flojoy-ai · flojoy-bot · Aug 12, 2023 · Aug 14, 2023 · Aug 14, 2023
diff --git a/docs/nodes/EXTRACTORS/DATAFRAME/EXTRACT_COLUMNS/a1-[autogen]/docstring.txt b/docs/nodes/EXTRACTORS/DATAFRAME/EXTRACT_COLUMNS/a1-[autogen]/docstring.txt
@@ -1,12 +1,17 @@
 
 The EXTRACT_COLUMNS node takes an input dataframe/matrix and returns a dataframe/matrix with only the specified columns.
 
+Inputs
+------
+default : DataFrame|Matrix
+    Input to use as the table for column extraction
+
 Parameters
 ----------
 columns : list of str or list of int
-    The columns to extract from the input dataframe.
+    The columns to extract from the input dataframe
 
 Returns
 -------
-DataFrame | Matrix
-    The dataframe or matrix with only the specified columns.
+DataFrame|Matrix
+    DataFrame or Matrix with only the specified columns
diff --git a/docs/nodes/EXTRACTORS/FILE/OPEN_IMAGE/a1-[autogen]/docstring.txt b/docs/nodes/EXTRACTORS/FILE/OPEN_IMAGE/a1-[autogen]/docstring.txt
@@ -1,6 +1,10 @@
 
 The OPEN_IMAGE node loads an image file from disk and returns an image type DataContainer object.
 
+Inputs
+------
+default: None
+
 Parameters
 ----------
 file_path : str
@@ -9,3 +13,4 @@ file_path : str
 Returns
 -------
 Image
+    Image loaded from specified file path
diff --git a/docs/nodes/EXTRACTORS/FILE/OPEN_PARQUET/a1-[autogen]/docstring.txt b/docs/nodes/EXTRACTORS/FILE/OPEN_PARQUET/a1-[autogen]/docstring.txt
@@ -1,11 +1,16 @@
 
 The OPEN_PARQUET node loads a local file of the .parquet file format. It then returns the file as a pandas.Dataframe type.
 
+Inputs
+------
+default: None
+
 Parameters
 ----------
 file_path : str
-    path to the file to be loaded
+    File path to the .parquet file or an URL of a .parquet file.
 
 Returns
 -------
-Dataframe
+DataFrame
+    DataFrame loaded from .parquet file
diff --git a/docs/nodes/EXTRACTORS/FILE/READ_CSV/a1-[autogen]/docstring.txt b/docs/nodes/EXTRACTORS/FILE/READ_CSV/a1-[autogen]/docstring.txt
@@ -5,3 +5,8 @@ Parameters
 ----------
 file_path : str
     File path to the .csv file or an URL of a .csv file.
+
+Returns
+-------
+DataFrame
+    DataFrame loaded from .csv file
diff --git a/docs/nodes/EXTRACTORS/FILE/READ_S3/a1-[autogen]/docstring.txt b/docs/nodes/EXTRACTORS/FILE/READ_S3/a1-[autogen]/docstring.txt
@@ -1,6 +1,10 @@
 
 The READ_S3 node takes a S3_key name, S3 bucket name, and file name as input, and extracts the file from the specified bucket using the S3_key that was saved.
 
+Inputs
+------
+default: None
+
 Parameters
 ----------
 s3_name : str
@@ -13,3 +17,5 @@ file_name : str
 Returns
 -------
 DataFrame
+    DataFrame loaded from file in the specfied bucket
+
diff --git a/docs/nodes/GENERATORS/SAMPLE_IMAGES/SKLEARNIMAGE/a1-[autogen]/docstring.txt b/docs/nodes/GENERATORS/SAMPLE_IMAGES/SKLEARNIMAGE/a1-[autogen]/docstring.txt
@@ -3,3 +3,14 @@ The SKLEARNIMAGE node is designed to load example images from scikit-image.
 
