From b86df435ddff9b0a2af6751b431ceee0d72d83cf Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Fri, 30 Sep 2022 03:20:09 -0300 Subject: [PATCH 01/17] add captura recursos --- pipelines/rj_smtr/constants.py | 7 + .../rj_smtr/projeto_subsidio_sppo/flow.py | 107 +++++++ .../rj_smtr/projeto_subsidio_sppo/tasks.py | 276 ++++++++++++++++++ 3 files changed, 390 insertions(+) create mode 100644 pipelines/rj_smtr/projeto_subsidio_sppo/flow.py create mode 100644 pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py diff --git a/pipelines/rj_smtr/constants.py b/pipelines/rj_smtr/constants.py index 010f37d49..eedf983be 100644 --- a/pipelines/rj_smtr/constants.py +++ b/pipelines/rj_smtr/constants.py @@ -120,6 +120,13 @@ class constants(Enum): # pylint: disable=c0103 "key_column": "trip_id", }, } + + # SUBSIDIO SPPO # + SUBSIDIO_SPPO_DATASET_ID = "projeto_subsidio_sppo" + SUBSIDIO_SPPO_RECURSO_TABLE_ID = "recursos_filtrada" + SUBSIDIO_SPPO_RECURSO_API_BASE_URL = "https://api.movidesk.com/public/v1/tickets?" + SUBSIDIO_SPPO_RECURSO_API_SECRET_PATH = "sppo_subsidio_recursos_api" + # RDO FTPS_SECRET_PATH = "smtr_rdo_ftps" diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/flow.py b/pipelines/rj_smtr/projeto_subsidio_sppo/flow.py new file mode 100644 index 000000000..cc87e99f8 --- /dev/null +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/flow.py @@ -0,0 +1,107 @@ +# -*- coding: utf-8 -*- +""" +Flows for projeto_subsidio_sppo +""" + +from prefect import Parameter, case +from prefect.run_configs import KubernetesRun +from prefect.storage import GCS +from prefect.tasks.control_flow import merge + +# EMD Imports # + +from pipelines.constants import constants as emd_constants +from pipelines.utils.tasks import ( + rename_current_flow_run_now_time, +) +from pipelines.utils.decorators import Flow + +# from pipelines.utils.execute_dbt_model.tasks import get_k8s_dbt_client + +# SMTR Imports # + +from pipelines.rj_smtr.constants import constants +from pipelines.rj_smtr.tasks import ( + create_date_hour_partition, + create_local_partition_path, + get_current_timestamp, + # get_local_dbt_client, + get_raw, + parse_timestamp_to_string, + save_raw_local, + save_treated_local, + upload_logs_to_bq, + bq_upload, +) + +# from pipelines.rj_smtr.schedules import ( +# every_day_hour_six, +# ) + +from pipelines.rj_smtr.projeto_subsidio_sppo.tasks import ( + pre_treatment_subsidio_sppo_recursos, +) + +# Flows # + +with Flow( + "SMTR - Subsídio SPPO - Recursos Captura", code_owners=["caio", "fernanda"] +) as subsidio_sppo_recursos: + + # Get run parameters # + date_range_start = Parameter("date_range_start", default=False) + date_range_end = Parameter("date_range_end", default=False) + + # SETUP # + timestamp = get_current_timestamp() + + rename_flow_run = rename_current_flow_run_now_time( + prefix="Subsídio SPPO - Recursos Captura: ", now_time=timestamp + ) + + partitions = create_date_hour_partition(timestamp) + + filename = parse_timestamp_to_string(timestamp) + + filepath = create_local_partition_path( + dataset_id=constants.SUBSIDIO_SPPO_DATASET_ID.value, + table_id=constants.SUBSIDIO_SPPO_RECURSO_TABLE_ID.value, + filename=filename, + partitions=partitions, + ) + + # EXTRACT # + raw_status = get_raw(date_range_start, date_range_end) + + raw_filepath = save_raw_local(status=raw_status, file_path=filepath) + + # TREAT + treated_status = pre_treatment_subsidio_sppo_recursos( + status=raw_status, timestamp=timestamp + ) + + treated_filepath = save_treated_local(status=treated_status, file_path=filepath) + + # LOAD + error = bq_upload( + dataset_id=constants.SUBSIDIO_SPPO_DATASET_ID.value, + table_id=constants.SUBSIDIO_SPPO_RECURSO_TABLE_ID.value, + filepath=treated_filepath, + raw_filepath=raw_filepath, + partitions=partitions, + status=treated_status, + ) + + upload_logs_to_bq( + dataset_id=constants.SUBSIDIO_SPPO_DATASET_ID.value, + parent_table_id=constants.SUBSIDIO_SPPO_RECURSO_TABLE_ID.value, + error=error, + timestamp=timestamp, + ) + +subsidio_sppo_recursos.storage = GCS(emd_constants.GCS_FLOWS_BUCKET.value) +subsidio_sppo_recursos.run_config = KubernetesRun( + image=emd_constants.DOCKER_IMAGE.value, + labels=[emd_constants.RJ_SMTR_DEV_AGENT_LABEL.value], +) +# subsidio_sppo_recursos.schedule = every_day_hour_six diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py new file mode 100644 index 000000000..c4466f0b9 --- /dev/null +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -0,0 +1,276 @@ +# -*- coding: utf-8 -*- + +import datetime +import requests +import pandas as pd +import numpy as np +import time +from typing import Dict +from prefect import task + +# EMD Imports # + +from pipelines.utils.utils import log, get_vault_secret + +# SMTR Imports # + +from pipelines.rj_smtr.constants import constants + +# Tasks # + +custom_fields = { + "Serviço:\n": "servico_recurso", + "Linha:\n": "linha_recurso", + "Número de ordem, se aplicável:\n": "id_veiculo", + "Hora de Início da Viagem, se aplicável:\n": "datetime_partida", + "Hora do fim da viagem, se aplicável:\n": "datetime_chegada", + "Dia da Viagem:\n": "data", + "Direção do Serviço:\n": "sentido", +} + + +@task +def create_api_url_recursos( + date_range_start, date_range_end, skip, top=1000 +) -> Dict: # pylint: disable=E501 + """ + Returns movidesk URL to get the requests over date range. + """ + date_range_start = pd.to_datetime(date_range_start, utc=True).strftime( + "%Y-%m-%dT%H:%M:%S.%MZ" + ) + date_range_end = pd.to_datetime(date_range_end, utc=True).strftime( + "%Y-%m-%dT%H:%M:%S.%MZ" + ) + + url = constants.SUBSIDIO_SPPO_RECURSO_API_BASE_URL.value + token = get_vault_secret(constants.SUBSIDIO_SPPO_RECURSO_API_SECRET_PATH.value)[ + "data" + ]["token"] + url += f"token={token}" + + status = " or ".join( + [f"baseStatus eq '{s}'" for s in ["New", "InAttendance", "Resolved"]] + ) + st = date_range_start + et = date_range_end + filters = f"category eq 'Recurso' and createdDate ge {st} and createdDate lt {et}" + + params = { + "select": "id,protocol,createdDate,baseStatus,servicefull", + "filter": f"({filters}) and ({status})", + "expand": "actions($select=id,description,htmlDescription,createdDate)", + "orderby": "createdDate%20desc", + "top": top, + "skip": skip, + } + + for k, v in params.items(): + url += f"&${k}={v}" + + return url + + +@task +def get_raw( + date_range_start: datetime, date_range_end: datetime, top: int = 1000 +) -> Dict: + """ + Returns a dataframe with all recurso data from movidesk api until date. + """ + exit = False + skip = 0 + df = pd.DataFrame() + + while not exit: + # captura recursos da api + try: + url = create_api_url_recursos(date_range_start, date_range_end, skip) + r = requests.get(url) + r = r.json() + except Exception as error: + return {"data": pd.DataFrame(), "error": error} # TODO: add retry policy + + for recurso in r: + # filtra recurso de viagem individual + if recurso["serviceFull"] == [ + "SPPO", + "Viagem Individual - Recurso Viagens Subsídio", + ]: + # busca acoes do recurso + for action in recurso["actions"]: + row = { + "id_recurso": recurso["protocol"], + "data_recurso": action["createdDate"], + } + # recupera dados de viagem informados no recurso + if action["description"][:16] == "Dados informados": + for field, col in custom_fields.items(): + try: + row.update( + { + col: action["description"] + .split(field)[1] + .split("\n\xa0\n")[0] + } + ) + except Exception as error: + # TODO: check if need this break + return {"data": pd.DataFrame(), "error": error} + # salva apenas ultimo dado de viagem informado no recurso + df = df.append(row, ignore_index=True) + # itera os proximos recursos do periodo + if len(r) == top: + skip += top + time.sleep(6) + else: + exit = True + + # print(df.head()) + if len(df) == 0: + return {"data": list(), "erro": "Empty data"} + return {"data": df.set_index("id_recurso", drop=True), "error": None} + + +# linha_recurso ja nao tem espaco, esta em uppercase +def split_servico(x): + + tipo_servico = ["A", "N", "V", "P", "R", "E", "D", "B", "C", "F", "G"] + variacao_servico = ["A", "B", "C", "D"] + + if len(x) == 0: + # print("1:", x) + return "", "" + # servico nao inicia com S + if x[0] != "S": + # print("2:", x[0]) + return "", "" + # tipo servico invalido + if x[1] not in tipo_servico: + # print("3:", x[1]) + return "", "" + if len(x) == 2: + # print("4:", x) + return x, "" + # variacao servico invalida + if x[2] not in variacao_servico: + # print("5:", x[2]) + return "", "" + if len(x) == 3: + # print("6:", x) + return x[:2], x[2] + # padrao nao identificado + return "", "" + + +def routes_type_cross_validation(df) -> pd.DataFrame: + """ + Validate linha and servico informed with patterns for route names. + """ + # selecionando linha_recurso no formato esperado + df["linha"] = df["linha_recurso"].str.upper().str.replace(" ", "") + + # padroniza servico informado + df["tipo_servico"] = ( + "S" + df["servico_recurso"].str.extract(r"(^[a-zA-Z])") + ).replace("SA", "") + + # extrai o numero da linha_recurso + df["numero_linha_recurso"] = df["linha"].str.extract(r"(LECD)").fillna("") + df[ + "linha_recurso" + ].str.extract(r"(\d+)") + # filtra numero de linha valida (ate 4 digitos) + df["aux"] = df["linha_recurso"].str.extract(r"(\d+)") + df = df[(df["aux"].str.len() >= 2) & (df["aux"].str.len() <= 4)].drop(columns="aux") + + # extrai servico da linha informada (caso tenha) + df["servico_linha_recurso"] = ( + df["linha"] + .str.split("-") + .str[0] + .str.replace("LECD", "") + .str.extract(r"([a-zA-Z]+)") + ) + # caso nao tenha servico na linha (e nao seja LECD), usa o tipo servico informado + df["servico_linha_recurso"] = df["servico_linha_recurso"].fillna(df["tipo_servico"]) + # remove variacao das lecds (so aceito se for regular) + df.loc[ + df["numero_linha_recurso"].str.startswith("LECD"), + "servico_linha_recurso", + ] = "" + + # verifica servico e variacao compostos na linha_recurso + df["servico_linha_recurso"], df["variacao_servico_linha_recurso"] = zip( + *df["servico_linha_recurso"].fillna("").apply(lambda x: split_servico(x)) + ) + + # filtra servicos validos + df = df[df["servico_linha_recurso"] == df["tipo_servico"]] + df["servico"] = ( + df["tipo_servico"] + + df["variacao_servico_linha_recurso"] + + df["numero_linha_recurso"] + ) + + return df.drop( + columns=[ + "linha", + "tipo_servico", + "variacao_servico_linha_recurso", + "numero_linha_recurso", + "servico_linha_recurso", + ] + ) + + +# @task +def pre_treatment_subsidio_sppo_recursos(status: dict, timestamp: datetime) -> Dict: + """ + Treat recursos requested from movidesk api. + """ + if status["error"] is not None: + return {"data": pd.DataFrame(), "error": status["error"]} + + if status["data"] == []: + # log("Data is empty, skipping treatment...") + return {"data": pd.DataFrame(), "error": status["error"]} + + df = status["data"] + + # filtrando não nulos + df = df.dropna() + + # seleciona número de ordem no formato esperado + df = df[df["id_veiculo"].str.match(pat=r"(^\d{5})")] + + # selecionando data no formato esperado + df = df[df["data"].str.match(pat=r"(^\d{2}/\d{2}/\d{4}$)")] + + # seleciona datetime_chegada e datetime_partida no formato esperado + df = df[df["datetime_partida"].str.match(pat=r"(^\d{2}:\d{2}$)")] + df = df[df["datetime_chegada"].str.match(pat=r"(^\d{2}:\d{2}$)")] + + # validando data inicio < data fim e aplicando data type à colunas + df["datetime_chegada"] = pd.to_datetime( + df["data"].str.cat(df["datetime_chegada"], sep=" ") + ).dt.strftime("%Y-%m-%d %H:%M:%S") + df["datetime_partida"] = pd.to_datetime( + df["data"].str.cat(df["datetime_partida"], sep=" ") + ).dt.strftime("%Y-%m-%d %H:%M:%S") + df["data_recurso"] = pd.to_datetime(df["data_recurso"]).dt.strftime( + "%Y-%m-%d %H:%M:%S" + ) + + # verifica se a data de partida é valida + df = df[df["datetime_partida"] < df["datetime_chegada"]] + df = df[df["datetime_partida"] <= df["data_recurso"]] + + df["data"] = pd.to_datetime(df["data"]).dt.strftime("%Y-%m-%d") + df["sentido"] = df["sentido"].str.extract(r"(^[VIC])") + + # verifica linha e servico informados + for col in ["servico_recurso", "linha_recurso"]: + df[col] = df[col].str.replace("\xa0", " ").str.replace("\n", "") + df = routes_type_cross_validation(df) + + return {"data": df, "error": None} From b373c638f1054a9954c13492885ae2bd47eea7e8 Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Fri, 30 Sep 2022 14:12:51 -0300 Subject: [PATCH 02/17] add status column --- pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index c4466f0b9..35d0fd0e9 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -101,6 +101,7 @@ def get_raw( for action in recurso["actions"]: row = { "id_recurso": recurso["protocol"], + "status_recurso": recurso["baseStatus"], "data_recurso": action["createdDate"], } # recupera dados de viagem informados no recurso From aee06306b1cac084e2ed379416b2f748946cd1cf Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Fri, 30 Sep 2022 14:15:02 -0300 Subject: [PATCH 03/17] fix and register flows --- pipelines/rj_smtr/__init__.py | 1 + pipelines/rj_smtr/projeto_subsidio_sppo/{flow.py => flows.py} | 0 2 files changed, 1 insertion(+) rename pipelines/rj_smtr/projeto_subsidio_sppo/{flow.py => flows.py} (100%) diff --git a/pipelines/rj_smtr/__init__.py b/pipelines/rj_smtr/__init__.py index 9e926f83b..5c877f89f 100644 --- a/pipelines/rj_smtr/__init__.py +++ b/pipelines/rj_smtr/__init__.py @@ -14,3 +14,4 @@ from pipelines.rj_smtr.br_rj_riodejaneiro_brt_gps.flows import * from pipelines.rj_smtr.materialize_to_datario.flows import * from pipelines.rj_smtr.registros_ocr_rir.flows import * +from pipelines.rj_smtr.projeto_subsidio_sppo.flows import * diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/flow.py b/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py similarity index 100% rename from pipelines/rj_smtr/projeto_subsidio_sppo/flow.py rename to pipelines/rj_smtr/projeto_subsidio_sppo/flows.py From 4a19ece13292d16156c3ca7d0e40d75388bab5fc Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Mon, 3 Oct 2022 14:26:11 -0300 Subject: [PATCH 04/17] add log and fix map --- pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index 35d0fd0e9..2a9989dd3 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -44,6 +44,8 @@ def create_api_url_recursos( ) url = constants.SUBSIDIO_SPPO_RECURSO_API_BASE_URL.value + log("URL Base: ", url) + token = get_vault_secret(constants.SUBSIDIO_SPPO_RECURSO_API_SECRET_PATH.value)[ "data" ]["token"] @@ -52,9 +54,10 @@ def create_api_url_recursos( status = " or ".join( [f"baseStatus eq '{s}'" for s in ["New", "InAttendance", "Resolved"]] ) - st = date_range_start - et = date_range_end - filters = f"category eq 'Recurso' and createdDate ge {st} and createdDate lt {et}" + dt = date_range_start + de = date_range_end + + filters = f"category eq 'Recurso' and createdDate ge {dt} and createdDate lt {de}" params = { "select": "id,protocol,createdDate,baseStatus,servicefull", @@ -64,6 +67,7 @@ def create_api_url_recursos( "top": top, "skip": skip, } + log("URL Parameters: ", params) for k, v in params.items(): url += f"&${k}={v}" @@ -267,7 +271,7 @@ def pre_treatment_subsidio_sppo_recursos(status: dict, timestamp: datetime) -> D df = df[df["datetime_partida"] <= df["data_recurso"]] df["data"] = pd.to_datetime(df["data"]).dt.strftime("%Y-%m-%d") - df["sentido"] = df["sentido"].str.extract(r"(^[VIC])") + df["sentido"] = df["sentido"].map({"Ida": "I", "Volta": "V", "Circular": "C"}) # verifica linha e servico informados for col in ["servico_recurso", "linha_recurso"]: From f74b3c8c2d9a304458a7b586aa7186ddc04ba1d1 Mon Sep 17 00:00:00 2001 From: Gabriel MOC Date: Mon, 3 Oct 2022 18:56:49 -0300 Subject: [PATCH 05/17] change url and get_raw to get values from customfields (#208) * change url and get_raw to get values from customfields * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * Update pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py * remove unused description fields Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Fernanda Scovino --- .../rj_smtr/projeto_subsidio_sppo/tasks.py | 35 ++++++++++--------- 1 file changed, 19 insertions(+), 16 deletions(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index 2a9989dd3..12bade343 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -62,7 +62,7 @@ def create_api_url_recursos( params = { "select": "id,protocol,createdDate,baseStatus,servicefull", "filter": f"({filters}) and ({status})", - "expand": "actions($select=id,description,htmlDescription,createdDate)", + "expand": "actions($select=id,createdDate),customFieldValues($expand=items)", "orderby": "createdDate%20desc", "top": top, "skip": skip, @@ -104,24 +104,27 @@ def get_raw( # busca acoes do recurso for action in recurso["actions"]: row = { + "data_recurso": action["createdDate"], + } + # recupera dados de viagem informados no recurso + row.update( + { "id_recurso": recurso["protocol"], "status_recurso": recurso["baseStatus"], - "data_recurso": action["createdDate"], + "id_veiculo": recurso["customFieldValues"][7]["value"], + "data": recurso["customFieldValues"][1]["value"], + "datetime_partida": recurso["customFieldValues"][2]["value"], + "datetime_chegada": recurso["customFieldValues"][3]["value"], + "servico": recurso["customFieldValues"][5]["value"], + "tipo_servico": recurso["customFieldValues"][6]["items"][0][ + "customFieldItem" + ], + "sentido": recurso["customFieldValues"][8]["items"][0][ + "customFieldItem" + ], } - # recupera dados de viagem informados no recurso - if action["description"][:16] == "Dados informados": - for field, col in custom_fields.items(): - try: - row.update( - { - col: action["description"] - .split(field)[1] - .split("\n\xa0\n")[0] - } - ) - except Exception as error: - # TODO: check if need this break - return {"data": pd.DataFrame(), "error": error} + ) + # salva apenas ultimo dado de viagem informado no recurso df = df.append(row, ignore_index=True) # itera os proximos recursos do periodo From e82db43147d4b3515a79f285c5f842c4dec6ede3 Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Mon, 3 Oct 2022 19:58:56 -0300 Subject: [PATCH 06/17] fix task source --- pipelines/rj_smtr/projeto_subsidio_sppo/flows.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py b/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py index cc87e99f8..4c8a2bdf2 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py @@ -26,7 +26,6 @@ create_local_partition_path, get_current_timestamp, # get_local_dbt_client, - get_raw, parse_timestamp_to_string, save_raw_local, save_treated_local, @@ -39,6 +38,7 @@ # ) from pipelines.rj_smtr.projeto_subsidio_sppo.tasks import ( + get_raw, pre_treatment_subsidio_sppo_recursos, ) From 834867158133965b23b5e701d1abe340100e08f1 Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Mon, 3 Oct 2022 20:27:57 -0300 Subject: [PATCH 07/17] add init --- pipelines/rj_smtr/projeto_subsidio_sppo/__init__.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 pipelines/rj_smtr/projeto_subsidio_sppo/__init__.py diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/__init__.py b/pipelines/rj_smtr/projeto_subsidio_sppo/__init__.py new file mode 100644 index 000000000..e69de29bb From 134f929512a24e9a0db58bfa5c84315303c27f82 Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Mon, 3 Oct 2022 20:28:19 -0300 Subject: [PATCH 08/17] remove old fields dict --- pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index 12bade343..9e58a1c8a 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -18,16 +18,6 @@ # Tasks # -custom_fields = { - "Serviço:\n": "servico_recurso", - "Linha:\n": "linha_recurso", - "Número de ordem, se aplicável:\n": "id_veiculo", - "Hora de Início da Viagem, se aplicável:\n": "datetime_partida", - "Hora do fim da viagem, se aplicável:\n": "datetime_chegada", - "Dia da Viagem:\n": "data", - "Direção do Serviço:\n": "sentido", -} - @task def create_api_url_recursos( From 249fff6eb4c52a3d41559a38980f9292777adcdb Mon Sep 17 00:00:00 2001 From: hellcassius Date: Tue, 4 Oct 2022 14:22:25 -0300 Subject: [PATCH 09/17] minor fixes --- pipelines/rj_smtr/projeto_subsidio_sppo/flows.py | 4 ++-- pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py | 10 +++++----- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py b/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py index 4c8a2bdf2..bfd03b9bc 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py @@ -49,8 +49,8 @@ ) as subsidio_sppo_recursos: # Get run parameters # - date_range_start = Parameter("date_range_start", default=False) - date_range_end = Parameter("date_range_end", default=False) + date_range_start = Parameter("date_range_start", default="2022-10-03 00:00:00") + date_range_end = Parameter("date_range_end", default="2022-10-04 00:00:00") # SETUP # timestamp = get_current_timestamp() diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index 9e58a1c8a..c99ab6fc3 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -19,7 +19,7 @@ # Tasks # -@task +# @task def create_api_url_recursos( date_range_start, date_range_end, skip, top=1000 ) -> Dict: # pylint: disable=E501 @@ -34,7 +34,7 @@ def create_api_url_recursos( ) url = constants.SUBSIDIO_SPPO_RECURSO_API_BASE_URL.value - log("URL Base: ", url) + log(f"URL Base: {url}") token = get_vault_secret(constants.SUBSIDIO_SPPO_RECURSO_API_SECRET_PATH.value)[ "data" @@ -57,7 +57,7 @@ def create_api_url_recursos( "top": top, "skip": skip, } - log("URL Parameters: ", params) + log(f"URL Parameters: {params}") for k, v in params.items(): url += f"&${k}={v}" @@ -126,7 +126,7 @@ def get_raw( # print(df.head()) if len(df) == 0: - return {"data": list(), "erro": "Empty data"} + return {"data": list(), "error": "Empty data"} return {"data": df.set_index("id_recurso", drop=True), "error": None} @@ -221,7 +221,7 @@ def routes_type_cross_validation(df) -> pd.DataFrame: ) -# @task +@task def pre_treatment_subsidio_sppo_recursos(status: dict, timestamp: datetime) -> Dict: """ Treat recursos requested from movidesk api. From 844122b84601902a586c0a98c3e38e48e98b8c81 Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Wed, 5 Oct 2022 18:04:22 -0300 Subject: [PATCH 10/17] fix lint and remove treatment - moved to dbt --- .../rj_smtr/projeto_subsidio_sppo/flows.py | 7 +- .../rj_smtr/projeto_subsidio_sppo/tasks.py | 321 +++++++----------- 2 files changed, 124 insertions(+), 204 deletions(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py b/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py index bfd03b9bc..ac61f8fcc 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/flows.py @@ -3,10 +3,9 @@ Flows for projeto_subsidio_sppo """ -from prefect import Parameter, case +from prefect import Parameter from prefect.run_configs import KubernetesRun from prefect.storage import GCS -from prefect.tasks.control_flow import merge # EMD Imports # @@ -38,7 +37,7 @@ # ) from pipelines.rj_smtr.projeto_subsidio_sppo.tasks import ( - get_raw, + get_raw_recursos, pre_treatment_subsidio_sppo_recursos, ) @@ -71,7 +70,7 @@ ) # EXTRACT # - raw_status = get_raw(date_range_start, date_range_end) + raw_status = get_raw_recursos(date_range_start, date_range_end) raw_filepath = save_raw_local(status=raw_status, file_path=filepath) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index c99ab6fc3..f83e2a9ea 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -1,11 +1,15 @@ # -*- coding: utf-8 -*- +""" +Tasks for projeto_subsidio_sppo +""" -import datetime +# pylint: disable=too-many-arguments,broad-except + +# import datetime +from typing import Dict, List +import time import requests import pandas as pd -import numpy as np -import time -from typing import Dict from prefect import task # EMD Imports # @@ -19,210 +23,137 @@ # Tasks # -# @task -def create_api_url_recursos( - date_range_start, date_range_end, skip, top=1000 -) -> Dict: # pylint: disable=E501 +def create_api_url_recursos(date_range_start, date_range_end, skip=0, top=1000) -> Dict: """ Returns movidesk URL to get the requests over date range. """ - date_range_start = pd.to_datetime(date_range_start, utc=True).strftime( - "%Y-%m-%dT%H:%M:%S.%MZ" - ) - date_range_end = pd.to_datetime(date_range_end, utc=True).strftime( - "%Y-%m-%dT%H:%M:%S.%MZ" - ) - url = constants.SUBSIDIO_SPPO_RECURSO_API_BASE_URL.value - log(f"URL Base: {url}") - token = get_vault_secret(constants.SUBSIDIO_SPPO_RECURSO_API_SECRET_PATH.value)[ "data" ]["token"] + url += f"token={token}" status = " or ".join( [f"baseStatus eq '{s}'" for s in ["New", "InAttendance", "Resolved"]] ) - dt = date_range_start - de = date_range_end - filters = f"category eq 'Recurso' and createdDate ge {dt} and createdDate lt {de}" + start = pd.to_datetime(date_range_start, utc=True).strftime("%Y-%m-%dT%H:%M:%S.%MZ") + end = pd.to_datetime(date_range_end, utc=True).strftime("%Y-%m-%dT%H:%M:%S.%MZ") + + category = "category eq 'Recurso'" + dates = f"createdDate ge {start} and createdDate lt {end}" + service = "serviceFirstLevel eq 'Viagem Individual - Recurso Viagens Subsídio'" params = { - "select": "id,protocol,createdDate,baseStatus,servicefull", - "filter": f"({filters}) and ({status})", - "expand": "actions($select=id,createdDate),customFieldValues($expand=items)", + "select": "id,protocol,createdDate,baseStatus,serviceSecondLevel,customFieldValues", + "filter": f"({category} and {dates} and {service}) and ({status})", + "expand": "customFieldValues($expand=items)", "orderby": "createdDate%20desc", "top": top, "skip": skip, } + log(f"URL Parameters: {params}") - for k, v in params.items(): - url += f"&${k}={v}" + for param, value in params.items(): + url += f"&${param}={value}" return url +def request_data(url: str, headers: dict = None) -> Dict: + """ + Return response json and error. + """ + data = None + + # Get data from API + try: + response = requests.get( + url, headers=headers, timeout=constants.MAX_TIMEOUT_SECONDS.value + ) + error = None + except Exception as exp: + error = exp + log(f"[CATCHED] Task failed with error: \n{error}", level="error") + return {"data": None, "error": error} + + # Check data results + if response.ok: # status code is less than 400 + data = response.json() + + return {"data": data, "error": error} + + @task -def get_raw( - date_range_start: datetime, date_range_end: datetime, top: int = 1000 +def get_raw_recursos( + date_range_start: str, date_range_end: str, skip: int = 0, top: int = 1000 ) -> Dict: """ Returns a dataframe with all recurso data from movidesk api until date. """ - exit = False - skip = 0 - df = pd.DataFrame() - - while not exit: - # captura recursos da api - try: - url = create_api_url_recursos(date_range_start, date_range_end, skip) - r = requests.get(url) - r = r.json() - except Exception as error: - return {"data": pd.DataFrame(), "error": error} # TODO: add retry policy - - for recurso in r: - # filtra recurso de viagem individual - if recurso["serviceFull"] == [ - "SPPO", - "Viagem Individual - Recurso Viagens Subsídio", - ]: - # busca acoes do recurso - for action in recurso["actions"]: - row = { - "data_recurso": action["createdDate"], - } - # recupera dados de viagem informados no recurso - row.update( - { - "id_recurso": recurso["protocol"], - "status_recurso": recurso["baseStatus"], - "id_veiculo": recurso["customFieldValues"][7]["value"], - "data": recurso["customFieldValues"][1]["value"], - "datetime_partida": recurso["customFieldValues"][2]["value"], - "datetime_chegada": recurso["customFieldValues"][3]["value"], - "servico": recurso["customFieldValues"][5]["value"], - "tipo_servico": recurso["customFieldValues"][6]["items"][0][ - "customFieldItem" - ], - "sentido": recurso["customFieldValues"][8]["items"][0][ - "customFieldItem" - ], - } - ) + all_records = False + data = {} + + while not all_records: + url = create_api_url_recursos(date_range_start, date_range_end, skip) + status = request_data(url) + + if status["error"] is not None: + return {"data": None, "error": status["error"]} + + data.update(status) - # salva apenas ultimo dado de viagem informado no recurso - df = df.append(row, ignore_index=True) # itera os proximos recursos do periodo - if len(r) == top: + if len(status["data"]) == top: skip += top time.sleep(6) else: - exit = True - - # print(df.head()) - if len(df) == 0: - return {"data": list(), "error": "Empty data"} - return {"data": df.set_index("id_recurso", drop=True), "error": None} - - -# linha_recurso ja nao tem espaco, esta em uppercase -def split_servico(x): - - tipo_servico = ["A", "N", "V", "P", "R", "E", "D", "B", "C", "F", "G"] - variacao_servico = ["A", "B", "C", "D"] - - if len(x) == 0: - # print("1:", x) - return "", "" - # servico nao inicia com S - if x[0] != "S": - # print("2:", x[0]) - return "", "" - # tipo servico invalido - if x[1] not in tipo_servico: - # print("3:", x[1]) - return "", "" - if len(x) == 2: - # print("4:", x) - return x, "" - # variacao servico invalida - if x[2] not in variacao_servico: - # print("5:", x[2]) - return "", "" - if len(x) == 3: - # print("6:", x) - return x[:2], x[2] - # padrao nao identificado - return "", "" - - -def routes_type_cross_validation(df) -> pd.DataFrame: + all_records = True + + if len(status["data"]) == 0: + return {"data": status["data"], "error": "Empty data"} + + return {"data": status["data"], "error": None} + + +def get_custom_fields(custom_fields: List) -> Dict: """ - Validate linha and servico informed with patterns for route names. + Return customFields dict. """ - # selecionando linha_recurso no formato esperado - df["linha"] = df["linha_recurso"].str.upper().str.replace(" ", "") - - # padroniza servico informado - df["tipo_servico"] = ( - "S" + df["servico_recurso"].str.extract(r"(^[a-zA-Z])") - ).replace("SA", "") - - # extrai o numero da linha_recurso - df["numero_linha_recurso"] = df["linha"].str.extract(r"(LECD)").fillna("") + df[ - "linha_recurso" - ].str.extract(r"(\d+)") - # filtra numero de linha valida (ate 4 digitos) - df["aux"] = df["linha_recurso"].str.extract(r"(\d+)") - df = df[(df["aux"].str.len() >= 2) & (df["aux"].str.len() <= 4)].drop(columns="aux") - - # extrai servico da linha informada (caso tenha) - df["servico_linha_recurso"] = ( - df["linha"] - .str.split("-") - .str[0] - .str.replace("LECD", "") - .str.extract(r"([a-zA-Z]+)") - ) - # caso nao tenha servico na linha (e nao seja LECD), usa o tipo servico informado - df["servico_linha_recurso"] = df["servico_linha_recurso"].fillna(df["tipo_servico"]) - # remove variacao das lecds (so aceito se for regular) - df.loc[ - df["numero_linha_recurso"].str.startswith("LECD"), - "servico_linha_recurso", - ] = "" - - # verifica servico e variacao compostos na linha_recurso - df["servico_linha_recurso"], df["variacao_servico_linha_recurso"] = zip( - *df["servico_linha_recurso"].fillna("").apply(lambda x: split_servico(x)) - ) + map_field = { + 111867: "data", + 111868: "hora_partida", + 111869: "hora_chegada", + 111871: "servico", + 111873: "id_veiculo", + 111872: "tipo_servico", + 111901: "sentido", + } - # filtra servicos validos - df = df[df["servico_linha_recurso"] == df["tipo_servico"]] - df["servico"] = ( - df["tipo_servico"] - + df["variacao_servico_linha_recurso"] - + df["numero_linha_recurso"] - ) + row = {} + for field in custom_fields: - return df.drop( - columns=[ - "linha", - "tipo_servico", - "variacao_servico_linha_recurso", - "numero_linha_recurso", - "servico_linha_recurso", - ] - ) + if field["customFieldId"] in custom_fields.keys(): + # dropdown field + if field["items"]: + row.update( + { + map_field[field["customFieldId"]]: field["items"][0][ + "customFieldItem" + ] + } + ) + # other fields + else: + row.update({map_field[field["customFieldId"]]: field["value"]}) + + return row @task -def pre_treatment_subsidio_sppo_recursos(status: dict, timestamp: datetime) -> Dict: +def pre_treatment_subsidio_sppo_recursos(status: dict, timestamp: str) -> Dict: """ Treat recursos requested from movidesk api. """ @@ -233,42 +164,32 @@ def pre_treatment_subsidio_sppo_recursos(status: dict, timestamp: datetime) -> D # log("Data is empty, skipping treatment...") return {"data": pd.DataFrame(), "error": status["error"]} - df = status["data"] + data = pd.DataFrame() + for item in status["data"]: + row = { + "modo": item["serviceSecondLevel"], + "id_recurso": item["id"], + "protocolo": item["protocol"], + "status": item["baseStatus"], + "data_recurso": item["createdDate"], + } + row.update(get_custom_fields(item["customFieldValues"])) + data = data.append(row, ignore_index=True) - # filtrando não nulos - df = df.dropna() - - # seleciona número de ordem no formato esperado - df = df[df["id_veiculo"].str.match(pat=r"(^\d{5})")] - - # selecionando data no formato esperado - df = df[df["data"].str.match(pat=r"(^\d{2}/\d{2}/\d{4}$)")] - - # seleciona datetime_chegada e datetime_partida no formato esperado - df = df[df["datetime_partida"].str.match(pat=r"(^\d{2}:\d{2}$)")] - df = df[df["datetime_chegada"].str.match(pat=r"(^\d{2}:\d{2}$)")] - - # validando data inicio < data fim e aplicando data type à colunas - df["datetime_chegada"] = pd.to_datetime( - df["data"].str.cat(df["datetime_chegada"], sep=" ") - ).dt.strftime("%Y-%m-%d %H:%M:%S") - df["datetime_partida"] = pd.to_datetime( - df["data"].str.cat(df["datetime_partida"], sep=" ") - ).dt.strftime("%Y-%m-%d %H:%M:%S") - df["data_recurso"] = pd.to_datetime(df["data_recurso"]).dt.strftime( - "%Y-%m-%d %H:%M:%S" - ) + data["timestamp_captura"] = timestamp - # verifica se a data de partida é valida - df = df[df["datetime_partida"] < df["datetime_chegada"]] - df = df[df["datetime_partida"] <= df["data_recurso"]] + # filtrando não nulos + data = data.dropna() - df["data"] = pd.to_datetime(df["data"]).dt.strftime("%Y-%m-%d") - df["sentido"] = df["sentido"].map({"Ida": "I", "Volta": "V", "Circular": "C"}) + # converte datas + data["data_recurso"] = ( + pd.to_datetime(data["data_recurso"]) + .dt.tz_localize(tz="America/Sao_Paulo") + .map(lambda x: x.isoformat()) + ) + data["data"] = pd.to_datetime(data["data"]).dt.strftime("%Y-%m-%d") - # verifica linha e servico informados - for col in ["servico_recurso", "linha_recurso"]: - df[col] = df[col].str.replace("\xa0", " ").str.replace("\n", "") - df = routes_type_cross_validation(df) + # remove caracteres de campo aberto que quebram o schema + data["servico"] = data["servico"].str.replace("\xa0", " ").str.replace("\n", "") - return {"data": df, "error": None} + return {"data": data, "error": None} From 6b1c6000e813a889b84d3f28a7f6876031cd3c06 Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Wed, 5 Oct 2022 18:40:50 -0300 Subject: [PATCH 11/17] add status to capture + hotfix --- pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index f83e2a9ea..53786e137 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -34,9 +34,10 @@ def create_api_url_recursos(date_range_start, date_range_end, skip=0, top=1000) url += f"token={token}" - status = " or ".join( - [f"baseStatus eq '{s}'" for s in ["New", "InAttendance", "Resolved"]] + baseStatus = " or ".join( + [f"baseStatus eq '{s}'" for s in ["New", "InAttendance", "Resolved", "Closed"]] ) + status = "status ne 'Resolvido Automaticamente'" start = pd.to_datetime(date_range_start, utc=True).strftime("%Y-%m-%dT%H:%M:%S.%MZ") end = pd.to_datetime(date_range_end, utc=True).strftime("%Y-%m-%dT%H:%M:%S.%MZ") @@ -47,7 +48,7 @@ def create_api_url_recursos(date_range_start, date_range_end, skip=0, top=1000) params = { "select": "id,protocol,createdDate,baseStatus,serviceSecondLevel,customFieldValues", - "filter": f"({category} and {dates} and {service}) and ({status})", + "filter": f"({category} and {dates} and {service} and {status}) and ({baseStatus})", "expand": "customFieldValues($expand=items)", "orderby": "createdDate%20desc", "top": top, @@ -135,7 +136,7 @@ def get_custom_fields(custom_fields: List) -> Dict: row = {} for field in custom_fields: - if field["customFieldId"] in custom_fields.keys(): + if field["customFieldId"] in map_field.keys(): # dropdown field if field["items"]: row.update( From 48575b16d439c4bf70d0c559c1a8334c39397122 Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Thu, 6 Oct 2022 10:27:32 -0300 Subject: [PATCH 12/17] fix data column + status iteration --- .../rj_smtr/projeto_subsidio_sppo/tasks.py | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index 53786e137..c5ce176a1 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -95,28 +95,28 @@ def get_raw_recursos( Returns a dataframe with all recurso data from movidesk api until date. """ all_records = False - data = {} + status = {"data": {}, "error": None} while not all_records: url = create_api_url_recursos(date_range_start, date_range_end, skip) - status = request_data(url) + current_status = request_data(url) - if status["error"] is not None: - return {"data": None, "error": status["error"]} + if current_status["error"] is not None: + return {"data": None, "error": current_status["error"]} - data.update(status) + status["data"] += current_status["data"] # itera os proximos recursos do periodo - if len(status["data"]) == top: + if len(current_status["data"]) == top: skip += top time.sleep(6) else: all_records = True if len(status["data"]) == 0: - return {"data": status["data"], "error": "Empty data"} + status["error"] = "Empty data" - return {"data": status["data"], "error": None} + return status def get_custom_fields(custom_fields: List) -> Dict: @@ -124,7 +124,7 @@ def get_custom_fields(custom_fields: List) -> Dict: Return customFields dict. """ map_field = { - 111867: "data", + 111867: "data_viagem", 111868: "hora_partida", 111869: "hora_chegada", 111871: "servico", @@ -188,7 +188,7 @@ def pre_treatment_subsidio_sppo_recursos(status: dict, timestamp: str) -> Dict: .dt.tz_localize(tz="America/Sao_Paulo") .map(lambda x: x.isoformat()) ) - data["data"] = pd.to_datetime(data["data"]).dt.strftime("%Y-%m-%d") + data["data_viagem"] = pd.to_datetime(data["data_viagem"]).dt.strftime("%Y-%m-%d") # remove caracteres de campo aberto que quebram o schema data["servico"] = data["servico"].str.replace("\xa0", " ").str.replace("\n", "") From 1e30fe26a0ea47e1ad87ef521820ecf28401a897 Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Thu, 6 Oct 2022 10:32:54 -0300 Subject: [PATCH 13/17] fix pylint --- pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index c5ce176a1..1608a55cb 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -3,7 +3,7 @@ Tasks for projeto_subsidio_sppo """ -# pylint: disable=too-many-arguments,broad-except +# pylint: disable=too-many-arguments,broad-except,too-many-locals # import datetime from typing import Dict, List @@ -32,9 +32,7 @@ def create_api_url_recursos(date_range_start, date_range_end, skip=0, top=1000) "data" ]["token"] - url += f"token={token}" - - baseStatus = " or ".join( + base_status = " or ".join( [f"baseStatus eq '{s}'" for s in ["New", "InAttendance", "Resolved", "Closed"]] ) status = "status ne 'Resolvido Automaticamente'" @@ -48,7 +46,7 @@ def create_api_url_recursos(date_range_start, date_range_end, skip=0, top=1000) params = { "select": "id,protocol,createdDate,baseStatus,serviceSecondLevel,customFieldValues", - "filter": f"({category} and {dates} and {service} and {status}) and ({baseStatus})", + "filter": f"({category} and {dates} and {service} and {status}) and ({base_status})", "expand": "customFieldValues($expand=items)", "orderby": "createdDate%20desc", "top": top, @@ -57,6 +55,8 @@ def create_api_url_recursos(date_range_start, date_range_end, skip=0, top=1000) log(f"URL Parameters: {params}") + url += f"token={token}" + for param, value in params.items(): url += f"&${param}={value}" @@ -136,7 +136,7 @@ def get_custom_fields(custom_fields: List) -> Dict: row = {} for field in custom_fields: - if field["customFieldId"] in map_field.keys(): + if field["customFieldId"] in map_field: # dropdown field if field["items"]: row.update( From afe2ece29b6c8b3681a7c82884911fd4e9788fdb Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Thu, 6 Oct 2022 10:51:39 -0300 Subject: [PATCH 14/17] fix list --- pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index 1608a55cb..a4714e8f9 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -95,7 +95,7 @@ def get_raw_recursos( Returns a dataframe with all recurso data from movidesk api until date. """ all_records = False - status = {"data": {}, "error": None} + status = {"data": [], "error": None} while not all_records: url = create_api_url_recursos(date_range_start, date_range_end, skip) From ab653e40a86a1b2e1adbea1455c68a3a62fcb2d5 Mon Sep 17 00:00:00 2001 From: Fernanda Scovino Date: Thu, 6 Oct 2022 15:07:19 -0300 Subject: [PATCH 15/17] fix status --- pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py index a4714e8f9..c0ca6d11d 100644 --- a/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py +++ b/pipelines/rj_smtr/projeto_subsidio_sppo/tasks.py @@ -45,7 +45,7 @@ def create_api_url_recursos(date_range_start, date_range_end, skip=0, top=1000) service = "serviceFirstLevel eq 'Viagem Individual - Recurso Viagens Subsídio'" params = { - "select": "id,protocol,createdDate,baseStatus,serviceSecondLevel,customFieldValues", + "select": "id,protocol,createdDate,status,serviceSecondLevel,customFieldValues", "filter": f"({category} and {dates} and {service} and {status}) and ({base_status})", "expand": "customFieldValues($expand=items)", "orderby": "createdDate%20desc", @@ -171,7 +171,7 @@ def pre_treatment_subsidio_sppo_recursos(status: dict, timestamp: str) -> Dict: "modo": item["serviceSecondLevel"], "id_recurso": item["id"], "protocolo": item["protocol"], - "status": item["baseStatus"], + "status": item["status"], "data_recurso": item["createdDate"], } row.update(get_custom_fields(item["customFieldValues"])) From 00df392e0989c510a394c687fab83fc55817fdf1 Mon Sep 17 00:00:00 2001 From: Carolina Gomes Date: Thu, 26 Oct 2023 15:43:37 -0300 Subject: [PATCH 16/17] Revert "conflitos resolvidos" This reverts commit affb7ce18aad71aa2a0ef74393f8147a7675638f, reversing changes made to 0041cbb42fc3924fb1527f523df7d2cd12ce06ef. --- .flake8 | 2 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pipelines/rj_smtr/constants.py | 271 +- pipelines/rj_smtr/example/flows.py | 2 + pipelines/rj_smtr/flows.py | 339 +- .../rj_smtr/materialize_to_datario/flows.py | 80 +- .../rj_smtr/projeto_subsidio_sppo/flows.py | 245 +- .../rj_smtr/projeto_subsidio_sppo/tasks.py | 9 +- pipelines/rj_smtr/registros_ocr_rir/flows.py | 2 - pipelines/rj_smtr/schedules.py | 58 +- pipelines/rj_smtr/tasks.py | 904 +- pipelines/rj_smtr/utils.py | 647 +- pipelines/rj_smtr/veiculo/__init__.py | 0 pipelines/rj_smtr/veiculo/constants.py | 71 - pipelines/rj_smtr/veiculo/flows.py | 245 - pipelines/rj_smtr/veiculo/tasks.py | 197 - pipelines/utils/__init__.py | 2 - pipelines/utils/backfill_flow/flows.py | 1 + pipelines/utils/constants.py | 1 - pipelines/utils/custom.py | 65 +- pipelines/utils/dump_datario/flows.py | 34 +- pipelines/utils/dump_datario/tasks.py | 163 +- pipelines/utils/dump_datario/utils.py | 91 +- pipelines/utils/dump_db/flows.py | 11 +- pipelines/utils/dump_db/tasks.py | 283 +- pipelines/utils/dump_db/utils.py | 28 +- .../utils/dump_earth_engine_asset/__init__.py | 0 .../utils/dump_earth_engine_asset/flows.py | 93 - .../utils/dump_earth_engine_asset/tasks.py | 252 - pipelines/utils/dump_to_gcs/flows.py | 1 + pipelines/utils/dump_to_gcs/tasks.py | 1 - pipelines/utils/dump_url/flows.py | 9 +- pipelines/utils/dump_url/tasks.py | 2 - pipelines/utils/dump_url/utils.py | 2 + pipelines/utils/execute_dbt_model/flows.py | 5 +- pipelines/utils/execute_dbt_model/tasks.py | 57 +- pipelines/utils/georeference/__init__.py | 0 pipelines/utils/georeference/flows.py | 163 - pipelines/utils/georeference/tasks.py | 149 - pipelines/utils/georeference/utils.py | 33 - pipelines/utils/policy_matrix/__init__.py | 0 pipelines/utils/policy_matrix/flows.py | 76 - pipelines/utils/policy_matrix/tasks.py | 136 - pipelines/utils/predict_flow/flows.py | 3 +- pipelines/utils/tasks.py | 36 +- pipelines/utils/utils.py | 240 +- pipelines/utils/whatsapp_bot/flows.py | 1 + .../__init__.py | 4 +- .../constants.py | 41 + .../cookiecutter.json | 4 + .../{{cookiecutter.project_name}}/flows.py | 78 + .../schedules.py | 88 + .../{{cookiecutter.project_name}}/tasks.py | 60 + .../{{cookiecutter.project_name}}/utils.py | 31 + .../__init__.py | 0 .../constants.py | 41 + .../{{cookiecutter.workspace_name}}/flows.py | 78 + .../schedules.py | 88 + .../{{cookiecutter.workspace_name}}/tasks.py | 60 + .../{{cookiecutter.workspace_name}}/utils.py | 31 + poetry.lock | 7735 +++++++---------- pyproject.toml | 60 +- requirements-cli.txt | 2 +- requirements-deploy.txt | 2 +- scripts/lint.py | 9 +- .../__init__.py | 0 tests/test_sanity.py | 12 + 287 files changed, 5338 insertions(+), 27524 deletions(-) create mode 100644 manage.py delete mode 100644 pipelines/rj_cetrio/__init__.py delete mode 100644 pipelines/rj_cetrio/dump_db_pit/flows.py delete mode 100644 pipelines/rj_cetrio/dump_db_pit/schedules.py delete mode 100644 pipelines/rj_cor/meteorologia/meteorologia_redemet/__init__.py delete mode 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rj_rioaguas/clima_fluviometro/nivel_lagoa}/__init__.py (100%) create mode 100644 pipelines/rj_rioaguas/clima_fluviometro/nivel_lagoa/flows.py create mode 100644 pipelines/rj_rioaguas/clima_fluviometro/nivel_lagoa/tasks.py rename pipelines/{rj_cor/meteorologia/radar/precipitacao/src => rj_rioaguas/nivel_lamina_agua}/__init__.py (100%) rename pipelines/rj_rioaguas/{saneamento_drenagem => }/nivel_lamina_agua/flows.py (95%) create mode 100644 pipelines/rj_rioaguas/nivel_lamina_agua/schedules.py rename pipelines/rj_rioaguas/{saneamento_drenagem => }/nivel_lamina_agua/tasks.py (87%) delete mode 100644 pipelines/rj_rioaguas/saneamento_drenagem/nivel_lagoa/__init__.py delete mode 100644 pipelines/rj_rioaguas/saneamento_drenagem/nivel_lagoa/flows.py delete mode 100644 pipelines/rj_rioaguas/saneamento_drenagem/nivel_lagoa/schedules.py delete mode 100644 pipelines/rj_rioaguas/saneamento_drenagem/nivel_lagoa/tasks.py delete mode 100644 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create mode 100644 pipelines/{{cookiecutter.project_name}}/constants.py create mode 100644 pipelines/{{cookiecutter.project_name}}/cookiecutter.json create mode 100644 pipelines/{{cookiecutter.project_name}}/flows.py create mode 100644 pipelines/{{cookiecutter.project_name}}/schedules.py create mode 100644 pipelines/{{cookiecutter.project_name}}/tasks.py create mode 100644 pipelines/{{cookiecutter.project_name}}/utils.py rename pipelines/{rj_escritorio/birthday_notification => {{cookiecutter.project_name}}/{{cookiecutter.workspace_name}}}/__init__.py (100%) create mode 100644 pipelines/{{cookiecutter.project_name}}/{{cookiecutter.workspace_name}}/constants.py create mode 100644 pipelines/{{cookiecutter.project_name}}/{{cookiecutter.workspace_name}}/flows.py create mode 100644 pipelines/{{cookiecutter.project_name}}/{{cookiecutter.workspace_name}}/schedules.py create mode 100644 pipelines/{{cookiecutter.project_name}}/{{cookiecutter.workspace_name}}/tasks.py create mode 100644 pipelines/{{cookiecutter.project_name}}/{{cookiecutter.workspace_name}}/utils.py rename {pipelines/rj_escritorio/comunicacao_executiva_dbt => tests}/__init__.py (100%) create mode 100644 tests/test_sanity.py diff --git a/.flake8 b/.flake8 index b3a4eac82..d744d65db 100644 --- a/.flake8 +++ b/.flake8 @@ -1,3 +1,5 @@ [flake8] ignore = E203,F401,F403,W503 +exclude = + *cookiecutter* max-line-length = 100 \ No newline at end of file diff --git a/.github/workflows/cd_staging.yaml b/.github/workflows/cd_staging.yaml index 090e88d27..dcc53ec6d 100644 --- a/.github/workflows/cd_staging.yaml +++ b/.github/workflows/cd_staging.yaml @@ -25,10 +25,9 @@ env: jobs: build-container: - if: startsWith(github.head_ref, 'staging/') || contains( github.event.pull_request.labels.