From ffa79aecfb9cc7f4896744ca9ac27a4808910044 Mon Sep 17 00:00:00 2001 From: aleksandra-kim Date: Thu, 8 Jul 2021 21:43:16 +0200 Subject: [PATCH 1/2] plotted all gwp uncertainty distributions and added to readme --- .gitignore | 1 + README.md | 6 +- dev/gwp_uncertainties.html | 67 --- dev/gwp_uncertainties.ipynb | 695 ------------------------------- dev/plot_gwp_uncertainties.ipynb | 331 +++++++++++++++ docs/gwp_uncertainties.svg | 1 + 6 files changed, 338 insertions(+), 763 deletions(-) delete mode 100644 dev/gwp_uncertainties.html delete mode 100644 dev/gwp_uncertainties.ipynb create mode 100644 dev/plot_gwp_uncertainties.ipynb create mode 100644 docs/gwp_uncertainties.svg diff --git a/.gitignore b/.gitignore index fe6d068..a19c7bb 100644 --- a/.gitignore +++ b/.gitignore @@ -133,3 +133,4 @@ dmypy.json # local files .idea/* +dev/write_files diff --git a/README.md b/README.md index b8e4429..3c23f95 100644 --- a/README.md +++ b/README.md @@ -29,4 +29,8 @@ Uncertainties in the inputs `x` are given in Table 8.SM.12 and Section 8.3.1. Un A special case is carbon monoxide, where we assume uniform distribution (see Table 8.A.4). Note that -- Uncertainties in NOx and VOC have not been taken into account in the current implementation. GWP values are given in Tables 8.A.3 and 8.A.5 respectively. \ No newline at end of file +- Uncertainties in NOx and VOC have not been taken into account in the current implementation. GWP values are given in Tables 8.A.3 and 8.A.5 respectively. + +Results +======= +![flows_gwp_uncertainties](docs/gwp_uncertainties.svg) diff --git a/dev/gwp_uncertainties.html b/dev/gwp_uncertainties.html deleted file mode 100644 index e81ab04..0000000 --- a/dev/gwp_uncertainties.html +++ /dev/null @@ -1,67 +0,0 @@ - - - -
-
- - \ No newline at end of file diff --git a/dev/gwp_uncertainties.ipynb b/dev/gwp_uncertainties.ipynb deleted file mode 100644 index 7090667..0000000 --- a/dev/gwp_uncertainties.ipynb +++ /dev/null @@ -1,695 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# import brightway2 as bw\n", - "import bw2calc as bc\n", - "import bw2data as bd\n", - "import numpy as np\n", - "import json, os\n", - "from scipy.stats import norm\n", - "import plotly.graph_objects as go\n", - "from plotly.subplots import make_subplots\n", - "\n", - "#Local files\n", - "from import_databases import *" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "project = \"GSA for protocol\"\n", - "bd.projects.set_current(project)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('IPCC 2013', 'climate change', 'GTP 100a'),\n", - " ('IPCC 2013', 'climate change', 'GTP 20a'),\n", - " ('IPCC 2013', 'climate change', 'GWP 100a'),\n", - " ('IPCC 2013', 'climate change', 'GWP 20a'),\n", - " ('IPCC 2013 no LT', 'climate change', 'GTP 100a'),\n", - " ('IPCC 2013 no LT', 'climate change', 'GTP 20a'),\n", - " ('IPCC 2013 no LT', 'climate change', 'GWP 100a'),\n", - " ('IPCC 2013 no LT', 'climate change', 'GWP 20a')]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[m for m in bd.methods if 'IPCC 2013' in str(m)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "%%time\n", - "co = bd.Database('CH consumption 1.0')\n", - "demand_act = co.search('food sector')[0]\n", - "# demand_act = co.search('beef')[0]\n", - "demand = {demand_act: 1}\n", - "method = ('IPCC 2013', 'climate change', 'GWP 100a')\n", - "print(demand_act)\n", - "\n", - "lca = bc.LCA(demand, method)\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Contribution analysis" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_substances = 20\n", - "characterized_substances = lca.characterized_inventory.toarray().sum(axis=1)\n", - "inds = np.argsort(abs(characterized_substances))[::-1][:num_substances]\n", - "characterized_substances[inds]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sum(characterized_substances[inds])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "codes = []\n", - "for ind in inds:\n", - " act = bd.get_activity(lca.reverse_dict()[2][ind])\n", - " codes.append((act['database'], act['code']))\n", - " print(act)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Uncertainties from IPCC 2013 report and it's supplementary materials (SM)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# ghg_std_multiplier_dict = {}\n", - "\n", - "# # 1. Time horizon, same for all cases, already specified in the LCIA method naming\n", - "# H = int( re.findall(r'\\d+', method[-1])[0]) \n", - "\n", - "# # 2. Conversion from ppbv (part per billion), page 8SM-15\n", - "# Ma = 28.97 # [kg/kmol], molecular weight of air \n", - "# Tm = 5.1352e18 # [kg] total mass of atmosphere\n", - "# ppbv_to_kg = lambda M: (Ma/M*1e9/Tm) # conversion, where M is molecular weight of the considered substance\n", - "\n", - "# # 3. Compute function f uncertainty given derivatives d = partial_df/partial_dx*delta_x, and normalize\n", - "# compute_delta_f = lambda nominal_value, *d: np.sqrt(np.sum(np.power(d,2))) / nominal_value\n", - "\n", - "# # 4. General expression for AGWP, equation 8.SM.9\n", - "# # A - radiative efficiency, [W/m2/ppb]\n", - "# # tau - lifetime, [yr]\n", - "# # H - time horizon, [yr]\n", - "# # mult - multiplier specific to each substance, in many cases is equal to 1\n", - "# compute_agwp_general = lambda A, tau, mult: A*tau*(1-np.exp(-H/tau)) * mult\n", - "\n", - "# # 5. Print computed +-delta_x uncertainty values as percentage of the expected value \\bar{x} \n", - "# # for a 90% confidence interval and its standard deviation\n", - "# print_uncertainties = lambda x_name, x_delta, x_std: print(\n", - "# \"{:10s} -> delta = {:6.3f}%, std = {:5.3f}*mean\".format(x_name, x_delta*100, x_std)\n", - "# )\n", - "\n", - "# # 6. Compute standard deviation x_std given +-delta_x values for confidence interval with a certain confidence level\n", - "# conf_interval_to_std = lambda conf_level, conf_interval: conf_interval / norm.ppf(0.5 + conf_level / 2)\n", - "\n", - "# def compute_co2_agwp(time_horizon):\n", - "# # 1. Carbon dioxide, AGWP expression is not general, instead taken from equation 8.SM.23\n", - "# # Constants are from Joos et al, 2013, https://doi.org/10.5194/acp-13-2793-2013\n", - "# # time_horizon is given in years\n", - "# M_co2 = 44.01\n", - "# A_co2 = 1.37e-5 * ppbv_to_kg(M_co2)\n", - "# delta_A_co2 = 0.1\n", - "# if time_horizon==20:\n", - "# I_co2 = 14.2\n", - "# delta_I_co2 = (16.3-12.2)/2/I_co2\n", - "# elif time_horizon==100:\n", - "# I_co2 = 52.4 \n", - "# delta_I_co2 = (65.2-39.5)/2/I_co2\n", - "# elif time_horizon==500:\n", - "# I_co2 = 184\n", - "# delta_I_co2 = (235-132)/2/I_co2\n", - "# co2_agwp = A_co2*I_co2\n", - "# co2_ddelta_A = I_co2*delta_A_co2*A_co2\n", - "# co2_ddelta_I = A_co2*delta_I_co2*I_co2\n", - "# co2_delta_agwp = compute_delta_f(co2_agwp, co2_ddelta_A, co2_ddelta_I)\n", - "# conf_level = 0.9\n", - "# co2_std_agwp = conf_interval_to_std(conf_level, co2_delta_agwp)\n", - "# return co2_delta_agwp, co2_std_agwp, co2_agwp\n", - "# co2_delta_agwp, co2_std_agwp, co2_agwp = compute_co2_agwp(H)\n", - "# print_uncertainties('CO2 agwp', co2_delta_agwp, co2_std_agwp)\n", - "\n", - "# # 2. Uncertainty values for AGWP, chapter 8.SM.12, equation 8.SM.24\n", - "# # delta values for A and tau are taken from table 8.SM.12\n", - "# compute_gwp = lambda x_agwp: x_agwp/co2_agwp\n", - "# compute_dgwp_dA = lambda A, tau, mult, delta_A: \\\n", - "# mult * tau / co2_agwp * (1 - np.exp(-H/tau)) * delta_A*A\n", - "# compute_dgwp_dtau = lambda A, tau, mult, delta_tau: \\\n", - "# mult * A / co2_agwp * (1 - np.exp(-H/tau) - H/tau*np.exp(-H/tau) ) * delta_tau*tau\n", - "# compute_dgwp_dgwpco2 = lambda x_agwp: \\\n", - "# 1 / co2_agwp**2 * x_agwp * co2_delta_agwp*co2_agwp\n", - "\n", - "# def compute_delta_gwp(x_name, M, A, tau, delta_A, delta_tau, mult=1, *dgwp_dy):\n", - "# x_agwp = compute_agwp_general (A, tau, mult)\n", - "# x_gwp = compute_gwp(x_agwp)\n", - "# x_dgwp_dA = compute_dgwp_dA (A, tau, mult, delta_A)\n", - "# x_dgwp_dtau = compute_dgwp_dtau(A, tau, mult, delta_tau)\n", - "# x_dgwp_dgwpco2 = compute_dgwp_dgwpco2(x_agwp)\n", - "# x_delta_gwp = compute_delta_f(\n", - "# x_gwp,\n", - "# x_dgwp_dA, \n", - "# x_dgwp_dtau, \n", - "# x_dgwp_dgwpco2,\n", - "# *dgwp_dy,\n", - "# )\n", - "# conf_level = 0.9\n", - "# x_std_multiplier_gwp = conf_interval_to_std(conf_level, x_delta_gwp)\n", - "# print_uncertainties(x_name, x_delta_gwp, x_std_multiplier_gwp)\n", - "# return x_delta_gwp, x_std_multiplier_gwp\n", - "\n", - "# Methane CH4, AGWP taken from chapter 8.SM.11.3.2, equation 8.SM.19\n", - "M_ch4 = 16.04\n", - "A_ch4 = 3.63e-4 * ppbv_to_kg(M_ch4)\n", - "tau = 12.4\n", - "f1 = 0.5 # due to effects on ozone\n", - "f2 = 0.15 # due to stratospheric H2O\n", - "mult = 1+f1+f2\n", - "delta_A = 0.1\n", - "delta_tau = 0.1857\n", - "delta_f1 = 0.6\n", - "delta_f2 = 0.7143\n", - "ch4_dgwp_df1 = A_ch4*tau / co2_agwp * (1 - np.exp(-H/tau)) * delta_f1*f1\n", - "ch4_dgwp_df2 = A_ch4*tau / co2_agwp * (1 - np.exp(-H/tau)) * delta_f2*f2\n", - "ch4_delta_gwp, ch4_std_multiplier_gwp = compute_delta_gwp(\n", - " 'CH4', M_ch4, A_ch4, tau, delta_A, delta_tau, mult, ch4_dgwp_df1, ch4_dgwp_df2\n", - ")\n", - "ghg_std_multiplier_dict['Methane, fossil'] = ch4_std_multiplier_gwp\n", - "ghg_std_multiplier_dict['Methane, non-fossil'] = ch4_std_multiplier_gwp\n", - "ghg_std_multiplier_dict['Methane, from soil or biomass stock'] = ch4_std_multiplier_gwp\n", - "\n", - "# Dinitrogen monoxide, table 8.A.1\n", - "M = 44.013\n", - "A = 3.00e-3 * ppbv_to_kg(M)\n", - "tau = 121\n", - "f1 = 0.5 # due to effects on ozone\n", - "f2 = 0.15 # due to stratospheric H2O\n", - "RE_ch4 = A_ch4\n", - "RE_n2o = A\n", - "mult = 1-0.36*(1+f1+f2)*RE_ch4/RE_n2o\n", - "delta_A = 0.1\n", - "delta_tau = 0.1299\n", - "delta_f1 = 0.6\n", - "delta_f2 = 0.7143\n", - "n2o_dgwp_df1 = -0.36*RE_ch4/RE_n2o*A*tau / co2_agwp * (1 - np.exp(-H/tau)) * delta_f1*f1\n", - "n2o_dgwp_df2 = -0.36*RE_ch4/RE_n2o*A*tau / co2_agwp * (1 - np.exp(-H/tau)) * delta_f2*f2\n", - "n2o_delta_gwp, n2o_std_multiplier_gwp = compute_delta_gwp(\n", - " 'N2O', M, A, tau, delta_A, delta_tau, mult, n2o_dgwp_df1, n2o_dgwp_df2\n", - ")\n", - "ghg_std_multiplier_dict['Dinitrogen monoxide'] = n2o_std_multiplier_gwp\n", - "\n", - "# Carbon monoxide, table 8.A.4, doi:10.1016/j.atmosenv.2009.04.044\n", - "# ghg_std_multiplier_dict['Carbon monoxide'] = nox_std_multiplier_gwp\n", - "\n", - "\n", - "# # CFC-11\n", - "# M = 137.37\n", - "# A = 0.26 * ppbv_to_kg(M)\n", - "# tau = 45\n", - "# mult = 1\n", - "# delta_A = 0.1\n", - "# delta_tau = 0.2255\n", - "# cfc11_agwp = compute_agwp_general (A, tau, mult)\n", - "# cfc11_gwp = compute_gwp(cfc11_agwp)\n", - "# cfc11_dgwp_dA = compute_dgwp_dA (A, tau, mult, delta_A)\n", - "# cfc11_dgwp_dtau = compute_dgwp_dtau(A, tau, mult, delta_tau)\n", - "# cfc11_dgwp_dgwpco2 = compute_dgwp_dgwpco2(cfc11_agwp)\n", - "# cfc11_delta_gwp, cfc11_std_multiplier_gwp = compute_delta_gwp('CFC-11', M, A, tau, delta_A, delta_tau, mult)\n", - "\n", - "# # CFC-12\n", - "# M = 120.91\n", - "# A = 0.32 * ppbv_to_kg(M)\n", - "# tau = 100\n", - "# mult = 1\n", - "# delta_A = 0.1\n", - "# delta_tau = 0.2876\n", - "# cfc12_delta_gwp, cfc12_std_multiplier_gwp = compute_delta_gwp('CFC-12', M, A, tau, delta_A, delta_tau, mult)\n", - "\n", - "# # Ethane, 1,1,1,2-tetrafluoro-, HFC-134a\n", - "# M = 102.03\n", - "# A = 0.16 * ppbv_to_kg(M)\n", - "# tau = 13.4\n", - "# mult = 1\n", - "# delta_A = 0.1\n", - "# delta_tau = 0.179\n", - "# hfc134a_delta_gwp, hfc134a_std_multiplier_gwp = compute_delta_gwp('HFC-134a', M, A, tau, delta_A, delta_tau, mult)\n", - "# ghg_std_multiplier_dict['Ethane, 