rename iSparrow to faunanet

birdnet_custom_v2.4/default.yml

@@ -17,11 +17,11 @@ Analysis:
   pattern: ".wav"
   check_time: 1
   delete_recordings: "never"
-  model_dir: ~/iSparrow/models 
+  model_dir: ~/faunanet/models 
 
 Data: 
-  input: ~/iSparrow_data
-  output: ~/iSparrow_output
+  input: ~/faunanet_data
+  output: ~/faunanet_output
   Preprocessor:
     sample_rate: 48000
     overlap: 0.0

birdnet_custom_v2.4/model.py

@@ -8,8 +8,8 @@ except Exception:
 
 from birdnetlib.analyzer import AnalyzerConfigurationError
 
-from iSparrow.model_base import ModelBase
-from iSparrow import utils
+from faunanet.model_base import ModelBase
+from faunanet import utils
 
 
 class Model(ModelBase):
@@ -193,12 +193,12 @@ class Model(ModelBase):
         return confidence
 
     @classmethod
-    def from_cfg(cls, iSparrow_dir: str, cfg: dict):
+    def from_cfg(cls, faunanet_dir: str, cfg: dict):
         """
         from_cfg Create a new instance from a dictionary containing keyword arguments. Usually loaded from a config file.
 
         Args:
-            iSparrow_dir (str): Installation directory of the iSparrow package
+            faunanet_dir (str): Installation directory of the faunanet package
             cfg (dict): Dictionary containing the keyword arguments
 
         Returns:
@@ -207,9 +207,9 @@ class Model(ModelBase):
 
         # preprocess config because we need two models here
         cfg["default_model_path"] = str(
-            Path(iSparrow_dir) / Path("models") / Path("birdnet_default")
+            Path(faunanet_dir) / Path("models") / Path("birdnet_default")
         )
         cfg["model_path"] = str(
-            Path(iSparrow_dir) / Path("models") / Path(cfg["model_path"])
+            Path(faunanet_dir) / Path("models") / Path(cfg["model_path"])
         )
         return cls(**cfg)

birdnet_custom_v2.4/preprocessor.py

@@ -1,5 +1,5 @@
 import numpy as np
-import iSparrow.preprocessor_base as ppb
+import faunanet.preprocessor_base as ppb
 
 
 class Preprocessor(ppb.PreprocessorBase):

birdnet_default_v2.4/default.yml

@@ -17,11 +17,11 @@ Analysis:
   pattern: ".wav"
   check_time: 1
   delete_recordings: "never"
-  model_dir: ~/iSparrow/models 
+  model_dir: ~/faunanet/models 
 
 Data: 
-  input: ~/iSparrow_data
-  output: ~/iSparrow_output
+  input: ~/faunanet_data
+  output: ~/faunanet_output
   Preprocessor:
     sample_rate: 48000
     overlap: 0.0

birdnet_default_v2.4/model.py

@@ -1,9 +1,9 @@
 from pathlib import Path
 import numpy as np
 
-from iSparrow.model_base import ModelBase
+from faunanet.model_base import ModelBase
 
-# from iSparrow import utils
+# from faunanet import utils
 
 
 class Model(ModelBase):
@@ -92,19 +92,19 @@ class Model(ModelBase):
         return confidence
 
     @classmethod
-    def from_cfg(cls, iSparrow_folder: str, cfg: dict):
+    def from_cfg(cls, faunanet_folder: str, cfg: dict):
         """
         from_cfg Create a new instance from a dictionary containing keyword arguments. Usually loaded from a config file.
 
         Args:
-            iSparrow_dir (str): Installation directory of the iSparrow package
+            faunanet_dir (str): Installation directory of the faunanet package
             cfg (dict): Dictionary containing the keyword arguments
 
         Returns:
             Model: New model instance created with the supplied kwargs.
         """
         cfg["model_path"] = str(
-            Path(iSparrow_folder) / Path("models") / cfg["model_path"]
+            Path(faunanet_folder) / Path("models") / cfg["model_path"]
         )
 
         return cls(**cfg)

birdnet_default_v2.4/preprocessor.py

@@ -1,5 +1,5 @@
 import numpy as np
-import iSparrow.preprocessor_base as ppb
+import faunanet.preprocessor_base as ppb
 
 
 class Preprocessor(ppb.PreprocessorBase):

google_bird_classification/default.yml

@@ -19,11 +19,11 @@ Analysis:
   pattern: ".wav"
   check_time: 1
   delete_recordings: "never"
-  model_dir: ~/iSparrow/models 
+  model_dir: ~/faunanet/models 
 
