str:\n bytes_io = io.BytesIO()\n np.save(bytes_io, data, allow_pickle=False)\n return base64.b64encode(zlib.compress(bytes_io.getvalue())).decode('utf-8')","def default_decoder(obj):\n\n if isinstance(obj, dict):\n if '__ndarray__' in obj:\n data = base64.b64decode(obj['__ndarray__'])\n return np.frombuffer(data, obj['dtype']).reshape(obj['shape'])\n else:\n for k, v in obj.items():\n obj[k] = default_decoder(v)\n elif isinstance(obj, bytes):\n return pickle.loads(obj)\n\n return obj","def _encodeArray(self, array):\n\n # Actually, we want dtype,naxis,axNlen,base64(array)\n return base64.b64encode(array.tostring())","def base64_decode_image(inStr):\n imgDat, imgType, imgShape = json.loads(inStr)\n imgDat = bytes(imgDat, encoding=\"utf-8\")\n\n imgDat = base64_decode_array(imgDat, imgType)\n imgDat = imgDat.reshape(imgShape)\n return imgDat","def dic_pickle_dumps_and_b64(data):\n for i in data:\n data[i] = base64.b64encode(pickle.dumps(data[i]))\n return data","def serialize(self, data):\n if isinstance(data, dict):\n return json.dumps(\n {\n key: value.tolist() if isinstance(value, np.ndarray) else value\n for key, value in data.items()\n }\n )\n\n if hasattr(data, \"read\"):\n return data.read()\n\n if isinstance(data, np.ndarray):\n return json.dumps(data.tolist())\n\n return json.dumps(data)","def get_data_structure_representation(self) -> dict:\n byte_buff = self.get_rle()\n encoding = \"RLE\"\n\n if len(byte_buff) > self.grid_size[0] * self.grid_size[1] * 4:\n encoding = \"RAW\"\n byte_buff = self.get_raw()\n print(\"RAW ran\")\n else:\n print(\"RLE ran\")\n\n json_dict = {\n \"encoding\": encoding,\n \"nodes\": [base64.b64encode(bytes(byte_buff)).decode()],\n \"dimensions\": [self.grid_size[0], self.grid_size[1]]\n }\n\n return json_dict","def rec_to_dict(arr):\n\n return dict(zip(arr.dtype.names, arr))","def to_dict(self):\n config = {\n 'dtype': self.dtype,\n 'unit': self.unit,\n 'max_shape': self.max_shape,\n 'min_shape': self.min_shape,\n 'ndim': self.ndim,\n 'max_value': self.max_value,\n 'min_value': self.min_value,\n 'depends_on': self.depends_on,\n }\n return {'node_type': 'Array', 'config': config}","def base64_data(cls, val):\n return cls('base64_data', val)","def np_to_base64(img_np):\n img = Image.fromarray(img_np.astype('uint8'), 'RGB')\n buffered = BytesIO()\n img.save(buffered, format=\"PNG\")\n return u\"data:image/png;base64,\" + base64.b64encode(buffered.getvalue()).decode(\"ascii\")","def encode(self, obj):\n def hint_tuples(item):\n \"\"\"\n If the object to be serialized is a tuple, or contains tuples,\n replace tuples with a dictionary that can be reconstructed on\n the receiver\n\n Args:\n self (object): Object that will be JSON serialized\n\n Returns:\n object: Object without tuples\n \"\"\"\n if isinstance(item, tuple):\n return {'__tuple__': True, 'items': item}\n if isinstance(item, list):\n return [hint_tuples(e) for e in item]\n if isinstance(item, dict):\n return {key: hint_tuples(value) for key, value in item.items()}\n return item\n\n return super(MultiDimensionalArrayEncoder, self).encode(hint_tuples(obj))","def to_dict(self):\n\n shape = super().to_dict()\n shape[\"filename\"] = self.filename\n shape[\"scale\"] = self.scale.tolist()\n return shape","def _metatize_HashableNDArray(obj):\n return obj","def to_internal_value(self, data):\n if isinstance(data, str) and data.startswith('data:image'):\n # Found image is encoded, and must be decoded\n format, imgstr = data.split(';base64,')\n ext = format.split('/')[-1] # Extract file extension\n id = uuid.uuid4()\n data = ContentFile(base64.b64decode(imgstr), name = id.urn[9:] + '.' + ext)\n return super(Base64ImageField, self).to_internal_value(data)","def __init__(self, data, from_base64=False, **kwargs):\n super().__init__(data, **kwargs)\n self.type_name = 'base64'\n self.encoding = kwargs.get('encoding', 'original')\n # TODO data should be bytes only\n if from_base64:\n self.data = data\n else:\n self.data = base64.b64encode(data).decode()","def _todict(self, matobj, filetype):\n self.filetype = filetype\n self.dict = {}\n self.index = 0\n for strg in matobj._fieldnames:\n self.elem = matobj.__dict__[strg]\n if isinstance(self.elem, scipy.io.matlab.mio5_params.mat_struct):\n self.dict[strg] = self._todict(self.elem, self.filetype)\n elif isinstance(self.elem, numpy.ndarray):\n self.parameter_list = []\n self.dict[strg] = self._tolist(self.elem, self.parameter_list)\n if self.filetype == \".mat\":\n if len(self.elem)>0:\n if isinstance(self.elem[0], numpy.ndarray):\n if strg == 'immagini':\n self.dict['arrayx'] = len(self.elem[0])\n self.dict['arrayy'] = len(self.elem)\n if self.parameter_list:\n if strg == 'immagini':\n self.dict['numshots'] = len(self.parameter_list) / (len(self.elem[0]) * len(self.elem))\n elif self.filetype == \".dat\":\n self.parameter_list = []\n self.dict[strg] = self._tolist(self.elem, self.parameter_list)\n if strg == 'roi_points':\n self.dict['arrayx'] = self.elem[2] - self.elem[0]\n self.dict['arrayy'] = self.elem[3] - self.elem[1]\n # if isinstance(self.elem[0], numpy.ndarray):\n # if self.parameter_list and self.dict['arrayx'] and self.dict['arrayy']:\n # self.dict['numshots'] = len(self.parameter_list) / (self.dict['arrayx'] * self.dict['arrayy'])\n\n else:\n print \"you done fucked up\"\n else:\n self.dict[strg] = self.elem\n return self.dict","def _serialize(self, data):\n data = [np.array(j) for j in data]\n self._data_shape_list = [j.shape for j in data]\n serialized_data = [j.ravel() for j in data]\n serialized_data = np.hstack(serialized_data)\n return serialized_data","def as_dict(self, key: str, output_type=torch.Tensor, dtype=None) -> dict:\n if output_type not in (torch.Tensor, np.ndarray):\n raise ValueError(f\"output_type must be torch.Tensor or np.ndarray, got {output_type}.\")\n return {\n key: self.get_array(output_type=output_type, dtype=dtype),\n PostFix.meta(key): self.meta,\n PostFix.transforms(key): self.applied_operations,\n }","def to_geom_dict(eltype, data, kdims, vdims, interface=None):\n from . import Dataset\n\n xname, yname = (kd.name for kd in kdims[:2])\n if isinstance(data, dict):\n data = {k: v if isscalar(v) else _asarray(v) for k, v in data.items()}\n return data\n new_el = Dataset(data, kdims, vdims)\n if new_el.interface is interface:\n return new_el.data\n new_dict = {}\n for d in new_el.dimensions():\n if d in (xname, yname):\n scalar = False\n else:\n scalar = new_el.interface.isscalar(new_el, d)\n vals = new_el.dimension_values(d, not scalar)\n new_dict[d.name] = vals[0] if scalar else vals\n return new_dict","def _encode_value(value):\n # leave numbers alone\n if isinstance(value, (int, long, float)):\n return value\n\n # leave Nones alone (they turn into null in JSON)\n if value is None:\n return value\n\n # convert datetime to str\n if isinstance(value, datetime.datetime):\n # return, don't go through truncation\n return str(value)\n\n # represent image as base64-encoded bytes\n import binascii\n from graphlab.data_structures.image import Image\n if isinstance(value, Image):\n image_format = None\n if value._format_enum == 0:\n image_format = 'jpeg'\n elif value._format_enum == 1:\n image_format = 'png'\n elif value._format_enum == 2:\n image_format = 'raw'\n if image_format is not None:\n ret = {\n 'type': 'image',\n 'width': value._width,\n 'height': value._height,\n 'channels': value._channels,\n 'format': image_format,\n 'id': id(value)\n }\n if image_format in ('jpeg', 'png'):\n ret.update({\n 'value': 'image/%s;base64,%s' % (image_format, binascii.b2a_base64(value._image_data))\n })\n elif image_format == 'raw':\n ret.update({\n 'value': list(value._image_data)\n })\n return ret\n\n # fallback case for images the browser does not know how to display\n # just convert to str and treat like any other type\n value = str(value)\n\n # convert strings to unicode (assumes utf-8 encoding, replaces invalid\n # characters with ?\n if isinstance(value, str) and sys.version_info.major == 2:\n value = unicode(value, encoding='utf-8', errors='replace')\n\n # get the array into a list so it is JSON serializable\n if isinstance(value, array.array):\n value = value.tolist()\n\n # truncate to 10 elements first\n if isinstance(value, (array.array, list)):\n value = value[:10]\n elif isinstance(value, dict):\n keys = value.keys()[:10]\n truncated = {}\n for key in keys:\n truncated[key] = value[key]\n value = truncated\n\n # get dict/list values properly encoded inside before dumping to str\n if isinstance(value, list):\n value = [_encode_value(v) for v in value]\n elif isinstance(value, dict):\n value = {_encode_value(k): _encode_value(v) for (k,v) in six.iteritems(value)}\n\n # json serialize dict/list types to convert to string\n if isinstance(value, (dict, list)):\n value = _to_json(value)\n\n # truncate via textwrap (will break on word boundaries if possible)\n wrapped = textwrap.wrap(value, 18)\n if len(wrapped) == 0:\n return ''\n\n return '%s%s' % (\n wrapped[0],\n '' if len(wrapped) == 1 else ' ...'\n )","def _serialize_buffer(buffer, array_serialization=None):\n if array_serialization == 'binary':\n # WARNING: in NumPy 1.9, tostring() has been renamed to tobytes()\n # but tostring() is still here for now for backward compatibility.\n return buffer.ravel().tostring()\n elif array_serialization == 'base64':\n return {'storage_type': 'base64',\n 'buffer': base64.b64encode(buffer).decode('ascii')\n }\n raise ValueError(\"The array serialization method should be 'binary' or \"\n \"'base64'.\")","def array_to_dict(self, x):\n d = {}\n n_fit_p = len(self.fit_parameters)\n n_nui_p = len(self.nuisance_parameters)\n n_wc = len(self.fit_wc_names)\n d['fit_parameters'] = { p: x[i] for i, p in enumerate(self.fit_parameters) }\n d['nuisance_parameters'] = { p: x[i + n_fit_p] for i, p in enumerate(self.nuisance_parameters) }\n d['fit_wc'] = { p: x[i + n_fit_p + n_nui_p] for i, p in enumerate(self.fit_wc_names) }\n return d","def proc_attr(inp):\n dic = {}\n for att in inp.attrs.keys():\n if getattr(inp.attrs[att], \"dtype\", None) is None:\n dic[att] = inp.attrs[att]\n elif inp.attrs[att].dtype.char == 'S':\n dic[att] = [\n x.strip() for x in inp.attrs[att].tostring().decode('ascii').split(',')\n ]\n else:\n dic[att] = (\n inp.attrs[att][0]\n if isinstance(inp.attrs[att],np.ndarray) and\n inp.attrs[att].size==1\n else inp.attrs[att]\n )\n return dic\n pass","def base64_2_data(s: str) -> np.ndarray:\n saved_bytes = io.BytesIO(zlib.decompress(base64.b64decode(s)))\n return np.load(saved_bytes)","def convertdataTOimage(data):\n data = data.partition(\",\")[2]\n padding = len(data)%4\n data += \"=\"*padding\n image = Image.open(BytesIO(b64decode(data)))\n return image","def to_dict(\n self,\n attributes: Iterable[str] = (\"xyz\", \"viewdir\", \"imgsz\", \"f\", \"c\", \"k\", \"p\"),\n ) -> Dict[str, tuple]:\n return {key: helpers.numpy_to_native(getattr(self, key)) for key in attributes}","def __init__(self, dtype=None, content_type=\"application/x-npy\"):\n super(NumpySerializer, self).__init__(content_type=content_type)\n self.dtype = dtype","def dic_b64_and_pickle_loads(data):\n for i in data:\n data[i] = pickle.loads(base64.b64decode(data[i]))\n return data","def convert_to_base64(image_file):\n with open(image_file, 'rb') as f:\n jpeg_bytes = base64.b64encode(f.read()).decode('utf-8')\n predict_request = '{\"instances\" : [{\"b64\": \"%s\"}]}' % jpeg_bytes\n # Write JSON to file\n with open(OUTPUT_FILE, 'w') as f:\n f.write(predict_request)\n return predict_request","def _metatize_ndarray(obj):\n return metatize(obj.view(HashableNDArray))","def __json_encode__(self) -> Dict[str, Any]:\n return {\"figure\": self.figure, \"name\": self.name, \"metadata\": self.metadata}","def numpy_recarray_to_dict(data, keyfield_name):\n numrec = data.size\n fielddtype = data.dtype.fields\n names = fielddtype.keys()\n formats = []\n output_dict = {}\n for name in names:\n formats.append(fielddtype[name][0])\n\n for dataindex in np.arange(numrec):\n record_entry = {}\n for name in names:\n # TODO: remove keyfield_name from this list\n record_entry[name] = data[dataindex][name].tolist()\n output_dict[data[dataindex][keyfield_name]] = record_entry\n return output_dict","def base64_decode_array(inStr, dtype):\n return np.frombuffer(base64.decodestring(inStr), dtype=dtype)","def annotation_to_dict(self):\r\n out_dict = {'bbox_center': self.bbox_center,\r\n 'bbox_size': np.array(self.bbox_size, dtype=\"float64\").tolist(),\r\n 'rotation': self.get_box_angle(),\r\n 'label': self.classname}\r\n return out_dict","def b64_json_enc(data):\n json_str = json.dumps(data)\n return base64.b64encode(json_str.encode()).decode()","def _todict(matobj):\n dict = {}\n for strg in matobj._fieldnames:\n elem = matobj.__dict__[strg]\n if isinstance(elem, sio.matlab.mio5_params.mat_struct):\n dict[strg] = _todict(elem)\n elif isinstance(elem, np.ndarray):\n dict[strg] = _tolist(elem)\n else:\n dict[strg] = elem\n return dict","def custom_encode(obj):\n if isinstance(obj, DictionaryMethods):\n key = '__Dictionary__'\n return {key: [list(obj), obj.alpha, obj.pat, obj.pat_args,\n obj.auto_fields]}\n elif isinstance(obj, Entry):\n return obj.data\n else:\n raise TypeError(\"obj {!r} of type {}\".format(obj, type(obj)))","def getbase64(nparr,):\n if type(nparr) == type({}):\n nparr = nparr['img']\n im = Image.fromarray(nparr)\n buf = BytesIO()\n im.save(buf,format=\"JPEG\")\n return base64.b64encode(buf.getvalue()).decode('ascii')","def characterize_object(obj_name, obj_data):\n if isinstance(obj_data, h5py.Dataset):\n summary['shapes'][obj_name] = obj_data.shape\n # note that this will break if the hdf5 file contains non-numeric datasets\n summary['sums'][obj_name] = obj_data[...].sum()\n print(\"dataset %s version = %s\" % (obj_name, hdf5_file.get_version(obj_name)))","def serialize_image(self, image):\r\n result = {\r\n 'pixels': image.tobytes(),\r\n 'size': image.size,\r\n 'mode': image.mode\r\n }\r\n return result","def as_array_dict(self):\n out = {}\n if self.get('material', None):\n out['material'] = self['material']\n if self.get('vertices', None):\n out['vertices'] = np.asarray(\n [[v.get(k, np.NaN) for k in 'xyzw']\n for v in self['vertices']])\n out['vertex_colors'] = np.asarray(\n [[v.get(k, np.NaN) for k in ['red', 'green', 'blue']]\n for v in self['vertices']])\n if np.all(np.isnan(out['vertices'][:, 3])):\n out['vertices'] = out['vertices'][:, :3]\n if np.all(np.isnan(out['vertex_colors'])):\n out.pop('vertex_colors')\n if self.get('params', None):\n out['params'] = np.asarray(\n [[v.get(k, np.NaN) for k in 'uvw']\n for v in self['params']])\n if np.all(np.isnan(out['params'][:, 2])):\n out['params'] = out['params'][:, :2]\n if self.get('normals', None):\n out['normals'] = np.asarray(\n [[v[k] for k in 'ijk'] for v in self['normals']])\n if self.get('texcoords', None):\n out['texcoords'] = np.asarray(\n [[v.get(k, np.NaN) for k in 'uvw']\n for v in self['texcoords']])\n if np.all(np.isnan(out['texcoords'][:, 1:])):\n out['texcoords'] = out['texcoords'][:, :1]\n elif np.all(np.isnan(out['texcoords'][:, 2])):\n out['texcoords'] = out['texcoords'][:, :2]\n if self.get('lines', None):\n out['lines'] = np.NaN * np.ones(\n (len(self['lines']), len(max(self['lines'], key=len))),\n dtype='int32')\n for i, vlist in enumerate(self['lines']):\n for j, v in enumerate(vlist):\n out['lines'][i, j] = v['vertex_index']\n if self.get('faces', None):\n face_shp = (len(self['faces']), len(max(self['faces'], key=len)))\n out['faces'] = np.NaN * np.ones(face_shp, dtype='int32')\n out['face_texcoords'] = np.NaN * np.ones(face_shp, dtype='int32')\n out['face_normals'] = np.NaN * np.ones(face_shp, dtype='int32')\n for i, vlist in enumerate(self['faces']):\n for j, v in enumerate(vlist):\n out['faces'][i, j] = v['vertex_index']\n out['face_texcoords'][i, j] = v.get(\n 'texcoord_index', np.NaN)\n out['face_normals'][i, j] = v.get('normal_index', np.NaN)\n if np.all(np.isnan(out['face_texcoords'])):\n out.pop('face_texcoords')\n if np.all(np.isnan(out['face_normals'])):\n out.pop('face_normals')\n if self.get('points', None):\n out['points'] = np.NaN * np.ones(\n (len(self['points']), len(max(self['points'], key=len))),\n dtype='int32')\n for i, vlist in enumerate(self['points']):\n out['points'][i, :len(vlist)] = vlist\n if self.get('curves', None):\n def fkey(x):\n return len(x['vertex_indices'])\n out['curves'] = np.NaN * np.ones(\n (len(self['curves']), len(max(self['curves'], key=fkey))),\n dtype='int32')\n out['curve_params'] = np.zeros((out['curves'].shape[0], 2))\n for i, curv in enumerate(self['curves']):\n out['curve_params'][i, :] = [curv['starting_param'],\n curv['ending_param']]\n out['curves'][i, :fkey(curv)] = curv['vertex_indices']\n if self.get('curve2Ds', None):\n out['curve2Ds'] = np.NaN * np.ones(\n (len(self['curve2Ds']), len(max(self['curve2Ds'], key=len))),\n dtype='int32')\n for i, vlist in enumerate(self['curve2Ds']):\n out['curve2Ds'][i, :len(vlist)] = vlist\n if self.get('surfaces', None):\n def fkey(x):\n return len(x['vertex_indices'])\n surf_shp = (len(self['surfaces']), len(max(self['surfaces'], key=fkey)))\n out['surfaces'] = np.NaN * np.ones(surf_shp, dtype='int32')\n out['surface_params'] = np.zeros((surf_shp[0], 4))\n out['surface_texcoords'] = np.NaN * np.ones(surf_shp, dtype='int32')\n out['surface_normals'] = np.NaN * np.ones(surf_shp, dtype='int32')\n for i, surf in enumerate(self['surfaces']):\n out['surface_params'][i, :] = [surf['starting_param_u'],\n surf['ending_param_u'],\n surf['starting_param_v'],\n surf['ending_param_v']]\n for j, v in enumerate(surf['vertex_indices']):\n out['surfaces'][i, j] = v['vertex_index']\n out['surface_texcoords'][i, j] = v.get(\n 'texcoord_index', np.NaN)\n out['surface_normals'][i, j] = v.get(\n 'normal_index', np.NaN)\n if np.all(np.isnan(out['surface_texcoords'])):\n out.pop('surface_texcoords')\n if np.all(np.isnan(out['surface_normals'])):\n out.pop('surface_normals')\n return out","def to_dict(self):\n\n shape = super().to_dict()\n shape[\"scale\"] = self.scale.tolist()\n return shape","def test_to_plain_python_obj_numpy(test_input, expected):\n output = r.to_plain_python_obj(test_input)\n assert output == expected\n # We should not get a json conversion error\n json.dumps(output)","def clean(imagedata):\n\n if isinstance(imagedata, ndarray):\n imagedata = [imagedata]\n\n outdict = [array_to_im(im) for im in imagedata]\n\n return {'images': outdict}","def todict(self):\n return dict(self.array)","def test_dict2ndarray(self):\n from ..io.util import _dict2ndarray\n x = np.arange(10)\n y = np.arange(10)*2\n z = np.arange(20).reshape((10, 2))\n self.assertEqual(z.shape, (10, 2))\n d = dict(x=x, y=y, z=z)\n nddata = _dict2ndarray(d)\n self.assertTrue((nddata['x'] == x).all())\n self.assertTrue((nddata['y'] == y).all())\n self.assertTrue((nddata['z'] == z).all())\n nddata = _dict2ndarray(d, columns=['x', 'y'])\n self.assertTrue((nddata['x'] == x).all())\n self.assertTrue((nddata['y'] == y).all())\n with self.assertRaises(ValueError) as ex:\n (nddata['z'] == z).all()\n self.assertEqual(ex.exception.args[0], 'no field of name z')","def data64(self) -> str:\n return Image.encode64(self.data)","def base64_to_numpy_image(b64: str) -> np.ndarray:\n image = np.array(Image.open(BytesIO(base64.b64decode(b64))))\n return image","def base64_encode_array(inArray):\n return base64.b64encode(inArray)","def clean(imagedata):\n if asarray(imagedata).ndim not in set((2, 3)):\n raise Exception(\"Input must be two or three dimensional\")\n\n outdict = [array_to_im(imagedata)]\n\n return {'images': outdict}","def clean(imagedata):\n if asarray(imagedata).ndim not in set((2, 3)):\n raise Exception(\"Input must be two or three dimensional\")\n\n outdict = [array_to_im(imagedata)]\n\n return {'images': outdict}","def _raw_data(self):\n data = self.datasource.as_array()\n if not isinstance(data, np.ndarray):\n raise TypeError(\"The data you try to load is no numpy array!\")\n if data.ndim != 2:\n raise ValueError(\"The data array you try to load does not have 2 \"\n \"dimensions!\")\n data = data.copy(order='C')\n ident = hashlib.md5(data).hexdigest()\n if self._dataident is None:\n self._dataident = ident\n elif self._dataident != ident:\n raise ValueError(\"The data you try to load from '%s' has changed \"\n \"since the last time. Please, check the \"\n \"datasource of the record '%s'.\"\n % (self.datasource.name, self.name))\n\n return data","def test_format_signature_numpy():","def data64(self, value: str) -> None:\n self.data = Image.decode64(value)","def as_correct_dtype(obj: ndarray, required_dtype: dtype) -> ndarray:\n if obj.dtype != required_dtype:\n return obj.astype(required_dtype)\n return obj","def data_prep_reg_go_def(xdata):\n xdata = np.asarray( xdata )\n x_dct = {'inputs': xdata}\n return x_dct","def pandas_typecast(self) -> dict:\n res = {}\n for feat in self.data_features:\n res[feat.key] = ApiForm.typecast(feat.dtype)\n return res","def _encode_data_dict(self, data_dict: dict) -> dict:\n\n # If we have _BYTES_DATA_KEY or _FS_CHECKPOINT_KEY in the data dict,\n # that means this is a directory checkpoint which has already been\n # converted into bytes. We don't want to double-encode it.\n # See the definition of super().__getstate__().\n if _BYTES_DATA_KEY in data_dict or _FS_CHECKPOINT_KEY in data_dict:\n return data_dict\n\n for k, v in data_dict.items():\n # Only check for attribute as we want to support\n # DDP, FSDP and any future approaches\n if isinstance(v, Module) and hasattr(v, \"module\"):\n data_dict[k] = v.module\n elif isinstance(v, dict):\n # We could limit this only to the MODEL_KEY, but we'd\n # miss any extra user-specified keys. This should be a\n # noop with anything but DDP/FSDP module state dicts.\n data_dict[k] = consume_prefix_in_state_dict_if_present_not_in_place(\n v, \"module.\"\n )\n\n # Convert the checkpoint dict to bytes, so that any GPU tensors that\n # are in the checkpoint dict can be properly deserialized on the\n # driver side, even if the driver does not have access to a GPU device.\n _buffer = io.BytesIO()\n torch.save(\n data_dict,\n _buffer,\n pickle_module=ray_pickle,\n pickle_protocol=pickle.HIGHEST_PROTOCOL\n # Using pickle.HIGHEST_PROTOCOL here because it's 5 for Python 3.8+,\n # but 4 for 3.7. For backward compatibility, we are not using\n # ray.cloudpickle because its default protocol is always 5.\n )\n return {ENCODED_DATA_KEY: _buffer.getvalue()}","def _decode_data_dict(self, data_dict: dict) -> dict:\n if ENCODED_DATA_KEY not in data_dict:\n return data_dict\n\n encoded_data = data_dict[ENCODED_DATA_KEY]\n _buffer = io.BytesIO(encoded_data)\n data_dict = torch.load(\n _buffer,\n map_location=\"cpu\"\n # Not using ray.cloudpickle here as it doesn't\n # define an Unpickler (as it is not necessary).\n )\n return data_dict","def _convert_data(self, data):\n if isinstance(data, Tensor):\n data = data.asnumpy()\n elif isinstance(data, list):\n data = np.array(data)\n elif isinstance(data, np.ndarray):\n pass\n else:\n raise TypeError('Input data type must be tensor, list or numpy.ndarray')\n return data","def as_str(self) -> str:\n return dumps(self.as_dict(), cls=NumpyEncoder)","def deserialise_image(data):\n if \"data:image\" in data:\n data = data[data.find(\",\") + 1:]\n\n return Image.open(io.BytesIO(base64.urlsafe_b64decode(data)))","def strip_dict(self, inp_d):\r\n inp_d = dict([(k, numpy.complex(inp_d[k]))\r\n for k in inp_d if type(k) == str and\r\n k.isalnum() and\r\n isinstance(inp_d[k], numbers.Number)])\r\n return inp_d","def encode_data_readable(cls, obj, typedef):\n return cls.encode_data(obj, typedef)","def __array__(self):\n return dict2rec(self)","def save_img_base64(_preds):\n img = Image.fromarray(_preds)\n buff = BytesIO()\n img.save(buff, format=\"JPEG\")\n return base64.b64encode(buff.getvalue())","def test_24_to_json_string(self):\n r = Rectangle(10, 7, 2, 8)\n d = r.to_dictionary()\n json_d = Base.to_json_string([d])\n self.assertEqual(type(json_d), str)\n self.assertEqual(\n d, {'height': 7, 'id': 1, 'width': 10, 'x': 2, 'y': 8})","def base64(self):\n image = self.png.getvalue()\n return base64.encodestring(image).decode('utf-8')","def _to_dict(self, item):\n if isinstance(item, Buffer):\n ret = {}\n fields = item._all_fields()\n for field in fields:\n ret[field.attr_name()] = self._to_dict(getattr(item, field.attr_name()))\n return ret\n\n if isinstance(item, Struct):\n ret = {}\n for field in item._container_.fields:\n if hasattr(field, 'name'):\n ret[field.name] = self._to_dict(field.get_value(item))\n elif isinstance(field, FieldListContainer):\n for inner_field in field.fields:\n if not isinstance(inner_field, AnonymousField):\n ret[inner_field.name] = self._to_dict(inner_field.get_value(item))\n return ret\n\n if isinstance(item, bytearray):\n return '0x' + binascii.hexlify(item) if item else ''\n\n if isinstance(item, list):\n return [self._to_dict(x) for x in item]\n\n return item","def serialize(obj):\n return np.fromstring(pickle.dumps(obj), dtype=np.uint8).astype(np.float32)","def serialize(obj):\n return np.fromstring(pickle.dumps(obj), dtype=np.uint8).astype(np.float32)","def as_dict(self):\n # Get the properties inherited from Data\n DataND_dict = super(DataND, self).as_dict()\n DataND_dict[\"axes\"] = list()\n for obj in self.axes:\n DataND_dict[\"axes\"].append(obj.as_dict())\n DataND_dict[\"normalizations\"] = self.normalizations\n DataND_dict[\"FTparameters\"] = self.FTparameters\n if self.values is None:\n DataND_dict[\"values\"] = None\n else:\n DataND_dict[\"values\"] = self.values.tolist()\n # The class name is added to the dict fordeserialisation purpose\n # Overwrite the mother class name\n DataND_dict[\"__class__\"] = \"DataND\"\n return DataND_dict","def asdict():\n pass","def normalize_config(config):\n return json.loads(json.dumps(config, cls=NumpyEncoder))","def json_serialize_numpy_array(array):\n return json.dumps(json_ready_numpy_array(array))","def json_serialize_numpy_array(array):\n return json.dumps(json_ready_numpy_array(array))","def serialize_ndarrays(d):\n def dict_handler(d):\n return d.items()\n\n handlers = {list: enumerate, tuple: enumerate,\n set: enumerate, frozenset: enumerate,\n dict: dict_handler}\n\n def serialize(o):\n for typ, handler in handlers.items():\n if isinstance(o, typ):\n for key, val in handler(o):\n if isinstance(val, np.ndarray):\n o[key] = val.tolist()\n else:\n o[key] = serialize_ndarrays(o[key])\n return o\n\n return serialize(d)","def convertImage(img):\n return '\\\\includegraphicsdata{%s}' % \":\".join([\n 'data',\n img.contentType,\n \"base64,%s\" % img.data.encode(\"base64\").replace(\"\\n\", \"\"),\n ])","def as_dict(self):\n if self._feat_matrix is not None:\n feature_matrix = self._feat_matrix.tolist()\n else:\n feature_matrix = self._feat_matrix\n if self.regression_data is not None:\n reg_data = jsanitize(asdict(self.regression_data))\n else:\n reg_data = None\n\n ce_d = {\n \"@module\": self.__class__.__module__,\n \"@class\": self.__class__.__name__,\n \"cluster_subspace\": self.cluster_subspace.as_dict(),\n \"coefs\": self.coefs.tolist(),\n \"regression_data\": reg_data,\n \"feature_matrix\": feature_matrix,\n }\n return ce_d","def to_image_data(data):\n \n # removing image\n if not data:\n return u''\n\n # image path (not changed)\n if data[0:5] != u'data:':\n return None\n \n # TODO: better MIME handling\n mime = data[5:data.index(u';')].lower()\n img = data[data.index(u',') + 1:].decode('base64')\n \n return mime, img","def rawtodictonary(rawstring):\n\n if is_python3():\n rawstring = bytes(rawstring).decode('utf-8') \n \n raw = rawstring.split(NULL)[:-2]\n\n data = {}\n for i in range(0,len(raw) - 1,2):\n\n key,val = raw[i], raw[i+1]\n keyel = key.split(\"\\x1c\")\n\n if len(keyel) == 1:\n if key in data:\n data[key][None] = val\n else:\n data[key]=val\n else:\n if keyel[0] in data and not isinstance(data[keyel[0]], dict):\n data[keyel[0]]={ None: data[keyel[0]] }\n\n try:\n data[keyel[0]][keyel[1]] = val\n except TypeError:\n data[keyel[0]] = {keyel[1] : val}\n except KeyError:\n data[keyel[0]] = {keyel[1] : val}\n\n return data","def generate_json_from_base64_image(base64_image):\n # Content image\n content_json_obj = {'content': base64_image}\n\n # Detection type.\n feature_json_obj = [{'type': get_detection_type(5),\n 'maxResults': 50,\n }\n ]\n\n # Now add it to the request\n request_list = []\n request_list.append({\n 'features': feature_json_obj,\n 'image': content_json_obj,\n })\n\n # To json\n data = json.dumps({'requests': request_list})\n\n return data","def serialize_numpy(self, buff, numpy):\n try:\n _x = self.name\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self.compatibility\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self.display_name\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self.description\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self.namespace\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self.icon.resource_name\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self.icon.format\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self.icon.data\n length = len(_x)\n # - if encoded as a list instead, serialize as bytes instead of string\n if type(_x) in [list, tuple]:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n length = len(self.remappings)\n buff.write(_struct_I.pack(length))\n for val1 in self.remappings:\n _x = val1.remap_from\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = val1.remap_to\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = self.parameters\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n buff.write(_struct_i.pack(self.max))\n _x = self.pairing.rapp\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n length = len(self.pairing.remappings)\n buff.write(_struct_I.pack(length))\n for val1 in self.pairing.remappings:\n _x = val1.remap_from\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = val1.remap_to\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n length = len(self.pairing.parameters)\n buff.write(_struct_I.pack(length))\n for val1 in self.pairing.parameters:\n _x = val1.key\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n _x = val1.value\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n buff.write(_struct_i.pack(self.hash))\n _x = self.role\n length = len(_x)\n if python3 or type(_x) == unicode:\n _x = _x.encode('utf-8')\n length = len(_x)\n if python3:\n buff.write(struct.pack('<I%sB'%length, length, *_x))\n else:\n buff.write(struct.pack('<I%ss'%length, length, _x))\n except struct.error as se: self._check_types(struct.error(\"%s: '%s' when writing '%s'\" % (type(se), str(se), str(locals().get('_x', self)))))\n except TypeError as te: self._check_types(ValueError(\"%s: '%s' when writing '%s'\" % (type(te), str(te), str(locals().get('_x', self)))))","def array_to_dict(arr: np.ndarray, domain: Optional[np.ndarray] = None) -> DictStrNum:\n\n if domain is None:\n keys, counts = np.unique(numpy_array(arr), return_counts=True)\n out_dict = dict(zip(keys, counts))\n else:\n out_dict = {}\n for d in np.unique(domain):\n arr_d = arr[domain == d]\n keys_d, counts_d = np.unique(numpy_array(arr_d), return_counts=True)\n out_dict[d] = dict(zip(keys_d, counts_d))\n\n return out_dict","def data_to_dict(data: Data) -> Dict[str, Any]:\n properties = data.serialize_init_args(obj=data)\n properties[\"__type\"] = data.__class__.__name__\n return properties","def data_to_dict(self):\n return self.data.copy()","def encode_data(data):\n bytes = json.dumps(data).encode('utf-8').encode('base64').replace('\\n', '')\n assert len(bytes) < 250 * 1024\n return bytes","def from_base64(x):\n\treturn numpy.fromstring(\n\t\tbinascii.a2b_base64(x),\n\t\tdtype=numpy.uint8\n\t)","def to_dictionary(self):\n x = super().to_dictionary()\n y = x.copy()\n y[\"size\"] = self.width\n if y[\"height\"] and y[\"width\"]:\n del y[\"height\"]\n del y[\"width\"]\n return(y)"],"string":"[\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n if obj.flags['C_CONTIGUOUS']:\\n obj_data = obj.data\\n else:\\n cont_obj = np.ascontiguousarray(obj)\\n assert(cont_obj.flags['C_CONTIGUOUS'])\\n obj_data = cont_obj.data\\n data_b64 = base64.b64encode(obj_data)\\n return dict(__ndarray__=data_b64,\\n dtype=str(obj.dtype),\\n shape=obj.shape)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n if obj.flags['C_CONTIGUOUS']:\\n obj_data = obj.data\\n else:\\n cont_obj = np.ascontiguousarray(obj)\\n assert(cont_obj.flags['C_CONTIGUOUS'])\\n obj_data = cont_obj.data\\n data_b64 = base64.b64encode(obj_data)\\n return dict(__ndarray__=data_b64,\\n dtype=str(obj.dtype),\\n shape=obj.shape)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n if obj.flags['C_CONTIGUOUS']:\\n obj_data = obj.data\\n else:\\n cont_obj = np.ascontiguousarray(obj)\\n assert (cont_obj.flags['C_CONTIGUOUS'])\\n obj_data = cont_obj.data\\n data_b64 = base64.b64encode(obj_data).decode(\\\"ascii\\\")\\n return dict(__ndarray__=data_b64,\\n dtype=str(obj.dtype),\\n shape=obj.shape)\\n # Let the base class default method raise the TypeError\\n return super().default(obj)\",\n \"def default(self, obj):\\n if isinstance(obj, np.ndarray):\\n if obj.flags['C_CONTIGUOUS']:\\n obj_data = obj.data\\n else:\\n cont_obj = np.ascontiguousarray(obj)\\n assert(cont_obj.flags['C_CONTIGUOUS'])\\n obj_data = cont_obj.data\\n data_b64 = base64.b64encode(obj_data)\\n return dict(__ndarray__=data_b64,\\n dtype=str(obj.dtype),\\n shape=obj.shape)\\n elif isinstance(obj, np.generic):\\n return np.asscalar(obj)\\n # Let the base class default method raise the TypeError\\n return json.JSONEncoder(self, obj)\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n todecode = dct['__ndarray__'].encode(\\\"ascii\\\")\\n data = base64.b64decode(todecode)\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def _json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def json_numpy_obj_hook(dct):\\n if isinstance(dct, dict) and '__ndarray__' in dct:\\n data = base64.b64decode(dct['__ndarray__'])\\n return np.frombuffer(data, dct['dtype']).reshape(dct['shape'])\\n return dct\",\n \"def default_encoder(obj):\\n\\n if isinstance(obj, np.ndarray):\\n data_b64 = base64.b64encode(np.ascontiguousarray(obj).data)\\n return dict(__ndarray__=data_b64, dtype=str(obj.dtype), shape=obj.shape)\\n\\n if isinstance(obj, dict):\\n result = dict()\\n for k, v in obj.items():\\n result[k] = default_encoder(v)\\n\\n return result\\n\\n return obj\",\n \"def base64_encode_image(inArray):\\n imgDat = [base64_encode_array(inArray).decode(\\\"utf-8\\\")]\\n imgType = str(inArray.dtype)\\n imgShape = inArray.shape\\n return json.dumps([ imgDat, imgType, imgShape ])\",\n \"def data_2_base64(data: np.ndarray) -> str:\\n bytes_io = io.BytesIO()\\n np.save(bytes_io, data, allow_pickle=False)\\n return base64.b64encode(zlib.compress(bytes_io.getvalue())).decode('utf-8')\",\n \"def default_decoder(obj):\\n\\n if isinstance(obj, dict):\\n if '__ndarray__' in obj:\\n data = base64.b64decode(obj['__ndarray__'])\\n return np.frombuffer(data, obj['dtype']).reshape(obj['shape'])\\n else:\\n for k, v in obj.items():\\n obj[k] = default_decoder(v)\\n elif isinstance(obj, bytes):\\n return pickle.loads(obj)\\n\\n return obj\",\n \"def _encodeArray(self, array):\\n\\n # Actually, we want dtype,naxis,axNlen,base64(array)\\n return base64.b64encode(array.tostring())\",\n \"def base64_decode_image(inStr):\\n imgDat, imgType, imgShape = json.loads(inStr)\\n imgDat = bytes(imgDat, encoding=\\\"utf-8\\\")\\n\\n imgDat = base64_decode_array(imgDat, imgType)\\n imgDat = imgDat.reshape(imgShape)\\n return imgDat\",\n \"def dic_pickle_dumps_and_b64(data):\\n for i in data:\\n data[i] = base64.b64encode(pickle.dumps(data[i]))\\n return data\",\n \"def serialize(self, data):\\n if isinstance(data, dict):\\n return json.dumps(\\n {\\n key: value.tolist() if isinstance(value, np.ndarray) else value\\n for key, value in data.items()\\n }\\n )\\n\\n if hasattr(data, \\\"read\\\"):\\n return data.read()\\n\\n if isinstance(data, np.ndarray):\\n return json.dumps(data.tolist())\\n\\n return json.dumps(data)\",\n \"def get_data_structure_representation(self) -> dict:\\n byte_buff = self.get_rle()\\n encoding = \\\"RLE\\\"\\n\\n if len(byte_buff) > self.grid_size[0] * self.grid_size[1] * 4:\\n encoding = \\\"RAW\\\"\\n byte_buff = self.get_raw()\\n print(\\\"RAW ran\\\")\\n else:\\n print(\\\"RLE ran\\\")\\n\\n json_dict = {\\n \\\"encoding\\\": encoding,\\n \\\"nodes\\\": [base64.b64encode(bytes(byte_buff)).decode()],\\n \\\"dimensions\\\": [self.grid_size[0], self.grid_size[1]]\\n }\\n\\n return json_dict\",\n \"def rec_to_dict(arr):\\n\\n return dict(zip(arr.dtype.names, arr))\",\n \"def to_dict(self):\\n config = {\\n 'dtype': self.dtype,\\n 'unit': self.unit,\\n 'max_shape': self.max_shape,\\n 'min_shape': self.min_shape,\\n 'ndim': self.ndim,\\n 'max_value': self.max_value,\\n 'min_value': self.min_value,\\n 'depends_on': self.depends_on,\\n }\\n return {'node_type': 'Array', 'config': config}\",\n \"def base64_data(cls, val):\\n return cls('base64_data', val)\",\n \"def np_to_base64(img_np):\\n img = Image.fromarray(img_np.astype('uint8'), 'RGB')\\n buffered = BytesIO()\\n img.save(buffered, format=\\\"PNG\\\")\\n return u\\\"data:image/png;base64,\\\" + base64.b64encode(buffered.getvalue()).decode(\\\"ascii\\\")\",\n \"def encode(self, obj):\\n def hint_tuples(item):\\n \\\"\\\"\\\"\\n If the object to be serialized is a tuple, or contains tuples,\\n replace tuples with a dictionary that can be reconstructed on\\n the receiver\\n\\n Args:\\n self (object): Object that will be JSON serialized\\n\\n Returns:\\n object: Object without tuples\\n \\\"\\\"\\\"\\n if isinstance(item, tuple):\\n return {'__tuple__': True, 'items': item}\\n if isinstance(item, list):\\n return [hint_tuples(e) for e in item]\\n if isinstance(item, dict):\\n return {key: hint_tuples(value) for key, value in item.items()}\\n return item\\n\\n return super(MultiDimensionalArrayEncoder, self).encode(hint_tuples(obj))\",\n \"def to_dict(self):\\n\\n shape = super().to_dict()\\n shape[\\\"filename\\\"] = self.filename\\n shape[\\\"scale\\\"] = self.scale.tolist()\\n return shape\",\n \"def _metatize_HashableNDArray(obj):\\n return obj\",\n \"def to_internal_value(self, data):\\n if isinstance(data, str) and data.startswith('data:image'):\\n # Found image is encoded, and must be decoded\\n format, imgstr = data.split(';base64,')\\n ext = format.split('/')[-1] # Extract file extension\\n id = uuid.uuid4()\\n data = ContentFile(base64.b64decode(imgstr), name = id.urn[9:] + '.' + ext)\\n return super(Base64ImageField, self).to_internal_value(data)\",\n \"def __init__(self, data, from_base64=False, **kwargs):\\n super().__init__(data, **kwargs)\\n self.type_name = 'base64'\\n self.encoding = kwargs.get('encoding', 'original')\\n # TODO data should be bytes only\\n if from_base64:\\n self.data = data\\n else:\\n self.data = base64.b64encode(data).decode()\",\n \"def _todict(self, matobj, filetype):\\n self.filetype = filetype\\n self.dict = {}\\n self.index = 0\\n for strg in matobj._fieldnames:\\n self.elem = matobj.__dict__[strg]\\n if isinstance(self.elem, scipy.io.matlab.mio5_params.mat_struct):\\n self.dict[strg] = self._todict(self.elem, self.filetype)\\n elif isinstance(self.elem, numpy.ndarray):\\n self.parameter_list = []\\n self.dict[strg] = self._tolist(self.elem, self.parameter_list)\\n if self.filetype == \\\".mat\\\":\\n if len(self.elem)>0:\\n if isinstance(self.elem[0], numpy.ndarray):\\n if strg == 'immagini':\\n self.dict['arrayx'] = len(self.elem[0])\\n self.dict['arrayy'] = len(self.elem)\\n if self.parameter_list:\\n if strg == 'immagini':\\n self.dict['numshots'] = len(self.parameter_list) / (len(self.elem[0]) * len(self.elem))\\n elif self.filetype == \\\".dat\\\":\\n self.parameter_list = []\\n self.dict[strg] = self._tolist(self.elem, self.parameter_list)\\n if strg == 'roi_points':\\n self.dict['arrayx'] = self.elem[2] - self.elem[0]\\n self.dict['arrayy'] = self.elem[3] - self.elem[1]\\n # if isinstance(self.elem[0], numpy.ndarray):\\n # if self.parameter_list and self.dict['arrayx'] and self.dict['arrayy']:\\n # self.dict['numshots'] = len(self.parameter_list) / (self.dict['arrayx'] * self.dict['arrayy'])\\n\\n else:\\n print \\\"you done fucked up\\\"\\n else:\\n self.dict[strg] = self.elem\\n return self.dict\",\n \"def _serialize(self, data):\\n data = [np.array(j) for j in data]\\n self._data_shape_list = [j.shape for j in data]\\n serialized_data = [j.ravel() for j in data]\\n serialized_data = np.hstack(serialized_data)\\n return serialized_data\",\n \"def as_dict(self, key: str, output_type=torch.Tensor, dtype=None) -> dict:\\n if output_type not in (torch.Tensor, np.ndarray):\\n raise ValueError(f\\\"output_type must be torch.Tensor or np.ndarray, got {output_type}.\\\")\\n return {\\n key: self.get_array(output_type=output_type, dtype=dtype),\\n PostFix.meta(key): self.meta,\\n PostFix.transforms(key): self.applied_operations,\\n }\",\n \"def to_geom_dict(eltype, data, kdims, vdims, interface=None):\\n from . import Dataset\\n\\n xname, yname = (kd.name for kd in kdims[:2])\\n if isinstance(data, dict):\\n data = {k: v if isscalar(v) else _asarray(v) for k, v in data.items()}\\n return data\\n new_el = Dataset(data, kdims, vdims)\\n if new_el.interface is interface:\\n return new_el.data\\n new_dict = {}\\n for d in new_el.dimensions():\\n if d in (xname, yname):\\n scalar = False\\n else:\\n scalar = new_el.interface.isscalar(new_el, d)\\n vals = new_el.dimension_values(d, not scalar)\\n new_dict[d.name] = vals[0] if scalar else vals\\n return new_dict\",\n \"def _encode_value(value):\\n # leave numbers alone\\n if isinstance(value, (int, long, float)):\\n return value\\n\\n # leave Nones alone (they turn into null in JSON)\\n if value is None:\\n return value\\n\\n # convert datetime to str\\n if isinstance(value, datetime.datetime):\\n # return, don't go through truncation\\n return str(value)\\n\\n # represent image as base64-encoded bytes\\n import binascii\\n from graphlab.data_structures.image import Image\\n if isinstance(value, Image):\\n image_format = None\\n if value._format_enum == 0:\\n image_format = 'jpeg'\\n elif value._format_enum == 1:\\n image_format = 'png'\\n elif value._format_enum == 2:\\n image_format = 'raw'\\n if image_format is not None:\\n ret = {\\n 'type': 'image',\\n 'width': value._width,\\n 'height': value._height,\\n 'channels': value._channels,\\n 'format': image_format,\\n 'id': id(value)\\n }\\n if image_format in ('jpeg', 'png'):\\n ret.update({\\n 'value': 'image/%s;base64,%s' % (image_format, binascii.b2a_base64(value._image_data))\\n })\\n elif image_format == 'raw':\\n ret.update({\\n 'value': list(value._image_data)\\n })\\n return ret\\n\\n # fallback case for images the browser does not know how to display\\n # just convert to str and treat like any other type\\n value = str(value)\\n\\n # convert strings to unicode (assumes utf-8 encoding, replaces invalid\\n # characters with ?\\n if isinstance(value, str) and sys.version_info.major == 2:\\n value = unicode(value, encoding='utf-8', errors='replace')\\n\\n # get the array into a list so it is JSON serializable\\n if isinstance(value, array.array):\\n value = value.tolist()\\n\\n # truncate to 10 elements first\\n if isinstance(value, (array.array, list)):\\n value = value[:10]\\n elif isinstance(value, dict):\\n keys = value.keys()[:10]\\n truncated = {}\\n for key in keys:\\n truncated[key] = value[key]\\n value = truncated\\n\\n # get dict/list values properly encoded inside before dumping to str\\n if isinstance(value, list):\\n value = [_encode_value(v) for v in value]\\n elif isinstance(value, dict):\\n value = {_encode_value(k): _encode_value(v) for (k,v) in six.iteritems(value)}\\n\\n # json serialize dict/list types to convert to string\\n if isinstance(value, (dict, list)):\\n value = _to_json(value)\\n\\n # truncate via textwrap (will break on word boundaries if possible)\\n wrapped = textwrap.wrap(value, 18)\\n if len(wrapped) == 0:\\n return ''\\n\\n return '%s%s' % (\\n wrapped[0],\\n '' if len(wrapped) == 1 else ' ...'\\n )\",\n \"def _serialize_buffer(buffer, array_serialization=None):\\n if array_serialization == 'binary':\\n # WARNING: in NumPy 1.9, tostring() has been renamed to tobytes()\\n # but tostring() is still here for now for backward compatibility.\\n return buffer.ravel().tostring()\\n elif array_serialization == 'base64':\\n return {'storage_type': 'base64',\\n 'buffer': base64.b64encode(buffer).decode('ascii')\\n }\\n raise ValueError(\\\"The array serialization method should be 'binary' or \\\"\\n \\\"'base64'.\\\")\",\n \"def array_to_dict(self, x):\\n d = {}\\n n_fit_p = len(self.fit_parameters)\\n n_nui_p = len(self.nuisance_parameters)\\n n_wc = len(self.fit_wc_names)\\n d['fit_parameters'] = { p: x[i] for i, p in enumerate(self.fit_parameters) }\\n d['nuisance_parameters'] = { p: x[i + n_fit_p] for i, p in enumerate(self.nuisance_parameters) }\\n d['fit_wc'] = { p: x[i + n_fit_p + n_nui_p] for i, p in enumerate(self.fit_wc_names) }\\n return d\",\n \"def proc_attr(inp):\\n dic = {}\\n for att in inp.attrs.keys():\\n if getattr(inp.attrs[att], \\\"dtype\\\", None) is None:\\n dic[att] = inp.attrs[att]\\n elif inp.attrs[att].dtype.char == 'S':\\n dic[att] = [\\n x.strip() for x in inp.attrs[att].tostring().decode('ascii').split(',')\\n ]\\n else:\\n dic[att] = (\\n inp.attrs[att][0]\\n if isinstance(inp.attrs[att],np.ndarray) and\\n inp.attrs[att].size==1\\n else inp.attrs[att]\\n )\\n return dic\\n pass\",\n \"def base64_2_data(s: str) -> np.ndarray:\\n saved_bytes = io.BytesIO(zlib.decompress(base64.b64decode(s)))\\n return np.load(saved_bytes)\",\n \"def convertdataTOimage(data):\\n data = data.partition(\\\",\\\")[2]\\n padding = len(data)%4\\n data += \\\"=\\\"*padding\\n image = Image.open(BytesIO(b64decode(data)))\\n return image\",\n \"def to_dict(\\n self,\\n attributes: Iterable[str] = (\\\"xyz\\\", \\\"viewdir\\\", \\\"imgsz\\\", \\\"f\\\", \\\"c\\\", \\\"k\\\", \\\"p\\\"),\\n ) -> Dict[str, tuple]:\\n return {key: helpers.numpy_to_native(getattr(self, key)) for key in attributes}\",\n \"def __init__(self, dtype=None, content_type=\\\"application/x-npy\\\"):\\n super(NumpySerializer, self).__init__(content_type=content_type)\\n self.dtype = dtype\",\n \"def dic_b64_and_pickle_loads(data):\\n for i in data:\\n data[i] = pickle.loads(base64.b64decode(data[i]))\\n return data\",\n \"def convert_to_base64(image_file):\\n with open(image_file, 'rb') as f:\\n jpeg_bytes = base64.b64encode(f.read()).decode('utf-8')\\n predict_request = '{\\\"instances\\\" : [{\\\"b64\\\": \\\"%s\\\"}]}' % jpeg_bytes\\n # Write JSON to file\\n with open(OUTPUT_FILE, 'w') as f:\\n f.write(predict_request)\\n return predict_request\",\n \"def _metatize_ndarray(obj):\\n return metatize(obj.view(HashableNDArray))\",\n \"def __json_encode__(self) -> Dict[str, Any]:\\n return {\\\"figure\\\": self.figure, \\\"name\\\": self.name, \\\"metadata\\\": self.metadata}\",\n \"def numpy_recarray_to_dict(data, keyfield_name):\\n numrec = data.size\\n fielddtype = data.dtype.fields\\n names = fielddtype.keys()\\n formats = []\\n output_dict = {}\\n for name in names:\\n formats.append(fielddtype[name][0])\\n\\n for dataindex in np.arange(numrec):\\n record_entry = {}\\n for name in names:\\n # TODO: remove keyfield_name from this list\\n record_entry[name] = data[dataindex][name].tolist()\\n output_dict[data[dataindex][keyfield_name]] = record_entry\\n return output_dict\",\n \"def base64_decode_array(inStr, dtype):\\n return np.frombuffer(base64.decodestring(inStr), dtype=dtype)\",\n \"def annotation_to_dict(self):\\r\\n out_dict = {'bbox_center': self.bbox_center,\\r\\n 'bbox_size': np.array(self.bbox_size, dtype=\\\"float64\\\").tolist(),\\r\\n 'rotation': self.get_box_angle(),\\r\\n 'label': self.classname}\\r\\n return out_dict\",\n \"def b64_json_enc(data):\\n json_str = json.dumps(data)\\n return base64.b64encode(json_str.encode()).decode()\",\n \"def _todict(matobj):\\n dict = {}\\n for strg in matobj._fieldnames:\\n elem = matobj.__dict__[strg]\\n if isinstance(elem, sio.matlab.mio5_params.mat_struct):\\n dict[strg] = _todict(elem)\\n elif isinstance(elem, np.ndarray):\\n dict[strg] = _tolist(elem)\\n else:\\n dict[strg] = elem\\n return dict\",\n \"def custom_encode(obj):\\n if isinstance(obj, DictionaryMethods):\\n key = '__Dictionary__'\\n return {key: [list(obj), obj.alpha, obj.pat, obj.pat_args,\\n obj.auto_fields]}\\n elif isinstance(obj, Entry):\\n return obj.data\\n else:\\n raise TypeError(\\\"obj {!r} of type {}\\\".format(obj, type(obj)))\",\n \"def getbase64(nparr,):\\n if type(nparr) == type({}):\\n nparr = nparr['img']\\n im = Image.fromarray(nparr)\\n buf = BytesIO()\\n im.save(buf,format=\\\"JPEG\\\")\\n return base64.b64encode(buf.getvalue()).decode('ascii')\",\n \"def characterize_object(obj_name, obj_data):\\n if isinstance(obj_data, h5py.Dataset):\\n summary['shapes'][obj_name] = obj_data.shape\\n # note that this will break if the hdf5 file contains non-numeric datasets\\n summary['sums'][obj_name] = obj_data[...].sum()\\n print(\\\"dataset %s version = %s\\\" % (obj_name, hdf5_file.get_version(obj_name)))\",\n \"def serialize_image(self, image):\\r\\n result = {\\r\\n 'pixels': image.tobytes(),\\r\\n 'size': image.size,\\r\\n 'mode': image.mode\\r\\n }\\r\\n return result\",\n \"def as_array_dict(self):\\n out = {}\\n if self.get('material', None):\\n out['material'] = self['material']\\n if self.get('vertices', None):\\n out['vertices'] = np.asarray(\\n [[v.get(k, np.NaN) for k in 'xyzw']\\n for v in self['vertices']])\\n out['vertex_colors'] = np.asarray(\\n [[v.get(k, np.NaN) for k in ['red', 'green', 'blue']]\\n for v in self['vertices']])\\n if np.all(np.isnan(out['vertices'][:, 3])):\\n out['vertices'] = out['vertices'][:, :3]\\n if np.all(np.isnan(out['vertex_colors'])):\\n out.pop('vertex_colors')\\n if self.get('params', None):\\n out['params'] = np.asarray(\\n [[v.get(k, np.NaN) for k in 'uvw']\\n for v in self['params']])\\n if np.all(np.isnan(out['params'][:, 2])):\\n out['params'] = out['params'][:, :2]\\n if self.get('normals', None):\\n out['normals'] = np.asarray(\\n [[v[k] for k in 'ijk'] for v in self['normals']])\\n if self.get('texcoords', None):\\n out['texcoords'] = np.asarray(\\n [[v.get(k, np.NaN) for k in 'uvw']\\n for v in self['texcoords']])\\n if np.all(np.isnan(out['texcoords'][:, 1:])):\\n out['texcoords'] = out['texcoords'][:, :1]\\n elif np.all(np.isnan(out['texcoords'][:, 2])):\\n out['texcoords'] = out['texcoords'][:, :2]\\n if self.get('lines', None):\\n out['lines'] = np.NaN * np.ones(\\n (len(self['lines']), len(max(self['lines'], key=len))),\\n dtype='int32')\\n for i, vlist in enumerate(self['lines']):\\n for j, v in enumerate(vlist):\\n out['lines'][i, j] = v['vertex_index']\\n if self.get('faces', None):\\n face_shp = (len(self['faces']), len(max(self['faces'], key=len)))\\n out['faces'] = np.NaN * np.ones(face_shp, dtype='int32')\\n out['face_texcoords'] = np.NaN * np.ones(face_shp, dtype='int32')\\n out['face_normals'] = np.NaN * np.ones(face_shp, dtype='int32')\\n for i, vlist in enumerate(self['faces']):\\n for j, v in enumerate(vlist):\\n out['faces'][i, j] = v['vertex_index']\\n out['face_texcoords'][i, j] = v.get(\\n 'texcoord_index', np.NaN)\\n out['face_normals'][i, j] = v.get('normal_index', np.NaN)\\n if np.all(np.isnan(out['face_texcoords'])):\\n out.pop('face_texcoords')\\n if np.all(np.isnan(out['face_normals'])):\\n out.pop('face_normals')\\n if self.get('points', None):\\n out['points'] = np.NaN * np.ones(\\n (len(self['points']), len(max(self['points'], key=len))),\\n dtype='int32')\\n for i, vlist in enumerate(self['points']):\\n out['points'][i, :len(vlist)] = vlist\\n if self.get('curves', None):\\n def fkey(x):\\n return len(x['vertex_indices'])\\n out['curves'] = np.NaN * np.ones(\\n (len(self['curves']), len(max(self['curves'], key=fkey))),\\n dtype='int32')\\n out['curve_params'] = np.zeros((out['curves'].shape[0], 2))\\n for i, curv in enumerate(self['curves']):\\n out['curve_params'][i, :] = [curv['starting_param'],\\n curv['ending_param']]\\n out['curves'][i, :fkey(curv)] = curv['vertex_indices']\\n if self.get('curve2Ds', None):\\n out['curve2Ds'] = np.NaN * np.ones(\\n (len(self['curve2Ds']), len(max(self['curve2Ds'], key=len))),\\n dtype='int32')\\n for i, vlist in enumerate(self['curve2Ds']):\\n out['curve2Ds'][i, :len(vlist)] = vlist\\n if self.get('surfaces', None):\\n def fkey(x):\\n return len(x['vertex_indices'])\\n surf_shp = (len(self['surfaces']), len(max(self['surfaces'], key=fkey)))\\n out['surfaces'] = np.NaN * np.ones(surf_shp, dtype='int32')\\n out['surface_params'] = np.zeros((surf_shp[0], 4))\\n out['surface_texcoords'] = np.NaN * np.ones(surf_shp, dtype='int32')\\n out['surface_normals'] = np.NaN * np.ones(surf_shp, dtype='int32')\\n for i, surf in enumerate(self['surfaces']):\\n out['surface_params'][i, :] = [surf['starting_param_u'],\\n surf['ending_param_u'],\\n surf['starting_param_v'],\\n surf['ending_param_v']]\\n for j, v in enumerate(surf['vertex_indices']):\\n out['surfaces'][i, j] = v['vertex_index']\\n out['surface_texcoords'][i, j] = v.get(\\n 'texcoord_index', np.NaN)\\n out['surface_normals'][i, j] = v.get(\\n 'normal_index', np.NaN)\\n if np.all(np.isnan(out['surface_texcoords'])):\\n out.pop('surface_texcoords')\\n if np.all(np.isnan(out['surface_normals'])):\\n out.pop('surface_normals')\\n return out\",\n \"def to_dict(self):\\n\\n shape = super().to_dict()\\n shape[\\\"scale\\\"] = self.scale.tolist()\\n return shape\",\n \"def test_to_plain_python_obj_numpy(test_input, expected):\\n output = r.to_plain_python_obj(test_input)\\n assert output == expected\\n # We should not get a json conversion error\\n json.dumps(output)\",\n \"def clean(imagedata):\\n\\n if isinstance(imagedata, ndarray):\\n imagedata = [imagedata]\\n\\n outdict = [array_to_im(im) for im in imagedata]\\n\\n return {'images': outdict}\",\n \"def todict(self):\\n return dict(self.array)\",\n \"def test_dict2ndarray(self):\\n from ..io.util import _dict2ndarray\\n x = np.arange(10)\\n y = np.arange(10)*2\\n z = np.arange(20).reshape((10, 2))\\n self.assertEqual(z.shape, (10, 2))\\n d = dict(x=x, y=y, z=z)\\n nddata = _dict2ndarray(d)\\n self.assertTrue((nddata['x'] == x).all())\\n self.assertTrue((nddata['y'] == y).all())\\n self.assertTrue((nddata['z'] == z).all())\\n nddata = _dict2ndarray(d, columns=['x', 'y'])\\n self.assertTrue((nddata['x'] == x).all())\\n self.assertTrue((nddata['y'] == y).all())\\n with self.assertRaises(ValueError) as ex:\\n (nddata['z'] == z).all()\\n self.assertEqual(ex.exception.args[0], 'no field of name z')\",\n \"def data64(self) -> str:\\n return Image.encode64(self.data)\",\n \"def base64_to_numpy_image(b64: str) -> np.ndarray:\\n image = np.array(Image.open(BytesIO(base64.b64decode(b64))))\\n return image\",\n \"def base64_encode_array(inArray):\\n return base64.b64encode(inArray)\",\n \"def clean(imagedata):\\n if asarray(imagedata).ndim not in set((2, 3)):\\n raise Exception(\\\"Input must be two or three dimensional\\\")\\n\\n outdict = [array_to_im(imagedata)]\\n\\n return {'images': outdict}\",\n \"def clean(imagedata):\\n if asarray(imagedata).ndim not in set((2, 3)):\\n raise Exception(\\\"Input must be two or three dimensional\\\")\\n\\n outdict = [array_to_im(imagedata)]\\n\\n return {'images': outdict}\",\n \"def _raw_data(self):\\n data = self.datasource.as_array()\\n if not isinstance(data, np.ndarray):\\n raise TypeError(\\\"The data you try to load is no numpy array!