YACWC

README.md

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+First, download python
+Then, in terminal, execute 
+`pip install tensorflow`
+

run_me.py

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+import tensorflow as tf
+import numpy as np
+interpreter = tf.lite.Interpreter(model_path="geo_v46.tflite")
+
+# Get input and output tensors.
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+interpreter.allocate_tensors()
+
+print(input_details)
+# This prints the following.  Output is trimmed to not overwhelm
+# [{'name': 'serving_default_longitude:0',
+#   'index': 0,
+#   'shape': array([1], dtype=int32),
+#   'dtype': numpy.float32 }
+#
+#  {'name': 'serving_default_week_of_year:0',
+#   'index': 1,
+#   'shape': array([1], dtype=int32),
+#   'dtype': numpy.float32 }
+#
+#  {'name': 'serving_default_latitude:0',
+#   'index': 2,
+#   'shape': array([1], dtype=int32),
+#   'dtype': numpy.float32 }
+
+print(output_details)
+# This prints the following.  Output is trimmed to not overwhelm
+# [{'dtype': <class 'numpy.float32'>,
+#   'index': 88,
+#   'shape': array([   1, 2728], dtype=int32)}]
+
+# %%
+with open('labels_species.txt','r') as ff:
+    output_label = ff.read().split('\n')
+
+
+# This is for Ann Arbor
+print('\n\n')
+print('Ann Arbor')
+interpreter.set_tensor(0, [np.float32(  -83.75) ])
+interpreter.set_tensor(1, [np.float32( 1.0 ) ])   #First week of the year
+interpreter.set_tensor(2, [np.float32(  42.29 )] )
+interpreter.invoke()
+output_probabilities_ish = interpreter.get_tensor(88).squeeze()
+sorted_idx = output_probabilities_ish.argsort()[::-1]
+
+for id_rank, rank in zip(sorted_idx, range(10)):
+    print(rank, output_label[id_rank], output_probabilities_ish[id_rank])
+
+
+#     Ann Arbor
+# 0 Junco hyemalis 0.32183477
+# 1 Cardinalis cardinalis 0.31213352
+# 2 Passer domesticus 0.28218403
+# 3 Dryobates pubescens 0.27699217
+# 4 Poecile atricapillus 0.2743776
+# 5 Corvus brachyrhynchos 0.27222255
+# 6 Zenaida macroura 0.26566097
+# 7 Branta canadensis 0.2610584
+# 8 Melanerpes carolinus 0.25785306
+# 9 Sitta carolinensis 0.25774997
+
+
+
+
+
+
+# Repeat for New Zealand in North Island near Tāne Mahuta
+print('\n\n')
+print('New Zealand')
+interpreter.set_tensor(0, [np.float32( 173.53 ) ])
+interpreter.set_tensor(1, [np.float32( 1.0 ) ])   #First week of the year
+interpreter.set_tensor(2, [np.float32( -35.59 )] )
+interpreter.invoke()
+output_probabilities_ish = interpreter.get_tensor(88).squeeze()
+sorted_idx = output_probabilities_ish.argsort()[::-1]
+
+for id_rank, rank in zip(sorted_idx, range(10)):
+    print(rank, output_label[id_rank], output_probabilities_ish[id_rank])
+
+    
+# New Zealand
+# 0 Acridotheres tristis 0.6020632
+# 1 Passer domesticus 0.47801244
+# 2 Hirundo neoxena 0.47301206
+# 3 Turdus merula 0.42792457
+# 4 Chroicocephalus novaehollandiae 0.38255733
+# 5 Todiramphus sanctus 0.35976177
+# 6 Zosterops lateralis 0.3565269
+# 7 Prosthemadera novaeseelandiae 0.35628808
+# 8 Larus dominicanus 0.34348693
+# 9 Gerygone igata 0.33886477
+
+
+#Repeat for Osa peninsula near guest house
+print('\n\n')
+print('Osa Peninsula')
+interpreter.set_tensor(0, [np.float32( -83.34 ) ])
+interpreter.set_tensor(1, [np.float32( 1.0 ) ])   #First week of the year
+interpreter.set_tensor(2, [np.float32( 8.40 )] )
+interpreter.invoke()
+output_probabilities_ish = interpreter.get_tensor(88).squeeze()
+sorted_idx = output_probabilities_ish.argsort()[::-1]
+
+for id_rank, rank in zip(sorted_idx, range(10)):
+    print(rank, output_label[id_rank], output_probabilities_ish[id_rank])
+
+
+# Osa Peninsula
+# 0 Ara macao 0.5610521
+# 1 Ramphastos ambiguus 0.52797556
+# 2 Ramphocelus passerinii 0.45787573
+# 3 Tyrannus melancholicus 0.34361967
+# 4 Poliocrania exsul 0.33964986
+# 5 Cantorchilus semibadius 0.3127423
+# 6 Thamnophilus bridgesi 0.30603442
+# 7 Daptrius chimachima 0.30467013
+# 8 Pitangus sulphuratus 0.3044303
