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How to use test_exclusions method in autotest

Best Python code snippet using autotest_python

01_🌍_Map.py

Source:01_🌍_Map.py

1# Dependencies2import streamlit as st3from google.oauth2 import service_account4import json, tempfile5import pandas as pd6import os7# Set page config8st.set_page_config(page_title = "Map output", layout="wide")9from streamlit_folium import folium_static, st_folium10import ee11import geemap.eefolium as geemap12from utils import *13# Load in datasets (that aren't in GEE)14polys_list = load_csv_list("constituencies_names.csv")[1:]15lad_list = load_csv_list("local_authorities_name.csv")[1:]16# service_account = st.secrets['service_account']17junkstring = '''18data = {}19data['type'] = st.secrets['other_keys']['type']20data['project_id'] = st.secrets['other_keys']['project_id']21data['private_key_id'] = st.secrets['other_keys']['private_key_id']22data['private_key'] = st.secrets['other_keys']['private_key']23data['client_email'] = st.secrets['other_keys']['client_email']24data['client_id'] = st.secrets['other_keys']['client_id']25data['auth_uri'] = st.secrets['other_keys']['auth_uri']26data['token_uri'] = st.secrets['other_keys']['token_uri']27data['auth_provider_x509_cert_url'] = st.secrets['other_keys']['auth_provider_x509_cert_url']28data['client_x509_cert_url'] = st.secrets['other_keys']['client_x509_cert_url']29tfile = tempfile.NamedTemporaryFile(mode="w+")30json.dump(data, tfile)31tfile.flush()32credentials = ee.ServiceAccountCredentials(service_account, tfile.name)33ee.Initialize(credentials)34tfile = tempfile.NamedTemporaryFile(mode="w+")35json.dump(data, tfile)36tfile.flush()37credentials = ee.ServiceAccountCredentials(service_account, tfile.name)38ee.Initialize(credentials)'''39#credentials = service_account.Credentials.from_service_account_info(st.secrets['username'], st.secrets["gcp_service_account"])40ee.Initialize()41# Intialize earth engine42#ee.Initialize()#st.secrets['EARTHENGINE_TOKEN'])43# Exclusion zones44exclusions_dict = {"Wind Speed": ee.Image('projects/data-sunlight-311713/assets/wind_cutoff').lt(1),45"Slope": ee.Terrain.slope(ee.Image("USGS/SRTMGL1_003")).lt(15),46"Slope > 10": ee.Terrain.slope(ee.Image("USGS/SRTMGL1_003")).lt(10),47"Transmission Lines":ee.FeatureCollection('projects/data-sunlight-311713/assets/transmission').reduceToImage(properties= ['FEATCODE'], reducer= ee.Reducer.first()).unmask().lt(1),48"Roads": ee.FeatureCollection('projects/data-sunlight-311713/assets/UK_Roads_Buffer_200m').reduceToImage(properties= ['FEATCODE'], reducer= ee.Reducer.first()).unmask().lt(1),49"Peatland": ee.FeatureCollection('projects/data-sunlight-311713/assets/merged_peatlands').reduceToImage(properties = ['Shape__Are'], reducer= ee.Reducer.first()).unmask().lt(1),50"Woodlands": ee.FeatureCollection('projects/data-sunlight-311713/assets/woodlands').reduceToImage(properties= ['FEATCODE'], reducer = ee.Reducer.first()).unmask().lt(1),51"Cycle Paths": ee.FeatureCollection('projects/data-sunlight-311713/assets/cyclenet').reduceToImage(properties= ['FID'], reducer= ee.Reducer.first()).unmask().lt(1),52"Railway": ee.FeatureCollection('projects/data-sunlight-311713/assets/traintracks').reduceToImage(properties = ['FEATCODE'], reducer = ee.Reducer.first()).unmask().lt(1),53"Areas of Natural Beauty": ee.FeatureCollection("projects/data-sunlight-311713/assets/Areas_of_Outstanding_Natural_Beauty_England").reduceToImage(properties= ['stat_area'], reducer= ee.Reducer.first()).unmask().lt(1),54"Protected Areas": ee.FeatureCollection("projects/data-sunlight-311713/assets/gb_protected_areas_nobuffer").reduceToImage(properties= ['Shape_Area'], reducer= ee.Reducer.first()).unmask().gt(0).eq(0),55"Surface Water":ee.FeatureCollection('projects/data-sunlight-311713/assets/UK_SurfaceWater_Area_Buffer_50m').reduceToImage(properties= ['FEATCODE'], reducer= ee.Reducer.first()).unmask().lt(1),56"Cultural Sites": ee.FeatureCollection('projects/data-sunlight-311713/assets/england_culturalsites').reduceToImage(properties = ['ListEntry'], reducer = ee.Reducer.first()).unmask().lt(1),57"Parks and Green Space": ee.FeatureCollection("projects/data-sunlight-311713/assets/GreenspaceEngArea").reduceToImage(properties= ['areaHa'], reducer= ee.Reducer.first()).unmask().lt(1),58"Functional