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Ecology","sub_category":"Circular Economy and Waste","monthly_downloads":40,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"# Santiago.jl\n\n[![version](https://juliahub.com/docs/Santiago/version.svg)](https://juliahub.com/ui/Packages/Santiago/JPJQH)\n[![Build\nStatus](https://github.com/santiago-sanitation-systems/Santiago.jl/workflows/CI/badge.svg)](https://github.com/santiago-sanitation-systems/Santiago.jl/actions)\n[![codecov](https://codecov.io/gh/santiago-sanitation-systems/Santiago.jl/branch/master/graph/badge.svg?token=GWBV5M4Z13)](https://codecov.io/gh/santiago-sanitation-systems/Santiago.jl)\n\n`Santiago` (SANitation sysTem Alternative GeneratOr) is a Julia package to generate appropriate sanitation system options. It is able to\n- find all possible systems given a set of sanitation technologies;\n- assess the appropriateness of a technology in a given case (context);\n- assess the overall appropriateness of a sanitation system in a given context;\n- calculate (optionally with uncertainly quantification) the massflows for each system for\n  total `phosphor`, total `nitrogen`, `totalsolids`, and `water`;\n- select a meaningful subset of systems for the given case.\n\nFor non-research applications we recommend to use `Santiago` via the web app [sanichoice.net](https://www.sanichoice.net/).\n\n# Citation\nSpuhler, D., Scheidegger, A., Maurer, M., 2021. Ex-ante quantification of nutrient, total solids, and water flows in sanitation systems. Journal of Environmental Management 280, 111785. https://doi.org/10.1016/j.jenvman.2020.111785\n\n# Installation\n\n1. Install [Julia](https://julialang.org/) version \u003e= 1.6.\n\n2. Install the `Santiago` package from the Julia prompt:\n```Julia\n] add Santiago\n```\n\n3. To edit Julia files you may also want to install [Visual Studio\nCode](https://code.visualstudio.com/) and its [Julia\nExtension](https://www.julia-vscode.org/docs/stable/). Alternatively, see the [Julia\nhome page](https://julialang.org/) for support for other editors.\n\n# Usage\n\nThe example below demonstrates the typical steps needed to identify\nsanitation systems appropriate for a given case. See the references below for a\nclarification of the terminology and the recommended embedding in the\nstrategic planning process.\n\nMost functions have a documentation string attached that can be accessed with\n`?functionname` on the Julia prompt.\n\nFor reproducibility it is a good idea to create a separate _Julia project_\n(similar like `virtualenv` in Python) for\nevery analysis, see [here](https://julialang.github.io/Pkg.jl/v1/environments/).\n\n## Minimal Example\n\n```Julia\nusing Santiago\n\n# -----------\n# 1) Import technologies\n\n# we use the test data that come with the package\ninput_tech_file = joinpath(pkgdir(Santiago), \"test/example_techs.json\")\ninput_case_file = joinpath(pkgdir(Santiago), \"test/example_case.json\")\n\nsources, additional_sources, techs = import_technologies(input_tech_file)\n\n# -----------\n# 2) Build all systems\n\nallSys = build_systems(sources, techs);\n\n# number of found systems\nlength(allSys)\n\n\n# The computations can be accelerated by setting max_candidates to a low number.\n# However, this will result only in a *stochastic* subset of all possible systems!\nallSys = build_systems(sources, techs, max_candidates=100);\n\n\n# -----------\n# 3) Calculate system properties\n\ntas, tas_components = appropriateness(input_tech_file, input_case_file)\n\nsysappscore!.(allSys)\nntechs!.(allSys)\nnconnections!.(allSys)\nconnectivity!.(allSys)\ntemplate!.(allSys)\n\n# see all properties of the first system\nallSys[1].properties\n\n# -----------\n# 4) Mass flows\n\n# Inputs for different sources in kg/year/person equivalent.\n# See references below.