{
  "__type": "IngestedDoc",
  "__tag": 4010,
  "_content": {
    "Notes": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "The "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "center"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " argument only affects the calculation of the central value around which the MAD is calculated. That is, passing in "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "center=np.mean"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " will calculate the MAD around the mean - it will not calculate the "
            },
            {
              "__type": "Emphasis",
              "__tag": 4047,
              "children": [
                {
                  "__type": "Text",
                  "__tag": 4046,
                  "value": "mean"
                }
              ]
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " absolute deviation."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "The input array may contain "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "inf",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", but if "
            },
            {
              "__type": "ParamRef",
              "__tag": 4071,
              "name": "center"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " returns "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "inf",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ", the corresponding MAD for that data will be "
            },
            {
              "__type": "InlineRole",
              "__tag": 4003,
              "value": "nan",
              "domain": null,
              "role": null,
              "inventory": null
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Beginning in SciPy 1.9, "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.matrix"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " inputs (not recommended for new code) are converted to "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.ndarray"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " before the calculation is performed. In this case, the output will be a scalar or "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.ndarray"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " of appropriate shape rather than a 2D "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.matrix"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ". Similarly, while masked elements of masked arrays are ignored, the output will be a scalar or "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.ndarray"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " rather than a masked array with "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "mask=False"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Strong",
              "__tag": 4048,
              "children": [
                {
                  "__type": "Text",
                  "__tag": 4046,
                  "value": "Array API Standard Support"
                }
              ]
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "CrossRef",
              "__tag": 4002,
              "value": "median_abs_deviation",
              "reference": {
                "__type": "LocalRef",
                "__tag": 4022,
                "kind": "module",
                "path": "scipy.stats._stats_py:median_abs_deviation"
              },
              "kind": "module"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "SCIPY_ARRAY_API=1"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": " and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported."
            }
          ]
        },
        {
          "__type": "Code",
          "__tag": 4050,
          "value": "====================  ====================  ====================\nLibrary               CPU                   GPU\n====================  ====================  ====================\nNumPy                 ✅                     n/a                 \nCuPy                  n/a                   ✅                   \nPyTorch               ✅                     ✅                   \nJAX                   ⛔                     ⛔                   \nDask                  ⛔                     n/a                 \n====================  ====================  ====================",
          "execution_status": null
        },
        {
          "__type": "Blockquote",
          "__tag": 4059,
          "children": [
            {
              "__type": "Paragraph",
              "__tag": 4045,
              "children": [
                {
                  "__type": "Text",
                  "__tag": 4046,
                  "value": "See "
                },
                {
                  "__type": "InlineRole",
                  "__tag": 4003,
                  "value": "dev-arrayapi",
                  "domain": null,
                  "role": "ref",
                  "inventory": null
                },
                {
                  "__type": "Text",
                  "__tag": 4046,
                  "value": " for more information."
                }
              ]
            }
          ]
        }
      ],
      "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": [
        {
          "__type": "Parameters",
          "__tag": 4026,
          "children": [
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "mad",
              "annotation": "scalar or ndarray",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "If "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "axis=None"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ", a scalar is returned. If the input contains integers or floats of smaller precision than "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "np.float64"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ", then the output data-type is "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "np.float64"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ". Otherwise, the output data-type is the same as that of the input."
                    }
                  ]
                }
              ]
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Summary": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "Compute the median absolute deviation of the data along the given axis."
            }
          ]
        }
      ],
      "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": [
        {
          "__type": "Parameters",
          "__tag": 4026,
          "children": [
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "x",
              "annotation": "array_like",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Input array or object that can be converted to an array."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "axis",
              "annotation": "int or None, default: 0",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "If an int, the axis of the input along which to compute the statistic. The statistic of each axis-slice (e.g. row) of the input will appear in a corresponding element of the output. If "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "None"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ", the input will be raveled before computing the statistic."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "center",
              "annotation": "callable, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "A function that will return the central value. The default is to use np.median. Any user defined function used will need to have the function signature "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "func(arr, axis)"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "scale",
              "annotation": "scalar or str, optional",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "The numerical value of scale will be divided out of the final result. The default is 1.0. The string \"normal\" is also accepted, and results in "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "scale"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " being the inverse of the standard normal quantile function at 0.75, which is approximately 0.67449. Array-like scale is also allowed, as long as it broadcasts correctly to the output such that "
                    },
                    {
                      "__type": "InlineCode",
                      "__tag": 4051,
                      "value": "out / scale"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " is a valid operation. The output dimensions depend on the input array, "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "x"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": ", and the "
                    },
                    {
                      "__type": "ParamRef",
                      "__tag": 4071,
                      "name": "axis"
                    },
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": " argument."