Sites": ee.FeatureCollection('projects/data-sunlight-311713/assets/Functional_sites').reduceToImage(properties= ['FEATCODE'], reducer= ee.Reducer.first()).unmask().lt(1),59"Built Up Areas": get_build_up_area_buffer(100).unmask().lt(1),60"Existing Solar PV": ee.FeatureCollection('projects/data-sunlight-311713/assets/solar_pv').reduceToImage(properties= ['FID'], reducer = ee.Reducer.first()).unmask().lt(1),61"Existing Other Renewable":ee.FeatureCollection('projects/data-sunlight-311713/assets/other_renewables').reduceToImage(properties= ['FID'], reducer= ee.Reducer.first()).unmask().lt(1),62"Existing Onshore Wind": ee.FeatureCollection('projects/data-sunlight-311713/assets/onshore_wind').reduceToImage(properties= ['FID'], reducer= ee.Reducer.first()).unmask().lt(1),63"Flood Risk Zone 2": ee.FeatureCollection('users/Josh_Redmond/EA_FloodMapForPlanningRiversAndSeaFloodZone2_SHP_Full').reduceToImage(properties= ['st_area_sh'], reducer= ee.Reducer.first()).unmask().lt(1),64"Flood Risk Zone 3": ee.FeatureCollection('users/Josh_Redmond/EA_FloodMapForPlanningRiversAndSeaFloodZone3_SHP_Full').reduceToImage(properties= ['st_area_sh'], reducer= ee.Reducer.first()).unmask().lt(1)65}66test_exclusions = list(exclusions_dict.keys())67wind_exclusions = ["Wind Speed",68"Transmission Lines",69"Aircraft Flightpath",70"Noise",71"Built Up Areas",72"Slope",73"Existing Onshore Wind",74"Existing Other Renewable"]75common_exclusions = ["Roads",76"Railway",77#"Public Rights of Way",78#"Heritage Sites",79"Peatland",80"Protected Areas",81"Areas of Natural Beauty",82"Woodlands",83"Cycle Paths",84"Surface Water",85"Cultural Sites",86"Parks and Green Space",87"Functional Sites",88]89solar_exclusions = ["Slope > 10",90"Existing Solar PV"]91# Streamlit formatting92#st.title("UK Renewables Map", anchor=None)93geometry_mode = st.selectbox("Local Area Type", ['Constituencies', 'Local Authorities'])94mode = st.radio("Power Option", ["ð Solar", "ð¨ Wind"])95with st.form("Parameters"):96    st.header("Map Options")97    with st.container():98       # col1, col2 = st.columns(2)99        #with col1:100       # with col2:101        if geometry_mode == "Constituencies":102  103            area = st.selectbox("Area", polys_list) #on_change =area_change_callback, args={"Cheshire", uk_adm2, m})104            105        else:106            area =st.selectbox("Area", lad_list)107        st.session_state['geometry'] = area108        st.header("Toggle Exclusion Criteria")109        radio_button = st.radio("Scenarios", ["Maximum Exclusions", "Allow on Peatland", "Custom"])110        #   if radio_button == "Custom":111        with st.expander("Options"):112            exclusion_buttons = {}113            if mode == "ð¨ Wind":114                key_list = common_exclusions+wind_exclusions115                #test_exclusions = list({exclusions_dict[k] for k in key_list}.keys())116                test_exclusions = key_list117                exclusion_options = test_exclusions118            else:119                key_list = common_exclusions+solar_exclusions120                test_exclusions = key_list121             #   test_exclusions = list({exclusions_dict[k] for k in key_list}.keys())122                exclusion_options = test_exclusions123            for ex in exclusion_options:124                #st.write(ex)125                x = st.checkbox(ex)126                exclusion_buttons[ex] = x127                128        if radio_button == "Maximum Exclusions":129            exclusion_buttons = {key:True for key in list(exclusions_dict.keys())}130        if radio_button == "Allow on Peatland":131            exclusion_buttons = {key:True for key in list(exclusions_dict.keys())}132            exclusion_buttons['Peatland'] = False133                134        #st.multiselect("Toggleable Criteria", wind_exclusions+common_exclusions)135        go_button = st.form_submit_button("Draw Map")136# Save exclusions buttons output in session state to display between pages137#exclusion_buttons_side = pd.DataFrame.from_dict(exclusion_buttons, orient = "index")138#if 'exclusion_buttons_side' not in st.session_state:139#    st.session_state['exclusion_buttons_side'] = exclusion_buttons_side140#if