feat: snapshot tests

Makefile

@@ -20,9 +20,15 @@ format:
 lock:
 	CUSTOM_COMPILE_COMMAND="make lock" uv pip compile --upgrade --generate-hashes requirements/base.in -o requirements.txt
 
+lock-package:
+	CUSTOM_COMPILE_COMMAND="make lock" uv pip compile --upgrade-package $(PACKAGE) --generate-hashes --emit-build-options requirements/base.in requirements/deploy.in -o requirements.txt
+
 lock-dev:
 	CUSTOM_COMPILE_COMMAND="make lock-dev" uv pip compile --upgrade --generate-hashes requirements/dev.in -o requirements-dev.txt
 
+lock-dev-package:
+	CUSTOM_COMPILE_COMMAND="make lock-dev" uv pip compile --upgrade-package $(PACKAGE) --generate-hashes requirements/dev.in -o requirements-dev.txt
+
 build:
 	echo "Building TK..."
 
@@ -35,9 +41,16 @@ clean:
 
 test: clean check_rebuild
 	if [ -z "$(PATTERN)" ]; then \
-		$(DC_RUN_CMD) pytest soil_id; \
+		$(DC_RUN_CMD) pytest soil_id -vv; \
+	else \
+		$(DC_RUN_CMD) pytest soil_id -vv -k $(PATTERN); \
+	fi
+
+test_update_snapshots: clean check_rebuild
+	if [ -z "$(PATTERN)" ]; then \
+		$(DC_RUN_CMD) pytest soil_id --snapshot-update; \
 	else \
-		$(DC_RUN_CMD) pytest soil_id -k $(PATTERN); \
+		$(DC_RUN_CMD) pytest soil_id --snapshot-update -k $(PATTERN); \
 	fi
 
 test-verbose:

requirements-dev.txt

@@ -174,6 +174,7 @@ pytest==8.4.1 \
     # via
     #   -r requirements/dev.in
     #   pytest-profiling
+    #   syrupy
 pytest-profiling==1.8.1 \
     --hash=sha256:3dd8713a96298b42d83de8f5951df3ada3e61b3e5d2a06956684175529e17aea \
     --hash=sha256:3f171fa69d5c82fa9aab76d66abd5f59da69135c37d6ae5bf7557f1b154cb08d
@@ -265,6 +266,10 @@ soupsieve==2.7 \
     --hash=sha256:6e60cc5c1ffaf1cebcc12e8188320b72071e922c2e897f737cadce79ad5d30c4 \
     --hash=sha256:ad282f9b6926286d2ead4750552c8a6142bc4c783fd66b0293547c8fe6ae126a
     # via beautifulsoup4
+syrupy==4.9.1 \
+    --hash=sha256:b7d0fcadad80a7d2f6c4c71917918e8ebe2483e8c703dfc8d49cdbb01081f9a4 \
+    --hash=sha256:b94cc12ed0e5e75b448255430af642516842a2374a46936dd2650cfb6dd20eda
+    # via -r requirements/dev.in
 tqdm==4.67.1 \
     --hash=sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2 \
     --hash=sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2

requirements/dev.in

@@ -4,3 +4,4 @@ ruff
 pre-commit
 pytest
 pytest-profiling
+syrupy

soil_id/db.py

@@ -353,7 +353,8 @@ def extract_hwsd2_data(connection, lon, lat, buffer_dist, table_name):
                 BOOL_OR(ST_Intersects(shape, {point})) AS pt_intersect
             FROM hwsd2_segment
             WHERE ST_DWithin(shape, {point}, {buffer_dist})
-            GROUP BY hwsd2_id;
+            GROUP BY hwsd2_id
+            ORDER BY hwsd2_id;
         """
 
