diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml index e91a2a0b65..e34aef58ae 100644 --- a/.github/workflows/deploy.yml +++ b/.github/workflows/deploy.yml @@ -18,6 +18,7 @@ jobs: uses: actions/checkout@v4 with: fetch-depth: 0 + submodules: true - name: 🔗 Install wget for Windows if: matrix.os == 'windows-latest' @@ -57,13 +58,16 @@ jobs: run: bash ./package_antares_web.sh working-directory: scripts - - name: 📜 Install changelog requirements - run: npm install -g auto-changelog + - name: Add installer to package + shell: bash + run: | + pip install hatch + ./package_antares_installer.sh + working-directory: scripts - - name: 📜️ Generate changelog file + - name: 📜️ Copy changelog file run: | - auto-changelog -l false --hide-empty-releases - mv CHANGELOG.md dist/package/CHANGELOG.md + cp docs/CHANGELOG.md dist/package/CHANGELOG.md - name: 📦 Archive Antares Desktop for Windows if: matrix.os == 'windows-latest' diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 0000000000..553c74283c --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "installer"] + path = installer + url = https://github.com/AntaresSimulatorTeam/antares-web-installer.git diff --git a/antarest/core/exceptions.py b/antarest/core/exceptions.py index 8358cc385d..adb38cd21b 100644 --- a/antarest/core/exceptions.py +++ b/antarest/core/exceptions.py @@ -392,6 +392,20 @@ def __str__(self) -> str: return self.detail +class OutputSubFolderNotFound(HTTPException): + """ + Exception raised when an output sub folders do not exist + """ + + def __init__(self, output_id: str, mc_root: str) -> None: + message = f"The output '{output_id}' sub-folder '{mc_root}' does not exist" + super().__init__(HTTPStatus.NOT_FOUND, message) + + def __str__(self) -> str: + """Return a string representation of the exception.""" + return self.detail + + class BadZipBinary(HTTPException): def __init__(self, message: str) -> None: super().__init__(HTTPStatus.UNSUPPORTED_MEDIA_TYPE, message) @@ -446,6 +460,11 @@ def __init__(self, message: str) -> None: super().__init__(HTTPStatus.UNPROCESSABLE_ENTITY, message) +class MCRootNotHandled(HTTPException): + def __init__(self, message: str) -> None: + super().__init__(HTTPStatus.UNPROCESSABLE_ENTITY, message) + + class MatrixWidthMismatchError(HTTPException): def __init__(self, message: str) -> None: super().__init__(HTTPStatus.UNPROCESSABLE_ENTITY, message) diff --git a/antarest/launcher/service.py b/antarest/launcher/service.py index d3a439e17c..8c8e88905d 100644 --- a/antarest/launcher/service.py +++ b/antarest/launcher/service.py @@ -2,7 +2,7 @@ import logging import os import shutil -from datetime import datetime, timedelta +from datetime import datetime from http import HTTPStatus from pathlib import Path from typing import Dict, List, Optional, cast @@ -59,7 +59,6 @@ def __init__(self, engine: str): ) -ORPHAN_JOBS_VISIBILITY_THRESHOLD = 10 # days LAUNCHER_PARAM_NAME_SUFFIX = "output_suffix" EXECUTION_INFO_FILE = "execution_info.ini" @@ -305,7 +304,6 @@ def _filter_from_user_permission(self, job_results: List[JobResult], user: Optio if not user: return [] - orphan_visibility_threshold = datetime.utcnow() - timedelta(days=ORPHAN_JOBS_VISIBILITY_THRESHOLD) allowed_job_results = [] study_ids = [job_result.study_id for job_result in job_results] @@ -330,9 +328,7 @@ def _filter_from_user_permission(self, job_results: List[JobResult], user: Optio raising=False, ): allowed_job_results.append(job_result) - elif ( - user and (user.is_site_admin() or user.is_admin_token()) - ) or job_result.creation_date >= orphan_visibility_threshold: + elif user and (user.is_site_admin() or user.is_admin_token()): allowed_job_results.append(job_result) return allowed_job_results diff --git a/antarest/study/business/aggregator_management.py b/antarest/study/business/aggregator_management.py index 57a2cce48e..ac88b9d82b 100644 --- a/antarest/study/business/aggregator_management.py +++ b/antarest/study/business/aggregator_management.py @@ -6,7 +6,7 @@ import numpy as np import pandas as pd -from antarest.core.exceptions import FileTooLargeError, OutputNotFound +from antarest.core.exceptions import FileTooLargeError, MCRootNotHandled, OutputNotFound, OutputSubFolderNotFound from antarest.study.storage.rawstudy.ini_reader import IniReader from antarest.study.storage.rawstudy.model.filesystem.matrix.date_serializer import ( FactoryDateSerializer, @@ -14,8 +14,7 @@ ) from antarest.study.storage.rawstudy.model.filesystem.matrix.matrix import MatrixFrequency -TEMPLATE_PARTS = "output/{sim_id}/economy/mc-ind" -"""Template for the path to reach the output data.""" +MC_TEMPLATE_PARTS = "output/{sim_id}/economy/{mc_root}" HORIZON_TEMPLATE = "output/{sim_id}/about-the-study/parameters.ini" # noinspection SpellCheckingInspection MCYEAR_COL = "mcYear" @@ -28,23 +27,48 @@ """Column name for the time index.""" TIME_COL = "time" """Column name for the timestamp.""" +CLUSTER_ID_COL = "cluster" +"""Column name for the cluster id.""" MC_YEAR_INDEX = 0 """Index in path parts starting from the Monte Carlo year to determine the Monte Carlo year.""" -AREA_OR_LINK_INDEX = 2 -"""Index in path parts starting from the Monte Carlo year to determine the area/link name.""" +AREA_OR_LINK_INDEX__IND, AREA_OR_LINK_INDEX__ALL = 2, 1 +"""Indexes in path parts starting from the output root `economy//mc-(ind/all)` to determine the area/link name.""" +PRODUCTION_COLUMN_NAME = "production" +PRODUCTION_COLUMN_REGEX = "mwh" +CLUSTER_ID_COMPONENT = 0 +ACTUAL_COLUMN_COMPONENT = 1 +DUMMY_COMPONENT = 2 logger = logging.getLogger(__name__) -class AreasQueryFile(str, Enum): +class MCRoot(str, Enum): + MC_IND = "mc-ind" + MC_ALL = "mc-all" + + +class MCIndAreasQueryFile(str, Enum): + VALUES = "values" + DETAILS = "details" + DETAILS_ST_STORAGE = "details-STstorage" + DETAILS_RES = "details-res" + + +class MCAllAreasQueryFile(str, Enum): VALUES = "values" DETAILS = "details" DETAILS_ST_STORAGE = "details-STstorage" DETAILS_RES = "details-res" + ID = "id" + + +class MCIndLinksQueryFile(str, Enum): + VALUES = "values" -class LinksQueryFile(str, Enum): +class MCAllLinksQueryFile(str, Enum): VALUES = "values" + ID = "id" def _checks_estimated_size(nb_files: int, df_bytes_size: int, nb_files_checked: int) -> None: @@ -54,28 +78,84 @@ def _checks_estimated_size(nb_files: int, df_bytes_size: int, nb_files_checked: raise FileTooLargeError(estimated_df_size, maximum_size) +def _columns_ordering(df_cols: t.List[str], column_name: str, is_details: bool, mc_root: MCRoot) -> t.Sequence[str]: + # original columns + org_cols = df_cols.copy() + if is_details: + org_cols = [col for col in org_cols if col != CLUSTER_ID_COL and col != TIME_ID_COL] + if mc_root == MCRoot.MC_IND: + new_column_order = ( + [column_name] + ([CLUSTER_ID_COL] if is_details else []) + [MCYEAR_COL, TIME_ID_COL, TIME_COL] + org_cols + ) + elif mc_root == MCRoot.MC_ALL: + org_cols = [col for col in org_cols if col not in {column_name, MCYEAR_COL, TIME_COL}] + new_column_order = [column_name] + ([CLUSTER_ID_COL] if is_details else []) + [TIME_ID_COL, TIME_COL] + org_cols + else: + raise MCRootNotHandled(f"Unknown Monte Carlo root: {mc_root}") + + return new_column_order + + +def _infer_production_column(cols: t.Sequence[str]) -> t.Optional[str]: + return next((c for c in cols if PRODUCTION_COLUMN_REGEX in c.lower().strip()), None) + + +def _infer_time_id(df: pd.DataFrame, is_details: bool) -> t.List[int]: + if is_details: + return df[TIME_ID_COL].tolist() + else: + return list(range(1, len(df) + 1)) + + +def _filtered_files_listing( + folders_to_check: t.List[Path], + query_file: str, + frequency: str, +) -> t.Dict[str, t.MutableSequence[str]]: + filtered_files: t.Dict[str, t.MutableSequence[str]] = {} + for folder_path in folders_to_check: + for file in folder_path.iterdir(): + if file.stem == f"{query_file}-{frequency}": + filtered_files.setdefault(folder_path.name, []).append(file.name) + return filtered_files + + class AggregatorManager: def __init__( self, study_path: Path, output_id: str, - query_file: t.Union[AreasQueryFile, LinksQueryFile], + query_file: t.Union[MCIndAreasQueryFile, MCAllAreasQueryFile, MCIndLinksQueryFile, MCAllLinksQueryFile], frequency: MatrixFrequency, - mc_years: t.Sequence[int], - columns_names: t.Sequence[str], ids_to_consider: t.Sequence[str], + columns_names: t.Sequence[str], + mc_years: t.Optional[t.Sequence[int]] = None, ): - self.study_path: Path = study_path - self.output_id: str = output_id - self.query_file: t.Union[AreasQueryFile, LinksQueryFile] = query_file - self.frequency: MatrixFrequency = frequency - self.mc_years: t.Sequence[int] = mc_years - self.columns_names: t.Sequence[str] = columns_names - self.ids_to_consider: t.Sequence[str] = ids_to_consider - self.output_type = "areas" if isinstance(query_file, AreasQueryFile) else "links" - self.mc_ind_path = self.study_path / TEMPLATE_PARTS.format(sim_id=self.output_id) - - def _parse_output_file(self, file_path: Path) -> pd.DataFrame: + self.study_path = study_path + self.output_id = output_id + self.query_file = query_file + self.frequency = frequency + self.mc_years = mc_years + self.columns_names = columns_names + self.ids_to_consider = ids_to_consider + self.output_type = ( + "areas" + if (isinstance(query_file, MCIndAreasQueryFile) or isinstance(query_file, MCAllAreasQueryFile)) + else "links" + ) + self.mc_ind_path = self.study_path / MC_TEMPLATE_PARTS.format( + sim_id=self.output_id, mc_root=MCRoot.MC_IND.value + ) + self.mc_all_path = self.study_path / MC_TEMPLATE_PARTS.format( + sim_id=self.output_id, mc_root=MCRoot.MC_ALL.value + ) + self.mc_root = ( + MCRoot.MC_IND + if (isinstance(query_file, MCIndAreasQueryFile) or isinstance(query_file, MCIndLinksQueryFile)) + else MCRoot.MC_ALL + ) + + def _parse_output_file(self, file_path: Path, normalize_column_name: bool = True) -> pd.DataFrame: csv_file = pd.read_csv( file_path, sep="\t", @@ -88,13 +168,20 @@ def _parse_output_file(self, file_path: Path) -> pd.DataFrame: date, body = date_serializer.extract_date(csv_file) df = rename_unnamed(body).astype(float) + df.index = date + + if not normalize_column_name: + return df + # normalize columns names new_cols = [] for col in body.columns: - name_to_consider = col[0] if self.query_file.value == AreasQueryFile.VALUES else " ".join(col) + if self.mc_root == MCRoot.MC_IND: + name_to_consider = col[0] if self.query_file.value == MCIndAreasQueryFile.VALUES else " ".join(col) + else: + name_to_consider = " ".join([col[0], col[2]]) new_cols.append(name_to_consider.upper().strip()) - df.index = date df.columns = pd.Index(new_cols) return df @@ -113,51 +200,148 @@ def _filter_ids(self, folder_path: Path) -> t.List[str]: return links_ids def _gather_all_files_to_consider(self) -> t.Sequence[Path]: - # Monte Carlo years filtering - all_mc_years = [d.name for d in self.mc_ind_path.iterdir()] - if self.mc_years: - all_mc_years = [year for year in all_mc_years if int(year) in self.mc_years] - if not all_mc_years: - return [] - - # Links / Areas ids filtering - - # The list of areas and links is the same whatever the MC year under consideration: - # Therefore we choose the first year by default avoiding useless scanning directory operations. - first_mc_year = all_mc_years[0] - areas_or_links_ids = self._filter_ids(self.mc_ind_path / first_mc_year / self.output_type) - - # Frequency and query file filtering - folders_to_check = [self.mc_ind_path / first_mc_year / self.output_type / id for id in areas_or_links_ids] - filtered_files: t.Dict[str, t.MutableSequence[str]] = {} - for folder_path in folders_to_check: - for file in folder_path.iterdir(): - if file.stem == f"{self.query_file.value}-{self.frequency}": - filtered_files.setdefault(folder_path.name, []).append(file.name) - - # Loop on MC years to return the whole list of files - all_output_files = [ - self.mc_ind_path / mc_year / self.output_type / area_or_link / file - for mc_year in all_mc_years - for area_or_link, files in filtered_files.items() - for file in files - ] + if self.mc_root == MCRoot.MC_IND: + # Monte Carlo years filtering + all_mc_years = [d.name for d in self.mc_ind_path.iterdir()] + if self.mc_years: + all_mc_years = [year for year in all_mc_years if int(year) in self.mc_years] + if not all_mc_years: + return [] + + # Links / Areas ids filtering + + # The list of areas and links is the same whatever the MC year under consideration: + # Therefore we choose the first year by default avoiding useless scanning directory operations. + first_mc_year = all_mc_years[0] + areas_or_links_ids = self._filter_ids(self.mc_ind_path / first_mc_year / self.output_type) + + # Frequency and query file filtering + folders_to_check = [self.mc_ind_path / first_mc_year / self.output_type / id for id in areas_or_links_ids] + filtered_files = _filtered_files_listing(folders_to_check, self.query_file, self.frequency) + + # Loop on MC years to return the whole list of files + all_output_files = [ + self.mc_ind_path / mc_year / self.output_type / area_or_link / file + for mc_year in all_mc_years + for area_or_link, files in filtered_files.items() + for file in files + ] + elif self.mc_root == MCRoot.MC_ALL: + # Links / Areas ids filtering + areas_or_links_ids = self._filter_ids(self.mc_all_path / self.output_type) + + # Frequency and query file filtering + folders_to_check = [self.mc_all_path / self.output_type / id for id in areas_or_links_ids] + filtered_files = _filtered_files_listing(folders_to_check, self.query_file, self.frequency) + + # Loop to return the whole list of files + all_output_files = [ + self.mc_all_path / self.output_type / area_or_link / file + for area_or_link, files in filtered_files.items() + for file in files + ] + else: + raise MCRootNotHandled(f"Unknown Monte Carlo root: {self.mc_root}") return all_output_files + def columns_filtering(self, df: pd.DataFrame, is_details: bool) -> pd.DataFrame: + # columns filtering + lower_case_columns = [c.lower() for c in self.columns_names] + if lower_case_columns: + if is_details: + filtered_columns = [CLUSTER_ID_COL, TIME_ID_COL] + [ + c for c in df.columns.tolist() if any(regex in c.lower() for regex in lower_case_columns) + ] + elif self.mc_root == MCRoot.MC_ALL: + filtered_columns = [ + c for c in df.columns.tolist() if any(regex in c.lower() for regex in lower_case_columns) + ] + else: + filtered_columns = [c for c in df.columns.tolist() if c.lower() in lower_case_columns] + df = df.loc[:, filtered_columns] + return df + + def _process_df(self, file_path: Path, is_details: bool) -> pd.DataFrame: + """ + Process the output file to return a DataFrame with the correct columns and values + - In the case of a details file, the DataFrame, the columns include two parts cluster name + actual column name + - In other cases, the DataFrame, the columns include only the actual column name + + Thus, the DataFrame is normalized to have the real columns names in both cases. And a new column is added to + for the details file to record the cluster id. + + Args: + file_path: the file Path to extract the data Frame from + is_details: whether the file is a details file or not + + Returns: + the DataFrame with the correct columns and values + """ + + if is_details: + # extract the data frame from the file without processing the columns + un_normalized_df = self._parse_output_file(file_path, normalize_column_name=False) + # number of rows in the data frame + df_len = len(un_normalized_df) + cluster_dummy_product_cols = sorted( + set([(x[CLUSTER_ID_COMPONENT], x[DUMMY_COMPONENT]) for x in un_normalized_df.columns]) + ) + # actual columns without the cluster id (NODU, production etc.) + actual_cols = sorted(set(un_normalized_df.columns.map(lambda x: x[ACTUAL_COLUMN_COMPONENT]))) + + # using a dictionary to build the new data frame with the base columns (NO2, production etc.) + # and the cluster id and time id + new_obj: t.Dict[str, t.Any] = {k: [] for k in [CLUSTER_ID_COL, TIME_ID_COL] + actual_cols} + + # loop over the cluster id to extract the values of the actual columns + for cluster_id, dummy_component in cluster_dummy_product_cols: + for actual_col in actual_cols: + col_values = un_normalized_df[(cluster_id, actual_col, dummy_component)].tolist() # type: ignore + new_obj[actual_col] += col_values + new_obj[CLUSTER_ID_COL] += [cluster_id for _ in range(df_len)] + new_obj[TIME_ID_COL] += list(range(1, df_len + 1)) + + # check if there is a production column to rename it to `PRODUCTION_COLUMN_NAME` + prod_col = _infer_production_column(actual_cols) + if prod_col is not None: + new_obj[PRODUCTION_COLUMN_NAME] = new_obj.pop(prod_col) + actual_cols.remove(prod_col) + + # reorganize the data frame + # first the production column if it exists + add_prod = [PRODUCTION_COLUMN_NAME] if prod_col is not None else [] + columns_order = [CLUSTER_ID_COL, TIME_ID_COL] + add_prod + list(actual_cols) + df = pd.DataFrame(new_obj).reindex(columns=columns_order).sort_values(by=[TIME_ID_COL, CLUSTER_ID_COL]) + df.index = pd.Index(list(range(1, len(df) + 1))) + + return df + + else: + # just extract the data frame from the file by just merging the columns components + return self._parse_output_file(file_path) + def _build_dataframe(self, files: t.Sequence[Path], horizon: int) -> pd.DataFrame: + if self.mc_root not in [MCRoot.MC_IND, MCRoot.MC_ALL]: + raise MCRootNotHandled(f"Unknown Monte Carlo root: {self.mc_root}") + is_details = self.query_file in [ + MCIndAreasQueryFile.DETAILS, + MCAllAreasQueryFile.DETAILS, + MCIndAreasQueryFile.DETAILS_ST_STORAGE, + MCAllAreasQueryFile.DETAILS_ST_STORAGE, + MCIndAreasQueryFile.DETAILS_RES, + MCAllAreasQueryFile.DETAILS_RES, + ] final_df = pd.DataFrame() nb_files = len(files) for k, file_path in enumerate(files): - df = self._parse_output_file(file_path) + df = self._process_df(file_path, is_details) # columns filtering - if self.columns_names: - filtered_columns = [c for c in df.columns.tolist() if c in self.columns_names] - df = df.loc[:, filtered_columns] + df = self.columns_filtering(df, is_details) # if no columns, no need to continue list_of_df_columns = df.columns.tolist() - if not list_of_df_columns: + if not list_of_df_columns or set(list_of_df_columns) == {CLUSTER_ID_COL, TIME_ID_COL}: return pd.DataFrame() # checks if the estimated dataframe size does not exceed the limit @@ -167,22 +351,26 @@ def _build_dataframe(self, files: t.Sequence[Path], horizon: int) -> pd.DataFram estimated_binary_size = final_df.memory_usage().sum() _checks_estimated_size(nb_files, estimated_binary_size, k) - # add column for links/areas - relative_path_parts = file_path.relative_to(self.mc_ind_path).parts column_name = AREA_COL if self.output_type == "areas" else LINK_COL - new_column_order = [column_name, MCYEAR_COL, TIME_ID_COL, TIME_COL] + list_of_df_columns - df[column_name] = relative_path_parts[AREA_OR_LINK_INDEX] - - # add column to record the Monte Carlo year - df[MCYEAR_COL] = int(relative_path_parts[MC_YEAR_INDEX]) + new_column_order = _columns_ordering(list_of_df_columns, column_name, is_details, self.mc_root) + + if self.mc_root == MCRoot.MC_IND: + # add column for links/areas + relative_path_parts = file_path.relative_to(self.mc_ind_path).parts + df[column_name] = relative_path_parts[AREA_OR_LINK_INDEX__IND] + # add column to record the Monte Carlo year + df[MCYEAR_COL] = int(relative_path_parts[MC_YEAR_INDEX]) + else: + # add column for links/areas + relative_path_parts = file_path.relative_to(self.mc_all_path).parts + df[column_name] = relative_path_parts[AREA_OR_LINK_INDEX__ALL] # add a column for the time id - df[TIME_ID_COL] = list(range(1, len(df) + 1)) + df[TIME_ID_COL] = _infer_time_id(df, is_details) # add horizon column df[TIME_COL] = horizon - # Reorganize the columns - df = df.reindex(columns=new_column_order) + df = df.reindex(columns=pd.Index(new_column_order)) final_df = pd.concat([final_df, df], ignore_index=True) @@ -191,23 +379,38 @@ def _build_dataframe(self, files: t.Sequence[Path], horizon: int) -> pd.DataFram return final_df + def _check_mc_root_folder_exists(self) -> None: + if self.mc_root == MCRoot.MC_IND: + if not self.mc_ind_path.exists(): + raise OutputSubFolderNotFound(self.output_id, f"economy/{MCRoot.MC_IND.value}") + elif self.mc_root == MCRoot.MC_ALL: + if not self.mc_all_path.exists(): + raise OutputSubFolderNotFound(self.output_id, f"economy/{MCRoot.MC_ALL.value}") + else: + raise MCRootNotHandled(f"Unknown Monte Carlo root: {self.mc_root}") + def aggregate_output_data(self) -> pd.DataFrame: """ Aggregates the output data of a study and returns it as a DataFrame """ - # Checks if mc-ind results exist - if not self.mc_ind_path.exists(): + output_folder = (self.mc_ind_path or self.mc_all_path).parent.parent + + # checks if the output folder exists + if not output_folder.exists(): raise OutputNotFound(self.output_id) + # checks if the mc root folder exists + self._check_mc_root_folder_exists() + + # filters files to consider + all_output_files = sorted(self._gather_all_files_to_consider()) + # Retrieves the horizon from the study output horizon_path = self.study_path / HORIZON_TEMPLATE.format(sim_id=self.output_id) launching_config = IniReader().read(horizon_path) horizon = launching_config.get("general", {}).get("horizon", 2018) - # filters files to consider - all_output_files = sorted(self._gather_all_files_to_consider()) - logger.info( f"Parsing {len(all_output_files)} {self.frequency.value} files" f"to build the aggregated output for study `{self.study_path.name}`" diff --git a/antarest/study/business/binding_constraint_management.py b/antarest/study/business/binding_constraint_management.py index f84aecb8ff..bf23d5ccbc 100644 --- a/antarest/study/business/binding_constraint_management.py +++ b/antarest/study/business/binding_constraint_management.py @@ -25,6 +25,9 @@ from antarest.study.business.utils import execute_or_add_commands from antarest.study.model import Study from antarest.study.storage.rawstudy.model.filesystem.config.binding_constraint import ( + DEFAULT_GROUP, + DEFAULT_OPERATOR, + DEFAULT_TIMESTEP, BindingConstraintFrequency, BindingConstraintOperator, ) @@ -44,7 +47,6 @@ default_bc_weekly_daily as default_bc_weekly_daily_86, ) from antarest.study.storage.variantstudy.model.command.create_binding_constraint import ( - DEFAULT_GROUP, EXPECTED_MATRIX_SHAPES, BindingConstraintMatrices, BindingConstraintPropertiesBase, @@ -480,8 +482,8 @@ def constraint_model_adapter(constraint: t.Mapping[str, t.Any], version: int) -> "id": constraint["id"], "name": constraint["name"], "enabled": constraint.get("enabled", True), - "time_step": constraint.get("type", BindingConstraintFrequency.HOURLY), - "operator": constraint.get("operator", BindingConstraintOperator.EQUAL), + "time_step": constraint.get("type", DEFAULT_TIMESTEP), + "operator": constraint.get("operator", DEFAULT_OPERATOR), "comments": constraint.get("comments", ""), "terms": constraint.get("terms", []), } @@ -704,8 +706,7 @@ def create_binding_constraint( if bc_id in {bc.id for bc in self.get_binding_constraints(study)}: raise DuplicateConstraintName(f"A binding constraint with the same name already exists: {bc_id}.") - # TODO: the default operator should be fixed somewhere so this condition can be consistent - check_attributes_coherence(data, version, data.operator or BindingConstraintOperator.EQUAL) + check_attributes_coherence(data, version, data.operator or DEFAULT_OPERATOR) new_constraint = {"name": data.name, **json.loads(data.json(exclude={"terms", "name"}, exclude_none=True))} args = { @@ -719,7 +720,7 @@ def create_binding_constraint( # Validates the matrices. Needed when the study is a variant because we only append the command to the list if isinstance(study, VariantStudy): - time_step = data.time_step or BindingConstraintFrequency.HOURLY + time_step = data.time_step or DEFAULT_TIMESTEP command.validates_and_fills_matrices( time_step=time_step, specific_matrices=None, version=version, create=True ) diff --git a/antarest/study/service.py b/antarest/study/service.py index 1143b3a309..8e4b537e19 100644 --- a/antarest/study/service.py +++ b/antarest/study/service.py @@ -49,7 +49,13 @@ from antarest.matrixstore.matrix_editor import MatrixEditInstruction from antarest.study.business.adequacy_patch_management import AdequacyPatchManager from antarest.study.business.advanced_parameters_management import AdvancedParamsManager -from antarest.study.business.aggregator_management import AggregatorManager, AreasQueryFile, LinksQueryFile +from antarest.study.business.aggregator_management import ( + AggregatorManager, + MCAllAreasQueryFile, + MCAllLinksQueryFile, + MCIndAreasQueryFile, + MCIndLinksQueryFile, +) from antarest.study.business.allocation_management import AllocationManager from antarest.study.business.area_management import AreaCreationDTO, AreaInfoDTO, AreaManager, AreaType, UpdateAreaUi from antarest.study.business.areas.hydro_management import HydroManager @@ -372,24 +378,25 @@ def aggregate_output_data( self, uuid: str, output_id: str, - query_file: t.Union[AreasQueryFile, LinksQueryFile], + query_file: t.Union[MCIndAreasQueryFile, MCAllAreasQueryFile, MCIndLinksQueryFile, MCAllLinksQueryFile], frequency: MatrixFrequency, - mc_years: t.Sequence[int], columns_names: t.Sequence[str], ids_to_consider: t.Sequence[str], params: RequestParameters, + mc_years: t.Optional[t.Sequence[int]] = None, ) -> pd.DataFrame: """ Aggregates output data based on several filtering conditions + Args: uuid: study uuid output_id: simulation output ID - query_file: which types of data to retrieve ("values", "details", "details-st-storage", "details-res") + query_file: which types of data to retrieve: "values", "details", "details-st-storage", "details-res", "ids" frequency: yearly, monthly, weekly, daily or hourly. - mc_years: list of monte-carlo years, if empty, all years are selected - columns_names: columns to be selected, if empty, all columns are selected + columns_names: regexes (if details) or columns to be selected, if empty, all columns are selected ids_to_consider: list of areas or links ids to consider, if empty, all areas are selected params: request parameters + mc_years: list of monte-carlo years, if empty, all years are selected (only for mc-ind) Returns: the aggregated data as a DataFrame @@ -397,17 +404,9 @@ def aggregate_output_data( study = self.get_study(uuid) assert_permission(params.user, study, StudyPermissionType.READ) study_path = self.storage_service.raw_study_service.get_study_path(study) - # fmt: off aggregator_manager = AggregatorManager( - study_path, - output_id, - query_file, - frequency, - mc_years, - columns_names, - ids_to_consider + study_path, output_id, query_file, frequency, ids_to_consider, columns_names, mc_years ) - # fmt: on return aggregator_manager.aggregate_output_data() def get_logs( diff --git a/antarest/study/storage/rawstudy/model/filesystem/config/binding_constraint.py b/antarest/study/storage/rawstudy/model/filesystem/config/binding_constraint.py index 11749cf456..c81d66ed5c 100644 --- a/antarest/study/storage/rawstudy/model/filesystem/config/binding_constraint.py +++ b/antarest/study/storage/rawstudy/model/filesystem/config/binding_constraint.py @@ -2,6 +2,8 @@ Object model used to read and update binding constraint configuration. """ +import typing as t + from antarest.study.business.enum_ignore_case import EnumIgnoreCase @@ -35,3 +37,17 @@ class BindingConstraintOperator(EnumIgnoreCase): GREATER = "greater" BOTH = "both" EQUAL = "equal" + + +OPERATOR_MATRICES_MAP: t.Dict[BindingConstraintOperator, t.List[str]] = { + BindingConstraintOperator.EQUAL: ["eq"], + BindingConstraintOperator.GREATER: ["gt"], + BindingConstraintOperator.LESS: ["lt"], + BindingConstraintOperator.BOTH: ["lt", "gt"], +} + + +DEFAULT_GROUP = "default" +"""Default group for binding constraints (since v8.7).""" +DEFAULT_OPERATOR = BindingConstraintOperator.EQUAL +DEFAULT_TIMESTEP = BindingConstraintFrequency.HOURLY diff --git a/antarest/study/storage/rawstudy/model/filesystem/config/files.py b/antarest/study/storage/rawstudy/model/filesystem/config/files.py index b74b0f2daf..b8140b8c87 100644 --- a/antarest/study/storage/rawstudy/model/filesystem/config/files.py +++ b/antarest/study/storage/rawstudy/model/filesystem/config/files.py @@ -10,14 +10,18 @@ from antarest.core.model import JSON from antarest.study.storage.rawstudy.ini_reader import IniReader -from antarest.study.storage.rawstudy.model.filesystem.config.binding_constraint import BindingConstraintFrequency +from antarest.study.storage.rawstudy.model.filesystem.config.binding_constraint import ( + DEFAULT_GROUP, + DEFAULT_OPERATOR, + DEFAULT_TIMESTEP, + BindingConstraintFrequency, +) from antarest.study.storage.rawstudy.model.filesystem.config.exceptions import ( SimulationParsingError, XpansionParsingError, ) from antarest.study.storage.rawstudy.model.filesystem.config.field_validators import extract_filtering from antarest.study.storage.rawstudy.model.filesystem.config.model import ( - DEFAULT_GROUP, Area, BindingConstraintDTO, DistrictSet, @@ -212,11 +216,14 @@ def _parse_bindings(root: Path) -> t.List[BindingConstraintDTO]: # contains a set of strings in the following format: "area.cluster" cluster_set = set() # Default value for time_step - time_step = BindingConstraintFrequency.HOURLY + time_step = bind.get("type", DEFAULT_TIMESTEP) + # Default value for operator + operator = bind.get("operator", DEFAULT_OPERATOR) + # Default value for group + group = bind.get("group", DEFAULT_GROUP) + # Build areas and clusters based on terms for key in bind: - if key == "type": - time_step = BindingConstraintFrequency(bind[key]) - elif "%" in key: + if "%" in key: areas = key.split("%", 1) area_set.add(areas[0]) area_set.add(areas[1]) @@ -224,13 +231,8 @@ def _parse_bindings(root: Path) -> t.List[BindingConstraintDTO]: cluster_set.add(key) area_set.add(key.split(".", 1)[0]) - group = bind.get("group", DEFAULT_GROUP) bc = BindingConstraintDTO( - id=bind["id"], - areas=area_set, - clusters=cluster_set, - time_step=time_step, - group=group, + id=bind["id"], areas=area_set, clusters=cluster_set, time_step=time_step, operator=operator, group=group ) output_list.append(bc) diff --git a/antarest/study/storage/rawstudy/model/filesystem/config/model.py b/antarest/study/storage/rawstudy/model/filesystem/config/model.py index 612c50e786..46e02bb051 100644 --- a/antarest/study/storage/rawstudy/model/filesystem/config/model.py +++ b/antarest/study/storage/rawstudy/model/filesystem/config/model.py @@ -7,15 +7,18 @@ from antarest.core.utils.utils import DTO from antarest.study.business.enum_ignore_case import EnumIgnoreCase -from .binding_constraint import BindingConstraintFrequency +from .binding_constraint import ( + DEFAULT_GROUP, + DEFAULT_OPERATOR, + DEFAULT_TIMESTEP, + BindingConstraintFrequency, + BindingConstraintOperator, +) from .field_validators import extract_filtering from .renewable import RenewableConfigType from .st_storage import STStorageConfigType from .thermal import ThermalConfigType -DEFAULT_GROUP = "default" -"""Default group for binding constraints (since v8.7).""" - class EnrModelling(EnumIgnoreCase): """ @@ -121,15 +124,18 @@ class BindingConstraintDTO(BaseModel): Attributes: id: The ID of the binding constraint. - group: The group for the scenario of BC (optional, required since v8.7). areas: List of area IDs on which the BC applies (links or clusters). clusters: List of thermal cluster IDs on which the BC applies (format: "area.cluster"). + time_step: The time_step of the BC + operator: The operator of the BC + group: The group for the scenario of BC (optional, required since v8.7). """ id: str areas: t.Set[str] clusters: t.Set[str] - time_step: BindingConstraintFrequency + time_step: BindingConstraintFrequency = DEFAULT_TIMESTEP + operator: BindingConstraintOperator = DEFAULT_OPERATOR # since v8.7 group: str = DEFAULT_GROUP diff --git a/antarest/study/storage/rawstudy/model/filesystem/lazy_node.py b/antarest/study/storage/rawstudy/model/filesystem/lazy_node.py index 7e47affbc9..c27b3b647f 100644 --- a/antarest/study/storage/rawstudy/model/filesystem/lazy_node.py +++ b/antarest/study/storage/rawstudy/model/filesystem/lazy_node.py @@ -1,4 +1,3 @@ -import shutil import typing as t from abc import ABC, abstractmethod from dataclasses import dataclass @@ -6,7 +5,6 @@ from pathlib import Path from zipfile import ZipFile -from antarest.core.exceptions import ChildNotFoundError from antarest.study.storage.rawstudy.model.filesystem.config.model import FileStudyTreeConfig from antarest.study.storage.rawstudy.model.filesystem.context import ContextServer from antarest.study.storage.rawstudy.model.filesystem.inode import G, INode, S, V @@ -109,22 +107,6 @@ def delete(self, url: t.Optional[t.List[str]] = None) -> None: elif self.config.path.exists(): self.config.path.unlink() - def _infer_path(self) -> Path: - if self.get_link_path().exists(): - return self.get_link_path() - elif self.config.path.exists(): - return self.config.path - else: - raise ChildNotFoundError( - f"Neither link file {self.get_link_path} nor matrix file {self.config.path} exists" - ) - - def _infer_target_path(self, is_link: bool) -> Path: - if is_link: - return self.get_link_path() - else: - return self.config.path - def get_link_path(self) -> Path: path = self.config.path.parent / (self.config.path.name + ".link") return path @@ -144,16 +126,6 @@ def save(self, data: t.Union[str, bytes, S], url: t.Optional[t.List[str]] = None self.get_link_path().unlink() return None - def rename_file(self, target: "LazyNode[t.Any, t.Any, t.Any]") -> None: - target_path = target._infer_target_path(self.get_link_path().exists()) - target_path.unlink(missing_ok=True) - self._infer_path().rename(target_path) - - def copy_file(self, target: "LazyNode[t.Any, t.Any, t.Any]") -> None: - target_path = target._infer_target_path(self.get_link_path().exists()) - target_path.unlink(missing_ok=True) - shutil.copy(self._infer_path(), target_path) - def get_lazy_content( self, url: t.Optional[t.List[str]] = None, diff --git a/antarest/study/storage/rawstudy/model/filesystem/matrix/input_series_matrix.py b/antarest/study/storage/rawstudy/model/filesystem/matrix/input_series_matrix.py index a68b0f521e..affaedc23e 100644 --- a/antarest/study/storage/rawstudy/model/filesystem/matrix/input_series_matrix.py +++ b/antarest/study/storage/rawstudy/model/filesystem/matrix/input_series_matrix.py @@ -1,4 +1,5 @@ import logging +import shutil from pathlib import Path from typing import Any, List, Optional, Union, cast @@ -73,11 +74,11 @@ def parse( raise ChildNotFoundError(f"File '{relpath}' not found in the study '{study_id}'") from e stopwatch.log_elapsed(lambda x: logger.info(f"Matrix parsed in {x}s")) - matrix.dropna(how="any", axis=1, inplace=True) + final_matrix = matrix.dropna(how="any", axis=1) if return_dataframe: - return matrix + return final_matrix - data = cast(JSON, matrix.to_dict(orient="split")) + data = cast(JSON, final_matrix.to_dict(orient="split")) stopwatch.log_elapsed(lambda x: logger.info(f"Matrix to dict in {x}s")) return data @@ -100,3 +101,20 @@ def check_errors( if self.nb_columns and len(data) != self.nb_columns: errors.append(f"{self.config.path}: Data was wrong size. expected {self.nb_columns} get {len(data)}") return errors + + def _infer_path(self) -> Path: + if self.get_link_path().exists(): + return self.get_link_path() + elif self.config.path.exists(): + return self.config.path + raise ChildNotFoundError(f"Neither link file {self.get_link_path()} nor matrix file {self.config.path} exists") + + def rename_file(self, target: str) -> None: + target_path = self.config.path.parent.joinpath(f"{target}{''.join(self._infer_path().suffixes)}") + target_path.unlink(missing_ok=True) + self._infer_path().rename(target_path) + + def copy_file(self, target: str) -> None: + target_path = self.config.path.parent.joinpath(f"{target}{''.join(self._infer_path().suffixes)}") + target_path.unlink(missing_ok=True) + shutil.copy(self._infer_path(), target_path) diff --git a/antarest/study/storage/rawstudy/model/filesystem/root/input/bindingconstraints/bindingcontraints.py b/antarest/study/storage/rawstudy/model/filesystem/root/input/bindingconstraints/bindingcontraints.py index 8ef625200c..51426d7e9e 100644 --- a/antarest/study/storage/rawstudy/model/filesystem/root/input/bindingconstraints/bindingcontraints.py +++ b/antarest/study/storage/rawstudy/model/filesystem/root/input/bindingconstraints/bindingcontraints.py @@ -1,4 +1,7 @@ -from antarest.study.storage.rawstudy.model.filesystem.config.binding_constraint import BindingConstraintFrequency +from antarest.study.storage.rawstudy.model.filesystem.config.binding_constraint import ( + OPERATOR_MATRICES_MAP, + BindingConstraintFrequency, +) from antarest.study.storage.rawstudy.model.filesystem.folder_node import FolderNode from antarest.study.storage.rawstudy.model.filesystem.inode import TREE from antarest.study.storage.rawstudy.model.filesystem.matrix.input_series_matrix import InputSeriesMatrix @@ -52,7 +55,8 @@ def build(self) -> TREE: } children = {} for binding in self.config.bindings: - for term in ["lt", "gt", "eq"]: + terms = OPERATOR_MATRICES_MAP[binding.operator] + for term in terms: matrix_id = f"{binding.id}_{term}" children[matrix_id] = InputSeriesMatrix( self.context, diff --git a/antarest/study/storage/variantstudy/business/utils_binding_constraint.py b/antarest/study/storage/variantstudy/business/utils_binding_constraint.py index f236cfbca3..8d7464c5aa 100644 --- a/antarest/study/storage/variantstudy/business/utils_binding_constraint.py +++ b/antarest/study/storage/variantstudy/business/utils_binding_constraint.py @@ -1,6 +1,10 @@ import typing as t -from antarest.study.storage.rawstudy.model.filesystem.config.binding_constraint import BindingConstraintFrequency +from antarest.study.storage.rawstudy.model.filesystem.config.binding_constraint import ( + DEFAULT_TIMESTEP, + BindingConstraintFrequency, + BindingConstraintOperator, +) from antarest.study.storage.rawstudy.model.filesystem.config.model import BindingConstraintDTO, FileStudyTreeConfig @@ -8,16 +12,14 @@ def parse_bindings_coeffs_and_save_into_config( bd_id: str, study_data_config: FileStudyTreeConfig, coeffs: t.Mapping[str, t.Union[t.Literal["hourly", "daily", "weekly"], t.Sequence[float]]], + operator: BindingConstraintOperator, + time_step: BindingConstraintFrequency, group: str, ) -> None: if bd_id not in [bind.id for bind in study_data_config.bindings]: areas_set = set() clusters_set = set() - # Default time_step value - time_step = BindingConstraintFrequency.HOURLY for k, v in coeffs.items(): - if k == "type": - time_step = BindingConstraintFrequency(v) if "%" in k: areas_set |= set(k.split("%")) elif "." in k: @@ -28,6 +30,7 @@ def parse_bindings_coeffs_and_save_into_config( group=group, areas=areas_set, clusters=clusters_set, + operator=operator, time_step=time_step, ) study_data_config.bindings.append(bc) diff --git a/antarest/study/storage/variantstudy/model/command/create_binding_constraint.py b/antarest/study/storage/variantstudy/model/command/create_binding_constraint.py index ca8d786d6a..e917a48850 100644 --- a/antarest/study/storage/variantstudy/model/command/create_binding_constraint.py +++ b/antarest/study/storage/variantstudy/model/command/create_binding_constraint.py @@ -9,6 +9,9 @@ from antarest.matrixstore.model import MatrixData from antarest.study.business.all_optional_meta import AllOptionalMetaclass from antarest.study.storage.rawstudy.model.filesystem.config.binding_constraint import ( + DEFAULT_GROUP, + DEFAULT_OPERATOR, + DEFAULT_TIMESTEP, BindingConstraintFrequency, BindingConstraintOperator, ) @@ -24,8 +27,6 @@ from antarest.study.storage.variantstudy.model.command.icommand import MATCH_SIGNATURE_SEPARATOR, ICommand from antarest.study.storage.variantstudy.model.model import CommandDTO -DEFAULT_GROUP = "default" - MatrixType = t.List[t.List[MatrixData]] EXPECTED_MATRIX_SHAPES = { @@ -80,8 +81,8 @@ def check_matrix_values(time_step: BindingConstraintFrequency, values: MatrixTyp class BindingConstraintPropertiesBase(BaseModel, extra=Extra.forbid, allow_population_by_field_name=True): enabled: bool = True - time_step: BindingConstraintFrequency = Field(BindingConstraintFrequency.HOURLY, alias="type") - operator: BindingConstraintOperator = BindingConstraintOperator.EQUAL + time_step: BindingConstraintFrequency = Field(DEFAULT_TIMESTEP, alias="type") + operator: BindingConstraintOperator = DEFAULT_OPERATOR comments: str = "" @classmethod @@ -337,18 +338,21 @@ def apply_binding_constraint( [str(coeff_val) for coeff_val in self.coeffs[link_or_cluster]] ) - group = self.group or DEFAULT_GROUP - parse_bindings_coeffs_and_save_into_config( - bd_id, - study_data.config, - self.coeffs or {}, - group=group, - ) study_data.tree.save( binding_constraints, ["input", "bindingconstraints", "bindingconstraints"], ) + existing_constraint = binding_constraints[new_key] + current_operator = self.operator or BindingConstraintOperator( + existing_constraint.get("operator", DEFAULT_OPERATOR) + ) + group = self.group or existing_constraint.get("group", DEFAULT_GROUP) + time_step = self.time_step or BindingConstraintFrequency(existing_constraint.get("type", DEFAULT_TIMESTEP)) + parse_bindings_coeffs_and_save_into_config( + bd_id, study_data.config, self.coeffs or {}, operator=current_operator, time_step=time_step, group=group + ) + if version >= 870: # When all BC of a given group are removed, the group should be removed from the scenario builder old_groups = old_groups or set() @@ -370,14 +374,13 @@ def apply_binding_constraint( BindingConstraintOperator.BOTH: [(self.less_term_matrix, "lt"), (self.greater_term_matrix, "gt")], } - current_operator = self.operator or BindingConstraintOperator(binding_constraints[new_key]["operator"]) - for matrix_term, matrix_alias in operator_matrices_map[current_operator]: if matrix_term: if not isinstance(matrix_term, str): # pragma: no cover raise TypeError(repr(matrix_term)) if version >= 870: - study_data.tree.save(matrix_term, ["input", "bindingconstraints", f"{bd_id}_{matrix_alias}"]) + matrix_id = f"{bd_id}_{matrix_alias}" + study_data.tree.save(matrix_term, ["input", "bindingconstraints", matrix_id]) return CommandOutput(status=True) @@ -395,10 +398,14 @@ class CreateBindingConstraint(AbstractBindingConstraintCommand): def _apply_config(self, study_data_config: FileStudyTreeConfig) -> t.Tuple[CommandOutput, t.Dict[str, t.Any]]: bd_id = transform_name_to_id(self.name) group = self.group or DEFAULT_GROUP + operator = self.operator or DEFAULT_OPERATOR + time_step = self.time_step or DEFAULT_TIMESTEP parse_bindings_coeffs_and_save_into_config( bd_id, study_data_config, self.coeffs or {}, + operator=operator, + time_step=time_step, group=group, ) return CommandOutput(status=True), {} diff --git a/antarest/study/storage/variantstudy/model/command/remove_binding_constraint.py b/antarest/study/storage/variantstudy/model/command/remove_binding_constraint.py index 33805f6c73..d2bd4de9ea 100644 --- a/antarest/study/storage/variantstudy/model/command/remove_binding_constraint.py +++ b/antarest/study/storage/variantstudy/model/command/remove_binding_constraint.py @@ -1,13 +1,11 @@ from typing import Any, Dict, List, Tuple from antarest.core.model import JSON +from antarest.study.storage.rawstudy.model.filesystem.config.binding_constraint import DEFAULT_GROUP from antarest.study.storage.rawstudy.model.filesystem.config.model import FileStudyTreeConfig from antarest.study.storage.rawstudy.model.filesystem.factory import FileStudy from antarest.study.storage.variantstudy.model.command.common import CommandName, CommandOutput -from antarest.study.storage.variantstudy.model.command.create_binding_constraint import ( - DEFAULT_GROUP, - remove_bc_from_scenario_builder, -) +from antarest.study.storage.variantstudy.model.command.create_binding_constraint import remove_bc_from_scenario_builder from antarest.study.storage.variantstudy.model.command.icommand import MATCH_SIGNATURE_SEPARATOR, ICommand from antarest.study.storage.variantstudy.model.model import CommandDTO @@ -50,8 +48,11 @@ def _apply(self, study_data: FileStudy) -> CommandOutput: if study_data.config.version < 870: study_data.tree.delete(["input", "bindingconstraints", self.id]) else: + existing_files = study_data.tree.get(["input", "bindingconstraints"], depth=1) for term in ["lt", "gt", "eq"]: - study_data.tree.delete(["input", "bindingconstraints", f"{self.id}_{term}"]) + matrix_id = f"{self.id}_{term}" + if matrix_id in existing_files: + study_data.tree.delete(["input", "bindingconstraints", matrix_id]) # When all BC of a given group are removed, the group should be removed from the scenario builder old_groups = {bd.get("group", DEFAULT_GROUP).lower() for bd in binding_constraints.values()} diff --git a/antarest/study/storage/variantstudy/model/command/remove_cluster.py b/antarest/study/storage/variantstudy/model/command/remove_cluster.py index 8f959a2ef1..a9460f4025 100644 --- a/antarest/study/storage/variantstudy/model/command/remove_cluster.py +++ b/antarest/study/storage/variantstudy/model/command/remove_cluster.py @@ -193,9 +193,11 @@ def _remove_cluster_from_binding_constraints(self, study_data: FileStudy) -> Non matrix_suffixes = ["_lt", "_gt", "_eq"] if study_data.config.version >= 870 else [""] + existing_files = study_data.tree.get(["input", "bindingconstraints"], depth=1) for bc_index, bc in bc_to_remove.items(): for suffix in matrix_suffixes: - # noinspection SpellCheckingInspection - study_data.tree.delete(["input", "bindingconstraints", f"{bc['id']}{suffix}"]) + matrix_id = f"{bc['id']}{suffix}" + if matrix_id in existing_files: + study_data.tree.delete(["input", "bindingconstraints", matrix_id]) study_data.tree.save(binding_constraints, url) diff --git a/antarest/study/storage/variantstudy/model/command/update_binding_constraint.py b/antarest/study/storage/variantstudy/model/command/update_binding_constraint.py index a879e4977e..ad21a7dd38 100644 --- a/antarest/study/storage/variantstudy/model/command/update_binding_constraint.py +++ b/antarest/study/storage/variantstudy/model/command/update_binding_constraint.py @@ -2,17 +2,17 @@ import typing as t from antarest.core.model import JSON -from antarest.matrixstore.model import MatrixData from antarest.study.storage.rawstudy.model.filesystem.config.binding_constraint import ( + DEFAULT_GROUP, + OPERATOR_MATRICES_MAP, BindingConstraintFrequency, BindingConstraintOperator, ) -from antarest.study.storage.rawstudy.model.filesystem.config.model import FileStudyTreeConfig +from antarest.study.storage.rawstudy.model.filesystem.config.model import BindingConstraintDTO, FileStudyTreeConfig from antarest.study.storage.rawstudy.model.filesystem.factory import FileStudy -from antarest.study.storage.rawstudy.model.filesystem.lazy_node import LazyNode +from antarest.study.storage.rawstudy.model.filesystem.matrix.input_series_matrix import InputSeriesMatrix from antarest.study.storage.variantstudy.model.command.common import CommandName, CommandOutput from antarest.study.storage.variantstudy.model.command.create_binding_constraint import ( - DEFAULT_GROUP, AbstractBindingConstraintCommand, TermMatrices, create_binding_constraint_config, @@ -20,27 +20,21 @@ from antarest.study.storage.variantstudy.model.command.icommand import MATCH_SIGNATURE_SEPARATOR, ICommand from antarest.study.storage.variantstudy.model.model import CommandDTO -MatrixType = t.List[t.List[MatrixData]] - -ALIAS_OPERATOR_MAP = { - BindingConstraintOperator.EQUAL: "eq", - BindingConstraintOperator.LESS: "lt", - BindingConstraintOperator.GREATER: "gt", -} - def update_matrices_names( file_study: FileStudy, - binding_constraint_id: str, + bc_id: str, existing_operator: BindingConstraintOperator, new_operator: BindingConstraintOperator, ) -> None: """ Update the matrix file name according to the new operator. + Due to legacy matrices generation, we need to check if the new matrix file already exists + and if it does, we need to first remove it before renaming the existing matrix file Args: file_study: the file study - binding_constraint_id: the binding constraint ID + bc_id: the binding constraint ID existing_operator: the existing operator new_operator: the new operator @@ -48,17 +42,6 @@ def update_matrices_names( NotImplementedError: if the case is not handled """ - parent_folder_node = file_study.tree.get_node(["input", "bindingconstraints"]) - matrix_lt = parent_folder_node.get_node([f"{binding_constraint_id}_lt"]) - assert isinstance(matrix_lt, LazyNode), f"Node type not handled yet: LazyNode expected, got {type(matrix_lt)}" - matrix_eq = parent_folder_node.get_node([f"{binding_constraint_id}_eq"]) - assert isinstance(matrix_eq, LazyNode), f"Node type not handled yet: LazyNode expected, got {type(matrix_eq)}" - matrix_gt = parent_folder_node.get_node([f"{binding_constraint_id}_gt"]) - assert isinstance(matrix_gt, LazyNode), f"Node type not handled yet: LazyNode expected, got {type(matrix_gt)}" - - # Due to legacy matrices generation, we need to check if the new matrix file already exists - # and if it does, we need to first remove it before renaming the existing matrix file - handled_operators = [ BindingConstraintOperator.EQUAL, BindingConstraintOperator.LESS, @@ -72,37 +55,31 @@ def update_matrices_names( ) elif existing_operator == new_operator: return # nothing to do - elif existing_operator != BindingConstraintOperator.BOTH and new_operator != BindingConstraintOperator.BOTH: - matrix_node = parent_folder_node.get_node([f"{binding_constraint_id}_{ALIAS_OPERATOR_MAP[existing_operator]}"]) - assert isinstance( - matrix_node, LazyNode - ), f"Node type not handled yet: LazyNode expected, got {type(matrix_node)}" - new_matrix_node = parent_folder_node.get_node([f"{binding_constraint_id}_{ALIAS_OPERATOR_MAP[new_operator]}"]) - assert isinstance( - new_matrix_node, LazyNode - ), f"Node type not handled yet: LazyNode expected, got {type(new_matrix_node)}" - matrix_node.rename_file(new_matrix_node) + + parent_folder_node = file_study.tree.get_node(["input", "bindingconstraints"]) + error_msg = "Unhandled node type, expected InputSeriesMatrix, got " + if existing_operator != BindingConstraintOperator.BOTH and new_operator != BindingConstraintOperator.BOTH: + current_node = parent_folder_node.get_node([f"{bc_id}_{OPERATOR_MATRICES_MAP[existing_operator][0]}"]) + assert isinstance(current_node, InputSeriesMatrix), f"{error_msg}{type(current_node)}" + current_node.rename_file(f"{bc_id}_{OPERATOR_MATRICES_MAP[new_operator][0]}") elif new_operator == BindingConstraintOperator.BOTH: + current_node = parent_folder_node.get_node([f"{bc_id}_{OPERATOR_MATRICES_MAP[existing_operator][0]}"]) + assert isinstance(current_node, InputSeriesMatrix), f"{error_msg}{type(current_node)}" if existing_operator == BindingConstraintOperator.EQUAL: - matrix_eq.rename_file(matrix_lt) - matrix_gt.delete() - # copy the matrix lt to gt - matrix_lt.copy_file(matrix_gt) - elif existing_operator == BindingConstraintOperator.LESS: - matrix_gt.delete() - matrix_lt.copy_file(matrix_gt) + current_node.copy_file(f"{bc_id}_gt") + current_node.rename_file(f"{bc_id}_lt") else: - matrix_lt.delete() - matrix_gt.copy_file(matrix_lt) + term = "lt" if existing_operator == BindingConstraintOperator.GREATER else "gt" + current_node.copy_file(f"{bc_id}_{term}") else: - if new_operator == BindingConstraintOperator.EQUAL: - # we may retrieve the mean of the two matrices, but here we just copy the lt matrix - matrix_lt.rename_file(matrix_eq) - matrix_gt.delete() - elif new_operator == BindingConstraintOperator.LESS: - matrix_gt.delete() + current_node = parent_folder_node.get_node([f"{bc_id}_lt"]) + assert isinstance(current_node, InputSeriesMatrix), f"{error_msg}{type(current_node)}" + if new_operator == BindingConstraintOperator.GREATER: + current_node.delete() else: - matrix_lt.delete() + parent_folder_node.get_node([f"{bc_id}_gt"]).delete() + if new_operator == BindingConstraintOperator.EQUAL: + current_node.rename_file(f"{bc_id}_eq") class UpdateBindingConstraint(AbstractBindingConstraintCommand): @@ -123,6 +100,31 @@ class UpdateBindingConstraint(AbstractBindingConstraintCommand): id: str def _apply_config(self, study_data: FileStudyTreeConfig) -> t.Tuple[CommandOutput, t.Dict[str, t.Any]]: + index = next(i for i, bc in enumerate(study_data.bindings) if bc.id == self.id) + existing_constraint = study_data.bindings[index] + areas_set = existing_constraint.areas + clusters_set = existing_constraint.clusters + if self.coeffs: + areas_set = set() + clusters_set = set() + for j in self.coeffs.keys(): + if "%" in j: + areas_set |= set(j.split("%")) + elif "." in j: + clusters_set.add(j) + areas_set.add(j.split(".")