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "nan_policy",
              "annotation": "{'propagate', 'omit', 'raise'}",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "Defines how to handle input NaNs."
                    }
                  ]
                },
                {
                  "__type": "BulletList",
                  "__tag": 4053,
                  "ordered": false,
                  "start": 1,
                  "children": [
                    {
                      "__type": "ListItem",
                      "__tag": 4054,
                      "children": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "InlineCode",
                              "__tag": 4051,
                              "value": "propagate"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": ": if a NaN is present in the axis slice (e.g. row) along   which the  statistic is computed, the corresponding entry of the output   will be NaN."
                            }
                          ]
                        }
                      ]
                    },
                    {
                      "__type": "ListItem",
                      "__tag": 4054,
                      "children": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "InlineCode",
                              "__tag": 4051,
                              "value": "omit"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": ": NaNs will be omitted when performing the calculation.   If insufficient data remains in the axis slice along which the   statistic is computed, the corresponding entry of the output will be   NaN."
                            }
                          ]
                        }
                      ]
                    },
                    {
                      "__type": "ListItem",
                      "__tag": 4054,
                      "children": [
                        {
                          "__type": "Paragraph",
                          "__tag": 4045,
                          "children": [
                            {
                              "__type": "InlineCode",
                              "__tag": 4051,
                              "value": "raise"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": ": if a NaN is present, a "
                            },
                            {
                              "__type": "InlineCode",
                              "__tag": 4051,
                              "value": "ValueError"
                            },
                            {
                              "__type": "Text",
                              "__tag": 4046,
                              "value": " will be raised."
                            }
                          ]
                        }
                      ]
                    }
                  ]
                }
              ]
            },
            {
              "__type": "DocParam",
              "__tag": 4016,
              "name": "keepdims",
              "annotation": "bool, default: False",
              "desc": [
                {
                  "__type": "Paragraph",
                  "__tag": 4045,
                  "children": [
                    {
                      "__type": "Text",
                      "__tag": 4046,
                      "value": "If this is set to True, the axes which are reduced are left in the result as dimensions with size one. With this option, the result will broadcast correctly against the input array."
                    }
                  ]
                }
              ]
            }
          ]
        }
      ],
      "title": [],
      "level": 0,
      "target": null
    },
    "Extended Summary": {
      "__type": "Section",
      "__tag": 4015,
      "children": [
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "The median absolute deviation (MAD, "
            },
            {
              "__type": "FootnoteReference",
              "__tag": 4066,
              "label": "1"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": ") computes the median over the absolute deviations from the median. It is a measure of dispersion similar to the standard deviation but more robust to outliers "
            },
            {
              "__type": "FootnoteReference",
              "__tag": 4066,
              "label": "2"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "."
            }
          ]
        },
        {
          "__type": "Paragraph",
          "__tag": 4045,
          "children": [
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "The MAD of an empty array is "
            },
            {
              "__type": "InlineCode",
              "__tag": 4051,
              "value": "np.nan"
            },
            {
              "__type": "Text",
              "__tag": 4046,
              "value": "."
