n\\n df = np.zeros([M+1, 1])\\n\\n df[:, 0] = np.array([[np.mean([(y.flatten()[i] - targets.flatten()[i]) * temp[i][j] for i in range(0, N)]) for j in range(0, M + 1)],])\\n\\n # df = np.matrix([[np.mean([(y[i] - targets[i]) * temp[i][j] for i in range(0, N)]) for j in range(0, M + 1)],])\\n\\n #####################################################################\\n # END OF YOUR CODE #\\n #####################################################################\\n return f, df, y\",\n \"def logistic_predict(weights, data):\\n\\n # TODO: Finish this function\\n\\n return y\",\n \"def logistic_reg(training_data):\\r\\n \\r\\n \\\"\\\"\\\" Setting guesses for minimum and maximum values of regularization parameter then\\r\\n find the value of parameter that minimizes error on cross validation data. If\\r\\n local minimum is found the return this model. If not, extend minimum or maximum \\r\\n appropriately and repeat \\\"\\\"\\\"\\r\\n from sklearn.linear_model import LogisticRegression\\r\\n C_min = 1.0e-5\\r\\n C_max = 1.0e5\\r\\n regularization_flag = 1 # To set 1 until local minimum is found\\r\\n regularization_param = 0\\r\\n \\r\\n# while regularization_flag != 0:\\r\\n# regularization_param, regularization_flag = set_reg_param(training_data, cv_data, alpha_min, alpha_max)\\r\\n# if regularization_flag == -1:\\r\\n# \\\"\\\"\\\" The local minimum is at point less than alpha_min \\\"\\\"\\\"\\r\\n# alpha_min = alpha_min * 0.3\\r\\n# if regularization_flag == 1:\\r\\n# \\\"\\\"\\\" The local minimum is at point greater then alpha_max \\\"\\\"\\\"\\r\\n# alpha_max = alpha_max * 3\\r\\n \\r\\n lr = LogisticRegression (C=C_max, random_state=0)\\r\\n lr.fit(training_data.X, training_data.y)\\r\\n return lr, C_max\",\n \"def logreg(mode, vectorizer, training_dir, test_dir):\\n # 1. load the training dataset\\n logging.basicConfig(level=logging.INFO)\\n logging.info(\\\"loading training dataset\\\")\\n x_train, y_train_age, y_train_gender, y_train_occ, _ = \\\\\\n load_dataset(training_dir, mode, vectorizer)\\n\\n # 2. train models\\n logging.info(\\\"fitting model age\\\")\\n age_model = LogisticRegression(multi_class='multinomial', solver=\\\"newton-cg\\\")\\n age_model.fit(x_train, y_train_age)\\n logging.info(\\\"fitting model gender\\\")\\n gender_model = LogisticRegression(multi_class='multinomial', solver=\\\"newton-cg\\\")\\n gender_model.fit(x_train, y_train_gender)\\n logging.info(\\\"fitting model acc\\\")\\n occ_model = LogisticRegression(multi_class='multinomial', solver=\\\"newton-cg\\\")\\n occ_model.fit(x_train, y_train_occ)\\n\\n # 3. load the test dataset\\n logging.info(\\\"loading test dataset ...\\\")\\n x_test, y_test_age, y_test_gender, y_test_occ, cid = \\\\\\n load_dataset(test_dir, mode, vectorizer)\\n\\n # 4. Predict and Evaluate\\n logging.info(\\\"predicting\\\")\\n age_pred = age_model.predict(x_test)\\n gender_pred = gender_model.predict(x_test)\\n occ_pred = occ_model.predict(x_test)\\n output_labels = [{\\\"id\\\": i, \\\"birthyear\\\": int(a), \\\"gender\\\": inv_g_dict[g], \\\"occupation\\\": inv_o_dict[o]}\\n for i, a, g, o in zip(cid, age_pred, gender_pred, occ_pred)]\\n\\n open(\\\"labels.ndjson\\\", \\\"w\\\").writelines(\\n [json.dumps(x) + \\\"\\\\n\\\" for x in output_labels]\\n )\",\n \"def log_reg(x, y, s):\\n usx = np.array(x)\\n usy = np.array(y)\\n\\n # split data into train and validation set\\n x_train, x_test, y_train, y_test = train_test_split(usx, usy, test_size=s)\\n cls_log = LogisticRegression()\\n cls_log.fit(x_train, y_train)\\n y_predict = cls_log.predict(x_test)\\n\\n # select only the probabilities of being fraud\\n y_pred_prob = cls_log.predict_proba(x_test)[:, 1]\\n return y_predict, y_test, y_pred_prob\",\n \"def set_initial_params(model: LogisticRegression):\\n n_classes = 15 # threat types\\n n_features = 33 # Number of features in dataset\\n model.classes_ = np.array([i for i in range(15)])\\n\\n model.coef_ = np.zeros((n_classes, n_features))\\n if model.fit_intercept:\\n model.intercept_ = np.zeros((n_classes,))\",\n \"def run_regression(train_embeds, train_labels, test_embeds, test_labels):\\n np.random.seed(1)\\n from sklearn.linear_model import SGDClassifier\\n from sklearn.dummy import DummyClassifier\\n from sklearn.metrics import f1_score\\n dummy = DummyClassifier()\\n dummy.fit(train_embeds, train_labels)\\n log = SGDClassifier(loss=\\\"log\\\", n_jobs=10, tol=1e-3)\\n log.fit(train_embeds, train_labels)\\n print(\\\"F1 score:\\\", f1_score(test_labels, log.predict(test_embeds), average=\\\"micro\\\"))\\n print(\\\"Random baseline f1 score:\\\", f1_score(test_labels, dummy.predict(test_embeds), average=\\\"micro\\\"))\",\n \"def test_intent_classifier_train(self):\\n pass\",\n \"def sklearn_model(train_data):\\n X, y = train_data\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\")\\n model = LogisticRegression(\\n multi_class=\\\"multinomial\\\", solver=\\\"lbfgs\\\", max_iter=1000\\n )\\n model.fit(X, y)\\n return model\",\n \"def train_logistic_regression(x_train, y_train, learning_rate, fit_intercept=False, max_iter=500):\\r\\n if fit_intercept:\\r\\n intercept = np.ones(x_train.shape[0], 1)\\r\\n x_train = np.hstack((intercept, x_train)) # hstacks merges 2 arrays column wise\\r\\n weights = np.zeros(x_train.shape[1])\\r\\n for iteration in range(max_iter):\\r\\n weights = update_weights(x_train, y_train, weights, learning_rate)\\r\\n # printing cost for every 100 iterations\\r\\n if iteration % 100 == 0:\\r\\n print(calculate_cost(x_train, y_train, weights))\\r\\n return weights\",\n \"def linear_regression(X, Y, Xs_test, Ys_test):\\n\\n ## YOUR CODE HERE\\n #################\\n return 0\",\n \"def main():\\n logging.basicConfig(filename=\\\"regression_model.log\\\", level=logging.INFO)\\n\\n # Define seed for random processes\\n SEED = 42\\n\\n # File path\\n filename = \\\"../data/movies_data.csv\\\"\\n\\n # Name of the title column\\n title = \\\"Titre\\\"\\n\\n # Name of the target variable\\n target = \\\"Rentabilité (%)\\\"\\n\\n # List of feature names\\n features = [\\n \\\"Popularité genre\\\",\\n \\\"Popularité thème\\\",\\n \\\"Rareté émotion\\\",\\n \\\"Référence\\\",\\n \\\"Budget (M$)\\\",\\n ]\\n from sklearn.linear_model import LinearRegression\\n\\n # Create a Linear Regression model\\n model = LinearRegression()\\n\\n # Create a RegressionModel object\\n regressor = RegressionModel(\\n model, features, target, title, feature_to_encode=\\\"Genre\\\"\\n )\\n\\n # Load the data\\n regressor.load_data(filename)\\n\\n # Preprocess the data\\n regressor.preprocess()\\n\\n # Train the model and calculate the mean MSE\\n metrics = regressor.train_and_evaluate(seed=SEED, test_size=0.1)\\n\\n logging.info(f\\\"Metrics: {metrics}\\\")\\n print(f\\\"Metrics: {metrics}\\\")\\n\\n # Make predictions and save them to a file\\n regressor.make_predictions(metrics=metrics)\",\n \"def train_logistic_regression(train_exs: List[SentimentExample],\\n feat_extractor: FeatureExtractor) -> LogisticRegressionClassifier:\\n indexer = feat_extractor.get_indexer()\\n weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\\n learning_rate = 0.1\\n for i in range(15):\\n for ex in train_exs:\\n features_of_str = feat_extractor.extract_features(ex.words, False)\\n expo = math.exp(np.dot(weights, features_of_str))\\n possibility = expo / (1 + expo)\\n gradient_of_w = np.dot(ex.label - possibility, features_of_str)\\n weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\\n return LogisticRegressionClassifier(weights, feat_extractor)\\n\\n # Methods for plotting average training loss\\n\\n # x = np.arange(0, 14)\\n # # learning_rate = 1\\n # indexer = feat_extractor.get_indexer()\\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\\n # avrg_losses = np.zeros(14)\\n # for i in range(15):\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\\n # weights = np.add(weights, gradient_of_w)\\n # loss = 0\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\\n # avrg_losses[i - 1] = loss / train_exs.__len__()\\n # plt.plot(x, avrg_losses)\\n #\\n # # learning_rate = 0.01\\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\\n # learning_rate = 0.01\\n # avrg_losses = np.zeros(14)\\n # for i in range(15):\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\\n # weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\\n # loss = 0\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\\n # avrg_losses[i - 1] = loss / train_exs.__len__()\\n # plt.plot(x, avrg_losses)\\n #\\n # # learning_rate = 0.1\\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\\n # learning_rate = 0.1\\n # avrg_losses = np.zeros(14)\\n # for i in range(15):\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\\n # weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\\n # loss = 0\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\\n # avrg_losses[i - 1] = loss / train_exs.__len__()\\n # plt.plot(x, avrg_losses)\\n # plt.xlabel('Epochs')\\n # plt.ylabel('Average Training Loss')\\n # plt.legend(['step size 1', 'step size 0.01', 'step size 0.1'], loc='upper left')\\n # plt.show()\\n # return LogisticRegressionClassifier(weights, feat_extractor)\",\n \"def TrainingAlgorithm():\\n urlFile = 'C:\\\\\\\\Users\\\\\\\\pokef\\\\\\\\Documents\\\\\\\\CybersecurityProg\\\\\\\\badandgoodurls.csv' # Replace this with data file path\\n urlCSV = pd.read_csv(urlFile, ',',error_bad_lines=False)\\n urlDF = pd.DataFrame(urlCSV)\\n \\n urlData = np.array(urlDF)\\n \\n labels = [d[1] for d in urlData]\\n corpus = [d[0] for d in urlData]\\n vectorizer = TfidfVectorizer(tokenizer=getTokens) # get a vector for each URL but use our tokenizer\\n X = vectorizer.fit_transform(corpus)\\n X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2, random_state=42)\\n \\n logisticReg = LogisticRegression()\\n logisticReg.fit(X_train, y_train)\\n print(logisticReg.score(X_test, y_test)) # print accuracy score\\n return vectorizer, logisticReg\",\n \"def model_performance_comparison(self, yvar, prev_group, C_FP, C_FN):\\n trn_bl_df = self.reweigh()\\n # sample_weights = self.reweigh(bl_df=trn_bl_df)\\n\\n s_weights = trn_bl_df.instance_weights\\n print(s_weights)\\n\\n trainset = trn_bl_df.convert_to_dataframe()[0]\\n testset = trainset\\n\\n X = trainset.loc[:, trainset.columns != yvar]\\n y = trainset[yvar]\\n #X_test = testset.loc[:, trainset.columns != yvar]\\n #y_test = testset[yvar]\\n\\n X_test = X\\n y_test = y\\n\\n clf_ww = sklearn.linear_model.LogisticRegression(random_state=999).fit(X, y, sample_weight=s_weights)\\n clf_wow = sklearn.linear_model.LogisticRegression(random_state=999).fit(X, y)\\n\\n output_probabilities_to_csv(model=clf_ww, x_test=X_test, path='probs_ww_withprvgroup.csv',priv_group_col=trainset[prev_group], actuals=y_test)\\n output_probabilities_to_csv(model=clf_wow, x_test=X_test, path='probs_wow_withprvgroup.csv', priv_group_col=trainset[prev_group], actuals=y_test)\\n\\n print(\\\"------------------------------------------\\\")\\n print(\\\"Accuracy of Vanila Logistic Model\\\")\\n print(\\\"------------------------------------------\\\")\\n print(\\\"Without Weights : \\\", round(clf_wow.score(X_test, y_test), 3))\\n print(\\\"With Weights : \\\", round(clf_ww.score(X_test, y_test), 3))\\n\\n X_test_age1 = testset.loc[:, trainset.columns != yvar][testset[prev_group] == 1.0]\\n y_test_age1 = testset[yvar][testset[prev_group] == 1.0]\\n X_test_age0 = testset.loc[:, trainset.columns != yvar][testset[prev_group] == 0.0]\\n y_test_age0 = testset[yvar][testset[prev_group] == 0.0]\\n\\n wow = round(abs(clf_wow.score(X_test_age0, y_test_age0) - clf_wow.score(X_test_age1, y_test_age1)), 3)\\n ww = round(abs(clf_ww.score(X_test_age0, y_test_age0) - clf_ww.score(X_test_age1, y_test_age1)), 3)\\n\\n #output_probabilities_to_csv(model=clf_ww, x_test=X_test_age0, path='probs_unpriv_ww.csv')\\n #output_probabilities_to_csv(model=clf_ww, x_test=X_test_age1, path='probs_priv_ww.csv')\\n #output_probabilities_to_csv(model=clf_wow, x_test=X_test_age0, path='probs_unpriv_wow.csv')\\n #output_probabilities_to_csv(model=clf_wow, x_test=X_test_age1, path='probs_priv_wow.csv')\\n\\n print(\\\"\\\")\\n print(\\\"\\\")\\n print(\\\"--------------------------------------------------------------\\\")\\n print(\\\"Difference in accuracy between privileged and unprivileged\\\")\\n print(\\\"--------------------------------------------------------------\\\")\\n print(\\\"without weights : \\\", wow)\\n print(\\\"with weights : \\\", ww)\\n\\n Ypredclf = clf_ww.predict(X_test)\\n Ypredclf2 = clf_wow.predict(X_test)\\n withw = confusion_matrix(y_test, Ypredclf)\\n without = confusion_matrix(y_test, Ypredclf2)\\n print(\\\"\\\")\\n print(\\\"\\\")\\n print(\\\"--------------------------------------------------------------\\\")\\n print(\\\"Confusion Matrix\\\")\\n print(\\\"--------------------------------------------------------------\\\")\\n print(\\\"without weights\\\")\\n print(without)\\n print(\\\"\\\")\\n print(\\\"\\\")\\n print(\\\"with weights\\\")\\n print(withw)\\n\\n a, b, c, d = without.ravel() #(tn, fp, fn, tp)\\n a1, b1, c1, d1 = withw.ravel() #(tn, fp, fn, tp)\\n\\n withweights = b1 * C_FP + c1 * C_FN\\n withoutweights = b * C_FP + c * C_FN\\n\\n print(\\\"\\\")\\n print(\\\"\\\")\\n print(\\\"cost with weights: \\\", withweights)\\n print(\\\"cost without weights: \\\", withoutweights)\\n print(\\\"Has cost decreased after reweighing?\\\", withweights < withoutweights)\\n\\n print('')\\n print('SUMMARY TABLE')\\n\\n cost = fr.CostingFairness(input_dataframe=self.data,\\n label_names=['credit'],\\n protected_attribute_names=['Age_previliged'],\\n trained_model=clf_ww)\\n\\n metrics_table = self.generate_pre_train_metrics_table(model_without_weights=clf_wow,\\n model_with_weights=clf_ww,\\n test_set=testset,\\n target=yvar,\\n privileged=prev_group,\\n false_positive_cost=C_FP,\\n false_negative_cost=C_FN)\\n priv_diff_table = generate_privileged_diff(metrics_table)\\n delta_table = generate_delta_table(metrics_table)\\n costs_table = cost.return_cost_fairness_accuracy_optimised()\\n\\n # pdf = PDF()\\n # pdf.add_page()\\n # pdf.write_table_to_pdf(metrics_table)\\n # pdf.write_table_to_pdf(priv_diff_table)\\n # pdf.write_table_to_pdf(delta_table)\\n # pdf.output('TEST01.pdf', 'F')\\n\\n print(\\\"\\\")\\n print(\\\"What we see is interesting, after re-weighing the bias of the model has decreased significantly by {}%, \\\"\\n \\\"with a very slight decrease in accuracy as shown earlier\\\".format(round((wow - ww) * 100)))\\n\\n return metrics_table, priv_diff_table, delta_table, costs_table\",\n \"def __init__(self, log=True, normalize=False):\\r\\n self.model = LinearRegression(normalize=normalize)\\r\\n self.log = log\",\n \"def fit_test(self):\",\n \"def do_stats_model(x, y):\\n Xx = sm.add_constant(x)\\n sm_logit = sm.Logit(y, Xx)\\n result = sm_logit.fit()\\n print result.summary()\\n result.pred_table()\\n # linear model\\n print \\\"linear regression model:\\\\n\\\"\\n sm_linear = sm.OLS(y, Xx)\\n result = sm_linear.fit()\\n print result.summary()\",\n \"def getInputSpecification(cls):\\n specs = super(LogisticRegression, cls).getInputSpecification()\\n specs.description = r\\\"\\\"\\\"The \\\\xmlNode{LogisticRegression} is\\n a logit, MaxEnt classifier.\\n In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme\\n if the ``multi_class'' option is set to ``ovr'', and uses the cross-entropy loss if the\\n ``multi_class'' option is set to ``multinomial''. (Currently the ``multinomial'' option\\n is supported only by the ``lbfgs'', ``sag'', ``saga'' and ``newton-cg'' solvers.)\\n This class implements regularized logistic regression using the ``liblinear'' library, ``newton-cg'',\\n ``sag'', ``saga'' and ``lbfgs'' solvers. Regularization is applied by default. It can handle both dense and sparse input.\\n The ``newton-cg'', ``sag'', and ``lbfgs'' solvers support only L2 regularization with primal formulation,\\n or no regularization. The ``liblinear'' solver supports both L1 and L2 regularization, with a dual formulation\\n only for the L2 penalty. The Elastic-Net regularization is only supported by the ``saga'' solver.\\n \\\\zNormalizationPerformed{LogisticRegression}\\n \\\"\\\"\\\"\\n specs.addSub(InputData.parameterInputFactory(\\\"penalty\\\", contentType=InputTypes.makeEnumType(\\\"penalty\\\", \\\"penaltyType\\\",['l1','l2', 'elasticnet', 'none']),\\n descr=r\\\"\\\"\\\"Used to specify the norm used in the penalization. The newton-cg, sag and lbfgs solvers\\n support only l2 penalties. elasticnet is only supported by the saga solver. If none (\\n not supported by the liblinear solver), no regularization is applied.\\\"\\\"\\\", default='l2'))\\n specs.addSub(InputData.parameterInputFactory(\\\"dual\\\", contentType=InputTypes.BoolType,\\n descr=r\\\"\\\"\\\"Select the algorithm to either solve the dual or primal optimization problem.\\n Prefer dual=False when $n_samples > n_features$.\\\"\\\"\\\", default=True))\\n specs.addSub(InputData.parameterInputFactory('C', contentType=InputTypes.FloatType,\\n descr=r\\\"\\\"\\\"Regularization parameter. The strength of the regularization is inversely\\n proportional to C.Must be strictly positive.\\\"\\\"\\\", default=1.0))\\n specs.addSub(InputData.parameterInputFactory(\\\"tol\\\", contentType=InputTypes.FloatType,\\n descr=r\\\"\\\"\\\"Tolerance for stopping criterion\\\"\\\"\\\", default=1e-4))\\n specs.addSub(InputData.parameterInputFactory(\\\"fit_intercept\\\", contentType=InputTypes.BoolType,\\n descr=r\\\"\\\"\\\"Whether to calculate the intercept for this model. Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function.\\\"\\\"\\\", default=True))\\n specs.addSub(InputData.parameterInputFactory(\\\"intercept_scaling\\\", contentType=InputTypes.FloatType,\\n descr=r\\\"\\\"\\\"When fit_intercept is True, instance vector x becomes $[x, intercept_scaling]$,\\n i.e. a “synthetic” feature with constant value equals to intercept_scaling is appended\\n to the instance vector. The intercept becomes $intercept_scaling * synthetic_feature_weight$\\n \\\\nb the synthetic feature weight is subject to $l1/l2$ regularization as all other features.\\n To lessen the effect of regularization on synthetic feature weight (and therefore on the intercept)\\n $intercept_scaling$ has to be increased.\\\"\\\"\\\", default=1.))\\n specs.addSub(InputData.parameterInputFactory(\\\"solver\\\", contentType=InputTypes.makeEnumType(\\\"solver\\\", \\\"solverType\\\",['newton-cg','lbfgs', 'liblinear','sag','saga']),\\n descr=r\\\"\\\"\\\"Algorithm to use in the optimization problem.\\n \\\\begin{itemize}\\n \\\\item For small datasets, ``liblinear'' is a good choice, whereas ``sag'' and ``saga'' are faster for large ones.\\n \\\\item For multiclass problems, only ``newton-cg'', ``sag'', ``saga'' and ``lbfgs'' handle multinomial loss; `\\n `liblinear'' is limited to one-versus-rest schemes.\\n \\\\item ``newton-cg'', ``lbfgs'', ``sag'' and ``saga'' handle L2 or no penalty\\n \\\\item ``liblinear'' and ``saga'' also handle L1 penalty\\n \\\\item ``saga'' also supports ``elasticnet'' penalty\\n \\\\item ``liblinear'' does not support setting penalty=``none''\\n \\\\end{itemize}\\\"\\\"\\\", default='lbfgs'))\\n specs.addSub(InputData.parameterInputFactory(\\\"max_iter\\\", contentType=InputTypes.IntegerType,\\n descr=r\\\"\\\"\\\"Hard limit on iterations within solver.``-1'' for no limit\\\"\\\"\\\", default=100))\\n specs.addSub(InputData.parameterInputFactory(\\\"multi_class\\\", contentType=InputTypes.makeEnumType(\\\"multiClass\\\", \\\"multiClassType\\\",['auto','ovr', 'multinomial']),\\n descr=r\\\"\\\"\\\"If the option chosen is ``ovr'', then a binary problem is fit for each label. For ``multinomial''\\n the loss minimised is the multinomial loss fit across the entire probability distribution, even when the\\n data is binary. ``multinomial' is unavailable when solver=``liblinear''. ``auto'' selects ``ovr'' if the data is\\n binary, or if solver=``liblinear'', and otherwise selects ``multinomial''.\\\"\\\"\\\", default='auto'))\\n specs.addSub(InputData.parameterInputFactory(\\\"l1_ratio\\\", contentType=InputTypes.FloatType,\\n descr=r\\\"\\\"\\\"The Elastic-Net mixing parameter, with $0 <= l1_ratio <= 1$. Only used if penalty=``elasticnet''.\\n Setting $l1_ratio=0$ is equivalent to using penalty=``l2'', while setting $l1_ratio=1$ is equivalent to using\\n $penalty=``l1''$. For $0 < l1_ratio <1$, the penalty is a combination of L1 and L2.\\\"\\\"\\\", default=0.5))\\n specs.addSub(InputData.parameterInputFactory(\\\"class_weight\\\", contentType=InputTypes.makeEnumType(\\\"classWeight\\\", \\\"classWeightType\\\",['balanced']),\\n descr=r\\\"\\\"\\\"If not given, all classes are supposed to have weight one.\\n The “balanced” mode uses the values of y to automatically adjust weights\\n inversely proportional to class frequencies in the input data\\\"\\\"\\\", default=None))\\n specs.addSub(InputData.parameterInputFactory(\\\"random_state\\\", contentType=InputTypes.IntegerType,\\n descr=r\\\"\\\"\\\"Used when solver == ‘sag’, ‘saga’ or ‘liblinear’ to shuffle the data.\\\"\\\"\\\",\\n default=None))\\n return specs\",\n \"def utest_SGD_Test():\\n model_fname = \\\"../work/model\\\"\\n # test binary classification.\\n if False:\\n #test_fname = \\\"../work/train.bz2\\\"\\n test_fname = \\\"../work/rcv1_test.binary.bz2\\\"\\n if True:\\n test_fname = \\\"../work/iris_multi.train\\\"\\n test_logreg(model_fname,test_fname,prob=True,acc=True)\\n pass\",\n \"def logistic_fit(self, penalty: str = 'l2', c: float = 1.0):\\r\\n self.LogisticModel = LogisticRegression(solver='liblinear', penalty=penalty, C=c).fit(self.x, self.y)\",\n \"def train(self, trainfile):\\r\\n sentences_emb,labels=self.read_data(trainfile)\\r\\n logReg = LogisticRegression(penalty=\\\"l2\\\",C = 10, multi_class='auto',solver='newton-cg')\\r\\n logReg.fit(sentences_emb,labels)\\r\\n self.clf=logReg\",\n \"def execute(self, parameters, messages):\\n try:\\n importlib.reload (arcsdm.logisticregression)\\n except :\\n reload(arcsdm.logisticregression);\\n arcsdm.logisticregression.Execute(self, parameters, messages)\\n return\\n \\n #execute_tool(arcsdm.logisticregression.Execute, self, parameters, messages)\",\n \"def test_1():\\n clf1 = mord.OrdinalRidge(alpha=0.)\\n clf1.fit(X, y)\\n\\n clf2 = mord.LogisticAT(alpha=0.)\\n clf2.fit(X, y)\\n\\n # the score is - absolute error, 0 is perfect\\n # assert clf1.score(X, y) < clf2.score(X, y)\\n\\n clf3 = mord.LogisticSE(alpha=0.)\\n clf3.fit(X, y)\\n pred3 = clf3.predict(X)\\n pred2 = clf2.predict(X)\\n\\n # check that it predicts better than the surrogate\\n # for other loss\\n assert np.abs(pred2 - y).mean() <= np.abs(pred3 - y).mean()\\n # # the score is - absolute error, 0 is perfect\\n # assert_almost_equal(clf.score(X, y), 0., places=2)\\n #\\n # clf = mord.LogisticIT(alpha=0.)\\n # clf.fit(X, y)\\n # # the score is classification error, 1 is perfect\\n # assert_almost_equal(clf.score(X, y), 1., places=2)\\n\\n # test on sparse matrices\\n X_sparse = sparse.csr_matrix(X)\\n clf4 = mord.LogisticAT(alpha=0.)\\n clf4.fit(X_sparse, y)\\n pred4 = clf4.predict(X_sparse)\\n assert metrics.mean_absolute_error(y, pred4) < 1.\",\n \"def test_xy(self):\\n x = np.array([[1,3], [2,8], [1,3]])\\n y = np.array([1,1,-1])\\n lro = LogisticRegressionOptimiser(x,y)\\n expected = np.array([[1,3], [2,8], [-1,-3]])\\n for i in 0,1,2:\\n for j in 0,1:\\n self.assertEqual(lro.xy[i][j], expected[i][j])\",\n \"def logistic(self, data, weights, biases):\\n\\n state_weight_prods = np.dot(data, weights)\\n print(-state_weight_prods - biases)\\n activations = 1.0 / (1 + np.exp(-state_weight_prods - biases))\\n plt.plot(state_weight_prods, activations)\\n plt.show()\\n return activations\",\n \"def train(self):\\n df = self.df\\n self.scaler = MinMaxScaler()\\n self.scaler.fit(df)\\n df[df.columns] = self.scaler.transform(df)\\n\\n\\n X_train, y_train = get_X_y(df, self.n_days, self.length , self.style)\\n X_train = np.array(X_train)\\n X_train.shape = (X_train.shape[0], X_train.shape[2])\\n\\n self.clf = LogisticRegression().fit(X_train, y_train)\\n\\n #es = EarlyStopping(monitor = 'accuracy',mode = 'min' , verbose = 1, patience = 100, restore_best_weights = True)\",\n \"def learn(self, Xtrain, ytrain):\",\n \"def test_train(self):\\n print \\\"x=\\\",self.trainer.train()\",\n \"def test_sklearn_log_loss(self):\\n X, y = datasets.load_breast_cancer(return_X_y=True)\\n\\n star = sb.BoostingClassifier(\\n loss=sb.losses.LogLoss(),\\n init_estimator=ScikitLearnLogOdds(),\\n base_estimator=tree.DecisionTreeRegressor(max_depth=3, random_state=42),\\n base_estimator_is_tree=True,\\n n_estimators=30,\\n learning_rate=0.1,\\n )\\n star = star.fit(X, y)\\n\\n scikit = ensemble.GradientBoostingClassifier(\\n loss='deviance',\\n max_depth=3,\\n n_estimators=30,\\n learning_rate=0.1,\\n random_state=42\\n )\\n scikit = scikit.fit(X, y)\\n\\n for y1, y2 in zip(star.iter_predict_proba(X), scikit.staged_predict_proba(X)):\\n np.testing.assert_allclose(y1, y2, rtol=1e-5)\",\n \"def on_train_end(self, logs={}):\\n LOSSES.append(self.losses)\\n print(self.j)\\n (x_test, y_test) = get_test_data(self.j)\\n y_pred = model.predict(x_test)\\n y_pred = y_pred.squeeze()\\n y_pred[y_pred < 0.5] = 0\\n y_pred[y_pred >= 0.5] = 1\\n print(y_pred)\\n\\n y_test = y_test.squeeze()\\n print(y_test)\\n confmat = confusion_matrix(y_test,y_pred)\\n print(confmat)\\n calc_TSS(confmat,2)\",\n \"def test_test_regression_model(self):\\n model = RegressionTestModel()\\n example = {'x_1': 3, 'x_2': 2}\\n pred = list(model.predict([example]))[0]\\n self.assertEqual(pred['score'], 20)\",\n \"def logistic(weights, data, targets, hyperparameters):\\n \\n t = np.transpose(np.repeat(np.reshape(weights[:-1], (len(weights)-1, 1)), len(data), axis = 1))\\n f_e = data * t\\n z_sums = np.sum(f_e, axis=1)\\n y = sigmoid(z_sums +weights[-1])\\n f = np.sum(np.log(1 + np.exp(-z_sums - weights[-1])) + (1 - np.transpose(targets)) * (z_sums + weights[-1]))\\n df = np.sum(data * np.transpose(((-np.exp(-z_sums - weights[-1]) / (1 + np.exp(-z_sums - weights[-1]))) + (1 - np.transpose(targets)))), axis = 0)\\n df = np.append(df, np.sum(np.transpose(((-np.exp(-z_sums - weights[-1]) / (1 + np.exp(-z_sums - weights[-1]))) + (1 - np.transpose(targets)))), axis = 0))\\n df = np.reshape(df, ((len(df), 1)))\\n\\n return f, df, np.reshape(y, (len(y), 1))\",\n \"def test_draw():\\n b_time = datetime.datetime.now()\\n print('Begining reading data')\\n DATA_TRAIN_PATH = get_filepath('train')\\n y, tX, ids = load_csv_data(DATA_TRAIN_PATH)\\n print(\\\"Finish loading in {s} seconds\\\".\\n format(s=(datetime.datetime.now() - b_time).total_seconds()))\\n\\n # data, _, _ = standardize(tX)\\n\\n # test_bias(y)\\n # nb_pc = 5\\n # print(\\\"test the PCA with {} elements\\\".format(nb_pc))\\n # pcs, pc_data = pca_transform(data, nb_pc, concatenate=False)\\n #\\n # print(\\\"get interactions\\\")\\n # interaction = interactions(data, range(0, 10))\\n # interaction, _, _ = standardize(interaction)\\n # print(\\\"select first 10 data entry with pc data\\\")\\n # data = np.c_[data[:, 0:10], pc_data]\\n # data = np.c_[data, interaction]\\n # # Begin the least square sgd\\n # e_time = datetime.datetime.now()\\n # print(\\\"Finish data reading in {s} seconds\\\".\\n # format(s=(e_time - b_time).total_seconds()))\\n data, _, _ = compose_complex_features_further(tX, intercept=True,\\n interaction=True,\\n log=True,\\n sqrt=True,\\n power=True,\\n pca=True)\\n train, valid = draw_balanced_subsample(y, data, trainsize=6000)\\n # t_data, _, _ = compose_complex_features_further(test_x, intercept=True,\\n # interaction=True,\\n # log=True,\\n # sqrt=True,\\n # power=True,\\n # pca=True)\\n\\n\\n logistic = LogisticRegression(train=train, validation=valid, regularizer='Lasso', regularizer_p=0.5)\\n result = logistic.train(lr=0.01, decay=0.5, early_stop=400, max_iters=2000)\\n print(result)\",\n \"def linear_regresstion_action(X_train=\\\"not defined\\\", X_test=\\\"not defined\\\", y_train=\\\"not defined\\\", y_test=\\\"not defined\\\",\\r\\n input_data=\\\"not defined\\\"):\\r\\n if \\\"not defined\\\" in [X_train, X_test, y_train, y_test]:\\r\\n X_train, X_test, y_train, y_test = splitting.splitting_data()\\r\\n\\r\\n if input_data == \\\"not defined\\\":\\r\\n raise ValueError(\\\"please provide input data\\\")\\r\\n\\r\\n linreg = LinearRegression()\\r\\n grid = {\\r\\n \\\"normalize\\\": [\\\"True\\\", \\\"False\\\"],\\r\\n }\\r\\n\\r\\n model = RandomizedSearchCV(linreg, grid, random_state=1007486)\\r\\n model.fit(X_train, y_train)\\r\\n y_pred = model.predict(X_test)\\r\\n predicted_units = model.predict(input_data)\\r\\n\\r\\n # assert score > 0.6, \\\"fuck this model is too bad!!!\\\"\\r\\n return y_pred, predicted_units\",\n \"def run_model(Xp_train=None,y_train=None,Xp_test=None,y_test=None,mt='bl', params={}):\\n models = { \\n \\\"bl\\\": lambda : None,\\n \\\"lr\\\": LinearRegression,\\n \\\"dtr\\\": DecisionTreeRegressor,\\n \\\"gbr\\\": GradientBoostingRegressor\\n }\\n\\n # Select Model, returns None for baseline\\n model = models[mt](**params)\\n\\n # Fit Model and make predictions\\n if mt != 'bl': # If model not baseline\\n # Dummy encoding , (each category of n levels or attributes is converted into n-1 dichotomous variables)\\n X_train = pd.get_dummies(Xp_train, columns=['type', 'duration','location'],drop_first=True)\\n X_test = pd.get_dummies(Xp_test, columns=['type', 'duration','location'],drop_first=True)\\n model.fit(X_train,y_train)\\n # Make Predictions\\n y_pred = model.predict(X_test)\\n #print X_test.shape\\n y_predtr = model.predict(X_train) \\n else: # Compute baseline\\n y_pred = np.median(y_train).repeat(len(y_test))\\n y_predtr = np.median(y_train).repeat(len(y_train))\\n \\n \\n\\n # Report metrics\\n print(\\\"Model name: %s\\\" % (model.__class__.__name__ if model else \\\"Price Median Baseline\\\"))\\n if mt != 'bl':\\n print(\\\"hyper-parameters: \\\" + \\\", \\\".join( \\\"{0}: {1}\\\".format(k,v) for (k,v) in params.items() ) )\\n print(\\\"Mean absolute error training set: %.2f\\\" % mean_absolute_error(y_train, y_predtr)) \\n print(\\\"Mean absolute error testing set: %.2f \\\\n\\\" % mean_absolute_error(y_test, y_pred))\",\n \"def setUp(self):\\n\\n self.ach_model = FundingSources.get_ach_model()\\n self.verify = self.get_basic_verification()\",\n \"def test_04_predict(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-predict-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n\\n ## update the log\\n y_pred = [0]\\n y_proba = [0.6,0.4]\\n runtime = \\\"00:00:02\\\"\\n model_version = 0.1\\n country = \\\"india\\\"\\n target_date = '2018-01-05'\\n\\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\\n model_version, test=True, prefix=LOG_PREFIX)\\n\\n df = pd.read_csv(log_file)\\n logged_y_pred = [literal_eval(i) for i in df['y_pred'].copy()][-1]\\n self.assertEqual(y_pred,logged_y_pred)\",\n \"def test_intent_classifier_add_training_samples(self):\\n pass\",\n \"def logistic_pen(weights, data, targets, hyperparameters):\\n\\n # TODO: Finish this function\\n\\n return f, df, y\",\n \"def logistic_regression(X, Y):\\n m, n = X.shape\\n theta = np.zeros(n)\\n learning_rate = 10\\n\\n i = 0\\n while True:\\n i += 1\\n prev_theta = theta\\n grad = calc_grad(X, Y, theta)\\n theta = theta - learning_rate * grad\\n if i % 10000 == 0:\\n print('Finished %d iterations' % i)\\n # plot decision boundary for the ith iteration listed in i_lst\\n i_lst = [1, 2, 3, 10, 100, 200, 500, 1000, 10000, 30370, 40000, 50000]\\n if i in i_lst:\\n save_path = \\\"output/p01_b_a\\\" + str(i) + \\\".png\\\"\\n plot(X, Y, theta, save_path)\\n if np.linalg.norm(prev_theta - theta) < 1e-15:\\n print('Converged in %d iterations' % i)\\n break\\n return\",\n \"def train(self, X, y, reg=1e-4, lr=0.1, epochs=5, batch_size=16):\\n self.classes = np.sort(np.unique(y))\\n self.models = []\\n ### YOUR CODE HERE 5-10 lines\\n for classs in range(len(self.classes)):\\n model = LogisticClassifierTwoClass()\\n z = np.array([1 if label == classs else 0 for label in y])\\n model.train(X, z, reg=reg, lr=lr, epochs=epochs, batch_size=batch_size)\\n self.models.append(model)\\n ### END CODE\\n assert len(self.models) == len(self.classes)\",\n \"def _train(self, log_prob):\\n raise NotImplementedError\",\n \"def logistic_predict(self, x: np.array) -> np.array:\\r\\n if self.LogisticModel is None:\\r\\n print('Logistic Model not trained, please run logistic_fit first!')\\r\\n return None\\r\\n else:\\r\\n return self.LogisticModel.predict(x)\",\n \"def train_classifier(X, y, Cs=10):\\n cls = LogisticRegressionCV(Cs=Cs, random_state=0, solver='lbfgs', max_iter=10000)\\n cls.fit(X, y)\\n return cls\",\n \"def fit(self, x, y):\\n # Note Logistic Regression Runtime\\n start_time = time.time()\\n\\n # Converting Pandas DataFrame to Numpy arrays\\n if not type(x).__module__ == np.__name__:\\n x = x.to_numpy()\\n if not type(y).__module__ == np.__name__:\\n y = y.to_numpy()\\n\\n # Insert a column of 1 in the feature vector X for the bias term in the weights\\n x = np.insert(x,0,1,axis=1)\\n \\n # Verify dimension of input\\n if len(x) != len(y):\\n print(\\\"The number of input features vector must be to be the same as the number of target variables\\\")\\n else:\\n losses = self.gradient_descent(x,y)\\n\\n # Note end time\\n end_time = time.time()\\n\\n # Log runtime\\n print(\\\"Logistic Regression training time: {0:.2f}s\\\".format(end_time - start_time))\\n \\n return losses\",\n \"def test_train_model_attributes_good():\\n\\tdf = pd.read_csv(\\\"test/sample_features.csv\\\")\\n\\n\\ty_train = df['price']\\n\\tX_train = df.loc[:, df.columns != 'price']\\n\\n\\tparams = {'n_estimators': 5, 'random_state': 2}\\n\\trf_test = train(X_train, y_train, params)\\n\\n\\t# test model attributes\\n\\tassert rf_test.get_params()['n_estimators'] == 5\\n\\tassert rf_test.get_params()['random_state'] == 2\",\n \"def logistic_regression_model_by_features(xTrain, yTrain, features, iter_step, resolution, initial_w0, step, max_iters):\\r\\n\\r\\n model = lgm.LogisticRegressionModel(initial_w0=initial_w0,\\r\\n initial_weights=[0.0] * len(features))\\r\\n\\r\\n # Extend xTrains and xTest with 1 at [0]\\r\\n xTrain = [[1] + x for x in xTrain]\\r\\n\\r\\n for i, iters in enumerate([iter_step] * resolution):\\r\\n fit_tic = time.time()\\r\\n model.fit(xTrain, yTrain, iterations=iters, step=step)\\r\\n fit_toc = time.time() - fit_tic\\r\\n iter_cnt = iter_step * (i + 1)\\r\\n print(\\\"Took {} sec. Fitted data for {} iterations\\\".format(fit_toc, iter_cnt))\\r\\n\\r\\n return model\",\n \"def test_training(self):\\n warnings.filterwarnings('ignore')\\n example_args = example_args_parser()\\n example_args.unittest = True\\n # prepare data\\n example_args.stage = 'prepare'\\n example_wrapper(example_args)\\n # train goalDNN model\\n example_args.stage = 'train'\\n example_args.model = 'goalDNN'\\n example_wrapper(example_args)\\n # train cVAE model\\n example_args.model = 'cVAE'\\n example_wrapper(example_args)\\n # train gcVAE model\\n example_args.model = 'gcVAE'\\n example_wrapper(example_args)\\n # cVAE harmonization\\n example_args.stage = 'predict'\\n example_args.model = 'cVAE'\\n example_wrapper(example_args)\\n # gcVAE harmonization\\n example_args.model = 'gcVAE'\\n example_wrapper(example_args)\\n # goalDNN prediction\\n example_args.model = 'goalDNN'\\n example_wrapper(example_args)\\n # XGBoost\\n example_args.stage = 'train'\\n example_args.model = 'XGBoost'\\n example_wrapper(example_args)\\n # compare with reference results\\n check_args = check_results_args_parser()\\n check_args.unittest = True\\n check_reference_results(check_args)\",\n \"def test_Bernoulli_NB_estimators():\",\n \"def test(self, X, y):\\n\\t\\tself.test_X = X\\n\\t\\tself.test_y = y\\n\\n\\t\\tclassifier = self.classifier.fit(self.X, self.y)\\n\\t\\ty_pred = classifier.predict(X) \\t\\t\\t# class prediction\\n\\t\\ty_prob = classifier.predict_proba(X)\\t# probability of each class\\n\\t\\tself.test_metrics = ModelMetrics(classifier, y, y_pred, y_prob, 'holdout')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77214766","0.7520839","0.7343993","0.7281926","0.7180691","0.71483684","0.71165","0.7098579","0.7064817","0.7052712","0.7048455","0.6993554","0.6963736","0.68801945","0.6828811","0.6816357","0.6768154","0.67567813","0.664204","0.6568265","0.6563794","0.6554172","0.64779526","0.64485836","0.6448569","0.6403184","0.63547915","0.6352554","0.634104","0.6336133","0.63331455","0.6301849","0.62874186","0.6286688","0.62722516","0.62658906","0.62619275","0.6243209","0.62367594","0.6204225","0.6197872","0.61926174","0.6180349","0.61594886","0.6148429","0.6147551","0.6147348","0.6144202","0.61167216","0.60997266","0.60631156","0.6060308","0.6049722","0.6048337","0.6033322","0.60251486","0.6021502","0.6019998","0.59980184","0.5972307","0.59520984","0.59209275","0.59156126","0.58866715","0.58844256","0.5865369","0.58500993","0.5842562","0.58300674","0.5827996","0.5791111","0.57878447","0.57850313","0.57789904","0.5775066","0.57748836","0.57676065","0.57672733","0.5745383","0.5738361","0.5734134","0.5723578","0.57190114","0.57104224","0.5696934","0.5695937","0.5693404","0.56921464","0.5691727","0.5689757","0.56875676","0.56864566","0.56857115","0.5678553","0.5675523","0.5674174","0.5668673","0.5658888","0.5658293","0.56581455"],"string":"[\n \"0.77214766\",\n \"0.7520839\",\n \"0.7343993\",\n \"0.7281926\",\n \"0.7180691\",\n \"0.71483684\",\n \"0.71165\",\n \"0.7098579\",\n \"0.7064817\",\n \"0.7052712\",\n \"0.7048455\",\n \"0.6993554\",\n \"0.6963736\",\n \"0.68801945\",\n \"0.6828811\",\n \"0.6816357\",\n \"0.6768154\",\n \"0.67567813\",\n \"0.664204\",\n \"0.6568265\",\n \"0.6563794\",\n \"0.6554172\",\n \"0.64779526\",\n \"0.64485836\",\n \"0.6448569\",\n \"0.6403184\",\n \"0.63547915\",\n \"0.6352554\",\n \"0.634104\",\n \"0.6336133\",\n \"0.63331455\",\n \"0.6301849\",\n \"0.62874186\",\n \"0.6286688\",\n \"0.62722516\",\n \"0.62658906\",\n \"0.62619275\",\n \"0.6243209\",\n \"0.62367594\",\n \"0.6204225\",\n \"0.6197872\",\n \"0.61926174\",\n \"0.6180349\",\n \"0.61594886\",\n \"0.6148429\",\n \"0.6147551\",\n \"0.6147348\",\n \"0.6144202\",\n \"0.61167216\",\n \"0.60997266\",\n \"0.60631156\",\n \"0.6060308\",\n \"0.6049722\",\n \"0.6048337\",\n \"0.6033322\",\n \"0.60251486\",\n \"0.6021502\",\n \"0.6019998\",\n \"0.59980184\",\n \"0.5972307\",\n \"0.59520984\",\n \"0.59209275\",\n \"0.59156126\",\n \"0.58866715\",\n \"0.58844256\",\n \"0.5865369\",\n \"0.58500993\",\n \"0.5842562\",\n \"0.58300674\",\n \"0.5827996\",\n \"0.5791111\",\n \"0.57878447\",\n \"0.57850313\",\n \"0.57789904\",\n \"0.5775066\",\n \"0.57748836\",\n \"0.57676065\",\n \"0.57672733\",\n \"0.5745383\",\n \"0.5738361\",\n \"0.5734134\",\n \"0.5723578\",\n \"0.57190114\",\n \"0.57104224\",\n \"0.5696934\",\n \"0.5695937\",\n \"0.5693404\",\n \"0.56921464\",\n \"0.5691727\",\n \"0.5689757\",\n \"0.56875676\",\n \"0.56864566\",\n \"0.56857115\",\n \"0.5678553\",\n \"0.5675523\",\n \"0.5674174\",\n \"0.5668673\",\n \"0.5658888\",\n \"0.5658293\",\n \"0.56581455\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69252974"},"document_rank":{"kind":"string","value":"13"}}},{"rowIdx":86687,"cells":{"query":{"kind":"string","value":"Test Case for Predict for Logistic Regression"},"document":{"kind":"string","value":"def test_predict(self):\n \n\n model ,vec, x_testing=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\"text\", method=\"count\")\n \n model2 ,vec_tfidf, x_testing2=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\"text\", method=\"tfidf\")\n \n \n \"\"\" Test correct data types and corrrect range of predicted values (1,0) for predict with countVectorizer\"\"\" \n \n self.assertIsInstance(predict(model,x_testing),\n np.ndarray)\n \n self.assertTrue(([0,1] ==np.unique(predict(model2,x_testing2))).all())\n\n \n \"\"\" Test correct data types and corrrect range of predicted values (1,0) for predict with tfidfVectorizer\"\"\" \n \n self.assertIsInstance(predict(model,x_testing),\n np.ndarray)\n \n self.assertTrue(([0,1] ==np.unique(predict(model2,x_testing2))).all())"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def logistic_predict(weights, data):\n\n # TODO: Finish this function\n\n return y","def logistic_predict(self, x: np.array) -> np.array:\r\n if self.LogisticModel is None:\r\n print('Logistic Model not trained, please run logistic_fit first!')\r\n return None\r\n else:\r\n return self.LogisticModel.predict(x)","def test_class_logistic(\n y_train_dep,\n y_test_dep,\n y_train_preds_lr,\n y_test_preds_lr):\n # Ensure the function works\n try:\n cls.classification_logistic_results(\n y_train_dep, y_test_dep, y_train_preds_lr, y_test_preds_lr\n )\n logging.info(\n \"Successfully Plotting Classification Results using logistic regression\"\n )\n except Exception as err:\n logging.error(\"Errors in plotting logistic classification results\")\n raise err\n # Ensure the output exists\n try:\n assert os.path.isfile(\"images/results/assessment_logistic.png\")\n except AssertionError as err:\n logging.error(\"Errors in plotting logistic classification file\")\n raise err","def test_04_predict(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-predict-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n\n ## update the log\n y_pred = [0]\n y_proba = [0.6,0.4]\n runtime = \"00:00:02\"\n model_version = 0.1\n country = \"india\"\n target_date = '2018-01-05'\n\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\n model_version, test=True, prefix=LOG_PREFIX)\n\n df = pd.read_csv(log_file)\n logged_y_pred = [literal_eval(i) for i in df['y_pred'].copy()][-1]\n self.assertEqual(y_pred,logged_y_pred)","def test_logistic_regression(x, y, tune):\n # Perform classification without tuning\n lrc = LogisticRegression()\n pipeline = create_pipeline(lrc)\n return accuracy(pipeline, x, y)","def test_train_logist(x_train_variable, y_train_dep):\n # Ensure the function works\n try:\n lrc = cls.train_logistic(x_train_variable, y_train_dep)\n logging.info(\"Successful Logistic Model\")\n except Exception as err:\n logging.error(\"Errors in Fitting the Logistic Regression\")\n raise err\n return lrc","def fit_and_predict_LR(X_train, Y_train, X_test):\n\n # Import the package\n from sklearn.linear_model import LogisticRegression\n\n #referenced to sklearn documentation \n \n # fit the model... \n clf = LogisticRegression().fit(X_train, Y_train) \n # make predictions \n predicted_LR = clf.predict(X_test)\n return predicted_LR\n ### END SOLUTION ### ","def run_logistic_regression(training, testing, feature_cols, outcome_col):\n if 'intercept' not in training.columns:\n training['intercept'] = 1\n if 'intercept' not in testing.columns:\n testing['intercept'] = 1\n intercept_feature_cols = feature_cols + ['intercept']\n logit = sm.Logit(training[outcome_col], training[intercept_feature_cols])\n fitted_logit_model = logit.fit()\n logit_diagnostics = get_diagnostics(testing[outcome_col], testing[intercept_feature_cols], fitted_logit_model, model_type = 'logit')\n predicted_logit_probs = fitted_logit_model.predict(testing[intercept_feature_cols])\n\n return fitted_logit_model, logit_diagnostics, predicted_logit_probs","def test_logistic_regression_c_parameter(params, X_train, X_test, y_train, y_test):","def LogisticRegression_sklearn(X_train, X_test, y_train, y_test):\n\n\tlog_reg = LogisticRegression()\n\tlog_reg.fit(X_train, y_train.ravel())\n\tyPred =log_reg.predict(X_test)\n\n\t#Printing metrics of the logistic regression model\n\tprint('Accuracy:', metrics.accuracy_score(y_test, yPred))\n\tprint('Precision:', metrics.precision_score(y_test, yPred))\n\tprint('Recall', metrics.recall_score(y_test, yPred))\n\n\t#confusion matrix\n\n\tconfusionMatrix = matrix.confusion_matrix(y_test, yPred)\n\tsb.heatmap(pd.DataFrame(confusionMatrix), annot= True, fmt='g')\n\tplt.title('Confustion matrix with default value 1')\n\tplt.ylabel('True values')\n\tplt.xlabel('Predicted values')\n\tplt.show()","def train_logistic_regression(train_x, train_y):\n\n logistic_regression_model = LogisticRegression(penalty='l2', C=1.0)\n logistic_regression_model.fit(train_x, train_y)\n return logistic_regression_model","def train_logistic_regression(train_exs: List[SentimentExample], feat_extractor: FeatureExtractor) -> LogisticRegressionClassifier:\n lr = LogisticRegressionClassifier(feat_extractor.corpus_length, feat_extractor)\n alpha = 1e0\n # beta = 1e-4\n for epoch in range(8):\n loss = 0.\n acc = 0\n indices = np.arange(len(train_exs))\n np.random.shuffle(indices)\n for i in indices:\n feat = feat_extractor.feats[i]\n sentimentExample = train_exs[i]\n y = sentimentExample.label\n z = 1 / (1 + np.exp(-feat.dot(np.expand_dims(lr.w, axis=1))))[0, 0]\n loss += -y * np.log(z) - (1 - y) * np.log(1 - z) \\\n # + beta * np.expand_dims(lr.w, axis=0).dot(np.expand_dims(lr.w, axis=1))[0, 0]\n predict = int(feat.dot(np.expand_dims(lr.w, axis=1))[0, 0] > 0)\n acc += (predict == y)\n grad = (z - y) * feat.toarray()[0] # + 2 * beta * lr.w\n lr.w = lr.w - alpha * grad\n print(\"epoch {:d}, loss: {:f}, accuracy: {:f}\".format(epoch, loss / len(train_exs), acc / len(train_exs)))\n\n for i in indices:\n feat = feat_extractor.feats[i]\n sentimentExample = train_exs[i]\n y = sentimentExample.label\n z = 1 / (1 + np.exp(-feat.dot(np.expand_dims(lr.w, axis=1))))[0, 0]\n loss += -y * np.log(z) - (1 - y) * np.log(1 - z)\n print(\"training loss: {:f}\".format(loss / len(train_exs)))\n\n return lr","def run_logistic(X_train, X_test, y_train, y_test, C=1, penalty = 'l2', solver = 'lbfgs'):\n \n logreg = LogisticRegression(fit_intercept=True, C=C, penalty = penalty, solver = solver)\n logreg.fit(X_train, y_train)\n get_scores(logreg, X_train, X_test, y_train, y_test)","def create_logistic_regression():\n\n pause_data = shuffle(pd.read_csv(sys.argv[1]))\n pause_data = pause_data.replace([np.inf, -np.inf], np.nan).dropna()\n # X = pause_data.drop([HAS_DEMENTIA, TRANSCRIPT_ID], axis=1)\n X = pause_data[MEMORY_FEATURES]\n y = pause_data[HAS_DEMENTIA]\n split_tracker = []\n rskf = RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=36851234)\n # n_repeats 10 too\n for train_index, test_index in rskf.split(X, y):\n X_train, X_test = X.iloc[list(train_index)], X.iloc[list(test_index)]\n y_train, y_test = y.iloc[list(train_index)], y.iloc[list(test_index)]\n logmodel = LogisticRegression()\n logmodel.fit(X_train, y_train)\n predictions = logmodel.predict(X_test)\n split_tracker.append({\n TRAIN: train_index,\n TEST: test_index,\n PREDICTIONS: predictions,\n Y_TEST: y_test\n })\n accuracy = []\n f1 = []\n auc = []\n print(\"Predictions\", split_tracker[0])\n for predictions in split_tracker:\n # print(classification_report(predictions[Y_TEST], predictions[PREDICTIONS]))\n accuracy.append(accuracy_score(predictions[Y_TEST], predictions[PREDICTIONS]))\n f1.append(f1_score(predictions[Y_TEST], predictions[PREDICTIONS]))\n auc.append(roc_auc_score(predictions[Y_TEST], predictions[PREDICTIONS]))\n print(accuracy)\n accuracy = np.array(accuracy)\n f1 = np.array(f1)\n auc = np.array(auc)\n print(len(accuracy))\n print('mean accuracy: ', accuracy.mean())\n print('mean f1 score: ', f1.mean())\n print('mean auc: ', auc.mean())","def trainRegressionModel(X,y):\n # # instantiate a logistic regression model, and fit with X and y\n # model = LogisticRegression()\n # model = model.fit(X, y)\n # # check the accuracy on the training set\n # print(model.score(X, y))\n #X['intercept'] = 1.0\n #del X['isCapitalized']\n #del X['isNN']\n #del X['isNNP']\n #del X['isJJ']\n #del X['isUpper']\n #del X['isPrecedingIN']\n logit = sm.Logit(y, X)\n result = logit.fit()\n print(result.summary())\n print(result.conf_int())\n model = LogisticRegression()\n model = model.fit(X, y)\n print(model.score(X, y))\n print(y.mean())\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n model2 = LogisticRegression()\n model2.fit(X_train, y_train)\n # predict class labels for the test set\n predicted = model.predict(X_test)\n print(predicted)\n for i in predicted:\n if i==1:\n print(\"Test:\"+str(i))\n print(max(predicted))\n #generate class probabilities\n probs = model2.predict_proba(X_test)\n print(probs)\n # generate evaluation metrics\n print(\"Accuracy: \"+str(metrics.accuracy_score(y_test, predicted)))\n print(\"AUC: \"+str(metrics.roc_auc_score(y_test, probs[:, 1])))\n print(metrics.confusion_matrix(y_test, predicted))\n print(metrics.classification_report(y_test, predicted))\n\n from sklearn.cross_validation import cross_val_score\n # evaluate the model using 10-fold cross-validation\n scores = cross_val_score(LogisticRegression(), X, y, scoring='accuracy', cv=10)\n print(scores)\n print(scores.mean())","def test_03_predict(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-predict-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n if os.path.exists(log_file):\n os.remove(log_file)\n \n ## update the log\n y_pred = [0]\n y_proba = [0.6,0.4]\n runtime = \"00:00:02\"\n model_version = 0.1\n country = \"india\"\n target_date = '2018-01-05'\n\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\n model_version, test=True, prefix=LOG_PREFIX)\n \n self.assertTrue(os.path.exists(log_file))","def _predict(self, testX):\n pass","def predict_logit(self, x):\n with torch.no_grad():\n y_ = self.tr_model(x)\n return y_","def predict(self, x_test):\n x_test_norm = self.normalize(x_test)\n y_pred = self.logistic_function(x_test_norm, self.b0, self.b1)\n return np.array([1 if p >= 0.5 else 0 for p in y_pred])","def test_predictor():","def predictions(logits):\n # TODO implement predictions\n return logits","def testLogRegres(weights, dev_x, dev_y):\n predict_y = []\n numSamples, numFeatures = np.shape(dev_x)\n for i in range(numSamples):\n if sigmoid(dev_x[i, :] * weights) > 0.5:\n label = 1\n else:\n label = 0\n predict_y.append(label)\n print('Congratulations, testing complete!')\n return predict_y","def train_logisticRegression(data: np.array, labels: np.array)->None:\n\n n_examples = np.size(data, 0)\n n_features = np.size(data, 1)\n n_categories = np.size(labels, 1)\n\n data = np.hstack((np.ones((n_examples, 1)), data))\n\n print(data[0:5, :])\n\n X_train, X_test, y_train, y_test, idx_test = split_data(data, labels, 0.7)\n\n convergence_goal = 1e-3\n learning_rate = 0.01\n\n theta = np.random.uniform(size=((n_features+1, n_categories)))\n\n for i in range(n_categories):\n\n cost_var = 1\n\n previous_cost = 1e6\n iterations = 0\n cost_to_plot = []\n\n while cost_var > convergence_goal:\n iterations += 1\n cost, grad = costFunction(X_train, y_train[:, i], theta[:, i])\n theta[:, i] = update_theta(theta[:, i], grad, learning_rate)\n cost_var = previous_cost - cost\n previous_cost = cost\n if iterations == 1: cost_var = 1\n cost_to_plot.append(cost)\n # print(cost)\n\n plt.plot(range(iterations), cost_to_plot, 'g-', label = 'cost')\n plt.xlabel('iterations')\n plt.ylabel('cost')\n # plt.show()\n\n predictions = lrPredict(theta, X_test)\n\n print(predictions[0:5, :])\n print(y_test[0:5, :])\n\n accuracy = np.mean([p == l for p, l in zip(predictions, y_test)])\n print(\"Accuracy = {}\".format(accuracy))\n\n pass","def test_using_predict(self):\n [X, labels, Y] = self.gen_data()\n # Call algorithm\n bias = multiLogReg(self.sds.from_numpy(\n X), self.sds.from_numpy(Y), verbose=False).compute()\n\n [m, y_pred, acc] = multiLogRegPredict(self.sds.from_numpy(\n X), self.sds.from_numpy(bias), self.sds.from_numpy(Y), verbose=False).compute()\n\n self.assertTrue(acc > 98)","def test_test_regression_model(self):\n model = RegressionTestModel()\n example = {'x_1': 3, 'x_2': 2}\n pred = list(model.predict([example]))[0]\n self.assertEqual(pred['score'], 20)","def predict(self, model, x_test):\n pass","def test(self) -> None:\n\n self._predictions = self._lr.predict(self._X_test)","def train_logistic_regression(X_train_input, y_train_input, C=1):\r\n from sklearn.linear_model import LogisticRegression\r\n logr_clf = LogisticRegression(C=C)\r\n logr_clf.fit(X_train_input, y_train_input)\r\n return logr_clf","def testLogisticRegression_TensorData(self):\n language_column = tf.contrib.layers.sparse_column_with_hash_bucket(\n 'language', hash_bucket_size=20)\n feature_columns = [\n tf.contrib.layers.embedding_column(language_column, dimension=1),\n tf.contrib.layers.real_valued_column('age')\n ]\n\n classifier = tf.contrib.learn.DNNClassifier(\n n_classes=2,\n feature_columns=feature_columns,\n hidden_units=[3, 3],\n config=tf.contrib.learn.RunConfig(tf_random_seed=1))\n\n classifier.fit(input_fn=_input_fn, steps=100)\n\n scores = classifier.evaluate(input_fn=_input_fn, steps=1)\n self.assertGreater(scores['accuracy'], 0.9)\n self.assertLess(scores['loss'], 0.3)\n predict_input_fn = functools.partial(_input_fn, num_epochs=1)\n predictions = list(\n classifier.predict(input_fn=predict_input_fn, as_iterable=True))\n self.assertListEqual(predictions, [1, 0, 0])","def test_predict(self):\n self.regression_single.predict(self.X_test)\n self.assertTrue(len(self.regression_single.y_pred))\n self.regression_boston.predict(self.boston_x_test)\n self.assertTrue(len(self.regression_boston.y_pred))","def logistic(weights, data, targets, hyperparameters):\n\n # TODO: Finish this function\n\n return f, df, y","def test_predict():\n df = pd.DataFrame(dict(x=[1.0, 2.0, 3.0, 4.0]))\n\n class Model(Poisson):\n dv = \"y\"\n features = dict(x=dict(transformer=lambda x: x.x, prior=dist.Normal(0, 1)))\n\n config = {\"samples\": {\"x\": onp.ones((2, 10))}}\n model = Model.from_dict(config)\n pred = model.predict(df)\n log_pred = onp.log(pred).round(2)\n assert df.x.astype(\"float32\").equals(log_pred.astype(\"float32\"))","def predict():\n import trace\n trace.predict()","def test_none_alpha_results() -> None:\n estimator = LogisticRegression()\n estimator.fit(X, y)\n y_pred_est = estimator.predict(X)\n mapie = MapieClassifier(estimator=estimator, cv=\"prefit\")\n mapie.fit(X, y)\n y_pred_mapie = mapie.predict(X)\n np.testing.assert_allclose(y_pred_est, y_pred_mapie)","def log_reg(x_train, y_train):\n\n log_reg_classifier = LogisticRegression(max_iter=1000, solver='lbfgs')\n log_reg_classifier.fit(x_train, y_train)\n return log_reg_classifier\n\n # log_reg_classifier.fit(x_train, y_train)","def test_predict_proba_nonnegative():\n\n def check_for_negative_prob(proba):\n for p in np.ravel(proba):\n assert np.round(p,7) >= 0\n\n clf = mord.LogisticAT(alpha=0.)\n clf.fit(X, y)\n check_for_negative_prob(clf.predict_proba(X))\n\n clf2 = mord.LogisticIT(alpha=0.)\n clf2.fit(X, y)\n check_for_negative_prob(clf2.predict_proba(X))\n\n clf3 = mord.LogisticSE(alpha=0.)\n clf3.fit(X, y)\n check_for_negative_prob(clf3.predict_proba(X))","def logistic_predict(weights, data):\n #####################################################################\n # TODO: #\n # Given the weights and bias, compute the probabilities predicted #\n # by the logistic classifier. #\n #####################################################################\n \n y = None\n N = len(data)\n M = len(weights) - 1\n temp = np.ones([N, M + 1])\n temp[: N, : M] = np.array(data)\n data = temp\n\n\n\n # print(np.matmul(data, weights).shape)\n z = np.matmul(data, weights)\n y = sigmoid(z)\n #####################################################################\n # END OF YOUR CODE #\n #####################################################################\n return y","def test_logistic():\n r=np.random.normal(size=20)\n assert np.isclose( ilogistic(logistic(r)),r ).all()","def __init__(self, reg_penalty='l2', reg_inv=1.0, k_fold=5, random_state=0):\n print(\"Initialize model Logistic Regression\")\n self.reg_penalty = reg_penalty\n self.reg_inv = reg_inv\n self.k_fold = k_fold\n self.random_state = random_state\n self.model = sklearn.linear_model.LogisticRegression(penalty=self.reg_penalty,\n C=self.reg_inv,\n max_iter=1000, \n random_state=self.random_state)","def predict_proba(self, X):\n return super(SGDLogistic, self).predict(X)","def test_model(preds, target):\n ### START CODE HERE (Replace instances of 'None' with your code) ###\n print(preds.shape, target.shape)\n total_log_ppx = np.sum(preds * tl.one_hot(target, preds.shape[-1]), axis= -1) # HINT: tl.one_hot() should replace one of the Nones\n print(total_log_ppx.shape)\n \n non_pad = 1.0 - np.equal(target, 0) # You should check if the target equals 0\n ppx = total_log_ppx * non_pad # Get rid of the padding\n\n log_ppx = np.sum(ppx) / np.sum(non_pad)\n ### END CODE HERE ###\n \n return -log_ppx","def predict(self, params, test):\n vect_logit = np.vectorize(logit)\n vect_comp = np.vectorize(complement)\n n_test = np.shape(test)[0]\n prior = self.model_prior\n conditional = self.model_conditional\n K = len(prior)\n p = np.shape(conditional)[1]\n test_p = np.shape(test)[1]\n if p != test_p:\n print \"Error: Number of training and testing features differ\"\n return\n preds = []\n weight = vect_logit(conditional)\n # Stores weight matrix\n condition_comp = vect_comp(conditional)\n intercept = np.log(\n prior) + np.sum(np.apply_along_axis(np.log, 1, condition_comp), axis=1)\n # Stores intercept vector\n weight = weight.transpose()\n classifier = test.dot(weight) + intercept\n preds = np.argmax(classifier, axis=1) + 1\n # Add 1 because the classes have a 1-based index\n preds = preds.reshape(n_test, 1)\n return preds","def main():\r\n x = [\r\n [ 1,1 ], [ 0,0 ], [ 1,0 ], [ 0,0 ], [ 0,0 ], [ 0,0 ],\r\n [ 0,0 ], [ 0,0 ], [ 1,1 ], [ 0,0 ], [ 0,0 ], [ 1,0 ],\r\n [ 1,0 ], [ 0,0 ], [ 1,1 ], [ 0,0 ], [ 1,0 ], [ 0,0 ]\r\n ]\r\n\r\n # Encoding of the correct classes for the training material\r\n y = [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0]\r\n b = BinaryLogisticRegression(x, y)\r\n b.fit()\r\n b.print_result()","def train_model(self):\r\n alpha, accuracy_rate = self.select_model()\r\n # Initialize logistic regression with alpha(learning rate)\r\n lg = logisticregression(C=alpha)\r\n # Train the model.\r\n lg.fit(self.training_data, self.target_data)\r\n # Save the trained model as .pkl file.\r\n joblib.dump(value=lg, filename=self.intention_id+'.pkl', compress=1)\r\n print \"Estimated Parameters of Logistic Regression\"\r\n # Estimated parameters of logistic regression.\r\n print lg.get_params()","def _train(self, log_prob):\n raise NotImplementedError","def test_valid_prediction(alpha: Any) -> None:\n model = LogisticRegression(multi_class=\"multinomial\")\n model.fit(X_toy, y_toy)\n mapie = MapieClassifier(estimator=model, cv=\"prefit\")\n mapie.fit(X_toy, y_toy)\n mapie.predict(X_toy, alpha=alpha)","def predict(self, X):","def predict(self, X):","def predict(self, X):\n ...","def predict(self, X):\n ...","def predict(self, X):\n ...","def create_regression(f_train, f_test, l_train, l_test):\n model = LogisticRegression()\n\n # train it on training data\n model.fit(f_train, l_train)\n\n # gather the model's predictions for train\n train_predictions = model.predict(f_train)\n\n # gather the model's predictions for test\n test_predictions = model.predict(f_test)\n\n # calculate accuaracy of train\n print('Reg Train Accuracy: ', accuracy_score(l_train, train_predictions))\n\n # calculate accuracy of test\n print('Reg Test Accuracy: ', accuracy_score(l_test, test_predictions))\n\n return model","def predict_1(trained_model, X_test, y_test):\n # Predict with test data\n start_time = timeit.default_timer()\n test_prediction = trained_model.predict(X_test)\n end_time = timeit.default_timer()\n time = end_time - start_time\n speed = int(X_test.shape[0] / time)\n \n # Get loss and accuracy\n test_loss, test_accuracy = trained_model.evaluate(X_test, y_test)\n \n # Prepare results messages\n msg_time = \"prediction time: {:.3f}s ({}px/s)\\n\".format(time, speed)\n msg_test_loss = \"test_loss: {:.3f}\\n\".format(test_loss)\n msg_test_acc = \"test_accuracy: {:.3f}\\n\\n\".format(test_accuracy)\n \n # Write results messages\n with open(OUTPUT_FILE, 'a') as f:\n f.write(msg_time)\n f.write(msg_test_loss)\n f.write(msg_test_acc)","def predict(self, X_):\n X = np.c_[np.ones(X_.shape[0]), X_] # Add one for bias to the first columns\n predictions = np.zeros(X.shape[0])\n ### YOUR CODE HERE\n z = X.dot(self.w)\n predictions = log_reg.logistic(z)\n predictions = np.round(predictions)\n ### END CODE\n assert predictions.shape == (X.shape[0],)\n return predictions.astype('int64')","def logistic_predict(weights, data):\n\n # TODO: Finish this function\n data = mod_data(data)\n z_array = get_z_array(weights, data)\n return sigmoid(z_array)","def decision_function(self, Xtt):\n # predict decision score on test dataset\n self.logger.info(\n self.__name__ + ' predicts decision scores on {:d} samples.'.format(Xtt.shape[0]))","def predict_evidences(self, X):","def predict_probs(self, x):\n # Note start time\n start_time = time.time()\n\n # Insert a column of 1 in the vector X\n x = np.insert(x,0,1,axis=1)\n\n # Initiate array to store predictions\n predictions = []\n\n # Get the probability value of being in class 1 for each training example\n for _ in x: \n predictions.append(self.logistic_function(self.weights.dot(_)))\n\n # Note end time\n end_time = time.time()\n\n # Log Predict Runtime\n print(\"Logistic Regression predict time: {0:.2f}s\".format(end_time - start_time))\n\n return predictions","def train_logistic_regression(train_exs: List[SentimentExample],\n feat_extractor: FeatureExtractor) -> LogisticRegressionClassifier:\n indexer = feat_extractor.get_indexer()\n weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\n learning_rate = 0.1\n for i in range(15):\n for ex in train_exs:\n features_of_str = feat_extractor.extract_features(ex.words, False)\n expo = math.exp(np.dot(weights, features_of_str))\n possibility = expo / (1 + expo)\n gradient_of_w = np.dot(ex.label - possibility, features_of_str)\n weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\n return LogisticRegressionClassifier(weights, feat_extractor)\n\n # Methods for plotting average training loss\n\n # x = np.arange(0, 14)\n # # learning_rate = 1\n # indexer = feat_extractor.get_indexer()\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\n # avrg_losses = np.zeros(14)\n # for i in range(15):\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\n # weights = np.add(weights, gradient_of_w)\n # loss = 0\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\n # avrg_losses[i - 1] = loss / train_exs.__len__()\n # plt.plot(x, avrg_losses)\n #\n # # learning_rate = 0.01\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\n # learning_rate = 0.01\n # avrg_losses = np.zeros(14)\n # for i in range(15):\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\n # weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\n # loss = 0\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\n # avrg_losses[i - 1] = loss / train_exs.__len__()\n # plt.plot(x, avrg_losses)\n #\n # # learning_rate = 0.1\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\n # learning_rate = 0.1\n # avrg_losses = np.zeros(14)\n # for i in range(15):\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\n # weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\n # loss = 0\n # for ex in train_exs:\n # features_of_str = feat_extractor.extract_features(ex.words, False)\n # expo = math.exp(np.dot(weights, features_of_str))\n # possibility = expo / (1 + expo)\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\n # avrg_losses[i - 1] = loss / train_exs.__len__()\n # plt.plot(x, avrg_losses)\n # plt.xlabel('Epochs')\n # plt.ylabel('Average Training Loss')\n # plt.legend(['step size 1', 'step size 0.01', 'step size 0.1'], loc='upper left')\n # plt.show()\n # return LogisticRegressionClassifier(weights, feat_extractor)","def logistic(weights, data, targets, hyperparameters):\n y = logistic_predict(weights, data)\n\n #####################################################################\n # TODO: #\n # Given weights and data, return the averaged loss over all data #\n # points, gradient of parameters, and the probabilities given by #\n # logistic regression. #\n #####################################################################\n f = None\n df = None\n\n f = evaluate(targets, y)[0]\n\n N = len(data)\n M = len(weights) - 1 \n temp = np.ones([N, M + 1])\n temp[: N, : M] = np.array(data)\n\n\n df = np.zeros([M+1, 1])\n\n df[:, 0] = np.array([[np.mean([(y.flatten()[i] - targets.flatten()[i]) * temp[i][j] for i in range(0, N)]) for j in range(0, M + 1)],])\n\n # df = np.matrix([[np.mean([(y[i] - targets[i]) * temp[i][j] for i in range(0, N)]) for j in range(0, M + 1)],])\n\n #####################################################################\n # END OF YOUR CODE #\n #####################################################################\n return f, df, y","def fit_logistic_regression():\n\n logger.debug(\"Running the fit_logistic_regression function now\")\n\n #Loading the configuration\n with open(os.path.join(\"config\",\"config.yml\"), \"r\") as f:\n config = yaml.safe_load(f)\n\n #Loading and pre processing the data\n logger.debug(\"Loading and pre processing the data\")\n train_df = load_data(config[\"load_data\"][\"train_file\"])\n train_df = pre_process_data(train_df, resample = True, resample_count = 500000)\n\n #Defining Pipeline\n pipeline = Pipeline([\n ('tfidf', TfidfVectorizer(analyzer='word', token_pattern=r'[A-Za-z0-9@-]+')),\n ('model', LogisticRegression(random_state=12345, verbose = 1, solver = 'saga')),\n ])\n\n #Defining parameters to vary\n parameters = {\n 'tfidf__max_df': (0.25, 0.5, 0.75),\n 'tfidf__max_features': (None, 5000, 10000, 50000),\n 'tfidf__ngram_range': ((1, 1), (1, 2)),\n 'model__C': (0.01, 1, 100)\n }\n\n scoring_list = [\"accuracy\", \"f1\", \"precision\", \"recall\", \"roc_auc\"]\n \n #Performing 5fold CV to determine best hyperparameters\n model = GridSearchCV(pipeline, parameters, cv=5,\n n_jobs=-1, verbose=1, scoring=scoring_list, refit='f1',)\n\n t0 = datetime.datetime.now()\n\n model.fit(train_df[\"Review\"].tolist(), train_df[\"Ratings\"].to_numpy())\n \n logger.info(\"Grid Search performed in {}\".format(str(datetime.datetime.now()-t0)))\n\n #Saving results\n res_df = pd.DataFrame(model.cv_results_)\n res_df.to_csv(os.path.join(config[\"summary_stats\"][\"save_location\"], \"LogisticRegressionResults.csv\"))\n \n #Saving the model\n pickle.dump(model, open(os.path.join(config[\"models\"][\"save_location\"], \"LogisticRegression.pkl\"),'wb'))\n\n return","def train(self):\n feature = Feature(trained=False)\n classifier = LogisticRegression(\n penalty='l2',\n max_iter=100,\n solver='liblinear',\n random_state=self.RAND_SEED)\n\n true_labels = []\n predicted_labels = []\n\n for subj in self.subjects:\n print(subj)\n # preprocess training and testing set\n self.dataset_gen(subject=subj, valid=False)\n\n # train and predict\n pipeline_steps = [('vectorized', feature.vector)]\n if self.istfidf:\n pipeline_steps.append(('tf-idf', feature.tfidftransform))\n if self.islda == 'small':\n pipeline_steps.append(('lda', feature.ldatransform_small))\n elif self.islda == 'large':\n pipeline_steps.append(('lda', feature.ldatransform_large))\n else:\n pass\n if self.isnorm:\n pipeline_steps.append(('scalar', StandardScaler(with_mean=False)))\n pipeline_steps.append(('clf', classifier))\n model = Pipeline(steps=pipeline_steps)\n\n model.fit(self.X_train, self.y_train)\n\n predicted = model.predict(self.X_test)\n # hamming\n predicted_labels.append(predicted)\n true_labels.append(self.y_test)\n\n true_matrix, pred_matrix = np.array(true_labels, int).T, np.array(predicted_labels, int).T\n true_matrix[true_matrix == -1] = 0\n pred_matrix[pred_matrix == -1] = 0\n\n evaluation = Evaluation(self.subjects)\n evaluation.model_evaluate(true_matrix=true_matrix, pred_matrix=pred_matrix, model_name=self.modelname)","def train(references, corpus):\n sim_vectors, classes = mod.model(references, corpus, labeled=True)\n pred = lm.LogisticRegression()\n pred.fit(sum(sim_vectors, []), classes)\n return pred","def logistic_predict(weights, data):\n \n t = np.transpose(np.repeat(np.reshape(weights[:-1], (len(weights)-1, 1)), len(data), axis = 1))\n f_e = data * t\n z_sums = np.sum(f_e, axis=1) \n \n x = -z_sums - weights[-1]\n y = sigmoid(z_sums + weights[-1])\n\n return np.reshape(y, (len(y), 1))","def predict(test_features, test_labels, network, config, log_date, log_timestamp):\n # set network to eval mode\n network.eval()\n # helper objects\n test_preds = []\n test_gt = []\n\n # initialize test dataset and loader\n dataset = torch.utils.data.TensorDataset(torch.from_numpy(test_features).float(), torch.from_numpy(test_labels))\n testloader = DataLoader(dataset,\n batch_size=config['batch_size'],\n num_workers=2,\n shuffle=False,\n )\n\n with torch.no_grad():\n # iterate over test dataset\n for i, (x, y) in enumerate(testloader):\n # send x and y to GPU\n inputs, targets = x.to(config['gpu']), y.to(config['gpu'])\n\n # send inputs through network to get predictions and calculate softmax probabilities\n test_output = network(inputs)\n test_output = torch.nn.functional.softmax(test_output, dim=1)\n\n # create predictions and append them to final list\n y_preds = np.argmax(test_output.cpu().numpy(), axis=-1)\n y_true = targets.cpu().numpy().flatten()\n test_preds = np.concatenate((np.array(test_preds, int), np.array(y_preds, int)))\n test_gt = np.concatenate((np.array(test_gt, int), np.array(y_true, int)))\n\n cls = np.array(range(config['nb_classes']))\n print('\\nTEST RESULTS: ')\n print(\"Avg. Accuracy: {0}\".format(jaccard_score(test_gt, test_preds, average='macro')))\n print(\"Avg. Precision: {0}\".format(precision_score(test_gt, test_preds, average='macro')))\n print(\"Avg. Recall: {0}\".format(recall_score(test_gt, test_preds, average='macro')))\n print(\"Avg. F1: {0}\".format(f1_score(test_gt, test_preds, average='macro')))\n\n print(\"\\nTEST RESULTS (PER CLASS): \")\n print(\"Accuracy: {0}\".format(jaccard_score(test_gt, test_preds, average=None, labels=cls)))\n print(\"Precision: {0}\".format(precision_score(test_gt, test_preds, average=None, labels=cls)))\n print(\"Recall: {0}\".format(recall_score(test_gt, test_preds, average=None, labels=cls)))\n print(\"F1: {0}\".format(f1_score(test_gt, test_preds, average=None, labels=cls)))\n\n if config['save_test_preds']:\n mkdir_if_missing(os.path.join('logs', log_date, log_timestamp))\n np.save(os.path.join('logs', log_date, log_timestamp, 'test_preds.npy'), test_output)","def test_predict():\n\n tpot_obj = TPOTClassifier()\n\n try:\n tpot_obj.predict(testing_features)\n assert False # Should be unreachable\n except ValueError:\n pass","def predict_and_test(weight, test_data, test_label, regression= \"logistic\"):\n pred_test, _ = get_output(weight, test_data, regression= regression)\n acc = get_accuracy(pred_test, test_label, regression= regression)\n return acc, pred_test","def predict_log_proba(self, X):\n X = _validate_X(X)\n return self.best_estimator_.predict_log_proba(X)","def predict():\n to_predict = np.zeros(5).reshape(1, 5)\n features = ['is_male', 'num_interactions_with_cust_service', 'late_on_payment', 'age', 'years_in_contract']\n for i, feat in enumerate(features):\n if request.args.get(feat) is not None:\n to_predict[0][i] = request.args.get(feat)\n\n response = clf2.predict(to_predict)\n\n if response:\n return \"The customer is likely to churn\"\n else:\n return \"He is a loyal customer\"","def test():\n # load dataset and model\n X, observed_y = load_data('../data/dev.txt')\n\n model = pickle.load(open('test.model', 'rb'))\n model.traverse()\n\n # predict labels for dataset\n preds = model.predict(X)\n\n # print(preds)\n # output model predictions\n np.savetxt('test.predictions', preds, fmt='%s')","def predict(model, X_testing):\n predictions = model.predict(X_testing)\n\n return predictions","def log_reg(x, y, s):\n usx = np.array(x)\n usy = np.array(y)\n\n # split data into train and validation set\n x_train, x_test, y_train, y_test = train_test_split(usx, usy, test_size=s)\n cls_log = LogisticRegression()\n cls_log.fit(x_train, y_train)\n y_predict = cls_log.predict(x_test)\n\n # select only the probabilities of being fraud\n y_pred_prob = cls_log.predict_proba(x_test)[:, 1]\n return y_predict, y_test, y_pred_prob","def predict(x, clf):\n\n y_pred, y_prob = clf.predict(x), clf.predict_proba(x)\n return y_pred, y_prob","def scoring_function(self, model, y_true, y_predicted_probability):","def test_setup_log_reg_classifier(self):\n \n\n model ,vec, x_testing=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\"text\", method=\"count\")\n \n model2 ,vec_tfidf, x_testing2=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\"text\", method=\"tfidf\")\n \n \n \"\"\" Test correct data types for countVectorizer\"\"\" \n \n self.assertIsInstance(vec,\n sklearn.feature_extraction.text.CountVectorizer)\n \n self.assertIsInstance(x_testing, scipy.sparse.csr.csr_matrix)\n \n self.assertIsInstance(model, sklearn.linear_model.LogisticRegression)\n \n \"\"\" Test correct data types TfidfVectorizer\"\"\" \n \n self.assertIsInstance(vec_tfidf,\n sklearn.feature_extraction.text.TfidfVectorizer)\n \n self.assertIsInstance(x_testing2, scipy.sparse.csr.csr_matrix)\n \n self.assertIsInstance(model2, sklearn.linear_model.LogisticRegression)\n \n \n \"\"\" Test correct behaviour for wrong method\"\"\" \n \n self.assertTrue(setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\"text\", method=\"ijfsiohf\"),\n 1)","def predict(self, Xtt):\n # predict outputs for test dataset\n self.logger.info(\n self.__name__ + ' predicts on {:d} samples.'.format(Xtt.shape[0]))\n pass","def predict(x_tst, model):\n\n predictions = model.predict(x_tst)\n return predictions","def test_test_model(self):\n\n dataset = ClassificationTestDataset()\n model = ClassificationTestModel(dataset)\n preds = list(model.predict(dataset.examples))\n self.assertEqual(np.argmax(preds[0]['preds']), 2)\n self.assertEqual(np.argmax(preds[1]['preds']), 1)\n self.assertEqual(np.argmax(preds[2]['preds']), 4)\n self.assertEqual(np.argmax(preds[3]['preds']), 3)","def predict_labels_logistic(self, weights, data):\n y_pred = sigmoid(np.dot(data, weights))\n y_pred[np.where(y_pred < 0.5)] = 0\n y_pred[np.where(y_pred >= 0.5)] = 1\n return y_pred","def on_predict_begin(self, logs=None):","def on_predict_begin(self, logs=None):","def predict(self, Xtest):\n ytest = np.zeros(Xtest.shape[0], dtype=int)\n\n ### YOUR CODE HERE\n sig = np.dot(Xtest, self.weights)\n sig = utils.sigmoid(sig)\n #print (sig)\n sig = np.round(sig)\n #print (sig)\n for i in range (0, ytest.shape[0]):\n ytest[i] = int(sig[i])\n ### END YOUR CODE\n #print (ytest)\n assert len(ytest) == Xtest.shape[0]\n return ytest","def predict_4(trained_model, X_test, y_test):\n # Predict with test data\n start_time = timeit.default_timer()\n trained_model.predict(X_test)\n end_time = timeit.default_timer()\n time = end_time - start_time\n speed = int(X_test.shape[0] / time)\n \n # Get loss and accuracy\n test_accuracy = trained_model.score(X_test, y_test)\n \n # Prepare results messages\n msg_time = \"prediction time: {:.3f}s ({}px/s)\\n\".format(time, speed)\n msg_test_acc = \"test_accuracy: {:.3f}\\n\\n\".format(test_accuracy)\n \n # Write results messages\n with open(OUTPUT_FILE, 'a') as f:\n f.write(msg_time)\n f.write(msg_test_acc)","def test_classifier_age_estimator(x, class_model):\n return class_model.predict(x)","def testLogisticRegression_MatrixData(self):\n cont_features = [\n tf.contrib.layers.real_valued_column('feature', dimension=4)]\n\n classifier = tf.contrib.learn.DNNClassifier(\n feature_columns=cont_features,\n hidden_units=[3, 3],\n config=tf.contrib.learn.RunConfig(tf_random_seed=1))\n\n classifier.fit(input_fn=_iris_input_logistic_fn, steps=100)\n scores = classifier.evaluate(input_fn=_iris_input_logistic_fn, steps=1)\n self.assertGreater(scores['accuracy'], 0.9)\n self.assertLess(scores['loss'], 0.3)","def __test_model(self, test, model, expected):\n prediction = model.predict(test)\n message = \"Prediction:\", *prediction, \"| Actual:\", expected\n self.__logger.info(message)","def test(self):\n y_list = []\n y_hat_list = []\n for ex_dict in ut.TEST_LIST:\n y_list.append(ex_dict[1])\n y_hat_list.append(self.predict(ex_dict[0]))\n acc = ut.compute_accuracy(y_hat_list, y_list)\n return y_hat_list, acc","def predict(self, predPoints=None):","def logistic(weights, data, targets, hyperparameters):\n # TODO: Finish this function\n n_data = len(data)\n dim_data = len(data[0])\n\n f = 0\n y = logistic_predict(weights, data)\n\n data = mod_data(data)\n\n # dl/dw_j = SUM(x_ij * (t_i - (1 - sigmoid(z))))\n df = np.dot(data.T, (1.0 * targets) - (1 - y))\n\n # to calculate f, we need to sum the negative log of all y iff target is 0 and (1-y) iff target is 1\n f = -1.0 * np.dot(targets.T, np.log(1 - y)) - 1.0 * np.dot(1 - targets.T, np.log(y))\n\n # calculate P(C=0|x_i) for all x_i \n return f[0,0], df, y","def predict(self) :\n y_pred = np.dot(self.W.T,self.X_test) + self.b \n if self.thr!=-1 :\n y_pred[y_pred <= self.thr] = -1\n y_pred[y_pred > self.thr] = 1\n y_pred = y_pred.astype(\"int\")\n corr = 0\n for i in range(y_pred.shape[1]) :\n if y_pred[:,i]==self.y_test[:,i] :\n corr += 1\n accu = (corr / y_pred.shape[1])*100\n print(\"ACCURACY : {}\".format(accu))\n else :\n rmse = np.sqrt(np.sum(np.square(self.y_test - y_pred)) / y_pred.shape[1])\n print(\"RMSE : {}\".format(rmse))","def predict(self, X):\n pass","def predict(self, X):\n pass","def predict(self, X):\n pass","def ovo_test(dic, x_test, y_test, nb_classes):\n TP = FP = TN = FN = 0\n correct_predict = 0\n for index, value in enumerate(x_test):\n predict = {elem : 0 for elem in nb_classes}\n for key in dic:\n predict[key[0] if dic[key].predict([value]) else key[1]] += 1\n predicted_value = max(predict.items(), key=operator.itemgetter(1))[0]\n if predicted_value == y_test[index]:\n correct_predict += 1\n TP += 1\n else:\n FP += 1\n \n return correct_predict, TP, FP, TN, FN","def train_predict_and_results(data, clf):\n tra_x, tst_x, tra_y, tst_y = data\n clf.fit(tra_x, tra_y)\n prd_y = clf.predict(tst_x)\n cnf = confusion_matrix(tst_y, prd_y)\n print (\"Classifier: %s \\tAccuracy score:%7.2f %%\"\n \"\\tTN:%5d FP:%5d FN:%5d TP:%5d\"\n % (clf.name, accuracy_score(tst_y, prd_y) * 100,\n cnf[0][0], cnf[0][1], cnf[1][0], cnf[1][1]))","def predict(self, Xtest):\n ytest = np.dot(Xtest, self.weights)\n return ytest","def predict(test_dataset,test_tX,weights):\n for idx, dataset in enumerate(test_tX):\n test_dataset[idx]['Prediction'] = predict_labels(weights[idx],dataset)\n return test_dataset","def predict(self, test_data):\n return self.leader.predict(test_data)","def analysis(houses:pd.DataFrame) -> None:\n \n \"\"\"\n #Me just trying to fit the data without any outside influences\n f= f'SELLER_HOUSE ~ SQFT_PER + PRICE + C(LOCATION)' \n result= smf.logit(formula= str(f), data= houses).fit()\n print(result.summary2())\n y= ['SELLER_HOUSE']\n x= ['SQFT_PER', 'PRICE', 'LOC_699 - Not Defined', 'LOC_AA - Airport Area', 'LOC_CG - Columbus Grove',\n 'LOC_CV - Cypress Village', 'LOC_EASTW - Eastwood', 'LOC_EC - El Camino Real', 'LOC_GP - Great Park',\n 'LOC_IRSP - Irvine Spectrum', 'LOC_LGA - Laguna Altura', 'LOC_NK - Northpark', 'LOC_NW - Northwood', \n 'LOC_OC - Oak Creek', 'LOC_OH - Orchard Hills', 'LOC_OT - Orangetree', 'LOC_PS - Portola Springs', \n 'LOC_QH - Quail Hill', 'LOC_SH - Shady Canyon', 'LOC_SJ - Rancho San Joaquin', 'LOC_STG - Stonegate', \n 'LOC_Stonegate', 'LOC_TR - Turtle Rock', 'LOC_TRG - Turtle Ridge', 'LOC_UP - University Park',\n 'LOC_UT - University Town Center', 'LOC_WB - Woodbridge', 'LOC_WD - Woodbury', \n 'LOC_WI - West Irvine', 'LOC_WN - Walnut (Irvine)', 'LOC_WP - Westpark']\n x_train, x_test, y_train, y_test= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\n logreg = LogisticRegression()\n logreg.fit(x_train, y_train.values.ravel())\n y_pred= logreg.predict(x_test)\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test, y_test), 3))\n # This model is really bad\n \n \"\"\"\n \n \"\"\n houses= houses.drop(['DAYS_ON_MARKET', 'ADDRESS', 'LOCATION',\n 'STATUS', 'PROPERTY_TYPE', 'ZIP_CODE'], axis= 1)\n columns= houses.columns.values.tolist()\n y= ['SELLER_HOUSE']\n x= [i for i in columns if i not in y]\n \n # Over Sampling Using SMOTE \n x_train, _, y_train, _= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\n x_columns= x_train.columns\n \n os= SMOTE(random_state= 0)\n os_x, os_y= os.fit_sample(x_train, y_train)\n os_x= pd.DataFrame(data= os_x, columns= x_columns)\n os_y= pd.DataFrame(data= os_y, columns= y)\n \n \n #Recursive Feature Elimination\n logreg= LogisticRegression(max_iter= 600)\n rfe= RFE(logreg, 20)\n rfe= rfe.fit(os_x, os_y.values.ravel())\n \n lst= [i for count, i in enumerate(x) if rfe.support_[count] == True]\n X= os_x[lst]\n Y= os_y['SELLER_HOUSE']\n \n \n #logit_model= sm.Logit(Y, X)\n #result= logit_model.fit()\n #print(result.summary2()) # Model choosen by RCE\n \n #These are features have a p-value less than 0.05\n final_x= ['BATHS', 'ZIP_92602.0', 'ZIP_92618.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\n #final_x= ['ZIP_92602.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\n X2= os_x[final_x]\n \n logit_model2= sm.Logit(Y, X2)\n result2= logit_model2.fit()\n print(result2.summary2()) # Final Model\n \n x_train2, x_test2, y_train2, y_test2= train_test_split(X2, Y, test_size= 0.3, random_state= 500)\n logreg = LogisticRegression()\n logreg.fit(x_train2, y_train2)\n \n y_pred= logreg.predict(x_test2)\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test2, y_test2), 2))\n \n conf_matrix= confusion_matrix(y_test2, y_pred)\n print(conf_matrix)\n # So 22+61 correct predictions and 13+44 wrong predictions\n \n logit_roc_auc = roc_auc_score(y_test2, logreg.predict(x_test2))\n fpr, tpr, _ = roc_curve(y_test2, logreg.predict_proba(x_test2)[:,1])\n plt.figure()\n plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n plt.plot([0, 1], [0, 1],'r--')\n plt.xlim([0.0, 1.0])\n plt.ylim([0.0, 1.05])\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n plt.title('Receiver operating characteristic')\n plt.legend(loc=\"lower right\")\n plt.show()\n \"\"","def predict(self, X):\n # ! You could simply call the hypothesis here\n predictions= self._hypothesis(X)\n #empty_predictions = np.zeros((1,X.shape[1]))\n return predictions"],"string":"[\n \"def logistic_predict(weights, data):\\n\\n # TODO: Finish this function\\n\\n return y\",\n \"def logistic_predict(self, x: np.array) -> np.array:\\r\\n if self.LogisticModel is None:\\r\\n print('Logistic Model not trained, please run logistic_fit first!')\\r\\n return None\\r\\n else:\\r\\n return self.LogisticModel.predict(x)\",\n \"def test_class_logistic(\\n y_train_dep,\\n y_test_dep,\\n y_train_preds_lr,\\n y_test_preds_lr):\\n # Ensure the function works\\n try:\\n cls.classification_logistic_results(\\n y_train_dep, y_test_dep, y_train_preds_lr, y_test_preds_lr\\n )\\n logging.info(\\n \\\"Successfully Plotting Classification Results using logistic regression\\\"\\n )\\n except Exception as err:\\n logging.error(\\\"Errors in plotting logistic classification results\\\")\\n raise err\\n # Ensure the output exists\\n try:\\n assert os.path.isfile(\\\"images/results/assessment_logistic.png\\\")\\n except AssertionError as err:\\n logging.error(\\\"Errors in plotting logistic classification file\\\")\\n raise err\",\n \"def test_04_predict(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-predict-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n\\n ## update the log\\n y_pred = [0]\\n y_proba = [0.6,0.4]\\n runtime = \\\"00:00:02\\\"\\n model_version = 0.1\\n country = \\\"india\\\"\\n target_date = '2018-01-05'\\n\\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\\n model_version, test=True, prefix=LOG_PREFIX)\\n\\n df = pd.read_csv(log_file)\\n logged_y_pred = [literal_eval(i) for i in df['y_pred'].copy()][-1]\\n self.assertEqual(y_pred,logged_y_pred)\",\n \"def test_logistic_regression(x, y, tune):\\n # Perform classification without tuning\\n lrc = LogisticRegression()\\n pipeline = create_pipeline(lrc)\\n return accuracy(pipeline, x, y)\",\n \"def test_train_logist(x_train_variable, y_train_dep):\\n # Ensure the function works\\n try:\\n lrc = cls.train_logistic(x_train_variable, y_train_dep)\\n logging.info(\\\"Successful Logistic Model\\\")\\n except Exception as err:\\n logging.error(\\\"Errors in Fitting the Logistic Regression\\\")\\n raise err\\n return lrc\",\n \"def fit_and_predict_LR(X_train, Y_train, X_test):\\n\\n # Import the package\\n from sklearn.linear_model import LogisticRegression\\n\\n #referenced to sklearn documentation \\n \\n # fit the model... \\n clf = LogisticRegression().fit(X_train, Y_train) \\n # make predictions \\n predicted_LR = clf.predict(X_test)\\n return predicted_LR\\n ### END SOLUTION ### \",\n \"def run_logistic_regression(training, testing, feature_cols, outcome_col):\\n if 'intercept' not in training.columns:\\n training['intercept'] = 1\\n if 'intercept' not in testing.columns:\\n testing['intercept'] = 1\\n intercept_feature_cols = feature_cols + ['intercept']\\n logit = sm.Logit(training[outcome_col], training[intercept_feature_cols])\\n fitted_logit_model = logit.fit()\\n logit_diagnostics = get_diagnostics(testing[outcome_col], testing[intercept_feature_cols], fitted_logit_model, model_type = 'logit')\\n predicted_logit_probs = fitted_logit_model.predict(testing[intercept_feature_cols])\\n\\n return fitted_logit_model, logit_diagnostics, predicted_logit_probs\",\n \"def test_logistic_regression_c_parameter(params, X_train, X_test, y_train, y_test):\",\n \"def LogisticRegression_sklearn(X_train, X_test, y_train, y_test):\\n\\n\\tlog_reg = LogisticRegression()\\n\\tlog_reg.fit(X_train, y_train.ravel())\\n\\tyPred =log_reg.predict(X_test)\\n\\n\\t#Printing metrics of the logistic regression model\\n\\tprint('Accuracy:', metrics.accuracy_score(y_test, yPred))\\n\\tprint('Precision:', metrics.precision_score(y_test, yPred))\\n\\tprint('Recall', metrics.recall_score(y_test, yPred))\\n\\n\\t#confusion matrix\\n\\n\\tconfusionMatrix = matrix.confusion_matrix(y_test, yPred)\\n\\tsb.heatmap(pd.DataFrame(confusionMatrix), annot= True, fmt='g')\\n\\tplt.title('Confustion matrix with default value 1')\\n\\tplt.ylabel('True values')\\n\\tplt.xlabel('Predicted values')\\n\\tplt.show()\",\n \"def train_logistic_regression(train_x, train_y):\\n\\n logistic_regression_model = LogisticRegression(penalty='l2', C=1.0)\\n logistic_regression_model.fit(train_x, train_y)\\n return logistic_regression_model\",\n \"def train_logistic_regression(train_exs: List[SentimentExample], feat_extractor: FeatureExtractor) -> LogisticRegressionClassifier:\\n lr = LogisticRegressionClassifier(feat_extractor.corpus_length, feat_extractor)\\n alpha = 1e0\\n # beta = 1e-4\\n for epoch in range(8):\\n loss = 0.\\n acc = 0\\n indices = np.arange(len(train_exs))\\n np.random.shuffle(indices)\\n for i in indices:\\n feat = feat_extractor.feats[i]\\n sentimentExample = train_exs[i]\\n y = sentimentExample.label\\n z = 1 / (1 + np.exp(-feat.dot(np.expand_dims(lr.w, axis=1))))[0, 0]\\n loss += -y * np.log(z) - (1 - y) * np.log(1 - z) \\\\\\n # + beta * np.expand_dims(lr.w, axis=0).dot(np.expand_dims(lr.w, axis=1))[0, 0]\\n predict = int(feat.dot(np.expand_dims(lr.w, axis=1))[0, 0] > 0)\\n acc += (predict == y)\\n grad = (z - y) * feat.toarray()[0] # + 2 * beta * lr.w\\n lr.w = lr.w - alpha * grad\\n print(\\\"epoch {:d}, loss: {:f}, accuracy: {:f}\\\".format(epoch, loss / len(train_exs), acc / len(train_exs)))\\n\\n for i in indices:\\n feat = feat_extractor.feats[i]\\n sentimentExample = train_exs[i]\\n y = sentimentExample.label\\n z = 1 / (1 + np.exp(-feat.dot(np.expand_dims(lr.w, axis=1))))[0, 0]\\n loss += -y * np.log(z) - (1 - y) * np.log(1 - z)\\n print(\\\"training loss: {:f}\\\".format(loss / len(train_exs)))\\n\\n return lr\",\n \"def run_logistic(X_train, X_test, y_train, y_test, C=1, penalty = 'l2', solver = 'lbfgs'):\\n \\n logreg = LogisticRegression(fit_intercept=True, C=C, penalty = penalty, solver = solver)\\n logreg.fit(X_train, y_train)\\n get_scores(logreg, X_train, X_test, y_train, y_test)\",\n \"def create_logistic_regression():\\n\\n pause_data = shuffle(pd.read_csv(sys.argv[1]))\\n pause_data = pause_data.replace([np.inf, -np.inf], np.nan).dropna()\\n # X = pause_data.drop([HAS_DEMENTIA, TRANSCRIPT_ID], axis=1)\\n X = pause_data[MEMORY_FEATURES]\\n y = pause_data[HAS_DEMENTIA]\\n split_tracker = []\\n rskf = RepeatedStratifiedKFold(n_splits=10, n_repeats=2, random_state=36851234)\\n # n_repeats 10 too\\n for train_index, test_index in rskf.split(X, y):\\n X_train, X_test = X.iloc[list(train_index)], X.iloc[list(test_index)]\\n y_train, y_test = y.iloc[list(train_index)], y.iloc[list(test_index)]\\n logmodel = LogisticRegression()\\n logmodel.fit(X_train, y_train)\\n predictions = logmodel.predict(X_test)\\n split_tracker.append({\\n TRAIN: train_index,\\n TEST: test_index,\\n PREDICTIONS: predictions,\\n Y_TEST: y_test\\n })\\n accuracy = []\\n f1 = []\\n auc = []\\n print(\\\"Predictions\\\", split_tracker[0])\\n for predictions in split_tracker:\\n # print(classification_report(predictions[Y_TEST], predictions[PREDICTIONS]))\\n accuracy.append(accuracy_score(predictions[Y_TEST], predictions[PREDICTIONS]))\\n f1.append(f1_score(predictions[Y_TEST], predictions[PREDICTIONS]))\\n auc.append(roc_auc_score(predictions[Y_TEST], predictions[PREDICTIONS]))\\n print(accuracy)\\n accuracy = np.array(accuracy)\\n f1 = np.array(f1)\\n auc = np.array(auc)\\n print(len(accuracy))\\n print('mean accuracy: ', accuracy.mean())\\n print('mean f1 score: ', f1.mean())\\n print('mean auc: ', auc.mean())\",\n \"def trainRegressionModel(X,y):\\n # # instantiate a logistic regression model, and fit with X and y\\n # model = LogisticRegression()\\n # model = model.fit(X, y)\\n # # check the accuracy on the training set\\n # print(model.score(X, y))\\n #X['intercept'] = 1.0\\n #del X['isCapitalized']\\n #del X['isNN']\\n #del X['isNNP']\\n #del X['isJJ']\\n #del X['isUpper']\\n #del X['isPrecedingIN']\\n logit = sm.Logit(y, X)\\n result = logit.fit()\\n print(result.summary())\\n print(result.conf_int())\\n model = LogisticRegression()\\n model = model.fit(X, y)\\n print(model.score(X, y))\\n print(y.mean())\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\\n model2 = LogisticRegression()\\n model2.fit(X_train, y_train)\\n # predict class labels for the test set\\n predicted = model.predict(X_test)\\n print(predicted)\\n for i in predicted:\\n if i==1:\\n print(\\\"Test:\\\"+str(i))\\n print(max(predicted))\\n #generate class probabilities\\n probs = model2.predict_proba(X_test)\\n print(probs)\\n # generate evaluation metrics\\n print(\\\"Accuracy: \\\"+str(metrics.accuracy_score(y_test, predicted)))\\n print(\\\"AUC: \\\"+str(metrics.roc_auc_score(y_test, probs[:, 1])))\\n print(metrics.confusion_matrix(y_test, predicted))\\n print(metrics.classification_report(y_test, predicted))\\n\\n from sklearn.cross_validation import cross_val_score\\n # evaluate the model using 10-fold cross-validation\\n scores = cross_val_score(LogisticRegression(), X, y, scoring='accuracy', cv=10)\\n print(scores)\\n print(scores.mean())\",\n \"def test_03_predict(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-predict-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n if os.path.exists(log_file):\\n os.remove(log_file)\\n \\n ## update the log\\n y_pred = [0]\\n y_proba = [0.6,0.4]\\n runtime = \\\"00:00:02\\\"\\n model_version = 0.1\\n country = \\\"india\\\"\\n target_date = '2018-01-05'\\n\\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\\n model_version, test=True, prefix=LOG_PREFIX)\\n \\n self.assertTrue(os.path.exists(log_file))\",\n \"def _predict(self, testX):\\n pass\",\n \"def predict_logit(self, x):\\n with torch.no_grad():\\n y_ = self.tr_model(x)\\n return y_\",\n \"def predict(self, x_test):\\n x_test_norm = self.normalize(x_test)\\n y_pred = self.logistic_function(x_test_norm, self.b0, self.b1)\\n return np.array([1 if p >= 0.5 else 0 for p in y_pred])\",\n \"def test_predictor():\",\n \"def predictions(logits):\\n # TODO implement predictions\\n return logits\",\n \"def testLogRegres(weights, dev_x, dev_y):\\n predict_y = []\\n numSamples, numFeatures = np.shape(dev_x)\\n for i in range(numSamples):\\n if sigmoid(dev_x[i, :] * weights) > 0.5:\\n label = 1\\n else:\\n label = 0\\n predict_y.append(label)\\n print('Congratulations, testing complete!')\\n return predict_y\",\n \"def train_logisticRegression(data: np.array, labels: np.array)->None:\\n\\n n_examples = np.size(data, 0)\\n n_features = np.size(data, 1)\\n n_categories = np.size(labels, 1)\\n\\n data = np.hstack((np.ones((n_examples, 1)), data))\\n\\n print(data[0:5, :])\\n\\n X_train, X_test, y_train, y_test, idx_test = split_data(data, labels, 0.7)\\n\\n convergence_goal = 1e-3\\n learning_rate = 0.01\\n\\n theta = np.random.uniform(size=((n_features+1, n_categories)))\\n\\n for i in range(n_categories):\\n\\n cost_var = 1\\n\\n previous_cost = 1e6\\n iterations = 0\\n cost_to_plot = []\\n\\n while cost_var > convergence_goal:\\n iterations += 1\\n cost, grad = costFunction(X_train, y_train[:, i], theta[:, i])\\n theta[:, i] = update_theta(theta[:, i], grad, learning_rate)\\n cost_var = previous_cost - cost\\n previous_cost = cost\\n if iterations == 1: cost_var = 1\\n cost_to_plot.append(cost)\\n # print(cost)\\n\\n plt.plot(range(iterations), cost_to_plot, 'g-', label = 'cost')\\n plt.xlabel('iterations')\\n plt.ylabel('cost')\\n # plt.show()\\n\\n predictions = lrPredict(theta, X_test)\\n\\n print(predictions[0:5, :])\\n print(y_test[0:5, :])\\n\\n accuracy = np.mean([p == l for p, l in zip(predictions, y_test)])\\n print(\\\"Accuracy = {}\\\".format(accuracy))\\n\\n pass\",\n \"def test_using_predict(self):\\n [X, labels, Y] = self.gen_data()\\n # Call algorithm\\n bias = multiLogReg(self.sds.from_numpy(\\n X), self.sds.from_numpy(Y), verbose=False).compute()\\n\\n [m, y_pred, acc] = multiLogRegPredict(self.sds.from_numpy(\\n X), self.sds.from_numpy(bias), self.sds.from_numpy(Y), verbose=False).compute()\\n\\n self.assertTrue(acc > 98)\",\n \"def test_test_regression_model(self):\\n model = RegressionTestModel()\\n example = {'x_1': 3, 'x_2': 2}\\n pred = list(model.predict([example]))[0]\\n self.assertEqual(pred['score'], 20)\",\n \"def predict(self, model, x_test):\\n pass\",\n \"def test(self) -> None:\\n\\n self._predictions = self._lr.predict(self._X_test)\",\n \"def train_logistic_regression(X_train_input, y_train_input, C=1):\\r\\n from sklearn.linear_model import LogisticRegression\\r\\n logr_clf = LogisticRegression(C=C)\\r\\n logr_clf.fit(X_train_input, y_train_input)\\r\\n return logr_clf\",\n \"def testLogisticRegression_TensorData(self):\\n language_column = tf.contrib.layers.sparse_column_with_hash_bucket(\\n 'language', hash_bucket_size=20)\\n feature_columns = [\\n tf.contrib.layers.embedding_column(language_column, dimension=1),\\n tf.contrib.layers.real_valued_column('age')\\n ]\\n\\n classifier = tf.contrib.learn.DNNClassifier(\\n n_classes=2,\\n feature_columns=feature_columns,\\n hidden_units=[3, 3],\\n config=tf.contrib.learn.RunConfig(tf_random_seed=1))\\n\\n classifier.fit(input_fn=_input_fn, steps=100)\\n\\n scores = classifier.evaluate(input_fn=_input_fn, steps=1)\\n self.assertGreater(scores['accuracy'], 0.9)\\n self.assertLess(scores['loss'], 0.3)\\n predict_input_fn = functools.partial(_input_fn, num_epochs=1)\\n predictions = list(\\n classifier.predict(input_fn=predict_input_fn, as_iterable=True))\\n self.assertListEqual(predictions, [1, 0, 0])\",\n \"def test_predict(self):\\n self.regression_single.predict(self.X_test)\\n self.assertTrue(len(self.regression_single.y_pred))\\n self.regression_boston.predict(self.boston_x_test)\\n self.assertTrue(len(self.regression_boston.y_pred))\",\n \"def logistic(weights, data, targets, hyperparameters):\\n\\n # TODO: Finish this function\\n\\n return f, df, y\",\n \"def test_predict():\\n df = pd.DataFrame(dict(x=[1.0, 2.0, 3.0, 4.0]))\\n\\n class Model(Poisson):\\n dv = \\\"y\\\"\\n features = dict(x=dict(transformer=lambda x: x.x, prior=dist.Normal(0, 1)))\\n\\n config = {\\\"samples\\\": {\\\"x\\\": onp.ones((2, 10))}}\\n model = Model.from_dict(config)\\n pred = model.predict(df)\\n log_pred = onp.log(pred).round(2)\\n assert df.x.astype(\\\"float32\\\").equals(log_pred.astype(\\\"float32\\\"))\",\n \"def predict():\\n import trace\\n trace.predict()\",\n \"def test_none_alpha_results() -> None:\\n estimator = LogisticRegression()\\n estimator.fit(X, y)\\n y_pred_est = estimator.predict(X)\\n mapie = MapieClassifier(estimator=estimator, cv=\\\"prefit\\\")\\n mapie.fit(X, y)\\n y_pred_mapie = mapie.predict(X)\\n np.testing.assert_allclose(y_pred_est, y_pred_mapie)\",\n \"def log_reg(x_train, y_train):\\n\\n log_reg_classifier = LogisticRegression(max_iter=1000, solver='lbfgs')\\n log_reg_classifier.fit(x_train, y_train)\\n return log_reg_classifier\\n\\n # log_reg_classifier.fit(x_train, y_train)\",\n \"def test_predict_proba_nonnegative():\\n\\n def check_for_negative_prob(proba):\\n for p in np.ravel(proba):\\n assert np.round(p,7) >= 0\\n\\n clf = mord.LogisticAT(alpha=0.)\\n clf.fit(X, y)\\n check_for_negative_prob(clf.predict_proba(X))\\n\\n clf2 = mord.LogisticIT(alpha=0.)\\n clf2.fit(X, y)\\n check_for_negative_prob(clf2.predict_proba(X))\\n\\n clf3 = mord.LogisticSE(alpha=0.)\\n clf3.fit(X, y)\\n check_for_negative_prob(clf3.predict_proba(X))\",\n \"def logistic_predict(weights, data):\\n #####################################################################\\n # TODO: #\\n # Given the weights and bias, compute the probabilities predicted #\\n # by the logistic classifier. #\\n #####################################################################\\n \\n y = None\\n N = len(data)\\n M = len(weights) - 1\\n temp = np.ones([N, M + 1])\\n temp[: N, : M] = np.array(data)\\n data = temp\\n\\n\\n\\n # print(np.matmul(data, weights).shape)\\n z = np.matmul(data, weights)\\n y = sigmoid(z)\\n #####################################################################\\n # END OF YOUR CODE #\\n #####################################################################\\n return y\",\n \"def test_logistic():\\n r=np.random.normal(size=20)\\n assert np.isclose( ilogistic(logistic(r)),r ).all()\",\n \"def __init__(self, reg_penalty='l2', reg_inv=1.0, k_fold=5, random_state=0):\\n print(\\\"Initialize model Logistic Regression\\\")\\n self.reg_penalty = reg_penalty\\n self.reg_inv = reg_inv\\n self.k_fold = k_fold\\n self.random_state = random_state\\n self.model = sklearn.linear_model.LogisticRegression(penalty=self.reg_penalty,\\n C=self.reg_inv,\\n max_iter=1000, \\n random_state=self.random_state)\",\n \"def predict_proba(self, X):\\n return super(SGDLogistic, self).predict(X)\",\n \"def test_model(preds, target):\\n ### START CODE HERE (Replace instances of 'None' with your code) ###\\n print(preds.shape, target.shape)\\n total_log_ppx = np.sum(preds * tl.one_hot(target, preds.shape[-1]), axis= -1) # HINT: tl.one_hot() should replace one of the Nones\\n print(total_log_ppx.shape)\\n \\n non_pad = 1.0 - np.equal(target, 0) # You should check if the target equals 0\\n ppx = total_log_ppx * non_pad # Get rid of the padding\\n\\n log_ppx = np.sum(ppx) / np.sum(non_pad)\\n ### END CODE HERE ###\\n \\n return -log_ppx\",\n \"def predict(self, params, test):\\n vect_logit = np.vectorize(logit)\\n vect_comp = np.vectorize(complement)\\n n_test = np.shape(test)[0]\\n prior = self.model_prior\\n conditional = self.model_conditional\\n K = len(prior)\\n p = np.shape(conditional)[1]\\n test_p = np.shape(test)[1]\\n if p != test_p:\\n print \\\"Error: Number of training and testing features differ\\\"\\n return\\n preds = []\\n weight = vect_logit(conditional)\\n # Stores weight matrix\\n condition_comp = vect_comp(conditional)\\n intercept = np.log(\\n prior) + np.sum(np.apply_along_axis(np.log, 1, condition_comp), axis=1)\\n # Stores intercept vector\\n weight = weight.transpose()\\n classifier = test.dot(weight) + intercept\\n preds = np.argmax(classifier, axis=1) + 1\\n # Add 1 because the classes have a 1-based index\\n preds = preds.reshape(n_test, 1)\\n return preds\",\n \"def main():\\r\\n x = [\\r\\n [ 1,1 ], [ 0,0 ], [ 1,0 ], [ 0,0 ], [ 0,0 ], [ 0,0 ],\\r\\n [ 0,0 ], [ 0,0 ], [ 1,1 ], [ 0,0 ], [ 0,0 ], [ 1,0 ],\\r\\n [ 1,0 ], [ 0,0 ], [ 1,1 ], [ 0,0 ], [ 1,0 ], [ 0,0 ]\\r\\n ]\\r\\n\\r\\n # Encoding of the correct classes for the training material\\r\\n y = [1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0]\\r\\n b = BinaryLogisticRegression(x, y)\\r\\n b.fit()\\r\\n b.print_result()\",\n \"def train_model(self):\\r\\n alpha, accuracy_rate = self.select_model()\\r\\n # Initialize logistic regression with alpha(learning rate)\\r\\n lg = logisticregression(C=alpha)\\r\\n # Train the model.\\r\\n lg.fit(self.training_data, self.target_data)\\r\\n # Save the trained model as .pkl file.\\r\\n joblib.dump(value=lg, filename=self.intention_id+'.pkl', compress=1)\\r\\n print \\\"Estimated Parameters of Logistic Regression\\\"\\r\\n # Estimated parameters of logistic regression.\\r\\n print lg.get_params()\",\n \"def _train(self, log_prob):\\n raise NotImplementedError\",\n \"def test_valid_prediction(alpha: Any) -> None:\\n model = LogisticRegression(multi_class=\\\"multinomial\\\")\\n model.fit(X_toy, y_toy)\\n mapie = MapieClassifier(estimator=model, cv=\\\"prefit\\\")\\n mapie.fit(X_toy, y_toy)\\n mapie.predict(X_toy, alpha=alpha)\",\n \"def predict(self, X):\",\n \"def predict(self, X):\",\n \"def predict(self, X):\\n ...\",\n \"def predict(self, X):\\n ...\",\n \"def predict(self, X):\\n ...\",\n \"def create_regression(f_train, f_test, l_train, l_test):\\n model = LogisticRegression()\\n\\n # train it on training data\\n model.fit(f_train, l_train)\\n\\n # gather the model's predictions for train\\n train_predictions = model.predict(f_train)\\n\\n # gather the model's predictions for test\\n test_predictions = model.predict(f_test)\\n\\n # calculate accuaracy of train\\n print('Reg Train Accuracy: ', accuracy_score(l_train, train_predictions))\\n\\n # calculate accuracy of test\\n print('Reg Test Accuracy: ', accuracy_score(l_test, test_predictions))\\n\\n return model\",\n \"def predict_1(trained_model, X_test, y_test):\\n # Predict with test data\\n start_time = timeit.default_timer()\\n test_prediction = trained_model.predict(X_test)\\n end_time = timeit.default_timer()\\n time = end_time - start_time\\n speed = int(X_test.shape[0] / time)\\n \\n # Get loss and accuracy\\n test_loss, test_accuracy = trained_model.evaluate(X_test, y_test)\\n \\n # Prepare results messages\\n msg_time = \\\"prediction time: {:.3f}s ({}px/s)\\\\n\\\".format(time, speed)\\n msg_test_loss = \\\"test_loss: {:.3f}\\\\n\\\".format(test_loss)\\n msg_test_acc = \\\"test_accuracy: {:.3f}\\\\n\\\\n\\\".format(test_accuracy)\\n \\n # Write results messages\\n with open(OUTPUT_FILE, 'a') as f:\\n f.write(msg_time)\\n f.write(msg_test_loss)\\n f.write(msg_test_acc)\",\n \"def predict(self, X_):\\n X = np.c_[np.ones(X_.shape[0]), X_] # Add one for bias to the first columns\\n predictions = np.zeros(X.shape[0])\\n ### YOUR CODE HERE\\n z = X.dot(self.w)\\n predictions = log_reg.logistic(z)\\n predictions = np.round(predictions)\\n ### END CODE\\n assert predictions.shape == (X.shape[0],)\\n return predictions.astype('int64')\",\n \"def logistic_predict(weights, data):\\n\\n # TODO: Finish this function\\n data = mod_data(data)\\n z_array = get_z_array(weights, data)\\n return sigmoid(z_array)\",\n \"def decision_function(self, Xtt):\\n # predict decision score on test dataset\\n self.logger.info(\\n self.__name__ + ' predicts decision scores on {:d} samples.'.format(Xtt.shape[0]))\",\n \"def predict_evidences(self, X):\",\n \"def predict_probs(self, x):\\n # Note start time\\n start_time = time.time()\\n\\n # Insert a column of 1 in the vector X\\n x = np.insert(x,0,1,axis=1)\\n\\n # Initiate array to store predictions\\n predictions = []\\n\\n # Get the probability value of being in class 1 for each training example\\n for _ in x: \\n predictions.append(self.logistic_function(self.weights.dot(_)))\\n\\n # Note end time\\n end_time = time.time()\\n\\n # Log Predict Runtime\\n print(\\\"Logistic Regression predict time: {0:.2f}s\\\".format(end_time - start_time))\\n\\n return predictions\",\n \"def train_logistic_regression(train_exs: List[SentimentExample],\\n feat_extractor: FeatureExtractor) -> LogisticRegressionClassifier:\\n indexer = feat_extractor.get_indexer()\\n weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\\n learning_rate = 0.1\\n for i in range(15):\\n for ex in train_exs:\\n features_of_str = feat_extractor.extract_features(ex.words, False)\\n expo = math.exp(np.dot(weights, features_of_str))\\n possibility = expo / (1 + expo)\\n gradient_of_w = np.dot(ex.label - possibility, features_of_str)\\n weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\\n return LogisticRegressionClassifier(weights, feat_extractor)\\n\\n # Methods for plotting average training loss\\n\\n # x = np.arange(0, 14)\\n # # learning_rate = 1\\n # indexer = feat_extractor.get_indexer()\\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\\n # avrg_losses = np.zeros(14)\\n # for i in range(15):\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\\n # weights = np.add(weights, gradient_of_w)\\n # loss = 0\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\\n # avrg_losses[i - 1] = loss / train_exs.__len__()\\n # plt.plot(x, avrg_losses)\\n #\\n # # learning_rate = 0.01\\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\\n # learning_rate = 0.01\\n # avrg_losses = np.zeros(14)\\n # for i in range(15):\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\\n # weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\\n # loss = 0\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\\n # avrg_losses[i - 1] = loss / train_exs.__len__()\\n # plt.plot(x, avrg_losses)\\n #\\n # # learning_rate = 0.1\\n # weights = np.transpose(np.zeros(indexer.__len__(), dtype=int))\\n # learning_rate = 0.1\\n # avrg_losses = np.zeros(14)\\n # for i in range(15):\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # gradient_of_w = np.dot(ex.label - possibility, features_of_str)\\n # weights = np.add(weights, np.dot(learning_rate, gradient_of_w))\\n # loss = 0\\n # for ex in train_exs:\\n # features_of_str = feat_extractor.extract_features(ex.words, False)\\n # expo = math.exp(np.dot(weights, features_of_str))\\n # possibility = expo / (1 + expo)\\n # loss += -(ex.label * math.log(possibility) + (1 - ex.label) * math.log(1 - possibility))\\n # avrg_losses[i - 1] = loss / train_exs.__len__()\\n # plt.plot(x, avrg_losses)\\n # plt.xlabel('Epochs')\\n # plt.ylabel('Average Training Loss')\\n # plt.legend(['step size 1', 'step size 0.01', 'step size 0.1'], loc='upper left')\\n # plt.show()\\n # return LogisticRegressionClassifier(weights, feat_extractor)\",\n \"def logistic(weights, data, targets, hyperparameters):\\n y = logistic_predict(weights, data)\\n\\n #####################################################################\\n # TODO: #\\n # Given weights and data, return the averaged loss over all data #\\n # points, gradient of parameters, and the probabilities given by #\\n # logistic regression. #\\n #####################################################################\\n f = None\\n df = None\\n\\n f = evaluate(targets, y)[0]\\n\\n N = len(data)\\n M = len(weights) - 1 \\n temp = np.ones([N, M + 1])\\n temp[: N, : M] = np.array(data)\\n\\n\\n df = np.zeros([M+1, 1])\\n\\n df[:, 0] = np.array([[np.mean([(y.flatten()[i] - targets.flatten()[i]) * temp[i][j] for i in range(0, N)]) for j in range(0, M + 1)],])\\n\\n # df = np.matrix([[np.mean([(y[i] - targets[i]) * temp[i][j] for i in range(0, N)]) for j in range(0, M + 1)],])\\n\\n #####################################################################\\n # END OF YOUR CODE #\\n #####################################################################\\n return f, df, y\",\n \"def fit_logistic_regression():\\n\\n logger.debug(\\\"Running the fit_logistic_regression function now\\\")\\n\\n #Loading the configuration\\n with open(os.path.join(\\\"config\\\",\\\"config.yml\\\"), \\\"r\\\") as f:\\n config = yaml.safe_load(f)\\n\\n #Loading and pre processing the data\\n logger.debug(\\\"Loading and pre processing the data\\\")\\n train_df = load_data(config[\\\"load_data\\\"][\\\"train_file\\\"])\\n train_df = pre_process_data(train_df, resample = True, resample_count = 500000)\\n\\n #Defining Pipeline\\n pipeline = Pipeline([\\n ('tfidf', TfidfVectorizer(analyzer='word', token_pattern=r'[A-Za-z0-9@-]+')),\\n ('model', LogisticRegression(random_state=12345, verbose = 1, solver = 'saga')),\\n ])\\n\\n #Defining parameters to vary\\n parameters = {\\n 'tfidf__max_df': (0.25, 0.5, 0.75),\\n 'tfidf__max_features': (None, 5000, 10000, 50000),\\n 'tfidf__ngram_range': ((1, 1), (1, 2)),\\n 'model__C': (0.01, 1, 100)\\n }\\n\\n scoring_list = [\\\"accuracy\\\", \\\"f1\\\", \\\"precision\\\", \\\"recall\\\", \\\"roc_auc\\\"]\\n \\n #Performing 5fold CV to determine best hyperparameters\\n model = GridSearchCV(pipeline, parameters, cv=5,\\n n_jobs=-1, verbose=1, scoring=scoring_list, refit='f1',)\\n\\n t0 = datetime.datetime.now()\\n\\n model.fit(train_df[\\\"Review\\\"].tolist(), train_df[\\\"Ratings\\\"].to_numpy())\\n \\n logger.info(\\\"Grid Search performed in {}\\\".format(str(datetime.datetime.now()-t0)))\\n\\n #Saving results\\n res_df = pd.DataFrame(model.cv_results_)\\n res_df.to_csv(os.path.join(config[\\\"summary_stats\\\"][\\\"save_location\\\"], \\\"LogisticRegressionResults.csv\\\"))\\n \\n #Saving the model\\n pickle.dump(model, open(os.path.join(config[\\\"models\\\"][\\\"save_location\\\"], \\\"LogisticRegression.pkl\\\"),'wb'))\\n\\n return\",\n \"def train(self):\\n feature = Feature(trained=False)\\n classifier = LogisticRegression(\\n penalty='l2',\\n max_iter=100,\\n solver='liblinear',\\n random_state=self.RAND_SEED)\\n\\n true_labels = []\\n predicted_labels = []\\n\\n for subj in self.subjects:\\n print(subj)\\n # preprocess training and testing set\\n self.dataset_gen(subject=subj, valid=False)\\n\\n # train and predict\\n pipeline_steps = [('vectorized', feature.vector)]\\n if self.istfidf:\\n pipeline_steps.append(('tf-idf', feature.tfidftransform))\\n if self.islda == 'small':\\n pipeline_steps.append(('lda', feature.ldatransform_small))\\n elif self.islda == 'large':\\n pipeline_steps.append(('lda', feature.ldatransform_large))\\n else:\\n pass\\n if self.isnorm:\\n pipeline_steps.append(('scalar', StandardScaler(with_mean=False)))\\n pipeline_steps.append(('clf', classifier))\\n model = Pipeline(steps=pipeline_steps)\\n\\n model.fit(self.X_train, self.y_train)\\n\\n predicted = model.predict(self.X_test)\\n # hamming\\n predicted_labels.append(predicted)\\n true_labels.append(self.y_test)\\n\\n true_matrix, pred_matrix = np.array(true_labels, int).T, np.array(predicted_labels, int).T\\n true_matrix[true_matrix == -1] = 0\\n pred_matrix[pred_matrix == -1] = 0\\n\\n evaluation = Evaluation(self.subjects)\\n evaluation.model_evaluate(true_matrix=true_matrix, pred_matrix=pred_matrix, model_name=self.modelname)\",\n \"def train(references, corpus):\\n sim_vectors, classes = mod.model(references, corpus, labeled=True)\\n pred = lm.LogisticRegression()\\n pred.fit(sum(sim_vectors, []), classes)\\n return pred\",\n \"def logistic_predict(weights, data):\\n \\n t = np.transpose(np.repeat(np.reshape(weights[:-1], (len(weights)-1, 1)), len(data), axis = 1))\\n f_e = data * t\\n z_sums = np.sum(f_e, axis=1) \\n \\n x = -z_sums - weights[-1]\\n y = sigmoid(z_sums + weights[-1])\\n\\n return np.reshape(y, (len(y), 1))\",\n \"def predict(test_features, test_labels, network, config, log_date, log_timestamp):\\n # set network to eval mode\\n network.eval()\\n # helper objects\\n test_preds = []\\n test_gt = []\\n\\n # initialize test dataset and loader\\n dataset = torch.utils.data.TensorDataset(torch.from_numpy(test_features).float(), torch.from_numpy(test_labels))\\n testloader = DataLoader(dataset,\\n batch_size=config['batch_size'],\\n num_workers=2,\\n shuffle=False,\\n )\\n\\n with torch.no_grad():\\n # iterate over test dataset\\n for i, (x, y) in enumerate(testloader):\\n # send x and y to GPU\\n inputs, targets = x.to(config['gpu']), y.to(config['gpu'])\\n\\n # send inputs through network to get predictions and calculate softmax probabilities\\n test_output = network(inputs)\\n test_output = torch.nn.functional.softmax(test_output, dim=1)\\n\\n # create predictions and append them to final list\\n y_preds = np.argmax(test_output.cpu().numpy(), axis=-1)\\n y_true = targets.cpu().numpy().flatten()\\n test_preds = np.concatenate((np.array(test_preds, int), np.array(y_preds, int)))\\n test_gt = np.concatenate((np.array(test_gt, int), np.array(y_true, int)))\\n\\n cls = np.array(range(config['nb_classes']))\\n print('\\\\nTEST RESULTS: ')\\n print(\\\"Avg. Accuracy: {0}\\\".format(jaccard_score(test_gt, test_preds, average='macro')))\\n print(\\\"Avg. Precision: {0}\\\".format(precision_score(test_gt, test_preds, average='macro')))\\n print(\\\"Avg. Recall: {0}\\\".format(recall_score(test_gt, test_preds, average='macro')))\\n print(\\\"Avg. F1: {0}\\\".format(f1_score(test_gt, test_preds, average='macro')))\\n\\n print(\\\"\\\\nTEST RESULTS (PER CLASS): \\\")\\n print(\\\"Accuracy: {0}\\\".format(jaccard_score(test_gt, test_preds, average=None, labels=cls)))\\n print(\\\"Precision: {0}\\\".format(precision_score(test_gt, test_preds, average=None, labels=cls)))\\n print(\\\"Recall: {0}\\\".format(recall_score(test_gt, test_preds, average=None, labels=cls)))\\n print(\\\"F1: {0}\\\".format(f1_score(test_gt, test_preds, average=None, labels=cls)))\\n\\n if config['save_test_preds']:\\n mkdir_if_missing(os.path.join('logs', log_date, log_timestamp))\\n np.save(os.path.join('logs', log_date, log_timestamp, 'test_preds.npy'), test_output)\",\n \"def test_predict():\\n\\n tpot_obj = TPOTClassifier()\\n\\n try:\\n tpot_obj.predict(testing_features)\\n assert False # Should be unreachable\\n except ValueError:\\n pass\",\n \"def predict_and_test(weight, test_data, test_label, regression= \\\"logistic\\\"):\\n pred_test, _ = get_output(weight, test_data, regression= regression)\\n acc = get_accuracy(pred_test, test_label, regression= regression)\\n return acc, pred_test\",\n \"def predict_log_proba(self, X):\\n X = _validate_X(X)\\n return self.best_estimator_.predict_log_proba(X)\",\n \"def predict():\\n to_predict = np.zeros(5).reshape(1, 5)\\n features = ['is_male', 'num_interactions_with_cust_service', 'late_on_payment', 'age', 'years_in_contract']\\n for i, feat in enumerate(features):\\n if request.args.get(feat) is not None:\\n to_predict[0][i] = request.args.get(feat)\\n\\n response = clf2.predict(to_predict)\\n\\n if response:\\n return \\\"The customer is likely to churn\\\"\\n else:\\n return \\\"He is a loyal customer\\\"\",\n \"def test():\\n # load dataset and model\\n X, observed_y = load_data('../data/dev.txt')\\n\\n model = pickle.load(open('test.model', 'rb'))\\n model.traverse()\\n\\n # predict labels for dataset\\n preds = model.predict(X)\\n\\n # print(preds)\\n # output model predictions\\n np.savetxt('test.predictions', preds, fmt='%s')\",\n \"def predict(model, X_testing):\\n predictions = model.predict(X_testing)\\n\\n return predictions\",\n \"def log_reg(x, y, s):\\n usx = np.array(x)\\n usy = np.array(y)\\n\\n # split data into train and validation set\\n x_train, x_test, y_train, y_test = train_test_split(usx, usy, test_size=s)\\n cls_log = LogisticRegression()\\n cls_log.fit(x_train, y_train)\\n y_predict = cls_log.predict(x_test)\\n\\n # select only the probabilities of being fraud\\n y_pred_prob = cls_log.predict_proba(x_test)[:, 1]\\n return y_predict, y_test, y_pred_prob\",\n \"def predict(x, clf):\\n\\n y_pred, y_prob = clf.predict(x), clf.predict_proba(x)\\n return y_pred, y_prob\",\n \"def scoring_function(self, model, y_true, y_predicted_probability):\",\n \"def test_setup_log_reg_classifier(self):\\n \\n\\n model ,vec, x_testing=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\\\"text\\\", method=\\\"count\\\")\\n \\n model2 ,vec_tfidf, x_testing2=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\\\"text\\\", method=\\\"tfidf\\\")\\n \\n \\n \\\"\\\"\\\" Test correct data types for countVectorizer\\\"\\\"\\\" \\n \\n self.assertIsInstance(vec,\\n sklearn.feature_extraction.text.CountVectorizer)\\n \\n self.assertIsInstance(x_testing, scipy.sparse.csr.csr_matrix)\\n \\n self.assertIsInstance(model, sklearn.linear_model.LogisticRegression)\\n \\n \\\"\\\"\\\" Test correct data types TfidfVectorizer\\\"\\\"\\\" \\n \\n self.assertIsInstance(vec_tfidf,\\n sklearn.feature_extraction.text.TfidfVectorizer)\\n \\n self.assertIsInstance(x_testing2, scipy.sparse.csr.csr_matrix)\\n \\n self.assertIsInstance(model2, sklearn.linear_model.LogisticRegression)\\n \\n \\n \\\"\\\"\\\" Test correct behaviour for wrong method\\\"\\\"\\\" \\n \\n self.assertTrue(setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,\\\"text\\\", method=\\\"ijfsiohf\\\"),\\n 1)\",\n \"def predict(self, Xtt):\\n # predict outputs for test dataset\\n self.logger.info(\\n self.__name__ + ' predicts on {:d} samples.'.format(Xtt.shape[0]))\\n pass\",\n \"def predict(x_tst, model):\\n\\n predictions = model.predict(x_tst)\\n return predictions\",\n \"def test_test_model(self):\\n\\n dataset = ClassificationTestDataset()\\n model = ClassificationTestModel(dataset)\\n preds = list(model.predict(dataset.examples))\\n self.assertEqual(np.argmax(preds[0]['preds']), 2)\\n self.assertEqual(np.argmax(preds[1]['preds']), 1)\\n self.assertEqual(np.argmax(preds[2]['preds']), 4)\\n self.assertEqual(np.argmax(preds[3]['preds']), 3)\",\n \"def predict_labels_logistic(self, weights, data):\\n y_pred = sigmoid(np.dot(data, weights))\\n y_pred[np.where(y_pred < 0.5)] = 0\\n y_pred[np.where(y_pred >= 0.5)] = 1\\n return y_pred\",\n \"def on_predict_begin(self, logs=None):\",\n \"def on_predict_begin(self, logs=None):\",\n \"def predict(self, Xtest):\\n ytest = np.zeros(Xtest.shape[0], dtype=int)\\n\\n ### YOUR CODE HERE\\n sig = np.dot(Xtest, self.weights)\\n sig = utils.sigmoid(sig)\\n #print (sig)\\n sig = np.round(sig)\\n #print (sig)\\n for i in range (0, ytest.shape[0]):\\n ytest[i] = int(sig[i])\\n ### END YOUR CODE\\n #print (ytest)\\n assert len(ytest) == Xtest.shape[0]\\n return ytest\",\n \"def predict_4(trained_model, X_test, y_test):\\n # Predict with test data\\n start_time = timeit.default_timer()\\n trained_model.predict(X_test)\\n end_time = timeit.default_timer()\\n time = end_time - start_time\\n speed = int(X_test.shape[0] / time)\\n \\n # Get loss and accuracy\\n test_accuracy = trained_model.score(X_test, y_test)\\n \\n # Prepare results messages\\n msg_time = \\\"prediction time: {:.3f}s ({}px/s)\\\\n\\\".format(time, speed)\\n msg_test_acc = \\\"test_accuracy: {:.3f}\\\\n\\\\n\\\".format(test_accuracy)\\n \\n # Write results messages\\n with open(OUTPUT_FILE, 'a') as f:\\n f.write(msg_time)\\n f.write(msg_test_acc)\",\n \"def test_classifier_age_estimator(x, class_model):\\n return class_model.predict(x)\",\n \"def testLogisticRegression_MatrixData(self):\\n cont_features = [\\n tf.contrib.layers.real_valued_column('feature', dimension=4)]\\n\\n classifier = tf.contrib.learn.DNNClassifier(\\n feature_columns=cont_features,\\n hidden_units=[3, 3],\\n config=tf.contrib.learn.RunConfig(tf_random_seed=1))\\n\\n classifier.fit(input_fn=_iris_input_logistic_fn, steps=100)\\n scores = classifier.evaluate(input_fn=_iris_input_logistic_fn, steps=1)\\n self.assertGreater(scores['accuracy'], 0.9)\\n self.assertLess(scores['loss'], 0.3)\",\n \"def __test_model(self, test, model, expected):\\n prediction = model.predict(test)\\n message = \\\"Prediction:\\\", *prediction, \\\"| Actual:\\\", expected\\n self.__logger.info(message)\",\n \"def test(self):\\n y_list = []\\n y_hat_list = []\\n for ex_dict in ut.TEST_LIST:\\n y_list.append(ex_dict[1])\\n y_hat_list.append(self.predict(ex_dict[0]))\\n acc = ut.compute_accuracy(y_hat_list, y_list)\\n return y_hat_list, acc\",\n \"def predict(self, predPoints=None):\",\n \"def logistic(weights, data, targets, hyperparameters):\\n # TODO: Finish this function\\n n_data = len(data)\\n dim_data = len(data[0])\\n\\n f = 0\\n y = logistic_predict(weights, data)\\n\\n data = mod_data(data)\\n\\n # dl/dw_j = SUM(x_ij * (t_i - (1 - sigmoid(z))))\\n df = np.dot(data.T, (1.0 * targets) - (1 - y))\\n\\n # to calculate f, we need to sum the negative log of all y iff target is 0 and (1-y) iff target is 1\\n f = -1.0 * np.dot(targets.T, np.log(1 - y)) - 1.0 * np.dot(1 - targets.T, np.log(y))\\n\\n # calculate P(C=0|x_i) for all x_i \\n return f[0,0], df, y\",\n \"def predict(self) :\\n y_pred = np.dot(self.W.T,self.X_test) + self.b \\n if self.thr!=-1 :\\n y_pred[y_pred <= self.thr] = -1\\n y_pred[y_pred > self.thr] = 1\\n y_pred = y_pred.astype(\\\"int\\\")\\n corr = 0\\n for i in range(y_pred.shape[1]) :\\n if y_pred[:,i]==self.y_test[:,i] :\\n corr += 1\\n accu = (corr / y_pred.shape[1])*100\\n print(\\\"ACCURACY : {}\\\".format(accu))\\n else :\\n rmse = np.sqrt(np.sum(np.square(self.y_test - y_pred)) / y_pred.shape[1])\\n print(\\\"RMSE : {}\\\".format(rmse))\",\n \"def predict(self, X):\\n pass\",\n \"def predict(self, X):\\n pass\",\n \"def predict(self, X):\\n pass\",\n \"def ovo_test(dic, x_test, y_test, nb_classes):\\n TP = FP = TN = FN = 0\\n correct_predict = 0\\n for index, value in enumerate(x_test):\\n predict = {elem : 0 for elem in nb_classes}\\n for key in dic:\\n predict[key[0] if dic[key].predict([value]) else key[1]] += 1\\n predicted_value = max(predict.items(), key=operator.itemgetter(1))[0]\\n if predicted_value == y_test[index]:\\n correct_predict += 1\\n TP += 1\\n else:\\n FP += 1\\n \\n return correct_predict, TP, FP, TN, FN\",\n \"def train_predict_and_results(data, clf):\\n tra_x, tst_x, tra_y, tst_y = data\\n clf.fit(tra_x, tra_y)\\n prd_y = clf.predict(tst_x)\\n cnf = confusion_matrix(tst_y, prd_y)\\n print (\\\"Classifier: %s \\\\tAccuracy score:%7.2f %%\\\"\\n \\\"\\\\tTN:%5d FP:%5d FN:%5d TP:%5d\\\"\\n % (clf.name, accuracy_score(tst_y, prd_y) * 100,\\n cnf[0][0], cnf[0][1], cnf[1][0], cnf[1][1]))\",\n \"def predict(self, Xtest):\\n ytest = np.dot(Xtest, self.weights)\\n return ytest\",\n \"def predict(test_dataset,test_tX,weights):\\n for idx, dataset in enumerate(test_tX):\\n test_dataset[idx]['Prediction'] = predict_labels(weights[idx],dataset)\\n return test_dataset\",\n \"def predict(self, test_data):\\n return self.leader.predict(test_data)\",\n \"def analysis(houses:pd.DataFrame) -> None:\\n \\n \\\"\\\"\\\"\\n #Me just trying to fit the data without any outside influences\\n f= f'SELLER_HOUSE ~ SQFT_PER + PRICE + C(LOCATION)' \\n result= smf.logit(formula= str(f), data= houses).fit()\\n print(result.summary2())\\n y= ['SELLER_HOUSE']\\n x= ['SQFT_PER', 'PRICE', 'LOC_699 - Not Defined', 'LOC_AA - Airport Area', 'LOC_CG - Columbus Grove',\\n 'LOC_CV - Cypress Village', 'LOC_EASTW - Eastwood', 'LOC_EC - El Camino Real', 'LOC_GP - Great Park',\\n 'LOC_IRSP - Irvine Spectrum', 'LOC_LGA - Laguna Altura', 'LOC_NK - Northpark', 'LOC_NW - Northwood', \\n 'LOC_OC - Oak Creek', 'LOC_OH - Orchard Hills', 'LOC_OT - Orangetree', 'LOC_PS - Portola Springs', \\n 'LOC_QH - Quail Hill', 'LOC_SH - Shady Canyon', 'LOC_SJ - Rancho San Joaquin', 'LOC_STG - Stonegate', \\n 'LOC_Stonegate', 'LOC_TR - Turtle Rock', 'LOC_TRG - Turtle Ridge', 'LOC_UP - University Park',\\n 'LOC_UT - University Town Center', 'LOC_WB - Woodbridge', 'LOC_WD - Woodbury', \\n 'LOC_WI - West Irvine', 'LOC_WN - Walnut (Irvine)', 'LOC_WP - Westpark']\\n x_train, x_test, y_train, y_test= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\\n logreg = LogisticRegression()\\n logreg.fit(x_train, y_train.values.ravel())\\n y_pred= logreg.predict(x_test)\\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test, y_test), 3))\\n # This model is really bad\\n \\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\n houses= houses.drop(['DAYS_ON_MARKET', 'ADDRESS', 'LOCATION',\\n 'STATUS', 'PROPERTY_TYPE', 'ZIP_CODE'], axis= 1)\\n columns= houses.columns.values.tolist()\\n y= ['SELLER_HOUSE']\\n x= [i for i in columns if i not in y]\\n \\n # Over Sampling Using SMOTE \\n x_train, _, y_train, _= train_test_split(houses[x], houses[y], test_size= 0.3, random_state= 500)\\n x_columns= x_train.columns\\n \\n os= SMOTE(random_state= 0)\\n os_x, os_y= os.fit_sample(x_train, y_train)\\n os_x= pd.DataFrame(data= os_x, columns= x_columns)\\n os_y= pd.DataFrame(data= os_y, columns= y)\\n \\n \\n #Recursive Feature Elimination\\n logreg= LogisticRegression(max_iter= 600)\\n rfe= RFE(logreg, 20)\\n rfe= rfe.fit(os_x, os_y.values.ravel())\\n \\n lst= [i for count, i in enumerate(x) if rfe.support_[count] == True]\\n X= os_x[lst]\\n Y= os_y['SELLER_HOUSE']\\n \\n \\n #logit_model= sm.Logit(Y, X)\\n #result= logit_model.fit()\\n #print(result.summary2()) # Model choosen by RCE\\n \\n #These are features have a p-value less than 0.05\\n final_x= ['BATHS', 'ZIP_92602.0', 'ZIP_92618.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\\n #final_x= ['ZIP_92602.0', 'LOC_699 - Not Defined', 'LOC_TR - Turtle Rock', 'LOC_WD - Woodbury']\\n X2= os_x[final_x]\\n \\n logit_model2= sm.Logit(Y, X2)\\n result2= logit_model2.fit()\\n print(result2.summary2()) # Final Model\\n \\n x_train2, x_test2, y_train2, y_test2= train_test_split(X2, Y, test_size= 0.3, random_state= 500)\\n logreg = LogisticRegression()\\n logreg.fit(x_train2, y_train2)\\n \\n y_pred= logreg.predict(x_test2)\\n print('Accuracy of logistic regression classifier on test set:', round(logreg.score(x_test2, y_test2), 2))\\n \\n conf_matrix= confusion_matrix(y_test2, y_pred)\\n print(conf_matrix)\\n # So 22+61 correct predictions and 13+44 wrong predictions\\n \\n logit_roc_auc = roc_auc_score(y_test2, logreg.predict(x_test2))\\n fpr, tpr, _ = roc_curve(y_test2, logreg.predict_proba(x_test2)[:,1])\\n plt.figure()\\n plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\\n plt.plot([0, 1], [0, 1],'r--')\\n plt.xlim([0.0, 1.0])\\n plt.ylim([0.0, 1.05])\\n plt.xlabel('False Positive Rate')\\n plt.ylabel('True Positive Rate')\\n plt.title('Receiver operating characteristic')\\n plt.legend(loc=\\\"lower right\\\")\\n plt.show()\\n \\\"\\\"\",\n \"def predict(self, X):\\n # ! You could simply call the hypothesis here\\n predictions= self._hypothesis(X)\\n #empty_predictions = np.zeros((1,X.shape[1]))\\n return predictions\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7305153","0.7237397","0.7183373","0.7152834","0.71376544","0.7132555","0.7074927","0.6997127","0.69198036","0.69073516","0.68978494","0.68912554","0.687313","0.6825163","0.68070877","0.67960995","0.67626137","0.67519486","0.674025","0.673961","0.67051345","0.6683692","0.6647519","0.66062915","0.65934134","0.6589576","0.65775514","0.6565161","0.6551202","0.65209574","0.65125924","0.65000725","0.647641","0.64565444","0.64509493","0.6450137","0.6444138","0.6440995","0.64309657","0.6425478","0.64223266","0.64105266","0.63972694","0.63818955","0.6374675","0.6348432","0.6321486","0.6321486","0.63203007","0.63203007","0.63203007","0.6320177","0.63110286","0.62957567","0.6284975","0.62816465","0.62561077","0.6242806","0.6242763","0.6237855","0.6225538","0.6222252","0.6213863","0.6213561","0.62053293","0.620482","0.61916214","0.6191054","0.6183332","0.61583745","0.6151508","0.6140515","0.61388695","0.6137847","0.6131657","0.611555","0.61138916","0.6110393","0.6107944","0.61053854","0.61053854","0.61041653","0.6100035","0.6092407","0.60793835","0.60765547","0.60742706","0.60704154","0.6068002","0.60655135","0.6047259","0.6047259","0.6047259","0.60471684","0.60463846","0.6042555","0.6034196","0.60248697","0.6020108","0.6017549"],"string":"[\n \"0.7305153\",\n \"0.7237397\",\n \"0.7183373\",\n \"0.7152834\",\n \"0.71376544\",\n \"0.7132555\",\n \"0.7074927\",\n \"0.6997127\",\n \"0.69198036\",\n \"0.69073516\",\n \"0.68978494\",\n \"0.68912554\",\n \"0.687313\",\n \"0.6825163\",\n \"0.68070877\",\n \"0.67960995\",\n \"0.67626137\",\n \"0.67519486\",\n \"0.674025\",\n \"0.673961\",\n \"0.67051345\",\n \"0.6683692\",\n \"0.6647519\",\n \"0.66062915\",\n \"0.65934134\",\n \"0.6589576\",\n \"0.65775514\",\n \"0.6565161\",\n \"0.6551202\",\n \"0.65209574\",\n \"0.65125924\",\n \"0.65000725\",\n \"0.647641\",\n \"0.64565444\",\n \"0.64509493\",\n \"0.6450137\",\n \"0.6444138\",\n \"0.6440995\",\n \"0.64309657\",\n \"0.6425478\",\n \"0.64223266\",\n \"0.64105266\",\n \"0.63972694\",\n \"0.63818955\",\n \"0.6374675\",\n \"0.6348432\",\n \"0.6321486\",\n \"0.6321486\",\n \"0.63203007\",\n \"0.63203007\",\n \"0.63203007\",\n \"0.6320177\",\n \"0.63110286\",\n \"0.62957567\",\n \"0.6284975\",\n \"0.62816465\",\n \"0.62561077\",\n \"0.6242806\",\n \"0.6242763\",\n \"0.6237855\",\n \"0.6225538\",\n \"0.6222252\",\n \"0.6213863\",\n \"0.6213561\",\n \"0.62053293\",\n \"0.620482\",\n \"0.61916214\",\n \"0.6191054\",\n \"0.6183332\",\n \"0.61583745\",\n \"0.6151508\",\n \"0.6140515\",\n \"0.61388695\",\n \"0.6137847\",\n \"0.6131657\",\n \"0.611555\",\n \"0.61138916\",\n \"0.6110393\",\n \"0.6107944\",\n \"0.61053854\",\n \"0.61053854\",\n \"0.61041653\",\n \"0.6100035\",\n \"0.6092407\",\n \"0.60793835\",\n \"0.60765547\",\n \"0.60742706\",\n \"0.60704154\",\n \"0.6068002\",\n \"0.60655135\",\n \"0.6047259\",\n \"0.6047259\",\n \"0.6047259\",\n \"0.60471684\",\n \"0.60463846\",\n \"0.6042555\",\n \"0.6034196\",\n \"0.60248697\",\n \"0.6020108\",\n \"0.6017549\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6600049"},"document_rank":{"kind":"string","value":"24"}}},{"rowIdx":86688,"cells":{"query":{"kind":"string","value":"Initialize database connection and sessionmaker"},"document":{"kind":"string","value":"def __init__(self):\n engine = db_connect()\n create_tables(engine)\n self.Session = sessionmaker(bind=engine)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def init_db(self):\n\n # The user can provide a custom string\n if self.database is None:\n self.logger.error(\"You must provide a database url, exiting.\")\n sys.exit(1)\n\n self.engine = create_engine(self.database, convert_unicode=True)\n self.session = scoped_session(\n sessionmaker(autocommit=False, autoflush=False, bind=self.engine)\n )\n\n # Database Setup\n Base.query = self.session.query_property()\n\n # import all modules here that might define models so that\n # they will be registered properly on the metadata. Otherwise\n # you will have to import them first before calling init_db()\n import expfactory.database.models\n\n self.Base = Base\n self.Base.metadata.create_all(bind=self.engine)","def init_database(cls):\n conn = config.db_connection_string(Settings)\n cls.Engine = create_engine(conn, echo=Settings.get('DEBUG'))\n cls.Session = sessionmaker(bind=cls.Engine)\n return cls","def init_db(self):\n self.db_config = databaseutils.process_db_config(self.config['db'])\n\n from sqlalchemy import create_engine\n from sqlalchemy.orm import sessionmaker, scoped_session\n self.engine = create_engine(self.db_config.constr, pool_recycle=3600)\n self.session = scoped_session(sessionmaker(bind=self.engine))\n\n # Make sure tables are created\n DB_Base.metadata.create_all(self.engine)","def init_database(self):\n engine = create_engine('sqlite:///todo.db?check_same_thread=False')\n self.Base.metadata.create_all(engine)\n self.session = sessionmaker(bind=engine)()","def __init__(self):\n engine = db_connect()\n self.Session = sessionmaker(bind=engine)","def init_db(self):\n self.db_config = databaseutils.process_db_config(self.config['db'])\n\n from sqlalchemy import create_engine\n from sqlalchemy.orm import sessionmaker, scoped_session\n self.engine = create_engine(self.db_config.constr, pool_recycle=3600)\n self.session = scoped_session(sessionmaker(bind=self.engine))\n\n # Make sure tables are created\n DB_Base.metadata.create_all(self.engine)\n\n if self.sqlite_file is not None:\n dbname = 'sqlite:///%s' % self.sqlite_file\n self.sqlite_engine = create_engine(dbname, echo=False)\n self.sqlite_session = scoped_session(sessionmaker(bind=self.sqlite_engine))\n DB_Base.metadata.create_all(self.sqlite_engine)\n logger.info('Using SQLite %s' % self.sqlite_engine)","def initialize_database():\n\n global DatabaseSession\n from config import config\n\n engine = create_engine(\n config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_URI, \n **config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_OPTIONS)\n\n DatabaseSession = sessionmaker(bind=engine)\n saq.db = scoped_session(DatabaseSession)","def __init__(self):\n engine = db_connect()\n create_table(engine)\n self.Session = sessionmaker(bind=engine)","def __init__(self):\n engine = db_connect()\n create_table(engine)\n self.Session = sessionmaker(bind=engine)","def __init__(self):\n engine = db_connect()\n create_table(engine)\n self.Session = sessionmaker(bind=engine)\n logging.info(\"****SaveRestaurantsPipeline: database connected****\")","def init_database(self):\n # init_database(self.engine)","def __init__(self):\n engine = db_connect()\n create_reals_table(engine)\n self.Session = sessionmaker(bind=engine)","def init_database(self):\n init_database(self.engine)","def __init__(self):\n engine = db_connect()\n create_table(engine)\n self.session = sessionmaker(bind=engine)","def __init__(self):\n engine = db_connect()\n create_table(engine)\n self.Session = sessionmaker(bind=engine)\n logging.info(\"****DuplicatesPipeline: database connected****\")","def init(self):\n self.db.connect()\n try:\n self.db.create_tables([JambiModel], safe=True)\n JambiModel.create(ref='0')\n self.logger.info('Database initialized')\n except IntegrityError:\n self.logger.info('Database was already initialized')\n self.db.close()","def database_setup():\n Base.metadata.create_all(bind=engine)\n db = LocalSession()\n try:\n populate_from_json(db, Vector, str(VECTORS))\n populate_from_json(db, Gender, str(GENDERS))\n populate_from_json(db, Tag, str(TAGS))\n finally:\n db.close()","def __init__(self):\n engine = create_engine(\"postgresql://postgres:1@localhost:5432/postgres\")\n session_class = sessionmaker(bind=engine)\n self.session = session_class()","def init_db(connection, echo):\r\n\r\n # create the database tables as defined\r\n engine = create_engine(connection, echo=echo)\r\n Base.metadata.create_all(engine)\r\n\r\n # create a session\r\n Base.metadata.bind = engine\r\n BaseSession = sessionmaker(bind=engine)\r\n session = BaseSession()\r\n\r\n # set the shared Model session\r\n Model.use_session(session)\r\n\r\n return (engine, session)","def init_db():\n return SQLAlchemy(app, session_options={\n 'expire_on_commit': False,\n })","def initdb():\n db.drop_all()\n db.configure_mappers()\n db.create_all()\n db.session.commit()","def __init__(self):\r\n self.conn = create_connection(DATABASE_PATH)","def init_db():\n db.drop_all()\n db.configure_mappers()\n db.create_all()\n db.session.commit()","def init():\n print(\"Executing initialization\")\n print(db.dsn)\n cursor = yield momoko.Op(\n db.execute,\n \"\"\"\n DROP SCHEMA public CASCADE;\n CREATE SCHEMA public;\n CREATE TABLE game\n (\n game_id text PRIMARY KEY,\n players integer,\n state bytea,\n timestamp timestamp\n );\n CREATE UNIQUE INDEX ix_game_id\n ON game\n (game_id);\n CREATE INDEX ix_timestamp\n ON game\n (timestamp);\n \"\"\")\n try:\n print(cursor.fetchall())\n except psycopg2.ProgrammingError:\n pass\n io = ioloop.IOLoop.instance()\n io.stop()","def _initTestingDB(): \n from sqlalchemy import create_engine\n engine = create_engine('sqlite://')\n from .models import (\n Base,\n TodoUser,\n )\n DBSession.configure(bind=engine)\n Base.metadata.create_all(engine)\n \n return DBSession","def initialize():\n \n db.connect()\n db.create_tables([Product], safe=True)","def setup():\n global connection\n connection = MySQLdb.connect(host=config.get('mysql.host'),\n user=config.get('mysql.user'),\n passwd=config.get('mysql.password'),\n db=config.get('mysql.db'),\n ssl={'ca' : config.get('mysql.cert')})\n init_model(connection)","def initialize():\n DATABASE.connect()\n DATABASE.create_tables([User], safe=True)\n DATABASE.close()","def init_db():\n # We are setting the module variables here for the first time, so disable the warning\n global DB_USER_TABLE # pylint: disable=global-variable-undefined\n global DB_CUSTOMER_TABLE # pylint: disable=global-variable-undefined\n global DB_USER_CUSTOMER_RELS_TABLE # pylint: disable=global-variable-undefined\n global DB_TICKET_TABLE # pylint: disable=global-variable-undefined\n global DB_COMMENT_TABLE # pylint: disable=global-variable-undefined\n\n db = TinyDB(app.config['DB_NAME'])\n\n DB_USER_TABLE = db.table('users')\n DB_CUSTOMER_TABLE = db.table('customers')\n DB_USER_CUSTOMER_RELS_TABLE = db.table('user_customer_rels')\n DB_TICKET_TABLE = db.table('tickets')\n DB_COMMENT_TABLE = db.table('comments')","def setup_db_conn():\n # TODO update so DB does not have to be hard coded\n # Currently DB is hardcoded","def __post_init__(self):\n self.dbase = databases.Database(\n self.dsn,\n min_size=self.min_size,\n max_size=self.max_size\n )\n self.engine, self.meta = self.get_engine_metadata()","def __init__(self):\n self.__engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'.\n format(getenv('HBNB_MYSQL_USER'),\n getenv('HBNB_MYSQL_PWD'),\n getenv('HBNB_MYSQL_HOST'),\n getenv('HBNB_MYSQL_DB')),\n pool_pre_ping=True)\n\n if getenv('HBNB_ENV') == 'test':\n \"\"\" Drop all tables\"\"\"\n Base.metadata.drop_all(self.__engine)\n\n Base.metadata.create_all(self.__engine)\n Session = sessionmaker(self.__engine)\n self.__session = Session()","def init_db(engine):\n base = declarative_base(engine)\n Session = sessionmaker(bind=engine)\n session = Session()\n base.metadata.reflect(bind = engine)\n connection = engine.raw_connection()\n cursor = connection.cursor()\n return session, base, connection, cursor","def initialize():\n DATABASE.connect()\n DATABASE.drop_tables([Journal], safe=True)\n DATABASE.create_tables([Journal], safe=True)\n DATABASE.close()","def __init__(self):\n engine = connect_to_db()\n create_lyrics_table(engine) #declarative base stuff\n self.Session = sessionmaker(bind=engine)\n\n # self.create_connection()\n # self.create_table()","def initialize():\n DATABASE.connect()\n DATABASE.create_tables([User, Entry], safe=True)\n DATABASE.close()","def init_db(cls, app):\n cls.logger.info(\"Initializing database\")\n cls.app = app\n # This is where we initialize SQLAlchemy from the Flask app\n db.init_app(app)\n app.app_context().push()\n db.create_all() # make our sqlalchemy tables","def init_db(cls, app):\n cls.logger.info(\"Initializing database\")\n cls.app = app\n # This is where we initialize SQLAlchemy from the Flask app\n db.init_app(app)\n app.app_context().push()\n db.create_all() # make our sqlalchemy tables","def _init_db():\n import alembic.config\n import alembic.command\n alembic_cfg = alembic.config.Config('alembic.ini')\n alembic_cfg.attributes['configure_logger'] = False\n alembic.command.upgrade(alembic_cfg, 'head')\n _reset_db(get_test_db_session())","def setup_db(config):\n # create the URL from the configuration\n db_url = URL(**config)\n db = create_engine(db_url)\n Session = sessionmaker(db)\n # Link the relational model to the database\n Model.metadata.create_all(db)\n\n return db, Session","def _init_db(self):\n cursor = self._main_connection.cursor()\n cursor.execute(self.sql[\"create_table\"])\n self._main_connection.commit()","def init_db():\n import cerbereapp.models\n Base.metadata.create_all(bind=engine)","def bootstrap(self):\n\n self.db = connection_manager.get(DbConnection, host=self.ip, port=3306, user=self.user, password=self.password)\n\n self.connected = True","def connect(cls):\n engine = create_engine(cls._build_uri(), echo=False)\n cls.Session = sessionmaker()\n cls.Session.configure(bind=engine)","def prepare_database(config):\n global Session\n engine = sqlalchemy.create_engine(config.db_string)\n session_factory = sqlalchemy.orm.sessionmaker(bind=engine)\n Session = sqlalchemy.orm.scoped_session(session_factory)","def setup(db_file):\n global session\n\n db_conn = \"sqlite:///%s\" % db_file\n logger.info(\"DB Connection: %s\" % db_conn)\n engine = create_engine(db_conn, connect_args={'check_same_thread':False})\n engine.Echo = True\n Base.metadata.create_all(engine)\n\n Session = scoped_session(sessionmaker(bind=engine))\n session = Session()\n print \"DB Connection: %s\" % db_conn","def setup_db():\n\n engine = config['tg.app_globals'].sa_engine\n # model.init_model(engine)\n # model.metadata.create_all(engine)","def init_database():\n database.init(DATABASE_NAME)\n database.connect()\n database.execute_sql('PRAGMA foreign_keys = ON')\n if not database.table_exists([Customer]):\n database.create_tables([Customer])\n database.close()","def setup_database(self):\n self.db.setup_database()","def create_db():\n init_postgres(current_app.config['SQLALCHEMY_DATABASE_URI'])","def initialize():\n\n db.connect() # Se conecta\n db.create_tables([Entry], safe=True) # Crea las tablas\n # safe=true evita crear modelos ya creados","def setup_db():\n engine = create_engine(settings.DATABASE)\n ModelBase.metadata.bind = engine\n ModelBase.metadata.create_all(engine)\n\n return scoped_session(sessionmaker(bind=engine))","def __init__(self):\n with open('config.json') as config:\n data = json.load(config)\n\n password = self.decode_password(data['db']['password'])\n db_conn_string = 'postgresql://' + data['db']['username'] + ':' + password + '@' + \\\n data['db']['hostname'] + ':' + data['db']['port'] + '/' + data['db']['database']\n\n self.engine = create_engine(db_conn_string)\n try:\n conn = self.engine.connect()\n if conn is not None:\n print(\"-I- Successful Database Connection\")\n except Exception as e:\n print(\"-W- \" + str(e))","def setup_session():\n print(\"Setting up session\")\n engine = setup_engine()\n Base.metadata.bin = engine\n\n DBSession = sessionmaker(bind=engine)\n session = DBSession()\n\n return session","def initialize_database():\n # Create the schema\n Base.metadata.create_all(engine)\n\n # Create a connection/database session\n session = Session()\n\n # Now, create a few restaurants:\n cupcake = Restaurant(name=\"Cupcakes\")\n five_guys = Restaurant(name=\"Five Guys\")\n ihop = Restaurant(name=\"IHOP\")\n\n # And a few users:\n mike = User(name=\"Mike\")\n ryan = User(name=\"Ryan\")\n\n # And finally a few votes:\n mike.preferences.append(Preference(vote=\"+1\", restaurant=five_guys))\n ryan.preferences.append(Preference(vote=\"+0\", restaurant=five_guys))\n ryan.preferences.append(Preference(vote=\"-0\", restaurant=cupcake))\n\n session.add(mike)\n session.add(ryan)\n session.add(ihop)\n\n session.commit()\n\n session.close()","def initdb(uri=\"sqlite:///packagedata.db\"):\n engine = sqlalchemy.create_engine(uri)\n Base.metadata.create_all(bind=engine)\n Session = sessionmaker(bind=engine)\n return Session","def __init__(self):\n self._connection = get_db_connection()","def init_database(use_mysql=False, dbname=\"sbs\"):\n #engine = create_engine('sqlite:///:memory:', echo=False)\n # \"mysql+mysqldb://{user}:{password}@{host}:{port}/{dbname}\"\n if use_mysql:\n db_setup = dict(user=os.environ.get('MYSQL_LOGIN'),\n password=os.environ.get('MYSQL_PASSWORD'),\n host=\"127.0.0.1\",\n port=os.environ.get('MYSQL_PORT', 3006),\n dbname=dbname\n )\n mysql_setup = \"mysql+mysqldb://{user}:{password}@{host}:{port}/{dbname}?charset=utf8\".format(**db_setup)\n engine = create_engine(mysql_setup, echo=False)\n else:\n engine = create_engine('sqlite:///data.sqlite', echo=False)\n event.Base.metadata.create_all(engine)\n gameinfomodel.Base.metadata.create_all(engine)\n playerinfo.Base.metadata.create_all(engine)\n teaminfomodel.Base.metadata.create_all(engine)\n Session = sessionmaker(bind=engine)\n session = Session()\n return session","def init_neo4j_db():\n try:\n app.neo4j_driver = GraphDatabase.driver(\n app.config[\"NEO4J_URI\"],\n auth=(app.config[\"NEO4J_USER\"], app.config[\"NEO4J_PWD\"])\n )\n except AuthError:\n app.neo4j_driver = None\n app._neo4j_up = True\n except:\n app.neo4j_driver = None\n app._neo4j_up = False","def initialize_database():\n # TODO: Refactor the funtime library\n this.db = Store(this.host).create_lib(this.store_name).get_store()","def init_db() -> None: \n \n Base.metadata.create_all(bind=engine)","def initialize():\n db.connect()\n db.create_tables([Entry], safe=True)","def initialize():\n db.connect()\n db.create_tables([Entry], safe=True)","def setup_db(self) -> None:\n conn = mysql.connector.connect(\n user=self.app.config[\"DATABASE_USER\"], password=self.app.config[\"DATABASE_PASSWORD\"],\n host=self.app.config[\"DATABASE_HOST\"], port=self.app.config[\"DATABASE_PORT\"], raise_on_warnings=True\n )\n try:\n cursor = conn.cursor()\n cursor.execute(\n \"CREATE DATABASE IF NOT EXISTS {} CHARACTER SET utf8\".format(self.app.config[\"DATABASE_NAME\"])\n )\n conn.commit()\n except:\n raise\n else:\n with self.DBManager(self.app) as connection:\n for model in sorted(lib.get_subclasses(lib.models.Model), key=lambda x: x.index):\n model.setup_table(connection=connection)\n finally:\n conn.close()","def init_db():\n current_app.logger.info('Creating database...')\n db.drop_all()\n db.create_all()\n db.session.commit()","def __init__(self, config):\n\n engine = self.__my_create_engine(config)\n\n if not engine:\n raise Exception(\"No engine created\")\n\n engine.connect()\n #metadata = MetaData(bind=engine)\n Session = sessionmaker(bind=engine)\n\n # Set the objects to work with\n self.session = Session()","def initialize(self) -> None:\n # First, establish a connection to the specified database\n try:\n self._connect_to_db()\n except psycopg2.OperationalError: # specified database does not exist\n with psycopg2.connect(database=DATABASE_ENV[\"POSTGRES_DB\"],\n user=self.dbuser, password=self.dbpassword,\n host=self.dbhost, port=str(self.dbport)) as con:\n with con.cursor() as cur:\n con.autocommit = True # cannot create db inside a transaction\n cur.execute(f'CREATE DATABASE \"{self.dbname}\"')\n con.autocommit = False\n self._connect_to_db() # try again\n\n # Second, create the necessary database table, only if required\n with self._connection.cursor() as cur:\n cur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS \"{self.MESSAGE_TABLE_NAME}\" (\n id SERIAL PRIMARY KEY,\n key CHAR(4) NOT NULL,\n value REAL NOT NULL,\n ts TIMESTAMP NOT NULL,\n tz TEXT NOT NULL\n );\n \"\"\")\n self._connection.commit()","def dbinit( *args, **kwargs ):","def __init_database(self):\n from admin.database import init_db\n init_db()","def setup(self):\n self.load_connection_info(self.ini_filename)\n if self.conn_info:\n self.logger.info('Load connection info of Postgres')\n\n psql_connection_info = f\"dbname={self.conn_info['dbname']} \" \\\n f\"user={self.conn_info['user']} \" \\\n f\"password={self.conn_info['password']} \" \\\n f\"port={self.conn_info['port']}\" \n \n check_db = self.create_db(psql_connection_info)\n\n connection = psycopg2.connect((\n f\"dbname=password_manager \" \\\n f\"user={self.conn_info['user']} \" \\\n f\"password={self.conn_info['password']} \" \\\n f\"port={self.conn_info['port']}\")) \n cursor = connection.cursor()\n\n if check_db:\n self.logger.info('Database has been created')\n\n check_tables = self.create_tables(connection, \n cursor, \n self.sql_query_table_person, \n self.sql_query_table_login_data)\n \n if check_tables:\n self.logger.info('Tables have been created')\n else:\n self.logger.info('Tables do not exist')\n else:\n self.logger.info('Database does not exist')\n \n connection.close()\n cursor.close()\n else:\n self.logger.info('Connection to Postgres could not esablished')","def __init__(self):\n self._setup()\n # Encryption/decryption cipher handler\n self.__cipher = self.__get_cipher()\n # Setup the engine for the sqlite database\n self._engine = create_engine(self.db_uri)\n # Configure the SQLAlchemy metadata\n self._metadata = MetaData()\n self._metadata.bind = self._engine\n self._load_db()\n # Configure the auto-mapping base model\n self._base = automap_base(metadata=self._metadata)\n self._base.prepare()\n # Setup a session generator for database connections\n self._session = sessionmaker(bind=self._engine)","def connect_db():\n engine = create_engine('sqlite:///app.db', echo=True)\n Base.metadata.bind = engine\n DBSession = sessionmaker(bind=engine)\n session = DBSession()\n return session","def __init__(self):\n\t\tself.obtainDatabaseConnection()","def connect_db(self):\n try:\n self.connection = self.engine.connect()\n except Exception:\n self.print_std_error()","def init() -> sqlite3.Connection:\n try:\n with open(DATABASE_INIT) as reader:\n sql = reader.read()\n connection = sqlite3.connect(DATABASE, check_same_thread=False)\n connection.executescript(sql)\n return connection\n except FileNotFoundError:\n log.error(DatabaseInitializeError())\n exit(1)","def init_db():\n db.drop_all()\n db.create_all()\n\n print(\"Initialized Connect 4 Database.\")","def __init__(self, connect: str):\n\n # create engine\n self._engine = create_engine(connect)\n self._engine.echo = False\n event.listen(self._engine, 'checkout', Database._checkout_listener)\n\n # and metadata\n MetaData(self._engine)\n\n # and session\n self._session = sessionmaker(bind=self._engine)\n\n # create tables\n Base.metadata.create_all(self._engine, checkfirst=True)","def connect():\n # global ENGINE\n # global Session\n\n # ENGINE = create_engine(\"sqlite:///ratings.db\", echo=True)\n # Session = sessionmaker(bind=ENGINE)\n\n # return Session()\n pass","def init_db():\n\tdb.drop_all()\n\tdb.create_all()\n\n\tprint(\"Initialized Database.\")\n\treturn","async def initialize(self, *, only_init_tables: bool=False):\n self.pool = await aiomysql.create_pool(\n host=self.host, user=self.user, password=self.passwd,\n loop=self.loop)\n db_exists = await self._check_db_exists()\n if not db_exists:\n await self._create_db()\n db_initialized = False\n else:\n db_initialized = await self._check_db_initialized()\n # We close the pool and create a new one because aiomysql doesn't\n # provide an easy way to change the active database for an entire\n # pool, just individual connections.\n self.pool.terminate()\n self.pool = await aiomysql.create_pool(\n host=self.host, user=self.user, password=self.passwd,\n db=self.dbname, loop=self.loop)\n if not db_initialized:\n await self._init_db(only_init_tables)\n await self._upgrade_db()\n log.msg('Database initialized')","def initialize_connection():\n session = imdb.IMDb()\n return session","def __init__(self, connection_url, echo=False):\n if not connection_url:\n raise ValueError('No database connection URL provided.')\n engine = create_engine(connection_url, echo=echo)\n PipelineRun.metadata.create_all(engine)\n self.session_factory = sessionmaker(bind=engine)","def setup_database():\n\n user = 'bard'\n password = 'STORY'\n database = 'story'\n DSN = f\"postgresql://{user}:{password}@postgres:5432/{database}\"\n engine = create_engine(DSN)\n register_tables(engine)\n return engine","def __init__(self):\n user = os.getenv('URL_MYSQL_USER')\n passwd = os.getenv('URL_MYSQL_PWD')\n host = os.getenv('URL_MYSQL_HOST')\n database = os.getenv('URL_MYSQL_DB')\n self.__engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'\n .format(user, passwd, host, database))","def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n self.database_path = \"postgres://{}:{}@{}/{}\".format('postgres', 'postgres', 'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","def initialize():\n db.connect()\n db.create_tables([Expense], safe=True)","def connectToDB():\r\n Base = declarative_base()\r\n engine = create_engine('sqlite:///DBLatency.db', echo=True)\r\n Base.metadata.bind = engine\r\n DBSession = sessionmaker(bind=engine)\r\n session = DBSession()\r\n return session","def _setup_db(app: FastAPI) -> None:\n engine = create_async_engine(str(settings.db_url), echo=settings.db_echo)\n session_factory = async_scoped_session(\n sessionmaker(\n engine,\n expire_on_commit=False,\n class_=AsyncSession,\n ),\n scopefunc=current_task,\n )\n app.state.db_engine = engine\n app.state.db_session_factory = session_factory","def setup_db():\n logger.info('Setting up db')\n setup_all_db()\n setup_emails()","def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n #self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n self.database_path = 'postgresql+psycopg2://{}:{}@{}/{}'.format('postgres','picasso0', 'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"Capstone\"\n self.database_path = \"postgres://{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app , self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n\n self.db.create_all()","def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"trivia_test\"\n self.database_path = \"postgres://{}:{}@{}/{}\".format('student',\n 'student', 'localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","def init_db(database_url, **database_connect_options):\n engine = create_engine(database_url,\n convert_unicode=True,\n **database_connect_options)\n\n Base.metadata.reflect(engine)\n Base.metadata.create_all(engine)\n session = scoped_session(sessionmaker(autocommit=False,\n autoflush=False,\n bind=engine))\n Base.query = session.query_property()\n\n return session","def init_postgres(testing):\n if 'POSTGRESQL_DATABASE_URI' in app.config:\n if not testing:\n # not testing will use request context as scope\n # for sqlalchemy Session object\n from flask import _app_ctx_stack\n import pgsqlutils.base as pgbase\n from pgsqlutils.base import get_db_conf, init_db_conn\n from sqlalchemy.orm import sessionmaker, scoped_session\n dbconf = get_db_conf()\n dbconf.DATABASE_URI = app.config['POSTGRESQL_DATABASE_URI']\n # monkey patching to replace default session\n # by a sessing handled by flask\n pgbase.Session = scoped_session(\n sessionmaker(),\n scopefunc=_app_ctx_stack.__ident_func__)\n init_db_conn()\n else:\n # Testing will use current thread as scope for Session\n from pgsqlutils.base import get_db_conf, init_db_conn\n dbconf = get_db_conf()\n dbconf.DATABASE_URI = app.config['POSTGRESQL_DATABASE_URI']\n init_db_conn()","def _initialize_db():\n conn, c = _get_db_connection()\n\n with open(str(SCHEMA_PATH)) as f:\n c.executescript(f.read())\n\n conn.close()","def bootstrap():\n Base.metadata.create_all(engine)","def setUp(self):\n self.app = create_app()\n self.client = self.app.test_client\n self.database_name = \"databasename\"\n self.database_path = \"postgresql://postgres:usman@{}/{}\".format('localhost:5432', self.database_name)\n setup_db(self.app, self.database_path)\n\n # binds the app to the current context\n with self.app.app_context():\n self.db = SQLAlchemy()\n self.db.init_app(self.app)\n # create all tables\n self.db.create_all()","def _connect(self):\n\n assert not self.__is_connected\n\n self._db_engine = create_engine(self._database.url)\n\n from sqlalchemy import event\n\n if self._database.driver == 'sqlite':\n @event.listens_for(self._db_engine, \"connect\")\n def do_connect(dbapi_connection, *args, **kwargs):\n # disable pysqlite's emitting of the BEGIN statement entirely.\n # also stops it from emitting COMMIT before any DDL.\n dbapi_connection.isolation_level = None\n\n @event.listens_for(self._db_engine, \"begin\")\n def do_begin(conn):\n # emit our own BEGIN\n conn.execute(\"BEGIN\")\n\n self._db_connection = self._db_engine.connect()\n self._db_transaction = self._db_connection.begin()\n\n self._orm_session = Session(\n bind=self._db_connection,\n transaction=self,\n )\n self._orm_session_proxy = weakref.proxy(self._orm_session)\n\n self.__is_connected = True","def setUp(self):\n self.a = backend.dbconnection.DBConnect()","def __init__(self, dialect, database, username=None, password=None,\n host=None, port=None, query=None):\n db_url = URL(drivername=dialect, database=database, username=username,\n password=password, host=host, port=port, query=query)\n self.engine = create_engine(db_url)\n self.session = None\n self.get_session = sessionmaker(bind=self.engine,\n expire_on_commit=False)\n\n # create ORM objects for the tables\n self._define_tables()"],"string":"[\n \"def init_db(self):\\n\\n # The user can provide a custom string\\n if self.database is None:\\n self.logger.error(\\\"You must provide a database url, exiting.\\\")\\n sys.exit(1)\\n\\n self.engine = create_engine(self.database, convert_unicode=True)\\n self.session = scoped_session(\\n sessionmaker(autocommit=False, autoflush=False, bind=self.engine)\\n )\\n\\n # Database Setup\\n Base.query = self.session.query_property()\\n\\n # import all modules here that might define models so that\\n # they will be registered properly on the metadata. Otherwise\\n # you will have to import them first before calling init_db()\\n import expfactory.database.models\\n\\n self.Base = Base\\n self.Base.metadata.create_all(bind=self.engine)\",\n \"def init_database(cls):\\n conn = config.db_connection_string(Settings)\\n cls.Engine = create_engine(conn, echo=Settings.get('DEBUG'))\\n cls.Session = sessionmaker(bind=cls.Engine)\\n return cls\",\n \"def init_db(self):\\n self.db_config = databaseutils.process_db_config(self.config['db'])\\n\\n from sqlalchemy import create_engine\\n from sqlalchemy.orm import sessionmaker, scoped_session\\n self.engine = create_engine(self.db_config.constr, pool_recycle=3600)\\n self.session = scoped_session(sessionmaker(bind=self.engine))\\n\\n # Make sure tables are created\\n DB_Base.metadata.create_all(self.engine)\",\n \"def init_database(self):\\n engine = create_engine('sqlite:///todo.db?check_same_thread=False')\\n self.Base.metadata.create_all(engine)\\n self.session = sessionmaker(bind=engine)()\",\n \"def __init__(self):\\n engine = db_connect()\\n self.Session = sessionmaker(bind=engine)\",\n \"def init_db(self):\\n self.db_config = databaseutils.process_db_config(self.config['db'])\\n\\n from sqlalchemy import create_engine\\n from sqlalchemy.orm import sessionmaker, scoped_session\\n self.engine = create_engine(self.db_config.constr, pool_recycle=3600)\\n self.session = scoped_session(sessionmaker(bind=self.engine))\\n\\n # Make sure tables are created\\n DB_Base.metadata.create_all(self.engine)\\n\\n if self.sqlite_file is not None:\\n dbname = 'sqlite:///%s' % self.sqlite_file\\n self.sqlite_engine = create_engine(dbname, echo=False)\\n self.sqlite_session = scoped_session(sessionmaker(bind=self.sqlite_engine))\\n DB_Base.metadata.create_all(self.sqlite_engine)\\n logger.info('Using SQLite %s' % self.sqlite_engine)\",\n \"def initialize_database():\\n\\n global DatabaseSession\\n from config import config\\n\\n engine = create_engine(\\n config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_URI, \\n **config[saq.CONFIG['global']['instance_type']].SQLALCHEMY_DATABASE_OPTIONS)\\n\\n DatabaseSession = sessionmaker(bind=engine)\\n saq.db = scoped_session(DatabaseSession)\",\n \"def __init__(self):\\n engine = db_connect()\\n create_table(engine)\\n self.Session = sessionmaker(bind=engine)\",\n \"def __init__(self):\\n engine = db_connect()\\n create_table(engine)\\n self.Session = sessionmaker(bind=engine)\",\n \"def __init__(self):\\n engine = db_connect()\\n create_table(engine)\\n self.Session = sessionmaker(bind=engine)\\n logging.info(\\\"****SaveRestaurantsPipeline: database connected****\\\")\",\n \"def init_database(self):\\n # init_database(self.engine)\",\n \"def __init__(self):\\n engine = db_connect()\\n create_reals_table(engine)\\n self.Session = sessionmaker(bind=engine)\",\n \"def init_database(self):\\n init_database(self.engine)\",\n \"def __init__(self):\\n engine = db_connect()\\n create_table(engine)\\n self.session = sessionmaker(bind=engine)\",\n \"def __init__(self):\\n engine = db_connect()\\n create_table(engine)\\n self.Session = sessionmaker(bind=engine)\\n logging.info(\\\"****DuplicatesPipeline: database connected****\\\")\",\n \"def init(self):\\n self.db.connect()\\n try:\\n self.db.create_tables([JambiModel], safe=True)\\n JambiModel.create(ref='0')\\n self.logger.info('Database initialized')\\n except IntegrityError:\\n self.logger.info('Database was already initialized')\\n self.db.close()\",\n \"def database_setup():\\n Base.metadata.create_all(bind=engine)\\n db = LocalSession()\\n try:\\n populate_from_json(db, Vector, str(VECTORS))\\n populate_from_json(db, Gender, str(GENDERS))\\n populate_from_json(db, Tag, str(TAGS))\\n finally:\\n db.close()\",\n \"def __init__(self):\\n engine = create_engine(\\\"postgresql://postgres:1@localhost:5432/postgres\\\")\\n session_class = sessionmaker(bind=engine)\\n self.session = session_class()\",\n \"def init_db(connection, echo):\\r\\n\\r\\n # create the database tables as defined\\r\\n engine = create_engine(connection, echo=echo)\\r\\n Base.metadata.create_all(engine)\\r\\n\\r\\n # create a session\\r\\n Base.metadata.bind = engine\\r\\n BaseSession = sessionmaker(bind=engine)\\r\\n session = BaseSession()\\r\\n\\r\\n # set the shared Model session\\r\\n Model.use_session(session)\\r\\n\\r\\n return (engine, session)\",\n \"def init_db():\\n return SQLAlchemy(app, session_options={\\n 'expire_on_commit': False,\\n })\",\n \"def initdb():\\n db.drop_all()\\n db.configure_mappers()\\n db.create_all()\\n db.session.commit()\",\n \"def __init__(self):\\r\\n self.conn = create_connection(DATABASE_PATH)\",\n \"def init_db():\\n db.drop_all()\\n db.configure_mappers()\\n db.create_all()\\n db.session.commit()\",\n \"def init():\\n print(\\\"Executing initialization\\\")\\n print(db.dsn)\\n cursor = yield momoko.Op(\\n db.execute,\\n \\\"\\\"\\\"\\n DROP SCHEMA public CASCADE;\\n CREATE SCHEMA public;\\n CREATE TABLE game\\n (\\n game_id text PRIMARY KEY,\\n players integer,\\n state bytea,\\n timestamp timestamp\\n );\\n CREATE UNIQUE INDEX ix_game_id\\n ON game\\n (game_id);\\n CREATE INDEX ix_timestamp\\n ON game\\n (timestamp);\\n \\\"\\\"\\\")\\n try:\\n print(cursor.fetchall())\\n except psycopg2.ProgrammingError:\\n pass\\n io = ioloop.IOLoop.instance()\\n io.stop()\",\n \"def _initTestingDB(): \\n from sqlalchemy import create_engine\\n engine = create_engine('sqlite://')\\n from .models import (\\n Base,\\n TodoUser,\\n )\\n DBSession.configure(bind=engine)\\n Base.metadata.create_all(engine)\\n \\n return DBSession\",\n \"def initialize():\\n \\n db.connect()\\n db.create_tables([Product], safe=True)\",\n \"def setup():\\n global connection\\n connection = MySQLdb.connect(host=config.get('mysql.host'),\\n user=config.get('mysql.user'),\\n passwd=config.get('mysql.password'),\\n db=config.get('mysql.db'),\\n ssl={'ca' : config.get('mysql.cert')})\\n init_model(connection)\",\n \"def initialize():\\n DATABASE.connect()\\n DATABASE.create_tables([User], safe=True)\\n DATABASE.close()\",\n \"def init_db():\\n # We are setting the module variables here for the first time, so disable the warning\\n global DB_USER_TABLE # pylint: disable=global-variable-undefined\\n global DB_CUSTOMER_TABLE # pylint: disable=global-variable-undefined\\n global DB_USER_CUSTOMER_RELS_TABLE # pylint: disable=global-variable-undefined\\n global DB_TICKET_TABLE # pylint: disable=global-variable-undefined\\n global DB_COMMENT_TABLE # pylint: disable=global-variable-undefined\\n\\n db = TinyDB(app.config['DB_NAME'])\\n\\n DB_USER_TABLE = db.table('users')\\n DB_CUSTOMER_TABLE = db.table('customers')\\n DB_USER_CUSTOMER_RELS_TABLE = db.table('user_customer_rels')\\n DB_TICKET_TABLE = db.table('tickets')\\n DB_COMMENT_TABLE = db.table('comments')\",\n \"def setup_db_conn():\\n # TODO update so DB does not have to be hard coded\\n # Currently DB is hardcoded\",\n \"def __post_init__(self):\\n self.dbase = databases.Database(\\n self.dsn,\\n min_size=self.min_size,\\n max_size=self.max_size\\n )\\n self.engine, self.meta = self.get_engine_metadata()\",\n \"def __init__(self):\\n self.__engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'.\\n format(getenv('HBNB_MYSQL_USER'),\\n getenv('HBNB_MYSQL_PWD'),\\n getenv('HBNB_MYSQL_HOST'),\\n getenv('HBNB_MYSQL_DB')),\\n pool_pre_ping=True)\\n\\n if getenv('HBNB_ENV') == 'test':\\n \\\"\\\"\\\" Drop all tables\\\"\\\"\\\"\\n Base.metadata.drop_all(self.