[0]) + group = self.group or existing_constraint.group + operator = self.operator or existing_constraint.operator + time_step = self.time_step or existing_constraint.time_step + new_constraint = BindingConstraintDTO( + id=self.id, + group=group, + areas=areas_set, + clusters=clusters_set, + operator=operator, + time_step=time_step, + ) + study_data.bindings[index] = new_constraint return CommandOutput(status=True), {} def _find_binding_config(self, binding_constraints: t.Mapping[str, JSON]) -> t.Optional[t.Tuple[str, JSON]]: @@ -154,9 +156,11 @@ def _apply(self, study_data: FileStudy) -> CommandOutput: # rename matrices if the operator has changed for version >= 870 if self.operator and study_data.config.version >= 870: existing_operator = BindingConstraintOperator(actual_cfg.get("operator")) - new_operator = BindingConstraintOperator(self.operator) + new_operator = self.operator update_matrices_names(study_data, self.id, existing_operator, new_operator) + self._apply_config(study_data.config) + updated_matrices = [ term for term in [m.value for m in TermMatrices] if hasattr(self, term) and getattr(self, term) ] diff --git a/antarest/study/web/raw_studies_blueprint.py b/antarest/study/web/raw_studies_blueprint.py index 93c2f018dc..425c67e6b5 100644 --- a/antarest/study/web/raw_studies_blueprint.py +++ b/antarest/study/web/raw_studies_blueprint.py @@ -18,7 +18,12 @@ from antarest.core.utils.utils import sanitize_string, sanitize_uuid from antarest.core.utils.web import APITag from antarest.login.auth import Auth -from antarest.study.business.aggregator_management import AreasQueryFile, LinksQueryFile +from antarest.study.business.aggregator_management import ( + MCAllAreasQueryFile, + MCAllLinksQueryFile, + MCIndAreasQueryFile, + MCIndLinksQueryFile, +) from antarest.study.service import StudyService from antarest.study.storage.df_download import TableExportFormat, export_file from antarest.study.storage.rawstudy.model.filesystem.matrix.matrix import MatrixFrequency @@ -175,14 +180,14 @@ def get_study( return Response(content=json_response, media_type="application/json") @bp.get( - "/studies/{uuid}/areas/aggregate/{output_id}", + "/studies/{uuid}/areas/aggregate/mc-ind/{output_id}", tags=[APITag.study_raw_data], - summary="Retrieve Aggregated Areas Raw Data from Study Output", + summary="Retrieve Aggregated Areas Raw Data from Study Economy MCs individual Outputs", ) def aggregate_areas_raw_data( uuid: str, output_id: str, - query_file: AreasQueryFile, + query_file: MCIndAreasQueryFile, frequency: MatrixFrequency, mc_years: str = "", areas_ids: str = "", @@ -195,13 +200,14 @@ def aggregate_areas_raw_data( Create an aggregation of areas raw data Parameters: + - `uuid`: study ID - `output_id`: the output ID aka the simulation ID - `query_file`: "values", "details", "details-STstorage", "details-res" - `frequency`: "hourly", "daily", "weekly", "monthly", "annual" - `mc_years`: which Monte Carlo years to be selected. If empty, all are selected (comma separated) - `areas_ids`: which areas to be selected. If empty, all are selected (comma separated) - - `columns_names`: which columns to be selected. If empty, all are selected (comma separated) + - `columns_names`: names or regexes (if `query_file` is of type `details`) to select columns (comma separated) - `export_format`: Returned file format (csv by default). Returns: @@ -223,10 +229,10 @@ def aggregate_areas_raw_data( output_id=output_id, query_file=query_file, frequency=frequency, - mc_years=[int(mc_year) for mc_year in _split_comma_separated_values(mc_years)], columns_names=_split_comma_separated_values(columns_names), ids_to_consider=_split_comma_separated_values(areas_ids), params=parameters, + mc_years=[int(mc_year) for mc_year in _split_comma_separated_values(mc_years)], ) download_name = f"aggregated_output_{uuid}_{output_id}{export_format.suffix}" @@ -244,14 +250,14 @@ def aggregate_areas_raw_data( ) @bp.get( - "/studies/{uuid}/links/aggregate/{output_id}", + "/studies/{uuid}/links/aggregate/mc-ind/{output_id}", tags=[APITag.study_raw_data], - summary="Retrieve Aggregated Links Raw Data from Study Output", + summary="Retrieve Aggregated Links Raw Data from Study Economy MCs individual Outputs", ) def aggregate_links_raw_data( uuid: str, output_id: str, - query_file: LinksQueryFile, + query_file: MCIndLinksQueryFile, frequency: MatrixFrequency, mc_years: str = "", links_ids: str = "", @@ -263,13 +269,14 @@ def aggregate_links_raw_data( Create an aggregation of links raw data Parameters: + - `uuid`: study ID - `output_id`: the output ID aka the simulation ID - `query_file`: "values" (currently the only available option) - `frequency`: "hourly", "daily", "weekly", "monthly", "annual" - `mc_years`: which Monte Carlo years to be selected. If empty, all are selected (comma separated) - `links_ids`: which links to be selected (ex: "be - fr"). If empty, all are selected (comma separated) - - `columns_names`: which columns to be selected. If empty, all are selected (comma separated) + - `columns_names`: names or regexes (if `query_file` is of type `details`) to select columns (comma separated) - `export_format`: Returned file format (csv by default). Returns: @@ -291,7 +298,140 @@ def aggregate_links_raw_data( output_id=output_id, query_file=query_file, frequency=frequency, + columns_names=_split_comma_separated_values(columns_names), + ids_to_consider=_split_comma_separated_values(links_ids), + params=parameters, mc_years=[int(mc_year) for mc_year in _split_comma_separated_values(mc_years)], + ) + + download_name = f"aggregated_output_{uuid}_{output_id}{export_format.suffix}" + download_log = f"Exporting aggregated output data for study '{uuid}' as {export_format} file" + + return export_file( + df_matrix, + study_service.file_transfer_manager, + export_format, + True, + True, + download_name, + download_log, + current_user, + ) + + @bp.get( + "/studies/{uuid}/areas/aggregate/mc-all/{output_id}", + tags=[APITag.study_raw_data], + summary="Retrieve Aggregated Areas Raw Data from Study Economy MCs All Outputs", + ) + def aggregate_areas_raw_data__all( + uuid: str, + output_id: str, + query_file: MCAllAreasQueryFile, + frequency: MatrixFrequency, + areas_ids: str = "", + columns_names: str = "", + export_format: TableExportFormat = DEFAULT_EXPORT_FORMAT, # type: ignore + current_user: JWTUser = Depends(auth.get_current_user), + ) -> FileResponse: + # noinspection SpellCheckingInspection + """ + Create an aggregation of areas raw data in mc-all + + Parameters: + + - `uuid`: study ID + - `output_id`: the output ID aka the simulation ID + - `query_file`: "values", "details", "details-STstorage", "details-res", "id" + - `frequency`: "hourly", "daily", "weekly", "monthly", "annual" + - `areas_ids`: which areas to be selected. If empty, all are selected (comma separated) + - `columns_names`: names or regexes (if `query_file` is of type `details`) to select columns (comma separated) + - `export_format`: Returned file format (csv by default). + + Returns: + FileResponse that corresponds to a dataframe with the aggregated areas raw data + """ + logger.info( + f"Aggregating areas output data for study {uuid}, output {output_id}," + f"from files '{query_file}-{frequency}.txt'", + extra={"user": current_user.id}, + ) + + # Avoid vulnerabilities by sanitizing the `uuid` and `output_id` parameters + uuid = sanitize_uuid(uuid) + output_id = sanitize_string(output_id) + + parameters = RequestParameters(user=current_user) + df_matrix = study_service.aggregate_output_data( + uuid, + output_id=output_id, + query_file=query_file, + frequency=frequency, + columns_names=_split_comma_separated_values(columns_names), + ids_to_consider=_split_comma_separated_values(areas_ids), + params=parameters, + ) + + download_name = f"aggregated_output_{uuid}_{output_id}{export_format.suffix}" + download_log = f"Exporting aggregated output data for study '{uuid}' as {export_format} file" + + return export_file( + df_matrix, + study_service.file_transfer_manager, + export_format, + True, + True, + download_name, + download_log, + current_user, + ) + + @bp.get( + "/studies/{uuid}/links/aggregate/mc-all/{output_id}", + tags=[APITag.study_raw_data], + summary="Retrieve Aggregated Links Raw Data from Study Economy MC-All Outputs", + ) + def aggregate_links_raw_data__all( + uuid: str, + output_id: str, + query_file: MCAllLinksQueryFile, + frequency: MatrixFrequency, + links_ids: str = "", + columns_names: str = "", + export_format: TableExportFormat = DEFAULT_EXPORT_FORMAT, # type: ignore + current_user: JWTUser = Depends(auth.get_current_user), + ) -> FileResponse: + """ + Create an aggregation of links in mc-all + + Parameters: + + - `uuid`: study ID + - `output_id`: the output ID aka the simulation ID + - `query_file`: "values", "id" + - `frequency`: "hourly", "daily", "weekly", "monthly", "annual" + - `links_ids`: which links to be selected (ex: "be - fr"). If empty, all are selected (comma separated) + - `columns_names`: names or regexes (if `query_file` is of type `details`) to select columns (comma separated) + - `export_format`: Returned file format (csv by default). + + Returns: + FileResponse that corresponds to a dataframe with the aggregated links raw data + """ + logger.info( + f"Aggregating links mc-all data for study {uuid}, output {output_id}," + f"from files '{query_file}-{frequency}.txt'", + extra={"user": current_user.id}, + ) + + # Avoid vulnerabilities by sanitizing the `uuid` and `output_id` parameters + uuid = sanitize_uuid(uuid) + output_id = sanitize_string(output_id) + + parameters = RequestParameters(user=current_user) + df_matrix = study_service.aggregate_output_data( + uuid, + output_id=output_id, + query_file=query_file, + frequency=frequency, columns_names=_split_comma_separated_values(columns_names), ids_to_consider=_split_comma_separated_values(links_ids), params=parameters, @@ -329,6 +469,7 @@ def edit_study( > NOTE: use the PUT endpoint to upload a file. Parameters: + - `uuid`: The UUID of the study. - `path`: The path to the data to update. Defaults to "/". - `data`: The formatted data to be posted. Defaults to an empty string. @@ -362,6 +503,7 @@ def replace_study_file( Update raw data for a study by posting a raw file. Parameters: + - `uuid`: The UUID of the study. - `path`: The path to the data to update. Defaults to "/". - `file`: The raw file to be posted (e.g. a CSV file opened in binary mode). @@ -425,6 +567,7 @@ def get_matrix( Download a matrix in a given format. Parameters: + - `uuid`: study ID - `matrix_path`: Relative path of the matrix to download. - `export_format`: Returned file format (csv by default). diff --git a/docs/install/2-DEPLOY.md b/docs/install/2-DEPLOY.md index 5946e362f9..1136e24410 100644 --- a/docs/install/2-DEPLOY.md +++ b/docs/install/2-DEPLOY.md @@ -80,3 +80,31 @@ You can download the latest version here: - [For Windows](https://github.com/AntaresSimulatorTeam/AntaREST/releases/download/v2.5.0/AntaresWeb-windows-latest.zip) - [For Ubuntu](https://github.com/AntaresSimulatorTeam/AntaREST/releases/download/v2.5.0/AntaresWeb-ubuntu-latest.zip) + +The local application contains an installer program that you can use to manage the installation. +This program will keep your local data while updating the functional parts of the server. + +### GUI installer (windows only) + +Double-click on the installer executable and follow the instructions. +In case you already have a local application, choose your current application path +in order to update it. + +### CLI installer (linux only) + +Open a new command prompt or powershell instance. +Run the following command: + +``` +AntaresWebInstaller -t +``` + +where `` is the directory where you want to install the Antares Web Desktop. + +Note that you can specify an existing directory as value of `TARGET_DIR`, in which case the installer will update the +existing installation. + +By default, the installer will generate shortcuts and launch the server at the end of the installation, but you +optionally can decide to deactivate these steps with `--no-shortcut` and `--no-launch`. + +Run ```AntaresWebInstaller --help``` for more options. \ No newline at end of file diff --git a/installer b/installer new file mode 160000 index 0000000000..e7552a8a66 --- /dev/null +++ b/installer @@ -0,0 +1 @@ +Subproject commit e7552a8a66bef72f9e4b8fc6e1b274895f15a180 diff --git a/requirements-dev.txt b/requirements-dev.txt index e7ff79c736..5ea6126c6c 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -11,6 +11,7 @@ pyinstaller-hooks-contrib==2024.6 # of the corresponding implementation libraries used in production (in `requirements.txt`). pandas-stubs~=1.4.0 +types-paramiko~=3.4.0 types-psycopg2~=2.9.4 types-redis~=4.1.2 types-requests~=2.27.1 diff --git a/scripts/package_antares_installer.sh b/scripts/package_antares_installer.sh new file mode 100755 index 0000000000..0050a3edb5 --- /dev/null +++ b/scripts/package_antares_installer.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +# Antares Desktop Installer +# +# This script must be run by ̀.github/worflows/deploy.yml`. +# It builds the installer application and stores it in the package directory. + +SCRIPT_DIR=$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" &>/dev/null && pwd -P) +PROJECT_DIR=$(dirname -- "${SCRIPT_DIR}") +INSTALLER_DIR="${PROJECT_DIR}/installer" +DIST_DIR="${PROJECT_DIR}/dist/package" + +# build package +echo "INFO: Generating the Installer..." + +pushd ${INSTALLER_DIR} +if [[ "$OSTYPE" == "msys"* ]]; then + # For windows we build the GUI version + hatch run pyinstaller:build_gui AntaresWebInstaller + mv dist/AntaresWebInstaller ${DIST_DIR} +else + # For linux we build the command line version + hatch run pyinstaller:build_cli AntaresWebInstallerCLI + mv dist/AntaresWebInstallerCLI ${DIST_DIR} +fi +popd diff --git a/tests/integration/assets/STA-mini.7z b/tests/integration/assets/STA-mini.7z index 10a4d3fdbf..4bae2fe52f 100644 Binary files a/tests/integration/assets/STA-mini.7z and b/tests/integration/assets/STA-mini.7z differ diff --git a/tests/integration/assets/STA-mini.zip b/tests/integration/assets/STA-mini.zip index f9ce08baf6..c201008ff5 100644 Binary files a/tests/integration/assets/STA-mini.zip and b/tests/integration/assets/STA-mini.zip differ diff --git a/tests/integration/filesystem_blueprint/test_filesystem_endpoints.py b/tests/integration/filesystem_blueprint/test_filesystem_endpoints.py index a547313896..ba33b51d9f 100644 --- a/tests/integration/filesystem_blueprint/test_filesystem_endpoints.py +++ b/tests/integration/filesystem_blueprint/test_filesystem_endpoints.py @@ -256,8 +256,8 @@ def test_lifecycle( { "path": str(ext_workspace_path / "STA-mini"), "file_type": "directory", - "file_count": IntegerRange(900, 1000), # 918 - "size_bytes": IntegerRange(7_000_000, 9_000_000), # nt: 7_741_619, posix: 8_597_683 + "file_count": IntegerRange(1000, 1100), # 1043 + "size_bytes": IntegerRange(9_000_000, 11_000_000), # 10_428_620 "created": AnyIsoDateTime(), "accessed": AnyIsoDateTime(), "modified": AnyIsoDateTime(), @@ -415,7 +415,7 @@ def test_size_of_studies( sizes.append(actual[0]["size_bytes"]) # Check the sizes - # The size of the new study should be between 140 and 300 KB. - # The suze of 'STA-mini' should be between 7 and 9 MB. + # The size of the new study should be between 140 and 350 KB. + # The suze of 'STA-mini' should be between 9 and 11 MB. sizes.sort() - assert sizes == [IntegerRange(140_000, 300_000), IntegerRange(7_000_000, 9_000_000)] + assert sizes == [IntegerRange(140_000, 350_000), IntegerRange(9_000_000, 11_000_000)] diff --git a/tests/integration/launcher_blueprint/test_launcher_local.py b/tests/integration/launcher_blueprint/test_launcher_local.py index 08d1175889..aeff69e315 100644 --- a/tests/integration/launcher_blueprint/test_launcher_local.py +++ b/tests/integration/launcher_blueprint/test_launcher_local.py @@ -122,3 +122,44 @@ def test_get_launcher_time_limit( "description": "Unknown solver configuration: 'unknown'", "exception": "UnknownSolverConfig", } + + def test_jobs_permissions( + self, + client: TestClient, + user_access_token: str, + admin_access_token: str, + ) -> None: + # create an admin study with no permissions + res = client.post( + "/v1/studies", + headers={"Authorization": f"Bearer {admin_access_token}"}, + params={"name": "study_admin"}, + ) + res.raise_for_status() + # get the study_id + study_id = res.json() + + # launch a job with the admin user + res = client.post( + f"/v1/launcher/run/{study_id}", + headers={"Authorization": f"Bearer {admin_access_token}"}, + json={"launcher": "local"}, + ) + res.raise_for_status() + job_id = res.json()["job_id"] + + # check that the user cannot see the job + res = client.get( + "/v1/launcher/jobs", + headers={"Authorization": f"Bearer {user_access_token}"}, + ) + res.raise_for_status() + assert job_id not in [job.get("id") for job in res.json()] + + # check that the admin can see the job + res = client.get( + "/v1/launcher/jobs", + headers={"Authorization": f"Bearer {admin_access_token}"}, + ) + res.raise_for_status() + assert job_id in [job.get("id") for job in res.json()] diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-01-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-01-all.result.tsv new file mode 100644 index 0000000000..5232984b37 --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-01-all.result.tsv @@ -0,0 +1,15 @@ +area timeId time OV. COST EXP OP. COST EXP OP. COST STD OP. COST MIN OP. COST MAX MRG. PRICE EXP MRG. PRICE STD MRG. PRICE MIN MRG. PRICE MAX CO2 EMIS. EXP BALANCE EXP BALANCE STD BALANCE MIN BALANCE MAX ROW BAL. VALUES PSP EXP MISC. NDG EXP LOAD EXP LOAD STD LOAD MIN LOAD MAX H. ROR EXP H. ROR STD H. ROR MIN H. ROR MAX WIND EXP WIND STD WIND MIN WIND MAX SOLAR EXP SOLAR STD SOLAR MIN SOLAR MAX NUCLEAR EXP NUCLEAR STD NUCLEAR MIN NUCLEAR MAX LIGNITE EXP LIGNITE STD LIGNITE MIN LIGNITE MAX COAL EXP COAL STD COAL MIN COAL MAX GAS EXP GAS STD GAS MIN GAS MAX OIL EXP OIL STD OIL MIN OIL MAX MIX. FUEL EXP MIX. FUEL STD MIX. FUEL MIN MIX. FUEL MAX MISC. DTG EXP MISC. DTG STD MISC. DTG MIN MISC. DTG MAX H. STOR EXP H. STOR STD H. STOR MIN H. STOR MAX H. PUMP EXP H. PUMP STD H. PUMP MIN H. PUMP MAX H. LEV EXP H. LEV STD H. LEV MIN H. LEV MAX H. INFL EXP H. INFL STD H. INFL MIN H. INFL MAX H. OVFL EXP H. OVFL STD H. OVFL MIN H. OVFL MAX H. VAL EXP H. VAL STD H. VAL MIN H. VAL MAX H. COST EXP H. COST STD H. COST MIN H. COST MAX UNSP. ENRG EXP UNSP. ENRG STD UNSP. ENRG MIN UNSP. ENRG MAX SPIL. ENRG EXP SPIL. ENRG STD SPIL. ENRG MIN SPIL. ENRG MAX LOLD EXP LOLD STD LOLD MIN LOLD MAX LOLP VALUES AVL DTG EXP AVL DTG STD AVL DTG MIN AVL DTG MAX DTG MRG EXP DTG MRG STD DTG MRG MIN DTG MRG MAX MAX MRG EXP MAX MRG STD MAX MRG MIN MAX MRG MAX NP COST EXP NP COST STD NP COST MIN NP COST MAX NODU EXP NODU STD NODU MIN NODU MAX +de 1 2030 282000.0 282000.0 0.0 282000.0 282000.0 11.66 0.0 11.66 11.66 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27600.0 0.0 27600.0 27600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27600.0 0.0 27600.0 27600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 404400.0 0.0 404400.0 404400.0 404400.0 0.0 404400.0 404400.0 0.0 0.0 0.0 0.0 26.0 0.0 26.0 26.0 +de 2 2030 1252000.0 1252000.0 0.0 1252000.0 1252000.0 23.33 0.0 23.33 23.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 85200.0 0.0 85200.0 85200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 85200.0 0.0 85200.0 85200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 346800.0 0.0 346800.0 346800.0 346800.0 0.0 346800.0 346800.0 0.0 0.0 0.0 0.0 55.0 0.0 55.0 55.0 +de 3 2030 2910000.0 2910000.0 0.0 2910000.0 2910000.0 35.0 0.0 35.0 35.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 142800.0 0.0 142800.0 142800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 142800.0 0.0 142800.0 142800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 289200.0 0.0 289200.0 289200.0 289200.0 0.0 289200.0 289200.0 0.0 0.0 0.0 0.0 83.0 0.0 83.0 83.0 +de 4 2030 5256000.0 5256000.0 0.0 5256000.0 5256000.0 46.67 0.0 46.67 46.67 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 200400.0 0.0 200400.0 200400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 200400.0 0.0 200400.0 200400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 231600.0 0.0 231600.0 231600.0 231600.0 0.0 231600.0 231600.0 0.0 0.0 0.0 0.0 111.0 0.0 111.0 111.0 +de 5 2030 8290000.0 8290000.0 0.0 8290000.0 8290000.0 58.33 0.0 58.33 58.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 258000.0 0.0 258000.0 258000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 258000.0 0.0 258000.0 258000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 174000.0 0.0 174000.0 174000.0 174000.0 0.0 174000.0 174000.0 0.0 0.0 0.0 0.0 139.0 0.0 139.0 139.0 +de 6 2030 12018000.0 12018000.0 0.0 12018000.0 12018000.0 71.67 0.0 71.67 71.67 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 315600.0 0.0 315600.0 315600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 315600.0 0.0 315600.0 315600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 116400.0 0.0 116400.0 116400.0 116400.0 0.0 116400.0 116400.0 0.0 0.0 0.0 0.0 170.0 0.0 170.0 170.0 +de 7 2030 16444000.0 16444000.0 0.0 16444000.0 16444000.0 83.33 0.0 83.33 83.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 373200.0 0.0 373200.0 373200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 373200.0 0.0 373200.0 373200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 58800.0 0.0 58800.0 58800.0 58800.0 0.0 58800.0 58800.0 0.0 0.0 0.0 0.0 199.0 0.0 199.0 199.0 +es 1 2030 282000.0 282000.0 0.0 282000.0 282000.0 11.66 0.0 11.66 11.66 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27600.0 0.0 27600.0 27600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27600.0 0.0 27600.0 27600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 404400.0 0.0 404400.0 404400.0 404400.0 0.0 404400.0 404400.0 0.0 0.0 0.0 0.0 26.0 0.0 26.0 26.0 +es 2 2030 1252000.0 1252000.0 0.0 1252000.0 1252000.0 23.33 0.0 23.33 23.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 85200.0 0.0 85200.0 85200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 85200.0 0.0 85200.0 85200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 346800.0 0.0 346800.0 346800.0 346800.0 0.0 346800.0 346800.0 0.0 0.0 0.0 0.0 55.0 0.0 55.0 55.0 +es 3 2030 2910000.0 2910000.0 0.0 2910000.0 2910000.0 35.0 0.0 35.0 35.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 142800.0 0.0 142800.0 142800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 142800.0 0.0 142800.0 142800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 289200.0 0.0 289200.0 289200.0 289200.0 0.0 289200.0 289200.0 0.0 0.0 0.0 0.0 83.0 0.0 83.0 83.0 +es 4 2030 5256000.0 5256000.0 0.0 5256000.0 5256000.0 46.67 0.0 46.67 46.67 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 200400.0 0.0 200400.0 200400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 200400.0 0.0 200400.0 200400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 231600.0 0.0 231600.0 231600.0 231600.0 0.0 231600.0 231600.0 0.0 0.0 0.0 0.0 111.0 0.0 111.0 111.0 +es 5 2030 8290000.0 8290000.0 0.0 8290000.0 8290000.0 58.33 0.0 58.33 58.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 258000.0 0.0 258000.0 258000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 258000.0 0.0 258000.0 258000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 174000.0 0.0 174000.0 174000.0 174000.0 0.0 174000.0 174000.0 0.0 0.0 0.0 0.0 139.0 0.0 139.0 139.0 +es 6 2030 12018000.0 12018000.0 0.0 12018000.0 12018000.0 71.67 0.0 71.67 71.67 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 315600.0 0.0 315600.0 315600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 315600.0 0.0 315600.0 315600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 116400.0 0.0 116400.0 116400.0 116400.0 0.0 116400.0 116400.0 0.0 0.0 0.0 0.0 170.0 0.0 170.0 170.0 +es 7 2030 16444000.0 16444000.0 0.0 16444000.0 16444000.0 83.33 0.0 83.33 83.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 373200.0 0.0 373200.0 373200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 373200.0 0.0 373200.0 373200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 58800.0 0.0 58800.0 58800.0 58800.0 0.0 58800.0 58800.0 0.0 0.0 0.0 0.0 199.0 0.0 199.0 199.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-02-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-02-all.result.tsv new file mode 100644 index 0000000000..1cd8562aac --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-02-all.result.tsv @@ -0,0 +1,10 @@ +area cluster timeId time production NODU NP Cost - Euro +de 01_solar 1 2030 315000.0 167.0 0.0 +de 02_wind_on 1 2030 275000.0 147.0 0.0 +de 03_wind_off 1 2030 235000.0 127.0 0.0 +de 04_res 1 2030 195000.0 107.0 0.0 +de 05_nuclear 1 2030 155000.0 87.0 0.0 +de 06_coal 1 2030 115000.0 67.0 0.0 +de 07_gas 1 2030 75000.0 47.0 0.0 +de 08_non-res 1 2030 35000.0 27.0 0.0 +de 09_hydro_pump 1 2030 2800.0 7.0 0.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-02.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-02.result.tsv index 54ce752e07..e8f08094b2 100644 --- a/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-02.result.tsv +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-02.result.tsv @@ -1,1009 +1,9073 @@ -area mcYear timeId time 01_SOLAR MWH 02_WIND_ON MWH 03_WIND_OFF MWH 04_RES MWH 05_NUCLEAR MWH 06_COAL MWH 07_GAS MWH 08_NON-RES MWH 09_HYDRO_PUMP MWH 01_SOLAR NP COST - EURO 02_WIND_ON NP COST - EURO 03_WIND_OFF NP COST - EURO 04_RES NP COST - EURO 05_NUCLEAR NP COST - EURO 06_COAL NP COST - EURO 07_GAS NP COST - EURO 08_NON-RES NP COST - EURO 09_HYDRO_PUMP NP COST - EURO 01_SOLAR NODU 02_WIND_ON NODU 03_WIND_OFF NODU 04_RES NODU 05_NUCLEAR NODU 06_COAL NODU 07_GAS NODU 08_NON-RES NODU 09_HYDRO_PUMP NODU -de 1 1 2030 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 2 2030 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 3 2030 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 4 2030 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 5 2030 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 6 2030 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 7 2030 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 8 2030 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 9 2030 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 10 2030 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 11 2030 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 12 2030 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 13 2030 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 14 2030 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 15 2030 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 16 2030 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 17 2030 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 18 2030 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 19 2030 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 20 2030 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 21 2030 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 22 2030 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 23 2030 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 24 2030 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 25 2030 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 26 2030 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 27 2030 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 28 2030 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 29 2030 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 30 2030 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 31 2030 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 32 2030 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 33 2030 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 34 2030 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 35 2030 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 36 2030 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 37 2030 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 38 2030 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 39 2030 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 40 2030 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 41 2030 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 42 2030 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 43 2030 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 44 2030 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 45 2030 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 46 2030 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 47 2030 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 48 2030 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 49 2030 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 50 2030 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 51 2030 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 52 2030 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 53 2030 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 54 2030 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 55 2030 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 56 2030 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 57 2030 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 58 2030 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 59 2030 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 60 2030 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 61 2030 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 62 2030 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 63 2030 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 64 2030 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 65 2030 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 66 2030 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 67 2030 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 68 2030 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 69 2030 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 70 2030 