1,1,1,2-tetrafluoro-, HFC-134a'] = hfc134a_std_multiplier_gwp\n", - "\n", - "# Sulfur hexafluoride\n", - "M = 146.06\n", - "A = 0.57 * ppbv_to_kg(M)\n", - "tau = 3200\n", - "mult = 1\n", - "delta_A = 0.1 # TODO chosen arbitrarily\n", - "delta_tau = 0.2 # TODO chosen arbitrarily\n", - "sf6_delta_gwp, sf6_std_multiplier_gwp = compute_delta_gwp('SF6', M, A, tau, delta_A, delta_tau, mult)\n", - "ghg_std_multiplier_dict['Sulfur hexafluoride'] = sf6_std_multiplier_gwp\n", - "\n", - "# # Ethane, hexafluoro-, HFC-116, chemical formula C2F6\n", - "# M = 138.01\n", - "# A = 0.25 * ppbv_to_kg(M)\n", - "# tau = 10000\n", - "# mult = 1\n", - "# delta_A = 0.1 # TODO chosen arbitrarily\n", - "# delta_tau = 0.2 # TODO chosen arbitrarily\n", - "# hfc116_delta_gwp, hfc116_std_multiplier_gwp = compute_delta_gwp('HFC-116', M, A, tau, delta_A, delta_tau, mult)\n", - "# ghg_std_multiplier_dict['Ethane, hexafluoro-, HFC-116'] = hfc116_std_multiplier_gwp\n", - "\n", - "# # Methane, tetrafluoro-, R-14\n", - "# M = 88.0043\n", - "# A = 0.09 * ppbv_to_kg(M)\n", - "# tau = 50000\n", - "# mult = 1\n", - "# delta_A = 0.1 # TODO chosen arbitrarily\n", - "# delta_tau = 0.2 # TODO chosen arbitrarily\n", - "# r14_delta_gwp, r14_std_multiplier_gwp = compute_delta_gwp('R-14', M, A, tau, delta_A, delta_tau, mult)\n", - "# ghg_std_multiplier_dict['Methane, tetrafluoro-, R-14'] = r14_std_multiplier_gwp\n", - "\n", - "# # Methane, bromotrifluoro-, Halon 1301\n", - "# M = 148.91\n", - "# A = 0.3 * ppbv_to_kg(M)\n", - "# tau = 65\n", - "# mult = 1\n", - "# delta_A = 0.1 # TODO chosen arbitrarily\n", - "# delta_tau = 0.2 # TODO chosen arbitrarily\n", - "# halon1301_delta_gwp, halon1301_std_multiplier_gwp = compute_delta_gwp('Halon-1301', M, A, tau, delta_A, delta_tau, mult)\n", - "# ghg_std_multiplier_dict['Methane, bromotrifluoro-, Halon 1301'] = halon1301_std_multiplier_gwp" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# New bw method with uncertainties" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import stats_arrays as sa\n", - "bw_method = bd.Method(method)\n", - "\n", - "flows_certain_list, flows_uncertain_list = [], []\n", - "for flow in bw_method.load():\n", - " if flow[0] not in codes:\n", - " flows_certain_list.append(flow)\n", - " else:\n", - " act = bd.get_activity(flow[0])\n", - " if 'Carbon dioxide' in act['name']:\n", - " flows_certain_list.append(flow)\n", - " elif 'Carbon monoxide, fossil' in act['name'] or \\\n", - " 'Carbon monoxide, from soil or biomass stock' in act['name']:\n", - " oxidation = 1.5714\n", - " min_ = 2 + oxidation\n", - " max_ = 3.3 + oxidation\n", - " flow_uncertain = (\n", - " flow[0], # flow key = (database, code)\n", - " {\n", - " 'amount': flow[1], \n", - " 'minimum': min_, \n", - " 'maximum': max_, \n", - " 'uncertainty type': sa.UniformUncertainty.id, # assumption from the ipcc report\n", - " }\n", - " )\n", - " flows_uncertain_list.append(flow_uncertain)\n", - " else:\n", - " ghg_gwp = flow[1]\n", - " ghg_std = ghg_gwp*ghg_std_multiplier_dict[act['name']]\n", - " flow_uncertain = (\n", - " flow[0], # flow key = (database, code)\n", - " {\n", - " 'amount': ghg_gwp, # static value of gwp\n", - " 'uncertainty type': sa.NormalUncertainty.id, # assumption from the ipcc report\n", - " 'loc': ghg_gwp, # mean value that is equal to the static one\n", - " 'scale': ghg_std, # standard deviation\n", - " }\n", - " )\n", - " flows_uncertain_list.append(flow_uncertain)\n", - "flows_list = flows_certain_list + flows_uncertain_list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "uncertain_method = method + ('uncertain',)\n", - "uncertain_bw_method = bd.Method(uncertain_method)\n", - "try:\n", - " uncertain_bw_method.deregister()\n", - "except:\n", - " pass\n", - "uncertain_bw_method.register(\n", - " **{\n", - " 'unit': bw_method.metadata['unit'],\n", - " 'num_cfs':len(flows_list),\n", - " 'abbreviation':'nonexistent',\n", - " 'description':'based on IPCC 2013 method but with uncertainties',\n", - " 'filename':'nonexistent'\n", - " }\n", - ")\n", - "uncertain_bw_method.write(flows_list)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "lca = bc.LCA(demand, uncertain_method)\n", - "lca.lci()\n", - "lca.lcia()\n", - "lca.score" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# uncertain_tech = lca.tech_params[lca.tech_params['uncertainty_type']>1]\n", - "# uncertain_bio = lca.bio_params[lca.bio_params['uncertainty_type']>1]\n", - "# uncertain_cf = lca.cf_params[lca.cf_params['uncertainty_type']>1]\n", - "# num_params = len(uncertain_tech) + len(uncertain_bio) + len(uncertain_cf)\n", - "# len(uncertain_tech), len(uncertain_bio), len(uncertain_cf)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# iterations = 20\n", - "# path_base = Path(\"/Users/akim/PycharmProjects/gsa_framework/dev/write_files/\")\n", - "# # path_base = Path('/data/user/kim_a/paper_gsa')\n", - "# write_dir = path_base / \"protocol_gsa\" / \"lca_model_food_num_params\".format(num_params)\n", - "# if flag_generate_scores_dict:\n", - "# model = LCAModel(demand, method, write_dir) # generate scores_dict\n", - "# del model\n", - "# model = LCAModel(demand, method, write_dir, num_params=num_params)\n", - "# gsa_seed = 92374523" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%%time\n", - "iterations = 4000\n", - "mc_certain_gwp = bc.ParallelMonteCarlo(demand, method, iterations)\n", - "lca_scores_certain_gwp = np.array(mc_certain_gwp.calculate())\n", - "\n", - "mc_uncertain_gwp = bc.ParallelMonteCarlo(demand, uncertain_method, iterations)\n", - "lca_scores_uncertain_gwp = np.array(mc_uncertain_gwp.calculate())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# %%time\n", - "# iterations = 200\n", - "# seed = 234534\n", - "# mc_certain_gwp1 = bc.MonteCarloLCA(demand, method, seed=seed)\n", - "# lca_scores_certain_gwp = [next(mc_certain_gwp1) for _ in