 Data: 
-  input: ~/iSparrow_data
-  output: ~/iSparrow_output
+  input: ~/faunanet_data
+  output: ~/faunanet_output
   Preprocessor:
     sample_rate: 32000
     overlap: 0.0

google_bird_classification/model.py

@@ -1,7 +1,7 @@
 from pathlib import Path
 import numpy as np
 import tensorflow as tf
-from iSparrow.model_base import ModelBase
+from faunanet.model_base import ModelBase
 import pandas as pd
 
 
@@ -48,12 +48,12 @@ class Model(ModelBase):
         return results
 
     @classmethod
-    def from_cfg(cls, iSparrow_folder: str, cfg: dict):
+    def from_cfg(cls, faunanet_folder: str, cfg: dict):
         """
         from_cfg Create a new instance from a dictionary containing keyword arguments. Usually loaded from a config file.
 
         Args:
-            iSparrow_dir (str): Installation directory of the iSparrow package
+            faunanet_dir (str): Installation directory of the faunanet package
             cfg (dict): Dictionary containing the keyword arguments
 
         Returns:
@@ -61,7 +61,7 @@ class Model(ModelBase):
         """
 
         cfg["model_path"] = str(
-            Path(iSparrow_folder) / Path("models") / Path(cfg["model_path"])
+            Path(faunanet_folder) / Path("models") / Path(cfg["model_path"])
         )
 
         return cls(**cfg)

google_bird_classification/preprocessor.py

@@ -3,7 +3,7 @@ import numpy as np
 
 from tensorflow.signal import frame as tf_split_signal_into_chunks
 
-from iSparrow import preprocessor_base as ppb
+from faunanet import preprocessor_base as ppb
 
 
 # README: work in progress - will be completed in separate issue

google_perch_lite/default.yml

@@ -19,11 +19,11 @@ Analysis:
   pattern: ".wav"
   check_time: 1
   delete_recordings: "never"
-  model_dir: ~/iSparrow/models 
+  model_dir: ~/faunanet/models 
 
 Data: 
-  input: ~/iSparrow_data
-  output: ~/iSparrow_output
+  input: ~/faunanet_data
+  output: ~/faunanet_output
   Preprocessor:
     sample_rate: 32000
     overlap: 0.0

google_perch_lite/model.py

@@ -1,12 +1,12 @@
-from iSparrow.model_base import ModelBase
+from faunanet.model_base import ModelBase
 
 try:
     import tflite_runtime.interpreter as tflite
 except ImportError:
     import tensorflow.lite as tflite
 
-from iSparrow import utils
-from iSparrow import ModelBase
+from faunanet import utils
+from faunanet import ModelBase
 
 import numpy as np
 from pathlib import Path
@@ -15,10 +15,10 @@ from scipy.special import softmax
 
 class Model(ModelBase):
     """
-    Model Implementation of a iSparrow model that uses the google perch tflite model.
+    Model Implementation of a faunanet model that uses the google perch tflite model.
 
     Args:
-        ModelBase (iSparrow.ModelBase): Model base class that provides the interface through which to interact with iSparrow.
+        ModelBase (faunanet.ModelBase): Model base class that provides the interface through which to interact with faunanet.
     """
 
     def __init__(self, model_path: str, num_threads: int = 1, **kwargs):
@@ -77,19 +77,19 @@ class Model(ModelBase):
         return confidence
 
     @classmethod
-    def from_cfg(cls, iSparrow_folder: str, cfg: dict):
+    def from_cfg(cls, faunanet_folder: str, cfg: dict):
         """
         from_cfg Create a new instance from a dictionary containing keyword arguments. Usually loaded from a config file.
 
         Args:
-            iSparrow_dir (str): Installation directory of the iSparrow package
+            faunanet_dir (str): Installation directory of the faunanet package
             cfg (dict): Dictionary containing the keyword arguments
 
         Returns:
             Model: New model instance created with the supplied kwargs.
         """
         cfg["model_name"] = str(
-            Path(iSparrow_folder) / Path("models") / cfg["model_name"]
+            Path(faunanet_folder) / Path("models") / cfg["model_name"]
         )
 
         return cls(**cfg)

google_perch_lite/preprocessor.py

@@ -1,5 +1,5 @@
 import numpy as np
-import iSparrow.preprocessor_base as ppb
+import faunanet.preprocessor_base as ppb
 
 
 class Preprocessor(ppb.PreprocessorBase):