*.name, 'staging') + if: startsWith(github.head_ref, 'staging/') name: Register flows (staging) runs-on: ubuntu-latest - steps: - uses: actions/checkout@v2 diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 378042b12..2c747d783 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -48,3 +48,6 @@ jobs: with: message: "${{ steps.code-tree-analysis.outputs.pr-message }}" GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + - name: Run tests + run: | + pytest diff --git a/.github/workflows/lint_python.yaml b/.github/workflows/lint_python.yaml index 826633534..82c1dd773 100644 --- a/.github/workflows/lint_python.yaml +++ b/.github/workflows/lint_python.yaml @@ -12,13 +12,24 @@ jobs: - name: Set up Python uses: actions/setup-python@v2 with: - python-version: "3.9.x" + python-version: "3.8.x" - name: Set up Poetry and upgrade pip run: | pip install -U pip poetry - name: Install flows run: | - poetry install - - name: Lint with flake8 - run: | - poetry run lint \ No newline at end of file + pip install --prefer-binary . + - name: Lint flows + uses: gabriel-milan/action-pylint@v1 + with: + GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}} + path: "pipelines/" + fail: true + pr-message: true + - name: Lint tests + uses: gabriel-milan/action-pylint@v1 + with: + GITHUB_TOKEN: ${{secrets.GITHUB_TOKEN}} + path: "tests/" + fail: true + pr-message: true diff --git a/.github/workflows/scripts/code_tree_analysis.py b/.github/workflows/scripts/code_tree_analysis.py index aa026c6c5..dc8638ec0 100644 --- a/.github/workflows/scripts/code_tree_analysis.py +++ b/.github/workflows/scripts/code_tree_analysis.py @@ -444,6 +444,7 @@ def identify_code_owners(files: List[str]) -> List[str]: if __name__ == "__main__": + # Assert arguments. if len(sys.argv) not in [2, 3]: print(f"Usage: python {sys.argv[0]} [--write-to-file]") @@ -464,7 +465,6 @@ def identify_code_owners(files: List[str]) -> List[str]: if file_.endswith(".py") and file_.startswith("pipelines") and "cookiecutter" not in file_ - and Path(file_).exists() ] print("We're interested in these files:") for file_ in changed_files: diff --git a/.github/workflows/scripts/register_flows.py b/.github/workflows/scripts/register_flows.py index 440337a6b..0083bde60 100644 --- a/.github/workflows/scripts/register_flows.py +++ b/.github/workflows/scripts/register_flows.py @@ -104,11 +104,7 @@ def build_and_register( # pylint: disable=too-many-branches while attempts < max_retries: attempts += 1 try: - ( - flow_id, - flow_version, - is_new, - ) = register_serialized_flow( + (flow_id, flow_version, is_new,) = register_serialized_flow( client=client, serialized_flow=serialized_flow, project_id=project_id, @@ -484,8 +480,8 @@ def get_affected_flows(fpath: str = None): for flow in declared_flows: try: flows.append(eval(flow)) - except Exception as exc: - logger.exception(f"Could not evaluate {flow}: {exc}") + except Exception: + logger.warning(f"Could not evaluate {flow}") return flows diff --git a/.gitignore b/.gitignore index 20149a425..0860c96d0 100644 --- a/.gitignore +++ b/.gitignore @@ -5,13 +5,7 @@ replit.nix test_local.py pylint.txt test.py -test/* -test/*.ipynb -test/*.csv setup.py -.vscode/* -*.hdf -*.DS_Store # Byte-compiled / optimized / DLL files diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index a06393020..90dd6ddcc 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,19 +1,20 @@ repos: - - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.4.0 +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 hooks: - - id: check-added-large-files # prevents adding large files - - id: detect-private-key # detects private keys - - id: fix-byte-order-marker # fixes BOM - - id: fix-encoding-pragma # fixes encoding pragma - - id: no-commit-to-branch # prevents committing to protected branches - - id: trailing-whitespace # prevents trailing whitespace - - repo: https://github.com/psf/black - rev: 23.7.0 + - id: check-added-large-files # prevents adding large files + - id: detect-private-key # detects private keys + - id: fix-byte-order-marker # fixes BOM + - id: fix-encoding-pragma # fixes encoding pragma + - id: no-commit-to-branch # prevents committing to protected branches + - id: trailing-whitespace # prevents trailing whitespace +- repo: https://github.com/psf/black + rev: 22.10.0 hooks: - - id: black + - id: black language_version: python3.9 - - repo: https://github.com/PyCQA/flake8 - rev: 6.1.0 + exclude: 'pipelines\/\{\{cookiecutter\.project_name\}\}.*' +- repo: https://github.com/PyCQA/flake8 + rev: 5.0.4 hooks: - - id: flake8 + - id: flake8 diff --git a/.pylintrc b/.pylintrc index ec63b0bd3..fd1acb8d6 100644 --- a/.pylintrc +++ b/.pylintrc @@ -20,7 +20,7 @@ fail-on= fail-under=10.0 # Files or directories to be skipped. They should be base names, not paths. -ignore= +ignore={{cookiecutter.project_name}} # Add files or directories matching the regex patterns to the ignore-list. The # regex matches against paths. diff --git a/LICENSE b/LICENSE index 62a22f8b0..f288702d2 100644 --- a/LICENSE +++ b/LICENSE @@ -1,8 +1,3 @@ -**Attention:** This repository contains part of the code under the GPL-3.0 license and part under an EULA license. -All code under the EULA license will have a header indicating that it is proprietary. -See the respective topics under LICENSE for the terms and conditions of each license. - - GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 @@ -677,89 +672,3 @@ may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read . - - -########################################################################### - - - END USER LICENSE AGREEMENT (EULA) - - IMPORTANT - PLEASE READ CAREFULLY BEFORE DOWNLOADING, INSTALLING OR USING -ANY PART OF THIS CODE: - - This End User License Agreement ("EULA") is entered into between the -Municipality of Rio de Janeiro ("Licensor") and any individual or entity -that downloads, installs or uses Licensor code ("Licensee") made available in -this directory and in its subdirectories. - - BY DOWNLOADING, INSTALLING, COPYING OR USING ANY PART OF THIS CODE, YOU -AGREE TO BE BOUND BY THE TERMS OF THIS EULA. IF YOU DO NOT AGREE TO THE TERMS -OF THIS EULA, DO NOT DOWNLOAD, INSTALL OR USE THE PRODUCT. - - - 1. License Grant. - - Licensor grants Licensee a non-exclusive, non-transferable license to -use this code for personal or commercial purposes, subject to the terms and -conditions of this Agreement. This license does not grant you the right to -distribute, modify, rent, lease or sublicense the product. - - - 2. Restrictions - - Licensee may not reproduce, distribute, modify, reverse engineer, create -derivative works or otherwise use or exploit any part of this code, particularly -that which is specifically identified by Licensor as proprietary or confidential -information ("Restricted Code"). Licensee acknowledges that any breach of this -restriction may cause Licensor irreparable harm and that Licensor may seek -injunctive relief in addition to any other available legal or equitable remedies. - - - 3. Property right - - The Product is the property of Licensor and is protected by copyright laws and -international treaty provisions. Licensor retains all right, title and interest in -and to the Code, including all intellectual property rights therein. You agree to -protect the Product from unauthorized use, reproduction or distribution. This -Agreement does not transfer any ownership rights to Licensee. - - - 4. Limitation of Liability - - IN NO EVENT SHALL LICENSOR BE LIABLE FOR ANY INDIRECT, INCIDENTAL, SPECIAL, -CONSEQUENTIAL OR PUNITIVE DAMAGES, OR ANY DAMAGES RESULTING FROM LOSS OF USE, DATA -OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHERWISE, ARISING OUT -OF OR IN CONNECTION WITH WITH THE USE OR PERFORMANCE OF THE PRODUCT. - - - 5. Disclaimer of Warranties - - THE PRODUCT IS PROVIDED WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, -INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS -FOR A PARTICULAR PURPOSE, OR NON-INFRINGEMENT. LICENSOR DOES NOT WARRANT THAT THE -PRODUCT WILL MEET YOUR REQUIREMENTS OR THAT THE OPERATION OF THE PRODUCT WILL BE -UNINTERRUPTED OR ERROR-FREE. - - - 6. Applicable law - - This EULA will be governed by and constructed in accordance with the laws of -the state of Rio de Janeiro, without giving effect to any choice of law or conflict -of law provisions. - - - 7. Total agreement - - This Agreement constitutes the entire agreement between the parties with -respect to the subject matter herein and supersedes all prior or contemporaneous -communications and proposals, oral or written, between the parties. - - - By downloading, installing or using the code, Licensee acknowledges that -Licensee has read and understood this Agreement and agrees to be bound by its -terms and conditions. - - If you have any questions about this EULA or the Product, please contact us -at escritoriodedados@gmail.com - - END OF TERMS AND CONDITIONS \ No newline at end of file diff --git a/README.md b/README.md index 64df2ad0b..9d83724f0 100644 --- a/README.md +++ b/README.md @@ -5,6 +5,7 @@ da Prefeitura do Rio de Janeiro. O repositório é gerido pelo Escritório Municipal de Dados (EMD) e alimentado de forma colaborativa com as equipes de dados e tecnologia das Secretarias. + > 💜 Todo o código é desenvolvido em Python utilizando o software livre [Prefect](https://prefect.io/). ## Configuração de ambiente para desenvolvimento @@ -16,12 +17,6 @@ dados e tecnologia das Secretarias. - `pip` - (Opcional, mas recomendado) Um ambiente virtual para desenvolvimento (`miniconda`, `virtualenv` ou similares) -### Licenças - -Este repositório contém parte do código sob a licença GPL-3.0 e parte sob uma licença EULA. -Todo código sob a licença EULA terá um cabeçalho indicando que é proprietário. -Consulte os respectivos tópicos em LICENÇA para os termos e condições de cada licença. - ### Procedimentos - Clonar esse repositório @@ -93,7 +88,7 @@ constants.py # valores constantes para todos os órgãos ``` -### Adicionando órgãos e projetos (descontinuado) +### Adicionando órgãos e projetos O script `manage.py` é responsável por criar e listar projetos desse repositório. Para usá-lo, no entanto, você deve instalar as dependências em `requirements-cli.txt`: diff --git a/backfill.py b/backfill.py index 8734e3e22..8e8ba1392 100644 --- a/backfill.py +++ b/backfill.py @@ -64,6 +64,7 @@ def create_timestamp_parameters( if __name__ == "__main__": + timestamp_parameters = create_timestamp_parameters( start=BACKFILL_START, end=BACKFILL_END, diff --git a/code_owners.yaml b/code_owners.yaml index 775494551..2726ae568 100644 --- a/code_owners.yaml +++ b/code_owners.yaml @@ -19,9 +19,6 @@ pipelines: - hellcassius - fernandascovino - eng-rodrigocunha - - borismarinho - - pixuimpou - - lingsv rj_escritorio: owners: - gabriel-milan diff --git a/manage.py b/manage.py new file mode 100644 index 000000000..730537fcf --- /dev/null +++ b/manage.py @@ -0,0 +1,290 @@ +# -*- coding: utf-8 -*- +from os import path +from pathlib import Path +import pkgutil +from sys import exit +from uuid import uuid4 + +import typer +from cookiecutter.main import cookiecutter +from loguru import logger +from string_utils import is_snake_case + +CONSTANTS_FILE_PATH = Path(__file__).parent / "pipelines" / "constants.py" +CONSTANTS_FIND_AGENTS_TEXT = "M9w=k-b_\n" +COOKIECUTTER_PATH_AGENCY = Path(__file__).parent / "pipelines" +COOKIECUTTER_PATH_PROJECT = ( + Path(__file__).parent / "pipelines" / "{{cookiecutter.project_name}}" +) +FLOWS_FILE_PATH = Path(__file__).parent / "pipelines" / "flows.py" +PIPELINES_PATH = Path(__file__).parent / "pipelines" + +app = typer.Typer() + + +@logger.catch +def append_text(original_text: str, text_to_add: str, after_text: str = None): + """ + Appends text to a string after a given text. + + :param original_text: The original text. + :param text_to_add: The text to add. + :param after_text: The text after which the text to add should be added. + :return: The original text with the text to add appended. + """ + if after_text is None: + return original_text + text_to_add + else: + return original_text.replace(after_text, after_text + text_to_add) + + +@logger.catch +def add_import(project_name: str): + """ + Adds an import to the project's flows file. + + :param project_name: The project name. + :return: The original text with the import appended. + """ + with open(FLOWS_FILE_PATH, "r") as flows_file: + flows_text = flows_file.read() + + flows_text = append_text( + flows_text, f"from pipelines.{project_name}.flows import *\n" + ) + + with open(FLOWS_FILE_PATH, "w") as flows_file: + flows_file.write(flows_text) + + +@logger.catch +def add_agency_import(agency_name: str, project_name: str): + """ + Adds an import to the agency's __init__ file. + + :param agency_name: The agency name. + :param project_name: The project name. + :return: The original text with the import appended. + """ + path = PIPELINES_PATH / agency_name / "__init__.py" + with open(path, "r") as flows_file: + flows_text = flows_file.read() + + flows_text = append_text( + flows_text, + f"from pipelines.{agency_name}.{project_name}.flows import *\n", + ) + + with open(path, "w") as flows_file: + flows_file.write(flows_text) + + +@logger.catch +def add_agent(project_name: str): + """ + Adds the agent to the constants file. + + :param project_name: The name of the project. + """ + with open(CONSTANTS_FILE_PATH, "r") as file: + file_text = file.read() + + file_text = append_text( + file_text, + f' {project_name.upper()}_AGENT_LABEL = "{uuid4()}"\n', + CONSTANTS_FIND_AGENTS_TEXT, + ) + + with open(CONSTANTS_FILE_PATH, "w") as file: + file.write(file_text) + + +@logger.catch +def cut_agency_cookie(agency_name: str): + """ + Runs the cookiecutter command. + + :param project_name: The name of the project. + """ + cookiecutter( + str(COOKIECUTTER_PATH_AGENCY), + no_input=True, + extra_context={ + "project_name": agency_name, + "workspace_name": "example", + }, + output_dir=str(COOKIECUTTER_PATH_AGENCY), + ) + delete_file(COOKIECUTTER_PATH_AGENCY / agency_name / "cookiecutter.json") + + +@logger.catch +def cut_project_cookie(agency_name: str, project_name: str): + """ + Runs the cookiecutter command. + + :param project_name: The name of the project. + """ + cookiecutter( + str(COOKIECUTTER_PATH_PROJECT), + no_input=True, + extra_context={ + "project_name": agency_name, + "workspace_name": project_name, + }, + output_dir=str(COOKIECUTTER_PATH_AGENCY / agency_name), + ) + + +@logger.catch +def delete_file(fname: str): + """ + Deletes a file. + + :param fname: The name of the file. + """ + if path.exists(fname): + Path(fname).unlink() + + +@logger.catch +def single_word_valid(input: str) -> bool: + """ + - Must have only letters and numbers + - Must not start with a number + """ + return input.isalnum() and not input[0].isdigit() + + +@logger.catch +def name_already_exists(input: str) -> bool: + """ + Checks if the name already exists. + + :param input: The input. + :return: True if the name already exists, False otherwise. + """ + try: + import pipelines + except ImportError: + logger.error("Não foi possível importar as pipelines.") + exit(1) + pkgpath = path.dirname(pipelines.__file__) + return input in [name for _, name, _ in pkgutil.iter_modules([pkgpath])] + + +@logger.catch +def check_name(input: str) -> str: + """ + Checks if the name is valid. + + :param input: The input. + :return: The input if it is valid, otherwise an error message. + """ + # Must be either valid as a single word or a snake case name + valid = single_word_valid(input) or is_snake_case(input) + + # Also, must not already exist + valid = valid and not name_already_exists(input) + + if valid: + return input + else: + logger.error( + f"O nome {input} é inválido. Ele deve estar em snake_case e ser único" + ) + exit(1) + + +@logger.catch +def agency_must_exist(input: str) -> str: + """ + Checks whether the agency exists. + + :param input: The input. + :return: The input if it exists, otherwise an error message. + """ + try: + import pipelines + except ImportError: + logger.error("Não foi possível importar as pipelines.") + exit(1) + pkgpath = path.dirname(pipelines.__file__) + agencies = [name for _, name, _ in pkgutil.iter_modules([pkgpath])] + if input in agencies: + return input + else: + logger.error(f"A agência {input} não existe.") + exit(1) + + +@logger.catch +def project_must_not_exist_in_agency(input: str, agency: str) -> str: + """ + Checks whether the project exists in the agency. + + :param input: The input. + :param agency: The agency. + :return: The input if it exists, otherwise an error message. + """ + try: + import pipelines + except ImportError: + logger.error("Não foi possível importar as pipelines.") + exit(1) + pkgpath = path.dirname(pipelines.__file__) + agencies = [name for _, name, _ in pkgutil.iter_modules([pkgpath])] + if agency in agencies: + projects = [ + name for _, name, _ in pkgutil.iter_modules([Path(pkgpath) / agency]) + ] + if input in projects: + logger.error(f"O projeto {input} já existe na agência {agency}.") + exit(1) + return input + else: + logger.error(f"O órgão {agency} não foi encontrado.") + exit(1) + + +@app.command() +def add_agency(agency_name: str = typer.Argument(..., callback=check_name)): + """ + Cria um novo órgão usando o cookiecutter. + """ + add_agent(agency_name) + add_import(agency_name) + cut_agency_cookie(agency_name) + + +@app.command() +def add_project( + agency_name: str = typer.Argument(..., callback=agency_must_exist), + project_name: str = typer.Argument(...), +): + """ + Cria um novo projeto no órgão usando o cookiecutter. + """ + project_must_not_exist_in_agency(project_name, agency_name) + add_agency_import(agency_name, project_name) + cut_project_cookie(agency_name, project_name) + + +@app.command() +def list_projects(): + """ + Lista os projetos cadastrados e nomes reservados + """ + try: + import pipelines + except ImportError: + logger.error("Não foi possível importar as pipelines.") + exit(1) + pkgpath = path.dirname(pipelines.__file__) + logger.info("Projetos cadastrados e nomes reservados:") + for _, name, _ in pkgutil.iter_modules([pkgpath]): + logger.info(f"- {name}") + + +if __name__ == "__main__": + app() diff --git a/pipelines/constants.py b/pipelines/constants.py index 900e2ebf9..fec150310 100644 --- a/pipelines/constants.py +++ b/pipelines/constants.py @@ -35,8 +35,6 @@ class constants(Enum): # pylint: disable=c0103 RJ_SEGOVI_AGENT_LABEL = "rj-segovi" - RJ_SEOP_AGENT_LABEL = "rj-seop" - RJ_COR_AGENT_LABEL = "rj-cor" RJ_RIOAGUAS_AGENT_LABEL = "rj-rioaguas" @@ -45,20 +43,10 @@ class constants(Enum): # pylint: disable=c0103 RJ_SMS_AGENT_LABEL = "rj-sms" - RJ_SMS_DEV_AGENT_LABEL = "rj-sms-dev" - RJ_SMI_AGENT_LABEL = "rj-smi" RJ_SECONSERVA_AGENT_LABEL = "rj-seconserva" - RJ_CETRIO_AGENT_LABEL = "rj-cetrio" - - RJ_SETUR_AGENT_LABEL = "rj-setur" - - RJ_IPLANRIO_AGENT_LABEL = "rj-iplanrio" - - RJ_PGM_AGENT_LABEL = "rj-pgm" - ###################################### # Other constants ###################################### @@ -98,10 +86,6 @@ class constants(Enum): # pylint: disable=c0103 "user_id": "272581753829326849", "type": "user_nickname", }, - "joao": { - "user_id": "692742616416256019", - "type": "user_nickname", - }, "fernanda": { "user_id": "692709168221650954", "type": "user_nickname", @@ -123,27 +107,7 @@ class constants(Enum): # pylint: disable=c0103 "type": "user_nickname", }, "rodrigo": { - "user_id": "1031636163804545094", - "type": "user_nickname", - }, - "boris": { - "user_id": "1109195532884262934", - "type": "user_nickname", - }, - "thiago": { - "user_id": "404716070088343552", - "type": "user_nickname", - }, - "andre": { - "user_id": "369657115012366336", - "type": "user_nickname", - }, - "rafaelpinheiro": { - "user_id": "1131538976101109772", - "type": "user_nickname", - }, - "carolinagomes": { - "user_id": "620000269392019469", + "user_id": "21031636163804545094", "type": "user_nickname", }, } diff --git a/pipelines/flows.py b/pipelines/flows.py index 55e643f5e..53ec04048 100644 --- a/pipelines/flows.py +++ b/pipelines/flows.py @@ -4,19 +4,13 @@ """ # pylint: disable=W0401,W0614,W0611 ############################################################################### -# Automatically managed, please do not touch # +# Automatically managed, please do not touch ############################################################################### from pipelines.datario import * from pipelines.rj_cor import * -from pipelines.rj_cetrio import * from pipelines.rj_escritorio import * -from pipelines.rj_iplanrio import * -from pipelines.rj_pgm import * -from pipelines.rj_rioaguas import * -from pipelines.rj_seconserva import * from pipelines.rj_segovi import * -from pipelines.rj_seop import * -from pipelines.rj_setur import * +from pipelines.rj_rioaguas import * from pipelines.rj_sme import * from pipelines.rj_smfp import * from pipelines.rj_smi import * diff --git a/pipelines/rj_cetrio/__init__.py b/pipelines/rj_cetrio/__init__.py deleted file mode 100644 index 2f7c24753..000000000 --- a/pipelines/rj_cetrio/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Prefect flows for rj_cetrio project in RJ -""" - -from pipelines.rj_cetrio.dump_db_pit.flows import * diff --git a/pipelines/rj_cetrio/dump_db_pit/flows.py b/pipelines/rj_cetrio/dump_db_pit/flows.py deleted file mode 100644 index 6c088c506..000000000 --- a/pipelines/rj_cetrio/dump_db_pit/flows.py +++ /dev/null @@ -1,42 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Database dumping flows for segovi project -""" - -from copy import deepcopy - -from prefect.run_configs import KubernetesRun -from prefect.storage import GCS - -from pipelines.constants import constants - -from pipelines.rj_cetrio.dump_db_pit.schedules import ( - cetrio_daily_update_schedule, -) -from pipelines.utils.dump_db.flows import dump_sql_flow -from pipelines.utils.utils import set_default_parameters - -cetrio_flow = deepcopy(dump_sql_flow) -cetrio_flow.name = "CETRIO: OCR - Ingerir tabelas de banco SQL" -cetrio_flow.storage = GCS(constants.GCS_FLOWS_BUCKET.value) -cetrio_flow.run_config = KubernetesRun( - image=constants.DOCKER_IMAGE.value, - labels=[ - constants.RJ_CETRIO_AGENT_LABEL.value, - ], -) - -cetrio_default_parameters = { - "db_database": "DWOCR_Staging", - "db_host": "10.39.64.50", - "db_port": "1433", - "db_type": "sql_server", - "vault_secret_path": "cet-rio-ocr-staging", - "dataset_id": "transporte_rodoviario_radar_transito", - "mode": "dev", -} -cetrio_flow = set_default_parameters( - cetrio_flow, default_parameters=cetrio_default_parameters -) - -cetrio_flow.schedule = cetrio_daily_update_schedule diff --git a/pipelines/rj_cetrio/dump_db_pit/schedules.py b/pipelines/rj_cetrio/dump_db_pit/schedules.py deleted file mode 100644 index 257ce3aa9..000000000 --- a/pipelines/rj_cetrio/dump_db_pit/schedules.py +++ /dev/null @@ -1,62 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Schedules for the database dump pipeline -""" - -from datetime import datetime, timedelta - -import pytz -from prefect.schedules import Schedule -from pipelines.constants import constants -from pipelines.utils.dump_db.utils import generate_dump_db_schedules -from pipelines.utils.utils import untuple_clocks as untuple - -##################################### -# -# CETRIO Schedules -# -##################################### - -cetrio_queries = { - "fluxo_veiculos": { - "dump_mode": "overwrite", - "execute_query": "SELECT f.CodCET AS cod_cet, \ - e.Logradouro, \ - e.BAIRRO, \ - e.NEQUIPEMP AS codigo_equipamento, \ - e.LOCEQUIP AS local_equipamento, \ - e.Latitude, \ - e.Longitude, \ - e.AP, d.Data, \ - t.Horario24 AS periodo, \ - CONVERT(time(0), t.PeriodoInicio) AS hora_inicio, \ - CONVERT(time(0), t.PeriodoTermino) AS hora_termino, \ - f.TotalRegistros AS total_registro, \ - f.TotalPlacas AS total_placas \ - FROM DWOCR.dbo.Fact_OCRS AS f \ - INNER JOIN DWOCR.dbo.Dim_Equipamento AS e \ - ON f.CodCET = e.CodCET \ - INNER JOIN DWOCR.dbo.Dim_Tempo AS t \ - ON t.CodTempo = f.CodTempo \ - INNER JOIN DWOCR.dbo.Dim_Data AS d \ - ON d.Data = t.Data;", - "materialize_after_dump": True, - }, -} - -cetrio_clocks = generate_dump_db_schedules( - interval=timedelta(days=1), - start_date=datetime(2023, 3, 1, 22, 0, tzinfo=pytz.timezone("America/Sao_Paulo")), - labels=[ - constants.RJ_CETRIO_AGENT_LABEL.value, - ], - db_database="DWOCR_Staging", - db_host="10.39.64.50", - db_port="1433", - db_type="sql_server", - dataset_id="transporte_rodoviario_radar_transito", - vault_secret_path="cet-rio-ocr-staging", - table_parameters=cetrio_queries, -) - -cetrio_daily_update_schedule = Schedule(clocks=untuple(cetrio_clocks)) diff --git a/pipelines/rj_cor/__init__.py b/pipelines/rj_cor/__init__.py index 34ffc0e76..5577cf8c2 100644 --- a/pipelines/rj_cor/__init__.py +++ b/pipelines/rj_cor/__init__.py @@ -4,10 +4,7 @@ """ from pipelines.rj_cor.bot_semaforo.flows import * from pipelines.rj_cor.meteorologia.meteorologia_inmet.flows import * -from pipelines.rj_cor.meteorologia.meteorologia_redemet.flows import * from pipelines.rj_cor.meteorologia.precipitacao_alertario.flows import * -from pipelines.rj_cor.meteorologia.precipitacao_cemaden.flows import * from pipelines.rj_cor.meteorologia.satelite.flows import * from pipelines.rj_cor.meteorologia.precipitacao_websirene.flows import * -from pipelines.rj_cor.meteorologia.radar.precipitacao.flows import * from pipelines.rj_cor.comando.eventos.flows import * diff --git a/pipelines/rj_cor/bot_semaforo/flows.py b/pipelines/rj_cor/bot_semaforo/flows.py index caa412c20..5fbe7df5b 100644 --- a/pipelines/rj_cor/bot_semaforo/flows.py +++ b/pipelines/rj_cor/bot_semaforo/flows.py @@ -24,6 +24,7 @@ "gabriel", ], ) as cet_telegram_flow: + secret_path = Parameter("secret_path") # Get credentials for Telegram diff --git a/pipelines/rj_cor/bot_semaforo/tasks.py b/pipelines/rj_cor/bot_semaforo/tasks.py index 2a671594b..67199af45 100644 --- a/pipelines/rj_cor/bot_semaforo/tasks.py +++ b/pipelines/rj_cor/bot_semaforo/tasks.py @@ -126,7 +126,6 @@ def format_message(dataframe: pd.DataFrame) -> pd.Series: """ Formats the message before sending it. """ - # Create a link for eath alert on google maps def map_link(dataframe: pd.DataFrame): url = ( diff --git a/pipelines/rj_cor/comando/eventos/constants.py b/pipelines/rj_cor/comando/eventos/constants.py index a3da25cb0..6312e6735 100644 --- a/pipelines/rj_cor/comando/eventos/constants.py +++ b/pipelines/rj_cor/comando/eventos/constants.py @@ -1,5 +1,4 @@ # -*- coding: utf-8 -*- -# flake8: noqa: E501 """ Constant values for the rj_cor.comando projects """ @@ -18,121 +17,3 @@ class constants(Enum): # pylint: disable=c0103 TABLE_ID_ATIVIDADES_EVENTOS = "ocorrencias_orgaos_responsaveis" TABLE_ID_POPS = "procedimento_operacional_padrao" TABLE_ID_ATIVIDADES_POPS = "procedimento_operacional_padrao_orgaos_responsaveis" - RAIN_DASHBOARD_FLOW_SCHEDULE_PARAMETERS = { - "redis_data_key": "data_alagamento_recente_comando", - "redis_update_key": "data_update_alagamento_recente_comando", - "query_data": """ - WITH - alagamentos AS ( - SELECT - id_evento, - CASE WHEN id_pop IN ("6", "31", "32") THEN 3 -- "Alagamento" - WHEN id_pop = "5" THEN 2 -- "Bolsão d'água" - WHEN id_pop = "33" THEN 1 -- "Lâmina d'água" - END AS tipo, - ST_GEOGPOINT(CAST(longitude AS FLOAT64), - CAST(latitude AS FLOAT64)) AS geometry - FROM `rj-cor.adm_cor_comando_staging.ocorrencias` - WHERE id_pop IN ("5", "6", "31", "32", "33") - AND data_particao >= DATE_TRUNC(TIMESTAMP_SUB(CURRENT_DATETIME("America/Sao_Paulo"), INTERVAL 1 day), day) - AND CAST(data_inicio AS DATETIME) >= TIMESTAMP_SUB(CURRENT_DATETIME("America/Sao_Paulo"), INTERVAL 1 day) - AND data_fim IS NULL - ), - final_table AS ( - SELECT - h3_grid.id AS id_h3, - nome AS bairro, - COALESCE(MAX(tipo), 0) AS tipo - FROM `rj-cor.dados_mestres.h3_grid_res8` h3_grid - INNER JOIN `rj-cor.dados_mestres.bairro` - ON ST_CONTAINS(`rj-cor.dados_mestres.bairro`.geometry, ST_CENTROID(h3_grid.geometry)) - LEFT JOIN alagamentos - ON ST_CONTAINS(h3_grid.geometry, alagamentos.geometry) - GROUP BY id_h3, bairro - ) - - SELECT - id_h3, - bairro, - tipo AS qnt_alagamentos, - CASE - WHEN tipo = 3 THEN "extremamente crítico" --"Alagamento" - WHEN tipo = 2 THEN "crítico" -- "Bolsão d'água" - WHEN tipo = 1 THEN "pouco crítico" --"Lâmina d'água" - ELSE "sem alagamento" - END AS status, - CASE - WHEN tipo = 1 THEN '#DAECFB'--'#00CCFF' - WHEN tipo = 2 THEN '#A9CBE8'--'#BFA230' - WHEN tipo = 3 THEN '#125999'--'#E0701F' - ELSE '#ffffff' - END AS color - FROM final_table - """, - "query_update": """ - SELECT date_trunc(current_datetime("America/Sao_Paulo"), minute) AS last_update - """, - } - RAIN_DASHBOARD_LAST_2H_FLOW_SCHEDULE_PARAMETERS = { - "redis_data_key": "data_alagamento_passado_comando", - "redis_update_key": "data_update_alagamento_passado_comando", - "query_data": """ - WITH - alagamentos AS ( - SELECT - id_evento, - CASE WHEN id_pop IN ("6", "31", "32") THEN 3 -- "Alagamento" - WHEN id_pop = "5" THEN 2 -- "Bolsão d'água" - WHEN id_pop = "33" THEN 1 -- "Lâmina d'água" - END AS tipo, - ST_GEOGPOINT(CAST(longitude AS FLOAT64), - CAST(latitude AS FLOAT64)) AS geometry - FROM `rj-cor.adm_cor_comando_staging.ocorrencias` - WHERE id_pop IN ("5", "6", "31", "32", "33") - AND data_particao >= DATE_TRUNC(TIMESTAMP_SUB(CURRENT_DATETIME("America/Sao_Paulo"), INTERVAL 120 MINUTE), day) - AND (CAST(data_fim AS DATETIME) >= TIMESTAMP_SUB(CURRENT_DATETIME("America/Sao_Paulo"), INTERVAL 120 MINUTE) - OR data_fim IS NULL) - -- AND data_particao >= DATE_TRUNC(TIMESTAMP_SUB(CAST("2022-04-01 04:39:15" as datetime), INTERVAL 120 MINUTE), day) - -- AND (CAST(data_fim AS DATETIME) >= TIMESTAMP_SUB(CAST("2022-04-01 04:39:15" as datetime), INTERVAL 120 MINUTE) - -- OR data_fim IS NULL) - ), - final_table AS ( - SELECT - h3_grid.id AS id_h3, - nome AS bairro, - COALESCE(MAX(tipo), 0) AS tipo - FROM `rj-cor.dados_mestres.h3_grid_res8` h3_grid - INNER JOIN `rj-cor.dados_mestres.bairro` - ON ST_CONTAINS(`rj-cor.dados_mestres.bairro`.geometry, ST_CENTROID(h3_grid.geometry)) - LEFT JOIN alagamentos - ON ST_CONTAINS(h3_grid.geometry, alagamentos.geometry) - GROUP BY id_h3, bairro - ) - - SELECT - id_h3, - bairro, - CASE - WHEN tipo = 3 THEN "Alagamento" - WHEN tipo = 2 THEN "Bolsão d'água" - WHEN tipo = 1 THEN "Lâmina d'água" - ELSE "sem alagamento" - END AS qnt_alagamentos, - CASE - WHEN tipo = 3 THEN "extremamente crítico" --"Alagamento" - WHEN tipo = 2 THEN "crítico" -- "Bolsão d'água" - WHEN tipo = 1 THEN "pouco crítico" --"Lâmina d'água" - ELSE "sem alagamento" - END AS status, - CASE - WHEN tipo = 1 THEN '#DAECFB'--'#00CCFF' - WHEN tipo = 2 THEN '#A9CBE8'--'#BFA230' - WHEN tipo = 3 THEN '#125999'--'#E0701F' - ELSE '#ffffff' - END AS color - FROM final_table - """, - "query_update": """ - SELECT date_trunc(current_datetime("America/Sao_Paulo"), minute) AS last_update - """, - } diff --git a/pipelines/rj_cor/comando/eventos/flows.py b/pipelines/rj_cor/comando/eventos/flows.py index bc4abca20..26db5b114 100644 --- a/pipelines/rj_cor/comando/eventos/flows.py +++ b/pipelines/rj_cor/comando/eventos/flows.py @@ -31,9 +31,6 @@ get_on_redis, save_on_redis, ) -from pipelines.rj_escritorio.rain_dashboard.constants import ( - constants as rain_dashboard_constants, -) from pipelines.utils.constants import constants as utils_constants from pipelines.utils.decorators import Flow from pipelines.utils.dump_db.constants import constants as dump_db_constants @@ -49,6 +46,7 @@ "paty", ], ) as rj_cor_comando_eventos_flow: + dump_mode = Parameter("dump_mode", default="append", required=False) # Materialization parameters @@ -61,9 +59,6 @@ materialize_to_datario = Parameter( "materialize_to_datario", default=False, required=False ) - TRIGGER_RAIN_DASHBOARD_UPDATE = Parameter( - "trigger_rain_dashboard_update", default=False, required=False - ) # Dump to GCS after? Should only dump to GCS if materializing to datario dump_to_gcs = Parameter("dump_to_gcs", default=False, required=False) @@ -107,7 +102,6 @@ dataset_id=dataset_id, table_id=table_id_eventos, dump_mode=dump_mode, - biglake_table=False, wait=eventos_path, ) @@ -116,7 +110,6 @@ dataset_id=dataset_id, table_id=table_id_atividades_eventos, dump_mode=dump_mode, - biglake_table=False, wait=atividade_eventos_path, ) @@ -247,50 +240,6 @@ stream_logs=True, raise_final_state=True, ) - with case(TRIGGER_RAIN_DASHBOARD_UPDATE, True): - # Trigger rain dashboard update flow run - rain_radar_dashboard_update_flow = create_flow_run( - flow_name=rain_dashboard_constants.RAIN_DASHBOARD_FLOW_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters=comando_constants.RAIN_DASHBOARD_FLOW_SCHEDULE_PARAMETERS.value, # noqa - labels=[ - "rj-cor", - ], - run_name="Update radar rain dashboard data (triggered by cor_comando flow)", # noqa - task_args=dict( - skip_on_upstream_skip=False, - ), - ) - rain_radar_dashboard_update_flow.set_upstream(task_upload_eventos) - - wait_for_rain_dashboard_update = wait_for_flow_run( - flow_run_id=rain_radar_dashboard_update_flow, - stream_states=True, - stream_logs=True, - raise_final_state=False, - ) - - # Trigger rain dashboard update last 2h flow run - rain_radar_dashboard_last_2h_update_flow = create_flow_run( - flow_name=rain_dashboard_constants.RAIN_DASHBOARD_FLOW_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters=comando_constants.RAIN_DASHBOARD_LAST_2H_FLOW_SCHEDULE_PARAMETERS.value, # noqa - labels=[ - "rj-cor", - ], - run_name="Update radar rain dashboard data (triggered by cor_comando flow for last 2h)", # noqa - task_args=dict( - skip_on_upstream_skip=False, - ), - ) - rain_radar_dashboard_last_2h_update_flow.set_upstream(task_upload_eventos) - - wait_for_rain_dashboard_last_2h_update = wait_for_flow_run( - flow_run_id=rain_radar_dashboard_last_2h_update_flow, - stream_states=True, - stream_logs=True, - raise_final_state=False, - ) rj_cor_comando_eventos_flow.storage = GCS(constants.GCS_FLOWS_BUCKET.value) rj_cor_comando_eventos_flow.run_config = KubernetesRun( @@ -355,7 +304,6 @@ data_path=path_pops, dataset_id=dataset_id, table_id=table_id_pops, - biglake_table=False, dump_mode=dump_mode, ) @@ -368,7 +316,6 @@ data_path=path_atividades_pops, dataset_id=dataset_id, table_id=table_id_atividades_pops, - biglake_table=False, dump_mode="overwrite", ) diff --git a/pipelines/rj_cor/comando/eventos/schedules.py b/pipelines/rj_cor/comando/eventos/schedules.py index 9d37ba82f..3fa12ae80 100644 --- a/pipelines/rj_cor/comando/eventos/schedules.py +++ b/pipelines/rj_cor/comando/eventos/schedules.py @@ -10,7 +10,7 @@ every_hour = Schedule( clocks=[ IntervalClock( - interval=timedelta(minutes=15), + interval=timedelta(hours=1), start_date=datetime(2022, 7, 19, 12, 50, 0), labels=[ constants.RJ_COR_AGENT_LABEL.value, diff --git a/pipelines/rj_cor/comando/eventos/tasks.py b/pipelines/rj_cor/comando/eventos/tasks.py index 7cd78302c..8b1de3abd 100644 --- a/pipelines/rj_cor/comando/eventos/tasks.py +++ b/pipelines/rj_cor/comando/eventos/tasks.py @@ -1,6 +1,5 @@ # -*- coding: utf-8 -*- -# pylint: disable=R0914,W0613,W0102,W0613,R0912,R0915,E1136,E1137,W0702 -# flake8: noqa: E722 +# pylint: disable=R0914,W0613,W0102,W0613,R0912,R0915 """ Tasks for comando """ @@ -35,8 +34,6 @@ def get_date_interval( """ If `date_interval_text` is provided, parse it for the date interval. Else, get the date interval from Redis. - Example of date_interval_text to use when selecting type string: - {"inicio": "2023-04-19 08:54:41.0", "fim": "2023-04-19 10:40:41.0"} """ if date_interval_text: log(f">>>>>>>>>>> Date interval was provided: {date_interval_text}") @@ -235,15 +232,10 @@ def download_eventos(date_interval, wait=None) -> Tuple[pd.DataFrame, str]: eventos_datas_cols = ["data_inicio", "data_fim"] atividades_eventos_datas_cols = ["data_chegada", "data_inicio", "data_fim"] - eventos[eventos_datas_cols] = eventos[eventos_datas_cols].fillna( - "1970-01-01 00:00:00" - ) + eventos[eventos_datas_cols] = eventos[eventos_datas_cols].fillna("1970-01-01") atividades_evento[atividades_eventos_datas_cols] = atividades_evento[ atividades_eventos_datas_cols - ].fillna("1970-01-01 00:00:00") - - # Treat id_pop col - eventos["id_pop"] = eventos["id_pop"].astype(float).astype(int) + ].fillna("1970-01-01") # Fixa colunas e ordem eventos = eventos[eventos_cols].drop_duplicates() @@ -267,14 +259,10 @@ def get_pops() -> pd.DataFrame: pops = pd.DataFrame(response["objeto"]) pops["id"] = pops["id"].astype("int") - pops = pops.rename({"id": "id_pop", "titulo": "pop_titulo"}, axis=1).sort_values( - "id_pop" - ) - pops["pop_titulo"] = pops[ - "pop_titulo" - ].str.capitalize() # pylint: disable=unsubscriptable-object, E1137 + pops = pops.rename({"id": "id_pop", "titulo": "pop_titulo"}, axis=1) + pops["pop_titulo"] = pops["pop_titulo"].str.capitalize() - return pops[["id_pop", "pop_titulo"]] # pylint: disable=unsubscriptable-object + return pops[["id_pop", "pop_titulo"]] @task(nout=2) @@ -291,18 +279,13 @@ def get_atividades_pops(pops: pd.DataFrame, redis_pops: list) -> pd.DataFrame: pop_ids = pops["id_pop"].unique() - # remove pop_id 0 - pop_ids = [i for i in pop_ids if i not in [0, "0"]] - atividades_pops = [] for pop_id in pop_ids: log(f">>>>>>> Requesting POP's activities for pop_id: {pop_id}") response = get_url(url=url + f"?popId={pop_id}", token=auth_token) tentativa = 0 - while ( - "error" in response.keys() or response == {"response": None} - ) and tentativa <= 5: + while "error" in response.keys() and tentativa <= 5: log( f">>>>>>> Requesting POP's activities for pop_id: {pop_id} Time: {tentativa+1}" ) @@ -310,22 +293,16 @@ def get_atividades_pops(pops: pd.DataFrame, redis_pops: list) -> pd.DataFrame: response = get_url(url=url + f"?popId={pop_id}", token=auth_token) tentativa += 1 - if ( - "error" in response.keys() or response == {"response": None} - ) and tentativa > 5: + if "error" in response.keys() and tentativa > 5: continue - try: - row_template = { - "pop_titulo": response["pop"], - "id_pop": pop_id, - "sigla": "", - "orgao": "", - "acao": "", - } - except: - log(f"Problem on response {response}") - + row_template = { + "pop_titulo": response["pop"], + "id_pop": pop_id, + "sigla": "", + "orgao": "", + "acao": "", + } for activity in response["atividades"]: row = deepcopy(row_template) row["sigla"] = activity["sigla"] @@ -334,7 +311,6 @@ def get_atividades_pops(pops: pd.DataFrame, redis_pops: list) -> pd.DataFrame: atividades_pops.append(row) dataframe = pd.DataFrame(atividades_pops) - dataframe = dataframe.sort_values(["id_pop", "sigla", "acao"]) for i in ["sigla", "orgao", "acao"]: dataframe[i] = dataframe[i].str.capitalize() diff --git a/pipelines/rj_cor/meteorologia/meteorologia_inmet/flows.py b/pipelines/rj_cor/meteorologia/meteorologia_inmet/flows.py index 68c53842d..bdbda5c02 100644 --- a/pipelines/rj_cor/meteorologia/meteorologia_inmet/flows.py +++ b/pipelines/rj_cor/meteorologia/meteorologia_inmet/flows.py @@ -1,5 +1,4 @@ # -*- coding: utf-8 -*- -# pylint: disable=C0103, E1120 """ Flows for meteorologia_inmet """ @@ -14,7 +13,7 @@ from pipelines.utils.constants import constants as utils_constants from pipelines.rj_cor.meteorologia.meteorologia_inmet.tasks import ( get_dates, - # slice_data, + slice_data, download, tratar_dados, salvar_dados, @@ -35,14 +34,11 @@ "paty", ], ) as cor_meteorologia_meteorologia_inmet: - DATASET_ID = "clima_estacao_meteorologica" + + DATASET_ID = "meio_ambiente_clima" TABLE_ID = "meteorologia_inmet" DUMP_MODE = "append" - # data_inicio e data_fim devem ser strings no formato "YYYY-MM-DD" - data_inicio = Parameter("data_inicio", default="", required=False) - data_fim = Parameter("data_fim", default="", required=False) - # Materialization parameters MATERIALIZE_AFTER_DUMP = Parameter( "materialize_after_dump", default=False, required=False @@ -61,11 +57,13 @@ default=dump_to_gcs_constants.MAX_BYTES_PROCESSED_PER_TABLE.value, ) - data_inicio_, data_fim_, backfill = get_dates(data_inicio, data_fim) - # data = slice_data(current_time=CURRENT_TIME) - dados = download(data_inicio_, data_fim_) - dados = tratar_dados(dados, backfill) - PATH = salvar_dados(dados=dados) + CURRENT_TIME, YESTERDAY = get_dates() + + data = slice_data(current_time=CURRENT_TIME) + + dados = download(data=data, yesterday=YESTERDAY) + dados, partitions = tratar_dados(dados=dados) + PATH = salvar_dados(dados=dados, partitions=partitions, data=data) # Create table in BigQuery UPLOAD_TABLE = create_table_and_upload_to_gcs( diff --git a/pipelines/rj_cor/meteorologia/meteorologia_inmet/tasks.py b/pipelines/rj_cor/meteorologia/meteorologia_inmet/tasks.py index b72371134..48441a09a 100644 --- a/pipelines/rj_cor/meteorologia/meteorologia_inmet/tasks.py +++ b/pipelines/rj_cor/meteorologia/meteorologia_inmet/tasks.py @@ -4,6 +4,7 @@ """ from datetime import datetime, timedelta import json +import os from pathlib import Path from typing import Tuple, Union @@ -13,33 +14,20 @@ import requests from pipelines.constants import constants -from pipelines.utils.utils import get_vault_secret, log, to_partitions -from pipelines.rj_cor.meteorologia.precipitacao_alertario.utils import ( - parse_date_columns, -) +from pipelines.utils.utils import log # from pipelines.rj_cor.meteorologia.meteorologia_inmet.meteorologia_utils import converte_timezone -@task(nout=3) -def get_dates(data_inicio: str, data_fim: str) -> Tuple[str, str]: +@task(nout=2) +def get_dates() -> Tuple[str, str]: """ - Task para obter o dia de início e o de fim. - Se nenhuma data foi passada a data_inicio corresponde a ontem - e data_fim a hoje e não estamos fazendo backfill. - Caso contrário, retorna as datas inputadas mos parâmetros do flow. + Task para obter o dia atual e o anterior """ # segundo o manual do inmet o dado vem em UTC - log(f"data de inicio e fim antes do if {data_inicio} {data_fim}") - if data_inicio == "": - data_fim = pendulum.now("UTC").format("YYYY-MM-DD") - data_inicio = pendulum.yesterday("UTC").format("YYYY-MM-DD") - backfill = 0 - else: - backfill = 1 - log(f"data de inicio e fim dps do if {data_inicio} {data_fim}") - - return data_inicio, data_fim, backfill + current_time = pendulum.now("UTC").format("YYYY-MM-DD") + yesterday = pendulum.yesterday("UTC").format("YYYY-MM-DD") + return current_time, yesterday @task() @@ -58,7 +46,7 @@ def slice_data(current_time: str) -> str: max_retries=constants.TASK_MAX_RETRIES.value, retry_delay=timedelta(seconds=constants.TASK_RETRY_DELAY.value), ) -def download(data_inicio: str, data_fim: str) -> pd.DataFrame: +def download(data: str, yesterday: str) -> pd.DataFrame: """ Faz o request na data especificada e retorna dados """ @@ -80,14 +68,9 @@ def download(data_inicio: str, data_fim: str) -> pd.DataFrame: # Trazer desde o dia anterior evita problemas quando já é outro dia # no UTC, visto que ele só traria dados do novo dia e substituiria # no arquivo da partição do dia atual no nosso timezone - - dicionario = get_vault_secret("inmet_api") - token = dicionario["data"]["token"] - raw = [] for id_estacao in estacoes_unicas: - base_url = "https://apitempo.inmet.gov.br/token/estacao" - url = f"{base_url}/{data_inicio}/{data_fim}/{id_estacao}/{token}" + url = f"https://apitempo.inmet.gov.br/estacao/{yesterday}/{data}/{id_estacao}" res = requests.get(url) if res.status_code != 200: log(f"Problema no id: {id_estacao}, {res.status_code}, {url}") @@ -104,8 +87,8 @@ def download(data_inicio: str, data_fim: str) -> pd.DataFrame: return dados -@task -def tratar_dados(dados: pd.DataFrame, backfill: bool = 0) -> pd.DataFrame: +@task(nout=2) +def tratar_dados(dados: pd.DataFrame) -> Tuple[pd.DataFrame, str]: """ Renomeia colunas e filtra dados com a hora do timestamp de execução """ @@ -209,6 +192,9 @@ def converte_timezone(data: str, horario: str) -> str: # Pegar o dia no nosso timezone como partição br_timezone = pendulum.now("America/Sao_Paulo").format("YYYY-MM-DD") + ano = br_timezone[:4] + mes = str(int(br_timezone[5:7])) + dia = str(int(br_timezone[8:10])) # Define colunas que serão salvas dados = dados[ @@ -236,35 +222,33 @@ def converte_timezone(data: str, horario: str) -> str: ] ] - if not backfill: - # Seleciona apenas dados daquele dia (devido à UTC) - dados = dados[dados["data"] == br_timezone] + # Seleciona apenas dados daquele dia (devido à UTC) + dados = dados[dados["data"] == br_timezone] # Remove linhas com todos os dados nan dados = dados.dropna(subset=float_cols, how="all") + partitions = f"ano={ano}/mes={mes}/dia={dia}" + log(f">>> partitions{partitions}") print(">>>> max hora ", dados[~dados.temperatura.isna()].horario.max()) - return dados + return dados, partitions @task -def salvar_dados(dados: pd.DataFrame) -> Union[str, Path]: +def salvar_dados(dados: pd.DataFrame, partitions: str, data: str) -> Union[str, Path]: """ Salvar dados em csv """ + base_path = Path(os.getcwd(), "data", "meteorologia_inmet", "output") - prepath = Path("/tmp/precipitacao_alertario/") - prepath.mkdir(parents=True, exist_ok=True) + partition_path = Path(base_path, partitions) - partition_column = "data" - dataframe, partitions = parse_date_columns(dados, partition_column) + if not os.path.exists(partition_path): + os.makedirs(partition_path) - # Cria partições a partir da data - to_partitions( - data=dataframe, - partition_columns=partitions, - savepath=prepath, - data_type="csv", - ) - log(f"[DEBUG] Files saved on {prepath}") - return prepath + filename = str(partition_path / f"dados_{data}.csv") + + log(f"Saving {filename}") + # dados.to_csv(filename, index=False) + dados.to_csv(r"{}".format(filename), index=False) + return base_path diff --git a/pipelines/rj_cor/meteorologia/meteorologia_redemet/__init__.py b/pipelines/rj_cor/meteorologia/meteorologia_redemet/__init__.py deleted file mode 100644 index 1515dbb06..000000000 --- a/pipelines/rj_cor/meteorologia/meteorologia_redemet/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Prefect flows for meteorologia_redemet project -""" -############################################################################### -# Automatically managed, please do not touch -############################################################################### diff --git a/pipelines/rj_cor/meteorologia/meteorologia_redemet/flows.py b/pipelines/rj_cor/meteorologia/meteorologia_redemet/flows.py deleted file mode 100644 index 95cf5f029..000000000 --- a/pipelines/rj_cor/meteorologia/meteorologia_redemet/flows.py +++ /dev/null @@ -1,143 +0,0 @@ -# -*- coding: utf-8 -*- -# pylint: disable=C0103, E1120 -""" -Flows for meteorologia_redemet -""" -from datetime import timedelta - -from prefect import case, Parameter -from prefect.run_configs import KubernetesRun -from prefect.storage import GCS -from prefect.tasks.prefect import create_flow_run, wait_for_flow_run - -from pipelines.constants import constants -from pipelines.utils.constants import constants as utils_constants -from pipelines.rj_cor.meteorologia.meteorologia_redemet.tasks import ( - get_dates, - download, - tratar_dados, - salvar_dados, -) -from pipelines.rj_cor.meteorologia.meteorologia_redemet.schedules import hour_schedule -from pipelines.utils.decorators import Flow -from pipelines.utils.dump_db.constants import constants as dump_db_constants -from pipelines.utils.dump_to_gcs.constants import constants as dump_to_gcs_constants -from pipelines.utils.tasks import ( - create_table_and_upload_to_gcs, - get_current_flow_labels, -) - - -with Flow( - name="COR: Meteorologia - Meteorologia REDEMET", - code_owners=[ - "richardg867", - "paty", - ], -) as cor_meteorologia_meteorologia_redemet: - DATASET_ID = "clima_estacao_meteorologica" - TABLE_ID = "meteorologia_redemet" - DUMP_MODE = "append" - - # data_inicio e data_fim devem ser strings no formato "YYYY-MM-DD" - data_inicio = Parameter("data_inicio", default="", required=False) - data_fim = Parameter("data_fim", default="", required=False) - - # Materialization parameters - MATERIALIZE_AFTER_DUMP = Parameter( - "materialize_after_dump", default=False, required=False - ) - MATERIALIZE_TO_DATARIO = Parameter( - "materialize_to_datario", default=False, required=False - ) - MATERIALIZATION_MODE = Parameter("mode", default="dev", required=False) - - # Dump to GCS after? Should only dump to GCS if materializing to datario - DUMP_TO_GCS = Parameter("dump_to_gcs", default=False, required=False) - - MAXIMUM_BYTES_PROCESSED = Parameter( - "maximum_bytes_processed", - required=False, - default=dump_to_gcs_constants.MAX_BYTES_PROCESSED_PER_TABLE.value, - ) - - data_inicio_, data_fim_, backfill = get_dates(data_inicio, data_fim) - # data = slice_data(current_time=CURRENT_TIME) - dados = download(data_inicio_, data_fim_) - dados = tratar_dados(dados, backfill) - PATH = salvar_dados(dados=dados) - - # Create table in BigQuery - UPLOAD_TABLE = create_table_and_upload_to_gcs( - data_path=PATH, - dataset_id=DATASET_ID, - table_id=TABLE_ID, - dump_mode=DUMP_MODE, - wait=PATH, - ) - - # Trigger DBT flow run - with case(MATERIALIZE_AFTER_DUMP, True): - current_flow_labels = get_current_flow_labels() - materialization_flow = create_flow_run( - flow_name=utils_constants.FLOW_EXECUTE_DBT_MODEL_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters={ - "dataset_id": DATASET_ID, - "table_id": TABLE_ID, - "mode": MATERIALIZATION_MODE, - "materialize_to_datario": MATERIALIZE_TO_DATARIO, - }, - labels=current_flow_labels, - run_name=f"Materialize {DATASET_ID}.{TABLE_ID}", - ) - - materialization_flow.set_upstream(UPLOAD_TABLE) - - wait_for_materialization = wait_for_flow_run( - materialization_flow, - stream_states=True, - stream_logs=True, - raise_final_state=True, - ) - - wait_for_materialization.max_retries = ( - dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_ATTEMPTS.value - ) - wait_for_materialization.retry_delay = timedelta( - seconds=dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_INTERVAL.value - ) - - with case(DUMP_TO_GCS, True): - # Trigger Dump to GCS flow run with project id as datario - dump_to_gcs_flow = create_flow_run( - flow_name=utils_constants.FLOW_DUMP_TO_GCS_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters={ - "project_id": "datario", - "dataset_id": DATASET_ID, - "table_id": TABLE_ID, - "maximum_bytes_processed": MAXIMUM_BYTES_PROCESSED, - }, - labels=[ - "datario", - ], - run_name=f"Dump to GCS {DATASET_ID}.{TABLE_ID}", - ) - dump_to_gcs_flow.set_upstream(wait_for_materialization) - - wait_for_dump_to_gcs = wait_for_flow_run( - dump_to_gcs_flow, - stream_states=True, - stream_logs=True, - raise_final_state=True, - ) - - -# para rodar na cloud -cor_meteorologia_meteorologia_redemet.storage = GCS(constants.GCS_FLOWS_BUCKET.value) -cor_meteorologia_meteorologia_redemet.run_config = KubernetesRun( - image=constants.DOCKER_IMAGE.value, - labels=[constants.RJ_COR_AGENT_LABEL.value], -) -cor_meteorologia_meteorologia_redemet.schedule = hour_schedule diff --git a/pipelines/rj_cor/meteorologia/meteorologia_redemet/schedules.py b/pipelines/rj_cor/meteorologia/meteorologia_redemet/schedules.py deleted file mode 100644 index e180adc28..000000000 --- a/pipelines/rj_cor/meteorologia/meteorologia_redemet/schedules.py +++ /dev/null @@ -1,21 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Schedules for meteorologia_redemet -Rodar a cada 1 hora -""" -from datetime import timedelta, datetime -from prefect.schedules import Schedule -from prefect.schedules.clocks import IntervalClock -from pipelines.constants import constants - -hour_schedule = Schedule( - clocks=[ - IntervalClock( - interval=timedelta(hours=1), - start_date=datetime(2023, 1, 1, 0, 12, 0), - labels=[ - constants.RJ_COR_AGENT_LABEL.value, - ], - ), - ] -) diff --git a/pipelines/rj_cor/meteorologia/meteorologia_redemet/tasks.py b/pipelines/rj_cor/meteorologia/meteorologia_redemet/tasks.py deleted file mode 100644 index 90f6dcbc2..000000000 --- a/pipelines/rj_cor/meteorologia/meteorologia_redemet/tasks.py +++ /dev/null @@ -1,201 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Tasks for meteorologia_redemet -""" -from datetime import timedelta -import json -from pathlib import Path -from typing import Tuple, Union - -import pandas as pd -import pendulum -from prefect import task -import requests - -from pipelines.constants import constants -from pipelines.utils.utils import ( - get_vault_secret, - log, - to_partitions, - parse_date_columns, -) - - -@task(nout=3) -def get_dates(data_inicio: str, data_fim: str) -> Tuple[str, str]: - """ - Task para obter o dia de início e o de fim. - Se nenhuma data foi passada a data_inicio corresponde a ontem - e data_fim a hoje e não estamos fazendo backfill. - Caso contrário, retorna as datas inputadas mos parâmetros do flow. - """ - # a API sempre retorna o dado em UTC - log(f"data de inicio e fim antes do if {data_inicio} {data_fim}") - if data_inicio == "": - data_fim = pendulum.now("UTC").format("YYYY-MM-DD") - data_inicio = pendulum.yesterday("UTC").format("YYYY-MM-DD") - backfill = 0 - else: - backfill = 1 - log(f"data de inicio e fim dps do if {data_inicio} {data_fim}") - - return data_inicio, data_fim, backfill - - -@task( - max_retries=constants.TASK_MAX_RETRIES.value, - retry_delay=timedelta(seconds=constants.TASK_RETRY_DELAY.value), -) -def download(data_inicio: str, data_fim: str) -> pd.DataFrame: - """ - Faz o request na data especificada e retorna dados - """ - - # Lista com as estações da cidade do Rio de Janeiro - estacoes_unicas = [ - "SBAF", - "SBGL", - "SBJR", - "SBRJ", - "SBSC", - ] - - dicionario = get_vault_secret("redemet-token") - - # Converte datas em int para cálculo de faixas. - data_inicio_int = int(data_inicio.replace("-", "")) - data_fim_int = int(data_fim.replace("-", "")) - - raw = [] - for id_estacao in estacoes_unicas: - base_url = f"https://api-redemet.decea.mil.br/aerodromos/info?api_key={dicionario['data']['token']}" # noqa - for data in range(data_inicio_int, data_fim_int + 1): - for hora in range(24): - url = f"{base_url}&localidade={id_estacao}&datahora={data:06}{hora:02}" - res = requests.get(url) - if res.status_code != 200: - log(f"Problema no id: {id_estacao}, {res.status_code}, {url}") - continue - res_data = json.loads(res.text) - if res_data["status"] is not True: - log(f"Problema no id: {id_estacao}, {res_data['message']}, {url}") - continue - if "data" not in res_data["data"]: - # Sem dados para esse horario - continue - raw.append(res_data) - - # Extrai objetos de dados - raw = [res_data["data"] for res_data in raw] - - # converte para dados - dados = pd.DataFrame(raw) - - return dados - - -@task -def tratar_dados(dados: pd.DataFrame, backfill: bool = 0) -> pd.DataFrame: - """ - Renomeia colunas e filtra dados com a hora do timestamp de execução - """ - - drop_cols = ["nome", "cidade", "lon", "lat", "localizacao", "tempoImagem", "metar"] - # Checa se todas estão no df - drop_cols = [c for c in drop_cols if c in dados.columns] - - # Remove colunas que já temos os dados em outras tabelas - dados = dados.drop(drop_cols, axis=1) - - # Adequando nome das variáveis - rename_cols = { - "localidade": "id_estacao", - "ur": "umidade", - } - - dados = dados.rename(columns=rename_cols) - - # Converte horário de UTC para America/Sao Paulo - formato = "DD/MM/YYYY HH:mm(z)" - dados["data"] = dados["data"].apply( - lambda x: pendulum.from_format(x, formato) - .in_tz("America/Sao_Paulo") - .format(formato) - ) - - # Ordenamento de variáveis - chaves_primarias = ["id_estacao", "data"] - demais_cols = [c for c in dados.columns if c not in chaves_primarias] - - dados = dados[chaves_primarias + demais_cols] - - # Converte variáveis que deveriam ser int para int - dados["temperatura"] = dados["temperatura"].apply( - lambda x: None if x[:-2] == "NIL" else int(x[:-2]) - ) - dados["umidade"] = dados["umidade"].apply( - lambda x: None if "%" not in x else int(x[:-1]) - ) - - dados["data"] = pd.to_datetime(dados.data, format="%d/%m/%Y %H:%M(%Z)") - - # Pegar o dia no nosso timezone como partição - br_timezone = pendulum.now("America/Sao_Paulo").format("YYYY-MM-DD") - - # Define colunas que serão salvas - dados = dados[ - [ - "id_estacao", - "data", - "temperatura", - "umidade", - "condicoes_tempo", - "ceu", - "teto", - "visibilidade", - ] - ] - - # Remover dados duplicados - dados = dados.drop_duplicates(subset=["id_estacao", "data"]) - - log(f"Dados antes do filtro dia:\n{dados[['id_estacao', 'data']]}") - - if not backfill: - # Seleciona apenas dados daquele dia (devido à UTC) - dados = dados[dados["data"].dt.date.astype(str) == br_timezone] - - log(f">>>> min hora {dados[~dados.temperatura.isna()].data.min()}") - log(f">>>> max hora {dados[~dados.temperatura.isna()].data.max()}") - - # Remover fuso horário - dados["data"] = dados["data"].dt.strftime("%Y-%m-%d %H:%M:%S") - dados.rename(columns={"data": "data_medicao"}, inplace=True) - - # Capitalizar os dados da coluna céu - dados["ceu"] = dados["ceu"].str.capitalize() - - return dados - - -@task -def salvar_dados(dados: pd.DataFrame) -> Union[str, Path]: - """ - Salvar dados em csv - """ - - prepath = Path("/tmp/meteorologia_redemet/") - prepath.mkdir(parents=True, exist_ok=True) - - partition_column = "data_medicao" - dataframe, partitions = parse_date_columns(dados, partition_column) - - # Cria partições a partir da data - to_partitions( - data=dataframe, - partition_columns=partitions, - savepath=prepath, - data_type="csv", - ) - log(f"[DEBUG] Files saved on {prepath}") - return prepath diff --git a/pipelines/rj_cor/meteorologia/precipitacao_alertario/constants.py b/pipelines/rj_cor/meteorologia/precipitacao_alertario/constants.py deleted file mode 100644 index ece03065a..000000000 --- a/pipelines/rj_cor/meteorologia/precipitacao_alertario/constants.py +++ /dev/null @@ -1,165 +0,0 @@ -# -*- coding: utf-8 -*- -# flake8: noqa: E501 -""" -Constant values for the rj_cor.meteorologia.precipitacao_alertario project -""" - -from enum import Enum - - -class constants(Enum): # pylint: disable=c0103 - """ - Constant values for the precipitacao_alertario project - """ - - RAIN_DASHBOARD_LAST_2H_FLOW_SCHEDULE_PARAMETERS = { - "redis_data_key": "data_chuva_passado_alertario", - "redis_update_key": "data_update_chuva_passado_alertario", - "query_data": """ - WITH - last_update_date AS ( - SELECT - CAST(MAX(data_particao) AS DATETIME) AS last_update - FROM `rj-cor.clima_pluviometro.taxa_precipitacao_alertario` - WHERE data_particao >= DATE_SUB(CURRENT_DATETIME('America/Sao_Paulo'), INTERVAL 2 DAY) - ), - alertario AS ( -- seleciona as últimas 2h de medição antes da última atualização - SELECT - id_estacao, - acumulado_chuva_15_min, - CURRENT_DATE('America/Sao_Paulo') as data, - data_particao, - DATETIME(CONCAT(data_particao," ", horario)) AS data_update, - FROM `rj-cor.clima_pluviometro.taxa_precipitacao_alertario` - INNER JOIN last_update_date lup ON 1=1 - WHERE data_particao >= DATE_SUB(CURRENT_DATE('America/Sao_Paulo'), INTERVAL 2 DAY) - AND CAST(CONCAT(data_particao, " ", horario) AS DATETIME) >= DATE_SUB(lup.last_update, INTERVAL 2 HOUR) - ), - - last_measurements AS (-- soma a quantidade chuva das últimas 2h - SELECT - a.id_estacao, - "alertario" AS sistema, - MAX(a.data_update) AS data_update, - SUM(a.acumulado_chuva_15_min) AS acumulado_chuva_15_min, - FROM alertario a - GROUP BY a.id_estacao, sistema - ), - - h3_chuvas AS ( -- calcula qnt de chuva para cada h3 - SELECT - h3.*, - lm.id_estacao, - lm.acumulado_chuva_15_min, - lm.acumulado_chuva_15_min/power(h3.dist,5) AS p1_15min, - 1/power(h3.dist,5) AS inv_dist - FROM ( - WITH centroid_h3 AS ( - SELECT - *, - ST_CENTROID(geometry) AS geom - FROM `rj-cor.dados_mestres.h3_grid_res8` - ), - - estacoes_pluviometricas AS ( - SELECT - id_estacao AS id, - estacao, - "alertario" AS sistema, - ST_GEOGPOINT(CAST(longitude AS FLOAT64), - CAST(latitude AS FLOAT64)) AS geom - FROM `rj-cor.clima_pluviometro.estacoes_alertario` - ), - - estacoes_mais_proximas AS ( -- calcula distância das estações para cada centróide do h3 - SELECT AS VALUE s - FROM ( - SELECT - ARRAY_AGG( - STRUCT( - a.id, b.id, b.estacao, - ST_DISTANCE(a.geom, b.geom), - b.sistema - ) - ORDER BY ST_DISTANCE(a.geom, b.geom) - ) AS ar - FROM (SELECT id, geom FROM centroid_h3) a - CROSS JOIN( - SELECT id, estacao, sistema, geom - FROM estacoes_pluviometricas - WHERE geom is not null - ) b - WHERE a.id <> b.id - GROUP BY a.id - ) ab - CROSS JOIN UNNEST(ab.ar) s - ) - - SELECT - *, - row_number() OVER (PARTITION BY id_h3 ORDER BY dist) AS ranking - FROM estacoes_mais_proximas - ORDER BY id_h3, ranking) h3 - LEFT JOIN last_measurements lm - ON lm.id_estacao=h3.id_estacao AND lm.sistema=h3.sistema - ), - - h3_media AS ( -- calcula média de chuva para as 3 estações mais próximas - SELECT - id_h3, - CAST(sum(p1_15min)/sum(inv_dist) AS DECIMAL) AS chuva_15min, - STRING_AGG(estacao ORDER BY estacao) estacoes - FROM h3_chuvas - -- WHERE ranking < 4 - GROUP BY id_h3 - ), - - final_table AS ( - SELECT - h3_media.id_h3, - h3_media.estacoes, - nome AS bairro, - cast(round(h3_media.chuva_15min,2) AS decimal) AS chuva_15min, - FROM h3_media - LEFT JOIN `rj-cor.dados_mestres.h3_grid_res8` h3_grid - ON h3_grid.id=h3_media.id_h3 - INNER JOIN `rj-cor.dados_mestres.bairro` - ON ST_CONTAINS(`rj-cor.dados_mestres.bairro`.geometry, ST_CENTROID(h3_grid.geometry)) - ) - - SELECT - final_table.id_h3, - bairro, - chuva_15min, - estacoes, - CASE - WHEN chuva_15min> 0 AND chuva_15min<= 10 THEN 'chuva fraca' - WHEN chuva_15min> 10 AND chuva_15min<= 50 THEN 'chuva moderada' - WHEN chuva_15min> 50 AND chuva_15min<= 100 THEN 'chuva forte' - WHEN chuva_15min> 100 THEN 'chuva muito forte' - ELSE 'sem chuva' - END AS status, - CASE - WHEN chuva_15min> 0 AND chuva_15min<= 10 THEN '#DAECFB'--'#00CCFF' - WHEN chuva_15min> 1 AND chuva_15min<= 50 THEN '#A9CBE8'--'#BFA230' - WHEN chuva_15min> 50 AND chuva_15min<= 100 THEN '#77A9D5'--'#E0701F' - WHEN chuva_15min> 100 THEN '#125999'--'#FF0000' - ELSE '#ffffff' - END AS color - FROM final_table - """, - "query_update": """ - SELECT - MAX( - DATETIME( - CONCAT(data_particao," ", horario) - ) - ) AS last_update - FROM `rj-cor.clima_pluviometro.taxa_precipitacao_alertario` - WHERE data_particao> DATE_SUB(CURRENT_DATE('America/Sao_Paulo'), INTERVAL 2 DAY) - """, - } diff --git a/pipelines/rj_cor/meteorologia/precipitacao_alertario/flows.py b/pipelines/rj_cor/meteorologia/precipitacao_alertario/flows.py index 1d3f45166..f3e4381f1 100644 --- a/pipelines/rj_cor/meteorologia/precipitacao_alertario/flows.py +++ b/pipelines/rj_cor/meteorologia/precipitacao_alertario/flows.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- # pylint: disable=C0103 """ -Flows for precipitacao_alertario. +Flows for precipitacao_alertario """ from datetime import timedelta @@ -12,22 +12,13 @@ from pipelines.constants import constants from pipelines.utils.constants import constants as utils_constants -from pipelines.utils.custom import wait_for_flow_run_with_timeout -from pipelines.rj_cor.meteorologia.precipitacao_alertario.constants import ( - constants as alertario_constants, -) from pipelines.rj_cor.meteorologia.precipitacao_alertario.tasks import ( - check_to_run_dbt, tratar_dados, salvar_dados, - save_last_dbt_update, ) from pipelines.rj_cor.meteorologia.precipitacao_alertario.schedules import ( minute_schedule, ) -from pipelines.rj_escritorio.rain_dashboard.constants import ( - constants as rain_dashboard_constants, -) from pipelines.utils.decorators import Flow from pipelines.utils.dump_db.constants import constants as dump_db_constants from pipelines.utils.dump_to_gcs.constants import constants as dump_to_gcs_constants @@ -36,17 +27,13 @@ get_current_flow_labels, ) -wait_for_flow_run_with_2min_timeout = wait_for_flow_run_with_timeout( - timeout=timedelta(minutes=2) -) - with Flow( name="COR: Meteorologia - Precipitacao ALERTARIO", code_owners=[ "paty", ], - # skip_if_running=True, ) as cor_meteorologia_precipitacao_alertario: + DATASET_ID = "clima_pluviometro" TABLE_ID = "taxa_precipitacao_alertario" DUMP_MODE = "append" @@ -59,9 +46,6 @@ "materialize_to_datario", default=False, required=False ) MATERIALIZATION_MODE = Parameter("mode", default="dev", required=False) - TRIGGER_RAIN_DASHBOARD_UPDATE = Parameter( - "trigger_rain_dashboard_update", default=False, required=False - ) # Dump to GCS after? Should only dump to GCS if materializing to datario DUMP_TO_GCS = Parameter("dump_to_gcs", default=False, required=False) @@ -72,14 +56,16 @@ default=dump_to_gcs_constants.MAX_BYTES_PROCESSED_PER_TABLE.value, ) - dados, empty_data = tratar_dados( - dataset_id=DATASET_ID, - table_id=TABLE_ID, - mode=MATERIALIZATION_MODE, + dados, empty_data, current_time = tratar_dados( + dataset_id=DATASET_ID, table_id=TABLE_ID ) + # If dataframe is empty stop flow + with case(empty_data, True): + MATERIALIZE_AFTER_DUMP = False + with case(empty_data, False): - path = salvar_dados(dados=dados) + path = salvar_dados(dados=dados, current_time=current_time) # Create table in BigQuery UPLOAD_TABLE = create_table_and_upload_to_gcs( data_path=path, @@ -89,120 +75,63 @@ wait=path, ) - run_dbt = check_to_run_dbt( - dataset_id=DATASET_ID, - table_id=TABLE_ID, - mode=MATERIALIZATION_MODE, - ) - run_dbt.set_upstream(UPLOAD_TABLE) - - with case(run_dbt, True): - # Trigger DBT flow run - with case(MATERIALIZE_AFTER_DUMP, True): - current_flow_labels = get_current_flow_labels() - materialization_flow = create_flow_run( - flow_name=utils_constants.FLOW_EXECUTE_DBT_MODEL_NAME.value, + # Trigger DBT flow run + with case(MATERIALIZE_AFTER_DUMP, True): + current_flow_labels = get_current_flow_labels() + materialization_flow = create_flow_run( + flow_name=utils_constants.FLOW_EXECUTE_DBT_MODEL_NAME.value, + project_name=constants.PREFECT_DEFAULT_PROJECT.value, + parameters={ + "dataset_id": DATASET_ID, + "table_id": TABLE_ID, + "mode": MATERIALIZATION_MODE, + "materialize_to_datario": MATERIALIZE_TO_DATARIO, + }, + labels=current_flow_labels, + run_name=f"Materialize {DATASET_ID}.{TABLE_ID}", + ) + + materialization_flow.set_upstream(UPLOAD_TABLE) + + wait_for_materialization = wait_for_flow_run( + materialization_flow, + stream_states=True, + stream_logs=True, + raise_final_state=True, + ) + + wait_for_materialization.max_retries = ( + dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_ATTEMPTS.value + ) + wait_for_materialization.retry_delay = timedelta( + seconds=dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_INTERVAL.value + ) + + with case(DUMP_TO_GCS, True): + # Trigger Dump to GCS flow run with project id as datario + dump_to_gcs_flow = create_flow_run( + flow_name=utils_constants.FLOW_DUMP_TO_GCS_NAME.value, project_name=constants.PREFECT_DEFAULT_PROJECT.value, parameters={ + "project_id": "datario", "dataset_id": DATASET_ID, "table_id": TABLE_ID, - "mode": MATERIALIZATION_MODE, - "materialize_to_datario": MATERIALIZE_TO_DATARIO, + "maximum_bytes_processed": MAXIMUM_BYTES_PROCESSED, }, - labels=current_flow_labels, - run_name=f"Materialize {DATASET_ID}.{TABLE_ID}", + labels=[ + "datario", + ], + run_name=f"Dump to GCS {DATASET_ID}.{TABLE_ID}", ) + dump_to_gcs_flow.set_upstream(wait_for_materialization) - current_flow_labels.set_upstream(run_dbt) - materialization_flow.set_upstream(current_flow_labels) - - wait_for_materialization = wait_for_flow_run_with_2min_timeout( - flow_run_id=materialization_flow, + wait_for_dump_to_gcs = wait_for_flow_run( + dump_to_gcs_flow, stream_states=True, stream_logs=True, raise_final_state=True, ) - last_dbt_update = save_last_dbt_update( - dataset_id=DATASET_ID, - table_id=TABLE_ID, - mode=MATERIALIZATION_MODE, - wait=wait_for_materialization, - ) - - wait_for_materialization.max_retries = ( - dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_ATTEMPTS.value - ) - wait_for_materialization.retry_delay = timedelta( - seconds=dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_INTERVAL.value - ) - - with case(TRIGGER_RAIN_DASHBOARD_UPDATE, True): - # Trigger rain dashboard update flow run - rain_dashboard_update_flow = create_flow_run( - flow_name=rain_dashboard_constants.RAIN_DASHBOARD_FLOW_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters=rain_dashboard_constants.RAIN_DASHBOARD_FLOW_SCHEDULE_PARAMETERS.value, # noqa - labels=[ - "rj-escritorio-dev", - ], - run_name="Update rain dashboard data (triggered by precipitacao_alertario flow)", # noqa - ) - rain_dashboard_update_flow.set_upstream(wait_for_materialization) - - wait_for_rain_dashboard_update = wait_for_flow_run( - flow_run_id=rain_dashboard_update_flow, - stream_states=True, - stream_logs=True, - raise_final_state=False, - ) - - # Trigger rain dashboard update last 2h flow run - rain_dashboard_last_2h_update_flow = create_flow_run( - flow_name=rain_dashboard_constants.RAIN_DASHBOARD_FLOW_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters=alertario_constants.RAIN_DASHBOARD_LAST_2H_FLOW_SCHEDULE_PARAMETERS.value, # noqa - labels=[ - "rj-escritorio-dev", - ], - run_name="Update rain dashboard data (triggered by precipitacao_alertario last 2h flow)", # noqa - ) - rain_dashboard_last_2h_update_flow.set_upstream( - wait_for_materialization - ) - - wait_for_rain_dashboard_last_2h_update = wait_for_flow_run( - flow_run_id=rain_dashboard_last_2h_update_flow, - stream_states=True, - stream_logs=True, - raise_final_state=False, - ) - - with case(DUMP_TO_GCS, True): - # Trigger Dump to GCS flow run with project id as datario - dump_to_gcs_flow = create_flow_run( - flow_name=utils_constants.FLOW_DUMP_TO_GCS_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters={ - "project_id": "datario", - "dataset_id": DATASET_ID, - "table_id": TABLE_ID, - "maximum_bytes_processed": MAXIMUM_BYTES_PROCESSED, - }, - labels=[ - "datario", - ], - run_name=f"Dump to GCS {DATASET_ID}.{TABLE_ID}", - ) - dump_to_gcs_flow.set_upstream(wait_for_materialization) - - wait_for_dump_to_gcs = wait_for_flow_run_with_2min_timeout( - flow_run_id=dump_to_gcs_flow, - stream_states=True, - stream_logs=True, - raise_final_state=True, - ) - # para rodar na cloud cor_meteorologia_precipitacao_alertario.storage = GCS(constants.GCS_FLOWS_BUCKET.value) cor_meteorologia_precipitacao_alertario.run_config = KubernetesRun( diff --git a/pipelines/rj_cor/meteorologia/precipitacao_alertario/schedules.py b/pipelines/rj_cor/meteorologia/precipitacao_alertario/schedules.py index 84d3768e2..c39bbe103 100644 --- a/pipelines/rj_cor/meteorologia/precipitacao_alertario/schedules.py +++ b/pipelines/rj_cor/meteorologia/precipitacao_alertario/schedules.py @@ -2,7 +2,7 @@ # pylint: disable=C0103 """ Schedules for precipitacao_alertario -Rodar a cada 1 minuto +Rodar a cada 5 minutos """ from datetime import timedelta, datetime from prefect.schedules import Schedule @@ -12,18 +12,11 @@ minute_schedule = Schedule( clocks=[ IntervalClock( - interval=timedelta(minutes=5), - start_date=datetime(2021, 1, 1, 0, 1, 0), + interval=timedelta(minutes=15), + start_date=datetime(2021, 1, 1, 0, 5, 0), labels=[ constants.RJ_COR_AGENT_LABEL.value, ], - parameter_defaults={ - # "trigger_rain_dashboard_update": True, - "materialize_after_dump": True, - "mode": "prod", - "materialize_to_datario": True, - "dump_to_gcs": False, - }, ), ] ) diff --git a/pipelines/rj_cor/meteorologia/precipitacao_alertario/tasks.py b/pipelines/rj_cor/meteorologia/precipitacao_alertario/tasks.py index 002d7cd33..f2a62d24c 100644 --- a/pipelines/rj_cor/meteorologia/precipitacao_alertario/tasks.py +++ b/pipelines/rj_cor/meteorologia/precipitacao_alertario/tasks.py @@ -1,9 +1,10 @@ # -*- coding: utf-8 -*- -# pylint: disable=C0103,R0914 +# pylint: disable=C0103 """ Tasks for precipitacao_alertario """ from datetime import timedelta +import os from pathlib import Path from typing import Union, Tuple @@ -11,39 +12,29 @@ import pandas as pd import pendulum from prefect import task - import pandas_read_xml as pdx # from prefect import context from pipelines.constants import constants -from pipelines.rj_cor.meteorologia.precipitacao_alertario.utils import ( - parse_date_columns, - treat_date_col, -) -from pipelines.utils.utils import ( - build_redis_key, - compare_dates_between_tables_redis, - log, - to_partitions, - save_str_on_redis, - save_updated_rows_on_redis, -) +from pipelines.rj_cor.meteorologia.utils import save_updated_rows_on_redis +from pipelines.utils.utils import log @task( - nout=2, + nout=3, max_retries=constants.TASK_MAX_RETRIES.value, retry_delay=timedelta(seconds=constants.TASK_RETRY_DELAY.value), ) -def tratar_dados( - dataset_id: str, table_id: str, mode: str = "dev" -) -> Tuple[pd.DataFrame, bool]: +def tratar_dados(dataset_id: str, table_id: str) -> Tuple[pd.DataFrame, bool]: """ Renomeia colunas e filtra dados com a hora e minuto do timestamp de execução mais próximo à este """ + # Hora atual no formato YYYYMMDDHHmm para criar partições + current_time = pendulum.now("America/Sao_Paulo").strftime("%Y%m%d%H%M") + url = "http://alertario.rio.rj.gov.br/upload/xml/Chuvas.xml" dados = pdx.read_xml(url, ["estacoes"]) dados = pdx.fully_flatten(dados) @@ -72,16 +63,7 @@ def tratar_dados( # Converte de UTC para horário São Paulo dados["data_medicao_utc"] = pd.to_datetime(dados["data_medicao_utc"]) - - see_cols = ["data_medicao_utc", "id_estacao", "acumulado_chuva_15_min"] - log(f"DEBUG: data utc {dados[see_cols]}") - - date_format = "%Y-%m-%d %H:%M:%S" - dados["data_medicao"] = dados["data_medicao_utc"].dt.strftime(date_format) - - see_cols = ["data_medicao", "id_estacao", "acumulado_chuva_15_min"] - - dados.data_medicao = dados.data_medicao.apply(treat_date_col) + dados["data_medicao"] = dados["data_medicao_utc"].dt.strftime("%Y-%m-%d %H:%M:%S") # Alterando valores ND, '-' e np.nan para NULL dados.replace(["ND", "-", np.nan], [None, None, None], inplace=True) @@ -103,42 +85,12 @@ def tratar_dados( dados.sort_values(["id_estacao", "data_medicao"] + float_cols, inplace=True) dados.drop_duplicates(subset=["id_estacao", "data_medicao"], keep="first") + log(f"uniquesss df >>>, {type(dados.id_estacao.unique()[0])}") dados["id_estacao"] = dados["id_estacao"].astype(str) - # Ajustando dados da meia-noite que vem sem o horário - for index, row in dados.iterrows(): - try: - date = pd.to_datetime(row["data_medicao"], format="%Y-%m-%d %H:%M:%S") - except ValueError: - date = pd.to_datetime(row["data_medicao"]) + pd.DateOffset(hours=0) - - dados.at[index, "data_medicao"] = date.strftime("%Y-%m-%d %H:%M:%S") + dados = save_updated_rows_on_redis(dados, dataset_id, table_id, mode="dev") - log(f"Dataframe before comparing with last data saved on redis {dados.head()}") - - dados = save_updated_rows_on_redis( - dados, - dataset_id, - table_id, - unique_id="id_estacao", - date_column="data_medicao", - date_format=date_format, - mode=mode, - ) - - log(f"Dataframe after comparing with last data saved on redis {dados.head()}") - - empty_data = dados.shape[0] == 0 - - # Save max date on redis to compare this with last dbt run - if not empty_data: - max_date = str(dados["data_medicao"].max()) - redis_key = build_redis_key(dataset_id, table_id, name="last_update", mode=mode) - log(f"Dataframe is not empty. Redis key: {redis_key} and new date: {max_date}") - save_str_on_redis(redis_key, "date", max_date) - else: - # If df is empty stop flow on flows.py - log(f"Dataframe is empty. Skipping update flow for datetime {date}.") + dados["id_estacao"] = dados["id_estacao"].astype(int) # Fixar ordem das colunas dados = dados[ @@ -153,71 +105,33 @@ def tratar_dados( ] ] - return dados, empty_data + # If df is empty stop flow + empty_data = dados.shape[0] == 0 + log(f"[DEBUG]: dataframe is empty: {empty_data}") + + return dados, empty_data, current_time @task -def salvar_dados(dados: pd.DataFrame) -> Union[str, Path]: +def salvar_dados(dados: pd.DataFrame, current_time: str) -> Union[str, Path]: """ Salvar dados tratados em csv para conseguir subir pro GCP """ - prepath = Path("/tmp/precipitacao_alertario/") - prepath.mkdir(parents=True, exist_ok=True) - - partition_column = "data_medicao" - dataframe, partitions = parse_date_columns(dados, partition_column) - current_time = pendulum.now("America/Sao_Paulo").strftime("%Y%m%d%H%M") - - # Cria partições a partir da data - to_partitions( - data=dataframe, - partition_columns=partitions, - savepath=prepath, - data_type="csv", - suffix=current_time, - ) - log(f"Files saved on {prepath}") - return prepath + ano = current_time[:4] + mes = str(int(current_time[4:6])) + dia = str(int(current_time[6:8])) + partitions = os.path.join(f"ano={ano}", f"mes={mes}", f"dia={dia}") + base_path = os.path.join(os.getcwd(), "data", "precipitacao_alertario", "output") -@task -def save_last_dbt_update( - dataset_id: str, - table_id: str, - mode: str = "dev", - wait=None, # pylint: disable=unused-argument -) -> None: - """ - Save on dbt last timestamp where it was updated - """ - now = pendulum.now("America/Sao_Paulo").to_datetime_string() - redis_key = build_redis_key(dataset_id, table_id, name="dbt_last_update", mode=mode) - save_str_on_redis(redis_key, "date", now) + partition_path = os.path.join(base_path, partitions) + if not os.path.exists(partition_path): + os.makedirs(partition_path) -@task(skip_on_upstream_skip=False) -def check_to_run_dbt( - dataset_id: str, - table_id: str, - mode: str = "dev", -) -> bool: - """ - It will run even if its upstream tasks skip. - """ + filename = os.path.join(partition_path, f"dados_{current_time}.csv") - key_table_1 = build_redis_key( - dataset_id, table_id, name="dbt_last_update", mode=mode - ) - key_table_2 = build_redis_key(dataset_id, table_id, name="last_update", mode=mode) - - format_date_table_1 = "YYYY-MM-DD HH:mm:SS" - format_date_table_2 = "YYYY-MM-DD HH:mm:SS" - - # Returns true if date saved on table_2 (alertario) is bigger than - # the date saved on table_1 (dbt). - run_dbt = compare_dates_between_tables_redis( - key_table_1, format_date_table_1, key_table_2, format_date_table_2 - ) - log(f">>>> debug data alertario > data dbt: {run_dbt}") - return run_dbt + log(f"Saving {filename}") + dados.to_csv(filename, index=False) + return base_path diff --git a/pipelines/rj_cor/meteorologia/precipitacao_alertario/utils.py b/pipelines/rj_cor/meteorologia/precipitacao_alertario/utils.py deleted file mode 100644 index 3c6253a28..000000000 --- a/pipelines/rj_cor/meteorologia/precipitacao_alertario/utils.py +++ /dev/null @@ -1,64 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Utils for precipitacao_alertario -""" -from typing import List, Tuple - -import numpy as np -import pandas as pd - - -def parse_date_columns( - dataframe: pd.DataFrame, partition_date_column: str -) -> Tuple[pd.DataFrame, List[str]]: - """ - Parses the date columns to the partition format. Reformatado para manter o formato utilizado - quando os dados foram salvos pela primeira vez (ano, mes, dia). - """ - ano_col = "ano" - mes_col = "mes" - data_col = "dia" - cols = [ano_col, mes_col, data_col] - for col in cols: - if col in dataframe.columns: - raise ValueError(f"Column {col} already exists, please review your model.") - - dataframe[partition_date_column] = dataframe[partition_date_column].astype(str) - dataframe[data_col] = pd.to_datetime( - dataframe[partition_date_column], errors="coerce" - ) - - dataframe[ano_col] = ( - dataframe[data_col] - .dt.year.fillna(-1) - .astype(int) - .astype(str) - .replace("-1", np.nan) - ) - - dataframe[mes_col] = ( - dataframe[data_col] - .dt.month.fillna(-1) - .astype(int) - .astype(str) - .replace("-1", np.nan) - ) - - dataframe[data_col] = ( - dataframe[data_col] - .dt.day.fillna(-1) - .astype(int) - .astype(str) - .replace("-1", np.nan) - ) - - return dataframe, [ano_col, mes_col, data_col] - - -def treat_date_col(row): - """ - Add zero hour if date cames without it - """ - if len(row) == 10: - row = row + " 00:00:00" - return row diff --git a/pipelines/rj_cor/meteorologia/precipitacao_cemaden/constants.py b/pipelines/rj_cor/meteorologia/precipitacao_cemaden/constants.py deleted file mode 100644 index 72c72861a..000000000 --- a/pipelines/rj_cor/meteorologia/precipitacao_cemaden/constants.py +++ /dev/null @@ -1,165 +0,0 @@ -# -*- coding: utf-8 -*- -# flake8: noqa: E501 -""" -Constant values for the rj_cor.meteorologia.precipitacao_cemaden project -""" - -from enum import Enum - - -class constants(Enum): # pylint: disable=c0103 - """ - Constant values for the precipitacao_cemaden project - """ - - RAIN_DASHBOARD_LAST_2H_FLOW_SCHEDULE_PARAMETERS = { - "redis_data_key": "data_chuva_passado_cemaden", - "redis_update_key": "data_update_chuva_passado_cemaden", - "query_data": """ - WITH - last_update_date AS ( - SELECT - CAST(MAX(data_particao) AS DATETIME) AS last_update - FROM `rj-cor.clima_pluviometro.taxa_precipitacao_cemaden` - WHERE data_particao >= DATE_SUB(CURRENT_DATETIME('America/Sao_Paulo'), INTERVAL 2 DAY) - ), - cemaden AS ( -- seleciona as últimas 2h de medição antes da última atualização - SELECT - id_estacao, - acumulado_chuva_1_h, - CURRENT_DATE('America/Sao_Paulo') as data, - data_particao, - DATETIME(CONCAT(data_particao," ", horario)) AS data_update, - FROM `rj-cor.clima_pluviometro.taxa_precipitacao_cemaden` - INNER JOIN last_update_date lup ON 1=1 - WHERE data_particao >= DATE_SUB(CURRENT_DATE('America/Sao_Paulo'), INTERVAL 2 DAY) - AND CAST(CONCAT(data_particao, " ", horario) AS DATETIME) >= DATE_SUB(lup.last_update, INTERVAL 2 HOUR) - ), - - last_measurements AS (-- soma a quantidade chuva das últimas 2h - SELECT - a.id_estacao, - "cemaden" AS sistema, - MAX(a.data_update) AS data_update, - SUM(a.acumulado_chuva_1_h) AS acumulado_chuva_1_h, - FROM cemaden a - GROUP BY a.id_estacao, sistema - ), - - h3_chuvas AS ( -- calcula qnt de chuva para cada h3 - SELECT - h3.*, - lm.id_estacao, - lm.acumulado_chuva_1_h, - lm.acumulado_chuva_1_h/power(h3.dist,5) AS p1_1h, - 1/power(h3.dist,5) AS inv_dist - FROM ( - WITH centroid_h3 AS ( - SELECT - *, - ST_CENTROID(geometry) AS geom - FROM `rj-cor.dados_mestres.h3_grid_res8` - ), - - estacoes_pluviometricas AS ( - SELECT - id_estacao AS id, - estacao, - "cemaden" AS sistema, - ST_GEOGPOINT(CAST(longitude AS FLOAT64), - CAST(latitude AS FLOAT64)) AS geom - FROM `rj-cor.clima_pluviometro.estacoes_cemaden` - ), - - estacoes_mais_proximas AS ( -- calcula distância das estações para cada centróide do h3 - SELECT AS VALUE s - FROM ( - SELECT - ARRAY_AGG( - STRUCT( - a.id, b.id, b.estacao, - ST_DISTANCE(a.geom, b.geom), - b.sistema - ) - ORDER BY ST_DISTANCE(a.geom, b.geom) - ) AS ar - FROM (SELECT id, geom FROM centroid_h3) a - CROSS JOIN( - SELECT id, estacao, sistema, geom - FROM estacoes_pluviometricas - WHERE geom is not null - ) b - WHERE a.id <> b.id - GROUP BY a.id - ) ab - CROSS JOIN UNNEST(ab.ar) s - ) - - SELECT - *, - row_number() OVER (PARTITION BY id_h3 ORDER BY dist) AS ranking - FROM estacoes_mais_proximas - ORDER BY id_h3, ranking) h3 - LEFT JOIN last_measurements lm - ON lm.id_estacao=h3.id_estacao AND lm.sistema=h3.sistema - ), - - h3_media AS ( -- calcula média de chuva para as 3 estações mais próximas - SELECT - id_h3, - CAST(sum(p1_1h)/sum(inv_dist) AS DECIMAL) AS chuva_1h, - STRING_AGG(estacao ORDER BY estacao) estacoes - FROM h3_chuvas - -- WHERE ranking < 4 - GROUP BY id_h3 - ), - - final_table AS ( - SELECT - h3_media.id_h3, - h3_media.estacoes, - nome AS bairro, - cast(round(h3_media.chuva_1h,2) AS decimal) AS chuva_1h, - FROM h3_media - LEFT JOIN `rj-cor.dados_mestres.h3_grid_res8` h3_grid - ON h3_grid.id=h3_media.id_h3 - INNER JOIN `rj-cor.dados_mestres.bairro` - ON ST_CONTAINS(`rj-cor.dados_mestres.bairro`.geometry, ST_CENTROID(h3_grid.geometry)) - ) - - SELECT - final_table.id_h3, - bairro, - chuva_1h, - estacoes, - CASE - WHEN chuva_1h> 0 AND chuva_1h<= 10 THEN 'chuva fraca' - WHEN chuva_1h> 10 AND chuva_1h<= 50 THEN 'chuva moderada' - WHEN chuva_1h> 50 AND chuva_1h<= 100 THEN 'chuva forte' - WHEN chuva_1h> 100 THEN 'chuva muito forte' - ELSE 'sem chuva' - END AS status, - CASE - WHEN chuva_1h> 0 AND chuva_1h<= 10 THEN '#DAECFB'--'#00CCFF' - WHEN chuva_1h> 1 AND chuva_1h<= 50 THEN '#A9CBE8'--'#BFA230' - WHEN chuva_1h> 50 AND chuva_1h<= 100 THEN '#77A9D5'--'#E0701F' - WHEN chuva_1h> 100 THEN '#125999'--'#FF0000' - ELSE '#ffffff' - END AS color - FROM final_table - """, - "query_update": """ - SELECT - MAX( - DATETIME( - CONCAT(data_particao," ", horario) - ) - ) AS last_update - FROM `rj-cor.clima_pluviometro.taxa_precipitacao_cemaden` - WHERE data_particao> DATE_SUB(CURRENT_DATE('America/Sao_Paulo'), INTERVAL 2 DAY) - """, - } diff --git a/pipelines/rj_cor/meteorologia/precipitacao_cemaden/flows.py b/pipelines/rj_cor/meteorologia/precipitacao_cemaden/flows.py deleted file mode 100644 index d4f8f0810..000000000 --- a/pipelines/rj_cor/meteorologia/precipitacao_cemaden/flows.py +++ /dev/null @@ -1,191 +0,0 @@ -# -*- coding: utf-8 -*- -# pylint: disable=C0103 -""" -Flows for precipitacao_cemaden. -""" -from datetime import timedelta - -from prefect import case, Parameter -from prefect.run_configs import KubernetesRun -from prefect.storage import GCS -from prefect.tasks.prefect import create_flow_run, wait_for_flow_run - -from pipelines.constants import constants -from pipelines.utils.constants import constants as utils_constants -from pipelines.utils.custom import wait_for_flow_run_with_timeout -from pipelines.rj_cor.meteorologia.precipitacao_cemaden.constants import ( - constants as cemaden_constants, -) -from pipelines.rj_cor.meteorologia.precipitacao_cemaden.tasks import ( - tratar_dados, - salvar_dados, -) -from pipelines.rj_cor.meteorologia.precipitacao_cemaden.schedules import ( - minute_schedule, -) -from pipelines.rj_escritorio.rain_dashboard.constants import ( - constants as rain_dashboard_constants, -) -from pipelines.utils.decorators import Flow -from pipelines.utils.dump_db.constants import constants as dump_db_constants -from pipelines.utils.dump_to_gcs.constants import constants as dump_to_gcs_constants -from pipelines.utils.tasks import ( - create_table_and_upload_to_gcs, - get_current_flow_labels, -) - -wait_for_flow_run_with_2min_timeout = wait_for_flow_run_with_timeout( - timeout=timedelta(minutes=2) -) - -with Flow( - name="COR: Meteorologia - Precipitacao CEMADEN", - code_owners=[ - "richardg867", - "paty", - ], - # skip_if_running=True, -) as cor_meteorologia_precipitacao_cemaden: - DATASET_ID = "clima_pluviometro" - TABLE_ID = "taxa_precipitacao_cemaden" - DUMP_MODE = "append" - - # Materialization parameters - MATERIALIZE_AFTER_DUMP = Parameter( - "materialize_after_dump", default=False, required=False - ) - MATERIALIZE_TO_DATARIO = Parameter( - "materialize_to_datario", default=False, required=False - ) - MATERIALIZATION_MODE = Parameter("mode", default="dev", required=False) - TRIGGER_RAIN_DASHBOARD_UPDATE = Parameter( - "trigger_rain_dashboard_update", default=False, required=False - ) - - # Dump to GCS after? Should only dump to GCS if materializing to datario - DUMP_TO_GCS = Parameter("dump_to_gcs", default=False, required=False) - - MAXIMUM_BYTES_PROCESSED = Parameter( - "maximum_bytes_processed", - required=False, - default=dump_to_gcs_constants.MAX_BYTES_PROCESSED_PER_TABLE.value, - ) - - dados = tratar_dados( - dataset_id=DATASET_ID, - table_id=TABLE_ID, - mode=MATERIALIZATION_MODE, - ) - path = salvar_dados(dados=dados) - - # Create table in BigQuery - UPLOAD_TABLE = create_table_and_upload_to_gcs( - data_path=path, - dataset_id=DATASET_ID, - table_id=TABLE_ID, - dump_mode=DUMP_MODE, - wait=path, - ) - - # Trigger DBT flow run - with case(MATERIALIZE_AFTER_DUMP, True): - current_flow_labels = get_current_flow_labels() - materialization_flow = create_flow_run( - flow_name=utils_constants.FLOW_EXECUTE_DBT_MODEL_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters={ - "dataset_id": DATASET_ID, - "table_id": TABLE_ID, - "mode": MATERIALIZATION_MODE, - "materialize_to_datario": MATERIALIZE_TO_DATARIO, - }, - labels=current_flow_labels, - run_name=f"Materialize {DATASET_ID}.