From 204c43d14d281ffaf0ec8dd45f9ea57ab8801811 Mon Sep 17 00:00:00 2001 From: Emie Elvire Sabumukama <67650132+emieelvire@users.noreply.github.com> Date: Thu, 16 May 2024 05:03:44 +0000 Subject: [PATCH 1/5] tax before refundable credits breakdown using hardcoded adds --- app.py | 93 +++++++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 79 insertions(+), 14 deletions(-) diff --git a/app.py b/app.py index fe7136c..2ddf000 100644 --- a/app.py +++ b/app.py @@ -6,6 +6,7 @@ StateCode, ) from policyengine_us.variables.household.income.household.household_benefits import household_benefits as HouseholdBenefits +from policyengine_us.variables.household.income.household.household_tax_before_refundable_credits import household_tax_before_refundable_credits as HouseholdTaxBeforeRefundableCredits import numpy as np import pandas as pd import hashlib @@ -69,14 +70,13 @@ def get_programs(state_code, head_employment_income, spouse_employment_income=No } simulation = Simulation(situation=situation) - #benefits breakdown - benefits_categories = HouseholdBenefits.adds - household_net_income = int(simulation.calculate("household_net_income", YEAR)) household_benefits = int(simulation.calculate("household_benefits", YEAR)) household_refundable_tax_credits = int(simulation.calculate("household_refundable_tax_credits", int(YEAR))) household_tax_before_refundable_credits = int(simulation.calculate("household_tax_before_refundable_credits", int(YEAR))) + # benefits breakdown + benefits_categories = HouseholdBenefits.adds benefits_dic ={} for benefit in benefits_categories: try: @@ -85,8 +85,28 @@ def get_programs(state_code, head_employment_income, spouse_employment_income=No benefit_amount = 0 benefits_dic[benefit]=benefit_amount + + # tax before refundable credits breakdown + # tax_before_refundable_credits = HouseholdTaxBeforeRefundableCredits.adds + tax_adds = [ + "employee_payroll_tax", + "self_employment_tax", + "income_tax_before_refundable_credits", + "flat_tax", + "household_state_tax_before_refundable_credits", + ] + + tax_bf_r_credits_dic = {} + for tax in tax_adds: + try: + tax_amount = int(simulation.calculate(tax, YEAR)[0]) + except ValueError: + tax_amount = 0 + + tax_bf_r_credits_dic[tax]=tax_amount + - return [household_net_income ,household_benefits ,household_refundable_tax_credits,household_tax_before_refundable_credits, benefits_dic] + return [household_net_income ,household_benefits ,household_refundable_tax_credits,household_tax_before_refundable_credits, benefits_dic, tax_bf_r_credits_dic] def get_categorized_programs(state_code, head_employment_income, spouse_employment_income, children_ages): programs_married = get_programs(state_code, head_employment_income, spouse_employment_income, children_ages) @@ -209,10 +229,10 @@ def get_marital_values(state_code, spouse, children_ages, tax_unit): programs = get_categorized_programs(state_code, head_employment_income, spouse_employment_income, children_ages) # benefits breakdowns - benefits_categories = programs[0][-1].keys() - benefits_married = programs[0][-1].values() - benefits_head = programs[1][-1].values() - benefits_spouse = programs[2][-1].values() + benefits_categories = programs[0][-2].keys() + benefits_married = programs[0][-2].values() + benefits_head = programs[1][-2].values() + benefits_spouse = programs[2][-2].values() benefits_separate = [x + y for x, y in zip(benefits_head, benefits_spouse)] benefits_delta = [x - y for x, y in zip(benefits_married, benefits_separate)] benefits_delta_percent = [(x - y) / x if x != 0 else 0 for x, y in zip(benefits_married, benefits_separate)] @@ -223,20 +243,34 @@ def get_marital_values(state_code, spouse, children_ages, tax_unit): formatted_benefits_delta = list(map(lambda x: "${:,}".format(round(x)), benefits_delta)) formatted_benefits_delta_percent = list(map(lambda x: "{:.1%}".format(x), benefits_delta_percent)) + # tax bf refundable credits breakdowns + tax_categories = programs[0][-1].keys() + tax_married = programs[0][-1].values() + tax_head = programs[1][-1].values() + tax_spouse = programs[2][-1].values() + tax_separate = [x + y for x, y in zip(tax_head, tax_spouse)] + tax_delta = [x - y for x, y in zip(tax_married, tax_separate)] + tax_delta_percent = [(x - y) / x if x != 0 else 0 for x, y in zip(tax_married, tax_separate)] + + # format tax breakdowns + formatted_tax_married = list(map(lambda x: "${:,}".format(round(x)), tax_married)) + formatted_tax_separate = list(map(lambda x: "${:,}".format(round(x)), tax_separate)) + formatted_tax_delta = list(map(lambda x: "${:,}".format(round(x)), tax_delta)) + formatted_tax_delta_percent = list(map(lambda x: "{:.1%}".format(x), tax_delta_percent)) + # married programs - married_programs = programs[0][:-1] # we exclude the last element which is the dictionary of benefits breakdown + married_programs = programs[0][:-2] # we exclude the last element which is the dictionary of benefits breakdown formatted_married_programs = list(map(lambda x: "${:,}".format(round(x)), married_programs)) # separate programs - head_separate = programs[1][:-1] # we exclude the last element which is the dictionary of benefits breakdown - spouse_separate = programs[2][:-1] # we exclude the last element which is the dictionary of benefits breakdown + head_separate = programs[1][:-2] # we exclude the last element which is the dictionary of benefits breakdown + spouse_separate = programs[2][:-2] # we exclude the last element which is the dictionary of benefits breakdown separate = [x + y for x, y in zip(head_separate, spouse_separate)] formatted_separate = list(map(lambda x: "${:,}".format(round(x)), separate)) # delta delta = [x - y for x, y in zip(married_programs, separate)] delta_percent = [(x - y) / x if x != 0 and x != 0 else 0 for x, y in zip(married_programs, separate)] - formatted_delta = list(map(lambda x: "${:,}".format(round(x)), delta)) formatted_delta_percent = list(map(lambda x: "{:.1%}".format(x), delta_percent)) @@ -286,17 +320,48 @@ def summarize_marriage_bonus(marriage_bonus): # filter benefits to keep only the non-zero values benefits_df = pd.DataFrame(benefits_table) filtered_benefits_df = benefits_df[(benefits_df['Not Married'] != "$0") | (benefits_df['Married'] != "$0")] + + # Tax breakdown table + tax_table = { + 'Program': tax_categories, + 'Not Married': formatted_tax_separate, + 'Married': formatted_tax_married, + 'Delta': formatted_tax_delta, + 'Delta Percentage': formatted_tax_delta_percent + + } + # filter benefits to keep only the non-zero values + tax_df = pd.DataFrame(tax_table) + filtered_tax_df = tax_df[(tax_df['Not Married'] != "$0") | (tax_df['Married'] != "$0")] # Display the tables in Streamlit - if not filtered_benefits_df.empty: # if we have benefits + if not filtered_benefits_df.empty and not filtered_tax_df.empty: # if we have benefits and taxes + tab1, tab2, tab3 = st.tabs(["Summary", "Benefits Breakdown", "Tax Before Refundable Credits Breakdown"]) + with tab1: + st.dataframe(table_data, hide_index=True) + + with tab2: + st.dataframe(filtered_benefits_df, hide_index=True) + + with tab3: + st.dataframe(filtered_tax_df, hide_index=True) + elif not filtered_benefits_df.empty and filtered_tax_df.empty: # we have benefits but no tax before refundable credits tab1, tab2 = st.tabs(["Summary", "Benefits Breakdown"]) with tab1: st.dataframe(table_data, hide_index=True) with tab2: st.dataframe(filtered_benefits_df, hide_index=True) + + elif filtered_benefits_df.empty and not filtered_tax_df.empty: # we have tax before refundable credits but no benefits + tab1, tab2 = st.tabs(["Summary", "Tax Before Refundable Credits Breakdown"]) + with tab1: + st.dataframe(table_data, hide_index=True) + + with tab2: + st.dataframe(filtered_tax_df, hide_index=True) - else: # if we don't have benefits, display just the main table + else: # if we don't have benefits or tax