\\\")\\n if data.ndim != 2:\\n raise ValueError(\\\"The data array you try to load does not have 2 \\\"\\n \\\"dimensions!\\\")\\n data = data.copy(order='C')\\n ident = hashlib.md5(data).hexdigest()\\n if self._dataident is None:\\n self._dataident = ident\\n elif self._dataident != ident:\\n raise ValueError(\\\"The data you try to load from '%s' has changed \\\"\\n \\\"since the last time. Please, check the \\\"\\n \\\"datasource of the record '%s'.\\\"\\n % (self.datasource.name, self.name))\\n\\n return data\",\n \"def test_format_signature_numpy():\",\n \"def data64(self, value: str) -> None:\\n self.data = Image.decode64(value)\",\n \"def as_correct_dtype(obj: ndarray, required_dtype: dtype) -> ndarray:\\n if obj.dtype != required_dtype:\\n return obj.astype(required_dtype)\\n return obj\",\n \"def data_prep_reg_go_def(xdata):\\n xdata = np.asarray( xdata )\\n x_dct = {'inputs': xdata}\\n return x_dct\",\n \"def pandas_typecast(self) -> dict:\\n res = {}\\n for feat in self.data_features:\\n res[feat.key] = ApiForm.typecast(feat.dtype)\\n return res\",\n \"def _encode_data_dict(self, data_dict: dict) -> dict:\\n\\n # If we have _BYTES_DATA_KEY or _FS_CHECKPOINT_KEY in the data dict,\\n # that means this is a directory checkpoint which has already been\\n # converted into bytes. We don't want to double-encode it.\\n # See the definition of super().__getstate__().\\n if _BYTES_DATA_KEY in data_dict or _FS_CHECKPOINT_KEY in data_dict:\\n return data_dict\\n\\n for k, v in data_dict.items():\\n # Only check for attribute as we want to support\\n # DDP, FSDP and any future approaches\\n if isinstance(v, Module) and hasattr(v, \\\"module\\\"):\\n data_dict[k] = v.module\\n elif isinstance(v, dict):\\n # We could limit this only to the MODEL_KEY, but we'd\\n # miss any extra user-specified keys. This should be a\\n # noop with anything but DDP/FSDP module state dicts.\\n data_dict[k] = consume_prefix_in_state_dict_if_present_not_in_place(\\n v, \\\"module.\\\"\\n )\\n\\n # Convert the checkpoint dict to bytes, so that any GPU tensors that\\n # are in the checkpoint dict can be properly deserialized on the\\n # driver side, even if the driver does not have access to a GPU device.\\n _buffer = io.BytesIO()\\n torch.save(\\n data_dict,\\n _buffer,\\n pickle_module=ray_pickle,\\n pickle_protocol=pickle.HIGHEST_PROTOCOL\\n # Using pickle.HIGHEST_PROTOCOL here because it's 5 for Python 3.8+,\\n # but 4 for 3.7. For backward compatibility, we are not using\\n # ray.cloudpickle because its default protocol is always 5.\\n )\\n return {ENCODED_DATA_KEY: _buffer.getvalue()}\",\n \"def _decode_data_dict(self, data_dict: dict) -> dict:\\n if ENCODED_DATA_KEY not in data_dict:\\n return data_dict\\n\\n encoded_data = data_dict[ENCODED_DATA_KEY]\\n _buffer = io.BytesIO(encoded_data)\\n data_dict = torch.load(\\n _buffer,\\n map_location=\\\"cpu\\\"\\n # Not using ray.cloudpickle here as it doesn't\\n # define an Unpickler (as it is not necessary).\\n )\\n return data_dict\",\n \"def _convert_data(self, data):\\n if isinstance(data, Tensor):\\n data = data.asnumpy()\\n elif isinstance(data, list):\\n data = np.array(data)\\n elif isinstance(data, np.ndarray):\\n pass\\n else:\\n raise TypeError('Input data type must be tensor, list or numpy.ndarray')\\n return data\",\n \"def as_str(self) -> str:\\n return dumps(self.as_dict(), cls=NumpyEncoder)\",\n \"def deserialise_image(data):\\n if \\\"data:image\\\" in data:\\n data = data[data.find(\\\",\\\") + 1:]\\n\\n return Image.open(io.BytesIO(base64.urlsafe_b64decode(data)))\",\n \"def strip_dict(self, inp_d):\\r\\n inp_d = dict([(k, numpy.complex(inp_d[k]))\\r\\n for k in inp_d if type(k) == str and\\r\\n k.isalnum() and\\r\\n isinstance(inp_d[k], numbers.Number)])\\r\\n return inp_d\",\n \"def encode_data_readable(cls, obj, typedef):\\n return cls.encode_data(obj, typedef)\",\n \"def __array__(self):\\n return dict2rec(self)\",\n \"def save_img_base64(_preds):\\n img = Image.fromarray(_preds)\\n buff = BytesIO()\\n img.save(buff, format=\\\"JPEG\\\")\\n return base64.b64encode(buff.getvalue())\",\n \"def test_24_to_json_string(self):\\n r = Rectangle(10, 7, 2, 8)\\n d = r.to_dictionary()\\n json_d = Base.to_json_string([d])\\n self.assertEqual(type(json_d), str)\\n self.assertEqual(\\n d, {'height': 7, 'id': 1, 'width': 10, 'x': 2, 'y': 8})\",\n \"def base64(self):\\n image = self.png.getvalue()\\n return base64.encodestring(image).decode('utf-8')\",\n \"def _to_dict(self, item):\\n if isinstance(item, Buffer):\\n ret = {}\\n fields = item._all_fields()\\n for field in fields:\\n ret[field.attr_name()] = self._to_dict(getattr(item, field.attr_name()))\\n return ret\\n\\n if isinstance(item, Struct):\\n ret = {}\\n for field in item._container_.fields:\\n if hasattr(field, 'name'):\\n ret[field.name] = self._to_dict(field.get_value(item))\\n elif isinstance(field, FieldListContainer):\\n for inner_field in field.fields:\\n if not isinstance(inner_field, AnonymousField):\\n ret[inner_field.name] = self._to_dict(inner_field.get_value(item))\\n return ret\\n\\n if isinstance(item, bytearray):\\n return '0x' + binascii.hexlify(item) if item else ''\\n\\n if isinstance(item, list):\\n return [self._to_dict(x) for x in item]\\n\\n return item\",\n \"def serialize(obj):\\n return np.fromstring(pickle.dumps(obj), dtype=np.uint8).astype(np.float32)\",\n \"def serialize(obj):\\n return np.fromstring(pickle.dumps(obj), dtype=np.uint8).astype(np.float32)\",\n \"def as_dict(self):\\n # Get the properties inherited from Data\\n DataND_dict = super(DataND, self).as_dict()\\n DataND_dict[\\\"axes\\\"] = list()\\n for obj in self.axes:\\n DataND_dict[\\\"axes\\\"].append(obj.as_dict())\\n DataND_dict[\\\"normalizations\\\"] = self.normalizations\\n DataND_dict[\\\"FTparameters\\\"] = self.FTparameters\\n if self.values is None:\\n DataND_dict[\\\"values\\\"] = None\\n else:\\n DataND_dict[\\\"values\\\"] = self.values.tolist()\\n # The class name is added to the dict fordeserialisation purpose\\n # Overwrite the mother class name\\n DataND_dict[\\\"__class__\\\"] = \\\"DataND\\\"\\n return DataND_dict\",\n \"def asdict():\\n pass\",\n \"def normalize_config(config):\\n return json.loads(json.dumps(config, cls=NumpyEncoder))\",\n \"def json_serialize_numpy_array(array):\\n return json.dumps(json_ready_numpy_array(array))\",\n \"def json_serialize_numpy_array(array):\\n return json.dumps(json_ready_numpy_array(array))\",\n \"def serialize_ndarrays(d):\\n def dict_handler(d):\\n return d.items()\\n\\n handlers = {list: enumerate, tuple: enumerate,\\n set: enumerate, frozenset: enumerate,\\n dict: dict_handler}\\n\\n def serialize(o):\\n for typ, handler in handlers.items():\\n if isinstance(o, typ):\\n for key, val in handler(o):\\n if isinstance(val, np.ndarray):\\n o[key] = val.tolist()\\n else:\\n o[key] = serialize_ndarrays(o[key])\\n return o\\n\\n return serialize(d)\",\n \"def convertImage(img):\\n return '\\\\\\\\includegraphicsdata{%s}' % \\\":\\\".join([\\n 'data',\\n img.contentType,\\n \\\"base64,%s\\\" % img.data.encode(\\\"base64\\\").replace(\\\"\\\\n\\\", \\\"\\\"),\\n ])\",\n \"def as_dict(self):\\n if self._feat_matrix is not None:\\n feature_matrix = self._feat_matrix.tolist()\\n else:\\n feature_matrix = self._feat_matrix\\n if self.regression_data is not None:\\n reg_data = jsanitize(asdict(self.regression_data))\\n else:\\n reg_data = None\\n\\n ce_d = {\\n \\\"@module\\\": self.__class__.__module__,\\n \\\"@class\\\": self.__class__.__name__,\\n \\\"cluster_subspace\\\": self.cluster_subspace.as_dict(),\\n \\\"coefs\\\": self.coefs.tolist(),\\n \\\"regression_data\\\": reg_data,\\n \\\"feature_matrix\\\": feature_matrix,\\n }\\n return ce_d\",\n \"def to_image_data(data):\\n \\n # removing image\\n if not data:\\n return u''\\n\\n # image path (not changed)\\n if data[0:5] != u'data:':\\n return None\\n \\n # TODO: better MIME handling\\n mime = data[5:data.index(u';')].lower()\\n img = data[data.index(u',') + 1:].decode('base64')\\n \\n return mime, img\",\n \"def rawtodictonary(rawstring):\\n\\n if is_python3():\\n rawstring = bytes(rawstring).decode('utf-8') \\n \\n raw = rawstring.split(NULL)[:-2]\\n\\n data = {}\\n for i in range(0,len(raw) - 1,2):\\n\\n key,val = raw[i], raw[i+1]\\n keyel = key.split(\\\"\\\\x1c\\\")\\n\\n if len(keyel) == 1:\\n if key in data:\\n data[key][None] = val\\n else:\\n data[key]=val\\n else:\\n if keyel[0] in data and not isinstance(data[keyel[0]], dict):\\n data[keyel[0]]={ None: data[keyel[0]] }\\n\\n try:\\n data[keyel[0]][keyel[1]] = val\\n except TypeError:\\n data[keyel[0]] = {keyel[1] : val}\\n except KeyError:\\n data[keyel[0]] = {keyel[1] : val}\\n\\n return data\",\n \"def generate_json_from_base64_image(base64_image):\\n # Content image\\n content_json_obj = {'content': base64_image}\\n\\n # Detection type.\\n feature_json_obj = [{'type': get_detection_type(5),\\n 'maxResults': 50,\\n }\\n ]\\n\\n # Now add it to the request\\n request_list = []\\n request_list.append({\\n 'features': feature_json_obj,\\n 'image': content_json_obj,\\n })\\n\\n # To json\\n data = json.dumps({'requests': request_list})\\n\\n return data\",\n \"def serialize_numpy(self, buff, numpy):\\n try:\\n _x = self.name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self.compatibility\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self.display_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self.description\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self.namespace\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self.icon.resource_name\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self.icon.format\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self.icon.data\\n length = len(_x)\\n # - if encoded as a list instead, serialize as bytes instead of string\\n if type(_x) in [list, tuple]:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n length = len(self.remappings)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.remappings:\\n _x = val1.remap_from\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = val1.remap_to\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = self.parameters\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n buff.write(_struct_i.pack(self.max))\\n _x = self.pairing.rapp\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n length = len(self.pairing.remappings)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.pairing.remappings:\\n _x = val1.remap_from\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = val1.remap_to\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n length = len(self.pairing.parameters)\\n buff.write(_struct_I.pack(length))\\n for val1 in self.pairing.parameters:\\n _x = val1.key\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n _x = val1.value\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n buff.write(_struct_i.pack(self.hash))\\n _x = self.role\\n length = len(_x)\\n if python3 or type(_x) == unicode:\\n _x = _x.encode('utf-8')\\n length = len(_x)\\n if python3:\\n buff.write(struct.pack('<I%sB'%length, length, *_x))\\n else:\\n buff.write(struct.pack('<I%ss'%length, length, _x))\\n except struct.error as se: self._check_types(struct.error(\\\"%s: '%s' when writing '%s'\\\" % (type(se), str(se), str(locals().get('_x', self)))))\\n except TypeError as te: self._check_types(ValueError(\\\"%s: '%s' when writing '%s'\\\" % (type(te), str(te), str(locals().get('_x', self)))))\",\n \"def array_to_dict(arr: np.ndarray, domain: Optional[np.ndarray] = None) -> DictStrNum:\\n\\n if domain is None:\\n keys, counts = np.unique(numpy_array(arr), return_counts=True)\\n out_dict = dict(zip(keys, counts))\\n else:\\n out_dict = {}\\n for d in np.unique(domain):\\n arr_d = arr[domain == d]\\n keys_d, counts_d = np.unique(numpy_array(arr_d), return_counts=True)\\n out_dict[d] = dict(zip(keys_d, counts_d))\\n\\n return out_dict\",\n \"def data_to_dict(data: Data) -> Dict[str, Any]:\\n properties = data.serialize_init_args(obj=data)\\n properties[\\\"__type\\\"] = data.__class__.__name__\\n return properties\",\n \"def data_to_dict(self):\\n return self.data.copy()\",\n \"def encode_data(data):\\n bytes = json.dumps(data).encode('utf-8').encode('base64').replace('\\\\n', '')\\n assert len(bytes) < 250 * 1024\\n return bytes\",\n \"def from_base64(x):\\n\\treturn numpy.fromstring(\\n\\t\\tbinascii.a2b_base64(x),\\n\\t\\tdtype=numpy.uint8\\n\\t)\",\n \"def to_dictionary(self):\\n x = super().to_dictionary()\\n y = x.copy()\\n y[\\\"size\\\"] = self.width\\n if y[\\\"height\\\"] and y[\\\"width\\\"]:\\n del y[\\\"height\\\"]\\n del y[\\\"width\\\"]\\n return(y)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7285015","0.7285015","0.7259372","0.7155377","0.7137759","0.6932167","0.6918994","0.6918994","0.6918994","0.64177775","0.6341543","0.6310709","0.5843858","0.5809174","0.58036816","0.5672971","0.5655243","0.56076634","0.55190325","0.5500997","0.5490805","0.548376","0.54784197","0.5421398","0.5393345","0.5384954","0.5380262","0.53676224","0.5365789","0.5345899","0.5329232","0.53206044","0.5301104","0.5271653","0.5263623","0.5255082","0.5254482","0.522863","0.5218855","0.52163655","0.5214114","0.52058655","0.5200694","0.5181299","0.51528436","0.51510924","0.514911","0.5136091","0.5136086","0.5116953","0.5112874","0.51114","0.51111835","0.50967425","0.5084372","0.50831515","0.50599444","0.50584424","0.5057565","0.5054458","0.50472414","0.50449646","0.50449646","0.5041881","0.50348467","0.50214595","0.5016177","0.50094545","0.5005731","0.5005719","0.50001377","0.4998667","0.49945298","0.4992592","0.4987687","0.49854028","0.49740136","0.49737224","0.4969055","0.4960554","0.49538487","0.4946149","0.4946149","0.4945708","0.49435577","0.49431947","0.49384588","0.49384588","0.49264348","0.4920747","0.49181026","0.4917326","0.4914777","0.49110502","0.49099576","0.4903829","0.49012208","0.48999593","0.48991987","0.48925754","0.4886793"],"string":"[\n \"0.7285015\",\n \"0.7285015\",\n \"0.7259372\",\n \"0.7155377\",\n \"0.7137759\",\n \"0.6932167\",\n \"0.6918994\",\n \"0.6918994\",\n \"0.6918994\",\n \"0.64177775\",\n \"0.6341543\",\n \"0.6310709\",\n \"0.5843858\",\n \"0.5809174\",\n \"0.58036816\",\n \"0.5672971\",\n \"0.5655243\",\n \"0.56076634\",\n \"0.55190325\",\n \"0.5500997\",\n \"0.5490805\",\n \"0.548376\",\n \"0.54784197\",\n \"0.5421398\",\n \"0.5393345\",\n \"0.5384954\",\n \"0.5380262\",\n \"0.53676224\",\n \"0.5365789\",\n \"0.5345899\",\n \"0.5329232\",\n \"0.53206044\",\n \"0.5301104\",\n \"0.5271653\",\n \"0.5263623\",\n \"0.5255082\",\n \"0.5254482\",\n \"0.522863\",\n \"0.5218855\",\n \"0.52163655\",\n \"0.5214114\",\n \"0.52058655\",\n \"0.5200694\",\n \"0.5181299\",\n \"0.51528436\",\n \"0.51510924\",\n \"0.514911\",\n \"0.5136091\",\n \"0.5136086\",\n \"0.5116953\",\n \"0.5112874\",\n \"0.51114\",\n \"0.51111835\",\n \"0.50967425\",\n \"0.5084372\",\n \"0.50831515\",\n \"0.50599444\",\n \"0.50584424\",\n \"0.5057565\",\n \"0.5054458\",\n \"0.50472414\",\n \"0.50449646\",\n \"0.50449646\",\n \"0.5041881\",\n \"0.50348467\",\n \"0.50214595\",\n \"0.5016177\",\n \"0.50094545\",\n \"0.5005731\",\n \"0.5005719\",\n \"0.50001377\",\n \"0.4998667\",\n \"0.49945298\",\n \"0.4992592\",\n \"0.4987687\",\n \"0.49854028\",\n \"0.49740136\",\n \"0.49737224\",\n \"0.4969055\",\n \"0.4960554\",\n \"0.49538487\",\n \"0.4946149\",\n \"0.4946149\",\n \"0.4945708\",\n \"0.49435577\",\n \"0.49431947\",\n \"0.49384588\",\n \"0.49384588\",\n \"0.49264348\",\n \"0.4920747\",\n \"0.49181026\",\n \"0.4917326\",\n \"0.4914777\",\n \"0.49110502\",\n \"0.49099576\",\n \"0.4903829\",\n \"0.49012208\",\n \"0.48999593\",\n \"0.48991987\",\n \"0.48925754\",\n \"0.4886793\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77978,"cells":{"query":{"kind":"string","value":"Read dense Jester dataset and split train/test data randomly."},"document":{"kind":"string","value":"def read_jester_data(fname='jester-dense-subset-100x20.csv'):\n logging.info('reading data')\n try:\n data = pd.read_csv(fname)\n except IOError as err:\n print str(err)\n url = 'https://gist.github.com/macks22/b40ac9c685e920ad3ca2'\n print 'download from: %s' % url\n sys.exit(DATA_NOT_FOUND)\n\n # Calculate split sizes.\n logging.info('splitting train/test sets')\n n, m = data.shape # # users, # jokes\n N = n * m # # cells in matrix\n test_size = N / 10 # use 10% of data as test set\n train_size = N - test_size # and remainder for training\n\n # Prepare train/test ndarrays.\n train = data.copy().values\n test = np.ones(data.shape) * np.nan\n\n # Draw random sample of training data to use for testing.\n tosample = np.where(~np.isnan(train)) # only sample non-missing values\n idx_pairs = zip(tosample[0], tosample[1]) # zip row/col indices\n indices = np.arange(len(idx_pairs)) # indices of row/col index pairs\n sample = np.random.choice(indices, replace=False, size=test_size) # draw sample\n\n # Transfer random sample from train set to test set.\n for idx in sample:\n idx_pair = idx_pairs[idx] # retrieve sampled index pair\n test[idx_pair] = train[idx_pair] # transfer to test set\n train[idx_pair] = np.nan # remove from train set\n\n # Verify everything worked properly\n assert(np.isnan(train).sum() == test_size)\n assert(np.isnan(test).sum() == train_size)\n\n # Return the two numpy ndarrays\n return train, test"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def run_train_test_split():\n # Load all documents\n conn = sq.connect(config.DB_FILE)\n documents = pd.read_sql_query('select pubmed_id, review_id, included, title, abstract from article ', conn)\n\n # Identify unique review IDs\n review_ids = documents['review_id'].unique()\n\n # Set seed for random sampling\n np.random.seed(2)\n\n # List of Reviews in the partial data set and full data set\n partial_set = list(np.random.choice(review_ids, 10, replace=False))\n full_set = list(review_ids.copy())\n\n # Load array (X) and labels (Y) of all documents\n with (open(config.DOC_TERM_MATRIX, \"rb\")) as openfile:\n X = pickle.load(openfile)\n\n y = documents['included']\n\n # Train-test split of the partial dataset\n train_test_split(X, y, partial_set, 'min_max', 'partial', review_ids)\n train_test_split(X, y, partial_set, 'tf_idf', 'partial', review_ids)\n\n # Train-test split of the full dataset\n train_test_split(X, y, full_set, 'min_max', 'full', review_ids)\n train_test_split(X, y, full_set, 'tf_idf', 'full', review_ids)","def generate_train_test(self):\n x, y = self.read_data()\n x_train, y_train, x_test, y_test = self.sample_data(x, y)\n self.train = (x_train, y_train)\n self.test = (x_test, y_test)","def split_data(dataset, test_size=0.5):\n shuffled_data = np.random.RandomState(seed=721).permutation(dataset)\n train_set = shuffled_data[: int(len(dataset) * (1 - test_size)), :]\n test_set = shuffled_data[int(len(dataset) * (1 - test_size)):, :]\n return train_set, test_set","def split_dataset(dataset, test_size):\r\n random.shuffle(dataset)\r\n \r\n rating_negativ = []\r\n rating_positiv = []\r\n \r\n for row in dataset:\r\n if int(row[1]) == 0:\r\n rating_negativ.append(row)\r\n elif int(row[1]) == 1:\r\n rating_positiv.append(row)\r\n\r\n random.shuffle(rating_positiv)\r\n random.shuffle(rating_negativ) \r\n \r\n neg_train_data, neg_val_data = train_test_split(rating_negativ, test_size=test_size)\r\n pos_train_data, pos_val_data = train_test_split(rating_positiv, test_size=test_size)\r\n \r\n train_data = neg_train_data + pos_train_data\r\n val_data = neg_val_data + pos_val_data\r\n \r\n random.shuffle(train_data)\r\n random.shuffle(val_data)\r\n \r\n return train_data, val_data","def load_data(input_file, test_size, random_seed ):\n ratings = np.genfromtxt(input_file, delimiter=',')\n #ratings = ratings[:200,:]\n train_masks = np.zeros_like(ratings)\n test_masks = np.zeros_like(ratings)\n pairs = list()\n for i in range(ratings.shape[0]):\n for j in range(ratings.shape[1]):\n pairs.append((i,j))\n train, test = train_test_split(pairs ,test_size = test_size, random_state= random_seed)\n for user,item in train:\n train_masks[user, item]= 1\n for user,book in test:\n test_masks[user, item]= 1 \n return ratings, train_masks, test_masks","def split_data_into_train_and_test(raw_training_data):\n train_set, test_set = train_test_split(raw_training_data, test_size=0.2, random_state=42)\n return train_set, test_set","def read_in_and_split_data(iris_data):\n iris_data = datasets.load_iris()\n data = iris_data['data']\n targets = iris_data['target']\n train_data, test_data, train_targets, test_targets = train_test_split(data, targets, test_size=0.1) \n return (train_data, test_data, train_targets, test_targets)","def get_dldata(filepath, dlTrainCorpusPath, dlTestCorpusPath, seed=2018, batch_size=16):\r\n\tf = open(\"record/synthetic and academic datasets/testcases_train.pkl\",'rb') #get the testcase ids of train sets and test sets\r\n\ttestcases += pickle.load(f) \r\n\tf.close()\r\n\r\n\tf = open(\"record/synthetic and academic datasets/testcases_test.pkl\",'rb')\r\n\ttestcases += pickle.load(f)\r\n\tf.close()\r\n\t\r\n print(\"produce train dataset...\") \r\n N = 6\r\n num = list(range(N))\r\n for i in num:\r\n train_set = [[], [], [], [], [], []]\r\n for folder_train in folders_train[int(i*len(folders_train)/N) : int((i+1)*len(folders_train)/N)]:\r\n if not folder_train in os.listdir(filepath):\r\n continue\r\n print(\"\\r\"+str(folder_train), end='')\r\n for filename in os.listdir(os.path.join(filepath, folder_train)):\r\n f = open(filepath + folder_train + '/' + filename, 'rb')\r\n data = pickle.load(f)\r\n f.close()\r\n if len(data[0][0]) > MAXLEN:\r\n data[2] = [x for x in data[2] if x <= MAXLEN]\r\n data[0] = cutdata(data[0][0])\r\n if data[0] == None:\r\n continue \r\n for n in range(len(data)):\r\n train_set[n].append(data[n])\r\n train_set[-1].append(folder_train+\"/\"+filename)\r\n f_train = open(dlTrainCorpusPath + \"train_\" + str(i)+ \"_0818.pkl\", 'wb')\r\n pickle.dump(train_set, f_train)\r\n f_train.close()\r\n\r\n del train_set \r\n gc.collect() \r\n\r\n print(\"\\nproduce test dataset...\")\r\n N = 6\r\n num = list(range(N))\r\n for i in num:\r\n test_set = [[], [], [], [], [], []]\r\n for folder_test in folders_test[int(i*len(folders_test)/N) : int((i+1)*len(folders_test)/N)]:\r\n if not folder_test in os.listdir(filepath):\r\n continue\r\n print(\"\\r\"+str(folder_test), end='')\r\n for filename in os.listdir(os.path.join(filepath, folder_test)):\r\n f = open(filepath + folder_test + '/' + filename, 'rb')\r\n data = pickle.load(f)\r\n f.close()\r\n if len(data[0][0]) > MAXLEN:\r\n data[2] = [x for x in data[2] if x <= MAXLEN]\r\n data[0] = cutdata(data[0][0])\r\n if data[0] == None:\r\n continue \r\n for n in range(len(data)):\r\n test_set[n].append(data[n])\r\n test_set[-1].append(folder_test+\"/\"+filename)\r\n \r\n f_test = open(dlTestCorpusPath + \"test_\" + str(i)+ \"_0124.pkl\", 'wb')\r\n pickle.dump(test_set, f_test)\r\n f_test.close()\r\n\r\n del test_set\r\n gc.collect()\r\n return","def split_dataset(self, test_size=0.20):\n\t\t(self.training_data, self.test_data, self.training_labels, self.test_labels) = train_test_split(self.training_data, self.training_labels, test_size=test_size)","def train_test_split(self):\n random.seed(self.args.seed)\n nodes = [node for node in range(self.ncount)]\n random.shuffle(nodes)\n self.train_nodes = torch.LongTensor(nodes[0:self.args.training_size])\n self.validation_nodes = torch.LongTensor(nodes[self.args.training_size:self.args.training_size+self.args.validation_size])\n self.test_nodes = torch.LongTensor(nodes[self.args.training_size+self.args.validation_size:])","def read_and_split_sets():\n gen_train_test_sets(\"Data_Sent_Embds/en_sent.pkl\", \"Data_Sent_Embd_Splitted/en_train.pkl\",\n \"Data_Sent_Embd_Splitted/en_test.pkl\")\n gen_train_test_sets(\"Data_Sent_Embds/es_sent.pkl\", \"Data_Sent_Embd_Splitted/es_train.pkl\",\n \"Data_Sent_Embd_Splitted/es_test.pkl\")\n gen_train_test_sets(\"Data_Sent_Embds/pr_sent.pkl\", \"Data_Sent_Embd_Splitted/pr_train.pkl\",\n \"Data_Sent_Embd_Splitted/pr_test.pkl\")","def build_toy_dataset(N):\n y_data = np.random.uniform(-10.5, 10.5, N)\n r_data = np.random.normal(size=N) # random noise\n x_data = np.sin(0.75 * y_data) * 7.0 + y_data * 0.5 + r_data * 1.0\n x_data = x_data.reshape((N, 1))\n return train_test_split(x_data, y_data, random_state=42)","def split_dataset(df, test_size, seed):\r\n ncols = np.size(df, 1)\r\n X = df.iloc[:, range(0, ncols - 1)]\r\n Y = df.iloc[:, ncols - 1]\r\n x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)\r\n y_train = get_dummies(y_train) # One-hot encoding\r\n y_test = get_dummies(y_test)\r\n return x_train, x_test, y_train, y_test","def train_test_set_split(dataset, dataset_name, test_size=0.1):\n train_indices_path = './' + dataset_name + '_train_indices(' + str(test_size) + ').txt'\n test_indices_path = './' + dataset_name + '_test_indices(' + str(test_size) + ').txt'\n try:\n train_indices = []\n test_indices = []\n file = open(train_indices_path, 'rt', encoding='utf-8')\n while True:\n line = file.readline()\n if not line:\n break\n train_indices.append(int(line[:-1]))\n file.close()\n file = open(test_indices_path, 'rt', encoding='utf-8')\n while True:\n line = file.readline()\n if not line:\n break\n test_indices.append(int(line[:-1]))\n file.close()\n train_labels = [dataset.targets[i] for i in train_indices]\n except FileNotFoundError:\n indices = np.arange(len(dataset))\n labels = np.array(dataset.targets)\n train_indices, test_indices, train_labels, _ = train_test_split(\n indices, labels, test_size=test_size, stratify=labels\n )\n file = open(train_indices_path, 'wt', encoding='utf-8')\n for i in train_indices:\n line = str(i) + '\\n'\n file.write(line)\n file.close()\n file = open(test_indices_path, 'wt', encoding='utf-8')\n for i in test_indices:\n line = str(i) + '\\n'\n file.write(line)\n file.close()\n\n train_set = torch.utils.data.Subset(dataset, indices=train_indices)\n test_set = torch.utils.data.Subset(dataset, indices=test_indices)\n return train_set, test_set, train_labels","def load_occupancy_dataset(trainsize=500, testsize=1000):\n filename = 'datasets/numericsequence.csv'\n dataset = loadcsv(filename)\n trainset, testset = splitdataset(dataset, trainsize, testsize)\n return trainset, testset","def load_mnist(path='./', test_size=0.3, random_state = 123):\n \n np.random.seed(random_state)\n if 'X_train.npy' not in os.listdir(path=path) or 'y_train.npy' not in os.listdir(path=path):\n print (\"Train dataset not found. Downloading...\")\n os.system(\"curl -L -o train.zip {}\".format(TRAIN_DATA_LINK))\n os.system(\"unzip train.zip\")\n os.system(\"tar -xf trainingSet.tar.gz\")\n images = []\n labels = []\n for class_name in os.listdir('./trainingSet'):\n if 'ipynb' not in class_name and '.DS' not in class_name:\n for image_name in os.listdir('./trainingSet/{}'.format(class_name)):\n image = imread('./trainingSet/{}/{}'.format(class_name, image_name))\n images.append(image)\n labels.append(int(class_name))\n X_train = np.array(images)\n y_train = np.array(labels)\n\n permutation = np.random.permutation(X_train.shape[0])\n X_train = X_train[permutation]\n y_train = y_train[permutation]\n\n with open('X_train.npy', 'wb') as f:\n np.save(f, X_train)\n with open('y_train.npy', 'wb') as f:\n np.save(f, y_train)\n os.system(\"rm -rf trainingSet\")\n os.system(\"rm -rf train.zip\")\n os.system(\"rm -rf trainingSet.tar.gz\")\n else:\n X_train = np.load('X_train.npy')\n y_train = np.load('y_train.npy')\n\n if 'X_test.npy' not in os.listdir(path=path) or 'y_test.npy' not in os.listdir(path=path):\n print (\"Test dataset not found. Downloading...\")\n os.system(\"curl -L -o test.zip {}\".format(TEST_DATA_LINK))\n os.system(\"unzip test.zip\")\n os.system(\"tar -xf trainingSample.tar.gz\")\n images = []\n labels = []\n for class_name in os.listdir('./trainingSample'):\n if 'ipynb' not in class_name and '.DS' not in class_name:\n for image_name in os.listdir('./trainingSample/{}'.format(class_name)):\n image = imread('./trainingSample/{}/{}'.format(class_name, image_name))\n images.append(image)\n labels.append(int(class_name))\n X_test = np.array(images)\n y_test = np.array(labels)\n with open('X_test.npy', 'wb') as f:\n np.save(f, X_test)\n with open('y_test.npy', 'wb') as f:\n np.save(f, y_test)\n\n os.system(\"rm -rf trainingSample\")\n os.system(\"rm -rf test.zip\")\n os.system(\"rm -rf trainingSet.tar.gz\")\n\n else:\n X_test = np.load('X_test.npy')\n y_test = np.load('y_test.npy')\n\n return X_train, X_test, y_train, y_test","def train_test_split(dataset, split):\r\n train = list()\r\n train_size = split * len(dataset)\r\n dataset_copy = list(dataset) \r\n while len(train) < train_size:\r\n index = randrange(len(dataset_copy))\r\n train.append(dataset_copy.pop(index))\r\n return train, dataset_copy","def split_data(X, y, test_size, random_state):\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)\n\n return X_train, X_test, y_train, y_test","def split_dataset(dataset, test_size):\n train_data = dataset.skip(test_size).shuffle(SHUFFLE_BUFFER_SIZE)\n train_data = train_data.padded_batch(BATCH_SIZE)\n \n test_data = dataset.take(test_size)\n test_data = test_data.padded_batch(BATCH_SIZE)\n \n return train_data, test_data","def data_split(self, test_size=0.2, stratify=None):\n return train_test_split(\n self, test_size=test_size, random_state=42, stratify=stratify\n )","def generate_data(self,seed):\n X, y = make_classification( n_samples = 250, random_state = seed )\n # Add bias term\n X = np.concatenate( ( np.ones( ( 250, 1 ) ), X ), axis = 1 )\n self.X_train, self.X_test, self.y_train, self.y_test = train_test_split( \n X, y, test_size = 50, random_state = seed )","def load_training_set():\n global training_set\n f = gzip.open('mnist.pkl.gz', 'rb')\n train, valid, test = cPickle.load(f)\n [training_set, training_labels] = train\n [validation_set, validation_labels] = valid\n [testing_set, testing_labels] = test\n training_set = np.concatenate((training_set, validation_set))\n f.close()\n np.random.shuffle(training_set)","def splitData(filename, testing_set_percentage):\n matFile = sio.loadmat(filename)\n data = matFile['mydata']\n\n np.savetxt('test.out', data, fmt='%d', delimiter=',') # X is an array\n print('BEFORE SHUFFLE', data)\n\n np.random.shuffle(data)\n\n np.savetxt('test_after.out', data, fmt='%d', delimiter=',') # X is an array\n\n print('AFTER SHUFFLE', data)\n\n r, c = np.array(data).shape\n\n testing_set_size = int(r * testing_set_percentage)\n training_set_size = r - testing_set_size\n\n train_data = data[:training_set_size]\n test_data = data[training_set_size:]\n\n return train_data, test_data","def get_random(everything_path, split_ratio=0.5, seed=0):\n # Loading data\n with h5py.File(everything_path, 'r') as data:\n labels = data['labels'].value\n \n # Splitting classes\n fri_i = np.where(labels==1)[0]\n frii_i = np.where(labels==2)[0]\n rand_i = np.where(labels==0)[0]\n\n # Shuffling\n fri_i = shuffle(fri_i, random_state=seed)\n frii_i = shuffle(frii_i, random_state=seed)\n rand_i = shuffle(rand_i, random_state=seed)\n\n # Splitting into training and testing sets\n cut = int(np.round(split_ratio * fri_i.shape[0]))\n train_fri = fri_i[cut:]\n test_fri = fri_i[:cut]\n\n cut = int(np.round(split_ratio * frii_i.shape[0]))\n train_frii = frii_i[cut:]\n test_frii = frii_i[:cut]\n\n cut = int(np.round(split_ratio * rand_i.shape[0]))\n train_rand = rand_i[cut:]\n test_rand = rand_i[:cut]\n\n train_i = np.concatenate((train_fri, train_frii, train_rand), axis=0)\n test_i = np.concatenate((test_fri, test_frii, test_rand), axis=0)\n\n return train_i, test_i","def split_train_test(ratings):\r\n ratings = ratings.sample(frac=1).reset_index(drop=True)\r\n train_user_list = []\r\n train_item_list = []\r\n train_rating_list = []\r\n test_user_list = []\r\n test_item_list = []\r\n test_rating_list = []\r\n user_pool = set(ratings['userId'].unique())\r\n for idx in user_pool:\r\n flag = 0\r\n items = ratings[ratings['userId']==idx][['itemId','rating']]\r\n for i, row in items.iterrows():\r\n if flag == 0:\r\n test_user_list.append(int(idx))\r\n test_item_list.append(int(row['itemId']))\r\n test_rating_list.append(row['rating'])\r\n flag = 1\r\n else:\r\n train_user_list.append(int(idx))\r\n train_item_list.append(int(row['itemId']))\r\n train_rating_list.append(row['rating'])\r\n\r\n train = pd.DataFrame({'userId': train_user_list, 'itemId': train_item_list, 'rating': train_rating_list}, columns=['userId', 'itemId', 'rating'])\r\n test = pd.DataFrame({'userId': test_user_list, 'itemId': test_item_list, 'rating': test_rating_list}, columns=['userId', 'itemId', 'rating'])\r\n return [train, test]\r\n \r\n\r\n \r\n #train, test = train_test_split(ratings, test_size=0.1, shuffle=True)\r\n #return [train, test]\r","def readData(path_to_dataset, train_size=0.8, validation_size=0.2):\n data = pd.read_csv(os.path.join(path_to_dataset, 'training_set_rel3.tsv'), sep='\\t', encoding='ISO-8859-1')\n # Drop columns that has null value \n data = data.dropna(axis=1)\n # Only take 4 columns of data from the dataset: essay_id, essay_set, essay, domain1_score\n data = data[['essay_id', 'essay_set', 'essay', 'domain1_score']]\n # Perform 80:20 train-test split on the training data\n train_set, test_set = train_test_split(data, train_size=train_size, random_state=0)\n # Split the 80% training set further into 60:20\n training_set, validation_set = train_test_split(train_set, test_size=validation_size, random_state=0)\n return training_set, test_set, validation_set","def load_data(test_split=0.1):\n global _data\n random.shuffle(_data)\n idx = int(len(_data) * (1 - test_split))\n x_train, y_train = np.array([d[:4] for d in _data[:idx]]), np.array([name_index[d[4]] for d in _data[:idx]])\n x_test, y_test = np.array([d[:4] for d in _data[idx:]]), np.array([name_index[d[4]] for d in _data[idx:]])\n return (x_train, y_train), (x_test, y_test)","def make_testing_training(data, percent_training, random_split=False, seed=None):\n ## Making testing and training sets\n data['computed Case Date/Time Closed'] = pd.to_datetime(data['Case Date/Time Closed'])\n ordered_data = data.sort(columns=['computed Case Date/Time Closed'])\n np.random.seed(seed=seed) \n nrows, ncols = ordered_data.shape\n\n if random_split:\n training_indices = np.random.choice(ordered_data.index, size=int(nrows*percent_training), replace=False)\n training = ordered_data.ix[training_indices]\n testing = ordered_data[~data['case_id'].isin(training['case_id'])]\n else: # split by date\n training_stop_index = int(percent_training * nrows)\n training = ordered_data[:training_stop_index]\n testing = ordered_data[training_stop_index:]\n\n return training, testing","def _load_dataset(self, path):\n\t\twhile True:\n\t\t\t\n\t\t\ttry:\n\t\t\t\tX_test = np.load(\"data/X_test.npy\")\n\t\t\t\tY_test = np.load(\"data/Y_test.npy\")\n\t\t\t\tbreak\n\n\t\t\texcept FileNotFoundError:\n\n\t\t\t\tX_test = np.zeros((10000,64,64,3))\n\t\t\t\tY_test = []\n\n\t\t\t\t\n\t\t\t\twith open(path, 'rb') as fo:\n\t\t\t\t\ttemp_element = pickle.load(fo, encoding='bytes')\n\n\t\t\t\ttemp_data = temp_element[b'data']\n\t\t\t\tY_test.extend(temp_element[b'labels'])\n\n\t\t\t\tfor j in range(10000):\n\t\t\t\t\tX_test[j] = self._reshape(temp_data[j])\n\n\t\t\t\tY_test = np.eye(10)[np.array(Y_test)]\n\t\t\t\t\n\t\t\t\tnp.save(\"./data/X_test\", X_test)\n\t\t\t\tnp.save(\"./data/Y_test\", Y_test)\n\n\t\t\t\tbreak\n\n\n\t\treturn X_test, Y_test","def load_all(test_num=100):\n\ttrain_data = pd.read_csv(\n\t\tconfig.train_rating, \n\t\tsep='\\t', header=None, names=['user', 'item'], \n\t\tusecols=[0, 1], dtype={0: np.int32, 1: np.int32})\n\n\tuser_num = train_data['user'].max() + 1\n\titem_num = train_data['item'].max() + 1\n\n\ttrain_data = train_data.values.tolist()\n\n\t# load ratings as a dok matrix\n\ttrain_mat = sp.dok_matrix((user_num, item_num), dtype=np.float32)\n\tfor x in train_data:\n\t\ttrain_mat[x[0], x[1]] = 1.0\n\n\ttest_data = []\n\twith open(config.test_negative, 'r') as fd:\n\t\tline = fd.readline()\n\t\twhile line != None and line != '':\n\t\t\tarr = line.split('\\t')\n\t\t\tu = eval(arr[0])[0]\n\t\t\ttest_data.append([u, eval(arr[0])[1]])\n\t\t\tfor i in arr[1:]:\n\t\t\t\ttest_data.append([u, int(i)])\n\t\t\tline = fd.readline()\n\treturn train_data, test_data, user_num, item_num, train_mat","def test_split_data():\n from parrot import process_input_data as pid\n\n data_file = os.path.abspath(\"../data/seq_class_dataset.tsv\")\n train, val, test = pid.split_data(data_file, datatype='sequence',\n problem_type='classification', num_classes=3)\n\n assert (len(train) == 210) and (len(val) == 45) and (len(test) == 45) and (len(train[0]) == 3)","def init_data(dataset_config: dict):\n # train and dev will be in random order, test may be ordered according to labels\n if dataset_config[\"name\"] == \"CoLA\":\n train, dev, test, num_classes = load_cola(dataset_config)\n elif dataset_config[\"name\"] == \"AGNews\":\n train, dev, test, num_classes = load_ag_news(dataset_config)\n elif dataset_config[\"name\"] == \"DBPedia\":\n train, dev, test, num_classes = load_dbpedia(dataset_config)\n elif dataset_config[\"name\"] == \"YRF\":\n train, dev, test, num_classes = load_yrf(dataset_config)\n else:\n raise NameError(f\"Dataset {dataset_config['name']} not implemented.\")\n # etc.\n\n # shrink size if debugging\n if dataset_config[\"debug\"]:\n # choose a random subset using huggingface select function\n train = train.select(random.sample(range(len(train)), k=200))\n dev = dev.select(random.sample(range(len(dev)), k=40))\n test = test.select(random.sample(range(len(test)), k=200))\n\n # create class imbalance\n random.seed(dataset_config[\"seed\"])\n if dataset_config[\"pool_balance\"] == \"balanced\":\n pass\n elif dataset_config[\"pool_balance\"] == \"imbalanced\":\n train = train.filter(lambda example: create_imbalanced_dataset(example, dataset_config[\"imbalance_prop\"], dataset_config['imbalance_cls']))\n else:\n NameError(f\"pool_balance = {dataset_config['pool_balance']} not allowed\")\n\n if dataset_config[\"dev_balance\"] == \"balanced\":\n pass\n elif dataset_config[\"dev_balance\"] == \"imbalanced\":\n dev = dev.filter(lambda example: create_imbalanced_dataset(example, dataset_config[\"imbalance_prop\"], dataset_config['imbalance_cls']))\n else:\n NameError(f\"dev_balance = {dataset_config['dev_balance']} not allowed\")\n\n # get seed labelled pool indices (using the same seed data every time)\n random.seed(dataset_config[\"seed\"])\n if dataset_config[\"seed_balance\"] == \"balanced\":\n # this is random (will have some variance vs pool)\n indices = list(range(len(train)))\n unlabelled_pool_idx, labelled_pool_idx = split(\n indices,\n random_state=dataset_config[\"seed\"],\n test_size=dataset_config[\"seed_size\"]\n )\n elif dataset_config[\"seed_balance\"] == \"stratified\":\n # this is the same as the underlying train set (which may be unbalanced)\n indices = list(range(len(train)))\n unlabelled_pool_idx, labelled_pool_idx = split(\n indices,\n random_state=dataset_config[\"seed\"],\n test_size=dataset_config[\"seed_size\"],\n stratify=train['label']\n )\n elif dataset_config[\"seed_balance\"] == \"imbalanced\":\n # artificially sample an imbalanced seed set from the pool\n unlabelled_pool_idx, labelled_pool_idx = create_imbalanced_seed(\n train,\n num_classes,\n dataset_config[\"seed_size\"],\n dataset_config['imbalance_prop'],\n dataset_config['imbalance_cls']\n )\n else:\n raise NameError(f\"seed_balance = {dataset_config['seed_balance']} not allowed\")\n\n return train, dev, test, num_classes, labelled_pool_idx, unlabelled_pool_idx","def read(train_path, test_path, label_name):\n train_dataset = pd.read_csv(train_path)\n test_dataset = pd.read_csv(test_path)\n\n train_labels = train_dataset.pop(label_name)\n\n imputer = DataFrameImputer().fit(train_dataset)\n train_dataset = imputer.transform(train_dataset)\n test_dataset = imputer.transform(test_dataset)\n\n train_dataset = pd.get_dummies(train_dataset)\n test_dataset = pd.get_dummies(test_dataset)\n\n train_dataset = train_dataset.drop(train_dataset.columns.difference(test_dataset.columns), axis=1)\n test_dataset = test_dataset.drop(test_dataset.columns.difference(train_dataset.columns), axis=1)\n\n scaler = StandardScaler().fit(train_dataset)\n train_dataset = scaler.transform(train_dataset)\n test_dataset = scaler.transform(test_dataset)\n\n return train_dataset, train_labels, test_dataset","def load_test_data():\r\n X_test = np.load('data/test/X_test.npy')\r\n scaling_test = np.load('data/test/scaling_test.npy')\r\n ids_test = np.load('data/test/ids_test.npy')\r\n y_test = np.load('data/test/y_test.npy')\r\n\r\n seed = np.random.randint(1, 10e6)\r\n np.random.seed(seed)\r\n np.random.shuffle(X_test)\r\n np.random.seed(seed)\r\n np.random.shuffle(scaling_test)\r\n np.random.seed(seed)\r\n np.random.shuffle(ids_test)\r\n np.random.seed(seed)\r\n np.random.shuffle(y_test)\r\n\r\n return X_test, scaling_test, ids_test, y_test","def split_data(path=None, features = None, targets = None, test_size=None, random_state=None, **kwargs):\n\n # read in preprocessed data and seperate by features and target variables\n model_data = pd.read_csv(path)\n features = model_data[features]\n targets = model_data[targets]\n\n # split data using random state\n X_train, X_test, y_train, y_test = model_selection.train_test_split(\n features, targets, test_size=test_size, random_state=random_state)\n\n # four target classifiers\n y_train_IE = y_train['I-E']\n y_train_NS = y_train['N-S']\n y_train_TF = y_train['T-F']\n y_train_JP = y_train['J-P']\n\n y_test_IE = y_test['I-E']\n y_test_NS = y_test['N-S']\n y_test_TF = y_test['T-F']\n y_test_JP = y_test['J-P']\n\n logger.info('train and test splitted with test size of %s', test_size)\n logger.warning('response variable y is divided into four classifiers, so you will see four y_train and four y_test.')\n return X_train, X_test, y_train_IE, y_train_NS, y_train_TF, y_train_JP, y_test_IE, y_test_NS, y_test_TF, y_test_JP","def _load_dataset(self, path):\n\t\twhile True:\n\t\t\t\n\t\t\ttry:\n\t\t\t\tX_train = np.load(\"data/X_train.npy\")\n\t\t\t\tX_val = np.load(\"data/X_val.npy\")\n\t\t\t\tY_train = np.load(\"data/Y_train.npy\")\n\t\t\t\tY_val = np.load(\"data/Y_val.npy\")\n\t\t\t\tbreak\n\n\t\t\texcept FileNotFoundError:\n\n\t\t\t\tdata_temp = np.zeros((50000,64,64,3))\n\t\t\t\tlabel_temp = []\n\n\t\t\t\tfor i in range(5):\n\n\t\t\t\t\tfile = path + str(i+1)\n\t\t\t\t\twith open(file, 'rb') as fo:\n\t\t\t\t\t\ttemp_element = pickle.load(fo, encoding='bytes')\n\n\t\t\t\t\ttemp_data = temp_element[b'data']\n\t\t\t\t\tlabel_temp.extend(temp_element[b'labels'])\n\n\t\t\t\t\tfor j in range(10000):\n\t\t\t\t\t\tdata_temp[j+(i*10000)] = self._reshape(temp_data[j])\n\n\t\t\t\tlabel_temp = np.eye(10)[np.array(label_temp)]\n\n\t\t\t\tnp.random.seed(123)\n\t\t\t\tpermutations = list(np.random.permutation(50000))\n\t\t\t\tX = data_temp[permutations, :, : , :] \n\t\t\t\tY = label_temp[permutations, :]\n\t\t\t\tX_train = X[0:40000, :, :, :] \n\t\t\t\tY_train = Y[0:40000, :]\n\t\t\t\tX_val = X[40000:50000, :, :, :] \n\t\t\t\tY_val = Y[40000:50000, :]\n\n\t\t\t\tnp.save(\"./data/X_train\", X_train)\n\t\t\t\tnp.save(\"./data/X_val\", X_val)\n\t\t\t\tnp.save(\"./data/Y_train\", Y_train)\n\t\t\t\tnp.save(\"./data/Y_val\", Y_val)\n\t\t\t\tbreak\n\n\t\treturn X_train, X_val, Y_train, Y_val","def __split_dataset(self):\n self.train, self.valid, _, _ = train_test_split(self.data, self.data, test_size=0.2)\n self.valid, self.test, _, _ = train_test_split(self.valid, self.valid, test_size=0.5)","def load_train_test_transactions(train_size=0.7):\n X, y = features_target_split()\n X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=train_size, random_state=7)\n print('\\nTraining and testing data creation successful\\n')\n return X_train, X_test, y_train,y_test","def split_training_testing(X, Y, gnd, negative=10000, per=0.05):\n df_x = pd.DataFrame(X)\n df_x['y'] = Y\n df_x['gnd'] = gnd\n df_x.sort_values(by=['y'], inplace=True, ascending=False)\n frac_positive = (df_x[df_x['y'] == 1].shape[0])/float(df_x.shape[0])\n split = int(frac_positive * per * df_x.shape[0])\n df_x.reset_index(drop=True, inplace=True)\n fraud = df_x[df_x['y'] == 1]\n # Shuffle inplace\n fraud = fraud.sample(frac=1, random_state=0).reset_index(drop=True)\n test = fraud.iloc[:split]\n train_ = fraud.iloc[split:]\n train = pd.concat([train_, df_x.iloc[fraud.shape[0]:].sample(n = negative, random_state=0)], ignore_index=True)\n # Shuffle inplace\n train = train.sample(frac=1, random_state=0).reset_index(drop=True)\n #train = randomSample(train, negative)\n y_train = train['y'].as_matrix()\n y_train_gnd = train['gnd'].as_matrix()\n train = train.drop(['y'], axis=1)\n train = train.drop(['gnd'], axis=1)\n \n y_test = test['y'].as_matrix()\n y_test_gnd = test['gnd'].as_matrix()\n test = test.drop(['y'], axis=1)\n test = test.drop(['gnd'], axis=1)\n return train.as_matrix(), y_train, y_train_gnd, test.as_matrix(), y_test, y_test_gnd","def train_test(in_path, train_out_path, test_out_path):\n df = pd.read_csv(in_path, sep='|')\n rng = RandomState()\n\n train = df.sample(frac=0.8, random_state=rng)\n test = df.loc[~df.index.isin(train.index)]\n\n train.to_csv(train_out_path, sep='|', index = None, header=True)\n test.to_csv(test_out_path, sep='|', index = None, header=True)","def create_sets(test, data, test_size=0.2, write=False):\n y_test = test['y_old']\n X_test = test.drop('y_old', 1)\n y_data = data['y_old']\n X_data = data.drop('y_old', 1)\n X_train, X_val, y_train, y_val = train_test_split(X_data, y_data, test_size=test_size, random_state=123)\n if write:\n pickle.dump((X_train, X_val, y_train, y_val), open(obj_save_path+'train_val_df.p', 'wb'))\n #X_train, X_val, y_train, y_val = pickle.load(open(obj_save_path+'train_val_df.p', 'rb'))\n return X_train, y_train, X_val, y_val, X_test, y_test","def test_dataset_from_file(train_dataset):\n dummy = \"justo. Praesent luctus. Curabitur egestas nunc sed libero. Proin sed\"\n assert train_dataset[0][0] == dummy\n assert train_dataset[0][1] == '6'","def test_full_dataset_from_file(full_dataset):\n train_dummy = \"justo. Praesent luctus. Curabitur egestas nunc sed libero. Proin sed\"\n val_dummy = \"malesuada. Integer id magna et ipsum cursus vestibulum. Mauris magna.\"\n\n assert full_dataset.train[0][0] == train_dummy\n assert full_dataset.train[0][1] == '6'\n\n assert full_dataset.val[0][0] == val_dummy\n assert full_dataset.val[0][1] == '8'\n\n assert full_dataset[0][0] == train_dummy\n assert full_dataset[100][0] == val_dummy","def train_test_split(filename: str, split=0.5) -> tuple:\n training_set = []\n test_set = []\n content = load_from_csv(filename)\n for _, value in enumerate(content):\n if random.random() < split:\n training_set.append(value)\n else:\n test_set.append(value)\n return training_set, test_set","def get_train_test_split(path=\"./data/train_test_split.json\"):\n with open(path) as f:\n train_test_split = json.load(f)\n return train_test_split","def Train_data():\n print (\"loading train data ...\")\n time_start = time.time()\n data_root = '/media/keziwen/86AA9651AA963E1D'\n with h5py.File(join(data_root, './data/train_real2.h5')) as f:\n data_real = f['train_real'][:]\n num, nt, ny, nx = data_real.shape\n data_real = np.transpose(data_real, (0, 1, 3, 2))\n with h5py.File(join(data_root, './data/train_imag2.h5')) as f:\n data_imag = f['train_imag'][:]\n num, nt, ny, nx = data_imag.shape\n data_imag = np.transpose(data_imag, (0, 1, 3, 2))\n data = data_real+1j*data_imag\n num_train = 15000\n num_validate = 2000\n train_data = data[0:num_train]\n validate_data = data[num_train:num_train+num_validate]\n\n train_data = np.random.permutation(train_data)\n\n time_end = time.time()\n print ('dataset has been created using {}s'.format(time_end-time_start))\n return train_data, validate_data","def data_set_maker():\n\n # crate a folder in your code directory and name it: \"files\". put the .npy files iside that folder\n\n x_all = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\n y_all = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\n\n # split the data into 10% validation-set and 90% training set\n raw_train, raw_valid, y_train, y_valid = train_test_split(x_all, y_all, test_size=0.2, random_state=43)\n return raw_train, raw_valid, y_train, y_valid","def train_val_test_split(\n reframed, n_hours, n_features, target_var=\"temp\", ascending_sampling=False\n):\n\n n_train_hours = int(reframed.shape[0] * 0.6)\n n_val_hours = int(reframed.shape[0] * 0.2)\n\n if ascending_sampling:\n print(\"Sampleamos datasets de pasado a futuro\")\n train = reframed.iloc[:n_train_hours, :]\n validation = reframed.iloc[n_train_hours : n_train_hours + n_val_hours, :]\n test = reframed.iloc[n_train_hours + n_val_hours :, :]\n else:\n print(\"Sampleamos datasets de futuro a pasado\")\n train = reframed.iloc[-n_train_hours:, :]\n validation = reframed.iloc[-(n_train_hours + n_val_hours) : -n_train_hours :, :]\n test = reframed.iloc[: -(n_train_hours + n_val_hours), :]\n\n # split into input and outputs\n n_obs = n_hours * n_features\n\n target_col = \"{}_t\".format(target_var)\n out_model_name = \"{}.hdf5\".format(target_var)\n # target_var = 'prcp(t)'\n train_X, train_y = train.iloc[:, :n_obs].values, train[target_col].values\n val_X, val_y = validation.iloc[:, :n_obs].values, validation[target_col].values\n test_X, test_y = test.iloc[:, :n_obs].values, test[target_col].values\n print(train_X.shape, len(train_X), train_y.shape)\n\n # reshape input to be 3D [samples, timesteps, features]\n train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))\n val_X = val_X.reshape((val_X.shape[0], n_hours, n_features))\n test_X = test_X.reshape((test_X.shape[0], n_hours, n_features))\n print(\n train_X.shape,\n train_y.shape,\n val_X.shape,\n val_y.shape,\n test_X.shape,\n test_y.shape,\n )\n\n return {\n \"trainset\": {\"X\": train_X, \"y\": train_y},\n \"valset\": {\"X\": val_X, \"y\": val_y},\n \"testset\": {\"X\": test_X, \"y\": test_y},\n }","def data_set_maker():\n\n # crate a folder in your code directory and name it: \"files\". put the .npy files iside that folder\n path = os.getcwd() # reads the current path\n x_train = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\n y_train = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\n x_test = np.load(path + '/files/tinyX_test.npy', 'r') # reads the input file\n x_train, y_train = shuffle(x_train, y_train)\n\n return x_train, y_train, x_test","def get_train_dev_examples(self, data_dir, seed, split=0.1, dom=-1):\n X, Y = [], []\n lines = self.read_txt(os.path.join(data_dir, \"train.txt\"))\n for line in lines:\n columns = line.rstrip().split(\" \")\n Y.append(columns[0].split(\":\")[0])\n X.append(\" \".join(columns[1:]))\n X_train, X_dev, Y_train, Y_dev = train_test_split(X, Y, test_size=split, stratify=Y, random_state=seed)\n train_examples = self._create_examples(zip(X_train, Y_train), \"train\", dom)\n dev_examples = self._create_examples(zip(X_dev, Y_dev), \"dev\", dom)\n return train_examples, dev_examples","def split_dataset(X, Y, train_size=0.8):\n if train_size != 1.0:\n return train_test_split(\n X, Y,\n train_size=train_size,\n stratify=Y\n )\n else:\n X_, Y_ = shuffle(\n X, Y\n )\n return X_, [], Y_, []","def test_train_split(X, y, test_size=0.2):\n idx = 0\n length_of_X = len(X)\n y_test = []\n X_test = []\n \n while idx < length_of_X*test_size:\n random_number_gen = np.random.randint(low=0, high=len(X))\n y_test.append(y[random_number_gen])\n X_test.append(X[random_number_gen])\n X = np.delete(X, random_number_gen, axis=0)\n y = np.delete(y, random_number_gen, axis=0)\n idx += 1\n return X, np.array(X_test), y, np.array(y_test)","def load_dataset():\n temp = gzip.open('mnist.pkl.gz')\n train, val , test = pickle.load(temp,encoding='latin1')\n temp.close()\n train_inp = [np.reshape(x, (784,1)) for x in train[0]]\n train_outp = [one_hot(y) for y in train[1]]\n training_data = zip(train_inp, train_outp)\n validation_inp = [np.reshape(x, (784, 1)) for x in val[0]]\n validation_data = zip(validation_inp, val[1])\n test_inp = [np.reshape(x, (784, 1)) for x in test[0]]\n test_data = zip(test_inp, test[1])\n return (training_data,validation_data,test_data)","def load_susy(trainsize=500, testsize=1000):\n filename = 'datasets/susysubset.csv'\n dataset = loadcsv(filename)\n trainset, testset = splitdataset(dataset, trainsize, testsize)\n return trainset, testset","def train_test_split(self, test_size=0.1, random_state=0, random_rate_label=True):\r\n data = self._shuffle_set(random_state)\r\n if test_size < 1:\r\n threshold = int(data.shape[0] * test_size)\r\n else:\r\n threshold = test_size\r\n dataset = Set(data[:, :-1], data[:, -1])\r\n if random_rate_label:\r\n split_index = np.random.randint(dataset.labels.shape[0], size=threshold)\r\n else:\r\n split_index = dataset._split_set_index(size=int(threshold / self.targets_count))\r\n X_test = dataset.features[split_index, :]\r\n y_test = dataset.labels[split_index]\r\n X_train = np.delete(dataset.features, split_index, axis=0)\r\n y_train = np.delete(dataset.labels, split_index, axis=0)\r\n return X_train, y_train, X_test, y_test","def test_data_split_good():\n\tdf_true = pd.read_csv(\"test/sample_data.csv\")\n\tdf_test = df_true.copy()\n\ttarget = df_true['price']\n\tfeatures = df_true.loc[:, df_true.columns != 'price']\n\n\tX_train_true, X_test_true, y_train_true, y_test_true = train_test_split(features, target, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ttest_size=0.4, random_state=0)\n\n\tsplit_data = {'test_size': 0.4, 'random_state': 0}\n\tX_train_test, X_test_test, y_train_test, y_test_test = data_split(df_test, 'price', split_data)\n\n\tassert X_train_true.equals(X_train_test)\n\tassert y_train_true.equals(y_train_test)\n\tassert X_test_true.equals(X_test_test)\n\tassert y_test_true.equals(y_test_test)","def load_data(train_size=30000, random_state=0):\n print(\"Loading adult data from alibi.\")\n np.random.seed(random_state)\n\n data = alibi.datasets.fetch_adult()\n\n # mix input data\n data_perm = np.random.permutation(np.c_[data.data, data.target])\n data.data = data_perm[:, :-1]\n data.target = data_perm[:, -1]\n\n # perform train / test split\n X_train, y_train = data.data[:train_size, :], data.target[:train_size]\n X_test, y_test = data.data[train_size:, :], data.target[train_size:]\n\n return data, X_train, y_train, X_test, y_test","def create_dataset(base_path, batch_size, is_train):\n columns_list = [\"feature\", \"label\"]\n num_consumer = 4\n\n if is_train:\n path = os.path.join(base_path, 'aclImdb_train.mindrecord0')\n else:\n path = os.path.join(base_path, 'aclImdb_test.mindrecord0')\n\n data_set = ds.MindDataset(path, columns_list, num_consumer)\n ds.config.set_seed(0)\n data_set = data_set.shuffle(buffer_size=data_set.get_dataset_size())\n data_set = data_set.batch(batch_size, drop_remainder=True)\n return data_set","def load_dataset(data_dir, img_size):\n global input_set\n global test_set\n\n imgs = []\n img_files = os.listdir(data_dir)\n for img in img_files:\n # try:\n tmp = scipy.misc.imread(data_dir + \"/\" + img)\n x, y, z = tmp.shape # shape : width * length * chanel\n coords_x = int(x / img_size) # 坐标\n coords_y = int(y / img_size) #\n coords = [(q, r) for q in range(coords_x) for r in range(coords_y)] # 列表 x * y\n for coord in coords:\n imgs.append((data_dir + \"/\" + img, coord)) # 为列表添加文件目录\n # except BaseException:\n # print(\"oops\")\n test_size = min(10, int(len(imgs) * 0.2))\n random.shuffle(imgs)\n test_set = imgs[:test_size]\n train_set_X = imgs[test_size:][:200]\n train_set = imgs[test_size:][200:400]\n return","def _split_train_tst(self):\n num_samples = self.Y.shape[0]\n mapper_file = self.checkpointer.get_mapper_file_location()\n if not self.checkpointer.is_mapper_checkpointed():\n print 'No mapper checkpoint found. Fresh loading in progress ...'