+# 9 Amazona autumnalis 0.29769742
+
+
+# %%
+import matplotlib.pyplot as plt
+from matplotlib import cm
+from matplotlib.patches import Polygon
+import geopandas as gpd
+
+# This is a way to see what the model thinks is the distribution of Ara Macao
+long_range = [-86, -82.5]
+lat_range = [7.5, 12]
+plt.close('all')
+long_points = np.linspace(*long_range,150)
+lat_points = np.linspace(*lat_range, 100)
+
+long_grid, lat_grid = np.meshgrid(long_points, lat_points)
+
+flat_long_grid = long_grid.flatten()
+flat_lat_grid = lat_grid.flatten()
+
+# The developers did not enable "batching" so we have to loop through which is very slow
+probs = list()
+for c_long, c_lat in zip(flat_long_grid, flat_lat_grid):  
+    interpreter.set_tensor(0, [np.float32(c_long)])
+    interpreter.set_tensor(1, [np.float32(1)])
+    interpreter.set_tensor(2, [np.float32(c_lat)])
+    interpreter.invoke()
+    macaw_prob = interpreter.get_tensor(88).squeeze()[output_label.index('Ara macao')]
+    probs.append(macaw_prob)
+
+prob_grid = np.reshape(probs, long_grid.shape)
+
+
+boundaries = gpd.read_file('gadm41_CRI.gpkg')
+cr_hull = list(boundaries.geometry)[0].convex_hull
+
+all_coords = [np.asarray(list(x.exterior.coords)) for x in list(boundaries.geometry)[0].geoms]
+
+
+X = long_grid
+Y = lat_grid
+Z = prob_grid
+fig = plt.figure()
+ax = fig.add_subplot(1,1,1)
+ax.contourf(X, Y, Z, cmap='Greens')
+ax.set_xlim(long_range)
+ax.set_ylim(lat_range)
+for cr_coords in all_coords:
+    ax.add_patch(Polygon(cr_coords, alpha=0.5))
+ax.set_aspect(1.0)
+fig.savefig('output_macaw.png')
+
+
+# %%
+
+# This is a way to see what the model thinks is the distribution of Ara Macao
+long_range = [-130,-60]
+lat_range = [22, 50]
+plt.close('all')
+long_points = np.linspace(*long_range,150)
+lat_points = np.linspace(*lat_range, 100)
+
+long_grid, lat_grid = np.meshgrid(long_points, lat_points)
+
+flat_long_grid = long_grid.flatten()
+flat_lat_grid = lat_grid.flatten()
+
+# The developers did not enable "batching" so we have to loop through which is very slow
+probs = list()
+for c_long, c_lat in zip(flat_long_grid, flat_lat_grid):  
+    interpreter.set_tensor(0, [np.float32(c_long)])
+    interpreter.set_tensor(1, [np.float32(1)])
+    interpreter.set_tensor(2, [np.float32(c_lat)])
+    interpreter.invoke()
+    cardinal_prob = interpreter.get_tensor(88).squeeze()[output_label.index('Cardinalis cardinalis')]
+    probs.append(cardinal_prob)
+    
+
+prob_grid = np.reshape(probs, long_grid.shape)
+
+
+boundaries = gpd.read_file('gadm41_USA.gpkg')
+cr_hull = list(boundaries.geometry)[0].convex_hull
+
+all_coords = [np.asarray(list(x.exterior.coords)) for x in list(boundaries.geometry)[0].geoms]
+
+
+X = long_grid
+Y = lat_grid
+Z = prob_grid
+fig = plt.figure()
+ax = fig.add_subplot(1,1,1)
+ax.contourf(X, Y, Z, cmap='Greens')
+ax.set_xlim(long_range)
+ax.set_ylim(lat_range)
+
+from matplotlib.collections import PatchCollection
+patches = list()
+for cr_coords in tqdm(all_coords):
+    patches.append(Polygon(cr_coords, alpha=0.5))
+ax.add_collection(PatchCollection(patches, linewidth=0.5, facecolor='none',edgecolor='black'))
+
+ax.set_aspect(1.0)
+fig.savefig('output_cardinal.png')
+
+
+
+
+def create_labels_species():
+# This code was used to generate the labels_specis_name file that is read in to understand what species each index corresponds to.
+# labels_2025.txt was acquired by unzipping the geo_v46.tflite file
+    import sqlite3
+    con = sqlite3.connect("merlin_room")
+    cursor  = con.cursor()
+    code_name_list = cursor.execute("SELECT speciesCode, scientificName FROM taxon;").fetchall()
+    code_name_map = {x[0]: x[1] for x in code_name_list}
+
+    with open('labels_2025.txt','r') as lab:
+        labels = lab.read().split('\n')
+    species_index_label =  [code_name_map.get(r,'N/A') for r in labels ]
+    with open('labels_species.txt','w') as ff:
+        ff.write('\n'.join(species_index_label))
+
+
+