before refundable credits, display just the main table st.dataframe(table_data, hide_index=True) def calculate_bonus(): From 37f707c3cd6087a3c004f4d5bd419deba4ec9285 Mon Sep 17 00:00:00 2001 From: Emie Elvire Sabumukama <67650132+emieelvire@users.noreply.github.com> Date: Thu, 16 May 2024 06:08:02 +0000 Subject: [PATCH 2/5] refundable tax credits without import --- app.py | 136 +++++++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 114 insertions(+), 22 deletions(-) diff --git a/app.py b/app.py index 2ddf000..ed889f4 100644 --- a/app.py +++ b/app.py @@ -7,6 +7,7 @@ ) from policyengine_us.variables.household.income.household.household_benefits import household_benefits as HouseholdBenefits from policyengine_us.variables.household.income.household.household_tax_before_refundable_credits import household_tax_before_refundable_credits as HouseholdTaxBeforeRefundableCredits +from policyengine_us.variables.household.income.household.household_refundable_tax_credits import household_refundable_tax_credits as HouseholdRefundableTaxCredits import numpy as np import pandas as pd import hashlib @@ -105,8 +106,25 @@ def get_programs(state_code, head_employment_income, spouse_employment_income=No tax_bf_r_credits_dic[tax]=tax_amount + # refundable tax breakdown + # refundable_tax_categories = HouseholdRefundableTaxCredits.adds + refundable_tax_categories = [ + "income_tax_refundable_credits", + "household_refundable_state_tax_credits", + ] + refundable_tax_dic ={} + for refundable_tax in refundable_tax_categories: + try: + refundable_tax_amount = int(simulation.calculate(refundable_tax, YEAR)[0]) + except ValueError: + refundable_tax_amount = 0 + + refundable_tax_dic[refundable_tax]=refundable_tax_amount + + print(refundable_tax_dic) + - return [household_net_income ,household_benefits ,household_refundable_tax_credits,household_tax_before_refundable_credits, benefits_dic, tax_bf_r_credits_dic] + return [household_net_income ,household_benefits ,household_refundable_tax_credits,household_tax_before_refundable_credits, benefits_dic, tax_bf_r_credits_dic, refundable_tax_dic] def get_categorized_programs(state_code, head_employment_income, spouse_employment_income, children_ages): programs_married = get_programs(state_code, head_employment_income, spouse_employment_income, children_ages) @@ -229,10 +247,10 @@ def get_marital_values(state_code, spouse, children_ages, tax_unit): programs = get_categorized_programs(state_code, head_employment_income, spouse_employment_income, children_ages) # benefits breakdowns - benefits_categories = programs[0][-2].keys() - benefits_married = programs[0][-2].values() - benefits_head = programs[1][-2].values() - benefits_spouse = programs[2][-2].values() + benefits_categories = programs[0][-3].keys() + benefits_married = programs[0][-3].values() + benefits_head = programs[1][-3].values() + benefits_spouse = programs[2][-3].values() benefits_separate = [x + y for x, y in zip(benefits_head, benefits_spouse)] benefits_delta = [x - y for x, y in zip(benefits_married, benefits_separate)] benefits_delta_percent = [(x - y) / x if x != 0 else 0 for x, y in zip(benefits_married, benefits_separate)] @@ -244,10 +262,10 @@ def get_marital_values(state_code, spouse, children_ages, tax_unit): formatted_benefits_delta_percent = list(map(lambda x: "{:.1%}".format(x), benefits_delta_percent)) # tax bf refundable credits breakdowns - tax_categories = programs[0][-1].keys() - tax_married = programs[0][-1].values() - tax_head = programs[1][-1].values() - tax_spouse = programs[2][-1].values() + tax_categories = programs[0][-2].keys() + tax_married = programs[0][-2].values() + tax_head = programs[1][-2].values() + tax_spouse = programs[2][-2].values() tax_separate = [x + y for x, y in zip(tax_head, tax_spouse)] tax_delta = [x - y for x, y in zip(tax_married, tax_separate)] tax_delta_percent = [(x - y) / x if x != 0 else 0 for x, y in zip(tax_married, tax_separate)] @@ -258,13 +276,28 @@ def get_marital_values(state_code, spouse, children_ages, tax_unit): formatted_tax_delta = list(map(lambda x: "${:,}".format(round(x)), tax_delta)) formatted_tax_delta_percent = list(map(lambda x: "{:.1%}".format(x), tax_delta_percent)) + # refundable tax credits breakdowns + refundable_tax_categories = programs[0][-1].keys() + refundable_tax_married = programs[0][-1].values() + refundable_tax_head = programs[1][-1].values() + refundable_tax_spouse = programs[2][-1].values() + refundable_tax_separate = [x + y for x, y in zip(refundable_tax_head, refundable_tax_spouse)] + refundable_tax_delta = [x - y for x, y in zip(refundable_tax_married, refundable_tax_separate)] + refundable_tax_delta_percent = [(x - y) / x if x != 0 else 0 for x, y in zip(refundable_tax_married, refundable_tax_separate)] + + # format refundable tax credits breakdowns + formatted_refundable_tax_married = list(map(lambda x: "${:,}".format(round(x)), refundable_tax_married)) + formatted_refundable_tax_separate = list(map(lambda x: "${:,}".format(round(x)), refundable_tax_separate)) + formatted_refundable_tax_delta = list(map(lambda x: "${:,}".format(round(x)), refundable_tax_delta)) + formatted_refundable_tax_delta_percent = list(map(lambda x: "{:.1%}".format(x), refundable_tax_delta_percent)) + # married programs - married_programs = programs[0][:-2] # we exclude the last element which is the dictionary of benefits breakdown + married_programs = programs[0][:-3] # we exclude the last element which is the dictionary of benefits breakdown formatted_married_programs = list(map(lambda x: "${:,}".format(round(x)), married_programs)) # separate programs - head_separate = programs[1][:-2] # we exclude the last element which is the dictionary of benefits breakdown - spouse_separate = programs[2][:-2] # we exclude the last element which is the dictionary of benefits breakdown + head_separate = programs[1][:-3] # we exclude the last element which is the dictionary of benefits breakdown + spouse_separate = programs[2][:-3] # we exclude the last element which is the dictionary of benefits breakdown separate = [x + y for x, y in zip(head_separate, spouse_separate)] formatted_separate = list(map(lambda x: "${:,}".format(round(x)), separate)) @@ -321,31 +354,82 @@ def summarize_marriage_bonus(marriage_bonus): benefits_df = pd.DataFrame(benefits_table) filtered_benefits_df = benefits_df[(benefits_df['Not Married'] != "$0") | (benefits_df['Married'] != "$0")] - # Tax breakdown table - tax_table = { + # Tax Before Refundable Credits breakdown table + tax_bf_refundable_credits_table = { 'Program': tax_categories, 'Not Married': formatted_tax_separate, 'Married': formatted_tax_married, 'Delta': formatted_tax_delta, 'Delta Percentage': formatted_tax_delta_percent + } + # filter tax before refundable credits to keep only the non-zero values + tax_before_refundable_credits_df = pd.DataFrame(tax_bf_refundable_credits_table) + filtered_tax_df = tax_before_refundable_credits_df[(tax_before_refundable_credits_df['Not Married'] != "$0") | (tax_before_refundable_credits_df['Married'] != "$0")] + + # Refundable Tax Credits breakdown table + refundable_tax_table = { + 'Program': refundable_tax_categories, + 'Not Married': formatted_refundable_tax_separate, + 'Married': formatted_refundable_tax_married, + 'Delta': formatted_refundable_tax_delta, + 'Delta Percentage': formatted_refundable_tax_delta_percent + } # filter benefits to keep only the non-zero values - tax_df = pd.DataFrame(tax_table) - filtered_tax_df = tax_df[(tax_df['Not Married'] != "$0") | (tax_df['Married'] != "$0")] + refundable_tax_df = pd.DataFrame(refundable_tax_table) + filtered_refundable_tax_df = refundable_tax_df[(refundable_tax_df['Not Married'] != "$0") | (refundable_tax_df['Married'] != "$0")] # Display the tables in Streamlit - if not filtered_benefits_df.empty and not filtered_tax_df.empty: # if we have benefits