\n # Now shuffle the data\n sample_id = range(num_samples)\n random.shuffle(sample_id)\n print 'Dumping the mapper shuffle for reuse.'\n Pickle.dump(sample_id, open(mapper_file, 'wb'))\n print 'Dump complete. Moving Forward...'\n else:\n print 'Mapper Checkpoint found... Reading from mapper dump'\n sample_id = Pickle.load(open(mapper_file, 'rb'))\n print 'Mapping unpickling complete.. Moving forward...'\n\n self.X_fwd = self.X_fwd[sample_id]\n self.X_bwd = self.X_bwd[sample_id]\n self.Y = self.Y[sample_id]\n # Now divide the data into test ans train set\n test_fraction = 0.01\n self.test_size = int(test_fraction * num_samples)\n self.train_size = num_samples - self.test_size\n # Forward review\n self.X_trn_fwd = self.X_fwd[0:self.train_size]\n self.X_tst_fwd = self.X_fwd[self.train_size:num_samples]\n # Backward review\n self.X_trn_bwd = self.X_bwd[0:self.train_size]\n self.X_tst_bwd = self.X_bwd[self.train_size:num_samples]\n # Summary\n self.Y_trn = self.Y[0:self.train_size]\n self.Y_tst = self.Y[self.train_size:num_samples]","def train_test_model_batch():\n train=learning.Train_kmer_clf()\n train.run()","def my_train_test_split(act_my_data, act_test_size=0.5):\r\n act_train_df, act_test_df = train_test_split(act_my_data,\r\n test_size=act_test_size)\r\n return act_train_df, act_test_df","def get_training_and_testing_sets(data, Y):\r\n data = pd.concat([data, Y], axis=1)\r\n x,y=data.shape\r\n train_X_sub1=data[0:x//6]\r\n dev_X_sub1 = data[x//6:x//6 + x//12]\r\n test_X_sub1 = data[x//6 + x//12:x//3]\r\n\r\n train_X_sub2 = data[x//3:x//3+x//6]\r\n dev_X_sub2 = data[x//6 + x//3:x//3 + x//6 + x//12]\r\n test_X_sub2 = data[x//3 + x//6 + x//12:2*x//3]\r\n\r\n train_X_sub3 = data[2*x//3:(2*x//3) +x//6]\r\n dev_X_sub3 = data[x//6 + 2*x//3: (2*x//3) + x//6 + x//12]\r\n test_X_sub3 = data[2*x//3 + x//6 + x//12:x]\r\n\r\n train_X=train_X_sub1.append(train_X_sub2,ignore_index = True)\r\n train_X =train_X.append(train_X_sub3,ignore_index = True)\r\n dev_X= dev_X_sub1.append(dev_X_sub2,ignore_index = True)\r\n dev_X = dev_X.append(dev_X_sub3,ignore_index = True)\r\n test_X = test_X_sub1.append(test_X_sub2,ignore_index = True)\r\n test_X = test_X.append(test_X_sub3,ignore_index = True)\r\n\r\n\r\n train_X = util.shuffle(train_X)\r\n train_X = train_X.reset_index(drop=True)\r\n\r\n dev_X = util.shuffle(dev_X)\r\n dev_X = dev_X.reset_index(drop=True)\r\n\r\n test_X = util.shuffle(test_X)\r\n test_X = test_X.reset_index(drop=True)\r\n\r\n train_X_final=train_X\r\n dev_X_final = dev_X\r\n test_X_final = test_X\r\n x, y = train_X_final.shape\r\n train_X = train_X_final.iloc[:, 0:y - 1]\r\n train_Y = train_X_final.iloc[:, y - 1]\r\n\r\n x, y = test_X_final.shape\r\n test_X = test_X_final.iloc[:, 0:y - 1]\r\n test_Y = test_X_final.iloc[:, y - 1]\r\n\r\n x, y = dev_X_final.shape\r\n dev_X = dev_X_final.iloc[:, 0:y - 1]\r\n dev_Y = dev_X_final.iloc[:, y - 1]\r\n\r\n return train_X, train_Y, dev_X,dev_Y,test_X, test_Y","def train_test_split_data(data, test_size=0.25):\n \n y = data['fraud'].values\n X = data.drop(columns=['fraud','description','name','org_desc','org_name','payee_name']).values\n \n return train_test_split(X, y, test_size=test_size, stratify=y, shuffle=True)","def split_data(self,test=False):\n shuffle_index = torch.randperm(self.train_target.shape[0])\n load = shuffle_index.shape[0]\n train_input_shuffle = self.train_input[shuffle_index]\n train_target_shuffle = self.train_target[shuffle_index]\n train_classes_shuffle = self.train_classes[shuffle_index]\n index_train = self.index_for_equal_class(train_target_shuffle[:load//2])\n train_input = train_input_shuffle[index_train]\n train_target = train_target_shuffle[index_train]\n train_classes = train_classes_shuffle[index_train]\n if not test:\n index_test = self.index_for_equal_class( train_target_shuffle[load//2:]) + load//2\n test_input = train_input_shuffle[index_test]\n test_target = train_target_shuffle[index_test]\n test_classes = train_classes_shuffle[index_test]\n else:\n index_test = self.index_for_equal_class(self.test_target)\n test_input = self.test_input[index_test]\n test_target = self.test_target[index_test]\n test_classes = self.test_classes[index_test]\n train_input, mean, std = normalize(train_input)\n test_input, _, _ = normalize(test_input,mean,std)\n return train_input, train_target, train_classes ,test_input ,test_target ,test_classes","def dataset(self, file, latent_dim = 4, pivot = 0.2):\n data_df = pd.read_csv(file, sep=\"::\", engine='python',\n names=['UserId', 'MovieId', 'Rating', 'Timestamp'])\n print(len(data_df))\n data_df['avg_score'] = data_df.groupby(by='UserId')['Rating'].transform('mean')\n # feature columns\n user_num, item_num = data_df['UserId'].max() + 1, data_df['MovieId'].max() + 1\n feature_columns = [[self.denseFeature('avg_score')],\n [self.sparseFeature('user_id', user_num, latent_dim),\n self.sparseFeature('item_id', item_num, latent_dim)]]\n # split train dataset and test dataset\n watch_count = data_df.groupby(by='UserId')['MovieId'].agg('count')\n print(\"分割后\"+str(pivot*100)+\"%作为数据集\\n\")\n test_df = pd.concat([data_df[data_df.UserId == i].iloc[int((1 - pivot) * watch_count[i]):] for i in (watch_count.index)], axis=0)\n print(test_df.head())\n test_df = test_df.reset_index()\n train_df = data_df.drop(labels=test_df['index'])\n # 删除非需求列\n train_df = train_df.drop(['Timestamp'], axis=1).sample(frac=1.).reset_index(drop=True)\n test_df = test_df.drop(['index', 'Timestamp'], axis=1).sample(frac=1.).reset_index(drop=True)\n train_X = [train_df['avg_score'].values, train_df[['UserId', 'MovieId']].values]\n train_y = train_df['Rating'].values.astype('int32')\n test_X = [test_df['avg_score'].values, test_df[['UserId', 'MovieId']].values]\n test_y = test_df['Rating'].values.astype('int32')\n return feature_columns, (train_X, train_y), (test_X, test_y)","def gen_train_val_test_split(self, splits=[0.7, 0.2, 0.1], random_state=42):\n patient_dirs = os.listdir(self.out_dir)\n msk_list, img_list, patient_list, is_tumor = [], [], [], []\n\n for patient in patient_dirs:\n patient_folder = os.path.join(self.out_dir, patient)\n if not os.path.isdir(patient_folder):\n continue\n\n patient_root = os.path.join(self.out_dir, patient)\n for file in os.listdir(patient_root):\n if \"mask\" not in file:\n patient_list.append(patient)\n img_list.append(os.path.join(patient_root, file))\n msk_list.append(os.path.join(patient_root, file[:file.find(\".npy\")] + \"_mask.npy\"))\n \n img = np.load(msk_list[-1])\n if np.mean(img) > 0:\n is_tumor.append(1)\n else:\n is_tumor.append(0)\n \n data = pd.DataFrame(data={\"Patient\": patient_list, \"Image\": img_list, \"Mask\": msk_list, \"Tumor Present\": is_tumor})\n self.train_df, self.valid_df, self.test_df = self._split_by_patients(data, val_split=splits[1], test_split=splits[2], random_state=random_state)\n # print(len(train), len(val), len(test))\n # self.train_df = data[data[\"Patient\"].isin(train)]\n # self.valid_df = data[data[\"Patient\"].isin(val)]\n # self.test_df = data[data[\"Patient\"].isin(test)]","def make_data_splits(samples, params, RESULTSDIR, num_experiments):\n # TODO: Switch to .mat from .pickle so that these lists are easier to read\n # and change.\n\n partition = {}\n if params[\"load_valid\"] is None:\n # Set random seed if included in params\n if params[\"data_split_seed\"] is not None:\n np.random.seed(params[\"data_split_seed\"])\n\n all_inds = np.arange(len(samples))\n\n # extract random inds from each set for validation\n v = params[\"num_validation_per_exp\"]\n valid_inds = []\n if params[\"valid_exp\"] is not None and params[\"num_validation_per_exp\"] > 0:\n all_valid_inds = []\n for e in params[\"valid_exp\"]:\n tinds = [\n i for i in range(len(samples)) if int(samples[i].split(\"_\")[0]) == e\n ]\n all_valid_inds = all_valid_inds + tinds\n valid_inds = valid_inds + list(\n np.random.choice(tinds, (v,), replace=False)\n )\n valid_inds = list(np.sort(valid_inds))\n\n train_inds = list(set(all_inds) - set(all_valid_inds))#[i for i in all_inds if i not in all_valid_inds]\n elif params[\"num_validation_per_exp\"] > 0: # if 0, do not perform validation\n for e in range(num_experiments):\n tinds = [\n i for i in range(len(samples)) if int(samples[i].split(\"_\")[0]) == e\n ]\n valid_inds = valid_inds + list(\n np.random.choice(tinds, (v,), replace=False)\n )\n valid_inds = list(np.sort(valid_inds))\n\n train_inds = [i for i in all_inds if i not in valid_inds]\n elif params[\"valid_exp\"] is not None:\n raise Exception(\"Need to set num_validation_per_exp in using valid_exp\")\n else:\n train_inds = all_inds\n\n assert (set(valid_inds) & set(train_inds)) == set()\n\n train_samples = samples[train_inds]\n train_inds = []\n if params[\"valid_exp\"] is not None:\n train_expts = [f for f in range(num_experiments) if f not in params[\"valid_exp\"]]\n else:\n train_expts = np.arange(num_experiments)\n\n print(\"TRAIN EXPTS: {}\".format(train_expts))\n\n if params[\"num_train_per_exp\"] is not None:\n # Then sample randomly without replacement from training sampleIDs\n for e in train_expts:\n tinds = [\n i for i in range(len(train_samples)) if int(train_samples[i].split(\"_\")[0]) == e\n ]\n print(e)\n print(len(tinds))\n train_inds = train_inds + list(\n np.random.choice(tinds, (params[\"num_train_per_exp\"],), replace=False)\n )\n train_inds = list(np.sort(train_inds))\n else:\n train_inds = np.arange(len(train_samples))\n\n \n\n partition[\"valid_sampleIDs\"] = samples[valid_inds]\n partition[\"train_sampleIDs\"] = train_samples[train_inds]\n\n # Save train/val inds\n with open(os.path.join(RESULTSDIR, \"val_samples.pickle\"), \"wb\") as f:\n cPickle.dump(partition[\"valid_sampleIDs\"], f)\n\n with open(os.path.join(RESULTSDIR, \"train_samples.pickle\"), \"wb\") as f:\n cPickle.dump(partition[\"train_sampleIDs\"], f)\n else:\n # Load validation samples from elsewhere\n with open(os.path.join(params[\"load_valid\"], \"val_samples.pickle\"), \"rb\",) as f:\n partition[\"valid_sampleIDs\"] = cPickle.load(f)\n partition[\"train_sampleIDs\"] = [\n f for f in samples if f not in partition[\"valid_sampleIDs\"]\n ]\n\n # Reset any seeding so that future batch shuffling, etc. are not tied to this seed\n if params[\"data_split_seed\"] is not None:\n np.random.seed()\n\n return partition","def test_data():\n batch_size = 10\n input_dim = 28\n test_data = np.random.rand(batch_size, input_dim)\n\n return test_data","def train_test_split_file(filename, test_size = 0.2, new_category = True):\n\tlogthis.say('Train test set separation starts.')\n\tdf: pd.DataFrame = pd.read_csv(filename, sep=';')\n\tif new_category:\n\t\ttrain, test = train_test_split(df, test_size=test_size, random_state=42, stratify=df['Label'])\n\telse:\n\t\ttrain = df.drop_duplicates(subset=['Text'], keep=False)\n\t\ttest = df[df.duplicated(subset=['Text'], keep=False)]\n\ttrain.to_csv(filename.replace('.csv', '_train.csv'), sep=';', index=False)\n\ttest.to_csv(filename.replace('.csv', '_test.csv'), sep=';', index=False)\n\tlogthis.say('Train test set separation done.')","def train_test_split(ratings):\n test = set(range(len(ratings))[::1000])\n train = sorted(set(range(len(ratings))) - test)\n test = sorted(test)\n return ratings.iloc[train], ratings.iloc[test]","def split_data(self, val_size=0.1, test_size=0.5):\n df = pd.read_csv(self.csv_path, delimiter=';')\n train, val = train_test_split(df, test_size=val_size)\n val, test = train_test_split(df, test_size=test_size)\n return train, val, test","def load_data():\n X = load_pickle(config['image_paths']['train_images_pickle'])\n y = load_train_labels()\n y = to_categorical(y)\n test_indices = np.random.choice(len(X), int(len(X) * float(config['model']['test_size'])), replace=False)\n X_train = np.asarray([e for idx, e in enumerate(X) if idx not in test_indices])\n X_test = np.asarray([e for idx, e in enumerate(X) if idx in test_indices])\n y_train = np.asarray([e for idx, e in enumerate(y) if idx not in test_indices])\n y_test = np.asarray([e for idx, e in enumerate(y) if idx in test_indices])\n return X_train, y_train, X_test, y_test","def separate(self):\n print(\"start dataset separating\")\n sum = 0\n for i in tqdm(range(len(self.itemlen))):\n il = self.itemlen[i]\n if il < 3:\n sum += il\n continue\n rarr = list(range(sum, sum+il))\n random.shuffle(rarr)\n self.train.append({\n 'input': self.input[rarr[0]],\n 'label': self.label[i]\n })\n self.val.append({\n 'input': self.input[rarr[1]],\n 'label': self.label[i]\n })\n for j in range(2, len(rarr)):\n self.test.append({\n 'input': self.input[rarr[j]],\n 'label': self.label[i]\n })\n sum += il","def load_data(self):\n with open('data/fordTrain.csv') as f:\n data = csv.reader(f, delimiter=',')\n train = [x for i, x in enumerate(data) if i > 0] \n # Extract features and target variable separately\n trainx = [x[3:] for x in train]\n trainy = [x[2] for x in train]\n\n with open('data/fordTest.csv') as f:\n data = csv.reader(f, delimiter=',')\n testx = [x[3:] for i, x in enumerate(data) if i > 0] \n\n with open('data/Solution.csv') as f:\n data = csv.reader(f, delimiter=',')\n testy = [x[2] for i, x in enumerate(data) if i > 0] \n\n # Extract features and target variable, convert to numpy array\n trainx = np.asarray(trainx, dtype=np.float32)\n trainy = np.asarray(trainy, dtype=np.int8)\n testx = np.asarray(testx, dtype=np.float32)\n testy = np.asarray(testy, dtype=np.int8)\n\n # Return training and test sets\n trainSet = Dataset(trainx, trainy)\n testSet = Dataset(testx, testy)\n return trainSet, testSet","def train_test_split_drifters():\n df = process_raw_df()\n ids = np.unique(df.index.get_level_values(level=0))\n rng = np.random.default_rng(seed=1)\n train_ids = np.sort(rng.choice(ids, size=len(ids)//2, replace=False))\n test_ids = np.sort(np.setdiff1d(ids, train_ids))\n train_df = df[df.index.get_level_values(level=0).isin(train_ids)].copy()\n test_df = df[df.index.get_level_values(level=0).isin(test_ids)].copy()\n return train_df, test_df","def create_train_test(option, transform, params, split=0.2):\r\n clip_im_dir = option.clip_im_dir\r\n matting_dir = option.matting_dir\r\n csv_path = option.csv_path\r\n \r\n print(\"create datasets\")\r\n \r\n \r\n data_df = pd.read_csv(csv_path)\r\n # data_df = MergeDataframe(clip_im_dir, matting_dir)\r\n \r\n #separate data in training and test data (20/80)\r\n train_df, test_df = train_test_split(data_df, test_size=split)\r\n \r\n #search right Dataset class\r\n package_dir = Path(src.dataset.__file__).resolve().parent\r\n\r\n for (_, module_name, _) in iter_modules([package_dir]):\r\n # print(module_name, self.ComType)\r\n if option.dataset.lower() == module_name.lower() :\r\n modelModule = importlib.import_module(\".\"+module_name)\r\n break\r\n \r\n # train data\r\n training_set = modelModule(train_df, clip_im_dir, matting_dir, transform, transform)\r\n train_loader = DataLoader(training_set, **params)\r\n \r\n \r\n #test data\r\n testing_set = modelModule(test_df, clip_im_dir, matting_dir, transform, transform)\r\n test_loader = DataLoader(testing_set, **params)\r\n \r\n return train_loader, test_loader","def load_davis_dataset():\n trainn_fold = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'folds', 'train_fold_setting1.txt')))\n train_fold = []\n for e in zip(*trainn_fold):\n for ee in e:\n train_fold.append(ee)\n #train_fold = [ee for e in trainn_fold for ee in e]\n test_fold = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'folds', 'test_fold_setting1.txt')))\n ligands = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'ligands_can.txt')),\n object_pairs_hook=OrderedDict)\n proteins = json.load(\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'proteins.txt')),\n object_pairs_hook=OrderedDict)\n \n affinity = pickle.load(open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'Y'), \n 'rb'), encoding='latin1')\n smiles_lst, protein_lst = [], []\n\n for k in ligands.keys():\n smiles = ligands[k]\n smiles_lst.append(smiles)\n for k in proteins.keys():\n protein_lst.append(proteins[k])\n\n affinity = [-np.log10(y / 1e9) for y in affinity]\n affinity = np.asarray(affinity)\n \n os.makedirs(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'processed'), exist_ok=True)\n train_test_dataset = []\n for split in ['train', 'test']:\n split_dir = os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'processed', split)\n os.makedirs(split_dir, exist_ok=True)\n fold = train_fold if split == 'train' else test_fold\n rows, cols = np.where(np.isnan(affinity) == False)\n rows, cols = rows[fold], cols[fold]\n \n data_lst = [[] for _ in range(1)]\n for idx in range(len(rows)):\n data = {}\n data['smiles'] = smiles_lst[rows[idx]]\n data['protein'] = protein_lst[cols[idx]]\n af = affinity[rows[idx], cols[idx]]\n data['aff'] = af\n\n data_lst[idx % 1].append(data)\n random.shuffle(data_lst)\n train_test_dataset.append(data_lst[0])\n return train_test_dataset","def get_uci_datasets(\n name, split_seed=0, test_fraction=0.10, train_frac=1.0, combine_val_train=False\n):\n # load full dataset\n load_funs = {\n \"naval\": _load_naval,\n \"protein\": _load_protein,\n \"crime\": _load_crime,\n \"energy\": _load_app_energy,\n }\n print(\"Loading dataset {}....\".format(name))\n if name == \"depth\":\n (X_train, y_train), (X_test, y_test) = load_funs[name]()\n y_scale = np.array([[1.0]])\n return (X_train, y_train), (X_test, y_test), y_scale\n\n X, y = load_funs[name]()\n X = X.astype(np.float32)\n y = y.astype(np.float32)\n\n # We create the train and test sets with 90% and 10% of the data\n\n if split_seed == -1: # Do not shuffle!\n permutation = range(X.shape[0])\n else:\n rs = np.random.RandomState(split_seed)\n permutation = rs.permutation(X.shape[0])\n\n size_train = int(np.round(X.shape[0] * (1 - test_fraction)))\n index_train = permutation[0:size_train]\n index_test = permutation[size_train:]\n\n X_train = X[index_train, :]\n X_test = X[index_test, :]\n if name == \"depth\":\n y_train = y[index_train]\n y_test = y[index_test]\n else:\n y_train = y[index_train, None]\n y_test = y[index_test, None]\n\n if train_frac != 1.0:\n rs = np.random.RandomState(split_seed)\n permutation = rs.permutation(X_train.shape[0])\n n_train = int(train_frac * len(X_train))\n X_train = X_train[:n_train]\n y_train = y_train[:n_train]\n\n if split_seed == -1: # Do not shuffle!\n permutation = range(X_train.shape[0])\n else:\n rs = np.random.RandomState(split_seed)\n permutation = rs.permutation(X_train.shape[0])\n\n if combine_val_train:\n val_fraction = 0.0\n else:\n val_fraction = 0.10\n size_train = int(np.round(X_train.shape[0] * (1 - val_fraction)))\n index_train = permutation[0:size_train]\n index_val = permutation[size_train:]\n\n X_new_train = X_train[index_train, :]\n X_val = X_train[index_val, :]\n\n y_new_train = y_train[index_train]\n y_val = y_train[index_val]\n\n print(\"Done loading dataset {}\".format(name))\n\n def standardize(data):\n mu = data.mean(axis=0, keepdims=1)\n scale = data.std(axis=0, keepdims=1)\n scale[scale < 1e-10] = 1.0\n\n data = (data - mu) / scale\n return data, mu, scale\n\n # Standardize\n X_new_train, x_train_mu, x_train_scale = standardize(X_new_train)\n X_test = (X_test - x_train_mu) / x_train_scale\n y_new_train, y_train_mu, y_train_scale = standardize(y_new_train)\n y_test = (y_test - y_train_mu) / y_train_scale\n X_val = (X_val - x_train_mu) / x_train_scale\n y_val = (y_val - y_train_mu) / y_train_scale\n\n train = TensorDataset(\n torch.Tensor(X_new_train).type(torch.float64),\n torch.Tensor(y_new_train).type(torch.float64),\n )\n\n val = TensorDataset(\n torch.Tensor(X_val).type(torch.float64),\n torch.Tensor(y_val).type(torch.float64),\n )\n\n test = TensorDataset(\n torch.Tensor(X_test).type(torch.float64),\n torch.Tensor(y_test).type(torch.float64),\n )\n in_size = X_train[0].shape\n target_size = y_train[0].shape\n\n return train, val, test, in_size, target_size, y_train_scale","def prepare_dataset():\n with open('gold-posts.txt', encoding='utf-8') as f:\n posts = f.readlines()\n with open('gold-labels.txt', encoding='utf-8') as f:\n labels = f.readlines()\n\n def to_cat(x: str) -> int:\n if x == 'p':\n return 1\n elif x == 'n':\n return 2\n else:\n return 0\n X = np.array([x.strip() for x in posts])\n y = np.array([to_cat(x.strip()) for x in labels])\n\n # DOES NOT WORK - too imbalanced\n #skf = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\n #for train_index, test_index in skf.split(X, y):\n # X_train, X_test = X[train_index], X[test_index]\n # y_train, y_test = y[train_index], y[test_index]\n # break\n\n # WORKS better\n trI, teI = balanced_split(y)\n\n train_texts = X[trI].tolist()\n train_labels = y[trI].tolist()\n valid_texts = X[teI].tolist()\n valid_labels = y[teI].tolist()\n return train_texts, train_labels, valid_texts, valid_labels","def split_train_and_test_with_py_datasets(data_set, batch_size=cfg['batch_size'], test_size=0.2, num_works=4,\n pin_memory=True):\n num_dataset = len(data_set)\n indices = list(range(num_dataset))\n split = int(np.floor(test_size * num_dataset))\n\n train_idx, test_idx = indices[split:], indices[:split]\n train_sampler = SubsetRandomSampler(train_idx)\n test_sampler = SubsetRandomSampler(test_idx)\n\n train_loader = torch.utils.data.DataLoader(\n dataset=data_set, batch_size=batch_size, sampler=train_sampler, num_workers=num_works,\n pin_memory=pin_memory\n )\n\n test_loader = torch.utils.data.DataLoader(\n dataset=data_set, batch_size=batch_size, sampler=test_sampler, num_workers=num_works,\n pin_memory=pin_memory\n )\n\n return train_loader, test_loader","def prepare_dataset(data_path, test_size=0.2, validation_size=0.2):\r\n\r\n # load dataset\r\n if data_path.endswith('json'):\r\n X, y = load_data_from_json(data_path)\r\n else:\r\n X, y = load_data_from_fold(data_path)\r\n # create train, validation, test split\r\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)\r\n X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)\r\n\r\n # add an axis to nd array\r\n X_train = X_train[..., np.newaxis]\r\n X_test = X_test[..., np.newaxis]\r\n X_validation = X_validation[..., np.newaxis]\r\n\r\n return X_train, y_train, X_validation, y_validation, X_test, y_test","def split_test_training(data_path, sequence_length):\n\n # logic for loading the CSV, using 'result' (2nd) column as basis for prediction\n with open(data_path) as f:\n record = csv.reader(f, delimiter=\",\")\n next(record, None)\n spat = []\n nb_of_values = 0\n for line in record:\n spat.append(float(line[2]))\n nb_of_values += 1\n\n # break file into chunks based on sequence length\n result = []\n for index in range(len(spat) - sequence_length):\n result.append(spat[index: index + sequence_length])\n result = np.array(result)\n\n # divide set into 20% for test, 80% for training\n row = int(round(0.8 * result.shape[0]))\n train = result[:row, :]\n np.random.shuffle(train)\n X_train = train[:, :-1]\n y_train = train[:, -1]\n X_test = result[row:, :-1]\n y_test = result[row:, -1]\n X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n\n return [X_train, y_train, X_test, y_test]","def GetDataset():\n x_train = []\n x_test = []\n y_train = []\n y_test = []\n\n classes1 = set()\n classes2 = set()\n for f in GetInputFiles():\n class1, class2, fold, fname = f.split('\\\\')[-4:]\n classes1.add(class1)\n classes2.add(class2)\n class1 = class1.split('_')[0]\n class2 = class2.split('_')[0]\n\n x = ReadAndTokenize(f)\n y = [int(class1 == 'positive'), int(class2 == 'truthful')]\n if fold == 'fold4':\n x_test.append(x)\n y_test.append(y)\n else:\n x_train.append(x)\n y_train.append(y)\n\n ### Make numpy arrays.\n x_test = MakeDesignMatrix(x_test)\n x_train = MakeDesignMatrix(x_train)\n y_test = numpy.array(y_test, dtype='float32')\n y_train = numpy.array(y_train, dtype='float32')\n\n dataset = (x_train, y_train, x_test, y_test)\n with open('dataset.pkl', 'wb') as fout:\n pickle.dump(dataset, fout)\n return dataset","def train_and_test(self, data):\n\n np.random.shuffle(data)\n datalist = self.unpack_data(data)\n\n logger.info('[*] 75-25 partition of datasets ...')\n\n markline1 = math.floor(0.75*(len(datalist['features'])))\n markline2 = math.floor(0.75*len(datalist['labels']))\n\n train_features = datalist['features'][:(markline1)]\n test_features = datalist['features'][(markline1):]\n \n train_labels = datalist['labels'][:(markline2)]\n test_labels = datalist['labels'][(markline2):]\n\n logger.info('[*] Training started with 75% Dataset ...')\n\n self.knn_model.fit(train_features, train_labels)\n\n logger.info('[*] Testing started with 25% Dataset ...')\n print('\\n/---------------Accuracy----------------/') \n \n accuracy = self.knn_model.score(train_features, train_labels)\n print('Test set accuracy {:.2f} %'.format(accuracy*100))\n\n if accuracy < 0.40:\n logger.warning('[-.-!] Thanks for tryin\\' but this machine ain\\'t learning.')\n\n return True","def create_dummy_data():\n data = loadmat(join(project_root, './data/lungs.mat'))['seq']\n nx, ny, nt = data.shape\n ny_red = 8\n sl = ny//ny_red\n data_t = np.transpose(data, (2, 0, 1))\n\n data_t[:, :, :sl*4]\n train_slice = data_t[:, :, :sl*4]\n validate_slice = data_t[:, :, ny//2:ny//2+ny//4]\n test_slice = data_t[:, :, ny//2+ny//4]\n\n # Synthesize data by extracting patches\n train = np.array([data_t[..., i:i+sl] for i in np.random.randint(0, sl*3, 20)])\n validate = np.array([data_t[..., i:i+sl] for i in (sl*4, sl*5)])\n test = np.array([data_t[..., i:i+sl] for i in (sl*6, sl*7)])\n\n return train, validate, test","def splitData(data, class_label, seed, ratio):\n\t\n\trandom.seed(seed)\n\tsubset = data.clone()\n\tsize_data = subset.data.shape[0]\n\tn = int(np.floor(size_data * ratio)) # number of datasets in train\n\tindex = random.sample(range(1, size_data), n)\n\tsplit_list = [item for item in [0] for i in range(size_data)]\n\t\n\tfor i in index:\n\t\tsplit_list[i]=1\n\t\n\treturn split_list #returns list of indeces where 0 is test and 1 is training data ","def load_train_data():\r\n X_train = np.load('data/train/X_train.npy')\r\n scaling_train = np.load('data/train/scaling_train.npy')\r\n ids_train = np.load('data/train/ids_train.npy')\r\n y_train = np.load('data/train/y_train.npy')\r\n\r\n seed = np.random.randint(1, 10e6)\r\n np.random.seed(seed)\r\n np.random.shuffle(X_train)\r\n np.random.seed(seed)\r\n np.random.shuffle(scaling_train)\r\n np.random.seed(seed)\r\n np.random.shuffle(ids_train)\r\n np.random.seed(seed)\r\n np.random.shuffle(y_train)\r\n\r\n return X_train, scaling_train, ids_train, y_train","def train_test_split(df, random_state=42):\n if len(df) < 3:\n print('no bueno')\n train, test = train_test_split(df, test_size=.2, random_state=random_state)\n train, val = train_test_split(train, test_size=.2, random_state=random_state)\n return train, test, val","def read_data_sets(data_path, fake_data=False, one_hot=False,\n validation_size=5000, source_url={},\n augment=False,\n percentage_train=100.,\n unbalance=False, unbalance_dict={\"percentage\": 20, \"label1\": 0, \"label2\": 8},\n ):\n\n class DataSets(object):\n pass\n\n data_sets = DataSets()\n\n if fake_data:\n data_sets.train = DataSet([], [], fake_data=True, one_hot=True)\n data_sets.validation = DataSet([], [], fake_data=True, one_hot=True)\n data_sets.test = DataSet([], [], fake_data=True, one_hot=True)\n return data_sets\n\n if not source_url: # empty string check\n if 'fashion' in data_path:\n source_url = DEFAULT_SOURCE_URL_FASHION\n else:\n source_url = DEFAULT_SOURCE_URL_MNIST\n\n if 'fashion' in data_path or 'mnist' in data_path: # mnist or fashion\n train_images, train_labels, val_images, val_labels, test_images, test_labels = \\\n load_mnist(data_path, validation_size, source_url, one_hot)\n reshape = True\n else:\n train_images, train_labels, val_images, val_labels, test_images, test_labels = \\\n load_medical_data(data_path)\n reshape = False\n\n # add random permutation to train & validation\n np.random.seed(42)\n\n n_train = train_images.shape[0]\n perm = np.random.permutation(n_train)\n train_images = train_images[perm]\n train_labels = train_labels[perm]\n\n n_val = val_images.shape[0]\n perm = np.random.permutation(n_val)\n val_images = val_images[perm]\n val_labels = val_labels[perm]\n\n # For experiments with data-augmentation\n if augment:\n if 'fashion' in data_path: # rotations +-10 and horizontal flips\n augmented_images, augmented_labels = augment_data(train_images, train_labels, hflip=True)\n elif 'mnist' in data_path: # rotations +-10\n augmented_images, augmented_labels = augment_data(train_images, train_labels, hflip=False)\n train_images = np.concatenate([train_images, np.expand_dims(augmented_images, 3)])\n train_labels = np.concatenate([train_labels, augmented_labels])\n # for the medical datasets, you can use the \"augment\" argument while doing patch extraction\n\n # For experiments with limited amount of data\n if percentage_train != 100.:\n train_size = int(0.01*percentage_train*train_images.shape[0])\n Xtrain_images, Xval_images, ytrain, yval = train_test_split(train_images, train_labels, train_size=train_size)\n train_images = Xtrain_images\n train_labels = ytrain\n\n # For experiments with class-imbalance distribution\n if unbalance:\n n_classes = len(np.unique(np.argmax(train_labels, 1)))\n reduceto = 0.01*unbalance_dict['percentage']\n label1 = unbalance_dict['label1']\n label2 = unbalance_dict['label2']\n\n pick_ids = []\n newsize = 0\n all_classes = np.arange(0, n_classes)\n all_classes = np.delete(all_classes, np.where(all_classes == label1)[0])\n all_classes = np.delete(all_classes, np.where(all_classes == label2)[0])\n\n for lab in [label1, label2]:\n allids = np.where(np.argmax(train_labels, 1) == lab)[0]\n selectedids = np.random.choice(allids, int(reduceto * allids.shape[0]), replace=False)\n pick_ids.append(selectedids)\n newsize += len(selectedids)\n\n new_ids = convert_list_to_array(pick_ids, newsize)\n\n other_ids = []\n othersize = 0\n for lab in all_classes.tolist():\n selectedids = np.where(np.argmax(train_labels, 1) == lab)[0]\n other_ids.append(selectedids)\n othersize += len(selectedids)\n\n keep_ids = convert_list_to_array(other_ids, othersize)\n\n # new_ids: contains the indices of the reduced (imbalance) classes\n # keep_ids: contains the indices of the rest (keep the same class distribution)\n resulting_ids = np.concatenate((new_ids, keep_ids))\n np.random.shuffle(resulting_ids)\n\n train_images = train_images[resulting_ids, ...]\n train_labels = train_labels[resulting_ids, ...]\n\n data_sets.train = DataSet(train_images, train_labels, fake_data=True, one_hot=True, reshape=reshape)\n data_sets.validation = DataSet(val_images, val_labels, fake_data=True, one_hot=True, reshape=reshape)\n data_sets.test = DataSet(test_images, test_labels, fake_data=True, one_hot=True, reshape=reshape)\n\n return data_sets","def import_data(seed: object = 42) -> object:\n\n # Read input data\n df = pd.read_csv(\"x_train_gr_smpl.csv\").astype(int)\n\n # label data-frame rows based on sample data\n for x in range(10):\n index = ~pd.read_csv(\"y_train_smpl_%s.csv\" % x, squeeze=True).astype(bool) # reversed flags (~)\n df.loc[index, 'label'] = str(x)\n\n input_data_ordered = df.iloc[:, 0:2304].to_numpy()\n output_data_ordered = df.iloc[:, 2304].to_numpy()\n\n # Randomise instance order (forcing the same result each time)\n np.random.seed(seed)\n permutation = np.random.permutation(df.shape[0])\n\n # Create base input and output arrays\n input_data = input_data_ordered[permutation]\n output_data = output_data_ordered[permutation]\n\n return input_data, output_data, df, input_data_ordered, output_data_ordered","def train_test(X_train, y_train, test_size=0.2, random_state=32, stratify=None):\n X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=test_size, random_state=random_state, stratify=stratify)\n \n return X_train, X_test, y_train, y_test","def data_split(config_path: Text) -> None:\n\n config = yaml.safe_load(open(config_path))\n\n dataset = pd.read_csv(config['featurize']['features_data'])\n train_dataset, test_dataset = train_test_split(\n dataset, \n test_size = config['data_split']['test_size'],\n random_state = config['data_split']['random_state']\n )\n\n train_csv_path = config['data_split']['train_path']\n test_csv_path = config['data_split']['test_path']\n train_dataset.to_csv(train_csv_path, index=False)\n test_dataset.to_csv(test_csv_path, index=False)","def train_val_test_split(data):\n raise NotImplementedError","def split_data(raw_data, output_pref):\n train_data = output_pref + \".train\"\n test_data = output_pref + \".test\"\n random.shuffle(raw_data)\n with open(train_data, \"w\", encoding=\"utf8\") as fw1:\n with open(test_data, \"w\", encoding=\"utf8\") as fw2:\n with open(train_data + \".raw\", \"w\", encoding=\"utf8\") as fw3:\n with open(test_data + \".raw\", \"w\", encoding=\"utf8\") as fw4:\n for idx, (line, item) in enumerate(raw_data):\n if idx < 1000:\n fw2.write(line + \"\\n\")\n fw4.write(\"\\t\".join([str(i) for i in item]) + \"\\n\")\n else:\n fw1.write(line + \"\\n\")\n fw3.write(\"\\t\".join([str(i) for i in item]) + \"\\n\")","def refresh_test_dataset(self):\n inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label = self.build_data(self.reader, self.test_items, self.option.max_path_length)\n self.test_dataset = CodeDataset(inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label)","def split_test_and_train_data(self, test_size=0.3, view=0):\n X_train, X_test, y_train, y_test = train_test_split(self.data[view], self.class_data, test_size=test_size)\n return X_train, X_test, y_train, y_test","def split(self, test_size=0.25, random_state=None):\n self.train_index, self.test_index = ms.train_test_split(\n self.data.index, test_size=test_size, random_state=random_state)","def split_data(data, test_size):\r\n ntest = int(round(len(data) * (1 - test_size)))+1\r\n \r\n train, test = data[:ntest], data[ntest:]\r\n \r\n return train,test","def load_dataset():\n\n train_dataset = h5py.File('datasets/train_catvnoncat.h5', \"r\")\n train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n\n test_dataset = h5py.File('datasets/test_catvnoncat.h5', \"r\")\n test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n\n classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n \n train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n \n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes"],"string":"[\n \"def run_train_test_split():\\n # Load all documents\\n conn = sq.connect(config.DB_FILE)\\n documents = pd.read_sql_query('select pubmed_id, review_id, included, title, abstract from article ', conn)\\n\\n # Identify unique review IDs\\n review_ids = documents['review_id'].unique()\\n\\n # Set seed for random sampling\\n np.random.seed(2)\\n\\n # List of Reviews in the partial data set and full data set\\n partial_set = list(np.random.choice(review_ids, 10, replace=False))\\n full_set = list(review_ids.copy())\\n\\n # Load array (X) and labels (Y) of all documents\\n with (open(config.DOC_TERM_MATRIX, \\\"rb\\\")) as openfile:\\n X = pickle.load(openfile)\\n\\n y = documents['included']\\n\\n # Train-test split of the partial dataset\\n train_test_split(X, y, partial_set, 'min_max', 'partial', review_ids)\\n train_test_split(X, y, partial_set, 'tf_idf', 'partial', review_ids)\\n\\n # Train-test split of the full dataset\\n train_test_split(X, y, full_set, 'min_max', 'full', review_ids)\\n train_test_split(X, y, full_set, 'tf_idf', 'full', review_ids)\",\n \"def generate_train_test(self):\\n x, y = self.read_data()\\n x_train, y_train, x_test, y_test = self.sample_data(x, y)\\n self.train = (x_train, y_train)\\n self.test = (x_test, y_test)\",\n \"def split_data(dataset, test_size=0.5):\\n shuffled_data = np.random.RandomState(seed=721).permutation(dataset)\\n train_set = shuffled_data[: int(len(dataset) * (1 - test_size)), :]\\n test_set = shuffled_data[int(len(dataset) * (1 - test_size)):, :]\\n return train_set, test_set\",\n \"def split_dataset(dataset, test_size):\\r\\n random.shuffle(dataset)\\r\\n \\r\\n rating_negativ = []\\r\\n rating_positiv = []\\r\\n \\r\\n for row in dataset:\\r\\n if int(row[1]) == 0:\\r\\n rating_negativ.append(row)\\r\\n elif int(row[1]) == 1:\\r\\n rating_positiv.append(row)\\r\\n\\r\\n random.shuffle(rating_positiv)\\r\\n random.shuffle(rating_negativ) \\r\\n \\r\\n neg_train_data, neg_val_data = train_test_split(rating_negativ, test_size=test_size)\\r\\n pos_train_data, pos_val_data = train_test_split(rating_positiv, test_size=test_size)\\r\\n \\r\\n train_data = neg_train_data + pos_train_data\\r\\n val_data = neg_val_data + pos_val_data\\r\\n \\r\\n random.shuffle(train_data)\\r\\n random.shuffle(val_data)\\r\\n \\r\\n return train_data, val_data\",\n \"def load_data(input_file, test_size, random_seed ):\\n ratings = np.genfromtxt(input_file, delimiter=',')\\n #ratings = ratings[:200,:]\\n train_masks = np.zeros_like(ratings)\\n test_masks = np.zeros_like(ratings)\\n pairs = list()\\n for i in range(ratings.shape[0]):\\n for j in range(ratings.shape[1]):\\n pairs.append((i,j))\\n train, test = train_test_split(pairs ,test_size = test_size, random_state= random_seed)\\n for user,item in train:\\n train_masks[user, item]= 1\\n for user,book in test:\\n test_masks[user, item]= 1 \\n return ratings, train_masks, test_masks\",\n \"def split_data_into_train_and_test(raw_training_data):\\n train_set, test_set = train_test_split(raw_training_data, test_size=0.2, random_state=42)\\n return train_set, test_set\",\n \"def read_in_and_split_data(iris_data):\\n iris_data = datasets.load_iris()\\n data = iris_data['data']\\n targets = iris_data['target']\\n train_data, test_data, train_targets, test_targets = train_test_split(data, targets, test_size=0.1) \\n return (train_data, test_data, train_targets, test_targets)\",\n \"def get_dldata(filepath, dlTrainCorpusPath, dlTestCorpusPath, seed=2018, batch_size=16):\\r\\n\\tf = open(\\\"record/synthetic and academic datasets/testcases_train.pkl\\\",'rb') #get the testcase ids of train sets and test sets\\r\\n\\ttestcases += pickle.load(f) \\r\\n\\tf.close()\\r\\n\\r\\n\\tf = open(\\\"record/synthetic and academic datasets/testcases_test.pkl\\\",'rb')\\r\\n\\ttestcases += pickle.load(f)\\r\\n\\tf.close()\\r\\n\\t\\r\\n print(\\\"produce train dataset...\\\") \\r\\n N = 6\\r\\n num = list(range(N))\\r\\n for i in num:\\r\\n train_set = [[], [], [], [], [], []]\\r\\n for folder_train in folders_train[int(i*len(folders_train)/N) : int((i+1)*len(folders_train)/N)]:\\r\\n if not folder_train in os.listdir(filepath):\\r\\n continue\\r\\n print(\\\"\\\\r\\\"+str(folder_train), end='')\\r\\n for filename in os.listdir(os.path.join(filepath, folder_train)):\\r\\n f = open(filepath + folder_train + '/' + filename, 'rb')\\r\\n data = pickle.load(f)\\r\\n f.close()\\r\\n if len(data[0][0]) > MAXLEN:\\r\\n data[2] = [x for x in data[2] if x <= MAXLEN]\\r\\n data[0] = cutdata(data[0][0])\\r\\n if data[0] == None:\\r\\n continue \\r\\n for n in range(len(data)):\\r\\n train_set[n].append(data[n])\\r\\n train_set[-1].append(folder_train+\\\"/\\\"+filename)\\r\\n f_train = open(dlTrainCorpusPath + \\\"train_\\\" + str(i)+ \\\"_0818.pkl\\\", 'wb')\\r\\n pickle.dump(train_set, f_train)\\r\\n f_train.close()\\r\\n\\r\\n del train_set \\r\\n gc.collect() \\r\\n\\r\\n print(\\\"\\\\nproduce test dataset...\\\")\\r\\n N = 6\\r\\n num = list(range(N))\\r\\n for i in num:\\r\\n test_set = [[], [], [], [], [], []]\\r\\n for folder_test in folders_test[int(i*len(folders_test)/N) : int((i+1)*len(folders_test)/N)]:\\r\\n if not folder_test in os.listdir(filepath):\\r\\n continue\\r\\n print(\\\"\\\\r\\\"+str(folder_test), end='')\\r\\n for filename in os.listdir(os.path.join(filepath, folder_test)):\\r\\n f = open(filepath + folder_test + '/' + filename, 'rb')\\r\\n data = pickle.load(f)\\r\\n f.close()\\r\\n if len(data[0][0]) > MAXLEN:\\r\\n data[2] = [x for x in data[2] if x <= MAXLEN]\\r\\n data[0] = cutdata(data[0][0])\\r\\n if data[0] == None:\\r\\n continue \\r\\n for n in range(len(data)):\\r\\n test_set[n].append(data[n])\\r\\n test_set[-1].append(folder_test+\\\"/\\\"+filename)\\r\\n \\r\\n f_test = open(dlTestCorpusPath + \\\"test_\\\" + str(i)+ \\\"_0124.pkl\\\", 'wb')\\r\\n pickle.dump(test_set, f_test)\\r\\n f_test.close()\\r\\n\\r\\n del test_set\\r\\n gc.collect()\\r\\n return\",\n \"def split_dataset(self, test_size=0.20):\\n\\t\\t(self.training_data, self.test_data, self.training_labels, self.test_labels) = train_test_split(self.training_data, self.training_labels, test_size=test_size)\",\n \"def train_test_split(self):\\n random.seed(self.args.seed)\\n nodes = [node for node in range(self.ncount)]\\n random.shuffle(nodes)\\n self.train_nodes = torch.LongTensor(nodes[0:self.args.training_size])\\n self.validation_nodes = torch.LongTensor(nodes[self.args.training_size:self.args.training_size+self.args.validation_size])\\n self.test_nodes = torch.LongTensor(nodes[self.args.training_size+self.args.validation_size:])\",\n \"def read_and_split_sets():\\n gen_train_test_sets(\\\"Data_Sent_Embds/en_sent.pkl\\\", \\\"Data_Sent_Embd_Splitted/en_train.pkl\\\",\\n \\\"Data_Sent_Embd_Splitted/en_test.pkl\\\")\\n gen_train_test_sets(\\\"Data_Sent_Embds/es_sent.pkl\\\", \\\"Data_Sent_Embd_Splitted/es_train.pkl\\\",\\n \\\"Data_Sent_Embd_Splitted/es_test.pkl\\\")\\n gen_train_test_sets(\\\"Data_Sent_Embds/pr_sent.pkl\\\", \\\"Data_Sent_Embd_Splitted/pr_train.pkl\\\",\\n \\\"Data_Sent_Embd_Splitted/pr_test.pkl\\\")\",\n \"def build_toy_dataset(N):\\n y_data = np.random.uniform(-10.5, 10.5, N)\\n r_data = np.random.normal(size=N) # random noise\\n x_data = np.sin(0.75 * y_data) * 7.0 + y_data * 0.5 + r_data * 1.0\\n x_data = x_data.reshape((N, 1))\\n return train_test_split(x_data, y_data, random_state=42)\",\n \"def split_dataset(df, test_size, seed):\\r\\n ncols = np.size(df, 1)\\r\\n X = df.iloc[:, range(0, ncols - 1)]\\r\\n Y = df.iloc[:, ncols - 1]\\r\\n x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)\\r\\n y_train = get_dummies(y_train) # One-hot encoding\\r\\n y_test = get_dummies(y_test)\\r\\n return x_train, x_test, y_train, y_test\",\n \"def train_test_set_split(dataset, dataset_name, test_size=0.1):\\n train_indices_path = './' + dataset_name + '_train_indices(' + str(test_size) + ').txt'\\n test_indices_path = './' + dataset_name + '_test_indices(' + str(test_size) + ').txt'\\n try:\\n train_indices = []\\n test_indices = []\\n file = open(train_indices_path, 'rt', encoding='utf-8')\\n while True:\\n line = file.readline()\\n if not line:\\n break\\n train_indices.append(int(line[:-1]))\\n file.close()\\n file = open(test_indices_path, 'rt', encoding='utf-8')\\n while True:\\n line = file.readline()\\n if not line:\\n break\\n test_indices.append(int(line[:-1]))\\n file.close()\\n train_labels = [dataset.targets[i] for i in train_indices]\\n except FileNotFoundError:\\n indices = np.arange(len(dataset))\\n labels = np.array(dataset.targets)\\n train_indices, test_indices, train_labels, _ = train_test_split(\\n indices, labels, test_size=test_size, stratify=labels\\n )\\n file = open(train_indices_path, 'wt', encoding='utf-8')\\n for i in train_indices:\\n line = str(i) + '\\\\n'\\n file.write(line)\\n file.close()\\n file = open(test_indices_path, 'wt', encoding='utf-8')\\n for i in test_indices:\\n line = str(i) + '\\\\n'\\n file.write(line)\\n file.close()\\n\\n train_set = torch.utils.data.Subset(dataset, indices=train_indices)\\n test_set = torch.utils.data.Subset(dataset, indices=test_indices)\\n return train_set, test_set, train_labels\",\n \"def load_occupancy_dataset(trainsize=500, testsize=1000):\\n filename = 'datasets/numericsequence.csv'\\n dataset = loadcsv(filename)\\n trainset, testset = splitdataset(dataset, trainsize, testsize)\\n return trainset, testset\",\n \"def load_mnist(path='./', test_size=0.3, random_state = 123):\\n \\n np.random.seed(random_state)\\n if 'X_train.npy' not in os.listdir(path=path) or 'y_train.npy' not in os.listdir(path=path):\\n print (\\\"Train dataset not found. Downloading...\\\")\\n os.system(\\\"curl -L -o train.zip {}\\\".format(TRAIN_DATA_LINK))\\n os.system(\\\"unzip train.zip\\\")\\n os.system(\\\"tar -xf trainingSet.tar.gz\\\")\\n images = []\\n labels = []\\n for class_name in os.listdir('./trainingSet'):\\n if 'ipynb' not in class_name and '.DS' not in class_name:\\n for image_name in os.listdir('./trainingSet/{}'.format(class_name)):\\n image = imread('./trainingSet/{}/{}'.format(class_name, image_name))\\n images.append(image)\\n labels.append(int(class_name))\\n X_train = np.array(images)\\n y_train = np.array(labels)\\n\\n permutation = np.random.permutation(X_train.shape[0])\\n X_train = X_train[permutation]\\n y_train = y_train[permutation]\\n\\n with open('X_train.npy', 'wb') as f:\\n np.save(f, X_train)\\n with open('y_train.npy', 'wb') as f:\\n np.save(f, y_train)\\n os.system(\\\"rm -rf trainingSet\\\")\\n os.system(\\\"rm -rf train.zip\\\")\\n os.system(\\\"rm -rf trainingSet.tar.gz\\\")\\n else:\\n X_train = np.load('X_train.npy')\\n y_train = np.load('y_train.npy')\\n\\n if 'X_test.npy' not in os.listdir(path=path) or 'y_test.npy' not in os.listdir(path=path):\\n print (\\\"Test dataset not found. Downloading...\\\")\\n os.system(\\\"curl -L -o test.zip {}\\\".format(TEST_DATA_LINK))\\n os.system(\\\"unzip test.zip\\\")\\n os.system(\\\"tar -xf trainingSample.tar.gz\\\")\\n images = []\\n labels = []\\n for class_name in os.listdir('./trainingSample'):\\n if 'ipynb' not in class_name and '.DS' not in class_name:\\n for image_name in os.listdir('./trainingSample/{}'.format(class_name)):\\n image = imread('./trainingSample/{}/{}'.format(class_name, image_name))\\n images.append(image)\\n labels.append(int(class_name))\\n X_test = np.array(images)\\n y_test = np.array(labels)\\n with open('X_test.npy', 'wb') as f:\\n np.save(f, X_test)\\n with open('y_test.npy', 'wb') as f:\\n np.save(f, y_test)\\n\\n os.system(\\\"rm -rf trainingSample\\\")\\n os.system(\\\"rm -rf test.zip\\\")\\n os.system(\\\"rm -rf trainingSet.tar.gz\\\")\\n\\n else:\\n X_test = np.load('X_test.npy')\\n y_test = np.load('y_test.npy')\\n\\n return X_train, X_test, y_train, y_test\",\n \"def train_test_split(dataset, split):\\r\\n train = list()\\r\\n train_size = split * len(dataset)\\r\\n dataset_copy = list(dataset) \\r\\n while len(train) < train_size:\\r\\n index = randrange(len(dataset_copy))\\r\\n train.append(dataset_copy.pop(index))\\r\\n return train, dataset_copy\",\n \"def split_data(X, y, test_size, random_state):\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=random_state)\\n\\n return X_train, X_test, y_train, y_test\",\n \"def split_dataset(dataset, test_size):\\n train_data = dataset.skip(test_size).shuffle(SHUFFLE_BUFFER_SIZE)\\n train_data = train_data.padded_batch(BATCH_SIZE)\\n \\n test_data = dataset.take(test_size)\\n test_data = test_data.padded_batch(BATCH_SIZE)\\n \\n return train_data, test_data\",\n \"def data_split(self, test_size=0.2, stratify=None):\\n return train_test_split(\\n self, test_size=test_size, random_state=42, stratify=stratify\\n )\",\n \"def generate_data(self,seed):\\n X, y = make_classification( n_samples = 250, random_state = seed )\\n # Add bias term\\n X = np.concatenate( ( np.ones( ( 250, 1 ) ), X ), axis = 1 )\\n self.X_train, self.X_test, self.y_train, self.y_test = train_test_split( \\n X, y, test_size = 50, random_state = seed )\",\n \"def load_training_set():\\n global training_set\\n f = gzip.open('mnist.pkl.gz', 'rb')\\n train, valid, test = cPickle.load(f)\\n [training_set, training_labels] = train\\n [validation_set, validation_labels] = valid\\n [testing_set, testing_labels] = test\\n training_set = np.concatenate((training_set, validation_set))\\n f.close()\\n np.random.shuffle(training_set)\",\n \"def splitData(filename, testing_set_percentage):\\n matFile = sio.loadmat(filename)\\n data = matFile['mydata']\\n\\n np.savetxt('test.out', data, fmt='%d', delimiter=',') # X is an array\\n print('BEFORE SHUFFLE', data)\\n\\n np.random.shuffle(data)\\n\\n np.savetxt('test_after.out', data, fmt='%d', delimiter=',') # X is an array\\n\\n print('AFTER SHUFFLE', data)\\n\\n r, c = np.array(data).shape\\n\\n testing_set_size = int(r * testing_set_percentage)\\n training_set_size = r - testing_set_size\\n\\n train_data = data[:training_set_size]\\n test_data = data[training_set_size:]\\n\\n return train_data, test_data\",\n \"def get_random(everything_path, split_ratio=0.5, seed=0):\\n # Loading data\\n with h5py.File(everything_path, 'r') as data:\\n labels = data['labels'].value\\n \\n # Splitting classes\\n fri_i = np.where(labels==1)[0]\\n frii_i = np.where(labels==2)[0]\\n rand_i = np.where(labels==0)[0]\\n\\n # Shuffling\\n fri_i = shuffle(fri_i, random_state=seed)\\n frii_i = shuffle(frii_i, random_state=seed)\\n rand_i = shuffle(rand_i, random_state=seed)\\n\\n # Splitting into training and testing sets\\n cut = int(np.round(split_ratio * fri_i.shape[0]))\\n train_fri = fri_i[cut:]\\n test_fri = fri_i[:cut]\\n\\n cut = int(np.round(split_ratio * frii_i.shape[0]))\\n train_frii = frii_i[cut:]\\n test_frii = frii_i[:cut]\\n\\n cut = int(np.round(split_ratio * rand_i.shape[0]))\\n train_rand = rand_i[cut:]\\n test_rand = rand_i[:cut]\\n\\n train_i = np.concatenate((train_fri, train_frii, train_rand), axis=0)\\n test_i = np.concatenate((test_fri, test_frii, test_rand), axis=0)\\n\\n return train_i, test_i\",\n \"def split_train_test(ratings):\\r\\n ratings = ratings.sample(frac=1).reset_index(drop=True)\\r\\n train_user_list = []\\r\\n train_item_list = []\\r\\n train_rating_list = []\\r\\n test_user_list = []\\r\\n test_item_list = []\\r\\n test_rating_list = []\\r\\n user_pool = set(ratings['userId'].unique())\\r\\n for idx in user_pool:\\r\\n flag = 0\\r\\n items = ratings[ratings['userId']==idx][['itemId','rating']]\\r\\n for i, row in items.iterrows():\\r\\n if flag == 0:\\r\\n test_user_list.append(int(idx))\\r\\n test_item_list.append(int(row['itemId']))\\r\\n test_rating_list.append(row['rating'])\\r\\n flag = 1\\r\\n else:\\r\\n train_user_list.append(int(idx))\\r\\n train_item_list.append(int(row['itemId']))\\r\\n train_rating_list.append(row['rating'])\\r\\n\\r\\n train = pd.DataFrame({'userId': train_user_list, 'itemId': train_item_list, 'rating': train_rating_list}, columns=['userId', 'itemId', 'rating'])\\r\\n test = pd.DataFrame({'userId': test_user_list, 'itemId': test_item_list, 'rating': test_rating_list}, columns=['userId', 'itemId', 'rating'])\\r\\n return [train, test]\\r\\n \\r\\n\\r\\n \\r\\n #train, test = train_test_split(ratings, test_size=0.1, shuffle=True)\\r\\n #return [train, test]\\r\",\n \"def readData(path_to_dataset, train_size=0.8, validation_size=0.2):\\n data = pd.read_csv(os.path.join(path_to_dataset, 'training_set_rel3.tsv'), sep='\\\\t', encoding='ISO-8859-1')\\n # Drop columns that has null value \\n data = data.dropna(axis=1)\\n # Only take 4 columns of data from the dataset: essay_id, essay_set, essay, domain1_score\\n data = data[['essay_id', 'essay_set', 'essay', 'domain1_score']]\\n # Perform 80:20 train-test split on the training data\\n train_set, test_set = train_test_split(data, train_size=train_size, random_state=0)\\n # Split the 80% training set further into 60:20\\n training_set, validation_set = train_test_split(train_set, test_size=validation_size, random_state=0)\\n return training_set, test_set, validation_set\",\n \"def load_data(test_split=0.1):\\n global _data\\n random.shuffle(_data)\\n idx = int(len(_data) * (1 - test_split))\\n x_train, y_train = np.array([d[:4] for d in _data[:idx]]), np.array([name_index[d[4]] for d in _data[:idx]])\\n x_test, y_test = np.array([d[:4] for d in _data[idx:]]), np.array([name_index[d[4]] for d in _data[idx:]])\\n return (x_train, y_train), (x_test, y_test)\",\n \"def make_testing_training(data, percent_training, random_split=False, seed=None):\\n ## Making testing and training sets\\n data['computed Case Date/Time Closed'] = pd.to_datetime(data['Case Date/Time Closed'])\\n ordered_data = data.sort(columns=['computed Case Date/Time Closed'])\\n np.random.seed(seed=seed) \\n nrows, ncols = ordered_data.shape\\n\\n if random_split:\\n training_indices = np.random.choice(ordered_data.index, size=int(nrows*percent_training), replace=False)\\n training = ordered_data.ix[training_indices]\\n testing = ordered_data[~data['case_id'].isin(training['case_id'])]\\n else: # split by date\\n training_stop_index = int(percent_training * nrows)\\n training = ordered_data[:training_stop_index]\\n testing = ordered_data[training_stop_index:]\\n\\n return training, testing\",\n \"def _load_dataset(self, path):\\n\\t\\twhile True:\\n\\t\\t\\t\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tX_test = np.load(\\\"data/X_test.npy\\\")\\n\\t\\t\\t\\tY_test = np.load(\\\"data/Y_test.npy\\\")\\n\\t\\t\\t\\tbreak\\n\\n\\t\\t\\texcept FileNotFoundError:\\n\\n\\t\\t\\t\\tX_test = np.zeros((10000,64,64,3))\\n\\t\\t\\t\\tY_test = []\\n\\n\\t\\t\\t\\t\\n\\t\\t\\t\\twith open(path, 'rb') as fo:\\n\\t\\t\\t\\t\\ttemp_element = pickle.load(fo, encoding='bytes')\\n\\n\\t\\t\\t\\ttemp_data = temp_element[b'data']\\n\\t\\t\\t\\tY_test.extend(temp_element[b'labels'])\\n\\n\\t\\t\\t\\tfor j in range(10000):\\n\\t\\t\\t\\t\\tX_test[j] = self._reshape(temp_data[j])\\n\\n\\t\\t\\t\\tY_test = np.eye(10)[np.array(Y_test)]\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tnp.save(\\\"./data/X_test\\\", X_test)\\n\\t\\t\\t\\tnp.save(\\\"./data/Y_test\\\", Y_test)\\n\\n\\t\\t\\t\\tbreak\\n\\n\\n\\t\\treturn X_test, Y_test\",\n \"def load_all(test_num=100):\\n\\ttrain_data = pd.read_csv(\\n\\t\\tconfig.train_rating, \\n\\t\\tsep='\\\\t', header=None, names=['user', 'item'], \\n\\t\\tusecols=[0, 1], dtype={0: np.int32, 1: np.int32})\\n\\n\\tuser_num = train_data['user'].max() + 1\\n\\titem_num = train_data['item'].max() + 1\\n\\n\\ttrain_data = train_data.values.tolist()\\n\\n\\t# load ratings as a dok matrix\\n\\ttrain_mat = sp.dok_matrix((user_num, item_num), dtype=np.float32)\\n\\tfor x in train_data:\\n\\t\\ttrain_mat[x[0], x[1]] = 1.0\\n\\n\\ttest_data = []\\n\\twith open(config.test_negative, 'r') as fd:\\n\\t\\tline = fd.readline()\\n\\t\\twhile line != None and line != '':\\n\\t\\t\\tarr = line.split('\\\\t')\\n\\t\\t\\tu = eval(arr[0])[0]\\n\\t\\t\\ttest_data.append([u, eval(arr[0])[1]])\\n\\t\\t\\tfor i in arr[1:]:\\n\\t\\t\\t\\ttest_data.append([u, int(i)])\\n\\t\\t\\tline = fd.readline()\\n\\treturn train_data, test_data, user_num, item_num, train_mat\",\n \"def test_split_data():\\n from parrot import process_input_data as pid\\n\\n data_file = os.path.abspath(\\\"../data/seq_class_dataset.tsv\\\")\\n train, val, test = pid.split_data(data_file, datatype='sequence',\\n problem_type='classification', num_classes=3)\\n\\n assert (len(train) == 210) and (len(val) == 45) and (len(test) == 45) and (len(train[0]) == 3)\",\n \"def init_data(dataset_config: dict):\\n # train and dev will be in random order, test may be ordered according to labels\\n if dataset_config[\\\"name\\\"] == \\\"CoLA\\\":\\n train, dev, test, num_classes = load_cola(dataset_config)\\n elif dataset_config[\\\"name\\\"] == \\\"AGNews\\\":\\n train, dev, test, num_classes = load_ag_news(dataset_config)\\n elif dataset_config[\\\"name\\\"] == \\\"DBPedia\\\":\\n train, dev, test, num_classes = load_dbpedia(dataset_config)\\n elif dataset_config[\\\"name\\\"] == \\\"YRF\\\":\\n train, dev, test, num_classes = load_yrf(dataset_config)\\n else:\\n raise NameError(f\\\"Dataset {dataset_config['name']} not implemented.