go_button:141#    st.session_state['exclusion_buttons_side'] = exclusion_buttons_side142# Make the true/false dict emojis143#torf = {True : "â", False : "â"}144#display_df = st.session_state['exclusion_buttons_side']145#display_df[0] = display_df[0].map(torf)146#display_df = display_df.style.hide_columns()147#st.sidebar.write(display_df.to_html(), unsafe_allow_html=True)148#st.sidebar.dataframe(display_df)149if go_button:150    m = geemap.Map(center=[55.3, 0], zoom=6)151    if geometry_mode == "Constituencies":152        uk_adm2_all = ee.FeatureCollection("projects/data-sunlight-311713/assets/Westminster_Parliamentary_Constituencies_December_2019_Boundaries_UK_BUC")#.filter(f"pcon19nm == '{area}'")153        uk_adm2 = uk_adm2_all.filter(f"pcon19nm == '{area}'")154    else:155        uk_adm2_all = ee.FeatureCollection("projects/data-sunlight-311713/assets/local_authorities_UK")#.filter(f"pcon19nm == '{area}'")156        uk_adm2 = uk_adm2_all.filter(f"LAD21NM == '{area}'")157    m.centerObject(uk_adm2)158    image_exclusion = []159    for x in exclusion_buttons.keys():160    # st.write(x)161    # st.write(exclusion_buttons[x])162        if exclusion_buttons[x]:163            image_exclusion.append(exclusions_dict[x])164    #     st.write(exclusions_dict[x])165    if mode == "ð Solar":166        power = ee.Image('projects/data-sunlight-311713/assets/PV_Average')167        minvis = 500168        maxvis = 1500169    else:170        power = ee.Image('projects/data-sunlight-311713/assets/wind_power')171        minvis = 1172        maxvis = 1000173    power = compute_exclusions(image_exclusion, power)174    power = power.updateMask(power.gt(0))175    st.session_state['power'] = power176    power = power.clip(uk_adm2)177    st.session_state['bounds'] = uk_adm2#_all178    empty = ee.Image().byte()179    outline = empty.paint(180    featureCollection= uk_adm2,181    color= 1,182    width= 3183    )184    m.addLayer(outline, {}, f"{area}", True, 0.5)185    m.addLayer(power, {"min":minvis, "max":maxvis, "palette":['#140b34', '#84206b', '#e55c30', '#f6d746']})186    m.add_colorbar(colors=['#140b34', '#84206b', '#e55c30', '#f6d746'], vmin=minvis, vmax=maxvis, layer_name="Potential Power")187    m.addLayerControl() 188    if mode == 'ð Solar':189        st.write("Map Power Units in kWh/kWp")190    else:191        st.write("Map Power Units in W/M2")192    folium_static(m, width=800, height=700)193    try:194        os.remove("test_csv.csv")195    except:196        pass197    #power = st.session_state['power']198    geom_mode = st.session_state['geometry']199    geemap.zonal_statistics(power.gt(0).multiply(ee.Image.constant(30)), st.session_state['bounds'] , "test_csv.csv", statistics_type='SUM', scale=30)200    # try:201    # Page title202    #st.title("UK Renewables Table")203    # Read in county names204    constituencies = pd.read_csv("test_csv.csv")205    #st.write(print(constituencies))206    constituencies['Wind Energy Estimate (GW)'] = constituencies['sum']/1000 * 19.8 / 1000207    constituencies['Solar Energy Estimate (GW)'] = constituencies['sum']/1000 * 200 / 1000208    constituencies['Total Area Available for Devleopment (Km/2)'] = constituencies['sum']/1000 209    try:210        constituencies = constituencies.rename(columns = {"pcon19nm" : "Constituency"})211        #constituencies.set_index(constituencies['Constituency'])212        constituencies = constituencies[['Constituency', 'Wind Energy Estimate (GW)', 'Solar Energy Estimate (GW)', 'Total Area Available for Devleopment (Km/2)']]213    except:214        constituencies = constituencies.rename(columns = {"LAD21NM" : "Local Authority"})215        #constituencies.set_index(constituencies['Local Authority'])216        constituencies = constituencies[['Local Authority', 'Wind Energy Estimate (GW)', 'Solar Energy Estimate (GW)', 'Total Area Available for Devleopment (Km/2)']]217    # constituencies = constituencies["sum"]218    st.dataframe(constituencies)219    @st.cache220    def convert_df(df):221        return df.to_csv().encode('utf-8')222    csvDownload = convert_df(constituencies)223    st.download_button(224    "Download .csv",225    csvDownload,226    "Renewable Energy Potential.csv",227    "text/csv",...