         # Use GeoPandas to execute the main query and load results into a GeoDataFrame.

soil_id/global_soil.py

@@ -105,7 +105,7 @@ def list_soils_global(connection, lon, lat, buffer_dist=100000):
     ##############################################################################################
     ExpCoeff = -0.00036888  # Decays to 0.25 @ 10km
     loc_scores = []
-    mucompdata_grouped = mucompdata_pd.groupby(["mukey", "cokey"], sort=False)
+    mucompdata_grouped = mucompdata_pd.groupby(["mukey", "cokey"])
 
     for (mukey, cokey), group in mucompdata_grouped:
         loc_score = calculate_location_score(group, ExpCoeff)
@@ -124,7 +124,7 @@ def list_soils_global(connection, lon, lat, buffer_dist=100000):
     mucompdata_pd["Index"] = mucompdata_pd.index
 
     # Group by component name
-    mucompdata_grouped = mucompdata_pd.groupby("compname", sort=False)
+    mucompdata_grouped = mucompdata_pd.groupby("compname")
 
     # Take at most 12 groups
     mucompdata_comp_grps = [group for _, group in mucompdata_grouped][:12]
@@ -234,7 +234,7 @@ def list_soils_global(connection, lon, lat, buffer_dist=100000):
     )
 
     # Group data by cokey for texture
-    muhorzdata_group_cokey = list(muhorzdata_pd.groupby("cokey", sort=False))
+    muhorzdata_group_cokey = list(muhorzdata_pd.groupby("cokey"))
     pd.set_option("display.max_rows", None)
     pd.set_option("display.max_columns", None)
     pd.set_option("display.width", None)
@@ -367,7 +367,7 @@ def list_soils_global(connection, lon, lat, buffer_dist=100000):
 
     # Create index for component instance display
     mucompdata_comp_grps_list = []
-    mucompdata_comp_grps = [group for _, group in mucompdata_pd.groupby("compname_grp", sort=False)]
+    mucompdata_comp_grps = [group for _, group in mucompdata_pd.groupby("compname_grp")]
 
     for group in mucompdata_comp_grps:
         group = group.sort_values("distance").reset_index(drop=True)
@@ -772,7 +772,7 @@ def rank_soils_global(
 
     # Horizon Data Similarity
     if soilIDRank_output_pd is not None:
-        cokey_groups = [group for _, group in soilIDRank_output_pd.groupby("compname", sort=False)]
+        cokey_groups = [group for _, group in soilIDRank_output_pd.groupby("compname")]
 
         # Create lists to store component statuses
         Comp_Rank_Status, Comp_Missing_Status, Comp_name = [], [], []
@@ -1087,7 +1087,7 @@ def normalize(arr):
 
     # Sorting and reindexing of Data-only score
     soilIDList_data = []
-    D_final_comp_grps = [g for _, g in D_final_horz.groupby("compname_grp", sort=False)]
+    D_final_comp_grps = [g for _, g in D_final_horz.groupby("compname_grp")]
 
     for comp_grps_temp in D_final_comp_grps:
         comp_grps_temp = comp_grps_temp.sort_values("Score_Data", ascending=False).reset_index(
@@ -1163,7 +1163,7 @@ def normalize(arr):
     soilIDList_out = []
 
     # Group by 'compname_grp'
-    for _, comp_grps_temp in D_final_loc.groupby("compname_grp", sort=False):
+    for _, comp_grps_temp in D_final_loc.groupby("compname_grp"):
         comp_grps_temp = comp_grps_temp.sort_values("Score_Data_Loc", ascending=False).reset_index(
             drop=True
         )

soil_id/soil_sim.py

@@ -128,7 +128,7 @@ def soil_sim(muhorzdata_pd):
     agg_data = []
 
     # Group data by compname_grp
-    sim_group_compname = [group for _, group in sim.groupby("compname_grp", sort=False)]
+    sim_group_compname = [group for _, group in sim.groupby("compname_grp")]
     for index, group in enumerate(sim_group_compname):
         # aggregate data into 0-30 and 30-100 or bottom depth
         group_ag = slice_and_aggregate_soil_data(group[sim_data_columns])