\\\")\\n # etc.\\n\\n # shrink size if debugging\\n if dataset_config[\\\"debug\\\"]:\\n # choose a random subset using huggingface select function\\n train = train.select(random.sample(range(len(train)), k=200))\\n dev = dev.select(random.sample(range(len(dev)), k=40))\\n test = test.select(random.sample(range(len(test)), k=200))\\n\\n # create class imbalance\\n random.seed(dataset_config[\\\"seed\\\"])\\n if dataset_config[\\\"pool_balance\\\"] == \\\"balanced\\\":\\n pass\\n elif dataset_config[\\\"pool_balance\\\"] == \\\"imbalanced\\\":\\n train = train.filter(lambda example: create_imbalanced_dataset(example, dataset_config[\\\"imbalance_prop\\\"], dataset_config['imbalance_cls']))\\n else:\\n NameError(f\\\"pool_balance = {dataset_config['pool_balance']} not allowed\\\")\\n\\n if dataset_config[\\\"dev_balance\\\"] == \\\"balanced\\\":\\n pass\\n elif dataset_config[\\\"dev_balance\\\"] == \\\"imbalanced\\\":\\n dev = dev.filter(lambda example: create_imbalanced_dataset(example, dataset_config[\\\"imbalance_prop\\\"], dataset_config['imbalance_cls']))\\n else:\\n NameError(f\\\"dev_balance = {dataset_config['dev_balance']} not allowed\\\")\\n\\n # get seed labelled pool indices (using the same seed data every time)\\n random.seed(dataset_config[\\\"seed\\\"])\\n if dataset_config[\\\"seed_balance\\\"] == \\\"balanced\\\":\\n # this is random (will have some variance vs pool)\\n indices = list(range(len(train)))\\n unlabelled_pool_idx, labelled_pool_idx = split(\\n indices,\\n random_state=dataset_config[\\\"seed\\\"],\\n test_size=dataset_config[\\\"seed_size\\\"]\\n )\\n elif dataset_config[\\\"seed_balance\\\"] == \\\"stratified\\\":\\n # this is the same as the underlying train set (which may be unbalanced)\\n indices = list(range(len(train)))\\n unlabelled_pool_idx, labelled_pool_idx = split(\\n indices,\\n random_state=dataset_config[\\\"seed\\\"],\\n test_size=dataset_config[\\\"seed_size\\\"],\\n stratify=train['label']\\n )\\n elif dataset_config[\\\"seed_balance\\\"] == \\\"imbalanced\\\":\\n # artificially sample an imbalanced seed set from the pool\\n unlabelled_pool_idx, labelled_pool_idx = create_imbalanced_seed(\\n train,\\n num_classes,\\n dataset_config[\\\"seed_size\\\"],\\n dataset_config['imbalance_prop'],\\n dataset_config['imbalance_cls']\\n )\\n else:\\n raise NameError(f\\\"seed_balance = {dataset_config['seed_balance']} not allowed\\\")\\n\\n return train, dev, test, num_classes, labelled_pool_idx, unlabelled_pool_idx\",\n \"def read(train_path, test_path, label_name):\\n train_dataset = pd.read_csv(train_path)\\n test_dataset = pd.read_csv(test_path)\\n\\n train_labels = train_dataset.pop(label_name)\\n\\n imputer = DataFrameImputer().fit(train_dataset)\\n train_dataset = imputer.transform(train_dataset)\\n test_dataset = imputer.transform(test_dataset)\\n\\n train_dataset = pd.get_dummies(train_dataset)\\n test_dataset = pd.get_dummies(test_dataset)\\n\\n train_dataset = train_dataset.drop(train_dataset.columns.difference(test_dataset.columns), axis=1)\\n test_dataset = test_dataset.drop(test_dataset.columns.difference(train_dataset.columns), axis=1)\\n\\n scaler = StandardScaler().fit(train_dataset)\\n train_dataset = scaler.transform(train_dataset)\\n test_dataset = scaler.transform(test_dataset)\\n\\n return train_dataset, train_labels, test_dataset\",\n \"def load_test_data():\\r\\n X_test = np.load('data/test/X_test.npy')\\r\\n scaling_test = np.load('data/test/scaling_test.npy')\\r\\n ids_test = np.load('data/test/ids_test.npy')\\r\\n y_test = np.load('data/test/y_test.npy')\\r\\n\\r\\n seed = np.random.randint(1, 10e6)\\r\\n np.random.seed(seed)\\r\\n np.random.shuffle(X_test)\\r\\n np.random.seed(seed)\\r\\n np.random.shuffle(scaling_test)\\r\\n np.random.seed(seed)\\r\\n np.random.shuffle(ids_test)\\r\\n np.random.seed(seed)\\r\\n np.random.shuffle(y_test)\\r\\n\\r\\n return X_test, scaling_test, ids_test, y_test\",\n \"def split_data(path=None, features = None, targets = None, test_size=None, random_state=None, **kwargs):\\n\\n # read in preprocessed data and seperate by features and target variables\\n model_data = pd.read_csv(path)\\n features = model_data[features]\\n targets = model_data[targets]\\n\\n # split data using random state\\n X_train, X_test, y_train, y_test = model_selection.train_test_split(\\n features, targets, test_size=test_size, random_state=random_state)\\n\\n # four target classifiers\\n y_train_IE = y_train['I-E']\\n y_train_NS = y_train['N-S']\\n y_train_TF = y_train['T-F']\\n y_train_JP = y_train['J-P']\\n\\n y_test_IE = y_test['I-E']\\n y_test_NS = y_test['N-S']\\n y_test_TF = y_test['T-F']\\n y_test_JP = y_test['J-P']\\n\\n logger.info('train and test splitted with test size of %s', test_size)\\n logger.warning('response variable y is divided into four classifiers, so you will see four y_train and four y_test.')\\n return X_train, X_test, y_train_IE, y_train_NS, y_train_TF, y_train_JP, y_test_IE, y_test_NS, y_test_TF, y_test_JP\",\n \"def _load_dataset(self, path):\\n\\t\\twhile True:\\n\\t\\t\\t\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tX_train = np.load(\\\"data/X_train.npy\\\")\\n\\t\\t\\t\\tX_val = np.load(\\\"data/X_val.npy\\\")\\n\\t\\t\\t\\tY_train = np.load(\\\"data/Y_train.npy\\\")\\n\\t\\t\\t\\tY_val = np.load(\\\"data/Y_val.npy\\\")\\n\\t\\t\\t\\tbreak\\n\\n\\t\\t\\texcept FileNotFoundError:\\n\\n\\t\\t\\t\\tdata_temp = np.zeros((50000,64,64,3))\\n\\t\\t\\t\\tlabel_temp = []\\n\\n\\t\\t\\t\\tfor i in range(5):\\n\\n\\t\\t\\t\\t\\tfile = path + str(i+1)\\n\\t\\t\\t\\t\\twith open(file, 'rb') as fo:\\n\\t\\t\\t\\t\\t\\ttemp_element = pickle.load(fo, encoding='bytes')\\n\\n\\t\\t\\t\\t\\ttemp_data = temp_element[b'data']\\n\\t\\t\\t\\t\\tlabel_temp.extend(temp_element[b'labels'])\\n\\n\\t\\t\\t\\t\\tfor j in range(10000):\\n\\t\\t\\t\\t\\t\\tdata_temp[j+(i*10000)] = self._reshape(temp_data[j])\\n\\n\\t\\t\\t\\tlabel_temp = np.eye(10)[np.array(label_temp)]\\n\\n\\t\\t\\t\\tnp.random.seed(123)\\n\\t\\t\\t\\tpermutations = list(np.random.permutation(50000))\\n\\t\\t\\t\\tX = data_temp[permutations, :, : , :] \\n\\t\\t\\t\\tY = label_temp[permutations, :]\\n\\t\\t\\t\\tX_train = X[0:40000, :, :, :] \\n\\t\\t\\t\\tY_train = Y[0:40000, :]\\n\\t\\t\\t\\tX_val = X[40000:50000, :, :, :] \\n\\t\\t\\t\\tY_val = Y[40000:50000, :]\\n\\n\\t\\t\\t\\tnp.save(\\\"./data/X_train\\\", X_train)\\n\\t\\t\\t\\tnp.save(\\\"./data/X_val\\\", X_val)\\n\\t\\t\\t\\tnp.save(\\\"./data/Y_train\\\", Y_train)\\n\\t\\t\\t\\tnp.save(\\\"./data/Y_val\\\", Y_val)\\n\\t\\t\\t\\tbreak\\n\\n\\t\\treturn X_train, X_val, Y_train, Y_val\",\n \"def __split_dataset(self):\\n self.train, self.valid, _, _ = train_test_split(self.data, self.data, test_size=0.2)\\n self.valid, self.test, _, _ = train_test_split(self.valid, self.valid, test_size=0.5)\",\n \"def load_train_test_transactions(train_size=0.7):\\n X, y = features_target_split()\\n X_train, X_test, y_train, y_test = train_test_split(X,y,train_size=train_size, random_state=7)\\n print('\\\\nTraining and testing data creation successful\\\\n')\\n return X_train, X_test, y_train,y_test\",\n \"def split_training_testing(X, Y, gnd, negative=10000, per=0.05):\\n df_x = pd.DataFrame(X)\\n df_x['y'] = Y\\n df_x['gnd'] = gnd\\n df_x.sort_values(by=['y'], inplace=True, ascending=False)\\n frac_positive = (df_x[df_x['y'] == 1].shape[0])/float(df_x.shape[0])\\n split = int(frac_positive * per * df_x.shape[0])\\n df_x.reset_index(drop=True, inplace=True)\\n fraud = df_x[df_x['y'] == 1]\\n # Shuffle inplace\\n fraud = fraud.sample(frac=1, random_state=0).reset_index(drop=True)\\n test = fraud.iloc[:split]\\n train_ = fraud.iloc[split:]\\n train = pd.concat([train_, df_x.iloc[fraud.shape[0]:].sample(n = negative, random_state=0)], ignore_index=True)\\n # Shuffle inplace\\n train = train.sample(frac=1, random_state=0).reset_index(drop=True)\\n #train = randomSample(train, negative)\\n y_train = train['y'].as_matrix()\\n y_train_gnd = train['gnd'].as_matrix()\\n train = train.drop(['y'], axis=1)\\n train = train.drop(['gnd'], axis=1)\\n \\n y_test = test['y'].as_matrix()\\n y_test_gnd = test['gnd'].as_matrix()\\n test = test.drop(['y'], axis=1)\\n test = test.drop(['gnd'], axis=1)\\n return train.as_matrix(), y_train, y_train_gnd, test.as_matrix(), y_test, y_test_gnd\",\n \"def train_test(in_path, train_out_path, test_out_path):\\n df = pd.read_csv(in_path, sep='|')\\n rng = RandomState()\\n\\n train = df.sample(frac=0.8, random_state=rng)\\n test = df.loc[~df.index.isin(train.index)]\\n\\n train.to_csv(train_out_path, sep='|', index = None, header=True)\\n test.to_csv(test_out_path, sep='|', index = None, header=True)\",\n \"def create_sets(test, data, test_size=0.2, write=False):\\n y_test = test['y_old']\\n X_test = test.drop('y_old', 1)\\n y_data = data['y_old']\\n X_data = data.drop('y_old', 1)\\n X_train, X_val, y_train, y_val = train_test_split(X_data, y_data, test_size=test_size, random_state=123)\\n if write:\\n pickle.dump((X_train, X_val, y_train, y_val), open(obj_save_path+'train_val_df.p', 'wb'))\\n #X_train, X_val, y_train, y_val = pickle.load(open(obj_save_path+'train_val_df.p', 'rb'))\\n return X_train, y_train, X_val, y_val, X_test, y_test\",\n \"def test_dataset_from_file(train_dataset):\\n dummy = \\\"justo. Praesent luctus. Curabitur egestas nunc sed libero. Proin sed\\\"\\n assert train_dataset[0][0] == dummy\\n assert train_dataset[0][1] == '6'\",\n \"def test_full_dataset_from_file(full_dataset):\\n train_dummy = \\\"justo. Praesent luctus. Curabitur egestas nunc sed libero. Proin sed\\\"\\n val_dummy = \\\"malesuada. Integer id magna et ipsum cursus vestibulum. Mauris magna.\\\"\\n\\n assert full_dataset.train[0][0] == train_dummy\\n assert full_dataset.train[0][1] == '6'\\n\\n assert full_dataset.val[0][0] == val_dummy\\n assert full_dataset.val[0][1] == '8'\\n\\n assert full_dataset[0][0] == train_dummy\\n assert full_dataset[100][0] == val_dummy\",\n \"def train_test_split(filename: str, split=0.5) -> tuple:\\n training_set = []\\n test_set = []\\n content = load_from_csv(filename)\\n for _, value in enumerate(content):\\n if random.random() < split:\\n training_set.append(value)\\n else:\\n test_set.append(value)\\n return training_set, test_set\",\n \"def get_train_test_split(path=\\\"./data/train_test_split.json\\\"):\\n with open(path) as f:\\n train_test_split = json.load(f)\\n return train_test_split\",\n \"def Train_data():\\n print (\\\"loading train data ...\\\")\\n time_start = time.time()\\n data_root = '/media/keziwen/86AA9651AA963E1D'\\n with h5py.File(join(data_root, './data/train_real2.h5')) as f:\\n data_real = f['train_real'][:]\\n num, nt, ny, nx = data_real.shape\\n data_real = np.transpose(data_real, (0, 1, 3, 2))\\n with h5py.File(join(data_root, './data/train_imag2.h5')) as f:\\n data_imag = f['train_imag'][:]\\n num, nt, ny, nx = data_imag.shape\\n data_imag = np.transpose(data_imag, (0, 1, 3, 2))\\n data = data_real+1j*data_imag\\n num_train = 15000\\n num_validate = 2000\\n train_data = data[0:num_train]\\n validate_data = data[num_train:num_train+num_validate]\\n\\n train_data = np.random.permutation(train_data)\\n\\n time_end = time.time()\\n print ('dataset has been created using {}s'.format(time_end-time_start))\\n return train_data, validate_data\",\n \"def data_set_maker():\\n\\n # crate a folder in your code directory and name it: \\\"files\\\". put the .npy files iside that folder\\n\\n x_all = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\\n y_all = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\\n\\n # split the data into 10% validation-set and 90% training set\\n raw_train, raw_valid, y_train, y_valid = train_test_split(x_all, y_all, test_size=0.2, random_state=43)\\n return raw_train, raw_valid, y_train, y_valid\",\n \"def train_val_test_split(\\n reframed, n_hours, n_features, target_var=\\\"temp\\\", ascending_sampling=False\\n):\\n\\n n_train_hours = int(reframed.shape[0] * 0.6)\\n n_val_hours = int(reframed.shape[0] * 0.2)\\n\\n if ascending_sampling:\\n print(\\\"Sampleamos datasets de pasado a futuro\\\")\\n train = reframed.iloc[:n_train_hours, :]\\n validation = reframed.iloc[n_train_hours : n_train_hours + n_val_hours, :]\\n test = reframed.iloc[n_train_hours + n_val_hours :, :]\\n else:\\n print(\\\"Sampleamos datasets de futuro a pasado\\\")\\n train = reframed.iloc[-n_train_hours:, :]\\n validation = reframed.iloc[-(n_train_hours + n_val_hours) : -n_train_hours :, :]\\n test = reframed.iloc[: -(n_train_hours + n_val_hours), :]\\n\\n # split into input and outputs\\n n_obs = n_hours * n_features\\n\\n target_col = \\\"{}_t\\\".format(target_var)\\n out_model_name = \\\"{}.hdf5\\\".format(target_var)\\n # target_var = 'prcp(t)'\\n train_X, train_y = train.iloc[:, :n_obs].values, train[target_col].values\\n val_X, val_y = validation.iloc[:, :n_obs].values, validation[target_col].values\\n test_X, test_y = test.iloc[:, :n_obs].values, test[target_col].values\\n print(train_X.shape, len(train_X), train_y.shape)\\n\\n # reshape input to be 3D [samples, timesteps, features]\\n train_X = train_X.reshape((train_X.shape[0], n_hours, n_features))\\n val_X = val_X.reshape((val_X.shape[0], n_hours, n_features))\\n test_X = test_X.reshape((test_X.shape[0], n_hours, n_features))\\n print(\\n train_X.shape,\\n train_y.shape,\\n val_X.shape,\\n val_y.shape,\\n test_X.shape,\\n test_y.shape,\\n )\\n\\n return {\\n \\\"trainset\\\": {\\\"X\\\": train_X, \\\"y\\\": train_y},\\n \\\"valset\\\": {\\\"X\\\": val_X, \\\"y\\\": val_y},\\n \\\"testset\\\": {\\\"X\\\": test_X, \\\"y\\\": test_y},\\n }\",\n \"def data_set_maker():\\n\\n # crate a folder in your code directory and name it: \\\"files\\\". put the .npy files iside that folder\\n path = os.getcwd() # reads the current path\\n x_train = np.load(path + '/files/tinyX.npy', 'r') # reads the input file\\n y_train = np.load(path + '/files/tinyY.npy', 'r') # reads the input file\\n x_test = np.load(path + '/files/tinyX_test.npy', 'r') # reads the input file\\n x_train, y_train = shuffle(x_train, y_train)\\n\\n return x_train, y_train, x_test\",\n \"def get_train_dev_examples(self, data_dir, seed, split=0.1, dom=-1):\\n X, Y = [], []\\n lines = self.read_txt(os.path.join(data_dir, \\\"train.txt\\\"))\\n for line in lines:\\n columns = line.rstrip().split(\\\" \\\")\\n Y.append(columns[0].split(\\\":\\\")[0])\\n X.append(\\\" \\\".join(columns[1:]))\\n X_train, X_dev, Y_train, Y_dev = train_test_split(X, Y, test_size=split, stratify=Y, random_state=seed)\\n train_examples = self._create_examples(zip(X_train, Y_train), \\\"train\\\", dom)\\n dev_examples = self._create_examples(zip(X_dev, Y_dev), \\\"dev\\\", dom)\\n return train_examples, dev_examples\",\n \"def split_dataset(X, Y, train_size=0.8):\\n if train_size != 1.0:\\n return train_test_split(\\n X, Y,\\n train_size=train_size,\\n stratify=Y\\n )\\n else:\\n X_, Y_ = shuffle(\\n X, Y\\n )\\n return X_, [], Y_, []\",\n \"def test_train_split(X, y, test_size=0.2):\\n idx = 0\\n length_of_X = len(X)\\n y_test = []\\n X_test = []\\n \\n while idx < length_of_X*test_size:\\n random_number_gen = np.random.randint(low=0, high=len(X))\\n y_test.append(y[random_number_gen])\\n X_test.append(X[random_number_gen])\\n X = np.delete(X, random_number_gen, axis=0)\\n y = np.delete(y, random_number_gen, axis=0)\\n idx += 1\\n return X, np.array(X_test), y, np.array(y_test)\",\n \"def load_dataset():\\n temp = gzip.open('mnist.pkl.gz')\\n train, val , test = pickle.load(temp,encoding='latin1')\\n temp.close()\\n train_inp = [np.reshape(x, (784,1)) for x in train[0]]\\n train_outp = [one_hot(y) for y in train[1]]\\n training_data = zip(train_inp, train_outp)\\n validation_inp = [np.reshape(x, (784, 1)) for x in val[0]]\\n validation_data = zip(validation_inp, val[1])\\n test_inp = [np.reshape(x, (784, 1)) for x in test[0]]\\n test_data = zip(test_inp, test[1])\\n return (training_data,validation_data,test_data)\",\n \"def load_susy(trainsize=500, testsize=1000):\\n filename = 'datasets/susysubset.csv'\\n dataset = loadcsv(filename)\\n trainset, testset = splitdataset(dataset, trainsize, testsize)\\n return trainset, testset\",\n \"def train_test_split(self, test_size=0.1, random_state=0, random_rate_label=True):\\r\\n data = self._shuffle_set(random_state)\\r\\n if test_size < 1:\\r\\n threshold = int(data.shape[0] * test_size)\\r\\n else:\\r\\n threshold = test_size\\r\\n dataset = Set(data[:, :-1], data[:, -1])\\r\\n if random_rate_label:\\r\\n split_index = np.random.randint(dataset.labels.shape[0], size=threshold)\\r\\n else:\\r\\n split_index = dataset._split_set_index(size=int(threshold / self.targets_count))\\r\\n X_test = dataset.features[split_index, :]\\r\\n y_test = dataset.labels[split_index]\\r\\n X_train = np.delete(dataset.features, split_index, axis=0)\\r\\n y_train = np.delete(dataset.labels, split_index, axis=0)\\r\\n return X_train, y_train, X_test, y_test\",\n \"def test_data_split_good():\\n\\tdf_true = pd.read_csv(\\\"test/sample_data.csv\\\")\\n\\tdf_test = df_true.copy()\\n\\ttarget = df_true['price']\\n\\tfeatures = df_true.loc[:, df_true.columns != 'price']\\n\\n\\tX_train_true, X_test_true, y_train_true, y_test_true = train_test_split(features, target, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\ttest_size=0.4, random_state=0)\\n\\n\\tsplit_data = {'test_size': 0.4, 'random_state': 0}\\n\\tX_train_test, X_test_test, y_train_test, y_test_test = data_split(df_test, 'price', split_data)\\n\\n\\tassert X_train_true.equals(X_train_test)\\n\\tassert y_train_true.equals(y_train_test)\\n\\tassert X_test_true.equals(X_test_test)\\n\\tassert y_test_true.equals(y_test_test)\",\n \"def load_data(train_size=30000, random_state=0):\\n print(\\\"Loading adult data from alibi.\\\")\\n np.random.seed(random_state)\\n\\n data = alibi.datasets.fetch_adult()\\n\\n # mix input data\\n data_perm = np.random.permutation(np.c_[data.data, data.target])\\n data.data = data_perm[:, :-1]\\n data.target = data_perm[:, -1]\\n\\n # perform train / test split\\n X_train, y_train = data.data[:train_size, :], data.target[:train_size]\\n X_test, y_test = data.data[train_size:, :], data.target[train_size:]\\n\\n return data, X_train, y_train, X_test, y_test\",\n \"def create_dataset(base_path, batch_size, is_train):\\n columns_list = [\\\"feature\\\", \\\"label\\\"]\\n num_consumer = 4\\n\\n if is_train:\\n path = os.path.join(base_path, 'aclImdb_train.mindrecord0')\\n else:\\n path = os.path.join(base_path, 'aclImdb_test.mindrecord0')\\n\\n data_set = ds.MindDataset(path, columns_list, num_consumer)\\n ds.config.set_seed(0)\\n data_set = data_set.shuffle(buffer_size=data_set.get_dataset_size())\\n data_set = data_set.batch(batch_size, drop_remainder=True)\\n return data_set\",\n \"def load_dataset(data_dir, img_size):\\n global input_set\\n global test_set\\n\\n imgs = []\\n img_files = os.listdir(data_dir)\\n for img in img_files:\\n # try:\\n tmp = scipy.misc.imread(data_dir + \\\"/\\\" + img)\\n x, y, z = tmp.shape # shape : width * length * chanel\\n coords_x = int(x / img_size) # 坐标\\n coords_y = int(y / img_size) #\\n coords = [(q, r) for q in range(coords_x) for r in range(coords_y)] # 列表 x * y\\n for coord in coords:\\n imgs.append((data_dir + \\\"/\\\" + img, coord)) # 为列表添加文件目录\\n # except BaseException:\\n # print(\\\"oops\\\")\\n test_size = min(10, int(len(imgs) * 0.2))\\n random.shuffle(imgs)\\n test_set = imgs[:test_size]\\n train_set_X = imgs[test_size:][:200]\\n train_set = imgs[test_size:][200:400]\\n return\",\n \"def _split_train_tst(self):\\n num_samples = self.Y.shape[0]\\n mapper_file = self.checkpointer.get_mapper_file_location()\\n if not self.checkpointer.is_mapper_checkpointed():\\n print 'No mapper checkpoint found. Fresh loading in progress ...'\\n # Now shuffle the data\\n sample_id = range(num_samples)\\n random.shuffle(sample_id)\\n print 'Dumping the mapper shuffle for reuse.'\\n Pickle.dump(sample_id, open(mapper_file, 'wb'))\\n print 'Dump complete. Moving Forward...'\\n else:\\n print 'Mapper Checkpoint found... Reading from mapper dump'\\n sample_id = Pickle.load(open(mapper_file, 'rb'))\\n print 'Mapping unpickling complete.. Moving forward...'\\n\\n self.X_fwd = self.X_fwd[sample_id]\\n self.X_bwd = self.X_bwd[sample_id]\\n self.Y = self.Y[sample_id]\\n # Now divide the data into test ans train set\\n test_fraction = 0.01\\n self.test_size = int(test_fraction * num_samples)\\n self.train_size = num_samples - self.test_size\\n # Forward review\\n self.X_trn_fwd = self.X_fwd[0:self.train_size]\\n self.X_tst_fwd = self.X_fwd[self.train_size:num_samples]\\n # Backward review\\n self.X_trn_bwd = self.X_bwd[0:self.train_size]\\n self.X_tst_bwd = self.X_bwd[self.train_size:num_samples]\\n # Summary\\n self.Y_trn = self.Y[0:self.train_size]\\n self.Y_tst = self.Y[self.train_size:num_samples]\",\n \"def train_test_model_batch():\\n train=learning.Train_kmer_clf()\\n train.run()\",\n \"def my_train_test_split(act_my_data, act_test_size=0.5):\\r\\n act_train_df, act_test_df = train_test_split(act_my_data,\\r\\n test_size=act_test_size)\\r\\n return act_train_df, act_test_df\",\n \"def get_training_and_testing_sets(data, Y):\\r\\n data = pd.concat([data, Y], axis=1)\\r\\n x,y=data.shape\\r\\n train_X_sub1=data[0:x//6]\\r\\n dev_X_sub1 = data[x//6:x//6 + x//12]\\r\\n test_X_sub1 = data[x//6 + x//12:x//3]\\r\\n\\r\\n train_X_sub2 = data[x//3:x//3+x//6]\\r\\n dev_X_sub2 = data[x//6 + x//3:x//3 + x//6 + x//12]\\r\\n test_X_sub2 = data[x//3 + x//6 + x//12:2*x//3]\\r\\n\\r\\n train_X_sub3 = data[2*x//3:(2*x//3) +x//6]\\r\\n dev_X_sub3 = data[x//6 + 2*x//3: (2*x//3) + x//6 + x//12]\\r\\n test_X_sub3 = data[2*x//3 + x//6 + x//12:x]\\r\\n\\r\\n train_X=train_X_sub1.append(train_X_sub2,ignore_index = True)\\r\\n train_X =train_X.append(train_X_sub3,ignore_index = True)\\r\\n dev_X= dev_X_sub1.append(dev_X_sub2,ignore_index = True)\\r\\n dev_X = dev_X.append(dev_X_sub3,ignore_index = True)\\r\\n test_X = test_X_sub1.append(test_X_sub2,ignore_index = True)\\r\\n test_X = test_X.append(test_X_sub3,ignore_index = True)\\r\\n\\r\\n\\r\\n train_X = util.shuffle(train_X)\\r\\n train_X = train_X.reset_index(drop=True)\\r\\n\\r\\n dev_X = util.shuffle(dev_X)\\r\\n dev_X = dev_X.reset_index(drop=True)\\r\\n\\r\\n test_X = util.shuffle(test_X)\\r\\n test_X = test_X.reset_index(drop=True)\\r\\n\\r\\n train_X_final=train_X\\r\\n dev_X_final = dev_X\\r\\n test_X_final = test_X\\r\\n x, y = train_X_final.shape\\r\\n train_X = train_X_final.iloc[:, 0:y - 1]\\r\\n train_Y = train_X_final.iloc[:, y - 1]\\r\\n\\r\\n x, y = test_X_final.shape\\r\\n test_X = test_X_final.iloc[:, 0:y - 1]\\r\\n test_Y = test_X_final.iloc[:, y - 1]\\r\\n\\r\\n x, y = dev_X_final.shape\\r\\n dev_X = dev_X_final.iloc[:, 0:y - 1]\\r\\n dev_Y = dev_X_final.iloc[:, y - 1]\\r\\n\\r\\n return train_X, train_Y, dev_X,dev_Y,test_X, test_Y\",\n \"def train_test_split_data(data, test_size=0.25):\\n \\n y = data['fraud'].values\\n X = data.drop(columns=['fraud','description','name','org_desc','org_name','payee_name']).values\\n \\n return train_test_split(X, y, test_size=test_size, stratify=y, shuffle=True)\",\n \"def split_data(self,test=False):\\n shuffle_index = torch.randperm(self.train_target.shape[0])\\n load = shuffle_index.shape[0]\\n train_input_shuffle = self.train_input[shuffle_index]\\n train_target_shuffle = self.train_target[shuffle_index]\\n train_classes_shuffle = self.train_classes[shuffle_index]\\n index_train = self.index_for_equal_class(train_target_shuffle[:load//2])\\n train_input = train_input_shuffle[index_train]\\n train_target = train_target_shuffle[index_train]\\n train_classes = train_classes_shuffle[index_train]\\n if not test:\\n index_test = self.index_for_equal_class( train_target_shuffle[load//2:]) + load//2\\n test_input = train_input_shuffle[index_test]\\n test_target = train_target_shuffle[index_test]\\n test_classes = train_classes_shuffle[index_test]\\n else:\\n index_test = self.index_for_equal_class(self.test_target)\\n test_input = self.test_input[index_test]\\n test_target = self.test_target[index_test]\\n test_classes = self.test_classes[index_test]\\n train_input, mean, std = normalize(train_input)\\n test_input, _, _ = normalize(test_input,mean,std)\\n return train_input, train_target, train_classes ,test_input ,test_target ,test_classes\",\n \"def dataset(self, file, latent_dim = 4, pivot = 0.2):\\n data_df = pd.read_csv(file, sep=\\\"::\\\", engine='python',\\n names=['UserId', 'MovieId', 'Rating', 'Timestamp'])\\n print(len(data_df))\\n data_df['avg_score'] = data_df.groupby(by='UserId')['Rating'].transform('mean')\\n # feature columns\\n user_num, item_num = data_df['UserId'].max() + 1, data_df['MovieId'].max() + 1\\n feature_columns = [[self.denseFeature('avg_score')],\\n [self.sparseFeature('user_id', user_num, latent_dim),\\n self.sparseFeature('item_id', item_num, latent_dim)]]\\n # split train dataset and test dataset\\n watch_count = data_df.groupby(by='UserId')['MovieId'].agg('count')\\n print(\\\"分割后\\\"+str(pivot*100)+\\\"%作为数据集\\\\n\\\")\\n test_df = pd.concat([data_df[data_df.UserId == i].iloc[int((1 - pivot) * watch_count[i]):] for i in (watch_count.index)], axis=0)\\n print(test_df.head())\\n test_df = test_df.reset_index()\\n train_df = data_df.drop(labels=test_df['index'])\\n # 删除非需求列\\n train_df = train_df.drop(['Timestamp'], axis=1).sample(frac=1.).reset_index(drop=True)\\n test_df = test_df.drop(['index', 'Timestamp'], axis=1).sample(frac=1.).reset_index(drop=True)\\n train_X = [train_df['avg_score'].values, train_df[['UserId', 'MovieId']].values]\\n train_y = train_df['Rating'].values.astype('int32')\\n test_X = [test_df['avg_score'].values, test_df[['UserId', 'MovieId']].values]\\n test_y = test_df['Rating'].values.astype('int32')\\n return feature_columns, (train_X, train_y), (test_X, test_y)\",\n \"def gen_train_val_test_split(self, splits=[0.7, 0.2, 0.1], random_state=42):\\n patient_dirs = os.listdir(self.out_dir)\\n msk_list, img_list, patient_list, is_tumor = [], [], [], []\\n\\n for patient in patient_dirs:\\n patient_folder = os.path.join(self.out_dir, patient)\\n if not os.path.isdir(patient_folder):\\n continue\\n\\n patient_root = os.path.join(self.out_dir, patient)\\n for file in os.listdir(patient_root):\\n if \\\"mask\\\" not in file:\\n patient_list.append(patient)\\n img_list.append(os.path.join(patient_root, file))\\n msk_list.append(os.path.join(patient_root, file[:file.find(\\\".npy\\\")] + \\\"_mask.npy\\\"))\\n \\n img = np.load(msk_list[-1])\\n if np.mean(img) > 0:\\n is_tumor.append(1)\\n else:\\n is_tumor.append(0)\\n \\n data = pd.DataFrame(data={\\\"Patient\\\": patient_list, \\\"Image\\\": img_list, \\\"Mask\\\": msk_list, \\\"Tumor Present\\\": is_tumor})\\n self.train_df, self.valid_df, self.test_df = self._split_by_patients(data, val_split=splits[1], test_split=splits[2], random_state=random_state)\\n # print(len(train), len(val), len(test))\\n # self.train_df = data[data[\\\"Patient\\\"].isin(train)]\\n # self.valid_df = data[data[\\\"Patient\\\"].isin(val)]\\n # self.test_df = data[data[\\\"Patient\\\"].isin(test)]\",\n \"def make_data_splits(samples, params, RESULTSDIR, num_experiments):\\n # TODO: Switch to .mat from .pickle so that these lists are easier to read\\n # and change.\\n\\n partition = {}\\n if params[\\\"load_valid\\\"] is None:\\n # Set random seed if included in params\\n if params[\\\"data_split_seed\\\"] is not None:\\n np.random.seed(params[\\\"data_split_seed\\\"])\\n\\n all_inds = np.arange(len(samples))\\n\\n # extract random inds from each set for validation\\n v = params[\\\"num_validation_per_exp\\\"]\\n valid_inds = []\\n if params[\\\"valid_exp\\\"] is not None and params[\\\"num_validation_per_exp\\\"] > 0:\\n all_valid_inds = []\\n for e in params[\\\"valid_exp\\\"]:\\n tinds = [\\n i for i in range(len(samples)) if int(samples[i].split(\\\"_\\\")[0]) == e\\n ]\\n all_valid_inds = all_valid_inds + tinds\\n valid_inds = valid_inds + list(\\n np.random.choice(tinds, (v,), replace=False)\\n )\\n valid_inds = list(np.sort(valid_inds))\\n\\n train_inds = list(set(all_inds) - set(all_valid_inds))#[i for i in all_inds if i not in all_valid_inds]\\n elif params[\\\"num_validation_per_exp\\\"] > 0: # if 0, do not perform validation\\n for e in range(num_experiments):\\n tinds = [\\n i for i in range(len(samples)) if int(samples[i].split(\\\"_\\\")[0]) == e\\n ]\\n valid_inds = valid_inds + list(\\n np.random.choice(tinds, (v,), replace=False)\\n )\\n valid_inds = list(np.sort(valid_inds))\\n\\n train_inds = [i for i in all_inds if i not in valid_inds]\\n elif params[\\\"valid_exp\\\"] is not None:\\n raise Exception(\\\"Need to set num_validation_per_exp in using valid_exp\\\")\\n else:\\n train_inds = all_inds\\n\\n assert (set(valid_inds) & set(train_inds)) == set()\\n\\n train_samples = samples[train_inds]\\n train_inds = []\\n if params[\\\"valid_exp\\\"] is not None:\\n train_expts = [f for f in range(num_experiments) if f not in params[\\\"valid_exp\\\"]]\\n else:\\n train_expts = np.arange(num_experiments)\\n\\n print(\\\"TRAIN EXPTS: {}\\\".format(train_expts))\\n\\n if params[\\\"num_train_per_exp\\\"] is not None:\\n # Then sample randomly without replacement from training sampleIDs\\n for e in train_expts:\\n tinds = [\\n i for i in range(len(train_samples)) if int(train_samples[i].split(\\\"_\\\")[0]) == e\\n ]\\n print(e)\\n print(len(tinds))\\n train_inds = train_inds + list(\\n np.random.choice(tinds, (params[\\\"num_train_per_exp\\\"],), replace=False)\\n )\\n train_inds = list(np.sort(train_inds))\\n else:\\n train_inds = np.arange(len(train_samples))\\n\\n \\n\\n partition[\\\"valid_sampleIDs\\\"] = samples[valid_inds]\\n partition[\\\"train_sampleIDs\\\"] = train_samples[train_inds]\\n\\n # Save train/val inds\\n with open(os.path.join(RESULTSDIR, \\\"val_samples.pickle\\\"), \\\"wb\\\") as f:\\n cPickle.dump(partition[\\\"valid_sampleIDs\\\"], f)\\n\\n with open(os.path.join(RESULTSDIR, \\\"train_samples.pickle\\\"), \\\"wb\\\") as f:\\n cPickle.dump(partition[\\\"train_sampleIDs\\\"], f)\\n else:\\n # Load validation samples from elsewhere\\n with open(os.path.join(params[\\\"load_valid\\\"], \\\"val_samples.pickle\\\"), \\\"rb\\\",) as f:\\n partition[\\\"valid_sampleIDs\\\"] = cPickle.load(f)\\n partition[\\\"train_sampleIDs\\\"] = [\\n f for f in samples if f not in partition[\\\"valid_sampleIDs\\\"]\\n ]\\n\\n # Reset any seeding so that future batch shuffling, etc. are not tied to this seed\\n if params[\\\"data_split_seed\\\"] is not None:\\n np.random.seed()\\n\\n return partition\",\n \"def test_data():\\n batch_size = 10\\n input_dim = 28\\n test_data = np.random.rand(batch_size, input_dim)\\n\\n return test_data\",\n \"def train_test_split_file(filename, test_size = 0.2, new_category = True):\\n\\tlogthis.say('Train test set separation starts.')\\n\\tdf: pd.DataFrame = pd.read_csv(filename, sep=';')\\n\\tif new_category:\\n\\t\\ttrain, test = train_test_split(df, test_size=test_size, random_state=42, stratify=df['Label'])\\n\\telse:\\n\\t\\ttrain = df.drop_duplicates(subset=['Text'], keep=False)\\n\\t\\ttest = df[df.duplicated(subset=['Text'], keep=False)]\\n\\ttrain.to_csv(filename.replace('.csv', '_train.csv'), sep=';', index=False)\\n\\ttest.to_csv(filename.replace('.csv', '_test.csv'), sep=';', index=False)\\n\\tlogthis.say('Train test set separation done.')\",\n \"def train_test_split(ratings):\\n test = set(range(len(ratings))[::1000])\\n train = sorted(set(range(len(ratings))) - test)\\n test = sorted(test)\\n return ratings.iloc[train], ratings.iloc[test]\",\n \"def split_data(self, val_size=0.1, test_size=0.5):\\n df = pd.read_csv(self.csv_path, delimiter=';')\\n train, val = train_test_split(df, test_size=val_size)\\n val, test = train_test_split(df, test_size=test_size)\\n return train, val, test\",\n \"def load_data():\\n X = load_pickle(config['image_paths']['train_images_pickle'])\\n y = load_train_labels()\\n y = to_categorical(y)\\n test_indices = np.random.choice(len(X), int(len(X) * float(config['model']['test_size'])), replace=False)\\n X_train = np.asarray([e for idx, e in enumerate(X) if idx not in test_indices])\\n X_test = np.asarray([e for idx, e in enumerate(X) if idx in test_indices])\\n y_train = np.asarray([e for idx, e in enumerate(y) if idx not in test_indices])\\n y_test = np.asarray([e for idx, e in enumerate(y) if idx in test_indices])\\n return X_train, y_train, X_test, y_test\",\n \"def separate(self):\\n print(\\\"start dataset separating\\\")\\n sum = 0\\n for i in tqdm(range(len(self.itemlen))):\\n il = self.itemlen[i]\\n if il < 3:\\n sum += il\\n continue\\n rarr = list(range(sum, sum+il))\\n random.shuffle(rarr)\\n self.train.append({\\n 'input': self.input[rarr[0]],\\n 'label': self.label[i]\\n })\\n self.val.append({\\n 'input': self.input[rarr[1]],\\n 'label': self.label[i]\\n })\\n for j in range(2, len(rarr)):\\n self.test.append({\\n 'input': self.input[rarr[j]],\\n 'label': self.label[i]\\n })\\n sum += il\",\n \"def load_data(self):\\n with open('data/fordTrain.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n train = [x for i, x in enumerate(data) if i > 0] \\n # Extract features and target variable separately\\n trainx = [x[3:] for x in train]\\n trainy = [x[2] for x in train]\\n\\n with open('data/fordTest.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n testx = [x[3:] for i, x in enumerate(data) if i > 0] \\n\\n with open('data/Solution.csv') as f:\\n data = csv.reader(f, delimiter=',')\\n testy = [x[2] for i, x in enumerate(data) if i > 0] \\n\\n # Extract features and target variable, convert to numpy array\\n trainx = np.asarray(trainx, dtype=np.float32)\\n trainy = np.asarray(trainy, dtype=np.int8)\\n testx = np.asarray(testx, dtype=np.float32)\\n testy = np.asarray(testy, dtype=np.int8)\\n\\n # Return training and test sets\\n trainSet = Dataset(trainx, trainy)\\n testSet = Dataset(testx, testy)\\n return trainSet, testSet\",\n \"def train_test_split_drifters():\\n df = process_raw_df()\\n ids = np.unique(df.index.get_level_values(level=0))\\n rng = np.random.default_rng(seed=1)\\n train_ids = np.sort(rng.choice(ids, size=len(ids)//2, replace=False))\\n test_ids = np.sort(np.setdiff1d(ids, train_ids))\\n train_df = df[df.index.get_level_values(level=0).isin(train_ids)].copy()\\n test_df = df[df.index.get_level_values(level=0).isin(test_ids)].copy()\\n return train_df, test_df\",\n \"def create_train_test(option, transform, params, split=0.2):\\r\\n clip_im_dir = option.clip_im_dir\\r\\n matting_dir = option.matting_dir\\r\\n csv_path = option.csv_path\\r\\n \\r\\n print(\\\"create datasets\\\")\\r\\n \\r\\n \\r\\n data_df = pd.read_csv(csv_path)\\r\\n # data_df = MergeDataframe(clip_im_dir, matting_dir)\\r\\n \\r\\n #separate data in training and test data (20/80)\\r\\n train_df, test_df = train_test_split(data_df, test_size=split)\\r\\n \\r\\n #search right Dataset class\\r\\n package_dir = Path(src.dataset.__file__).resolve().parent\\r\\n\\r\\n for (_, module_name, _) in iter_modules([package_dir]):\\r\\n # print(module_name, self.ComType)\\r\\n if option.dataset.lower() == module_name.lower() :\\r\\n modelModule = importlib.import_module(\\\".\\\"+module_name)\\r\\n break\\r\\n \\r\\n # train data\\r\\n training_set = modelModule(train_df, clip_im_dir, matting_dir, transform, transform)\\r\\n train_loader = DataLoader(training_set, **params)\\r\\n \\r\\n \\r\\n #test data\\r\\n testing_set = modelModule(test_df, clip_im_dir, matting_dir, transform, transform)\\r\\n test_loader = DataLoader(testing_set, **params)\\r\\n \\r\\n return train_loader, test_loader\",\n \"def load_davis_dataset():\\n trainn_fold = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'folds', 'train_fold_setting1.txt')))\\n train_fold = []\\n for e in zip(*trainn_fold):\\n for ee in e:\\n train_fold.append(ee)\\n #train_fold = [ee for e in trainn_fold for ee in e]\\n test_fold = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'folds', 'test_fold_setting1.txt')))\\n ligands = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'ligands_can.txt')),\\n object_pairs_hook=OrderedDict)\\n proteins = json.load(\\n open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'proteins.txt')),\\n object_pairs_hook=OrderedDict)\\n \\n affinity = pickle.load(open(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'Y'), \\n 'rb'), encoding='latin1')\\n smiles_lst, protein_lst = [], []\\n\\n for k in ligands.keys():\\n smiles = ligands[k]\\n smiles_lst.append(smiles)\\n for k in proteins.keys():\\n protein_lst.append(proteins[k])\\n\\n affinity = [-np.log10(y / 1e9) for y in affinity]\\n affinity = np.asarray(affinity)\\n \\n os.makedirs(os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'processed'), exist_ok=True)\\n train_test_dataset = []\\n for split in ['train', 'test']:\\n split_dir = os.path.join('dataset', 'regression', 'benchmark', 'DAVIStest', 'processed', split)\\n os.makedirs(split_dir, exist_ok=True)\\n fold = train_fold if split == 'train' else test_fold\\n rows, cols = np.where(np.isnan(affinity) == False)\\n rows, cols = rows[fold], cols[fold]\\n \\n data_lst = [[] for _ in range(1)]\\n for idx in range(len(rows)):\\n data = {}\\n data['smiles'] = smiles_lst[rows[idx]]\\n data['protein'] = protein_lst[cols[idx]]\\n af = affinity[rows[idx], cols[idx]]\\n data['aff'] = af\\n\\n data_lst[idx % 1].append(data)\\n random.shuffle(data_lst)\\n train_test_dataset.append(data_lst[0])\\n return train_test_dataset\",\n \"def get_uci_datasets(\\n name, split_seed=0, test_fraction=0.10, train_frac=1.0, combine_val_train=False\\n):\\n # load full dataset\\n load_funs = {\\n \\\"naval\\\": _load_naval,\\n \\\"protein\\\": _load_protein,\\n \\\"crime\\\": _load_crime,\\n \\\"energy\\\": _load_app_energy,\\n }\\n print(\\\"Loading dataset {}....\\\".format(name))\\n if name == \\\"depth\\\":\\n (X_train, y_train), (X_test, y_test) = load_funs[name]()\\n y_scale = np.array([[1.0]])\\n return (X_train, y_train), (X_test, y_test), y_scale\\n\\n X, y = load_funs[name]()\\n X = X.astype(np.float32)\\n y = y.astype(np.float32)\\n\\n # We create the train and test sets with 90% and 10% of the data\\n\\n if split_seed == -1: # Do not shuffle!\\n permutation = range(X.shape[0])\\n else:\\n rs = np.random.RandomState(split_seed)\\n permutation = rs.permutation(X.shape[0])\\n\\n size_train = int(np.round(X.shape[0] * (1 - test_fraction)))\\n index_train = permutation[0:size_train]\\n index_test = permutation[size_train:]\\n\\n X_train = X[index_train, :]\\n X_test = X[index_test, :]\\n if name == \\\"depth\\\":\\n y_train = y[index_train]\\n y_test = y[index_test]\\n else:\\n y_train = y[index_train, None]\\n y_test = y[index_test, None]\\n\\n if train_frac != 1.0:\\n rs = np.random.RandomState(split_seed)\\n permutation = rs.permutation(X_train.shape[0])\\n n_train = int(train_frac * len(X_train))\\n X_train = X_train[:n_train]\\n y_train = y_train[:n_train]\\n\\n if split_seed == -1: # Do not shuffle!\\n permutation = range(X_train.shape[0])\\n else:\\n rs = np.random.RandomState(split_seed)\\n permutation = rs.permutation(X_train.shape[0])\\n\\n if combine_val_train:\\n val_fraction = 0.0\\n else:\\n val_fraction = 0.10\\n size_train = int(np.round(X_train.shape[0] * (1 - val_fraction)))\\n index_train = permutation[0:size_train]\\n index_val = permutation[size_train:]\\n\\n X_new_train = X_train[index_train, :]\\n X_val = X_train[index_val, :]\\n\\n y_new_train = y_train[index_train]\\n y_val = y_train[index_val]\\n\\n print(\\\"Done loading dataset {}\\\".format(name))\\n\\n def standardize(data):\\n mu = data.mean(axis=0, keepdims=1)\\n scale = data.std(axis=0, keepdims=1)\\n scale[scale < 1e-10] = 1.0\\n\\n data = (data - mu) / scale\\n return data, mu, scale\\n\\n # Standardize\\n X_new_train, x_train_mu, x_train_scale = standardize(X_new_train)\\n X_test = (X_test - x_train_mu) / x_train_scale\\n y_new_train, y_train_mu, y_train_scale = standardize(y_new_train)\\n y_test = (y_test - y_train_mu) / y_train_scale\\n X_val = (X_val - x_train_mu) / x_train_scale\\n y_val = (y_val - y_train_mu) / y_train_scale\\n\\n train = TensorDataset(\\n torch.Tensor(X_new_train).type(torch.float64),\\n torch.Tensor(y_new_train).type(torch.float64),\\n )\\n\\n val = TensorDataset(\\n torch.Tensor(X_val).type(torch.float64),\\n torch.Tensor(y_val).type(torch.float64),\\n )\\n\\n test = TensorDataset(\\n torch.Tensor(X_test).type(torch.float64),\\n torch.Tensor(y_test).type(torch.float64),\\n )\\n in_size = X_train[0].shape\\n target_size = y_train[0].shape\\n\\n return train, val, test, in_size, target_size, y_train_scale\",\n \"def prepare_dataset():\\n with open('gold-posts.txt', encoding='utf-8') as f:\\n posts = f.readlines()\\n with open('gold-labels.txt', encoding='utf-8') as f:\\n labels = f.readlines()\\n\\n def to_cat(x: str) -> int:\\n if x == 'p':\\n return 1\\n elif x == 'n':\\n return 2\\n else:\\n return 0\\n X = np.array([x.strip() for x in posts])\\n y = np.array([to_cat(x.strip()) for x in labels])\\n\\n # DOES NOT WORK - too imbalanced\\n #skf = StratifiedKFold(n_splits=5, random_state=None, shuffle=False)\\n #for train_index, test_index in skf.split(X, y):\\n # X_train, X_test = X[train_index], X[test_index]\\n # y_train, y_test = y[train_index], y[test_index]\\n # break\\n\\n # WORKS better\\n trI, teI = balanced_split(y)\\n\\n train_texts = X[trI].tolist()\\n train_labels = y[trI].tolist()\\n valid_texts = X[teI].tolist()\\n valid_labels = y[teI].tolist()\\n return train_texts, train_labels, valid_texts, valid_labels\",\n \"def split_train_and_test_with_py_datasets(data_set, batch_size=cfg['batch_size'], test_size=0.2, num_works=4,\\n pin_memory=True):\\n num_dataset = len(data_set)\\n indices = list(range(num_dataset))\\n split = int(np.floor(test_size * num_dataset))\\n\\n train_idx, test_idx = indices[split:], indices[:split]\\n train_sampler = SubsetRandomSampler(train_idx)\\n test_sampler = SubsetRandomSampler(test_idx)\\n\\n train_loader = torch.utils.data.DataLoader(\\n dataset=data_set, batch_size=batch_size, sampler=train_sampler, num_workers=num_works,\\n pin_memory=pin_memory\\n )\\n\\n test_loader = torch.utils.data.DataLoader(\\n dataset=data_set, batch_size=batch_size, sampler=test_sampler, num_workers=num_works,\\n pin_memory=pin_memory\\n )\\n\\n return train_loader, test_loader\",\n \"def prepare_dataset(data_path, test_size=0.2, validation_size=0.2):\\r\\n\\r\\n # load dataset\\r\\n if data_path.endswith('json'):\\r\\n X, y = load_data_from_json(data_path)\\r\\n else:\\r\\n X, y = load_data_from_fold(data_path)\\r\\n # create train, validation, test split\\r\\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)\\r\\n X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)\\r\\n\\r\\n # add an axis to nd array\\r\\n X_train = X_train[..., np.newaxis]\\r\\n X_test = X_test[..., np.newaxis]\\r\\n X_validation = X_validation[..., np.newaxis]\\r\\n\\r\\n return X_train, y_train, X_validation, y_validation, X_test, y_test\",\n \"def split_test_training(data_path, sequence_length):\\n\\n # logic for loading the CSV, using 'result' (2nd) column as basis for prediction\\n with open(data_path) as f:\\n record = csv.reader(f, delimiter=\\\",\\\")\\n next(record, None)\\n spat = []\\n nb_of_values = 0\\n for line in record:\\n spat.append(float(line[2]))\\n nb_of_values += 1\\n\\n # break file into chunks based on sequence length\\n result = []\\n for index in range(len(spat) - sequence_length):\\n result.append(spat[index: index + sequence_length])\\n result = np.array(result)\\n\\n # divide set into 20% for test, 80% for training\\n row = int(round(0.8 * result.shape[0]))\\n train = result[:row, :]\\n np.random.shuffle(train)\\n X_train = train[:, :-1]\\n y_train = train[:, -1]\\n X_test = result[row:, :-1]\\n y_test = result[row:, -1]\\n X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\\n X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\\n\\n return [X_train, y_train, X_test, y_test]\",\n \"def GetDataset():\\n x_train = []\\n x_test = []\\n y_train = []\\n y_test = []\\n\\n classes1 = set()\\n classes2 = set()\\n for f in GetInputFiles():\\n class1, class2, fold, fname = f.split('\\\\\\\\')[-4:]\\n classes1.add(class1)\\n classes2.add(class2)\\n class1 = class1.split('_')[0]\\n class2 = class2.split('_')[0]\\n\\n x = ReadAndTokenize(f)\\n y = [int(class1 == 'positive'), int(class2 == 'truthful')]\\n if fold == 'fold4':\\n x_test.append(x)\\n y_test.append(y)\\n else:\\n x_train.append(x)\\n y_train.append(y)\\n\\n ### Make numpy arrays.\\n x_test = MakeDesignMatrix(x_test)\\n x_train = MakeDesignMatrix(x_train)\\n y_test = numpy.array(y_test, dtype='float32')\\n y_train = numpy.array(y_train, dtype='float32')\\n\\n dataset = (x_train, y_train, x_test, y_test)\\n with open('dataset.pkl', 'wb') as fout:\\n pickle.dump(dataset, fout)\\n return dataset\",\n \"def train_and_test(self, data):\\n\\n np.random.shuffle(data)\\n datalist = self.unpack_data(data)\\n\\n logger.info('[*] 75-25 partition of datasets ...')\\n\\n markline1 = math.floor(0.75*(len(datalist['features'])))\\n markline2 = math.floor(0.75*len(datalist['labels']))\\n\\n train_features = datalist['features'][:(markline1)]\\n test_features = datalist['features'][(markline1):]\\n \\n train_labels = datalist['labels'][:(markline2)]\\n test_labels = datalist['labels'][(markline2):]\\n\\n logger.info('[*] Training started with 75% Dataset ...')\\n\\n self.knn_model.fit(train_features, train_labels)\\n\\n logger.info('[*] Testing started with 25% Dataset ...')\\n print('\\\\n/---------------Accuracy----------------/') \\n \\n accuracy = self.knn_model.score(train_features, train_labels)\\n print('Test set accuracy {:.2f} %'.format(accuracy*100))\\n\\n if accuracy < 0.40:\\n logger.warning('[-.-!] Thanks for tryin\\\\' but this machine ain\\\\'t learning.')\\n\\n return True\",\n \"def create_dummy_data():\\n data = loadmat(join(project_root, './data/lungs.mat'))['seq']\\n nx, ny, nt = data.shape\\n ny_red = 8\\n sl = ny//ny_red\\n data_t = np.transpose(data, (2, 0, 1))\\n\\n data_t[:, :, :sl*4]\\n train_slice = data_t[:, :, :sl*4]\\n validate_slice = data_t[:, :, ny//2:ny//2+ny//4]\\n test_slice = data_t[:, :, ny//2+ny//4]\\n\\n # Synthesize data by extracting patches\\n train = np.array([data_t[..., i:i+sl] for i in np.random.randint(0, sl*3, 20)])\\n validate = np.array([data_t[..., i:i+sl] for i in (sl*4, sl*5)])\\n test = np.array([data_t[..., i:i+sl] for i in (sl*6, sl*7)])\\n\\n return train, validate, test\",\n \"def splitData(data, class_label, seed, ratio):\\n\\t\\n\\trandom.seed(seed)\\n\\tsubset = data.clone()\\n\\tsize_data = subset.data.shape[0]\\n\\tn = int(np.floor(size_data * ratio)) # number of datasets in train\\n\\tindex = random.sample(range(1, size_data), n)\\n\\tsplit_list = [item for item in [0] for i in range(size_data)]\\n\\t\\n\\tfor i in index:\\n\\t\\tsplit_list[i]=1\\n\\t\\n\\treturn split_list #returns list of indeces where 0 is test and 1 is training data \",\n \"def load_train_data():\\r\\n X_train = np.load('data/train/X_train.npy')\\r\\n scaling_train = np.load('data/train/scaling_train.npy')\\r\\n ids_train = np.load('data/train/ids_train.npy')\\r\\n y_train = np.load('data/train/y_train.npy')\\r\\n\\r\\n seed = np.random.randint(1, 10e6)\\r\\n np.random.seed(seed)\\r\\n np.random.shuffle(X_train)\\r\\n np.random.seed(seed)\\r\\n np.random.shuffle(scaling_train)\\r\\n np.random.seed(seed)\\r\\n np.random.shuffle(ids_train)\\r\\n np.random.seed(seed)\\r\\n np.random.shuffle(y_train)\\r\\n\\r\\n return X_train, scaling_train, ids_train, y_train\",\n \"def train_test_split(df, random_state=42):\\n if len(df) < 3:\\n print('no bueno')\\n train, test = train_test_split(df, test_size=.2, random_state=random_state)\\n train, val = train_test_split(train, test_size=.2, random_state=random_state)\\n return train, test, val\",\n \"def read_data_sets(data_path, fake_data=False, one_hot=False,\\n validation_size=5000, source_url={},\\n augment=False,\\n percentage_train=100.,\\n unbalance=False, unbalance_dict={\\\"percentage\\\": 20, \\\"label1\\\": 0, \\\"label2\\\": 8},\\n ):\\n\\n class DataSets(object):\\n pass\\n\\n data_sets = DataSets()\\n\\n if fake_data:\\n data_sets.train = DataSet([], [], fake_data=True, one_hot=True)\\n data_sets.validation = DataSet([], [], fake_data=True, one_hot=True)\\n data_sets.test = DataSet([], [], fake_data=True, one_hot=True)\\n return data_sets\\n\\n if not source_url: # empty string check\\n if 'fashion' in data_path:\\n source_url = DEFAULT_SOURCE_URL_FASHION\\n else:\\n source_url = DEFAULT_SOURCE_URL_MNIST\\n\\n if 'fashion' in data_path or 'mnist' in data_path: # mnist or fashion\\n train_images, train_labels, val_images, val_labels, test_images, test_labels = \\\\\\n load_mnist(data_path, validation_size, source_url, one_hot)\\n reshape = True\\n else:\\n train_images, train_labels, val_images, val_labels, test_images, test_labels = \\\\\\n load_medical_data(data_path)\\n reshape = False\\n\\n # add random permutation to train & validation\\n np.random.seed(42)\\n\\n n_train = train_images.shape[0]\\n perm = np.random.permutation(n_train)\\n train_images = train_images[perm]\\n train_labels = train_labels[perm]\\n\\n n_val = val_images.shape[0]\\n perm = np.random.permutation(n_val)\\n val_images = val_images[perm]\\n val_labels = val_labels[perm]\\n\\n # For experiments with data-augmentation\\n if augment:\\n if 'fashion' in data_path: # rotations +-10 and horizontal flips\\n augmented_images, augmented_labels = augment_data(train_images, train_labels, hflip=True)\\n elif 'mnist' in data_path: # rotations +-10\\n augmented_images, augmented_labels = augment_data(train_images, train_labels, hflip=False)\\n train_images = np.concatenate([train_images, np.expand_dims(augmented_images, 3)])\\n train_labels = np.concatenate([train_labels, augmented_labels])\\n # for the medical datasets, you can use the \\\"augment\\\" argument while doing patch extraction\\n\\n # For experiments with limited amount of data\\n if percentage_train != 100.:\\n train_size = int(0.01*percentage_train*train_images.shape[0])\\n Xtrain_images, Xval_images, ytrain, yval = train_test_split(train_images, train_labels, train_size=train_size)\\n train_images = Xtrain_images\\n train_labels = ytrain\\n\\n # For experiments with class-imbalance distribution\\n if unbalance:\\n n_classes = len(np.unique(np.argmax(train_labels, 1)))\\n reduceto = 0.01*unbalance_dict['percentage']\\n label1 = unbalance_dict['label1']\\n label2 = unbalance_dict['label2']\\n\\n pick_ids = []\\n newsize = 0\\n all_classes = np.arange(0, n_classes)\\n all_classes = np.delete(all_classes, np.where(all_classes == label1)[0])\\n all_classes = np.delete(all_classes, np.where(all_classes == label2)[0])\\n\\n for lab in [label1, label2]:\\n allids = np.where(np.argmax(train_labels, 1) == lab)[0]\\n selectedids = np.random.choice(allids, int(reduceto * allids.shape[0]), replace=False)\\n pick_ids.append(selectedids)\\n newsize += len(selectedids)\\n\\n new_ids = convert_list_to_array(pick_ids, newsize)\\n\\n other_ids = []\\n othersize = 0\\n for lab in all_classes.tolist():\\n selectedids = np.where(np.argmax(train_labels, 1) == lab)[0]\\n other_ids.append(selectedids)\\n othersize += len(selectedids)\\n\\n keep_ids = convert_list_to_array(other_ids, othersize)\\n\\n # new_ids: contains the indices of the reduced (imbalance) classes\\n # keep_ids: contains the indices of the rest (keep the same class distribution)\\n resulting_ids = np.concatenate((new_ids, keep_ids))\\n np.random.shuffle(resulting_ids)\\n\\n train_images = train_images[resulting_ids, ...]\\n train_labels = train_labels[resulting_ids, ...]