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 71 2030 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 72 2030 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 73 2030 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 74 2030 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 75 2030 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 76 2030 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 77 2030 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 78 2030 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 79 2030 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 80 2030 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 81 2030 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 82 2030 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 83 2030 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 84 2030 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 85 2030 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 86 2030 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 87 2030 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 88 2030 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 89 2030 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 90 2030 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 91 2030 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 92 2030 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 93 2030 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 94 2030 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 95 2030 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 96 2030 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 97 2030 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 98 2030 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 99 2030 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 100 2030 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 101 2030 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 102 2030 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 103 2030 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 104 2030 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 105 2030 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 106 2030 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 107 2030 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 108 2030 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 109 2030 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 110 2030 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 111 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 112 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 113 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 114 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 115 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 116 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 117 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 118 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 119 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 120 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 121 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 122 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 123 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 124 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 125 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 126 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 127 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 128 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 129 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 130 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 131 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 132 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 133 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 134 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 135 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 136 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 137 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 138 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 139 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 140 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 141 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 142 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 143 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 144 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 145 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 146 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 147 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 148 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 149 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 150 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 151 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 152 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 153 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 154 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 155 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 156 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 157 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 158 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 159 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 160 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 161 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 162 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 163 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 164 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 165 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 166 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 167 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 168 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 169 2030 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 170 2030 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 171 2030 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 172 2030 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 173 2030 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 174 2030 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 175 2030 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 176 2030 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 177 2030 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 178 2030 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 179 2030 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 180 2030 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 181 2030 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 182 2030 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 183 2030 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 184 2030 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 185 2030 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 186 2030 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 187 2030 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 188 2030 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 189 2030 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 190 2030 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 191 2030 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 192 2030 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 193 2030 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 194 2030 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 195 2030 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 196 2030 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 197 2030 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 198 2030 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 199 2030 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 200 2030 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 201 2030 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 202 2030 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 203 2030 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 204 2030 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 205 2030 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 206 2030 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 207 2030 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 208 2030 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 209 2030 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 210 2030 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 211 2030 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 212 2030 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 213 2030 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 214 2030 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 215 2030 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 216 2030 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 217 2030 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 218 2030 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 219 2030 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 220 2030 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 221 2030 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 222 2030 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 223 2030 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 224 2030 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 225 2030 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 226 2030 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 227 2030 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 228 2030 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 229 2030 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -de 1 230 2030 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 231 2030 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 232 2030 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 233 2030 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 234 2030 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 235 2030 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 236 2030 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 237 2030 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 238 2030 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 239 2030 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 240 2030 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 241 2030 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 242 2030 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 243 2030 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 244 2030 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 245 2030 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 246 2030 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 247 2030 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 248 2030 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 249 2030 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -de 1 250 2030 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 251 2030 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 252 2030 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 253 2030 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 254 2030 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 255 2030 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 256 2030 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 257 2030 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 258 2030 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 259 2030 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 260 2030 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 261 2030 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 262 2030 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 263 2030 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 264 2030 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 265 2030 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 266 2030 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 267 2030 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 268 2030 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 269 2030 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -de 1 270 2030 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 271 2030 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 272 2030 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 273 2030 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 274 2030 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 275 2030 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 276 2030 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 277 2030 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 278 2030 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 279 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 280 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 281 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 282 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 283 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 284 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 285 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 286 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 287 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 288 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 289 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -de 1 290 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 291 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 292 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 293 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 294 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 295 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 296 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 297 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 298 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 299 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 300 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 301 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 302 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 303 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 304 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 305 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 306 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 307 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 308 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 309 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -de 1 310 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 311 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 312 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 313 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 314 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 315 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 316 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 317 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 318 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 319 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 320 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 321 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 322 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 323 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 324 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 325 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 326 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 327 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 328 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 329 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -de 1 330 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 331 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 332 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 333 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 334 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 335 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -de 1 336 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 1 2030 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 2 2030 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 3 2030 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 4 2030 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 5 2030 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 6 2030 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 7 2030 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 8 2030 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 9 2030 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 10 2030 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 11 2030 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 12 2030 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 13 2030 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 14 2030 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 15 2030 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 16 2030 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 17 2030 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 18 2030 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 19 2030 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 20 2030 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 21 2030 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 22 2030 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 23 2030 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 24 2030 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 25 2030 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 26 2030 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 27 2030 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 28 2030 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 29 2030 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 30 2030 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 31 2030 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 32 2030 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 33 2030 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 34 2030 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 35 2030 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 36 2030 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 37 2030 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 38 2030 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 39 2030 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 40 2030 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 41 2030 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 42 2030 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 43 2030 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 44 2030 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 45 2030 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 46 2030 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 47 2030 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 48 2030 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 49 2030 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 50 2030 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 51 2030 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 52 2030 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 53 2030 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 54 2030 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 55 2030 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 56 2030 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 57 2030 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 58 2030 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 59 2030 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 60 2030 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 61 2030 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 62 2030 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 63 2030 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 64 2030 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 65 2030 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 66 2030 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 67 2030 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 68 2030 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 69 2030 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 70 2030 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 71 2030 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 72 2030 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 73 2030 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 74 2030 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 75 2030 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 76 2030 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 77 2030 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 78 2030 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 79 2030 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 80 2030 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 81 2030 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 82 2030 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 83 2030 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 84 2030 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 85 2030 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 86 2030 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 87 2030 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 88 2030 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 89 2030 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 90 2030 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 91 2030 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 92 2030 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 93 2030 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 94 2030 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 95 2030 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 96 2030 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 97 2030 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 98 2030 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 99 2030 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 100 2030 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 101 2030 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 102 2030 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 103 2030 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 104 2030 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 105 2030 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 106 2030 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 107 2030 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 108 2030 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 109 2030 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 110 2030 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 111 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 112 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 113 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 114 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 115 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 116 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 117 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 118 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 119 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 120 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 121 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 122 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 123 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 124 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 125 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 126 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 127 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 128 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 129 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 130 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 131 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 132 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 133 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 134 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 135 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 136 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 137 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 138 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 139 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 140 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 141 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 142 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 143 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 144 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 145 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 146 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 147 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 148 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 149 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 150 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 151 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 152 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 153 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 154 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 155 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 156 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 157 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 158 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 159 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 160 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 161 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 162 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 163 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 164 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 165 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 166 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 167 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 168 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 169 2030 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 170 2030 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 171 2030 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 172 2030 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 173 2030 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 174 2030 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 175 2030 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 176 2030 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 177 2030 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 178 2030 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 179 2030 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 180 2030 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 181 2030 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 182 2030 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 183 2030 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 184 2030 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 185 2030 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 186 2030 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 187 2030 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 188 2030 