 Examples can be found here:
 https://scikit-image.org/docs/stable/auto_examples/index.html
+
+Parameters
+----------
+img_key : str
+    The name of the image to be loader from scikit-image.
+
+Outputs
+-------
+Image
+    DataContainer containing image loaded from scikit-image.
+
diff --git a/docs/nodes/GENERATORS/SAMPLE_IMAGES/SKLEARNIMAGE/a1-[autogen]/python_code.txt b/docs/nodes/GENERATORS/SAMPLE_IMAGES/SKLEARNIMAGE/a1-[autogen]/python_code.txt
@@ -3,7 +3,7 @@ from skimage import data
 from typing import Literal
 
 
-@flojoy(node_type="default", deps={"scikit-image": "0.21.0"})
+@flojoy(deps={"scikit-image": "0.21.0"})
 def SKLEARNIMAGE(
     img_key: Literal[
         "astronaut",
@@ -52,6 +52,7 @@ def SKLEARNIMAGE(
     ] = "astronaut"
 ) -> Image:
 
+
     img_array = getattr(data, img_key)()
 
     if len(img_array.shape) == 2:

diff --git a/docs/nodes/LOADERS/CLOUD_DATABASE/LOADER/a1-[autogen]/python_code.txt b/docs/nodes/LOADERS/CLOUD_DATABASE/LOADER/a1-[autogen]/python_code.txt
@@ -15,7 +15,7 @@ def LOADER(
     measurement_id: Optional[str] = None,
     dc_id: Optional[str] = None,
 ) -> DataContainer:
-    api_key = get_env_var("FLOJOY_CLOUD_API_KEY")
+    api_key = get_env_var("FLOJOY_CLOUD_KEY")
 
     if api_key is None:
         raise KeyError("Frontier API key is not found!")

diff --git a/docs/nodes/LOADERS/LOCAL_FILE_SYSTEM/LOCAL_FILE/a1-[autogen]/docstring.txt b/docs/nodes/LOADERS/LOCAL_FILE_SYSTEM/LOCAL_FILE/a1-[autogen]/docstring.txt
@@ -11,5 +11,5 @@ file_path : str
 Returns
 -------
 Image|DataFrame
-    type 'image' for file_type 'image'
-    type 'dataframe' for file_type 'json', 'csv', 'excel', 'xml'
+    Image for file_type 'image'
+    DataFrame for file_type 'json', 'csv', 'excel', 'xml'
diff --git a/docs/nodes/LOADERS/LOCAL_FILE_SYSTEM/OPEN_MATLAB/a1-[autogen]/docstring.txt b/docs/nodes/LOADERS/LOCAL_FILE_SYSTEM/OPEN_MATLAB/a1-[autogen]/docstring.txt
@@ -10,4 +10,5 @@ file_path : str
 
 Returns
 -------
-Dataframe
+DataFrame
+    DataFrame loaded from the .mat file
diff --git a/docs/nodes/TRANSFORMERS/ARITHMETIC/ADD/a1-[autogen]/python_code.txt b/docs/nodes/TRANSFORMERS/ARITHMETIC/ADD/a1-[autogen]/python_code.txt
@@ -1,6 +1,6 @@
 import numpy as np
 from flojoy import flojoy, OrderedPair, Scalar, Vector
-from nodes.TRANSFORMERS.ARITHMETIC.utils.arithmetic_utils import get_val
+from flojoy_nodes.TRANSFORMERS.ARITHMETIC.utils.arithmetic_utils import get_val
 from functools import reduce
 
 
@@ -9,6 +9,7 @@ def ADD(
     a: OrderedPair | Scalar | Vector, b: list[OrderedPair | Scalar | Vector]
 ) -> OrderedPair | Scalar | Vector:
 
+
     initial = get_val(a)
     seq = map(lambda dc: get_val(dc), b)
     y = reduce(lambda u, v: np.add(u, v), seq, initial)

diff --git a/docs/nodes/TRANSFORMERS/ARITHMETIC/DIVIDE/a1-[autogen]/python_code.txt b/docs/nodes/TRANSFORMERS/ARITHMETIC/DIVIDE/a1-[autogen]/python_code.txt
@@ -1,6 +1,6 @@
 import numpy as np
 from flojoy import flojoy, OrderedPair, Scalar, Vector
-from nodes.TRANSFORMERS.ARITHMETIC.utils.arithmetic_utils import get_val
+from flojoy_nodes.TRANSFORMERS.ARITHMETIC.utils.arithmetic_utils import get_val
 from functools import reduce
 
 
@@ -9,6 +9,7 @@ def DIVIDE(
     a: OrderedPair | Scalar | Vector, b: list[OrderedPair | Scalar | Vector]
 ) -> OrderedPair | Scalar | Vector:
 