\\n\\n data_sets.train = DataSet(train_images, train_labels, fake_data=True, one_hot=True, reshape=reshape)\\n data_sets.validation = DataSet(val_images, val_labels, fake_data=True, one_hot=True, reshape=reshape)\\n data_sets.test = DataSet(test_images, test_labels, fake_data=True, one_hot=True, reshape=reshape)\\n\\n return data_sets\",\n \"def import_data(seed: object = 42) -> object:\\n\\n # Read input data\\n df = pd.read_csv(\\\"x_train_gr_smpl.csv\\\").astype(int)\\n\\n # label data-frame rows based on sample data\\n for x in range(10):\\n index = ~pd.read_csv(\\\"y_train_smpl_%s.csv\\\" % x, squeeze=True).astype(bool) # reversed flags (~)\\n df.loc[index, 'label'] = str(x)\\n\\n input_data_ordered = df.iloc[:, 0:2304].to_numpy()\\n output_data_ordered = df.iloc[:, 2304].to_numpy()\\n\\n # Randomise instance order (forcing the same result each time)\\n np.random.seed(seed)\\n permutation = np.random.permutation(df.shape[0])\\n\\n # Create base input and output arrays\\n input_data = input_data_ordered[permutation]\\n output_data = output_data_ordered[permutation]\\n\\n return input_data, output_data, df, input_data_ordered, output_data_ordered\",\n \"def train_test(X_train, y_train, test_size=0.2, random_state=32, stratify=None):\\n X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=test_size, random_state=random_state, stratify=stratify)\\n \\n return X_train, X_test, y_train, y_test\",\n \"def data_split(config_path: Text) -> None:\\n\\n config = yaml.safe_load(open(config_path))\\n\\n dataset = pd.read_csv(config['featurize']['features_data'])\\n train_dataset, test_dataset = train_test_split(\\n dataset, \\n test_size = config['data_split']['test_size'],\\n random_state = config['data_split']['random_state']\\n )\\n\\n train_csv_path = config['data_split']['train_path']\\n test_csv_path = config['data_split']['test_path']\\n train_dataset.to_csv(train_csv_path, index=False)\\n test_dataset.to_csv(test_csv_path, index=False)\",\n \"def train_val_test_split(data):\\n raise NotImplementedError\",\n \"def split_data(raw_data, output_pref):\\n train_data = output_pref + \\\".train\\\"\\n test_data = output_pref + \\\".test\\\"\\n random.shuffle(raw_data)\\n with open(train_data, \\\"w\\\", encoding=\\\"utf8\\\") as fw1:\\n with open(test_data, \\\"w\\\", encoding=\\\"utf8\\\") as fw2:\\n with open(train_data + \\\".raw\\\", \\\"w\\\", encoding=\\\"utf8\\\") as fw3:\\n with open(test_data + \\\".raw\\\", \\\"w\\\", encoding=\\\"utf8\\\") as fw4:\\n for idx, (line, item) in enumerate(raw_data):\\n if idx < 1000:\\n fw2.write(line + \\\"\\\\n\\\")\\n fw4.write(\\\"\\\\t\\\".join([str(i) for i in item]) + \\\"\\\\n\\\")\\n else:\\n fw1.write(line + \\\"\\\\n\\\")\\n fw3.write(\\\"\\\\t\\\".join([str(i) for i in item]) + \\\"\\\\n\\\")\",\n \"def refresh_test_dataset(self):\\n inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label = self.build_data(self.reader, self.test_items, self.option.max_path_length)\\n self.test_dataset = CodeDataset(inputs_id, inputs_starts, inputs_paths, inputs_ends, inputs_label)\",\n \"def split_test_and_train_data(self, test_size=0.3, view=0):\\n X_train, X_test, y_train, y_test = train_test_split(self.data[view], self.class_data, test_size=test_size)\\n return X_train, X_test, y_train, y_test\",\n \"def split(self, test_size=0.25, random_state=None):\\n self.train_index, self.test_index = ms.train_test_split(\\n self.data.index, test_size=test_size, random_state=random_state)\",\n \"def split_data(data, test_size):\\r\\n ntest = int(round(len(data) * (1 - test_size)))+1\\r\\n \\r\\n train, test = data[:ntest], data[ntest:]\\r\\n \\r\\n return train,test\",\n \"def load_dataset():\\n\\n train_dataset = h5py.File('datasets/train_catvnoncat.h5', \\\"r\\\")\\n train_set_x_orig = np.array(train_dataset[\\\"train_set_x\\\"][:]) # your train set features\\n train_set_y_orig = np.array(train_dataset[\\\"train_set_y\\\"][:]) # your train set labels\\n\\n test_dataset = h5py.File('datasets/test_catvnoncat.h5', \\\"r\\\")\\n test_set_x_orig = np.array(test_dataset[\\\"test_set_x\\\"][:]) # your test set features\\n test_set_y_orig = np.array(test_dataset[\\\"test_set_y\\\"][:]) # your test set labels\\n\\n classes = np.array(test_dataset[\\\"list_classes\\\"][:]) # the list of classes\\n \\n train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\\n test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\\n \\n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6528886","0.64985585","0.6490711","0.64342165","0.6429354","0.6394741","0.6342159","0.6324972","0.6307256","0.62858176","0.6265146","0.6257298","0.6205019","0.620443","0.6178737","0.6150126","0.611777","0.60921246","0.60828966","0.6066437","0.60641325","0.6062602","0.6052017","0.6048466","0.60388833","0.60363877","0.6036025","0.6022706","0.60132146","0.6010124","0.6007949","0.600368","0.59971565","0.59842634","0.59818965","0.5975935","0.5975586","0.5974247","0.59728575","0.59506327","0.59487695","0.5945407","0.594357","0.5943059","0.59427035","0.5942429","0.5940767","0.5937666","0.5937126","0.59342295","0.5934049","0.5927177","0.591105","0.5898492","0.5895472","0.58923954","0.5875478","0.5870853","0.5869927","0.5869446","0.5865473","0.5865393","0.5865221","0.5863001","0.58591807","0.5850938","0.5843976","0.5828823","0.58277386","0.582771","0.5823803","0.582334","0.58202505","0.58179647","0.58077407","0.5807287","0.5806373","0.58045286","0.58045214","0.58042854","0.58040273","0.5799598","0.57986367","0.5798504","0.5796021","0.5792348","0.5791928","0.5790073","0.578846","0.57867426","0.578275","0.57819533","0.5764724","0.5763196","0.57592964","0.5758974","0.5750545","0.5748394","0.57445556","0.5743058"],"string":"[\n \"0.6528886\",\n \"0.64985585\",\n \"0.6490711\",\n \"0.64342165\",\n \"0.6429354\",\n \"0.6394741\",\n \"0.6342159\",\n \"0.6324972\",\n \"0.6307256\",\n \"0.62858176\",\n \"0.6265146\",\n \"0.6257298\",\n \"0.6205019\",\n \"0.620443\",\n \"0.6178737\",\n \"0.6150126\",\n \"0.611777\",\n \"0.60921246\",\n \"0.60828966\",\n \"0.6066437\",\n \"0.60641325\",\n \"0.6062602\",\n \"0.6052017\",\n \"0.6048466\",\n \"0.60388833\",\n \"0.60363877\",\n \"0.6036025\",\n \"0.6022706\",\n \"0.60132146\",\n \"0.6010124\",\n \"0.6007949\",\n \"0.600368\",\n \"0.59971565\",\n \"0.59842634\",\n \"0.59818965\",\n \"0.5975935\",\n \"0.5975586\",\n \"0.5974247\",\n \"0.59728575\",\n \"0.59506327\",\n \"0.59487695\",\n \"0.5945407\",\n \"0.594357\",\n \"0.5943059\",\n \"0.59427035\",\n \"0.5942429\",\n \"0.5940767\",\n \"0.5937666\",\n \"0.5937126\",\n \"0.59342295\",\n \"0.5934049\",\n \"0.5927177\",\n \"0.591105\",\n \"0.5898492\",\n \"0.5895472\",\n \"0.58923954\",\n \"0.5875478\",\n \"0.5870853\",\n \"0.5869927\",\n \"0.5869446\",\n \"0.5865473\",\n \"0.5865393\",\n \"0.5865221\",\n \"0.5863001\",\n \"0.58591807\",\n \"0.5850938\",\n \"0.5843976\",\n \"0.5828823\",\n \"0.58277386\",\n \"0.582771\",\n \"0.5823803\",\n \"0.582334\",\n \"0.58202505\",\n \"0.58179647\",\n \"0.58077407\",\n \"0.5807287\",\n \"0.5806373\",\n \"0.58045286\",\n \"0.58045214\",\n \"0.58042854\",\n \"0.58040273\",\n \"0.5799598\",\n \"0.57986367\",\n \"0.5798504\",\n \"0.5796021\",\n \"0.5792348\",\n \"0.5791928\",\n \"0.5790073\",\n \"0.578846\",\n \"0.57867426\",\n \"0.578275\",\n \"0.57819533\",\n \"0.5764724\",\n \"0.5763196\",\n \"0.57592964\",\n \"0.5758974\",\n \"0.5750545\",\n \"0.5748394\",\n \"0.57445556\",\n \"0.5743058\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74138194"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77979,"cells":{"query":{"kind":"string","value":"Construct the Probabilistic Matrix Factorization model using pymc3. Note that the `testval` param for U and V initialize the model away from 0 using a small amount of Gaussian noise."},"document":{"kind":"string","value":"def build_pmf_model(train, alpha=2, dim=10, std=0.01):\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n # Low precision reflects uncertainty; prevents overfitting.\n # We use point estimates from the data to intialize.\n # Set to mean variance across users and items.\n alpha_u = 1 / train.var(axis=1).mean()\n alpha_v = 1 / train.var(axis=0).mean()\n\n logging.info('building the PMF model')\n n, m = train.shape\n with pm.Model() as pmf:\n U = pm.MvNormal(\n 'U', mu=0, tau=alpha_u * np.eye(dim),\n shape=(n, dim), testval=np.random.randn(n, dim) * std)\n V = pm.MvNormal(\n 'V', mu=0, tau=alpha_v * np.eye(dim),\n shape=(m, dim), testval=np.random.randn(m, dim) * std)\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones(train.shape),\n observed=train)\n\n logging.info('done building PMF model')\n return pmf"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def model_test(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in, models_fit, label):\n # Generate fake data with some \"true\" parameters\n (D_vec, Ninv) = gen_data(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in)\n Ninv_sqrt = np.matrix(linalg.sqrtm(Ninv))\n (dust_params, sync_params, cmb_params) = params_in\n (dust_amp, sync_amp, cmb_amp) = amps_in\n \n # Beam model\n beam_mat = np.identity(3*len(nu))\n\n # Set-up MCMC\n dust_guess = np.array([1.6, 20.])\n sync_guess = np.array([-3.])\n cmb_guess = np.array([])\n guess = np.concatenate((dust_guess, sync_guess, cmb_guess))\n #ndim = len(dust_guess) + len(sync_guess) + len(cmb_guess)\n \n # Run MCMC sampler on this model\n t0 = time.time()\n dust_params_out, sync_params_out, cmb_params_out, samples \\\n = mcmc(guess, nu, D_vec, Ninv, beam_mat, models_fit, label)\n print \"MCMC run in %d sec.\" % (time.time() - t0)\n \n # Estimate error on recovered CMB amplitudes\n (F_fg, F_cmb, F) = F_matrix(nu, dust_params_out, sync_params_out, cmb_params_out, models_fit)\n H = F_fg.T*Ninv*F_fg\n x_mat = np.linalg.inv(F.T*beam_mat.T*Ninv*beam_mat*F)*F.T*beam_mat.T*Ninv*D_vec # Equation A3\n \n U, Lambda, VT = np.linalg.svd(Ninv_sqrt*F_fg, full_matrices=False) # Equation A14\n \n print \"-\"*30\n print \"F_cmb.T\", F_cmb.T.shape\n print \"Ninv_sqrt\", Ninv_sqrt.shape\n print \"F_cmb\", F_cmb.shape\n print \"I\", np.identity(U.shape[0]).shape\n print \"U\", U.shape\n print \"U.T\", U.T.shape\n print \"-\"*30\n \n \n \n N_eff_inv_cmb = F_cmb.T*Ninv_sqrt*(np.matrix(np.identity(U.shape[0])) - U*U.T)*Ninv_sqrt*F_cmb # Equation A16\n N_eff_cmb = np.linalg.inv(N_eff_inv_cmb)\n cmb_noise = np.array([N_eff_cmb[0,0], N_eff_cmb[1,1], N_eff_cmb[2,2]])\n\n gls_cmb = x_mat[0:3,0]\n cmb_chisq = (np.matrix(cmb_amp).T - gls_cmb).T*N_eff_inv_cmb*(np.matrix(cmb_amp).T - gls_cmb)\n \n # Output triangle plots for dust\n if label != None:\n if (models_fit[0] == 'mbb' and models_fit[1] == 'pow'):\n if (models_in[0] == 'mbb'):\n fig = corner.corner(samples, truths=[dust_params[0], dust_params[1], sync_params[0]],\n labels=[r\"$\\beta_d$\", r\"$T_d$\",r\"$\\alpha_s$\"])\n else :\n fig = corner.corner(samples, labels=[r\"$\\beta_d$\", r\"$T_d$\",r\"$\\alpha_s$\"])\n else :\n print 'Error! Not configured for this plot!'\n exit()\n fig.savefig('triangle_' + label + '.png')\n plt.close('all')\n \n # Run multinest sampler\n #multinest(nu, D_vec, Ninv, beam_mat, ndim, models_fit, label)\n \n return gls_cmb, cmb_chisq, cmb_noise","def compute_pmf(X_inp, rank, c1, c2):\n\n X_arr = []\n u_arr = []\n v_arr = []\n d_arr = []\n\n X = deepcopy(X_inp)\n\n v_init = np.ones((np.shape(X)[1],1))\n\n for i in range(rank):\n X_arr.append(X)\n (d,u,v) = compute_factor(X, v_init, c1, c2)\n\n\n assert abs(npla.norm(u) - 1 ) < 1e-3\n assert abs(npla.norm(v) - 1 ) < 1e-3\n\n d_arr.append(d)\n u_arr.append(u)\n v_arr.append(v)\n\n toSub = np.outer(u,v)\n assert np.shape(toSub) == np.shape(X)\n X -= d * toSub\n\n return (X_arr, u_arr, v_arr, d_arr)","def build_mod_bpmf_model(train, alpha=2, dim=10, std=0.01):\n n, m = train.shape\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\n\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n # We will use separate priors for sigma and correlation matrix.\n # In order to convert the upper triangular correlation values to a\n # complete correlation matrix, we need to construct an index matrix:\n n_elem = dim * (dim - 1) / 2\n tri_index = np.zeros([dim, dim], dtype=int)\n tri_index[np.triu_indices(dim, k=1)] = np.arange(n_elem)\n tri_index[np.triu_indices(dim, k=1)[::-1]] = np.arange(n_elem)\n\n logging.info('building the BPMF model')\n with pm.Model() as bpmf:\n # Specify user feature matrix\n sigma_u = pm.Uniform('sigma_u', shape=dim)\n corr_triangle_u = pm.LKJCorr(\n 'corr_u', n=1, p=dim,\n testval=np.random.randn(n_elem) * std)\n\n corr_matrix_u = corr_triangle_u[tri_index]\n corr_matrix_u = t.fill_diagonal(corr_matrix_u, 1)\n cov_matrix_u = t.diag(sigma_u).dot(corr_matrix_u.dot(t.diag(sigma_u)))\n lambda_u = t.nlinalg.matrix_inverse(cov_matrix_u)\n\n mu_u = pm.Normal(\n 'mu_u', mu=0, tau=beta_0 * t.diag(lambda_u), shape=dim,\n testval=np.random.randn(dim) * std)\n U = pm.MvNormal(\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\n testval=np.random.randn(n, dim) * std)\n\n # Specify item feature matrix\n sigma_v = pm.Uniform('sigma_v', shape=dim)\n corr_triangle_v = pm.LKJCorr(\n 'corr_v', n=1, p=dim,\n testval=np.random.randn(n_elem) * std)\n\n corr_matrix_v = corr_triangle_v[tri_index]\n corr_matrix_v = t.fill_diagonal(corr_matrix_v, 1)\n cov_matrix_v = t.diag(sigma_v).dot(corr_matrix_v.dot(t.diag(sigma_v)))\n lambda_v = t.nlinalg.matrix_inverse(cov_matrix_v)\n\n mu_v = pm.Normal(\n 'mu_v', mu=0, tau=beta_0 * t.diag(lambda_v), shape=dim,\n testval=np.random.randn(dim) * std)\n V = pm.MvNormal(\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\n testval=np.random.randn(m, dim) * std)\n\n # Specify rating likelihood function\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\n observed=train)\n\n logging.info('done building the BPMF model')\n return bpmf","def gen_fpm_cube_from_LUT(mp, modelType):\n # Different variables for compact and full models\n if modelType.lower() == 'compact':\n # size = (Ndiel, Nmetal, mp.Nsbp)\n complexTransCube = mp.complexTransCompact\n lamVec = mp.sbp_centers\n elif modelType.lower() == 'full':\n # size = (Ndiel,Nmetal,mp.Nsbp*mp.Nwpsbp)\n complexTransCube = mp.complexTransFull\n lamVec = mp.full.lambdas\n else:\n raise ValueError('modelType must specify full or compact model.')\n Nlam = len(lamVec)\n\n t_diel_bias = mp.t_diel_bias_nm*1e-9 # bias thickness of dielectric [m]\n\n # Generate thickness profiles of each layer\n # Metal layer profile [m]\n DM8surf = falco.hlc.gen_fpm_surf_from_cube(mp.dm8, modelType)\n # Dielectric layer profile [m]\n DM9surf = t_diel_bias + falco.hlc.gen_fpm_surf_from_cube(mp.dm9,\n modelType)\n Neta, Nxi = DM8surf.shape\n\n if not (DM8surf.shape == DM9surf.shape):\n raise ValueError('FPM surf arrays for DM 8 and 9 must be same size!')\n\n # Obtain indices of nearest thickness values in the complex tran datacube.\n DM8transInd = falco.hlc.discretize_fpm_surf(DM8surf, mp.t_metal_nm_vec,\n mp.dt_metal_nm)\n DM9transInd = falco.hlc.discretize_fpm_surf(DM9surf, mp.t_diel_nm_vec,\n mp.dt_diel_nm)\n\n # Look up values\n fpmCube = np.zeros((Neta, Nxi, Nlam), dtype=complex)\n for ilam in range(Nlam):\n for ix in range(Nxi):\n for iy in range(Neta):\n ind_metal = DM8transInd[iy, ix]\n ind_diel = DM9transInd[iy, ix]\n fpmCube[iy, ix, ilam] = complexTransCube[ind_diel, ind_metal,\n ilam]\n\n return fpmCube, DM8surf, DM9surf","def build_bpmf_model(train, alpha=2, dim=10, std=0.01):\n n, m = train.shape\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\n\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n logging.info('building the BPMF model')\n with pm.Model() as bpmf:\n # Specify user feature matrix\n lambda_u = pm.Wishart(\n 'lambda_u', n=dim, V=np.eye(dim), shape=(dim, dim),\n testval=np.random.randn(dim, dim) * std)\n mu_u = pm.Normal(\n 'mu_u', mu=0, tau=beta_0 * lambda_u, shape=dim,\n testval=np.random.randn(dim) * std)\n U = pm.MvNormal(\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\n testval=np.random.randn(n, dim) * std)\n\n # Specify item feature matrix\n lambda_v = pm.Wishart(\n 'lambda_v', n=dim, V=np.eye(dim), shape=(dim, dim),\n testval=np.random.randn(dim, dim) * std)\n mu_v = pm.Normal(\n 'mu_v', mu=0, tau=beta_0 * lambda_v, shape=dim,\n testval=np.random.randn(dim) * std)\n V = pm.MvNormal(\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\n testval=np.random.randn(m, dim) * std)\n\n # Specify rating likelihood function\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\n observed=train)\n\n logging.info('done building the BPMF model')\n return bpmf","def _cp3(X, n_components, tol, max_iter, init_type, random_state=None):\n\n if len(X.shape) != 3:\n raise ValueError(\"CP3 decomposition only supports 3 dimensions!\")\n\n if init_type == \"random\":\n A, B, C = _random_init(X, n_components, random_state)\n elif init_type == \"hosvd\":\n A, B, C = _hosvd_init(X, n_components)\n grams = [np.dot(arr.T, arr) for arr in (A, B, C)]\n err = 1E10\n\n for itr in range(max_iter):\n err_old = err\n A = matricize(X, 0).dot(kr(C, B)).dot(linalg.pinv(grams[1] * grams[2]))\n if itr == 0:\n normalization = np.sqrt((A ** 2).sum(axis=0))\n else:\n normalization = A.max(axis=0)\n normalization[normalization < 1] = 1\n A /= normalization\n grams[0] = np.dot(A.T, A)\n\n B = matricize(X, 1).dot(kr(C, A)).dot(linalg.pinv(grams[0] * grams[2]))\n if itr == 0:\n normalization = np.sqrt((B ** 2).sum(axis=0))\n else:\n normalization = B.max(axis=0)\n normalization[normalization < 1] = 1\n B /= normalization\n grams[1] = np.dot(B.T, B)\n\n C = matricize(X, 2).dot(kr(B, A)).dot(linalg.pinv(grams[0] * grams[1]))\n if itr == 0:\n normalization = np.sqrt((C ** 2).sum(axis=0))\n else:\n normalization = C.max(axis=0)\n normalization[normalization < 1] = 1\n C /= normalization\n grams[2] = np.dot(C.T, C)\n\n err = linalg.norm(matricize(X, 0) - np.dot(A, kr(C, B).T)) ** 2\n thresh = np.abs(err - err_old) / err_old\n if thresh < tol:\n break\n\n return A, B, C","def emulator(pca, gp_model, params):\n # Weights prediction\n pred_weights = gp_predict(gp_model, params)\n\n # Inverse PCA (pred_weights * basis + mean)\n reconstructed = pca.inverse_transform(pred_weights)\n return reconstructed","def prob3():\n #set up the matrices\n solvers.options['show_progress'] = False\n c = np.array([4., 7., 6., 8., 8., 9.])\n\n G = np.array([[1.,1.,0.,0.,0.,0.],\n [-1.,-1.,0.,0.,0.,0.],\n [0.,0.,1.,1.,0.,0.],\n [0.,0.,-1.,-1.,0.,0.],\n [0.,0.,0.,0.,1.,1.],\n [0.,0.,0.,0.,-1.,-1.],\n [1.,0.,1.,0.,1.,0.],\n [-1.,0.,-1.,0.,-1.,0.],\n [0.,1.,0.,1.,0.,1.],\n [0.,-1.,0.,-1.,0.,-1.]])\n G = np.vstack((G, -1*np.eye(6)))\n h = np.array([7,-7,2,-2,4,-4,5,-5,8,-8,0,0,0,0,0,0],dtype=\"float\")\n\n #convert the matrices\n c = matrix(c)\n G = matrix(G)\n h = matrix(h)\n #solve the matrices\n sol = solvers.lp(c, G, h)\n return np.ravel(sol['x']), sol['primal objective']","def proba_fm(m_pred,f_pred, dataset):\n p = np.zeros(10)\n if dataset == 'cifar10':\n for i in range(10):\n if i <4:\n if i <2:\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:2]))\n else:\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[2:4]))\n else:\n if i <6:\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[4:6]))\n elif i <8:\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[6:8]))\n else:\n p[i] = (m_pred[4])*(f_pred[i]/np.sum(f_pred[8:]))\n else :\n for i in range(10):\n if i <5:\n if i <3:\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:3]))\n else:\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[3:5]))\n else:\n if i <8:\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[5:8]))\n else:\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[8:]))\n return(p)","def milp(mdp, maxV, zeroConstraints=()):\n m = Model()\n m.setParam('OutputFlag', False)\n\n # convert notation to previous implementation\n S = mdp.S\n A = mdp.A\n R = mdp.rFuncs\n psi = mdp.psi\n T = mdp.T\n alpha = mdp.alpha\n gamma = mdp.gamma\n\n # useful constants\n rLen = len(R)\n M = 10000 # a large number\n Sr = range(len(S))\n Ar = range(len(A))\n\n # decision variables\n x = m.addVars(len(S), len(A), lb=0, name='x')\n z = m.addVars(rLen, vtype=GRB.BINARY, name='z')\n y = m.addVars(rLen, name='y')\n\n # constraints on y\n for i in range(rLen):\n m.addConstr(y[i] <= sum([x[s, a] * R[i](S[s], A[a]) for s in Sr for a in Ar]) - maxV[i] + (1 - z[i]) * M)\n m.addConstr(y[i] <= z[i] * M)\n\n # constraints on x (valid occupancy)\n for sp in Sr:\n m.addConstr(sum(x[s, a] * ((s == sp) - gamma * T(S[s], A[a], S[sp])) for s in Sr for a in Ar) == alpha(S[sp]))\n\n # == constraints\n for consIdx in range(len(zeroConstraints)):\n m.addConstr(sum(x[S.index(s), A.index(a)] for s, a in zeroConstraints[consIdx]) == 0)\n # obj\n m.setObjective(sum([psi[i] * y[i] for i in xrange(rLen)]), GRB.MAXIMIZE)\n\n m.optimize()\n\n pi = {(S[s], A[a]): x[s, a].X for s in Sr for a in Ar}\n\n if m.status == GRB.Status.OPTIMAL:\n # return feasible being true and the obj value, opt pi\n # .X attribute is to retrieve the value of the variable\n return pi\n else:\n # simply return infeasible\n raise Exception('milp problem optimal solution not found' + m.status)","def build_CNNflexi_Modular(input):\n\n params_3D = input['params']\n sample_shape = input['sample_shape']\n inputA = Input(shape=sample_shape[1:])\n clf = params_3D['clf']\n\n # Model head\n for i_layers in range(0,params_3D['n_layers']):\n if i_layers == 0:\n x = conv3D_block(params_3D,inputA)\n else:\n x = conv3D_block(params_3D, x)\n\n # Classifiers\n if clf == 'dense':\n x = Flatten()(x)\n x = Dense(256, activation='relu', kernel_initializer='he_uniform',activity_regularizer=l1(params_3D['l1_den']))(x)\n x = Dense(128, activation='relu', kernel_initializer='he_uniform',activity_regularizer=l1(params_3D['l1_den']))(x)\n x = Dense(64, activation='relu', kernel_initializer='he_uniform',activity_regularizer=l1(params_3D['l1_den']))(x)\n elif clf == 'gap':\n x = GlobalAveragePooling3D()(x)\n else:\n raise('Invalid classifier choice!')\n\n\n model = Model(inputs=inputA, outputs=x)\n return model","def create_pyomo_model():\n model = po.ConcreteModel()\n model.t = pod.ContinuousSet(bounds=(0, 1))\n model.tau = po.Var()\n\n model.ca = po.Var(model.t, bounds=(0, 50)) # set of concentrations in reaction mixture\n model.dca_dt = pod.DerivativeVar(model.ca, wrt=model.t)\n\n model.beta = po.Var() # model parameters\n\n def _material_balance(m, t):\n return m.dca_dt[t] / m.tau == -m.beta * m.ca[t]\n model.material_balance = po.Constraint(model.t, rule=_material_balance)\n\n simulator = pod.Simulator(model, \"casadi\")\n\n return model, simulator","def gen_fpm_from_LUT(mp, si, wi, modelType):\n # Different variables for compact and full models\n if modelType.lower() == 'compact':\n # size = (Ndiel, Nmetal, mp.Nsbp)\n complexTransCube = mp.complexTransCompact\n # Index of the wavelength in mp.sbp_centers\n ilam = si\n elif modelType.lower() == 'full':\n # size = (Ndiel,Nmetal,mp.Nsbp*mp.Nwpsbp)\n complexTransCube = mp.complexTransFull\n # Index of the wavelength in mp.lam_array\n ilam = (si-1)*mp.Nwpsbp + wi\n else:\n raise ValueError('modelType must specify full or compact model.')\n\n t_diel_bias = mp.t_diel_bias_nm*1e-9 # bias thickness of dielectric [m]\n\n # Generate thickness profiles of each layer\n # Metal layer profile [m]\n DM8surf = falco.hlc.gen_fpm_surf_from_cube(mp.dm8, modelType)\n # Dielectric layer profile [m]\n DM9surf = t_diel_bias + falco.hlc.gen_fpm_surf_from_cube(mp.dm9, modelType)\n Nxi = DM8surf.shape[1]\n Neta = DM8surf.shape[0]\n\n if not (DM8surf.size == DM9surf.size):\n raise ValueError('FPM surf arrays for DM 8 and 9 must be same size!')\n\n # Obtain indices of nearest thickness values in the complex tran datacube.\n DM8transInd = falco.hlc.discretize_fpm_surf(DM8surf, mp.t_metal_nm_vec,\n mp.dt_metal_nm)\n DM9transInd = falco.hlc.discretize_fpm_surf(DM9surf, mp.t_diel_nm_vec,\n mp.dt_diel_nm)\n\n # Look up values\n fpm = np.zeros((Neta, Nxi), dtype=complex)\n for ix in range(Nxi):\n for iy in range(Neta):\n ind_metal = DM8transInd[iy, ix]\n ind_diel = DM9transInd[iy, ix]\n fpm[iy, ix] = complexTransCube[ind_diel, ind_metal, ilam]\n\n return fpm","def proba(c_pred,m_pred,f_pred, dataset):\n p = np.zeros(10)\n if dataset == 'cifar10':\n for i in range(10):\n if i <4:\n if i <2:\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:2]))\n elif i <4:\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[2:4]))\n if i >=4:\n if i <6:\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[4:6]))\n elif i <8:\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[6:8]))\n elif i <10:\n p[i] = c_pred[1]*(m_pred[4]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[8:10]))\n else :\n for i in range(10):\n if i <5:\n if i <3:\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:3]))\n elif i <5:\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[3:5]))\n if i >=5:\n if i <8:\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[5:8]))\n elif i <10:\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[8:]))\n return(p)","def __init__(self, n_components=5, user=\"user_0\", model=\"gaussian\"):\n self.user = user\n self.n_components = n_components\n\n if(model==\"gaussian\"):\n self.model = hmm.GaussianHMM(n_components=n_components, covariance_type=\"diag\", \\\n init_params=\"cm\", params=\"cmt\")\n elif(model==\"GMMHMM\"):\n self.model = hmm.GMMHMM(n_components=n_components, n_mix=3, covariance_type=\"diag\", \\\n init_params=\"cm\", params=\"cmt\")\n self.model.gmms_ = [sklearn.mixture.GaussianMixture()]*3\n\n self.model.startprob_ = np.concatenate(([1],np.zeros(n_components-1)))\n self.model.transmat_ = self.compute_trans_matrix( n_components )\n\n self.overall_accuracy = 0","def _initialize_fembv_varx_random(X, n_components, memory=0,\n u=None, random_state=None,\n random_Theta=True):\n rng = check_random_state(random_state)\n\n n_samples, n_features = X.shape\n max_memory = np.max(memory)\n n_terms = n_samples - max_memory\n if u is None:\n n_external = 0\n else:\n n_external = u.shape[1]\n\n Gamma = right_stochastic_matrix(\n (n_terms, n_components), random_state=rng)\n\n xb = X.mean(axis=0)\n Theta = [{'mu': xb.copy(), 'memory': memory,\n 'At': None, 'Bt': None,\n 'Sigma': np.identity(n_features)}] * n_components\n\n for k in range(n_components):\n if memory > 0:\n Theta[k]['At'] = np.empty((memory * n_features, n_features))\n for i in range(memory):\n if random_Theta:\n Theta[k]['At'][i * n_features:\n (i + 1) * n_features] = rng.uniform(\n size=(n_features, n_features))\n else:\n Theta[k]['At'][i * n_features:\n (i + 1) * n_features] = np.identity(\n n_features)\n if u is not None:\n if random_Theta:\n Theta[k]['Bt'] = rng.uniform(size=(n_external, n_features))\n else:\n Theta[k]['Bt'] = np.zeros((n_external, n_features))\n\n return Gamma, Theta","def initialize_cplex_problem(model, num_threads=1, lpmethod=0, adv=2):\n # type: (compass.models.MetabolicModel)\n\n # Create the Problem first\n # Easier to modify existing problem and re-solve\n problem = cplex.Cplex()\n problem.set_log_stream(None) # Suppress output\n problem.set_error_stream(None) # Suppress errors\n problem.set_warning_stream(None) # Suppress Warnings\n problem.set_results_stream(None) # Suppress results to output\n\n # Set Parameters for the Cplex solver\n problem.parameters.emphasis.numerical.set(True)\n problem.parameters.threads.set(num_threads)\n problem.parameters.preprocessing.reduce.set(3) #Turning on primal and dual preprocessing also enables some reoptimization features\n problem.parameters.advance.set(adv) #Will presolve advanced basis again\n problem.parameters.barrier.convergetol.set(1e-12) #default is 1e-8, minimum is 1e-12.\n problem.parameters.simplex.tolerances.optimality.set(1e-9) #default 1e-6, minimum is 1e-9\n problem.parameters.lpmethod.set(lpmethod) #default lets CPLEX choose the method\n\n # Add variables\n reactions = list(model.reactions.values())\n problem.variables.add(\n names=[x.id for x in reactions],\n ub=[x.upper_bound for x in reactions],\n lb=[x.lower_bound for x in reactions],)\n\n # Add constraints\n\n # Add stoichiometry constraints\n c_lin_expr, c_senses, c_rhs, c_names = (\n utils.get_steadystate_constraints(model))\n\n problem.linear_constraints.add(\n lin_expr=c_lin_expr,\n senses=c_senses,\n rhs=c_rhs,\n names=c_names)\n\n # Initialize the objective\n utils.reset_objective(problem)\n\n return problem","def get_estimates(model):\n from itertools import product\n from pymc3 import summary\n\n subjects = model.data['subject'].unique().astype(np.int)\n parameters = ['v', 'gamma', 's', 'tau', 't0']\n estimates = pd.DataFrame()\n MAP = extract_modes(model.trace)\n combinations = list(product(*[model.design['factor_conditions'][factor]\n for factor in model.design['factors']]))\n subject_template = pd.DataFrame({factor: [combination[f]\n for combination in combinations]\n for f, factor\n in enumerate(model.design['factors'])},\n index=np.zeros(1))\n if model.type == 'hierarchical':\n summary_table = summary(model.trace[0])\n elif model.type == 'individual':\n summary_tables = [summary(trace)\n for trace in model.trace]\n else:\n raise ValueError(\n 'Model type not understood. Make sure \"make_model\" has already been called.')\n for subject in subjects:\n subject_estimates = subject_template.copy()\n subject_estimates.loc[:, 'subject'] = np.array([subject])\n for parameter in parameters:\n subject_template[parameter] = np.nan\n subject_template[parameter + '_hpd_2.5'] = np.nan\n subject_template[parameter + '_hpd_97.5'] = np.nan\n subject_template[parameter] = np.nan\n\n dependence = model.design[parameter]['dependence']\n if dependence is None:\n # Parameter is fixed\n if model.type == 'hierarchical':\n # add participant paramaters\n subject_estimates[parameter] = MAP[0][parameter][subject][0]\n subject_estimates[parameter + '_hpd_2.5'] = summary_table.loc[parameter +\n '__{}_0'.format(subject), 'hpd_2.5']\n subject_estimates[parameter + '_hpd_97.5'] = summary_table.loc[parameter +\n '__{}_0'.format(subject), 'hpd_97.5']\n # add population parameters\n if (parameter + '_mu') in summary_table.index:\n subject_estimates[parameter +\n '_mu'] = summary_table.loc[parameter + '_mu', 'mean']\n subject_estimates[parameter +\n '_mu_hpd_2.5'] = summary_table.loc[parameter + '_mu', 'hpd_2.5']\n subject_estimates[parameter +\n '_mu_hpd_97.5'] = summary_table.loc[parameter + '_mu', 'hpd_97.5']\n\n elif model.type == 'individual':\n # add participant paramaters\n subject_estimates[parameter] = MAP[subject][parameter][0][0]\n subject_estimates[parameter +\n '_hpd_2.5'] = summary_tables[subject].loc[parameter + '__0_0', 'hpd_2.5']\n subject_estimates[parameter +\n '_hpd_97.5'] = summary_tables[subject].loc[parameter + '__0_0', 'hpd_97.5']\n else:\n # Parameter has dependence\n conditions = model.design[parameter]['conditions']\n for condition in conditions:\n if condition not in model.data.loc[model.data['subject'] == subject, dependence].values:\n subject_estimates = subject_estimates.drop(subject_estimates[subject_estimates[dependence] == condition].index,\n axis=0)\n else:\n # Check if subject is in condition\n if subject in model.design[parameter][condition]['subjects']:\n parameter_condition = parameter + '_' + condition\n if model.type == 'hierarchical':\n index = model.design[parameter][condition]['subject_mapping'][subject]\n # extract participant parameters\n estimate = MAP[parameter_condition][index]\n hpd25 = summary_table.loc[parameter_condition +\n '__{}'.format(index), 'hpd_2.5']\n hpd975 = summary_table.loc[parameter_condition +\n '__{}'.format(index), 'hpd_97.5']\n # extract population parameters\n if (parameter_condition + '_mu') in summary_table.index:\n pop_estimate = summary_table.loc[parameter_condition + '_mu', 'mean']\n pop_hpd25 = summary_table.loc[parameter_condition +\n '_mu', 'hpd_2.5']\n pop_hpd975 = summary_table.loc[parameter_condition +\n '_mu', 'hpd_97.5']\n\n elif model.type == 'individual':\n if model.design[parameter]['type'] == 'between':\n estimate = MAP[subject][parameter]\n hpd25 = summary_tables[subject].loc[parameter +\n '__0_0', 'hpd_2.5']\n hpd975 = summary_tables[subject].loc[parameter +\n '__0_0', 'hpd_97.5']\n elif model.design[parameter]['type'] == 'within':\n estimate = MAP[subject][parameter_condition]\n hpd25 = summary_tables[subject].loc[parameter_condition +\n '__0_0', 'hpd_2.5']\n hpd975 = summary_tables[subject].loc[parameter_condition +\n '__0_0', 'hpd_97.5']\n else:\n raise ValueError('Parameter dependence not understood for {}: {} ({}).'.format(\n parameter, dependence, condition))\n else:\n raise ValueError(\n 'Model type not understood. Make sure \"make_model\" has already been called.')\n # add participant parameters\n subject_estimates.loc[subject_estimates[dependence]\n == condition, parameter] = estimate\n subject_estimates.loc[subject_estimates[dependence]\n == condition, parameter + '_hpd_2.5'] = hpd25\n subject_estimates.loc[subject_estimates[dependence]\n == condition, parameter + '_hpd_97.5'] = hpd975\n # add population parameters\n if model.type == 'hierarchical':\n subject_estimates.loc[subject_estimates[dependence]\n == condition, parameter + '_mu'] = pop_estimate\n subject_estimates.loc[subject_estimates[dependence]\n == condition, parameter + '_mu_hpd_2.5'] = pop_hpd25\n subject_estimates.loc[subject_estimates[dependence] ==\n condition, parameter + '_mu_hpd_97.5'] = pop_hpd975\n\n estimates = pd.concat([estimates, subject_estimates])\n\n estimates.reset_index(inplace=True, drop=True)\n return estimates","def run_model_three():\n\n pos_map = pickle.load(open('pos_map.p', \"rb\"))\n vocab = build_vocab(training_directory)\n word2vec = get_w2vec(wordtovec)\n\n train_out_list = prep_data_lexsub(training_directory, \"mod3_train_prepped.txt\", 3, pos_map, vocab, word2vec)\n test_out_list = prep_data_lexsub(testing_directory, \"mod3_test_prepped.txt\", 3, pos_map, vocab, word2vec)\n\n posset = get_posset()\n vocab.add(\"<s>\")\n vocab.add(\"<UNK>\")\n convert_to_svm('mod3_train_prepped.txt', \"mod3_train.svm\", posset, vocab)\n convert_to_svm('mod3_test_prepped.txt', \"mod3_test.svm\", posset, vocab)\n\n p_labels, p_acc, p_vals = train_test_model(\"mod3_train.svm\", \"mod3_test.svm\")\n with open(\"modelscores.txt\", \"a\") as text_file:\n text_file.write(str(p_acc[0]))\n text_file.write(\"\\n\")\n pickle.dump(p_labels, open('mod3_p_labels.p', 'wb'))","def abcmodel_matrix(\n S0: float, P: np.ndarray, a: float, b: float, c: float\n) -> np.ndarray:\n P = P[:, np.newaxis] if len(P.shape) == 1 else P\n t = len(P) # number of timesteps\n\n # Calculate the contribution of initial storage.\n # Shape: (t, 1)\n b_vec = np.array([[c * (1 - c) ** i for i in range(t)]]).T\n\n # Calculate the contribution of Precip / previous storage.\n # Shape: (t, t)\n A = np.eye(t) * (1 - a - b)\n\n # set the off diagonal elements:\n # ac(1 - c) ** (row_ix + 1) - (col_x + 2)\n row_ix = np.tril_indices_from(A, k=-1)[0]\n col_ix = np.tril_indices_from(A, k=-1)[1]\n A[(row_ix, col_ix)] = a * c * (1 - c) ** ((row_ix + 1) - (col_ix + 2))\n\n # Calculate Simulated Discharge (Q)\n Qsim = (A @ P) + (S0 * b_vec)\n\n assert Qsim.shape == (t, 1)\n return Qsim","def cloudModel(PsPs, PcPc):\n PsPc = 1 - PsPs\n PcPs = 1 - PcPc\n \"\"\"\n transition matrix -- don't really need...\n \"\"\"\n P = np.array([[PsPs, PsPc],\n [PcPs, PcPc]])\n \"\"\"\n initial condintion\n \"\"\"\n sunny = True\n t0 = np.random.choice([False, True])\n \"\"\"\n run realisation\n \"\"\"\n clear = []\n t = t0\n for k in xrange(365):\n # predict tomorrow\n if t == sunny:\n t1 = bool(scipy.stats.bernoulli.rvs(PsPs))\n else:\n t1 = not bool(scipy.stats.bernoulli.rvs(PcPc))\n clear.append(t1)\n t = t1\n clear = np.array(clear)\n return clear","def make_male_3D_model\\\n (TABLE_info, m1_male_crvs, m2_male_left_crvs, m2_male_right_crvs,\\\n m3_male_left_crvs, m3_male_right_crvs, m4_male_crvs):\n \"\"\"\n 1 Get t_m from TABLE_info\n \"\"\"\n width = TABLE_info[0]\n t_m = TABLE_info[2]\n\n \"\"\"\n 2 Get crvs from list.\n \"\"\"\n # m1\n m1_male_upper_crv = m1_male_crvs[0]\n m1_male_middle_crv = m1_male_crvs[1]\n m1_male_lower_crv = m1_male_crvs[2]\n\n # m2\n m2_male_left_upper_crv = m2_male_left_crvs[0]\n m2_male_left_middle_crv = m2_male_left_crvs[1]\n m2_male_left_lower_crv = m2_male_left_crvs[2]\n\n m2_male_right_upper_crv = m2_male_right_crvs[0]\n m2_male_right_middle_crv = m2_male_right_crvs[1]\n m2_male_right_lower_crv = m2_male_right_crvs[2]\n\n # m3\n m3_male_left_upper_crv = m3_male_left_crvs[0]\n m3_male_left_middle_crv = m3_male_left_crvs[1]\n m3_male_left_lower_crv = m3_male_left_crvs[2]\n\n m3_male_right_upper_crv = m3_male_right_crvs[0]\n m3_male_right_middle_crv = m3_male_right_crvs[1]\n m3_male_right_lower_crv = m3_male_right_crvs[2]\n\n # m4\n m4_male_upper_crv = m4_male_crvs[0]\n m4_male_middle_crv = m4_male_crvs[1]\n m4_male_lower_crv = m4_male_crvs[2]\n\n \"\"\"\n 3 Make 3D.\n \"\"\"\n # path\n start = (0, 0, 0)\n end = (0, 0, t_m)\n path = rs.AddLine(start, end)\n\n # m1\n m1_male_upper_model = rs.ExtrudeCurve(m1_male_upper_crv, path)\n m1_male_middle_model = rs.ExtrudeCurve(m1_male_middle_crv, path)\n m1_male_lower_model = rs.ExtrudeCurve(m1_male_lower_crv, path)\n\n rs.CapPlanarHoles(m1_male_upper_model)\n rs.CapPlanarHoles(m1_male_middle_model)\n rs.CapPlanarHoles(m1_male_lower_model)\n\n # m2 left\n m2_male_left_upper_model = rs.ExtrudeCurve(m2_male_left_upper_crv, path)\n m2_male_left_middle_model = rs.ExtrudeCurve(m2_male_left_middle_crv, path)\n m2_male_left_lower_model = rs.ExtrudeCurve(m2_male_left_lower_crv, path)\n\n rs.CapPlanarHoles(m2_male_left_upper_model)\n rs.CapPlanarHoles(m2_male_left_middle_model)\n rs.CapPlanarHoles(m2_male_left_lower_model)\n\n # m2 right\n m2_male_right_upper_model = rs.ExtrudeCurve(m2_male_right_upper_crv, path)\n m2_male_right_middle_model = rs.ExtrudeCurve(m2_male_right_middle_crv, path)\n m2_male_right_lower_model = rs.ExtrudeCurve(m2_male_right_lower_crv, path)\n\n rs.CapPlanarHoles(m2_male_right_upper_model)\n rs.CapPlanarHoles(m2_male_right_middle_model)\n rs.CapPlanarHoles(m2_male_right_lower_model)\n\n # m3 left\n m3_male_left_upper_model = rs.ExtrudeCurve(m3_male_left_upper_crv, path)\n m3_male_left_middle_model = rs.ExtrudeCurve(m3_male_left_middle_crv, path)\n m3_male_left_lower_model = rs.ExtrudeCurve(m3_male_left_lower_crv, path)\n\n rs.CapPlanarHoles(m3_male_left_upper_model)\n rs.CapPlanarHoles(m3_male_left_middle_model)\n rs.CapPlanarHoles(m3_male_left_lower_model)\n\n # m3 right\n m3_male_right_upper_model = rs.ExtrudeCurve(m3_male_right_upper_crv, path)\n m3_male_right_middle_model = rs.ExtrudeCurve(m3_male_right_middle_crv, path)\n m3_male_right_lower_model = rs.ExtrudeCurve(m3_male_right_lower_crv, path)\n\n rs.CapPlanarHoles(m3_male_right_upper_model)\n rs.CapPlanarHoles(m3_male_right_middle_model)\n rs.CapPlanarHoles(m3_male_right_lower_model)\n\n # m4\n m4_male_upper_model = rs.ExtrudeCurve(m4_male_upper_crv, path)\n m4_male_middle_model = rs.ExtrudeCurve(m4_male_middle_crv, path)\n m4_male_lower_model = rs.ExtrudeCurve(m4_male_lower_crv, path)\n\n rs.CapPlanarHoles(m4_male_upper_model)\n rs.CapPlanarHoles(m4_male_middle_model)\n rs.CapPlanarHoles(m4_male_lower_model)\n\n male_upper_models =\\\n [m1_male_upper_model, m2_male_left_upper_model, m2_male_right_upper_model,\\\n m3_male_left_upper_model, m3_male_right_upper_model, m4_male_upper_model]\n\n male_middle_models =\\\n [m1_male_middle_model, m2_male_left_middle_model, m2_male_right_middle_model,\\\n m3_male_left_middle_model, m3_male_right_middle_model, m4_male_middle_model]\n\n male_lower_models =\\\n [m1_male_lower_model, m2_male_left_lower_model, m2_male_right_lower_model,\\\n m3_male_left_lower_model, m3_male_right_lower_model, m4_male_lower_model]\n\n # move objects\n trans_upper = (0, 0, 2 * t_m)\n trans_middle = (0, 0, t_m)\n rs.MoveObjects(male_upper_models, trans_upper)\n rs.MoveObjects(male_middle_models, trans_middle)\n\n\n # deploy models\n O = (0, 0, 0)\n angle = 90\n rs.RotateObjects(male_upper_models, O, angle, None, False)\n rs.RotateObjects(male_middle_models, O, angle, None, False)\n rs.RotateObjects(male_lower_models, O, angle, None, False)\n\n axis = (1, 0, 0)\n rs.RotateObjects(male_upper_models, O, angle, axis, False)\n rs.RotateObjects(male_middle_models, O, angle, axis, False)\n rs.RotateObjects(male_lower_models, O, angle, axis, False)\n\n trans = (-1.5 * width, 0, 0)\n rs.MoveObjects(male_upper_models, trans)\n rs.MoveObjects(male_middle_models, trans)\n rs.MoveObjects(male_lower_models, trans)\n\n rs.DeleteObject(path)\n\n male_models = [male_upper_models, male_middle_models, male_lower_models]","def run_pycma(self):\n\n self.mug_pipeline.set_folder_names(self.folder_name)\n self.mug_pipeline.set_optimizer_type(OptimizerType.PYCMA)\n\n self.mug_initial_poses = []\n\n for i in range(self.num_mugs):\n self.mug_initial_poses += \\\n RollPitchYaw(np.random.uniform(0.0, 2.0*np.pi, size=3)).ToQuaternion().wxyz().tolist() + \\\n [np.random.uniform(-0.1, 0.1), np.random.uniform(-0.1, 0.1), np.random.uniform(0.1, 0.2)]\n\n print(self.mug_initial_poses, flush=True)\n\n iter_num = 0\n\n start_time = time.time()\n elapsed_time = 0\n\n manager = Manager()\n self.all_probabilities = manager.list()\n all_probabilities_lock = manager.Lock()\n\n self.total_iterations = manager.Value('d', 0)\n self.num_counterexamples = manager.Value('d', 0)\n\n self.model_number = manager.Value('d', 0)\n model_number_lock = manager.Lock()\n\n counter_lock = manager.Lock()\n\n file_q = manager.Queue()\n\n filename = '{}/results.csv'.format(self.folder_name)\n watcher = Process(target=self.listener, args=(file_q, filename))\n watcher.start()\n\n # TODO: share GPU for inference using model.share_memory()\n\n es = cma.CMAEvolutionStrategy(self.mug_initial_poses, 1.0/3.0,\n {'bounds': [-1.0, 1.0], 'verb_disp': 1, 'popsize': self.num_processes})\n\n while not es.stop():\n try:\n ep = EvalParallel3(self.run_inference, number_of_processes=self.num_processes)\n lst = range(iter_num, iter_num + self.num_processes)\n X = es.ask()\n elapsed_time = time.time() - start_time\n jobs = ep(X, lst=lst, args=(self.mug_pipeline, self.all_probabilities,\n self.total_iterations, self.num_counterexamples,\n self.model_number, model_number_lock, counter_lock,\n all_probabilities_lock, file_q))\n except FoundCounterexample:\n print('FOUND COUNTEREXAMPLE EXCEPTION', flush=True)\n self.mug_initial_poses = []\n\n for i in range(self.num_mugs):\n self.mug_initial_poses += \\\n RollPitchYaw(np.random.uniform(0., 2.*np.pi, size=3)).ToQuaternion().wxyz().tolist() + \\\n [np.random.uniform(-0.1, 0.1), np.random.uniform(-0.1, 0.1), np.random.uniform(0.1, 0.2)]\n\n es = cma.CMAEvolutionStrategy(self.mug_initial_poses, 1.0/3.0,\n {'bounds': [-1.0, 1.0], 'verb_disp': 1, 'popsize': self.num_processes})\n # except torch.multiprocessing.context.TimeoutError:\n # print('timed out!', flush=True)\n # break\n except FoundMaxCounterexamples:\n print('found {} counterexamples!'.format(self.max_counterexamples), flush=True)\n break\n except:\n print(\"Unhandled unnamed exception in pycma\", flush=True)\n raise\n\n iter_num += self.num_processes\n torch.cuda.empty_cache()\n print('calling ep.terminate()', flush=True)\n ep.terminate()\n\n elapsed_time = time.time() - start_time\n print('ran for {} minutes! total number of iterations is {}, with {} sec/image'.format(\n elapsed_time/60.0, self.total_iterations.value, elapsed_time/self.total_iterations.value), flush=True)\n file_q.put('kill')\n print('probabilities:', self.all_probabilities, flush=True)\n es.result_pretty()\n\n sys.stdout.flush()","def proba_middle(c_pred,m_pred,dataset):\n if dataset =='cifar10':\n p = np.zeros(5)\n for i in range(5):\n if i in [0,1]:\n p[i] = c_pred[0]*(m_pred[i]/(m_pred[0]+m_pred[1]))\n if i in [2,3,4]:\n p[i] = c_pred[1]*(m_pred[i]/(m_pred[2]+m_pred[3]++m_pred[4]))\n else :\n p = np.zeros(4)\n for i in range(4):\n if i in [0,1]:\n p[i] = c_pred[0]*(m_pred[i]/(m_pred[0]+m_pred[1]))\n if i in [2,3]:\n p[i] = c_pred[1]*(m_pred[i]/(m_pred[2]+m_pred[3]))\n return(p)","def U_MP9301(ps, **kw) -> MAG:\n g, i = kw.get(\"g\", \"g\"), kw.get(\"i\", \"i\")\n gmi = kw.get(\"gmi\", \"g-i\")\n\n c = data.loc[\"U_MP9301\"]\n\n if gmi in ps.colnames:\n gmi = ps[gmi]\n else:\n gmi = ps[g] - ps[i]\n\n ind = (c[\"lb\"] < gmi) & (gmi < c[\"ub\"])\n if not all(ind):\n warnings.warn(\"MCg1.U: not all .3 mag < (g-i)_ps < 1.5 mag\")\n\n g_ps = ps[g]\n\n u_cfht = (\n g_ps\n + c[\"c0\"]\n + (c[\"c1\"] * gmi)\n + (c[\"c2\"] * gmi ** 2)\n + (c[\"c3\"] * gmi ** 3)\n )\n\n return u_cfht","def prob3():\n#raise NotImplementedError(\"Problem 3 Incomplete\")\n n = np.linspace(5000,500000,100)\n h = lambda x : x > 10\n MC_estimates = []\n for N in xrange(5000,505000,5000):\n X = np.random.gamma(9,scale=0.5,size=N)\n MC = 1./N*np.sum(h(X))\n MC_estimates.append(MC)\n MC_estimates = np.array(MC_estimates)\n MC_est = prob2()\n exact = np.array([1-stats.gamma(a=9,scale=0.5).cdf(10)]*n.shape[0])\n print n.shape,MC_estimates.shape\n plt.plot(n,abs(MC_estimates-exact),'r-',label=\"Monte Carlo\")\n plt.plot(n,abs(MC_est-exact),'b-',label=\"Importance\")\n plt.title(\"Error or Approx.\")\n plt.legend(loc=1)\n plt.show()","def test(model_params, dataset_test, testing_params, log_directory, device, cuda_available=True,\n metric_fns=None):\n # DATA LOADER\n test_loader = DataLoader(dataset_test, batch_size=testing_params[\"batch_size\"],\n shuffle=False, pin_memory=True,\n collate_fn=imed_loader_utils.imed_collate,\n num_workers=0)\n\n # LOAD TRAIN MODEL\n fname_model = os.path.join(log_directory, \"best_model.pt\")\n print('\\nLoading model: {}'.format(fname_model))\n model = torch.load(fname_model, map_location=device)\n if cuda_available:\n model.cuda()\n model.eval()\n\n # CREATE OUTPUT FOLDER\n path_3Dpred = os.path.join(log_directory, 'pred_masks')\n if not os.path.isdir(path_3Dpred):\n os.makedirs(path_3Dpred)\n\n # METRIC MANAGER\n metric_mgr = imed_metrics.MetricManager(metric_fns)\n\n # UNCERTAINTY SETTINGS\n if (testing_params['uncertainty']['epistemic'] or testing_params['uncertainty']['aleatoric']) and \\\n testing_params['uncertainty']['n_it'] > 0:\n n_monteCarlo = testing_params['uncertainty']['n_it']\n testing_params['uncertainty']['applied'] = True\n print('\\nComputing model uncertainty over {} iterations.'.format(n_monteCarlo))\n else:\n testing_params['uncertainty']['applied'] = False\n n_monteCarlo = 1\n\n for i_monteCarlo in range(n_monteCarlo):\n preds_npy, gt_npy = run_inference(test_loader, model, model_params, testing_params, path_3Dpred,\n cuda_available, i_monteCarlo)\n metric_mgr(preds_npy, gt_npy)\n\n # COMPUTE UNCERTAINTY MAPS\n if n_monteCarlo > 1:\n imed_utils.run_uncertainty(ifolder=path_3Dpred)\n\n metrics_dict = metric_mgr.get_results()\n metric_mgr.reset()\n print(metrics_dict)\n return metrics_dict","def calc_zchi2_batch(spectra, tdata, weights, flux, wflux, nz, nbasis, solve_matrices_algorithm=\"PCA\", use_gpu=False, fullprecision=True):\n zchi2 = np.zeros(nz)\n if (weights.sum() == 0):\n zchi2[:] = 9e99\n zcoeff = np.zeros((nz, nbasis))\n return (zchi2, zcoeff)\n if (use_gpu):\n global cp\n import cupy as cp\n #On the GPU, all operations are batch operations for all templates\n #in parallel.\n\n #1) batch_dot_product_sparse will compute dot products of all\n #spectra with all templates in batch and return a 3D array of\n #size (nz x ncols x nbasis).\n Tbs = batch_dot_product_sparse(spectra, tdata, nz, use_gpu)\n if (cp_memcheck):\n #Free memory on consumer grade GPUs with low resources\n mpool = cp.get_default_memory_pool()\n mpool.free_all_blocks()\n\n #2) On the GPU, M and y are computed for all templates at once\n #CUPY swapaxes is the equivalent of the transpose in CPU mode\n #and the @ matrix multiplication operator performs a dot\n #product for each template.\n\n ###!!! NOTE - there are 3 different options for calculating the\n ### M and y arrays -\n ### A) Straight CUPY, which works well on perlmutter with a\n ### runtime of 6.2s on 1 GPU and 2.0s on 4 GPUs, but is\n ### unusably slow on Volta generation GPUs (16.8s for only\n ### 10 targets on a 1660 Super).\n ### B) calc_M_y_batch, the custom CUDA kernel, which is the\n ### fastest at 2.9s on 1 GPU and 0.7s on 4 GPUs (and 0.7s\n ### for 10 targets on a 1660 Super) but is the most difficult\n ### from a maintenance perspective\n ### C) Use the calc_batch_dot_product_3d3d_gpu kernel to offload\n ### only the matrix multiplication for M (and transpose of\n ### Tbs) but use CUPY for everything else. This strikes a\n ### middle ground that is very maintainable but removes the\n ### bottleneck of the CUPY Volta issue. 5.7s on 1 GPU and\n ### 1.8s on 4 GPUs on Perlmutter; 1.6s for 10 targets on\n ### 1660 Super.\n ###!!! NOTE - uncomment the 2 lines below to run (A)\n #all_M = Tbs.swapaxes(-2, -1) @ (weights[None, :, None] * Tbs)\n #all_y = (Tbs.swapaxes(-2, -1) @ wflux)\n ###!!! NOTE - uncomment the below line to run (B)\n #(all_M, all_y) = calc_M_y_batch(Tbs, weights, wflux, nz, nbasis)\n ###!!! NOTE - uncomment the 2 lines below to run (C)\n all_M = calc_batch_dot_product_3d3d_gpu(Tbs, (weights[None, :, None] * Tbs), transpose_a=True, fullprecision=fullprecision)\n all_y = (Tbs.swapaxes(-2, -1) @ wflux)\n ###!!! NOTE - uncomment the 2 lines below to run an alternative\n ### version of (C) that does the transpose on the CPU - this seems\n ### to needlessly waste time though\n #all_M = calc_batch_dot_product_3d3d_gpu(cp.ascontiguousarray(Tbs.swapaxes(-2, -1)), (weights[None, :, None] * Tbs))\n #all_y = (Tbs.swapaxes(-2, -1) @ wflux)\n\n #3) Use new helper method solve_matrices to use appropriate method\n #for this template to solve for zcoeff in batch for all_M and all_y.\n #There is no Error thrown by cupy's version of linalg.solve so just\n #need to catch NotImplementedError.\n try:\n zcoeff = solve_matrices(all_M, all_y, solve_algorithm=solve_matrices_algorithm, use_gpu=True)\n except NotImplementedError:\n zchi2[:] = 9e99\n zcoeff = np.zeros((nz, nbasis))\n return (zchi2, zcoeff)\n\n #4) calc_batch_dot_product_3d2d will compute the dot product\n #of Tbs and zcoeff for all templates in parallel.\n #It is the same as model[i,:,:] = Tbs[i,:,:].dot(zcoeff[i,:])\n model = calc_batch_dot_product_3d2d(Tbs, zcoeff, use_gpu)\n\n #5) On the GPU, (flux-model)*(flux-model) is faster than\n #(flux-model)**2. The @ matrix multiplication operator performs\n #a dot product for each template. get() copies the data back\n #from the GPU to the numpy array allocated for zchi2.\n zchi2[:] = (((flux - model)*(flux-model)) @ weights).get()\n #Copy data from GPU to numpy arrays\n zcoeff = zcoeff.get()\n\n if (cp_memcheck):\n #Free memory on consumer grade GPUs with low resources\n del Tbs\n del all_M\n del all_y\n del model\n mpool = cp.get_default_memory_pool()\n mpool.free_all_blocks()\n else:\n zcoeff = np.zeros((nz, nbasis))\n #On the CPU, the templates are looped over and all operations\n #are performed on one template at a time.\n\n for i in range(nz):\n #1) dot_product_sparse_one will compute dot products of all\n #spectra with ONE template and return a 2D array of size\n #(ncols x nbasis)\n Tb = dot_product_sparse_one(spectra, tdata, i)\n\n #2) On the CPU, M and y are computed for each template\n M = Tb.T.dot(np.multiply(weights[:,None], Tb))\n y = Tb.T.dot(wflux)\n\n #3) Use new helper method solve_matrices to use appropriate method\n #for this template to solve for zcoeff for each M, y.\n #Catch LinAlgError and NotImplementedError\n try:\n zcoeff[i,:] = solve_matrices(M, y, solve_algorithm=solve_matrices_algorithm, use_gpu=False)\n except np.linalg.LinAlgError:\n zchi2[i] = 9e99\n continue\n except NotImplementedError:\n zchi2[i] = 9e99\n continue\n\n #4) Calculate dot products individually for each template\n model = Tb.dot(zcoeff[i,:])\n\n #5) Calculate this zchi2 element individually for each template\n zchi2[i] = np.dot( (flux - model)**2, weights )\n return (zchi2, zcoeff)","def jointUncertaintyMilp(mdp, oldPi, oldZC, unknownFeatStates, costOfQuery):\n m = Model()\n m.setParam('OutputFlag', False)\n\n # convert notation to previous implementation\n S = mdp.S\n A = mdp.A\n R = mdp.rFuncs\n psi = mdp.psi\n T = mdp.T\n alpha = mdp.alpha\n gamma = mdp.gamma\n\n # useful constants\n rLen = len(R)\n M = 10000 # a large number\n Sr = range(len(S))\n Ar = range(len(A))\n\n # decision variables\n x = m.addVars(len(S), len(A), lb=0, name='x')\n y = m.addVars(rLen, name='y')\n # y prime, a helper variable\n y0 = m.addVars(rLen, name='y0', lb=0)\n\n # oldPi is a mapping from state, action (in S x A) to occupancy\n # to be consistent with x, convert it to a mapping from (s, a) where s in Sr, a in Ar\n oldX = {(s, a): oldPi[S[s], A[a]] for s in Sr for a in Ar}\n\n # integer variables\n zR = m.addVars(rLen, vtype=GRB.BINARY, name='zR')\n zC = m.addVars(len(unknownFeatStates), vtype=GRB.BINARY, name='zC')\n # zCNew indicates the newly changed features by x. note that it does not need to be constrained as integers\n zCNew = m.addVars(len(unknownFeatStates), lb=0, name='zCNew')\n\n zSafe = m.addVar(vtype=GRB.BINARY, name='zSafe')\n\n V = lambda x_local, r: sum([x_local[s, a] * r(S[s], A[a]) for s in Sr for a in Ar])\n\n # (a) flow conservation constraint\n for sp in Sr:\n m.addConstr(sum(x[s, a] * ((s == sp) - gamma * T(S[s], A[a], S[sp])) for s in Sr for a in Ar) == alpha(S[sp]))\n\n # (b) is encoded in the transition function\n\n for consIdx in range(len(unknownFeatStates)):\n # (c) unknown features can be changed\n m.addConstr(M * zC[consIdx] >= sum(x[S.index(s), A.index(a)] for s in unknownFeatStates[consIdx] for a in A))\n # (d) constrain z^{new}_\\phi, note that lb of zCNew is 0\n m.addConstr(zCNew[consIdx] >= zC[consIdx] - oldZC[consIdx])\n\n # (e) constraints on y^0_r\n m.addConstr(sum(zC[idx] for idx in range(len(oldZC)) if oldZC[idx] == 1) <= sum(oldZC) - 1 + zSafe * M)\n for i in range(rLen):\n m.addConstr(y0[i] >= V(oldX, R[i]) - (1 - zSafe) * M)\n\n # (f) constraints on y_r\n for i in range(rLen):\n m.addConstr(y[i] <= V(x, R[i]) - y0[i] + (1 - zR[i]) * M)\n m.addConstr(y[i] <= 0 + zR[i] * M)\n\n # obj\n m.setObjective(sum([psi[i] * y[i] for i in xrange(rLen)])\n - sum(zC[idx] * costOfQuery for idx in range(len(unknownFeatStates))),\n GRB.MAXIMIZE)\n\n m.optimize()\n\n pi = {(S[s], A[a]): x[s, a].X for s in Sr for a in Ar}\n\n if config.VERBOSE:\n # print decision variables other than pi for debugging\n print 'oldZC', oldZC\n print 'zC', [zC[consIdx].X for consIdx in range(len(unknownFeatStates))]\n print 'y0 values', [y0[rIdx].X for rIdx in range(rLen)]\n print 'y values', [y[rIdx].X for rIdx in range(rLen)]\n\n if m.status == GRB.Status.OPTIMAL:\n # return feasible being true and the obj value, opt pi\n # .X attribute is to retrieve the value of the variable\n return pi\n else:\n # simply return infeasible\n raise Exception('milp problem optimal solution not found' + m.status)","def train(self, prob: SvmUProblem):\n # Define variables\n self.prob = prob\n x = self.prob.X\n y = self.prob.Y\n\n # Define the inputs to CVXOPT - See Appendix G.6\n P_a = prob.yi_yj * prob.xi_xj\n P_b = prob.gamma * prob.xstari_xstarj\n P_c = prob.sigma * prob.yi_yj * prob.xstarstari_xstarstarj\n\n P_row_0 = np.hstack((P_a + P_b, P_a))\n P_row_0 = np.hstack((P_row_0, P_b))\n P_row_0 = np.hstack((P_row_0, np.zeros((prob.num, prob.num))))\n P_row_1 = np.hstack((P_a, P_a + P_c))\n P_row_1 = np.hstack((P_row_1, np.zeros((prob.num, prob.num))))\n P_row_1 = np.hstack((P_row_1, P_c))\n P_row_2 = np.hstack((P_b, np.zeros((prob.num, prob.num))))\n P_row_2 = np.hstack((P_row_2, P_b))\n P_row_2 = np.hstack((P_row_2, np.zeros((prob.num, prob.num))))\n P_row_3 = np.hstack((np.zeros((prob.num, prob.num)), P_c))\n P_row_3 = np.hstack((P_row_3, np.zeros((prob.num, prob.num))))\n P_row_3 = np.hstack((P_row_3, P_c))\n\n P = np.vstack((P_row_0, P_row_1))\n P = np.vstack((P, P_row_2))\n P = np.vstack((P, P_row_3))\n\n q = np.hstack((np.repeat(-1, prob.num), np.repeat(-1, prob.num)))\n q = np.hstack((q, np.zeros(prob.num)))\n q = np.hstack((q, np.zeros(prob.num)))\n\n positive_eye = np.eye(prob.num, dtype='d')\n negative_eye = -np.eye(prob.num, dtype='d')\n zeros = np.zeros((prob.num, prob.num))\n\n # g1 = -a <= 0\n g1 = np.hstack((negative_eye, zeros))\n g1 = np.hstack((g1, zeros))\n g1 = np.hstack((g1, zeros))\n\n # g2 = -n <= 0\n g2 = np.hstack((zeros, negative_eye))\n g2 = np.hstack((g2, zeros))\n g2 = np.hstack((g2, zeros))\n\n # g3 = -d <= C\n g3 = np.hstack((zeros, zeros))\n g3 = np.hstack((g3, negative_eye))\n g3 = np.hstack((g3, zeros))\n\n # g4 = n + e <= Del C\n g4 = np.hstack((zeros, positive_eye))\n g4 = np.hstack((g4, zeros))\n g4 = np.hstack((g4, positive_eye))\n\n # g5 = -e <= C\n g5 = np.hstack((zeros, zeros))\n g5 = np.hstack((g5, zeros))\n g5 = np.hstack((g5, negative_eye))\n\n G = np.vstack((g1, g2))\n G = np.vstack((G, g3))\n G = np.vstack((G, g4))\n G = np.vstack((G, g5))\n\n h1 = np.zeros((prob.num, 1))\n h2 = np.zeros((prob.num, 1))\n h3 = np.repeat(prob.C, prob.num).reshape(-1, 1)\n h4 = np.repeat((prob.delta * prob.C), prob.num).reshape(-1, 1)\n h5 = np.repeat(prob.C, prob.num).reshape(-1, 1)\n h = np.vstack((h1, h2))\n h = np.vstack((h, h3))\n h = np.vstack((h, h4))\n h = np.vstack((h, h5))\n\n A1 = np.hstack((prob.Y, -prob.Y))\n A1 = np.hstack((A1, np.zeros(2 * prob.num)))\n A2 = np.hstack((-np.ones(prob.num), np.zeros(prob.num)))\n A2 = np.hstack((A2, -np.ones(prob.num)))\n A2 = np.hstack((A2, np.zeros(prob.num)))\n A3 = np.hstack((np.zeros(prob.num), -prob.Y))\n A3 = np.hstack((A3, np.zeros(prob.num)))\n A3 = np.hstack((A3, -prob.Y))\n\n A = np.vstack((A1, A2))\n A = np.vstack((A, A3))\n\n b = np.zeros(3)\n b = b.reshape(-1, 1)\n\n P = matrix(P, tc='d')\n q = matrix(q, tc='d')\n G = matrix(G, tc='d')\n h = matrix(h, tc='d')\n A = matrix(A, tc='d')\n b = matrix(b, tc='d')\n\n # Solve optimization problem using CVXOPT\n solvers.options['show_progress'] = False\n sol = solvers.qp(P, q, G, h, A, b)\n alphas_etas_deltas_epsilons = np.array(sol['x'])\n self.alphas = alphas_etas_deltas_epsilons[:prob.num]\n self.etas = alphas_etas_deltas_epsilons[prob.num:(2 * prob.num)]\n self.deltas = alphas_etas_deltas_epsilons[(2 * prob.num):(3 * prob.num)]\n self.epsilons = alphas_etas_deltas_epsilons[-prob.num:]\n\n # Populate Classifier object to be returned\n clf = Classifier()\n clf.b = self.get_b\n clf.alphas = np.asarray(self.alphas + self.etas)\n clf.xs = x\n clf.ys = y\n clf.kern = prob.xkernel\n return clf","def maximize_loglik_fcmnl_MPEC(model_params: Union[CupidParams, CupidParamsCSHeteroxy, CupidParamsFcmnl],\n x_init: np.ndarray,\n lower: Optional[np.ndarray] = None,\n upper: Optional[np.ndarray] = None,\n checkgrad: Optional[bool] = False,\n verbose: Optional[bool] = False,\n fixed_vars: Optional[List[int]] = None,\n fixed_vals: Optional[List[float]] = None,\n options: Optional[Dict] = {'iprint': 1}) -> Tuple[float, np.ndarray, int]:\n n_paramsU = x_init.size\n bases_surplus = model_params.bases_surplus\n ncat_men, ncat_women, n_bases = bases_surplus.shape\n n_pars_b_men, n_pars_b_women = model_params.n_pars_b_men, model_params.n_pars_b_women\n n_pars_b = n_pars_b_men + n_pars_b_women\n n_thetas = n_pars_b + n_bases\n\n try:\n kc = KN_new()\n except:\n bs_error_abort(\"Failed to find a valid Knitro license.