__engine)\\n\\n Base.metadata.create_all(self.__engine)\\n Session = sessionmaker(self.__engine)\\n self.__session = Session()\",\n \"def init_db(engine):\\n base = declarative_base(engine)\\n Session = sessionmaker(bind=engine)\\n session = Session()\\n base.metadata.reflect(bind = engine)\\n connection = engine.raw_connection()\\n cursor = connection.cursor()\\n return session, base, connection, cursor\",\n \"def initialize():\\n DATABASE.connect()\\n DATABASE.drop_tables([Journal], safe=True)\\n DATABASE.create_tables([Journal], safe=True)\\n DATABASE.close()\",\n \"def __init__(self):\\n engine = connect_to_db()\\n create_lyrics_table(engine) #declarative base stuff\\n self.Session = sessionmaker(bind=engine)\\n\\n # self.create_connection()\\n # self.create_table()\",\n \"def initialize():\\n DATABASE.connect()\\n DATABASE.create_tables([User, Entry], safe=True)\\n DATABASE.close()\",\n \"def init_db(cls, app):\\n cls.logger.info(\\\"Initializing database\\\")\\n cls.app = app\\n # This is where we initialize SQLAlchemy from the Flask app\\n db.init_app(app)\\n app.app_context().push()\\n db.create_all() # make our sqlalchemy tables\",\n \"def init_db(cls, app):\\n cls.logger.info(\\\"Initializing database\\\")\\n cls.app = app\\n # This is where we initialize SQLAlchemy from the Flask app\\n db.init_app(app)\\n app.app_context().push()\\n db.create_all() # make our sqlalchemy tables\",\n \"def _init_db():\\n import alembic.config\\n import alembic.command\\n alembic_cfg = alembic.config.Config('alembic.ini')\\n alembic_cfg.attributes['configure_logger'] = False\\n alembic.command.upgrade(alembic_cfg, 'head')\\n _reset_db(get_test_db_session())\",\n \"def setup_db(config):\\n # create the URL from the configuration\\n db_url = URL(**config)\\n db = create_engine(db_url)\\n Session = sessionmaker(db)\\n # Link the relational model to the database\\n Model.metadata.create_all(db)\\n\\n return db, Session\",\n \"def _init_db(self):\\n cursor = self._main_connection.cursor()\\n cursor.execute(self.sql[\\\"create_table\\\"])\\n self._main_connection.commit()\",\n \"def init_db():\\n import cerbereapp.models\\n Base.metadata.create_all(bind=engine)\",\n \"def bootstrap(self):\\n\\n self.db = connection_manager.get(DbConnection, host=self.ip, port=3306, user=self.user, password=self.password)\\n\\n self.connected = True\",\n \"def connect(cls):\\n engine = create_engine(cls._build_uri(), echo=False)\\n cls.Session = sessionmaker()\\n cls.Session.configure(bind=engine)\",\n \"def prepare_database(config):\\n global Session\\n engine = sqlalchemy.create_engine(config.db_string)\\n session_factory = sqlalchemy.orm.sessionmaker(bind=engine)\\n Session = sqlalchemy.orm.scoped_session(session_factory)\",\n \"def setup(db_file):\\n global session\\n\\n db_conn = \\\"sqlite:///%s\\\" % db_file\\n logger.info(\\\"DB Connection: %s\\\" % db_conn)\\n engine = create_engine(db_conn, connect_args={'check_same_thread':False})\\n engine.Echo = True\\n Base.metadata.create_all(engine)\\n\\n Session = scoped_session(sessionmaker(bind=engine))\\n session = Session()\\n print \\\"DB Connection: %s\\\" % db_conn\",\n \"def setup_db():\\n\\n engine = config['tg.app_globals'].sa_engine\\n # model.init_model(engine)\\n # model.metadata.create_all(engine)\",\n \"def init_database():\\n database.init(DATABASE_NAME)\\n database.connect()\\n database.execute_sql('PRAGMA foreign_keys = ON')\\n if not database.table_exists([Customer]):\\n database.create_tables([Customer])\\n database.close()\",\n \"def setup_database(self):\\n self.db.setup_database()\",\n \"def create_db():\\n init_postgres(current_app.config['SQLALCHEMY_DATABASE_URI'])\",\n \"def initialize():\\n\\n db.connect() # Se conecta\\n db.create_tables([Entry], safe=True) # Crea las tablas\\n # safe=true evita crear modelos ya creados\",\n \"def setup_db():\\n engine = create_engine(settings.DATABASE)\\n ModelBase.metadata.bind = engine\\n ModelBase.metadata.create_all(engine)\\n\\n return scoped_session(sessionmaker(bind=engine))\",\n \"def __init__(self):\\n with open('config.json') as config:\\n data = json.load(config)\\n\\n password = self.decode_password(data['db']['password'])\\n db_conn_string = 'postgresql://' + data['db']['username'] + ':' + password + '@' + \\\\\\n data['db']['hostname'] + ':' + data['db']['port'] + '/' + data['db']['database']\\n\\n self.engine = create_engine(db_conn_string)\\n try:\\n conn = self.engine.connect()\\n if conn is not None:\\n print(\\\"-I- Successful Database Connection\\\")\\n except Exception as e:\\n print(\\\"-W- \\\" + str(e))\",\n \"def setup_session():\\n print(\\\"Setting up session\\\")\\n engine = setup_engine()\\n Base.metadata.bin = engine\\n\\n DBSession = sessionmaker(bind=engine)\\n session = DBSession()\\n\\n return session\",\n \"def initialize_database():\\n # Create the schema\\n Base.metadata.create_all(engine)\\n\\n # Create a connection/database session\\n session = Session()\\n\\n # Now, create a few restaurants:\\n cupcake = Restaurant(name=\\\"Cupcakes\\\")\\n five_guys = Restaurant(name=\\\"Five Guys\\\")\\n ihop = Restaurant(name=\\\"IHOP\\\")\\n\\n # And a few users:\\n mike = User(name=\\\"Mike\\\")\\n ryan = User(name=\\\"Ryan\\\")\\n\\n # And finally a few votes:\\n mike.preferences.append(Preference(vote=\\\"+1\\\", restaurant=five_guys))\\n ryan.preferences.append(Preference(vote=\\\"+0\\\", restaurant=five_guys))\\n ryan.preferences.append(Preference(vote=\\\"-0\\\", restaurant=cupcake))\\n\\n session.add(mike)\\n session.add(ryan)\\n session.add(ihop)\\n\\n session.commit()\\n\\n session.close()\",\n \"def initdb(uri=\\\"sqlite:///packagedata.db\\\"):\\n engine = sqlalchemy.create_engine(uri)\\n Base.metadata.create_all(bind=engine)\\n Session = sessionmaker(bind=engine)\\n return Session\",\n \"def __init__(self):\\n self._connection = get_db_connection()\",\n \"def init_database(use_mysql=False, dbname=\\\"sbs\\\"):\\n #engine = create_engine('sqlite:///:memory:', echo=False)\\n # \\\"mysql+mysqldb://{user}:{password}@{host}:{port}/{dbname}\\\"\\n if use_mysql:\\n db_setup = dict(user=os.environ.get('MYSQL_LOGIN'),\\n password=os.environ.get('MYSQL_PASSWORD'),\\n host=\\\"127.0.0.1\\\",\\n port=os.environ.get('MYSQL_PORT', 3006),\\n dbname=dbname\\n )\\n mysql_setup = \\\"mysql+mysqldb://{user}:{password}@{host}:{port}/{dbname}?charset=utf8\\\".format(**db_setup)\\n engine = create_engine(mysql_setup, echo=False)\\n else:\\n engine = create_engine('sqlite:///data.sqlite', echo=False)\\n event.Base.metadata.create_all(engine)\\n gameinfomodel.Base.metadata.create_all(engine)\\n playerinfo.Base.metadata.create_all(engine)\\n teaminfomodel.Base.metadata.create_all(engine)\\n Session = sessionmaker(bind=engine)\\n session = Session()\\n return session\",\n \"def init_neo4j_db():\\n try:\\n app.neo4j_driver = GraphDatabase.driver(\\n app.config[\\\"NEO4J_URI\\\"],\\n auth=(app.config[\\\"NEO4J_USER\\\"], app.config[\\\"NEO4J_PWD\\\"])\\n )\\n except AuthError:\\n app.neo4j_driver = None\\n app._neo4j_up = True\\n except:\\n app.neo4j_driver = None\\n app._neo4j_up = False\",\n \"def initialize_database():\\n # TODO: Refactor the funtime library\\n this.db = Store(this.host).create_lib(this.store_name).get_store()\",\n \"def init_db() -> None: \\n \\n Base.metadata.create_all(bind=engine)\",\n \"def initialize():\\n db.connect()\\n db.create_tables([Entry], safe=True)\",\n \"def initialize():\\n db.connect()\\n db.create_tables([Entry], safe=True)\",\n \"def setup_db(self) -> None:\\n conn = mysql.connector.connect(\\n user=self.app.config[\\\"DATABASE_USER\\\"], password=self.app.config[\\\"DATABASE_PASSWORD\\\"],\\n host=self.app.config[\\\"DATABASE_HOST\\\"], port=self.app.config[\\\"DATABASE_PORT\\\"], raise_on_warnings=True\\n )\\n try:\\n cursor = conn.cursor()\\n cursor.execute(\\n \\\"CREATE DATABASE IF NOT EXISTS {} CHARACTER SET utf8\\\".format(self.app.config[\\\"DATABASE_NAME\\\"])\\n )\\n conn.commit()\\n except:\\n raise\\n else:\\n with self.DBManager(self.app) as connection:\\n for model in sorted(lib.get_subclasses(lib.models.Model), key=lambda x: x.index):\\n model.setup_table(connection=connection)\\n finally:\\n conn.close()\",\n \"def init_db():\\n current_app.logger.info('Creating database...')\\n db.drop_all()\\n db.create_all()\\n db.session.commit()\",\n \"def __init__(self, config):\\n\\n engine = self.__my_create_engine(config)\\n\\n if not engine:\\n raise Exception(\\\"No engine created\\\")\\n\\n engine.connect()\\n #metadata = MetaData(bind=engine)\\n Session = sessionmaker(bind=engine)\\n\\n # Set the objects to work with\\n self.session = Session()\",\n \"def initialize(self) -> None:\\n # First, establish a connection to the specified database\\n try:\\n self._connect_to_db()\\n except psycopg2.OperationalError: # specified database does not exist\\n with psycopg2.connect(database=DATABASE_ENV[\\\"POSTGRES_DB\\\"],\\n user=self.dbuser, password=self.dbpassword,\\n host=self.dbhost, port=str(self.dbport)) as con:\\n with con.cursor() as cur:\\n con.autocommit = True # cannot create db inside a transaction\\n cur.execute(f'CREATE DATABASE \\\"{self.dbname}\\\"')\\n con.autocommit = False\\n self._connect_to_db() # try again\\n\\n # Second, create the necessary database table, only if required\\n with self._connection.cursor() as cur:\\n cur.execute(f\\\"\\\"\\\"\\n CREATE TABLE IF NOT EXISTS \\\"{self.MESSAGE_TABLE_NAME}\\\" (\\n id SERIAL PRIMARY KEY,\\n key CHAR(4) NOT NULL,\\n value REAL NOT NULL,\\n ts TIMESTAMP NOT NULL,\\n tz TEXT NOT NULL\\n );\\n \\\"\\\"\\\")\\n self._connection.commit()\",\n \"def dbinit( *args, **kwargs ):\",\n \"def __init_database(self):\\n from admin.database import init_db\\n init_db()\",\n \"def setup(self):\\n self.load_connection_info(self.ini_filename)\\n if self.conn_info:\\n self.logger.info('Load connection info of Postgres')\\n\\n psql_connection_info = f\\\"dbname={self.conn_info['dbname']} \\\" \\\\\\n f\\\"user={self.conn_info['user']} \\\" \\\\\\n f\\\"password={self.conn_info['password']} \\\" \\\\\\n f\\\"port={self.conn_info['port']}\\\" \\n \\n check_db = self.create_db(psql_connection_info)\\n\\n connection = psycopg2.connect((\\n f\\\"dbname=password_manager \\\" \\\\\\n f\\\"user={self.conn_info['user']} \\\" \\\\\\n f\\\"password={self.conn_info['password']} \\\" \\\\\\n f\\\"port={self.conn_info['port']}\\\")) \\n cursor = connection.cursor()\\n\\n if check_db:\\n self.logger.info('Database has been created')\\n\\n check_tables = self.create_tables(connection, \\n cursor, \\n self.sql_query_table_person, \\n self.sql_query_table_login_data)\\n \\n if check_tables:\\n self.logger.info('Tables have been created')\\n else:\\n self.logger.info('Tables do not exist')\\n else:\\n self.logger.info('Database does not exist')\\n \\n connection.close()\\n cursor.close()\\n else:\\n self.logger.info('Connection to Postgres could not esablished')\",\n \"def __init__(self):\\n self._setup()\\n # Encryption/decryption cipher handler\\n self.__cipher = self.__get_cipher()\\n # Setup the engine for the sqlite database\\n self._engine = create_engine(self.db_uri)\\n # Configure the SQLAlchemy metadata\\n self._metadata = MetaData()\\n self._metadata.bind = self._engine\\n self._load_db()\\n # Configure the auto-mapping base model\\n self._base = automap_base(metadata=self._metadata)\\n self._base.prepare()\\n # Setup a session generator for database connections\\n self._session = sessionmaker(bind=self._engine)\",\n \"def connect_db():\\n engine = create_engine('sqlite:///app.db', echo=True)\\n Base.metadata.bind = engine\\n DBSession = sessionmaker(bind=engine)\\n session = DBSession()\\n return session\",\n \"def __init__(self):\\n\\t\\tself.obtainDatabaseConnection()\",\n \"def connect_db(self):\\n try:\\n self.connection = self.engine.connect()\\n except Exception:\\n self.print_std_error()\",\n \"def init() -> sqlite3.Connection:\\n try:\\n with open(DATABASE_INIT) as reader:\\n sql = reader.read()\\n connection = sqlite3.connect(DATABASE, check_same_thread=False)\\n connection.executescript(sql)\\n return connection\\n except FileNotFoundError:\\n log.error(DatabaseInitializeError())\\n exit(1)\",\n \"def init_db():\\n db.drop_all()\\n db.create_all()\\n\\n print(\\\"Initialized Connect 4 Database.\\\")\",\n \"def __init__(self, connect: str):\\n\\n # create engine\\n self._engine = create_engine(connect)\\n self._engine.echo = False\\n event.listen(self._engine, 'checkout', Database._checkout_listener)\\n\\n # and metadata\\n MetaData(self._engine)\\n\\n # and session\\n self._session = sessionmaker(bind=self._engine)\\n\\n # create tables\\n Base.metadata.create_all(self._engine, checkfirst=True)\",\n \"def connect():\\n # global ENGINE\\n # global Session\\n\\n # ENGINE = create_engine(\\\"sqlite:///ratings.db\\\", echo=True)\\n # Session = sessionmaker(bind=ENGINE)\\n\\n # return Session()\\n pass\",\n \"def init_db():\\n\\tdb.drop_all()\\n\\tdb.create_all()\\n\\n\\tprint(\\\"Initialized Database.\\\")\\n\\treturn\",\n \"async def initialize(self, *, only_init_tables: bool=False):\\n self.pool = await aiomysql.create_pool(\\n host=self.host, user=self.user, password=self.passwd,\\n loop=self.loop)\\n db_exists = await self._check_db_exists()\\n if not db_exists:\\n await self._create_db()\\n db_initialized = False\\n else:\\n db_initialized = await self._check_db_initialized()\\n # We close the pool and create a new one because aiomysql doesn't\\n # provide an easy way to change the active database for an entire\\n # pool, just individual connections.\\n self.pool.terminate()\\n self.pool = await aiomysql.create_pool(\\n host=self.host, user=self.user, password=self.passwd,\\n db=self.dbname, loop=self.loop)\\n if not db_initialized:\\n await self._init_db(only_init_tables)\\n await self._upgrade_db()\\n log.msg('Database initialized')\",\n \"def initialize_connection():\\n session = imdb.IMDb()\\n return session\",\n \"def __init__(self, connection_url, echo=False):\\n if not connection_url:\\n raise ValueError('No database connection URL provided.')\\n engine = create_engine(connection_url, echo=echo)\\n PipelineRun.metadata.create_all(engine)\\n self.session_factory = sessionmaker(bind=engine)\",\n \"def setup_database():\\n\\n user = 'bard'\\n password = 'STORY'\\n database = 'story'\\n DSN = f\\\"postgresql://{user}:{password}@postgres:5432/{database}\\\"\\n engine = create_engine(DSN)\\n register_tables(engine)\\n return engine\",\n \"def __init__(self):\\n user = os.getenv('URL_MYSQL_USER')\\n passwd = os.getenv('URL_MYSQL_PWD')\\n host = os.getenv('URL_MYSQL_HOST')\\n database = os.getenv('URL_MYSQL_DB')\\n self.__engine = create_engine('mysql+mysqldb://{}:{}@{}/{}'\\n .format(user, passwd, host, database))\",\n \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format('postgres', 'postgres', 'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"def initialize():\\n db.connect()\\n db.create_tables([Expense], safe=True)\",\n \"def connectToDB():\\r\\n Base = declarative_base()\\r\\n engine = create_engine('sqlite:///DBLatency.db', echo=True)\\r\\n Base.metadata.bind = engine\\r\\n DBSession = sessionmaker(bind=engine)\\r\\n session = DBSession()\\r\\n return session\",\n \"def _setup_db(app: FastAPI) -> None:\\n engine = create_async_engine(str(settings.db_url), echo=settings.db_echo)\\n session_factory = async_scoped_session(\\n sessionmaker(\\n engine,\\n expire_on_commit=False,\\n class_=AsyncSession,\\n ),\\n scopefunc=current_task,\\n )\\n app.state.db_engine = engine\\n app.state.db_session_factory = session_factory\",\n \"def setup_db():\\n logger.info('Setting up db')\\n setup_all_db()\\n setup_emails()\",\n \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n #self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n self.database_path = 'postgresql+psycopg2://{}:{}@{}/{}'.format('postgres','picasso0', 'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"Capstone\\\"\\n self.database_path = \\\"postgres://{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app , self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n\\n self.db.create_all()\",\n \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"trivia_test\\\"\\n self.database_path = \\\"postgres://{}:{}@{}/{}\\\".format('student',\\n 'student', 'localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"def init_db(database_url, **database_connect_options):\\n engine = create_engine(database_url,\\n convert_unicode=True,\\n **database_connect_options)\\n\\n Base.metadata.reflect(engine)\\n Base.metadata.create_all(engine)\\n session = scoped_session(sessionmaker(autocommit=False,\\n autoflush=False,\\n bind=engine))\\n Base.query = session.query_property()\\n\\n return session\",\n \"def init_postgres(testing):\\n if 'POSTGRESQL_DATABASE_URI' in app.config:\\n if not testing:\\n # not testing will use request context as scope\\n # for sqlalchemy Session object\\n from flask import _app_ctx_stack\\n import pgsqlutils.base as pgbase\\n from pgsqlutils.base import get_db_conf, init_db_conn\\n from sqlalchemy.orm import sessionmaker, scoped_session\\n dbconf = get_db_conf()\\n dbconf.DATABASE_URI = app.config['POSTGRESQL_DATABASE_URI']\\n # monkey patching to replace default session\\n # by a sessing handled by flask\\n pgbase.Session = scoped_session(\\n sessionmaker(),\\n scopefunc=_app_ctx_stack.__ident_func__)\\n init_db_conn()\\n else:\\n # Testing will use current thread as scope for Session\\n from pgsqlutils.base import get_db_conf, init_db_conn\\n dbconf = get_db_conf()\\n dbconf.DATABASE_URI = app.config['POSTGRESQL_DATABASE_URI']\\n init_db_conn()\",\n \"def _initialize_db():\\n conn, c = _get_db_connection()\\n\\n with open(str(SCHEMA_PATH)) as f:\\n c.executescript(f.read())\\n\\n conn.close()\",\n \"def bootstrap():\\n Base.metadata.create_all(engine)\",\n \"def setUp(self):\\n self.app = create_app()\\n self.client = self.app.test_client\\n self.database_name = \\\"databasename\\\"\\n self.database_path = \\\"postgresql://postgres:usman@{}/{}\\\".format('localhost:5432', self.database_name)\\n setup_db(self.app, self.database_path)\\n\\n # binds the app to the current context\\n with self.app.app_context():\\n self.db = SQLAlchemy()\\n self.db.init_app(self.app)\\n # create all tables\\n self.db.create_all()\",\n \"def _connect(self):\\n\\n assert not self.__is_connected\\n\\n self._db_engine = create_engine(self._database.url)\\n\\n from sqlalchemy import event\\n\\n if self._database.driver == 'sqlite':\\n @event.listens_for(self._db_engine, \\\"connect\\\")\\n def do_connect(dbapi_connection, *args, **kwargs):\\n # disable pysqlite's emitting of the BEGIN statement entirely.\\n # also stops it from emitting COMMIT before any DDL.\\n dbapi_connection.isolation_level = None\\n\\n @event.listens_for(self._db_engine, \\\"begin\\\")\\n def do_begin(conn):\\n # emit our own BEGIN\\n conn.execute(\\\"BEGIN\\\")\\n\\n self._db_connection = self._db_engine.connect()\\n self._db_transaction = self._db_connection.begin()\\n\\n self._orm_session = Session(\\n bind=self._db_connection,\\n transaction=self,\\n )\\n self._orm_session_proxy = weakref.proxy(self._orm_session)\\n\\n self.__is_connected = True\",\n \"def setUp(self):\\n self.a = backend.dbconnection.DBConnect()\",\n \"def __init__(self, dialect, database, username=None, password=None,\\n host=None, port=None, query=None):\\n db_url = URL(drivername=dialect, database=database, username=username,\\n password=password, host=host, port=port, query=query)\\n self.engine = create_engine(db_url)\\n self.session = None\\n self.get_session = sessionmaker(bind=self.engine,\\n expire_on_commit=False)\\n\\n # create ORM objects for the tables\\n self._define_tables()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77571905","0.77500427","0.77498686","0.77142614","0.7675466","0.7598643","0.75940216","0.75440794","0.75440794","0.753408","0.7526113","0.7511898","0.7495771","0.7474186","0.737677","0.73386455","0.73346627","0.7311713","0.72757417","0.7271616","0.7243954","0.72419906","0.7233685","0.72113234","0.7200269","0.71980953","0.71647763","0.71329296","0.7132054","0.71019447","0.7101764","0.7092895","0.7090498","0.70825523","0.7075029","0.7025197","0.7024565","0.7024565","0.7019031","0.70183665","0.7011483","0.6993204","0.6977371","0.6975236","0.69680566","0.6958181","0.6952793","0.6940881","0.6924623","0.69244164","0.6915345","0.69061047","0.6901581","0.6892202","0.6887314","0.6882271","0.68799466","0.6869892","0.6836489","0.6827135","0.6826746","0.682568","0.682568","0.68230635","0.68148136","0.68139267","0.68125045","0.68097526","0.680571","0.67995095","0.67933446","0.67883974","0.6782679","0.6780524","0.677905","0.6776679","0.67604","0.6750724","0.67303014","0.6724494","0.67158747","0.67113954","0.67064476","0.6670589","0.6657258","0.6655613","0.6651786","0.66501206","0.6649615","0.6648423","0.6644832","0.66444534","0.66440094","0.6640162","0.66345084","0.66323763","0.6626832","0.66262805","0.6622611","0.66210663"],"string":"[\n \"0.77571905\",\n \"0.77500427\",\n \"0.77498686\",\n \"0.77142614\",\n \"0.7675466\",\n \"0.7598643\",\n \"0.75940216\",\n \"0.75440794\",\n \"0.75440794\",\n \"0.753408\",\n \"0.7526113\",\n \"0.7511898\",\n \"0.7495771\",\n \"0.7474186\",\n \"0.737677\",\n \"0.73386455\",\n \"0.73346627\",\n \"0.7311713\",\n \"0.72757417\",\n \"0.7271616\",\n \"0.7243954\",\n \"0.72419906\",\n \"0.7233685\",\n \"0.72113234\",\n \"0.7200269\",\n \"0.71980953\",\n \"0.71647763\",\n \"0.71329296\",\n \"0.7132054\",\n \"0.71019447\",\n \"0.7101764\",\n \"0.7092895\",\n \"0.7090498\",\n \"0.70825523\",\n \"0.7075029\",\n \"0.7025197\",\n \"0.7024565\",\n \"0.7024565\",\n \"0.7019031\",\n \"0.70183665\",\n \"0.7011483\",\n \"0.6993204\",\n \"0.6977371\",\n \"0.6975236\",\n \"0.69680566\",\n \"0.6958181\",\n \"0.6952793\",\n \"0.6940881\",\n \"0.6924623\",\n \"0.69244164\",\n \"0.6915345\",\n \"0.69061047\",\n \"0.6901581\",\n \"0.6892202\",\n \"0.6887314\",\n \"0.6882271\",\n \"0.68799466\",\n \"0.6869892\",\n \"0.6836489\",\n \"0.6827135\",\n \"0.6826746\",\n \"0.682568\",\n \"0.682568\",\n \"0.68230635\",\n \"0.68148136\",\n \"0.68139267\",\n \"0.68125045\",\n \"0.68097526\",\n \"0.680571\",\n \"0.67995095\",\n \"0.67933446\",\n \"0.67883974\",\n \"0.6782679\",\n \"0.6780524\",\n \"0.677905\",\n \"0.6776679\",\n \"0.67604\",\n \"0.6750724\",\n \"0.67303014\",\n \"0.6724494\",\n \"0.67158747\",\n \"0.67113954\",\n \"0.67064476\",\n \"0.6670589\",\n \"0.6657258\",\n \"0.6655613\",\n \"0.6651786\",\n \"0.66501206\",\n \"0.6649615\",\n \"0.6648423\",\n \"0.6644832\",\n \"0.66444534\",\n \"0.66440094\",\n \"0.6640162\",\n \"0.66345084\",\n \"0.66323763\",\n \"0.6626832\",\n \"0.66262805\",\n \"0.6622611\",\n \"0.66210663\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.79214334"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":86689,"cells":{"query":{"kind":"string","value":"Let's Authenticate the Banks Note This is using docstrings for specifications."},"document":{"kind":"string","value":"def predict_note_authentication(totalyearlycompensation,safety,classifier):\n \n prediction=classifier.predict([[totalyearlycompensation,safety]])\n print(prediction)\n return prediction"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def auth():\n pass","def auth():\n pass","def authorization():\n pass","def authenticate(self, username, password, consumerKey, consumerSecret):\r\n pass","def authenticate():\n return Response(\n '''Login Required - email acaceres@0-sec.net for access or DM him @_hyp3ri0n on Twitter. Please do so from a corporate email or\n with some kind of proof that you are a security engineer, academic, or need this data for research purposes (e.g. email from a corporate or .edu email or provide\n a linkedin or twitter account showing as much)\n You have to login with proper credentials''', 401, {'WWW-Authenticate': 'Basic realm=\"Login Required - email acaceres@0-sec.net for access or DM him @_hyp3ri0n on Twitter. Please do so from a corporate email or with some kind of proof that you are a security engineer, academic, or need this data for research purposes (e.g. email from a corporate or .edu email or provide a linkedin or twitter account showing as much)\"'})","def authenticate_bluetooth(self):\n data = self.blu.main()\n if bool(data) == True:\n authentication = self.client.validate_mac(\n data[\"mac_address\"], data[\"email\"]).decode(\"utf-8\")\n if authentication == \"valid\":\n self.current_email = data[\"email\"]\n self.unlock_time = round(datetime.now().timestamp())\n self.display_successful_unlock_eng()\n elif authentication == \"invalid\":\n print(self.INVALID_USER)\n time.sleep(3)\n self.display_main()\n else:\n self.display_eng()","def authenticate():\n return Response('Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )","def authenticate(credentials):","def auth(self):\n\n self.name = self.config[\"sname\"]\n self.numeric = self.config[\"numeric\"]\n\n passwd = self.config[\"password\"]\n\n now = int(time.time())\n\n self.send_line(\"PASS :%s\" % passwd)\n self.send_line(\"SERVER %s 1 %d %d J10 %s]]] 0 :Gravelir Services\" %\\\n (self.name, now, now, self.numeric))","def authenticate():\r\n return Response(\r\n 'Could not verify your access level for that URL.\\n'\r\n 'You have to login with proper credentials', 401,\r\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL, need token.\\n', 403,\n {'WWW-Authenticate': 'Basic realm=\"token Required\"'})","def authenticate():\n\treturn Response(\n\t'Could not verify your access level for that URL.\\n'\n\t'You have to login with proper credentials', 401,\n\t{'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticator():","def authenticate():\n\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n get_auth_headers())","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials.', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def check_auth():","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def _authenticate(self):\n\t\tfrom getpass import getpass\n\t\tpassword = getpass()\n\t\tself.msg('nickserv', 'identify %s' % password)","def main():\n\n config_file = 'auth_demo.cfg'\n config = ConfigParser.SafeConfigParser({\n 'username':'',\n })\n config.read(config_file)\n if not config.has_section('auth_demo_login'):\n config.add_section('auth_demo_login')\n\n username = config.get('auth_demo_login','username')\n password = None\n if username != '':\n password = keyring.get_password('auth_demo_login', username)\n\n if password == None or not auth(username, password):\n\n while 1:\n username = raw_input(\"Username:\\n\")\n password = getpass.getpass(\"Password:\\n\")\n\n if auth(username, password):\n break\n else:\n print \"Authorization failed.\"\n \n # store the username\n config.set('auth_demo_login', 'username', username)\n config.write(open(config_file, 'w'))\n\n # store the password\n keyring.set_password('auth_demo_login', username, password)\n\n # the stuff that needs authorization here\n print \"Authorization successful.\"","def test_authentication():\n\n c = GoodreadsClient()\n assert_equals(c.user['name'], 'Jan Skus')","def Login(self):\n self.Send(self.EncryptString('login\\n'))\n\n # 'Please provide your membership number to authenticate:'\n print self.DecryptString(self.Recv(4096))\n\n # Flag 1.\n self.Send(self.EncryptString(self.flag_1))\n\n # 'Ah, I see, sir has a basic account. In that case, your limited cloud\n # execution access has been provisioned.'\n print self.DecryptString(self.Recv(4096))\n\n # 'Will there by anything else today, sir?'\n print self.DecryptString(self.Recv(4096))","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials',\n 401,\n {\n 'WWW-Authenticate': 'Basic realm=\"Login Required\"'\n }\n )","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )","def authenticate_password(self, secret=\"\"):\r\n #hexstr = binascii.b2a_hex(secret)\r\n self.sendAndRecv(\"AUTHENTICATE \\\"%s\\\"\\r\\n\"%secret)","def authenticate():\n return Response(\n 'You have to login with proper credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return \"Please log in... Log in page\"","def apply_auth():\n\tclient = BaiduOpenApi()\n\tapi = client.device.code\n\tresp = client.device.code.get(response_type=\"device_code\", scope=\"netdisk\")\n\t# open grant page and wait for user confirm\n\twebbrowser.open_new_tab(r\"http://openapi.baidu.com/device?code=%s\"%resp[\"user_code\"])\n\t# yield to main\n\tyield\n\t# main will tell user to confirm and it will take a while\n\t# polling to wait server back\n\tpolling_tokens(resp[\"device_code\"], resp[\"interval\"], resp[\"expires_in\"])","def login(self):","def authenticate():\n return Response('Not Authorized', 401, {'WWW-Authenticate': 'Basic realm=\"api\"'})","def authenticate():\n return flask.Response('Login required.', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return send_msg(\n 401,\n 'Must be connected',\n headers={'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def login():","def login():","def login():\n pass","def authentication_hook(self):\n pass","def __init__(self):\n\n self._authorize()","def authenticate( self ):\n\n print(\"Getting new token\")\n self.getFrob()\n self.getAuthKey()\n self.getToken()\n self.cacheToken()","def fill_authorization_basic(user, pass_word):\n return user, pass_word","def __init__(__self__, *,\n password: pulumi.Input[str],\n server: pulumi.Input[str],\n type: pulumi.Input[str],\n username: pulumi.Input[str]):\n pulumi.set(__self__, \"password\", password)\n pulumi.set(__self__, \"server\", server)\n pulumi.set(__self__, \"type\", 'BasicAuth')\n pulumi.set(__self__, \"username\", username)","def authenticate():\n return abort(401)","def test_login(self):\n\n print('\\n\\nEnter a valid LendingClub account information...')\n email = input('Email:')\n password = getpass.getpass()\n\n self.assertTrue(self.session.authenticate(email, password))\n print('Authentication successful')","def main(username, pw):\n pass","def test_authenticate(self):\n a = Authenticator( self.logger )\n authentication_id = a.authenticate(\n self.patron_barcode, self.api_url_root, self.api_key, self.partnership_id, self.university_code )\n self.assertEqual(\n 27, len(authentication_id) )","def Login(self):\n raise NotImplementedError()","def CR_authentication():\n \n # create a random 10 character string\n choices = string.letters + string.digits + string.punctuation;\n randomString = ''.join(random.choice(choices) for i in range(10))\n session['challenge'] = randomString\n \n return Response('Access failed.', 401, {'WWW-Authenticate': str.format('Basic realm=\\\"Protected iStreet event data; Challenge: {0}\\\"', randomString)})","def authenticate():\n return Response(\n 'Could not verify your access level for that URL.\\n'\n 'You have to login with Web Manager credentials', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def authenticate():\n return Response(\n 'Could not verify your credentials for that url', 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'})","def test_01_authenticated(self):\r\n res = self.signin(email=self.email_addr, password=self.password)\r\n res = self.app.get(self.url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"The CKAN exporter should be available for the owner of the app\"\r\n assert dom.find(id=\"ckan\") is not None, err_msg\r\n\r\n self.signout()\r\n\r\n self.signin(email=self.email_addr2, password=self.password)\r\n res = self.app.get(self.url, follow_redirects=True)\r\n dom = BeautifulSoup(res.data)\r\n err_msg = \"The CKAN exporter should be ONLY available for the owner of the app\"\r\n assert dom.find(id=\"ckan\") is None, err_msg","def authenticate(self, username: str, password: str) -> Optional[str]:","def authenticate(self, username: str, password: str) -> Optional[str]:","def test_read_o_auth_client_authorization(self):\n pass","def mifare_auth_b(self,address,key_b):\n if self._uid == False:\n raise RuntimeError(\"No Mifare card currently activated.\")\n if len(self._uid) == 4:\n uid = self._uid\n elif len(self._uid) == 7: # 10-byte UID cards don't exist yet.\n uid = self._uid[3:7] # Sequence 1, keep it simple.\n self.in_data_exchange(bytearray([MIFARE_COMMAND_AUTH_B,address]) + key_b + uid)","def login_bot(self):\n pass","def authenticate(self):\n self.connection.authenticate()","def authenticate(self):\n return Response(\n 'Could not verify your access level for that URL.\\nYou have to login with proper credentials',\n 401,\n {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )","def authorize(username, password):\n\ts = '%s:%s' % (username, password)\n\tPAYLOAD_HEADERS['Authorization'] = b'Basic ' + base64.b64encode(s.encode('ascii'))","def authenticate(self):\n # Check if we already have access token and secret\n if not os.path.exists(self.sTOKEN_FILE):\n # 1) Obtain Request token\n oauth = OAuth1(self.apiKey, client_secret=self.apiKeySecret, callback_uri='oob')\n r = requests.post(url=self.sREQUEST_TOKEN_URL, auth=oauth)\n credentials = parse_qs(r.content)\n resource_owner_key = credentials.get('oauth_token')[0]\n resource_owner_secret = credentials.get('oauth_token_secret')[0]\n\n # 2) Obtain authorization for the user to access resources\n # Redirect the user to /authorize and get the callback\n authorize_url = self.sAUTHORIZE_URL + '?oauth_token=' + resource_owner_key + \\\n '&oauth_consumer_key=' + self.apiKey + \\\n '&Access=Full&Permissions=Modify'\n\n print 'Please go here and authorize,', authorize_url\n verifier = raw_input('Please enter the six-digit PIN code: ')\n\n # 3) Obtain final access token\n oauth = OAuth1(self.apiKey, client_secret = self.apiKeySecret,\n resource_owner_key = resource_owner_key,\n resource_owner_secret = resource_owner_secret,\n verifier=verifier)\n r = requests.post(url=self.sACCESS_TOKEN_URL, auth=oauth)\n\n credentials = parse_qs(r.content)\n access_token = credentials.get('oauth_token')[0]\n access_token_secret = credentials.get('oauth_token_secret')[0]\n\n # Store access token so we can use it later\n with open(self.sTOKEN_FILE, 'w') as f:\n json.dump({'access_token': access_token,\n 'access_token_secret': access_token_secret}, f)\n\n else:\n with open(self.sTOKEN_FILE, 'r') as f:\n tokens = json.load(f)\n access_token = tokens.get('access_token')\n access_token_secret = tokens.get('access_token_secret')\n\n # store the file access token details for use in other methods\n self.accessToken = access_token\n self.accessTokenSecret = access_token_secret","def auth():\n\n if current_user.is_authenticated:\n\n # OAuth is only necesary when we don't have a user's API\n if not current_user.zoter_api:\n\n def get_auth_url():\n request_token, request_token_secret = zoteroAuth.get_request_token()\n session['request_token'] = request_token\n session['request_token_secret'] = request_token_secret\n auth_url = zoteroAuth.get_authorize_url(request_token)\n return auth_url\n\n flash('Hi! {}, please visit <a href=\"{}\" target=\"new\">here</a> for authentication.'.format(current_user.username, get_auth_url()))\n return redirect(url_for('bookshelf'))\n \n else:\n flash('You already have an API key!')\n return redirect(url_for('sync'))","def _get_auth_string(self):","def auth(self, username, password):\n return False","def authn_and_authz():\n authentication()\n authorization()","def _auth():\n with open('api-credentials.txt', 'r') as f:\n lines = f.read().splitlines()\n credentials = lines[0] + ':' + lines[1]\n return 'Basic ' + base64.b64encode(credentials.encode()).decode()","def interactive_authorize():\n username = raw_input(\"Enter username: \")\n password = raw_input(\"Enter password: \")\n authorize(username, password)","def auth(self, user):","def test_authenticate(self, mock_set_token: Mock) -> None:\n # Authenticate using Exonet client.\n ExonetClient(\"kaSD0ffAD1ldSA92A0KODkaksda02KDAK\")\n\n # Check mock calls.\n assert mock_set_token.call_args[0][0] == \"kaSD0ffAD1ldSA92A0KODkaksda02KDAK\"\n assert mock_set_token.call_count == 1","def __init__(self):\n self.auth()","def __init__(self,account_name, username, password):\n self.account_name = account_name\n self.username = username\n self.password = password","def authenticate(self):\n\n # Choose a random user agent string from the most popular\n agent_strings = [\n 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36',\n 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36',\n 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36'\n ]\n headers = {'User-Agent': choice(agent_strings)}\n\n # Spin up a requests session\n with requests.Session() as s:\n s.headers.update(headers)\n\n login_page = s.get('https://alta.registries.gov.ab.ca/SpinII/').content\n soup = BeautifulSoup(login_page, 'html.parser')\n\n login_payload = {\n 'uctrlFullHeader:ShutdownAlert1:Hidden1':'',\n 'uctrlFullHeader:ShutdownAlert1:Hidden2':'',\n 'JavascriptEnabled':1,\n 'uctrlLogon:txtLogonName':'',\n 'uctrlLogon:txtPassword':'',\n 'uctrlLogon:cmdLogonGuest.x':59,\n 'uctrlLogon:cmdLogonGuest.y':26\n }\n login_payload['__EVENTTARGET'] = soup.select_one('#__EVENTTARGET')['value']\n login_payload['__EVENTARGUMENT'] = soup.select_one('#__EVENTARGUMENT')['value']\n login_payload['__VIEWSTATE'] = soup.select_one('#__VIEWSTATE')['value']\n\n sleep(2)\n legal_notice_page = s.post('https://alta.registries.gov.ab.ca/SpinII/logon.aspx', \\\n data=login_payload).content\n soup = BeautifulSoup(legal_notice_page, 'html.parser')\n\n login_payload['__VIEWSTATE'] = soup.select_one('#__VIEWSTATE')['value']\n login_payload['cmdYES.x'] = 55\n login_payload['cmdYES.y'] = 12\n del login_payload['__EVENTARGUMENT']\n del login_payload['__EVENTTARGET']\n\n sleep(2)\n confirm_guest_page = s.post('https://alta.registries.gov.ab.ca/SpinII/legalnotice.aspx', \\\n data=login_payload).content\n soup = BeautifulSoup(confirm_guest_page, 'html.parser')\n\n if len(soup.find_all(text='You are logged on as a Guest.')) > 0:\n return s","def authenticate():\n return Response(\n '', 401, {'WWW-Authenticate': 'Basic realm=\"Login Required\"'}\n )","def __init__(self,account,username, password):\n self.account = account\n self.username = username\n self.password = password","def main():\t\n\t# read in short term auth\n\tf = open('./input.txt', 'r')\n\tshort_term_auth = f.read()\n\n\tlong_term_access_token = long_term_token(short_term_auth)\n\tprint(long_term_access_token)\n\tprint('run program like normal now')","def auth_token(self):","def __init__(self, login=\"user\", password=\"password\"):\n try:\n kerberos\n except NameError:\n log(\"Kerberos Python package is not\"\n \" installed. Can't proceed with Kerberos\"\n \" authorization.\", logLevel.ERROR)\n sys.exit(4)\n\n super(KerberizedCDSInvenioConnector, self).__init__(\"user\",\n \"password\")"],"string":"[\n \"def auth():\\n pass\",\n \"def auth():\\n pass\",\n \"def authorization():\\n pass\",\n \"def authenticate(self, username, password, consumerKey, consumerSecret):\\r\\n pass\",\n \"def authenticate():\\n return Response(\\n '''Login Required - email acaceres@0-sec.net for access or DM him @_hyp3ri0n on Twitter. Please do so from a corporate email or\\n with some kind of proof that you are a security engineer, academic, or need this data for research purposes (e.g. email from a corporate or .edu email or provide\\n a linkedin or twitter account showing as much)\\n You have to login with proper credentials''', 401, {'WWW-Authenticate': 'Basic realm=\\\"Login Required - email acaceres@0-sec.net for access or DM him @_hyp3ri0n on Twitter. Please do so from a corporate email or with some kind of proof that you are a security engineer, academic, or need this data for research purposes (e.g. email from a corporate or .edu email or provide a linkedin or twitter account showing as much)\\\"'})\",\n \"def authenticate_bluetooth(self):\\n data = self.blu.main()\\n if bool(data) == True:\\n authentication = self.client.validate_mac(\\n data[\\\"mac_address\\\"], data[\\\"email\\\"]).decode(\\\"utf-8\\\")\\n if authentication == \\\"valid\\\":\\n self.current_email = data[\\\"email\\\"]\\n self.unlock_time = round(datetime.now().timestamp())\\n self.display_successful_unlock_eng()\\n elif authentication == \\\"invalid\\\":\\n print(self.INVALID_USER)\\n time.sleep(3)\\n self.display_main()\\n else:\\n self.display_eng()\",\n \"def authenticate():\\n return Response('Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'}\\n )\",\n \"def authenticate(credentials):\",\n \"def auth(self):\\n\\n self.name = self.config[\\\"sname\\\"]\\n self.numeric = self.config[\\\"numeric\\\"]\\n\\n passwd = self.config[\\\"password\\\"]\\n\\n now = int(time.time())\\n\\n self.send_line(\\\"PASS :%s\\\" % passwd)\\n self.send_line(\\\"SERVER %s 1 %d %d J10 %s]]] 0 :Gravelir Services\\\" %\\\\\\n (self.name, now, now, self.numeric))\",\n \"def authenticate():\\r\\n return Response(\\r\\n 'Could not verify your access level for that URL.\\\\n'\\r\\n 'You have to login with proper credentials', 401,\\r\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL, need token.\\\\n', 403,\\n {'WWW-Authenticate': 'Basic realm=\\\"token Required\\\"'})\",\n \"def authenticate():\\n\\treturn Response(\\n\\t'Could not verify your access level for that URL.\\\\n'\\n\\t'You have to login with proper credentials', 401,\\n\\t{'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticator():\",\n \"def authenticate():\\n\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n get_auth_headers())\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials.', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def check_auth():\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def _authenticate(self):\\n\\t\\tfrom getpass import getpass\\n\\t\\tpassword = getpass()\\n\\t\\tself.msg('nickserv', 'identify %s' % password)\",\n \"def main():\\n\\n config_file = 'auth_demo.cfg'\\n config = ConfigParser.SafeConfigParser({\\n 'username':'',\\n })\\n config.read(config_file)\\n if not config.has_section('auth_demo_login'):\\n config.add_section('auth_demo_login')\\n\\n username = config.get('auth_demo_login','username')\\n password = None\\n if username != '':\\n password = keyring.get_password('auth_demo_login', username)\\n\\n if password == None or not auth(username, password):\\n\\n while 1:\\n username = raw_input(\\\"Username:\\\\n\\\")\\n password = getpass.getpass(\\\"Password:\\\\n\\\")\\n\\n if auth(username, password):\\n break\\n else:\\n print \\\"Authorization failed.\\\"\\n \\n # store the username\\n config.set('auth_demo_login', 'username', username)\\n config.write(open(config_file, 'w'))\\n\\n # store the password\\n keyring.set_password('auth_demo_login', username, password)\\n\\n # the stuff that needs authorization here\\n print \\\"Authorization successful.\\\"\",\n \"def test_authentication():\\n\\n c = GoodreadsClient()\\n assert_equals(c.user['name'], 'Jan Skus')\",\n \"def Login(self):\\n self.Send(self.EncryptString('login\\\\n'))\\n\\n # 'Please provide your membership number to authenticate:'\\n print self.DecryptString(self.Recv(4096))\\n\\n # Flag 1.\\n self.Send(self.EncryptString(self.flag_1))\\n\\n # 'Ah, I see, sir has a basic account. In that case, your limited cloud\\n # execution access has been provisioned.'\\n print self.DecryptString(self.Recv(4096))\\n\\n # 'Will there by anything else today, sir?'\\n print self.DecryptString(self.Recv(4096))\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials',\\n 401,\\n {\\n 'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'\\n }\\n )\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'}\\n )\",\n \"def authenticate_password(self, secret=\\\"\\\"):\\r\\n #hexstr = binascii.b2a_hex(secret)\\r\\n self.sendAndRecv(\\\"AUTHENTICATE \\\\\\\"%s\\\\\\\"\\\\r\\\\n\\\"%secret)\",\n \"def authenticate():\\n return Response(\\n 'You have to login with proper credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return \\\"Please log in... Log in page\\\"\",\n \"def apply_auth():\\n\\tclient = BaiduOpenApi()\\n\\tapi = client.device.code\\n\\tresp = client.device.code.get(response_type=\\\"device_code\\\", scope=\\\"netdisk\\\")\\n\\t# open grant page and wait for user confirm\\n\\twebbrowser.open_new_tab(r\\\"http://openapi.baidu.com/device?code=%s\\\"%resp[\\\"user_code\\\"])\\n\\t# yield to main\\n\\tyield\\n\\t# main will tell user to confirm and it will take a while\\n\\t# polling to wait server back\\n\\tpolling_tokens(resp[\\\"device_code\\\"], resp[\\\"interval\\\"], resp[\\\"expires_in\\\"])\",\n \"def login(self):\",\n \"def authenticate():\\n return Response('Not Authorized', 401, {'WWW-Authenticate': 'Basic realm=\\\"api\\\"'})\",\n \"def authenticate():\\n return flask.Response('Login required.', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return send_msg(\\n 401,\\n 'Must be connected',\\n headers={'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def login():\",\n \"def login():\",\n \"def login():\\n pass\",\n \"def authentication_hook(self):\\n pass\",\n \"def __init__(self):\\n\\n self._authorize()\",\n \"def authenticate( self ):\\n\\n print(\\\"Getting new token\\\")\\n self.getFrob()\\n self.getAuthKey()\\n self.getToken()\\n self.cacheToken()\",\n \"def fill_authorization_basic(user, pass_word):\\n return user, pass_word\",\n \"def __init__(__self__, *,\\n password: pulumi.Input[str],\\n server: pulumi.Input[str],\\n type: pulumi.Input[str],\\n username: pulumi.Input[str]):\\n pulumi.set(__self__, \\\"password\\\", password)\\n pulumi.set(__self__, \\\"server\\\", server)\\n pulumi.set(__self__, \\\"type\\\", 'BasicAuth')\\n pulumi.set(__self__, \\\"username\\\", username)\",\n \"def authenticate():\\n return abort(401)\",\n \"def test_login(self):\\n\\n print('\\\\n\\\\nEnter a valid LendingClub account information...')\\n email = input('Email:')\\n password = getpass.getpass()\\n\\n self.assertTrue(self.session.authenticate(email, password))\\n print('Authentication successful')\",\n \"def main(username, pw):\\n pass\",\n \"def test_authenticate(self):\\n a = Authenticator( self.logger )\\n authentication_id = a.authenticate(\\n self.patron_barcode, self.api_url_root, self.api_key, self.partnership_id, self.university_code )\\n self.assertEqual(\\n 27, len(authentication_id) )\",\n \"def Login(self):\\n raise NotImplementedError()\",\n \"def CR_authentication():\\n \\n # create a random 10 character string\\n choices = string.letters + string.digits + string.punctuation;\\n randomString = ''.join(random.choice(choices) for i in range(10))\\n session['challenge'] = randomString\\n \\n return Response('Access failed.', 401, {'WWW-Authenticate': str.format('Basic realm=\\\\\\\"Protected iStreet event data; Challenge: {0}\\\\\\\"', randomString)})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your access level for that URL.\\\\n'\\n 'You have to login with Web Manager credentials', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def authenticate():\\n return Response(\\n 'Could not verify your credentials for that url', 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'})\",\n \"def test_01_authenticated(self):\\r\\n res = self.signin(email=self.email_addr, password=self.password)\\r\\n res = self.app.get(self.url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"The CKAN exporter should be available for the owner of the app\\\"\\r\\n assert dom.find(id=\\\"ckan\\\") is not None, err_msg\\r\\n\\r\\n self.signout()\\r\\n\\r\\n self.signin(email=self.email_addr2, password=self.password)\\r\\n res = self.app.get(self.url, follow_redirects=True)\\r\\n dom = BeautifulSoup(res.data)\\r\\n err_msg = \\\"The CKAN exporter should be ONLY available for the owner of the app\\\"\\r\\n assert dom.find(id=\\\"ckan\\\") is None, err_msg\",\n \"def authenticate(self, username: str, password: str) -> Optional[str]:\",\n \"def authenticate(self, username: str, password: str) -> Optional[str]:\",\n \"def test_read_o_auth_client_authorization(self):\\n pass\",\n \"def mifare_auth_b(self,address,key_b):\\n if self._uid == False:\\n raise RuntimeError(\\\"No Mifare card currently activated.\\\")\\n if len(self._uid) == 4:\\n uid = self._uid\\n elif len(self._uid) == 7: # 10-byte UID cards don't exist yet.\\n uid = self._uid[3:7] # Sequence 1, keep it simple.\\n self.in_data_exchange(bytearray([MIFARE_COMMAND_AUTH_B,address]) + key_b + uid)\",\n \"def login_bot(self):\\n pass\",\n \"def authenticate(self):\\n self.connection.authenticate()\",\n \"def authenticate(self):\\n return Response(\\n 'Could not verify your access level for that URL.\\\\nYou have to login with proper credentials',\\n 401,\\n {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'}\\n )\",\n \"def authorize(username, password):\\n\\ts = '%s:%s' % (username, password)\\n\\tPAYLOAD_HEADERS['Authorization'] = b'Basic ' + base64.b64encode(s.encode('ascii'))\",\n \"def authenticate(self):\\n # Check if we already have access token and secret\\n if not os.path.exists(self.sTOKEN_FILE):\\n # 1) Obtain Request token\\n oauth = OAuth1(self.apiKey, client_secret=self.apiKeySecret, callback_uri='oob')\\n r = requests.post(url=self.sREQUEST_TOKEN_URL, auth=oauth)\\n credentials = parse_qs(r.content)\\n resource_owner_key = credentials.get('oauth_token')[0]\\n resource_owner_secret = credentials.get('oauth_token_secret')[0]\\n\\n # 2) Obtain authorization for the user to access resources\\n # Redirect the user to /authorize and get the callback\\n authorize_url = self.sAUTHORIZE_URL + '?oauth_token=' + resource_owner_key + \\\\\\n '&oauth_consumer_key=' + self.apiKey + \\\\\\n '&Access=Full&Permissions=Modify'\\n\\n print 'Please go here and authorize,', authorize_url\\n verifier = raw_input('Please enter the six-digit PIN code: ')\\n\\n # 3) Obtain final access token\\n oauth = OAuth1(self.apiKey, client_secret = self.apiKeySecret,\\n resource_owner_key = resource_owner_key,\\n resource_owner_secret = resource_owner_secret,\\n verifier=verifier)\\n r = requests.post(url=self.sACCESS_TOKEN_URL, auth=oauth)\\n\\n credentials = parse_qs(r.content)\\n access_token = credentials.get('oauth_token')[0]\\n access_token_secret = credentials.get('oauth_token_secret')[0]\\n\\n # Store access token so we can use it later\\n with open(self.sTOKEN_FILE, 'w') as f:\\n json.dump({'access_token': access_token,\\n 'access_token_secret': access_token_secret}, f)\\n\\n else:\\n with open(self.sTOKEN_FILE, 'r') as f:\\n tokens = json.load(f)\\n access_token = tokens.get('access_token')\\n access_token_secret = tokens.get('access_token_secret')\\n\\n # store the file access token details for use in other methods\\n self.accessToken = access_token\\n self.accessTokenSecret = access_token_secret\",\n \"def auth():\\n\\n if current_user.is_authenticated:\\n\\n # OAuth is only necesary when we don't have a user's API\\n if not current_user.zoter_api:\\n\\n def get_auth_url():\\n request_token, request_token_secret = zoteroAuth.get_request_token()\\n session['request_token'] = request_token\\n session['request_token_secret'] = request_token_secret\\n auth_url = zoteroAuth.get_authorize_url(request_token)\\n return auth_url\\n\\n flash('Hi! {}, please visit <a href=\\\"{}\\\" target=\\\"new\\\">here</a> for authentication.'.format(current_user.username, get_auth_url()))\\n return redirect(url_for('bookshelf'))\\n \\n else:\\n flash('You already have an API key!')\\n return redirect(url_for('sync'))\",\n \"def _get_auth_string(self):\",\n \"def auth(self, username, password):\\n return False\",\n \"def authn_and_authz():\\n authentication()\\n authorization()\",\n \"def _auth():\\n with open('api-credentials.txt', 'r') as f:\\n lines = f.read().splitlines()\\n credentials = lines[0] + ':' + lines[1]\\n return 'Basic ' + base64.b64encode(credentials.encode()).decode()\",\n \"def interactive_authorize():\\n username = raw_input(\\\"Enter username: \\\")\\n password = raw_input(\\\"Enter password: \\\")\\n authorize(username, password)\",\n \"def auth(self, user):\",\n \"def test_authenticate(self, mock_set_token: Mock) -> None:\\n # Authenticate using Exonet client.\\n ExonetClient(\\\"kaSD0ffAD1ldSA92A0KODkaksda02KDAK\\\")\\n\\n # Check mock calls.\\n assert mock_set_token.call_args[0][0] == \\\"kaSD0ffAD1ldSA92A0KODkaksda02KDAK\\\"\\n assert mock_set_token.call_count == 1\",\n \"def __init__(self):\\n self.auth()\",\n \"def __init__(self,account_name, username, password):\\n self.account_name = account_name\\n self.username = username\\n self.password = password\",\n \"def authenticate(self):\\n\\n # Choose a random user agent string from the most popular\\n agent_strings = [\\n 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.113 Safari/537.36',\\n 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36',\\n 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.90 Safari/537.36'\\n ]\\n headers = {'User-Agent': choice(agent_strings)}\\n\\n # Spin up a requests session\\n with requests.Session() as s:\\n s.headers.update(headers)\\n\\n login_page = s.get('https://alta.registries.gov.ab.ca/SpinII/').content\\n soup = BeautifulSoup(login_page, 'html.parser')\\n\\n login_payload = {\\n 'uctrlFullHeader:ShutdownAlert1:Hidden1':'',\\n 'uctrlFullHeader:ShutdownAlert1:Hidden2':'',\\n 'JavascriptEnabled':1,\\n 'uctrlLogon:txtLogonName':'',\\n 'uctrlLogon:txtPassword':'',\\n 'uctrlLogon:cmdLogonGuest.x':59,\\n 'uctrlLogon:cmdLogonGuest.y':26\\n }\\n login_payload['__EVENTTARGET'] = soup.select_one('#__EVENTTARGET')['value']\\n login_payload['__EVENTARGUMENT'] = soup.select_one('#__EVENTARGUMENT')['value']\\n login_payload['__VIEWSTATE'] = soup.select_one('#__VIEWSTATE')['value']\\n\\n sleep(2)\\n legal_notice_page = s.post('https://alta.registries.gov.ab.ca/SpinII/logon.aspx', \\\\\\n data=login_payload).content\\n soup = BeautifulSoup(legal_notice_page, 'html.parser')\\n\\n login_payload['__VIEWSTATE'] = soup.select_one('#__VIEWSTATE')['value']\\n login_payload['cmdYES.x'] = 55\\n login_payload['cmdYES.y'] = 12\\n del login_payload['__EVENTARGUMENT']\\n del login_payload['__EVENTTARGET']\\n\\n sleep(2)\\n confirm_guest_page = s.post('https://alta.registries.gov.ab.ca/SpinII/legalnotice.aspx', \\\\\\n data=login_payload).content\\n soup = BeautifulSoup(confirm_guest_page, 'html.parser')\\n\\n if len(soup.find_all(text='You are logged on as a Guest.')) > 0:\\n return s\",\n \"def authenticate():\\n return Response(\\n '', 401, {'WWW-Authenticate': 'Basic realm=\\\"Login Required\\\"'}\\n )\",\n \"def __init__(self,account,username, password):\\n self.account = account\\n self.username = username\\n self.password = password\",\n \"def main():\\t\\n\\t# read in short term auth\\n\\tf = open('./input.txt', 'r')\\n\\tshort_term_auth = f.read()\\n\\n\\tlong_term_access_token = long_term_token(short_term_auth)\\n\\tprint(long_term_access_token)\\n\\tprint('run program like normal now')\",\n \"def auth_token(self):\",\n \"def __init__(self, login=\\\"user\\\", password=\\\"password\\\"):\\n try:\\n kerberos\\n except NameError:\\n log(\\\"Kerberos Python package is not\\\"\\n \\\" installed. Can't proceed with Kerberos\\\"\\n \\\" authorization.\\\", logLevel.ERROR)\\n sys.exit(4)\\n\\n super(KerberizedCDSInvenioConnector, self).__init__(\\\"user\\\",\\n \\\"password\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6442201","0.6442201","0.61614424","0.5995388","0.59081537","0.58965886","0.5888043","0.58654636","0.5845789","0.58425903","0.58413416","0.58286655","0.58205444","0.5803473","0.58030915","0.5792578","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57841116","0.57711077","0.57546794","0.57534885","0.5693552","0.568416","0.56834745","0.56834745","0.56834745","0.56834745","0.56834745","0.56834745","0.56834745","0.56834745","0.5679225","0.56615216","0.56519127","0.5650823","0.5650396","0.5643723","0.5586596","0.5583293","0.55583364","0.555649","0.555649","0.5556005","0.55223346","0.5522215","0.5514407","0.5513395","0.55082625","0.550182","0.5492445","0.54861176","0.545861","0.5456759","0.54516613","0.5449599","0.5448585","0.54410887","0.5424602","0.5424602","0.5421433","0.5416408","0.5403286","0.5385974","0.5359474","0.53540385","0.5350544","0.535025","0.5344429","0.5340384","0.53387165","0.5336879","0.5332162","0.53287137","0.5312592","0.53111976","0.5309166","0.5309129","0.5308473","0.5302345","0.5294167","0.5294048","0.52858204"],"string":"[\n \"0.6442201\",\n \"0.6442201\",\n \"0.61614424\",\n \"0.5995388\",\n \"0.59081537\",\n \"0.58965886\",\n \"0.5888043\",\n \"0.58654636\",\n \"0.5845789\",\n \"0.58425903\",\n \"0.58413416\",\n \"0.58286655\",\n \"0.58205444\",\n \"0.5803473\",\n \"0.58030915\",\n \"0.5792578\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57841116\",\n \"0.57711077\",\n \"0.57546794\",\n \"0.57534885\",\n \"0.5693552\",\n \"0.568416\",\n \"0.56834745\",\n \"0.56834745\",\n \"0.56834745\",\n \"0.56834745\",\n \"0.56834745\",\n \"0.56834745\",\n \"0.56834745\",\n \"0.56834745\",\n \"0.5679225\",\n \"0.56615216\",\n \"0.56519127\",\n \"0.5650823\",\n \"0.5650396\",\n \"0.5643723\",\n \"0.5586596\",\n \"0.5583293\",\n \"0.55583364\",\n \"0.555649\",\n \"0.555649\",\n \"0.5556005\",\n \"0.55223346\",\n \"0.5522215\",\n \"0.5514407\",\n \"0.5513395\",\n \"0.55082625\",\n \"0.550182\",\n \"0.5492445\",\n \"0.54861176\",\n \"0.545861\",\n \"0.5456759\",\n \"0.54516613\",\n \"0.5449599\",\n \"0.5448585\",\n \"0.54410887\",\n \"0.5424602\",\n \"0.5424602\",\n \"0.5421433\",\n \"0.5416408\",\n \"0.5403286\",\n \"0.5385974\",\n \"0.5359474\",\n \"0.53540385\",\n \"0.5350544\",\n \"0.535025\",\n \"0.5344429\",\n \"0.5340384\",\n \"0.53387165\",\n \"0.5336879\",\n \"0.5332162\",\n \"0.53287137\",\n \"0.5312592\",\n \"0.53111976\",\n \"0.5309166\",\n \"0.5309129\",\n \"0.5308473\",\n \"0.5302345\",\n \"0.5294167\",\n \"0.5294048\",\n \"0.52858204\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":86690,"cells":{"query":{"kind":"string","value":"Recenters an image from the origin to the middle of the display image"},"document":{"kind":"string","value":"def center(image):\n\tsize = image.shape\n\thalf = int(np.ceil(size[0]/2))\n\timage = np.roll(np.roll(image, half, 0), half, 1)\n\treturn image"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def on_image(self, image):","def center_image(image):\r\n image.anchor_x = image.width / 2\r\n image.anchor_y = image.height / 2","def center_image(image):\r\n image.anchor_x = image.width / 2\r\n image.anchor_y = image.height / 2","def center_image(image):\n image.anchor_x = image.width/2\n image.anchor_y = image.height/2","def adjust(self, image):\n ...","def pos_image(image, x,y):\n image.anchor_x = x\n image.anchor_y = y","def query_image_tile(self, coord):","def cut_main_screen(im):\n top = 0\n left = 0\n bottom = 980\n right = 1350\n return im[top:bottom, left:right].copy()","def load_image_centered(filename):\n im = pyglet.image.load(filename)\n im.anchor_x = im.width // 2\n im.anchor_y = im.height // 2\n return im","def update_canvas_display_image_from_full_image(self):\n\n full_image_rect = (0, 0, self.image_reader.full_image_ny, self.image_reader.full_image_nx)\n self.update_canvas_display_image_from_full_image_rect(full_image_rect)","def transform(self, previousimage):","def center_image_anchor(image):\n image.anchor_x = image.height/2\n image.anchor_y = image.height/2","def draw_image(self):\n self.PDF.saveState()\n self.PDF.scale(1, -1)\n # self.PDF.drawImage(\n # LOGO, 490, -78, width=80, preserveAspectRatio=True, mask=\"auto\"\n # )\n self.PDF.restoreState()","def show_image(self):\n cv2.imshow('Image', self.__diff_image())\n cv2.waitKey()","def update_image(self):\n if self.first is None or self.second is None:\n LOG.warn(\"No images set yet\")\n else:\n pos = self.slider.value()\n moved = np.roll(self.second, self.second.shape[0] / 2 - pos, axis=0)\n self.image_item.setImage(moved - self.first)","def __draw_image(self):\n if self.image_name is not None:\n img = mpimg.imread(self.image_name)\n extent = (0.5, self.xmax+0.5, -0.5, self.ymax-0.5)\n self.ax.imshow(img, extent=extent, origin='lower',\n alpha=self.image_alpha)","def display_image(self, img, img_pos):\n image = tk.Label(self.top, image=img)\n image.grid(row=img_pos[0], column=img_pos[1],\n columnspan=img_pos[2], rowspan=img_pos[3])","def _setup_new_image(self):\n\n if not self._viewer is None:\n if not self._input_image is None:\n self._viewer.SetInput(self._input_image)\n else:\n self._viewer.SetInput(self._dummy_image_source.GetOutput())\n\n ii = self._viewer.GetInput()\n \n ii.UpdateInformation()\n ii.Update()\n range = ii.GetScalarRange()\n self._viewer.SetColorWindow(range[1] - range[0])\n self._viewer.SetColorLevel(0.5 * (range[1] + range[0]))\n \n icp = self._view_frame._image_control_panel\n icp.slider.SetRange(self._viewer.GetSliceMin(),\n self._viewer.GetSliceMax())\n icp.slider.SetValue(self._viewer.GetSliceMin())\n \n #self._viewer.UpdateDisplayExtent()\n self._viewer.GetRenderer().ResetCamera()","def update(self):\n self.imagecounter +=1\n if self.imagecounter > 7:\n self.imagecounter = 0\n self.image = pygame.image.load(self.pictures[self.imagecounter])\n self.rect = self.image.get_rect()\n self.rect.left = self.x\n self.rect.top = self.y","def overlay_image(image_to_show, image_to_add, directory=None):\n \n direcx = \"/Documents/1.4.5 Images/\"#put a condition pls\n if directory == None:\n directory = os.getcwd()\n print directory\n new_image = PIL.Image.open(directory + direcx + image_to_show)\n new_imager = PIL.Image.open(directory + direcx + image_to_show)\n new_imager.show()\n new_image2 = PIL.Image.open(directory + direcx + image_to_add)\n new_image2.show()\n \n width, height = new_imager.size\n print str(width) + \" \" + str(height)\n \n new_image22 = new_image2.resize((width,height), PIL.Image.ANTIALIAS)\n new_image22.show()\n\n new_imager.paste(new_image22, (0,0), new_image22)\n new_imager.show()\n #cohrt = PIL.Image.blend(new_imager, new_image22, alpha=0.5)\n #cohrt.show() #broked","def __show_image(self):\n box_image = self.canvas.coords(self.container) # get image area\n box_canvas = (self.canvas.canvasx(0), # get visible area of the canvas\n self.canvas.canvasy(0),\n self.canvas.canvasx(self.canvas.winfo_width()),\n self.canvas.canvasy(self.canvas.winfo_height()))\n box_img_int = tuple(map(int, box_image)) # convert to integer or it will not work properly\n # Get scroll region box\n box_scroll = [min(box_img_int[0], box_canvas[0]), min(box_img_int[1], box_canvas[1]),\n max(box_img_int[2], box_canvas[2]), max(box_img_int[3], box_canvas[3])]\n # Horizontal part of the image is in the visible area\n if box_scroll[0] == box_canvas[0] and box_scroll[2] == box_canvas[2]:\n box_scroll[0] = box_img_int[0]\n box_scroll[2] = box_img_int[2]\n # Vertical part of the image is in the visible area\n if box_scroll[1] == box_canvas[1] and box_scroll[3] == box_canvas[3]:\n box_scroll[1] = box_img_int[1]\n box_scroll[3] = box_img_int[3]\n # Convert scroll region to tuple and to integer\n self.canvas.configure(scrollregion=tuple(map(int, box_scroll))) # set scroll region\n x1 = max(box_canvas[0] - box_image[0], 0) # get coordinates (x1,y1,x2,y2) of the image tile\n y1 = max(box_canvas[1] - box_image[1], 0)\n x2 = min(box_canvas[2], box_image[2]) - box_image[0]\n y2 = min(box_canvas[3], box_image[3]) - box_image[1]\n if int(x2 - x1) > 0 and int(y2 - y1) > 0: # show image if it in the visible area\n if self.__huge and self.__curr_img < 0: # show huge image\n h = int((y2 - y1) / self.imscale) # height of the tile band\n self.__tile[1][3] = h # set the tile band height\n self.__tile[2] = self.__offset + self.imwidth * int(y1 / self.imscale) * 3\n self.__image.close()\n self.__image = Image.open(self.path) # reopen / reset image\n self.__image.size = (self.imwidth, h) # set size of the tile band\n self.__image.tile = [self.__tile]\n image = self.__image.crop((int(x1 / self.imscale), 0, int(x2 / self.imscale), h))\n else: # show normal image\n image = self.__pyramid[max(0, self.__curr_img)].crop( # crop current img from pyramid\n (int(x1 / self.__scale), int(y1 / self.__scale),\n int(x2 / self.__scale), int(y2 / self.__scale)))\n #\n imagetk = ImageTk.PhotoImage(image.resize((int(x2 - x1), int(y2 - y1)), self.__filter))\n imageid = self.canvas.create_image(max(box_canvas[0], box_img_int[0]),\n max(box_canvas[1], box_img_int[1]),\n anchor='nw', image=imagetk)\n self.canvas.lower(imageid) # set image into background\n if self.last_image_id != 0:\n self.canvas.delete(self.last_image_id)\n\n self.last_image_id = imageid\n self.canvas.imagetk = imagetk # keep an extra reference to prevent garbage-collection","def positionZoomed(self):\n rect_size = self._image.get_rect()\n\n if rect_size.width > 720:\n self.x = (int((720 - rect_size.width ) / 2))\n\n if rect_size.height > 720:\n self.y = (int((720 - rect_size.height) / 2))","def update(self):\n cv2.imshow(self.window_name, self.map.get_crop())","def update_current_image(self):\n\n rect = (0, 0, self.variables.canvas_width, self.variables.canvas_height)\n if self.variables.canvas_image_object is not None:\n self.variables.canvas_image_object.update_canvas_display_image_from_canvas_rect(rect)\n self.set_image_from_numpy_array(self.variables.canvas_image_object.display_image)\n self.update()","def __show_image(self):\n box_image = self.canvas_image.coords(self.container) # get image area\n box_canvas = (self.canvas_image.canvasx(0), # get visible area of the canvas\n self.canvas_image.canvasy(0),\n self.canvas_image.canvasx(self.canvas_image.winfo_width()),\n self.canvas_image.canvasy(self.canvas_image.winfo_height()))\n self.box_img_int = tuple(map(int, box_image)) # convert to integer or it will not work properly\n \n # Get scroll region box\n box_scroll = [min(self.box_img_int[0], box_canvas[0]), min(self.box_img_int[1], box_canvas[1]),\n max(self.box_img_int[2], box_canvas[2]), max(self.box_img_int[3], box_canvas[3])]\n # Horizontal part of the image is in the visible area\n if box_scroll[0] == box_canvas[0] and box_scroll[2] == box_canvas[2]:\n box_scroll[0] = self.box_img_int[0]\n box_scroll[2] = self.box_img_int[2]\n # Vertical part of the image is in the visible area\n if box_scroll[1] == box_canvas[1] and box_scroll[3] == box_canvas[3]:\n box_scroll[1] = self.box_img_int[1]\n box_scroll[3] = self.box_img_int[3]\n # Convert scroll region to tuple and to integer\n self.canvas_image.configure(scrollregion=tuple(map(int, box_scroll))) # set scroll region\n x1 = max(box_canvas[0] - box_image[0], 0) # get coordinates (x1,y1,x2,y2) of the image tile\n y1 = max(box_canvas[1] - box_image[1], 0)\n x2 = min(box_canvas[2], box_image[2]) - box_image[0]\n y2 = min(box_canvas[3], box_image[3]) - box_image[1]\n if int(x2 - x1) > 0 and int(y2 - y1) > 0: # show image if it in the visible area\n image = self.__pyramid[max(0, self.__curr_img)].crop( # crop current img from pyramid\n (int(x1 / self.__scale), int(y1 / self.__scale),\n int(x2 / self.__scale), int(y2 / self.__scale)))\n #\n imagetk = ImageTk.PhotoImage(image.resize((int(x2 - x1), int(y2 - y1)), self.__filter))\n self.imageid = self.canvas_image.create_image(max(box_canvas[0], self.box_img_int[0]),\n max(box_canvas[1], self.box_img_int[1]),\n anchor='nw', image=imagetk)\n self.canvas_image.lower(self.imageid) # set image into background\n self.canvas_image.imagetk = imagetk # keep an extra reference to prevent garbage-collection","def draw_image(self):\n \n pixel_array = self.imageprepare(self.image_path)\n newArr = self.reshape_pixel_array(pixel_array)\n plt.imshow(newArr, interpolation='nearest')\n plt.savefig('MNIST_IMAGE.png')#save MNIST image\n plt.show()#Show / plot that image","def on_draw_over_image(self):","def displayImg(self):\r\n\r\n\t# If you want to skip n frames, set value to 0 to see all images\r\n\tSKIP = 4500\r\n for idx in range(len(self.centers)):\r\n\t if idx < SKIP:\r\n\t\tcontinue\r\n file_left = self.lefts[idx][5]\r\n file_center = self.centers[idx][5]\r\n file_right = self.rights[idx][5]\r\n\r\n img_left = cv2.imread(os.path.join(self.pathDir, file_left), \\\r\n cv2.IMREAD_COLOR)\r\n img_center = cv2.imread(os.path.join(self.pathDir, file_center), \\\r\n cv2.IMREAD_COLOR)\r\n img_right = cv2.imread(os.path.join(self.pathDir, file_right), \\\r\n cv2.IMREAD_COLOR)\r\n\r\n\t #Resize the image to 50%\r\n img_l = cv2.resize(img_left, None, fx=0.5, fy=0.5, \\\r\n interpolation = cv2.INTER_LINEAR)\r\n img_c = cv2.resize(img_center, None, fx=0.5, fy=0.5, \\\r\n interpolation = cv2.INTER_LINEAR)\r\n img_r = cv2.resize(img_right, None, fx=0.5, fy=0.5, \\\r\n interpolation = cv2.INTER_LINEAR)\r\n \r\n height, width = img_c.shape[:2]\r\n new_img = np.zeros((height, width*3, img_c.shape[2]),\r\n np.uint8)\r\n\r\n #Adding sequence numbers and Time\r\n\t #Left\r\n strTime = self.timestampToStr(self.lefts[idx][1])\r\n\t self.putTextToImg(img_l, self.lefts[idx][0], strTime, height)\r\n\t #Center\r\n\t strTime = self.timestampToStr(self.centers[idx][1])\r\n\t self.putTextToImg(img_c, self.centers[idx][0], strTime, height)\r\n\t #Right\r\n\t strTime = self.timestampToStr(self.rights[idx][1])\r\n\t self.putTextToImg(img_r, self.rights[idx][0], strTime, height)\r\n\t \r\n\t angle = float(self.angles_at_timestamps[idx])\r\n\t speed = float(self.speed_at_timestamps[idx])\r\n\r\n\t print \"speed: %f - angle: %f\" % (speed, angle)\r\n\r\n\t self.draw_path_on(img_c, speed, angle)\r\n\r\n\t #Generate the new image\r\n for i in range(height):\r\n new_img[i] = np.concatenate((img_l[i, : ], img_c[i, : ], \\\r\n img_r[i, : ]))\r\n \r\n\r\n cv2.imshow('Udacity Challenge 2 - Viewer', new_img)\r\n key = cv2.waitKey(30)\r\n\r\n # Press q to exit\r\n if key == ord('q'):\r\n break\r\n\r\n cv2.destroyAllWindows()","def main():\n original = SimpleImage('images/mt-rainier.jpg')\n original.show()\n reflected = make_reflected('images/mt-rainier.jpg')\n reflected.show()","def update_image(self):\n self.image = Image.fromarray(self.img)","def update_image(self):\n if self.filenames:\n pos = self.slider.value()\n proj, flat, dark, theta = dx.read_aps_32id(self.filenames, proj=(pos, pos+1))\n if self.ffc_correction:\n image = proj[0,:,:].astype(np.float)/flat[0,:,:].astype(np.float)\n else:\n image = proj[0,:,:].astype(np.float)\n self.image_item.setImage(image)","def show(image):\n fig = pyplot.figure()\n axis = fig.add_subplot(1, 1, 1)\n imgplot = axis.imshow(image)\n imgplot.set_interpolation('nearest')\n axis.xaxis.set_ticks_position('top')\n axis.yaxis.set_ticks_position('left')\n pyplot.show()","def overlay_image(image_to_show, image_to_add, directory=None):\n \n direcx = \"/Documents/1.4.5 Images/\"#put a condition pls\n if directory == None:\n directory = os.getcwd()\n print directory\n new_image = PIL.Image.open(directory + direcx + image_to_show)\n new_imager = PIL.Image.open(directory + direcx + image_to_show)\n new_imager.show()\n new_image2 = PIL.Image.open(directory + direcx + image_to_add)\n new_image2.show()\n width, height = new_imager.size\n print str(width) + \" \" + str(height)\n #print new_image\n #print new_image2\n #if image_to_show == \"\":\n # print_directory_list2()\n # return \"Use one of these\"\n new_image22 = new_image2.resize((width,height), PIL.Image.ANTIALIAS)\n new_image22.show()\n\n new_imager.paste(new_image22, (width, height), new_image22)\n new_imager.show()\n #cohrt = PIL.Image.blend(new_imager, new_image22, alpha=0.5)\n #cohrt.show()","def _render_static_image_annotation(self):\n cv2.rectangle(self._image,\n (0,0), (640, 40),\n (0, 0, 0),\n -1)\n \n cv2.putText(self._image,self._current_mode, (40, 25),\n cv2.FONT_HERSHEY_SIMPLEX, 0.7, 255, 2)\n\n cv2.putText(self._image, time.asctime(), (400, 460),\n cv2.FONT_HERSHEY_SIMPLEX, 0.7, 255, 2)","def other_object(img):\n return img[500:570, 1000:1070]","def view(self):\n window = tk.Tk()\n label = tk.Label(window)\n label.pack()\n img = self.get_tkimage()\n label[\"image\"] = label.img = img\n window.mainloop()","def img_cb(self, image):\n self.last_img = image\n self.is_new_img = True","def _center_image(image):\n\n dim_x, dim_y = image.shape[0:2]\n\n x = int(dim_x/2)\n off_x = int(dim_x/4)\n xmin = x - off_x\n xmax = x + off_x\n\n y = int(dim_y/2)\n off_y = int(dim_y/4)\n ymin = y - off_y\n ymax = y + off_y\n\n return(image[xmin:xmax, ymin:ymax])","def resting(self):\n self.image = self.image_list[self.index]\n\n if self.rect.y % 32 != 0:\n self.correct_position(self.rect.y)\n if self.rect.x % 32 != 0:\n self.correct_position(self.rect.x)","def crop_image(image):\r\n return image[40:-20, :]","def update(self, *args):\n\n # change picture every 100 milliseconds\n now = pygame.time.get_ticks()\n if now - self.last_update > 100:\n self.index = self.index ^ 1\n self.image = self.images[self.index]\n self.last_update = now\n prom = self.rect.center\n self.rect = self.image.get_rect()\n self.rect.center = prom\n\n self.rect.x -= args[0]","def getimage(self):","def newAvatarImage(self, imgPath, imgName): \n img = ocempgui.draw.Image.load_image(imgPath)\n if not self.images[imgName]: \n imgOcemp = guiobjects.OcempImageMapTransparent(img)\n imgOcemp.topleft = 528, 114\n self.window.add_child(imgOcemp)\n self.images[imgName] = imgOcemp\n else:\n self.images[imgName].picture = img","def imshow(img):\n imadd(img)\n plt.ion()\n plt.show()","def LKTrackerImageToImage(imageOld, pixelCoordsOld, imageNew,\n pixelCoordsNew, windowSize):\n # imageOld = cv2.cvtColor(imageOld, cv2.COLOR_BGR2GRAY)\n # imageNew = cv2.cvtColor(imageNew, cv2.COLOR_BGR2GRAY)\n\n # Get top left corner of window.\n\n topLeftX1, topLeftY1 = pixelCoordsOld - windowSize // 2\n topLeftX2, topLeftY2 = pixelCoordsNew - windowSize // 2\n\n # Compute horizontal and vertical gradients for the original frame.\n gx = utils.pixelDiffImages(imageOld,\n topLeftX1,\n topLeftY1,\n imageOld,\n topLeftX1,\n topLeftY1 - 1,\n windowSize,\n windowSize)\n \n gy = utils.pixelDiffImages(imageOld,\n topLeftX1,\n topLeftY1,\n imageOld,\n topLeftX1 - 1,\n topLeftY1,\n windowSize,\n windowSize)\n \n # Compute difference between original and new frames.\n diff = utils.pixelDiffImages(imageOld,\n topLeftX1,\n topLeftY1,\n imageNew,\n topLeftX2,\n topLeftY2,\n windowSize,\n windowSize)\n\n # Compute components of Harris matrix.\n Ixx = gx ** 2\n Iyy = gy ** 2\n Ixy = gx * gy\n\n # Compute Gaussian kernel for weighting pixels in the window.\n gkern = np.outer(signal.gaussian(windowSize, 2.5),\n signal.gaussian(windowSize, 2.5))\n\n # Construct matrices and solve the matrix-vector equation to get the\n # movement of the pixel.\n Z = np.array([[np.sum(Ixx * gkern), np.sum(Ixy * gkern)],\n [np.sum(Ixy * gkern), np.sum(Iyy * gkern)]])\n b = np.array([np.sum(diff * gx * gkern), np.sum(diff * gy * gkern)])\n d = np.linalg.solve(Z, b)\n\n # Compute new position of pixel\n return pixelCoordsNew + d[: : -1]","def rot_center(self):\n x, y = pygame.mouse.get_pos()\n relx, rely = x - self.rect.x, y - self.rect.y\n angle = math.atan2(relx, rely)\n angle = math.degrees(angle)\n orig_rect = self.image_original.get_rect()\n rot_image = pygame.transform.rotate(self.image_original, angle)\n rot_rect = orig_rect.copy()\n rot_rect.center = rot_image.get_rect().center\n rot_image = rot_image.subsurface(rot_rect).copy()\n return rot_image","def offset(img, y_s, y_e, x_s, x_e):\n if y_s < 0:\n y_s = 0\n if y_e > img.shape[0]:\n y_e = img.shape[0]\n if x_s < 0:\n x_s = 0\n if x_e > img.shape[1]:\n x_e = img.shape[1]\n new_img = img[y_s:y_e, x_s:x_e]\n return new_img","def update_pos(self):\n self.imgx=self.pathX[min(self.x,len(self.pathX)-1)]\\\n [min(self.y,len(self.pathX[self.x])-1)]\n self.imgy=self.pathY[min(self.x,len(self.pathY)-1)]\\\n [min(self.y,len(self.pathY[self.x])-1)]","def display(self):\n image_qt = ImageQt.ImageQt(self.view_state.get_image())\n self.imageLabel.setPixmap(QtGui.QPixmap.fromImage(image_qt))\n self.imageLabel.adjustSize()","def small_image(self):\n pass","def full_photo():\n top = Toplevel()\n top.title(\"Full APOD Photo\")\n top.iconbitmap('10.APOD Viewer/rocket.ico')\n\n #Load the full image to the top image\n img_label = Label(top, image=full_img)\n img_label.pack()","def display(self):\n display(self.image)","def show(image):\n fig = pyplot.figure()\n ax = fig.add_subplot(1,1,1)\n imgplot = ax.imshow(image, cmap=mpl.cm.Greys)\n imgplot.set_interpolation('nearest')\n ax.xaxis.set_ticks_position('top')\n ax.yaxis.set_ticks_position('left')\n pyplot.show()","def update_image(self, cv_img):\n\t\tqt_img = self.ImageEdits(cv_img)\n\t\tself.camera.setPixmap(qt_img)","def aktualisiere(self):\n if(self.zeichnung == None):\n self.zeichnung = self.leinwand.create_image(self.x, self.y, \n image=self.grafik)\n delta_x = self.zielX - self.x\n delta_y = self.zielY - self.y\n self.leinwand.move(self.zeichnung, delta_x, delta_y)\n\n self.x = self.zielX\n self.y = self.zielY","def extract_thumbnail(img, joints):\n x_min, y_min = np.min(joints, axis=0)\n x_max, y_max = np.max(joints, axis=0)\n [h, w, _] = img.shape\n\n if x_min > 20:\n x_min -= 20\n if y_min > 20:\n y_min -= 20\n\n if x_max+20 < w:\n x_max += 20\n if y_max+20 < h:\n y_max += 20\n\n return img[y_min:y_max, x_min:x_max, :]","def set_current(self, image_name):\n # Sets the position of the crop\n self.j ,self.i = 0, 0\n\n # loads the image\n self.image = convert2int(tifffile.imread(image_name)).astype(numpy.float32)\n\n # Computes the number of crops in x and y\n self.ny = numpy.ceil(self.image.shape[0] / self.step)\n self.nx = numpy.ceil(self.image.shape[1] / self.step)\n\n # rescale the image\n self.image -= self.image_min\n self.image /= (0.8 * (self.image_max - self.image_min))\n self.image = numpy.clip(self.image, 0, 1)","def showimage(image):\n mplt.figure()\n mplt.imshow(image)\n mplt.show()","def image_proc(self):\r\n res_erode, res_in_rect = self._get_res_image()\r\n\r\n Moment_rect = cv2.moments(res_in_rect)\r\n if (Moment_rect[\"m00\"] != 0):\r\n self.rect_x = int(Moment_rect[\"m10\"] / Moment_rect[\"m00\"])\r\n self.rect_y = int(Moment_rect[\"m01\"] / Moment_rect[\"m00\"])\r\n\r\n # afin de trouver les centres de la rectangulaire.\r\n cnts, hierarchy = cv2.findContours(res_erode,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\r\n for i in cnts:\r\n #Moment = cv2.moments(cnts)\r\n moment = cv2.moments(res_erode)\r\n self.cx = int(moment[\"m10\"] / moment[\"m00\"])\r\n self.cy = int(moment[\"m01\"] / moment[\"m00\"])\r\n cv2.circle(output_image,(self.cx,self.cy),7,(255,255,255),-1)\r\n cv2.putText(output_image, \"center\", (self.cx, self.cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\r\n #print(cX,cY)\r","def display(self):\n\t\tself.imgDisplay.set_from_pixbuf(self.getVisible())\n\t\tgc.collect()","def show_to_window(self):\n if self.normal_mode:\n self.show_image.show_original_image(\n self.image, self.width_original_image)\n self.show_image.show_result_image(\n self.image, self.width_result_image, self.angle)\n\n else:\n if self.panorama_mode:\n image = draw_polygon(\n self.image.copy(),\n self.mapX_pano,\n self.mapY_pano)\n mapX = np.load(\n './plugins/Thread_inspection/view_image/maps_pano/mapX.npy')\n mapY = np.load(\n './plugins/Thread_inspection/view_image/maps_pano/mapY.npy')\n rho = self.panorama.rho\n\n self.result_image = cv2.remap(\n self.image,\n mapX,\n mapY,\n cv2.INTER_CUBIC)\n self.result_image = self.result_image[round(\n rho + round(self.moildev.getRhoFromAlpha(30))):self.h, 0:self.w]\n # print(self.width_result_image)\n else:\n image = draw_polygon(self.image.copy(), self.mapX, self.mapY)\n self.result_image = cv2.remap(\n self.image,\n self.mapX,\n self.mapY,\n cv2.INTER_CUBIC)\n self.show_image.show_original_image(\n image, self.width_original_image)\n self.show_image.show_result_image(\n self.result_image, self.width_result_image, self.angle)","def show_image(self):\n cv2.imshow(self.config.DISPLAY_NAME, self.image)","def test_plot_img_with_auto_cut_coords(display_mode):\n data = np.zeros((20, 20, 20))\n data[3:-3, 3:-3, 3:-3] = 1\n img = Nifti1Image(data, np.eye(4))\n plot_img(img, cut_coords=None, display_mode=display_mode, black_bg=True)\n plt.close()","def show(self, screen):\n x_display = self.xy_position[0] * constants.CELL_SIZE\n y_display = self.xy_position[1] * constants.CELL_SIZE\n screen.blit(self.image, (x_display, y_display))","def update(self):\n rect = self.img_rect.center\n self.font_img = self.font.render(self.text, True, color1)\n self.img_rect = self.font_img.get_rect()\n # self.img_rect.center = [self.title_img.get_width() + self.font_img.get_width()//2 + padding, 5*Y//8]\n self.img_rect.center = rect","def pix_center(self):\n return self._pix_center","def show(image):\n from matplotlib import pyplot\n import matplotlib as mpl\n fig = pyplot.figure()\n ax = fig.add_subplot(1,1,1)\n imgplot = ax.imshow(image, cmap=mpl.cm.Greys)\n imgplot.set_interpolation('nearest')\n ax.xaxis.set_ticks_position('top')\n ax.yaxis.set_ticks_position('left')\n pyplot.show()","def show(image):\n from matplotlib import pyplot\n import matplotlib as mpl\n fig = pyplot.figure()\n ax = fig.add_subplot(1,1,1)\n imgplot = ax.imshow(image, cmap=mpl.cm.Greys)\n imgplot.set_interpolation('nearest')\n ax.xaxis.set_ticks_position('top')\n ax.yaxis.set_ticks_position('left')\n pyplot.show()","def show(image):\n from matplotlib import pyplot\n import matplotlib as mpl\n fig = pyplot.figure()\n ax = fig.add_subplot(1,1,1)\n imgplot = ax.imshow(image, cmap=mpl.cm.Greys)\n imgplot.set_interpolation('nearest')\n ax.xaxis.set_ticks_position('top')\n ax.yaxis.set_ticks_position('left')\n pyplot.show()","def showImage(self, image):\n \n self.image = img","def display_image(img, label):\n cv2.imshow(label,img)\n cv2.waitKey(0)\n cv2.destroyAllWindows()","def _observation(self, img):\n img = self.crop(img)\n img = imresize(img, self.img_size)\n if self.grayscale:\n img = img.mean(-1, keepdims=True)\n img = np.transpose(img, (2, 0, 1)) # reshape from (h,w,colors) to (colors,h,w)\n img = img.astype('float32') / 255.\n img = np.squeeze(img)\n return img","def display_cropped_img(i):\n image = PIL.Image.open(testing_img_paths[i])\n image = image.crop(box=(313,99,825,611))\n image = image.resize((256,256))\n display(image)","def process(self, image):","def template_zone(self):\n ratio = 32\n shape = [self.cam.height, self.cam.width]\n img = self.cam.frame[shape[0] / 2 - 3 * shape[0] / ratio:shape[0] / 2 + 3 * shape[0] / ratio,\n shape[1] / 2 - 3 * shape[1] / ratio:shape[1] / 2 + 3 * shape[1] / ratio]\n return img","def img_extent(img):\n# get the image coordinates in pixels\n px0 = img.meta['crpix1']\n py0 = img.meta['crpix2']\n# get the image coordinates in arcsec \n ax0 = img.meta['crval1']\n ay0 = img.meta['crval2']\n# get the image scale in arcsec \n axd = img.meta['cdelt1']\n ayd = img.meta['cdelt2']\n#get the number of pixels\n tx,ty = img.data.shape\n#get the max and min x and y values\n minx,maxx = px0-tx,tx-px0\n miny,maxy = py0-ty,ty-py0\n#convert to arcsec\n maxx,minx = maxx*axd,minx*axd\n maxy,miny = maxy*ayd,miny*ayd\n\n\n return maxx,minx,maxy,miny","def blitme(self):\n self.screen.blit(self.image, self.rect)\n # print('y = ' + str(self.rect.centery))\n # print('x = ' + str(self.rect.centerx))","def _update_screen(self) -> None:\n # setup the source image with an alpha channel\n alpha = 255 * np.ones_like(self.image[..., 0:1])\n image = np.concatenate([self._image, alpha], axis=-1).astype(np.uint8)\n # setup the super pixel segmentations\n super_pixels = np.zeros_like(self.image)\n super_pixels = mark_boundaries(\n super_pixels,\n self._super_pixel_segments,\n self._super_pixel_color\n )\n # concatenate the first channel of sup as the alpha channel\n super_pixels = [super_pixels, super_pixels[..., 0:1]]\n super_pixels = np.concatenate(super_pixels, axis=-1).astype(np.uint8)\n # setup the segmentation image with an alpha channel scaled by the\n # opacity parameter of the application [0, 9]\n intensity = 255 * (self._opacity / 9)\n intensity = intensity * np.ones_like(self._segmentation[..., 0:1])\n segmentation = np.concatenate([self._segmentation, intensity], axis=-1)\n segmentation = segmentation.astype(np.uint8)\n # send the images to the window\n self._view.show([image, segmentation, super_pixels])","def image_cb(self, msg):\n self.has_image = True\n self.camera_image = msg\n self.get_light_state()","def show_img(self):\n if self.image is not None:\n cv2.imshow(self.image_window, self.image)\n cv2.waitKey(1)\n else:\n rospy.loginfo(\"No image to show yet\")","def show_image(f, x, y):\n window_surface.blit(f, (x, y))","def _set_anchor_center(img):\n img.anchor_x = int(img.width / 2)\n img.anchor_y = int(img.height / 2)","def displayImages(self):\n\n plt.figure(figsize=(8,6))\n plt.subplot(1,2,1)\n plt.imshow( self.original_image, cmap=\"gray\")\n plt.title(\"Original Image\")\n plt.subplot(1,2,2)\n plt.imshow( self.blurred_image, cmap=\"gray\")\n plt.title(\"Blurred Image\")","def __move_to(self, event):\n self.canvas_image.scan_dragto(event.x, event.y, gain=1)\n self.to_coord = (event.x, event.y)\n self.__show_image() # zoom tile and show it on the canvas","def rot_center(self):\n loc = self.rect.center\n self.image = pygame.transform.rotate(self.current_sprite_alpha, self.rot)\n self.rect = self.image.get_rect()\n self.rect.center = loc","def setUp(self):\r\n center = [1,1]\r\n size = 1\r\n self.imageinfo = ImageInfo(center, size)","def setUp(self):\r\n center = [1,1]\r\n size = 1\r\n self.imageinfo = ImageInfo(center, size)","def setUp(self):\r\n center = [1,1]\r\n size = 1\r\n self.imageinfo = ImageInfo(center, size)","def setUp(self):\r\n center = [1,1]\r\n size = 1\r\n self.imageinfo = ImageInfo(center, size)","def setUp(self):\r\n center = [1,1]\r\n size = 1\r\n self.imageinfo = ImageInfo(center, size)","def display_image(X):\n\n\tim = X.reshape(28, 28)\n\ttemp = plt.imshow(im)\n\tplt.show()","def zoom_augmentation():\n # Get the width and the height of the zoomed version\n x_len, y_len = np.random.randint(250, 350, size=2)\n # Get left upper ,right and lower bound of the pixels in the original image\n left = np.random.randint(x_size-x_len)\n upper = np.random.randint(y_size-y_len)\n right, lower = left + x_len, upper+y_len\n # Crops the box and resizes it to the original image size\n box = (left, upper, right, lower)\n return lambda image: image.transform(image.size, Image.EXTENT, box)","def get_current_image(self):\n raise NotImplementedError","def show_image(self, name, img, loc=(0,0), anchor='nw',\n scale=1, background=False, interpolation=cv2.INTER_LINEAR):\n if name in self._images:\n self._canvas.delete(self._images[name][0])\n self._images[name] = [img, None, -1] # image (original size), img_tk (shown), item_id\n img = cv2.resize(img, (int(round(img.shape[1]*scale)),\n int(round(img.shape[0]*scale))), interpolation=interpolation)\n self._images[name][1] = ImageTk.PhotoImage(image=Image.fromarray(img))\n self._images[name][2] = self._canvas.create_image(loc[0], loc[1],\n anchor=anchor, image=self._images[name][1])\n if background:\n self._width = img.shape[1]\n self._height = img.shape[0]\n self._canvas.config(width=self._width,\n height=self._height)\n return self._images[name][1].width(), self._images[name][1].height()","def image_cb(self, msg):\n self.has_image = True\n self.camera_image = msg","def crop_center(image, cropx, cropy):\n x,y = image.shape\n startx = x//2 - cropx//2\n starty = y//2 - cropy//2 \n return image[startx:startx+cropx, starty:starty+cropy]","def fotoXO(self):\n\t\ttry:\n\t\t\timageP = self.cam.get_image()\n\t\t\tself.image = pygame.transform.flip(imageP, True, False)\n\t\texcept:\n\t\t\tpass\n\t\treturn self.image","def show_centre_of_bbox(self, image, objects):\n for obj in objects:\n image = cv2.circle(image, \n (int(obj.centre_cords[0] * self.x), int(obj.centre_cords[1] * self.y)), \n radius=5, \n color=AXE_COLOR, \n thickness=-1)\n \n return image","def update_img(self):\n self.img = np.array(self.image)","def _drawimage(self, item, pos, image):\n x, y = pos\n self.cv.coords(item, (x * self.xscale, -y * self.yscale))\n self.cv.itemconfig(item, image=image)","def image(self):\n return self.any_image(-1)"],"string":"[\n \"def on_image(self, image):\",\n \"def center_image(image):\\r\\n image.anchor_x = image.width / 2\\r\\n image.anchor_y = image.height / 2\",\n \"def center_image(image):\\r\\n image.anchor_x = image.width / 2\\r\\n image.anchor_y = image.height / 2\",\n \"def center_image(image):\\n image.anchor_x = image.width/2\\n image.anchor_y = image.height/2\",\n \"def adjust(self, image):\\n ...\",\n \"def pos_image(image, x,y):\\n image.anchor_x = x\\n image.anchor_y = y\",\n \"def query_image_tile(self, coord):\",\n \"def cut_main_screen(im):\\n top = 0\\n left = 0\\n bottom = 980\\n right = 1350\\n return im[top:bottom, left:right].copy()\",\n \"def load_image_centered(filename):\\n im = pyglet.image.load(filename)\\n im.anchor_x = im.width // 2\\n im.anchor_y = im.height // 2\\n return im\",\n \"def update_canvas_display_image_from_full_image(self):\\n\\n full_image_rect = (0, 0, self.image_reader.full_image_ny, self.image_reader.full_image_nx)\\n self.update_canvas_display_image_from_full_image_rect(full_image_rect)\",\n \"def transform(self, previousimage):\",\n \"def center_image_anchor(image):\\n image.anchor_x = image.height/2\\n image.anchor_y = image.height/2\",\n \"def draw_image(self):\\n self.PDF.saveState()\\n self.PDF.scale(1, -1)\\n # self.PDF.drawImage(\\n # LOGO, 490, -78, width=80, preserveAspectRatio=True, mask=\\\"auto\\\"\\n # )\\n self.PDF.restoreState()\",\n \"def show_image(self):\\n cv2.imshow('Image', self.__diff_image())\\n cv2.waitKey()\",\n \"def update_image(self):\\n if self.first is None or self.second is None:\\n LOG.warn(\\\"No images set yet\\\")\\n else:\\n pos = self.slider.value()\\n moved = np.roll(self.second, self.second.shape[0] / 2 - pos, axis=0)\\n self.image_item.setImage(moved - self.first)\",\n \"def __draw_image(self):\\n if self.image_name is not None:\\n img = mpimg.imread(self.image_name)\\n extent = (0.5, self.xmax+0.5, -0.5, self.ymax-0.5)\\n self.ax.imshow(img, extent=extent, origin='lower',\\n alpha=self.image_alpha)\",\n \"def display_image(self, img, img_pos):\\n image = tk.Label(self.top, image=img)\\n image.grid(row=img_pos[0], column=img_pos[1],\\n columnspan=img_pos[2], rowspan=img_pos[3])\",\n \"def _setup_new_image(self):\\n\\n if not self._viewer is None:\\n if not self._input_image is None:\\n self._viewer.SetInput(self._input_image)\\n else:\\n self._viewer.SetInput(self._dummy_image_source.GetOutput())\\n\\n ii = self._viewer.GetInput()\\n \\n ii.UpdateInformation()\\n ii.Update()\\n range = ii.GetScalarRange()\\n self._viewer.SetColorWindow(range[1] - range[0])\\n self._viewer.SetColorLevel(0.5 * (range[1] + range[0]))\\n \\n icp = self._view_frame._image_control_panel\\n icp.slider.SetRange(self._viewer.GetSliceMin(),\\n self._viewer.GetSliceMax())\\n icp.slider.SetValue(self._viewer.GetSliceMin())\\n \\n #self._viewer.UpdateDisplayExtent()\\n self._viewer.GetRenderer().ResetCamera()\",\n \"def update(self):\\n self.imagecounter +=1\\n if self.imagecounter > 7:\\n self.imagecounter = 0\\n self.image = pygame.image.load(self.pictures[self.imagecounter])\\n self.rect = self.image.get_rect()\\n self.rect.left = self.x\\n self.rect.top = self.y\",\n \"def overlay_image(image_to_show, image_to_add, directory=None):\\n \\n direcx = \\\"/Documents/1.4.5 Images/\\\"#put a condition pls\\n if directory == None:\\n directory = os.getcwd()\\n print directory\\n new_image = PIL.Image.open(directory + direcx + image_to_show)\\n new_imager = PIL.Image.open(directory + direcx + image_to_show)\\n new_imager.show()\\n new_image2 = PIL.Image.open(directory + direcx + image_to_add)\\n new_image2.show()\\n \\n width, height = new_imager.size\\n print str(width) + \\\" \\\" + str(height)\\n \\n new_image22 = new_image2.resize((width,height), PIL.Image.ANTIALIAS)\\n new_image22.show()\\n\\n new_imager.paste(new_image22, (0,0), new_image22)\\n new_imager.show()\\n #cohrt = PIL.Image.blend(new_imager, new_image22, alpha=0.5)\\n #cohrt.show() #broked\",\n \"def __show_image(self):\\n box_image = self.canvas.coords(self.container) # get image area\\n box_canvas = (self.canvas.canvasx(0), # get visible area of the canvas\\n self.canvas.canvasy(0),\\n self.canvas.canvasx(self.canvas.winfo_width()),\\n self.canvas.canvasy(self.canvas.winfo_height()))\\n box_img_int = tuple(map(int, box_image)) # convert to integer or it will not work properly\\n # Get scroll region box\\n box_scroll = [min(box_img_int[0], box_canvas[0]), min(box_img_int[1], box_canvas[1]),\\n max(box_img_int[2], box_canvas[2]), max(box_img_int[3], box_canvas[3])]\\n # Horizontal part of the image is in the visible area\\n if box_scroll[0] == box_canvas[0] and box_scroll[2] == box_canvas[2]:\\n box_scroll[0] = box_img_int[0]\\n box_scroll[2] = box_img_int[2]\\n # Vertical part of the image is in the visible area\\n if box_scroll[1] == box_canvas[1] and box_scroll[3] == box_canvas[3]:\\n box_scroll[1] = box_img_int[1]\\n box_scroll[3] = box_img_int[3]\\n # Convert scroll region to tuple and to integer\\n self.canvas.configure(scrollregion=tuple(map(int, box_scroll))) # set scroll region\\n x1 = max(box_canvas[0] - box_image[0], 0) # get coordinates (x1,y1,x2,y2) of the image tile\\n y1 = max(box_canvas[1] - box_image[1], 0)\\n x2 = min(box_canvas[2], box_image[2]) - box_image[0]\\n y2 = min(box_canvas[3], box_image[3]) - box_image[1]\\n if int(x2 - x1) > 0 and int(y2 - y1) > 0: # show image if it in the visible area\\n if self.__huge and self.__curr_img < 0: # show huge image\\n h = int((y2 - y1) / self.imscale) # height of the tile band\\n self.__tile[1][3] = h # set the tile band height\\n self.__tile[2] = self.__offset + self.imwidth * int(y1 / self.imscale) * 3\\n self.__image.close()\\n self.__image = Image.open(self.path) # reopen / reset image\\n self.__image.size = (self.imwidth, h) # set size of the tile band\\n self.__image.tile = [self.__tile]\\n image = self.__image.crop((int(x1 / self.imscale), 0, int(x2 / self.imscale), h))\\n else: # show normal image\\n image = self.__pyramid[max(0, self.__curr_img)].crop( # crop current img from pyramid\\n (int(x1 / self.__scale), int(y1 / self.__scale),\\n int(x2 / self.__scale), int(y2 / self.__scale)))\\n #\\n imagetk = ImageTk.PhotoImage(image.resize((int(x2 - x1), int(y2 - y1)), self.__filter))\\n imageid = self.canvas.create_image(max(box_canvas[0], box_img_int[0]),\\n max(box_canvas[1], box_img_int[1]),\\n anchor='nw', image=imagetk)\\n self.canvas.lower(imageid) # set image into background\\n if self.last_image_id != 0:\\n self.canvas.delete(self.last_image_id)\\n\\n self.last_image_id = imageid\\n self.canvas.imagetk = imagetk # keep an extra reference to prevent garbage-collection\",\n \"def positionZoomed(self):\\n rect_size = self._image.get_rect()\\n\\n if rect_size.width > 720:\\n self.x = (int((720 - rect_size.width ) / 2))\\n\\n if rect_size.height > 720:\\n self.y = (int((720 - rect_size.height) / 2))\",\n \"def update(self):\\n cv2.imshow(self.window_name, self.map.get_crop())\",\n \"def update_current_image(self):\\n\\n rect = (0, 0, self.variables.canvas_width, self.variables.canvas_height)\\n if self.variables.canvas_image_object is not None:\\n self.variables.canvas_image_object.update_canvas_display_image_from_canvas_rect(rect)\\n self.set_image_from_numpy_array(self.variables.canvas_image_object.display_image)\\n self.update()\",\n \"def __show_image(self):\\n box_image = self.canvas_image.coords(self.container) # get image area\\n box_canvas = (self.canvas_image.canvasx(0), # get visible area of the canvas\\n self.canvas_image.canvasy(0),\\n self.canvas_image.canvasx(self.canvas_image.winfo_width()),\\n self.canvas_image.canvasy(self.canvas_image.winfo_height()))\\n self.box_img_int = tuple(map(int, box_image)) # convert to integer or it will not work properly\\n \\n # Get scroll region box\\n box_scroll = [min(self.box_img_int[0], box_canvas[0]), min(self.box_img_int[1], box_canvas[1]),\\n max(self.box_img_int[2], box_canvas[2]), max(self.box_img_int[3], box_canvas[3])]\\n # Horizontal part of the image is in the visible area\\n if box_scroll[0] == box_canvas[0] and box_scroll[2] == box_canvas[2]:\\n box_scroll[0] = self.box_img_int[0]\\n box_scroll[2] = self.box_img_int[2]\\n # Vertical part of the image is in the visible area\\n if box_scroll[1] == box_canvas[1] and box_scroll[3] == box_canvas[3]:\\n box_scroll[1] = self.box_img_int[1]\\n box_scroll[3] = self.box_img_int[3]\\n # Convert scroll region to tuple and to integer\\n self.canvas_image.configure(scrollregion=tuple(map(int, box_scroll))) # set scroll region\\n x1 = max(box_canvas[0] - box_image[0], 0) # get coordinates (x1,y1,x2,y2) of the image tile\\n y1 = max(box_canvas[1] - box_image[1], 0)\\n x2 = min(box_canvas[2], box_image[2]) - box_image[0]\\n y2 = min(box_canvas[3], box_image[3]) - box_image[1]\\n if int(x2 - x1) > 0 and int(y2 - y1) > 0: # show image if it in the visible area\\n image = self.__pyramid[max(0, self.__curr_img)].crop( # crop current img from pyramid\\n (int(x1 / self.__scale), int(y1 / self.__scale),\\n int(x2 / self.__scale), int(y2 / self.__scale)))\\n #\\n imagetk = ImageTk.PhotoImage(image.resize((int(x2 - x1), int(y2 - y1)), self.__filter))\\n self.imageid = self.canvas_image.create_image(max(box_canvas[0], self.box_img_int[0]),\\n max(box_canvas[1], self.box_img_int[1]),\\n anchor='nw', image=imagetk)\\n self.canvas_image.lower(self.imageid) # set image into background\\n self.canvas_image.imagetk = imagetk # keep an extra reference to prevent garbage-collection\",\n \"def draw_image(self):\\n \\n pixel_array = self.imageprepare(self.image_path)\\n newArr = self.reshape_pixel_array(pixel_array)\\n plt.imshow(newArr, interpolation='nearest')\\n plt.savefig('MNIST_IMAGE.png')#save MNIST image\\n plt.show()#Show / plot that image\",\n \"def on_draw_over_image(self):\",\n \"def displayImg(self):\\r\\n\\r\\n\\t# If you want to skip n frames, set value to 0 to see all images\\r\\n\\tSKIP = 4500\\r\\n for idx in range(len(self.centers)):\\r\\n\\t if idx < SKIP:\\r\\n\\t\\tcontinue\\r\\n file_left = self.lefts[idx][5]\\r\\n file_center = self.centers[idx][5]\\r\\n file_right = self.rights[idx][5]\\r\\n\\r\\n img_left = cv2.imread(os.path.join(self.pathDir, file_left), \\\\\\r\\n cv2.IMREAD_COLOR)\\r\\n img_center = cv2.imread(os.path.join(self.pathDir, file_center), \\\\\\r\\n cv2.IMREAD_COLOR)\\r\\n img_right = cv2.imread(os.path.join(self.pathDir, file_right), \\\\\\r\\n cv2.IMREAD_COLOR)\\r\\n\\r\\n\\t #Resize the image to 50%\\r\\n img_l = cv2.resize(img_left, None, fx=0.5, fy=0.5, \\\\\\r\\n interpolation = cv2.INTER_LINEAR)\\r\\n img_c = cv2.resize(img_center, None, fx=0.5, fy=0.5, \\\\\\r\\n interpolation = cv2.INTER_LINEAR)\\r\\n img_r = cv2.resize(img_right, None, fx=0.5, fy=0.5, \\\\\\r\\n interpolation = cv2.INTER_LINEAR)\\r\\n \\r\\n height, width = img_c.shape[:2]\\r\\n new_img = np.zeros((height, width*3, img_c.shape[2]),\\r\\n np.uint8)\\r\\n\\r\\n #Adding sequence numbers and Time\\r\\n\\t #Left\\r\\n strTime = self.timestampToStr(self.lefts[idx][1])\\r\\n\\t self.putTextToImg(img_l, self.lefts[idx][0], strTime, height)\\r\\n\\t #Center\\r\\n\\t strTime = self.timestampToStr(self.centers[idx][1])\\r\\n\\t self.putTextToImg(img_c, self.centers[idx][0], strTime, height)\\r\\n\\t #Right\\r\\n\\t strTime = self.timestampToStr(self.rights[idx][1])\\r\\n\\t self.putTextToImg(img_r, self.rights[idx][0], strTime, height)\\r\\n\\t \\r\\n\\t angle = float(self.angles_at_timestamps[idx])\\r\\n\\t speed = float(self.speed_at_timestamps[idx])\\r\\n\\r\\n\\t print \\\"speed: %f - angle: %f\\\" % (speed, angle)\\r\\n\\r\\n\\t self.draw_path_on(img_c, speed, angle)\\r\\n\\r\\n\\t #Generate the new image\\r\\n for i in range(height):\\r\\n new_img[i] = np.concatenate((img_l[i, : ], img_c[i, : ], \\\\\\r\\n img_r[i, : ]))\\r\\n \\r\\n\\r\\n cv2.imshow('Udacity Challenge 2 - Viewer', new_img)\\r\\n key = cv2.waitKey(30)\\r\\n\\r\\n # Press q to exit\\r\\n if key == ord('q'):\\r\\n break\\r\\n\\r\\n cv2.destroyAllWindows()\",\n \"def main():\\n original = SimpleImage('images/mt-rainier.jpg')\\n original.show()\\n reflected = make_reflected('images/mt-rainier.jpg')\\n reflected.show()\",\n \"def update_image(self):\\n self.image = Image.fromarray(self.img)\",\n \"def update_image(self):\\n if self.filenames:\\n pos = self.slider.value()\\n proj, flat, dark, theta = dx.read_aps_32id(self.filenames, proj=(pos, pos+1))\\n if self.ffc_correction:\\n image = proj[0,:,:].astype(np.float)/flat[0,:,:].astype(np.float)\\n else:\\n image = proj[0,:,:].astype(np.float)\\n self.image_item.setImage(image)\",\n \"def show(image):\\n fig = pyplot.figure()\\n axis = fig.add_subplot(1, 1, 1)\\n imgplot = axis.imshow(image)\\n imgplot.set_interpolation('nearest')\\n axis.xaxis.set_ticks_position('top')\\n axis.yaxis.set_ticks_position('left')\\n pyplot.show()\",\n \"def overlay_image(image_to_show, image_to_add, directory=None):\\n \\n direcx = \\\"/Documents/1.4.5 Images/\\\"#put a condition pls\\n if directory == None:\\n directory = os.getcwd()\\n print directory\\n new_image = PIL.Image.open(directory + direcx + image_to_show)\\n new_imager = PIL.Image.open(directory + direcx + image_to_show)\\n new_imager.show()\\n new_image2 = PIL.Image.open(directory + direcx + image_to_add)\\n new_image2.show()\\n width, height = new_imager.size\\n print str(width) + \\\" \\\" + str(height)\\n #print new_image\\n #print new_image2\\n #if image_to_show == \\\"\\\":\\n # print_directory_list2()\\n # return \\\"Use one of these\\\"\\n new_image22 = new_image2.resize((width,height), PIL.Image.ANTIALIAS)\\n new_image22.show()\\n\\n new_imager.paste(new_image22, (width, height), new_image22)\\n new_imager.show()\\n #cohrt = PIL.Image.blend(new_imager, new_image22, alpha=0.5)\\n #cohrt.show()\",\n \"def _render_static_image_annotation(self):\\n cv2.rectangle(self._image,\\n (0,0), (640, 40),\\n (0, 0, 0),\\n -1)\\n \\n cv2.putText(self._image,self._current_mode, (40, 25),\\n cv2.FONT_HERSHEY_SIMPLEX, 0.7, 255, 2)\\n\\n cv2.putText(self._image, time.asctime(), (400, 460),\\n cv2.FONT_HERSHEY_SIMPLEX, 0.7, 255, 2)\",\n \"def other_object(img):\\n return img[500:570, 1000:1070]\",\n \"def view(self):\\n window = tk.Tk()\\n label = tk.Label(window)\\n label.pack()\\n img = self.get_tkimage()\\n label[\\\"image\\\"] = label.img = img\\n window.mainloop()\",\n \"def img_cb(self, image):\\n self.last_img = image\\n self.is_new_img = True\",\n \"def _center_image(image):\\n\\n dim_x, dim_y = image.shape[0:2]\\n\\n x = int(dim_x/2)\\n off_x = int(dim_x/4)\\n xmin = x - off_x\\n xmax = x + off_x\\n\\n y = int(dim_y/2)\\n off_y = int(dim_y/4)\\n ymin = y - off_y\\n ymax = y + off_y\\n\\n return(image[xmin:xmax, ymin:ymax])\",\n \"def resting(self):\\n self.image = self.image_list[self.index]\\n\\n if self.rect.y % 32 != 0:\\n self.correct_position(self.rect.y)\\n if self.rect.x % 32 != 0:\\n self.correct_position(self.rect.x)\",\n \"def crop_image(image):\\r\\n return image[40:-20, :]\",\n \"def update(self, *args):\\n\\n # change picture every 100 milliseconds\\n now = pygame.time.get_ticks()\\n if now - self.last_update > 100:\\n self.index = self.index ^ 1\\n self.image = self.images[self.index]\\n self.last_update = now\\n prom = self.rect.center\\n self.rect = self.image.get_rect()\\n self.rect.center = prom\\n\\n self.rect.x -= args[0]\",\n \"def getimage(self):\",\n \"def newAvatarImage(self, imgPath, imgName): \\n img = ocempgui.draw.Image.load_image(imgPath)\\n if not self.images[imgName]: \\n imgOcemp = guiobjects.OcempImageMapTransparent(img)\\n imgOcemp.topleft = 528, 114\\n self.window.add_child(imgOcemp)\\n self.images[imgName] = imgOcemp\\n else:\\n self.images[imgName].picture = img\",\n \"def imshow(img):\\n imadd(img)\\n plt.ion()\\n plt.show()\",\n \"def LKTrackerImageToImage(imageOld, pixelCoordsOld, imageNew,\\n pixelCoordsNew, windowSize):\\n # imageOld = cv2.cvtColor(imageOld, cv2.COLOR_BGR2GRAY)\\n # imageNew = cv2.cvtColor(imageNew, cv2.COLOR_BGR2GRAY)\\n\\n # Get top left corner of window.\\n\\n topLeftX1, topLeftY1 = pixelCoordsOld - windowSize // 2\\n topLeftX2, topLeftY2 = pixelCoordsNew - windowSize // 2\\n\\n # Compute horizontal and vertical gradients for the original frame.\\n gx = utils.pixelDiffImages(imageOld,\\n topLeftX1,\\n topLeftY1,\\n imageOld,\\n topLeftX1,\\n topLeftY1 - 1,\\n windowSize,\\n windowSize)\\n \\n gy = utils.pixelDiffImages(imageOld,\\n topLeftX1,\\n topLeftY1,\\n imageOld,\\n topLeftX1 - 1,\\n topLeftY1,\\n windowSize,\\n windowSize)\\n \\n # Compute difference between original and new frames.\\n diff = utils.pixelDiffImages(imageOld,\\n topLeftX1,\\n topLeftY1,\\n imageNew,\\n topLeftX2,\\n topLeftY2,\\n windowSize,\\n windowSize)\\n\\n # Compute components of Harris matrix.\\n Ixx = gx ** 2\\n Iyy = gy ** 2\\n Ixy = gx * gy\\n\\n # Compute Gaussian kernel for weighting pixels in the window.\\n gkern = np.outer(signal.gaussian(windowSize, 2.5),\\n signal.gaussian(windowSize, 2.5))\\n\\n # Construct matrices and solve the matrix-vector equation to get the\\n # movement of the pixel.\\n Z = np.array([[np.sum(Ixx * gkern), np.sum(Ixy * gkern)],\\n [np.sum(Ixy * gkern), np.sum(Iyy * gkern)]])\\n b = np.array([np.sum(diff * gx * gkern), np.sum(diff * gy * gkern)])\\n d = np.linalg.solve(Z, b)\\n\\n # Compute new position of pixel\\n return pixelCoordsNew + d[: : -1]\",\n \"def rot_center(self):\\n x, y = pygame.mouse.get_pos()\\n relx, rely = x - self.rect.x, y - self.rect.y\\n angle = math.atan2(relx, rely)\\n angle = math.degrees(angle)\\n orig_rect = self.image_original.get_rect()\\n rot_image = pygame.transform.rotate(self.image_original, angle)\\n rot_rect = orig_rect.copy()\\n rot_rect.center = rot_image.get_rect().center\\n rot_image = rot_image.subsurface(rot_rect).copy()\\n return rot_image\",\n \"def offset(img, y_s, y_e, x_s, x_e):\\n if y_s < 0:\\n y_s = 0\\n if y_e > img.shape[0]:\\n y_e = img.shape[0]\\n if x_s < 0:\\n x_s = 0\\n if x_e > img.shape[1]:\\n x_e = img.shape[1]\\n new_img = img[y_s:y_e, x_s:x_e]\\n return new_img\",\n \"def update_pos(self):\\n self.imgx=self.pathX[min(self.x,len(self.pathX)-1)]\\\\\\n [min(self.y,len(self.pathX[self.x])-1)]\\n self.imgy=self.pathY[min(self.x,len(self.pathY)-1)]\\\\\\n [min(self.y,len(self.pathY[self.x])-1)]\",\n \"def display(self):\\n image_qt = ImageQt.ImageQt(self.view_state.get_image())\\n self.imageLabel.setPixmap(QtGui.QPixmap.fromImage(image_qt))\\n self.imageLabel.adjustSize()\",\n \"def small_image(self):\\n pass\",\n \"def full_photo():\\n top = Toplevel()\\n top.title(\\\"Full APOD Photo\\\")\\n top.iconbitmap('10.APOD Viewer/rocket.ico')\\n\\n #Load the full image to the top image\\n img_label = Label(top, image=full_img)\\n img_label.pack()\",\n \"def display(self):\\n display(self.image)\",\n \"def show(image):\\n fig = pyplot.figure()\\n ax = fig.add_subplot(1,1,1)\\n imgplot = ax.imshow(image, cmap=mpl.cm.Greys)\\n imgplot.set_interpolation('nearest')\\n ax.xaxis.set_ticks_position('top')\\n ax.yaxis.set_ticks_position('left')\\n pyplot.show()\",\n \"def update_image(self, cv_img):\\n\\t\\tqt_img = self.ImageEdits(cv_img)\\n\\t\\tself.camera.setPixmap(qt_img)\",\n \"def aktualisiere(self):\\n if(self.zeichnung == None):\\n self.zeichnung = self.leinwand.create_image(self.x, self.y, \\n image=self.grafik)\\n delta_x = self.zielX - self.x\\n delta_y = self.zielY - self.y\\n self.leinwand.move(self.zeichnung, delta_x, delta_y)\\n\\n self.x = self.zielX\\n self.y = self.zielY\",\n \"def extract_thumbnail(img, joints):\\n x_min, y_min = np.min(joints, axis=0)\\n x_max, y_max = np.max(joints, axis=0)\\n [h, w, _] = img.shape\\n\\n if x_min > 20:\\n x_min -= 20\\n if y_min > 20:\\n y_min -= 20\\n\\n if x_max+20 < w:\\n x_max += 20\\n if y_max+20 < h:\\n y_max += 20\\n\\n return img[y_min:y_max, x_min:x_max, :]\",\n \"def set_current(self, image_name):\\n # Sets the position of the crop\\n self.j ,self.i = 0, 0\\n\\n # loads the image\\n self.image = convert2int(tifffile.imread(image_name)).astype(numpy.float32)\\n\\n # Computes the number of crops in x and y\\n self.ny = numpy.ceil(self.image.shape[0] / self.step)\\n self.nx = numpy.ceil(self.image.shape[1] / self.step)\\n\\n # rescale the image\\n self.image -= self.image_min\\n self.image /= (0.8 * (self.image_max - self.image_min))\\n self.image = numpy.clip(self.image, 0, 1)\",\n \"def showimage(image):\\n mplt.figure()\\n mplt.imshow(image)\\n mplt.show()\",\n \"def image_proc(self):\\r\\n res_erode, res_in_rect = self._get_res_image()\\r\\n\\r\\n Moment_rect = cv2.moments(res_in_rect)\\r\\n if (Moment_rect[\\\"m00\\\"] != 0):\\r\\n self.rect_x = int(Moment_rect[\\\"m10\\\"] / Moment_rect[\\\"m00\\\"])\\r\\n self.rect_y = int(Moment_rect[\\\"m01\\\"] / Moment_rect[\\\"m00\\\"])\\r\\n\\r\\n # afin de trouver les centres de la rectangulaire.\\r\\n cnts, hierarchy = cv2.findContours(res_erode,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\\r\\n for i in cnts:\\r\\n #Moment = cv2.moments(cnts)\\r\\n moment = cv2.moments(res_erode)\\r\\n self.cx = int(moment[\\\"m10\\\"] / moment[\\\"m00\\\"])\\r\\n self.cy = int(moment[\\\"m01\\\"] / moment[\\\"m00\\\"])\\r\\n cv2.circle(output_image,(self.cx,self.cy),7,(255,255,255),-1)\\r\\n cv2.putText(output_image, \\\"center\\\", (self.cx, self.cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)\\r\\n #print(cX,cY)\\r\",\n \"def display(self):\\n\\t\\tself.imgDisplay.set_from_pixbuf(self.getVisible())\\n\\t\\tgc.collect()\",\n \"def show_to_window(self):\\n if self.normal_mode:\\n self.show_image.show_original_image(\\n self.image, self.width_original_image)\\n self.show_image.show_result_image(\\n self.image, self.width_result_image, self.angle)\\n\\n else:\\n if self.panorama_mode:\\n image = draw_polygon(\\n self.image.copy(),\\n self.mapX_pano,\\n self.mapY_pano)\\n mapX = np.load(\\n './plugins/Thread_inspection/view_image/maps_pano/mapX.npy')\\n mapY = np.load(\\n './plugins/Thread_inspection/view_image/maps_pano/mapY.npy')\\n rho = self.panorama.rho\\n\\n self.result_image = cv2.remap(\\n self.image,\\n mapX,\\n mapY,\\n cv2.INTER_CUBIC)\\n self.result_image = self.result_image[round(\\n rho + round(self.moildev.getRhoFromAlpha(30))):self.h, 0:self.w]\\n # print(self.width_result_image)\\n else:\\n image = draw_polygon(self.image.copy(), self.mapX, self.mapY)\\n self.result_image = cv2.remap(\\n self.image,\\n self.mapX,\\n self.mapY,\\n cv2.INTER_CUBIC)\\n self.show_image.show_original_image(\\n image, self.width_original_image)\\n self.show_image.show_result_image(\\n self.result_image, self.width_result_image, self.angle)\",\n \"def show_image(self):\\n cv2.imshow(self.config.DISPLAY_NAME, self.image)\",\n \"def test_plot_img_with_auto_cut_coords(display_mode):\\n data = np.zeros((20, 20, 20))\\n data[3:-3, 3:-3, 3:-3] = 1\\n img = Nifti1Image(data, np.eye(4))\\n plot_img(img, cut_coords=None, display_mode=display_mode, black_bg=True)\\n plt.close()\",\n \"def show(self, screen):\\n x_display = self.xy_position[0] * constants.CELL_SIZE\\n y_display = self.xy_position[1] * constants.CELL_SIZE\\n screen.blit(self.image, (x_display, y_display))\",\n \"def update(self):\\n rect = self.img_rect.center\\n self.font_img = self.font.render(self.text, True, color1)\\n self.img_rect = self.font_img.get_rect()\\n # self.img_rect.center = [self.title_img.get_width() + self.font_img.get_width()//2 + padding, 5*Y//8]\\n self.img_rect.center = rect\",\n \"def pix_center(self):\\n return self._pix_center\",\n \"def show(image):\\n from matplotlib import pyplot\\n import matplotlib as mpl\\n fig = pyplot.figure()\\n ax = fig.add_subplot(1,1,1)\\n imgplot = ax.imshow(image, cmap=mpl.cm.Greys)\\n imgplot.set_interpolation('nearest')\\n ax.xaxis.set_ticks_position('top')\\n ax.yaxis.set_ticks_position('left')\\n pyplot.show()\",\n \"def show(image):\\n from matplotlib import pyplot\\n import matplotlib as mpl\\n fig = pyplot.figure()\\n ax = fig.add_subplot(1,1,1)\\n imgplot = ax.imshow(image, cmap=mpl.cm.Greys)\\n imgplot.set_interpolation('nearest')\\n ax.xaxis.set_ticks_position('top')\\n ax.yaxis.set_ticks_position('left')\\n pyplot.show()\",\n \"def show(image):\\n from matplotlib import pyplot\\n import matplotlib as mpl\\n fig = pyplot.figure()\\n ax = fig.add_subplot(1,1,1)\\n imgplot = ax.imshow(image, cmap=mpl.cm.Greys)\\n imgplot.set_interpolation('nearest')\\n ax.xaxis.set_ticks_position('top')\\n ax.yaxis.set_ticks_position('left')\\n pyplot.show()\",\n \"def showImage(self, image):\\n \\n self.image = img\",\n \"def display_image(img, label):\\n cv2.imshow(label,img)\\n cv2.waitKey(0)\\n cv2.destroyAllWindows()\",\n \"def _observation(self, img):\\n img = self.crop(img)\\n img = imresize(img, self.img_size)\\n if self.grayscale:\\n img = img.mean(-1, keepdims=True)\\n img = np.transpose(img, (2, 0, 1)) # reshape from (h,w,colors) to (colors,h,w)\\n img = img.astype('float32') / 255.\\n img = np.squeeze(img)\\n return img\",\n \"def display_cropped_img(i):\\n image = PIL.Image.open(testing_img_paths[i])\\n image = image.crop(box=(313,99,825,611))\\n image = image.resize((256,256))\\n display(image)\",\n \"def process(self, image):\",\n \"def template_zone(self):\\n ratio = 32\\n shape = [self.cam.height, self.cam.width]\\n img = self.cam.frame[shape[0] / 2 - 3 * shape[0] / ratio:shape[0] / 2 + 3 * shape[0] / ratio,\\n shape[1] / 2 - 3 * shape[1] / ratio:shape[1] / 2 + 3 * shape[1] / ratio]\\n return img\",\n \"def img_extent(img):\\n# get the image coordinates in pixels\\n px0 = img.meta['crpix1']\\n py0 = img.meta['crpix2']\\n# get the image coordinates in arcsec \\n ax0 = img.meta['crval1']\\n ay0 = img.meta['crval2']\\n# get the image scale in arcsec \\n axd = img.meta['cdelt1']\\n ayd = img.meta['cdelt2']\\n#get the number of pixels\\n tx,ty = img.data.shape\\n#get the max and min x and y values\\n minx,maxx = px0-tx,tx-px0\\n miny,maxy = py0-ty,ty-py0\\n#convert to arcsec\\n maxx,minx = maxx*axd,minx*axd\\n maxy,miny = maxy*ayd,miny*ayd\\n\\n\\n return maxx,minx,maxy,miny\",\n \"def blitme(self):\\n self.screen.blit(self.image, self.rect)\\n # print('y = ' + str(self.rect.centery))\\n # print('x = ' + str(self.rect.centerx))\",\n \"def _update_screen(self) -> None:\\n # setup the source image with an alpha channel\\n alpha = 255 * np.ones_like(self.image[..., 0:1])\\n image = np.concatenate([self._image, alpha], axis=-1).astype(np.uint8)\\n # setup the super pixel segmentations\\n super_pixels = np.zeros_like(self.image)\\n super_pixels = mark_boundaries(\\n super_pixels,\\n self._super_pixel_segments,\\n self._super_pixel_color\\n )\\n # concatenate the first channel of sup as the alpha channel\\n super_pixels = [super_pixels, super_pixels[..., 0:1]]\\n super_pixels = np.concatenate(super_pixels, axis=-1).astype(np.uint8)\\n # setup the segmentation image with an alpha channel scaled by the\\n # opacity parameter of the application [0, 9]\\n intensity = 255 * (self._opacity / 9)\\n intensity = intensity * np.ones_like(self._segmentation[..., 0:1])\\n segmentation = np.concatenate([self._segmentation, intensity], axis=-1)\\n segmentation = segmentation.astype(np.uint8)\\n # send the images to the window\\n self._view.show([image, segmentation, super_pixels])\",\n \"def image_cb(self, msg):\\n self.has_image = True\\n self.camera_image = msg\\n self.get_light_state()\",\n \"def show_img(self):\\n if self.image is not None:\\n cv2.imshow(self.image_window, self.image)\\n cv2.waitKey(1)\\n else:\\n rospy.loginfo(\\\"No image to show yet\\\")\",\n \"def show_image(f, x, y):\\n window_surface.blit(f, (x, y))\",\n \"def _set_anchor_center(img):\\n img.anchor_x = int(img.width / 2)\\n img.anchor_y = int(img.height / 2)\",\n \"def displayImages(self):\\n\\n plt.figure(figsize=(8,6))\\n plt.subplot(1,2,1)\\n plt.imshow( self.original_image, cmap=\\\"gray\\\")\\n plt.title(\\\"Original Image\\\")\\n plt.subplot(1,2,2)\\n plt.imshow( self.blurred_image, cmap=\\\"gray\\\")\\n plt.title(\\\"Blurred Image\\\")\",\n \"def __move_to(self, event):\\n self.canvas_image.scan_dragto(event.x, event.y, gain=1)\\n self.to_coord = (event.x, event.y)\\n self.__show_image() # zoom tile and show it on the canvas\",\n \"def rot_center(self):\\n loc = self.rect.center\\n self.image = pygame.transform.rotate(self.current_sprite_alpha, self.rot)\\n self.rect = self.image.get_rect()\\n self.rect.center = loc\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def setUp(self):\\r\\n center = [1,1]\\r\\n size = 1\\r\\n self.imageinfo = ImageInfo(center, size)\",\n \"def display_image(X):\\n\\n\\tim = X.reshape(28, 28)\\n\\ttemp = plt.imshow(im)\\n\\tplt.show()\",\n \"def zoom_augmentation():\\n # Get the width and the height of the zoomed version\\n x_len, y_len = np.random.randint(250, 350, size=2)\\n # Get left upper ,right and lower bound of the pixels in the original image\\n left = np.random.randint(x_size-x_len)\\n upper = np.random.randint(y_size-y_len)\\n right, lower = left + x_len, upper+y_len\\n # Crops the box and resizes it to the original image size\\n box = (left, upper, right, lower)\\n return lambda image: image.transform(image.size, Image.EXTENT, box)\",\n \"def get_current_image(self):\\n raise NotImplementedError\",\n \"def show_image(self, name, img, loc=(0,0), anchor='nw',\\n scale=1, background=False, interpolation=cv2.INTER_LINEAR):\\n if name in self._images:\\n self._canvas.delete(self._images[name][0])\\n self._images[name] = [img, None, -1] # image (original size), img_tk (shown), item_id\\n img = cv2.resize(img, (int(round(img.shape[1]*scale)),\\n int(round(img.shape[0]*scale))), interpolation=interpolation)\\n self._images[name][1] = ImageTk.PhotoImage(image=Image.fromarray(img))\\n self._images[name][2] = self._canvas.create_image(loc[0], loc[1],\\n anchor=anchor, image=self._images[name][1])\\n if background:\\n self._width = img.shape[1]\\n self._height = img.shape[0]\\n self._canvas.config(width=self._width,\\n height=self._height)\\n return self._images[name][1].width(), self._images[name][1].height()\",\n \"def image_cb(self, msg):\\n self.has_image = True\\n self.camera_image = msg\",\n \"def crop_center(image, cropx, cropy):\\n x,y = image.shape\\n startx = x//2 - cropx//2\\n starty = y//2 - cropy//2 \\n return image[startx:startx+cropx, starty:starty+cropy]\",\n \"def fotoXO(self):\\n\\t\\ttry:\\n\\t\\t\\timageP = self.cam.get_image()\\n\\t\\t\\tself.image = pygame.transform.flip(imageP, True, False)\\n\\t\\texcept:\\n\\t\\t\\tpass\\n\\t\\treturn self.image\",\n \"def show_centre_of_bbox(self, image, objects):\\n for obj in objects:\\n image = cv2.circle(image, \\n (int(obj.centre_cords[0] * self.x), int(obj.centre_cords[1] * self.y)), \\n radius=5, \\n color=AXE_COLOR, \\n thickness=-1)\\n \\n return image\",\n \"def update_img(self):\\n self.img = np.array(self.image)\",\n \"def _drawimage(self, item, pos, image):\\n x, y = pos\\n self.cv.coords(item, (x * self.xscale, -y * self.yscale))\\n self.cv.itemconfig(item, image=image)\",\n \"def image(self):\\n return self.any_image(-1)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65491354","0.6542907","0.6542907","0.64663273","0.6463194","0.6424689","0.63045746","0.6273058","0.6259496","0.62448","0.6225228","0.6177235","0.6176828","0.6174159","0.61622965","0.61580944","0.6121791","0.60993665","0.6059796","0.6056057","0.60365945","0.6031962","0.6012734","0.6000552","0.59728634","0.59722567","0.5966817","0.5949587","0.5919378","0.59151673","0.58762413","0.5868779","0.58626956","0.5858683","0.58554083","0.5849206","0.5832329","0.58305436","0.5824223","0.58147055","0.5805754","0.5800372","0.5795915","0.5791792","0.57892853","0.5781823","0.57740414","0.5773421","0.57645166","0.57641894","0.57611823","0.5758428","0.5757747","0.5757498","0.57534605","0.5751432","0.5751387","0.5742017","0.57343906","0.57186604","0.57158905","0.57158864","0.5711506","0.569698","0.56823677","0.56767875","0.56656855","0.56656855","0.56656855","0.56648314","0.56632894","0.5657528","0.565413","0.56484056","0.56445414","0.5642764","0.5627978","0.5625409","0.5612049","0.5606881","0.56049985","0.5604298","0.5603991","0.55955285","0.5594648","0.5591527","0.5591527","0.5591527","0.5591527","0.5591527","0.55901223","0.55836874","0.5580433","0.5578321","0.55734324","0.5571352","0.55706483","0.5569337","0.5565726","0.5562672","0.55592203"],"string":"[\n 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\"0.5742017\",\n \"0.57343906\",\n \"0.57186604\",\n \"0.57158905\",\n \"0.57158864\",\n \"0.5711506\",\n \"0.569698\",\n \"0.56823677\",\n \"0.56767875\",\n \"0.56656855\",\n \"0.56656855\",\n \"0.56656855\",\n \"0.56648314\",\n \"0.56632894\",\n \"0.5657528\",\n \"0.565413\",\n \"0.56484056\",\n \"0.56445414\",\n \"0.5642764\",\n \"0.5627978\",\n \"0.5625409\",\n \"0.5612049\",\n \"0.5606881\",\n \"0.56049985\",\n \"0.5604298\",\n \"0.5603991\",\n \"0.55955285\",\n \"0.5594648\",\n \"0.5591527\",\n \"0.5591527\",\n \"0.5591527\",\n \"0.5591527\",\n \"0.5591527\",\n \"0.55901223\",\n \"0.55836874\",\n \"0.5580433\",\n \"0.5578321\",\n \"0.55734324\",\n \"0.5571352\",\n \"0.55706483\",\n \"0.5569337\",\n \"0.5565726\",\n \"0.5562672\",\n \"0.55592203\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":86691,"cells":{"query":{"kind":"string","value":"Returns the power spectrum function of an image"},"document":{"kind":"string","value":"def ps(image):\n\timage = image.astype(float)\n\tps_img = abs(pow(fft2(image), 2))\n\treturn ps_img"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def spectral_power(img, avg_window_size=None, log=True): #COMPLETE spectrum generator\r\n image = img.copy()\r\n # to avoid large spectral power at the 0 frequency :\r\n image -= np.mean(image)\r\n # wiener filter to reduce non physical variability in the spectral power\r\n if avg_window_size:\r\n N = avg_window_size\r\n image = wiener(image, (N, N))\r\n # compute the spectral power function. Place the 0 frequency-component in the center\r\n fshift = np.fft.fftshift(np.fft.fft2(image))\r\n spectrum = np.abs(fshift)**2\r\n if log:\r\n spectrum = 10*np.log(spectrum)\r\n return spectrum","def powerSpectrum(input, nfft):\n result = fft(input, nfft)\n result = np.power(np.absolute(result), 2)\n\n # myplot(result, 'Power Spectogram')\n\n return result","def get_spectrum_from_img(img):\n # Executa FFT em 2 dimensoes\n f = np.fft.fft2(img)\n # Desloca a frequencia zero para o centro do espectro\n fshift = np.fft.fftshift(f)\n\n return fshift","def powerSpectrum(input, nfft):\n freq = fft(input, nfft)\n return freq.real**2 + freq.imag**2","def power_spectrum_fn(p, fmag, otf):\n\n val = p[2]**2 * p[0]**2 * np.abs(fmag) ** (-2 * p[1]) * np.abs(otf)**2 + p[3]\n\n return val","def show_spectrum(image: np.ndarray):\n assert len(image.shape) == 2, 'image must be 2D'\n spectral = np.fft.fft2(image)\n spectral[0, 0] = 0 # Kill DC component\n spectrum = np.fft.fftshift(spectral) # Shift DC to center\n magnitude = np.log(np.abs(spectrum))\n plt.imshow(magnitude, cmap='gray')\n plt.show()","def fourierNP(img):\n\tgray = grayscale(img)\n\tf = np.fft.fft2(gray)\n\tfshift = np.fft.fftshift(f)\n\tmagnitude_spectrum = 20*np.log(np.abs(fshift)) # RETURN THIS\n return magnitude_spectrum","def get_power(frames, num_fft):\n #a = get_magnitude(frames, num_fft)\n #b = np.square(a)\n #print('max : ', np.max(a))\n #print('min : ', np.min(a))\n #print('sq max : ', np.max(b))\n #print('sq min : ', np.min(b))\n #print(a.shape)\n #print(b.shape)\n #return b/num_fft\n return np.square(get_magnitude(frames, num_fft) / np.sqrt(num_fft))","def return_power_spectrum(self, freq_signal, time_signal):\n\n\t\tfreq_signal **= 2 \n\t\tlen_fts = len(freq_signal)\n\t\tlen_signal = len(signal)\n\n\t\tif len_signal % 2:\n\t\t\tfreq_signal[1:len_fts] *= 2\n\n\t\telse:\n\t\t\tfreq_signal[1:len_fts-1] *= 2\n\n\t\treturn freq_signal","def get_spectral_features(image, img_type: str): # Apply on the whole image\r\n spectrum = spectral_power(image, log=False)\r\n bins_energy, _ = get_energy_bins(spectrum, num_bins=3) # Use images with outside val: NaN\r\n spectral_features = {img_type + \"low_freq_energy\": bins_energy[0],\r\n img_type + \"mid_freq_energy\": bins_energy[1], \r\n img_type + \"high_freq_energy\": bins_energy[2]}\r\n return spectral_features","def f_mw(f):\n return np.power(f, 2)","def _get_power_of_spectrum(U):\n \n _, n_samples, n_frames, n_frequencies = U.shape\n del _\n\n P = np.zeros([n_samples, n_samples, n_frequencies], dtype=np.complex64)\n P = np.sum(np.abs(U)**2, axis=2) / n_frames\n \n return P","def fourierCV(img):\n\tgray = grayscale(img)\n\tdft = cv2.dft(np.float32(gray), flags = cv2.DFT_COMPLEX_OUTPUT)\n\tdft_shift = np.fft.fftshift(dft)\n\tmagnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0], dft_shift[:,:,1]))\n return magnitude_spectrum","def power(image, kernel):\n\n image = old_div((image - image.mean()), image.std())\n return np.sqrt(ndi.convolve(image, np.real(kernel), mode='wrap') ** 2 +\n ndi.convolve(image, np.imag(kernel), mode='wrap') ** 2)","def bandwidth_power(\n x, fs, bands, dim=-2, n_overlap=None, nperseg=None, scaling=\"density\"\n):\n dim = fix_dim(dim, x.dim())\n if not x.is_complex():\n # convert the torch<1.8 \"complex\" format to a proper complex dtype\n assert x.shape[-1] == 2 # the last dim has size 2 for re and im\n x = x[..., 0] + 1j * x[..., 1] # makes into ndim-1\n\n if nperseg is None:\n nperseg = x.shape[dim]\n\n # 1. Welch\n window = torch.hamming_window(\n nperseg, periodic=False, dtype=x.real.dtype, device=x.device\n )\n ff, px = pwelch(x, dim, window, fs=fs, scaling=scaling, n_overlap=n_overlap)\n\n ff, px = fftshift(ff), fftshift(px, dim=dim)\n if not bands:\n # this needs to return a tensor that has the expected shape\n return (\n ff,\n px,\n torch.empty(\n *px.shape[:dim],\n *px.shape[dim + 1 :],\n 0,\n dtype=px.dtype,\n device=px.device,\n ),\n )\n\n # 2. Compute power within each band\n channel, df = [], 1.0 / (nperseg * fs) # noqa: F841\n for lo, hi in bands:\n (index,) = torch.nonzero(ff.gt(lo) & ff.lt(hi), as_tuple=True)\n power = torch.index_select(px, dim, index)\n channel.append(power.sum(dim=dim)) # * df)\n # end for\n\n # 3. get the power in decibel\n return ff, px, 10 * torch.log10(torch.stack(channel, dim=-1))","def get_noise_power(img_ft, fxs, fys, fmax):\n\n ps = np.abs(img_ft) ** 2\n\n # exclude regions of frequency space\n fxfx, fyfy = np.meshgrid(fxs, fys)\n ff = np.sqrt(fxfx ** 2 + fyfy ** 2)\n noise_power = np.mean(ps[ff > fmax])\n\n return noise_power","def wavelettf_greyscale(img, wavelet):\n\n # %% Converting the image to right datatype\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n img = np.float32(img)\n img /= 255\n\n # %% Extracting and returning the coefficients\n coeffs = pywt.dwt2(img, wavelet, axes=(0, 1))\n cA, (cH, cV, cD) = coeffs\n return coeffs","def _spectrum(self, energy):\n return self.pref*energy**2*self.occupation_number(energy/self.kT)","def spectrum(self):\r\n spectrum = tsa.cache_to_psd(self.cache, self.ij)\r\n\r\n return spectrum","def exponential(image: np.ndarray) -> np.ndarray:\n return np.power(image, 0.75).astype('uint8')","def snr_f(self, image):\n image_ps = self.pow_spec(image)\n noise_level = numpy.sum(self.rim*image_ps)/numpy.sum(self.rim)\n return numpy.sqrt(image_ps[int(self.size/2), int(self.size/2)]/noise_level)","def get_img_from_spectrum(spectrum):\n # Retorna a frequencia zero colocada no centro do espectro de volta a\n # posicao original.\n f_ishift = np.fft.ifftshift(spectrum)\n # Realiza FFT inversa\n img_back = np.fft.ifft2(f_ishift)\n # Realiza a conversao para valores somente reais\n img_back = np.abs(img_back)\n\n return img_back","def power_spectral_density(var):\n\n n = len(var)\n Y = np.fft.fft(var)/n # fft computing and normalization\n Y = Y[range(int(n/2))]\n k = np.arange(n)\n Ts = 30*60 # sampling interval (sec)\n Fs = 1./Ts\n T = n/Fs\n frq = k/T # two sides frequency range\n frq = frq[range(int(n/2))] # one side frequency range\n\n return frq, abs(Y)","def FFTpower(signal, timestep):\n \n # Compute the FFT of a real-valued signal. If N is the number \n # of points of the original signal, 'Nfreq' is (N/2+1).\n \n fourier = np.fft.rfft(signal)\n Ntime = len(signal)\n Nfreq = len(fourier)\n \n # Compute the power\n \n power = np.abs(fourier)**2 * 4.0 / Ntime**2\n \n # Compute the frequency array.\n # First compute an equidistant array that goes from 0 to 1 (included),\n # with in total as many points as in the 'fourier' array.\n # Then rescale the array that it goes from 0 to the Nyquist frequency\n # which is 0.5/timestep\n \n freq = np.arange(float(Nfreq)) / (Nfreq-1) * 0.5 / timestep\n \n # That's it!\n \n return (freq, power)","def DFTpower2(time, signal, freqs):\n \n powerSpectrum = np.zeros(len(freqs))\n\n for i, freq in enumerate(freqs):\n arg = 2.0 * np.pi * freq * time\n powerSpectrum[i] = np.sum(signal * np.cos(arg))**2 + np.sum(signal * np.sin(arg))**2\n\n powerSpectrum = powerSpectrum * 4.0 / len(time)**2\n return(powerSpectrum)","def mt_power_spectrum(s, sample_rate, window_size, low_bias=False, bandwidth=5.0):\n\n sample_length_bins = min(len(s), int(window_size * sample_rate))\n\n #break signal into chunks and estimate coherence for each chunk\n nchunks = int(np.floor(len(s) / float(sample_length_bins)))\n nleft = len(s) % sample_length_bins\n #ignore the last chunk if it's too short\n if nleft > (sample_length_bins / 2.0):\n nchunks += 1\n\n ps_freq = None\n ps_ests = list()\n for k in range(nchunks):\n si = k*sample_length_bins\n ei = min(len(s), si + sample_length_bins)\n print('si=%d, ei=%d, len(s)=%d' % (si, ei, len(s)))\n\n ps_freq,mt_ps,var = ntalg.multi_taper_psd(s[si:ei], Fs=sample_rate, adaptive=True, BW=bandwidth, jackknife=False,\n low_bias=low_bias, sides='onesided')\n ps_ests.append(mt_ps)\n\n ps_ests = np.array(ps_ests)\n\n ps_mean = ps_ests.mean(axis=0)\n ps_std = ps_ests.std(axis=0, ddof=1)\n\n return ps_freq,ps_mean,ps_std","def get_spectrum(self):\n return self.spectrum.copy()","def max_power_spectrum(sig, FS):\n\n if np.std(sig) == 0:\n return float(max(signal.welch(sig, int(FS), nperseg=len(sig))[1]))\n else:\n return float(max(signal.welch(sig/np.std(sig), int(FS), nperseg=len(sig))[1]))","def spectrum(self):\r\n f, spectrum = tsa.get_spectra(self.input.data, method=self.method)\r\n return spectrum","def compute_zp_spectral_function(self):\n nomegase = self.nomegase\n nkpt = self.nkpt\n nband = self.nband\n\n self.spectral_function = np.zeros((nomegase, nkpt, nband), dtype=float)\n\n omega = np.einsum('kn,l->knl', np.ones((nkpt, nband)), self.omegase)\n\n self.spectral_function = (\n (1 / np.pi) * np.abs(self.self_energy.imag) /\n ((omega - self.self_energy.real) ** 2 + self.self_energy.imag ** 2)\n )","def powerlaw_transform(I, gamma):\n for i in range(I.shape[0]): # image width\n for j in range(I.shape[1]): # image height\n I[i, j] = int(math.pow(I[i, j], gamma))","def intensity(self):\r\n return np.power(prb.amplitude, 2)","def mw_f(mw):\n return np.power(mw, 0.5)","def power2d(f,*kw):\n ft2=np.abs(np.fft.fft2(f))**2\n m=f.shape\n k=np.meshgrid(range(m[0]),range(m[1]))\n k=np.sqrt(k[0]**2+k[1]**2)\n a=2\n k0=1.0/a**0.5\n k1=1.0*a**0.5\n power=[]\n kk=[]\n while(k1 <= m[0]//2):\n kk.append((k0*k1)**0.5)\n w=np.where((k>k0) & (k <= k1))\n power.append(ft2[w].sum())\n k0=k1\n k1=k1*a\n pl.loglog(kk,power,*kw)","def _powerperdegree(self):\n return SHPowerSpectrum(self.coeffs)","def spectrum(ind, power, epower=None, ind_fit=None, power_fit=None,\n fit_label=\"Fit\", show=True, savepath=None):\n plt.errorbar(ind, power, yerr=epower, fmt='k.', capsize=2, alpha=0.2,\n zorder=0, label=\"WMAP data\")\n if power_fit is not None and ind_fit is not None:\n plt.plot(ind_fit, power_fit, 'r-', label=fit_label, zorder=1)\n plt.legend()\n if savepath is not None:\n plt.savefig(savepath)\n if show:\n plt.show()\n else:\n plt.close()","def fourier(img):\n return fourierCV(img)","def op(self, img):\n return self._mask * np.fft.fft2(img, norm=\"ortho\")","def PowerSpectralDensity(f):\n sky_averaging_constant = (20/3) # Sky Averaged <--- I got this from Jonathan's notes but I need\n # to check where he got it...\n L = 2.5*10**9 # Length of LISA arm\n f0 = 19.09*10**-3 \n\n Poms = ((1.5*10**-11)**2)*(1 + ((2*10**-3)/f)**4) # Optical Metrology Sensor\n Pacc = (3*10**-15)**2*(1 + (4*10**-3/(10*f))**2)*(1 + (f/(8*10**-3))**4) # Acceleration Noise\n Sc = 9*10**(-45)*f**(-7/3)*np.exp(-f**0.171 + 292*f*np.sin(1020*f)) * (1 \\\n + np.tanh(1680*(0.00215 - f))) \n\n PSD = (sky_averaging_constant)* ((10/(3*L**2))*(Poms + (4*Pacc)/((2*np.pi*f))**4)*(1 + 0.6*(f/f0)**2) + Sc) # PSD\n return PSD","def _powerperdegree(self):\n return SHCPowerSpectrum(self.coeffs)","def energy(image):\n #create sobel operators for gradient calculation\n sobel_x = np.array([\n [-1, 0, 1],\n [-2, 0, 2],\n [-1, 0, 1]])\n sobel_y = np.array([\n [-1, -2, -1],\n [0, 0, 0],\n [1, 2, 1]])\n #calculate gradients\n gradients_x = derive(image, sobel_x)\n gradients_y = derive(image, sobel_y)\n #calculate energy as defined in Avidan et al.\n energy_result = np.abs(gradients_x) + np.abs(gradients_y)\n return energy_result","def specPlot(image, qplot=True):\n # Load image, get dimensions\n im = readImage(image)\n L, W = im.shape\n xmin, xmax = (-W//2, W//2)\n ymin, ymax = (-L//2, L//2)\n xrng = range(xmin, xmax)\n yrng = range(ymin, ymax)\n\n # Calculate power spectrum\n spec = np.abs(fftshift(fft2(im)))**2\n\n # Calculate rotational average of spectrum\n [fx, fy] = np.meshgrid(xrng, yrng)\n sf = (np.sqrt(fx**2 + fy**2)).round().astype(int).flatten()\n rot_spec = np.bincount(sf, weights=spec.flatten()) / np.bincount(sf)\n rot_spec = rot_spec[1:min(L,W)//2]\n\n # Make a plot if requested\n if qplot:\n fig1, ax1 = plt.subplots()\n h = ax1.imshow(np.log10(spec), extent=[xmin, xmax, ymin, ymax])\n ax1.axis('off')\n cb = fig1.colorbar(h)\n cb.set_label(r'$\\log_{10}$(Energy)')\n\n fig2, ax2 = plt.subplots()\n ax2.loglog(np.arange(1, len(rot_spec)+1), rot_spec)\n ax2.set_xlabel('Spatial frequency (cycles/image)')\n ax2.set_ylabel('Energy')\n\n # Return\n if qplot:\n return spec, rot_spec, (fig1, ax1), (fig2, ax2)\n else:\n return spec, rot_spec","def calc_psf_fwhm_inpix_moffat(arr):\n\tmodel = fit_moffat(arr)\n\n\tfwhm = 2.* model.gamma * np.sqrt( 2.**(1./model.alpha) - 1. )\n\n\treturn fwhm","def calc_psd2d(self):\n print(\"Calculating 2D power spectral density ... \", end=\"\", flush=True)\n rows, cols = self.shape\n imgf = np.fft.fftshift(np.fft.fft2(self.image))\n # NOTE: normalize w.r.t. image size\n norm = rows * cols * self.pixel[0]**2\n self.psd2d = (np.abs(imgf) ** 2) / norm\n print(\"DONE\", flush=True)\n return self.psd2d","def get_power_spectrum(self, cosmo, nz1, nz2):\n t1 = ccl.WeakLensingTracer(cosmo, nz1)\n t2 = ccl.WeakLensingTracer(cosmo, nz2)\n return ccl.angular_cl(cosmo, t1, t2, self.ells)","def plot_power_spectrum_fit(img_ft, otf, options, pfit, frq_sim=None, mask=None, figsize=(20, 10), ttl_str=\"\"):\n\n if frq_sim is None:\n frq_sim = [0, 0]\n\n if mask is None:\n mask = np.ones(img_ft.shape, dtype=np.bool)\n\n # get physical data\n dx = options['pixel_size']\n dy = dx\n wavelength = options['wavelength']\n na = options['na']\n fmax = 1 / (0.5 * wavelength / na)\n\n # get frequency data\n fxs = tools.get_fft_frqs(img_ft.shape[1], dx)\n fys = tools.get_fft_frqs(img_ft.shape[0], dy)\n fxfx, fyfy = np.meshgrid(fxs, fys)\n ff_shift = np.sqrt((fxfx - frq_sim[0]) ** 2 + (fyfy - frq_sim[1]) ** 2)\n\n # get experimental and theoretical visualization\n ps_exp = np.abs(img_ft)**2\n ps_fit = power_spectrum_fn(pfit, ff_shift, otf)\n ps_fit_no_otf = power_spectrum_fn(list(pfit[:-1]) + [0], ff_shift, 1)\n\n # wienier filter, i.e. \"divide\" by otf\n snr = ps_fit_no_otf / pfit[-1]\n wfilter = np.abs(otf)**2 / (np.abs(otf)**4 + 1 / snr**2)\n ps_exp_deconvolved = ps_exp * wfilter\n ps_fit_deconvolved = ps_fit * wfilter\n\n # ######################\n # plot results\n # ######################\n extent = tools.get_extent(fys, fxs)\n\n fig = plt.figure(figsize=figsize)\n grid = plt.GridSpec(2, 10)\n\n sttl_str = \"\"\n if ttl_str != \"\":\n sttl_str += \"%s\\n\" % ttl_str\n sttl_str += 'm=%0.3g, A=%0.3g, alpha=%0.3f\\nnoise=%0.3g, frq sim = (%0.2f, %0.2f) 1/um' % \\\n (pfit[-2], pfit[0], pfit[1], pfit[-1], frq_sim[0], frq_sim[1])\n plt.suptitle(sttl_str)\n\n # ######################\n # plot power spectrum x otf vs frequency in 1D\n # ######################\n ax1 = plt.subplot(grid[0, :5])\n\n ax1.semilogy(ff_shift.ravel(), ps_exp.ravel(), 'k.')\n ax1.semilogy(ff_shift[mask].ravel(), ps_exp[mask].ravel(), 'b.')\n ax1.semilogy(ff_shift.ravel(), ps_fit.ravel(), 'r')\n\n ylims = ax1.get_ylim()\n ax1.set_ylim([ylims[0], 1.2 * np.max(ps_exp[mask].ravel())])\n\n ax1.set_xlabel('frequency (1/um)')\n ax1.set_ylabel('power spectrum')\n ax1.legend(['all data', 'data used to fit', 'fit'], loc=\"upper right\")\n ax1.title.set_text('m^2 A^2 |f|^{-2*alpha} |otf(f)|^2 + N')\n\n # ######################\n # plot az avg divided by otf in 1D\n # ######################\n ax2 = plt.subplot(grid[0, 5:])\n\n ax2.semilogy(ff_shift.ravel(), ps_exp_deconvolved.ravel(), 'k.')\n ax2.semilogy(ff_shift[mask].ravel(), ps_exp_deconvolved[mask].ravel(), 'b.')\n ax2.semilogy(ff_shift.ravel(), ps_fit_no_otf.ravel(), 'g')\n\n ylims = ax1.get_ylim()\n ax1.set_ylim([ylims[0], 1.2 * np.max(ps_exp_deconvolved[mask].ravel())])\n\n ax2.title.set_text('m^2 A^2 |k|^{-2*alpha} |otf|^4/(|otf|^4 + snr^2)')\n ax2.set_xlabel('|f - f_sim| (1/um)')\n ax2.set_ylabel('power spectrum')\n\n # ######################\n # plot 2D power spectrum\n # ######################\n ax3 = plt.subplot(grid[1, :2])\n\n ax3.imshow(ps_exp, interpolation=None, norm=PowerNorm(gamma=0.1), extent=extent)\n\n circ = matplotlib.patches.Circle((0, 0), radius=fmax, color='k', fill=0, ls='--')\n ax3.add_artist(circ)\n\n circ2 = matplotlib.patches.Circle((frq_sim[0], frq_sim[1]), radius=fmax, color='k', fill=0, ls='--')\n ax3.add_artist(circ2)\n\n ax3.set_xlabel('fx (1/um)')\n ax3.set_ylabel('fy (1/um)')\n ax3.title.set_text('raw power spectrum')\n\n # ######################\n # 2D fit\n # ######################\n ax5 = plt.subplot(grid[1, 2:4])\n ax5.imshow(ps_fit, interpolation=None, norm=PowerNorm(gamma=0.1), extent=extent)\n\n circ = matplotlib.patches.Circle((0, 0), radius=fmax, color='k', fill=0, ls='--')\n ax5.add_artist(circ)\n\n circ2 = matplotlib.patches.Circle((frq_sim[0], frq_sim[1]), radius=fmax, color='k', fill=0, ls='--')\n ax5.add_artist(circ2)\n\n ax5.set_xlabel('fx (1/um)')\n ax5.set_ylabel('fy (1/um)')\n ax5.title.set_text('2D fit')\n\n # ######################\n # plot 2D power spectrum divided by otf\n # ######################\n ax4 = plt.subplot(grid[1, 4:6])\n # ps_over_otf[mask == 0] = np.nan\n ax4.imshow(ps_exp_deconvolved, interpolation=None, norm=PowerNorm(gamma=0.1), extent=extent)\n\n circ = matplotlib.patches.Circle((0, 0), radius=fmax, color='k', fill=0, ls='--')\n ax4.add_artist(circ)\n\n circ2 = matplotlib.patches.Circle((frq_sim[0], frq_sim[1]), radius=fmax, color='k', fill=0, ls='--')\n ax4.add_artist(circ2)\n\n ax4.set_xlabel('fx (1/um)')\n ax4.set_ylabel('fy (1/um)')\n ax4.title.set_text('deconvolved power spectrum')\n\n # ######################\n # plot 2D power spectrum divided by otf with masked region\n # ######################\n ax4 = plt.subplot(grid[1, 6:8])\n # ps_over_otf[mask == 0] = np.nan\n ps_exp_deconvolved[mask == 0] = np.nan\n ax4.imshow(ps_exp_deconvolved, interpolation=None, norm=PowerNorm(gamma=0.1), extent=extent)\n\n circ = matplotlib.patches.Circle((0, 0), radius=fmax, color='k', fill=0, ls='--')\n ax4.add_artist(circ)\n\n circ2 = matplotlib.patches.Circle((frq_sim[0], frq_sim[1]), radius=fmax, color='k', fill=0, ls='--')\n ax4.add_artist(circ2)\n\n ax4.set_xlabel('fx (1/um)')\n ax4.set_ylabel('fy (1/um)')\n ax4.title.set_text('masked, deconvolved power spectrum')\n\n #\n ax4 = plt.subplot(grid[1, 8:])\n ax4.imshow(ps_fit_deconvolved, interpolation=None, norm=PowerNorm(gamma=0.1), extent=extent)\n\n circ = matplotlib.patches.Circle((0, 0), radius=fmax, color='k', fill=0, ls='--')\n ax4.add_artist(circ)\n\n circ2 = matplotlib.patches.Circle((frq_sim[0], frq_sim[1]), radius=fmax, color='k', fill=0, ls='--')\n ax4.add_artist(circ2)\n\n ax4.set_xlabel('fx (1/um)')\n ax4.set_ylabel('fy (1/um)')\n ax4.title.set_text('fit deconvolved')\n\n return fig","def DFT2(image):\n full_dft2 = DFT(DFT(image.transpose()).transpose())\n return full_dft2.astype(np.complex128)","def plasma_source_spectrum(energies, N0, T):\n return N0*numpy.exp(-energies/T)","def overf_power_spectrum(amp, index, f0, dt, n, cut_off=0):\n \n if cut_off < 0:\n raise ValueError(\"Low frequency cut off must not be negative.\")\n # Sometimes the fitting routines do something weird that causes\n # an overflow from a ridiculous index. Limit the index.\n index = max(index, -20)\n # Get the frequencies represented in the FFT.\n df = 1.0/dt/n\n freq = sp.arange(n, dtype=float)\n freq[n//2+1:] -= freq[-1] + 1\n freq = abs(freq)*df\n # 0th (mean) mode is meaningless has IR divergence. Deal with it later (in\n # the cut off.\n freq[0] = 1\n # Make the power spectrum.\n power = (freq/f0)**index\n power *= amp\n # Restore frequency of mean mode.\n freq[0] = 0\n # Find the power just above the cut off frequency.\n p_cut = power[sp.amin(sp.where(freq > cut_off)[0])]\n # Flatten off the power spectrum.\n power[freq <= cut_off] = p_cut\n return power","def power(self):\n return irradiance_on_plane(self.vnorm, self.h,\n self.date, self.lat) * self.s * self.eff","def compute_absolute_power(freqs, powers, band, method='sum'):\n\n _, band_powers = trim_spectrum(freqs, powers, band)\n abs_power = get_avg_func(method)(band_powers)\n\n return abs_power","def calc_psf_fwhm_inpix_gaussian(arr):\n\tmodel = fit_gaussian(arr)\n\n\tsigma = max(model.y_stddev, model.x_stddev)\n\tfwhm = 2.355 * sigma\n\n\treturn fwhm","def field_strength_to_power_flux(field: float) -> float:\n\n power = np.float_power(np.abs(field), 2)\n power *= (0.5 * speed_of_light * epsilon_0)\n\n return power","def powerlaw(E,alpha,A):\n\n\treturn A*E**alpha","def spectrum(self, wl: Union[float, ndarray]) -> Union[float, ndarray]:\n wlm = wl * 1e-9 # Wavelength to meters\n return 3.74183e-16 * wlm ** -5. / (np.exp(0.014388 / (wlm * self.temp)) - 1.)","def convolve_power(power_spectrum, window_power, axis=-1):\n \n data_corr = fft.ifft(power_spectrum, axis=axis)\n window_corr = fft.ifft(window_power, axis=axis)\n true_corr = data_corr * window_corr\n true_power = fft.fft(true_corr, axis=axis, overwrite_x=True)\n return true_power","def spectral(w, s=1.0):\n n_in, n_out = w.size()\n n = max(n_out, n_in)\n gain = s / math.sqrt(n)\n return w.normal_(0, 1).mul_(gain)","def spectrum(self,photon_energy):\n\n Eph = _validate_ene(photon_energy)\n\n spec = self.n0 * (self.weight_ee * self._emiss_ee(Eph)\n + self.weight_ep * self._emiss_ep(Eph))\n\n return spec","def get_spectrum(self, shcoeffs, nwins):\n for itaper in range(nwins):\n tapercoeffs = self._coeffs(itaper)\n modelcoeffs = shcoeffs.get_coeffs(normalization='4pi',kind='real')\n coeffs = SHMultiply(tapercoeffs,modelcoeffs)","def spectrum(G):\n\n try:\n return eigvalsh(G.todense())\n except ValueError:\n return eigvalsh(G)","def calc_power(field):\r\n\r\n poynt_in_points = 0.5*numpy.real(field.p * numpy.conj(field.vn))\r\n power = numpy.sum(poynt_in_points)\r\n power *= field.one_pixel_area\r\n return power","def bin_power(X,Band,Fs):\n\n\tC = fft(X)\n\tC = abs(C)\n\tPower =np.zeros(len(Band)-1);\n\tfor Freq_Index in range(0,len(Band)-1):\n\t\tFreq = float(Band[Freq_Index])\t\t\t\t\t\t\t\t\t\t## Xin Liu\n\t\tNext_Freq = float(Band[Freq_Index+1])\n\t\tPower[Freq_Index] = sum(C[np.floor(Freq/Fs*len(X)):np.floor(Next_Freq/Fs*len(X))])\n\tPower_Ratio = Power/sum(Power)\n\treturn Power, Power_Ratio","def deconvolve_power(power_spectrum, window_power, axis=-1):\n \n data_corr = fft.ifft(power_spectrum, axis=axis)\n window_corr = fft.ifft(window_power, axis=axis)\n true_corr = data_corr / window_corr\n true_power = fft.fft(true_corr, axis=axis, overwrite_x=True)\n return true_power","def compute_power_spectrum(self, k, p, smooth=False, for_corr=False, data_name=None, nopoly=False):\n\n # Get the basic power spectrum components\n if self.kvals is None or self.pksmooth is None or self.pkratio is None:\n ks = self.camb.ks\n pk_smooth_lin, pk_ratio = self.compute_basic_power_spectrum(p[\"om\"])\n else:\n ks = self.kvals\n pk_smooth_lin, pk_ratio = self.pksmooth, self.pkratio\n\n # We split for isotropic and anisotropic here for consistency with our previous isotropic convention, which\n # differs from our implementation of the Beutler2017 isotropic model quite a bit. This results in some duplication\n # of code and a few nested if statements, but it's perhaps more readable and a little faster (because we only\n # need one interpolation for the whole isotropic monopole, rather than separately for the smooth and wiggle components)\n\n if not for_corr:\n if \"b{0}\" not in p:\n p = self.deal_with_ndata(p, 0)\n\n if self.isotropic:\n pk = [np.zeros(len(k))]\n kprime = k if for_corr else k / p[\"alpha\"]\n if self.dilate_smooth:\n pk_smooth = splev(kprime, splrep(ks, pk_smooth_lin)) / (1.0 + kprime**2 * p[\"sigma_s\"] ** 2 / 2.0) ** 2\n else:\n pk_smooth = splev(k, splrep(ks, pk_smooth_lin)) / (1.0 + k**2 * p[\"sigma_s\"] ** 2 / 2.0) ** 2\n if not for_corr:\n pk_smooth *= p[\"b{0}\"]\n\n # Volume factor\n pk_smooth /= p[\"alpha\"] ** 3\n\n if smooth:\n propagator = np.ones(len(kprime))\n else:\n # Compute the propagator\n C = np.exp(-0.5 * kprime**2 * p[\"sigma_nl\"] ** 2)\n propagator = 1.0 + splev(kprime, splrep(ks, pk_ratio)) * C\n prefac = np.ones(len(kprime)) if smooth else propagator\n\n if for_corr or nopoly:\n poly = None\n pk[0] = pk_smooth * propagator\n else:\n shape, poly = self.add_poly(k, k, p, prefac, pk_smooth)\n if not self.marg:\n pk[0] = (pk_smooth + shape) * propagator\n\n else:\n\n epsilon = 0 if for_corr else p[\"epsilon\"]\n kprime = np.tile(k, (self.nmu, 1)).T if for_corr else np.outer(k / p[\"alpha\"], self.get_kprimefac(epsilon))\n muprime = self.mu if for_corr else self.get_muprime(epsilon)\n if self.dilate_smooth:\n fog = 1.0 / (1.0 + muprime**2 * kprime**2 * p[\"sigma_s\"] ** 2 / 2.0) ** 2\n reconfac = splev(kprime, splrep(self.camb.ks, self.camb.smoothing_kernel)) if self.recon_type.lower() == \"iso\" else 0.0\n kaiser_prefac = 1.0 + p[\"beta\"] * muprime**2 * (1.0 - reconfac)\n pk_smooth = kaiser_prefac**2 * splev(kprime, splrep(ks, pk_smooth_lin))\n else:\n ktile = np.tile(k, (self.nmu, 1)).T\n fog = 1.0 / (1.0 + muprime**2 * ktile**2 * p[\"sigma_s\"] ** 2 / 2.0) ** 2\n reconfac = splev(ktile, splrep(self.camb.ks, self.camb.smoothing_kernel)) if self.recon_type.lower() == \"iso\" else 0.0\n kaiser_prefac = 1.0 + p[\"beta\"] * muprime**2 * (1.0 - reconfac)\n pk_smooth = kaiser_prefac**2 * splev(ktile, splrep(ks, pk_smooth_lin))\n\n if not for_corr:\n pk_smooth *= p[\"b{0}\"]\n\n # Volume factor\n pk_smooth /= p[\"alpha\"] ** 3\n\n # Compute the propagator\n if smooth:\n pk2d = pk_smooth * fog\n else:\n C = np.exp(-0.5 * kprime**2 * (muprime**2 * p[\"sigma_nl_par\"] ** 2 + (1.0 - muprime**2) * p[\"sigma_nl_perp\"] ** 2))\n pk2d = pk_smooth * (fog + splev(kprime, splrep(ks, pk_ratio)) * C)\n\n pk0, pk2, pk4 = self.integrate_mu(pk2d)\n\n # Polynomial shape\n pk = [pk0, np.zeros(len(k)), pk2, np.zeros(len(k)), pk4, np.zeros(len(k))]\n\n if for_corr or nopoly:\n poly = None\n kprime = k\n else:\n shape, poly = self.add_poly(k, k, p, np.ones(len(k)), pk)\n if self.marg:\n pk = [np.zeros(len(k))] * 6\n else:\n for pole in self.poly_poles:\n pk[pole] += shape[pole]\n\n return kprime, pk, poly","def cpsd(self):\r\n self.welch_method = self.method\r\n self.welch_method['this_method'] = 'welch'\r\n self.welch_method['Fs'] = self.input.sampling_rate\r\n f, spectrum_welch = tsa.get_spectra(self.input.data,\r\n method=self.welch_method)\r\n\r\n return f, spectrum_welch","def signal_spectral(signal, FS):\n # check inputs\n if signal is None or signal == []:\n print(\"Signal is empty.\")\n\n # ensure numpy\n signal = np.array(signal)\n # f, spectrum = st.welch_spectrum(signal, sampling_rate=FS)\n spectrum = np.fft.fft(signal, FS)[:len(signal)//2]\n f = np.fft.fftfreq(len(signal))[:len(signal)//2]\n\n cum_ff = np.cumsum(spectrum)\n spect_diff = np.diff(spectrum)\n #energy, _ = st.signal_energy(spectrum, f)[:]\n\n args, names = [], []\n\n if dict['spectral_maxpeaks']['use'] == 'yes':\n # spectral_maxpeaks\n try:\n spectral_maxpeaks = np.sum([1 for nd in range(len(spect_diff[:-1])) if (spect_diff[nd+1]<0 and spect_diff[nd]>0)])\n except:\n spectral_maxpeaks = None\n args += [spectral_maxpeaks]\n names += ['spectral_maxpeaks']\n\n # if dict['spect_var']['use'] == 'yes':\n # # spect_variation\n # try:\n # spect_var = np.convolve(energy)\n # spect_var /= np.max(np.abs(spect_var))\n # except:\n # spect_var = None\n # args += [spect_var]\n # names += ['spect_var']\n\n if dict['curve_distance']['use'] == 'yes':\n # curve_distance\n try:\n curve_distance = np.sum(np.linspace(0, cum_ff[-1], len(cum_ff)) - cum_ff)\n except:\n curve_distance = None\n args += [curve_distance]\n names += ['curve_distance']\n\n if dict['spectral_roll_off']['use'] == 'yes':\n # spectral_roll_off\n try:\n spectral_roll_off = spectral_roll(f, spectrum, cum_ff, 0.95)[0]\n except:\n spectral_roll_off = None\n args += [spectral_roll_off]\n names += ['spectral_roll_off']\n\n if dict['spectral_roll_on']['use'] == 'yes':\n # spectral_roll_on\n try:\n spectral_roll_on = spectral_roll(f, spectrum, cum_ff, 0.05)[0]\n except:\n spectral_roll_on = None\n args += [spectral_roll_on]\n names += ['spectral_roll_on']\n\n if dict['spectral_dec']['use'] == 'yes':\n # spectral_decrease\n try:\n spectral_dec = (1/np.sum(spectrum)) * np.sum((spectrum[:] - spectrum[1])/np.linspace(1, len(spectrum), len(spectrum),1))\n except:\n spectral_dec = None\n args += [spectral_dec]\n names += ['spectral_dec']\n\n if dict['spectral_slope']['use'] == 'yes':\n # spectral_slope\n sum_f = np.sum(f)\n len_f = len(f)\n try:\n spectral_slope = (len_f * np.dot(f, spectrum) - sum_f * np.sum(spectrum)) / (len_f * np.dot(f, f) - sum_f ** 2)\n except:\n spectral_slope = None\n args += [spectral_slope]\n names += ['spectral_slope']\n\n sum_spectrum = np.sum(spectrum)\n norm_spectrum = spectrum / sum_spectrum\n # spectral_centroid\n try:\n spectral_centroid = np.dot(f, norm_spectrum)\n except:\n spectral_centroid = None\n\n # spectral_spread\n try:\n spectral_spread = np.dot(((f - spectral_centroid) ** 2), norm_spectrum)\n except:\n spectral_spread = None\n\n if dict['spectral_spread']['use'] == 'yes':\n args += [spectral_spread]\n names += ['spectral_spread']\n\n if dict['spectral_kurtosis']['use'] == 'yes':\n # spectral_kurtosis\n try:\n spectral_kurtosis = np.sum(((f - spectral_centroid) ** 4) * norm_spectrum) / (spectral_spread**2)\n except:\n spectral_kurtosis = None\n args += [spectral_kurtosis]\n names += ['spectral_kurtosis']\n\n if dict['spectral_skewness']['use'] == 'yes':\n # spectral_skewness\n try:\n spectral_skewness = np.sum(((f - spectral_centroid) ** 3) * norm_spectrum) / (spectral_spread ** (3 / 2))\n except:\n spectral_skewness = None\n args += [spectral_skewness]\n names += ['spectral_skewness']\n\n if dict['max_frequency']['use'] == 'yes':\n # max_frequency\n try:\n max_frequency = f[np.where(cum_ff > cum_ff[-1]*0.95)[0][0]]\n except:\n max_frequency = None\n args += [max_frequency]\n names += ['max_frequency']\n\n if dict['fundamental_frequency']['use'] == 'yes':\n # fundamental_frequency\n try:\n fundamental_frequency = f[np.where(cum_ff > cum_ff[-1]*0.5)[0][0]]\n except:\n fundamental_frequency = None\n args += [fundamental_frequency]\n names += ['fundamental_frequency']\n\n # if dict['max_power_spectrum']['use'] == 'yes':\n # # max_power_spectrum\n # try:\n # max_power_spectrum = np.max(spectrum)\n # except:\n # max_power_spectrum = None\n # args += max_power_spectrum\n # names += 'max_power_spectrum'\n\n # if dict['mean_power_spectrum']['use'] == 'yes':\n # # mean_power_spectrum\n # try:\n # mean_power_spectrum = np.mean(spectrum)\n # except:\n # mean_power_spectrum = None\n # args += mean_power_spectrum\n # names += 'mean_power_spectrum'\n #\n # if dict['spectral_skewness']['use'] == 'yes':\n # try:\n # spectral_skewness = np.mean(spectrum)\n # except:\n # spectral_skewness = None\n # args += spectral_skewness\n # names += 'spectral_skewness'\n #\n # if dict['spectral_kurtosis']['use'] == 'yes':\n # try:\n # spectral_kurtosis = np.mean(spectrum)\n # except:\n # spectral_kurtosis = None\n # args += spectral_kurtosis\n # names += 'spectral_kurtosis'\n\n # if dict['spectral_hist_']['use'] == 'yes':\n # # histogram\n # try:\n # _hist = list(np.histogram(spectrum, bins=int(np.sqrt(len(spectrum))), density=True)[0])\n # except:\n # if len(signal) > 1:\n # _hist = [None] * int(np.sqrt(len(signal)))\n # else:\n # _hist = [None]\n # args += [i for i in _hist]\n # names += ['spectral_hist_' + str(i) for i in range(len(_hist))]\n\n #return utils.ReturnTuple(tuple(args), tuple(names))\n return args, names","def energy_map(img):\n img_new = img.astype(float) #converting image to float\n total_energy = 0.0 # To store the sum of energy for all channels\n r,c,d = img.shape \n for i in range(d):\n dy = np.zeros([r, c], dtype=float) \n dx = np.zeros([r, c], dtype=float)\n if r > 1:\n dy = np.gradient(img_new[:,:,i], axis=0) #gradient along rows\n if c > 1:\n dx = np.gradient(img_new[:,:,i], axis=1) #gradient along columns\n total_energy += np.absolute(dy) + np.absolute(dx) \n return total_energy #Total energy map for entire image","def spectrum2D(path):\n image = cv2.imread(path,0)\n fftOriginal = np.fft.fft2(image)\n shiftedFFT = np.fft.fftshift(fftOriginal)\n shiftedFFTMagnitude = np.abs(shiftedFFT)\n ## AVERAGE RADIAL\n rows = image.shape[0]\n cols = image.shape[1]\n midRow = rows/2+1\n midCol = cols/2+1\n maxRadius = math.ceil(np.sqrt((midRow+2)**2 + (midCol+2)**2))\n radialProfile = np.zeros((maxRadius, 1))\n count = np.zeros((maxRadius, 1))\n for i in range(cols):\n for j in range(rows):\n radius = np.sqrt((j-midRow)**2 + (i-midCol)**2)\n thisIndex = math.ceil(radius) + 1\n radialProfile[thisIndex] = radialProfile[thisIndex] + shiftedFFTMagnitude[j,i]\n count[thisIndex] = count[thisIndex] + 1\n radialProfile_new = radialProfile / count\n for k in range(radialProfile_new.shape[0]):\n if math.isnan(radialProfile_new[k]):\n radialProfile_new[k] = 0.\n return radialProfile_new","def integrate_spectrum(self):\n flux = sum(self.spectrum)\n return flux","def FFTpowerdensity(signal, timestep):\n \n # Compute the FFT of a real-valued signal. If N is the number \n # of points of the original signal, 'Nfreq' is (N/2+1).\n \n fourier = np.fft.rfft(signal)\n Ntime = len(signal)\n Nfreq = len(fourier)\n \n # Compute the power density\n \n powerdensity = np.abs(fourier)**2 / Ntime * timestep\n \n # Compute the frequency array.\n # First compute an equidistant array that goes from 0 to 1 (included),\n # with in total as many points as in the 'fourier' array.\n # Then rescale the array that it goes from 0 to the Nyquist frequency\n # which is 0.5/timestep\n \n freq = np.arange(float(Nfreq)) / (Nfreq-1) * 0.5 / timestep\n \n # That's it!\n \n return (freq, powerdensity)","def compute_intensity(self, data):\n # Compute RMS intensity (as float to prevent overflow).\n data = self.audio_resample(data.astype(np.float32)**2)**0.5\n\n # Apply dynamic range compression.\n return data**self._exponent","def get_spectrum(self, path):\n raise NotImplementedError","def square_window_spectrum(N, Fs):\r\n f = get_freqs(Fs, N - 1)\r\n j = 0 + 1j\r\n a = -j * (N - 1) * f / 2\r\n b = np.sin(N * f / 2.0)\r\n c = np.sin(f / 2.0)\r\n make = np.exp(a) * b / c\r\n\r\n return f, make[1:] / make[1]","def power(source : Image, destination : Image = None, exponent : float = 1) -> Image:\n\n\n parameters = {\n \"src\":source,\n \"dst\": destination,\n \"exponent\":float(exponent)\n }\n\n execute(__file__, '../clij-opencl-kernels/kernels/power_' + str(len(destination.shape)) + 'd_x.cl', 'power_' + str(len(destination.shape)) + 'd', destination.shape, parameters)\n return destination","def apply_smoothstep(image):\n image_out = 3 * image**2 - 2 * image**3\n return image_out","def DFTpower(time, signal, f0=None, fn=None, df=None, full_output=False):\n\n freqs = np.arange(f0,fn,df)\n Ntime = len(time)\n Nfreq = int(np.ceil((fn-f0)/df))\n \n A = np.exp(1j*2.*pi*f0*time) * signal\n B = np.exp(1j*2.*pi*df*time)\n ft = np.zeros(Nfreq, complex) \n ft[0] = A.sum()\n for k in range(1,Nfreq):\n A *= B\n ft[k] = np.sum(A)\n \n if full_output:\n return freqs,ft**2*4.0/Ntime**2\n else:\n return freqs,(ft.real**2 + ft.imag**2) * 4.0 / Ntime**2","def smoothSpectrum(f, X_f, r_oct):\n X_f_out = np.zeros(np.shape(X_f))\n for n in range(np.shape(f)[0]):\n # standard deviation\n sigma = f[n] / r_oct / np.pi\n # Gaussian window with the center frequnecy f[n] an dstandard deviation\n w = np.exp( -(f-f[n])**2 / (2*sigma**2) )\n w = w / np.sum(w, axis=0)\n X_f_out[n] = np.sum(w * X_f)\n \n return X_f_out","def power_flux_to_field_strength(power: float) -> float:\n\n field_strength = (2 * power) / (speed_of_light * epsilon_0)\n field_strength = np.sqrt(field_strength)\n\n return field_strength","def spectral_brightness(data, fft_data):\n spec = np.abs(fft_data)\n weight_vec = np.log(np.linspace(1, 100, len(fft_data)))\n weight_vec = np.pi*weight_vec/weight_vec[-1]\n weight = np.cos(weight_vec)/2 + 0.5\n low_spec_sum = sum(spec*weight)\n high_spec_sum = sum(spec*(1-weight))\n if low_spec_sum == 0:\n return 1 # attention: if signal is a sine at fs/2 this is also hit\n return high_spec_sum/low_spec_sum","def chebyshev_coeffs(f, n):\n extrema = np.cos((np.pi * np.arange(2*n))/n)\n samples = f(extrema)\n coeffs = np.real(np.fft.fft(samples))[:n+1]\n coeffs /= n \n coeffs[0] /= 2\n coeffs[n] /= 2\n\n return coeffs","def fft_image(shape, sd=None, decay_power=1):\n sd = sd or 0.01\n # batch, h, w, ch = shape # tf style: [N, H, W, C]\n batch, ch, h, w = shape # torch style: [N, C, H, W]\n \n freqs = rfft2d_freqs(h, w)\n init_val_size = (2, batch, ch) + freqs.shape\n\n init_val = np.random.normal(size=init_val_size, scale=sd).astype(np.float32)\n # spectrum_real_imag_t = tf.Variable(init_val)\n spectrum_real_imag_t = Variable(torch.from_numpy(init_val))\n\n # spectrum_t = tf.complex(spectrum_real_imag_t[0], spectrum_real_imag_t[1])\n spectrum_t = torch.complex(spectrum_real_imag_t[0], spectrum_real_imag_t[1])\n\n # Scale the spectrum. First normalize energy, then scale by the square-root\n # of the number of pixels to get a unitary transformation.\n # This allows to use similar leanring rates to pixel-wise optimisation.\n scale = 1.0 / np.maximum(freqs, 1.0 / max(w, h)) ** decay_power\n scale *= np.sqrt(w * h)\n print(scale.shape, spectrum_t.shape)\n scaled_spectrum_t = torch.from_numpy(scale) * spectrum_t\n\n # convert complex scaled spectrum to shape (h, w, ch) image tensor\n # needs to transpose because irfft2d returns channels first\n # image_t = tf.transpose(tf.spectral.irfft2d(scaled_spectrum_t), (0, 2, 3, 1))\n image_t = torch.fft.irfft(scaled_spectrum_t) # shape: [N, C, H, W]\n\n # in case of odd spatial input dimensions we need to crop\n # image_t = image_t[:batch, :h, :w, :ch] # tf style\n image_t = image_t[:batch, :ch, :h, :w] # torch style\n image_t = image_t / 4.0 # TODO: is that a magic constant?\n return image_t","def power(x): \r\n return x(1)","def power_method(mat, start, maxit):\n result = start\n for i in xrange(maxit):\n result = mat*result\n result = result/np.linalg.norm(result)\n return result","def wavefunction(self, x):\n return ( float(1) / math.pi**(float(1)/4)) * math.exp( x**2 / float(-2))","def single_slit_diffraction_intensity(slit_width, wavelength, screen_distance, X):\n return ((np.sin((np.pi * slit_width * X) / (wavelength * screen_distance))) / (\n (np.pi * slit_width * X) / (wavelength * screen_distance))) ** 2","def match_power_spectrum(s, sample_rate, nsamps=5, isreal=False):\n\n #get FT of the signal\n sfft = fft(s)\n amplitude = np.abs(sfft)\n phase = np.angle(sfft)\n\n s_recon = np.zeros([nsamps, len(s)], dtype='complex128')\n for k in range(nsamps):\n #shuffle the phase\n np.random.shuffle(phase)\n #reconstruct the signal\n sfft_recon = amplitude*(np.cos(phase) + 1j*np.sin(phase))\n s_recon[k, :] = ifft(sfft_recon)\n\n if isreal:\n return np.real(s_recon)\n return s_recon","def show_spectrum(h, title=\"\"):\n H = fft2(h)\n\n # Remember to plot the abs of the fft2(h)\n plt.imshow(np.abs(H))\n plt.gray()\n plt.title(title)\n plt.show()","def get_camb_power(matpow):\n data = np.loadtxt(matpow)\n kk = data[:,0]\n ii = np.where(kk > 0.)\n return (kk[ii], data[:,1][ii])","def spectrum(self,photon_energy):\n outspecene = _validate_ene(photon_energy)\n\n self.specic = np.zeros(len(outspecene)) * u.Unit('1/(s eV)')\n\n for seed in self.seed_photon_fields:\n # Call actual computation, detached to allow changes in subclasses\n self.specic += self._calc_specic(seed,outspecene).to('1/(s eV)')\n\n self.specic = self.specic.to('1/(s eV)')\n\n return self.specic","def calc_psf_fwhm(arr, mode='moffat'):\n\n\tif mode == 'moffat':\n\t\treturn calc_psf_fwhm_inpix_moffat(arr)\n\telif mode == 'gaussian':\n\t\treturn calc_psf_fwhm_inpix_gaussian(arr)\n\telse:\n\t\traise ValueError(\"mode not recognized\")","def signal_power(signal_input, *arg):\r\n signal_T = np.array(signal_input)\r\n energy = np.abs(signal_T) ** 2\r\n if len(arg) < 1:\r\n power = np.mean(energy)\r\n else:\r\n power = np.mean(energy, axis=arg[0])\r\n return power","def specular_power(self) -> float:\n return self.GetSpecularPower()","def test_fft_spectrum_02():\n f, t, Sxx = _spectral_helper(x, x, fs=s_freq,\n window='hann',\n nperseg=x.shape[0],\n noverlap=0,\n nfft=None,\n return_onesided=True,\n mode='psd',\n scaling='spectrum')\n\n f0, Sxx0 = _fft(x, s_freq, detrend=None, taper='hann', scaling='energy', sides='one')\n\n assert_array_equal(f0, f)\n assert_array_almost_equal(Sxx0, Sxx[:, 0] * CORRECTION_FACTOR)","def get_camb_nu_power(matpow, transfer):\n matter = np.loadtxt(matpow)\n trans = np.loadtxt(transfer)\n #Adjust Fourier convention to match CAMB.\n tnufac = (trans[:,5]/trans[:,6])**2\n return matter[:,0], matter[:,1]*tnufac","def ggc_lsf(wave):\n coeffs = np.array([15.290, -6.079e-3, 9.472e-7, -4.395e-11])\n powers = np.arange(len(coeffs))\n fwhm = np.dot(coeffs, wave[None, :] ** powers[:, None])\n return fwhm","def powerSpectrum(self,l_edges,scale=None):\n\n\t\tassert not self._masked,\"Power spectrum calculation for masked maps is not allowed yet!\"\n\t\tassert l_edges is not None\n\n\t\tif self.side_angle.unit.physical_type==\"length\":\n\t\t\traise NotImplementedError(\"Power spectrum measurement not implemented yet if side physical unit is length!\")\n\n\t\tl = 0.5*(l_edges[:-1] + l_edges[1:])\n\n\t\t#Calculate the Fourier transform of the map with numpy FFT\n\t\tft_map = fftengine.rfft2(self.data)\n\n\t\t#Compute scaling coefficients\n\t\tif scale is not None:\n\t\t\tsc = scale(self.getEll())\n\t\telse:\n\t\t\tsc = None\n\n\t\t#Compute the power spectrum with the C backend implementation\n\t\tpower_spectrum = _topology.rfft2_azimuthal(ft_map,ft_map,self.side_angle.to(u.deg).value,l_edges,sc)\n\n\t\t#Output the power spectrum\n\t\treturn l,power_spectrum","def avg_power_spectro(Sxx_power):\r\n S_power_mean = np.mean(Sxx_power, axis=1)\r\n\r\n return S_power_mean","def scipy_smooth(img, sigma=5):\n return ndimage.gaussian_filter(img, sigma=sigma)","def plot_spectrum( data ):\n d = data[1]\n # rfft gives positive frequecies. Square to get power spectrum.\n fp = numpy.absolute( numpy.fft.rfft( d ) )**2\n freq = numpy.fft.fftfreq( d.shape[-1] )\n n = len(fp)\n\n # reshape stuff a bit. keep only positive freqs.\n fp = fp[1:-1]\n freq = freq[1:n-1]\n lrslope = linregress( numpy.log(freq[30:]), numpy.log(fp[30:]) )[0]\n fig = plt.figure()\n ax = fig.add_subplot(111)\n ax.loglog( freq, fp, label=\"Lin. reg.