feat(Prediction-Event): added new unique identifier hash_id

A new hash_id identifier has been added to prediction events, allowing the creation of a unique URL for the event that remains even after reprocessing.

Issue #1052

backend/tno/migrations/0006_auto_20240822_1847.py

@@ -0,0 +1,32 @@
+# Generated by Django 3.2.18 on 2024-08-22 18:47
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ("tno", "0005_auto_20240804_0208"),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name="occultation",
+            name="hash_id",
+            field=models.CharField(
+                blank=True,
+                default=None,
+                help_text="Unique hash identifier for the prediction event",
+                max_length=26,
+                null=True,
+                unique=True,
+                verbose_name="Hash ID",
+            ),
+        ),
+        migrations.AddIndex(
+            model_name="occultation",
+            index=models.Index(
+                fields=["hash_id"], name="tno_occulta_hash_id_ff09b7_idx"
+            ),
+        ),
+    ]

backend/tno/models/occultation.py

@@ -11,7 +11,15 @@
 # TODO Squash Migrations:
 # https://coderbook.com/@marcus/how-to-squash-and-merge-django-migrations/
 class Occultation(models.Model):
-
+    hash_id = models.CharField(
+        max_length=26,
+        verbose_name="Hash ID",
+        null=True,
+        blank=True,
+        default=None,
+        help_text="Unique hash identifier for the prediction event",
+        unique=True,
+    )
     # -------------------------------------------------
     # Identificação do Objeto
     # -------------------------------------------------
@@ -91,6 +99,7 @@ class Occultation(models.Model):
     # -------------------------------------------------
     # Informações da prediçao
     # -------------------------------------------------
+
     date_time = models.DateTimeField(
         verbose_name="Date Time", auto_now_add=False, null=False, blank=False
     )
@@ -963,6 +972,11 @@ class Meta:
                 ]
             ),
             # event indexes
+            models.Index(
+                fields=[
+                    "hash_id",
+                ]
+            ),
             models.Index(
                 fields=[
                     "date_time",

backend/tno/serializers.py

@@ -91,17 +91,25 @@ class Meta:
 
 
 class OccultationSerializer(serializers.ModelSerializer):
+    id = serializers.SerializerMethodField()
     map_url = serializers.SerializerMethodField()
 
     class Meta:
         model = Occultation
         exclude = (
+            "hash_id",
             "occ_path_min_longitude",
             "occ_path_max_longitude",
             "occ_path_min_latitude",
             "occ_path_max_latitude",
         )
 
+    def get_id(self, obj):
+        if obj.hash_id != None:
+            return obj.hash_id
+        else:
+            return obj.id
+
     def get_map_url(self, obj):
         request = self.context.get("request")
         relative_url = obj.get_map_relative_url()

backend/tno/views/occultation.py

@@ -9,6 +9,7 @@
 from dateutil.relativedelta import relativedelta
 from django.conf import settings
 from django.db.models import F, FloatField, Q, Value
+from django.shortcuts import get_object_or_404
 from drf_spectacular.utils import extend_schema, inline_serializer
 from rest_framework import serializers, viewsets
 from rest_framework.decorators import action
@@ -29,6 +30,9 @@ class CharInFilter(django_filters.BaseInFilter, django_filters.CharFilter):
 
 class OccultationFilter(django_filters.FilterSet):
 
+    hash_id = django_filters.CharFilter(
+        label="Hash ID", field_name="hash_id", lookup_expr="exact"
+    )
     date_time = django_filters.DateTimeFromToRangeFilter()
 
     name = CharInFilter(field_name="name", lookup_expr="in")
@@ -142,6 +146,8 @@ class Meta:
 class OccultationViewSet(viewsets.ReadOnlyModelViewSet):
     permission_classes = [AllowAny]
 
+    lookup_field = "hash_id"
+
     queryset = Occultation.objects.all()
     serializer_class = OccultationSerializer
 