range(iterations)]\n", - "\n", - "# mc_uncertain_gwp1 = bc.MonteCarloLCA(demand, uncertain_method, seed=seed)\n", - "# lca_scores_uncertain_gwp = [next(mc_uncertain_gwp1) for _ in range(iterations)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.std(lca_scores_certain_gwp), np.std(lca_scores_uncertain_gwp)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot LCA scores" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "num_bins = 60\n", - "bin_min = min(np.hstack([lca_scores_certain_gwp, lca_scores_uncertain_gwp]))\n", - "bin_max = max(np.hstack([lca_scores_certain_gwp, lca_scores_uncertain_gwp]))\n", - "bins_ = np.linspace(bin_min, bin_max, num_bins, endpoint=True)\n", - "freq_certain, bins_certain = np.histogram(lca_scores_certain_gwp, bins=bins_, density=True)\n", - "freq_uncertain, bins_uncertain = np.histogram(lca_scores_uncertain_gwp, bins=bins_, density=True)\n", - " \n", - "fig = go.Figure()\n", - "fig.add_trace(\n", - " go.Bar(\n", - " x=bins_certain,\n", - " y=freq_certain,\n", - " name='GWP values W/O uncertainties',\n", - " opacity=0.7,\n", - " ),\n", - ")\n", - "fig.add_trace(\n", - " go.Bar(\n", - " x=bins_uncertain,\n", - " y=freq_uncertain,\n", - " name='GWP values WITH uncertainties',\n", - " opacity=0.7,\n", - " ),\n", - ")\n", - "fig.update_layout(barmode=\"overlay\")\n", - "fig.update_yaxes(title_text='Frequency')\n", - "fig.update_xaxes(title_text='LCA scores, [kg CO2-eq]')\n", - "fig.show()\n", - "fig.write_html(\"write_files/lca_scores_gwp_uncertainties.html\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot GWP with uncertainties" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data = []\n", - "flow_names = []\n", - "iterations = 2000\n", - "for flow in flows_uncertain_list:\n", - " if flow[1]['uncertainty type'] == sa.NormalUncertainty.id:\n", - " x = np.random.normal(flow[1]['loc'], flow[1]['scale'], iterations)\n", - " elif flow[1]['uncertainty type'] == sa.UniformUncertainty.id:\n", - " x = (flow[1]['maximum'] - flow[1]['minimum'])*np.random.rand(iterations) + flow[1]['minimum']\n", - " act = bd.get_activity(flow[0])\n", - " flow_name = \"{} {}\".format(act['name'], act['categories'])[:45]\n", - " data.append(\n", - " {\n", - " 'name': flow_name,\n", - " 'static': flow[1]['amount'],\n", - " 'x': x\n", - " }\n", - " )\n", - " flow_names.append(flow_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n_plots = len(flows_uncertain_list)\n", - "ncols = 4\n", - "nrows = int(np.ceil(n_plots/ncols))\n", - "fig = make_subplots(\n", - " rows=nrows,\n", - " cols=ncols,\n", - " shared_yaxes=True,\n", - " subplot_titles=flow_names,\n", - ")\n", - "i = 0\n", - "for row in range(nrows):\n", - " for col in range(ncols):\n", - " if i < n_plots:\n", - " values = data[i]['x']\n", - " freq, bins = np.histogram(values, bins=num_bins)\n", - " fig.add_trace(\n", - " go.Bar(\n", - " x=bins,\n", - " y=freq,\n", - " showlegend=False,\n", - " marker_color = 'blue',\n", - " opacity=0.7,\n", - " ),\n", - " row=row+1,\n", - " col=col+1,\n", - " )\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=[data[i]['static']],\n", - " y=[0],\n", - " showlegend=False,\n", - " marker_color = 'red',\n", - " mode=\"markers\",\n", - " marker_symbol = 'x',\n", - " marker_size=15,\n", - " opacity=0.7,\n", - " ),\n", - " row=row+1,\n", - " col=col+1,\n", - " )\n", - " i += 1 \n", - "fig.update_yaxes(title_text=\"Frequency\", col=1)\n", - "fig.update_xaxes(title_text=\"GWP\", row=nrows)\n", - "fig.update_layout(\n", - " width=400*ncols, height=300*nrows,\n", - ")\n", - "fig.show()\n", - "fig.write_html(\"write_files/gwp_uncertainties.html\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "varInspector": { - "cols": { - "lenName": 16, - "lenType": 16, - "lenVar": 40 - }, - "kernels_config": { - "python": { - "delete_cmd_postfix": "", - "delete_cmd_prefix": "del ", - "library": "var_list.py", - "varRefreshCmd": "print(var_dic_list())" - }, - "r": { - "delete_cmd_postfix": ") ", - "delete_cmd_prefix": "rm(", - "library": "var_list.r", - "varRefreshCmd": "cat(var_dic_list()) " - } - }, - "types_to_exclude": [ - "module", - "function", - "builtin_function_or_method", - "instance", - "_Feature" - ], - "window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/dev/plot_gwp_uncertainties.ipynb b/dev/plot_gwp_uncertainties.ipynb new file mode 100644 index 0000000..ef06109 --- /dev/null +++ b/dev/plot_gwp_uncertainties.ipynb @@ -0,0 +1,331 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pickle\n", + "from pathlib import Path\n", + "import bw2calc as bc\n", + "import bw2data as bd\n", + "from gwp_uncertainties import add_bw_method_with_gwp_uncertainties\n", + "import plotly.graph_objects as go\n", + "import stats_arrays as sa\n", + "from plotly.subplots import make_subplots\n", + "\n", + "if __name__ == \"__main__\":\n", + "\n", + " time_horizon = 100 # TODO choose\n", + "\n", + " project = \"GSA for protocol\"\n", + " bd.projects.set_current(project)\n", + "\n", + " add_bw_method_with_gwp_uncertainties(time_horizon, verbose=True)\n", + "\n", + " co = bd.Database('CH consumption 1.0')\n", + " demand_act = co.search('food sector')[0]\n", + " demand = {demand_act: 1}\n", + " print(demand_act)\n", + " method = ('IPCC 2013', 'climate change', 'GWP {}a'.format(time_horizon))\n", + " uncertain_method = method + ('uncertain',)\n", + "\n", + " lca = bc.LCA(demand, method)\n", + " lca.lci()\n", + " lca.lcia()\n", + " print(\"{} LCA score with standard IPCC method\".format(lca.score))\n", + " ulca = bc.LCA(demand, uncertain_method)\n", + " ulca.lci()\n", + " ulca.lcia()\n", + " print(\"{} LCA score with uncertain IPCC method\".format(ulca.score))\n", + "\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "iterations = 2000\n", + "write_dir = Path(\"write_files\")\n", + "path_lca_scores_certain = write_dir / \"lca_scores_certain_gwp_{}.pickle\".format(iterations)\n", + "path_lca_scores_uncertain = write_dir / \"lca_scores_uncertain_gwp_{}.pickle\".format(iterations)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "if path_lca_scores_certain.exists():\n", + " with open(path_lca_scores_certain, 'rb') as f:\n", + " lca_scores_certain_gwp = pickle.load(f)\n", + "else:\n", + " mc_certain_gwp = bc.ParallelMonteCarlo(demand, method, iterations)\n", + " lca_scores_certain_gwp = np.array(mc_certain_gwp.calculate())\n", + " with open(path_lca_scores_certain, 'wb') as f:\n", + " pickle.dump(lca_scores_certain_gwp, f)\n", + "\n", + "if path_lca_scores_uncertain.exists():\n", + " with open(path_lca_scores_uncertain, 'rb') as f:\n", + " lca_scores_uncertain_gwp = pickle.load(f)\n", + "else:\n", + " mc_uncertain_gwp = bc.ParallelMonteCarlo(demand, uncertain_method, iterations)\n", + " lca_scores_uncertain_gwp = np.array(mc_uncertain_gwp.calculate())\n", + " with open(path_lca_scores_uncertain, 'wb') as f:\n", + " pickle.dump(lca_scores_uncertain_gwp, f)\n", + " \n", + "print(\"LCA scores W/O uncertainties in GWP -> std={}\".format(\n", + " np.std(lca_scores_certain_gwp))\n", + " )\n", + "print(\"LCA scores WITH uncertainties in GWP -> std={}\".format(\n", + " np.std(lca_scores_uncertain_gwp))\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot LCA scores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "num_bins = 60\n", + "bin_min = min(np.hstack([lca_scores_certain_gwp, lca_scores_uncertain_gwp]))\n", + "bin_max = max(np.hstack([lca_scores_certain_gwp, lca_scores_uncertain_gwp]))\n", + "bins_ = np.linspace(bin_min, bin_max, num_bins, endpoint=True)\n", + "freq_certain, bins_certain = np.histogram(lca_scores_certain_gwp, bins=bins_, density=True)\n", + "freq_uncertain, bins_uncertain = np.histogram(lca_scores_uncertain_gwp, bins=bins_, density=True)\n", + " \n", + "fig = go.Figure()\n", + "fig.add_trace(\n", + " go.Bar(\n", + " x=bins_certain,\n", + " y=freq_certain,\n", + " name='GWP values W/O uncertainties',\n", + " opacity=0.7,\n", + " ),\n", + ")\n", + "fig.add_trace(\n", + " go.Bar(\n", + " x=bins_uncertain,\n", + " y=freq_uncertain,\n", + " name='GWP values WITH uncertainties',\n", + " opacity=0.7,\n", + " ),\n", + ")\n", + "fig.update_layout(barmode=\"overlay\")\n", + "fig.update_yaxes(title_text='Frequency')\n", + "fig.update_xaxes(title_text='LCA scores, [kg CO2-eq]')\n", + "fig.show()\n", + "fig.write_html(\"write_files/lca_scores_gwp_uncertainties.html\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Plot GWP with uncertainties" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bw_uncertain_method = bd.Method(uncertain_method)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "all_flows = bw_uncertain_method.load()\n", + "uncertain_flows = []\n", + "inds = []\n", + "for i,flow in enumerate(all_flows):\n", + " try:\n", + " flow[1].get('uncertainty type')\n", + " flow_name = bd.get_activity(flow[0])['name']\n", + " if flow_name not in uncertain_flows:\n", + " uncertain_flows.append(flow_name)\n", + " inds.append(i)\n", + " except:\n", + " pass\n", + "# unique_uncertain_flows = set(uncertain_flows)\n", + "flows_uncertain_list = [all_flows[i] for i in inds]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = []\n", + "flow_names = []\n", + "iterations = 2000\n", + "for flow in flows_uncertain_list:\n", + " if flow[1]['uncertainty type'] == sa.NormalUncertainty.id:\n", + " x = np.random.normal(flow[1]['loc'], flow[1]['scale'], iterations)\n", + " elif flow[1]['uncertainty type'] == sa.UniformUncertainty.id:\n", + " x = (flow[1]['maximum'] - flow[1]['minimum'])*np.random.rand(iterations) + flow[1]['minimum']\n", + " act = bd.get_activity(flow[0])\n", + " flow_name = \"{} {}\".format(act['name'], act['categories'])[:45]\n", + " data.append(\n", + " {\n", + " 'name': flow_name,\n", + " 'static': flow[1]['amount'],\n", + " 'x': x\n", + " }\n", + " )\n", + " flow_names.append(flow_name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "n_plots = len(flows_uncertain_list)\n", + "ncols = 4\n", + "nrows = int(np.ceil(n_plots/ncols))\n", + "fig = make_subplots(\n", + " rows=nrows,\n", + " cols=ncols,\n", + " shared_yaxes=True,\n", + " subplot_titles=flow_names,\n", + ")\n", + "i = 0\n", + "for row in range(nrows):\n", + " for col in range(ncols):\n", + " if i < n_plots:\n", + " values = data[i]['x']\n", + " freq, bins = np.histogram(values, bins=num_bins)\n", + " fig.add_trace(\n", + " go.Bar(\n", + " x=bins,\n", + " y=freq,\n", + " showlegend=False,\n", + " marker_color = 'blue',\n", + " opacity=0.7,\n", + " ),\n", + " row=row+1,\n", + " col=col+1,\n", + " )\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=[data[i]['static']],\n", + " y=[0],\n", + " showlegend=False,\n", + " marker_color = 'red',\n", + " mode=\"markers\",\n", + " marker_symbol = 'x',\n", + " marker_size=15,\n", + " opacity=0.7,\n", + " ),\n", + " row=row+1,\n", + " col=col+1,\n", + " )\n", + " i += 1 \n", + "fig.update_yaxes(title_text=\"Frequency\", col=1)\n", + "fig.update_xaxes(title_text=\"GWP\", row=nrows)\n", + "fig.update_layout(\n", + " width=400*ncols, height=300*nrows,\n", + ")\n", + "fig.show()\n", + "fig.write_html(\"write_files/gwp_uncertainties.html\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig.write_image(\"write_files/gwp_uncertainties.svg\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "varInspector": { + "cols": { + "lenName": 16, + "lenType": 16, + "lenVar": 40 + }, + "kernels_config": { + "python": { + "delete_cmd_postfix": "", + "delete_cmd_prefix": "del ", + "library": "var_list.py", + "varRefreshCmd": "print(var_dic_list())" + }, + "r": { + "delete_cmd_postfix": ") ", + "delete_cmd_prefix": "rm(", + "library": "var_list.r", + "varRefreshCmd": "cat(var_dic_list()) " + } + }, + "types_to_exclude": [ + "module", + "function", + "builtin_function_or_method", + "instance", + "_Feature" + ], + "window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/gwp_uncertainties.svg b/docs/gwp_uncertainties.svg new file mode 100644 index 0000000..7059220 --- /dev/null +++ b/docs/gwp_uncertainties.svg @@ -0,0 +1 @@ +44.505010044.522.53510152025200300400050100500100015002000501001502002502000400060004000600080000501005001000501001502000.511.54k6k8k10k12k050100100020003000501002004006008004k6k8k10k12k0501005k10k15k200030004000500010203040501234050100100020002k4k6k8k10002000300010k15k20k0501005105k10k15k501001502002502004006008001000050100102030405020405101520204005010050010001500200025004k6k8k10k2000400060005k10k15k05010010k15k20k25k4k6k8k10k12k15k20k25k30k35kGWPGWPGWPGWPFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyCarbon monoxide, fossil ('air', 'low populatiCarbon monoxide, from soil or biomass stock (Carbon monoxide, non-fossil ('air', 'low popuChloroform ('air', 'low population density, lDinitrogen monoxide ('air', 'low population dEthane, 1,1,1,2-tetrafluoro-, HFC-134a ('air'Ethane, 1,1,1-trichloro-, HCFC-140 ('air', 'lEthane, 1,1,1-trifluoro-, HFC-143a ('air', 'lEthane, 1,1,2-trichloro-1,2,2-trifluoro-, CFCEthane, 1,1-dichloro-1-fluoro-, HCFC-141b ('aEthane, 1,1-difluoro-, HFC-152a ('air', 'low Ethane, 1,2-dichloro- ('air', 'low populationEthane, 1,2-dichloro-1,1,2,2-tetrafluoro-, CFEthane, 1-chloro-1,1-difluoro-, HCFC-142b ('aEthane, 2,2-dichloro-1,1,1-trifluoro-, HCFC-1Ethane, 2-chloro-1,1,1,2-tetrafluoro-, HCFC-1Ethane, chloropentafluoro-, CFC-115 ('air', 'Ethane, hexafluoro-, HFC-116 ('air', 'low popEthane, pentafluoro-, HFC-125 ('air', 'low poMethane ('air', 'urban air close to ground')Methane, bromo-, Halon 1001 ('air', 'low popuMethane, bromochlorodifluoro-, Halon 1211 ('aMethane, bromotrifluoro-, Halon 1301 ('air', Methane, chlorodifluoro-, HCFC-22 ('air', 'loMethane, chlorotrifluoro-, CFC-13 ('air', 'loMethane, dichloro-, HCC-30 ('air', 'low populMethane, dichlorodifluoro-, CFC-12 ('air', 'lMethane, dichlorofluoro-, HCFC-21 ('air', 'loMethane, difluoro-, HFC-32 ('air', 'low populMethane, fossil ('air', 'low population densiMethane, from soil or biomass stock ('air', 'Methane, monochloro-, R-40 ('air', 'low populMethane, non-fossil ('air', 'low population dMethane, tetrachloro-, R-10 ('air', 'low popuMethane, tetrafluoro-, R-14 ('air', 'low popuMethane, trichlorofluoro-, CFC-11 ('air', 'loMethane, trifluoro-, HFC-23 ('air', 'low popuNitrogen fluoride ('air', 'low population denPerfluoropentane ('air',)Sulfur hexafluoride ('air', 'low population d \ No newline at end of file From 68d7dae66ea0cf1220404b8111f4b28a3e9787d6 Mon Sep 17 00:00:00 2001 From: aleksandra-kim Date: Thu, 23 Sep 2021 11:54:45 +0200 Subject: [PATCH 2/2] added better figures --- README.md | 2 + dev/plot_paper2.py | 340 +++++++++++++++++++++++++++++++ docs/gwp_uncertainties.svg | 2 +- docs/gwp_uncertainties_table.png | Bin 0 -> 4160192 bytes 4 files changed, 343 insertions(+), 1 deletion(-) create mode 100644 dev/plot_paper2.py create mode 100644 docs/gwp_uncertainties_table.png diff --git a/README.md b/README.md index 3c23f95..38a1365 100644 --- a/README.md +++ b/README.md @@ -34,3 +34,5 @@ Note that Results ======= ![flows_gwp_uncertainties](docs/gwp_uncertainties.svg) + +![table values](docs/gwp_uncertainties_table.png) diff --git a/dev/plot_paper2.py b/dev/plot_paper2.py new file mode 100644 index 0000000..924abc7 --- /dev/null +++ b/dev/plot_paper2.py @@ -0,0 +1,340 @@ +import numpy as np +import pickle +from pathlib import Path +import bw2calc as bc +import bw2data as bd +from gwp_uncertainties import add_bw_method_with_gwp_uncertainties +import plotly.graph_objects as go +import stats_arrays as sa +from plotly.subplots import make_subplots +from dev.utils_paper_plotting import * +from decimal import Decimal + +show_figure = False +save_figure = True + +path_base = Path( +"/Users/akim/PycharmProjects/gsa-framework-master/dev/write_files/" +) +write_dir = path_base / "protocol_gsa" +write_dir_arr = write_dir / "arrays" +write_dir_fig = write_dir / "figures" + +fig_format = ["pdf", "svg"] +num_bins = 60 +color = color_darkgray_hex + + +time_horizon = 100 # TODO choose + +project = "GSA for protocol" +bd.projects.set_current(project) + +# add_bw_method_with_gwp_uncertainties(time_horizon, verbose=True) + +co = bd.Database('CH consumption 1.0') +# demand_act = co.search('food sector')[0] +# demand = {demand_act: 1} +# print(demand_act) +method = ('IPCC 2013', 'climate change', 'GWP {}a'.format(time_horizon)) +uncertain_method = method + ('uncertain',) +bw_uncertain_method = bd.Method(uncertain_method) + +all_flows = bw_uncertain_method.load() +uncertain_flows = [] +inds = [] +for i,flow in enumerate(all_flows): + try: + flow[1].get('uncertainty type') + flow_name = bd.get_activity(flow[0])['name'] + if flow_name not in uncertain_flows: + uncertain_flows.append(flow_name) + inds.append(i) + except: + pass +# unique_uncertain_flows = set(uncertain_flows) +flows_uncertain_list = [all_flows[i] for i in inds] + +def format_value(dict, dict_key): + if dict_key == "uncertainty type": + value = dict.get(dict_key) + if value == sa.NormalUncertainty.id: + value_str = "$\mathcal{N}$" + elif value == sa.LognormalUncertainty.id: + value_str = "$Log\mathcal{N}$" + elif value == sa.UniformUncertainty.id: + value_str = "\mathcal{U}$" + else: + value = dict.get(dict_key, None) + if value is not None: + value_str = '%.2e' % Decimal(value) + value_str = value_str.replace("+0", "") + value_str = value_str.replace("-0", "-") + else: + value_str = "--" + return value_str + +for flow in flows_uncertain_list: + flow_name = bd.get_activity(flow[0])['name'] + data = flow[1] + gwp = format_value(data, "amount") + uct = format_value(data, "uncertainty