\")\n\n KN_add_vars(kc, n_paramsU)\n\n # bounds, if any\n if lower is None:\n # not necessary since infinite\n KN_set_var_lobnds(kc, xLoBnds=np.full(n_paramsU, -KN_INFINITY))\n else:\n KN_set_var_lobnds(kc, xLoBnds=lower)\n if upper is None:\n KN_set_var_upbnds(kc, xUpBnds=np.full(n_paramsU, KN_INFINITY))\n else:\n KN_set_var_upbnds(kc, xUpBnds=upper)\n\n n_prod_categories = ncat_men * ncat_women\n # Add the constraints and set the rhs and coefficients\n n_cons = n_prod_categories\n KN_add_cons(kc, n_cons)\n KN_set_con_eqbnds(kc, cEqBnds=[0.0] * n_cons)\n\n # Define an initial point. If not set, Knitro will generate one.\n KN_set_var_primal_init_values(kc, xInitVals=x_init)\n\n if fixed_vars is not None:\n assert fixed_vals is not None\n KN_set_var_fxbnds(kc, fixed_vars, fixed_vals)\n\n cb = KN_add_eval_callback(kc, evalObj=True,\n indexCons=np.arange(n_prod_categories),\n funcCallback=log_likelihood_fcmnl_MPEC)\n\n KN_set_cb_user_params(kc, cb, model_params)\n\n # c(x,y) has derivatives in thetas, U(x,t), U(z,y)\n # dc(x,y)/dU(x,y) may only appear once\n n_args_jac = n_thetas + ncat_men + ncat_women - 1\n n_jac = n_cons * n_args_jac\n cbjacIndexCons = np.repeat(np.arange(n_prod_categories), n_args_jac)\n cbjacIndexVars = np.zeros(n_prod_categories * n_args_jac, int)\n i = 0\n for iman in range(ncat_men):\n for iwoman in range(ncat_women):\n # derivatives in thetas\n cbjacIndexVars[i:(i+n_thetas)] = np.arange(n_thetas)\n # derivatives in [iman, jwoman]\n cbjacIndexVars[(i + n_thetas):(i+n_thetas+ncat_women)] = \\\n n_thetas + iman*ncat_women + np.arange(ncat_women)\n # derivatives in [jman, iwoman] except [iman, iwoman]\n list_men = list(range(ncat_men))\n del list_men[iman]\n cbjacIndexVars[(i + n_thetas + ncat_women):(i+n_args_jac)] = \\\n n_thetas + iwoman + ncat_women*np.array(list_men, int)\n i += n_args_jac\n\n print(cbjacIndexCons.shape)\n print(cbjacIndexVars.shape)\n\n KN_set_cb_grad(kc, cb, objGradIndexVars=KN_DENSE,\n jacIndexCons=cbjacIndexCons, jacIndexVars=cbjacIndexVars,\n gradCallback=grad_log_likelihood_fcmnl_MPEC)\n\n KN_set_int_param(kc, KN_PARAM_OUTLEV, KN_OUTLEV_ALL)\n\n if checkgrad:\n # Perform a derivative check.\n KN_set_int_param(kc, KN_PARAM_DERIVCHECK, KN_DERIVCHECK_ALL)\n\n # Solve the problem.\n nStatus = KN_solve(kc)\n\n loglik_val, estimates = print_optimization_results_MPEC(kc)\n\n print_stars()\n print(f\" Value of log-likelihood: {loglik_val: > 8.3f}\\n\")\n print()\n\n return loglik_val, np.array(estimates), nStatus","def make_female_3D_model\\\n (TABLE_info, m1_female_crvs, m2_female_left_crvs, m2_female_right_crvs,\\\n m3_female_left_crvs, m3_female_right_crvs, m4_female_crvs):\n \"\"\"\n 1 Get t_m from TABLE_info\n \"\"\"\n width = TABLE_info[0]\n height = TABLE_info[1]\n t_m = TABLE_info[2]\n\n \"\"\"\n 2 Get crvs from list.\n \"\"\"\n # m1\n m1_female_upper_crv = m1_female_crvs[0]\n m1_female_middle_crv = m1_female_crvs[1]\n m1_female_lower_crv = m1_female_crvs[2]\n\n # m2\n m2_female_left_upper_crv = m2_female_left_crvs[0]\n m2_female_left_middle_crv = m2_female_left_crvs[1]\n m2_female_left_lower_crv = m2_female_left_crvs[2]\n\n m2_female_right_upper_crv = m2_female_right_crvs[0]\n m2_female_right_middle_crv = m2_female_right_crvs[1]\n m2_female_right_lower_crv = m2_female_right_crvs[2]\n\n # m3\n m3_female_left_upper_crv = m3_female_left_crvs[0]\n m3_female_left_middle_crv = m3_female_left_crvs[1]\n m3_female_left_lower_crv = m3_female_left_crvs[2]\n\n m3_female_right_upper_crv = m3_female_right_crvs[0]\n m3_female_right_middle_crv = m3_female_right_crvs[1]\n m3_female_right_lower_crv = m3_female_right_crvs[2]\n\n # m4\n m4_female_upper_crv = m4_female_crvs[0]\n m4_female_middle_crv = m4_female_crvs[1]\n m4_female_lower_crv = m4_female_crvs[2]\n\n \"\"\"\n 3 Make 3D.\n \"\"\"\n # path\n start = (0, 0, 0)\n end = (0, 0, t_m)\n path = rs.AddLine(start, end)\n\n # m1\n m1_female_upper_model = rs.ExtrudeCurve(m1_female_upper_crv, path)\n m1_female_middle_model = rs.ExtrudeCurve(m1_female_middle_crv, path)\n m1_female_lower_model = rs.ExtrudeCurve(m1_female_lower_crv, path)\n\n rs.CapPlanarHoles(m1_female_upper_model)\n rs.CapPlanarHoles(m1_female_middle_model)\n rs.CapPlanarHoles(m1_female_lower_model)\n\n # m2 left\n m2_female_left_upper_model = rs.ExtrudeCurve(m2_female_left_upper_crv, path)\n m2_female_left_middle_model = rs.ExtrudeCurve(m2_female_left_middle_crv, path)\n m2_female_left_lower_model = rs.ExtrudeCurve(m2_female_left_lower_crv, path)\n\n rs.CapPlanarHoles(m2_female_left_upper_model)\n rs.CapPlanarHoles(m2_female_left_middle_model)\n rs.CapPlanarHoles(m2_female_left_lower_model)\n\n # m2 right\n m2_female_right_upper_model = rs.ExtrudeCurve(m2_female_right_upper_crv, path)\n m2_female_right_middle_model = rs.ExtrudeCurve(m2_female_right_middle_crv, path)\n m2_female_right_lower_model = rs.ExtrudeCurve(m2_female_right_lower_crv, path)\n\n rs.CapPlanarHoles(m2_female_right_upper_model)\n rs.CapPlanarHoles(m2_female_right_middle_model)\n rs.CapPlanarHoles(m2_female_right_lower_model)\n\n # m3 left\n m3_female_left_upper_model = rs.ExtrudeCurve(m3_female_left_upper_crv, path)\n m3_female_left_middle_model = rs.ExtrudeCurve(m3_female_left_middle_crv, path)\n m3_female_left_lower_model = rs.ExtrudeCurve(m3_female_left_lower_crv, path)\n\n rs.CapPlanarHoles(m3_female_left_upper_model)\n rs.CapPlanarHoles(m3_female_left_middle_model)\n rs.CapPlanarHoles(m3_female_left_lower_model)\n\n # m3 right\n m3_female_right_upper_model = rs.ExtrudeCurve(m3_female_right_upper_crv, path)\n m3_female_right_middle_model = rs.ExtrudeCurve(m3_female_right_middle_crv, path)\n m3_female_right_lower_model = rs.ExtrudeCurve(m3_female_right_lower_crv, path)\n\n rs.CapPlanarHoles(m3_female_right_upper_model)\n rs.CapPlanarHoles(m3_female_right_middle_model)\n rs.CapPlanarHoles(m3_female_right_lower_model)\n\n # m4\n m4_female_upper_model = rs.ExtrudeCurve(m4_female_upper_crv, path)\n m4_female_middle_model = rs.ExtrudeCurve(m4_female_middle_crv, path)\n m4_female_lower_model = rs.ExtrudeCurve(m4_female_lower_crv, path)\n\n rs.CapPlanarHoles(m4_female_upper_model)\n rs.CapPlanarHoles(m4_female_middle_model)\n rs.CapPlanarHoles(m4_female_lower_model)\n\n female_upper_models =\\\n [m1_female_upper_model, m2_female_left_upper_model, m2_female_right_upper_model,\\\n m3_female_left_upper_model, m3_female_right_upper_model, m4_female_upper_model]\n\n female_middle_models =\\\n [m1_female_middle_model, m2_female_left_middle_model, m2_female_right_middle_model,\\\n m3_female_left_middle_model, m3_female_right_middle_model, m4_female_middle_model]\n\n female_lower_models =\\\n [m1_female_lower_model, m2_female_left_lower_model, m2_female_right_lower_model,\\\n m3_female_left_lower_model, m3_female_right_lower_model, m4_female_lower_model]\n\n # move objects\n trans_upper = (0, 0, 2 * t_m)\n trans_middle = (0, 0, t_m)\n rs.MoveObjects(female_upper_models, trans_upper)\n rs.MoveObjects(female_middle_models, trans_middle)\n\n\n # deploy models\n O = (0, 0, 0)\n angle = 90\n rs.RotateObjects(female_upper_models, O, angle, None, False)\n rs.RotateObjects(female_middle_models, O, angle, None, False)\n rs.RotateObjects(female_lower_models, O, angle, None, False)\n\n axis = (1, 0, 0)\n rs.RotateObjects(female_upper_models, O, angle, axis, False)\n rs.RotateObjects(female_middle_models, O, angle, axis, False)\n rs.RotateObjects(female_lower_models, O, angle, axis, False)\n\n rs.RotateObjects(female_upper_models, O, angle, None, False)\n rs.RotateObjects(female_middle_models, O, angle, None, False)\n rs.RotateObjects(female_lower_models, O, angle, None, False)\n\n trans = (-2 * width - 3 * t_m / 2, width / 2 - 3 * t_m / 2, 0)\n rs.MoveObjects(female_upper_models, trans)\n rs.MoveObjects(female_middle_models, trans)\n rs.MoveObjects(female_lower_models, trans)\n\n rs.DeleteObject(path)\n\n female_models = [female_upper_models, female_middle_models, female_lower_models]","def factor_mat(all_dat, f_num, iterations, regularization):\n\n\t# get # of users and # of items\n\t[u_num, i_num] = all_dat.shape\n\n\t# init user factors and item factors with random values\n\tu_fac = np.matrix(np.random.rand(u_num, f_num))\t# MxF\n\ti_fac = np.matrix(np.random.rand(i_num, f_num))\t# NxF\n\n\t# calculate the preference matrix\n\tpreference = cal_preference(all_dat)\n\n\t# calculate the confidence matrix\n\tconfidence = cal_confidence(all_dat)\n\t\n\t# recalculate the user factors and item factors using the alternating least square method\n\tfor itr in range(iterations):\n\t\tu_fac = alternate_ls(u_num, i_fac, preference, confidence, regularization)\n\t\t#print itr, \"u_fac\"\n\t\ti_fac = alternate_ls(i_num, u_fac, preference.T, confidence.T, regularization)\n\t\t#print itr, \"i_fac\"\n\t\n\t# save the output\n\tdf = pd.DataFrame(u_fac)\n\tdf.to_csv(\"tmp/u_fac.tmp\", index=False, header=False, sep='\\t', encoding='utf-8')\n\tdf = pd.DataFrame(i_fac.T)\n\tdf.to_csv(\"tmp/i_fac.tmp\", index=False, header=False, sep='\\t', encoding='utf-8')\n\n\t# an MxF user factor matrix and an FxN item factor matrix\n\treturn [u_fac, i_fac.T]","def initParams(self):\n sizes = [self.inputDim]+self.layerSizes+[self.outputDim]\n scales = [np.sqrt(6)/np.sqrt(n+m) for n,m in zip(sizes[:-1],sizes[1:])]\n self.stack = [[np.random.rand(m,n)*2*s-s,np.zeros((m,1))] \\\n for n,m,s in zip(sizes[:-1],sizes[1:],scales)]\n self.hActs_M = [cm.empty((s,self.maxBatch)) for s in sizes]\n\n if self.train:\n # Now assuming that all layers are the same size\n self.grad = [[cm.empty(w.shape),cm.empty(b.shape)] for w,b in self.stack]\n self.deltasC_M = cm.empty((self.outputDim,self.maxBatch))\n self.deltasOut_M = cm.empty((sizes[1],self.maxBatch)) \n self.deltasIn_M = cm.empty((sizes[1],self.maxBatch)) \n self.tmpGrad_M = cm.empty((self.layerSize,self.maxBatch))\n \n # Allocate memory once here and reuse\n # Store probs\n self.probs_M = cm.empty((self.outputDim,self.maxBatch))\n # Store col max\n self.rowVec_M = cm.empty((1,self.maxBatch))\n \n self.stack = [[cm.CUDAMatrix(w),cm.CUDAMatrix(b)]\n for w,b in self.stack]\n\n if self.temporalLayer > 0:\n # dummy bias used for temporal layer\n dummy = cm.empty((1,1))\n dummy.assign(0.0)\n\n scale = np.sqrt(6)/np.sqrt(self.layerSize*2)\n wtf = cm.CUDAMatrix(2*scale*np.random.rand(self.layerSize,\n self.layerSize)-scale)\n wtb = cm.CUDAMatrix(2*scale*np.random.rand(self.layerSize,\n self.layerSize)-scale)\n self.stack.append([wtf,dummy])\n self.stack.append([wtb,dummy])\n\n # forward and backward activations for temporal layer\n self.hActsFor_M = cm.empty((self.layerSize,self.maxBatch))\n self.hActsBack_M = cm.empty((self.layerSize,self.maxBatch))\n\n if self.train:\n dwtf = cm.empty(wtf.shape)\n self.grad.append([dwtf,dummy])\n dwtb = cm.empty(wtb.shape)\n self.grad.append([dwtb,dummy])\n\n self.tmpGradBack_M = cm.empty((self.layerSize,self.maxBatch))\n self.deltasFor_M = cm.empty((self.layerSize,self.maxBatch))\n self.deltasBack_M = cm.empty((self.layerSize,self.maxBatch))","def u_init(self):\n\n u0 = self.params.u0\n N = self.params.nparts\n\n u = self.dtype_u(((3, N), self.init[1], self.init[2]))\n\n if u0[2][0] != 1 or u0[3][0] != 1:\n raise ProblemError('so far only q = m = 1 is implemented')\n\n # set first particle to u0\n u.pos[0, 0] = u0[0][0]\n u.pos[1, 0] = u0[0][1]\n u.pos[2, 0] = u0[0][2]\n u.vel[0, 0] = u0[1][0]\n u.vel[1, 0] = u0[1][1]\n u.vel[2, 0] = u0[1][2]\n\n u.q[0] = u0[2][0]\n u.m[0] = u0[3][0]\n\n # initialize random seed\n np.random.seed(N)\n\n comx = u.pos[0, 0]\n comy = u.pos[1, 0]\n comz = u.pos[2, 0]\n\n for n in range(1, N):\n # draw 3 random variables in [-1,1] to shift positions\n r = np.random.random_sample(3) - 1\n u.pos[0, n] = r[0] + u0[0][0]\n u.pos[1, n] = r[1] + u0[0][1]\n u.pos[2, n] = r[2] + u0[0][2]\n\n # draw 3 random variables in [-5,5] to shift velocities\n r = np.random.random_sample(3) - 5\n u.vel[0, n] = r[0] + u0[1][0]\n u.vel[1, n] = r[1] + u0[1][1]\n u.vel[2, n] = r[2] + u0[1][2]\n\n u.q[n] = u0[2][0]\n u.m[n] = u0[3][0]\n\n # gather positions to check center\n comx += u.pos[0, n]\n comy += u.pos[1, n]\n comz += u.pos[2, n]\n\n # print('Center of positions:',comx/N,comy/N,comz/N)\n\n return u","def prediction(prediction_file_name, clusters_list, svd_use_flag):\n \n coords = misc_functions.getWindowCoords()\n \n test_users = range(coords[0], coords[2] + 1) \n test_items = range(coords[1], coords[3] + 1)\n \n #print \"len(test_users) = \", len(test_users)\n #print \"len(test_items) = \", len(test_items)\n #print \"test_items = \", test_items\n \n # this matrix to be written as result finally\n #misc_functions.step()\n prediction_matrix = zeros((len(test_users), len(test_items)), dtype = float)\n \n training_matrix = scipy.io.mmio.mmread(\"history.mtx\").tocsr()\n \n item_X_meta_matrix = scipy.io.mmio.mmread(\"../../../well_done/items-metas_global.mtx\").toarray()\n \n # getting meta matrices for corresponding using metas\n meta_ctr = 0\n meta_matrices = []\n for meta in METAS_TO_USE:\n if svd_use_flag:\n meta_matrice_file_name = \"users-\" + METAS_TO_USE[meta] + \".svd.mtx\"\n else:\n meta_matrice_file_name = \"users-\" + METAS_TO_USE[meta] + \".mtx\"\n exec(\"meta_matrices.append(scipy.io.mmio.mmread(\\\"\" + meta_matrice_file_name + \"\\\").toarray())\")\n\n #user_counter = 0\n #for user in test_users:\n for cur_cluster in clusters_list:\n \n #print \"cur_cluster[0] = \", cur_cluster[0]\n user = int (cur_cluster[0].split(\"\\t\")[1])\n #print \"user #\", user\n \n #user_metas = {} - changed to list because of problem with dimension\n user_metas = []\n \n values = zeros((len(METAS_TO_USE), len(test_items)), dtype = float)\n meta_ctr = 0\n for meta in METAS_TO_USE:\n \n #print \" meta_matrices = \", meta_matrices\n #print \" meta_matrices[meta_ctr] = \", meta_matrices[meta_ctr]\n user_vector = meta_matrices[meta_ctr][user]\n #print \" user_vector = \", user_vector\n #print \" len(user_metas) = \", len(user_metas)\n #print \" meta_ctr = \", meta_ctr\n #print \"meta = \", meta\n #misc_functions.step()\n \n # normalizing counts of visited metas to use them as weights later\n if max(user_vector) != 0:\n user_metas.append(1.0 * user_vector / max(user_vector))\n else:\n user_metas.append(zeros((len(user_vector), ), dtype = float))\n #print \" user_metas[meta_ctr] = \", user_metas[meta_ctr]\n #print \" user_metas[meta_ctr].shape = \", user_metas[meta_ctr].shape\n \n #for item in test_items:\n for cluster in cur_cluster[1 : ]:\n start_cluster_item = int(cluster.split(\"\\t\")[0])\n stop_cluster_item = int(cluster.split(\"\\t\")[2])\n \n cluster_items = range(start_cluster_item, stop_cluster_item + 1)\n \n for item in cluster_items:\n meta_value = item_X_meta_matrix[item, meta]\n \n # PRICE\n if meta == 8:\n meta_value = priceToPriceCat(meta_value)\n \n # CITY HEURISTIC\n if meta == 11:\n if user_metas[meta_ctr][meta_value - 1] < CITY_TRESHOLD:\n values[:, item - coords[1]] *= CITY_COEF\n \"\"\"\n # DAYTIME\n if meta == 17:\n meta_value = dayTime(meta_value)\n \"\"\"\n \n #print \" meta_value = \", meta_value\n #print \" item = \", item\n #step()\n values[meta_ctr][item - coords[1]] = (user_metas[meta_ctr])[meta_value - 1]\n \n \"\"\"HEURISTICS \"\"\"\n \n \n \n \n \n \"\"\"\\\\ HEURISTICS \"\"\"\n\n meta_ctr += 1\n #print \"values[:, 0:10] = \", values[:, 0:10]\n prediction_vector = numpy.sum(META_WEIGHTS * values, axis = 0)\n #print \"prediction_vector[0:10] = \", prediction_vector[0:10]\n #print \"sum(prediction_vector) = \", sum(prediction_vector)\n prediction_matrix[user - coords[0]] = prediction_vector\n \n #step()\n \n# ===== END OF MAIN CYCLE ===== \n\n result_matrix = scipy.sparse.csr_matrix(prediction_matrix)\n scipy.io.mmio.mmwrite(prediction_file_name, result_matrix, field = 'real', precision = 5)","def main():\n logger.info(\"=> creating model ...\")\n logger.info(\"Classes: %s\", cfg.classes)\n\n value_scale = 255\n mean = [0.485, 0.456, 0.406]\n mean = [item * value_scale for item in mean]\n std = [0.229, 0.224, 0.225]\n std = [item * value_scale for item in std]\n gray_folder = os.path.join(cfg.result_path, 'gray')\n color_folder = os.path.join(cfg.result_path, 'color')\n\n test_transform = pt_transform.Compose([pt_transform.Normalize(mean=mean, std=std, is_train=False)])\n\n if cfg.data_root[-1] == \"/\":\n val_list = cfg.data_root + cfg.val_list\n else:\n val_list = cfg.data_root + '/' + cfg.val_list\n\n test_data = pt_dataset.SemData(\n split='val', data_root=cfg.data_root,\n data_list=val_list,\n transform=test_transform)\n\n test_loader = ds.GeneratorDataset(test_data, column_names=[\"data\", \"label\"],\n shuffle=False)\n test_loader.batch(1)\n colors = numpy.loadtxt(cfg.color_txt).astype('uint8')\n\n from src.model import cpnet\n\n CPNet = cpnet.CPNet(\n prior_channels=256,\n proir__size=60,\n am_kernel_size=11,\n pretrained=True,\n pretrained_path=cfg.pretrain_path,\n deep_base=True\n )\n\n ms_checkpoint = load_checkpoint(cfg.ckpt)\n load_param_into_net(CPNet, ms_checkpoint, strict_load=True)\n CPNet.set_train(False)\n test(test_loader, test_data.data_list, CPNet, cfg.classes, mean, std, cfg.base_size, cfg.test_h,\n cfg.test_w, cfg.scales, gray_folder, color_folder, colors)\n if cfg.split != 'test':\n cal_acc(test_data.data_list, gray_folder, cfg.classes)","def _like3(init_par, alpha, delta, plx_obs, mualpha_obs, mudelta_obs, sigma_obs, ccoef, i):\r\n\r\n\tplx_mod, v, sigma_v = init_par[i], init_par[-4:-1], init_par[-1] \r\n\tp, q, r = normalTriad(alpha, delta)\r\n\tmualpha_mod = np.dot(np.transpose(p),v)*plx_mod/_A\r\n\tmudelta_mod = np.dot(np.transpose(q),v)*plx_mod/_A\r\n \t\r\n\tsigma_plx, sigma_mualpha, sigma_mudelta = sigma_obs\r\n\tr_plx_muRa, r_plx_muDec, r_muRa_muDec = ccoef[0], ccoef[1], ccoef[2] \r\n\t\r\n\tC = np.zeros((3,3),dtype=np.float64)\r\n\tC[0,0],C[1,1],C[2,2] = sigma_plx**2.,sigma_mualpha**2., sigma_mudelta**2.\r\n\tC[0,1], C[0,2] = r_plx_muRa*sigma_plx*sigma_mualpha, r_plx_muDec*sigma_plx*sigma_mudelta\r\n\tC[1,0], C[1,2] = r_plx_muRa*sigma_plx*sigma_mualpha, r_muRa_muDec*sigma_mualpha*sigma_mudelta\r\n\tC[2,0], C[2,1] = r_plx_muDec*sigma_plx*sigma_mudelta, r_muRa_muDec*sigma_mualpha*sigma_mudelta\r\n\r\n\tE = np.zeros((3,3),dtype=np.float64)\r\n\tE[1,1],E[2,2] = (sigma_v**2.)*(plx_mod/_A)**2., (sigma_v**2.)*(plx_mod/_A)**2.\r\n\t\r\n\tD = np.add(E,C)\r\n\tdetD = det(D) \r\n\tinvD = inv(D)\r\n\t\t\r\n\ta_c = np.array([plx_obs - plx_mod, mualpha_obs - mualpha_mod, mudelta_obs-mudelta_mod])\r\n\tg_func = row_matrix_col(a_c, a_c, invD)\r\n\t\r\n\t\r\n\treturn detD, g_func","def run(self, C, p0 = None):\n global algorithm \n algorithm = AdaptiveMM(self.g, C, p0 = p0, lambda0 = 2000)\n solve()","def c3d(self):\n model = Sequential()\n # 1st layer group\n model.add(Conv3D(64, 3, 3, 3, activation='relu',\n border_mode='same', name='conv1',\n subsample=(1, 1, 1),\n input_shape=self.input_shape))\n model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),\n border_mode='valid', name='pool1'))\n # 2nd layer group\n model.add(Conv3D(128, 3, 3, 3, activation='relu',\n border_mode='same', name='conv2',\n subsample=(1, 1, 1)))\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n border_mode='valid', name='pool2'))\n # 3rd layer group\n model.add(Conv3D(256, 3, 3, 3, activation='relu',\n border_mode='same', name='conv3a',\n subsample=(1, 1, 1)))\n model.add(Conv3D(256, 3, 3, 3, activation='relu',\n border_mode='same', name='conv3b',\n subsample=(1, 1, 1)))\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n border_mode='valid', name='pool3'))\n # 4th layer group\n model.add(Conv3D(512, 3, 3, 3, activation='relu',\n border_mode='same', name='conv4a',\n subsample=(1, 1, 1)))\n model.add(Conv3D(512, 3, 3, 3, activation='relu',\n border_mode='same', name='conv4b',\n subsample=(1, 1, 1)))\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n border_mode='valid', name='pool4'))\n\n # 5th layer group\n model.add(Conv3D(512, 3, 3, 3, activation='relu',\n border_mode='same', name='conv5a',\n subsample=(1, 1, 1)))\n model.add(Conv3D(512, 3, 3, 3, activation='relu',\n border_mode='same', name='conv5b',\n subsample=(1, 1, 1)))\n model.add(ZeroPadding3D(padding=(0, 1, 1)))\n # model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n # border_mode='valid', name='pool5', dim_ordering=\"tf\"))\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\n border_mode='same', name='pool5', dim_ordering=\"tf\"))\n model.add(Flatten())\n\n # FC layers group\n model.add(Dense(4096, activation='relu', name='fc6'))\n model.add(Dropout(0.5))\n model.add(Dense(4096, activation='relu', name='fc7'))\n model.add(Dropout(0.5))\n model.add(Dense(self.nb_classes, activation='softmax'))\n for layer in model.layers:\n print(layer.output_shape)\n return model","def create_model(num_vars, num_categs, hidden_dims, actfn=None):\n num_outputs = max(1, num_categs)\n num_inputs = num_vars\n actfn = get_activation_function(actfn)\n\n mask = InputMask(None)\n if num_categs > 0:\n pre_layers = EmbedLayer(num_vars=num_vars,\n num_categs=num_categs,\n hidden_dim=hidden_dims[0],\n input_mask=mask,\n sparse_embeds=(num_vars >= 50))\n num_inputs = pre_layers.hidden_dim\n pre_layers = [pre_layers, actfn()]\n else:\n pre_layers = mask\n\n mlps = MultivarMLP(input_dims=num_inputs,\n hidden_dims=hidden_dims,\n output_dims=num_outputs,\n extra_dims=[num_vars],\n actfn=actfn,\n pre_layers=pre_layers)\n return mlps","def CvM(self, using, dx=0.0001):\n pits = np.array(self.PIT(using=using,dx=dx))\n cvm_result = skgof.cvm_test(pits, stats.uniform())\n return cvm_result.statistic, cvm_result.pvalue","def inital_probabilities(self,fc,ec):\n matrix = np.empty(ec*fc)\n matrix = matrix.reshape(ec,fc)\n matrix.fill(1/fc)\n return matrix # repreents probability - p(e|f)","def fixedZMPchi2(self, pars):\n\t\tif not self.hasZMP and self.nZero > 0:\n\t\t\traise RuntimeError(\"No zero mode parameters set\")\n\t\tif pars is not None:\n\t\t\tself.setShapeParameters(pars)\n\t\ta,b,c = self.getOwnTheoryABC()\n\t\tA = np.zeros((2*self.nFunc, 2*self.nFunc))\n\t\tB = np.zeros((2*self.nFunc))\n\t\tC = c\n\t\tfor i in range(2*self.nZero):\n\t\t\tC += b[i]*self.zeroModeParameters[i]\n\t\t\tfor j in range(2*self.nZero):\n\t\t\t\tC += self.zeroModeParameters[i]*self.zeroModeParameters[j]*a[i,j]\n\t\tfor i in range(2*self.nFunc):\n\t\t\tB[i] += b[2*self.nZero+i]\n\t\t\tfor j in range(2*self.nZero):\n\t\t\t\tB[i] += (a[2*self.nZero+i,j]+a[j,2*self.nZero+i])*self.zeroModeParameters[j]\n\t\t\tfor j in range(2*self.nFunc):\n\t\t\t\tA[i,j] += a[2*self.nZero + i, 2*self.nZero+j]\n\t\tif self.ownPinv:\n\t\t\tcouplings = -np.dot(B, utils.pinv(np.transpose(A) + A, numLim = self.numLim))\n\t\telse:\n\t\t\tcouplings = -np.dot(B, la.pinv(np.transpose(A) + A))\n\t\treturn np.dot(couplings, np.dot(A,couplings)) + np.dot(B,couplings) + C","def do_PMC_ABC(self, n, real_data, prior, draws, num_keep, abc=None, criterion = 0.1\n , show=False, oneD='both',analytic_posterior = None, param_array = None):\n\n if abc is None:\n # Start from scratch\n print (\"Starting PMC from scratch\")\n restart = True\n Gaussprior = priors.TruncatedGaussian(prior[\"mean\"],prior[\"variance\"]\n ,prior[\"lower\"],prior[\"upper\"])\n\n abc = ABC.ABC(real_data = real_data, prior = Gaussprior, sess=n.sess\n , get_compressor=n.get_compressor, simulator=generate_data_ABC, seed=None\n , simulator_args=None, dictionary = self.validation_dictionary)\n\n else: # Use the abc object from the do_ABC() function\n print (\"Starting PMC from previous ABC or PMC result\")\n restart = False\n \n all_epsilon = abc.PMC(draws = draws, posterior = num_keep\n , criterion = criterion, at_once = True, save_sims = None, MLE = True) \n\n # plot output samples and histogram of approximate posterior\n def plot_samples_oneD():\n \n fig, ax = plt.subplots(2, 1, sharex = 'col', figsize = (10, 10))\n plt.subplots_adjust(hspace = 0)\n ax[0].scatter(abc.PMC_dict[\"parameters\"][:] , abc.PMC_dict[\"summaries\"][:]\n , s = 1, label = \"Accepted samples\", alpha=0.5)\n ax[0].axhline(abc.summary[0], color = 'black', linestyle = 'dashed', label = \"Summary of observed data\")\n ax[0].set_ylabel('Network output', labelpad = 0)\n # ax[0].set_ylim([np.min(s_[:]), np.max(s_[:])])\n ax[0].set_xlim([prior[\"lower\"][0], prior[\"upper\"][0]])\n \n ax[1].hist(abc.PMC_dict[\"parameters\"], np.linspace(prior[\"lower\"][0]\n , prior[\"upper\"][0], 100), histtype = u'step', density = True\n , linewidth = 1.5, label = \"PMC posterior\", color = 'k')\n\n ax[1].set_xlabel('$\\\\theta_1 = \\Omega_M$ ')\n ax[1].set_ylabel('$\\\\mathcal{P}(\\\\theta1|{\\\\bf d})$')\n ax[1].set_yticks([])\n\n if analytic_posterior is not None:\n ax[1].plot(param_array, analytic_posterior, linewidth = 1.5, color = 'C2'\n , label = \"Analytic posterior\")\n\n # Theta-fid\n ax[1].axvline(theta_fid[0], linestyle = \"dashed\", label = \"$\\\\theta_{fid}$\")\n\n ax[1].legend(frameon=False)\n\n # fig.suptitle(\"Only showing 1st network output summary out of %i \\n Full network output on real data: %s\"%(s_.shape[1],str(summary_)))\n fig.suptitle(\"Results of PMC\")\n\n plt.savefig(f'{self.figuredir}PMC_ABC_{self.modelversion}_1D.png')\n if show: plt.show()\n plt.close()\n\n # plot output samples and histogram of the accepted samples\n def plot_samples_twoD():\n sys.exit('There is only 1 dimension, cannot make corner plot')\n hist_kwargs = {} # add kwargs to give to matplotlib hist funct\n fig, ax = plt.subplots(2, 2, figsize = (10, 10))\n fig = corner.corner(theta_, fig=fig, truths = theta_fid\n , labels=['$\\\\theta_1$ (variance)']\n , plot_contours=True, range=prior, hist_kwargs=hist_kwargs)\n fig.suptitle(\"Approximate posterior after PMC ABC, num_keep = %i\"%num_keep)\n \n plt.savefig(f'{self.figuredir}PMC_ABC_{self.modelversion}_2D.png')\n if show: plt.show()\n plt.close()\n\n # Plot epsilon vs iterations\n def plot_epsilon():\n fig, ax = plt.subplots()\n ax.plot(all_epsilon,color='k',label='$\\epsilon$ values')\n plt.xlabel('Iteration')\n plt.ylabel('$\\epsilon$')\n plt.legend()\n \n plt.savefig(f'{self.figuredir}PMC_ABC_{self.modelversion}_epsilon.png')\n if show: plt.show()\n plt.close()\n\n if oneD == 'both':\n plot_samples_oneD()\n plot_samples_twoD()\n elif type(oneD) == bool:\n if oneD:\n plot_samples_oneD()\n else:\n plot_samples_twoD()\n else: \n raise ValueError('Allowed values for oneD are \"both\", True or False')\n\n plot_epsilon()\n\n return abc","def semi_analytical_marginal_probj(param_inst, cash_tt, solu_dict,\n cur_col_prefix_space='_'):\n\n choice_set_list = param_inst.model_option['choice_set_list']\n choice_names_use = param_inst.model_option['choice_names_full_use']\n choice_names_use = param_inst.model_option['choice_names_use']\n\n each_j_prob = solu_dict['each_j_prob']\n ktp_opti_allJ = solu_dict['ktp_opti_allJ']\n btp_opti_allJ = solu_dict['btp_opti_allJ']\n consumption_opti_allJ = solu_dict['consumption_opti_allJ']\n\n # btp_fb_opti_allJ = solu_dict['btp_fb_opti_allJ']\n # btp_ib_opti_allJ = solu_dict['btp_ib_opti_allJ']\n # btp_fs_opti_allJ = solu_dict['btp_fs_opti_allJ']\n # btp_il_opti_allJ = solu_dict['btp_il_opti_allJ']\n\n trans_prob_list = []\n simu_output_pd_allj = 0\n\n for ctr, choicej in enumerate(choice_set_list):\n\n cur_col_prefix = choice_names_use[ctr]\n logger.info('ctr,choicej,cur_col_prefix:\\n%s,%s,%s',\n str(ctr), str(choicej), str(cur_col_prefix))\n\n btp_opti = btp_opti_allJ[:, ctr]\n ktp_opti = ktp_opti_allJ[:, ctr]\n prob_cur = each_j_prob[:, ctr]\n consumption_opti = consumption_opti_allJ[:, ctr]\n\n '''\n Get columns at centered interpolating grid points for:\n 'cash_grid_centered'\n 'marginal_dist'\n 'btp_opti_grid',\n 'ktp_opti_grid',\n 'consumption_opti_grid' \n '''\n logger.info('Solve, P(COH|j), P(COH|COH,j), ctr:%s, name:%s', str(ctr), cur_col_prefix)\n simu_output_pd_curj, trans_prob_curj = condianaly.semi_analytical_marginal(\n param_inst,\n cash_tt, ktp_opti, btp_opti, consumption_opti,\n each_j_prob=prob_cur,\n trans_prob_only=True,\n cur_col_prefix=cur_col_prefix + cur_col_prefix_space)\n\n '''Store Choice J Transition Prob'''\n trans_prob_list.append(trans_prob_curj)\n\n '''Update Column Names'''\n if (ctr == 0):\n simu_output_pd_allj = simu_output_pd_curj\n else:\n '''Cumulate'''\n simu_output_pd_allj = pd.concat([simu_output_pd_allj,\n simu_output_pd_curj], axis=1)\n\n \"\"\"\n D. Add columns for each j of J for fbibfsil\n \"\"\"\n # D1. Get Columns from just created panda files, j specific columns\n steady_var_suffixes_dict = hardstring.get_steady_var_suffixes()\n btp_opti_grid_allJ_cols = [col for col in simu_output_pd_allj.columns if\n cur_col_prefix_space + steady_var_suffixes_dict['btp_opti_grid'] in col]\n ktp_opti_grid_allJ_cols = [col for col in simu_output_pd_allj.columns if\n cur_col_prefix_space + steady_var_suffixes_dict['ktp_opti_grid'] in col]\n\n # D2. Matrix from columns\n btp_opti_grid_allJ = simu_output_pd_allj[btp_opti_grid_allJ_cols].to_numpy()\n ktp_opti_grid_allJ = simu_output_pd_allj[ktp_opti_grid_allJ_cols].to_numpy()\n\n # D3. Get fb ib fs il specific matrixes\n btp_fb_opti_allJ, btp_ib_opti_allJ, btp_fs_opti_allJ, btp_il_opti_allJ = \\\n fbibfsis.genfibs_btpstack(choice_set_list, btp_opti_grid_allJ, ktp_opti_grid_allJ, param_inst)\n\n # D4. Add to simu_output_pd_allj panda\n fb_ib_fs_il_steady_var_key_list = ['btp_fb_opti_grid', 'btp_ib_opti_grid',\n 'btp_fs_opti_grid', 'btp_il_opti_grid']\n varnames_list = [choice_names_use[ctr] + cur_col_prefix_space + steady_var_suffixes_dict[fbibfsil_stdykey]\n for fbibfsil_stdykey in fb_ib_fs_il_steady_var_key_list\n for ctr, choicej in enumerate(choice_set_list)]\n\n # D5. Additional Panda columns with fb ib fs il information\n varnames = \",\".join(map(str, varnames_list))\n varmat = np.column_stack((btp_fb_opti_allJ, btp_ib_opti_allJ, btp_fs_opti_allJ, btp_il_opti_allJ))\n simu_output_pd_allj_fbibfsil = proj_sys_sup.debug_panda(varnames, varmat, export_panda=False, log=False)\n\n # D6. Concatenate together, join more columns together. \n simu_output_pd_allj = pd.concat([simu_output_pd_allj, simu_output_pd_allj_fbibfsil], axis=1)\n\n \"\"\"\n E0. Grid Column\n \"\"\"\n cash_grid_centered_cols = [col for col in simu_output_pd_allj.columns\n if steady_var_suffixes_dict['cash_grid_centered'] in col]\n simu_output_pd_allj['cash_grid_centered'] = simu_output_pd_allj[cash_grid_centered_cols[0]]\n\n \"\"\"\n E1. Adjust Probabilities due to Interpolation issue over J choices\n \"\"\"\n logger.info('simu_output_pd_allj.columns:\\n%s', simu_output_pd_allj.columns)\n prob_cols = [col for col in simu_output_pd_allj.columns if 'probJ_opti_grid' in col]\n logger.info('prob_cols:\\n%s', prob_cols)\n probJ_matrix = simu_output_pd_allj[prob_cols].to_numpy()\n logger.info('probJ_matrix:\\n%s', probJ_matrix)\n\n '''These are actually not needed, perfect symmatery'''\n probJ_matrix_rowsum = np.reshape(np.sum(probJ_matrix, axis=1), (-1, 1))\n logger.info('probJ_matrix_rowsum:\\n%s', probJ_matrix_rowsum)\n probJ_matrix_rescale_sum1 = probJ_matrix / probJ_matrix_rowsum\n logger.info('probJ_matrix_rescale_sum1:\\n%s', probJ_matrix_rescale_sum1)\n\n \"\"\"\n E2. Overall Conditional probabilities\n \"\"\"\n trans_prob_wgtJ = 0\n trans_prob_dict_allj = {}\n for ctr, choicej in enumerate(choice_set_list):\n '''E2a. Transition Probability current j'''\n trans_prob_curj = trans_prob_list[ctr]\n\n '''E2b. Choice Probability over J'''\n prob_opti_grid = probJ_matrix_rescale_sum1[:, ctr]\n\n '''E2c. Update current column with reweighted sum to 1 choice J prob'''\n simu_output_pd_allj[prob_cols[ctr]] = prob_opti_grid\n\n '''E2d. Weighted Discrete Transition Probability'''\n trans_prob_curj_wgted = trans_prob_curj * np.reshape(prob_opti_grid, (-1, 1))\n logger.debug('trans_prob_curj:\\n%s', trans_prob_curj)\n logger.debug('prob_opti_grid:\\n%s', prob_opti_grid)\n logger.debug('trans_prob_curj_wgted:\\n%s', trans_prob_curj_wgted)\n\n '''E2e. Update Column Names'''\n trans_prob_dict_allj[choicej] = trans_prob_curj\n if (ctr == 0):\n trans_prob_wgtJ = trans_prob_curj_wgted\n else:\n '''Cumulate'''\n trans_prob_wgtJ = trans_prob_wgtJ + trans_prob_curj_wgted\n\n logger.info('trans_prob_wgtJ:\\n%s', trans_prob_wgtJ)\n logger.info('np.sum(trans_prob_wgtJ):\\n%s', np.sum(trans_prob_wgtJ, axis=1))\n\n return simu_output_pd_allj, trans_prob_wgtJ, trans_prob_dict_allj","def generate_prediction_matrix(list_of_models, list_of_tfidf, X_test):\n n = len(X_test)\n m = len(list_of_models)\n \n prediction_matrix = np.ones((n,m))\n \n for i, model in enumerate(list_of_models):\n print(f\"Generating predictions for model: {i+1}\")\n model = list_of_models[i]\n tfidf = list_of_tfidf[i]\n try:\n X_test_tfidf = tfidf.transform(X_test)\n predictions = model.predict_proba(X_test_tfidf)[:,1]\n except Exception as e:\n print(str(e))\n print(predictions)\n prediction_matrix[:,i] *= predictions\n \n return prediction_matrix","def _pmatrix(kn_u, kn_d, thickness):\n p = np.zeros((kn_u.size, 4, 4), np.complex128)\n\n p0 = np.exp(complex(0, 1) * kn_u * thickness)\n p1 = np.exp(complex(0, 1) * kn_d * thickness)\n\n p[:, 0, 0] = 1 / p0\n p[:, 1, 1] = p0\n p[:, 2, 2] = 1 / p1\n p[:, 3, 3] = p1\n\n return p","def create_petsc_matrix(sizes, mtx_prealloc=None, comm=None):\n if comm is None:\n comm = PETSc.COMM_WORLD\n\n pmtx = PETSc.Mat()\n pmtx.create(comm)\n pmtx.setType('aij')\n\n pmtx.setSizes((sizes, sizes))\n\n if mtx_prealloc is not None:\n pmtx.setPreallocationCSR((mtx_prealloc.indptr, mtx_prealloc.indices))\n\n pmtx.setUp()\n\n return pmtx","def fit(self, Cui):\n\n _check_open_blas()\n\n users, items = Cui.shape\n\n self.user_vectors = np.random.normal(size=(users, self.num_factors))\\\n .astype(self.dtype)\n\n self.item_vectors = np.random.normal(size=(items, self.num_factors))\\\n .astype(self.dtype)\n\n '''\n use_native 是否使用原生的算法（这个原生和自定义的区别还有待观察）\n '''\n self.solver = implicit.als.least_squares if self.use_native else least_squares\n\n self.fit_partial(Cui)","def fit_cmle_mixt(copula, data, opti_method='SLSQP'):\n psd_obs = pseudo_obs(data)\n\n def log_likelihood(parameters):\n if copula.dim == 2:\n w1, param1, param2 = parameters\n params = [w1, param1, param2]\n else: # dim = 3\n w1, w2, w3, param1, param2, param3 = parameters\n params = [w1, w2, w3, param1, param2, param3]\n logl = -sum([ np.log(copula.get_pdf(psd_obs[0][i],psd_obs[1][i],params)) for i in range(0,len(psd_obs[0]))])\n return logl\n\n def con(parameters):\n w1, w2, w3, param1, param2, param3 = parameters\n return 1 - w1 - w2 - w3\n\n if copula.dim == 2:\n results = minimize(log_likelihood, copula.parameters_start, method=opti_method, bounds=copula.bounds_param) #options={'maxiter': 300})#.x[0]\n else:\n cons = {'type':'eq', 'fun': con}\n results = minimize(log_likelihood, copula.parameters_start, method=opti_method, bounds=copula.bounds_param, constraints=cons) #options={'maxiter': 300})#.x[0]\n\n\n print(\"method:\", opti_method, \"- success:\", results.success, \":\", results.message)\n if results.success == True:\n return (results.x, -results.fun)\n\n print(\"optimization failed\")\n return None","def make_critic(input_dim=model_config[\"data_dimension\"]):\n model = Sequential(name=\"critic\")\n model.add(Reshape((input_dim+(1,)), input_shape=input_dim))\n\n model.add(Conv3D(64, (5, 5, 5), padding='same'))\n model.add(LeakyReLU())\n\n model.add(Conv3D(128, (5, 5, 5), kernel_initializer='he_normal', strides=2))\n model.add(LeakyReLU())\n\n model.add(Conv3D(256, (5, 5, 5), kernel_initializer='he_normal', padding='same', strides=2))\n model.add(LeakyReLU())\n\n model.add(Conv3D(512, (5, 5, 5), kernel_initializer='he_normal', padding='same'))\n model.add(LeakyReLU())\n\n model.add(Flatten())\n\n model.add(Dense(1, kernel_initializer='he_normal'))\n return model","def SGHMC(gradU, p, eps=0.1, L=5, M=1, C=3, V=4):\n \n # randomly generate new auxiliary variable (momentum)\n r = np.random.randn()\n B = 0.5 * V * eps\n for i in range(L):\n r = r - gradU(p)*eps - r*C*eps + np.random.randn()*np.sqrt(2*(C-B)*eps)\n p += r/M* eps\n return p","def p_mx_c(pm,px,py,pyx_c,pym_c,beta):\n \n pmx_c = np.zeros((pm.size,px.size)) # P(M|X) matrix to be returned\n for mi in range(pm.size):\n for xi in range(px.size):\n pmx_c[mi,xi] = pm[mi] * np.exp(-beta * entropy(pyx_c[:,xi], pym_c[:,mi], base=2))\n z = pmx_c.sum(axis=0)\n pmx_c /= z #Normalize\n \n \t\n return pmx_c, z","def transform_pc3d(pcl_c3d, Ts, seq_n, K_cur, batch_n):\n\n ## need to transform: flat.uvb, flat.feature['xyz'], flat.feature['normal']\n ## no need to transform grid features\n \n assert batch_n % seq_n == 0 # mode==0\n n_group = batch_n // seq_n\n\n ## get relative pose\n T, R, t, target_id = relative_T(Ts, seq_n, batch_n)\n\n ## get accumulative length\n nb = pcl_c3d.flat.nb\n acc_b = []\n acc = 0\n acc_b.append( acc )\n for ib in range(batch_n):\n acc = acc + nb[ib]\n acc_b.append( acc )\n\n ## process flat features\n flat_xyz = pcl_c3d.flat.feature['xyz'] # 1*C*NB\n flat_normal = pcl_c3d.flat.feature['normal']\n trans_normal_list = []\n trans_xyz_list = []\n uvb_list = []\n new_nb = []\n for ib in range(batch_n):\n ## xyz\n trans_xyz = torch.matmul(R[ib], flat_xyz[:, :, acc_b[ib]:acc_b[ib+1]]) + t[ib]\n mask_positive = trans_xyz[0, 2, :] > 0\n trans_xyz = trans_xyz[:, :, mask_positive]\n trans_xyz_list.append(trans_xyz)\n new_nb.append(trans_xyz.shape[2])\n\n ## normal\n trans_normal = torch.matmul(R[ib], flat_normal[:, :, acc_b[ib]:acc_b[ib+1]])\n trans_normal = trans_normal[:, :, mask_positive]\n trans_normal_list.append(trans_normal)\n\n ## project to uv, add b\n uvb = torch.matmul(K_cur[ib], trans_xyz)\n uvb[:, :2] = uvb[:, :2] / uvb[:, [2]] #- 1 , commented because in dataset_read.py there is a K_mat2py() function converting K from matlab to python coordinate\n uvb[:, 2, :] = target_id[ib]\n uvb_list.append(uvb)\n\n ## construct the new object\n tr_pcl_c3d = PCL_C3D_Flat()\n tr_pcl_c3d.feature['xyz'] = torch.cat(trans_xyz_list, dim=2)\n tr_pcl_c3d.feature['normal'] = torch.cat(trans_normal_list, dim=2)\n tr_pcl_c3d.uvb = torch.cat(uvb_list, dim=2)\n tr_pcl_c3d.nb = new_nb\n\n for feat_key in pcl_c3d.flat.feature:\n if feat_key not in ['xyz', 'normal']:\n tr_pcl_c3d.feature[feat_key] = pcl_c3d.flat.feature[feat_key]\n\n return tr_pcl_c3d","def part3c_0():\n xs = exampleInput\n N = 10000\n\n difference = 0.0\n for ys, estimatedProb in submission.computeGibbsProbabilities( simpleCRF, \n submission.getCRFBlocks,\n submission.chooseGibbsCRF,\n xs, N ).iteritems():\n trueProb = nerUtils.computeProbability( simpleCRF, xs, ys )\n difference = abs( trueProb - estimatedProb )\n grader.requireIsLessThan( 5e-2, difference )","def get_model():\n model = ecole.scip.Model.from_file(str(DATA_DIR / \"bppc8-02.mps\"))\n model.disable_cuts()\n model.disable_presolve()\n model.set_param(\"randomization/permuteconss\", True)\n model.set_param(\"randomization/permutevars\", True)\n model.set_param(\"randomization/permutationseed\", 784)\n model.set_param(\"randomization/randomseedshift\", 784)\n model.set_param(\"randomization/lpseed\", 784)\n return model","def solve(P, M, N, C, items, constraints):\n Items = []\n weight_dict = dict()\n cost_dict = dict()\n earn_dict = dict()\n class_dict = dict()\n \n for i in items:\n Items.append(i[0])\n weight_dict[i[0]] = i[2]\n cost_dict[i[0]] = i[3]\n earn_dict[i[0]] = i[4] - i[3]\n class_dict[i[0]] = i[1]\n \n Class_dict = defaultdict(list)\n for key, value in sorted(class_dict.iteritems()):\n Class_dict[value].append(key)\n \n # Create the 'prob' variable to contain the problem data\n prob = LpProblem(\"The PICKITEMS Problem\", LpMaximize)\n # Two dictionary called 'x_vars' and 'y_vars' are created to contain the referenced Variables\n x_vars = LpVariable.dicts(\"\",Items,0,1,cat=LpInteger)\n y_vars = LpVariable.dicts(\"#\",range(N),0,1,cat=LpInteger)\n \n # The objection function is added to 'prob' first\n prob += lpSum([earn_dict[i]*x_vars[i] for i in Items]), \"Total money we can earn in this file\"\n \n # Constraints are added to 'prob'\n prob += lpSum([weight_dict[i]*x_vars[i] for i in Items]) <= P, \"WeightsRequirement\"\n prob += lpSum([cost_dict[i]*x_vars[i] for i in Items]) <= M, \"CostRequirement\"\n # the relations(constraint) between class and item (x and y)\n for num in range(C):\n for i in Class_dict[num]:\n prob += LpConstraint(x_vars[i]-y_vars[num] <= 0), \"\"\n # the relations for class constraints\n for constraint in constraints:\n prob += lpSum([y_vars[i] for i in constraint]) <= 1, \"\"\n # The problem is solved using PuLP's choice of Solver\n prob.solve()\n \n items_chosen = []\n for v in prob.variables():\n if v.varValue == 1 and v.name[0]!='#':\n items_chosen.append(v.name[1:])\n return items_chosen","def p_m(pmx_c,px):\n pm = np.zeros(pmx_c.shape[0])\n for mi in range(pm.size):\n for xi in range(px.size):\n pm[mi] += pmx_c[mi,xi]*px[xi]\n return pm","def run_pca(df, cols=None): \n import numpy as np\n import pandas as pd\n import matplotlib.pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n from sklearn.preprocessing import StandardScaler\n from sklearn.decomposition import PCA\n import mpld3\n\n # Define and markers to use for different categories\n groups_dict = {(u'D', 0):('Germany, unregulated', 'g', 'o'),\n (u'N', 0):('Norway, unregulated', 'b', 'o'),\n (u'D', 1):('Germany, regulated', 'g', '^'),\n (u'N', 1):('Norway, regulated', 'b', '^')}\n \n # Extract cols of interest\n cats = df[['country', 'regulated']]\n\n if cols:\n df = df[cols].astype(float)\n\n # Standardise the feature data\n feat_std = StandardScaler().fit_transform(df)\n\n # Setup PCA. Initially, choose to keep ALL components\n pca = PCA()\n\n # Fit model\n pca.fit(feat_std)\n\n # Get explained variances (in %)\n var_exp = 100*pca.explained_variance_ratio_\n cum_exp = np.cumsum(var_exp)\n\n # Get eigenvalues\n cov_mat = np.cov(feat_std.T)\n eig_vals, eig_vecs = np.linalg.eig(cov_mat)\n\n # Get number of EVs > 1 (Kaiser-Guttman criterion)\n # and print summary\n n_kgc = (eig_vals > 1).sum()\n print 'Variance explained by first %s PCs (%%):\\n' % n_kgc\n print var_exp[:n_kgc]\n print '\\nTotal: %.2f%%' % var_exp[:n_kgc].sum()\n \n # Plot\n fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n \n # Explained variance\n axes[0].bar(range(1, len(var_exp)+1), var_exp, \n align='center', label='Individual components')\n axes[0].plot(range(1, len(cum_exp)+1), cum_exp, \n 'r-o', label='Cumulative')\n axes[0].set_xlabel('Principal component')\n axes[0].set_ylabel('Variance explained (%)')\n axes[0].legend(loc='center right')\n \n # Eigenvalues\n axes[1].plot(range(1, len(eig_vals)+1), np.sort(eig_vals)[::-1], \n 'r-o', label='Eigenvalues')\n axes[1].axhline(1, c='k', ls='-', label='Kaiser-Guttman threshold')\n axes[1].set_xlabel('Principal component')\n axes[1].set_ylabel('Eigenvalue')\n axes[1].legend(loc='upper right') \n \n # PC loadings\n loads = pd.DataFrame(data=pca.components_, \n columns=df.columns,\n index=range(1, pca.components_.shape[0]+1)).T\n\n # Project into 2 and 3 components\n fig = plt.figure(figsize=(16, 6))\n \n # Plot 2 components\n ax = fig.add_subplot(1, 2, 1)\n \n # Refit the PCA, this time specifying 2 components\n # and transforming the result\n feat_reduced = PCA(n_components=2).fit_transform(feat_std)\n \n # Build df \n data = pd.DataFrame({'PC1':feat_reduced[:, 0],\n 'PC2':feat_reduced[:, 1],\n 'country':cats['country'],\n 'regulated':cats['regulated']}) \n\n groups = data.groupby(['country', 'regulated'])\n \n # Plot\n for name, group in groups:\n ax.scatter(group['PC1'], group['PC2'], s=60,\n label=groups_dict[name][0],\n c=groups_dict[name][1],\n marker=groups_dict[name][2])\n \n ax.set_xlabel('First principal component')\n ax.set_ylabel('Second principal component')\n ax.set_title('First two PCA directions')\n \n # Plot 3 components\n ax = fig.add_subplot(1, 2, 2, projection='3d', \n elev=-150, azim=135)\n\n # Refit the PCA, this time specifying 3 components\n # and transforming the result\n feat_reduced = PCA(n_components=3).fit_transform(feat_std)\n\n # Build df with colours\n data = pd.DataFrame({'PC1':feat_reduced[:, 0],\n 'PC2':feat_reduced[:, 1],\n 'PC3':feat_reduced[:, 2],\n 'country':cats['country'],\n 'regulated':cats['regulated']}) \n \n groups = data.groupby(['country', 'regulated'])\n \n # Plot\n for name, group in groups:\n ax.scatter(group['PC1'], group['PC2'], group['PC3'],\n label=groups_dict[name][0],\n c=groups_dict[name][1],\n marker=groups_dict[name][2],\n s=60)\n \n ax.set_title('First three PCA directions')\n ax.set_xlabel('First principal component')\n ax.set_ylabel('Second principal component')\n ax.set_zlabel('Third principal component')\n ax.legend(bbox_to_anchor=(0.15, -0.1), frameon=True)\n plt.show()\n\n return loads","def MPE(cpts, obs):\r\n s = z3.Optimize()\r\n # we store our variables in a dictionary because it makes retrieval easier\r\n var = {}\r\n for variable in cpts:\r\n var[variable[0]] = (Bool(variable[0]))\r\n # we use this loop to make our hard assertions\r\n for observations in obs:\r\n if observations != []:\r\n if observations[1] == '0':\r\n s.add(Not(var[observations[0]]))\r\n else:\r\n s.add(var[observations[0]])\r\n # call our helper function to add all of our soft assertions\r\n for cpt in cpts:\r\n weighted_clause(cpt, var, s)\r\n s.check()\r\n return s.model()","def create_vision_transformer_model_3d(input_image_size,\n number_of_classification_labels=1000,\n mode=\"classification\",\n patch_size=6,\n number_of_transformer_layers=8,\n transformer_units=[128, 64],\n projection_dimension=64,\n number_of_attention_heads=4,\n mlp_head_units=[2048, 1024],\n dropout_rate=0.5):\n\n inputs = Input(shape=input_image_size)\n\n patches = ExtractPatches3D(patch_size)(inputs)\n number_of_patches = ((input_image_size[0] * input_image_size[1] * input_image_size[2]) // (patch_size ** 3))\n encoded_patches = EncodePatches(number_of_patches,\n projection_dimension)(patches)\n\n for _ in range(number_of_transformer_layers):\n\n x1 = LayerNormalization(epsilon=1e-6)(encoded_patches)\n\n attention_output = MultiHeadAttention(num_heads=number_of_attention_heads,\n key_dim=projection_dimension,\n dropout=dropout_rate/5.0)(x1, x1)\n x2 = Add()([attention_output, encoded_patches])\n x3 = LayerNormalization(epsilon=1e-6)(x2)\n x3 = multilayer_perceptron(x3,\n hidden_units=transformer_units,\n dropout_rate=dropout_rate/5.0)\n encoded_patches = Add()([x3, x2])\n\n representation = LayerNormalization(epsilon=1e-6)(encoded_patches)\n representation = Flatten()(representation)\n representation = Dropout(dropout_rate)(representation)\n\n features = multilayer_perceptron(representation,\n hidden_units=mlp_head_units,\n dropout_rate=dropout_rate)\n\n layer_activation = ''\n if mode == 'classification':\n layer_activation = 'softmax'\n elif mode == 'regression':\n layer_activation = 'linear'\n elif mode == 'sigmoid':\n layer_activation = 'sigmoid'\n else:\n raise ValueError('mode must be either `classification` or `regression`.')\n\n outputs = Dense(number_of_classification_labels,\n activation=layer_activation)(features)\n\n model = Model(inputs=inputs, outputs=outputs)\n\n return model","def taucmm1_ncmm3(n):\n # Données issues de Kukharaskii : Solid States Communications 13, 1761 (1973) : Plasmon-phonon coupling in GaAs\n wp_cmm1 = np.array([156, 193, 202, 357, 692, 748, 802])\n tau_cmm1 = np.array([75, 84, 86, 88, 95, 102, 105])\n wp_n = convert_wpcmm1_dop(wp_cmm1)\n p = np.poly1d(np.polyfit(wp_n,tau_cmm1,3))\n return p(n)","def get_mc_pred(output,eng,frame,nImg):\n preds_2d = [];\n preds_3d = [];\n \n c,s = ut.calc_cent_scale(frame);\n \n center = matlab.double([list(c)],(1,2));\n scale = matlab.double([s],(1,1));\n \n for i in tqdm(range(0,nImg)):\n preds_2d.append(eng.transformMPII(output[\"W_final\"][2*i:2*(i+1)],center,scale,matlab.double([64,64],(1,2)),1));\n preds_3d.append(output[\"S_final\"][3*i:3*i + 3]);\n \n print(\"Converting estimates to Python format...\");\n preds_2d = np.array(preds_2d);\n preds_3d = np.array(preds_3d);\n \n preds_2d = preds_2d.swapaxes(1, 2);\n preds_3d = preds_3d.swapaxes(1, 2);\n \n return preds_2d, preds_3d;","def create_model(\n self,\n return_simulations: bool = False,\n return_rdatas: bool = False,\n ) -> Callable[[Union[Sequence, Mapping]], Mapping]:\n # parameter ids to consider\n x_free_ids = self.petab_problem.get_x_ids(free=True, fixed=False)\n\n # fixed parameters\n x_fixed_ids = self.petab_problem.get_x_ids(\n free=False, fixed=True)\n x_fixed_vals = self.petab_problem.get_x_nominal(\n scaled=True, free=False, fixed=True)\n\n # extract variables for improved pickling\n petab_problem = self.petab_problem\n amici_model = self.amici_model\n amici_solver = self.amici_solver\n prior_scales = self.prior_scales\n scaled_scales = self.scaled_scales\n\n if set(prior_scales.keys()) != set(x_free_ids):\n # this should not happen\n raise AssertionError(\"Parameter id mismatch\")\n\n # no gradients for pyabc\n amici_solver.setSensitivityOrder(0)\n\n def model(par: Union[Sequence, Mapping]) -> Mapping:\n \"\"\"The model function.\n\n Note: The parameters are assumed to be passed on prior scale.\n \"\"\"\n # copy since we add fixed parameters\n par = copy.deepcopy(par)\n\n # convenience to allow calling model not only with dicts\n if not isinstance(par, Mapping):\n par = {key: val for key, val in zip(x_free_ids, par)}\n\n # add fixed parameters\n for key, val in zip(x_fixed_ids, x_fixed_vals):\n par[key] = val\n\n # scale parameters whose priors are not on scale\n for key in prior_scales.keys():\n par[key] = rescale(\n val=par[key],\n origin_scale=prior_scales,\n target_scale=scaled_scales,\n )\n\n # simulate model\n sim = simulate_petab(\n petab_problem=petab_problem,\n amici_model=amici_model,\n solver=amici_solver,\n problem_parameters=par,\n scaled_parameters=True)\n\n # return values of interest\n ret = {'llh': sim[LLH]}\n if return_simulations:\n for i_rdata, rdata in enumerate(sim[RDATAS]):\n ret[f'y_{i_rdata}'] = rdata['y']\n if return_rdatas:\n ret[RDATAS] = sim[RDATAS]\n\n return ret\n\n return model","def simulate_qubit_pairs_3D_lattice(cbrt_n, m):\n # Initialize some variables\n n = np.power(cbrt_n, 3)\n x_max = 20\n d = np.arange(0, x_max + 1)\n k_matrix = np.zeros((m, x_max + 1))\n\n # Run the simulation a total of m times\n for i in range(m):\n start = time.time()\n\n sim = chp_py.CHP_Simulation(n)\n sims = []\n # Add a sim for depth 0\n sims.append(chp_py.CHP_Simulation(n, sim.state))\n\n for j in range(x_max):\n gates = utils.get_lattice_gates((cbrt_n, cbrt_n, cbrt_n), j)\n sim.apply_gates(gates)\n sims.append(chp_py.CHP_Simulation(n, sim.state))\n\n # This computes the collision_probability at different\n # depths in parallel\n k_matrix[i, :] = utils.sims_to_collision_probabilty(sims)\n\n end = time.time()\n print(\"Time elapsed = {:0.2f}, for sample = {}\".format(end - start, i))\n\n # Store everything in data\n data = {\"d\": d,\n \"k_matrix\": k_matrix,\n \"n\": n,\n \"m\": m}\n\n return data","def runSoftmaxOnMNIST_PCA_Cubic():\n print(\"[INFO] Running soft max on MNIST data with PCA 10 and Cubic Kernel...\")\n\n tempParameter = 1.0\n print(\"[INFO] Using tempParameter of:\", tempParameter)\n\n ### LOAD DATA ###\n trainX, trainY, testX, testY = getMNISTData()\n print(\"[INFO] Loaded in data...\")\n\n ### Find the 10-dimensional PCA representation of the training and test set ###\n n_components = 10\n pcs = principalComponents(trainX)\n train_pca10 = projectOntoPC(trainX, pcs, n_components)\n test_pca10 = projectOntoPC(testX, pcs, n_components)\n print(\"[INFO] Got PCA features...\")\n\n ### CONVERT TO CUBIC REPRESENTATION ###\n train_cube = cubicFeatures(train_pca10)\n test_cube = cubicFeatures(test_pca10)\n print(\"[INFO] Got cubic features...\")\n\n ### TRAIN THE MODEL ###\n start = time.time()\n theta, costFunctionHistory = softmaxRegression(train_cube, trainY, tempParameter, alpha=0.3, lambdaFactor=1.0e-4, k=10, numIterations=150)\n finish = time.time()\n print(\"[INFO] Finished training in %f secs\" % (finish-start))\n\n ### PLOTTING ###\n plotCostFunctionOverTime(costFunctionHistory)\n\n ### COMPUTE ERROR ###\n err = computeTestError(test_cube, testY, theta, tempParameter)\n print(\" ### TEST ERROR RESULTS ###\")\n print(\"Test Error (PCA10 w/ Cubic Features):\", err)\n\n return err","def marcovNuc (i = random.choice(stateSpace), step = 100):\n # matrix of transition probabilities\n #matrix = [[0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25]] \n matrix = [[0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1]] \n step += 1 # add one to the range because we remove it at the end\n sims = [] # List to hold the results of the Marcov chain\n sims.append(i) # append the seed value to the sims list\n for x in range(step):\n \n if sims[-1] == 'A':\n w = np.random.random() # Random number generator\n # the next set of if statements determine where the random number \n # sits on the number line of probabilities\n if matrix[0][0] > w:\n sims.append('A')\n elif matrix[0][1] + matrix[0][0] > w:\n sims.append('C')\n elif matrix[0][2] + matrix[0][1] + matrix[0][0] > w:\n sims.append('G')\n else:\n sims.append('T')\n elif sims[-1] == 'C':\n x = np.random.random()\n if matrix[1][0] > x:\n sims.append('A')\n elif matrix[1][1] + matrix[1][0] > x:\n sims.append('C')\n elif matrix[1][2] + matrix[1][1] + matrix[1][0] > x:\n sims.append('G')\n else:\n sims.append('T')\n \n elif sims[-1] == 'G':\n y = np.random.random()\n if matrix[2][0] > y:\n sims.append('A')\n elif matrix[2][1] + matrix[2][0] > y:\n sims.append('C')\n elif matrix[2][2] + matrix[2][1] + matrix[2][0] > y:\n sims.append('G')\n else:\n sims.append('T')\n\n else:\n z = np.random.random()\n if matrix[3][0] > z:\n sims.append('A')\n elif matrix[3][1] + matrix[3][0] > z:\n sims.append('C')\n elif matrix[3][2] + matrix[3][1] + matrix[3][0] > z:\n sims.append('G')\n else:\n sims.append('T')\n\n return sims[1:-1] # remove the initial value (the seed)","def fit_FITC(self, Kmm, Kmn, Knn_diag):\r\n num_inducing = Kmm.shape[0]\r\n\r\n \"\"\"\r\n Prior approximation parameters:\r\n q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)\r\n Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn\r\n \"\"\"\r\n Lm = jitchol(Kmm)\r\n Lmi = chol_inv(Lm)\r\n Kmmi = np.dot(Lmi.T,Lmi)\r\n P0 = Kmn.T\r\n KmnKnm = np.dot(P0.T, P0)\r\n KmmiKmn = np.dot(Kmmi,P0.T)\r\n Qnn_diag = np.sum(P0.T*KmmiKmn,-2)\r\n Diag0 = Knn_diag - Qnn_diag\r\n R0 = jitchol(Kmmi).T\r\n\r\n \"\"\"\r\n Posterior approximation: q(f|y) = N(f| mu, Sigma)\r\n Sigma = Diag + P*R.T*R*P.T + K\r\n mu = w + P*Gamma\r\n \"\"\"\r\n self.w = np.zeros(self.N)\r\n self.Gamma = np.zeros(num_inducing)\r\n mu = np.zeros(self.N)\r\n P = P0.copy()\r\n R = R0.copy()\r\n Diag = Diag0.copy()\r\n Sigma_diag = Knn_diag\r\n RPT0 = np.dot(R0,P0.T)\r\n\r\n \"\"\"\r\n Initial values - Cavity distribution parameters:\r\n q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}\r\n sigma_ = 1./tau_\r\n mu_ = v_/tau_\r\n \"\"\"\r\n self.tau_ = np.empty(self.N,dtype=float)\r\n self.v_ = np.empty(self.N,dtype=float)\r\n\r\n #Initial values - Marginal moments\r\n z = np.empty(self.N,dtype=float)\r\n self.Z_hat = np.empty(self.N,dtype=float)\r\n phi = np.empty(self.N,dtype=float)\r\n mu_hat = np.empty(self.N,dtype=float)\r\n sigma2_hat = np.empty(self.N,dtype=float)\r\n\r\n #Approximation\r\n epsilon_np1 = 1\r\n epsilon_np2 = 1\r\n \tself.iterations = 0\r\n self.np1 = [self.tau_tilde.copy()]\r\n self.np2 = [self.v_tilde.copy()]\r\n while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:\r\n update_order = np.random.permutation(self.N)\r\n for i in update_order:\r\n #Cavity distribution parameters\r\n self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]\r\n self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]\r\n #Marginal moments\r\n self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model_list[self.index[i]].moments_match(self._transf_data[i],self.tau_[i],self.v_[i])\r\n #Site parameters update\r\n Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])\r\n Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])\r\n self.tau_tilde[i] += Delta_tau\r\n self.v_tilde[i] += Delta_v\r\n #Posterior distribution parameters update\r\n dtd1 = Delta_tau*Diag[i] + 1.