@@ -211,33 +217,41 @@ def check_user_location_params(self, params):
 
         return lat, long, radius
 
-    # TODO: Estudar a possibilidade de um metodo asyncrono
-    # Teste Com metodo assincrono pode ser promissor!
-    # if None not in [lat, long, radius]:
-    #     # print(f"Latitude: {lat} Longitude: {long} Radius: {radius}")
-    #     job = group(
-    #         assync_visibility_from_coeff.s(
-    #             event_id=event.id,
-    #             latitude=lat,
-    #             longitude=long,
-    #             radius=radius,
-    #             date_time=event.date_time.isoformat(),
-    #             inputdict=event.occ_path_coeff,
-    #             # object_diameter=event.diameter,
-    #             # ring_diameter=event.diameter,
-    #             # n_elements= 1500,
-    #             # ignore_nighttime= False,
-    #             # latitudinal= False
-    #          ) for event in queryset)
-
-    #     result = job.apply_async()
-    #     while result.ready() == False:
-    #         print(f"Completed: {result.completed_count()}")
-    #         sleep(1)
-    # t1 = datetime.now()
-    # dt = t1 - t0
-    # logger.info(f"Query Completed in {humanize.naturaldelta(dt)}")
-    # return queryset
+    # TODO: Remover este metodo depois que todas as predições tiverem hash_id.
+    # Este metodo sobrescreve o metodo base que retorna o objeto pela lookup_field na url.
+    # Este metodo foi criado para permitir a busca pelo ID do objeto no banco de dados visando manter a compatibilidade com urls antigas.
+    def get_object(self):
+        """
+        Returns the object the view is displaying.
+
+        You may want to override this if you need to provide non-standard
+        queryset lookups.  Eg if objects are referenced using multiple
+        keyword arguments in the url conf.
+        """
+        queryset = self.filter_queryset(self.get_queryset())
+
+        # Perform the lookup filtering.
+        lookup_url_kwarg = self.lookup_url_kwarg or self.lookup_field
+
+        assert lookup_url_kwarg in self.kwargs, (
+            "Expected view %s to be called with a URL keyword argument "
+            'named "%s". Fix your URL conf, or set the `.lookup_field` '
+            "attribute on the view correctly."
+            % (self.__class__.__name__, lookup_url_kwarg)
+        )
+
+        try:
+            id = int(self.kwargs[lookup_url_kwarg])
+            filter_kwargs = {"pk": id}
+        except:
+            filter_kwargs = {self.lookup_field: self.kwargs[lookup_url_kwarg]}
+
+        obj = get_object_or_404(queryset, **filter_kwargs)
+
+        # May raise a permission denied
+        self.check_object_permissions(self.request, obj)
+
+        return obj
 
     def list(self, request):
         t0 = datetime.now()
@@ -369,7 +383,7 @@ def highlights_maps_stats(self, request):
         },
     )
     @action(detail=True, methods=["get"], permission_classes=(AllowAny,))
-    def get_or_create_map(self, request, pk):
+    def get_or_create_map(self, request, pk=None, hash_id=None):
         """Retorna o mapa para o evento de ocultação.
 
         Verifica se já existe mapa para o evento especifico para o ID.
@@ -500,7 +514,7 @@ def asteroids_with_prediction(self, request):
 
     @extend_schema(exclude=True)
     @action(detail=True, methods=["get"], permission_classes=(AllowAny,))
-    def get_star_by_event(self, request, pk=None):
+    def get_star_by_event(self, request, pk=None, hash_id=None):
         pre_occ = self.get_object()
 
         source_id = pre_occ.gaia_source_id

frontend/src/components/PredictionEventsDataGrid/EventsGrid.js

@@ -53,14 +53,6 @@ function PredictEventGrid() {
     return (
       <React.Fragment>
         <Grid container spacing={2}>
-          {/* <Grid item xs={12}>
-            <Stack direction='row' justifyContent='center' alignItems='center' spacing={1}>
-              <CircularProgress size='1rem' />
-              <Typography variant='body2' sx={{ mb: 2 }} color='text.secondary'>
-                {message}
-              </Typography>
-            </Stack>
-          </Grid> */}
           <Grid item xs={12}>
             <Skeleton variant='rectangular' width={'100%'} height={250} />
           </Grid>

frontend/src/services/api/Occultation.js

@@ -48,7 +48,12 @@ export const filter_by_location = ({
   return api.get('/occultations/filter_location/', { params })
 }
 
-export const getOccultationById = ({ id }) => api.get(`/occultations/${id}`).then((res) => res.data)
+export const getOccultationById = ({ id }) => {
+  if (!id) {
+    return
+  }
+  return api.get(`/occultations/${id}`).then((res) => res.data)
+}
 
 export const getNextTwenty = ({ page, pageSize, ordering }) => {
   const params = {

predict_occultation/src/asteroid/asteroid.py

@@ -981,6 +981,7 @@ def register_occultations(self, start_period: str, end_period: str, jobid: int):
                     "closest_approach_uncertainty",
                     "moon_illuminated_fraction",
                     "probability_of_centrality",
+                    "hash_id",
                 ]
             )
 