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 189 2030 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 190 2030 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 191 2030 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 192 2030 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 193 2030 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 194 2030 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 195 2030 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 196 2030 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 197 2030 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 198 2030 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 199 2030 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 200 2030 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 201 2030 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 202 2030 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 203 2030 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 204 2030 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 205 2030 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 206 2030 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 207 2030 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 208 2030 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 209 2030 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 210 2030 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 211 2030 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 212 2030 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 213 2030 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 214 2030 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 215 2030 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 216 2030 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 217 2030 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 218 2030 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 219 2030 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 220 2030 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 221 2030 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 222 2030 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 223 2030 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 224 2030 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 225 2030 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 226 2030 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 227 2030 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 228 2030 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 229 2030 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -fr 1 230 2030 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 231 2030 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 232 2030 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 233 2030 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 234 2030 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 235 2030 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 236 2030 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 237 2030 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 238 2030 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 239 2030 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 240 2030 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 241 2030 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 242 2030 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 243 2030 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 244 2030 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 245 2030 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 246 2030 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 247 2030 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 248 2030 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 249 2030 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -fr 1 250 2030 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 251 2030 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 252 2030 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 253 2030 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 254 2030 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 255 2030 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 256 2030 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 257 2030 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 258 2030 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 259 2030 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 260 2030 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 261 2030 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 262 2030 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 263 2030 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 264 2030 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 265 2030 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 266 2030 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 267 2030 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 268 2030 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 269 2030 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -fr 1 270 2030 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 271 2030 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 272 2030 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 273 2030 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 274 2030 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 275 2030 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 276 2030 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 277 2030 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 278 2030 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 279 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 280 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 281 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 282 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 283 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 284 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 285 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 286 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 287 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 288 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 289 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -fr 1 290 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 291 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 292 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 293 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 294 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 295 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 296 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 297 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 298 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 299 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 300 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 301 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 302 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 303 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 304 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 305 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 306 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 307 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 308 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 309 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -fr 1 310 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 311 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 312 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 313 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 314 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 315 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 316 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 317 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 318 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 319 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 320 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 321 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 322 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 323 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 324 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 325 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 326 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 327 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 328 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 329 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -fr 1 330 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 331 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 332 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 333 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 334 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 335 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -fr 1 336 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 1 2030 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 2 2030 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 3 2030 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 4 2030 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 5 2030 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 6 2030 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 7 2030 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 8 2030 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 9 2030 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 10 2030 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 11 2030 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 12 2030 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 13 2030 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 14 2030 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 15 2030 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 16 2030 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 17 2030 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 18 2030 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 19 2030 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 20 2030 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 21 2030 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 22 2030 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 23 2030 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 24 2030 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 25 2030 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 26 2030 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 27 2030 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 28 2030 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 29 2030 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 30 2030 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 31 2030 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 32 2030 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 33 2030 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 34 2030 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 35 2030 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 36 2030 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 37 2030 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 38 2030 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 39 2030 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 40 2030 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 41 2030 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 42 2030 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 43 2030 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 44 2030 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 45 2030 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 46 2030 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 47 2030 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 48 2030 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 49 2030 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 50 2030 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 51 2030 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 52 2030 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 53 2030 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 54 2030 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 55 2030 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 56 2030 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 57 2030 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 58 2030 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 59 2030 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 60 2030 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 61 2030 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 62 2030 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 63 2030 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 64 2030 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 65 2030 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 66 2030 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 67 2030 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 68 2030 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 69 2030 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 70 2030 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 71 2030 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 72 2030 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 73 2030 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 74 2030 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 75 2030 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 76 2030 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 77 2030 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 78 2030 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 79 2030 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 80 2030 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 81 2030 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 82 2030 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 83 2030 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 84 2030 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 85 2030 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 86 2030 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 87 2030 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 88 2030 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 89 2030 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 90 2030 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 91 2030 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 92 2030 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 93 2030 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 94 2030 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 95 2030 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 96 2030 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 97 2030 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 98 2030 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 99 2030 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 100 2030 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 101 2030 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 102 2030 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 103 2030 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 104 2030 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 105 2030 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 106 2030 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 107 2030 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 108 2030 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 109 2030 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 110 2030 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 111 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 112 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 113 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 114 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 115 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 116 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 117 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 118 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 119 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 120 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 121 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 122 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 123 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 124 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 125 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 126 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 127 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 128 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 129 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 130 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 131 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 132 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 133 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 134 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 135 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 136 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 137 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 138 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 139 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 140 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 141 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 142 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 143 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 144 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 145 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 146 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 147 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 148 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 149 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 150 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 151 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 152 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 153 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 154 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 155 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 156 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 157 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 158 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 159 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 160 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 161 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 162 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 163 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 164 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 165 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 166 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 167 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 168 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 169 2030 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 170 2030 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 171 2030 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 172 2030 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 173 2030 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 174 2030 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 175 2030 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 176 2030 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 177 2030 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 178 2030 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 179 2030 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 180 2030 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 181 2030 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 182 2030 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 183 2030 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 184 2030 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 185 2030 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 186 2030 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 187 2030 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 188 2030 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 189 2030 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 190 2030 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 191 2030 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 192 2030 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 193 2030 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 194 2030 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 195 2030 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 196 2030 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 197 2030 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 198 2030 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 199 2030 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 200 2030 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 201 2030 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 202 2030 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 203 2030 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 204 2030 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 205 2030 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 206 2030 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 207 2030 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 208 2030 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 209 2030 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 210 2030 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 211 2030 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 212 2030 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 213 2030 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 214 2030 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 215 2030 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 216 2030 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 217 2030 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 218 2030 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 219 2030 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 220 2030 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 221 2030 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 222 2030 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 223 2030 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 224 2030 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 225 2030 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 226 2030 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 227 2030 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 228 2030 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 229 2030 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 -it 1 230 2030 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 231 2030 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 232 2030 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 233 2030 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 234 2030 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 235 2030 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 236 2030 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 237 2030 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 238 2030 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 239 2030 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 240 2030 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 241 2030 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 242 2030 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 243 2030 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 244 2030 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 245 2030 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 246 2030 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 247 2030 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 248 2030 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 249 2030 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 -it 1 250 2030 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 251 2030 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 252 2030 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 253 2030 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 254 2030 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 255 2030 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 256 2030 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 257 2030 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 258 2030 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 259 2030 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 260 2030 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 261 2030 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 262 2030 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 263 2030 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 264 2030 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 265 2030 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 266 2030 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 267 2030 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 268 2030 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 269 2030 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 -it 1 270 2030 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 271 2030 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 272 2030 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 273 2030 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 274 2030 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 275 2030 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 276 2030 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 277 2030 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 278 2030 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 279 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 280 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 281 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 282 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 283 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 284 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 285 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 286 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 287 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 288 2030 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 289 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 -it 1 290 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 291 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 292 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 293 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 294 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 295 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 296 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 297 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 298 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 299 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 300 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 301 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 302 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 303 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 304 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 305 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 306 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 307 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 308 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 309 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 -it 1 310 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 311 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 312 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 313 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 314 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 315 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 316 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 317 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 318 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 319 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 320 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 321 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 322 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 323 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 324 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 325 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 326 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 327 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 328 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 1900.