Delete upstream demos.

examples/Image processing.lynxkite.json

@@ -1,303 +0,0 @@
-{
-  "edges": [
-    {
-      "id": "xy-edge__Open image 1output-View image 1image",
-      "source": "Open image 1",
-      "sourceHandle": "output",
-      "target": "View image 1",
-      "targetHandle": "image"
-    },
-    {
-      "id": "xy-edge__To grayscale 1output-View image 2image",
-      "source": "To grayscale 1",
-      "sourceHandle": "output",
-      "target": "View image 2",
-      "targetHandle": "image"
-    },
-    {
-      "id": "xy-edge__Blur 1output-To grayscale 1image",
-      "source": "Blur 1",
-      "sourceHandle": "output",
-      "target": "To grayscale 1",
-      "targetHandle": "image"
-    },
-    {
-      "id": "Open image 1 Flip vertically 1",
-      "source": "Open image 1",
-      "sourceHandle": "output",
-      "target": "Flip vertically 1",
-      "targetHandle": "image"
-    },
-    {
-      "id": "Flip vertically 1 Blur 1",
-      "source": "Flip vertically 1",
-      "sourceHandle": "output",
-      "target": "Blur 1",
-      "targetHandle": "image"
-    }
-  ],
-  "env": "Pillow",
-  "nodes": [
-    {
-      "data": {
-        "__execution_delay": 0.0,
-        "collapsed": null,
-        "display": null,
-        "error": null,
-        "input_metadata": null,
-        "meta": {
-          "inputs": [],
-          "name": "Open image",
-          "outputs": [
-            {
-              "name": "output",
-              "position": "right",
-              "type": {
-                "type": "None"
-              }
-            }
-          ],
-          "params": [
-            {
-              "default": null,
-              "name": "filename",
-              "type": {
-                "type": "<class 'str'>"
-              }
-            }
-          ],
-          "type": "basic"
-        },
-        "params": {
-          "filename": "https://media.licdn.com/dms/image/v2/C4E03AQEq4tdJKQiNHQ/profile-displayphoto-shrink_200_200/profile-displayphoto-shrink_200_200/0/1657270040827?e=2147483647&v=beta&t=lDxix0_0-_K7NUFqgPdzxY5-P7f73bWpPS_XRre842c"
-        },
-        "status": "done",
-        "title": "Open image"
-      },
-      "dragHandle": ".bg-primary",
-      "height": 222.0,
-      "id": "Open image 1",
-      "parentId": null,
-      "position": {
-        "x": -316.51795927908694,
-        "y": 122.80901061526373
-      },
-      "type": "basic",
-      "width": 422.0
-    },
-    {
-      "data": {
-        "display": "data:image/jpeg;base64,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",
-        "error": null,
-        "input_metadata": null,
-        "meta": {
-          "inputs": [
-            {
-              "name": "image",
-              "position": "left",
-              "type": {
-                "type": "<module 'PIL.Image' from '/media/nvme/darabos/lynxkite-2024/.venv/lib/python3.11/site-packages/PIL/Image.py'>"
-              }
-            }
-          ],
-          "name": "View image",
-          "outputs": [],
-          "params": [],
-          "type": "image"
-        },
-        "params": {},
-        "status": "done",
-        "title": "View image"
-      },
-      "dragHandle": ".bg-primary",
-      "height": 288.0,
-      "id": "View image 1",
-      "parentId": null,
-      "position": {
-        "x": 371.2152385614552,
-        "y": -243.68185336918702
-      },
-      "type": "image",
-      "width": 265.0
-    },
-    {
-      "data": {
-        "display": "data:image/jpeg;base64,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",
-        "error": null,
-        "input_metadata": null,
-        "meta": {
-          "inputs": [
-            {
-              "name": "image",
-              "position": "left",
-              "type": {
-                "type": "<module 'PIL.Image' from '/media/nvme/darabos/lynxkite-2024/.venv/lib/python3.11/site-packages/PIL/Image.py'>"
-              }
-            }
-          ],
-          "name": "View image",
-          "outputs": [],
-          "params": [],
-          "type": "image"