            }
          ]
        },
        {
          "__type": "Admonition",
          "__tag": 4056,
          "kind": "versionadded",
          "base_type": "neutral",
          "children": [
            {
              "__type": "AdmonitionTitle",
              "__tag": 4055,
              "children": [
                {
                  "__type": "Text",
                  "__tag": 4046,
                  "value": "versionadded 1.5.0"
                }
              ]
            }
          ]
        }
      ],
      "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/stats/_stats_py.py",
  "item_line": 3234,
  "item_type": "function",
  "aliases": [
    "scipy.stats.median_abs_deviation"
  ],
  "example_section_data": {
    "__type": "Section",
    "__tag": 4015,
    "children": [
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "When comparing the behavior of `median_abs_deviation` with ``np.std``,\nthe latter is affected when we change a single value of an array to have an\noutlier value while the MAD hardly changes:\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "import numpy as np\nfrom scipy import stats\nx = stats.norm.rvs(size=100, scale=1, random_state=123456)\n",
        "execution_status": "success"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "x.std()\nstats.median_abs_deviation(x)\n",
        "execution_status": "failure"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "x[0] = 345.6\n",
        "execution_status": "success"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "x.std()\nstats.median_abs_deviation(x)\n",
        "execution_status": "failure"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nAxis handling example:\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "x = np.array([[10, 7, 4], [3, 2, 1]])\nx\nstats.median_abs_deviation(x)\n",
        "execution_status": "success"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "stats.median_abs_deviation(x, axis=None)\n",
        "execution_status": "failure"
      },
      {
        "__type": "Text",
        "__tag": 4046,
        "value": "\nScale normal example:\n\n"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "x = stats.norm.rvs(size=1000000, scale=2, random_state=123456)\n",
        "execution_status": "success"
      },
      {
        "__type": "Code",
        "__tag": 4050,
        "value": "stats.median_abs_deviation(x)\nstats.median_abs_deviation(x, scale='normal')\n",
        "execution_status": "failure"
      }
    ],
    "title": [],
    "level": 0,
    "target": null
  },
  "see_also": [
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "numpy.median",
        "reference": {
          "__type": "RefInfo",
          "__tag": 4000,
          "module": "current-module",
          "version": "current-version",
          "kind": "to-resolve",
          "path": "numpy.median"
        },
        "kind": "module"
      },
      "descriptions": [],
      "type": "func"
    },
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "numpy.std",
        "reference": {
          "__type": "RefInfo",
          "__tag": 4000,
          "module": "current-module",
          "version": "current-version",
          "kind": "to-resolve",
          "path": "numpy.std"
        },
        "kind": "module"
      },
      "descriptions": [],
      "type": "func"
    },
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "numpy.var",
        "reference": {
          "__type": "RefInfo",
          "__tag": 4000,
          "module": "current-module",
          "version": "current-version",
          "kind": "to-resolve",
          "path": "numpy.var"
        },
        "kind": "module"
      },
      "descriptions": [],
      "type": "func"
    },
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "scipy.stats.iqr",
        "reference": {
          "__type": "LocalRef",
          "__tag": 4022,
          "kind": "module",
          "path": "scipy.stats._stats_py:iqr"
        },
        "kind": "module"
      },
      "descriptions": [],
      "type": "func"
    },
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "scipy.stats.tmean",
        "reference": {
          "__type": "LocalRef",
          "__tag": 4022,
          "kind": "module",
          "path": "scipy.stats._stats_py:tmean"
        },
        "kind": "module"
      },
      "descriptions": [],
      "type": "func"
    },
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "scipy.stats.tstd",
        "reference": {
          "__type": "LocalRef",
          "__tag": 4022,
          "kind": "module",
          "path": "scipy.stats._stats_py:tstd"
        },
        "kind": "module"
      },
      "descriptions": [],
      "type": "func"
    },
    {
      "__type": "SeeAlsoItem",
      "__tag": 4028,
      "name": {
        "__type": "CrossRef",
        "__tag": 4002,
        "value": "scipy.stats.tvar",
        "reference": {
          "__type": "LocalRef",
          "__tag": 4022,
          "kind": "module",
          "path": "scipy.stats._stats_py:tvar"
        },
        "kind": "module"
      },
      "descriptions": [],
      "type": "func"
    }
  ],
  "signature": {
    "__type": "SignatureNode",
    "__tag": 4029,
    "kind": "function",
    "parameters": [
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "x",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": {
          "__type": "Empty",
          "__tag": 4031
        }
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "axis",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "0"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "center",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "None"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "scale",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "1.0"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "nan_policy",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "POSITIONAL_OR_KEYWORD",
        "default": "propagate"
      },
      {
        "__type": "SigParam",
        "__tag": 4030,
        "name": "keepdims",
        "annotation": {
          "__type": "Empty",
          "__tag": 4031
        },
        "kind": "KEYWORD_ONLY",
        "default": "False"
      }
    ],
    "return_annotation": {
      "__type": "Empty",
      "__tag": 4031
    },
    "target_name": "median_abs_deviation"
  },
  "references": [
    ".. [1] \"Median absolute deviation\",",
    "       https://en.wikipedia.org/wiki/Median_absolute_deviation",
    ".. [2] \"Robust measures of scale\",",
    "       https://en.wikipedia.org/wiki/Robust_measures_of_scale"
  ],
  "qa": "scipy.stats._stats_py:median_abs_deviation",
  "arbitrary": [],
  "local_refs": [
    "axis",
    "center",
    "keepdims",
    "mad",
    "nan_policy",
    "scale",
    "x"
  ]
}