=\"+str(round( lrslope,1 )) )\n ax.legend( loc='lower left' )\n return fig","def get_power(self, kf, mean_fluxes):\n mf = None\n flux_arr = np.empty(shape=(self.len(),np.size(kf)))\n for (i,ss) in enumerate(self.spectrae):\n if mean_fluxes is not None:\n mf = mean_fluxes[i]\n kf_sim, flux_power_sim = ss.get_flux_power_1D(\"H\",1,1215, mean_flux_desired=mf)\n #Rebin flux power to have desired k bins\n rebinned=scipy.interpolate.interpolate.interp1d(kf_sim,flux_power_sim)\n ii = np.where(kf > kf_sim[0])\n ff = flux_power_sim[0]*np.ones_like(kf)\n ff[ii] = rebinned(kf[ii])\n flux_arr[i] = ff\n flux_arr = np.ravel(flux_arr)\n assert np.shape(flux_arr) == (self.len()*np.size(kf),)\n self.drop_table()\n return flux_arr"],"string":"[\n \"def spectral_power(img, avg_window_size=None, log=True): #COMPLETE spectrum generator\\r\\n image = img.copy()\\r\\n # to avoid large spectral power at the 0 frequency :\\r\\n image -= np.mean(image)\\r\\n # wiener filter to reduce non physical variability in the spectral power\\r\\n if avg_window_size:\\r\\n N = avg_window_size\\r\\n image = wiener(image, (N, N))\\r\\n # compute the spectral power function. Place the 0 frequency-component in the center\\r\\n fshift = np.fft.fftshift(np.fft.fft2(image))\\r\\n spectrum = np.abs(fshift)**2\\r\\n if log:\\r\\n spectrum = 10*np.log(spectrum)\\r\\n return spectrum\",\n \"def powerSpectrum(input, nfft):\\n result = fft(input, nfft)\\n result = np.power(np.absolute(result), 2)\\n\\n # myplot(result, 'Power Spectogram')\\n\\n return result\",\n \"def get_spectrum_from_img(img):\\n # Executa FFT em 2 dimensoes\\n f = np.fft.fft2(img)\\n # Desloca a frequencia zero para o centro do espectro\\n fshift = np.fft.fftshift(f)\\n\\n return fshift\",\n \"def powerSpectrum(input, nfft):\\n freq = fft(input, nfft)\\n return freq.real**2 + freq.imag**2\",\n \"def power_spectrum_fn(p, fmag, otf):\\n\\n val = p[2]**2 * p[0]**2 * np.abs(fmag) ** (-2 * p[1]) * np.abs(otf)**2 + p[3]\\n\\n return val\",\n \"def show_spectrum(image: np.ndarray):\\n assert len(image.shape) == 2, 'image must be 2D'\\n spectral = np.fft.fft2(image)\\n spectral[0, 0] = 0 # Kill DC component\\n spectrum = np.fft.fftshift(spectral) # Shift DC to center\\n magnitude = np.log(np.abs(spectrum))\\n plt.imshow(magnitude, cmap='gray')\\n plt.show()\",\n \"def fourierNP(img):\\n\\tgray = grayscale(img)\\n\\tf = np.fft.fft2(gray)\\n\\tfshift = np.fft.fftshift(f)\\n\\tmagnitude_spectrum = 20*np.log(np.abs(fshift)) # RETURN THIS\\n return magnitude_spectrum\",\n \"def get_power(frames, num_fft):\\n #a = get_magnitude(frames, num_fft)\\n #b = np.square(a)\\n #print('max : ', np.max(a))\\n #print('min : ', np.min(a))\\n #print('sq max : ', np.max(b))\\n #print('sq min : ', np.min(b))\\n #print(a.shape)\\n #print(b.shape)\\n #return b/num_fft\\n return np.square(get_magnitude(frames, num_fft) / np.sqrt(num_fft))\",\n \"def return_power_spectrum(self, freq_signal, time_signal):\\n\\n\\t\\tfreq_signal **= 2 \\n\\t\\tlen_fts = len(freq_signal)\\n\\t\\tlen_signal = len(signal)\\n\\n\\t\\tif len_signal % 2:\\n\\t\\t\\tfreq_signal[1:len_fts] *= 2\\n\\n\\t\\telse:\\n\\t\\t\\tfreq_signal[1:len_fts-1] *= 2\\n\\n\\t\\treturn freq_signal\",\n \"def get_spectral_features(image, img_type: str): # Apply on the whole image\\r\\n spectrum = spectral_power(image, log=False)\\r\\n bins_energy, _ = get_energy_bins(spectrum, num_bins=3) # Use images with outside val: NaN\\r\\n spectral_features = {img_type + \\\"low_freq_energy\\\": bins_energy[0],\\r\\n img_type + \\\"mid_freq_energy\\\": bins_energy[1], \\r\\n img_type + \\\"high_freq_energy\\\": bins_energy[2]}\\r\\n return spectral_features\",\n \"def f_mw(f):\\n return np.power(f, 2)\",\n \"def _get_power_of_spectrum(U):\\n \\n _, n_samples, n_frames, n_frequencies = U.shape\\n del _\\n\\n P = np.zeros([n_samples, n_samples, n_frequencies], dtype=np.complex64)\\n P = np.sum(np.abs(U)**2, axis=2) / n_frames\\n \\n return P\",\n \"def fourierCV(img):\\n\\tgray = grayscale(img)\\n\\tdft = cv2.dft(np.float32(gray), flags = cv2.DFT_COMPLEX_OUTPUT)\\n\\tdft_shift = np.fft.fftshift(dft)\\n\\tmagnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0], dft_shift[:,:,1]))\\n return magnitude_spectrum\",\n \"def power(image, kernel):\\n\\n image = old_div((image - image.mean()), image.std())\\n return np.sqrt(ndi.convolve(image, np.real(kernel), mode='wrap') ** 2 +\\n ndi.convolve(image, np.imag(kernel), mode='wrap') ** 2)\",\n \"def bandwidth_power(\\n x, fs, bands, dim=-2, n_overlap=None, nperseg=None, scaling=\\\"density\\\"\\n):\\n dim = fix_dim(dim, x.dim())\\n if not x.is_complex():\\n # convert the torch<1.8 \\\"complex\\\" format to a proper complex dtype\\n assert x.shape[-1] == 2 # the last dim has size 2 for re and im\\n x = x[..., 0] + 1j * x[..., 1] # makes into ndim-1\\n\\n if nperseg is None:\\n nperseg = x.shape[dim]\\n\\n # 1. Welch\\n window = torch.hamming_window(\\n nperseg, periodic=False, dtype=x.real.dtype, device=x.device\\n )\\n ff, px = pwelch(x, dim, window, fs=fs, scaling=scaling, n_overlap=n_overlap)\\n\\n ff, px = fftshift(ff), fftshift(px, dim=dim)\\n if not bands:\\n # this needs to return a tensor that has the expected shape\\n return (\\n ff,\\n px,\\n torch.empty(\\n *px.shape[:dim],\\n *px.shape[dim + 1 :],\\n 0,\\n dtype=px.dtype,\\n device=px.device,\\n ),\\n )\\n\\n # 2. Compute power within each band\\n channel, df = [], 1.0 / (nperseg * fs) # noqa: F841\\n for lo, hi in bands:\\n (index,) = torch.nonzero(ff.gt(lo) & ff.lt(hi), as_tuple=True)\\n power = torch.index_select(px, dim, index)\\n channel.append(power.sum(dim=dim)) # * df)\\n # end for\\n\\n # 3. get the power in decibel\\n return ff, px, 10 * torch.log10(torch.stack(channel, dim=-1))\",\n \"def get_noise_power(img_ft, fxs, fys, fmax):\\n\\n ps = np.abs(img_ft) ** 2\\n\\n # exclude regions of frequency space\\n fxfx, fyfy = np.meshgrid(fxs, fys)\\n ff = np.sqrt(fxfx ** 2 + fyfy ** 2)\\n noise_power = np.mean(ps[ff > fmax])\\n\\n return noise_power\",\n \"def wavelettf_greyscale(img, wavelet):\\n\\n # %% Converting the image to right datatype\\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\\n img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\\n img = np.float32(img)\\n img /= 255\\n\\n # %% Extracting and returning the coefficients\\n coeffs = pywt.dwt2(img, wavelet, axes=(0, 1))\\n cA, (cH, cV, cD) = coeffs\\n return coeffs\",\n \"def _spectrum(self, energy):\\n return self.pref*energy**2*self.occupation_number(energy/self.kT)\",\n \"def spectrum(self):\\r\\n spectrum = tsa.cache_to_psd(self.cache, self.ij)\\r\\n\\r\\n return spectrum\",\n \"def exponential(image: np.ndarray) -> np.ndarray:\\n return np.power(image, 0.75).astype('uint8')\",\n \"def snr_f(self, image):\\n image_ps = self.pow_spec(image)\\n noise_level = numpy.sum(self.rim*image_ps)/numpy.sum(self.rim)\\n return numpy.sqrt(image_ps[int(self.size/2), int(self.size/2)]/noise_level)\",\n \"def get_img_from_spectrum(spectrum):\\n # Retorna a frequencia zero colocada no centro do espectro de volta a\\n # posicao original.\\n f_ishift = np.fft.ifftshift(spectrum)\\n # Realiza FFT inversa\\n img_back = np.fft.ifft2(f_ishift)\\n # Realiza a conversao para valores somente reais\\n img_back = np.abs(img_back)\\n\\n return img_back\",\n \"def power_spectral_density(var):\\n\\n n = len(var)\\n Y = np.fft.fft(var)/n # fft computing and normalization\\n Y = Y[range(int(n/2))]\\n k = np.arange(n)\\n Ts = 30*60 # sampling interval (sec)\\n Fs = 1./Ts\\n T = n/Fs\\n frq = k/T # two sides frequency range\\n frq = frq[range(int(n/2))] # one side frequency range\\n\\n return frq, abs(Y)\",\n \"def FFTpower(signal, timestep):\\n \\n # Compute the FFT of a real-valued signal. If N is the number \\n # of points of the original signal, 'Nfreq' is (N/2+1).\\n \\n fourier = np.fft.rfft(signal)\\n Ntime = len(signal)\\n Nfreq = len(fourier)\\n \\n # Compute the power\\n \\n power = np.abs(fourier)**2 * 4.0 / Ntime**2\\n \\n # Compute the frequency array.\\n # First compute an equidistant array that goes from 0 to 1 (included),\\n # with in total as many points as in the 'fourier' array.\\n # Then rescale the array that it goes from 0 to the Nyquist frequency\\n # which is 0.5/timestep\\n \\n freq = np.arange(float(Nfreq)) / (Nfreq-1) * 0.5 / timestep\\n \\n # That's it!\\n \\n return (freq, power)\",\n \"def DFTpower2(time, signal, freqs):\\n \\n powerSpectrum = np.zeros(len(freqs))\\n\\n for i, freq in enumerate(freqs):\\n arg = 2.0 * np.pi * freq * time\\n powerSpectrum[i] = np.sum(signal * np.cos(arg))**2 + np.sum(signal * np.sin(arg))**2\\n\\n powerSpectrum = powerSpectrum * 4.0 / len(time)**2\\n return(powerSpectrum)\",\n \"def mt_power_spectrum(s, sample_rate, window_size, low_bias=False, bandwidth=5.0):\\n\\n sample_length_bins = min(len(s), int(window_size * sample_rate))\\n\\n #break signal into chunks and estimate coherence for each chunk\\n nchunks = int(np.floor(len(s) / float(sample_length_bins)))\\n nleft = len(s) % sample_length_bins\\n #ignore the last chunk if it's too short\\n if nleft > (sample_length_bins / 2.0):\\n nchunks += 1\\n\\n ps_freq = None\\n ps_ests = list()\\n for k in range(nchunks):\\n si = k*sample_length_bins\\n ei = min(len(s), si + sample_length_bins)\\n print('si=%d, ei=%d, len(s)=%d' % (si, ei, len(s)))\\n\\n ps_freq,mt_ps,var = ntalg.multi_taper_psd(s[si:ei], Fs=sample_rate, adaptive=True, BW=bandwidth, jackknife=False,\\n low_bias=low_bias, sides='onesided')\\n ps_ests.append(mt_ps)\\n\\n ps_ests = np.array(ps_ests)\\n\\n ps_mean = ps_ests.mean(axis=0)\\n ps_std = ps_ests.std(axis=0, ddof=1)\\n\\n return ps_freq,ps_mean,ps_std\",\n \"def get_spectrum(self):\\n return self.spectrum.copy()\",\n \"def max_power_spectrum(sig, FS):\\n\\n if np.std(sig) == 0:\\n return float(max(signal.welch(sig, int(FS), nperseg=len(sig))[1]))\\n else:\\n return float(max(signal.welch(sig/np.std(sig), int(FS), nperseg=len(sig))[1]))\",\n \"def spectrum(self):\\r\\n f, spectrum = tsa.get_spectra(self.input.data, method=self.method)\\r\\n return spectrum\",\n \"def compute_zp_spectral_function(self):\\n nomegase = self.nomegase\\n nkpt = self.nkpt\\n nband = self.nband\\n\\n self.spectral_function = np.zeros((nomegase, nkpt, nband), dtype=float)\\n\\n omega = np.einsum('kn,l->knl', np.ones((nkpt, nband)), self.omegase)\\n\\n self.spectral_function = (\\n (1 / np.pi) * np.abs(self.self_energy.imag) /\\n ((omega - self.self_energy.real) ** 2 + self.self_energy.imag ** 2)\\n )\",\n \"def powerlaw_transform(I, gamma):\\n for i in range(I.shape[0]): # image width\\n for j in range(I.shape[1]): # image height\\n I[i, j] = int(math.pow(I[i, j], gamma))\",\n \"def intensity(self):\\r\\n return np.power(prb.amplitude, 2)\",\n \"def mw_f(mw):\\n return np.power(mw, 0.5)\",\n \"def power2d(f,*kw):\\n ft2=np.abs(np.fft.fft2(f))**2\\n m=f.shape\\n k=np.meshgrid(range(m[0]),range(m[1]))\\n k=np.sqrt(k[0]**2+k[1]**2)\\n a=2\\n k0=1.0/a**0.5\\n k1=1.0*a**0.5\\n power=[]\\n kk=[]\\n while(k1 <= m[0]//2):\\n kk.append((k0*k1)**0.5)\\n w=np.where((k>k0) & (k <= k1))\\n power.append(ft2[w].sum())\\n k0=k1\\n k1=k1*a\\n pl.loglog(kk,power,*kw)\",\n \"def _powerperdegree(self):\\n return SHPowerSpectrum(self.coeffs)\",\n \"def spectrum(ind, power, epower=None, ind_fit=None, power_fit=None,\\n fit_label=\\\"Fit\\\", show=True, savepath=None):\\n plt.errorbar(ind, power, yerr=epower, fmt='k.', capsize=2, alpha=0.2,\\n zorder=0, label=\\\"WMAP data\\\")\\n if power_fit is not None and ind_fit is not None:\\n plt.plot(ind_fit, power_fit, 'r-', label=fit_label, zorder=1)\\n plt.legend()\\n if savepath is not None:\\n plt.savefig(savepath)\\n if show:\\n plt.show()\\n else:\\n plt.close()\",\n \"def fourier(img):\\n return fourierCV(img)\",\n \"def op(self, img):\\n return self._mask * np.fft.fft2(img, norm=\\\"ortho\\\")\",\n \"def PowerSpectralDensity(f):\\n sky_averaging_constant = (20/3) # Sky Averaged <--- I got this from Jonathan's notes but I need\\n # to check where he got it...\\n L = 2.5*10**9 # Length of LISA arm\\n f0 = 19.09*10**-3 \\n\\n Poms = ((1.5*10**-11)**2)*(1 + ((2*10**-3)/f)**4) # Optical Metrology Sensor\\n Pacc = (3*10**-15)**2*(1 + (4*10**-3/(10*f))**2)*(1 + (f/(8*10**-3))**4) # Acceleration Noise\\n Sc = 9*10**(-45)*f**(-7/3)*np.exp(-f**0.171 + 292*f*np.sin(1020*f)) * (1 \\\\\\n + np.tanh(1680*(0.00215 - f))) \\n\\n PSD = (sky_averaging_constant)* ((10/(3*L**2))*(Poms + (4*Pacc)/((2*np.pi*f))**4)*(1 + 0.6*(f/f0)**2) + Sc) # PSD\\n return PSD\",\n \"def _powerperdegree(self):\\n return SHCPowerSpectrum(self.coeffs)\",\n \"def energy(image):\\n #create sobel operators for gradient calculation\\n sobel_x = np.array([\\n [-1, 0, 1],\\n [-2, 0, 2],\\n [-1, 0, 1]])\\n sobel_y = np.array([\\n [-1, -2, -1],\\n [0, 0, 0],\\n [1, 2, 1]])\\n #calculate gradients\\n gradients_x = derive(image, sobel_x)\\n gradients_y = derive(image, sobel_y)\\n #calculate energy as defined in Avidan et al.\\n energy_result = np.abs(gradients_x) + np.abs(gradients_y)\\n return energy_result\",\n \"def specPlot(image, qplot=True):\\n # Load image, get dimensions\\n im = readImage(image)\\n L, W = im.shape\\n xmin, xmax = (-W//2, W//2)\\n ymin, ymax = (-L//2, L//2)\\n xrng = range(xmin, xmax)\\n yrng = range(ymin, ymax)\\n\\n # Calculate power spectrum\\n spec = np.abs(fftshift(fft2(im)))**2\\n\\n # Calculate rotational average of spectrum\\n [fx, fy] = np.meshgrid(xrng, yrng)\\n sf = (np.sqrt(fx**2 + fy**2)).round().astype(int).flatten()\\n rot_spec = np.bincount(sf, weights=spec.flatten()) / np.bincount(sf)\\n rot_spec = rot_spec[1:min(L,W)//2]\\n\\n # Make a plot if requested\\n if qplot:\\n fig1, ax1 = plt.subplots()\\n h = ax1.imshow(np.log10(spec), extent=[xmin, xmax, ymin, ymax])\\n ax1.axis('off')\\n cb = fig1.colorbar(h)\\n cb.set_label(r'$\\\\log_{10}$(Energy)')\\n\\n fig2, ax2 = plt.subplots()\\n ax2.loglog(np.arange(1, len(rot_spec)+1), rot_spec)\\n ax2.set_xlabel('Spatial frequency (cycles/image)')\\n ax2.set_ylabel('Energy')\\n\\n # Return\\n if qplot:\\n return spec, rot_spec, (fig1, ax1), (fig2, ax2)\\n else:\\n return spec, rot_spec\",\n \"def calc_psf_fwhm_inpix_moffat(arr):\\n\\tmodel = fit_moffat(arr)\\n\\n\\tfwhm = 2.* model.gamma * np.sqrt( 2.**(1./model.alpha) - 1. )\\n\\n\\treturn fwhm\",\n \"def calc_psd2d(self):\\n print(\\\"Calculating 2D power spectral density ... \\\", end=\\\"\\\", flush=True)\\n rows, cols = self.shape\\n imgf = np.fft.fftshift(np.fft.fft2(self.image))\\n # NOTE: normalize w.r.t. image size\\n norm = rows * cols * self.pixel[0]**2\\n self.psd2d = (np.abs(imgf) ** 2) / norm\\n print(\\\"DONE\\\", flush=True)\\n return self.psd2d\",\n \"def get_power_spectrum(self, cosmo, nz1, nz2):\\n t1 = ccl.WeakLensingTracer(cosmo, nz1)\\n t2 = ccl.WeakLensingTracer(cosmo, nz2)\\n return ccl.angular_cl(cosmo, t1, t2, self.ells)\",\n \"def plot_power_spectrum_fit(img_ft, otf, options, pfit, frq_sim=None, mask=None, figsize=(20, 10), ttl_str=\\\"\\\"):\\n\\n if frq_sim is None:\\n frq_sim = [0, 0]\\n\\n if mask is None:\\n mask = np.ones(img_ft.shape, dtype=np.bool)\\n\\n # get physical data\\n dx = options['pixel_size']\\n dy = dx\\n wavelength = options['wavelength']\\n na = options['na']\\n fmax = 1 / (0.5 * wavelength / na)\\n\\n # get frequency data\\n fxs = tools.get_fft_frqs(img_ft.shape[1], dx)\\n fys = tools.get_fft_frqs(img_ft.shape[0], dy)\\n fxfx, fyfy = np.meshgrid(fxs, fys)\\n ff_shift = np.sqrt((fxfx - frq_sim[0]) ** 2 + (fyfy - frq_sim[1]) ** 2)\\n\\n # get experimental and theoretical visualization\\n ps_exp = np.abs(img_ft)**2\\n ps_fit = power_spectrum_fn(pfit, ff_shift, otf)\\n ps_fit_no_otf = power_spectrum_fn(list(pfit[:-1]) + [0], ff_shift, 1)\\n\\n # wienier filter, i.e. \\\"divide\\\" by otf\\n snr = ps_fit_no_otf / pfit[-1]\\n wfilter = np.abs(otf)**2 / (np.abs(otf)**4 + 1 / snr**2)\\n ps_exp_deconvolved = ps_exp * wfilter\\n ps_fit_deconvolved = ps_fit * wfilter\\n\\n # ######################\\n # plot results\\n # ######################\\n extent = tools.get_extent(fys, fxs)\\n\\n fig = plt.figure(figsize=figsize)\\n grid = plt.GridSpec(2, 10)\\n\\n sttl_str = \\\"\\\"\\n if ttl_str != \\\"\\\":\\n sttl_str += \\\"%s\\\\n\\\" % ttl_str\\n sttl_str += 'm=%0.3g, A=%0.3g, alpha=%0.3f\\\\nnoise=%0.3g, frq sim = (%0.2f, %0.2f) 1/um' % \\\\\\n (pfit[-2], pfit[0], pfit[1], pfit[-1], frq_sim[0], frq_sim[1])\\n plt.suptitle(sttl_str)\\n\\n # ######################\\n # plot power spectrum x otf vs frequency in 1D\\n # ######################\\n ax1 = plt.subplot(grid[0, :5])\\n\\n ax1.semilogy(ff_shift.ravel(), ps_exp.ravel(), 'k.')\\n ax1.semilogy(ff_shift[mask].ravel(), ps_exp[mask].ravel(), 'b.')\\n ax1.semilogy(ff_shift.ravel(), ps_fit.ravel(), 'r')\\n\\n ylims = ax1.get_ylim()\\n ax1.set_ylim([ylims[0], 1.2 * np.max(ps_exp[mask].ravel())])\\n\\n ax1.set_xlabel('frequency (1/um)')\\n ax1.set_ylabel('power spectrum')\\n ax1.legend(['all data', 'data used to fit', 'fit'], loc=\\\"upper right\\\")\\n ax1.title.set_text('m^2 A^2 |f|^{-2*alpha} |otf(f)|^2 + N')\\n\\n # ######################\\n # plot az avg divided by otf in 1D\\n # ######################\\n ax2 = plt.subplot(grid[0, 5:])\\n\\n ax2.semilogy(ff_shift.ravel(), ps_exp_deconvolved.ravel(), 'k.')\\n ax2.semilogy(ff_shift[mask].ravel(), ps_exp_deconvolved[mask].ravel(), 'b.')\\n ax2.semilogy(ff_shift.ravel(), ps_fit_no_otf.ravel(), 'g')\\n\\n ylims = ax1.get_ylim()\\n ax1.set_ylim([ylims[0], 1.2 * np.max(ps_exp_deconvolved[mask].ravel())])\\n\\n ax2.title.set_text('m^2 A^2 |k|^{-2*alpha} |otf|^4/(|otf|^4 + snr^2)')\\n ax2.set_xlabel('|f - f_sim| (1/um)')\\n ax2.set_ylabel('power spectrum')\\n\\n # ######################\\n # plot 2D power spectrum\\n # ######################\\n ax3 = plt.subplot(grid[1, :2])\\n\\n ax3.imshow(ps_exp, interpolation=None, norm=PowerNorm(gamma=0.1), extent=extent)\\n\\n circ = matplotlib.patches.Circle((0, 0), radius=fmax, color='k', fill=0, ls='--')\\n ax3.add_artist(circ)\\n\\n circ2 = matplotlib.patches.Circle((frq_sim[0], frq_sim[1]), radius=fmax, color='k', fill=0, ls='--')\\n ax3.add_artist(circ2)\\n\\n ax3.set_xlabel('fx (1/um)')\\n ax3.set_ylabel('fy (1/um)')\\n ax3.title.set_text('raw power spectrum')\\n\\n # ######################\\n # 2D fit\\n # ######################\\n ax5 = plt.subplot(grid[1, 2:4])\\n ax5.imshow(ps_fit, interpolation=None, norm=PowerNorm(gamma=0.1), extent=extent)\\n\\n circ = matplotlib.patches.Circle((0, 0), radius=fmax, color='k', fill=0, ls='--')\\n ax5.add_artist(circ)\\n\\n circ2 = matplotlib.patches.Circle((frq_sim[0], frq_sim[1]), radius=fmax, color='k', fill=0, ls='--')\\n ax5.add_artist(circ2)\\n\\n ax5.set_xlabel('fx (1/um)')\\n ax5.set_ylabel('fy (1/um)')\\n ax5.title.set_text('2D fit')\\n\\n # ######################\\n # plot 2D power spectrum divided by otf\\n # ######################\\n ax4 = plt.subplot(grid[1, 4:6])\\n # ps_over_otf[mask == 0] = np.nan\\n ax4.imshow(ps_exp_deconvolved, interpolation=None, norm=PowerNorm(gamma=0.1), extent=extent)\\n\\n circ = matplotlib.patches.Circle((0, 0), radius=fmax, color='k', fill=0, ls='--')\\n ax4.add_artist(circ)\\n\\n circ2 = matplotlib.patches.Circle((frq_sim[0], frq_sim[1]), radius=fmax, color='k', fill=0, ls='--')\\n ax4.add_artist(circ2)\\n\\n ax4.set_xlabel('fx (1/um)')\\n ax4.set_ylabel('fy (1/um)')\\n ax4.title.set_text('deconvolved power spectrum')\\n\\n # ######################\\n # plot 2D power spectrum divided by otf with masked region\\n # ######################\\n ax4 = plt.subplot(grid[1, 6:8])\\n # ps_over_otf[mask == 0] = np.nan\\n ps_exp_deconvolved[mask == 0] = np.nan\\n ax4.imshow(ps_exp_deconvolved, interpolation=None, norm=PowerNorm(gamma=0.1), extent=extent)\\n\\n circ = matplotlib.patches.Circle((0, 0), radius=fmax, color='k', fill=0, ls='--')\\n ax4.add_artist(circ)\\n\\n circ2 = matplotlib.patches.Circle((frq_sim[0], frq_sim[1]), radius=fmax, color='k', fill=0, ls='--')\\n ax4.add_artist(circ2)\\n\\n ax4.set_xlabel('fx (1/um)')\\n ax4.set_ylabel('fy (1/um)')\\n ax4.title.set_text('masked, deconvolved power spectrum')\\n\\n #\\n ax4 = plt.subplot(grid[1, 8:])\\n ax4.imshow(ps_fit_deconvolved, interpolation=None, norm=PowerNorm(gamma=0.1), extent=extent)\\n\\n circ = matplotlib.patches.Circle((0, 0), radius=fmax, color='k', fill=0, ls='--')\\n ax4.add_artist(circ)\\n\\n circ2 = matplotlib.patches.Circle((frq_sim[0], frq_sim[1]), radius=fmax, color='k', fill=0, ls='--')\\n ax4.add_artist(circ2)\\n\\n ax4.set_xlabel('fx (1/um)')\\n ax4.set_ylabel('fy (1/um)')\\n ax4.title.set_text('fit deconvolved')\\n\\n return fig\",\n \"def DFT2(image):\\n full_dft2 = DFT(DFT(image.transpose()).transpose())\\n return full_dft2.astype(np.complex128)\",\n \"def plasma_source_spectrum(energies, N0, T):\\n return N0*numpy.exp(-energies/T)\",\n \"def overf_power_spectrum(amp, index, f0, dt, n, cut_off=0):\\n \\n if cut_off < 0:\\n raise ValueError(\\\"Low frequency cut off must not be negative.\\\")\\n # Sometimes the fitting routines do something weird that causes\\n # an overflow from a ridiculous index. Limit the index.\\n index = max(index, -20)\\n # Get the frequencies represented in the FFT.\\n df = 1.0/dt/n\\n freq = sp.arange(n, dtype=float)\\n freq[n//2+1:] -= freq[-1] + 1\\n freq = abs(freq)*df\\n # 0th (mean) mode is meaningless has IR divergence. Deal with it later (in\\n # the cut off.\\n freq[0] = 1\\n # Make the power spectrum.\\n power = (freq/f0)**index\\n power *= amp\\n # Restore frequency of mean mode.\\n freq[0] = 0\\n # Find the power just above the cut off frequency.\\n p_cut = power[sp.amin(sp.where(freq > cut_off)[0])]\\n # Flatten off the power spectrum.\\n power[freq <= cut_off] = p_cut\\n return power\",\n \"def power(self):\\n return irradiance_on_plane(self.vnorm, self.h,\\n self.date, self.lat) * self.s * self.eff\",\n \"def compute_absolute_power(freqs, powers, band, method='sum'):\\n\\n _, band_powers = trim_spectrum(freqs, powers, band)\\n abs_power = get_avg_func(method)(band_powers)\\n\\n return abs_power\",\n \"def calc_psf_fwhm_inpix_gaussian(arr):\\n\\tmodel = fit_gaussian(arr)\\n\\n\\tsigma = max(model.y_stddev, model.x_stddev)\\n\\tfwhm = 2.355 * sigma\\n\\n\\treturn fwhm\",\n \"def field_strength_to_power_flux(field: float) -> float:\\n\\n power = np.float_power(np.abs(field), 2)\\n power *= (0.5 * speed_of_light * epsilon_0)\\n\\n return power\",\n \"def powerlaw(E,alpha,A):\\n\\n\\treturn A*E**alpha\",\n \"def spectrum(self, wl: Union[float, ndarray]) -> Union[float, ndarray]:\\n wlm = wl * 1e-9 # Wavelength to meters\\n return 3.74183e-16 * wlm ** -5. / (np.exp(0.014388 / (wlm * self.temp)) - 1.)\",\n \"def convolve_power(power_spectrum, window_power, axis=-1):\\n \\n data_corr = fft.ifft(power_spectrum, axis=axis)\\n window_corr = fft.ifft(window_power, axis=axis)\\n true_corr = data_corr * window_corr\\n true_power = fft.fft(true_corr, axis=axis, overwrite_x=True)\\n return true_power\",\n \"def spectral(w, s=1.0):\\n n_in, n_out = w.size()\\n n = max(n_out, n_in)\\n gain = s / math.sqrt(n)\\n return w.normal_(0, 1).mul_(gain)\",\n \"def spectrum(self,photon_energy):\\n\\n Eph = _validate_ene(photon_energy)\\n\\n spec = self.n0 * (self.weight_ee * self._emiss_ee(Eph)\\n + self.weight_ep * self._emiss_ep(Eph))\\n\\n return spec\",\n \"def get_spectrum(self, shcoeffs, nwins):\\n for itaper in range(nwins):\\n tapercoeffs = self._coeffs(itaper)\\n modelcoeffs = shcoeffs.get_coeffs(normalization='4pi',kind='real')\\n coeffs = SHMultiply(tapercoeffs,modelcoeffs)\",\n \"def spectrum(G):\\n\\n try:\\n return eigvalsh(G.todense())\\n except ValueError:\\n return eigvalsh(G)\",\n \"def calc_power(field):\\r\\n\\r\\n poynt_in_points = 0.5*numpy.real(field.p * numpy.conj(field.vn))\\r\\n power = numpy.sum(poynt_in_points)\\r\\n power *= field.one_pixel_area\\r\\n return power\",\n \"def bin_power(X,Band,Fs):\\n\\n\\tC = fft(X)\\n\\tC = abs(C)\\n\\tPower =np.zeros(len(Band)-1);\\n\\tfor Freq_Index in range(0,len(Band)-1):\\n\\t\\tFreq = float(Band[Freq_Index])\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t## Xin Liu\\n\\t\\tNext_Freq = float(Band[Freq_Index+1])\\n\\t\\tPower[Freq_Index] = sum(C[np.floor(Freq/Fs*len(X)):np.floor(Next_Freq/Fs*len(X))])\\n\\tPower_Ratio = Power/sum(Power)\\n\\treturn Power, Power_Ratio\",\n \"def deconvolve_power(power_spectrum, window_power, axis=-1):\\n \\n data_corr = fft.ifft(power_spectrum, axis=axis)\\n window_corr = fft.ifft(window_power, axis=axis)\\n true_corr = data_corr / window_corr\\n true_power = fft.fft(true_corr, axis=axis, overwrite_x=True)\\n return true_power\",\n \"def compute_power_spectrum(self, k, p, smooth=False, for_corr=False, data_name=None, nopoly=False):\\n\\n # Get the basic power spectrum components\\n if self.kvals is None or self.pksmooth is None or self.pkratio is None:\\n ks = self.camb.ks\\n pk_smooth_lin, pk_ratio = self.compute_basic_power_spectrum(p[\\\"om\\\"])\\n else:\\n ks = self.kvals\\n pk_smooth_lin, pk_ratio = self.pksmooth, self.pkratio\\n\\n # We split for isotropic and anisotropic here for consistency with our previous isotropic convention, which\\n # differs from our implementation of the Beutler2017 isotropic model quite a bit. This results in some duplication\\n # of code and a few nested if statements, but it's perhaps more readable and a little faster (because we only\\n # need one interpolation for the whole isotropic monopole, rather than separately for the smooth and wiggle components)\\n\\n if not for_corr:\\n if \\\"b{0}\\\" not in p:\\n p = self.deal_with_ndata(p, 0)\\n\\n if self.isotropic:\\n pk = [np.zeros(len(k))]\\n kprime = k if for_corr else k / p[\\\"alpha\\\"]\\n if self.dilate_smooth:\\n pk_smooth = splev(kprime, splrep(ks, pk_smooth_lin)) / (1.0 + kprime**2 * p[\\\"sigma_s\\\"] ** 2 / 2.0) ** 2\\n else:\\n pk_smooth = splev(k, splrep(ks, pk_smooth_lin)) / (1.0 + k**2 * p[\\\"sigma_s\\\"] ** 2 / 2.0) ** 2\\n if not for_corr:\\n pk_smooth *= p[\\\"b{0}\\\"]\\n\\n # Volume factor\\n pk_smooth /= p[\\\"alpha\\\"] ** 3\\n\\n if smooth:\\n propagator = np.ones(len(kprime))\\n else:\\n # Compute the propagator\\n C = np.exp(-0.5 * kprime**2 * p[\\\"sigma_nl\\\"] ** 2)\\n propagator = 1.0 + splev(kprime, splrep(ks, pk_ratio)) * C\\n prefac = np.ones(len(kprime)) if smooth else propagator\\n\\n if for_corr or nopoly:\\n poly = None\\n pk[0] = pk_smooth * propagator\\n else:\\n shape, poly = self.add_poly(k, k, p, prefac, pk_smooth)\\n if not self.marg:\\n pk[0] = (pk_smooth + shape) * propagator\\n\\n else:\\n\\n epsilon = 0 if for_corr else p[\\\"epsilon\\\"]\\n kprime = np.tile(k, (self.nmu, 1)).T if for_corr else np.outer(k / p[\\\"alpha\\\"], self.get_kprimefac(epsilon))\\n muprime = self.mu if for_corr else self.get_muprime(epsilon)\\n if self.dilate_smooth:\\n fog = 1.0 / (1.0 + muprime**2 * kprime**2 * p[\\\"sigma_s\\\"] ** 2 / 2.0) ** 2\\n reconfac = splev(kprime, splrep(self.camb.ks, self.camb.smoothing_kernel)) if self.recon_type.lower() == \\\"iso\\\" else 0.0\\n kaiser_prefac = 1.0 + p[\\\"beta\\\"] * muprime**2 * (1.0 - reconfac)\\n pk_smooth = kaiser_prefac**2 * splev(kprime, splrep(ks, pk_smooth_lin))\\n else:\\n ktile = np.tile(k, (self.nmu, 1)).T\\n fog = 1.0 / (1.0 + muprime**2 * ktile**2 * p[\\\"sigma_s\\\"] ** 2 / 2.0) ** 2\\n reconfac = splev(ktile, splrep(self.camb.ks, self.camb.smoothing_kernel)) if self.recon_type.lower() == \\\"iso\\\" else 0.0\\n kaiser_prefac = 1.0 + p[\\\"beta\\\"] * muprime**2 * (1.0 - reconfac)\\n pk_smooth = kaiser_prefac**2 * splev(ktile, splrep(ks, pk_smooth_lin))\\n\\n if not for_corr:\\n pk_smooth *= p[\\\"b{0}\\\"]\\n\\n # Volume factor\\n pk_smooth /= p[\\\"alpha\\\"] ** 3\\n\\n # Compute the propagator\\n if smooth:\\n pk2d = pk_smooth * fog\\n else:\\n C = np.exp(-0.5 * kprime**2 * (muprime**2 * p[\\\"sigma_nl_par\\\"] ** 2 + (1.0 - muprime**2) * p[\\\"sigma_nl_perp\\\"] ** 2))\\n pk2d = pk_smooth * (fog + splev(kprime, splrep(ks, pk_ratio)) * C)\\n\\n pk0, pk2, pk4 = self.integrate_mu(pk2d)\\n\\n # Polynomial shape\\n pk = [pk0, np.zeros(len(k)), pk2, np.zeros(len(k)), pk4, np.zeros(len(k))]\\n\\n if for_corr or nopoly:\\n poly = None\\n kprime = k\\n else:\\n shape, poly = self.add_poly(k, k, p, np.ones(len(k)), pk)\\n if self.marg:\\n pk = [np.zeros(len(k))] * 6\\n else:\\n for pole in self.poly_poles:\\n pk[pole] += shape[pole]\\n\\n return kprime, pk, poly\",\n \"def cpsd(self):\\r\\n self.welch_method = self.method\\r\\n self.welch_method['this_method'] = 'welch'\\r\\n self.welch_method['Fs'] = self.input.sampling_rate\\r\\n f, spectrum_welch = tsa.get_spectra(self.input.data,\\r\\n method=self.welch_method)\\r\\n\\r\\n return f, spectrum_welch\",\n \"def signal_spectral(signal, FS):\\n # check inputs\\n if signal is None or signal == []:\\n print(\\\"Signal is empty.\\\")\\n\\n # ensure numpy\\n signal = np.array(signal)\\n # f, spectrum = st.welch_spectrum(signal, sampling_rate=FS)\\n spectrum = np.fft.fft(signal, FS)[:len(signal)//2]\\n f = np.fft.fftfreq(len(signal))[:len(signal)//2]\\n\\n cum_ff = np.cumsum(spectrum)\\n spect_diff = np.diff(spectrum)\\n #energy, _ = st.signal_energy(spectrum, f)[:]\\n\\n args, names = [], []\\n\\n if dict['spectral_maxpeaks']['use'] == 'yes':\\n # spectral_maxpeaks\\n try:\\n spectral_maxpeaks = np.sum([1 for nd in range(len(spect_diff[:-1])) if (spect_diff[nd+1]<0 and spect_diff[nd]>0)])\\n except:\\n spectral_maxpeaks = None\\n args += [spectral_maxpeaks]\\n names += ['spectral_maxpeaks']\\n\\n # if dict['spect_var']['use'] == 'yes':\\n # # spect_variation\\n # try:\\n # spect_var = np.convolve(energy)\\n # spect_var /= np.max(np.abs(spect_var))\\n # except:\\n # spect_var = None\\n # args += [spect_var]\\n # names += ['spect_var']\\n\\n if dict['curve_distance']['use'] == 'yes':\\n # curve_distance\\n try:\\n curve_distance = np.sum(np.linspace(0, cum_ff[-1], len(cum_ff)) - cum_ff)\\n except:\\n curve_distance = None\\n args += [curve_distance]\\n names += ['curve_distance']\\n\\n if dict['spectral_roll_off']['use'] == 'yes':\\n # spectral_roll_off\\n try:\\n spectral_roll_off = spectral_roll(f, spectrum, cum_ff, 0.95)[0]\\n except:\\n spectral_roll_off = None\\n args += [spectral_roll_off]\\n names += ['spectral_roll_off']\\n\\n if dict['spectral_roll_on']['use'] == 'yes':\\n # spectral_roll_on\\n try:\\n spectral_roll_on = spectral_roll(f, spectrum, cum_ff, 0.05)[0]\\n except:\\n spectral_roll_on = None\\n args += [spectral_roll_on]\\n names += ['spectral_roll_on']\\n\\n if dict['spectral_dec']['use'] == 'yes':\\n # spectral_decrease\\n try:\\n spectral_dec = (1/np.sum(spectrum)) * np.sum((spectrum[:] - spectrum[1])/np.linspace(1, len(spectrum), len(spectrum),1))\\n except:\\n spectral_dec = None\\n args += [spectral_dec]\\n names += ['spectral_dec']\\n\\n if dict['spectral_slope']['use'] == 'yes':\\n # spectral_slope\\n sum_f = np.sum(f)\\n len_f = len(f)\\n try:\\n spectral_slope = (len_f * np.dot(f, spectrum) - sum_f * np.sum(spectrum)) / (len_f * np.dot(f, f) - sum_f ** 2)\\n except:\\n spectral_slope = None\\n args += [spectral_slope]\\n names += ['spectral_slope']\\n\\n sum_spectrum = np.sum(spectrum)\\n norm_spectrum = spectrum / sum_spectrum\\n # spectral_centroid\\n try:\\n spectral_centroid = np.dot(f, norm_spectrum)\\n except:\\n spectral_centroid = None\\n\\n # spectral_spread\\n try:\\n spectral_spread = np.dot(((f - spectral_centroid) ** 2), norm_spectrum)\\n except:\\n spectral_spread = None\\n\\n if dict['spectral_spread']['use'] == 'yes':\\n args += [spectral_spread]\\n names += ['spectral_spread']\\n\\n if dict['spectral_kurtosis']['use'] == 'yes':\\n # spectral_kurtosis\\n try:\\n spectral_kurtosis = np.sum(((f - spectral_centroid) ** 4) * norm_spectrum) / (spectral_spread**2)\\n except:\\n spectral_kurtosis = None\\n args += [spectral_kurtosis]\\n names += ['spectral_kurtosis']\\n\\n if dict['spectral_skewness']['use'] == 'yes':\\n # spectral_skewness\\n try:\\n spectral_skewness = np.sum(((f - spectral_centroid) ** 3) * norm_spectrum) / (spectral_spread ** (3 / 2))\\n except:\\n spectral_skewness = None\\n args += [spectral_skewness]\\n names += ['spectral_skewness']\\n\\n if dict['max_frequency']['use'] == 'yes':\\n # max_frequency\\n try:\\n max_frequency = f[np.where(cum_ff > cum_ff[-1]*0.95)[0][0]]\\n except:\\n max_frequency = None\\n args += [max_frequency]\\n names += ['max_frequency']\\n\\n if dict['fundamental_frequency']['use'] == 'yes':\\n # fundamental_frequency\\n try:\\n fundamental_frequency = f[np.where(cum_ff > cum_ff[-1]*0.5)[0][0]]\\n except:\\n fundamental_frequency = None\\n args += [fundamental_frequency]\\n names += ['fundamental_frequency']\\n\\n # if dict['max_power_spectrum']['use'] == 'yes':\\n # # max_power_spectrum\\n # try:\\n # max_power_spectrum = np.max(spectrum)\\n # except:\\n # max_power_spectrum = None\\n # args += max_power_spectrum\\n # names += 'max_power_spectrum'\\n\\n # if dict['mean_power_spectrum']['use'] == 'yes':\\n # # mean_power_spectrum\\n # try:\\n # mean_power_spectrum = np.mean(spectrum)\\n # except:\\n # mean_power_spectrum = None\\n # args += mean_power_spectrum\\n # names += 'mean_power_spectrum'\\n #\\n # if dict['spectral_skewness']['use'] == 'yes':\\n # try:\\n # spectral_skewness = np.mean(spectrum)\\n # except:\\n # spectral_skewness = None\\n # args += spectral_skewness\\n # names += 'spectral_skewness'\\n #\\n # if dict['spectral_kurtosis']['use'] == 'yes':\\n # try:\\n # spectral_kurtosis = np.mean(spectrum)\\n # except:\\n # spectral_kurtosis = None\\n # args += spectral_kurtosis\\n # names += 'spectral_kurtosis'\\n\\n # if dict['spectral_hist_']['use'] == 'yes':\\n # # histogram\\n # try:\\n # _hist = list(np.histogram(spectrum, bins=int(np.sqrt(len(spectrum))), density=True)[0])\\n # except:\\n # if len(signal) > 1:\\n # _hist = [None] * int(np.sqrt(len(signal)))\\n # else:\\n # _hist = [None]\\n # args += [i for i in _hist]\\n # names += ['spectral_hist_' + str(i) for i in range(len(_hist))]\\n\\n #return utils.ReturnTuple(tuple(args), tuple(names))\\n return args, names\",\n \"def energy_map(img):\\n img_new = img.astype(float) #converting image to float\\n total_energy = 0.0 # To store the sum of energy for all channels\\n r,c,d = img.shape \\n for i in range(d):\\n dy = np.zeros([r, c], dtype=float) \\n dx = np.zeros([r, c], dtype=float)\\n if r > 1:\\n dy = np.gradient(img_new[:,:,i], axis=0) #gradient along rows\\n if c > 1:\\n dx = np.gradient(img_new[:,:,i], axis=1) #gradient along columns\\n total_energy += np.absolute(dy) + np.absolute(dx) \\n return total_energy #Total energy map for entire image\",\n \"def spectrum2D(path):\\n image = cv2.imread(path,0)\\n fftOriginal = np.fft.fft2(image)\\n shiftedFFT = np.fft.fftshift(fftOriginal)\\n shiftedFFTMagnitude = np.abs(shiftedFFT)\\n ## AVERAGE RADIAL\\n rows = image.shape[0]\\n cols = image.shape[1]\\n midRow = rows/2+1\\n midCol = cols/2+1\\n maxRadius = math.ceil(np.sqrt((midRow+2)**2 + (midCol+2)**2))\\n radialProfile = np.zeros((maxRadius, 1))\\n count = np.zeros((maxRadius, 1))\\n for i in range(cols):\\n for j in range(rows):\\n radius = np.sqrt((j-midRow)**2 + (i-midCol)**2)\\n thisIndex = math.ceil(radius) + 1\\n radialProfile[thisIndex] = radialProfile[thisIndex] + shiftedFFTMagnitude[j,i]\\n count[thisIndex] = count[thisIndex] + 1\\n radialProfile_new = radialProfile / count\\n for k in range(radialProfile_new.shape[0]):\\n if math.isnan(radialProfile_new[k]):\\n radialProfile_new[k] = 0.\\n return radialProfile_new\",\n \"def integrate_spectrum(self):\\n flux = sum(self.spectrum)\\n return flux\",\n \"def FFTpowerdensity(signal, timestep):\\n \\n # Compute the FFT of a real-valued signal. If N is the number \\n # of points of the original signal, 'Nfreq' is (N/2+1).\\n \\n fourier = np.fft.rfft(signal)\\n Ntime = len(signal)\\n Nfreq = len(fourier)\\n \\n # Compute the power density\\n \\n powerdensity = np.abs(fourier)**2 / Ntime * timestep\\n \\n # Compute the frequency array.\\n # First compute an equidistant array that goes from 0 to 1 (included),\\n # with in total as many points as in the 'fourier' array.\\n # Then rescale the array that it goes from 0 to the Nyquist frequency\\n # which is 0.5/timestep\\n \\n freq = np.arange(float(Nfreq)) / (Nfreq-1) * 0.5 / timestep\\n \\n # That's it!\\n \\n return (freq, powerdensity)\",\n \"def compute_intensity(self, data):\\n # Compute RMS intensity (as float to prevent overflow).\\n data = self.audio_resample(data.astype(np.float32)**2)**0.5\\n\\n # Apply dynamic range compression.\\n return data**self._exponent\",\n \"def get_spectrum(self, path):\\n raise NotImplementedError\",\n \"def square_window_spectrum(N, Fs):\\r\\n f = get_freqs(Fs, N - 1)\\r\\n j = 0 + 1j\\r\\n a = -j * (N - 1) * f / 2\\r\\n b = np.sin(N * f / 2.0)\\r\\n c = np.sin(f / 2.0)\\r\\n make = np.exp(a) * b / c\\r\\n\\r\\n return f, make[1:] / make[1]\",\n \"def power(source : Image, destination : Image = None, exponent : float = 1) -> Image:\\n\\n\\n parameters = {\\n \\\"src\\\":source,\\n \\\"dst\\\": destination,\\n \\\"exponent\\\":float(exponent)\\n }\\n\\n execute(__file__, '../clij-opencl-kernels/kernels/power_' + str(len(destination.shape)) + 'd_x.cl', 'power_' + str(len(destination.shape)) + 'd', destination.shape, parameters)\\n return destination\",\n \"def apply_smoothstep(image):\\n image_out = 3 * image**2 - 2 * image**3\\n return image_out\",\n \"def DFTpower(time, signal, f0=None, fn=None, df=None, full_output=False):\\n\\n freqs = np.arange(f0,fn,df)\\n Ntime = len(time)\\n Nfreq = int(np.ceil((fn-f0)/df))\\n \\n A = np.exp(1j*2.*pi*f0*time) * signal\\n B = np.exp(1j*2.*pi*df*time)\\n ft = np.zeros(Nfreq, complex) \\n ft[0] = A.sum()\\n for k in range(1,Nfreq):\\n A *= B\\n ft[k] = np.sum(A)\\n \\n if full_output:\\n return freqs,ft**2*4.0/Ntime**2\\n else:\\n return freqs,(ft.real**2 + ft.imag**2) * 4.0 / Ntime**2\",\n \"def smoothSpectrum(f, X_f, r_oct):\\n X_f_out = np.zeros(np.shape(X_f))\\n for n in range(np.shape(f)[0]):\\n # standard deviation\\n sigma = f[n] / r_oct / np.pi\\n # Gaussian window with the center frequnecy f[n] an dstandard deviation\\n w = np.exp( -(f-f[n])**2 / (2*sigma**2) )\\n w = w / np.sum(w, axis=0)\\n X_f_out[n] = np.sum(w * X_f)\\n \\n return X_f_out\",\n \"def power_flux_to_field_strength(power: float) -> float:\\n\\n field_strength = (2 * power) / (speed_of_light * epsilon_0)\\n field_strength = np.sqrt(field_strength)\\n\\n return field_strength\",\n \"def spectral_brightness(data, fft_data):\\n spec = np.abs(fft_data)\\n weight_vec = np.log(np.linspace(1, 100, len(fft_data)))\\n weight_vec = np.pi*weight_vec/weight_vec[-1]\\n weight = np.cos(weight_vec)/2 + 0.5\\n low_spec_sum = sum(spec*weight)\\n high_spec_sum = sum(spec*(1-weight))\\n if low_spec_sum == 0:\\n return 1 # attention: if signal is a sine at fs/2 this is also hit\\n return high_spec_sum/low_spec_sum\",\n \"def chebyshev_coeffs(f, n):\\n extrema = np.cos((np.pi * np.arange(2*n))/n)\\n samples = f(extrema)\\n coeffs = np.real(np.fft.fft(samples))[:n+1]\\n coeffs /= n \\n coeffs[0] /= 2\\n coeffs[n] /= 2\\n\\n return coeffs\",\n \"def fft_image(shape, sd=None, decay_power=1):\\n sd = sd or 0.01\\n # batch, h, w, ch = shape # tf style: [N, H, W, C]\\n batch, ch, h, w = shape # torch style: [N, C, H, W]\\n \\n freqs = rfft2d_freqs(h, w)\\n init_val_size = (2, batch, ch) + freqs.shape\\n\\n init_val = np.random.normal(size=init_val_size, scale=sd).astype(np.float32)\\n # spectrum_real_imag_t = tf.Variable(init_val)\\n spectrum_real_imag_t = Variable(torch.from_numpy(init_val))\\n\\n # spectrum_t = tf.complex(spectrum_real_imag_t[0], spectrum_real_imag_t[1])\\n spectrum_t = torch.complex(spectrum_real_imag_t[0], spectrum_real_imag_t[1])\\n\\n # Scale the spectrum. First normalize energy, then scale by the square-root\\n # of the number of pixels to get a unitary transformation.\\n # This allows to use similar leanring rates to pixel-wise optimisation.\\n scale = 1.0 / np.maximum(freqs, 1.0 / max(w, h)) ** decay_power\\n scale *= np.sqrt(w * h)\\n print(scale.shape, spectrum_t.shape)\\n scaled_spectrum_t = torch.from_numpy(scale) * spectrum_t\\n\\n # convert complex scaled spectrum to shape (h, w, ch) image tensor\\n # needs to transpose because irfft2d returns channels first\\n # image_t = tf.transpose(tf.spectral.irfft2d(scaled_spectrum_t), (0, 2, 3, 1))\\n image_t = torch.fft.irfft(scaled_spectrum_t) # shape: [N, C, H, W]\\n\\n # in case of odd spatial input dimensions we need to crop\\n # image_t = image_t[:batch, :h, :w, :ch] # tf style\\n image_t = image_t[:batch, :ch, :h, :w] # torch style\\n image_t = image_t / 4.0 # TODO: is that a magic constant?\\n return image_t\",\n \"def power(x): \\r\\n return x(1)\",\n \"def power_method(mat, start, maxit):\\n result = start\\n for i in xrange(maxit):\\n result = mat*result\\n result = result/np.linalg.norm(result)\\n return result\",\n \"def wavefunction(self, x):\\n return ( float(1) / math.pi**(float(1)/4)) * math.exp( x**2 / float(-2))\",\n \"def single_slit_diffraction_intensity(slit_width, wavelength, screen_distance, X):\\n return ((np.sin((np.pi * slit_width * X) / (wavelength * screen_distance))) / (\\n (np.pi * slit_width * X) / (wavelength * screen_distance))) ** 2\",\n \"def match_power_spectrum(s, sample_rate, nsamps=5, isreal=False):\\n\\n #get FT of the signal\\n sfft = fft(s)\\n amplitude = np.abs(sfft)\\n phase = np.angle(sfft)\\n\\n s_recon = np.zeros([nsamps, len(s)], dtype='complex128')\\n for k in range(nsamps):\\n #shuffle the phase\\n np.random.shuffle(phase)\\n #reconstruct the signal\\n sfft_recon = amplitude*(np.cos(phase) + 1j*np.sin(phase))\\n s_recon[k, :] = ifft(sfft_recon)\\n\\n if isreal:\\n return np.real(s_recon)\\n return s_recon\",\n \"def show_spectrum(h, title=\\\"\\\"):\\n H = fft2(h)\\n\\n # Remember to plot the abs of the fft2(h)\\n plt.imshow(np.abs(H))\\n plt.gray()\\n plt.title(title)\\n plt.show()\",\n \"def get_camb_power(matpow):\\n data = np.loadtxt(matpow)\\n kk = data[:,0]\\n ii = np.where(kk > 0.)\\n return (kk[ii], data[:,1][ii])\",\n \"def spectrum(self,photon_energy):\\n outspecene = _validate_ene(photon_energy)\\n\\n self.specic = np.zeros(len(outspecene)) * u.Unit('1/(s eV)')\\n\\n for seed in self.seed_photon_fields:\\n # Call actual computation, detached to allow changes in subclasses\\n self.specic += self._calc_specic(seed,outspecene).to('1/(s eV)')\\n\\n self.specic = self.specic.to('1/(s eV)')\\n\\n return self.specic\",\n \"def calc_psf_fwhm(arr, mode='moffat'):\\n\\n\\tif mode == 'moffat':\\n\\t\\treturn calc_psf_fwhm_inpix_moffat(arr)\\n\\telif mode == 'gaussian':\\n\\t\\treturn calc_psf_fwhm_inpix_gaussian(arr)\\n\\telse:\\n\\t\\traise ValueError(\\\"mode not recognized\\\")\",\n \"def signal_power(signal_input, *arg):\\r\\n signal_T = np.array(signal_input)\\r\\n energy = np.abs(signal_T) ** 2\\r\\n if len(arg) < 1:\\r\\n power = np.mean(energy)\\r\\n else:\\r\\n power = np.mean(energy, axis=arg[0])\\r\\n return power\",\n \"def specular_power(self) -> float:\\n return self.GetSpecularPower()\",\n \"def test_fft_spectrum_02():\\n f, t, Sxx = _spectral_helper(x, x, fs=s_freq,\\n window='hann',\\n nperseg=x.shape[0],\\n noverlap=0,\\n nfft=None,\\n return_onesided=True,\\n mode='psd',\\n scaling='spectrum')\\n\\n f0, Sxx0 = _fft(x, s_freq, detrend=None, taper='hann', scaling='energy', sides='one')\\n\\n assert_array_equal(f0, f)\\n assert_array_almost_equal(Sxx0, Sxx[:, 0] * CORRECTION_FACTOR)\",\n \"def get_camb_nu_power(matpow, transfer):\\n matter = np.loadtxt(matpow)\\n trans = np.loadtxt(transfer)\\n #Adjust Fourier convention to match CAMB.\\n tnufac = (trans[:,5]/trans[:,6])**2\\n return matter[:,0], matter[:,1]*tnufac\",\n \"def ggc_lsf(wave):\\n coeffs = np.array([15.290, -6.079e-3, 9.472e-7, -4.395e-11])\\n powers = np.arange(len(coeffs))\\n fwhm = np.dot(coeffs, wave[None, :] ** powers[:, None])\\n return fwhm\",\n \"def powerSpectrum(self,l_edges,scale=None):\\n\\n\\t\\tassert not self._masked,\\\"Power spectrum calculation for masked maps is not allowed yet!\\\"\\n\\t\\tassert l_edges is not None\\n\\n\\t\\tif self.side_angle.unit.physical_type==\\\"length\\\":\\n\\t\\t\\traise NotImplementedError(\\\"Power spectrum measurement not implemented yet if side physical unit is length!\\\")\\n\\n\\t\\tl = 0.5*(l_edges[:-1] + l_edges[1:])\\n\\n\\t\\t#Calculate the Fourier transform of the map with numpy FFT\\n\\t\\tft_map = fftengine.rfft2(self.data)\\n\\n\\t\\t#Compute scaling coefficients\\n\\t\\tif scale is not None:\\n\\t\\t\\tsc = scale(self.getEll())\\n\\t\\telse:\\n\\t\\t\\tsc = None\\n\\n\\t\\t#Compute the power spectrum with the C backend implementation\\n\\t\\tpower_spectrum = _topology.rfft2_azimuthal(ft_map,ft_map,self.side_angle.to(u.deg).value,l_edges,sc)\\n\\n\\t\\t#Output the power spectrum\\n\\t\\treturn l,power_spectrum\",\n \"def avg_power_spectro(Sxx_power):\\r\\n S_power_mean = np.mean(Sxx_power, axis=1)\\r\\n\\r\\n return S_power_mean\",\n \"def scipy_smooth(img, sigma=5):\\n return ndimage.gaussian_filter(img, sigma=sigma)\",\n \"def plot_spectrum( data ):\\n d = data[1]\\n # rfft gives positive frequecies. Square to get power spectrum.\\n fp = numpy.absolute( numpy.fft.rfft( d ) )**2\\n freq = numpy.fft.fftfreq( d.shape[-1] )\\n n = len(fp)\\n\\n # reshape stuff a bit. keep only positive freqs.\\n fp = fp[1:-1]\\n freq = freq[1:n-1]\\n lrslope = linregress( numpy.log(freq[30:]), numpy.log(fp[30:]) )[0]\\n fig = plt.figure()\\n ax = fig.add_subplot(111)\\n ax.loglog( freq, fp, label=\\\"Lin. reg.=\\\"+str(round( lrslope,1 )) )\\n ax.legend( loc='lower left' )\\n return fig\",\n \"def get_power(self, kf, mean_fluxes):\\n mf = None\\n flux_arr = np.empty(shape=(self.len(),np.size(kf)))\\n for (i,ss) in enumerate(self.spectrae):\\n if mean_fluxes is not None:\\n mf = mean_fluxes[i]\\n kf_sim, flux_power_sim = ss.get_flux_power_1D(\\\"H\\\",1,1215, mean_flux_desired=mf)\\n #Rebin flux power to have desired k bins\\n rebinned=scipy.interpolate.interpolate.interp1d(kf_sim,flux_power_sim)\\n ii = np.where(kf > kf_sim[0])\\n ff = flux_power_sim[0]*np.ones_like(kf)\\n ff[ii] = rebinned(kf[ii])\\n flux_arr[i] = ff\\n flux_arr = np.ravel(flux_arr)\\n assert np.shape(flux_arr) == (self.len()*np.size(kf),)\\n self.drop_table()\\n return flux_arr\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75860554","0.7297011","0.72364277","0.7204423","0.7068326","0.6855464","0.6534","0.64967054","0.64738446","0.63099605","0.62746674","0.6239556","0.62161505","0.6206272","0.61712384","0.61586595","0.61429554","0.61336607","0.61296254","0.601769","0.598229","0.5950988","0.5949658","0.5926232","0.59150684","0.58871436","0.5879494","0.5866421","0.58569485","0.5845522","0.5838766","0.5834952","0.58236384","0.5823084","0.5804672","0.57676417","0.57571375","0.5735388","0.57175195","0.5710356","0.57027173","0.56977546","0.56909645","0.56646335","0.56389755","0.563849","0.5630142","0.56082404","0.5595785","0.5589412","0.55819625","0.557912","0.5578824","0.55777043","0.55722463","0.55310065","0.55288374","0.55283874","0.5520984","0.5517459","0.5516295","0.5512915","0.5491033","0.54608166","0.5446469","0.5440995","0.54399943","0.54321754","0.5431606","0.5430334","0.54233915","0.542253","0.54179746","0.54178166","0.53970957","0.5396539","0.53811973","0.53624815","0.53576016","0.5355597","0.5339457","0.5331056","0.5328756","0.5317546","0.5317222","0.53161585","0.5315999","0.53138506","0.52633625","0.5262167","0.5262096","0.52598375","0.5258323","0.525683","0.5255516","0.5250418","0.5249645","0.5246059","0.52408195","0.52264273"],"string":"[\n \"0.75860554\",\n \"0.7297011\",\n \"0.72364277\",\n \"0.7204423\",\n \"0.7068326\",\n \"0.6855464\",\n \"0.6534\",\n \"0.64967054\",\n \"0.64738446\",\n \"0.63099605\",\n \"0.62746674\",\n \"0.6239556\",\n \"0.62161505\",\n \"0.6206272\",\n \"0.61712384\",\n \"0.61586595\",\n \"0.61429554\",\n \"0.61336607\",\n \"0.61296254\",\n \"0.601769\",\n \"0.598229\",\n \"0.5950988\",\n \"0.5949658\",\n \"0.5926232\",\n \"0.59150684\",\n \"0.58871436\",\n \"0.5879494\",\n \"0.5866421\",\n \"0.58569485\",\n \"0.5845522\",\n \"0.5838766\",\n \"0.5834952\",\n \"0.58236384\",\n \"0.5823084\",\n \"0.5804672\",\n \"0.57676417\",\n \"0.57571375\",\n \"0.5735388\",\n \"0.57175195\",\n \"0.5710356\",\n \"0.57027173\",\n \"0.56977546\",\n \"0.56909645\",\n \"0.56646335\",\n \"0.56389755\",\n \"0.563849\",\n \"0.5630142\",\n \"0.56082404\",\n \"0.5595785\",\n \"0.5589412\",\n \"0.55819625\",\n \"0.557912\",\n \"0.5578824\",\n \"0.55777043\",\n \"0.55722463\",\n \"0.55310065\",\n \"0.55288374\",\n \"0.55283874\",\n \"0.5520984\",\n \"0.5517459\",\n \"0.5516295\",\n \"0.5512915\",\n \"0.5491033\",\n \"0.54608166\",\n \"0.5446469\",\n \"0.5440995\",\n \"0.54399943\",\n \"0.54321754\",\n \"0.5431606\",\n \"0.5430334\",\n \"0.54233915\",\n \"0.542253\",\n \"0.54179746\",\n \"0.54178166\",\n \"0.53970957\",\n \"0.5396539\",\n \"0.53811973\",\n \"0.53624815\",\n \"0.53576016\",\n \"0.5355597\",\n \"0.5339457\",\n \"0.5331056\",\n \"0.5328756\",\n \"0.5317546\",\n \"0.5317222\",\n \"0.53161585\",\n \"0.5315999\",\n \"0.53138506\",\n \"0.52633625\",\n \"0.5262167\",\n \"0.5262096\",\n \"0.52598375\",\n \"0.5258323\",\n \"0.525683\",\n \"0.5255516\",\n \"0.5250418\",\n \"0.5249645\",\n \"0.5246059\",\n \"0.52408195\",\n \"0.52264273\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69459283"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":86692,"cells":{"query":{"kind":"string","value":"Returns the hash_value for any substring of a string using the precomputed hash table."},"document":{"kind":"string","value":"def get_hash_value(table, prime, multiplier, start, length):\n y = pow(multiplier, length, prime)\n hash_value = (table[start+length] - y*table[start]) % prime\n return hash_value"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def computeHash(string):\n\tif isBytes(string):\n\t\tstring = string.decode(\"latin-1\")\n\thash_ = 63689\n\tfor char in string:\n\t\thash_ = hash_ * 378551 + ord(char)\n\treturn hash_ % 65536","def hash(self, string):\n return self.__scaffydb.hash(string)","def hash(string):\n hs = 0\n for s in string:\n hs += ord(s)\n return hs","def hashstring(astring, tablesize):\n \n sum = 0\n for pos in range(len(astring)):\n # to account for anagrams, we give weightage to positions of the letters to give different hash values\n sum = sum + ord(astring[pos]) * (pos + 1)\n \n return sum % tablesize","def customHashFunc(str):\n return sum(ord(chr) for chr in str)%128","def get_256_hash_from_string(string):\n\n sha256 = hashlib.sha256()\n sha256.update(string.encode('utf-8'))\n\n return sha256.hexdigest()","def keyhash(string):\n return hashlib.sha1(string.encode('utf-8')).hexdigest()","def match(self, string, substr):\n hp = self.hash(substr)\n hh = self.hash(string[0:0+len(substr)])\n if hh == hp:\n if string[0:0+len(substr)] == substr:\n return 0\n for i in xrange(1, len(string) - len(substr) + 1):\n hh = hh - ord(string[i-1]) + ord(string[i + len(substr) -1])\n if hh == hp:\n if string[i:i+len(substr)] == substr:\n return i\n return -1","def strhash(s: str) -> int:\n h = hashlib.md5(s.encode('utf-8'))\n h = int(h.hexdigest(), base=16)\n return h","def _hash(self, string, hash_type):\n hash_types = {\n 'TABLE_OFFSET': 0,\n 'HASH_A': 1,\n 'HASH_B': 2,\n 'TABLE': 3\n }\n seed1 = 0x7FED7FED\n seed2 = 0xEEEEEEEE\n\n for ch in string.upper():\n if not isinstance(ch, int): ch = ord(ch)\n value = self.encryption_table[(hash_types[hash_type] << 8) + ch]\n seed1 = (value ^ (seed1 + seed2)) & 0xFFFFFFFF\n seed2 = ch + seed1 + seed2 + (seed2 << 5) + 3 & 0xFFFFFFFF\n\n return seed1","def custom_hash(str_in):\n \n PRIMES = [\n 2, 3, 5, 7, 11, 13,\n 17, 19, 23, 29, 31,\n 37, 41, 43, 47, 53,\n 59, 61, 67, 71, 73,\n 79, 83, 89, 97, 101\n ]\n\n LOWERCASE_Z_ASCII = ord('z')\n hash_count = 1\n\n for letter in str_in:\n try:\n hash_count *= PRIMES[ord(letter) - LOWERCASE_Z_ASCII]\n except IndexError:\n print(f'list index out of range: {letter} in {str_in}')\n \n return hash_count","def get_hash_code(s):\n h = 0\n n = len(s)\n for i, c in enumerate(s):\n h = h + ord(c) * 31 ** (n - 1 - i)\n return StrUtil.convert_4_bytes(h)","def hash(self, string):\n h = md5()\n h.update(string)\n return h.digest()","def hash_function(s):\n\n # O(n) over the key length\n # O(1) over the HASH_DATA_SIZE\n\n bytes_list = s.encode()\n\n total = 0\n\n\n for b in bytes_list: # O(n) over the length of the key\n total += b\n\n\n total &= 0xffffffff # 32 bit (8 f's)\n\n return total","def comparison(self):\n for i in xrange(len(self.string)-len(self.substring)+1):\n if self.hash(self.string[i:i+len(self.substring)]) == self.hash(self.substring):\n if self.string[i:i+len(self.substring)] == self.substring:\n return i\n return -1","def get_hash(s):\n hash_object = hashlib.md5(s.encode())\n return hash_object.hexdigest()","def get_hash(hash_function, x: str):\n hash_function.update(x.encode())\n return int.from_bytes(hash_function.digest(), byteorder=\"big\")","def myHash(string, base=91, mod=1000000321):\n value = 0\n for pos, elem in enumerate(string[::-1]): # считаем значение полинома\n value += ord(elem) * base**pos # в последней задаче сделано с помощью массива (динамика)\n return value % mod","def hash_djb2(string):\n hashval = ctypes.c_uint(5381)\n for char in string:\n hashval.value = ((hashval.value << 5) + hashval.value) + ord(char)\n return hashval.value & 0x7FFFFFFF","def chord_hash(input_string):\n h = hashlib.sha1() # 160 bit string\n encoded_data = input_string.encode('utf-8')\n h.update(encoded_data)\n hex_string = h.hexdigest()\n hex_value = int(hex_string, 16)\n hash_integer_value = hex_value >> (160 - m)\n return hash_integer_value","def hash(self, text):\n hashval = 0\n for i in xrange(0, len(text)):\n hashval += ord(text[i])**i\n return hashval","def elf_hash(s):\n h = 0\n for c in s:\n h = (h << 4) + ord(c)\n t = (h & 0xF0000000)\n if t != 0:\n h = h ^ (t >> 24)\n h = h & ~t\n return h","def _precompute_substrings(self, p: int) -> List[int]:\n hash_vals: List[int] = [0]\n for i in range(len(self._s)):\n val = (hash_vals[i] * self.X + ord(self._s[i])) % p\n hash_vals.append(val)\n\n return hash_vals","def hash_string(input_str):\n input_b = str.encode(input_str)\n input_hash = hashlib.md5(input_b.lower())\n input_hash_str = input_hash.hexdigest()\n\n return input_hash_str","def compute_minhash(string: str) -> LeanMinHash:\n m = MinHash(num_perm=PERMUTATIONS)\n for d in string_encoder(string):\n m.update(d)\n return LeanMinHash(m)","def chunk_hashes(str):\n chunk = 1024*1024\n chunk_count = int(math.ceil(len(str)/float(chunk)))\n chunks = [str[i*chunk:(i+1)*chunk] for i in range(chunk_count)]\n return [hashlib.sha256(x).digest() for x in chunks]","def hash_str(string):\n\n return hmac.new(secret, string).hexdigest()","def fasthash(string):\r\n md4 = hashlib.new(\"md4\")\r\n md4.update(string)\r\n return md4.hexdigest()","def _s_hash(fn, data: str):\n\n return fn(_b(data)).hexdigest()","def hash_function_2(key: str) -> int:\n hash, index = 0, 0\n index = 0\n for letter in key:\n hash += (index + 1) * ord(letter)\n index += 1\n return hash","def _hash_value(value):\n return hashlib.md5(value.encode('utf-8')).hexdigest()[:9]","def hashing_info(string):#KEY HASHING FUNCTION\n nodeInfo = string.encode('utf-8')\n\n #md5 -> 2^7 = 128 bits\n hash_object = hashlib.md5()\n hash_object.update(nodeInfo)\n\n tmp = hash_object.hexdigest()\n tmp = int(tmp,16)\n\n result = tmp >> (128-16)\n return result","def search_hash(word_input,hashtable):\n\n if word_input in hashtable:\n return hashtable[word_input]\n else:\n return None","def hash_me(cls, p_str, p_len=64):\n v_hash = str()\n v_len = EC.SHA256 if p_len is None else EC.SHA256 if p_len not in EC.HASH_ALGO else p_len\n if v_len == EC.SHA512:\n v_hash = hashlib.sha512()\n elif v_len == EC.SHA256:\n v_hash = hashlib.sha256()\n elif v_len == EC.SHA224:\n v_hash = hashlib.sha224()\n elif v_len == EC.SHA1:\n v_hash = hashlib.sha1()\n\n v_hash.update(p_str.encode(\"utf-8\"))\n return v_hash.hexdigest()","def hash(plainString):\n result = plainString\n for i in range(0,12):\n result = hashHelp(result)\n return result","def hash_function(self, x):\n if not x:\n return -1\n hashed_value = 0\n\n for char in x:\n hashed_value = 181 * hashed_value + ord(char)\n\n return hashed_value % self.capacity","def kmp_algo(inp_string: str, substr: str) -> Optional[int]:\n\n pi = _prefix_fun(substr)\n i, j = 0, 0\n while i <= len(inp_string)-len(substr):\n if inp_string[i] == substr[j]:\n first_occurrence = i\n while j < len(substr):\n if inp_string[i] != substr[j]:\n j = pi[j-1]\n break\n i += 1\n j += 1\n else:\n return first_occurrence\n else:\n i += 1\n return None","def hash_function_1(key: str) -> int:\n hash = 0\n for letter in key:\n hash += ord(letter)\n return hash","def sha256_2_string(string_to_hash):\n\n # Solution for (1a)\n import hashlib\n first_sha = hashlib.sha256(string_to_hash.encode(\"utf8\"))\n second_sha = hashlib.sha256(first_sha.digest())\n return second_sha.hexdigest()\n\n # Placeholder for (1a)\n return \"deadbeef\"","def djb_hash(s):\n h = 5381\n for c in s:\n h = h * 33 + ord(c)\n return h & 0xffffffff","def str_to_hash(self, param):\n param = param.encode('utf-8')\n my_hash = hashlib.md5(param)\n return my_hash.hexdigest()","def _substring_occurrences(\n cls, in_str: str, substrings: Iterable[str]\n ) -> Dict[str, List[int]]:\n occurrences = {}\n for substring in substrings:\n occurrences[substring] = list(findall(substring, in_str))\n return occurrences","def calculate_rule_hash(self, rule):\r\n hash_strings = []\r\n condition_string_prefaces = (\"$\", \"!\", \"#\", \"@\")\r\n # dictionary for substitutions\r\n string_substitutions = {}\r\n all_strings = []\r\n # original code used md5\r\n # m = hashlib.md5()\r\n m = hashlib.sha3_256()\r\n # Adding all string contents to the list\r\n if 'strings' in rule:\r\n for s in rule['strings']:\r\n if s['type'] == \"byte\":\r\n # original code just needed to append the converted hex code as a string. We need to create the dictionary entries for substitutions as well\r\n # hash_strings.append(re.sub(r'[^a-fA-F\\?0-9]+', '', s['value']))\r\n byte_code_string = re.sub(r'[^a-fA-F\\?0-9]+', '', s['value'])\r\n dict_entry = {s['name']: byte_code_string}\r\n string_substitutions.update(dict_entry)\r\n hash_strings.append(byte_code_string)\r\n else:\r\n # The following line was the only portion of this else statement in the original code\r\n # This change takes modifiers into account for string arguments\r\n # hash_strings.append(s['value'])\r\n string_and_modifiers = []\r\n string_and_modifiers.append(s['value'])\r\n if 'modifiers' in s:\r\n for modifier in s['modifiers']:\r\n string_and_modifiers.append(modifier)\r\n string_and_modifiers = \" \".join(string_and_modifiers)\r\n all_strings.append(\"$\"+string_and_modifiers)\r\n dict_entry = {s['name']: string_and_modifiers}\r\n string_substitutions.update(dict_entry)\r\n #hash_strings.append(\"$\"+string_and_modifiers)\r\n all_strings = self.resort_stings_add_commas(all_strings)\r\n # Adding the components of the condition to the list (except the variables)\r\n all_wild_card_1 = \"\\$\\*\"\r\n all_wild_card_2 = \"them\"\r\n for e in rule['condition_terms']:\r\n if re.match(all_wild_card_1, e) or re.match(all_wild_card_2, e):\r\n hash_strings.extend(all_strings)\r\n elif e.startswith(condition_string_prefaces):\r\n if len(e) > 1:\r\n string_preface, string_name = e[:1], e[1:]\r\n string_name = \"$\" + string_name\r\n if e.endswith(\"*\"):\r\n hash_strings.extend(self.resort_stings_add_commas(self.regex_match_string_names_for_values(string_preface, string_name, string_substitutions)))\r\n #hash_strings.extend(\"Pull all the matching strings\")\r\n else:\r\n if string_name in string_substitutions:\r\n substituted = string_preface + string_substitutions[string_name]\r\n hash_strings.append(substituted)\r\n else:\r\n hash_strings.append(e)\r\n else:\r\n hash_strings.append(e)\r\n else:\r\n hash_strings.append(e)\r\n # Generate a hash from the sorted contents\r\n #hash_strings.sort()\r\n m.update(\"\".join(hash_strings).encode(\"ascii\"))\r\n return m.hexdigest()","def set_hashes(s, hash_function=_crc32_hash):\n return {(hash_function(e), e) for e in s}","def get_encoded_minhash(string: str) -> str:\n return encode_minhash(compute_minhash(string))","def query_lsh(lsh: MinHashLSH, string: str) -> List[str]:\n mhash = compute_minhash(string)\n return lsh.query(mhash)","def hash_string(password):\n return hash(password)","def hashHelp(plainString):\n # hashed = plainString\n n = len(plainString) // 4\n\n a = plainString[0:n]\n b = plainString[n : n * 2]\n c = plainString[n * 2 : n * 3]\n d = plainString[n * 3 : len(plainString)]\n\n if len(a) == 0 or len(b) == 0 or len(c) == 0 or len(d) == 0:\n raise Exception(\"Error string not long enough, use at least 4 characters\")\n\n numA = numToBin(functionF(a,d))\n numB = numToBin(strToNum(b))\n numC = numToBin(bitShift(c))\n numD = numToBin(functionG(a,b,c,d))\n\n # hashed = A + B + numC + D\n # hashed = numA + '|' + numB + '|' + numC + '|' + numD\n hashed = text_from_bits(numD + numA + numB + numC)\n hashed = numbersToLetters(hashed)\n return hashed","def hash_string(to_hash):\n\n chars = string.printable\n\n hashed = \"\"\n\n total = 1\n\n counter = 1\n\n for letter in to_hash:\n\n total *= (chars.index(letter) * counter * len(to_hash)*13)\n\n counter += 1\n\n if counter%3 == 0:\n\n total *= total\n\n total = str(total)[:30]\n\n temp_int = \"\"\n\n for i in range(len(total)):\n\n temp_int += total[i]\n\n if i % 2 != 0:\n\n hashed += chars[int(temp_int)]\n\n temp_int = \"\"\n\n return hashed","def hash(self, searchkey):\n return searchkey % self.n","def hash_string(self):\n return self._hash_string","def hashing(word) :\r\n ans = hashlib.sha256(word.encode())\r\n return ans.hexdigest()","def hash_key(self,key: str) -> int: \n \n total = 0\n prime = 3\n \n for index, char in enumerate(key,start=1):\n \n total += ord(char)*(prime)**index\n \n return total % self.buckets","def hashWord(self, word):\n return self.func(word).hexdigest()","def hash_str(c, hash_length):\n if isinstance(c, float):\n if numpy.isnan(c):\n return c\n raise ValueError(f\"numpy.nan expected, not {c}\")\n m = hashlib.sha256()\n m.update(c.encode(\"utf-8\"))\n r = m.hexdigest()\n if len(r) >= hash_length:\n return r[:hash_length]\n return r","def _key_hash(self, key):\n\n split_key = key.strip(' ').split(' ')[1]\n return int(split_key)","def precompute_hashes(text, pattern_len, p, x):\n\n if len(text) < pattern_len or pattern_len == 0:\n return []\n\n hashes = [ 0 ] * (len(text) - pattern_len + 1)\n\n slice = text[len(text) - pattern_len:]\n hashes[len(text) - pattern_len] = SearchPattern.poly_hash(\n slice, p, x)\n\n y = 1\n for i in range(1, pattern_len + 1):\n y = (y * x) % p\n for i in range(len(text) - pattern_len - 1, -1, -1):\n hashes[i] = (x * hashes[i + 1] + ord(text[i]) -\n y * ord(text[i + pattern_len])) % p\n\n return hashes","def _hash_function(self, key):\n h = 0\n a = 31\n table_size = self.size\n for i in range(len(key)):\n h = (h * a + ord(key[i])) % table_size\n return h","def hash_value(self, value):\n return hash(value) % self.size","def hash(self, key):\n key = str(key)\n h = 0\n a = self.hash_base\n for i in range(len(str(key))):\n h = (h * a + ord(key[i])) % self.table_capacity\n return h","def hash_function(input_tuple):\n return hash(input_tuple)","def hashing(self, key):\n #return hash(key) % self.__size\n if type(key) == str:\n hashVal = 0\n for i, c in enumerate(key):\n hashVal += int(ord(c)) * 31 ** (len(key) -1 - i)\n return hashVal % self.__size\n return key % self.__size","def from_string(the_string):\n hash = hashlib.md5()\n\n buf = StringIO.StringIO(the_string)\n for block in iter(lambda: buf.read(AWS_UPLOAD_PART_SIZE), \"\"):\n hash.update(block)\n return hash.hexdigest()","def _gethash(self, invalue) -> int:\n return hash(invalue) % self.capacity","def hash_string(\r\n k: bytes,\r\n e: str,\r\n) -> bytes:\r\n return hmac.new(k, e.encode('utf-8'), hashlib.sha256).digest()","def HashValue(self) -> _n_0_t_3[_n_0_t_9]:","def getSubstring(str):\n\tresultStr=[]\n\ttempStr=\"\"\n\tfor i in range(len(str)-1):\n\t\t#result[a]=a in result and result[a]+1 or 1\n\t\ttempStr=str[i]\n\t\tfor b in str[i+1:]:\n\t\t\tif b in tempStr:\n\t\t\t\tbreak\n\t\t\telse: tempStr+=b\n\n\t\tresultStr.append(tempStr)\n\treturn sorted(resultStr,key=lambda x:len(x),reverse=True)[0]","def __polynomial_hash(self, s, base = 31, max_size=168):\r\n digest = 0\r\n max_size = 168\r\n for c in s: digest = base * digest + ord(c)\r\n digest &= 2 ** max_size - 1 \r\n return hex(digest).rstrip('L')","def minhash(s, t, n):\n X = union_signature(s, t, n)\n Y = intersection_signature(X, s, t)\n return len(Y) / len(X)","def calculate_weighted_hash(cls, word):\n\n hash_value = 0\n for char in word:\n hash_value += cls.alpha_lookup[char.lower()]\n return hash_value","def h(x):\n\n hasher = hashlib.sha256()\n hasher.update(x)\n return hasher.digest()","def fnvhash(a): \n h = 2166136261 \n for i in a: \n t = (h * 16777619) & 0xffffffffL \n h = t ^ i \n return h","def hash_string_to_int(\r\n k: bytes,\r\n e: str,\r\n) -> int:\r\n return int.from_bytes(hash_string(k, e), 'big')","def hax(string):\r\n hax_dict = { }\r\n return hax_dict.get(string, string)","def apply_hash (self, s):\r\n m = md5()\r\n m.update (s)\r\n d = m.digest()\r\n # base64.encodestring tacks on an extra linefeed.\r\n return encodestring (d)[:-1]","def gen_hash(s: str) -> str:\n\n m = hashlib.md5()\n m.update(bytes(s, encoding = 'utf8'))\n hash_code = str(m.hexdigest())\n\n return hash_code","def main():\n inputStr = input(\"Enter string to be hashed: \")\n print(f\"Hashed string: {hash(inputStr.replace(' ', ''))}\")","def hash_dump_string(dump_string):\n return hashlib.sha256(six.binary_type().join([dump_string])).hexdigest()","def _hash(self, key):\n if self.function == 'fnv':\n h = 2166136261\n for i in range(len(key)):\n h = (h * 16777619) ^ ord(key[i])\n return h\n elif self.function == 'add':\n h = 0\n for i in range(len(key)):\n h += ord(key[i])\n return h","def __str_to_hash(string_to_hash: str, errors: str = 'ignore') -> str:\n string_hash = string_to_hash.encode(encoding=\"utf-8\", errors=errors)\n return hashlib.md5(string_hash).hexdigest()","def hash_str_length(self):\n return self.hash_byte_length() * 2","def hash_function_1(key):\n hash = 0\n for i in key:\n hash = hash + ord(i)\n return hash","def _hash(self, hashKey):\n return hashKey % self.size","def cache_find_hashed(item: str) -> str:\n\treturn cache_find(md5(item))","def calc_statistics_hash(self) -> bytes:\n return b\"somehash\"","def get_checksum(str):\n hash_object = hashlib.sha1(b'%s' % str)\n hex_dig = hash_object.hexdigest()\n return hex_dig","def _reduce(self, hash):\n summation = 0\n for char in hash:\n summation += ord(char)\n return summation % self._size","def shist(search_string):\n # gets the full dict\n my_string = str(search_string)\n history_dict = hist()\n # creates the final history dict\n final_history_dict = {}\n for key in history_dict:\n if my_string in history_dict[key]:\n final_history_dict[key] = history_dict[key]\n return final_history_dict","def minhash(self, s, ii, mask):\n fn = self._hash_function(ii)\n return min([fn(x) & mask for x in s])","def _rehash(self, hashKey, integer):\n return ( hashKey + integer * integer ) % self.size","def get_hash(thing):\n n = hashlib.sha256()\n \n if isinstance(thing,str):\n n.update(thing.encode('utf-8' ))\n elif isinstance(thing, bytes):\n n.update(thing)\n elif isinstance(thing,BeautifulSoup):\n n.update(get_hash(str(thing)))\n else:\n raise RuntimeError(\"unknown type: {}\".format(str(type(thing))))\n \n return(n.digest())","def _build_hash_table(arr: [str]):\n ht = {}\n for cur_str in arr:\n\n anagram = cur_str[::-1]\n if cur_str in ht.keys():\n # This string is an anagram of some previous\n # Increase anagram count for hash table item\n (original, orig_cnt, anag_cnt) = ht[cur_str]\n ht[cur_str] = (original, orig_cnt, anag_cnt + 1)\n elif anagram in ht.keys():\n # This string equals to some prevoius\n # Increase original count for hash table item\n (original, orig_cnt, anag_cnt) = ht[anagram]\n ht[anagram] = (original, orig_cnt+1, anag_cnt)\n else:\n # This string is new\n ht[anagram] = (cur_str, 1, 0)\n return ht","def poly_hash(text, p, x):\n hash = 0\n for ch in reversed(text):\n hash = (hash * x + ord(ch)) % p\n\n return hash","def hash_f(x: Text) -> Tuple[Text, Text]:\n return \"hash\", x.lower()","def find_idx(string, substring):\n idx = string.find(substring)\n while idx != -1:\n yield idx\n idx = string.find(substring, idx+1)","def slophash(val):\n\n if not val:\n return None\n else:\n return sha256(val.encode('utf8')).hexdigest()[0:10]","def digest(string):\n return md5(string.encode(\"utf-8\")).hexdigest()","def get_string_sha256(str_to_convert):\n hasher = hashlib.sha256()\n hasher.update(bytearray(str_to_convert.encode('ascii')))\n return base64.b64encode(hasher.digest())","async def get_hash(identifier):\n return hashlib.md5(identifier.encode('utf8')).hexdigest()","def distinct_substrigns(str1, k):\n hash_map = {}\n n = len(str1)\n start = 0 \n end = 1\n hash_map[str1[start]] = 1\n res = [] \n\n while end < n:\n print (\"hash_map is :\", hash_map)\n if str1[end] not in hash_map: \n hash_map[str1[end]] = 1\n else:\n while str1[start]!=str1[end]:\n del hash_map[str1[start]]\n start += 1\n start += 1\n\n if end-start+1 == k:\n temp = str1[start:end+1]\n if temp not in res:\n res.append(temp)\n del hash_map[str1[start]]\n start += 1\n end += 1\n #print(end,start)\n return res"],"string":"[\n \"def computeHash(string):\\n\\tif isBytes(string):\\n\\t\\tstring = string.decode(\\\"latin-1\\\")\\n\\thash_ = 63689\\n\\tfor char in string:\\n\\t\\thash_ = hash_ * 378551 + ord(char)\\n\\treturn hash_ % 65536\",\n \"def hash(self, string):\\n return self.__scaffydb.hash(string)\",\n \"def hash(string):\\n hs = 0\\n for s in string:\\n hs += ord(s)\\n return hs\",\n \"def hashstring(astring, tablesize):\\n \\n sum = 0\\n for pos in range(len(astring)):\\n # to account for anagrams, we give weightage to positions of the letters to give different hash values\\n sum = sum + ord(astring[pos]) * (pos + 1)\\n \\n return sum % tablesize\",\n \"def customHashFunc(str):\\n return sum(ord(chr) for chr in str)%128\",\n \"def get_256_hash_from_string(string):\\n\\n sha256 = hashlib.sha256()\\n sha256.update(string.encode('utf-8'))\\n\\n return sha256.hexdigest()\",\n \"def keyhash(string):\\n return hashlib.sha1(string.encode('utf-8')).hexdigest()\",\n \"def match(self, string, substr):\\n hp = self.hash(substr)\\n hh = self.hash(string[0:0+len(substr)])\\n if hh == hp:\\n if string[0:0+len(substr)] == substr:\\n return 0\\n for i in xrange(1, len(string) - len(substr) + 1):\\n hh = hh - ord(string[i-1]) + ord(string[i + len(substr) -1])\\n if hh == hp:\\n if string[i:i+len(substr)] == substr:\\n return i\\n return -1\",\n \"def strhash(s: str) -> int:\\n h = hashlib.md5(s.encode('utf-8'))\\n h = int(h.hexdigest(), base=16)\\n return h\",\n \"def _hash(self, string, hash_type):\\n hash_types = {\\n 'TABLE_OFFSET': 0,\\n 'HASH_A': 1,\\n 'HASH_B': 2,\\n 'TABLE': 3\\n }\\n seed1 = 0x7FED7FED\\n seed2 = 0xEEEEEEEE\\n\\n for ch in string.upper():\\n if not isinstance(ch, int): ch = ord(ch)\\n value = self.encryption_table[(hash_types[hash_type] << 8) + ch]\\n seed1 = (value ^ (seed1 + seed2)) & 0xFFFFFFFF\\n seed2 = ch + seed1 + seed2 + (seed2 << 5) + 3 & 0xFFFFFFFF\\n\\n return seed1\",\n \"def custom_hash(str_in):\\n \\n PRIMES = [\\n 2, 3, 5, 7, 11, 13,\\n 17, 19, 23, 29, 31,\\n 37, 41, 43, 47, 53,\\n 59, 61, 67, 71, 73,\\n 79, 83, 89, 97, 101\\n ]\\n\\n LOWERCASE_Z_ASCII = ord('z')\\n hash_count = 1\\n\\n for letter in str_in:\\n try:\\n hash_count *= PRIMES[ord(letter) - LOWERCASE_Z_ASCII]\\n except IndexError:\\n print(f'list index out of range: {letter} in {str_in}')\\n \\n return hash_count\",\n \"def get_hash_code(s):\\n h = 0\\n n = len(s)\\n for i, c in enumerate(s):\\n h = h + ord(c) * 31 ** (n - 1 - i)\\n return StrUtil.convert_4_bytes(h)\",\n \"def hash(self, string):\\n h = md5()\\n h.update(string)\\n return h.digest()\",\n \"def hash_function(s):\\n\\n # O(n) over the key length\\n # O(1) over the HASH_DATA_SIZE\\n\\n bytes_list = s.encode()\\n\\n total = 0\\n\\n\\n for b in bytes_list: # O(n) over the length of the key\\n total += b\\n\\n\\n total &= 0xffffffff # 32 bit (8 f's)\\n\\n return total\",\n \"def comparison(self):\\n for i in xrange(len(self.string)-len(self.substring)+1):\\n if self.hash(self.string[i:i+len(self.substring)]) == self.hash(self.substring):\\n if self.string[i:i+len(self.substring)] == self.substring:\\n return i\\n return -1\",\n \"def get_hash(s):\\n hash_object = hashlib.md5(s.encode())\\n return hash_object.hexdigest()\",\n \"def get_hash(hash_function, x: str):\\n hash_function.update(x.encode())\\n return int.from_bytes(hash_function.digest(), byteorder=\\\"big\\\")\",\n \"def myHash(string, base=91, mod=1000000321):\\n value = 0\\n for pos, elem in enumerate(string[::-1]): # считаем значение полинома\\n value += ord(elem) * base**pos # в последней задаче сделано с помощью массива (динамика)\\n return value % mod\",\n \"def hash_djb2(string):\\n hashval = ctypes.c_uint(5381)\\n for char in string:\\n hashval.value = ((hashval.value << 5) + hashval.value) + ord(char)\\n return hashval.value & 0x7FFFFFFF\",\n \"def chord_hash(input_string):\\n h = hashlib.sha1() # 160 bit string\\n encoded_data = input_string.encode('utf-8')\\n h.update(encoded_data)\\n hex_string = h.hexdigest()\\n hex_value = int(hex_string, 16)\\n hash_integer_value = hex_value >> (160 - m)\\n return hash_integer_value\",\n \"def hash(self, text):\\n hashval = 0\\n for i in xrange(0, len(text)):\\n hashval += ord(text[i])**i\\n return hashval\",\n \"def elf_hash(s):\\n h = 0\\n for c in s:\\n h = (h << 4) + ord(c)\\n t = (h & 0xF0000000)\\n if t != 0:\\n h = h ^ (t >> 24)\\n h = h & ~t\\n return h\",\n \"def _precompute_substrings(self, p: int) -> List[int]:\\n hash_vals: List[int] = [0]\\n for i in range(len(self._s)):\\n val = (hash_vals[i] * self.X + ord(self._s[i])) % p\\n hash_vals.append(val)\\n\\n return hash_vals\",\n \"def hash_string(input_str):\\n input_b = str.encode(input_str)\\n input_hash = hashlib.md5(input_b.lower())\\n input_hash_str = input_hash.hexdigest()\\n\\n return input_hash_str\",\n \"def compute_minhash(string: str) -> LeanMinHash:\\n m = MinHash(num_perm=PERMUTATIONS)\\n for d in string_encoder(string):\\n m.update(d)\\n return LeanMinHash(m)\",\n \"def chunk_hashes(str):\\n chunk = 1024*1024\\n chunk_count = int(math.ceil(len(str)/float(chunk)))\\n chunks = [str[i*chunk:(i+1)*chunk] for i in range(chunk_count)]\\n return [hashlib.sha256(x).digest() for x in chunks]\",\n \"def hash_str(string):\\n\\n return hmac.new(secret, string).hexdigest()\",\n \"def fasthash(string):\\r\\n md4 = hashlib.new(\\\"md4\\\")\\r\\n md4.update(string)\\r\\n return md4.hexdigest()\",\n \"def _s_hash(fn, data: str):\\n\\n return fn(_b(data)).hexdigest()\",\n \"def hash_function_2(key: str) -> int:\\n hash, index = 0, 0\\n index = 0\\n for letter in key:\\n hash += (index + 1) * ord(letter)\\n index += 1\\n return hash\",\n \"def _hash_value(value):\\n return hashlib.md5(value.encode('utf-8')).hexdigest()[:9]\",\n \"def hashing_info(string):#KEY HASHING FUNCTION\\n nodeInfo = string.encode('utf-8')\\n\\n #md5 -> 2^7 = 128 bits\\n hash_object = hashlib.md5()\\n hash_object.update(nodeInfo)\\n\\n tmp = hash_object.hexdigest()\\n tmp = int(tmp,16)\\n\\n result = tmp >> (128-16)\\n return result\",\n \"def search_hash(word_input,hashtable):\\n\\n if word_input in hashtable:\\n return hashtable[word_input]\\n else:\\n return None\",\n \"def hash_me(cls, p_str, p_len=64):\\n v_hash = str()\\n v_len = EC.SHA256 if p_len is None else EC.SHA256 if p_len not in EC.HASH_ALGO else p_len\\n if v_len == EC.SHA512:\\n v_hash = hashlib.sha512()\\n elif v_len == EC.SHA256:\\n v_hash = hashlib.sha256()\\n elif v_len == EC.SHA224:\\n v_hash = hashlib.sha224()\\n elif v_len == EC.SHA1:\\n v_hash = hashlib.sha1()\\n\\n v_hash.update(p_str.encode(\\\"utf-8\\\"))\\n return v_hash.hexdigest()\",\n \"def hash(plainString):\\n result = plainString\\n for i in range(0,12):\\n result = hashHelp(result)\\n return result\",\n \"def hash_function(self, x):\\n if not x:\\n return -1\\n hashed_value = 0\\n\\n for char in x:\\n hashed_value = 181 * hashed_value + ord(char)\\n\\n return hashed_value % self.capacity\",\n \"def kmp_algo(inp_string: str, substr: str) -> Optional[int]:\\n\\n pi = _prefix_fun(substr)\\n i, j = 0, 0\\n while i <= len(inp_string)-len(substr):\\n if inp_string[i] == substr[j]:\\n first_occurrence = i\\n while j < len(substr):\\n if inp_string[i] != substr[j]:\\n j = pi[j-1]\\n break\\n i += 1\\n j += 1\\n else:\\n return first_occurrence\\n else:\\n i += 1\\n return None\",\n \"def hash_function_1(key: str) -> int:\\n hash = 0\\n for letter in key:\\n hash += ord(letter)\\n return hash\",\n \"def sha256_2_string(string_to_hash):\\n\\n # Solution for (1a)\\n import hashlib\\n first_sha = hashlib.sha256(string_to_hash.encode(\\\"utf8\\\"))\\n second_sha = hashlib.sha256(first_sha.digest())\\n return second_sha.hexdigest()\\n\\n # Placeholder for (1a)\\n return \\\"deadbeef\\\"\",\n \"def djb_hash(s):\\n h = 5381\\n for c in s:\\n h = h * 33 + ord(c)\\n return h & 0xffffffff\",\n \"def str_to_hash(self, param):\\n param = param.encode('utf-8')\\n my_hash = hashlib.md5(param)\\n return my_hash.hexdigest()\",\n \"def _substring_occurrences(\\n cls, in_str: str, substrings: Iterable[str]\\n ) -> Dict[str, List[int]]:\\n occurrences = {}\\n for substring in substrings:\\n occurrences[substring] = list(findall(substring, in_str))\\n return occurrences\",\n \"def calculate_rule_hash(self, rule):\\r\\n hash_strings = []\\r\\n condition_string_prefaces = (\\\"$\\\", \\\"!\\\", \\\"#\\\", \\\"@\\\")\\r\\n # dictionary for substitutions\\r\\n string_substitutions = {}\\r\\n all_strings = []\\r\\n # original code used md5\\r\\n # m = hashlib.md5()\\r\\n m = hashlib.sha3_256()\\r\\n # Adding all string contents to the list\\r\\n if 'strings' in rule:\\r\\n for s in rule['strings']:\\r\\n if s['type'] == \\\"byte\\\":\\r\\n # original code just needed to append the converted hex code as a string. We need to create the dictionary entries for substitutions as well\\r\\n # hash_strings.append(re.sub(r'[^a-fA-F\\\\?0-9]+', '', s['value']))\\r\\n byte_code_string = re.sub(r'[^a-fA-F\\\\?0-9]+', '', s['value'])\\r\\n dict_entry = {s['name']: byte_code_string}\\r\\n string_substitutions.update(dict_entry)\\r\\n hash_strings.append(byte_code_string)\\r\\n else:\\r\\n # The following line was the only portion of this else statement in the original code\\r\\n # This change takes modifiers into account for string arguments\\r\\n # hash_strings.append(s['value'])\\r\\n string_and_modifiers = []\\r\\n string_and_modifiers.append(s['value'])\\r\\n if 'modifiers' in s:\\r\\n for modifier in s['modifiers']:\\r\\n string_and_modifiers.append(modifier)\\r\\n string_and_modifiers = \\\" \\\".join(string_and_modifiers)\\r\\n all_strings.append(\\\"$\\\"+string_and_modifiers)\\r\\n dict_entry = {s['name']: string_and_modifiers}\\r\\n string_substitutions.update(dict_entry)\\r\\n #hash_strings.append(\\\"$\\\"+string_and_modifiers)\\r\\n all_strings = self.resort_stings_add_commas(all_strings)\\r\\n # Adding the components of the condition to the list (except the variables)\\r\\n all_wild_card_1 = \\\"\\\\$\\\\*\\\"\\r\\n all_wild_card_2 = \\\"them\\\"\\r\\n for e in rule['condition_terms']:\\r\\n if re.match(all_wild_card_1, e) or re.match(all_wild_card_2, e):\\r\\n hash_strings.extend(all_strings)\\r\\n elif e.startswith(condition_string_prefaces):\\r\\n if len(e) > 1:\\r\\n string_preface, string_name = e[:1], e[1:]\\r\\n string_name = \\\"$\\\" + string_name\\r\\n if e.endswith(\\\"*\\\"):\\r\\n hash_strings.extend(self.resort_stings_add_commas(self.regex_match_string_names_for_values(string_preface, string_name, string_substitutions)))\\r\\n #hash_strings.extend(\\\"Pull all the matching strings\\\")\\r\\n else:\\r\\n if string_name in string_substitutions:\\r\\n substituted = string_preface + string_substitutions[string_name]\\r\\n hash_strings.append(substituted)\\r\\n else:\\r\\n hash_strings.append(e)\\r\\n else:\\r\\n hash_strings.append(e)\\r\\n else:\\r\\n hash_strings.append(e)\\r\\n # Generate a hash from the sorted contents\\r\\n #hash_strings.sort()\\r\\n m.update(\\\"\\\".join(hash_strings).encode(\\\"ascii\\\"))\\r\\n return m.hexdigest()\",\n \"def set_hashes(s, hash_function=_crc32_hash):\\n return {(hash_function(e), e) for e in s}\",\n \"def get_encoded_minhash(string: str) -> str:\\n return encode_minhash(compute_minhash(string))\",\n \"def query_lsh(lsh: MinHashLSH, string: str) -> List[str]:\\n mhash = compute_minhash(string)\\n return lsh.query(mhash)\",\n \"def hash_string(password):\\n return hash(password)\",\n \"def hashHelp(plainString):\\n # hashed = plainString\\n n = len(plainString) // 4\\n\\n a = plainString[0:n]\\n b = plainString[n : n * 2]\\n c = plainString[n * 2 : n * 3]\\n d = plainString[n * 3 : len(plainString)]\\n\\n if len(a) == 0 or len(b) == 0 or len(c) == 0 or len(d) == 0:\\n raise Exception(\\\"Error string not long enough, use at least 4 characters\\\")\\n\\n numA = numToBin(functionF(a,d))\\n numB = numToBin(strToNum(b))\\n numC = numToBin(bitShift(c))\\n numD = numToBin(functionG(a,b,c,d))\\n\\n # hashed = A + B + numC + D\\n # hashed = numA + '|' + numB + '|' + numC + '|' + numD\\n hashed = text_from_bits(numD + numA + numB + numC)\\n hashed = numbersToLetters(hashed)\\n return hashed\",\n \"def hash_string(to_hash):\\n\\n chars = string.printable\\n\\n hashed = \\\"\\\"\\n\\n total = 1\\n\\n counter = 1\\n\\n for letter in to_hash:\\n\\n total *= (chars.index(letter) * counter * len(to_hash)*13)\\n\\n counter += 1\\n\\n if counter%3 == 0:\\n\\n total *= total\\n\\n total = str(total)[:30]\\n\\n temp_int = \\\"\\\"\\n\\n for i in range(len(total)):\\n\\n temp_int += total[i]\\n\\n if i % 2 != 0:\\n\\n hashed += chars[int(temp_int)]\\n\\n temp_int = \\\"\\\"\\n\\n return hashed\",\n \"def hash(self, searchkey):\\n return searchkey % self.n\",\n \"def hash_string(self):\\n return self._hash_string\",\n \"def hashing(word) :\\r\\n ans = hashlib.sha256(word.encode())\\r\\n return ans.hexdigest()\",\n \"def hash_key(self,key: str) -> int: \\n \\n total = 0\\n prime = 3\\n \\n for index, char in enumerate(key,start=1):\\n \\n total += ord(char)*(prime)**index\\n \\n return total % self.buckets\",\n \"def hashWord(self, word):\\n return self.func(word).hexdigest()\",\n \"def hash_str(c, hash_length):\\n if isinstance(c, float):\\n if numpy.isnan(c):\\n return c\\n raise ValueError(f\\\"numpy.nan expected, not {c}\\\")\\n m = hashlib.sha256()\\n m.update(c.encode(\\\"utf-8\\\"))\\n r = m.hexdigest()\\n if len(r) >= hash_length:\\n return r[:hash_length]\\n return r\",\n \"def _key_hash(self, key):\\n\\n split_key = key.strip(' ').split(' ')[1]\\n return int(split_key)\",\n \"def precompute_hashes(text, pattern_len, p, x):\\n\\n if len(text) < pattern_len or pattern_len == 0:\\n return []\\n\\n hashes = [ 0 ] * (len(text) - pattern_len + 1)\\n\\n slice = text[len(text) - pattern_len:]\\n hashes[len(text) - pattern_len] = SearchPattern.poly_hash(\\n slice, p, x)\\n\\n y = 1\\n for i in range(1, pattern_len + 1):\\n y = (y * x) % p\\n for i in range(len(text) - pattern_len - 1, -1, -1):\\n hashes[i] = (x * hashes[i + 1] + ord(text[i]) -\\n y * ord(text[i + pattern_len])) % p\\n\\n return hashes\",\n \"def _hash_function(self, key):\\n h = 0\\n a = 31\\n table_size = self.size\\n for i in range(len(key)):\\n h = (h * a + ord(key[i])) % table_size\\n return h\",\n \"def hash_value(self, value):\\n return hash(value) % self.size\",\n \"def hash(self, key):\\n key = str(key)\\n h = 0\\n a = self.hash_base\\n for i in range(len(str(key))):\\n h = (h * a + ord(key[i])) % self.table_capacity\\n return h\",\n \"def hash_function(input_tuple):\\n return hash(input_tuple)\",\n \"def hashing(self, key):\\n #return hash(key) % self.__size\\n if type(key) == str:\\n hashVal = 0\\n for i, c in enumerate(key):\\n hashVal += int(ord(c)) * 31 ** (len(key) -1 - i)\\n return hashVal % self.__size\\n return key % self.__size\",\n \"def from_string(the_string):\\n hash = hashlib.md5()\\n\\n buf = StringIO.StringIO(the_string)\\n for block in iter(lambda: buf.read(AWS_UPLOAD_PART_SIZE), \\\"\\\"):\\n hash.update(block)\\n return hash.hexdigest()\",\n \"def _gethash(self, invalue) -> int:\\n return hash(invalue) % self.capacity\",\n \"def hash_string(\\r\\n k: bytes,\\r\\n e: str,\\r\\n) -> bytes:\\r\\n return hmac.new(k, e.encode('utf-8'), hashlib.sha256).digest()\",\n \"def HashValue(self) -> _n_0_t_3[_n_0_t_9]:\",\n \"def getSubstring(str):\\n\\tresultStr=[]\\n\\ttempStr=\\\"\\\"\\n\\tfor i in range(len(str)-1):\\n\\t\\t#result[a]=a in result and result[a]+1 or 1\\n\\t\\ttempStr=str[i]\\n\\t\\tfor b in str[i+1:]:\\n\\t\\t\\tif b in tempStr:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\telse: tempStr+=b\\n\\n\\t\\tresultStr.append(tempStr)\\n\\treturn sorted(resultStr,key=lambda x:len(x),reverse=True)[0]\",\n \"def __polynomial_hash(self, s, base = 31, max_size=168):\\r\\n digest = 0\\r\\n max_size = 168\\r\\n for c in s: digest = base * digest + ord(c)\\r\\n digest &= 2 ** max_size - 1 \\r\\n return hex(digest).rstrip('L')\",\n \"def minhash(s, t, n):\\n X = union_signature(s, t, n)\\n Y = intersection_signature(X, s, t)\\n return len(Y) / len(X)\",\n \"def calculate_weighted_hash(cls, word):\\n\\n hash_value = 0\\n for char in word:\\n hash_value += cls.alpha_lookup[char.lower()]\\n return hash_value\",\n \"def h(x):\\n\\n hasher = hashlib.sha256()\\n hasher.update(x)\\n return hasher.digest()\",\n \"def fnvhash(a): \\n h = 2166136261 \\n for i in a: \\n t = (h * 16777619) & 0xffffffffL \\n h = t ^ i \\n return h\",\n \"def hash_string_to_int(\\r\\n k: bytes,\\r\\n e: str,\\r\\n) -> int:\\r\\n return int.from_bytes(hash_string(k, e), 'big')\",\n \"def hax(string):\\r\\n hax_dict = { }\\r\\n return hax_dict.get(string, string)\",\n \"def apply_hash (self, s):\\r\\n m = md5()\\r\\n m.update (s)\\r\\n d = m.digest()\\r\\n # base64.encodestring tacks on an extra linefeed.\\r\\n return encodestring (d)[:-1]\",\n \"def gen_hash(s: str) -> str:\\n\\n m = hashlib.md5()\\n m.update(bytes(s, encoding = 'utf8'))\\n hash_code = str(m.hexdigest())\\n\\n return hash_code\",\n \"def main():\\n inputStr = input(\\\"Enter string to be hashed: \\\")\\n print(f\\\"Hashed string: {hash(inputStr.replace(' ', ''))}\\\")\",\n \"def hash_dump_string(dump_string):\\n return hashlib.sha256(six.binary_type().join([dump_string])).hexdigest()\",\n \"def _hash(self, key):\\n if self.function == 'fnv':\\n h = 2166136261\\n for i in range(len(key)):\\n h = (h * 16777619) ^ ord(key[i])\\n return h\\n elif self.function == 'add':\\n h = 0\\n for i in range(len(key)):\\n h += ord(key[i])\\n return h\",\n \"def __str_to_hash(string_to_hash: str, errors: str = 'ignore') -> str:\\n string_hash = string_to_hash.encode(encoding=\\\"utf-8\\\", errors=errors)\\n return hashlib.md5(string_hash).hexdigest()\",\n \"def hash_str_length(self):\\n return self.hash_byte_length() * 2\",\n \"def hash_function_1(key):\\n hash = 0\\n for i in key:\\n hash = hash + ord(i)\\n return hash\",\n \"def _hash(self, hashKey):\\n return hashKey % self.size\",\n \"def cache_find_hashed(item: str) -> str:\\n\\treturn cache_find(md5(item))\",\n \"def calc_statistics_hash(self) -> bytes:\\n return b\\\"somehash\\\"\",\n \"def get_checksum(str):\\n hash_object = hashlib.sha1(b'%s' % str)\\n hex_dig = hash_object.hexdigest()\\n return hex_dig\",\n \"def _reduce(self, hash):\\n summation = 0\\n for char in hash:\\n summation += ord(char)\\n return summation % self._size\",\n \"def shist(search_string):\\n # gets the full dict\\n my_string = str(search_string)\\n history_dict = hist()\\n # creates the final history dict\\n final_history_dict = {}\\n for key in history_dict:\\n if my_string in history_dict[key]:\\n final_history_dict[key] = history_dict[key]\\n return final_history_dict\",\n \"def minhash(self, s, ii, mask):\\n fn = self._hash_function(ii)\\n return min([fn(x) & mask for x in s])\",\n \"def _rehash(self, hashKey, integer):\\n return ( hashKey + integer * integer ) % self.size\",\n \"def get_hash(thing):\\n n = hashlib.sha256()\\n \\n if isinstance(thing,str):\\n n.update(thing.encode('utf-8' ))\\n elif isinstance(thing, bytes):\\n n.update(thing)\\n elif isinstance(thing,BeautifulSoup):\\n n.update(get_hash(str(thing)))\\n else:\\n raise RuntimeError(\\\"unknown type: {}\\\".format(str(type(thing))))\\n \\n return(n.digest())\",\n \"def _build_hash_table(arr: [str]):\\n ht = {}\\n for cur_str in arr:\\n\\n anagram = cur_str[::-1]\\n if cur_str in ht.keys():\\n # This string is an anagram of some previous\\n # Increase anagram count for hash table item\\n (original, orig_cnt, anag_cnt) = ht[cur_str]\\n ht[cur_str] = (original, orig_cnt, anag_cnt + 1)\\n elif anagram in ht.keys():\\n # This string equals to some prevoius\\n # Increase original count for hash table item\\n (original, orig_cnt, anag_cnt) = ht[anagram]\\n ht[anagram] = (original, orig_cnt+1, anag_cnt)\\n else:\\n # This string is new\\n ht[anagram] = (cur_str, 1, 0)\\n return ht\",\n \"def poly_hash(text, p, x):\\n hash = 0\\n for ch in reversed(text):\\n hash = (hash * x + ord(ch)) % p\\n\\n return hash\",\n \"def hash_f(x: Text) -> Tuple[Text, Text]:\\n return \\\"hash\\\", x.lower()\",\n \"def find_idx(string, substring):\\n idx = string.find(substring)\\n while idx != -1:\\n yield idx\\n idx = string.find(substring, idx+1)\",\n \"def slophash(val):\\n\\n if not val:\\n return None\\n else:\\n return sha256(val.encode('utf8')).hexdigest()[0:10]\",\n \"def digest(string):\\n return md5(string.encode(\\\"utf-8\\\")).hexdigest()\",\n \"def get_string_sha256(str_to_convert):\\n hasher = hashlib.sha256()\\n hasher.update(bytearray(str_to_convert.encode('ascii')))\\n return base64.b64encode(hasher.digest())\",\n \"async def get_hash(identifier):\\n return hashlib.md5(identifier.encode('utf8')).hexdigest()\",\n \"def distinct_substrigns(str1, k):\\n hash_map = {}\\n n = len(str1)\\n start = 0 \\n end = 1\\n hash_map[str1[start]] = 1\\n res = [] \\n\\n while end < n:\\n print (\\\"hash_map is :\\\", hash_map)\\n if str1[end] not in hash_map: \\n hash_map[str1[end]] = 1\\n else:\\n while str1[start]!=str1[end]:\\n del hash_map[str1[start]]\\n start += 1\\n start += 1\\n\\n if end-start+1 == k:\\n temp = str1[start:end+1]\\n if temp not in res:\\n res.append(temp)\\n del hash_map[str1[start]]\\n start += 1\\n end += 1\\n #print(end,start)\\n return res\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.698711","0.666947","0.6521936","0.64672047","0.6333751","0.6230026","0.619084","0.6189764","0.6126085","0.6096258","0.6066745","0.60384375","0.6025499","0.6015478","0.59934574","0.5983955","0.5969096","0.5931997","0.5915404","0.590932","0.590201","0.58949053","0.58854634","0.581452","0.57773787","0.57503337","0.57376105","0.56938386","0.5690356","0.56843364","0.5628747","0.56151694","0.56144136","0.5596155","0.55805445","0.55785197","0.5546298","0.5534413","0.5496787","0.548706","0.54717416","0.5466661","0.5464694","0.5446593","0.54306984","0.5429179","0.54287744","0.5422655","0.5410028","0.54075354","0.53976417","0.5369141","0.5361776","0.53555065","0.53438485","0.5340869","0.531283","0.5306619","0.5302808","0.528721","0.52865386","0.5281391","0.52804583","0.52761674","0.52721137","0.52677655","0.5247638","0.5241618","0.52401364","0.523942","0.5232617","0.52177405","0.5216913","0.5212339","0.5212138","0.5206073","0.52043694","0.5203345","0.51943135","0.51901233","0.51814324","0.51761556","0.51706797","0.5169087","0.51650465","0.51608455","0.5154639","0.5147977","0.51432383","0.514171","0.5141465","0.51403123","0.51257277","0.51201093","0.5119322","0.51173186","0.5111225","0.5105933","0.509925","0.5093864"],"string":"[\n \"0.698711\",\n \"0.666947\",\n \"0.6521936\",\n \"0.64672047\",\n \"0.6333751\",\n \"0.6230026\",\n \"0.619084\",\n \"0.6189764\",\n \"0.6126085\",\n \"0.6096258\",\n \"0.6066745\",\n \"0.60384375\",\n \"0.6025499\",\n \"0.6015478\",\n \"0.59934574\",\n \"0.5983955\",\n \"0.5969096\",\n \"0.5931997\",\n \"0.5915404\",\n \"0.590932\",\n \"0.590201\",\n \"0.58949053\",\n \"0.58854634\",\n \"0.581452\",\n \"0.57773787\",\n \"0.57503337\",\n \"0.57376105\",\n \"0.56938386\",\n \"0.5690356\",\n \"0.56843364\",\n \"0.5628747\",\n \"0.56151694\",\n \"0.56144136\",\n \"0.5596155\",\n \"0.55805445\",\n \"0.55785197\",\n \"0.5546298\",\n \"0.5534413\",\n \"0.5496787\",\n \"0.548706\",\n \"0.54717416\",\n \"0.5466661\",\n \"0.5464694\",\n \"0.5446593\",\n \"0.54306984\",\n \"0.5429179\",\n \"0.54287744\",\n \"0.5422655\",\n \"0.5410028\",\n \"0.54075354\",\n \"0.53976417\",\n \"0.5369141\",\n \"0.5361776\",\n \"0.53555065\",\n \"0.53438485\",\n \"0.5340869\",\n \"0.531283\",\n \"0.5306619\",\n \"0.5302808\",\n \"0.528721\",\n \"0.52865386\",\n \"0.5281391\",\n \"0.52804583\",\n \"0.52761674\",\n \"0.52721137\",\n \"0.52677655\",\n \"0.5247638\",\n \"0.5241618\",\n \"0.52401364\",\n \"0.523942\",\n \"0.5232617\",\n \"0.52177405\",\n \"0.5216913\",\n \"0.5212339\",\n \"0.5212138\",\n \"0.5206073\",\n \"0.52043694\",\n \"0.5203345\",\n \"0.51943135\",\n \"0.51901233\",\n \"0.51814324\",\n \"0.51761556\",\n \"0.51706797\",\n \"0.5169087\",\n \"0.51650465\",\n \"0.51608455\",\n \"0.5154639\",\n \"0.5147977\",\n \"0.51432383\",\n \"0.514171\",\n \"0.5141465\",\n \"0.51403123\",\n \"0.51257277\",\n \"0.51201093\",\n \"0.5119322\",\n \"0.51173186\",\n \"0.5111225\",\n \"0.5105933\",\n \"0.509925\",\n \"0.5093864\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5547814"},"document_rank":{"kind":"string","value":"36"}}},{"rowIdx":86693,"cells":{"query":{"kind":"string","value":"Precompute hashtable for a string with 2 prime numbers and a multiplier. Using a rolling hash function, we can get the hashvalue for any substring in constant time."},"document":{"kind":"string","value":"def pre_compute_hashes(s, M1, M2, X):\n n = len(s)\n h1 = [0 for _ in range(n+1)]\n h2 = [0 for _ in range(n+1)]\n for i in range(1, n+1):\n ch = ord(s[i-1])\n h1[i] = (X*h1[i-1] + ch) % M1\n h2[i] = (X*h2[i-1] + ch) % M2\n return h1, h2"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _precompute_substrings(self, p: int) -> List[int]:\n hash_vals: List[int] = [0]\n for i in range(len(self._s)):\n val = (hash_vals[i] * self.X + ord(self._s[i])) % p\n hash_vals.append(val)\n\n return hash_vals","def get_hash_value(table, prime, multiplier, start, length):\n y = pow(multiplier, length, prime)\n hash_value = (table[start+length] - y*table[start]) % prime\n return hash_value","def precompute_hashes(text, pattern_len, p, x):\n\n if len(text) < pattern_len or pattern_len == 0:\n return []\n\n hashes = [ 0 ] * (len(text) - pattern_len + 1)\n\n slice = text[len(text) - pattern_len:]\n hashes[len(text) - pattern_len] = SearchPattern.poly_hash(\n slice, p, x)\n\n y = 1\n for i in range(1, pattern_len + 1):\n y = (y * x) % p\n for i in range(len(text) - pattern_len - 1, -1, -1):\n hashes[i] = (x * hashes[i + 1] + ord(text[i]) -\n y * ord(text[i + pattern_len])) % p\n\n return hashes","def hashstring(astring, tablesize):\n \n sum = 0\n for pos in range(len(astring)):\n # to account for anagrams, we give weightage to positions of the letters to give different hash values\n sum = sum + ord(astring[pos]) * (pos + 1)\n \n return sum % tablesize","def myHash(string, base=91, mod=1000000321):\n value = 0\n for pos, elem in enumerate(string[::-1]): # считаем значение полинома\n value += ord(elem) * base**pos # в последней задаче сделано с помощью массива (динамика)\n return value % mod","def simple_hash(integer, primes):\n result = 0\n result = ((result + integer + primes[0]) * primes[1]) % primes[2]\n return result","def computeHash(string):\n\tif isBytes(string):\n\t\tstring = string.decode(\"latin-1\")\n\thash_ = 63689\n\tfor char in string:\n\t\thash_ = hash_ * 378551 + ord(char)\n\treturn hash_ % 65536","def poly_hash(text, p, x):\n hash = 0\n for ch in reversed(text):\n hash = (hash * x + ord(ch)) % p\n\n return hash","def hash_double(d):\n # initialize table\n table = [\"-\"] * 19\n # consider each integer k in the input\n for k in d:\n # if k is already in the table this is a duplicate so move to next integer in the input\n # note this check for a duplicate is using the functionality of python rather than checking using a linear probe\n if k in table:\n continue\n # apply the hash function\n i = (6 * k + 3) % 19\n t = i\n # initialize count that checks whether linear probe has considered each bucket and is now full\n count = 0\n j = 0\n # while bucket is already filled\n s = 11 - (k % 11)\n while table[i] != '-':\n j += 1\n # move to next bucket\n i = (t + j*s) % 19\n # increment count\n count += 1\n\n # if table is full\n if count >= 18:\n # can return table as nothing further can be added\n break\n\n # Ensure table[i] is empty so k can be added here\n if table[i] == '-':\n table[i] = k\n\n # now each part of the input has been considered return the table\n return table","def customHashFunc(str):\n return sum(ord(chr) for chr in str)%128","def _rehash(self, hashKey, integer):\n return ( hashKey + integer * integer ) % self.size","def _hash_function(self, key):\n h = 0\n a = 31\n table_size = self.size\n for i in range(len(key)):\n h = (h * a + ord(key[i])) % table_size\n return h","def h_python(key, N):\n return hash(key) % N","def custom_hash(str_in):\n \n PRIMES = [\n 2, 3, 5, 7, 11, 13,\n 17, 19, 23, 29, 31,\n 37, 41, 43, 47, 53,\n 59, 61, 67, 71, 73,\n 79, 83, 89, 97, 101\n ]\n\n LOWERCASE_Z_ASCII = ord('z')\n hash_count = 1\n\n for letter in str_in:\n try:\n hash_count *= PRIMES[ord(letter) - LOWERCASE_Z_ASCII]\n except IndexError:\n print(f'list index out of range: {letter} in {str_in}')\n \n return hash_count","def hash_key(self,key: str) -> int: \n \n total = 0\n prime = 3\n \n for index, char in enumerate(key,start=1):\n \n total += ord(char)*(prime)**index\n \n return total % self.buckets","def hash(plainString):\n result = plainString\n for i in range(0,12):\n result = hashHelp(result)\n return result","def mkPerfHash(keys, Hash):\n f1, f2, G = generate_hash(keys, Hash)\n return lambda k: (G[f1(k)] + G[f2(k)]) % len(G)","def _hash_function(self, k):\n return (hash(k) * self._scale + self._shift) % self._prime % len(self._table)","def hash_function_2(key: str) -> int:\n hash, index = 0, 0\n index = 0\n for letter in key:\n hash += (index + 1) * ord(letter)\n index += 1\n return hash","def compute_minhash(string: str) -> LeanMinHash:\n m = MinHash(num_perm=PERMUTATIONS)\n for d in string_encoder(string):\n m.update(d)\n return LeanMinHash(m)","def hash_quadratic(d):\n # initialize table\n table = [\"-\"] * 19\n # consider each integer k in the input\n for k in d:\n # if k is already in the table this is a duplicate so move to next integer in the input\n # note this check for a duplicate is using the functionality of python rather than checking using a linear probe\n if k in table:\n continue\n # apply the hash function\n i = (6 * k + 3) % 19\n t = i\n # initialize count that checks whether linear probe has considered each bucket and is now full\n count = 0\n j = 0\n # while bucket is already filled\n\n while table[i] != '-':\n j += 1\n # move to next bucket\n i = (t + j ** 2) % 19\n # increment count\n count += 1\n\n # if table is full\n if count >= 18:\n # can return table as nothing further can be added\n break\n\n # Ensure table[i] is empty so k can be added here\n if table[i] == '-':\n table[i] = k\n\n # now each part of the input has been considered return the table\n return table","def prime_mod_hash(self, key: T) -> int:\n return (self.a * self.encode(key)) % self.table_size","def perfect_hash(num):\n return ((num+OFFSET)*(SIZE/PERIOD)) % (SIZE+1) + 1","def _gethash(self, invalue) -> int:\n return hash(invalue) % self.capacity","def hash_function(self, x):\n if not x:\n return -1\n hashed_value = 0\n\n for char in x:\n hashed_value = 181 * hashed_value + ord(char)\n\n return hashed_value % self.capacity","def hashHelp(plainString):\n # hashed = plainString\n n = len(plainString) // 4\n\n a = plainString[0:n]\n b = plainString[n : n * 2]\n c = plainString[n * 2 : n * 3]\n d = plainString[n * 3 : len(plainString)]\n\n if len(a) == 0 or len(b) == 0 or len(c) == 0 or len(d) == 0:\n raise Exception(\"Error string not long enough, use at least 4 characters\")\n\n numA = numToBin(functionF(a,d))\n numB = numToBin(strToNum(b))\n numC = numToBin(bitShift(c))\n numD = numToBin(functionG(a,b,c,d))\n\n # hashed = A + B + numC + D\n # hashed = numA + '|' + numB + '|' + numC + '|' + numD\n hashed = text_from_bits(numD + numA + numB + numC)\n hashed = numbersToLetters(hashed)\n return hashed","def search(self, L: int, a: int, modulus: int, n: int, nums: List[int]) -> str:\n # compute the hash of string S[:L]\n h = 0\n for i in range(L):\n h = (h * a + nums[i]) % modulus\n \n # already seen hashes of strings of length L\n seen = {h} \n # const value to be used often : a**L % modulus\n aL = pow(a, L, modulus) \n for start in range(1, n - L + 1):\n # compute rolling hash in O(1) time\n h = (h * a - nums[start - 1] * aL + nums[start + L - 1]) % modulus\n if h in seen:\n return start\n seen.add(h)\n return -1","def _hash(self, string, hash_type):\n hash_types = {\n 'TABLE_OFFSET': 0,\n 'HASH_A': 1,\n 'HASH_B': 2,\n 'TABLE': 3\n }\n seed1 = 0x7FED7FED\n seed2 = 0xEEEEEEEE\n\n for ch in string.upper():\n if not isinstance(ch, int): ch = ord(ch)\n value = self.encryption_table[(hash_types[hash_type] << 8) + ch]\n seed1 = (value ^ (seed1 + seed2)) & 0xFFFFFFFF\n seed2 = ch + seed1 + seed2 + (seed2 << 5) + 3 & 0xFFFFFFFF\n\n return seed1","def preprocess_hashes(tex):\n blocks = catlist()\n rx = hash_rx\n m = rx.search(tex)\n while m:\n if len(m.group(2)) > 40:\n tex2htm.warn(\"Possible runaway hash: {}\".format(text_sample(m.group(2))))\n raise(None)\n blocks.append(tex[:m.start()])\n blocks.append(re.sub(r'(^|[^\\\\])%', r'\\1\\%', m.group(0)))\n tex = tex[m.end():]\n m = rx.search(tex)\n blocks.append(tex)\n return \"\".join(blocks)","def compute_phash(im):\n return imagehash.phash(ensure_pil(im))","def rehash(self):\n primes = [3, 7, 11, 17, 23, 29, 37, 47, 59, 71, 89, 107, 131, 163, 197, 239, 293, 353, 431, 521, 631, 761,\n 919, 1103, 1327, 1597, 1931, 2333, 2801, 3371, 4049, 4861, 5839, 7013, 8419, 10103, 12143, 14591,\n 17519, 21023, 25229, 30293, 36353, 43627, 52361, 62851, 75431, 90523, 108631, 130363, 156437,\n 187751, 225307, 270371, 324449, 389357, 467237, 560689, 672827, 807403, 968897, 1162687, 1395263,\n 1674319, 2009191, 2411033, 2893249, 3471899, 4166287, 4999559, 5999471, 7199369]\n\n temp_list = []\n for i in self.array:\n if i is not None:\n temp_list.append(i)\n\n self.table_capacity = 2 * self.table_capacity\n\n\n try:\n for i in range(len(primes)):\n if primes[i] > self.table_capacity:\n self.table_capacity = primes[i]\n break\n elif i == (len(primes) -1) :\n if not primes[i] > self.table_capacity:\n raise ValueError(\"There is no prime in the list such that prime > \" + self.table_capacity)\n except Exception as error:\n print(error)\n\n self.array = [None] * self.table_capacity\n self.count = 0\n for element in temp_list:\n self.insert(element[0], element[1])\n\n self.rehash_count += 1","def elf_hash(s):\n h = 0\n for c in s:\n h = (h << 4) + ord(c)\n t = (h & 0xF0000000)\n if t != 0:\n h = h ^ (t >> 24)\n h = h & ~t\n return h","def _build_hash_table(arr: [str]):\n ht = {}\n for cur_str in arr:\n\n anagram = cur_str[::-1]\n if cur_str in ht.keys():\n # This string is an anagram of some previous\n # Increase anagram count for hash table item\n (original, orig_cnt, anag_cnt) = ht[cur_str]\n ht[cur_str] = (original, orig_cnt, anag_cnt + 1)\n elif anagram in ht.keys():\n # This string equals to some prevoius\n # Increase original count for hash table item\n (original, orig_cnt, anag_cnt) = ht[anagram]\n ht[anagram] = (original, orig_cnt+1, anag_cnt)\n else:\n # This string is new\n ht[anagram] = (cur_str, 1, 0)\n return ht","def getHash(expression):\n h = {}\n for i in range(0, len(expression), 2):\n sign = expression[i]\n char = expression[i+1]\n if char not in h:\n h[char] = 0\n if sign == \"+\":\n h[char] += 1\n else:\n h[char] -= 1\n if h[char] == 0:\n del h[char]\n return h","def calHash(n, m):\n return int(m*BloomFilter.ln2/n)","def hash(self, text):\n hashval = 0\n for i in xrange(0, len(text)):\n hashval += ord(text[i])**i\n return hashval","def hash_function(s):\n\n # O(n) over the key length\n # O(1) over the HASH_DATA_SIZE\n\n bytes_list = s.encode()\n\n total = 0\n\n\n for b in bytes_list: # O(n) over the length of the key\n total += b\n\n\n total &= 0xffffffff # 32 bit (8 f's)\n\n return total","def fn(k):\n seen = set()\n for i in range(len(s)-k+1): \n val = (prefix[i+k] - prefix[i]*fac[k]) % MOD \n if val in seen: return True # rolling hash (ver. Monte Carlo)\n seen.add(val)\n return False","def hash_function_multiplication(key, array_size, a):\n temp = a * key \n temp = temp - int(temp) \n\n return int(array_size * temp)","def compute_hash(self, key: int):\n return key % 42","def hash_function_2(key):\n hash = 0\n index = 0\n for i in key:\n hash = hash + (index + 1) * ord(i)\n index = index + 1\n return hash","def prehash(key):\n\n return hash(key)","def hash(string):\n hs = 0\n for s in string:\n hs += ord(s)\n return hs","def chunk_hashes(str):\n chunk = 1024*1024\n chunk_count = int(math.ceil(len(str)/float(chunk)))\n chunks = [str[i*chunk:(i+1)*chunk] for i in range(chunk_count)]\n return [hashlib.sha256(x).digest() for x in chunks]","def concatHash(h1: int, h2: int, len2: int, mod=10**11 + 7, base=1313131) -> int:\r\n return (h1 * pow(base, len2, mod) + h2) % mod","def calculate_rule_hash(self, rule):\r\n hash_strings = []\r\n condition_string_prefaces = (\"$\", \"!\", \"#\", \"@\")\r\n # dictionary for substitutions\r\n string_substitutions = {}\r\n all_strings = []\r\n # original code used md5\r\n # m = hashlib.md5()\r\n m = hashlib.sha3_256()\r\n # Adding all string contents to the list\r\n if 'strings' in rule:\r\n for s in rule['strings']:\r\n if s['type'] == \"byte\":\r\n # original code just needed to append the converted hex code as a string. We need to create the dictionary entries for substitutions as well\r\n # hash_strings.append(re.sub(r'[^a-fA-F\\?0-9]+', '', s['value']))\r\n byte_code_string = re.sub(r'[^a-fA-F\\?0-9]+', '', s['value'])\r\n dict_entry = {s['name']: byte_code_string}\r\n string_substitutions.update(dict_entry)\r\n hash_strings.append(byte_code_string)\r\n else:\r\n # The following line was the only portion of this else statement in the original code\r\n # This change takes modifiers into account for string arguments\r\n # hash_strings.append(s['value'])\r\n string_and_modifiers = []\r\n string_and_modifiers.append(s['value'])\r\n if 'modifiers' in s:\r\n for modifier in s['modifiers']:\r\n string_and_modifiers.append(modifier)\r\n string_and_modifiers = \" \".join(string_and_modifiers)\r\n all_strings.append(\"$\"+string_and_modifiers)\r\n dict_entry = {s['name']: string_and_modifiers}\r\n string_substitutions.update(dict_entry)\r\n #hash_strings.append(\"$\"+string_and_modifiers)\r\n all_strings = self.resort_stings_add_commas(all_strings)\r\n # Adding the components of the condition to the list (except the variables)\r\n all_wild_card_1 = \"\\$\\*\"\r\n all_wild_card_2 = \"them\"\r\n for e in rule['condition_terms']:\r\n if re.match(all_wild_card_1, e) or re.match(all_wild_card_2, e):\r\n hash_strings.extend(all_strings)\r\n elif e.startswith(condition_string_prefaces):\r\n if len(e) > 1:\r\n string_preface, string_name = e[:1], e[1:]\r\n string_name = \"$\" + string_name\r\n if e.endswith(\"*\"):\r\n hash_strings.extend(self.resort_stings_add_commas(self.regex_match_string_names_for_values(string_preface, string_name, string_substitutions)))\r\n #hash_strings.extend(\"Pull all the matching strings\")\r\n else:\r\n if string_name in string_substitutions:\r\n substituted = string_preface + string_substitutions[string_name]\r\n hash_strings.append(substituted)\r\n else:\r\n hash_strings.append(e)\r\n else:\r\n hash_strings.append(e)\r\n else:\r\n hash_strings.append(e)\r\n # Generate a hash from the sorted contents\r\n #hash_strings.sort()\r\n m.update(\"\".join(hash_strings).encode(\"ascii\"))\r\n return m.hexdigest()","def __polynomial_hash(self, s, base = 31, max_size=168):\r\n digest = 0\r\n max_size = 168\r\n for c in s: digest = base * digest + ord(c)\r\n digest &= 2 ** max_size - 1 \r\n return hex(digest).rstrip('L')","def _hash_func(self, key: int) -> int:\n return key % self.capacity","def hash_function_2(key):\n\n hash = 0\n index = 0\n for i in key:\n hash = hash + (index + 1) * ord(i)\n index = index + 1\n return hash","def hash_function_2(key):\n\n hash = 0\n index = 0\n for i in key:\n hash = hash + (index + 1) * ord(i)\n index = index + 1\n return hash","def hash_function_2(key):\n\n hash = 0\n index = 0\n for i in key:\n hash = hash + (index + 1) * ord(i)\n index = index + 1\n return hash","def hash_string(to_hash):\n\n chars = string.printable\n\n hashed = \"\"\n\n total = 1\n\n counter = 1\n\n for letter in to_hash:\n\n total *= (chars.index(letter) * counter * len(to_hash)*13)\n\n counter += 1\n\n if counter%3 == 0:\n\n total *= total\n\n total = str(total)[:30]\n\n temp_int = \"\"\n\n for i in range(len(total)):\n\n temp_int += total[i]\n\n if i % 2 != 0:\n\n hashed += chars[int(temp_int)]\n\n temp_int = \"\"\n\n return hashed","def hash_function_1(key: str) -> int:\n hash = 0\n for letter in key:\n hash += ord(letter)\n return hash","def hash(self, key):\n key = str(key)\n h = 0\n a = self.hash_base\n for i in range(len(str(key))):\n h = (h * a + ord(key[i])) % self.table_capacity\n return h","def hash(self, searchkey):\n return searchkey % self.n","def hash(token, num_buckets):\n return murmurhash3_32(token, positive=True) % num_buckets","def fasthash(string):\r\n md4 = hashlib.new(\"md4\")\r\n md4.update(string)\r\n return md4.hexdigest()","def hash_djb2(string):\n hashval = ctypes.c_uint(5381)\n for char in string:\n hashval.value = ((hashval.value << 5) + hashval.value) + ord(char)\n return hashval.value & 0x7FFFFFFF","def h(x):\n\n hasher = hashlib.sha256()\n hasher.update(x)\n return hasher.digest()","def get_hash_code(s):\n h = 0\n n = len(s)\n for i, c in enumerate(s):\n h = h + ord(c) * 31 ** (n - 1 - i)\n return StrUtil.convert_4_bytes(h)","def compute_phash_int(im):\n return phash2int(compute_phash(im))","def hashing_info(string):#KEY HASHING FUNCTION\n nodeInfo = string.encode('utf-8')\n\n #md5 -> 2^7 = 128 bits\n hash_object = hashlib.md5()\n hash_object.update(nodeInfo)\n\n tmp = hash_object.hexdigest()\n tmp = int(tmp,16)\n\n result = tmp >> (128-16)\n return result","def hashing(self, key):\n #return hash(key) % self.__size\n if type(key) == str:\n hashVal = 0\n for i, c in enumerate(key):\n hashVal += int(ord(c)) * 31 ** (len(key) -1 - i)\n return hashVal % self.__size\n return key % self.__size","def knot_hash(s: str) -> str:\n inp = [ord(c) for c in s] + [17, 31, 73, 47, 23]\n seq = [*range(256)]\n skip, pos = 0, 0\n for _ in range(64):\n seq, skip, pos = knot_hash_round(inp, seq, skip, pos, in_place=False)\n seq = [reduce(lambda a, b: a ^ b, seq[i:i + 16]) for i in range(0, 256, 16)]\n return ''.join(hex(k)[2:].zfill(2) for k in seq)","def chord_hash(input_string):\n h = hashlib.sha1() # 160 bit string\n encoded_data = input_string.encode('utf-8')\n h.update(encoded_data)\n hex_string = h.hexdigest()\n hex_value = int(hex_string, 16)\n hash_integer_value = hex_value >> (160 - m)\n return hash_integer_value","def hash(self, string):\n return self.__scaffydb.hash(string)","def fnvhash(a): \n h = 2166136261 \n for i in a: \n t = (h * 16777619) & 0xffffffffL \n h = t ^ i \n return h","def HashAlgorithm(self) -> _n_7_t_0:","def hash_to_table(self, hash_vals):\r\n\t\treturn int(sum([(hash_vals[i] * self._random_r[i]) \\\r\n\t\t\t\t\t\tfor i in range(self._lsh_nums)]) % self._c)","def calculate_weighted_hash(cls, word):\n\n hash_value = 0\n for char in word:\n hash_value += cls.alpha_lookup[char.lower()]\n return hash_value","def slowfun(x, y):\n tmp_key = str(x) + \"_\" + str(y)\n\n # Does the lookup key exist in lookup_dict?\n if tmp_key in lookup_dict:\n # Found key, return precalculated value\n return lookup_dict[tmp_key]\n\n # Generate \n v = math.pow(x, y)\n v = math.factorial(v)\n v //= (x + y)\n v %= 982451653\n \n lookup_dict[tmp_key] = v\n\n return v","def hash_value(self, x, h_num):\n a, b, p = self.hfs[h_num]\n return (a * x + b) % p % self.m","def _hash(self) -> None:\r\n # for a unit cube there are 8 possible hashes\r\n # returns the tuple of with all 8 hashes\r\n\r\n self.hashes[\"aaa\"] = P[P[P[self.xi] + self.yi] + self.zi]\r\n self.hashes[\"aab\"] = P[P[P[self.xi] + self.yi] + self._inc(self.zi)]\r\n self.hashes[\"aba\"] = P[P[P[self.xi] + self._inc(self.yi)] + self.zi]\r\n self.hashes[\"abb\"] = P[P[P[self.xi] + self._inc(self.yi)] + self._inc(self.zi)]\r\n self.hashes[\"baa\"] = P[P[P[self._inc(self.xi)] + self.yi] + self.zi]\r\n self.hashes[\"bab\"] = P[P[P[self._inc(self.xi)] + self.yi] + self._inc(self.zi)]\r\n self.hashes[\"bba\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self.zi]\r\n self.hashes[\"bbb\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self._inc(self.zi)]","def random_h_function(key, N):\n\n if key is None:\n raise TypeError(\"Key must not be None\")\n\n\n hashcode = 0\n for i in range(len(key)):\n hashcode = (71 * hashcode + ord(key[i])) % N\n return hashcode","def process_hash_regularizer(input, coeff = 0.001, name ='hash'):\n with tf.variable_scope(name) as scope:\n if coeff > 0:\n hash_regularizer = tf.nn.l2_loss( tf.abs(input) - 1 ) \n tf.summary.scalar(name + '_hash_penalty', hash_regularizer) \n hash_regularizer_obj = coeff * hash_regularizer\n tf.add_to_collection(name + '_objectives', hash_regularizer_obj)\n tf.summary.scalar(name + '_weighted_hash_penalty', hash_regularizer_obj)\n\n for i in range(CODEWORD_LENGTH):\n tf.summary.histogram(name + '_hash_code' + str(i),input[:,i])","def hash(x):\r\n return (randint(1,5*c)*x + randint(1,5*c))%c","def patterns_hash(patterns):\n return hash(\"\".join(map(str, patterns)))\n # return sum((3**(5 * i) + 1) * (p + 1) for i, p in enumerate(patterns))","def hshingle(s, k):\n for s in shingle(s, k):\n yield hash(s)","def _reduce(self, hash):\n summation = 0\n for char in hash:\n summation += ord(char)\n return summation % self._size","def get_num_slots(self):\n Return the load factor for this hash table.\n\n Implement this.\n \"\"\"\n return self.elements / self.capacity\n\n\n def fnv1(self, key):\n \"\"\"\n FNV-1 Hash, 64-bit\n\n Implement this, and/or DJB2.pyy\n \"\"\"\n\n # Your code here\n\n\n def djb2(self, key):\n \"\"\"\n DJB2 hash, 32-bit\n\n Implement this, and/or FNV-1.\n \"\"\"\n # Your code here\n\n\n def hash_index(self, key):\n \"\"\"\n Take an arbitrary key and return a valid integer index\n between within the storage capacity of the hash table.\n \"\"\"\n #return self.fnv1(key) % self.capacity\n<<<<<<< Updated upstream\n return self.djb2(key) % self.capacity\n=======\n return self.djb2(key) % len(self.storage)\n>>>>>>> Stashed changes\n\n def put(self, key, value):\n \"\"\"\n Store the value with the given key.\n\n Hash collisions should be handled with Linked List Chaining.\n\n Implement this.\n \"\"\"\n<<<<<<< Updated upstream\n # Your code here\n=======\n # need to account for if the key value is the same \n\n i = self.hash_index(key)\n if not self.storage[i]:\n hte = HashTableEntry(key, value)\n self.storage[i] = hte\n self.elements += 1\n hte.head = HashTableEntry(key, value)\n elif self.storage[i] and self.storage[i].key != key:\n self.storage[i].insert_at_head(HashTableEntry(key, value))\n>>>>>>> Stashed changes\n\n\n def delete(self, key):\n \"\"\"\n Remove the value stored with the given key.\n\n Print a warning if the key is not found.\n\n Implement this.\n \"\"\"\n<<<<<<< Updated upstream\n # Your code here\n=======\n i = self.hash_index(key)\n node = self.storage[i]\n prev = None\n if node.key == key:\n self.storage[i] = node.next\n return\n while node != None:\n if node.key == key:\n prev.next = node.next\n self.storage[i].next = None\n return\n prev = node\n node = node.next\n self.elements -= 1\n return\n>>>>>>> Stashed changes\n\n\n def get(self, key):\n \"\"\"\n Retrieve the value stored with the given key.\n\n Returns None if the key is not found.\n\n Implement this.\n \"\"\"\n<<<<<<< Updated upstream\n # Your code here\n=======\n # - find the index in the hash table for the key\n i = self.hash_index(key)\n # - search the list for that key\n if not self.storage[i]:\n return None\n else:\n if self.storage[i].find_key(key) == key:\n return self.storage[i].value\n>>>>>>> Stashed changes\n\n\n def resize(self, new_capacity):\n \"\"\"\n Changes the capacity of the hash table and\n rehashes all key/value pairs.\n\n Implement this.\n \"\"\"\n<<<<<<< Updated upstream\n # Your code here\n=======\n prev_storage = self.storage\n self.capacity = new_cap\n self.storage = [None] * new_cap\n for i in range(len(prev_storage)):\n prev = prev_storage[i]\n if prev:\n while prev:\n if prev.key:\n self.put(prev.key, prev.value)\n prev = prev.next\n\n>>>>>>> Stashed changes\n\n\n\nif __name__ == \"__main__\":\n ht = HashTable(8)\n\n ht.put(\"line_1\", \"'Twas brillig, and the slithy toves\")\n ht.put(\"line_2\", \"Did gyre and gimble in the wabe:\")\n ht.put(\"line_3\", \"All mimsy were the borogoves,\")\n ht.put(\"line_4\", \"And the mome raths outgrabe.\")\n ht.put(\"line_5\", '\"Beware the Jabberwock, my son!')\n ht.put(\"line_6\", \"The jaws that bite, the claws that catch!\")\n ht.put(\"line_7\", \"Beware the Jubjub bird, and shun\")\n ht.put(\"line_8\", 'The frumious Bandersnatch!\"')\n ht.put(\"line_9\", \"He took his vorpal sword in hand;\")\n ht.put(\"line_10\", \"Long time the manxome foe he sought--\")\n ht.put(\"line_11\", \"So rested he by the Tumtum tree\")\n ht.put(\"line_12\", \"And stood awhile in thought.\")\n\n print(\"\")\n\n # Test storing beyond capacity\n for i in range(1, 13):\n print(ht.get(f\"line_{i}\"))\n\n # Test resizing\n old_capacity = ht.get_num_slots()\n ht.resize(ht.capacity * 2)\n new_capacity = ht.get_num_slots()\n\n print(f\"\\nResized from {old_capacity} to {new_capacity}.\\n\")\n\n # Test if data intact after resizing\n for i in range(1, 13):\n print(ht.get(f\"line_{i}\"))\n\n print(\"\")","def hash_function_1(key):\n hash = 0\n for i in key:\n hash = hash + ord(i)\n return hash","def linear_probing(hash, A, x):\n i = 0\n s = len(A)\n while A[getRemainder(hash(x)+i, s)] is not None:\n i = i + 1\n index = getRemainder(hash(x)+i, s)\n print(f'Linear Probing index is: {index}')\n A[index] = x\n print(f'insertion of {x}: {A}')","def r_soft_hash(x):\n if abs(x) < 1e-9:return 0\n # round it to some number of bits\n b = ns.round(ns.log(abs(x)) / ns.log(2))\n gran = 2**(b-30)\n return ns.round(x / gran) * gran","def set_hashes(s, hash_function=_crc32_hash):\n return {(hash_function(e), e) for e in s}","def minhash(s, t, n):\n X = union_signature(s, t, n)\n Y = intersection_signature(X, s, t)\n return len(Y) / len(X)","def keyhash(string):\n return hashlib.sha1(string.encode('utf-8')).hexdigest()","def djb_hash(s):\n h = 5381\n for c in s:\n h = h * 33 + ord(c)\n return h & 0xffffffff","def hashing(file,pp):\n\n def myhash(instring):\n # sdbm hash\n res = 0\n for t in instring:\n res = (ord(t) + (res<<6) + (res<<16) - res) % 2**32\n return res\n\n return hex(myhash(file.replace('\\\\','/')+\":\"+pp))","def sparse_hash(data, rounds=1, suffix=None):\n data = data if suffix is None else data + suffix\n hash = [x for x in range(256)]\n hash_length = len(hash)\n pos = 0\n skip = 0\n\n for round in range(rounds):\n for instruction in data:\n\n # reverse the number of hash digits from the pos\n collector = []\n\n new_pos = 0\n for index in range(pos, pos+instruction):\n if index < hash_length:\n collector.append(hash[index])\n else:\n if new_pos >= hash_length:\n new_pos = 0\n collector.append(hash[new_pos])\n new_pos += 1\n\n to_reverse = list(reversed(collector))\n\n # put the numbers back\n new_pos = 0\n for index, item in enumerate(to_reverse, pos):\n new_pos = index if index is pos else (new_pos + 1)\n if new_pos >= hash_length:\n new_pos = 0\n hash[new_pos] = item\n\n # step to next hash index\n pos += instruction + skip\n if pos >= hash_length:\n special = pos - hash_length\n pos = 0\n special = special % hash_length\n for item in range(special):\n pos += 1\n\n skip += 1\n return hash","def _hash(self, hashKey):\n return hashKey % self.size","def h_rolling(key, N, p=53, m=2**64):\n if type(key) == str:\n s = 0\n for i in range(len(key)):\n s += ord(key[i]) * p**i\n s = s % m\n return s % N\n elif type(N) == int:\n s = 0\n try:\n len(key)\n except:\n raise ValueError\n \n for i in range(len(key)):\n s += ord(key[i]) * p**i\n s = s % m\n return s % N\n else:\n raise ValueError","def test_hash(self):\r\n self.assertEqual(processor_hash('test'), 'GqNJWF7X7L07nEhqMAZ+OVyks1Y=')\r\n self.assertEqual(processor_hash('edx '), '/KowheysqM2PFYuxVKg0P8Flfk4=')","def search_hash(word_input,hashtable):\n\n if word_input in hashtable:\n return hashtable[word_input]\n else:\n return None","def H2( curve, in_str ):\n return curve.hash_to_point( \"H2_salt%s\" % in_str )","def compute_ahash(im):\n return imagehash.average_hash(ensure_pil(im))","def hash(x) -> int:\n pass","def rehash(self, key) -> int:\n return (key + 1) % self.size","def _minhash_from_text(self, text):\n minhash = MinHash(self._config.num_perm)\n for word in self._shingles_from_text(text):\n minhash.update(word.encode('utf8'))\n return minhash","def _hash(self):\r\n MAX = sys.maxint\r\n MASK = 2 * MAX + 1\r\n n = len(self)\r\n h = 1927868237 * (n + 1)\r\n h &= MASK\r\n for x in self:\r\n hx = hash(x)\r\n h ^= (hx ^ (hx << 16) ^ 89869747) * 3644798167\r\n h &= MASK\r\n h = h * 69069 + 907133923\r\n h &= MASK\r\n if h > MAX:\r\n h -= MASK + 1\r\n if h == -1:\r\n h = 590923713\r\n return h","def sherlockAndAnagrams(s):\n counter, len_s = 0, len(s)\n \n print(f\"Original string: {s}. length: {len_s}\")\n for chunk in range(1, len_s):\n arr = []\n for i in range(0, len_s - chunk + 1):\n ex1 = \"\".join(s[i:i+chunk])\n ex = \"\".join(sorted(s[i:i+chunk]))\n print(f\"chunk: {chunk}, i: {i}, sub: {ex1}, sub_sorted: {ex}\")\n arr.append(ex)\n\n c = Counter(arr)\n for key in c:\n val = c[key]\n if val > 1:\n counter += (val * (val - 1)) // 2\n \n print(f\"chunk: {chunk}, # counter: {counter}, Counter: {str(c)}\\n\")\n print(f\"Result: {counter}.\")\n return counter"],"string":"[\n \"def _precompute_substrings(self, p: int) -> List[int]:\\n hash_vals: List[int] = [0]\\n for i in range(len(self._s)):\\n val = (hash_vals[i] * self.X + ord(self._s[i])) % p\\n hash_vals.append(val)\\n\\n return hash_vals\",\n \"def get_hash_value(table, prime, multiplier, start, length):\\n y = pow(multiplier, length, prime)\\n hash_value = (table[start+length] - y*table[start]) % prime\\n return hash_value\",\n \"def precompute_hashes(text, pattern_len, p, x):\\n\\n if len(text) < pattern_len or pattern_len == 0:\\n return []\\n\\n hashes = [ 0 ] * (len(text) - pattern_len + 1)\\n\\n slice = text[len(text) - pattern_len:]\\n hashes[len(text) - pattern_len] = SearchPattern.poly_hash(\\n slice, p, x)\\n\\n y = 1\\n for i in range(1, pattern_len + 1):\\n y = (y * x) % p\\n for i in range(len(text) - pattern_len - 1, -1, -1):\\n hashes[i] = (x * hashes[i + 1] + ord(text[i]) -\\n y * ord(text[i + pattern_len])) % p\\n\\n return hashes\",\n \"def hashstring(astring, tablesize):\\n \\n sum = 0\\n for pos in range(len(astring)):\\n # to account for anagrams, we give weightage to positions of the letters to give different hash values\\n sum = sum + ord(astring[pos]) * (pos + 1)\\n \\n return sum % tablesize\",\n \"def myHash(string, base=91, mod=1000000321):\\n value = 0\\n for pos, elem in enumerate(string[::-1]): # считаем значение полинома\\n value += ord(elem) * base**pos # в последней задаче сделано с помощью массива (динамика)\\n return value % mod\",\n \"def simple_hash(integer, primes):\\n result = 0\\n result = ((result + integer + primes[0]) * primes[1]) % primes[2]\\n return result\",\n \"def computeHash(string):\\n\\tif isBytes(string):\\n\\t\\tstring = string.decode(\\\"latin-1\\\")\\n\\thash_ = 63689\\n\\tfor char in string:\\n\\t\\thash_ = hash_ * 378551 + ord(char)\\n\\treturn hash_ % 65536\",\n \"def poly_hash(text, p, x):\\n hash = 0\\n for ch in reversed(text):\\n hash = (hash * x + ord(ch)) % p\\n\\n return hash\",\n \"def hash_double(d):\\n # initialize table\\n table = [\\\"-\\\"] * 19\\n # consider each integer k in the input\\n for k in d:\\n # if k is already in the table this is a duplicate so move to next integer in the input\\n # note this check for a duplicate is using the functionality of python rather than checking using a linear probe\\n if k in table:\\n continue\\n # apply the hash function\\n i = (6 * k + 3) % 19\\n t = i\\n # initialize count that checks whether linear probe has considered each bucket and is now full\\n count = 0\\n j = 0\\n # while bucket is already filled\\n s = 11 - (k % 11)\\n while table[i] != '-':\\n j += 1\\n # move to next bucket\\n i = (t + j*s) % 19\\n # increment count\\n count += 1\\n\\n # if table is full\\n if count >= 18:\\n # can return table as nothing further can be added\\n break\\n\\n # Ensure table[i] is empty so k can be added here\\n if table[i] == '-':\\n table[i] = k\\n\\n # now each part of the input has been considered return the table\\n return table\",\n \"def customHashFunc(str):\\n return sum(ord(chr) for chr in str)%128\",\n \"def _rehash(self, hashKey, integer):\\n return ( hashKey + integer * integer ) % self.size\",\n \"def _hash_function(self, key):\\n h = 0\\n a = 31\\n table_size = self.size\\n for i in range(len(key)):\\n h = (h * a + ord(key[i])) % table_size\\n return h\",\n \"def h_python(key, N):\\n return hash(key) % N\",\n \"def custom_hash(str_in):\\n \\n PRIMES = [\\n 2, 3, 5, 7, 11, 13,\\n 17, 19, 23, 29, 31,\\n 37, 41, 43, 47, 53,\\n 59, 61, 67, 71, 73,\\n 79, 83, 89, 97, 101\\n ]\\n\\n LOWERCASE_Z_ASCII = ord('z')\\n hash_count = 1\\n\\n for letter in str_in:\\n try:\\n hash_count *= PRIMES[ord(letter) - LOWERCASE_Z_ASCII]\\n except IndexError:\\n print(f'list index out of range: {letter} in {str_in}')\\n \\n return hash_count\",\n \"def hash_key(self,key: str) -> int: \\n \\n total = 0\\n prime = 3\\n \\n for index, char in enumerate(key,start=1):\\n \\n total += ord(char)*(prime)**index\\n \\n return total % self.buckets\",\n \"def hash(plainString):\\n result = plainString\\n for i in range(0,12):\\n result = hashHelp(result)\\n return result\",\n \"def mkPerfHash(keys, Hash):\\n f1, f2, G = generate_hash(keys, Hash)\\n return lambda k: (G[f1(k)] + G[f2(k)]) % len(G)\",\n \"def _hash_function(self, k):\\n return (hash(k) * self._scale + self._shift) % self._prime % len(self._table)\",\n \"def hash_function_2(key: str) -> int:\\n hash, index = 0, 0\\n index = 0\\n for letter in key:\\n hash += (index + 1) * ord(letter)\\n index += 1\\n return hash\",\n \"def compute_minhash(string: str) -> LeanMinHash:\\n m = MinHash(num_perm=PERMUTATIONS)\\n for d in string_encoder(string):\\n m.update(d)\\n return LeanMinHash(m)\",\n \"def hash_quadratic(d):\\n # initialize table\\n table = [\\\"-\\\"] * 19\\n # consider each integer k in the input\\n for k in d:\\n # if k is already in the table this is a duplicate so move to next integer in the input\\n # note this check for a duplicate is using the functionality of python rather than checking using a linear probe\\n if k in table:\\n continue\\n # apply the hash function\\n i = (6 * k + 3) % 19\\n t = i\\n # initialize count that checks whether linear probe has considered each bucket and is now full\\n count = 0\\n j = 0\\n # while bucket is already filled\\n\\n while table[i] != '-':\\n j += 1\\n # move to next bucket\\n i = (t + j ** 2) % 19\\n # increment count\\n count += 1\\n\\n # if table is full\\n if count >= 18:\\n # can return table as nothing further can be added\\n break\\n\\n # Ensure table[i] is empty so k can be added here\\n if table[i] == '-':\\n table[i] = k\\n\\n # now each part of the input has been considered return the table\\n return table\",\n \"def prime_mod_hash(self, key: T) -> int:\\n return (self.a * self.encode(key)) % self.table_size\",\n \"def perfect_hash(num):\\n return ((num+OFFSET)*(SIZE/PERIOD)) % (SIZE+1) + 1\",\n \"def _gethash(self, invalue) -> int:\\n return hash(invalue) % self.capacity\",\n \"def hash_function(self, x):\\n if not x:\\n return -1\\n hashed_value = 0\\n\\n for char in x:\\n hashed_value = 181 * hashed_value + ord(char)\\n\\n return hashed_value % self.capacity\",\n \"def hashHelp(plainString):\\n # hashed = plainString\\n n = len(plainString) // 4\\n\\n a = plainString[0:n]\\n b = plainString[n : n * 2]\\n c = plainString[n * 2 : n * 3]\\n d = plainString[n * 3 : len(plainString)]\\n\\n if len(a) == 0 or len(b) == 0 or len(c) == 0 or len(d) == 0:\\n raise Exception(\\\"Error string not long enough, use at least 4 characters\\\")\\n\\n numA = numToBin(functionF(a,d))\\n numB = numToBin(strToNum(b))\\n numC = numToBin(bitShift(c))\\n numD = numToBin(functionG(a,b,c,d))\\n\\n # hashed = A + B + numC + D\\n # hashed = numA + '|' + numB + '|' + numC + '|' + numD\\n hashed = text_from_bits(numD + numA + numB + numC)\\n hashed = numbersToLetters(hashed)\\n return hashed\",\n \"def search(self, L: int, a: int, modulus: int, n: int, nums: List[int]) -> str:\\n # compute the hash of string S[:L]\\n h = 0\\n for i in range(L):\\n h = (h * a + nums[i]) % modulus\\n \\n # already seen hashes of strings of length L\\n seen = {h} \\n # const value to be used often : a**L % modulus\\n aL = pow(a, L, modulus) \\n for start in range(1, n - L + 1):\\n # compute rolling hash in O(1) time\\n h = (h * a - nums[start - 1] * aL + nums[start + L - 1]) % modulus\\n if h in seen:\\n return start\\n seen.add(h)\\n return -1\",\n \"def _hash(self, string, hash_type):\\n hash_types = {\\n 'TABLE_OFFSET': 0,\\n 'HASH_A': 1,\\n 'HASH_B': 2,\\n 'TABLE': 3\\n }\\n seed1 = 0x7FED7FED\\n seed2 = 0xEEEEEEEE\\n\\n for ch in string.upper():\\n if not isinstance(ch, int): ch = ord(ch)\\n value = self.encryption_table[(hash_types[hash_type] << 8) + ch]\\n seed1 = (value ^ (seed1 + seed2)) & 0xFFFFFFFF\\n seed2 = ch + seed1 + seed2 + (seed2 << 5) + 3 & 0xFFFFFFFF\\n\\n return seed1\",\n \"def preprocess_hashes(tex):\\n blocks = catlist()\\n rx = hash_rx\\n m = rx.search(tex)\\n while m:\\n if len(m.group(2)) > 40:\\n tex2htm.warn(\\\"Possible runaway hash: {}\\\".format(text_sample(m.group(2))))\\n raise(None)\\n blocks.append(tex[:m.start()])\\n blocks.append(re.sub(r'(^|[^\\\\\\\\])%', r'\\\\1\\\\%', m.group(0)))\\n tex = tex[m.end():]\\n m = rx.search(tex)\\n blocks.append(tex)\\n return \\\"\\\".join(blocks)\",\n \"def compute_phash(im):\\n return imagehash.phash(ensure_pil(im))\",\n \"def rehash(self):\\n primes = [3, 7, 11, 17, 23, 29, 37, 47, 59, 71, 89, 107, 131, 163, 197, 239, 293, 353, 431, 521, 631, 761,\\n 919, 1103, 1327, 1597, 1931, 2333, 2801, 3371, 4049, 4861, 5839, 7013, 8419, 10103, 12143, 14591,\\n 17519, 21023, 25229, 30293, 36353, 43627, 52361, 62851, 75431, 90523, 108631, 130363, 156437,\\n 187751, 225307, 270371, 324449, 389357, 467237, 560689, 672827, 807403, 968897, 1162687, 1395263,\\n 1674319, 2009191, 2411033, 2893249, 3471899, 4166287, 4999559, 5999471, 7199369]\\n\\n temp_list = []\\n for i in self.array:\\n if i is not None:\\n temp_list.append(i)\\n\\n self.table_capacity = 2 * self.table_capacity\\n\\n\\n try:\\n for i in range(len(primes)):\\n if primes[i] > self.table_capacity:\\n self.table_capacity = primes[i]\\n break\\n elif i == (len(primes) -1) :\\n if not primes[i] > self.table_capacity:\\n raise ValueError(\\\"There is no prime in the list such that prime > \\\" + self.table_capacity)\\n except Exception as error:\\n print(error)\\n\\n self.array = [None] * self.table_capacity\\n self.count = 0\\n for element in temp_list:\\n self.insert(element[0], element[1])\\n\\n self.rehash_count += 1\",\n \"def elf_hash(s):\\n h = 0\\n for c in s:\\n h = (h << 4) + ord(c)\\n t = (h & 0xF0000000)\\n if t != 0:\\n h = h ^ (t >> 24)\\n h = h & ~t\\n return h\",\n \"def _build_hash_table(arr: [str]):\\n ht = {}\\n for cur_str in arr:\\n\\n anagram = cur_str[::-1]\\n if cur_str in ht.keys():\\n # This string is an anagram of some previous\\n # Increase anagram count for hash table item\\n (original, orig_cnt, anag_cnt) = ht[cur_str]\\n ht[cur_str] = (original, orig_cnt, anag_cnt + 1)\\n elif anagram in ht.keys():\\n # This string equals to some prevoius\\n # Increase original count for hash table item\\n (original, orig_cnt, anag_cnt) = ht[anagram]\\n ht[anagram] = (original, orig_cnt+1, anag_cnt)\\n else:\\n # This string is new\\n ht[anagram] = (cur_str, 1, 0)\\n return ht\",\n \"def getHash(expression):\\n h = {}\\n for i in range(0, len(expression), 2):\\n sign = expression[i]\\n char = expression[i+1]\\n if char not in h:\\n h[char] = 0\\n if sign == \\\"+\\\":\\n h[char] += 1\\n else:\\n h[char] -= 1\\n if h[char] == 0:\\n del h[char]\\n return h\",\n \"def calHash(n, m):\\n return int(m*BloomFilter.ln2/n)\",\n \"def hash(self, text):\\n hashval = 0\\n for i in xrange(0, len(text)):\\n hashval += ord(text[i])**i\\n return hashval\",\n \"def hash_function(s):\\n\\n # O(n) over the key length\\n # O(1) over the HASH_DATA_SIZE\\n\\n bytes_list = s.encode()\\n\\n total = 0\\n\\n\\n for b in bytes_list: # O(n) over the length of the key\\n total += b\\n\\n\\n total &= 0xffffffff # 32 bit (8 f's)\\n\\n return total\",\n \"def fn(k):\\n seen = set()\\n for i in range(len(s)-k+1): \\n val = (prefix[i+k] - prefix[i]*fac[k]) % MOD \\n if val in seen: return True # rolling hash (ver. Monte Carlo)\\n seen.add(val)\\n return False\",\n \"def hash_function_multiplication(key, array_size, a):\\n temp = a * key \\n temp = temp - int(temp) \\n\\n return int(array_size * temp)\",\n \"def compute_hash(self, key: int):\\n return key % 42\",\n \"def hash_function_2(key):\\n hash = 0\\n index = 0\\n for i in key:\\n hash = hash + (index + 1) * ord(i)\\n index = index + 1\\n return hash\",\n \"def prehash(key):\\n\\n return hash(key)\",\n \"def hash(string):\\n hs = 0\\n for s in string:\\n hs += ord(s)\\n return hs\",\n \"def chunk_hashes(str):\\n chunk = 1024*1024\\n chunk_count = int(math.ceil(len(str)/float(chunk)))\\n chunks = [str[i*chunk:(i+1)*chunk] for i in range(chunk_count)]\\n return [hashlib.sha256(x).digest() for x in chunks]\",\n \"def concatHash(h1: int, h2: int, len2: int, mod=10**11 + 7, base=1313131) -> int:\\r\\n return (h1 * pow(base, len2, mod) + h2) % mod\",\n \"def calculate_rule_hash(self, rule):\\r\\n hash_strings = []\\r\\n condition_string_prefaces = (\\\"$\\\", \\\"!\\\", \\\"#\\\", \\\"@\\\")\\r\\n # dictionary for substitutions\\r\\n string_substitutions = {}\\r\\n all_strings = []\\r\\n # original code used md5\\r\\n # m = hashlib.md5()\\r\\n m = hashlib.sha3_256()\\r\\n # Adding all string contents to the list\\r\\n if 'strings' in rule:\\r\\n for s in rule['strings']:\\r\\n if s['type'] == \\\"byte\\\":\\r\\n # original code just needed to append the converted hex code as a string. We need to create the dictionary entries for substitutions as well\\r\\n # hash_strings.append(re.sub(r'[^a-fA-F\\\\?0-9]+', '', s['value']))\\r\\n byte_code_string = re.sub(r'[^a-fA-F\\\\?0-9]+', '', s['value'])\\r\\n dict_entry = {s['name']: byte_code_string}\\r\\n string_substitutions.update(dict_entry)\\r\\n hash_strings.append(byte_code_string)\\r\\n else:\\r\\n # The following line was the only portion of this else statement in the original code\\r\\n # This change takes modifiers into account for string arguments\\r\\n # hash_strings.append(s['value'])\\r\\n string_and_modifiers = []\\r\\n string_and_modifiers.append(s['value'])\\r\\n if 'modifiers' in s:\\r\\n for modifier in s['modifiers']:\\r\\n string_and_modifiers.append(modifier)\\r\\n string_and_modifiers = \\\" \\\".join(string_and_modifiers)\\r\\n all_strings.append(\\\"$\\\"+string_and_modifiers)\\r\\n dict_entry = {s['name']: string_and_modifiers}\\r\\n string_substitutions.update(dict_entry)\\r\\n #hash_strings.append(\\\"$\\\"+string_and_modifiers)\\r\\n all_strings = self.resort_stings_add_commas(all_strings)\\r\\n # Adding the components of the condition to the list (except the variables)\\r\\n all_wild_card_1 = \\\"\\\\$\\\\*\\\"\\r\\n all_wild_card_2 = \\\"them\\\"\\r\\n for e in rule['condition_terms']:\\r\\n if re.match(all_wild_card_1, e) or re.match(all_wild_card_2, e):\\r\\n hash_strings.extend(all_strings)\\r\\n elif e.startswith(condition_string_prefaces):\\r\\n if len(e) > 1:\\r\\n string_preface, string_name = e[:1], e[1:]\\r\\n string_name = \\\"$\\\" + string_name\\r\\n if e.endswith(\\\"*\\\"):\\r\\n hash_strings.extend(self.resort_stings_add_commas(self.regex_match_string_names_for_values(string_preface, string_name, string_substitutions)))\\r\\n #hash_strings.extend(\\\"Pull all the matching strings\\\")\\r\\n else:\\r\\n if string_name in string_substitutions:\\r\\n substituted = string_preface + string_substitutions[string_name]\\r\\n hash_strings.append(substituted)\\r\\n else:\\r\\n hash_strings.append(e)\\r\\n else:\\r\\n hash_strings.append(e)\\r\\n else:\\r\\n hash_strings.append(e)\\r\\n # Generate a hash from the sorted contents\\r\\n #hash_strings.sort()\\r\\n m.update(\\\"\\\".join(hash_strings).encode(\\\"ascii\\\"))\\r\\n return m.hexdigest()\",\n \"def __polynomial_hash(self, s, base = 31, max_size=168):\\r\\n digest = 0\\r\\n max_size = 168\\r\\n for c in s: digest = base * digest + ord(c)\\r\\n digest &= 2 ** max_size - 1 \\r\\n return hex(digest).rstrip('L')\",\n \"def _hash_func(self, key: int) -> int:\\n return key % self.capacity\",\n \"def hash_function_2(key):\\n\\n hash = 0\\n index = 0\\n for i in key:\\n hash = hash + (index + 1) * ord(i)\\n index = index + 1\\n return hash\",\n \"def hash_function_2(key):\\n\\n hash = 0\\n index = 0\\n for i in key:\\n hash = hash + (index + 1) * ord(i)\\n index = index + 1\\n return hash\",\n \"def hash_function_2(key):\\n\\n hash = 0\\n index = 0\\n for i in key:\\n hash = hash + (index + 1) * ord(i)\\n index = index + 1\\n return hash\",\n \"def hash_string(to_hash):\\n\\n chars = string.printable\\n\\n hashed = \\\"\\\"\\n\\n total = 1\\n\\n counter = 1\\n\\n for letter in to_hash:\\n\\n total *= (chars.index(letter) * counter * len(to_hash)*13)\\n\\n counter += 1\\n\\n if counter%3 == 0:\\n\\n total *= total\\n\\n total = str(total)[:30]\\n\\n temp_int = \\\"\\\"\\n\\n for i in range(len(total)):\\n\\n temp_int += total[i]\\n\\n if i % 2 != 0:\\n\\n hashed += chars[int(temp_int)]\\n\\n temp_int = \\\"\\\"\\n\\n return hashed\",\n \"def hash_function_1(key: str) -> int:\\n hash = 0\\n for letter in key:\\n hash += ord(letter)\\n return hash\",\n \"def hash(self, key):\\n key = str(key)\\n h = 0\\n a = self.hash_base\\n for i in range(len(str(key))):\\n h = (h * a + ord(key[i])) % self.table_capacity\\n return h\",\n \"def hash(self, searchkey):\\n return searchkey % self.n\",\n \"def hash(token, num_buckets):\\n return murmurhash3_32(token, positive=True) % num_buckets\",\n \"def fasthash(string):\\r\\n md4 = hashlib.new(\\\"md4\\\")\\r\\n md4.update(string)\\r\\n return md4.hexdigest()\",\n \"def hash_djb2(string):\\n hashval = ctypes.c_uint(5381)\\n for char in string:\\n hashval.value = ((hashval.value << 5) + hashval.value) + ord(char)\\n return hashval.value & 0x7FFFFFFF\",\n \"def h(x):\\n\\n hasher = hashlib.sha256()\\n hasher.update(x)\\n return hasher.digest()\",\n \"def get_hash_code(s):\\n h = 0\\n n = len(s)\\n for i, c in enumerate(s):\\n h = h + ord(c) * 31 ** (n - 1 - i)\\n return StrUtil.convert_4_bytes(h)\",\n \"def compute_phash_int(im):\\n return phash2int(compute_phash(im))\",\n \"def hashing_info(string):#KEY HASHING FUNCTION\\n nodeInfo = string.encode('utf-8')\\n\\n #md5 -> 2^7 = 128 bits\\n hash_object = hashlib.md5()\\n hash_object.update(nodeInfo)\\n\\n tmp = hash_object.hexdigest()\\n tmp = int(tmp,16)\\n\\n result = tmp >> (128-16)\\n return result\",\n \"def hashing(self, key):\\n #return hash(key) % self.__size\\n if type(key) == str:\\n hashVal = 0\\n for i, c in enumerate(key):\\n hashVal += int(ord(c)) * 31 ** (len(key) -1 - i)\\n return hashVal % self.__size\\n return key % self.__size\",\n \"def knot_hash(s: str) -> str:\\n inp = [ord(c) for c in s] + [17, 31, 73, 47, 23]\\n seq = [*range(256)]\\n skip, pos = 0, 0\\n for _ in range(64):\\n seq, skip, pos = knot_hash_round(inp, seq, skip, pos, in_place=False)\\n seq = [reduce(lambda a, b: a ^ b, seq[i:i + 16]) for i in range(0, 256, 16)]\\n return ''.join(hex(k)[2:].zfill(2) for k in seq)\",\n \"def chord_hash(input_string):\\n h = hashlib.sha1() # 160 bit string\\n encoded_data = input_string.encode('utf-8')\\n h.update(encoded_data)\\n hex_string = h.hexdigest()\\n hex_value = int(hex_string, 16)\\n hash_integer_value = hex_value >> (160 - m)\\n return hash_integer_value\",\n \"def hash(self, string):\\n return self.__scaffydb.hash(string)\",\n \"def fnvhash(a): \\n h = 2166136261 \\n for i in a: \\n t = (h * 16777619) & 0xffffffffL \\n h = t ^ i \\n return h\",\n \"def HashAlgorithm(self) -> _n_7_t_0:\",\n \"def hash_to_table(self, hash_vals):\\r\\n\\t\\treturn int(sum([(hash_vals[i] * self._random_r[i]) \\\\\\r\\n\\t\\t\\t\\t\\t\\tfor i in range(self._lsh_nums)]) % self._c)\",\n \"def calculate_weighted_hash(cls, word):\\n\\n hash_value = 0\\n for char in word:\\n hash_value += cls.alpha_lookup[char.lower()]\\n return hash_value\",\n \"def slowfun(x, y):\\n tmp_key = str(x) + \\\"_\\\" + str(y)\\n\\n # Does the lookup key exist in lookup_dict?\\n if tmp_key in lookup_dict:\\n # Found key, return precalculated value\\n return lookup_dict[tmp_key]\\n\\n # Generate \\n v = math.pow(x, y)\\n v = math.factorial(v)\\n v //= (x + y)\\n v %= 982451653\\n \\n lookup_dict[tmp_key] = v\\n\\n return v\",\n \"def hash_value(self, x, h_num):\\n a, b, p = self.hfs[h_num]\\n return (a * x + b) % p % self.m\",\n \"def _hash(self) -> None:\\r\\n # for a unit cube there are 8 possible hashes\\r\\n # returns the tuple of with all 8 hashes\\r\\n\\r\\n self.hashes[\\\"aaa\\\"] = P[P[P[self.xi] + self.yi] + self.zi]\\r\\n self.hashes[\\\"aab\\\"] = P[P[P[self.xi] + self.yi] + self._inc(self.zi)]\\r\\n self.hashes[\\\"aba\\\"] = P[P[P[self.xi] + self._inc(self.yi)] + self.zi]\\r\\n self.hashes[\\\"abb\\\"] = P[P[P[self.xi] + self._inc(self.yi)] + self._inc(self.zi)]\\r\\n self.hashes[\\\"baa\\\"] = P[P[P[self._inc(self.xi)] + self.yi] + self.zi]\\r\\n self.hashes[\\\"bab\\\"] = P[P[P[self._inc(self.xi)] + self.yi] + self._inc(self.zi)]\\r\\n self.hashes[\\\"bba\\\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self.zi]\\r\\n self.hashes[\\\"bbb\\\"] = P[P[P[self._inc(self.xi)] + self._inc(self.yi)] + self._inc(self.zi)]\",\n \"def random_h_function(key, N):\\n\\n if key is None:\\n raise TypeError(\\\"Key must not be None\\\")\\n\\n\\n hashcode = 0\\n for i in range(len(key)):\\n hashcode = (71 * hashcode + ord(key[i])) % N\\n return hashcode\",\n \"def process_hash_regularizer(input, coeff = 0.001, name ='hash'):\\n with tf.variable_scope(name) as scope:\\n if coeff > 0:\\n hash_regularizer = tf.nn.l2_loss( tf.abs(input) - 1 ) \\n tf.summary.scalar(name + '_hash_penalty', hash_regularizer) \\n hash_regularizer_obj = coeff * hash_regularizer\\n tf.add_to_collection(name + '_objectives', hash_regularizer_obj)\\n tf.summary.scalar(name + '_weighted_hash_penalty', hash_regularizer_obj)\\n\\n for i in range(CODEWORD_LENGTH):\\n tf.summary.histogram(name + '_hash_code' + str(i),input[:,i])\",\n \"def hash(x):\\r\\n return (randint(1,5*c)*x + randint(1,5*c))%c\",\n \"def patterns_hash(patterns):\\n return hash(\\\"\\\".join(map(str, patterns)))\\n # return sum((3**(5 * i) + 1) * (p + 1) for i, p in enumerate(patterns))\",\n \"def hshingle(s, k):\\n for s in shingle(s, k):\\n yield hash(s)\",\n \"def _reduce(self, hash):\\n summation = 0\\n for char in hash:\\n summation += ord(char)\\n return summation % self._size\",\n \"def get_num_slots(self):\\n Return the load factor for this hash table.\\n\\n Implement this.\\n \\\"\\\"\\\"\\n return self.elements / self.capacity\\n\\n\\n def fnv1(self, key):\\n \\\"\\\"\\\"\\n FNV-1 Hash, 64-bit\\n\\n Implement this, and/or DJB2.pyy\\n \\\"\\\"\\\"\\n\\n # Your code here\\n\\n\\n def djb2(self, key):\\n \\\"\\\"\\\"\\n DJB2 hash, 32-bit\\n\\n Implement this, and/or FNV-1.\\n \\\"\\\"\\\"\\n # Your code here\\n\\n\\n def hash_index(self, key):\\n \\\"\\\"\\\"\\n Take an arbitrary key and return a valid integer index\\n between within the storage capacity of the hash table.\\n \\\"\\\"\\\"\\n #return self.fnv1(key) % self.capacity\\n<<<<<<< Updated upstream\\n return self.djb2(key) % self.capacity\\n=======\\n return self.djb2(key) % len(self.storage)\\n>>>>>>> Stashed changes\\n\\n def put(self, key, value):\\n \\\"\\\"\\\"\\n Store the value with the given key.\\n\\n Hash collisions should be handled with Linked List Chaining.\\n\\n Implement this.\\n \\\"\\\"\\\"\\n<<<<<<< Updated upstream\\n # Your code here\\n=======\\n # need to account for if the key value is the same \\n\\n i = self.hash_index(key)\\n if not self.storage[i]:\\n hte = HashTableEntry(key, value)\\n self.storage[i] = hte\\n self.elements += 1\\n hte.head = HashTableEntry(key, value)\\n elif self.storage[i] and self.storage[i].key != key:\\n self.storage[i].insert_at_head(HashTableEntry(key, value))\\n>>>>>>> Stashed changes\\n\\n\\n def delete(self, key):\\n \\\"\\\"\\\"\\n Remove the value stored with the given key.\\n\\n Print a warning if the key is not found.\\n\\n Implement this.\\n \\\"\\\"\\\"\\n<<<<<<< Updated upstream\\n # Your code here\\n=======\\n i = self.hash_index(key)\\n node = self.storage[i]\\n prev = None\\n if node.key == key:\\n self.storage[i] = node.next\\n return\\n while node != None:\\n if node.key == key:\\n prev.next = node.next\\n self.storage[i].next = None\\n return\\n prev = node\\n node = node.next\\n self.elements -= 1\\n return\\n>>>>>>> Stashed changes\\n\\n\\n def get(self, key):\\n \\\"\\\"\\\"\\n Retrieve the value stored with the given key.\\n\\n Returns None if the key is not found.\\n\\n Implement this.\\n \\\"\\\"\\\"\\n<<<<<<< Updated upstream\\n # Your code here\\n=======\\n # - find the index in the hash table for the key\\n i = self.hash_index(key)\\n # - search the list for that key\\n if not self.storage[i]:\\n return None\\n else:\\n if self.storage[i].find_key(key) == key:\\n return self.storage[i].value\\n>>>>>>> Stashed changes\\n\\n\\n def resize(self, new_capacity):\\n \\\"\\\"\\\"\\n Changes the capacity of the hash table and\\n rehashes all key/value pairs.\\n\\n Implement this.\\n \\\"\\\"\\\"\\n<<<<<<< Updated upstream\\n # Your code here\\n=======\\n prev_storage = self.storage\\n self.capacity = new_cap\\n self.storage = [None] * new_cap\\n for i in range(len(prev_storage)):\\n prev = prev_storage[i]\\n if prev:\\n while prev:\\n if prev.key:\\n self.put(prev.key, prev.value)\\n prev = prev.next\\n\\n>>>>>>> Stashed changes\\n\\n\\n\\nif __name__ == \\\"__main__\\\":\\n ht = HashTable(8)\\n\\n ht.put(\\\"line_1\\\", \\\"'Twas brillig, and the slithy toves\\\")\\n ht.put(\\\"line_2\\\", \\\"Did gyre and gimble in the wabe:\\\")\\n ht.put(\\\"line_3\\\", \\\"All mimsy were the borogoves,\\\")\\n ht.put(\\\"line_4\\\", \\\"And the mome raths outgrabe.\\\")\\n ht.put(\\\"line_5\\\", '\\\"Beware the Jabberwock, my son!')\\n ht.put(\\\"line_6\\\", \\\"The jaws that bite, the claws that catch!\\\")\\n ht.put(\\\"line_7\\\", \\\"Beware the Jubjub bird, and shun\\\")\\n ht.put(\\\"line_8\\\", 'The frumious Bandersnatch!\\\"')\\n ht.put(\\\"line_9\\\", \\\"He took his vorpal sword in hand;\\\")\\n ht.put(\\\"line_10\\\", \\\"Long time the manxome foe he sought--\\\")\\n ht.put(\\\"line_11\\\", \\\"So rested he by the Tumtum tree\\\")\\n ht.put(\\\"line_12\\\", \\\"And stood awhile in thought.\\\")\\n\\n print(\\\"\\\")\\n\\n # Test storing beyond capacity\\n for i in range(1, 13):\\n print(ht.get(f\\\"line_{i}\\\"))\\n\\n # Test resizing\\n old_capacity = ht.get_num_slots()\\n ht.resize(ht.capacity * 2)\\n new_capacity = ht.get_num_slots()\\n\\n print(f\\\"\\\\nResized from {old_capacity} to {new_capacity}.\\\\n\\\")\\n\\n # Test if data intact after resizing\\n for i in range(1, 13):\\n print(ht.get(f\\\"line_{i}\\\"))\\n\\n print(\\\"\\\")\",\n \"def hash_function_1(key):\\n hash = 0\\n for i in key:\\n hash = hash + ord(i)\\n return hash\",\n \"def linear_probing(hash, A, x):\\n i = 0\\n s = len(A)\\n while A[getRemainder(hash(x)+i, s)] is not None:\\n i = i + 1\\n index = getRemainder(hash(x)+i, s)\\n print(f'Linear Probing index is: {index}')\\n A[index] = x\\n print(f'insertion of {x}: {A}')\",\n \"def r_soft_hash(x):\\n if abs(x) < 1e-9:return 0\\n # round it to some number of bits\\n b = ns.round(ns.log(abs(x)) / ns.log(2))\\n gran = 2**(b-30)\\n return ns.round(x / gran) * gran\",\n \"def set_hashes(s, hash_function=_crc32_hash):\\n return {(hash_function(e), e) for e in s}\",\n \"def minhash(s, t, n):\\n X = union_signature(s, t, n)\\n Y = intersection_signature(X, s, t)\\n return len(Y) / len(X)\",\n \"def keyhash(string):\\n return hashlib.sha1(string.encode('utf-8')).hexdigest()\",\n \"def djb_hash(s):\\n h = 5381\\n for c in s:\\n h = h * 33 + ord(c)\\n return h & 0xffffffff\",\n \"def hashing(file,pp):\\n\\n def myhash(instring):\\n # sdbm hash\\n res = 0\\n for t in instring:\\n res = (ord(t) + (res<<6) + (res<<16) - res) % 2**32\\n return res\\n\\n return hex(myhash(file.replace('\\\\\\\\','/')+\\\":\\\"+pp))\",\n \"def sparse_hash(data, rounds=1, suffix=None):\\n data = data if suffix is None else data + suffix\\n hash = [x for x in range(256)]\\n hash_length = len(hash)\\n pos = 0\\n skip = 0\\n\\n for round in range(rounds):\\n for instruction in data:\\n\\n # reverse the number of hash digits from the pos\\n collector = []\\n\\n new_pos = 0\\n for index in range(pos, pos+instruction):\\n if index < hash_length:\\n collector.append(hash[index])\\n else:\\n if new_pos >= hash_length:\\n new_pos = 0\\n collector.append(hash[new_pos])\\n new_pos += 1\\n\\n to_reverse = list(reversed(collector))\\n\\n # put the numbers back\\n new_pos = 0\\n for index, item in enumerate(to_reverse, pos):\\n new_pos = index if index is pos else (new_pos + 1)\\n if new_pos >= hash_length:\\n new_pos = 0\\n hash[new_pos] = item\\n\\n # step to next hash index\\n pos += instruction + skip\\n if pos >= hash_length:\\n special = pos - hash_length\\n pos = 0\\n special = special % hash_length\\n for item in range(special):\\n pos += 1\\n\\n skip += 1\\n return hash\",\n \"def _hash(self, hashKey):\\n return hashKey % self.size\",\n \"def h_rolling(key, N, p=53, m=2**64):\\n if type(key) == str:\\n s = 0\\n for i in range(len(key)):\\n s += ord(key[i]) * p**i\\n s = s % m\\n return s % N\\n elif type(N) == int:\\n s = 0\\n try:\\n len(key)\\n except:\\n raise ValueError\\n \\n for i in range(len(key)):\\n s += ord(key[i]) * p**i\\n s = s % m\\n return s % N\\n else:\\n raise ValueError\",\n \"def test_hash(self):\\r\\n self.assertEqual(processor_hash('test'), 'GqNJWF7X7L07nEhqMAZ+OVyks1Y=')\\r\\n self.assertEqual(processor_hash('edx '), '/KowheysqM2PFYuxVKg0P8Flfk4=')\",\n \"def search_hash(word_input,hashtable):\\n\\n if word_input in hashtable:\\n return hashtable[word_input]\\n else:\\n return None\",\n \"def H2( curve, in_str ):\\n return curve.hash_to_point( \\\"H2_salt%s\\\" % in_str )\",\n \"def compute_ahash(im):\\n return imagehash.average_hash(ensure_pil(im))\",\n \"def hash(x) -> int:\\n pass\",\n \"def rehash(self, key) -> int:\\n return (key + 1) % self.size\",\n \"def _minhash_from_text(self, text):\\n minhash = MinHash(self._config.num_perm)\\n for word in self._shingles_from_text(text):\\n minhash.update(word.encode('utf8'))\\n return minhash\",\n \"def _hash(self):\\r\\n MAX = sys.maxint\\r\\n MASK = 2 * MAX + 1\\r\\n n = len(self)\\r\\n h = 1927868237 * (n + 1)\\r\\n h &= MASK\\r\\n for x in self:\\r\\n hx = hash(x)\\r\\n h ^= (hx ^ (hx << 16) ^ 89869747) * 3644798167\\r\\n h &= MASK\\r\\n h = h * 69069 + 907133923\\r\\n h &= MASK\\r\\n if h > MAX:\\r\\n h -= MASK + 1\\r\\n if h == -1:\\r\\n h = 590923713\\r\\n return h\",\n \"def sherlockAndAnagrams(s):\\n counter, len_s = 0, len(s)\\n \\n print(f\\\"Original string: {s}. length: {len_s}\\\")\\n for chunk in range(1, len_s):\\n arr = []\\n for i in range(0, len_s - chunk + 1):\\n ex1 = \\\"\\\".join(s[i:i+chunk])\\n ex = \\\"\\\".join(sorted(s[i:i+chunk]))\\n print(f\\\"chunk: {chunk}, i: {i}, sub: {ex1}, sub_sorted: {ex}\\\")\\n arr.append(ex)\\n\\n c = Counter(arr)\\n for key in c:\\n val = c[key]\\n if val > 1:\\n counter += (val * (val - 1)) // 2\\n \\n print(f\\\"chunk: {chunk}, # counter: {counter}, Counter: {str(c)}\\\\n\\\")\\n print(f\\\"Result: {counter}.\\\")\\n return counter\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7161889","0.68627113","0.664224","0.656542","0.6429222","0.6295608","0.62773484","0.6263875","0.6260028","0.6208387","0.6194183","0.61931044","0.615499","0.61416006","0.6121987","0.60881466","0.6085557","0.6082059","0.6034449","0.59991634","0.59969115","0.5987632","0.5973045","0.59574956","0.5954722","0.5949508","0.5946395","0.593777","0.59085816","0.58892155","0.5863827","0.5842274","0.5787009","0.5786908","0.57752043","0.57744265","0.5768597","0.5712562","0.5681212","0.56589836","0.56548995","0.56538427","0.5621999","0.56134164","0.5610026","0.5607922","0.56017476","0.5594552","0.55905205","0.55905205","0.55905205","0.55895233","0.55857843","0.55609095","0.55513865","0.5538856","0.55291814","0.5524769","0.5502071","0.54881686","0.5478223","0.5472835","0.54726523","0.54519325","0.5448867","0.54473865","0.54456013","0.54306364","0.54101366","0.5402815","0.54020125","0.54005986","0.5397171","0.5375123","0.5370232","0.53663224","0.53615093","0.53613955","0.5357809","0.5346506","0.53454745","0.53367007","0.5336113","0.5334758","0.5330892","0.53140455","0.5313344","0.530176","0.5274684","0.5273305","0.52651453","0.526471","0.52621776","0.5257532","0.52569866","0.525028","0.52495533","0.524805","0.52323365","0.52313554"],"string":"[\n \"0.7161889\",\n \"0.68627113\",\n \"0.664224\",\n \"0.656542\",\n \"0.6429222\",\n \"0.6295608\",\n \"0.62773484\",\n \"0.6263875\",\n \"0.6260028\",\n \"0.6208387\",\n \"0.6194183\",\n \"0.61931044\",\n \"0.615499\",\n \"0.61416006\",\n \"0.6121987\",\n \"0.60881466\",\n \"0.6085557\",\n \"0.6082059\",\n \"0.6034449\",\n \"0.59991634\",\n \"0.59969115\",\n \"0.5987632\",\n \"0.5973045\",\n \"0.59574956\",\n \"0.5954722\",\n \"0.5949508\",\n \"0.5946395\",\n \"0.593777\",\n \"0.59085816\",\n \"0.58892155\",\n \"0.5863827\",\n \"0.5842274\",\n \"0.5787009\",\n \"0.5786908\",\n \"0.57752043\",\n \"0.57744265\",\n \"0.5768597\",\n \"0.5712562\",\n \"0.5681212\",\n \"0.56589836\",\n \"0.56548995\",\n \"0.56538427\",\n \"0.5621999\",\n \"0.56134164\",\n \"0.5610026\",\n \"0.5607922\",\n \"0.56017476\",\n \"0.5594552\",\n \"0.55905205\",\n \"0.55905205\",\n \"0.55905205\",\n \"0.55895233\",\n \"0.55857843\",\n \"0.55609095\",\n \"0.55513865\",\n \"0.5538856\",\n \"0.55291814\",\n \"0.5524769\",\n \"0.5502071\",\n \"0.54881686\",\n \"0.5478223\",\n \"0.5472835\",\n \"0.54726523\",\n \"0.54519325\",\n \"0.5448867\",\n \"0.54473865\",\n \"0.54456013\",\n \"0.54306364\",\n \"0.54101366\",\n \"0.5402815\",\n \"0.54020125\",\n \"0.54005986\",\n \"0.5397171\",\n \"0.5375123\",\n \"0.5370232\",\n \"0.53663224\",\n \"0.53615093\",\n \"0.53613955\",\n \"0.5357809\",\n \"0.5346506\",\n \"0.53454745\",\n \"0.53367007\",\n \"0.5336113\",\n \"0.5334758\",\n \"0.5330892\",\n \"0.53140455\",\n \"0.5313344\",\n \"0.530176\",\n \"0.5274684\",\n \"0.5273305\",\n \"0.52651453\",\n \"0.526471\",\n \"0.52621776\",\n \"0.5257532\",\n \"0.52569866\",\n \"0.525028\",\n \"0.52495533\",\n \"0.524805\",\n \"0.52323365\",\n \"0.52313554\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69907576"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":86694,"cells":{"query":{"kind":"string","value":"Uses binary search to find the number of mismatches. It finds the leftmost mismatch and then looks at the substring hash after that index position and continues until the number of mismatches are more than k or the start pointer is more than or equal to end."},"document":{"kind":"string","value":"def find_num_matches(p1, p2, t1, t2, m1, m2, x, k, len_p, i):\n start = 0\n for mismatch in range(k):\n # print(f'start: {start}, id: {id}')\n id = start\n end = len_p-1\n while start <= end:\n mid = start + (end-start)//2\n p_h1, p_h2 = get_hash_value(\n p1, m1, x, id, mid-start), get_hash_value(p2, m2, x, id, mid-start)\n s_h1, s_h2 = get_hash_value(\n t1, m1, x, i+id, mid-start), get_hash_value(t2, m2, x, i+id, mid-start)\n if p_h1 == s_h1 and p_h2 == s_h2: # move to the right half, no mismatch yet\n start = mid + 1\n else:\n end = mid - 1\n # when loop exits, start - 1 is the index of the mismatch.\n if start == len_p: # found the last mismatch\n return True\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def lcs_hamming_only_matches(s1: str, s2: str, k: int, length: int, matches_lst: List):\n count = 0\n for i in range(0, len(s1) - length + 1):\n for j in range(0, len(s2) - length + 1):\n sub1 = s1[i: i + length]\n sub2 = s2[j: j + length]\n result = hamming_distance(sub1, sub2)\n if result <= k:\n matches_lst.append([i, j, result, sub1, sub2])\n count += 1\n # print(\"total matches: \" + str(count))\n return count","def kmp_search(full_str, sub_str):\n n, m = len(full_str), len(sub_str)\n result = []\n pi = get_partial_match(sub_str)\n begin, matched = 0, 0\n while begin <= (n - m):\n if matched < m and full_str[begin + matched] == sub_str[matched]:\n matched += 1\n if matched == m:\n result.append(begin)\n else:\n if matched == 0:\n begin += 1\n else:\n begin += (matched - pi[matched - 1])\n matched = pi[matched - 1]\n return result","def comparison(self):\n for i in xrange(len(self.string)-len(self.substring)+1):\n if self.hash(self.string[i:i+len(self.substring)]) == self.hash(self.substring):\n if self.string[i:i+len(self.substring)] == self.substring:\n return i\n return -1","def detectMiss(n, k):\n k -= 1\n nlog = math.ceil(math.log(n,2))\n nloglessone = nlog - 1\n lower_base = 2**nloglessone - 1\n lowerbaseBack = lower_base\n diff_list = [2**i for i in range(nloglessone-1, 0, -1)]\n diff_lit_back = diff_list[1:-1]\n if lower_base == k and 2**nlog -1 == n:\n return(k)\n matchres = False\n while(lower_base <= k and len(diff_list)>0 ):\n if lower_base == k:\n matchres = True\n break\n else:\n addVal = diff_list[0]\n lower_base += addVal\n diff_list.remove(addVal)\n if matchres:\n k -= 1\n if k%2==0:\n return(k)\n else:\n while(len(diff_lit_back)>0 and lowerbaseBack<k):\n newAddVal = diff_lit_back[0]\n lowerbaseBack = lowerbaseBack+newAddVal\n postitionIndx = lowerbaseBack + newAddVal\n if lowerbaseBack == k and postitionIndx <= n:\n return(k)\n elif lowerbaseBack == k and postitionIndx > n:\n return(k-1)\n else:\n pass\n diff_lit_back.remove(newAddVal)\n\n return(k)","def distinct_substrigns(str1, k):\n hash_map = {}\n n = len(str1)\n start = 0 \n end = 1\n hash_map[str1[start]] = 1\n res = [] \n\n while end < n:\n print (\"hash_map is :\", hash_map)\n if str1[end] not in hash_map: \n hash_map[str1[end]] = 1\n else:\n while str1[start]!