{TABLE_ID}", - ) - - materialization_flow.set_upstream(current_flow_labels) - - wait_for_materialization = wait_for_flow_run_with_2min_timeout( - flow_run_id=materialization_flow, - stream_states=True, - stream_logs=True, - raise_final_state=True, - ) - wait_for_materialization.max_retries = ( - dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_ATTEMPTS.value - ) - wait_for_materialization.retry_delay = timedelta( - seconds=dump_db_constants.WAIT_FOR_MATERIALIZATION_RETRY_INTERVAL.value - ) - - with case(TRIGGER_RAIN_DASHBOARD_UPDATE, True): - # Trigger rain dashboard update flow run - rain_dashboard_update_flow = create_flow_run( - flow_name=rain_dashboard_constants.RAIN_DASHBOARD_FLOW_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters=rain_dashboard_constants.RAIN_DASHBOARD_FLOW_SCHEDULE_PARAMETERS.value, # noqa - labels=[ - "rj-escritorio-dev", - ], - run_name="Update rain dashboard data (triggered by precipitacao_cemaden flow)", # noqa - ) - rain_dashboard_update_flow.set_upstream(wait_for_materialization) - - wait_for_rain_dashboard_update = wait_for_flow_run( - flow_run_id=rain_dashboard_update_flow, - stream_states=True, - stream_logs=True, - raise_final_state=False, - ) - - # Trigger rain dashboard update last 2h flow run - rain_dashboard_last_2h_update_flow = create_flow_run( - flow_name=rain_dashboard_constants.RAIN_DASHBOARD_FLOW_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters=cemaden_constants.RAIN_DASHBOARD_LAST_2H_FLOW_SCHEDULE_PARAMETERS.value, # noqa - labels=[ - "rj-escritorio-dev", - ], - run_name="Update rain dashboard data (triggered by precipitacao_cemaden last 2h flow)", # noqa - ) - rain_dashboard_last_2h_update_flow.set_upstream(wait_for_materialization) - - wait_for_rain_dashboard_last_2h_update = wait_for_flow_run( - flow_run_id=rain_dashboard_last_2h_update_flow, - stream_states=True, - stream_logs=True, - raise_final_state=False, - ) - - with case(DUMP_TO_GCS, True): - # Trigger Dump to GCS flow run with project id as datario - dump_to_gcs_flow = create_flow_run( - flow_name=utils_constants.FLOW_DUMP_TO_GCS_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters={ - "project_id": "datario", - "dataset_id": DATASET_ID, - "table_id": TABLE_ID, - "maximum_bytes_processed": MAXIMUM_BYTES_PROCESSED, - }, - labels=[ - "datario", - ], - run_name=f"Dump to GCS {DATASET_ID}.{TABLE_ID}", - ) - dump_to_gcs_flow.set_upstream(wait_for_materialization) - - wait_for_dump_to_gcs = wait_for_flow_run_with_2min_timeout( - flow_run_id=dump_to_gcs_flow, - stream_states=True, - stream_logs=True, - raise_final_state=True, - ) - -# para rodar na cloud -cor_meteorologia_precipitacao_cemaden.storage = GCS(constants.GCS_FLOWS_BUCKET.value) -cor_meteorologia_precipitacao_cemaden.run_config = KubernetesRun( - image=constants.DOCKER_IMAGE.value, - labels=[constants.RJ_COR_AGENT_LABEL.value], -) -cor_meteorologia_precipitacao_cemaden.schedule = minute_schedule diff --git a/pipelines/rj_cor/meteorologia/precipitacao_cemaden/schedules.py b/pipelines/rj_cor/meteorologia/precipitacao_cemaden/schedules.py deleted file mode 100644 index f3e2c8a77..000000000 --- a/pipelines/rj_cor/meteorologia/precipitacao_cemaden/schedules.py +++ /dev/null @@ -1,30 +0,0 @@ -# -*- coding: utf-8 -*- -# pylint: disable=C0103 -""" -Schedules for precipitacao_cemaden -Rodar a cada 1 minuto -""" -from datetime import timedelta, datetime -from prefect.schedules import Schedule -from prefect.schedules.clocks import IntervalClock -from pipelines.constants import constants - -minute_schedule = Schedule( - clocks=[ - IntervalClock( - interval=timedelta(minutes=5), - start_date=datetime(2023, 1, 1, 0, 1, 0), - labels=[ - constants.RJ_COR_AGENT_LABEL.value, - ], - parameter_defaults={ - # "trigger_rain_dashboard_update": True, - # "materialize_after_dump": True, - "materialize_after_dump": False, - # "mode": "prod", - "materialize_to_datario": False, - "dump_to_gcs": False, - }, - ), - ] -) diff --git a/pipelines/rj_cor/meteorologia/precipitacao_cemaden/tasks.py b/pipelines/rj_cor/meteorologia/precipitacao_cemaden/tasks.py deleted file mode 100644 index 686692d7c..000000000 --- a/pipelines/rj_cor/meteorologia/precipitacao_cemaden/tasks.py +++ /dev/null @@ -1,180 +0,0 @@ -# -*- coding: utf-8 -*- -# pylint: disable=C0103 -""" -Tasks for precipitacao_cemaden -""" -from datetime import timedelta -from pathlib import Path -from typing import Union, Tuple - -import numpy as np -import pandas as pd -import pendulum -from prefect import task -from prefect.engine.signals import ENDRUN -from prefect.engine.state import Skipped - -# from prefect import context - -from pipelines.constants import constants -from pipelines.rj_cor.meteorologia.precipitacao_cemaden.utils import ( - parse_date_columns, -) -from pipelines.utils.utils import ( - log, - to_partitions, - save_updated_rows_on_redis, -) - - -@task( - nout=2, - max_retries=constants.TASK_MAX_RETRIES.value, - retry_delay=timedelta(seconds=constants.TASK_RETRY_DELAY.value), -) -def tratar_dados( - dataset_id: str, table_id: str, mode: str = "dev" -) -> Tuple[pd.DataFrame, bool]: - """ - Renomeia colunas e filtra dados com a hora e minuto do timestamp - de execução mais próximo à este - """ - - url = "http://sjc.salvar.cemaden.gov.br/resources/graficos/interativo/getJson2.php?uf=RJ" - dados = pd.read_json(url) - - drop_cols = [ - "uf", - "codibge", - "cidade", - "nomeestacao", - "tipoestacao", - "status", - ] - rename_cols = { - "idestacao": "id_estacao", - "ultimovalor": "acumulado_chuva_10_min", - "datahoraUltimovalor": "data_medicao_utc", - "acc1hr": "acumulado_chuva_1_h", - "acc3hr": "acumulado_chuva_3_h", - "acc6hr": "acumulado_chuva_6_h", - "acc12hr": "acumulado_chuva_12_h", - "acc24hr": "acumulado_chuva_24_h", - "acc48hr": "acumulado_chuva_48_h", - "acc72hr": "acumulado_chuva_72_h", - "acc96hr": "acumulado_chuva_96_h", - } - - dados = ( - dados[(dados["codibge"] == 3304557) & (dados["tipoestacao"] == 1)] - .drop(drop_cols, axis=1) - .rename(rename_cols, axis=1) - ) - log(f"\n[DEBUG]: df.head() {dados.head()}") - - # Converte de UTC para horário São Paulo - dados["data_medicao_utc"] = pd.to_datetime(dados["data_medicao_utc"], dayfirst=True) - - see_cols = ["data_medicao_utc", "id_estacao", "acumulado_chuva_1_h"] - log(f"DEBUG: data utc {dados[see_cols]}") - - date_format = "%Y-%m-%d %H:%M:%S" - dados["data_medicao"] = dados["data_medicao_utc"].dt.strftime(date_format) - - log(f"DEBUG: df dtypes {dados.dtypes}") - see_cols = ["data_medicao", "id_estacao", "acumulado_chuva_1_h"] - log(f"DEBUG: data {dados[see_cols]}") - - # Alterando valores '-' e np.nan para NULL - dados.replace(["-", np.nan], [0, None], inplace=True) - - # Altera valores negativos para None - float_cols = [ - "acumulado_chuva_10_min", - "acumulado_chuva_1_h", - "acumulado_chuva_3_h", - "acumulado_chuva_6_h", - "acumulado_chuva_12_h", - "acumulado_chuva_24_h", - "acumulado_chuva_48_h", - "acumulado_chuva_72_h", - "acumulado_chuva_96_h", - ] - dados[float_cols] = np.where(dados[float_cols] < 0, None, dados[float_cols]) - - # Elimina linhas em que o id_estacao é igual mantendo a de menor valor nas colunas float - dados.sort_values(["id_estacao", "data_medicao"] + float_cols, inplace=True) - dados.drop_duplicates(subset=["id_estacao", "data_medicao"], keep="first") - - # Ajustando dados da meia-noite que vem sem o horário - for index, row in dados.iterrows(): - try: - date = pd.to_datetime(row["data_medicao"], format="%Y-%m-%d %H:%M:%S") - except ValueError: - date = pd.to_datetime(row["data_medicao"]) + pd.DateOffset(hours=0) - - dados.at[index, "data_medicao"] = date.strftime("%Y-%m-%d %H:%M:%S") - - log(f"Dataframe before comparing with last data saved on redis {dados.head()}") - - dados = save_updated_rows_on_redis( - dados, - dataset_id, - table_id, - unique_id="id_estacao", - date_column="data_medicao", - date_format=date_format, - mode=mode, - ) - - log(f"Dataframe after comparing with last data saved on redis {dados.head()}") - - # If df is empty stop flow - if dados.shape[0] == 0: - skip_text = "No new data available on API" - log(skip_text) - raise ENDRUN(state=Skipped(skip_text)) - - # Fixar ordem das colunas - dados = dados[ - [ - "id_estacao", - "data_medicao", - "acumulado_chuva_10_min", - "acumulado_chuva_1_h", - "acumulado_chuva_3_h", - "acumulado_chuva_6_h", - "acumulado_chuva_12_h", - "acumulado_chuva_24_h", - "acumulado_chuva_48_h", - "acumulado_chuva_72_h", - "acumulado_chuva_96_h", - ] - ] - - return dados - - -@task -def salvar_dados(dados: pd.DataFrame) -> Union[str, Path]: - """ - Salvar dados tratados em csv para conseguir subir pro GCP - """ - - prepath = Path("/tmp/precipitacao_cemaden/") - prepath.mkdir(parents=True, exist_ok=True) - - partition_column = "data_medicao" - dataframe, partitions = parse_date_columns(dados, partition_column) - current_time = pendulum.now("America/Sao_Paulo").strftime("%Y%m%d%H%M") - - # Cria partições a partir da data - to_partitions( - data=dataframe, - partition_columns=partitions, - savepath=prepath, - data_type="csv", - suffix=current_time, - ) - log(f"[DEBUG] Files saved on {prepath}") - return prepath diff --git a/pipelines/rj_cor/meteorologia/precipitacao_cemaden/utils.py b/pipelines/rj_cor/meteorologia/precipitacao_cemaden/utils.py deleted file mode 100644 index 7b2af3764..000000000 --- a/pipelines/rj_cor/meteorologia/precipitacao_cemaden/utils.py +++ /dev/null @@ -1,55 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Utils for precipitacao_cemaden -""" -from typing import List, Tuple - -import numpy as np -import pandas as pd - - -def parse_date_columns( - dataframe: pd.DataFrame, partition_date_column: str -) -> Tuple[pd.DataFrame, List[str]]: - """ - Parses the date columns to the partition format. Reformatado para manter o formato utilizado - quando os dados foram salvos pela primeira vez (ano, mes, dia). - """ - ano_col = "ano" - mes_col = "mes" - data_col = "dia" - cols = [ano_col, mes_col, data_col] - for col in cols: - if col in dataframe.columns: - raise ValueError(f"Column {col} already exists, please review your model.") - - dataframe[partition_date_column] = dataframe[partition_date_column].astype(str) - dataframe[data_col] = pd.to_datetime( - dataframe[partition_date_column], errors="coerce" - ) - - dataframe[ano_col] = ( - dataframe[data_col] - .dt.year.fillna(-1) - .astype(int) - .astype(str) - .replace("-1", np.nan) - ) - - dataframe[mes_col] = ( - dataframe[data_col] - .dt.month.fillna(-1) - .astype(int) - .astype(str) - .replace("-1", np.nan) - ) - - dataframe[data_col] = ( - dataframe[data_col] - .dt.day.fillna(-1) - .astype(int) - .astype(str) - .replace("-1", np.nan) - ) - - return dataframe, [ano_col, mes_col, data_col] diff --git a/pipelines/rj_cor/meteorologia/precipitacao_websirene/flows.py b/pipelines/rj_cor/meteorologia/precipitacao_websirene/flows.py index e10335749..3fe5c73f3 100644 --- a/pipelines/rj_cor/meteorologia/precipitacao_websirene/flows.py +++ b/pipelines/rj_cor/meteorologia/precipitacao_websirene/flows.py @@ -1,5 +1,4 @@ # -*- coding: utf-8 -*- -# pylint: disable=C0103 """ Flows for precipitacao_alertario """ @@ -10,8 +9,7 @@ from pipelines.constants import constants from pipelines.rj_cor.meteorologia.precipitacao_websirene.tasks import ( - download_dados, - tratar_dados, + download_tratar_dados, salvar_dados, ) from pipelines.rj_cor.meteorologia.precipitacao_websirene.schedules import ( @@ -24,13 +22,13 @@ get_current_flow_labels, ) - with Flow( "COR: Meteorologia - Precipitacao WEBSIRENE", code_owners=[ "paty", ], ) as cor_meteorologia_precipitacao_websirene: + DATASET_ID = "clima_pluviometro" TABLE_ID = "taxa_precipitacao_websirene" DUMP_MODE = "append" @@ -44,50 +42,42 @@ ) MATERIALIZATION_MODE = Parameter("mode", default="dev", required=False) - dataframe = download_dados() - dataframe, empty_data = tratar_dados( - dfr=dataframe, + DFR = download_tratar_dados() + PATH = salvar_dados(dfr=DFR) + + # Create table in BigQuery + UPLOAD_TABLE = create_table_and_upload_to_gcs( + data_path=PATH, dataset_id=DATASET_ID, table_id=TABLE_ID, - mode=MATERIALIZATION_MODE, + dump_mode=DUMP_MODE, + wait=PATH, ) - with case(empty_data, False): - PATH = salvar_dados(dfr=dataframe) - - # Create table in BigQuery - UPLOAD_TABLE = create_table_and_upload_to_gcs( - data_path=PATH, - dataset_id=DATASET_ID, - table_id=TABLE_ID, - dump_mode=DUMP_MODE, - wait=PATH, + # Trigger DBT flow run + with case(MATERIALIZE_AFTER_DUMP, True): + current_flow_labels = get_current_flow_labels() + materialization_flow = create_flow_run( + flow_name=utils_constants.FLOW_EXECUTE_DBT_MODEL_NAME.value, + project_name=constants.PREFECT_DEFAULT_PROJECT.value, + parameters={ + "dataset_id": DATASET_ID, + "table_id": TABLE_ID, + "mode": MATERIALIZATION_MODE, + "materialize_to_datario": MATERIALIZE_TO_DATARIO, + }, + labels=current_flow_labels, + run_name=f"Materialize {DATASET_ID}.{TABLE_ID}", ) - # Trigger DBT flow run - with case(MATERIALIZE_AFTER_DUMP, True): - current_flow_labels = get_current_flow_labels() - materialization_flow = create_flow_run( - flow_name=utils_constants.FLOW_EXECUTE_DBT_MODEL_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters={ - "dataset_id": DATASET_ID, - "table_id": TABLE_ID, - "mode": MATERIALIZATION_MODE, - "materialize_to_datario": MATERIALIZE_TO_DATARIO, - }, - labels=current_flow_labels, - run_name=f"Materialize {DATASET_ID}.{TABLE_ID}", - ) - - materialization_flow.set_upstream(UPLOAD_TABLE) + materialization_flow.set_upstream(UPLOAD_TABLE) - wait_for_materialization = wait_for_flow_run( - materialization_flow, - stream_states=True, - stream_logs=True, - raise_final_state=True, - ) + wait_for_materialization = wait_for_flow_run( + materialization_flow, + stream_states=True, + stream_logs=True, + raise_final_state=True, + ) # para rodar na cloud cor_meteorologia_precipitacao_websirene.storage = GCS(constants.GCS_FLOWS_BUCKET.value) diff --git a/pipelines/rj_cor/meteorologia/precipitacao_websirene/tasks.py b/pipelines/rj_cor/meteorologia/precipitacao_websirene/tasks.py index 9e93d4e95..43d10e256 100644 --- a/pipelines/rj_cor/meteorologia/precipitacao_websirene/tasks.py +++ b/pipelines/rj_cor/meteorologia/precipitacao_websirene/tasks.py @@ -3,53 +3,29 @@ Tasks for precipitacao_alertario """ from datetime import timedelta +import os from pathlib import Path -from typing import Union, Tuple +from typing import Union import pandas as pd -import pendulum from prefect import task import pandas_read_xml as pdx from pipelines.constants import constants -from pipelines.utils.utils import ( - log, - to_partitions, - parse_date_columns, - save_updated_rows_on_redis, -) @task( max_retries=constants.TASK_MAX_RETRIES.value, retry_delay=timedelta(seconds=constants.TASK_RETRY_DELAY.value), ) -def download_dados() -> pd.DataFrame: +def download_tratar_dados() -> pd.DataFrame: """ - Faz o request e salva em um dataframe + Faz o request e salva dados localmente """ # Acessar url websirene url = "http://websirene.rio.rj.gov.br/xml/chuvas.xml" - dfr = pdx.read_xml(url, ["estacoes"]) - return dfr - - -@task( - nout=2, - max_retries=constants.TASK_MAX_RETRIES.value, - retry_delay=timedelta(seconds=constants.TASK_RETRY_DELAY.value), -) -def tratar_dados( - dfr: pd.DataFrame, - dataset_id: str, - table_id: str, - mode: str = "dev", -) -> Tuple[pd.DataFrame, bool]: - """ - Faz o request e salva dados localmente - """ drop_cols = [ "@hora", "estacao|@nome", @@ -69,20 +45,34 @@ def tratar_dados( "estacao|chuvas|@mes": "acumulado_chuva_mes", } + dfr = pdx.read_xml(url, ["estacoes"]) dfr = pdx.fully_flatten(dfr).drop(drop_cols, axis=1).rename(rename_cols, axis=1) # Converte de UTC para horário São Paulo - date_format = "%Y-%m-%d %H:%M:%S" dfr["data_medicao_utc"] = pd.to_datetime(dfr["data_medicao_utc"]) dfr["data_medicao"] = ( dfr["data_medicao_utc"] .dt.tz_convert("America/Sao_Paulo") - .dt.strftime(date_format) + .dt.strftime("%Y-%m-%d %H:%M:%S") ) dfr["data_medicao"] = pd.to_datetime(dfr["data_medicao"]) dfr = dfr.drop(["data_medicao_utc"], axis=1) + # Ordenação de variáveis + cols_order = [ + "id_estacao", + "data_medicao", + "acumulado_chuva_15_min", + "acumulado_chuva_1_h", + "acumulado_chuva_4_h", + "acumulado_chuva_24_h", + "acumulado_chuva_96_h", + "acumulado_chuva_mes", + ] + + dfr = dfr[cols_order] + # Converte variáveis que deveriam ser float para float float_cols = [ "acumulado_chuva_15_min", @@ -94,21 +84,7 @@ def tratar_dados( ] dfr[float_cols] = dfr[float_cols].apply(pd.to_numeric, errors="coerce") - dfr = save_updated_rows_on_redis( - dfr, - dataset_id, - table_id, - unique_id="id_estacao", - date_column="data_medicao", - date_format=date_format, - mode=mode, - ) - - # If df is empty stop flow - empty_data = dfr.shape[0] == 0 - log(f"[DEBUG]: dataframe is empty: {empty_data}") - - return dfr, empty_data + return dfr @task @@ -117,34 +93,25 @@ def salvar_dados(dfr: pd.DataFrame) -> Union[str, Path]: Salvar dados tratados em csv para conseguir subir pro GCP """ - # Ordenação de variáveis - cols_order = [ - "id_estacao", - "data_medicao", - "acumulado_chuva_15_min", - "acumulado_chuva_1_h", - "acumulado_chuva_4_h", - "acumulado_chuva_24_h", - "acumulado_chuva_96_h", - "acumulado_chuva_mes", - ] + # Pegar o dia máximo que aparece na base como partição + max_date = str(dfr["data_medicao"].max()) + ano = max_date[:4] + mes = str(int(max_date[5:7])) + data = str(max_date[:10]) - dfr = dfr[cols_order] + partitions = os.path.join( + f"ano_particao={ano}", f"mes_particao={mes}", f"data_particao={data}" + ) - prepath = Path("/tmp/precipitacao_websirene/") - prepath.mkdir(parents=True, exist_ok=True) + base_path = os.path.join(os.getcwd(), "data", "precipitacao_websirene", "output") - partition_column = "data_medicao" - dataframe, partitions = parse_date_columns(dfr, partition_column) - current_time = pendulum.now("America/Sao_Paulo").strftime("%Y%m%d%H%M") + partition_path = os.path.join(base_path, partitions) - # Cria partições a partir da data - to_partitions( - data=dataframe, - partition_columns=partitions, - savepath=prepath, - data_type="csv", - suffix=current_time, - ) - log(f"[DEBUG] Files saved on {prepath}") - return prepath + if not os.path.exists(partition_path): + os.makedirs(partition_path) + + filename = os.path.join(partition_path, f"dados_{max_date}.csv") + + print(f"Saving {filename}") + dfr.to_csv(filename, index=False) + return base_path diff --git a/pipelines/rj_cor/meteorologia/radar/precipitacao/constants.py b/pipelines/rj_cor/meteorologia/radar/precipitacao/constants.py deleted file mode 100644 index cc4b23058..000000000 --- a/pipelines/rj_cor/meteorologia/radar/precipitacao/constants.py +++ /dev/null @@ -1,148 +0,0 @@ -# -*- coding: utf-8 -*- -# flake8: noqa: E501 -""" -Constants for the rain dashboard pipeline -""" -from enum import Enum - - -class constants(Enum): # pylint: disable=c0103 - """ - Constants for the rain dashboard pipeline - """ - - DATASET_ID = "clima_radar" - TABLE_ID = "taxa_precipitacao_guaratiba" - RAIN_DASHBOARD_FLOW_NAME = ( - "EMD: Atualizar dados de chuva provinientes de radar na api.dados.rio" - ) - RAIN_DASHBOARD_FLOW_SCHEDULE_PARAMETERS = { - "redis_data_key": "data_chuva_recente_radar_inea", - "redis_update_key": "data_update_chuva_recente_radar_inea", - "query_data": """ - WITH - last_update_date AS ( - SELECT - MAX(data_medicao) AS last_update - FROM `rj-cor.clima_radar_staging.taxa_precipitacao_guaratiba` - WHERE data_particao>= CAST(DATE_SUB(CURRENT_DATE('America/Sao_Paulo'), INTERVAL 1 DAY) AS STRING) - ), - final_table AS ( - SELECT - id_h3, - case - when id_h3 = "88a8a07735fffff" then "Barra da Tijuca" - when id_h3 = "88a8a07ab5fffff" then "Ipanema" - when id_h3 = "88a8a078e1fffff" then "Copacabana" - when id_h3 = "88a8a03959fffff" then "Barra de Guaratiba" - when id_h3 = "88a8a039d1fffff" then "Guaratiba" - when id_h3 = "88a8a06817fffff" then "Ribeira" - when id_h3 = "88a8a068e9fffff" then "Zumbi" - else bairro end as bairro, - CAST(predictions AS FLOAT64) AS chuva_15min, - "Guaratiba" AS estacoes, - FROM `rj-cor.clima_radar_staging.taxa_precipitacao_guaratiba` tx - INNER JOIN last_update_date lud ON lud.last_update = tx.data_medicao - WHERE id_h3 not in ("88a8a079ddfffff", "88a8a068e5fffff", "88a8a06995fffff", "88a8a07a5dfffff") - ) - SELECT - id_h3, - bairro, - CASE - WHEN chuva_15min<= 0.2 THEN 0.0 ELSE ROUND(chuva_15min, 2) END AS chuva_15min, - estacoes, - CASE - WHEN chuva_15min> 0.2 AND chuva_15min<= 1.25 THEN 'chuva fraca' - WHEN chuva_15min> 1.25 AND chuva_15min<= 6.25 THEN 'chuva moderada' - WHEN chuva_15min> 6.25 AND chuva_15min<= 12.5 THEN 'chuva forte' - WHEN chuva_15min> 12.5 THEN 'chuva muito forte' - ELSE 'sem chuva' - END AS status, - CASE - WHEN chuva_15min> 0 AND chuva_15min<= 1.25 THEN '#DAECFB'--'#00CCFF' - WHEN chuva_15min> 1.25 AND chuva_15min<= 6.25 THEN '#A9CBE8'--'#BFA230' - WHEN chuva_15min> 6.25 AND chuva_15min<= 12.5 THEN '#77A9D5'--'#E0701F' - WHEN chuva_15min> 12.5 THEN '#125999'--'#FF0000' - ELSE '#ffffff' - END AS color - FROM final_table - """, - "query_update": """ - SELECT - DATETIME(MAX(data_medicao)) AS last_update - FROM `rj-cor.clima_radar_staging.taxa_precipitacao_guaratiba` - WHERE data_particao>= CAST(DATE_SUB(CURRENT_DATE('America/Sao_Paulo'), INTERVAL 1 DAY) AS STRING) - """, - } - # Modificar query para últimas 2h - RAIN_DASHBOARD_LAST_2H_FLOW_SCHEDULE_PARAMETERS = { - "redis_data_key": "data_chuva_passado_radar_inea", - "redis_update_key": "data_update_chuva_passado_radar_inea", - "query_data": """ - WITH - last_update_date AS ( - SELECT - CAST(MAX(data_medicao) AS DATETIME) AS last_update - FROM `rj-cor.clima_radar_staging.taxa_precipitacao_guaratiba` - WHERE data_particao>= CAST(DATE_SUB(CURRENT_DATETIME('America/Sao_Paulo'), INTERVAL 1 DAY) AS STRING) - ), - select_data_each_15_min AS ( - SELECT distinct - id_h3, - case - when id_h3 = "88a8a07735fffff" then "Barra da Tijuca" - when id_h3 = "88a8a07ab5fffff" then "Ipanema" - when id_h3 = "88a8a078e1fffff" then "Copacabana" - when id_h3 = "88a8a03959fffff" then "Barra de Guaratiba" - when id_h3 = "88a8a039d1fffff" then "Guaratiba" - when id_h3 = "88a8a06817fffff" then "Ribeira" - when id_h3 = "88a8a068e9fffff" then "Zumbi" - else bairro end as bairro, - CASE WHEN - CAST(predictions AS FLOAT64) <= 0.2 - THEN 0.0 ELSE CAST(predictions AS FLOAT64) END AS chuva_15min, -- retira ruídos antes de somar - ROW_NUMBER() OVER (PARTITION BY id_h3, bairro ORDER BY data_medicao DESC) as row_num - FROM `rj-cor.clima_radar_staging.taxa_precipitacao_guaratiba` tx - INNER JOIN last_update_date lup ON 1=1 - WHERE tx.data_particao>= CAST(DATE_SUB(CURRENT_DATETIME('America/Sao_Paulo'), INTERVAL 1 DAY) AS STRING) - AND CAST(tx.data_medicao AS DATETIME)>= DATE_SUB(lup.last_update, INTERVAL 2 HOUR) - AND id_h3 not in ("88a8a079ddfffff", "88a8a068e5fffff", "88a8a06995fffff", "88a8a07a5dfffff") - ), - final_table AS ( - SELECT - id_h3, - bairro, - SUM(chuva_15min) AS chuva_15min, - "Guaratiba" AS estacoes, - FROM select_data_each_15_min - WHERE row_num in (1, 4, 7, 10) - GROUP BY id_h3, bairro, estacoes - ) - SELECT - id_h3, - bairro, - ROUND(chuva_15min, 2) AS chuva_15min, - estacoes, - CASE - WHEN chuva_15min> 0 AND chuva_15min<= 10 THEN 'chuva fraca' - WHEN chuva_15min> 10 AND chuva_15min<= 50 THEN 'chuva moderada' - WHEN chuva_15min> 50 AND chuva_15min<= 100 THEN 'chuva forte' - WHEN chuva_15min> 100 THEN 'chuva muito forte' - ELSE 'sem chuva' - END AS status, - CASE - WHEN chuva_15min> 0 AND chuva_15min<= 10 THEN '#DAECFB'--'#00CCFF' - WHEN chuva_15min> 1 AND chuva_15min<= 50 THEN '#A9CBE8'--'#BFA230' - WHEN chuva_15min> 50 AND chuva_15min<= 100 THEN '#77A9D5'--'#E0701F' - WHEN chuva_15min> 100 THEN '#125999'--'#FF0000' - ELSE '#ffffff' - END AS color - FROM final_table - """, - "query_update": """ - SELECT - DATETIME(MAX(data_medicao)) AS last_update - FROM `rj-cor.clima_radar_staging.taxa_precipitacao_guaratiba` - WHERE data_particao>= CAST(DATE_SUB(CURRENT_DATE('America/Sao_Paulo'), INTERVAL 1 DAY) AS STRING) - """, - } diff --git a/pipelines/rj_cor/meteorologia/radar/precipitacao/coordinates/h3_grid_res8.csv b/pipelines/rj_cor/meteorologia/radar/precipitacao/coordinates/h3_grid_res8.csv deleted file mode 100644 index 2f92bb589..000000000 --- a/pipelines/rj_cor/meteorologia/radar/precipitacao/coordinates/h3_grid_res8.csv +++ /dev/null @@ -1,1592 +0,0 @@ --2.293410147020551548e+01, -4.323948128495234045e+01 --2.291129054646219743e+01, -4.358621582003632966e+01 --2.288727559185135618e+01, -4.367166330603929936e+01 --2.284222865386863077e+01, -4.325264984679955660e+01 --2.303374150540408749e+01, -4.358614213421510186e+01 --2.298125236769831758e+01, -4.352031603165712426e+01 --2.291287599328492774e+01, -4.369994725604080088e+01 --2.300778809376465617e+01, -4.345334428498176749e+01 --2.288724572362094278e+01, -4.331519583785799199e+01 --2.278710238065767157e+01, -4.322060102952733729e+01 --2.292848908829721566e+01, -4.340224512033364590e+01 --2.300578555820118609e+01, -4.328730869365964651e+01 --2.290941757018417135e+01, -4.342022348948746924e+01 --2.287930854180844520e+01, -4.325968427332374944e+01 --2.284224128681028532e+01, -4.355687320276479824e+01 --2.293249136330296878e+01, -4.317796729417186441e+01 --2.291039940119531693e+01, -4.373368453487372420e+01 --2.293151278753397193e+01, -4.352524938416907929e+01 --2.290919410870504436e+01, -4.336797172477588447e+01 --2.294680026349281121e+01, -4.322749010201188469e+01 --2.287570181395795998e+01, -4.364065297733282733e+01 --2.300075866564054650e+01, -4.330255763816330017e+01 --2.286035910885583888e+01, -4.363413129294590220e+01 --2.297986162784269837e+01, -4.320676331642838619e+01 --2.294310128290114292e+01, -4.355626366117731862e+01 --2.290883781463325519e+01, -4.361995716237551335e+01 --2.296723077861097195e+01, -4.352304733762287015e+01 --2.283660268570409713e+01, -4.341538833767476291e+01 --2.292532186560711338e+01, -4.358348630926622747e+01 --2.292213257820608519e+01, -4.340823805964744508e+01 --2.290869589219764180e+01, -4.326346189859471991e+01 --2.296885725178419690e+01, -4.363679538983277695e+01 --2.287761846717181768e+01, -4.345018444030436200e+01 --2.287912595432685592e+01, -4.351168071805165738e+01 --2.298403165689924421e+01, -4.359107043368639722e+01 --2.296214653616544155e+01, -4.323400503816132101e+01 --2.299224759559766795e+01, -4.339456719030007292e+01 --2.300928863820143988e+01, -4.351485303714934361e+01 --2.296901279054155864e+01, -4.333253162376151124e+01 --2.287145221520868077e+01, -4.350842099934122587e+01 --2.283852096581628643e+01, -4.327714685995885446e+01 --2.283528611988480606e+01, -4.340613773579675438e+01 --2.290791569912527592e+01, -4.376742040120625887e+01 --2.294482950208858441e+01, -4.372224456142810567e+01 --2.299811799457199868e+01, -4.328405044652895839e+01 --2.296522148905818739e+01, -4.371352980465541549e+01 --2.292889555361210085e+01, -4.350674664899292310e+01 --2.293674045547994211e+01, -4.356225439892039475e+01 --2.295366771550907359e+01, -4.368251616414643479e+01 --2.293807306047655814e+01, -4.326723986359758811e+01 --2.287499039183345317e+01, -4.343168239637201111e+01 --2.301151687322397166e+01, -4.342884514875858315e+01 --2.280617906027571706e+01, -4.320261739690066349e+01 --2.292454063041990153e+01, -4.337448898612358761e+01 --2.291664530398309907e+01, -4.367545874011378970e+01 --2.293918069880770005e+01, -4.352850956696061502e+01 --2.287014174226564478e+01, -4.349917042476455720e+01 --2.287162890941389648e+01, -4.325642694461613758e+01 --2.298479533757264548e+01, -4.344356498046466442e+01 --2.292547442164087101e+01, -4.363572964243608965e+01 --2.282050333696537336e+01, -4.325213004192583810e+01 --2.287276212282521470e+01, -4.351767153715634606e+01 --2.288436083942784549e+01, -4.354868301142041531e+01 --2.301414005948857167e+01, -4.344735121174252157e+01 --2.300815714337804252e+01, -4.355785433564328457e+01 --2.291901779458567034e+01, -4.328522445641434757e+01 --2.289856163620227036e+01, -4.359819561092927387e+01 --2.295976862168589960e+01, -4.326776027647799339e+01 --2.297158551220287137e+01, -4.370754117839062758e+01 --2.288831134816535595e+01, -4.327219375963840520e+01 --2.292009828459624288e+01, -4.354648249575060248e+01 --2.302068808468493089e+01, -4.349361577284638969e+01 --2.292183895922173065e+01, -4.371245888901784582e+01 --2.286358089994925180e+01, -4.345291701733015799e+01 --2.284725923830584549e+01, -4.323740376016917253e+01 --2.284580284449871357e+01, -4.348014163031692902e+01 --2.281043492064817002e+01, -4.328261995798191464e+01 --2.291211642627970235e+01, -4.318670276082157500e+01 --2.283109407712154137e+01, -4.332613868617934116e+01 --2.304009624222191377e+01, -4.358015084927313154e+01 --2.297296608026849896e+01, -4.336029036817304672e+01 --2.292907312850962853e+01, -4.355899391905609264e+01 --2.289247243940361187e+01, -4.370866184793059261e+01 --2.287872028500569854e+01, -4.340718782626681360e+01 --2.295606262674734666e+01, -4.329226147009386949e+01 --2.290753458313110258e+01, -4.361070674759223920e+01 --2.297376113606048520e+01, -4.356930618503027119e+01 --2.290568671544108525e+01, -4.344471890450633822e+01 --2.293197591027201199e+01, -4.332549232723204113e+01 --2.287345191888076812e+01, -4.337018259822411892e+01 --2.285361510082891101e+01, -4.323140903291145776e+01 --2.295390849615272799e+01, -4.337827187004968721e+01 --2.297033112042536729e+01, -4.334178456681371472e+01 --2.289911336686065724e+01, -4.339846135023869778e+01 --2.286946669064306903e+01, -4.369887734438875526e+01 --2.294732030407186940e+01, -4.333200901097638535e+01 --2.286226703642680036e+01, -4.344366623230768454e+01 --2.287798370235801571e+01, -4.325043225978738803e+01 --2.295092714094572628e+01, -4.361177077650496869e+01 --2.296241264419443340e+01, -4.328626650407613141e+01 --2.286556683642358578e+01, -4.367112930707160245e+01 --2.295743308742507338e+01, -4.365802555109811323e+01 --2.296373380616228488e+01, -4.329551958032802617e+01 --2.286526464104584733e+01, -4.356665345250582533e+01 --2.289970330027088607e+01, -4.355520339178888634e+01 --2.283348621241816190e+01, -4.329239197748519530e+01 --2.296451729862121738e+01, -4.320024873295012924e+01 --2.295653980834055474e+01, -4.339677680713932517e+01 --2.280672162006634807e+01, -4.330711502435089244e+01 --2.287824630930355596e+01, -4.330268616341938070e+01 --2.298647734387122910e+01, -4.355732407476539692e+01 --2.287650511853977164e+01, -4.349317934784557593e+01 --2.294586704727135640e+01, -4.362701163258179093e+01 --2.281068854693254266e+01, -4.333486705221743307e+01 --2.293616018552777192e+01, -4.340550414001716462e+01 --2.288958667242863143e+01, -4.358568468302288323e+01 --2.293468645901508651e+01, -4.375272045458620340e+01 --2.287293755227230108e+01, -4.356991367418982009e+01 --2.284468164310881022e+01, -4.352313321640736632e+01 --2.288362277333138550e+01, -4.374838500738290037e+01 --2.286156506234035746e+01, -4.328691915295251391e+01 --2.296835265991431285e+01, -4.348004743615655343e+01 --2.289439133426847306e+01, -4.321394251406950104e+01 --2.289708834320669695e+01, -4.353670195443071833e+01 --2.291637202004114116e+01, -4.326671948615438623e+01 --2.295806445219288250e+01, -4.345828338829431203e+01 --2.288168599691196547e+01, -4.322593309243120530e+01 --2.294574644818991516e+01, -4.327049763886007128e+01 --2.281490188278267084e+01, -4.316287320591397503e+01 --2.281811945061129876e+01, -4.328587722685922046e+01 --2.285963761485189849e+01, -4.342516456258405100e+01 --2.282315440240815718e+01, -4.327063235304568423e+01 --2.294701676419304093e+01, -4.358401740203117214e+01 --2.291748309500190217e+01, -4.352798035456354597e+01 --2.298435015457386754e+01, -4.333904882348395660e+01 --2.299356185063321689e+01, -4.340382014630633734e+01 --2.294976993519668795e+01, -4.365476409813850722e+01 --2.286115552007191454e+01, -4.348666034966201011e+01 --2.299820031421104005e+01, -4.364058982433875400e+01 --2.297668185787602724e+01, -4.333579018828015705e+01 --2.284691253032815794e+01, -4.343714830423427031e+01 --2.292253543252024173e+01, -4.351273801391830887e+01 --2.290247235031895556e+01, -4.362594658387757107e+01 --2.295496584029356057e+01, -4.369176676177789886e+01 --2.299114860640522906e+01, -4.343757197756658428e+01 --2.301972771858355671e+01, -4.358887204681627736e+01 --2.287830544889466822e+01, -4.365915243168520021e+01 --2.294797194953098796e+01, -4.348877164470729184e+01 --2.291979401963848417e+01, -4.318995961393147809e+01 --2.285610242786678370e+01, -4.350190177066328090e+01 --2.284860863933221253e+01, -4.355088327465661990e+01 --2.281149152996452401e+01, -4.323962199487150571e+01 --2.287476985774248561e+01, -4.337943395077879671e+01 --2.284355403230114234e+01, -4.326190132859304782e+01 --2.287668845306243171e+01, -4.354542292521200153e+01 --2.288988693310628619e+01, -4.333369948429363205e+01 --2.300757783527141243e+01, -4.340108581076329841e+01 --2.293412775499622924e+01, -4.354375196876397069e+01 --2.286205011226298467e+01, -4.339141934340536721e+01 --2.286527345223699825e+01, -4.326242148317352587e+01 --2.298753263860365692e+01, -4.321002071542130096e+01 --2.283396898991038526e+01, -4.339688710248989167e+01 --2.302573183575222870e+01, -4.347837021717542427e+01 --2.298566781236432988e+01, -4.334830195730007318e+01 --2.288301219603896897e+01, -4.323518527188377192e+01 --2.298237359313198880e+01, -4.347731507106654192e+01 --2.294784250747621712e+01, -4.318448110878164670e+01 --2.286686735378865976e+01, -4.368037869872092216e+01 --2.289888254514955790e+01, -4.334620953053985914e+01 --2.301392798637342807e+01, -4.339509195642639128e+01 --2.279450028162698416e+01, -4.317160762794732420e+01 --2.292417243720222686e+01, -4.362647909013190173e+01 --2.292879810083064385e+01, -4.320246953050017424e+01 --2.285984357938121647e+01, -4.347740979570905040e+01 --2.303992842802247054e+01, -4.352789540351971453e+01 --2.285216728895847282e+01, -4.347415049393096353e+01 --2.293939579272304385e+01, -4.327649267539263889e+01 --2.297975270177692764e+01, -4.345881028005261726e+01 --2.290020140213997024e+01, -4.335546137956409041e+01 --2.283480943963823151e+01, -4.330164314272619919e+01 --2.305150025979702733e+01, -4.355891496558461284e+01 --2.291299079105332481e+01, -4.375218368386138224e+01 --2.301206855633953907e+01, -4.358561089699333024e+01 --2.286095260803891094e+01, -4.343441541389828586e+01 --2.302868809229262936e+01, -4.360138555332503074e+01 --2.282932772735804505e+01, -4.351661399110475514e+01 --2.300100142028342631e+01, -4.335481963022024132e+01 --2.285017246780288147e+01, -4.330815836922074169e+01 --2.297358807254611435e+01, -4.351705570786125321e+01 --2.293001283486511355e+01, -4.346374871272747242e+01 --2.296946297298200790e+01, -4.343704579413100930e+01 --2.286770674682710336e+01, -4.353291257538420211e+01 --2.297489550728454333e+01, -4.352630768393471783e+01 --2.291686775429809941e+01, -4.337123032022528690e+01 --2.289648006902468680e+01, -4.337995810790994256e+01 --2.286639763192084374e+01, -4.352366220438552347e+01 --2.296289346075022664e+01, -4.339078337169057420e+01 --2.292244863155573142e+01, -4.320846538096588318e+01 --2.295885680780845206e+01, -4.371951827481196062e+01 --2.290083342957381518e+01, -4.351220934800890205e+01 --2.295468186736382066e+01, -4.358727820239499806e+01 --2.291935984644069890e+01, -4.374619617269116389e+01 --2.294492152171804022e+01, -4.336576059818436590e+01 --2.292980410894676879e+01, -4.341149710324310007e+01 --2.300339706845291587e+01, -4.332106472561822130e+01 --2.299152883494981481e+01, -4.354208070696228106e+01 --2.294812515569055478e+01, -4.323674322483486776e+01 --2.289464582585507202e+01, -4.357044426501072820e+01 --2.291291319668051685e+01, -4.334347420298580289e+01 --2.300494897170408848e+01, -4.338257942871611306e+01 --2.298063413915555842e+01, -4.336354921841268606e+01 --2.289069129445703155e+01, -4.323844245181943791e+01 --2.299134729299647262e+01, -4.348982750766386829e+01 --2.300701406738101440e+01, -4.360085380589323023e+01 --2.291486564003971438e+01, -4.350947806223273773e+01 --2.297559877309845788e+01, -4.337879605332228294e+01 --2.294742087436162592e+01, -4.374074488278563422e+01 --2.281969268801490003e+01, -4.334737554210506971e+01 --2.300122701915949719e+01, -4.340707951565934763e+01 --2.291687362203498424e+01, -4.377993203517733889e+01 --2.293514690342330553e+01, -4.319647353489683894e+01 --2.295825716491744828e+01, -4.351053526891627143e+01 --2.281837184001847163e+01, -4.333812482561233992e+01 --2.292140502971819416e+01, -4.355573350845997282e+01 --2.290606651760641199e+01, -4.354921289514788896e+01 --2.297750867420344179e+01, -4.354481152248256137e+01 --2.285253055019567370e+01, -4.357863298217038306e+01 --2.289095753663867328e+01, -4.329069785872849963e+01 --2.290206989785826153e+01, -4.321719954431829791e+01 --2.291242907527525929e+01, -4.354322224594024249e+01 --2.290339622635369921e+01, -4.322645206027705456e+01 --2.295432942480510263e+01, -4.348277967385996590e+01 --2.287032328051559915e+01, -4.355141320278881523e+01 --2.303894694718008296e+01, -4.362315156251160175e+01 --2.285917449240021782e+01, -4.332066751563008467e+01 --2.302722190268822899e+01, -4.353987943376989023e+01 --2.297275707079875673e+01, -4.366454866294391479e+01 --2.299177061467635852e+01, -4.329004578263852920e+01 --2.291879097300257584e+01, -4.353723144429108771e+01 --2.278045259622721019e+01, -4.317434659042388745e+01 --2.287323721376730390e+01, -4.367439047306555722e+01 --2.298891589009874536e+01, -4.352357642497675982e+01 --2.294292316907692353e+01, -4.350401510829292562e+01 --2.292401682028812715e+01, -4.357423541569983882e+01 --2.284273696317261582e+01, -4.335714957163912686e+01 --2.299413950820330044e+01, -4.356058483482971866e+01 --2.298106343114397276e+01, -4.346806269289506020e+01 --2.291404507455954231e+01, -4.365695839093025654e+01 --2.292662634419625434e+01, -4.359273716215417238e+01 --2.294777185416995380e+01, -4.343651964649652797e+01 --2.280911012949099970e+01, -4.327336905643164044e+01 --2.301807057901365638e+01, -4.347511005304954068e+01 --2.296573155030463198e+01, -4.346154308660985777e+01 --2.287583700117465568e+01, -4.369288938871267192e+01 --2.293725028293791723e+01, -4.336250191057676773e+01 --2.295451420586018898e+01, -4.353503014091320011e+01 --2.298908642505591260e+01, -4.357582786230945260e+01 --2.299859861615783174e+01, -4.338857338409218301e+01 --2.294535107897129933e+01, -4.347026799262346941e+01 --2.296744003611478746e+01, -4.327101816834539960e+01 --2.287538024181728602e+01, -4.353617250057377674e+01 --2.293543438864276851e+01, -4.355300320338805165e+01 --2.287081434689907056e+01, -4.335167980492017392e+01 --2.291107368921413112e+01, -4.322970925945939058e+01 --2.294468106400443119e+01, -4.331350367093875064e+01 --2.287407146504981270e+01, -4.352692203772785007e+01 --2.298648339695120058e+01, -4.325303158621430555e+01 --2.291634862829964092e+01, -4.357097489190585549e+01 --2.296479441836264002e+01, -4.325251165011820831e+01 --2.300325896833665595e+01, -4.362534801298681941e+01 --2.289756312297177487e+01, -4.333695765320376125e+01 --2.301937961596071958e+01, -4.348436293071036829e+01 --2.296529725057472859e+01, -4.335703158846361305e+01 --2.283957620622090090e+01, -4.323414681340325671e+01 --2.285494150419881265e+01, -4.324066077820923226e+01 --2.296392484189744465e+01, -4.370427921156032625e+01 --2.296234619774747543e+01, -4.359053907185399623e+01 --2.292933532905624361e+01, -4.330698737022103018e+01 --2.294177586055425522e+01, -4.324273876936961614e+01 --2.301786771481684468e+01, -4.342285167188310879e+01 --2.299429777105184058e+01, -4.361283495008849798e+01 --2.295367234489883046e+01, -4.332601470685479939e+01 --2.291794456833835625e+01, -4.368470884674544408e+01 --2.303600846857564122e+01, -4.350013654159653242e+01 --2.296179563324738737e+01, -4.343378630602408919e+01 --2.292607525411304081e+01, -4.343599353465958046e+01 --2.293568777706733997e+01, -4.330099287096851413e+01 --2.300065624972030065e+01, -4.360684445491359895e+01 --2.285366667300501575e+01, -4.353564308271666050e+01 --2.288922989094471205e+01, -4.348119674007445212e+01 --2.300536460687624185e+01, -4.348709528289969484e+01 --2.279982083026465389e+01, -4.320861208636218720e+01 --2.300471541909428552e+01, -4.333031822907184250e+01 --2.284842638011311777e+01, -4.349864226073351148e+01 --2.289995654454339657e+01, -4.330320772702306442e+01 --2.300440861984903407e+01, -4.358234981628860538e+01 --2.295817046823457375e+01, -4.320624495292853595e+01 --2.287799609872035944e+01, -4.355467331116055618e+01 --2.279877256346651393e+01, -4.325161027244405432e+01 --2.299547505578916784e+01, -4.326554315457629230e+01 --2.302478042804187197e+01, -4.357362856762142655e+01 --2.304154625320192551e+01, -4.364165602202984218e+01 --2.296068623794018748e+01, -4.347678755184546873e+01 --2.291027848296004521e+01, -4.368144735881471519e+01 --2.295563923849591603e+01, -4.349203157495678340e+01 --2.307316356847915628e+01, -4.355944533465521573e+01 --2.288987514851197247e+01, -4.369016266944312576e+01 --2.294946144983983416e+01, -4.355027277069886082e+01 --2.302103004533161013e+01, -4.359812424371817485e+01 --2.284337151459149950e+01, -4.351388312129874691e+01 --2.291224592023074180e+01, -4.349097562261529504e+01 --2.288206968324592694e+01, -4.363466403990012310e+01 --2.300909860537518625e+01, -4.346259713905580924e+01 --2.287946333231738549e+01, -4.361616414487406956e+01 --2.296330575567521493e+01, -4.349529157490281506e+01 --2.291742472259409880e+01, -4.322371381903695919e+01 --2.292041078591178760e+01, -4.365096934851037247e+01 --2.300231783814406938e+01, -4.336407292579880135e+01 --2.293656414205170435e+01, -4.351000664759872194e+01 --2.288679892342997491e+01, -4.351494050631792732e+01 --2.295630978149578283e+01, -4.334452018322178901e+01 --2.290576631973329569e+01, -4.319269867693077458e+01 --2.289754286929754556e+01, -4.369342412990369695e+01 --2.297428271009360046e+01, -4.336954322551516583e+01 --2.293484493541055613e+01, -4.339625203159143751e+01 --2.288076679078070086e+01, -4.362541411381523915e+01 --2.293329535226037663e+01, -4.333474476512655826e+01 --2.284537495899483162e+01, -4.337565148873837018e+01 --2.284485600599325394e+01, -4.357537278587972196e+01 --2.295281407876345625e+01, -4.342127465514467843e+01 --2.295236902302278281e+01, -4.367326552067472534e+01 --2.292775107422491487e+01, -4.324547665758168336e+01 --2.294846953998307271e+01, -4.364551332003794215e+01 --2.301190181846384419e+01, -4.353335802188924930e+01 --2.299508217045025305e+01, -4.346532997964943945e+01 --2.278681099926941656e+01, -4.316835159944657363e+01 --2.292313595524612779e+01, -4.372170880896168654e+01 --2.302347673580515774e+01, -4.356437615805538854e+01 --2.301956224753258340e+01, -4.353661869311492438e+01 --2.296199628034199947e+01, -4.348603958117701751e+01 --2.285459016767893914e+01, -4.344040723327115927e+01 --2.288460225487991551e+01, -4.329669208464513730e+01 --2.279346193696747491e+01, -4.321460663061305496e+01 --2.290127789216663956e+01, -4.331245980877248769e+01 --2.292702353963326090e+01, -4.374945827960707589e+01 --2.292537021515148510e+01, -4.327922973888629343e+01 --2.287063516218958625e+01, -4.365589137579642909e+01 --2.283027489349496975e+01, -4.316938551416354386e+01 --2.291051149929818465e+01, -4.337722361052551889e+01 --2.301825628113536837e+01, -4.352736612766172186e+01 --2.290895354991861765e+01, -4.331571783221183125e+01 --2.289117407721041531e+01, -4.369941228204226746e+01 --2.286468337954277175e+01, -4.340992145718002604e+01 --2.295544039895825605e+01, -4.343977908810781230e+01 --2.291806421007779804e+01, -4.373694648573562915e+01 --2.292572824551422883e+01, -4.374020850473807087e+01 --2.293716676146891587e+01, -4.371898260235997213e+01 --2.289258495248421355e+01, -4.376089613746767526e+01 --2.286599916606187222e+01, -4.341917246686509912e+01 --2.289165373940817361e+01, -4.344745173602857591e+01 --2.294045120005205618e+01, -4.323348576638709062e+01 --2.287188970018263134e+01, -4.330868009492547088e+01 --2.292453671868389975e+01, -4.378319442418806062e+01 --2.298961738371554375e+01, -4.337606118624626816e+01 --2.297015775936653981e+01, -4.364604652497363446e+01 --2.278814231128858481e+01, -4.317760264567469619e+01 --2.294030388901382977e+01, -4.348551178417322660e+01 --2.294440701039575714e+01, -4.356551494803401425e+01 --2.287048775311055593e+01, -4.360365354869292531e+01 --2.298533454984961466e+01, -4.360032209461255803e+01 --2.297470837693940027e+01, -4.347405514002077354e+01 --2.299313997706657275e+01, -4.365583117046571004e+01 --2.293444131081872683e+01, -4.364824139824082749e+01 --2.291557991229785785e+01, -4.377068263500302692e+01 --2.293899339900931622e+01, -4.347626006850854452e+01 --2.286632483289184492e+01, -4.321941914134589524e+01 --2.301676097390901532e+01, -4.346585714034632986e+01 --2.300424061571385437e+01, -4.353009742011172278e+01 --2.289333942354371487e+01, -4.356119373573812936e+01 --2.298610686362212974e+01, -4.345281755462322337e+01 --2.280564695748870108e+01, -4.335011037304091275e+01 --2.292961242553036172e+01, -4.376795768089790784e+01 --2.292496545425757049e+01, -4.347899227232685604e+01 --2.280484953604554121e+01, -4.319336619415184941e+01 --2.286897799934447306e+01, -4.323792308831072262e+01 --2.285809597674297677e+01, -4.336366594396476870e+01 --2.293964849197205780e+01, -4.332875063374361702e+01 --2.284645663372038271e+01, -4.333265434575307751e+01 --2.297931727889604403e+01, -4.335429623694829360e+01 --2.281992498949746917e+01, -4.339962079145417562e+01 --2.280828717420794050e+01, -4.336861128257827147e+01 --2.290842113402967328e+01, -4.321120401074423256e+01 --2.284620309803955607e+01, -4.328040421995897447e+01 --2.299968443512481286e+01, -4.334556630587182013e+01 --2.293975881818511553e+01, -4.373748274738527897e+01 --2.287309591519301222e+01, -4.362215334838382574e+01 --2.304905508839999939e+01, -4.359266473596544955e+01 --2.282101297243067251e+01, -4.335662623023227980e+01 --2.301545080071216987e+01, -4.345660419308318012e+01 --2.286620693213296818e+01, -4.347141848528583807e+01 --2.299054044089122684e+01, -4.363732831786289523e+01 --2.287700391443667058e+01, -4.364990272657451698e+01 --2.304400186145739937e+01, -4.360790837958624877e+01 --2.286657126738856860e+01, -4.357590353744848954e+01 --2.301467332262401655e+01, -4.360411506802900306e+01 --2.297405587452223230e+01, -4.367379966480931586e+01 --2.298251203460074521e+01, -4.322527069086489604e+01 --2.293172197053915440e+01, -4.327323487457207563e+01 --2.292870087418624081e+01, -4.345449702050246543e+01 --2.303113537066701610e+01, -4.356763717315317308e+01 --2.290475909473407867e+01, -4.353996206549400227e+01 --2.296087282295770038e+01, -4.352903881534859920e+01 --2.288484959481732872e+01, -4.334894473118588110e+01 --2.284801069790592720e+01, -4.339415328647243797e+01 --2.299487553830982733e+01, -4.341307307098887946e+01 --2.294716857732014503e+01, -4.363626249802501889e+01 --2.293878898708886638e+01, -4.342400826216874066e+01 --2.289360146598970047e+01, -4.330920185614814244e+01 --2.295989320745825779e+01, -4.362428298001522364e+01 --2.287519385256597459e+01, -4.348392860854931286e+01 --2.288791816901425946e+01, -4.347194574449429183e+01 --2.296319057870654134e+01, -4.319099520990658903e+01 --2.293593253866065496e+01, -4.335324955728134455e+01 --2.288548912615069142e+01, -4.350568974719675452e+01 --2.298830142700212420e+01, -4.336680813916376565e+01 --2.299022264644167279e+01, -4.353282858499316887e+01 --2.287930317872612918e+01, -4.356392365793751509e+01 --2.297404416996910115e+01, -4.331728402850978199e+01 --2.299093277326959850e+01, -4.338531420345254475e+01 --2.282539362334574307e+01, -4.348886414880745832e+01 --2.289072492101502476e+01, -4.354269255913541059e+01 --2.293856746117705470e+01, -4.337175423470768720e+01 --2.298157883178810224e+01, -4.362481547840167906e+01 --2.285864310102354224e+01, -4.321616227288082968e+01 --2.287585267582590021e+01, -4.333643494959477493e+01 --2.292065491554486201e+01, -4.375544581049588544e+01 --2.297781927272563252e+01, -4.364930800476921746e+01 --2.288112873757270194e+01, -4.337344103103193760e+01 --2.290259867470257049e+01, -4.332171186396906393e+01 --2.284381366660414159e+01, -4.331415195574722787e+01 --2.292515221051365515e+01, -4.353124049032562937e+01 --2.303504371940592677e+01, -4.359539455374109451e+01 --2.286787732822325481e+01, -4.358515358211519697e+01 --2.301345235096871278e+01, -4.329056701178674160e+01 --2.281572845355460188e+01, -4.331962330945897577e+01 --2.293700858119530039e+01, -4.331024548516147377e+01 --2.294832079036718397e+01, -4.359326856820746343e+01 --2.287692395820329949e+01, -4.329343436981226745e+01 --2.280936680122842475e+01, -4.332561640333199193e+01 --2.301433497192872935e+01, -4.349960820663594774e+01 --2.288828106303952836e+01, -4.357643432509990333e+01 --2.295418930839412397e+01, -4.317848467852196848e+01 --2.298145869951957465e+01, -4.326828072479920451e+01 --2.291423194810337449e+01, -4.335272627087930175e+01 --2.297897520467387267e+01, -4.360631254682359526e+01 --2.289884145682063021e+01, -4.370267383127512772e+01 --2.298301425749878746e+01, -4.368631245850260569e+01 --2.301597485286773903e+01, -4.361336709114082311e+01 --2.293834113810527242e+01, -4.367599292808254319e+01 --2.288803899825278521e+01, -4.321993787599762982e+01 --2.296157785799457329e+01, -4.338153079334936280e+01 --2.301524179701020145e+01, -4.340434522642605941e+01 --2.298663687478770257e+01, -4.360957371397544335e+01 --2.290588516171228761e+01, -4.349696707083818836e+01 --2.287104404094739962e+01, -4.340392908594996157e+01 --2.293832881951945879e+01, -4.331949807291398002e+01 --2.285626734374656976e+01, -4.324991250070041104e+01 --2.285941458644026980e+01, -4.337291710697250835e+01 --2.294203956004068345e+01, -4.329499822445752244e+01 --2.287056757926591644e+01, -4.329942842335252351e+01 --2.298698490319611665e+01, -4.335755506268752413e+01 --2.292669248530666692e+01, -4.328848230813035514e+01 --2.299396410187327078e+01, -4.350833227127487390e+01 --2.289808752459288854e+01, -4.318944186885158132e+01 --2.284909734459305497e+01, -4.335115662535601189e+01 --2.293787270378911103e+01, -4.351925812594662091e+01 --2.290498881583736690e+01, -4.328796122092795429e+01 --2.283877573891031787e+01, -4.332939648061892512e+01 --2.290025071244315669e+01, -4.376415823535479888e+01 --2.296048252712028770e+01, -4.342453397942067994e+01 --2.288857565337127298e+01, -4.368091301061489418e+01 --2.297187641429729510e+01, -4.340329510192005102e+01 --2.300974470734010069e+01, -4.331506973071962818e+01 --2.293429308499825581e+01, -4.359599793680439461e+01 --2.296769389410585660e+01, -4.332327865310431037e+01 --2.291971739405493480e+01, -4.344198594574121586e+01 --2.305410662706819025e+01, -4.357742063378641006e+01 --2.283438357288950016e+01, -4.350137352607818286e+01 --2.302852696071203908e+01, -4.354913205262658948e+01 --2.302608355157800446e+01, -4.358288093700990373e+01 --2.288843689508311741e+01, -4.362867494808474333e+01 --2.297732767552705369e+01, -4.349255960499941409e+01 --2.290897887753541440e+01, -4.367219734129334086e+01 --2.291027399752062621e+01, -4.332496998316967307e+01 --2.294928153464963927e+01, -4.349802341737053268e+01 --2.287388202129286086e+01, -4.347467783376702499e+01 --2.281200972929471504e+01, -4.334411767339001642e+01 --2.282233269319803881e+01, -4.336587688951182429e+01 --2.305280372805461653e+01, -4.356816781963376428e+01 --2.294223585909661622e+01, -4.370374404843988714e+01 --2.289054104730248795e+01, -4.349044770030933194e+01 --2.293263505717809636e+01, -4.348225199337345970e+01 --2.280380533519871733e+01, -4.323636520997325761e+01 --2.298778216842572775e+01, -4.356657598844012824e+01 --2.289725693224878356e+01, -4.358894520308827225e+01 --2.295870392226944290e+01, -4.331076735295918922e+01 --2.297319054655653048e+01, -4.341254771265845847e+01 --2.292614302494872547e+01, -4.318396352642081126e+01 --2.296924644909374180e+01, -4.338478978704300459e+01 --2.281756354325651515e+01, -4.318137614342390407e+01 --2.298852339299472902e+01, -4.341906647662635521e+01 --2.291860601783725571e+01, -4.348498402306974242e+01 --2.297794887696708699e+01, -4.370155239614241793e+01 --2.295119527314434293e+01, -4.371625626759008298e+01 --2.289932709872701366e+01, -4.345071099151682148e+01 --2.294105399511904508e+01, -4.374673274708841575e+01 --2.302965622470084739e+01, -4.350612902095713963e+01 --2.292645974977639867e+01, -4.354049167183533342e+01 --2.292156676774485646e+01, -4.360797785725252851e+01 --2.293197583839115694e+01, -4.368198174332402317e+01 --2.284822706693957528e+01, -4.344639890243228564e+01 --2.298872821340517447e+01, -4.347132260082702970e+01 --2.304123394361957367e+01, -4.353714828254506841e+01 --2.294340617834541618e+01, -4.366075335804983837e+01 --2.292112160808808241e+01, -4.319921250777377253e+01 --2.289384451004505650e+01, -4.336145474529894983e+01 --2.280696734753538379e+01, -4.335936084230401377e+01 --2.287815930791994035e+01, -4.360691413355846180e+01 --2.293282055460505831e+01, -4.353450069553026935e+01 --2.299441082285250104e+01, -4.330855273043094655e+01 --2.283901345795899829e+01, -4.338164403795958890e+01 --2.292694154819655949e+01, -4.334073874852727215e+01 --2.286577433185894392e+01, -4.336692423586212897e+01 --2.292324335172837024e+01, -4.377394493672603915e+01 --2.288232656063648207e+01, -4.373913585794273473e+01 --2.296609733181769997e+01, -4.356604545020048391e+01 --2.285305417926188554e+01, -4.337890982931605066e+01 --2.292171141015581171e+01, -4.366021967966327821e+01 --2.291598357701602851e+01, -4.346648118397103389e+01 --2.294470577969924108e+01, -4.367000395754786979e+01 --2.295712713899698443e+01, -4.355353327566736255e+01 --2.296981851889433202e+01, -4.323726261322740783e+01 --2.294353296440122492e+01, -4.371299432864687162e+01 --2.292560988083252838e+01, -4.368797040314739633e+01 --2.302330331720444079e+01, -4.351212134861426506e+01 --2.295843275472164535e+01, -4.356278478460247072e+01 --2.295341906678032728e+01, -4.327375548521110460e+01 --2.286803084694878763e+01, -4.363739210075956265e+01 --2.285785283143701463e+01, -4.331141608872325577e+01 --2.300626368727011339e+01, -4.339183263508974875e+01 --2.292103025830801499e+01, -4.345123757855141378e+01 --2.278947306113784421e+01, -4.318685367238770567e+01 --2.299770170833317096e+01, -4.348383514252483195e+01 --2.297245619961685748e+01, -4.356005449351179237e+01 --2.296793061628249077e+01, -4.337553708386927553e+01 --2.289316166303728650e+01, -4.350894951281477319e+01 --2.283456529599618889e+01, -4.355361325801779060e+01 --2.290391100463948604e+01, -4.368743582211960330e+01 --2.285700593434668804e+01, -4.340666276316972727e+01 --2.288610276565835733e+01, -4.371464970861759269e+01 --2.299283445555852623e+01, -4.355133279040187233e+01 --2.291266470911407183e+01, -4.329121902703036540e+01 --2.281596989565198541e+01, -4.337186936580867780e+01 --2.300293397019241937e+01, -4.352084503847822816e+01 --2.299618865856130157e+01, -4.342232596386519106e+01 --2.294096759848557454e+01, -4.333800316716796885e+01 --2.297028863904367313e+01, -4.369829044961802822e+01 --2.290457468080708381e+01, -4.348771592538285802e+01 --2.291991638900359973e+01, -4.349423538954140867e+01 --2.297164888369008295e+01, -4.335103748177840544e+01 --2.301059551243796975e+01, -4.352410554831686085e+01 --2.299333500194315505e+01, -4.335156075846531820e+01 --2.293727465965788426e+01, -4.377122003107428583e+01 --2.284513543215877363e+01, -4.332340316430939708e+01 --2.302442256936345544e+01, -4.346911721151191443e+01 --2.291417389805931393e+01, -4.370919713478188839e+01 --2.294612547209616693e+01, -4.373149474630162814e+01 --2.290261162493063196e+01, -4.367818593937410299e+01 --2.287973243586142758e+01, -4.372063741321147745e+01 --2.289427933640885371e+01, -4.346595408604902389e+01 --2.296716904077267074e+01, -4.321875571643345637e+01 --2.287367550652211534e+01, -4.342243132501895531e+01 --2.296755617637542812e+01, -4.362754421123087667e+01 --2.290631095900911518e+01, -4.329721345060855953e+01 --2.296495232233263906e+01, -4.360904172557211922e+01 --2.299265598130675770e+01, -4.349907990761378329e+01 --2.288740033619941627e+01, -4.372389909132123620e+01 --2.290810267138438050e+01, -4.341097185215094356e+01 --2.298041790147965813e+01, -4.366781032179217448e+01 --2.287630471207214455e+01, -4.344093343496120951e+01 --2.290737182309485576e+01, -4.325420947380786885e+01 --2.286489419854001426e+01, -4.346216776848355323e+01 --2.295551692732687243e+01, -4.318773812353903452e+01 --2.291467150749251758e+01, -4.345722971230834020e+01 --2.294210600958370350e+01, -4.365150271384642622e+01 --2.299184049314380829e+01, -4.364657976615127666e+01 --2.293314023395228318e+01, -4.363899079954603621e+01 --2.294839067770408292e+01, -4.328900343081387092e+01 --2.286901529638037189e+01, -4.354216290834596492e+01 --2.291477170419690168e+01, -4.320520833185404541e+01 --2.285721800318589914e+01, -4.345890858285115854e+01 --2.281281824023196947e+01, -4.324887308869666924e+01 --2.290415458131406012e+01, -4.338321675194251981e+01 --2.291924326570514481e+01, -4.369395890742790556e+01 --2.284487884704767424e+01, -4.327115278647359986e+01 --2.287843452390632848e+01, -4.371138811888378228e+01 --2.292950324710249888e+01, -4.371572071155021177e+01 --2.299805125771124281e+01, -4.358834072921997205e+01 --2.290910285569178839e+01, -4.372443488739197193e+01 --2.293935088846812320e+01, -4.358075667078139759e+01 --2.298418018676122898e+01, -4.364331823862475801e+01 --2.301477229893844267e+01, -4.329982081295784013e+01 --2.297114240877218450e+01, -4.324651602790928706e+01 --2.294815492759241593e+01, -4.354102131419141841e+01 --2.299991310138960543e+01, -4.339782646538169786e+01 --2.294623836380218762e+01, -4.337501301716288538e+01 --2.286134136961628727e+01, -4.353890296084121303e+01 --2.304514707685244090e+01, -4.356490668802057797e+01 --2.297954356052463964e+01, -4.340655435470877421e+01 --2.297228007037481490e+01, -4.350780369456445129e+01 --2.305019622235942833e+01, -4.354966207212122953e+01 --2.289128964841972902e+01, -4.375164694959465095e+01 --2.295222946522234153e+01, -4.362102181766204012e+01 --2.280645885813986951e+01, -4.325486718028945887e+01 --2.297056171512085498e+01, -4.339404245988743014e+01 --2.299573007653180312e+01, -4.331780616503306192e+01 --2.291373615933921215e+01, -4.355247316715092865e+01 --2.286972746081735863e+01, -4.339467791919842909e+01 --2.291182832433216987e+01, -4.338647546634577878e+01 --2.283373612633302585e+01, -4.334464058428891775e+01 --2.289871008614268177e+01, -4.365043601942092266e+01 --2.286841031588125617e+01, -4.338542672161179325e+01 --2.287533233051835779e+01, -4.323192816324425536e+01 --2.292717350161509060e+01, -4.339299310618905281e+01 --2.292344736952635387e+01, -4.341748991684149672e+01 --2.289492258332419894e+01, -4.331845381552556518e+01 --2.295259199035828601e+01, -4.336901935628149829e+01 --2.287960641726838418e+01, -4.366840209218305802e+01 --2.286816730521620045e+01, -4.368962804465387251e+01 --2.281016425676594039e+01, -4.323037087829701619e+01 --2.292758608689152666e+01, -4.349749522614227715e+01 --2.299750121132141700e+01, -4.343157882445284201e+01 --2.297506550485214305e+01, -4.357855783660287585e+01 --2.288043552400914038e+01, -4.352093134799735452e+01 --2.292738834724691444e+01, -4.344524529431907212e+01 --2.287427756254965416e+01, -4.327493070640301909e+01 --2.290413219608077355e+01, -4.379190575945141717e+01 --2.300946151608451373e+01, -4.356710656275650706e+01 --2.293037896785071439e+01, -4.356824498380294841e+01 --2.285853107373054272e+01, -4.346815920661142485e+01 --2.290154510734737059e+01, -4.377340746077868516e+01 --2.302461008086985572e+01, -4.352137408128133700e+01 --2.299919090540203825e+01, -4.354534135338451506e+01 --2.289624313567410141e+01, -4.332770574803824104e+01 --2.298303192989188659e+01, -4.332979566172169683e+01 --2.290763253705884495e+01, -4.330646565453065477e+01 --2.301581732663160551e+01, -4.356111521221244942e+01 --2.289821348966552961e+01, -4.349370735073706840e+01 --2.289836962553631139e+01, -4.324169970303252342e+01 --2.281944334518981776e+01, -4.329512819970405957e+01 --2.284009671097817318e+01, -4.333864753903223033e+01 --2.295039658259755910e+01, -4.345502375988861132e+01 --2.292642631137565346e+01, -4.323622387181573146e+01 --2.290437317696367359e+01, -4.343546745861659986e+01 --2.289494399448328821e+01, -4.367492458844265002e+01 --2.290780574322181451e+01, -4.371518519189393004e+01 --2.284858541094690310e+01, -4.324665538552975619e+01 --2.297911887116302765e+01, -4.365855918557875981e+01 --2.303634536441158431e+01, -4.360464693195404351e+01 --2.288963472475821703e+01, -4.328144582165106158e+01 --2.285741301094893174e+01, -4.351115212404305765e+01 --2.292562233653458392e+01, -4.333148643376720344e+01 --2.281333034824712414e+01, -4.335336826636749663e+01 --2.301129866196444240e+01, -4.337658532429746572e+01 --2.285590436781328805e+01, -4.344965792491036893e+01 --2.289364370731826526e+01, -4.366567474931655823e+01 --2.292140001238204405e+01, -4.325147188414914723e+01 --2.302086764548625553e+01, -4.354587122031983171e+01 --2.292190549589594184e+01, -4.335598474690545601e+01 --2.290273381909947048e+01, -4.373042265217135594e+01 --2.295956527751778253e+01, -4.351978706081447257e+01 --2.300180089901844127e+01, -4.356384566416637938e+01 --2.288639576019372512e+01, -4.341044663679780058e+01 --2.280114956121996883e+01, -4.321786314934110607e+01 --2.293039890387515811e+01, -4.326398215944236370e+01 --2.293381941595595919e+01, -4.318722042454715648e+01 --2.294048812704246032e+01, -4.353776097015848734e+01 --2.294195905344590258e+01, -4.359925878049022430e+01 --2.291555013399828766e+01, -4.336197831011990189e+01 --2.295207279302385572e+01, -4.356877556637797255e+01 --2.283321924659961510e+01, -4.324014136885644177e+01 --2.291157752156420102e+01, -4.369069733055779636e+01 --2.287453224853049960e+01, -4.332718335852895564e+01 --2.293145091610032438e+01, -4.322097545129754081e+01 --2.289447112228343073e+01, -4.351820036412114945e+01 --2.287893165706625709e+01, -4.345943541191933690e+01 --2.289275489453974188e+01, -4.340445406824778019e+01 --2.293090601718444077e+01, -4.377720730635628854e+01 --2.288973944755114331e+01, -4.363792496413812927e+01 --2.289143875748691670e+01, -4.339520258762453153e+01 --2.303208339150627282e+01, -4.347237735253370516e+01 --2.290362148999711422e+01, -4.358295525260653136e+01 --2.281860718860696835e+01, -4.339037034668400139e+01 --2.293768234244348392e+01, -4.346700831775029883e+01 --2.304889161580599577e+01, -4.354040913972605864e+01 --2.290662107962861427e+01, -4.375817103812413222e+01 --2.298542256811844453e+01, -4.329604097169874422e+01 --2.281728882396940250e+01, -4.338111987130815095e+01 --2.302311273471378428e+01, -4.345986417111769384e+01 --2.298720993527325618e+01, -4.340981367773800059e+01 --2.296422304124387281e+01, -4.314798396626705568e+01 --2.291050323672681444e+01, -4.378591897612731998e+01 --2.293053637894351482e+01, -4.362048947192690918e+01 --2.288941682092731611e+01, -4.353344191276934083e+01 --2.287076551000618707e+01, -4.370812659744378692e+01 --2.305035662046524081e+01, -4.360191733691154070e+01 --2.277912095306861673e+01, -4.316509564263928667e+01 --2.293637071938201544e+01, -4.345775653238072067e+01 --2.295189900423799756e+01, -4.351652685352836869e+01 --2.297822919838059974e+01, -4.339730161840157052e+01 --2.286073263163601155e+01, -4.338216824028010166e+01 --2.290699968816667109e+01, -4.345397031694970735e+01 --2.294080527347730225e+01, -4.364225202541964421e+01 --2.287321125662679933e+01, -4.331793174014504899e+01 --2.292377508997219948e+01, -4.321771823302530180e+01 --2.284206082129494320e+01, -4.350463298914729648e+01 --2.288220997488062736e+01, -4.333044138606243223e+01 --2.281253662672711258e+01, -4.319662256235445597e+01 --2.296442014490551387e+01, -4.345229086010100872e+01 --2.281917695945191227e+01, -4.324287885102194906e+01 --2.296509546603649810e+01, -4.366128707270620879e+01 --2.291428563528335616e+01, -4.376143318440379915e+01 --2.294149503906222876e+01, -4.319047739428197019e+01 --2.289779700061911072e+01, -4.338920974435072964e+01 --2.290945888939695507e+01, -4.316819710980000480e+01 --2.297536329724978899e+01, -4.332653712204147212e+01 --2.291547123582464351e+01, -4.371844696629922566e+01 --2.291530776104937317e+01, -4.330972365289228065e+01 --2.289334133249928627e+01, -4.325694693686731540e+01 --2.292585734896704253e+01, -4.338374106129158747e+01 --2.291398651772798090e+01, -4.330047135292579696e+01 --2.297616561042314487e+01, -4.323126672493550160e+01 --2.291875038430001510e+01, -4.323296652997851197e+01 --2.282553501552609987e+01, -4.323688446739115676e+01 --2.292166130782211653e+01, -4.330372932613620662e+01 --2.290719507664033827e+01, -4.350621818012471209e+01 --2.290443806559117235e+01, -4.318344597931848483e+01 --2.289227978373852679e+01, -4.329994987038831766e+01 --2.289624371516408630e+01, -4.368417438213230497e+01 --2.282869482980241216e+01, -4.335988426461657497e+01 --2.282076667642471435e+01, -4.330437914690615742e+01 --2.285327540284913184e+01, -4.343115650841556175e+01 --2.285497533369280276e+01, -4.354489319397374203e+01 --2.290507666097062867e+01, -4.364444701887815370e+01 --2.297008338887578915e+01, -4.328952458898208988e+01 --2.290737337428003073e+01, -4.355846368591114270e+01 --2.292365428847521613e+01, -4.346974074232645791e+01 --2.299559918710025741e+01, -4.362208661769783191e+01 --2.295286112390114042e+01, -4.316923121393351437e+01 --2.278547912515036344e+01, -4.315910053418555492e+01 --2.294684783832756736e+01, -4.353176981921494360e+01 --2.293542589914935803e+01, -4.324873416789491642e+01 --2.284405624316395844e+01, -4.336640054486830564e+01 --2.304790235764707163e+01, -4.363566553220285726e+01 --2.282524976785821380e+01, -4.318463245851195609e+01 --2.300866706715180143e+01, -4.335807857254604869e+01 --2.296661421675385739e+01, -4.336628435084863753e+01 --2.297208680924939728e+01, -4.345555053511172616e+01 --2.293089578851164489e+01, -4.336849552270292918e+01 --2.299788505645998526e+01, -4.353608913970193584e+01 --2.295170809679034463e+01, -4.346427576584359542e+01 --2.296853798501797073e+01, -4.353229918410291788e+01 --2.280351944923131313e+01, -4.318411497106514219e+01 --2.291794855154301302e+01, -4.332822817310863428e+01 --2.281386581906214062e+01, -4.320587386303333943e+01 --2.281440591522309802e+01, -4.331037251076269712e+01 --2.300603320246932526e+01, -4.333957170503272494e+01 --2.302834866348606013e+01, -4.349687612238045631e+01 --2.285832205693228403e+01, -4.341591367884716846e+01 --2.295522443550239799e+01, -4.338752435382978945e+01 --2.296082174607110105e+01, -4.322475169824077312e+01 --2.291780783677604205e+01, -4.363246855410869784e+01 --2.284141711908815608e+01, -4.334789856953300102e+01 --2.291662843901030300e+01, -4.331897592644736505e+01 --2.279583126236939705e+01, -4.318085877256134353e+01 --2.297799985178048487e+01, -4.334504322674327881e+01 --2.294383051241384308e+01, -4.340876322993814540e+01 --2.284033138072255298e+01, -4.339089482720028457e+01 --2.287956809582848550e+01, -4.331193793098499611e+01 --2.280247772955445740e+01, -4.322711419070555650e+01 --2.293168424042208997e+01, -4.357749600866467432e+01 --2.289595166211158883e+01, -4.357969475428810568e+01 --2.296266161853284515e+01, -4.333852597753019609e+01 --2.295447378428664464e+01, -4.323074753441659368e+01 --2.296104228416117365e+01, -4.358128768317794055e+01 --2.280804449225034247e+01, -4.331636572721573941e+01 --2.297378848981938759e+01, -4.326502278742964336e+01 --2.304920138429320176e+01, -4.364491783022530313e+01 --2.286412013083389994e+01, -4.360964286332149698e+01 --2.291446027745507052e+01, -4.340497908626895907e+01 --2.304253889033313030e+01, -4.354640112329192192e+01 --2.281465040372208719e+01, -4.336261883066774914e+01 --2.300405546044606098e+01, -4.347784262617532391e+01 --2.281902677009965785e+01, -4.349485480879178567e+01 --2.299069979179825651e+01, -4.333305427212615513e+01 --2.297881440625449656e+01, -4.355406338399240695e+01 --2.298287979332402742e+01, -4.363406688017364843e+01 --2.288697488769997079e+01, -4.356718392687048436e+01 --2.290974769404544276e+01, -4.322045664598034165e+01 --2.292907527174758897e+01, -4.325472942028473256e+01 --2.297581711005815563e+01, -4.343105283832291263e+01 --2.288936542859072887e+01, -4.322919017501406103e+01 --2.298383638849105637e+01, -4.323452434530474164e+01 --2.285196007339029833e+01, -4.342190575082577908e+01 --2.286445549290701251e+01, -4.335767294866352728e+01 --2.293277647583292733e+01, -4.323022837925907425e+01 --2.294645864006708535e+01, -4.342726754002409706e+01 --2.303096321739428731e+01, -4.351538188287334208e+01 --2.294916989403205676e+01, -4.319373443560652248e+01 --2.302217247493187102e+01, -4.355512370879409900e+01 --2.288220665550958444e+01, -4.368690127741314200e+01 --2.295412752219785801e+01, -4.343052688798214689e+01 --2.297077517465501728e+01, -4.344629818138912469e+01 --2.303731569043583960e+01, -4.350938953256301289e+01 --2.298013627685526927e+01, -4.325902726001064735e+01 --2.283587123323716739e+01, -4.325864416189273953e+01 --2.287608723176186842e+01, -4.338868527329228897e+01 --2.296740204048483136e+01, -4.357529701068131800e+01 --2.297853557511258416e+01, -4.319750959739712926e+01 --2.282497044351793036e+01, -4.338437811959931878e+01 --2.305915647652344092e+01, -4.356217607518677681e+01 --2.291504267703556863e+01, -4.356172404930016029e+01 --2.294908450170212433e+01, -4.344577171994551179e+01 --2.293243245188221380e+01, -4.343000097342693522e+01 --2.283831497118109866e+01, -4.352912353468364870e+01 --2.303879323098568221e+01, -4.357089825748689549e+01 --2.288322102418371529e+01, -4.359167445841861621e+01 --2.290143693178430340e+01, -4.372117309289069453e+01 --2.294093818623723990e+01, -4.369449372128889308e+01 --2.302233180335537099e+01, -4.360737639916661834e+01 --2.301106328920690203e+01, -4.332432335698707249e+01 --2.300516536703561243e+01, -4.343483847559583921e+01 --2.288102978145901290e+01, -4.372988665972502531e+01 --2.285041685376882725e+01, -4.336040772255083908e+01 --2.304530259616590016e+01, -4.361716080640109539e+01 --2.286883070406160101e+01, -4.348991981390840067e+01 --2.296249547219108678e+01, -4.364278511390507731e+01 --2.282892283995138527e+01, -4.341212973631966321e+01 --2.291239911946678376e+01, -4.323896185069880715e+01 --2.298912813919478282e+01, -4.327153873327927158e+01 --2.287424383976424025e+01, -4.357916385018406658e+01 --2.286129693335134405e+01, -4.323466602496382194e+01 --2.288566290111597112e+01, -4.325368956273705834e+01 --2.286246689573684066e+01, -4.349591086798812967e+01 --2.288597496401551368e+01, -4.366241355619821718e+01 --2.285759095957014253e+01, -4.356339330002769827e+01 --2.285347956680585568e+01, -4.348340095512521231e+01 --2.297272447610145463e+01, -4.330803090816761625e+01 --2.291078794017429843e+01, -4.317744994506814749e+01 --2.292384410473991707e+01, -4.352198927085925817e+01 --2.296310817253114323e+01, -4.344303859974495907e+01 --2.290310924687892680e+01, -4.317419326187921769e+01 --2.294995727970263033e+01, -4.335051424071053106e+01 --2.302180233186865621e+01, -4.345061109647524944e+01 --2.288999377764254817e+01, -4.374239771229144225e+01 --2.290283742028939784e+01, -4.337396499074775846e+01 --2.295337761383148845e+01, -4.357802690458532879e+01 --2.296625453320371335e+01, -4.361829298964995161e+01 --2.295314866025631417e+01, -4.322149429195273029e+01 --2.290366610761177313e+01, -4.327870896597125494e+01 --2.281652251518584151e+01, -4.322437640093767897e+01 --2.297131276094251362e+01, -4.360305144865195359e+01 --2.282686162342331571e+01, -4.324613577840595013e+01 --2.292747084543161051e+01, -4.319321653862946420e+01 --2.285085444632093044e+01, -4.346489999776271418e+01 --2.299901062563404253e+01, -4.349308767096143669e+01 --2.294600096705902814e+01, -4.332275635450425710e+01 --2.290403013955348754e+01, -4.373967216312495765e+01 --2.300842555817185797e+01, -4.330581607775413744e+01 --2.283718069197832534e+01, -4.357211265873547745e+01 --2.297665177766920053e+01, -4.369230153189055699e+01 --2.296348621209699914e+01, -4.354754221039588913e+01 --2.288529302869838489e+01, -4.345344364922679148e+01 --2.300195789323439755e+01, -4.361609625514386579e+01 --2.294010253839998725e+01, -4.343326027492995678e+01 --2.299596794357468710e+01, -4.337006713040741346e+01 --2.296262762674185964e+01, -4.369502857149019803e+01 --2.285122381172270067e+01, -4.356938311943959974e+01 --2.294272794175221364e+01, -4.345176420141164186e+01 --2.299150639684094344e+01, -4.323778203699696121e+01 --2.286394850998112815e+01, -4.325316968706881227e+01 --2.297601830990377181e+01, -4.348330739024259373e+01 --2.291169537966991498e+01, -4.374293413385743889e+01 --2.300274574613026957e+01, -4.346858993426771889e+01 --2.283569460361805881e+01, -4.351062356597945779e+01 --2.288869734021497138e+01, -4.373314842603976160e+01 --2.291314458374007046e+01, -4.339572729175439036e+01 --2.286166188933680132e+01, -4.364338086264302774e+01 --2.284249133712441804e+01, -4.330490072054880102e+01 --2.287665829857171573e+01, -4.324118022293339436e+01 --2.299003803700751547e+01, -4.348057507190751636e+01 --2.289542978662631256e+01, -4.317093665037344152e+01 --2.282977220690316145e+01, -4.331688769599368527e+01 --2.283083806719350051e+01, -4.327388957098150968e+01 --2.293012479107658663e+01, -4.321172250155037631e+01 --2.307158593530918367e+01, -4.319358481726442989e+01 --2.298386599124672358e+01, -4.353882012992867345e+01 --2.285628342923615719e+01, -4.355414326657137991e+01 --2.288748695509811881e+01, -4.336744796249679013e+01 --2.295937562853530167e+01, -4.346753548739047091e+01 --2.281889352934566162e+01, -4.319062758284953674e+01 --2.304628069560900983e+01, -4.352190316007004611e+01 --2.290195202132744257e+01, -4.346921352789446047e+01 --2.284730020554441765e+01, -4.354163329356826040e+01 --2.300798119581901346e+01, -4.350560048886907083e+01 --2.291650551201134434e+01, -4.362321809182383703e+01 --2.283719638131839957e+01, -4.326789552303822717e+01 --2.294442348469167214e+01, -4.326124470608866801e+01 --2.287163069916787350e+01, -4.356066345823074215e+01 --2.290492642074888963e+01, -4.359220579222575509e+01 --2.294336059497572933e+01, -4.330425096076236002e+01 --2.300012461410377185e+01, -4.345008444683231374e+01 --2.292298221627971344e+01, -4.331298172208922637e+01 --2.296820899075464695e+01, -4.317574506503145670e+01 --2.299465175635659620e+01, -4.336081395897728896e+01 --2.297748973223869839e+01, -4.324052025959613132e+01 --2.286862904452502931e+01, -4.343767438941976877e+01 --2.303748965077705790e+01, -4.356164562566362264e+01 --2.283001421572652845e+01, -4.336913501881747379e+01 --2.295756038650583974e+01, -4.371026781759259450e+01 --2.291073190326116915e+01, -4.342947509465371070e+01 --2.296584345266624183e+01, -4.320950223528662804e+01 --2.286336702825420986e+01, -4.340067041586605967e+01 --2.294863907497552447e+01, -4.334126163987259872e+01 --2.283700506973708499e+01, -4.351987356900858828e+01 --2.305049984145898989e+01, -4.365417008435245805e+01 --2.297844140509642585e+01, -4.344955783302152952e+01 --2.296119462365390262e+01, -4.363353406860521488e+01 --2.291335887198150445e+01, -4.344797820654640930e+01 --2.292677583913454242e+01, -4.364498015134065412e+01 --2.303243872246978441e+01, -4.357688967385838197e+01 --2.293747486944269554e+01, -4.341475621703704491e+01 --2.298938133620105617e+01, -4.332380098726408590e+01 --2.280513237808358440e+01, -4.324561620666199957e+01 --2.294989850977464130e+01, -4.370700589884773990e+01 --2.297994470448945137e+01, -4.351106392618901708e+01 --2.290532589308392630e+01, -4.374892162526980144e+01 --2.298806231367700192e+01, -4.331454767525041660e+01 --2.288698740692843714e+01, -4.326294167317291084e+01 --2.286994313633622866e+01, -4.344692530132503094e+01 --2.304758644020105507e+01, -4.353115616888155870e+01 --2.294071795926709711e+01, -4.328574546250676036e+01 --2.294971194358378597e+01, -4.329825628903122237e+01 --2.292054139602054619e+01, -4.370320892167931248e+01 --2.303618550166006074e+01, -4.355239295428558677e+01 --2.301282875030392816e+01, -4.343809819680682693e+01 --2.297863647374404295e+01, -4.350181178380902480e+01 --2.294161381239153030e+01, -4.349476346426209972e+01 --2.292627605375152555e+01, -4.348824376709285389e+01 --2.297511068085213282e+01, -4.327427613130293338e+01 --2.293559665687470073e+01, -4.360524883911825356e+01 --2.288902588049310438e+01, -4.342894925165894904e+01 --2.298085735558207077e+01, -4.341580705955117025e+01 --2.290623078514825650e+01, -4.360145629071498519e+01 --2.301302673240426699e+01, -4.349035549301316905e+01 --2.292776672246331415e+01, -4.354974281490618893e+01 --2.289863463190079784e+01, -4.329395561920319579e+01 --2.283637592012568618e+01, -4.336314236950148882e+01 --2.284669311059787233e+01, -4.338490240276713195e+01 --2.296347076028544265e+01, -4.324325835561467102e+01 --2.298793863164970119e+01, -4.361882529129286468e+01 --2.282391955063377509e+01, -4.317538090104724091e+01 --2.295182297049120024e+01, -4.321224102767469333e+01 --2.293505852989068572e+01, -4.344850471288215488e+01 --2.299674790934385626e+01, -4.357908880472099611e+01 --2.284973730072655940e+01, -4.350789252180857147e+01 --2.298368318767546015e+01, -4.348656741408463233e+01 --2.295103264345464567e+01, -4.330750912143217590e+01 --2.291204567054901631e+01, -4.343872666716743680e+01 --2.293111856349722544e+01, -4.342074905443494970e+01 --2.292793025599140222e+01, -4.360198797388163428e+01 --2.294514485945961013e+01, -4.341801540101051415e+01 --2.289669991353341416e+01, -4.343220832010750598e+01 --2.290326363399025666e+01, -4.347846474424090246e+01 --2.292807668961976120e+01, -4.365423061636368374e+01 --2.289636412632418683e+01, -4.373641026051272007e+01 --2.290472199010415721e+01, -4.323570455416655989e+01 --2.284932772085197072e+01, -4.340340413937208552e+01 --2.290214255065272297e+01, -4.352146029144238071e+01 --2.298923982037204183e+01, -4.362807682608274007e+01 --2.288063281685433736e+01, -4.326893626306011953e+01 --2.293436640720334552e+01, -4.329174023081764489e+01 --2.300831593850029932e+01, -4.361010573854591854e+01 --2.293780018252386910e+01, -4.321497969358882330e+01 --2.287981056938612667e+01, -4.336418955406116282e+01 --2.283133303776411438e+01, -4.337838574351813037e+01 --2.291259513769510292e+01, -4.359546645041643131e+01 --2.293586988181129982e+01, -4.370973245800136198e+01 --2.295694848522176912e+01, -4.350128344013632642e+01 --2.284449022962925469e+01, -4.347089126193942832e+01 --2.298431158307560196e+01, -4.369556345803554365e+01 --2.289704409306682820e+01, -4.323244732893207498e+01 --2.291765401306697214e+01, -4.358022569448600336e+01 --2.285677680261139599e+01, -4.335441475173910675e+01 --2.292481463945135900e+01, -4.317471049435685870e+01 --2.291580786509150158e+01, -4.316220081332931358e+01 --2.291950129784408219e+01, -4.338973425110089011e+01 --2.289538547262960222e+01, -4.342295693497144526e+01 --2.298011957061394028e+01, -4.356331520634993382e+01 --2.290678720692833892e+01, -4.340172018312644298e+01 --2.285388446362646775e+01, -4.328366165096310425e+01 --2.304284505227480295e+01, -4.365090818644365811e+01 --2.298741782225487285e+01, -4.346207009490479578e+01 --2.283791868730147101e+01, -4.342463890764184242e+01 --2.289839610475365816e+01, -4.354595269246975420e+01 --2.294860117868258342e+01, -4.369775548299047330e+01 --2.295499134633692861e+01, -4.333526745891150256e+01 --2.286181612145833242e+01, -4.333917028803011107e+01 --2.295049671506008693e+01, -4.320298774206506920e+01 --2.289012205518443110e+01, -4.338595107613683410e+01 --2.291112142490839787e+01, -4.353397128615018374e+01 --2.295894495270662716e+01, -4.336302554667319953e+01 --2.287400579826682900e+01, -4.322267608120227322e+01 --2.295378709646684001e+01, -4.373475686180245248e+01 --2.284752678520829861e+01, -4.328965562856679128e+01 --2.288771110298325695e+01, -4.341969796029871276e+01 --2.294755463961131881e+01, -4.338426540632387685e+01 --2.288376337904221458e+01, -4.339194389530281626e+01 --2.286262300366294298e+01, -4.324391786750476996e+01 --2.291577540541057445e+01, -4.341423084940719690e+01 --2.288592427162342702e+01, -4.330594397435753251e+01 --2.285545706411294375e+01, -4.334516353077781758e+01 --2.293950397008962838e+01, -4.363300129325142507e+01 --2.299018237725818281e+01, -4.322852828516108303e+01 --2.295127491818591281e+01, -4.335976681300764568e+01 --2.288191564152238655e+01, -4.358242423204851690e+01 --2.286151015866783354e+01, -4.359114316072915329e+01 --2.300710584176968609e+01, -4.329656239857329325e+01 --2.290998538877246204e+01, -4.357696514883738104e+01 --2.284164873929451645e+01, -4.340014558580585202e+01 --2.301917982185445766e+01, -4.343210484637543090e+01 --2.282628847293574381e+01, -4.339362868944299834e+01 --2.303488078369894154e+01, -4.354314024383518245e+01 --2.299299578675945455e+01, -4.360358324026284293e+01 --2.284093307978287157e+01, -4.354762335204365797e+01 --2.287849183632122418e+01, -4.335493804784319849e+01 --2.298410184521913990e+01, -4.328678758112589264e+01 --2.300254036940127023e+01, -4.341633253444256013e+01 --2.298548001203628388e+01, -4.365256955327289745e+01 --2.282341164859067462e+01, -4.332288096245308395e+01 --2.293374577403541181e+01, -4.343925285973676864e+01 --2.300162675662872758e+01, -4.351159261942046186e+01 --2.293339150727211262e+01, -4.374347059186199971e+01 --2.290637796647740032e+01, -4.365369717084316648e+01 --2.300143546399436190e+01, -4.345933720765923169e+01 --2.282801692377181624e+01, -4.350736408021314361e+01 --2.295153237391735601e+01, -4.315997773025637940e+01 --2.292430255920645976e+01, -4.332223409146036630e+01 --2.282844977303361489e+01, -4.330763667903040215e+01 --2.286731438774480196e+01, -4.342842344443906910e+01 --2.288244634081375395e+01, -4.338269247827322772e+01 --2.290391889208345688e+01, -4.333096389213039146e+01 --2.288583009171091476e+01, -4.361017478737841202e+01 --2.295150006872119874e+01, -4.341202239007775177e+01 --2.291617465065926851e+01, -4.351872922705005919e+01 --2.279611837539220431e+01, -4.323310849634596309e+01 --2.296479205566695470e+01, -4.355679384994464698e+01 --2.283307197761703122e+01, -4.349212344978673173e+01 --2.300961724110353046e+01, -4.361935762912253978e+01 --2.283962430788476894e+01, -4.353837346252264950e+01 --2.282258877051875956e+01, -4.316612932418099291e+01 --2.278281369089350150e+01, -4.314059834849412312e+01 --2.282033847528842330e+01, -4.350410466401866927e+01 --2.297907385516962719e+01, -4.330203601358373078e+01 --2.294326228527046752e+01, -4.360850977333235790e+01 --2.285173579868086691e+01, -4.336965879069824581e+01 --2.288337200964926055e+01, -4.364391392264678871e+01 --2.294666179761637181e+01, -4.347951983629752704e+01 --2.299679680861092734e+01, -4.327479681285108626e+01 --2.298118711436169548e+01, -4.321601701441188226e+01 --2.279716168076972949e+01, -4.319010989749003926e+01 --2.298674272429361309e+01, -4.330529433656779048e+01 --2.290767870535345452e+01, -4.366294727847552792e+01 --2.296026168862445971e+01, -4.337227818484939945e+01 --2.288305296662478128e+01, -4.353943249516379410e+01 --2.289578001569495669e+01, -4.352745117815381093e+01 --2.297484092236074460e+01, -4.322201316794944859e+01 --2.288327967345841429e+01, -4.328744016920318671e+01 --2.290074300468684143e+01, -4.320794700677278399e+01 --2.296984462403107230e+01, -4.354155099256589523e+01 --2.289571799595457335e+01, -4.322319493245024802e+01 --2.287295351813283517e+01, -4.326567883756558786e+01 --2.297115069558557465e+01, -4.355080276252959948e+01 --2.292301146743271190e+01, -4.366946996614168341e+01 --2.298171313838555463e+01, -4.332054247249585899e+01 --2.303470067809062627e+01, -4.349088351428120802e+01 --2.286685375429170008e+01, -4.332392540171190376e+01 --2.299377154999908512e+01, -4.345607734617541240e+01 --2.294179498842851572e+01, -4.354701233505809199e+01 --2.305179772908139668e+01, -4.366342229410209796e+01 --2.304270055752153112e+01, -4.359865591080507841e+01 --2.288035923348454048e+01, -4.321668089093797249e+01 --2.298760856598573099e+01, -4.351432422739541295e+01 --2.279849153675806761e+01, -4.319936100225105946e+01 --2.296704238866277237e+01, -4.347079527878916139e+01 --2.288266562688255235e+01, -4.343494141836401923e+01 --2.280432600413547561e+01, -4.334085987527102191e+01 --2.293525501366055863e+01, -4.350075513239907110e+01 --2.293304447167128757e+01, -4.328248756519135299e+01 --2.281785001883571340e+01, -4.323362763719845958e+01 --2.297535411016665918e+01, -4.368305062128551697e+01 --2.293598084316432661e+01, -4.376197026781753863e+01 --2.298589591029584156e+01, -4.340056084721414464e+01 --2.297351566812018930e+01, -4.321275958912062976e+01 --2.292026308285378278e+01, -4.359872717764136496e+01 --2.297246573243664258e+01, -4.325576941947088017e+01 --2.292286988588230301e+01, -4.361722849491005860e+01 --2.299415273617481503e+01, -4.325628947218725529e+01 --2.292194941732807223e+01, -4.376469539866807423e+01 --2.293079990125351486e+01, -4.372497071986224881e+01 --2.298255946330566957e+01, -4.352956809973110097e+01 --2.305654851328597488e+01, -4.354367006631884607e+01 --2.287740404087191592e+01, -4.339793656528238586e+01 --2.294282219337794260e+01, -4.319973060271494347e+01 --2.293065590251474006e+01, -4.331623986210274779e+01 --2.290650806384795501e+01, -4.370593544886131809e+01 --2.300031897508758760e+01, -4.350234016342075449e+01 --2.294887034907863921e+01, -4.339351776518494574e+01 --2.282580321461368200e+01, -4.328913456669988591e+01 --2.287030373646152981e+01, -4.324717502803705571e+01 --2.286377770631034423e+01, -4.350516135020547637e+01 --2.295973780313115853e+01, -4.357203625393339763e+01 --2.299657863951473047e+01, -4.352683688780127369e+01 --2.294414878226761445e+01, -4.320898379046935389e+01 --2.298488063442224316e+01, -4.319151306080879493e+01 --2.293327442020969897e+01, -4.369123202800152228e+01 --2.303357549695794404e+01, -4.353388749479474740e+01 --2.306681191804708320e+01, -4.356543725406549328e+01 --2.295106976289763523e+01, -4.366401483184478849e+01 --2.286508795172684216e+01, -4.351441179583195407e+01 --2.296134295280443283e+01, -4.332927312994680591e+01 --2.289740844020394306e+01, -4.364118595713714654e+01 --2.288480462865449994e+01, -4.370540027841054354e+01 --2.285104765645663250e+01, -4.351714274615210343e+01 --2.289675893780894711e+01, -4.318018926944086644e+01 --2.288060969301501402e+01, -4.357317396506081053e+01 --2.298516017596402961e+01, -4.324377797724874739e+01 --2.301076532042636913e+01, -4.357635875003393267e+01 --2.292081722095647223e+01, -4.339898617090647548e+01 --2.288713377644842595e+01, -4.361942488900423598e+01 --2.298348324417218791e+01, -4.343431237291149927e+01 --2.291504828494893076e+01, -4.325746696452881679e+01 --2.302998950934361488e+01, -4.361063779928714013e+01 --2.292923360092849450e+01, -4.361123874396654543e+01 --2.301564264326868425e+01, -4.350886088393987450e+01 --2.292801418996425511e+01, -4.329773485206941075e+01 --2.297218984777032702e+01, -4.320350598893175942e+01 --2.297140421571446112e+01, -4.329877776149753998e+01 --2.291840396377684641e+01, -4.343273427962564170e+01 --2.282208944424772668e+01, -4.331363006798323312e+01 --2.286542426855417176e+01, -4.361889265180946040e+01 --2.291159387979711326e+01, -4.333422210692178567e+01 --2.302704053381481231e+01, -4.348762318762575063e+01 --2.284954103895925570e+01, -4.345564946710243959e+01 --2.301020442831455526e+01, -4.341959206808025584e+01 --2.289952374255628698e+01, -4.350295836761308976e+01 --2.288286783485771281e+01, -4.348718811957379415e+01 --2.287179211703448445e+01, -4.361290346964018738e+01 --2.295675270897859832e+01, -4.344903125503931562e+01 --2.290920975151197325e+01, -4.377666971403452578e+01 --2.288467376992698732e+01, -4.365316376157342404e+01 --2.298278055566354539e+01, -4.327753416533183639e+01 --2.283769497107095958e+01, -4.337239321856594643e+01 --2.289731215430688849e+01, -4.328470348579527638e+01 --2.286313608941767583e+01, -4.334842163255878233e+01 --2.298630067416749867e+01, -4.350507199273142334e+01 --2.289969459329424240e+01, -4.325095205426914191e+01 --2.295018549213745374e+01, -4.340277009326369040e+01 --2.292831826632595948e+01, -4.375870800515428272e+01 --2.280883642070538286e+01, -4.322111973945554553e+01 --2.281679499275765721e+01, -4.327662622885378596e+01 --2.293067668931128722e+01, -4.367273141283725835e+01 --2.288452584093465703e+01, -4.360092464368921128e+01 --2.297651910623068616e+01, -4.364005677984560094e+01 --2.298499221470903109e+01, -4.349581972146705056e+01 --2.297521837174142689e+01, -4.363080551128999929e+01 --2.295353122207212593e+01, -4.363027281618190756e+01 --2.289034009265684588e+01, -4.343820051039632091e+01 --2.283984498666221086e+01, -4.328639817217236185e+01 --2.298195043249202740e+01, -4.337280217065399057e+01 --2.292443238403108907e+01, -4.373095868102900141e+01 --2.285256054627671318e+01, -4.327441012097079920e+01 --2.290305907280051301e+01, -4.342621597976272341e+01 --2.290001116561895600e+01, -4.365968603754290456e+01 --2.290962393609804693e+01, -4.347247303956846309e+01 --2.295949639007121590e+01, -4.321549833633561377e+01 --2.299544399281981555e+01, -4.356983683976350363e+01 --2.285235744723786766e+01, -4.352639293328211778e+01 --2.289377023502877506e+01, -4.371791136662623245e+01 --2.289234285373231614e+01, -4.365642486523589838e+01 --2.286395744925965801e+01, -4.355740332776907309e+01 --2.299246036196215215e+01, -4.344682467865364828e+01 --2.297643230546671234e+01, -4.328352945060806434e+01 --2.290131167856940309e+01, -4.366893601102127320e+01 --2.295626339731337140e+01, -4.370101731308728432e+01 --2.296552296615148947e+01, -4.340928843596710607e+01 --2.283194764094912799e+01, -4.353511370085583110e+01 --2.291274410961768382e+01, -4.364770814934219345e+01 --2.291708996753998662e+01, -4.342348258068687272e+01 --2.294730327993078589e+01, -4.368850502050010221e+01 --2.296003149269666821e+01, -4.367652715235256977e+01 --2.284116844378771205e+01, -4.329564945919640451e+01 --2.283812535338598337e+01, -4.347688237525600385e+01 --2.296379575300638720e+01, -4.365203611543273610e+01 --2.296870618160409805e+01, -4.358454853090494652e+01 --2.298983628339409435e+01, -4.342831924339668603e+01 --2.282605436656424658e+01, -4.334138266964242803e+01 --2.292431095767934579e+01, -4.367872020746367667e+01 --2.294385695533518543e+01, -4.315672101093552016e+01 --2.285872302897812602e+01, -4.352040244052063400e+01 --2.281705042850905585e+01, -4.332887408123625761e+01 --2.294962424930608691e+01, -4.360251969319275389e+01 --2.290063984288440935e+01, -4.345996227682571345e+01 --2.296611751032851245e+01, -4.326176492118929673e+01 --2.287193647096808391e+01, -4.366514094689285486e+01 --2.293674976259587339e+01, -4.325798702760415182e+01 --2.293689966172464167e+01, -4.361449969961874729e+01 --2.306550856166344587e+01, -4.355618417782179819e+01 --2.294571217084785530e+01, -4.357476619514600458e+01 --2.291769518994284027e+01, -4.327597198361039688e+01 --2.293988407331101342e+01, -4.338100652919167288e+01 --2.296364954382592671e+01, -4.359979041947941170e+01 --2.295483241143148589e+01, -4.363952377158256724e+01 --2.296953650901846800e+01, -4.318499872640450121e+01 --2.291534547270244460e+01, -4.366620858801478988e+01 --2.304775298706695708e+01, -4.358341209384667536e+01 --2.288433783079660344e+01, -4.324443742881320674e+01 --2.298217058348663855e+01, -4.342505973244620776e+01 --2.299067363025330124e+01, -4.368957436419476892e+01 --2.286296410413419977e+01, -4.365263038855085398e+01 --2.302983145005971366e+01, -4.355838463258181292e+01 --2.295301904421472727e+01, -4.347352773732816189e+01 --2.280987655385874291e+01, -4.317811989995465183e+01 --2.295582095602162909e+01, -4.354428172761019056e+01 --2.293574182050857502e+01, -4.365749195288314155e+01 --2.290547117688268841e+01, -4.339246848289620573e+01 --2.295920702443672567e+01, -4.316323452136045802e+01 --2.296420849682471399e+01, -4.340003591939063909e+01 --2.284074956328469952e+01, -4.349538282043504012e+01 --2.292033983390144414e+01, -4.329447690408375848e+01 --2.292826019412494176e+01, -4.334999103525818498e+01 --2.299527165463089773e+01, -4.351758459816478108e+01 --2.294120011927295621e+01, -4.339025879354633020e+01 --2.285568924716181272e+01, -4.339741181603398701e+01 --2.297261519903375770e+01, -4.361230284521106171e+01 --2.304660276158355359e+01, -4.362641319076736579e+01 --2.285281589428564075e+01, -4.332666100457712588e+01 --2.296876199565323162e+01, -4.328027139110383814e+01 --2.286933328749528727e+01, -4.364664176025804210e+01 --2.298142416721738712e+01, -4.357256698907290371e+01 --2.295785461460055643e+01, -4.340602922949591402e+01 --2.288397961044488582e+01, -4.344419255050375028e+01 --2.290831209507431510e+01, -4.346322169546449032e+01 --2.290100992969362892e+01, -4.356445405190610387e+01 --2.296397971781203040e+01, -4.334777879719662508e+01 --2.294423195900484203e+01, -4.351326671578346605e+01 --2.294456494990022932e+01, -4.361776072419023365e+01 --2.294554018211007573e+01, -4.352251828625156804e+01 --2.284991101986826934e+01, -4.325590698777178034e+01 --2.291926809857983116e+01, -4.333748039239368666e+01 --2.296505440184862579e+01, -4.330477263090502760e+01 --2.296053544154499804e+01, -4.317248810362907818e+01 --2.295474112978548575e+01, -4.328300849016338958e+01 --2.291729508048620190e+01, -4.347573262105220948e+01 --2.300571162780932966e+01, -4.359160183164669178e+01 --2.302199598512117262e+01, -4.350286857897513215e+01 --2.285064417936901648e+01, -4.341265496098390742e+01 --2.296639461121827352e+01, -4.367053798524346320e+01 --2.296109091601295660e+01, -4.327701340263194396e+01 --2.300363368863986935e+01, -4.337332619212492801e+01 --2.302738610634949978e+01, -4.359213326573851788e+01 --2.303226964150154643e+01, -4.352463470764668330e+01 --2.293020445384797057e+01, -4.351599803516257481e+01 --2.302049136089366854e+01, -4.344135798806699711e+01 --2.282951314932585873e+01, -4.326463833068340392e+01 --2.290655763110954268e+01, -4.334946786541785002e+01 --2.282365185024960041e+01, -4.337512751946155731e+01 --2.284428176359574181e+01, -4.341864700918307562e+01 --2.290231599295721665e+01, -4.357370467233933198e+01 --2.284777727122102675e+01, -4.334190549959600958e+01 --2.293704176295849706e+01, -4.366674246299102435e+01 --2.291818480893567056e+01, -4.338048230071306222e+01 --2.288135107807763191e+01, -4.342569025328983656e+01 --2.294706884588344309e+01, -4.327975054726264403e+01 --2.290981320830359991e+01, -4.352472028826283434e+01 --2.300455947496358178e+01, -4.363459972796035657e+01 --2.286049558909661528e+01, -4.332991891555979436e+01 --2.300667318535561634e+01, -4.349634790395840866e+01 --2.301320755621440384e+01, -4.354261045738417835e+01 --2.289296682068087208e+01, -4.345670292807352553e+01 --2.282737488005911430e+01, -4.335063348139748740e+01 --2.287213341524386934e+01, -4.336093121611048673e+01 --2.280750802185216841e+01, -4.321186857882930354e+01 --2.284354892890203104e+01, -4.356612301420413047e+01 --2.298039378012228795e+01, -4.331128925628900106e+01 --2.293352911915764381e+01, -4.338699989224226528e+01 --2.289610622786635830e+01, -4.363193585117340945e+01 --2.286024147876875645e+01, -4.327766752651109528e+01 --2.284296553360787030e+01, -4.340939631329417381e+01 --2.279744575075778457e+01, -4.324235939584050215e+01 --2.295320688857736968e+01, -4.352577851605852999e+01 --2.287717253844536103e+01, -4.334568651286031837e+01 --2.300049618627632952e+01, -4.355459352836678733e+01 --2.283613210317237119e+01, -4.331089428198367131e+01 --2.290151969387568442e+01, -4.336471319979416705e+01 --2.298517195145677050e+01, -4.354807212176751818e+01 --2.294600481358202515e+01, -4.367925451185861618e+01 --2.300735041851084262e+01, -4.334882515301834616e+01 --2.296637443118568811e+01, -4.331402565532464166e+01 --2.286659783036191484e+01, -4.327167325533664410e+01 --2.286020432435058680e+01, -4.358189324758849637e+01 --2.301694974635918456e+01, -4.351811352444260450e+01 --2.296849406287164541e+01, -4.322800917590792835e+01 --2.289104143378875733e+01, -4.364717493668246107e+01 --2.285123606500112814e+01, -4.326515856641291435e+01 --2.285413676131514293e+01, -4.333591228156306840e+01 --2.304497438209437021e+01, -4.351265011377391545e+01 --2.294360411342582751e+01, -4.335650814987074853e+01 --2.293883903442987204e+01, -4.317197091731070202e+01 --2.282760593843590158e+01, -4.340287922851048563e+01 --2.300554669312830214e+01, -4.353934976383860800e+01 --2.297097150083565609e+01, -4.349855164452651479e+01 --2.289466550453776605e+01, -4.326619914414492030e+01 --2.299935403785801569e+01, -4.359759261277823583e+01 --2.288856661080447452e+01, -4.332444767466413538e+01 --2.291676800651831414e+01, -4.372769675011107182e+01 --2.294065525449042653e+01, -4.359000774614589346e+01 --2.291895883127311251e+01, -4.358947645655850778e+01 --2.288195652742714614e+01, -4.327818822851408953e+01 --2.289350010424938020e+01, -4.361343551013386843e+01 --2.289407046627619735e+01, -4.341370551752413576e+01 --2.296186329299140283e+01, -4.318174166663865066e+01 --2.289559128652613040e+01, -4.347520520947288958e+01 --2.297691426921637259e+01, -4.338804885111188270e+01 --2.301609167957192881e+01, -4.330907458822567691e+01 --2.286709260620611417e+01, -4.337617549367227099e+01 --2.297391706932127065e+01, -4.362155419958445890e+01 --2.295762765030808694e+01, -4.335377287930316470e+01 --2.297339787669944400e+01, -4.346480285481639783e+01 --2.289480344919355659e+01, -4.362268570201170093e+01 --2.304024688481601402e+01, -4.363240381389176292e+01 --2.292122619391972904e+01, -4.350348671998496997e+01 --2.285759261940331655e+01, -4.325916419990267769e+01 --2.291093521117210230e+01, -4.348172434877945847e+01 --2.295598555355662640e+01, -4.359652945932494106e+01 --2.295249146872921742e+01, -4.372550658873559826e+01 --2.289203245524211283e+01, -4.355194316695235557e+01 --2.281414438749925466e+01, -4.325812415929029697e+01 --2.288566814647452574e+01, -4.355793348881665850e+01 --2.289986577390788369e+01, -4.360744597732912808e+01 --2.286288808174664311e+01, -4.329617075417560557e+01 --2.307446658413713081e+01, -4.356869850220186891e+01 --2.297086346138008395e+01, -4.319425236786550215e+01 --2.283265129584778919e+01, -4.338763643823631355e+01 --2.296815020429637144e+01, -4.342779337381971771e+01 --2.293457243470401252e+01, -4.370048226638792954e+01 --2.298458131812884275e+01, -4.339130798553718904e+01 --2.287439914752214420e+01, -4.363140318444199295e+01 --2.288507985219115781e+01, -4.340119528163842944e+01 --2.289801378892132888e+01, -4.344145967245002993e+01 --2.282473328938551305e+01, -4.333213182983357825e+01 --2.289598911182978469e+01, -4.327545132728168653e+01 --2.293183858997268842e+01, -4.362974015728074306e+01 --2.298677926908602487e+01, -4.366182082363603456e+01 --2.285891733110024049e+01, -4.326841587533369449e+01 --2.293647382563565529e+01, -4.320572662473826853e+01 --2.284991650807997488e+01, -4.356013321677441041e+01 --2.288660588157685893e+01, -4.346269471405103246e+01 --2.290520981763292951e+01, -4.369668565877594091e+01 --2.281120687163607386e+01, -4.318737124116743331e+01 --2.292272397862085853e+01, -4.326072452641322030e+01 --2.281308281358267109e+01, -4.330112168562965280e+01 --2.296132984365402763e+01, -4.368577788492698488e+01 --2.284559742919137193e+01, -4.342789767299048975e+01 --2.290345110572785714e+01, -4.353071119743145800e+01 --2.286751910066212545e+01, -4.348066916725478848e+01 --2.295873257393267153e+01, -4.366727637424897779e+01 --2.293221273683574069e+01, -4.337774772245195720e+01 --2.292007548091891422e+01, -4.324221921851006556e+01 --2.287554956157910269e+01, -4.358841398573154180e+01 --2.299704876318181235e+01, -4.332705957279565467e+01 --2.294403979366134294e+01, -4.346101611416742116e+01 --2.285889792462985426e+01, -4.357264329386072887e+01 --2.286553242779041284e+01, -4.331467387903761335e+01 --2.285520781698195592e+01, -4.329291315590747047e+01 --2.287125666311201400e+01, -4.345617617967277369e+01 --2.300207815061255801e+01, -4.331181119515446909e+01 --2.289201659578279902e+01, -4.324769470593133747e+01 --2.288350592525059923e+01, -4.369615080118181538e+01 --2.285149446309225851e+01, -4.331740970030227800e+01 --2.297000975511131315e+01, -4.359380001038921648e+01 --2.289252588279404677e+01, -4.335220302009339122e+01 --2.294141552330977518e+01, -4.344251225483837686e+01 --2.293298894615935524e+01, -4.358674699315917422e+01 --2.291144257794031347e+01, -4.363845786373242674e+01 --2.292937697303310074e+01, -4.366348103702318895e+01 --2.288810815504390206e+01, -4.352419122833617138e+01 --2.297720895624034299e+01, -4.318825585780075471e+01 --2.296217980117237190e+01, -4.353829053203649124e+01 --2.286792164428728924e+01, -4.328092500307575818e+01 --2.292510098326848578e+01, -4.322697106346943485e+01 --2.289506746402316040e+01, -4.372716083764753137e+01 --2.286949471391374189e+01, -4.334242836513544006e+01 --2.292322334604052614e+01, -4.336523688116741226e+01 --2.284616251801926978e+01, -4.358462251730975368e+01 --2.295859122366769967e+01, -4.361503184861714288e+01 --2.291816676443283995e+01, -4.378918138444510788e+01 --2.295235277724888334e+01, -4.331676192753420906e+01 --2.294309995545698300e+01, -4.325199174943099933e+01 --2.298326615883869906e+01, -4.338205509318964204e+01 --2.301171792476037226e+01, -4.348110274355394722e+01 --2.290377478889660523e+01, -4.363519682306230862e+01 --2.294251559899609560e+01, -4.339951102728927879e+01 --2.299728356353066872e+01, -4.337932027227323317e+01 --2.299309100221149649e+01, -4.329929926947189500e+01 --2.295077324291264276e+01, -4.325524940217125192e+01 --2.290867966469024708e+01, -4.356771443730166027e+01 --2.291609849587007020e+01, -4.321446108616795811e+01 --2.299836688273525809e+01, -4.333631295323609578e+01 --2.300998314832527214e+01, -4.336733196313326033e+01 --2.287256962478625866e+01, -4.346542702398078717e+01 --2.295728867234589643e+01, -4.360578067489302612e+01 --2.300385315204868419e+01, -4.342558552124887683e+01 --2.290709400923591588e+01, -4.320195135423354316e+01 --2.291344434764370064e+01, -4.319595555657775066e+01 --2.304414328197161055e+01, -4.366016030665094405e+01 --2.286672784061380170e+01, -4.362814239778276715e+01 --2.294228613899261404e+01, -4.334725567270449176e+01 --2.289120669045867729e+01, -4.334295126626416561e+01 --2.300310504356406938e+01, -4.357309776030107429e+01 --2.283063796643895316e+01, -4.352586386481295477e+01 --2.291389916240698454e+01, -4.360471703949570355e+01 --2.287713604565647074e+01, -4.370213877722361673e+01 --2.295916885421591047e+01, -4.341528162041716854e+01 --2.285383672343420969e+01, -4.358788280448478503e+01 --2.299943861360447173e+01, -4.329330405512722990e+01 --2.281175914870158294e+01, -4.329187083454198870e+01 --2.299917563523857211e+01, -4.324103979999346592e+01 --2.291846586627622884e+01, -4.318070669992157917e+01 --2.299950002514666991e+01, -4.364984136240617119e+01 --2.290013947767044300e+01, -4.371192348576470010e+01 --2.301040854906224453e+01, -4.347184995874071944e+01 --2.295059055290704464e+01, -4.350727515380494737e+01 --2.290116934530141535e+01, -4.361669630180588086e+01 --2.287453739052389778e+01, -4.368363995383273135e+01 --2.293394531868091235e+01, -4.349150358082992085e+01 --2.283587327640285736e+01, -4.356286297817314335e+01 --2.287685471765247058e+01, -4.359766408035029883e+01 --2.297767253922282293e+01, -4.359706101798170863e+01 --2.288353006727226102e+01, -4.333969307260955617e+01 --2.293820209948418665e+01, -4.362375051782380098e+01 --2.290101899627178383e+01, -4.326020438215945063e+01 --2.304384326809882921e+01, -4.355565392527790181e+01 --2.292957827425237483e+01, -4.335924329347754735e+01 --2.293846307361431158e+01, -4.372823269898187704e+01 --2.286264969209484121e+01, -4.354815316372022238e+01 --2.288880478769916138e+01, -4.337669953426682667e+01 --2.293912597401897102e+01, -4.322423274096598789e+01 --2.297881328773889820e+01, -4.324977377144871582e+01 --2.290234283440042873e+01, -4.326945668622097685e+01 --2.282447909019989751e+01, -4.327988347229710797e+01 --2.285437199544454501e+01, -4.338816083792199407e+01 --2.296899119778472809e+01, -4.368903967418134471e+01 --2.303764644035746656e+01, -4.361389926837163244e+01 --2.293804595544321501e+01, -4.357150555487889676e+01 --2.289895575059646760e+01, -4.375490895984808759e+01 --2.291372398473461658e+01, -4.324821441921609733e+01 --2.284884990848550146e+01, -4.329890701181481916e+01 --2.283505630519055529e+01, -4.335389149124842589e+01 --2.283325675082324580e+01, -4.354436349875138745e+01 --2.289219619309778508e+01, -4.360418527602181626e+01 --2.301261360800270239e+01, -4.338583865555602159e+01 --2.288616855744842837e+01, -4.335819636130904797e+01 --2.291520262053373003e+01, -4.361396758679217101e+01 --2.283745420295242567e+01, -4.332014539477534498e+01 --2.286924489394472815e+01, -4.329017672590848775e+01 --2.299881319652418910e+01, -4.344083165226935961e+01 --2.301337122376001432e+01, -4.359486300315243312e+01 --2.292404737956675476e+01, -4.326997714481974100e+01 --2.304645031652979625e+01, -4.357415941103762691e+01 --2.284599120678155870e+01, -4.353238327399117225e+01 --2.300685220237118500e+01, -4.354860206917653898e+01 --2.285653060627488387e+01, -4.330216463532160276e+01 --2.295613303323691312e+01, -4.364877468338196564e+01 --2.301712135916958246e+01, -4.357036753058115153e+01 --2.296592300487484195e+01, -4.351379545360800449e+01 --2.305785277954654333e+01, -4.355292309030415510e+01 --2.300647701429622671e+01, -4.344409139700093192e+01 --2.293132422921765468e+01, -4.347300037051189747e+01 --2.296966236399370231e+01, -4.348929955822978854e+01 --2.298780605138417243e+01, -4.326228517171870180e+01 --2.290174440301798242e+01, -4.341696446842694712e+01 --2.296769318848865638e+01, -4.367978885256250265e+01 --2.299690003484114342e+01, -4.363133824260861360e+01 --2.282712677558104275e+01, -4.329838563577171584e+01 --2.290523854424162664e+01, -4.334021589277408282e+01 --2.296461466387458472e+01, -4.350454353254057338e+01 --2.290604718904079817e+01, -4.324495702550432696e+01 --2.288003596409648566e+01, -4.341643905576335527e+01 --2.291001939628067774e+01, -4.327271429938235769e+01 --2.292271120830366016e+01, -4.356498448193703865e+01 --2.289185163802185485e+01, -4.349969862471679249e+01 --2.295738355759795724e+01, -4.330151442452000055e+01 --2.296002372069414577e+01, -4.332002025492896280e+01 --2.282670555574582849e+01, -4.349811413262005999e+01 --2.284711489468194046e+01, -4.348939196340897695e+01 --2.286281542751703100e+01, -4.360039303280077405e+01 --2.292234255647005980e+01, -4.346048917757389773e+01 --2.295209643779995545e+01, -4.326450245571952280e+01 --2.283848754265782688e+01, -4.358136229922298099e+01 --2.279479043743902622e+01, -4.322385757444277488e+01 --2.299639222325011545e+01, -4.347458257859341302e+01 --2.300889141564164220e+01, -4.341033895525432484e+01 --2.304139868442181438e+01, -4.358940340053994333e+01 --2.288155634097508440e+01, -4.347793725203617754e+01 --2.301842482317492866e+01, -4.357961980894319964e+01 --2.289941554690865999e+01, -4.319869444812305659e+01 --2.302591627605264435e+01, -4.353062677649401024e+01 --2.297775336359520182e+01, -4.329278274486254929e+01 --2.288088931771012469e+01, -4.332118967202681148e+01 --2.287236005620455970e+01, -4.341318022138420929e+01 --2.291355606320844274e+01, -4.350022686059378429e+01 --2.289516257214432926e+01, -4.337070644139858899e+01 --2.290042916768249981e+01, -4.340771292509153767e+01 --2.292820602560687604e+01, -4.370647065567246869e+01 --2.298620691966937457e+01, -4.320076689848280438e+01 --2.291014047959095734e+01, -4.362920753458286782e+01 --2.290787615261978516e+01, -4.335871980957920613e+01 --2.283943774062646881e+01, -4.348613261564393184e+01 --2.290850442552585520e+01, -4.351546925276029754e+01 --2.299044966031484449e+01, -4.328079227041344268e+01 --2.294944948218668657e+01, -4.324599632504881441e+01 --2.289690267096693077e+01, -4.348445629786295541e+01 --2.303862234360635597e+01, -4.351864248669822643e+01 --2.291910959476405907e+01, -4.364171897316488469e+01 --2.288024428168933966e+01, -4.346868634932396702e+01 --2.283175981786637365e+01, -4.348287333758712236e+01 --2.295579834251332940e+01, -4.324000075458362602e+01 --2.291134233527557384e+01, -4.328196667568839473e+01 --2.299039011369762875e+01, -4.358507969589113173e+01 --2.287781581914855522e+01, -4.350243005117372519e+01 --2.299201768040134297e+01, -4.334230752935410180e+01 --2.286003248188898596e+01, -4.352965271961402749e+01 --2.294016731938978637e+01, -4.318122416565301336e+01 --2.293963994588582622e+01, -4.368524334767580797e+01 --2.286817451635539911e+01, -4.333317689723860155e+01 --2.289089171580280180e+01, -4.359493500015748424e+01 --2.288090681949445937e+01, -4.367765170758629978e+01 --2.283241538362078771e+01, -4.333538964910518132e+01 --2.301655503984671114e+01, -4.341359846507248932e+01 --2.287560104259592819e+01, -4.328418255064605802e+01 --2.292690823682932688e+01, -4.369722055271093808e+01 --2.285479127979720104e+01, -4.349265138086332172e+01 --2.285997029911518652e+01, -4.322541415992839831e+01 --2.292476159485078568e+01, -4.342674174200629977e+01 --2.301451272562511363e+01, -4.355186285431937421e+01 --2.295844576128141412e+01, -4.325850712609694426e+01 --2.297636930592761217e+01, -4.358780944774741783e+01 --2.300446470753410111e+01, -4.327805496349591152e+01 --2.293209598799748861e+01, -4.373422068012676078e+01 --2.297620237452524705e+01, -4.353555962230258558e+01 --2.286918282348541354e+01, -4.359440358602397225e+01 --2.288417876327145351e+01, -4.349643895145468520e+01 --2.281519444857152834e+01, -4.321512514272187389e+01 --2.295684398063008302e+01, -4.319699154850213318e+01 --2.288174452813020210e+01, -4.353018194052875600e+01 --2.286421053691937644e+01, -4.330542232969809646e+01 --2.298027730221685516e+01, -4.361556403379097446e+01 --2.292058708005329848e+01, -4.334673258382009209e+01 --2.283216242158044551e+01, -4.328314078674286947e+01 --2.294547480566222930e+01, -4.321823695706251556e+01 --2.295712233486775844e+01, -4.324925395197124800e+01 --2.298272819600052941e+01, -4.358181873167914944e+01 --2.297712954116821393e+01, -4.344030535228415602e+01 --2.299169323428677103e+01, -4.359433148870298425e+01 --2.295076740500514134e+01, -4.355952418825511074e+01 --2.296683686866415641e+01, -4.341854092093759476e+01 --2.297450411183221064e+01, -4.342180029162022947e+01 --2.297145769905915458e+01, -4.365529761617132465e+01 --2.293461422841238573e+01, -4.334399717530381224e+01 --2.299282984983596023e+01, -4.324703576616658296e+01 --2.289766022186938699e+01, -4.374565963474015717e+01 diff --git a/pipelines/rj_cor/meteorologia/radar/precipitacao/coordinates/h3_grid_res8_2.csv b/pipelines/rj_cor/meteorologia/radar/precipitacao/coordinates/h3_grid_res8_2.csv deleted file mode 100644 index c59cc7c39..000000000 --- a/pipelines/rj_cor/meteorologia/radar/precipitacao/coordinates/h3_grid_res8_2.csv +++ /dev/null @@ -1,1592 +0,0 @@ --22.934101470205515,-43.23948128495234,88a8a06141fffff,Tijuca --22.911290546462197,-43.58621582003633,88a8a02829fffff,Inhoaíba --22.887275591851356,-43.6716633060393,88a8a028d9fffff,Santa Cruz --22.842228653868631,-43.252649846799557,88a8a06f4dfffff,Ramos --23.033741505404087,-43.5861421342151,88a8a039c1fffff,Guaratiba --22.981252367698318,-43.520316031657124,88a8a076b1fffff,Guaratiba --22.912875993284928,-43.6999472560408,88a8a02a9bfffff,Santa Cruz --23.007788093764656,-43.453344284981767,88a8a07447fffff,Recreio dos Bandeirantes --22.887245723620943,-43.315195837857992,88a8a060e5fffff,Quintino Bocaiúva --22.787102380657672,-43.220601029527337,88a8a06893fffff,Galeão --22.928489088297216,-43.402245120333646,88a8a075a5fffff,Jacarepaguá --23.005785558201186,-43.287308693659647,88a8a071b9fffff,Joá --22.909417570184171,-43.420223489487469,88a8a0674bfffff,Realengo --22.879308541808445,-43.259684273323749,88a8a061e1fffff,Higienópolis --22.842241286810285,-43.5568732027648,88a8a029a7fffff,Campo Grande --22.932491363302969,-43.177967294171864,88a8a06a49fffff,Catete --22.910399401195317,-43.733684534873724,88a8a021b1fffff,Santa Cruz --22.931512787533972,-43.525249384169079,88a8a07493fffff,Campo Grande --22.909194108705044,-43.367971724775884,88a8a062b1fffff,Tanque --22.946800263492811,-43.227490102011885,88a8a0789bfffff,Alto da Boa Vista --22.87570181395796,-43.640652977332827,88a8a0288dfffff,Paciência --23.000758665640546,-43.3025576381633,88a8a07197fffff,Itanhangá --22.860359108855839,-43.6341312929459,88a8a028abfffff,Paciência --22.9798616278427,-43.206763316428386,88a8a078cbfffff,Lagoa --22.943101282901143,-43.556263661177319,88a8a02b3dfffff,Campo Grande --22.908837814633255,-43.619957162375513,88a8a0281dfffff,Cosmos --22.967230778610972,-43.52304733762287,88a8a074d9fffff,Guaratiba --22.836602685704097,-43.415388337674763,88a8a06553fffff,Parque Anchieta --22.925321865607113,-43.583486309266227,88a8a02861fffff,Campo Grande --22.922132578206085,-43.408238059647445,88a8a075a7fffff,Jacarepaguá --22.908695892197642,-43.26346189859472,88a8a06119fffff,Engenho Novo --22.968857251784197,-43.636795389832777,88a8a02a0bfffff,Guaratiba --22.877618467171818,-43.450184440304362,88a8a06627fffff,Padre Miguel --22.879125954326856,-43.511680718051657,88a8a066cdfffff,Santíssimo --22.984031656899244,-43.5910704336864,88a8a039b3fffff,Guaratiba --22.962146536165442,-43.234005038161321,88a8a0632dfffff,Alto da Boa Vista --22.992247595597668,-43.394567190300073,88a8a07509fffff,Barra da Tijuca --23.00928863820144,-43.514853037149344,88a8a0768dfffff,Vargem Grande --22.969012790541559,-43.332531623761511,88a8a06203fffff,Jacarepaguá --22.871452215208681,-43.508420999341226,88a8a066c5fffff,Santíssimo --22.838520965816286,-43.277146859958854,88a8a061b7fffff,Penha --22.835286119884806,-43.406137735796754,88a8a06557fffff,Parque Anchieta --22.907915699125276,-43.767420401206259,88a8a02e45fffff,Santa Cruz --22.944829502088584,-43.722244561428106,88a8a021ebfffff,Santa Cruz --22.998117994572,-43.284050446528958,88a8a071b1fffff,São Conrado --22.965221489058187,-43.713529804655415,88a8a02111fffff,Santa Cruz --22.9288955536121,-43.506746648992923,88a8a074bbfffff,Campo Grande --22.936740455479942,-43.562254398920395,88a8a02b31fffff,Campo Grande --22.953667715509074,-43.682516164146435,88a8a02135fffff,Santa Cruz --22.938073060476558,-43.267239863597588,88a8a06333fffff,Andaraí --22.874990391833453,-43.431682396372011,88a8a06711fffff,Realengo --23.011516873223972,-43.428845148758583,88a8a0746dfffff,Barra da Tijuca --22.806179060275717,-43.202617396900663,88a8a0688bfffff,Jardim Guanabara --22.9245406304199,-43.374488986123588,88a8a06283fffff,Taquara --22.9166453039831,-43.67545874011379,88a8a02a83fffff,Santa Cruz --22.9391806988077,-43.528509566960615,88a8a0749bfffff,Campo Grande --22.870141742265645,-43.499170424764557,88a8a066e9fffff,Senador Camará --22.871628909413896,-43.256426944616138,88a8a061e7fffff,Bonsucesso --22.984795337572645,-43.443564980464664,88a8a0742bfffff,Vargem Pequena --22.925474421640871,-43.63572964243609,88a8a0285bfffff,Paciência --22.820503336965373,-43.252130041925838,88a8a06f0dfffff,Galeão --22.872762122825215,-43.517671537156346,88a8a066c1fffff,Santíssimo --22.884360839427845,-43.548683011420415,88a8a0293bfffff,Campo Grande --23.014140059488572,-43.447351211742522,88a8a07445fffff,Recreio dos Bandeirantes --23.008157143378043,-43.557854335643285,88a8a039adfffff,Guaratiba --22.91901779458567,-43.285224456414348,88a8a06029fffff,Jacarepaguá --22.89856163620227,-43.598195610929274,88a8a0283dfffff,Cosmos --22.9597686216859,-43.267760276477993,88a8a06303fffff,Alto da Boa Vista --22.971585512202871,-43.707541178390628,88a8a0211dfffff,Sepetiba --22.888311348165356,-43.272193759638405,88a8a061c5fffff,Cachambi --22.920098284596243,-43.5464824957506,88a8a02943fffff,Campo Grande --23.020688084684931,-43.49361577284639,88a8a076edfffff,Vargem Grande --22.921838959221731,-43.712458889017846,88a8a021a1fffff,Santa Cruz --22.863580899949252,-43.452917017330158,88a8a06441fffff,Padre Miguel --22.847259238305845,-43.237403760169173,88a8a06a91fffff,Cidade Universitária --22.845802844498714,-43.480141630316929,88a8a06453fffff,Gericinó --22.81043492064817,-43.282619957981915,88a8a06f19fffff,Cordovil --22.912116426279702,-43.186702760821575,88a8a06a55fffff,Centro --22.831094077121541,-43.326138686179341,88a8a06e09fffff,Irajá --23.040096242221914,-43.580150849273132,88a8a039cdfffff,Guaratiba --22.9729660802685,-43.360290368173047,88a8a07525fffff,Jacarepaguá --22.929073128509629,-43.558993919056093,88a8a02b37fffff,Campo Grande --22.892472439403612,-43.708661847930593,88a8a02f47fffff,Santa Cruz --22.8787202850057,-43.407187826266814,88a8a06707fffff,Vila Militar --22.956062626747347,-43.292261470093869,88a8a0631bfffff,Alto da Boa Vista --22.907534583131103,-43.610706747592239,88a8a02803fffff,Cosmos --22.973761136060485,-43.569306185030271,88a8a02b43fffff,Guaratiba --22.905686715441085,-43.444718904506338,88a8a06665fffff,Realengo --22.931975910272012,-43.325492327232041,88a8a06051fffff,Jacarepaguá --22.873451918880768,-43.370182598224119,88a8a06099fffff,Marechal Hermes --22.853615100828911,-43.231409032911458,88a8a06a9dfffff,Cidade Universitária --22.953908496152728,-43.378271870049687,88a8a062cbfffff,Curicica --22.970331120425367,-43.341784566813715,88a8a0621dfffff,Anil --22.899113366860657,-43.3984613502387,88a8a0676bfffff,Jardim Sulacap --22.869466690643069,-43.698877344388755,88a8a02f2bfffff,Santa Cruz --22.947320304071869,-43.332009010976385,88a8a06233fffff,Anil --22.8622670364268,-43.443666232307685,88a8a06445fffff,Realengo --22.877983702358016,-43.250432259787388,88a8a061e5fffff,Manguinhos --22.950927140945726,-43.611770776504969,88a8a02a23fffff,Guaratiba --22.962412644194433,-43.286266504076131,88a8a06319fffff,Alto da Boa Vista --22.865566836423586,-43.6711293070716,88a8a02899fffff,Santa Cruz --22.957433087425073,-43.658025551098113,88a8a02a1bfffff,Guaratiba --22.963733806162285,-43.295519580328026,88a8a06225fffff,Alto da Boa Vista --22.865264641045847,-43.566653452505825,88a8a029e3fffff,Campo Grande --22.899703300270886,-43.555203391788886,88a8a0291dfffff,Campo Grande --22.833486212418162,-43.292391977485195,88a8a06e65fffff,Brás de Pina --22.964517298621217,-43.200248732950129,88a8a07889fffff,Lagoa --22.956539808340555,-43.396776807139325,88a8a07533fffff,Jacarepaguá --22.806721620066348,-43.307115024350892,88a8a06e23fffff,Vigário Geral --22.878246309303556,-43.302686163419381,88a8a061d1fffff,Pilares --22.986477343871229,-43.5573240747654,88a8a02b4dfffff,Guaratiba --22.876505118539772,-43.493179347845576,88a8a06617fffff,Senador Camará --22.945867047271356,-43.627011632581791,88a8a02a31fffff,Santa Cruz --22.810688546932543,-43.334867052217433,88a8a06e15fffff,Parque Colúmbia --22.936160185527772,-43.405504140017165,88a8a075adfffff,Jacarepaguá --22.889586672428631,-43.585684683022883,88a8a029c9fffff,Campo Grande --22.934686459015087,-43.7527204545862,88a8a021d7fffff,Santa Cruz --22.8729375522723,-43.56991367418982,88a8a029ebfffff,Campo Grande --22.84468164310881,-43.523133216407366,88a8a06683fffff,Campo Grande --22.883622773331386,-43.7483850073829,88a8a02e2dfffff,Santa Cruz --22.861565062340357,-43.286919152952514,88a8a06183fffff,Inhaúma --22.968352659914313,-43.480047436156553,88a8a074e9fffff,Vargem Grande --22.894391334268473,-43.2139425140695,88a8a06127fffff,Caju --22.897088343206697,-43.536701954430718,88a8a02907fffff,Senador Vasconcelos --22.916372020041141,-43.266719486154386,88a8a06153fffff,Engenho Novo --22.958064452192882,-43.458283388294312,88a8a075d9fffff,Vargem Grande --22.881685996911965,-43.225933092431205,88a8a06acbfffff,Caju --22.945746448189915,-43.270497638860071,88a8a0633bfffff,Alto da Boa Vista --22.814901882782671,-43.162873205913975,88a8a068e5fffff, --22.8181194506113,-43.28587722685922,88a8a06f53fffff,Cordovil --22.8596376148519,-43.425164562584051,88a8a0646dfffff,Realengo --22.823154402408157,-43.270632353045684,88a8a06f55fffff,Penha --22.947016764193041,-43.584017402031172,88a8a02b11fffff,Guaratiba --22.917483095001902,-43.527980354563546,88a8a0296bfffff,Campo Grande --22.984350154573868,-43.339048823483957,88a8a06255fffff,Itanhangá --22.993561850633217,-43.403820146306337,88a8a07555fffff,Barra da Tijuca --22.949769935196688,-43.654764098138507,88a8a02a13fffff,Santa Cruz --22.861155520071915,-43.48666034966201,88a8a066e5fffff,Vila Kennedy --22.99820031421104,-43.640589824338754,88a8a038b5fffff,Pedra de Guaratiba --22.976681857876027,-43.335790188280157,88a8a0620bfffff,Jacarepaguá --22.846912530328158,-43.43714830423427,88a8a06463fffff,Realengo --22.922535432520242,-43.512738013918309,88a8a0296dfffff,Campo Grande --22.902472350318956,-43.625946583877571,88a8a02811fffff,Cosmos --22.954965840293561,-43.6917667617779,88a8a02131fffff,Sepetiba --22.991148606405229,-43.437571977566584,88a8a07429fffff,Vargem Pequena --23.019727718583557,-43.588872046816277,88a8a03989fffff,Guaratiba --22.878305448894668,-43.6591524316852,88a8a028d5fffff,Paciência --22.947971949530988,-43.488771644707292,88a8a07485fffff,Vargem Grande --22.919794019638484,-43.189959613931478,88a8a06a5dfffff,Santa Teresa --22.856102427866784,-43.501901770663281,88a8a066e3fffff,Bangu --22.848608639332213,-43.55088327465662,88a8a029a5fffff,Campo Grande --22.811491529964524,-43.239621994871506,88a8a06f29fffff,Galeão --22.874769857742486,-43.3794339507788,88a8a06725fffff,Campo dos Afonsos --22.843554032301142,-43.261901328593048,88a8a06f49fffff,Olaria --22.876688453062432,-43.545422925212,88a8a02933fffff,Campo Grande --22.889886933106286,-43.333699484293632,88a8a060ebfffff,Cascadura --23.007577835271412,-43.4010858107633,88a8a0754bfffff,Barra da Tijuca --22.934127754996229,-43.543751968763971,88a8a02949fffff,Campo Grande --22.862050112262985,-43.391419343405367,88a8a06549fffff,Deodoro --22.865273452237,-43.262421483173526,88a8a061a9fffff,Complexo do Alemão --22.987532638603657,-43.2100207154213,88a8a07ab5fffff, --22.833968989910385,-43.396887102489892,88a8a0650bfffff,Parque Anchieta --23.025731835752229,-43.478370217175424,88a8a07631fffff,Recreio dos Bandeirantes --22.98566781236433,-43.348301957300073,88a8a06251fffff,Barra da Tijuca --22.883012196038969,-43.235185271883772,88a8a06137fffff,Caju --22.982373593131989,-43.477315071066542,88a8a07411fffff,Vargem Pequena --22.947842507476217,-43.184481108781647,88a8a078abfffff,Botafogo --22.86686735378866,-43.680378698720922,88a8a02f25fffff,Santa Cruz --22.898882545149558,-43.346209530539859,88a8a060c1fffff,Praça Seca --23.013927986373428,-43.395091956426391,88a8a07549fffff,Barra da Tijuca --22.794500281626984,-43.171607627947324,88a8a068a1fffff,Freguesia (Ilha) --22.924172437202227,-43.6264790901319,88a8a02851fffff,Paciência --22.928798100830644,-43.202469530500174,88a8a078b3fffff,Rio Comprido --22.859843579381216,-43.47740979570905,88a8a06459fffff,Bangu --23.039928428022471,-43.527895403519715,88a8a03923fffff,Grumari --22.852167288958473,-43.474150493930964,88a8a06451fffff,Bangu --22.939395792723044,-43.276492675392639,88a8a0606dfffff,Grajaú --22.979752701776928,-43.458810280052617,88a8a07439fffff,Vargem Pequena --22.90020140213997,-43.35546137956409,88a8a060cbfffff,Praça Seca --22.834809439638232,-43.3016431427262,88a8a06e61fffff,Brás de Pina --23.051500259797027,-43.558914965584613,88a8a0391dfffff,Guaratiba --22.912990791053325,-43.752183683861382,88a8a02197fffff,Santa Cruz --23.012068556339539,-43.58561089699333,88a8a03981fffff,Guaratiba --22.860952608038911,-43.434415413898286,88a8a06469fffff,Realengo --23.028688092292629,-43.601385553325031,88a8a039ddfffff,Guaratiba --22.829327727358045,-43.516613991104755,88a8a066b1fffff,Bangu --23.001001420283426,-43.354819630220241,88a8a070b3fffff,Barra da Tijuca --22.850172467802881,-43.308158369220742,88a8a06193fffff,Vila Kosmos --22.973588072546114,-43.517055707861253,88a8a076b7fffff,Guaratiba --22.930012834865114,-43.463748712727472,88a8a0759bfffff,Jacarepaguá --22.969462972982008,-43.437045794131009,88a8a075c9fffff,Camorim --22.867706746827103,-43.5329125753842,88a8a066ddfffff,Santíssimo --22.974895507284543,-43.526307683934718,88a8a076b3fffff,Guaratiba --22.9168677542981,-43.371230320225287,88a8a062b9fffff,Tanque --22.896480069024687,-43.379958107909943,88a8a06765fffff,Jardim Sulacap --22.866397631920844,-43.523662204385523,88a8a066c3fffff,Santíssimo --22.962893460750227,-43.390783371690574,88a8a07531fffff,Jacarepaguá --22.922448631555731,-43.208465380965883,88a8a06165fffff,Rio Comprido --22.958856807808452,-43.719518274811961,88a8a02113fffff,Santa Cruz --22.900833429573815,-43.5122093480089,88a8a0292dfffff,Campo Grande --22.954681867363821,-43.587278202395,88a8a02b19fffff,Guaratiba --22.9193598464407,-43.746196172691164,88a8a02195fffff,Santa Cruz --22.94492152171804,-43.365760598184366,88a8a062c7fffff,Cidade de Deus --22.929804108946769,-43.4114971032431,88a8a075a1fffff,Jacarepaguá --23.003397068452916,-43.321064725618221,88a8a0624dfffff,Barra da Tijuca --22.991528834949815,-43.542080706962281,88a8a07691fffff,Guaratiba --22.948125155690555,-43.236743224834868,88a8a06327fffff,Alto da Boa Vista --22.894645825855072,-43.570444265010728,88a8a0291bfffff,Campo Grande --22.912913196680517,-43.3434742029858,88a8a062a7fffff,Tanque --23.004948971704088,-43.382579428716113,88a8a07545fffff,Barra da Tijuca --22.980634139155558,-43.363549218412686,88a8a0752dfffff,Barra da Tijuca --22.890691294457032,-43.238442451819438,88a8a06131fffff,Benfica --22.991347292996473,-43.489827507663868,88a8a076a5fffff,Vargem Pequena --23.007014067381014,-43.60085380589323,88a8a0399dfffff,Guaratiba --22.914865640039714,-43.509478062232738,88a8a02965fffff,Campo Grande --22.975598773098458,-43.378796053322283,88a8a0752bfffff,Barra da Tijuca --22.947420874361626,-43.740744882785634,88a8a021c3fffff,Santa Cruz --22.8196926880149,-43.34737554210507,88a8a06e19fffff,Parque Colúmbia --23.001227019159497,-43.407079515659348,88a8a0755dfffff,Barra da Tijuca --22.916873622034984,-43.779932035177339,88a8a02e49fffff,Santa Cruz --22.935146903423306,-43.196473534896839,88a8a078b1fffff,Santa Teresa --22.958257164917448,-43.510535268916271,88a8a074d5fffff,Vargem Grande --22.818371840018472,-43.33812482561234,88a8a06e1dfffff,Acari --22.921405029718194,-43.555733508459973,88a8a0295dfffff,Campo Grande --22.906066517606412,-43.549212895147889,88a8a0290bfffff,Campo Grande --22.977508674203442,-43.544811522482561,88a8a02b69fffff,Guaratiba --22.852530550195674,-43.578632982170383,88a8a02987fffff,Campo Grande --22.890957536638673,-43.2906978587285,88a8a061cbfffff,Engenho de Dentro --22.902069897858262,-43.2171995443183,88a8a06121fffff,São Cristóvão --22.912429075275259,-43.543222245940242,88a8a02909fffff,Campo Grande --22.9033962263537,-43.226452060277055,88a8a0612bfffff,São Cristóvão --22.954329424805103,-43.482779673859966,88a8a074e3fffff,Vargem Grande --22.8703232805156,-43.551413202788815,88a8a029e5fffff,Campo Grande --23.038946947180083,-43.6231515625116,88a8a038edfffff,Guaratiba --22.859174492400218,-43.320667515630085,88a8a060a7fffff,Vicente de Carvalho --23.027221902688229,-43.53987943376989,88a8a03937fffff,Barra de Guaratiba --22.972757070798757,-43.664548662943915,88a8a0212dfffff,Guaratiba --22.991770614676359,-43.290045782638529,88a8a071b3fffff,Itanhangá --22.918790973002576,-43.537231444291088,88a8a02947fffff,Campo Grande --22.78045259622721,-43.174346590423887,88a8a3d249fffff,Freguesia (Ilha) --22.873237213767304,-43.674390473065557,88a8a028d3fffff,Santa Cruz --22.988915890098745,-43.52357642497676,88a8a076b9fffff,Guaratiba --22.942923169076924,-43.504015108292926,88a8a07483fffff,Campo Grande --22.924016820288127,-43.574235415699839,88a8a02865fffff,Campo Grande --22.842736963172616,-43.357149571639127,88a8a06565fffff,Honório Gurgel --22.9941395082033,-43.560584834829719,88a8a039a7fffff,Ilha de Guaratiba --22.981063431143973,-43.46806269289506,88a8a07415fffff,Vargem Pequena --22.914045074559542,-43.656958390930257,88a8a02aabfffff,Santa Cruz --22.926626344196254,-43.592737162154172,88a8a0286bfffff,Campo Grande --22.947771854169954,-43.436519646496528,88a8a07589fffff,Jacarepaguá --22.809110129491,-43.27336905643164,88a8a06f1dfffff,Cordovil --23.018070579013656,-43.475110053049541,88a8a07637fffff,Recreio dos Bandeirantes --22.965731550304632,-43.461543086609858,88a8a07433fffff,Vargem Grande --22.875837001174656,-43.692889388712672,88a8a02f29fffff,Santa Cruz --22.937250282937917,-43.362501910576768,88a8a0628dfffff,Taquara --22.954514205860189,-43.5350301409132,88a8a02b2dfffff,Guaratiba --22.989086425055913,-43.575827862309453,88a8a039b5fffff,Ilha de Guaratiba --22.998598616157832,-43.388573384092183,88a8a07547fffff,Barra da Tijuca --22.9453510789713,-43.470267992623469,88a8a074adfffff,Vargem Grande --22.967440036114787,-43.2710181683454,88a8a0630bfffff,Alto da Boa Vista --22.875380241817286,-43.536172500573777,88a8a02937fffff,Campo Grande --22.935434388642769,-43.553003203388052,88a8a02b35fffff,Campo Grande --22.870814346899071,-43.351679804920174,88a8a06083fffff,Osvaldo Cruz --22.911073689214131,-43.229709259459391,88a8a06105fffff,Maracanã --22.944681064004431,-43.313503670938751,88a8a0604bfffff,Jacarepaguá --22.874071465049813,-43.52692203772785,88a8a066cbfffff,Santíssimo --22.9864833969512,-43.253031586214306,88a8a06345fffff,Rocinha --22.916348628299641,-43.570974891905855,88a8a0295bfffff,Campo Grande --22.96479441836264,-43.252511650118208,88a8a06305fffff,Alto da Boa Vista --23.003258968336656,-43.625348012986819,88a8a038a7fffff,Guaratiba --22.897563122971775,-43.336957653203761,88a8a060c5fffff,Quintino Bocaiúva --23.01937961596072,-43.484362930710368,88a8a07633fffff,Recreio dos Bandeirantes --22.965297250574729,-43.357031588463613,88a8a0621bfffff,Jacarepaguá --22.8395762062209,-43.234146813403257,88a8a06a97fffff,Cidade Universitária --22.854941504198813,-43.240660778209232,88a8a06a99fffff,Maré --22.963924841897445,-43.704279211560326,88a8a02115fffff,Sepetiba --22.962346197747475,-43.590539071853996,88a8a02b53fffff,Guaratiba --22.929335329056244,-43.30698737022103,88a8a06009fffff,Jacarepaguá --22.941775860554255,-43.242738769369616,88a8a06149fffff,Tijuca --23.017867714816845,-43.422851671883109,88a8a0773bfffff,Barra da Tijuca --22.994297771051841,-43.6128349500885,88a8a03993fffff,Guaratiba --22.95367234489883,-43.3260147068548,88a8a06231fffff,Jacarepaguá --22.917944568338356,-43.684708846745444,88a8a02a9dfffff,Santa Cruz --23.036008468575641,-43.500136541596532,88a8a07611fffff,Recreio dos Bandeirantes --22.961795633247387,-43.433786306024089,88a8a075c1fffff,Camorim --22.926075254113041,-43.43599353465958,88a8a075b9fffff,Jacarepaguá --22.93568777706734,-43.300992870968514,88a8a06047fffff,Jacarepaguá --23.0006562497203,-43.6068444549136,88a8a03991fffff,Guaratiba --22.853666673005016,-43.535643082716661,88a8a066d7fffff,Campo Grande --22.889229890944712,-43.481196740074452,88a8a06603fffff,Senador Camará --23.005364606876242,-43.487095282899695,88a8a0745bfffff,Recreio dos Bandeirantes --22.799820830264654,-43.208612086362187,88a8a0689dfffff,Portuguesa --23.004715419094286,-43.330318229071842,88a8a06249fffff,Barra da Tijuca --22.848426380113118,-43.498642260733511,88a8a066a9fffff,Bangu --22.899956544543397,-43.303207727023064,88a8a06031fffff,Encantado --23.004408619849034,-43.582349816288605,88a8a03987fffff,Guaratiba --22.958170468234574,-43.206244952928536,88a8a0788bfffff,Jardim Botânico --22.877996098720359,-43.554673311160556,88a8a029edfffff,Campo Grande --22.798772563466514,-43.251610272444054,88a8a06f3dfffff,Galeão --22.995475055789168,-43.265543154576292,88a8a06349fffff,São Conrado --23.024780428041872,-43.573628567621427,88a8a039ebfffff,Guaratiba --23.041546253201926,-43.641656022029842,88a8a038c5fffff,Guaratiba --22.960686237940187,-43.476787551845469,88a8a074e1fffff,Vargem Grande --22.910278482960045,-43.681447358814715,88a8a02a95fffff,Santa Cruz --22.955639238495916,-43.492031574956783,88a8a0748dfffff,Vargem Grande --23.073163568479156,-43.559445334655216,88a8a0395dfffff,Barra de Guaratiba --22.889875148511972,-43.690162669443126,88a8a02f61fffff,Santa Cruz --22.949461449839834,-43.550272770698861,88a8a02b2bfffff,Guaratiba --23.02103004533161,-43.598124243718175,88a8a039d5fffff,Guaratiba --22.8433715145915,-43.513883121298747,88a8a06687fffff,Bangu --22.912245920230742,-43.490975622615295,88a8a0665dfffff,Senador Camará --22.882069683245927,-43.634664039900123,88a8a028ebfffff,Paciência --23.009098605375186,-43.462597139055809,88a8a07443fffff,Recreio dos Bandeirantes --22.879463332317385,-43.61616414487407,88a8a028e5fffff,Campo Grande --22.963305755675215,-43.495291574902815,88a8a074c7fffff,Vargem Grande --22.9174247225941,-43.223713819036959,88a8a06163fffff,Maracanã --22.920410785911788,-43.650969348510372,88a8a02aa9fffff,Paciência --23.002317838144069,-43.3640729257988,88a8a0756dfffff,Barra da Tijuca --22.936564142051704,-43.510006647598722,88a8a07495fffff,Campo Grande --22.886798923429975,-43.514940506317927,88a8a02927fffff,Santíssimo --22.956309781495783,-43.344520183221789,88a8a06217fffff,Anil --22.905766319733296,-43.192698676930775,88a8a06a57fffff,Centro --22.897542869297546,-43.6934241299037,88a8a02f69fffff,Santa Cruz --22.9742827100936,-43.369543225515166,88a8a07521fffff,Jacarepaguá --22.934844935410556,-43.396252031591438,88a8a062d3fffff,Jacarepaguá --22.8807667907807,-43.625414113815239,88a8a028e1fffff,Campo Grande --22.933295352260377,-43.334744765126558,88a8a0605bfffff,Freguesia (Jacarepaguá) --22.845374958994832,-43.37565148873837,88a8a0656bfffff,Guadalupe --22.844856005993254,-43.575372785879722,88a8a029bdfffff,Campo Grande --22.952814078763456,-43.421274655144678,88a8a075ebfffff,Jacarepaguá --22.952369023022783,-43.673265520674725,88a8a02ac9fffff,Santa Cruz --22.927751074224915,-43.245476657581683,88a8a06143fffff,Andaraí --22.948469539983073,-43.645513320037942,88a8a02a17fffff,Santa Cruz --23.011901818463844,-43.533358021889249,88a8a076d5fffff,Guaratiba --22.995082170450253,-43.465329979649439,88a8a0740bfffff,Recreio dos Bandeirantes --22.786810999269417,-43.168351599446574,88a8a068a7fffff,Freguesia (Ilha) --22.923135955246128,-43.721708808961687,88a8a021abfffff,Santa Cruz --23.023476735805158,-43.564376158055389,88a8a039e1fffff,Guaratiba --23.019562247532583,-43.536618693114924,88a8a076ddfffff,Barra de Guaratiba --22.961996280342,-43.486039581177018,88a8a074ebfffff,Vargem Grande --22.854590167678939,-43.440407233271159,88a8a0646bfffff,Realengo --22.884602254879916,-43.296692084645137,88a8a061ddfffff,Pilares --22.793461936967475,-43.214606630613055,88a8a06891fffff,Galeão --22.90127789216664,-43.312459808772488,88a8a0603bfffff,Piedade --22.927023539633261,-43.749458279607076,88a8a0219dfffff,Santa Cruz --22.925370215151485,-43.279229738886293,88a8a06067fffff,Jacarepaguá --22.870635162189586,-43.655891375796429,88a8a0288bfffff,Paciência --22.83027489349497,-43.169385514163544,88a8a06817fffff, --22.910511499298185,-43.377223610525519,88a8a062bbfffff,Tanque --23.018256281135368,-43.527366127661722,88a8a076c3fffff,Recreio dos Bandeirantes --22.908953549918618,-43.315717832211831,88a8a06015fffff,Água Santa --22.891174077210415,-43.699412282042267,88a8a02f6bfffff,Santa Cruz --22.864683379542772,-43.409921457180026,88a8a06731fffff,Vila Militar --22.955440398958256,-43.439779088107812,88a8a075c3fffff,Camorim --22.918064210077798,-43.736946485735629,88a8a021b9fffff,Santa Cruz --22.925728245514229,-43.740208504738071,88a8a02183fffff,Santa Cruz --22.937166761468916,-43.718982602359972,88a8a021e3fffff,Santa Cruz --22.892584952484214,-43.760896137467675,88a8a02e63fffff,Santa Cruz --22.865999166061872,-43.4191724668651,88a8a0673bfffff,Magalhães Bastos --22.891653739408174,-43.447451736028576,88a8a0662dfffff,Padre Miguel --22.940451200052056,-43.233485766387091,88a8a0614dfffff,Tijuca --22.871889700182631,-43.308680094925471,88a8a061d3fffff,Tomás Coelho --22.9245367186839,-43.783194424188061,88a8a020a3fffff,Santa Cruz --22.989617383715544,-43.376061186246268,88a8a07563fffff,Barra da Tijuca --22.97015775936654,-43.646046524973634,88a8a02a57fffff,Guaratiba --22.788142311288585,-43.177602645674696,88a8a068a3fffff,Freguesia (Ilha) --22.94030388901383,-43.485511784173227,88a8a074abfffff,Vargem Grande --22.944407010395757,-43.565514948034014,88a8a02b39fffff,Guaratiba --22.870487753110556,-43.603653548692925,88a8a029d1fffff,Campo Grande --22.985334549849615,-43.600322094612558,88a8a02a6dfffff,Guaratiba --22.9747083769394,-43.474055140020774,88a8a07417fffff,Vargem Grande --22.993139977066573,-43.65583117046571,88a8a038b3fffff,Guaratiba --22.934441310818727,-43.648241398240827,88a8a02ae1fffff,Santa Cruz --22.915579912297858,-43.770682635003027,88a8a02e4dfffff,Santa Cruz --22.938993399009316,-43.476260068508545,88a8a074a1fffff,Vargem Grande --22.866324832891845,-43.219419141345895,88a8a06a89fffff,Cidade Universitária --23.016760973909015,-43.46585714034633,88a8a0744bfffff,Recreio dos Bandeirantes --23.004240615713854,-43.530097420111723,88a8a0768bfffff,Guaratiba --22.893339423543715,-43.561193735738129,88a8a02911fffff,Campo Grande --22.98610686362213,-43.452817554623223,88a8a07407fffff,Vargem Pequena --22.8056469574887,-43.350110373040913,88a8a06e13fffff,Pavuna --22.929612425530362,-43.767957680897908,88a8a020a5fffff,Santa Cruz --22.92496545425757,-43.478992272326856,88a8a06649fffff,Senador Camará --22.804849536045541,-43.193366194151849,88a8a06881fffff,Jardim Carioca --22.868977999344473,-43.237923088310723,88a8a06ad1fffff,Maré --22.858095976742977,-43.363665943964769,88a8a06097fffff,Marechal Hermes --22.939648491972058,-43.328750633743617,88a8a06059fffff,Freguesia (Jacarepaguá) --22.846456633720383,-43.332654345753078,88a8a06e4bfffff,Colégio --22.979317278896044,-43.354296236948294,88a8a06253fffff,Barra da Tijuca --22.819924989497469,-43.399620791454176,88a8a06515fffff,Anchieta --22.80828717420794,-43.368611282578271,88a8a06ec9fffff,Pavuna --22.908421134029673,-43.211204010744233,88a8a0612dfffff,Praça da Bandeira --22.846203098039556,-43.280404219958974,88a8a061b1fffff,Penha --22.999684435124813,-43.34556630587182,88a8a070b7fffff,Barra da Tijuca --22.939758818185116,-43.737482747385279,88a8a02189fffff,Santa Cruz --22.873095915193012,-43.622153348383826,88a8a028e7fffff,Paciência --23.0490550884,-43.59266473596545,88a8a03823fffff,Guaratiba --22.821012972430673,-43.35662623023228,88a8a06525fffff,Pavuna --23.01545080071217,-43.45660419308318,88a8a07441fffff,Recreio dos Bandeirantes --22.866206932132968,-43.471418485285838,88a8a06637fffff,Bangu --22.990540440891227,-43.637328317862895,88a8a02a4bfffff,Pedra de Guaratiba --22.877003914436671,-43.649902726574517,88a8a02889fffff,Paciência --23.0440018614574,-43.607908379586249,88a8a03831fffff,Guaratiba --22.866571267388569,-43.57590353744849,88a8a0298dfffff,Campo Grande --23.014673322624017,-43.604115068029,88a8a039d7fffff,Guaratiba --22.974055874522232,-43.673799664809316,88a8a02129fffff,Guaratiba --22.982512034600745,-43.225270690864896,88a8a07ab3fffff,Leblon --22.931721970539154,-43.273234874572076,88a8a06065fffff,Grajaú --22.928700874186241,-43.454497020502465,88a8a07591fffff,Jacarepaguá --23.031135370667016,-43.567637173153173,88a8a039e9fffff,Guaratiba --22.904759094734079,-43.539962065494,88a8a02901fffff,Campo Grande --22.9608728229577,-43.5290388153486,88a8a074dbfffff,Guaratiba --22.884849594817329,-43.348944731185881,88a8a06089fffff,Campinho --22.848010697905927,-43.394153286472438,88a8a06543fffff,Deodoro --22.994875538309827,-43.413073070988879,88a8a07551fffff,Barra da Tijuca --22.947168577320145,-43.636262498025019,88a8a02a3bfffff,Santa Cruz --22.938788987088866,-43.424008262168741,88a8a07585fffff,Jacarepaguá --22.8936014659897,-43.309201856148142,88a8a06033fffff,Piedade --22.959893207458258,-43.624282980015224,88a8a02a07fffff,Guaratiba --22.875193852565975,-43.483928608549313,88a8a0663bfffff,Senador Camará --22.887918169014259,-43.471945744494292,88a8a06607fffff,Bangu --22.963190578706541,-43.190995209906589,88a8a0788dfffff,Copacabana --22.935932538660655,-43.353249557281345,88a8a062e3fffff,Pechincha --22.885489126150691,-43.505689747196755,88a8a0661bfffff,Santíssimo --22.988301427002124,-43.366808139163766,88a8a07567fffff,Barra da Tijuca --22.990222646441673,-43.532828584993169,88a8a07695fffff,Guaratiba --22.879303178726129,-43.563923657937515,88a8a029e9fffff,Campo Grande --22.9740441699691,-43.317284028509782,88a8a06205fffff,Itanhangá --22.9909327732696,-43.385314203452545,88a8a0750dfffff,Barra da Tijuca --22.825393623345743,-43.488864148807458,88a8a064cdfffff,Bangu --22.890724921015025,-43.542692559135411,88a8a02939fffff,Senador Vasconcelos --22.938567461177055,-43.371754234707687,88a8a06289fffff,Taquara --22.981578831788102,-43.624815478401679,88a8a02a47fffff,Guaratiba --22.858643101023542,-43.21616227288083,88a8a06a81fffff,Cidade Universitária --22.8758526758259,-43.336434949594775,88a8a06085fffff,Madureira --22.920654915544862,-43.755445810495885,88a8a02191fffff,Santa Cruz --22.977819272725633,-43.649308004769217,88a8a02a51fffff,Guaratiba --22.881128737572702,-43.373441031031938,88a8a060d3fffff,Vila Valqueire --22.90259867470257,-43.321711863969064,88a8a06017fffff,Piedade --22.843813666604142,-43.314151955747228,88a8a06e45fffff,Vila da Penha --22.925152210513655,-43.531240490325629,88a8a02945fffff,Campo Grande --23.035043719405927,-43.595394553741095,88a8a039cbfffff,Guaratiba --22.867877328223255,-43.5851535821152,88a8a02989fffff,Campo Grande --23.013452350968713,-43.290567011786742,88a8a07183fffff,Joá --22.815728453554602,-43.319623309458976,88a8a06e07fffff,Jardim América --22.9370085811953,-43.310245485161474,88a8a06043fffff,Jacarepaguá --22.948320790367184,-43.593268568207463,88a8a02b1bfffff,Guaratiba --22.8769239582033,-43.293434369812267,88a8a061d5fffff,Pilares --22.809366801228425,-43.325616403331992,88a8a06e39fffff,Jardim América --23.014334971928729,-43.499608206635948,88a8a076e1fffff,Vargem Grande --22.888281063039528,-43.5764343250999,88a8a029cdfffff,Campo Grande --22.954189308394124,-43.178484678521968,88a8a078a9fffff,Botafogo --22.981458699519575,-43.268280724799205,88a8a06343fffff,Alto da Boa Vista --22.914231948103374,-43.3527262708793,88a8a062a3fffff,Tanque --22.978975204673873,-43.606312546823595,88a8a02a61fffff,Guaratiba --22.89884145682063,-43.702673831275128,88a8a02f45fffff,Santa Cruz --22.983014257498787,-43.686312458502606,88a8a0210dfffff,Sepetiba --23.015974852867739,-43.613367091140823,88a8a039d3fffff,Guaratiba --22.938341138105272,-43.675992928082543,88a8a02ac3fffff,Santa Cruz --22.888038998252785,-43.21993787599763,88a8a06ac9fffff,Caju --22.961577857994573,-43.381530793349363,88a8a07535fffff,Jacarepaguá --23.0152417970102,-43.404345226426059,88a8a07735fffff, --22.986636874787703,-43.609573713975443,88a8a02a69fffff,Guaratiba --22.905885161712288,-43.496967070838188,88a8a06651fffff,Senador Camará --22.8710440409474,-43.403929085949962,88a8a0673dfffff,Vila Militar --22.938328819519459,-43.31949807291398,88a8a0605dfffff,Jacarepaguá --22.85626734374657,-43.249912500700411,88a8a061a5fffff,Ramos --22.85941458644027,-43.372917106972508,88a8a06093fffff,Marechal Hermes --22.942039560040683,-43.294998224457522,88a8a06045fffff,Jacarepaguá --22.870567579265916,-43.299428423352524,88a8a061d7fffff,Tomás Coelho --22.986984903196117,-43.357555062687524,88a8a0625bfffff,Barra da Tijuca --22.926692485306667,-43.288482308130355,88a8a06063fffff,Jacarepaguá --22.993964101873271,-43.508332271274874,88a8a076abfffff,Vargem Grande --22.898087524592889,-43.189441868851581,88a8a06a1dfffff,Gamboa --22.849097344593055,-43.351156625356012,88a8a060b3fffff,Rocha Miranda --22.937872703789111,-43.519258125946621,88a8a07491fffff,Campo Grande --22.904988815837367,-43.287961220927954,88a8a06023fffff,Méier --22.838775738910318,-43.329396480618925,88a8a06e43fffff,Colégio --22.900250712443157,-43.7641582353548,88a8a02e6bfffff,Santa Cruz --22.960482527120288,-43.42453397942068,88a8a075c5fffff,Jacarepaguá --22.888575653371273,-43.680913010614894,88a8a02f65fffff,Santa Cruz --22.971876414297295,-43.403295101920051,88a8a07515fffff,Jacarepaguá --23.0097447073401,-43.315069730719628,88a8a0719bfffff,Barra da Tijuca --22.934293084998256,-43.595997936804395,88a8a02845fffff,Campo Grande --22.967693894105857,-43.32327865310431,88a8a06207fffff,Jacarepaguá --22.919717394054935,-43.441985945741216,88a8a075bbfffff,Jacarepaguá --23.05410662706819,-43.57742063378641,88a8a03825fffff,Guaratiba --22.8343835728895,-43.501373526078183,88a8a066a3fffff,Bangu --23.028526960712039,-43.549132052626589,88a8a03933fffff,Barra de Guaratiba --23.026083551578004,-43.582880937009904,88a8a039c7fffff,Guaratiba --22.888436895083117,-43.628674948084743,88a8a028e9fffff,Paciência --22.977327675527054,-43.492559604999414,88a8a074cdfffff,Vargem Grande --22.908978877535414,-43.672197341293341,88a8a02ab9fffff,Santa Cruz --22.910273997520626,-43.324969983169673,88a8a06011fffff,Quintino Bocaiúva --22.949281534649639,-43.498023417370533,88a8a07481fffff,Vargem Grande --22.873882021292861,-43.474677833767025,88a8a06631fffff,Bangu --22.812009729294715,-43.344117673390016,88a8a06e11fffff,Parque Colúmbia --22.822332693198039,-43.365876889511824,88a8a06521fffff,Costa Barros --23.052803728054617,-43.568167819633764,88a8a03919fffff,Guaratiba --22.942235859096616,-43.703744048439887,88a8a021e5fffff,Santa Cruz --22.890541047302488,-43.490447700309332,88a8a0661dfffff,Senador Camará --22.932635057178096,-43.48225199337346,88a8a074a3fffff,Campo Grande --22.803805335198717,-43.236365209973258,88a8a06f21fffff,Galeão --22.987782168425728,-43.566575988440128,88a8a02b49fffff,Ilha de Guaratiba --22.897256932248784,-43.588945203088272,88a8a02823fffff,Campo Grande --22.958703922269443,-43.310767352959189,88a8a06223fffff,Jacarepaguá --22.97319054655653,-43.412547712658458,88a8a07511fffff,Jacarepaguá --22.926143024948725,-43.183963526420811,88a8a06a4bfffff,Catete --22.969246449093742,-43.384789787043005,88a8a0753dfffff,Jacarepaguá --22.817563543256515,-43.181376143423904,88a8a068ebfffff,Pitangueiras --22.988523392994729,-43.419066476626355,88a8a07553fffff,Camorim --22.918606017837256,-43.484984023069742,88a8a0664bfffff,Senador Camará --22.977948876967087,-43.701552396142418,88a8a0210bfffff,Sepetiba --22.951195273144343,-43.716256267590083,88a8a021e9fffff,Santa Cruz --22.899327098727014,-43.450710991516821,88a8a06667fffff,Realengo --22.941053995119045,-43.746732747088416,88a8a021d5fffff,Santa Cruz --23.029656224700847,-43.50612902095714,88a8a07613fffff,Recreio dos Bandeirantes --22.9264597497764,-43.540491671835333,88a8a02941fffff,Campo Grande --22.921566767744856,-43.607977857252529,88a8a02809fffff,Cosmos --22.931975838391157,-43.681981743324023,88a8a02ad5fffff,Santa Cruz --22.848227066939575,-43.446398902432286,88a8a0640dfffff,Realengo --22.988728213405174,-43.47132260082703,88a8a0741dfffff,Vargem Pequena --23.041233943619574,-43.537148282545068,88a8a0393dfffff,Grumari --22.943406178345416,-43.660753358049838,88a8a02ac5fffff,Santa Cruz --22.921121608088082,-43.199212507773773,88a8a06a59fffff,Catumbi --22.893844510045056,-43.36145474529895,88a8a060ddfffff,Praça Seca --22.806967347535384,-43.359360842304014,88a8a06ecdfffff,Pavuna --22.87815930791994,-43.606914133558462,88a8a029d9fffff,Campo Grande --22.932820554605058,-43.534500695530269,88a8a0294dfffff,Campo Grande --22.9944108228525,-43.308552730430947,88a8a06269fffff,Itanhangá --22.839013457959,-43.381644037959589,88a8a0650dfffff,Guadalupe --22.926941548196559,-43.340738748527272,88a8a062adfffff,Freguesia (Jacarepaguá) --22.865774331858944,-43.366924235862129,88a8a06091fffff,Marechal Hermes --22.92324335172837,-43.773944936726039,88a8a020a7fffff,Santa Cruz --22.882326560636482,-43.739135857942735,88a8a02f53fffff,Santa Cruz --22.9660973318177,-43.566045450200484,88a8a02b09fffff,Guaratiba --22.853054179261886,-43.378909829316051,88a8a06545fffff,Deodoro --22.921711410155812,-43.660219679663278,88a8a02a85fffff,Santa Cruz --22.915983577016029,-43.466481183971034,88a8a06645fffff,Jacarepaguá --22.944705779699241,-43.67000395754787,88a8a02ac1fffff,Santa Cruz --22.957127138996984,-43.553533275667363,88a8a02b05fffff,Guaratiba --22.969818518894332,-43.237262613227408,88a8a06367fffff,Jardim Botânico --22.943532964401225,-43.712994328646872,88a8a021e1fffff,Santa Cruz --22.925609880832528,-43.687970403147396,88a8a02ad7fffff,Santa Cruz --23.023303317204441,-43.512121348614265,88a8a076c5fffff,Recreio dos Bandeirantes --22.958432754721645,-43.562784784602471,88a8a02b01fffff,Guaratiba --22.953419066780327,-43.273755485211105,88a8a06315fffff,Alto da Boa Vista --22.868030846948788,-43.637392100759563,88a8a02885fffff,Paciência --22.857852831437015,-43.311416088723256,88a8a0619bfffff,Vicente de Carvalho --23.006263687270113,-43.391832635089749,88a8a07541fffff,Barra da Tijuca --22.921030258308015,-43.451237578551414,88a8a07597fffff,Jacarepaguá --22.789473061137844,-43.186853672387706,88a8a068bdfffff,Tauá --22.997701708333171,-43.483835142524832,88a8a07453fffff,Vargem Pequena --22.972456199616857,-43.560054493511792,88a8a02b47fffff,Guaratiba --22.967930616282491,-43.375537083869276,88a8a07523fffff,Jacarepaguá --22.893161663037287,-43.508949512814773,88a8a02925fffff,Santíssimo --22.834565295996189,-43.553613258017791,88a8a3934dfffff,Campo Grande --22.903911004639486,-43.6874358221196,88a8a02a97fffff,Santa Cruz --22.857005934346688,-43.406662763169727,88a8a06737fffff,Vila Militar --22.886102765658357,-43.714649708617593,88a8a02f09fffff,Santa Cruz --22.992834455558526,-43.551332790401872,88a8a0769bfffff,Ilha de Guaratiba --22.912664709114072,-43.291219027030365,88a8a0602bfffff,Lins de Vasconcelos --22.815969895651985,-43.371869365808678,88a8a06523fffff,Pavuna --23.002933970192419,-43.520845038478228,88a8a07681fffff,Vargem Grande --22.9961886585613,-43.422325963865191,88a8a0755bfffff,Barra da Tijuca --22.940967598485575,-43.338003167167969,88a8a062e5fffff,Freguesia (Jacarepaguá) --22.970288639043673,-43.698290449618028,88a8a02103fffff,Sepetiba --22.904574680807084,-43.487715925382858,88a8a06655fffff,Senador Camará --22.9199163890036,-43.494235389541409,88a8a074b7fffff,Senador Camará --22.971648883690083,-43.351037481778405,88a8a06219fffff,Jacarepaguá --23.01059551243797,-43.524105548316861,88a8a07689fffff,Recreio dos Bandeirantes --22.993335001943155,-43.351560758465318,88a8a06259fffff,Barra da Tijuca --22.937274659657884,-43.771220031074286,88a8a020adfffff,Santa Cruz --22.845135432158774,-43.3234031643094,88a8a06e41fffff,Irajá --23.024422569363455,-43.469117211511914,88a8a07635fffff,Recreio dos Bandeirantes --22.914173898059314,-43.709197134781888,88a8a021a7fffff,Santa Cruz --22.946125472096167,-43.731494746301628,88a8a021c7fffff,Santa Cruz --22.902611624930632,-43.6781859393741,88a8a02abbfffff,Santa Cruz --22.879732435861428,-43.720637413211477,88a8a02f0bfffff,Santa Cruz --22.894279336408854,-43.465954086049024,88a8a06605fffff,Bangu --22.967169040772671,-43.218755716433456,88a8a078d1fffff,Lagoa --22.873675506522115,-43.422431325018955,88a8a06715fffff,Realengo --22.967556176375428,-43.627544211230877,88a8a02a01fffff,Guaratiba --22.906310959009115,-43.29721345060856,88a8a0603dfffff,Engenho de Dentro --22.964952322332639,-43.609041725572119,88a8a02a29fffff,Guaratiba --22.992655981306758,-43.499079907613783,88a8a076a1fffff,Vargem Grande --22.887400336199416,-43.723899091321236,88a8a02f55fffff,Santa Cruz --22.90810267138438,-43.410971852150944,88a8a06741fffff,Taquara --22.980417901479658,-43.667810321792174,88a8a02167fffff,Guaratiba --22.876304712072145,-43.44093343496121,88a8a0671bfffff,Realengo --22.907371823094856,-43.254209473807869,88a8a0611dfffff,Riachuelo --22.864894198540014,-43.462167768483553,88a8a0644bfffff,Bangu --22.955516927326872,-43.187738123539035,88a8a07885fffff,Botafogo --22.914671507492518,-43.45722971230834,88a8a06669fffff,Realengo --22.942106009583703,-43.651502713846426,88a8a02ae9fffff,Santa Cruz --22.991840493143808,-43.646579766151277,88a8a038b7fffff,Guaratiba --22.933140233952283,-43.638990799546036,88a8a02ae5fffff,Santa Cruz --22.948390677704083,-43.289003430813871,88a8a06313fffff,Alto da Boa Vista --22.869015296380372,-43.542162908345965,88a8a066d9fffff,Campo Grande --22.9147717041969,-43.205208331854045,88a8a06a5bfffff,Estácio --22.8572180031859,-43.458908582851159,88a8a06443fffff,Bangu --22.812818240231969,-43.248873088696669,88a8a06f05fffff,Galeão --22.90415458131406,-43.38321675194252,88a8a0676dfffff,Jardim Sulacap --22.919243265705145,-43.693958907427906,88a8a02a99fffff,Santa Cruz --22.844878847047674,-43.2711527864736,88a8a061b5fffff,Olaria --22.878434523906328,-43.711388118883782,88a8a02f01fffff,Santa Cruz --22.9295032471025,-43.715720711550212,88a8a021a9fffff,Santa Cruz --22.998051257711243,-43.588340729219972,88a8a039b9fffff,Ilha de Guaratiba --22.909102855691788,-43.724434887391972,88a8a021b5fffff,Santa Cruz --22.939350888468123,-43.5807566707814,88a8a02b17fffff,Guaratiba --22.984180186761229,-43.643318238624758,88a8a02a5dfffff,Guaratiba --23.014772298938443,-43.29982081295784,88a8a0719dfffff,Barra da Tijuca --22.971142408772184,-43.246516027909287,88a8a06363fffff,Alto da Boa Vista --22.948154927592416,-43.541021314191418,88a8a02b21fffff,Guaratiba --22.999913101389605,-43.3978264653817,88a8a07543fffff,Barra da Tijuca --22.946238363802188,-43.375013017162885,88a8a062c3fffff,Taquara --22.861341369616287,-43.538902960841213,88a8a066d1fffff,Santíssimo --23.045147076852441,-43.564906688020578,88a8a03911fffff,Guaratiba --22.97954356052464,-43.406554354708774,88a8a0751dfffff,Barra da Tijuca --22.972280070374815,-43.507803694564451,88a8a074cbfffff,Vargem Grande --23.050196222359428,-43.54966207212123,88a8a03903fffff,Grumari --22.891289648419729,-43.751646949594651,88a8a02e67fffff,Santa Cruz --22.952229465222342,-43.62102181766204,88a8a02a3dfffff,Guaratiba --22.80645885813987,-43.254867180289459,88a8a06f07fffff,Galeão --22.970561715120855,-43.39404245988743,88a8a07539fffff,Jacarepaguá --22.995730076531803,-43.317806165033062,88a8a06245fffff,Itanhangá --22.913736159339212,-43.552473167150929,88a8a02955fffff,Campo Grande --22.869727460817359,-43.394677919198429,88a8a06723fffff,Vila Militar --22.91182832433217,-43.386475466345779,88a8a06297fffff,Taquara --22.833736126333026,-43.344640584288918,88a8a06e51fffff,Coelho Neto --22.898710086142682,-43.650436019420923,88a8a028c9fffff,Paciência --22.868410315881256,-43.385426721611793,88a8a06727fffff,Vila Militar --22.875332330518358,-43.231928163244255,88a8a06addfffff,Caju --22.927173501615091,-43.392993106189053,88a8a06299fffff,Jacarepaguá --22.923447369526354,-43.4174899168415,88a8a075a3fffff,Jacarepaguá --22.8949225833242,-43.318453815525565,88a8a060edfffff,Quintino Bocaiúva --22.952591990358286,-43.3690193562815,88a8a062c1fffff,Jacarepaguá --22.879606417268384,-43.668402092183058,88a8a028d1fffff,Santa Cruz --22.8681673052162,-43.689628044653873,88a8a02f21fffff,Santa Cruz --22.81016425676594,-43.230370878297016,88a8a06f2dfffff,Galeão --22.927586086891527,-43.497495226142277,88a8a074b1fffff,Campo Grande --22.997501211321417,-43.431578824452842,88a8a07467fffff,Recreio dos Bandeirantes --22.975065504852143,-43.578557836602876,88a8a02b5dfffff,Guaratiba --22.88043552400914,-43.520931347997355,88a8a066c9fffff,Santíssimo --22.927388347246914,-43.445245294319072,88a8a07595fffff,Jacarepaguá --22.874277562549654,-43.274930706403019,88a8a0618dfffff,Inhaúma --22.904132196080774,-43.791905759451417,88a8a02e5dfffff,Santa Cruz --23.009461516084514,-43.567106562756507,88a8a039a9fffff,Guaratiba --22.930378967850714,-43.568244983802948,88a8a02b33fffff,Campo Grande --22.858531073730543,-43.468159206611425,88a8a0645dfffff,Bangu --22.901545107347371,-43.773407460778685,88a8a02e47fffff,Santa Cruz --23.024610080869856,-43.521374081281337,88a8a076c1fffff,Recreio dos Bandeirantes --22.999190905402038,-43.545341353384515,88a8a07699fffff,Ilha de Guaratiba --22.8962431356741,-43.327705748038241,88a8a060e9fffff,Quintino Bocaiúva --22.983031929891887,-43.3297956617217,88a8a06209fffff,Itanhangá --22.907632537058845,-43.306465654530655,88a8a06039fffff,Água Santa --23.015817326631606,-43.561115212212449,88a8a039e7fffff,Guaratiba --22.89821348966553,-43.493707350737068,88a8a06657fffff,Senador Camará --22.898369625536311,-43.241699703032523,88a8a06139fffff,Benfica --22.819443345189818,-43.29512819970406,88a8a06e2dfffff,Cordovil --22.840096710978173,-43.33864753903223,88a8a06e5dfffff,Colégio --22.950396582597559,-43.455023759888611,88a8a075d1fffff,Vargem Grande --22.926426311375653,-43.236223871815731,88a8a06147fffff,Tijuca --22.904373176963674,-43.4354674586166,88a8a06759fffff,Realengo --22.894943994483288,-43.67492458844265,88a8a02ab3fffff,Santa Cruz --22.907805743221815,-43.71518519189393,88a8a02f49fffff,Santa Cruz --22.848585410946903,-43.246655385529756,88a8a06a9bfffff,Maré --22.979118871163028,-43.65855918557876,88a8a02a5bfffff,Guaratiba --23.036345364411584,-43.604646931954044,88a8a03837fffff,Guaratiba --22.889634724758217,-43.281445821651062,88a8a061c1fffff,Cachambi --22.857413010948932,-43.511152124043058,88a8a0668dfffff,Bangu --22.925622336534584,-43.3314864337672,88a8a06053fffff,Freguesia (Jacarepaguá) --22.813330348247124,-43.3533682663675,88a8a06e1bfffff,Pavuna --23.011298661964442,-43.376585324297466,88a8a07093fffff,Barra da Tijuca --22.855904367813288,-43.449657924910369,88a8a06447fffff,Realengo --22.893643707318265,-43.665674749316558,88a8a02ab7fffff,Santa Cruz --22.921400012382044,-43.251471884149147,88a8a06155fffff,Andaraí --23.020867645486256,-43.545871220319832,88a8a076d9fffff,Barra de Guaratiba --22.921905495895942,-43.355984746905456,88a8a062abfffff,Tanque --22.90273381909947,-43.730422652171356,88a8a021b7fffff,Santa Cruz --22.959565277517783,-43.519787060814473,88a8a074d1fffff,Guaratiba --23.001800899018441,-43.563845664166379,88a8a039a1fffff,Ilha de Guaratiba --22.886395760193725,-43.4104466367978,88a8a06701fffff,Realengo --22.801149561219969,-43.217863149341106,88a8a06899fffff,Galeão --22.930398903875158,-43.263982159442364,88a8a06159fffff,Grajaú --22.933819415955959,-43.187220424547156,88a8a078b5fffff,Laranjeiras --22.94048812704246,-43.537760970158487,88a8a02b27fffff,Campo Grande --22.941959053445903,-43.599258780490224,88a8a0284dfffff,Guaratiba --22.915550133998288,-43.3619783101199,88a8a062bdfffff,Tanque --22.952072793023856,-43.568775566377973,88a8a02b03fffff,Guaratiba --22.833219246599615,-43.240141368856442,88a8a06f69fffff,Galeão --22.9115775215642,-43.690697330557796,88a8a02a91fffff,Santa Cruz --22.8745322485305,-43.327183358528956,88a8a060a9fffff,Engenheiro Leal --22.931450916100324,-43.220975451297541,88a8a06169fffff,Tijuca --22.894471122283431,-43.518200364121149,88a8a02921fffff,Senador Vasconcelos --22.878931657066257,-43.459435411919337,88a8a06623fffff,Bangu --22.892754894539742,-43.40445406824778,88a8a0670dfffff,Jardim Sulacap --22.930906017184441,-43.777207306356289,88a8a020a1fffff,Santa Cruz --22.889739447551143,-43.637924964138129,88a8a028c5fffff,Paciência --22.891438757486917,-43.395202587624532,88a8a06763fffff,Jardim Sulacap --23.032083391506273,-43.472377352533705,88a8a0763dfffff,Recreio dos Bandeirantes --22.903621489997114,-43.582955252606531,88a8a02821fffff,Inhoaíba --22.818607188606968,-43.390370346684,88a8a06539fffff,Anchieta --22.937682342443484,-43.4670083177503,88a8a074a5fffff,Jacarepaguá --23.048891615805996,-43.540409139726059,88a8a03907fffff,Grumari --22.906621079628614,-43.758171038124132,88a8a02e69fffff,Santa Cruz --22.985422568118445,-43.296040971698744,88a8a06265fffff,Itanhangá --22.817288823969403,-43.381119871308151,88a8a0653dfffff,Pavuna --23.023112734713784,-43.459864171117694,88a8a07449fffff,Recreio dos Bandeirantes --22.987209935273256,-43.409813677738,88a8a07557fffff,Barra da Tijuca --22.964223041243873,-43.147983966267056,88a8a079ddfffff, --22.910503236726814,-43.78591897612732,88a8a02e4bfffff,Santa Cruz --22.930536378943515,-43.620489471926909,88a8a0285dfffff,Cosmos --22.889416820927316,-43.533441912769341,88a8a0293dfffff,Senador Vasconcelos --22.870765510006187,-43.708126597443787,88a8a02f07fffff,Santa Cruz --23.050356620465241,-43.601917336911541,88a8a0383dfffff,Guaratiba --22.779120953068617,-43.165095642639287,88a8a3d24dfffff,Freguesia (Ilha) --22.936370719382015,-43.457756532380721,88a8a07599fffff,Jacarepaguá --22.951899004237998,-43.516526853528369,88a8a074d7fffff,Campo Grande --22.9782291983806,-43.397301618401571,88a8a07503fffff,Barra da Tijuca --22.860732631636012,-43.3821682402801,88a8a0654dfffff,Deodoro --22.906999688166671,-43.453970316949707,88a8a06661fffff,Realengo --22.940805273477302,-43.642252025419644,88a8a02aedfffff,Santa Cruz --22.8732112566268,-43.317931740145049,88a8a060adfffff,Cavalcanti --22.9237750899722,-43.2177182330253,88a8a06161fffff,Tijuca --22.842060821294943,-43.5046329891473,88a8a066abfffff,Bangu --22.882209974880627,-43.330441386062432,88a8a060e3fffff,Cascadura --22.812536626727113,-43.196622562354456,88a8a06889fffff,Jardim Guanabara --22.964420144905514,-43.452290860101009,88a8a07437fffff,Vargem Pequena --22.819176959451912,-43.242878851021949,88a8a06f63fffff,Galeão --22.965095466036498,-43.661287072706209,88a8a02125fffff,Guaratiba --22.914285635283356,-43.7614331844038,88a8a02193fffff,Santa Cruz --22.941495039062229,-43.19047739428197,88a8a078bdfffff,Laranjeiras --22.897797000619111,-43.38920974435073,88a8a06761fffff,Jardim Sulacap --22.909458889396955,-43.168197109800005,88a8a06a0dfffff,Centro --22.975363297249789,-43.326537122041472,88a8a06201fffff,Itanhangá --22.915471235824644,-43.718446966299226,88a8a021a3fffff,Santa Cruz --22.915307761049373,-43.309723652892281,88a8a06003fffff,Água Santa --22.893341332499286,-43.256946936867315,88a8a06117fffff,Jacaré --22.925857348967043,-43.383741061291587,88a8a0629dfffff,Taquara --22.913986517727981,-43.3004713529258,88a8a06007fffff,Engenho de Dentro --22.976165610423145,-43.2312667249355,88a8a06365fffff,Gávea --22.918750384300015,-43.232966529978512,88a8a0610dfffff,Maracanã --22.8255350155261,-43.236884467391157,88a8a06f61fffff,Galeão --22.921661307822117,-43.303729326136207,88a8a06001fffff,Jacarepaguá --22.907195076640338,-43.506218180124712,88a8a0665bfffff,Campo Grande --22.904438065591172,-43.183445979318485,88a8a06a0bfffff,Centro --22.892279783738527,-43.299949870388318,88a8a06037fffff,Encantado --22.896243715164086,-43.684174382132305,88a8a02f6dfffff,Santa Cruz --22.828694829802412,-43.359884264616575,88a8a0652dfffff,Barros Filho --22.820766676424714,-43.304379146906157,88a8a06e29fffff,Parada de Lucas --22.853275402849132,-43.431156508415562,88a8a06461fffff,Realengo --22.854975333692803,-43.544893193973742,88a8a066d3fffff,Campo Grande --22.905076660970629,-43.644447018878154,88a8a02aa7fffff,Paciência --22.970083388875789,-43.28952458898209,88a8a06353fffff,Alto da Boa Vista --22.907373374280031,-43.558463685911143,88a8a02957fffff,Campo Grande --22.923654288475216,-43.469740742326458,88a8a0664dfffff,Senador Camará --22.995599187100257,-43.622086617697832,88a8a02a4dfffff,Guaratiba --22.95286112390114,-43.169231213933514,88a8a078adfffff,Urca --22.785479125150363,-43.159100534185555,88a8a0699bfffff,Freguesia (Ilha) --22.946847838327567,-43.531769819214944,88a8a02b25fffff,Guaratiba --22.935425899149358,-43.248734167894916,88a8a0614bfffff,Tijuca --22.844056243163958,-43.366400544868306,88a8a06561fffff,Guadalupe --23.047902357647072,-43.635665532202857,88a8a03813fffff,Guaratiba --22.825249767858214,-43.184632458511956,88a8a068c5fffff,Cacuia --23.0086670671518,-43.358078572546049,88a8a070bbfffff,Barra da Tijuca --22.966614216753857,-43.366284350848638,88a8a07527fffff,Jacarepaguá --22.972086809249397,-43.455550535111726,88a8a07431fffff,Vargem Pequena --22.930895788511645,-43.368495522702929,88a8a06281fffff,Taquara --22.997885056459985,-43.536089139701936,88a8a0769dfffff,Guaratiba --22.951708096790345,-43.464275765843595,88a8a075dbfffff,Vargem Grande --22.968537985017971,-43.532299184102918,88a8a02b65fffff,Guaratiba --22.803519449231313,-43.184114971065142,88a8a06885fffff,Tauá --22.917948551543013,-43.328228173108634,88a8a06019fffff,Freguesia (Jacarepaguá) --22.813865819062141,-43.205873863033339,88a8a068d5fffff,Jardim Guanabara --22.814405915223098,-43.3103725107627,88a8a06e2bfffff,Vigário Geral --23.006033202469325,-43.339571705032725,88a8a070b5fffff,Barra da Tijuca --23.02834866348606,-43.496876122380456,88a8a07617fffff,Recreio dos Bandeirantes --22.858322056932284,-43.415913678847168,88a8a06733fffff,Realengo --22.955224435502398,-43.387524353829789,88a8a07537fffff,Curicica --22.9608217460711,-43.224751698240773,88a8a078d3fffff,Jardim Botânico --22.917807836776042,-43.6324685541087,88a8a02853fffff,Paciência --22.841417119088156,-43.347898569533,88a8a06e59fffff,Rocha Miranda --22.916628439010303,-43.318975926447365,88a8a0601dfffff,Água Santa --22.795831262369397,-43.180858772561344,88a8a068abfffff,Tauá --22.977999851780485,-43.345043226743279,88a8a06257fffff,Jacarepaguá --22.943830512413843,-43.408763229938145,88a8a075e7fffff,Jacarepaguá --22.840331380722553,-43.390894827200285,88a8a06509fffff,Deodoro --22.879568095828485,-43.311937930984996,88a8a061dbfffff,Piedade --22.802477729554457,-43.227114190705556,88a8a06f25fffff,Galeão --22.93168424042209,-43.577496008664674,88a8a0286dfffff,Campo Grande --22.895951662111589,-43.579694754288106,88a8a02827fffff,Campo Grande --22.962661618532845,-43.338525977530196,88a8a06215fffff,Anil --22.954473784286645,-43.230747534416594,88a8a06325fffff,Alto da Boa Vista --22.961042284161174,-43.581287683177941,88a8a02b57fffff,Guaratiba --22.808044492250342,-43.316365727215739,88a8a06e3dfffff,Vigário Geral --22.973788489819388,-43.265022787429643,88a8a06309fffff,Alto da Boa Vista --23.0492013842932,-43.6449178302253,88a8a038cdfffff,Guaratiba --22.8641201308339,-43.6096428633215,88a8a029d3fffff,Campo Grande --22.914460277455071,-43.404979086268959,88a8a0674dfffff,Taquara --23.04253889033313,-43.546401123291922,88a8a03939fffff,Grumari --22.814650403722087,-43.362618830667749,88a8a06527fffff,Pavuna --23.004055460446061,-43.477842626175324,88a8a07451fffff,Recreio dos Bandeirantes --22.819026770099658,-43.494854808791786,88a8a064c1fffff,Bangu --22.990699791798257,-43.333054272126155,88a8a06243fffff,Barra da Tijuca --22.978814406254497,-43.554063383992407,88a8a02b45fffff,Guaratiba --22.982879793324027,-43.634066880173648,88a8a02a43fffff,Guaratiba --22.886974887699971,-43.567183926870484,88a8a02913fffff,Campo Grande --22.909747694045443,-43.220456645980342,88a8a06129fffff,São Cristóvão --22.929075271747589,-43.254729420284733,88a8a0615dfffff,Andaraí --22.975817110058156,-43.431052838322913,88a8a07427fffff,Camorim --22.889365428590729,-43.229190175014061,88a8a06135fffff,Vasco da Gama --22.983836388491056,-43.234524345304742,88a8a0636dfffff,Leblon --22.8519600733903,-43.421905750825779,88a8a06465fffff,Realengo --22.864455492907013,-43.357672948663527,88a8a06095fffff,Bento Ribeiro --22.932776475832927,-43.230228379259074,88a8a06145fffff,Tijuca --22.946458640067085,-43.4272675400241,88a8a0758dfffff,Jacarepaguá --23.030963217394287,-43.515381882873342,88a8a076cdfffff,Recreio dos Bandeirantes --22.949169894032057,-43.193734435606522,88a8a07887fffff,Botafogo --23.022172474931871,-43.5551237087941,88a8a039e5fffff,Guaratiba --22.882206655509584,-43.686901277413142,88a8a02f67fffff,Santa Cruz --22.954127522197858,-43.430526887982147,88a8a075c7fffff,Jacarepaguá --22.970775174655017,-43.446298181389125,88a8a07435fffff,Vargem Pequena --23.03731569043584,-43.509389532563013,88a8a0761bfffff,Grumari --22.980136276855269,-43.259027260010647,88a8a06347fffff,São Conrado --22.835871233237167,-43.25864416189274,88a8a06f41fffff,Penha --22.876087231761868,-43.388685273292289,88a8a06721fffff,Campo dos Afonsos --22.967402040484831,-43.575297010681318,88a8a02b55fffff,Guaratiba --22.978535575112584,-43.197509597397129,88a8a078c1fffff,Ipanema --22.82497044351793,-43.384378119599319,88a8a06507fffff,Anchieta --23.059156476523441,-43.562176075186777,88a8a03957fffff,Barra de Guaratiba --22.915042677035569,-43.56172404930016,88a8a02951fffff,Campo Grande --22.949084501702124,-43.445771719945512,88a8a075d5fffff,Camorim --22.932432451882214,-43.430000973426935,88a8a07587fffff,Jacarepaguá --22.8383149711811,-43.529123534683649,88a8a06695fffff,Campo Grande --23.038793230985682,-43.570898257486895,88a8a03913fffff,Guaratiba --22.883221024183715,-43.591674458418616,88a8a029cbfffff,Campo Grande --22.901436931784303,-43.721173092890695,88a8a02f4bfffff,Santa Cruz --22.94093818623724,-43.694493721288893,88a8a02ad9fffff,Santa Cruz --23.022331803355371,-43.607376399166618,88a8a039d1fffff, --23.011063289206902,-43.324323356987072,88a8a070a7fffff,Barra da Tijuca --23.005165367035612,-43.434838475595839,88a8a07461fffff,Barra da Tijuca --22.881029781459013,-43.729886659725025,88a8a02f57fffff,Santa Cruz --22.850416853768827,-43.360407722550839,88a8a0656dfffff,Marechal Hermes --23.0453025961659,-43.617160806401095,88a8a0383bfffff,Guaratiba --22.8688307040616,-43.4899198139084,88a8a066edfffff,Senador Camará --22.962495472191087,-43.642785113905077,88a8a02a1dfffff,Guaratiba --22.828922839951385,-43.412129736319663,88a8a06519fffff,Anchieta --22.912399119466784,-43.238961850698807,88a8a06101fffff,Maracanã --22.989128139194783,-43.271538733279272,88a8a0634bfffff,São Conrado --22.87424383976424,-43.579163850184067,88a8a029c7fffff,Campo Grande --22.861296933351344,-43.234666024963822,88a8a06ad7fffff,Cidade Universitária --22.885662901115971,-43.253689562737058,88a8a061edfffff,Jacarezinho --22.862466895736841,-43.49591086798813,88a8a066e1fffff,Bangu --22.885974964015514,-43.662413556198217,88a8a028ddfffff,Santa Cruz --22.857590959570143,-43.5633933000277,88a8a029a9fffff,Campo Grande --22.853479566805856,-43.483400955125212,88a8a0645bfffff,Vila Kennedy --22.972724476101455,-43.308030908167616,88a8a06229fffff,Itanhangá --22.9107879401743,-43.177449945068147,88a8a06a09fffff,Centro --22.923844104739917,-43.521989270859258,88a8a02969fffff,Campo Grande --22.963108172531143,-43.443038599744959,88a8a075cbfffff,Camorim --22.903109246878927,-43.174193261879218,88a8a06a01fffff,Centro --22.94995727970263,-43.350514240710531,88a8a062e9fffff,Freguesia (Jacarepaguá) --23.021802331868656,-43.450611096475249,88a8a0744dfffff,Recreio dos Bandeirantes --22.889993777642548,-43.742397712291442,88a8a02f5bfffff,Santa Cruz --22.902837420289398,-43.373964990747758,88a8a062b3fffff,Tanque --22.953377613831488,-43.578026904585329,88a8a02b1dfffff,Guaratiba --22.966254533203713,-43.618292989649952,88a8a02a05fffff,Guaratiba --22.953148660256314,-43.22149429195273,88a8a07899fffff,Alto da Boa Vista --22.903666107611773,-43.278708965971255,88a8a06027fffff,Méier --22.816522515185842,-43.224376400937679,88a8a068dbfffff,Galeão --22.971312760942514,-43.603051448651954,88a8a02a67fffff,Guaratiba --22.826861623423316,-43.24613577840595,88a8a06f6bfffff,Galeão --22.927470845431611,-43.193216538629464,88a8a078b7fffff,Santa Teresa --22.85085444632093,-43.464899997762714,88a8a06455fffff,Bangu --22.999010625634043,-43.493087670961437,88a8a076adfffff,Recreio dos Bandeirantes --22.946000967059028,-43.322756354504257,88a8a06237fffff,Jacarepaguá --22.904030139553488,-43.739672163124958,88a8a021b3fffff,Santa Cruz --23.008425558171858,-43.305816077754137,88a8a07191fffff,Barra da Tijuca --22.837180691978325,-43.572112658735477,88a8a029b5fffff,Campo Grande --22.9766517776692,-43.692301531890557,88a8a02101fffff,Sepetiba --22.963486212097,-43.547542210395889,88a8a02b63fffff,Guaratiba --22.885293028698385,-43.453443649226791,88a8a06621fffff,Padre Miguel --23.001957893234398,-43.616096255143866,88a8a0399bfffff,Guaratiba --22.940102538399987,-43.433260274929957,88a8a07581fffff,Jacarepaguá --22.995967943574687,-43.370067130407413,88a8a07561fffff,Barra da Tijuca --22.96262762674186,-43.6950285714902,88a8a02139fffff,Sepetiba --22.8512238117227,-43.5693831194396,88a8a029abfffff,Campo Grande --22.942727941752214,-43.451764201411642,88a8a075d7fffff,Jacarepaguá --22.991506396840943,-43.237782036996961,88a8a07a97fffff,Vidigal --22.863948509981128,-43.253169687068812,88a8a061adfffff,Bonsucesso --22.976018309903772,-43.483307390242594,88a8a07413fffff,Vargem Grande --22.911695379669915,-43.742934133857439,88a8a021bbfffff,Santa Cruz --23.00274574613027,-43.468589934267719,88a8a07455fffff,Recreio dos Bandeirantes --22.835694603618059,-43.510623565979458,88a8a066bdfffff,Bangu --22.888697340214971,-43.733148426039762,88a8a02f51fffff,Santa Cruz --22.91314458374007,-43.39572729175439,88a8a06293fffff,Taquara --22.8616618893368,-43.643380862643028,88a8a02887fffff,Paciência --22.842491337124418,-43.3049007205488,88a8a06e69fffff,Vila da Penha --22.876658298571716,-43.241180222933394,88a8a06ad9fffff,Maré --22.990038037007515,-43.480575071907516,88a8a07419fffff,Vargem Pequena --22.895429786626313,-43.170936650373442,88a8a06a07fffff,Centro --22.829772206903161,-43.316887695993685,88a8a06e0dfffff,Vista Alegre --22.8308380671935,-43.27388957098151,88a8a06f5dfffff,Penha --22.930124791076587,-43.211722501550376,88a8a0616dfffff,Rio Comprido --23.071585935309184,-43.19358481726443,88a8a07a5dfffff,Ipanema --22.983865991246724,-43.538820129928673,88a8a07697fffff,Guaratiba --22.856283429236157,-43.55414326657138,88a8a029adfffff,Campo Grande --22.887486955098119,-43.36744796249679,88a8a060d1fffff,Vila Valqueire --22.9593756285353,-43.467535487390471,88a8a074e5fffff,Vargem Grande --22.818893529345662,-43.190627582849537,88a8a068c7fffff,Jardim Guanabara --23.04628069560901,-43.521903160070046,88a8a03921fffff,Grumari --22.901952021327443,-43.46921352789446,88a8a0660dfffff,Bangu --22.847300205544418,-43.54163329356826,88a8a06699fffff,Campo Grande --23.007981195819013,-43.505600488869071,88a8a076e3fffff,Vargem Grande --22.916505512011344,-43.623218091823837,88a8a02857fffff,Cosmos --22.8371963813184,-43.267895523038227,88a8a06f4bfffff,Penha --22.944423484691672,-43.261244706088668,88a8a06331fffff,Tijuca --22.871630699167874,-43.560663458230742,88a8a029e1fffff,Campo Grande --22.90492642074889,-43.592205792225755,88a8a0282bfffff,Inhoaíba --22.943360594975729,-43.30425096076236,88a8a06041fffff,Jacarepaguá --23.000124614103772,-43.450084446832314,88a8a0740dfffff,Recreio dos Bandeirantes --22.922982216279713,-43.312981722089226,88a8a0600bfffff,Jacarepaguá --22.968208990754647,-43.175745065031457,88a8a078e1fffff, --22.994651756356596,-43.360813958977289,88a8a07565fffff,Barra da Tijuca --22.9774897322387,-43.240520259596131,88a8a06361fffff,Alto da Boa Vista --22.868629044525029,-43.437674389419769,88a8a06713fffff,Realengo --23.037489650777058,-43.561645625663623,88a8a03917fffff,Guaratiba --22.830014215726528,-43.369135018817474,88a8a06529fffff,Costa Barros --22.95756038650584,-43.710267817592594,88a8a02117fffff,Santa Cruz --22.910731903261169,-43.429475094653711,88a8a075b7fffff,Jacarepaguá --22.965843452666242,-43.209502235286628,88a8a078d5fffff,Lagoa --22.86336702825421,-43.40067041586606,88a8a06735fffff,Vila Militar --22.948639074975524,-43.3412616398726,88a8a062edfffff,Freguesia (Jacarepaguá) --22.837005069737085,-43.519873569008588,88a8a066b9fffff,Bangu --23.05049984145899,-43.654170084352458,88a8a038c9fffff,Guaratiba --22.978441405096426,-43.44955783302153,88a8a0743dfffff,Vargem Pequena --22.961194623653903,-43.633534068605215,88a8a02a03fffff,Guaratiba --22.913358871981504,-43.447978206546409,88a8a0666dfffff,Jacarepaguá --22.926775839134542,-43.644980151340654,88a8a02ae7fffff,Santa Cruz --23.032438722469784,-43.576889673858382,88a8a039c5fffff,Guaratiba --22.937474869442696,-43.414756217037045,88a8a075a9fffff,Jacarepaguá --22.989381336201056,-43.323800987264086,88a8a06247fffff,Itanhangá --22.805132378083584,-43.245616206662,88a8a06f2bfffff,Galeão --22.949898509774641,-43.70700589884774,88a8a021edfffff,Santa Cruz --22.979944704489451,-43.511063926189017,88a8a076b5fffff,Vargem Grande --22.905325893083926,-43.7489216252698,88a8a02e6dfffff,Santa Cruz --22.988062313677002,-43.314547675250417,88a8a0626bfffff,Itanhangá --22.886987406928437,-43.262941673172911,88a8a061e9fffff,Jacarezinho --22.869943136336229,-43.446925301325031,88a8a0644dfffff,Padre Miguel --23.047586440201055,-43.531156168881559,88a8a0392bfffff,Grumari --22.940717959267097,-43.28574546250676,88a8a06069fffff,Jacarepaguá --22.949711943583786,-43.298256289031222,88a8a0604dfffff,Alto da Boa Vista --22.920541396020546,-43.703208921679312,88a8a021a5fffff,Santa Cruz --23.036185501660061,-43.552392954285587,88a8a0393bfffff,Barra de Guaratiba --23.012828750303928,-43.438098196806827,88a8a07469fffff,Barra da Tijuca --22.978636473744043,-43.501811783809025,88a8a074c9fffff,Vargem Grande --22.94161381239153,-43.4947634642621,88a8a07487fffff,Vargem Grande --22.926276053751526,-43.488243767092854,88a8a074b5fffff,Senador Camará --22.975110680852133,-43.274276131302933,88a8a06355fffff,Alto da Boa Vista --22.9355966568747,-43.605248839118254,88a8a02841fffff,Guaratiba --22.889025880493104,-43.428949251658949,88a8a06757fffff,Realengo --22.980857355582071,-43.41580705955117,88a8a07519fffff,Camorim --22.906230785148257,-43.601456290714985,88a8a02807fffff,Inhoaíba --23.013026732404267,-43.490355493013169,88a8a076e5fffff,Recreio dos Bandeirantes --22.927766722463314,-43.549742814906189,88a8a0294bfffff,Campo Grande --22.898634631900798,-43.293955619203196,88a8a06035fffff,Engenho de Dentro --22.836375920125686,-43.363142369501489,88a8a06567fffff,Barros Filho --22.846693110597872,-43.384902402767132,88a8a06547fffff,Deodoro --22.963470760285443,-43.243258355614671,88a8a06329fffff,Alto da Boa Vista --22.9879386316497,-43.618825291292865,88a8a02a45fffff,Guaratiba --22.823919550633775,-43.175380901047241,88a8a068e9fffff, --22.9518229704912,-43.212241027674693,88a8a0789dfffff,Santa Teresa --22.935058529890686,-43.448504712882155,88a8a0759dfffff,Jacarepaguá --22.996747909343856,-43.579088804720996,88a8a039bdfffff,Ilha de Guaratiba --22.849737300726559,-43.507892521808571,88a8a06685fffff,Bangu --22.98368318767546,-43.486567414084632,88a8a0741bfffff,Vargem Grande --22.951032643454646,-43.307509121432176,88a8a06049fffff,Jacarepaguá --22.912045670549016,-43.438726667167437,88a8a075b3fffff,Jacarepaguá --22.931118563497225,-43.42074905443495,88a8a075abfffff,Jacarepaguá --22.927930255991402,-43.601987973881634,88a8a02847fffff,Inhoaíba --22.94514485945961,-43.418015401010514,88a8a075e3fffff,Jacarepaguá --22.896699913533414,-43.432208320107506,88a8a06751fffff,Realengo --22.903263633990257,-43.4784647442409,88a8a06609fffff,Senador Camará --22.928076689619761,-43.654230616363684,88a8a02ae3fffff,Santa Cruz --22.896364126324187,-43.73641026051272,88a8a02f59fffff,Santa Cruz --22.904721990104157,-43.23570455416656,88a8a06107fffff,Mangueira --22.849327720851971,-43.403404139372086,88a8a0655dfffff,Vila Militar --22.902142550652723,-43.521460291442381,88a8a02929fffff,Campo Grande --22.989239820372042,-43.62807682608274,88a8a02a41fffff,Guaratiba --22.880632816854337,-43.26893626306012,88a8a061ebfffff,Del Castilho --22.934366407203346,-43.291740230817645,88a8a0606bfffff,Jacarepaguá --23.0083159385003,-43.610105738545919,88a8a03999fffff,Guaratiba --22.937800182523869,-43.214979693588823,88a8a07897fffff,Rio Comprido --22.879810569386127,-43.364189554061163,88a8a060d7fffff,Vila Valqueire --22.831333037764114,-43.37838574351813,88a8a06505fffff,Guadalupe --22.912595137695103,-43.595466450416431,88a8a02805fffff,Inhoaíba --22.9358698818113,-43.709732458001362,88a8a021e7fffff,Santa Cruz --22.956948485221769,-43.501283440136326,88a8a07489fffff,Vargem Grande --22.844490229629255,-43.470891261939428,88a8a06457fffff,Gericinó --22.984311583075602,-43.695563458035544,88a8a02109fffff,Sepetiba --22.897044093066828,-43.232447328932075,88a8a0613dfffff,São Cristóvão --22.917654013066972,-43.580225694486,88a8a02867fffff,Campo Grande --22.856776802611396,-43.354414751739107,88a8a060bbfffff,Bento Ribeiro --22.924814639451359,-43.174710494356859,88a8a06a41fffff,Glória --22.9158078650915,-43.162200813329314,88a8a06a6bfffff,Centro --22.919501297844082,-43.38973425110089,88a8a06291fffff,Taquara --22.895385472629602,-43.422956934971445,88a8a06755fffff,Realengo --22.98011957061394,-43.563315206349934,88a8a02b41fffff,Guaratiba --22.906787206928339,-43.401720183126443,88a8a06745fffff,Taquara --22.853884463626468,-43.283661650963104,88a8a061b9fffff,Olaria --23.042845052274803,-43.650908186443658,88a8a038c1fffff,Guaratiba --22.987417822254873,-43.462070094904796,88a8a07403fffff,Vargem Pequena --22.837918687301471,-43.424638907641842,88a8a0642dfffff,Parque Anchieta --22.898396104753658,-43.545952692469754,88a8a02903fffff,Campo Grande --22.948601178682583,-43.697755482990473,88a8a02133fffff,Santa Cruz --22.954991346336929,-43.3352674589115,88a8a0623bfffff,Anil --22.861816121458332,-43.339170288030111,88a8a060bdfffff,Turiaçú --22.950496715060087,-43.202987742065069,88a8a07883fffff,Humaitá --22.890122055184431,-43.385951076136834,88a8a06767fffff,Jardim Sulacap --22.911121424908398,-43.533971286150184,88a8a0290dfffff,Campo Grande --22.958944952706627,-43.3630255466732,88a8a062cdfffff,Jacarepaguá --22.874005798266829,-43.222676081202273,88a8a06ac3fffff,Caju --22.95378709646684,-43.734756861802452,88a8a021c1fffff,Santa Cruz --22.8475267852083,-43.289655628566791,88a8a061bbfffff,Penha --22.887711102983257,-43.419697960298713,88a8a0670bfffff,Realengo --22.947554639611319,-43.384265406323877,88a8a062ddfffff,Curicica --22.883763379042215,-43.391943895302816,88a8a06729fffff,Campo dos Afonsos --22.862623003662943,-43.24391786750477,88a8a06ad3fffff,Maré --22.915775405410574,-43.4142308494072,88a8a06749fffff,Taquara --22.885924271623427,-43.305943974357533,88a8a061d9fffff,Piedade --22.855457064112944,-43.345163530777818,88a8a060b1fffff,Rocha Miranda --22.939503970089628,-43.633001293251425,88a8a02a33fffff,Santa Cruz --22.990182377258183,-43.228528285161083,88a8a07abbfffff,Vidigal --22.951274918185913,-43.359766813007646,88a8a062c5fffff,Cidade de Deus --22.881915641522387,-43.582424232048517,88a8a029c1fffff,Campo Grande --22.861510158667834,-43.591143160729153,88a8a0298bfffff,Campo Grande --23.007105841769686,-43.296562398573293,88a8a07195fffff,Barra da Tijuca --22.909985388772462,-43.576965148837381,88a8a0282dfffff,Campo Grande --22.841648739294516,-43.400145585805852,88a8a06555fffff,Ricardo de Albuquerque --23.019179821854458,-43.432104846375431,88a8a07717fffff,Barra da Tijuca --22.826288472935744,-43.393628689443,88a8a06503fffff,Anchieta --23.034880783698942,-43.543140243835182,88a8a03931fffff,Barra de Guaratiba --22.992995786759455,-43.603583240262843,88a8a03997fffff,Guaratiba --22.840933079782872,-43.547623352043658,88a8a0669bfffff,Campo Grande --22.878491836321224,-43.3549380478432,88a8a0608bfffff,Osvaldo Cruz --22.98410184521914,-43.286787581125893,88a8a06359fffff,Alto da Boa Vista --23.00254036940127,-43.41633253444256,88a8a07559fffff,Barra da Tijuca --22.985480012036284,-43.6525695532729,88a8a02a59fffff,Guaratiba --22.823411648590675,-43.322880962453084,88a8a06e01fffff,Irajá --22.933745774035412,-43.439252859736769,88a8a07583fffff,Jacarepaguá --23.001626756628728,-43.511592619420462,88a8a07685fffff,Vargem Grande --22.933391507272113,-43.743470591862,88a8a0218bfffff,Santa Cruz --22.9063779664774,-43.653697170843166,88a8a02aa3fffff,Paciência --23.001435463994362,-43.459337207659232,88a8a07409fffff,Recreio dos Bandeirantes --22.828016923771816,-43.507364080213144,88a8a066b5fffff,Bangu --22.951532373917356,-43.159977730256379,88a8a079d3fffff,Urca --22.92430255920646,-43.322234091460366,88a8a06057fffff,Jacarepaguá --22.828449773033615,-43.3076366790304,88a8a06e63fffff,Cordovil --22.867314387744802,-43.428423444439069,88a8a06717fffff,Realengo --22.882446340813754,-43.382692478273228,88a8a0672dfffff,Campo dos Afonsos --22.903918892083457,-43.330963892130391,88a8a06013fffff,Quintino Bocaiúva --22.885830091710915,-43.610174787378412,88a8a02833fffff,Cosmos --22.9515000687212,-43.412022390077752,88a8a075e1fffff,Jacarepaguá --22.916174650659269,-43.518729227050059,88a8a02961fffff,Campo Grande --22.796118375392204,-43.233108496345963,88a8a06f27fffff,Galeão --22.964792055666955,-43.556793849944647,88a8a02b0dfffff,Guaratiba --22.833071977617031,-43.492123449786732,88a8a066a7fffff,Bangu --23.00961724110353,-43.61935762912254,88a8a038a5fffff,Guaratiba --22.839624307884769,-43.53837346252265,88a8a06691fffff,Campo Grande --22.82258877051876,-43.166129324180993,88a8a068edfffff,Ribeira --22.7828136908935,-43.140598348494123,88a8a06995fffff, --22.820338475288423,-43.504104664018669,88a8a064cbfffff,Bangu --22.979073855169627,-43.302036013583731,88a8a06267fffff,Itanhangá --22.943262285270468,-43.608509773332358,88a8a02849fffff,Guaratiba --22.851735798680867,-43.369658790698246,88a8a06569fffff,Marechal Hermes --22.883372009649261,-43.643913922646789,88a8a028c7fffff,Paciência --22.946661797616372,-43.479519836297527,88a8a074a9fffff,Vargem Grande --22.996796808610927,-43.274796812851086,88a8a071b5fffff,São Conrado --22.981187114361695,-43.216017014411882,88a8a07ab7fffff,Leblon --22.797161680769729,-43.190109897490039,88a8a06887fffff,Tauá --22.986742724293613,-43.30529433656779,88a8a06261fffff,Itanhangá --22.907678705353455,-43.662947278475528,88a8a02abdfffff,Santa Cruz --22.96026168862446,-43.3722781848494,88a8a062c9fffff,Jacarepaguá --22.883052966624781,-43.539432495163794,88a8a02931fffff,Campo Grande --22.895780015694957,-43.527451178153811,88a8a0292bfffff,Senador Vasconcelos --22.974840922360745,-43.222013167949449,88a8a078d9fffff,Lagoa --22.883279673458414,-43.287440169203187,88a8a061c3fffff,Engenho de Dentro --22.900743004686841,-43.207947006772784,88a8a06125fffff,Santo Cristo --22.969844624031072,-43.541550992565895,88a8a02b61fffff,Guaratiba --22.895717995954573,-43.223194932450248,88a8a06123fffff,Vasco da Gama --22.872953518132835,-43.265678837565588,88a8a061e3fffff,Higienópolis --22.971150695585575,-43.5508027625296,88a8a02b6bfffff,Guaratiba --22.923011467432712,-43.669469966141683,88a8a02a81fffff,Santa Cruz --22.981713138385555,-43.320542472495859,88a8a0620dfffff,Itanhangá --23.034700678090626,-43.490883514281208,88a8a07615fffff,Recreio dos Bandeirantes --22.8668537542917,-43.323925401711904,88a8a060a1fffff,Cavalcanti --22.993771549999085,-43.456077346175412,88a8a07401fffff,Vargem Pequena --22.941794988428516,-43.547012335058092,88a8a02b23fffff,Campo Grande --23.051797729081397,-43.6634222941021,88a8a03ab5fffff,Guaratiba --23.042700557521531,-43.598655910805078,88a8a03835fffff,Guaratiba --22.88035923348454,-43.216680890937972,88a8a06ac1fffff,Caju --22.987608565985731,-43.514324227395413,88a8a076bdfffff,Vargem Grande --22.798491536758068,-43.199361002251059,88a8a06883fffff,Moneró --22.967042388662772,-43.470795278789161,88a8a074edfffff,Vargem Grande --22.882665626882552,-43.434941418364019,88a8a06719fffff,Realengo --22.804326004135476,-43.340859875271022,88a8a06e17fffff,Pavuna --22.935255013660559,-43.500755132399071,88a8a074b9fffff,Campo Grande --22.933044471671288,-43.282487565191353,88a8a06061fffff,Jacarepaguá --22.817850018835713,-43.23362763719846,88a8a06f67fffff,Galeão --22.975354110166659,-43.683050621285517,88a8a02105fffff,Sepetiba --22.935980843164327,-43.761970267817539,88a8a021d3fffff,Santa Cruz --22.985895910295842,-43.400560847214145,88a8a0750bfffff,Barra da Tijuca --22.973515668120189,-43.21275958912063,88a8a078ddfffff,Lagoa --22.920263082853783,-43.598727177641365,88a8a0280dfffff,Inhoaíba --22.972465732436643,-43.25576941947088,88a8a0630dfffff,Alto da Boa Vista --22.922869885882303,-43.617228494910059,88a8a02855fffff,Cosmos --22.994152736174815,-43.256289472187255,88a8a0634dfffff,São Conrado --22.921949417328072,-43.764695398668074,88a8a0219bfffff,Santa Cruz --22.930799901253515,-43.724970719862249,88a8a02185fffff,Santa Cruz --22.98255946330567,-43.5295680997311,88a8a076bbfffff,Guaratiba --23.056548513285975,-43.543670066318846,88a8a03901fffff,Grumari --22.877404040871916,-43.397936565282386,88a8a0672bfffff,Vila Militar --22.942822193377943,-43.199730602714943,88a8a078b9fffff,Cosme Velho --22.93065590251474,-43.316239862102748,88a8a06055fffff,Jacarepaguá --22.906508063847955,-43.705935448861318,88a8a02f4dfffff,Santa Cruz --23.000318975087588,-43.502340163420754,88a8a076a9fffff,Vargem Grande --22.948870349078639,-43.393517765184946,88a8a062d9fffff,Jacarepaguá --22.825803214613682,-43.289134566699886,88a8a06f5bfffff,Brás de Pina --22.87030373646153,-43.247175028037056,88a8a06adbfffff,Bonsucesso --22.863777706310344,-43.505161350205476,88a8a066ebfffff,Bangu --22.959737803131159,-43.5720362539334,88a8a02b0bfffff,Guaratiba --22.99657863951473,-43.526836887801274,88a8a07683fffff,Guaratiba --22.944148782267614,-43.208983790469354,88a8a07895fffff,Santa Teresa --22.984880634422243,-43.191513060808795,88a8a078cdfffff,Copacabana --22.9332744202097,-43.691232028001522,88a8a02ad1fffff,Santa Cruz --23.033575496957944,-43.533887494794747,88a8a03935fffff,Recreio dos Bandeirantes --23.066811918047083,-43.565437254065493,88a8a03951fffff,Barra de Guaratiba --22.951069762897635,-43.664014831844788,88a8a02acdfffff,Santa Cruz --22.865087951726842,-43.514411795831954,88a8a066c7fffff,Santíssimo --22.961342952804433,-43.329273129946806,88a8a06239fffff,Jacarepaguá --22.897408440203943,-43.641185957137147,88a8a028cdfffff,Paciência --22.8848046286545,-43.705400278410544,88a8a02f0dfffff,Santa Cruz --22.851047656456632,-43.5171427461521,88a8a06681fffff,Campo Grande --22.896758937808947,-43.180189269440866,88a8a06a03fffff,Centro --22.880609693015014,-43.573173965060811,88a8a029c5fffff,Campo Grande --22.98516017596403,-43.243777977248747,88a8a06369fffff,Gávea --23.010765320426369,-43.576358750033933,88a8a03985fffff,Guaratiba --22.920817220956472,-43.398986170906475,88a8a0629bfffff,Jacarepaguá --22.887133776448426,-43.619424889004236,88a8a028edfffff,Paciência --22.983483244172188,-43.4343123729115,88a8a07421fffff,Camorim --22.915048284948931,-43.257466964528817,88a8a06157fffff,Vila Isabel --23.029989509343615,-43.61063779928714,88a8a039d9fffff,Guaratiba --22.929233600928494,-43.611238743966545,88a8a02843fffff,Cosmos --23.015642643268684,-43.508860883939874,88a8a076ebfffff,Vargem Grande --22.928014189964255,-43.297734852069411,88a8a0600dfffff,Jacarepaguá --22.972189847770327,-43.203505988931759,88a8a078c3fffff,Lagoa --22.971404215714461,-43.29877776149754,88a8a0622dfffff,Alto da Boa Vista --22.918403963776846,-43.432734279625642,88a8a075b1fffff,Jacarepaguá --22.822089444247727,-43.313630067983233,88a8a06e05fffff,Parada de Lucas --22.865424268554172,-43.61889265180946,88a8a028adfffff,Campo Grande --22.911593879797113,-43.334222106921786,88a8a0601bfffff,Freguesia (Jacarepaguá) --23.027040533814812,-43.487623187625751,88a8a0763bfffff,Recreio dos Bandeirantes --22.849541038959256,-43.45564946710244,88a8a06409fffff,Realengo --23.010204428314555,-43.419592068080256,88a8a07733fffff,Barra da Tijuca --22.899523742556287,-43.50295836761309,88a8a06653fffff,Senador Camará --22.882867834857713,-43.487188119573794,88a8a06615fffff,Senador Camará --22.871792117034484,-43.612903469640187,88a8a029dbfffff,Campo Grande --22.9567527089786,-43.449031255039316,88a8a075ddfffff,Vargem Grande --22.909209751511973,-43.776669714034526,88a8a02e41fffff,Santa Cruz --22.884673769926987,-43.653163761573424,88a8a028c3fffff,Santa Cruz --22.982780555663545,-43.277534165331836,88a8a0635dfffff,São Conrado --22.83769497107096,-43.372393218565946,88a8a06563fffff,Guadalupe --22.897312154306888,-43.284703485795276,88a8a061c9fffff,Todos os Santos --22.863136089417676,-43.348421632558782,88a8a060b9fffff,Osvaldo Cruz --22.9863006741675,-43.505071992731423,88a8a076a3fffff,Vargem Grande --22.899694593294242,-43.250952054269142,88a8a06115fffff,Rocha --22.950185492137454,-43.40277009326369,88a8a075e5fffff,Jacarepaguá --22.928318266325959,-43.758708005154283,88a8a02199fffff,Santa Cruz --22.808836420705383,-43.221119739455546,88a8a068d3fffff,Galeão --22.816794992757657,-43.276626228853786,88a8a06f57fffff,Penha Circular --22.930676689311287,-43.672731412837258,88a8a02a89fffff,Santa Cruz --22.884525840934657,-43.600924643689211,88a8a02837fffff,Campo Grande --22.976519106230686,-43.6400567798456,88a8a02a55fffff,Guaratiba --22.984992214709031,-43.495819721467051,88a8a076a7fffff,Vargem Grande --22.975218371741427,-43.63080551129,88a8a02a09fffff,Guaratiba --22.953531222072126,-43.630272816181908,88a8a02a39fffff,Guaratiba --22.890340092656846,-43.438200510396321,88a8a06753fffff,Realengo --22.839844986662211,-43.286398172172362,88a8a061b3fffff,Penha Circular --22.981950432492027,-43.372802170653991,88a8a07529fffff,Barra da Tijuca --22.924432384031089,-43.730958681029,88a8a02187fffff,Santa Cruz --22.852560546276713,-43.2744101209708,88a8a061bdfffff,Olaria --22.903059072800513,-43.426215979762723,88a8a0675dfffff,Realengo --22.900011165618956,-43.659686037542905,88a8a02ab5fffff,Santa Cruz --22.909623936098047,-43.472473039568463,88a8a06647fffff,Bangu --22.959496390071216,-43.215498336335614,88a8a078d7fffff,Jardim Botânico --22.995443992819816,-43.569836839763504,88a8a039a3fffff,Ilha de Guaratiba --22.852357447237868,-43.526392933282118,88a8a0668bfffff,Campo Grande --22.893770235028775,-43.717911366626232,88a8a02f43fffff,Santa Cruz --22.892342853732316,-43.6564248652359,88a8a028cbfffff,Santa Cruz --22.863957449259658,-43.557403327769073,88a8a029e7fffff,Campo Grande --22.992460361962152,-43.446824678653648,88a8a07405fffff,Vargem Pequena --22.976432305466712,-43.283529450608064,88a8a06351fffff,Alto da Boa Vista --22.901311678569403,-43.668936011021273,88a8a02ab1fffff,Santa Cruz --22.956263397313371,-43.701017313087284,88a8a0213bfffff,Sepetiba --22.965522966151489,-43.409288435967106,88a8a07517fffff,Jacarepaguá --22.831947640949128,-43.535113700855831,88a8a06697fffff,Campo Grande --22.912744109617684,-43.647708149342193,88a8a02aa1fffff,Paciência --22.917089967539987,-43.423482580686873,88a8a075b5fffff,Jacarepaguá --22.947303279930786,-43.6885050205001,88a8a02137fffff,Sepetiba --22.960031492696668,-43.67652715235257,88a8a02123fffff,Santa Cruz --22.841168443787712,-43.295649459196405,88a8a06e6dfffff,Penha Circular --22.838125353385983,-43.476882375256004,88a8a06419fffff,Gericinó --22.963795753006387,-43.652036115432736,88a8a02a19fffff,Guaratiba --22.968706181604098,-43.584548530904947,88a8a02b51fffff,Guaratiba --22.989836283394094,-43.428319243396686,88a8a0742dfffff,Camorim --22.826054366564247,-43.341382669642428,88a8a06e57fffff,Acari --22.924310957679346,-43.678720207463677,88a8a02a8bfffff,Santa Cruz --22.943856955335185,-43.15672101093552,88a8a07999fffff,Urca --22.858723028978126,-43.520402440520634,88a8a06689fffff,Campo Grande --22.817050428509056,-43.328874081236258,88a8a06e03fffff,Irajá --22.949624249306087,-43.602519693192754,88a8a02a27fffff,Guaratiba --22.900639842884409,-43.459962276825713,88a8a06663fffff,Bangu --22.966117510328512,-43.2617649211893,88a8a06301fffff,Alto da Boa Vista --22.871936470968084,-43.665140946892855,88a8a028d7fffff,Santa Cruz --22.936749762595873,-43.257987027604152,88a8a06337fffff,Andaraí --22.936899661724642,-43.614499699618747,88a8a0284bfffff,Santa Cruz --23.065508561663446,-43.5561841778218,88a8a03955fffff,Grumari --22.945712170847855,-43.574766195146005,88a8a02b15fffff,Campo Grande --22.91769518994284,-43.2759719836104,88a8a0602dfffff,Engenho Novo --22.939884073311013,-43.381006529191673,88a8a062d5fffff,Curicica --22.963649543825927,-43.599790419479412,88a8a02a2dfffff,Guaratiba --22.954832411431486,-43.639523771582567,88a8a02a15fffff,Santa Cruz --22.969536509018468,-43.1849987264045,88a8a078ebfffff,Copacabana --22.915345472702445,-43.66620858801479,88a8a02a87fffff,Santa Cruz --23.047752987066957,-43.583412093846675,88a8a03827fffff,Guaratiba --22.884337830796603,-43.244437428813207,88a8a06133fffff,Manguinhos --22.982170583486639,-43.425059732446208,88a8a07425fffff,Camorim --22.9906736302533,-43.689574364194769,88a8a02147fffff,Sepetiba --22.8629641041342,-43.652630388550854,88a8a02883fffff,Paciência --23.029831450059714,-43.558384632581813,88a8a039edfffff,Barra de Guaratiba --22.953019044214727,-43.473527737328162,88a8a074e7fffff,Vargem Grande --22.809876553858743,-43.178119899954652,88a8a068e3fffff,Praia da Bandeira --22.955820956021629,-43.544281727610191,88a8a02b29fffff,Guaratiba --22.935741820508575,-43.657491952883142,88a8a02aebfffff,Santa Cruz --22.905471176882688,-43.392468482896206,88a8a06769fffff,Taquara --22.959207024436726,-43.163234521360458,88a8a079dbfffff,Urca --22.964208496824714,-43.400035919390639,88a8a0753bfffff,Jacarepaguá --22.8407495632847,-43.49538282043504,88a8a066a1fffff,Bangu --22.920339833901444,-43.294476904083758,88a8a06005fffff,Lins de Vasconcelos --22.928260194124942,-43.349991035258185,88a8a062a9fffff,Pechincha --22.995271654630898,-43.517584598164781,88a8a07687fffff,Vargem Grande --22.941200119272956,-43.39025879354633,88a8a062d1fffff,Jacarepaguá --22.855689247161813,-43.397411816033987,88a8a0654bfffff,Vila Militar --22.972615199033758,-43.612302845211062,88a8a02a63fffff,Guaratiba --23.046602761583554,-43.626413190767366,88a8a03817fffff,Guaratiba --22.852815894285641,-43.326661004577126,88a8a06e49fffff,Irajá --22.968761995653232,-43.280271391103838,88a8a06357fffff,Alto da Boa Vista --22.869333287495287,-43.646641760258042,88a8a02881fffff,Paciência --22.981424167217387,-43.572566989072904,88a8a02b4bfffff,Guaratiba --22.957854614600556,-43.406029229495914,88a8a075edfffff,Jacarepaguá --22.883979610444886,-43.44419255050375,88a8a06625fffff,Padre Miguel --22.908312095074315,-43.46322169546449,88a8a0666bfffff,Realengo --22.901009929693629,-43.564454051906104,88a8a02919fffff,Campo Grande --22.96397971781203,-43.347778797196625,88a8a06211fffff,Jacarepaguá --22.944231959004842,-43.513266715783466,88a8a0749dfffff,Campo Grande --22.944564949900229,-43.617760724190234,88a8a02a35fffff,Santa Cruz --22.945540182110076,-43.522518286251568,88a8a07499fffff,Campo Grande --22.849911019868269,-43.25590698777178,88a8a061a7fffff,Ramos --22.919268098579831,-43.337480392393687,88a8a062a5fffff,Freguesia (Jacarepaguá) --22.965054401848626,-43.304772630905028,88a8a06221fffff,Jacarepaguá --22.960535441544998,-43.172488103629078,88a8a078e7fffff,Copacabana --22.954741129785486,-43.28300849016339,88a8a06311fffff,Alto da Boa Vista --22.917295080486202,-43.475732621052209,88a8a06641fffff,Senador Camará --23.00571162780933,-43.591601831646692,88a8a03983fffff,Guaratiba --23.021995985121173,-43.502868578975132,88a8a076e9fffff,Recreio dos Bandeirantes --22.850644179369016,-43.412654960983907,88a8a06559fffff,Vila Militar --22.966394611218274,-43.670537985243463,88a8a02121fffff,Santa Cruz --22.961090916012957,-43.277013402631944,88a8a0631dfffff,Alto da Boa Vista --23.003633688639869,-43.373326192124928,88a8a07569fffff,Barra da Tijuca --23.0273861063495,-43.592133265738518,88a8a039c3fffff,Guaratiba --23.032269641501546,-43.524634707646683,88a8a076c9fffff,Recreio dos Bandeirantes --22.930204453847971,-43.515998035162575,88a8a07497fffff,Campo Grande --23.020491360893669,-43.441357988067,88a8a07713fffff,Barra da Tijuca --22.829513149325859,-43.264638330683404,88a8a06f43fffff,Penha --22.906557631109543,-43.34946786541785,88a8a060c9fffff,Praça Seca --22.8236518502496,-43.375127519461557,88a8a0652bfffff,Costa Barros --22.844281763595742,-43.418647009183076,88a8a0655bfffff,Vila Militar --22.902315992957217,-43.573704672339332,88a8a02825fffff,Campo Grande --22.847777271221027,-43.34190549959601,88a8a060b7fffff,Colégio --22.937041762958497,-43.666742462991024,88a8a02ac7fffff,Santa Cruz --22.918184808935671,-43.380482300713062,88a8a06295fffff,Taquara --22.881351078077632,-43.425690253289837,88a8a0671dfffff,Realengo --22.947068845883443,-43.279750547262644,88a8a06317fffff,Alto da Boa Vista --22.9098132083036,-43.524720288262834,88a8a02963fffff,Campo Grande --23.004559474963582,-43.634599727960357,88a8a038a3fffff,Pedra de Guaratiba --22.860495589096615,-43.329918915559794,88a8a060a3fffff,Vaz Lobo --23.006673185355616,-43.496347903958409,88a8a076e7fffff,Vargem Grande --23.013207556214404,-43.542610457384178,88a8a076d1fffff,Guaratiba --22.892966820680872,-43.456702928073526,88a8a06629fffff,Bangu --22.827374880059114,-43.350633481397487,88a8a06e53fffff,Coelho Neto --22.872133415243869,-43.360931216110487,88a8a0609dfffff,Bento Ribeiro --22.807508021852168,-43.2118685788293,88a8a068d7fffff,Jardim Guanabara --22.843548928902031,-43.56612301420413,88a8a029a3fffff,Campo Grande --22.980393780122288,-43.311289256289,88a8a06263fffff,Itanhangá --22.933529119157644,-43.386999892242265,88a8a062d7fffff,Jacarepaguá --22.896106227866358,-43.631935851173409,88a8a02813fffff,Paciência --22.860241478768756,-43.277667526511095,88a8a06187fffff,Complexo do Alemão --22.84296553360787,-43.409396313294174,88a8a06551fffff,Ricardo de Albuquerque --22.797445750757785,-43.2423593958405,88a8a06f23fffff,Galeão --22.95320688857737,-43.52577851605853,88a8a074d3fffff,Guaratiba --22.877172538445361,-43.345686512860318,88a8a06081fffff,Madureira --23.00049618627633,-43.554593528366787,88a8a039a5fffff,Ilha de Guaratiba --22.836132103172371,-43.310894281983671,88a8a06e6bfffff,Brás de Pina --22.901519693875684,-43.364713199794167,88a8a062b7fffff,Praça Seca --22.985171951456771,-43.548072121767518,88a8a07693fffff,Guaratiba --22.946004813582025,-43.679254511858616,88a8a02acbfffff,Santa Cruz --23.007350418510843,-43.348825153018346,88a8a070b1fffff,Barra da Tijuca --22.966374431185688,-43.314025655324642,88a8a0622bfffff,Jacarepaguá --22.866597830361915,-43.271673255336644,88a8a06185fffff,Complexo do Alemão --22.860204324350587,-43.581893247588496,88a8a02981fffff,Campo Grande --23.016949746359185,-43.518113524442605,88a8a076c7fffff,Recreio dos Bandeirantes --22.968494062871645,-43.228009175907928,88a8a078dbfffff,Jardim Botânico --22.891041433788757,-43.647174936682461,88a8a028c1fffff,Paciência --22.851236065001128,-43.265158566412914,88a8a061a3fffff,Olaria --22.854136761315143,-43.335912281563068,88a8a060b5fffff,Irajá --23.04497438209437,-43.512650113773915,88a8a03925fffff,Grumari --22.943604113425828,-43.356508149870749,88a8a062ebfffff,Freguesia (Jacarepaguá) --22.938839034429872,-43.1719709173107,88a8a078a7fffff,Flamengo --22.8276059384359,-43.402879228510486,88a8a0651dfffff,Parque Anchieta --23.005546693128302,-43.539349763838608,88a8a076d7fffff,Guaratiba --22.970971500835656,-43.498551644526515,88a8a074c1fffff,Vargem Grande --22.894665504537766,-43.26619914414492,88a8a06113fffff,Cachambi --22.999354037858016,-43.597592612778236,88a8a03995fffff,Guaratiba --22.888566610804475,-43.324447674664135,88a8a060e1fffff,Quintino Bocaiúva --22.916768006518314,-43.727696750111072,88a8a021bdfffff,Santa Cruz --22.940655254490427,-43.590007746145893,88a8a02b13fffff,Guaratiba --22.918958831273113,-43.589476456558508,88a8a02863fffff,Inhoaíba --22.881956527427146,-43.27818822851409,88a8a061c7fffff,Del Castilho --22.89350010424938,-43.613435510133868,88a8a0283bfffff,Cosmos --22.894070466276197,-43.413705517524136,88a8a06709fffff,Realengo --22.961863292991403,-43.181741666638651,88a8a078e3fffff,Copacabana --22.89559128652613,-43.47520520947289,88a8a06601fffff,Bangu --22.976914269216373,-43.388048851111883,88a8a07507fffff,Barra da Tijuca --23.016091679571929,-43.309074588225677,88a8a07199fffff,Barra da Tijuca --22.867092606206114,-43.376175493672271,88a8a0609bfffff,Marechal Hermes --22.973917069321271,-43.621554199584459,88a8a02a0dfffff,Guaratiba --22.957627650308087,-43.353772879303165,88a8a06213fffff,Gardênia Azul --22.973397876699444,-43.4648028548164,88a8a0743bfffff,Vargem Pequena --22.894803449193557,-43.6226857020117,88a8a02817fffff,Paciência --23.040246884816014,-43.632403813891763,88a8a038e9fffff,Guaratiba --22.921226193919729,-43.50348671998497,88a8a074b3fffff,Campo Grande --22.857592619403317,-43.259164199902678,88a8a061a1fffff,Ramos --22.910935211172102,-43.481724348779458,88a8a06643fffff,Senador Camará --22.955985553556626,-43.596529459324941,88a8a02a25fffff,Guaratiba --22.952491468729217,-43.7255065887356,88a8a021c5fffff,Santa Cruz --22.892032455242113,-43.551943166952356,88a8a02915fffff,Campo Grande --22.814144387499255,-43.2581241592903,88a8a06f01fffff,Galeão --22.885668146474526,-43.557933488816658,88a8a02917fffff,Campo Grande --22.899865773907884,-43.607445977329128,88a8a02839fffff,Cosmos --22.862888081746643,-43.296170754175606,88a8a0619dfffff,Engenho da Rainha --23.074466584137131,-43.568698502201869,88a8a03959fffff, --22.970863461380084,-43.1942523678655,88a8a078c7fffff,Copacabana --22.832651295847789,-43.387636438236314,88a8a06501fffff,Ricardo de Albuquerque --22.968150204296371,-43.427793373819718,88a8a075cdfffff,Camorim --22.934572434704013,-43.70048226638793,88a8a02adbfffff,Santa Cruz --22.984581318128843,-43.391307985537189,88a8a07501fffff,Barra da Tijuca --22.874399147522144,-43.631403184441993,88a8a028e3fffff,Paciência --22.885079852191158,-43.401195281638429,88a8a06705fffff,Vila Militar --22.898013788921329,-43.44145967245003,88a8a0675bfffff,Realengo --22.824733289385513,-43.332131829833578,88a8a06e0bfffff,Irajá --22.895989111829785,-43.275451327281687,88a8a061cdfffff,Méier --22.931838589972688,-43.629740157280743,88a8a02859fffff,Paciência --22.986779269086025,-43.661820823636035,88a8a02165fffff,Guaratiba --22.85891733110024,-43.268415875333694,88a8a061abfffff,Complexo do Alemão --22.936473825635655,-43.205726624738269,88a8a078bbfffff,Rio Comprido --22.849916508079975,-43.56013321677441,88a8a029a1fffff,Campo Grande --22.886605881576859,-43.462694714051032,88a8a0662bfffff,Bangu --22.90520981763293,-43.696685658775941,88a8a02a93fffff,Santa Cruz --22.811206871636074,-43.187371241167433,88a8a0688dfffff,Cacuia --22.922723978620859,-43.26072452641322,88a8a06151fffff,Grajaú --22.813082813582671,-43.301121685629653,88a8a06e21fffff,Vigário Geral --22.961329843654028,-43.685777884926985,88a8a0213dfffff,Sepetiba --22.845597429191372,-43.42789767299049,88a8a06467fffff,Parque Anchieta --22.903451105727857,-43.530711197431458,88a8a02905fffff,Senador Vasconcelos --22.867519100662125,-43.480669167254788,88a8a06633fffff,Bangu --22.958732573932672,-43.667276374248978,88a8a02127fffff,Santa Cruz --22.932212736835741,-43.377747722451957,88a8a0628bfffff,Taquara --22.920075480918914,-43.242219218510066,88a8a06109fffff,Vila Isabel --22.875549561579103,-43.588413985731542,88a8a029c3fffff,Campo Grande --22.997048763181812,-43.327059572795655,88a8a06241fffff,Barra da Tijuca --22.944039793661343,-43.461016114167421,88a8a075d3fffff,Jacarepaguá --22.858897924629854,-43.572643293860729,88a8a02985fffff,Campo Grande --22.865532427790413,-43.314673879037613,88a8a060a5fffff,Cavalcanti --22.855207816981956,-43.29291315590747,88a8a06195fffff,Engenho da Rainha --22.871256663112014,-43.456176179672774,88a8a06449fffff,Bangu --23.002078150612558,-43.311811195154469,88a8a07193fffff,Barra da Tijuca --22.8920165957828,-43.247694705931337,88a8a0613bfffff,Rocha --22.8835059252506,-43.696150801181815,88a8a02f63fffff,Santa Cruz --22.851494463092259,-43.317409700302278,88a8a06e4dfffff,Vicente de Carvalho --22.970009755111313,-43.593800010389216,88a8a02b5bfffff,Guaratiba --22.892525882794047,-43.352203020093391,88a8a060c3fffff,Praça Seca --22.941415523309775,-43.442512254838377,88a8a0758bfffff,Jacarepaguá --22.932988946159355,-43.586746993159174,88a8a02869fffff,Guaratiba --22.911442577940313,-43.638457863732427,88a8a02aa5fffff,Paciência --22.9293769730331,-43.663481037023189,88a8a02a8dfffff,Santa Cruz --22.888108155043902,-43.524191228336171,88a8a02923fffff,Senador Vasconcelos --22.977208956240343,-43.188255857800755,88a8a078c5fffff,Copacabana --22.962179801172372,-43.538290532036491,88a8a02b67fffff,Guaratiba --22.867921644287289,-43.280925003075758,88a8a06181fffff,Inhaúma --22.925100983268486,-43.226971063469435,88a8a0616bfffff,Tijuca --22.89506746402316,-43.727160837647531,88a8a02f5dfffff,Santa Cruz --22.869494713913742,-43.34242836513544,88a8a06087fffff,Madureira --22.923223346040526,-43.365236881167412,88a8a06287fffff,Taquara --22.84616251801927,-43.584622517309754,88a8a029b9fffff,Campo Grande --22.9585912236677,-43.615031848617143,88a8a02a2bfffff,Guaratiba --22.91816676443284,-43.789181384445108,88a8a020b5fffff,Santa Cruz --22.952352777248883,-43.316761927534209,88a8a06235fffff,Jacarepaguá --22.943099955456983,-43.251991749431,88a8a06335fffff,Tijuca --22.9832661588387,-43.382055093189642,88a8a07505fffff,Barra da Tijuca --23.011717924760372,-43.481102743553947,88a8a07459fffff,Recreio dos Bandeirantes --22.903774788896605,-43.635196823062309,88a8a0281bfffff,Paciência --22.942515598996096,-43.399511027289279,88a8a062dbfffff,Jacarepaguá --22.997283563530669,-43.379320272273233,88a8a0756bfffff,Barra da Tijuca --22.993091002211496,-43.299299269471895,88a8a0626dfffff,Itanhangá --22.950773242912643,-43.255249402171252,88a8a0633dfffff,Alto da Boa Vista --22.908679664690247,-43.56771443730166,88a8a02953fffff,Campo Grande --22.91609849587007,-43.214461086167958,88a8a06167fffff,Tijuca --22.998366882735258,-43.336312953236096,88a8a0624bfffff,Barra da Tijuca --23.009983148325272,-43.36733196313326,88a8a07097fffff,Barra da Tijuca --22.872569624786259,-43.465427023980787,88a8a06635fffff,Bangu --22.957288672345896,-43.605780674893026,88a8a02a21fffff,Guaratiba --23.003853152048684,-43.425585521248877,88a8a07465fffff,Barra da Tijuca --22.907094009235916,-43.201951354233543,88a8a06a53fffff,Cidade Nova --22.9134443476437,-43.195955556577751,88a8a06a51fffff,Cidade Nova --23.044143281971611,-43.660160306650944,88a8a038cbfffff,Guaratiba --22.8667278406138,-43.628142397782767,88a8a028a9fffff,Paciência --22.942286138992614,-43.347255672704492,88a8a062e1fffff,Freguesia (Jacarepaguá) --22.891206690458677,-43.342951266264166,88a8a060c7fffff,Praça Seca --23.003105043564069,-43.573097760301074,88a8a039abfffff,Guaratiba --22.830637966438953,-43.525863864812955,88a8a066bbfffff,Bangu --22.913899162406985,-43.6047170394957,88a8a02801fffff,Inhoaíba --22.877136045656471,-43.702138777223617,88a8a02f05fffff,Santa Cruz --22.95916885421591,-43.415281620417169,88a8a075e9fffff,Jacarepaguá --22.85383672343421,-43.587882804484785,88a8a02983fffff,Campo Grande --22.999438613604472,-43.29330405512723,88a8a071bbfffff,Barra da Tijuca --22.811759148701583,-43.291870834541989,88a8a06e25fffff,Parada de Lucas --22.999175635238572,-43.241039799993466,88a8a07a91fffff,Vidigal --22.918465866276229,-43.180706699921579,88a8a06a43fffff,Santa Teresa --22.99950002514667,-43.649841362406171,88a8a038b1fffff,Pedra de Guaratiba --22.900139477670443,-43.7119234857647,88a8a02f41fffff,Santa Cruz --23.010408549062245,-43.471849958740719,88a8a0745dfffff,Recreio dos Bandeirantes --22.950590552907045,-43.507275153804947,88a8a0748bfffff,Vargem Grande --22.901169345301415,-43.616696301805881,88a8a02815fffff,Cosmos --22.874537390523898,-43.683639953832731,88a8a02f2dfffff,Santa Cruz --22.933945318680912,-43.491503580829921,88a8a074bdfffff,Campo Grande --22.835873276402857,-43.562862978173143,88a8a39349fffff,Campo Grande --22.876854717652471,-43.5976640803503,88a8a029ddfffff,Campo Grande --22.977672539222823,-43.597061017981709,88a8a02a65fffff,Guaratiba --22.883530067272261,-43.339693072609556,88a8a0608dfffff,Cascadura --22.938202099484187,-43.6237505178238,88a8a02a37fffff,Santa Cruz --22.901018996271784,-43.260204382159451,88a8a06111fffff,Sampaio --23.043843268098829,-43.5556539252779,88a8a03915fffff,Guaratiba --22.929578274252375,-43.359243293477547,88a8a06285fffff,Pechincha --22.938463073614312,-43.728232698981877,88a8a0218dfffff,Santa Cruz --22.862649692094841,-43.548153163720222,88a8a066dbfffff,Campo Grande --22.888804787699161,-43.376699534266827,88a8a060dbfffff,Jardim Sulacap --22.939125974018971,-43.224232740965988,88a8a07893fffff,Alto da Boa Vista --22.978813287738898,-43.249773771448716,88a8a0636bfffff,Alto da Boa Vista --22.902342834400429,-43.269456686220977,88a8a0611bfffff,Engenho Novo --22.824479090199898,-43.279883472297108,88a8a06f51fffff,Penha Circular --22.854371995444545,-43.388160837921994,88a8a06541fffff,Deodoro --22.968991197784728,-43.689039674181345,88a8a02107fffff,Sepetiba --23.037646440357467,-43.613899268371632,88a8a03833fffff,Guaratiba --22.938045955443215,-43.5715055548789,88a8a02b3bfffff,Campo Grande --22.898955750596468,-43.754908959848088,88a8a02e61fffff,Santa Cruz --22.913723984734617,-43.2482144192161,88a8a0610bfffff,Vila Isabel --22.8488499084855,-43.298907011814819,88a8a06197fffff,Penha Circular --22.835056305190555,-43.353891491248426,88a8a06e5bfffff,Coelho Neto --22.833256750823246,-43.544363498751387,88a8a06693fffff,Campo Grande --22.892196193097785,-43.604185276021816,88a8a02831fffff,Cosmos --23.012613608002702,-43.385838655556022,88a8a0754dfffff,Barra da Tijuca --22.886168557448428,-43.358196361309048,88a8a060d5fffff,Vila Valqueire --22.91520262053373,-43.613967586792171,88a8a0280bfffff,Cosmos --22.837454202952426,-43.320145394775345,88a8a06e47fffff,Irajá --22.869244893944728,-43.290176725908488,88a8a0618bfffff,Inhaúma --22.998813196524189,-43.44083165226936,88a8a07463fffff,Recreio dos Bandeirantes --23.013371223760014,-43.594863003152433,88a8a0398bfffff,Guaratiba --22.924047379566755,-43.269977144819741,88a8a0615bfffff,Grajaú --23.046450316529796,-43.574159411037627,88a8a0391bfffff,Guaratiba --22.845991206781559,-43.532383273991172,88a8a0669dfffff,Campo Grande --23.006852202371185,-43.548602069176539,88a8a076d3fffff,Ilha de Guaratiba --22.856530606274884,-43.3021646353216,88a8a06191fffff,Vila Kosmos --22.956133033236913,-43.648774683381966,88a8a02a11fffff,Guaratiba --23.017121359169582,-43.570367530581152,88a8a039e3fffff,Guaratiba --22.965923004874842,-43.513795453608004,88a8a074ddfffff,Vargem Grande --23.057852779546543,-43.552923090304155,88a8a0390bfffff,Barra de Guaratiba --23.006477014296227,-43.444091397000932,88a8a0746bfffff,Recreio dos Bandeirantes --22.931324229217655,-43.4730003705119,88a8a074a7fffff,Senador Camará --22.969662363993702,-43.489299558229789,88a8a074c5fffff,Vargem Grande --22.987806051384172,-43.2622851717187,88a8a06341fffff,São Conrado --22.901744403017982,-43.416964468426947,88a8a06743fffff,Jardim Sulacap --22.967693188488656,-43.6797888525625,88a8a0212bfffff,Sepetiba --22.996900034841143,-43.631338242608614,88a8a02a49fffff,Pedra de Guaratiba --22.827126775581043,-43.298385635771716,88a8a06e67fffff,Cordovil --22.905238544241627,-43.340215892774083,88a8a060cdfffff,Praça Seca --22.964614663874585,-43.504543532540573,88a8a074c3fffff,Vargem Grande --22.906047189040798,-43.244957025504327,88a8a06103fffff,São Francisco Xavier --22.880035964096486,-43.416439055763355,88a8a06703fffff,Realengo --22.910019396280678,-43.272714299382358,88a8a06025fffff,Engenho Novo --22.92271120830366,-43.564984481937039,88a8a02959fffff,Campo Grande --22.891851638021855,-43.499698624716792,88a8a06619fffff,Senador Camará --22.957383557597957,-43.30151442452,88a8a06227fffff,Jacarepaguá --22.960023720694146,-43.320020254928963,88a8a0623dfffff,Jacarepaguá --22.826705555745828,-43.49811413262006,88a8a064c9fffff,Bangu --22.84711489468194,-43.489391963408977,88a8a066adfffff,Bangu --22.862815427517031,-43.600393032800774,88a8a029d7fffff,Campo Grande --22.92234255647006,-43.4604891775739,88a8a07593fffff,Jacarepaguá --22.952096437799955,-43.264502455719523,88a8a06339fffff,Alto da Boa Vista --22.838487542657827,-43.581362299222981,88a8a029b1fffff,Campo Grande --22.794790437439026,-43.223857574442775,88a8a0689bfffff,Galeão --22.996392223250115,-43.474582578593413,88a8a07457fffff,Recreio dos Bandeirantes --23.008891415641642,-43.410338955254325,88a8a07737fffff,Barra da Tijuca --23.041398684421814,-43.589403400539943,88a8a039c9fffff,Guaratiba --22.881556340975084,-43.477937252036178,88a8a06639fffff,Bangu --23.018424823174929,-43.5796198089432,88a8a0398dfffff,Guaratiba --22.89941554690866,-43.198694448123057,88a8a06a19fffff,Santo Cristo --23.025916276052644,-43.53062677649401,88a8a076cbfffff,Recreio dos Bandeirantes --22.977753363595202,-43.292782744862549,88a8a0635bfffff,Alto da Boa Vista --22.880889317710125,-43.321189672026811,88a8a060e7fffff,Cascadura --22.87236005620456,-43.413180221384209,88a8a06739fffff,Magalhães Bastos --22.913556063208443,-43.500226860593784,88a8a06659fffff,Campo Grande --22.895162572144329,-43.370706441398589,88a8a060d9fffff,Vila Valqueire --22.9004291676825,-43.407712925091538,88a8a06747fffff,Jardim Sulacap --22.928206025606876,-43.706470655672469,88a8a021adfffff,Santa Cruz --22.986206919669375,-43.200766898482804,88a8a078c9fffff,Ipanema --22.910140479590957,-43.629207534582868,88a8a02819fffff,Paciência --22.907876152619785,-43.358719809579206,88a8a062b5fffff,Praça Seca --22.839437740626469,-43.486132615643932,88a8a066a5fffff,Bangu --22.908504425525855,-43.5154692527603,88a8a02967fffff,Campo Grande --22.990449660314844,-43.280792270413443,88a8a071b7fffff,São Conrado --22.949449482186687,-43.245996325048814,88a8a06323fffff,Alto da Boa Vista --22.896902670966931,-43.484456297862955,88a8a0660bfffff,Senador Camará --23.038622343606356,-43.518642486698226,88a8a03927fffff,Grumari --22.919109594764059,-43.641718973164885,88a8a02aadfffff,Paciência --22.88024428168934,-43.468686349323967,88a8a0663dfffff,Bangu --22.831759817866374,-43.482873337587122,88a8a0641bfffff,Gericinó --22.955798342513329,-43.240000754583626,88a8a06321fffff,Alto da Boa Vista --22.911342335275574,-43.281966675688395,88a8a06021fffff,Lins de Vasconcelos --22.990390113697629,-43.585079695891132,88a8a039b1fffff,Guaratiba --22.877815819148555,-43.502430051173725,88a8a06613fffff,Senador Camará --22.992017680401343,-43.3423075293541,88a8a0625dfffff,Barra da Tijuca --22.860032481888986,-43.529652719614027,88a8a066d5fffff,Campo Grande --22.940167319389786,-43.181224165653013,88a8a078a3fffff,Botafogo --22.939639945885826,-43.685243347675808,88a8a02addfffff,Santa Cruz --22.8681745163554,-43.3331768972386,88a8a060abfffff,Madureira --22.890891715802802,-43.594935000157484,88a8a02835fffff,Inhoaíba --22.880906819494459,-43.6776517075863,88a8a028dbfffff,Santa Cruz --22.832415383620788,-43.335389649105181,88a8a06e55fffff,Irajá --23.016555039846711,-43.413598465072489,88a8a07731fffff,Barra da Tijuca --22.875601042595928,-43.284182550646058,88a8a06189fffff,Inhaúma --22.926908236829327,-43.697220552710938,88a8a02ad3fffff,Santa Cruz --22.8547912797972,-43.492651380863322,88a8a066e7fffff,Vila Kennedy --22.859970299115187,-43.2254141599284,88a8a06a8bfffff,Cidade Universitária --22.924761594850786,-43.4267417420063,88a8a075bdfffff,Jacarepaguá --23.014512725625114,-43.551862854319374,88a8a076dbfffff,Guaratiba --22.958445761281414,-43.258507126096944,88a8a06307fffff,Alto da Boa Vista --22.976369305927612,-43.587809447747418,88a8a02b59fffff,Guaratiba --23.0044647075341,-43.278054963495912,88a8a071bdfffff,Joá --22.932095987997489,-43.734220680126761,88a8a02181fffff,Santa Cruz --22.976202374525247,-43.535559622302586,88a8a02b6dfffff,Guaratiba --22.869182823485414,-43.594403586023972,88a8a029d5fffff,Campo Grande --22.884178763271454,-43.496438951454685,88a8a06611fffff,Senador Camará --22.815194448571528,-43.215125142721874,88a8a068d1fffff,Galeão --22.956843980630083,-43.196991548502133,88a8a07881fffff,Humaitá --22.881744528130202,-43.530181940528756,88a8a02935fffff,Santíssimo --22.864210536919376,-43.3054223296981,88a8a06199fffff,Tomás Coelho --22.980277302216855,-43.615564033790974,88a8a02a6bfffff,Guaratiba --22.9205870800533,-43.346732583820092,88a8a062a1fffff,Pechincha --22.832162421580446,-43.283140786742869,88a8a06f59fffff,Penha Circular --22.945474805662229,-43.218236957062516,88a8a07891fffff,Santa Teresa --22.957122334867758,-43.249253951971248,88a8a0632bfffff,Alto da Boa Vista --22.982728196000529,-43.581818731679149,88a8a039b7fffff,Guaratiba --22.977129541168214,-43.440305352284156,88a8a07423fffff,Vargem Pequena --22.991693234286771,-43.594331488702984,88a8a039bbfffff,Guaratiba --22.950767405005141,-43.559524188255111,88a8a02b07fffff,Guaratiba --22.966836868664156,-43.418540920937595,88a8a07513fffff,Jacarepaguá --22.974504111832211,-43.421800291620229,88a8a0751bfffff,Camorim --22.971457699059155,-43.655297616171325,88a8a02a53fffff,Guaratiba --22.934614228412386,-43.343997175303812,88a8a062e7fffff,Freguesia (Jacarepaguá) --22.99282984983596,-43.247035766166583,88a8a07a93fffff,Rocinha --22.897660221869387,-43.745659634740157,88a8a02e65fffff,Santa Cruz diff --git a/pipelines/rj_cor/meteorologia/radar/precipitacao/flows.py b/pipelines/rj_cor/meteorologia/radar/precipitacao/flows.py deleted file mode 100644 index c45c68f20..000000000 --- a/pipelines/rj_cor/meteorologia/radar/precipitacao/flows.py +++ /dev/null @@ -1,144 +0,0 @@ -# -*- coding: utf-8 -*- -# flake8: noqa: E501 -# pylint: disable=C0103 -""" -Flows for setting rain dashboard using radar data. -""" -from prefect import case, Parameter -from prefect.run_configs import KubernetesRun -from prefect.storage import GCS -from prefect.tasks.prefect import create_flow_run, wait_for_flow_run - -from pipelines.constants import constants -from pipelines.rj_cor.meteorologia.radar.precipitacao.schedules import TIME_SCHEDULE -from pipelines.rj_cor.meteorologia.radar.precipitacao.constants import ( - constants as radar_constants, -) -from pipelines.rj_cor.meteorologia.radar.precipitacao.tasks import ( - change_predict_rain_specs, - download_files_storage, - get_filenames_storage, - save_data, - run_model, -) -from pipelines.rj_cor.tasks import ( - get_on_redis, - save_on_redis, -) -from pipelines.rj_escritorio.rain_dashboard.constants import ( - constants as rain_dashboard_constants, -) -from pipelines.utils.decorators import Flow - -from pipelines.utils.tasks import create_table_and_upload_to_gcs - - -with Flow( - name="COR: Meteorologia - Precipitacao RADAR", - code_owners=[ - "paty", - ], - # skip_if_running=True, -) as cor_meteorologia_precipitacao_radar_flow: - # Prefect Parameters - MODE = Parameter("mode", default="prod") - RADAR_NAME = Parameter("radar_name", default="gua") - TRIGGER_RAIN_DASHBOARD_UPDATE = Parameter( - "trigger_rain_dashboard_update", default=True, required=False - ) - - # Other Parameters - DATASET_ID = radar_constants.DATASET_ID.value - TABLE_ID = radar_constants.TABLE_ID.value - DUMP_MODE = "append" - BASE_PATH = "pipelines/rj_cor/meteorologia/radar/precipitacao/" - - # Tasks - BUCKET_NAME = "rj-escritorio-dev" - files_saved_redis = get_on_redis(DATASET_ID, TABLE_ID, mode=MODE) - files_on_storage_list = get_filenames_storage( - BUCKET_NAME, RADAR_NAME, files_saved_redis - ) - - download_files_task = download_files_storage( - bucket_name=BUCKET_NAME, - files_to_download=files_on_storage_list, - destination_path=f"{BASE_PATH}radar_data/", - ) - change_json_task = change_predict_rain_specs( - files_to_model=files_on_storage_list, - destination_path=f"{BASE_PATH}radar_data/", - ) - download_files_task.set_upstream(change_json_task) - dfr = run_model(wait=download_files_task) - # dfr.set_upstream(download_files_task) - # run_model_task.set_upstream(change_json_task) - save_data_path = save_data(dfr) - upload_table = create_table_and_upload_to_gcs( - data_path=save_data_path, - dataset_id=DATASET_ID, - table_id=TABLE_ID, - dump_mode=DUMP_MODE, - ) - # upload_table.set_upstream(run_model_task) - - # Save new filenames on redis - save_last_update_redis = save_on_redis( - DATASET_ID, - TABLE_ID, - MODE, - files_on_storage_list, - keep_last=3, - wait=upload_table, - ) - # save_last_update_redis.set_upstream(upload_table) - - with case(TRIGGER_RAIN_DASHBOARD_UPDATE, True): - # Trigger rain dashboard update flow run - rain_radar_dashboard_update_flow = create_flow_run( - flow_name=rain_dashboard_constants.RAIN_DASHBOARD_FLOW_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters=radar_constants.RAIN_DASHBOARD_FLOW_SCHEDULE_PARAMETERS.value, # noqa - labels=[ - "rj-cor", - ], - run_name="Update radar rain dashboard data (triggered by precipitacao_radar flow)", # noqa - ) - rain_radar_dashboard_update_flow.set_upstream(upload_table) - - wait_for_rain_dashboard_update = wait_for_flow_run( - flow_run_id=rain_radar_dashboard_update_flow, - stream_states=True, - stream_logs=True, - raise_final_state=False, - ) - # Trigger rain dashboard update last 2h flow run - rain_dashboard_last_2h_update_flow = create_flow_run( - flow_name=rain_dashboard_constants.RAIN_DASHBOARD_FLOW_NAME.value, - project_name=constants.PREFECT_DEFAULT_PROJECT.value, - parameters=radar_constants.RAIN_DASHBOARD_LAST_2H_FLOW_SCHEDULE_PARAMETERS.value, # noqa - labels=[ - "rj-cor", - ], - run_name="Update radar rain dashboard data (triggered by precipitacao_radar last 2h flow)", # noqa - ) - rain_dashboard_last_2h_update_flow.set_upstream(upload_table) - - wait_for_rain_dashboard_last_2h_update = wait_for_flow_run( - flow_run_id=rain_dashboard_last_2h_update_flow, - stream_states=True, - stream_logs=True, - raise_final_state=False, - ) - -cor_meteorologia_precipitacao_radar_flow.storage = GCS(constants.GCS_FLOWS_BUCKET.value) -cor_meteorologia_precipitacao_radar_flow.run_config = KubernetesRun( - image=constants.DOCKER_IMAGE.value, - labels=[constants.RJ_COR_AGENT_LABEL.value], - cpu_request=1, - cpu_limit=1, - memory_request="2Gi", - memory_limit="3Gi", -) - -cor_meteorologia_precipitacao_radar_flow.schedule = TIME_SCHEDULE diff 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