and taxes - tab1, tab2, tab3 = st.tabs(["Summary", "Benefits Breakdown", "Tax Before Refundable Credits Breakdown"]) + if not filtered_benefits_df.empty and not filtered_tax_df.empty and not filtered_refundable_tax_df.empty: # all tabs + tab1, tab2, tab3, tab4 = st.tabs(["Summary", "Benefits Breakdown", "Refundable Tax Credits", "Tax Before Refundable Credits Breakdown"]) with tab1: st.dataframe(table_data, hide_index=True) with tab2: st.dataframe(filtered_benefits_df, hide_index=True) + + with tab3: + st.dataframe(filtered_refundable_tax_df, hide_index=True) + with tab4: + st.dataframe(filtered_tax_df, hide_index=True) + + elif not filtered_benefits_df.empty and not filtered_tax_df.empty and filtered_refundable_tax_df.empty: # tab 2 and tab 4 + tab1, tab2, tab4 = st.tabs(["Summary", "Benefits Breakdown", "Tax Before Refundable Credits Breakdown"]) + with tab1: + st.dataframe(table_data, hide_index=True) + + with tab2: + st.dataframe(filtered_benefits_df, hide_index=True) + + with tab4: + st.dataframe(filtered_tax_df, hide_index=True) + + elif not filtered_benefits_df.empty and filtered_tax_df.empty and not filtered_refundable_tax_df.empty: # tab 2 and tab 3 + tab1, tab2, tab3 = st.tabs(["Summary", "Benefits Breakdown", "Refundable Tax Credits Breakdown"]) + with tab1: + st.dataframe(table_data, hide_index=True) + + with tab2: + st.dataframe(filtered_benefits_df, hide_index=True) + + with tab3: + st.dataframe(filtered_refundable_tax_df, hide_index=True) + + elif filtered_benefits_df.empty and not filtered_tax_df.empty and not filtered_refundable_tax_df.empty: # tab 3 and tab 4 + tab1, tab3, tab4 = st.tabs(["Summary", "Refundable Tax Credits Breakdown", "Tax Before Refundable Credits Breakdown"]) + with tab1: + st.dataframe(table_data, hide_index=True) + with tab3: + st.dataframe(filtered_refundable_tax_df, hide_index=True) + + with tab4: st.dataframe(filtered_tax_df, hide_index=True) - elif not filtered_benefits_df.empty and filtered_tax_df.empty: # we have benefits but no tax before refundable credits + + + elif not filtered_benefits_df.empty and filtered_tax_df.empty and filtered_refundable_tax_df.empty: # tab 2 tab1, tab2 = st.tabs(["Summary", "Benefits Breakdown"]) with tab1: st.dataframe(table_data, hide_index=True) @@ -353,14 +437,22 @@ def summarize_marriage_bonus(marriage_bonus): with tab2: st.dataframe(filtered_benefits_df, hide_index=True) - elif filtered_benefits_df.empty and not filtered_tax_df.empty: # we have tax before refundable credits but no benefits - tab1, tab2 = st.tabs(["Summary", "Tax Before Refundable Credits Breakdown"]) + elif filtered_benefits_df.empty and not filtered_tax_df.empty and filtered_refundable_tax_df.empty: # tab 4 + tab1, tab4 = st.tabs(["Summary", "Tax Before Refundable Credits Breakdown"]) with tab1: st.dataframe(table_data, hide_index=True) - with tab2: + with tab4: st.dataframe(filtered_tax_df, hide_index=True) + elif filtered_benefits_df.empty and filtered_tax_df.empty and not filtered_refundable_tax_df.empty: # tab 3 + tab1, tab3 = st.tabs(["Summary", "Refundable Tax Credits Breakdown"]) + with tab1: + st.dataframe(table_data, hide_index=True) + + with tab3: + st.dataframe(filtered_refundable_tax_df, hide_index=True) + else: # if we don't have benefits or tax before refundable credits, display just the main table st.dataframe(table_data, hide_index=True) From a2cfb284def77554e0c030093ba41e3e9e61c5a9 Mon Sep 17 00:00:00 2001 From: Emie Elvire Sabumukama <67650132+emieelvire@users.noreply.github.com> Date: Thu, 16 May 2024 06:33:52 +0000 Subject: [PATCH 3/5] add breakdown word on table column --- app.