=str1[end]:\n del hash_map[str1[start]]\n start += 1\n start += 1\n\n if end-start+1 == k:\n temp = str1[start:end+1]\n if temp not in res:\n res.append(temp)\n del hash_map[str1[start]]\n start += 1\n end += 1\n #print(end,start)\n return res","def subStringMatchExact(target,key,length):\r\n index = find(target,key)\r\n if index < 0:\r\n return ()\r\n else:\r\n matches = subStringMatchExact(target[index+len(key):len(target)],key,length)\r\n index += (length - len(target))\r\n matches += (index,)\r\n print matches\r\n return matches","def _matched_len(self, string, idx1, idx2):\n\n str_len = len(string)\n if idx1 == idx2:\n return str_len - idx1\n counter = 0\n while idx1 < str_len and idx2 < str_len and string[idx1] == string[idx2]:\n counter += 1\n idx1 += 1\n idx2 += 1\n\n return counter","def match(self, string, substr):\n hp = self.hash(substr)\n hh = self.hash(string[0:0+len(substr)])\n if hh == hp:\n if string[0:0+len(substr)] == substr:\n return 0\n for i in xrange(1, len(string) - len(substr) + 1):\n hh = hh - ord(string[i-1]) + ord(string[i + len(substr) -1])\n if hh == hp:\n if string[i:i+len(substr)] == substr:\n return i\n return -1","def kmp_algo(inp_string: str, substr: str) -> Optional[int]:\n\n pi = _prefix_fun(substr)\n i, j = 0, 0\n while i <= len(inp_string)-len(substr):\n if inp_string[i] == substr[j]:\n first_occurrence = i\n while j < len(substr):\n if inp_string[i] != substr[j]:\n j = pi[j-1]\n break\n i += 1\n j += 1\n else:\n return first_occurrence\n else:\n i += 1\n return None","def kbest_matches(self, k=1, minlen=2, buffer=0):\n ki = 0\n while k is None or ki < k:\n idx = None\n lcm = None\n while idx is None:\n idx = np.unravel_index(np.argmax(self._wp, axis=None), self._wp.shape)\n if idx[0] == 0 or idx[1] == 0:\n return None\n r, c = idx\n lcm = LCMatch(self, r, c)\n for (x, y) in lcm.path:\n x += 1\n y += 1\n if len(self._wp.mask.shape) > 0 and self._wp.mask[x, y] is True: # True means invalid\n # print('found path contains masked, restart')\n lcm = None\n idx = None\n break\n else:\n self._wp[x, y] = ma.masked\n if len(lcm.path) < minlen:\n # print('found path too short, restart')\n lcm = None\n idx = None\n if buffer > 0 and lcm is not None:\n miny, maxy = 0, self._wp.shape[1] - 1\n minx, maxx = 0, self._wp.shape[0] - 1\n for (x, y) in lcm.path:\n xx = x + 1\n for yy in range(max(miny, y + 1 - buffer), min(maxy, y + 1 + buffer)):\n self._wp[xx, yy] = ma.masked\n yy = y + 1\n for xx in range(max(minx, x + 1 - buffer), min(maxx, x + 1 + buffer)):\n self._wp[xx, yy] = ma.masked\n if lcm is not None:\n ki += 1\n yield lcm","def lcs_hamming_only_matches_with_many_k(s1: str, s2: str, ks: List[int], length: int, matches_lst: List, query_name, target_name):\n file_name = './chromo_matches_' + str(length) + '.txt'\n f = open(file_name, mode='a')\n count = [0, 0, 0]\n for i in range(0, len(s1) - length + 1):\n for j in range(0, len(s2) - length + 1):\n sub1 = s1[i: i + length]\n sub2 = s2[j: j + length]\n result = hamming_distance(sub1, sub2)\n if result <= 0:\n # matches_lst.append([query_name, target_name, i, j, result, sub1, sub2])\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\n')\n count[0] += 1\n if result <= 1:\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\n')\n count[1] += 1\n if result <= 2:\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\n')\n count[2] += 1\n\n # print(\"total matches: \" + str(count))\n f.close()\n return count","def index_equals_value_search2(arr):\n start = 0\n end = len(arr) - 1\n while start <= end:\n mid = int((end + start)/2)\n print('mid = {}'.format(mid))\n if arr[mid] == mid:\n return mid\n elif arr[mid] < mid:\n start = mid + 1\n elif arr[mid] > mid:\n end = mid - 1\n return -1","def find_mismatching_pair(s):\n i = 0\n j = len(s) - 1\n while i < j and s[i] == s[j]:\n i += 1\n j -= 1\n return i, j","def fn(i, k, lower, upper):\n if k == len(evil): return 0 # boundary condition \n if i == n: return 1 \n lo = ascii_lowercase.index(s1[i]) if lower else 0\n hi = ascii_lowercase.index(s2[i]) if upper else 25\n \n ans = 0\n for x in range(lo, hi+1): \n kk = k \n while kk and evil[kk] != ascii_lowercase[x]: \n kk = lps[kk-1]\n if evil[kk] == ascii_lowercase[x]: kk += 1\n ans += fn(i+1, kk, lower and x == lo, upper and x == hi)\n return ans","def overlappingKmers(s, k=15, overlap=11, includeFinalPeptide=True, returnStartInds=False):\n inds = [i for i in range(0, len(s), k-overlap) if i+k < len(s)]\n\n if includeFinalPeptide and not s[-k:] == s[inds[-1]:inds[-1]+k]:\n inds.append(len(s)-k)\n\n mers = [s[i:i+k] for i in inds]\n\n if returnStartInds:\n return mers, inds\n else:\n return mers","def fn(k):\n seen = set()\n for i in range(len(s)-k+1): \n val = (prefix[i+k] - prefix[i]*fac[k]) % MOD \n if val in seen: return True # rolling hash (ver. Monte Carlo)\n seen.add(val)\n return False","def solve(k, text, pattern):\n # print(k, text, pattern)\n base = pow(10, 9)\n M1 = base + 7\n M2 = base + 9\n X = 263\n len_p = len(pattern)\n len_t = len(text)\n pattern1, pattern2 = pre_compute_hashes(pattern, M1, M2, X)\n text1, text2 = pre_compute_hashes(text, M1, M2, X)\n res = []\n p_hash1, p_hash2 = get_hash_value(\n pattern1, M1, X, 0, len_p), get_hash_value(pattern2, M2, X, 0, len_p)\n for i in range(len_t-len_p+1): # all possible candidates\n subs_hash1 = get_hash_value(text1, M1, X, i, len_p)\n subs_hash2 = get_hash_value(text2, M2, X, i, len_p)\n is_valid = find_num_matches(pattern1, pattern2, text1, text2, M1, M2, X, k, len_p, i)\n if is_valid:\n res.append(i)\n return res","def brute_force_string_matching(text: str, pattern: str) -> int:\n n = len(text)\n m = len(pattern)\n for i in range(n-m):\n j = 0\n while j < m and pattern[j] == text[j+i]:\n j += 1\n if j == m:\n return i\n return -1","def _index_q_list_in_k_list(q_list, k_list):\r\n q_list_length = len(q_list)\r\n k_list_length = len(k_list)\r\n for idx in range(k_list_length - q_list_length + 1):\r\n t = [q == k for q, k in zip(q_list, k_list[idx: idx + q_list_length])]\r\n # print(idx, t)\r\n if all(t):\r\n # print(idx)\r\n idx_start = idx\r\n return idx_start","def kbest_matches(self, k=1, overlap=0):\n self.align()\n matching = np.array(self.matching)\n maxv = np.ceil(np.max(matching) + 1)\n matching[:min(len(self.query) - 1, overlap)] = maxv\n ki = 0\n while k is None or ki < k:\n best_idx = np.argmin(matching)\n if best_idx == 0 or np.isinf(matching[best_idx]) or matching[best_idx] == maxv:\n # No more matches found\n break\n match = self.get_match(best_idx)\n b, e = match.segment\n cur_overlap = min(overlap, e - b - 1)\n mb, me = best_idx + 1 - (e - b) + cur_overlap, best_idx + 1\n if np.isinf(np.max(matching[mb:me])):\n # No overlapping matches\n matching[best_idx] = maxv\n continue\n matching[mb:me] = np.inf\n ki += 1\n yield match","def followup(self,s,k):\n if not s or k==0:\n return 0\n # using the maxheap to save the lastoccurence of the char\n maxheap=[]\n # window size record the k distince char\n inwindow={}\n j,maxlen=0,1\n # also can read char from the api\n for i in xrange(len(s)):\n ch=s[i]\n if len(inwindow)==k and (ch not in inwindow):\n idx,first=heapq.heappop(maxheap)\n del inwindow[first]\n j=idx+1\n if ch in inwindow:\n #del maxheap[-inwindow[ch]]\n for idx in xrange(len(maxheap)):\n if maxheap[idx][1]==ch:\n maxheap[idx],maxheap[-1]=maxheap[-1],maxheap[idx]\n maxheap.pop()\n heapq.heapify(maxheap)\n break\n inwindow[ch]=i\n heapq.heappush(maxheap,(i,ch))\n maxlen=max(maxlen,(i-j+1))\n return maxlen","def rh_get_match(x, y, k, d=7, hash_size=\"automatic\"):\n n = len(x)\n m = len(y)\n\n if hash_size == \"automatic\":\n q = n * m\n else:\n q = hash_size\n\n output = list()\n\n for i in range(n - k + 1):\n matches = rabin_karp_matcher(\n target=y,\n potential=x[i:i + k],\n d=d,\n q=q\n )\n\n for match in matches:\n output.append((i, match))\n\n return output","def compute_fwd(p1, p2):\n for k in range(len(p2)):\n if p1[k] != p2[k]:\n break\n return(k)","def length_of_longest_substring(arr, k):\n window_start = 0\n max_repeat_times = 0\n frequency_map = {0: 0, 1: 0}\n len_longest = 0\n\n for window_end in range(len(arr)):\n right_char = arr[window_end]\n left_char = arr[window_start]\n frequency_map[right_char] += 1\n max_repeat_times = frequency_map[0]\n\n if max_repeat_times > k:\n frequency_map[left_char] -= 1\n window_start += 1\n len_longest = max(len_longest, window_end - window_start + 1)\n\n return len_longest","def split_at_conflict(scaff1, scaff2, k):\n linker = \"link_conflict5\"\n p1 = scaff1.index(k)\n p2 = scaff2.index(k)\n \n l_split1=0\n r_split1=0\n l_split2=0\n r_split2=0\n out1=[]\n out2=[]\n out3=[]\n out_scaffs = []\n\n for q in range(len(scaff2)-p2-1):\n if scaff1[p1+q] != scaff2[p2+q]:\n r_split1 = p1+q-1\n r_split2 = p2+q-1\n break\n for q in range(p2):\n if scaff1[p1-q] != scaff2[p2-q]:\n l_split1 = p1-q+2\n l_split2 = p2-q+2\n break\n\n if l_split1 and r_split1: \n out1 = scaff1[:l_split1]\n out1.append(linker)\n out2 = scaff1[l_split1:r_split1]\n out2.insert(0,linker)\n out2.append(linker)\n out3 = scaff1[r_split1:]\n out3.insert(0,linker)\n out_scaffs.extend([out1,out2,out3])\n \n elif l_split1:\n out1 = scaff1[:l_split1]\n out1.append(linker)\n out3 = scaff1[l_split1:]\n out3.insert(0,linker)\n out_scaffs.extend([out1,out3]) \n \n elif r_split1:\n out1 = scaff1[:r_split1]\n out1.append(linker)\n out3 = scaff1[r_split1:]\n out3.insert(0,linker)\n out_scaffs.extend([out1,out3])\n \n else:\n paf(\"NB! Expected conflict not found\")\n\n if l_split2 and r_split2:\n out1 = scaff2[:l_split2]\n out1.append(linker)\n out2 = scaff2[l_split2:r_split2]\n out2.insert(0,linker)\n out2.append(linker)\n out3 = scaff2[r_split2:]\n out3.insert(0,linker)\n out_scaffs.extend([out1,out2,out3])\n \n elif l_split2:\n out2 = scaff2[:l_split2]\n out2.append(linker)\n out3 = scaff2[l_split2:]\n out3.insert(0,linker)\n out_scaffs.extend([out2,out3])\n \n elif r_split2:\n out2 = scaff2[:r_split2]\n out2.append(linker)\n out3 = scaff2[r_split2:]\n out3.insert(0,linker)\n out_scaffs.extend([out2,out3])\n \n else:\n paf(\"NB! Expected conflict not found\")\n\n return out_scaffs","def ends(self, s):\n\t\tlength = len(s)\n\t\tif s[length - 1] != self.b[self.k]: # tiny speed-up\n\t\t\treturn 0\n\t\tif length > (self.k - self.k0 + 1):\n\t\t\treturn 0\n\t\tif self.b[self.k-length+1:self.k+1] != s:\n\t\t\treturn 0\n\t\tself.j = self.k - length\n\t\treturn 1","def indapproxpattern(pattern, string, nummismatch):\n\n indarr = []\n# substringarr = []\n numchars = len(pattern)\n\n for i in xrange(0, len(string) - numchars + 1):\n \n substring = patterncount.subtext(string, i, numchars)\n \n if hammingdist(pattern, substring) <= nummismatch:\n \n indarr.append(i)\n# substringarr.append(substring)\n \n return indarr","def binary_search_h(cls, student_list, name, start, end):\n midpoint = (start + end) // 2\n mid_name = student_list[midpoint].name\n if name == mid_name:\n return midpoint\n elif abs(end - start) == 1:\n return cls.NOT_FOUND\n else:\n if name > mid_name:\n start = midpoint + 1\n return cls.binary_search_h(student_list, name, start, end)\n else:\n end = midpoint - 1\n return cls.binary_search_h(student_list, name, start, end)","def _find_right_bound(morps, begin, max_key_len):\n len_sum = len(morps[begin].lemma())\n for idx in range(begin+1, len(morps)):\n len_sum += len(morps[idx].lemma())\n if len_sum > max_key_len:\n return idx\n return len(morps)","def regular_get_match(x, y, k):\n n = len(x)\n m = len(y)\n\n output = list()\n\n for i in range(n - k + 1):\n matches = non_rolling_matcher(\n target=y,\n potential=x[i:i + k]\n )\n\n for match in matches:\n output.append((i, match))\n\n return output","def kmp_search(S, w):\n\n m = 0 # beginning of the current match in S\n i = 0 # position of the current character in w\n T = _compute_kmp_table(w)\n\n while m + i < len(S):\n if w[i] == S[m + i]: # next character matches\n i += 1\n if i == len(w): # we have a complete match\n yield m\n # we can now skip the pos in S based on T[i]\n # go to the char we just matched, and step back T[i] steps\n m = m + i - T[i]\n i = T[i]\n assert i != -1\n else: # There is a character mismatch\n if T[i] == -1:\n # We use the same logic for advancing m, since T[i] = -1\n m = m + i + 1\n # but we have to set i to be the start of w now\n # When T[i] == -1, assume we havent matched any characters and move to the next char\n i = 0\n else:\n # Use the same logic to advance m\n m = m + i - T[i]\n\n # Then assume we have matched T[i] characters up till now\n # the only difference between T[i] = 0 and T[i] = -1 is whether or\n # not we advance a character\n i = T[i]\n assert i != -1","def get_closest(occurences, content, k=25):\n result = []\n o = occurences[0] #get first\n for idx in o:\n res = 0\n for i in range(1, len(occurences)): #other than first\n oo = occurences[i]\n where = bisect_left(oo, idx)\n #try both, after and before the binary searched index (if exists)\n try:\n res += min(abs(oo[min(len(oo)-1, where+1)]-idx), abs(oo[min(len(oo)-1, where)]-idx))\n except:\n print(\"Something went wrong here\")\n result.append((res, idx))\n \n #if res < best:\n # best = res\n # best_where = (idx)\n\n result = sorted(result)\n final = []\n \n for score, i in tqdm(result):\n f = False\n for ii in final:\n if abs(i-ii) <= 200:\n f = True\n break\n if not f:\n final.append(i)\n\n paragraphs = []\n\n for idx in final:\n paragraphs.append(get_para(content, idx))\n\n print(\"DONE HERE\")\n return paragraphs[:k]","def better_matching(self, pattern, offsets=False, slice_len=5):\n first_occurencies, counts_at = self.setup_better_match(slice_len)\n pattern = list(pattern)\n top, bottom = 0, self.tl\n while top <= bottom:\n if pattern:\n current = pattern.pop()\n if counts_at(bottom+1)[current] - counts_at(top)[current] > 0:\n #if current in self.last_col[top:bottom+1]:\n top = first_occurencies[current] + counts_at(top)[current]\n bottom = first_occurencies[current] + counts_at(bottom + 1)[current] - 1\n else:\n if offsets:\n return []\n else:\n return 0\n else:\n if offsets:\n return self.offsets(top, bottom)\n else:\n return bottom - top + 1","def kmp_algo(inp_string: str, substr: str) -> Optional[int]:\n # 1. Сравниваем `i-й` элемент строки и `j-й` элемент шаблона\n # 2. Если они равны - увеличиваем оба счетчика (и проверяем условие выхода)\n # 3. Если не равны – значит мы нашли несовпадение где-то в середине шаблона,\n # нужно вычислить, с какого символа шаблона нужно продолжать. `j = pi(j-1)`\n pref_fun = _prefix_fun(substr) # префикс таблица\n i = j = 0 # индексы элементов строки и подстроки\n while i < len(inp_string) and j < len(substr):\n if substr[j] == inp_string[i]:\n i += 1\n j += 1\n elif j == 0:\n i += 1\n else:\n j = pref_fun[j - 1]\n else:\n if j == len(substr):\n return i - j\n return None","def find_substring(string):\n sub_s = \"\"\n if len(string) == 1:\n print(1)\n for k in range(0, len(string) // 2):\n sub_s = sub_s[:k] + string[k]\n pos = 0\n next_pos = string.find(sub_s, pos + k + 1)\n count = 1\n while next_pos != -1 and next_pos == pos + k + 1:\n count += 1\n pos += k + 1\n next_pos = string.find(sub_s, pos + k + 1)\n if next_pos == -1 and pos == len(string) - k - 1:\n return count\n return 0","def binarysearch(a, i):\n l = 0\n r = len(a) - 1\n \n while l < r:\n m = l + (r-l)/2\n \n if a[m] == i:\n return m\n elif a[m] < i:\n l = m + 1\n else:\n r = m\n \n if l > 0 and abs(i - a[l-1]) < abs(i- a[l]):\n return l-1\n \n return l","def subStringMatchExact(target,key):\r\n index = find(target,key)\r\n #print 'here',target,key,index\r\n if index < 0 or len(key) <= 0 or len(target) <= 0:\r\n return ()\r\n matches = subStringMatchExact(target[index+len(key):len(target)],key)\r\n offset = index + len(key)\r\n temp_matches = ()\r\n #print matches\r\n if matches:\r\n for x in range(0, len(matches)) :\r\n temp_matches += ((matches[x] + offset),)\r\n #matches.insert(0,index)\r\n temp_matches = (index,) + temp_matches\r\n return temp_matches","def search(self, L: int, a: int, modulus: int, n: int, nums: List[int]) -> str:\n # compute the hash of string S[:L]\n h = 0\n for i in range(L):\n h = (h * a + nums[i]) % modulus\n \n # already seen hashes of strings of length L\n seen = {h} \n # const value to be used often : a**L % modulus\n aL = pow(a, L, modulus) \n for start in range(1, n - L + 1):\n # compute rolling hash in O(1) time\n h = (h * a - nums[start - 1] * aL + nums[start + L - 1]) % modulus\n if h in seen:\n return start\n seen.add(h)\n return -1","def longest_prefix_length(s, i, j):\n l = 0\n while (i+l < len(s)) and (j+l < len(s)):\n if s[i+l] != s[j+l]:\n break\n l += 1\n return l","def compare_eq_len(a, b):\n n = len(a)\n m = len(b)\n\n # slide b across a from left to right till from just overlapping till full overlap\n overlap = 0 # stores length of the overlap\n lconcat = \"\" # this stores the shortest common superstring\n for j in range(m):\n starta = 0\n enda = j+1\n startb = m - (j+1)\n endb = m\n if a[starta:enda] == b[startb:endb]:\n # if an overlap is found, check if it is larger than the previously detected one\n # print(\"overlap found\")\n if len(a[starta:enda]) > overlap: \n overlap = len(a[starta:enda]) \n lconcat = b + a[enda:] # this is the current shortest common superstring\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\n\n # print(\"-\")\n\n # slide b across a so that b starts from one element past a after full overlap\n rconcat = \"\"\n for j in range(m-1):\n starta = j+1\n enda = m\n startb = 0\n endb = m - (j+1)\n if a[starta:enda] == b[startb:endb]:\n if len(a[starta:enda]) > overlap: # if there is a bigger overlap then save it \n # print(\"overlap found\")\n overlap = len(a[starta:enda]) \n rconcat = a + b[endb:]\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\n\n # after checking for overlaps there may be 1 or no shortest common\n # superstrings stored in both lconcat and rconcat. Choose the shortest one if it exists\n # or the concatenation of a and b if there are no overlaps. We may have to make some\n # arbitrary choices here.\n\n if not lconcat and not rconcat: # both lconcat and rconcat are empty, no overlaps\n superstring = a + b # append b to a (could prepend here too, this is an arbitrary choice)\n elif lconcat and not rconcat: # lconcat contains overlap and rconcat is empty\n superstring = lconcat \n elif rconcat and not lconcat: # rconcat contains overlap and lconcat is empty\n superstring = rconcat\n elif rconcat and lconcat and (len(lconcat) <= len(rconcat)): # use lconcat if it is shorter or equal len to rconat\n superstring = lconcat\n elif rconcat and lconcat and (len(rconcat) < len(lconcat)): # use rconcat only if it is shorter than lconat\n superstring = rconcat\n return superstring","def ApproxStrMatch(pattern, text, d, debug = False):\n\tcount = 0\n\tindex = []\n\tfor i in range(len(text)-len(pattern)+1):\n\t\tif debug:\n\t\t\tprint text[i:i+len(pattern)]\n\t\t\tprint HammingDist(text[i:i+len(pattern)], pattern)\n\t\tif HammingDist(text[i:i+len(pattern)], pattern) <= d:\n\t\t\tcount += 1\n\t\t\tindex.append(i)\n\treturn index","def fn(ss):\n i = cnt = 0\n for ch in s: \n if ss[i] == ch: \n i += 1\n if i == len(ss): \n if (cnt := cnt + 1) == k: return True \n i = 0\n return False","def find_kmp(T, P):\n n, m = len(T), len(P) # introduce convenient notations\n if m == 0: # trivial search for empty string\n return 0\n fail = compute_kmp_fail(P) # rely on utility to precompute\n j = 0 # index into text\n k = 0 # index into pattern\n while j < n:\n if T[j] == P[k]: # P[0:k+1] matched thus far\n if k == m-1: # match is complete\n return j - m + 1\n else: # try to extend match\n j += 1\n k += 1\n elif k > 0:\n k = fail[k-1] # reuse suffix of P[0:k]\n else:\n j += 1\n return -1 # reached end without match","def binsearch(alist, key, start, end):\n mid = len(alist) // 2\n if start > end:\n return None\n elif start < end:\n return binsearch(alist, key, start, mid-1)\n else:\n return mid","def Overlap(a, b, min_length=3):\n start = 0\n while True:\n # look for b's prefix in a\n start = a.find(b[:min_length], start) # Python Docs: string.find(value, start, end)\n if start == -1:\n return 0\n\n if b.startswith(a[start:]):\n return len(a) - start \n start += 1","def longest_common_substring_iterative_bottom_up(s1, s2):\n\n cache = [[0] * (len(s2)+1) for _ in range(-1, len(s1))]\n\n for i in range(1, len(s1)+1):\n for j in range(1, len(s2)+1):\n\n if s1[i-1] == s2[j-1]:\n\n # previous count is cache[i-1][j-1]\n cache[i][j] = cache[i-1][j-1] + 1\n else:\n cache[i][j] = max(cache[i-1][j-1],\n max(cache[i-1][j],\n cache[i][j-1]))\n\n return cache[-1][-1]","def findIndicesOfStartsWithEndsWith(target, wordListCustomSorted):\n n = len(wordListCustomSorted)\n firstInd = lastInd = n // 2\n if target == firstAndLastChars(wordListCustomSorted[0]):\n firstInd = 0\n elif target < firstAndLastChars(wordListCustomSorted[0]):\n return (-1, -1)\n if target == firstAndLastChars(wordListCustomSorted[n-1]):\n lastInd = n-1\n elif target > firstAndLastChars(wordListCustomSorted[n-1]):\n return (-1, -1)\n def firstIndIsMatch(firstInd):\n if firstInd == 0:\n return True\n else:\n return firstAndLastChars(wordListCustomSorted[firstInd]) == target and \\\n firstAndLastChars(wordListCustomSorted[firstInd-1]) < target\n def lastIndIsMatch(lastInd):\n if lastInd == n-1:\n return True\n else:\n return firstAndLastChars(wordListCustomSorted[lastInd]) == target and \\\n firstAndLastChars(wordListCustomSorted[lastInd+1]) > target\n\n lb = 1\n ub = n-1\n while not firstIndIsMatch(firstInd):\n if lb >= ub:\n return (-1, -1)\n if target > firstAndLastChars(wordListCustomSorted[firstInd]):\n lb = firstInd + 1\n else:\n ub = firstInd\n firstInd = (lb + ub) // 2\n lb = firstInd\n ub = n-2\n while not lastIndIsMatch(lastInd):\n if lb > ub:\n return (-1, -1)\n if target >= firstAndLastChars(wordListCustomSorted[lastInd]):\n lb = lastInd + 1\n else:\n ub = lastInd\n lastInd = (lb + ub) // 2\n return firstInd, lastInd","def karp_rabin_search(s, t):\n rs = RollingHash(len(s))\n rt = RollingHash(len(s))\n for c in s:\n rs.append(c)\n for c in t[:len(s)]:\n rt.append(c)\n if rs.hash == rt.hash:\n return True\n for i in range(len(s), len(t)):\n rt.pop_left()\n rt.append(t[i])\n if rs.hash == rt.hash:\n return True\n return False","def compute_kmp_fail(P):\n m = len(P)\n fail = [0]*m # by default, presume overlap of 0 everywhere\n j = 1\n k = 0\n while j < m: # compute f(j) during this pass, if nonzero\n if P[j] == P[k]: # k+1 characters match thus far\n fail[j] = k+1\n j += 1\n k += 1\n elif k > 0: # k follows a matching prefix\n k = fail[k-1]\n else: # no match found starting at j\n j += 1\n return fail","def missing_binary_search(item_list):\n first = 0\n last = len(item_list)-1\n while first < last:\n mid = (first + last)//2\n if mid not in item_list:\n return mid\n if mid - first != item_list[mid] - item_list[first]:\n last = mid\n elif last - mid != item_list[last] - item_list[mid]:\n first = mid\n else:\n return None","def overlap(a, b, min_length=3):\n start = 0 # start all the way at the left\n\n while True:\n start = a.find(b[:min_length], start) # look for b's suffix i a\n if start == -1: # no more occurrences to right\n return 0\n # found occurrence; check for full suffix/prefix match\n if b.startswith(a[start:]):\n return len(a) - start\n start += 1 # move just paat previous match","def find_string(n, c_length, start=None):\n \n c = range(c_length)\n if start is None:\n i = get_minimum(n)\n else:\n i = start\n\n strings = [e for e in generate_strings(n, c)]\n while True:\n for x, s in enumerate(generate_strings(i, c)):\n if check_string(s, strings):\n return s\n\n if x % 1000000 == 0:\n print x\n i += 1\n print \"processing %s\" % i","def search(self, key):\r\n left = 0 \r\n right = self.num_keys()\r\n while right > left:\r\n mid = (left + right)//2\r\n if self.keys[mid] >= key:\r\n right = mid\r\n else:\r\n left = mid + 1\r\n return left","def overlap(s1, s2):\n j = len(s2) - 1\n while j >= 0 and not s1.endswith(s2[:j + 1]):\n j -= 1\n return j","def overlap(s1, s2):\n for i in range(min(len(s1), len(s2)), 0, -1):\n if s1.endswith(s2[:i]):\n return i\n return 0","def match_len(chain):\n previous,current = chain[-2:]\n previous = [ x for x in reversed(str(previous).split('.')) ]\n current = [ x for x in reversed(str(current).split('.')) ]\n\n max_length = min(( len(x) for x in (previous,current) ))\n i = 0\n while i < max_length:\n if previous[i] != current[i]:\n return i\n i += 1\n return i","def KMP_algo(inp_string: str, substr: str):\n\n\tdef prefix_fun(prefix_str: str) -> list:\n\t\t\"\"\"\n\t\tPrefix function for KMP\n\n\t\t:param prefix_str: dubstring for prefix function\n\t\t:return: prefix values table\n\t\t\"\"\"\n\n\n\t\tf = []\n\n\t\tfor i in range(len(prefix_str)):\n\t\t\tj = i\n\t\t\toffset = 0\n\t\t\twhile j > 0:\n\t\t\t\tj -= 1\n\t\t\t\t# print(i, j, offset, prefix_str[j], prefix_str[i-offset])\n\t\t\t\tif prefix_str[j] == prefix_str[i - offset]:\n\t\t\t\t\toffset += 1\n\t\t\t\telse:\n\t\t\t\t\tj += offset\n\t\t\t\t\toffset = 0\n\t\t\tf.append(offset)\n\t\t\t# print('append', offset)\n\n\t\treturn f\n\n\t'''\n\t\tВажный Комментарий: первоначально был сделан вариант, который ищет не только первое, но и \n\t\tпоследующие вхождения подстроки в строку и много тестовых выводов. Не удалял. \n\t\tПоэтому в коде есть костыли и некоторая непричёсанность =) \t\t\n\t'''\n\n\n\tf = prefix_fun(substr)\n\t# print(f)\n\n\ti = 0\n\tj = 0\n\tdone = False\n\n\twhile not done: # (input(\"Exit - 0\") != '0')\n\t\tinitf = False\n\t\tdone = False\n\n\t\ta = inp_string[i:i + 1]\n\t\tb = substr[j:j + 1]\n\t\t# print('a=', a, 'b=', b)\n\n\t\tif a == b:\n\t\t\ti += 1\n\t\t\tj += 1\n\t\telif j <= 0:\n\t\t\ti += 1\n\t\telse:\n\t\t\tj = f[j - 1]\n\t\t# print('i=', i, 'j=', j)\n\t\t# print(inp_string[:i])\n\t\t# print(substr[:j])\n\n\t\tif len(inp_string) == i:\n\t\t\t# print(\"Конец поиска : конец строки\")\n\t\t\tdone = True\n\n\t\tif len(substr) == j:\n\t\t\t# print(\"Подстрока найдена!\", i - j)\n\t\t\tinitf = True\n\n\t\t\treturn i - j # если закомментировать этот return, то будет искать последующие вхождеия\n\n\t\tif initf:\n\t\t\ti += 1\n\t\t\tj = 0\n\n\treturn None","def crackSafe(self, n: int, k: int) -> str:","def findChildB(T,k):\r\n for i in range(len(T.data)):\r\n if k < T.data[i].word:\r\n return i\r\n return len(T.data)","def kmp_strstr(haystack, needle):\r\n def computeLPSArray(word):\r\n # The explaination of this function is in _LPSArray()\r\n lps = [0] * len(word)\r\n wordIdx = 0\r\n delta = 1\r\n while delta < len(word):\r\n while delta < len(word) and word[delta] == word[wordIdx]:\r\n lps[delta] = wordIdx + 1\r\n wordIdx += 1\r\n delta += 1\r\n if wordIdx != 0:\r\n wordIdx = lps[wordIdx-1]\r\n else:\r\n delta += 1\r\n return lps\r\n\r\n # handle special cases\r\n if haystack == needle:\r\n return [0]\r\n elif not needle:\r\n return [0]\r\n elif not haystack:\r\n return []\r\n # init lps table\r\n lps = computeLPSArray(needle)\r\n ans = []\r\n # main loop\r\n i_haystack = 0\r\n i_needle = 0\r\n while i_haystack < len(haystack):\r\n if haystack[i_haystack] == needle[i_needle]:\r\n i_haystack += 1\r\n i_needle += 1\r\n # print i_needle, i_haystack\r\n if i_needle == len(needle):\r\n ans.append(i_haystack - i_needle)\r\n i_needle = lps[i_needle-1]\r\n elif i_needle != 0:\r\n i_needle = lps[i_needle-1]\r\n else:\r\n i_haystack += 1\r\n\r\n return ans","def subStringMatchExact(target, key):\n\tanswer_tuple = () # initialize the tuple we will return\n\tstart = 0 # use this initial the starting point for find()\n\twhile find(target, key, start) >=0:\n\t\tstart = find(target, key, start)\n\t\tanswer_tuple += (start,)\n\t\tstart+=1\n\treturn answer_tuple","def subStringMatchExact(target, key):\n\tanswer_tuple = () # initialize the tuple we will return\n\tstart = 0 # use this initial the starting point for find()\n\twhile find(target, key, start) >=0:\n\t\tstart = find(target, key, start)\n\t\tanswer_tuple += (start,)\n\t\tstart+=1\n\treturn answer_tuple","def needle_diff(left_struc, right_struc, key, minimal=True):\n if type(left_struc) not in (list, tuple):\n return keyset_diff(left_struc, right_struc, key, minimal)\n assert type(right_struc) in (list, tuple)\n\n down_col = 0\n lastrow = [\n [[key + [sub_i]] for sub_i in range(i)]\n for i in range(len(left_struc), -1, -1)\n ]\n\n def modify_cand():\n \"\"\"Build the candidate diff that involves (potentially) modifying an\n element.\"\"\"\n if col_i + 1 < len(lastrow):\n return (lastrow[col_i + 1] +\n diff(left_elem, right_elem, key=key + [left_i],\n minimal=minimal, verbose=False))\n\n def delete_cand():\n \"\"\"Build the candidate diff that involves deleting an element.\"\"\"\n if row:\n return row[0] + [[key + [left_i]]]\n\n def append_cand():\n \"\"\"Build the candidate diff that involves appending an element.\"\"\"\n if col_i == down_col:\n return (lastrow[col_i] +\n [[key + [append_key(lastrow[col_i], left_struc, key)],\n right_elem]])\n\n for right_i, right_elem in enumerate(right_struc):\n first_left_i = min(right_i, len(left_struc) - 1)\n left_elems = left_struc[first_left_i:]\n row = []\n\n for left_i, left_elem in enumerate(left_elems, first_left_i):\n col_i = len(left_struc) - left_i - 1\n cands = [c for c in (modify_cand(), delete_cand(), append_cand())\n if c is not None]\n winner = min(cands, key=lambda d: len(compact_json_dumps(d)))\n row.insert(0, winner)\n\n lastrow = row\n return winner","def overlap(a, b, min_length=3):\r\n start = 0 # start all the way at the left\r\n while True:\r\n start = a.find(b[:min_length], start) # look for b's prefix in a\r\n if start == -1: # no more occurrences to right\r\n return 0\r\n # found occurrence; check for full suffix/prefix match\r\n if b.startswith(a[start:]):\r\n return len(a)-start\r\n start += 1 # move just past previous match\r","def is_possible(strs, k):\n for i in xrange(len(strs) / k):\n rows = strs[i * k: (i + 1) * k]\n for j in xrange(len(strs[0]) / k):\n piece = [row[j * k: (j + 1) * k] for row in rows]\n target = piece[0]\n for s in piece:\n if s != target:\n return \"Impossible\"\n return \"Possible\"","def find_similar_hashes(self):\n matches = self.hasher.find_matches(self.__offset_hash_map.values())\n print(\" Found {} matches.\".format(len(matches)))\n\n print(\" Formatting the found matches. This takes some time ...\")\n self.__matched_offsets = self.__to_offset_list(matches, self.__offset_hash_map)\n\n return","def get_mismatch_matrix(k,m):\n words = get_words(k)\n N = len(words)\n\n mismatch_matrix = np.zeros((N, N))\n for i in range(N):\n for j in range(i, N):\n if Levenshtein.hamming(words[i], words[j]) <= m:\n mismatch_matrix[i,j] = 1/2\n mismatch_matrix[j,i] = 1/2\n\n return mismatch_matrix","def binary_search(a,x):\n start = 0 \n end = len(a)-1\n\n while start<= end:\n mid = (start+end)//2 \n if x == a[mid]:\n return mid\n elif x < a[mid]:\n end = mid-1\n else:\n start = mid+1\n return -1","def binary_search(seq, key): # find the most closer one to the key but smaller than the key\n# begin/end /not exist\n\n\n begin = 0\n end = len(seq)-1\n if key > seq[end]:\n return end\n while begin <= end:\n mid1 = (begin + end)//2\n mid2 = (begin + end)//2 + 1\n tmp1 = seq[mid1]\n tmp2 = seq[mid2]\n if key > tmp1 and key > tmp2:\n begin = mid1\n elif key < tmp1:\n end = mid1\n elif key > tmp1 and key < tmp2:\n return mid1\n elif key == tmp1:\n end = mid1 - 1\n return -1","def rabin_karp_search(pattern, s, d=5294212309, q=9743212277):\n m, n = len(pattern), len(s)\n # h = pow(d,m-1)%q\n # mod as we go\n h = 1\n for _ in range(m-1):\n h = (d * h)%q\n p, t = 0, 0\n result = []\n for i in range(m): \n p = (d*p+ord(pattern[i]))%q\n t = (d*t+ord(s[i]))%q\n for k in range(n-m+1):\n if p == t: # check character by character\n match = True\n for i in range(m):\n if pattern[i] != s[k+i]:\n match = False\n break\n if match:\n result = result + [k]\n if k < n-m:\n t = (t-h*ord(s[k]))%q \n t = (t*d+ord(s[k+m]))%q \n t = (t+q)%q \n return result","def scs_of_pair(a, b):\n # compare two strings where the text a is longer than the pattern b\n n = len(a)\n m = len(b)\n\n # check the args are the right way round\n if m > n:\n raise ValueError(\"string b is longer than string a\")\n\n # slide b across a from left to right till from just overlapping till full overlap\n overlap = 0 # stores length of the overlap\n lconcat = \"\" # stores the curretn shortest common superstring\n for j in range(n): \n starta = 0 if (j+1) <= m else ((j+1) - m) \n enda = j+1 \n startb = (m - (j+1)) if (j+1) < m else 0 \n endb = m \n if a[starta:enda] == b[startb:endb]:\n # print(\"overlap found\")\n if len(a[starta:enda]) > overlap: # if there is a bigger overlap then save it \n overlap = len(a[starta:enda]) \n lconcat = b + a[enda:]\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\n\n # print(\"-\")\n rconcat = \"\"\n for j in range(m - 1):\n starta = (n - m) + (j + 1) \n enda = n \n startb = 0 \n endb = m - (j+1) \n if a[starta:enda] == b[startb:endb]:\n # print(\"overlap found\")\n if len(a[starta:enda]) > overlap: # if there is a bigger overlap then save it \n overlap = len(a[starta:enda]) \n rconcat = a + b[endb:]\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\n\n # after checking for overlaps there may be 1 or no shortest common\n # superstrings stored in both lconcat and rconcat. Choose the shortest one if it exists\n # or the concatenation of a and b if there are no overlaps. We may have to make some\n # arbitrary choices here.\n\n if not lconcat and not rconcat: # both lconcat and rconcat are empty, no overlaps\n superstring = a + b # append b to a (could prepend here too)\n elif lconcat and not rconcat: # lconcat contains overlap and rconcat is empty\n superstring = lconcat\n elif rconcat and not lconcat: # rconcat contains overlap and lconcat is empty\n superstring = rconcat\n elif rconcat and lconcat and (len(lconcat) <= len(rconcat)): # use lconcat if it is shorter or equal len to rconat\n superstring = lconcat\n elif rconcat and lconcat and (len(rconcat) < len(lconcat)): # use rconcat only if it is shorter than lconat\n superstring = rconcat\n return superstring","def freq_words_with_mismatches(seq, k, d):\n # Frequency analysis so we don't generate mismatches for the same k-mer more than once.\n kmer_freq = defaultdict(int)\n for i in xrange(len(seq)-k+1):\n kmer_freq[seq[i:i+k]] += 1\n\n # Get all of the mismatches for each unique k-mer in the sequence, appearing freq times.\n mismatch_count = defaultdict(int)\n for kmer, freq in kmer_freq.iteritems():\n for mismatch in kmer_mismatches(kmer, d):\n mismatch_count[mismatch] += freq\n\n # Computing the maximum value is somewhat time consuming to repeat, so only do it once!\n max_count = max(mismatch_count.values())\n return sorted([kmer for kmer, count in mismatch_count.iteritems() if count == max_count])","def binaryLSearch(array, start, end, anchor):\n if start >= end:\n raise ValueError(\"start index {s} cannot be greater than end index {e}\".format(s=start, e=end))\n elif start == end - 1:\n if array[start] > anchor:\n return start\n else:\n return None\n\n # now assume start <= end - 2\n\n mid = (start + end) // 2 # guaranteed start < mid < end\n if array[mid - 1] >= anchor:\n return binaryLSearch(array, start, mid, anchor)\n else:\n return binaryLSearch(array, mid, end, anchor)","def getHash(key, hashseed, m, k):\n salt = str(hashseed)\n hashed_password = hashlib.sha256((key + salt).encode('utf-8')).hexdigest()\n #print hashed_password\n if k > 32: raise Exception(\"k should be less than 32\")\n if k <= 1: raise Exception(\"k should be more than 2\")\n if k > m: raise Exception(\"k should be less than m\")\n\n # it cuts 4 byte from the hashed_password, so the value is 0xFFFFFFFF\n assert(m < 0xFFFFFFFF)\n hashedPasswordLength = len(hashed_password)\n assert(hashedPasswordLength/4 > k)\n\n result = set()\n index = 0\n\n # make the non-overwrapping hash value below m\n while True:\n value = int(hashed_password[index:index+4], 16) % m\n index += 4\n result.add(value)\n\n if len(result) == k: break\n\n \n if index >= hashedPasswordLength:\n print (index)\n print (hashedPasswordLength)\n\n assert index < hashedPasswordLength\n return list(result)","def longest_common_substring_bottom_up(s1, s2):\n lcs_array = lcs(s1, s2)\n n = len(s1)\n m = len(s2)\n l = lcs_array[n][m]\n result = \"\"\n while l != 0:\n if s1[n - 1] == s2[m - 1]:\n result = s1[n - 1] + result\n l -= 1\n n -= 1\n m -= 1\n elif lcs_array[n][m - 1] >= lcs_array[n - 1][m]:\n m -= 1\n else:\n n -= 1\n return result","def kSimilarity(self, s1: str, s2: str) -> int:\n def neighbors(s: str) -> Iterator[str]:\n i = 0\n while s[i] == s2[i]: # Skip the already swapped chars.\n i += 1\n\n t = list(s)\n for j in range(i + 1, N):\n # Make a swap.\n if s[j] == s2[i] and s[j] != s2[j]:\n t[i], t[j] = t[j], t[i]\n yield ''.join(t)\n t[i], t[j] = t[j], t[i]\n\n N = len(s1)\n queue = deque([(s1, 0)])\n visited = {s1}\n while queue:\n s, swapCnt = queue.popleft()\n if s == s2:\n return swapCnt\n\n swapCnt += 1\n for neighbor in neighbors(s):\n if neighbor not in visited:\n visited.add(neighbor)\n queue.append((neighbor, swapCnt))","def search_entry_equal_to_its_index(A):\n L, R = 0, len(A) -1\n\n while L <= R:\n M = L + (R - L) // 2\n if A[M] > M:\n R = M - 1\n elif A[M] == M:\n return M\n else:\n # A[M] < M\n L = M + 1\n\n return -1","def kmerNeighbors(text,k):\r\n L=set()\r\n for i in range(0,len(text)-k+1):\r\n for d in range(0,k+1):\r\n L.update(Neighbors(kmer(text,i,k),d))\r\n D=dict()\r\n for l in L:\r\n D[l]=minHamm(text,l)\r\n return D","def lcs_pairwise(s1: str, s2: str, k: int, length: int):\n\n for i in range(0, len(s1) - length + 1):\n for j in range(0, len(s2) - length + 1):\n sub1 = s1[i: i + length]\n sub2 = s2[j: j + length]\n result = pairwise2.align.globalxx(sub1, sub2)\n if result[0][2] >= length - k - 1:\n print(str(i) + \" \" + str(j) + \" \" + str(length - result[0][2]) + \" \" +\n sub1 + \" \" + sub2, end=\"\\t\")\n else:\n print(str(i) + \" \" + str(j) + \" N N N\", end=\"\\t\")\n print(\"\")","def solve(n, k):\n o = [True] + [False] * n + [True] # whether it's occupied\n # initialize Ls and Rs\n Ls = [-1] + [i for i in range(n)] + [-1]\n Rs = Ls[::-1] # Ls reversed\n\n for i in range(k):\n # find where min(Ls,Rs) is maximal\n min_ = 0\n for j in range(n+1):\n if not o[j]:\n if min(Ls[j], Rs[j]) > min_:\n min_ = min(Ls[j], Rs[j])\n\n # check if there is more than one\n pos = None\n max_ = 0\n for j in range(n+1):\n if not o[j]:\n if min(Ls[j], Rs[j]) == min_:\n if (pos is None) or (pos is not None and max(Ls[j], Rs[j]) > max_):\n pos = j\n max_ = max(Ls[j], Rs[j])\n\n # take pos\n maxs = max(Rs[pos], Ls[pos])\n mins = min(Rs[pos], Ls[pos])\n\n o[pos] = True\n Rs[pos] = -1\n Ls[pos] = -1\n\n # update Ls and Rs\n l = pos # index of first occupied left to position taken\n while l > 0 and not o[l - 1]:\n l -= 1\n for x in range(pos - l):\n Ls[l + x] = x\n Rs[l + x] = pos - l - x - 1\n\n r = pos # index of first occupied right to position taken\n while r <= n and not o[r + 1]:\n r += 1\n for x in range(r - pos):\n Ls[pos + x + 1] = x\n Rs[pos + x + 1] = r - pos - x - 1\n\n return str(maxs) + \" \" + str(mins)","def _bin_search_recursive(self, v, start, end):\n if end < start:\n return start\n\n mid = (start + end) / 2\n if self.values[mid] == v:\n return mid\n elif self.values[mid] < v:\n return self._bin_search_recursive(v, mid + 1, end)\n else:\n return self._bin_search_recursive(v, start, mid - 1)","def get_2away_pairs(local_index_to_kmer, k):\n\n #These are the base cases for the recursion. If k==1, the kmers obviously can't differ in exactly two bases, so return an empty list. if k==2, return every pair of indices where the kmers at those indices differ at exactly two bases.\n if k == 1:\n return []\n if k == 2:\n return [(i, j) for (i,j) in combinations(local_index_to_kmer, 2) if local_index_to_kmer[i][0] != local_index_to_kmer[j][0] and local_index_to_kmer[i][1] != local_index_to_kmer[j][1]]\n\n #Get the two halves of the kmer\n k_L = k//2\n k_R = k-k_L\n\n #initialize dictionaries in which the key is the hash of half of the kmer, and the value is a list of indices of the kmers with that same hash\n kmer_L_hashes = defaultdict(list)\n kmer_R_hashes = defaultdict(list)\n\n #initialize pairs, which will be returned by get_1away_pairs\n pairs = []\n\n #initialize dictionaries containing the left halves and the right halves (since we will have to check cases where the left half differs by 1 and the right half differs by 1)\n local_index_to_kmer_L = {}\n local_index_to_kmer_R = {}\n\n #for each kmer, calculate its left hash and right hash, then add its index to the corresponding entries of the dictionary\n for i, kmer in local_index_to_kmer.items():\n kmer_L = kmer[:k_L]\n kmer_R = kmer[k_L:]\n local_index_to_kmer_L[i] = kmer_L\n local_index_to_kmer_R[i] = kmer_R\n kmer_L_hashes[kmer_to_int(kmer_L)] += [i]\n kmer_R_hashes[kmer_to_int(kmer_R)] += [i]\n\n #for each left hash in which there are multiple kmers with that left hash, find the list of pairs in which the right half differs by 2. (aka, if left half matches, recurse on right half).\n for kmer_L_hash_indices in kmer_L_hashes.values(): #same in first half\n if len(kmer_L_hash_indices) > 1:\n pairs += get_2away_pairs({kmer_L_hash_index:local_index_to_kmer[kmer_L_hash_index][k_L:] for kmer_L_hash_index in kmer_L_hash_indices}, k_R) #differ by 2 in right half\n\n #for each right hash in which there are multiple kmers with that right hash, find the list of pairs in which the left half differs by 2. (aka, if right half matches, recurse on left half).\n for kmer_R_hash_indices in kmer_R_hashes.values(): #same in second half\n if len(kmer_R_hash_indices) > 1:\n pairs += get_2away_pairs({kmer_R_hash_index:local_index_to_kmer[kmer_R_hash_index][:k_L] for kmer_R_hash_index in kmer_R_hash_indices}, k_L) #differ by 2 in left half\n\n #Find matching pairs where the left half is one away, and the right half is one away\n possible_pairs_L = set(get_1away_pairs(local_index_to_kmer_L,k_L))\n possible_pairs_R = set(get_1away_pairs(local_index_to_kmer_R,k_R))\n pairs += list(possible_pairs_L.intersection(possible_pairs_R))\n return(pairs)","def find_pair_with_sum_1(arr, k):\n n = len(arr)\n\n def find_pivot(arr):\n start = 0\n end = len(arr) - 1\n\n if arr[end] > arr[start]:\n return 0\n\n while end >= start:\n mid = start + (end-start)//2\n\n if arr[mid] > arr[mid+1]:\n return mid+1\n if arr[mid] < arr[mid-1]:\n return mid\n\n if arr[mid] > arr[start]:\n start = mid+1\n else:\n end = mid-1\n\n smallest_index = find_pivot(arr)\n print(\"Pivot is at \", smallest_index)\n largest_index = smallest_index-1\n\n while smallest_index != largest_index:\n curr_sum = arr[smallest_index] + arr[largest_index]\n\n if curr_sum == k:\n return (arr[smallest_index], arr[largest_index])\n\n if curr_sum > k:\n largest_index = (largest_index - 1) % n\n else:\n smallest_index = (smallest_index + 1) % n\n\n return \"Not Found\"","def countSubStringMatch(target,key):\n count = 0\n for i in range(0,len(target)-len(key)):\n if target[i:i+len(key)] == key:\n count += 1\n return count","def minhash(s, t, n):\n X = union_signature(s, t, n)\n Y = intersection_signature(X, s, t)\n return len(Y) / len(X)","def binarySearch(A, k):\n \n #TODO: Implement without using python's in-built functiondef binary(A, k):\n def bSearch(A, k, low, high):\n if high == low:\n if A[low] == k:\n return low\n else:\n return -1\n mid = (low + high)//2\n if A[mid] == k:\n return mid\n elif A[mid] > k:\n if low == mid:\n return -1\n else:\n return bSearch(A, k, low, mid-1)\n else:\n return bSearch(A, k, mid+1, high)\n if isinstance(A, list) == False or isinstance(k, int) == False:\n return -1\n else:\n if len(A) == 0:\n return -1\n else:\n x = bSearch(A, k, 0, len(A)-1)\n return x","def is_k_anagram(s1: str, s2: str, k: int) -> tuple:\n if len(s1) != len(s2):\n return False, None, None\n\n counter_map = Counter(s1)\n extra = list()\n changes = 0\n for char in s2:\n if counter_map[char] > 0:\n counter_map[char] -= 1\n else:\n extra.append(char)\n changes += 1\n\n if changes > k:\n return False, None, None\n diff = [c for c in counter_map if counter_map[c] > 0]\n return True, extra, diff","def fn(i1, i2):\n if i1 == len(word1): return len(word2) - i2\n if i2 == len(word2): return len(word1) - i1\n if word1[i1] == word2[i2]: return fn(i1+1, i2+1)\n return 1 + min(fn(i1+1,i2), fn(i1, i2+1))","def rotated_array_search(input_list, number):\n start_index = 0\n end_index = len(input_list) - 1\n while start_index <= end_index:\n mid_idx = (start_index + end_index)//2 \n left = input_list[:mid_idx]\n right = input_list[mid_idx:]\n try: \n if input_list[mid_idx] == number:\n return mid_idx\n found = False\n if number >= left[0] and number <= left[-1]:\n start_index = 0\n end_index = mid_idx - 1\n found = True\n else:# number >= right[0] and number <= right[-1]: \n start_index = mid_idx + 1\n\n if not found:\n if number >= right[0] and number <= right[-1]: \n start_index = mid_idx + 1\n else:\n end_index = mid_idx + 1\n except IndexError: #if element not found, return -1\n return -1\n return -1","def calculate_score(s1, s2, l1, l2, startpoint):\n\n matched = \"\" # to hold string displaying alignments\n score = 0\n for i in range(l2):\n if (i + startpoint) < l1:\n if s1[i + startpoint] == s2[i]: # if the bases match\n matched = matched + \"*\" # * indicates a match\n score = score + 1\n else:\n matched = matched + \"-\" # - indicates no match\n\n return score","def find_brute(T, P):\n n, m = len(T), len(P) # introduce convenient notations\n for i in range(n-m+1): # try every potential starting index within T\n k = 0 # an index into pattern P\n while k < m and P[k] == T[i+k]: # kth character of P matches\n k += 1\n if k == m: # if we reached the end of pattern,\n return i # substring T[i:i+m] matches P\n return -1 # failed to find a match starting with any i","def method3(self, startPos, endPos, k):\n diff = abs(startPos - endPos)\n if diff > k:\n return 0\n dp = [[0 for _ in range(2 * k + 1)] for _ in range(k + 1)]\n # shift k steps to avoid negative index, all the second index need to add shift\n shift = k\n dp[0][0 + shift] = 1\n\n for t in range(1, k+1):\n for p in range(-k, k + 1):\n #if 0 <= p - 1 + shift <= k:\n if p - 1 >= -k:\n dp[t][p + shift] += dp[t-1][p - 1 + shift]\n #if 0 <= p + 1 + shift <= k:\n if p + 1 <= k:\n dp[t][p + shift] += dp[t-1][p + 1 + shift]\n return dp[k][diff+shift] % (10**9+7)","def best_pairing(current_end, end_dict, inverse_dict, blast_hits, l_min_score, r_min_score):\n #this duplicates part of trio_hits - should try to rewrite that to use this function\n \n l_flange = int(end_dict[current_end][1])\n l_contig = end_dict[current_end][0]\n \n #first find blast hits for the target scaffold end\n left_matches = []\n for hit in blast_hits:\n if hit[0] == l_contig and int(hit[11]) >= l_min_score:\n left_matches.append(hit)\n \n link_count = {}\n \n #then find other ends with correctly oriented hits adjacent to the target hits\n for slink in end_dict:\n link = end_dict[slink][0]\n \n right_matches = []\n\n for hit in blast_hits:\n if hit[0] == link and int(hit[11]) >= r_min_score: \n right_matches.append(hit)\n \n for lhit in left_matches:\n for rhit in right_matches:\n srhit = inverse_dict[rhit[0]]\n r_flange = end_dict[srhit][1]\n joint_flange = l_flange + r_flange\n \n if lhit[1] == rhit[1]:\n lh_start = int(lhit[8])\n lh_end = int(lhit[9])\n rh_start = int(rhit[8])\n rh_end = int(rhit[9])\n\n if abs(lh_start - rh_start) < joint_flange + 3000:\n if (lh_end - lh_start)/(rh_end - rh_start) < 0:\n if abs(lh_end - rh_end) > abs(lh_start - rh_start):\n link_score = int(lhit[11]) * int(rhit[11])\n if not link in link_count: \n link_count[link] = link_score\n elif link_score > link_count[link]:\n link_count[link] = link_score\n return link_count","def solve(self):\n # init the 1000\n self.buf_init()\n\n keys = 0\n while keys != 64:\n # check if possible key\n res = find_in_a_row(self.buffer.pop(0))\n if res:\n for b in self.buffer:\n # if found a 5 in a row\n if match_in_a_row(b, c=res):\n keys += 1\n break\n\n # add new one to end and iterate\n hash_str = '{}{}'.format(self.salt, self.forward_idx).encode()\n self.buffer.append(md5(hash_str).hexdigest())\n self.forward_idx += 1\n self.idx += 1\n\n return self.idx - 1","def find_substring(pattern, target):\n # Eliminate trivial cases.\n n = len(target)\n m = len(pattern)\n if (not n or not m or m > n):\n return False\n #\n # Search by comparing hashes.\n pattern_hash = hash(pattern)\n for string_start in range(n - m + 1):\n string_end = string_start + m\n if pattern_hash == hash(target[string_start:string_end]):\n return True\n return False","def find_pairs_in_list(arr, k, l):\n i = 0\n j = 0\n n = len(arr)\n container = Container()\n container.inc(arr[i])\n\n while j < n and i <= j:\n\n values = set()\n for m in range(l+1):\n values.add(arr[i] + m)\n values.add(arr[i] - m)\n\n for value in values:\n if (container.count(value) > 0 and value != arr[i]) or \\\n (container.count(arr[i]) > 1):\n return True\n\n if j < n:\n j += 1\n container.inc(arr[j])\n if j - i > k or j == n-1:\n container.dec(arr[i])\n i += 1\n\n return False","def longest_common_substring_backtrack(s1, s2, i, j, count):\n if i == len(s1) or j == len(s2):\n return 0\n\n if s1[i-1] == s2[j-1]:\n count = 1 + longest_common_substring_backtrack(s1, s2, i+1, j+1, count)\n\n else:\n count = max(count,\n max(longest_common_substring_backtrack(s1, s2, i+1, j, count),\n longest_common_substring_backtrack(s1, s2, i, j+1, count)))\n\n return count","def frequent_words_with_mismatches(text, k, d):\n\n patterns = []\n freq_map = {}\n n = len(text)\n for i in range(n - k + 1):\n pattern = text[i:i + k]\n pattern_rc = reverse_complement(pattern)\n neighborhood = neighbors(pattern, d) + neighbors(pattern_rc, d)\n for j in range(len(neighborhood)):\n neighbor = neighborhood[j]\n if neighbor not in freq_map.keys():\n freq_map[neighbor] = 1\n else:\n freq_map[neighbor] = freq_map[neighbor] + 1\n m = max_map(freq_map)\n for key in freq_map.keys():\n if freq_map[key] == m:\n patterns.append(key)\n return patterns","def findKthNumber(self, m: int, n: int, k: int) -> int:\n l, r = 1, m * n\n while l < r:\n mid = l + ((r - l) >> 1)\n\n # Check if there are k or more values that are less than mid.\n # For each row, its elements look like 1*i, 2*i, ... n*i, so the\n # largest number that is less than x will be x // i. But if x is\n # too large for the current row, the total count for this row\n # will be n instead.\n if sum(min(mid // r, n) for r in range(1, m + 1)) >= k:\n # mid is our candidate.\n r = mid\n else:\n l = mid + 1\n\n return l","def find_known_index(cryptf):\n ct1 = cryptf(b'A')\n ct2 = cryptf(b'B')\n bsz = find_blocksize(cryptf)\n\n # find where are plaintext block is by comparing two ciphertexts with equal size inputs\n blocknum = -1\n for i, (blk1, blk2) in enumerate(zip(chunks(ct1, bsz), chunks(ct2, bsz))):\n if blk1 != blk2:\n blocknum = i + 1\n break\n else:\n raise Exception(\"Could not find delta block!\")\n\n nextblock = chunks(ct1, bsz)[blocknum]\n\n # find offset by creating blocks of equal size but different last byte\n # when the next block differs, we know we've passed a block boundary\n offset = -1\n for i in range(bsz+1):\n nextblock1 = chunks(cryptf(b'A'*i+b'A'), bsz)[blocknum]\n nextblock2 = chunks(cryptf(b'A'*i+b'B'), bsz)[blocknum]\n if nextblock1 != nextblock2:\n offset = i\n break\n else:\n raise Exception(\"Could not find alignment offset!\")\n\n return blocknum, offset"],"string":"[\n \"def lcs_hamming_only_matches(s1: str, s2: str, k: int, length: int, matches_lst: List):\\n count = 0\\n for i in range(0, len(s1) - length + 1):\\n for j in range(0, len(s2) - length + 1):\\n sub1 = s1[i: i + length]\\n sub2 = s2[j: j + length]\\n result = hamming_distance(sub1, sub2)\\n if result <= k:\\n matches_lst.append([i, j, result, sub1, sub2])\\n count += 1\\n # print(\\\"total matches: \\\" + str(count))\\n return count\",\n \"def kmp_search(full_str, sub_str):\\n n, m = len(full_str), len(sub_str)\\n result = []\\n pi = get_partial_match(sub_str)\\n begin, matched = 0, 0\\n while begin <= (n - m):\\n if matched < m and full_str[begin + matched] == sub_str[matched]:\\n matched += 1\\n if matched == m:\\n result.append(begin)\\n else:\\n if matched == 0:\\n begin += 1\\n else:\\n begin += (matched - pi[matched - 1])\\n matched = pi[matched - 1]\\n return result\",\n \"def comparison(self):\\n for i in xrange(len(self.string)-len(self.substring)+1):\\n if self.hash(self.string[i:i+len(self.substring)]) == self.hash(self.substring):\\n if self.string[i:i+len(self.substring)] == self.substring:\\n return i\\n return -1\",\n \"def detectMiss(n, k):\\n k -= 1\\n nlog = math.ceil(math.log(n,2))\\n nloglessone = nlog - 1\\n lower_base = 2**nloglessone - 1\\n lowerbaseBack = lower_base\\n diff_list = [2**i for i in range(nloglessone-1, 0, -1)]\\n diff_lit_back = diff_list[1:-1]\\n if lower_base == k and 2**nlog -1 == n:\\n return(k)\\n matchres = False\\n while(lower_base <= k and len(diff_list)>0 ):\\n if lower_base == k:\\n matchres = True\\n break\\n else:\\n addVal = diff_list[0]\\n lower_base += addVal\\n diff_list.remove(addVal)\\n if matchres:\\n k -= 1\\n if k%2==0:\\n return(k)\\n else:\\n while(len(diff_lit_back)>0 and lowerbaseBack<k):\\n newAddVal = diff_lit_back[0]\\n lowerbaseBack = lowerbaseBack+newAddVal\\n postitionIndx = lowerbaseBack + newAddVal\\n if lowerbaseBack == k and postitionIndx <= n:\\n return(k)\\n elif lowerbaseBack == k and postitionIndx > n:\\n return(k-1)\\n else:\\n pass\\n diff_lit_back.remove(newAddVal)\\n\\n return(k)\",\n \"def distinct_substrigns(str1, k):\\n hash_map = {}\\n n = len(str1)\\n start = 0 \\n end = 1\\n hash_map[str1[start]] = 1\\n res = [] \\n\\n while end < n:\\n print (\\\"hash_map is :\\\", hash_map)\\n if str1[end] not in hash_map: \\n hash_map[str1[end]] = 1\\n else:\\n while str1[start]!=str1[end]:\\n del hash_map[str1[start]]\\n start += 1\\n start += 1\\n\\n if end-start+1 == k:\\n temp = str1[start:end+1]\\n if temp not in res:\\n res.append(temp)\\n del hash_map[str1[start]]\\n start += 1\\n end += 1\\n #print(end,start)\\n return res\",\n \"def subStringMatchExact(target,key,length):\\r\\n index = find(target,key)\\r\\n if index < 0:\\r\\n return ()\\r\\n else:\\r\\n matches = subStringMatchExact(target[index+len(key):len(target)],key,length)\\r\\n index += (length - len(target))\\r\\n matches += (index,)\\r\\n print matches\\r\\n return matches\",\n \"def _matched_len(self, string, idx1, idx2):\\n\\n str_len = len(string)\\n if idx1 == idx2:\\n return str_len - idx1\\n counter = 0\\n while idx1 < str_len and idx2 < str_len and string[idx1] == string[idx2]:\\n counter += 1\\n idx1 += 1\\n idx2 += 1\\n\\n return counter\",\n \"def match(self, string, substr):\\n hp = self.hash(substr)\\n hh = self.hash(string[0:0+len(substr)])\\n if hh == hp:\\n if string[0:0+len(substr)] == substr:\\n return 0\\n for i in xrange(1, len(string) - len(substr) + 1):\\n hh = hh - ord(string[i-1]) + ord(string[i + len(substr) -1])\\n if hh == hp:\\n if string[i:i+len(substr)] == substr:\\n return i\\n return -1\",\n \"def kmp_algo(inp_string: str, substr: str) -> Optional[int]:\\n\\n pi = _prefix_fun(substr)\\n i, j = 0, 0\\n while i <= len(inp_string)-len(substr):\\n if inp_string[i] == substr[j]:\\n first_occurrence = i\\n while j < len(substr):\\n if inp_string[i] != substr[j]:\\n j = pi[j-1]\\n break\\n i += 1\\n j += 1\\n else:\\n return first_occurrence\\n else:\\n i += 1\\n return None\",\n \"def kbest_matches(self, k=1, minlen=2, buffer=0):\\n ki = 0\\n while k is None or ki < k:\\n idx = None\\n lcm = None\\n while idx is None:\\n idx = np.unravel_index(np.argmax(self._wp, axis=None), self._wp.shape)\\n if idx[0] == 0 or idx[1] == 0:\\n return None\\n r, c = idx\\n lcm = LCMatch(self, r, c)\\n for (x, y) in lcm.path:\\n x += 1\\n y += 1\\n if len(self._wp.mask.shape) > 0 and self._wp.mask[x, y] is True: # True means invalid\\n # print('found path contains masked, restart')\\n lcm = None\\n idx = None\\n break\\n else:\\n self._wp[x, y] = ma.masked\\n if len(lcm.path) < minlen:\\n # print('found path too short, restart')\\n lcm = None\\n idx = None\\n if buffer > 0 and lcm is not None:\\n miny, maxy = 0, self._wp.shape[1] - 1\\n minx, maxx = 0, self._wp.shape[0] - 1\\n for (x, y) in lcm.path:\\n xx = x + 1\\n for yy in range(max(miny, y + 1 - buffer), min(maxy, y + 1 + buffer)):\\n self._wp[xx, yy] = ma.masked\\n yy = y + 1\\n for xx in range(max(minx, x + 1 - buffer), min(maxx, x + 1 + buffer)):\\n self._wp[xx, yy] = ma.masked\\n if lcm is not None:\\n ki += 1\\n yield lcm\",\n \"def lcs_hamming_only_matches_with_many_k(s1: str, s2: str, ks: List[int], length: int, matches_lst: List, query_name, target_name):\\n file_name = './chromo_matches_' + str(length) + '.txt'\\n f = open(file_name, mode='a')\\n count = [0, 0, 0]\\n for i in range(0, len(s1) - length + 1):\\n for j in range(0, len(s2) - length + 1):\\n sub1 = s1[i: i + length]\\n sub2 = s2[j: j + length]\\n result = hamming_distance(sub1, sub2)\\n if result <= 0:\\n # matches_lst.append([query_name, target_name, i, j, result, sub1, sub2])\\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\\\n')\\n count[0] += 1\\n if result <= 1:\\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\\\n')\\n count[1] += 1\\n if result <= 2:\\n f.write(query_name + ',' + target_name + ',' + str(i) + ',' + str(j) + ',' + str(k)+ ',' + str(sub1) + ',' + sub2 + '\\\\n')\\n count[2] += 1\\n\\n # print(\\\"total matches: \\\" + str(count))\\n f.close()\\n return count\",\n \"def index_equals_value_search2(arr):\\n start = 0\\n end = len(arr) - 1\\n while start <= end:\\n mid = int((end + start)/2)\\n print('mid = {}'.format(mid))\\n if arr[mid] == mid:\\n return mid\\n elif arr[mid] < mid:\\n start = mid + 1\\n elif arr[mid] > mid:\\n end = mid - 1\\n return -1\",\n \"def find_mismatching_pair(s):\\n i = 0\\n j = len(s) - 1\\n while i < j and s[i] == s[j]:\\n i += 1\\n j -= 1\\n return i, j\",\n \"def fn(i, k, lower, upper):\\n if k == len(evil): return 0 # boundary condition \\n if i == n: return 1 \\n lo = ascii_lowercase.index(s1[i]) if lower else 0\\n hi = ascii_lowercase.index(s2[i]) if upper else 25\\n \\n ans = 0\\n for x in range(lo, hi+1): \\n kk = k \\n while kk and evil[kk] != ascii_lowercase[x]: \\n kk = lps[kk-1]\\n if evil[kk] == ascii_lowercase[x]: kk += 1\\n ans += fn(i+1, kk, lower and x == lo, upper and x == hi)\\n return ans\",\n \"def overlappingKmers(s, k=15, overlap=11, includeFinalPeptide=True, returnStartInds=False):\\n inds = [i for i in range(0, len(s), k-overlap) if i+k < len(s)]\\n\\n if includeFinalPeptide and not s[-k:] == s[inds[-1]:inds[-1]+k]:\\n inds.append(len(s)-k)\\n\\n mers = [s[i:i+k] for i in inds]\\n\\n if returnStartInds:\\n return mers, inds\\n else:\\n return mers\",\n \"def fn(k):\\n seen = set()\\n for i in range(len(s)-k+1): \\n val = (prefix[i+k] - prefix[i]*fac[k]) % MOD \\n if val in seen: return True # rolling hash (ver. Monte Carlo)\\n seen.add(val)\\n return False\",\n \"def solve(k, text, pattern):\\n # print(k, text, pattern)\\n base = pow(10, 9)\\n M1 = base + 7\\n M2 = base + 9\\n X = 263\\n len_p = len(pattern)\\n len_t = len(text)\\n pattern1, pattern2 = pre_compute_hashes(pattern, M1, M2, X)\\n text1, text2 = pre_compute_hashes(text, M1, M2, X)\\n res = []\\n p_hash1, p_hash2 = get_hash_value(\\n pattern1, M1, X, 0, len_p), get_hash_value(pattern2, M2, X, 0, len_p)\\n for i in range(len_t-len_p+1): # all possible candidates\\n subs_hash1 = get_hash_value(text1, M1, X, i, len_p)\\n subs_hash2 = get_hash_value(text2, M2, X, i, len_p)\\n is_valid = find_num_matches(pattern1, pattern2, text1, text2, M1, M2, X, k, len_p, i)\\n if is_valid:\\n res.append(i)\\n return res\",\n \"def brute_force_string_matching(text: str, pattern: str) -> int:\\n n = len(text)\\n m = len(pattern)\\n for i in range(n-m):\\n j = 0\\n while j < m and pattern[j] == text[j+i]:\\n j += 1\\n if j == m:\\n return i\\n return -1\",\n \"def _index_q_list_in_k_list(q_list, k_list):\\r\\n q_list_length = len(q_list)\\r\\n k_list_length = len(k_list)\\r\\n for idx in range(k_list_length - q_list_length + 1):\\r\\n t = [q == k for q, k in zip(q_list, k_list[idx: idx + q_list_length])]\\r\\n # print(idx, t)\\r\\n if all(t):\\r\\n # print(idx)\\r\\n idx_start = idx\\r\\n return idx_start\",\n \"def kbest_matches(self, k=1, overlap=0):\\n self.align()\\n matching = np.array(self.matching)\\n maxv = np.ceil(np.max(matching) + 1)\\n matching[:min(len(self.query) - 1, overlap)] = maxv\\n ki = 0\\n while k is None or ki < k:\\n best_idx = np.argmin(matching)\\n if best_idx == 0 or np.isinf(matching[best_idx]) or matching[best_idx] == maxv:\\n # No more matches found\\n break\\n match = self.get_match(best_idx)\\n b, e = match.segment\\n cur_overlap = min(overlap, e - b - 1)\\n mb, me = best_idx + 1 - (e - b) + cur_overlap, best_idx + 1\\n if np.isinf(np.max(matching[mb:me])):\\n # No overlapping matches\\n matching[best_idx] = maxv\\n continue\\n matching[mb:me] = np.inf\\n ki += 1\\n yield match\",\n \"def followup(self,s,k):\\n if not s or k==0:\\n return 0\\n # using the maxheap to save the lastoccurence of the char\\n maxheap=[]\\n # window size record the k distince char\\n inwindow={}\\n j,maxlen=0,1\\n # also can read char from the api\\n for i in xrange(len(s)):\\n ch=s[i]\\n if len(inwindow)==k and (ch not in inwindow):\\n idx,first=heapq.heappop(maxheap)\\n del inwindow[first]\\n j=idx+1\\n if ch in inwindow:\\n #del maxheap[-inwindow[ch]]\\n for idx in xrange(len(maxheap)):\\n if maxheap[idx][1]==ch:\\n maxheap[idx],maxheap[-1]=maxheap[-1],maxheap[idx]\\n maxheap.pop()\\n heapq.heapify(maxheap)\\n break\\n inwindow[ch]=i\\n heapq.heappush(maxheap,(i,ch))\\n maxlen=max(maxlen,(i-j+1))\\n return maxlen\",\n \"def rh_get_match(x, y, k, d=7, hash_size=\\\"automatic\\\"):\\n n = len(x)\\n m = len(y)\\n\\n if hash_size == \\\"automatic\\\":\\n q = n * m\\n else:\\n q = hash_size\\n\\n output = list()\\n\\n for i in range(n - k + 1):\\n matches = rabin_karp_matcher(\\n target=y,\\n potential=x[i:i + k],\\n d=d,\\n q=q\\n )\\n\\n for match in matches:\\n output.append((i, match))\\n\\n return output\",\n \"def compute_fwd(p1, p2):\\n for k in range(len(p2)):\\n if p1[k] != p2[k]:\\n break\\n return(k)\",\n \"def length_of_longest_substring(arr, k):\\n window_start = 0\\n max_repeat_times = 0\\n frequency_map = {0: 0, 1: 0}\\n len_longest = 0\\n\\n for window_end in range(len(arr)):\\n right_char = arr[window_end]\\n left_char = arr[window_start]\\n frequency_map[right_char] += 1\\n max_repeat_times = frequency_map[0]\\n\\n if max_repeat_times > k:\\n frequency_map[left_char] -= 1\\n window_start += 1\\n len_longest = max(len_longest, window_end - window_start + 1)\\n\\n return len_longest\",\n \"def split_at_conflict(scaff1, scaff2, k):\\n linker = \\\"link_conflict5\\\"\\n p1 = scaff1.index(k)\\n p2 = scaff2.index(k)\\n \\n l_split1=0\\n r_split1=0\\n l_split2=0\\n r_split2=0\\n out1=[]\\n out2=[]\\n out3=[]\\n out_scaffs = []\\n\\n for q in range(len(scaff2)-p2-1):\\n if scaff1[p1+q] != scaff2[p2+q]:\\n r_split1 = p1+q-1\\n r_split2 = p2+q-1\\n break\\n for q in range(p2):\\n if scaff1[p1-q] != scaff2[p2-q]:\\n l_split1 = p1-q+2\\n l_split2 = p2-q+2\\n break\\n\\n if l_split1 and r_split1: \\n out1 = scaff1[:l_split1]\\n out1.append(linker)\\n out2 = scaff1[l_split1:r_split1]\\n out2.insert(0,linker)\\n out2.append(linker)\\n out3 = scaff1[r_split1:]\\n out3.insert(0,linker)\\n out_scaffs.extend([out1,out2,out3])\\n \\n elif l_split1:\\n out1 = scaff1[:l_split1]\\n out1.append(linker)\\n out3 = scaff1[l_split1:]\\n out3.insert(0,linker)\\n out_scaffs.extend([out1,out3]) \\n \\n elif r_split1:\\n out1 = scaff1[:r_split1]\\n out1.append(linker)\\n out3 = scaff1[r_split1:]\\n out3.insert(0,linker)\\n out_scaffs.extend([out1,out3])\\n \\n else:\\n paf(\\\"NB! Expected conflict not found\\\")\\n\\n if l_split2 and r_split2:\\n out1 = scaff2[:l_split2]\\n out1.append(linker)\\n out2 = scaff2[l_split2:r_split2]\\n out2.insert(0,linker)\\n out2.append(linker)\\n out3 = scaff2[r_split2:]\\n out3.insert(0,linker)\\n out_scaffs.extend([out1,out2,out3])\\n \\n elif l_split2:\\n out2 = scaff2[:l_split2]\\n out2.append(linker)\\n out3 = scaff2[l_split2:]\\n out3.insert(0,linker)\\n out_scaffs.extend([out2,out3])\\n \\n elif r_split2:\\n out2 = scaff2[:r_split2]\\n out2.append(linker)\\n out3 = scaff2[r_split2:]\\n out3.insert(0,linker)\\n out_scaffs.extend([out2,out3])\\n \\n else:\\n paf(\\\"NB! Expected conflict not found\\\")\\n\\n return out_scaffs\",\n \"def ends(self, s):\\n\\t\\tlength = len(s)\\n\\t\\tif s[length - 1] != self.b[self.k]: # tiny speed-up\\n\\t\\t\\treturn 0\\n\\t\\tif length > (self.k - self.k0 + 1):\\n\\t\\t\\treturn 0\\n\\t\\tif self.b[self.k-length+1:self.k+1] != s:\\n\\t\\t\\treturn 0\\n\\t\\tself.j = self.k - length\\n\\t\\treturn 1\",\n \"def indapproxpattern(pattern, string, nummismatch):\\n\\n indarr = []\\n# substringarr = []\\n numchars = len(pattern)\\n\\n for i in xrange(0, len(string) - numchars + 1):\\n \\n substring = patterncount.subtext(string, i, numchars)\\n \\n if hammingdist(pattern, substring) <= nummismatch:\\n \\n indarr.append(i)\\n# substringarr.append(substring)\\n \\n return indarr\",\n \"def binary_search_h(cls, student_list, name, start, end):\\n midpoint = (start + end) // 2\\n mid_name = student_list[midpoint].name\\n if name == mid_name:\\n return midpoint\\n elif abs(end - start) == 1:\\n return cls.NOT_FOUND\\n else:\\n if name > mid_name:\\n start = midpoint + 1\\n return cls.binary_search_h(student_list, name, start, end)\\n else:\\n end = midpoint - 1\\n return cls.binary_search_h(student_list, name, start, end)\",\n \"def _find_right_bound(morps, begin, max_key_len):\\n len_sum = len(morps[begin].lemma())\\n for idx in range(begin+1, len(morps)):\\n len_sum += len(morps[idx].lemma())\\n if len_sum > max_key_len:\\n return idx\\n return len(morps)\",\n \"def regular_get_match(x, y, k):\\n n = len(x)\\n m = len(y)\\n\\n output = list()\\n\\n for i in range(n - k + 1):\\n matches = non_rolling_matcher(\\n target=y,\\n potential=x[i:i + k]\\n )\\n\\n for match in matches:\\n output.append((i, match))\\n\\n return output\",\n \"def kmp_search(S, w):\\n\\n m = 0 # beginning of the current match in S\\n i = 0 # position of the current character in w\\n T = _compute_kmp_table(w)\\n\\n while m + i < len(S):\\n if w[i] == S[m + i]: # next character matches\\n i += 1\\n if i == len(w): # we have a complete match\\n yield m\\n # we can now skip the pos in S based on T[i]\\n # go to the char we just matched, and step back T[i] steps\\n m = m + i - T[i]\\n i = T[i]\\n assert i != -1\\n else: # There is a character mismatch\\n if T[i] == -1:\\n # We use the same logic for advancing m, since T[i] = -1\\n m = m + i + 1\\n # but we have to set i to be the start of w now\\n # When T[i] == -1, assume we havent matched any characters and move to the next char\\n i = 0\\n else:\\n # Use the same logic to advance m\\n m = m + i - T[i]\\n\\n # Then assume we have matched T[i] characters up till now\\n # the only difference between T[i] = 0 and T[i] = -1 is whether or\\n # not we advance a character\\n i = T[i]\\n assert i != -1\",\n \"def get_closest(occurences, content, k=25):\\n result = []\\n o = occurences[0] #get first\\n for idx in o:\\n res = 0\\n for i in range(1, len(occurences)): #other than first\\n oo = occurences[i]\\n where = bisect_left(oo, idx)\\n #try both, after and before the binary searched index (if exists)\\n try:\\n res += min(abs(oo[min(len(oo)-1, where+1)]-idx), abs(oo[min(len(oo)-1, where)]-idx))\\n except:\\n print(\\\"Something went wrong here\\\")\\n result.append((res, idx))\\n \\n #if res < best:\\n # best = res\\n # best_where = (idx)\\n\\n result = sorted(result)\\n final = []\\n \\n for score, i in tqdm(result):\\n f = False\\n for ii in final:\\n if abs(i-ii) <= 200:\\n f = True\\n break\\n if not f:\\n final.append(i)\\n\\n paragraphs = []\\n\\n for idx in final:\\n paragraphs.append(get_para(content, idx))\\n\\n print(\\\"DONE HERE\\\")\\n return paragraphs[:k]\",\n \"def better_matching(self, pattern, offsets=False, slice_len=5):\\n first_occurencies, counts_at = self.setup_better_match(slice_len)\\n pattern = list(pattern)\\n top, bottom = 0, self.tl\\n while top <= bottom:\\n if pattern:\\n current = pattern.pop()\\n if counts_at(bottom+1)[current] - counts_at(top)[current] > 0:\\n #if current in self.last_col[top:bottom+1]:\\n top = first_occurencies[current] + counts_at(top)[current]\\n bottom = first_occurencies[current] + counts_at(bottom + 1)[current] - 1\\n else:\\n if offsets:\\n return []\\n else:\\n return 0\\n else:\\n if offsets:\\n return self.offsets(top, bottom)\\n else:\\n return bottom - top + 1\",\n \"def kmp_algo(inp_string: str, substr: str) -> Optional[int]:\\n # 1. Сравниваем `i-й` элемент строки и `j-й` элемент шаблона\\n # 2. Если они равны - увеличиваем оба счетчика (и проверяем условие выхода)\\n # 3. Если не равны – значит мы нашли несовпадение где-то в середине шаблона,\\n # нужно вычислить, с какого символа шаблона нужно продолжать. `j = pi(j-1)`\\n pref_fun = _prefix_fun(substr) # префикс таблица\\n i = j = 0 # индексы элементов строки и подстроки\\n while i < len(inp_string) and j < len(substr):\\n if substr[j] == inp_string[i]:\\n i += 1\\n j += 1\\n elif j == 0:\\n i += 1\\n else:\\n j = pref_fun[j - 1]\\n else:\\n if j == len(substr):\\n return i - j\\n return None\",\n \"def find_substring(string):\\n sub_s = \\\"\\\"\\n if len(string) == 1:\\n print(1)\\n for k in range(0, len(string) // 2):\\n sub_s = sub_s[:k] + string[k]\\n pos = 0\\n next_pos = string.find(sub_s, pos + k + 1)\\n count = 1\\n while next_pos != -1 and next_pos == pos + k + 1:\\n count += 1\\n pos += k + 1\\n next_pos = string.find(sub_s, pos + k + 1)\\n if next_pos == -1 and pos == len(string) - k - 1:\\n return count\\n return 0\",\n \"def binarysearch(a, i):\\n l = 0\\n r = len(a) - 1\\n \\n while l < r:\\n m = l + (r-l)/2\\n \\n if a[m] == i:\\n return m\\n elif a[m] < i:\\n l = m + 1\\n else:\\n r = m\\n \\n if l > 0 and abs(i - a[l-1]) < abs(i- a[l]):\\n return l-1\\n \\n return l\",\n \"def subStringMatchExact(target,key):\\r\\n index = find(target,key)\\r\\n #print 'here',target,key,index\\r\\n if index < 0 or len(key) <= 0 or len(target) <= 0:\\r\\n return ()\\r\\n matches = subStringMatchExact(target[index+len(key):len(target)],key)\\r\\n offset = index + len(key)\\r\\n temp_matches = ()\\r\\n #print matches\\r\\n if matches:\\r\\n for x in range(0, len(matches)) :\\r\\n temp_matches += ((matches[x] + offset),)\\r\\n #matches.insert(0,index)\\r\\n temp_matches = (index,) + temp_matches\\r\\n return temp_matches\",\n \"def search(self, L: int, a: int, modulus: int, n: int, nums: List[int]) -> str:\\n # compute the hash of string S[:L]\\n h = 0\\n for i in range(L):\\n h = (h * a + nums[i]) % modulus\\n \\n # already seen hashes of strings of length L\\n seen = {h} \\n # const value to be used often : a**L % modulus\\n aL = pow(a, L, modulus) \\n for start in range(1, n - L + 1):\\n # compute rolling hash in O(1) time\\n h = (h * a - nums[start - 1] * aL + nums[start + L - 1]) % modulus\\n if h in seen:\\n return start\\n seen.add(h)\\n return -1\",\n \"def longest_prefix_length(s, i, j):\\n l = 0\\n while (i+l < len(s)) and (j+l < len(s)):\\n if s[i+l] != s[j+l]:\\n break\\n l += 1\\n return l\",\n \"def compare_eq_len(a, b):\\n n = len(a)\\n m = len(b)\\n\\n # slide b across a from left to right till from just overlapping till full overlap\\n overlap = 0 # stores length of the overlap\\n lconcat = \\\"\\\" # this stores the shortest common superstring\\n for j in range(m):\\n starta = 0\\n enda = j+1\\n startb = m - (j+1)\\n endb = m\\n if a[starta:enda] == b[startb:endb]:\\n # if an overlap is found, check if it is larger than the previously detected one\\n # print(\\\"overlap found\\\")\\n if len(a[starta:enda]) > overlap: \\n overlap = len(a[starta:enda]) \\n lconcat = b + a[enda:] # this is the current shortest common superstring\\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\\n\\n # print(\\\"-\\\")\\n\\n # slide b across a so that b starts from one element past a after full overlap\\n rconcat = \\\"\\\"\\n for j in range(m-1):\\n starta = j+1\\n enda = m\\n startb = 0\\n endb = m - (j+1)\\n if a[starta:enda] == b[startb:endb]:\\n if len(a[starta:enda]) > overlap: # if there is a bigger overlap then save it \\n # print(\\\"overlap found\\\")\\n overlap = len(a[starta:enda]) \\n rconcat = a + b[endb:]\\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\\n\\n # after checking for overlaps there may be 1 or no shortest common\\n # superstrings stored in both lconcat and rconcat. Choose the shortest one if it exists\\n # or the concatenation of a and b if there are no overlaps. We may have to make some\\n # arbitrary choices here.\\n\\n if not lconcat and not rconcat: # both lconcat and rconcat are empty, no overlaps\\n superstring = a + b # append b to a (could prepend here too, this is an arbitrary choice)\\n elif lconcat and not rconcat: # lconcat contains overlap and rconcat is empty\\n superstring = lconcat \\n elif rconcat and not lconcat: # rconcat contains overlap and lconcat is empty\\n superstring = rconcat\\n elif rconcat and lconcat and (len(lconcat) <= len(rconcat)): # use lconcat if it is shorter or equal len to rconat\\n superstring = lconcat\\n elif rconcat and lconcat and (len(rconcat) < len(lconcat)): # use rconcat only if it is shorter than lconat\\n superstring = rconcat\\n return superstring\",\n \"def ApproxStrMatch(pattern, text, d, debug = False):\\n\\tcount = 0\\n\\tindex = []\\n\\tfor i in range(len(text)-len(pattern)+1):\\n\\t\\tif debug:\\n\\t\\t\\tprint text[i:i+len(pattern)]\\n\\t\\t\\tprint HammingDist(text[i:i+len(pattern)], pattern)\\n\\t\\tif HammingDist(text[i:i+len(pattern)], pattern) <= d:\\n\\t\\t\\tcount += 1\\n\\t\\t\\tindex.append(i)\\n\\treturn index\",\n \"def fn(ss):\\n i = cnt = 0\\n for ch in s: \\n if ss[i] == ch: \\n i += 1\\n if i == len(ss): \\n if (cnt := cnt + 1) == k: return True \\n i = 0\\n return False\",\n \"def find_kmp(T, P):\\n n, m = len(T), len(P) # introduce convenient notations\\n if m == 0: # trivial search for empty string\\n return 0\\n fail = compute_kmp_fail(P) # rely on utility to precompute\\n j = 0 # index into text\\n k = 0 # index into pattern\\n while j < n:\\n if T[j] == P[k]: # P[0:k+1] matched thus far\\n if k == m-1: # match is complete\\n return j - m + 1\\n else: # try to extend match\\n j += 1\\n k += 1\\n elif k > 0:\\n k = fail[k-1] # reuse suffix of P[0:k]\\n else:\\n j += 1\\n return -1 # reached end without match\",\n \"def binsearch(alist, key, start, end):\\n mid = len(alist) // 2\\n if start > end:\\n return None\\n elif start < end:\\n return binsearch(alist, key, start, mid-1)\\n else:\\n return mid\",\n \"def Overlap(a, b, min_length=3):\\n start = 0\\n while True:\\n # look for b's prefix in a\\n start = a.find(b[:min_length], start) # Python Docs: string.find(value, start, end)\\n if start == -1:\\n return 0\\n\\n if b.startswith(a[start:]):\\n return len(a) - start \\n start += 1\",\n \"def longest_common_substring_iterative_bottom_up(s1, s2):\\n\\n cache = [[0] * (len(s2)+1) for _ in range(-1, len(s1))]\\n\\n for i in range(1, len(s1)+1):\\n for j in range(1, len(s2)+1):\\n\\n if s1[i-1] == s2[j-1]:\\n\\n # previous count is cache[i-1][j-1]\\n cache[i][j] = cache[i-1][j-1] + 1\\n else:\\n cache[i][j] = max(cache[i-1][j-1],\\n max(cache[i-1][j],\\n cache[i][j-1]))\\n\\n return cache[-1][-1]\",\n \"def findIndicesOfStartsWithEndsWith(target, wordListCustomSorted):\\n n = len(wordListCustomSorted)\\n firstInd = lastInd = n // 2\\n if target == firstAndLastChars(wordListCustomSorted[0]):\\n firstInd = 0\\n elif target < firstAndLastChars(wordListCustomSorted[0]):\\n return (-1, -1)\\n if target == firstAndLastChars(wordListCustomSorted[n-1]):\\n lastInd = n-1\\n elif target > firstAndLastChars(wordListCustomSorted[n-1]):\\n return (-1, -1)\\n def firstIndIsMatch(firstInd):\\n if firstInd == 0:\\n return True\\n else:\\n return firstAndLastChars(wordListCustomSorted[firstInd]) == target and \\\\\\n firstAndLastChars(wordListCustomSorted[firstInd-1]) < target\\n def lastIndIsMatch(lastInd):\\n if lastInd == n-1:\\n return True\\n else:\\n return firstAndLastChars(wordListCustomSorted[lastInd]) == target and \\\\\\n firstAndLastChars(wordListCustomSorted[lastInd+1]) > target\\n\\n lb = 1\\n ub = n-1\\n while not firstIndIsMatch(firstInd):\\n if lb >= ub:\\n return (-1, -1)\\n if target > firstAndLastChars(wordListCustomSorted[firstInd]):\\n lb = firstInd + 1\\n else:\\n ub = firstInd\\n firstInd = (lb + ub) // 2\\n lb = firstInd\\n ub = n-2\\n while not lastIndIsMatch(lastInd):\\n if lb > ub:\\n return (-1, -1)\\n if target >= firstAndLastChars(wordListCustomSorted[lastInd]):\\n lb = lastInd + 1\\n else:\\n ub = lastInd\\n lastInd = (lb + ub) // 2\\n return firstInd, lastInd\",\n \"def karp_rabin_search(s, t):\\n rs = RollingHash(len(s))\\n rt = RollingHash(len(s))\\n for c in s:\\n rs.append(c)\\n for c in t[:len(s)]:\\n rt.append(c)\\n if rs.hash == rt.hash:\\n return True\\n for i in range(len(s), len(t)):\\n rt.pop_left()\\n rt.append(t[i])\\n if rs.hash == rt.hash:\\n return True\\n return False\",\n \"def compute_kmp_fail(P):\\n m = len(P)\\n fail = [0]*m # by default, presume overlap of 0 everywhere\\n j = 1\\n k = 0\\n while j < m: # compute f(j) during this pass, if nonzero\\n if P[j] == P[k]: # k+1 characters match thus far\\n fail[j] = k+1\\n j += 1\\n k += 1\\n elif k > 0: # k follows a matching prefix\\n k = fail[k-1]\\n else: # no match found starting at j\\n j += 1\\n return fail\",\n \"def missing_binary_search(item_list):\\n first = 0\\n last = len(item_list)-1\\n while first < last:\\n mid = (first + last)//2\\n if mid not in item_list:\\n return mid\\n if mid - first != item_list[mid] - item_list[first]:\\n last = mid\\n elif last - mid != item_list[last] - item_list[mid]:\\n first = mid\\n else:\\n return None\",\n \"def overlap(a, b, min_length=3):\\n start = 0 # start all the way at the left\\n\\n while True:\\n start = a.find(b[:min_length], start) # look for b's suffix i a\\n if start == -1: # no more occurrences to right\\n return 0\\n # found occurrence; check for full suffix/prefix match\\n if b.startswith(a[start:]):\\n return len(a) - start\\n start += 1 # move just paat previous match\",\n \"def find_string(n, c_length, start=None):\\n \\n c = range(c_length)\\n if start is None:\\n i = get_minimum(n)\\n else:\\n i = start\\n\\n strings = [e for e in generate_strings(n, c)]\\n while True:\\n for x, s in enumerate(generate_strings(i, c)):\\n if check_string(s, strings):\\n return s\\n\\n if x % 1000000 == 0:\\n print x\\n i += 1\\n print \\\"processing %s\\\" % i\",\n \"def search(self, key):\\r\\n left = 0 \\r\\n right = self.num_keys()\\r\\n while right > left:\\r\\n mid = (left + right)//2\\r\\n if self.keys[mid] >= key:\\r\\n right = mid\\r\\n else:\\r\\n left = mid + 1\\r\\n return left\",\n \"def overlap(s1, s2):\\n j = len(s2) - 1\\n while j >= 0 and not s1.endswith(s2[:j + 1]):\\n j -= 1\\n return j\",\n \"def overlap(s1, s2):\\n for i in range(min(len(s1), len(s2)), 0, -1):\\n if s1.endswith(s2[:i]):\\n return i\\n return 0\",\n \"def match_len(chain):\\n previous,current = chain[-2:]\\n previous = [ x for x in reversed(str(previous).split('.')) ]\\n current = [ x for x in reversed(str(current).split('.')) ]\\n\\n max_length = min(( len(x) for x in (previous,current) ))\\n i = 0\\n while i < max_length:\\n if previous[i] != current[i]:\\n return i\\n i += 1\\n return i\",\n \"def KMP_algo(inp_string: str, substr: str):\\n\\n\\tdef prefix_fun(prefix_str: str) -> list:\\n\\t\\t\\\"\\\"\\\"\\n\\t\\tPrefix function for KMP\\n\\n\\t\\t:param prefix_str: dubstring for prefix function\\n\\t\\t:return: prefix values table\\n\\t\\t\\\"\\\"\\\"\\n\\n\\n\\t\\tf = []\\n\\n\\t\\tfor i in range(len(prefix_str)):\\n\\t\\t\\tj = i\\n\\t\\t\\toffset = 0\\n\\t\\t\\twhile j > 0:\\n\\t\\t\\t\\tj -= 1\\n\\t\\t\\t\\t# print(i, j, offset, prefix_str[j], prefix_str[i-offset])\\n\\t\\t\\t\\tif prefix_str[j] == prefix_str[i - offset]:\\n\\t\\t\\t\\t\\toffset += 1\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tj += offset\\n\\t\\t\\t\\t\\toffset = 0\\n\\t\\t\\tf.append(offset)\\n\\t\\t\\t# print('append', offset)\\n\\n\\t\\treturn f\\n\\n\\t'''\\n\\t\\tВажный Комментарий: первоначально был сделан вариант, который ищет не только первое, но и \\n\\t\\tпоследующие вхождения подстроки в строку и много тестовых выводов. Не удалял. \\n\\t\\tПоэтому в коде есть костыли и некоторая непричёсанность =) \\t\\t\\n\\t'''\\n\\n\\n\\tf = prefix_fun(substr)\\n\\t# print(f)\\n\\n\\ti = 0\\n\\tj = 0\\n\\tdone = False\\n\\n\\twhile not done: # (input(\\\"Exit - 0\\\") != '0')\\n\\t\\tinitf = False\\n\\t\\tdone = False\\n\\n\\t\\ta = inp_string[i:i + 1]\\n\\t\\tb = substr[j:j + 1]\\n\\t\\t# print('a=', a, 'b=', b)\\n\\n\\t\\tif a == b:\\n\\t\\t\\ti += 1\\n\\t\\t\\tj += 1\\n\\t\\telif j <= 0:\\n\\t\\t\\ti += 1\\n\\t\\telse:\\n\\t\\t\\tj = f[j - 1]\\n\\t\\t# print('i=', i, 'j=', j)\\n\\t\\t# print(inp_string[:i])\\n\\t\\t# print(substr[:j])\\n\\n\\t\\tif len(inp_string) == i:\\n\\t\\t\\t# print(\\\"Конец поиска : конец строки\\\")\\n\\t\\t\\tdone = True\\n\\n\\t\\tif len(substr) == j:\\n\\t\\t\\t# print(\\\"Подстрока найдена!\\\", i - j)\\n\\t\\t\\tinitf = True\\n\\n\\t\\t\\treturn i - j # если закомментировать этот return, то будет искать последующие вхождеия\\n\\n\\t\\tif initf:\\n\\t\\t\\ti += 1\\n\\t\\t\\tj = 0\\n\\n\\treturn None\",\n \"def crackSafe(self, n: int, k: int) -> str:\",\n \"def findChildB(T,k):\\r\\n for i in range(len(T.data)):\\r\\n if k < T.data[i].word:\\r\\n return i\\r\\n return len(T.data)\",\n \"def kmp_strstr(haystack, needle):\\r\\n def computeLPSArray(word):\\r\\n # The explaination of this function is in _LPSArray()\\r\\n lps = [0] * len(word)\\r\\n wordIdx = 0\\r\\n delta = 1\\r\\n while delta < len(word):\\r\\n while delta < len(word) and word[delta] == word[wordIdx]:\\r\\n lps[delta] = wordIdx + 1\\r\\n wordIdx += 1\\r\\n delta += 1\\r\\n if wordIdx != 0:\\r\\n wordIdx = lps[wordIdx-1]\\r\\n else:\\r\\n delta += 1\\r\\n return lps\\r\\n\\r\\n # handle special cases\\r\\n if haystack == needle:\\r\\n return [0]\\r\\n elif not needle:\\r\\n return [0]\\r\\n elif not haystack:\\r\\n return []\\r\\n # init lps table\\r\\n lps = computeLPSArray(needle)\\r\\n ans = []\\r\\n # main loop\\r\\n i_haystack = 0\\r\\n i_needle = 0\\r\\n while i_haystack < len(haystack):\\r\\n if haystack[i_haystack] == needle[i_needle]:\\r\\n i_haystack += 1\\r\\n i_needle += 1\\r\\n # print i_needle, i_haystack\\r\\n if i_needle == len(needle):\\r\\n ans.append(i_haystack - i_needle)\\r\\n i_needle = lps[i_needle-1]\\r\\n elif i_needle != 0:\\r\\n i_needle = lps[i_needle-1]\\r\\n else:\\r\\n i_haystack += 1\\r\\n\\r\\n return ans\",\n \"def subStringMatchExact(target, key):\\n\\tanswer_tuple = () # initialize the tuple we will return\\n\\tstart = 0 # use this initial the starting point for find()\\n\\twhile find(target, key, start) >=0:\\n\\t\\tstart = find(target, key, start)\\n\\t\\tanswer_tuple += (start,)\\n\\t\\tstart+=1\\n\\treturn answer_tuple\",\n \"def subStringMatchExact(target, key):\\n\\tanswer_tuple = () # initialize the tuple we will return\\n\\tstart = 0 # use this initial the starting point for find()\\n\\twhile find(target, key, start) >=0:\\n\\t\\tstart = find(target, key, start)\\n\\t\\tanswer_tuple += (start,)\\n\\t\\tstart+=1\\n\\treturn answer_tuple\",\n \"def needle_diff(left_struc, right_struc, key, minimal=True):\\n if type(left_struc) not in (list, tuple):\\n return keyset_diff(left_struc, right_struc, key, minimal)\\n assert type(right_struc) in (list, tuple)\\n\\n down_col = 0\\n lastrow = [\\n [[key + [sub_i]] for sub_i in range(i)]\\n for i in range(len(left_struc), -1, -1)\\n ]\\n\\n def modify_cand():\\n \\\"\\\"\\\"Build the candidate diff that involves (potentially) modifying an\\n element.\\\"\\\"\\\"\\n if col_i + 1 < len(lastrow):\\n return (lastrow[col_i + 1] +\\n diff(left_elem, right_elem, key=key + [left_i],\\n minimal=minimal, verbose=False))\\n\\n def delete_cand():\\n \\\"\\\"\\\"Build the candidate diff that involves deleting an element.\\\"\\\"\\\"\\n if row:\\n return row[0] + [[key + [left_i]]]\\n\\n def append_cand():\\n \\\"\\\"\\\"Build the candidate diff that involves appending an element.\\\"\\\"\\\"\\n if col_i == down_col:\\n return (lastrow[col_i] +\\n [[key + [append_key(lastrow[col_i], left_struc, key)],\\n right_elem]])\\n\\n for right_i, right_elem in enumerate(right_struc):\\n first_left_i = min(right_i, len(left_struc) - 1)\\n left_elems = left_struc[first_left_i:]\\n row = []\\n\\n for left_i, left_elem in enumerate(left_elems, first_left_i):\\n col_i = len(left_struc) - left_i - 1\\n cands = [c for c in (modify_cand(), delete_cand(), append_cand())\\n if c is not None]\\n winner = min(cands, key=lambda d: len(compact_json_dumps(d)))\\n row.insert(0, winner)\\n\\n lastrow = row\\n return winner\",\n \"def overlap(a, b, min_length=3):\\r\\n start = 0 # start all the way at the left\\r\\n while True:\\r\\n start = a.find(b[:min_length], start) # look for b's prefix in a\\r\\n if start == -1: # no more occurrences to right\\r\\n return 0\\r\\n # found occurrence; check for full suffix/prefix match\\r\\n if b.startswith(a[start:]):\\r\\n return len(a)-start\\r\\n start += 1 # move just past previous match\\r\",\n \"def is_possible(strs, k):\\n for i in xrange(len(strs) / k):\\n rows = strs[i * k: (i + 1) * k]\\n for j in xrange(len(strs[0]) / k):\\n piece = [row[j * k: (j + 1) * k] for row in rows]\\n target = piece[0]\\n for s in piece:\\n if s != target:\\n return \\\"Impossible\\\"\\n return \\\"Possible\\\"\",\n \"def find_similar_hashes(self):\\n matches = self.hasher.find_matches(self.__offset_hash_map.values())\\n print(\\\" Found {} matches.\\\".format(len(matches)))\\n\\n print(\\\" Formatting the found matches. This takes some time ...\\\")\\n self.__matched_offsets = self.__to_offset_list(matches, self.__offset_hash_map)\\n\\n return\",\n \"def get_mismatch_matrix(k,m):\\n words = get_words(k)\\n N = len(words)\\n\\n mismatch_matrix = np.zeros((N, N))\\n for i in range(N):\\n for j in range(i, N):\\n if Levenshtein.hamming(words[i], words[j]) <= m:\\n mismatch_matrix[i,j] = 1/2\\n mismatch_matrix[j,i] = 1/2\\n\\n return mismatch_matrix\",\n \"def binary_search(a,x):\\n start = 0 \\n end = len(a)-1\\n\\n while start<= end:\\n mid = (start+end)//2 \\n if x == a[mid]:\\n return mid\\n elif x < a[mid]:\\n end = mid-1\\n else:\\n start = mid+1\\n return -1\",\n \"def binary_search(seq, key): # find the most closer one to the key but smaller than the key\\n# begin/end /not exist\\n\\n\\n begin = 0\\n end = len(seq)-1\\n if key > seq[end]:\\n return end\\n while begin <= end:\\n mid1 = (begin + end)//2\\n mid2 = (begin + end)//2 + 1\\n tmp1 = seq[mid1]\\n tmp2 = seq[mid2]\\n if key > tmp1 and key > tmp2:\\n begin = mid1\\n elif key < tmp1:\\n end = mid1\\n elif key > tmp1 and key < tmp2:\\n return mid1\\n elif key == tmp1:\\n end = mid1 - 1\\n return -1\",\n \"def rabin_karp_search(pattern, s, d=5294212309, q=9743212277):\\n m, n = len(pattern), len(s)\\n # h = pow(d,m-1)%q\\n # mod as we go\\n h = 1\\n for _ in range(m-1):\\n h = (d * h)%q\\n p, t = 0, 0\\n result = []\\n for i in range(m): \\n p = (d*p+ord(pattern[i]))%q\\n t = (d*t+ord(s[i]))%q\\n for k in range(n-m+1):\\n if p == t: # check character by character\\n match = True\\n for i in range(m):\\n if pattern[i] != s[k+i]:\\n match = False\\n break\\n if match:\\n result = result + [k]\\n if k < n-m:\\n t = (t-h*ord(s[k]))%q \\n t = (t*d+ord(s[k+m]))%q \\n t = (t+q)%q \\n return result\",\n \"def scs_of_pair(a, b):\\n # compare two strings where the text a is longer than the pattern b\\n n = len(a)\\n m = len(b)\\n\\n # check the args are the right way round\\n if m > n:\\n raise ValueError(\\\"string b is longer than string a\\\")\\n\\n # slide b across a from left to right till from just overlapping till full overlap\\n overlap = 0 # stores length of the overlap\\n lconcat = \\\"\\\" # stores the curretn shortest common superstring\\n for j in range(n): \\n starta = 0 if (j+1) <= m else ((j+1) - m) \\n enda = j+1 \\n startb = (m - (j+1)) if (j+1) < m else 0 \\n endb = m \\n if a[starta:enda] == b[startb:endb]:\\n # print(\\\"overlap found\\\")\\n if len(a[starta:enda]) > overlap: # if there is a bigger overlap then save it \\n overlap = len(a[starta:enda]) \\n lconcat = b + a[enda:]\\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\\n\\n # print(\\\"-\\\")\\n rconcat = \\\"\\\"\\n for j in range(m - 1):\\n starta = (n - m) + (j + 1) \\n enda = n \\n startb = 0 \\n endb = m - (j+1) \\n if a[starta:enda] == b[startb:endb]:\\n # print(\\\"overlap found\\\")\\n if len(a[starta:enda]) > overlap: # if there is a bigger overlap then save it \\n overlap = len(a[starta:enda]) \\n rconcat = a + b[endb:]\\n # print(starta, enda, startb, endb, a[starta:enda], b[startb:endb])\\n\\n # after checking for overlaps there may be 1 or no shortest common\\n # superstrings stored in both lconcat and rconcat. Choose the shortest one if it exists\\n # or the concatenation of a and b if there are no overlaps. We may have to make some\\n # arbitrary choices here.\\n\\n if not lconcat and not rconcat: # both lconcat and rconcat are empty, no overlaps\\n superstring = a + b # append b to a (could prepend here too)\\n elif lconcat and not rconcat: # lconcat contains overlap and rconcat is empty\\n superstring = lconcat\\n elif rconcat and not lconcat: # rconcat contains overlap and lconcat is empty\\n superstring = rconcat\\n elif rconcat and lconcat and (len(lconcat) <= len(rconcat)): # use lconcat if it is shorter or equal len to rconat\\n superstring = lconcat\\n elif rconcat and lconcat and (len(rconcat) < len(lconcat)): # use rconcat only if it is shorter than lconat\\n superstring = rconcat\\n return superstring\",\n \"def freq_words_with_mismatches(seq, k, d):\\n # Frequency analysis so we don't generate mismatches for the same k-mer more than once.\\n kmer_freq = defaultdict(int)\\n for i in xrange(len(seq)-k+1):\\n kmer_freq[seq[i:i+k]] += 1\\n\\n # Get all of the mismatches for each unique k-mer in the sequence, appearing freq times.\\n mismatch_count = defaultdict(int)\\n for kmer, freq in kmer_freq.iteritems():\\n for mismatch in kmer_mismatches(kmer, d):\\n mismatch_count[mismatch] += freq\\n\\n # Computing the maximum value is somewhat time consuming to repeat, so only do it once!\\n max_count = max(mismatch_count.values())\\n return sorted([kmer for kmer, count in mismatch_count.iteritems() if count == max_count])\",\n \"def binaryLSearch(array, start, end, anchor):\\n if start >= end:\\n raise ValueError(\\\"start index {s} cannot be greater than end index {e}\\\".format(s=start, e=end))\\n elif start == end - 1:\\n if array[start] > anchor:\\n return start\\n else:\\n return None\\n\\n # now assume start <= end - 2\\n\\n mid = (start + end) // 2 # guaranteed start < mid < end\\n if array[mid - 1] >= anchor:\\n return binaryLSearch(array, start, mid, anchor)\\n else:\\n return binaryLSearch(array, mid, end, anchor)\",\n \"def getHash(key, hashseed, m, k):\\n salt = str(hashseed)\\n hashed_password = hashlib.sha256((key + salt).encode('utf-8')).hexdigest()\\n #print hashed_password\\n if k > 32: raise Exception(\\\"k should be less than 32\\\")\\n if k <= 1: raise Exception(\\\"k should be more than 2\\\")\\n if k > m: raise Exception(\\\"k should be less than m\\\")\\n\\n # it cuts 4 byte from the hashed_password, so the value is 0xFFFFFFFF\\n assert(m < 0xFFFFFFFF)\\n hashedPasswordLength = len(hashed_password)\\n assert(hashedPasswordLength/4 > k)\\n\\n result = set()\\n index = 0\\n\\n # make the non-overwrapping hash value below m\\n while True:\\n value = int(hashed_password[index:index+4], 16) % m\\n index += 4\\n result.add(value)\\n\\n if len(result) == k: break\\n\\n \\n if index >= hashedPasswordLength:\\n print (index)\\n print (hashedPasswordLength)\\n\\n assert index < hashedPasswordLength\\n return list(result)\",\n \"def longest_common_substring_bottom_up(s1, s2):\\n lcs_array = lcs(s1, s2)\\n n = len(s1)\\n m = len(s2)\\n l = lcs_array[n][m]\\n result = \\\"\\\"\\n while l != 0:\\n if s1[n - 1] == s2[m - 1]:\\n result = s1[n - 1] + result\\n l -= 1\\n n -= 1\\n m -= 1\\n elif lcs_array[n][m - 1] >= lcs_array[n - 1][m]:\\n m -= 1\\n else:\\n n -= 1\\n return result\",\n \"def kSimilarity(self, s1: str, s2: str) -> int:\\n def neighbors(s: str) -> Iterator[str]:\\n i = 0\\n while s[i] == s2[i]: # Skip the already swapped chars.\\n i += 1\\n\\n t = list(s)\\n for j in range(i + 1, N):\\n # Make a swap.\\n if s[j] == s2[i] and s[j] != s2[j]:\\n t[i], t[j] = t[j], t[i]\\n yield ''.join(t)\\n t[i], t[j] = t[j], t[i]\\n\\n N = len(s1)\\n queue = deque([(s1, 0)])\\n visited = {s1}\\n while queue:\\n s, swapCnt = queue.popleft()\\n if s == s2:\\n return swapCnt\\n\\n swapCnt += 1\\n for neighbor in neighbors(s):\\n if neighbor not in visited:\\n visited.add(neighbor)\\n queue.append((neighbor, swapCnt))\",\n \"def search_entry_equal_to_its_index(A):\\n L, R = 0, len(A) -1\\n\\n while L <= R:\\n M = L + (R - L) // 2\\n if A[M] > M:\\n R = M - 1\\n elif A[M] == M:\\n return M\\n else:\\n # A[M] < M\\n L = M + 1\\n\\n return -1\",\n \"def kmerNeighbors(text,k):\\r\\n L=set()\\r\\n for i in range(0,len(text)-k+1):\\r\\n for d in range(0,k+1):\\r\\n L.update(Neighbors(kmer(text,i,k),d))\\r\\n D=dict()\\r\\n for l in L:\\r\\n D[l]=minHamm(text,l)\\r\\n return D\",\n \"def lcs_pairwise(s1: str, s2: str, k: int, length: int):\\n\\n for i in range(0, len(s1) - length + 1):\\n for j in range(0, len(s2) - length + 1):\\n sub1 = s1[i: i + length]\\n sub2 = s2[j: j + length]\\n result = pairwise2.align.globalxx(sub1, sub2)\\n if result[0][2] >= length - k - 1:\\n print(str(i) + \\\" \\\" + str(j) + \\\" \\\" + str(length - result[0][2]) + \\\" \\\" +\\n sub1 + \\\" \\\" + sub2, end=\\\"\\\\t\\\")\\n else:\\n print(str(i) + \\\" \\\" + str(j) + \\\" N N N\\\", end=\\\"\\\\t\\\")\\n print(\\\"\\\")\",\n \"def solve(n, k):\\n o = [True] + [False] * n + [True] # whether it's occupied\\n # initialize Ls and Rs\\n Ls = [-1] + [i for i in range(n)] + [-1]\\n Rs = Ls[::-1] # Ls reversed\\n\\n for i in range(k):\\n # find where min(Ls,Rs) is maximal\\n min_ = 0\\n for j in range(n+1):\\n if not o[j]:\\n if min(Ls[j], Rs[j]) > min_:\\n min_ = min(Ls[j], Rs[j])\\n\\n # check if there is more than one\\n pos = None\\n max_ = 0\\n for j in range(n+1):\\n if not o[j]:\\n if min(Ls[j], Rs[j]) == min_:\\n if (pos is None) or (pos is not None and max(Ls[j], Rs[j]) > max_):\\n pos = j\\n max_ = max(Ls[j], Rs[j])\\n\\n # take pos\\n maxs = max(Rs[pos], Ls[pos])\\n mins = min(Rs[pos], Ls[pos])\\n\\n o[pos] = True\\n Rs[pos] = -1\\n Ls[pos] = -1\\n\\n # update Ls and Rs\\n l = pos # index of first occupied left to position taken\\n while l > 0 and not o[l - 1]:\\n l -= 1\\n for x in range(pos - l):\\n Ls[l + x] = x\\n Rs[l + x] = pos - l - x - 1\\n\\n r = pos # index of first occupied right to position taken\\n while r <= n and not o[r + 1]:\\n r += 1\\n for x in range(r - pos):\\n Ls[pos + x + 1] = x\\n Rs[pos + x + 1] = r - pos - x - 1\\n\\n return str(maxs) + \\\" \\\" + str(mins)\",\n \"def _bin_search_recursive(self, v, start, end):\\n if end < start:\\n return start\\n\\n mid = (start + end) / 2\\n if self.values[mid] == v:\\n return mid\\n elif self.values[mid] < v:\\n return self._bin_search_recursive(v, mid + 1, end)\\n else:\\n return self._bin_search_recursive(v, start, mid - 1)\",\n \"def get_2away_pairs(local_index_to_kmer, k):\\n\\n #These are the base cases for the recursion. If k==1, the kmers obviously can't differ in exactly two bases, so return an empty list. if k==2, return every pair of indices where the kmers at those indices differ at exactly two bases.\\n if k == 1:\\n return []\\n if k == 2:\\n return [(i, j) for (i,j) in combinations(local_index_to_kmer, 2) if local_index_to_kmer[i][0] != local_index_to_kmer[j][0] and local_index_to_kmer[i][1] != local_index_to_kmer[j][1]]\\n\\n #Get the two halves of the kmer\\n k_L = k//2\\n k_R = k-k_L\\n\\n #initialize dictionaries in which the key is the hash of half of the kmer, and the value is a list of indices of the kmers with that same hash\\n kmer_L_hashes = defaultdict(list)\\n kmer_R_hashes = defaultdict(list)\\n\\n #initialize pairs, which will be returned by get_1away_pairs\\n pairs = []\\n\\n #initialize dictionaries containing the left halves and the right halves (since we will have to check cases where the left half differs by 1 and the right half differs by 1)\\n local_index_to_kmer_L = {}\\n local_index_to_kmer_R = {}\\n\\n #for each kmer, calculate its left hash and right hash, then add its index to the corresponding entries of the dictionary\\n for i, kmer in local_index_to_kmer.items():\\n kmer_L = kmer[:k_L]\\n kmer_R = kmer[k_L:]\\n local_index_to_kmer_L[i] = kmer_L\\n local_index_to_kmer_R[i] = kmer_R\\n kmer_L_hashes[kmer_to_int(kmer_L)] += [i]\\n kmer_R_hashes[kmer_to_int(kmer_R)] += [i]\\n\\n #for each left hash in which there are multiple kmers with that left hash, find the list of pairs in which the right half differs by 2. (aka, if left half matches, recurse on right half).\\n for kmer_L_hash_indices in kmer_L_hashes.values(): #same in first half\\n if len(kmer_L_hash_indices) > 1:\\n pairs += get_2away_pairs({kmer_L_hash_index:local_index_to_kmer[kmer_L_hash_index][k_L:] for kmer_L_hash_index in kmer_L_hash_indices}, k_R) #differ by 2 in right half\\n\\n #for each right hash in which there are multiple kmers with that right hash, find the list of pairs in which the left half differs by 2. (aka, if right half matches, recurse on left half).\\n for kmer_R_hash_indices in kmer_R_hashes.values(): #same in second half\\n if len(kmer_R_hash_indices) > 1:\\n pairs += get_2away_pairs({kmer_R_hash_index:local_index_to_kmer[kmer_R_hash_index][:k_L] for kmer_R_hash_index in kmer_R_hash_indices}, k_L) #differ by 2 in left half\\n\\n #Find matching pairs where the left half is one away, and the right half is one away\\n possible_pairs_L = set(get_1away_pairs(local_index_to_kmer_L,k_L))\\n possible_pairs_R = set(get_1away_pairs(local_index_to_kmer_R,k_R))\\n pairs += list(possible_pairs_L.intersection(possible_pairs_R))\\n return(pairs)\",\n \"def find_pair_with_sum_1(arr, k):\\n n = len(arr)\\n\\n def find_pivot(arr):\\n start = 0\\n end = len(arr) - 1\\n\\n if arr[end] > arr[start]:\\n return 0\\n\\n while end >= start:\\n mid = start + (end-start)//2\\n\\n if arr[mid] > arr[mid+1]:\\n return mid+1\\n if arr[mid] < arr[mid-1]:\\n return mid\\n\\n if arr[mid] > arr[start]:\\n start = mid+1\\n else:\\n end = mid-1\\n\\n smallest_index = find_pivot(arr)\\n print(\\\"Pivot is at \\\", smallest_index)\\n largest_index = smallest_index-1\\n\\n while smallest_index != largest_index:\\n curr_sum = arr[smallest_index] + arr[largest_index]\\n\\n if curr_sum == k:\\n return (arr[smallest_index], arr[largest_index])\\n\\n if curr_sum > k:\\n largest_index = (largest_index - 1) % n\\n else:\\n smallest_index = (smallest_index + 1) % n\\n\\n return \\\"Not Found\\\"\",\n \"def countSubStringMatch(target,key):\\n count = 0\\n for i in range(0,len(target)-len(key)):\\n if target[i:i+len(key)] == key:\\n count += 1\\n return count\",\n \"def minhash(s, t, n):\\n X = union_signature(s, t, n)\\n Y = intersection_signature(X, s, t)\\n return len(Y) / len(X)\",\n \"def binarySearch(A, k):\\n \\n #TODO: Implement without using python's in-built functiondef binary(A, k):\\n def bSearch(A, k, low, high):\\n if high == low:\\n if A[low] == k:\\n return low\\n else:\\n return -1\\n mid = (low + high)//2\\n if A[mid] == k:\\n return mid\\n elif A[mid] > k:\\n if low == mid:\\n return -1\\n else:\\n return bSearch(A, k, low, mid-1)\\n else:\\n return bSearch(A, k, mid+1, high)\\n if isinstance(A, list) == False or isinstance(k, int) == False:\\n return -1\\n else:\\n if len(A) == 0:\\n return -1\\n else:\\n x = bSearch(A, k, 0, len(A)-1)\\n return x\",\n \"def is_k_anagram(s1: str, s2: str, k: int) -> tuple:\\n if len(s1) != len(s2):\\n return False, None, None\\n\\n counter_map = Counter(s1)\\n extra = list()\\n changes = 0\\n for char in s2:\\n if counter_map[char] > 0:\\n counter_map[char] -= 1\\n else:\\n extra.append(char)\\n changes += 1\\n\\n if changes > k:\\n return False, None, None\\n diff = [c for c in counter_map if counter_map[c] > 0]\\n return True, extra, diff\",\n \"def fn(i1, i2):\\n if i1 == len(word1): return len(word2) - i2\\n if i2 == len(word2): return len(word1) - i1\\n if word1[i1] == word2[i2]: return fn(i1+1, i2+1)\\n return 1 + min(fn(i1+1,i2), fn(i1, i2+1))\",\n \"def rotated_array_search(input_list, number):\\n start_index = 0\\n end_index = len(input_list) - 1\\n while start_index <= end_index:\\n mid_idx = (start_index + end_index)//2 \\n left = input_list[:mid_idx]\\n right = input_list[mid_idx:]\\n try: \\n if input_list[mid_idx] == number:\\n return mid_idx\\n found = False\\n if number >= left[0] and number <= left[-1]:\\n start_index = 0\\n end_index = mid_idx - 1\\n found = True\\n else:# number >= right[0] and number <= right[-1]: \\n start_index = mid_idx + 1\\n\\n if not found:\\n if number >= right[0] and number <= right[-1]: \\n start_index = mid_idx + 1\\n else:\\n end_index = mid_idx + 1\\n except IndexError: #if element not found, return -1\\n return -1\\n return -1\",\n \"def calculate_score(s1, s2, l1, l2, startpoint):\\n\\n matched = \\\"\\\" # to hold string displaying alignments\\n score = 0\\n for i in range(l2):\\n if (i + startpoint) < l1:\\n if s1[i + startpoint] == s2[i]: # if the bases match\\n matched = matched + \\\"*\\\" # * indicates a match\\n score = score + 1\\n else:\\n matched = matched + \\\"-\\\" # - indicates no match\\n\\n return score\",\n \"def find_brute(T, P):\\n n, m = len(T), len(P) # introduce convenient notations\\n for i in range(n-m+1): # try every potential starting index within T\\n k = 0 # an index into pattern P\\n while k < m and P[k] == T[i+k]: # kth character of P matches\\n k += 1\\n if k == m: # if we reached the end of pattern,\\n return i # substring T[i:i+m] matches P\\n return -1 # failed to find a match starting with any i\",\n \"def method3(self, startPos, endPos, k):\\n diff = abs(startPos - endPos)\\n if diff > k:\\n return 0\\n dp = [[0 for _ in range(2 * k + 1)] for _ in range(k + 1)]\\n # shift k steps to avoid negative index, all the second index need to add shift\\n shift = k\\n dp[0][0 + shift] = 1\\n\\n for t in range(1, k+1):\\n for p in range(-k, k + 1):\\n #if 0 <= p - 1 + shift <= k:\\n if p - 1 >= -k:\\n dp[t][p + shift] += dp[t-1][p - 1 + shift]\\n #if 0 <= p + 1 + shift <= k:\\n if p + 1 <= k:\\n dp[t][p + shift] += dp[t-1][p + 1 + shift]\\n return dp[k][diff+shift] % (10**9+7)\",\n \"def best_pairing(current_end, end_dict, inverse_dict, blast_hits, l_min_score, r_min_score):\\n #this duplicates part of trio_hits - should try to rewrite that to use this function\\n \\n l_flange = int(end_dict[current_end][1])\\n l_contig = end_dict[current_end][0]\\n \\n #first find blast hits for the target scaffold end\\n left_matches = []\\n for hit in blast_hits:\\n if hit[0] == l_contig and int(hit[11]) >= l_min_score:\\n left_matches.append(hit)\\n \\n link_count = {}\\n \\n #then find other ends with correctly oriented hits adjacent to the target hits\\n for slink in end_dict:\\n link = end_dict[slink][0]\\n \\n right_matches = []\\n\\n for hit in blast_hits:\\n if hit[0] == link and int(hit[11]) >= r_min_score: \\n right_matches.append(hit)\\n \\n for lhit in left_matches:\\n for rhit in right_matches:\\n srhit = inverse_dict[rhit[0]]\\n r_flange = end_dict[srhit][1]\\n joint_flange = l_flange + r_flange\\n \\n if lhit[1] == rhit[1]:\\n lh_start = int(lhit[8])\\n lh_end = int(lhit[9])\\n rh_start = int(rhit[8])\\n rh_end = int(rhit[9])\\n\\n if abs(lh_start - rh_start) < joint_flange + 3000:\\n if (lh_end - lh_start)/(rh_end - rh_start) < 0:\\n if abs(lh_end - rh_end) > abs(lh_start - rh_start):\\n link_score = int(lhit[11]) * int(rhit[11])\\n if not link in link_count: \\n link_count[link] = link_score\\n elif link_score > link_count[link]:\\n link_count[link] = link_score\\n return link_count\",\n \"def solve(self):\\n # init the 1000\\n self.buf_init()\\n\\n keys = 0\\n while keys != 64:\\n # check if possible key\\n res = find_in_a_row(self.buffer.pop(0))\\n if res:\\n for b in self.buffer:\\n # if found a 5 in a row\\n if match_in_a_row(b, c=res):\\n keys += 1\\n break\\n\\n # add new one to end and iterate\\n hash_str = '{}{}'.format(self.salt, self.forward_idx).encode()\\n self.buffer.append(md5(hash_str).hexdigest())\\n self.forward_idx += 1\\n self.idx += 1\\n\\n return self.idx - 1\",\n \"def find_substring(pattern, target):\\n # Eliminate trivial cases.\\n n = len(target)\\n m = len(pattern)\\n if (not n or not m or m > n):\\n return False\\n #\\n # Search by comparing hashes.\\n pattern_hash = hash(pattern)\\n for string_start in range(n - m + 1):\\n string_end = string_start + m\\n if pattern_hash == hash(target[string_start:string_end]):\\n return True\\n return False\",\n \"def find_pairs_in_list(arr, k, l):\\n i = 0\\n j = 0\\n n = len(arr)\\n container = Container()\\n container.inc(arr[i])\\n\\n while j < n and i <= j:\\n\\n values = set()\\n for m in range(l+1):\\n values.add(arr[i] + m)\\n values.add(arr[i] - m)\\n\\n for value in values:\\n if (container.count(value) > 0 and value != arr[i]) or \\\\\\n (container.count(arr[i]) > 1):\\n return True\\n\\n if j < n:\\n j += 1\\n container.inc(arr[j])\\n if j - i > k or j == n-1:\\n container.dec(arr[i])\\n i += 1\\n\\n return False\",\n \"def longest_common_substring_backtrack(s1, s2, i, j, count):\\n if i == len(s1) or j == len(s2):\\n return 0\\n\\n if s1[i-1] == s2[j-1]:\\n count = 1 + longest_common_substring_backtrack(s1, s2, i+1, j+1, count)\\n\\n else:\\n count = max(count,\\n max(longest_common_substring_backtrack(s1, s2, i+1, j, count),\\n longest_common_substring_backtrack(s1, s2, i, j+1, count)))\\n\\n return count\",\n \"def frequent_words_with_mismatches(text, k, d):\\n\\n patterns = []\\n freq_map = {}\\n n = len(text)\\n for i in range(n - k + 1):\\n pattern = text[i:i + k]\\n pattern_rc = reverse_complement(pattern)\\n neighborhood = neighbors(pattern, d) + neighbors(pattern_rc, d)\\n for j in range(len(neighborhood)):\\n neighbor = neighborhood[j]\\n if neighbor not in freq_map.keys():\\n freq_map[neighbor] = 1\\n else:\\n freq_map[neighbor] = freq_map[neighbor] + 1\\n m = max_map(freq_map)\\n for key in freq_map.keys():\\n if freq_map[key] == m:\\n patterns.append(key)\\n return patterns\",\n \"def findKthNumber(self, m: int, n: int, k: int) -> int:\\n l, r = 1, m * n\\n while l < r:\\n mid = l + ((r - l) >> 1)\\n\\n # Check if there are k or more values that are less than mid.\\n # For each row, its elements look like 1*i, 2*i, ... n*i, so the\\n # largest number that is less than x will be x // i. But if x is\\n # too large for the current row, the total count for this row\\n # will be n instead.\\n if sum(min(mid // r, n) for r in range(1, m + 1)) >= k:\\n # mid is our candidate.\\n r = mid\\n else:\\n l = mid + 1\\n\\n return l\",\n \"def find_known_index(cryptf):\\n ct1 = cryptf(b'A')\\n ct2 = cryptf(b'B')\\n bsz = find_blocksize(cryptf)\\n\\n # find where are plaintext block is by comparing two ciphertexts with equal size inputs\\n blocknum = -1\\n for i, (blk1, blk2) in enumerate(zip(chunks(ct1, bsz), chunks(ct2, bsz))):\\n if blk1 != blk2:\\n blocknum = i + 1\\n break\\n else:\\n raise Exception(\\\"Could not find delta block!\\\")\\n\\n nextblock = chunks(ct1, bsz)[blocknum]\\n\\n # find offset by creating blocks of equal size but different last byte\\n # when the next block differs, we know we've passed a block boundary\\n offset = -1\\n for i in range(bsz+1):\\n nextblock1 = chunks(cryptf(b'A'*i+b'A'), bsz)[blocknum]\\n nextblock2 = chunks(cryptf(b'A'*i+b'B'), bsz)[blocknum]\\n if nextblock1 != nextblock2:\\n offset = i\\n break\\n else:\\n raise Exception(\\\"Could not find alignment offset!\\\")\\n\\n return blocknum, offset\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6563195","0.63334215","0.619655","0.6072905","0.5992598","0.5792656","0.57802635","0.5761363","0.57300216","0.5706402","0.56756544","0.5673622","0.56674","0.5588823","0.55755645","0.557284","0.5536065","0.5526799","0.55215424","0.55209804","0.5507269","0.54943544","0.5494262","0.5481268","0.54761773","0.5468075","0.5444503","0.54357845","0.5420464","0.54155046","0.5412011","0.5400879","0.53974277","0.53899837","0.53802747","0.5378721","0.5370008","0.53650784","0.53388757","0.5325569","0.52926785","0.52857494","0.52803123","0.52759445","0.52757436","0.5270265","0.5267761","0.5250319","0.5249184","0.5246828","0.5243347","0.5232479","0.5222525","0.52197784","0.5216477","0.52032477","0.5199638","0.51960224","0.51800925","0.51699704","0.5168958","0.5168958","0.51679635","0.51620156","0.5149581","0.51425135","0.5139666","0.513533","0.513453","0.5134307","0.5128063","0.5127148","0.51269156","0.512436","0.512372","0.51229954","0.5122528","0.5122213","0.5115626","0.5114999","0.51140225","0.5113896","0.51052386","0.51018304","0.5099044","0.5096058","0.5095378","0.50908124","0.508863","0.5086501","0.5070042","0.50691956","0.5067554","0.5063141","0.5057723","0.5047866","0.5038475","0.5027659","0.5017436","0.50154954"],"string":"[\n \"0.6563195\",\n \"0.63334215\",\n \"0.619655\",\n \"0.6072905\",\n \"0.5992598\",\n \"0.5792656\",\n \"0.57802635\",\n \"0.5761363\",\n \"0.57300216\",\n \"0.5706402\",\n \"0.56756544\",\n \"0.5673622\",\n \"0.56674\",\n \"0.5588823\",\n \"0.55755645\",\n \"0.557284\",\n \"0.5536065\",\n \"0.5526799\",\n \"0.55215424\",\n \"0.55209804\",\n \"0.5507269\",\n \"0.54943544\",\n \"0.5494262\",\n \"0.5481268\",\n \"0.54761773\",\n \"0.5468075\",\n \"0.5444503\",\n \"0.54357845\",\n \"0.5420464\",\n \"0.54155046\",\n \"0.5412011\",\n \"0.5400879\",\n \"0.53974277\",\n \"0.53899837\",\n \"0.53802747\",\n \"0.5378721\",\n \"0.5370008\",\n \"0.53650784\",\n \"0.53388757\",\n \"0.5325569\",\n \"0.52926785\",\n \"0.52857494\",\n \"0.52803123\",\n \"0.52759445\",\n \"0.52757436\",\n \"0.5270265\",\n \"0.5267761\",\n \"0.5250319\",\n \"0.5249184\",\n \"0.5246828\",\n \"0.5243347\",\n \"0.5232479\",\n \"0.5222525\",\n \"0.52197784\",\n \"0.5216477\",\n \"0.52032477\",\n \"0.5199638\",\n \"0.51960224\",\n \"0.51800925\",\n \"0.51699704\",\n \"0.5168958\",\n \"0.5168958\",\n \"0.51679635\",\n \"0.51620156\",\n \"0.5149581\",\n \"0.51425135\",\n \"0.5139666\",\n \"0.513533\",\n \"0.513453\",\n \"0.5134307\",\n \"0.5128063\",\n \"0.5127148\",\n \"0.51269156\",\n \"0.512436\",\n \"0.512372\",\n \"0.51229954\",\n \"0.5122528\",\n \"0.5122213\",\n \"0.5115626\",\n \"0.5114999\",\n \"0.51140225\",\n \"0.5113896\",\n \"0.51052386\",\n \"0.51018304\",\n \"0.5099044\",\n \"0.5096058\",\n \"0.5095378\",\n \"0.50908124\",\n \"0.508863\",\n \"0.5086501\",\n \"0.5070042\",\n \"0.50691956\",\n \"0.5067554\",\n \"0.5063141\",\n \"0.5057723\",\n \"0.5047866\",\n \"0.5038475\",\n \"0.5027659\",\n \"0.5017436\",\n \"0.50154954\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6899368"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":86695,"cells":{"query":{"kind":"string","value":"Driver function to return number of occurences of a pattern in a text with at most k mismatches. Returns the start index of occurence as well as the number of mismatches."},"document":{"kind":"string","value":"def solve(k, text, pattern):\n # print(k, text, pattern)\n base = pow(10, 9)\n M1 = base + 7\n M2 = base + 9\n X = 263\n len_p = len(pattern)\n len_t = len(text)\n pattern1, pattern2 = pre_compute_hashes(pattern, M1, M2, X)\n text1, text2 = pre_compute_hashes(text, M1, M2, X)\n res = []\n p_hash1, p_hash2 = get_hash_value(\n pattern1, M1, X, 0, len_p), get_hash_value(pattern2, M2, X, 0, len_p)\n for i in range(len_t-len_p+1): # all possible candidates\n subs_hash1 = get_hash_value(text1, M1, X, i, len_p)\n subs_hash2 = get_hash_value(text2, M2, X, i, len_p)\n is_valid = find_num_matches(pattern1, pattern2, text1, text2, M1, M2, X, k, len_p, i)\n if is_valid:\n res.append(i)\n return res"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def frequent_words_with_mismatches(text, k, d):\n\n patterns = []\n freq_map = {}\n n = len(text)\n for i in range(n - k + 1):\n pattern = text[i:i + k]\n pattern_rc = reverse_complement(pattern)\n neighborhood = neighbors(pattern, d) + neighbors(pattern_rc, d)\n for j in range(len(neighborhood)):\n neighbor = neighborhood[j]\n if neighbor not in freq_map.keys():\n freq_map[neighbor] = 1\n else:\n freq_map[neighbor] = freq_map[neighbor] + 1\n m = max_map(freq_map)\n for key in freq_map.keys():\n if freq_map[key] == m:\n patterns.append(key)\n return patterns","def _n_matches(gold_tokens, pred_tokens):\n matcher = difflib.SequenceMatcher(None, gold_tokens, pred_tokens)\n return sum(match.size for match in matcher.get_matching_blocks())","def count_occurrences(text, pattern, d=0):\n return len(find_occurrences(text, pattern, d))","def test_count_mismatches(self):\r\n self.assertEqual(count_mismatches('GG', 'GG', 10), 0)\r\n self.assertEqual(count_mismatches('GGG', 'AAA', 10), 3)\r\n self.assertEqual(count_mismatches('GGG', 'AAA', 1), 2)","def CountAppStrMatch(pattern, text, d, debug = False):\n\tcount = 0\n\tif debug:\n\t\tprint len(text)-len(pattern)+1\n\tfor i in range(len(text)-len(pattern)+1):\n\t\tif debug:\n\t\t\tprint text[i:i+len(pattern)]\n\t\t\tprint HammingDist(text[i:i+len(pattern)], pattern)\n\t\tif HammingDist(text[i:i+len(pattern)], pattern) <= d:\n\t\t\tcount += 1\n\treturn count","def better_clumps_finding(text, k, t, L):\n frequent_patterns = []\n clumps = [0 for i in range(0, 4**k)]\n first_subtext = text[:L]\n freq_array = compute_freq(first_subtext, k)\n for index, freq in enumerate(freq_array):\n if freq >= t:\n clumps[index] = 1\n for i in range(1, len(text) - L + 1):\n old_kmer = text[i - 1:i - 1 + k]\n old_kmer_number = pattern_to_number(old_kmer)\n freq_array[old_kmer_number] -= 1\n new_kmer = text[i + L:i + L + k]\n new_kmer_number = pattern_to_number(new_kmer)\n freq_array[new_kmer_number] += 1\n if freq_array[new_kmer_number] >= t:\n clumps[new_kmer_number] = 1\n for index, clump in enumerate(clumps):\n if clump == 1:\n pattern = number_to_pattern(index, k)\n frequent_patterns.append(pattern) \n return frequent_patterns","def frequent_words(text, k):\n frequent_patterns = []\n count = {}\n for i in range(0, len(text)-k+1):\n pattern = text[i:i+k]\n count[i] = pattern_count(text, pattern)\n max_count = max(count.values()) if count else 0\n for i in range(0, len(text)-k+1):\n pattern = text[i:i+k]\n if count[i] == max_count and pattern not in frequent_patterns:\n frequent_patterns.append(text[i:i+k])\n return frequent_patterns","def freq_words_with_mismatches(seq, k, d):\n # Frequency analysis so we don't generate mismatches for the same k-mer more than once.\n kmer_freq = defaultdict(int)\n for i in xrange(len(seq)-k+1):\n kmer_freq[seq[i:i+k]] += 1\n\n # Get all of the mismatches for each unique k-mer in the sequence, appearing freq times.\n mismatch_count = defaultdict(int)\n for kmer, freq in kmer_freq.iteritems():\n for mismatch in kmer_mismatches(kmer, d):\n mismatch_count[mismatch] += freq\n\n # Computing the maximum value is somewhat time consuming to repeat, so only do it once!\n max_count = max(mismatch_count.values())\n return sorted([kmer for kmer, count in mismatch_count.iteritems() if count == max_count])","def find_num_matches(p1, p2, t1, t2, m1, m2, x, k, len_p, i):\n start = 0\n for mismatch in range(k):\n # print(f'start: {start}, id: {id}')\n id = start\n end = len_p-1\n while start <= end:\n mid = start + (end-start)//2\n p_h1, p_h2 = get_hash_value(\n p1, m1, x, id, mid-start), get_hash_value(p2, m2, x, id, mid-start)\n s_h1, s_h2 = get_hash_value(\n t1, m1, x, i+id, mid-start), get_hash_value(t2, m2, x, i+id, mid-start)\n if p_h1 == s_h1 and p_h2 == s_h2: # move to the right half, no mismatch yet\n start = mid + 1\n else:\n end = mid - 1\n # when loop exits, start - 1 is the index of the mismatch.\n if start == len_p: # found the last mismatch\n return True\n return False","def pattern_count(text, pattern):\n\n count = 0\n len_text = len(text)\n len_pattern = len(pattern)\n for i in range(len_text - len_pattern):\n if pattern in text[i:i + len_pattern]:\n count = count + 1\n else:\n continue\n return count","def pattern_count(text, pattern):\n return len([i\n for i in range(0, len(text) - len(pattern) + 1)\n if text[i:i + len(pattern)] == pattern])","def compute_freq(text, k):\n freq_array = [0 for i in range(0, 4**k)]\n for i in range(0, len(text) - k + 1):\n pattern = text[i:i + k]\n j = pattern_to_number(pattern)\n freq_array[j] += 1\n # return ' '.join([str(i) for i in freq_array])\n return freq_array","def frequent_words_t(text, k, t):\n frequent_patterns = []\n count = {}\n for i in range(0, len(text)-k+1):\n pattern = text[i:i+k]\n count[i] = pattern_count(text, pattern)\n if count[i] >= t and pattern not in frequent_patterns:\n frequent_patterns.append(text[i:i+k])\n return frequent_patterns","def brute_force_string_matching(text: str, pattern: str) -> int:\n n = len(text)\n m = len(pattern)\n for i in range(n-m):\n j = 0\n while j < m and pattern[j] == text[j+i]:\n j += 1\n if j == m:\n return i\n return -1","def indapproxpattern(pattern, string, nummismatch):\n\n indarr = []\n# substringarr = []\n numchars = len(pattern)\n\n for i in xrange(0, len(string) - numchars + 1):\n \n substring = patterncount.subtext(string, i, numchars)\n \n if hammingdist(pattern, substring) <= nummismatch:\n \n indarr.append(i)\n# substringarr.append(substring)\n \n return indarr","def count_pattern(sentence, pattern):\n n = len(pattern)\n counter = 0\n for i in range(len(sentence) - n + 1):\n if sentence[i:i+n] == pattern:\n counter += 1\n\n return counter","def mismatch(pattern, pattern_2):\n mismatch = 0\n for n, i in zip(pattern, pattern_2):\n if n != i:\n mismatch += 1\n return mismatch","def get_num_mismatches(sequence, ref_genome, position):\n characters = list(sequence)\n num_mismatches = 0\n for i in range(0, len(characters)):\n if position + i >= len(ref_genome):\n break\n if characters[i] != ref_genome[position + i]:\n num_mismatches += 1\n\n return num_mismatches","def count(pattern, string, overlapping=True, sensitive=True, regexp=False):\n return len(SE.findall(pattern, string, overlapping, sensitive, regexp))","def faster_frequent_words(text, k):\n frequent_patterns = []\n freq_array = compute_freq(text, k)\n max_count = max(freq_array)\n for i in range(0, len(text)-k+1):\n if freq_array[i] == max_count:\n pattern = number_to_pattern(i, k)\n frequent_patterns.append(pattern)\n return frequent_patterns","def PatternCount(text, pattern):\n\n count = 0\n for i in range(0, len(text)-len(pattern)+1):\n if text[i:i+len(pattern)] == pattern:\n count += 1\n return count","def get_pattern_count(sequence, pattern):\n return len(re.findall(r'(?=' + pattern + ')', sequence))","def count_match_mismatch(data, index):\n\tsubjects = set()\n\tnew_data = []\n\tfor line in data:\n\t\tif line[\"mark_name\"] == \"match_3D7_\" + str(index):\n\t\t\tsubjects.add(line[\"subject\"])\n\t\t\tnew_data.append(line)\n\tmatch_map = {}\n\tmismatch_map = {}\t\n\tfor subject in subjects:\n\t\tmatch_map[subject] = 0\n\t\tmismatch_map[subject] = 0\t\t\n\t\tfor line in new_data:\n\t\t\tif line[\"subject\"] == subject and line[\"mark_value\"] == \"1\":\n\t\t\t\tmatch_map[subject] = 1\n\t\t\tif line[\"subject\"] == subject and line[\"mark_value\"] == \"0\":\n\t\t\t\tmismatch_map[subject] = 1\t\t\t\t\n\tmatch_count = 0\n\tmismatch_count = 0\t\n\tfor subject, value in match_map.items():\n\t\tmatch_count += value\n\tfor subject, value in mismatch_map.items():\n\t\tmismatch_count += value\t\t\n\treturn (match_count, mismatch_count)","def frequent_words_by_sorting(text, k):\n frequent_patterns = []\n index = []\n count = []\n for i in range(0, len(text) - k + 1):\n pattern = text[i:i + k]\n index[i] = pattern_to_number(pattern)\n count[i] = 1\n sorted_index = sorted(index)\n for i in range(0, len(text) - k + 1):\n if sorted_index[i] == sorted_index[i-1]:\n count[i] = count[i -1] + 1\n max_count = max(count)\n for i in range(0, len(text) - k + 1):\n if count[i] == max_count:\n pattern = number_to_pattern(sorted_index[i], k)\n frequent_patterns.append(pattern)\n return frequent_patterns","def ApproxStrMatch(pattern, text, d, debug = False):\n\tcount = 0\n\tindex = []\n\tfor i in range(len(text)-len(pattern)+1):\n\t\tif debug:\n\t\t\tprint text[i:i+len(pattern)]\n\t\t\tprint HammingDist(text[i:i+len(pattern)], pattern)\n\t\tif HammingDist(text[i:i+len(pattern)], pattern) <= d:\n\t\t\tcount += 1\n\t\t\tindex.append(i)\n\treturn index","def count_kmers(dna, k):\n kmer_count = Counter()\n for i in range(len(dna)):\n kmer = dna[i:(i+k)]\n if len(kmer) == k:\n kmer_count[kmer] += 1\n return kmer_count","def find_occurrences(text, pattern, d=0):\n idx_of_last_pattern = len(text) - len(pattern)\n return [i for i in range(idx_of_last_pattern + 1) if hamming(text[i:i + len(pattern)], pattern) <= d]","def clumps_finding(text, k, t, L):\n frequent_patterns = []\n clumps = [0 for i in range(0, 4**k)]\n for i in range(0, len(text) - L + 1):\n subtext = text[i:i + L]\n freq_array = compute_freq(subtext, k)\n for index, freq in enumerate(freq_array):\n if freq >= t:\n clumps[index] = 1\n for index, clump in enumerate(clumps):\n if clump == 1:\n pattern = number_to_pattern(index, k)\n frequent_patterns.append(pattern)\n return frequent_patterns","def better_matching(self, pattern, offsets=False, slice_len=5):\n first_occurencies, counts_at = self.setup_better_match(slice_len)\n pattern = list(pattern)\n top, bottom = 0, self.tl\n while top <= bottom:\n if pattern:\n current = pattern.pop()\n if counts_at(bottom+1)[current] - counts_at(top)[current] > 0:\n #if current in self.last_col[top:bottom+1]:\n top = first_occurencies[current] + counts_at(top)[current]\n bottom = first_occurencies[current] + counts_at(bottom + 1)[current] - 1\n else:\n if offsets:\n return []\n else:\n return 0\n else:\n if offsets:\n return self.offsets(top, bottom)\n else:\n return bottom - top + 1","def mismatch_search(haystack, needle, mismatches=1):\n haystack = haystack.encode('utf-8');\n needle = needle.encode('utf-8');\n if not using_tre:\n raise RBSError(\"tre isn't loaded.\")\n fz = tre.Fuzzyness(maxerr=mismatches, maxsub=mismatches,\n maxdel=0, maxins=0)\n needle = \".*(%s).*\" % needle\n pt = tre.compile(needle, tre.EXTENDED)\n incidence = 0\n while True:\n m = pt.search(haystack, fz)\n if m:\n index = m.groups()[1][1]-1\n incidence += 1\n haystack = haystack[:index]\n else:\n break\n return incidence","def support_count(pattern, D):\n support_count = 0\n tmp_p = set(pattern)\n for transaction in D:\n if tmp_p <= set(transaction):\n support_count += 1\n return support_count","def num_of_errors(text):\n tool = language_check.LanguageTool('en-US')\n matches = tool.check(text)\n errors = len(matches)\n return errors","def lcs_hamming_only_matches(s1: str, s2: str, k: int, length: int, matches_lst: List):\n count = 0\n for i in range(0, len(s1) - length + 1):\n for j in range(0, len(s2) - length + 1):\n sub1 = s1[i: i + length]\n sub2 = s2[j: j + length]\n result = hamming_distance(sub1, sub2)\n if result <= k:\n matches_lst.append([i, j, result, sub1, sub2])\n count += 1\n # print(\"total matches: \" + str(count))\n return count","def frequency_table(text, k):\n\n freq_map = {}\n len_text = len(text)\n for i in range(len_text - k + 1):\n pattern = text[i:i + k]\n if pattern not in freq_map:\n freq_map[pattern] = 1\n else:\n freq_map[pattern] = freq_map[pattern] + 1\n return freq_map","def get_kth_ugly_number(k):\n count = 0; i = 0\n while count < k:\n i += 1\n if is_ugly(i):\n count += 1\n return i","def sorted_frequent_words(text: str, k: int) -> Set[str]:\n frequent_patterns = set()\n index = []\n count = []\n for i in range(len(text) - k + 1):\n pattern = text[i:i+k]\n index.append(pattern_to_number(pattern))\n count.append(1)\n\n sorted_index = sorted(index)\n\n for i in range(1, len(text) - k + 1):\n if sorted_index[i] == sorted_index[i-1]:\n count[i] = count[i-1] + 1\n\n max_count = max(count)\n\n for i in range(len(text) - k + 1):\n if count[i] == max_count:\n pattern = number_to_pattern(sorted_index[i], k)\n frequent_patterns.add(pattern)\n\n return frequent_patterns","def _matched_len(self, string, idx1, idx2):\n\n str_len = len(string)\n if idx1 == idx2:\n return str_len - idx1\n counter = 0\n while idx1 < str_len and idx2 < str_len and string[idx1] == string[idx2]:\n counter += 1\n idx1 += 1\n idx2 += 1\n\n return counter","def n_errors(gold_tokens, pred_tokens):\n return len(gold_tokens) + len(pred_tokens) - 2 * _n_matches(gold_tokens, pred_tokens)","def find_all_occurrences_brute_force(pattern, text):\n\n result = []\n\n if len(text) < len(pattern):\n return result\n\n for i in range(0, len(text) - len(pattern) + 1):\n matched = True\n\n k = 0\n for j in range(i, i + len(pattern)):\n if pattern[k] != text[j]:\n matched = False\n break\n k += 1\n\n if matched:\n result.append(i)\n\n return result","def CountOccurrences(pattern, bwt, starts, occ_counts_before):\n # Implement this function yourself\n return 0","def find_clumps(text, k, len_win, t):\n\n patterns = []\n len_text = len(text)\n for i in range(len_text - len_win + 1):\n window = text[i:i + len_win]\n freq_map = frequency_table(window, k)\n for key in freq_map.keys():\n if freq_map[key] >= t and key not in patterns:\n patterns.append(key)\n return patterns","def str_search(pattern, text):\n N, M = len(text), len(pattern)\n i = 0 \n while i <= N-M:\n j = 0 \n while j < M:\n if text[i + j] != pattern[j]:\n break \n j += 1\n if j == M:\n return i\n i += 1\n return N","def _find_max_number_of_grouping(cls, reserved_seats, k):\n # print(reserved_seats)\n n = len(reserved_seats)\n count_groups = 0\n count_empty_contigous_seats = 0\n i = 0\n while i < n:\n if reserved_seats[i] != 0:\n # print('continue', i)\n count_empty_contigous_seats = 0\n i += 1\n continue\n\n count_empty_contigous_seats += 1\n # print('empty', i, count_empty_contigous_seats)\n if count_empty_contigous_seats >= k:\n count_groups += 1\n # print('found', i, count_groups)\n\n if ((i + 1) % len(cls._PLANE_ROW)) == 0:\n # print('new row', i)\n count_empty_contigous_seats = 0\n\n i += 1\n\n return count_groups","def kbest_matches(self, k=1, overlap=0):\n self.align()\n matching = np.array(self.matching)\n maxv = np.ceil(np.max(matching) + 1)\n matching[:min(len(self.query) - 1, overlap)] = maxv\n ki = 0\n while k is None or ki < k:\n best_idx = np.argmin(matching)\n if best_idx == 0 or np.isinf(matching[best_idx]) or matching[best_idx] == maxv:\n # No more matches found\n break\n match = self.get_match(best_idx)\n b, e = match.segment\n cur_overlap = min(overlap, e - b - 1)\n mb, me = best_idx + 1 - (e - b) + cur_overlap, best_idx + 1\n if np.isinf(np.max(matching[mb:me])):\n # No overlapping matches\n matching[best_idx] = maxv\n continue\n matching[mb:me] = np.inf\n ki += 1\n yield match","def allele_match_counts(self, map_hit):\n if map_hit.NM == 0:\n l = self.allele_end - self.allele_start + 1\n return l, l\n\n # If there are mismatches, then we can use the extended cigar string to\n # work out how many of those mismatches are in the allele.\n # Example cigar to remind which way round I and D are:\n # read: AGT--TGATCAAGTAC\n # ref: AGTGATGATC----AC\n # cigar: 3M2D5M4I2M\n probe_pos = map_hit.q_st\n total_positions = 0\n matches = 0\n\n if map_hit.strand == -1:\n map_hit.cigar.reverse()\n\n for length, operator in map_hit.cigar:\n if probe_pos > self.allele_end:\n break\n\n if operator == 7 or operator == 8: # 7,8 are \"=\",\"X\" == match/mismatch\n for i in range(length):\n if self.allele_start <= probe_pos <= self.allele_end:\n if operator == 7:\n matches += 1\n total_positions += 1\n probe_pos += 1\n if probe_pos > self.allele_end:\n break\n elif operator == 1: # 1 = I = insertion\n if self.allele_start <= probe_pos <= self.allele_end:\n total_positions += length\n probe_pos += length\n elif operator == 2: # 2 = D = deletion\n if self.allele_start <= probe_pos <= self.allele_end:\n total_positions += length\n else:\n raise RuntimeError(\n f\"Unexpected cigar operator number {operator} with length {length} from cigar\"\n )\n\n if map_hit.strand == -1:\n map_hit.cigar.reverse()\n\n return matches, total_positions","def detectMiss(n, k):\n k -= 1\n nlog = math.ceil(math.log(n,2))\n nloglessone = nlog - 1\n lower_base = 2**nloglessone - 1\n lowerbaseBack = lower_base\n diff_list = [2**i for i in range(nloglessone-1, 0, -1)]\n diff_lit_back = diff_list[1:-1]\n if lower_base == k and 2**nlog -1 == n:\n return(k)\n matchres = False\n while(lower_base <= k and len(diff_list)>0 ):\n if lower_base == k:\n matchres = True\n break\n else:\n addVal = diff_list[0]\n lower_base += addVal\n diff_list.remove(addVal)\n if matchres:\n k -= 1\n if k%2==0:\n return(k)\n else:\n while(len(diff_lit_back)>0 and lowerbaseBack<k):\n newAddVal = diff_lit_back[0]\n lowerbaseBack = lowerbaseBack+newAddVal\n postitionIndx = lowerbaseBack + newAddVal\n if lowerbaseBack == k and postitionIndx <= n:\n return(k)\n elif lowerbaseBack == k and postitionIndx > n:\n return(k-1)\n else:\n pass\n diff_lit_back.remove(newAddVal)\n\n return(k)","def _count_chunks(matches):\n i = 0\n chunks = 1\n while (i < len(matches) - 1):\n if (matches[i + 1][0] == matches[i][0] + 1) and (matches[i + 1][1] == matches[i][1] + 1):\n i += 1\n continue\n i += 1\n chunks += 1\n return chunks","def count_sequence_mismatches(seq):\n trans_table = str.maketrans('ACGT', 'TGCA')\n half_len = len(seq) // 2\n second_half = seq[-half_len:].translate(trans_table)\n mismatches = 0\n for i in range(half_len):\n if seq[i] != second_half[-i - 1]:\n mismatches += 1\n return mismatches","def str_search_two(pattern, text):\n N, M = len(text), len(pattern)\n i, j = 0, 0 \n while i < N and j < M:\n if text[i] == pattern[j]:\n j += 1\n else:\n i -= j\n j = 0 \n i += 1\n if j == M:\n return i - M\n else:\n return N","def string_num_matches(str1, str2):\n sm = edit_distance.SequenceMatcher(a=str1, b=str2)\n return sm.matches()","def count(r, c, k):\n\t\tif 0 <= r < M and 0 <= c < N:\n\t\t\tif A[r][c] == target[k]:\n\t\t\t\t\"*** YOUR CODE HERE ***\"\n\t\t\t\treturn memoized_count(r, c, k)\n\t\t\telse:\n\t\t\t\treturn 0\n\t\telse:\n\t\t\treturn 0","def get_count(self):\n\n return len(self._pattern)","def count_segments(markers) -> int:\n cnt = Counter()\n for row in markers:\n cnt.update(row)\n n_cnt = dict(takewhile(lambda x: x[1] >= 10, cnt.most_common()))\n del n_cnt[1]\n del n_cnt[-1]\n return len(n_cnt.keys())","def pattern_count(DNA, pattern, start=0, end=0, mutation_thresh=0):\n if start < 0 or start >= len(DNA):\n raise ValueError(\"The starting position should be between 0 and the size \" + \\\n \"of the DNA\")\n\n k = len(pattern)\n count = 0\n end = len(DNA) - k + 1 if end == 0 else end\n\n for i in range(0, end):\n if hamming_distance(DNA[i:i+k], pattern) <= mutation_thresh:\n count += 1\n\n return count","def better_solution(self, S: str, words: List[str]) -> int:\n def encode(S):\n return zip(*[(k, len(list(v))) for k, v in groupby(S)])\n\n k, v = encode(S)\n count = 0\n for word in words:\n kk, vv = encode(word)\n if k != kk:\n continue\n count += int(all(c1 >= max(c2, 3) or c1 == c2 for c1, c2 in zip(v, vv)))\n return count","def find_clumps(text, k, L, t):\n clumps = []\n k_mers = frequent_words_t(text, k, t)\n for k_mer in k_mers:\n positions = find_position(k_mer, text)\n for position in positions:\n subtext = text[position:position + L]\n count = pattern_count(subtext, k_mer)\n if count >= t and k_mer not in clumps:\n clumps.append(k_mer)\n return clumps","def frequency_array(text, k):\r\n freq_list=[]\r\n p_list=pattern_list(k)\r\n for i in p_list:\r\n freq_list.append(PatternCount(i,text))\r\n return freq_list","def hits_and_misses(guess_seq, true_seq):\n num_hits = sum(guess_token == true_token\n for guess_token, true_token\n in zip(guess_seq, true_seq))\n num_misses = sum((Counter(guess_seq) & Counter(true_seq)).values()) - num_hits\n return num_hits, num_misses","def regular_get_match(x, y, k):\n n = len(x)\n m = len(y)\n\n output = list()\n\n for i in range(n - k + 1):\n matches = non_rolling_matcher(\n target=y,\n potential=x[i:i + k]\n )\n\n for match in matches:\n output.append((i, match))\n\n return output","def count(text):\n return len(text)","def count_until(match_value):","def get_nmismatches(bases_mask):\n # Check mask is valid\n if not bases_mask_is_valid(bases_mask):\n raise Exception(\"'%s': not a valid bases mask\" % bases_mask)\n # Total the length of all index reads\n index_length = 0\n for read in bases_mask.upper().split(','):\n if read.startswith('I'):\n try:\n i = read.index('N')\n read = read[:i]\n except ValueError:\n pass\n try:\n index_length += int(read[1:])\n except ValueError:\n index_length += len(read)\n # Return number of mismatches\n if index_length >= 6:\n return 1\n else:\n return 0","def __len__(self: TokenMatcher) -> int:\n return len(self._patterns)","def count_kmers(file_name, k, verbose=False):\n if verbose:\n start = time.time()\n print('Counting kmers in {}'.format(file_name))\n total_kmers = 0\n with open(file_name, 'r') as f:\n line_num = 0\n for line in f:\n if line_num % 4 == 1: # dna sequence\n total_kmers += len(line) - k # eliminate new-line\n line_num += 1\n if verbose:\n end = time.time()\n print('{} kmers are counted in {:.2f} seconds'.format(\n total_kmers, end - start))\n return total_kmers","def __naive_matching(pattern, text):\n matched_positions = []\n n = len(text)\n m = len(pattern)\n # Last possible pattern starting position in the text is n - m\n for pos in range((n - m) + 1):\n if pattern == text[pos:(pos+m)]:\n matched_positions.append(pos)\n return matched_positions","def __len__(self) -> int:\n n_fuzzy_patterns = sum(len(p[\"patterns\"]) for p in self.fuzzy_patterns.values())\n n_regex_patterns = sum(len(p[\"patterns\"]) for p in self.regex_patterns.values())\n return n_fuzzy_patterns + n_regex_patterns","def match_specific_string(input_data: list, keyword: str) -> int:\n number_of_words = 0\n for element in input_data:\n number_of_words += len(re.findall(keyword, element, re.IGNORECASE))\n return number_of_words","def distance_between_pattern_and_strings(pattern, dna):\n\n k = len(pattern)\n distance = 0\n\n for text in dna:\n hamming_distance = 1000000\n for i in range(len(text) - k + 1):\n if hamming_distance > compute_hamming_distance(pattern, text[i:i + k]):\n hamming_distance = compute_hamming_distance(pattern, text[i:i + k])\n distance = distance + hamming_distance\n return distance","def count_regexp_occ(regexp=\"\", text=None):\n return len(re.findall(regexp, text))","def count_regexp_occ(regexp=\"\", text=None):\n return len(re.findall(regexp, text))","def __count_of_keyword_matches__(self, citation_string, key_string):\n matches = 0\n for keyword in self.dict_of_keywords.get(key_string):\n pattern = re.compile(keyword)\n if pattern.search(citation_string):\n matches += 1\n return matches","def count_kmers(seq, k=3):\n # Start with an empty dictionary\n counts = {}\n # Calculate how many kmers of length k there are\n num_kmers = len(str(seq)) - k + 1\n # Loop over the kmer start positions\n for i in range(num_kmers):\n # Slice the string to get the kmer\n kmer = str(seq)[i:i+k]\n # Add the kmer to the dictionary if it's not there\n if kmer not in counts:\n counts[kmer] = 0\n # Increment the count for this kmer\n counts[kmer] += 1\n # Return the final counts\n return counts","def test_suite():\n test(count(\"is\", \"Mississippi\") == 2)\n test(count(\"an\", \"banana\") == 2)\n test(count(\"ana\", \"banana\") == 2)\n test(count(\"nana\", \"banana\") == 1)\n test(count(\"nanan\", \"banana\") == 0)\n test(count(\"aaa\", \"aaaaaa\") == 4)","def hit_rate_at_k(rs, k):\n if k < 1 or k > len(rs[0]):\n raise ValueError('k value must be >=1 and < Max Rank')\n hits = 0\n for r in rs:\n if np.sum(r[:k]) > 0: hits += 1\n\n return hits / len(rs)","def get_multi_pattern_count(word, patterns):\n\n distinct_positions = set()\n for pattern in patterns:\n result = Util.find_all_occurrences_knuth_morris_pratt(pattern,\n word)\n distinct_positions |= set(result)\n\n return distinct_positions","def CountOccurrences(pattern, bwt, starts, occ_counts_before, suffix_array):\r\n # 0$ 1A 2T 3G 4C\r\n letters = {'$':0, 'A':1, 'T':2, 'G':3, 'C':4}\r\n top=0\r\n bottom = len(bwt)-1\r\n matches_index = []\r\n while True:\r\n if len(pattern)!=0:\r\n char = pattern[-1]\r\n j = letters[char]\r\n pattern = pattern[:-1]\r\n found=False\r\n for i in range(top,bottom+1):\r\n if bwt[i] == char:\r\n top = occ_counts_before[i][j] + starts[char] -1\r\n bottom = occ_counts_before[bottom][j] + starts[char] -1\r\n found = True\r\n break\r\n\r\n if found==False:\r\n return matches_index\r\n # when pattern is finished proccessing\r\n else:\r\n for i in range(top, bottom+1):\r\n matches_index.append(suffix_array[i])\r\n return matches_index\r\n\r\n return matches_index","def parse_file_count(path, args):\n try:\n fisier = open(path, 'r')\n except IOError:\n print(\"Nu am putut deschide fisierul :\", path)\n return\n n_found = 0\n pattern = args.pattern\n for line in fisier:\n if args.ignore_case:\n line = line.lower()\n pattern = pattern.lower()\n n_found += line.count(pattern)\n\n fisier.close()\n return n_found","def measure_the_work(pattern_to_find):\n recipes = [3, 7]\n keys = [int(key) for key in pattern_to_find]\n elf1 = 0\n elf2 = 1\n not_found = True\n\n while not_found:\n elf1_value = recipes[elf1]\n elf2_value = recipes[elf2]\n\n recipe_sum = elf1_value + elf2_value\n\n if recipe_sum > 9:\n recipe_string = f\"{recipe_sum:02d}\"\n recipes.append(int(recipe_string[:1]))\n recipes.append(int(recipe_string[1:]))\n else:\n recipes.append(recipe_sum)\n\n elf1 = loop_around(1 + elf1 + elf1_value, len(recipes))\n elf2 = loop_around(1 + elf2 + elf2_value, len(recipes))\n if recipes[-1] == keys[-1] or recipes[-2] == keys[-1]:\n\n if pattern_to_find in ''.join(map(str, recipes[-(len(keys) + 2):])):\n not_found = False\n if recipes[-1] == keys[-1]:\n return len(recipes) - len(keys)\n else:\n return len(recipes) - len(keys) - 1","def test_ababab():\n assert part_01.count_for('ababab', 2) == 0\n assert part_01.count_for('ababab', 3) == 1","def numKLenSubstrNoRepeats(self, S, K):\n return self.for_loop(S, K)","def pk(y_true, y_pred, k):\n \n # if k is 0, return 0. we should never have this\n # as k is always >= 1\n if k == 0:\n return 0\n # we are interested only in top-k predictions\n y_pred = y_pred[:k]\n \n # convert predictions to set\n pred_set = set(y_pred)\n \n # convert actual values to set\n true_set = set(y_true)\n \n # find common values\n common_values = pred_set.intersection(true_set)\n \n # return length of common values over k\n return len(common_values) / len(y_pred[:k])","def all_matches(self, pattern):\n\n pat_len = len(pattern)\n if pat_len > self.text_len:\n raise ValueError(\"Pattern length is bigger than text\")\n\n bad_char_table = self._bad_char_table(pattern)\n L = self._good_suffix_table_one(pattern)\n H = self._good_suffix_table_two(pattern)\n indexes = []\n\n pat_end_ind = pat_len - 1\n prev_end = -1\n # prev_end - previous index of pattern end relative to text (for Galil's rule)\n # p - index of char in pattern\n # t - index of char in text\n while pat_end_ind < self.text_len:\n p = pat_len - 1\n t = pat_end_ind\n while p >= 0 and t > prev_end and pattern[p] == self.text[t]:\n p -= 1\n t -= 1\n if p == -1 or t == prev_end: # Matched or holds Galil's rule\n indexes.append(pat_end_ind - pat_len + 1)\n pat_end_ind += pat_len - H[1] if pat_len > 1 else 1\n else:\n char_shift = self._bad_char_shift(self.text[t], bad_char_table)\n if p+1 == pat_len:\n suffix_shift = 1\n elif L[p] == -1:\n suffix_shift = pat_len - H[p+1]\n else:\n suffix_shift = pat_len - L[p]\n shift = max(char_shift, suffix_shift)\n prev_end = pat_end_ind if shift >= p+1 else prev_end #update parameter for Galil's rule\n pat_end_ind += shift\n\n return indexes","def recCountString():\r\n target = raw_input(\"Enter target string: \")\r\n key = raw_input(\"Enter key string: \")\r\n matches = subStringMatchExact(target,key)\r\n print \"match(es) =\",matches","def frequent_words(text, k):\n\n frequent_patterns = []\n freq_map = frequency_table(text, k)\n max_val = max_map(freq_map)\n for key in freq_map.keys():\n if freq_map[key] == max_val:\n frequent_patterns.append(key)\n return frequent_patterns","def naive_with_counts(p, t):\n occurences = []\n num_alignments = 0\n num_character_comparisons = 0\n for i in range(len(t) - len(p) + 1):\n match = True\n for j in range(len(p)):\n if t[i + j] != p[j]:\n match = False\n break\n if match:\n occurences.append(i)\n num_alignments += 1\n num_character_comparisons += (j + 1)\n return occurences, num_alignments, num_character_comparisons","def count(self):\n return len(self.find())","def kmp(text, pattern):\n pattern = list(pattern)\n length = len(pattern)\n shifts = [1] * (length + 1)\n shift = 1\n for pos, pat in enumerate(pattern):\n while shift <= pos and pat != pattern[pos-shift]:\n shift += shifts[pos-shift]\n shifts[pos+1] = shift\n\n start_pos = 0\n match_len = 0\n for c in text:\n while match_len == length or match_len >= 0 and pattern[match_len] != c:\n start_pos += shifts[match_len]\n match_len -= shifts[match_len]\n match_len += 1\n if match_len == length:\n yield start_pos","def count_mismatches(seq1, seq2, max_mm):\r\n mm = 0\r\n for i in range(min(len(seq2), len(seq1))):\r\n if seq1[i] != seq2[i]:\r\n mm += 1\r\n if mm > max_mm:\r\n return mm\r\n return mm","def count_k(n, k):\n if n == 0:\n return 1\n elif n < 0:\n return 0\n else:\n total = 0\n i = 1\n while i <= k:\n total += count_k(n - i, k)\n i += 1\n return total","def length_of_longest_substring(arr, k):\n window_start = 0\n max_repeat_times = 0\n frequency_map = {0: 0, 1: 0}\n len_longest = 0\n\n for window_end in range(len(arr)):\n right_char = arr[window_end]\n left_char = arr[window_start]\n frequency_map[right_char] += 1\n max_repeat_times = frequency_map[0]\n\n if max_repeat_times > k:\n frequency_map[left_char] -= 1\n window_start += 1\n len_longest = max(len_longest, window_end - window_start + 1)\n\n return len_longest","def __count_text(text, limit=None):\n\n count = 0\n is_text = True\n for i, c in enumerate(text):\n if is_text and c == '\\33':\n is_text = False\n\n if is_text:\n count += 1\n if limit is not None and count == limit:\n return i + 1\n\n if not is_text and c == 'm':\n is_text = True\n\n if limit is not None:\n return len(text)\n else:\n return count","def utr3_motif_counts(self, pattern):\n return len(re.findall(pattern.upper(), self.three_prime_utr_sequence.upper()))","def kbest_matches(self, k=1, minlen=2, buffer=0):\n ki = 0\n while k is None or ki < k:\n idx = None\n lcm = None\n while idx is None:\n idx = np.unravel_index(np.argmax(self._wp, axis=None), self._wp.shape)\n if idx[0] == 0 or idx[1] == 0:\n return None\n r, c = idx\n lcm = LCMatch(self, r, c)\n for (x, y) in lcm.path:\n x += 1\n y += 1\n if len(self._wp.mask.shape) > 0 and self._wp.mask[x, y] is True: # True means invalid\n # print('found path contains masked, restart')\n lcm = None\n idx = None\n break\n else:\n self._wp[x, y] = ma.masked\n if len(lcm.path) < minlen:\n # print('found path too short, restart')\n lcm = None\n idx = None\n if buffer > 0 and lcm is not None:\n miny, maxy = 0, self._wp.shape[1] - 1\n minx, maxx = 0, self._wp.shape[0] - 1\n for (x, y) in lcm.path:\n xx = x + 1\n for yy in range(max(miny, y + 1 - buffer), min(maxy, y + 1 + buffer)):\n self._wp[xx, yy] = ma.masked\n yy = y + 1\n for xx in range(max(minx, x + 1 - buffer), min(maxx, x + 1 + buffer)):\n self._wp[xx, yy] = ma.masked\n if lcm is not None:\n ki += 1\n yield lcm","def contig_complementary_score(s1, s2) -> int:\n matcher = SequenceMatcher()\n matcher.set_seq1(str(s1))\n matcher.set_seq2(str(s2.complement()))\n _, _, size = matcher.find_longest_match(0, len(s1), 0, len(s2))\n return size","def is_k_anagram(s1: str, s2: str, k: int) -> tuple:\n if len(s1) != len(s2):\n return False, None, None\n\n counter_map = Counter(s1)\n extra = list()\n changes = 0\n for char in s2:\n if counter_map[char] > 0:\n counter_map[char] -= 1\n else:\n extra.append(char)\n changes += 1\n\n if changes > k:\n return False, None, None\n diff = [c for c in counter_map if counter_map[c] > 0]\n return True, extra, diff","def countSubStringMatch(target,key):\n count = 0\n for i in range(0,len(target)-len(key)):\n if target[i:i+len(key)] == key:\n count += 1\n return count","def keyword_length(text):\n text = scrub_string(text)\n a = [fabs(IC(text, ncol) - ENGLISH_IC) for ncol in range(1, MAX_LEN)]\n return a.index(min(a)) + 1","def searchKnuthMorris(pattern,text):\n if isinstance(pattern, str) == False:\n raise Exception(\"The pattern is not of type string\")\n if isinstance(text,str) == False:\n raise Exception(\"The text is not of type string\")\n if pattern is None or text is None or pattern == \"\" or text == \"\":\n raise Exception(\"One of the strings is not defined correctly\")\n if len(pattern) > len(text):\n raise Exception(\"The pattern string is supposed to be shorter than the text\")\n\n i = 0\n j = 0\n\n # First, we compute the LPS array\n arrayLPS = computeLPSArray(pattern)\n\n result = list()\n \n # Check character by character the text \n # NOTE: differently from the naive approach, that would\n # require us to go back and re-analyze parts of the text\n # that we previously analyzed, in this approach\n # the i index is never decremented, allowing us to \n # examine the string text just once\n while i < len(text): \n # If the two characters match, increment both indexes\n if text[i] == pattern[j]:\n i += 1\n j += 1\n \n # If we were able to match the whole pattern, \n # store the index where the pattern starts. \n if j == len(pattern):\n result.append(i-j)\n j = arrayLPS[j-1]\n \n # Otherwise if they do not match and j is not equal to 0, check\n # the value stored in the previous cell [j-1] and let j get\n # that value\n elif text[i] != pattern[j]:\n if j!=0:\n j = arrayLPS[j-1]\n # Otherwise (if they do not match and j is equal to 0) increment i\n else:\n i += 1\n return result","def match(self):\r\n results = []\r\n pattern = self.pattern\r\n text = self.text\r\n m = len(self.pattern)\r\n n = len(self.text)\r\n p = self._prefix\r\n k = 0\r\n for i in range(n):\r\n while k > 0 and text[i] != pattern[k]:\r\n k = p[k-1]\r\n if pattern[k] == text[i]:\r\n k = k+1\r\n if k == m:\r\n results.append(i-m+1)\r\n k = p[k-1]\r\n return results","def support_difference_count(m, m_hat):\n m_nnz, m_hat_nnz, intersection_nnz = _nonzero_intersection(m, m_hat)\n return int((m_nnz + m_hat_nnz - (2 * intersection_nnz)) / 2.0)"],"string":"[\n \"def frequent_words_with_mismatches(text, k, d):\\n\\n patterns = []\\n freq_map = {}\\n n = len(text)\\n for i in range(n - k + 1):\\n pattern = text[i:i + k]\\n pattern_rc = reverse_complement(pattern)\\n neighborhood = neighbors(pattern, d) + neighbors(pattern_rc, d)\\n for j in range(len(neighborhood)):\\n neighbor = neighborhood[j]\\n if neighbor not in freq_map.keys():\\n freq_map[neighbor] = 1\\n else:\\n freq_map[neighbor] = freq_map[neighbor] + 1\\n m = max_map(freq_map)\\n for key in freq_map.keys():\\n if freq_map[key] == m:\\n patterns.append(key)\\n return patterns\",\n \"def _n_matches(gold_tokens, pred_tokens):\\n matcher = difflib.SequenceMatcher(None, gold_tokens, pred_tokens)\\n return sum(match.size for match in matcher.get_matching_blocks())\",\n \"def count_occurrences(text, pattern, d=0):\\n return len(find_occurrences(text, pattern, d))\",\n \"def test_count_mismatches(self):\\r\\n self.assertEqual(count_mismatches('GG', 'GG', 10), 0)\\r\\n self.assertEqual(count_mismatches('GGG', 'AAA', 10), 3)\\r\\n self.assertEqual(count_mismatches('GGG', 'AAA', 1), 2)\",\n \"def CountAppStrMatch(pattern, text, d, debug = False):\\n\\tcount = 0\\n\\tif debug:\\n\\t\\tprint len(text)-len(pattern)+1\\n\\tfor i in range(len(text)-len(pattern)+1):\\n\\t\\tif debug:\\n\\t\\t\\tprint text[i:i+len(pattern)]\\n\\t\\t\\tprint HammingDist(text[i:i+len(pattern)], pattern)\\n\\t\\tif HammingDist(text[i:i+len(pattern)], pattern) <= d:\\n\\t\\t\\tcount += 1\\n\\treturn count\",\n \"def better_clumps_finding(text, k, t, L):\\n frequent_patterns = []\\n clumps = [0 for i in range(0, 4**k)]\\n first_subtext = text[:L]\\n freq_array = compute_freq(first_subtext, k)\\n for index, freq in enumerate(freq_array):\\n if freq >= t:\\n clumps[index] = 1\\n for i in range(1, len(text) - L + 1):\\n old_kmer = text[i - 1:i - 1 + k]\\n old_kmer_number = pattern_to_number(old_kmer)\\n freq_array[old_kmer_number] -= 1\\n new_kmer = text[i + L:i + L + k]\\n new_kmer_number = pattern_to_number(new_kmer)\\n freq_array[new_kmer_number] += 1\\n if freq_array[new_kmer_number] >= t:\\n clumps[new_kmer_number] = 1\\n for index, clump in enumerate(clumps):\\n if clump == 1:\\n pattern = number_to_pattern(index, k)\\n frequent_patterns.append(pattern) \\n return frequent_patterns\",\n \"def frequent_words(text, k):\\n frequent_patterns = []\\n count = {}\\n for i in range(0, len(text)-k+1):\\n pattern = text[i:i+k]\\n count[i] = pattern_count(text, pattern)\\n max_count = max(count.values()) if count else 0\\n for i in range(0, len(text)-k+1):\\n pattern = text[i:i+k]\\n if count[i] == max_count and pattern not in frequent_patterns:\\n frequent_patterns.append(text[i:i+k])\\n return frequent_patterns\",\n \"def freq_words_with_mismatches(seq, k, d):\\n # Frequency analysis so we don't generate mismatches for the same k-mer more than once.\\n kmer_freq = defaultdict(int)\\n for i in xrange(len(seq)-k+1):\\n kmer_freq[seq[i:i+k]] += 1\\n\\n # Get all of the mismatches for each unique k-mer in the sequence, appearing freq times.\\n mismatch_count = defaultdict(int)\\n for kmer, freq in kmer_freq.iteritems():\\n for mismatch in kmer_mismatches(kmer, d):\\n mismatch_count[mismatch] += freq\\n\\n # Computing the maximum value is somewhat time consuming to repeat, so only do it once!\\n max_count = max(mismatch_count.values())\\n return sorted([kmer for kmer, count in mismatch_count.iteritems() if count == max_count])\",\n \"def find_num_matches(p1, p2, t1, t2, m1, m2, x, k, len_p, i):\\n start = 0\\n for mismatch in range(k):\\n # print(f'start: {start}, id: {id}')\\n id = start\\n end = len_p-1\\n while start <= end:\\n mid = start + (end-start)//2\\n p_h1, p_h2 = get_hash_value(\\n p1, m1, x, id, mid-start), get_hash_value(p2, m2, x, id, mid-start)\\n s_h1, s_h2 = get_hash_value(\\n t1, m1, x, i+id, mid-start), get_hash_value(t2, m2, x, i+id, mid-start)\\n if p_h1 == s_h1 and p_h2 == s_h2: # move to the right half, no mismatch yet\\n start = mid + 1\\n else:\\n end = mid - 1\\n # when loop exits, start - 1 is the index of the mismatch.\\n if start == len_p: # found the last mismatch\\n return True\\n return False\",\n \"def pattern_count(text, pattern):\\n\\n count = 0\\n len_text = len(text)\\n len_pattern = len(pattern)\\n for i in range(len_text - len_pattern):\\n if pattern in text[i:i + len_pattern]:\\n count = count + 1\\n else:\\n continue\\n return count\",\n \"def pattern_count(text, pattern):\\n return len([i\\n for i in range(0, len(text) - len(pattern) + 1)\\n if text[i:i + len(pattern)] == pattern])\",\n \"def compute_freq(text, k):\\n freq_array = [0 for i in range(0, 4**k)]\\n for i in range(0, len(text) - k + 1):\\n pattern = text[i:i + k]\\n j = pattern_to_number(pattern)\\n freq_array[j] += 1\\n # return ' '.join([str(i) for i in freq_array])\\n return freq_array\",\n \"def frequent_words_t(text, k, t):\\n frequent_patterns = []\\n count = {}\\n for i in range(0, len(text)-k+1):\\n pattern = text[i:i+k]\\n count[i] = pattern_count(text, pattern)\\n if count[i] >= t and pattern not in frequent_patterns:\\n frequent_patterns.append(text[i:i+k])\\n return frequent_patterns\",\n \"def brute_force_string_matching(text: str, pattern: str) -> int:\\n n = len(text)\\n m = len(pattern)\\n for i in range(n-m):\\n j = 0\\n while j < m and pattern[j] == text[j+i]:\\n j += 1\\n if j == m:\\n return i\\n return -1\",\n \"def indapproxpattern(pattern, string, nummismatch):\\n\\n indarr = []\\n# substringarr = []\\n numchars = len(pattern)\\n\\n for i in xrange(0, len(string) - numchars + 1):\\n \\n substring = patterncount.subtext(string, i, numchars)\\n \\n if hammingdist(pattern, substring) <= nummismatch:\\n \\n indarr.append(i)\\n# substringarr.append(substring)\\n \\n return indarr\",\n \"def count_pattern(sentence, pattern):\\n n = len(pattern)\\n counter = 0\\n for i in range(len(sentence) - n + 1):\\n if sentence[i:i+n] == pattern:\\n counter += 1\\n\\n return counter\",\n \"def mismatch(pattern, pattern_2):\\n mismatch = 0\\n for n, i in zip(pattern, pattern_2):\\n if n != i:\\n mismatch += 1\\n return mismatch\",\n \"def get_num_mismatches(sequence, ref_genome, position):\\n characters = list(sequence)\\n num_mismatches = 0\\n for i in range(0, len(characters)):\\n if position + i >= len(ref_genome):\\n break\\n if characters[i] != ref_genome[position + i]:\\n num_mismatches += 1\\n\\n return num_mismatches\",\n \"def count(pattern, string, overlapping=True, sensitive=True, regexp=False):\\n return len(SE.findall(pattern, string, overlapping, sensitive, regexp))\",\n \"def faster_frequent_words(text, k):\\n frequent_patterns = []\\n freq_array = compute_freq(text, k)\\n max_count = max(freq_array)\\n for i in range(0, len(text)-k+1):\\n if freq_array[i] == max_count:\\n pattern = number_to_pattern(i, k)\\n frequent_patterns.append(pattern)\\n return frequent_patterns\",\n \"def PatternCount(text, pattern):\\n\\n count = 0\\n for i in range(0, len(text)-len(pattern)+1):\\n if text[i:i+len(pattern)] == pattern:\\n count += 1\\n return count\",\n \"def get_pattern_count(sequence, pattern):\\n return len(re.findall(r'(?=' + pattern + ')', sequence))\",\n \"def count_match_mismatch(data, index):\\n\\tsubjects = set()\\n\\tnew_data = []\\n\\tfor line in data:\\n\\t\\tif line[\\\"mark_name\\\"] == \\\"match_3D7_\\\" + str(index):\\n\\t\\t\\tsubjects.add(line[\\\"subject\\\"])\\n\\t\\t\\tnew_data.append(line)\\n\\tmatch_map = {}\\n\\tmismatch_map = {}\\t\\n\\tfor subject in subjects:\\n\\t\\tmatch_map[subject] = 0\\n\\t\\tmismatch_map[subject] = 0\\t\\t\\n\\t\\tfor line in new_data:\\n\\t\\t\\tif line[\\\"subject\\\"] == subject and line[\\\"mark_value\\\"] == \\\"1\\\":\\n\\t\\t\\t\\tmatch_map[subject] = 1\\n\\t\\t\\tif line[\\\"subject\\\"] == subject and line[\\\"mark_value\\\"] == \\\"0\\\":\\n\\t\\t\\t\\tmismatch_map[subject] = 1\\t\\t\\t\\t\\n\\tmatch_count = 0\\n\\tmismatch_count = 0\\t\\n\\tfor subject, value in match_map.items():\\n\\t\\tmatch_count += value\\n\\tfor subject, value in mismatch_map.items():\\n\\t\\tmismatch_count += value\\t\\t\\n\\treturn (match_count, mismatch_count)\",\n \"def frequent_words_by_sorting(text, k):\\n frequent_patterns = []\\n index = []\\n count = []\\n for i in range(0, len(text) - k + 1):\\n pattern = text[i:i + k]\\n index[i] = pattern_to_number(pattern)\\n count[i] = 1\\n sorted_index = sorted(index)\\n for i in range(0, len(text) - k + 1):\\n if sorted_index[i] == sorted_index[i-1]:\\n count[i] = count[i -1] + 1\\n max_count = max(count)\\n for i in range(0, len(text) - k + 1):\\n if count[i] == max_count:\\n pattern = number_to_pattern(sorted_index[i], k)\\n frequent_patterns.append(pattern)\\n return frequent_patterns\",\n \"def ApproxStrMatch(pattern, text, d, debug = False):\\n\\tcount = 0\\n\\tindex = []\\n\\tfor i in range(len(text)-len(pattern)+1):\\n\\t\\tif debug:\\n\\t\\t\\tprint text[i:i+len(pattern)]\\n\\t\\t\\tprint HammingDist(text[i:i+len(pattern)], pattern)\\n\\t\\tif HammingDist(text[i:i+len(pattern)], pattern) <= d:\\n\\t\\t\\tcount += 1\\n\\t\\t\\tindex.append(i)\\n\\treturn index\",\n \"def count_kmers(dna, k):\\n kmer_count = Counter()\\n for i in range(len(dna)):\\n kmer = dna[i:(i+k)]\\n if len(kmer) == k:\\n kmer_count[kmer] += 1\\n return kmer_count\",\n \"def find_occurrences(text, pattern, d=0):\\n idx_of_last_pattern = len(text) - len(pattern)\\n return [i for i in range(idx_of_last_pattern + 1) if hamming(text[i:i + len(pattern)], pattern) <= d]\",\n \"def clumps_finding(text, k, t, L):\\n frequent_patterns = []\\n clumps = [0 for i in range(0, 4**k)]\\n for i in range(0, len(text) - L + 1):\\n subtext = text[i:i + L]\\n freq_array = compute_freq(subtext, k)\\n for index, freq in enumerate(freq_array):\\n if freq >= t:\\n clumps[index] = 1\\n for index, clump in enumerate(clumps):\\n if clump == 1:\\n pattern = number_to_pattern(index, k)\\n frequent_patterns.append(pattern)\\n return frequent_patterns\",\n \"def better_matching(self, pattern, offsets=False, slice_len=5):\\n first_occurencies, counts_at = self.setup_better_match(slice_len)\\n pattern = list(pattern)\\n top, bottom = 0, self.tl\\n while top <= bottom:\\n if pattern:\\n current = pattern.pop()\\n if counts_at(bottom+1)[current] - counts_at(top)[current] > 0:\\n #if current in self.last_col[top:bottom+1]:\\n top = first_occurencies[current] + counts_at(top)[current]\\n bottom = first_occurencies[current] + counts_at(bottom + 1)[current] - 1\\n else:\\n if offsets:\\n return []\\n else:\\n return 0\\n else:\\n if offsets:\\n return self.offsets(top, bottom)\\n else:\\n return bottom - top + 1\",\n \"def mismatch_search(haystack, needle, mismatches=1):\\n haystack = haystack.encode('utf-8');\\n needle = needle.encode('utf-8');\\n if not using_tre:\\n raise RBSError(\\\"tre isn't loaded.\\\")\\n fz = tre.Fuzzyness(maxerr=mismatches, maxsub=mismatches,\\n maxdel=0, maxins=0)\\n needle = \\\".*(%s).*\\\" % needle\\n pt = tre.compile(needle, tre.EXTENDED)\\n incidence = 0\\n while True:\\n m = pt.search(haystack, fz)\\n if m:\\n index = m.groups()[1][1]-1\\n incidence += 1\\n haystack = haystack[:index]\\n else:\\n break\\n return incidence\",\n \"def support_count(pattern, D):\\n support_count = 0\\n tmp_p = set(pattern)\\n for transaction in D:\\n if tmp_p <= set(transaction):\\n support_count += 1\\n return support_count\",\n \"def num_of_errors(text):\\n tool = language_check.LanguageTool('en-US')\\n matches = tool.check(text)\\n errors = len(matches)\\n return errors\",\n \"def lcs_hamming_only_matches(s1: str, s2: str, k: int, length: int, matches_lst: List):\\n count = 0\\n for i in range(0, len(s1) - length + 1):\\n for j in range(0, len(s2) - length + 1):\\n sub1 = s1[i: i + length]\\n sub2 = s2[j: j + length]\\n result = hamming_distance(sub1, sub2)\\n if result <= k:\\n matches_lst.append([i, j, result, sub1, sub2])\\n count += 1\\n # print(\\\"total matches: \\\" + str(count))\\n return count\",\n \"def frequency_table(text, k):\\n\\n freq_map = {}\\n len_text = len(text)\\n for i in range(len_text - k + 1):\\n pattern = text[i:i + k]\\n if pattern not in freq_map:\\n freq_map[pattern] = 1\\n else:\\n freq_map[pattern] = freq_map[pattern] + 1\\n return freq_map\",\n \"def get_kth_ugly_number(k):\\n count = 0; i = 0\\n while count < k:\\n i += 1\\n if is_ugly(i):\\n count += 1\\n return i\",\n \"def sorted_frequent_words(text: str, k: int) -> Set[str]:\\n frequent_patterns = set()\\n index = []\\n count = []\\n for i in range(len(text) - k + 1):\\n pattern = text[i:i+k]\\n index.append(pattern_to_number(pattern))\\n count.append(1)\\n\\n sorted_index = sorted(index)\\n\\n for i in range(1, len(text) - k + 1):\\n if sorted_index[i] == sorted_index[i-1]:\\n count[i] = count[i-1] + 1\\n\\n max_count = max(count)\\n\\n for i in range(len(text) - k + 1):\\n if count[i] == max_count:\\n pattern = number_to_pattern(sorted_index[i], k)\\n frequent_patterns.add(pattern)\\n\\n return frequent_patterns\",\n \"def _matched_len(self, string, idx1, idx2):\\n\\n str_len = len(string)\\n if idx1 == idx2:\\n return str_len - idx1\\n counter = 0\\n while idx1 < str_len and idx2 < str_len and string[idx1] == string[idx2]:\\n counter += 1\\n idx1 += 1\\n idx2 += 1\\n\\n return counter\",\n \"def n_errors(gold_tokens, pred_tokens):\\n return len(gold_tokens) + len(pred_tokens) - 2 * _n_matches(gold_tokens, pred_tokens)\",\n \"def find_all_occurrences_brute_force(pattern, text):\\n\\n result = []\\n\\n if len(text) < len(pattern):\\n return result\\n\\n for i in range(0, len(text) - len(pattern) + 1):\\n matched = True\\n\\n k = 0\\n for j in range(i, i + len(pattern)):\\n if pattern[k] != text[j]:\\n matched = False\\n break\\n k += 1\\n\\n if matched:\\n result.append(i)\\n\\n return result\",\n \"def CountOccurrences(pattern, bwt, starts, occ_counts_before):\\n # Implement this function yourself\\n return 0\",\n \"def find_clumps(text, k, len_win, t):\\n\\n patterns = []\\n len_text = len(text)\\n for i in range(len_text - len_win + 1):\\n window = text[i:i + len_win]\\n freq_map = frequency_table(window, k)\\n for key in freq_map.keys():\\n if freq_map[key] >= t and key not in patterns:\\n patterns.append(key)\\n return patterns\",\n \"def str_search(pattern, text):\\n N, M = len(text), len(pattern)\\n i = 0 \\n while i <= N-M:\\n j = 0 \\n while j < M:\\n if text[i + j] != pattern[j]:\\n break \\n j += 1\\n if j == M:\\n return i\\n i += 1\\n return N\",\n \"def _find_max_number_of_grouping(cls, reserved_seats, k):\\n # print(reserved_seats)\\n n = len(reserved_seats)\\n count_groups = 0\\n count_empty_contigous_seats = 0\\n i = 0\\n while i < n:\\n if reserved_seats[i] != 0:\\n # print('continue', i)\\n count_empty_contigous_seats = 0\\n i += 1\\n continue\\n\\n count_empty_contigous_seats += 1\\n # print('empty', i, count_empty_contigous_seats)\\n if count_empty_contigous_seats >= k:\\n count_groups += 1\\n # print('found', i, count_groups)\\n\\n if ((i + 1) % len(cls._PLANE_ROW)) == 0:\\n # print('new row', i)\\n count_empty_contigous_seats = 0\\n\\n i += 1\\n\\n return count_groups\",\n \"def kbest_matches(self, k=1, overlap=0):\\n self.align()\\n matching = np.array(self.matching)\\n maxv = np.ceil(np.max(matching) + 1)\\n matching[:min(len(self.query) - 1, overlap)] = maxv\\n ki = 0\\n while k is None or ki < k:\\n best_idx = np.argmin(matching)\\n if best_idx == 0 or np.isinf(matching[best_idx]) or matching[best_idx] == maxv:\\n # No more matches found\\n break\\n match = self.get_match(best_idx)\\n b, e = match.segment\\n cur_overlap = min(overlap, e - b - 1)\\n mb, me = best_idx + 1 - (e - b) + cur_overlap, best_idx + 1\\n if np.isinf(np.max(matching[mb:me])):\\n # No overlapping matches\\n matching[best_idx] = maxv\\n continue\\n matching[mb:me] = np.inf\\n ki += 1\\n yield match\",\n \"def allele_match_counts(self, map_hit):\\n if map_hit.NM == 0:\\n l = self.allele_end - self.allele_start + 1\\n return l, l\\n\\n # If there are mismatches, then we can use the extended cigar string to\\n # work out how many of those mismatches are in the allele.\\n # Example cigar to remind which way round I and D are:\\n # read: AGT--TGATCAAGTAC\\n # ref: AGTGATGATC----AC\\n # cigar: 3M2D5M4I2M\\n probe_pos = map_hit.q_st\\n total_positions = 0\\n matches = 0\\n\\n if map_hit.strand == -1:\\n map_hit.cigar.reverse()\\n\\n for length, operator in map_hit.cigar:\\n if probe_pos > self.allele_end:\\n break\\n\\n if operator == 7 or operator == 8: # 7,8 are \\\"=\\\",\\\"X\\\" == match/mismatch\\n for i in range(length):\\n if self.allele_start <= probe_pos <= self.allele_end:\\n if operator == 7:\\n matches += 1\\n total_positions += 1\\n probe_pos += 1\\n if probe_pos > self.allele_end:\\n break\\n elif operator == 1: # 1 = I = insertion\\n if self.allele_start <= probe_pos <= self.allele_end:\\n total_positions += length\\n probe_pos += length\\n elif operator == 2: # 2 = D = deletion\\n if self.allele_start <= probe_pos <= self.allele_end:\\n total_positions += length\\n else:\\n raise RuntimeError(\\n f\\\"Unexpected cigar operator number {operator} with length {length} from cigar\\\"\\n )\\n\\n if map_hit.strand == -1:\\n map_hit.cigar.reverse()\\n\\n return matches, total_positions\",\n \"def detectMiss(n, k):\\n k -= 1\\n nlog = math.ceil(math.log(n,2))\\n nloglessone = nlog - 1\\n lower_base = 2**nloglessone - 1\\n lowerbaseBack = lower_base\\n diff_list = [2**i for i in range(nloglessone-1, 0, -1)]\\n diff_lit_back = diff_list[1:-1]\\n if lower_base == k and 2**nlog -1 == n:\\n return(k)\\n matchres = False\\n while(lower_base <= k and len(diff_list)>0 ):\\n if lower_base == k:\\n matchres = True\\n break\\n else:\\n addVal = diff_list[0]\\n lower_base += addVal\\n diff_list.remove(addVal)\\n if matchres:\\n k -= 1\\n if k%2==0:\\n return(k)\\n else:\\n while(len(diff_lit_back)>0 and lowerbaseBack<k):\\n newAddVal = diff_lit_back[0]\\n lowerbaseBack = lowerbaseBack+newAddVal\\n postitionIndx = lowerbaseBack + newAddVal\\n if lowerbaseBack == k and postitionIndx <= n:\\n return(k)\\n elif lowerbaseBack == k and postitionIndx > n:\\n return(k-1)\\n else:\\n pass\\n diff_lit_back.remove(newAddVal)\\n\\n return(k)\",\n \"def _count_chunks(matches):\\n i = 0\\n chunks = 1\\n while (i < len(matches) - 1):\\n if (matches[i + 1][0] == matches[i][0] + 1) and (matches[i + 1][1] == matches[i][1] + 1):\\n i += 1\\n continue\\n i += 1\\n chunks += 1\\n return chunks\",\n \"def count_sequence_mismatches(seq):\\n trans_table = str.maketrans('ACGT', 'TGCA')\\n half_len = len(seq) // 2\\n second_half = seq[-half_len:].translate(trans_table)\\n mismatches = 0\\n for i in range(half_len):\\n if seq[i] != second_half[-i - 1]:\\n mismatches += 1\\n return mismatches\",\n \"def str_search_two(pattern, text):\\n N, M = len(text), len(pattern)\\n i, j = 0, 0 \\n while i < N and j < M:\\n if text[i] == pattern[j]:\\n j += 1\\n else:\\n i -= j\\n j = 0 \\n i += 1\\n if j == M:\\n return i - M\\n else:\\n return N\",\n \"def string_num_matches(str1, str2):\\n sm = edit_distance.SequenceMatcher(a=str1, b=str2)\\n return sm.matches()\",\n \"def count(r, c, k):\\n\\t\\tif 0 <= r < M and 0 <= c < N:\\n\\t\\t\\tif A[r][c] == target[k]:\\n\\t\\t\\t\\t\\\"*** YOUR CODE HERE ***\\\"\\n\\t\\t\\t\\treturn memoized_count(r, c, k)\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn 0\\n\\t\\telse:\\n\\t\\t\\treturn 0\",\n \"def get_count(self):\\n\\n return len(self._pattern)\",\n \"def count_segments(markers) -> int:\\n cnt = Counter()\\n for row in markers:\\n cnt.update(row)\\n n_cnt = dict(takewhile(lambda x: x[1] >= 10, cnt.most_common()))\\n del n_cnt[1]\\n del n_cnt[-1]\\n return len(n_cnt.keys())\",\n \"def pattern_count(DNA, pattern, start=0, end=0, mutation_thresh=0):\\n if start < 0 or start >= len(DNA):\\n raise ValueError(\\\"The starting position should be between 0 and the size \\\" + \\\\\\n \\\"of the DNA\\\")\\n\\n k = len(pattern)\\n count = 0\\n end = len(DNA) - k + 1 if end == 0 else end\\n\\n for i in range(0, end):\\n if hamming_distance(DNA[i:i+k], pattern) <= mutation_thresh:\\n count += 1\\n\\n return count\",\n \"def better_solution(self, S: str, words: List[str]) -> int:\\n def encode(S):\\n return zip(*[(k, len(list(v))) for k, v in groupby(S)])\\n\\n k, v = encode(S)\\n count = 0\\n for word in words:\\n kk, vv = encode(word)\\n if k != kk:\\n continue\\n count += int(all(c1 >= max(c2, 3) or c1 == c2 for c1, c2 in zip(v, vv)))\\n return count\",\n \"def find_clumps(text, k, L, t):\\n clumps = []\\n k_mers = frequent_words_t(text, k, t)\\n for k_mer in k_mers:\\n positions = find_position(k_mer, text)\\n for position in positions:\\n subtext = text[position:position + L]\\n count = pattern_count(subtext, k_mer)\\n if count >= t and k_mer not in clumps:\\n clumps.append(k_mer)\\n return clumps\",\n \"def frequency_array(text, k):\\r\\n freq_list=[]\\r\\n p_list=pattern_list(k)\\r\\n for i in p_list:\\r\\n freq_list.append(PatternCount(i,text))\\r\\n return freq_list\",\n \"def hits_and_misses(guess_seq, true_seq):\\n num_hits = sum(guess_token == true_token\\n for guess_token, true_token\\n in zip(guess_seq, true_seq))\\n num_misses = sum((Counter(guess_seq) & Counter(true_seq)).values()) - num_hits\\n return num_hits, num_misses\",\n \"def regular_get_match(x, y, k):\\n n = len(x)\\n m = len(y)\\n\\n output = list()\\n\\n for i in range(n - k + 1):\\n matches = non_rolling_matcher(\\n target=y,\\n potential=x[i:i + k]\\n )\\n\\n for match in matches:\\n output.append((i, match))\\n\\n return output\",\n \"def count(text):\\n return len(text)\",\n \"def count_until(match_value):\",\n \"def get_nmismatches(bases_mask):\\n # Check mask is valid\\n if not bases_mask_is_valid(bases_mask):\\n raise Exception(\\\"'%s': not a valid bases mask\\\" % bases_mask)\\n # Total the length of all index reads\\n index_length = 0\\n for read in bases_mask.upper().split(','):\\n if read.startswith('I'):\\n try:\\n i = read.index('N')\\n read = read[:i]\\n except ValueError:\\n pass\\n try:\\n index_length += int(read[1:])\\n except ValueError:\\n index_length += len(read)\\n # Return number of mismatches\\n if index_length >= 6:\\n return 1\\n else:\\n return 0\",\n \"def __len__(self: TokenMatcher) -> int:\\n return len(self._patterns)\",\n \"def count_kmers(file_name, k, verbose=False):\\n if verbose:\\n start = time.time()\\n print('Counting kmers in {}'.format(file_name))\\n total_kmers = 0\\n with open(file_name, 'r') as f:\\n line_num = 0\\n for line in f:\\n if line_num % 4 == 1: # dna sequence\\n total_kmers += len(line) - k # eliminate new-line\\n line_num += 1\\n if verbose:\\n end = time.time()\\n print('{} kmers are counted in {:.2f} seconds'.format(\\n total_kmers, end - start))\\n return total_kmers\",\n \"def __naive_matching(pattern, text):\\n matched_positions = []\\n n = len(text)\\n m = len(pattern)\\n # Last possible pattern starting position in the text is n - m\\n for pos in range((n - m) + 1):\\n if pattern == text[pos:(pos+m)]:\\n matched_positions.append(pos)\\n return matched_positions\",\n \"def __len__(self) -> int:\\n n_fuzzy_patterns = sum(len(p[\\\"patterns\\\"]) for p in self.fuzzy_patterns.values())\\n n_regex_patterns = sum(len(p[\\\"patterns\\\"]) for p in self.regex_patterns.values())\\n return n_fuzzy_patterns + n_regex_patterns\",\n \"def match_specific_string(input_data: list, keyword: str) -> int:\\n number_of_words = 0\\n for element in input_data:\\n number_of_words += len(re.findall(keyword, element, re.IGNORECASE))\\n return number_of_words\",\n \"def distance_between_pattern_and_strings(pattern, dna):\\n\\n k = len(pattern)\\n distance = 0\\n\\n for text in dna:\\n hamming_distance = 1000000\\n for i in range(len(text) - k + 1):\\n if hamming_distance > compute_hamming_distance(pattern, text[i:i + k]):\\n hamming_distance = compute_hamming_distance(pattern, text[i:i + k])\\n distance = distance + hamming_distance\\n return distance\",\n \"def count_regexp_occ(regexp=\\\"\\\", text=None):\\n return len(re.findall(regexp, text))\",\n \"def count_regexp_occ(regexp=\\\"\\\", text=None):\\n return len(re.findall(regexp, text))\",\n \"def __count_of_keyword_matches__(self, citation_string, key_string):\\n matches = 0\\n for keyword in self.dict_of_keywords.get(key_string):\\n pattern = re.compile(keyword)\\n if pattern.search(citation_string):\\n matches += 1\\n return matches\",\n \"def count_kmers(seq, k=3):\\n # Start with an empty dictionary\\n counts = {}\\n # Calculate how many kmers of length k there are\\n num_kmers = len(str(seq)) - k + 1\\n # Loop over the kmer start positions\\n for i in range(num_kmers):\\n # Slice the string to get the kmer\\n kmer = str(seq)[i:i+k]\\n # Add the kmer to the dictionary if it's not there\\n if kmer not in counts:\\n counts[kmer] = 0\\n # Increment the count for this kmer\\n counts[kmer] += 1\\n # Return the final counts\\n return counts\",\n \"def test_suite():\\n test(count(\\\"is\\\", \\\"Mississippi\\\") == 2)\\n test(count(\\\"an\\\", \\\"banana\\\") == 2)\\n test(count(\\\"ana\\\", \\\"banana\\\") == 2)\\n test(count(\\\"nana\\\", \\\"banana\\\") == 1)\\n test(count(\\\"nanan\\\", \\\"banana\\\") == 0)\\n test(count(\\\"aaa\\\", \\\"aaaaaa\\\") == 4)\",\n \"def hit_rate_at_k(rs, k):\\n if k < 1 or k > len(rs[0]):\\n raise ValueError('k value must be >=1 and < Max Rank')\\n hits = 0\\n for r in rs:\\n if np.sum(r[:k]) > 0: hits += 1\\n\\n return hits / len(rs)\",\n \"def get_multi_pattern_count(word, patterns):\\n\\n distinct_positions = set()\\n for pattern in patterns:\\n result = Util.find_all_occurrences_knuth_morris_pratt(pattern,\\n word)\\n distinct_positions |= set(result)\\n\\n return distinct_positions\",\n \"def CountOccurrences(pattern, bwt, starts, occ_counts_before, suffix_array):\\r\\n # 0$ 1A 2T 3G 4C\\r\\n letters = {'$':0, 'A':1, 'T':2, 'G':3, 'C':4}\\r\\n top=0\\r\\n bottom = len(bwt)-1\\r\\n matches_index = []\\r\\n while True:\\r\\n if len(pattern)!