\ninput_masses = Dict(\"Dry.toilet\" =\u003e Dict(\"phosphor\" =\u003e 0.548,\n                                         \"nitrogen\" =\u003e 4.550,\n                                         \"totalsolids\" =\u003e 32.12,\n                                         \"water\" =\u003e 547.1),\n                    \"Pour.flush\" =\u003e Dict(\"phosphor\" =\u003e 0.548,\n                                         \"nitrogen\" =\u003e 4.55,\n                                         \"totalsolids\" =\u003e 32.12,\n                                         \"water\" =\u003e 1277.1),\n                    \"Cistern.flush\" =\u003e Dict(\"phosphor\" =\u003e 0.548,\n                                            \"nitrogen\" =\u003e 4.55,\n                                            \"totalsolids\" =\u003e 32.12,\n                                            \"water\" =\u003e 22447.1),\n                    # Urine diversion dry toilet\n                    \"Uddt\" =\u003e Dict(\"phosphor\" =\u003e 0.548,\n                                   \"nitrogen\" =\u003e 4.55,\n                                   \"totalsolids\" =\u003e 32.12,\n                                   \"water\" =\u003e 547.1)\n                    )\n\n\n# Calculate massflows with 20 Mont Carlo iterations (probably not enough)\n# for all systems and save to system properties\nmassflow_summary_parallel!(allSys, input_masses, n=20);\n\n# Alternatively, the non-parallelized version can be used:\n# massflow_summary!.(allSys, Ref(input_masses), n=20);\n\n# If the flows of every technology is of interest, set 'techflows=true'.\n# The default is 'false' as this produces as very large amount of additional data!\nmassflow_summary_parallel!(allSys, input_masses, n=20, techflows=true);\n\n# Examples how to extract results\nallSys[2].properties[\"massflow_stats\"][\"entered\"]\nallSys[2].properties[\"massflow_stats\"][\"recovery_ratio\"]\nallSys[2].properties[\"massflow_stats\"][\"recovered\"]\n\nallSys[2].properties[\"massflow_stats\"][\"lost\"][:,\"air loss\",:]\nallSys[2].properties[\"massflow_stats\"][\"lost\"][:,:,\"mean\"]\nallSys[2].properties[\"massflow_stats\"][\"lost\"][:,:,\"q_0.5\"]\n\n# -----------\n# 5) select a subset of systems\n\n# For example, select eight systems for further investigation\nselectedSys = select_systems(allSys, 8)\n\n# We can also include or exclude technologies\nselect_systems(allSys, 8, techs_exclude=[\"Pour.flush\", \"wsp_3_trans\"])\nselect_systems(allSys, 8, techs_include=[\"Pour.flush\"])\n\n# Similar for templates\nselect_systems(allSys, 8, templates_exclude=[\"ST.3\", \"ST.15\"])\nselect_systems(allSys, 8, templates_include=[\"ST.17\"])\n\n# By default the systems are selected by the `\"sysappscore\"` but other\n# properties can be used too. For example, here we prefer short systems:\nselect_systems(allSys, 8, target=\"ntechs\", maximize=false)\n\n# Or systems with a high phosphor recovery (run massflow calculation first):\nselect_systems(allSys, 8, target=\"phosphor\" =\u003e \"recovery_ratio\")\n\n# By default the returned systems are diverse while having a good\n# target score. You can ignore the diversity requirement to get the\n# systems with the best target scores by setting\n# the `selection_type` to \"ranking\".\nselect_systems(allSys, 10, selection_type=\"ranking\")\n\n# This helper function returns the systems with matching IDs:\npick_systems(allSys, [\"003s-QbnU-FvGB\", \"0JLD-YQbJ-SGAu\"])\n\n# Investigate how techs and templates are used\ntemplates_per_tech(allSys)\ntechs_per_template(allSys)\n\n# -----------\n# 6) write some properties in a DataFrame for further analysis\n\ndf = properties_dataframe(selectedSys,\n                          massflow_selection = [\"recovered | water | mean\",\n                                                \"recovered | water | sd\",\n                                                \"lost | water | air loss| q_0.5\",\n                                                \"entered | water\"])\n\nsize(df)\nnames(df)\n\n# or you could simply export all properties (\u003e 400!)\ndf = properties_dataframe(allSys, massflow_selection = \"all\")\n\n# export as csv\nimport CSV  # the package 'CSV' needs to be installed separately\nCSV.write(\"mysystems.csv\", df)\n\n\n# -----------\n# 7) create a visualization of a system as pdf\n\n# First write a dot file\ndot_file(selectedSys[1], \"system.dot\")\n\n# Then, convert it to pdf (The program `graphviz` must be installed on the system)\nrun(`dot -Tpdf system.dot -o system.pdf`)\n\n\n# -----------\n# 8) export to JSON\n\n# Note, the JSON export is designed to interface other applications,\n# but not for serialization.