+
     initial = get_val(a)
     seq = map(lambda dc: get_val(dc), b)
     y = reduce(lambda u, v: np.divide(u, v), seq, initial)

diff --git a/docs/nodes/TRANSFORMERS/ARITHMETIC/MULTIPLY/a1-[autogen]/python_code.txt b/docs/nodes/TRANSFORMERS/ARITHMETIC/MULTIPLY/a1-[autogen]/python_code.txt
@@ -1,6 +1,6 @@
 import numpy as np
 from flojoy import OrderedPair, flojoy, Scalar, Vector
-from nodes.TRANSFORMERS.ARITHMETIC.utils.arithmetic_utils import get_val
+from flojoy_nodes.TRANSFORMERS.ARITHMETIC.utils.arithmetic_utils import get_val
 from functools import reduce
 
 
@@ -9,6 +9,7 @@ def MULTIPLY(
     a: OrderedPair | Scalar | Vector, b: list[OrderedPair | Scalar | Vector]
 ) -> OrderedPair | Scalar | Vector:
 
+
     initial = get_val(a)
     seq = map(lambda dc: get_val(dc), b)
     y = reduce(lambda u, v: np.multiply(u, v), seq, initial)

diff --git a/docs/nodes/TRANSFORMERS/ARITHMETIC/SUBTRACT/a1-[autogen]/python_code.txt b/docs/nodes/TRANSFORMERS/ARITHMETIC/SUBTRACT/a1-[autogen]/python_code.txt
@@ -1,6 +1,6 @@
 import numpy as np
 from flojoy import flojoy, OrderedPair, Scalar, Vector
-from nodes.TRANSFORMERS.ARITHMETIC.utils.arithmetic_utils import get_val
+from flojoy_nodes.TRANSFORMERS.ARITHMETIC.utils.arithmetic_utils import get_val
 from functools import reduce
 
 
@@ -9,6 +9,7 @@ def SUBTRACT(
     a: OrderedPair | Scalar | Vector, b: list[OrderedPair | Scalar | Vector]
 ) -> OrderedPair | Scalar | Vector:
 
+
     initial = get_val(a)
     seq = map(lambda dc: get_val(dc), b)
     y = reduce(lambda u, v: np.subtract(u, v), seq, initial)

diff --git a/docs/nodes/TRANSFORMERS/CALCULUS/DOUBLE_INDEFINITE_INTEGRAL/a1-[autogen]/python_code.txt b/docs/nodes/TRANSFORMERS/CALCULUS/DOUBLE_INDEFINITE_INTEGRAL/a1-[autogen]/python_code.txt
@@ -2,28 +2,24 @@ from flojoy import flojoy, OrderedTriple, Matrix
 import numpy as np
 
 
+def contains_only_numbers(column, colName):
+    for i in range(0, len(column)):
+        if not isinstance(column.item(i), (int, float)):
+            raise ValueError(
+                f"The value {column.item(i)} in column {colName} is of type {type(column.item(i))}. The OrderedTriple need to contain only int or float values."
+            )
+
+
 @flojoy
 def DOUBLE_INDEFINITE_INTEGRAL(
     default: OrderedTriple, width: int = 3, height: int = 3
 ) -> Matrix:
 
 
-    def contains_only_numbers(column):
-        return all(isinstance(value, (np.int_, np.float_)) for value in column)
-
     if np.divide(len(default.x), width) == height:
-        if not contains_only_numbers(default.x):
-            raise ValueError(
-                "There is some values that are not of type int or float. The OrderedTriple need to contain only int or float values."
-            )
-        elif not contains_only_numbers(default.y):
-            raise ValueError(
-                "There is some values that are not of type int or float. The OrderedTriple need to contain only int or float values."
-            )
-        elif not contains_only_numbers(default.z):
-            raise ValueError(
-                "There is some values that are not of type int or float. The OrderedTriple need to contain only int or float values."
-            )
+        contains_only_numbers(default.x, "x")
+        contains_only_numbers(default.y, "y")
+        contains_only_numbers(default.z, "z")
 
         input_x = np.reshape(default.x, (height, width))
         input_y = np.reshape(default.y, (height, width))