-        },
-        "params": {},
-        "status": "done",
-        "title": "View image"
-      },
-      "dragHandle": ".bg-primary",
-      "height": 456.0,
-      "id": "View image 2",
-      "parentId": null,
-      "position": {
-        "x": 1068.1904563045216,
-        "y": 313.7040149772122
-      },
-      "type": "image",
-      "width": 398.0
-    },
-    {
-      "data": {
-        "__execution_delay": null,
-        "collapsed": true,
-        "display": null,
-        "error": null,
-        "input_metadata": null,
-        "meta": {
-          "inputs": [
-            {
-              "name": "image",
-              "position": "left",
-              "type": {
-                "type": "<module 'PIL.Image' from '/media/nvme/darabos/lynxkite-2024/.venv/lib/python3.11/site-packages/PIL/Image.py'>"
-              }
-            }
-          ],
-          "name": "To grayscale",
-          "outputs": [
-            {
-              "name": "output",
-              "position": "right",
-              "type": {
-                "type": "None"
-              }
-            }
-          ],
-          "params": [],
-          "type": "basic"
-        },
-        "params": {},
-        "status": "done",
-        "title": "To grayscale"
-      },
-      "dragHandle": ".bg-primary",
-      "height": 200.0,
-      "id": "To grayscale 1",
-      "parentId": null,
-      "position": {
-        "x": 788.18031953735,
-        "y": 541.1434137066244
-      },
-      "type": "basic",
-      "width": 200.0
-    },
-    {
-      "data": {
-        "__execution_delay": 0.0,
-        "collapsed": null,
-        "display": null,
-        "error": null,
-        "input_metadata": null,
-        "meta": {
-          "inputs": [
-            {
-              "name": "image",
-              "position": "left",
-              "type": {
-                "type": "<module 'PIL.Image' from '/media/nvme/darabos/lynxkite-2024/.venv/lib/python3.11/site-packages/PIL/Image.py'>"
-              }
-            }
-          ],
-          "name": "Blur",
-          "outputs": [
-            {
-              "name": "output",
-              "position": "right",
-              "type": {
-                "type": "None"
-              }
-            }
-          ],
-          "params": [
-            {
-              "default": 5.0,
-              "name": "radius",
-              "type": {
-                "type": "<class 'float'>"
-              }
-            }
-          ],
-          "type": "basic"
-        },
-        "params": {
-          "radius": "5"
-        },
-        "status": "done",
-        "title": "Blur"
-      },
-      "dragHandle": ".bg-primary",
-      "height": 200.0,
-      "id": "Blur 1",
-      "parentId": null,
-      "position": {
-        "x": 505.15961556359304,
-        "y": 539.8477981917164
-      },
-      "type": "basic",
-      "width": 200.0
-    },
-    {
-      "data": {
-        "__execution_delay": null,
-        "collapsed": true,
-        "display": null,
-        "error": null,
-        "input_metadata": null,
-        "meta": {
-          "inputs": [
-            {
-              "name": "image",
-              "position": "left",
-              "type": {
-                "type": "<module 'PIL.Image' from '/media/nvme/darabos/lynxkite-2024/.venv/lib/python3.11/site-packages/PIL/Image.py'>"
-              }
-            }
-          ],
-          "name": "Flip vertically",
-          "outputs": [
-            {
-              "name": "output",
-              "position": "right",
-              "type": {
-                "type": "None"
-              }
-            }
-          ],
-          "params": [],
-          "type": "basic"
-        },
-        "params": {},
-        "status": "done",
-        "title": "Flip vertically"
-      },
-      "dragHandle": ".bg-primary",
-      "height": 200.0,
-      "id": "Flip vertically 1",
-      "position": {
-        "x": 148.51544517498044,
-        "y": 288.98657171134255
-      },
-      "type": "basic",
-      "width": 200.0
-    }
-  ]
-}