=0:\\r\\n char = pattern[-1]\\r\\n j = letters[char]\\r\\n pattern = pattern[:-1]\\r\\n found=False\\r\\n for i in range(top,bottom+1):\\r\\n if bwt[i] == char:\\r\\n top = occ_counts_before[i][j] + starts[char] -1\\r\\n bottom = occ_counts_before[bottom][j] + starts[char] -1\\r\\n found = True\\r\\n break\\r\\n\\r\\n if found==False:\\r\\n return matches_index\\r\\n # when pattern is finished proccessing\\r\\n else:\\r\\n for i in range(top, bottom+1):\\r\\n matches_index.append(suffix_array[i])\\r\\n return matches_index\\r\\n\\r\\n return matches_index\",\n \"def parse_file_count(path, args):\\n try:\\n fisier = open(path, 'r')\\n except IOError:\\n print(\\\"Nu am putut deschide fisierul :\\\", path)\\n return\\n n_found = 0\\n pattern = args.pattern\\n for line in fisier:\\n if args.ignore_case:\\n line = line.lower()\\n pattern = pattern.lower()\\n n_found += line.count(pattern)\\n\\n fisier.close()\\n return n_found\",\n \"def measure_the_work(pattern_to_find):\\n recipes = [3, 7]\\n keys = [int(key) for key in pattern_to_find]\\n elf1 = 0\\n elf2 = 1\\n not_found = True\\n\\n while not_found:\\n elf1_value = recipes[elf1]\\n elf2_value = recipes[elf2]\\n\\n recipe_sum = elf1_value + elf2_value\\n\\n if recipe_sum > 9:\\n recipe_string = f\\\"{recipe_sum:02d}\\\"\\n recipes.append(int(recipe_string[:1]))\\n recipes.append(int(recipe_string[1:]))\\n else:\\n recipes.append(recipe_sum)\\n\\n elf1 = loop_around(1 + elf1 + elf1_value, len(recipes))\\n elf2 = loop_around(1 + elf2 + elf2_value, len(recipes))\\n if recipes[-1] == keys[-1] or recipes[-2] == keys[-1]:\\n\\n if pattern_to_find in ''.join(map(str, recipes[-(len(keys) + 2):])):\\n not_found = False\\n if recipes[-1] == keys[-1]:\\n return len(recipes) - len(keys)\\n else:\\n return len(recipes) - len(keys) - 1\",\n \"def test_ababab():\\n assert part_01.count_for('ababab', 2) == 0\\n assert part_01.count_for('ababab', 3) == 1\",\n \"def numKLenSubstrNoRepeats(self, S, K):\\n return self.for_loop(S, K)\",\n \"def pk(y_true, y_pred, k):\\n \\n # if k is 0, return 0. we should never have this\\n # as k is always >= 1\\n if k == 0:\\n return 0\\n # we are interested only in top-k predictions\\n y_pred = y_pred[:k]\\n \\n # convert predictions to set\\n pred_set = set(y_pred)\\n \\n # convert actual values to set\\n true_set = set(y_true)\\n \\n # find common values\\n common_values = pred_set.intersection(true_set)\\n \\n # return length of common values over k\\n return len(common_values) / len(y_pred[:k])\",\n \"def all_matches(self, pattern):\\n\\n pat_len = len(pattern)\\n if pat_len > self.text_len:\\n raise ValueError(\\\"Pattern length is bigger than text\\\")\\n\\n bad_char_table = self._bad_char_table(pattern)\\n L = self._good_suffix_table_one(pattern)\\n H = self._good_suffix_table_two(pattern)\\n indexes = []\\n\\n pat_end_ind = pat_len - 1\\n prev_end = -1\\n # prev_end - previous index of pattern end relative to text (for Galil's rule)\\n # p - index of char in pattern\\n # t - index of char in text\\n while pat_end_ind < self.text_len:\\n p = pat_len - 1\\n t = pat_end_ind\\n while p >= 0 and t > prev_end and pattern[p] == self.text[t]:\\n p -= 1\\n t -= 1\\n if p == -1 or t == prev_end: # Matched or holds Galil's rule\\n indexes.append(pat_end_ind - pat_len + 1)\\n pat_end_ind += pat_len - H[1] if pat_len > 1 else 1\\n else:\\n char_shift = self._bad_char_shift(self.text[t], bad_char_table)\\n if p+1 == pat_len:\\n suffix_shift = 1\\n elif L[p] == -1:\\n suffix_shift = pat_len - H[p+1]\\n else:\\n suffix_shift = pat_len - L[p]\\n shift = max(char_shift, suffix_shift)\\n prev_end = pat_end_ind if shift >= p+1 else prev_end #update parameter for Galil's rule\\n pat_end_ind += shift\\n\\n return indexes\",\n \"def recCountString():\\r\\n target = raw_input(\\\"Enter target string: \\\")\\r\\n key = raw_input(\\\"Enter key string: \\\")\\r\\n matches = subStringMatchExact(target,key)\\r\\n print \\\"match(es) =\\\",matches\",\n \"def frequent_words(text, k):\\n\\n frequent_patterns = []\\n freq_map = frequency_table(text, k)\\n max_val = max_map(freq_map)\\n for key in freq_map.keys():\\n if freq_map[key] == max_val:\\n frequent_patterns.append(key)\\n return frequent_patterns\",\n \"def naive_with_counts(p, t):\\n occurences = []\\n num_alignments = 0\\n num_character_comparisons = 0\\n for i in range(len(t) - len(p) + 1):\\n match = True\\n for j in range(len(p)):\\n if t[i + j] != p[j]:\\n match = False\\n break\\n if match:\\n occurences.append(i)\\n num_alignments += 1\\n num_character_comparisons += (j + 1)\\n return occurences, num_alignments, num_character_comparisons\",\n \"def count(self):\\n return len(self.find())\",\n \"def kmp(text, pattern):\\n pattern = list(pattern)\\n length = len(pattern)\\n shifts = [1] * (length + 1)\\n shift = 1\\n for pos, pat in enumerate(pattern):\\n while shift <= pos and pat != pattern[pos-shift]:\\n shift += shifts[pos-shift]\\n shifts[pos+1] = shift\\n\\n start_pos = 0\\n match_len = 0\\n for c in text:\\n while match_len == length or match_len >= 0 and pattern[match_len] != c:\\n start_pos += shifts[match_len]\\n match_len -= shifts[match_len]\\n match_len += 1\\n if match_len == length:\\n yield start_pos\",\n \"def count_mismatches(seq1, seq2, max_mm):\\r\\n mm = 0\\r\\n for i in range(min(len(seq2), len(seq1))):\\r\\n if seq1[i] != seq2[i]:\\r\\n mm += 1\\r\\n if mm > max_mm:\\r\\n return mm\\r\\n return mm\",\n \"def count_k(n, k):\\n if n == 0:\\n return 1\\n elif n < 0:\\n return 0\\n else:\\n total = 0\\n i = 1\\n while i <= k:\\n total += count_k(n - i, k)\\n i += 1\\n return total\",\n \"def length_of_longest_substring(arr, k):\\n window_start = 0\\n max_repeat_times = 0\\n frequency_map = {0: 0, 1: 0}\\n len_longest = 0\\n\\n for window_end in range(len(arr)):\\n right_char = arr[window_end]\\n left_char = arr[window_start]\\n frequency_map[right_char] += 1\\n max_repeat_times = frequency_map[0]\\n\\n if max_repeat_times > k:\\n frequency_map[left_char] -= 1\\n window_start += 1\\n len_longest = max(len_longest, window_end - window_start + 1)\\n\\n return len_longest\",\n \"def __count_text(text, limit=None):\\n\\n count = 0\\n is_text = True\\n for i, c in enumerate(text):\\n if is_text and c == '\\\\33':\\n is_text = False\\n\\n if is_text:\\n count += 1\\n if limit is not None and count == limit:\\n return i + 1\\n\\n if not is_text and c == 'm':\\n is_text = True\\n\\n if limit is not None:\\n return len(text)\\n else:\\n return count\",\n \"def utr3_motif_counts(self, pattern):\\n return len(re.findall(pattern.upper(), self.three_prime_utr_sequence.upper()))\",\n \"def kbest_matches(self, k=1, minlen=2, buffer=0):\\n ki = 0\\n while k is None or ki < k:\\n idx = None\\n lcm = None\\n while idx is None:\\n idx = np.unravel_index(np.argmax(self._wp, axis=None), self._wp.shape)\\n if idx[0] == 0 or idx[1] == 0:\\n return None\\n r, c = idx\\n lcm = LCMatch(self, r, c)\\n for (x, y) in lcm.path:\\n x += 1\\n y += 1\\n if len(self._wp.mask.shape) > 0 and self._wp.mask[x, y] is True: # True means invalid\\n # print('found path contains masked, restart')\\n lcm = None\\n idx = None\\n break\\n else:\\n self._wp[x, y] = ma.masked\\n if len(lcm.path) < minlen:\\n # print('found path too short, restart')\\n lcm = None\\n idx = None\\n if buffer > 0 and lcm is not None:\\n miny, maxy = 0, self._wp.shape[1] - 1\\n minx, maxx = 0, self._wp.shape[0] - 1\\n for (x, y) in lcm.path:\\n xx = x + 1\\n for yy in range(max(miny, y + 1 - buffer), min(maxy, y + 1 + buffer)):\\n self._wp[xx, yy] = ma.masked\\n yy = y + 1\\n for xx in range(max(minx, x + 1 - buffer), min(maxx, x + 1 + buffer)):\\n self._wp[xx, yy] = ma.masked\\n if lcm is not None:\\n ki += 1\\n yield lcm\",\n \"def contig_complementary_score(s1, s2) -> int:\\n matcher = SequenceMatcher()\\n matcher.set_seq1(str(s1))\\n matcher.set_seq2(str(s2.complement()))\\n _, _, size = matcher.find_longest_match(0, len(s1), 0, len(s2))\\n return size\",\n \"def is_k_anagram(s1: str, s2: str, k: int) -> tuple:\\n if len(s1) != len(s2):\\n return False, None, None\\n\\n counter_map = Counter(s1)\\n extra = list()\\n changes = 0\\n for char in s2:\\n if counter_map[char] > 0:\\n counter_map[char] -= 1\\n else:\\n extra.append(char)\\n changes += 1\\n\\n if changes > k:\\n return False, None, None\\n diff = [c for c in counter_map if counter_map[c] > 0]\\n return True, extra, diff\",\n \"def countSubStringMatch(target,key):\\n count = 0\\n for i in range(0,len(target)-len(key)):\\n if target[i:i+len(key)] == key:\\n count += 1\\n return count\",\n \"def keyword_length(text):\\n text = scrub_string(text)\\n a = [fabs(IC(text, ncol) - ENGLISH_IC) for ncol in range(1, MAX_LEN)]\\n return a.index(min(a)) + 1\",\n \"def searchKnuthMorris(pattern,text):\\n if isinstance(pattern, str) == False:\\n raise Exception(\\\"The pattern is not of type string\\\")\\n if isinstance(text,str) == False:\\n raise Exception(\\\"The text is not of type string\\\")\\n if pattern is None or text is None or pattern == \\\"\\\" or text == \\\"\\\":\\n raise Exception(\\\"One of the strings is not defined correctly\\\")\\n if len(pattern) > len(text):\\n raise Exception(\\\"The pattern string is supposed to be shorter than the text\\\")\\n\\n i = 0\\n j = 0\\n\\n # First, we compute the LPS array\\n arrayLPS = computeLPSArray(pattern)\\n\\n result = list()\\n \\n # Check character by character the text \\n # NOTE: differently from the naive approach, that would\\n # require us to go back and re-analyze parts of the text\\n # that we previously analyzed, in this approach\\n # the i index is never decremented, allowing us to \\n # examine the string text just once\\n while i < len(text): \\n # If the two characters match, increment both indexes\\n if text[i] == pattern[j]:\\n i += 1\\n j += 1\\n \\n # If we were able to match the whole pattern, \\n # store the index where the pattern starts. \\n if j == len(pattern):\\n result.append(i-j)\\n j = arrayLPS[j-1]\\n \\n # Otherwise if they do not match and j is not equal to 0, check\\n # the value stored in the previous cell [j-1] and let j get\\n # that value\\n elif text[i] != pattern[j]:\\n if j!=0:\\n j = arrayLPS[j-1]\\n # Otherwise (if they do not match and j is equal to 0) increment i\\n else:\\n i += 1\\n return result\",\n \"def match(self):\\r\\n results = []\\r\\n pattern = self.pattern\\r\\n text = self.text\\r\\n m = len(self.pattern)\\r\\n n = len(self.text)\\r\\n p = self._prefix\\r\\n k = 0\\r\\n for i in range(n):\\r\\n while k > 0 and text[i] != pattern[k]:\\r\\n k = p[k-1]\\r\\n if pattern[k] == text[i]:\\r\\n k = k+1\\r\\n if k == m:\\r\\n results.append(i-m+1)\\r\\n k = p[k-1]\\r\\n return results\",\n \"def support_difference_count(m, m_hat):\\n m_nnz, m_hat_nnz, intersection_nnz = _nonzero_intersection(m, m_hat)\\n return int((m_nnz + m_hat_nnz - (2 * intersection_nnz)) / 2.0)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6649192","0.6579769","0.6411769","0.63241273","0.62982893","0.62874293","0.6251507","0.6247955","0.6232768","0.62261176","0.6216543","0.6216298","0.6168045","0.6126884","0.6091661","0.6048104","0.6023427","0.60232127","0.6001935","0.59950364","0.5993816","0.59459305","0.59360576","0.5933066","0.5926173","0.5914956","0.58649075","0.58619463","0.5842085","0.57988405","0.5793785","0.57766575","0.577309","0.57584745","0.5737162","0.5701908","0.5657092","0.5654822","0.56399673","0.5636228","0.56314254","0.5601308","0.55574465","0.55539024","0.5538619","0.55331016","0.5502281","0.54918015","0.54876906","0.5482958","0.5480058","0.54611194","0.545986","0.5454756","0.5424775","0.5419278","0.53980386","0.53918016","0.5380502","0.53804815","0.537782","0.537564","0.53674823","0.5363213","0.5362478","0.53597003","0.53544545","0.5350326","0.5348769","0.5348769","0.53485835","0.5343998","0.53439164","0.53392744","0.5332776","0.5324192","0.5320346","0.5317222","0.53087044","0.5302056","0.53002286","0.5298648","0.5288856","0.52808565","0.52673554","0.5256672","0.52545434","0.52538097","0.5241838","0.52399254","0.52240825","0.5216967","0.5206996","0.52069265","0.5199405","0.5197354","0.51970327","0.5194159","0.51886415","0.5184674"],"string":"[\n \"0.6649192\",\n \"0.6579769\",\n \"0.6411769\",\n \"0.63241273\",\n \"0.62982893\",\n \"0.62874293\",\n \"0.6251507\",\n \"0.6247955\",\n \"0.6232768\",\n \"0.62261176\",\n \"0.6216543\",\n \"0.6216298\",\n \"0.6168045\",\n \"0.6126884\",\n \"0.6091661\",\n \"0.6048104\",\n \"0.6023427\",\n \"0.60232127\",\n \"0.6001935\",\n \"0.59950364\",\n \"0.5993816\",\n \"0.59459305\",\n \"0.59360576\",\n \"0.5933066\",\n \"0.5926173\",\n \"0.5914956\",\n \"0.58649075\",\n \"0.58619463\",\n \"0.5842085\",\n \"0.57988405\",\n \"0.5793785\",\n \"0.57766575\",\n \"0.577309\",\n \"0.57584745\",\n \"0.5737162\",\n \"0.5701908\",\n \"0.5657092\",\n \"0.5654822\",\n \"0.56399673\",\n \"0.5636228\",\n \"0.56314254\",\n \"0.5601308\",\n \"0.55574465\",\n \"0.55539024\",\n \"0.5538619\",\n \"0.55331016\",\n \"0.5502281\",\n \"0.54918015\",\n \"0.54876906\",\n \"0.5482958\",\n \"0.5480058\",\n \"0.54611194\",\n \"0.545986\",\n \"0.5454756\",\n \"0.5424775\",\n \"0.5419278\",\n \"0.53980386\",\n \"0.53918016\",\n \"0.5380502\",\n \"0.53804815\",\n \"0.537782\",\n \"0.537564\",\n \"0.53674823\",\n \"0.5363213\",\n \"0.5362478\",\n \"0.53597003\",\n \"0.53544545\",\n \"0.5350326\",\n \"0.5348769\",\n \"0.5348769\",\n \"0.53485835\",\n \"0.5343998\",\n \"0.53439164\",\n \"0.53392744\",\n \"0.5332776\",\n \"0.5324192\",\n \"0.5320346\",\n \"0.5317222\",\n \"0.53087044\",\n \"0.5302056\",\n \"0.53002286\",\n \"0.5298648\",\n \"0.5288856\",\n \"0.52808565\",\n \"0.52673554\",\n \"0.5256672\",\n \"0.52545434\",\n \"0.52538097\",\n \"0.5241838\",\n \"0.52399254\",\n \"0.52240825\",\n \"0.5216967\",\n \"0.5206996\",\n \"0.52069265\",\n \"0.5199405\",\n \"0.5197354\",\n \"0.51970327\",\n \"0.5194159\",\n \"0.51886415\",\n \"0.5184674\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6189535"},"document_rank":{"kind":"string","value":"12"}}},{"rowIdx":86696,"cells":{"query":{"kind":"string","value":"Calculate the mse for vector e."},"document":{"kind":"string","value":"def calculate_mse(e):\r\n return 1/2*np.mean(e**2)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def calculate_mse(e):\n return 1/2*np.mean(e.dot(e))","def compute_MSE(e):\n\n return 1/2*np.mean(e**2)","def compute_RMSE(e):\n \"\"\"Corresponds to sqrt(2*MSE)\"\"\"\n \n return np.sqrt(2*compute_MSE(e))","def _mse(self):\n error = self._input * self._weights - self._label\n sum_ = 0.0\n for i in range(self._input.shape[0]):\n sum_ += error[i, 0]**2\n return sum_/self._input.shape[0]","def compute_mse(y, tx, w):\n print(\"y.shape = \", y.shape)\n print(\"tx.shape = \", tx.shape)\n e = y - tx.dot(w)\n print(\"e.shape = \", e.shape)\n mse = e.T.dot(e) / (2 * len(e))\n return mse","def _calc_msve(self):\n v = []\n for state in self._env.state_iterator():\n feature_vector = self._features.vector(state)\n v.append(utils.state_value(feature_vector, self.theta))\n\n self.msve.append(utils.rmse(v, self._true_values))","def compute_mse(y, tx, w):\n e = y - tx.dot(w)\n mse = e.dot(e) / (2 * len(e))\n return mse","def compute_mse(y, tx, w):\n e = y - tx@w\n mse = e.T.dot(e) /(2*len(e))\n return mse","def compute_mse(y, tx, w):\n e = y-tx@w\n return 1/(2*y.shape[0])*e.transpose()@e","def d_mse(x, y):\n\n return 2 * (x - y) / x.size(0) / x.size(1)","def mse(self, data, *args, **kwargs):\n return self._mse(np.array(data), *args, **kwargs)","def mse(self, X, Y):\n\t\treturn mse_k(X, to_1_of_K(Y))","def compute_mse(y, tx, w):\n e = y[:, np.newaxis] - tx @ w\n return (e * e).sum() / (2.0 * len(y))","def mse(x, y):\n\n return (x - y).pow(2).sum(dim=1, keepdim=True).mean() / x.size(1)","def evse(self, data, *args, **kwargs):\n darr = np.array(data)\n d = darr if len(darr.shape) == 1 else darr[0] / darr[1]\n return (d - self.evs(darr, *args, **kwargs))**2","def mse(actual,expected):\n return np.mean(se(actual,expected))","def rmse_from_mse (mse):\n return np.sqrt(2*mse)","def mse(o, r):\r\n\r\n return np.mean(np.square((np.abs(o).astype(float) - np.abs(r).astype(float))))","def rmse(self):\n lam = self.lam()\n weights = lam / lam.sum()\n weighted_var = self.var() * weights\n rmse = np.sqrt(weighted_var.sum())\n return rmse","def mse ( target_array ):\n return np.mean ( ( target_array - np.mean ( target_array ) ) ** 2 )\n # End mse()","def negative_mse ( target_array ):\n return -1 * mse ( target_array )\n # End negative_mse()","def _mse(self, weights):\n error = self._input * weights - self._label\n sum_ = 0.0\n for i in range(self._input.shape[0]):\n sum_ += error[i, 0]**2\n return sum_ / self._input.shape[0]","def mse(predicted, actual):\n diff = predicted - actual\n return np.average(diff * diff, axis=0)","def compute_rmse(y, tx, w):\n return np.sqrt(2*compute_mse(y,tx,w))","def rmse (lmse) :\n return np.sqrt(2*lmse)","def mse(X, Y, W):\n\n # TODO\n mse = np.sum((X@W-Y)**2)/(2*X.shape[0])\n # END TODO\n\n return mse","def ivrmse(self):\n return (self.model_error_iv()**2).mean()**.5","def mse(self):\n xs, ys = self.R.nonzero()\n predicted = self.full_matrix()\n error = 0\n for x, y in zip(xs, ys):\n # print(predicted[x, y], self.R[x, y] )\n error += pow(self.R[x, y] - predicted[x, y], 2)\n return np.sqrt(error)","def MSE(actual, noisy):\n mean_squared_error(actual, noisy)","def mse(self, x_tensors=None):\n\n return self.se(x_tensors)/self.y.size","def rmse(x, y):\n return mse(x, y) ** .5","def mse(real, predicted):\n # Calculate the mse\n N = len(real)\n mse = (1 / N) * np.sum((real - predicted) ** 2)\n return mse","def mse(response_vector, prediction_vector):\n return np.power(response_vector - prediction_vector, 2).mean()","def calc_mse(data, ax=0):\n return ((data[:, 0] - data[:, 1]) ** 2).mean(axis=ax)","def mse(a, b):\n a = numpy(a)\n b = numpy(b)\n return ((a - b) ** 2).sum()","def compute_rmse(y, tx, w):\n return np.sqrt(2 * compute_mse(y, tx, w))","def compute_mae(e):\n return np.mean(np.abs(e))","def MSE(a,b,axis):\n return ((a-b)**2).mean(axis=axis)","def compute_MAE(e):\n\n return np.mean(np.abs(e))","def mse (vec1, vec2):\n sum = 0.0 #Initializes sum to 0\n count = len(vec1) #Number of total elements in each vector\n for i in range(count):\n sum += (vec2[i]-vec1[i])**2 #Adds the square of the difference between the values at each position in the two vectors\n return sum/count","def calculate_mae(e):\n return np.mean(np.abs(e))","def mse(observed, predicted):\n return np.sqrt(np.mean((observed - predicted)**2))","def compute_mse(theta_0, theta_1, data):\n\n mse=0\n sum=0\n quad=0\n i=0\n\n while(i < len(data)):\n h = theta_0 + (theta_1*data[i][0]) \n aux = h - data[i][1]\n quad = aux**2\n sum = sum + quad\n i = i + 1\n\n mse = sum/len(data)\n\n return mse","def loss_mse(self, coeffs, x_values, y_values):\n return np.mean(pow(self.f(x_values, coeffs) - y_values, 2))","def rmse(self):\n return (self.model_error()**2).mean()**.5","def calculate_mse(in_img, out_img):\n error = 0\n for x in range(in_img.shape[0]):\n for y in range(in_img.shape[1]):\n error += (in_img[x, y] - out_img[x, y]) ** 2\n return error / (in_img.shape[0] * in_img.shape[1])","def mse(t, y, rmse=False):\n\tmse = mean_squared_error(t, y)\n\tif rmse:\n\t\tmse = mse ** 0.5\n\treturn mse","def compute_loss_mse(y, tx, w):\n e = y - tx.dot(w)\n\n return e.dot(e.T)/(2*len(e))","def mse(targets: List[float], preds: List[float]) -> float:\n return mean_squared_error(targets, preds)","def irmse(self) -> float:\n # Getting the gradient of the observed data\n obs_len = self.true.size\n obs_grad = self.true[1:obs_len] - self.true[0:obs_len - 1]\n\n # Standard deviation of the gradient\n obs_grad_std = np.std(obs_grad, ddof=1)\n\n # Divide RMSE by the standard deviation of the gradient of the observed data\n return float(self.rmse() / obs_grad_std)","def rmse(self):\n y_pred, y_true = self._finalize_labels_and_prediction()\n\n return np.sqrt(F.mse_loss(y_pred, y_true).cpu().item())","def rmse(self):\n y_pred, y_true = self._finalize_labels_and_prediction()\n\n return np.sqrt(F.mse_loss(y_pred, y_true).cpu().item())","def mse(A, B):\n return ((A - B) ** 2).mean(axis=0)","def mse(result, expected):\n total_square_sum = 0\n for index1 in range(0, len(result)):\n total_square_sum += (result[index1] - expected[index1]) ** 2\n return total_square_sum / float(len(result))","def MSE(X, y, w):\r\n n = X.shape[0]\r\n f = X @ w\r\n J = np.sum(np.power((y - f), 2)) / n\r\n return J","def _calc_Em(self):\n return (self.parameters.E0 +\n self.x * sqrt2 * self.parameters.sigma * self.mt)","def decomposed_mse(self) -> float:\n e_std = np.std(self.true)\n s_std = np.std(self.predicted)\n\n bias_squared = self.bias() ** 2\n sdsd = (e_std - s_std) ** 2\n lcs = 2 * e_std * s_std * (1 - self.corr_coeff())\n\n decomposed_mse = bias_squared + sdsd + lcs\n\n return float(decomposed_mse)","def mse(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n return np.mean(np.power(y_true - y_pred, 2))","def mse(gt, pred):\n return np.mean((gt - pred) ** 2)","def mse(gt, pred):\n return np.mean((gt - pred) ** 2)","def compute_mse_loss(y, tx, w):\n e = y - np.dot(tx, w)\n N = e.size\n return (1./(2*N)) * np.sum(np.square(e))","def rmse(self, X, y) -> float:\n return np.sqrt(np.sum(self.l2_error(X, y)))","def mse(datax,datay,w):\n return np.mean((datax.dot(w.T)-datay)**2)","def computeMse(data_target, Y):\n if data_target.shape != Y.shape:\n print \"the shapes does not correspond\",\n print data_target.shape,\n print Y.shape\n exit(-1)\n return np.sum(np.square(data_target - Y) / Y.shape[0])","def vsq_from_E(E):\n Ej=E*1.6021*10**-22\n m=1.674929*10**-27\n return (2.*Ej)/m","def mse(sim, obs, dim=\"time\"):\n # wrap numpy function\n kwargs = dict(\n input_core_dims=[[dim], [dim]], dask=\"parallelized\", output_dtypes=[float]\n )\n mse = xr.apply_ufunc(_mse, sim, obs, **kwargs)\n mse.name = \"mse\"\n return mse","def mse_loss(self, x, y):\n loss = tf.reduce_mean(tf.square(x - y))\n return loss","def mae(t, y):\n\treturn mean_absolute_error(t, y)","def nrmse(self) -> float:\n return float(self.rmse() / (np.max(self.true) - np.min(self.true)))","def MSE(self, theta):\n V = np.array((theta * self.mu_phi).sum(axis=1))\n return np.mean((V - self.mu_accum_r) ** 2)","def MeanSqError(self):\r\n\t\treturn self.mse","def mse_k(self, X, Y):\n\t\treturn np.power(Y - self.predict_soft(X), 2).sum(1).mean(0)","def mse(y, yhat):\n return 0.5 * jnp.mean((y - yhat)**2)","def rmse(self, weights=None) -> float:\n return sqrt(np.average((self.true - self.predicted) ** 2, axis=0, weights=weights))","def estimate_minee(self, X=None, Y=None):\n dXY, dX, dY = self.forward_minee(X, Y)\n return dXY - dX - dY","def seToSE( x ):\n x = asarray(x,dtype=float)\n if x.shape != (6,):\n raise ValueError(\"shape must be (6,); got %s\" % str(x.shape))\n #\n return expM(screw(x))","def _mse_loss(reference: tf.Tensor, estimate: tf.Tensor) -> tf.Tensor:\n return tf.reduce_mean(tf.square(reference - estimate), axis=-1)","def calcSE(self):\n # Make sure the Variance is already calculated\n if not hasattr(self,'ZmAreaVar'):\n self.calcZmAreaVar()\n # Standard Error = Square Root of Variance\n self.SE = self.ZmAreaVar ** 0.5\n # Should I be returning the value also?\n # Or just setting the attribute?\n return self.SE","def calcSE(self):\n # Make sure the Variance is already calculated\n if not hasattr(self,'ZmAreaVar'):\n self.calcZmAreaVar()\n # Standard Error = Square Root of Variance\n self.SE = self.ZmAreaVar ** 0.5\n # Should I be returning the value also?\n # Or just setting the attribute?\n return self.SE","def ev2ve(eV): \n return cv*np.sqrt( eV*(eV+2.e0*mec2))/(eV+mec2)","def calculate_E0(self) -> float:\n noisy = self.kernel_eigenvectors_[-1].copy()\n np.random.shuffle(noisy)\n\n kernel_eigenvectors = self.kernel_eigenvectors_[:-1]\n kernel_eigenvectors.append(noisy)\n\n eigenvectors_matrix = scipy.sparse.csr_matrix(\n np.column_stack([eigenvector for eigenvector in kernel_eigenvectors])\n )\n\n if len(kernel_eigenvectors) == 2:\n ev0 = kernel_eigenvectors[0]\n ev1 = kernel_eigenvectors[1]\n _, Gamma, _ = scipy.sparse.linalg.svds(\n ev0.T @ ev1, k=self.n_jointly_smooth_functions, which=\"LM\"\n )\n else:\n _, Gamma, _ = scipy.sparse.linalg.svds(\n eigenvectors_matrix, k=self.n_jointly_smooth_functions, which=\"LM\"\n )\n\n Gamma.sort()\n gamma2 = Gamma[-2]\n E0 = (1 + gamma2) / 2\n return E0","def mse(x1, x2, axis=0):\n x1 = np.asanyarray(x1)\n x2 = np.asanyarray(x2)\n return np.mean((x1 - x2) ** 2, axis=axis)","def sse(x, y):\n return sum(se(x, y))","def calculate_mse(img0, img1):\n mse = skm.mean_squared_error(img0, img1)\n return mse","def log_mse():\n trainer = {\n 'loss_weights': {\n 'si-sdr': 0.0,\n 'log-mse': 1.0,\n }\n }","def bse_re(self):\n p = self.model.exog.shape[1]\n return np.sqrt(self.scale * np.diag(self.cov_params())[p:])","def _get_mse(controller: PIDController, num_pts: float, dt: float) -> float:\n mse = 0\n for _ in range(num_pts):\n controller.step(dt)\n err = controller.get_error()\n mse += err**2\n mse /= num_pts\n return mse","def rmse(sim, obs, dim=\"time\"):\n rmse = np.sqrt(mse(sim, obs, dim=dim))\n rmse.name = \"rmse\"\n return rmse","def mse(self, y):\n return T.mean((self.y_pred - y) ** 2)","def _mse2(self, trace, **inputs):\n exp = np.dot(inputs['gwas_gen'],\n trace['beta_med'].mean(axis=0).T)\n phen_pred = exp * trace['alpha'].mean()\n mse = np.mean((inputs['gwas_phen'] - phen_pred) ** 2)\n return mse","def nmse(actual: np.ndarray, predicted: np.ndarray):\n return np.mean(np.square(actual - predicted)) / (np.mean(actual) * np.mean(predicted) + np.finfo(float).eps)","def error_MSE(resid):\n if resid.ndim == 2:\n return (norm(np.asarray(resid).ravel())**2)/float(resid.shape[1])\n elif resid.ndim == 1:\n return (norm(np.asarray(resid).ravel())**2)\n else:\n raise Exception(\"array passed to error_MSE has incorrect shape\")","def compute_loss_mse(y, tx, w):\n y = np.array([y]).T.reshape([len(y), 1])\n w = np.array([w]).T.reshape([len(w), 1])\n \n e = y - np.matmul(tx,w) \n \n return (1/(2 * y.shape[0])) * (e.T.dot(e))","def bse_fe(self):\n p = self.model.exog.shape[1]\n return np.sqrt(np.diag(self.cov_params())[0:p])","def E2V(E):\n# for energy in mev returns velocity in m/s\n return sqrt(E/5.227e-6)","def calc_sigma(evs):\n\n sigma = zeros(vecLen-1, dtype='d')\n sigma[0] = absolute(evs[0] - evs[1])\n\n for j in range(1, vecLen-1):\n term1 = absolute(evs[j] - evs[j-1])\n term2 = absolute(evs[j+1] - evs[j])\n sigma[j] = 0.5 * (term1 + term2)\n if sigma[j] is 0.0:\n print \"Degenerate mode at: \", j\n del j\n return sigma","def rmse(y_preds: ndarray, y_actual: ndarray) -> float:\n\n return np.sqrt(np.mean(np.power(y_preds - y_actual, 2)))","def mse(mat1, mat2):\n\tmse = 0\n\tw, h = mat1.shape\n\tif mat1.shape != mat2.shape:\n\t\treturn -1\n\tprint(\"inside mse\")\n\tprint(mat1)\n\tprint(mat2)\n\tfor i in range(w):\n\t\tfor j in range(h):\n\t\t\tmse += \tpow((int(mat1[i,j]) - int(mat2[i,j])), 2)\n\treturn mse/ (w*h)","def gmae(self) -> float:\n return _geometric_mean(np.abs(self._error()))","def vFrmE(E):\n Ej=E*1.6021*10**-22\n m=1.674929*10**-27\n v=np.sqrt((2.*Ej)/m)\n return(v)"],"string":"[\n \"def calculate_mse(e):\\n return 1/2*np.mean(e.dot(e))\",\n \"def compute_MSE(e):\\n\\n return 1/2*np.mean(e**2)\",\n \"def compute_RMSE(e):\\n \\\"\\\"\\\"Corresponds to sqrt(2*MSE)\\\"\\\"\\\"\\n \\n return np.sqrt(2*compute_MSE(e))\",\n \"def _mse(self):\\n error = self._input * self._weights - self._label\\n sum_ = 0.0\\n for i in range(self._input.shape[0]):\\n sum_ += error[i, 0]**2\\n return sum_/self._input.shape[0]\",\n \"def compute_mse(y, tx, w):\\n print(\\\"y.shape = \\\", y.shape)\\n print(\\\"tx.shape = \\\", tx.shape)\\n e = y - tx.dot(w)\\n print(\\\"e.shape = \\\", e.shape)\\n mse = e.T.dot(e) / (2 * len(e))\\n return mse\",\n \"def _calc_msve(self):\\n v = []\\n for state in self._env.state_iterator():\\n feature_vector = self._features.vector(state)\\n v.append(utils.state_value(feature_vector, self.theta))\\n\\n self.msve.append(utils.rmse(v, self._true_values))\",\n \"def compute_mse(y, tx, w):\\n e = y - tx.dot(w)\\n mse = e.dot(e) / (2 * len(e))\\n return mse\",\n \"def compute_mse(y, tx, w):\\n e = y - tx@w\\n mse = e.T.dot(e) /(2*len(e))\\n return mse\",\n \"def compute_mse(y, tx, w):\\n e = y-tx@w\\n return 1/(2*y.shape[0])*e.transpose()@e\",\n \"def d_mse(x, y):\\n\\n return 2 * (x - y) / x.size(0) / x.size(1)\",\n \"def mse(self, data, *args, **kwargs):\\n return self._mse(np.array(data), *args, **kwargs)\",\n \"def mse(self, X, Y):\\n\\t\\treturn mse_k(X, to_1_of_K(Y))\",\n \"def compute_mse(y, tx, w):\\n e = y[:, np.newaxis] - tx @ w\\n return (e * e).sum() / (2.0 * len(y))\",\n \"def mse(x, y):\\n\\n return (x - y).pow(2).sum(dim=1, keepdim=True).mean() / x.size(1)\",\n \"def evse(self, data, *args, **kwargs):\\n darr = np.array(data)\\n d = darr if len(darr.shape) == 1 else darr[0] / darr[1]\\n return (d - self.evs(darr, *args, **kwargs))**2\",\n \"def mse(actual,expected):\\n return np.mean(se(actual,expected))\",\n \"def rmse_from_mse (mse):\\n return np.sqrt(2*mse)\",\n \"def mse(o, r):\\r\\n\\r\\n return np.mean(np.square((np.abs(o).astype(float) - np.abs(r).astype(float))))\",\n \"def rmse(self):\\n lam = self.lam()\\n weights = lam / lam.sum()\\n weighted_var = self.var() * weights\\n rmse = np.sqrt(weighted_var.sum())\\n return rmse\",\n \"def mse ( target_array ):\\n return np.mean ( ( target_array - np.mean ( target_array ) ) ** 2 )\\n # End mse()\",\n \"def negative_mse ( target_array ):\\n return -1 * mse ( target_array )\\n # End negative_mse()\",\n \"def _mse(self, weights):\\n error = self._input * weights - self._label\\n sum_ = 0.0\\n for i in range(self._input.shape[0]):\\n sum_ += error[i, 0]**2\\n return sum_ / self._input.shape[0]\",\n \"def mse(predicted, actual):\\n diff = predicted - actual\\n return np.average(diff * diff, axis=0)\",\n \"def compute_rmse(y, tx, w):\\n return np.sqrt(2*compute_mse(y,tx,w))\",\n \"def rmse (lmse) :\\n return np.sqrt(2*lmse)\",\n \"def mse(X, Y, W):\\n\\n # TODO\\n mse = np.sum((X@W-Y)**2)/(2*X.shape[0])\\n # END TODO\\n\\n return mse\",\n \"def ivrmse(self):\\n return (self.model_error_iv()**2).mean()**.5\",\n \"def mse(self):\\n xs, ys = self.R.nonzero()\\n predicted = self.full_matrix()\\n error = 0\\n for x, y in zip(xs, ys):\\n # print(predicted[x, y], self.R[x, y] )\\n error += pow(self.R[x, y] - predicted[x, y], 2)\\n return np.sqrt(error)\",\n \"def MSE(actual, noisy):\\n mean_squared_error(actual, noisy)\",\n \"def mse(self, x_tensors=None):\\n\\n return self.se(x_tensors)/self.y.size\",\n \"def rmse(x, y):\\n return mse(x, y) ** .5\",\n \"def mse(real, predicted):\\n # Calculate the mse\\n N = len(real)\\n mse = (1 / N) * np.sum((real - predicted) ** 2)\\n return mse\",\n \"def mse(response_vector, prediction_vector):\\n return np.power(response_vector - prediction_vector, 2).mean()\",\n \"def calc_mse(data, ax=0):\\n return ((data[:, 0] - data[:, 1]) ** 2).mean(axis=ax)\",\n \"def mse(a, b):\\n a = numpy(a)\\n b = numpy(b)\\n return ((a - b) ** 2).sum()\",\n \"def compute_rmse(y, tx, w):\\n return np.sqrt(2 * compute_mse(y, tx, w))\",\n \"def compute_mae(e):\\n return np.mean(np.abs(e))\",\n \"def MSE(a,b,axis):\\n return ((a-b)**2).mean(axis=axis)\",\n \"def compute_MAE(e):\\n\\n return np.mean(np.abs(e))\",\n \"def mse (vec1, vec2):\\n sum = 0.0 #Initializes sum to 0\\n count = len(vec1) #Number of total elements in each vector\\n for i in range(count):\\n sum += (vec2[i]-vec1[i])**2 #Adds the square of the difference between the values at each position in the two vectors\\n return sum/count\",\n \"def calculate_mae(e):\\n return np.mean(np.abs(e))\",\n \"def mse(observed, predicted):\\n return np.sqrt(np.mean((observed - predicted)**2))\",\n \"def compute_mse(theta_0, theta_1, data):\\n\\n mse=0\\n sum=0\\n quad=0\\n i=0\\n\\n while(i < len(data)):\\n h = theta_0 + (theta_1*data[i][0]) \\n aux = h - data[i][1]\\n quad = aux**2\\n sum = sum + quad\\n i = i + 1\\n\\n mse = sum/len(data)\\n\\n return mse\",\n \"def loss_mse(self, coeffs, x_values, y_values):\\n return np.mean(pow(self.f(x_values, coeffs) - y_values, 2))\",\n \"def rmse(self):\\n return (self.model_error()**2).mean()**.5\",\n \"def calculate_mse(in_img, out_img):\\n error = 0\\n for x in range(in_img.shape[0]):\\n for y in range(in_img.shape[1]):\\n error += (in_img[x, y] - out_img[x, y]) ** 2\\n return error / (in_img.shape[0] * in_img.shape[1])\",\n \"def mse(t, y, rmse=False):\\n\\tmse = mean_squared_error(t, y)\\n\\tif rmse:\\n\\t\\tmse = mse ** 0.5\\n\\treturn mse\",\n \"def compute_loss_mse(y, tx, w):\\n e = y - tx.dot(w)\\n\\n return e.dot(e.T)/(2*len(e))\",\n \"def mse(targets: List[float], preds: List[float]) -> float:\\n return mean_squared_error(targets, preds)\",\n \"def irmse(self) -> float:\\n # Getting the gradient of the observed data\\n obs_len = self.true.size\\n obs_grad = self.true[1:obs_len] - self.true[0:obs_len - 1]\\n\\n # Standard deviation of the gradient\\n obs_grad_std = np.std(obs_grad, ddof=1)\\n\\n # Divide RMSE by the standard deviation of the gradient of the observed data\\n return float(self.rmse() / obs_grad_std)\",\n \"def rmse(self):\\n y_pred, y_true = self._finalize_labels_and_prediction()\\n\\n return np.sqrt(F.mse_loss(y_pred, y_true).cpu().item())\",\n \"def rmse(self):\\n y_pred, y_true = self._finalize_labels_and_prediction()\\n\\n return np.sqrt(F.mse_loss(y_pred, y_true).cpu().item())\",\n \"def mse(A, B):\\n return ((A - B) ** 2).mean(axis=0)\",\n \"def mse(result, expected):\\n total_square_sum = 0\\n for index1 in range(0, len(result)):\\n total_square_sum += (result[index1] - expected[index1]) ** 2\\n return total_square_sum / float(len(result))\",\n \"def MSE(X, y, w):\\r\\n n = X.shape[0]\\r\\n f = X @ w\\r\\n J = np.sum(np.power((y - f), 2)) / n\\r\\n return J\",\n \"def _calc_Em(self):\\n return (self.parameters.E0 +\\n self.x * sqrt2 * self.parameters.sigma * self.mt)\",\n \"def decomposed_mse(self) -> float:\\n e_std = np.std(self.true)\\n s_std = np.std(self.predicted)\\n\\n bias_squared = self.bias() ** 2\\n sdsd = (e_std - s_std) ** 2\\n lcs = 2 * e_std * s_std * (1 - self.corr_coeff())\\n\\n decomposed_mse = bias_squared + sdsd + lcs\\n\\n return float(decomposed_mse)\",\n \"def mse(y_true: np.ndarray, y_pred: np.ndarray) -> float:\\n return np.mean(np.power(y_true - y_pred, 2))\",\n \"def mse(gt, pred):\\n return np.mean((gt - pred) ** 2)\",\n \"def mse(gt, pred):\\n return np.mean((gt - pred) ** 2)\",\n \"def compute_mse_loss(y, tx, w):\\n e = y - np.dot(tx, w)\\n N = e.size\\n return (1./(2*N)) * np.sum(np.square(e))\",\n \"def rmse(self, X, y) -> float:\\n return np.sqrt(np.sum(self.l2_error(X, y)))\",\n \"def mse(datax,datay,w):\\n return np.mean((datax.dot(w.T)-datay)**2)\",\n \"def computeMse(data_target, Y):\\n if data_target.shape != Y.shape:\\n print \\\"the shapes does not correspond\\\",\\n print data_target.shape,\\n print Y.shape\\n exit(-1)\\n return np.sum(np.square(data_target - Y) / Y.shape[0])\",\n \"def vsq_from_E(E):\\n Ej=E*1.6021*10**-22\\n m=1.674929*10**-27\\n return (2.*Ej)/m\",\n \"def mse(sim, obs, dim=\\\"time\\\"):\\n # wrap numpy function\\n kwargs = dict(\\n input_core_dims=[[dim], [dim]], dask=\\\"parallelized\\\", output_dtypes=[float]\\n )\\n mse = xr.apply_ufunc(_mse, sim, obs, **kwargs)\\n mse.name = \\\"mse\\\"\\n return mse\",\n \"def mse_loss(self, x, y):\\n loss = tf.reduce_mean(tf.square(x - y))\\n return loss\",\n \"def mae(t, y):\\n\\treturn mean_absolute_error(t, y)\",\n \"def nrmse(self) -> float:\\n return float(self.rmse() / (np.max(self.true) - np.min(self.true)))\",\n \"def MSE(self, theta):\\n V = np.array((theta * self.mu_phi).sum(axis=1))\\n return np.mean((V - self.mu_accum_r) ** 2)\",\n \"def MeanSqError(self):\\r\\n\\t\\treturn self.mse\",\n \"def mse_k(self, X, Y):\\n\\t\\treturn np.power(Y - self.predict_soft(X), 2).sum(1).mean(0)\",\n \"def mse(y, yhat):\\n return 0.5 * jnp.mean((y - yhat)**2)\",\n \"def rmse(self, weights=None) -> float:\\n return sqrt(np.average((self.true - self.predicted) ** 2, axis=0, weights=weights))\",\n \"def estimate_minee(self, X=None, Y=None):\\n dXY, dX, dY = self.forward_minee(X, Y)\\n return dXY - dX - dY\",\n \"def seToSE( x ):\\n x = asarray(x,dtype=float)\\n if x.shape != (6,):\\n raise ValueError(\\\"shape must be (6,); got %s\\\" % str(x.shape))\\n #\\n return expM(screw(x))\",\n \"def _mse_loss(reference: tf.Tensor, estimate: tf.Tensor) -> tf.Tensor:\\n return tf.reduce_mean(tf.square(reference - estimate), axis=-1)\",\n \"def calcSE(self):\\n # Make sure the Variance is already calculated\\n if not hasattr(self,'ZmAreaVar'):\\n self.calcZmAreaVar()\\n # Standard Error = Square Root of Variance\\n self.SE = self.ZmAreaVar ** 0.5\\n # Should I be returning the value also?\\n # Or just setting the attribute?\\n return self.SE\",\n \"def calcSE(self):\\n # Make sure the Variance is already calculated\\n if not hasattr(self,'ZmAreaVar'):\\n self.calcZmAreaVar()\\n # Standard Error = Square Root of Variance\\n self.SE = self.ZmAreaVar ** 0.5\\n # Should I be returning the value also?\\n # Or just setting the attribute?\\n return self.SE\",\n \"def ev2ve(eV): \\n return cv*np.sqrt( eV*(eV+2.e0*mec2))/(eV+mec2)\",\n \"def calculate_E0(self) -> float:\\n noisy = self.kernel_eigenvectors_[-1].copy()\\n np.random.shuffle(noisy)\\n\\n kernel_eigenvectors = self.kernel_eigenvectors_[:-1]\\n kernel_eigenvectors.append(noisy)\\n\\n eigenvectors_matrix = scipy.sparse.csr_matrix(\\n np.column_stack([eigenvector for eigenvector in kernel_eigenvectors])\\n )\\n\\n if len(kernel_eigenvectors) == 2:\\n ev0 = kernel_eigenvectors[0]\\n ev1 = kernel_eigenvectors[1]\\n _, Gamma, _ = scipy.sparse.linalg.svds(\\n ev0.T @ ev1, k=self.n_jointly_smooth_functions, which=\\\"LM\\\"\\n )\\n else:\\n _, Gamma, _ = scipy.sparse.linalg.svds(\\n eigenvectors_matrix, k=self.n_jointly_smooth_functions, which=\\\"LM\\\"\\n )\\n\\n Gamma.sort()\\n gamma2 = Gamma[-2]\\n E0 = (1 + gamma2) / 2\\n return E0\",\n \"def mse(x1, x2, axis=0):\\n x1 = np.asanyarray(x1)\\n x2 = np.asanyarray(x2)\\n return np.mean((x1 - x2) ** 2, axis=axis)\",\n \"def sse(x, y):\\n return sum(se(x, y))\",\n \"def calculate_mse(img0, img1):\\n mse = skm.mean_squared_error(img0, img1)\\n return mse\",\n \"def log_mse():\\n trainer = {\\n 'loss_weights': {\\n 'si-sdr': 0.0,\\n 'log-mse': 1.0,\\n }\\n }\",\n \"def bse_re(self):\\n p = self.model.exog.shape[1]\\n return np.sqrt(self.scale * np.diag(self.cov_params())[p:])\",\n \"def _get_mse(controller: PIDController, num_pts: float, dt: float) -> float:\\n mse = 0\\n for _ in range(num_pts):\\n controller.step(dt)\\n err = controller.get_error()\\n mse += err**2\\n mse /= num_pts\\n return mse\",\n \"def rmse(sim, obs, dim=\\\"time\\\"):\\n rmse = np.sqrt(mse(sim, obs, dim=dim))\\n rmse.name = \\\"rmse\\\"\\n return rmse\",\n \"def mse(self, y):\\n return T.mean((self.y_pred - y) ** 2)\",\n \"def _mse2(self, trace, **inputs):\\n exp = np.dot(inputs['gwas_gen'],\\n trace['beta_med'].mean(axis=0).T)\\n phen_pred = exp * trace['alpha'].mean()\\n mse = np.mean((inputs['gwas_phen'] - phen_pred) ** 2)\\n return mse\",\n \"def nmse(actual: np.ndarray, predicted: np.ndarray):\\n return np.mean(np.square(actual - predicted)) / (np.mean(actual) * np.mean(predicted) + np.finfo(float).eps)\",\n \"def error_MSE(resid):\\n if resid.ndim == 2:\\n return (norm(np.asarray(resid).ravel())**2)/float(resid.shape[1])\\n elif resid.ndim == 1:\\n return (norm(np.asarray(resid).ravel())**2)\\n else:\\n raise Exception(\\\"array passed to error_MSE has incorrect shape\\\")\",\n \"def compute_loss_mse(y, tx, w):\\n y = np.array([y]).T.reshape([len(y), 1])\\n w = np.array([w]).T.reshape([len(w), 1])\\n \\n e = y - np.matmul(tx,w) \\n \\n return (1/(2 * y.shape[0])) * (e.T.dot(e))\",\n \"def bse_fe(self):\\n p = self.model.exog.shape[1]\\n return np.sqrt(np.diag(self.cov_params())[0:p])\",\n \"def E2V(E):\\n# for energy in mev returns velocity in m/s\\n return sqrt(E/5.227e-6)\",\n \"def calc_sigma(evs):\\n\\n sigma = zeros(vecLen-1, dtype='d')\\n sigma[0] = absolute(evs[0] - evs[1])\\n\\n for j in range(1, vecLen-1):\\n term1 = absolute(evs[j] - evs[j-1])\\n term2 = absolute(evs[j+1] - evs[j])\\n sigma[j] = 0.5 * (term1 + term2)\\n if sigma[j] is 0.0:\\n print \\\"Degenerate mode at: \\\", j\\n del j\\n return sigma\",\n \"def rmse(y_preds: ndarray, y_actual: ndarray) -> float:\\n\\n return np.sqrt(np.mean(np.power(y_preds - y_actual, 2)))\",\n \"def mse(mat1, mat2):\\n\\tmse = 0\\n\\tw, h = mat1.shape\\n\\tif mat1.shape != mat2.shape:\\n\\t\\treturn -1\\n\\tprint(\\\"inside mse\\\")\\n\\tprint(mat1)\\n\\tprint(mat2)\\n\\tfor i in range(w):\\n\\t\\tfor j in range(h):\\n\\t\\t\\tmse += \\tpow((int(mat1[i,j]) - int(mat2[i,j])), 2)\\n\\treturn mse/ (w*h)\",\n \"def gmae(self) -> float:\\n return _geometric_mean(np.abs(self._error()))\",\n \"def vFrmE(E):\\n Ej=E*1.6021*10**-22\\n m=1.674929*10**-27\\n v=np.sqrt((2.*Ej)/m)\\n return(v)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8627569","0.8492956","0.7717357","0.7474573","0.73560566","0.73297226","0.725569","0.72040147","0.7180668","0.716424","0.715627","0.7137266","0.71137995","0.71091557","0.71063846","0.70705134","0.70670843","0.70613927","0.70559907","0.7045481","0.70399845","0.7000987","0.6988783","0.6975244","0.6938725","0.68984145","0.689767","0.6895254","0.68909615","0.6883307","0.6872525","0.6860589","0.6857577","0.6853891","0.684799","0.6839126","0.68386453","0.6806303","0.6764161","0.67631465","0.67603576","0.67489725","0.66673","0.6655289","0.6652651","0.66451156","0.6627243","0.6625776","0.66046154","0.6594294","0.6593656","0.6593656","0.6593254","0.6544808","0.6541368","0.6533959","0.64920115","0.64834595","0.6483152","0.6483152","0.64789593","0.6470935","0.64579105","0.64510936","0.64457107","0.6384288","0.638278","0.63792276","0.6379205","0.63632715","0.63567406","0.63343436","0.6329064","0.63182193","0.62957215","0.62923765","0.6291367","0.6276919","0.6276919","0.62745076","0.62701714","0.62592614","0.62575746","0.62535423","0.6246377","0.62407327","0.62340504","0.62231916","0.6222999","0.6220677","0.6216933","0.62096494","0.6198518","0.61869156","0.61853427","0.6178273","0.6172159","0.61686605","0.61603504","0.6156893"],"string":"[\n \"0.8627569\",\n \"0.8492956\",\n \"0.7717357\",\n \"0.7474573\",\n \"0.73560566\",\n \"0.73297226\",\n \"0.725569\",\n \"0.72040147\",\n \"0.7180668\",\n \"0.716424\",\n \"0.715627\",\n \"0.7137266\",\n \"0.71137995\",\n \"0.71091557\",\n \"0.71063846\",\n \"0.70705134\",\n \"0.70670843\",\n \"0.70613927\",\n \"0.70559907\",\n \"0.7045481\",\n \"0.70399845\",\n \"0.7000987\",\n \"0.6988783\",\n \"0.6975244\",\n \"0.6938725\",\n \"0.68984145\",\n \"0.689767\",\n \"0.6895254\",\n \"0.68909615\",\n \"0.6883307\",\n \"0.6872525\",\n \"0.6860589\",\n \"0.6857577\",\n \"0.6853891\",\n \"0.684799\",\n \"0.6839126\",\n \"0.68386453\",\n \"0.6806303\",\n \"0.6764161\",\n \"0.67631465\",\n \"0.67603576\",\n \"0.67489725\",\n \"0.66673\",\n \"0.6655289\",\n \"0.6652651\",\n \"0.66451156\",\n \"0.6627243\",\n \"0.6625776\",\n \"0.66046154\",\n \"0.6594294\",\n \"0.6593656\",\n \"0.6593656\",\n \"0.6593254\",\n \"0.6544808\",\n \"0.6541368\",\n \"0.6533959\",\n \"0.64920115\",\n \"0.64834595\",\n \"0.6483152\",\n \"0.6483152\",\n \"0.64789593\",\n \"0.6470935\",\n \"0.64579105\",\n \"0.64510936\",\n \"0.64457107\",\n \"0.6384288\",\n \"0.638278\",\n \"0.63792276\",\n \"0.6379205\",\n \"0.63632715\",\n \"0.63567406\",\n \"0.63343436\",\n \"0.6329064\",\n \"0.63182193\",\n \"0.62957215\",\n \"0.62923765\",\n \"0.6291367\",\n \"0.6276919\",\n \"0.6276919\",\n \"0.62745076\",\n \"0.62701714\",\n \"0.62592614\",\n \"0.62575746\",\n \"0.62535423\",\n \"0.6246377\",\n \"0.62407327\",\n \"0.62340504\",\n \"0.62231916\",\n \"0.6222999\",\n \"0.6220677\",\n \"0.6216933\",\n \"0.62096494\",\n \"0.6198518\",\n \"0.61869156\",\n \"0.61853427\",\n \"0.6178273\",\n \"0.6172159\",\n \"0.61686605\",\n \"0.61603504\",\n \"0.6156893\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.86390364"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":86697,"cells":{"query":{"kind":"string","value":"Initialise UI Data Manager and Response Handler"},"document":{"kind":"string","value":"def __init__(self, iface, configManager):\n\n QObject.__init__(self)\n self.iface = iface\n self._queues = None\n self._dockWindow = None\n self._currentMapTool = None\n self.rclParent = None\n self.currentRevItem = None\n self.actions = []\n if Controller._instance == None:\n Controller._instance = self\n self.uidm = UiDataManager(self.iface, self)\n self.RespHandler = ResponseHandler(self.iface, self.uidm)\n self._config_ui = configManager\n\n self.refLayer = None\n self.adrlayer = None\n self.revLayer = None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self):\n self.websock_handlers = {}\n self.ajax_handlers = {'__dashboard__': self.get_dashboard_ui}\n self.dashboard_handlers = {}","def init_ui(self):\n raise NotImplementedError","def init_ui(self):\n raise NotImplementedError","def prepare_UI(self):","def _initUI(self) -> None:\n self._createActions()\n self._addActionsToMoveButtons()\n self._createToolBar()\n self._createStatusBar()\n self._createMainContextMenu()","def init_ui(self):\n raise NotImplementedError(\"This is an abstract method.\")","def _init_ui(self):\r\n\t\t\r\n\t\tself.input_frame = Input(self)\r\n\t\tself.input_frame.pack()\r\n\t\t\r\n\t\tbutton_ok = Button(self, text = \"Ping\", command = self._go)\r\n\t\tbutton_ok.pack()\r\n\t\t\r\n\t\tself.result_frame = Result(self)\r\n\t\tself.result_frame.pack()","def __init__(self):\n\n # TODO: Add login and data grab logic","def InitUI(self):\n\t\tself._initMenuBar()\n\t\tself._initLayout()\n\t\t\n\t\t# Bindings\n\t\tself.Bind(wx.EVT_BUTTON, self.OnButtonClicked)\n\t\t\n\t\t# We can't even start without an input file\n\t\tself.OnOpen(None)","def __init__(self):\n # setup the database session\n engine = create_engine('sqlite:///%s'%os.path.join(APP_DIR,config.get('Inkcut','database_dir'),config.get('Inkcut','database_name')))\n\n Session.configure(bind=engine)\n self.session = Session()\n\n self.job = None\n self.ui = {\n 'main_window':MainWindow(self),\n 'device_dialog':DeviceDialog(self),\n }\n self.statusbar = self.ui['main_window'].widgets['statusbar']","def initUI(self):\n # Setting the main layout as Vertical.\n self.mainLayout = QHBoxLayout()\n\n # Create title.\n self.title = QLabel(self.__name + \" : \")\n\n # Add description as tooltip.\n self.title.setToolTip(self.__description)\n\n # Add title to main layout.\n self.mainLayout.addWidget(self.title)\n\n # Create ComboBox.\n self.dropDown = QComboBox()\n\n # Add datas to drop down.\n self.dropDown.addItems(self.__datas)\n\n # Set default index to dropdown.\n self.dropDown.setCurrentIndex(self.__currentValue)\n\n # Connect dropdown with update method.\n self.dropDown.currentIndexChanged.connect(self.changeCurrentValue)\n\n # Add ComboBox to main layout.\n self.mainLayout.addWidget(self.dropDown)\n\n # Add the main layout to the window.\n self.setLayout(self.mainLayout)","def init_UI(self):\n\n self.master.title(\"Search for different companies\")\n self.master.geometry(\"400x400\")\n\n self.label_combobox = Label(self, text=\"Search by\")\n self.label_combobox.pack()\n\n self.combo_searching_options = Combobox(self, state=\"readonly\")\n self.combo_searching_options['values'] = self.combobox_values\n self.combo_searching_options.pack()\n\n self.label_input = Label(self, text=\"Entry the value\")\n self.label_input.pack()\n\n self.user_input = Entry(self, width=40)\n self.user_input.pack()\n\n self.btn_submit = Button(self, text=\"Submit\", command=self.submit)\n self.btn_submit.pack()\n\n self.text_area = scrolledtext.ScrolledText(self)\n self.text_area.pack()\n\n sys.stdout = RedirectOutputText(self.text_area)\n\n self.btn_back = Button(self, text=\"Back\", command=self.go_back)\n self.btn_back.pack()","def __init__(self):\r\n super().__init__()\r\n self.init_ui()","def __init__(self, **kwargs):\r\n\r\n self.def_ms = modulestore()\r\n self.is_using_mongo = True\r\n if isinstance(self.def_ms, XMLModuleStore):\r\n self.is_using_mongo = False\r\n self.msg = u''\r\n self.datatable = []\r\n super(SysadminDashboardView, self).__init__(**kwargs)","def initUI(self):\n self.logger.debug('Setting up the Measurement GUI')\n self.setWindowTitle(self.title)\n\n self.show()\n\n self.make_combobox_scanner()\n self.make_combobox_movements()\n self.make_combobox_configurate()\n self.make_combobox_basic()","def do_init(self):\n\n pass","async def init(self):\n self.base_tamplates = {}\n self.preparing_task = None\n self.app = aioweb.Application()\n self.runner = aioweb.AppRunner(self.app)","def initialization(self):\n super().initialization()\n self.register_uniq_ids([self.id_modal, self.id_modal_close, self.id_wip_button])\n\n # Register modules\n self.modules = [self.mod_table, self.mod_cache, self.mod_upload]","def initUI(self):\n centralwidget = QtWidgets.QWidget()\n self.setCentralWidget(centralwidget)\n\n person_table = PersonTable(self)\n person_table.embed(self.sql)\n\n # Box Layout to organize our GUI\n lay = QtWidgets.QVBoxLayout(centralwidget)\n lay.addWidget(person_table)\n self.setGeometry(0, 0, person_table.width() + 20, person_table.height() + 20)\n self.person_table = person_table\n self.show()","def init_context_data(self):\n pass","async def _init(self, **kwargs):","def setUp(self):\n self.ui = UI()","def initialize_core(self):\n # set current session unique id\n self.conf.set(\"accesspoint\", \"current_session\", self.currentSessionID)\n # set interface for shared connection from params\n self.conf.set(\"accesspoint\", \"interface_net\", self.parse_args.interface_net)\n \n if self.parse_args.interface:\n self.conf.set(\"accesspoint\", \"interface\", self.parse_args.interface)\n\n self.all_modules = module_list\n\n # intialize the LoggerManager\n # TODO: this change solve IndexError: list index out of range\n # but not a definitive solution\n self.logger_manager = LoggerManager(self)\n self.coreui = DefaultController(self)\n\n # print(self.coreui.Plugins)\n self.proxy_controller = self.coreui.getController(\"proxy_controller\")\n self.mitm_controller = self.coreui.getController(\"mitm_controller\")\n self.wireless_controller = self.coreui.getController(\"wireless_controller\")\n self.dhcp_controller = self.coreui.getController(\"dhcp_controller\")\n self.dns_controller = self.coreui.getController(\"dns_controller\")\n self.uiwid_controller = self.coreui.getController(\"ui_controller\")\n\n self.parser_list_func = {\n # parser_set_proxy is default extend class\n \"parser_set_proxy\": self.proxy_controller.pumpkinproxy,\n \"parser_set_plugin\": self.mitm_controller.sniffkin3,\n \"parser_set_mode\": self.wireless_controller.Settings,\n \"parser_set_security\": self.wireless_controller.Settings,\n \"parser_set_hostapd_config\": self.wireless_controller.Settings,\n \"parser_set_dhcpconf\": self.wireless_controller.Settings,\n \"parser_set_dhcpmode\": self.dns_controller.Active,\n }\n self.parser_autcomplete_func = {}\n\n # hook function (plugins and proxies)\n self.intialize_hook_func(self.proxy_controller)\n self.intialize_hook_func(self.mitm_controller)\n\n # register autocomplete set security command\n self.parser_autcomplete_func[\n \"parser_set_security\"\n ] = self.wireless_controller.Settings.getCommandsSecurity\n self.parser_autcomplete_func[\n \"parser_set_hostapd_config\"\n ] = self.wireless_controller.Settings.getCommandsHostapd\n self.parser_autcomplete_func[\n \"parser_set_dhcpconf\"\n ] = self.wireless_controller.Settings.getCommandsDhcpConf\n self.parser_autcomplete_func[\n \"parser_set_dhcpmode\"\n ] = self.dns_controller.getCommandsDhcpMode\n\n self.commands = {\n \"interface\": \"interface\",\n \"interface_net\": \"interface_net\",\n \"ssid\": \"ssid\",\n \"bssid\": \"bssid\",\n \"channel\": \"channel\",\n \"proxy\": None, # only for settings proxy\n \"plugin\": None, # only for settings plugin\n \"mode\": None, # only for settings mdoe\n \"dhcpconf\": None, # only for settings dhcpconf\n \"dhcpmode\": None, # only for settings dhcpmode\n \"security\": \"enable_security\",\n \"hostapd_config\": \"enable_hostapd_config\",\n }\n\n # get all command plugins and proxies\n for ctr_name, ctr_instance in self.coreui.getController(None).items():\n if hasattr(ctr_instance, \"getInfo\"):\n for plugin_name, plugins_info in ctr_instance.getInfo().items():\n self.commands[plugin_name] = \"\"\n\n self.threads = {\"RogueAP\": [], \"Modules\": {}}","def init(self, **kwargs):\n self._d = {}\n self._th = None\n self._run = True\n self.load()","def __init__(self):\n self._create_options()\n self._create_sections()","def setup(cls):\n super().setup()\n cls.http_handler = cast(\n HttpHandler, cls._skill.skill_context.handlers.http_handler\n )\n cls.logger = cls._skill.skill_context.logger\n\n cls.http_dialogues = cast(\n HttpDialogues, cls._skill.skill_context.http_dialogues\n )\n\n cls.get_method = \"get\"\n cls.post_method = \"post\"\n cls.url = \"some_url\"\n cls.version = \"some_version\"\n cls.headers = \"some_headers\"\n cls.body = b\"some_body\"\n cls.sender = \"fetchai/some_skill:0.1.0\"\n cls.skill_id = str(cls._skill.skill_context.skill_id)\n\n cls.status_code = 100\n cls.status_text = \"some_status_text\"\n\n cls.content = b\"some_content\"\n cls.list_of_messages = (\n DialogueMessage(\n HttpMessage.Performative.REQUEST,\n {\n \"method\": cls.get_method,\n \"url\": cls.url,\n \"version\": cls.version,\n \"headers\": cls.headers,\n \"body\": cls.body,\n },\n ),\n )","def initCentralUic(self):\n self.initFileTableWidget()\n self.initViewerStack()\n self.splitter.setSizes([150, 850])","def __init__(self, menu_manager):\n\n\t\tself.menu_manager = menu_manager\n\t\tself.uiCoordinator = menu_manager.uiCoordinator\n\t\tself.highscore = menu_manager.user.highscore\n\t\tself.name = menu_manager.user.username\n\t\tself.high_scores = menu_manager.con.getHighscores(\"DESC\")\n\t\tself.level = menu_manager.level\n\t\tself.open_window = False\n\n\t\tself.setupWindow()\n\t\tself.setupInfo()\n\t\tself.setupBinds()","def init(self, userdata, conn):\r\n pass","def initialize(self, request, response):\n super(FSMHandler, self).initialize(request, response)\n # pylint: disable-msg=W0201\n # - this is the preferred location to initialize the handler in the webapp framework\n self.fsm = None","def initialise(self):\n self.set_up()","def load_ui(self):\n\n load_data_err = self.load_all_services()\n if load_data_err:\n self.dlg.uLabelWarning.setText(load_data_err)\n self.dlg.uLabelWarning.show()\n self.update_cache = False\n else:\n self.dlg.uLabelWarning.hide()","def do_startup(self):\n \n import json\n\n GLib.set_application_name(\"Deity\")\n Gtk.Application.do_startup(self)\n \n settings = self.get_settings()\n\n menub = Gtk.MenuButton(name=\"input-menu_button\",\n use_popover=True)\n\n headerbar = Gtk.HeaderBar(name=\"input-headerbar\",\n show_close_button=True,\n title=\"Deity\")\n\n main_grid = Gtk.Grid(name=\"input-main_grid\")\n\n statusbar = Gtk.Box(name=\"input-statusbar\",\n orientation=0,\n spacing=2)\n statusbar.pack_start(self.statuslabel, 1, 1, 1)\n\n self.connector.connect(\"query-status\", self.show_output)\n self.connector.connect(\"query-waiting\",\n lambda wid, count: self.statuslabel.set_text(\n f\"Queries on hold : {count}\"))\n self.connector.connect(\"request\", print)\n\n headerbar.pack_end(menub)\n\n main_grid.attach(self.iogrid.get_widget(), 0, 0, 1, 1)\n main_grid.attach(statusbar, 0, 1, 1, 1)\n\n self.output_window.add(self.get_placeholder_image())\n\n self.window.set_titlebar(headerbar)\n self.window.set_default_icon_from_file(\"artwork/Logo.png\")\n self.window.add(main_grid)\n\n self.window.connect(\"key-press-event\", self.parse_keypress)\n self.window.connect(\"delete-event\", self.request_quit)\n \n self.other[\"connector\"] = self.connector\n self.other[\"headerbar\"] = headerbar\n self.other[\"history\"] = self.history\n self.other[\"input-window\"] = self.window\n self.other[\"iogrid\"] = self.iogrid\n self.other[\"plugins\"] = self.get_plugins(settings[\"enabled-plugins\"])\n self.other[\"statusbar\"] = statusbar\n self.other[\"statuslabel\"] = self.statuslabel\n self.other[\"output-notebook\"] = self.notebook\n self.other[\"output-window\"] = self.output_window\n self.other[\"main-grid\"] = main_grid\n self.other[\"menu_button\"] = menub\n \n self.apply_settings(settings)\n self.current_prompt = self.iogrid.add_prompt()\n\n self.window.set_application(self)\n self.output_window.set_application(self)\n\n self.output_window.move(800, 150)\n self.window.move(75, 160)","def __init__(self):\n\n super().__init__()\n\n scripts_handler = ScriptsTemplateHandler()\n application_handler = ApplicationTemplateHandler()\n scripts_handler.set_next(application_handler)\n\n self._handlers = DTO(project=[scripts_handler, application_handler])","def setup_response_collector(self):\n pass","def onStart(self):\n self.register_form(JobManager.id, JobManager,\n title=\"JobManager\", shortcut=\"^D\", name=\"JobManager\")\n self.register_form(SystemOverviewForm.id, SystemOverviewForm,\n title=\"System Overview\", shortcut=\"^O\", name=\"System Overview\")\n self.register_form(StateManagerForm.id, StateManagerForm,\n title=\"State Manager\", shortcut=\"^T\", name=\"State Manager\")\n self.register_form(ModuleRunnerForm.id, ModuleRunnerForm,\n title=\"Module Runner\", shortcut=\"^T\", name=\"Module Runner\")\n self.init_forms()","def init_ui(self):\n self.master.title(\"Backbone\")\n self.master.geometry(\"300x150\")\n\n self.pack(fill=BOTH, expand=1)\n\n self.btn_upload_file = Button(self, text=\"Upload file\", command=self.upload_file)\n self.btn_upload_file.place(x=90, y=10)\n\n self.btn_create_training_file = Button(self, text=\"Create & upload training file\",\n command=self.create_training_file)\n self.btn_create_training_file.place(x=30, y=40)\n\n self.btn_run_algorithm = Button(self, text=\"Run algorithm\", command=self.run_algorithm)\n self.btn_run_algorithm.place(x=80, y=70)\n\n self.btn_view_results = Button(self, text=\"View Results\", command=self.view_results)\n self.btn_view_results.place(x=85, y=100)","def __init__(self):\n self.model = Model()\n self.view = View()\n\n self.server = Server(msg_handler=self.msg_handler,\n err_handler=self.err_handler,\n conn_handler=self.conn_handler,\n quit_handler=self.quit_handler)\n self.server.start()\n\n self.view.frame.onclose(self.server.close)","def __init__(self):\n self.window = Tk() # The main window\n self.__initialize_variables__() # Initialize the variables\n self.__initialize_menu__() # Initialize the Menu\n self.__initialize_status_bar__()\n self.__initialize_gui__() # Initialize the GUI widgets","def initializeUI(self):\n self.setStyleSheet(abstyle)\n self.setGeometry(140, 100, 860, 484)\n self.setWindowTitle('Emotions Data View')\n self.setupModelView()","async def init(self) -> None:","async def init(self) -> None:","def setup_method(self):\n MANAGER._tables = {}\n MANAGER._views = {}","def setup_ui(self):\n #super(Dialog, self).__init__()\n self.createFormGroupBox()\n\n buttonBox = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\n buttonBox.accepted.connect(self.check_input)\n buttonBox.rejected.connect(self.reject)\n\n mainLayout = QVBoxLayout()\n mainLayout.addWidget(self.formGroupBox)\n mainLayout.addWidget(buttonBox)\n self.setLayout(mainLayout)\n\n self.make_connections()\n\n self.setWindowTitle(\"Load Data ...\")","def setDataRequestHandler(self, handler):\n self.dataRequestHandler = handler","def setup(self):\n print(\"Inside setup\")\n self.fetch_from_db()\n # Don't assign to the function output. Assing it to the function object so that whenever we\n # do some changes to layout, it will reflect without server restarting\n app.layout = self.get_root_layout\n return app.server","def _setup_ui(self):\n\n self.window = ui.Widget()\n self.window.dimensions = ui.normalize_dimension((\n 0, 0,\n self.normalized_screen_resolution[0],\n self.normalized_screen_resolution[1]\n ))\n self.window.background_color = ImageColor.getcolor('#000000', 'RGB')\n\n interface_frame = ui.Widget(parent=self.window)\n interface_frame.dimensions = ui.normalize_dimension((\n self.preview_renderer.window[2],\n 0,\n self.normalized_screen_resolution[0] - self.preview_renderer.window[2],\n self.normalized_screen_resolution[1]\n ))\n interface_frame.background_color = ImageColor.getcolor('#ffffff', 'RGB')\n\n number = ui.LabelWidget(\"\",\n name=NAME_GET_STARTED,\n parent=interface_frame,\n align=\"center\",\n font_color=(0, 0, 0, 255))\n number.dimensions = (\n 5, 5,\n interface_frame.width - 10,\n interface_frame.height - 10\n )","def initService(self):","def __init__(self, root, handle_after_logout, food_service):\n self._root = root\n self._handle_after_logout = handle_after_logout\n self.food_service = food_service\n\n self._ingredient = None\n self._date = None\n\n self._frame = None\n self._ingredients_frame = None\n self._ingredients_view = None\n self._message_var = None\n\n super().__init__(self._frame)\n\n self._initialize()","def __init__(self):\n\t\tself.satchmo_variation_manager = adminviews.variation_manager\n\t\tadminviews.variation_list = self.variation_list\n\t\tadminviews.variation_manager = self.variation_manager\n\t\t# listeners.add_toolbar_context = self.add_my_toolbar_context#(listeners.add_toolbar_context)","def setUp(self):\n self.response = {\n \"id\": \"753859569859690509\",\n \"last_message_id\": \"754623102561812511\",\n \"type\": 0,\n \"name\": \"general\",\n \"position\": 4,\n \"parent_id\": \"753859569859690506\",\n \"topic\": None,\n \"guild_id\": \"753859568764977194\",\n \"permission_overwrites\": [],\n \"nsfw\": False,\n \"rate_limit_per_user\": 0,\n }\n self.app = {}","def onInit(self):\n pass","def init(self):\n self.dispatcher.start()\n self.replyer.start()","def preloop(self):\n\n self.manager = FacilityManager()","def init_ui(self):\n self.panel_sizer = wx.BoxSizer(wx.VERTICAL)\n self.figure_bmp = wx.StaticBitmap(self, wx.ID_ANY,\n bitmap=self.controller.empty_bitmap(self.bitmap_width,\n self.bitmap_height),\n pos=wx.DefaultPosition, size=wx.DefaultSize)\n self.panel_sizer.Add(self.figure_bmp, ui_defaults.ctrl_pct, wx.CENTER,\n ui_defaults.widget_margin)\n self.SetSizerAndFit(self.panel_sizer)","def ui_setup(self):\n loader = QUiLoader()\n file = QFile('./user_interface/form/main_window.ui')\n file.open(QFile.ReadOnly)\n self._window = loader.load(file)\n file.close()\n\n status_bar = QStatusBar(self._window)\n status_bar.showMessage(__copyright__)\n self._window.setStatusBar(status_bar)\n self._window.setWindowIcon(QIcon('./user_interface/media/bucketing_icon.jpeg'))\n self._window.setWindowTitle('PySide2 Project - Basic UI Framework')\n\n self._option_panel = OptionPanel()\n self._option_panel.add_button('DekBan', './user_interface/media/dekban.png')\n self._option_panel.add_button('Charlie', './user_interface/media/charlie.jpeg')\n self._option_panel.add_button('Simon', './user_interface/media/Simon.jpeg')\n\n # Add widget to main layout\n main_layout = self._window.main_layout\n main_layout.itemAtPosition(0, 0).setAlignment(QtCore.Qt.AlignCenter)\n main_layout.itemAtPosition(0, 1).setAlignment(QtCore.Qt.AlignVCenter)\n main_layout.addWidget(self._option_panel, 2, 0, 1, 1)\n\n # Add page widget to stack\n self._pages['item'] = ItemWidget()\n self._pages['text1'] = TextPage(text=PAUSE_TEXT)\n self._pages['text2'] = TextPage(text=STOP_TEXT)\n\n for index, name in enumerate(self._pages):\n print('pages {} : {} page'.format(index, name))\n self._window.widget_stack.addWidget(self._pages[name].widget)\n\n self._window.widget_stack.setCurrentIndex(0)\n\n # Build up signal / slot\n self._option_panel.currentItemChanged.connect(self.set_page)","def init_UI(self):\n # widgets\n self.modeComboBox = QtWidgets.QComboBox()\n self.updateBtn = QtWidgets.QPushButton('Force Update')\n self.pauseBtn = QtWidgets.QPushButton()\n self.pauseBtn.setCheckable(True)\n\n self.clearBtn = QtWidgets.QPushButton()\n self.clearBtn.setIcon(QtGui.QIcon(IconPaths.ICON_CLEAR_FILES))\n self.filterBtn = QtWidgets.QToolButton()\n self.filterBtn.setIcon(QtGui.QIcon(IconPaths.ICON_FILTER))\n self.filterBtn.setMinimumWidth(35)\n self.filterBtn.setStyleSheet('QToolButton::menu-indicator {subcontrol-position: center right; height: 7px}')\n self.filterBtn.setPopupMode(QtWidgets.QToolButton.InstantPopup)\n self.prefBtn = QtWidgets.QToolButton()\n self.prefBtn.setIcon(QtGui.QIcon(IconPaths.ICON_SETTINGS))\n #self.pBar = QtWidgets.QProgressBar()\n self.itemView = LocalizeView()\n self.itemView.setModel(self.proxy_model)\n self.autoScrollCB = QtWidgets.QCheckBox('Auto scroll to localizing files')\n self.autoScrollCB.setChecked(True)\n\n # tweak sizes so the widgets all line up vertically with Nuke's style\n self.modeComboBox.setMinimumHeight(self.updateBtn.sizeHint().height())\n self.pauseBtn.setMaximumSize(self.updateBtn.sizeHint())\n self.clearBtn.setMaximumSize(self.updateBtn.sizeHint())\n\n # mode menu\n self.modeLabel = QtWidgets.QLabel('Mode')\n self.modeComboBox.addItems(['On', 'Manual', 'Off'])\n\n # update menu\n self.updateMenu = QtWidgets.QMenu()\n self.act_forceUpdateAll = QtWidgets.QAction('All', self)\n self.act_forceUpdateSelectedNodes = QtWidgets.QAction('Selected', self)\n self.act_forceUpdateOnDemand = QtWidgets.QAction('On demand only', self) \n self.updateMenu.addAction(self.act_forceUpdateAll)\n self.updateMenu.addAction(self.act_forceUpdateSelectedNodes)\n self.updateMenu.addAction(self.act_forceUpdateOnDemand)\n self.updateBtn.setMenu(self.updateMenu)\n\n # clear menu\n self.clearMenu = QtWidgets.QMenu()\n self.clearMenu.addAction(QtWidgets.QAction('All local files', self, triggered=self.__delete_all_local_files))\n self.clearMenu.addAction(QtWidgets.QAction('Unused local files', self, triggered=_open_delete_dialog))\n self.clearBtn.setMenu(self.clearMenu)\n\n # filter menu\n self.filterMenu = QtWidgets.QMenu(self.filterBtn)\n self.act_filter_all = QtWidgets.QAction('All', self.filterMenu, checkable=True)\n self.act_filter_in_progress = QtWidgets.QAction('In Progress', self.filterMenu, checkable=True)\n self.act_filter_up_to_date = QtWidgets.QAction('Up to date', self.filterMenu, checkable=True)\n self.act_filter_out_of_date = QtWidgets.QAction('Out of date', self.filterMenu, checkable=True)\n self.act_filter_from_source = QtWidgets.QAction('Reading from source', self.filterMenu, checkable=True)\n self.act_filter_disabled = QtWidgets.QAction('Disabled', self.filterMenu, checkable=True)\n self.act_filter_not_localized = QtWidgets.QAction('Not Localized', self.filterMenu, checkable=True)\n\n self.act_filter_in_progress.setData(Status.IN_PROGRESS)\n self.act_filter_up_to_date.setData(Status.UP_TO_DATE)\n self.act_filter_out_of_date.setData(Status.OUT_OF_DATE)\n self.act_filter_from_source.setData(Status.READ_FROM_SOURCE)\n self.act_filter_disabled.setData(Status.DISABLED)\n self.act_filter_not_localized.setData(Status.NOT_LOCALIZED)\n for act in (self.act_filter_all, self.act_filter_in_progress, self.act_filter_up_to_date, self.act_filter_out_of_date,\n self.act_filter_from_source, self.act_filter_disabled, self.act_filter_not_localized):\n self.filterMenu.addAction(act)\n self.filterBtn.setMenu(self.filterMenu)\n\n # tooltips\n self.modeComboBox.setToolTip('Sets the global localization mode.\\nThis is the same as using the options in the Cache/Localization/Mode menu.')\n self.updateBtn.setToolTip('Forces the update of localized files.\\nThis is the same as using the options in the Cache/Localization/Force Update menu.')\n self.pauseBtn.setToolTip('Pauses/Resumes file localization.\\nThis is the same as Cache/Localization/Pause.')\n self.clearBtn.setToolTip('''Allows for clearing localized files.\\nTwo modes are supported:\n \"All local files\" - this will delete all files in {}\n \"Unused local files\" - this will only delete unused local files (same as Cache/Localization/Clear Unused Local Files)'''.format(nuke.toNode('preferences')['localCachePath'].evaluate()))\n self.filterBtn.setToolTip('Sets a view filter the table.')\n self.prefBtn.setToolTip('Open the preferences.')\n\n # layouts\n layout = QtWidgets.QVBoxLayout()\n btnLayout = QtWidgets.QHBoxLayout()\n btnLayout.addWidget(self.modeLabel)\n btnLayout.addWidget(self.modeComboBox)\n btnLayout.addWidget(self.updateBtn)\n btnLayout.addWidget(self.pauseBtn)\n btnLayout.addWidget(self.clearBtn)\n btnLayout.addStretch()\n btnLayout.addWidget(self.filterBtn)\n btnLayout.addWidget(self.prefBtn)\n layout.addLayout(btnLayout)\n #layout.addWidget(self.pBar)\n layout.addWidget(self.itemView)\n layout.addWidget(self.autoScrollCB)\n layout.setAlignment(self.autoScrollCB, QtCore.Qt.AlignRight)\n self.setLayout(layout)","def start(self):\n self.ioloop.add_callback(self.get_data)\n logging.info(\"[DataSource] Started\")","def _handler_init(self):\r\n\t\tself._handlers[\"player-join\"] = FunctionDelegate()\r\n\t\tself._handlers[\"player-quit\"] = FunctionDelegate()\r\n\t\tself._handlers[\"game-start\"] = FunctionDelegate()\r\n\t\tself._handlers[\"game-stop\"] = FunctionDelegate()","async def initialize(self):","def setup(self):\n self.ui.setup_window()","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def init(self):\n pass","def on_load(self):\n self.__init__()","def __init__(self, request, response):\n # calling super class constructor\n super(TaskboardHandler, self).__init__(request=request, response=response)","def __init__(self, module_manager):\n self._module_manager = module_manager\n\n \n self._config = DefaultConfigClass()\n\n # modules should toggle this variable to True once they have\n # initialised and shown their view once.\n self.view_initialised = False","def initUI(self):\n\n self.wid = RosGenWidget()\n self.setCentralWidget(self.wid)\n menubar = self.menuBar()\n fileMenu = menubar.addMenu('&Файл')\n editMenu = menubar.addMenu('&Редактирование')\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\n self.statusbar = self.statusBar()\n self.statusbar.showMessage('Ожидание данных')\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\n self.setGeometry(600, 200, 700, 400)\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\n self.show()","def _initialize(self):\n self.send_init_command()","def __init__(self, db_handle):\n self.cur = db_handle.database().get_cursor()\n self.question_handler = db_handle.question()\n self.answer_handler = db_handle.answer()\n self.user_handler = db_handle.user()","def msg_app_init(self,msg):\r\n #load the main window layouts (in aui mixin class)\r\n self.frame.LoadLayouts()","def setUpTestData(cls):\n cls.emulate_off_api_manager_categories()\n cls.emulate_off_api_manager_products()\n cls.db_manager = Command()","def _init(self):\n pass","def init_layout(self):\n pass","def _initialise_run(self) -> None:","def init(self):\n self._service_store = ServiceStore(self.driver, self.network)\n self._emulator = NetworkEmulator(self.store, self.driver)","def setup_ui(self, datamodels):\n self.datamodels = []#Use self.add_data_display() to append\n self.dataviews = []\n\n self.centralwidget = QtGui.QWidget(self)\n #self.centralwidget.setMinimumSize(600, 400)\n\n #self.scrollarea = QScrollArea(self)\n self.setWidgetResizable(True)\n\n #Create layout\n self.verticalLayout = QtGui.QVBoxLayout(self.centralwidget)\n\n #Create couple of elements\n self.addDataDisplays = QtGui.QPushButton(self) #Draw (+) button to add data displays\n self.addDataDisplays.setText(\"+\")\n\n #self.verticalLayout.addWidget(self.addDataDisplays)\n\n for model in datamodels:\n self.add_data_display(model)\n #self.datamodels.append(model)\n\n self.addDataDisplays.clicked.connect(self.add_data_display)\n\n #add elements to layout\n self.setGeometry(self.geometry())\n self.setWidget(self.centralwidget)","def init(self):\n\t\tsp_addcallback(self.sp_callback)\n\t\tself.downloader.start()","def init_view(self):\n self.view_map = self.ctx.clientmap","def __init__(self):\n self.admin = Admin()\n self.current_user = self.admin.username\n Controller.generate_random_roles()\n self.admin_cli_handler = AdminCLIHandler(self)\n self.user_cli_handler = UserCLIHandler(self)\n self.user_to_action_mapping = {\n 'admin': {\n 0: self.admin_cli_handler.help,\n 1: self.admin_cli_handler.relogin,\n 2: self.admin_cli_handler.view_all_roles,\n 3: self.admin_cli_handler.new_user,\n 4: self.admin_cli_handler.new_role,\n 5: self.admin_cli_handler.edit_role,\n 6: self.admin_cli_handler.delete_role,\n 7: self.admin_cli_handler.view_request,\n 8: self.admin_cli_handler.delete_user,\n 9: self.admin_cli_handler.list_users,\n 10: self.admin_cli_handler.assign_role_to_user\n },\n 'user': {\n 0: self.user_cli_handler.help,\n 1: self.user_cli_handler.relogin,\n 2: self.user_cli_handler.view_all_roles,\n 3: self.user_cli_handler.request_new_role,\n 4: self.user_cli_handler.view_my_roles\n }\n }\n\n print(f\"\"\"\\n\\n\n Hi! You are logged in as {self.admin.username}\n Enter 0(numerical zero) for help\n Enter Ctrl+C to exit!\n \"\"\")","def init_app(self):\n \n tk_multi_depedencies = self.import_module(\"tk_multi_depedencies\")\n \n # register commands:\n display_name = self.get_setting(\"display_name\")\n \n # \"Publish Render\" ---> publish_render\n command_name = display_name.lower().replace(\" \", \"_\")\n if command_name.endswith(\"...\"):\n command_name = command_name[:-3]\n params = {\"short_name\": command_name, \n \"title\": \"%s...\" % display_name,\n \"description\": \"Publishing dependencies into Shotgun\"}\n\n show_dialog_callback = lambda: self.engine.show_dialog(display_name, self, tk_multi_depedencies.Dialog, self)\n \n self.log_debug(\"Registering command for tk-multi-dependencies\")\n self.engine.register_command(\"%s...\" % display_name, \n show_dialog_callback, \n params)","def _post_initialisations(self):\n # Init the settings module.\n self.dummy_for_settings = SectionConfig(self.app.id, self.__class__.__name__)\n global settings\n settings = self.dummy_for_settings\n\n self.dummy_for_options = OptionConfig(self.app.id)\n global options\n options = self.dummy_for_options\n\n # Bind message boxes.\n self.MessageBox = MessageBox(self)\n self.msg = self.MessageBox.Message\n self.are_you_sure = self.MessageBox.are_you_sure\n\n # Set previous size and state.\n width = settings.get('width', 350)\n height = settings.get('height', 350)\n self.set_title(self.app.localizedname)\n self.resize(width, height)\n if settings.get_bool('maximized', False):\n self.maximize()\n # Load any other settings here.\n self.load_xinput_devices()","def __init__(self, master):\n self.ports = list(serial.tools.list_ports.comports())\n self.master = master # initialize Root window\n self.master.title('Zeng.ltd Dashboard') # set title for Root window\n self.master.geometry(\"1000x700\") # set size and location for Root window\n self.master.configure(background=\"lightgrey\")\n self.mainframe = ttk.Notebook(self.master,\n padding=\"0 0 0 0\") # left top right bottem create mainframe in Root winow\n self.mainframe.grid(column=0, row=0, sticky=(N, W, S, E)) # set mainframe to root windows size\n self.master.protocol(\"WM_DELETE_WINDOW\", lambda: self.quit())\n\n menubar = Menu(self.master) # create a menubar\n\n # display the menu\n self.master.config(menu=menubar)\n\n # List of all our views\n self.views = {} # Empty dict\n\n # The back-end process\n self.pyctrl = PyCtrl(self)\n self.pyctrl.start()","def init_and_toast(self, msg):\n html = render_to_string(\"include/hardware_admin_init.html\", {\n 'hw_list': ItemType.objects.all(),\n })\n return JsonResponse({\n 'content': html,\n 'msg': msg\n })","def __init__(self):\n self.model = self.load_model()\n self.form_html = self.create_form_html()","def __init__(self, main_logger = None):\n\t\tself.log = main_logger or logger.init_logger(\"mandrill\")\n\n\t\tself.http_client = AsyncHTTPClient()\n\t\tself.key = config.MANDRILL_KEY","def readResponseDB(self):\n self.widget = ResponseFilePicker(self, self.database_api)\n self.widget.show()","def on_init(self):\n self.write_log(\"策略初始化\")\n self.exchange_load_bar(self.exchange)","def setup(self):\n\t\tScriptedLoadableModuleWidget.setup(self)\n\n\t\tself._loadUI()\n\n\t\t# Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's\n\t\t# \"mrmlSceneChanged(vtkMRMLScene*)\" signal in is connected to each MRML widget's.\n\t\t# \"setMRMLScene(vtkMRMLScene*)\" slot.\n\t\t\n\n\t\t# Create logic class. Logic implements all computations that should be possible to run\n\t\t# in batch mode, without a graphical user interface.\n\t\tself.logic = postopProgrammingLogic()\n\n\t\t# Connections\n\t\tself._setupConnections()","def __init__(self):\n # Passing the class make this Python 2 and Python 3 compatible\n super(MayaSceneLevelGeneratorUI, self).__init__(parent=maya_main_window())\n\n # Create the generators needed\n self._level_gen = level.LevelGenerator([blocks.BlockFile(\"\", blk_type) for blk_type in VALID_BLOCK_TYPES])\n self._scene_gen = MayaSceneLevelGenerator(None) # Fill in level at button press time\n\n # Window things\n self.setWindowTitle(\"Maya Scene Level Generator\")\n self.resize(500, 200)\n self.setWindowFlags(self.windowFlags() ^ PySide2.QtCore.Qt.WindowContextHelpButtonHint)\n\n # Set up for the first time\n self._create_widgets()\n self._create_layout()\n self._refresh_view()\n self._create_connections() # Order matters, since refreshing triggers connections\n\n print(self._level_gen.block_list) # TODO delete","def user_init(self):\n pass","def setUp(self):\n\n self.logger_stats = DataScreen()","def _init_(self):\n self.res = {}","def setup(self):\r\n ScriptedLoadableModuleWidget.setup(self)\r\n\r\n # Load widget from .ui file (created by Qt Designer).\r\n # Additional widgets can be instantiated manually and added to self.layout.\r\n uiWidget = slicer.util.loadUI(self.resourcePath('UI/RecordHerniaData.ui'))\r\n self.layout.addWidget(uiWidget)\r\n self.ui = slicer.util.childWidgetVariables(uiWidget)\r\n\r\n # Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's\r\n # \"mrmlSceneChanged(vtkMRMLScene*)\" signal in is connected to each MRML widget's.\r\n # \"setMRMLScene(vtkMRMLScene*)\" slot.\r\n uiWidget.setMRMLScene(slicer.mrmlScene)\r\n\r\n # Create logic class. Logic implements all computations that should be possible to run\r\n # in batch mode, without a graphical user interface.\r\n self.logic = TMSRecordDataModuleLogic()\r\n self.recordingStarted = False\r\n self.camerasStarted = False\r\n self.moduleDir = os.path.dirname(slicer.modules.tmsrecorddatamodule.path)\r\n self.logic.setupScene()\r\n\r\n # Buttons\r\n self.ui.StartStopRecordingButton.connect('clicked(bool)', self.onStartStopRecordingClicked)\r\n self.ui.startCamerasButton.connect('clicked(bool)',self.onStartStopCamerasClicked)","def _init(self):\n self.wx_menu = wx.Menu()"],"string":"[\n \"def __init__(self):\\n self.websock_handlers = {}\\n self.ajax_handlers = {'__dashboard__': self.get_dashboard_ui}\\n self.dashboard_handlers = {}\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def prepare_UI(self):\",\n \"def _initUI(self) -> None:\\n self._createActions()\\n self._addActionsToMoveButtons()\\n self._createToolBar()\\n self._createStatusBar()\\n self._createMainContextMenu()\",\n \"def init_ui(self):\\n raise NotImplementedError(\\\"This is an abstract method.\\\")\",\n \"def _init_ui(self):\\r\\n\\t\\t\\r\\n\\t\\tself.input_frame = Input(self)\\r\\n\\t\\tself.input_frame.pack()\\r\\n\\t\\t\\r\\n\\t\\tbutton_ok = Button(self, text = \\\"Ping\\\", command = self._go)\\r\\n\\t\\tbutton_ok.pack()\\r\\n\\t\\t\\r\\n\\t\\tself.result_frame = Result(self)\\r\\n\\t\\tself.result_frame.pack()\",\n \"def __init__(self):\\n\\n # TODO: Add login and data grab logic\",\n \"def InitUI(self):\\n\\t\\tself._initMenuBar()\\n\\t\\tself._initLayout()\\n\\t\\t\\n\\t\\t# Bindings\\n\\t\\tself.Bind(wx.EVT_BUTTON, self.OnButtonClicked)\\n\\t\\t\\n\\t\\t# We can't even start without an input file\\n\\t\\tself.OnOpen(None)\",\n \"def __init__(self):\\n # setup the database session\\n engine = create_engine('sqlite:///%s'%os.path.join(APP_DIR,config.get('Inkcut','database_dir'),config.get('Inkcut','database_name')))\\n\\n Session.configure(bind=engine)\\n self.session = Session()\\n\\n self.job = None\\n self.ui = {\\n 'main_window':MainWindow(self),\\n 'device_dialog':DeviceDialog(self),\\n }\\n self.statusbar = self.ui['main_window'].widgets['statusbar']\",\n \"def initUI(self):\\n # Setting the main layout as Vertical.\\n self.mainLayout = QHBoxLayout()\\n\\n # Create title.\\n self.title = QLabel(self.__name + \\\" : \\\")\\n\\n # Add description as tooltip.\\n self.title.setToolTip(self.__description)\\n\\n # Add title to main layout.\\n self.mainLayout.addWidget(self.title)\\n\\n # Create ComboBox.\\n self.dropDown = QComboBox()\\n\\n # Add datas to drop down.\\n self.dropDown.addItems(self.__datas)\\n\\n # Set default index to dropdown.\\n self.dropDown.setCurrentIndex(self.__currentValue)\\n\\n # Connect dropdown with update method.\\n self.dropDown.currentIndexChanged.connect(self.changeCurrentValue)\\n\\n # Add ComboBox to main layout.\\n self.mainLayout.addWidget(self.dropDown)\\n\\n # Add the main layout to the window.\\n self.setLayout(self.mainLayout)\",\n \"def init_UI(self):\\n\\n self.master.title(\\\"Search for different companies\\\")\\n self.master.geometry(\\\"400x400\\\")\\n\\n self.label_combobox = Label(self, text=\\\"Search by\\\")\\n self.label_combobox.pack()\\n\\n self.combo_searching_options = Combobox(self, state=\\\"readonly\\\")\\n self.combo_searching_options['values'] = self.combobox_values\\n self.combo_searching_options.pack()\\n\\n self.label_input = Label(self, text=\\\"Entry the value\\\")\\n self.label_input.pack()\\n\\n self.user_input = Entry(self, width=40)\\n self.user_input.pack()\\n\\n self.btn_submit = Button(self, text=\\\"Submit\\\", command=self.submit)\\n self.btn_submit.pack()\\n\\n self.text_area = scrolledtext.ScrolledText(self)\\n self.text_area.pack()\\n\\n sys.stdout = RedirectOutputText(self.text_area)\\n\\n self.btn_back = Button(self, text=\\\"Back\\\", command=self.go_back)\\n self.btn_back.pack()\",\n \"def __init__(self):\\r\\n super().__init__()\\r\\n self.init_ui()\",\n \"def __init__(self, **kwargs):\\r\\n\\r\\n self.def_ms = modulestore()\\r\\n self.is_using_mongo = True\\r\\n if isinstance(self.def_ms, XMLModuleStore):\\r\\n self.is_using_mongo = False\\r\\n self.msg = u''\\r\\n self.datatable = []\\r\\n super(SysadminDashboardView, self).__init__(**kwargs)\",\n \"def initUI(self):\\n self.logger.debug('Setting up the Measurement GUI')\\n self.setWindowTitle(self.title)\\n\\n self.show()\\n\\n self.make_combobox_scanner()\\n self.make_combobox_movements()\\n self.make_combobox_configurate()\\n self.make_combobox_basic()\",\n \"def do_init(self):\\n\\n pass\",\n \"async def init(self):\\n self.base_tamplates = {}\\n self.preparing_task = None\\n self.app = aioweb.Application()\\n self.runner = aioweb.AppRunner(self.app)\",\n \"def initialization(self):\\n super().initialization()\\n self.register_uniq_ids([self.id_modal, self.id_modal_close, self.id_wip_button])\\n\\n # Register modules\\n self.modules = [self.mod_table, self.mod_cache, self.mod_upload]\",\n \"def initUI(self):\\n centralwidget = QtWidgets.QWidget()\\n self.setCentralWidget(centralwidget)\\n\\n person_table = PersonTable(self)\\n person_table.embed(self.sql)\\n\\n # Box Layout to organize our GUI\\n lay = QtWidgets.QVBoxLayout(centralwidget)\\n lay.addWidget(person_table)\\n self.setGeometry(0, 0, person_table.width() + 20, person_table.height() + 20)\\n self.person_table = person_table\\n self.show()\",\n \"def init_context_data(self):\\n pass\",\n \"async def _init(self, **kwargs):\",\n \"def setUp(self):\\n self.ui = UI()\",\n \"def initialize_core(self):\\n # set current session unique id\\n self.conf.set(\\\"accesspoint\\\", \\\"current_session\\\", self.currentSessionID)\\n # set interface for shared connection from params\\n self.conf.set(\\\"accesspoint\\\", \\\"interface_net\\\", self.parse_args.interface_net)\\n \\n if self.parse_args.interface:\\n self.conf.set(\\\"accesspoint\\\", \\\"interface\\\", self.parse_args.interface)\\n\\n self.all_modules = module_list\\n\\n # intialize the LoggerManager\\n # TODO: this change solve IndexError: list index out of range\\n # but not a definitive solution\\n self.logger_manager = LoggerManager(self)\\n self.coreui = DefaultController(self)\\n\\n # print(self.coreui.Plugins)\\n self.proxy_controller = self.coreui.getController(\\\"proxy_controller\\\")\\n self.mitm_controller = self.coreui.getController(\\\"mitm_controller\\\")\\n self.wireless_controller = self.coreui.getController(\\\"wireless_controller\\\")\\n self.dhcp_controller = self.coreui.getController(\\\"dhcp_controller\\\")\\n self.dns_controller = self.coreui.getController(\\\"dns_controller\\\")\\n self.uiwid_controller = self.coreui.getController(\\\"ui_controller\\\")\\n\\n self.parser_list_func = {\\n # parser_set_proxy is default extend class\\n \\\"parser_set_proxy\\\": self.proxy_controller.pumpkinproxy,\\n \\\"parser_set_plugin\\\": self.mitm_controller.sniffkin3,\\n \\\"parser_set_mode\\\": self.wireless_controller.Settings,\\n \\\"parser_set_security\\\": self.wireless_controller.Settings,\\n \\\"parser_set_hostapd_config\\\": self.wireless_controller.Settings,\\n \\\"parser_set_dhcpconf\\\": self.wireless_controller.Settings,\\n \\\"parser_set_dhcpmode\\\": self.dns_controller.Active,\\n }\\n self.parser_autcomplete_func = {}\\n\\n # hook function (plugins and proxies)\\n self.intialize_hook_func(self.proxy_controller)\\n self.intialize_hook_func(self.mitm_controller)\\n\\n # register autocomplete set security command\\n self.parser_autcomplete_func[\\n \\\"parser_set_security\\\"\\n ] = self.wireless_controller.Settings.getCommandsSecurity\\n self.parser_autcomplete_func[\\n \\\"parser_set_hostapd_config\\\"\\n ] = self.wireless_controller.Settings.getCommandsHostapd\\n self.parser_autcomplete_func[\\n \\\"parser_set_dhcpconf\\\"\\n ] = self.wireless_controller.Settings.getCommandsDhcpConf\\n self.parser_autcomplete_func[\\n \\\"parser_set_dhcpmode\\\"\\n ] = self.dns_controller.getCommandsDhcpMode\\n\\n self.commands = {\\n \\\"interface\\\": \\\"interface\\\",\\n \\\"interface_net\\\": \\\"interface_net\\\",\\n \\\"ssid\\\": \\\"ssid\\\",\\n \\\"bssid\\\": \\\"bssid\\\",\\n \\\"channel\\\": \\\"channel\\\",\\n \\\"proxy\\\": None, # only for settings proxy\\n \\\"plugin\\\": None, # only for settings plugin\\n \\\"mode\\\": None, # only for settings mdoe\\n \\\"dhcpconf\\\": None, # only for settings dhcpconf\\n \\\"dhcpmode\\\": None, # only for settings dhcpmode\\n \\\"security\\\": \\\"enable_security\\\",\\n \\\"hostapd_config\\\": \\\"enable_hostapd_config\\\",\\n }\\n\\n # get all command plugins and proxies\\n for ctr_name, ctr_instance in self.coreui.getController(None).items():\\n if hasattr(ctr_instance, \\\"getInfo\\\"):\\n for plugin_name, plugins_info in ctr_instance.getInfo().items():\\n self.commands[plugin_name] = \\\"\\\"\\n\\n self.threads = {\\\"RogueAP\\\": [], \\\"Modules\\\": {}}\",\n \"def init(self, **kwargs):\\n self._d = {}\\n self._th = None\\n self._run = True\\n self.load()\",\n \"def __init__(self):\\n self._create_options()\\n self._create_sections()\",\n \"def setup(cls):\\n super().setup()\\n cls.http_handler = cast(\\n HttpHandler, cls._skill.skill_context.handlers.http_handler\\n )\\n cls.logger = cls._skill.skill_context.logger\\n\\n cls.http_dialogues = cast(\\n HttpDialogues, cls._skill.skill_context.http_dialogues\\n )\\n\\n cls.get_method = \\\"get\\\"\\n cls.post_method = \\\"post\\\"\\n cls.url = \\\"some_url\\\"\\n cls.version = \\\"some_version\\\"\\n cls.headers = \\\"some_headers\\\"\\n cls.body = b\\\"some_body\\\"\\n cls.sender = \\\"fetchai/some_skill:0.1.0\\\"\\n cls.skill_id = str(cls._skill.skill_context.skill_id)\\n\\n cls.status_code = 100\\n cls.status_text = \\\"some_status_text\\\"\\n\\n cls.content = b\\\"some_content\\\"\\n cls.list_of_messages = (\\n DialogueMessage(\\n HttpMessage.Performative.REQUEST,\\n {\\n \\\"method\\\": cls.get_method,\\n \\\"url\\\": cls.url,\\n \\\"version\\\": cls.version,\\n \\\"headers\\\": cls.headers,\\n \\\"body\\\": cls.body,\\n },\\n ),\\n )\",\n \"def initCentralUic(self):\\n self.initFileTableWidget()\\n self.initViewerStack()\\n self.splitter.setSizes([150, 850])\",\n \"def __init__(self, menu_manager):\\n\\n\\t\\tself.menu_manager = menu_manager\\n\\t\\tself.uiCoordinator = menu_manager.uiCoordinator\\n\\t\\tself.highscore = menu_manager.user.highscore\\n\\t\\tself.name = menu_manager.user.username\\n\\t\\tself.high_scores = menu_manager.con.getHighscores(\\\"DESC\\\")\\n\\t\\tself.level = menu_manager.level\\n\\t\\tself.open_window = False\\n\\n\\t\\tself.setupWindow()\\n\\t\\tself.setupInfo()\\n\\t\\tself.setupBinds()\",\n \"def init(self, userdata, conn):\\r\\n pass\",\n \"def initialize(self, request, response):\\n super(FSMHandler, self).initialize(request, response)\\n # pylint: disable-msg=W0201\\n # - this is the preferred location to initialize the handler in the webapp framework\\n self.fsm = None\",\n \"def initialise(self):\\n self.set_up()\",\n \"def load_ui(self):\\n\\n load_data_err = self.load_all_services()\\n if load_data_err:\\n self.dlg.uLabelWarning.setText(load_data_err)\\n self.dlg.uLabelWarning.show()\\n self.update_cache = False\\n else:\\n self.dlg.uLabelWarning.hide()\",\n \"def do_startup(self):\\n \\n import json\\n\\n GLib.set_application_name(\\\"Deity\\\")\\n Gtk.Application.do_startup(self)\\n \\n settings = self.get_settings()\\n\\n menub = Gtk.MenuButton(name=\\\"input-menu_button\\\",\\n use_popover=True)\\n\\n headerbar = Gtk.HeaderBar(name=\\\"input-headerbar\\\",\\n show_close_button=True,\\n title=\\\"Deity\\\")\\n\\n main_grid = Gtk.Grid(name=\\\"input-main_grid\\\")\\n\\n statusbar = Gtk.Box(name=\\\"input-statusbar\\\",\\n orientation=0,\\n spacing=2)\\n statusbar.pack_start(self.statuslabel, 1, 1, 1)\\n\\n self.connector.connect(\\\"query-status\\\", self.show_output)\\n self.connector.connect(\\\"query-waiting\\\",\\n lambda wid, count: self.statuslabel.set_text(\\n f\\\"Queries on hold : {count}\\\"))\\n self.connector.connect(\\\"request\\\", print)\\n\\n headerbar.pack_end(menub)\\n\\n main_grid.attach(self.iogrid.get_widget(), 0, 0, 1, 1)\\n main_grid.attach(statusbar, 0, 1, 1, 1)\\n\\n self.output_window.add(self.get_placeholder_image())\\n\\n self.window.set_titlebar(headerbar)\\n self.window.set_default_icon_from_file(\\\"artwork/Logo.png\\\")\\n self.window.add(main_grid)\\n\\n self.window.connect(\\\"key-press-event\\\", self.parse_keypress)\\n self.window.connect(\\\"delete-event\\\", self.request_quit)\\n \\n self.other[\\\"connector\\\"] = self.connector\\n self.other[\\\"headerbar\\\"] = headerbar\\n self.other[\\\"history\\\"] = self.history\\n self.other[\\\"input-window\\\"] = self.window\\n self.other[\\\"iogrid\\\"] = self.iogrid\\n self.other[\\\"plugins\\\"] = self.get_plugins(settings[\\\"enabled-plugins\\\"])\\n self.other[\\\"statusbar\\\"] = statusbar\\n self.other[\\\"statuslabel\\\"] = self.statuslabel\\n self.other[\\\"output-notebook\\\"] = self.notebook\\n self.other[\\\"output-window\\\"] = self.output_window\\n self.other[\\\"main-grid\\\"] = main_grid\\n self.other[\\\"menu_button\\\"] = menub\\n \\n self.apply_settings(settings)\\n self.current_prompt = self.iogrid.add_prompt()\\n\\n self.window.set_application(self)\\n self.output_window.set_application(self)\\n\\n self.output_window.move(800, 150)\\n self.window.move(75, 160)\",\n \"def __init__(self):\\n\\n super().__init__()\\n\\n scripts_handler = ScriptsTemplateHandler()\\n application_handler = ApplicationTemplateHandler()\\n scripts_handler.set_next(application_handler)\\n\\n self._handlers = DTO(project=[scripts_handler, application_handler])\",\n \"def setup_response_collector(self):\\n pass\",\n \"def onStart(self):\\n self.register_form(JobManager.id, JobManager,\\n title=\\\"JobManager\\\", shortcut=\\\"^D\\\", name=\\\"JobManager\\\")\\n self.register_form(SystemOverviewForm.id, SystemOverviewForm,\\n title=\\\"System Overview\\\", shortcut=\\\"^O\\\", name=\\\"System Overview\\\")\\n self.register_form(StateManagerForm.id, StateManagerForm,\\n title=\\\"State Manager\\\", shortcut=\\\"^T\\\", name=\\\"State Manager\\\")\\n self.register_form(ModuleRunnerForm.id, ModuleRunnerForm,\\n title=\\\"Module Runner\\\", shortcut=\\\"^T\\\", name=\\\"Module Runner\\\")\\n self.init_forms()\",\n \"def init_ui(self):\\n self.master.title(\\\"Backbone\\\")\\n self.master.geometry(\\\"300x150\\\")\\n\\n self.pack(fill=BOTH, expand=1)\\n\\n self.btn_upload_file = Button(self, text=\\\"Upload file\\\", command=self.upload_file)\\n self.btn_upload_file.place(x=90, y=10)\\n\\n self.btn_create_training_file = Button(self, text=\\\"Create & upload training file\\\",\\n command=self.create_training_file)\\n self.btn_create_training_file.place(x=30, y=40)\\n\\n self.btn_run_algorithm = Button(self, text=\\\"Run algorithm\\\", command=self.run_algorithm)\\n self.btn_run_algorithm.place(x=80, y=70)\\n\\n self.btn_view_results = Button(self, text=\\\"View Results\\\", command=self.view_results)\\n self.btn_view_results.place(x=85, y=100)\",\n \"def __init__(self):\\n self.model = Model()\\n self.view = View()\\n\\n self.server = Server(msg_handler=self.msg_handler,\\n err_handler=self.err_handler,\\n conn_handler=self.conn_handler,\\n quit_handler=self.quit_handler)\\n self.server.start()\\n\\n self.view.frame.onclose(self.server.close)\",\n \"def __init__(self):\\n self.window = Tk() # The main window\\n self.__initialize_variables__() # Initialize the variables\\n self.__initialize_menu__() # Initialize the Menu\\n self.__initialize_status_bar__()\\n self.__initialize_gui__() # Initialize the GUI widgets\",\n \"def initializeUI(self):\\n self.setStyleSheet(abstyle)\\n self.setGeometry(140, 100, 860, 484)\\n self.setWindowTitle('Emotions Data View')\\n self.setupModelView()\",\n \"async def init(self) -> None:\",\n \"async def init(self) -> None:\",\n \"def setup_method(self):\\n MANAGER._tables = {}\\n MANAGER._views = {}\",\n \"def setup_ui(self):\\n #super(Dialog, self).__init__()\\n self.createFormGroupBox()\\n\\n buttonBox = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\\n buttonBox.accepted.connect(self.check_input)\\n buttonBox.rejected.connect(self.reject)\\n\\n mainLayout = QVBoxLayout()\\n mainLayout.addWidget(self.formGroupBox)\\n mainLayout.addWidget(buttonBox)\\n self.setLayout(mainLayout)\\n\\n self.make_connections()\\n\\n self.setWindowTitle(\\\"Load Data ...\\\")\",\n \"def setDataRequestHandler(self, handler):\\n self.dataRequestHandler = handler\",\n \"def setup(self):\\n print(\\\"Inside setup\\\")\\n self.fetch_from_db()\\n # Don't assign to the function output. Assing it to the function object so that whenever we\\n # do some changes to layout, it will reflect without server restarting\\n app.layout = self.get_root_layout\\n return app.server\",\n \"def _setup_ui(self):\\n\\n self.window = ui.Widget()\\n self.window.dimensions = ui.normalize_dimension((\\n 0, 0,\\n self.normalized_screen_resolution[0],\\n self.normalized_screen_resolution[1]\\n ))\\n self.window.background_color = ImageColor.getcolor('#000000', 'RGB')\\n\\n interface_frame = ui.Widget(parent=self.window)\\n interface_frame.dimensions = ui.normalize_dimension((\\n self.preview_renderer.window[2],\\n 0,\\n self.normalized_screen_resolution[0] - self.preview_renderer.window[2],\\n self.normalized_screen_resolution[1]\\n ))\\n interface_frame.background_color = ImageColor.getcolor('#ffffff', 'RGB')\\n\\n number = ui.LabelWidget(\\\"\\\",\\n name=NAME_GET_STARTED,\\n parent=interface_frame,\\n align=\\\"center\\\",\\n font_color=(0, 0, 0, 255))\\n number.dimensions = (\\n 5, 5,\\n interface_frame.width - 10,\\n interface_frame.height - 10\\n )\",\n \"def initService(self):\",\n \"def __init__(self, root, handle_after_logout, food_service):\\n self._root = root\\n self._handle_after_logout = handle_after_logout\\n self.food_service = food_service\\n\\n self._ingredient = None\\n self._date = None\\n\\n self._frame = None\\n self._ingredients_frame = None\\n self._ingredients_view = None\\n self._message_var = None\\n\\n super().__init__(self._frame)\\n\\n self._initialize()\",\n \"def __init__(self):\\n\\t\\tself.satchmo_variation_manager = adminviews.variation_manager\\n\\t\\tadminviews.variation_list = self.variation_list\\n\\t\\tadminviews.variation_manager = self.variation_manager\\n\\t\\t# listeners.add_toolbar_context = self.add_my_toolbar_context#(listeners.add_toolbar_context)\",\n \"def setUp(self):\\n self.response = {\\n \\\"id\\\": \\\"753859569859690509\\\",\\n \\\"last_message_id\\\": \\\"754623102561812511\\\",\\n \\\"type\\\": 0,\\n \\\"name\\\": \\\"general\\\",\\n \\\"position\\\": 4,\\n \\\"parent_id\\\": \\\"753859569859690506\\\",\\n \\\"topic\\\": None,\\n \\\"guild_id\\\": \\\"753859568764977194\\\",\\n \\\"permission_overwrites\\\": [],\\n \\\"nsfw\\\": False,\\n \\\"rate_limit_per_user\\\": 0,\\n }\\n self.app = {}\",\n \"def onInit(self):\\n pass\",\n \"def init(self):\\n self.dispatcher.start()\\n self.replyer.start()\",\n \"def preloop(self):\\n\\n self.manager = FacilityManager()\",\n \"def init_ui(self):\\n self.panel_sizer = wx.BoxSizer(wx.VERTICAL)\\n self.figure_bmp = wx.StaticBitmap(self, wx.ID_ANY,\\n bitmap=self.controller.empty_bitmap(self.bitmap_width,\\n self.bitmap_height),\\n pos=wx.DefaultPosition, size=wx.DefaultSize)\\n self.panel_sizer.Add(self.figure_bmp, ui_defaults.ctrl_pct, wx.CENTER,\\n ui_defaults.widget_margin)\\n self.SetSizerAndFit(self.panel_sizer)\",\n \"def ui_setup(self):\\n loader = QUiLoader()\\n file = QFile('./user_interface/form/main_window.ui')\\n file.open(QFile.ReadOnly)\\n self._window = loader.load(file)\\n file.close()\\n\\n status_bar = QStatusBar(self._window)\\n status_bar.showMessage(__copyright__)\\n self._window.setStatusBar(status_bar)\\n self._window.setWindowIcon(QIcon('./user_interface/media/bucketing_icon.jpeg'))\\n self._window.setWindowTitle('PySide2 Project - Basic UI Framework')\\n\\n self._option_panel = OptionPanel()\\n self._option_panel.add_button('DekBan', './user_interface/media/dekban.png')\\n self._option_panel.add_button('Charlie', './user_interface/media/charlie.jpeg')\\n self._option_panel.add_button('Simon', './user_interface/media/Simon.jpeg')\\n\\n # Add widget to main layout\\n main_layout = self._window.main_layout\\n main_layout.itemAtPosition(0, 0).setAlignment(QtCore.Qt.AlignCenter)\\n main_layout.itemAtPosition(0, 1).setAlignment(QtCore.Qt.AlignVCenter)\\n main_layout.addWidget(self._option_panel, 2, 0, 1, 1)\\n\\n # Add page widget to stack\\n self._pages['item'] = ItemWidget()\\n self._pages['text1'] = TextPage(text=PAUSE_TEXT)\\n self._pages['text2'] = TextPage(text=STOP_TEXT)\\n\\n for index, name in enumerate(self._pages):\\n print('pages {} : {} page'.format(index, name))\\n self._window.widget_stack.addWidget(self._pages[name].widget)\\n\\n self._window.widget_stack.setCurrentIndex(0)\\n\\n # Build up signal / slot\\n self._option_panel.currentItemChanged.connect(self.set_page)\",\n \"def init_UI(self):\\n # widgets\\n self.modeComboBox = QtWidgets.QComboBox()\\n self.updateBtn = QtWidgets.QPushButton('Force Update')\\n self.pauseBtn = QtWidgets.QPushButton()\\n self.pauseBtn.setCheckable(True)\\n\\n self.clearBtn = QtWidgets.QPushButton()\\n self.clearBtn.setIcon(QtGui.QIcon(IconPaths.ICON_CLEAR_FILES))\\n self.filterBtn = QtWidgets.QToolButton()\\n self.filterBtn.setIcon(QtGui.QIcon(IconPaths.ICON_FILTER))\\n self.filterBtn.setMinimumWidth(35)\\n self.filterBtn.setStyleSheet('QToolButton::menu-indicator {subcontrol-position: center right; height: 7px}')\\n self.filterBtn.setPopupMode(QtWidgets.QToolButton.InstantPopup)\\n self.prefBtn = QtWidgets.QToolButton()\\n self.prefBtn.setIcon(QtGui.QIcon(IconPaths.ICON_SETTINGS))\\n #self.pBar = QtWidgets.QProgressBar()\\n self.itemView = LocalizeView()\\n self.itemView.setModel(self.proxy_model)\\n self.autoScrollCB = QtWidgets.QCheckBox('Auto scroll to localizing files')\\n self.autoScrollCB.setChecked(True)\\n\\n # tweak sizes so the widgets all line up vertically with Nuke's style\\n self.modeComboBox.setMinimumHeight(self.updateBtn.sizeHint().height())\\n self.pauseBtn.setMaximumSize(self.updateBtn.sizeHint())\\n self.clearBtn.setMaximumSize(self.updateBtn.sizeHint())\\n\\n # mode menu\\n self.modeLabel = QtWidgets.QLabel('Mode')\\n self.modeComboBox.addItems(['On', 'Manual', 'Off'])\\n\\n # update menu\\n self.updateMenu = QtWidgets.QMenu()\\n self.act_forceUpdateAll = QtWidgets.QAction('All', self)\\n self.act_forceUpdateSelectedNodes = QtWidgets.QAction('Selected', self)\\n self.act_forceUpdateOnDemand = QtWidgets.QAction('On demand only', self) \\n self.updateMenu.addAction(self.act_forceUpdateAll)\\n self.updateMenu.addAction(self.act_forceUpdateSelectedNodes)\\n self.updateMenu.addAction(self.act_forceUpdateOnDemand)\\n self.updateBtn.setMenu(self.updateMenu)\\n\\n # clear menu\\n self.clearMenu = QtWidgets.QMenu()\\n self.clearMenu.addAction(QtWidgets.QAction('All local files', self, triggered=self.__delete_all_local_files))\\n self.clearMenu.addAction(QtWidgets.QAction('Unused local files', self, triggered=_open_delete_dialog))\\n self.clearBtn.setMenu(self.clearMenu)\\n\\n # filter menu\\n self.filterMenu = QtWidgets.QMenu(self.filterBtn)\\n self.act_filter_all = QtWidgets.QAction('All', self.filterMenu, checkable=True)\\n self.act_filter_in_progress = QtWidgets.QAction('In Progress', self.filterMenu, checkable=True)\\n self.act_filter_up_to_date = QtWidgets.QAction('Up to date', self.filterMenu, checkable=True)\\n self.act_filter_out_of_date = QtWidgets.QAction('Out of date', self.filterMenu, checkable=True)\\n self.act_filter_from_source = QtWidgets.QAction('Reading from source', self.filterMenu, checkable=True)\\n self.act_filter_disabled = QtWidgets.QAction('Disabled', self.filterMenu, checkable=True)\\n self.act_filter_not_localized = QtWidgets.QAction('Not Localized', self.filterMenu, checkable=True)\\n\\n self.act_filter_in_progress.setData(Status.IN_PROGRESS)\\n self.act_filter_up_to_date.setData(Status.UP_TO_DATE)\\n self.act_filter_out_of_date.setData(Status.OUT_OF_DATE)\\n self.act_filter_from_source.setData(Status.READ_FROM_SOURCE)\\n self.act_filter_disabled.setData(Status.DISABLED)\\n self.act_filter_not_localized.setData(Status.NOT_LOCALIZED)\\n for act in (self.act_filter_all, self.act_filter_in_progress, self.act_filter_up_to_date, self.act_filter_out_of_date,\\n self.act_filter_from_source, self.act_filter_disabled, self.act_filter_not_localized):\\n self.filterMenu.addAction(act)\\n self.filterBtn.setMenu(self.filterMenu)\\n\\n # tooltips\\n self.modeComboBox.setToolTip('Sets the global localization mode.\\\\nThis is the same as using the options in the Cache/Localization/Mode menu.')\\n self.updateBtn.setToolTip('Forces the update of localized files.\\\\nThis is the same as using the options in the Cache/Localization/Force Update menu.')\\n self.pauseBtn.setToolTip('Pauses/Resumes file localization.\\\\nThis is the same as Cache/Localization/Pause.')\\n self.clearBtn.setToolTip('''Allows for clearing localized files.\\\\nTwo modes are supported:\\n \\\"All local files\\\" - this will delete all files in {}\\n \\\"Unused local files\\\" - this will only delete unused local files (same as Cache/Localization/Clear Unused Local Files)'''.format(nuke.toNode('preferences')['localCachePath'].evaluate()))\\n self.filterBtn.setToolTip('Sets a view filter the table.')\\n self.prefBtn.setToolTip('Open the preferences.')\\n\\n # layouts\\n layout = QtWidgets.QVBoxLayout()\\n btnLayout = QtWidgets.QHBoxLayout()\\n btnLayout.addWidget(self.modeLabel)\\n btnLayout.addWidget(self.modeComboBox)\\n btnLayout.addWidget(self.updateBtn)\\n btnLayout.addWidget(self.pauseBtn)\\n btnLayout.addWidget(self.clearBtn)\\n btnLayout.addStretch()\\n btnLayout.addWidget(self.filterBtn)\\n btnLayout.addWidget(self.prefBtn)\\n layout.addLayout(btnLayout)\\n #layout.addWidget(self.pBar)\\n layout.addWidget(self.itemView)\\n layout.addWidget(self.autoScrollCB)\\n layout.setAlignment(self.autoScrollCB, QtCore.Qt.AlignRight)\\n self.setLayout(layout)\",\n \"def start(self):\\n self.ioloop.add_callback(self.get_data)\\n logging.info(\\\"[DataSource] Started\\\")\",\n \"def _handler_init(self):\\r\\n\\t\\tself._handlers[\\\"player-join\\\"] = FunctionDelegate()\\r\\n\\t\\tself._handlers[\\\"player-quit\\\"] = FunctionDelegate()\\r\\n\\t\\tself._handlers[\\\"game-start\\\"] = FunctionDelegate()\\r\\n\\t\\tself._handlers[\\\"game-stop\\\"] = FunctionDelegate()\",\n \"async def initialize(self):\",\n \"def setup(self):\\n self.ui.setup_window()\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def init(self):\\n pass\",\n \"def on_load(self):\\n self.__init__()\",\n \"def __init__(self, request, response):\\n # calling super class constructor\\n super(TaskboardHandler, self).__init__(request=request, response=response)\",\n \"def __init__(self, module_manager):\\n self._module_manager = module_manager\\n\\n \\n self._config = DefaultConfigClass()\\n\\n # modules should toggle this variable to True once they have\\n # initialised and shown their view once.\\n self.view_initialised = False\",\n \"def initUI(self):\\n\\n self.wid = RosGenWidget()\\n self.setCentralWidget(self.wid)\\n menubar = self.menuBar()\\n fileMenu = menubar.addMenu('&Файл')\\n editMenu = menubar.addMenu('&Редактирование')\\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\\n self.statusbar = self.statusBar()\\n self.statusbar.showMessage('Ожидание данных')\\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\\n self.setGeometry(600, 200, 700, 400)\\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\\n self.show()\",\n \"def _initialize(self):\\n self.send_init_command()\",\n \"def __init__(self, db_handle):\\n self.cur = db_handle.database().get_cursor()\\n self.question_handler = db_handle.question()\\n self.answer_handler = db_handle.answer()\\n self.user_handler = db_handle.user()\",\n \"def msg_app_init(self,msg):\\r\\n #load the main window layouts (in aui mixin class)\\r\\n self.frame.LoadLayouts()\",\n \"def setUpTestData(cls):\\n cls.emulate_off_api_manager_categories()\\n cls.emulate_off_api_manager_products()\\n cls.db_manager = Command()\",\n \"def _init(self):\\n pass\",\n \"def init_layout(self):\\n pass\",\n \"def _initialise_run(self) -> None:\",\n \"def init(self):\\n self._service_store = ServiceStore(self.driver, self.network)\\n self._emulator = NetworkEmulator(self.store, self.driver)\",\n \"def setup_ui(self, datamodels):\\n self.datamodels = []#Use self.add_data_display() to append\\n self.dataviews = []\\n\\n self.centralwidget = QtGui.QWidget(self)\\n #self.centralwidget.setMinimumSize(600, 400)\\n\\n #self.scrollarea = QScrollArea(self)\\n self.setWidgetResizable(True)\\n\\n #Create layout\\n self.verticalLayout = QtGui.QVBoxLayout(self.centralwidget)\\n\\n #Create couple of elements\\n self.addDataDisplays = QtGui.QPushButton(self) #Draw (+) button to add data displays\\n self.addDataDisplays.setText(\\\"+\\\")\\n\\n #self.verticalLayout.addWidget(self.addDataDisplays)\\n\\n for model in datamodels:\\n self.add_data_display(model)\\n #self.datamodels.append(model)\\n\\n self.addDataDisplays.clicked.connect(self.add_data_display)\\n\\n #add elements to layout\\n self.setGeometry(self.geometry())\\n self.setWidget(self.centralwidget)\",\n \"def init(self):\\n\\t\\tsp_addcallback(self.sp_callback)\\n\\t\\tself.downloader.start()\",\n \"def init_view(self):\\n self.view_map = self.ctx.clientmap\",\n \"def __init__(self):\\n self.admin = Admin()\\n self.current_user = self.admin.username\\n Controller.generate_random_roles()\\n self.admin_cli_handler = AdminCLIHandler(self)\\n self.user_cli_handler = UserCLIHandler(self)\\n self.user_to_action_mapping = {\\n 'admin': {\\n 0: self.admin_cli_handler.help,\\n 1: self.admin_cli_handler.relogin,\\n 2: self.admin_cli_handler.view_all_roles,\\n 3: self.admin_cli_handler.new_user,\\n 4: self.admin_cli_handler.new_role,\\n 5: self.admin_cli_handler.edit_role,\\n 6: self.admin_cli_handler.delete_role,\\n 7: self.admin_cli_handler.view_request,\\n 8: self.admin_cli_handler.delete_user,\\n 9: self.admin_cli_handler.list_users,\\n 10: self.admin_cli_handler.assign_role_to_user\\n },\\n 'user': {\\n 0: self.user_cli_handler.help,\\n 1: self.user_cli_handler.relogin,\\n 2: self.user_cli_handler.view_all_roles,\\n 3: self.user_cli_handler.request_new_role,\\n 4: self.user_cli_handler.view_my_roles\\n }\\n }\\n\\n print(f\\\"\\\"\\\"\\\\n\\\\n\\n Hi! You are logged in as {self.admin.username}\\n Enter 0(numerical zero) for help\\n Enter Ctrl+C to exit!\\n \\\"\\\"\\\")\",\n \"def init_app(self):\\n \\n tk_multi_depedencies = self.import_module(\\\"tk_multi_depedencies\\\")\\n \\n # register commands:\\n display_name = self.get_setting(\\\"display_name\\\")\\n \\n # \\\"Publish Render\\\" ---> publish_render\\n command_name = display_name.lower().replace(\\\" \\\", \\\"_\\\")\\n if command_name.endswith(\\\"...\\\"):\\n command_name = command_name[:-3]\\n params = {\\\"short_name\\\": command_name, \\n \\\"title\\\": \\\"%s...\\\" % display_name,\\n \\\"description\\\": \\\"Publishing dependencies into Shotgun\\\"}\\n\\n show_dialog_callback = lambda: self.engine.show_dialog(display_name, self, tk_multi_depedencies.Dialog, self)\\n \\n self.log_debug(\\\"Registering command for tk-multi-dependencies\\\")\\n self.engine.register_command(\\\"%s...\\\" % display_name, \\n show_dialog_callback, \\n params)\",\n \"def _post_initialisations(self):\\n # Init the settings module.\\n self.dummy_for_settings = SectionConfig(self.app.id, self.__class__.__name__)\\n global settings\\n settings = self.dummy_for_settings\\n\\n self.dummy_for_options = OptionConfig(self.app.id)\\n global options\\n options = self.dummy_for_options\\n\\n # Bind message boxes.\\n self.MessageBox = MessageBox(self)\\n self.msg = self.MessageBox.Message\\n self.are_you_sure = self.MessageBox.are_you_sure\\n\\n # Set previous size and state.\\n width = settings.get('width', 350)\\n height = settings.get('height', 350)\\n self.set_title(self.app.localizedname)\\n self.resize(width, height)\\n if settings.get_bool('maximized', False):\\n self.maximize()\\n # Load any other settings here.\\n self.load_xinput_devices()\",\n \"def __init__(self, master):\\n self.ports = list(serial.tools.list_ports.comports())\\n self.master = master # initialize Root window\\n self.master.title('Zeng.ltd Dashboard') # set title for Root window\\n self.master.geometry(\\\"1000x700\\\") # set size and location for Root window\\n self.master.configure(background=\\\"lightgrey\\\")\\n self.mainframe = ttk.Notebook(self.master,\\n padding=\\\"0 0 0 0\\\") # left top right bottem create mainframe in Root winow\\n self.mainframe.grid(column=0, row=0, sticky=(N, W, S, E)) # set mainframe to root windows size\\n self.master.protocol(\\\"WM_DELETE_WINDOW\\\", lambda: self.quit())\\n\\n menubar = Menu(self.master) # create a menubar\\n\\n # display the menu\\n self.master.config(menu=menubar)\\n\\n # List of all our views\\n self.views = {} # Empty dict\\n\\n # The back-end process\\n self.pyctrl = PyCtrl(self)\\n self.pyctrl.start()\",\n \"def init_and_toast(self, msg):\\n html = render_to_string(\\\"include/hardware_admin_init.html\\\", {\\n 'hw_list': ItemType.objects.all(),\\n })\\n return JsonResponse({\\n 'content': html,\\n 'msg': msg\\n })\",\n \"def __init__(self):\\n self.model = self.load_model()\\n self.form_html = self.create_form_html()\",\n \"def __init__(self, main_logger = None):\\n\\t\\tself.log = main_logger or logger.init_logger(\\\"mandrill\\\")\\n\\n\\t\\tself.http_client = AsyncHTTPClient()\\n\\t\\tself.key = config.MANDRILL_KEY\",\n \"def readResponseDB(self):\\n self.widget = ResponseFilePicker(self, self.database_api)\\n self.widget.show()\",\n \"def on_init(self):\\n self.write_log(\\\"策略初始化\\\")\\n self.exchange_load_bar(self.exchange)\",\n \"def setup(self):\\n\\t\\tScriptedLoadableModuleWidget.setup(self)\\n\\n\\t\\tself._loadUI()\\n\\n\\t\\t# Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's\\n\\t\\t# \\\"mrmlSceneChanged(vtkMRMLScene*)\\\" signal in is connected to each MRML widget's.\\n\\t\\t# \\\"setMRMLScene(vtkMRMLScene*)\\\" slot.\\n\\t\\t\\n\\n\\t\\t# Create logic class. Logic implements all computations that should be possible to run\\n\\t\\t# in batch mode, without a graphical user interface.\\n\\t\\tself.logic = postopProgrammingLogic()\\n\\n\\t\\t# Connections\\n\\t\\tself._setupConnections()\",\n \"def __init__(self):\\n # Passing the class make this Python 2 and Python 3 compatible\\n super(MayaSceneLevelGeneratorUI, self).__init__(parent=maya_main_window())\\n\\n # Create the generators needed\\n self._level_gen = level.LevelGenerator([blocks.BlockFile(\\\"\\\", blk_type) for blk_type in VALID_BLOCK_TYPES])\\n self._scene_gen = MayaSceneLevelGenerator(None) # Fill in level at button press time\\n\\n # Window things\\n self.setWindowTitle(\\\"Maya Scene Level Generator\\\")\\n self.resize(500, 200)\\n self.setWindowFlags(self.windowFlags() ^ PySide2.QtCore.Qt.WindowContextHelpButtonHint)\\n\\n # Set up for the first time\\n self._create_widgets()\\n self._create_layout()\\n self._refresh_view()\\n self._create_connections() # Order matters, since refreshing triggers connections\\n\\n print(self._level_gen.block_list) # TODO delete\",\n \"def user_init(self):\\n pass\",\n \"def setUp(self):\\n\\n self.logger_stats = DataScreen()\",\n \"def _init_(self):\\n self.res = {}\",\n \"def setup(self):\\r\\n ScriptedLoadableModuleWidget.setup(self)\\r\\n\\r\\n # Load widget from .ui file (created by Qt Designer).\\r\\n # Additional widgets can be instantiated manually and added to self.layout.\\r\\n uiWidget = slicer.util.loadUI(self.resourcePath('UI/RecordHerniaData.ui'))\\r\\n self.layout.addWidget(uiWidget)\\r\\n self.ui = slicer.util.childWidgetVariables(uiWidget)\\r\\n\\r\\n # Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's\\r\\n # \\\"mrmlSceneChanged(vtkMRMLScene*)\\\" signal in is connected to each MRML widget's.\\r\\n # \\\"setMRMLScene(vtkMRMLScene*)\\\" slot.\\r\\n uiWidget.setMRMLScene(slicer.mrmlScene)\\r\\n\\r\\n # Create logic class. Logic implements all computations that should be possible to run\\r\\n # in batch mode, without a graphical user interface.\\r\\n self.logic = TMSRecordDataModuleLogic()\\r\\n self.recordingStarted = False\\r\\n self.camerasStarted = False\\r\\n self.moduleDir = os.path.dirname(slicer.modules.tmsrecorddatamodule.path)\\r\\n self.logic.setupScene()\\r\\n\\r\\n # Buttons\\r\\n self.ui.StartStopRecordingButton.connect('clicked(bool)', self.onStartStopRecordingClicked)\\r\\n self.ui.startCamerasButton.connect('clicked(bool)',self.onStartStopCamerasClicked)\",\n \"def _init(self):\\n self.wx_menu = wx.Menu()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.56793326\",\n \"0.56783015\",\n \"0.56767124\",\n \"0.56668055\",\n \"0.56660783\",\n \"0.56660783\",\n \"0.56660783\",\n \"0.56660783\",\n \"0.56660783\",\n \"0.56660783\",\n \"0.56660783\",\n \"0.56660783\",\n \"0.5660091\",\n \"0.5650568\",\n \"0.5648301\",\n \"0.56349903\",\n \"0.5631568\",\n \"0.5619361\",\n \"0.561183\",\n \"0.5608623\",\n \"0.56030136\",\n \"0.56015205\",\n \"0.5597639\",\n \"0.55975324\",\n \"0.55868655\",\n \"0.5582321\",\n \"0.5572517\",\n \"0.5572248\",\n \"0.55714345\",\n \"0.5569192\",\n \"0.55672264\",\n \"0.5561664\",\n \"0.5553741\",\n \"0.55487615\",\n \"0.55467266\",\n \"0.55462027\",\n \"0.5539639\",\n \"0.55341375\",\n \"0.55282027\",\n \"0.5528108\",\n \"0.55272955\",\n \"0.5525144\",\n \"0.5525013\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.55337137"},"document_rank":{"kind":"string","value":"95"}}},{"rowIdx":86698,"cells":{"query":{"kind":"string","value":"Set up UI within QGIS"},"document":{"kind":"string","value":"def initGui(self):\n\n # set srs\n self._displayCrs = QgsCoordinateReferenceSystem()\n self._displayCrs.createFromOgcWmsCrs('EPSG:4167') \n self.iface.mapCanvas().mapSettings().setDestinationCrs(self._displayCrs)\n\n # init layerManager\n self._layerManager = LayerManager(self.iface, self)\n self._layerManager.registerFunctions()\n # init Highlighter\n self.highlighter = FeatureHighlighter(self.iface, self._layerManager, self)\n\n # Build an action list from QGIS navigation toolbar\n actionList = self.iface.mapNavToolToolBar().actions()\n self.actions = self.iface.mapNavToolToolBar().actions()\n\n # Main address editing window\n self._loadaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/loadaddress.png'), \n 'QGIS-AIMS-Plugin', self.iface.mainWindow())\n self._loadaction.setWhatsThis('Open the QGIS-AIMS-Plugin')\n self._loadaction.setStatusTip('Open the QGIS-AIMS-Plugin')\n self._loadaction.triggered.connect(self.loadQueues)\n self._loadaction.triggered.connect(self.loadLayers)\n self._loadaction.triggered.connect(self.enableAddressLayer)\n self._loadaction.triggered.connect(self.startDM)\n self._loadaction.triggered.connect(self.disableConfig)\n \n # Config Dialog\n self._configdialog = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/config.png'), \n 'AIMS Configuration', self.iface.mainWindow())\n self._configdialog.setWhatsThis('Configure AIMS')\n self._configdialog.setStatusTip('Configure AIMS')\n self._configdialog.triggered.connect(self.configureAims)\n\n self.config_stacked_widget = self._config_ui.uStackedWidget\n self.config_list_options = self._config_ui.uListOptions\n self.config_list_options.itemClicked.connect(self.showConfigSelectedOption)\n\n item = QListWidgetItem(\"User Config\")\n image_path = ':/plugins/QGIS-AIMS-Plugin/resources/config.png'\n item.setIcon(QIcon(image_path))\n self.config_list_options.addItem(item)\n\n item = QListWidgetItem(\"Dev Config\")\n image_path = ':/plugins/QGIS-AIMS-Plugin/resources/config.png'\n item.setIcon(QIcon(image_path))\n self.config_list_options.addItem(item)\n\n # Create new address tool\n self._createnewaddressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/newaddresspoint.png'), \n 'Create AIMS Feature', self.iface.mainWindow())\n self._createnewaddressaction.setWhatsThis('Create AIMS Feature')\n self._createnewaddressaction.setStatusTip('Create AIMS Feature')\n self._createnewaddressaction.setEnabled(False)\n self._createnewaddressaction.setCheckable(True)\n self._createnewaddressaction.triggered.connect( self.startNewAddressTool )\n self._createnewaddresstool = CreateNewAddressTool( self.iface, self._layerManager, self)\n self._createnewaddresstool.setAction( self._createnewaddressaction )\n self.actions.append(self._createnewaddressaction)\n\n # Delete address point\n self._deladdressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/deleteaddress.png'), \n 'Delete AIMS Feature', self.iface.mainWindow())\n self._deladdressaction.setWhatsThis('Delete AIMS Feature')\n self._deladdressaction.setStatusTip('Delete AIMS Feature')\n self._deladdressaction.setEnabled(False)\n self._deladdressaction.setCheckable(True)\n self._deladdressaction.triggered.connect( self.startDelAddressTool )\n self._deladdtool = DelAddressTool( self.iface, self._layerManager, self)\n self._deladdtool.setAction( self._deladdressaction ) \n self.actions.append(self._deladdressaction)\n\n # Move address\n self._moveaddressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/moveaddress.png'), \n 'Move AIMS Feature(s)', self.iface.mainWindow())\n self._moveaddressaction.setWhatsThis('Move AIMS Feature(s)')\n self._moveaddressaction.setStatusTip('Move AIMS Feature(s)')\n self._moveaddressaction.setEnabled(False)\n self._moveaddressaction.setCheckable(True)\n self._moveaddressaction.triggered.connect( self.startMoveAddressTool )\n self._moveaddtool = MoveAddressTool( self.iface, self._layerManager, self)\n self._moveaddtool.setAction( self._moveaddressaction ) \n self.actions.append(self._moveaddressaction)\n \n # Update address\n self._updateaddressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/updateaddress.png'), \n 'Update AIMS Feature', self.iface.mainWindow())\n self._updateaddressaction.setWhatsThis('Update AIMS Feature')\n self._updateaddressaction.setStatusTip('Update AIMS Feature')\n self._updateaddressaction.setEnabled(False)\n self._updateaddressaction.setCheckable(True)\n self._updateaddressaction.triggered.connect( self.startUpdateAddressTool )\n self._updateaddtool = UpdateAddressTool( self.iface, self._layerManager, self)\n self._updateaddtool.setAction( self._updateaddressaction ) \n self.actions.append(self._updateaddressaction)\n\n # RCL tool -- Not a QAction as triggered from many palaces but not the toolbar\n self._rcltool = GetRcl(self.iface, self._layerManager, self)\n\n # UpdateReview Position tool -- Not a QAction as triggered initiated from review queue form\n self._updateReviewPos = UpdateReviewPosition(self.iface, self._layerManager, self)\n\n # Address lineage\n \"\"\"\n self._lineageaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/lineage.png'), \n 'Build Lineage Relationships Between Features', self.iface.mainWindow())\n self._lineageaction.setWhatsThis('Build Lineage Relationships Between Features')\n self._lineageaction.setStatusTip('Build Lineage Relationships Between Features')\n self._lineageaction.setEnabled(False)\n self._lineageaction.setCheckable(True)\n self._lineagetool = LineageTool( self.iface, self._layerManager, self)\n self._lineageaction.triggered.connect(self._lineagetool.setEnabled)\n self.actions.append(self._lineageaction)\n \"\"\"\n\n # Address highlighter\n self._highlightaction = QAction(QIcon(\":/plugins/QGIS-AIMS-Plugin/resources/addresshighlight.png\"), \n \"Electoral address highlighter\", self.iface.mainWindow())\n self._highlightaction.setWhatsThis(\"Turn the electoral address highlighter on or off\")\n self._highlightaction.setStatusTip(\"Turn the electoral address highlighter on or off\") \n self._highlightaction.setText('Highlightaction')\n self._highlightaction.setEnabled(False)\n self._highlightaction.setCheckable(True)\n self._highlightaction.toggled.connect( self.highlighter.setEnabled )\n\n # Add to own toolbar\n self._toolbar = self.iface.addToolBar('QGIS-AIMS-Plugin')\n self._toolbar.addAction(self._createnewaddressaction)\n self._toolbar.addAction(self._deladdressaction)\n self._toolbar.addAction(self._updateaddressaction)\n self._toolbar.addAction(self._moveaddressaction)\n #self._toolbar.addAction(self._lineageaction)\n self._toolbar.addAction(self._highlightaction)\n\n # Add actions to menu\n self.iface.addToolBarIcon(self._loadaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._loadaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._createnewaddressaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._deladdressaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._updateaddressaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._moveaddressaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._highlightaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._configdialog)\n\n # capture maptool selection changes\n QObject.connect(self.iface.mapCanvas(), SIGNAL( \"mapToolSet(QgsMapTool *)\" ), self.mapToolChanged)\n\n # Add actions from QGIS attributes toolbar (handling QWidgetActions)\n tmpActionList = self.iface.attributesToolBar().actions()\n for action in tmpActionList:\n if isinstance(action, QWidgetAction):\n actionList.extend( action.defaultWidget().actions() )\n else:\n actionList.append( action )\n # ... could add other toolbars' action lists...\n\n # Build a group with actions from actionList\n group = QActionGroup( self.iface.mainWindow() )\n group.setExclusive(True)\n for qgisAction in actionList:\n group.addAction( qgisAction )\n\n # Add our own actions\n for action in self.actions:\n if action.text() == 'Highlightaction': continue\n group.addAction( action )"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def setup(self):\n self.ui.setup_window()","def initUI(self):\n \n self.setWindowTitle(\"Intecol Flir camera\")\n self.setGeometry(300, 100, 1012, 622)","def inicialUI(self):\r\n\r\n self.setGeometry(500, 500, 500, 500)\r\n self.setWindownTitle(\"Pesquisa\")\r\n self.displayWidgets()\r\n\r\n self.show()","def initGui(self):\n self.sketchButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'sketch.svg'),\n text=self.tr('Sketch on map'),\n callback=self.sketchAction,\n parent=self.iface.mainWindow(),\n object_name='mSketchAction')\n self.penButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'pen.svg'),\n text=self.tr('Draw line on map'),\n callback=self.penAction,\n parent=self.iface.mainWindow(),\n object_name='mPenAction')\n self.canvasButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'canvas.svg'),\n text=self.tr('Color and width canvas'),\n callback=None,\n parent=self.iface.mainWindow())\n self.eraseButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'erase.svg'),\n text=self.tr('Erase sketches'),\n callback=self.eraseAction,\n parent=self.iface.mainWindow(),\n object_name='mEraseAction')\n self.removeButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'remove.svg'),\n text=self.tr('Remove all sketches'),\n callback=self.removeSketchesAction,\n parent=self.iface.mainWindow(),\n object_name='mRemoveAllSketches')\n self.noteButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'note.svg'),\n text=self.tr('Add text annotations to sketches'),\n callback=None,\n parent=self.iface.mainWindow(),\n object_name='mAddTextAnnotations')\n self.convertButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'toLayer.svg'),\n text=self.tr('Convert annotations to Memory Layer'),\n callback=self.toMemoryLayerAction,\n parent=self.iface.mainWindow(),\n object_name='mConvertAnnotationsToMemoryLayer')\n self.saveButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'inbox.svg'),\n text=self.tr('Save sketches to file'),\n callback=self.saveAction,\n parent=self.iface.mainWindow(),\n object_name='mSaveSketchesToFile')\n self.loadButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'outbox.svg'),\n text=self.tr('Load sketches from file'),\n callback=self.loadAction,\n parent=self.iface.mainWindow(),\n object_name='mLoadSketchesFromFile')\n self.canvasButton.setMenu(self.canvasMenu())\n self.noteButton.setCheckable(True)\n self.penButton.setCheckable(True)\n self.sketchButton.setCheckable(True)\n self.eraseButton.setCheckable(True)\n self.geoSketches = []\n self.dumLayer = QgsVectorLayer(\"Point?crs=EPSG:4326\", \"temporary_points\", \"memory\")\n self.pressed = None\n self.previousPoint = None\n self.previousMoved = None\n self.gestures = 0\n self.points = 0\n self.currentColor = QColor(\"#aa0000\")\n self.currentWidth = 5\n self.annotation = sketchNoteDialog(self.iface)\n self.annotatatedSketch = None\n self.sketchEnabled(None)\n self.iface.projectRead.connect(self.projectReadAction)\n self.iface.newProjectCreated.connect(self.newProjectCreatedAction)\n QgsProject.instance().legendLayersAdded.connect(self.notSavedProjectAction)","def init_ui(self):\n raise NotImplementedError","def init_ui(self):\n raise NotImplementedError","def initGui(self):\n # Create an action to start capturing polygons and add it to the digitize toolbar\n icon = QIcon()\n icon.addPixmap(QPixmap(_fromUtf8(\":/plugins/olainterface/lo-logo.png\")), QIcon.Normal, QIcon.Off)\n self.openDialogAction = QAction(icon, QString(\"Land Observatory\"), self.iface.mainWindow())\n self.iface.pluginToolBar().addAction(self.openDialogAction)\n\n # Connect to signals for button behaviour\n self.connect(self.openDialogAction, SIGNAL(\"triggered()\"), self.openDialog)","def setUp(self):\n self.dialog = wtss_qgisDialog(None)","def initGui(self):\n\n icon_path = ':/plugins/EU_Mapper/EUICON.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Generate EU Map'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def initUI(self):\n self.logger.debug('Setting up the Measurement GUI')\n self.setWindowTitle(self.title)\n\n self.show()\n\n self.make_combobox_scanner()\n self.make_combobox_movements()\n self.make_combobox_configurate()\n self.make_combobox_basic()","def initGui(self):\n self.run_icon = QIcon(\n os.path.join(self.plugin_dir, \"resources\", \"icon_add_service.svg\")\n )\n self.fav_icon = QIcon(\n os.path.join(self.plugin_dir, \"resources\", \"pdok_icon_bookmark.svg\")\n )\n\n self.del_icon = QIcon(\n os.path.join(self.plugin_dir, \"resources\", \"pdok_icon_delete.svg\")\n )\n\n self.run_action = QAction(self.run_icon, PLUGIN_NAME, self.iface.mainWindow())\n self.run_button = QToolButton()\n self.run_button.setMenu(QMenu())\n self.run_button.setPopupMode(QToolButton.MenuButtonPopup)\n self.run_button.setDefaultAction(self.run_action)\n\n self.services_loaded = False\n\n self.run_action.triggered.connect(self.run)\n self.setup_fq_checkboxes()\n\n # Add toolbar button and menu item\n self.toolbar = self.iface.addToolBar(PLUGIN_NAME)\n self.toolbar.setObjectName(PLUGIN_NAME)\n self.toolbar.addWidget(self.run_button)\n\n # Set default layer loading behaviour\n self.default_tree_locations = {\n \"wms\": \"top\",\n \"wmts\": \"bottom\",\n \"wfs\": \"top\",\n \"wcs\": \"top\",\n }\n\n self.add_fav_actions_to_toolbar_button()\n\n self.toolbar_search = QLineEdit()\n\n def toolbar_search_mouse_event():\n self.toolbar_search.selectAll()\n self.timer_toolbar_search.start()\n\n self.toolbar_search.mousePressEvent = lambda _: toolbar_search_mouse_event()\n\n self.toolbar_search.setMaximumWidth(200)\n self.toolbar_search.setAlignment(Qt.AlignLeft)\n self.toolbar_search.setPlaceholderText(\"Zoek in PDOK Locatieserver\")\n self.toolbar.addWidget(self.toolbar_search)\n self.timer_toolbar_search = QTimer()\n self.timer_toolbar_search.setSingleShot(True)\n self.timer_toolbar_search.setInterval(200)\n self.timer_toolbar_search.timeout.connect(self.toolbar_search_get_suggestions)\n self.toolbar_search.textEdited.connect(\n lambda: self.timer_toolbar_search.start()\n )\n\n eraser_icon = QIcon(\n os.path.join(self.plugin_dir, \"resources\", \"icon_remove_cross.svg\")\n )\n self.clean_ls_search_action = QAction(\n eraser_icon, \"Cleanup\", self.erase_address()\n )\n\n if not self.show_ls_feature():\n self.toolbar.addAction(self.clean_ls_search_action)\n\n self.clean_ls_search_action.triggered.connect(self.erase_address)\n self.clean_ls_search_action.setEnabled(False)\n self.iface.addPluginToMenu(f\"&{PLUGIN_NAME}\", self.run_action)\n\n self.about_action = QAction(self.run_icon, \"About\", self.iface.mainWindow())\n self.about_action.setWhatsThis(f\"{PLUGIN_NAME} About\")\n self.iface.addPluginToMenu(f\"&{PLUGIN_NAME}\", self.about_action)\n\n self.about_action.triggered.connect(self.about)\n self.dlg.ui.btnLoadLayer.clicked.connect(lambda: self.load_layer(\"default\"))\n self.dlg.ui.btnLoadLayerTop.clicked.connect(lambda: self.load_layer(\"top\"))\n self.dlg.ui.btnLoadLayerBottom.clicked.connect(\n lambda: self.load_layer(\"bottom\")\n )\n\n self.dlg.ui.pushButton.clicked.connect(self.toggle_all_fq_checkboxes)\n\n self.dlg.geocoder_search.returnPressed.connect(\n self.ls_dialog_get_suggestions_and_remove_pointer\n )\n self.timer_geocoder_search = QTimer()\n self.timer_geocoder_search.setSingleShot(True)\n self.timer_geocoder_search.setInterval(200)\n self.timer_geocoder_search.timeout.connect(\n self.ls_dialog_get_suggestions_and_remove_pointer\n )\n self.dlg.geocoder_search.textEdited.connect(\n lambda: self.timer_geocoder_search.start()\n )\n\n self.dlg.geocoder_search.setPlaceholderText(\n \"Zoek in PDOK Locatieserver, bv postcode of postcode huisnummer\"\n )\n\n self.dlg.geocoderResultSearch.textChanged.connect(self.filter_geocoder_result)\n self.dlg.geocoderResultSearch.setPlaceholderText(\n \"een of meer zoekwoorden uit resultaat\"\n )\n # connect all fq checkboxes with suggest, so upon a change in fq filter we re-search\n for cbx in self.fq_checkboxes.keys():\n cbx.stateChanged.connect(self.ls_dialog_get_suggestions_and_remove_pointer)\n self.run(True)\n\n # set to hidden when no layer selected\n self.dlg.ui.layer_info.setHidden(True)\n self.dlg.ui.layer_options_groupbox.setHidden(True)","def initializeUI(self):\n self.setStyleSheet(abstyle)\n self.setGeometry(140, 100, 860, 484)\n self.setWindowTitle('Emotions Data View')\n self.setupModelView()","def initGui(self):\n #icon_path = ':/plugins/QGisImajnetPlugin/icon.png'\n icon_path = ':/plugins/imajnet-qgis-plugin/resources/img/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Imajnet plugin'),\n callback=self.run,\n parent=self.iface.mainWindow())\n \n self._closestImageAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/closestImageMode.png',\n text=self.tr(u'Closest image'),\n callback=self.on_closestImage_click,\n checkable_flag=True,\n enabled_flag=False,\n add_to_menu=False,\n add_to_toolbar=True,\n status_tip=self.tr(u'Click on the map and get the closest image'),\n whats_this=self.tr(u'Imajnet closest image tool'),\n parent=self.iface.mainWindow())\n \n self._clickModeAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/clickMode.png',\n text=self.tr(u'Click mode'),\n callback=self.on_clickMode_click,\n checkable_flag=True,\n enabled_flag=False,\n add_to_menu=False,\n add_to_toolbar=True,\n status_tip=self.tr(u'Click on an object and get an image oriented towards that object'),\n whats_this=self.tr(u'Imajnet click mode tool'),\n parent=self.iface.mainWindow())\n \n \n self._aboutAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/about.png',\n text=self.tr(u'About'),\n callback=self.on_about_click,\n enabled_flag=True,\n add_to_menu=True,\n add_to_toolbar=False,\n parent=self.iface.mainWindow())\n \n self._helpAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/help.png',\n text=self.tr(u'Help'),\n callback=self.on_help_click,\n enabled_flag=True,\n add_to_menu=True,\n add_to_toolbar=True,\n parent=self.iface.mainWindow())\n \n self._disconnectAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/logout.png',\n text=self.tr(u'Disconnect'),\n callback=self.on_disconnect_click,\n enabled_flag=False,\n add_to_menu=True,\n add_to_toolbar=False,\n parent=self.iface.mainWindow())\n \n self._debugModeAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/debug.png',\n text=self.tr(u'Debug'),\n callback=self.on_debug_click,\n enabled_flag=False,\n add_to_menu=self.isDevMode(),\n add_to_toolbar=False,\n parent=self.iface.mainWindow())\n debugShortcutKeys = QKeySequence(\"Ctrl+I\")\n self._debugModeAction.setShortcut(debugShortcutKeys)\n #self._debugModeAction.setVisible(False)\n \n \n self._jumpForwardAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\n text=self.tr(u'jump forward'),\n callback=self.jumpForward,\n enabled_flag=True,\n add_to_menu=False,\n add_to_toolbar=False,\n parent=self.iface.mainWindow())\n jumpForwardShortcutKeys = QKeySequence(\"PgUp\")\n self._jumpForwardAction.setShortcut(jumpForwardShortcutKeys)\n \n self._jumpBackwardAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\n text=self.tr(u'jump backward'),\n callback=self.jumpBackward,\n enabled_flag=True,\n add_to_menu=False,\n add_to_toolbar=False,\n parent=self.iface.mainWindow()) \n jumpBackwardShortcutKeys = QKeySequence(\"PgDown\")\n self._jumpBackwardAction.setShortcut(jumpBackwardShortcutKeys)\n \n \n self._jumpForwardShortAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\n text=self.tr(u'jump forward short'),\n callback=self.jumpForwardShort,\n enabled_flag=True,\n add_to_menu=False,\n add_to_toolbar=False,\n parent=self.iface.mainWindow())\n jumpForwardShortShortcutKeys = QKeySequence(\"Shift+PgUp\")\n self._jumpForwardShortAction.setShortcut(jumpForwardShortShortcutKeys)\n \n self._jumpBackwardShortAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\n text=self.tr(u'jump backward short'),\n callback=self.jumpBackwardShort,\n enabled_flag=True,\n add_to_menu=False,\n add_to_toolbar=False,\n parent=self.iface.mainWindow()) \n jumpBackwardShortShortcutKeys = QKeySequence(\"Shift+PgDown\")\n self._jumpBackwardShortAction.setShortcut(jumpBackwardShortShortcutKeys)","def prepare_UI(self):","def initGui(self):\n icon = QIcon(\":/plugins/DEMto3D/icons/demto3d.png\")\n text = self.tr(\"DEM 3D printing\")\n parent = self.iface.mainWindow()\n self.action = QAction(icon, text, parent)\n self.action.setObjectName(text)\n self.action.setStatusTip(text)\n self.action.triggered.connect(self.run)\n\n self.iface.addRasterToolBarIcon(self.action)\n self.iface.addPluginToRasterMenu(self.menu, self.action)","def initGui(self):\n\n icon_path = ':/plugins/Isovist/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Isovist'),\n callback=self.run,\n parent=self.iface.mainWindow())\n \n #example Code\n #result = QObject.connect(self.clickTool, SIGNAL(\"canvasClicked(const QgsPoint &, Qt::MouseButton)\"), self.handleMouseDown)\n #QMessageBox.information( self.iface.mainWindow(),\"Info\", \"connect = %s\"%str(result) )","def initGui(self):\n self.network_layer_notifier = QgepLayerNotifier(\n self.iface.mainWindow(), [\"vw_network_node\", \"vw_network_segment\"]\n )\n self.wastewater_networkelement_layer_notifier = QgepLayerNotifier(\n self.iface.mainWindow(), [\"vw_wastewater_node\", \"vw_qgep_reach\"]\n )\n self.toolbarButtons = []\n\n # Create toolbar button\n self.profileAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/wastewater-profile.svg\")),\n self.tr(\"Profile\"),\n self.iface.mainWindow(),\n )\n self.profileAction.setWhatsThis(self.tr(\"Reach trace\"))\n self.profileAction.setEnabled(False)\n self.profileAction.setCheckable(True)\n self.profileAction.triggered.connect(self.profileToolClicked)\n\n self.downstreamAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/wastewater-downstream.svg\")),\n self.tr(\"Downstream\"),\n self.iface.mainWindow(),\n )\n self.downstreamAction.setWhatsThis(self.tr(\"Downstream reaches\"))\n self.downstreamAction.setEnabled(False)\n self.downstreamAction.setCheckable(True)\n self.downstreamAction.triggered.connect(self.downstreamToolClicked)\n\n self.upstreamAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/wastewater-upstream.svg\")),\n self.tr(\"Upstream\"),\n self.iface.mainWindow(),\n )\n self.upstreamAction.setWhatsThis(self.tr(\"Upstream reaches\"))\n self.upstreamAction.setEnabled(False)\n self.upstreamAction.setCheckable(True)\n self.upstreamAction.triggered.connect(self.upstreamToolClicked)\n\n self.wizardAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/wizard.svg\")),\n \"Wizard\",\n self.iface.mainWindow(),\n )\n self.wizardAction.setWhatsThis(self.tr(\"Create new manholes and reaches\"))\n self.wizardAction.setEnabled(False)\n self.wizardAction.setCheckable(True)\n self.wizardAction.triggered.connect(self.wizard)\n\n self.connectNetworkElementsAction = QAction(\n QIcon(\n os.path.join(\n plugin_root_path(), \"icons/link-wastewater-networkelement.svg\"\n )\n ),\n QApplication.translate(\"qgepplugin\", \"Connect wastewater networkelements\"),\n self.iface.mainWindow(),\n )\n self.connectNetworkElementsAction.setEnabled(False)\n self.connectNetworkElementsAction.setCheckable(True)\n self.connectNetworkElementsAction.triggered.connect(self.connectNetworkElements)\n\n self.refreshNetworkTopologyAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/refresh-network.svg\")),\n \"Refresh network topology\",\n self.iface.mainWindow(),\n )\n self.refreshNetworkTopologyAction.setWhatsThis(\n self.tr(\"Refresh network topology\")\n )\n self.refreshNetworkTopologyAction.setEnabled(False)\n self.refreshNetworkTopologyAction.setCheckable(False)\n self.refreshNetworkTopologyAction.triggered.connect(\n self.refreshNetworkTopologyActionClicked\n )\n\n self.aboutAction = QAction(self.tr(\"About\"), self.iface.mainWindow())\n self.aboutAction.triggered.connect(self.about)\n\n self.settingsAction = QAction(self.tr(\"Settings\"), self.iface.mainWindow())\n self.settingsAction.triggered.connect(self.showSettings)\n\n self.importAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/interlis_import.svg\")),\n self.tr(\"Import from interlis\"),\n self.iface.mainWindow(),\n )\n self.importAction.setWhatsThis(self.tr(\"Import from interlis\"))\n self.importAction.setEnabled(False)\n self.importAction.setCheckable(False)\n self.importAction.triggered.connect(self.actionImportClicked)\n\n self.exportAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/interlis_export.svg\")),\n self.tr(\"Export to interlis\"),\n self.iface.mainWindow(),\n )\n self.exportAction.setWhatsThis(self.tr(\"Export from interlis\"))\n self.exportAction.setEnabled(False)\n self.exportAction.setCheckable(False)\n self.exportAction.triggered.connect(self.actionExportClicked)\n\n self.datamodelInitToolAction = QAction(\n self.tr(\"Datamodel tool\"), self.iface.mainWindow()\n )\n self.datamodelInitToolAction.triggered.connect(self.showDatamodelInitTool)\n\n # Add toolbar button and menu item\n self.toolbar = QToolBar(QApplication.translate(\"qgepplugin\", \"QGEP\"))\n self.toolbar.addAction(self.profileAction)\n self.toolbar.addAction(self.upstreamAction)\n self.toolbar.addAction(self.downstreamAction)\n self.toolbar.addAction(self.wizardAction)\n self.toolbar.addAction(self.refreshNetworkTopologyAction)\n self.toolbar.addAction(self.connectNetworkElementsAction)\n\n self.iface.addPluginToMenu(\"&QGEP\", self.profileAction)\n self.iface.addPluginToMenu(\"&QGEP\", self.settingsAction)\n self.iface.addPluginToMenu(\"&QGEP\", self.aboutAction)\n\n admin_mode = QSettings().value(\"/QGEP/AdminMode\", False)\n # seems QGIS loads True as \"true\" on restart ?!\n if admin_mode and admin_mode != \"false\":\n self.toolbar.addAction(self.importAction)\n self.toolbar.addAction(self.exportAction)\n self.iface.addPluginToMenu(\"&QGEP\", self.datamodelInitToolAction)\n\n self.iface.addToolBar(self.toolbar)\n\n # Local array of buttons to enable / disable based on context\n self.toolbarButtons.append(self.profileAction)\n self.toolbarButtons.append(self.upstreamAction)\n self.toolbarButtons.append(self.downstreamAction)\n self.toolbarButtons.append(self.wizardAction)\n self.toolbarButtons.append(self.refreshNetworkTopologyAction)\n self.toolbarButtons.append(self.importAction)\n self.toolbarButtons.append(self.exportAction)\n\n self.network_layer_notifier.layersAvailable.connect(self.onLayersAvailable)\n self.network_layer_notifier.layersUnavailable.connect(self.onLayersUnavailable)\n\n # Init the object maintaining the network\n self.network_analyzer = QgepGraphManager()\n self.network_analyzer.message_emitted.connect(\n self.iface.messageBar().pushMessage\n )\n # Create the map tool for profile selection\n self.profile_tool = QgepProfileMapTool(\n self.iface, self.profileAction, self.network_analyzer\n )\n self.profile_tool.profileChanged.connect(self.onProfileChanged)\n\n self.upstream_tree_tool = QgepTreeMapTool(\n self.iface, self.upstreamAction, self.network_analyzer\n )\n self.upstream_tree_tool.setDirection(\"upstream\")\n self.upstream_tree_tool.treeChanged.connect(self.onTreeChanged)\n self.downstream_tree_tool = QgepTreeMapTool(\n self.iface, self.downstreamAction, self.network_analyzer\n )\n self.downstream_tree_tool.setDirection(\"downstream\")\n self.downstream_tree_tool.treeChanged.connect(self.onTreeChanged)\n\n self.maptool_connect_networkelements = QgepMapToolConnectNetworkElements(\n self.iface, self.connectNetworkElementsAction\n )\n\n self.wastewater_networkelement_layer_notifier.layersAvailableChanged.connect(\n self.connectNetworkElementsAction.setEnabled\n )\n\n self.processing_provider = QgepProcessingProvider()\n QgsApplication.processingRegistry().addProvider(self.processing_provider)\n\n self.network_layer_notifier.layersAdded([])","def initializeUI(self):\n self.setGeometry(100, 100, 450, 300)\n self.setWindowTitle('Model and View Example')\n\n self.setupModelView()\n\n self.show()","def _init_ui(self):\n self.setWindowTitle(\"HB Havens: resultaten\")\n self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowContextHelpButtonHint)\n\n self.setLayout(QtWidgets.QVBoxLayout())\n\n # Create figure\n self.figure = Figure(figsize=(4,4))\n self.ax = self.figure.add_subplot()\n\n self.ax.grid()\n self.ax.spines['right'].set_visible(False)\n self.ax.spines['top'].set_visible(False)\n self.ax.tick_params(axis='y', color='0.75')\n self.ax.tick_params(axis='x', color='0.75')\n self.ax.set_aspect(1)\n\n # Add canvas\n self.canvas = FigureCanvasQTAgg(self.figure)\n\n # this is the Navigation widget\n # it takes the Canvas widget and a parent\n self.layout().addWidget(self.canvas)\n\n # Add location selection\n hbox = QtWidgets.QHBoxLayout()\n label = QtWidgets.QLabel('Locatie:')\n label.setFixedWidth(80)\n hbox.addWidget(label)\n self.location_combobox = QtWidgets.QComboBox()\n self.location_combobox.addItems(self.result_locations)\n self.location_combobox.setCurrentIndex(self.locid)\n self.location_combobox.currentIndexChanged.connect(self._set_location)\n hbox.addWidget(self.location_combobox)\n self.layout().addLayout(hbox)\n\n # Add parameter selection\n hbox = QtWidgets.QHBoxLayout()\n label = QtWidgets.QLabel('Parameter:')\n label.setFixedWidth(80)\n hbox.addWidget(label)\n self.parameter_combobox = QtWidgets.QComboBox()\n self.input_parameters = self.modelunctab.mainmodel.hydraulic_loads.result_columns[:]\n self.parameter_combobox.addItems(self.input_parameters)\n self.parameter_combobox.currentIndexChanged.connect(self._set_parameter)\n self.parameter_combobox.setCurrentIndex(0)\n self._set_parameter()\n self.figure.tight_layout()\n hbox.addWidget(self.parameter_combobox)\n self.layout().addLayout(hbox)\n\n # Line\n line = QtWidgets.QFrame()\n line.setFrameShape(QtWidgets.QFrame.HLine)\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\n\n self.layout().addWidget(line)\n\n # Add ok/close\n self.closebutton = QtWidgets.QPushButton('Sluiten')\n self.closebutton.clicked.connect(self.close)\n self.layout().addWidget(self.closebutton, 0, QtCore.Qt.AlignRight)\n\n self.layout().setSizeConstraint(QtWidgets.QLayout.SetFixedSize)","def init_ui(self):\n self.parent.title(\"Roku Player Controller\")\n self.style.theme_use(\"default\")","def initGui(self):\n\n self.action = QAction(QIcon(self.plugdir + '/icon.png'), 'MGB Water Balance', self.iface.mainWindow())\n self.action.triggered.connect(self.run)\n self.iface.addToolBarIcon(self.action)\n self.iface.addPluginToMenu('&IPH - Plugins', self.action)","def initializeUI(self):\n self.setGeometry(100, 100, 300, 200)\n self.setWindowTitle('Event Handling Example')\n\n self.show()","def initGui(self):\n\n icon_path = ':/plugins/AreaPrinter/mountainIcon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'AreaPrinter'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n\tself.add_action(\n icon_path,\n text=self.tr(u'Grid Convergence'),\n callback=self.runTools,\n parent=self.iface.mainWindow())","def initGui(self):\r\n\r\n # Create help action \r\n self.helpAction = QAction( QIcon(\":/plugins/layercombinations/about.png\"), u\"Help\", self.iface.mainWindow())\r\n # connect the action \r\n self.helpAction.triggered.connect( self.showHelp )\r\n # Add menu item\r\n self.iface.addPluginToMenu(u\"&Layer Combinations\", self.helpAction)\r\n\r\n # Create the action that allows to change the widget type\r\n self.changeWidgetAction = QAction(\"Change widget type\", self.iface.mainWindow())\r\n self.changeWidgetAction.triggered.connect( self.changeWidget )\r\n self.iface.addPluginToMenu(u\"&Layer Combinations\", self.changeWidgetAction)\r\n\r\n # Create the action that will toggle the plugin panel\r\n self.action = QAction(QIcon(\":/plugins/layercombinations/icon.png\"), \"Show/hide the Layer Combinations widgets\", self.iface.mainWindow())\r\n self.action.triggered.connect( self.widget.toggle )\r\n # Add toolbar button and menu item\r\n self.iface.addToolBarIcon(self.action)\r\n self.iface.addPluginToMenu(u\"&Layer Combinations\", self.action)\r\n\r\n\r\n # Add the widget to the mainWindow\r\n self.widget.addToiFace(self.iface)","def __init__(self):\n super(QTUIProject, self).__init__()\n self.setupUi(self)\n self.assignWidgets()\n self.show()\n self.SlotsJsonName = \"Slots Assets Folder\"\n self.BingoJsonName = \"Bingo Assets Folder\"","def init_ui(self):\n raise NotImplementedError(\"This is an abstract method.\")","def initGui(self):\n from p4_view import Gui\n self.updateStatus(\"Launching GUI...\")\n self.gui = Gui(self, self.lmap)\n self.gui.setStart(self.cfg[\"START\"])\n self.gui.setGoal(self.cfg[\"GOAL\"])\n self.gui.setPossGoals(self.cfg[\"POSS_GOALS\"])\n #GHD\n self.gui.setMapName(self.cfg[\"MAP_FILE\"])\n self.updateStatus(\"OK\")\n self.gui.mainloop()","def initGui(self):\n\n icon_path = ':/plugins/qgsCsvAzmDist2LatLon/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'csvAzmDist2LatLon'),\n callback=self.run,\n parent=self.iface.mainWindow())\n \n self.dlg.pbInCsv.clicked.connect(self.select_input_file)\n self.dlg.pbOutCsv.clicked.connect(self.select_output_file)\n self.dlg.pbCalc.clicked.connect(self.calculations)","def initGui(self):\n\n icon_path = ''\n self.add_action(\n icon_path,\n text=self.tr(u'圖資操作處理工具v3.0'),\n callback=self.run,\n parent=self.iface.mainWindow())\n \n \n\n #照片EXIF定位ImportPhotos初始參數\n self.clickPhotos = self.add_action(\n icon_path,\n text=self.tr(u'檢視照片'),\n callback=self.mouseClick,\n parent=self.iface.mainWindow())\n \n self.clickPhotos.setCheckable(True)\n self.clickPhotos.setEnabled(True)\n # self.iface.addPluginToMenu(TFB_Tools3.description(), self.action)\n self.listPhotos = []\n self.layernamePhotos = [] \n self.toolMouseClick = MouseClick(self.canvas, self)\n \n\n self.fields = ['ID', 'Name', 'Date', 'Time', 'Lon', 'Lat', 'Altitude', 'North', 'Azimuth', 'Camera Maker',\n 'Camera Model', 'Title', 'Comment', 'Path', 'RelPath', 'Timestamp']\n\n self.extension_switch = {\n \".shp\": \"ESRI Shapefile\",\n \".geojson\": \"GeoJSON\",\n \".gpkg\":\"GPKG\",\n \".csv\": \"CSV\",\n \".kml\": \"KML\",\n \".tab\": \"MapInfo File\"\n }\n\n self.extension_switch2 = {\n \"ESRI Shapefile (*.shp *.SHP)\": \".shp\",\n \"GeoJSON (*.geojson *.GEOJSON)\": \".geojson\",\n \"GeoPackage (*.gpkg *.GPKG)\":\".gpkg\",\n \"Comma Separated Value (*.csv *.CSV)\": \".csv\",\n \"Keyhole Markup Language (*.kml *.KML)\": \".kml\",\n \"Mapinfo TAB (*.tab *.TAB)\": \".tab\"\n }\n\n self.extension_switch_types = {\n \".shp\": \"ESRI Shapefile\",\n \".geojson\": \"GeoJSON\",\n \".gpkg\":\"GPKG\",\n \".csv\": \"CSV\",\n \".kml\": \"KML\",\n \".tab\": \"MapInfo File\"\n }\n\n ############################\n\n self.pointEmitter = QgsMapToolEmitPoint(self.iface.mapCanvas())\n self.iface.mapCanvas().setMapTool( self.pointEmitter )","def initGui(self):\n\n icon_path = ':/plugins/PluginChoucas/img/loaddata.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Chargement de données de montagne'),\n callback=self.run,\n parent=self.iface.mainWindow())\n \n icon_path = ':/plugins/PluginChoucas/img/loupe.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Recherche par mots sur clé'),\n callback=self.search,\n parent=self.iface.mainWindow())\n \n icon_path = ':/plugins/PluginChoucas/img/EmpriseZoneEtude.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Emprise ZE'),\n callback=self.loadZoneEtude,\n parent=self.iface.mainWindow())\n \n \n #icon_path = ':/plugins/PluginChoucas/img/landmark.png'\n #self.add_action(\n # icon_path,\n # text=self.tr(u'Afficher la description de l itinéraire'),\n # callback=self.displayDescription,\n # parent=self.iface.mainWindow())","def __setup_ui(self):\n self.pixel_label = QLabel(\"\", self)\n self.pixel_label.setFixedWidth(100)\n self.pixel_coords_label = QLabel(\"\", self)\n self.statusBar().addPermanentWidget(self.pixel_coords_label)\n self.statusBar().addPermanentWidget(self.pixel_label)\n\n self.current_fps_label = QLabel(\"\", self)\n self.statusBar().addPermanentWidget(self.current_fps_label)\n\n self.toolbar = self.addToolBar(\"default\")\n self.toolbar.setMovable(False)\n self.setContextMenuPolicy(Qt.NoContextMenu)\n\n exit_act = QAction(QIcon.fromTheme('exit'), 'Exit', self)\n exit_act.setShortcut('Ctrl+Q')\n exit_act.setStatusTip(\"Exit application\")\n exit_act.triggered.connect(self.app.quit)\n self.toolbar.addAction(exit_act)\n\n preferences_action = QAction(QIcon.fromTheme(\"preferences-desktop\"),\n \"Preferences\", self)\n preferences_action.setStatusTip(\"Open preferences dialog\")\n preferences_action.triggered.connect(self.open_preferences)\n self.toolbar.addAction(preferences_action)\n\n self.device_label = QLabel(\"Device:\")\n self.device_combo = QComboBox(self)\n self.device_combo.setSizeAdjustPolicy(QComboBox.AdjustToContents)\n # self.device_combo.setMinimumWidth(300)\n self.device_combo.activated[str].connect(self.on_device_selected)\n self.toolbar.addWidget(self.device_label)\n self.toolbar.addWidget(self.device_combo)\n\n self.format_label = QLabel(\"Format:\")\n self.format_combo = QComboBox(self)\n self.format_combo.setSizeAdjustPolicy(QComboBox.AdjustToContents)\n self.format_combo.setMinimumWidth(150)\n self.format_combo.activated[str].connect(self.on_format_selected)\n self.toolbar.addWidget(self.format_label)\n self.toolbar.addWidget(self.format_combo)\n\n self.resolution_label = QLabel(\"Resolution:\")\n self.resolution_combo = TcamComboBox(self, \"Select Resolution\")\n self.resolution_combo.setSizeAdjustPolicy(QComboBox.AdjustToContents)\n self.resolution_combo.activated[str].connect(self.on_resolution_selected)\n self.toolbar.addWidget(self.resolution_label)\n self.toolbar.addWidget(self.resolution_combo)\n\n self.fps_label = QLabel(\"FPS:\")\n self.fps_combo = TcamComboBox(self, \"Select FPS:\")\n self.fps_combo.setSizeAdjustPolicy(QComboBox.AdjustToContents)\n self.fps_combo.activated[str].connect(self.on_fps_selected)\n self.toolbar.addWidget(self.fps_label)\n self.toolbar.addWidget(self.fps_combo)\n\n self.save_image = QAction(\"Save Image\", self)\n self.save_image.setIcon(QIcon.fromTheme(\"insert-image\"))\n\n self.save_image.triggered.connect(self.save_image_action)\n self.toolbar.addAction(self.save_image)\n\n self.fit_to_win = QAction(\"Fit To Window\", self)\n self.fit_to_win.setIcon(QIcon.fromTheme(\"zoom-fit-best\"))\n\n self.fit_to_win.triggered.connect(self.fit_to_window)\n self.toolbar.addAction(self.fit_to_win)\n\n self.props_action = QAction(\"\", self)\n self.props_action.setText(\"Properties\")\n self.props_action.setVisible(False)\n self.props_action.triggered.connect(self.toggle_properties_dialog)\n self.toolbar.addAction(self.props_action)\n\n self.recording_action = QAction(\"\", self)\n self.recording_action.setIcon(QIcon.fromTheme(\"media-record\"))\n self.recording_action.setIconText(\"Start recording\")\n self.recording_action.setText(\"Start recording\")\n self.recording_action.triggered.connect(self.start_recording_video)\n self.toolbar.addAction(self.recording_action)\n\n self.set_device_menus_enabled(False)\n\n self.view = None","def initGui(self):\n\n icon_path = ':/plugins/PolygonByPolarCoordinates/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'PolygonByPolarCoordinates'),\n callback=self.run,\n parent=self.iface.mainWindow())","def initGui(self):\n\n icon_path = ':/plugins/roadsalt_mapbuilder/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'&Road Salt Map Builder'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def initGui(self):\n\n\t\ticon_path = ':/plugins/main_index_extractor/icon.png'\n\t\tself.add_action(\n\t\t\ticon_path,\n\t\t\ttext=self.tr(u'Extract VI from Sentinel-2 data'),\n\t\t\tcallback=self.run,\n\t\t\tparent=self.iface.mainWindow())","def initUI(self):\n\n self.wid = RosGenWidget()\n self.setCentralWidget(self.wid)\n menubar = self.menuBar()\n fileMenu = menubar.addMenu('&Файл')\n editMenu = menubar.addMenu('&Редактирование')\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\n self.statusbar = self.statusBar()\n self.statusbar.showMessage('Ожидание данных')\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\n self.setGeometry(600, 200, 700, 400)\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\n self.show()","def initGui(self): # pylint:disable=invalid-name\n\n icon_path = os.path.join(self.plugin_dir, \"icons\")\n\n self.add_action(\n os.path.join(icon_path, \"icon.png\"),\n text=self.translate(\"Load Data\"),\n callback=self.run,\n parent=self.iface.mainWindow(),\n )\n\n # Plugin Dialog\n self.dlg.uListOptions.itemClicked.connect(self.show_selected_option)\n self.dlg.uListOptions.itemClicked.emit(self.dlg.uListOptions.item(0))\n\n model = QStandardItemModel()\n self.dlg.uCRSCombo.setModel(model)\n self.dlg.uCRSCombo.currentIndexChanged.connect(self.layer_crs_selected)\n\n self.dlg.uLabelWarning.setStyleSheet(\"color:red\")\n self.dlg.uWarningSettings.setStyleSheet(\"color:red\")\n\n self.add_item(title=\"ALL\", icon=os.path.join(icon_path, \"all.png\"))\n self.add_item(title=\"WFS\", icon=os.path.join(icon_path, \"wfs.png\"))\n self.add_item(title=\"WMTS\", icon=os.path.join(icon_path, \"wmts.png\"))\n self.add_item(title=\"Settings\", icon=os.path.join(icon_path, \"settings.png\"))\n self.add_item(title=\"Help\", icon=os.path.join(icon_path, \"help.png\"))\n self.add_item(title=\"About\", icon=os.path.join(icon_path, \"about.png\"))\n\n # set table model\n self.set_table_model_view()\n\n # set help html\n self.dlg.hHelpHtml.setOpenExternalLinks(True)\n help_file = os.path.join(self.plugin_dir, \"help.html\")\n with open(help_file, \"r\", encoding=\"utf-8\") as file:\n help_html = file.read()\n help_html.format(self.plugin_dir)\n self.dlg.hHelpHtml.setHtml(help_html.format(icon_path))\n\n # set about html\n self.dlg.hAboutHtml.setOpenExternalLinks(True)\n about_file = os.path.join(self.plugin_dir, \"about.html\")\n with open(about_file, \"r\", encoding=\"utf-8\") as file:\n about_html = file.read()\n about_html.format(self.plugin_dir)\n self.dlg.hAboutHtml.setHtml(about_html.format(icon_path))\n\n # populate settings\n # default data services to combo\n\n self.dlg.uComboBoxDomain.addItems(SER)\n\n # settings signals\n self.dlg.uBtnAddDomain.clicked.connect(self.add_new_domain)\n for entry in range(1, 11):\n getattr(self.dlg, \"uBtnSaveDomain{0}\".format(entry)).clicked.connect(\n self.save_domain\n )\n getattr(self.dlg, \"uBtnRemoveDomain{0}\".format(entry)).clicked.connect(\n self.save_domain\n )\n self.load_settings()","def initGui(self):\n\n icon_path = ':/plugins/LidarProcessor/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'LiDAR Operations'),\n callback=self.run,\n parent=self.iface.mainWindow())","def _initUI(self) -> None:\n self._createActions()\n self._addActionsToMoveButtons()\n self._createToolBar()\n self._createStatusBar()\n self._createMainContextMenu()","def main():\n \n cities, coordinates, speedlimits, adjlist = data_for_app()\n \n ui(cities, coordinates, speedlimits, adjlist)","def iniciaUI(self):\n\n self.setGeometry(100,100, 300, 200)\n self.setWindowTitle(\"Formulario\")\n self.displayWidgets()\n\n self.show()","def init_ui(self):\n # Create GUI elements, set them in dict structure\n labelwidth = 150\n\n # Add parameter line edit for Factor Tm to Tp\n\n # Add line edit with browsebutton for swan result folder\n self.input_elements['hares folder'] = widgets.ExtendedLineEdit(\n label='HARES uitvoerbestanden folder:',\n labelwidth=labelwidth,\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_hares_folder)\n )\n\n\n self.setLayout(QtWidgets.QVBoxLayout())\n self.layout().setSpacing(10)\n\n for _, item in self.input_elements.items():\n self.layout().addWidget(item)\n\n line = QtWidgets.QFrame()\n line.setFrameShape(QtWidgets.QFrame.HLine)\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\n self.layout().addWidget(line)\n\n # OK and Cancel buttons\n self.generateButton = QtWidgets.QPushButton('Start lezen uitvoerbestanden')\n self.generateButton.setDefault(True)\n self.generateButton.clicked.connect(self.generate)\n\n self.cancelButton = QtWidgets.QPushButton('Annuleren')\n self.cancelButton.setAutoDefault(False)\n self.cancelButton.clicked.connect(self.cancel)\n\n button_box = QtWidgets.QDialogButtonBox(QtCore.Qt.Horizontal, self)\n button_box.addButton(self.generateButton, QtWidgets.QDialogButtonBox.ActionRole)\n button_box.addButton(self.cancelButton, QtWidgets.QDialogButtonBox.RejectRole)\n button_box.accepted.connect(QtWidgets.QDialog.accept)\n\n self.layout().addWidget(button_box)","def initGui(self):\n\n icon_path = ':/plugins/Hybriddekning/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Hybriddekning'),\n callback=self.run,\n parent=self.iface.mainWindow())","def _setup_ui(self):\n\n self.window = ui.Widget()\n self.window.dimensions = ui.normalize_dimension((\n 0, 0,\n self.normalized_screen_resolution[0],\n self.normalized_screen_resolution[1]\n ))\n self.window.background_color = ImageColor.getcolor('#000000', 'RGB')\n\n interface_frame = ui.Widget(parent=self.window)\n interface_frame.dimensions = ui.normalize_dimension((\n self.preview_renderer.window[2],\n 0,\n self.normalized_screen_resolution[0] - self.preview_renderer.window[2],\n self.normalized_screen_resolution[1]\n ))\n interface_frame.background_color = ImageColor.getcolor('#ffffff', 'RGB')\n\n number = ui.LabelWidget(\"\",\n name=NAME_GET_STARTED,\n parent=interface_frame,\n align=\"center\",\n font_color=(0, 0, 0, 255))\n number.dimensions = (\n 5, 5,\n interface_frame.width - 10,\n interface_frame.height - 10\n )","def initGui(self):\n\n icon_path = ':/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'PacSafe'),\n callback=self.run,\n parent=self.iface.mainWindow())","def initGui(self):\n\n icon_path = ':/plugins/trialPurpose/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'trial'),\n callback=self.run,\n parent=self.iface.mainWindow())","def initGui(self):\n\n icon_path = ':/plugins/cctv_mapper/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'CCTV Mapper'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def init_ui(self):\n # Create GUI elements, set them in dict structure\n labelwidth = 150\n\n # Add parameter line edit for Factor Tm to Tp\n self.input_elements['factor Tm Tp'] = widgets.ParameterInputLine(\n label='Factor Tm naar Tp:',\n labelwidth=labelwidth,\n unitlabel='(NVT: Tp aanwezig)' if 'Tp' in self.hydraulic_loads.columns else '',\n validator=QtGui.QDoubleValidator(0.01, 99.99, 20),\n )\n\n if 'Tp' in self.hydraulic_loads.columns or self.parent_tab.step != 'I1':\n self.input_elements['factor Tm Tp'].set_enabled(False)\n\n # Add line edit with browsebutton for Master template\n self.input_elements['mastertemplate'] = widgets.ExtendedLineEdit(\n label='Master template bestand:',\n labelwidth=labelwidth,\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_master_template)\n )\n\n # Add line edit with browsebutton for depth file\n self.input_elements['depthfile'] = widgets.ExtendedLineEdit(\n label='Bathymetry bestand:',\n labelwidth=labelwidth,\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_bathymetry_file)\n )\n\n # Add line edit with browsebutton for swan result folder\n self.input_elements['swanfolder'] = widgets.ExtendedLineEdit(\n label='SWAN uitvoer folder:',\n labelwidth=labelwidth,\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_swan_folder)\n )\n\n\n self.setLayout(QtWidgets.QVBoxLayout())\n self.layout().setSpacing(10)\n\n for _, item in self.input_elements.items():\n self.layout().addWidget(item)\n\n line = QtWidgets.QFrame()\n line.setFrameShape(QtWidgets.QFrame.HLine)\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\n self.layout().addWidget(line)\n\n # OK and Cancel buttons\n self.generateButton = QtWidgets.QPushButton('Genereer invoer')\n self.generateButton.setDefault(True)\n self.generateButton.clicked.connect(self.generate)\n\n self.cancelButton = QtWidgets.QPushButton('Annuleren')\n self.cancelButton.setAutoDefault(False)\n self.cancelButton.clicked.connect(self.cancel)\n\n button_box = QtWidgets.QDialogButtonBox(QtCore.Qt.Horizontal, self)\n button_box.addButton(self.generateButton, QtWidgets.QDialogButtonBox.ActionRole)\n button_box.addButton(self.cancelButton, QtWidgets.QDialogButtonBox.RejectRole)\n button_box.accepted.connect(QtWidgets.QDialog.accept)\n\n self.layout().addWidget(button_box)","def __init__(self):\r\n super().