\n\nopen(\"system_export.json\", \"w\") do f\n    JSON3.write(f, selectedSys)\nend\n```\n\n\n## Input format\n\nTypically the information on the case specification and the available\ntechnologies are provided via files. `Santiago` can only import JSON\nfiles. The structure must match these examples:\n\n- Technologies: [`example_techs.json`](test/example_techs.json)\n- Case: [`example_case.json`](test/example_case.json)\n\nMany tools are available to browse and edit JSON files. For example,\nFirefox renders JSON files nicely, or Visual Studio allows for editing.\n\n\n## Logging\n\nBy default, `Santiago` prints only few information. This can be\nadapted by the logging level. With the package `LoggingExtras.jl` (needs to\nbe installed extra)\ndifferent logging levels can be used for the console output and the log file:\n\n```Julia\nusing Logging\nusing LoggingExtras\n\n# - on console show only infos and errors, write everything in the logfile 'mylogfile.log'\nmylogger = TeeLogger(\n    MinLevelLogger(FileLogger(\"mylogfile.log\"), Logging.Debug),  # logs to file\n    MinLevelLogger(ConsoleLogger(), Logging.Info)                # logs to console\n)\nglobal_logger(mylogger)\n\n... use Santiago functions ...\n```\n\n## Update systems for a new case profile\n\nThe generation of all systems is computationally intense. The code\nbelow demonstrates how to first generate all systems without case\ninformation and later update the system scores with case data.\n\n```Julia\nusing Serialization\n\n## 1) build systems without case information and cache result\n\nsources, additional_sources, techs = import_technologies(tech_file)\n\nif isfile(\"mycachfile.jls\")\n    allSys, sources, additional_sources, techs = deserialize(\"mycachfile.jls\")\nelse\n    allSys = build_systems(sources, techs)\n    ...\n    massflow_summary!.(allSys, Ref(input_masses), n=100);\n    ...\n    serialize(\"mycachfile.jls\", (allSys, sources, additional_sources, techs)) \n    # note: we need to save the techs in order to ensure the link to from systems to tech properties (tas)\nend\n\nsysappscore!.(allSys) # all are '-1.0' because no case profile was defined yet\n\n## 2) read case file and update sysappscore\n\ntas, tas_components = appropriateness(tech_file, case_file);\nupdate_appropriateness!(sources, tas)\nupdate_appropriateness!(additional_sources, tas)\nupdate_appropriateness!(techs, tas)\n\nsysappscore!.(allSys)  # now we have the updated SAS\n\n## 3) select systems\n\nfewSys = select_systems(allSys, 6)\n\n## 4) scale massflows for 100 people\n\nscale_massflows!.(fewSys, 100)\n\n```\nThe slowest parts are `build_systems` and\n`massflow_summary!`. Therefore we could cache the output as shown in this\nexample. Steps 2 and 4 are fast and can be quickly adapted to new cases.\n\n\n## Multi-threading\n\nThe functions `build_systems` and especially\n`massflow_summary_parallel!` benefit from multi-threading. As this may\ninvolves some overhead, benchmarking is recommended. See the official\n[documentation](https://docs.julialang.org/en/v1/manual/parallel-computing/#man-multithreading-1)\nhow to control the number of threads.\n\n\n\n# References\n\nSpuhler, D., Scheidegger, A., Maurer, M., 2018. Generation of\nsanitation system options for urban planning considering novel\ntechnologies. Water Research 145,\n259–278. https://doi.org/10.1016/j.watres.2018.08.021\n\nSpuhler, D., Scheidegger, A., Maurer, M., 2020. Comparative analysis\nof sanitation systems for resource recovery: influence of\nconfigurations and single technology components. Water\nResearch 116281. https://doi.org/10.1016/j.watres.2020.116281\n\nSpuhler, D., Scheidegger, A., Maurer, M., 2021. Ex-ante quantification\nof nutrient, total solids, and water flows in sanitation\nsystems. Journal of Environmental Management 280, 111785.\nhttps://doi.org/10.1016/j.jenvman.2020.111785\n\n\n# License\n\nThe `Santiago.jl` package is free software: you can redistribute it and/or modify\nit under the terms of the GNU Affero General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU Affero General Public License for more details.\n\nYou should have received a copy of the GNU Affero General Public License\nalong with this program.  If not, see \u003chttps://www.gnu.org/licenses/\u003e.\n\nCopyright 2020, Dorothee Spuhler at Eawag. 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