examples/Image table.lynxkite.json

@@ -1,364 +0,0 @@
-{
-  "edges": [
-    {
-      "id": "Example image table 1 View tables 1",
-      "source": "Example image table 1",
-      "sourceHandle": "output",
-      "target": "View tables 1",
-      "targetHandle": "bundle"
-    },
-    {
-      "id": "Import CSV 1 Draw molecules 1",
-      "source": "Import CSV 1",
-      "sourceHandle": "output",
-      "target": "Draw molecules 1",
-      "targetHandle": "df"
-    },
-    {
-      "id": "Draw molecules 1 View tables 2",
-      "source": "Draw molecules 1",
-      "sourceHandle": "output",
-      "target": "View tables 2",
-      "targetHandle": "bundle"
-    }
-  ],
-  "env": "LynxKite Graph Analytics",
-  "nodes": [
-    {
-      "data": {
-        "__execution_delay": null,
-        "collapsed": false,
-        "display": null,
-        "error": null,
-        "input_metadata": [],
-        "meta": {
-          "color": "orange",
-          "inputs": [],
-          "name": "Example image table",
-          "outputs": [
-            {
-              "name": "output",
-              "position": "right",
-              "type": {
-                "type": "None"
-              }
-            }
-          ],
-          "params": [],
-          "type": "basic"
-        },
-        "params": {},
-        "status": "done",
-        "title": "Example image table"
-      },
-      "dragHandle": ".bg-primary",
-      "height": 200.0,
-      "id": "Example image table 1",
-      "position": {
-        "x": 356.1935187064265,
-        "y": 224.8472733628614
-      },
-      "type": "basic",
-      "width": 280.0
-    },
-    {
-      "data": {
-        "display": {
-          "dataframes": {
-            "df": {
-              "columns": [
-                "names",
-                "images"
-              ],
-              "data": [
-                [
-                  "svg",
-                  "<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 64 64\" enable-background=\"new 0 0 64 64\"><path d=\"M56 2 18.8 42.909 8 34.729 2 34.729 18.8 62 62 2z\"/></svg>"
-                ],
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"
-                ],
-                [
-                  "caffeine",
-                  "CN1C=NC2=C1C(=O)N(C(=O)N2C)C",
-                  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"
-                ],
-                [
-                  "\u03b1-d-glucopyranose",
-                  "C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O)O)O)O)O",
-                  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"
-                ]
-              ]
-            }
-          },
-          "other": {},
-          "relations": []
-        },
-        "error": null,
-        "input_metadata": [
-          {
-            "dataframes": {
-              "df": {
-                "columns": [
-                  "image",
-                  "name",
-                  "smiles"
-                ]
-              }
-            },
-            "other": {},
-            "relations": []
-          }
-        ],
-        "meta": {
-          "color": "orange",
-          "inputs": [
-            {
-              "name": "bundle",
-              "position": "left",
-              "type": {
-                "type": "<class 'lynxkite_graph_analytics.core.Bundle'>"
-              }
-            }
-          ],
-          "name": "View tables",
-          "outputs": [],
-          "params": [
-            {
-              "default": 100,
-              "name": "limit",
-              "type": {
-                "type": "<class 'int'>"
-              }
-            }
-          ],
-          "type": "table_view"
-        },
-        "params": {
-          "limit": 100.0
-        },
-        "status": "planned",
-        "title": "View tables"
-      },
-      "dragHandle": ".bg-primary",
-      "height": 418.0,
-      "id": "View tables 2",
-      "position": {
-        "x": 815.4121289519509,
-        "y": -330.8232285057863
-      },
-      "type": "table_view",
-      "width": 1116.0
-    },
-    {
-      "data": {
-        "__execution_delay": 0.0,
-        "collapsed": null,
-        "display": null,
-        "error": "module 'rdkit.Chem' has no attribute 'Draw'",
-        "input_metadata": [
-          {}
-        ],
-        "meta": {
-          "color": "orange",
-          "inputs": [
-            {
-              "name": "df",
-              "position": "left",
-              "type": {
-                "type": "<class 'pandas.core.frame.DataFrame'>"
-              }
-            }
-          ],
-          "name": "Draw molecules",
-          "outputs": [
-            {
-              "name": "output",
-              "position": "right",
-              "type": {
-                "type": "None"
-              }
-            }
-          ],
-          "params": [
-            {
-              "default": null,
-              "name": "smiles_column",
-              "type": {
-                "type": "<class 'str'>"
-              }
-            },
-            {
-              "default": "image",
-              "name": "image_column",
-              "type": {
-                "type": "<class 'str'>"
-              }
-            }
-          ],
-          "type": "basic"
-        },
-        "params": {
-          "image_column": "image",
-          "smiles_column": "smiles"
-        },
-        "status": "done",
-        "title": "Draw molecules"
-      },
-      "dragHandle": ".bg-primary",
-      "height": 296.0,
-      "id": "Draw molecules 1",
-      "position": {
-        "x": 351.1956913898301,
-        "y": -235.00831568554486
-      },
-      "type": "basic",
-      "width": 212.0
-    }
-  ]
-}