@@ -996,6 +997,7 @@ def register_occultations(self, start_period: str, end_period: str, jobid: int):
             )
             data.seek(0)
 
+            # rowcount = dao.import_occultations(list(df.columns), data)
             rowcount = dao.import_occultations(data)
 
             del df

predict_occultation/src/dao/occultation.py

@@ -40,12 +40,18 @@ def delete_by_asteroid_name_period(
             )
             return rows
 
+    # def import_occultations(self, columns: list, data):
     def import_occultations(self, data):
 
         # Sql Copy com todas as colunas que vão ser importadas e o formato do csv.
         # IMPORTANTE! A ORDEM DAS COLUNAS PRECISA SER IDENTICA A COMO ESTA NO DB!
+        # sql = (
+        #     f"COPY {self.tbl} ({', '.join(columns)}) "
+        #     "FROM STDIN with (FORMAT CSV, DELIMITER '|', HEADER);"
+        # )
+
         sql = (
-            f"COPY {self.tbl} (name, number, date_time, gaia_source_id, ra_star_candidate, dec_star_candidate, ra_target, dec_target, closest_approach, position_angle, velocity, delta, g, j_star, h, k_star, long, loc_t, off_ra, off_dec, proper_motion, ct, multiplicity_flag, e_ra, e_dec, pmra, pmdec, ra_star_deg, dec_star_deg, ra_target_deg, dec_target_deg, created_at, apparent_diameter, aphelion, apparent_magnitude, dec_star_to_date, dec_star_with_pm, dec_target_apparent, diameter, e_dec_target, e_ra_target, eccentricity, ephemeris_version, g_mag_vel_corrected, h_mag_vel_corrected, inclination, instant_uncertainty, magnitude_drop, perihelion, ra_star_to_date, ra_star_with_pm, ra_target_apparent, rp_mag_vel_corrected, semimajor_axis, have_path_coeff, occ_path_max_longitude, occ_path_min_longitude, occ_path_coeff, occ_path_is_nightside, occ_path_max_latitude, occ_path_min_latitude, base_dynclass, bsp_planetary, bsp_source, catalog, dynclass, job_id, leap_seconds, nima, obs_source, orb_ele_source, predict_step, albedo, albedo_err_max, albedo_err_min, alias, arg_perihelion, astorb_dynbaseclass, astorb_dynsubclass, density, density_err_max, density_err_min, diameter_err_max, diameter_err_min, epoch, last_obs_included, long_asc_node, mass, mass_err_max, mass_err_min, mean_anomaly, mean_daily_motion, mpc_critical_list, pha_flag, principal_designation, rms, g_star, h_star, event_duration, moon_separation, sun_elongation, closest_approach_uncertainty, moon_illuminated_fraction, probability_of_centrality) "
+            f"COPY {self.tbl} (name, number, date_time, gaia_source_id, ra_star_candidate, dec_star_candidate, ra_target, dec_target, closest_approach, position_angle, velocity, delta, g, j_star, h, k_star, long, loc_t, off_ra, off_dec, proper_motion, ct, multiplicity_flag, e_ra, e_dec, pmra, pmdec, ra_star_deg, dec_star_deg, ra_target_deg, dec_target_deg, created_at, apparent_diameter, aphelion, apparent_magnitude, dec_star_to_date, dec_star_with_pm, dec_target_apparent, diameter, e_dec_target, e_ra_target, eccentricity, ephemeris_version, g_mag_vel_corrected, h_mag_vel_corrected, inclination, instant_uncertainty, magnitude_drop, perihelion, ra_star_to_date, ra_star_with_pm, ra_target_apparent, rp_mag_vel_corrected, semimajor_axis, have_path_coeff, occ_path_max_longitude, occ_path_min_longitude, occ_path_coeff, occ_path_is_nightside, occ_path_max_latitude, occ_path_min_latitude, base_dynclass, bsp_planetary, bsp_source, catalog, dynclass, job_id, leap_seconds, nima, obs_source, orb_ele_source, predict_step, albedo, albedo_err_max, albedo_err_min, alias, arg_perihelion, astorb_dynbaseclass, astorb_dynsubclass, density, density_err_max, density_err_min, diameter_err_max, diameter_err_min, epoch, last_obs_included, long_asc_node, mass, mass_err_max, mass_err_min, mean_anomaly, mean_daily_motion, mpc_critical_list, pha_flag, principal_designation, rms, g_star, h_star, event_duration, moon_separation, sun_elongation, closest_approach_uncertainty, moon_illuminated_fraction, probability_of_centrality, hash_id) "
             "FROM STDIN with (FORMAT CSV, DELIMITER '|', HEADER);"
         )
 

predict_occultation/src/predict_occultation/pipeline/library.py

@@ -1,7 +1,10 @@
 #!/usr/bin/python2.7
 # -*- coding: utf-8 -*-
+import base64
+import hashlib
 import os
 import re
+from datetime import datetime, timedelta
 
 import astropy.units as u
 import numpy as np
@@ -11,6 +14,46 @@
 from scipy.interpolate import interp1d
 
