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remove write requests from ER notebook #346

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Nov 29, 2024
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remove write requests from ER notebook #346

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307 changes: 0 additions & 307 deletions doc/source/notebooks/01. IO/EarthRanger_IO.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -44,10 +44,7 @@
"import os\n",
"import sys\n",
"\n",
"import geopandas as gpd\n",
"import pandas as pd\n",
"from erclient.client import ERClientException\n",
"from shapely.geometry import Point\n",
"\n",
"import ecoscope\n",
"\n",
Expand Down Expand Up @@ -210,181 +207,6 @@
"events_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## POST `Event`\n",
"\n",
"First delete any existing events:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_event_types = pd.DataFrame(er_io.get_event_types())\n",
"carcass_id = df_event_types[df_event_types[\"value\"] == \"carcass_rep\"][\"id\"].values[0]\n",
"carcass_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" event_df = er_io.get_events(\n",
" event_type=[carcass_id], # Carcass\n",
" ).reset_index()\n",
" for i, value in event_df.iterrows():\n",
" er_io.delete_event(event_id=event_df.loc[i, \"id\"])\n",
"except AssertionError:\n",
" print(\"No events exist yet\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define events to be uploaded:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"new_event = [\n",
" {\n",
" \"event_details\": {\n",
" \"carcassrep_ageofanimal\": None,\n",
" \"carcassrep_ageofcarcass\": None,\n",
" \"carcassrep_causeofdeath\": None,\n",
" \"carcassrep_sex\": None,\n",
" \"carcassrep_species\": None,\n",
" \"carcassrep_trophystatus\": None,\n",
" },\n",
" \"event_type\": \"carcass_rep\",\n",
" \"icon_id\": \"carcass_rep\",\n",
" \"id\": \"e29f9078-ee0a-4f06-b685-92e9ff266e9b\",\n",
" \"location\": {\"latitude\": -27.12759, \"longitude\": -109.40804},\n",
" \"priority\": 100,\n",
" \"state\": \"resolved\",\n",
" \"time\": pd.Timestamp(\"2022-03-15 15:44:00-0700\"),\n",
" \"title\": \"Carcass\",\n",
" }\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Read events as dataframe:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"new_event_df = pd.DataFrame.from_dict(new_event)\n",
"new_event_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Upload:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"try:\n",
" er_io.post_event(events=new_event_df.iloc[[0]])\n",
"except ERClientException as e:\n",
" print(e)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PATCH `Event`\n",
"\n",
"Define updated values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"updated_event = pd.DataFrame(\n",
" [\n",
" {\n",
" \"event_details\": {\n",
" \"carcassrep_ageofanimal\": \"adult\",\n",
" \"carcassrep_ageofcarcass\": \"less_than_a_day\",\n",
" \"carcassrep_causeofdeath\": \"unnaturalshot\",\n",
" \"carcassrep_sex\": \"female\",\n",
" \"carcassrep_species\": \"elephant\",\n",
" \"carcassrep_trophystatus\": \"removed\",\n",
" }\n",
" }\n",
" ]\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get and display current values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"event_df = er_io.get_events(\n",
" event_type=[\"b0d66402-34cb-4e9a-9715-2ac4eeebb574\"], # Carcass\n",
").reset_index()\n",
"\n",
"event_df.loc[0, \"event_details\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Patch and confirm values have been updated"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"id_to_patch = event_df.loc[0, \"id\"]\n",
"er_io.patch_event(event_id=id_to_patch, events=updated_event).loc[0, \"event_details\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -688,135 +510,6 @@
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## POST `Observation`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First delete any existing observations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"elephants = er_io.get_subjectgroup_observations(\n",
" subject_group_name=\"Elephants\", since=pd.Timestamp(\"2022-01-01\").isoformat()\n",
")\n",
"\n",
"if not elephants.empty:\n",
" for observation_id in elephants[\"extra__id\"].unique():\n",
" er_io.delete_observation(observation_id)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create new observations from existing sources"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_sources = er_io.get_sources()\n",
"habiba_source_id = df_sources[df_sources[\"manufacturer_id\"] == \"ST-Habiba\"][\"id\"].values[0]\n",
"salif_keita_source_id = df_sources[df_sources[\"manufacturer_id\"] == \"ST-SalifKeita\"][\"id\"].values[0]\n",
"habiba_source_id, salif_keita_source_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"observations = [\n",
" {\n",
" \"fixtime\": pd.Timestamp(\"2023-04-24 11:03:31\").isoformat(),\n",
" \"geometry\": Point(1.99, 2.99),\n",
" \"source_id\": habiba_source_id, # Habiba's source\n",
" },\n",
" {\n",
" \"fixtime\": pd.Timestamp(\"2023-02-01 14:02:05\").isoformat(),\n",
" \"geometry\": Point(1.345, 2.3303),\n",
" \"source_id\": habiba_source_id, # Habiba's source\n",
" },\n",
" {\n",
" \"fixtime\": pd.Timestamp(\"2023-03-21 06:03:27\").isoformat(),\n",
" \"geometry\": Point(3.4455, -3.4554),\n",
" \"source_id\": salif_keita_source_id, # Salif Keita's source\n",
" },\n",
"]\n",
"\n",
"gdf = gpd.GeoDataFrame.from_dict(observations)\n",
"gdf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Post observations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"er_io.post_observations(observations=gdf, source_id_col=\"source_id\", recorded_at_col=\"fixtime\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## POST `SubjectSource`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_subjects = er_io.get_subjects()\n",
"salif_keita_id = df_subjects[df_subjects[\"name\"] == \"Salif Keita\"][\"id\"].values[0]\n",
"\n",
"df_sources = er_io.get_sources()\n",
"salif_keita_source_id = df_sources[df_sources[\"manufacturer_id\"] == \"ST-SalifKeita\"][\"id\"].values[0]\n",
"\n",
"salif_keita_id, salif_keita_source_id"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"er_io.post_subjectsource(\n",
" salif_keita_id, # Salif Keita subject ID\n",
" source_id=salif_keita_source_id, # Salif Keita's source\n",
" lower_bound_assigned_range=pd.Timestamp.utcnow().isoformat(),\n",
" upper_bound_assigned_range=(pd.Timestamp.utcnow() + pd.Timedelta(days=9000)).isoformat(),\n",
" additional={},\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down
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