{
  "__type": "IngestedDoc",
  "__tag": 4010,
  "_content": {
    "Notes": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Warns": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Raises": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Yields": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Methods": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Returns": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Summary": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "No Docstrings"
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Receives": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Warnings": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Attributes": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Parameters": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Extended Summary": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    },
    "Other Parameters": {
      "__type": "Section",
      "__tag": 4015,
      "children": [],
      "title": [],
      "level": 0,
      "target": null
    }
  },
  "_ordered_sections": [
    "Summary",
    "Extended Summary",
    "Parameters",
    "Attributes",
    "Methods",
    "Returns",
    "Yields",
    "Receives",
    "Other Parameters",
    "Raises",
    "Warns",
    "Warnings",
    "Notes"
  ],
  "item_file": "/scipy/odr/_odrpack.py",
  "item_line": 0,
  "item_type": "module",
  "aliases": [
    "scipy.odr._odrpack"
  ],
  "example_section_data": {
    "__type": "Section",
    "__tag": 4015,
    "children": [],
    "title": [],
    "level": 0,
    "target": null
  },
  "see_also": [],
  "signature": null,
  "references": null,
  "qa": "scipy.odr._odrpack",
  "arbitrary": [
    {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Python wrappers for Orthogonal Distance Regression (ODRPACK)."
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "BulletList",
          "__tag": 4053,
          "ordered": false,
          "start": 1,
          "children": [
            {
              "__type": "ListItem",
              "__tag": 4054,
              "children": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Array formats -- FORTRAN stores its arrays in memory column first, i.e., an   array element A(i, j, k) will be next to A(i+1, j, k). In C and, consequently,   NumPy, arrays are stored row first: A[i, j, k] is next to A[i, j, k+1]. For   efficiency and convenience, the input and output arrays of the fitting   function (and its Jacobians) are passed to FORTRAN without transposition.   Therefore, where the ODRPACK documentation says that the X array is of shape   (N, M), it will be passed to the Python function as an array of shape (M, N).   If M==1, the 1-D case, then nothing matters; if M>1, then your   Python functions will be dealing with arrays that are indexed in reverse of   the ODRPACK documentation. No real issue, but watch out for your indexing of   the Jacobians: the i,jth elements (@f_i/@x_j) evaluated at the nth   observation will be returned as jacd[j, i, n]. Except for the Jacobians, it   really is easier to deal with x[0] and x[1] than x[:,0] and x[:,1]. Of course,   you can always use the transpose() function from SciPy explicitly."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "ListItem",
              "__tag": 4054,
              "children": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Examples -- See the accompanying file test/test.py for examples of how to set   up fits of your own. Some are taken from the User's Guide; some are from   other sources."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "ListItem",
              "__tag": 4054,
              "children": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Models -- Some common models are instantiated in the accompanying module   models.py . Contributions are welcome."
                    }
                  ]
                }
              ]
            }
          ]
        }
      ],
      "title": [
        {
          "__type": "Text",
          "__tag": 4046,
          "value": "Notes"
        }
      ],
      "level": 0,
      "target": null
    },
    {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "BulletList",
          "__tag": 4053,
          "ordered": false,
          "start": 1,
          "children": [
            {
              "__type": "ListItem",
              "__tag": 4054,
              "children": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Thanks to Arnold Moene and Gerard Vermeulen for fixing some killer bugs."
                    }
                  ]
                }
              ]
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Robert Kern robert.kern@gmail.com"
            }
          ]
        }
      ],
      "title": [
        {
          "__type": "Text",
          "__tag": 4046,
          "value": "Credits"
        }
      ],
      "level": 0,
      "target": null
    }
  ],
  "local_refs": []
}