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 329 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 -it 1 330 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 100.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 331 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 332 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 300.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 333 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 334 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 500.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 335 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 -it 1 336 2030 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 2000.0 700.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +area cluster mcYear timeId time production NODU NP Cost - Euro +de 01_solar 1 1 2030 0.0 0.0 0.0 +de 02_wind_on 1 1 2030 0.0 0.0 0.0 +de 03_wind_off 1 1 2030 0.0 0.0 0.0 +de 04_res 1 1 2030 0.0 0.0 0.0 +de 05_nuclear 1 1 2030 0.0 0.0 0.0 +de 06_coal 1 1 2030 0.0 0.0 0.0 +de 07_gas 1 1 2030 0.0 0.0 0.0 +de 08_non-res 1 1 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 1 2030 0.0 0.0 0.0 +de 01_solar 1 2 2030 100.0 1.0 0.0 +de 02_wind_on 1 2 2030 0.0 0.0 0.0 +de 03_wind_off 1 2 2030 0.0 0.0 0.0 +de 04_res 1 2 2030 0.0 0.0 0.0 +de 05_nuclear 1 2 2030 0.0 0.0 0.0 +de 06_coal 1 2 2030 0.0 0.0 0.0 +de 07_gas 1 2 2030 0.0 0.0 0.0 +de 08_non-res 1 2 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 2 2030 0.0 0.0 0.0 +de 01_solar 1 3 2030 200.0 1.0 0.0 +de 02_wind_on 1 3 2030 0.0 0.0 0.0 +de 03_wind_off 1 3 2030 0.0 0.0 0.0 +de 04_res 1 3 2030 0.0 0.0 0.0 +de 05_nuclear 1 3 2030 0.0 0.0 0.0 +de 06_coal 1 3 2030 0.0 0.0 0.0 +de 07_gas 1 3 2030 0.0 0.0 0.0 +de 08_non-res 1 3 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 3 2030 0.0 0.0 0.0 +de 01_solar 1 4 2030 300.0 1.0 0.0 +de 02_wind_on 1 4 2030 0.0 0.0 0.0 +de 03_wind_off 1 4 2030 0.0 0.0 0.0 +de 04_res 1 4 2030 0.0 0.0 0.0 +de 05_nuclear 1 4 2030 0.0 0.0 0.0 +de 06_coal 1 4 2030 0.0 0.0 0.0 +de 07_gas 1 4 2030 0.0 0.0 0.0 +de 08_non-res 1 4 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 4 2030 0.0 0.0 0.0 +de 01_solar 1 5 2030 400.0 1.0 0.0 +de 02_wind_on 1 5 2030 0.0 0.0 0.0 +de 03_wind_off 1 5 2030 0.0 0.0 0.0 +de 04_res 1 5 2030 0.0 0.0 0.0 +de 05_nuclear 1 5 2030 0.0 0.0 0.0 +de 06_coal 1 5 2030 0.0 0.0 0.0 +de 07_gas 1 5 2030 0.0 0.0 0.0 +de 08_non-res 1 5 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 5 2030 0.0 0.0 0.0 +de 01_solar 1 6 2030 500.0 1.0 0.0 +de 02_wind_on 1 6 2030 0.0 0.0 0.0 +de 03_wind_off 1 6 2030 0.0 0.0 0.0 +de 04_res 1 6 2030 0.0 0.0 0.0 +de 05_nuclear 1 6 2030 0.0 0.0 0.0 +de 06_coal 1 6 2030 0.0 0.0 0.0 +de 07_gas 1 6 2030 0.0 0.0 0.0 +de 08_non-res 1 6 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 6 2030 0.0 0.0 0.0 +de 01_solar 1 7 2030 600.0 1.0 0.0 +de 02_wind_on 1 7 2030 0.0 0.0 0.0 +de 03_wind_off 1 7 2030 0.0 0.0 0.0 +de 04_res 1 7 2030 0.0 0.0 0.0 +de 05_nuclear 1 7 2030 0.0 0.0 0.0 +de 06_coal 1 7 2030 0.0 0.0 0.0 +de 07_gas 1 7 2030 0.0 0.0 0.0 +de 08_non-res 1 7 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 7 2030 0.0 0.0 0.0 +de 01_solar 1 8 2030 700.0 1.0 0.0 +de 02_wind_on 1 8 2030 0.0 0.0 0.0 +de 03_wind_off 1 8 2030 0.0 0.0 0.0 +de 04_res 1 8 2030 0.0 0.0 0.0 +de 05_nuclear 1 8 2030 0.0 0.0 0.0 +de 06_coal 1 8 2030 0.0 0.0 0.0 +de 07_gas 1 8 2030 0.0 0.0 0.0 +de 08_non-res 1 8 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 8 2030 0.0 0.0 0.0 +de 01_solar 1 9 2030 800.0 1.0 0.0 +de 02_wind_on 1 9 2030 0.0 0.0 0.0 +de 03_wind_off 1 9 2030 0.0 0.0 0.0 +de 04_res 1 9 2030 0.0 0.0 0.0 +de 05_nuclear 1 9 2030 0.0 0.0 0.0 +de 06_coal 1 9 2030 0.0 0.0 0.0 +de 07_gas 1 9 2030 0.0 0.0 0.0 +de 08_non-res 1 9 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 9 2030 0.0 0.0 0.0 +de 01_solar 1 10 2030 900.0 1.0 0.0 +de 02_wind_on 1 10 2030 0.0 0.0 0.0 +de 03_wind_off 1 10 2030 0.0 0.0 0.0 +de 04_res 1 10 2030 0.0 0.0 0.0 +de 05_nuclear 1 10 2030 0.0 0.0 0.0 +de 06_coal 1 10 2030 0.0 0.0 0.0 +de 07_gas 1 10 2030 0.0 0.0 0.0 +de 08_non-res 1 10 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 10 2030 0.0 0.0 0.0 +de 01_solar 1 11 2030 1000.0 1.0 0.0 +de 02_wind_on 1 11 2030 0.0 0.0 0.0 +de 03_wind_off 1 11 2030 0.0 0.0 0.0 +de 04_res 1 11 2030 0.0 0.0 0.0 +de 05_nuclear 1 11 2030 0.0 0.0 0.0 +de 06_coal 1 11 2030 0.0 0.0 0.0 +de 07_gas 1 11 2030 0.0 0.0 0.0 +de 08_non-res 1 11 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 11 2030 0.0 0.0 0.0 +de 01_solar 1 12 2030 1100.0 1.0 0.0 +de 02_wind_on 1 12 2030 0.0 0.0 0.0 +de 03_wind_off 1 12 2030 0.0 0.0 0.0 +de 04_res 1 12 2030 0.0 0.0 0.0 +de 05_nuclear 1 12 2030 0.0 0.0 0.0 +de 06_coal 1 12 2030 0.0 0.0 0.0 +de 07_gas 1 12 2030 0.0 0.0 0.0 +de 08_non-res 1 12 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 12 2030 0.0 0.0 0.0 +de 01_solar 1 13 2030 1200.0 1.0 0.0 +de 02_wind_on 1 13 2030 0.0 0.0 0.0 +de 03_wind_off 1 13 2030 0.0 0.0 0.0 +de 04_res 1 13 2030 0.0 0.0 0.0 +de 05_nuclear 1 13 2030 0.0 0.0 0.0 +de 06_coal 1 13 2030 0.0 0.0 0.0 +de 07_gas 1 13 2030 0.0 0.0 0.0 +de 08_non-res 1 13 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 13 2030 0.0 0.0 0.0 +de 01_solar 1 14 2030 1300.0 1.0 0.0 +de 02_wind_on 1 14 2030 0.0 0.0 0.0 +de 03_wind_off 1 14 2030 0.0 0.0 0.0 +de 04_res 1 14 2030 0.0 0.0 0.0 +de 05_nuclear 1 14 2030 0.0 0.0 0.0 +de 06_coal 1 14 2030 0.0 0.0 0.0 +de 07_gas 1 14 2030 0.0 0.0 0.0 +de 08_non-res 1 14 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 14 2030 0.0 0.0 0.0 +de 01_solar 1 15 2030 1400.0 1.0 0.0 +de 02_wind_on 1 15 2030 0.0 0.0 0.0 +de 03_wind_off 1 15 2030 0.0 0.0 0.0 +de 04_res 1 15 2030 0.0 0.0 0.0 +de 05_nuclear 1 15 2030 0.0 0.0 0.0 +de 06_coal 1 15 2030 0.0 0.0 0.0 +de 07_gas 1 15 2030 0.0 0.0 0.0 +de 08_non-res 1 15 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 15 2030 0.0 0.0 0.0 +de 01_solar 1 16 2030 1500.0 1.0 0.0 +de 02_wind_on 1 16 2030 0.0 0.0 0.0 +de 03_wind_off 1 16 2030 0.0 0.0 0.0 +de 04_res 1 16 2030 0.0 0.0 0.0 +de 05_nuclear 1 16 2030 0.0 0.0 0.0 +de 06_coal 1 16 2030 0.0 0.0 0.0 +de 07_gas 1 16 2030 0.0 0.0 0.0 +de 08_non-res 1 16 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 16 2030 0.0 0.0 0.0 +de 01_solar 1 17 2030 1600.0 1.0 0.0 +de 02_wind_on 1 17 2030 0.0 0.0 0.0 +de 03_wind_off 1 17 2030 0.0 0.0 0.0 +de 04_res 1 17 2030 0.0 0.0 0.0 +de 05_nuclear 1 17 2030 0.0 0.0 0.0 +de 06_coal 1 17 2030 0.0 0.0 0.0 +de 07_gas 1 17 2030 0.0 0.0 0.0 +de 08_non-res 1 17 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 17 2030 0.0 0.0 0.0 +de 01_solar 1 18 2030 1700.0 1.0 0.0 +de 02_wind_on 1 18 2030 0.0 0.0 0.0 +de 03_wind_off 1 18 2030 0.0 0.0 0.0 +de 04_res 1 18 2030 0.0 0.0 0.0 +de 05_nuclear 1 18 2030 0.0 0.0 0.0 +de 06_coal 1 18 2030 0.0 0.0 0.0 +de 07_gas 1 18 2030 0.0 0.0 0.0 +de 08_non-res 1 18 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 18 2030 0.0 0.0 0.0 +de 01_solar 1 19 2030 1800.0 1.0 0.0 +de 02_wind_on 1 19 2030 0.0 0.0 0.0 +de 03_wind_off 1 19 2030 0.0 0.0 0.0 +de 04_res 1 19 2030 0.0 0.0 0.0 +de 05_nuclear 1 19 2030 0.0 0.0 0.0 +de 06_coal 1 19 2030 0.0 0.0 0.0 +de 07_gas 1 19 2030 0.0 0.0 0.0 +de 08_non-res 1 19 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 19 2030 0.0 0.0 0.0 +de 01_solar 1 20 2030 1900.0 1.0 0.0 +de 02_wind_on 1 20 2030 0.0 0.0 0.0 +de 03_wind_off 1 20 2030 0.0 0.0 0.0 +de 04_res 1 20 2030 0.0 0.0 0.0 +de 05_nuclear 1 20 2030 0.0 0.0 0.0 +de 06_coal 1 20 2030 0.0 0.0 0.0 +de 07_gas 1 20 2030 0.0 0.0 0.0 +de 08_non-res 1 20 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 20 2030 0.0 0.0 0.0 +de 01_solar 1 21 2030 2000.0 1.0 0.0 +de 02_wind_on 1 21 2030 0.0 0.0 0.0 +de 03_wind_off 1 21 2030 0.0 0.0 0.0 +de 04_res 1 21 2030 0.0 0.0 0.0 +de 05_nuclear 1 21 2030 0.0 0.0 0.0 +de 06_coal 1 21 2030 0.0 0.0 0.0 +de 07_gas 1 21 2030 0.0 0.0 0.0 +de 08_non-res 1 21 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 21 2030 0.0 0.0 0.0 +de 01_solar 1 22 2030 2000.0 1.0 0.0 +de 02_wind_on 1 22 2030 100.0 1.0 0.0 +de 03_wind_off 1 22 2030 0.0 0.0 0.0 +de 04_res 1 22 2030 0.0 0.0 0.0 +de 05_nuclear 1 22 2030 0.0 0.0 0.0 +de 06_coal 1 22 2030 0.0 0.0 0.0 +de 07_gas 1 22 2030 0.0 0.0 0.0 +de 08_non-res 1 22 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 22 2030 0.0 0.0 0.0 +de 01_solar 1 23 2030 2000.0 1.0 0.0 +de 02_wind_on 1 23 2030 200.0 1.0 0.0 +de 03_wind_off 1 23 2030 0.0 0.0 0.0 +de 04_res 1 23 2030 0.0 0.0 0.0 +de 05_nuclear 1 23 2030 0.0 0.0 0.0 +de 06_coal 1 23 2030 0.0 0.0 0.0 +de 07_gas 1 23 2030 0.0 0.0 0.0 +de 08_non-res 1 23 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 23 2030 0.0 0.0 0.0 +de 01_solar 1 24 2030 2000.0 1.0 0.0 +de 02_wind_on 1 24 2030 300.0 1.0 0.0 +de 03_wind_off 1 24 2030 0.0 0.0 0.0 +de 04_res 1 24 2030 0.0 0.0 0.0 +de 05_nuclear 1 24 2030 0.0 0.0 0.0 +de 06_coal 1 24 2030 0.0 0.0 0.0 +de 07_gas 1 24 2030 0.0 0.0 0.0 +de 08_non-res 1 24 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 24 2030 0.0 0.0 0.0 +de 01_solar 1 25 2030 2000.0 1.0 0.0 +de 02_wind_on 1 25 2030 400.0 1.0 0.0 +de 03_wind_off 1 25 2030 0.0 0.0 0.0 +de 04_res 1 25 2030 0.0 0.0 0.0 +de 05_nuclear 1 25 2030 0.0 0.0 0.0 +de 06_coal 1 25 2030 0.0 0.0 0.0 +de 07_gas 1 25 2030 0.0 0.0 0.0 +de 08_non-res 1 25 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 25 2030 0.0 0.0 0.0 +de 01_solar 1 26 2030 2000.0 1.0 0.0 +de 02_wind_on 1 26 2030 500.0 1.0 0.0 +de 03_wind_off 1 26 2030 0.0 0.0 0.0 +de 04_res 1 26 2030 0.0 0.0 0.0 +de 05_nuclear 1 26 2030 0.0 0.0 0.0 +de 06_coal 1 26 2030 0.0 0.0 0.0 +de 07_gas 1 26 2030 0.0 0.0 0.0 +de 08_non-res 1 26 2030 0.0 0.0 0.0 +de 09_hydro_pump 1 26 2030 0.0 0.0 0.0 +de 01_solar 1 27 2030 2000.0 1.0 0.0 +de 02_wind_on 1 27 2030 600.0 1.0 0.0 +de 03_wind_off 1 27 2030 0.0 0.0 0.0 +de 04_res 1 27 2030 0.0 0.0 0.0 +de 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2030 2000.0 1.0 0.0 +it 04_res 1 330 2030 2000.0 1.0 0.0 +it 05_nuclear 1 330 2030 2000.0 1.0 0.0 +it 06_coal 1 330 2030 2000.0 1.0 0.0 +it 07_gas 1 330 2030 2000.0 1.0 0.0 +it 08_non-res 1 330 2030 2000.0 1.0 0.0 +it 09_hydro_pump 1 330 2030 100.0 1.0 0.0 +it 01_solar 1 331 2030 2000.0 1.0 0.0 +it 02_wind_on 1 331 2030 2000.0 1.0 0.0 +it 03_wind_off 1 331 2030 2000.0 1.0 0.0 +it 04_res 1 331 2030 2000.0 1.0 0.0 +it 05_nuclear 1 331 2030 2000.0 1.0 0.0 +it 06_coal 1 331 2030 2000.0 1.0 0.0 +it 07_gas 1 331 2030 2000.0 1.0 0.0 +it 08_non-res 1 331 2030 2000.0 1.0 0.0 +it 09_hydro_pump 1 331 2030 200.0 1.0 0.0 +it 01_solar 1 332 2030 2000.0 1.0 0.0 +it 02_wind_on 1 332 2030 2000.0 1.0 0.0 +it 03_wind_off 1 332 2030 2000.0 1.0 0.0 +it 04_res 1 332 2030 2000.0 1.0 0.0 +it 05_nuclear 1 332 2030 2000.0 1.0 0.0 +it 06_coal 1 332 2030 2000.0 1.0 0.0 +it 07_gas 1 332 2030 2000.0 1.0 0.0 +it 08_non-res 1 332 2030 2000.0 1.0 0.0 +it 09_hydro_pump 1 332 2030 300.0 1.0 0.0 +it 01_solar 1 333 2030 2000.0 1.0 0.0 +it 02_wind_on 1 333 2030 2000.0 1.0 0.0 +it 03_wind_off 1 333 2030 2000.0 1.0 0.0 +it 04_res 1 333 2030 2000.0 1.0 0.0 +it 05_nuclear 1 333 2030 2000.0 1.0 0.0 +it 06_coal 1 333 2030 2000.0 1.0 0.0 +it 07_gas 1 333 2030 2000.0 1.0 0.0 +it 08_non-res 1 333 2030 2000.0 1.0 0.0 +it 09_hydro_pump 1 333 2030 400.0 1.0 0.0 +it 01_solar 1 334 2030 2000.0 1.0 0.0 +it 02_wind_on 1 334 2030 2000.0 1.0 0.0 +it 03_wind_off 1 334 2030 2000.0 1.0 0.0 +it 04_res 1 334 2030 2000.0 1.0 0.0 +it 05_nuclear 1 334 2030 2000.0 1.0 0.0 +it 06_coal 1 334 2030 2000.0 1.0 0.0 +it 07_gas 1 334 2030 2000.0 1.0 0.0 +it 08_non-res 1 334 2030 2000.0 1.0 0.0 +it 09_hydro_pump 1 334 2030 500.0 1.0 0.0 +it 01_solar 1 335 2030 2000.0 1.0 0.0 +it 02_wind_on 1 335 2030 2000.0 1.0 0.0 +it 03_wind_off 1 335 2030 2000.0 1.0 0.0 +it 04_res 1 335 2030 2000.0 1.0 0.0 +it 05_nuclear 1 335 2030 2000.0 1.0 0.0 +it 06_coal 1 335 2030 2000.0 1.0 0.0 +it 07_gas 1 335 2030 2000.0 1.0 0.0 +it 08_non-res 1 335 2030 2000.0 1.0 0.0 +it 09_hydro_pump 1 335 2030 600.0 1.0 0.0 +it 01_solar 1 336 2030 2000.0 1.0 0.0 +it 02_wind_on 1 336 2030 2000.0 1.0 0.0 +it 03_wind_off 1 336 2030 2000.0 1.0 0.0 +it 04_res 1 336 2030 2000.0 1.0 0.0 +it 05_nuclear 1 336 2030 2000.0 1.0 0.0 +it 06_coal 1 336 2030 2000.0 1.0 0.0 +it 07_gas 1 336 2030 2000.0 1.0 0.0 +it 08_non-res 1 336 2030 2000.0 1.0 0.0 +it 09_hydro_pump 1 336 2030 700.0 1.0 0.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-03-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-03-all.result.tsv new file mode 100644 index 0000000000..4d0e4d75b0 --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-03-all.result.tsv @@ -0,0 +1,15 @@ +area timeId time OP. COST EXP OP. COST STD OP. COST MIN OP. COST MAX MRG. PRICE EXP MRG. PRICE STD MRG. PRICE MIN MRG. PRICE MAX +de 1 2030 282000.0 0.0 282000.0 282000.0 11.66 0.0 11.66 11.66 +de 2 2030 1252000.0 0.0 1252000.0 1252000.0 23.33 0.0 23.33 23.33 +de 3 2030 2910000.0 0.0 2910000.0 2910000.0 35.0 0.0 35.0 35.0 +de 4 2030 5256000.0 0.0 5256000.0 5256000.0 46.67 0.0 46.67 46.67 +de 5 2030 8290000.0 0.0 8290000.0 8290000.0 58.33 0.0 58.33 58.33 +de 6 2030 12018000.0 0.0 12018000.0 12018000.0 71.67 0.0 71.67 71.67 +de 7 2030 16444000.0 0.0 16444000.0 16444000.0 83.33 0.0 83.33 83.33 +es 1 2030 282000.0 0.0 282000.0 282000.0 11.66 0.0 11.66 11.66 +es 2 2030 1252000.0 0.0 1252000.0 1252000.0 23.33 0.0 23.33 23.33 +es 3 2030 2910000.0 0.0 2910000.0 2910000.0 35.0 0.0 35.0 35.0 +es 4 2030 5256000.0 0.0 5256000.0 5256000.0 46.67 0.0 46.67 46.67 +es 5 2030 8290000.0 0.0 8290000.0 8290000.0 58.33 0.0 58.33 58.33 +es 6 2030 12018000.0 0.0 12018000.0 12018000.0 71.67 0.0 71.67 71.67 +es 7 2030 16444000.0 0.0 16444000.0 16444000.0 83.33 0.0 83.33 83.33 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-04-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-04-all.result.tsv new file mode 100644 index 0000000000..5232984b37 --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-04-all.result.tsv @@ -0,0 +1,15 @@ +area timeId time OV. COST EXP OP. COST EXP OP. COST STD OP. COST MIN OP. COST MAX MRG. PRICE EXP MRG. PRICE STD MRG. PRICE MIN MRG. PRICE MAX CO2 EMIS. EXP BALANCE EXP BALANCE STD BALANCE MIN BALANCE MAX ROW BAL. VALUES PSP EXP MISC. NDG EXP LOAD EXP LOAD STD LOAD MIN LOAD MAX H. ROR EXP H. ROR STD H. ROR MIN H. ROR MAX WIND EXP WIND STD WIND MIN WIND MAX SOLAR EXP SOLAR STD SOLAR MIN SOLAR MAX NUCLEAR EXP NUCLEAR STD NUCLEAR MIN NUCLEAR MAX LIGNITE EXP LIGNITE STD LIGNITE MIN LIGNITE MAX COAL EXP COAL STD COAL MIN COAL MAX GAS EXP GAS STD GAS MIN GAS MAX OIL EXP OIL STD OIL MIN OIL MAX MIX. FUEL EXP MIX. FUEL STD MIX. FUEL MIN MIX. FUEL MAX MISC. DTG EXP MISC. DTG STD MISC. DTG MIN MISC. DTG MAX H. STOR EXP H. STOR STD H. STOR MIN H. STOR MAX H. PUMP EXP H. PUMP STD H. PUMP MIN H. PUMP MAX H. LEV EXP H. LEV STD H. LEV MIN H. LEV MAX H. INFL EXP H. INFL STD H. INFL MIN H. INFL MAX H. OVFL EXP H. OVFL STD H. OVFL MIN H. OVFL MAX H. VAL EXP H. VAL STD H. VAL MIN H. VAL MAX H. COST EXP H. COST STD H. COST MIN H. COST MAX UNSP. ENRG EXP UNSP. ENRG STD UNSP. ENRG MIN UNSP. ENRG MAX SPIL. ENRG EXP SPIL. ENRG STD SPIL. ENRG MIN SPIL. ENRG MAX LOLD EXP LOLD STD LOLD MIN LOLD MAX LOLP VALUES AVL DTG EXP AVL DTG STD AVL DTG MIN AVL DTG MAX DTG MRG EXP DTG MRG STD DTG MRG MIN DTG MRG MAX MAX MRG EXP MAX MRG STD MAX MRG MIN MAX MRG MAX NP COST EXP NP COST STD NP COST MIN NP COST MAX NODU EXP NODU STD NODU MIN NODU MAX +de 1 2030 282000.0 282000.0 0.0 282000.0 282000.0 11.66 0.0 11.66 11.66 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27600.0 0.0 27600.0 27600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27600.0 0.0 27600.0 27600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 404400.0 0.0 404400.0 404400.0 404400.0 0.0 404400.0 404400.0 0.0 0.0 0.0 0.0 26.0 0.0 26.0 26.0 +de 2 2030 1252000.0 1252000.0 0.0 1252000.0 1252000.0 23.33 0.0 23.33 23.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 85200.0 0.0 85200.0 85200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 85200.0 0.0 85200.0 85200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 346800.0 0.0 346800.0 346800.0 346800.0 0.0 346800.0 346800.0 0.0 0.0 0.0 0.0 55.0 0.0 55.0 55.0 +de 3 2030 2910000.0 2910000.0 0.0 2910000.0 2910000.0 35.0 0.0 35.0 35.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 142800.0 0.0 142800.0 142800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 142800.0 0.0 142800.0 142800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 289200.0 0.0 289200.0 289200.0 289200.0 0.0 289200.0 289200.0 0.0 0.0 0.0 0.0 83.0 0.0 83.0 83.0 +de 4 2030 5256000.0 5256000.0 0.0 5256000.0 5256000.0 46.67 0.0 46.67 46.67 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 200400.0 0.0 200400.0 200400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 200400.0 0.0 200400.0 200400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 231600.0 0.0 231600.0 231600.0 231600.0 0.0 231600.0 231600.0 0.0 0.0 0.0 0.0 111.0 0.0 111.0 111.0 +de 5 2030 8290000.0 8290000.0 0.0 8290000.0 8290000.0 58.33 0.0 58.33 58.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 258000.0 0.0 258000.0 258000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 258000.0 0.0 258000.0 258000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 174000.0 0.0 174000.0 174000.0 174000.0 0.0 174000.0 174000.0 0.0 0.0 0.0 0.0 139.0 0.0 139.0 139.0 +de 6 2030 12018000.0 12018000.0 0.0 12018000.0 12018000.0 71.67 0.0 71.67 71.67 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 315600.0 0.0 315600.0 315600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 315600.0 0.0 315600.0 315600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 116400.0 0.0 116400.0 116400.0 116400.0 0.0 116400.0 116400.0 0.0 0.0 0.0 0.0 170.0 0.0 170.0 170.0 +de 7 2030 16444000.0 16444000.0 0.0 16444000.0 16444000.0 83.33 0.0 83.33 83.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 373200.0 0.0 373200.0 373200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 373200.0 0.0 373200.0 373200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 58800.0 0.0 58800.0 58800.0 58800.0 0.0 58800.0 58800.0 0.0 0.0 0.0 0.0 199.0 0.0 199.0 199.0 +es 1 2030 282000.0 282000.0 0.0 282000.0 282000.0 11.66 0.0 11.66 11.66 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27600.0 0.0 27600.0 27600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 27600.0 0.0 27600.0 27600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 404400.0 0.0 404400.0 404400.0 404400.0 0.0 404400.0 404400.0 0.0 0.0 0.0 0.0 26.0 0.0 26.0 26.0 +es 2 2030 1252000.0 1252000.0 0.0 1252000.0 1252000.0 23.33 0.0 23.33 23.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 85200.0 0.0 85200.0 85200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 85200.0 0.0 85200.0 85200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 346800.0 0.0 346800.0 346800.0 346800.0 0.0 346800.0 346800.0 0.0 0.0 0.0 0.0 55.0 0.0 55.0 55.0 +es 3 2030 2910000.0 2910000.0 0.0 2910000.0 2910000.0 35.0 0.0 35.0 35.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 142800.0 0.0 142800.0 142800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 142800.0 0.0 142800.0 142800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 289200.0 0.0 289200.0 289200.0 289200.0 0.0 289200.0 289200.0 0.0 0.0 0.0 0.0 83.0 0.0 83.0 83.0 +es 4 2030 5256000.0 5256000.0 0.0 5256000.0 5256000.0 46.67 0.0 46.67 46.67 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 200400.0 0.0 200400.0 200400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 200400.0 0.0 200400.0 200400.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 231600.0 0.0 231600.0 231600.0 231600.0 0.0 231600.0 231600.0 0.0 0.0 0.0 0.0 111.0 0.0 111.0 111.0 +es 5 2030 8290000.0 8290000.0 0.0 8290000.0 8290000.0 58.33 0.0 58.33 58.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 258000.0 0.0 258000.0 258000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 258000.0 0.0 258000.0 258000.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 174000.0 0.0 174000.0 174000.0 174000.0 0.0 174000.0 174000.0 0.0 0.0 0.0 0.0 139.0 0.0 139.0 139.0 +es 6 2030 12018000.0 12018000.0 0.0 12018000.0 12018000.0 71.67 0.0 71.67 71.67 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 315600.0 0.0 315600.0 315600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 315600.0 0.0 315600.0 315600.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 116400.0 0.0 116400.0 116400.0 116400.0 0.0 116400.0 116400.0 0.0 0.0 0.0 0.0 170.0 0.0 170.0 170.0 +es 7 2030 16444000.0 16444000.0 0.0 16444000.0 16444000.0 83.33 0.0 83.33 83.33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 373200.0 0.0 373200.0 373200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 373200.0 0.0 373200.0 373200.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 432000.0 0.0 432000.0 432000.0 58800.0 0.0 58800.0 58800.0 58800.0 0.0 58800.0 58800.0 0.0 0.0 0.0 0.0 199.0 0.0 199.0 199.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-05-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-05-all.result.tsv new file mode 100644 index 0000000000..c4361bb160 --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-05-all.result.tsv @@ -0,0 +1,3 @@ +area timeId time OV. COST EXP OP. COST EXP OP. COST STD OP. COST MIN OP. COST MAX MRG. PRICE EXP MRG. PRICE STD MRG. PRICE MIN MRG. PRICE MAX CO2 EMIS. EXP BALANCE EXP BALANCE STD BALANCE MIN BALANCE MAX ROW BAL. VALUES PSP EXP MISC. NDG EXP LOAD EXP LOAD STD LOAD MIN LOAD MAX H. ROR EXP H. ROR STD H. ROR MIN H. ROR MAX WIND EXP WIND STD WIND MIN WIND MAX SOLAR EXP SOLAR STD SOLAR MIN SOLAR MAX NUCLEAR EXP NUCLEAR STD NUCLEAR MIN NUCLEAR MAX LIGNITE EXP LIGNITE STD LIGNITE MIN LIGNITE MAX COAL EXP COAL STD COAL MIN COAL MAX GAS EXP GAS STD GAS MIN GAS MAX OIL EXP OIL STD OIL MIN OIL MAX MIX. FUEL EXP MIX. FUEL STD MIX. FUEL MIN MIX. FUEL MAX MISC. DTG EXP MISC. DTG STD MISC. DTG MIN MISC. DTG MAX H. STOR EXP H. STOR STD H. STOR MIN H. STOR MAX H. PUMP EXP H. PUMP STD H. PUMP MIN H. PUMP MAX H. LEV EXP H. LEV STD H. LEV MIN H. LEV MAX H. INFL EXP H. INFL STD H. INFL MIN H. INFL MAX H. OVFL EXP H. OVFL STD H. OVFL MIN H. OVFL MAX H. VAL EXP H. VAL STD H. VAL MIN H. VAL MAX H. COST EXP H. COST STD H. COST MIN H. COST MAX UNSP. ENRG EXP UNSP. ENRG STD UNSP. ENRG MIN UNSP. ENRG MAX SPIL. ENRG EXP SPIL. ENRG STD SPIL. ENRG MIN SPIL. ENRG MAX LOLD EXP LOLD STD LOLD MIN LOLD MAX LOLP VALUES AVL DTG EXP AVL DTG STD AVL DTG MIN AVL DTG MAX DTG MRG EXP DTG MRG STD DTG MRG MIN DTG MRG MAX MAX MRG EXP MAX MRG STD MAX MRG MIN MAX MRG MAX NP COST EXP NP COST STD NP COST MIN NP COST MAX NODU EXP NODU STD NODU MIN NODU MAX +de 1 2030 46452000.0 46452000.0 0.0 46452000.0 46452000.0 47.14 0.0 47.14 47.14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1402800.0 0.0 1402800.0 1402800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1402800.0 0.0 1402800.0 1402800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3024000.0 0.0 3024000.0 3024000.0 1621200.0 0.0 1621200.0 1621200.0 1621200.0 0.0 1621200.0 1621200.0 0.0 0.0 0.0 0.0 783.0 0.0 783.0 783.0 +es 1 2030 46452000.0 46452000.0 0.0 46452000.0 46452000.0 47.14 0.0 47.14 47.14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1402800.0 0.0 1402800.0 1402800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1402800.0 0.0 1402800.0 1402800.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3024000.0 0.0 3024000.0 3024000.0 1621200.0 0.0 1621200.0 1621200.0 1621200.0 0.0 1621200.0 1621200.0 0.0 0.0 0.0 0.0 783.0 0.0 783.0 783.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-06-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-06-all.result.tsv new file mode 100644 index 0000000000..67ce2dcb67 --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-06-all.result.tsv @@ -0,0 +1,15 @@ +area timeId time OP. COST MIN OP. COST MAX MRG. PRICE MIN MRG. PRICE MAX BALANCE MIN BALANCE MAX LOAD MIN LOAD MAX H. ROR MIN H. ROR MAX WIND MIN WIND MAX SOLAR MIN SOLAR MAX NUCLEAR MIN NUCLEAR MAX LIGNITE MIN LIGNITE MAX COAL MIN COAL MAX GAS MIN GAS MAX OIL MIN OIL MAX MIX. FUEL MIN MIX. FUEL MAX MISC. DTG MIN MISC. DTG MAX H. STOR MIN H. STOR MAX H. PUMP MIN H. PUMP MAX H. LEV MIN H. LEV MAX H. INFL MIN H. INFL MAX H. OVFL MIN H. OVFL MAX H. VAL MIN H. VAL MAX H. COST MIN H. COST MAX UNSP. ENRG MIN UNSP. ENRG MAX SPIL. ENRG MIN SPIL. ENRG MAX LOLD MIN LOLD MAX AVL DTG MIN AVL DTG MAX DTG MRG MIN DTG MRG MAX MAX MRG MIN MAX MRG MAX NP COST MIN NP COST MAX NODU MIN NODU MAX +de 1 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de 2 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de 3 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de 4 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de 5 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de 6 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de 7 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +es 1 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +es 2 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +es 3 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +es 4 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +es 5 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +es 6 2030 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +es 7 2030 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b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-07-all.result.tsv new file mode 100644 index 0000000000..f658354e14 --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-07-all.result.tsv @@ -0,0 +1,15 @@ +area timeId time OV. COST EXP OP. COST EXP OP. COST STD OP. COST MIN OP. COST MAX H. COST EXP H. COST STD H. COST MIN H. 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336 2030 1.0 0.0 +it 09_hydro_pump 2 336 2030 1.0 0.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-08-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-08-all.result.tsv new file mode 100644 index 0000000000..565b614f8e --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_areas_raw_data/test-08-all.result.tsv @@ -0,0 +1,19 @@ +area cluster timeId time NODU NP Cost - Euro +de 01_solar 1 2030 167.0 0.0 +de 02_wind_on 1 2030 147.0 0.0 +de 03_wind_off 1 2030 127.0 0.0 +de 04_res 1 2030 107.0 0.0 +de 05_nuclear 1 2030 87.0 0.0 +de 06_coal 1 2030 67.0 0.0 +de 07_gas 1 2030 47.0 0.0 +de 08_non-res 1 2030 27.0 0.0 +de 09_hydro_pump 1 2030 7.0 0.0 +es 01_solar 1 2030 167.0 0.0 +es 02_wind_on 1 2030 147.0 0.0 +es 03_wind_off 1 2030 127.0 0.0 +es 04_res 1 2030 107.0 0.0 +es 05_nuclear 1 2030 87.0 0.0 +es 06_coal 1 2030 67.0 0.0 +es 07_gas 1 2030 47.0 0.0 +es 08_non-res 1 2030 27.0 0.0 +es 09_hydro_pump 1 2030 7.0 0.