examples/Model definition.lynxkite.json

@@ -1,671 +0,0 @@
-{
-  "edges": [
-    {
-      "id": "MSE loss 2 Optimizer 2",
-      "source": "MSE loss 2",
-      "sourceHandle": "output",
-      "target": "Optimizer 2",
-      "targetHandle": "loss"
-    },
-    {
-      "id": "Activation 1 Repeat 1",
-      "source": "Activation 1",
-      "sourceHandle": "output",
-      "target": "Repeat 1",
-      "targetHandle": "input"
-    },
-    {
-      "id": "Linear 1 Activation 1",
-      "source": "Linear 1",
-      "sourceHandle": "output",
-      "target": "Activation 1",
-      "targetHandle": "x"
-    },
-    {
-      "id": "Repeat 1 Linear 1",
-      "source": "Repeat 1",
-      "sourceHandle": "output",
-      "target": "Linear 1",
-      "targetHandle": "x"
-    },
-    {
-      "id": "Input: tensor 1 Linear 1",
-      "source": "Input: tensor 1",
-      "sourceHandle": "output",
-      "target": "Linear 1",
-      "targetHandle": "x"
-    },
-    {
-      "id": "Constant vector 1 Add 1",
-      "source": "Constant vector 1",
-      "sourceHandle": "output",
-      "target": "Add 1",
-      "targetHandle": "b"
-    },
-    {
-      "id": "Input: tensor 3 Add 1",
-      "source": "Input: tensor 3",
-      "sourceHandle": "output",
-      "target": "Add 1",
-      "targetHandle": "a"
-    },
-    {
-      "id": "Add 1 MSE loss 2",
-      "source": "Add 1",
-      "sourceHandle": "output",
-      "target": "MSE loss 2",
-      "targetHandle": "y"
-    },
-    {
-      "id": "Activation 1 Output 1",
-      "source": "Activation 1",
-      "sourceHandle": "output",
-      "target": "Output 1",
-      "targetHandle": "x"
-    },
-    {
-      "id": "Output 1 MSE loss 2",
-      "source": "Output 1",
-      "sourceHandle": "x",
-      "target": "MSE loss 2",
-      "targetHandle": "x"
-    }
-  ],
-  "env": "PyTorch model",
-  "nodes": [
-    {
-      "data": {
-        "__execution_delay": 0.0,
-        "collapsed": null,
-        "display": null,
-        "error": null,
-        "input_metadata": null,
-        "meta": {
-          "categories": [],
-          "color": "green",
-          "doc": null,
-          "id": "Optimizer",
-          "inputs": [
-            {
-              "name": "loss",
-              "position": "bottom",
-              "type": {
-                "type": "tensor"
-              }
-            }
-          ],
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examples/Multi-output demo.lynxkite.json

@@ -1,301 +0,0 @@
-{
-  "edges": [
-    {
-      "id": "Multi-output example 1 one View tables 1 bundle",
-      "source": "Multi-output example 1",
-      "sourceHandle": "one",
-      "target": "View tables 1",
-      "targetHandle": "bundle"
-    },
-    {
-      "id": "Multi-output example 1 two View tables 2 bundle",
-      "source": "Multi-output example 1",
-      "sourceHandle": "two",
-      "target": "View tables 2",
-      "targetHandle": "bundle"
-    }
-  ],
-  "env": "LynxKite Graph Analytics",
-  "nodes": [
-    {
-      "data": {
-        "__execution_delay": 0.0,
-        "collapsed": false,
-        "display": null,
-        "error": null,
-        "input_metadata": [],
-        "meta": {
-          "categories": [
-            "Examples"
-          ],
-          "color": "orange",
-          "doc": [
-            {
-              "kind": "text",
-              "value": "Returns two outputs. Also demonstrates Numpy-style docstrings."
-            },
-            {
-              "kind": "parameters",
-              "value": [
-                {
-                  "annotation": "int",
-                  "description": "Number of elements in output \"one\".",
-                  "name": "a_limit"
-                },
-                {
-                  "annotation": "int",
-                  "description": "Number of elements in output \"two\".",
-                  "name": "b_limit"
-                }
-              ]
-            },
-            {
-              "kind": "returns",
-              "value": [
-                {
-                  "annotation": "A dict with two DataFrames in it.",
-                  "description": "",
-                  "name": ""
-                }
-              ]
-            }
-          ],
-          "id": "Examples > Multi-output example",
-          "inputs": [],
-          "name": "Multi-output example",
-          "outputs": [
-            {
-              "name": "one",
-              "position": "right",
-              "type": {
-                "type": "None"
-              }
-            },
-            {
-              "name": "two",
-              "position": "right",
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-        "status": "done",
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-        "op_id": "View tables",
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-          "name": "View tables",
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-          "type": "table_view"
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-        "op_id": "View tables",
-        "params": {
-          "limit": 100.0
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-        "status": "done",
-        "title": "View tables"
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-      "dragHandle": ".drag-handle",
-      "height": 215.0,
-      "id": "View tables 2",
-      "position": {
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-      "width": 225.0
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examples/fake_data.py