__init__()\r\n self.init_ui()","def initGui(self):\n\n icon_path = ':/plugins/FlowMapArrowCurver/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Flow Map Curved Arrow Calculator'),\n callback=self.run,\n parent=self.iface.mainWindow())","def initUI(self):\n centralwidget = QtWidgets.QWidget()\n self.setCentralWidget(centralwidget)\n\n person_table = PersonTable(self)\n person_table.embed(self.sql)\n\n # Box Layout to organize our GUI\n lay = QtWidgets.QVBoxLayout(centralwidget)\n lay.addWidget(person_table)\n self.setGeometry(0, 0, person_table.width() + 20, person_table.height() + 20)\n self.person_table = person_table\n self.show()","def createUI(self):\n\n q.getQItem(windowID, QtWidgets.QWidget)\n cmds.setParent(q.fullPath)\n\n # ################################################\n # Active Render Layer\n\n # cmds.separator(height=12, style='none')\n addFrameLayout(\n '%s_frameLayoutLayers' % windowID,\n 'Visible Render Layer', collapsable=False,\n labelVisible=False,\n marginHeight=0\n )\n\n addRowLayout(\n '%s_rowLayoutActiveRenderLayer' % windowID,\n 4,\n columnAlign4=('left', 'left', 'right', 'right'),\n columnAttach4=('left', 'both', 'right', 'right'),\n columnWidth4=(\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.775,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075\n )\n )\n\n\n addButton('%s_addNewLayer' % windowID, 'New', rsAddNewLayer,\n image='RS_create_layer', size=(21, 21))\n addOptionMenu('%s_selectActiveLayer' % windowID,\n 'Active Layer ', (), rsSelectActiveLayer)\n addButton('rsOpenRenderSetupWindow', 'Render Setup',\n rsOpenRenderSetupWindow, image='render_setup.png',\n size=(21, 21))\n addButton('rsOpenUnifiedRenderGlobals', 'Render Globals',\n rsOpenUnifiedRenderGlobals, image='render_setup.png',\n size=(21, 21))\n\n # ################################################\n # Work Render Layers\n\n cmds.setParent(q.fullPath)\n addFrameLayout('%s_frameLayoutLayersB' % windowID,\n 'Work Render Layer', collapsable=False,\n labelVisible=False, marginHeight=0)\n addRowLayout('%s_rowLayoutVisibleRenderLayer' % windowID, 3,\n columnAlign3=('left', 'left', 'right'),\n columnAttach3=('left', 'both', 'right'),\n columnWidth3=((WINDOW_WIDTH - FRAME_MARGIN * 2)\n * 0.075, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.85,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075))\n\n cmds.separator()\n addOptionMenu('%s_selectVisibleLayer' % windowID,\n 'Visible Layer ', (), rsSelectVisibleLayer)\n cmds.separator()\n\n cmds.setParent(q.fullPath)\n cmds.separator(height=12, style='none')\n\n # ################################################\n # Collections\n\n addFrameLayout('%s_frameLayout02' % windowID, 'Collections',\n labelVisible=False, marginHeight=0)\n\n addRowLayout(\n '%s_rowLayout02' % windowID,\n 6,\n columnAlign6=('left', 'left', 'left', 'left', 'left', 'left'),\n columnAttach6=('both', 'both', 'right', 'right', 'right', 'right'),\n columnWidth6=(\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.18,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.18,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.415,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075,\n )\n )\n\n addButton('rsAddCollection', 'Add', rsAddCollection)\n addButton('rsRemoveCollection', 'Remove', rsRemoveCollection)\n addButton('rsSelectShapes', 'Select Shapes', rsSelectShapes,\n image='selectObject.png', size=(21, 21))\n addButton('rsRenameShader', 'Rename Shader', rsRenameShader,\n size=(21, 21), image='QR_rename.png')\n addButton('rsDuplicateShader', 'Duplicate Shader',\n duplicateShader, size=(21, 21), image='newPreset.png')\n addButton('rsRefreshUI', 'Refresh', rsRefreshUI, size=(21, 21),\n image='QR_refresh.png')\n\n # ###########################\n # Filter List\n\n cmds.setParent('%s_frameLayout02' % windowID)\n addRowLayout('%s_rowLayout03' % windowID, 2,\n columnAlign2=('left', 'left'),\n columnAttach2=('both', 'both'),\n columnWidth2=((WINDOW_WIDTH - FRAME_MARGIN * 2)\n * 0.6, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.42))\n\n addTextField('%s_filterShaderList' % windowID, 'Search',\n rsFilterShaderList_off, rsFilterShaderList_off,\n window.updateUI)\n addOptionMenu('rsShaderGroups', '|', (), rsShaderGroups)\n\n # ###########################\n # The shaders scroll list\n\n cmds.setParent('%s_frameLayout02' % windowID)\n addRowLayout('%s_rowLayout04' % windowID, 1, columnAlign1='both', columnAttach1='both', columnWidth1=WINDOW_WIDTH\n + 12)\n addTextScrollList('%s_ShaderScrollList' % windowID, (),\n rsShaderScrollList_doubleClick,\n rsShaderScrollList_onSelect,\n rsShaderScrollList_deleteKey)\n\n # Add popup menu:\n\n cmds.popupMenu('rsShaderScrollListPopupMenu',\n parent='%s_ShaderScrollList' % windowID,\n allowOptionBoxes=False, markingMenu=True,\n postMenuCommand=postMenuCommand)\n cmds.menuItem('%s_popupMenuItem02' % windowID,\n label='Duplicate Shader', command=duplicateShader)\n cmds.menuItem(divider=True)\n cmds.menuItem('%s_popupMenuItem04' % windowID,\n label='Graph Shader')\n cmds.menuItem(divider=True)\n cmds.menuItem('%s_popupMenuItem03' % windowID,\n label='Select Shader')\n cmds.menuItem(divider=True)\n cmds.menuItem('%s_popupMenuItem05' % windowID,\n label='Select Assigned Shapes')\n cmds.menuItem('%s_popupMenuItem06' % windowID,\n label='Select Assigned Transforms')\n\n # ##################################################\n # Arnold Property Overrides\n\n cmds.setParent('%s_frameLayout02' % windowID)\n cmds.columnLayout(\n '%s_columnLayout20' % windowID,\n width=WINDOW_WIDTH - FRAME_MARGIN * 2,\n columnAlign='left',\n columnAttach=('left', 0),\n adjustableColumn=False,\n rowSpacing=0,\n )\n\n cmds.separator(parent='%s_columnLayout20' % windowID, height=4,\n style='none')\n\n addRowLayout('%s_rowLayout05' % windowID, 2,\n columnAlign2=('left', 'both'),\n columnAttach2=('left', 'right'),\n columnWidth2=((WINDOW_WIDTH - FRAME_MARGIN * 2)\n * 0.75, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.25))\n addText('%s_textArnoldPropertyOverridesLabel' % windowID,\n 'Apply Arnold Property Overrides', 'plainLabelFont')\n addCheckBox('rsArnoldPropertyOverridesCheckBox', '',\n rsArnoldPropertyOverridesCheckBox,\n rsArnoldPropertyOverridesCheckBox)\n cmds.separator(parent='%s_columnLayout20' % windowID, height=4,\n style='none')\n\n # Column Layout to toggle\n\n cmds.setParent('%s_columnLayout20' % windowID)\n cmds.columnLayout(\n '%s_columnLayout02' % windowID,\n width=WINDOW_WIDTH - FRAME_MARGIN * 2,\n columnAlign='left',\n columnAttach=('left', 0),\n adjustableColumn=False,\n rowSpacing=0,\n )\n\n addCheckboxes('%s_columnLayout02' % windowID)\n cmds.columnLayout('%s_columnLayout02' % windowID, edit=True,\n visible=False)\n\n # #################################################\n # Shader Override\n\n cmds.setParent('%s_frameLayout02' % windowID)\n cmds.columnLayout(\n '%s_columnLayout21' % windowID,\n width=WINDOW_WIDTH - FRAME_MARGIN * 2,\n columnAlign='left',\n columnAttach=('left', 0),\n adjustableColumn=False,\n rowSpacing=0,\n )\n cmds.separator(parent='%s_columnLayout21' % windowID, height=4,\n style='none')\n addRowLayout('%s_rowLayout06' % windowID, 2,\n columnAlign2=('left', 'right'),\n columnAttach2=('left', 'right'),\n columnWidth2=((WINDOW_WIDTH - FRAME_MARGIN * 2)\n * 0.75, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.25))\n addText('%s_shaderOverrideLabel' % windowID, 'Shader Override',\n 'plainLabelFont')\n addCheckBox('%s_shaderOverrideCheckbox' % windowID, '',\n rsShaderOverrideCheckbox, rsShaderOverrideCheckbox)\n cmds.separator(parent='%s_columnLayout21' % windowID, height=4,\n style='none')\n\n cmds.setParent('%s_columnLayout21' % windowID)\n cmds.columnLayout(\n '%s_columnLayout03' % windowID,\n width=WINDOW_WIDTH - FRAME_MARGIN * 2,\n columnAlign='left',\n columnAttach=('both', 4),\n adjustableColumn=True,\n rowSpacing=0,\n )\n cmds.setParent('%s_columnLayout03' % windowID)\n addOptionMenu('%s_optionMenu02' % windowID, 'Select: ', (),\n rsShaderOverridesMenu)\n\n global selectedShaderOverride\n\n # default selection\n\n selectedShaderOverride = SHADER_OVERRIDE_OPTIONS[0]['ui']\n cmds.columnLayout('%s_columnLayout03' % windowID, edit=True,\n visible=False)\n\n # #################################################\n\n cmds.setParent(q.fullPath)\n cmds.separator(height=10, style='none')\n\n # #################################################\n # Extras\n\n addFrameLayout('%s_frameLayout50' % windowID, 'Extras',\n collapsable=True, marginHeight=0,\n labelVisible=False)\n\n # #################################################\n # Add & Assign Shader Groups\n\n addFrameLayout(\n '%s_frameLayout05' % windowID,\n 'Add & Assign Shader Groups',\n collapsable=True,\n marginWidth=0,\n marginHeight=0,\n collapse=False,\n labelVisible=True,\n )\n\n # Add the renamer window\n\n self.gwCustomRenamer = CustomRenamer()\n self.gwCustomRenamer.createUI()\n\n # #################################################\n # AutoConnect\n\n cmds.setParent('%s_frameLayout50' % windowID)\n\n addFrameLayout(\n '%s_frameLayout03' % windowID,\n 'Adobe Connector',\n collapsable=True,\n marginWidth=0,\n marginHeight=0,\n collapse=True,\n labelVisible=True,\n )\n addRowLayout('%s_rowLayout07', 3, columnAlign3=('left', 'left',\n 'left'), columnAttach3=('both', 'both', 'both'),\n columnWidth3=((WINDOW_WIDTH - FRAME_MARGIN * 2)\n * 0.4, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.3,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.3))\n addButton('updateConnections', '> Update Connections <',\n updateConnections)\n addButton('uvSnapshot', 'UV Snapshot', uvSnapshot)\n addButton('editTexture', 'Edit Texture', editTexture)\n\n # After Effects\n\n cmds.setParent('%s_frameLayout03' % windowID)\n addRowLayout('%s_rowLayout11' % windowID, 2,\n columnAlign2=('left', 'left'),\n columnAttach2=('both', 'both'),\n columnWidth2=((WINDOW_WIDTH - FRAME_MARGIN * 2)\n * 0.4, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.6))\n addText('%s_text90' % windowID, 'Send to After Effects:')\n addButton('makeCompButton', 'Send to After Effects', rsMakeComp)\n\n # #################################################\n # Render Setup /\n # Output settings\n\n cmds.setParent('%s_frameLayout50' % windowID)\n addFrameLayout(\n '%s_frameLayout04' % windowID,\n 'Output Settings',\n collapsable=True,\n marginWidth=0,\n marginHeight=0,\n collapse=True,\n labelVisible=True,\n )\n addRowLayout('%s_rowLayout08' % windowID, 1,\n columnAlign1='center', columnAttach1='both',\n columnWidth1=WINDOW_WIDTH - FRAME_MARGIN * 2)\n addButton('%s_revealOutputDirectory' % windowID,\n 'Output path not set yet', rsRevealOutputDirectory)\n\n cmds.setParent('%s_frameLayout04' % windowID)\n addRowLayout('%s_rowLayout09' % windowID, 3,\n columnAlign3=('left', 'right', 'right'),\n columnAttach3=('left', 'right', 'right'),\n columnWidth3=((WINDOW_WIDTH - FRAME_MARGIN * 2)\n * 0.8, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.14,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.06))\n\n addOptionMenu('%s_optionMenu05' % windowID, '', (),\n rsSelectOutputTemplate)\n addOptionMenu('%s_outputVersionMenu' % windowID, '', (),\n rsSelectOutputVersion)\n cmds.menuItem(label='v001')\n\n cmds.setParent('%s_rowLayout09' % windowID)\n addButton('%s_incrementOutputVersionButton' % windowID, '+1',\n rsIncrementOutputVersion, size=(21, 21))\n\n cmds.setParent('%s_frameLayout04' % windowID)\n addRowLayout('%s_rowLayout10' % windowID, 2,\n columnAlign2=('left', 'left'),\n columnAttach2=('both', 'right'),\n columnWidth2=((WINDOW_WIDTH - FRAME_MARGIN * 2)\n * 0.7, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.3))\n addOptionMenu('%s_optionMenu03' % windowID, 'Format:', (),\n rsOutputTemplatesMenu)\n addOptionMenu('%s_optionMenu06' % windowID, '', (),\n rsSetFPSMenu)\n\n cmds.setParent('%s_frameLayout04' % windowID)\n addRowLayout('%s_rowLayout12' % windowID, 4,\n columnAlign4=('right', 'left', 'right', 'left'),\n columnAttach4=('both', 'both', 'both', 'both'),\n columnWidth4=((WINDOW_WIDTH - FRAME_MARGIN * 2)\n * 0.50, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.15,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.20,\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.15))\n\n addText('%s_setInFrameLabel' % windowID, 'In Frame ')\n addTextField('%s_setInFrame' % windowID, '', setInFrame,\n setInFrame, setInFrame)\n\n addText('%s_setOutFrameLabel' % windowID, 'Out Frame ')\n addTextField('%s_setOutFrame' % windowID, '', setOutFrame,\n setOutFrame, setOutFrame)","def initGui(self):\n\n icon_path = ':/plugins/HERE_RTTI_Archive_Miner/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Mapping GPS trace to Traffic Archive'),\n callback=self.run,\n parent=self.iface.mainWindow())\n init_config_path = './here_rtti_miner_config.ini'\n if os.path.exists(init_config_path):\n init_config = open(init_config_path, mode='r')\n f = init_config.readlines()\n if len(f) == 3:\n self.iface.messageBar().pushMessage(\"Info\", '{}\\\\here_rtti_miner_config.ini loaded.'.format(\n os.path.abspath(os.path.curdir)), level=QgsMessageBar.INFO, duration=1)\n app_id = (f[0].replace('\\n', ''))\n self.dlg.app_id_textbox.setPlainText(app_id)\n app_code = (f[1].replace('\\n', ''))\n self.dlg.app_code_textbox.setPlainText(app_code)\n archive_path = (f[2].replace('\\n', ''))\n self.dlg.archive_textbox.setPlainText(archive_path)\n\n \"\"\"Checkbox Listener\"\"\"\n self.cb = self.dlg.date_time_override_checkBox\n self.cb.toggle()\n self.cb.setChecked(False)\n self.dlg.connect(self.cb, QtCore.SIGNAL('stateChanged(int)'), self.change_state)\n\n \"\"\"DTM preload\"\"\"\n self.dtm = self.dlg.dateTimeEdit\n # get current date and time\n now = QtCore.QDateTime.currentDateTime()\n # set current date and time to the object\n self.dtm.setDateTime(now)","def _initUI(self):\n\n vlayout = QtWidgets.QVBoxLayout()\n\n # Description\n #----------------------------------------------------------------\n hlayout = QtWidgets.QHBoxLayout()\n\n label = QtWidgets.QLabel()\n label.setText('Locatie:')\n label.setFixedWidth(100)\n hlayout.addWidget(label)\n\n label = QtWidgets.QLabel()\n label.setText(self.name)\n hlayout.addWidget(label)\n hlayout.setSpacing(10)\n\n vlayout.addLayout(hlayout)\n\n # Exportnaam\n #----------------------------------------------------------------\n self.exportname = ParameterInputLine(label='Exportnaam:', labelwidth=100)\n self.exportname.LineEdit.setMinimumWidth(200)\n vlayout.addLayout(self.exportname.layout)\n\n # Exportdatabase\n #----------------------------------------------------------------\n self.exportpath = ExtendedLineEdit(label='SQLite-database:', labelwidth=100, browsebutton=True)\n self.exportpath.BrowseButton.clicked.connect(self._get_path_database)\n vlayout.addLayout(self.exportpath.layout)\n\n # Line\n line = QtWidgets.QFrame()\n line.setFrameShape(QtWidgets.QFrame.HLine)\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\n\n vlayout.addWidget(line)\n\n # Buttons\n #----------------------------------------------------------------\n hbox = QtWidgets.QHBoxLayout()\n hbox.addItem(QtWidgets.QSpacerItem(0, 0, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Maximum))\n # Add ok/close\n self.closebutton = QtWidgets.QPushButton('Sluiten')\n self.closebutton.clicked.connect(self.close)\n hbox.addWidget(self.closebutton)\n # Add ok/close\n self.savebutton = QtWidgets.QPushButton('Opslaan')\n self.savebutton.clicked.connect(self._save)\n hbox.addWidget(self.savebutton)\n\n vlayout.addLayout(hbox)\n\n # Add layout to widget\n self.setLayout(vlayout)","def initGui(self):\n\n icon_path = ':/plugins/convertTo3D/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Convert to 3D'),\n callback=self.run,\n parent=self.iface.mainWindow())","def initGui(self):\n\n icon_path = ':/plugins/Integracion/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u''),\n callback=self.run,\n parent=self.iface.mainWindow())","def iniciaUI(self):\n\n self.setGeometry(100,100, 250, 250)\n self.setWindowTitle(\"Login\")\n self.displayWidgets()\n\n self.show()","def initUI(self):\n # Setting the main layout as Vertical.\n self.mainLayout = QHBoxLayout()\n\n # Create title.\n self.title = QLabel(self.__name + \" : \")\n\n # Add description as tooltip.\n self.title.setToolTip(self.__description)\n\n # Add title to main layout.\n self.mainLayout.addWidget(self.title)\n\n # Create ComboBox.\n self.dropDown = QComboBox()\n\n # Add datas to drop down.\n self.dropDown.addItems(self.__datas)\n\n # Set default index to dropdown.\n self.dropDown.setCurrentIndex(self.__currentValue)\n\n # Connect dropdown with update method.\n self.dropDown.currentIndexChanged.connect(self.changeCurrentValue)\n\n # Add ComboBox to main layout.\n self.mainLayout.addWidget(self.dropDown)\n\n # Add the main layout to the window.\n self.setLayout(self.mainLayout)","def initGui(self):\n\n icon_path = ':/plugins/new_raptor/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Add New Raptor nest'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def launchUI():\n app = QtWidgets.QApplication(sys.argv)\n ui = ClientFileManager()\n ui.resize(1200, 650)\n ui.show()\n sys.exit(app.exec_())","def createUserInterface(self):\n\n\t\tself.__layout = self.__parent.createUserInterface()\n\n\t\tstep_label = qt.QLabel( 'Choose the volume you would like to threshold. If you are calculating a subtraction map, check the \\\"Calculate Subtraction Map\\\" box and select a post-contrast image.' )\n\t\tstep_label.setWordWrap(True)\n\t\tself.__primaryGroupBox = qt.QGroupBox()\n\t\tself.__primaryGroupBox.setTitle('Information')\n\t\tself.__primaryGroupBoxLayout = qt.QFormLayout(self.__primaryGroupBox)\n\n\t\tself.__subtractionMappingGroupBox = qt.QGroupBox()\n\t\tself.__subtractionMappingGroupBox.setTitle('Volume Selection')\n\t\tself.__subtractionMappingGroupBoxLayout = qt.QFormLayout(self.__subtractionMappingGroupBox)\n\n\t\tbaselineScanLabel = qt.QLabel( 'Primary / Pre-Contrast Image:' )\n\t\tself.__baselineVolumeSelector = slicer.qMRMLNodeComboBox()\n\t\tself.__baselineVolumeSelector.toolTip = \"Select the volume you wish to threshold. If you are calculating a subtraction map, this will be the pre-contrast scan.\"\n\t\tself.__baselineVolumeSelector.nodeTypes = ['vtkMRMLScalarVolumeNode']\n\t\tself.__baselineVolumeSelector.setMRMLScene(slicer.mrmlScene)\n\t\tself.__baselineVolumeSelector.addEnabled = 0\n\n\t\tsubtractionMappingLabel = qt.QLabel( 'Calculate Subtraction Map:' )\n\t\tself.__enableSubtractionMapping = qt.QCheckBox()\n\t\tself.__enableSubtractionMapping.checked = False\n\t\tself.__enableSubtractionMapping.setToolTip(\"Check if you would like to calculate a subtraction map\")\n\t\tself.__enableSubtractionMapping.connect('clicked()', self.setSubtractionMapping)\n\n\t\tfollowupScanLabel = qt.QLabel( 'Post-Contrast Image:' )\n\t\tself.__followupVolumeSelector = slicer.qMRMLNodeComboBox()\n\t\tself.__followupVolumeSelector.toolTip = \"Choose the post-contrast scan\"\n\t\tself.__followupVolumeSelector.nodeTypes = ['vtkMRMLScalarVolumeNode']\n\t\tself.__followupVolumeSelector.setMRMLScene(slicer.mrmlScene)\n\t\tself.__followupVolumeSelector.addEnabled = 0\n\t\tself.__followupVolumeSelector.enabled = 0\n\n\t\tself.__layout.addRow(self.__primaryGroupBox)\n\t\tself.__primaryGroupBoxLayout.addRow( step_label )\n\t\tself.__subtractionMappingGroupBoxLayout.addRow( baselineScanLabel, self.__baselineVolumeSelector )\n\n\t\tself.__layout.addRow(self.__subtractionMappingGroupBox)\n\t\tself.__subtractionMappingGroupBoxLayout.addRow( subtractionMappingLabel, self.__enableSubtractionMapping )\n\t\tself.__subtractionMappingGroupBoxLayout.addRow( followupScanLabel, self.__followupVolumeSelector )\n\n\t\tself.updateWidgetFromParameters(self.parameterNode())\n\n\t\t# This timer is a trick to wait for buttons to load BEFORE deleting them.\n\t\tqt.QTimer.singleShot(0, self.killButton)","def _init_ui(self):\n\n hlayout = QtWidgets.QHBoxLayout()\n\n label = QtWidgets.QLabel('Kies een normtraject:')\n\n hlayout.addWidget(label)\n\n self.section_combobox = QtWidgets.QComboBox()\n self.section_combobox.setFixedWidth(60)\n self.section_ids = sorted([''] + io.geometry.import_section_ids(self.datadir))\n self.section_combobox.addItems(self.section_ids)\n\n hlayout.addWidget(self.section_combobox)\n\n self.add_button = QtWidgets.QPushButton('Toevoegen', clicked=self._add_flooddefence)\n\n hlayout.addWidget(self.add_button)\n\n vlayout = QtWidgets.QVBoxLayout()\n vlayout.addLayout(hlayout)\n\n line = QtWidgets.QFrame()\n line.setFrameShape(QtWidgets.QFrame.HLine)\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\n\n vlayout.addWidget(line)\n\n self.close_button = QtWidgets.QPushButton('Sluiten', clicked=self.close)\n vlayout.addWidget(self.close_button, 0, QtCore.Qt.AlignRight)\n\n self.setLayout(vlayout)\n\n self.setWindowTitle(\"HB Havens: normtrajecten\")\n self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowContextHelpButtonHint)","def init_ui(self):\n self.master.title(\"Backbone\")\n self.master.geometry(\"300x150\")\n\n self.pack(fill=BOTH, expand=1)\n\n self.btn_upload_file = Button(self, text=\"Upload file\", command=self.upload_file)\n self.btn_upload_file.place(x=90, y=10)\n\n self.btn_create_training_file = Button(self, text=\"Create & upload training file\",\n command=self.create_training_file)\n self.btn_create_training_file.place(x=30, y=40)\n\n self.btn_run_algorithm = Button(self, text=\"Run algorithm\", command=self.run_algorithm)\n self.btn_run_algorithm.place(x=80, y=70)\n\n self.btn_view_results = Button(self, text=\"View Results\", command=self.view_results)\n self.btn_view_results.place(x=85, y=100)","def init_UI(self):\n # widgets\n self.modeComboBox = QtWidgets.QComboBox()\n self.updateBtn = QtWidgets.QPushButton('Force Update')\n self.pauseBtn = QtWidgets.QPushButton()\n self.pauseBtn.setCheckable(True)\n\n self.clearBtn = QtWidgets.QPushButton()\n self.clearBtn.setIcon(QtGui.QIcon(IconPaths.ICON_CLEAR_FILES))\n self.filterBtn = QtWidgets.QToolButton()\n self.filterBtn.setIcon(QtGui.QIcon(IconPaths.ICON_FILTER))\n self.filterBtn.setMinimumWidth(35)\n self.filterBtn.setStyleSheet('QToolButton::menu-indicator {subcontrol-position: center right; height: 7px}')\n self.filterBtn.setPopupMode(QtWidgets.QToolButton.InstantPopup)\n self.prefBtn = QtWidgets.QToolButton()\n self.prefBtn.setIcon(QtGui.QIcon(IconPaths.ICON_SETTINGS))\n #self.pBar = QtWidgets.QProgressBar()\n self.itemView = LocalizeView()\n self.itemView.setModel(self.proxy_model)\n self.autoScrollCB = QtWidgets.QCheckBox('Auto scroll to localizing files')\n self.autoScrollCB.setChecked(True)\n\n # tweak sizes so the widgets all line up vertically with Nuke's style\n self.modeComboBox.setMinimumHeight(self.updateBtn.sizeHint().height())\n self.pauseBtn.setMaximumSize(self.updateBtn.sizeHint())\n self.clearBtn.setMaximumSize(self.updateBtn.sizeHint())\n\n # mode menu\n self.modeLabel = QtWidgets.QLabel('Mode')\n self.modeComboBox.addItems(['On', 'Manual', 'Off'])\n\n # update menu\n self.updateMenu = QtWidgets.QMenu()\n self.act_forceUpdateAll = QtWidgets.QAction('All', self)\n self.act_forceUpdateSelectedNodes = QtWidgets.QAction('Selected', self)\n self.act_forceUpdateOnDemand = QtWidgets.QAction('On demand only', self) \n self.updateMenu.addAction(self.act_forceUpdateAll)\n self.updateMenu.addAction(self.act_forceUpdateSelectedNodes)\n self.updateMenu.addAction(self.act_forceUpdateOnDemand)\n self.updateBtn.setMenu(self.updateMenu)\n\n # clear menu\n self.clearMenu = QtWidgets.QMenu()\n self.clearMenu.addAction(QtWidgets.QAction('All local files', self, triggered=self.__delete_all_local_files))\n self.clearMenu.addAction(QtWidgets.QAction('Unused local files', self, triggered=_open_delete_dialog))\n self.clearBtn.setMenu(self.clearMenu)\n\n # filter menu\n self.filterMenu = QtWidgets.QMenu(self.filterBtn)\n self.act_filter_all = QtWidgets.QAction('All', self.filterMenu, checkable=True)\n self.act_filter_in_progress = QtWidgets.QAction('In Progress', self.filterMenu, checkable=True)\n self.act_filter_up_to_date = QtWidgets.QAction('Up to date', self.filterMenu, checkable=True)\n self.act_filter_out_of_date = QtWidgets.QAction('Out of date', self.filterMenu, checkable=True)\n self.act_filter_from_source = QtWidgets.QAction('Reading from source', self.filterMenu, checkable=True)\n self.act_filter_disabled = QtWidgets.QAction('Disabled', self.filterMenu, checkable=True)\n self.act_filter_not_localized = QtWidgets.QAction('Not Localized', self.filterMenu, checkable=True)\n\n self.act_filter_in_progress.setData(Status.IN_PROGRESS)\n self.act_filter_up_to_date.setData(Status.UP_TO_DATE)\n self.act_filter_out_of_date.setData(Status.OUT_OF_DATE)\n self.act_filter_from_source.setData(Status.READ_FROM_SOURCE)\n self.act_filter_disabled.setData(Status.DISABLED)\n self.act_filter_not_localized.setData(Status.NOT_LOCALIZED)\n for act in (self.act_filter_all, self.act_filter_in_progress, self.act_filter_up_to_date, self.act_filter_out_of_date,\n self.act_filter_from_source, self.act_filter_disabled, self.act_filter_not_localized):\n self.filterMenu.addAction(act)\n self.filterBtn.setMenu(self.filterMenu)\n\n # tooltips\n self.modeComboBox.setToolTip('Sets the global localization mode.\\nThis is the same as using the options in the Cache/Localization/Mode menu.')\n self.updateBtn.setToolTip('Forces the update of localized files.\\nThis is the same as using the options in the Cache/Localization/Force Update menu.')\n self.pauseBtn.setToolTip('Pauses/Resumes file localization.\\nThis is the same as Cache/Localization/Pause.')\n self.clearBtn.setToolTip('''Allows for clearing localized files.\\nTwo modes are supported:\n \"All local files\" - this will delete all files in {}\n \"Unused local files\" - this will only delete unused local files (same as Cache/Localization/Clear Unused Local Files)'''.format(nuke.toNode('preferences')['localCachePath'].evaluate()))\n self.filterBtn.setToolTip('Sets a view filter the table.')\n self.prefBtn.setToolTip('Open the preferences.')\n\n # layouts\n layout = QtWidgets.QVBoxLayout()\n btnLayout = QtWidgets.QHBoxLayout()\n btnLayout.addWidget(self.modeLabel)\n btnLayout.addWidget(self.modeComboBox)\n btnLayout.addWidget(self.updateBtn)\n btnLayout.addWidget(self.pauseBtn)\n btnLayout.addWidget(self.clearBtn)\n btnLayout.addStretch()\n btnLayout.addWidget(self.filterBtn)\n btnLayout.addWidget(self.prefBtn)\n layout.addLayout(btnLayout)\n #layout.addWidget(self.pBar)\n layout.addWidget(self.itemView)\n layout.addWidget(self.autoScrollCB)\n layout.setAlignment(self.autoScrollCB, QtCore.Qt.AlignRight)\n self.setLayout(layout)","def ui_setup(self):\n loader = QUiLoader()\n file = QFile('./user_interface/form/main_window.ui')\n file.open(QFile.ReadOnly)\n self._window = loader.load(file)\n file.close()\n\n status_bar = QStatusBar(self._window)\n status_bar.showMessage(__copyright__)\n self._window.setStatusBar(status_bar)\n self._window.setWindowIcon(QIcon('./user_interface/media/bucketing_icon.jpeg'))\n self._window.setWindowTitle('PySide2 Project - Basic UI Framework')\n\n self._option_panel = OptionPanel()\n self._option_panel.add_button('DekBan', './user_interface/media/dekban.png')\n self._option_panel.add_button('Charlie', './user_interface/media/charlie.jpeg')\n self._option_panel.add_button('Simon', './user_interface/media/Simon.jpeg')\n\n # Add widget to main layout\n main_layout = self._window.main_layout\n main_layout.itemAtPosition(0, 0).setAlignment(QtCore.Qt.AlignCenter)\n main_layout.itemAtPosition(0, 1).setAlignment(QtCore.Qt.AlignVCenter)\n main_layout.addWidget(self._option_panel, 2, 0, 1, 1)\n\n # Add page widget to stack\n self._pages['item'] = ItemWidget()\n self._pages['text1'] = TextPage(text=PAUSE_TEXT)\n self._pages['text2'] = TextPage(text=STOP_TEXT)\n\n for index, name in enumerate(self._pages):\n print('pages {} : {} page'.format(index, name))\n self._window.widget_stack.addWidget(self._pages[name].widget)\n\n self._window.widget_stack.setCurrentIndex(0)\n\n # Build up signal / slot\n self._option_panel.currentItemChanged.connect(self.set_page)","def setup_gui(self):\n central_widget = QWidget(self)\n central_widget.setObjectName('central_widget')\n self.label = QLabel('Hello World')\n self.input_field = QLineEdit()\n change_button = QPushButton('Change text')\n close_button = QPushButton('close')\n quit_button = QPushButton('quit')\n central_layout = QVBoxLayout()\n button_layout = QHBoxLayout()\n central_layout.addWidget(self.label)\n central_layout.addWidget(self.input_field)\n # a separate layout to display buttons horizontal\n button_layout.addWidget(change_button)\n button_layout.addWidget(close_button)\n button_layout.addWidget(quit_button)\n central_layout.addLayout(button_layout)\n central_widget.setLayout(central_layout)\n self.setCentralWidget(central_widget)\n # create a system tray icon. Uncomment the second form, to have an\n # icon assigned, otherwise you will only be seeing an empty space in\n # system tray\n self.systemtrayicon = QSystemTrayIcon(self)\n self.systemtrayicon.show()\n # set a fancy icon\n self.systemtrayicon.setIcon(QIcon.fromTheme('help-browser'))\n change_button.clicked.connect(self.change_text)\n quit_button.clicked.connect(QApplication.instance().quit)\n close_button.clicked.connect(self.hide)\n # show main window, if the system tray icon was clicked\n self.systemtrayicon.activated.connect(self.icon_activated)","def _init_ui(self):\n hlayout = QtWidgets.QHBoxLayout()\n\n hlayout.addWidget(QtWidgets.QLabel('Kies een normtraject:'))\n\n self.section_combobox = QtWidgets.QComboBox()\n self.section_combobox.setFixedWidth(60)\n self._update_combobox()\n\n hlayout.addWidget(self.section_combobox)\n\n self.remove_button = QtWidgets.QPushButton('Verwijderen', clicked=self._del_flooddefence)\n hlayout.addWidget(self.remove_button)\n\n vlayout = QtWidgets.QVBoxLayout()\n vlayout.addLayout(hlayout)\n\n line = QtWidgets.QFrame()\n line.setFrameShape(QtWidgets.QFrame.HLine)\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\n\n vlayout.addWidget(line)\n\n self.close_button = QtWidgets.QPushButton('Sluiten', clicked=self.close)\n vlayout.addWidget(self.close_button, 0, QtCore.Qt.AlignRight)\n\n self.setLayout(vlayout)\n\n self.setWindowTitle(\"HB Havens: normtrajecten\")\n self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowContextHelpButtonHint)","def initGui(self):\n\n icon_path = ':/plugins/ActualizacionCatastral/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Act. Catastral'),\n callback=self.run,\n parent=self.iface.mainWindow())","def initUI(self):\n\n grid = QGridLayout()\n grid.addWidget(self.from_currency_label, 0, 0, Qt.AlignRight)\n grid.addWidget(self.from_currency, 0, 1)\n grid.addWidget(self.to_currency_label, 0, 2, Qt.AlignRight)\n grid.addWidget(self.to_currency, 0, 3)\n grid.addWidget(self.from_amount_label, 1, 0)\n grid.addWidget(self.from_amount, 1, 1)\n grid.addWidget(self.to_amount_label, 1, 2)\n grid.addWidget(self.to_amount, 1, 3)\n\n grid.addWidget(self.from_calendar, 2, 0, 1, 2)\n grid.addWidget(self.to_calendar, 2, 2, 1, 2)\n\n grid.addWidget(self.rates_plot, 3, 0, 1, 4)\n grid.addWidget(self.graph_hint, 4, 0, 1, 4)\n\n self.rates_plot.showGrid(x=True, y=True)\n self.rates_plot.setLabel('left', 'Rate')\n self.rates_plot.setLabel('bottom', 'Days')\n self.legend = self.rates_plot.addLegend()\n\n self.setLayout(grid)\n self.setWindowTitle('Currency Converter - Assignment 1 - Arnaud Bourget - 2981151')\n\n self.from_currency.currentIndexChanged.connect(self.updateUI)\n self.to_currency.currentIndexChanged.connect(self.updateUI)\n self.from_amount.valueChanged.connect(self.fromAmountHandler)\n self.from_calendar.selectionChanged.connect(self.fromCalendarHandler)\n self.to_calendar.selectionChanged.connect(self.toCalendarHandler)\n\n self.show()","def __init__(self, parent=None):\n super(Representative, self).__init__(parent)\n self.setupUi(self)","def setUp(self):\n self.ui = UI()","def _init_ui(self):\r\n\t\t\r\n\t\tself.input_frame = Input(self)\r\n\t\tself.input_frame.pack()\r\n\t\t\r\n\t\tbutton_ok = Button(self, text = \"Ping\", command = self._go)\r\n\t\tbutton_ok.pack()\r\n\t\t\r\n\t\tself.result_frame = Result(self)\r\n\t\tself.result_frame.pack()","def init_UI(self):\n\n self.master.title(\"Search for different companies\")\n self.master.geometry(\"400x400\")\n\n self.label_combobox = Label(self, text=\"Search by\")\n self.label_combobox.pack()\n\n self.combo_searching_options = Combobox(self, state=\"readonly\")\n self.combo_searching_options['values'] = self.combobox_values\n self.combo_searching_options.pack()\n\n self.label_input = Label(self, text=\"Entry the value\")\n self.label_input.pack()\n\n self.user_input = Entry(self, width=40)\n self.user_input.pack()\n\n self.btn_submit = Button(self, text=\"Submit\", command=self.submit)\n self.btn_submit.pack()\n\n self.text_area = scrolledtext.ScrolledText(self)\n self.text_area.pack()\n\n sys.stdout = RedirectOutputText(self.text_area)\n\n self.btn_back = Button(self, text=\"Back\", command=self.go_back)\n self.btn_back.pack()","def start_ui(self):\n\t\tself.start_animation()\n\t\tself.app.exec()","def setup_gui(self):\n # if there are more than 1 visualizer we need to assure that there\n # will not be tag conflicts\n BaseRealTimeVisualizer.setup_gui_lock.acquire()\n # look for valid tag\n dpg.create_context()\n\n self.id = 0\n while dpg.does_item_exist(f'main_window_{self.id}'):\n self.id += 1\n\n with dpg.texture_registry(show=False):\n dpg.add_dynamic_texture(\n width=self.width,\n height=self.height,\n default_value=np.zeros((self.width, self.height, 3)),\n tag=f'input_image_texture_{self.id}',\n )\n\n with dpg.window(\n tag=f'main_window_{self.id}',\n no_title_bar=True,\n autosize=True\n ):\n dpg.add_image(\n texture_tag=f'input_image_texture_{self.id}',\n tag=f'image_render_{self.id}',\n pos=(_PADDING, _PADDING)\n )\n\n dpg.set_global_font_scale(_FONT_SCALE)\n\n if self.id == 0:\n dpg.set_primary_window(f'main_window_{self.id}', True)\n dpg.create_viewport(\n title=self.title,\n width=self.width + _PADDING*2,\n height=self.height + _PADDING*2,\n resizable=True\n )\n dpg.setup_dearpygui()\n dpg.show_viewport()\n elif self.id == 1:\n dpg.set_primary_window('main_window_0', False)\n\n BaseRealTimeVisualizer.setup_gui_lock.release()","def initGui(self):\n\n icon_path = ':/plugins/TMD/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Weather Today'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def ui(self):\n return ui","def _setupUi(self):\n self.setupUi(self)\n self.twTree.setStyleSheet(\"background-color: rgb(200, 200, 200)\")","def initUI(self):\n startbtn = QPushButton(\"Start Recroding\", self)\n startbtn.move(30, 50)\n\n stopbtn = QPushButton(\"Stop Recording\", self)\n stopbtn.move(150, 50)\n\n initbtn = QPushButton(\"Initilize\", self)\n initbtn.move(30, 100)\n\n plotbtn = QPushButton(\"Plot\", self)\n plotbtn.move(150, 100)\n\n startbtn.clicked.connect(self.start_recording)\n stopbtn.clicked.connect(self.stop_recording)\n initbtn.clicked.connect(self.init_recording)\n plotbtn.clicked.connect(self.plot_signals)\n\n self.statusBar()\n self.statusBar().showMessage('Click Init')\n\n self.setGeometry(300, 300, 290, 150)\n self.setWindowTitle('Recorder 1.0')\n self.setWindowIcon(QIcon(\"./Static/Images/icon.jpg\"))\n self.show()","def initCentralUic(self):\n self.initFileTableWidget()\n self.initViewerStack()\n self.splitter.setSizes([150, 850])","def configure_ui(self):\n self.setWindowIcon(self.MAIN_ICON)\n self.setWindowModality(Qt.ApplicationModal)","def run(self):\n\n # Create the dialog with elements (after translation) and keep reference\n # Only create GUI ONCE in callback, so that it will only load when the plugin is started\n if self.first_start == True:\n self.first_start = False\n self.dlg = RoadSaltMapDialog()\n self.dlg.setWindowTitle(\"Road Salt Map Builder\")\n#map 1\n self.dlg.tb_outMap1.clicked.connect(self.select_outputmap1_file)\n self.dlg.tb_inBasin_1.clicked.connect(self.select_basin1_file)\n self.dlg.tb_inRoad_1.clicked.connect(self.select_road1_file)\n self.dlg.pb_ok_1.clicked.connect(self.run_method_1)\n#map 2\n self.dlg.tb_outMap2.clicked.connect(self.select_outputmap2_file)\n self.dlg.tb_inBasin_2.clicked.connect(self.select_basin2_file)\n self.dlg.tb_inRoad_2.clicked.connect(self.select_road2_file)\n self.dlg.tb_inSalt.clicked.connect(self.select_salt_file)\n self.dlg.pb_ok_2.clicked.connect(self.run_method_2)\n self.dlg.cb_colDate.currentIndexChanged.connect(self.change_interval)\n\n # Fetch the currently loaded layers\n layers = QgsProject.instance().layerTreeRoot().children()\n # Clear the contents of the comboBox and lineEdit from previous runs\n#map 1\n self.dlg.l_outMap1.clear()\n self.dlg.l_inBasin_1.clear()\n self.dlg.l_inRoad_1.clear()\n#map 2\n self.dlg.l_outMap2.clear()\n self.dlg.l_inBasin_2.clear()\n self.dlg.l_inRoad_2.clear()\n self.dlg.l_inSalt.clear()\n self.dlg.cb_colSalt.clear()\n self.dlg.cb_colDate.clear()\n self.dlg.de_starttime.clear()\n self.dlg.de_endtime.clear()\n\n # show the dialog\n self.dlg.show()\n # Run the dialog event loop\n result = self.dlg.exec_()\n # See if OK was pressed\n if result:\n # Do something useful here - delete the line containing pass and\n # substitute with your code.\n pass","def __init__(self):\n self.s = QSettings()\n self.p = QgsProject.instance()","def main():\r\n qApp = QtGui.QApplication(sys.argv)\r\n qApp.setApplicationName(\"Visualization Tool\")\r\n qApp.setOrganizationName(\"Marquette University Speechlab\")\r\n qApp.setWindowIcon(QtGui.QIcon(\":/vtIcon.ico\"))\r\n form = MainWindow()\r\n form.show()\r\n sys.exit(qApp.exec_())","def __init__(self, windowId, windowTitle, windowDimensions):\n # check if another copy of the window exists\n if cmds.window(windowId, exists=True):\n cmds.deleteUI(windowId)\n\n # load furtility\n mel.eval(\"ta_loadFurtilityAndMPCSolver()\")\n\n # initialize main window features\n super(nHairToolset, self).__init__(getMayaMainPtr())\n self.setObjectName(windowId)\n self.setWindowTitle(windowTitle)\n self.setAttribute(QtCore.Qt.WA_DeleteOnClose)\n self.setFixedSize(windowDimensions[0], windowDimensions[1])\n self.centerWindow()\n # style setup\n self.style = QtGui.QStyleFactory.create('plastique')\n self.setStyle(self.style)\n\n # add layout and widgets\n centerWidget = QtGui.QWidget(self)\n # setup central widget to hold all elements\n centerWidget.setLayout(QtGui.QVBoxLayout(centerWidget))\n self.setCentralWidget(centerWidget)\n\n # create the tab widget to hold the tabs\n innerTabs = QtGui.QTabWidget()\n innerTabs.setFocusPolicy(QtCore.Qt.NoFocus)\n centerWidget.layout().addWidget(innerTabs)\n\n # first Tab\n mainControlsTab = QtGui.QWidget()\n mainControlsTab.setLayout(QtGui.QHBoxLayout())\n mainControlsTab.layout().setAlignment(QtCore.Qt.AlignLeft)\n mainControlsTab.layout()\n #----------------------------------------------------------------------------------------------------------#\n # first column for the main tab\n mainTabColumn1_Layout = QtGui.QVBoxLayout()\n mainTabColumn1_Layout.setAlignment(QtCore.Qt.AlignTop)\n innerTabs.addTab(mainControlsTab, 'Main Functions')\n\n # rigFix options group box\n rigFxGrpBox = customGroupBox('RigFx Options', (240,160), self.style.objectName(), 0, 0)\n rigFixNameField = self.createLabeledNameField('Name: ', 'enter a rigFx name...')\n buildRigFxBtn = QtGui.QPushButton('Build RigFx')\n updateSetsBtn = QtGui.QPushButton('Update Sets')\n motionMultBtn = QtGui.QPushButton('MotionMultiplier')\n # populate rigFx groupBox\n rigFxGrpBox.layout().addLayout(rigFixNameField)\n rigFxGrpBox.layout().addWidget(buildRigFxBtn)\n rigFxGrpBox.layout().addWidget(updateSetsBtn)\n rigFxGrpBox.layout().addWidget(motionMultBtn)\n rigFxGrpBox.layout().addStretch(True)\n\n # nHair groups options groupBox\n nHairGroupsGrpBox = customGroupBox('nHair Groups', (240,330), self.style.objectName(), 0, 0)\n nHairGroupList = QtGui.QListWidget()\n nHairGroupCreationField = self.createLabeledNameField('Group Name:', 'name your group...')\n nHairGroupCreateBtn = QtGui.QPushButton('Create')\n nHairGroupDeleteBtn = QtGui.QPushButton('Delete')\n nHairGroupControlsBtn = QtGui.QPushButton('Create Controls')\n nHairGroupWindControlsBtn = QtGui.QPushButton('Create Wind Controls')\n # add the nHair group elements to the corresponding groupbox\n nHairGroupsGrpBox.layout().addWidget(nHairGroupList)\n nHairGroupsGrpBox.layout().addLayout(nHairGroupCreationField)\n nHairGroupsGrpBox.layout().addWidget(nHairGroupCreateBtn)\n nHairGroupsGrpBox.layout().addWidget(nHairGroupDeleteBtn)\n nHairGroupsGrpBox.layout().addWidget(nHairGroupControlsBtn)\n nHairGroupsGrpBox.layout().addWidget(nHairGroupWindControlsBtn)\n\n # little nucleus groupboxer\n nucleusGroupBox = customGroupBox('Nucleus', (240, 48), self.style.objectName(), 0, 0, QtGui.QHBoxLayout())\n nucleusGroupBox.layout().setAlignment(QtCore.Qt.AlignCenter)\n nucleusSateLabel = QtGui.QLabel('State: ')\n nucleusOnRadioBtn = QtGui.QRadioButton('On')\n nucleusOffRadioBtn = QtGui.QRadioButton('Off')\n nucleusOnRadioBtn.setChecked(True)\n # add widgets to groupBox\n nucleusGroupBox.layout().addWidget(nucleusSateLabel)\n nucleusGroupBox.layout().addItem(QtGui.QSpacerItem(20,2))\n nucleusGroupBox.layout().addWidget(nucleusOnRadioBtn)\n nucleusGroupBox.layout().addItem(QtGui.QSpacerItem(20,2))\n nucleusGroupBox.layout().addWidget(nucleusOffRadioBtn)\n\n # nHair groupBox\n nHairToolBox = customGroupBox('nHair Tools', [240,200], self.style.objectName(), 0, 0, QtGui.QGridLayout())\n nHairToolBox.layout().setAlignment(QtCore.Qt.AlignTop)\n createHairBtn = customIconButton(':/hairCreate.png',(40,40), 'Create Hair')\n paintHairBtn = customIconButton(':/hairPaint.png',(40,40), 'Paint Hair Tool')\n mkDynCurvesBtn = customIconButton(':/hairDynamicCurves.png',(40,40), 'Make Selected Curves Dynamic')\n interactiveBtn = customIconButton(':/interactivePlayback.png',(40,40), 'Interactive Playback')\n currentPosDispBtn = customIconButton(':/hairDisplayCurrent.png',(40,40), 'Display Current Position')\n startPosDispBtn = customIconButton(':/hairDisplayStart.png',(40,40), 'Display Start Position')\n restPosDispBtn = customIconButton(':/hairDisplayRest.png',(40,40), 'Display Rest Position')\n nCacheCreateBtn = customIconButton(':/nClothCacheCreate.png',(40,40), 'Create nCache')\n nCacheDeleteBtn = customIconButton(':/nClothCacheDelete.png',(40,40), 'Delete nCache')\n passiveColliderBtn = customIconButton(':/nClothCreatePassive.png',(40,40), 'Delete nCache')\n # add them to groupBox\n nHairToolBox.layout().addWidget(createHairBtn, 0,0)\n nHairToolBox.layout().addWidget(paintHairBtn, 0,1)\n nHairToolBox.layout().addWidget(mkDynCurvesBtn, 0,2)\n nHairToolBox.layout().addWidget(interactiveBtn, 0,3)\n nHairToolBox.layout().addWidget(currentPosDispBtn, 0,4)\n nHairToolBox.layout().addWidget(startPosDispBtn, 1,0)\n nHairToolBox.layout().addWidget(restPosDispBtn, 1,1)\n nHairToolBox.layout().addWidget(nCacheCreateBtn, 1,2)\n nHairToolBox.layout().addWidget(nCacheDeleteBtn, 1,3)\n nHairToolBox.layout().addWidget(passiveColliderBtn, 1,4)\n\n # add the first column widgets to the corresponding layout\n mainTabColumn1_Layout.addWidget(rigFxGrpBox)\n mainTabColumn1_Layout.addWidget(nHairGroupsGrpBox)\n mainTabColumn1_Layout.addWidget(nucleusGroupBox)\n mainTabColumn1_Layout.addWidget(nHairToolBox)\n\n #----------------------------------------------------------------------------------------------------------#\n # second column for the main tab\n mainTabColumn2_Layout = QtGui.QVBoxLayout()\n mainTabColumn2_Layout.setAlignment(QtCore.Qt.AlignTop)\n\n # control list set\n controlsGroupBox = customGroupBox('Controls', (220, 160), self.style.objectName())\n controlList = QtGui.QListWidget()\n # add the control list to the groupbox\n controlsGroupBox.layout().addWidget(controlList)\n\n # dynamic node list\n dynamicNodesGroupBox = customGroupBox('Dynamic nodes', (220, 160), self.style.objectName())\n dynamicNodeList = QtGui.QListWidget()\n # add the dynamic node list to the groupbox\n dynamicNodesGroupBox.layout().addWidget(dynamicNodeList)\n\n # collider and constraint nodes list\n colConstGroupBox = customGroupBox('Colliders and Consraints', (220, 160), self.style.objectName())\n colConstNodeList = QtGui.QListWidget()\n # add the colliders and constraints list to the groupbox\n colConstGroupBox.layout().addWidget(colConstNodeList)\n\n ## force nodes list\n #forcesGroupBox = customGroupBox('Forces', (200, 140), self.style.objectName())\n #forcesNodeList = QtGui.QListWidget()\n ## add the force list to the groupbox\n #forcesGroupBox.layout().addWidget(forcesNodeList)\n\n # add the second column widgets to the second column\n mainTabColumn2_Layout.addWidget(controlsGroupBox)\n mainTabColumn2_Layout.addWidget(dynamicNodesGroupBox)\n mainTabColumn2_Layout.addWidget(colConstGroupBox)\n #----------------------------------------------------------------------------------------------------------#\n\n # add the main tab columns to the first tab\n mainControlsTab.layout().addLayout(mainTabColumn1_Layout)\n mainControlsTab.layout().addItem(QtGui.QSpacerItem(20,0))\n mainControlsTab.layout().addLayout(mainTabColumn2_Layout)\n\n # second tab\n secondaryControlsTab = QtGui.QWidget(innerTabs)\n innerTabs.addTab(secondaryControlsTab, 'Secondary')","def initUI(self) -> None:\n ratio = 70\n width_to_set = (ratio * self.get_current_window_info()[0]) / 100.0\n height_to_set = (ratio * self.get_current_window_info()[1]) / 100.0\n self.setGeometry(200, 100, width_to_set, height_to_set)\n self.createTable()\n # Add box layout, add table to box layout and add box layout to widget\n self.layout = QVBoxLayout()\n self.layout.addWidget(self.tableWidget)\n self.setLayout(self.layout)\n self.setWindowTitle('View files')\n self.show()","def initialize_gui(self) -> None:\n # pymol.Qt provides the PyQt5 interface\n from PyQt5 import QtWidgets\n from PyQt5.uic import loadUi\n # from pymol.Qt.utils import loadUi\n\n # populate the QMainWindow from our *.ui file\n uifile = os.path.join(os.path.dirname(__file__), 'PyMOL-KVFinder-web-tools.ui')\n loadUi(uifile, self)\n\n # ScrollBars binded to QListWidgets in Descriptors\n scroll_bar_volume = QtWidgets.QScrollBar(self)\n self.volume_list.setVerticalScrollBar(scroll_bar_volume)\n scroll_bar_area = QtWidgets.QScrollBar(self)\n self.area_list.setVerticalScrollBar(scroll_bar_area)\n scroll_bar_residues = QtWidgets.QScrollBar(self)\n self.residues_list.setVerticalScrollBar(scroll_bar_residues)\n\n # about text\n self.about_text.setHtml(about_text)\n\n ########################\n ### Buttons Callback ###\n ########################\n\n # hook up QMainWindow buttons callbacks\n self.button_run.clicked.connect(self.run)\n self.button_exit.clicked.connect(self.close)\n self.button_restore.clicked.connect(self.restore)\n self.button_grid.clicked.connect(self.show_grid)\n \n # hook up Parameters button callbacks\n self.button_browse.clicked.connect(self.select_directory)\n self.refresh_input.clicked.connect(lambda: self.refresh(self.input))\n \n # hook up Search Space button callbacks\n # Box Adjustment\n self.button_draw_box.clicked.connect(self.set_box)\n self.button_delete_box.clicked.connect(self.delete_box)\n self.button_redraw_box.clicked.connect(self.redraw_box)\n self.button_box_adjustment_help.clicked.connect(self.box_adjustment_help)\n # Ligand Adjustment\n self.refresh_ligand.clicked.connect(lambda: self.refresh(self.ligand))\n\n # hook up methods to results tab\n # Jobs\n self.available_jobs.currentIndexChanged.connect(self.fill_job_information)\n self.button_show_job.clicked.connect(self.show_id)\n self.button_add_job_id.clicked.connect(self.add_id)\n # Visualization\n self.button_browse_results.clicked.connect(self.select_results_file)\n self.button_load_results.clicked.connect(self.load_results)\n self.volume_list.itemSelectionChanged.connect(lambda list1=self.volume_list, list2=self.area_list: self.show_cavities(list1, list2))\n self.area_list.itemSelectionChanged.connect(lambda list1=self.area_list, list2=self.volume_list: self.show_cavities(list1, list2))\n self.residues_list.itemSelectionChanged.connect(self.show_residues)","def setupOptionsFrame(self):\n\n # CPU / CUDA options\n self.device.addItems([\"cuda\", \"cpu\"])\n self.scriptedEffect.addLabeledOptionsWidget(\"Device:\", self.device)\n\n self.modality.addItems([\"CT\", \"MRI\"])\n self.scriptedEffect.addLabeledOptionsWidget(\"Modality:\", self.modality)\n\n # Add ROI options\n self.roiSelector.nodeTypes = ['vtkMRMLMarkupsROINode']\n self.roiSelector.noneEnabled = True\n self.roiSelector.setMRMLScene(slicer.mrmlScene)\n self.scriptedEffect.addLabeledOptionsWidget(\"ROI: \", self.roiSelector)\n\n # Toggle ROI visibility button\n toggleROIVisibilityButton = qt.QPushButton(\"Toggle ROI Visibility\")\n toggleROIVisibilityButton.objectName = self.__class__.__name__ + 'ToggleROIVisibility'\n toggleROIVisibilityButton.setToolTip(\"Toggle selected ROI visibility\")\n toggleROIVisibilityButton.connect('clicked()', self.toggleROIVisibility)\n self.scriptedEffect.addOptionsWidget(toggleROIVisibilityButton)\n\n # Apply button\n applyButton = qt.QPushButton(\"Apply\")\n applyButton.objectName = self.__class__.__name__ + 'Apply'\n applyButton.setToolTip(\"Extract liver from input volume\")\n applyButton.connect('clicked()', self.onApply)\n self.scriptedEffect.addOptionsWidget(applyButton)","def setup(self):\n self.ui_manager.purge_ui_elements()\n y_slot = self.window.height // 12\n\n ui_input_box = arcade.gui.UIInputBox(\n center_x=self.window.width // 2,\n center_y=y_slot * 7,\n width=250\n )\n ui_input_box.set_style_attrs(\n bg_color=(66, 179, 208),\n bg_color_hover=(112, 212, 238),\n bg_color_focus=(255, 228, 14)\n )\n ui_input_box.text = self.name\n ui_input_box.cursor_index = len(ui_input_box.text)\n self.ui_manager.add_ui_element(ui_input_box)\n\n button = buttons.ExitButton(\n 'Exit',\n center_x=self.window.width // 2,\n center_y=y_slot * 1,\n width=250\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(135, 21, 25),\n bg_color_press=(122, 21, 24),\n )\n self.ui_manager.add_ui_element(button)\n\n button = buttons.AuthorButton(\"Author\",\n center_x=self.window.width // 2,\n center_y=y_slot * 2,\n width=250,\n user=ui_input_box\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(88, 196, 96),\n bg_color_press=(28, 71, 32),\n )\n self.ui_manager.add_ui_element(button)\n\n button = buttons.ResultButton(\"Results\",\n center_x=self.window.width // 2,\n center_y=y_slot * 3,\n width=250,\n user=ui_input_box,\n level = \"level1\"\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(88, 196, 96),\n bg_color_press=(28, 71, 32),\n )\n self.ui_manager.add_ui_element(button)\n\n button = buttons.RulesButton(\"Rules\",\n center_x=self.window.width // 2,\n center_y=y_slot * 4,\n width=250,\n user=ui_input_box\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(88, 196, 96),\n bg_color_press=(28, 71, 32),\n )\n self.ui_manager.add_ui_element(button)\n\n button = buttons.LevelButton(\"Play level 1\",\n center_x=self.window.width // 2,\n center_y=y_slot * 6,\n width=250,\n user=ui_input_box,\n level=\"level1\"\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(88, 196, 96),\n bg_color_press=(28, 71, 32),\n )\n self.ui_manager.add_ui_element(button)\n\n button = buttons.LevelButton(\"Play level 2\",\n center_x=self.window.width // 2,\n center_y=y_slot * 5,\n width=250,\n user=ui_input_box,\n level = \"level2\"\n )\n button.set_style_attrs(\n bg_color=(51, 139, 57),\n bg_color_hover=(88, 196, 96),\n bg_color_press=(28, 71, 32),\n )\n self.ui_manager.add_ui_element(button)","def initUI(self):\n\n lbl_names = ['Название проекта', 'Версия', 'Директория', 'Описание', 'Автор', 'Почта', 'Дополнительные зависимости', 'Название ноды']\n param_list = ['motor_driver', '0.0.0', '/home/mitya/catkin_ws/src/', 'The motor_driver package', 'D. Potapov',\n 'potapov627@yandex.ru', 'nav_msgs, geometry_msgs, tf, ', 'motor_driver_node']\n labels = []\n for name in lbl_names:\n labels.append(QLabel(name))\n for i, ph in zip(range(len(labels)), param_list):\n ed_line = QLineEdit()\n if i == 1:\n ed_line.setValidator(QRegExpValidator(QRegExp(\"^([0-9\\.])*[0-9]$\")))\n elif i == 5:\n ed_line.setValidator(QRegExpValidator(QRegExp(\"^([a-z0-9_-]+\\.)*[a-z0-9_-]+@[a-z0-9_-]+(\\.[a-z0-9_-]+)*\\.[a-z]{2,6}$\")))\n ed_line.setPlaceholderText(ph)\n if i != 0:\n ed_line.textEdited.connect(self.change_data)\n else:\n ed_line.textEdited.connect(self.change_pkg_name)\n self.full_ed_lines.append(ed_line)\n grid = QGridLayout()\n grid.setSpacing(5)\n for i in range(1, len(labels) + 1):\n for j in range(0, 2):\n if j == 0:\n grid.addWidget(labels[i - 1], i, j)\n else:\n grid.addWidget(self.full_ed_lines[i - 1], i, j)\n ch_dirButton = QPushButton(self)\n ch_dirButton.setIcon(QIcon('./icons/open_folder.png'))\n ch_dirButton.clicked.connect(self.ch_dirDialog)\n grid.addWidget(ch_dirButton, 3, 3)\n genButton = QPushButton(\"Сгенерировать\")\n genButton.clicked.connect(self.generate)\n grid.addWidget(genButton, len(labels) + 2, 1)\n self.setLayout(grid)\n self.setMinimumSize(700, 400)\n self.show()","def setUp(self):\n self.plugin = attributePainter(IFACE)\n self.vector = QgsVectorLayer(os.path.join(self.plugin.plugin_dir,\"test\",\"testdata.gpkg|layername=testdata\"),\"testdata\",\"ogr\")","def __init__(self, viewer: geoviewer.GeoGraphViewer) -> None:\n super().__init__(viewer=viewer)\n\n widget = self._create_settings_widget()\n self.children = [widget]","def setup_ui(self):\n\t\t\n\t\t# CREATION DU LAYOUT\n\t\tself.layout = QtWidgets.QHBoxLayout(self) #le layout prend la fenetre principal en argument donc notre self\n\t\t\n\t\t# CREATION DES WIDGETS\n\t\tself.cbb_devisesFrom = QtWidgets.QComboBox() #combobox (liste deroulante) pour choisir la devise From\n\t\tself.spn_montant = QtWidgets.QSpinBox() #spinbox (zone affichage) du montant a convertir\n\t\tself.cbb_devisesTo = QtWidgets.QComboBox() #cbb pour choisir la devise To\n\t\tself.spn_montantConverti = QtWidgets.QSpinBox() #spn du montant converti\n\t\tself.btn_inverser = QtWidgets.QPushButton(\"Inverser devises\") #bouton pour inverser les devises\n\t\t\n\t\t# AJOUT AU LAYOUT\n\t\tself.layout.addWidget(self.cbb_devisesFrom)\n\t\tself.layout.addWidget(self.spn_montant)\n\t\tself.layout.addWidget(self.cbb_devisesTo)\n\t\tself.layout.addWidget(self.spn_montantConverti)\n\t\tself.layout.addWidget(self.btn_inverser)","def initGui(self):\n\n icon_path = ':/plugins/save_attributes/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u''),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def run(self):\n\n # Create the dialog with elements (after translation) and keep reference\n # Only create GUI ONCE in callback, so that it will only load when the plugin is started\n if self.first_start == True:\n self.first_start = False\n self.dlg = NewRaptorDialog()\n\n #\n self.dlg.cmbSpecies.currentTextChanged.connect(self.evt_cmbSpecies_changed)\n\n\n mc = self.iface.mapCanvas()\n\n self.dlg.spbLatitude.setValue(mc.center().y())\n self.dlg.spbLongitude.setValue(mc.center().x())\n self.dlg.dteLast.setDate(QDate.currentDate())\n\n\n # Lista de camada do mapa\n map_layers = []\n for lyr in mc.layers():\n map_layers.append(lyr.name())\n\n # Coloca o nome dos layrs em uma caixa de mensagem, com o titulo camadas\n #QMessageBox.information(self.dlg, \"Layers\", str(map_layers))\n\n # cria uma caixa de mensagem caso não achar alguams das layrs descritas abaixo\n missing_layers = []\n if not 'Raptor Nests' in map_layers:\n missing_layers.append('Raptor Nests')\n if not 'Raptor Buffer' in map_layers:\n missing_layers.append('Raptor Buffer')\n if not 'Linear Buffer' in map_layers:\n missing_layers.append('Linear Buffer')\n\n if missing_layers:\n msg = 'The following layres are missing from ths project\\n'\n for lyr in missing_layers:\n msg += f'\\n {lyr}'\n QMessageBox.critical(self.dlg, 'Missing layers', msg)\n # o return barra a execução das demais linhas de códigos\n return\n\n\n # show the dialog\n self.dlg.show()\n # Run the dialog event loop\n result = self.dlg.exec_()\n # See if OK was pressed\n if result:\n # Do something useful here - delete the line containing pass and\n # substitute with your code.\n\n # irá retornar uma lista com o as camdas com nome Raptor Nests, mas iremos pegar apenas a primeira camadd\n lyrNests = QgsProject.instance().mapLayersByName('Raptor Nests')[0]\n lyrBuffer = QgsProject.instance().mapLayersByName('Raptor Buffer')[0]\n lyrLinear = QgsProject.instance().mapLayersByName('Linear Buffer')[0]\n # Irá pegar o ultimo valor de índice\n idxNestID = lyrNests.fields().indexOf('Nest_ID')\n valNestID = lyrNests.maximumValue(idxNestID) + 1\n\n # Irá pegar os valores que foram digitados na caixa de texto\n valLat = self.dlg.spbLatitude.value()\n valLng = self.dlg.spbLongitude.value()\n valSpecies = self.dlg.cmbSpecies.currentText()\n valBuffer = self.dlg.spbBuffer.value()\n valStatus = self.dlg.cmbStatus.currentText()\n valLast = self.dlg.dteLast.date()\n QMessageBox.information(self.dlg, 'Message', f'New Nest ID: {valNestID}\\n \\nLatitude: {valLat} '\n f'\\nLongitude: {valLng} \\nSpecies: {valSpecies} '\n f'\\nBuffer: {valBuffer} \\nStatus: {valStatus} \\nLast: {valLast}')\n # cria um novo objeto da classe QgsFeature, no qual a classe terá os mesmoa campos do lyrnet.fildes\n # (objeto da Layer Raptor Nests)\n ftrNest = QgsFeature(lyrNests.fields())\n # Irá começar a adicoanr atributos de acordo com o nome dos campos da layer Raptor Nests\n ftrNest.setAttribute('lat_y_dd', valLat)\n ftrNest.setAttribute('long_x_dd', valLng)\n ftrNest.setAttribute('recentspec', valSpecies)\n ftrNest.setAttribute('buf_dist', valBuffer)\n ftrNest.setAttribute('recentstat', valStatus)\n ftrNest.setAttribute('lastsurvey', valLast)\n ftrNest.setAttribute('Nest_ID', valNestID)\n\n # colcoando o tipo de geometria\n geom = QgsGeometry(QgsPoint(valLng, valLat))\n ftrNest.setGeometry(geom)\n\n # Cada tipo de dados pode ter um provedor diferente, o qgis tem extensões para varios tipos de provedor,\n # a função abaixo pega o provedor e inclui os dados de acordo com o proveor\n pr = lyrNests.dataProvider()\n pr.addFeatures([ftrNest])\n lyrNests.reload()\n\n # Adiconando a camada de buffer\n pr = lyrBuffer.dataProvider()\n buffer = geom.buffer(valBuffer, 10)\n ftrNest.setGeometry(buffer)\n pr.addFeatures([ftrNest])\n lyrBuffer.reload()\n\n dlgTable = DlgTable()\n dlgTable.setWindowTitle(f\"Impacts Table for Nest {valNestID}\")\n\n # Find linear projects that will impacted and report then in the table\n bb = buffer.boundingBox()\n linears = lyrLinear.getFeatures(bb)\n for linear in linears:\n valID = linear.attribute('Project')\n valType = linear.attribute('type')\n valDistance = linear.geometry().distance(geom)\n if valDistance < valBuffer:\n #populate table with Linear data\n row = dlgTable.tblImpacts.rowCount()\n dlgTable.tblImpacts.insertRow(row)\n dlgTable.tblImpacts.setItem(row, 0, QTableWidgetItem(str(valID)))\n dlgTable.tblImpacts.setItem(row, 1, QTableWidgetItem(valType))\n twi = QTableWidgetItem(f\"{valDistance:4.5f}\")\n twi.setTextAlignment(QtCore.Qt.AlignRight)\n dlgTable.tblImpacts.setItem(row, 2, twi)\n\n dlgTable.tblImpacts.sortItems(2)\n dlgTable.show()\n dlgTable.exec_()\n\n else:\n QMessageBox.information(self.dlg, \"Message\", \"Should only run if cancelled\")","def run(self):\n self.iface.actionShowPythonDialog().trigger()\n python_console = self.iface.mainWindow().findChild(QDockWidget, 'PythonConsole')\n if not python_console.isVisible():\n python_console.setVisible(True)\n layers = self.iface.legendInterface().layers()\n layer_list = []\n for layer in layers:\n layer_list.append(layer.name())\n self.dlg.trace_file_list.addItems(layer_list)\n # show the dialog\n self.dlg.show()\n # Run the dialog event loop\n result = self.dlg.exec_()\n # See if OK was pressed\n if result:\n f = open('./here_rtti_miner_config.ini', mode='w')\n app_id = self.dlg.app_id_textbox.toPlainText()\n f.write(app_id + '\\n')\n app_code = self.dlg.app_code_textbox.toPlainText()\n f.write(app_code + '\\n')\n archive_path = self.dlg.archive_textbox.toPlainText()\n f.write(archive_path + '\\n')\n f.close()\n\n \"\"\"Getting Source Of Target Layer\"\"\"\n self.app_id = self.dlg.app_id_textbox.toPlainText()\n self.app_code = self.dlg.app_code_textbox.toPlainText()\n self.archive_path = self.dlg.archive_textbox.toPlainText()\n selected_layer_index = self.dlg.trace_file_list.currentIndex()\n self.mia_image(self.app_id, self.app_code)\n def mia_autorefresh():\n self.mia_image(self.app_id, self.app_code)\n self.mia_image(self.app_id, self.app_code)\n self.iface.mapCanvas().extentsChanged.connect(mia_autorefresh)\n if selected_layer_index >= 0:\n selected_layer = layers[selected_layer_index]\n selected_layer_source = selected_layer.source()\n selected_layer_source_file = selected_layer_source.split('|')[0]\n selected_layer_source_file_type = selected_layer_source_file.split('.')[-1].upper()\n\n \"\"\"Accessing HERE Route Match Extension\"\"\"\n print('Calling HERE RME...')\n app_id = self.dlg.app_id_textbox.toPlainText()\n app_code = self.dlg.app_code_textbox.toPlainText()\n rme_url = 'http://rme.cit.api.here.com/2/matchroute.json?routemode=car&access,gate,oneway,thrutraf,turn&filetype={}&app_id={}&app_code={}&attributes=LINK_TMC_FC1(*),LINK_TMC_FC2(*),LINK_TMC_FC3(*),LINK_TMC_FC4(*),LINK_TMC_FC5(*)'.format(\n selected_layer_source_file_type, app_id, app_code)\n payload = open(selected_layer_source_file, mode='r').read()\n r = requests.post(rme_url, data=payload)\n r.encoding = 'utf-8'\n rme_result = json.loads(r.text)\n print('RMC result captured.\\nStart Parsing...')\n rme_result_db = self.rme_result_parsing(selected_layer_source_file,\n selected_layer_source_file_type.upper(), rme_result)\n output_result = self.here_rtti_mapping(selected_layer_source_file, rme_result_db)\n print('Opening results.')\n uri = 'file:///{}?delimiter={}&xField={}&yField={}'.format(output_result.replace('\\\\', '/'), ',',\n 'lon_matched', 'lat_matched')\n vlayer = QgsVectorLayer(uri, 'output_result', \"delimitedtext\")\n QgsMapLayerRegistry.instance().addMapLayer(vlayer)\n vlayer.loadNamedStyle(resolve('render_result.qml'))\n order = self.iface.layerTreeCanvasBridge().customLayerOrder()\n order.insert(0, order.pop(order.index(vlayer.id())))\n self.iface.layerTreeCanvasBridge().setCustomLayerOrder(order)\n self.iface.layerTreeCanvasBridge().setHasCustomLayerOrder(True)\n print('Results opened.')\n else:\n self.iface.messageBar().pushMessage(\"Error\",\n 'GPS trace is not loaded, please load GPS trace in GPX/KML/NMEA/CSV format.',\n level=QgsMessageBar.CRITICAL)","def initGui(self):\n\n icon_path = ':/plugins/buienradar_plugin/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Buienradar Plugin'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def mainWidget(self):\n raise RuntimeError('Not implemented')","def init_widget(self):","def initGui(self):\n\n icon_path = ':/plugins/OD/icon.png'\n self.add_action(\n icon_path,\n text='OD',\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def initGui(self):\n\n icon_path = ':/plugins/pit_removal/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'remove pits'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True"],"string":"[\n \"def setup(self):\\n self.ui.setup_window()\",\n \"def initUI(self):\\n \\n self.setWindowTitle(\\\"Intecol Flir camera\\\")\\n self.setGeometry(300, 100, 1012, 622)\",\n \"def inicialUI(self):\\r\\n\\r\\n self.setGeometry(500, 500, 500, 500)\\r\\n self.setWindownTitle(\\\"Pesquisa\\\")\\r\\n self.displayWidgets()\\r\\n\\r\\n self.show()\",\n \"def initGui(self):\\n self.sketchButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'sketch.svg'),\\n text=self.tr('Sketch on map'),\\n callback=self.sketchAction,\\n parent=self.iface.mainWindow(),\\n object_name='mSketchAction')\\n self.penButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'pen.svg'),\\n text=self.tr('Draw line on map'),\\n callback=self.penAction,\\n parent=self.iface.mainWindow(),\\n object_name='mPenAction')\\n self.canvasButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'canvas.svg'),\\n text=self.tr('Color and width canvas'),\\n callback=None,\\n parent=self.iface.mainWindow())\\n self.eraseButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'erase.svg'),\\n text=self.tr('Erase sketches'),\\n callback=self.eraseAction,\\n parent=self.iface.mainWindow(),\\n object_name='mEraseAction')\\n self.removeButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'remove.svg'),\\n text=self.tr('Remove all sketches'),\\n callback=self.removeSketchesAction,\\n parent=self.iface.mainWindow(),\\n object_name='mRemoveAllSketches')\\n self.noteButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'note.svg'),\\n text=self.tr('Add text annotations to sketches'),\\n callback=None,\\n parent=self.iface.mainWindow(),\\n object_name='mAddTextAnnotations')\\n self.convertButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'toLayer.svg'),\\n text=self.tr('Convert annotations to Memory Layer'),\\n callback=self.toMemoryLayerAction,\\n parent=self.iface.mainWindow(),\\n object_name='mConvertAnnotationsToMemoryLayer')\\n self.saveButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'inbox.svg'),\\n text=self.tr('Save sketches to file'),\\n callback=self.saveAction,\\n parent=self.iface.mainWindow(),\\n object_name='mSaveSketchesToFile')\\n self.loadButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'outbox.svg'),\\n text=self.tr('Load sketches from file'),\\n callback=self.loadAction,\\n parent=self.iface.mainWindow(),\\n object_name='mLoadSketchesFromFile')\\n self.canvasButton.setMenu(self.canvasMenu())\\n self.noteButton.setCheckable(True)\\n self.penButton.setCheckable(True)\\n self.sketchButton.setCheckable(True)\\n self.eraseButton.setCheckable(True)\\n self.geoSketches = []\\n self.dumLayer = QgsVectorLayer(\\\"Point?crs=EPSG:4326\\\", \\\"temporary_points\\\", \\\"memory\\\")\\n self.pressed = None\\n self.previousPoint = None\\n self.previousMoved = None\\n self.gestures = 0\\n self.points = 0\\n self.currentColor = QColor(\\\"#aa0000\\\")\\n self.currentWidth = 5\\n self.annotation = sketchNoteDialog(self.iface)\\n self.annotatatedSketch = None\\n self.sketchEnabled(None)\\n self.iface.projectRead.connect(self.projectReadAction)\\n self.iface.newProjectCreated.connect(self.newProjectCreatedAction)\\n QgsProject.instance().legendLayersAdded.connect(self.notSavedProjectAction)\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def initGui(self):\\n # Create an action to start capturing polygons and add it to the digitize toolbar\\n icon = QIcon()\\n icon.addPixmap(QPixmap(_fromUtf8(\\\":/plugins/olainterface/lo-logo.png\\\")), QIcon.Normal, QIcon.Off)\\n self.openDialogAction = QAction(icon, QString(\\\"Land Observatory\\\"), self.iface.mainWindow())\\n self.iface.pluginToolBar().addAction(self.openDialogAction)\\n\\n # Connect to signals for button behaviour\\n self.connect(self.openDialogAction, SIGNAL(\\\"triggered()\\\"), self.openDialog)\",\n \"def setUp(self):\\n self.dialog = wtss_qgisDialog(None)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/EU_Mapper/EUICON.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Generate EU Map'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def initUI(self):\\n self.logger.debug('Setting up the Measurement GUI')\\n self.setWindowTitle(self.title)\\n\\n self.show()\\n\\n self.make_combobox_scanner()\\n self.make_combobox_movements()\\n self.make_combobox_configurate()\\n self.make_combobox_basic()\",\n \"def initGui(self):\\n self.run_icon = QIcon(\\n os.path.join(self.plugin_dir, \\\"resources\\\", \\\"icon_add_service.svg\\\")\\n )\\n self.fav_icon = QIcon(\\n os.path.join(self.plugin_dir, \\\"resources\\\", \\\"pdok_icon_bookmark.svg\\\")\\n )\\n\\n self.del_icon = QIcon(\\n os.path.join(self.plugin_dir, \\\"resources\\\", \\\"pdok_icon_delete.svg\\\")\\n )\\n\\n self.run_action = QAction(self.run_icon, PLUGIN_NAME, self.iface.mainWindow())\\n self.run_button = QToolButton()\\n self.run_button.setMenu(QMenu())\\n self.run_button.setPopupMode(QToolButton.MenuButtonPopup)\\n self.run_button.setDefaultAction(self.run_action)\\n\\n self.services_loaded = False\\n\\n self.run_action.triggered.connect(self.run)\\n self.setup_fq_checkboxes()\\n\\n # Add toolbar button and menu item\\n self.toolbar = self.iface.addToolBar(PLUGIN_NAME)\\n self.toolbar.setObjectName(PLUGIN_NAME)\\n self.toolbar.addWidget(self.run_button)\\n\\n # Set default layer loading behaviour\\n self.default_tree_locations = {\\n \\\"wms\\\": \\\"top\\\",\\n \\\"wmts\\\": \\\"bottom\\\",\\n \\\"wfs\\\": \\\"top\\\",\\n \\\"wcs\\\": \\\"top\\\",\\n }\\n\\n self.add_fav_actions_to_toolbar_button()\\n\\n self.toolbar_search = QLineEdit()\\n\\n def toolbar_search_mouse_event():\\n self.toolbar_search.selectAll()\\n self.timer_toolbar_search.start()\\n\\n self.toolbar_search.mousePressEvent = lambda _: toolbar_search_mouse_event()\\n\\n self.toolbar_search.setMaximumWidth(200)\\n self.toolbar_search.setAlignment(Qt.AlignLeft)\\n self.toolbar_search.setPlaceholderText(\\\"Zoek in PDOK Locatieserver\\\")\\n self.toolbar.addWidget(self.toolbar_search)\\n self.timer_toolbar_search = QTimer()\\n self.timer_toolbar_search.setSingleShot(True)\\n self.timer_toolbar_search.setInterval(200)\\n self.timer_toolbar_search.timeout.connect(self.toolbar_search_get_suggestions)\\n self.toolbar_search.textEdited.connect(\\n lambda: self.timer_toolbar_search.start()\\n )\\n\\n eraser_icon = QIcon(\\n os.path.join(self.plugin_dir, \\\"resources\\\", \\\"icon_remove_cross.svg\\\")\\n )\\n self.clean_ls_search_action = QAction(\\n eraser_icon, \\\"Cleanup\\\", self.erase_address()\\n )\\n\\n if not self.show_ls_feature():\\n self.toolbar.addAction(self.clean_ls_search_action)\\n\\n self.clean_ls_search_action.triggered.connect(self.erase_address)\\n self.clean_ls_search_action.setEnabled(False)\\n self.iface.addPluginToMenu(f\\\"&{PLUGIN_NAME}\\\", self.run_action)\\n\\n self.about_action = QAction(self.run_icon, \\\"About\\\", self.iface.mainWindow())\\n self.about_action.setWhatsThis(f\\\"{PLUGIN_NAME} About\\\")\\n self.iface.addPluginToMenu(f\\\"&{PLUGIN_NAME}\\\", self.about_action)\\n\\n self.about_action.triggered.connect(self.about)\\n self.dlg.ui.btnLoadLayer.clicked.connect(lambda: self.load_layer(\\\"default\\\"))\\n self.dlg.ui.btnLoadLayerTop.clicked.connect(lambda: self.load_layer(\\\"top\\\"))\\n self.dlg.ui.btnLoadLayerBottom.clicked.connect(\\n lambda: self.load_layer(\\\"bottom\\\")\\n )\\n\\n self.dlg.ui.pushButton.clicked.connect(self.toggle_all_fq_checkboxes)\\n\\n self.dlg.geocoder_search.returnPressed.connect(\\n self.ls_dialog_get_suggestions_and_remove_pointer\\n )\\n self.timer_geocoder_search = QTimer()\\n self.timer_geocoder_search.setSingleShot(True)\\n self.timer_geocoder_search.setInterval(200)\\n self.timer_geocoder_search.timeout.connect(\\n self.ls_dialog_get_suggestions_and_remove_pointer\\n )\\n self.dlg.geocoder_search.textEdited.connect(\\n lambda: self.timer_geocoder_search.start()\\n )\\n\\n self.dlg.geocoder_search.setPlaceholderText(\\n \\\"Zoek in PDOK Locatieserver, bv postcode of postcode huisnummer\\\"\\n )\\n\\n self.dlg.geocoderResultSearch.textChanged.connect(self.filter_geocoder_result)\\n self.dlg.geocoderResultSearch.setPlaceholderText(\\n \\\"een of meer zoekwoorden uit resultaat\\\"\\n )\\n # connect all fq checkboxes with suggest, so upon a change in fq filter we re-search\\n for cbx in self.fq_checkboxes.keys():\\n cbx.stateChanged.connect(self.ls_dialog_get_suggestions_and_remove_pointer)\\n self.run(True)\\n\\n # set to hidden when no layer selected\\n self.dlg.ui.layer_info.setHidden(True)\\n self.dlg.ui.layer_options_groupbox.setHidden(True)\",\n \"def initializeUI(self):\\n self.setStyleSheet(abstyle)\\n self.setGeometry(140, 100, 860, 484)\\n self.setWindowTitle('Emotions Data View')\\n self.setupModelView()\",\n \"def initGui(self):\\n #icon_path = ':/plugins/QGisImajnetPlugin/icon.png'\\n icon_path = ':/plugins/imajnet-qgis-plugin/resources/img/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Imajnet plugin'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n \\n self._closestImageAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/closestImageMode.png',\\n text=self.tr(u'Closest image'),\\n callback=self.on_closestImage_click,\\n checkable_flag=True,\\n enabled_flag=False,\\n add_to_menu=False,\\n add_to_toolbar=True,\\n status_tip=self.tr(u'Click on the map and get the closest image'),\\n whats_this=self.tr(u'Imajnet closest image tool'),\\n parent=self.iface.mainWindow())\\n \\n self._clickModeAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/clickMode.png',\\n text=self.tr(u'Click mode'),\\n callback=self.on_clickMode_click,\\n checkable_flag=True,\\n enabled_flag=False,\\n add_to_menu=False,\\n add_to_toolbar=True,\\n status_tip=self.tr(u'Click on an object and get an image oriented towards that object'),\\n whats_this=self.tr(u'Imajnet click mode tool'),\\n parent=self.iface.mainWindow())\\n \\n \\n self._aboutAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/about.png',\\n text=self.tr(u'About'),\\n callback=self.on_about_click,\\n enabled_flag=True,\\n add_to_menu=True,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow())\\n \\n self._helpAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/help.png',\\n text=self.tr(u'Help'),\\n callback=self.on_help_click,\\n enabled_flag=True,\\n add_to_menu=True,\\n add_to_toolbar=True,\\n parent=self.iface.mainWindow())\\n \\n self._disconnectAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/logout.png',\\n text=self.tr(u'Disconnect'),\\n callback=self.on_disconnect_click,\\n enabled_flag=False,\\n add_to_menu=True,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow())\\n \\n self._debugModeAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/debug.png',\\n text=self.tr(u'Debug'),\\n callback=self.on_debug_click,\\n enabled_flag=False,\\n add_to_menu=self.isDevMode(),\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow())\\n debugShortcutKeys = QKeySequence(\\\"Ctrl+I\\\")\\n self._debugModeAction.setShortcut(debugShortcutKeys)\\n #self._debugModeAction.setVisible(False)\\n \\n \\n self._jumpForwardAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\\n text=self.tr(u'jump forward'),\\n callback=self.jumpForward,\\n enabled_flag=True,\\n add_to_menu=False,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow())\\n jumpForwardShortcutKeys = QKeySequence(\\\"PgUp\\\")\\n self._jumpForwardAction.setShortcut(jumpForwardShortcutKeys)\\n \\n self._jumpBackwardAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\\n text=self.tr(u'jump backward'),\\n callback=self.jumpBackward,\\n enabled_flag=True,\\n add_to_menu=False,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow()) \\n jumpBackwardShortcutKeys = QKeySequence(\\\"PgDown\\\")\\n self._jumpBackwardAction.setShortcut(jumpBackwardShortcutKeys)\\n \\n \\n self._jumpForwardShortAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\\n text=self.tr(u'jump forward short'),\\n callback=self.jumpForwardShort,\\n enabled_flag=True,\\n add_to_menu=False,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow())\\n jumpForwardShortShortcutKeys = QKeySequence(\\\"Shift+PgUp\\\")\\n self._jumpForwardShortAction.setShortcut(jumpForwardShortShortcutKeys)\\n \\n self._jumpBackwardShortAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\\n text=self.tr(u'jump backward short'),\\n callback=self.jumpBackwardShort,\\n enabled_flag=True,\\n add_to_menu=False,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow()) \\n jumpBackwardShortShortcutKeys = QKeySequence(\\\"Shift+PgDown\\\")\\n self._jumpBackwardShortAction.setShortcut(jumpBackwardShortShortcutKeys)\",\n \"def prepare_UI(self):\",\n \"def initGui(self):\\n icon = QIcon(\\\":/plugins/DEMto3D/icons/demto3d.png\\\")\\n text = self.tr(\\\"DEM 3D printing\\\")\\n parent = self.iface.mainWindow()\\n self.action = QAction(icon, text, parent)\\n self.action.setObjectName(text)\\n self.action.setStatusTip(text)\\n self.action.triggered.connect(self.run)\\n\\n self.iface.addRasterToolBarIcon(self.action)\\n self.iface.addPluginToRasterMenu(self.menu, self.action)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/Isovist/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Isovist'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n \\n #example Code\\n #result = QObject.connect(self.clickTool, SIGNAL(\\\"canvasClicked(const QgsPoint &, Qt::MouseButton)\\\"), self.handleMouseDown)\\n #QMessageBox.information( self.iface.mainWindow(),\\\"Info\\\", \\\"connect = %s\\\"%str(result) )\",\n \"def initGui(self):\\n self.network_layer_notifier = QgepLayerNotifier(\\n self.iface.mainWindow(), [\\\"vw_network_node\\\", \\\"vw_network_segment\\\"]\\n )\\n self.wastewater_networkelement_layer_notifier = QgepLayerNotifier(\\n self.iface.mainWindow(), [\\\"vw_wastewater_node\\\", \\\"vw_qgep_reach\\\"]\\n )\\n self.toolbarButtons = []\\n\\n # Create toolbar button\\n self.profileAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/wastewater-profile.svg\\\")),\\n self.tr(\\\"Profile\\\"),\\n self.iface.mainWindow(),\\n )\\n self.profileAction.setWhatsThis(self.tr(\\\"Reach trace\\\"))\\n self.profileAction.setEnabled(False)\\n self.profileAction.setCheckable(True)\\n self.profileAction.triggered.connect(self.profileToolClicked)\\n\\n self.downstreamAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/wastewater-downstream.svg\\\")),\\n self.tr(\\\"Downstream\\\"),\\n self.iface.mainWindow(),\\n )\\n self.downstreamAction.setWhatsThis(self.tr(\\\"Downstream reaches\\\"))\\n self.downstreamAction.setEnabled(False)\\n self.downstreamAction.setCheckable(True)\\n self.downstreamAction.triggered.connect(self.downstreamToolClicked)\\n\\n self.upstreamAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/wastewater-upstream.svg\\\")),\\n self.tr(\\\"Upstream\\\"),\\n self.iface.mainWindow(),\\n )\\n self.upstreamAction.setWhatsThis(self.tr(\\\"Upstream reaches\\\"))\\n self.upstreamAction.setEnabled(False)\\n self.upstreamAction.setCheckable(True)\\n self.upstreamAction.triggered.connect(self.upstreamToolClicked)\\n\\n self.wizardAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/wizard.svg\\\")),\\n \\\"Wizard\\\",\\n self.iface.mainWindow(),\\n )\\n self.wizardAction.setWhatsThis(self.tr(\\\"Create new manholes and reaches\\\"))\\n self.wizardAction.setEnabled(False)\\n self.wizardAction.setCheckable(True)\\n self.wizardAction.triggered.connect(self.wizard)\\n\\n self.connectNetworkElementsAction = QAction(\\n QIcon(\\n os.path.join(\\n plugin_root_path(), \\\"icons/link-wastewater-networkelement.svg\\\"\\n )\\n ),\\n QApplication.translate(\\\"qgepplugin\\\", \\\"Connect wastewater networkelements\\\"),\\n self.iface.mainWindow(),\\n )\\n self.connectNetworkElementsAction.setEnabled(False)\\n self.connectNetworkElementsAction.setCheckable(True)\\n self.connectNetworkElementsAction.triggered.connect(self.connectNetworkElements)\\n\\n self.refreshNetworkTopologyAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/refresh-network.svg\\\")),\\n \\\"Refresh network topology\\\",\\n self.iface.mainWindow(),\\n )\\n self.refreshNetworkTopologyAction.setWhatsThis(\\n self.tr(\\\"Refresh network topology\\\")\\n )\\n self.refreshNetworkTopologyAction.setEnabled(False)\\n self.refreshNetworkTopologyAction.setCheckable(False)\\n self.refreshNetworkTopologyAction.triggered.connect(\\n self.refreshNetworkTopologyActionClicked\\n )\\n\\n self.aboutAction = QAction(self.tr(\\\"About\\\"), self.iface.mainWindow())\\n self.aboutAction.triggered.connect(self.about)\\n\\n self.settingsAction = QAction(self.tr(\\\"Settings\\\"), self.iface.mainWindow())\\n self.settingsAction.triggered.connect(self.showSettings)\\n\\n self.importAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/interlis_import.svg\\\")),\\n self.tr(\\\"Import from interlis\\\"),\\n self.iface.mainWindow(),\\n )\\n self.importAction.setWhatsThis(self.tr(\\\"Import from interlis\\\"))\\n self.importAction.setEnabled(False)\\n self.importAction.setCheckable(False)\\n self.importAction.triggered.connect(self.actionImportClicked)\\n\\n self.exportAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/interlis_export.svg\\\")),\\n self.tr(\\\"Export to interlis\\\"),\\n self.iface.mainWindow(),\\n )\\n self.exportAction.setWhatsThis(self.tr(\\\"Export from interlis\\\"))\\n self.exportAction.setEnabled(False)\\n self.exportAction.setCheckable(False)\\n self.exportAction.triggered.connect(self.actionExportClicked)\\n\\n self.datamodelInitToolAction = QAction(\\n self.tr(\\\"Datamodel tool\\\"), self.iface.mainWindow()\\n )\\n self.datamodelInitToolAction.triggered.connect(self.showDatamodelInitTool)\\n\\n # Add toolbar button and menu item\\n self.toolbar = QToolBar(QApplication.translate(\\\"qgepplugin\\\", \\\"QGEP\\\"))\\n self.toolbar.addAction(self.profileAction)\\n self.toolbar.addAction(self.upstreamAction)\\n self.toolbar.addAction(self.downstreamAction)\\n self.toolbar.addAction(self.wizardAction)\\n self.toolbar.addAction(self.refreshNetworkTopologyAction)\\n self.toolbar.addAction(self.connectNetworkElementsAction)\\n\\n self.iface.addPluginToMenu(\\\"&QGEP\\\", self.profileAction)\\n self.iface.addPluginToMenu(\\\"&QGEP\\\", self.settingsAction)\\n self.iface.addPluginToMenu(\\\"&QGEP\\\", self.aboutAction)\\n\\n admin_mode = QSettings().value(\\\"/QGEP/AdminMode\\\", False)\\n # seems QGIS loads True as \\\"true\\\" on restart ?!\\n if admin_mode and admin_mode != \\\"false\\\":\\n self.toolbar.addAction(self.importAction)\\n self.toolbar.addAction(self.exportAction)\\n self.iface.addPluginToMenu(\\\"&QGEP\\\", self.datamodelInitToolAction)\\n\\n self.iface.addToolBar(self.toolbar)\\n\\n # Local array of buttons to enable / disable based on context\\n self.toolbarButtons.append(self.profileAction)\\n self.toolbarButtons.append(self.upstreamAction)\\n self.toolbarButtons.append(self.downstreamAction)\\n self.toolbarButtons.append(self.wizardAction)\\n self.toolbarButtons.append(self.refreshNetworkTopologyAction)\\n self.toolbarButtons.append(self.importAction)\\n self.toolbarButtons.append(self.exportAction)\\n\\n self.network_layer_notifier.layersAvailable.connect(self.onLayersAvailable)\\n self.network_layer_notifier.layersUnavailable.connect(self.onLayersUnavailable)\\n\\n # Init the object maintaining the network\\n self.network_analyzer = QgepGraphManager()\\n self.network_analyzer.message_emitted.connect(\\n self.iface.messageBar().pushMessage\\n )\\n # Create the map tool for profile selection\\n self.profile_tool = QgepProfileMapTool(\\n self.iface, self.profileAction, self.network_analyzer\\n )\\n self.profile_tool.profileChanged.connect(self.onProfileChanged)\\n\\n self.upstream_tree_tool = QgepTreeMapTool(\\n self.iface, self.upstreamAction, self.network_analyzer\\n )\\n self.upstream_tree_tool.setDirection(\\\"upstream\\\")\\n self.upstream_tree_tool.treeChanged.connect(self.onTreeChanged)\\n self.downstream_tree_tool = QgepTreeMapTool(\\n self.iface, self.downstreamAction, self.network_analyzer\\n )\\n self.downstream_tree_tool.setDirection(\\\"downstream\\\")\\n self.downstream_tree_tool.treeChanged.connect(self.onTreeChanged)\\n\\n self.maptool_connect_networkelements = QgepMapToolConnectNetworkElements(\\n self.iface, self.connectNetworkElementsAction\\n )\\n\\n self.wastewater_networkelement_layer_notifier.layersAvailableChanged.connect(\\n self.connectNetworkElementsAction.setEnabled\\n )\\n\\n self.processing_provider = QgepProcessingProvider()\\n QgsApplication.processingRegistry().addProvider(self.processing_provider)\\n\\n self.network_layer_notifier.layersAdded([])\",\n \"def initializeUI(self):\\n self.setGeometry(100, 100, 450, 300)\\n self.setWindowTitle('Model and View Example')\\n\\n self.setupModelView()\\n\\n self.show()\",\n \"def _init_ui(self):\\n self.setWindowTitle(\\\"HB Havens: resultaten\\\")\\n self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowContextHelpButtonHint)\\n\\n self.setLayout(QtWidgets.QVBoxLayout())\\n\\n # Create figure\\n self.figure = Figure(figsize=(4,4))\\n self.ax = self.figure.add_subplot()\\n\\n self.ax.grid()\\n self.ax.spines['right'].set_visible(False)\\n self.ax.spines['top'].set_visible(False)\\n self.ax.tick_params(axis='y', color='0.75')\\n self.ax.tick_params(axis='x', color='0.75')\\n self.ax.set_aspect(1)\\n\\n # Add canvas\\n self.canvas = FigureCanvasQTAgg(self.figure)\\n\\n # this is the Navigation widget\\n # it takes the Canvas widget and a parent\\n self.layout().addWidget(self.canvas)\\n\\n # Add location selection\\n hbox = QtWidgets.QHBoxLayout()\\n label = QtWidgets.QLabel('Locatie:')\\n label.setFixedWidth(80)\\n hbox.addWidget(label)\\n self.location_combobox = QtWidgets.QComboBox()\\n self.location_combobox.addItems(self.result_locations)\\n self.location_combobox.setCurrentIndex(self.locid)\\n self.location_combobox.currentIndexChanged.connect(self._set_location)\\n hbox.addWidget(self.location_combobox)\\n self.layout().addLayout(hbox)\\n\\n # Add parameter selection\\n hbox = QtWidgets.QHBoxLayout()\\n label = QtWidgets.QLabel('Parameter:')\\n label.setFixedWidth(80)\\n hbox.addWidget(label)\\n self.parameter_combobox = QtWidgets.QComboBox()\\n self.input_parameters = self.modelunctab.mainmodel.hydraulic_loads.result_columns[:]\\n self.parameter_combobox.addItems(self.input_parameters)\\n self.parameter_combobox.currentIndexChanged.connect(self._set_parameter)\\n self.parameter_combobox.setCurrentIndex(0)\\n self._set_parameter()\\n self.figure.tight_layout()\\n hbox.addWidget(self.parameter_combobox)\\n self.layout().addLayout(hbox)\\n\\n # Line\\n line = QtWidgets.QFrame()\\n line.setFrameShape(QtWidgets.QFrame.HLine)\\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\\n\\n self.layout().addWidget(line)\\n\\n # Add ok/close\\n self.closebutton = QtWidgets.QPushButton('Sluiten')\\n self.closebutton.clicked.connect(self.close)\\n self.layout().addWidget(self.closebutton, 0, QtCore.Qt.AlignRight)\\n\\n self.layout().setSizeConstraint(QtWidgets.QLayout.SetFixedSize)\",\n \"def init_ui(self):\\n self.parent.title(\\\"Roku Player Controller\\\")\\n self.style.theme_use(\\\"default\\\")\",\n \"def initGui(self):\\n\\n self.action = QAction(QIcon(self.plugdir + '/icon.png'), 'MGB Water Balance', self.iface.mainWindow())\\n self.action.triggered.connect(self.run)\\n self.iface.addToolBarIcon(self.action)\\n self.iface.addPluginToMenu('&IPH - Plugins', self.action)\",\n \"def initializeUI(self):\\n self.setGeometry(100, 100, 300, 200)\\n self.setWindowTitle('Event Handling Example')\\n\\n self.show()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/AreaPrinter/mountainIcon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'AreaPrinter'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n\\tself.add_action(\\n icon_path,\\n text=self.tr(u'Grid Convergence'),\\n callback=self.runTools,\\n parent=self.iface.mainWindow())\",\n \"def initGui(self):\\r\\n\\r\\n # Create help action \\r\\n self.helpAction = QAction( QIcon(\\\":/plugins/layercombinations/about.png\\\"), u\\\"Help\\\", self.iface.mainWindow())\\r\\n # connect the action \\r\\n self.helpAction.triggered.connect( self.showHelp )\\r\\n # Add menu item\\r\\n self.iface.addPluginToMenu(u\\\"&Layer Combinations\\\", self.helpAction)\\r\\n\\r\\n # Create the action that allows to change the widget type\\r\\n self.changeWidgetAction = QAction(\\\"Change widget type\\\", self.iface.mainWindow())\\r\\n self.changeWidgetAction.triggered.connect( self.changeWidget )\\r\\n self.iface.addPluginToMenu(u\\\"&Layer Combinations\\\", self.changeWidgetAction)\\r\\n\\r\\n # Create the action that will toggle the plugin panel\\r\\n self.action = QAction(QIcon(\\\":/plugins/layercombinations/icon.png\\\"), \\\"Show/hide the Layer Combinations widgets\\\", self.iface.mainWindow())\\r\\n self.action.triggered.connect( self.widget.toggle )\\r\\n # Add toolbar button and menu item\\r\\n self.iface.addToolBarIcon(self.action)\\r\\n self.iface.addPluginToMenu(u\\\"&Layer Combinations\\\", self.action)\\r\\n\\r\\n\\r\\n # Add the widget to the mainWindow\\r\\n self.widget.addToiFace(self.iface)\",\n \"def __init__(self):\\n super(QTUIProject, self).__init__()\\n self.setupUi(self)\\n self.assignWidgets()\\n self.show()\\n self.SlotsJsonName = \\\"Slots Assets Folder\\\"\\n self.BingoJsonName = \\\"Bingo Assets Folder\\\"\",\n \"def init_ui(self):\\n raise NotImplementedError(\\\"This is an abstract method.\\\")\",\n \"def initGui(self):\\n from p4_view import Gui\\n self.updateStatus(\\\"Launching GUI...\\\")\\n self.gui = Gui(self, self.lmap)\\n self.gui.setStart(self.cfg[\\\"START\\\"])\\n self.gui.setGoal(self.cfg[\\\"GOAL\\\"])\\n self.gui.setPossGoals(self.cfg[\\\"POSS_GOALS\\\"])\\n #GHD\\n self.gui.setMapName(self.cfg[\\\"MAP_FILE\\\"])\\n self.updateStatus(\\\"OK\\\")\\n self.gui.mainloop()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/qgsCsvAzmDist2LatLon/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'csvAzmDist2LatLon'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n \\n self.dlg.pbInCsv.clicked.connect(self.select_input_file)\\n self.dlg.pbOutCsv.clicked.connect(self.select_output_file)\\n self.dlg.pbCalc.clicked.connect(self.calculations)\",\n \"def initGui(self):\\n\\n icon_path = ''\\n self.add_action(\\n icon_path,\\n text=self.tr(u'圖資操作處理工具v3.0'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n \\n \\n\\n #照片EXIF定位ImportPhotos初始參數\\n self.clickPhotos = self.add_action(\\n icon_path,\\n text=self.tr(u'檢視照片'),\\n callback=self.mouseClick,\\n parent=self.iface.mainWindow())\\n \\n self.clickPhotos.setCheckable(True)\\n self.clickPhotos.setEnabled(True)\\n # self.iface.addPluginToMenu(TFB_Tools3.description(), self.action)\\n self.listPhotos = []\\n self.layernamePhotos = [] \\n self.toolMouseClick = MouseClick(self.canvas, self)\\n \\n\\n self.fields = ['ID', 'Name', 'Date', 'Time', 'Lon', 'Lat', 'Altitude', 'North', 'Azimuth', 'Camera Maker',\\n 'Camera Model', 'Title', 'Comment', 'Path', 'RelPath', 'Timestamp']\\n\\n self.extension_switch = {\\n \\\".shp\\\": \\\"ESRI Shapefile\\\",\\n \\\".geojson\\\": \\\"GeoJSON\\\",\\n \\\".gpkg\\\":\\\"GPKG\\\",\\n \\\".csv\\\": \\\"CSV\\\",\\n \\\".kml\\\": \\\"KML\\\",\\n \\\".tab\\\": \\\"MapInfo File\\\"\\n }\\n\\n self.extension_switch2 = {\\n \\\"ESRI Shapefile (*.shp *.SHP)\\\": \\\".shp\\\",\\n \\\"GeoJSON (*.geojson *.GEOJSON)\\\": \\\".geojson\\\",\\n \\\"GeoPackage (*.gpkg *.GPKG)\\\":\\\".gpkg\\\",\\n \\\"Comma Separated Value (*.csv *.CSV)\\\": \\\".csv\\\",\\n \\\"Keyhole Markup Language (*.kml *.KML)\\\": \\\".kml\\\",\\n \\\"Mapinfo TAB (*.tab *.TAB)\\\": \\\".tab\\\"\\n }\\n\\n self.extension_switch_types = {\\n \\\".shp\\\": \\\"ESRI Shapefile\\\",\\n \\\".geojson\\\": \\\"GeoJSON\\\",\\n \\\".gpkg\\\":\\\"GPKG\\\",\\n \\\".csv\\\": \\\"CSV\\\",\\n \\\".kml\\\": \\\"KML\\\",\\n \\\".tab\\\": \\\"MapInfo File\\\"\\n }\\n\\n ############################\\n\\n self.pointEmitter = QgsMapToolEmitPoint(self.iface.mapCanvas())\\n self.iface.mapCanvas().setMapTool( self.pointEmitter )\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/PluginChoucas/img/loaddata.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Chargement de données de montagne'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n \\n icon_path = ':/plugins/PluginChoucas/img/loupe.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Recherche par mots sur clé'),\\n callback=self.search,\\n parent=self.iface.mainWindow())\\n \\n icon_path = ':/plugins/PluginChoucas/img/EmpriseZoneEtude.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Emprise ZE'),\\n callback=self.loadZoneEtude,\\n parent=self.iface.mainWindow())\\n \\n \\n #icon_path = ':/plugins/PluginChoucas/img/landmark.png'\\n #self.add_action(\\n # icon_path,\\n # text=self.tr(u'Afficher la description de l itinéraire'),\\n # callback=self.displayDescription,\\n # parent=self.iface.mainWindow())\",\n \"def __setup_ui(self):\\n self.pixel_label = QLabel(\\\"\\\", self)\\n self.pixel_label.setFixedWidth(100)\\n self.pixel_coords_label = QLabel(\\\"\\\", self)\\n self.statusBar().addPermanentWidget(self.pixel_coords_label)\\n self.statusBar().addPermanentWidget(self.pixel_label)\\n\\n self.current_fps_label = QLabel(\\\"\\\", self)\\n self.statusBar().addPermanentWidget(self.current_fps_label)\\n\\n self.toolbar = self.addToolBar(\\\"default\\\")\\n self.toolbar.setMovable(False)\\n self.setContextMenuPolicy(Qt.NoContextMenu)\\n\\n exit_act = QAction(QIcon.fromTheme('exit'), 'Exit', self)\\n exit_act.setShortcut('Ctrl+Q')\\n exit_act.setStatusTip(\\\"Exit application\\\")\\n exit_act.triggered.connect(self.app.quit)\\n self.toolbar.addAction(exit_act)\\n\\n preferences_action = QAction(QIcon.fromTheme(\\\"preferences-desktop\\\"),\\n \\\"Preferences\\\", self)\\n preferences_action.setStatusTip(\\\"Open preferences dialog\\\")\\n preferences_action.triggered.connect(self.open_preferences)\\n self.toolbar.addAction(preferences_action)\\n\\n self.device_label = QLabel(\\\"Device:\\\")\\n self.device_combo = QComboBox(self)\\n self.device_combo.setSizeAdjustPolicy(QComboBox.AdjustToContents)\\n # self.device_combo.setMinimumWidth(300)\\n self.device_combo.activated[str].connect(self.on_device_selected)\\n self.toolbar.addWidget(self.device_label)\\n self.toolbar.addWidget(self.device_combo)\\n\\n self.format_label = QLabel(\\\"Format:\\\")\\n self.format_combo = QComboBox(self)\\n self.format_combo.setSizeAdjustPolicy(QComboBox.AdjustToContents)\\n self.format_combo.setMinimumWidth(150)\\n self.format_combo.activated[str].connect(self.on_format_selected)\\n self.toolbar.addWidget(self.format_label)\\n self.toolbar.addWidget(self.format_combo)\\n\\n self.resolution_label = QLabel(\\\"Resolution:\\\")\\n self.resolution_combo = TcamComboBox(self, \\\"Select Resolution\\\")\\n self.resolution_combo.setSizeAdjustPolicy(QComboBox.AdjustToContents)\\n self.resolution_combo.activated[str].connect(self.on_resolution_selected)\\n self.toolbar.addWidget(self.resolution_label)\\n self.toolbar.addWidget(self.resolution_combo)\\n\\n self.fps_label = QLabel(\\\"FPS:\\\")\\n self.fps_combo = TcamComboBox(self, \\\"Select FPS:\\\")\\n self.fps_combo.setSizeAdjustPolicy(QComboBox.AdjustToContents)\\n self.fps_combo.activated[str].connect(self.on_fps_selected)\\n self.toolbar.addWidget(self.fps_label)\\n self.toolbar.addWidget(self.fps_combo)\\n\\n self.save_image = QAction(\\\"Save Image\\\", self)\\n self.save_image.setIcon(QIcon.fromTheme(\\\"insert-image\\\"))\\n\\n self.save_image.triggered.connect(self.save_image_action)\\n self.toolbar.addAction(self.save_image)\\n\\n self.fit_to_win = QAction(\\\"Fit To Window\\\", self)\\n self.fit_to_win.setIcon(QIcon.fromTheme(\\\"zoom-fit-best\\\"))\\n\\n self.fit_to_win.triggered.connect(self.fit_to_window)\\n self.toolbar.addAction(self.fit_to_win)\\n\\n self.props_action = QAction(\\\"\\\", self)\\n self.props_action.setText(\\\"Properties\\\")\\n self.props_action.setVisible(False)\\n self.props_action.triggered.connect(self.toggle_properties_dialog)\\n self.toolbar.addAction(self.props_action)\\n\\n self.recording_action = QAction(\\\"\\\", self)\\n self.recording_action.setIcon(QIcon.fromTheme(\\\"media-record\\\"))\\n self.recording_action.setIconText(\\\"Start recording\\\")\\n self.recording_action.setText(\\\"Start recording\\\")\\n self.recording_action.triggered.connect(self.start_recording_video)\\n self.toolbar.addAction(self.recording_action)\\n\\n self.set_device_menus_enabled(False)\\n\\n self.view = None\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/PolygonByPolarCoordinates/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'PolygonByPolarCoordinates'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/roadsalt_mapbuilder/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'&Road Salt Map Builder'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def initGui(self):\\n\\n\\t\\ticon_path = ':/plugins/main_index_extractor/icon.png'\\n\\t\\tself.add_action(\\n\\t\\t\\ticon_path,\\n\\t\\t\\ttext=self.tr(u'Extract VI from Sentinel-2 data'),\\n\\t\\t\\tcallback=self.run,\\n\\t\\t\\tparent=self.iface.mainWindow())\",\n \"def initUI(self):\\n\\n self.wid = RosGenWidget()\\n self.setCentralWidget(self.wid)\\n menubar = self.menuBar()\\n fileMenu = menubar.addMenu('&Файл')\\n editMenu = menubar.addMenu('&Редактирование')\\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\\n self.statusbar = self.statusBar()\\n self.statusbar.showMessage('Ожидание данных')\\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\\n self.setGeometry(600, 200, 700, 400)\\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\\n self.show()\",\n \"def initGui(self): # pylint:disable=invalid-name\\n\\n icon_path = os.path.join(self.plugin_dir, \\\"icons\\\")\\n\\n self.add_action(\\n os.path.join(icon_path, \\\"icon.png\\\"),\\n text=self.translate(\\\"Load Data\\\"),\\n callback=self.run,\\n parent=self.iface.mainWindow(),\\n )\\n\\n # Plugin Dialog\\n self.dlg.uListOptions.itemClicked.connect(self.show_selected_option)\\n self.dlg.uListOptions.itemClicked.emit(self.dlg.uListOptions.item(0))\\n\\n model = QStandardItemModel()\\n self.dlg.uCRSCombo.setModel(model)\\n self.dlg.uCRSCombo.currentIndexChanged.connect(self.layer_crs_selected)\\n\\n self.dlg.uLabelWarning.setStyleSheet(\\\"color:red\\\")\\n self.dlg.uWarningSettings.setStyleSheet(\\\"color:red\\\")\\n\\n self.add_item(title=\\\"ALL\\\", icon=os.path.join(icon_path, \\\"all.png\\\"))\\n self.add_item(title=\\\"WFS\\\", icon=os.path.join(icon_path, \\\"wfs.png\\\"))\\n self.add_item(title=\\\"WMTS\\\", icon=os.path.join(icon_path, \\\"wmts.png\\\"))\\n self.add_item(title=\\\"Settings\\\", icon=os.path.join(icon_path, \\\"settings.png\\\"))\\n self.add_item(title=\\\"Help\\\", icon=os.path.join(icon_path, \\\"help.png\\\"))\\n self.add_item(title=\\\"About\\\", icon=os.path.join(icon_path, \\\"about.png\\\"))\\n\\n # set table model\\n self.set_table_model_view()\\n\\n # set help html\\n self.dlg.hHelpHtml.setOpenExternalLinks(True)\\n help_file = os.path.join(self.plugin_dir, \\\"help.html\\\")\\n with open(help_file, \\\"r\\\", encoding=\\\"utf-8\\\") as file:\\n help_html = file.read()\\n help_html.format(self.plugin_dir)\\n self.dlg.hHelpHtml.setHtml(help_html.format(icon_path))\\n\\n # set about html\\n self.dlg.hAboutHtml.setOpenExternalLinks(True)\\n about_file = os.path.join(self.plugin_dir, \\\"about.html\\\")\\n with open(about_file, \\\"r\\\", encoding=\\\"utf-8\\\") as file:\\n about_html = file.read()\\n about_html.format(self.plugin_dir)\\n self.dlg.hAboutHtml.setHtml(about_html.format(icon_path))\\n\\n # populate settings\\n # default data services to combo\\n\\n self.dlg.uComboBoxDomain.addItems(SER)\\n\\n # settings signals\\n self.dlg.uBtnAddDomain.clicked.connect(self.add_new_domain)\\n for entry in range(1, 11):\\n getattr(self.dlg, \\\"uBtnSaveDomain{0}\\\".format(entry)).clicked.connect(\\n self.save_domain\\n )\\n getattr(self.dlg, \\\"uBtnRemoveDomain{0}\\\".format(entry)).clicked.connect(\\n self.save_domain\\n )\\n self.load_settings()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/LidarProcessor/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'LiDAR Operations'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def _initUI(self) -> None:\\n self._createActions()\\n self._addActionsToMoveButtons()\\n self._createToolBar()\\n self._createStatusBar()\\n self._createMainContextMenu()\",\n \"def main():\\n \\n cities, coordinates, speedlimits, adjlist = data_for_app()\\n \\n ui(cities, coordinates, speedlimits, adjlist)\",\n \"def iniciaUI(self):\\n\\n self.setGeometry(100,100, 300, 200)\\n self.setWindowTitle(\\\"Formulario\\\")\\n self.displayWidgets()\\n\\n self.show()\",\n \"def init_ui(self):\\n # Create GUI elements, set them in dict structure\\n labelwidth = 150\\n\\n # Add parameter line edit for Factor Tm to Tp\\n\\n # Add line edit with browsebutton for swan result folder\\n self.input_elements['hares folder'] = widgets.ExtendedLineEdit(\\n label='HARES uitvoerbestanden folder:',\\n labelwidth=labelwidth,\\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_hares_folder)\\n )\\n\\n\\n self.setLayout(QtWidgets.QVBoxLayout())\\n self.layout().setSpacing(10)\\n\\n for _, item in self.input_elements.items():\\n self.layout().addWidget(item)\\n\\n line = QtWidgets.QFrame()\\n line.setFrameShape(QtWidgets.QFrame.HLine)\\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\\n self.layout().addWidget(line)\\n\\n # OK and Cancel buttons\\n self.generateButton = QtWidgets.QPushButton('Start lezen uitvoerbestanden')\\n self.generateButton.setDefault(True)\\n self.generateButton.clicked.connect(self.generate)\\n\\n self.cancelButton = QtWidgets.QPushButton('Annuleren')\\n self.cancelButton.setAutoDefault(False)\\n self.cancelButton.clicked.connect(self.cancel)\\n\\n button_box = QtWidgets.QDialogButtonBox(QtCore.Qt.Horizontal, self)\\n button_box.addButton(self.generateButton, QtWidgets.QDialogButtonBox.ActionRole)\\n button_box.addButton(self.cancelButton, QtWidgets.QDialogButtonBox.RejectRole)\\n button_box.accepted.connect(QtWidgets.QDialog.accept)\\n\\n self.layout().addWidget(button_box)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/Hybriddekning/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Hybriddekning'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def _setup_ui(self):\\n\\n self.window = ui.Widget()\\n self.window.dimensions = ui.normalize_dimension((\\n 0, 0,\\n self.normalized_screen_resolution[0],\\n self.normalized_screen_resolution[1]\\n ))\\n self.window.background_color = ImageColor.getcolor('#000000', 'RGB')\\n\\n interface_frame = ui.Widget(parent=self.window)\\n interface_frame.dimensions = ui.normalize_dimension((\\n self.preview_renderer.window[2],\\n 0,\\n self.normalized_screen_resolution[0] - self.preview_renderer.window[2],\\n self.normalized_screen_resolution[1]\\n ))\\n interface_frame.background_color = ImageColor.getcolor('#ffffff', 'RGB')\\n\\n number = ui.LabelWidget(\\\"\\\",\\n name=NAME_GET_STARTED,\\n parent=interface_frame,\\n align=\\\"center\\\",\\n font_color=(0, 0, 0, 255))\\n number.dimensions = (\\n 5, 5,\\n interface_frame.width - 10,\\n interface_frame.height - 10\\n )\",\n \"def initGui(self):\\n\\n icon_path = ':/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'PacSafe'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/trialPurpose/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'trial'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/cctv_mapper/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'CCTV Mapper'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def init_ui(self):\\n # Create GUI elements, set them in dict structure\\n labelwidth = 150\\n\\n # Add parameter line edit for Factor Tm to Tp\\n self.input_elements['factor Tm Tp'] = widgets.ParameterInputLine(\\n label='Factor Tm naar Tp:',\\n labelwidth=labelwidth,\\n unitlabel='(NVT: Tp aanwezig)' if 'Tp' in self.hydraulic_loads.columns else '',\\n validator=QtGui.QDoubleValidator(0.01, 99.99, 20),\\n )\\n\\n if 'Tp' in self.hydraulic_loads.columns or self.parent_tab.step != 'I1':\\n self.input_elements['factor Tm Tp'].set_enabled(False)\\n\\n # Add line edit with browsebutton for Master template\\n self.input_elements['mastertemplate'] = widgets.ExtendedLineEdit(\\n label='Master template bestand:',\\n labelwidth=labelwidth,\\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_master_template)\\n )\\n\\n # Add line edit with browsebutton for depth file\\n self.input_elements['depthfile'] = widgets.ExtendedLineEdit(\\n label='Bathymetry bestand:',\\n labelwidth=labelwidth,\\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_bathymetry_file)\\n )\\n\\n # Add line edit with browsebutton for swan result folder\\n self.input_elements['swanfolder'] = widgets.ExtendedLineEdit(\\n label='SWAN uitvoer folder:',\\n labelwidth=labelwidth,\\n browsebutton=QtWidgets.QPushButton('...', clicked=self.select_swan_folder)\\n )\\n\\n\\n self.setLayout(QtWidgets.QVBoxLayout())\\n self.layout().setSpacing(10)\\n\\n for _, item in self.input_elements.items():\\n self.layout().addWidget(item)\\n\\n line = QtWidgets.QFrame()\\n line.setFrameShape(QtWidgets.QFrame.HLine)\\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\\n self.layout().addWidget(line)\\n\\n # OK and Cancel buttons\\n self.generateButton = QtWidgets.QPushButton('Genereer invoer')\\n self.generateButton.setDefault(True)\\n self.generateButton.clicked.connect(self.generate)\\n\\n self.cancelButton = QtWidgets.QPushButton('Annuleren')\\n self.cancelButton.setAutoDefault(False)\\n self.cancelButton.clicked.connect(self.cancel)\\n\\n button_box = QtWidgets.QDialogButtonBox(QtCore.Qt.Horizontal, self)\\n button_box.addButton(self.generateButton, QtWidgets.QDialogButtonBox.ActionRole)\\n button_box.addButton(self.cancelButton, QtWidgets.QDialogButtonBox.RejectRole)\\n button_box.accepted.connect(QtWidgets.QDialog.accept)\\n\\n self.layout().addWidget(button_box)\",\n \"def __init__(self):\\r\\n super().__init__()\\r\\n self.init_ui()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/FlowMapArrowCurver/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Flow Map Curved Arrow Calculator'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def initUI(self):\\n centralwidget = QtWidgets.QWidget()\\n self.setCentralWidget(centralwidget)\\n\\n person_table = PersonTable(self)\\n person_table.embed(self.sql)\\n\\n # Box Layout to organize our GUI\\n lay = QtWidgets.QVBoxLayout(centralwidget)\\n lay.addWidget(person_table)\\n self.setGeometry(0, 0, person_table.width() + 20, person_table.height() + 20)\\n self.person_table = person_table\\n self.show()\",\n \"def createUI(self):\\n\\n q.getQItem(windowID, QtWidgets.QWidget)\\n cmds.setParent(q.fullPath)\\n\\n # ################################################\\n # Active Render Layer\\n\\n # cmds.separator(height=12, style='none')\\n addFrameLayout(\\n '%s_frameLayoutLayers' % windowID,\\n 'Visible Render Layer', collapsable=False,\\n labelVisible=False,\\n marginHeight=0\\n )\\n\\n addRowLayout(\\n '%s_rowLayoutActiveRenderLayer' % windowID,\\n 4,\\n columnAlign4=('left', 'left', 'right', 'right'),\\n columnAttach4=('left', 'both', 'right', 'right'),\\n columnWidth4=(\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.775,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075\\n )\\n )\\n\\n\\n addButton('%s_addNewLayer' % windowID, 'New', rsAddNewLayer,\\n image='RS_create_layer', size=(21, 21))\\n addOptionMenu('%s_selectActiveLayer' % windowID,\\n 'Active Layer ', (), rsSelectActiveLayer)\\n addButton('rsOpenRenderSetupWindow', 'Render Setup',\\n rsOpenRenderSetupWindow, image='render_setup.png',\\n size=(21, 21))\\n addButton('rsOpenUnifiedRenderGlobals', 'Render Globals',\\n rsOpenUnifiedRenderGlobals, image='render_setup.png',\\n size=(21, 21))\\n\\n # ################################################\\n # Work Render Layers\\n\\n cmds.setParent(q.fullPath)\\n addFrameLayout('%s_frameLayoutLayersB' % windowID,\\n 'Work Render Layer', collapsable=False,\\n labelVisible=False, marginHeight=0)\\n addRowLayout('%s_rowLayoutVisibleRenderLayer' % windowID, 3,\\n columnAlign3=('left', 'left', 'right'),\\n columnAttach3=('left', 'both', 'right'),\\n columnWidth3=((WINDOW_WIDTH - FRAME_MARGIN * 2)\\n * 0.075, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.85,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075))\\n\\n cmds.separator()\\n addOptionMenu('%s_selectVisibleLayer' % windowID,\\n 'Visible Layer ', (), rsSelectVisibleLayer)\\n cmds.separator()\\n\\n cmds.setParent(q.fullPath)\\n cmds.separator(height=12, style='none')\\n\\n # ################################################\\n # Collections\\n\\n addFrameLayout('%s_frameLayout02' % windowID, 'Collections',\\n labelVisible=False, marginHeight=0)\\n\\n addRowLayout(\\n '%s_rowLayout02' % windowID,\\n 6,\\n columnAlign6=('left', 'left', 'left', 'left', 'left', 'left'),\\n columnAttach6=('both', 'both', 'right', 'right', 'right', 'right'),\\n columnWidth6=(\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.18,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.18,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.415,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.075,\\n )\\n )\\n\\n addButton('rsAddCollection', 'Add', rsAddCollection)\\n addButton('rsRemoveCollection', 'Remove', rsRemoveCollection)\\n addButton('rsSelectShapes', 'Select Shapes', rsSelectShapes,\\n image='selectObject.png', size=(21, 21))\\n addButton('rsRenameShader', 'Rename Shader', rsRenameShader,\\n size=(21, 21), image='QR_rename.png')\\n addButton('rsDuplicateShader', 'Duplicate Shader',\\n duplicateShader, size=(21, 21), image='newPreset.png')\\n addButton('rsRefreshUI', 'Refresh', rsRefreshUI, size=(21, 21),\\n image='QR_refresh.png')\\n\\n # ###########################\\n # Filter List\\n\\n cmds.setParent('%s_frameLayout02' % windowID)\\n addRowLayout('%s_rowLayout03' % windowID, 2,\\n columnAlign2=('left', 'left'),\\n columnAttach2=('both', 'both'),\\n columnWidth2=((WINDOW_WIDTH - FRAME_MARGIN * 2)\\n * 0.6, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.42))\\n\\n addTextField('%s_filterShaderList' % windowID, 'Search',\\n rsFilterShaderList_off, rsFilterShaderList_off,\\n window.updateUI)\\n addOptionMenu('rsShaderGroups', '|', (), rsShaderGroups)\\n\\n # ###########################\\n # The shaders scroll list\\n\\n cmds.setParent('%s_frameLayout02' % windowID)\\n addRowLayout('%s_rowLayout04' % windowID, 1, columnAlign1='both', columnAttach1='both', columnWidth1=WINDOW_WIDTH\\n + 12)\\n addTextScrollList('%s_ShaderScrollList' % windowID, (),\\n rsShaderScrollList_doubleClick,\\n rsShaderScrollList_onSelect,\\n rsShaderScrollList_deleteKey)\\n\\n # Add popup menu:\\n\\n cmds.popupMenu('rsShaderScrollListPopupMenu',\\n parent='%s_ShaderScrollList' % windowID,\\n allowOptionBoxes=False, markingMenu=True,\\n postMenuCommand=postMenuCommand)\\n cmds.menuItem('%s_popupMenuItem02' % windowID,\\n label='Duplicate Shader', command=duplicateShader)\\n cmds.menuItem(divider=True)\\n cmds.menuItem('%s_popupMenuItem04' % windowID,\\n label='Graph Shader')\\n cmds.menuItem(divider=True)\\n cmds.menuItem('%s_popupMenuItem03' % windowID,\\n label='Select Shader')\\n cmds.menuItem(divider=True)\\n cmds.menuItem('%s_popupMenuItem05' % windowID,\\n label='Select Assigned Shapes')\\n cmds.menuItem('%s_popupMenuItem06' % windowID,\\n label='Select Assigned Transforms')\\n\\n # ##################################################\\n # Arnold Property Overrides\\n\\n cmds.setParent('%s_frameLayout02' % windowID)\\n cmds.columnLayout(\\n '%s_columnLayout20' % windowID,\\n width=WINDOW_WIDTH - FRAME_MARGIN * 2,\\n columnAlign='left',\\n columnAttach=('left', 0),\\n adjustableColumn=False,\\n rowSpacing=0,\\n )\\n\\n cmds.separator(parent='%s_columnLayout20' % windowID, height=4,\\n style='none')\\n\\n addRowLayout('%s_rowLayout05' % windowID, 2,\\n columnAlign2=('left', 'both'),\\n columnAttach2=('left', 'right'),\\n columnWidth2=((WINDOW_WIDTH - FRAME_MARGIN * 2)\\n * 0.75, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.25))\\n addText('%s_textArnoldPropertyOverridesLabel' % windowID,\\n 'Apply Arnold Property Overrides', 'plainLabelFont')\\n addCheckBox('rsArnoldPropertyOverridesCheckBox', '',\\n rsArnoldPropertyOverridesCheckBox,\\n rsArnoldPropertyOverridesCheckBox)\\n cmds.separator(parent='%s_columnLayout20' % windowID, height=4,\\n style='none')\\n\\n # Column Layout to toggle\\n\\n cmds.setParent('%s_columnLayout20' % windowID)\\n cmds.columnLayout(\\n '%s_columnLayout02' % windowID,\\n width=WINDOW_WIDTH - FRAME_MARGIN * 2,\\n columnAlign='left',\\n columnAttach=('left', 0),\\n adjustableColumn=False,\\n rowSpacing=0,\\n )\\n\\n addCheckboxes('%s_columnLayout02' % windowID)\\n cmds.columnLayout('%s_columnLayout02' % windowID, edit=True,\\n visible=False)\\n\\n # #################################################\\n # Shader Override\\n\\n cmds.setParent('%s_frameLayout02' % windowID)\\n cmds.columnLayout(\\n '%s_columnLayout21' % windowID,\\n width=WINDOW_WIDTH - FRAME_MARGIN * 2,\\n columnAlign='left',\\n columnAttach=('left', 0),\\n adjustableColumn=False,\\n rowSpacing=0,\\n )\\n cmds.separator(parent='%s_columnLayout21' % windowID, height=4,\\n style='none')\\n addRowLayout('%s_rowLayout06' % windowID, 2,\\n columnAlign2=('left', 'right'),\\n columnAttach2=('left', 'right'),\\n columnWidth2=((WINDOW_WIDTH - FRAME_MARGIN * 2)\\n * 0.75, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.25))\\n addText('%s_shaderOverrideLabel' % windowID, 'Shader Override',\\n 'plainLabelFont')\\n addCheckBox('%s_shaderOverrideCheckbox' % windowID, '',\\n rsShaderOverrideCheckbox, rsShaderOverrideCheckbox)\\n cmds.separator(parent='%s_columnLayout21' % windowID, height=4,\\n style='none')\\n\\n cmds.setParent('%s_columnLayout21' % windowID)\\n cmds.columnLayout(\\n '%s_columnLayout03' % windowID,\\n width=WINDOW_WIDTH - FRAME_MARGIN * 2,\\n columnAlign='left',\\n columnAttach=('both', 4),\\n adjustableColumn=True,\\n rowSpacing=0,\\n )\\n cmds.setParent('%s_columnLayout03' % windowID)\\n addOptionMenu('%s_optionMenu02' % windowID, 'Select: ', (),\\n rsShaderOverridesMenu)\\n\\n global selectedShaderOverride\\n\\n # default selection\\n\\n selectedShaderOverride = SHADER_OVERRIDE_OPTIONS[0]['ui']\\n cmds.columnLayout('%s_columnLayout03' % windowID, edit=True,\\n visible=False)\\n\\n # #################################################\\n\\n cmds.setParent(q.fullPath)\\n cmds.separator(height=10, style='none')\\n\\n # #################################################\\n # Extras\\n\\n addFrameLayout('%s_frameLayout50' % windowID, 'Extras',\\n collapsable=True, marginHeight=0,\\n labelVisible=False)\\n\\n # #################################################\\n # Add & Assign Shader Groups\\n\\n addFrameLayout(\\n '%s_frameLayout05' % windowID,\\n 'Add & Assign Shader Groups',\\n collapsable=True,\\n marginWidth=0,\\n marginHeight=0,\\n collapse=False,\\n labelVisible=True,\\n )\\n\\n # Add the renamer window\\n\\n self.gwCustomRenamer = CustomRenamer()\\n self.gwCustomRenamer.createUI()\\n\\n # #################################################\\n # AutoConnect\\n\\n cmds.setParent('%s_frameLayout50' % windowID)\\n\\n addFrameLayout(\\n '%s_frameLayout03' % windowID,\\n 'Adobe Connector',\\n collapsable=True,\\n marginWidth=0,\\n marginHeight=0,\\n collapse=True,\\n labelVisible=True,\\n )\\n addRowLayout('%s_rowLayout07', 3, columnAlign3=('left', 'left',\\n 'left'), columnAttach3=('both', 'both', 'both'),\\n columnWidth3=((WINDOW_WIDTH - FRAME_MARGIN * 2)\\n * 0.4, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.3,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.3))\\n addButton('updateConnections', '> Update Connections <',\\n updateConnections)\\n addButton('uvSnapshot', 'UV Snapshot', uvSnapshot)\\n addButton('editTexture', 'Edit Texture', editTexture)\\n\\n # After Effects\\n\\n cmds.setParent('%s_frameLayout03' % windowID)\\n addRowLayout('%s_rowLayout11' % windowID, 2,\\n columnAlign2=('left', 'left'),\\n columnAttach2=('both', 'both'),\\n columnWidth2=((WINDOW_WIDTH - FRAME_MARGIN * 2)\\n * 0.4, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.6))\\n addText('%s_text90' % windowID, 'Send to After Effects:')\\n addButton('makeCompButton', 'Send to After Effects', rsMakeComp)\\n\\n # #################################################\\n # Render Setup /\\n # Output settings\\n\\n cmds.setParent('%s_frameLayout50' % windowID)\\n addFrameLayout(\\n '%s_frameLayout04' % windowID,\\n 'Output Settings',\\n collapsable=True,\\n marginWidth=0,\\n marginHeight=0,\\n collapse=True,\\n labelVisible=True,\\n )\\n addRowLayout('%s_rowLayout08' % windowID, 1,\\n columnAlign1='center', columnAttach1='both',\\n columnWidth1=WINDOW_WIDTH - FRAME_MARGIN * 2)\\n addButton('%s_revealOutputDirectory' % windowID,\\n 'Output path not set yet', rsRevealOutputDirectory)\\n\\n cmds.setParent('%s_frameLayout04' % windowID)\\n addRowLayout('%s_rowLayout09' % windowID, 3,\\n columnAlign3=('left', 'right', 'right'),\\n columnAttach3=('left', 'right', 'right'),\\n columnWidth3=((WINDOW_WIDTH - FRAME_MARGIN * 2)\\n * 0.8, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.14,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.06))\\n\\n addOptionMenu('%s_optionMenu05' % windowID, '', (),\\n rsSelectOutputTemplate)\\n addOptionMenu('%s_outputVersionMenu' % windowID, '', (),\\n rsSelectOutputVersion)\\n cmds.menuItem(label='v001')\\n\\n cmds.setParent('%s_rowLayout09' % windowID)\\n addButton('%s_incrementOutputVersionButton' % windowID, '+1',\\n rsIncrementOutputVersion, size=(21, 21))\\n\\n cmds.setParent('%s_frameLayout04' % windowID)\\n addRowLayout('%s_rowLayout10' % windowID, 2,\\n columnAlign2=('left', 'left'),\\n columnAttach2=('both', 'right'),\\n columnWidth2=((WINDOW_WIDTH - FRAME_MARGIN * 2)\\n * 0.7, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.3))\\n addOptionMenu('%s_optionMenu03' % windowID, 'Format:', (),\\n rsOutputTemplatesMenu)\\n addOptionMenu('%s_optionMenu06' % windowID, '', (),\\n rsSetFPSMenu)\\n\\n cmds.setParent('%s_frameLayout04' % windowID)\\n addRowLayout('%s_rowLayout12' % windowID, 4,\\n columnAlign4=('right', 'left', 'right', 'left'),\\n columnAttach4=('both', 'both', 'both', 'both'),\\n columnWidth4=((WINDOW_WIDTH - FRAME_MARGIN * 2)\\n * 0.50, (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.15,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.20,\\n (WINDOW_WIDTH - FRAME_MARGIN * 2) * 0.15))\\n\\n addText('%s_setInFrameLabel' % windowID, 'In Frame ')\\n addTextField('%s_setInFrame' % windowID, '', setInFrame,\\n setInFrame, setInFrame)\\n\\n addText('%s_setOutFrameLabel' % windowID, 'Out Frame ')\\n addTextField('%s_setOutFrame' % windowID, '', setOutFrame,\\n setOutFrame, setOutFrame)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/HERE_RTTI_Archive_Miner/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Mapping GPS trace to Traffic Archive'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n init_config_path = './here_rtti_miner_config.ini'\\n if os.path.exists(init_config_path):\\n init_config = open(init_config_path, mode='r')\\n f = init_config.readlines()\\n if len(f) == 3:\\n self.iface.messageBar().pushMessage(\\\"Info\\\", '{}\\\\\\\\here_rtti_miner_config.ini loaded.'.format(\\n os.path.abspath(os.path.curdir)), level=QgsMessageBar.INFO, duration=1)\\n app_id = (f[0].replace('\\\\n', ''))\\n self.dlg.app_id_textbox.setPlainText(app_id)\\n app_code = (f[1].replace('\\\\n', ''))\\n self.dlg.app_code_textbox.setPlainText(app_code)\\n archive_path = (f[2].replace('\\\\n', ''))\\n self.dlg.archive_textbox.setPlainText(archive_path)\\n\\n \\\"\\\"\\\"Checkbox Listener\\\"\\\"\\\"\\n self.cb = self.dlg.date_time_override_checkBox\\n self.cb.toggle()\\n self.cb.setChecked(False)\\n self.dlg.connect(self.cb, QtCore.SIGNAL('stateChanged(int)'), self.change_state)\\n\\n \\\"\\\"\\\"DTM preload\\\"\\\"\\\"\\n self.dtm = self.dlg.dateTimeEdit\\n # get current date and time\\n now = QtCore.QDateTime.currentDateTime()\\n # set current date and time to the object\\n self.dtm.setDateTime(now)\",\n \"def _initUI(self):\\n\\n vlayout = QtWidgets.QVBoxLayout()\\n\\n # Description\\n #----------------------------------------------------------------\\n hlayout = QtWidgets.QHBoxLayout()\\n\\n label = QtWidgets.QLabel()\\n label.setText('Locatie:')\\n label.setFixedWidth(100)\\n hlayout.addWidget(label)\\n\\n label = QtWidgets.QLabel()\\n label.setText(self.name)\\n hlayout.addWidget(label)\\n hlayout.setSpacing(10)\\n\\n vlayout.addLayout(hlayout)\\n\\n # Exportnaam\\n #----------------------------------------------------------------\\n self.exportname = ParameterInputLine(label='Exportnaam:', labelwidth=100)\\n self.exportname.LineEdit.setMinimumWidth(200)\\n vlayout.addLayout(self.exportname.layout)\\n\\n # Exportdatabase\\n #----------------------------------------------------------------\\n self.exportpath = ExtendedLineEdit(label='SQLite-database:', labelwidth=100, browsebutton=True)\\n self.exportpath.BrowseButton.clicked.connect(self._get_path_database)\\n vlayout.addLayout(self.exportpath.layout)\\n\\n # Line\\n line = QtWidgets.QFrame()\\n line.setFrameShape(QtWidgets.QFrame.HLine)\\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\\n\\n vlayout.addWidget(line)\\n\\n # Buttons\\n #----------------------------------------------------------------\\n hbox = QtWidgets.QHBoxLayout()\\n hbox.addItem(QtWidgets.QSpacerItem(0, 0, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Maximum))\\n # Add ok/close\\n self.closebutton = QtWidgets.QPushButton('Sluiten')\\n self.closebutton.clicked.connect(self.close)\\n hbox.addWidget(self.closebutton)\\n # Add ok/close\\n self.savebutton = QtWidgets.QPushButton('Opslaan')\\n self.savebutton.clicked.connect(self._save)\\n hbox.addWidget(self.savebutton)\\n\\n vlayout.addLayout(hbox)\\n\\n # Add layout to widget\\n self.setLayout(vlayout)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/convertTo3D/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Convert to 3D'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/Integracion/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u''),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def iniciaUI(self):\\n\\n self.setGeometry(100,100, 250, 250)\\n self.setWindowTitle(\\\"Login\\\")\\n self.displayWidgets()\\n\\n self.show()\",\n \"def initUI(self):\\n # Setting the main layout as Vertical.\\n self.mainLayout = QHBoxLayout()\\n\\n # Create title.\\n self.title = QLabel(self.__name + \\\" : \\\")\\n\\n # Add description as tooltip.\\n self.title.setToolTip(self.__description)\\n\\n # Add title to main layout.\\n self.mainLayout.addWidget(self.title)\\n\\n # Create ComboBox.\\n self.dropDown = QComboBox()\\n\\n # Add datas to drop down.\\n self.dropDown.addItems(self.__datas)\\n\\n # Set default index to dropdown.\\n self.dropDown.setCurrentIndex(self.__currentValue)\\n\\n # Connect dropdown with update method.\\n self.dropDown.currentIndexChanged.connect(self.changeCurrentValue)\\n\\n # Add ComboBox to main layout.\\n self.mainLayout.addWidget(self.dropDown)\\n\\n # Add the main layout to the window.\\n self.setLayout(self.mainLayout)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/new_raptor/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Add New Raptor nest'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def launchUI():\\n app = QtWidgets.QApplication(sys.argv)\\n ui = ClientFileManager()\\n ui.resize(1200, 650)\\n ui.show()\\n sys.exit(app.exec_())\",\n \"def createUserInterface(self):\\n\\n\\t\\tself.__layout = self.__parent.createUserInterface()\\n\\n\\t\\tstep_label = qt.QLabel( 'Choose the volume you would like to threshold. If you are calculating a subtraction map, check the \\\\\\\"Calculate Subtraction Map\\\\\\\" box and select a post-contrast image.' )\\n\\t\\tstep_label.setWordWrap(True)\\n\\t\\tself.__primaryGroupBox = qt.QGroupBox()\\n\\t\\tself.__primaryGroupBox.setTitle('Information')\\n\\t\\tself.__primaryGroupBoxLayout = qt.QFormLayout(self.__primaryGroupBox)\\n\\n\\t\\tself.__subtractionMappingGroupBox = qt.QGroupBox()\\n\\t\\tself.__subtractionMappingGroupBox.setTitle('Volume Selection')\\n\\t\\tself.__subtractionMappingGroupBoxLayout = qt.QFormLayout(self.__subtractionMappingGroupBox)\\n\\n\\t\\tbaselineScanLabel = qt.QLabel( 'Primary / Pre-Contrast Image:' )\\n\\t\\tself.__baselineVolumeSelector = slicer.qMRMLNodeComboBox()\\n\\t\\tself.__baselineVolumeSelector.toolTip = \\\"Select the volume you wish to threshold. If you are calculating a subtraction map, this will be the pre-contrast scan.\\\"\\n\\t\\tself.__baselineVolumeSelector.nodeTypes = ['vtkMRMLScalarVolumeNode']\\n\\t\\tself.__baselineVolumeSelector.setMRMLScene(slicer.mrmlScene)\\n\\t\\tself.__baselineVolumeSelector.addEnabled = 0\\n\\n\\t\\tsubtractionMappingLabel = qt.QLabel( 'Calculate Subtraction Map:' )\\n\\t\\tself.__enableSubtractionMapping = qt.QCheckBox()\\n\\t\\tself.__enableSubtractionMapping.checked = False\\n\\t\\tself.__enableSubtractionMapping.setToolTip(\\\"Check if you would like to calculate a subtraction map\\\")\\n\\t\\tself.__enableSubtractionMapping.connect('clicked()', self.setSubtractionMapping)\\n\\n\\t\\tfollowupScanLabel = qt.QLabel( 'Post-Contrast Image:' )\\n\\t\\tself.__followupVolumeSelector = slicer.qMRMLNodeComboBox()\\n\\t\\tself.__followupVolumeSelector.toolTip = \\\"Choose the post-contrast scan\\\"\\n\\t\\tself.__followupVolumeSelector.nodeTypes = ['vtkMRMLScalarVolumeNode']\\n\\t\\tself.__followupVolumeSelector.setMRMLScene(slicer.mrmlScene)\\n\\t\\tself.__followupVolumeSelector.addEnabled = 0\\n\\t\\tself.__followupVolumeSelector.enabled = 0\\n\\n\\t\\tself.__layout.addRow(self.__primaryGroupBox)\\n\\t\\tself.__primaryGroupBoxLayout.addRow( step_label )\\n\\t\\tself.__subtractionMappingGroupBoxLayout.addRow( baselineScanLabel, self.__baselineVolumeSelector )\\n\\n\\t\\tself.__layout.addRow(self.__subtractionMappingGroupBox)\\n\\t\\tself.__subtractionMappingGroupBoxLayout.addRow( subtractionMappingLabel, self.__enableSubtractionMapping )\\n\\t\\tself.__subtractionMappingGroupBoxLayout.addRow( followupScanLabel, self.__followupVolumeSelector )\\n\\n\\t\\tself.updateWidgetFromParameters(self.parameterNode())\\n\\n\\t\\t# This timer is a trick to wait for buttons to load BEFORE deleting them.\\n\\t\\tqt.QTimer.singleShot(0, self.killButton)\",\n \"def _init_ui(self):\\n\\n hlayout = QtWidgets.QHBoxLayout()\\n\\n label = QtWidgets.QLabel('Kies een normtraject:')\\n\\n hlayout.addWidget(label)\\n\\n self.section_combobox = QtWidgets.QComboBox()\\n self.section_combobox.setFixedWidth(60)\\n self.section_ids = sorted([''] + io.geometry.import_section_ids(self.datadir))\\n self.section_combobox.addItems(self.section_ids)\\n\\n hlayout.addWidget(self.section_combobox)\\n\\n self.add_button = QtWidgets.QPushButton('Toevoegen', clicked=self._add_flooddefence)\\n\\n hlayout.addWidget(self.add_button)\\n\\n vlayout = QtWidgets.QVBoxLayout()\\n vlayout.addLayout(hlayout)\\n\\n line = QtWidgets.QFrame()\\n line.setFrameShape(QtWidgets.QFrame.HLine)\\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\\n\\n vlayout.addWidget(line)\\n\\n self.close_button = QtWidgets.QPushButton('Sluiten', clicked=self.close)\\n vlayout.addWidget(self.close_button, 0, QtCore.Qt.AlignRight)\\n\\n self.setLayout(vlayout)\\n\\n self.setWindowTitle(\\\"HB Havens: normtrajecten\\\")\\n self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowContextHelpButtonHint)\",\n \"def init_ui(self):\\n self.master.title(\\\"Backbone\\\")\\n self.master.geometry(\\\"300x150\\\")\\n\\n self.pack(fill=BOTH, expand=1)\\n\\n self.btn_upload_file = Button(self, text=\\\"Upload file\\\", command=self.upload_file)\\n self.btn_upload_file.place(x=90, y=10)\\n\\n self.btn_create_training_file = Button(self, text=\\\"Create & upload training file\\\",\\n command=self.create_training_file)\\n self.btn_create_training_file.place(x=30, y=40)\\n\\n self.btn_run_algorithm = Button(self, text=\\\"Run algorithm\\\", command=self.run_algorithm)\\n self.btn_run_algorithm.place(x=80, y=70)\\n\\n self.btn_view_results = Button(self, text=\\\"View Results\\\", command=self.view_results)\\n self.btn_view_results.place(x=85, y=100)\",\n \"def init_UI(self):\\n # widgets\\n self.modeComboBox = QtWidgets.QComboBox()\\n self.updateBtn = QtWidgets.QPushButton('Force Update')\\n self.pauseBtn = QtWidgets.QPushButton()\\n self.pauseBtn.setCheckable(True)\\n\\n self.clearBtn = QtWidgets.QPushButton()\\n self.clearBtn.setIcon(QtGui.QIcon(IconPaths.ICON_CLEAR_FILES))\\n self.filterBtn = QtWidgets.QToolButton()\\n self.filterBtn.setIcon(QtGui.QIcon(IconPaths.ICON_FILTER))\\n self.filterBtn.setMinimumWidth(35)\\n self.filterBtn.setStyleSheet('QToolButton::menu-indicator {subcontrol-position: center right; height: 7px}')\\n self.filterBtn.setPopupMode(QtWidgets.QToolButton.InstantPopup)\\n self.prefBtn = QtWidgets.QToolButton()\\n self.prefBtn.setIcon(QtGui.QIcon(IconPaths.ICON_SETTINGS))\\n #self.pBar = QtWidgets.QProgressBar()\\n self.itemView = LocalizeView()\\n self.itemView.setModel(self.proxy_model)\\n self.autoScrollCB = QtWidgets.QCheckBox('Auto scroll to localizing files')\\n self.autoScrollCB.setChecked(True)\\n\\n # tweak sizes so the widgets all line up vertically with Nuke's style\\n self.modeComboBox.setMinimumHeight(self.updateBtn.sizeHint().height())\\n self.pauseBtn.setMaximumSize(self.updateBtn.sizeHint())\\n self.clearBtn.setMaximumSize(self.updateBtn.sizeHint())\\n\\n # mode menu\\n self.modeLabel = QtWidgets.QLabel('Mode')\\n self.modeComboBox.addItems(['On', 'Manual', 'Off'])\\n\\n # update menu\\n self.updateMenu = QtWidgets.QMenu()\\n self.act_forceUpdateAll = QtWidgets.QAction('All', self)\\n self.act_forceUpdateSelectedNodes = QtWidgets.QAction('Selected', self)\\n self.act_forceUpdateOnDemand = QtWidgets.QAction('On demand only', self) \\n self.updateMenu.addAction(self.act_forceUpdateAll)\\n self.updateMenu.addAction(self.act_forceUpdateSelectedNodes)\\n self.updateMenu.addAction(self.act_forceUpdateOnDemand)\\n self.updateBtn.setMenu(self.updateMenu)\\n\\n # clear menu\\n self.clearMenu = QtWidgets.QMenu()\\n self.clearMenu.addAction(QtWidgets.QAction('All local files', self, triggered=self.__delete_all_local_files))\\n self.clearMenu.addAction(QtWidgets.QAction('Unused local files', self, triggered=_open_delete_dialog))\\n self.clearBtn.setMenu(self.clearMenu)\\n\\n # filter menu\\n self.filterMenu = QtWidgets.QMenu(self.filterBtn)\\n self.act_filter_all = QtWidgets.QAction('All', self.filterMenu, checkable=True)\\n self.act_filter_in_progress = QtWidgets.QAction('In Progress', self.filterMenu, checkable=True)\\n self.act_filter_up_to_date = QtWidgets.QAction('Up to date', self.filterMenu, checkable=True)\\n self.act_filter_out_of_date = QtWidgets.QAction('Out of date', self.filterMenu, checkable=True)\\n self.act_filter_from_source = QtWidgets.QAction('Reading from source', self.filterMenu, checkable=True)\\n self.act_filter_disabled = QtWidgets.QAction('Disabled', self.filterMenu, checkable=True)\\n self.act_filter_not_localized = QtWidgets.QAction('Not Localized', self.filterMenu, checkable=True)\\n\\n self.act_filter_in_progress.setData(Status.IN_PROGRESS)\\n self.act_filter_up_to_date.setData(Status.UP_TO_DATE)\\n self.act_filter_out_of_date.setData(Status.OUT_OF_DATE)\\n self.act_filter_from_source.setData(Status.READ_FROM_SOURCE)\\n self.act_filter_disabled.setData(Status.DISABLED)\\n self.act_filter_not_localized.setData(Status.NOT_LOCALIZED)\\n for act in (self.act_filter_all, self.act_filter_in_progress, self.act_filter_up_to_date, self.act_filter_out_of_date,\\n self.act_filter_from_source, self.act_filter_disabled, self.act_filter_not_localized):\\n self.filterMenu.addAction(act)\\n self.filterBtn.setMenu(self.filterMenu)\\n\\n # tooltips\\n self.modeComboBox.setToolTip('Sets the global localization mode.\\\\nThis is the same as using the options in the Cache/Localization/Mode menu.')\\n self.updateBtn.setToolTip('Forces the update of localized files.\\\\nThis is the same as using the options in the Cache/Localization/Force Update menu.')\\n self.pauseBtn.setToolTip('Pauses/Resumes file localization.\\\\nThis is the same as Cache/Localization/Pause.')\\n self.clearBtn.setToolTip('''Allows for clearing localized files.\\\\nTwo modes are supported:\\n \\\"All local files\\\" - this will delete all files in {}\\n \\\"Unused local files\\\" - this will only delete unused local files (same as Cache/Localization/Clear Unused Local Files)'''.format(nuke.toNode('preferences')['localCachePath'].evaluate()))\\n self.filterBtn.setToolTip('Sets a view filter the table.')\\n self.prefBtn.setToolTip('Open the preferences.')\\n\\n # layouts\\n layout = QtWidgets.QVBoxLayout()\\n btnLayout = QtWidgets.QHBoxLayout()\\n btnLayout.addWidget(self.modeLabel)\\n btnLayout.addWidget(self.modeComboBox)\\n btnLayout.addWidget(self.updateBtn)\\n btnLayout.addWidget(self.pauseBtn)\\n btnLayout.addWidget(self.clearBtn)\\n btnLayout.addStretch()\\n btnLayout.addWidget(self.filterBtn)\\n btnLayout.addWidget(self.prefBtn)\\n layout.addLayout(btnLayout)\\n #layout.addWidget(self.pBar)\\n layout.addWidget(self.itemView)\\n layout.addWidget(self.autoScrollCB)\\n layout.setAlignment(self.autoScrollCB, QtCore.Qt.AlignRight)\\n self.setLayout(layout)\",\n \"def ui_setup(self):\\n loader = QUiLoader()\\n file = QFile('./user_interface/form/main_window.ui')\\n file.open(QFile.ReadOnly)\\n self._window = loader.load(file)\\n file.close()\\n\\n status_bar = QStatusBar(self._window)\\n status_bar.showMessage(__copyright__)\\n self._window.setStatusBar(status_bar)\\n self._window.setWindowIcon(QIcon('./user_interface/media/bucketing_icon.jpeg'))\\n self._window.setWindowTitle('PySide2 Project - Basic UI Framework')\\n\\n self._option_panel = OptionPanel()\\n self._option_panel.add_button('DekBan', './user_interface/media/dekban.png')\\n self._option_panel.add_button('Charlie', './user_interface/media/charlie.jpeg')\\n self._option_panel.add_button('Simon', './user_interface/media/Simon.jpeg')\\n\\n # Add widget to main layout\\n main_layout = self._window.main_layout\\n main_layout.itemAtPosition(0, 0).setAlignment(QtCore.Qt.AlignCenter)\\n main_layout.itemAtPosition(0, 1).setAlignment(QtCore.Qt.AlignVCenter)\\n main_layout.addWidget(self._option_panel, 2, 0, 1, 1)\\n\\n # Add page widget to stack\\n self._pages['item'] = ItemWidget()\\n self._pages['text1'] = TextPage(text=PAUSE_TEXT)\\n self._pages['text2'] = TextPage(text=STOP_TEXT)\\n\\n for index, name in enumerate(self._pages):\\n print('pages {} : {} page'.format(index, name))\\n self._window.widget_stack.addWidget(self._pages[name].widget)\\n\\n self._window.widget_stack.setCurrentIndex(0)\\n\\n # Build up signal / slot\\n self._option_panel.currentItemChanged.connect(self.set_page)\",\n \"def setup_gui(self):\\n central_widget = QWidget(self)\\n central_widget.setObjectName('central_widget')\\n self.label = QLabel('Hello World')\\n self.input_field = QLineEdit()\\n change_button = QPushButton('Change text')\\n close_button = QPushButton('close')\\n quit_button = QPushButton('quit')\\n central_layout = QVBoxLayout()\\n button_layout = QHBoxLayout()\\n central_layout.addWidget(self.label)\\n central_layout.addWidget(self.input_field)\\n # a separate layout to display buttons horizontal\\n button_layout.addWidget(change_button)\\n button_layout.addWidget(close_button)\\n button_layout.addWidget(quit_button)\\n central_layout.addLayout(button_layout)\\n central_widget.setLayout(central_layout)\\n self.setCentralWidget(central_widget)\\n # create a system tray icon. Uncomment the second form, to have an\\n # icon assigned, otherwise you will only be seeing an empty space in\\n # system tray\\n self.systemtrayicon = QSystemTrayIcon(self)\\n self.systemtrayicon.show()\\n # set a fancy icon\\n self.systemtrayicon.setIcon(QIcon.fromTheme('help-browser'))\\n change_button.clicked.connect(self.change_text)\\n quit_button.clicked.connect(QApplication.instance().quit)\\n close_button.clicked.connect(self.hide)\\n # show main window, if the system tray icon was clicked\\n self.systemtrayicon.activated.connect(self.icon_activated)\",\n \"def _init_ui(self):\\n hlayout = QtWidgets.QHBoxLayout()\\n\\n hlayout.addWidget(QtWidgets.QLabel('Kies een normtraject:'))\\n\\n self.section_combobox = QtWidgets.QComboBox()\\n self.section_combobox.setFixedWidth(60)\\n self._update_combobox()\\n\\n hlayout.addWidget(self.section_combobox)\\n\\n self.remove_button = QtWidgets.QPushButton('Verwijderen', clicked=self._del_flooddefence)\\n hlayout.addWidget(self.remove_button)\\n\\n vlayout = QtWidgets.QVBoxLayout()\\n vlayout.addLayout(hlayout)\\n\\n line = QtWidgets.QFrame()\\n line.setFrameShape(QtWidgets.QFrame.HLine)\\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\\n\\n vlayout.addWidget(line)\\n\\n self.close_button = QtWidgets.QPushButton('Sluiten', clicked=self.close)\\n vlayout.addWidget(self.close_button, 0, QtCore.Qt.AlignRight)\\n\\n self.setLayout(vlayout)\\n\\n self.setWindowTitle(\\\"HB Havens: normtrajecten\\\")\\n self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowContextHelpButtonHint)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/ActualizacionCatastral/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Act. Catastral'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def initUI(self):\\n\\n grid = QGridLayout()\\n grid.addWidget(self.from_currency_label, 0, 0, Qt.AlignRight)\\n grid.addWidget(self.from_currency, 0, 1)\\n grid.addWidget(self.to_currency_label, 0, 2, Qt.AlignRight)\\n grid.addWidget(self.to_currency, 0, 3)\\n grid.addWidget(self.from_amount_label, 1, 0)\\n grid.addWidget(self.from_amount, 1, 1)\\n grid.addWidget(self.to_amount_label, 1, 2)\\n grid.addWidget(self.to_amount, 1, 3)\\n\\n grid.addWidget(self.from_calendar, 2, 0, 1, 2)\\n grid.addWidget(self.to_calendar, 2, 2, 1, 2)\\n\\n grid.addWidget(self.rates_plot, 3, 0, 1, 4)\\n grid.addWidget(self.graph_hint, 4, 0, 1, 4)\\n\\n self.rates_plot.showGrid(x=True, y=True)\\n self.rates_plot.setLabel('left', 'Rate')\\n self.rates_plot.setLabel('bottom', 'Days')\\n self.legend = self.rates_plot.addLegend()\\n\\n self.setLayout(grid)\\n self.setWindowTitle('Currency Converter - Assignment 1 - Arnaud Bourget - 2981151')\\n\\n self.from_currency.currentIndexChanged.connect(self.updateUI)\\n self.to_currency.currentIndexChanged.connect(self.updateUI)\\n self.from_amount.valueChanged.connect(self.fromAmountHandler)\\n self.from_calendar.selectionChanged.connect(self.fromCalendarHandler)\\n self.to_calendar.selectionChanged.connect(self.toCalendarHandler)\\n\\n self.show()\",\n \"def __init__(self, parent=None):\\n super(Representative, self).__init__(parent)\\n self.setupUi(self)\",\n \"def setUp(self):\\n self.ui = UI()\",\n \"def _init_ui(self):\\r\\n\\t\\t\\r\\n\\t\\tself.input_frame = Input(self)\\r\\n\\t\\tself.input_frame.pack()\\r\\n\\t\\t\\r\\n\\t\\tbutton_ok = Button(self, text = \\\"Ping\\\", command = self._go)\\r\\n\\t\\tbutton_ok.pack()\\r\\n\\t\\t\\r\\n\\t\\tself.result_frame = Result(self)\\r\\n\\t\\tself.result_frame.pack()\",\n \"def init_UI(self):\\n\\n self.master.title(\\\"Search for different companies\\\")\\n self.master.geometry(\\\"400x400\\\")\\n\\n self.label_combobox = Label(self, text=\\\"Search by\\\")\\n self.label_combobox.pack()\\n\\n self.combo_searching_options = Combobox(self, state=\\\"readonly\\\")\\n self.combo_searching_options['values'] = self.combobox_values\\n self.combo_searching_options.pack()\\n\\n self.label_input = Label(self, text=\\\"Entry the value\\\")\\n self.label_input.pack()\\n\\n self.user_input = Entry(self, width=40)\\n self.user_input.pack()\\n\\n self.btn_submit = Button(self, text=\\\"Submit\\\", command=self.submit)\\n self.btn_submit.pack()\\n\\n self.text_area = scrolledtext.ScrolledText(self)\\n self.text_area.pack()\\n\\n sys.stdout = RedirectOutputText(self.text_area)\\n\\n self.btn_back = Button(self, text=\\\"Back\\\", command=self.go_back)\\n self.btn_back.pack()\",\n \"def start_ui(self):\\n\\t\\tself.start_animation()\\n\\t\\tself.app.exec()\",\n \"def setup_gui(self):\\n # if there are more than 1 visualizer we need to assure that there\\n # will not be tag conflicts\\n BaseRealTimeVisualizer.setup_gui_lock.acquire()\\n # look for valid tag\\n dpg.create_context()\\n\\n self.id = 0\\n while dpg.does_item_exist(f'main_window_{self.id}'):\\n self.id += 1\\n\\n with dpg.texture_registry(show=False):\\n dpg.add_dynamic_texture(\\n width=self.width,\\n height=self.height,\\n default_value=np.zeros((self.width, self.height, 3)),\\n tag=f'input_image_texture_{self.id}',\\n )\\n\\n with dpg.window(\\n tag=f'main_window_{self.id}',\\n no_title_bar=True,\\n autosize=True\\n ):\\n dpg.add_image(\\n texture_tag=f'input_image_texture_{self.id}',\\n tag=f'image_render_{self.id}',\\n pos=(_PADDING, _PADDING)\\n )\\n\\n dpg.set_global_font_scale(_FONT_SCALE)\\n\\n if self.id == 0:\\n dpg.set_primary_window(f'main_window_{self.id}', True)\\n dpg.create_viewport(\\n title=self.title,\\n width=self.width + _PADDING*2,\\n height=self.height + _PADDING*2,\\n resizable=True\\n )\\n dpg.setup_dearpygui()\\n dpg.show_viewport()\\n elif self.id == 1:\\n dpg.set_primary_window('main_window_0', False)\\n\\n BaseRealTimeVisualizer.setup_gui_lock.release()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/TMD/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Weather Today'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def ui(self):\\n return ui\",\n \"def _setupUi(self):\\n self.setupUi(self)\\n self.twTree.setStyleSheet(\\\"background-color: rgb(200, 200, 200)\\\")\",\n \"def initUI(self):\\n startbtn = QPushButton(\\\"Start Recroding\\\", self)\\n startbtn.move(30, 50)\\n\\n stopbtn = QPushButton(\\\"Stop Recording\\\", self)\\n stopbtn.move(150, 50)\\n\\n initbtn = QPushButton(\\\"Initilize\\\", self)\\n initbtn.move(30, 100)\\n\\n plotbtn = QPushButton(\\\"Plot\\\", self)\\n plotbtn.move(150, 100)\\n\\n startbtn.clicked.connect(self.start_recording)\\n stopbtn.clicked.connect(self.stop_recording)\\n initbtn.clicked.connect(self.init_recording)\\n plotbtn.clicked.connect(self.plot_signals)\\n\\n self.statusBar()\\n self.statusBar().showMessage('Click Init')\\n\\n self.setGeometry(300, 300, 290, 150)\\n self.setWindowTitle('Recorder 1.0')\\n self.setWindowIcon(QIcon(\\\"./Static/Images/icon.jpg\\\"))\\n self.show()\",\n \"def initCentralUic(self):\\n self.initFileTableWidget()\\n self.initViewerStack()\\n self.splitter.setSizes([150, 850])\",\n \"def configure_ui(self):\\n self.setWindowIcon(self.MAIN_ICON)\\n self.setWindowModality(Qt.ApplicationModal)\",\n \"def run(self):\\n\\n # Create the dialog with elements (after translation) and keep reference\\n # Only create GUI ONCE in callback, so that it will only load when the plugin is started\\n if self.first_start == True:\\n self.first_start = False\\n self.dlg = RoadSaltMapDialog()\\n self.dlg.setWindowTitle(\\\"Road Salt Map Builder\\\")\\n#map 1\\n self.dlg.tb_outMap1.clicked.connect(self.select_outputmap1_file)\\n self.dlg.tb_inBasin_1.clicked.connect(self.select_basin1_file)\\n self.dlg.tb_inRoad_1.clicked.connect(self.select_road1_file)\\n self.dlg.pb_ok_1.clicked.connect(self.run_method_1)\\n#map 2\\n self.dlg.tb_outMap2.clicked.connect(self.select_outputmap2_file)\\n self.dlg.tb_inBasin_2.clicked.connect(self.select_basin2_file)\\n self.dlg.tb_inRoad_2.clicked.connect(self.select_road2_file)\\n self.dlg.tb_inSalt.clicked.connect(self.select_salt_file)\\n self.dlg.pb_ok_2.clicked.connect(self.run_method_2)\\n self.dlg.cb_colDate.currentIndexChanged.connect(self.change_interval)\\n\\n # Fetch the currently loaded layers\\n layers = QgsProject.instance().layerTreeRoot().children()\\n # Clear the contents of the comboBox and lineEdit from previous runs\\n#map 1\\n self.dlg.l_outMap1.clear()\\n self.dlg.l_inBasin_1.clear()\\n self.dlg.l_inRoad_1.clear()\\n#map 2\\n self.dlg.l_outMap2.clear()\\n self.dlg.l_inBasin_2.clear()\\n self.dlg.l_inRoad_2.clear()\\n self.dlg.l_inSalt.clear()\\n self.dlg.cb_colSalt.clear()\\n self.dlg.cb_colDate.clear()\\n self.dlg.de_starttime.clear()\\n self.dlg.de_endtime.clear()\\n\\n # show the dialog\\n self.dlg.show()\\n # Run the dialog event loop\\n result = self.dlg.exec_()\\n # See if OK was pressed\\n if result:\\n # Do something useful here - delete the line containing pass and\\n # substitute with your code.\\n pass\",\n \"def __init__(self):\\n self.s = QSettings()\\n self.p = QgsProject.instance()\",\n \"def main():\\r\\n qApp = QtGui.QApplication(sys.argv)\\r\\n qApp.setApplicationName(\\\"Visualization Tool\\\")\\r\\n qApp.setOrganizationName(\\\"Marquette University Speechlab\\\")\\r\\n qApp.setWindowIcon(QtGui.QIcon(\\\":/vtIcon.ico\\\"))\\r\\n form = MainWindow()\\r\\n form.show()\\r\\n sys.exit(qApp.exec_())\",\n \"def __init__(self, windowId, windowTitle, windowDimensions):\\n # check if another copy of the window exists\\n if cmds.window(windowId, exists=True):\\n cmds.deleteUI(windowId)\\n\\n # load furtility\\n mel.eval(\\\"ta_loadFurtilityAndMPCSolver()\\\")\\n\\n # initialize main window features\\n super(nHairToolset, self).__init__(getMayaMainPtr())\\n self.setObjectName(windowId)\\n self.setWindowTitle(windowTitle)\\n self.setAttribute(QtCore.Qt.WA_DeleteOnClose)\\n self.setFixedSize(windowDimensions[0], windowDimensions[1])\\n self.centerWindow()\\n # style setup\\n self.style = QtGui.QStyleFactory.create('plastique')\\n self.setStyle(self.style)\\n\\n # add layout and widgets\\n centerWidget = QtGui.QWidget(self)\\n # setup central widget to hold all elements\\n centerWidget.setLayout(QtGui.QVBoxLayout(centerWidget))\\n self.setCentralWidget(centerWidget)\\n\\n # create the tab widget to hold the tabs\\n innerTabs = QtGui.QTabWidget()\\n innerTabs.setFocusPolicy(QtCore.Qt.NoFocus)\\n centerWidget.layout().addWidget(innerTabs)\\n\\n # first Tab\\n mainControlsTab = QtGui.QWidget()\\n mainControlsTab.setLayout(QtGui.QHBoxLayout())\\n mainControlsTab.layout().setAlignment(QtCore.Qt.AlignLeft)\\n mainControlsTab.layout()\\n #----------------------------------------------------------------------------------------------------------#\\n # first column for the main tab\\n mainTabColumn1_Layout = QtGui.QVBoxLayout()\\n mainTabColumn1_Layout.setAlignment(QtCore.Qt.AlignTop)\\n innerTabs.addTab(mainControlsTab, 'Main Functions')\\n\\n # rigFix options group box\\n rigFxGrpBox = customGroupBox('RigFx Options', (240,160), self.style.objectName(), 0, 0)\\n rigFixNameField = self.createLabeledNameField('Name: ', 'enter a rigFx name...')\\n buildRigFxBtn = QtGui.QPushButton('Build RigFx')\\n updateSetsBtn = QtGui.QPushButton('Update Sets')\\n motionMultBtn = QtGui.QPushButton('MotionMultiplier')\\n # populate rigFx groupBox\\n rigFxGrpBox.layout().addLayout(rigFixNameField)\\n rigFxGrpBox.layout().addWidget(buildRigFxBtn)\\n rigFxGrpBox.layout().addWidget(updateSetsBtn)\\n rigFxGrpBox.layout().addWidget(motionMultBtn)\\n rigFxGrpBox.layout().addStretch(True)\\n\\n # nHair groups options groupBox\\n nHairGroupsGrpBox = customGroupBox('nHair Groups', (240,330), self.style.objectName(), 0, 0)\\n nHairGroupList = QtGui.QListWidget()\\n nHairGroupCreationField = self.createLabeledNameField('Group Name:', 'name your group...')\\n nHairGroupCreateBtn = QtGui.QPushButton('Create')\\n nHairGroupDeleteBtn = QtGui.QPushButton('Delete')\\n nHairGroupControlsBtn = QtGui.QPushButton('Create Controls')\\n nHairGroupWindControlsBtn = QtGui.QPushButton('Create Wind Controls')\\n # add the nHair group elements to the corresponding groupbox\\n nHairGroupsGrpBox.layout().addWidget(nHairGroupList)\\n nHairGroupsGrpBox.layout().addLayout(nHairGroupCreationField)\\n nHairGroupsGrpBox.layout().addWidget(nHairGroupCreateBtn)\\n nHairGroupsGrpBox.layout().addWidget(nHairGroupDeleteBtn)\\n nHairGroupsGrpBox.layout().addWidget(nHairGroupControlsBtn)\\n nHairGroupsGrpBox.layout().addWidget(nHairGroupWindControlsBtn)\\n\\n # little nucleus groupboxer\\n nucleusGroupBox = customGroupBox('Nucleus', (240, 48), self.style.objectName(), 0, 0, QtGui.QHBoxLayout())\\n nucleusGroupBox.layout().setAlignment(QtCore.Qt.AlignCenter)\\n nucleusSateLabel = QtGui.QLabel('State: ')\\n nucleusOnRadioBtn = QtGui.QRadioButton('On')\\n nucleusOffRadioBtn = QtGui.QRadioButton('Off')\\n nucleusOnRadioBtn.setChecked(True)\\n # add widgets to groupBox\\n nucleusGroupBox.layout().addWidget(nucleusSateLabel)\\n nucleusGroupBox.layout().addItem(QtGui.QSpacerItem(20,2))\\n nucleusGroupBox.layout().addWidget(nucleusOnRadioBtn)\\n nucleusGroupBox.layout().addItem(QtGui.QSpacerItem(20,2))\\n nucleusGroupBox.layout().addWidget(nucleusOffRadioBtn)\\n\\n # nHair groupBox\\n nHairToolBox = customGroupBox('nHair Tools', [240,200], self.style.objectName(), 0, 0, QtGui.QGridLayout())\\n nHairToolBox.layout().setAlignment(QtCore.Qt.AlignTop)\\n createHairBtn = customIconButton(':/hairCreate.png',(40,40), 'Create Hair')\\n paintHairBtn = customIconButton(':/hairPaint.png',(40,40), 'Paint Hair Tool')\\n mkDynCurvesBtn = customIconButton(':/hairDynamicCurves.png',(40,40), 'Make Selected Curves Dynamic')\\n interactiveBtn = customIconButton(':/interactivePlayback.png',(40,40), 'Interactive Playback')\\n currentPosDispBtn = customIconButton(':/hairDisplayCurrent.png',(40,40), 'Display Current Position')\\n startPosDispBtn = customIconButton(':/hairDisplayStart.png',(40,40), 'Display Start Position')\\n restPosDispBtn = customIconButton(':/hairDisplayRest.png',(40,40), 'Display Rest Position')\\n nCacheCreateBtn = customIconButton(':/nClothCacheCreate.png',(40,40), 'Create nCache')\\n nCacheDeleteBtn = customIconButton(':/nClothCacheDelete.png',(40,40), 'Delete nCache')\\n passiveColliderBtn = customIconButton(':/nClothCreatePassive.png',(40,40), 'Delete nCache')\\n # add them to groupBox\\n nHairToolBox.layout().addWidget(createHairBtn, 0,0)\\n nHairToolBox.layout().addWidget(paintHairBtn, 0,1)\\n nHairToolBox.layout().addWidget(mkDynCurvesBtn, 0,2)\\n nHairToolBox.layout().addWidget(interactiveBtn, 0,3)\\n nHairToolBox.layout().addWidget(currentPosDispBtn, 0,4)\\n nHairToolBox.layout().addWidget(startPosDispBtn, 1,0)\\n nHairToolBox.layout().addWidget(restPosDispBtn, 1,1)\\n nHairToolBox.layout().addWidget(nCacheCreateBtn, 1,2)\\n nHairToolBox.layout().addWidget(nCacheDeleteBtn, 1,3)\\n nHairToolBox.layout().addWidget(passiveColliderBtn, 1,4)\\n\\n # add the first column widgets to the corresponding layout\\n mainTabColumn1_Layout.addWidget(rigFxGrpBox)\\n mainTabColumn1_Layout.addWidget(nHairGroupsGrpBox)\\n mainTabColumn1_Layout.addWidget(nucleusGroupBox)\\n mainTabColumn1_Layout.addWidget(nHairToolBox)\\n\\n #----------------------------------------------------------------------------------------------------------#\\n # second column for the main tab\\n mainTabColumn2_Layout = QtGui.QVBoxLayout()\\n mainTabColumn2_Layout.setAlignment(QtCore.Qt.AlignTop)\\n\\n # control list set\\n controlsGroupBox = customGroupBox('Controls', (220, 160), self.style.objectName())\\n controlList = QtGui.QListWidget()\\n # add the control list to the groupbox\\n controlsGroupBox.layout().addWidget(controlList)\\n\\n # dynamic node list\\n dynamicNodesGroupBox = customGroupBox('Dynamic nodes', (220, 160), self.style.objectName())\\n dynamicNodeList = QtGui.QListWidget()\\n # add the dynamic node list to the groupbox\\n dynamicNodesGroupBox.layout().addWidget(dynamicNodeList)\\n\\n # collider and constraint nodes list\\n colConstGroupBox = customGroupBox('Colliders and Consraints', (220, 160), self.style.objectName())\\n colConstNodeList = QtGui.QListWidget()\\n # add the colliders and constraints list to the groupbox\\n colConstGroupBox.layout().addWidget(colConstNodeList)\\n\\n ## force nodes list\\n #forcesGroupBox = customGroupBox('Forces', (200, 140), self.style.objectName())\\n #forcesNodeList = QtGui.QListWidget()\\n ## add the force list to the groupbox\\n #forcesGroupBox.layout().addWidget(forcesNodeList)\\n\\n # add the second column widgets to the second column\\n mainTabColumn2_Layout.addWidget(controlsGroupBox)\\n mainTabColumn2_Layout.addWidget(dynamicNodesGroupBox)\\n mainTabColumn2_Layout.addWidget(colConstGroupBox)\\n #----------------------------------------------------------------------------------------------------------#\\n\\n # add the main tab columns to the first tab\\n mainControlsTab.layout().addLayout(mainTabColumn1_Layout)\\n mainControlsTab.layout().addItem(QtGui.QSpacerItem(20,0))\\n mainControlsTab.layout().addLayout(mainTabColumn2_Layout)\\n\\n # second tab\\n secondaryControlsTab = QtGui.QWidget(innerTabs)\\n innerTabs.addTab(secondaryControlsTab, 'Secondary')\",\n \"def initUI(self) -> None:\\n ratio = 70\\n width_to_set = (ratio * self.get_current_window_info()[0]) / 100.0\\n height_to_set = (ratio * self.get_current_window_info()[1]) / 100.0\\n self.setGeometry(200, 100, width_to_set, height_to_set)\\n self.createTable()\\n # Add box layout, add table to box layout and add box layout to widget\\n self.layout = QVBoxLayout()\\n self.layout.addWidget(self.tableWidget)\\n self.setLayout(self.layout)\\n self.setWindowTitle('View files')\\n self.show()\",\n \"def initialize_gui(self) -> None:\\n # pymol.Qt provides the PyQt5 interface\\n from PyQt5 import QtWidgets\\n from PyQt5.uic import loadUi\\n # from pymol.Qt.utils import loadUi\\n\\n # populate the QMainWindow from our *.ui file\\n uifile = os.path.join(os.path.dirname(__file__), 'PyMOL-KVFinder-web-tools.ui')\\n loadUi(uifile, self)\\n\\n # ScrollBars binded to QListWidgets in Descriptors\\n scroll_bar_volume = QtWidgets.QScrollBar(self)\\n self.volume_list.setVerticalScrollBar(scroll_bar_volume)\\n scroll_bar_area = QtWidgets.QScrollBar(self)\\n self.area_list.setVerticalScrollBar(scroll_bar_area)\\n scroll_bar_residues = QtWidgets.QScrollBar(self)\\n self.residues_list.setVerticalScrollBar(scroll_bar_residues)\\n\\n # about text\\n self.about_text.setHtml(about_text)\\n\\n ########################\\n ### Buttons Callback ###\\n ########################\\n\\n # hook up QMainWindow buttons callbacks\\n self.button_run.clicked.connect(self.run)\\n self.button_exit.clicked.connect(self.close)\\n self.button_restore.clicked.connect(self.restore)\\n self.button_grid.clicked.connect(self.show_grid)\\n \\n # hook up Parameters button callbacks\\n self.button_browse.clicked.connect(self.select_directory)\\n self.refresh_input.clicked.connect(lambda: self.refresh(self.input))\\n \\n # hook up Search Space button callbacks\\n # Box Adjustment\\n self.button_draw_box.clicked.connect(self.set_box)\\n self.button_delete_box.clicked.connect(self.delete_box)\\n self.button_redraw_box.clicked.connect(self.redraw_box)\\n self.button_box_adjustment_help.clicked.connect(self.box_adjustment_help)\\n # Ligand Adjustment\\n self.refresh_ligand.clicked.connect(lambda: self.refresh(self.ligand))\\n\\n # hook up methods to results tab\\n # Jobs\\n self.available_jobs.currentIndexChanged.connect(self.fill_job_information)\\n self.button_show_job.clicked.connect(self.show_id)\\n self.button_add_job_id.clicked.connect(self.add_id)\\n # Visualization\\n self.button_browse_results.clicked.connect(self.select_results_file)\\n self.button_load_results.clicked.connect(self.load_results)\\n self.volume_list.itemSelectionChanged.connect(lambda list1=self.volume_list, list2=self.area_list: self.show_cavities(list1, list2))\\n self.area_list.itemSelectionChanged.connect(lambda list1=self.area_list, list2=self.volume_list: self.show_cavities(list1, list2))\\n self.residues_list.itemSelectionChanged.connect(self.show_residues)\",\n \"def setupOptionsFrame(self):\\n\\n # CPU / CUDA options\\n self.device.addItems([\\\"cuda\\\", \\\"cpu\\\"])\\n self.scriptedEffect.addLabeledOptionsWidget(\\\"Device:\\\", self.device)\\n\\n self.modality.addItems([\\\"CT\\\", \\\"MRI\\\"])\\n self.scriptedEffect.addLabeledOptionsWidget(\\\"Modality:\\\", self.modality)\\n\\n # Add ROI options\\n self.roiSelector.nodeTypes = ['vtkMRMLMarkupsROINode']\\n self.roiSelector.noneEnabled = True\\n self.roiSelector.setMRMLScene(slicer.mrmlScene)\\n self.scriptedEffect.addLabeledOptionsWidget(\\\"ROI: \\\", self.roiSelector)\\n\\n # Toggle ROI visibility button\\n toggleROIVisibilityButton = qt.QPushButton(\\\"Toggle ROI Visibility\\\")\\n toggleROIVisibilityButton.objectName = self.__class__.__name__ + 'ToggleROIVisibility'\\n toggleROIVisibilityButton.setToolTip(\\\"Toggle selected ROI visibility\\\")\\n toggleROIVisibilityButton.connect('clicked()', self.toggleROIVisibility)\\n self.scriptedEffect.addOptionsWidget(toggleROIVisibilityButton)\\n\\n # Apply button\\n applyButton = qt.QPushButton(\\\"Apply\\\")\\n applyButton.objectName = self.__class__.__name__ + 'Apply'\\n applyButton.setToolTip(\\\"Extract liver from input volume\\\")\\n applyButton.connect('clicked()', self.onApply)\\n self.scriptedEffect.addOptionsWidget(applyButton)\",\n \"def setup(self):\\n self.ui_manager.purge_ui_elements()\\n y_slot = self.window.height // 12\\n\\n ui_input_box = arcade.gui.UIInputBox(\\n center_x=self.window.width // 2,\\n center_y=y_slot * 7,\\n width=250\\n )\\n ui_input_box.set_style_attrs(\\n bg_color=(66, 179, 208),\\n bg_color_hover=(112, 212, 238),\\n bg_color_focus=(255, 228, 14)\\n )\\n ui_input_box.text = self.name\\n ui_input_box.cursor_index = len(ui_input_box.text)\\n self.ui_manager.add_ui_element(ui_input_box)\\n\\n button = buttons.ExitButton(\\n 'Exit',\\n center_x=self.window.width // 2,\\n center_y=y_slot * 1,\\n width=250\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(135, 21, 25),\\n bg_color_press=(122, 21, 24),\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = buttons.AuthorButton(\\\"Author\\\",\\n center_x=self.window.width // 2,\\n center_y=y_slot * 2,\\n width=250,\\n user=ui_input_box\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(88, 196, 96),\\n bg_color_press=(28, 71, 32),\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = buttons.ResultButton(\\\"Results\\\",\\n center_x=self.window.width // 2,\\n center_y=y_slot * 3,\\n width=250,\\n user=ui_input_box,\\n level = \\\"level1\\\"\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(88, 196, 96),\\n bg_color_press=(28, 71, 32),\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = buttons.RulesButton(\\\"Rules\\\",\\n center_x=self.window.width // 2,\\n center_y=y_slot * 4,\\n width=250,\\n user=ui_input_box\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(88, 196, 96),\\n bg_color_press=(28, 71, 32),\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = buttons.LevelButton(\\\"Play level 1\\\",\\n center_x=self.window.width // 2,\\n center_y=y_slot * 6,\\n width=250,\\n user=ui_input_box,\\n level=\\\"level1\\\"\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(88, 196, 96),\\n bg_color_press=(28, 71, 32),\\n )\\n self.ui_manager.add_ui_element(button)\\n\\n button = buttons.LevelButton(\\\"Play level 2\\\",\\n center_x=self.window.width // 2,\\n center_y=y_slot * 5,\\n width=250,\\n user=ui_input_box,\\n level = \\\"level2\\\"\\n )\\n button.set_style_attrs(\\n bg_color=(51, 139, 57),\\n bg_color_hover=(88, 196, 96),\\n bg_color_press=(28, 71, 32),\\n )\\n self.ui_manager.add_ui_element(button)\",\n \"def initUI(self):\\n\\n lbl_names = ['Название проекта', 'Версия', 'Директория', 'Описание', 'Автор', 'Почта', 'Дополнительные зависимости', 'Название ноды']\\n param_list = ['motor_driver', '0.0.0', '/home/mitya/catkin_ws/src/', 'The motor_driver package', 'D. Potapov',\\n 'potapov627@yandex.ru', 'nav_msgs, geometry_msgs, tf, ', 'motor_driver_node']\\n labels = []\\n for name in lbl_names:\\n labels.append(QLabel(name))\\n for i, ph in zip(range(len(labels)), param_list):\\n ed_line = QLineEdit()\\n if i == 1:\\n ed_line.setValidator(QRegExpValidator(QRegExp(\\\"^([0-9\\\\.])*[0-9]$\\\")))\\n elif i == 5:\\n ed_line.setValidator(QRegExpValidator(QRegExp(\\\"^([a-z0-9_-]+\\\\.)*[a-z0-9_-]+@[a-z0-9_-]+(\\\\.[a-z0-9_-]+)*\\\\.[a-z]{2,6}$\\\")))\\n ed_line.setPlaceholderText(ph)\\n if i != 0:\\n ed_line.textEdited.connect(self.change_data)\\n else:\\n ed_line.textEdited.connect(self.change_pkg_name)\\n self.full_ed_lines.append(ed_line)\\n grid = QGridLayout()\\n grid.setSpacing(5)\\n for i in range(1, len(labels) + 1):\\n for j in range(0, 2):\\n if j == 0:\\n grid.addWidget(labels[i - 1], i, j)\\n else:\\n grid.addWidget(self.full_ed_lines[i - 1], i, j)\\n ch_dirButton = QPushButton(self)\\n ch_dirButton.setIcon(QIcon('./icons/open_folder.png'))\\n ch_dirButton.clicked.connect(self.ch_dirDialog)\\n grid.addWidget(ch_dirButton, 3, 3)\\n genButton = QPushButton(\\\"Сгенерировать\\\")\\n genButton.clicked.connect(self.generate)\\n grid.addWidget(genButton, len(labels) + 2, 1)\\n self.setLayout(grid)\\n self.setMinimumSize(700, 400)\\n self.show()\",\n \"def setUp(self):\\n self.plugin = attributePainter(IFACE)\\n self.vector = QgsVectorLayer(os.path.join(self.plugin.plugin_dir,\\\"test\\\",\\\"testdata.gpkg|layername=testdata\\\"),\\\"testdata\\\",\\\"ogr\\\")\",\n \"def __init__(self, viewer: geoviewer.GeoGraphViewer) -> None:\\n super().