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/app.py b/app.py index ed889f4..dbcdc68 100644 --- a/app.py +++ b/app.py @@ -120,8 +120,7 @@ def get_programs(state_code, head_employment_income, spouse_employment_income=No refundable_tax_amount = 0 refundable_tax_dic[refundable_tax]=refundable_tax_amount - - print(refundable_tax_dic) + return [household_net_income ,household_benefits ,household_refundable_tax_credits,household_tax_before_refundable_credits, benefits_dic, tax_bf_r_credits_dic, refundable_tax_dic] @@ -382,7 +381,7 @@ def summarize_marriage_bonus(marriage_bonus): # Display the tables in Streamlit if not filtered_benefits_df.empty and not filtered_tax_df.empty and not filtered_refundable_tax_df.empty: # all tabs - tab1, tab2, tab3, tab4 = st.tabs(["Summary", "Benefits Breakdown", "Refundable Tax Credits", "Tax Before Refundable Credits Breakdown"]) + tab1, tab2, tab3, tab4 = st.tabs(["Summary", "Benefits Breakdown", "Refundable Tax Credits Breakdown", "Tax Before Refundable Credits Breakdown"]) with tab1: st.dataframe(table_data, hide_index=True) From f166cb312f351344eb738e79a7b3e9bdc75512e1 Mon Sep 17 00:00:00 2001 From: Emie Elvire Sabumukama <67650132+emieelvire@users.noreply.github.com> Date: Fri, 31 May 2024 12:52:15 +0000 Subject: [PATCH 4/5] formatted breakdown --- app.py | 30 +- error.txt | 796 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 820 insertions(+), 6 deletions(-) create mode 100644 error.txt diff --git a/app.py b/app.py index 43fbbc4..fcc5350 100644 --- a/app.py +++ b/app.py @@ -8,6 +8,7 @@ from policyengine_us.variables.household.income.household.household_benefits import household_benefits as HouseholdBenefits from policyengine_us.variables.household.income.household.household_tax_before_refundable_credits import household_tax_before_refundable_credits as HouseholdTaxBeforeRefundableCredits from policyengine_us.variables.household.income.household.household_refundable_tax_credits import household_refundable_tax_credits as HouseholdRefundableTaxCredits +from policyengine_us.variables.gov.states.tax.income.state_income_tax_before_refundable_credits import state_income_tax_before_refundable_credits import numpy as np import pandas as pd import hashlib @@ -89,12 +90,13 @@ def get_programs(state_code, head_employment_income, spouse_employment_income=No # tax before refundable credits breakdown # tax_before_refundable_credits = HouseholdTaxBeforeRefundableCredits.adds + tax_adds = [ "employee_payroll_tax", "self_employment_tax", "income_tax_before_refundable_credits", "flat_tax", - "household_state_tax_before_refundable_credits", + "household_state_tax_before_refundable_credits" ] tax_bf_r_credits_dic = {} @@ -105,6 +107,17 @@ def get_programs(state_code, head_employment_income, spouse_employment_income=No tax_amount = 0 tax_bf_r_credits_dic[tax]=tax_amount + + state_income_taxes = state_income_tax_before_refundable_credits.adds + for state_income_tax in state_income_taxes: + try: + state_tax_amount = int(simulation.calculate(state_income_tax, YEAR)[0]) + except ValueError: + state_tax_amount = 0 + + if state_tax_amount: + tax_bf_r_credits_dic[tax]=state_tax_amount + # refundable tax breakdown # refundable_tax_categories = HouseholdRefundableTaxCredits.adds @@ -120,7 +133,6 @@ def get_programs(state_code, head_employment_income, spouse_employment_income=No refundable_tax_amount = 0 refundable_tax_dic[refundable_tax]=refundable_tax_amount - return [household_net_income ,household_benefits ,household_refundable_tax_credits,household_tax_before_refundable_credits, benefits_dic, tax_bf_r_credits_dic, refundable_tax_dic] @@ -131,6 +143,9 @@ def get_categorized_programs(state_code, head_employment_income, spouse_employme programs_spouse = get_programs(state_code, spouse_employment_income, None, {}) # Pass an empty dictionary for children_ages return [programs_married, programs_head, programs_spouse] +def format_breakdown(lst): + return [item.replace('_', ' ').title() for item in lst] + # Create a function to get net income for household def get_marital_values(state_code, spouse, children_ages, tax_unit): # Start by adding the single head. @@ -249,6 +264,7 @@ def get_marital_values(state_code, spouse, children_ages, tax_unit): # benefits