@@ -1,21 +0,0 @@
-from lynxkite.core.ops import op
-from faker import Faker  # ty: ignore[unresolved-import]
-import pandas as pd
-
-faker = Faker()
-
-
-@op("LynxKite Graph Analytics", "Fake data")
-def fake(*, n=10):
-    """Creates a DataFrame with random-generated names and postal addresses.
-
-    Parameters:
-        n: Number of rows to create.
-    """
-    df = pd.DataFrame(
-        {
-            "name": [faker.name() for _ in range(n)],
-            "address": [faker.address() for _ in range(n)],
-        }
-    )
-    return df

examples/make_image_table.py

@@ -1,11 +0,0 @@
-from lynxkite.core.ops import op
-import pandas as pd
-import base64
-
-
-@op("LynxKite Graph Analytics", "Example image table")
-def make_image_table():
-    svg = '<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 64 64" enable-background="new 0 0 64 64"><path d="M56 2 18.8 42.909 8 34.729 2 34.729 18.8 62 62 2z"/></svg>'
-    data = "data:image/svg+xml;base64," + base64.b64encode(svg.encode("utf-8")).decode("utf-8")
-    http = "https://upload.wikimedia.org/wikipedia/commons/2/2e/Emojione_BW_2714.svg"
-    return pd.DataFrame({"names": ["svg", "data", "http"], "images": [svg, data, http]})

examples/matplotlib_example.py

@@ -1,34 +0,0 @@
-# From https://matplotlib.org/stable/gallery/images_contours_and_fields/contour_corner_mask.html
-import matplotlib.pyplot as plt
-import numpy as np
-from lynxkite.core.ops import op
-
-
-@op("LynxKite Graph Analytics", "Matplotlib example", view="matplotlib")
-def example():
-    # Data to plot.
-    x, y = np.meshgrid(np.arange(7), np.arange(10))
-    z = np.sin(0.5 * x) * np.cos(0.52 * y)
-
-    # Mask various z values.
-    mask = np.zeros_like(z, dtype=bool)
-    mask[2, 3:5] = True
-    mask[3:5, 4] = True
-    mask[7, 2] = True
-    mask[5, 0] = True
-    mask[0, 6] = True
-    z = np.ma.array(z, mask=mask)
-    print(z)
-
-    corner_masks = [False, True]
-    fig, axs = plt.subplots(ncols=2)
-    for ax, corner_mask in zip(axs, corner_masks):
-        cs = ax.contourf(x, y, z, corner_mask=corner_mask)
-        ax.contour(cs, colors="k")
-        ax.set_title(f"{corner_mask=}")
-
-        # Plot grid.
-        ax.grid(c="k", ls="-", alpha=0.3)
-
-        # Indicate masked points with red circles.
-        ax.plot(np.ma.array(x, mask=~mask), y, "ro")

examples/multi_output_demo.py

@@ -1,24 +0,0 @@
-from lynxkite.core.ops import op
-import pandas as pd
-
-
-@op("LynxKite Graph Analytics", "Examples", "Multi-output example", outputs=["one", "two"])
-def multi_output(*, a_limit=4, b_limit=10):
-    """
-    Returns two outputs. Also demonstrates Numpy-style docstrings.
-
-    Parameters
-    ----------
-    a_limit : int
-        Number of elements in output "one".
-    b_limit : int
-        Number of elements in output "two".
-
-    Returns
-    -------
-    A dict with two DataFrames in it.
-    """
-    return {
-        "one": pd.DataFrame({"a": range(a_limit)}),
-        "two": pd.DataFrame({"b": range(b_limit)}),
-    }