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-01-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-01-all.result.tsv new file mode 100644 index 0000000000..a7e863a3ee --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-01-all.result.tsv @@ -0,0 +1,365 @@ +link timeId time FLOW LIN. EXP FLOW LIN. STD FLOW LIN. MIN FLOW LIN. MAX UCAP LIN. EXP UCAP LIN. STD UCAP LIN. MIN UCAP LIN. MAX LOOP FLOW VALUES FLOW QUAD. VALUES CONG. FEE (ALG.) EXP CONG. FEE (ALG.) STD CONG. FEE (ALG.) MIN CONG. FEE (ALG.) MAX CONG. FEE (ABS.) EXP CONG. FEE (ABS.) STD CONG. FEE (ABS.) MIN CONG. FEE (ABS.) MAX MARG. COST EXP MARG. COST STD MARG. COST MIN MARG. COST MAX CONG. PROB + VALUES CONG. PROB - VALUES HURDLE COST EXP HURDLE COST STD HURDLE COST MIN HURDLE COST MAX +de - fr 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 24 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 25 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 26 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - 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fr 332 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 333 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 334 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 335 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 336 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 337 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 338 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 339 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 340 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 341 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 342 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 343 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 344 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 345 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 346 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 347 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 348 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 349 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 350 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 351 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 352 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 353 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 354 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 355 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 356 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 357 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 358 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 359 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 360 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 361 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 362 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 363 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 364 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-02-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-02-all.result.tsv new file mode 100644 index 0000000000..c57daa2450 --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-02-all.result.tsv @@ -0,0 +1,13 @@ +link timeId time FLOW LIN. EXP FLOW LIN. STD FLOW LIN. MIN FLOW LIN. MAX UCAP LIN. EXP UCAP LIN. STD UCAP LIN. MIN UCAP LIN. MAX LOOP FLOW VALUES FLOW QUAD. VALUES CONG. FEE (ALG.) EXP CONG. FEE (ALG.) STD CONG. FEE (ALG.) MIN CONG. FEE (ALG.) MAX CONG. FEE (ABS.) EXP CONG. FEE (ABS.) STD CONG. FEE (ABS.) MIN CONG. FEE (ABS.) MAX MARG. COST EXP MARG. COST STD MARG. COST MIN MARG. COST MAX CONG. PROB + VALUES CONG. PROB - VALUES HURDLE COST EXP HURDLE COST STD HURDLE COST MIN HURDLE COST MAX +de - fr 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-03-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-03-all.result.tsv new file mode 100644 index 0000000000..a7e863a3ee --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-03-all.result.tsv @@ -0,0 +1,365 @@ +link timeId time FLOW LIN. EXP FLOW LIN. STD FLOW LIN. MIN FLOW LIN. MAX UCAP LIN. EXP UCAP LIN. STD UCAP LIN. MIN UCAP LIN. MAX LOOP FLOW VALUES FLOW QUAD. VALUES CONG. FEE (ALG.) EXP CONG. FEE (ALG.) STD CONG. FEE (ALG.) MIN CONG. FEE (ALG.) MAX CONG. FEE (ABS.) EXP CONG. FEE (ABS.) STD CONG. FEE (ABS.) MIN CONG. FEE (ABS.) MAX MARG. COST EXP MARG. COST STD MARG. COST MIN MARG. COST MAX CONG. PROB + VALUES CONG. PROB - VALUES HURDLE COST EXP HURDLE COST STD HURDLE COST MIN HURDLE COST MAX +de - fr 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 20 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 21 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 22 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 23 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 24 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 25 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 26 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 27 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 28 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 29 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 30 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 31 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 32 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 33 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 34 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 35 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 36 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 37 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 38 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 39 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 40 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 41 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 42 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 43 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 44 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 45 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 46 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 47 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 48 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 49 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 50 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 51 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 52 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 53 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 54 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 55 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 56 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 57 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 58 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 59 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 60 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 61 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 62 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 63 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 64 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 65 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 66 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 67 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 68 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 69 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 70 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 71 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - 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fr 364 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-04-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-04-all.result.tsv new file mode 100644 index 0000000000..c57daa2450 --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-04-all.result.tsv @@ -0,0 +1,13 @@ +link timeId time FLOW LIN. EXP FLOW LIN. STD FLOW LIN. MIN FLOW LIN. MAX UCAP LIN. EXP UCAP LIN. STD UCAP LIN. MIN UCAP LIN. MAX LOOP FLOW VALUES FLOW QUAD. VALUES CONG. FEE (ALG.) EXP CONG. FEE (ALG.) STD CONG. FEE (ALG.) MIN CONG. FEE (ALG.) MAX CONG. FEE (ABS.) EXP CONG. FEE (ABS.) STD CONG. FEE (ABS.) MIN CONG. FEE (ABS.) MAX MARG. COST EXP MARG. COST STD MARG. COST MIN MARG. COST MAX CONG. PROB + VALUES CONG. PROB - VALUES HURDLE COST EXP HURDLE COST STD HURDLE COST MIN HURDLE COST MAX +de - fr 1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 +de - fr 12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 diff --git a/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-05-all.result.tsv b/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-05-all.result.tsv new file mode 100644 index 0000000000..504ad467d7 --- /dev/null +++ b/tests/integration/raw_studies_blueprint/assets/aggregate_links_raw_data/test-05-all.result.tsv @@ -0,0 +1,365 @@ +link timeId time FLOW LIN. MIN FLOW LIN. MAX UCAP LIN. MIN UCAP LIN. MAX CONG. FEE (ALG.) MIN CONG. FEE (ALG.) MAX CONG. FEE (ABS.) MIN CONG. FEE (ABS.) MAX MARG. COST MIN MARG. COST MAX HURDLE COST MIN HURDLE COST MAX +de - fr 1 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 2 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 3 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 4 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 5 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 6 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 7 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 8 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 9 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 10 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 11 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 12 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 13 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 14 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 15 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 16 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 17 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 18 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 19 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 20 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 21 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 22 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 23 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 24 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 25 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 26 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 27 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 28 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 29 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 30 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 31 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 32 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 33 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 34 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 35 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 36 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 37 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 38 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 39 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 40 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 41 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 42 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 43 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 44 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 45 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 46 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 47 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 48 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 49 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 50 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 51 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 52 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 53 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 54 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 55 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 56 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 57 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 58 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 59 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 60 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 61 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 62 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 63 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 64 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 65 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 66 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 67 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 68 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 69 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 70 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 71 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 72 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 73 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 74 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 75 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 76 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 77 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 78 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 79 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 80 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 81 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 82 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 83 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 84 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 85 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 86 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 87 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 88 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 89 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 90 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 91 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 92 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 93 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 94 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 95 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 96 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 97 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 98 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 99 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 100 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 101 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 102 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 103 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 104 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 105 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 106 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 107 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 108 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 109 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 110 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 111 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 112 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 113 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 114 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 115 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 116 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 117 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 118 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - fr 119 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 +de - 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COST CONG. 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fr 344 0.0 0.0 0.0 0.0 0.0 +de - fr 345 0.0 0.0 0.0 0.0 0.0 +de - fr 346 0.0 0.0 0.0 0.0 0.0 +de - fr 347 0.0 0.0 0.0 0.0 0.0 +de - fr 348 0.0 0.0 0.0 0.0 0.0 +de - fr 349 0.0 0.0 0.0 0.0 0.0 +de - fr 350 0.0 0.0 0.0 0.0 0.0 +de - fr 351 0.0 0.0 0.0 0.0 0.0 +de - fr 352 0.0 0.0 0.0 0.0 0.0 +de - fr 353 0.0 0.0 0.0 0.0 0.0 +de - fr 354 0.0 0.0 0.0 0.0 0.0 +de - fr 355 0.0 0.0 0.0 0.0 0.0 +de - fr 356 0.0 0.0 0.0 0.0 0.0 +de - fr 357 0.0 0.0 0.0 0.0 0.0 +de - fr 358 0.0 0.0 0.0 0.0 0.0 +de - fr 359 0.0 0.0 0.0 0.0 0.0 +de - fr 360 0.0 0.0 0.0 0.0 0.0 +de - fr 361 0.0 0.0 0.0 0.0 0.0 +de - fr 362 0.0 0.0 0.0 0.0 0.0 +de - fr 363 0.0 0.0 0.0 0.0 0.0 +de - fr 364 0.0 0.0 0.0 0.0 0.0 diff --git a/tests/integration/raw_studies_blueprint/test_aggregate_raw_data.py b/tests/integration/raw_studies_blueprint/test_aggregate_raw_data.py index 1dd53fdfe2..591791c8ee 100644 --- a/tests/integration/raw_studies_blueprint/test_aggregate_raw_data.py +++ b/tests/integration/raw_studies_blueprint/test_aggregate_raw_data.py @@ -1,20 +1,28 @@ import io +import shutil +from pathlib import Path import numpy as np import pandas as pd import pytest from starlette.testclient import TestClient -from antarest.study.business.aggregator_management import AreasQueryFile, LinksQueryFile +from antarest.study.business.aggregator_management import ( + MCAllAreasQueryFile, + MCAllLinksQueryFile, + MCIndAreasQueryFile, + MCIndLinksQueryFile, +) from antarest.study.storage.df_download import TableExportFormat from antarest.study.storage.rawstudy.model.filesystem.matrix.matrix import MatrixFrequency from tests.integration.raw_studies_blueprint.assets import ASSETS_DIR -AREAS_REQUESTS = [ +# define the requests parameters for the `economy/mc-ind` outputs aggregation +AREAS_REQUESTS__IND = [ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": AreasQueryFile.VALUES, + "query_file": MCIndAreasQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "mc_years": "", "areas_ids": "", @@ -25,7 +33,7 @@ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": AreasQueryFile.DETAILS, + "query_file": MCIndAreasQueryFile.DETAILS, "frequency": MatrixFrequency.HOURLY, "mc_years": "1", "areas_ids": "de,fr,it", @@ -36,7 +44,7 @@ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": AreasQueryFile.VALUES, + "query_file": MCIndAreasQueryFile.VALUES, "frequency": MatrixFrequency.WEEKLY, "mc_years": "1,2", "areas_ids": "", @@ -47,7 +55,7 @@ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": AreasQueryFile.VALUES, + "query_file": MCIndAreasQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "mc_years": "2", "areas_ids": "es,fr,de", @@ -58,7 +66,7 @@ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": AreasQueryFile.VALUES, + "query_file": MCIndAreasQueryFile.VALUES, "frequency": MatrixFrequency.ANNUAL, "mc_years": "", "areas_ids": "", @@ -66,13 +74,31 @@ }, "test-05.result.tsv", ), + ( + { + "output_id": "20201014-1425eco-goodbye", + "query_file": MCIndAreasQueryFile.VALUES, + "frequency": MatrixFrequency.HOURLY, + "columns_names": "COSt,NODu", + }, + "test-06.result.tsv", + ), + ( + { + "output_id": "20201014-1425eco-goodbye", + "query_file": MCIndAreasQueryFile.DETAILS, + "frequency": MatrixFrequency.HOURLY, + "columns_names": "COSt,NODu", + }, + "test-07.result.tsv", + ), ] -LINKS_REQUESTS = [ +LINKS_REQUESTS__IND = [ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": LinksQueryFile.VALUES, + "query_file": MCIndLinksQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "mc_years": "", "columns_names": "", @@ -82,7 +108,7 @@ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": LinksQueryFile.VALUES, + "query_file": MCIndLinksQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "mc_years": "1", "columns_names": "", @@ -92,30 +118,39 @@ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": LinksQueryFile.VALUES, + "query_file": MCIndLinksQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "mc_years": "1,2", - "columns_names": "UCAP LIN.,FLOW QUAD.", + "columns_names": "UCAP LIn.,FLOw qUAD.", }, "test-03.result.tsv", ), ( { "output_id": "20201014-1425eco-goodbye", - "query_file": LinksQueryFile.VALUES, + "query_file": MCIndLinksQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "mc_years": "1", "links_ids": "de - fr", }, "test-04.result.tsv", ), + ( + { + "output_id": "20201014-1425eco-goodbye", + "query_file": MCIndLinksQueryFile.VALUES, + "frequency": MatrixFrequency.HOURLY, + "columns_names": "MArG. COsT,CONG. PRoB +", + }, + "test-05.result.tsv", + ), ] -SAME_REQUEST_DIFFERENT_FORMATS = [ +SAME_REQUEST_DIFFERENT_FORMATS__IND = [ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": LinksQueryFile.VALUES, + "query_file": MCIndLinksQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "format": "csv", }, @@ -124,7 +159,7 @@ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": LinksQueryFile.VALUES, + "query_file": MCIndLinksQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "format": "tsv", }, @@ -133,7 +168,7 @@ ( { "output_id": "20201014-1425eco-goodbye", - "query_file": LinksQueryFile.VALUES, + "query_file": MCIndLinksQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "format": "xlsx", }, @@ -142,28 +177,28 @@ ] -INCOHERENT_REQUESTS_BODIES = [ +INCOHERENT_REQUESTS_BODIES__IND = [ { "output_id": "20201014-1425eco-goodbye", - "query_file": AreasQueryFile.VALUES, + "query_file": MCIndAreasQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "mc_years": "123456789", }, { "output_id": "20201014-1425eco-goodbye", - "query_file": LinksQueryFile.VALUES, + "query_file": MCIndLinksQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "columns_names": "fake_col", }, { "output_id": "20201014-1425eco-goodbye", - "query_file": AreasQueryFile.VALUES, + "query_file": MCIndAreasQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "links_ids": "fake_id", }, ] -WRONGLY_TYPED_REQUESTS = [ +WRONGLY_TYPED_REQUESTS__IND = [ { "output_id": "20201014-1425eco-goodbye", "query_file": "fake_query_file", @@ -171,20 +206,229 @@ }, { "output_id": "20201014-1425eco-goodbye", - "query_file": AreasQueryFile.VALUES, + "query_file": MCIndAreasQueryFile.VALUES, "frequency": "fake_frequency", }, { "output_id": "20201014-1425eco-goodbye", - "query_file": AreasQueryFile.VALUES, + "query_file": MCIndAreasQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, "format": "fake_format", }, ] +# define the requests parameters for the `economy/mc-all` outputs aggregation +AREAS_REQUESTS__ALL = [ + ( + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "areas_ids": "", + "columns_names": "", + }, + "test-01-all.result.tsv", + ), + ( + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.DETAILS, + "frequency": MatrixFrequency.MONTHLY, + "areas_ids": "de,fr,it", + "columns_names": "", + }, + "test-02-all.result.tsv", + ), + ( + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "areas_ids": "", + "columns_names": "OP. CoST,MRG. PrICE", + }, + "test-03-all.result.tsv", + ), + ( + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "areas_ids": "es,fr,de", + "columns_names": "", + }, + "test-04-all.result.tsv", + ), + ( + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.VALUES, + "frequency": MatrixFrequency.MONTHLY, + "areas_ids": "", + "columns_names": "", + }, + "test-05-all.result.tsv", + ), + ( + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.ID, + "frequency": MatrixFrequency.DAILY, + "areas_ids": "", + "columns_names": "", + }, + "test-06-all.result.tsv", + ), + ( + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "columns_names": "COsT,NoDU", + }, + "test-07-all.result.tsv", + ), + ( + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.DETAILS, + "frequency": MatrixFrequency.MONTHLY, + "columns_names": "COsT,NoDU", + }, + "test-08-all.result.tsv", + ), +] + +LINKS_REQUESTS__ALL = [ + ( + { + "output_id": "20241807-1540eco-extra-outputs", + "query_file": MCAllLinksQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "columns_names": "", + }, + "test-01-all.result.tsv", + ), + ( + { + "output_id": "20241807-1540eco-extra-outputs", + "query_file": MCAllLinksQueryFile.VALUES, + "frequency": MatrixFrequency.MONTHLY, + "columns_names": "", + }, + "test-02-all.result.tsv", + ), + ( + { + "output_id": "20241807-1540eco-extra-outputs", + "query_file": MCAllLinksQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "columns_names": "", + }, + "test-03-all.result.tsv", + ), + ( + { + "output_id": "20241807-1540eco-extra-outputs", + "query_file": MCAllLinksQueryFile.VALUES, + "frequency": MatrixFrequency.MONTHLY, + "links_ids": "de - fr", + }, + "test-04-all.result.tsv", + ), + ( + { + "output_id": "20241807-1540eco-extra-outputs", + "query_file": MCAllLinksQueryFile.ID, + "frequency": MatrixFrequency.DAILY, + "links_ids": "", + }, + "test-05-all.result.tsv", + ), + ( + { + "output_id": "20241807-1540eco-extra-outputs", + "query_file": MCAllLinksQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "columns_names": "MARG. COsT,CONG. ProB +", + }, + "test-06-all.result.tsv", + ), +] + +SAME_REQUEST_DIFFERENT_FORMATS__ALL = [ + ( + { + "output_id": "20241807-1540eco-extra-outputs", + "query_file": MCAllLinksQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "format": "csv", + }, + "test-01-all.result.tsv", + ), + ( + { + "output_id": "20241807-1540eco-extra-outputs", + "query_file": MCAllLinksQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "format": "tsv", + }, + "test-01-all.result.tsv", + ), + ( + { + "output_id": "20241807-1540eco-extra-outputs", + "query_file": MCAllLinksQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "format": "xlsx", + }, + "test-01-all.result.tsv", + ), +] + + +INCOHERENT_REQUESTS_BODIES__ALL = [ + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + }, + { + "output_id": "20201014-1427eco", + "query_file": MCAllLinksQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "columns_names": "fake_col", + }, + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.VALUES, + "frequency": MatrixFrequency.MONTHLY, + "links_ids": "fake_id", + }, +] + +WRONGLY_TYPED_REQUESTS__ALL = [ + { + "output_id": "20201014-1427eco", + "query_file": "fake_query_file", + "frequency": MatrixFrequency.MONTHLY, + }, + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.VALUES, + "frequency": "fake_frequency", + }, + { + "output_id": "20201014-1427eco", + "query_file": MCAllAreasQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "format": "fake_format", + }, +] + @pytest.mark.integration_test -class TestRawDataAggregation: +class TestRawDataAggregationMCInd: """ Check the aggregation of Raw Data from studies outputs """ @@ -200,9 +444,9 @@ def test_area_aggregation( """ client.headers = {"Authorization": f"Bearer {user_access_token}"} - for params, expected_result_filename in AREAS_REQUESTS: + for params, expected_result_filename in AREAS_REQUESTS__IND: output_id = params.pop("output_id") - res = client.get(f"/v1/studies/{internal_study_id}/areas/aggregate/{output_id}", params=params) + res = client.get(f"/v1/studies/{internal_study_id}/areas/aggregate/mc-ind/{output_id}", params=params) assert res.status_code == 200, res.json() content = io.BytesIO(res.content) df = pd.read_csv(content, index_col=0, sep=",") @@ -229,9 +473,9 @@ def test_links_aggregation( """ client.headers = {"Authorization": f"Bearer {user_access_token}"} - for params, expected_result_filename in LINKS_REQUESTS: + for params, expected_result_filename in LINKS_REQUESTS__IND: output_id = params.pop("output_id") - res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/{output_id}", params=params) + res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/mc-ind/{output_id}", params=params) assert res.status_code == 200, res.json() content = io.BytesIO(res.content) df = pd.read_csv(content, index_col=0, sep=",") @@ -258,9 +502,9 @@ def test_different_formats( """ client.headers = {"Authorization": f"Bearer {user_access_token}"} - for params, expected_result_filename in SAME_REQUEST_DIFFERENT_FORMATS: + for params, expected_result_filename in SAME_REQUEST_DIFFERENT_FORMATS__IND: output_id = params.pop("output_id") - res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/{output_id}", params=params) + res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/mc-ind/{output_id}", params=params) assert res.status_code == 200, res.json() content = io.BytesIO(res.content) export_format = params["format"] @@ -289,9 +533,9 @@ def test_aggregation_with_incoherent_bodies( Asserts that requests with incoherent bodies don't crash but send empty dataframes """ client.headers = {"Authorization": f"Bearer {user_access_token}"} - for params in INCOHERENT_REQUESTS_BODIES: + for params in INCOHERENT_REQUESTS_BODIES__IND: output_id = params.pop("output_id") - res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/{output_id}", params=params) + res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/mc-ind/{output_id}", params=params) assert res.status_code == 200, res.json() content = io.BytesIO(res.content) df = pd.read_csv(content, index_col=0, sep=",") @@ -302,9 +546,9 @@ def test_wrongly_typed_request(self, client: TestClient, user_access_token: str, Asserts that wrongly typed requests send an HTTP 422 Exception """ client.headers = {"Authorization": f"Bearer {user_access_token}"} - for params in WRONGLY_TYPED_REQUESTS: + for params in WRONGLY_TYPED_REQUESTS__IND: output_id = params.pop("output_id") - res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/{output_id}", params=params) + res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/mc-ind/{output_id}", params=params) assert res.status_code == 422 assert res.json()["exception"] == "RequestValidationError" @@ -316,9 +560,9 @@ def test_aggregation_with_wrong_output(self, client: TestClient, user_access_tok # test for areas res = client.get( - f"/v1/studies/{internal_study_id}/areas/aggregate/unknown_id", + f"/v1/studies/{internal_study_id}/areas/aggregate/mc-ind/unknown_id", params={ - "query_file": AreasQueryFile.VALUES, + "query_file": MCIndAreasQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, }, ) @@ -328,12 +572,271 @@ def test_aggregation_with_wrong_output(self, client: TestClient, user_access_tok # test for links res = client.get( - f"/v1/studies/{internal_study_id}/links/aggregate/unknown_id", + f"/v1/studies/{internal_study_id}/links/aggregate/mc-ind/unknown_id", params={ - "query_file": LinksQueryFile.VALUES, + "query_file": MCIndLinksQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY, }, ) assert res.status_code == 404, res.json() assert res.json()["exception"] == "OutputNotFound" assert "unknown_id" in res.json()["description"], "The output_id should be in the message" + + def test_empty_columns(self, client: TestClient, user_access_token: str, internal_study_id: str): + """ + Asserts that requests get an empty dataframe when columns are not existing + """ + client.headers = {"Authorization": f"Bearer {user_access_token}"} + + # test for areas + res = client.get( + f"/v1/studies/{internal_study_id}/areas/aggregate/mc-ind/20201014-1425eco-goodbye", + params={ + "query_file": MCIndAreasQueryFile.DETAILS, + "frequency": MatrixFrequency.HOURLY, + "columns_names": "fake_col", + }, + ) + assert res.status_code == 200, res.json() + df = pd.read_csv(io.BytesIO(res.content), index_col=0, sep=",") + assert df.empty + + # test for links + res = client.get( + f"/v1/studies/{internal_study_id}/links/aggregate/mc-ind/20201014-1425eco-goodbye", + params={ + "query_file": MCIndLinksQueryFile.VALUES, + "frequency": MatrixFrequency.HOURLY, + "columns_names": "fake_col", + }, + ) + assert res.status_code == 200, res.json() + df = pd.read_csv(io.BytesIO(res.content), index_col=0, sep=",") + assert df.empty + + def test_non_existing_folder( + self, tmp_path: Path, client: TestClient, user_access_token: str, internal_study_id: str + ): + """ + Asserts that requests with non-existing folders send an HTTP 404 Exception + """ + client.headers = {"Authorization": f"Bearer {user_access_token}"} + + # the mc-ind folder + mc_ind_folder = tmp_path.joinpath("ext_workspace/STA-mini/output/20201014-1425eco-goodbye/economy/mc-ind") + # delete the folder + shutil.rmtree(mc_ind_folder) + res = client.get( + f"/v1/studies/{internal_study_id}/areas/aggregate/mc-ind/20201014-1425eco-goodbye", + params={"query_file": MCIndAreasQueryFile.VALUES, "frequency": MatrixFrequency.HOURLY}, + ) + assert res.status_code == 404, res.json() + assert "economy/mc-ind" in res.json()["description"] + assert res.json()["exception"] == "OutputSubFolderNotFound" + + +@pytest.mark.integration_test +class TestRawDataAggregationMCAll: + """ + Check the aggregation of Raw Data from studies outputs in `economy/mc-all` + """ + + def test_area_aggregation( + self, + client: TestClient, + user_access_token: str, + internal_study_id: str, + ): + """ + Test the aggregation of areas data + """ + client.headers = {"Authorization": f"Bearer {user_access_token}"} + + for params, expected_result_filename in AREAS_REQUESTS__ALL: + output_id = params.pop("output_id") + res = client.get(f"/v1/studies/{internal_study_id}/areas/aggregate/mc-all/{output_id}", params=params) + assert res.status_code == 200, res.json() + content = io.BytesIO(res.content) + df = pd.read_csv(content, index_col=0, sep=",") + resource_file = ASSETS_DIR.joinpath(f"aggregate_areas_raw_data/{expected_result_filename}") + resource_file.parent.mkdir(exist_ok=True, parents=True) + if not resource_file.exists(): + # create the resource to add it to non-regression tests + df.to_csv(resource_file, sep="\t", index=False) + expected_df = pd.read_csv(resource_file, sep="\t", header=0) + expected_df = expected_df.replace({np.nan: None}) + # cast types of expected_df to match df + for col in expected_df.columns: + expected_df[col] = expected_df[col].astype(df[col].dtype) + pd.testing.assert_frame_equal(df, expected_df) + + def test_links_aggregation( + self, + client: TestClient, + user_access_token: str, + internal_study_id: str, + ): + """ + Test the aggregation of links data + """ + client.headers = {"Authorization": f"Bearer {user_access_token}"} + + for params, expected_result_filename in LINKS_REQUESTS__ALL: + output_id = params.pop("output_id") + res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/mc-all/{output_id}", params=params) + assert res.status_code == 200, res.json() + content = io.BytesIO(res.content) + df = pd.read_csv(content, index_col=0, sep=",") + resource_file = ASSETS_DIR.joinpath(f"aggregate_links_raw_data/{expected_result_filename}") + resource_file.parent.mkdir(exist_ok=True, parents=True) + if not resource_file.exists(): + # create the resource to add it to non-regression tests + df.to_csv(resource_file, sep="\t", index=False) + expected_df = pd.read_csv(resource_file, sep="\t", header=0) + expected_df = expected_df.replace({np.nan: None}) + # cast types of expected_df to match df + for col in expected_df.columns: + expected_df[col] = expected_df[col].astype(df[col].dtype) + pd.testing.assert_frame_equal(df, expected_df) + + def test_different_formats( + self, + client: TestClient, + user_access_token: str, + internal_study_id: str, + ): + """ + Tests that all formats work and produce the same result + """ + client.headers = {"Authorization": f"Bearer {user_access_token}"} + + for params, expected_result_filename in SAME_REQUEST_DIFFERENT_FORMATS__ALL: + output_id = params.pop("output_id") + res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/mc-all/{output_id}", params=params) + assert res.status_code == 200, res.json() + content = io.BytesIO(res.content) + export_format = params["format"] + if export_format == TableExportFormat.CSV.value: + df = pd.read_csv(content, index_col=0, sep=",") + elif export_format == TableExportFormat.TSV.value: + df = pd.read_csv(content, index_col=0, sep="\t") + else: + df = pd.read_excel(content, index_col=0) # type: ignore + resource_file = ASSETS_DIR.joinpath(f"aggregate_links_raw_data/{expected_result_filename}") + resource_file.parent.mkdir(exist_ok=True, parents=True) + if not resource_file.exists(): + # create the resource to add it to non-regression tests + df.to_csv(resource_file, sep="\t", index=False) + expected_df = pd.read_csv(resource_file, sep="\t", header=0) + expected_df = expected_df.replace({np.nan: None}) + # cast types of expected_df to match df + for col in expected_df.columns: + expected_df[col] = expected_df[col].astype(df[col].dtype) + pd.testing.assert_frame_equal(df, expected_df) + + def test_aggregation_with_incoherent_bodies( + self, client: TestClient, user_access_token: str, internal_study_id: str + ): + """ + Asserts that requests with incoherent bodies don't crash but send empty dataframes + """ + client.headers = {"Authorization": f"Bearer {user_access_token}"} + for params in INCOHERENT_REQUESTS_BODIES__ALL: + output_id = params.pop("output_id") + res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/mc-all/{output_id}", params=params) + assert res.status_code == 200, res.json() + content = io.BytesIO(res.content) + df = pd.read_csv(content, index_col=0, sep=",") + assert df.empty + + def test_wrongly_typed_request(self, client: TestClient, user_access_token: str, internal_study_id: str): + """ + Asserts that wrongly typed requests send an HTTP 422 Exception + """ + client.headers = {"Authorization": f"Bearer {user_access_token}"} + for params in WRONGLY_TYPED_REQUESTS__ALL: + output_id = params.pop("output_id") + res = client.get(f"/v1/studies/{internal_study_id}/links/aggregate/mc-all/{output_id}", params=params) + assert res.status_code == 422 + assert res.json()["exception"] == "RequestValidationError" + + def test_aggregation_with_wrong_output(self, client: TestClient, user_access_token: str, internal_study_id: str): + """ + Asserts that requests with wrong output send an HTTP 422 Exception + """ + client.headers = {"Authorization": f"Bearer {user_access_token}"} + + # test for areas + res = client.get( + f"/v1/studies/{internal_study_id}/areas/aggregate/mc-all/unknown_id", + params={ + "query_file": MCIndAreasQueryFile.VALUES, + "frequency": MatrixFrequency.HOURLY, + }, + ) + assert res.status_code == 404, res.json() + assert res.json()["exception"] == "OutputNotFound" + assert "unknown_id" in res.json()["description"], "The output_id should be in the message" + + # test for links + res = client.get( + f"/v1/studies/{internal_study_id}/links/aggregate/mc-all/unknown_id", + params={ + "query_file": MCIndLinksQueryFile.VALUES, + "frequency": MatrixFrequency.HOURLY, + }, + ) + assert res.status_code == 404, res.json() + assert res.json()["exception"] == "OutputNotFound" + assert "unknown_id" in res.json()["description"], "The output_id should be in the message" + + def test_empty_columns(self, client: TestClient, user_access_token: str, internal_study_id: str): + """ + Asserts that requests get an empty dataframe when columns are not existing + """ + + client.headers = {"Authorization": f"Bearer {user_access_token}"} + + # test for areas + res = client.get( + f"/v1/studies/{internal_study_id}/areas/aggregate/mc-all/20201014-1427eco", + params={ + "query_file": MCAllAreasQueryFile.DETAILS, + "frequency": MatrixFrequency.MONTHLY, + "columns_names": "fake_col", + }, + ) + assert res.status_code == 200, res.json() + df = pd.read_csv(io.BytesIO(res.content), index_col=0, sep=",") + assert df.empty + + # test for links + res = client.get( + f"/v1/studies/{internal_study_id}/links/aggregate/mc-all/20241807-1540eco-extra-outputs", + params={ + "query_file": MCAllLinksQueryFile.VALUES, + "frequency": MatrixFrequency.DAILY, + "columns_names": "fake_col", + }, + ) + assert res.status_code == 200, res.json() + df = pd.read_csv(io.BytesIO(res.content), index_col=0, sep=",") + assert df.empty + + def test_non_existing_folder( + self, tmp_path: Path, client: TestClient, user_access_token: str, internal_study_id: str + ): + """ + Test that an error 404 is raised when the `economy/mc-all` folder does not exist + """ + client.headers = {"Authorization": f"Bearer {user_access_token}"} + mc_all_path = tmp_path.joinpath("ext_workspace/STA-mini/output/20241807-1540eco-extra-outputs/economy/mc-all") + # delete the folder + shutil.rmtree(mc_all_path) + res = client.get( + f"/v1/studies/{internal_study_id}/links/aggregate/mc-all/20241807-1540eco-extra-outputs", + params={"query_file": MCAllLinksQueryFile.VALUES, "frequency": MatrixFrequency.DAILY}, + ) + assert res.status_code == 404, res.json() + assert "economy/mc-all" in res.json()["description"] + assert res.json()["exception"] == "OutputSubFolderNotFound" diff --git a/tests/integration/studies_blueprint/assets/test_synthesis/raw_study.synthesis.json b/tests/integration/studies_blueprint/assets/test_synthesis/raw_study.synthesis.json index 04ce3ddbec..80ffa29e32 100644 --- a/tests/integration/studies_blueprint/assets/test_synthesis/raw_study.synthesis.json +++ b/tests/integration/studies_blueprint/assets/test_synthesis/raw_study.synthesis.json @@ -1589,6 +1589,18 @@ ], "archived": true, "xpansion": "" + }, + "20241807-1540eco-extra-outputs": { + "name": "extra-outputs", + "date": "20241807-1540", + "mode": "economy", + "nbyears": 1, + "synthesis": true, + "by_year": true, + "error": false, + "playlist": [], + "archived": false, + "xpansion": "" } }, "bindings": [], diff --git a/tests/integration/studies_blueprint/test_disk_usage.py b/tests/integration/studies_blueprint/test_disk_usage.py index dcfb48a5b7..d2257a40fd 100644 --- a/tests/integration/studies_blueprint/test_disk_usage.py +++ b/tests/integration/studies_blueprint/test_disk_usage.py @@ -27,8 +27,8 @@ def test_disk_usage_endpoint( headers=user_headers, ) assert res.status_code == 200, res.json() - disk_usage = res.json() # currently: 7.47 Mio on Ubuntu - assert 7 * 1024 * 1024 < disk_usage < 8 * 1024 * 1024 + disk_usage = res.json() # currently: 8.75 Mio on Ubuntu + assert 8 * 1024 * 1024 < disk_usage < 9 * 1024 * 1024 # Copy the study in managed workspace in order to create a variant res = client.post( diff --git a/tests/integration/study_data_blueprint/test_binding_constraints.py b/tests/integration/study_data_blueprint/test_binding_constraints.py index 554468989f..64e839c6fa 100644 --- a/tests/integration/study_data_blueprint/test_binding_constraints.py +++ b/tests/integration/study_data_blueprint/test_binding_constraints.py @@ -680,6 +680,16 @@ def test_for_version_870(self, client: TestClient, user_access_token: str, study else: assert data == np.zeros((matrix_lt3.shape[0], 1)).tolist() + # Checks that we only see existing matrices inside the Debug View + res = client.get(f"/v1/studies/{study_id}/raw", params={"path": "/input/bindingconstraints", "depth": 1}) + assert res.status_code in {200, 201} + assert res.json() == { + f"{bc_id_wo_group}_lt": {}, + f"{bc_id_w_group}_gt": {}, + f"{bc_id_w_matrix}_eq": {}, + "bindingconstraints": {}, + } + # ============================= # CONSTRAINT TERM MANAGEMENT # ============================= diff --git a/tests/integration/test_integration.py b/tests/integration/test_integration.py index 08aa099dd1..2119f96658 100644 --- a/tests/integration/test_integration.py +++ b/tests/integration/test_integration.py @@ -81,7 +81,7 @@ def test_main(client: TestClient, admin_access_token: str) -> None: headers={"Authorization": f'Bearer {george_credentials["access_token"]}'}, ) res_output = res.json() - assert len(res_output) == 5 + assert len(res_output) == 6 res = client.get( f"/v1/studies/{study_id}/outputs/20201014-1427eco/variables", @@ -178,7 +178,7 @@ def test_main(client: TestClient, admin_access_token: str) -> None: f"/v1/studies/{study_id}/outputs", headers={"Authorization": f'Bearer {george_credentials["access_token"]}'}, ) - assert len(res.json()) == 4 + assert len(res.json()) == 5 # study creation created = client.post( @@ -1400,7 +1400,7 @@ def test_area_management(client: TestClient, admin_access_token: str) -> None: res_links = client.get(f"/v1/studies/{study_id}/links") assert res_links.json() == [] - res = client.put( + client.put( f"/v1/studies/{study_id}/areas/area%201/ui", json={"x": 100, "y": 100, "color_rgb": [255, 0, 100]}, ) @@ -1662,7 +1662,7 @@ def test_import(client: TestClient, admin_access_token: str, internal_study_id: f"/v1/studies/{internal_study_id}/outputs", headers={"Authorization": f'Bearer {george_credentials["access_token"]}'}, ) - assert len(res.json()) == 6 + assert len(res.json()) == 7 # tests outputs import for .7z output_path_seven_zip = ASSETS_DIR / "output_adq.7z" @@ -1675,7 +1675,7 @@ def test_import(client: TestClient, admin_access_token: str, internal_study_id: f"/v1/studies/{internal_study_id}/outputs", headers={"Authorization": f'Bearer {george_credentials["access_token"]}'}, ) - assert len(res.json()) == 7 + assert len(res.json()) == 8 # test matrices import for .zip and .7z files matrices_zip_path = ASSETS_DIR / "matrices.zip" diff --git a/tests/integration/test_integration_token_end_to_end.py b/tests/integration/test_integration_token_end_to_end.py index bdab6a036d..66957d1a1e 100644 --- a/tests/integration/test_integration_token_end_to_end.py +++ b/tests/integration/test_integration_token_end_to_end.py @@ -197,7 +197,7 @@ def wait_unit_finished() -> bool: # read a result res = client.get(f"/v1/studies/{study_id}/outputs", headers=bot_headers) - assert len(res.json()) == 5 + assert len(res.json()) == 6 first_output_name = res.json()[0]["name"] res = client.get( f"/v1/studies/{study_id}/raw", @@ -212,7 +212,7 @@ def wait_unit_finished() -> bool: # remove output client.delete(f"/v1/studies/{study_id}/outputs/{first_output_name}", headers=bot_headers) res = client.get(f"/v1/studies/{study_id}/outputs", headers=bot_headers) - assert len(res.json()) == 4 + assert len(res.json()) == 5 # delete variant res = client.delete(f"/v1/studies/{variant_id}", headers=bot_headers) diff --git a/tests/launcher/test_service.py b/tests/launcher/test_service.py index 9095673070..6998df5667 100644 --- a/tests/launcher/test_service.py +++ b/tests/launcher/test_service.py @@ -40,13 +40,7 @@ LauncherParametersDTO, LogType, ) -from antarest.launcher.service import ( - EXECUTION_INFO_FILE, - LAUNCHER_PARAM_NAME_SUFFIX, - ORPHAN_JOBS_VISIBILITY_THRESHOLD, - JobNotFound, - LauncherService, -) +from antarest.launcher.service import EXECUTION_INFO_FILE, LAUNCHER_PARAM_NAME_SUFFIX, JobNotFound, LauncherService from antarest.login.auth import Auth from antarest.login.model import Identity from antarest.study.model import OwnerInfo, PublicMode, Study, StudyMetadataDTO @@ -257,7 +251,7 @@ def test_service_get_jobs_from_database(self, db_session) -> None: job_status=JobStatus.SUCCESS, msg="Hello, World!", exit_code=0, - creation_date=now - timedelta(days=ORPHAN_JOBS_VISIBILITY_THRESHOLD + 1), + creation_date=now - timedelta(days=11), owner=identity_instance, ) ] @@ -308,7 +302,7 @@ def test_service_get_jobs_from_database(self, db_session) -> None: ) ), ) - == returned_faked_execution_results + == [] ) with pytest.raises(UserHasNotPermissionError): diff --git a/tests/storage/repository/filesystem/matrix/test_input_series_matrix.py b/tests/storage/repository/filesystem/matrix/test_input_series_matrix.py index b6ac49fce1..1bc9df122a 100644 --- a/tests/storage/repository/filesystem/matrix/test_input_series_matrix.py +++ b/tests/storage/repository/filesystem/matrix/test_input_series_matrix.py @@ -1,3 +1,5 @@ +import itertools +import shutil import textwrap import typing as t from pathlib import Path @@ -92,3 +94,71 @@ def test_save(self, my_study_config: FileStudyTreeConfig) -> None: """ ) assert actual == expected + + +class TestCopyAndRenameFile: + node: InputSeriesMatrix + fake_node: InputSeriesMatrix + target: str + file: Path + link: Path + modified_file: Path + modified_link: Path + + def _set_up(self, tmp_path: Path) -> None: + self.file = tmp_path / "my-study" / "lazy.txt" + self.file.parent.mkdir() + + self.link = self.file.parent / f"{self.file.name}.link" + self.link.write_text("Link: Mock File Content") + + config = FileStudyTreeConfig(study_path=self.file.parent, path=self.file, version=-1, study_id="") + context = ContextServer(matrix=Mock(), resolver=Mock()) + self.node = InputSeriesMatrix(context=context, config=config) + + self.modified_file = self.file.parent / "lazy_modified.txt" + self.modified_link = self.file.parent / f"{self.modified_file.name}.link" + config2 = FileStudyTreeConfig(study_path=self.file.parent, path=self.modified_file, version=-1, study_id="") + self.fake_node = InputSeriesMatrix(context=Mock(), config=config2) + self.target = self.modified_file.stem + + def _checks_behavior(self, rename: bool, target_is_link: bool): + # Asserts `_infer_path` fails if there's no file + with pytest.raises(ChildNotFoundError): + self.fake_node._infer_path() + + # Checks `copy_file`, `rename_file` methods + if target_is_link: + assert not self.modified_link.exists() + assert self.link.exists() + assert not self.file.exists() + assert not self.modified_file.exists() + + self.node.rename_file(self.target) if rename else self.node.copy_file(self.target) + + assert not self.link.exists() if rename else self.link.exists() + assert self.modified_link.exists() + assert not self.file.exists() + assert not self.modified_file.exists() + assert self.modified_link.read_text() == "Link: Mock File Content" + + else: + content = b"No Link: Mock File Content" + self.node.save(content) + assert self.file.read_text() == content.decode("utf-8") + assert not self.link.exists() + assert not self.modified_file.exists() + + self.node.rename_file(self.target) if rename else self.node.copy_file(self.target) + + assert not self.link.exists() + assert not self.file.exists() if rename else self.file.exists() + assert self.modified_file.exists() + assert not self.modified_link.exists() + assert self.modified_file.read_text() == content.decode("utf-8") + + def test_copy_and_rename_file(self, tmp_path: Path): + for rename, target_is_link in itertools.product([True, False], repeat=2): + self._set_up(tmp_path) + self._checks_behavior(rename, target_is_link) + shutil.rmtree(tmp_path / "my-study") diff --git a/tests/storage/repository/filesystem/test_lazy_node.py b/tests/storage/repository/filesystem/test_lazy_node.py index a2c72415f5..f899d32fa3 100644 --- a/tests/storage/repository/filesystem/test_lazy_node.py +++ b/tests/storage/repository/filesystem/test_lazy_node.py @@ -2,8 +2,6 @@ from typing import List, Optional from unittest.mock import Mock -import pytest - from antarest.study.storage.rawstudy.model.filesystem.config.model import FileStudyTreeConfig from antarest.study.storage.rawstudy.model.filesystem.context import ContextServer from antarest.study.storage.rawstudy.model.filesystem.lazy_node import LazyNode @@ -140,113 +138,3 @@ def test_save_txt(tmp_path: Path): assert file.read_text() == content assert not link.exists() resolver.resolve.assert_called_once_with(content) - - -@pytest.mark.parametrize("target_is_link", [True, False]) -def test_rename_file(tmp_path: Path, target_is_link: bool): - file = tmp_path / "my-study/lazy.txt" - file.parent.mkdir() - - link = file.parent / f"{file.name}.link" - link.write_text("Link: Mock File Content") - - resolver = Mock() - resolver.resolve.return_value = None - - resolver2 = Mock() - resolver2.resolve.return_value = None - - config = FileStudyTreeConfig(study_path=file, path=file, version=-1, study_id="") - context = ContextServer(matrix=Mock(), resolver=resolver) - node = MockLazyNode(context=context, config=config) - - renaming_file = file.parent / "lazy_rename.txt" - renaming_link = file.parent / f"{renaming_file.name}.link" - config2 = FileStudyTreeConfig(study_path=renaming_file, path=renaming_file, version=-1, study_id="") - context2 = ContextServer(matrix=Mock(), resolver=resolver2) - target = MockLazyNode(context=context2, config=config2) - - if target_is_link: - assert not renaming_link.exists() - assert link.exists() - assert not file.exists() - assert not renaming_file.exists() - - node.rename_file(target) - - assert not link.exists() - assert renaming_link.exists() - assert not file.exists() - assert not renaming_file.exists() - assert renaming_link.read_text() == "Link: Mock File Content" - - else: - content = "No Link: Mock File Content" - node.save(content) - assert file.read_text() == content - assert not link.exists() - assert not renaming_file.exists() - resolver.resolve.assert_called_once_with(content) - - node.rename_file(target) - - assert not link.exists() - assert not file.exists() - assert renaming_file.exists() - assert not renaming_link.exists() - assert renaming_file.read_text() == "No Link: Mock File Content" - - -@pytest.mark.parametrize("target_is_link", [True, False]) -def test_copy_file(tmp_path: Path, target_is_link: bool): - file = tmp_path / "my-study/lazy.txt" - file.parent.mkdir() - - link = file.parent / f"{file.name}.link" - link.write_text("Link: Mock File Content") - - resolver = Mock() - resolver.resolve.return_value = None - - resolver2 = Mock() - resolver2.resolve.return_value = None - - config = FileStudyTreeConfig(study_path=file, path=file, version=-1, study_id="") - context = ContextServer(matrix=Mock(), resolver=resolver) - node = MockLazyNode(context=context, config=config) - - copied_file = file.parent / "lazy_copy.txt" - copied_link = file.parent / f"{copied_file.name}.link" - config2 = FileStudyTreeConfig(study_path=copied_file, path=copied_file, version=-1, study_id="") - context2 = ContextServer(matrix=Mock(), resolver=resolver2) - target = MockLazyNode(context=context2, config=config2) - - if target_is_link: - assert not copied_link.exists() - assert link.exists() - assert not file.exists() - assert not copied_file.exists() - - node.copy_file(target) - - assert link.exists() - assert copied_link.exists() - assert not file.exists() - assert not copied_file.exists() - assert copied_link.read_text() == "Link: Mock File Content" - - else: - content = "No Link: Mock File Content" - node.save(content) - assert file.read_text() == content - assert not link.exists() - assert not copied_file.exists() - resolver.resolve.assert_called_once_with(content) - - node.copy_file(target) - - assert not link.exists() - assert file.exists() - assert copied_file.exists() - assert not copied_link.exists() - assert copied_file.read_text() == "No Link: Mock File Content" diff --git a/tests/variantstudy/model/command/test_manage_binding_constraints.py b/tests/variantstudy/model/command/test_manage_binding_constraints.py index f3f6460400..d4034658e3 100644 --- a/tests/variantstudy/model/command/test_manage_binding_constraints.py +++ b/tests/variantstudy/model/command/test_manage_binding_constraints.py @@ -589,7 +589,7 @@ def test__update_matrices_names( # update matrices names update_matrices_names( file_study=empty_study, - binding_constraint_id="bd_rename_matrices", + bc_id="bd_rename_matrices", existing_operator=existing_operator, new_operator=new_operator, )