breakdowns benefits_categories = programs[0][-3].keys() + formatted_benefits_categories = format_breakdown(benefits_categories) benefits_married = programs[0][-3].values() benefits_head = programs[1][-3].values() benefits_spouse = programs[2][-3].values() @@ -264,6 +280,7 @@ def get_marital_values(state_code, spouse, children_ages, tax_unit): # tax bf refundable credits breakdowns tax_categories = programs[0][-2].keys() + formatted_tax_categories = format_breakdown(tax_categories) tax_married = programs[0][-2].values() tax_head = programs[1][-2].values() tax_spouse = programs[2][-2].values() @@ -279,6 +296,7 @@ def get_marital_values(state_code, spouse, children_ages, tax_unit): # refundable tax credits breakdowns refundable_tax_categories = programs[0][-1].keys() + formatted_refundable_tax_categories = format_breakdown(refundable_tax_categories) refundable_tax_married = programs[0][-1].values() refundable_tax_head = programs[1][-1].values() refundable_tax_spouse = programs[2][-1].values() @@ -308,7 +326,7 @@ def get_marital_values(state_code, spouse, children_ages, tax_unit): formatted_delta = list(map(lambda x: "${:,}".format(round(x)), delta)) formatted_delta_percent = list(map(lambda x: "{:.1%}".format(x), delta_percent)) - programs = ["Net Income", "Benefits", "Refundable tax credits", "Taxes before refundable credits"] + programs = ["Net Income", "Benefits", "Refundable Tax Credits", "Taxes Before Refundable Credits"] # Determine marriage penalty or bonus, and extent in dollars and percentage. @@ -344,7 +362,7 @@ def summarize_marriage_bonus(marriage_bonus): # Benefits breakdown table benefits_table = { - 'Program': benefits_categories, + 'Program': formatted_benefits_categories, 'Not Married': formatted_benefits_separate, 'Married': formatted_benefits_married, 'Delta': formatted_benefits_delta, @@ -357,7 +375,7 @@ def summarize_marriage_bonus(marriage_bonus): # Tax Before Refundable Credits breakdown table tax_bf_refundable_credits_table = { - 'Program': tax_categories, + 'Program': formatted_tax_categories, 'Not Married': formatted_tax_separate, 'Married': formatted_tax_married, 'Delta': formatted_tax_delta, @@ -370,7 +388,7 @@ def summarize_marriage_bonus(marriage_bonus): # Refundable Tax Credits breakdown table refundable_tax_table = { - 'Program': refundable_tax_categories, + 'Program': formatted_refundable_tax_categories, 'Not Married': formatted_refundable_tax_separate, 'Married': formatted_refundable_tax_married, 'Delta': formatted_refundable_tax_delta, diff --git a/error.txt b/error.txt new file mode 100644 index 0000000..4a78ed6 --- /dev/null +++ b/error.txt @@ -0,0 +1,796 @@ +Collecting policyengine-us + Using cached policyengine_us-0.777.7-py3-none-any.whl.metadata (2.5 kB) +Collecting click==8.1.3 (from policyengine-us) + Using cached click-8.1.3-py3-none-any.whl.metadata (3.2 kB) +Collecting h5py (from policyengine-us) + Using cached h5py-3.11.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB) +Collecting microdf-python (from policyengine-us) + Using cached microdf_python-0.3.0-py3-none-any.whl.metadata (437 bytes) +Requirement already satisfied: pandas in /opt/conda/lib/python3.12/site-packages (from policyengine-us) (2.2.1) +Collecting pathlib (from policyengine-us) + Using cached pathlib-1.0.1-py3-none-any.whl.metadata (5.1 kB) +Collecting policyengine-core<3,>=2.21.5 (from policyengine-us) + Using cached policyengine_core-2.21.8-py3-none-any.whl.metadata (2.7 kB) +Collecting pytest (from policyengine-us) + Using cached pytest-8.2.1-py3-none-any.whl.metadata (7.6 kB) +Collecting pytest-dependency (from policyengine-us) + Using cached pytest-dependency-0.6.0.tar.gz (19 kB) + Preparing metadata (setup.py): started + Preparing metadata (setup.py): finished with status 'done' +Collecting pyyaml (from policyengine-us) + Using cached PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.1 kB) +Requirement already satisfied: requests in /opt/conda/lib/python3.12/site-packages (from policyengine-us) (2.31.0) +INFO: pip is looking at multiple versions of policyengine-us to determine which version is compatible with other requirements. This could take a while. +Collecting policyengine-us + Using cached policyengine_us-0.777.6-py3-none-any.whl.metadata (2.5 kB) + Using cached policyengine_us-0.777.5-py3-none-any.whl.metadata (2.5 kB) + Using cached policyengine_us-0.777.4-py3-none-any.whl.metadata (2.5 kB) + Using cached policyengine_us-0.777.3-py3-none-any.whl.metadata (2.5 kB) + Using cached policyengine_us-0.777.2-py3-none-any.whl.metadata (2.5 kB) + Using cached policyengine_us-0.777.1-py3-none-any.whl.metadata (2.5 kB) + Using cached policyengine_us-0.777.0-py3-none-any.whl.metadata (2.5 kB) +INFO: pip is still looking at multiple versions of policyengine-us to determine which version is compatible with other requirements. 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policyengine_us-0.343.0-py3-none-any.whl.metadata (3.3 kB) + Using cached policyengine_us-0.342.0-py3-none-any.whl.metadata (3.2 kB) +Collecting synthimpute (from policyengine-us) + Using cached synthimpute-0.1-py3-none-any.whl.metadata (451 bytes) +Collecting tables (from policyengine-us) + Using cached tables-3.9.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.3 kB) +Collecting tabulate (from policyengine-us) + Using cached tabulate-0.9.0-py3-none-any.whl.metadata (34 kB) +Requirement already satisfied: tqdm in /opt/conda/lib/python3.12/site-packages (from policyengine-us) (4.65.0) +Collecting numpy<1.25 (from policyengine-core<3,>=2.21.5->policyengine-us) + Using cached numpy-1.24.4.tar.gz (10.9 MB) + Installing build dependencies: started + Installing build dependencies: finished with status 'done' + Getting requirements to build wheel: started + Getting requirements to build wheel: finished with status 'error' From 6aec07b72d1f7a6b8fb18134069b6fa7b4634e42 Mon Sep 17 00:00:00 2001 From: Emie Elvire Sabumukama <67650132+emieelvire@users.noreply.github.com> Date: Fri, 31 May 2024 12:57:17 +0000 Subject: [PATCH 5/5] remove error file --- error.txt | 796 ------------------------------------------------------ 1 file changed, 796 deletions(-) delete mode 100644 error.txt diff --git a/error.txt b/error.txt deleted file mode 100644 index 4a78ed6..0000000 --- a/error.txt +++ /dev/null @@ -1,796 +0,0 @@ -Collecting policyengine-us - Using cached policyengine_us-0.777.7-py3-none-any.whl.metadata (2.5 kB) -Collecting click==8.1.3 (from policyengine-us) - Using cached click-8.1.3-py3-none-any.whl.metadata (3.2 kB) -Collecting h5py (from policyengine-us) - Using cached h5py-3.11.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.5 kB) -Collecting microdf-python (from policyengine-us) - Using cached microdf_python-0.3.0-py3-none-any.whl.metadata (437 bytes) -Requirement already satisfied: pandas in /opt/conda/lib/python3.12/site-packages (from policyengine-us) (2.2.1) -Collecting pathlib (from policyengine-us) - Using cached pathlib-1.0.1-py3-none-any.whl.metadata (5.1 kB) -Collecting policyengine-core<3,>=2.21.5 (from policyengine-us) - Using cached policyengine_core-2.21.8-py3-none-any.whl.metadata (2.7 kB) -Collecting pytest (from policyengine-us) - Using cached pytest-8.2.1-py3-none-any.whl.metadata (7.6 kB) -Collecting pytest-dependency (from policyengine-us) - Using cached pytest-dependency-0.6.0.tar.gz (19 kB) - Preparing metadata (setup.py): started - Preparing metadata (setup.py): finished with status 'done' -Collecting pyyaml (from policyengine-us) - Using cached PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.1 kB) -Requirement already satisfied: requests in /opt/conda/lib/python3.12/site-packages (from policyengine-us) (2.31.0) -INFO: pip is looking at multiple versions of policyengine-us to determine which version is compatible with other requirements. 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