type") + min_ = format_value(data, "minimum") + max_ = format_value(data, "maximum") + std = format_value(data, "scale") + print("{} & & {} & {} & {} & {} & {} \\\\".format(flow_name, gwp, uct, std, min_, max_)) + +opacity = 0.65 +iterations = 2000 + +np.random.seed(23423) + +if save_figure or show_figure: + ncols = 5 + nrows = 8 + flow_names = [] + i = 0 + for flow in flows_uncertain_list: + flow_name = bd.get_activity(flow[0])['name'] + flow_names.append(flow_name) + + flow_names_short = { + 'Carbon monoxide, fossil': r"$\text{1. CO, fossil}$", + 'Carbon monoxide, from soil or biomass stock': r"$\text{2. CO, from soil}$", + 'Carbon monoxide, non-fossil': r"$\text{3. CO, non-fossil}$", + 'Chloroform': r"$\text{4. CHCl}_3$", + 'Dinitrogen monoxide': r"$\text{5. N}_2\text{O}$", + 'Ethane, 1,1,1,2-tetrafluoro-, HFC-134a': r"$\text{6. HFC-134a}$", + 'Ethane, 1,1,1-trichloro-, HCFC-140': r"$\text{7. HCFC-140}$", + 'Ethane, 1,1,1-trifluoro-, HFC-143a': r"$\text{8. HFC-143a}$", + 'Ethane, 1,1,2-trichloro-1,2,2-trifluoro-, CFC-113': r"$\text{9. CFC-113}$", + 'Ethane, 1,1-dichloro-1-fluoro-, HCFC-141b': r"$\text{10. HCFC-141b}$", + 'Ethane, 1,1-difluoro-, HFC-152a': r"$\text{11. HFC-152a}$", + 'Ethane, 1,2-dichloro-': r"$\text{12. CH}_2\text{ClCH}_2\text{Cl}$", + 'Ethane, 1,2-dichloro-1,1,2,2-tetrafluoro-, CFC-114': r"$\text{13. CFC-114}$", + 'Ethane, 1-chloro-1,1-difluoro-, HCFC-142b': r"$\text{14. HCFC-142b}$", + 'Ethane, 2,2-dichloro-1,1,1-trifluoro-, HCFC-123': r"$\text{15. HCFC-123}$", + 'Ethane, 2-chloro-1,1,1,2-tetrafluoro-, HCFC-124': r"$\text{16. HCFC-124}$", + 'Ethane, chloropentafluoro-, CFC-115': r"$\text{17. CFC-115}$", + 'Ethane, hexafluoro-, HFC-116': r"$\text{18. HFC-116}$", + 'Ethane, pentafluoro-, HFC-125': r"$\text{19. HFC-125}$", + 'Methane': r"$\text{20. CH}_4$", + 'Methane, bromo-, Halon 1001': r"$\text{21. Halon 1001}$", + 'Methane, bromochlorodifluoro-, Halon 1211': r"$\text{22. Halon 1211}$", + 'Methane, bromotrifluoro-, Halon 1301': r"$\text{23. Halon 1301}$", + 'Methane, chlorodifluoro-, HCFC-22': r"$\text{24. HCFC-22}$", + 'Methane, chlorotrifluoro-, CFC-13': r"$\text{25. CFC-13}$", + 'Methane, dichloro-, HCC-30': r"$\text{26. HCC-30}$", + 'Methane, dichlorodifluoro-, CFC-12': r"$\text{27. CFC-12}$", + 'Methane, dichlorofluoro-, HCFC-21': r"$\text{28. HCFC-21}$", + 'Methane, difluoro-, HFC-32': r"$\text{29. HFC-32}$", + 'Methane, fossil': r"$\text{30. CH}_4\text{, fossil}$", + 'Methane, from soil or biomass stock': r"$\text{31. CH}_4\text{, from soil}$", + 'Methane, monochloro-, R-40': r"$\text{32. R-40}$", + 'Methane, non-fossil': r"$\text{33. CH}_4\text{, non-fossil}$", + 'Methane, tetrachloro-, R-10': r"$\text{34. R-10}$", + 'Methane, tetrafluoro-, R-14': r"$\text{35. R-14}$", + 'Methane, trichlorofluoro-, CFC-11': r"$\text{36. CFC-11}$", + 'Methane, trifluoro-, HFC-23': r"$\text{37. HFC-23}$", + 'Nitrogen fluoride': r"$\text{38. NF}_3$", + 'Perfluoropentane': r"$\text{39. n-C}_5\text{F}_{12}$", + 'Sulfur hexafluoride': r"$\text{40. SF}_6$", + } + + flow_ticks = { + 'Dinitrogen monoxide': { + "tickvals": [200, 300], + "ticktext": ['200', '300'], + }, + 'Ethane, 1,1,1,2-tetrafluoro-, HFC-134a': { + "tickvals": [800, 1600], + "ticktext": ["800", "1'600"], + }, + 'Ethane, 1,1,1-trichloro-, HCFC-140': { + "tickvals": [100, 200], + "ticktext": ["100", "200"], + }, + 'Ethane, 1,1,1-trifluoro-, HFC-143a': { + "tickvals": [3000, 6000], + "ticktext": ["3'000", "6'000"], + }, + 'Ethane, 1,1,2-trichloro-1,2,2-trifluoro-, CFC-113': { + "tickvals": [4000, 7000], + "ticktext": ["4'000", "7'000"], + }, + 'Ethane, 1,1-dichloro-1-fluoro-, HCFC-141b': { + "tickvals": [500, 1000], + "ticktext": ["500", "1'000"], + }, + 'Ethane, 1,1-difluoro-, HFC-152a': { + "tickvals": [100, 200], + "ticktext": ["100", "200"], + }, + 'Ethane, 1,2-dichloro-1,1,2,2-tetrafluoro-, CFC-114': { + "tickvals": [6000, 10000], + "ticktext": ["6'000", "10'000"], + }, + 'Ethane, 1-chloro-1,1-difluoro-, HCFC-142b': { + "tickvals": [1500, 2500], + "ticktext": ["1'500", "2'500"], + }, + 'Ethane, chloropentafluoro-, CFC-115': { + "tickvals": [6000, 10000], + "ticktext": ["6'000", "10'000"], + }, + 'Ethane, hexafluoro-, HFC-116': { + "tickvals": [7000, 14000], + "ticktext": ["7'000", "14'000"], + }, + 'Ethane, pentafluoro-, HFC-125': { + "tickvals": [2000, 4000], + "ticktext": ["2'000", "4'000"], + }, + 'Methane, bromochlorodifluoro-, Halon 1211': { + "tickvals": [1000, 2000], + "ticktext": ["1'000", "2'000"], + }, + 'Methane, bromotrifluoro-, Halon 1301': { + "tickvals": [4000, 8000], + "ticktext": ["4'000", "8'000"], + }, + 'Methane, chlorodifluoro-, HCFC-22': { + "tickvals": [1000, 2000], + "ticktext": ["1'000", "2'000"], + }, + 'Methane, chlorotrifluoro-, CFC-13': { + "tickvals": [10000, 18000], + "ticktext": ["10'000", "18'000"], + }, + 'Methane, dichlorodifluoro-, CFC-12': { + "tickvals": [7000, 14000], + "ticktext": ["7'000", "14'000"], + }, + 'Methane, dichlorofluoro-, HCFC-21': { + "tickvals": [100, 200], + "ticktext": ["100", "200"], + }, + 'Methane, difluoro-, HFC-32': { + "tickvals": [500, 1000], + "ticktext": ["500", "1'000"], + }, + 'Methane, tetrachloro-, R-10': { + "tickvals": [1000, 2000], + "ticktext": ["1'000", "2'000"], + }, + 'Methane, tetrafluoro-, R-14': { + "tickvals": [4000, 8000], + "ticktext": ["4'000", "8'000"], + }, + 'Methane, trichlorofluoro-, CFC-11': { + "tickvals": [3000, 6000], + "ticktext": ["3'000", "6'000"], + }, + 'Methane, trifluoro-, HFC-23': { + "tickvals": [8000, 16000], + "ticktext": ["8'000", "16'000"], + }, + 'Nitrogen fluoride': { + "tickvals": [10000, 20000], + "ticktext": ["10'000", "20'000"], + }, + 'Perfluoropentane': { + "tickvals": [6000, 10000], + "ticktext": ["6'000", "10'000"], + }, + 'Sulfur hexafluoride': { + "tickvals": [15000, 30000], + "ticktext": ["15'000", "30'000"], + }, + } + + flow_names_plot = [flow_names_short[n] for n in flow_names] + + fig = make_subplots( + rows=nrows, cols=ncols, + subplot_titles=flow_names_plot, + horizontal_spacing=0.08, + vertical_spacing=0.08, + ) + + i = 0 + for row in range(1,nrows+1): + for col in range(1,ncols+1): + flow = flows_uncertain_list[i] + flow_name = bd.get_activity(flow[0])['name'] + if flow[1]['uncertainty type'] == sa.NormalUncertainty.id: + x = np.random.normal(flow[1]['loc'], flow[1]['scale'], iterations) + elif flow[1]['uncertainty type'] == sa.UniformUncertainty.id: + x = (flow[1]['maximum'] - flow[1]['minimum'])*np.random.rand(iterations) + flow[1]['minimum'] + bin_min = min(x) + bin_max = max(x) + bins_ = np.linspace(bin_min, bin_max, num_bins, endpoint=True) + freq, bins = np.histogram(x, bins=bins_) + fig.add_trace( + go.Scatter( + x=bins, + y=freq, + opacity=opacity, + line=dict(color=color, width=1, shape="hvh"), + showlegend=False, + fill="tozeroy", + ), + row=row, + col=col, + ) + if flow_name in flow_ticks: + fig.update_xaxes( + tickvals=flow_ticks[flow_name]['tickvals'], + ticktext=flow_ticks[flow_name]['ticktext'], + row=row, + col=col, + ) + i += 1 + # Both + fig.update_xaxes(title_text="GWP, [kg CO2-eq]", row=nrows, title_standoff=25,) + fig.update_yaxes(title_text="Frequency", col=1, title_standoff=25,) + fig.update_xaxes( + showgrid=True, + gridwidth=1, + gridcolor=color_gray_hex, + zeroline=True, + zerolinewidth=1, + zerolinecolor=color_gray_hex, + showline=True, + linewidth=1, + linecolor=color_gray_hex, + ) + fig.update_yaxes( + showgrid=True, + gridwidth=1, + gridcolor=color_gray_hex, + zeroline=True, + zerolinewidth=1, + zerolinecolor=color_black_hex, + showline=True, + linewidth=1, + linecolor=color_gray_hex, + ) + fig.update_layout( + width=ncols*180, + height=nrows*120, + paper_bgcolor="rgba(255,255,255,1)", + plot_bgcolor="rgba(255,255,255,1)", + legend=dict( + x=0.85, + y=1.2, + orientation="v", + xanchor="center", + font=dict(size=14), + # bgcolor=color_lightgray_hex, + bordercolor=color_darkgray_hex, + borderwidth=1, + ), + margin=dict(l=0, r=0, t=20, b=0), + ) + if show_figure: fig.show() + if save_figure: save_fig(fig, "gwp_uncertainty", fig_format, write_dir_fig) + + + +print() \ No newline at end of file diff --git a/docs/gwp_uncertainties.svg b/docs/gwp_uncertainties.svg index 7059220..2dacc91 100644 --- a/docs/gwp_uncertainties.svg +++ b/docs/gwp_uncertainties.svg @@ -1 +1 @@ -44.505010044.522.53510152025200300400050100500100015002000501001502002502000400060004000600080000501005001000501001502000.511.54k6k8k10k12k050100100020003000501002004006008004k6k8k10k12k0501005k10k15k200030004000500010203040501234050100100020002k4k6k8k10002000300010k15k20k0501005105k10k15k501001502002502004006008001000050100102030405020405101520204005010050010001500200025004k6k8k10k2000400060005k10k15k05010010k15k20k25k4k6k8k10k12k15k20k25k30k35kGWPGWPGWPGWPFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyCarbon monoxide, fossil ('air', 'low populatiCarbon monoxide, from soil or biomass stock (Carbon monoxide, non-fossil ('air', 'low popuChloroform ('air', 'low population density, lDinitrogen monoxide ('air', 'low population dEthane, 1,1,1,2-tetrafluoro-, HFC-134a ('air'Ethane, 1,1,1-trichloro-, HCFC-140 ('air', 'lEthane, 1,1,1-trifluoro-, HFC-143a ('air', 'lEthane, 1,1,2-trichloro-1,2,2-trifluoro-, CFCEthane, 1,1-dichloro-1-fluoro-, HCFC-141b ('aEthane, 1,1-difluoro-, HFC-152a ('air', 'low Ethane, 1,2-dichloro- ('air', 'low populationEthane, 1,2-dichloro-1,1,2,2-tetrafluoro-, CFEthane, 1-chloro-1,1-difluoro-, HCFC-142b ('aEthane, 2,2-dichloro-1,1,1-trifluoro-, HCFC-1Ethane, 2-chloro-1,1,1,2-tetrafluoro-, HCFC-1Ethane, chloropentafluoro-, CFC-115 ('air', 'Ethane, hexafluoro-, HFC-116 ('air', 'low popEthane, pentafluoro-, HFC-125 ('air', 'low poMethane ('air', 'urban air close to ground')Methane, bromo-, Halon 1001 ('air', 'low popuMethane, bromochlorodifluoro-, Halon 1211 ('aMethane, bromotrifluoro-, Halon 1301 ('air', Methane, chlorodifluoro-, HCFC-22 ('air', 'loMethane, chlorotrifluoro-, CFC-13 ('air', 'loMethane, dichloro-, HCC-30 ('air', 'low populMethane, dichlorodifluoro-, CFC-12 ('air', 'lMethane, dichlorofluoro-, HCFC-21 ('air', 'loMethane, difluoro-, HFC-32 ('air', 'low populMethane, fossil ('air', 'low population densiMethane, from soil or biomass stock ('air', 'Methane, monochloro-, R-40 ('air', 'low populMethane, non-fossil ('air', 'low population dMethane, tetrachloro-, R-10 ('air', 'low popuMethane, tetrafluoro-, R-14 ('air', 'low popuMethane, trichlorofluoro-, CFC-11 ('air', 'loMethane, trifluoro-, HFC-23 ('air', 'low popuNitrogen fluoride ('air', 'low population denPerfluoropentane ('air',)Sulfur hexafluoride ('air', 'low population d \ No newline at end of file +44.50204044.50102030402.53010203040101520250501002003000204060808001'6000204060801002000501003'0006'0000501004'0007'0000501005001'0000501001002000204060800.510501006'00010'0000501001'5002'500050100501000501002004006008000501006'00010'0000501007'00014'0000501002'0004'00002040608020400501001230501001'0002'0000204060804'0008'0000501001'0002'00005010010'00018'000050100510150501007'00014'0000501001002000501005001'0000501002040050100102030405005010051015020406080102030400501001'0002'0000501004'0008'0000501003'0006'0000501008'00016'00002040608010'00020'0000501006'00010'00005010015'00030'000050100GWP, [kg CO2-eq]GWP, [kg CO2-eq]GWP, [kg CO2-eq]GWP, [kg CO2-eq]GWP, [kg CO2-eq]FrequencyFrequencyFrequencyFrequencyFrequencyFrequencyFrequencyFrequency \ No newline at end of file diff --git a/docs/gwp_uncertainties_table.png b/docs/gwp_uncertainties_table.png new file mode 100644 index 0000000000000000000000000000000000000000..ec9e05384909ddf0691ea3815fe4b773d95eb388 GIT binary patch literal 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