__init__(viewer=viewer)\\n\\n widget = self._create_settings_widget()\\n self.children = [widget]\",\n \"def setup_ui(self):\\n\\t\\t\\n\\t\\t# CREATION DU LAYOUT\\n\\t\\tself.layout = QtWidgets.QHBoxLayout(self) #le layout prend la fenetre principal en argument donc notre self\\n\\t\\t\\n\\t\\t# CREATION DES WIDGETS\\n\\t\\tself.cbb_devisesFrom = QtWidgets.QComboBox() #combobox (liste deroulante) pour choisir la devise From\\n\\t\\tself.spn_montant = QtWidgets.QSpinBox() #spinbox (zone affichage) du montant a convertir\\n\\t\\tself.cbb_devisesTo = QtWidgets.QComboBox() #cbb pour choisir la devise To\\n\\t\\tself.spn_montantConverti = QtWidgets.QSpinBox() #spn du montant converti\\n\\t\\tself.btn_inverser = QtWidgets.QPushButton(\\\"Inverser devises\\\") #bouton pour inverser les devises\\n\\t\\t\\n\\t\\t# AJOUT AU LAYOUT\\n\\t\\tself.layout.addWidget(self.cbb_devisesFrom)\\n\\t\\tself.layout.addWidget(self.spn_montant)\\n\\t\\tself.layout.addWidget(self.cbb_devisesTo)\\n\\t\\tself.layout.addWidget(self.spn_montantConverti)\\n\\t\\tself.layout.addWidget(self.btn_inverser)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/save_attributes/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u''),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def run(self):\\n\\n # Create the dialog with elements (after translation) and keep reference\\n # Only create GUI ONCE in callback, so that it will only load when the plugin is started\\n if self.first_start == True:\\n self.first_start = False\\n self.dlg = NewRaptorDialog()\\n\\n #\\n self.dlg.cmbSpecies.currentTextChanged.connect(self.evt_cmbSpecies_changed)\\n\\n\\n mc = self.iface.mapCanvas()\\n\\n self.dlg.spbLatitude.setValue(mc.center().y())\\n self.dlg.spbLongitude.setValue(mc.center().x())\\n self.dlg.dteLast.setDate(QDate.currentDate())\\n\\n\\n # Lista de camada do mapa\\n map_layers = []\\n for lyr in mc.layers():\\n map_layers.append(lyr.name())\\n\\n # Coloca o nome dos layrs em uma caixa de mensagem, com o titulo camadas\\n #QMessageBox.information(self.dlg, \\\"Layers\\\", str(map_layers))\\n\\n # cria uma caixa de mensagem caso não achar alguams das layrs descritas abaixo\\n missing_layers = []\\n if not 'Raptor Nests' in map_layers:\\n missing_layers.append('Raptor Nests')\\n if not 'Raptor Buffer' in map_layers:\\n missing_layers.append('Raptor Buffer')\\n if not 'Linear Buffer' in map_layers:\\n missing_layers.append('Linear Buffer')\\n\\n if missing_layers:\\n msg = 'The following layres are missing from ths project\\\\n'\\n for lyr in missing_layers:\\n msg += f'\\\\n {lyr}'\\n QMessageBox.critical(self.dlg, 'Missing layers', msg)\\n # o return barra a execução das demais linhas de códigos\\n return\\n\\n\\n # show the dialog\\n self.dlg.show()\\n # Run the dialog event loop\\n result = self.dlg.exec_()\\n # See if OK was pressed\\n if result:\\n # Do something useful here - delete the line containing pass and\\n # substitute with your code.\\n\\n # irá retornar uma lista com o as camdas com nome Raptor Nests, mas iremos pegar apenas a primeira camadd\\n lyrNests = QgsProject.instance().mapLayersByName('Raptor Nests')[0]\\n lyrBuffer = QgsProject.instance().mapLayersByName('Raptor Buffer')[0]\\n lyrLinear = QgsProject.instance().mapLayersByName('Linear Buffer')[0]\\n # Irá pegar o ultimo valor de índice\\n idxNestID = lyrNests.fields().indexOf('Nest_ID')\\n valNestID = lyrNests.maximumValue(idxNestID) + 1\\n\\n # Irá pegar os valores que foram digitados na caixa de texto\\n valLat = self.dlg.spbLatitude.value()\\n valLng = self.dlg.spbLongitude.value()\\n valSpecies = self.dlg.cmbSpecies.currentText()\\n valBuffer = self.dlg.spbBuffer.value()\\n valStatus = self.dlg.cmbStatus.currentText()\\n valLast = self.dlg.dteLast.date()\\n QMessageBox.information(self.dlg, 'Message', f'New Nest ID: {valNestID}\\\\n \\\\nLatitude: {valLat} '\\n f'\\\\nLongitude: {valLng} \\\\nSpecies: {valSpecies} '\\n f'\\\\nBuffer: {valBuffer} \\\\nStatus: {valStatus} \\\\nLast: {valLast}')\\n # cria um novo objeto da classe QgsFeature, no qual a classe terá os mesmoa campos do lyrnet.fildes\\n # (objeto da Layer Raptor Nests)\\n ftrNest = QgsFeature(lyrNests.fields())\\n # Irá começar a adicoanr atributos de acordo com o nome dos campos da layer Raptor Nests\\n ftrNest.setAttribute('lat_y_dd', valLat)\\n ftrNest.setAttribute('long_x_dd', valLng)\\n ftrNest.setAttribute('recentspec', valSpecies)\\n ftrNest.setAttribute('buf_dist', valBuffer)\\n ftrNest.setAttribute('recentstat', valStatus)\\n ftrNest.setAttribute('lastsurvey', valLast)\\n ftrNest.setAttribute('Nest_ID', valNestID)\\n\\n # colcoando o tipo de geometria\\n geom = QgsGeometry(QgsPoint(valLng, valLat))\\n ftrNest.setGeometry(geom)\\n\\n # Cada tipo de dados pode ter um provedor diferente, o qgis tem extensões para varios tipos de provedor,\\n # a função abaixo pega o provedor e inclui os dados de acordo com o proveor\\n pr = lyrNests.dataProvider()\\n pr.addFeatures([ftrNest])\\n lyrNests.reload()\\n\\n # Adiconando a camada de buffer\\n pr = lyrBuffer.dataProvider()\\n buffer = geom.buffer(valBuffer, 10)\\n ftrNest.setGeometry(buffer)\\n pr.addFeatures([ftrNest])\\n lyrBuffer.reload()\\n\\n dlgTable = DlgTable()\\n dlgTable.setWindowTitle(f\\\"Impacts Table for Nest {valNestID}\\\")\\n\\n # Find linear projects that will impacted and report then in the table\\n bb = buffer.boundingBox()\\n linears = lyrLinear.getFeatures(bb)\\n for linear in linears:\\n valID = linear.attribute('Project')\\n valType = linear.attribute('type')\\n valDistance = linear.geometry().distance(geom)\\n if valDistance < valBuffer:\\n #populate table with Linear data\\n row = dlgTable.tblImpacts.rowCount()\\n dlgTable.tblImpacts.insertRow(row)\\n dlgTable.tblImpacts.setItem(row, 0, QTableWidgetItem(str(valID)))\\n dlgTable.tblImpacts.setItem(row, 1, QTableWidgetItem(valType))\\n twi = QTableWidgetItem(f\\\"{valDistance:4.5f}\\\")\\n twi.setTextAlignment(QtCore.Qt.AlignRight)\\n dlgTable.tblImpacts.setItem(row, 2, twi)\\n\\n dlgTable.tblImpacts.sortItems(2)\\n dlgTable.show()\\n dlgTable.exec_()\\n\\n else:\\n QMessageBox.information(self.dlg, \\\"Message\\\", \\\"Should only run if cancelled\\\")\",\n \"def run(self):\\n self.iface.actionShowPythonDialog().trigger()\\n python_console = self.iface.mainWindow().findChild(QDockWidget, 'PythonConsole')\\n if not python_console.isVisible():\\n python_console.setVisible(True)\\n layers = self.iface.legendInterface().layers()\\n layer_list = []\\n for layer in layers:\\n layer_list.append(layer.name())\\n self.dlg.trace_file_list.addItems(layer_list)\\n # show the dialog\\n self.dlg.show()\\n # Run the dialog event loop\\n result = self.dlg.exec_()\\n # See if OK was pressed\\n if result:\\n f = open('./here_rtti_miner_config.ini', mode='w')\\n app_id = self.dlg.app_id_textbox.toPlainText()\\n f.write(app_id + '\\\\n')\\n app_code = self.dlg.app_code_textbox.toPlainText()\\n f.write(app_code + '\\\\n')\\n archive_path = self.dlg.archive_textbox.toPlainText()\\n f.write(archive_path + '\\\\n')\\n f.close()\\n\\n \\\"\\\"\\\"Getting Source Of Target Layer\\\"\\\"\\\"\\n self.app_id = self.dlg.app_id_textbox.toPlainText()\\n self.app_code = self.dlg.app_code_textbox.toPlainText()\\n self.archive_path = self.dlg.archive_textbox.toPlainText()\\n selected_layer_index = self.dlg.trace_file_list.currentIndex()\\n self.mia_image(self.app_id, self.app_code)\\n def mia_autorefresh():\\n self.mia_image(self.app_id, self.app_code)\\n self.mia_image(self.app_id, self.app_code)\\n self.iface.mapCanvas().extentsChanged.connect(mia_autorefresh)\\n if selected_layer_index >= 0:\\n selected_layer = layers[selected_layer_index]\\n selected_layer_source = selected_layer.source()\\n selected_layer_source_file = selected_layer_source.split('|')[0]\\n selected_layer_source_file_type = selected_layer_source_file.split('.')[-1].upper()\\n\\n \\\"\\\"\\\"Accessing HERE Route Match Extension\\\"\\\"\\\"\\n print('Calling HERE RME...')\\n app_id = self.dlg.app_id_textbox.toPlainText()\\n app_code = self.dlg.app_code_textbox.toPlainText()\\n rme_url = 'http://rme.cit.api.here.com/2/matchroute.json?routemode=car&access,gate,oneway,thrutraf,turn&filetype={}&app_id={}&app_code={}&attributes=LINK_TMC_FC1(*),LINK_TMC_FC2(*),LINK_TMC_FC3(*),LINK_TMC_FC4(*),LINK_TMC_FC5(*)'.format(\\n selected_layer_source_file_type, app_id, app_code)\\n payload = open(selected_layer_source_file, mode='r').read()\\n r = requests.post(rme_url, data=payload)\\n r.encoding = 'utf-8'\\n rme_result = json.loads(r.text)\\n print('RMC result captured.\\\\nStart Parsing...')\\n rme_result_db = self.rme_result_parsing(selected_layer_source_file,\\n selected_layer_source_file_type.upper(), rme_result)\\n output_result = self.here_rtti_mapping(selected_layer_source_file, rme_result_db)\\n print('Opening results.')\\n uri = 'file:///{}?delimiter={}&xField={}&yField={}'.format(output_result.replace('\\\\\\\\', '/'), ',',\\n 'lon_matched', 'lat_matched')\\n vlayer = QgsVectorLayer(uri, 'output_result', \\\"delimitedtext\\\")\\n QgsMapLayerRegistry.instance().addMapLayer(vlayer)\\n vlayer.loadNamedStyle(resolve('render_result.qml'))\\n order = self.iface.layerTreeCanvasBridge().customLayerOrder()\\n order.insert(0, order.pop(order.index(vlayer.id())))\\n self.iface.layerTreeCanvasBridge().setCustomLayerOrder(order)\\n self.iface.layerTreeCanvasBridge().setHasCustomLayerOrder(True)\\n print('Results opened.')\\n else:\\n self.iface.messageBar().pushMessage(\\\"Error\\\",\\n 'GPS trace is not loaded, please load GPS trace in GPX/KML/NMEA/CSV format.',\\n level=QgsMessageBar.CRITICAL)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/buienradar_plugin/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Buienradar Plugin'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def mainWidget(self):\\n raise RuntimeError('Not implemented')\",\n \"def init_widget(self):\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/OD/icon.png'\\n self.add_action(\\n icon_path,\\n text='OD',\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/pit_removal/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'remove pits'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72260803","0.7211928","0.7206275","0.71976244","0.7164867","0.7164867","0.7149143","0.7126986","0.70783645","0.7038808","0.70362467","0.7032511","0.70274544","0.698081","0.69556254","0.69539404","0.69419616","0.6932011","0.69111043","0.69000256","0.6899709","0.6897077","0.6883799","0.6867602","0.6846346","0.6835323","0.68007356","0.67941254","0.67927265","0.6788358","0.6777231","0.67511666","0.6750748","0.67198277","0.66896254","0.66713125","0.6665501","0.6658642","0.65942866","0.65922016","0.659175","0.65808123","0.6544756","0.6543705","0.6541473","0.6534597","0.65343493","0.6525658","0.65224165","0.6516919","0.6507016","0.64956236","0.64944196","0.64643985","0.6456944","0.64548373","0.6449513","0.64396673","0.6432242","0.6427231","0.642106","0.6397821","0.6392589","0.63838816","0.6335574","0.63311046","0.6327827","0.632666","0.63256043","0.63238484","0.63220835","0.6312294","0.6302792","0.63010293","0.62978953","0.6289814","0.6287012","0.6281287","0.6276492","0.6274792","0.62728834","0.62520355","0.62482864","0.62457436","0.62438434","0.6234505","0.62120205","0.6211064","0.6209695","0.617586","0.6169372","0.615579","0.61458856","0.6140702","0.61339974","0.61317754","0.6123085","0.61157393","0.6111159","0.6109092"],"string":"[\n \"0.72260803\",\n \"0.7211928\",\n \"0.7206275\",\n \"0.71976244\",\n \"0.7164867\",\n \"0.7164867\",\n \"0.7149143\",\n \"0.7126986\",\n \"0.70783645\",\n \"0.7038808\",\n \"0.70362467\",\n \"0.7032511\",\n \"0.70274544\",\n \"0.698081\",\n \"0.69556254\",\n \"0.69539404\",\n \"0.69419616\",\n \"0.6932011\",\n \"0.69111043\",\n \"0.69000256\",\n \"0.6899709\",\n \"0.6897077\",\n \"0.6883799\",\n \"0.6867602\",\n \"0.6846346\",\n \"0.6835323\",\n \"0.68007356\",\n \"0.67941254\",\n \"0.67927265\",\n \"0.6788358\",\n \"0.6777231\",\n \"0.67511666\",\n \"0.6750748\",\n \"0.67198277\",\n \"0.66896254\",\n \"0.66713125\",\n \"0.6665501\",\n \"0.6658642\",\n \"0.65942866\",\n \"0.65922016\",\n \"0.659175\",\n \"0.65808123\",\n \"0.6544756\",\n \"0.6543705\",\n \"0.6541473\",\n \"0.6534597\",\n \"0.65343493\",\n \"0.6525658\",\n \"0.65224165\",\n \"0.6516919\",\n \"0.6507016\",\n \"0.64956236\",\n \"0.64944196\",\n \"0.64643985\",\n \"0.6456944\",\n \"0.64548373\",\n \"0.6449513\",\n \"0.64396673\",\n \"0.6432242\",\n \"0.6427231\",\n \"0.642106\",\n \"0.6397821\",\n \"0.6392589\",\n \"0.63838816\",\n \"0.6335574\",\n \"0.63311046\",\n \"0.6327827\",\n \"0.632666\",\n \"0.63256043\",\n \"0.63238484\",\n \"0.63220835\",\n \"0.6312294\",\n \"0.6302792\",\n \"0.63010293\",\n \"0.62978953\",\n \"0.6289814\",\n \"0.6287012\",\n \"0.6281287\",\n \"0.6276492\",\n \"0.6274792\",\n \"0.62728834\",\n \"0.62520355\",\n \"0.62482864\",\n \"0.62457436\",\n \"0.62438434\",\n \"0.6234505\",\n \"0.62120205\",\n \"0.6211064\",\n \"0.6209695\",\n \"0.617586\",\n \"0.6169372\",\n \"0.615579\",\n \"0.61458856\",\n \"0.6140702\",\n \"0.61339974\",\n \"0.61317754\",\n \"0.6123085\",\n \"0.61157393\",\n \"0.6111159\",\n \"0.6109092\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7779239"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":86699,"cells":{"query":{"kind":"string","value":"Initialise Loading of the queue widgets into QGIS"},"document":{"kind":"string","value":"def loadQueues( self ):\n queues = self.Queues()\n if not queues.isVisible():\n queues.parent().show()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def initGui(self):\n\n # set srs\n self._displayCrs = QgsCoordinateReferenceSystem()\n self._displayCrs.createFromOgcWmsCrs('EPSG:4167') \n self.iface.mapCanvas().mapSettings().setDestinationCrs(self._displayCrs)\n\n # init layerManager\n self._layerManager = LayerManager(self.iface, self)\n self._layerManager.registerFunctions()\n # init Highlighter\n self.highlighter = FeatureHighlighter(self.iface, self._layerManager, self)\n\n # Build an action list from QGIS navigation toolbar\n actionList = self.iface.mapNavToolToolBar().actions()\n self.actions = self.iface.mapNavToolToolBar().actions()\n\n # Main address editing window\n self._loadaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/loadaddress.png'), \n 'QGIS-AIMS-Plugin', self.iface.mainWindow())\n self._loadaction.setWhatsThis('Open the QGIS-AIMS-Plugin')\n self._loadaction.setStatusTip('Open the QGIS-AIMS-Plugin')\n self._loadaction.triggered.connect(self.loadQueues)\n self._loadaction.triggered.connect(self.loadLayers)\n self._loadaction.triggered.connect(self.enableAddressLayer)\n self._loadaction.triggered.connect(self.startDM)\n self._loadaction.triggered.connect(self.disableConfig)\n \n # Config Dialog\n self._configdialog = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/config.png'), \n 'AIMS Configuration', self.iface.mainWindow())\n self._configdialog.setWhatsThis('Configure AIMS')\n self._configdialog.setStatusTip('Configure AIMS')\n self._configdialog.triggered.connect(self.configureAims)\n\n self.config_stacked_widget = self._config_ui.uStackedWidget\n self.config_list_options = self._config_ui.uListOptions\n self.config_list_options.itemClicked.connect(self.showConfigSelectedOption)\n\n item = QListWidgetItem(\"User Config\")\n image_path = ':/plugins/QGIS-AIMS-Plugin/resources/config.png'\n item.setIcon(QIcon(image_path))\n self.config_list_options.addItem(item)\n\n item = QListWidgetItem(\"Dev Config\")\n image_path = ':/plugins/QGIS-AIMS-Plugin/resources/config.png'\n item.setIcon(QIcon(image_path))\n self.config_list_options.addItem(item)\n\n # Create new address tool\n self._createnewaddressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/newaddresspoint.png'), \n 'Create AIMS Feature', self.iface.mainWindow())\n self._createnewaddressaction.setWhatsThis('Create AIMS Feature')\n self._createnewaddressaction.setStatusTip('Create AIMS Feature')\n self._createnewaddressaction.setEnabled(False)\n self._createnewaddressaction.setCheckable(True)\n self._createnewaddressaction.triggered.connect( self.startNewAddressTool )\n self._createnewaddresstool = CreateNewAddressTool( self.iface, self._layerManager, self)\n self._createnewaddresstool.setAction( self._createnewaddressaction )\n self.actions.append(self._createnewaddressaction)\n\n # Delete address point\n self._deladdressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/deleteaddress.png'), \n 'Delete AIMS Feature', self.iface.mainWindow())\n self._deladdressaction.setWhatsThis('Delete AIMS Feature')\n self._deladdressaction.setStatusTip('Delete AIMS Feature')\n self._deladdressaction.setEnabled(False)\n self._deladdressaction.setCheckable(True)\n self._deladdressaction.triggered.connect( self.startDelAddressTool )\n self._deladdtool = DelAddressTool( self.iface, self._layerManager, self)\n self._deladdtool.setAction( self._deladdressaction ) \n self.actions.append(self._deladdressaction)\n\n # Move address\n self._moveaddressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/moveaddress.png'), \n 'Move AIMS Feature(s)', self.iface.mainWindow())\n self._moveaddressaction.setWhatsThis('Move AIMS Feature(s)')\n self._moveaddressaction.setStatusTip('Move AIMS Feature(s)')\n self._moveaddressaction.setEnabled(False)\n self._moveaddressaction.setCheckable(True)\n self._moveaddressaction.triggered.connect( self.startMoveAddressTool )\n self._moveaddtool = MoveAddressTool( self.iface, self._layerManager, self)\n self._moveaddtool.setAction( self._moveaddressaction ) \n self.actions.append(self._moveaddressaction)\n \n # Update address\n self._updateaddressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/updateaddress.png'), \n 'Update AIMS Feature', self.iface.mainWindow())\n self._updateaddressaction.setWhatsThis('Update AIMS Feature')\n self._updateaddressaction.setStatusTip('Update AIMS Feature')\n self._updateaddressaction.setEnabled(False)\n self._updateaddressaction.setCheckable(True)\n self._updateaddressaction.triggered.connect( self.startUpdateAddressTool )\n self._updateaddtool = UpdateAddressTool( self.iface, self._layerManager, self)\n self._updateaddtool.setAction( self._updateaddressaction ) \n self.actions.append(self._updateaddressaction)\n\n # RCL tool -- Not a QAction as triggered from many palaces but not the toolbar\n self._rcltool = GetRcl(self.iface, self._layerManager, self)\n\n # UpdateReview Position tool -- Not a QAction as triggered initiated from review queue form\n self._updateReviewPos = UpdateReviewPosition(self.iface, self._layerManager, self)\n\n # Address lineage\n \"\"\"\n self._lineageaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/lineage.png'), \n 'Build Lineage Relationships Between Features', self.iface.mainWindow())\n self._lineageaction.setWhatsThis('Build Lineage Relationships Between Features')\n self._lineageaction.setStatusTip('Build Lineage Relationships Between Features')\n self._lineageaction.setEnabled(False)\n self._lineageaction.setCheckable(True)\n self._lineagetool = LineageTool( self.iface, self._layerManager, self)\n self._lineageaction.triggered.connect(self._lineagetool.setEnabled)\n self.actions.append(self._lineageaction)\n \"\"\"\n\n # Address highlighter\n self._highlightaction = QAction(QIcon(\":/plugins/QGIS-AIMS-Plugin/resources/addresshighlight.png\"), \n \"Electoral address highlighter\", self.iface.mainWindow())\n self._highlightaction.setWhatsThis(\"Turn the electoral address highlighter on or off\")\n self._highlightaction.setStatusTip(\"Turn the electoral address highlighter on or off\") \n self._highlightaction.setText('Highlightaction')\n self._highlightaction.setEnabled(False)\n self._highlightaction.setCheckable(True)\n self._highlightaction.toggled.connect( self.highlighter.setEnabled )\n\n # Add to own toolbar\n self._toolbar = self.iface.addToolBar('QGIS-AIMS-Plugin')\n self._toolbar.addAction(self._createnewaddressaction)\n self._toolbar.addAction(self._deladdressaction)\n self._toolbar.addAction(self._updateaddressaction)\n self._toolbar.addAction(self._moveaddressaction)\n #self._toolbar.addAction(self._lineageaction)\n self._toolbar.addAction(self._highlightaction)\n\n # Add actions to menu\n self.iface.addToolBarIcon(self._loadaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._loadaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._createnewaddressaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._deladdressaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._updateaddressaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._moveaddressaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._highlightaction)\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._configdialog)\n\n # capture maptool selection changes\n QObject.connect(self.iface.mapCanvas(), SIGNAL( \"mapToolSet(QgsMapTool *)\" ), self.mapToolChanged)\n\n # Add actions from QGIS attributes toolbar (handling QWidgetActions)\n tmpActionList = self.iface.attributesToolBar().actions()\n for action in tmpActionList:\n if isinstance(action, QWidgetAction):\n actionList.extend( action.defaultWidget().actions() )\n else:\n actionList.append( action )\n # ... could add other toolbars' action lists...\n\n # Build a group with actions from actionList\n group = QActionGroup( self.iface.mainWindow() )\n group.setExclusive(True)\n for qgisAction in actionList:\n group.addAction( qgisAction )\n\n # Add our own actions\n for action in self.actions:\n if action.text() == 'Highlightaction': continue\n group.addAction( action )","def init_widget(self):","def initGui(self):\n self.run_icon = QIcon(\n os.path.join(self.plugin_dir, \"resources\", \"icon_add_service.svg\")\n )\n self.fav_icon = QIcon(\n os.path.join(self.plugin_dir, \"resources\", \"pdok_icon_bookmark.svg\")\n )\n\n self.del_icon = QIcon(\n os.path.join(self.plugin_dir, \"resources\", \"pdok_icon_delete.svg\")\n )\n\n self.run_action = QAction(self.run_icon, PLUGIN_NAME, self.iface.mainWindow())\n self.run_button = QToolButton()\n self.run_button.setMenu(QMenu())\n self.run_button.setPopupMode(QToolButton.MenuButtonPopup)\n self.run_button.setDefaultAction(self.run_action)\n\n self.services_loaded = False\n\n self.run_action.triggered.connect(self.run)\n self.setup_fq_checkboxes()\n\n # Add toolbar button and menu item\n self.toolbar = self.iface.addToolBar(PLUGIN_NAME)\n self.toolbar.setObjectName(PLUGIN_NAME)\n self.toolbar.addWidget(self.run_button)\n\n # Set default layer loading behaviour\n self.default_tree_locations = {\n \"wms\": \"top\",\n \"wmts\": \"bottom\",\n \"wfs\": \"top\",\n \"wcs\": \"top\",\n }\n\n self.add_fav_actions_to_toolbar_button()\n\n self.toolbar_search = QLineEdit()\n\n def toolbar_search_mouse_event():\n self.toolbar_search.selectAll()\n self.timer_toolbar_search.start()\n\n self.toolbar_search.mousePressEvent = lambda _: toolbar_search_mouse_event()\n\n self.toolbar_search.setMaximumWidth(200)\n self.toolbar_search.setAlignment(Qt.AlignLeft)\n self.toolbar_search.setPlaceholderText(\"Zoek in PDOK Locatieserver\")\n self.toolbar.addWidget(self.toolbar_search)\n self.timer_toolbar_search = QTimer()\n self.timer_toolbar_search.setSingleShot(True)\n self.timer_toolbar_search.setInterval(200)\n self.timer_toolbar_search.timeout.connect(self.toolbar_search_get_suggestions)\n self.toolbar_search.textEdited.connect(\n lambda: self.timer_toolbar_search.start()\n )\n\n eraser_icon = QIcon(\n os.path.join(self.plugin_dir, \"resources\", \"icon_remove_cross.svg\")\n )\n self.clean_ls_search_action = QAction(\n eraser_icon, \"Cleanup\", self.erase_address()\n )\n\n if not self.show_ls_feature():\n self.toolbar.addAction(self.clean_ls_search_action)\n\n self.clean_ls_search_action.triggered.connect(self.erase_address)\n self.clean_ls_search_action.setEnabled(False)\n self.iface.addPluginToMenu(f\"&{PLUGIN_NAME}\", self.run_action)\n\n self.about_action = QAction(self.run_icon, \"About\", self.iface.mainWindow())\n self.about_action.setWhatsThis(f\"{PLUGIN_NAME} About\")\n self.iface.addPluginToMenu(f\"&{PLUGIN_NAME}\", self.about_action)\n\n self.about_action.triggered.connect(self.about)\n self.dlg.ui.btnLoadLayer.clicked.connect(lambda: self.load_layer(\"default\"))\n self.dlg.ui.btnLoadLayerTop.clicked.connect(lambda: self.load_layer(\"top\"))\n self.dlg.ui.btnLoadLayerBottom.clicked.connect(\n lambda: self.load_layer(\"bottom\")\n )\n\n self.dlg.ui.pushButton.clicked.connect(self.toggle_all_fq_checkboxes)\n\n self.dlg.geocoder_search.returnPressed.connect(\n self.ls_dialog_get_suggestions_and_remove_pointer\n )\n self.timer_geocoder_search = QTimer()\n self.timer_geocoder_search.setSingleShot(True)\n self.timer_geocoder_search.setInterval(200)\n self.timer_geocoder_search.timeout.connect(\n self.ls_dialog_get_suggestions_and_remove_pointer\n )\n self.dlg.geocoder_search.textEdited.connect(\n lambda: self.timer_geocoder_search.start()\n )\n\n self.dlg.geocoder_search.setPlaceholderText(\n \"Zoek in PDOK Locatieserver, bv postcode of postcode huisnummer\"\n )\n\n self.dlg.geocoderResultSearch.textChanged.connect(self.filter_geocoder_result)\n self.dlg.geocoderResultSearch.setPlaceholderText(\n \"een of meer zoekwoorden uit resultaat\"\n )\n # connect all fq checkboxes with suggest, so upon a change in fq filter we re-search\n for cbx in self.fq_checkboxes.keys():\n cbx.stateChanged.connect(self.ls_dialog_get_suggestions_and_remove_pointer)\n self.run(True)\n\n # set to hidden when no layer selected\n self.dlg.ui.layer_info.setHidden(True)\n self.dlg.ui.layer_options_groupbox.setHidden(True)","def prepare_UI(self):","def initGui(self):\n self.sketchButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'sketch.svg'),\n text=self.tr('Sketch on map'),\n callback=self.sketchAction,\n parent=self.iface.mainWindow(),\n object_name='mSketchAction')\n self.penButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'pen.svg'),\n text=self.tr('Draw line on map'),\n callback=self.penAction,\n parent=self.iface.mainWindow(),\n object_name='mPenAction')\n self.canvasButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'canvas.svg'),\n text=self.tr('Color and width canvas'),\n callback=None,\n parent=self.iface.mainWindow())\n self.eraseButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'erase.svg'),\n text=self.tr('Erase sketches'),\n callback=self.eraseAction,\n parent=self.iface.mainWindow(),\n object_name='mEraseAction')\n self.removeButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'remove.svg'),\n text=self.tr('Remove all sketches'),\n callback=self.removeSketchesAction,\n parent=self.iface.mainWindow(),\n object_name='mRemoveAllSketches')\n self.noteButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'note.svg'),\n text=self.tr('Add text annotations to sketches'),\n callback=None,\n parent=self.iface.mainWindow(),\n object_name='mAddTextAnnotations')\n self.convertButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'toLayer.svg'),\n text=self.tr('Convert annotations to Memory Layer'),\n callback=self.toMemoryLayerAction,\n parent=self.iface.mainWindow(),\n object_name='mConvertAnnotationsToMemoryLayer')\n self.saveButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'inbox.svg'),\n text=self.tr('Save sketches to file'),\n callback=self.saveAction,\n parent=self.iface.mainWindow(),\n object_name='mSaveSketchesToFile')\n self.loadButton = self.add_action(\n path.join(self.plugin_dir, 'icons', 'outbox.svg'),\n text=self.tr('Load sketches from file'),\n callback=self.loadAction,\n parent=self.iface.mainWindow(),\n object_name='mLoadSketchesFromFile')\n self.canvasButton.setMenu(self.canvasMenu())\n self.noteButton.setCheckable(True)\n self.penButton.setCheckable(True)\n self.sketchButton.setCheckable(True)\n self.eraseButton.setCheckable(True)\n self.geoSketches = []\n self.dumLayer = QgsVectorLayer(\"Point?crs=EPSG:4326\", \"temporary_points\", \"memory\")\n self.pressed = None\n self.previousPoint = None\n self.previousMoved = None\n self.gestures = 0\n self.points = 0\n self.currentColor = QColor(\"#aa0000\")\n self.currentWidth = 5\n self.annotation = sketchNoteDialog(self.iface)\n self.annotatatedSketch = None\n self.sketchEnabled(None)\n self.iface.projectRead.connect(self.projectReadAction)\n self.iface.newProjectCreated.connect(self.newProjectCreatedAction)\n QgsProject.instance().legendLayersAdded.connect(self.notSavedProjectAction)","def initUI(self):\n self.logger.debug('Setting up the Measurement GUI')\n self.setWindowTitle(self.title)\n\n self.show()\n\n self.make_combobox_scanner()\n self.make_combobox_movements()\n self.make_combobox_configurate()\n self.make_combobox_basic()","def __init__(self):\n super(QTUIProject, self).__init__()\n self.setupUi(self)\n self.assignWidgets()\n self.show()\n self.SlotsJsonName = \"Slots Assets Folder\"\n self.BingoJsonName = \"Bingo Assets Folder\"","def initGui(self):\n\n icon_path = ':/plugins/HERE_RTTI_Archive_Miner/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Mapping GPS trace to Traffic Archive'),\n callback=self.run,\n parent=self.iface.mainWindow())\n init_config_path = './here_rtti_miner_config.ini'\n if os.path.exists(init_config_path):\n init_config = open(init_config_path, mode='r')\n f = init_config.readlines()\n if len(f) == 3:\n self.iface.messageBar().pushMessage(\"Info\", '{}\\\\here_rtti_miner_config.ini loaded.'.format(\n os.path.abspath(os.path.curdir)), level=QgsMessageBar.INFO, duration=1)\n app_id = (f[0].replace('\\n', ''))\n self.dlg.app_id_textbox.setPlainText(app_id)\n app_code = (f[1].replace('\\n', ''))\n self.dlg.app_code_textbox.setPlainText(app_code)\n archive_path = (f[2].replace('\\n', ''))\n self.dlg.archive_textbox.setPlainText(archive_path)\n\n \"\"\"Checkbox Listener\"\"\"\n self.cb = self.dlg.date_time_override_checkBox\n self.cb.toggle()\n self.cb.setChecked(False)\n self.dlg.connect(self.cb, QtCore.SIGNAL('stateChanged(int)'), self.change_state)\n\n \"\"\"DTM preload\"\"\"\n self.dtm = self.dlg.dateTimeEdit\n # get current date and time\n now = QtCore.QDateTime.currentDateTime()\n # set current date and time to the object\n self.dtm.setDateTime(now)","def initGui(self):\r\n\r\n # Create help action \r\n self.helpAction = QAction( QIcon(\":/plugins/layercombinations/about.png\"), u\"Help\", self.iface.mainWindow())\r\n # connect the action \r\n self.helpAction.triggered.connect( self.showHelp )\r\n # Add menu item\r\n self.iface.addPluginToMenu(u\"&Layer Combinations\", self.helpAction)\r\n\r\n # Create the action that allows to change the widget type\r\n self.changeWidgetAction = QAction(\"Change widget type\", self.iface.mainWindow())\r\n self.changeWidgetAction.triggered.connect( self.changeWidget )\r\n self.iface.addPluginToMenu(u\"&Layer Combinations\", self.changeWidgetAction)\r\n\r\n # Create the action that will toggle the plugin panel\r\n self.action = QAction(QIcon(\":/plugins/layercombinations/icon.png\"), \"Show/hide the Layer Combinations widgets\", self.iface.mainWindow())\r\n self.action.triggered.connect( self.widget.toggle )\r\n # Add toolbar button and menu item\r\n self.iface.addToolBarIcon(self.action)\r\n self.iface.addPluginToMenu(u\"&Layer Combinations\", self.action)\r\n\r\n\r\n # Add the widget to the mainWindow\r\n self.widget.addToiFace(self.iface)","def init_ui(self):\n raise NotImplementedError","def init_ui(self):\n raise NotImplementedError","def on_init(self, queue=None, **kwargs):\n self.queue = queue if queue else Queue()","def initGui(self):\n\n icon_path = ':/plugins/roadsalt_mapbuilder/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'&Road Salt Map Builder'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def __init__(self):\n Redrawable.all_instantiated_widgets.append(self)","def _initDialog(self):\n\n # ===== Configure focus policy ===== #\n self.setFocusPolicy(Qt.NoFocus)\n self.setFocus(False)\n\n # ===== Create movie parameters ===== #\n movieLabel = QLabel()\n self._movie = QMovie('./icons/loading.gif')\n movieLabel.setMovie(self._movie)\n\n # ===== Create layout ===== #\n layout = QVBoxLayout()\n layout.setContentsMargins(0, 0, 0, 0)\n layout.setSpacing(0)\n layout.addWidget(movieLabel)\n self.setLayout(layout)","def init_queues(self):\n for step in self.get_children():\n if step.kind == StepKinds.queue:\n step.init_object(self.context, None)","def _initJobs(self):\n pass","def _init_widgets(self):\n comps = self.ui.component_list\n comps.addItems(sorted(self._labels.keys()))\n data = self.ui.data_list\n data.addItems(sorted(self._data.keys()))","def _init_ui(self):\n self.setWindowTitle(\"HB Havens: resultaten\")\n self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowContextHelpButtonHint)\n\n self.setLayout(QtWidgets.QVBoxLayout())\n\n # Create figure\n self.figure = Figure(figsize=(4,4))\n self.ax = self.figure.add_subplot()\n\n self.ax.grid()\n self.ax.spines['right'].set_visible(False)\n self.ax.spines['top'].set_visible(False)\n self.ax.tick_params(axis='y', color='0.75')\n self.ax.tick_params(axis='x', color='0.75')\n self.ax.set_aspect(1)\n\n # Add canvas\n self.canvas = FigureCanvasQTAgg(self.figure)\n\n # this is the Navigation widget\n # it takes the Canvas widget and a parent\n self.layout().addWidget(self.canvas)\n\n # Add location selection\n hbox = QtWidgets.QHBoxLayout()\n label = QtWidgets.QLabel('Locatie:')\n label.setFixedWidth(80)\n hbox.addWidget(label)\n self.location_combobox = QtWidgets.QComboBox()\n self.location_combobox.addItems(self.result_locations)\n self.location_combobox.setCurrentIndex(self.locid)\n self.location_combobox.currentIndexChanged.connect(self._set_location)\n hbox.addWidget(self.location_combobox)\n self.layout().addLayout(hbox)\n\n # Add parameter selection\n hbox = QtWidgets.QHBoxLayout()\n label = QtWidgets.QLabel('Parameter:')\n label.setFixedWidth(80)\n hbox.addWidget(label)\n self.parameter_combobox = QtWidgets.QComboBox()\n self.input_parameters = self.modelunctab.mainmodel.hydraulic_loads.result_columns[:]\n self.parameter_combobox.addItems(self.input_parameters)\n self.parameter_combobox.currentIndexChanged.connect(self._set_parameter)\n self.parameter_combobox.setCurrentIndex(0)\n self._set_parameter()\n self.figure.tight_layout()\n hbox.addWidget(self.parameter_combobox)\n self.layout().addLayout(hbox)\n\n # Line\n line = QtWidgets.QFrame()\n line.setFrameShape(QtWidgets.QFrame.HLine)\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\n\n self.layout().addWidget(line)\n\n # Add ok/close\n self.closebutton = QtWidgets.QPushButton('Sluiten')\n self.closebutton.clicked.connect(self.close)\n self.layout().addWidget(self.closebutton, 0, QtCore.Qt.AlignRight)\n\n self.layout().setSizeConstraint(QtWidgets.QLayout.SetFixedSize)","def initGui(self):\n\n icon_path = ':/plugins/AreaPrinter/mountainIcon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'AreaPrinter'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n\tself.add_action(\n icon_path,\n text=self.tr(u'Grid Convergence'),\n callback=self.runTools,\n parent=self.iface.mainWindow())","def run(self):\n\n # Create the dialog with elements (after translation) and keep reference\n # Only create GUI ONCE in callback, so that it will only load when the plugin is started\n if self.first_start == True:\n self.first_start = False\n self.dlg = RoadSaltMapDialog()\n self.dlg.setWindowTitle(\"Road Salt Map Builder\")\n#map 1\n self.dlg.tb_outMap1.clicked.connect(self.select_outputmap1_file)\n self.dlg.tb_inBasin_1.clicked.connect(self.select_basin1_file)\n self.dlg.tb_inRoad_1.clicked.connect(self.select_road1_file)\n self.dlg.pb_ok_1.clicked.connect(self.run_method_1)\n#map 2\n self.dlg.tb_outMap2.clicked.connect(self.select_outputmap2_file)\n self.dlg.tb_inBasin_2.clicked.connect(self.select_basin2_file)\n self.dlg.tb_inRoad_2.clicked.connect(self.select_road2_file)\n self.dlg.tb_inSalt.clicked.connect(self.select_salt_file)\n self.dlg.pb_ok_2.clicked.connect(self.run_method_2)\n self.dlg.cb_colDate.currentIndexChanged.connect(self.change_interval)\n\n # Fetch the currently loaded layers\n layers = QgsProject.instance().layerTreeRoot().children()\n # Clear the contents of the comboBox and lineEdit from previous runs\n#map 1\n self.dlg.l_outMap1.clear()\n self.dlg.l_inBasin_1.clear()\n self.dlg.l_inRoad_1.clear()\n#map 2\n self.dlg.l_outMap2.clear()\n self.dlg.l_inBasin_2.clear()\n self.dlg.l_inRoad_2.clear()\n self.dlg.l_inSalt.clear()\n self.dlg.cb_colSalt.clear()\n self.dlg.cb_colDate.clear()\n self.dlg.de_starttime.clear()\n self.dlg.de_endtime.clear()\n\n # show the dialog\n self.dlg.show()\n # Run the dialog event loop\n result = self.dlg.exec_()\n # See if OK was pressed\n if result:\n # Do something useful here - delete the line containing pass and\n # substitute with your code.\n pass","def initGui(self):\n\n self.action = QAction(QIcon(self.plugdir + '/icon.png'), 'MGB Water Balance', self.iface.mainWindow())\n self.action.triggered.connect(self.run)\n self.iface.addToolBarIcon(self.action)\n self.iface.addPluginToMenu('&IPH - Plugins', self.action)","def initGui(self):\n\n icon_path = ':/plugins/PluginChoucas/img/loaddata.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Chargement de données de montagne'),\n callback=self.run,\n parent=self.iface.mainWindow())\n \n icon_path = ':/plugins/PluginChoucas/img/loupe.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Recherche par mots sur clé'),\n callback=self.search,\n parent=self.iface.mainWindow())\n \n icon_path = ':/plugins/PluginChoucas/img/EmpriseZoneEtude.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Emprise ZE'),\n callback=self.loadZoneEtude,\n parent=self.iface.mainWindow())\n \n \n #icon_path = ':/plugins/PluginChoucas/img/landmark.png'\n #self.add_action(\n # icon_path,\n # text=self.tr(u'Afficher la description de l itinéraire'),\n # callback=self.displayDescription,\n # parent=self.iface.mainWindow())","def initialize(self):\n super(QtBaseWidgetComponent, self).initialize()","def initGui(self):\n\n icon_path = ':/plugins/obstacle_faa_dof_db/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'ObstacleFAADigitialObstacleFileDB'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def initGui(self):\n\n icon_path = ':/plugins/EU_Mapper/EUICON.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Generate EU Map'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def __init__(self, dataset, training_data):\n super().__init__()\n self.threadpool = QThreadPool()\n self.setFixedSize(1000, 600)\n self.center()\n self.setWindowTitle('GA practice') \n self.show()\n #read the map and training data\n self.map_datalist = dataset.keys()\n self.map_data = dataset \n self.training_datalist = training_data.keys()\n self.training_data = training_data\n\n #creat file choosing area\n self.file_run_creation(self.map_datalist, self.training_datalist)\n \n self.operation_parameter_creation()\n self.ouput_text_creation()\n hbox = QHBoxLayout()\n vbox = QVBoxLayout()\n vbox.addWidget(self.file_run)\n vbox.addWidget(self.operation_type)\n vbox.addWidget(self.text_group_box)\n hbox.addLayout(vbox)\n self.m = PlotCanvas(self.map_data)\n hbox.addWidget(self.m)\n self.setLayout(hbox)","def init_UI(self):\n # widgets\n self.modeComboBox = QtWidgets.QComboBox()\n self.updateBtn = QtWidgets.QPushButton('Force Update')\n self.pauseBtn = QtWidgets.QPushButton()\n self.pauseBtn.setCheckable(True)\n\n self.clearBtn = QtWidgets.QPushButton()\n self.clearBtn.setIcon(QtGui.QIcon(IconPaths.ICON_CLEAR_FILES))\n self.filterBtn = QtWidgets.QToolButton()\n self.filterBtn.setIcon(QtGui.QIcon(IconPaths.ICON_FILTER))\n self.filterBtn.setMinimumWidth(35)\n self.filterBtn.setStyleSheet('QToolButton::menu-indicator {subcontrol-position: center right; height: 7px}')\n self.filterBtn.setPopupMode(QtWidgets.QToolButton.InstantPopup)\n self.prefBtn = QtWidgets.QToolButton()\n self.prefBtn.setIcon(QtGui.QIcon(IconPaths.ICON_SETTINGS))\n #self.pBar = QtWidgets.QProgressBar()\n self.itemView = LocalizeView()\n self.itemView.setModel(self.proxy_model)\n self.autoScrollCB = QtWidgets.QCheckBox('Auto scroll to localizing files')\n self.autoScrollCB.setChecked(True)\n\n # tweak sizes so the widgets all line up vertically with Nuke's style\n self.modeComboBox.setMinimumHeight(self.updateBtn.sizeHint().height())\n self.pauseBtn.setMaximumSize(self.updateBtn.sizeHint())\n self.clearBtn.setMaximumSize(self.updateBtn.sizeHint())\n\n # mode menu\n self.modeLabel = QtWidgets.QLabel('Mode')\n self.modeComboBox.addItems(['On', 'Manual', 'Off'])\n\n # update menu\n self.updateMenu = QtWidgets.QMenu()\n self.act_forceUpdateAll = QtWidgets.QAction('All', self)\n self.act_forceUpdateSelectedNodes = QtWidgets.QAction('Selected', self)\n self.act_forceUpdateOnDemand = QtWidgets.QAction('On demand only', self) \n self.updateMenu.addAction(self.act_forceUpdateAll)\n self.updateMenu.addAction(self.act_forceUpdateSelectedNodes)\n self.updateMenu.addAction(self.act_forceUpdateOnDemand)\n self.updateBtn.setMenu(self.updateMenu)\n\n # clear menu\n self.clearMenu = QtWidgets.QMenu()\n self.clearMenu.addAction(QtWidgets.QAction('All local files', self, triggered=self.__delete_all_local_files))\n self.clearMenu.addAction(QtWidgets.QAction('Unused local files', self, triggered=_open_delete_dialog))\n self.clearBtn.setMenu(self.clearMenu)\n\n # filter menu\n self.filterMenu = QtWidgets.QMenu(self.filterBtn)\n self.act_filter_all = QtWidgets.QAction('All', self.filterMenu, checkable=True)\n self.act_filter_in_progress = QtWidgets.QAction('In Progress', self.filterMenu, checkable=True)\n self.act_filter_up_to_date = QtWidgets.QAction('Up to date', self.filterMenu, checkable=True)\n self.act_filter_out_of_date = QtWidgets.QAction('Out of date', self.filterMenu, checkable=True)\n self.act_filter_from_source = QtWidgets.QAction('Reading from source', self.filterMenu, checkable=True)\n self.act_filter_disabled = QtWidgets.QAction('Disabled', self.filterMenu, checkable=True)\n self.act_filter_not_localized = QtWidgets.QAction('Not Localized', self.filterMenu, checkable=True)\n\n self.act_filter_in_progress.setData(Status.IN_PROGRESS)\n self.act_filter_up_to_date.setData(Status.UP_TO_DATE)\n self.act_filter_out_of_date.setData(Status.OUT_OF_DATE)\n self.act_filter_from_source.setData(Status.READ_FROM_SOURCE)\n self.act_filter_disabled.setData(Status.DISABLED)\n self.act_filter_not_localized.setData(Status.NOT_LOCALIZED)\n for act in (self.act_filter_all, self.act_filter_in_progress, self.act_filter_up_to_date, self.act_filter_out_of_date,\n self.act_filter_from_source, self.act_filter_disabled, self.act_filter_not_localized):\n self.filterMenu.addAction(act)\n self.filterBtn.setMenu(self.filterMenu)\n\n # tooltips\n self.modeComboBox.setToolTip('Sets the global localization mode.\\nThis is the same as using the options in the Cache/Localization/Mode menu.')\n self.updateBtn.setToolTip('Forces the update of localized files.\\nThis is the same as using the options in the Cache/Localization/Force Update menu.')\n self.pauseBtn.setToolTip('Pauses/Resumes file localization.\\nThis is the same as Cache/Localization/Pause.')\n self.clearBtn.setToolTip('''Allows for clearing localized files.\\nTwo modes are supported:\n \"All local files\" - this will delete all files in {}\n \"Unused local files\" - this will only delete unused local files (same as Cache/Localization/Clear Unused Local Files)'''.format(nuke.toNode('preferences')['localCachePath'].evaluate()))\n self.filterBtn.setToolTip('Sets a view filter the table.')\n self.prefBtn.setToolTip('Open the preferences.')\n\n # layouts\n layout = QtWidgets.QVBoxLayout()\n btnLayout = QtWidgets.QHBoxLayout()\n btnLayout.addWidget(self.modeLabel)\n btnLayout.addWidget(self.modeComboBox)\n btnLayout.addWidget(self.updateBtn)\n btnLayout.addWidget(self.pauseBtn)\n btnLayout.addWidget(self.clearBtn)\n btnLayout.addStretch()\n btnLayout.addWidget(self.filterBtn)\n btnLayout.addWidget(self.prefBtn)\n layout.addLayout(btnLayout)\n #layout.addWidget(self.pBar)\n layout.addWidget(self.itemView)\n layout.addWidget(self.autoScrollCB)\n layout.setAlignment(self.autoScrollCB, QtCore.Qt.AlignRight)\n self.setLayout(layout)","def initWidgets(self):\r\n if self.autoExampleWidgets:\r\n self.initExampleWidgets()","def initGui(self):\n self.network_layer_notifier = QgepLayerNotifier(\n self.iface.mainWindow(), [\"vw_network_node\", \"vw_network_segment\"]\n )\n self.wastewater_networkelement_layer_notifier = QgepLayerNotifier(\n self.iface.mainWindow(), [\"vw_wastewater_node\", \"vw_qgep_reach\"]\n )\n self.toolbarButtons = []\n\n # Create toolbar button\n self.profileAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/wastewater-profile.svg\")),\n self.tr(\"Profile\"),\n self.iface.mainWindow(),\n )\n self.profileAction.setWhatsThis(self.tr(\"Reach trace\"))\n self.profileAction.setEnabled(False)\n self.profileAction.setCheckable(True)\n self.profileAction.triggered.connect(self.profileToolClicked)\n\n self.downstreamAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/wastewater-downstream.svg\")),\n self.tr(\"Downstream\"),\n self.iface.mainWindow(),\n )\n self.downstreamAction.setWhatsThis(self.tr(\"Downstream reaches\"))\n self.downstreamAction.setEnabled(False)\n self.downstreamAction.setCheckable(True)\n self.downstreamAction.triggered.connect(self.downstreamToolClicked)\n\n self.upstreamAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/wastewater-upstream.svg\")),\n self.tr(\"Upstream\"),\n self.iface.mainWindow(),\n )\n self.upstreamAction.setWhatsThis(self.tr(\"Upstream reaches\"))\n self.upstreamAction.setEnabled(False)\n self.upstreamAction.setCheckable(True)\n self.upstreamAction.triggered.connect(self.upstreamToolClicked)\n\n self.wizardAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/wizard.svg\")),\n \"Wizard\",\n self.iface.mainWindow(),\n )\n self.wizardAction.setWhatsThis(self.tr(\"Create new manholes and reaches\"))\n self.wizardAction.setEnabled(False)\n self.wizardAction.setCheckable(True)\n self.wizardAction.triggered.connect(self.wizard)\n\n self.connectNetworkElementsAction = QAction(\n QIcon(\n os.path.join(\n plugin_root_path(), \"icons/link-wastewater-networkelement.svg\"\n )\n ),\n QApplication.translate(\"qgepplugin\", \"Connect wastewater networkelements\"),\n self.iface.mainWindow(),\n )\n self.connectNetworkElementsAction.setEnabled(False)\n self.connectNetworkElementsAction.setCheckable(True)\n self.connectNetworkElementsAction.triggered.connect(self.connectNetworkElements)\n\n self.refreshNetworkTopologyAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/refresh-network.svg\")),\n \"Refresh network topology\",\n self.iface.mainWindow(),\n )\n self.refreshNetworkTopologyAction.setWhatsThis(\n self.tr(\"Refresh network topology\")\n )\n self.refreshNetworkTopologyAction.setEnabled(False)\n self.refreshNetworkTopologyAction.setCheckable(False)\n self.refreshNetworkTopologyAction.triggered.connect(\n self.refreshNetworkTopologyActionClicked\n )\n\n self.aboutAction = QAction(self.tr(\"About\"), self.iface.mainWindow())\n self.aboutAction.triggered.connect(self.about)\n\n self.settingsAction = QAction(self.tr(\"Settings\"), self.iface.mainWindow())\n self.settingsAction.triggered.connect(self.showSettings)\n\n self.importAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/interlis_import.svg\")),\n self.tr(\"Import from interlis\"),\n self.iface.mainWindow(),\n )\n self.importAction.setWhatsThis(self.tr(\"Import from interlis\"))\n self.importAction.setEnabled(False)\n self.importAction.setCheckable(False)\n self.importAction.triggered.connect(self.actionImportClicked)\n\n self.exportAction = QAction(\n QIcon(os.path.join(plugin_root_path(), \"icons/interlis_export.svg\")),\n self.tr(\"Export to interlis\"),\n self.iface.mainWindow(),\n )\n self.exportAction.setWhatsThis(self.tr(\"Export from interlis\"))\n self.exportAction.setEnabled(False)\n self.exportAction.setCheckable(False)\n self.exportAction.triggered.connect(self.actionExportClicked)\n\n self.datamodelInitToolAction = QAction(\n self.tr(\"Datamodel tool\"), self.iface.mainWindow()\n )\n self.datamodelInitToolAction.triggered.connect(self.showDatamodelInitTool)\n\n # Add toolbar button and menu item\n self.toolbar = QToolBar(QApplication.translate(\"qgepplugin\", \"QGEP\"))\n self.toolbar.addAction(self.profileAction)\n self.toolbar.addAction(self.upstreamAction)\n self.toolbar.addAction(self.downstreamAction)\n self.toolbar.addAction(self.wizardAction)\n self.toolbar.addAction(self.refreshNetworkTopologyAction)\n self.toolbar.addAction(self.connectNetworkElementsAction)\n\n self.iface.addPluginToMenu(\"&QGEP\", self.profileAction)\n self.iface.addPluginToMenu(\"&QGEP\", self.settingsAction)\n self.iface.addPluginToMenu(\"&QGEP\", self.aboutAction)\n\n admin_mode = QSettings().value(\"/QGEP/AdminMode\", False)\n # seems QGIS loads True as \"true\" on restart ?!\n if admin_mode and admin_mode != \"false\":\n self.toolbar.addAction(self.importAction)\n self.toolbar.addAction(self.exportAction)\n self.iface.addPluginToMenu(\"&QGEP\", self.datamodelInitToolAction)\n\n self.iface.addToolBar(self.toolbar)\n\n # Local array of buttons to enable / disable based on context\n self.toolbarButtons.append(self.profileAction)\n self.toolbarButtons.append(self.upstreamAction)\n self.toolbarButtons.append(self.downstreamAction)\n self.toolbarButtons.append(self.wizardAction)\n self.toolbarButtons.append(self.refreshNetworkTopologyAction)\n self.toolbarButtons.append(self.importAction)\n self.toolbarButtons.append(self.exportAction)\n\n self.network_layer_notifier.layersAvailable.connect(self.onLayersAvailable)\n self.network_layer_notifier.layersUnavailable.connect(self.onLayersUnavailable)\n\n # Init the object maintaining the network\n self.network_analyzer = QgepGraphManager()\n self.network_analyzer.message_emitted.connect(\n self.iface.messageBar().pushMessage\n )\n # Create the map tool for profile selection\n self.profile_tool = QgepProfileMapTool(\n self.iface, self.profileAction, self.network_analyzer\n )\n self.profile_tool.profileChanged.connect(self.onProfileChanged)\n\n self.upstream_tree_tool = QgepTreeMapTool(\n self.iface, self.upstreamAction, self.network_analyzer\n )\n self.upstream_tree_tool.setDirection(\"upstream\")\n self.upstream_tree_tool.treeChanged.connect(self.onTreeChanged)\n self.downstream_tree_tool = QgepTreeMapTool(\n self.iface, self.downstreamAction, self.network_analyzer\n )\n self.downstream_tree_tool.setDirection(\"downstream\")\n self.downstream_tree_tool.treeChanged.connect(self.onTreeChanged)\n\n self.maptool_connect_networkelements = QgepMapToolConnectNetworkElements(\n self.iface, self.connectNetworkElementsAction\n )\n\n self.wastewater_networkelement_layer_notifier.layersAvailableChanged.connect(\n self.connectNetworkElementsAction.setEnabled\n )\n\n self.processing_provider = QgepProcessingProvider()\n QgsApplication.processingRegistry().addProvider(self.processing_provider)\n\n self.network_layer_notifier.layersAdded([])","def _initUI(self) -> None:\n self._createActions()\n self._addActionsToMoveButtons()\n self._createToolBar()\n self._createStatusBar()\n self._createMainContextMenu()","def perform_setup(self):\n # pylint: disable=attribute-defined-outside-init\n self.progress_bar_dialog.show()\n self.progress_bar_dialog.update_progress(0.1)\n\n self.builder = Gtk.Builder()\n self.builder.add_from_file(pkg_resources.resource_filename(\n 'qubes_config', 'new_qube.glade'))\n\n self.main_window = self.builder.get_object('main_window')\n self.qube_name: Gtk.Entry = self.builder.get_object('qube_name')\n self.qube_label_combo: Gtk.ComboBox = \\\n self.builder.get_object('qube_label')\n\n load_theme(widget=self.main_window,\n light_theme_path=pkg_resources.resource_filename(\n 'qubes_config', 'qubes-new-qube-light.css'),\n dark_theme_path=pkg_resources.resource_filename(\n 'qubes_config', 'qubes-new-qube-dark.css'))\n\n self.progress_bar_dialog.update_progress(0.1)\n\n self.template_handler = TemplateHandler(self.builder, self.qapp)\n\n self.progress_bar_dialog.update_progress(0.1)\n\n self.qube_type_app: Gtk.RadioButton = \\\n self.builder.get_object('qube_type_app')\n self.qube_type_template: Gtk.RadioButton = \\\n self.builder.get_object('qube_type_template')\n self.qube_type_standalone: Gtk.RadioButton = \\\n self.builder.get_object('qube_type_standalone')\n self.qube_type_disposable: Gtk.RadioButton = \\\n self.builder.get_object('qube_type_disposable')\n\n self.tooltips = {\n 'qube_type_app': self.builder.get_object('qube_type_app_q'),\n 'qube_type_template':\n self.builder.get_object('qube_type_template_q'),\n 'qube_type_standalone':\n self.builder.get_object('qube_type_standalone_q'),\n 'qube_type_disposable':\n self.builder.get_object('qube_type_disposable_q')\n }\n\n self.qube_type_app.connect('toggled', self._type_selected)\n self.qube_type_template.connect('toggled', self._type_selected)\n self.qube_type_standalone.connect('toggled', self._type_selected)\n self.qube_type_disposable.connect('toggled', self._type_selected)\n\n label_dict = {}\n for label in self.qapp.labels:\n label_dict[str(label)] = {\n 'icon': f'appvm-{label}',\n 'object': str(label)\n }\n\n self.qube_label_modeler = ImageListModeler(\n combobox=self.qube_label_combo,\n value_list=label_dict\n )\n\n self.qube_name.connect('changed', self._name_changed)\n\n self.progress_bar_dialog.update_progress(0.1)\n\n self.network_selector = NetworkSelector(self.builder, self.qapp)\n\n self.progress_bar_dialog.update_progress(0.1)\n\n self.app_box_handler = ApplicationBoxHandler(\n self.builder, self.template_handler)\n\n self.progress_bar_dialog.update_progress(0.1)\n\n self.advanced_handler = AdvancedHandler(self.builder, self.qapp)\n\n self.progress_bar_dialog.update_progress(0.1)\n\n self.create_button: Gtk.Button = \\\n self.builder.get_object('create_button')\n self.create_button.connect('clicked', self._do_create_qube)\n\n self.cancel_button: Gtk.Button = \\\n self.builder.get_object('cancel_button')\n self.cancel_button.connect('clicked', self._quit)\n\n self.viewport_handler = ViewportHandler(\n self.main_window, [self.builder.get_object('main_scrolled_window')])\n\n self.main_window.connect('delete-event', self._quit)\n\n self.progress_bar_dialog.update_progress(1)\n self.progress_bar_dialog.hide()","def initUI(self):\n startbtn = QPushButton(\"Start Recroding\", self)\n startbtn.move(30, 50)\n\n stopbtn = QPushButton(\"Stop Recording\", self)\n stopbtn.move(150, 50)\n\n initbtn = QPushButton(\"Initilize\", self)\n initbtn.move(30, 100)\n\n plotbtn = QPushButton(\"Plot\", self)\n plotbtn.move(150, 100)\n\n startbtn.clicked.connect(self.start_recording)\n stopbtn.clicked.connect(self.stop_recording)\n initbtn.clicked.connect(self.init_recording)\n plotbtn.clicked.connect(self.plot_signals)\n\n self.statusBar()\n self.statusBar().showMessage('Click Init')\n\n self.setGeometry(300, 300, 290, 150)\n self.setWindowTitle('Recorder 1.0')\n self.setWindowIcon(QIcon(\"./Static/Images/icon.jpg\"))\n self.show()","def load_objects(self, queue):\n pass","def initGui(self):\n #icon_path = ':/plugins/QGisImajnetPlugin/icon.png'\n icon_path = ':/plugins/imajnet-qgis-plugin/resources/img/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Imajnet plugin'),\n callback=self.run,\n parent=self.iface.mainWindow())\n \n self._closestImageAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/closestImageMode.png',\n text=self.tr(u'Closest image'),\n callback=self.on_closestImage_click,\n checkable_flag=True,\n enabled_flag=False,\n add_to_menu=False,\n add_to_toolbar=True,\n status_tip=self.tr(u'Click on the map and get the closest image'),\n whats_this=self.tr(u'Imajnet closest image tool'),\n parent=self.iface.mainWindow())\n \n self._clickModeAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/clickMode.png',\n text=self.tr(u'Click mode'),\n callback=self.on_clickMode_click,\n checkable_flag=True,\n enabled_flag=False,\n add_to_menu=False,\n add_to_toolbar=True,\n status_tip=self.tr(u'Click on an object and get an image oriented towards that object'),\n whats_this=self.tr(u'Imajnet click mode tool'),\n parent=self.iface.mainWindow())\n \n \n self._aboutAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/about.png',\n text=self.tr(u'About'),\n callback=self.on_about_click,\n enabled_flag=True,\n add_to_menu=True,\n add_to_toolbar=False,\n parent=self.iface.mainWindow())\n \n self._helpAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/help.png',\n text=self.tr(u'Help'),\n callback=self.on_help_click,\n enabled_flag=True,\n add_to_menu=True,\n add_to_toolbar=True,\n parent=self.iface.mainWindow())\n \n self._disconnectAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/logout.png',\n text=self.tr(u'Disconnect'),\n callback=self.on_disconnect_click,\n enabled_flag=False,\n add_to_menu=True,\n add_to_toolbar=False,\n parent=self.iface.mainWindow())\n \n self._debugModeAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/debug.png',\n text=self.tr(u'Debug'),\n callback=self.on_debug_click,\n enabled_flag=False,\n add_to_menu=self.isDevMode(),\n add_to_toolbar=False,\n parent=self.iface.mainWindow())\n debugShortcutKeys = QKeySequence(\"Ctrl+I\")\n self._debugModeAction.setShortcut(debugShortcutKeys)\n #self._debugModeAction.setVisible(False)\n \n \n self._jumpForwardAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\n text=self.tr(u'jump forward'),\n callback=self.jumpForward,\n enabled_flag=True,\n add_to_menu=False,\n add_to_toolbar=False,\n parent=self.iface.mainWindow())\n jumpForwardShortcutKeys = QKeySequence(\"PgUp\")\n self._jumpForwardAction.setShortcut(jumpForwardShortcutKeys)\n \n self._jumpBackwardAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\n text=self.tr(u'jump backward'),\n callback=self.jumpBackward,\n enabled_flag=True,\n add_to_menu=False,\n add_to_toolbar=False,\n parent=self.iface.mainWindow()) \n jumpBackwardShortcutKeys = QKeySequence(\"PgDown\")\n self._jumpBackwardAction.setShortcut(jumpBackwardShortcutKeys)\n \n \n self._jumpForwardShortAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\n text=self.tr(u'jump forward short'),\n callback=self.jumpForwardShort,\n enabled_flag=True,\n add_to_menu=False,\n add_to_toolbar=False,\n parent=self.iface.mainWindow())\n jumpForwardShortShortcutKeys = QKeySequence(\"Shift+PgUp\")\n self._jumpForwardShortAction.setShortcut(jumpForwardShortShortcutKeys)\n \n self._jumpBackwardShortAction = self.add_action(\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\n text=self.tr(u'jump backward short'),\n callback=self.jumpBackwardShort,\n enabled_flag=True,\n add_to_menu=False,\n add_to_toolbar=False,\n parent=self.iface.mainWindow()) \n jumpBackwardShortShortcutKeys = QKeySequence(\"Shift+PgDown\")\n self._jumpBackwardShortAction.setShortcut(jumpBackwardShortShortcutKeys)","def create_widgets(self):","def initialize(self):\n self.setWindowTitle(\"Playlist Maker\")\n self.setGeometry(0,0, 800, 494)\n self.mbox = QVBoxLayout()\n self.hbox = QHBoxLayout()\n self.hbtnbox = QHBoxLayout()","def Queues(self):\n if not self._queues:\n queues = AimsQueueWidget(self.iface.mainWindow())\n self._dockWindow = DockWindow(self.iface.mainWindow(),queues,\"AimsQueues\",\"Aims Queues\")\n self._queues = queues\n self._dockWindow.unloadPlugin.connect(self.unload)\n return self._queues","def initCentralUic(self):\n self.initFileTableWidget()\n self.initViewerStack()\n self.splitter.setSizes([150, 850])","def initUI(self):\n centralwidget = QtWidgets.QWidget()\n self.setCentralWidget(centralwidget)\n\n person_table = PersonTable(self)\n person_table.embed(self.sql)\n\n # Box Layout to organize our GUI\n lay = QtWidgets.QVBoxLayout(centralwidget)\n lay.addWidget(person_table)\n self.setGeometry(0, 0, person_table.width() + 20, person_table.height() + 20)\n self.person_table = person_table\n self.show()","def __init__(self):\n\n self.boxes = [\n urwid.AttrMap(urwid.Text('QMap'), 'menu header'),\n urwid.Divider('=')\n ]\n\n self.widget = urwid.Padding(None, align='center')\n self.update()\n\n BaseTimedWidgetWrap.__init__(self, self.widget)","def init_UI(self):\r\n\t\t# buttons\r\n\t\tself.dlg_btns.accepted.connect(self.on_ok_click)\r\n\t\tself.dlg_btns.rejected.connect(self.on_cancel_click)\r\n\t\tself.source_img_browse_btn.clicked.connect(self.on_source_img_browse_btn_click)\r\n\t\tself.sink_dir_browse_btn.clicked.connect(self.on_sink_dir_browse_btn_click)\r\n\r\n\t\t# layouts\r\n\t\tself.source_img_layout.addWidget(self.source_img_entry)\r\n\t\tself.source_img_layout.addWidget(self.source_img_browse_btn)\r\n\r\n\t\tself.sink_dir_layout.addWidget(self.sink_dir_entry)\r\n\t\tself.sink_dir_layout.addWidget(self.sink_dir_browse_btn)\r\n\r\n\t\tself.sink_db_name_layout.addWidget(self.sink_db_name_entry)\r\n\r\n\t\tself.form_layout.addRow(\"Image Array Path (.npy): \", self.source_img_layout)\r\n\t\t# if continuing an existing case\r\n\t\tif self.existing_case:\r\n\t\t\tself.source_db_browse_btn.clicked.connect(self.on_source_db_browse_btn_click)\r\n\t\t\tself.source_db_layout.addWidget(self.source_db_entry)\r\n\t\t\tself.source_db_layout.addWidget(self.source_db_browse_btn)\r\n\t\t\tself.form_layout.addRow(\"Source Database Path (.db): \", self.source_db_layout)\r\n\t\tself.form_layout.addRow(\"Sink Directory (folder): \", self.sink_dir_layout)\r\n\t\tself.form_layout.addRow(\"Sink Database Name: \", self.sink_db_name_layout)\r\n\r\n\t\tself.dlg_layout.addLayout(self.form_layout)\r\n\t\tself.dlg_layout.addWidget(self.dlg_btns)\r\n\r\n\t\t# set file dialog properties\r\n\t\tself.setLayout(self.dlg_layout)\r\n\t\tself.setGeometry(self.x, self.y, self.width, self.height)\r\n\t\tself.setWindowTitle(self.title)","def initGui(self):\n\n icon_path = ':/plugins/LidarProcessor/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'LiDAR Operations'),\n callback=self.run,\n parent=self.iface.mainWindow())","def initGui(self):\n\n icon_path = ''\n self.add_action(\n icon_path,\n text=self.tr(u'圖資操作處理工具v3.0'),\n callback=self.run,\n parent=self.iface.mainWindow())\n \n \n\n #照片EXIF定位ImportPhotos初始參數\n self.clickPhotos = self.add_action(\n icon_path,\n text=self.tr(u'檢視照片'),\n callback=self.mouseClick,\n parent=self.iface.mainWindow())\n \n self.clickPhotos.setCheckable(True)\n self.clickPhotos.setEnabled(True)\n # self.iface.addPluginToMenu(TFB_Tools3.description(), self.action)\n self.listPhotos = []\n self.layernamePhotos = [] \n self.toolMouseClick = MouseClick(self.canvas, self)\n \n\n self.fields = ['ID', 'Name', 'Date', 'Time', 'Lon', 'Lat', 'Altitude', 'North', 'Azimuth', 'Camera Maker',\n 'Camera Model', 'Title', 'Comment', 'Path', 'RelPath', 'Timestamp']\n\n self.extension_switch = {\n \".shp\": \"ESRI Shapefile\",\n \".geojson\": \"GeoJSON\",\n \".gpkg\":\"GPKG\",\n \".csv\": \"CSV\",\n \".kml\": \"KML\",\n \".tab\": \"MapInfo File\"\n }\n\n self.extension_switch2 = {\n \"ESRI Shapefile (*.shp *.SHP)\": \".shp\",\n \"GeoJSON (*.geojson *.GEOJSON)\": \".geojson\",\n \"GeoPackage (*.gpkg *.GPKG)\":\".gpkg\",\n \"Comma Separated Value (*.csv *.CSV)\": \".csv\",\n \"Keyhole Markup Language (*.kml *.KML)\": \".kml\",\n \"Mapinfo TAB (*.tab *.TAB)\": \".tab\"\n }\n\n self.extension_switch_types = {\n \".shp\": \"ESRI Shapefile\",\n \".geojson\": \"GeoJSON\",\n \".gpkg\":\"GPKG\",\n \".csv\": \"CSV\",\n \".kml\": \"KML\",\n \".tab\": \"MapInfo File\"\n }\n\n ############################\n\n self.pointEmitter = QgsMapToolEmitPoint(self.iface.mapCanvas())\n self.iface.mapCanvas().setMapTool( self.pointEmitter )","def _loader(q: Queue):\n black = '#000000'\n white = '#ffffff'\n log_h = 100\n logo_fp = APP.R.img('wet_logo.png')\n log_font_size = 9\n\n class Screen:\n \"\"\"\n uses win32 api to get active screen dimensions in pixels and inches, w/h ratio, dpi\n only works on windows\n \"\"\"\n _MM_TO_IN = 0.0393700787\n\n def __init__(self, window: tk.Tk) -> None:\n ctypes.windll.shcore.SetProcessDpiAwareness(1)\n self.resolution_w, self.resolution_h = [win32api.GetSystemMetrics(i) for i in [0, 1]]\n dc = ctypes.windll.user32.GetDC(window.winfo_id())\n self.width, self.height = [ctypes.windll.gdi32.GetDeviceCaps(dc, v) * self._MM_TO_IN for v in\n [4, 6]]\n self.w_h_ratio, self.dpi = self.width / self.height, int(self.resolution_w / self.width)\n log.debug(f'measured {repr(self)}')\n\n def __repr__(self) -> str:\n inches = f'{round(self.width, 2)}x{round(self.height, 2)}in'\n return f'Screen({self.resolution_w}x{self.resolution_h}px {self.dpi}dpi {inches})'\n\n class Logo(tk.Label):\n def __init__(self, parent, image) -> None:\n kwargs = dict(bg=black, anchor='n', image=image)\n super().__init__(parent, **kwargs)\n self.pack(fill=tk.BOTH, expand=1)\n\n class LogField(tk.Text):\n _max_length = 6\n\n @staticmethod\n def truncate(line: str, limit: int) -> str:\n \"\"\"\n if a string is longer than limit, return a substring from start-limit\n \"\"\"\n if len(line) > limit:\n return line[:limit]\n return line\n\n def add_line(self, line: str) -> None:\n self['state'] = 'normal'\n if self._length == self._max_length:\n self.delete(1.0, 2.0)\n self._length -= 1\n if self.index('end-1c') != '1.0':\n self.insert('end', '\\n')\n self.insert('end', self._truncate(line))\n self._length += 1\n self['state'] = 'disabled'\n\n def __init__(self, parent: 'Loader', w: int) -> None:\n self._length = 0\n\n self.font = font.nametofont('TkFixedFont')\n self.font.config(size=log_font_size)\n limit = int(w / self.font.measure('m')) - 1\n self._truncate = partial(self.truncate, limit=limit)\n\n super().__init__(parent, state='disabled', wrap='none', relief='flat', cursor='arrow',\n fg=white, bg=black, selectbackground=black, width=w,\n inactiveselectbackground=black, font=self.font, height=log_h)\n self.pack(fill=tk.BOTH, expand=1)\n\n class Loader(tk.Tk):\n # ? https://stackoverflow.com/a/16115616\n\n def quit(self) -> None:\n if self.poll_after:\n self.after_cancel(self.poll_after)\n\n super().quit()\n\n def __post_init__(self) -> None:\n self.screen = Screen(self)\n self._logo_img = PIL.ImageTk.PhotoImage(self._logo_img)\n\n s_w, s_h = self.screen.resolution_w, self.screen.resolution_h\n w, h = int(self._logo_img.width()), int(self._logo_img.height())\n x_off, y_off = int((s_w // 2) - (w // 2)), int((s_h // 2) - ((h + log_h) // 2))\n self.geometry(f'{w}x{h + log_h}+{x_off}+{y_off}')\n\n self.logo = Logo(self, self._logo_img)\n self.log = LogField(self, w)\n\n self.poll_after = self.after(100, self.poll)\n log.info('started polling')\n\n self.mainloop()\n\n def __init__(self, _q: Queue) -> None:\n self.q = _q\n self._logo_img = PIL.Image.open(logo_fp)\n\n super().__init__()\n self.protocol('WM_DELETE_WINDOW', self.quit)\n self.attributes('-topmost', True)\n self.overrideredirect(True)\n\n self.__post_init__()\n\n def poll(self) -> None:\n while 1:\n try:\n msg = self.q.get()\n\n except Empty:\n break\n\n else:\n self.log.add_line(msg)\n if msg == '$STOP_LOADER$':\n return self.quit()\n\n Loader(q)","def setup(self):\n ScriptedLoadableModuleWidget.setup(self)\n\n globalPath, _ = os.path.split( os.path.splitext( slicer.modules.regularizedfastmarching.path )[0] )\n self.globalPath = globalPath + \"/Resources/SegmentationFastMarching/\"\n self.volumesPath = self.globalPath + \"Volumes/\"\n self.seedsCsvPath = self.globalPath + \"SeedsLabels/\"\n self.seedsPath = self.globalPath + \"Seeds/\"\n self.regularizationsPath = self.globalPath + \"Regularizations/\"\n self.segmentationsPath = self.globalPath + \"Segmentations/\"\n\n self.logic = RegularizedFastMarchingLogic()\n # self.logic.previousVolumeName = None\n # self.logic.imgLabel = np.array([])\n # self.logic.previousImgIds = np.array([])\n # self.logic.imgDist = np.array([])\n\n # Hide the Slicer Logo to increase space\n slicer.util.findChild(slicer.util.mainWindow(), 'LogoLabel').visible = False\n \n #\n #region Restart Slicer Area\n #\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\n parametersCollapsibleButton.text = \"Reload\"\n self.layout.addWidget(parametersCollapsibleButton)\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\n self.restartSlicerButton = qt.QPushButton(\"Restart Slicer\")\n self.restartSlicerButton.connect('clicked(bool)', slicer.util.restart)\n parametersFormLayout.addRow(self.restartSlicerButton)\n #endregion \n\n #\n #region Load Data Area\n #\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\n parametersCollapsibleButton.text = \"Seeds\"\n self.layout.addWidget(parametersCollapsibleButton)\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\n \n #\n # File name volume .nii\n # \n # self.volueNameComboBox = qt.QComboBox() \n # self.volueNameComboBox = self.fillComboBox(self.volueNameComboBox, self.volumesPath, \".nii\")\n # parametersFormLayout.addRow(\"Volumes .nii: \", self.volueNameComboBox)\n \n #\n # Load volume buttons\n #\n # horizontalLayout = qt.QHBoxLayout()\n # self.loadBrainVolumeButton = qt.QPushButton(\"Load Example Volume\")\n # self.loadBrainVolumeButton.enabled = True\n # horizontalLayout.addWidget(self.loadBrainVolumeButton)\n \n #\n # Load custom volumes buttons\n #\n # self.loadCustomVolumeButton = qt.QPushButton(\"Load Selected Volume\")\n # self.loadCustomVolumeButton.enabled = True\n # horizontalLayout.addWidget(self.loadCustomVolumeButton) \n # parametersFormLayout.addRow(horizontalLayout)\n \n \n #\n # Add vertical spacing\n # \n # verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n # parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Seeds files and a text line to load / save seeds from the scene\n #\n horizontalLayout = qt.QHBoxLayout()\n fileNameSeedsLabel = qt.QLabel(\"Seeds: \")\n self.fileNameSeedsComboBox = qt.QComboBox() \n self.fileNameSeedsComboBox = self.fillComboBox(self.fileNameSeedsComboBox, self.seedsPath, \".seed\") \n self.fileNameSeedsLineEdit = qt.QLineEdit()\n horizontalLayout.addWidget(fileNameSeedsLabel)\n horizontalLayout.addWidget(self.fileNameSeedsComboBox)\n horizontalLayout.addWidget(self.fileNameSeedsLineEdit)\n self.fileNameSeedsComboBox.currentTextChanged.connect(self.setSelectedSeedsFile)\n self.setSelectedSeedsFile(self.fileNameSeedsComboBox.currentText)\n parametersFormLayout.addRow(horizontalLayout)\n \n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Save markers Button\n #\n horizontalLayout = qt.QHBoxLayout()\n self.saveMarkersButton = qt.QPushButton(\"Save markers\")\n self.saveMarkersButton.toolTip = \"Save the markers in a fcsv file\"\n self.saveMarkersButton.enabled = True\n horizontalLayout.addWidget(self.saveMarkersButton)\n \n #\n # Load markers Button\n #\n self.loadMarkersButton = qt.QPushButton(\"Load markers\")\n self.loadMarkersButton.toolTip = \"Load the markers from fcsv file.\"\n self.loadMarkersButton.enabled = True\n horizontalLayout.addWidget(self.loadMarkersButton) \n \n #\n # Clear markups Button\n #\n self.clearButton = qt.QPushButton(\"Clear all markups\")\n self.clearButton.toolTip = \"Remove all fiducial markers.\"\n self.clearButton.enabled = True\n horizontalLayout.addWidget(self.clearButton)\n parametersFormLayout.addRow(horizontalLayout)\n \n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 25, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Fiducial placement button\n #\n \"\"\"\n Add this markup in the markupsList and rename it depending on seeds combobox's value\n \"\"\"\n @vtk.calldata_type(vtk.VTK_INT)\n def onMarkupAdded(caller, event, index): \n markupsNode = caller\n # Label, Name\n seedTokens = self.currentSeedNameComboBox.currentText.replace(\" \", \"\").split(\":\")\n markupsNode.SetNthFiducialLabel(index, seedTokens[1])\n markupsNode.SetNthControlPointDescription(index, seedTokens[0])\n \n self.w=slicer.qSlicerMarkupsPlaceWidget()\n self.w.setMRMLScene(slicer.mrmlScene)\n \n markupsNode = slicer.mrmlScene.GetFirstNodeByName(\"MarkupsFiducial\")\n if markupsNode == None: \n markupsNode = slicer.mrmlScene.AddNewNodeByClass(\"vtkMRMLMarkupsFiducialNode\")\n markupsNode.CreateDefaultDisplayNodes()\n \n # On markup added event: format the added markup\n markupsNode.AddObserver(slicer.vtkMRMLMarkupsNode.PointPositionDefinedEvent, onMarkupAdded)\n self.w.setCurrentNode(markupsNode)\n # Hide all buttons and only show place button\n self.w.buttonsVisible=True\n self.w.placeButton().show()\n self.w.show()\n parametersFormLayout.addRow(self.w)\n \n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # File name data's seeds\n # \n self.fileNameDataComboBox = qt.QComboBox() \n self.fileNameDataComboBox = self.fillComboBox(self.fileNameDataComboBox, self.seedsCsvPath, \".csv\") \n self.fileNameDataComboBox.currentTextChanged.connect(self.setSeedsLabelFromFile)\n parametersFormLayout.addRow(\"Seeds labels csv: \", self.fileNameDataComboBox)\n\n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n horizontalLayout = qt.QHBoxLayout()\n # Seeds label \n self.seedsLabelText = qt.QLabel()\n self.seedsLabelText.text = \"Labels:\"\n horizontalLayout.addWidget(self.seedsLabelText)\n \n # ComboxBox containing names and labels\n self.currentSeedNameComboBox= qt.QComboBox()\n \n # Add labels depending on first csv file found\n self.setSeedsLabelFromFile(self.fileNameDataComboBox.currentText)\n horizontalLayout.addWidget(self.currentSeedNameComboBox)\n \n self.clearOrganButton = qt.QPushButton(\"Clear this organ\")\n self.clearOrganButton.toolTip = \"Remove all fiducial markers for this organ.\"\n self.clearOrganButton.enabled = True\n horizontalLayout.addWidget(self.clearOrganButton)\n parametersFormLayout.addRow(horizontalLayout)\n \n #endregion\n\n\n #\n #region Segmentation Parameters Area\n #\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\n parametersCollapsibleButton.text = \"Parameters\"\n self.layout.addWidget(parametersCollapsibleButton)\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\n\n #\n # input volume selector\n #\n self.inputSelector = slicer.qMRMLNodeComboBox()\n self.inputSelector.nodeTypes = [\"vtkMRMLScalarVolumeNode\"]\n self.inputSelector.selectNodeUponCreation = True\n self.inputSelector.addEnabled = False\n self.inputSelector.removeEnabled = False\n self.inputSelector.noneEnabled = False\n self.inputSelector.showHidden = False\n self.inputSelector.showChildNodeTypes = False\n self.inputSelector.setMRMLScene( slicer.mrmlScene )\n self.inputSelector.setToolTip( \"Pick the input to the algorithm.\" )\n parametersFormLayout.addRow(\"Input Volume: \", self.inputSelector)\n\n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Segmentation distance to stop the algo\n #\n self.distance = ctk.ctkSliderWidget()\n self.distance.singleStep = 10\n self.distance.minimum = 0\n self.distance.maximum = 1000\n self.distance.value = 100\n self.distance.setToolTip(\"Set the maximum geodesic distance allowed from a seed\")\n parametersFormLayout.addRow(\"Distance\", self.distance)\n \n #\n # Margin to build masks\n #\n self.marginMask = ctk.ctkSliderWidget()\n self.marginMask.singleStep = 1\n self.marginMask.minimum = 0\n self.marginMask.maximum = 50\n self.marginMask.value = 15\n self.marginMask.setToolTip(\"Margin used to build masks for each seed\")\n parametersFormLayout.addRow(\"Mask Margin (voxel)\", self.marginMask)\n\n #\n # Gamma to prevent the seeds to cross bridges between organs\n #\n self.gammaSlider = qt.QSlider(qt.Qt.Horizontal)\n self.gammaSlider.setRange(0, 1000)\n self.gammaSlider.setSingleStep(1)\n self.gammaSlider.setPageStep(10)\n self.gammaSlider.setValue(25)\n\n self.gammaSpinBox = qt.QDoubleSpinBox()\n self.gammaSpinBox.setDecimals(3)\n self.gammaSpinBox.setRange(0, 1)\n self.gammaSpinBox.setSingleStep(0.001)\n self.gammaSpinBox.setValue(0.025)\n\n self.gammaSlider.valueChanged.connect(self.setGammaValueFromSlider)\n self.gammaSpinBox.valueChanged.connect(self.setGammaValueFromSpinBox)\n \n horizontalLayout = qt.QHBoxLayout()\n horizontalLayout.addWidget(self.gammaSlider)\n horizontalLayout.addWidget(self.gammaSpinBox)\n\n parametersFormLayout.addRow(\"Regularization weight\", horizontalLayout)\n \n #\n # Regularization radius\n #\n self.regularizationDiameter = ctk.ctkSliderWidget()\n self.regularizationDiameter.singleStep = 1\n self.regularizationDiameter.minimum = 1\n self.regularizationDiameter.maximum = 20\n self.regularizationDiameter.value = 4\n self.regularizationDiameter.setToolTip(\"Margin used to build masks for each seed\")\n parametersFormLayout.addRow(\"Regularization Diameter (voxels)\", self.regularizationDiameter)\n \n #\n # Threshold to prevent the seeds to spread over a range intensities\n #\n self.minThresholdSlider = ctk.ctkSliderWidget()\n self.minThresholdSlider.singleStep = 1\n self.minThresholdSlider.minimum = 0\n self.minThresholdSlider.maximum = 255\n self.minThresholdSlider.value = 40\n self.minThresholdSlider.setToolTip(\"Seeds cannot spread below this value\")\n self.minThresholdSlider.valueChanged.connect(self.setMinThresholdValue)\n parametersFormLayout.addRow(\"Min Threshold (digital level)\", self.minThresholdSlider)\n\n self.maxThresholdSlider = ctk.ctkSliderWidget()\n self.maxThresholdSlider.singleStep = 1\n self.maxThresholdSlider.minimum = 0\n self.maxThresholdSlider.maximum = 255\n self.maxThresholdSlider.value = 255\n self.maxThresholdSlider.setToolTip(\"Seeds cannot spread over this value\")\n self.maxThresholdSlider.valueChanged.connect(self.setMaxThresholdValue)\n parametersFormLayout.addRow(\"Max Threshold (digital level)\", self.maxThresholdSlider)\n\n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Check box to keep or not last segmentation\n #\n self.removeLastSegmentationCheckBox = qt.QCheckBox(\"\")\n self.removeLastSegmentationCheckBox.setChecked(True)\n parametersFormLayout.addRow(\"Remove last segmentation\", self.removeLastSegmentationCheckBox)\n \n #\n # Show the background seed of the next segmentation\n #\n self.showBackGroundCheckBox = qt.QCheckBox(\"\")\n self.showBackGroundCheckBox.setChecked(False)\n parametersFormLayout.addRow(\"Show background\", self.showBackGroundCheckBox)\n \n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n \n #\n # Segment Button\n #\n self.segmentButton = qt.QPushButton(\"Segment\")\n self.segmentButton.toolTip = \"Run the algorithm.\"\n self.segmentButton.enabled = False\n parametersFormLayout.addRow(self.segmentButton)\n #endregion \n\n #\n #region Load Save segmentations\n #\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\n parametersCollapsibleButton.text = \"Segmentations\"\n self.layout.addWidget(parametersCollapsibleButton)\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\n\n #\n # Checkbox to save the segmentation using the labels \n #\n self.saveByLabelsCheckBox = qt.QCheckBox(\"\")\n self.saveByLabelsCheckBox.setChecked(True)\n parametersFormLayout.addRow(\"Save by labels\", self.saveByLabelsCheckBox)\n\n #\n # Checkbox to save the segmentation using the voxels intensities \n #\n self.saveByIntensitiesCheckBox = qt.QCheckBox(\"\")\n self.saveByIntensitiesCheckBox.setChecked(False)\n parametersFormLayout.addRow(\"Save by intensities\", self.saveByIntensitiesCheckBox)\n\n #\n # Checkbox to generate segments data \n #\n self.generateDataCsvCheckBox = qt.QCheckBox(\"\")\n self.generateDataCsvCheckBox.setChecked(False)\n parametersFormLayout.addRow(\"Generate segments data CSV\", self.generateDataCsvCheckBox)\n\n\n #\n # Name of the segmentation to save\n #\n self.saveSegmentationName = qt.QLineEdit()\n self.saveSegmentationName.textChanged.connect(self.onSaveSegmentationFileChange)\n parametersFormLayout.addRow(\"Segmentation name:\", self.saveSegmentationName) \n\n #\n # Save segmentationButton Button\n #\n self.saveSegmentationButton = qt.QPushButton(\"Save segmentation\")\n self.saveSegmentationButton.toolTip = \"Save this segmentation with used parameters.\"\n parametersFormLayout.addRow(self.saveSegmentationButton) \n\n #\n # Add vertical spacing\n # \n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\n parametersFormLayout.addItem(verticalSpacer)\n\n horizontalLayout = qt.QHBoxLayout()\n self.segmentationLoadLabelText = qt.QLabel()\n self.segmentationLoadLabelText.text = \"Segmentation files:\"\n horizontalLayout.addWidget(self.segmentationLoadLabelText)\n \n #\n # Segmentation files \n # \n self.segmentationFilesComboBox = qt.QComboBox() \n self.segmentationFilesComboBox = self.fillComboBox(self.segmentationFilesComboBox, self.segmentationsPath, \".seg.nrrd\")\n horizontalLayout.addWidget(self.segmentationFilesComboBox)\n parametersFormLayout.addRow(horizontalLayout)\n \n #\n # Load segmentationButton Button\n #\n self.loadSegmentationButton = qt.QPushButton(\"Load segmentation\")\n self.loadSegmentationButton.toolTip = \"Load the segmenation from this file.\"\n parametersFormLayout.addRow(self.loadSegmentationButton) \n #end region\n \n\n #\n #region Connections & Shortcuts\n #\n # self.loadBrainVolumeButton.connect('clicked(bool)', self.onLoadBrainVolumeButton)\n # self.loadCustomVolumeButton.connect('clicked(bool)', self.onLoadCustomVolumeButton)\n \n self.clearButton.connect('clicked(bool)', self.onClearButton)\n self.clearOrganButton.connect('clicked(bool)', self.onClearOrganButton)\n self.segmentButton.connect('clicked(bool)', self.onSegmentButton)\n self.saveSegmentationButton.connect('clicked(bool)', self.onSaveSegmentationButton)\n self.loadSegmentationButton.connect('clicked(bool)', self.onLoadSegmentationButton)\n self.saveMarkersButton.connect('clicked(bool)', self.onSaveMarkersButton)\n self.loadMarkersButton.connect('clicked(bool)', self.onLoadMarkersButton)\n self.inputSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.onSelect)\n \n # Keyboard shortcut bind: press ctrl button to add markup \n markupShortcut = qt.QShortcut(slicer.util.mainWindow())\n markupShortcut.setKey( qt.QKeySequence(\"w\") )\n markupShortcut.connect('activated()', self.addMarkupcallback)\n \n markupShortcut = qt.QShortcut(slicer.util.mainWindow())\n markupShortcut.setKey( qt.QKeySequence(\"x\") )\n markupShortcut.connect('activated()', self.addBgMarkupcallback)\n #endregion \n\n # Refresh Apply button state\n self.onSelect()\n \n # Refresh segmentation save button state\n self.onSaveSegmentationFileChange()","def initialize_gui(self) -> None:\n # pymol.Qt provides the PyQt5 interface\n from PyQt5 import QtWidgets\n from PyQt5.uic import loadUi\n # from pymol.Qt.utils import loadUi\n\n # populate the QMainWindow from our *.ui file\n uifile = os.path.join(os.path.dirname(__file__), 'PyMOL-KVFinder-web-tools.ui')\n loadUi(uifile, self)\n\n # ScrollBars binded to QListWidgets in Descriptors\n scroll_bar_volume = QtWidgets.QScrollBar(self)\n self.volume_list.setVerticalScrollBar(scroll_bar_volume)\n scroll_bar_area = QtWidgets.QScrollBar(self)\n self.area_list.setVerticalScrollBar(scroll_bar_area)\n scroll_bar_residues = QtWidgets.QScrollBar(self)\n self.residues_list.setVerticalScrollBar(scroll_bar_residues)\n\n # about text\n self.about_text.setHtml(about_text)\n\n ########################\n ### Buttons Callback ###\n ########################\n\n # hook up QMainWindow buttons callbacks\n self.button_run.clicked.connect(self.run)\n self.button_exit.clicked.connect(self.close)\n self.button_restore.clicked.connect(self.restore)\n self.button_grid.clicked.connect(self.show_grid)\n \n # hook up Parameters button callbacks\n self.button_browse.clicked.connect(self.select_directory)\n self.refresh_input.clicked.connect(lambda: self.refresh(self.input))\n \n # hook up Search Space button callbacks\n # Box Adjustment\n self.button_draw_box.clicked.connect(self.set_box)\n self.button_delete_box.clicked.connect(self.delete_box)\n self.button_redraw_box.clicked.connect(self.redraw_box)\n self.button_box_adjustment_help.clicked.connect(self.box_adjustment_help)\n # Ligand Adjustment\n self.refresh_ligand.clicked.connect(lambda: self.refresh(self.ligand))\n\n # hook up methods to results tab\n # Jobs\n self.available_jobs.currentIndexChanged.connect(self.fill_job_information)\n self.button_show_job.clicked.connect(self.show_id)\n self.button_add_job_id.clicked.connect(self.add_id)\n # Visualization\n self.button_browse_results.clicked.connect(self.select_results_file)\n self.button_load_results.clicked.connect(self.load_results)\n self.volume_list.itemSelectionChanged.connect(lambda list1=self.volume_list, list2=self.area_list: self.show_cavities(list1, list2))\n self.area_list.itemSelectionChanged.connect(lambda list1=self.area_list, list2=self.volume_list: self.show_cavities(list1, list2))\n self.residues_list.itemSelectionChanged.connect(self.show_residues)","def initGui(self):\n # Create an action to start capturing polygons and add it to the digitize toolbar\n icon = QIcon()\n icon.addPixmap(QPixmap(_fromUtf8(\":/plugins/olainterface/lo-logo.png\")), QIcon.Normal, QIcon.Off)\n self.openDialogAction = QAction(icon, QString(\"Land Observatory\"), self.iface.mainWindow())\n self.iface.pluginToolBar().addAction(self.openDialogAction)\n\n # Connect to signals for button behaviour\n self.connect(self.openDialogAction, SIGNAL(\"triggered()\"), self.openDialog)","def initUI(self):\n\n self.wid = RosGenWidget()\n self.setCentralWidget(self.wid)\n menubar = self.menuBar()\n fileMenu = menubar.addMenu('&Файл')\n editMenu = menubar.addMenu('&Редактирование')\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\n self.statusbar = self.statusBar()\n self.statusbar.showMessage('Ожидание данных')\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\n self.setGeometry(600, 200, 700, 400)\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\n self.show()","def initGui(self):\n\n icon_path = ':/plugins/qgsCsvAzmDist2LatLon/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'csvAzmDist2LatLon'),\n callback=self.run,\n parent=self.iface.mainWindow())\n \n self.dlg.pbInCsv.clicked.connect(self.select_input_file)\n self.dlg.pbOutCsv.clicked.connect(self.select_output_file)\n self.dlg.pbCalc.clicked.connect(self.calculations)","def create_widgets( self ):","def initUI(self):\n # Setting the main layout as Vertical.\n self.mainLayout = QHBoxLayout()\n\n # Create title.\n self.title = QLabel(self.__name + \" : \")\n\n # Add description as tooltip.\n self.title.setToolTip(self.__description)\n\n # Add title to main layout.\n self.mainLayout.addWidget(self.title)\n\n # Create ComboBox.\n self.dropDown = QComboBox()\n\n # Add datas to drop down.\n self.dropDown.addItems(self.__datas)\n\n # Set default index to dropdown.\n self.dropDown.setCurrentIndex(self.__currentValue)\n\n # Connect dropdown with update method.\n self.dropDown.currentIndexChanged.connect(self.changeCurrentValue)\n\n # Add ComboBox to main layout.\n self.mainLayout.addWidget(self.dropDown)\n\n # Add the main layout to the window.\n self.setLayout(self.mainLayout)","def __init__(self, *args, **kwargs):\n\n super(FlirCameraWidget, self).__init__(*args, **kwargs)\n #self.loadForm()\n self.window = Ui_MainWindow()\n self.window.setupUi(self)\n\n self.initUI()\n Styles(self)","def initGui(self):\n\n icon_path = ':/plugins/new_raptor/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Add New Raptor nest'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def __init__(self):\n self._queue_items = []","def initGui(self): # pylint:disable=invalid-name\n\n icon_path = os.path.join(self.plugin_dir, \"icons\")\n\n self.add_action(\n os.path.join(icon_path, \"icon.png\"),\n text=self.translate(\"Load Data\"),\n callback=self.run,\n parent=self.iface.mainWindow(),\n )\n\n # Plugin Dialog\n self.dlg.uListOptions.itemClicked.connect(self.show_selected_option)\n self.dlg.uListOptions.itemClicked.emit(self.dlg.uListOptions.item(0))\n\n model = QStandardItemModel()\n self.dlg.uCRSCombo.setModel(model)\n self.dlg.uCRSCombo.currentIndexChanged.connect(self.layer_crs_selected)\n\n self.dlg.uLabelWarning.setStyleSheet(\"color:red\")\n self.dlg.uWarningSettings.setStyleSheet(\"color:red\")\n\n self.add_item(title=\"ALL\", icon=os.path.join(icon_path, \"all.png\"))\n self.add_item(title=\"WFS\", icon=os.path.join(icon_path, \"wfs.png\"))\n self.add_item(title=\"WMTS\", icon=os.path.join(icon_path, \"wmts.png\"))\n self.add_item(title=\"Settings\", icon=os.path.join(icon_path, \"settings.png\"))\n self.add_item(title=\"Help\", icon=os.path.join(icon_path, \"help.png\"))\n self.add_item(title=\"About\", icon=os.path.join(icon_path, \"about.png\"))\n\n # set table model\n self.set_table_model_view()\n\n # set help html\n self.dlg.hHelpHtml.setOpenExternalLinks(True)\n help_file = os.path.join(self.plugin_dir, \"help.html\")\n with open(help_file, \"r\", encoding=\"utf-8\") as file:\n help_html = file.read()\n help_html.format(self.plugin_dir)\n self.dlg.hHelpHtml.setHtml(help_html.format(icon_path))\n\n # set about html\n self.dlg.hAboutHtml.setOpenExternalLinks(True)\n about_file = os.path.join(self.plugin_dir, \"about.html\")\n with open(about_file, \"r\", encoding=\"utf-8\") as file:\n about_html = file.read()\n about_html.format(self.plugin_dir)\n self.dlg.hAboutHtml.setHtml(about_html.format(icon_path))\n\n # populate settings\n # default data services to combo\n\n self.dlg.uComboBoxDomain.addItems(SER)\n\n # settings signals\n self.dlg.uBtnAddDomain.clicked.connect(self.add_new_domain)\n for entry in range(1, 11):\n getattr(self.dlg, \"uBtnSaveDomain{0}\".format(entry)).clicked.connect(\n self.save_domain\n )\n getattr(self.dlg, \"uBtnRemoveDomain{0}\".format(entry)).clicked.connect(\n self.save_domain\n )\n self.load_settings()","def assignWidgets(self):\n self.buttonBox.accepted.connect(self.runAll)\n self.buttonBox.rejected.connect(self.exitSystem)\n self.FilesButton.clicked.connect(self.FileToRun)\n self.OutputButton.clicked.connect(self.OutPutLocation)","def __init__(self):\r\n super().__init__()\r\n self.init_ui()","def initGui(self):\n from p4_view import Gui\n self.updateStatus(\"Launching GUI...\")\n self.gui = Gui(self, self.lmap)\n self.gui.setStart(self.cfg[\"START\"])\n self.gui.setGoal(self.cfg[\"GOAL\"])\n self.gui.setPossGoals(self.cfg[\"POSS_GOALS\"])\n #GHD\n self.gui.setMapName(self.cfg[\"MAP_FILE\"])\n self.updateStatus(\"OK\")\n self.gui.mainloop()","def process_widgets(self):\r\n\r\n self.runmode_menu.add_radiobutton(label=\"Graphical User Interface\", value=0, variable=self.gui_menu_var,\r\n command=self.disable_debugging_mode)\r\n self.runmode_menu.add_radiobutton(label=\"Command Line Interface\", value=1, variable=self.gui_menu_var,\r\n command=lambda gui=self: load_cli(self))\r\n self.runmode_menu.add_radiobutton(label=\"Debugging Mode (GUI + CLI)\", value=2, variable=self.gui_menu_var,\r\n command=self.enable_debugging_mode)\r\n\r\n # Placing all the submenus\r\n self.filemenu.add_cascade(label=\"Run Mode\", menu=self.runmode_menu)\r\n self.menubar.add_cascade(label=\"File\", menu=self.filemenu)\r\n\r\n self.config(menu=self.menubar) # Indicating that the \"menubar\" variable is the filemenu of the application\r\n\r\n self.folder_frame.pack()\r\n\r\n # self.folder_locator.pack(side=LEFT, padx=10, pady=10)\r\n\r\n self.media_folder_label.pack(side=LEFT, padx=10, pady=10)\r\n\r\n self.folder_button.pack(side=LEFT)\r\n\r\n self.path_frame_parent.pack(side=LEFT)\r\n\r\n self.search_frame.pack()\r\n\r\n self.search_frame.pack()\r\n self.search_entry.grid(row=0, column=0, padx=10, pady=20)\r\n self.search_button.grid(row=0, column=1, padx=5)\r\n # self.advanced_search_button.grid(row=0, column=2, padx=5)\r\n\r\n self.media_frame.pack()\r\n\r\n self.button_frame.pack()","def initGui(self):\n\n icon_path = ':/plugins/buienradar_plugin/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'Buienradar Plugin'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def _initialize_widgets(self):\n self.outer_board = [[Frame(self.root, bd = self.FRAME_BORDER_WIDTH, \n relief = self.FRAME_RELIEF) \n for _ in range(self.BOARD_DIM)] \n for _ in range(self.BOARD_DIM)]\n self.inner_boards = [[self._generate_inner_board(r, c) \n for c in range(self.BOARD_DIM)]\n for r in range(self.BOARD_DIM)]","def _run_delayed_gui_load_code(self):\n #Stop the timer.\n self._delayed_gui_timer.stop()\n print(f'_run_delayed_gui_load_code() called!')\n # Try to select the first combo item after they've loaded\n self.ui.contextSelectorWidget._trySelectFirstComboItem()","def initGui(self):\n icon = QIcon(\":/plugins/DEMto3D/icons/demto3d.png\")\n text = self.tr(\"DEM 3D printing\")\n parent = self.iface.mainWindow()\n self.action = QAction(icon, text, parent)\n self.action.setObjectName(text)\n self.action.setStatusTip(text)\n self.action.triggered.connect(self.run)\n\n self.iface.addRasterToolBarIcon(self.action)\n self.iface.addPluginToRasterMenu(self.menu, self.action)","def _init_ui(self):\n self.setGeometry(100, 100, 1020, 650)\n self.setWindowTitle('Diamond Puck Barcode Scanner')\n self.setWindowIcon(QtGui.QIcon('web.png'))\n\n self.init_menu_bar()\n\n # Barcode table - lists all the barcodes in a record\n self.barcodeTable = BarcodeTable(self._config)\n\n # Image frame - displays image of the currently selected scan record\n self.imageFrame = ImageFrame()\n\n # Scan record table - lists all the records in the store\n self.recordTable = ScanRecordTable(self.barcodeTable, self.imageFrame, self._config)\n\n self._btn_begin = QPushButton(\"Start Scan\")\n self._btn_begin.setStyleSheet(\"font-size:20pt;\")\n self._btn_begin.setFixedSize(150, 60)\n self._btn_begin.clicked.connect(self._start_live_capture)\n\n self._btn_stop = QPushButton(\"Stop Scan\")\n self._btn_stop.setStyleSheet(\"font-size:20pt\")\n self._btn_stop.setFixedSize(150, 60)\n self._btn_stop.clicked.connect(self._stop_live_capture)\n\n hbox_btn = QHBoxLayout()\n hbox_btn.addWidget(self._btn_begin)\n hbox_btn.addWidget(self._btn_stop)\n hbox_btn.addStretch()\n\n # Create layout\n hbox = QtGui.QHBoxLayout()\n hbox.setSpacing(10)\n hbox.addWidget(self.recordTable)\n hbox.addWidget(self.barcodeTable)\n hbox.addWidget(self.imageFrame)\n hbox.addStretch(1)\n\n vbox = QtGui.QVBoxLayout()\n vbox.addLayout(hbox_btn)\n vbox.addLayout(hbox)\n\n main_widget = QtGui.QWidget()\n main_widget.setLayout(vbox)\n self.setCentralWidget(main_widget)\n\n self.show()","def init_ui(self):\n raise NotImplementedError(\"This is an abstract method.\")","def initUI(self):\n\t\t#de button om het spel te beginnen\n\t\tself.playButton = QtGui.QPushButton(\"Play\", self)\n\t\tself.playButton.clicked.connect(self.playField)\n\t\tself.playButton.setStyleSheet(\"background-color: red;border-style: outset; border-radius:5px; font: bold 18px;\")\n\t\tself.playButton.move(210,100)\n\t\tself.playButton.resize(150, 50)\n\t\t\n\t\t#zakje chips\n\t\tself.chips = QtGui.QPixmap(\"chips.png\")\n\t\tself.lbl = QtGui.QLabel(self)\n\t\tself.lbl.setPixmap(self.chips)\n\t\tself.lbl.move(410, 300)\n\t\t\n\t\t#1 zak chips is nooit genoeg voor de heavy breathing cat\n\t\tself.chips2 = QtGui.QPixmap(\"chips.png\")\n\t\tself.lbl2 = QtGui.QLabel(self)\n\t\tself.lbl2.setPixmap(self.chips)\n\t\tself.lbl2.move(30, 30)\n\t\t\n\t\t#pikachu in een sailor pakje, want battleships\n\t\tself.pikachu = QtGui.QPixmap(\"pikachu.png\")\n\t\tself.lbl3 = QtGui.QLabel(self)\n\t\tself.lbl3.setPixmap(self.pikachu)\n\t\tself.lbl3.move(400,0)\n\t\t\n\t\t#een kat kan natuurlijk niet ontbreken\n\t\tself.kat = QtGui.QPixmap(\"hb2.jpeg\")\n\t\tself.lblKat = QtGui.QLabel(self)\n\t\tself.lblKat.setPixmap(self.kat)\n\t\tself.lblKat.move(0,230)\n\t\t\n\t\t#ons super vette logo #swek\n\t\tself.battlechipsPlaatje = QtGui.QPixmap(\"battleships.png\")\n\t\tself.lblbattle = QtGui.QLabel(self)\n\t\tself.lblbattle.setPixmap(self.battlechipsPlaatje)\n\t\tself.lblbattle.move(180,10)\n\t\t\n\t\t#le window\n\t\tself.setGeometry(150, 150, 600, 600)\n\t\tself.setWindowTitle(\"Menu\")\n\t\tself.setStyleSheet(\"background-color: #8e8f94\")\n\t\tself.show()","def initLayers(self):\n self.addRow(self.currentLayerData)\n self.selected = self.jobRow.child(0)\n self.setSelectedFromItem(self.selected)","def __init_widgets(self):\n self.add_events(Gdk.EventMask.ENTER_NOTIFY_MASK)\n\n headerbar = self.get_header_bar()\n scanner_image = Gtk.Image.new_from_icon_name(\"qrscanner-symbolic\", Gtk.IconSize.BUTTON)\n self.scan_button.add(scanner_image)\n\n self.add_button.get_style_context().add_class(\"suggested-action\")\n self.add_button.set_sensitive(False)\n self.add_button.set_can_default(True)\n\n headerbar.pack_end(self.add_button)\n headerbar.pack_end(self.scan_button)\n\n self.account_config.connect(\"changed\", self._on_account_config_changed)\n self.add_button.connect(\"clicked\", self._on_add)\n self.scan_button.connect(\"clicked\", self.account_config.scan_qr)\n self.get_content_area().pack_start(self.account_config, True, True, 0)","def __init__(self):\n self.queue = Queue()","def initGui(self):\n\n icon_path = ':/plugins/cctv_mapper/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'CCTV Mapper'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def init_UI(self):\n\n self.master.title(\"Search for different companies\")\n self.master.geometry(\"400x400\")\n\n self.label_combobox = Label(self, text=\"Search by\")\n self.label_combobox.pack()\n\n self.combo_searching_options = Combobox(self, state=\"readonly\")\n self.combo_searching_options['values'] = self.combobox_values\n self.combo_searching_options.pack()\n\n self.label_input = Label(self, text=\"Entry the value\")\n self.label_input.pack()\n\n self.user_input = Entry(self, width=40)\n self.user_input.pack()\n\n self.btn_submit = Button(self, text=\"Submit\", command=self.submit)\n self.btn_submit.pack()\n\n self.text_area = scrolledtext.ScrolledText(self)\n self.text_area.pack()\n\n sys.stdout = RedirectOutputText(self.text_area)\n\n self.btn_back = Button(self, text=\"Back\", command=self.go_back)\n self.btn_back.pack()","def create_widgets( self ):\n\n # photo preview.\n self.photoPreview = grafwidgets.MultiRubberBandedLabel( self.preview_pixmap )\n self.previewArea = grafwidgets.PhotoPreviewArea( self.photoPreview )\n\n # draw the art record regions.\n # XXX: factor this into a separate routine\n for art in self.art_records:\n if art[\"region\"] is not None:\n self.photoPreview.add_band( art[\"id\"], art[\"region\"] )\n\n # processing type.\n self.selectionBox = QComboBox()\n\n self.selectionBox.addItem( \"all\", \"all\" )\n for state in self.db.get_processing_states():\n self.selectionBox.addItem( state, state )\n\n self.selectionBox.activated.connect( self.selectionTypeActivation )\n\n self.selectionBoxLabel = QLabel( \"&Processing Type:\" )\n self.selectionBoxLabel.setBuddy( self.selectionBox )\n\n # art record selection.\n #\n # XXX: describe these.\n self.selectionView = SelectionView()\n self.selectionView.setModel( self.proxyArtModel )\n self.selectionView.activated.connect( self.selectionActivation )\n self.selectionView.selectionModel().selectionChanged.connect( self.recordSelectionChange )\n\n # record addition and removal buttons.\n self.newRecordButton = QPushButton( \"&New Record\" )\n self.newRecordButton.clicked.connect( self.create_new_record )\n\n self.deleteRecordButton = QPushButton( \"&Delete Record\" )\n self.deleteRecordButton.clicked.connect( self.delete_record )\n\n # we shouldn't be able to push the delete button until we have a\n # record selected.\n self.deleteRecordButton.setEnabled( False )\n\n # art record summary labels.\n self.artTypeLabel = QLabel()\n self.artSizeLabel = QLabel()\n self.artQualityLabel = QLabel()\n self.artDateLabel = QLabel()\n self.artArtistsLabel = QLabel()\n self.artAssociatesLabel = QLabel()\n self.artVandalsLabel = QLabel()\n self.artTagsLabel = QLabel()\n\n # photo record processing state.\n self.photoProcessingStateComboBox = QComboBox()\n for state in self.db.get_processing_states():\n self.photoProcessingStateComboBox.addItem( state, state )\n self.photoProcessingStateComboLabel = QLabel( \"Stat&e:\" )\n self.photoProcessingStateComboLabel.setBuddy( self.photoProcessingStateComboBox )\n\n # photo record tags.\n #\n # NOTE: our accelerator is chosen to match the ArtRecordEditor's\n # accelerator.\n #\n self.photoTagsLineEdit = QLineEdit( \"\" )\n self.photoTagsLabel = QLabel( \"Ta&gs:\" )\n self.photoTagsLabel.setBuddy( self.photoTagsLineEdit )\n\n # XXX: need to add\n #\n # * description","def __init__(self, showCTWM, showWHM, showRTLE, algorithmCTWM, algorithmWHM, tool_type_text, material_text,\n flutes_text, coating_text, cutting_type_text, wpmaterial_text, heat_text, tool_diameter_num,\n parent_img_path=None):\n\n # Initialize the widget and the main window\n QtWidgets.QMainWindow.__init__(self)\n self.setWindowTitle(\"Smart Sensing System\")\n #self.setFixedSize(1000, 450)\n self.widgetHolder = QtWidgets.QWidget()\n self.setCentralWidget(self.widgetHolder)\n\n # Set all the passed arguments as class variables\n self.parent_img_path = parent_img_path\n self.showCTWM = showCTWM\n self.showWHM = showWHM\n self.showRTLE = showRTLE\n self.tool_type_text = tool_type_text\n self.material_text = material_text\n self.flutes_text = flutes_text\n self.coating_text = coating_text\n self.cutting_type_text = cutting_type_text\n self.wpmaterial_text = wpmaterial_text\n self.heat_text = heat_text\n self.tool_diameter_num = tool_diameter_num\n\n\n # Pre-loading all images into dictionary\n self.images = dict()\n\n if self.showCTWM:\n self.images['imageFailureGrayCTWM'] = QPixmap(os.path.join(self.parent_img_path, \"failure_gray.jpg\"))\n self.images['imageAdvancedGrayCTWM'] = QPixmap(os.path.join(self.parent_img_path, \"advanced_gray.jpg\"))\n self.images['imageAverageGrayCTWM'] = QPixmap(os.path.join(self.parent_img_path, \"average_gray.jpg\"))\n self.images['imageGoodGrayCTWM'] = QPixmap(os.path.join(self.parent_img_path, \"good_gray.jpg\"))\n self.images['imageGoodGreenCTWM'] = QPixmap(os.path.join(self.parent_img_path, \"good_green.jpg\"))\n self.images['imageAverageYellowCTWM'] = QPixmap(os.path.join(self.parent_img_path, \"average_yellow.jpg\"))\n self.images['imageAdvancedOrangeCTWM'] = QPixmap(os.path.join(self.parent_img_path, \"advanced_orange.jpg\"))\n self.images['imageFailureRedCTWM'] = QPixmap(os.path.join(self.parent_img_path, \"failure_red.jpg\"))\n if self.showWHM:\n self.images['imageLevelFourWHMgray'] = QPixmap(os.path.join(self.parent_img_path, \"levelFour_gray.jpg\"))\n self.images['imageLevelThreeWHMgray'] = QPixmap(os.path.join(self.parent_img_path, \"levelThree_gray.jpg\"))\n self.images['imageLevelTwoWHMgray'] = QPixmap(os.path.join(self.parent_img_path, \"levelTwo_gray.jpg\"))\n self.images['imageLevelOneWHMgray'] = QPixmap(os.path.join(self.parent_img_path, \"levelOne_gray.jpg\"))\n self.images['imageLevelOneWHMgreen'] = QPixmap(os.path.join(self.parent_img_path, \"levelOne_green.jpg\"))\n self.images['imageLevelTwoWHMyellow'] = QPixmap(os.path.join(self.parent_img_path, \"levelTwo_yellow.jpg\"))\n self.images['imageLevelThreeWHMorange'] = QPixmap(os.path.join(self.parent_img_path, \"levelThree_orange.jpg\"))\n self.images['imageLevelFourWHMred'] = QPixmap(os.path.join(self.parent_img_path, \"levelFour_red.jpg\"))\n\n # Initialize file to save output to\n self.save_file_name = \"HTW\" + str(datetime.now().date())\n self.save_file = open(os.path.join(args.parent_img_path, self.save_file_name), \"w\")\n self.save_file.write(\"History of Tool Wear\" + \"\\n\")\n self.save_file.close()\n\n\n # Assigns the thread that runs the algorithm to the flag setPointsCTWM\n self.setPointsCTWM = algorithmCTWM\n # When the algorithm raises the flag 'finished', do the function \"updateCTWM\"\n self.setPointsCTWM.finished.connect(self.updateCTWM)\n\n # Assigns the thread that runs the algorithm to the flag setPointsWHM\n self.setPointsWHM = algorithmWHM\n # When the algorithm raises the flag 'finished', do the function \"updateWHM\"\n self.setPointsWHM.finished.connect(self.updateWHM)\n\n # Create a VBox called Main Layout that will contain all the other widgets\n self.mainLayout = QtWidgets.QVBoxLayout()\n self.widgetHolder.setLayout(self.mainLayout)\n\n ###############################################################################################################\n # #\n # FIRST ROW: IN AN HBOX #\n # Contains Title, Start, Stop, Reset, Save buttons, and Save directory #\n ###############################################################################################################\n self.firstRow = QtWidgets.QHBoxLayout()\n self.windowTitle = QtWidgets.QLabel(\"Smart Sensing System\")\n self.windowTitle.setContentsMargins(0, 0, 30, 0) # someLayout.setContentsMargins(left, top, right, bottom)\n self.font = QtGui.QFont(\"Corbel\", 16, QtGui.QFont.Bold, QtGui.QFont.StyleItalic)\n self.windowTitle.setFont(self.font)\n self.firstRow.addWidget(self.windowTitle)\n self.startButton = QtWidgets.QPushButton(\"Start\")\n self.startButton.setMinimumWidth(200)\n self.startButton.setEnabled(True)\n self.startButton.setStyleSheet(\"background-color: #009933\")\n self.startButton.clicked.connect(self.startButtonPressed)\n self.firstRow.addWidget(self.startButton)\n self.stopButton = QtWidgets.QPushButton(\"Stop\")\n self.stopButton.setMinimumWidth(200)\n self.stopButton.setEnabled(False)\n self.stopButton.setStyleSheet(\"background-color: #cc2900\")\n self.stopButton.clicked.connect(self.stopButtonPressed)\n self.firstRow.addWidget(self.stopButton)\n self.reset = QtWidgets.QPushButton(\"Reset\") # connection to clicked in init -> Clear CTWM and WHM Graphs\n self.reset.setEnabled(True)\n self.reset.setMaximumWidth(70)\n self.reset.clicked.connect(self.resetPressed)\n self.firstRow.addWidget(self.reset)\n\n self.addressBox = QtWidgets.QLineEdit()\n self.addressBox.setMaximumWidth(200)\n self.addressBox.setPlaceholderText(\"Address\")\n self.addressBox.setContentsMargins(30, 0, 0, 0) # someLayout.setContentsMargins(left, top, right, bottom)\n self.addressBox.setAlignment(QtCore.Qt.AlignCenter)\n self.firstRow.addWidget(self.addressBox)\n self.screenshotNum = 0 # For filename saving purposes\n self.save = QtWidgets.QPushButton(\"Save\")\n self.save.setMaximumWidth(70)\n self.save.clicked.connect(self.saveWindowState)\n self.firstRow.addWidget(self.save)\n self.mainLayout.addLayout(self.firstRow)\n\n ###############################################################################################################\n # #\n # SECOND ROW: IN AN HBOX #\n # For Cutting Tool Wear Monitoring and Workpiece Hardness Monitoring #\n ###############################################################################################################\n self.secondRow = QtWidgets.QHBoxLayout()\n # These are some configurations for the graph for user-viewing purposes\n pg.setConfigOption('background', '#d9d9d9')\n pg.setConfigOption('foreground', 'k')\n pg.setConfigOptions(antialias=True)\n # Set up pen to be used to draw the graphs\n self.pen = pg.mkPen(color='b', width=3)\n\n # Use a numpy array to keep track of the outcomes of the wear condition\n self.outcomesCTWM = np.array([])\n # Use a numpy array to keep track of the outcomes of the hardness condition\n self.outcomesWHM = np.array([])\n\n if self.showCTWM: # Show the Cutting Tool Wear Monitoring widget\n self.initCTWM()\n if self.showWHM: # Show the Workpiece Hardness Monitoring widget\n self.initWHM()\n self.mainLayout.addLayout(self.secondRow)\n\n ###############################################################################################################\n # #\n # THIRD ROW: IN AN VBOX #\n # For Remaining Tool Life Estimation #\n ###############################################################################################################\n if self.showRTLE: # Show the Remaining Tool Life Estimation widget\n self.initRTLE()\n\n ###############################################################################################################\n # #\n # FOURTH ROW: IN AN HBOX #\n # Contains RIT label and version number #\n ###############################################################################################################\n self.fourthRow = QtWidgets.QHBoxLayout()\n self.versionFont = QtGui.QFont(\"Corbel\", 10)\n self.version = QLabel(\"Version: 1.0\")\n self.version.setFont(self.versionFont)\n self.version.setAlignment(QtCore.Qt.AlignRight)\n self.fourthRow.addWidget(self.version)\n self.mainLayout.addLayout(self.fourthRow)\n\n # Show all these widgets\n self.show()\n\n ##############################################################################################################\n # #\n # ALL BUTTONS' FUNCTIONALITY #\n # #\n ###############################################################################################################","def setup(self):\n self.ui.setup_window()","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def __init__(self):\n self.queue = []","def gui_init(self):\n if not self.parent is None:\n self.parent.children.append(self)\n self.children = []\n self.priority = PRIORITY_GUI_ELEMENTS","def __init__(self):\n self.s = QSettings()\n self.p = QgsProject.instance()","def setup(self):\r\n ScriptedLoadableModuleWidget.setup(self)\r\n\r\n # Load widget from .ui file (created by Qt Designer).\r\n # Additional widgets can be instantiated manually and added to self.layout.\r\n uiWidget = slicer.util.loadUI(self.resourcePath('UI/RecordHerniaData.ui'))\r\n self.layout.addWidget(uiWidget)\r\n self.ui = slicer.util.childWidgetVariables(uiWidget)\r\n\r\n # Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's\r\n # \"mrmlSceneChanged(vtkMRMLScene*)\" signal in is connected to each MRML widget's.\r\n # \"setMRMLScene(vtkMRMLScene*)\" slot.\r\n uiWidget.setMRMLScene(slicer.mrmlScene)\r\n\r\n # Create logic class. Logic implements all computations that should be possible to run\r\n # in batch mode, without a graphical user interface.\r\n self.logic = TMSRecordDataModuleLogic()\r\n self.recordingStarted = False\r\n self.camerasStarted = False\r\n self.moduleDir = os.path.dirname(slicer.modules.tmsrecorddatamodule.path)\r\n self.logic.setupScene()\r\n\r\n # Buttons\r\n self.ui.StartStopRecordingButton.connect('clicked(bool)', self.onStartStopRecordingClicked)\r\n self.ui.startCamerasButton.connect('clicked(bool)',self.onStartStopCamerasClicked)","def initialize(self):\n super(QtMainWindow, self).initialize()\n self.update_menu_bar()","def __init__(self) -> None:\n self._queue = []","def initGui(self):\n\n icon_path = ':/plugins/pit_removal/icon.png'\n self.add_action(\n icon_path,\n text=self.tr(u'remove pits'),\n callback=self.run,\n parent=self.iface.mainWindow())\n\n # will be set False in run()\n self.first_start = True","def __init__(self):\r\n self.queue = []","def __init__(self):\r\n self.queue = []","def _init_ui(self):\n # Create GUI elements, set them in dict structure\n inf_symbol = u'\\u221E'\n gamma_symbol = u'\\u03B3'\n unicode_squared = u'\\u00B9'\n labelwidth=175\n\n if len(self.waterlevels) > 100:\n raise NotImplementedError('More than 100 water levels where discovered in the hydraulic loads. The method with PHAROS is not implemented for this number of loads. Recalculate the wave conditions at given water levels, or pick a method without PHAROS.')\n\n self.input_elements['hydraulic loads'] = {\n 'Hs_max': widgets.ParameterLabel(\n label='Max. significante golfhoogte:',\n labelwidth=labelwidth,\n value='{:.3f}'.format(self.Hs_max),\n unit='m'\n ),\n # 'Tp_max': widgets.ParameterLabel(\n # label='Maximale piekperiode',\n # labelwidth=150\n # ),\n 'factor Tm Tp': widgets.ParameterInputLine(\n label='Factor Tm naar Tp:',\n labelwidth=labelwidth,\n validator=QtGui.QDoubleValidator(0.01, 99.99, 20),\n ),\n 'water depth for wave length': widgets.ParameterInputLine(\n label='Waterdiepte voor golflengte:',\n labelwidth=labelwidth,\n unitlabel='m',\n validator=QtGui.QDoubleValidator(0.00, np.inf, 20),\n ),\n }\n\n self.input_elements['wave directions'] = {\n 'lowest': widgets.ParameterInputLine(\n label='Laagste waarde [0-360]:',\n labelwidth=labelwidth,\n unitlabel='graden (nautisch)',\n validator=QtGui.QDoubleValidator(0.00, 360.00, 20),\n ),\n 'highest': widgets.ParameterInputLine(\n label='Hoogste waarde [0-360]:',\n labelwidth=labelwidth,\n unitlabel='graden (nautisch)',\n validator=QtGui.QDoubleValidator(0.00, 360.00, 20),\n ),\n 'bin size': widgets.ParameterInputLine(\n label='Klassegrootte [1-360]:',\n labelwidth=labelwidth,\n unitlabel='graden',\n validator=QtGui.QDoubleValidator(1.00, 360.00, 20),\n )\n }\n\n self.input_elements['frequencies'] = {\n 'lowest': widgets.ParameterInputLine(\n label='Ondergrens [{:.3f} - {:.3f}]:'.format(*self.f_range),\n labelwidth=labelwidth,\n unitlabel='Hz',\n validator=QtGui.QDoubleValidator(self.f_range[0] - 0.01, self.f_range[1] + 0.01, 20),\n ),\n 'highest': widgets.ParameterInputLine(\n label='Bovengrens [{:.3f} - {:.3f}]:'.format(*self.f_range),\n labelwidth=labelwidth,\n unitlabel='Hz',\n validator=QtGui.QDoubleValidator(self.f_range[0] - 0.01, self.f_range[1] + 0.01, 20),\n ),\n 'number of bins': widgets.ParameterInputLine(\n label='Aantal klassen [1-50]:',\n labelwidth=labelwidth,\n validator=QtGui.QIntValidator(1, 50),\n ),\n 'scale': widgets.ComboboxInputLine(\n label='Frequentie schaal:',\n labelwidth=labelwidth,\n items=['lineair', 'logaritmisch'],\n )\n }\n\n self.input_elements['2d wave spectrum'] = {\n 'spread': widgets.ParameterInputLine(\n label='Spreiding [10-70]:',\n labelwidth=labelwidth,\n unitlabel='graden',\n validator=QtGui.QDoubleValidator(10.0, 70.0, 20),\n ),\n 'gamma': widgets.ParameterInputLine(\n label='JONSWAP peak\\nenhancement factor {} [1-7]:'.format(gamma_symbol),\n labelwidth=labelwidth,\n unitlabel='',\n validator=QtGui.QDoubleValidator(1.00, 7.00, 20),\n ),\n 'min energy': widgets.ParameterInputLine(\n label='Signaleringswaarde energie [0-{}]:'.format(inf_symbol),\n labelwidth=labelwidth,\n unitlabel='m{}s/degree'.format(unicode_squared),\n validator=QtGui.QDoubleValidator(0.00, 2.00, 20),\n )\n }\n\n self.input_elements['paths'] = {\n 'pharos folder': widgets.ExtendedLineEdit(\n label='Uitvoermap:',\n labelwidth=labelwidth,\n browsebutton=QtWidgets.QPushButton(\n '...',\n clicked=self._load_pharos_folder\n )\n ),\n 'schematisation folder': widgets.ExtendedLineEdit(\n label='Schematisatiemap:',\n labelwidth=labelwidth,\n browsebutton=QtWidgets.QPushButton(\n '...',\n clicked=self._load_schematisations_folder\n )\n )\n }\n\n self.input_elements['water levels'] = {\n 'checked': widgets.CheckBoxInput(\n labels=self.waterlevels,\n nrows=max(2, len(self.waterlevels) // 20),\n unitlabel='m + NAP'\n )\n }\n\n delta = u'\\u0394'\n self.input_elements['transformation'] = {\n 'dx': widgets.ParameterInputLine(\n label='{}x [RD + {}x = lokaal]:'.format(delta, delta),\n labelwidth=labelwidth,\n ),\n 'dy': widgets.ParameterInputLine(\n label='{}y [RD + {}y = lokaal]:'.format(delta, delta),\n labelwidth=labelwidth,\n )\n }\n\n\n # Define titles for groups\n titles = {\n 'hydraulic loads': 'Hydraulische belastingen',\n 'wave directions': 'Golfrichtingen',\n 'frequencies': 'Frequenties',\n '2d wave spectrum': '2D golfspectrum',\n 'paths': 'Paden',\n 'water levels': 'Te simuleren waterstanden',\n 'transformation': 'Transformatie voor coordinatenstelsel'\n }\n\n\n # Create base layout\n self.setLayout(QtWidgets.QVBoxLayout())\n # self.layout().setSpacing(10)\n\n for tag, title in titles.items():\n if tag in self.input_elements:\n group_layout = QtWidgets.QVBoxLayout()\n for _, item in self.input_elements[tag].items():\n group_layout.addWidget(item)\n\n # Add groupbox with title\n groupbox = QtWidgets.QGroupBox(title)\n groupbox.setLayout(group_layout)\n self.layout().addWidget(groupbox)\n\n\n line = QtWidgets.QFrame()\n line.setFrameShape(QtWidgets.QFrame.HLine)\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\n self.layout().addWidget(line)\n\n # OK and Cancel buttons\n\n self.generate_button = QtWidgets.QPushButton('Genereer tabel')\n self.generate_button.setDefault(True)\n # self.generate_button.setEnabled(False)\n self.generate_button.clicked.connect(self.generate)\n\n self.cancel_button = QtWidgets.QPushButton('Annuleren')\n self.cancel_button.setAutoDefault(False)\n self.cancel_button.clicked.connect(self.cancel)\n\n button_box = QtWidgets.QDialogButtonBox(QtCore.Qt.Horizontal, self)\n button_box.addButton(self.generate_button, QtWidgets.QDialogButtonBox.ActionRole)\n button_box.addButton(self.cancel_button, QtWidgets.QDialogButtonBox.RejectRole)\n\n button_box.accepted.connect(QtWidgets.QDialog.accept)\n # button_box.rejected.connect(QtWidgets.QDialog.reject)\n\n self.layout().addWidget(button_box)","def __init__(self):\n # setup the database session\n engine = create_engine('sqlite:///%s'%os.path.join(APP_DIR,config.get('Inkcut','database_dir'),config.get('Inkcut','database_name')))\n\n Session.configure(bind=engine)\n self.session = Session()\n\n self.job = None\n self.ui = {\n 'main_window':MainWindow(self),\n 'device_dialog':DeviceDialog(self),\n }\n self.statusbar = self.ui['main_window'].widgets['statusbar']","def initUI(self):\n\n grid = QGridLayout()\n grid.addWidget(self.from_currency_label, 0, 0, Qt.AlignRight)\n grid.addWidget(self.from_currency, 0, 1)\n grid.addWidget(self.to_currency_label, 0, 2, Qt.AlignRight)\n grid.addWidget(self.to_currency, 0, 3)\n grid.addWidget(self.from_amount_label, 1, 0)\n grid.addWidget(self.from_amount, 1, 1)\n grid.addWidget(self.to_amount_label, 1, 2)\n grid.addWidget(self.to_amount, 1, 3)\n\n grid.addWidget(self.from_calendar, 2, 0, 1, 2)\n grid.addWidget(self.to_calendar, 2, 2, 1, 2)\n\n grid.addWidget(self.rates_plot, 3, 0, 1, 4)\n grid.addWidget(self.graph_hint, 4, 0, 1, 4)\n\n self.rates_plot.showGrid(x=True, y=True)\n self.rates_plot.setLabel('left', 'Rate')\n self.rates_plot.setLabel('bottom', 'Days')\n self.legend = self.rates_plot.addLegend()\n\n self.setLayout(grid)\n self.setWindowTitle('Currency Converter - Assignment 1 - Arnaud Bourget - 2981151')\n\n self.from_currency.currentIndexChanged.connect(self.updateUI)\n self.to_currency.currentIndexChanged.connect(self.updateUI)\n self.from_amount.valueChanged.connect(self.fromAmountHandler)\n self.from_calendar.selectionChanged.connect(self.fromCalendarHandler)\n self.to_calendar.selectionChanged.connect(self.toCalendarHandler)\n\n self.show()","def __init__(self, iface, dialog_base, bbox_tool):\n QObject.__init__(self, None)\n self.iface = iface\n self.dialog_base = dialog_base\n self.bbox_tool = bbox_tool\n self.sources = {}\n self.geometries = {}\n self.types_dict = {}\n self.items = {}\n self.search_thread_pool = QThreadPool()\n self.json_thread_pool = QThreadPool()\n self.progress_message_bar = None\n self.progress_message_bar_widget = None\n self.json_progress_message_bar = None\n self.json_progress_message_bar_widget = None\n self.json_progress = None\n self.json_failed = False\n self.point_vector_layer = None\n self.line_vector_layer = None\n self.polygon_vector_layer = None\n self.dialog_base.aoi_button.clicked.connect(self.aoi_button_clicked)\n self.dialog_base.export_button.clicked.connect(self.export)\n self.bbox_tool.released.connect(self.search)\n # self.query_initial_sources()\n self.file_dict = {}\n self.directory = None\n self.written_first_line = False\n self.written_first_point = False\n self.written_first_polygon = False\n # locks\n self.search_lock = Lock()\n self.json_lock = Lock()","def init_toolbar(self):\n raise NotImplementedError","def start(self):\n self.__main_window = Tk()\n self.__start_loading_window()","def create_widgets( self ):\n\n self.selectionView = SelectionView()\n self.selectionView.setModel( self.proxyPhotosModel )\n self.selectionView.activated.connect( self.selectionActivation )\n self.selectionView.selectionModel().selectionChanged.connect( self.selectionChange )\n self.selectionView.setColumnHidden( self.ID_COLUMN, True ) # hide the ID\n\n self.selectionBox = QComboBox()\n\n self.selectionBox.addItem( \"all\", \"all\" )\n for state in self.db.get_processing_states():\n self.selectionBox.addItem( state, state )\n\n self.selectionBox.activated.connect( self.selectionTypeActivation )\n\n self.selectionBoxLabel = QLabel( \"&Processing Type:\" )\n self.selectionBoxLabel.setBuddy( self.selectionBox )\n\n self.previewArea = grafwidgets.PhotoPreviewArea()\n\n # informational labels for the photo record.\n self.infoStateLabel = QLabel()\n self.infoSummaryLabel = QLabel()\n self.infoLocationLabel = QLabel()\n self.infoTakenLabel = QLabel()\n self.infoTagsLabel = QLabel()\n\n # dock widget which will hold the selection layout once created\n # in create_layout, for now it gets an empty widget.\n self.selection_dock = QDockWidget()\n self.selection_dock.setFeatures( QDockWidget.DockWidgetMovable )\n self.selection_dock.setWidget( QWidget() )","def __init__(self, map_state):\n self.map_state = map_state\n self.image = map_prepare.GFX[\"misc\"][\"interface\"]\n self.make_widgets()","def __selectJobsSetup(self):\n\n select_btn = QtWidgets.QPushButton(\"Select:\")\n select_btn.setFocusPolicy(QtCore.Qt.NoFocus)\n self.__toolbar.addWidget(select_btn)\n select_btn.clicked.connect(self.__selectJobsHandle) # pylint: disable=no-member\n\n self._selectJobsEditBox = JobSelectEditBox(self)\n self.__toolbar.addWidget(self._selectJobsEditBox)\n self._selectJobsEditBox.returnPressed.connect(self.__selectJobsHandle) # pylint: disable=no-member\n\n clear_btn = QtWidgets.QPushButton(\"Clr\")\n clear_btn.setFocusPolicy(QtCore.Qt.NoFocus)\n clear_btn.setFixedWidth(24)\n self.__toolbar.addWidget(clear_btn)\n clear_btn.clicked.connect(self._selectJobsEditBox.actionClear) # pylint: disable=no-member\n\n mine_btn = QtWidgets.QPushButton(\"selectMine\")\n mine_btn.setFocusPolicy(QtCore.Qt.NoFocus)\n mine_btn.setFixedWidth(70)\n self.__toolbar.addWidget(mine_btn)\n mine_btn.clicked.connect(self.__selectJobsHandleMine) # pylint: disable=no-member","def __init__(self, *args, obj=None, **kwargs):\n super(MainWindow, self).__init__(*args, **kwargs)\n self.setupUi(self)\n\n # real-frequency grid\n self.realgrid = RealFrequencyGrid(wmax=float(self.max_real_freq.text()),\n nw=int(self.num_real_freq.text()),\n type=str(self.grid_type_combo.currentText()))\n self.connect_realgrid_button()\n self.connect_wmax()\n self.connect_nw()\n self.connect_grid_type()\n\n\n # self.connect_select_button()\n # self.connect_load_button()\n # self.connect_show_button()\n self.connect_load_button_text()\n self.connect_show_button_2()\n self.connect_select_button_2()\n\n # text display field and \"Do it\" button\n self.text_output.setReadOnly(True)\n self.connect_doit_button()\n\n # output data\n self.output_data = OutputData()\n self.connect_select_output_button()\n self.connect_save_button()","def __init__(self, sys_argv):\n super().__init__(sys_argv)\n log.debug(\"Supplying Thread information from init of QApplication\")\n self.setAttribute(Qt.AA_EnableHighDpiScaling)\n self.setStyle(\"Fusion\")\n self.mainWindow = MainWindow()\n self.mainWindow.setWindowTitle(\"jet-tracker\")\n # __location__ = os.getcwd()\n # File = open(__location__ + '\\jt_configs\\Geoo.qss', 'r')\n # with File:\n # qss = File.read()\n # self.setStyleSheet(qss)\n self.mainWindow.show()","def __init__(self):\n\n self.ai_list = []\n self.master = Tk()\n self.ui_draw = IntVar()\n\n self.populate_ai_list(self.ai_list)\n self.frame = Frame(self.master)\n self.build_ui()\n\n self.master.mainloop()"],"string":"[\n \"def initGui(self):\\n\\n # set srs\\n self._displayCrs = QgsCoordinateReferenceSystem()\\n self._displayCrs.createFromOgcWmsCrs('EPSG:4167') \\n self.iface.mapCanvas().mapSettings().setDestinationCrs(self._displayCrs)\\n\\n # init layerManager\\n self._layerManager = LayerManager(self.iface, self)\\n self._layerManager.registerFunctions()\\n # init Highlighter\\n self.highlighter = FeatureHighlighter(self.iface, self._layerManager, self)\\n\\n # Build an action list from QGIS navigation toolbar\\n actionList = self.iface.mapNavToolToolBar().actions()\\n self.actions = self.iface.mapNavToolToolBar().actions()\\n\\n # Main address editing window\\n self._loadaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/loadaddress.png'), \\n 'QGIS-AIMS-Plugin', self.iface.mainWindow())\\n self._loadaction.setWhatsThis('Open the QGIS-AIMS-Plugin')\\n self._loadaction.setStatusTip('Open the QGIS-AIMS-Plugin')\\n self._loadaction.triggered.connect(self.loadQueues)\\n self._loadaction.triggered.connect(self.loadLayers)\\n self._loadaction.triggered.connect(self.enableAddressLayer)\\n self._loadaction.triggered.connect(self.startDM)\\n self._loadaction.triggered.connect(self.disableConfig)\\n \\n # Config Dialog\\n self._configdialog = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/config.png'), \\n 'AIMS Configuration', self.iface.mainWindow())\\n self._configdialog.setWhatsThis('Configure AIMS')\\n self._configdialog.setStatusTip('Configure AIMS')\\n self._configdialog.triggered.connect(self.configureAims)\\n\\n self.config_stacked_widget = self._config_ui.uStackedWidget\\n self.config_list_options = self._config_ui.uListOptions\\n self.config_list_options.itemClicked.connect(self.showConfigSelectedOption)\\n\\n item = QListWidgetItem(\\\"User Config\\\")\\n image_path = ':/plugins/QGIS-AIMS-Plugin/resources/config.png'\\n item.setIcon(QIcon(image_path))\\n self.config_list_options.addItem(item)\\n\\n item = QListWidgetItem(\\\"Dev Config\\\")\\n image_path = ':/plugins/QGIS-AIMS-Plugin/resources/config.png'\\n item.setIcon(QIcon(image_path))\\n self.config_list_options.addItem(item)\\n\\n # Create new address tool\\n self._createnewaddressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/newaddresspoint.png'), \\n 'Create AIMS Feature', self.iface.mainWindow())\\n self._createnewaddressaction.setWhatsThis('Create AIMS Feature')\\n self._createnewaddressaction.setStatusTip('Create AIMS Feature')\\n self._createnewaddressaction.setEnabled(False)\\n self._createnewaddressaction.setCheckable(True)\\n self._createnewaddressaction.triggered.connect( self.startNewAddressTool )\\n self._createnewaddresstool = CreateNewAddressTool( self.iface, self._layerManager, self)\\n self._createnewaddresstool.setAction( self._createnewaddressaction )\\n self.actions.append(self._createnewaddressaction)\\n\\n # Delete address point\\n self._deladdressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/deleteaddress.png'), \\n 'Delete AIMS Feature', self.iface.mainWindow())\\n self._deladdressaction.setWhatsThis('Delete AIMS Feature')\\n self._deladdressaction.setStatusTip('Delete AIMS Feature')\\n self._deladdressaction.setEnabled(False)\\n self._deladdressaction.setCheckable(True)\\n self._deladdressaction.triggered.connect( self.startDelAddressTool )\\n self._deladdtool = DelAddressTool( self.iface, self._layerManager, self)\\n self._deladdtool.setAction( self._deladdressaction ) \\n self.actions.append(self._deladdressaction)\\n\\n # Move address\\n self._moveaddressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/moveaddress.png'), \\n 'Move AIMS Feature(s)', self.iface.mainWindow())\\n self._moveaddressaction.setWhatsThis('Move AIMS Feature(s)')\\n self._moveaddressaction.setStatusTip('Move AIMS Feature(s)')\\n self._moveaddressaction.setEnabled(False)\\n self._moveaddressaction.setCheckable(True)\\n self._moveaddressaction.triggered.connect( self.startMoveAddressTool )\\n self._moveaddtool = MoveAddressTool( self.iface, self._layerManager, self)\\n self._moveaddtool.setAction( self._moveaddressaction ) \\n self.actions.append(self._moveaddressaction)\\n \\n # Update address\\n self._updateaddressaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/updateaddress.png'), \\n 'Update AIMS Feature', self.iface.mainWindow())\\n self._updateaddressaction.setWhatsThis('Update AIMS Feature')\\n self._updateaddressaction.setStatusTip('Update AIMS Feature')\\n self._updateaddressaction.setEnabled(False)\\n self._updateaddressaction.setCheckable(True)\\n self._updateaddressaction.triggered.connect( self.startUpdateAddressTool )\\n self._updateaddtool = UpdateAddressTool( self.iface, self._layerManager, self)\\n self._updateaddtool.setAction( self._updateaddressaction ) \\n self.actions.append(self._updateaddressaction)\\n\\n # RCL tool -- Not a QAction as triggered from many palaces but not the toolbar\\n self._rcltool = GetRcl(self.iface, self._layerManager, self)\\n\\n # UpdateReview Position tool -- Not a QAction as triggered initiated from review queue form\\n self._updateReviewPos = UpdateReviewPosition(self.iface, self._layerManager, self)\\n\\n # Address lineage\\n \\\"\\\"\\\"\\n self._lineageaction = QAction(QIcon(':/plugins/QGIS-AIMS-Plugin/resources/lineage.png'), \\n 'Build Lineage Relationships Between Features', self.iface.mainWindow())\\n self._lineageaction.setWhatsThis('Build Lineage Relationships Between Features')\\n self._lineageaction.setStatusTip('Build Lineage Relationships Between Features')\\n self._lineageaction.setEnabled(False)\\n self._lineageaction.setCheckable(True)\\n self._lineagetool = LineageTool( self.iface, self._layerManager, self)\\n self._lineageaction.triggered.connect(self._lineagetool.setEnabled)\\n self.actions.append(self._lineageaction)\\n \\\"\\\"\\\"\\n\\n # Address highlighter\\n self._highlightaction = QAction(QIcon(\\\":/plugins/QGIS-AIMS-Plugin/resources/addresshighlight.png\\\"), \\n \\\"Electoral address highlighter\\\", self.iface.mainWindow())\\n self._highlightaction.setWhatsThis(\\\"Turn the electoral address highlighter on or off\\\")\\n self._highlightaction.setStatusTip(\\\"Turn the electoral address highlighter on or off\\\") \\n self._highlightaction.setText('Highlightaction')\\n self._highlightaction.setEnabled(False)\\n self._highlightaction.setCheckable(True)\\n self._highlightaction.toggled.connect( self.highlighter.setEnabled )\\n\\n # Add to own toolbar\\n self._toolbar = self.iface.addToolBar('QGIS-AIMS-Plugin')\\n self._toolbar.addAction(self._createnewaddressaction)\\n self._toolbar.addAction(self._deladdressaction)\\n self._toolbar.addAction(self._updateaddressaction)\\n self._toolbar.addAction(self._moveaddressaction)\\n #self._toolbar.addAction(self._lineageaction)\\n self._toolbar.addAction(self._highlightaction)\\n\\n # Add actions to menu\\n self.iface.addToolBarIcon(self._loadaction)\\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._loadaction)\\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._createnewaddressaction)\\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._deladdressaction)\\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._updateaddressaction)\\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._moveaddressaction)\\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._highlightaction)\\n self.iface.addPluginToMenu('&QGIS-AIMS-Plugin', self._configdialog)\\n\\n # capture maptool selection changes\\n QObject.connect(self.iface.mapCanvas(), SIGNAL( \\\"mapToolSet(QgsMapTool *)\\\" ), self.mapToolChanged)\\n\\n # Add actions from QGIS attributes toolbar (handling QWidgetActions)\\n tmpActionList = self.iface.attributesToolBar().actions()\\n for action in tmpActionList:\\n if isinstance(action, QWidgetAction):\\n actionList.extend( action.defaultWidget().actions() )\\n else:\\n actionList.append( action )\\n # ... could add other toolbars' action lists...\\n\\n # Build a group with actions from actionList\\n group = QActionGroup( self.iface.mainWindow() )\\n group.setExclusive(True)\\n for qgisAction in actionList:\\n group.addAction( qgisAction )\\n\\n # Add our own actions\\n for action in self.actions:\\n if action.text() == 'Highlightaction': continue\\n group.addAction( action )\",\n \"def init_widget(self):\",\n \"def initGui(self):\\n self.run_icon = QIcon(\\n os.path.join(self.plugin_dir, \\\"resources\\\", \\\"icon_add_service.svg\\\")\\n )\\n self.fav_icon = QIcon(\\n os.path.join(self.plugin_dir, \\\"resources\\\", \\\"pdok_icon_bookmark.svg\\\")\\n )\\n\\n self.del_icon = QIcon(\\n os.path.join(self.plugin_dir, \\\"resources\\\", \\\"pdok_icon_delete.svg\\\")\\n )\\n\\n self.run_action = QAction(self.run_icon, PLUGIN_NAME, self.iface.mainWindow())\\n self.run_button = QToolButton()\\n self.run_button.setMenu(QMenu())\\n self.run_button.setPopupMode(QToolButton.MenuButtonPopup)\\n self.run_button.setDefaultAction(self.run_action)\\n\\n self.services_loaded = False\\n\\n self.run_action.triggered.connect(self.run)\\n self.setup_fq_checkboxes()\\n\\n # Add toolbar button and menu item\\n self.toolbar = self.iface.addToolBar(PLUGIN_NAME)\\n self.toolbar.setObjectName(PLUGIN_NAME)\\n self.toolbar.addWidget(self.run_button)\\n\\n # Set default layer loading behaviour\\n self.default_tree_locations = {\\n \\\"wms\\\": \\\"top\\\",\\n \\\"wmts\\\": \\\"bottom\\\",\\n \\\"wfs\\\": \\\"top\\\",\\n \\\"wcs\\\": \\\"top\\\",\\n }\\n\\n self.add_fav_actions_to_toolbar_button()\\n\\n self.toolbar_search = QLineEdit()\\n\\n def toolbar_search_mouse_event():\\n self.toolbar_search.selectAll()\\n self.timer_toolbar_search.start()\\n\\n self.toolbar_search.mousePressEvent = lambda _: toolbar_search_mouse_event()\\n\\n self.toolbar_search.setMaximumWidth(200)\\n self.toolbar_search.setAlignment(Qt.AlignLeft)\\n self.toolbar_search.setPlaceholderText(\\\"Zoek in PDOK Locatieserver\\\")\\n self.toolbar.addWidget(self.toolbar_search)\\n self.timer_toolbar_search = QTimer()\\n self.timer_toolbar_search.setSingleShot(True)\\n self.timer_toolbar_search.setInterval(200)\\n self.timer_toolbar_search.timeout.connect(self.toolbar_search_get_suggestions)\\n self.toolbar_search.textEdited.connect(\\n lambda: self.timer_toolbar_search.start()\\n )\\n\\n eraser_icon = QIcon(\\n os.path.join(self.plugin_dir, \\\"resources\\\", \\\"icon_remove_cross.svg\\\")\\n )\\n self.clean_ls_search_action = QAction(\\n eraser_icon, \\\"Cleanup\\\", self.erase_address()\\n )\\n\\n if not self.show_ls_feature():\\n self.toolbar.addAction(self.clean_ls_search_action)\\n\\n self.clean_ls_search_action.triggered.connect(self.erase_address)\\n self.clean_ls_search_action.setEnabled(False)\\n self.iface.addPluginToMenu(f\\\"&{PLUGIN_NAME}\\\", self.run_action)\\n\\n self.about_action = QAction(self.run_icon, \\\"About\\\", self.iface.mainWindow())\\n self.about_action.setWhatsThis(f\\\"{PLUGIN_NAME} About\\\")\\n self.iface.addPluginToMenu(f\\\"&{PLUGIN_NAME}\\\", self.about_action)\\n\\n self.about_action.triggered.connect(self.about)\\n self.dlg.ui.btnLoadLayer.clicked.connect(lambda: self.load_layer(\\\"default\\\"))\\n self.dlg.ui.btnLoadLayerTop.clicked.connect(lambda: self.load_layer(\\\"top\\\"))\\n self.dlg.ui.btnLoadLayerBottom.clicked.connect(\\n lambda: self.load_layer(\\\"bottom\\\")\\n )\\n\\n self.dlg.ui.pushButton.clicked.connect(self.toggle_all_fq_checkboxes)\\n\\n self.dlg.geocoder_search.returnPressed.connect(\\n self.ls_dialog_get_suggestions_and_remove_pointer\\n )\\n self.timer_geocoder_search = QTimer()\\n self.timer_geocoder_search.setSingleShot(True)\\n self.timer_geocoder_search.setInterval(200)\\n self.timer_geocoder_search.timeout.connect(\\n self.ls_dialog_get_suggestions_and_remove_pointer\\n )\\n self.dlg.geocoder_search.textEdited.connect(\\n lambda: self.timer_geocoder_search.start()\\n )\\n\\n self.dlg.geocoder_search.setPlaceholderText(\\n \\\"Zoek in PDOK Locatieserver, bv postcode of postcode huisnummer\\\"\\n )\\n\\n self.dlg.geocoderResultSearch.textChanged.connect(self.filter_geocoder_result)\\n self.dlg.geocoderResultSearch.setPlaceholderText(\\n \\\"een of meer zoekwoorden uit resultaat\\\"\\n )\\n # connect all fq checkboxes with suggest, so upon a change in fq filter we re-search\\n for cbx in self.fq_checkboxes.keys():\\n cbx.stateChanged.connect(self.ls_dialog_get_suggestions_and_remove_pointer)\\n self.run(True)\\n\\n # set to hidden when no layer selected\\n self.dlg.ui.layer_info.setHidden(True)\\n self.dlg.ui.layer_options_groupbox.setHidden(True)\",\n \"def prepare_UI(self):\",\n \"def initGui(self):\\n self.sketchButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'sketch.svg'),\\n text=self.tr('Sketch on map'),\\n callback=self.sketchAction,\\n parent=self.iface.mainWindow(),\\n object_name='mSketchAction')\\n self.penButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'pen.svg'),\\n text=self.tr('Draw line on map'),\\n callback=self.penAction,\\n parent=self.iface.mainWindow(),\\n object_name='mPenAction')\\n self.canvasButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'canvas.svg'),\\n text=self.tr('Color and width canvas'),\\n callback=None,\\n parent=self.iface.mainWindow())\\n self.eraseButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'erase.svg'),\\n text=self.tr('Erase sketches'),\\n callback=self.eraseAction,\\n parent=self.iface.mainWindow(),\\n object_name='mEraseAction')\\n self.removeButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'remove.svg'),\\n text=self.tr('Remove all sketches'),\\n callback=self.removeSketchesAction,\\n parent=self.iface.mainWindow(),\\n object_name='mRemoveAllSketches')\\n self.noteButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'note.svg'),\\n text=self.tr('Add text annotations to sketches'),\\n callback=None,\\n parent=self.iface.mainWindow(),\\n object_name='mAddTextAnnotations')\\n self.convertButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'toLayer.svg'),\\n text=self.tr('Convert annotations to Memory Layer'),\\n callback=self.toMemoryLayerAction,\\n parent=self.iface.mainWindow(),\\n object_name='mConvertAnnotationsToMemoryLayer')\\n self.saveButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'inbox.svg'),\\n text=self.tr('Save sketches to file'),\\n callback=self.saveAction,\\n parent=self.iface.mainWindow(),\\n object_name='mSaveSketchesToFile')\\n self.loadButton = self.add_action(\\n path.join(self.plugin_dir, 'icons', 'outbox.svg'),\\n text=self.tr('Load sketches from file'),\\n callback=self.loadAction,\\n parent=self.iface.mainWindow(),\\n object_name='mLoadSketchesFromFile')\\n self.canvasButton.setMenu(self.canvasMenu())\\n self.noteButton.setCheckable(True)\\n self.penButton.setCheckable(True)\\n self.sketchButton.setCheckable(True)\\n self.eraseButton.setCheckable(True)\\n self.geoSketches = []\\n self.dumLayer = QgsVectorLayer(\\\"Point?crs=EPSG:4326\\\", \\\"temporary_points\\\", \\\"memory\\\")\\n self.pressed = None\\n self.previousPoint = None\\n self.previousMoved = None\\n self.gestures = 0\\n self.points = 0\\n self.currentColor = QColor(\\\"#aa0000\\\")\\n self.currentWidth = 5\\n self.annotation = sketchNoteDialog(self.iface)\\n self.annotatatedSketch = None\\n self.sketchEnabled(None)\\n self.iface.projectRead.connect(self.projectReadAction)\\n self.iface.newProjectCreated.connect(self.newProjectCreatedAction)\\n QgsProject.instance().legendLayersAdded.connect(self.notSavedProjectAction)\",\n \"def initUI(self):\\n self.logger.debug('Setting up the Measurement GUI')\\n self.setWindowTitle(self.title)\\n\\n self.show()\\n\\n self.make_combobox_scanner()\\n self.make_combobox_movements()\\n self.make_combobox_configurate()\\n self.make_combobox_basic()\",\n \"def __init__(self):\\n super(QTUIProject, self).__init__()\\n self.setupUi(self)\\n self.assignWidgets()\\n self.show()\\n self.SlotsJsonName = \\\"Slots Assets Folder\\\"\\n self.BingoJsonName = \\\"Bingo Assets Folder\\\"\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/HERE_RTTI_Archive_Miner/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Mapping GPS trace to Traffic Archive'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n init_config_path = './here_rtti_miner_config.ini'\\n if os.path.exists(init_config_path):\\n init_config = open(init_config_path, mode='r')\\n f = init_config.readlines()\\n if len(f) == 3:\\n self.iface.messageBar().pushMessage(\\\"Info\\\", '{}\\\\\\\\here_rtti_miner_config.ini loaded.'.format(\\n os.path.abspath(os.path.curdir)), level=QgsMessageBar.INFO, duration=1)\\n app_id = (f[0].replace('\\\\n', ''))\\n self.dlg.app_id_textbox.setPlainText(app_id)\\n app_code = (f[1].replace('\\\\n', ''))\\n self.dlg.app_code_textbox.setPlainText(app_code)\\n archive_path = (f[2].replace('\\\\n', ''))\\n self.dlg.archive_textbox.setPlainText(archive_path)\\n\\n \\\"\\\"\\\"Checkbox Listener\\\"\\\"\\\"\\n self.cb = self.dlg.date_time_override_checkBox\\n self.cb.toggle()\\n self.cb.setChecked(False)\\n self.dlg.connect(self.cb, QtCore.SIGNAL('stateChanged(int)'), self.change_state)\\n\\n \\\"\\\"\\\"DTM preload\\\"\\\"\\\"\\n self.dtm = self.dlg.dateTimeEdit\\n # get current date and time\\n now = QtCore.QDateTime.currentDateTime()\\n # set current date and time to the object\\n self.dtm.setDateTime(now)\",\n \"def initGui(self):\\r\\n\\r\\n # Create help action \\r\\n self.helpAction = QAction( QIcon(\\\":/plugins/layercombinations/about.png\\\"), u\\\"Help\\\", self.iface.mainWindow())\\r\\n # connect the action \\r\\n self.helpAction.triggered.connect( self.showHelp )\\r\\n # Add menu item\\r\\n self.iface.addPluginToMenu(u\\\"&Layer Combinations\\\", self.helpAction)\\r\\n\\r\\n # Create the action that allows to change the widget type\\r\\n self.changeWidgetAction = QAction(\\\"Change widget type\\\", self.iface.mainWindow())\\r\\n self.changeWidgetAction.triggered.connect( self.changeWidget )\\r\\n self.iface.addPluginToMenu(u\\\"&Layer Combinations\\\", self.changeWidgetAction)\\r\\n\\r\\n # Create the action that will toggle the plugin panel\\r\\n self.action = QAction(QIcon(\\\":/plugins/layercombinations/icon.png\\\"), \\\"Show/hide the Layer Combinations widgets\\\", self.iface.mainWindow())\\r\\n self.action.triggered.connect( self.widget.toggle )\\r\\n # Add toolbar button and menu item\\r\\n self.iface.addToolBarIcon(self.action)\\r\\n self.iface.addPluginToMenu(u\\\"&Layer Combinations\\\", self.action)\\r\\n\\r\\n\\r\\n # Add the widget to the mainWindow\\r\\n self.widget.addToiFace(self.iface)\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def init_ui(self):\\n raise NotImplementedError\",\n \"def on_init(self, queue=None, **kwargs):\\n self.queue = queue if queue else Queue()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/roadsalt_mapbuilder/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'&Road Salt Map Builder'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def __init__(self):\\n Redrawable.all_instantiated_widgets.append(self)\",\n \"def _initDialog(self):\\n\\n # ===== Configure focus policy ===== #\\n self.setFocusPolicy(Qt.NoFocus)\\n self.setFocus(False)\\n\\n # ===== Create movie parameters ===== #\\n movieLabel = QLabel()\\n self._movie = QMovie('./icons/loading.gif')\\n movieLabel.setMovie(self._movie)\\n\\n # ===== Create layout ===== #\\n layout = QVBoxLayout()\\n layout.setContentsMargins(0, 0, 0, 0)\\n layout.setSpacing(0)\\n layout.addWidget(movieLabel)\\n self.setLayout(layout)\",\n \"def init_queues(self):\\n for step in self.get_children():\\n if step.kind == StepKinds.queue:\\n step.init_object(self.context, None)\",\n \"def _initJobs(self):\\n pass\",\n \"def _init_widgets(self):\\n comps = self.ui.component_list\\n comps.addItems(sorted(self._labels.keys()))\\n data = self.ui.data_list\\n data.addItems(sorted(self._data.keys()))\",\n \"def _init_ui(self):\\n self.setWindowTitle(\\\"HB Havens: resultaten\\\")\\n self.setWindowFlags(self.windowFlags() & ~QtCore.Qt.WindowContextHelpButtonHint)\\n\\n self.setLayout(QtWidgets.QVBoxLayout())\\n\\n # Create figure\\n self.figure = Figure(figsize=(4,4))\\n self.ax = self.figure.add_subplot()\\n\\n self.ax.grid()\\n self.ax.spines['right'].set_visible(False)\\n self.ax.spines['top'].set_visible(False)\\n self.ax.tick_params(axis='y', color='0.75')\\n self.ax.tick_params(axis='x', color='0.75')\\n self.ax.set_aspect(1)\\n\\n # Add canvas\\n self.canvas = FigureCanvasQTAgg(self.figure)\\n\\n # this is the Navigation widget\\n # it takes the Canvas widget and a parent\\n self.layout().addWidget(self.canvas)\\n\\n # Add location selection\\n hbox = QtWidgets.QHBoxLayout()\\n label = QtWidgets.QLabel('Locatie:')\\n label.setFixedWidth(80)\\n hbox.addWidget(label)\\n self.location_combobox = QtWidgets.QComboBox()\\n self.location_combobox.addItems(self.result_locations)\\n self.location_combobox.setCurrentIndex(self.locid)\\n self.location_combobox.currentIndexChanged.connect(self._set_location)\\n hbox.addWidget(self.location_combobox)\\n self.layout().addLayout(hbox)\\n\\n # Add parameter selection\\n hbox = QtWidgets.QHBoxLayout()\\n label = QtWidgets.QLabel('Parameter:')\\n label.setFixedWidth(80)\\n hbox.addWidget(label)\\n self.parameter_combobox = QtWidgets.QComboBox()\\n self.input_parameters = self.modelunctab.mainmodel.hydraulic_loads.result_columns[:]\\n self.parameter_combobox.addItems(self.input_parameters)\\n self.parameter_combobox.currentIndexChanged.connect(self._set_parameter)\\n self.parameter_combobox.setCurrentIndex(0)\\n self._set_parameter()\\n self.figure.tight_layout()\\n hbox.addWidget(self.parameter_combobox)\\n self.layout().addLayout(hbox)\\n\\n # Line\\n line = QtWidgets.QFrame()\\n line.setFrameShape(QtWidgets.QFrame.HLine)\\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\\n\\n self.layout().addWidget(line)\\n\\n # Add ok/close\\n self.closebutton = QtWidgets.QPushButton('Sluiten')\\n self.closebutton.clicked.connect(self.close)\\n self.layout().addWidget(self.closebutton, 0, QtCore.Qt.AlignRight)\\n\\n self.layout().setSizeConstraint(QtWidgets.QLayout.SetFixedSize)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/AreaPrinter/mountainIcon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'AreaPrinter'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n\\tself.add_action(\\n icon_path,\\n text=self.tr(u'Grid Convergence'),\\n callback=self.runTools,\\n parent=self.iface.mainWindow())\",\n \"def run(self):\\n\\n # Create the dialog with elements (after translation) and keep reference\\n # Only create GUI ONCE in callback, so that it will only load when the plugin is started\\n if self.first_start == True:\\n self.first_start = False\\n self.dlg = RoadSaltMapDialog()\\n self.dlg.setWindowTitle(\\\"Road Salt Map Builder\\\")\\n#map 1\\n self.dlg.tb_outMap1.clicked.connect(self.select_outputmap1_file)\\n self.dlg.tb_inBasin_1.clicked.connect(self.select_basin1_file)\\n self.dlg.tb_inRoad_1.clicked.connect(self.select_road1_file)\\n self.dlg.pb_ok_1.clicked.connect(self.run_method_1)\\n#map 2\\n self.dlg.tb_outMap2.clicked.connect(self.select_outputmap2_file)\\n self.dlg.tb_inBasin_2.clicked.connect(self.select_basin2_file)\\n self.dlg.tb_inRoad_2.clicked.connect(self.select_road2_file)\\n self.dlg.tb_inSalt.clicked.connect(self.select_salt_file)\\n self.dlg.pb_ok_2.clicked.connect(self.run_method_2)\\n self.dlg.cb_colDate.currentIndexChanged.connect(self.change_interval)\\n\\n # Fetch the currently loaded layers\\n layers = QgsProject.instance().layerTreeRoot().children()\\n # Clear the contents of the comboBox and lineEdit from previous runs\\n#map 1\\n self.dlg.l_outMap1.clear()\\n self.dlg.l_inBasin_1.clear()\\n self.dlg.l_inRoad_1.clear()\\n#map 2\\n self.dlg.l_outMap2.clear()\\n self.dlg.l_inBasin_2.clear()\\n self.dlg.l_inRoad_2.clear()\\n self.dlg.l_inSalt.clear()\\n self.dlg.cb_colSalt.clear()\\n self.dlg.cb_colDate.clear()\\n self.dlg.de_starttime.clear()\\n self.dlg.de_endtime.clear()\\n\\n # show the dialog\\n self.dlg.show()\\n # Run the dialog event loop\\n result = self.dlg.exec_()\\n # See if OK was pressed\\n if result:\\n # Do something useful here - delete the line containing pass and\\n # substitute with your code.\\n pass\",\n \"def initGui(self):\\n\\n self.action = QAction(QIcon(self.plugdir + '/icon.png'), 'MGB Water Balance', self.iface.mainWindow())\\n self.action.triggered.connect(self.run)\\n self.iface.addToolBarIcon(self.action)\\n self.iface.addPluginToMenu('&IPH - Plugins', self.action)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/PluginChoucas/img/loaddata.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Chargement de données de montagne'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n \\n icon_path = ':/plugins/PluginChoucas/img/loupe.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Recherche par mots sur clé'),\\n callback=self.search,\\n parent=self.iface.mainWindow())\\n \\n icon_path = ':/plugins/PluginChoucas/img/EmpriseZoneEtude.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Emprise ZE'),\\n callback=self.loadZoneEtude,\\n parent=self.iface.mainWindow())\\n \\n \\n #icon_path = ':/plugins/PluginChoucas/img/landmark.png'\\n #self.add_action(\\n # icon_path,\\n # text=self.tr(u'Afficher la description de l itinéraire'),\\n # callback=self.displayDescription,\\n # parent=self.iface.mainWindow())\",\n \"def initialize(self):\\n super(QtBaseWidgetComponent, self).initialize()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/obstacle_faa_dof_db/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'ObstacleFAADigitialObstacleFileDB'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/EU_Mapper/EUICON.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Generate EU Map'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def __init__(self, dataset, training_data):\\n super().__init__()\\n self.threadpool = QThreadPool()\\n self.setFixedSize(1000, 600)\\n self.center()\\n self.setWindowTitle('GA practice') \\n self.show()\\n #read the map and training data\\n self.map_datalist = dataset.keys()\\n self.map_data = dataset \\n self.training_datalist = training_data.keys()\\n self.training_data = training_data\\n\\n #creat file choosing area\\n self.file_run_creation(self.map_datalist, self.training_datalist)\\n \\n self.operation_parameter_creation()\\n self.ouput_text_creation()\\n hbox = QHBoxLayout()\\n vbox = QVBoxLayout()\\n vbox.addWidget(self.file_run)\\n vbox.addWidget(self.operation_type)\\n vbox.addWidget(self.text_group_box)\\n hbox.addLayout(vbox)\\n self.m = PlotCanvas(self.map_data)\\n hbox.addWidget(self.m)\\n self.setLayout(hbox)\",\n \"def init_UI(self):\\n # widgets\\n self.modeComboBox = QtWidgets.QComboBox()\\n self.updateBtn = QtWidgets.QPushButton('Force Update')\\n self.pauseBtn = QtWidgets.QPushButton()\\n self.pauseBtn.setCheckable(True)\\n\\n self.clearBtn = QtWidgets.QPushButton()\\n self.clearBtn.setIcon(QtGui.QIcon(IconPaths.ICON_CLEAR_FILES))\\n self.filterBtn = QtWidgets.QToolButton()\\n self.filterBtn.setIcon(QtGui.QIcon(IconPaths.ICON_FILTER))\\n self.filterBtn.setMinimumWidth(35)\\n self.filterBtn.setStyleSheet('QToolButton::menu-indicator {subcontrol-position: center right; height: 7px}')\\n self.filterBtn.setPopupMode(QtWidgets.QToolButton.InstantPopup)\\n self.prefBtn = QtWidgets.QToolButton()\\n self.prefBtn.setIcon(QtGui.QIcon(IconPaths.ICON_SETTINGS))\\n #self.pBar = QtWidgets.QProgressBar()\\n self.itemView = LocalizeView()\\n self.itemView.setModel(self.proxy_model)\\n self.autoScrollCB = QtWidgets.QCheckBox('Auto scroll to localizing files')\\n self.autoScrollCB.setChecked(True)\\n\\n # tweak sizes so the widgets all line up vertically with Nuke's style\\n self.modeComboBox.setMinimumHeight(self.updateBtn.sizeHint().height())\\n self.pauseBtn.setMaximumSize(self.updateBtn.sizeHint())\\n self.clearBtn.setMaximumSize(self.updateBtn.sizeHint())\\n\\n # mode menu\\n self.modeLabel = QtWidgets.QLabel('Mode')\\n self.modeComboBox.addItems(['On', 'Manual', 'Off'])\\n\\n # update menu\\n self.updateMenu = QtWidgets.QMenu()\\n self.act_forceUpdateAll = QtWidgets.QAction('All', self)\\n self.act_forceUpdateSelectedNodes = QtWidgets.QAction('Selected', self)\\n self.act_forceUpdateOnDemand = QtWidgets.QAction('On demand only', self) \\n self.updateMenu.addAction(self.act_forceUpdateAll)\\n self.updateMenu.addAction(self.act_forceUpdateSelectedNodes)\\n self.updateMenu.addAction(self.act_forceUpdateOnDemand)\\n self.updateBtn.setMenu(self.updateMenu)\\n\\n # clear menu\\n self.clearMenu = QtWidgets.QMenu()\\n self.clearMenu.addAction(QtWidgets.QAction('All local files', self, triggered=self.__delete_all_local_files))\\n self.clearMenu.addAction(QtWidgets.QAction('Unused local files', self, triggered=_open_delete_dialog))\\n self.clearBtn.setMenu(self.clearMenu)\\n\\n # filter menu\\n self.filterMenu = QtWidgets.QMenu(self.filterBtn)\\n self.act_filter_all = QtWidgets.QAction('All', self.filterMenu, checkable=True)\\n self.act_filter_in_progress = QtWidgets.QAction('In Progress', self.filterMenu, checkable=True)\\n self.act_filter_up_to_date = QtWidgets.QAction('Up to date', self.filterMenu, checkable=True)\\n self.act_filter_out_of_date = QtWidgets.QAction('Out of date', self.filterMenu, checkable=True)\\n self.act_filter_from_source = QtWidgets.QAction('Reading from source', self.filterMenu, checkable=True)\\n self.act_filter_disabled = QtWidgets.QAction('Disabled', self.filterMenu, checkable=True)\\n self.act_filter_not_localized = QtWidgets.QAction('Not Localized', self.filterMenu, checkable=True)\\n\\n self.act_filter_in_progress.setData(Status.IN_PROGRESS)\\n self.act_filter_up_to_date.setData(Status.UP_TO_DATE)\\n self.act_filter_out_of_date.setData(Status.OUT_OF_DATE)\\n self.act_filter_from_source.setData(Status.READ_FROM_SOURCE)\\n self.act_filter_disabled.setData(Status.DISABLED)\\n self.act_filter_not_localized.setData(Status.NOT_LOCALIZED)\\n for act in (self.act_filter_all, self.act_filter_in_progress, self.act_filter_up_to_date, self.act_filter_out_of_date,\\n self.act_filter_from_source, self.act_filter_disabled, self.act_filter_not_localized):\\n self.filterMenu.addAction(act)\\n self.filterBtn.setMenu(self.filterMenu)\\n\\n # tooltips\\n self.modeComboBox.setToolTip('Sets the global localization mode.\\\\nThis is the same as using the options in the Cache/Localization/Mode menu.')\\n self.updateBtn.setToolTip('Forces the update of localized files.\\\\nThis is the same as using the options in the Cache/Localization/Force Update menu.')\\n self.pauseBtn.setToolTip('Pauses/Resumes file localization.\\\\nThis is the same as Cache/Localization/Pause.')\\n self.clearBtn.setToolTip('''Allows for clearing localized files.\\\\nTwo modes are supported:\\n \\\"All local files\\\" - this will delete all files in {}\\n \\\"Unused local files\\\" - this will only delete unused local files (same as Cache/Localization/Clear Unused Local Files)'''.format(nuke.toNode('preferences')['localCachePath'].evaluate()))\\n self.filterBtn.setToolTip('Sets a view filter the table.')\\n self.prefBtn.setToolTip('Open the preferences.')\\n\\n # layouts\\n layout = QtWidgets.QVBoxLayout()\\n btnLayout = QtWidgets.QHBoxLayout()\\n btnLayout.addWidget(self.modeLabel)\\n btnLayout.addWidget(self.modeComboBox)\\n btnLayout.addWidget(self.updateBtn)\\n btnLayout.addWidget(self.pauseBtn)\\n btnLayout.addWidget(self.clearBtn)\\n btnLayout.addStretch()\\n btnLayout.addWidget(self.filterBtn)\\n btnLayout.addWidget(self.prefBtn)\\n layout.addLayout(btnLayout)\\n #layout.addWidget(self.pBar)\\n layout.addWidget(self.itemView)\\n layout.addWidget(self.autoScrollCB)\\n layout.setAlignment(self.autoScrollCB, QtCore.Qt.AlignRight)\\n self.setLayout(layout)\",\n \"def initWidgets(self):\\r\\n if self.autoExampleWidgets:\\r\\n self.initExampleWidgets()\",\n \"def initGui(self):\\n self.network_layer_notifier = QgepLayerNotifier(\\n self.iface.mainWindow(), [\\\"vw_network_node\\\", \\\"vw_network_segment\\\"]\\n )\\n self.wastewater_networkelement_layer_notifier = QgepLayerNotifier(\\n self.iface.mainWindow(), [\\\"vw_wastewater_node\\\", \\\"vw_qgep_reach\\\"]\\n )\\n self.toolbarButtons = []\\n\\n # Create toolbar button\\n self.profileAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/wastewater-profile.svg\\\")),\\n self.tr(\\\"Profile\\\"),\\n self.iface.mainWindow(),\\n )\\n self.profileAction.setWhatsThis(self.tr(\\\"Reach trace\\\"))\\n self.profileAction.setEnabled(False)\\n self.profileAction.setCheckable(True)\\n self.profileAction.triggered.connect(self.profileToolClicked)\\n\\n self.downstreamAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/wastewater-downstream.svg\\\")),\\n self.tr(\\\"Downstream\\\"),\\n self.iface.mainWindow(),\\n )\\n self.downstreamAction.setWhatsThis(self.tr(\\\"Downstream reaches\\\"))\\n self.downstreamAction.setEnabled(False)\\n self.downstreamAction.setCheckable(True)\\n self.downstreamAction.triggered.connect(self.downstreamToolClicked)\\n\\n self.upstreamAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/wastewater-upstream.svg\\\")),\\n self.tr(\\\"Upstream\\\"),\\n self.iface.mainWindow(),\\n )\\n self.upstreamAction.setWhatsThis(self.tr(\\\"Upstream reaches\\\"))\\n self.upstreamAction.setEnabled(False)\\n self.upstreamAction.setCheckable(True)\\n self.upstreamAction.triggered.connect(self.upstreamToolClicked)\\n\\n self.wizardAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/wizard.svg\\\")),\\n \\\"Wizard\\\",\\n self.iface.mainWindow(),\\n )\\n self.wizardAction.setWhatsThis(self.tr(\\\"Create new manholes and reaches\\\"))\\n self.wizardAction.setEnabled(False)\\n self.wizardAction.setCheckable(True)\\n self.wizardAction.triggered.connect(self.wizard)\\n\\n self.connectNetworkElementsAction = QAction(\\n QIcon(\\n os.path.join(\\n plugin_root_path(), \\\"icons/link-wastewater-networkelement.svg\\\"\\n )\\n ),\\n QApplication.translate(\\\"qgepplugin\\\", \\\"Connect wastewater networkelements\\\"),\\n self.iface.mainWindow(),\\n )\\n self.connectNetworkElementsAction.setEnabled(False)\\n self.connectNetworkElementsAction.setCheckable(True)\\n self.connectNetworkElementsAction.triggered.connect(self.connectNetworkElements)\\n\\n self.refreshNetworkTopologyAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/refresh-network.svg\\\")),\\n \\\"Refresh network topology\\\",\\n self.iface.mainWindow(),\\n )\\n self.refreshNetworkTopologyAction.setWhatsThis(\\n self.tr(\\\"Refresh network topology\\\")\\n )\\n self.refreshNetworkTopologyAction.setEnabled(False)\\n self.refreshNetworkTopologyAction.setCheckable(False)\\n self.refreshNetworkTopologyAction.triggered.connect(\\n self.refreshNetworkTopologyActionClicked\\n )\\n\\n self.aboutAction = QAction(self.tr(\\\"About\\\"), self.iface.mainWindow())\\n self.aboutAction.triggered.connect(self.about)\\n\\n self.settingsAction = QAction(self.tr(\\\"Settings\\\"), self.iface.mainWindow())\\n self.settingsAction.triggered.connect(self.showSettings)\\n\\n self.importAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/interlis_import.svg\\\")),\\n self.tr(\\\"Import from interlis\\\"),\\n self.iface.mainWindow(),\\n )\\n self.importAction.setWhatsThis(self.tr(\\\"Import from interlis\\\"))\\n self.importAction.setEnabled(False)\\n self.importAction.setCheckable(False)\\n self.importAction.triggered.connect(self.actionImportClicked)\\n\\n self.exportAction = QAction(\\n QIcon(os.path.join(plugin_root_path(), \\\"icons/interlis_export.svg\\\")),\\n self.tr(\\\"Export to interlis\\\"),\\n self.iface.mainWindow(),\\n )\\n self.exportAction.setWhatsThis(self.tr(\\\"Export from interlis\\\"))\\n self.exportAction.setEnabled(False)\\n self.exportAction.setCheckable(False)\\n self.exportAction.triggered.connect(self.actionExportClicked)\\n\\n self.datamodelInitToolAction = QAction(\\n self.tr(\\\"Datamodel tool\\\"), self.iface.mainWindow()\\n )\\n self.datamodelInitToolAction.triggered.connect(self.showDatamodelInitTool)\\n\\n # Add toolbar button and menu item\\n self.toolbar = QToolBar(QApplication.translate(\\\"qgepplugin\\\", \\\"QGEP\\\"))\\n self.toolbar.addAction(self.profileAction)\\n self.toolbar.addAction(self.upstreamAction)\\n self.toolbar.addAction(self.downstreamAction)\\n self.toolbar.addAction(self.wizardAction)\\n self.toolbar.addAction(self.refreshNetworkTopologyAction)\\n self.toolbar.addAction(self.connectNetworkElementsAction)\\n\\n self.iface.addPluginToMenu(\\\"&QGEP\\\", self.profileAction)\\n self.iface.addPluginToMenu(\\\"&QGEP\\\", self.settingsAction)\\n self.iface.addPluginToMenu(\\\"&QGEP\\\", self.aboutAction)\\n\\n admin_mode = QSettings().value(\\\"/QGEP/AdminMode\\\", False)\\n # seems QGIS loads True as \\\"true\\\" on restart ?!