 
+def normalize_to_nearest_hour(dt):
+    # Ensure input is a datetime object
+    if not isinstance(dt, datetime):
+        raise TypeError("Input must be a datetime object")
+
+    # Extract the minute component
+    minute = dt.minute
+
+    # If minutes >= 30, round up to the next hour
+    if minute >= 30:
+        dt = dt + timedelta(hours=1)
+
+    # Normalize to the nearest hour by setting minutes and seconds to zero
+    normalized_dt = dt.replace(minute=0, second=0, microsecond=0)
+
+    return normalized_dt
+
+
+def generate_hash(name: str, source_id: int, date_time: datetime):
+    """
+    Generates a hash based on the given parameters.
+
+    Args:
+        name (str): The name parameter.
+        source_id (int): The source ID parameter.
+        date_time (datetime): The date and time parameter.
+
+    Returns:
+        str: The generated hash.
+
+    """
+    # Convert date and time of event to the nearest hour
+    nearest_hour = normalize_to_nearest_hour(date_time)
+    # Generate the identifier string with asteroid name, star gaia source id, and nearest hour
+    identifier = f"{name} {source_id} {nearest_hour.strftime('%Y-%m-%d %H:%M:%S')}"
+    md5 = hashlib.md5(identifier.encode("utf-8")).digest()
+    hash = base64.urlsafe_b64encode(md5).decode("utf-8").rstrip("=")
+    return hash
+
+
 def check_leapsec(filename):
     """
     Verifica se o arquivo leapSec existe

predict_occultation/src/predict_occultation/pipeline/occ_path_coeff.py

@@ -12,6 +12,7 @@
     asteroid_visual_magnitude,
     compute_magnitude_drop,
     dec_hms_to_deg,
+    generate_hash,
     get_apparent_diameter,
     get_closest_approach_uncertainty,
     get_event_duration,
@@ -144,6 +145,7 @@ def run_occultation_path_coeff(
         for row in df.to_dict(orient="records"):
 
             new_row = {
+                "hash_id": None,
                 "gaia_source_id": None,
                 "gaia_g_mag": None,
                 "apparent_magnitude": None,
@@ -338,7 +340,6 @@ def run_occultation_path_coeff(
 
         if len(coeff_paths) > 0:
             df_coeff = pd.DataFrame.from_dict(coeff_paths)
-
             df["gaia_source_id"] = df_coeff["gaia_source_id"]
             df["g_star"] = df_coeff["gaia_g_mag"]
             df["apparent_magnitude"] = df_coeff["apparent_magnitude"]
@@ -366,8 +367,11 @@ def run_occultation_path_coeff(
             df["occ_path_min_latitude"] = df_coeff["occ_path_min_latitude"]
 
             del df_coeff
+        # TODO: esse else pode ser removido, todas as colunas calculadas anteriormente precisam ser incluidas no df.
+        # Só entra neste else se não tiver nenhuma predição.
+        # O array coeff_paths na verdade está representando cada uma das predições.
         else:
-            df["gaia_source_id"] = None
+            # df["gaia_source_id"] = None
             df["g_star"] = None
             df["apparent_magnitude"] = None
             df["magnitude_drop"] = None
@@ -478,6 +482,12 @@ def run_occultation_path_coeff(
         # Converter as strings date_time do instante da ocultação em objetos datetime utc
         df["date_time"] = pd.to_datetime(df["date_time"], utc=True)
 
+        # Gera um hash unico para cada evento de predicao
+        df["hash_id"] = df.apply(
+            lambda x: generate_hash(x["name"], x["gaia_source_id"], x["date_time"]),
+            axis=1,
+        )
+
         # Altera a ordem das colunas para coincidir com a da tabela
         df = df.reindex(
             columns=[
@@ -587,6 +597,7 @@ def run_occultation_path_coeff(
                 "closest_approach_uncertainty",
                 "moon_illuminated_fraction",
                 "probability_of_centrality",
+                "hash_id",
             ]
         )