\r\n dii = Diag[i]\r\n Diag[i] = dii - (Delta_tau * dii**2.)/dtd1\r\n pi_ = P[i,:].reshape(1,num_inducing)\r\n P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_\r\n Rp_i = np.dot(R,pi_.T)\r\n RTR = np.dot(R.T,np.dot(np.eye(num_inducing) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))\r\n R = jitchol(RTR).T\r\n self.w[i] += (Delta_v - Delta_tau*self.w[i])*dii/dtd1\r\n self.Gamma += (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)\r\n RPT = np.dot(R,P.T)\r\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\r\n mu = self.w + np.dot(P,self.Gamma)\r\n self.iterations += 1\r\n #Sigma recomptutation with Cholesky decompositon\r\n Iplus_Dprod_i = 1./(1.+ Diag0 * self.tau_tilde)\r\n Diag = Diag0 * Iplus_Dprod_i\r\n P = Iplus_Dprod_i[:,None] * P0\r\n safe_diag = np.where(Diag0 < self.tau_tilde, self.tau_tilde/(1.+Diag0*self.tau_tilde), (1. - Iplus_Dprod_i)/Diag0)\r\n L = jitchol(np.eye(num_inducing) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))\r\n R,info = dtrtrs(L,R0,lower=1)\r\n RPT = np.dot(R,P.T)\r\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\r\n self.w = Diag * self.v_tilde\r\n self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))\r\n mu = self.w + np.dot(P,self.Gamma)\r\n epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N\r\n epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N\r\n self.np1.append(self.tau_tilde.copy())\r\n self.np2.append(self.v_tilde.copy())\r\n\r\n return self._compute_GP_variables()","def initialize_model(self, config_param_vals = None):\n self._is_initialized = True\n\n self.fmu.instantiate()\n self.fmu.reset()\n self.fmu.setupExperiment(startTime=self.start_time)\n if config_param_vals is not None:\n self._apply_config(config_param_vals)\n self.fmu.enterInitializationMode()\n self.fmu.exitInitializationMode()\n\n return","def Nmatrix(init_par, alpha, delta, obs, sigma_obs, ccoef, N):\n\tparallax, v, sigma_v = init_par[:-4], init_par[-4:-1], init_par[-1] \n\tplx_obs, mualpha_obs, mudelta_obs = obs[:, 0], obs[:, 1], obs[:, 2]\n\n\tp, q, r = normalTriad(alpha, delta)\n\tmualpha_mod = np.dot(np.transpose(p),v)*parallax/_A\n\tmudelta_mod = np.dot(np.transpose(q),v)*parallax/_A\n\t\n\tplx_mod, mualpha_mod, mudelta_mod = parallax, mualpha_mod, mudelta_mod\n\tsigma_plx, sigma_mualpha, sigma_mudelta = np.transpose(sigma_obs)\n\ta,like, expo, detD = np.ones(N),np.ones(N),np.ones(N), np.ones(N) \n\tC = np.zeros((3,3,N),dtype=np.float64)\n\tC[0,0,:],C[1,1,:],C[2,2,:] = sigma_plx**2.,sigma_mualpha**2., sigma_mudelta**2.\n\tcorr_coefficient_plx_mualpha, corr_coefficient_plx_mudelta, corr_coefficient_mualpha_mudelta = np.zeros(N), np.zeros(N), np.zeros(N)\n\tcorr_coefficient_plx_mualpha[:], corr_coefficient_plx_mudelta[:], corr_coefficient_mualpha_mudelta[:] = ccoef[:, 0], ccoef[:, 1], ccoef[:, 2] \n\t\n\tC[0,1,:], C[0,2,:] = corr_coefficient_plx_mualpha*sigma_plx*sigma_mualpha, corr_coefficient_plx_mudelta*sigma_plx*sigma_mudelta\n\tC[1,0,:], C[1,2,:] = corr_coefficient_plx_mualpha*sigma_plx*sigma_mualpha, corr_coefficient_mualpha_mudelta*sigma_mualpha*sigma_mudelta\n\tC[2,0,:], C[2,1,:] = corr_coefficient_plx_mudelta*sigma_plx*sigma_mudelta, corr_coefficient_mualpha_mudelta*sigma_mualpha*sigma_mudelta\n\tE = np.zeros((3,3,N),dtype=np.float64)\n\tE[1,1,:],E[2,2,:] = (sigma_v**2.)*(parallax/_A)**2., (sigma_v**2.)*(parallax/_A)**2.\n\tD,invD = np.zeros((3,3,N),dtype=np.float64),np.zeros((3,3,N),dtype=np.float64)\n\tD = np.add(E,C)\n\tfor i in range(N):\n\t\tdetD[i] = matrix_det(D[:,:,i]) \n\t\tinvD[:,:,i] = matrix_inv(D[:,:,i])\n\t\t\n\ta_c = np.ones((3,N))\n\ta_c = [plx_obs - plx_mod, mualpha_obs - mualpha_mod, mudelta_obs-mudelta_mod]\n\t\n\t\n\n\t\n\tcprime_pi, cprime_vx, cprime_vy, cprime_vz, = np.ones((3,N)), np.ones((3,N)), \\\n\t\t\t\t\t\t\tnp.ones((3,N)), np.ones((3,N)), \n\tcprime_pi[0,:] = 1.\n\tcprime_pi[1,:] = np.dot(np.transpose(p),v)/_A\n\tcprime_pi[2,:] = np.dot(np.transpose(q),v)/_A\n\t\n\tcprime_vx[0,:] = 0.\n\tcprime_vx[1,:] = -np.sin(alpha)*plx_mod/_A \n\tcprime_vx[2,:] = -np.sin(delta)*np.cos(alpha)*plx_mod/_A\n\n\t\n\tcprime_vy[0,:] = 0.\n\tcprime_vy[1,:] = np.cos(alpha)*plx_mod/_A \n\tcprime_vy[2,:] = -np.sin(delta)*np.sin(alpha)*plx_mod/_A\n\n\tcprime_vz[0,:] = 0.\n\tcprime_vz[1,:] = 0. \n\tcprime_vz[2,:] = np.cos(delta)*plx_mod/_A\n\n\tdlnd_dpi, dlnd_dsigmav = np.zeros(N), np.zeros(N)\n\tde_dpi, de_dsigmav = np.zeros(N), np.zeros(N)\n\t\n\n\t### See formula A.5 \n\tde_dpi[:] = ((sigma_v/_A)**2.)*2.*plx_mod[:]\n\tde_dsigmav[:] = ((plx_mod[:]/_A)**2.)*2.*sigma_v\n\t\n\tdlnd_dpi[:] = (invD[1,1,:] + invD[2,2,:])*de_dpi[:] \n\tdlnd_dsigmav[:] = (invD[1,1,:] + invD[2,2,:])*de_dsigmav[:]\n\t\n\t\n\t\n\t### See formula A.7\n\thess = np.zeros((N+4, N+4))\n\n\thess_diag_pi, hess_diag_pi_1, hess_diag_pi_2 = np.zeros(N), np.zeros(N), np.zeros(N)\n\thess_diag_pi_1[:] = invD[0, 0, :]*cprime_pi[0, :]*cprime_pi[0, :] + invD[0, 1, :]*cprime_pi[0, :]*cprime_pi[1, :] + invD[0, 2, :]*cprime_pi[0, :]*cprime_pi[2, :] + \\\n\t\t\t invD[1, 0, :]*cprime_pi[1, :]*cprime_pi[0, :] + invD[1, 1, :]*cprime_pi[1, :]*cprime_pi[1, :] + invD[1, 2, :]*cprime_pi[1, :]*cprime_pi[2, :] + \\\n\t\t \t invD[2, 0, :]*cprime_pi[2, :]*cprime_pi[0, :] + invD[2, 1, :]*cprime_pi[2, :]*cprime_pi[1, :] + invD[2, 2, :]*cprime_pi[2, :]*cprime_pi[2, :]\t\n\t\n\t\n\t#hess_diag_pi_2[:] = np.sum(0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dpi[:]) ### Check if it's with or without sum: without!\n\t# So correct formula is below.\n\thess_diag_pi_2[:] = (0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dpi[:])\n\thess_diag_pi[:] = hess_diag_pi_1[:] + hess_diag_pi_2[:]\t\n\n\t\n\thess_diag_vx, hess_diag_vy, hess_diag_vz, hess_diag_sigmav = np.zeros(N), np.zeros(N), np.zeros(N), np.zeros(N)\n\thess_pi_vx, hess_pi_vy, hess_pi_vz, hess_pi_sigmav = np.zeros(N), np.zeros(N), np.zeros(N), np.zeros(N)\n\thess_diag_vxi, hess_diag_vyi, hess_diag_vzi = np.zeros(N), np.zeros(N), np.zeros(N)\n\t\n\thess_diag_vxi[:] = invD[0, 0, :]*cprime_vx[0, :]*cprime_vx[0, :] + invD[0, 1, :]*cprime_vx[0, :]*cprime_vx[1, :] + invD[0, 2, :]*cprime_vx[0, :]*cprime_vx[2, :] + \\\n\t\t\t invD[1, 0, :]*cprime_vx[1, :]*cprime_vx[0, :] + invD[1, 1, :]*cprime_vx[1, :]*cprime_vx[1, :] + invD[1, 2, :]*cprime_vx[1, :]*cprime_vx[2, :] + \\\n\t\t\t invD[2, 0, :]*cprime_vx[2, :]*cprime_vx[0, :] + invD[2, 1, :]*cprime_vx[2, :]*cprime_vx[1, :] + invD[2, 2, :]*cprime_vx[2, :]*cprime_vx[2, :] \t\t\n\t\n\thess_diag_vyi[:] = invD[0, 0, :]*cprime_vy[0, :]*cprime_vy[0, :] + invD[0, 1, :]*cprime_vy[0, :]*cprime_vy[1, :] + invD[0, 2, :]*cprime_vy[0, :]*cprime_vy[2, :] +\\\n\t\t\t invD[1, 0, :]*cprime_vy[1, :]*cprime_vy[0, :] + invD[1, 1, :]*cprime_vy[1, :]*cprime_vy[1, :] + invD[1, 2, :]*cprime_vy[1, :]*cprime_vy[2, :] +\\\n\t\t\t invD[2, 0, :]*cprime_vy[2, :]*cprime_vy[0, :] + invD[2, 1, :]*cprime_vy[2, :]*cprime_vy[1, :] + invD[2, 2, :]*cprime_vy[2, :]*cprime_vy[2, :] \t\n\n\n\thess_diag_vzi[:] = invD[0, 0, :]*cprime_vz[0, :]*cprime_vz[0, :] + invD[0, 1, :]*cprime_vz[0, :]*cprime_vz[1, :] + invD[0, 2, :]*cprime_vz[0, :]*cprime_vz[2, :] +\\\n\t\t\t invD[1, 0, :]*cprime_vz[1, :]*cprime_vz[0, :] + invD[1, 1, :]*cprime_vz[1, :]*cprime_vz[1, :] + invD[1, 2, :]*cprime_vz[1, :]*cprime_vz[2, :] +\\\n\t\t\t invD[2, 0, :]*cprime_vz[2, :]*cprime_vz[0, :] + invD[2, 1, :]*cprime_vz[2, :]*cprime_vz[1, :] + invD[2, 2, :]*cprime_vz[2, :]*cprime_vz[2, :] \t\t\n\t\n\n\thess_pi_vx[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vx[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vx[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vx[2, :] +\\\n\t\t\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vx[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vx[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vx[2, :] +\\\n\t\t\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vx[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vx[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vx[2, :] \n\n\thess_pi_vy[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vy[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vy[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vy[2, :] +\\\n\t\t\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vy[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vy[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vy[2, :] +\\\n\t\t\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vy[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vy[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vy[2, :] \n\n\thess_pi_vz[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vz[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vz[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vz[2, :] +\\\n\t\t\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vz[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vz[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vz[2, :] +\\\n\t\t\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vz[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vz[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vz[2, :] \n\n\t\t\t\t\t\t\n\thess_diag_vx = np.sum(hess_diag_vxi)\n\thess_diag_vy = np.sum(hess_diag_vyi)\n\thess_diag_vz = np.sum(hess_diag_vzi)\t\n\t\n\thess_diag_sigmav = np.sum(0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dsigmav[:]*de_dsigmav[:])\n\thess_pi_sigmav[:] = 0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dsigmav[:] \n\n\thess_diag = np.concatenate((hess_diag_pi, np.array([hess_diag_vx, hess_diag_vy, hess_diag_vz, hess_diag_sigmav])))\n\t\n\tfor i in range(N+4):\n\t\thess[i, i] = hess_diag[i]\n\t\t\n\t\n\tfor j in range(N):\n\t\t\thess[j, -4] = hess_pi_vx[j]\n\t\t\thess[j, -3] = hess_pi_vy[j]\n\t\t\thess[j, -2] = hess_pi_vz[j]\n\t\t\thess[j, -1] = hess_pi_sigmav[j]\n\t\t\thess[-4, j] = hess_pi_vx[j]\n\t\t\thess[-3, j] = hess_pi_vy[j] \n\t\t\thess[-2, j] = hess_pi_vz[j]\n\t\t\thess[-1, j] = hess_pi_sigmav[j]\n\t\t\t\n\n\t\n\t\n\tpart_12, part_13, part_23 = np.zeros(N),np.zeros(N),np.zeros(N)\n\tfor ia in range(3):\n\t\tfor ib in range(3):\n\t\t\tpart_12[:] += invD[ia, ib, :]*cprime_vx[ia, :]*cprime_vy[ib, :] \n\t\t\tpart_13[:] += invD[ia, ib, :]*cprime_vx[ia, :]*cprime_vz[ib, :] \n\t\t\tpart_23[:] += invD[ia, ib, :]*cprime_vy[ia, :]*cprime_vz[ib, :] \t\t\t\t\n\n\thess[-4, -3] = np.sum(part_12)\n\thess[-3, -4] = hess[-4, -3]\n\t\n\thess[-4, -2] = np.sum(part_13)\n\thess[-2, -4] = hess[-4, -2]\n\n\thess[-3, -2] = np.sum(part_23)\n\thess[-2, -3] = hess[-3, -2]\n\n\t#### I am returning here the matrix Njk, which is defined as -E(H),\n\t#### where H is the hessian of the likelihood: therefore to obtain the real hessian, one\n\t#### should multiply this by '-1' (see function below.)\n\treturn hess ### See eq. 18","def setup_milp(model, target, remove_blocked=False, exclude_reaction_ids=set()):\n original_model = model\n model = model.copy()\n model.objective=target\n for i in heterologous_reactions.keys(): # turns off each heterologous reaction in order to get the only the native metabolic network.\n model.reactions.get_by_id(i).lower_bound=0\n model.reactions.get_by_id(i).upper_bound=0\n\n for r in model.reactions:\n if r.id.find('MetaCyc')>-1:\n r.type='heterologous'\n else:\n r.type='native'\n\n # Set the solver to Gurobi for the fastest result. Set to CPLEX if Gurobi is not available.\n if \"gurobi\" in cobra.util.solver.solvers.keys():\n logger.info(\"Changing solver to Gurobi and tweaking some parameters.\")\n if \"gurobi_interface\" not in model.solver.interface.__name__:\n model.solver = \"gurobi\"\n # The tolerances are set to the minimum value. This gives maximum precision.\n problem = model.solver.problem\n problem.params.NodeMethod = 1 # primal simplex node relaxation\n problem.params.FeasibilityTol = 1e-9 #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\n problem.params.OptimalityTol = 1e-3 #how sure the solver has to be about this optimum being really the best it has.\n problem.params.IntFeasTol = 1e-9 #If a value is set to an integer, how much may it still vary? > set smallest possible\n problem.params.MIPgapAbs = 1e-9\n problem.params.MIPgap = 1e-9\n problem.params.Threads=1 #In order to reduce memory usage (increased calculation time)\n problem.params.TimeLimit = 200 # Use max 200 seconds when called, return best solution after that\n problem.params.PoolSearchMode = 1 #0 for only finding the optimum, 1 for finding more solutions (but no quality guaranteed), 2 for finding the n best possible solutions\n problem.params.PoolSolutions = 10 # Number of solutions kept when finding the optimal solution\n problem.params.PoolGap = 0.9 # only store solutions within 90% of the optimal objective value\n\n elif \"cplex\" in cobra.util.solver.solvers.keys():\n logger.warning(\"Changing solver to CPLEX, as Gurobi is not available. This may cause a big slowdown and limit options afterwards.\")\n if \"cplex_interface\" not in model.solver.interface.__name__:\n model.solver = \"cplex\"\n # The tolerances are set to the minimum value. This gives maximum precision.\n problem = model.solver.problem\n problem.parameters.mip.strategy.startalgorithm.set(1) # primal simplex node relaxation\n problem.parameters.simplex.tolerances.feasibility.set(1e-9) #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\n problem.parameters.simplex.tolerances.optimality.set(1e-3) #possibly fine with 1e-3, try if allowed. Is how sure the solver has to be about this optimum being really the best it has.\n problem.parameters.mip.tolerances.integrality.set(1e-9) #If a value is set to an integer, how much may it still vary? > set smallest possible\n problem.parameters.mip.tolerances.absmipgap.set(1e-9)\n problem.parameters.mip.tolerances.mipgap.set(1e-9)\n problem.parameters.mip.pool.relgap.set(0.9) # For populate: find all solutions within 10% of the optimum for relgap = 0.1\n problem.parameters.timelimit.set(200) # Use max 200 seconds for solving\n problem.parameters.mip.limits.populate.set(20) # Find max 20 solutions (=default)\n\n else:\n logger.warning(\"You are trying to run 'Hamlet Hot Rig' with %s. This might not end well.\" %\n model.solver.interface.__name__.split(\".\")[-1])\n pass\n # Remove reactions that are blocked: no flux through these reactions possible. This will reduce the search space for the solver, if not done already.\n if remove_blocked:\n blocked_reactions = cameo.flux_analysis.analysis.find_blocked_reactions(model)\n model.remove_reactions(blocked_reactions)\n\n # Make dual\n model_with = model.copy() # This variable looks unnecessary, but is kept out of fear of messing stuff up\n model_with.optimize()\n dual_problem = convert_linear_problem_to_dual(model_with.solver)\n logger.debug(\"Dual problem successfully created\")\n\n # Combine primal and dual\n primal_problem = model.solver\n\n for var in dual_problem.variables: # All variables in the dual are copied to the primal\n var = primal_problem.interface.Variable.clone(var)\n primal_problem.add(var)\n for const in dual_problem.constraints: # All constraints in the dual are copied to the primal\n const = primal_problem.interface.Constraint.clone(const, model=primal_problem)\n primal_problem.add(const)\n logger.debug(\"Dual and primal combined\")\n\n dual_problem.optimize()\n\n # Dictionaries to hold the binary control variables:\n heterologous_y_vars = {} # 1 for 'knockin', 0 for inactive\n medium_y_vars = {} # 1 for medium addition (up to -10), 0 for no addition\n\n # Now the fun stuff\n constrained_dual_vars = set()\n\n # For the knockins and medium additions:\n for reaction in [r for r in model.reactions if r.type == \"heterologous\"]:\n # Add constraint variables\n interface = model.solver.interface\n y_var = interface.Variable(\"y_\" + reaction.id, type=\"binary\")\n\n # Constrain the primal: flux through reactions maximum within (-1000, 1000), or smaller boundaries defined before\n model.solver.add(interface.Constraint(reaction.flux_expression - 1000 * y_var, ub=0, name=\"primal_y_const_\"+reaction.id+\"_ub\"))\n model.solver.add(interface.Constraint(reaction.flux_expression + 1000 * y_var, lb=0, name=\"primal_y_const_\"+reaction.id+\"_lb\"))\n\n # Constrain the dual\n constrained_vars = []\n\n if reaction.upper_bound != 0:\n dual_forward_ub = model.solver.variables[\"dual_\" + reaction.forward_variable.name + \"_ub\"]\n model.solver.add(interface.Constraint(dual_forward_ub - 1000 * (1 - y_var), ub=0))\n constrained_vars.append(dual_forward_ub)\n if reaction.lower_bound != 0:\n dual_reverse_ub = model.solver.variables[\"dual_\" + reaction.reverse_variable.name + \"_ub\"]\n model.solver.add(interface.Constraint(dual_reverse_ub - 1000 * (1 - y_var), ub=0))\n constrained_vars.append(dual_reverse_ub)\n constrained_dual_vars.update(constrained_vars)\n\n # Add y variable to the corresponding modifications dictionary\n heterologous_y_vars[y_var] = reaction\n\n logger.debug(\"Control variables created\")\n\n # Add number of heterologous switch contraint constraint\n heterologous_turn_on = model.solver.interface.Constraint(\n optlang.symbolics.Add(*heterologous_y_vars), lb=0, ub=0, name=\"heterologous_reaction_constraint\"\n )\n model.solver.add(heterologous_turn_on)\n\n # Set the objective\n primal_objective = model.solver.objective\n dual_objective = interface.Objective.clone(\n dual_problem.objective, model=model.solver\n )\n\n switch_objective=interface.Objective(heterologous_turn_on.expression, direction='min')\n\n full_objective = interface.Objective(primal_objective.expression-dual_objective.expression, direction=\"max\")\n model.objective = full_objective\n\n return model,primal_objective,dual_objective,full_objective,switch_objective","def HM_taucmm1_ncmm3(n):\n \n #wp = np.array([1.5e18, 3e18])\n #tau_cmm1 = np.array([59, 70])\n #p = np.poly1d(np.polyfit(wp,tau_cmm1,1))\n \n #return p(n)\n return 70","def mcmc_fit(mc_model, mcmc_parameters):\n mcmc = pymc.MCMC(mc_model)\n mcmc.use_step_method(pymc.AdaptiveMetropolis, mcmc.theta_i)\n print('running MCMC')\n mcmc.sample(mcmc_parameters['points'], mcmc_parameters['burn'], mcmc_parameters['steps'],\n progress_bar=True)\n print('finished MCMC')\n return mcmc","def mc3(**kwargs):\n return _video_resnet('mc3',\n block=BasicBlock,\n conv_makers=[Conv3DSimple] * 2 + [Conv3DNoTemporal] * 2,\n layers=[NUM_LAYER, NUM_LAYER, NUM_LAYER, NUM_LAYER],\n stem=BasicStem, **kwargs)","def example():\n num_user, num_item, ratings = build_ticket()\n \n # suffle_data\n np.random.shuffle(ratings)\n\n # split data to training & validation\n train_pct = 0.9\n train_size = int(train_pct * len(ratings))\n train = ratings[:train_size]\n validation = ratings[train_size:]\n\n # params\n num_feature = 5\n bmf_model = BayesianMatrixFactorization(\n num_user, num_item, num_feature, train, validation, max_rating=1, min_rating=0, ratingsMatirx=ratings)\n\n start_time = time.clock()\n bmf_model.estimate(10)\n end_time = time.clock()\n \n mat = np.matrix(bmf_model.item_features)\n with open('../data/ticket/item_feature', 'w') as f:\n for line in mat:\n np.savetxt(f, line, fmt='%.5f')\n\n print \"time spend = %.3f\" % (end_time - start_time)\n\n return bmf_model","def _construct_mvn(self,\n loc_shape,\n diag_shape,\n update_shape,\n dtype=np.float32):\n if loc_shape is not None:\n loc = np.random.normal(size=loc_shape).astype(dtype)\n else:\n loc = None\n cov_diag_factor = np.random.uniform(\n low=1., high=2., size=diag_shape).astype(dtype)\n cov_perturb_factor = np.random.normal(\n size=update_shape).astype(dtype)\n return mvdplrc.MultivariateNormalDiagPlusLowRankCovariance(\n loc=loc,\n cov_diag_factor=cov_diag_factor,\n cov_perturb_factor=cov_perturb_factor)","def proposal_rule(cov, mean, npix):\n npix = int(npix)\n \n params = np.random.multivariate_normal(mean, cov)\n # check if parameters are in right domain\n \n params[:npix] = test_params(params[:npix], mean[:npix],\\\n cov[:npix,:npix], crit='Rpp')\n params[npix:-1] = test_params(params[npix:-1], mean[npix:-1],\\\n cov[npix:-1,npix:-1], crit='Pb')\n params[-1] = test_params(params[-1], mean[-1], cov[-1,-1], crit='psib')\n \n #print(params) \n return(params)","def get_element(self, PP=None, prec=None, maxD=None, maxC=None, cusp_form=False, ef=True, mp=1E-8, M0_set=None, Y_set=None,\n SetCs={}, use_mpmath=False, constant_term=\"pp\", use_temporary_file_storage=False, return_matrix=False):\n minprec = mp\n if PP is None:\n PP = self.smallest_pp() # Use the smallest discriminant possible\n if prec is None and maxD is None and maxC is None:\n raise ValueError(\"Need either desired precision, number of discriminants or number of coefficients!\")\n if maxD is not None:\n # Need the correct M0 to use for getting this many coefficients.\n prec = None\n M0_set = ceil(maxD / self.rank()) # should be approximately good\n for m in range(1, maxD * self.rank() + 1):\n min_d = maxD\n # We need to make sure that all discriminants below maxD are accounted for\n for r in self.index_set():\n D = D_from_rn(self.multiplier(), (r, m))\n if D < min_d:\n min_d = D\n if min_d >= maxD:\n M0_set = m\n break\n elif maxC is not None:\n prec = None\n M0_set = maxC\n P0 = self.get_principal_parts(PP)\n if len(P0) > 1:\n raise NotImplementedError(\"More than one principal part is not implemented yet\")\n P = P0[0]\n if self._verbose > 0:\n print(\"P={0}\".format(P))\n print(f\"prec={prec}\")\n print(\"M0_set={0}\".format(M0_set))\n F = VVHarmonicWeakMaassFormElement(self, P)\n if self._verbose > 0:\n sys.stdout.flush()\n if M0_set:\n mpmath.mp.dps = 53 # Do the estimate in low precision\n Y = mpmath.mpf(0.75)\n M = M0_set\n [er1, er2] = F.get_error_estimates(Y, M0_set)\n prec = max(er1, er2)\n if self._verbose > 0:\n print(f\"prec={prec}\")\n elif prec is not None:\n [Y, M] = self.get_Y_and_M(P, self._weight, prec)\n else:\n raise ValueError(\"Could not decide number of coefficients to compute from input!\")\n if self._verbose > 0:\n print(f\"prec={prec}\")\n print(f\"Y={Y}\")\n print(f\"M={M}\")\n print(f\"minprec={minprec}\")\n # if minprec is not None and prec > minprec:\n # prec=minprec\n # [Y,M]=self.get_Y_and_M(P,self._weight,minprec)\n Q = M + 10\n if Y_set and Y_set > 0 and Y_set < 0.866:\n Y = Y_set\n if self._verbose > 1:\n print(\"prec=\", prec)\n print(\"Y=\", Y)\n print(\"M=\", M)\n print(\"Q=\", Q)\n dold = mpmath.mp.dps\n mpmath.mp.dps = max(self._dprec, prec + 10)\n if self._verbose > 0:\n print(\"using {0} digits\".format(mpmath.mp.dps))\n print(\"P={0}\".format(P))\n print(\"ef={0}\".format(ef))\n if ef:\n if use_mpmath:\n Y = mpmath.mp.mpf(Y)\n W = vv_harmonic_wmwf_setupV_ef(self, P, Y, M, Q, mpmath.mp.mpf(self._weight))\n else:\n RF = RealField(self._prec)\n Y = RF(Y)\n mpmath.mp.dps = self._setupV_prec\n P = {'+': P}\n W = vv_harmonic_wmwf_setupV_mpc2(self, P, Y, M, Q)\n mpmath.mp.dps = self._prec\n else:\n W = vv_harmonic_wmwf_setupV(self, P, Y, M, Q, self._weight, self._sym_type, verbose=self._verbose)\n W['space'] = self\n W['PP'] = P\n if return_matrix:\n return W\n if use_temporary_file_storage:\n s = \"tmpWN\" + str(self.multiplier().weil_module().N()) + \"-\"\n dirv = [\"/tmp\", \".\"]\n try:\n for dirs in dirv:\n try:\n st = os.stat(dirs)\n [f, tmpfilename] = tempfile.mkstemp(prefix=s, dir=dirs, suffix='.sobj')\n os.close(f)\n raise StopIteration()\n except OSError:\n continue\n except StopIteration:\n pass\n if self._verbose > 0:\n print(\"tmpfilename={0}\".format(tmpfilename))\n try:\n save(W, tmpfilename)\n except MemoryError:\n print(\"Could not save to file!\")\n pass\n N = self.set_norm(P=PP, C=SetCs)\n if self._verbose > 0:\n print(\"N = {0}\".format(N))\n print(\"V.parent={0}\".format(W['V'].parent()))\n if use_mpmath:\n C = solve_system_for_vv_harmonic_weak_Maass_waveforms(W, N, deb=False)\n else:\n C = solve_system_for_vv_harmonic_weak_Maass_waveforms_new(self, W, N)\n if self._verbose > 0:\n print(\"C001={0}\".format(C[0][0][1]))\n CC = dict()\n # we need to shift the indices of C to the (semi-)correct indices\n # i.e. for efficiency we use integers instead of rationals\n F = list()\n for j in C.keys():\n CC[j] = dict()\n for k in C[j].keys():\n CC[j][self.multiplier().weil_module()._D_as_integers[k]] = C[j][k]\n F.append(VVHarmonicWeakMaassFormElement(self, P, CC[j], prec))\n mpmath.mp.dps = dold\n if(len(F) == 1):\n return F[0]\n else:\n return F","def train(self, prob: SvmProblem):\n # Define variables\n self.prob = prob\n C = self.prob.C\n gamma = self.prob.gamma\n\n # Define the inputs to CVXOPT - See Appendix G.2\n P1 = (self.prob.xi_xj * self.prob.yi_yj) + gamma * self.prob.xstari_xstarj\n P2 = gamma * self.prob.xstari_xstarj\n P11 = np.hstack((P1, P2))\n P22 = np.hstack((P2, P2))\n P = np.vstack((P11, P22))\n q = np.hstack((np.repeat(-1, self.prob.num), np.zeros(self.prob.num)))\n negative_eye = -np.eye(self.prob.num, dtype='d')\n zeros = np.zeros((self.prob.num, self.prob.num))\n g1 = np.hstack((negative_eye, zeros))\n g2 = np.hstack((zeros, negative_eye))\n G = np.vstack((g1, g2))\n h1 = np.zeros((self.prob.num, 1))\n h2 = np.repeat(C, self.prob.num).reshape(-1, 1)\n h = np.vstack((h1, h2))\n A1 = np.hstack((self.prob.Y, np.zeros(self.prob.num)))\n A2 = np.repeat(-1, 2 * self.prob.num)\n A = np.vstack((A1, A2))\n b = np.zeros(2)\n b = b.reshape(-1, 1)\n\n P = matrix(P, tc='d')\n q = matrix(q, tc='d')\n G = matrix(G, tc='d')\n h = matrix(h, tc='d')\n A = matrix(A, tc='d')\n b = matrix(b, tc='d')\n\n # Solve optimization problem using CVXOPT\n solvers.options['show_progress'] = False\n sol = solvers.qp(P, q, G, h, A, b)\n alphas_deltas = np.array(sol['x'])\n self.alphas = np.asarray(alphas_deltas[:self.prob.num])\n self.deltas = alphas_deltas[self.prob.num:]\n\n # Populate Classifier object to be returned\n clf = Classifier()\n clf.b = self.getB()\n clf.alphas = self.alphas\n clf.xs = self.prob.X\n clf.ys = self.prob.Y\n clf.kern = self.prob.xkernel\n return clf","def mcmc(data, uncert=None, func=None, indparams=[],\n params=None, pmin=None, pmax=None, stepsize=None,\n prior=None, priorlow=None, priorup=None,\n numit=10, nchains=10, walk='demc', wlike=False,\n leastsq=True, chisqscale=False, grtest=True, burnin=0,\n thinning=1, plots=False, savefile=None, savemodel=None,\n comm=None, resume=False, log=None, rms=False):\n\n # Import the model function:\n if type(func) in [list, tuple, np.ndarray]:\n if func[0] != 'hack':\n if len(func) == 3:\n sys.path.append(func[2])\n exec('from %s import %s as func'%(func[1], func[0]))\n elif not callable(func):\n mu.error(\"'func' must be either, a callable, or an iterable (list, \"\n \"tuple, or ndarray) of strings with the model function, file, \"\n \"and path names.\", log)\n\n if np.ndim(params) == 1: # Force it to be 2D (one for each chain)\n params = np.atleast_2d(params)\n nparams = len(params[0]) # Number of model params\n ndata = len(data) # Number of data values\n # Set default uncertainties:\n if uncert is None:\n uncert = np.ones(ndata)\n # Set default boundaries:\n if pmin is None:\n pmin = np.zeros(nparams) - np.inf\n if pmax is None:\n pmax = np.zeros(nparams) + np.inf\n # Set default stepsize:\n if stepsize is None:\n stepsize = 0.1 * np.abs(params[0])\n # Set prior parameter indices:\n if (prior is None) or (priorup is None) or (priorlow is None):\n prior = priorup = priorlow = np.zeros(nparams) # Zero arrays\n iprior = np.where(priorlow != 0)[0]\n ilog = np.where(priorlow < 0)[0]\n\n nfree = np.sum(stepsize > 0) # Number of free parameters\n chainlen = int(np.ceil(numit/nchains)) # Number of iterations per chain\n ifree = np.where(stepsize > 0)[0] # Free parameter indices\n ishare = np.where(stepsize < 0)[0] # Shared parameter indices\n # Number of model parameters (excluding wavelet parameters):\n if wlike:\n mpars = nparams - 3\n else:\n mpars = nparams\n\n # Intermediate steps to run GR test and print progress report:\n intsteps = chainlen / 10\n\n # Allocate arrays with variables:\n numaccept = np.zeros(nchains) # Number of accepted proposal jumps\n outbounds = np.zeros((nchains, nfree), np.int) # Out of bounds proposals\n allparams = np.zeros((nchains, nfree, chainlen)) # Parameter's record\n if savemodel is not None:\n allmodel = np.zeros((nchains, ndata, chainlen)) # Fit model\n\n if resume:\n oldparams = np.load(savefile)\n nold = np.shape(oldparams)[2] # Number of old-run iterations\n allparams = np.dstack((oldparams, allparams))\n if savemodel is not None:\n allmodel = np.dstack((np.load(savemodel), allmodel))\n # Set params to the last-iteration state of the previous run:\n params = np.repeat(params, nchains, 0)\n params[:,ifree] = oldparams[:,:,-1]\n else:\n nold = 0\n\n # Set MPI flag:\n mpi = comm is not None\n\n if mpi:\n from mpi4py import MPI\n # Send sizes info to other processes:\n array1 = np.asarray([mpars, chainlen], np.int)\n mu.comm_bcast(comm, array1, MPI.INT)\n\n # DEMC parameters:\n gamma = 2.4 / np.sqrt(2*nfree)\n gamma2 = 0.001 # Jump scale factor of support distribution\n\n # Least-squares minimization:\n if leastsq:\n fitargs = (params[0], func, data, uncert, indparams, stepsize, pmin, pmax,\n prior, priorlow, priorup)\n fitchisq, dummy = mf.modelfit(params[0,ifree], args=fitargs)\n fitbestp = np.copy(params[0, ifree])\n mu.msg(1, \"Least-squares best-fitting parameters: \\n{:s}\\n\\n\".\n format(str(fitbestp)), log)\n\n # Replicate to make one set for each chain: (nchains, nparams):\n if np.shape(params)[0] != nchains:\n params = np.repeat(params, nchains, 0)\n # Start chains with an initial jump:\n for p in ifree:\n # For each free param, use a normal distribution: \n params[1:, p] = np.random.normal(params[0, p], stepsize[p], nchains-1)\n # Stay within pmin and pmax boundaries:\n params[np.where(params[:, p] < pmin[p]), p] = pmin[p]\n params[np.where(params[:, p] > pmax[p]), p] = pmax[p]\n \n # Update shared parameters:\n for s in ishare:\n params[:, s] = params[:, -int(stepsize[s])-1]\n\n # Calculate chi-squared for model using current params:\n models = np.zeros((nchains, ndata))\n if mpi:\n # Scatter (send) parameters to func:\n mu.comm_scatter(comm, params[:,0:mpars].flatten(), MPI.DOUBLE)\n # Gather (receive) evaluated models:\n mpimodels = np.zeros(nchains*ndata, np.double)\n mu.comm_gather(comm, mpimodels)\n # Store them in models variable:\n models = np.reshape(mpimodels, (nchains, ndata))\n else:\n for c in np.arange(nchains):\n fargs = [params[c, 0:mpars]] + indparams # List of function's arguments\n models[c] = func(*fargs)\n\n # Calculate chi-squared for each chain:\n currchisq = np.zeros(nchains)\n c2 = np.zeros(nchains) # No-Jeffrey's chisq\n for c in np.arange(nchains):\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\n currchisq[c], c2[c] = dwt.wlikelihood(params[c, mpars:], models[c]-data,\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n else:\n currchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n\n # Scale data-uncertainties such that reduced chisq = 1:\n if chisqscale:\n chifactor = np.sqrt(np.amin(currchisq)/(ndata-nfree))\n uncert *= chifactor\n # Re-calculate chisq with the new uncertainties:\n for c in np.arange(nchains):\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\n currchisq[c], c2[c] = dwt.wlikelihood(params[c,mpars:], models[c]-data,\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n else:\n currchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n if leastsq:\n fitchisq = currchisq[0]\n\n # Get lowest chi-square and best fitting parameters:\n bestchisq = np.amin(c2)\n bestp = np.copy(params[np.argmin(c2)])\n bestmodel = np.copy(models[np.argmin(c2)])\n\n if savemodel is not None:\n allmodel[:,:,0] = models\n\n # Set up the random walks:\n if walk == \"mrw\":\n # Generate proposal jumps from Normal Distribution for MRW:\n mstep = np.random.normal(0, stepsize[ifree], (chainlen, nchains, nfree))\n elif walk == \"demc\":\n # Support random distribution:\n support = np.random.normal(0, stepsize[ifree], (chainlen, nchains, nfree))\n # Generate indices for the chains such r[c] != c:\n r1 = np.random.randint(0, nchains-1, (nchains, chainlen))\n r2 = np.random.randint(0, nchains-1, (nchains, chainlen))\n for c in np.arange(nchains):\n r1[c][np.where(r1[c]==c)] = nchains-1\n r2[c][np.where(r2[c]==c)] = nchains-1\n\n # Uniform random distribution for the Metropolis acceptance rule:\n unif = np.random.uniform(0, 1, (chainlen, nchains))\n\n # Proposed iteration parameters and chi-square (per chain):\n nextp = np.copy(params) # Proposed parameters\n nextchisq = np.zeros(nchains) # Chi square of nextp \n\n # Start loop:\n mu.msg(1, \"Start MCMC chains ({:s})\".format(time.ctime()), log)\n for i in np.arange(chainlen):\n # Proposal jump:\n if walk == \"mrw\":\n jump = mstep[i]\n elif walk == \"demc\":\n jump = (gamma * (params[r1[:,i]]-params[r2[:,i]])[:,ifree] +\n gamma2 * support[i] )\n # Propose next point:\n nextp[:,ifree] = params[:,ifree] + jump\n\n # Check it's within boundaries: \n outpars = np.asarray(((nextp < pmin) | (nextp > pmax))[:,ifree])\n outflag = np.any(outpars, axis=1)\n outbounds += ((nextp < pmin) | (nextp > pmax))[:,ifree]\n for p in ifree:\n nextp[np.where(nextp[:, p] < pmin[p]), p] = pmin[p]\n nextp[np.where(nextp[:, p] > pmax[p]), p] = pmax[p]\n\n # Update shared parameters:\n for s in ishare:\n nextp[:, s] = nextp[:, -int(stepsize[s])-1]\n\n # Evaluate the models for the proposed parameters:\n if mpi:\n mu.comm_scatter(comm, nextp[:,0:mpars].flatten(), MPI.DOUBLE)\n mu.comm_gather(comm, mpimodels)\n models = np.reshape(mpimodels, (nchains, ndata))\n else:\n for c in np.where(~outflag)[0]:\n fargs = [nextp[c, 0:mpars]] + indparams # List of function's arguments\n models[c] = func(*fargs)\n\n # Calculate chisq:\n for c in np.where(~outflag)[0]:\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\n nextchisq[c], c2[c] = dwt.wlikelihood(nextp[c,mpars:], models[c]-data,\n (nextp[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n else:\n nextchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\n (nextp[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n\n # Reject out-of-bound jumps:\n nextchisq[np.where(outflag)] = np.inf\n # Evaluate which steps are accepted and update values:\n accept = np.exp(0.5 * (currchisq - nextchisq))\n accepted = accept >= unif[i]\n if i >= burnin:\n numaccept += accepted\n # Update params and chi square:\n params [accepted] = nextp [accepted]\n currchisq[accepted] = nextchisq[accepted]\n\n # Check lowest chi-square:\n if np.amin(c2) < bestchisq:\n bestp = np.copy(params[np.argmin(c2)])\n bestmodel = np.copy(models[np.argmin(c2)])\n bestchisq = np.amin(c2)\n\n # Store current iteration values:\n allparams[:,:,i+nold] = params[:, ifree]\n if savemodel is not None:\n models[~accepted] = allmodel[~accepted,:,i+nold-1]\n allmodel[:,:,i+nold] = models\n \n # Print intermediate info:\n if ((i+1) % intsteps == 0) and (i > 0):\n mu.progressbar((i+1.0)/chainlen, log)\n mu.msg(1, \"Out-of-bound Trials:\\n {:s}\".\n format(np.sum(outbounds, axis=0)), log)\n mu.msg(1, \"Best Parameters: (chisq={:.4f})\\n{:s}\".\n format(bestchisq, str(bestp)), log)\n\n # Gelman-Rubin statistic:\n if grtest and (i+nold) > burnin:\n psrf = gr.convergetest(allparams[:, :, burnin:i+nold+1:thinning])\n mu.msg(1, \"Gelman-Rubin statistic for free parameters:\\n{:s}\".\n format(psrf), log)\n if np.all(psrf < 1.01):\n mu.msg(1, \"All parameters have converged to within 1% of unity.\", log)\n # Save current results:\n if savefile is not None:\n np.save(savefile, allparams[:,:,0:i+nold])\n if savemodel is not None:\n np.save(savemodel, allmodel[:,:,0:i+nold])\n\n # Stack together the chains:\n allstack = allparams[0, :, burnin:]\n for c in np.arange(1, nchains):\n allstack = np.hstack((allstack, allparams[c, :, burnin:]))\n # And the models:\n if savemodel is not None:\n modelstack = allmodel[0,:,burnin:]\n for c in np.arange(1, nchains):\n modelstack = np.hstack((modelstack, allmodel[c, :, burnin:]))\n\n # Print out Summary:\n mu.msg(1, \"\\nFin, MCMC Summary:\\n------------------\", log)\n\n nsample = (chainlen-burnin)*nchains # This sample\n ntotal = (nold+chainlen-burnin)*nchains\n BIC = bestchisq + nfree*np.log(ndata)\n redchisq = bestchisq/(ndata-nfree)\n sdr = np.std(bestmodel-data)\n\n fmtlen = len(str(ntotal))\n mu.msg(1, \"Burned in iterations per chain: {:{}d}\".\n format(burnin, fmtlen), log, 1)\n mu.msg(1, \"Number of iterations per chain: {:{}d}\".\n format(chainlen, fmtlen), log, 1)\n mu.msg(1, \"MCMC sample size: {:{}d}\".\n format(nsample, fmtlen), log, 1)\n mu.msg(resume, \"Total MCMC sample size: {:{}d}\".\n format(ntotal, fmtlen), log, 1)\n mu.msg(1, \"Acceptance rate: {:.2f}%\\n \".\n format(np.sum(numaccept)*100.0/nsample), log, 1)\n\n meanp = np.mean(allstack, axis=1) # Parameters mean\n uncertp = np.std(allstack, axis=1) # Parameter standard deviation\n mu.msg(1, \"Best-fit params Uncertainties Signal/Noise Sample \"\n \"Mean\", log, 1)\n for i in np.arange(nfree):\n mu.msg(1, \"{: 15.7e} {: 15.7e} {:12.2f} {: 15.7e}\".\n format(bestp[ifree][i], uncertp[i],\n np.abs(bestp[ifree][i])/uncertp[i], meanp[i]), log, 1)\n\n if leastsq and np.any(np.abs((bestp[ifree]-fitbestp)/fitbestp) > 1e-08):\n np.set_printoptions(precision=8)\n mu.warning(\"MCMC found a better fit than the minimizer:\\n\"\n \" MCMC best-fitting parameters: (chisq={:.8g})\\n {:s}\\n\"\n \" Minimizer best-fitting parameters: (chisq={:.8g})\\n\"\n \" {:s}\".format(bestchisq, str(bestp[ifree]), \n fitchisq, str(fitbestp)), log)\n\n fmtl = len(\"%.4f\"%BIC) # Length of string formatting\n mu.msg(1, \" \", log)\n if chisqscale:\n mu.msg(1, \"sqrt(reduced chi-squared) factor: {:{}.4f}\".\n format(chifactor, fmtl), log, 1)\n mu.msg(1, \"Best-parameter's chi-squared: {:{}.4f}\".\n format(bestchisq, fmtl), log, 1)\n mu.msg(1, \"Bayesian Information Criterion: {:{}.4f}\".\n format(BIC, fmtl), log, 1)\n mu.msg(1, \"Reduced chi-squared: {:{}.4f}\".\n format(redchisq, fmtl), log, 1)\n mu.msg(1, \"Standard deviation of residuals: {:.6g}\\n\".format(sdr), log, 1)\n\n\n if rms:\n rms, rmse, stderr, bs = ta.binrms(bestmodel-data)\n\n if plots:\n print(\"Plotting figures ...\")\n # Extract filename from savefile:\n if savefile is not None:\n if savefile.rfind(\".\") == -1:\n fname = savefile[savefile.rfind(\"/\")+1:] # Cut out file extention.\n else:\n fname = savefile[savefile.rfind(\"/\")+1:savefile.rfind(\".\")]\n else:\n fname = \"MCMC\"\n # Trace plot:\n mp.trace(allstack, thinning=thinning, savefile=fname+\"_trace.png\",\n sep=np.size(allstack[0])/nchains)\n # Pairwise posteriors:\n mp.pairwise(allstack, thinning=thinning, savefile=fname+\"_pairwise.png\")\n # Histograms:\n mp.histogram(allstack, thinning=thinning, savefile=fname+\"_posterior.png\")\n # RMS vs bin size:\n if rms:\n mp.RMS(bs, rms, stderr, rmse, binstep=len(bs)/500+1,\n savefile=fname+\"_RMS.png\")\n if indparams != [] and np.size(indparams[0]) == ndata:\n mp.modelfit(data, uncert, indparams[0], bestmodel,\n savefile=fname+\"_model.png\")\n\n # Save definitive results:\n if savefile is not None:\n np.save(savefile, allparams)\n if savemodel is not None:\n np.save(savemodel, allmodel)\n\n return allstack, bestp","def generate_C_for_m(t, spherical=False, sigma_m=1.0, eigen_th= 0.01):\n if not spherical:\n n_u= generate_n_u(t)\n tmp= np.random.rand(len(n_u), len(n_u))\n matrix= (tmp + tmp.T)/2.0\n eigv, eigw= np.linalg.eigh(matrix)\n eigv[eigv < eigen_th]= eigen_th\n eigv= eigv*(sigma_m*sigma_m)\n matrix= np.dot(np.dot(eigw.T, np.diag(eigv)), eigw)\n return matrix\n else:\n n_u= generate_n_u(t)\n matrix= np.eye(len(n_u))*(sigma_m*sigma_m)\n return matrix","def create_confusion_matrix(model, X_test, y_test, test_pred, title):\n # Confusion matrix\n class_names = [\"Skipped\", \"Not Skipped\"]\n print(\"Confusion Matrix: %s\" % title)\n print(confusion_matrix(y_test, test_pred))\n\n disp = plot_confusion_matrix(\n model,\n X_test,\n y_test,\n display_labels=class_names,\n cmap=plt.cm.Blues,\n normalize=\"true\",\n )\n disp.ax_.set_title(\"Confusion Matrix %s (Normalized)\" % title)\n\n print(\"Confusion Matrix %s (Normalized)\" % title)\n print(disp.confusion_matrix)\n\n plt.show()","def example_wbf_3d_2_models(iou_thr=0.55, draw_image=True):\n\n boxes_list = [\n [\n [0.00, 0.51, 0.41, 0.81, 0.91, 0.78],\n [0.10, 0.31, 0.45, 0.71, 0.61, 0.85],\n [0.01, 0.32, 0.55, 0.83, 0.93, 0.95],\n [0.02, 0.53, 0.11, 0.11, 0.94, 0.55],\n [0.03, 0.24, 0.34, 0.12, 0.35, 0.67],\n ],\n [\n [0.04, 0.56, 0.36, 0.84, 0.92, 0.82],\n [0.12, 0.33, 0.46, 0.72, 0.64, 0.75],\n [0.38, 0.66, 0.55, 0.79, 0.95, 0.90],\n [0.08, 0.49, 0.15, 0.21, 0.89, 0.67],\n ],\n ]\n scores_list = [\n [\n 0.9,\n 0.8,\n 0.2,\n 0.4,\n 0.7,\n ],\n [\n 0.5,\n 0.8,\n 0.7,\n 0.3,\n ]\n ]\n labels_list = [\n [\n 0,\n 1,\n 0,\n 1,\n 1,\n ],\n [\n 1,\n 1,\n 1,\n 0,\n ]\n ]\n weights = [2, 1]\n if draw_image:\n show_boxes(boxes_list, scores_list, labels_list)\n\n boxes, scores, labels = weighted_boxes_fusion_3d(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=0.0)\n\n if draw_image:\n show_boxes([boxes], [scores], [labels.astype(np.int32)])\n\n print(len(boxes))\n print(boxes)","def create_mtl_pt_model(model_args):\n global_model = torch.load(model_args['global_model_fn'])\n model = MTL_MIMIC_Model(model_args[\"input_dim\"],\n model_args[\"n_layers\"],\n model_args[\"units\"],\n model_args[\"num_dense_shared_layers\"],\n model_args[\"dense_shared_layer_size\"],\n model_args[\"n_multi_layers\"],\n model_args[\"multi_units\"],\n model_args[\"output_dim\"],\n model_args[\"tasks\"],\n lstm = global_model.lstm,\n shared = global_model.rest[:-2],\n )\n return model","def setUp(self):\n # Simple QP problem\n sp.random.seed(4)\n\n self.n = 30\n self.m = 30\n self.P = sparse.csc_matrix((self.n, self.n))\n self.q = np.zeros(self.n)\n self.A = sparse.random(self.m, self.n, density=1.0, format='csc')\n self.u = np.random.rand(self.m)\n self.l = self.u\n self.opts = {'verbose': False,\n 'eps_abs': 1e-06,\n 'eps_rel': 1e-06,\n 'scaling': True,\n 'alpha': 1.6,\n 'max_iter': 5000,\n 'polish': False,\n 'warm_start': True,\n 'polish_refine_iter': 4}\n self.model = osqp.OSQP()\n self.model.setup(P=self.P, q=self.q, A=self.A, l=self.l, u=self.u,\n **self.opts)","def generation_model(self, z, c): # P(x|z, c)\n z_c = torch.cat((z, c), dim=1)\n x_hat = self.decoder(z_c)\n return x_hat","def generation_model(self, z, c): # P(x|z, c)\n z_c = torch.cat((z, c), dim=1)\n x_hat = self.decoder(z_c)\n return x_hat","def fit_FITC(self, Kmm, Kmn, Knn_diag, epsilon=1e-3,power_ep=[1.,1.]):\r\n self.epsilon = epsilon\r\n self.eta, self.delta = power_ep\r\n\r\n num_inducing = Kmm.shape[0]\r\n\r\n \"\"\"\r\n Prior approximation parameters:\r\n q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)\r\n Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn\r\n \"\"\"\r\n Lm = jitchol(Kmm)\r\n Lmi = chol_inv(Lm)\r\n Kmmi = np.dot(Lmi.T,Lmi)\r\n P0 = Kmn.T\r\n KmnKnm = np.dot(P0.T, P0)\r\n KmmiKmn = np.dot(Kmmi,P0.T)\r\n Qnn_diag = np.sum(P0.T*KmmiKmn,-2)\r\n Diag0 = Knn_diag - Qnn_diag\r\n R0 = jitchol(Kmmi).T\r\n\r\n \"\"\"\r\n Posterior approximation: q(f|y) = N(f| mu, Sigma)\r\n Sigma = Diag + P*R.T*R*P.T + K\r\n mu = w + P*Gamma\r\n \"\"\"\r\n self.w = np.zeros(self.num_data)\r\n self.Gamma = np.zeros(num_inducing)\r\n mu = np.zeros(self.num_data)\r\n P = P0.copy()\r\n R = R0.copy()\r\n Diag = Diag0.copy()\r\n Sigma_diag = Knn_diag\r\n RPT0 = np.dot(R0,P0.T)\r\n\r\n \"\"\"\r\n Initial values - Cavity distribution parameters:\r\n q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}\r\n sigma_ = 1./tau_\r\n mu_ = v_/tau_\r\n \"\"\"\r\n self.tau_ = np.empty(self.num_data,dtype=float)\r\n self.v_ = np.empty(self.num_data,dtype=float)\r\n\r\n #Initial values - Marginal moments\r\n z = np.empty(self.num_data,dtype=float)\r\n self.Z_hat = np.empty(self.num_data,dtype=float)\r\n phi = np.empty(self.num_data,dtype=float)\r\n mu_hat = np.empty(self.num_data,dtype=float)\r\n sigma2_hat = np.empty(self.num_data,dtype=float)\r\n\r\n #Approximation\r\n epsilon_np1 = 1\r\n epsilon_np2 = 1\r\n \tself.iterations = 0\r\n self.np1 = [self.tau_tilde.copy()]\r\n self.np2 = [self.v_tilde.copy()]\r\n while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:\r\n update_order = np.random.permutation(self.num_data)\r\n for i in update_order:\r\n #Cavity distribution parameters\r\n self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]\r\n self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]\r\n #Marginal moments\r\n self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self.data[i],self.tau_[i],self.v_[i])\r\n #Site parameters update\r\n Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])\r\n Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])\r\n self.tau_tilde[i] += Delta_tau\r\n self.v_tilde[i] += Delta_v\r\n #Posterior distribution parameters update\r\n dtd1 = Delta_tau*Diag[i] + 1.\r\n dii = Diag[i]\r\n Diag[i] = dii - (Delta_tau * dii**2.)/dtd1\r\n pi_ = P[i,:].reshape(1,num_inducing)\r\n P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_\r\n Rp_i = np.dot(R,pi_.T)\r\n RTR = np.dot(R.T,np.dot(np.eye(num_inducing) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))\r\n R = jitchol(RTR).T\r\n self.w[i] += (Delta_v - Delta_tau*self.w[i])*dii/dtd1\r\n self.Gamma += (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)\r\n RPT = np.dot(R,P.T)\r\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\r\n mu = self.w + np.dot(P,self.Gamma)\r\n self.iterations += 1\r\n #Sigma recomptutation with Cholesky decompositon\r\n Iplus_Dprod_i = 1./(1.+ Diag0 * self.tau_tilde)\r\n Diag = Diag0 * Iplus_Dprod_i\r\n P = Iplus_Dprod_i[:,None] * P0\r\n safe_diag = np.where(Diag0 < self.tau_tilde, self.tau_tilde/(1.+Diag0*self.tau_tilde), (1. - Iplus_Dprod_i)/Diag0)\r\n L = jitchol(np.eye(num_inducing) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))\r\n R,info = dtrtrs(L,R0,lower=1)\r\n RPT = np.dot(R,P.T)\r\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\r\n self.w = Diag * self.v_tilde\r\n self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))\r\n mu = self.w + np.dot(P,self.Gamma)\r\n epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.num_data\r\n epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.num_data\r\n self.np1.append(self.tau_tilde.copy())\r\n self.np2.append(self.v_tilde.copy())\r\n\r\n return self._compute_GP_variables()","def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=None, optimize=True, plot=True):\r\n data = GPy.util.datasets.crescent_data(seed=seed)\r\n Y = data['Y']\r\n Y[Y.flatten()==-1] = 0\r\n\r\n if model_type == 'Full':\r\n m = GPy.models.GPClassification(data['X'], Y, kernel=kernel)\r\n\r\n elif model_type == 'DTC':\r\n m = GPy.models.SparseGPClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)\r\n m['.*len'] = 10.\r\n\r\n elif model_type == 'FITC':\r\n m = GPy.models.FITCClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)\r\n m['.*len'] = 3.\r\n\r\n if optimize:\r\n m.pseudo_EM()\r\n\r\n if plot:\r\n m.plot()\r\n\r\n print m\r\n return m","def build(obs_space: Box, action_space: Box, spec: Spec) -> MLPModel:\n model = MLPModel(obs_space, action_space, spec.network)\n model.initialize_parameters(spec.initializer)\n if spec.residual:\n model = ResidualStochasticModel(model)\n return model","def create_p3_feature(C3, C4, C5, feature_size=256):\n P5 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C5_reduced')(C5)\n P5_upsampled = layers.UpsampleLike(name='P5_upsampled')([P5, C4])\n\n # add P5 elementwise to C4\n P4 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C4_reduced')(C4)\n P4 = keras.layers.Add(name='P4_merged')([P5_upsampled, P4])\n P4_upsampled = layers.UpsampleLike(name='P4_upsampled')([P4, C3])\n\n # add P4 elementwise to C3\n P3 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C3_reduced')(C3)\n P3 = keras.layers.Add(name='P3_merged')([P4_upsampled, P3])\n P3 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='P3')(P3)\n\n return P3","def getMCMC(self,x,y,priors={},datamodel=None):\n import pymc\n from operator import isSequenceType\n from inspect import getargspec\n from types import MethodType\n\n if set(priors.keys()) != set(self.params):\n raise ValueError(\"input priors don't match function params\")\n\n d={}\n for k,v in priors.iteritems():\n if isinstance(v,pymc.StochasticBase):\n d[k]=v\n elif isSequenceType(v):\n if len(v) == 2:\n d[k]=pymc.distributions.Uniform(k,v[0],v[1])\n else:\n raise ValueError(\"couldn't understand sequence \"+str(v) )\n elif v > 0:\n d[k] = pymc.distributions.Normal(k,self.pardict[k],1.0/v/v)\n elif v == 0:\n d[k] = pymc.distributions.Poisson(k,self.pardict[k])\n else:\n raise ValueError(\"couldn't interpret prior \"+str(v))\n\n funcdict=dict(d)\n if type(self.f) is MethodType:\n xstr = getargspec(self.f)[0][1]\n else:\n assert callable(self.f),'object function is not a callable'\n xstr = getargspec(self.f)[0][0]\n funcdict[xstr]=x\n funcdet=pymc.Deterministic(name='f',eval=self.f,parents=funcdict,doc=\"FunctionModel1D function\")\n d['f'] = funcdet\n\n if type(datamodel) is tuple:\n distobj,dataarg,kwargs=datamodel\n if 'name' not in kwargs:\n kwargs['name'] = 'data'\n\n kwargs[dataarg] = funcdet\n kwargs['observed'] = True\n kwargs['value'] = y\n\n datamodel = distobj(**kwargs)\n\n d[datamodel.__name__] = datamodel\n\n #now if any stochastics are in the kwargs, assume they are part of the model\n for k, v in kwargs.iteritems():\n if isinstance(v, pymc.StochasticBase):\n d[k] = v\n else:\n if datamodel is None:\n sig = np.std(y)\n else:\n sig = np.array(datamodel)\n d['data'] = pymc.distributions.Normal('data',mu=funcdet,tau=1/sig/sig,observed=True,value=y)\n\n return pymc.MCMC(d)","def build_mlp(input_, config):\n current_input = input_\n print(current_input)\n for i in range(len(config['fcc_layers']) - 1):\n current_input = tf.keras.layers.Dense(\n units=config['fcc_layers'][i], activation='tanh',\n name='fcc_layer_{}'.format(i + 1))(current_input)\n current_input = tf.keras.layers.Dropout(\n rate=config['dropout'], name='dropout_{}'.format(i + 1))(current_input)\n cascade_embedding_layer = tf.keras.layers.Dense(\n units=config['fcc_layers'][-1], activation='tanh',\n name='cascade_embedding_layer')(current_input)\n cascade_embedding_layer_do = tf.keras.layers.Dropout(\n rate=config['dropout'],\n name='cascade_embedding_dropout')(cascade_embedding_layer)\n prediction_layer = tf.keras.layers.Dense(\n units=1, activation='sigmoid',\n name='prediction_layer')(cascade_embedding_layer_do)\n return cascade_embedding_layer, prediction_layer","def make_mlp_model():\n return snt.Sequential([\n snt.nets.MLP([LATENT_SIZE] * NUM_LAYERS, activate_final=True),\n snt.LayerNorm()\n ])","def __init__(self, input_size: int, output_size: int, old_cols: List[AlphaModule] = None, hidden_size=768,\n ptm=\"bert\", feature_size=768, require_proto=False, tokenizer=None, prob_l=-1) -> None:\n super(PTMClassifier_PNN, self).__init__()\n \n if old_cols is None:\n old_cols = []\n \n self.old_cols = []\n \n self.output_size = output_size\n self.hidden_size = hidden_size # default\n self.feature_size = feature_size\n \n self.fc1 = nn.Linear(self.hidden_size, 100)\n self.classifier = nn.Linear(100, self.output_size)\n \n if len(old_cols) > 0:\n self.old_fc1s = ListModule()\n self.base_1 = nn.Sequential(\n nn.Linear(100 * len(old_cols), 100),\n nn.ReLU(),\n )\n self.base_2 = nn.Sequential(\n nn.Linear(100 * len(old_cols), 100),\n nn.ReLU(),\n )\n \n self.adaptor1 = nn.Sequential(AlphaModule(100 * len(old_cols)),\n self.base_1)\n self.adaptor2 = nn.Sequential(AlphaModule(100 * len(old_cols)),\n self.base_2)\n \n for old_col in old_cols:\n self.old_fc1s.append(\n nn.Sequential(nn.Linear(self.hidden_size, 100), nn.ReLU()))\n self.old_fc1s[-1][0].load_state_dict(old_col.fc1.state_dict())\n \n # ptm\n self.ptm = ptm.lower()\n assert self.ptm in supported_ptm\n ptm_ = import_from(\"transformers\", supported_ptm[self.ptm][0] + \"Model\")\n \n self.encoder = ptm_.from_pretrained(supported_ptm[self.ptm][1], output_hidden_states=True)\n if tokenizer is not None:\n self.encoder.resize_token_embeddings(len(tokenizer))\n self.prob_l = prob_l\n \n self.require_proto = require_proto\n \n # todo : modify net into one network\n if self.require_proto:\n # prototype-based classification\n self.net = nn.Sequential(\n self.fc1\n )\n self.net_ = nn.Sequential(\n self.encoder,\n self.fc1,\n self.encoder_adaptor,\n nn.ReLU(),\n )\n else:\n self.classifier = nn.Linear(self.feature_size, self.output_size, bias=True)\n self.net = nn.Sequential(\n self.classifier\n )\n self.net_ = nn.Sequential(\n self.encoder,\n self.classifier\n )\n \n self.reset_parameters()","def MILP_1(self,xData,uData):\n print \"\\n\",\"*\"*80,\"\\n\",\"MILP 1: Parameter Estimation\\n\",\"*\"*80 \n model=Model(\"parameters\")\n outflow={}\n d={}\n bigM=500\n Q_out={}\n Q_in={}\n N=max(l[1] for l in xData.keys())\n print \"x Data size is\",N\n N=max(l[1] for l in uData.keys())\n print \"u Data size is\",N\n for l in self.links:\n l.d=model.addVar(lb=0,ub=200,obj=0*l.type==\"road\") \n for t in range(1,N):\n d[l,t]=model.addVar(lb=0,ub=200,obj=1)\n for k in l.outgoing:\n outflow[l,k,t]=model.addVar(lb=0,ub=200)\n self.c[l,k]=model.addVar(lb=20,ub=200)\n self.beta[l,k]=model.addVar(lb=0.2,ub=0.8)\n self.alpha[l,k]=model.addVar(lb=0,ub=1)\n self.M[l,k]=model.addVar(lb=0,ub=200)\n d[\"outflow-1\",l,k,t]=model.addVar(vtype=GRB.BINARY) \n d[\"outflow-2\",l,k,t]=model.addVar(vtype=GRB.BINARY) \n model.update()\n for t in range(1,N):\n for l in self.links:\n if True:\n Q_out[l,t]=LinExpr()\n Q_in[l,t]=LinExpr()\n Q_out[l,t].addConstant(0)\n Q_in[l,t].addConstant(0)\n for k in l.outgoing:\n model.addConstr(outflow[l,k,t]<=self.beta[l,k]*uData[l,t]*xData[l,t])\n model.addConstr(outflow[l,k,t]<=self.M[l,k])\n model.addConstr(outflow[l,k,t]<=self.c[l,k]-self.alpha[l,k]*xData[k,t])\n model.addConstr(outflow[l,k,t]>=self.beta[l,k]*uData[l,t]*xData[l,t]+bigM*d[\"outflow-1\",l,k,t]-bigM)\n model.addConstr(outflow[l,k,t]>=self.M[l,k]+bigM*d[\"outflow-2\",l,k,t]-bigM)\n model.addConstr(outflow[l,k,t]>=self.c[l,k]-self.alpha[l,k]*xData[k,t]-bigM*d[\"outflow-1\",l,k,t]-bigM*d[\"outflow-2\",l,k,t])\n Q_out[l,t].add(outflow[l,k,t])\n for k in l.incoming:\n Q_in[l,t].add(outflow[k,l,t])\n if l.type==\"road\":\n model.addConstr(xData[l,t+1]<=xData[l,t]- Q_out[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival) \n else:\n model.addConstr(xData[l,t+1]<=xData[l,t]- uData[l,t]*xData[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival)\n for l in self.links:\n sum=LinExpr()\n for k in l.outgoing:\n sum.add(self.beta[l,k])\n model.addConstr(sum>=0)\n \n# J=QuadExpr()\n# for l in self.links:\n# for t in range(1,N):\n# if l.type==\"road\":\n# J.add(d[l,t]*d[l,t])\n# model.setObjective(J)\n model.optimize()\n for l in self.links:\n l.d=l.d.X\n for k in l.outgoing:\n self.beta[l,k]=self.beta[l,k].X\n self.c[l,k]=self.c[l,k].X\n self.alpha[l,k]=self.alpha[l,k].X\n self.M[l,k]=self.M[l,k].X\n for l in self.links:\n for t in range(1,N):\n l.d=max(d[l,t].X,l.d)\n \n \n\n \n if True:\n for t in range(1,N):\n print \"*\"*80,\"time=\",t\n for l in self.links:\n print \"\\n\",l,\"x is\",xData[l,t],\"u is\",uData[l,t],\"x+ is\",xData[l,t+1]\n for k in l.outgoing:\n print k,\"beta:\",self.beta[l,k],\"outflow\",outflow[l,k,t].X","def __init__(\n self,\n n_words: int,\n n_topics: int = 3,\n prior_mu_loc: Union[None, tf.Variable, tf.Tensor] = None,\n prior_mu_scale_diag: Union[None, tf.Variable, tf.Tensor] = None,\n m_mu: Union[None, tf.Variable] = None,\n sigma_mu: Union[None, tf.Variable] = None,\n prior_ell_loc: Union[None, tf.Variable, tf.Tensor] = None,\n prior_ell_scale_diag: Union[None, tf.Variable, tf.Tensor] = None,\n m_ell: Union[None, tf.Variable] = None,\n sigma_ell: Union[None, tf.Variable] = None,\n topics_words_logits: Union[None, tf.Variable, tf.Tensor] = None,\n layer_sizes: List[Union[int, str]] = (300, 300, 300),\n activation: str = \"relu\",\n dtype: Union[np.dtype, tf.DType, str] = np.float64,\n jitter: float = 1e-5,\n validate_args: bool = False,\n allow_nan_stats: bool = False,\n name: str = \"CTMMuSigma\",\n ):\n super(CTMMuSigma, self).