\\n if admin_mode and admin_mode != \\\"false\\\":\\n self.toolbar.addAction(self.importAction)\\n self.toolbar.addAction(self.exportAction)\\n self.iface.addPluginToMenu(\\\"&QGEP\\\", self.datamodelInitToolAction)\\n\\n self.iface.addToolBar(self.toolbar)\\n\\n # Local array of buttons to enable / disable based on context\\n self.toolbarButtons.append(self.profileAction)\\n self.toolbarButtons.append(self.upstreamAction)\\n self.toolbarButtons.append(self.downstreamAction)\\n self.toolbarButtons.append(self.wizardAction)\\n self.toolbarButtons.append(self.refreshNetworkTopologyAction)\\n self.toolbarButtons.append(self.importAction)\\n self.toolbarButtons.append(self.exportAction)\\n\\n self.network_layer_notifier.layersAvailable.connect(self.onLayersAvailable)\\n self.network_layer_notifier.layersUnavailable.connect(self.onLayersUnavailable)\\n\\n # Init the object maintaining the network\\n self.network_analyzer = QgepGraphManager()\\n self.network_analyzer.message_emitted.connect(\\n self.iface.messageBar().pushMessage\\n )\\n # Create the map tool for profile selection\\n self.profile_tool = QgepProfileMapTool(\\n self.iface, self.profileAction, self.network_analyzer\\n )\\n self.profile_tool.profileChanged.connect(self.onProfileChanged)\\n\\n self.upstream_tree_tool = QgepTreeMapTool(\\n self.iface, self.upstreamAction, self.network_analyzer\\n )\\n self.upstream_tree_tool.setDirection(\\\"upstream\\\")\\n self.upstream_tree_tool.treeChanged.connect(self.onTreeChanged)\\n self.downstream_tree_tool = QgepTreeMapTool(\\n self.iface, self.downstreamAction, self.network_analyzer\\n )\\n self.downstream_tree_tool.setDirection(\\\"downstream\\\")\\n self.downstream_tree_tool.treeChanged.connect(self.onTreeChanged)\\n\\n self.maptool_connect_networkelements = QgepMapToolConnectNetworkElements(\\n self.iface, self.connectNetworkElementsAction\\n )\\n\\n self.wastewater_networkelement_layer_notifier.layersAvailableChanged.connect(\\n self.connectNetworkElementsAction.setEnabled\\n )\\n\\n self.processing_provider = QgepProcessingProvider()\\n QgsApplication.processingRegistry().addProvider(self.processing_provider)\\n\\n self.network_layer_notifier.layersAdded([])\",\n \"def _initUI(self) -> None:\\n self._createActions()\\n self._addActionsToMoveButtons()\\n self._createToolBar()\\n self._createStatusBar()\\n self._createMainContextMenu()\",\n \"def perform_setup(self):\\n # pylint: disable=attribute-defined-outside-init\\n self.progress_bar_dialog.show()\\n self.progress_bar_dialog.update_progress(0.1)\\n\\n self.builder = Gtk.Builder()\\n self.builder.add_from_file(pkg_resources.resource_filename(\\n 'qubes_config', 'new_qube.glade'))\\n\\n self.main_window = self.builder.get_object('main_window')\\n self.qube_name: Gtk.Entry = self.builder.get_object('qube_name')\\n self.qube_label_combo: Gtk.ComboBox = \\\\\\n self.builder.get_object('qube_label')\\n\\n load_theme(widget=self.main_window,\\n light_theme_path=pkg_resources.resource_filename(\\n 'qubes_config', 'qubes-new-qube-light.css'),\\n dark_theme_path=pkg_resources.resource_filename(\\n 'qubes_config', 'qubes-new-qube-dark.css'))\\n\\n self.progress_bar_dialog.update_progress(0.1)\\n\\n self.template_handler = TemplateHandler(self.builder, self.qapp)\\n\\n self.progress_bar_dialog.update_progress(0.1)\\n\\n self.qube_type_app: Gtk.RadioButton = \\\\\\n self.builder.get_object('qube_type_app')\\n self.qube_type_template: Gtk.RadioButton = \\\\\\n self.builder.get_object('qube_type_template')\\n self.qube_type_standalone: Gtk.RadioButton = \\\\\\n self.builder.get_object('qube_type_standalone')\\n self.qube_type_disposable: Gtk.RadioButton = \\\\\\n self.builder.get_object('qube_type_disposable')\\n\\n self.tooltips = {\\n 'qube_type_app': self.builder.get_object('qube_type_app_q'),\\n 'qube_type_template':\\n self.builder.get_object('qube_type_template_q'),\\n 'qube_type_standalone':\\n self.builder.get_object('qube_type_standalone_q'),\\n 'qube_type_disposable':\\n self.builder.get_object('qube_type_disposable_q')\\n }\\n\\n self.qube_type_app.connect('toggled', self._type_selected)\\n self.qube_type_template.connect('toggled', self._type_selected)\\n self.qube_type_standalone.connect('toggled', self._type_selected)\\n self.qube_type_disposable.connect('toggled', self._type_selected)\\n\\n label_dict = {}\\n for label in self.qapp.labels:\\n label_dict[str(label)] = {\\n 'icon': f'appvm-{label}',\\n 'object': str(label)\\n }\\n\\n self.qube_label_modeler = ImageListModeler(\\n combobox=self.qube_label_combo,\\n value_list=label_dict\\n )\\n\\n self.qube_name.connect('changed', self._name_changed)\\n\\n self.progress_bar_dialog.update_progress(0.1)\\n\\n self.network_selector = NetworkSelector(self.builder, self.qapp)\\n\\n self.progress_bar_dialog.update_progress(0.1)\\n\\n self.app_box_handler = ApplicationBoxHandler(\\n self.builder, self.template_handler)\\n\\n self.progress_bar_dialog.update_progress(0.1)\\n\\n self.advanced_handler = AdvancedHandler(self.builder, self.qapp)\\n\\n self.progress_bar_dialog.update_progress(0.1)\\n\\n self.create_button: Gtk.Button = \\\\\\n self.builder.get_object('create_button')\\n self.create_button.connect('clicked', self._do_create_qube)\\n\\n self.cancel_button: Gtk.Button = \\\\\\n self.builder.get_object('cancel_button')\\n self.cancel_button.connect('clicked', self._quit)\\n\\n self.viewport_handler = ViewportHandler(\\n self.main_window, [self.builder.get_object('main_scrolled_window')])\\n\\n self.main_window.connect('delete-event', self._quit)\\n\\n self.progress_bar_dialog.update_progress(1)\\n self.progress_bar_dialog.hide()\",\n \"def initUI(self):\\n startbtn = QPushButton(\\\"Start Recroding\\\", self)\\n startbtn.move(30, 50)\\n\\n stopbtn = QPushButton(\\\"Stop Recording\\\", self)\\n stopbtn.move(150, 50)\\n\\n initbtn = QPushButton(\\\"Initilize\\\", self)\\n initbtn.move(30, 100)\\n\\n plotbtn = QPushButton(\\\"Plot\\\", self)\\n plotbtn.move(150, 100)\\n\\n startbtn.clicked.connect(self.start_recording)\\n stopbtn.clicked.connect(self.stop_recording)\\n initbtn.clicked.connect(self.init_recording)\\n plotbtn.clicked.connect(self.plot_signals)\\n\\n self.statusBar()\\n self.statusBar().showMessage('Click Init')\\n\\n self.setGeometry(300, 300, 290, 150)\\n self.setWindowTitle('Recorder 1.0')\\n self.setWindowIcon(QIcon(\\\"./Static/Images/icon.jpg\\\"))\\n self.show()\",\n \"def load_objects(self, queue):\\n pass\",\n \"def initGui(self):\\n #icon_path = ':/plugins/QGisImajnetPlugin/icon.png'\\n icon_path = ':/plugins/imajnet-qgis-plugin/resources/img/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Imajnet plugin'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n \\n self._closestImageAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/closestImageMode.png',\\n text=self.tr(u'Closest image'),\\n callback=self.on_closestImage_click,\\n checkable_flag=True,\\n enabled_flag=False,\\n add_to_menu=False,\\n add_to_toolbar=True,\\n status_tip=self.tr(u'Click on the map and get the closest image'),\\n whats_this=self.tr(u'Imajnet closest image tool'),\\n parent=self.iface.mainWindow())\\n \\n self._clickModeAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/clickMode.png',\\n text=self.tr(u'Click mode'),\\n callback=self.on_clickMode_click,\\n checkable_flag=True,\\n enabled_flag=False,\\n add_to_menu=False,\\n add_to_toolbar=True,\\n status_tip=self.tr(u'Click on an object and get an image oriented towards that object'),\\n whats_this=self.tr(u'Imajnet click mode tool'),\\n parent=self.iface.mainWindow())\\n \\n \\n self._aboutAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/about.png',\\n text=self.tr(u'About'),\\n callback=self.on_about_click,\\n enabled_flag=True,\\n add_to_menu=True,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow())\\n \\n self._helpAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/help.png',\\n text=self.tr(u'Help'),\\n callback=self.on_help_click,\\n enabled_flag=True,\\n add_to_menu=True,\\n add_to_toolbar=True,\\n parent=self.iface.mainWindow())\\n \\n self._disconnectAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/logout.png',\\n text=self.tr(u'Disconnect'),\\n callback=self.on_disconnect_click,\\n enabled_flag=False,\\n add_to_menu=True,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow())\\n \\n self._debugModeAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/debug.png',\\n text=self.tr(u'Debug'),\\n callback=self.on_debug_click,\\n enabled_flag=False,\\n add_to_menu=self.isDevMode(),\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow())\\n debugShortcutKeys = QKeySequence(\\\"Ctrl+I\\\")\\n self._debugModeAction.setShortcut(debugShortcutKeys)\\n #self._debugModeAction.setVisible(False)\\n \\n \\n self._jumpForwardAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\\n text=self.tr(u'jump forward'),\\n callback=self.jumpForward,\\n enabled_flag=True,\\n add_to_menu=False,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow())\\n jumpForwardShortcutKeys = QKeySequence(\\\"PgUp\\\")\\n self._jumpForwardAction.setShortcut(jumpForwardShortcutKeys)\\n \\n self._jumpBackwardAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\\n text=self.tr(u'jump backward'),\\n callback=self.jumpBackward,\\n enabled_flag=True,\\n add_to_menu=False,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow()) \\n jumpBackwardShortcutKeys = QKeySequence(\\\"PgDown\\\")\\n self._jumpBackwardAction.setShortcut(jumpBackwardShortcutKeys)\\n \\n \\n self._jumpForwardShortAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\\n text=self.tr(u'jump forward short'),\\n callback=self.jumpForwardShort,\\n enabled_flag=True,\\n add_to_menu=False,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow())\\n jumpForwardShortShortcutKeys = QKeySequence(\\\"Shift+PgUp\\\")\\n self._jumpForwardShortAction.setShortcut(jumpForwardShortShortcutKeys)\\n \\n self._jumpBackwardShortAction = self.add_action(\\n ':/plugins/imajnet-qgis-plugin/resources/img/icon.png',\\n text=self.tr(u'jump backward short'),\\n callback=self.jumpBackwardShort,\\n enabled_flag=True,\\n add_to_menu=False,\\n add_to_toolbar=False,\\n parent=self.iface.mainWindow()) \\n jumpBackwardShortShortcutKeys = QKeySequence(\\\"Shift+PgDown\\\")\\n self._jumpBackwardShortAction.setShortcut(jumpBackwardShortShortcutKeys)\",\n \"def create_widgets(self):\",\n \"def initialize(self):\\n self.setWindowTitle(\\\"Playlist Maker\\\")\\n self.setGeometry(0,0, 800, 494)\\n self.mbox = QVBoxLayout()\\n self.hbox = QHBoxLayout()\\n self.hbtnbox = QHBoxLayout()\",\n \"def Queues(self):\\n if not self._queues:\\n queues = AimsQueueWidget(self.iface.mainWindow())\\n self._dockWindow = DockWindow(self.iface.mainWindow(),queues,\\\"AimsQueues\\\",\\\"Aims Queues\\\")\\n self._queues = queues\\n self._dockWindow.unloadPlugin.connect(self.unload)\\n return self._queues\",\n \"def initCentralUic(self):\\n self.initFileTableWidget()\\n self.initViewerStack()\\n self.splitter.setSizes([150, 850])\",\n \"def initUI(self):\\n centralwidget = QtWidgets.QWidget()\\n self.setCentralWidget(centralwidget)\\n\\n person_table = PersonTable(self)\\n person_table.embed(self.sql)\\n\\n # Box Layout to organize our GUI\\n lay = QtWidgets.QVBoxLayout(centralwidget)\\n lay.addWidget(person_table)\\n self.setGeometry(0, 0, person_table.width() + 20, person_table.height() + 20)\\n self.person_table = person_table\\n self.show()\",\n \"def __init__(self):\\n\\n self.boxes = [\\n urwid.AttrMap(urwid.Text('QMap'), 'menu header'),\\n urwid.Divider('=')\\n ]\\n\\n self.widget = urwid.Padding(None, align='center')\\n self.update()\\n\\n BaseTimedWidgetWrap.__init__(self, self.widget)\",\n \"def init_UI(self):\\r\\n\\t\\t# buttons\\r\\n\\t\\tself.dlg_btns.accepted.connect(self.on_ok_click)\\r\\n\\t\\tself.dlg_btns.rejected.connect(self.on_cancel_click)\\r\\n\\t\\tself.source_img_browse_btn.clicked.connect(self.on_source_img_browse_btn_click)\\r\\n\\t\\tself.sink_dir_browse_btn.clicked.connect(self.on_sink_dir_browse_btn_click)\\r\\n\\r\\n\\t\\t# layouts\\r\\n\\t\\tself.source_img_layout.addWidget(self.source_img_entry)\\r\\n\\t\\tself.source_img_layout.addWidget(self.source_img_browse_btn)\\r\\n\\r\\n\\t\\tself.sink_dir_layout.addWidget(self.sink_dir_entry)\\r\\n\\t\\tself.sink_dir_layout.addWidget(self.sink_dir_browse_btn)\\r\\n\\r\\n\\t\\tself.sink_db_name_layout.addWidget(self.sink_db_name_entry)\\r\\n\\r\\n\\t\\tself.form_layout.addRow(\\\"Image Array Path (.npy): \\\", self.source_img_layout)\\r\\n\\t\\t# if continuing an existing case\\r\\n\\t\\tif self.existing_case:\\r\\n\\t\\t\\tself.source_db_browse_btn.clicked.connect(self.on_source_db_browse_btn_click)\\r\\n\\t\\t\\tself.source_db_layout.addWidget(self.source_db_entry)\\r\\n\\t\\t\\tself.source_db_layout.addWidget(self.source_db_browse_btn)\\r\\n\\t\\t\\tself.form_layout.addRow(\\\"Source Database Path (.db): \\\", self.source_db_layout)\\r\\n\\t\\tself.form_layout.addRow(\\\"Sink Directory (folder): \\\", self.sink_dir_layout)\\r\\n\\t\\tself.form_layout.addRow(\\\"Sink Database Name: \\\", self.sink_db_name_layout)\\r\\n\\r\\n\\t\\tself.dlg_layout.addLayout(self.form_layout)\\r\\n\\t\\tself.dlg_layout.addWidget(self.dlg_btns)\\r\\n\\r\\n\\t\\t# set file dialog properties\\r\\n\\t\\tself.setLayout(self.dlg_layout)\\r\\n\\t\\tself.setGeometry(self.x, self.y, self.width, self.height)\\r\\n\\t\\tself.setWindowTitle(self.title)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/LidarProcessor/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'LiDAR Operations'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\",\n \"def initGui(self):\\n\\n icon_path = ''\\n self.add_action(\\n icon_path,\\n text=self.tr(u'圖資操作處理工具v3.0'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n \\n \\n\\n #照片EXIF定位ImportPhotos初始參數\\n self.clickPhotos = self.add_action(\\n icon_path,\\n text=self.tr(u'檢視照片'),\\n callback=self.mouseClick,\\n parent=self.iface.mainWindow())\\n \\n self.clickPhotos.setCheckable(True)\\n self.clickPhotos.setEnabled(True)\\n # self.iface.addPluginToMenu(TFB_Tools3.description(), self.action)\\n self.listPhotos = []\\n self.layernamePhotos = [] \\n self.toolMouseClick = MouseClick(self.canvas, self)\\n \\n\\n self.fields = ['ID', 'Name', 'Date', 'Time', 'Lon', 'Lat', 'Altitude', 'North', 'Azimuth', 'Camera Maker',\\n 'Camera Model', 'Title', 'Comment', 'Path', 'RelPath', 'Timestamp']\\n\\n self.extension_switch = {\\n \\\".shp\\\": \\\"ESRI Shapefile\\\",\\n \\\".geojson\\\": \\\"GeoJSON\\\",\\n \\\".gpkg\\\":\\\"GPKG\\\",\\n \\\".csv\\\": \\\"CSV\\\",\\n \\\".kml\\\": \\\"KML\\\",\\n \\\".tab\\\": \\\"MapInfo File\\\"\\n }\\n\\n self.extension_switch2 = {\\n \\\"ESRI Shapefile (*.shp *.SHP)\\\": \\\".shp\\\",\\n \\\"GeoJSON (*.geojson *.GEOJSON)\\\": \\\".geojson\\\",\\n \\\"GeoPackage (*.gpkg *.GPKG)\\\":\\\".gpkg\\\",\\n \\\"Comma Separated Value (*.csv *.CSV)\\\": \\\".csv\\\",\\n \\\"Keyhole Markup Language (*.kml *.KML)\\\": \\\".kml\\\",\\n \\\"Mapinfo TAB (*.tab *.TAB)\\\": \\\".tab\\\"\\n }\\n\\n self.extension_switch_types = {\\n \\\".shp\\\": \\\"ESRI Shapefile\\\",\\n \\\".geojson\\\": \\\"GeoJSON\\\",\\n \\\".gpkg\\\":\\\"GPKG\\\",\\n \\\".csv\\\": \\\"CSV\\\",\\n \\\".kml\\\": \\\"KML\\\",\\n \\\".tab\\\": \\\"MapInfo File\\\"\\n }\\n\\n ############################\\n\\n self.pointEmitter = QgsMapToolEmitPoint(self.iface.mapCanvas())\\n self.iface.mapCanvas().setMapTool( self.pointEmitter )\",\n \"def _loader(q: Queue):\\n black = '#000000'\\n white = '#ffffff'\\n log_h = 100\\n logo_fp = APP.R.img('wet_logo.png')\\n log_font_size = 9\\n\\n class Screen:\\n \\\"\\\"\\\"\\n uses win32 api to get active screen dimensions in pixels and inches, w/h ratio, dpi\\n only works on windows\\n \\\"\\\"\\\"\\n _MM_TO_IN = 0.0393700787\\n\\n def __init__(self, window: tk.Tk) -> None:\\n ctypes.windll.shcore.SetProcessDpiAwareness(1)\\n self.resolution_w, self.resolution_h = [win32api.GetSystemMetrics(i) for i in [0, 1]]\\n dc = ctypes.windll.user32.GetDC(window.winfo_id())\\n self.width, self.height = [ctypes.windll.gdi32.GetDeviceCaps(dc, v) * self._MM_TO_IN for v in\\n [4, 6]]\\n self.w_h_ratio, self.dpi = self.width / self.height, int(self.resolution_w / self.width)\\n log.debug(f'measured {repr(self)}')\\n\\n def __repr__(self) -> str:\\n inches = f'{round(self.width, 2)}x{round(self.height, 2)}in'\\n return f'Screen({self.resolution_w}x{self.resolution_h}px {self.dpi}dpi {inches})'\\n\\n class Logo(tk.Label):\\n def __init__(self, parent, image) -> None:\\n kwargs = dict(bg=black, anchor='n', image=image)\\n super().__init__(parent, **kwargs)\\n self.pack(fill=tk.BOTH, expand=1)\\n\\n class LogField(tk.Text):\\n _max_length = 6\\n\\n @staticmethod\\n def truncate(line: str, limit: int) -> str:\\n \\\"\\\"\\\"\\n if a string is longer than limit, return a substring from start-limit\\n \\\"\\\"\\\"\\n if len(line) > limit:\\n return line[:limit]\\n return line\\n\\n def add_line(self, line: str) -> None:\\n self['state'] = 'normal'\\n if self._length == self._max_length:\\n self.delete(1.0, 2.0)\\n self._length -= 1\\n if self.index('end-1c') != '1.0':\\n self.insert('end', '\\\\n')\\n self.insert('end', self._truncate(line))\\n self._length += 1\\n self['state'] = 'disabled'\\n\\n def __init__(self, parent: 'Loader', w: int) -> None:\\n self._length = 0\\n\\n self.font = font.nametofont('TkFixedFont')\\n self.font.config(size=log_font_size)\\n limit = int(w / self.font.measure('m')) - 1\\n self._truncate = partial(self.truncate, limit=limit)\\n\\n super().__init__(parent, state='disabled', wrap='none', relief='flat', cursor='arrow',\\n fg=white, bg=black, selectbackground=black, width=w,\\n inactiveselectbackground=black, font=self.font, height=log_h)\\n self.pack(fill=tk.BOTH, expand=1)\\n\\n class Loader(tk.Tk):\\n # ? https://stackoverflow.com/a/16115616\\n\\n def quit(self) -> None:\\n if self.poll_after:\\n self.after_cancel(self.poll_after)\\n\\n super().quit()\\n\\n def __post_init__(self) -> None:\\n self.screen = Screen(self)\\n self._logo_img = PIL.ImageTk.PhotoImage(self._logo_img)\\n\\n s_w, s_h = self.screen.resolution_w, self.screen.resolution_h\\n w, h = int(self._logo_img.width()), int(self._logo_img.height())\\n x_off, y_off = int((s_w // 2) - (w // 2)), int((s_h // 2) - ((h + log_h) // 2))\\n self.geometry(f'{w}x{h + log_h}+{x_off}+{y_off}')\\n\\n self.logo = Logo(self, self._logo_img)\\n self.log = LogField(self, w)\\n\\n self.poll_after = self.after(100, self.poll)\\n log.info('started polling')\\n\\n self.mainloop()\\n\\n def __init__(self, _q: Queue) -> None:\\n self.q = _q\\n self._logo_img = PIL.Image.open(logo_fp)\\n\\n super().__init__()\\n self.protocol('WM_DELETE_WINDOW', self.quit)\\n self.attributes('-topmost', True)\\n self.overrideredirect(True)\\n\\n self.__post_init__()\\n\\n def poll(self) -> None:\\n while 1:\\n try:\\n msg = self.q.get()\\n\\n except Empty:\\n break\\n\\n else:\\n self.log.add_line(msg)\\n if msg == '$STOP_LOADER$':\\n return self.quit()\\n\\n Loader(q)\",\n \"def setup(self):\\n ScriptedLoadableModuleWidget.setup(self)\\n\\n globalPath, _ = os.path.split( os.path.splitext( slicer.modules.regularizedfastmarching.path )[0] )\\n self.globalPath = globalPath + \\\"/Resources/SegmentationFastMarching/\\\"\\n self.volumesPath = self.globalPath + \\\"Volumes/\\\"\\n self.seedsCsvPath = self.globalPath + \\\"SeedsLabels/\\\"\\n self.seedsPath = self.globalPath + \\\"Seeds/\\\"\\n self.regularizationsPath = self.globalPath + \\\"Regularizations/\\\"\\n self.segmentationsPath = self.globalPath + \\\"Segmentations/\\\"\\n\\n self.logic = RegularizedFastMarchingLogic()\\n # self.logic.previousVolumeName = None\\n # self.logic.imgLabel = np.array([])\\n # self.logic.previousImgIds = np.array([])\\n # self.logic.imgDist = np.array([])\\n\\n # Hide the Slicer Logo to increase space\\n slicer.util.findChild(slicer.util.mainWindow(), 'LogoLabel').visible = False\\n \\n #\\n #region Restart Slicer Area\\n #\\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\\n parametersCollapsibleButton.text = \\\"Reload\\\"\\n self.layout.addWidget(parametersCollapsibleButton)\\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\\n self.restartSlicerButton = qt.QPushButton(\\\"Restart Slicer\\\")\\n self.restartSlicerButton.connect('clicked(bool)', slicer.util.restart)\\n parametersFormLayout.addRow(self.restartSlicerButton)\\n #endregion \\n\\n #\\n #region Load Data Area\\n #\\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\\n parametersCollapsibleButton.text = \\\"Seeds\\\"\\n self.layout.addWidget(parametersCollapsibleButton)\\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\\n \\n #\\n # File name volume .nii\\n # \\n # self.volueNameComboBox = qt.QComboBox() \\n # self.volueNameComboBox = self.fillComboBox(self.volueNameComboBox, self.volumesPath, \\\".nii\\\")\\n # parametersFormLayout.addRow(\\\"Volumes .nii: \\\", self.volueNameComboBox)\\n \\n #\\n # Load volume buttons\\n #\\n # horizontalLayout = qt.QHBoxLayout()\\n # self.loadBrainVolumeButton = qt.QPushButton(\\\"Load Example Volume\\\")\\n # self.loadBrainVolumeButton.enabled = True\\n # horizontalLayout.addWidget(self.loadBrainVolumeButton)\\n \\n #\\n # Load custom volumes buttons\\n #\\n # self.loadCustomVolumeButton = qt.QPushButton(\\\"Load Selected Volume\\\")\\n # self.loadCustomVolumeButton.enabled = True\\n # horizontalLayout.addWidget(self.loadCustomVolumeButton) \\n # parametersFormLayout.addRow(horizontalLayout)\\n \\n \\n #\\n # Add vertical spacing\\n # \\n # verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n # parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Seeds files and a text line to load / save seeds from the scene\\n #\\n horizontalLayout = qt.QHBoxLayout()\\n fileNameSeedsLabel = qt.QLabel(\\\"Seeds: \\\")\\n self.fileNameSeedsComboBox = qt.QComboBox() \\n self.fileNameSeedsComboBox = self.fillComboBox(self.fileNameSeedsComboBox, self.seedsPath, \\\".seed\\\") \\n self.fileNameSeedsLineEdit = qt.QLineEdit()\\n horizontalLayout.addWidget(fileNameSeedsLabel)\\n horizontalLayout.addWidget(self.fileNameSeedsComboBox)\\n horizontalLayout.addWidget(self.fileNameSeedsLineEdit)\\n self.fileNameSeedsComboBox.currentTextChanged.connect(self.setSelectedSeedsFile)\\n self.setSelectedSeedsFile(self.fileNameSeedsComboBox.currentText)\\n parametersFormLayout.addRow(horizontalLayout)\\n \\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Save markers Button\\n #\\n horizontalLayout = qt.QHBoxLayout()\\n self.saveMarkersButton = qt.QPushButton(\\\"Save markers\\\")\\n self.saveMarkersButton.toolTip = \\\"Save the markers in a fcsv file\\\"\\n self.saveMarkersButton.enabled = True\\n horizontalLayout.addWidget(self.saveMarkersButton)\\n \\n #\\n # Load markers Button\\n #\\n self.loadMarkersButton = qt.QPushButton(\\\"Load markers\\\")\\n self.loadMarkersButton.toolTip = \\\"Load the markers from fcsv file.\\\"\\n self.loadMarkersButton.enabled = True\\n horizontalLayout.addWidget(self.loadMarkersButton) \\n \\n #\\n # Clear markups Button\\n #\\n self.clearButton = qt.QPushButton(\\\"Clear all markups\\\")\\n self.clearButton.toolTip = \\\"Remove all fiducial markers.\\\"\\n self.clearButton.enabled = True\\n horizontalLayout.addWidget(self.clearButton)\\n parametersFormLayout.addRow(horizontalLayout)\\n \\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 25, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Fiducial placement button\\n #\\n \\\"\\\"\\\"\\n Add this markup in the markupsList and rename it depending on seeds combobox's value\\n \\\"\\\"\\\"\\n @vtk.calldata_type(vtk.VTK_INT)\\n def onMarkupAdded(caller, event, index): \\n markupsNode = caller\\n # Label, Name\\n seedTokens = self.currentSeedNameComboBox.currentText.replace(\\\" \\\", \\\"\\\").split(\\\":\\\")\\n markupsNode.SetNthFiducialLabel(index, seedTokens[1])\\n markupsNode.SetNthControlPointDescription(index, seedTokens[0])\\n \\n self.w=slicer.qSlicerMarkupsPlaceWidget()\\n self.w.setMRMLScene(slicer.mrmlScene)\\n \\n markupsNode = slicer.mrmlScene.GetFirstNodeByName(\\\"MarkupsFiducial\\\")\\n if markupsNode == None: \\n markupsNode = slicer.mrmlScene.AddNewNodeByClass(\\\"vtkMRMLMarkupsFiducialNode\\\")\\n markupsNode.CreateDefaultDisplayNodes()\\n \\n # On markup added event: format the added markup\\n markupsNode.AddObserver(slicer.vtkMRMLMarkupsNode.PointPositionDefinedEvent, onMarkupAdded)\\n self.w.setCurrentNode(markupsNode)\\n # Hide all buttons and only show place button\\n self.w.buttonsVisible=True\\n self.w.placeButton().show()\\n self.w.show()\\n parametersFormLayout.addRow(self.w)\\n \\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 20, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # File name data's seeds\\n # \\n self.fileNameDataComboBox = qt.QComboBox() \\n self.fileNameDataComboBox = self.fillComboBox(self.fileNameDataComboBox, self.seedsCsvPath, \\\".csv\\\") \\n self.fileNameDataComboBox.currentTextChanged.connect(self.setSeedsLabelFromFile)\\n parametersFormLayout.addRow(\\\"Seeds labels csv: \\\", self.fileNameDataComboBox)\\n\\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n horizontalLayout = qt.QHBoxLayout()\\n # Seeds label \\n self.seedsLabelText = qt.QLabel()\\n self.seedsLabelText.text = \\\"Labels:\\\"\\n horizontalLayout.addWidget(self.seedsLabelText)\\n \\n # ComboxBox containing names and labels\\n self.currentSeedNameComboBox= qt.QComboBox()\\n \\n # Add labels depending on first csv file found\\n self.setSeedsLabelFromFile(self.fileNameDataComboBox.currentText)\\n horizontalLayout.addWidget(self.currentSeedNameComboBox)\\n \\n self.clearOrganButton = qt.QPushButton(\\\"Clear this organ\\\")\\n self.clearOrganButton.toolTip = \\\"Remove all fiducial markers for this organ.\\\"\\n self.clearOrganButton.enabled = True\\n horizontalLayout.addWidget(self.clearOrganButton)\\n parametersFormLayout.addRow(horizontalLayout)\\n \\n #endregion\\n\\n\\n #\\n #region Segmentation Parameters Area\\n #\\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\\n parametersCollapsibleButton.text = \\\"Parameters\\\"\\n self.layout.addWidget(parametersCollapsibleButton)\\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\\n\\n #\\n # input volume selector\\n #\\n self.inputSelector = slicer.qMRMLNodeComboBox()\\n self.inputSelector.nodeTypes = [\\\"vtkMRMLScalarVolumeNode\\\"]\\n self.inputSelector.selectNodeUponCreation = True\\n self.inputSelector.addEnabled = False\\n self.inputSelector.removeEnabled = False\\n self.inputSelector.noneEnabled = False\\n self.inputSelector.showHidden = False\\n self.inputSelector.showChildNodeTypes = False\\n self.inputSelector.setMRMLScene( slicer.mrmlScene )\\n self.inputSelector.setToolTip( \\\"Pick the input to the algorithm.\\\" )\\n parametersFormLayout.addRow(\\\"Input Volume: \\\", self.inputSelector)\\n\\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Segmentation distance to stop the algo\\n #\\n self.distance = ctk.ctkSliderWidget()\\n self.distance.singleStep = 10\\n self.distance.minimum = 0\\n self.distance.maximum = 1000\\n self.distance.value = 100\\n self.distance.setToolTip(\\\"Set the maximum geodesic distance allowed from a seed\\\")\\n parametersFormLayout.addRow(\\\"Distance\\\", self.distance)\\n \\n #\\n # Margin to build masks\\n #\\n self.marginMask = ctk.ctkSliderWidget()\\n self.marginMask.singleStep = 1\\n self.marginMask.minimum = 0\\n self.marginMask.maximum = 50\\n self.marginMask.value = 15\\n self.marginMask.setToolTip(\\\"Margin used to build masks for each seed\\\")\\n parametersFormLayout.addRow(\\\"Mask Margin (voxel)\\\", self.marginMask)\\n\\n #\\n # Gamma to prevent the seeds to cross bridges between organs\\n #\\n self.gammaSlider = qt.QSlider(qt.Qt.Horizontal)\\n self.gammaSlider.setRange(0, 1000)\\n self.gammaSlider.setSingleStep(1)\\n self.gammaSlider.setPageStep(10)\\n self.gammaSlider.setValue(25)\\n\\n self.gammaSpinBox = qt.QDoubleSpinBox()\\n self.gammaSpinBox.setDecimals(3)\\n self.gammaSpinBox.setRange(0, 1)\\n self.gammaSpinBox.setSingleStep(0.001)\\n self.gammaSpinBox.setValue(0.025)\\n\\n self.gammaSlider.valueChanged.connect(self.setGammaValueFromSlider)\\n self.gammaSpinBox.valueChanged.connect(self.setGammaValueFromSpinBox)\\n \\n horizontalLayout = qt.QHBoxLayout()\\n horizontalLayout.addWidget(self.gammaSlider)\\n horizontalLayout.addWidget(self.gammaSpinBox)\\n\\n parametersFormLayout.addRow(\\\"Regularization weight\\\", horizontalLayout)\\n \\n #\\n # Regularization radius\\n #\\n self.regularizationDiameter = ctk.ctkSliderWidget()\\n self.regularizationDiameter.singleStep = 1\\n self.regularizationDiameter.minimum = 1\\n self.regularizationDiameter.maximum = 20\\n self.regularizationDiameter.value = 4\\n self.regularizationDiameter.setToolTip(\\\"Margin used to build masks for each seed\\\")\\n parametersFormLayout.addRow(\\\"Regularization Diameter (voxels)\\\", self.regularizationDiameter)\\n \\n #\\n # Threshold to prevent the seeds to spread over a range intensities\\n #\\n self.minThresholdSlider = ctk.ctkSliderWidget()\\n self.minThresholdSlider.singleStep = 1\\n self.minThresholdSlider.minimum = 0\\n self.minThresholdSlider.maximum = 255\\n self.minThresholdSlider.value = 40\\n self.minThresholdSlider.setToolTip(\\\"Seeds cannot spread below this value\\\")\\n self.minThresholdSlider.valueChanged.connect(self.setMinThresholdValue)\\n parametersFormLayout.addRow(\\\"Min Threshold (digital level)\\\", self.minThresholdSlider)\\n\\n self.maxThresholdSlider = ctk.ctkSliderWidget()\\n self.maxThresholdSlider.singleStep = 1\\n self.maxThresholdSlider.minimum = 0\\n self.maxThresholdSlider.maximum = 255\\n self.maxThresholdSlider.value = 255\\n self.maxThresholdSlider.setToolTip(\\\"Seeds cannot spread over this value\\\")\\n self.maxThresholdSlider.valueChanged.connect(self.setMaxThresholdValue)\\n parametersFormLayout.addRow(\\\"Max Threshold (digital level)\\\", self.maxThresholdSlider)\\n\\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Check box to keep or not last segmentation\\n #\\n self.removeLastSegmentationCheckBox = qt.QCheckBox(\\\"\\\")\\n self.removeLastSegmentationCheckBox.setChecked(True)\\n parametersFormLayout.addRow(\\\"Remove last segmentation\\\", self.removeLastSegmentationCheckBox)\\n \\n #\\n # Show the background seed of the next segmentation\\n #\\n self.showBackGroundCheckBox = qt.QCheckBox(\\\"\\\")\\n self.showBackGroundCheckBox.setChecked(False)\\n parametersFormLayout.addRow(\\\"Show background\\\", self.showBackGroundCheckBox)\\n \\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n \\n #\\n # Segment Button\\n #\\n self.segmentButton = qt.QPushButton(\\\"Segment\\\")\\n self.segmentButton.toolTip = \\\"Run the algorithm.\\\"\\n self.segmentButton.enabled = False\\n parametersFormLayout.addRow(self.segmentButton)\\n #endregion \\n\\n #\\n #region Load Save segmentations\\n #\\n parametersCollapsibleButton = ctk.ctkCollapsibleButton()\\n parametersCollapsibleButton.text = \\\"Segmentations\\\"\\n self.layout.addWidget(parametersCollapsibleButton)\\n parametersFormLayout = qt.QFormLayout(parametersCollapsibleButton)\\n\\n #\\n # Checkbox to save the segmentation using the labels \\n #\\n self.saveByLabelsCheckBox = qt.QCheckBox(\\\"\\\")\\n self.saveByLabelsCheckBox.setChecked(True)\\n parametersFormLayout.addRow(\\\"Save by labels\\\", self.saveByLabelsCheckBox)\\n\\n #\\n # Checkbox to save the segmentation using the voxels intensities \\n #\\n self.saveByIntensitiesCheckBox = qt.QCheckBox(\\\"\\\")\\n self.saveByIntensitiesCheckBox.setChecked(False)\\n parametersFormLayout.addRow(\\\"Save by intensities\\\", self.saveByIntensitiesCheckBox)\\n\\n #\\n # Checkbox to generate segments data \\n #\\n self.generateDataCsvCheckBox = qt.QCheckBox(\\\"\\\")\\n self.generateDataCsvCheckBox.setChecked(False)\\n parametersFormLayout.addRow(\\\"Generate segments data CSV\\\", self.generateDataCsvCheckBox)\\n\\n\\n #\\n # Name of the segmentation to save\\n #\\n self.saveSegmentationName = qt.QLineEdit()\\n self.saveSegmentationName.textChanged.connect(self.onSaveSegmentationFileChange)\\n parametersFormLayout.addRow(\\\"Segmentation name:\\\", self.saveSegmentationName) \\n\\n #\\n # Save segmentationButton Button\\n #\\n self.saveSegmentationButton = qt.QPushButton(\\\"Save segmentation\\\")\\n self.saveSegmentationButton.toolTip = \\\"Save this segmentation with used parameters.\\\"\\n parametersFormLayout.addRow(self.saveSegmentationButton) \\n\\n #\\n # Add vertical spacing\\n # \\n verticalSpacer = qt.QSpacerItem(0, 30, qt.QSizePolicy.Minimum, qt.QSizePolicy.Expanding)\\n parametersFormLayout.addItem(verticalSpacer)\\n\\n horizontalLayout = qt.QHBoxLayout()\\n self.segmentationLoadLabelText = qt.QLabel()\\n self.segmentationLoadLabelText.text = \\\"Segmentation files:\\\"\\n horizontalLayout.addWidget(self.segmentationLoadLabelText)\\n \\n #\\n # Segmentation files \\n # \\n self.segmentationFilesComboBox = qt.QComboBox() \\n self.segmentationFilesComboBox = self.fillComboBox(self.segmentationFilesComboBox, self.segmentationsPath, \\\".seg.nrrd\\\")\\n horizontalLayout.addWidget(self.segmentationFilesComboBox)\\n parametersFormLayout.addRow(horizontalLayout)\\n \\n #\\n # Load segmentationButton Button\\n #\\n self.loadSegmentationButton = qt.QPushButton(\\\"Load segmentation\\\")\\n self.loadSegmentationButton.toolTip = \\\"Load the segmenation from this file.\\\"\\n parametersFormLayout.addRow(self.loadSegmentationButton) \\n #end region\\n \\n\\n #\\n #region Connections & Shortcuts\\n #\\n # self.loadBrainVolumeButton.connect('clicked(bool)', self.onLoadBrainVolumeButton)\\n # self.loadCustomVolumeButton.connect('clicked(bool)', self.onLoadCustomVolumeButton)\\n \\n self.clearButton.connect('clicked(bool)', self.onClearButton)\\n self.clearOrganButton.connect('clicked(bool)', self.onClearOrganButton)\\n self.segmentButton.connect('clicked(bool)', self.onSegmentButton)\\n self.saveSegmentationButton.connect('clicked(bool)', self.onSaveSegmentationButton)\\n self.loadSegmentationButton.connect('clicked(bool)', self.onLoadSegmentationButton)\\n self.saveMarkersButton.connect('clicked(bool)', self.onSaveMarkersButton)\\n self.loadMarkersButton.connect('clicked(bool)', self.onLoadMarkersButton)\\n self.inputSelector.connect(\\\"currentNodeChanged(vtkMRMLNode*)\\\", self.onSelect)\\n \\n # Keyboard shortcut bind: press ctrl button to add markup \\n markupShortcut = qt.QShortcut(slicer.util.mainWindow())\\n markupShortcut.setKey( qt.QKeySequence(\\\"w\\\") )\\n markupShortcut.connect('activated()', self.addMarkupcallback)\\n \\n markupShortcut = qt.QShortcut(slicer.util.mainWindow())\\n markupShortcut.setKey( qt.QKeySequence(\\\"x\\\") )\\n markupShortcut.connect('activated()', self.addBgMarkupcallback)\\n #endregion \\n\\n # Refresh Apply button state\\n self.onSelect()\\n \\n # Refresh segmentation save button state\\n self.onSaveSegmentationFileChange()\",\n \"def initialize_gui(self) -> None:\\n # pymol.Qt provides the PyQt5 interface\\n from PyQt5 import QtWidgets\\n from PyQt5.uic import loadUi\\n # from pymol.Qt.utils import loadUi\\n\\n # populate the QMainWindow from our *.ui file\\n uifile = os.path.join(os.path.dirname(__file__), 'PyMOL-KVFinder-web-tools.ui')\\n loadUi(uifile, self)\\n\\n # ScrollBars binded to QListWidgets in Descriptors\\n scroll_bar_volume = QtWidgets.QScrollBar(self)\\n self.volume_list.setVerticalScrollBar(scroll_bar_volume)\\n scroll_bar_area = QtWidgets.QScrollBar(self)\\n self.area_list.setVerticalScrollBar(scroll_bar_area)\\n scroll_bar_residues = QtWidgets.QScrollBar(self)\\n self.residues_list.setVerticalScrollBar(scroll_bar_residues)\\n\\n # about text\\n self.about_text.setHtml(about_text)\\n\\n ########################\\n ### Buttons Callback ###\\n ########################\\n\\n # hook up QMainWindow buttons callbacks\\n self.button_run.clicked.connect(self.run)\\n self.button_exit.clicked.connect(self.close)\\n self.button_restore.clicked.connect(self.restore)\\n self.button_grid.clicked.connect(self.show_grid)\\n \\n # hook up Parameters button callbacks\\n self.button_browse.clicked.connect(self.select_directory)\\n self.refresh_input.clicked.connect(lambda: self.refresh(self.input))\\n \\n # hook up Search Space button callbacks\\n # Box Adjustment\\n self.button_draw_box.clicked.connect(self.set_box)\\n self.button_delete_box.clicked.connect(self.delete_box)\\n self.button_redraw_box.clicked.connect(self.redraw_box)\\n self.button_box_adjustment_help.clicked.connect(self.box_adjustment_help)\\n # Ligand Adjustment\\n self.refresh_ligand.clicked.connect(lambda: self.refresh(self.ligand))\\n\\n # hook up methods to results tab\\n # Jobs\\n self.available_jobs.currentIndexChanged.connect(self.fill_job_information)\\n self.button_show_job.clicked.connect(self.show_id)\\n self.button_add_job_id.clicked.connect(self.add_id)\\n # Visualization\\n self.button_browse_results.clicked.connect(self.select_results_file)\\n self.button_load_results.clicked.connect(self.load_results)\\n self.volume_list.itemSelectionChanged.connect(lambda list1=self.volume_list, list2=self.area_list: self.show_cavities(list1, list2))\\n self.area_list.itemSelectionChanged.connect(lambda list1=self.area_list, list2=self.volume_list: self.show_cavities(list1, list2))\\n self.residues_list.itemSelectionChanged.connect(self.show_residues)\",\n \"def initGui(self):\\n # Create an action to start capturing polygons and add it to the digitize toolbar\\n icon = QIcon()\\n icon.addPixmap(QPixmap(_fromUtf8(\\\":/plugins/olainterface/lo-logo.png\\\")), QIcon.Normal, QIcon.Off)\\n self.openDialogAction = QAction(icon, QString(\\\"Land Observatory\\\"), self.iface.mainWindow())\\n self.iface.pluginToolBar().addAction(self.openDialogAction)\\n\\n # Connect to signals for button behaviour\\n self.connect(self.openDialogAction, SIGNAL(\\\"triggered()\\\"), self.openDialog)\",\n \"def initUI(self):\\n\\n self.wid = RosGenWidget()\\n self.setCentralWidget(self.wid)\\n menubar = self.menuBar()\\n fileMenu = menubar.addMenu('&Файл')\\n editMenu = menubar.addMenu('&Редактирование')\\n self.create_menu_par('Менеджер подписчиков и издателей', self.wid.show_manager, fileMenu, 'Ctrl+M')\\n self.create_menu_par('Очистить', self.wid.clear_all_lines, editMenu, 'Ctrl+D')\\n self.create_menu_par('Загрузить данные из...', self.wid.open_fileDialog, fileMenu, 'Ctrl+F')\\n self.create_menu_par('Сохранить как...', self.wid.save_fileDialog, fileMenu, 'Ctrl+S')\\n self.create_menu_par('Выход', self.exit_app, fileMenu, 'Esc')\\n self.statusbar = self.statusBar()\\n self.statusbar.showMessage('Ожидание данных')\\n self.wid.msg2Statusbar[str].connect(self.statusbar.showMessage)\\n self.setGeometry(600, 200, 700, 400)\\n self.setWindowTitle('Генератор шаблонов ROS-приложения')\\n self.show()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/qgsCsvAzmDist2LatLon/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'csvAzmDist2LatLon'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n \\n self.dlg.pbInCsv.clicked.connect(self.select_input_file)\\n self.dlg.pbOutCsv.clicked.connect(self.select_output_file)\\n self.dlg.pbCalc.clicked.connect(self.calculations)\",\n \"def create_widgets( self ):\",\n \"def initUI(self):\\n # Setting the main layout as Vertical.\\n self.mainLayout = QHBoxLayout()\\n\\n # Create title.\\n self.title = QLabel(self.__name + \\\" : \\\")\\n\\n # Add description as tooltip.\\n self.title.setToolTip(self.__description)\\n\\n # Add title to main layout.\\n self.mainLayout.addWidget(self.title)\\n\\n # Create ComboBox.\\n self.dropDown = QComboBox()\\n\\n # Add datas to drop down.\\n self.dropDown.addItems(self.__datas)\\n\\n # Set default index to dropdown.\\n self.dropDown.setCurrentIndex(self.__currentValue)\\n\\n # Connect dropdown with update method.\\n self.dropDown.currentIndexChanged.connect(self.changeCurrentValue)\\n\\n # Add ComboBox to main layout.\\n self.mainLayout.addWidget(self.dropDown)\\n\\n # Add the main layout to the window.\\n self.setLayout(self.mainLayout)\",\n \"def __init__(self, *args, **kwargs):\\n\\n super(FlirCameraWidget, self).__init__(*args, **kwargs)\\n #self.loadForm()\\n self.window = Ui_MainWindow()\\n self.window.setupUi(self)\\n\\n self.initUI()\\n Styles(self)\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/new_raptor/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Add New Raptor nest'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def __init__(self):\\n self._queue_items = []\",\n \"def initGui(self): # pylint:disable=invalid-name\\n\\n icon_path = os.path.join(self.plugin_dir, \\\"icons\\\")\\n\\n self.add_action(\\n os.path.join(icon_path, \\\"icon.png\\\"),\\n text=self.translate(\\\"Load Data\\\"),\\n callback=self.run,\\n parent=self.iface.mainWindow(),\\n )\\n\\n # Plugin Dialog\\n self.dlg.uListOptions.itemClicked.connect(self.show_selected_option)\\n self.dlg.uListOptions.itemClicked.emit(self.dlg.uListOptions.item(0))\\n\\n model = QStandardItemModel()\\n self.dlg.uCRSCombo.setModel(model)\\n self.dlg.uCRSCombo.currentIndexChanged.connect(self.layer_crs_selected)\\n\\n self.dlg.uLabelWarning.setStyleSheet(\\\"color:red\\\")\\n self.dlg.uWarningSettings.setStyleSheet(\\\"color:red\\\")\\n\\n self.add_item(title=\\\"ALL\\\", icon=os.path.join(icon_path, \\\"all.png\\\"))\\n self.add_item(title=\\\"WFS\\\", icon=os.path.join(icon_path, \\\"wfs.png\\\"))\\n self.add_item(title=\\\"WMTS\\\", icon=os.path.join(icon_path, \\\"wmts.png\\\"))\\n self.add_item(title=\\\"Settings\\\", icon=os.path.join(icon_path, \\\"settings.png\\\"))\\n self.add_item(title=\\\"Help\\\", icon=os.path.join(icon_path, \\\"help.png\\\"))\\n self.add_item(title=\\\"About\\\", icon=os.path.join(icon_path, \\\"about.png\\\"))\\n\\n # set table model\\n self.set_table_model_view()\\n\\n # set help html\\n self.dlg.hHelpHtml.setOpenExternalLinks(True)\\n help_file = os.path.join(self.plugin_dir, \\\"help.html\\\")\\n with open(help_file, \\\"r\\\", encoding=\\\"utf-8\\\") as file:\\n help_html = file.read()\\n help_html.format(self.plugin_dir)\\n self.dlg.hHelpHtml.setHtml(help_html.format(icon_path))\\n\\n # set about html\\n self.dlg.hAboutHtml.setOpenExternalLinks(True)\\n about_file = os.path.join(self.plugin_dir, \\\"about.html\\\")\\n with open(about_file, \\\"r\\\", encoding=\\\"utf-8\\\") as file:\\n about_html = file.read()\\n about_html.format(self.plugin_dir)\\n self.dlg.hAboutHtml.setHtml(about_html.format(icon_path))\\n\\n # populate settings\\n # default data services to combo\\n\\n self.dlg.uComboBoxDomain.addItems(SER)\\n\\n # settings signals\\n self.dlg.uBtnAddDomain.clicked.connect(self.add_new_domain)\\n for entry in range(1, 11):\\n getattr(self.dlg, \\\"uBtnSaveDomain{0}\\\".format(entry)).clicked.connect(\\n self.save_domain\\n )\\n getattr(self.dlg, \\\"uBtnRemoveDomain{0}\\\".format(entry)).clicked.connect(\\n self.save_domain\\n )\\n self.load_settings()\",\n \"def assignWidgets(self):\\n self.buttonBox.accepted.connect(self.runAll)\\n self.buttonBox.rejected.connect(self.exitSystem)\\n self.FilesButton.clicked.connect(self.FileToRun)\\n self.OutputButton.clicked.connect(self.OutPutLocation)\",\n \"def __init__(self):\\r\\n super().__init__()\\r\\n self.init_ui()\",\n \"def initGui(self):\\n from p4_view import Gui\\n self.updateStatus(\\\"Launching GUI...\\\")\\n self.gui = Gui(self, self.lmap)\\n self.gui.setStart(self.cfg[\\\"START\\\"])\\n self.gui.setGoal(self.cfg[\\\"GOAL\\\"])\\n self.gui.setPossGoals(self.cfg[\\\"POSS_GOALS\\\"])\\n #GHD\\n self.gui.setMapName(self.cfg[\\\"MAP_FILE\\\"])\\n self.updateStatus(\\\"OK\\\")\\n self.gui.mainloop()\",\n \"def process_widgets(self):\\r\\n\\r\\n self.runmode_menu.add_radiobutton(label=\\\"Graphical User Interface\\\", value=0, variable=self.gui_menu_var,\\r\\n command=self.disable_debugging_mode)\\r\\n self.runmode_menu.add_radiobutton(label=\\\"Command Line Interface\\\", value=1, variable=self.gui_menu_var,\\r\\n command=lambda gui=self: load_cli(self))\\r\\n self.runmode_menu.add_radiobutton(label=\\\"Debugging Mode (GUI + CLI)\\\", value=2, variable=self.gui_menu_var,\\r\\n command=self.enable_debugging_mode)\\r\\n\\r\\n # Placing all the submenus\\r\\n self.filemenu.add_cascade(label=\\\"Run Mode\\\", menu=self.runmode_menu)\\r\\n self.menubar.add_cascade(label=\\\"File\\\", menu=self.filemenu)\\r\\n\\r\\n self.config(menu=self.menubar) # Indicating that the \\\"menubar\\\" variable is the filemenu of the application\\r\\n\\r\\n self.folder_frame.pack()\\r\\n\\r\\n # self.folder_locator.pack(side=LEFT, padx=10, pady=10)\\r\\n\\r\\n self.media_folder_label.pack(side=LEFT, padx=10, pady=10)\\r\\n\\r\\n self.folder_button.pack(side=LEFT)\\r\\n\\r\\n self.path_frame_parent.pack(side=LEFT)\\r\\n\\r\\n self.search_frame.pack()\\r\\n\\r\\n self.search_frame.pack()\\r\\n self.search_entry.grid(row=0, column=0, padx=10, pady=20)\\r\\n self.search_button.grid(row=0, column=1, padx=5)\\r\\n # self.advanced_search_button.grid(row=0, column=2, padx=5)\\r\\n\\r\\n self.media_frame.pack()\\r\\n\\r\\n self.button_frame.pack()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/buienradar_plugin/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'Buienradar Plugin'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def _initialize_widgets(self):\\n self.outer_board = [[Frame(self.root, bd = self.FRAME_BORDER_WIDTH, \\n relief = self.FRAME_RELIEF) \\n for _ in range(self.BOARD_DIM)] \\n for _ in range(self.BOARD_DIM)]\\n self.inner_boards = [[self._generate_inner_board(r, c) \\n for c in range(self.BOARD_DIM)]\\n for r in range(self.BOARD_DIM)]\",\n \"def _run_delayed_gui_load_code(self):\\n #Stop the timer.\\n self._delayed_gui_timer.stop()\\n print(f'_run_delayed_gui_load_code() called!')\\n # Try to select the first combo item after they've loaded\\n self.ui.contextSelectorWidget._trySelectFirstComboItem()\",\n \"def initGui(self):\\n icon = QIcon(\\\":/plugins/DEMto3D/icons/demto3d.png\\\")\\n text = self.tr(\\\"DEM 3D printing\\\")\\n parent = self.iface.mainWindow()\\n self.action = QAction(icon, text, parent)\\n self.action.setObjectName(text)\\n self.action.setStatusTip(text)\\n self.action.triggered.connect(self.run)\\n\\n self.iface.addRasterToolBarIcon(self.action)\\n self.iface.addPluginToRasterMenu(self.menu, self.action)\",\n \"def _init_ui(self):\\n self.setGeometry(100, 100, 1020, 650)\\n self.setWindowTitle('Diamond Puck Barcode Scanner')\\n self.setWindowIcon(QtGui.QIcon('web.png'))\\n\\n self.init_menu_bar()\\n\\n # Barcode table - lists all the barcodes in a record\\n self.barcodeTable = BarcodeTable(self._config)\\n\\n # Image frame - displays image of the currently selected scan record\\n self.imageFrame = ImageFrame()\\n\\n # Scan record table - lists all the records in the store\\n self.recordTable = ScanRecordTable(self.barcodeTable, self.imageFrame, self._config)\\n\\n self._btn_begin = QPushButton(\\\"Start Scan\\\")\\n self._btn_begin.setStyleSheet(\\\"font-size:20pt;\\\")\\n self._btn_begin.setFixedSize(150, 60)\\n self._btn_begin.clicked.connect(self._start_live_capture)\\n\\n self._btn_stop = QPushButton(\\\"Stop Scan\\\")\\n self._btn_stop.setStyleSheet(\\\"font-size:20pt\\\")\\n self._btn_stop.setFixedSize(150, 60)\\n self._btn_stop.clicked.connect(self._stop_live_capture)\\n\\n hbox_btn = QHBoxLayout()\\n hbox_btn.addWidget(self._btn_begin)\\n hbox_btn.addWidget(self._btn_stop)\\n hbox_btn.addStretch()\\n\\n # Create layout\\n hbox = QtGui.QHBoxLayout()\\n hbox.setSpacing(10)\\n hbox.addWidget(self.recordTable)\\n hbox.addWidget(self.barcodeTable)\\n hbox.addWidget(self.imageFrame)\\n hbox.addStretch(1)\\n\\n vbox = QtGui.QVBoxLayout()\\n vbox.addLayout(hbox_btn)\\n vbox.addLayout(hbox)\\n\\n main_widget = QtGui.QWidget()\\n main_widget.setLayout(vbox)\\n self.setCentralWidget(main_widget)\\n\\n self.show()\",\n \"def init_ui(self):\\n raise NotImplementedError(\\\"This is an abstract method.\\\")\",\n \"def initUI(self):\\n\\t\\t#de button om het spel te beginnen\\n\\t\\tself.playButton = QtGui.QPushButton(\\\"Play\\\", self)\\n\\t\\tself.playButton.clicked.connect(self.playField)\\n\\t\\tself.playButton.setStyleSheet(\\\"background-color: red;border-style: outset; border-radius:5px; font: bold 18px;\\\")\\n\\t\\tself.playButton.move(210,100)\\n\\t\\tself.playButton.resize(150, 50)\\n\\t\\t\\n\\t\\t#zakje chips\\n\\t\\tself.chips = QtGui.QPixmap(\\\"chips.png\\\")\\n\\t\\tself.lbl = QtGui.QLabel(self)\\n\\t\\tself.lbl.setPixmap(self.chips)\\n\\t\\tself.lbl.move(410, 300)\\n\\t\\t\\n\\t\\t#1 zak chips is nooit genoeg voor de heavy breathing cat\\n\\t\\tself.chips2 = QtGui.QPixmap(\\\"chips.png\\\")\\n\\t\\tself.lbl2 = QtGui.QLabel(self)\\n\\t\\tself.lbl2.setPixmap(self.chips)\\n\\t\\tself.lbl2.move(30, 30)\\n\\t\\t\\n\\t\\t#pikachu in een sailor pakje, want battleships\\n\\t\\tself.pikachu = QtGui.QPixmap(\\\"pikachu.png\\\")\\n\\t\\tself.lbl3 = QtGui.QLabel(self)\\n\\t\\tself.lbl3.setPixmap(self.pikachu)\\n\\t\\tself.lbl3.move(400,0)\\n\\t\\t\\n\\t\\t#een kat kan natuurlijk niet ontbreken\\n\\t\\tself.kat = QtGui.QPixmap(\\\"hb2.jpeg\\\")\\n\\t\\tself.lblKat = QtGui.QLabel(self)\\n\\t\\tself.lblKat.setPixmap(self.kat)\\n\\t\\tself.lblKat.move(0,230)\\n\\t\\t\\n\\t\\t#ons super vette logo #swek\\n\\t\\tself.battlechipsPlaatje = QtGui.QPixmap(\\\"battleships.png\\\")\\n\\t\\tself.lblbattle = QtGui.QLabel(self)\\n\\t\\tself.lblbattle.setPixmap(self.battlechipsPlaatje)\\n\\t\\tself.lblbattle.move(180,10)\\n\\t\\t\\n\\t\\t#le window\\n\\t\\tself.setGeometry(150, 150, 600, 600)\\n\\t\\tself.setWindowTitle(\\\"Menu\\\")\\n\\t\\tself.setStyleSheet(\\\"background-color: #8e8f94\\\")\\n\\t\\tself.show()\",\n \"def initLayers(self):\\n self.addRow(self.currentLayerData)\\n self.selected = self.jobRow.child(0)\\n self.setSelectedFromItem(self.selected)\",\n \"def __init_widgets(self):\\n self.add_events(Gdk.EventMask.ENTER_NOTIFY_MASK)\\n\\n headerbar = self.get_header_bar()\\n scanner_image = Gtk.Image.new_from_icon_name(\\\"qrscanner-symbolic\\\", Gtk.IconSize.BUTTON)\\n self.scan_button.add(scanner_image)\\n\\n self.add_button.get_style_context().add_class(\\\"suggested-action\\\")\\n self.add_button.set_sensitive(False)\\n self.add_button.set_can_default(True)\\n\\n headerbar.pack_end(self.add_button)\\n headerbar.pack_end(self.scan_button)\\n\\n self.account_config.connect(\\\"changed\\\", self._on_account_config_changed)\\n self.add_button.connect(\\\"clicked\\\", self._on_add)\\n self.scan_button.connect(\\\"clicked\\\", self.account_config.scan_qr)\\n self.get_content_area().pack_start(self.account_config, True, True, 0)\",\n \"def __init__(self):\\n self.queue = Queue()\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/cctv_mapper/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'CCTV Mapper'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def init_UI(self):\\n\\n self.master.title(\\\"Search for different companies\\\")\\n self.master.geometry(\\\"400x400\\\")\\n\\n self.label_combobox = Label(self, text=\\\"Search by\\\")\\n self.label_combobox.pack()\\n\\n self.combo_searching_options = Combobox(self, state=\\\"readonly\\\")\\n self.combo_searching_options['values'] = self.combobox_values\\n self.combo_searching_options.pack()\\n\\n self.label_input = Label(self, text=\\\"Entry the value\\\")\\n self.label_input.pack()\\n\\n self.user_input = Entry(self, width=40)\\n self.user_input.pack()\\n\\n self.btn_submit = Button(self, text=\\\"Submit\\\", command=self.submit)\\n self.btn_submit.pack()\\n\\n self.text_area = scrolledtext.ScrolledText(self)\\n self.text_area.pack()\\n\\n sys.stdout = RedirectOutputText(self.text_area)\\n\\n self.btn_back = Button(self, text=\\\"Back\\\", command=self.go_back)\\n self.btn_back.pack()\",\n \"def create_widgets( self ):\\n\\n # photo preview.\\n self.photoPreview = grafwidgets.MultiRubberBandedLabel( self.preview_pixmap )\\n self.previewArea = grafwidgets.PhotoPreviewArea( self.photoPreview )\\n\\n # draw the art record regions.\\n # XXX: factor this into a separate routine\\n for art in self.art_records:\\n if art[\\\"region\\\"] is not None:\\n self.photoPreview.add_band( art[\\\"id\\\"], art[\\\"region\\\"] )\\n\\n # processing type.\\n self.selectionBox = QComboBox()\\n\\n self.selectionBox.addItem( \\\"all\\\", \\\"all\\\" )\\n for state in self.db.get_processing_states():\\n self.selectionBox.addItem( state, state )\\n\\n self.selectionBox.activated.connect( self.selectionTypeActivation )\\n\\n self.selectionBoxLabel = QLabel( \\\"&Processing Type:\\\" )\\n self.selectionBoxLabel.setBuddy( self.selectionBox )\\n\\n # art record selection.\\n #\\n # XXX: describe these.\\n self.selectionView = SelectionView()\\n self.selectionView.setModel( self.proxyArtModel )\\n self.selectionView.activated.connect( self.selectionActivation )\\n self.selectionView.selectionModel().selectionChanged.connect( self.recordSelectionChange )\\n\\n # record addition and removal buttons.\\n self.newRecordButton = QPushButton( \\\"&New Record\\\" )\\n self.newRecordButton.clicked.connect( self.create_new_record )\\n\\n self.deleteRecordButton = QPushButton( \\\"&Delete Record\\\" )\\n self.deleteRecordButton.clicked.connect( self.delete_record )\\n\\n # we shouldn't be able to push the delete button until we have a\\n # record selected.\\n self.deleteRecordButton.setEnabled( False )\\n\\n # art record summary labels.\\n self.artTypeLabel = QLabel()\\n self.artSizeLabel = QLabel()\\n self.artQualityLabel = QLabel()\\n self.artDateLabel = QLabel()\\n self.artArtistsLabel = QLabel()\\n self.artAssociatesLabel = QLabel()\\n self.artVandalsLabel = QLabel()\\n self.artTagsLabel = QLabel()\\n\\n # photo record processing state.\\n self.photoProcessingStateComboBox = QComboBox()\\n for state in self.db.get_processing_states():\\n self.photoProcessingStateComboBox.addItem( state, state )\\n self.photoProcessingStateComboLabel = QLabel( \\\"Stat&e:\\\" )\\n self.photoProcessingStateComboLabel.setBuddy( self.photoProcessingStateComboBox )\\n\\n # photo record tags.\\n #\\n # NOTE: our accelerator is chosen to match the ArtRecordEditor's\\n # accelerator.\\n #\\n self.photoTagsLineEdit = QLineEdit( \\\"\\\" )\\n self.photoTagsLabel = QLabel( \\\"Ta&gs:\\\" )\\n self.photoTagsLabel.setBuddy( self.photoTagsLineEdit )\\n\\n # XXX: need to add\\n #\\n # * description\",\n \"def __init__(self, showCTWM, showWHM, showRTLE, algorithmCTWM, algorithmWHM, tool_type_text, material_text,\\n flutes_text, coating_text, cutting_type_text, wpmaterial_text, heat_text, tool_diameter_num,\\n parent_img_path=None):\\n\\n # Initialize the widget and the main window\\n QtWidgets.QMainWindow.__init__(self)\\n self.setWindowTitle(\\\"Smart Sensing System\\\")\\n #self.setFixedSize(1000, 450)\\n self.widgetHolder = QtWidgets.QWidget()\\n self.setCentralWidget(self.widgetHolder)\\n\\n # Set all the passed arguments as class variables\\n self.parent_img_path = parent_img_path\\n self.showCTWM = showCTWM\\n self.showWHM = showWHM\\n self.showRTLE = showRTLE\\n self.tool_type_text = tool_type_text\\n self.material_text = material_text\\n self.flutes_text = flutes_text\\n self.coating_text = coating_text\\n self.cutting_type_text = cutting_type_text\\n self.wpmaterial_text = wpmaterial_text\\n self.heat_text = heat_text\\n self.tool_diameter_num = tool_diameter_num\\n\\n\\n # Pre-loading all images into dictionary\\n self.images = dict()\\n\\n if self.showCTWM:\\n self.images['imageFailureGrayCTWM'] = QPixmap(os.path.join(self.parent_img_path, \\\"failure_gray.jpg\\\"))\\n self.images['imageAdvancedGrayCTWM'] = QPixmap(os.path.join(self.parent_img_path, \\\"advanced_gray.jpg\\\"))\\n self.images['imageAverageGrayCTWM'] = QPixmap(os.path.join(self.parent_img_path, \\\"average_gray.jpg\\\"))\\n self.images['imageGoodGrayCTWM'] = QPixmap(os.path.join(self.parent_img_path, \\\"good_gray.jpg\\\"))\\n self.images['imageGoodGreenCTWM'] = QPixmap(os.path.join(self.parent_img_path, \\\"good_green.jpg\\\"))\\n self.images['imageAverageYellowCTWM'] = QPixmap(os.path.join(self.parent_img_path, \\\"average_yellow.jpg\\\"))\\n self.images['imageAdvancedOrangeCTWM'] = QPixmap(os.path.join(self.parent_img_path, \\\"advanced_orange.jpg\\\"))\\n self.images['imageFailureRedCTWM'] = QPixmap(os.path.join(self.parent_img_path, \\\"failure_red.jpg\\\"))\\n if self.showWHM:\\n self.images['imageLevelFourWHMgray'] = QPixmap(os.path.join(self.parent_img_path, \\\"levelFour_gray.jpg\\\"))\\n self.images['imageLevelThreeWHMgray'] = QPixmap(os.path.join(self.parent_img_path, \\\"levelThree_gray.jpg\\\"))\\n self.images['imageLevelTwoWHMgray'] = QPixmap(os.path.join(self.parent_img_path, \\\"levelTwo_gray.jpg\\\"))\\n self.images['imageLevelOneWHMgray'] = QPixmap(os.path.join(self.parent_img_path, \\\"levelOne_gray.jpg\\\"))\\n self.images['imageLevelOneWHMgreen'] = QPixmap(os.path.join(self.parent_img_path, \\\"levelOne_green.jpg\\\"))\\n self.images['imageLevelTwoWHMyellow'] = QPixmap(os.path.join(self.parent_img_path, \\\"levelTwo_yellow.jpg\\\"))\\n self.images['imageLevelThreeWHMorange'] = QPixmap(os.path.join(self.parent_img_path, \\\"levelThree_orange.jpg\\\"))\\n self.images['imageLevelFourWHMred'] = QPixmap(os.path.join(self.parent_img_path, \\\"levelFour_red.jpg\\\"))\\n\\n # Initialize file to save output to\\n self.save_file_name = \\\"HTW\\\" + str(datetime.now().date())\\n self.save_file = open(os.path.join(args.parent_img_path, self.save_file_name), \\\"w\\\")\\n self.save_file.write(\\\"History of Tool Wear\\\" + \\\"\\\\n\\\")\\n self.save_file.close()\\n\\n\\n # Assigns the thread that runs the algorithm to the flag setPointsCTWM\\n self.setPointsCTWM = algorithmCTWM\\n # When the algorithm raises the flag 'finished', do the function \\\"updateCTWM\\\"\\n self.setPointsCTWM.finished.connect(self.updateCTWM)\\n\\n # Assigns the thread that runs the algorithm to the flag setPointsWHM\\n self.setPointsWHM = algorithmWHM\\n # When the algorithm raises the flag 'finished', do the function \\\"updateWHM\\\"\\n self.setPointsWHM.finished.connect(self.updateWHM)\\n\\n # Create a VBox called Main Layout that will contain all the other widgets\\n self.mainLayout = QtWidgets.QVBoxLayout()\\n self.widgetHolder.setLayout(self.mainLayout)\\n\\n ###############################################################################################################\\n # #\\n # FIRST ROW: IN AN HBOX #\\n # Contains Title, Start, Stop, Reset, Save buttons, and Save directory #\\n ###############################################################################################################\\n self.firstRow = QtWidgets.QHBoxLayout()\\n self.windowTitle = QtWidgets.QLabel(\\\"Smart Sensing System\\\")\\n self.windowTitle.setContentsMargins(0, 0, 30, 0) # someLayout.setContentsMargins(left, top, right, bottom)\\n self.font = QtGui.QFont(\\\"Corbel\\\", 16, QtGui.QFont.Bold, QtGui.QFont.StyleItalic)\\n self.windowTitle.setFont(self.font)\\n self.firstRow.addWidget(self.windowTitle)\\n self.startButton = QtWidgets.QPushButton(\\\"Start\\\")\\n self.startButton.setMinimumWidth(200)\\n self.startButton.setEnabled(True)\\n self.startButton.setStyleSheet(\\\"background-color: #009933\\\")\\n self.startButton.clicked.connect(self.startButtonPressed)\\n self.firstRow.addWidget(self.startButton)\\n self.stopButton = QtWidgets.QPushButton(\\\"Stop\\\")\\n self.stopButton.setMinimumWidth(200)\\n self.stopButton.setEnabled(False)\\n self.stopButton.setStyleSheet(\\\"background-color: #cc2900\\\")\\n self.stopButton.clicked.connect(self.stopButtonPressed)\\n self.firstRow.addWidget(self.stopButton)\\n self.reset = QtWidgets.QPushButton(\\\"Reset\\\") # connection to clicked in init -> Clear CTWM and WHM Graphs\\n self.reset.setEnabled(True)\\n self.reset.setMaximumWidth(70)\\n self.reset.clicked.connect(self.resetPressed)\\n self.firstRow.addWidget(self.reset)\\n\\n self.addressBox = QtWidgets.QLineEdit()\\n self.addressBox.setMaximumWidth(200)\\n self.addressBox.setPlaceholderText(\\\"Address\\\")\\n self.addressBox.setContentsMargins(30, 0, 0, 0) # someLayout.setContentsMargins(left, top, right, bottom)\\n self.addressBox.setAlignment(QtCore.Qt.AlignCenter)\\n self.firstRow.addWidget(self.addressBox)\\n self.screenshotNum = 0 # For filename saving purposes\\n self.save = QtWidgets.QPushButton(\\\"Save\\\")\\n self.save.setMaximumWidth(70)\\n self.save.clicked.connect(self.saveWindowState)\\n self.firstRow.addWidget(self.save)\\n self.mainLayout.addLayout(self.firstRow)\\n\\n ###############################################################################################################\\n # #\\n # SECOND ROW: IN AN HBOX #\\n # For Cutting Tool Wear Monitoring and Workpiece Hardness Monitoring #\\n ###############################################################################################################\\n self.secondRow = QtWidgets.QHBoxLayout()\\n # These are some configurations for the graph for user-viewing purposes\\n pg.setConfigOption('background', '#d9d9d9')\\n pg.setConfigOption('foreground', 'k')\\n pg.setConfigOptions(antialias=True)\\n # Set up pen to be used to draw the graphs\\n self.pen = pg.mkPen(color='b', width=3)\\n\\n # Use a numpy array to keep track of the outcomes of the wear condition\\n self.outcomesCTWM = np.array([])\\n # Use a numpy array to keep track of the outcomes of the hardness condition\\n self.outcomesWHM = np.array([])\\n\\n if self.showCTWM: # Show the Cutting Tool Wear Monitoring widget\\n self.initCTWM()\\n if self.showWHM: # Show the Workpiece Hardness Monitoring widget\\n self.initWHM()\\n self.mainLayout.addLayout(self.secondRow)\\n\\n ###############################################################################################################\\n # #\\n # THIRD ROW: IN AN VBOX #\\n # For Remaining Tool Life Estimation #\\n ###############################################################################################################\\n if self.showRTLE: # Show the Remaining Tool Life Estimation widget\\n self.initRTLE()\\n\\n ###############################################################################################################\\n # #\\n # FOURTH ROW: IN AN HBOX #\\n # Contains RIT label and version number #\\n ###############################################################################################################\\n self.fourthRow = QtWidgets.QHBoxLayout()\\n self.versionFont = QtGui.QFont(\\\"Corbel\\\", 10)\\n self.version = QLabel(\\\"Version: 1.0\\\")\\n self.version.setFont(self.versionFont)\\n self.version.setAlignment(QtCore.Qt.AlignRight)\\n self.fourthRow.addWidget(self.version)\\n self.mainLayout.addLayout(self.fourthRow)\\n\\n # Show all these widgets\\n self.show()\\n\\n ##############################################################################################################\\n # #\\n # ALL BUTTONS' FUNCTIONALITY #\\n # #\\n ###############################################################################################################\",\n \"def setup(self):\\n self.ui.setup_window()\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def __init__(self):\\n self.queue = []\",\n \"def gui_init(self):\\n if not self.parent is None:\\n self.parent.children.append(self)\\n self.children = []\\n self.priority = PRIORITY_GUI_ELEMENTS\",\n \"def __init__(self):\\n self.s = QSettings()\\n self.p = QgsProject.instance()\",\n \"def setup(self):\\r\\n ScriptedLoadableModuleWidget.setup(self)\\r\\n\\r\\n # Load widget from .ui file (created by Qt Designer).\\r\\n # Additional widgets can be instantiated manually and added to self.layout.\\r\\n uiWidget = slicer.util.loadUI(self.resourcePath('UI/RecordHerniaData.ui'))\\r\\n self.layout.addWidget(uiWidget)\\r\\n self.ui = slicer.util.childWidgetVariables(uiWidget)\\r\\n\\r\\n # Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's\\r\\n # \\\"mrmlSceneChanged(vtkMRMLScene*)\\\" signal in is connected to each MRML widget's.\\r\\n # \\\"setMRMLScene(vtkMRMLScene*)\\\" slot.\\r\\n uiWidget.setMRMLScene(slicer.mrmlScene)\\r\\n\\r\\n # Create logic class. Logic implements all computations that should be possible to run\\r\\n # in batch mode, without a graphical user interface.\\r\\n self.logic = TMSRecordDataModuleLogic()\\r\\n self.recordingStarted = False\\r\\n self.camerasStarted = False\\r\\n self.moduleDir = os.path.dirname(slicer.modules.tmsrecorddatamodule.path)\\r\\n self.logic.setupScene()\\r\\n\\r\\n # Buttons\\r\\n self.ui.StartStopRecordingButton.connect('clicked(bool)', self.onStartStopRecordingClicked)\\r\\n self.ui.startCamerasButton.connect('clicked(bool)',self.onStartStopCamerasClicked)\",\n \"def initialize(self):\\n super(QtMainWindow, self).initialize()\\n self.update_menu_bar()\",\n \"def __init__(self) -> None:\\n self._queue = []\",\n \"def initGui(self):\\n\\n icon_path = ':/plugins/pit_removal/icon.png'\\n self.add_action(\\n icon_path,\\n text=self.tr(u'remove pits'),\\n callback=self.run,\\n parent=self.iface.mainWindow())\\n\\n # will be set False in run()\\n self.first_start = True\",\n \"def __init__(self):\\r\\n self.queue = []\",\n \"def __init__(self):\\r\\n self.queue = []\",\n \"def _init_ui(self):\\n # Create GUI elements, set them in dict structure\\n inf_symbol = u'\\\\u221E'\\n gamma_symbol = u'\\\\u03B3'\\n unicode_squared = u'\\\\u00B9'\\n labelwidth=175\\n\\n if len(self.waterlevels) > 100:\\n raise NotImplementedError('More than 100 water levels where discovered in the hydraulic loads. The method with PHAROS is not implemented for this number of loads. Recalculate the wave conditions at given water levels, or pick a method without PHAROS.')\\n\\n self.input_elements['hydraulic loads'] = {\\n 'Hs_max': widgets.ParameterLabel(\\n label='Max. significante golfhoogte:',\\n labelwidth=labelwidth,\\n value='{:.3f}'.format(self.Hs_max),\\n unit='m'\\n ),\\n # 'Tp_max': widgets.ParameterLabel(\\n # label='Maximale piekperiode',\\n # labelwidth=150\\n # ),\\n 'factor Tm Tp': widgets.ParameterInputLine(\\n label='Factor Tm naar Tp:',\\n labelwidth=labelwidth,\\n validator=QtGui.QDoubleValidator(0.01, 99.99, 20),\\n ),\\n 'water depth for wave length': widgets.ParameterInputLine(\\n label='Waterdiepte voor golflengte:',\\n labelwidth=labelwidth,\\n unitlabel='m',\\n validator=QtGui.QDoubleValidator(0.00, np.inf, 20),\\n ),\\n }\\n\\n self.input_elements['wave directions'] = {\\n 'lowest': widgets.ParameterInputLine(\\n label='Laagste waarde [0-360]:',\\n labelwidth=labelwidth,\\n unitlabel='graden (nautisch)',\\n validator=QtGui.QDoubleValidator(0.00, 360.00, 20),\\n ),\\n 'highest': widgets.ParameterInputLine(\\n label='Hoogste waarde [0-360]:',\\n labelwidth=labelwidth,\\n unitlabel='graden (nautisch)',\\n validator=QtGui.QDoubleValidator(0.00, 360.00, 20),\\n ),\\n 'bin size': widgets.ParameterInputLine(\\n label='Klassegrootte [1-360]:',\\n labelwidth=labelwidth,\\n unitlabel='graden',\\n validator=QtGui.QDoubleValidator(1.00, 360.00, 20),\\n )\\n }\\n\\n self.input_elements['frequencies'] = {\\n 'lowest': widgets.ParameterInputLine(\\n label='Ondergrens [{:.3f} - {:.3f}]:'.format(*self.f_range),\\n labelwidth=labelwidth,\\n unitlabel='Hz',\\n validator=QtGui.QDoubleValidator(self.f_range[0] - 0.01, self.f_range[1] + 0.01, 20),\\n ),\\n 'highest': widgets.ParameterInputLine(\\n label='Bovengrens [{:.3f} - {:.3f}]:'.format(*self.f_range),\\n labelwidth=labelwidth,\\n unitlabel='Hz',\\n validator=QtGui.QDoubleValidator(self.f_range[0] - 0.01, self.f_range[1] + 0.01, 20),\\n ),\\n 'number of bins': widgets.ParameterInputLine(\\n label='Aantal klassen [1-50]:',\\n labelwidth=labelwidth,\\n validator=QtGui.QIntValidator(1, 50),\\n ),\\n 'scale': widgets.ComboboxInputLine(\\n label='Frequentie schaal:',\\n labelwidth=labelwidth,\\n items=['lineair', 'logaritmisch'],\\n )\\n }\\n\\n self.input_elements['2d wave spectrum'] = {\\n 'spread': widgets.ParameterInputLine(\\n label='Spreiding [10-70]:',\\n labelwidth=labelwidth,\\n unitlabel='graden',\\n validator=QtGui.QDoubleValidator(10.0, 70.0, 20),\\n ),\\n 'gamma': widgets.ParameterInputLine(\\n label='JONSWAP peak\\\\nenhancement factor {} [1-7]:'.format(gamma_symbol),\\n labelwidth=labelwidth,\\n unitlabel='',\\n validator=QtGui.QDoubleValidator(1.00, 7.00, 20),\\n ),\\n 'min energy': widgets.ParameterInputLine(\\n label='Signaleringswaarde energie [0-{}]:'.format(inf_symbol),\\n labelwidth=labelwidth,\\n unitlabel='m{}s/degree'.format(unicode_squared),\\n validator=QtGui.QDoubleValidator(0.00, 2.00, 20),\\n )\\n }\\n\\n self.input_elements['paths'] = {\\n 'pharos folder': widgets.ExtendedLineEdit(\\n label='Uitvoermap:',\\n labelwidth=labelwidth,\\n browsebutton=QtWidgets.QPushButton(\\n '...',\\n clicked=self._load_pharos_folder\\n )\\n ),\\n 'schematisation folder': widgets.ExtendedLineEdit(\\n label='Schematisatiemap:',\\n labelwidth=labelwidth,\\n browsebutton=QtWidgets.QPushButton(\\n '...',\\n clicked=self._load_schematisations_folder\\n )\\n )\\n }\\n\\n self.input_elements['water levels'] = {\\n 'checked': widgets.CheckBoxInput(\\n labels=self.waterlevels,\\n nrows=max(2, len(self.waterlevels) // 20),\\n unitlabel='m + NAP'\\n )\\n }\\n\\n delta = u'\\\\u0394'\\n self.input_elements['transformation'] = {\\n 'dx': widgets.ParameterInputLine(\\n label='{}x [RD + {}x = lokaal]:'.format(delta, delta),\\n labelwidth=labelwidth,\\n ),\\n 'dy': widgets.ParameterInputLine(\\n label='{}y [RD + {}y = lokaal]:'.format(delta, delta),\\n labelwidth=labelwidth,\\n )\\n }\\n\\n\\n # Define titles for groups\\n titles = {\\n 'hydraulic loads': 'Hydraulische belastingen',\\n 'wave directions': 'Golfrichtingen',\\n 'frequencies': 'Frequenties',\\n '2d wave spectrum': '2D golfspectrum',\\n 'paths': 'Paden',\\n 'water levels': 'Te simuleren waterstanden',\\n 'transformation': 'Transformatie voor coordinatenstelsel'\\n }\\n\\n\\n # Create base layout\\n self.setLayout(QtWidgets.QVBoxLayout())\\n # self.layout().setSpacing(10)\\n\\n for tag, title in titles.items():\\n if tag in self.input_elements:\\n group_layout = QtWidgets.QVBoxLayout()\\n for _, item in self.input_elements[tag].items():\\n group_layout.addWidget(item)\\n\\n # Add groupbox with title\\n groupbox = QtWidgets.QGroupBox(title)\\n groupbox.setLayout(group_layout)\\n self.layout().addWidget(groupbox)\\n\\n\\n line = QtWidgets.QFrame()\\n line.setFrameShape(QtWidgets.QFrame.HLine)\\n line.setFrameShadow(QtWidgets.QFrame.Sunken)\\n self.layout().addWidget(line)\\n\\n # OK and Cancel buttons\\n\\n self.generate_button = QtWidgets.QPushButton('Genereer tabel')\\n self.generate_button.setDefault(True)\\n # self.generate_button.setEnabled(False)\\n self.generate_button.clicked.connect(self.generate)\\n\\n self.cancel_button = QtWidgets.QPushButton('Annuleren')\\n self.cancel_button.setAutoDefault(False)\\n self.cancel_button.clicked.connect(self.cancel)\\n\\n button_box = QtWidgets.QDialogButtonBox(QtCore.Qt.Horizontal, self)\\n button_box.addButton(self.generate_button, QtWidgets.QDialogButtonBox.ActionRole)\\n button_box.addButton(self.cancel_button, QtWidgets.QDialogButtonBox.RejectRole)\\n\\n button_box.accepted.connect(QtWidgets.QDialog.accept)\\n # button_box.rejected.connect(QtWidgets.QDialog.reject)\\n\\n self.layout().addWidget(button_box)\",\n \"def __init__(self):\\n # setup the database session\\n engine = create_engine('sqlite:///%s'%os.path.join(APP_DIR,config.get('Inkcut','database_dir'),config.get('Inkcut','database_name')))\\n\\n Session.configure(bind=engine)\\n self.session = Session()\\n\\n self.job = None\\n self.ui = {\\n 'main_window':MainWindow(self),\\n 'device_dialog':DeviceDialog(self),\\n }\\n self.statusbar = self.ui['main_window'].widgets['statusbar']\",\n \"def initUI(self):\\n\\n grid = QGridLayout()\\n grid.addWidget(self.from_currency_label, 0, 0, Qt.AlignRight)\\n grid.addWidget(self.from_currency, 0, 1)\\n grid.addWidget(self.to_currency_label, 0, 2, Qt.AlignRight)\\n grid.addWidget(self.to_currency, 0, 3)\\n grid.addWidget(self.from_amount_label, 1, 0)\\n grid.addWidget(self.from_amount, 1, 1)\\n grid.addWidget(self.to_amount_label, 1, 2)\\n grid.addWidget(self.to_amount, 1, 3)\\n\\n grid.addWidget(self.from_calendar, 2, 0, 1, 2)\\n grid.addWidget(self.to_calendar, 2, 2, 1, 2)\\n\\n grid.addWidget(self.rates_plot, 3, 0, 1, 4)\\n grid.addWidget(self.graph_hint, 4, 0, 1, 4)\\n\\n self.rates_plot.showGrid(x=True, y=True)\\n self.rates_plot.setLabel('left', 'Rate')\\n self.rates_plot.setLabel('bottom', 'Days')\\n self.legend = self.rates_plot.addLegend()\\n\\n self.setLayout(grid)\\n self.setWindowTitle('Currency Converter - Assignment 1 - Arnaud Bourget - 2981151')\\n\\n self.from_currency.currentIndexChanged.connect(self.updateUI)\\n self.to_currency.currentIndexChanged.connect(self.updateUI)\\n self.from_amount.valueChanged.connect(self.fromAmountHandler)\\n self.from_calendar.selectionChanged.connect(self.fromCalendarHandler)\\n self.to_calendar.selectionChanged.connect(self.toCalendarHandler)\\n\\n self.show()\",\n \"def __init__(self, iface, dialog_base, bbox_tool):\\n QObject.__init__(self, None)\\n self.iface = iface\\n self.dialog_base = dialog_base\\n self.bbox_tool = bbox_tool\\n self.sources = {}\\n self.geometries = {}\\n self.types_dict = {}\\n self.items = {}\\n self.search_thread_pool = QThreadPool()\\n self.json_thread_pool = QThreadPool()\\n self.progress_message_bar = None\\n self.progress_message_bar_widget = None\\n self.json_progress_message_bar = None\\n self.json_progress_message_bar_widget = None\\n self.json_progress = None\\n self.json_failed = False\\n self.point_vector_layer = None\\n self.line_vector_layer = None\\n self.polygon_vector_layer = None\\n self.dialog_base.aoi_button.clicked.connect(self.aoi_button_clicked)\\n self.dialog_base.export_button.clicked.connect(self.export)\\n self.bbox_tool.released.connect(self.search)\\n # self.query_initial_sources()\\n self.file_dict = {}\\n self.directory = None\\n self.written_first_line = False\\n self.written_first_point = False\\n self.written_first_polygon = False\\n # locks\\n self.search_lock = Lock()\\n self.json_lock = Lock()\",\n \"def init_toolbar(self):\\n raise NotImplementedError\",\n \"def start(self):\\n self.__main_window = Tk()\\n self.__start_loading_window()\",\n \"def create_widgets( self ):\\n\\n self.selectionView = SelectionView()\\n self.selectionView.setModel( self.proxyPhotosModel )\\n self.selectionView.activated.connect( self.selectionActivation )\\n self.selectionView.selectionModel().selectionChanged.connect( self.selectionChange )\\n self.selectionView.setColumnHidden( self.ID_COLUMN, True ) # hide the ID\\n\\n self.selectionBox = QComboBox()\\n\\n self.selectionBox.addItem( \\\"all\\\", \\\"all\\\" )\\n for state in self.db.get_processing_states():\\n self.selectionBox.addItem( state, state )\\n\\n self.selectionBox.activated.connect( self.selectionTypeActivation )\\n\\n self.selectionBoxLabel = QLabel( \\\"&Processing Type:\\\" )\\n self.selectionBoxLabel.setBuddy( self.selectionBox )\\n\\n self.previewArea = grafwidgets.PhotoPreviewArea()\\n\\n # informational labels for the photo record.\\n self.infoStateLabel = QLabel()\\n self.infoSummaryLabel = QLabel()\\n self.infoLocationLabel = QLabel()\\n self.infoTakenLabel = QLabel()\\n self.infoTagsLabel = QLabel()\\n\\n # dock widget which will hold the selection layout once created\\n # in create_layout, for now it gets an empty widget.\\n self.selection_dock = QDockWidget()\\n self.selection_dock.setFeatures( QDockWidget.DockWidgetMovable )\\n self.selection_dock.setWidget( QWidget() )\",\n \"def __init__(self, map_state):\\n self.map_state = map_state\\n self.image = map_prepare.GFX[\\\"misc\\\"][\\\"interface\\\"]\\n self.make_widgets()\",\n \"def __selectJobsSetup(self):\\n\\n select_btn = QtWidgets.QPushButton(\\\"Select:\\\")\\n select_btn.setFocusPolicy(QtCore.Qt.NoFocus)\\n self.__toolbar.addWidget(select_btn)\\n select_btn.clicked.connect(self.__selectJobsHandle) # pylint: disable=no-member\\n\\n self._selectJobsEditBox = JobSelectEditBox(self)\\n self.__toolbar.addWidget(self._selectJobsEditBox)\\n self._selectJobsEditBox.returnPressed.connect(self.__selectJobsHandle) # pylint: disable=no-member\\n\\n clear_btn = QtWidgets.QPushButton(\\\"Clr\\\")\\n clear_btn.setFocusPolicy(QtCore.Qt.NoFocus)\\n clear_btn.setFixedWidth(24)\\n self.__toolbar.addWidget(clear_btn)\\n clear_btn.clicked.connect(self._selectJobsEditBox.actionClear) # pylint: disable=no-member\\n\\n mine_btn = QtWidgets.QPushButton(\\\"selectMine\\\")\\n mine_btn.setFocusPolicy(QtCore.Qt.NoFocus)\\n mine_btn.setFixedWidth(70)\\n self.__toolbar.addWidget(mine_btn)\\n mine_btn.clicked.connect(self.__selectJobsHandleMine) # pylint: disable=no-member\",\n \"def __init__(self, *args, obj=None, **kwargs):\\n super(MainWindow, self).__init__(*args, **kwargs)\\n self.setupUi(self)\\n\\n # real-frequency grid\\n self.realgrid = RealFrequencyGrid(wmax=float(self.max_real_freq.text()),\\n nw=int(self.num_real_freq.text()),\\n type=str(self.grid_type_combo.currentText()))\\n self.connect_realgrid_button()\\n self.connect_wmax()\\n self.connect_nw()\\n self.connect_grid_type()\\n\\n\\n # self.connect_select_button()\\n # self.connect_load_button()\\n # self.connect_show_button()\\n self.connect_load_button_text()\\n self.connect_show_button_2()\\n self.connect_select_button_2()\\n\\n # text display field and \\\"Do it\\\" button\\n self.text_output.setReadOnly(True)\\n self.connect_doit_button()\\n\\n # output data\\n self.output_data = OutputData()\\n self.connect_select_output_button()\\n self.connect_save_button()\",\n \"def __init__(self, sys_argv):\\n super().__init__(sys_argv)\\n log.debug(\\\"Supplying Thread information from init of QApplication\\\")\\n self.setAttribute(Qt.AA_EnableHighDpiScaling)\\n self.setStyle(\\\"Fusion\\\")\\n self.mainWindow = MainWindow()\\n self.mainWindow.setWindowTitle(\\\"jet-tracker\\\")\\n # __location__ = os.getcwd()\\n # File = open(__location__ + '\\\\jt_configs\\\\Geoo.qss', 'r')\\n # with File:\\n # qss = File.read()\\n # self.setStyleSheet(qss)\\n self.mainWindow.show()\",\n \"def __init__(self):\\n\\n self.ai_list = []\\n self.master = Tk()\\n self.ui_draw = IntVar()\\n\\n self.populate_ai_list(self.ai_list)\\n self.frame = Frame(self.master)\\n self.build_ui()\\n\\n self.master.mainloop()\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.67353976\",\n \"0.6553519\",\n \"0.65403384\",\n \"0.64762926\",\n \"0.63988465\",\n \"0.639814\",\n \"0.6305668\",\n \"0.62839437\",\n \"0.6229685\",\n \"0.6174939\",\n \"0.6174939\",\n \"0.6155415\",\n \"0.6138522\",\n \"0.6124276\",\n \"0.6120289\",\n \"0.6089418\",\n \"0.60719943\",\n \"0.6070405\",\n \"0.6062321\",\n \"0.605937\",\n \"0.60530907\",\n \"0.60440236\",\n \"0.6037443\",\n \"0.6020554\",\n \"0.6002176\",\n \"0.5989785\",\n \"0.59882593\",\n \"0.5985397\",\n \"0.5983207\",\n \"0.59769213\",\n \"0.5974041\",\n \"0.5971263\",\n \"0.59531856\",\n \"0.5952054\",\n \"0.5948913\",\n \"0.59421355\",\n \"0.59395593\",\n \"0.59215724\",\n \"0.59214765\",\n \"0.590634\",\n \"0.5890779\",\n \"0.58899325\",\n \"0.58875656\",\n \"0.588066\",\n \"0.58751774\",\n \"0.58751637\",\n \"0.5874457\",\n \"0.5859956\",\n \"0.5845231\",\n \"0.5839951\",\n \"0.5838624\",\n \"0.5829434\",\n \"0.58178574\",\n \"0.58112645\",\n \"0.58047605\",\n \"0.58030605\",\n \"0.5796536\",\n \"0.57903993\",\n \"0.57903695\",\n \"0.578963\",\n \"0.57860893\",\n \"0.57852614\",\n \"0.577931\",\n \"0.57758117\",\n \"0.57754207\",\n \"0.5762723\",\n \"0.575399\",\n \"0.5752997\",\n \"0.5750207\",\n \"0.57472754\",\n \"0.5726013\",\n \"0.57225114\",\n \"0.5720199\",\n \"0.5717498\",\n \"0.5715584\",\n \"0.57151264\",\n \"0.57151264\",\n \"0.57151264\",\n \"0.57151264\",\n \"0.57151264\",\n \"0.57108253\",\n \"0.5708572\",\n \"0.57018876\",\n \"0.569036\",\n \"0.568002\",\n \"0.5679418\",\n \"0.5672896\",\n \"0.5672896\",\n \"0.5665002\",\n \"0.56640595\",\n \"0.5660038\",\n \"0.5660028\",\n \"0.565856\",\n \"0.565741\",\n \"0.5656651\",\n \"0.5651649\",\n \"0.56280434\",\n \"0.56267226\",\n \"0.5622418\",\n \"0.5610623\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6407615"},"document_rank":{"kind":"string","value":"4"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":866,"numItemsPerPage":100,"numTotalItems":95772,"offset":86600,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NTkyNTE2MSwianRpIjoiYWM4NWMzOWYtOTc2Ni00ODg3LWExODAtN2ZiMTRiNDQ2OTUwIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODU5Mjg3NjEsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.ZRjdfd3wq-Qi54EuQkFWxzKjHrLdGe-TegEuVALOFGxKy8QnOu0F13azPeCbEqWkeKot5142vGFJRFJvvzuEBg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Produces permutations with nonadjacent chars swapped (up to 4 chars distance) | def longswapchar(word: str) -> Iterator[str]:
for first in range(0, len(word) - 2):
for second in range(first + 2, min(first + MAX_CHAR_DISTANCE, len(word))):
yield word[:first] + word[second] + word[first+1:second] + word[first] + word[second+1:] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def permute(s):\n output = []\n if len(s) <= 1:\n return s\n\n for i, letter in enumerate(s):\n # strip current letter from s and find perms on new string\n\n new_s = s[:i] + s[i + 1:]\n\n permutes = permute(new_s)\n\n for perm in permutes:\n output.append(let... | [
"0.6736689",
"0.65894395",
"0.6449836",
"0.6353029",
"0.6336718",
"0.63142693",
"0.62049276",
"0.6189887",
"0.61519015",
"0.6071711",
"0.60714144",
"0.5976132",
"0.5960588",
"0.5945252",
"0.5918505",
"0.5895274",
"0.5881311",
"0.58511114",
"0.58357",
"0.58307004",
"0.582559",... | 0.5421294 | 76 |
Produces permutations with chars replaced by adjacent chars on keyboard layout ("vat > cat") or downcased (if it was accidental uppercase). | def badcharkey(word: str, layout: str) -> Iterator[str]:
for i, c in enumerate(word):
before = word[:i]
after = word[i+1:]
if c != c.upper():
yield before + c.upper() + after
if not layout:
continue
pos = layout.find(c)
while pos != -1:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def permutor(text, permutation):\n scrambled = \"\"\n for x in permutation:\n scrambled = scrambled + text[x]\n return scrambled",
"def permute(s):\n output = []\n if len(s) <= 1:\n return s\n\n for i, letter in enumerate(s):\n # strip current letter from s and find perms o... | [
"0.6372465",
"0.63254774",
"0.6264477",
"0.6245013",
"0.604972",
"0.59293675",
"0.58192295",
"0.5791638",
"0.5778724",
"0.5763599",
"0.57522595",
"0.5729332",
"0.57102674",
"0.5680283",
"0.56723374",
"0.5573873",
"0.55528605",
"0.55336505",
"0.5531236",
"0.5504609",
"0.550429... | 0.54962444 | 22 |
Produces permutations with one char removed in all possible positions | def extrachar(word: str) -> Iterator[str]:
if len(word) < 2:
return
for i in range(0, len(word)):
yield word[:i] + word[i+1:] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def permute(s):\n output = []\n if len(s) <= 1:\n return s\n\n for i, letter in enumerate(s):\n # strip current letter from s and find perms on new string\n\n new_s = s[:i] + s[i + 1:]\n\n permutes = permute(new_s)\n\n for perm in permutes:\n output.append(let... | [
"0.71060157",
"0.6775533",
"0.650322",
"0.64496285",
"0.6383745",
"0.6379555",
"0.6363275",
"0.62668854",
"0.6234628",
"0.6207375",
"0.6204744",
"0.61904925",
"0.6178601",
"0.6170437",
"0.6127599",
"0.6112459",
"0.6085988",
"0.6070011",
"0.6062588",
"0.6038429",
"0.600483",
... | 0.0 | -1 |
Produces permutations with one char inserted in all possible possitions. | def forgotchar(word: str, trystring: str) -> Iterator[str]:
if not trystring:
return
for c in trystring:
for i in range(0, len(word)):
yield word[:i] + c + word[i:] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def permutations(iterable):\n pass",
"def permute(s):\n output = []\n if len(s) <= 1:\n return s\n\n for i, letter in enumerate(s):\n # strip current letter from s and find perms on new string\n\n new_s = s[:i] + s[i + 1:]\n\n permutes = permute(new_s)\n\n for perm ... | [
"0.68747985",
"0.68487537",
"0.675091",
"0.6745953",
"0.6665328",
"0.6615404",
"0.6543778",
"0.6540176",
"0.6488002",
"0.6486939",
"0.64503247",
"0.64461046",
"0.64378107",
"0.64356333",
"0.6427934",
"0.638743",
"0.6379941",
"0.6378034",
"0.6345018",
"0.6322257",
"0.6304166",... | 0.0 | -1 |
Produces permutations with one character moved by 2, 3 or 4 places forward or backward (not 1, | def movechar(word: str) -> Iterator[str]:
if len(word) < 2:
return
for frompos, char in enumerate(word):
for topos in range(frompos + 3, min(len(word), frompos + MAX_CHAR_DISTANCE + 1)):
yield word[:frompos] + word[frompos+1:topos] + char + word[topos:]
for frompos in reversed... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def permute(s):\n output = []\n if len(s) <= 1:\n return s\n\n for i, letter in enumerate(s):\n # strip current letter from s and find perms on new string\n\n new_s = s[:i] + s[i + 1:]\n\n permutes = permute(new_s)\n\n for perm in permutes:\n output.append(let... | [
"0.6871835",
"0.67360544",
"0.66985404",
"0.6541775",
"0.65291804",
"0.6452237",
"0.6439172",
"0.63887715",
"0.63329065",
"0.62688124",
"0.6244564",
"0.6234082",
"0.6229988",
"0.6199025",
"0.61834604",
"0.6168268",
"0.6122072",
"0.61141455",
"0.61071986",
"0.60252476",
"0.598... | 0.0 | -1 |
Produces permutations with accidental twoletterdoubling fixed (vacation > vacacation) | def doubletwochars(word: str) -> Iterator[str]:
if len(word) < 5:
return
# TODO: 1) for vacacation yields "vacation" twice, hunspell's algo kinda wiser
# 2) maybe just use regexp?..
for i in range(2, len(word)):
if word[i-2] == word[i] and word[i-3] == word[i-1]:
yield word... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perm_2_let():\r\n return {''.join(i) for i in permutations('abcdefghijklmnopqrstuvwxyz', 2)}\r\n # print(comb_2_let, sep='')\r",
"def exercise_b2_2():\r\n letters = ['a', 'e', 'i', 'o', 'u', 'u']\r\n combinations = list(permutations(letters))\r\n uniq_combinations = set(combinations)\r\n to... | [
"0.6638055",
"0.6480466",
"0.634991",
"0.63021815",
"0.6247458",
"0.6168677",
"0.61462843",
"0.6046891",
"0.5984386",
"0.59619355",
"0.58376366",
"0.5808831",
"0.58081603",
"0.5806433",
"0.57955134",
"0.5788775",
"0.5746197",
"0.574384",
"0.57248336",
"0.5721883",
"0.5707456"... | 0.54011095 | 58 |
Produces permutation of splitting in two words in all possible positions. | def twowords(word: str) -> Iterator[List[str]]:
for i in range(1, len(word)):
yield [word[:i], word[i:]] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def word_perms(word):\n\t# Question 4a: Generates all strings that are permutations of the letters in word\n\treturn {''.join(w) for w in permutations(word)}",
"def perm_2_let():\r\n return {''.join(i) for i in permutations('abcdefghijklmnopqrstuvwxyz', 2)}\r\n # print(comb_2_let, sep='')\r",
"def genera... | [
"0.6488516",
"0.6271073",
"0.62227064",
"0.61066955",
"0.60962975",
"0.60536313",
"0.59959745",
"0.5934376",
"0.5868344",
"0.5823151",
"0.5795944",
"0.5765678",
"0.57420015",
"0.5711072",
"0.5696769",
"0.5692085",
"0.5676564",
"0.5672385",
"0.5649941",
"0.5633519",
"0.5633268... | 0.5415966 | 39 |
Initialize MeSH search tool | def __init__(self, outprefix: str):
paths = PhenoXPaths(outprefix)
mesh_json_path = os.path.join(paths.data_dir, 'mesh.json')
self.mesh = dict()
if not os.path.exists(mesh_json_path):
mesh_bin_file = glob.glob(os.path.join(paths.data_dir, '*.bin'))
if mesh_bin_fi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, args, parsers):\n self.parsers = parsers\n self.search_fields = args.search_field if args.search_field else []",
"def __init__(self, command_name, search_term):\n\n BotCommand.__init__(self, command_name, 'all')\n self.search_term = search_term\n self.api_cli... | [
"0.6176019",
"0.6114654",
"0.6089348",
"0.6023115",
"0.60166216",
"0.5964663",
"0.5958271",
"0.58684164",
"0.5789062",
"0.5752446",
"0.57192004",
"0.56736904",
"0.56730086",
"0.56630135",
"0.56393933",
"0.56281126",
"0.562146",
"0.5617541",
"0.56095546",
"0.56027716",
"0.5574... | 0.0 | -1 |
Parse MeSH tree from bin file | def _parse_mesh_bin(self, bin_file, json_file):
def _chunks(filename, start):
"""
Split file into chunks
:param filename:
:param start:
:return:
"""
with open(filename, 'r') as f:
buffer = []
for... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __parse(self):\n # raw/objects: detect name, type, use major tag for type as parent node\n # raw/graphics: as object raw, but add TILE_PAGE\n # init: usually flat file, except\n # embark_profiles.txt: [PROFILE] is parent\n # interface.txt: [BIND] is parent (legacy will be... | [
"0.59550416",
"0.578597",
"0.5505974",
"0.544396",
"0.5331556",
"0.5322069",
"0.5312597",
"0.52813524",
"0.5267293",
"0.5237262",
"0.5228618",
"0.5199379",
"0.518287",
"0.5182435",
"0.5178287",
"0.5173431",
"0.5137329",
"0.51313764",
"0.5130195",
"0.5124704",
"0.5118085",
"... | 0.6865124 | 0 |
Split file into chunks | def _chunks(filename, start):
with open(filename, 'r') as f:
buffer = []
for line in f:
if line.startswith(start):
if buffer:
yield buffer
buffer = []
else:... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def split_file(self, input_file):\r\n file_list = [] \r\n with open(input_file, 'r', encoding='GB18030', errors='ignore') as f_in:\r\n data = f_in.readlines()\r\n lines_num = len(data)\r\n size = lines_num // self.num_workers # lines splitted in a chunk\r\n ... | [
"0.804088",
"0.7830803",
"0.75680953",
"0.74309623",
"0.74250346",
"0.7374043",
"0.7371516",
"0.7276274",
"0.7133963",
"0.7073844",
"0.70632815",
"0.70365644",
"0.7006685",
"0.69359064",
"0.6887627",
"0.68578815",
"0.6841715",
"0.6821044",
"0.68040067",
"0.68006545",
"0.67727... | 0.71220946 | 9 |
Find closest MeSH term | def lookup(self, query_text):
query = query_text.lower()
if query in self.mesh.keys():
return self.mesh[query]
else:
closest = difflib.get_close_matches(query, self.mesh.keys())
print('Did you mean?')
for ind, match in enumerate(closest):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_closest(self):\n m = mats.Materials(\"mats_test.json\", NoneVisited())\n self.assertEqual( '164 G. Canis Majoris', m.closest([0, 0, 0], ['Tungsten'])[1]['system'])\n self.assertEqual( '2MASS J10433563-5945136', m.closest([0, 0, 0], ['Germanium'])[1]['system'])",
"def closest_phrase(... | [
"0.6311742",
"0.60357434",
"0.6002968",
"0.5989886",
"0.59766924",
"0.59562415",
"0.58662575",
"0.5721969",
"0.5704622",
"0.5643911",
"0.5631589",
"0.56212884",
"0.5604074",
"0.55850166",
"0.5584442",
"0.5579262",
"0.5561027",
"0.55594707",
"0.5556232",
"0.5536082",
"0.552261... | 0.0 | -1 |
Takes in an infix expression, evaluates it, and returns the result | def calculator(infix_expr):
# Assign precedence values to operators
prec = {}
prec['^'] = 4
prec['*'] = 3
prec['/'] = 3
prec['+'] = 2
prec['-'] = 2
prec['('] = 1
# Instantiate stacks
operand_stack = Stack()
operator_stack = Stack()
try:
token_list = infix_expr.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def evaluate_infix(string):\n return postfix(infix_to_postfix(string))",
"def calculate_infix_expression(cls, expression):\n\t\tlogger.info(f\"in the calculate infix expression {expression}\")\n\t\telements = expression.split()\n\t\tstack = []\n\t\ttry:\n\t\t\tfor e in elements:\n\t\t\t\tif not e.isdigit() an... | [
"0.81387174",
"0.79848576",
"0.7524545",
"0.73664916",
"0.7191866",
"0.7085205",
"0.7037346",
"0.6990421",
"0.6908584",
"0.69054866",
"0.6903593",
"0.6898661",
"0.68923616",
"0.68888754",
"0.6804834",
"0.67953634",
"0.67565376",
"0.6755559",
"0.6743953",
"0.6695394",
"0.66859... | 0.721813 | 4 |
Helper function to do mathematical operations /+ | def do_math(operator, op1, op2):
if operator == "*":
return op1 * op2
if operator == "/":
return op1 / op2
if operator == "+":
return op1 + op2
if operator == "-":
return op1 - op2
if operator == "^":
return op1**(op2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def perform_operation(operator, num_1, num_2):\n\n if operator == \"*\":\n return num_1 * num_2\n if operator == \"+\":\n return num_1 + num_2\n if operator == \"-\":\n return num_1 - num_2\n if operator == \"/\":\n return num_1 / num_2",
"def calculate_expression(number1,... | [
"0.7359163",
"0.7181013",
"0.7104238",
"0.6985625",
"0.697319",
"0.697319",
"0.6970828",
"0.6933551",
"0.69055194",
"0.6828667",
"0.67890763",
"0.6654178",
"0.6600834",
"0.657938",
"0.6561923",
"0.65443194",
"0.6537193",
"0.6487585",
"0.6484884",
"0.6473484",
"0.6470769",
"... | 0.7230534 | 1 |
Adds gates to the input circuit to perform a thresholding operation, which discards singular values below some threshold, or minimum value. | def _threshold(self, circuit, register, ctrl_string, measure_flag_qubit=True):
# Determine the number of controls
ncontrols = (ctrl_string + "1").index("1")
# Edge case in which no operations are added
if ncontrols == 0:
return
# Make sure there is at least one cont... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def threshold(self,thresholdValue):\n # TO DO\n pass",
"def global_threshold(img, threshold_method):\n pass",
"def overlay_thresholding_function(threshold, positive=True):\n # from the interface class definition above, there will be 3 values\n # for the thresh type: inactive, less than, ... | [
"0.6542963",
"0.6380482",
"0.63642716",
"0.6296534",
"0.6028167",
"0.59781814",
"0.59754616",
"0.5960532",
"0.5943381",
"0.59409696",
"0.592669",
"0.59017485",
"0.585506",
"0.58209485",
"0.58209485",
"0.58209485",
"0.57742774",
"0.575426",
"0.57475746",
"0.57433945",
"0.57346... | 0.58375096 | 13 |
Returns the quantum circuit to recommend product(s) to a user. | def create_circuit(self,
user,
threshold,
measurements=True,
return_registers=False,
logical_barriers=False,
swaps=True):
# Make sure the user is valid
self._validate... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_recommend(self, user):\n return self.user_cf.calculate(target_user_id=user, user_n=self.user_n,\n item_n=self.item_n, type=2)",
"def classical_recommendation(self, user, rank, quantum_format=True):\n # Make sure the user and rank are ok\n self._v... | [
"0.6139024",
"0.5891252",
"0.574195",
"0.5702561",
"0.5693326",
"0.5630196",
"0.5525133",
"0.55186236",
"0.5515071",
"0.54674363",
"0.54653776",
"0.54438645",
"0.54074544",
"0.53887576",
"0.5363334",
"0.53183645",
"0.52956206",
"0.52643067",
"0.52258486",
"0.517817",
"0.51552... | 0.49878383 | 29 |
Runs the quantum circuit recommending products for the given user and returns the raw counts. | def run_and_return_counts(self, user, threshold, shots=10000):
circuit = self.create_circuit(user, threshold, measurements=True, logical_barriers=False)
job = execute(circuit, BasicAer.get_backend("qasm_simulator"), shots=shots)
results = job.result()
return results.get_counts() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_recommend(self, user):\n return self.user_cf.calculate(target_user_id=user, user_n=self.user_n,\n item_n=self.item_n, type=2)",
"def recommend(self, user, threshold, shots=10000, with_probabilities=True, products_as_ints=True):\n # Run the quantum recom... | [
"0.664292",
"0.6593653",
"0.60152787",
"0.5872946",
"0.5831164",
"0.5804622",
"0.57920045",
"0.56995636",
"0.5659857",
"0.5658142",
"0.5658142",
"0.5597596",
"0.5582271",
"0.55194044",
"0.55072933",
"0.54899436",
"0.5483319",
"0.54552543",
"0.53995514",
"0.53746325",
"0.53264... | 0.62393165 | 2 |
Returns a recommendation for a specified user. | def recommend(self, user, threshold, shots=10000, with_probabilities=True, products_as_ints=True):
# Run the quantum recommendation and get the counts
counts = self.run_and_return_counts(user, threshold, shots)
# Remove all outcomes with flag qubit measured as zero (assuming the flag qubit is m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_recommend(self, user):\n return self.user_cf.calculate(target_user_id=user, user_n=self.user_n,\n item_n=self.item_n, type=2)",
"def get_recommendations_for_user(self, user_id):\r\n\r\n sql_command = \"\"\"\r\n SELECT event_id, sc... | [
"0.82474965",
"0.752934",
"0.7385011",
"0.732243",
"0.72096705",
"0.70603764",
"0.70367336",
"0.6976795",
"0.6925429",
"0.67433727",
"0.672272",
"0.66872346",
"0.6640382",
"0.66097564",
"0.65273446",
"0.6522883",
"0.6514766",
"0.64301234",
"0.6375047",
"0.6346488",
"0.6334251... | 0.0 | -1 |
Returns a recommendation for a specified user via classical singular value decomposition. | def classical_recommendation(self, user, rank, quantum_format=True):
# Make sure the user and rank are ok
self._validate_user(user)
self._validate_rank(rank)
# Do the classical SVD
_, _, vmat = np.linalg.svd(self.matrix, full_matrices=True)
# Do the projection
r... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def recommend(self, user):\n K = self.n_sim_user\n N = self.n_rec_movie\n rank = dict()\n watched_movies = self.trainset[user]\n\n # v=similar user, wuv=similarity factor\n for v, wuv in sorted(self.user_sim_mat[user].items(),\n key=itemgetter(1... | [
"0.62279725",
"0.6218502",
"0.593269",
"0.58930665",
"0.570855",
"0.56177104",
"0.5597132",
"0.5564896",
"0.55526376",
"0.5535168",
"0.5519044",
"0.5508257",
"0.54996467",
"0.5496056",
"0.54412323",
"0.54394263",
"0.5439314",
"0.5430811",
"0.5413947",
"0.5373785",
"0.53643227... | 0.6778557 | 0 |
Validates a user (vector). If an invalid user for the recommendation system is given, a UserError is thrown. Else, nothing happens. | def _validate_user(self, user):
# Make sure the user is of the correct type
if not isinstance(user, (list, tuple, np.ndarray)):
raise UserVectorError("Invalid type for user. Accepted types are list, tuple, and numpy.ndarray.")
# Make sure the user vector has the correct length
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _validate_user(_):\n pass",
"def validate_by_user(self, user, value):\n if not self.validated and not self.destroyed:\n self.parent.logger.write(\"vote\", user, [value, self.createur.identifier])\n if value == 0:\n if self.validator.count(user) > 0:\n ... | [
"0.6562727",
"0.605157",
"0.5901822",
"0.5775897",
"0.57269454",
"0.56912977",
"0.56912977",
"0.5518426",
"0.5507653",
"0.5503397",
"0.55014575",
"0.54806924",
"0.5464648",
"0.5460626",
"0.54598135",
"0.53860456",
"0.5361218",
"0.53423893",
"0.53335655",
"0.53295267",
"0.5252... | 0.8340915 | 0 |
Throws a RankError if the rank is not valid, else nothing happens. | def _validate_rank(self, rank):
if rank <= 0 or rank > self.num_users:
raise RankError("Rank must be in the range 0 < rank <= number of users.") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _validate_rank(self, rank: int) -> None:\n if not isinstance(rank, int):\n raise TypeError(\"rank should be an integer\")\n if rank < 0:\n raise ValueError(\"rank cannot be negative\")\n if rank >= self.L**2:\n raise ValueError(f\"rank should be less than {... | [
"0.8221189",
"0.6799999",
"0.6629772",
"0.64921623",
"0.6485812",
"0.6354266",
"0.6354266",
"0.6263529",
"0.60272527",
"0.5991913",
"0.59675074",
"0.58497083",
"0.5840833",
"0.58285457",
"0.5822559",
"0.57317233",
"0.56980824",
"0.56085336",
"0.55671346",
"0.5528553",
"0.5522... | 0.86043864 | 0 |
Converts a decimal in base ten to a binary decimal string. | def _to_binary_decimal(decimal, nbits=5):
if 1 <= decimal < 0:
raise ValueError("Argument decimal should satisfy 0 <= decimal < 1.")
binary = ""
for ii in range(1, nbits + 1):
if decimal * 2 ** ii >= 1:
binary += "1"
decimal = (decimal * 1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def decimal_to_binary(num):\n binary_res = \"\"\n while num >= 1:\n binary_char = num % BINARY_BASE\n num = math.floor(num / BINARY_BASE)\n binary_res += str(binary_char)\n if len(binary_res) < REGISTER_SIZE:\n binary_res += \"0\" * (REGISTER_SIZE - len(binary_res))\n return... | [
"0.7807933",
"0.7628011",
"0.7184916",
"0.71676975",
"0.7100728",
"0.70524585",
"0.7034531",
"0.70182544",
"0.70076656",
"0.6989513",
"0.6979323",
"0.69152266",
"0.68483025",
"0.68307817",
"0.6806326",
"0.67578655",
"0.6750961",
"0.6747368",
"0.672164",
"0.6674812",
"0.663185... | 0.6931232 | 11 |
Returns the integer equivalent of the binary string. | def _binary_string_to_int(bitstring, big_endian=True):
if not big_endian:
bitstring = str(reversed(bitstring))
val = 0
nbits = len(bitstring)
for (n, bit) in enumerate(bitstring):
if bit == "1":
val += 2**(nbits - n - 1)
return val | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def bin2int(r: str) -> int:",
"def bin_to_int(bit_string):\r\n return int(''.join(bit_string), 2)",
"def bitstr_to_int(a):\n return int(a, 2)",
"def byte_str_to_int(str):\n return int.from_bytes(str, byteorder = \"big\")",
"def bitstring_to_int(bitstr):\n b_list = bitstr.tolist()\n mystring ... | [
"0.8224296",
"0.8092693",
"0.76653373",
"0.740448",
"0.7333371",
"0.73030263",
"0.7261367",
"0.7229864",
"0.70981485",
"0.70571816",
"0.7037208",
"0.6998326",
"0.69783306",
"0.68612695",
"0.68541133",
"0.684989",
"0.683415",
"0.6813389",
"0.68040365",
"0.67303544",
"0.6716656... | 0.7727533 | 2 |
Converts the rank to a ctrl_string for thresholding. | def _rank_to_ctrl_string(rank, length):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __rank_from_int_to_str(rank: int) -> str:\n return str(rank + 1)",
"def __str__(self):\n return str(self.rank)",
"def _threshold_to_control_string(self, threshold):\n # Make sure the threshold is ok\n if threshold < 0 or threshold > 1:\n raise ThresholdError(\"Argumen... | [
"0.6593619",
"0.6022002",
"0.56026626",
"0.5538748",
"0.53891945",
"0.5351683",
"0.5284331",
"0.52622",
"0.52310747",
"0.5225471",
"0.5217082",
"0.5201264",
"0.51567566",
"0.5155428",
"0.5123787",
"0.51021874",
"0.5084797",
"0.5050555",
"0.50300705",
"0.50247926",
"0.5008611"... | 0.8312594 | 0 |
Returns a control string for the threshold circuit which keeps all values strictly above the threshold. | def _threshold_to_control_string(self, threshold):
# Make sure the threshold is ok
if threshold < 0 or threshold > 1:
raise ThresholdError("Argument threshold must satisfy 0 <= threshold <= 1.")
# Compute the angle 0 <= theta <= 1 for this threshold singular value
theta = 1 ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_threshold_str(self):\n if self.unit == ThresholdUnit.PERCENTAGE:\n return f\"{self.threshold}%\"\n return self.threshold",
"def _strHard(self):\n if self.checkGreaterThanThreshold:\n operator += \">\"\n else:\n operator += \"<\"\n return \"(Hard) %s %... | [
"0.67489445",
"0.64910465",
"0.61631197",
"0.60861534",
"0.60524446",
"0.60228556",
"0.5994099",
"0.5945274",
"0.590314",
"0.590314",
"0.590314",
"0.590314",
"0.590314",
"0.58639866",
"0.5801137",
"0.5792392",
"0.5790814",
"0.5790814",
"0.57659495",
"0.5691014",
"0.5691014",
... | 0.7095868 | 0 |
Tigerholm version of IA. | def ka_tf(Y,t,voltage_clamp_func,voltage_clamp_params):
# g = gbar * n * h
v = voltage_clamp_func(t,voltage_clamp_params)
n = Y[0]
h = Y[1]
q10 = 1.0#3.3 # Preserved in case it is useful but disabled
ninf = (1.0/(1.0 + np.exp(-(v+5.4+15)/16.4)))**4
ntau = 0.25 + 10.04*np.exp((-(v+24.67)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def t_IAx(mt, x, defer=0, i=None, inc=1., method='udd'):\n\n return IA_x(mt=mt, x=x, x_first=x + 1 + defer, x_last=mt.w + 1, i=i, inc=inc, method=method)",
"def airl():\n algorithm = \"airl\"",
"def ij(ij, pol, ant) :\n s.ij(pol, ij, ant)",
"def t_nIAx(mt, x, n, defer=0, i=None, inc=1., method='udd'... | [
"0.6104706",
"0.6072912",
"0.5867349",
"0.57087487",
"0.567309",
"0.5646599",
"0.5618267",
"0.5607819",
"0.5509949",
"0.54581845",
"0.5440544",
"0.5368196",
"0.53348154",
"0.5330311",
"0.53295183",
"0.53151816",
"0.5309329",
"0.52920604",
"0.5260829",
"0.525207",
"0.5248973",... | 0.0 | -1 |
Jaffe et al. 1994 ICaL model. | def cal_ja(Y,t,voltage_clamp_func,voltage_clamp_params):
v = voltage_clamp_func(t,voltage_clamp_params)
m = Y[0]
tfa = 1.
ki = 0.001 # (mM)
cao = 2.5 # Davidson (mM)
" To do: make cai variable as an input like voltage "
cai = 1.e-4 # (mM) Roughly values (100 nM) from Intracellular ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ICA_log_likelihood(X, model):\n\n # TODO: YOUR CODE HERE",
"def construct_model():\n import lbann\n\n # Layer graph\n input = lbann.Input(target_mode='N/A', name='inp_data')\n # data is 64*64*4 images + 15 scalar + 5 param\n #inp_slice = lbann.Slice(input, axis=0, slice_points=\"0 16399 164... | [
"0.6196822",
"0.5908754",
"0.5809889",
"0.57607794",
"0.57542014",
"0.5594538",
"0.5566583",
"0.5541234",
"0.55340904",
"0.54170597",
"0.5401296",
"0.54003006",
"0.5380386",
"0.53723615",
"0.5342001",
"0.5331724",
"0.5316832",
"0.5313649",
"0.53117883",
"0.5307793",
"0.530736... | 0.0 | -1 |
Model of Ntype Ca current from Migliore 95 | def can_mi():
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Ncen(self, m):\n pass",
"def nits(self):",
"def front_column_model_p_gain():",
"def NuGrid_net(self,model_type='delay'):\n\n # Create list of masses and metallicites:\n self.masses = [12.0,15.0,20.0,25.0]\n self.metallicities = [0.02,0.01,0.006,0.001,0.0001]\t\t\n \n ... | [
"0.6159706",
"0.5985799",
"0.5833766",
"0.57632107",
"0.5715623",
"0.56937754",
"0.56754404",
"0.5638119",
"0.5625848",
"0.56154746",
"0.55823624",
"0.5574716",
"0.55289745",
"0.55031383",
"0.55031383",
"0.549705",
"0.5462524",
"0.5442102",
"0.5433495",
"0.543087",
"0.5421858... | 0.0 | -1 |
Kouranova Ih model with nonspecific current (reversal potential should be set at 30 mV | def hcn_kn(Y,t,voltage_clamp_func,voltage_clamp_params):
v = voltage_clamp_func(t,voltage_clamp_params)
n_s = Y[0]
n_f = Y[1]
ninf_s = 1/(1 + np.exp((v+87.2)/9.7))
ninf_f = ninf_s
if v > -70.0:
tau_ns = 300.0 + 542.0 * np.exp((v+25.0)/20.0)
tau_nf = 140.0 + 50.0 * np.exp(-(v+2... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def doParametersOfInterest(self):\n self.modelBuilder.doVar(\"kappa_W[1,0.0,2.0]\") \n self.modelBuilder.doVar(\"kappa_Z[1,0.0,2.0]\") \n self.modelBuilder.doVar(\"kappa_tau[1,0.0,3.0]\")\n self.modelBuilder.doVar(\"kappa_mu[1,0.0,5.0]\") \n self.modelBuilder.factory_(\"expr::kap... | [
"0.5959516",
"0.5938657",
"0.59188086",
"0.56832284",
"0.5678824",
"0.56349754",
"0.556212",
"0.5555892",
"0.5533694",
"0.5513207",
"0.55051756",
"0.54915553",
"0.5479147",
"0.54786277",
"0.5437813",
"0.5418036",
"0.5404291",
"0.5395422",
"0.53909826",
"0.53884333",
"0.538687... | 0.0 | -1 |
Tigerholm version of the Kouranova Ih model which is identical except that when you calculate the current you don't use a nonspecific reversal potential and instead split the current between Na+ and K+, 50/50. | def hcn_tf(Y,t,voltage_clamp_func,voltage_clamp_params):
v = voltage_clamp_func(t,voltage_clamp_params)
n_s = Y[0]
n_f = Y[1]
ninf_s = 1/(1 + np.exp((v+87.2)/9.7))
ninf_f = ninf_s
if v > -70.0:
tau_ns = 300.0 + 542.0 * np.exp((v+25.0)/20.0)
tau_nf = 140.0 + 50.0 * np.e... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_kwta_inhibition(self) -> None:\n top_m_units = self.units.top_k_net_indices(self.spec.k + 1)\n g_i_thr_m = self.units.g_i_thr(top_m_units[-1])\n g_i_thr_k = self.units.g_i_thr(top_m_units[-2])\n self.gc_i = g_i_thr_m + 0.5 * (g_i_thr_k - g_i_thr_m)",
"def calc_k(self):\n\t\n\... | [
"0.6207315",
"0.61734277",
"0.61404717",
"0.6069017",
"0.59344864",
"0.57818156",
"0.57013375",
"0.568654",
"0.5584831",
"0.5570212",
"0.55488724",
"0.5501513",
"0.54939854",
"0.5492646",
"0.5472838",
"0.547054",
"0.54563785",
"0.5454046",
"0.54503864",
"0.54393375",
"0.54393... | 0.0 | -1 |
Instantiate Basic Classes to call here. | def __init__(self, secret_key=None):
PayStackBase.__init__(self, secret_key=secret_key)
self.transaction = Transaction
self.customer = Customer
self.plan = Plan | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize(cls):",
"def setUpClass(cls):\n\n Base._Base__nb_objects = 0\n cls.b1 = Base()\n cls.b2 = Base()\n cls.b3 = Base(22)\n cls.b4 = Base(2.2)\n cls.b5 = Base(\"two\")\n cls.r1 = Rectangle(10, 7, 2, 8)\n cls.r2 = Rectangle(2, 4)",
"def __init__(... | [
"0.68417025",
"0.6666116",
"0.6656014",
"0.65786934",
"0.6541811",
"0.647474",
"0.6471347",
"0.6452271",
"0.64435893",
"0.64189386",
"0.64028394",
"0.6396954",
"0.6392208",
"0.63788474",
"0.63788474",
"0.63529116",
"0.6334275",
"0.63251996",
"0.6317408",
"0.631519",
"0.630211... | 0.0 | -1 |
This function returns a custom augmentations object for use with sequences via the load_sequences function in data_core.py. Please note that these augmentations have only been tested with RGB data between 0 and 1 and that order of operations is critical. e.g., blurs don't like missing data so shouldn't be applied befor... | def sample_custom_augmentations_constructor(num_features: int, window_radius: int) -> albumentations.Compose:
max_kernel = int(round(0.1 * window_radius))
max_hole_size = int(round(0.1 * window_radius))
additional_targets = [ADDITIONAL_TARGETS_KEY.format(idx) for idx in range(1, num_features)]
return a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_augmentation_sequence():\n # Macro to apply something with 50% chance\n sometimes = lambda aug: iaa.Sometimes(0.5, aug) # 50%\n rarely = lambda aug: iaa.Sometimes(0.1, aug) # 10%\n\n # Augmentation applied to every image\n # Augmentors sampled one value per channel\n aug_sequence = iaa.Se... | [
"0.72518003",
"0.7063507",
"0.6583223",
"0.6424683",
"0.6421103",
"0.6192685",
"0.6152167",
"0.6097807",
"0.6092612",
"0.6060356",
"0.60179466",
"0.60006166",
"0.59754187",
"0.5908761",
"0.5866812",
"0.5864484",
"0.58596736",
"0.58376133",
"0.58314306",
"0.58147705",
"0.57766... | 0.6154471 | 6 |
Returns true if the instance exists. | def _Exists(self):
cmd = util.GcloudCommand(self, 'beta', 'bigtable', 'instances', 'list')
cmd.flags['format'] = 'json'
cmd.flags['project'] = self.project
# The zone flag makes this command fail.
cmd.flags['zone'] = []
stdout, stderr, retcode = cmd.Issue(
suppress_warning=True, raise_on... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def exists(self):\n return True",
"def exists(self):\n return True",
"def singularity_exists(self):\n instances = Client.instances(quiet=self.quiet)\n for instance in instances:\n if self.pid in instance.name:\n return True\n return False",
"def ex... | [
"0.80422133",
"0.80422133",
"0.79262125",
"0.77907544",
"0.7623855",
"0.76222694",
"0.7574529",
"0.75404423",
"0.75287634",
"0.74621236",
"0.7362517",
"0.7278307",
"0.7277255",
"0.7208512",
"0.7166167",
"0.71638733",
"0.71490216",
"0.7146296",
"0.7137245",
"0.70719695",
"0.70... | 0.71871287 | 14 |
Generate Campaign Mailer File | def put(self, campaign_id):
campaign = Campaign.query.filter_by(public_id=campaign_id).first()
if campaign.candidates.count() > 0:
campaign.launch_task('generate_mailer_file')
return {'success': True, 'message': 'Initiated generate mailer file'}, 200
else:
api... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sendEmail(householdID):\n contactID = mdb.getContact(householdID)\n sqlq = \"\"\"\n SELECT Name, Surname, Address1, Address2, Town, Postcode, email, status\n FROM Contact\n WHERE idContact = '{}';\n \"\"\".format(contactID)\n result = mdb.getSQL(sqlq)[0]\n\n... | [
"0.62662476",
"0.6100366",
"0.6038897",
"0.60258704",
"0.6018833",
"0.5995741",
"0.59857935",
"0.59802675",
"0.59382075",
"0.59136933",
"0.5876253",
"0.58697945",
"0.58588535",
"0.585498",
"0.5701235",
"0.5700115",
"0.5661588",
"0.5602806",
"0.55970114",
"0.5536965",
"0.55360... | 0.6127983 | 1 |
Download Generated Campaign Mailer File | def get(self, campaign_id):
campaign = Campaign.query.filter_by(public_id=campaign_id).first()
if campaign:
return send_file(campaign.mailer_file)
else:
api.abort(404, message='Campaign does not exist', success=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_attachment(self, msg):\n path = None\n for part in msg.walk():\n if part.get_content_type() == 'application/pdf':\n\n time_prefix = datetime.datetime.now().strftime(\"%Y-%m-%dT%H:%M:%S\")\n filename = time_prefix+\"-\"+part.get_filename()\n ... | [
"0.6207587",
"0.61629426",
"0.61061794",
"0.60872763",
"0.6069316",
"0.6033814",
"0.6006443",
"0.5982717",
"0.5926988",
"0.5911624",
"0.5889739",
"0.5855126",
"0.5819009",
"0.57935196",
"0.5749433",
"0.5705398",
"0.56524426",
"0.5646966",
"0.5636374",
"0.5617726",
"0.5607355"... | 0.64709884 | 0 |
Assign Candidate Import to Campaign | def put(self, campaign_id, import_id):
try:
campaign = Campaign.query.filter_by(public_id=campaign_id).first()
candidate_import = CandidateImport.query.filter_by(public_id=import_id).first()
Candidate.query.filter_by(import_id=candidate_import.id).update({Candidate.campaign_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def put(self, campaign_id):\n campaign = Campaign.query.filter_by(public_id=campaign_id).first()\n if campaign.candidates.count() > 0:\n campaign.launch_task('generate_mailer_file')\n return {'success': True, 'message': 'Initiated generate mailer file'}, 200\n else:\n ... | [
"0.57850623",
"0.53894794",
"0.52772266",
"0.52463496",
"0.5215476",
"0.5212152",
"0.51909935",
"0.50752306",
"0.5036112",
"0.5028312",
"0.50166535",
"0.50076145",
"0.4974562",
"0.48906434",
"0.48841932",
"0.48716426",
"0.48612928",
"0.48469207",
"0.48326838",
"0.4820747",
"0... | 0.72279286 | 0 |
Print the list of filters in the pcigale database. | def list_filters():
with Database() as base:
filters = {name: base.get_filter(name) for name in
base.get_filter_names()}
name = Column(data=[filters[f].name for f in filters], name='Name')
description = Column(data=[filters[f].description for f in filters],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_filters():\n print('Hello! Let\\'s explore some US bikeshare data!')",
"def PrintFilterCollection(self):\n self._filter_collection.Print(self._output_writer)",
"def filters_show():\n log = slog()\n log.title_set('Filters applied')\n if self.args['table3D']... | [
"0.7513858",
"0.728271",
"0.7068839",
"0.656867",
"0.6391572",
"0.6353896",
"0.6288215",
"0.61509585",
"0.6072937",
"0.60103214",
"0.5993697",
"0.59286284",
"0.58995205",
"0.5839732",
"0.5807007",
"0.5801106",
"0.5794581",
"0.57741386",
"0.5759257",
"0.57586986",
"0.57420343"... | 0.7171043 | 2 |
Add filters to the pcigale database. | def add_filters(fnames):
with Database(writable=True) as base:
for fname in fnames:
with open(fname, 'r') as f_fname:
filter_name = f_fname.readline().strip('# \n\t')
filter_type = f_fname.readline().strip('# \n\t')
filter_description = f_fname.rea... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _add_filters(self, filters):\n self._env.filters['dateformat'] = dateformat\n self._env.filters.update(filters or {})",
"def apply_filters(self, new_filters):\n\t\tself.filters = new_filters",
"def apply_filters(self, filters):\n self._data = self.model.objects.filter(**filters)",
"d... | [
"0.66940427",
"0.6561778",
"0.6558746",
"0.6418302",
"0.64057815",
"0.62887925",
"0.6068981",
"0.60636497",
"0.6025763",
"0.6024873",
"0.600526",
"0.59792304",
"0.5913037",
"0.58850616",
"0.5872086",
"0.5865903",
"0.58526486",
"0.5840947",
"0.58158356",
"0.5815147",
"0.581483... | 0.63654387 | 5 |
Delete filters from the pcigale database | def del_filters(fnames):
with Database(writable=True) as base:
names = base.get_filter_names()
for fname in fnames:
if fname in names:
base.del_filter(fname)
print("Removing filter {}".format(fname))
else:
print("Filter {} not i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear_list_filter(self,*args,**kwargs):\n # import pdb;pdb.set_trace()\n if args:\n for arg in args:\n if isinstance(arg,SqliteTable):\n try:\n del session[self.HEADER_NAME][arg.table_name]\n except Exception a... | [
"0.6794046",
"0.6666188",
"0.64374053",
"0.6412906",
"0.6134906",
"0.612257",
"0.60874057",
"0.60324705",
"0.60267925",
"0.6019257",
"0.5974808",
"0.594915",
"0.59430444",
"0.59391993",
"0.5930648",
"0.58455044",
"0.5835066",
"0.58248544",
"0.5820826",
"0.58151543",
"0.578677... | 0.717233 | 0 |
Worker to plot filter transmission curves in parallel | def worker_plot(fname):
with Database() as base:
_filter = base.get_filter(fname)
plt.clf()
plt.plot(_filter.trans_table[0], _filter.trans_table[1], color='k')
plt.xlim(_filter.trans_table[0][0], _filter.trans_table[0][-1])
plt.minorticks_on()
plt.xlabel('Wavelength [nm]')
plt.ylabel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_filters(fnames):\n if len(fnames) == 0:\n with Database() as base:\n fnames = base.get_filter_names()\n with mp.Pool(processes=mp.cpu_count()) as pool:\n pool.map(worker_plot, fnames)",
"def doAllPlots ():\n #df = processIp (\"18-06-01-1-attack.pcap\", \"ec:1a:59:79:f4:... | [
"0.63512516",
"0.6205365",
"0.61081904",
"0.5971916",
"0.5961135",
"0.5878029",
"0.5855526",
"0.58490413",
"0.5817654",
"0.58115464",
"0.5803139",
"0.5800116",
"0.57768595",
"0.57581633",
"0.5753977",
"0.5733456",
"0.5699219",
"0.56965303",
"0.5688704",
"0.5669338",
"0.566890... | 0.69686276 | 0 |
Plot the filters provided as parameters. If not filter is given, then plot all the filters. | def plot_filters(fnames):
if len(fnames) == 0:
with Database() as base:
fnames = base.get_filter_names()
with mp.Pool(processes=mp.cpu_count()) as pool:
pool.map(worker_plot, fnames) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_filters(net, layer, x, y):\n filters = net.layers[layer].w.eval()\n fig = plt.figure()\n for j in range(len(filters)):\n ax = fig.add_subplot(y, x, j)\n ax.matshow(filters[j][0], cmap = matplotlib.cm.binary)\n plt.xticks(np.array([]))\n plt.yticks(np.array([]))\n pl... | [
"0.7144101",
"0.68334246",
"0.6760905",
"0.66760325",
"0.64315146",
"0.63357484",
"0.626112",
"0.6214689",
"0.6168091",
"0.6132944",
"0.61184967",
"0.61166304",
"0.6065469",
"0.60580444",
"0.60235727",
"0.59663427",
"0.5963482",
"0.5903638",
"0.58925027",
"0.5876012",
"0.5876... | 0.70423925 | 1 |
helper function to parse time | def parse_date(td):
resYear = float(td.days)/364.0 # get the number of years including the the numbers after the dot
resMonth = int((resYear - int(resYear))*364/30) # get the number of months, by multiply the number after the dot by 364 and divide by 30.
resYear = int(resYear)
return str(resYear)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_time(text):\n\n # When keyword is 'in' adds values to time\n if text[-3] == 'in':\n remind_time = time.gmtime(int(text[-2]) * int(text[-1]) + time.time())\n # Otherwise try to parse time as written\n else:\n remind_time = text[-1].replace(':', ' ') \\\n + \" \... | [
"0.76827943",
"0.76406664",
"0.7517842",
"0.7467294",
"0.7415888",
"0.73868084",
"0.7386573",
"0.7343121",
"0.73254955",
"0.73001826",
"0.7159429",
"0.70875233",
"0.70563596",
"0.704737",
"0.7032999",
"0.701714",
"0.70158684",
"0.70149547",
"0.70029277",
"0.6998369",
"0.69053... | 0.0 | -1 |
Reads ATP matches but does not parse time into datetime object | def readATPMatches(dirname):
allFiles = glob.glob(dirname + "/atp_matches_" + "20??.csv") ##restrict training set to matches from 2000s
matches = pd.DataFrame()
container = list()
for filen in allFiles:
df = pd.read_csv(filen,
index_col=None,
header=0)
container.append(df)
matches = pd.concat(con... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def test_process_read_custom_time(self):\n xknx = XKNX()\n self.datetime = DateTime(\n xknx,\n \"TestDateTime\",\n group_address=\"1/2/3\",\n broadcast_type=\"TIME\",\n localtime=False,\n respond_to_read=True,\n )\n\n ... | [
"0.6332722",
"0.62225044",
"0.6097771",
"0.6039601",
"0.6038429",
"0.6006697",
"0.5948585",
"0.5936493",
"0.59112996",
"0.58939725",
"0.5887772",
"0.5887216",
"0.5877503",
"0.585918",
"0.5835001",
"0.5828498",
"0.5826352",
"0.581287",
"0.58076936",
"0.5769214",
"0.57655585",
... | 0.0 | -1 |
Reads ATP matches and parses time into datetime object | def readATPMatchesParseTime(dirname):
allFiles = glob.glob(dirname + "/atp_matches_" + "20??.csv")
allFiles = allFiles[:-1] ## avoid 2017 since its incomplete
matches = pd.DataFrame()
container = list()
for filen in allFiles:
df = pd.read_csv(filen,
index_col=None,
header=0,
parse_dates=[5],... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _parse_time(time_string: str) -> datetime:\n\n # Strings with timezone (+01:00) in v2 are not easily parsed. But time\n # zones are not important here, so we just omit them.\n time_string = time_string.rsplit('+')[0]\n\n time_formats = [\n '%Y-%m-%dT%H:%M:%S.%fZ', # Defa... | [
"0.63215744",
"0.6218922",
"0.620877",
"0.60958487",
"0.60541457",
"0.60302633",
"0.6005715",
"0.5943619",
"0.59128654",
"0.59121895",
"0.58951235",
"0.58806336",
"0.58780116",
"0.58697003",
"0.5864516",
"0.5851861",
"0.5849143",
"0.5806065",
"0.5782346",
"0.5781294",
"0.5772... | 0.65270346 | 0 |
This is an intentionally bad condition that triggers an error | def condition(x):
return 'string' + x | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sanity_check(self):\n pass",
"def sanity_check(self):\n return True",
"def invalid(self):\n pass",
"def violated(self) -> bool:\n ...",
"def unexpectedException(self):",
"def unexpected_error(self, exception):",
"def test_case_01(self):\n if True:\n sel... | [
"0.7094991",
"0.70528525",
"0.7000832",
"0.6899215",
"0.68890953",
"0.682098",
"0.6796712",
"0.6757366",
"0.675611",
"0.6751527",
"0.6733074",
"0.6728573",
"0.6728573",
"0.65932304",
"0.6588249",
"0.656368",
"0.6537258",
"0.6537245",
"0.64931065",
"0.64808136",
"0.6459043",
... | 0.0 | -1 |
No. 1 tests collection for DeviceAssociation. | def test_deviceassociation_1(base_settings):
filename = base_settings["unittest_data_dir"] / "deviceassociation-example.json"
inst = deviceassociation.DeviceAssociation.parse_file(
filename, content_type="application/json", encoding="utf-8"
)
assert "DeviceAssociation" == inst.resource_type
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_instance(self):\n self.assertEqual(True, type(self.Test.defined_associations['things']) is pyperry.association.HasMany)",
"def test_get_devices1(self):\n pass",
"def test_defined_associations(self):\n self.assertEqual(True, len(self.Test.defined_associations) > 0)",
"def test_de... | [
"0.63186795",
"0.62924093",
"0.62830204",
"0.62830204",
"0.62830204",
"0.62792283",
"0.62792283",
"0.624757",
"0.61836016",
"0.611244",
"0.6044153",
"0.5977334",
"0.59670866",
"0.59656906",
"0.5937387",
"0.59067976",
"0.5894439",
"0.5884807",
"0.58664834",
"0.58437556",
"0.58... | 0.67872405 | 0 |
Time complexity is O(log N), since the problem can be reduced down to binary search. If A[j] > j, then no entry after j can satisfy the given criterion. This is because each element in the array is at least 1 greater than the previous element. For the same reason , if A[j] < j, no entry before j can satisfy the given c... | def search_entry_equal_to_its_index(A):
L, R = 0, len(A) -1
while L <= R:
M = L + (R - L) // 2
if A[M] > M:
R = M - 1
elif A[M] == M:
return M
else:
# A[M] < M
L = M + 1
return -1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def BS(self, arr, N, X):\n lo = 0\n hi = N - 1\n while lo <= hi:\n mid = (lo + hi) // 2\n if arr[mid] == X:\n return True\n elif arr[mid] < X:\n lo = mid + 1\n else:\n hi = mid - 1\n return False",
... | [
"0.6312722",
"0.62923104",
"0.6226362",
"0.6148673",
"0.6132592",
"0.6132072",
"0.60798305",
"0.60679054",
"0.60506034",
"0.6018032",
"0.6006477",
"0.6005336",
"0.5975313",
"0.59663606",
"0.5922118",
"0.5916837",
"0.5898111",
"0.58858323",
"0.588001",
"0.58537954",
"0.5833917... | 0.6494884 | 0 |
Handle GET requests for job | def retrieve(self, request, pk=None):
try:
job = Job.objects.get(pk=pk)
serializer = JobSerializer(job, context={'request': request})
return Response(serializer.data)
except Exception as ex:
return HttpResponseServerError(ex) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def do_GET(self):\n self._try_to_process_request(self._handle_get_request)",
"def on_get(self, req, resp):\n try:\n n_reqs = int(req.params.get('n', self.default_reqs))\n except ValueError:\n error_response(resp, 'ERROR: Incorrect number of requests')\n retur... | [
"0.7475271",
"0.70410204",
"0.6988128",
"0.6965615",
"0.6851276",
"0.67894495",
"0.6776669",
"0.67289686",
"0.6651133",
"0.6593948",
"0.6584175",
"0.6569812",
"0.65278816",
"0.6481121",
"0.6451629",
"0.6391111",
"0.6358613",
"0.6351677",
"0.6351061",
"0.6326234",
"0.6315531",... | 0.6014503 | 35 |
Handle PUT requests for a job | def update(self, request, pk=None):
job = Job.objects.get(pk=pk)
job.title = request.data["title"]
job.description = request.data["description"]
job.city = request.data["city"]
job.state = request.data["state"]
job.application = request.data["application"]
user =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_put(self, api, command):\n return self._make_request_from_command('PUT', command)",
"def put(self, *args, **kwargs):\n return self.handle_put_request()",
"def _put(self, *args, **kwargs):\n return self._request('put', *args, **kwargs)",
"def http_put(self, **kwargs):\n ... | [
"0.70080054",
"0.697571",
"0.6961433",
"0.68555117",
"0.68339956",
"0.6767455",
"0.67614263",
"0.67394274",
"0.6658202",
"0.65806633",
"0.65120596",
"0.6483549",
"0.6420429",
"0.6349386",
"0.62906986",
"0.6290243",
"0.6290243",
"0.6290243",
"0.62850165",
"0.6248225",
"0.62433... | 0.62344754 | 21 |
Handle DELETE requests for a single job | def destroy(self, request, pk=None):
try:
job = Job.objects.get(pk=pk)
job.delete()
return Response({}, status=status.HTTP_204_NO_CONTENT)
except Job.DoesNotExist as ex:
return Response({'message': ex.args[0]}, status=status.HTTP_404_NOT_FOUND)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _delete_job(self, job):",
"def delete(self):\n parser = reqparse.RequestParser()\n parser.add_argument(\"job_id\", type=str, location=\"form\")\n args = parser.parse_args()\n job_id = args[\"job_id\"]\n if job_id is None or job_id == \"\":\n return errors.all_err... | [
"0.804811",
"0.72077435",
"0.7132578",
"0.7118642",
"0.71037126",
"0.708545",
"0.68401533",
"0.6763502",
"0.67079043",
"0.668712",
"0.6683445",
"0.66702896",
"0.6655265",
"0.6599019",
"0.6597455",
"0.6576192",
"0.6546401",
"0.6521259",
"0.6478296",
"0.64718896",
"0.64297384",... | 0.70287615 | 6 |
Handle GET requests to profile | def list(self, request):
jobs = Job.objects.all()
city = self.request.query_params.get('city', None)
state = self.request.query_params.get('state', None)
# Support filtering jobs by user id
job = self.request.query_params.get('user', None)
if job is not None:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getProfile(self):\n # GET /profile\n debugMain('getProfile')\n return self._genericGet('/profile')",
"def get_profile(request):\n collected_values = {}\n\n # Only allow GET requests on this endpoint\n if request.method != 'GET':\n collected_values[\"success\"] = False\n ... | [
"0.7356478",
"0.7284313",
"0.714012",
"0.7091871",
"0.6971823",
"0.6945185",
"0.68961984",
"0.6872427",
"0.681282",
"0.6787994",
"0.67019415",
"0.6681623",
"0.66748136",
"0.66683257",
"0.6582885",
"0.65826267",
"0.6577597",
"0.6541843",
"0.64396584",
"0.6310587",
"0.62897474"... | 0.0 | -1 |
>>> h_d_n(123) (1, 2, 3) >>> h_d_n(321) (3, 2, 1) >>> h_d_n(90) (0, 9, 0) | def h_d_n(x:int) -> tuple:
return(x // 100, (x % 100) // 10, x % 10) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculateDeCrypt(asci: int, d: int, n: int) -> int:\n return pow(int(asci),d,n)",
"def d(n):\n return sum(divisors(n))",
"def d(s):\n return s + 1",
"def factorizacion_ds(n:int) -> Tuple[int,int]:\r\n if n<2:\r\n raise ValueError(\"n debe ser >=2\")\r\n d = n if not n&1 else (n - 1)... | [
"0.6135414",
"0.59373635",
"0.59330577",
"0.5917609",
"0.58610594",
"0.5802275",
"0.5800661",
"0.57471615",
"0.5747002",
"0.57074314",
"0.57021093",
"0.5676164",
"0.5650685",
"0.56254137",
"0.558355",
"0.55562544",
"0.5553181",
"0.55499274",
"0.55134493",
"0.55087435",
"0.548... | 0.719936 | 0 |
Instantiates a local feature extractor. | def __init__(self): | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def magic_init(cls, feature_path=FEATURES_DATA_PATH, raw_path=RAW_DATA_PATH,\n raw_label_filename='labels.csv'):\n\n from features import AVAILABLE_FEATURES\n out_path = feature_path\n if cls.dependency_feature_name:\n # source path is in feature path\n ... | [
"0.6501958",
"0.60083246",
"0.5784577",
"0.57365555",
"0.56559485",
"0.56446105",
"0.56006855",
"0.55886626",
"0.5558868",
"0.5509337",
"0.5506666",
"0.5467253",
"0.5453437",
"0.5414988",
"0.5393013",
"0.5385658",
"0.5385658",
"0.536459",
"0.535608",
"0.5349533",
"0.5327604",... | 0.0 | -1 |
Detect and describe local keypoints. | def get_local_des(self, img):
_, des =self.fe.detectAndCompute(img, None)
return des | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_keypoint_detection(init_env, config_file):\n run_all_steps(init_env, config_file)",
"def get_local_features(self, img):\n kp, des = self.fe.detectAndCompute(img, None)\n return kp, des",
"def surf_keypoint_detection(img):\n surf = cv2.xfeatures2d.SURF_create(510)\n kp, des = sur... | [
"0.63079906",
"0.61416626",
"0.61312884",
"0.6021436",
"0.57368505",
"0.5623367",
"0.55178356",
"0.5503452",
"0.5495695",
"0.5457657",
"0.5428592",
"0.54241955",
"0.5420307",
"0.54104996",
"0.53900355",
"0.53616196",
"0.53247434",
"0.5266665",
"0.5254872",
"0.5253049",
"0.523... | 0.0 | -1 |
Detect and describe local keypoints. | def get_local_features(self, img):
kp, des = self.fe.detectAndCompute(img, None)
return kp, des | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_keypoint_detection(init_env, config_file):\n run_all_steps(init_env, config_file)",
"def surf_keypoint_detection(img):\n surf = cv2.xfeatures2d.SURF_create(510)\n kp, des = surf.detectAndCompute(img, None)\n return des",
"def get_keypoints():\n # Keypoints are not available in the COCO ... | [
"0.63097143",
"0.61336166",
"0.60220844",
"0.5738497",
"0.5622902",
"0.55199105",
"0.55028194",
"0.5497425",
"0.545911",
"0.5430861",
"0.54248726",
"0.54204404",
"0.5411404",
"0.53913933",
"0.5363734",
"0.5326334",
"0.52683765",
"0.5255654",
"0.52534306",
"0.5239289",
"0.5189... | 0.6142685 | 1 |
Instantiates a local feature extractor. | def __init__(self, delf_dir):
self.des_dir = delf_dir | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def magic_init(cls, feature_path=FEATURES_DATA_PATH, raw_path=RAW_DATA_PATH,\n raw_label_filename='labels.csv'):\n\n from features import AVAILABLE_FEATURES\n out_path = feature_path\n if cls.dependency_feature_name:\n # source path is in feature path\n ... | [
"0.6501958",
"0.60083246",
"0.5784577",
"0.57365555",
"0.56559485",
"0.56446105",
"0.56006855",
"0.55886626",
"0.5558868",
"0.5509337",
"0.5506666",
"0.5467253",
"0.5453437",
"0.5414988",
"0.5393013",
"0.5385658",
"0.5385658",
"0.536459",
"0.535608",
"0.5349533",
"0.5327604",... | 0.0 | -1 |
Instantiates a local feature extractor. | def __init__(self, label_num, des_dir, des_dim=48):
self.label_num = label_num
self.des_dir = des_dir
self.des_dim = 48 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def magic_init(cls, feature_path=FEATURES_DATA_PATH, raw_path=RAW_DATA_PATH,\n raw_label_filename='labels.csv'):\n\n from features import AVAILABLE_FEATURES\n out_path = feature_path\n if cls.dependency_feature_name:\n # source path is in feature path\n ... | [
"0.6501958",
"0.60083246",
"0.5784577",
"0.57365555",
"0.56559485",
"0.56446105",
"0.56006855",
"0.55886626",
"0.5558868",
"0.5509337",
"0.5506666",
"0.5467253",
"0.5453437",
"0.5414988",
"0.5393013",
"0.5385658",
"0.5385658",
"0.536459",
"0.535608",
"0.5349533",
"0.5327604",... | 0.0 | -1 |
Sets up the argparse object for a specific dataset. | def init_cormorant_argparse(dataset):
parser = argparse.ArgumentParser(description='Cormorant network options.')
parser = setup_shared_args(parser)
# Datasets without additional arguments
if dataset.lower() in ["smp", "lba"]:
pass
# Datasets for classification tasks
elif dataset == "res"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse():\n parser = argparse.ArgumentParser()\n parser.add_argument('--dataset', default='ml-1m', help='which dataset to use')\n args = parser.parse_args()\n main(args)",
"def __init__(self):\n self.parser = argparse.ArgumentParser(prog='PROG')\n self.parser.add_argument(\"--idir\",... | [
"0.709905",
"0.6737217",
"0.6725684",
"0.6715105",
"0.66910356",
"0.6689633",
"0.66340303",
"0.6629519",
"0.6566277",
"0.65392494",
"0.64324933",
"0.6376761",
"0.6372934",
"0.6358038",
"0.63520265",
"0.6338287",
"0.6338084",
"0.633524",
"0.63297504",
"0.6312832",
"0.6287631",... | 0.70690745 | 1 |
Set up for the doc tests | def setUpClass(cls):
cls.fs_f = inspect.getmembers(FileStorage, inspect.isfunction) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup( self ):",
"def setup(self) -> None:",
"def setup(self):\n pass",
"def setUp(self):\n self.docCounter = 0",
"def setup(self):\n ...",
"def test_doc():\n pass",
"def setup(self):\n pass",
"def setup(self):\n pass",
"def setup(self):\n pass",
... | [
"0.7833533",
"0.7703642",
"0.7694024",
"0.7689729",
"0.7655827",
"0.7654702",
"0.76496744",
"0.76496744",
"0.76496744",
"0.76496744",
"0.76496744",
"0.76496744",
"0.76496744",
"0.76496744",
"0.76496744",
"0.76496744",
"0.76496744",
"0.76496744",
"0.7637151",
"0.7637151",
"0.7... | 0.0 | -1 |
Test that models/engine/file_storage.py conforms to PEP8. | def test_pep8_conformance_file_storage(self):
pep8s = pep8.StyleGuide(quiet=True)
result = pep8s.check_files(['models/engine/file_storage.py'])
self.assertEqual(result.total_errors, 0,
"Found code style errors (and warnings).") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_pep8_conformance_test_file_storage(self):\n pep8s = pep8.StyleGuide(quiet=True)\n result = pep8s.check_files(['tests/test_models/test_engine/\\\ntest_file_storage.py'])\n self.assertEqual(result.total_errors, 0,\n \"Found code style errors (and warnings).\")",
... | [
"0.7939913",
"0.740642",
"0.740642",
"0.7346395",
"0.7346395",
"0.66497564",
"0.6502688",
"0.6492406",
"0.63181925",
"0.6306742",
"0.6300503",
"0.6291657",
"0.6269766",
"0.62613714",
"0.6225302",
"0.62200075",
"0.61967963",
"0.61587316",
"0.615522",
"0.6145747",
"0.61391056",... | 0.8031939 | 0 |
Test tests/test_models/test_file_storage.py conforms to PEP8. | def test_pep8_conformance_test_file_storage(self):
pep8s = pep8.StyleGuide(quiet=True)
result = pep8s.check_files(['tests/test_models/test_engine/\
test_file_storage.py'])
self.assertEqual(result.total_errors, 0,
"Found code style errors (and warnings).") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_pep8_conformance_file_storage(self):\n pep8s = pep8.StyleGuide(quiet=True)\n result = pep8s.check_files(['models/engine/file_storage.py'])\n self.assertEqual(result.total_errors, 0,\n \"Found code style errors (and warnings).\")",
"def test_file_field():",
... | [
"0.750308",
"0.7247188",
"0.7087716",
"0.7005382",
"0.7005382",
"0.69971234",
"0.68696296",
"0.6790633",
"0.6790633",
"0.6779602",
"0.6669038",
"0.6602457",
"0.6590004",
"0.6556125",
"0.6523744",
"0.65025145",
"0.6485095",
"0.6340272",
"0.6330424",
"0.6330424",
"0.6236692",
... | 0.7583977 | 0 |
Test for the file_storage.py module docstring | def test_file_storage_module_docstring(self):
self.assertIsNot(file_storage.__doc__, None,
"file_storage.py needs a docstring")
self.assertTrue(len(file_storage.__doc__) >= 1,
"file_storage.py needs a docstring") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_file_storage_class_docstring(self):\n self.assertIsNot(FileStorage.__doc__, None,\n \"State class needs a docstring\")\n self.assertTrue(len(FileStorage.__doc__) >= 1,\n \"State class needs a docstring\")",
"def test_docstring(self):\n ... | [
"0.7408883",
"0.7304265",
"0.68893766",
"0.68893766",
"0.68422467",
"0.6720938",
"0.66850764",
"0.66716516",
"0.6499715",
"0.6474401",
"0.6443321",
"0.6381629",
"0.6381629",
"0.6371081",
"0.6322822",
"0.6282735",
"0.6265872",
"0.6205506",
"0.6188024",
"0.6153745",
"0.6124802"... | 0.8053633 | 0 |
Test for the FileStorage class docstring | def test_file_storage_class_docstring(self):
self.assertIsNot(FileStorage.__doc__, None,
"State class needs a docstring")
self.assertTrue(len(FileStorage.__doc__) >= 1,
"State class needs a docstring") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_file_storage_module_docstring(self):\n self.assertIsNot(file_storage.__doc__, None,\n \"file_storage.py needs a docstring\")\n self.assertTrue(len(file_storage.__doc__) >= 1,\n \"file_storage.py needs a docstring\")",
"def test_docstring(self)... | [
"0.7724605",
"0.7373347",
"0.7370406",
"0.70291007",
"0.70291007",
"0.70174724",
"0.69507873",
"0.68998235",
"0.6748319",
"0.6748319",
"0.6646066",
"0.6420071",
"0.6388432",
"0.6388432",
"0.6386928",
"0.6385066",
"0.62834305",
"0.6273837",
"0.6257467",
"0.6246449",
"0.6236639... | 0.79537505 | 0 |
Test for the presence of docstrings in FileStorage methods | def test_fs_func_docstrings(self):
for func in self.fs_f:
self.assertIsNot(func[1].__doc__, None,
"{:s} method needs a docstring".format(func[0]))
self.assertTrue(len(func[1].__doc__) >= 1,
"{:s} method needs a docstring".format(fu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_docstring(self):\n self.assertTrue(len(FileStorage.__doc__) > 1)\n self.assertTrue(len(FileStorage.all.__doc__) > 1)\n self.assertTrue(len(FileStorage.new.__doc__) > 1)\n self.assertTrue(len(FileStorage.save.__doc__) > 1)\n self.assertTrue(len(FileStorage.reload.__doc__)... | [
"0.7583647",
"0.73714226",
"0.726911",
"0.71747947",
"0.71257865",
"0.7060106",
"0.6927781",
"0.68431073",
"0.68117917",
"0.6764475",
"0.674331",
"0.6721562",
"0.67128253",
"0.6688601",
"0.66628176",
"0.6654613",
"0.6654613",
"0.66002995",
"0.6595453",
"0.6587535",
"0.6514702... | 0.7237537 | 3 |
Generator that checks types | def checks_type(self, itr, raises=None, warns=None, silent=None):
if raises is warns is silent is None:
# default behaviour decided at init (default is to raise TypeError)
raises = True
emit = self._actions[1 - first_true_index((raises, warns, silent))]
for i, obj in enu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_basic_types(self):\n\t\tyield self.check_setget(\"a_string\", \"some random string\")\n\t\tyield self.check_setget(\"an_integer\", 42)\n\t\tyield self.check_setget(\"a_long\", long(1<<30))\n\t\tyield self.check_setget(\"a_dict\", { \"foo\" : \"bar\", \"baz\" : \"quux\" })",
"def get_check_types():",
"... | [
"0.6941974",
"0.66699964",
"0.649637",
"0.6342046",
"0.63404155",
"0.62976897",
"0.62158614",
"0.62021077",
"0.61712503",
"0.61615604",
"0.61520094",
"0.60979986",
"0.6086096",
"0.60858196",
"0.60639775",
"0.60638964",
"0.6004825",
"0.5992021",
"0.59898406",
"0.5961027",
"0.5... | 0.661526 | 2 |
Main function to perform configs loading and wishart calculations | def main(template, wishart_neighbors, wishart_significance):
print("------------------ LOADING CONFIGURATIONS -------------------")
file_conf = environ.get('CONFIG')
try:
with open(file_conf, 'r') as file_conf:
configs = yaml.load(file_conf)
except Exception:
raise Exception... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n logger = logging.getLogger(__name__)\n logger.info('making final data set from raw data')\n get_user_config()",
"def load_config():\n # config variables are fetched from file, but if not, then here are defaults\n x_of_outer = 30\n y_of_outer = 30\n\n min_percent = 30\n max_p... | [
"0.7060118",
"0.69054794",
"0.67772466",
"0.67089844",
"0.6650186",
"0.66354215",
"0.66354215",
"0.6626167",
"0.6543456",
"0.6519847",
"0.646248",
"0.6450026",
"0.6423697",
"0.64213604",
"0.64095026",
"0.63988",
"0.6387472",
"0.6386118",
"0.6385381",
"0.6366049",
"0.6364681",... | 0.6297701 | 25 |
Check mysql doesn't exists. Test that we can trigger a mysql connection failure and we fail gracefully to ensure we don't break people without mysql | def test_mysql_connect_fail(self):
if test_migrations._is_backend_avail(
'mysql', 'kickstand_cifail', self.PASSWD, self.DATABASE):
self.fail("Shouldn't have connected") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_mysql_connect_fail(self):\n if _is_backend_avail('mysql', user=\"openstack_cifail\"):\n self.fail(\"Shouldn't have connected\")",
"def test_missing(server):\n\n assert \"non_existing_database\" not in server\n with pytest.raises(excepts.DBNotExists):\n server[\"non_existin... | [
"0.8019343",
"0.64847225",
"0.6414118",
"0.6394261",
"0.6361902",
"0.63209903",
"0.62543845",
"0.6211786",
"0.6167792",
"0.61182964",
"0.6047251",
"0.6041001",
"0.6038732",
"0.6023822",
"0.6023704",
"0.5986073",
"0.59562546",
"0.5951382",
"0.59086776",
"0.58945745",
"0.589357... | 0.7984295 | 1 |
Check postgres doesn't exists. Test that we can trigger a postgres connection failure and we fail gracefully to ensure we don't break people without postgres | def test_postgresql_connect_fail(self):
if test_migrations._is_backend_avail(
'postgres', 'kickstand_cifail', self.PASSWD, self.DATABASE):
self.fail("Shouldn't have connected") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_postgresql_connect_fail(self):\n if _is_backend_avail('postgresql', user=\"openstack_cifail\"):\n self.fail(\"Shouldn't have connected\")",
"def database_exists(database):\n try:\n test_connection_db = Database(database=database)\n test_connection_db.close()\n r... | [
"0.79075533",
"0.7150718",
"0.6752807",
"0.66953045",
"0.66447365",
"0.660909",
"0.6439612",
"0.63683623",
"0.6328746",
"0.6321043",
"0.6304608",
"0.6242608",
"0.6206685",
"0.61457425",
"0.6078942",
"0.6048207",
"0.603846",
"0.6034774",
"0.59535533",
"0.59409016",
"0.5940679"... | 0.7844154 | 1 |
Construct new File Counter. | def __init__(self, synapse_df):
self._file_df = synapse_df[synapse_df.type == "file"]
self._folder_df = synapse_df[synapse_df.type == "folder"]
self._init_file_types()
self._identify_archive_folders()
self._walk_files() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fileCounter(directory):",
"def _increment_file_counter(self):\n self._add_to_file_counter(1)",
"def _setcounter():\n fname = os.path.basename(camera.status.lastfile)\n tname = fname.split('.')[0]\n i = len(tname)-1\n if i > -1:\n while tname[i].isdigit() and i>-1:\n i = i - 1\n nname ... | [
"0.7541506",
"0.72807074",
"0.7094554",
"0.6776536",
"0.66911954",
"0.66372764",
"0.64699787",
"0.64583606",
"0.64356756",
"0.6371093",
"0.6329999",
"0.6246639",
"0.61441904",
"0.61257684",
"0.6021628",
"0.597491",
"0.5957693",
"0.5949097",
"0.5911655",
"0.59001577",
"0.58357... | 0.0 | -1 |
Get the File Type Counter. | def get_num_files(self, file_type):
return self.file_type_counter.get(file_type, 0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_filetype(filepath, fileinfo, temppath):\n features = collections.Counter()\n\n # skip files in temp directory (only record primary file type here)\n if filepath.startswith(temppath):\n return features\n\n # skip if no fileinfo\n if not fileinfo:\n return features\n\n feature... | [
"0.7075743",
"0.6808621",
"0.6684209",
"0.660871",
"0.6603232",
"0.65423536",
"0.6539293",
"0.6506061",
"0.6490176",
"0.6285142",
"0.62413156",
"0.61309546",
"0.6097419",
"0.6095863",
"0.60910076",
"0.60835075",
"0.605242",
"0.60425013",
"0.60141003",
"0.6012714",
"0.59846336... | 0.78032047 | 0 |
Get the Total File Size for the Specified File Type. | def get_total_file_size(self, file_type):
return self.file_size_counter.get(file_type, 0) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_num_files(self, file_type):\n return self.file_type_counter.get(file_type, 0)",
"def get_size(self, typ):\n return self.info.get_size(typ)",
"def get_file_size(self) -> int:\n return self.get_main_information()['FileSize']",
"def total_file_length(self):\n if self.is_multi... | [
"0.71219486",
"0.702957",
"0.7018137",
"0.6878474",
"0.6850464",
"0.67821544",
"0.6761017",
"0.6627649",
"0.6599945",
"0.65494007",
"0.6542609",
"0.65323985",
"0.6529978",
"0.652283",
"0.6522734",
"0.65056324",
"0.6504725",
"0.6465689",
"0.64619946",
"0.6411416",
"0.638301",
... | 0.88532853 | 0 |
Make sure the input data is correctly formatted. Return only correctly formatted rows. | def assert_data_format(data):
counter = {'normal': 0, 'malformatted': 0, 'total': 0}
dataOutput = []
try:
assert isinstance(data, list), 'Input must be a list'
except AssertionError as e:
logging.critical('Incorrect input')
raise AssertionError(e)
for row in data:
try... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_row_length(self):\n\n row_data = []\n for row in self._extract_data():\n if len(row) < self.row_length:\n row_data.append(row + [\"\" for _ in range(self.row_length - len(row))])\n else:\n row_data.append(row)\n\n return row_data",... | [
"0.6582768",
"0.65188223",
"0.64139277",
"0.61330855",
"0.5944692",
"0.5943652",
"0.5934933",
"0.5927441",
"0.5897106",
"0.5895621",
"0.58874494",
"0.5877053",
"0.5865555",
"0.58563167",
"0.58471286",
"0.58074796",
"0.5797014",
"0.5791906",
"0.57584476",
"0.5747523",
"0.57316... | 0.629423 | 3 |
Print the dimensions of the dataset. | def dataDimensions(data):
logging.info('Number of rows of data: %s' % len(data))
logging.info('Number of columns of data: %s' % len(data[1])) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dimensions():",
"def print_shape(self, data):\n print(data.shape)",
"def test_dimensions(self):\n\t\t\n\t\t# default\t\n\t\tdetails = self.watcher.describe()\n\t\tprint(details)\n\t\t\n\t\t# default\t\n\t\tdetails = self.watcher.describe(layers=[self.first_layer])\n\t\tprint(details)\n\t\tN = detail... | [
"0.72835773",
"0.7208219",
"0.70247895",
"0.6825681",
"0.6780157",
"0.6741044",
"0.67365885",
"0.66827595",
"0.6679423",
"0.6673389",
"0.6670542",
"0.66320324",
"0.66320324",
"0.6608273",
"0.6598985",
"0.65961164",
"0.6579692",
"0.6556183",
"0.6556135",
"0.65442944",
"0.65418... | 0.73739463 | 0 |
Convert a csv to a list of dictionaries. | def csvToDict(filepath):
data = []
with open(getcwd() + filepath, 'r') as dataset:
assert csv.Sniffer().has_header(dataset.read(9999)), 'No headers'
dataset.seek(0)
dialect = csv.Sniffer().sniff(dataset.read(99999))
dataset.seek(0)
reader = csv.DictReader(dataset, dialect... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def csv_to_dict(filename):\n data_list = []\n \n with open(filename, 'rb') as datafile:\n data_reader = csv.DictReader(datafile, delimiter = ',')\n for row in data_reader:\n data_list.append(row)\n\n return data_list",
"def read_csv_as_dicts(csv_input_file_name):\n input_t... | [
"0.79083765",
"0.7728302",
"0.7700437",
"0.76274085",
"0.75642544",
"0.7432599",
"0.7360535",
"0.72664535",
"0.7258198",
"0.7178364",
"0.7064394",
"0.7061235",
"0.6999389",
"0.6995503",
"0.69937056",
"0.6939701",
"0.6924568",
"0.6895539",
"0.68594724",
"0.6778694",
"0.6662602... | 0.7280546 | 7 |
Print a list of file and ask user to pick one. | def choose_file():
chdir(getcwd()+'/data/US')
f = []
for (dirpath, dirnames, filenames) in walk(getcwd()):
f.extend(filenames)
print('Which file do you want to work on?')
for i in f:
print(str(f.index(i)) + ' - ' + i)
while True:
try:
return f[int(input('Type ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select(files, file_type):\n k = 0\n print('== ' + file_type + ' List ==')\n for file in files:\n print(\"[\" + str(k) + \"]. \" + file)\n k += 1\n print('Select a ' + file_type + ' to continue')\n idx = int(input())\n return files[idx]",
"def Infor_file():\n \n import os... | [
"0.75064415",
"0.71939963",
"0.65890867",
"0.6564373",
"0.65554124",
"0.64916223",
"0.6449925",
"0.6444414",
"0.6431766",
"0.63376623",
"0.6304233",
"0.6280491",
"0.6218513",
"0.6209496",
"0.61802244",
"0.6176718",
"0.6160366",
"0.61584055",
"0.614337",
"0.61433536",
"0.61109... | 0.6795578 | 2 |
Essa fucao retorna os registros | def __repr__(self):
return "<User %s>"% self.username | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def regs() -> None:",
"def regenall(ctx):\n c = ctx.obj['client']\n if not c.login:\n return False\n\n r = requests.request(\"GET\", urljoin(c.BASE_URL, '/apiproxy/JobService.js'), params={'accesskey': c.login, 'method': 'GenerateAndPopulateAllSearches'})\n print(r.status_code, r.text)\n\n ... | [
"0.6523601",
"0.5854332",
"0.57901585",
"0.5744846",
"0.5733562",
"0.56205326",
"0.56156015",
"0.5540107",
"0.5526909",
"0.5510328",
"0.5385071",
"0.53573036",
"0.5337793",
"0.5313724",
"0.5296566",
"0.5269433",
"0.5258065",
"0.52537435",
"0.52228576",
"0.52224046",
"0.519734... | 0.0 | -1 |
Set the scroll region on the canvas | def set_scrollregion(self, event=None):
self.canvas.configure(scrollregion=self.canvas.bbox('all')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_configure(self, event):\n self.testCanvas.configure(scrollregion=self.testCanvas.bbox('all'))\n self.testCanvas.yview_moveto(1)",
"def __reconfig__(self, event):\r\n x, y = event.width//2, event.height//2\r\n self.canvas.config(scrollregion=(-x, -y, x, y))",
"def update_scrol... | [
"0.7618465",
"0.75891054",
"0.70867497",
"0.6937721",
"0.66451067",
"0.66319126",
"0.6626159",
"0.6525639",
"0.6470909",
"0.6445509",
"0.6432371",
"0.64242435",
"0.63335246",
"0.6312971",
"0.62980497",
"0.62910694",
"0.6274682",
"0.6224632",
"0.6214763",
"0.6129663",
"0.60695... | 0.82397264 | 0 |
Get the instance's families | def get_families(instance):
families = instance.data.get("families", [])
family = instance.data.get("family")
if family:
families.append(family)
return set(families) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_families(self):\n return self.__make_api_call('get/families')",
"def list_families(self):\n return self.__make_api_call('list/families')",
"def families(self):\n\n return [get_target_family_by_id(i) for i in self._family_ids]",
"def get_families(self):\n # Implemented from... | [
"0.7949288",
"0.7805857",
"0.730276",
"0.71409595",
"0.71386194",
"0.68486196",
"0.6693059",
"0.64362943",
"0.63239765",
"0.63228005",
"0.6175703",
"0.6123435",
"0.6111729",
"0.6105033",
"0.6057197",
"0.59759986",
"0.5844662",
"0.58399475",
"0.5798087",
"0.5798087",
"0.578143... | 0.8296791 | 0 |
Return the member nodes that are invalid | def get_invalid(cls, instance):
others = [i for i in list(instance.context) if
i is not instance and
set(cls.families) & get_families(i)]
if not others:
return []
other_ids = defaultdict(list)
for other in others:
for _id, mem... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validate(self, node):",
"def get_member_errors(rows):\n\n member_errors = []\n\n for row in rows:\n if row['svname'] not in ['FRONTEND', 'BACKEND'] and \\\n row['status'] not in ['UP', 'no check']:\n member_errors.append(row['pxname'] + ':' + row['svname'])\n\n return mem... | [
"0.6208121",
"0.61689955",
"0.61560327",
"0.6121829",
"0.5997731",
"0.58034176",
"0.57894987",
"0.57870746",
"0.5766943",
"0.5748134",
"0.5737588",
"0.5679277",
"0.56642914",
"0.5614621",
"0.5604182",
"0.5556039",
"0.55382854",
"0.55286926",
"0.54840016",
"0.5482115",
"0.5415... | 0.68075114 | 0 |
Workaround if cmd.png() doesn't work | def pnghack(filepath, width=2000, height=2000): #cmd.png() doesnt work with api
cmd.set('ray_trace_frames', 1) # Frames are raytraced before saving an image.
cmd.viewport(width, height) # Set resolution
cmd.mpng(filepath, 1, 1) # Use batch png mode with 1 frame only
cmd.mplay() # cmd.mpng needs the ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _repr_png_(self):\n mol = self.owner.mol\n keku = IPythonConsole.kekulizeStructures\n size = IPythonConsole.molSize\n opts = IPythonConsole.drawOptions\n return Draw._moltoimg(\n mol, size, self.aix, \"\", returnPNG=True, drawOptions=opts,\n kekulize=kek... | [
"0.6851574",
"0.6851574",
"0.64041066",
"0.6282898",
"0.62631154",
"0.6247078",
"0.62100506",
"0.60794604",
"0.6079245",
"0.5956906",
"0.5950246",
"0.5947135",
"0.59258497",
"0.59258497",
"0.5924405",
"0.57349074",
"0.5717621",
"0.5695675",
"0.56849134",
"0.56611705",
"0.5616... | 0.65783626 | 2 |
Create event for when application starts. | def __init__(self):
self.started = Event() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_start(self):\n pass",
"def on_start(self):\n pass",
"def on_start(self):\n pass",
"def on_start(self):\n pass",
"def on_start(self):\n pass",
"def on_start(self):\n pass",
"def on_start(self):\n pass",
"def on_start(self):\n pass",
"def... | [
"0.7306682",
"0.7306682",
"0.7306682",
"0.7306682",
"0.7306682",
"0.7306682",
"0.7306682",
"0.7306682",
"0.7121523",
"0.708508",
"0.69430435",
"0.6940174",
"0.6940174",
"0.68922603",
"0.66725594",
"0.66557467",
"0.66363394",
"0.66359836",
"0.6628001",
"0.6623074",
"0.6535528"... | 0.67195034 | 14 |
Called after the model is created. | def run(self):
self.started() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_model(self):\n pass",
"def init_model(self):\n pass",
"def initialize(self, model):\n pass",
"def post_build(self):\n pass",
"def afterInit(self):",
"def create_model(self):\n pass",
"def create_model(self):\n pass",
"def create_models( self ):... | [
"0.74985135",
"0.7452881",
"0.7244991",
"0.7007463",
"0.6994847",
"0.6924713",
"0.6924713",
"0.6918619",
"0.6884343",
"0.6876301",
"0.685897",
"0.6847569",
"0.68398577",
"0.6822226",
"0.6820061",
"0.6773574",
"0.6766544",
"0.6766544",
"0.671303",
"0.66979086",
"0.66924626",
... | 0.0 | -1 |
Encode a Caesar cipher. | def caesar_encode(self, text, key):
result_list = []
for char in text:
if char.isalpha():
if char.islower():
offset = ASCII_LOWER_OFFSET
else:
offset = ASCII_UPPER_OFFSET
char = chr((ord(char) - offset + ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encode(key, plain):\n print(\"ciphertext: \", end=\"\")\n\n # used variables\n pos = 0\n key_len = len(key)\n\n # loop over every character in the text\n for char in plain:\n key_pos = pos % key_len\n # leave non-alphabetical characters alone\n if not char.isalpha():\n ... | [
"0.7432236",
"0.73142827",
"0.7064377",
"0.7056149",
"0.69567156",
"0.68698937",
"0.6776725",
"0.6752809",
"0.6702963",
"0.660597",
"0.657546",
"0.65655595",
"0.65487605",
"0.6533035",
"0.65041655",
"0.6461819",
"0.64432335",
"0.639268",
"0.637906",
"0.63251495",
"0.6317241",... | 0.6664478 | 9 |
Overview of monthly spend by category for the last n months. | def monthly_overview():
df = (
monzo
[~monzo.category.isin(['general', 'transfer'])]
.pivot_table('amount', 'month', 'category',
aggfunc='sum', fill_value=0)
.reset_index()
.melt(id_vars=['month'], value_name='amount')
)
inc = df[df.category.eq('i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SumCostByMonthPerCategory(year, numberOfMonth, category):\n\n logs.logger.debug(\n \"Start to adds all amount of Cost objects based on the month of payment date and on the category.\")\n try:\n num_days = calendar.monthrange(year, numberOfMonth)[1]\n searchedCostByMonthFromDB = GetAl... | [
"0.6153713",
"0.58537513",
"0.58471185",
"0.5838708",
"0.56815994",
"0.5626167",
"0.5604199",
"0.5594761",
"0.55863005",
"0.5585975",
"0.55839974",
"0.55758244",
"0.55617726",
"0.55524075",
"0.55136883",
"0.55043674",
"0.54847926",
"0.54786766",
"0.5466748",
"0.54640615",
"0.... | 0.69854707 | 0 |
Ensure findlinks option endup being a list of strings. | def _fixup_find_links(find_links):
if isinstance(find_links, str):
return find_links.split()
assert isinstance(find_links, (tuple, list))
return find_links | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def produce_links_search(self, value_list:list) -> list:\n return [\n [self.produce_link_google(f) for f in value_list],\n [self.produce_link_qwant(f) for f in value_list],\n [self.produce_link_bing(f) for f in value_list],\n [self.produce_link_duckduckgo(f) for f... | [
"0.59844595",
"0.59079087",
"0.58242726",
"0.5810565",
"0.5720067",
"0.56380796",
"0.56306934",
"0.5554415",
"0.55288565",
"0.55027115",
"0.54880166",
"0.54853743",
"0.54810125",
"0.54488546",
"0.54431033",
"0.5396246",
"0.5343557",
"0.5296428",
"0.5295228",
"0.5291945",
"0.5... | 0.71301365 | 0 |
Fetch an egg needed for building. Use pip/wheel to fetch/build a wheel. | def fetch_build_egg(dist, req):
_DeprecatedInstaller.emit()
_warn_wheel_not_available(dist)
return _fetch_build_egg_no_warn(dist, req) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_essential(self):\n self.install_package(\"build-essential\")",
"def build():\n local('python' + python_version + ' setup.py bdist_egg')",
"def ensure_wheel():\n wheels = sorted(DIST.glob(\"*.whl\"))\n if not wheels:\n subprocess.check_call([\"pyproject-build\", \".\", \"--wheel... | [
"0.6031584",
"0.59851366",
"0.5983711",
"0.5879789",
"0.58144295",
"0.57931286",
"0.5760505",
"0.5605846",
"0.5510201",
"0.54663193",
"0.5444306",
"0.54275984",
"0.5427478",
"0.5391566",
"0.5365108",
"0.53289986",
"0.53218055",
"0.5299065",
"0.5275417",
"0.5265924",
"0.525300... | 0.7449293 | 0 |
Return a new requirement without the environment marker to avoid calling pip with something like `babel; extra == "i18n"`, which would always be ignored. | def strip_marker(req):
import pkg_resources # Delay import to avoid unnecessary side-effects
# create a copy to avoid mutating the input
req = pkg_resources.Requirement.parse(str(req))
req.marker = None
return req | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def avoid_pip_isolation(env: Mapping[str, str]) -> dict[str, str]:\n new_env = {k: v for k, v in env.items() if k != \"PYTHONNOUSERSITE\"}\n if \"PYTHONPATH\" not in new_env:\n return new_env\n\n new_env[\"PYTHONPATH\"] = os.pathsep.join(\n [\n path\n for path in new_en... | [
"0.6552357",
"0.5994064",
"0.589006",
"0.5876299",
"0.57701266",
"0.5718214",
"0.56937885",
"0.5686367",
"0.5629351",
"0.55940837",
"0.5577624",
"0.549431",
"0.54820436",
"0.5460692",
"0.54408735",
"0.5440247",
"0.5432639",
"0.5432031",
"0.54307127",
"0.54157686",
"0.53946084... | 0.75084823 | 0 |
Test Case for setup of Logistic Regression | def test_setup_log_reg_classifier(self):
model ,vec, x_testing=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,"text", method="count")
model2 ,vec_tfidf, x_testing2=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,"text", ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_logistic_regression_c_parameter(params, X_train, X_test, y_train, y_test):",
"def test_train_logist(x_train_variable, y_train_dep):\n # Ensure the function works\n try:\n lrc = cls.train_logistic(x_train_variable, y_train_dep)\n logging.info(\"Successful Logistic Model\")\n except... | [
"0.77214766",
"0.7520839",
"0.7343993",
"0.7281926",
"0.7180691",
"0.71483684",
"0.71165",
"0.7098579",
"0.7064817",
"0.7052712",
"0.7048455",
"0.6993554",
"0.6963736",
"0.68801945",
"0.6828811",
"0.6816357",
"0.6768154",
"0.67567813",
"0.664204",
"0.6568265",
"0.6563794",
... | 0.69252974 | 13 |
Test Case for Predict for Logistic Regression | def test_predict(self):
model ,vec, x_testing=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,"text", method="count")
model2 ,vec_tfidf, x_testing2=setup_log_reg_classifier(self.training_data, self.training_y, self.testing_data,"text", method="tfidf")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logistic_predict(weights, data):\n\n # TODO: Finish this function\n\n return y",
"def logistic_predict(self, x: np.array) -> np.array:\r\n if self.LogisticModel is None:\r\n print('Logistic Model not trained, please run logistic_fit first!')\r\n return None\r\n else:... | [
"0.7305153",
"0.7237397",
"0.7183373",
"0.7152834",
"0.71376544",
"0.7132555",
"0.7074927",
"0.6997127",
"0.69198036",
"0.69073516",
"0.68978494",
"0.68912554",
"0.687313",
"0.6825163",
"0.68070877",
"0.67960995",
"0.67626137",
"0.67519486",
"0.674025",
"0.673961",
"0.6705134... | 0.6600049 | 24 |
Initialize database connection and sessionmaker | def __init__(self):
engine = db_connect()
create_tables(engine)
self.Session = sessionmaker(bind=engine) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_db(self):\n\n # The user can provide a custom string\n if self.database is None:\n self.logger.error(\"You must provide a database url, exiting.\")\n sys.exit(1)\n\n self.engine = create_engine(self.database, convert_unicode=True)\n self.session = scoped_session(\n session... | [
"0.77571905",
"0.77500427",
"0.77498686",
"0.77142614",
"0.7675466",
"0.7598643",
"0.75940216",
"0.75440794",
"0.75440794",
"0.753408",
"0.7526113",
"0.7511898",
"0.7495771",
"0.7474186",
"0.737677",
"0.73386455",
"0.73346627",
"0.7311713",
"0.72757417",
"0.7271616",
"0.72439... | 0.79214334 | 0 |
Let's Authenticate the Banks Note This is using docstrings for specifications. | def predict_note_authentication(totalyearlycompensation,safety,classifier):
prediction=classifier.predict([[totalyearlycompensation,safety]])
print(prediction)
return prediction | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def auth():\n pass",
"def auth():\n pass",
"def authorization():\n pass",
"def authenticate(self, username, password, consumerKey, consumerSecret):\r\n pass",
"def authenticate():\n return Response(\n '''Login Required - email acaceres@0-sec.net for access or DM him @_hyp3ri0n on Twitter.... | [
"0.6442201",
"0.6442201",
"0.61614424",
"0.5995388",
"0.59081537",
"0.58965886",
"0.5888043",
"0.58654636",
"0.5845789",
"0.58425903",
"0.58413416",
"0.58286655",
"0.58205444",
"0.5803473",
"0.58030915",
"0.5792578",
"0.57841116",
"0.57841116",
"0.57841116",
"0.57841116",
"0.... | 0.0 | -1 |
Recenters an image from the origin to the middle of the display image | def center(image):
size = image.shape
half = int(np.ceil(size[0]/2))
image = np.roll(np.roll(image, half, 0), half, 1)
return image | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_image(self, image):",
"def center_image(image):\r\n image.anchor_x = image.width / 2\r\n image.anchor_y = image.height / 2",
"def center_image(image):\r\n image.anchor_x = image.width / 2\r\n image.anchor_y = image.height / 2",
"def center_image(image):\n image.anchor_x = image.width/2\... | [
"0.65491354",
"0.6542907",
"0.6542907",
"0.64663273",
"0.6463194",
"0.6424689",
"0.63045746",
"0.6273058",
"0.6259496",
"0.62448",
"0.6225228",
"0.6177235",
"0.6176828",
"0.6174159",
"0.61622965",
"0.61580944",
"0.6121791",
"0.60993665",
"0.6059796",
"0.6056057",
"0.60365945"... | 0.0 | -1 |
Returns the power spectrum function of an image | def ps(image):
image = image.astype(float)
ps_img = abs(pow(fft2(image), 2))
return ps_img | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def spectral_power(img, avg_window_size=None, log=True): #COMPLETE spectrum generator\r\n image = img.copy()\r\n # to avoid large spectral power at the 0 frequency :\r\n image -= np.mean(image)\r\n # wiener filter to reduce non physical variability in the spectral power\r\n if avg_window_size:\r\n ... | [
"0.75860554",
"0.7297011",
"0.72364277",
"0.7204423",
"0.7068326",
"0.6855464",
"0.6534",
"0.64967054",
"0.64738446",
"0.63099605",
"0.62746674",
"0.6239556",
"0.62161505",
"0.6206272",
"0.61712384",
"0.61586595",
"0.61429554",
"0.61336607",
"0.61296254",
"0.601769",
"0.59822... | 0.69459283 | 5 |
Returns the hash_value for any substring of a string using the precomputed hash table. | def get_hash_value(table, prime, multiplier, start, length):
y = pow(multiplier, length, prime)
hash_value = (table[start+length] - y*table[start]) % prime
return hash_value | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def computeHash(string):\n\tif isBytes(string):\n\t\tstring = string.decode(\"latin-1\")\n\thash_ = 63689\n\tfor char in string:\n\t\thash_ = hash_ * 378551 + ord(char)\n\treturn hash_ % 65536",
"def hash(self, string):\n return self.__scaffydb.hash(string)",
"def hash(string):\n hs = 0\n ... | [
"0.698711",
"0.666947",
"0.6521936",
"0.64672047",
"0.6333751",
"0.6230026",
"0.619084",
"0.6189764",
"0.6126085",
"0.6096258",
"0.6066745",
"0.60384375",
"0.6025499",
"0.6015478",
"0.59934574",
"0.5983955",
"0.5969096",
"0.5931997",
"0.5915404",
"0.590932",
"0.590201",
"0.... | 0.5547814 | 36 |
Precompute hashtable for a string with 2 prime numbers and a multiplier. Using a rolling hash function, we can get the hashvalue for any substring in constant time. | def pre_compute_hashes(s, M1, M2, X):
n = len(s)
h1 = [0 for _ in range(n+1)]
h2 = [0 for _ in range(n+1)]
for i in range(1, n+1):
ch = ord(s[i-1])
h1[i] = (X*h1[i-1] + ch) % M1
h2[i] = (X*h2[i-1] + ch) % M2
return h1, h2 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _precompute_substrings(self, p: int) -> List[int]:\n hash_vals: List[int] = [0]\n for i in range(len(self._s)):\n val = (hash_vals[i] * self.X + ord(self._s[i])) % p\n hash_vals.append(val)\n\n return hash_vals",
"def get_hash_value(table, prime, multiplier, start, ... | [
"0.7161889",
"0.68627113",
"0.664224",
"0.656542",
"0.6429222",
"0.6295608",
"0.62773484",
"0.6263875",
"0.6260028",
"0.6208387",
"0.6194183",
"0.61931044",
"0.615499",
"0.61416006",
"0.6121987",
"0.60881466",
"0.6085557",
"0.6082059",
"0.6034449",
"0.59991634",
"0.59969115",... | 0.69907576 | 1 |
Uses binary search to find the number of mismatches. It finds the leftmost mismatch and then looks at the substring hash after that index position and continues until the number of mismatches are more than k or the start pointer is more than or equal to end. | def find_num_matches(p1, p2, t1, t2, m1, m2, x, k, len_p, i):
start = 0
for mismatch in range(k):
# print(f'start: {start}, id: {id}')
id = start
end = len_p-1
while start <= end:
mid = start + (end-start)//2
p_h1, p_h2 = get_hash_value(
p1... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def lcs_hamming_only_matches(s1: str, s2: str, k: int, length: int, matches_lst: List):\n count = 0\n for i in range(0, len(s1) - length + 1):\n for j in range(0, len(s2) - length + 1):\n sub1 = s1[i: i + length]\n sub2 = s2[j: j + length]\n result = hamming_distance(s... | [
"0.6563195",
"0.63334215",
"0.619655",
"0.6072905",
"0.5992598",
"0.5792656",
"0.57802635",
"0.5761363",
"0.57300216",
"0.5706402",
"0.56756544",
"0.5673622",
"0.56674",
"0.5588823",
"0.55755645",
"0.557284",
"0.5536065",
"0.5526799",
"0.55215424",
"0.55209804",
"0.5507269",
... | 0.6899368 | 0 |
Driver function to return number of occurences of a pattern in a text with at most k mismatches. Returns the start index of occurence as well as the number of mismatches. | def solve(k, text, pattern):
# print(k, text, pattern)
base = pow(10, 9)
M1 = base + 7
M2 = base + 9
X = 263
len_p = len(pattern)
len_t = len(text)
pattern1, pattern2 = pre_compute_hashes(pattern, M1, M2, X)
text1, text2 = pre_compute_hashes(text, M1, M2, X)
res = []
p_hash1,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def frequent_words_with_mismatches(text, k, d):\n\n patterns = []\n freq_map = {}\n n = len(text)\n for i in range(n - k + 1):\n pattern = text[i:i + k]\n pattern_rc = reverse_complement(pattern)\n neighborhood = neighbors(pattern, d) + neighbors(pattern_rc, d)\n for j in ra... | [
"0.6649192",
"0.6579769",
"0.6411769",
"0.63241273",
"0.62982893",
"0.62874293",
"0.6251507",
"0.6247955",
"0.6232768",
"0.62261176",
"0.6216543",
"0.6216298",
"0.6168045",
"0.6126884",
"0.6091661",
"0.6048104",
"0.6023427",
"0.60232127",
"0.6001935",
"0.59950364",
"0.5993816... | 0.6189535 | 12 |
Calculate the mse for vector e. | def calculate_mse(e):
return 1/2*np.mean(e**2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_mse(e):\n return 1/2*np.mean(e.dot(e))",
"def compute_MSE(e):\n\n return 1/2*np.mean(e**2)",
"def compute_RMSE(e):\n \"\"\"Corresponds to sqrt(2*MSE)\"\"\"\n \n return np.sqrt(2*compute_MSE(e))",
"def _mse(self):\n error = self._input * self._weights - self._label\n ... | [
"0.8627569",
"0.8492956",
"0.7717357",
"0.7474573",
"0.73560566",
"0.73297226",
"0.725569",
"0.72040147",
"0.7180668",
"0.716424",
"0.715627",
"0.7137266",
"0.71137995",
"0.71091557",
"0.71063846",
"0.70705134",
"0.70670843",
"0.70613927",
"0.70559907",
"0.7045481",
"0.703998... | 0.86390364 | 0 |
Initialise UI Data Manager and Response Handler | def __init__(self, iface, configManager):
QObject.__init__(self)
self.iface = iface
self._queues = None
self._dockWindow = None
self._currentMapTool = None
self.rclParent = None
self.currentRevItem = None
self.actions = []
if Controller._instance ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n self.websock_handlers = {}\n self.ajax_handlers = {'__dashboard__': self.get_dashboard_ui}\n self.dashboard_handlers = {}",
"def init_ui(self):\n raise NotImplementedError",
"def init_ui(self):\n raise NotImplementedError",
"def prepare_UI(self):",
"... | [
"0.6750872",
"0.66436946",
"0.66436946",
"0.65866643",
"0.65207833",
"0.6492288",
"0.64365065",
"0.6331915",
"0.6310459",
"0.62631446",
"0.6223878",
"0.6221055",
"0.6193123",
"0.6126456",
"0.6115807",
"0.60736716",
"0.6054278",
"0.6013648",
"0.600479",
"0.59794086",
"0.597384... | 0.55337137 | 95 |
Set up UI within QGIS | def initGui(self):
# set srs
self._displayCrs = QgsCoordinateReferenceSystem()
self._displayCrs.createFromOgcWmsCrs('EPSG:4167')
self.iface.mapCanvas().mapSettings().setDestinationCrs(self._displayCrs)
# init layerManager
self._layerManager = LayerManager(self.iface, s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setup(self):\n self.ui.setup_window()",
"def initUI(self):\n \n self.setWindowTitle(\"Intecol Flir camera\")\n self.setGeometry(300, 100, 1012, 622)",
"def inicialUI(self):\r\n\r\n self.setGeometry(500, 500, 500, 500)\r\n self.setWindownTitle(\"Pesquisa\")\r\n ... | [
"0.72260803",
"0.7211928",
"0.7206275",
"0.71976244",
"0.7164867",
"0.7164867",
"0.7149143",
"0.7126986",
"0.70783645",
"0.7038808",
"0.70362467",
"0.7032511",
"0.70274544",
"0.698081",
"0.69556254",
"0.69539404",
"0.69419616",
"0.6932011",
"0.69111043",
"0.69000256",
"0.6899... | 0.7779239 | 0 |
Initialise Loading of the queue widgets into QGIS | def loadQueues( self ):
queues = self.Queues()
if not queues.isVisible():
queues.parent().show() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initGui(self):\n\n # set srs\n self._displayCrs = QgsCoordinateReferenceSystem()\n self._displayCrs.createFromOgcWmsCrs('EPSG:4167') \n self.iface.mapCanvas().mapSettings().setDestinationCrs(self._displayCrs)\n\n # init layerManager\n self._layerManager = LayerManager(... | [
"0.67353976",
"0.6553519",
"0.65403384",
"0.64762926",
"0.63988465",
"0.639814",
"0.6305668",
"0.62839437",
"0.6229685",
"0.6174939",
"0.6174939",
"0.6155415",
"0.6138522",
"0.6124276",
"0.6120289",
"0.6089418",
"0.60719943",
"0.6070405",
"0.6062321",
"0.605937",
"0.60530907"... | 0.6407615 | 4 |
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