examples/ode_lstm.py

@@ -1,54 +0,0 @@
-from lynxkite.core.ops import op_registration, LongStr
-from lynxkite_graph_analytics.core import Bundle
-from matplotlib import pyplot as plt
-import numpy as np
-import pandas as pd
-import json
-
-op = op_registration("LynxKite Graph Analytics")
-
-
-@op("Drop NA")
-def drop_na(df: pd.DataFrame):
-    return df.replace("", np.nan).dropna()
-
-
-@op("Sort by")
-def sort_by(df: pd.DataFrame, *, key_columns: str):
-    df = df.copy()
-    df.sort_values(
-        by=[k.strip() for k in key_columns.split(",")],
-        inplace=True,
-        ignore_index=True,
-    )
-    return df
-
-
-@op("Group by")
-def group_by(df: pd.DataFrame, *, key_columns: str, aggregation: LongStr):
-    key_columns = [k.strip() for k in key_columns.split(",")]
-    j = json.loads(aggregation)
-    for k, vs in j.items():
-        j[k] = [list if v == "list" else v for v in vs]
-    res = df.groupby(key_columns).agg(j).reset_index()
-    res.columns = ["_".join(col) for col in res.columns]
-    return res
-
-
-@op("Take first element of list")
-def take_first_element(df: pd.DataFrame, *, column: str):
-    df = df.copy()
-    df[f"{column}_first_element"] = df[column].apply(lambda x: x[0])
-    return df
-
-
-@op("Plot time series", view="matplotlib")
-def plot_time_series(bundle: Bundle, *, table_name: str, index: int, x_column: str, y_columns: str):
-    df = bundle.dfs[table_name]
-    y_columns = [y.strip() for y in y_columns.split(",")]
-    x = df[x_column].iloc[index]
-    for y_column in y_columns:
-        y = df[y_column].iloc[index]
-        plt.plot(x, y, "o-", label=y_column)
-    plt.xlabel(x_column)
-    plt.legend()

examples/requirements.txt

@@ -1,3 +0,0 @@
-# Example of a requirements.txt file. LynxKite will automatically install anything you put here.
-faker
-matplotlib