test_tournament_manager_factory.py

Source:test_tournament_manager_factory.py

1import unittest2import axelrod3class TestTournamentManagerFactory(unittest.TestCase):4    @classmethod5    def setUpClass(cls):6        cls.tmf = axelrod.TournamentManagerFactory7        cls.test_output_directory = './assets/'8        cls.test_with_ecological = True9        cls.test_rebuild_cache = False10        cls.test_cache_file = './cache.txt'11        cls.test_exclusions = ['basic_strategies', 'cheating_strategies']12        cls.test_kwargs = {13            'processes': 2,14            'turns': 10,15            'repetitions': 200,16            'noise': 017        }18        cls.expected_basic_strategies = axelrod.basic_strategies19        cls.expected_strategies = (20            axelrod.ordinary_strategies)21        cls.expected_cheating_strategies = axelrod.cheating_strategies22        cls.expected_all_strategies = (23            axelrod.ordinary_strategies +24            axelrod.cheating_strategies)25    def test_tournaments_dict(self):26        # Tests to ensure that the tournaments dictionary contains the correct27        # keys and values28        actual_basic_strategies = (29            self.tmf._tournaments_dict()['basic_strategies'])30        actual_strategies = self.tmf._tournaments_dict()['strategies']31        actual_cheating_strategies = (32            self.tmf._tournaments_dict()['cheating_strategies'])33        actual_all_strategies = self.tmf._tournaments_dict()['all_strategies']34        self.assertEqual(35            actual_basic_strategies, self.expected_basic_strategies)36        self.assertEqual(actual_strategies, self.expected_strategies)37        self.assertEqual(38            actual_cheating_strategies, self.expected_cheating_strategies)39        self.assertEqual(actual_all_strategies, self.expected_all_strategies)40        # Tests to ensure that the exclusions list works as intended41        with_exclusions = self.tmf._tournaments_dict(self.test_exclusions)42        self.assertFalse('basic_strategies' in with_exclusions)43        self.assertFalse('cheating_strategies' in with_exclusions)44        self.assertEqual(45            with_exclusions['strategies'], self.expected_strategies)46    def test_add_tournaments(self):47        mgr = axelrod.TournamentManager(48            self.test_output_directory,49            self.test_with_ecological)50        self.tmf._add_tournaments(mgr, self.test_exclusions, self.test_kwargs)51        self.assertEqual(len(mgr._tournaments), 2)52        self.assertIsInstance(mgr._tournaments[0], axelrod.Tournament)53        self.assertEqual(mgr._tournaments[0].name, 'strategies')54    def test_create_tournament_manager(self):55        mgr = self.tmf.create_tournament_manager(56            output_directory=self.test_output_directory,57            no_ecological=False,58            rebuild_cache=self.test_rebuild_cache,59            cache_file=self.test_cache_file,60            exclusions=self.test_exclusions,61            **self.test_kwargs)62        self.assertIsInstance(mgr, axelrod.TournamentManager)63        self.assertEqual(mgr._output_directory, self.test_output_directory)64        self.assertEqual(len(mgr._tournaments), 2)65        self.assertTrue(mgr._with_ecological)...

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