examples/uploads/example-pizza.md

@@ -1,136 +0,0 @@
-hello
-
-### 1. **Overview**
-
-This document outlines the pricing structure and available options for our pizza delivery service. The goal is to provide clear guidance on the pricing tiers, additional offerings, and optional extras to ensure consistency across all locations and platforms (phone, online, in-app). All pricing is based on current market trends, food costs, and competitive analysis.
-
----
-
-### 2. **Pizza Options**
-
-#### 2.1 **Size & Base Pricing**
-
-| Size             | Diameter   | Price (Cheese Pizza) |
-|------------------|------------|----------------------|
-| Small            | 10 inches  | $8.99                |
-| Medium           | 12 inches  | $11.99               |
-| Large            | 14 inches  | $14.99               |
-| Extra Large      | 16 inches  | $17.99               |
-
-**Note**: Cheese pizza pricing includes sauce and cheese. Toppings are additional (see section 2.3).
-
-#### 2.2 **Crust Options**
-
-| Crust Type       | Description                              | Price Adjustment |
-|------------------|------------------------------------------|------------------|
-| Classic Hand-Tossed | Soft, airy texture                     | No Change        |
-| Thin & Crispy    | Light and crunchy                        | No Change        |
-| Stuffed Crust    | Filled with mozzarella                   | +$2.00 (M-XL)    |
-| Gluten-Free      | 10" only; made with rice flour           | +$2.50 (Small Only) |
-
----
-
-### 3. **Toppings**
-
-#### 3.1 **Standard Toppings**
-**Price per topping:**
-
-- Small: $1.00
-- Medium: $1.50
-- Large: $2.00
-- Extra Large: $2.50
-
-| Topping          | Category       |
-|------------------|----------------|
-| Pepperoni        | Meat           |
-| Sausage          | Meat           |
-| Mushrooms        | Vegetable      |
-| Onions           | Vegetable      |
-| Bell Peppers     | Vegetable      |
-| Olives           | Vegetable      |
-| Extra Cheese     | Dairy          |
-
-#### 3.2 **Premium Toppings**
-**Price per topping:**
-
-- Small: $1.75
-- Medium: $2.25
-- Large: $2.75
-- Extra Large: $3.25
-
-| Topping          | Category       |
-|------------------|----------------|
-| Grilled Chicken  | Meat           |
-| Bacon            | Meat           |
-| Sun-Dried Tomatoes| Vegetable      |
-| Artichoke Hearts | Vegetable      |
-| Feta Cheese      | Dairy          |
-| Vegan Cheese     | Dairy Alternative |
-
----
-
-### 4. **Specialty Pizzas**
-
-Specialty pizzas include a combination of premium toppings and are available in all sizes. Prices below are for Medium size, with additional costs for upgrading to larger sizes.
-
-| Pizza Name           | Description                                        | Price (Medium)  |
-|----------------------|----------------------------------------------------|-----------------|
-| Meat Lover’s          | Pepperoni, sausage, bacon, ham                     | $16.99          |
-| Veggie Delight        | Mushrooms, bell peppers, onions, olives            | $14.99          |
-| BBQ Chicken           | BBQ sauce, grilled chicken, red onions, cilantro   | $17.99          |
-| Margherita            | Fresh mozzarella, tomatoes, basil                  | $15.99          |
-| Hawaiian              | Ham, pineapple                                     | $14.99          |
-
----
-
-### 5. **Additional Menu Items**
-
-#### 5.1 **Side Orders**
-
-| Item               | Description                          | Price         |
-|--------------------|--------------------------------------|---------------|
-| Garlic Breadsticks | Served with marinara dipping sauce   | $5.99         |
-| Chicken Wings      | Buffalo, BBQ, or plain (10 pieces)   | $9.99         |
-| Mozzarella Sticks  | Served with marinara (8 pieces)      | $6.99         |
-| Caesar Salad       | Romaine, croutons, Caesar dressing   | $7.99         |
-
-#### 5.2 **Desserts**
-
-| Item               | Description                          | Price         |
-|--------------------|--------------------------------------|---------------|
-| Chocolate Brownies | Chewy and rich (6 pieces)            | $4.99         |
-| Cinnamon Sticks    | Dusted with cinnamon sugar           | $5.99         |
-
----
-
-### 6. **Drinks**
-
-| Size               | Price         |
-|--------------------|---------------|
-| 20 oz Bottle       | $1.99         |
-| 2-Liter Bottle     | $3.50         |
-
-Available options: Coke, Diet Coke, Sprite, Root Beer, Lemonade.
-
----
-
-### 7. **Delivery Fees & Minimum Order**
-
-- **Delivery Fee**: $2.99
-- **Minimum Order**: $12.00
-
-*Note: Delivery fees and minimum order thresholds apply to all delivery orders within a 5-mile radius. Additional charges may apply for orders outside this zone.*
-
----
-
-### 8. **Promotions & Discounts**
-
-- **Monday Madness**: Buy one large pizza, get a second pizza for 50% off.
-- **Student Discount**: 10% off with valid student ID (pickup only).
-- **Family Deal**: 2 large pizzas, 1 side, and 2-liter soda for $29.99.
-
----
-
-### 9. **Conclusion**
-
-This pricing and menu structure is designed to offer a wide range of choices for our customers while maintaining competitive pricing and ensuring profitability. Please ensure all team members are familiar with the details in this document and implement it accordingly.

examples/uploads/molecules2.csv

@@ -1,4 +0,0 @@
-name,smiles
-ciprofloxacin,C1CNCCN1c(c2)c(F)cc3c2N(C4CC4)C=C(C3=O)C(=O)O
-caffeine,CN1C=NC2=C1C(=O)N(C(=O)N2C)C
-α-d-glucopyranose,C([C@@H]1[C@H]([C@@H]([C@H]([C@H](O1)O)O)O)O)O

examples/word2vec.py

@@ -1,27 +0,0 @@
-from lynxkite.core.ops import op
-import pandas as pd
-
-ENV = "LynxKite Graph Analytics"
-
-
-@op(ENV, "Word2vec for the top 1000 words", slow=True)
-def word2vec_1000():
-    import staticvectors  # ty: ignore[unresolved-import]
-
-    model = staticvectors.StaticVectors("neuml/word2vec-quantized")
-    df = pd.read_csv(
-        "https://gist.githubusercontent.com/deekayen/4148741/raw/98d35708fa344717d8eee15d11987de6c8e26d7d/1-1000.txt",
-        names=["word"],
-    )
-    df["embedding"] = model.embeddings(df.word.tolist()).tolist()
-    return df
-
-
-@op(ENV, "Take first N")
-def first_n(df: pd.DataFrame, *, n=10):
-    return df.head(n)
-
-
-@op(ENV, "Sample N")
-def sample_n(df: pd.DataFrame, *, n=10):
-    return df.sample(n)