__init__(\n n_topics=n_topics,\n n_words=n_words,\n topics_words_logits=topics_words_logits,\n layer_sizes=layer_sizes,\n activation=activation,\n jitter=jitter,\n dtype=dtype,\n validate_args=validate_args,\n allow_nan_stats=allow_nan_stats,\n name=name,\n )\n\n del self.prior_eta # unused\n\n dof = n_topics * (n_topics + 1) // 2\n self.bijector_ell = tfb.FillScaleTriL(diag_shift=tf.cast(1e-5, dtype))\n\n if prior_mu_loc is None:\n prior_mu_loc = np.random.standard_normal([1, n_topics]).astype(dtype=dtype)\n\n if prior_mu_scale_diag is None:\n prior_mu_scale_diag = tf.zeros([1, n_topics], dtype=dtype) + 0.01\n\n self.prior_mu = tfd.MultivariateNormalDiag(\n prior_mu_loc,\n scale_diag=prior_mu_scale_diag,\n validate_args=validate_args,\n allow_nan_stats=allow_nan_stats,\n name=\"prior_mu\",\n )\n\n if m_mu is None:\n m_mu = np.random.standard_normal([1, n_topics]).astype(dtype=dtype)\n m_mu = tf.Variable(m_mu, name=\"m_mu\")\n\n if sigma_mu is None:\n sigma_mu = tf.zeros([1, n_topics], dtype=dtype) + 0.01\n sigma_mu = tfp.util.TransformedVariable(\n sigma_mu,\n bijector=tfb.Chain([tfb.Shift(tf.cast(1e-3, dtype)), tfb.Softplus()]),\n name=\"sigma_mu\",\n )\n self.surrogate_posterior_mu = tfd.MultivariateNormalDiag(\n m_mu,\n scale_diag=sigma_mu,\n validate_args=validate_args,\n allow_nan_stats=allow_nan_stats,\n name=\"posterior_mu\",\n )\n\n if prior_ell_loc is None:\n prior_ell_loc = np.random.standard_normal([1, dof]).astype(dtype=dtype)\n\n if prior_ell_scale_diag is None:\n prior_ell_scale_diag = tf.zeros([1, dof], dtype=dtype) + 0.01\n\n self.prior_ell = tfd.MultivariateNormalDiag(\n prior_ell_loc,\n scale_diag=prior_ell_scale_diag,\n validate_args=validate_args,\n allow_nan_stats=allow_nan_stats,\n name=\"prior_ell\",\n )\n\n if m_ell is None:\n m_ell = np.random.standard_normal([1, dof]).astype(dtype=dtype)\n m_ell = tf.Variable(m_ell, name=\"m_ell\")\n\n if sigma_ell is None:\n sigma_ell = tf.zeros([1, dof], dtype=dtype) + 0.01\n sigma_ell = tfp.util.TransformedVariable(\n sigma_ell,\n bijector=tfb.Chain([tfb.Shift(tf.cast(1e-3, dtype)), tfb.Softplus()]),\n name=\"sigma_ell\",\n )\n self.surrogate_posterior_ell = tfd.MultivariateNormalDiag(\n m_ell,\n scale_diag=sigma_ell,\n validate_args=validate_args,\n allow_nan_stats=allow_nan_stats,\n name=\"posterior_ell\",\n )","def template_model():\n model_type = 'continuous' # either 'discrete' or 'continuous'\n model = do_mpc.model.Model(model_type)\n\n # Model variables:\n var1 = model.set_variable(var_type='_x', var_name='var1')\n var2 = model.set_variable(var_type='_x', var_name='var2')\n\n state = vertcat(var1,var2)\n state_dot = model.set_variable(var_type='_x', var_name='state_dot', shape=(2.1))\n\n input1 = model.set_variable(var_type='_u', var_name='input1')\n\n\n # Parameters:\n # define Parameters\n\n model.set_rhs('var1',state_dot[0])\n model.set_rhs('var2',state_dot[1])\n\n state_dot_rhs = vertcat(\n # rhs1,\n # rhs2)\n model.set_rhs('state_dot',state_dot_rhs)\n\n model.setup()\n\n return model","def init_model(args: argparse.Namespace, device) -> Tuple[MultivarMLP, AdjacencyMatrix]:\n model = create_model(num_vars=args.num_variables, \n num_categs=args.max_categories, \n hidden_dims=args.hidden_dims, \n actfn='leakyrelu')\n \n \n if args.data_parallel:\n device = torch.device(\"cuda:0\")\n print(\"Data parallel activated. Using %i GPUs\" % torch.cuda.device_count())\n model = nn.DataParallel(model)\n else:\n device = torch.device(\"cpu\")\n \n adj_matrix = AdjacencyMatrix(args.num_variables, device)\n return model, adj_matrix"],"string":"[\n \"def model_test(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in, models_fit, label):\\n # Generate fake data with some \\\"true\\\" parameters\\n (D_vec, Ninv) = gen_data(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in)\\n Ninv_sqrt = np.matrix(linalg.sqrtm(Ninv))\\n (dust_params, sync_params, cmb_params) = params_in\\n (dust_amp, sync_amp, cmb_amp) = amps_in\\n \\n # Beam model\\n beam_mat = np.identity(3*len(nu))\\n\\n # Set-up MCMC\\n dust_guess = np.array([1.6, 20.])\\n sync_guess = np.array([-3.])\\n cmb_guess = np.array([])\\n guess = np.concatenate((dust_guess, sync_guess, cmb_guess))\\n #ndim = len(dust_guess) + len(sync_guess) + len(cmb_guess)\\n \\n # Run MCMC sampler on this model\\n t0 = time.time()\\n dust_params_out, sync_params_out, cmb_params_out, samples \\\\\\n = mcmc(guess, nu, D_vec, Ninv, beam_mat, models_fit, label)\\n print \\\"MCMC run in %d sec.\\\" % (time.time() - t0)\\n \\n # Estimate error on recovered CMB amplitudes\\n (F_fg, F_cmb, F) = F_matrix(nu, dust_params_out, sync_params_out, cmb_params_out, models_fit)\\n H = F_fg.T*Ninv*F_fg\\n x_mat = np.linalg.inv(F.T*beam_mat.T*Ninv*beam_mat*F)*F.T*beam_mat.T*Ninv*D_vec # Equation A3\\n \\n U, Lambda, VT = np.linalg.svd(Ninv_sqrt*F_fg, full_matrices=False) # Equation A14\\n \\n print \\\"-\\\"*30\\n print \\\"F_cmb.T\\\", F_cmb.T.shape\\n print \\\"Ninv_sqrt\\\", Ninv_sqrt.shape\\n print \\\"F_cmb\\\", F_cmb.shape\\n print \\\"I\\\", np.identity(U.shape[0]).shape\\n print \\\"U\\\", U.shape\\n print \\\"U.T\\\", U.T.shape\\n print \\\"-\\\"*30\\n \\n \\n \\n N_eff_inv_cmb = F_cmb.T*Ninv_sqrt*(np.matrix(np.identity(U.shape[0])) - U*U.T)*Ninv_sqrt*F_cmb # Equation A16\\n N_eff_cmb = np.linalg.inv(N_eff_inv_cmb)\\n cmb_noise = np.array([N_eff_cmb[0,0], N_eff_cmb[1,1], N_eff_cmb[2,2]])\\n\\n gls_cmb = x_mat[0:3,0]\\n cmb_chisq = (np.matrix(cmb_amp).T - gls_cmb).T*N_eff_inv_cmb*(np.matrix(cmb_amp).T - gls_cmb)\\n \\n # Output triangle plots for dust\\n if label != None:\\n if (models_fit[0] == 'mbb' and models_fit[1] == 'pow'):\\n if (models_in[0] == 'mbb'):\\n fig = corner.corner(samples, truths=[dust_params[0], dust_params[1], sync_params[0]],\\n labels=[r\\\"$\\\\beta_d$\\\", r\\\"$T_d$\\\",r\\\"$\\\\alpha_s$\\\"])\\n else :\\n fig = corner.corner(samples, labels=[r\\\"$\\\\beta_d$\\\", r\\\"$T_d$\\\",r\\\"$\\\\alpha_s$\\\"])\\n else :\\n print 'Error! Not configured for this plot!'\\n exit()\\n fig.savefig('triangle_' + label + '.png')\\n plt.close('all')\\n \\n # Run multinest sampler\\n #multinest(nu, D_vec, Ninv, beam_mat, ndim, models_fit, label)\\n \\n return gls_cmb, cmb_chisq, cmb_noise\",\n \"def compute_pmf(X_inp, rank, c1, c2):\\n\\n X_arr = []\\n u_arr = []\\n v_arr = []\\n d_arr = []\\n\\n X = deepcopy(X_inp)\\n\\n v_init = np.ones((np.shape(X)[1],1))\\n\\n for i in range(rank):\\n X_arr.append(X)\\n (d,u,v) = compute_factor(X, v_init, c1, c2)\\n\\n\\n assert abs(npla.norm(u) - 1 ) < 1e-3\\n assert abs(npla.norm(v) - 1 ) < 1e-3\\n\\n d_arr.append(d)\\n u_arr.append(u)\\n v_arr.append(v)\\n\\n toSub = np.outer(u,v)\\n assert np.shape(toSub) == np.shape(X)\\n X -= d * toSub\\n\\n return (X_arr, u_arr, v_arr, d_arr)\",\n \"def build_mod_bpmf_model(train, alpha=2, dim=10, std=0.01):\\n n, m = train.shape\\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\\n\\n # Mean value imputation on training data.\\n train = train.copy()\\n nan_mask = np.isnan(train)\\n train[nan_mask] = train[~nan_mask].mean()\\n\\n # We will use separate priors for sigma and correlation matrix.\\n # In order to convert the upper triangular correlation values to a\\n # complete correlation matrix, we need to construct an index matrix:\\n n_elem = dim * (dim - 1) / 2\\n tri_index = np.zeros([dim, dim], dtype=int)\\n tri_index[np.triu_indices(dim, k=1)] = np.arange(n_elem)\\n tri_index[np.triu_indices(dim, k=1)[::-1]] = np.arange(n_elem)\\n\\n logging.info('building the BPMF model')\\n with pm.Model() as bpmf:\\n # Specify user feature matrix\\n sigma_u = pm.Uniform('sigma_u', shape=dim)\\n corr_triangle_u = pm.LKJCorr(\\n 'corr_u', n=1, p=dim,\\n testval=np.random.randn(n_elem) * std)\\n\\n corr_matrix_u = corr_triangle_u[tri_index]\\n corr_matrix_u = t.fill_diagonal(corr_matrix_u, 1)\\n cov_matrix_u = t.diag(sigma_u).dot(corr_matrix_u.dot(t.diag(sigma_u)))\\n lambda_u = t.nlinalg.matrix_inverse(cov_matrix_u)\\n\\n mu_u = pm.Normal(\\n 'mu_u', mu=0, tau=beta_0 * t.diag(lambda_u), shape=dim,\\n testval=np.random.randn(dim) * std)\\n U = pm.MvNormal(\\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\\n testval=np.random.randn(n, dim) * std)\\n\\n # Specify item feature matrix\\n sigma_v = pm.Uniform('sigma_v', shape=dim)\\n corr_triangle_v = pm.LKJCorr(\\n 'corr_v', n=1, p=dim,\\n testval=np.random.randn(n_elem) * std)\\n\\n corr_matrix_v = corr_triangle_v[tri_index]\\n corr_matrix_v = t.fill_diagonal(corr_matrix_v, 1)\\n cov_matrix_v = t.diag(sigma_v).dot(corr_matrix_v.dot(t.diag(sigma_v)))\\n lambda_v = t.nlinalg.matrix_inverse(cov_matrix_v)\\n\\n mu_v = pm.Normal(\\n 'mu_v', mu=0, tau=beta_0 * t.diag(lambda_v), shape=dim,\\n testval=np.random.randn(dim) * std)\\n V = pm.MvNormal(\\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\\n testval=np.random.randn(m, dim) * std)\\n\\n # Specify rating likelihood function\\n R = pm.Normal(\\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\\n observed=train)\\n\\n logging.info('done building the BPMF model')\\n return bpmf\",\n \"def gen_fpm_cube_from_LUT(mp, modelType):\\n # Different variables for compact and full models\\n if modelType.lower() == 'compact':\\n # size = (Ndiel, Nmetal, mp.Nsbp)\\n complexTransCube = mp.complexTransCompact\\n lamVec = mp.sbp_centers\\n elif modelType.lower() == 'full':\\n # size = (Ndiel,Nmetal,mp.Nsbp*mp.Nwpsbp)\\n complexTransCube = mp.complexTransFull\\n lamVec = mp.full.lambdas\\n else:\\n raise ValueError('modelType must specify full or compact model.')\\n Nlam = len(lamVec)\\n\\n t_diel_bias = mp.t_diel_bias_nm*1e-9 # bias thickness of dielectric [m]\\n\\n # Generate thickness profiles of each layer\\n # Metal layer profile [m]\\n DM8surf = falco.hlc.gen_fpm_surf_from_cube(mp.dm8, modelType)\\n # Dielectric layer profile [m]\\n DM9surf = t_diel_bias + falco.hlc.gen_fpm_surf_from_cube(mp.dm9,\\n modelType)\\n Neta, Nxi = DM8surf.shape\\n\\n if not (DM8surf.shape == DM9surf.shape):\\n raise ValueError('FPM surf arrays for DM 8 and 9 must be same size!')\\n\\n # Obtain indices of nearest thickness values in the complex tran datacube.\\n DM8transInd = falco.hlc.discretize_fpm_surf(DM8surf, mp.t_metal_nm_vec,\\n mp.dt_metal_nm)\\n DM9transInd = falco.hlc.discretize_fpm_surf(DM9surf, mp.t_diel_nm_vec,\\n mp.dt_diel_nm)\\n\\n # Look up values\\n fpmCube = np.zeros((Neta, Nxi, Nlam), dtype=complex)\\n for ilam in range(Nlam):\\n for ix in range(Nxi):\\n for iy in range(Neta):\\n ind_metal = DM8transInd[iy, ix]\\n ind_diel = DM9transInd[iy, ix]\\n fpmCube[iy, ix, ilam] = complexTransCube[ind_diel, ind_metal,\\n ilam]\\n\\n return fpmCube, DM8surf, DM9surf\",\n \"def build_bpmf_model(train, alpha=2, dim=10, std=0.01):\\n n, m = train.shape\\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\\n\\n # Mean value imputation on training data.\\n train = train.copy()\\n nan_mask = np.isnan(train)\\n train[nan_mask] = train[~nan_mask].mean()\\n\\n logging.info('building the BPMF model')\\n with pm.Model() as bpmf:\\n # Specify user feature matrix\\n lambda_u = pm.Wishart(\\n 'lambda_u', n=dim, V=np.eye(dim), shape=(dim, dim),\\n testval=np.random.randn(dim, dim) * std)\\n mu_u = pm.Normal(\\n 'mu_u', mu=0, tau=beta_0 * lambda_u, shape=dim,\\n testval=np.random.randn(dim) * std)\\n U = pm.MvNormal(\\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\\n testval=np.random.randn(n, dim) * std)\\n\\n # Specify item feature matrix\\n lambda_v = pm.Wishart(\\n 'lambda_v', n=dim, V=np.eye(dim), shape=(dim, dim),\\n testval=np.random.randn(dim, dim) * std)\\n mu_v = pm.Normal(\\n 'mu_v', mu=0, tau=beta_0 * lambda_v, shape=dim,\\n testval=np.random.randn(dim) * std)\\n V = pm.MvNormal(\\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\\n testval=np.random.randn(m, dim) * std)\\n\\n # Specify rating likelihood function\\n R = pm.Normal(\\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\\n observed=train)\\n\\n logging.info('done building the BPMF model')\\n return bpmf\",\n \"def _cp3(X, n_components, tol, max_iter, init_type, random_state=None):\\n\\n if len(X.shape) != 3:\\n raise ValueError(\\\"CP3 decomposition only supports 3 dimensions!\\\")\\n\\n if init_type == \\\"random\\\":\\n A, B, C = _random_init(X, n_components, random_state)\\n elif init_type == \\\"hosvd\\\":\\n A, B, C = _hosvd_init(X, n_components)\\n grams = [np.dot(arr.T, arr) for arr in (A, B, C)]\\n err = 1E10\\n\\n for itr in range(max_iter):\\n err_old = err\\n A = matricize(X, 0).dot(kr(C, B)).dot(linalg.pinv(grams[1] * grams[2]))\\n if itr == 0:\\n normalization = np.sqrt((A ** 2).sum(axis=0))\\n else:\\n normalization = A.max(axis=0)\\n normalization[normalization < 1] = 1\\n A /= normalization\\n grams[0] = np.dot(A.T, A)\\n\\n B = matricize(X, 1).dot(kr(C, A)).dot(linalg.pinv(grams[0] * grams[2]))\\n if itr == 0:\\n normalization = np.sqrt((B ** 2).sum(axis=0))\\n else:\\n normalization = B.max(axis=0)\\n normalization[normalization < 1] = 1\\n B /= normalization\\n grams[1] = np.dot(B.T, B)\\n\\n C = matricize(X, 2).dot(kr(B, A)).dot(linalg.pinv(grams[0] * grams[1]))\\n if itr == 0:\\n normalization = np.sqrt((C ** 2).sum(axis=0))\\n else:\\n normalization = C.max(axis=0)\\n normalization[normalization < 1] = 1\\n C /= normalization\\n grams[2] = np.dot(C.T, C)\\n\\n err = linalg.norm(matricize(X, 0) - np.dot(A, kr(C, B).T)) ** 2\\n thresh = np.abs(err - err_old) / err_old\\n if thresh < tol:\\n break\\n\\n return A, B, C\",\n \"def emulator(pca, gp_model, params):\\n # Weights prediction\\n pred_weights = gp_predict(gp_model, params)\\n\\n # Inverse PCA (pred_weights * basis + mean)\\n reconstructed = pca.inverse_transform(pred_weights)\\n return reconstructed\",\n \"def prob3():\\n #set up the matrices\\n solvers.options['show_progress'] = False\\n c = np.array([4., 7., 6., 8., 8., 9.])\\n\\n G = np.array([[1.,1.,0.,0.,0.,0.],\\n [-1.,-1.,0.,0.,0.,0.],\\n [0.,0.,1.,1.,0.,0.],\\n [0.,0.,-1.,-1.,0.,0.],\\n [0.,0.,0.,0.,1.,1.],\\n [0.,0.,0.,0.,-1.,-1.],\\n [1.,0.,1.,0.,1.,0.],\\n [-1.,0.,-1.,0.,-1.,0.],\\n [0.,1.,0.,1.,0.,1.],\\n [0.,-1.,0.,-1.,0.,-1.]])\\n G = np.vstack((G, -1*np.eye(6)))\\n h = np.array([7,-7,2,-2,4,-4,5,-5,8,-8,0,0,0,0,0,0],dtype=\\\"float\\\")\\n\\n #convert the matrices\\n c = matrix(c)\\n G = matrix(G)\\n h = matrix(h)\\n #solve the matrices\\n sol = solvers.lp(c, G, h)\\n return np.ravel(sol['x']), sol['primal objective']\",\n \"def proba_fm(m_pred,f_pred, dataset):\\n p = np.zeros(10)\\n if dataset == 'cifar10':\\n for i in range(10):\\n if i <4:\\n if i <2:\\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:2]))\\n else:\\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[2:4]))\\n else:\\n if i <6:\\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[4:6]))\\n elif i <8:\\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[6:8]))\\n else:\\n p[i] = (m_pred[4])*(f_pred[i]/np.sum(f_pred[8:]))\\n else :\\n for i in range(10):\\n if i <5:\\n if i <3:\\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:3]))\\n else:\\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[3:5]))\\n else:\\n if i <8:\\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[5:8]))\\n else:\\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[8:]))\\n return(p)\",\n \"def milp(mdp, maxV, zeroConstraints=()):\\n m = Model()\\n m.setParam('OutputFlag', False)\\n\\n # convert notation to previous implementation\\n S = mdp.S\\n A = mdp.A\\n R = mdp.rFuncs\\n psi = mdp.psi\\n T = mdp.T\\n alpha = mdp.alpha\\n gamma = mdp.gamma\\n\\n # useful constants\\n rLen = len(R)\\n M = 10000 # a large number\\n Sr = range(len(S))\\n Ar = range(len(A))\\n\\n # decision variables\\n x = m.addVars(len(S), len(A), lb=0, name='x')\\n z = m.addVars(rLen, vtype=GRB.BINARY, name='z')\\n y = m.addVars(rLen, name='y')\\n\\n # constraints on y\\n for i in range(rLen):\\n m.addConstr(y[i] <= sum([x[s, a] * R[i](S[s], A[a]) for s in Sr for a in Ar]) - maxV[i] + (1 - z[i]) * M)\\n m.addConstr(y[i] <= z[i] * M)\\n\\n # constraints on x (valid occupancy)\\n for sp in Sr:\\n m.addConstr(sum(x[s, a] * ((s == sp) - gamma * T(S[s], A[a], S[sp])) for s in Sr for a in Ar) == alpha(S[sp]))\\n\\n # == constraints\\n for consIdx in range(len(zeroConstraints)):\\n m.addConstr(sum(x[S.index(s), A.index(a)] for s, a in zeroConstraints[consIdx]) == 0)\\n # obj\\n m.setObjective(sum([psi[i] * y[i] for i in xrange(rLen)]), GRB.MAXIMIZE)\\n\\n m.optimize()\\n\\n pi = {(S[s], A[a]): x[s, a].X for s in Sr for a in Ar}\\n\\n if m.status == GRB.Status.OPTIMAL:\\n # return feasible being true and the obj value, opt pi\\n # .X attribute is to retrieve the value of the variable\\n return pi\\n else:\\n # simply return infeasible\\n raise Exception('milp problem optimal solution not found' + m.status)\",\n \"def build_CNNflexi_Modular(input):\\n\\n params_3D = input['params']\\n sample_shape = input['sample_shape']\\n inputA = Input(shape=sample_shape[1:])\\n clf = params_3D['clf']\\n\\n # Model head\\n for i_layers in range(0,params_3D['n_layers']):\\n if i_layers == 0:\\n x = conv3D_block(params_3D,inputA)\\n else:\\n x = conv3D_block(params_3D, x)\\n\\n # Classifiers\\n if clf == 'dense':\\n x = Flatten()(x)\\n x = Dense(256, activation='relu', kernel_initializer='he_uniform',activity_regularizer=l1(params_3D['l1_den']))(x)\\n x = Dense(128, activation='relu', kernel_initializer='he_uniform',activity_regularizer=l1(params_3D['l1_den']))(x)\\n x = Dense(64, activation='relu', kernel_initializer='he_uniform',activity_regularizer=l1(params_3D['l1_den']))(x)\\n elif clf == 'gap':\\n x = GlobalAveragePooling3D()(x)\\n else:\\n raise('Invalid classifier choice!')\\n\\n\\n model = Model(inputs=inputA, outputs=x)\\n return model\",\n \"def create_pyomo_model():\\n model = po.ConcreteModel()\\n model.t = pod.ContinuousSet(bounds=(0, 1))\\n model.tau = po.Var()\\n\\n model.ca = po.Var(model.t, bounds=(0, 50)) # set of concentrations in reaction mixture\\n model.dca_dt = pod.DerivativeVar(model.ca, wrt=model.t)\\n\\n model.beta = po.Var() # model parameters\\n\\n def _material_balance(m, t):\\n return m.dca_dt[t] / m.tau == -m.beta * m.ca[t]\\n model.material_balance = po.Constraint(model.t, rule=_material_balance)\\n\\n simulator = pod.Simulator(model, \\\"casadi\\\")\\n\\n return model, simulator\",\n \"def gen_fpm_from_LUT(mp, si, wi, modelType):\\n # Different variables for compact and full models\\n if modelType.lower() == 'compact':\\n # size = (Ndiel, Nmetal, mp.Nsbp)\\n complexTransCube = mp.complexTransCompact\\n # Index of the wavelength in mp.sbp_centers\\n ilam = si\\n elif modelType.lower() == 'full':\\n # size = (Ndiel,Nmetal,mp.Nsbp*mp.Nwpsbp)\\n complexTransCube = mp.complexTransFull\\n # Index of the wavelength in mp.lam_array\\n ilam = (si-1)*mp.Nwpsbp + wi\\n else:\\n raise ValueError('modelType must specify full or compact model.')\\n\\n t_diel_bias = mp.t_diel_bias_nm*1e-9 # bias thickness of dielectric [m]\\n\\n # Generate thickness profiles of each layer\\n # Metal layer profile [m]\\n DM8surf = falco.hlc.gen_fpm_surf_from_cube(mp.dm8, modelType)\\n # Dielectric layer profile [m]\\n DM9surf = t_diel_bias + falco.hlc.gen_fpm_surf_from_cube(mp.dm9, modelType)\\n Nxi = DM8surf.shape[1]\\n Neta = DM8surf.shape[0]\\n\\n if not (DM8surf.size == DM9surf.size):\\n raise ValueError('FPM surf arrays for DM 8 and 9 must be same size!')\\n\\n # Obtain indices of nearest thickness values in the complex tran datacube.\\n DM8transInd = falco.hlc.discretize_fpm_surf(DM8surf, mp.t_metal_nm_vec,\\n mp.dt_metal_nm)\\n DM9transInd = falco.hlc.discretize_fpm_surf(DM9surf, mp.t_diel_nm_vec,\\n mp.dt_diel_nm)\\n\\n # Look up values\\n fpm = np.zeros((Neta, Nxi), dtype=complex)\\n for ix in range(Nxi):\\n for iy in range(Neta):\\n ind_metal = DM8transInd[iy, ix]\\n ind_diel = DM9transInd[iy, ix]\\n fpm[iy, ix] = complexTransCube[ind_diel, ind_metal, ilam]\\n\\n return fpm\",\n \"def proba(c_pred,m_pred,f_pred, dataset):\\n p = np.zeros(10)\\n if dataset == 'cifar10':\\n for i in range(10):\\n if i <4:\\n if i <2:\\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:2]))\\n elif i <4:\\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[2:4]))\\n if i >=4:\\n if i <6:\\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[4:6]))\\n elif i <8:\\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[6:8]))\\n elif i <10:\\n p[i] = c_pred[1]*(m_pred[4]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[8:10]))\\n else :\\n for i in range(10):\\n if i <5:\\n if i <3:\\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:3]))\\n elif i <5:\\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[3:5]))\\n if i >=5:\\n if i <8:\\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[5:8]))\\n elif i <10:\\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[8:]))\\n return(p)\",\n \"def __init__(self, n_components=5, user=\\\"user_0\\\", model=\\\"gaussian\\\"):\\n self.user = user\\n self.n_components = n_components\\n\\n if(model==\\\"gaussian\\\"):\\n self.model = hmm.GaussianHMM(n_components=n_components, covariance_type=\\\"diag\\\", \\\\\\n init_params=\\\"cm\\\", params=\\\"cmt\\\")\\n elif(model==\\\"GMMHMM\\\"):\\n self.model = hmm.GMMHMM(n_components=n_components, n_mix=3, covariance_type=\\\"diag\\\", \\\\\\n init_params=\\\"cm\\\", params=\\\"cmt\\\")\\n self.model.gmms_ = [sklearn.mixture.GaussianMixture()]*3\\n\\n self.model.startprob_ = np.concatenate(([1],np.zeros(n_components-1)))\\n self.model.transmat_ = self.compute_trans_matrix( n_components )\\n\\n self.overall_accuracy = 0\",\n \"def _initialize_fembv_varx_random(X, n_components, memory=0,\\n u=None, random_state=None,\\n random_Theta=True):\\n rng = check_random_state(random_state)\\n\\n n_samples, n_features = X.shape\\n max_memory = np.max(memory)\\n n_terms = n_samples - max_memory\\n if u is None:\\n n_external = 0\\n else:\\n n_external = u.shape[1]\\n\\n Gamma = right_stochastic_matrix(\\n (n_terms, n_components), random_state=rng)\\n\\n xb = X.mean(axis=0)\\n Theta = [{'mu': xb.copy(), 'memory': memory,\\n 'At': None, 'Bt': None,\\n 'Sigma': np.identity(n_features)}] * n_components\\n\\n for k in range(n_components):\\n if memory > 0:\\n Theta[k]['At'] = np.empty((memory * n_features, n_features))\\n for i in range(memory):\\n if random_Theta:\\n Theta[k]['At'][i * n_features:\\n (i + 1) * n_features] = rng.uniform(\\n size=(n_features, n_features))\\n else:\\n Theta[k]['At'][i * n_features:\\n (i + 1) * n_features] = np.identity(\\n n_features)\\n if u is not None:\\n if random_Theta:\\n Theta[k]['Bt'] = rng.uniform(size=(n_external, n_features))\\n else:\\n Theta[k]['Bt'] = np.zeros((n_external, n_features))\\n\\n return Gamma, Theta\",\n \"def initialize_cplex_problem(model, num_threads=1, lpmethod=0, adv=2):\\n # type: (compass.models.MetabolicModel)\\n\\n # Create the Problem first\\n # Easier to modify existing problem and re-solve\\n problem = cplex.Cplex()\\n problem.set_log_stream(None) # Suppress output\\n problem.set_error_stream(None) # Suppress errors\\n problem.set_warning_stream(None) # Suppress Warnings\\n problem.set_results_stream(None) # Suppress results to output\\n\\n # Set Parameters for the Cplex solver\\n problem.parameters.emphasis.numerical.set(True)\\n problem.parameters.threads.set(num_threads)\\n problem.parameters.preprocessing.reduce.set(3) #Turning on primal and dual preprocessing also enables some reoptimization features\\n problem.parameters.advance.set(adv) #Will presolve advanced basis again\\n problem.parameters.barrier.convergetol.set(1e-12) #default is 1e-8, minimum is 1e-12.\\n problem.parameters.simplex.tolerances.optimality.set(1e-9) #default 1e-6, minimum is 1e-9\\n problem.parameters.lpmethod.set(lpmethod) #default lets CPLEX choose the method\\n\\n # Add variables\\n reactions = list(model.reactions.values())\\n problem.variables.add(\\n names=[x.id for x in reactions],\\n ub=[x.upper_bound for x in reactions],\\n lb=[x.lower_bound for x in reactions],)\\n\\n # Add constraints\\n\\n # Add stoichiometry constraints\\n c_lin_expr, c_senses, c_rhs, c_names = (\\n utils.get_steadystate_constraints(model))\\n\\n problem.linear_constraints.add(\\n lin_expr=c_lin_expr,\\n senses=c_senses,\\n rhs=c_rhs,\\n names=c_names)\\n\\n # Initialize the objective\\n utils.reset_objective(problem)\\n\\n return problem\",\n \"def get_estimates(model):\\n from itertools import product\\n from pymc3 import summary\\n\\n subjects = model.data['subject'].unique().astype(np.int)\\n parameters = ['v', 'gamma', 's', 'tau', 't0']\\n estimates = pd.DataFrame()\\n MAP = extract_modes(model.trace)\\n combinations = list(product(*[model.design['factor_conditions'][factor]\\n for factor in model.design['factors']]))\\n subject_template = pd.DataFrame({factor: [combination[f]\\n for combination in combinations]\\n for f, factor\\n in enumerate(model.design['factors'])},\\n index=np.zeros(1))\\n if model.type == 'hierarchical':\\n summary_table = summary(model.trace[0])\\n elif model.type == 'individual':\\n summary_tables = [summary(trace)\\n for trace in model.trace]\\n else:\\n raise ValueError(\\n 'Model type not understood. Make sure \\\"make_model\\\" has already been called.')\\n for subject in subjects:\\n subject_estimates = subject_template.copy()\\n subject_estimates.loc[:, 'subject'] = np.array([subject])\\n for parameter in parameters:\\n subject_template[parameter] = np.nan\\n subject_template[parameter + '_hpd_2.5'] = np.nan\\n subject_template[parameter + '_hpd_97.5'] = np.nan\\n subject_template[parameter] = np.nan\\n\\n dependence = model.design[parameter]['dependence']\\n if dependence is None:\\n # Parameter is fixed\\n if model.type == 'hierarchical':\\n # add participant paramaters\\n subject_estimates[parameter] = MAP[0][parameter][subject][0]\\n subject_estimates[parameter + '_hpd_2.5'] = summary_table.loc[parameter +\\n '__{}_0'.format(subject), 'hpd_2.5']\\n subject_estimates[parameter + '_hpd_97.5'] = summary_table.loc[parameter +\\n '__{}_0'.format(subject), 'hpd_97.5']\\n # add population parameters\\n if (parameter + '_mu') in summary_table.index:\\n subject_estimates[parameter +\\n '_mu'] = summary_table.loc[parameter + '_mu', 'mean']\\n subject_estimates[parameter +\\n '_mu_hpd_2.5'] = summary_table.loc[parameter + '_mu', 'hpd_2.5']\\n subject_estimates[parameter +\\n '_mu_hpd_97.5'] = summary_table.loc[parameter + '_mu', 'hpd_97.5']\\n\\n elif model.type == 'individual':\\n # add participant paramaters\\n subject_estimates[parameter] = MAP[subject][parameter][0][0]\\n subject_estimates[parameter +\\n '_hpd_2.5'] = summary_tables[subject].loc[parameter + '__0_0', 'hpd_2.5']\\n subject_estimates[parameter +\\n '_hpd_97.5'] = summary_tables[subject].loc[parameter + '__0_0', 'hpd_97.5']\\n else:\\n # Parameter has dependence\\n conditions = model.design[parameter]['conditions']\\n for condition in conditions:\\n if condition not in model.data.loc[model.data['subject'] == subject, dependence].values:\\n subject_estimates = subject_estimates.drop(subject_estimates[subject_estimates[dependence] == condition].index,\\n axis=0)\\n else:\\n # Check if subject is in condition\\n if subject in model.design[parameter][condition]['subjects']:\\n parameter_condition = parameter + '_' + condition\\n if model.type == 'hierarchical':\\n index = model.design[parameter][condition]['subject_mapping'][subject]\\n # extract participant parameters\\n estimate = MAP[parameter_condition][index]\\n hpd25 = summary_table.loc[parameter_condition +\\n '__{}'.format(index), 'hpd_2.5']\\n hpd975 = summary_table.loc[parameter_condition +\\n '__{}'.format(index), 'hpd_97.5']\\n # extract population parameters\\n if (parameter_condition + '_mu') in summary_table.index:\\n pop_estimate = summary_table.loc[parameter_condition + '_mu', 'mean']\\n pop_hpd25 = summary_table.loc[parameter_condition +\\n '_mu', 'hpd_2.5']\\n pop_hpd975 = summary_table.loc[parameter_condition +\\n '_mu', 'hpd_97.5']\\n\\n elif model.type == 'individual':\\n if model.design[parameter]['type'] == 'between':\\n estimate = MAP[subject][parameter]\\n hpd25 = summary_tables[subject].loc[parameter +\\n '__0_0', 'hpd_2.5']\\n hpd975 = summary_tables[subject].loc[parameter +\\n '__0_0', 'hpd_97.5']\\n elif model.design[parameter]['type'] == 'within':\\n estimate = MAP[subject][parameter_condition]\\n hpd25 = summary_tables[subject].loc[parameter_condition +\\n '__0_0', 'hpd_2.5']\\n hpd975 = summary_tables[subject].loc[parameter_condition +\\n '__0_0', 'hpd_97.5']\\n else:\\n raise ValueError('Parameter dependence not understood for {}: {} ({}).'.format(\\n parameter, dependence, condition))\\n else:\\n raise ValueError(\\n 'Model type not understood. Make sure \\\"make_model\\\" has already been called.')\\n # add participant parameters\\n subject_estimates.loc[subject_estimates[dependence]\\n == condition, parameter] = estimate\\n subject_estimates.loc[subject_estimates[dependence]\\n == condition, parameter + '_hpd_2.5'] = hpd25\\n subject_estimates.loc[subject_estimates[dependence]\\n == condition, parameter + '_hpd_97.5'] = hpd975\\n # add population parameters\\n if model.type == 'hierarchical':\\n subject_estimates.loc[subject_estimates[dependence]\\n == condition, parameter + '_mu'] = pop_estimate\\n subject_estimates.loc[subject_estimates[dependence]\\n == condition, parameter + '_mu_hpd_2.5'] = pop_hpd25\\n subject_estimates.loc[subject_estimates[dependence] ==\\n condition, parameter + '_mu_hpd_97.5'] = pop_hpd975\\n\\n estimates = pd.concat([estimates, subject_estimates])\\n\\n estimates.reset_index(inplace=True, drop=True)\\n return estimates\",\n \"def run_model_three():\\n\\n pos_map = pickle.load(open('pos_map.p', \\\"rb\\\"))\\n vocab = build_vocab(training_directory)\\n word2vec = get_w2vec(wordtovec)\\n\\n train_out_list = prep_data_lexsub(training_directory, \\\"mod3_train_prepped.txt\\\", 3, pos_map, vocab, word2vec)\\n test_out_list = prep_data_lexsub(testing_directory, \\\"mod3_test_prepped.txt\\\", 3, pos_map, vocab, word2vec)\\n\\n posset = get_posset()\\n vocab.add(\\\"<s>\\\")\\n vocab.add(\\\"<UNK>\\\")\\n convert_to_svm('mod3_train_prepped.txt', \\\"mod3_train.svm\\\", posset, vocab)\\n convert_to_svm('mod3_test_prepped.txt', \\\"mod3_test.svm\\\", posset, vocab)\\n\\n p_labels, p_acc, p_vals = train_test_model(\\\"mod3_train.svm\\\", \\\"mod3_test.svm\\\")\\n with open(\\\"modelscores.txt\\\", \\\"a\\\") as text_file:\\n text_file.write(str(p_acc[0]))\\n text_file.write(\\\"\\\\n\\\")\\n pickle.dump(p_labels, open('mod3_p_labels.p', 'wb'))\",\n \"def abcmodel_matrix(\\n S0: float, P: np.ndarray, a: float, b: float, c: float\\n) -> np.ndarray:\\n P = P[:, np.newaxis] if len(P.shape) == 1 else P\\n t = len(P) # number of timesteps\\n\\n # Calculate the contribution of initial storage.\\n # Shape: (t, 1)\\n b_vec = np.array([[c * (1 - c) ** i for i in range(t)]]).T\\n\\n # Calculate the contribution of Precip / previous storage.\\n # Shape: (t, t)\\n A = np.eye(t) * (1 - a - b)\\n\\n # set the off diagonal elements:\\n # ac(1 - c) ** (row_ix + 1) - (col_x + 2)\\n row_ix = np.tril_indices_from(A, k=-1)[0]\\n col_ix = np.tril_indices_from(A, k=-1)[1]\\n A[(row_ix, col_ix)] = a * c * (1 - c) ** ((row_ix + 1) - (col_ix + 2))\\n\\n # Calculate Simulated Discharge (Q)\\n Qsim = (A @ P) + (S0 * b_vec)\\n\\n assert Qsim.shape == (t, 1)\\n return Qsim\",\n \"def cloudModel(PsPs, PcPc):\\n PsPc = 1 - PsPs\\n PcPs = 1 - PcPc\\n \\\"\\\"\\\"\\n transition matrix -- don't really need...\\n \\\"\\\"\\\"\\n P = np.array([[PsPs, PsPc],\\n [PcPs, PcPc]])\\n \\\"\\\"\\\"\\n initial condintion\\n \\\"\\\"\\\"\\n sunny = True\\n t0 = np.random.choice([False, True])\\n \\\"\\\"\\\"\\n run realisation\\n \\\"\\\"\\\"\\n clear = []\\n t = t0\\n for k in xrange(365):\\n # predict tomorrow\\n if t == sunny:\\n t1 = bool(scipy.stats.bernoulli.rvs(PsPs))\\n else:\\n t1 = not bool(scipy.stats.bernoulli.rvs(PcPc))\\n clear.append(t1)\\n t = t1\\n clear = np.array(clear)\\n return clear\",\n \"def make_male_3D_model\\\\\\n (TABLE_info, m1_male_crvs, m2_male_left_crvs, m2_male_right_crvs,\\\\\\n m3_male_left_crvs, m3_male_right_crvs, m4_male_crvs):\\n \\\"\\\"\\\"\\n 1 Get t_m from TABLE_info\\n \\\"\\\"\\\"\\n width = TABLE_info[0]\\n t_m = TABLE_info[2]\\n\\n \\\"\\\"\\\"\\n 2 Get crvs from list.\\n \\\"\\\"\\\"\\n # m1\\n m1_male_upper_crv = m1_male_crvs[0]\\n m1_male_middle_crv = m1_male_crvs[1]\\n m1_male_lower_crv = m1_male_crvs[2]\\n\\n # m2\\n m2_male_left_upper_crv = m2_male_left_crvs[0]\\n m2_male_left_middle_crv = m2_male_left_crvs[1]\\n m2_male_left_lower_crv = m2_male_left_crvs[2]\\n\\n m2_male_right_upper_crv = m2_male_right_crvs[0]\\n m2_male_right_middle_crv = m2_male_right_crvs[1]\\n m2_male_right_lower_crv = m2_male_right_crvs[2]\\n\\n # m3\\n m3_male_left_upper_crv = m3_male_left_crvs[0]\\n m3_male_left_middle_crv = m3_male_left_crvs[1]\\n m3_male_left_lower_crv = m3_male_left_crvs[2]\\n\\n m3_male_right_upper_crv = m3_male_right_crvs[0]\\n m3_male_right_middle_crv = m3_male_right_crvs[1]\\n m3_male_right_lower_crv = m3_male_right_crvs[2]\\n\\n # m4\\n m4_male_upper_crv = m4_male_crvs[0]\\n m4_male_middle_crv = m4_male_crvs[1]\\n m4_male_lower_crv = m4_male_crvs[2]\\n\\n \\\"\\\"\\\"\\n 3 Make 3D.\\n \\\"\\\"\\\"\\n # path\\n start = (0, 0, 0)\\n end = (0, 0, t_m)\\n path = rs.AddLine(start, end)\\n\\n # m1\\n m1_male_upper_model = rs.ExtrudeCurve(m1_male_upper_crv, path)\\n m1_male_middle_model = rs.ExtrudeCurve(m1_male_middle_crv, path)\\n m1_male_lower_model = rs.ExtrudeCurve(m1_male_lower_crv, path)\\n\\n rs.CapPlanarHoles(m1_male_upper_model)\\n rs.CapPlanarHoles(m1_male_middle_model)\\n rs.CapPlanarHoles(m1_male_lower_model)\\n\\n # m2 left\\n m2_male_left_upper_model = rs.ExtrudeCurve(m2_male_left_upper_crv, path)\\n m2_male_left_middle_model = rs.ExtrudeCurve(m2_male_left_middle_crv, path)\\n m2_male_left_lower_model = rs.ExtrudeCurve(m2_male_left_lower_crv, path)\\n\\n rs.CapPlanarHoles(m2_male_left_upper_model)\\n rs.CapPlanarHoles(m2_male_left_middle_model)\\n rs.CapPlanarHoles(m2_male_left_lower_model)\\n\\n # m2 right\\n m2_male_right_upper_model = rs.ExtrudeCurve(m2_male_right_upper_crv, path)\\n m2_male_right_middle_model = rs.ExtrudeCurve(m2_male_right_middle_crv, path)\\n m2_male_right_lower_model = rs.ExtrudeCurve(m2_male_right_lower_crv, path)\\n\\n rs.CapPlanarHoles(m2_male_right_upper_model)\\n rs.CapPlanarHoles(m2_male_right_middle_model)\\n rs.CapPlanarHoles(m2_male_right_lower_model)\\n\\n # m3 left\\n m3_male_left_upper_model = rs.ExtrudeCurve(m3_male_left_upper_crv, path)\\n m3_male_left_middle_model = rs.ExtrudeCurve(m3_male_left_middle_crv, path)\\n m3_male_left_lower_model = rs.ExtrudeCurve(m3_male_left_lower_crv, path)\\n\\n rs.CapPlanarHoles(m3_male_left_upper_model)\\n rs.CapPlanarHoles(m3_male_left_middle_model)\\n rs.CapPlanarHoles(m3_male_left_lower_model)\\n\\n # m3 right\\n m3_male_right_upper_model = rs.ExtrudeCurve(m3_male_right_upper_crv, path)\\n m3_male_right_middle_model = rs.ExtrudeCurve(m3_male_right_middle_crv, path)\\n m3_male_right_lower_model = rs.ExtrudeCurve(m3_male_right_lower_crv, path)\\n\\n rs.CapPlanarHoles(m3_male_right_upper_model)\\n rs.CapPlanarHoles(m3_male_right_middle_model)\\n rs.CapPlanarHoles(m3_male_right_lower_model)\\n\\n # m4\\n m4_male_upper_model = rs.ExtrudeCurve(m4_male_upper_crv, path)\\n m4_male_middle_model = rs.ExtrudeCurve(m4_male_middle_crv, path)\\n m4_male_lower_model = rs.ExtrudeCurve(m4_male_lower_crv, path)\\n\\n rs.CapPlanarHoles(m4_male_upper_model)\\n rs.CapPlanarHoles(m4_male_middle_model)\\n rs.CapPlanarHoles(m4_male_lower_model)\\n\\n male_upper_models =\\\\\\n [m1_male_upper_model, m2_male_left_upper_model, m2_male_right_upper_model,\\\\\\n m3_male_left_upper_model, m3_male_right_upper_model, m4_male_upper_model]\\n\\n male_middle_models =\\\\\\n [m1_male_middle_model, m2_male_left_middle_model, m2_male_right_middle_model,\\\\\\n m3_male_left_middle_model, m3_male_right_middle_model, m4_male_middle_model]\\n\\n male_lower_models =\\\\\\n [m1_male_lower_model, m2_male_left_lower_model, m2_male_right_lower_model,\\\\\\n m3_male_left_lower_model, m3_male_right_lower_model, m4_male_lower_model]\\n\\n # move objects\\n trans_upper = (0, 0, 2 * t_m)\\n trans_middle = (0, 0, t_m)\\n rs.MoveObjects(male_upper_models, trans_upper)\\n rs.MoveObjects(male_middle_models, trans_middle)\\n\\n\\n # deploy models\\n O = (0, 0, 0)\\n angle = 90\\n rs.RotateObjects(male_upper_models, O, angle, None, False)\\n rs.RotateObjects(male_middle_models, O, angle, None, False)\\n rs.RotateObjects(male_lower_models, O, angle, None, False)\\n\\n axis = (1, 0, 0)\\n rs.RotateObjects(male_upper_models, O, angle, axis, False)\\n rs.RotateObjects(male_middle_models, O, angle, axis, False)\\n rs.RotateObjects(male_lower_models, O, angle, axis, False)\\n\\n trans = (-1.5 * width, 0, 0)\\n rs.MoveObjects(male_upper_models, trans)\\n rs.MoveObjects(male_middle_models, trans)\\n rs.MoveObjects(male_lower_models, trans)\\n\\n rs.DeleteObject(path)\\n\\n male_models = [male_upper_models, male_middle_models, male_lower_models]\",\n \"def run_pycma(self):\\n\\n self.mug_pipeline.set_folder_names(self.folder_name)\\n self.mug_pipeline.set_optimizer_type(OptimizerType.PYCMA)\\n\\n self.mug_initial_poses = []\\n\\n for i in range(self.num_mugs):\\n self.mug_initial_poses += \\\\\\n RollPitchYaw(np.random.uniform(0.0, 2.0*np.pi, size=3)).ToQuaternion().wxyz().tolist() + \\\\\\n [np.random.uniform(-0.1, 0.1), np.random.uniform(-0.1, 0.1), np.random.uniform(0.1, 0.2)]\\n\\n print(self.mug_initial_poses, flush=True)\\n\\n iter_num = 0\\n\\n start_time = time.time()\\n elapsed_time = 0\\n\\n manager = Manager()\\n self.all_probabilities = manager.list()\\n all_probabilities_lock = manager.Lock()\\n\\n self.total_iterations = manager.Value('d', 0)\\n self.num_counterexamples = manager.Value('d', 0)\\n\\n self.model_number = manager.Value('d', 0)\\n model_number_lock = manager.Lock()\\n\\n counter_lock = manager.Lock()\\n\\n file_q = manager.Queue()\\n\\n filename = '{}/results.csv'.format(self.folder_name)\\n watcher = Process(target=self.listener, args=(file_q, filename))\\n watcher.start()\\n\\n # TODO: share GPU for inference using model.share_memory()\\n\\n es = cma.CMAEvolutionStrategy(self.mug_initial_poses, 1.0/3.0,\\n {'bounds': [-1.0, 1.0], 'verb_disp': 1, 'popsize': self.num_processes})\\n\\n while not es.stop():\\n try:\\n ep = EvalParallel3(self.run_inference, number_of_processes=self.num_processes)\\n lst = range(iter_num, iter_num + self.num_processes)\\n X = es.ask()\\n elapsed_time = time.time() - start_time\\n jobs = ep(X, lst=lst, args=(self.mug_pipeline, self.all_probabilities,\\n self.total_iterations, self.num_counterexamples,\\n self.model_number, model_number_lock, counter_lock,\\n all_probabilities_lock, file_q))\\n except FoundCounterexample:\\n print('FOUND COUNTEREXAMPLE EXCEPTION', flush=True)\\n self.mug_initial_poses = []\\n\\n for i in range(self.num_mugs):\\n self.mug_initial_poses += \\\\\\n RollPitchYaw(np.random.uniform(0., 2.*np.pi, size=3)).ToQuaternion().wxyz().tolist() + \\\\\\n [np.random.uniform(-0.1, 0.1), np.random.uniform(-0.1, 0.1), np.random.uniform(0.1, 0.2)]\\n\\n es = cma.CMAEvolutionStrategy(self.mug_initial_poses, 1.0/3.0,\\n {'bounds': [-1.0, 1.0], 'verb_disp': 1, 'popsize': self.num_processes})\\n # except torch.multiprocessing.context.TimeoutError:\\n # print('timed out!', flush=True)\\n # break\\n except FoundMaxCounterexamples:\\n print('found {} counterexamples!'.format(self.max_counterexamples), flush=True)\\n break\\n except:\\n print(\\\"Unhandled unnamed exception in pycma\\\", flush=True)\\n raise\\n\\n iter_num += self.num_processes\\n torch.cuda.empty_cache()\\n print('calling ep.terminate()', flush=True)\\n ep.terminate()\\n\\n elapsed_time = time.time() - start_time\\n print('ran for {} minutes! total number of iterations is {}, with {} sec/image'.format(\\n elapsed_time/60.0, self.total_iterations.value, elapsed_time/self.total_iterations.value), flush=True)\\n file_q.put('kill')\\n print('probabilities:', self.all_probabilities, flush=True)\\n es.result_pretty()\\n\\n sys.stdout.flush()\",\n \"def proba_middle(c_pred,m_pred,dataset):\\n if dataset =='cifar10':\\n p = np.zeros(5)\\n for i in range(5):\\n if i in [0,1]:\\n p[i] = c_pred[0]*(m_pred[i]/(m_pred[0]+m_pred[1]))\\n if i in [2,3,4]:\\n p[i] = c_pred[1]*(m_pred[i]/(m_pred[2]+m_pred[3]++m_pred[4]))\\n else :\\n p = np.zeros(4)\\n for i in range(4):\\n if i in [0,1]:\\n p[i] = c_pred[0]*(m_pred[i]/(m_pred[0]+m_pred[1]))\\n if i in [2,3]:\\n p[i] = c_pred[1]*(m_pred[i]/(m_pred[2]+m_pred[3]))\\n return(p)\",\n \"def U_MP9301(ps, **kw) -> MAG:\\n g, i = kw.get(\\\"g\\\", \\\"g\\\"), kw.get(\\\"i\\\", \\\"i\\\")\\n gmi = kw.get(\\\"gmi\\\", \\\"g-i\\\")\\n\\n c = data.loc[\\\"U_MP9301\\\"]\\n\\n if gmi in ps.colnames:\\n gmi = ps[gmi]\\n else:\\n gmi = ps[g] - ps[i]\\n\\n ind = (c[\\\"lb\\\"] < gmi) & (gmi < c[\\\"ub\\\"])\\n if not all(ind):\\n warnings.warn(\\\"MCg1.U: not all .3 mag < (g-i)_ps < 1.5 mag\\\")\\n\\n g_ps = ps[g]\\n\\n u_cfht = (\\n g_ps\\n + c[\\\"c0\\\"]\\n + (c[\\\"c1\\\"] * gmi)\\n + (c[\\\"c2\\\"] * gmi ** 2)\\n + (c[\\\"c3\\\"] * gmi ** 3)\\n )\\n\\n return u_cfht\",\n \"def prob3():\\n#raise NotImplementedError(\\\"Problem 3 Incomplete\\\")\\n n = np.linspace(5000,500000,100)\\n h = lambda x : x > 10\\n MC_estimates = []\\n for N in xrange(5000,505000,5000):\\n X = np.random.gamma(9,scale=0.5,size=N)\\n MC = 1./N*np.sum(h(X))\\n MC_estimates.append(MC)\\n MC_estimates = np.array(MC_estimates)\\n MC_est = prob2()\\n exact = np.array([1-stats.gamma(a=9,scale=0.5).cdf(10)]*n.shape[0])\\n print n.shape,MC_estimates.shape\\n plt.plot(n,abs(MC_estimates-exact),'r-',label=\\\"Monte Carlo\\\")\\n plt.plot(n,abs(MC_est-exact),'b-',label=\\\"Importance\\\")\\n plt.title(\\\"Error or Approx.\\\")\\n plt.legend(loc=1)\\n plt.show()\",\n \"def test(model_params, dataset_test, testing_params, log_directory, device, cuda_available=True,\\n metric_fns=None):\\n # DATA LOADER\\n test_loader = DataLoader(dataset_test, batch_size=testing_params[\\\"batch_size\\\"],\\n shuffle=False, pin_memory=True,\\n collate_fn=imed_loader_utils.imed_collate,\\n num_workers=0)\\n\\n # LOAD TRAIN MODEL\\n fname_model = os.path.join(log_directory, \\\"best_model.pt\\\")\\n print('\\\\nLoading model: {}'.format(fname_model))\\n model = torch.load(fname_model, map_location=device)\\n if cuda_available:\\n model.cuda()\\n model.eval()\\n\\n # CREATE OUTPUT FOLDER\\n path_3Dpred = os.path.join(log_directory, 'pred_masks')\\n if not os.path.isdir(path_3Dpred):\\n os.makedirs(path_3Dpred)\\n\\n # METRIC MANAGER\\n metric_mgr = imed_metrics.MetricManager(metric_fns)\\n\\n # UNCERTAINTY SETTINGS\\n if (testing_params['uncertainty']['epistemic'] or testing_params['uncertainty']['aleatoric']) and \\\\\\n testing_params['uncertainty']['n_it'] > 0:\\n n_monteCarlo = testing_params['uncertainty']['n_it']\\n testing_params['uncertainty']['applied'] = True\\n print('\\\\nComputing model uncertainty over {} iterations.'.format(n_monteCarlo))\\n else:\\n testing_params['uncertainty']['applied'] = False\\n n_monteCarlo = 1\\n\\n for i_monteCarlo in range(n_monteCarlo):\\n preds_npy, gt_npy = run_inference(test_loader, model, model_params, testing_params, path_3Dpred,\\n cuda_available, i_monteCarlo)\\n metric_mgr(preds_npy, gt_npy)\\n\\n # COMPUTE UNCERTAINTY MAPS\\n if n_monteCarlo > 1:\\n imed_utils.run_uncertainty(ifolder=path_3Dpred)\\n\\n metrics_dict = metric_mgr.get_results()\\n metric_mgr.reset()\\n print(metrics_dict)\\n return metrics_dict\",\n \"def calc_zchi2_batch(spectra, tdata, weights, flux, wflux, nz, nbasis, solve_matrices_algorithm=\\\"PCA\\\", use_gpu=False, fullprecision=True):\\n zchi2 = np.zeros(nz)\\n if (weights.sum() == 0):\\n zchi2[:] = 9e99\\n zcoeff = np.zeros((nz, nbasis))\\n return (zchi2, zcoeff)\\n if (use_gpu):\\n global cp\\n import cupy as cp\\n #On the GPU, all operations are batch operations for all templates\\n #in parallel.\\n\\n #1) batch_dot_product_sparse will compute dot products of all\\n #spectra with all templates in batch and return a 3D array of\\n #size (nz x ncols x nbasis).\\n Tbs = batch_dot_product_sparse(spectra, tdata, nz, use_gpu)\\n if (cp_memcheck):\\n #Free memory on consumer grade GPUs with low resources\\n mpool = cp.get_default_memory_pool()\\n mpool.free_all_blocks()\\n\\n #2) On the GPU, M and y are computed for all templates at once\\n #CUPY swapaxes is the equivalent of the transpose in CPU mode\\n #and the @ matrix multiplication operator performs a dot\\n #product for each template.\\n\\n ###!!! NOTE - there are 3 different options for calculating the\\n ### M and y arrays -\\n ### A) Straight CUPY, which works well on perlmutter with a\\n ### runtime of 6.2s on 1 GPU and 2.0s on 4 GPUs, but is\\n ### unusably slow on Volta generation GPUs (16.8s for only\\n ### 10 targets on a 1660 Super).\\n ### B) calc_M_y_batch, the custom CUDA kernel, which is the\\n ### fastest at 2.9s on 1 GPU and 0.7s on 4 GPUs (and 0.7s\\n ### for 10 targets on a 1660 Super) but is the most difficult\\n ### from a maintenance perspective\\n ### C) Use the calc_batch_dot_product_3d3d_gpu kernel to offload\\n ### only the matrix multiplication for M (and transpose of\\n ### Tbs) but use CUPY for everything else. This strikes a\\n ### middle ground that is very maintainable but removes the\\n ### bottleneck of the CUPY Volta issue. 5.7s on 1 GPU and\\n ### 1.8s on 4 GPUs on Perlmutter; 1.6s for 10 targets on\\n ### 1660 Super.\\n ###!!! NOTE - uncomment the 2 lines below to run (A)\\n #all_M = Tbs.swapaxes(-2, -1) @ (weights[None, :, None] * Tbs)\\n #all_y = (Tbs.swapaxes(-2, -1) @ wflux)\\n ###!!! NOTE - uncomment the below line to run (B)\\n #(all_M, all_y) = calc_M_y_batch(Tbs, weights, wflux, nz, nbasis)\\n ###!!! NOTE - uncomment the 2 lines below to run (C)\\n all_M = calc_batch_dot_product_3d3d_gpu(Tbs, (weights[None, :, None] * Tbs), transpose_a=True, fullprecision=fullprecision)\\n all_y = (Tbs.swapaxes(-2, -1) @ wflux)\\n ###!!! NOTE - uncomment the 2 lines below to run an alternative\\n ### version of (C) that does the transpose on the CPU - this seems\\n ### to needlessly waste time though\\n #all_M = calc_batch_dot_product_3d3d_gpu(cp.ascontiguousarray(Tbs.swapaxes(-2, -1)), (weights[None, :, None] * Tbs))\\n #all_y = (Tbs.swapaxes(-2, -1) @ wflux)\\n\\n #3) Use new helper method solve_matrices to use appropriate method\\n #for this template to solve for zcoeff in batch for all_M and all_y.\\n #There is no Error thrown by cupy's version of linalg.solve so just\\n #need to catch NotImplementedError.\\n try:\\n zcoeff = solve_matrices(all_M, all_y, solve_algorithm=solve_matrices_algorithm, use_gpu=True)\\n except NotImplementedError:\\n zchi2[:] = 9e99\\n zcoeff = np.zeros((nz, nbasis))\\n return (zchi2, zcoeff)\\n\\n #4) calc_batch_dot_product_3d2d will compute the dot product\\n #of Tbs and zcoeff for all templates in parallel.\\n #It is the same as model[i,:,:] = Tbs[i,:,:].dot(zcoeff[i,:])\\n model = calc_batch_dot_product_3d2d(Tbs, zcoeff, use_gpu)\\n\\n #5) On the GPU, (flux-model)*(flux-model) is faster than\\n #(flux-model)**2. The @ matrix multiplication operator performs\\n #a dot product for each template. get() copies the data back\\n #from the GPU to the numpy array allocated for zchi2.\\n zchi2[:] = (((flux - model)*(flux-model)) @ weights).get()\\n #Copy data from GPU to numpy arrays\\n zcoeff = zcoeff.get()\\n\\n if (cp_memcheck):\\n #Free memory on consumer grade GPUs with low resources\\n del Tbs\\n del all_M\\n del all_y\\n del model\\n mpool = cp.get_default_memory_pool()\\n mpool.free_all_blocks()\\n else:\\n zcoeff = np.zeros((nz, nbasis))\\n #On the CPU, the templates are looped over and all operations\\n #are performed on one template at a time.\\n\\n for i in range(nz):\\n #1) dot_product_sparse_one will compute dot products of all\\n #spectra with ONE template and return a 2D array of size\\n #(ncols x nbasis)\\n Tb = dot_product_sparse_one(spectra, tdata, i)\\n\\n #2) On the CPU, M and y are computed for each template\\n M = Tb.T.dot(np.multiply(weights[:,None], Tb))\\n y = Tb.T.dot(wflux)\\n\\n #3) Use new helper method solve_matrices to use appropriate method\\n #for this template to solve for zcoeff for each M, y.\\n #Catch LinAlgError and NotImplementedError\\n try:\\n zcoeff[i,:] = solve_matrices(M, y, solve_algorithm=solve_matrices_algorithm, use_gpu=False)\\n except np.linalg.LinAlgError:\\n zchi2[i] = 9e99\\n continue\\n except NotImplementedError:\\n zchi2[i] = 9e99\\n continue\\n\\n #4) Calculate dot products individually for each template\\n model = Tb.dot(zcoeff[i,:])\\n\\n #5) Calculate this zchi2 element individually for each template\\n zchi2[i] = np.dot( (flux - model)**2, weights )\\n return (zchi2, zcoeff)\",\n \"def jointUncertaintyMilp(mdp, oldPi, oldZC, unknownFeatStates, costOfQuery):\\n m = Model()\\n m.setParam('OutputFlag', False)\\n\\n # convert notation to previous implementation\\n S = mdp.S\\n A = mdp.A\\n R = mdp.rFuncs\\n psi = mdp.psi\\n T = mdp.T\\n alpha = mdp.alpha\\n gamma = mdp.gamma\\n\\n # useful constants\\n rLen = len(R)\\n M = 10000 # a large number\\n Sr = range(len(S))\\n Ar = range(len(A))\\n\\n # decision variables\\n x = m.addVars(len(S), len(A), lb=0, name='x')\\n y = m.addVars(rLen, name='y')\\n # y prime, a helper variable\\n y0 = m.addVars(rLen, name='y0', lb=0)\\n\\n # oldPi is a mapping from state, action (in S x A) to occupancy\\n # to be consistent with x, convert it to a mapping from (s, a) where s in Sr, a in Ar\\n oldX = {(s, a): oldPi[S[s], A[a]] for s in Sr for a in Ar}\\n\\n # integer variables\\n zR = m.addVars(rLen, vtype=GRB.BINARY, name='zR')\\n zC = m.addVars(len(unknownFeatStates), vtype=GRB.BINARY, name='zC')\\n # zCNew indicates the newly changed features by x. note that it does not need to be constrained as integers\\n zCNew = m.addVars(len(unknownFeatStates), lb=0, name='zCNew')\\n\\n zSafe = m.addVar(vtype=GRB.BINARY, name='zSafe')\\n\\n V = lambda x_local, r: sum([x_local[s, a] * r(S[s], A[a]) for s in Sr for a in Ar])\\n\\n # (a) flow conservation constraint\\n for sp in Sr:\\n m.addConstr(sum(x[s, a] * ((s == sp) - gamma * T(S[s], A[a], S[sp])) for s in Sr for a in Ar) == alpha(S[sp]))\\n\\n # (b) is encoded in the transition function\\n\\n for consIdx in range(len(unknownFeatStates)):\\n # (c) unknown features can be changed\\n m.addConstr(M * zC[consIdx] >= sum(x[S.index(s), A.index(a)] for s in unknownFeatStates[consIdx] for a in A))\\n # (d) constrain z^{new}_\\\\phi, note that lb of zCNew is 0\\n m.addConstr(zCNew[consIdx] >= zC[consIdx] - oldZC[consIdx])\\n\\n # (e) constraints on y^0_r\\n m.addConstr(sum(zC[idx] for idx in range(len(oldZC)) if oldZC[idx] == 1) <= sum(oldZC) - 1 + zSafe * M)\\n for i in range(rLen):\\n m.addConstr(y0[i] >= V(oldX, R[i]) - (1 - zSafe) * M)\\n\\n # (f) constraints on y_r\\n for i in range(rLen):\\n m.addConstr(y[i] <= V(x, R[i]) - y0[i] + (1 - zR[i]) * M)\\n m.addConstr(y[i] <= 0 + zR[i] * M)\\n\\n # obj\\n m.setObjective(sum([psi[i] * y[i] for i in xrange(rLen)])\\n - sum(zC[idx] * costOfQuery for idx in range(len(unknownFeatStates))),\\n GRB.MAXIMIZE)\\n\\n m.optimize()\\n\\n pi = {(S[s], A[a]): x[s, a].X for s in Sr for a in Ar}\\n\\n if config.VERBOSE:\\n # print decision variables other than pi for debugging\\n print 'oldZC', oldZC\\n print 'zC', [zC[consIdx].X for consIdx in range(len(unknownFeatStates))]\\n print 'y0 values', [y0[rIdx].X for rIdx in range(rLen)]\\n print 'y values', [y[rIdx].X for rIdx in range(rLen)]\\n\\n if m.status == GRB.Status.OPTIMAL:\\n # return feasible being true and the obj value, opt pi\\n # .X attribute is to retrieve the value of the variable\\n return pi\\n else:\\n # simply return infeasible\\n raise Exception('milp problem optimal solution not found' + m.status)\",\n \"def train(self, prob: SvmUProblem):\\n # Define variables\\n self.prob = prob\\n x = self.prob.X\\n y = self.prob.Y\\n\\n # Define the inputs to CVXOPT - See Appendix G.6\\n P_a = prob.yi_yj * prob.xi_xj\\n P_b = prob.gamma * prob.xstari_xstarj\\n P_c = prob.sigma * prob.yi_yj * prob.xstarstari_xstarstarj\\n\\n P_row_0 = np.hstack((P_a + P_b, P_a))\\n P_row_0 = np.hstack((P_row_0, P_b))\\n P_row_0 = np.hstack((P_row_0, np.zeros((prob.num, prob.num))))\\n P_row_1 = np.hstack((P_a, P_a + P_c))\\n P_row_1 = np.hstack((P_row_1, np.zeros((prob.num, prob.num))))\\n P_row_1 = np.hstack((P_row_1, P_c))\\n P_row_2 = np.hstack((P_b, np.zeros((prob.num, prob.num))))\\n P_row_2 = np.hstack((P_row_2, P_b))\\n P_row_2 = np.hstack((P_row_2, np.zeros((prob.num, prob.num))))\\n P_row_3 = np.hstack((np.zeros((prob.num, prob.num)), P_c))\\n P_row_3 = np.hstack((P_row_3, np.zeros((prob.num, prob.num))))\\n P_row_3 = np.hstack((P_row_3, P_c))\\n\\n P = np.vstack((P_row_0, P_row_1))\\n P = np.vstack((P, P_row_2))\\n P = np.vstack((P, P_row_3))\\n\\n q = np.hstack((np.repeat(-1, prob.num), np.repeat(-1, prob.num)))\\n q = np.hstack((q, np.zeros(prob.num)))\\n q = np.hstack((q, np.zeros(prob.num)))\\n\\n positive_eye = np.eye(prob.num, dtype='d')\\n negative_eye = -np.eye(prob.num, dtype='d')\\n zeros = np.zeros((prob.num, prob.num))\\n\\n # g1 = -a <= 0\\n g1 = np.hstack((negative_eye, zeros))\\n g1 = np.hstack((g1, zeros))\\n g1 = np.hstack((g1, zeros))\\n\\n # g2 = -n <= 0\\n g2 = np.hstack((zeros, negative_eye))\\n g2 = np.hstack((g2, zeros))\\n g2 = np.hstack((g2, zeros))\\n\\n # g3 = -d <= C\\n g3 = np.hstack((zeros, zeros))\\n g3 = np.hstack((g3, negative_eye))\\n g3 = np.hstack((g3, zeros))\\n\\n # g4 = n + e <= Del C\\n g4 = np.hstack((zeros, positive_eye))\\n g4 = np.hstack((g4, zeros))\\n g4 = np.hstack((g4, positive_eye))\\n\\n # g5 = -e <= C\\n g5 = np.hstack((zeros, zeros))\\n g5 = np.hstack((g5, zeros))\\n g5 = np.hstack((g5, negative_eye))\\n\\n G = np.vstack((g1, g2))\\n G = np.vstack((G, g3))\\n G = np.vstack((G, g4))\\n G = np.vstack((G, g5))\\n\\n h1 = np.zeros((prob.num, 1))\\n h2 = np.zeros((prob.num, 1))\\n h3 = np.repeat(prob.C, prob.num).reshape(-1, 1)\\n h4 = np.repeat((prob.delta * prob.C), prob.num).reshape(-1, 1)\\n h5 = np.repeat(prob.C, prob.num).reshape(-1, 1)\\n h = np.vstack((h1, h2))\\n h = np.vstack((h, h3))\\n h = np.vstack((h, h4))\\n h = np.vstack((h, h5))\\n\\n A1 = np.hstack((prob.Y, -prob.Y))\\n A1 = np.hstack((A1, np.zeros(2 * prob.num)))\\n A2 = np.hstack((-np.ones(prob.num), np.zeros(prob.num)))\\n A2 = np.hstack((A2, -np.ones(prob.num)))\\n A2 = np.hstack((A2, np.zeros(prob.num)))\\n A3 = np.hstack((np.zeros(prob.num), -prob.Y))\\n A3 = np.hstack((A3, np.zeros(prob.num)))\\n A3 = np.hstack((A3, -prob.Y))\\n\\n A = np.vstack((A1, A2))\\n A = np.vstack((A, A3))\\n\\n b = np.zeros(3)\\n b = b.reshape(-1, 1)\\n\\n P = matrix(P, tc='d')\\n q = matrix(q, tc='d')\\n G = matrix(G, tc='d')\\n h = matrix(h, tc='d')\\n A = matrix(A, tc='d')\\n b = matrix(b, tc='d')\\n\\n # Solve optimization problem using CVXOPT\\n solvers.options['show_progress'] = False\\n sol = solvers.qp(P, q, G, h, A, b)\\n alphas_etas_deltas_epsilons = np.array(sol['x'])\\n self.alphas = alphas_etas_deltas_epsilons[:prob.num]\\n self.etas = alphas_etas_deltas_epsilons[prob.num:(2 * prob.num)]\\n self.deltas = alphas_etas_deltas_epsilons[(2 * prob.num):(3 * prob.num)]\\n self.epsilons = alphas_etas_deltas_epsilons[-prob.num:]\\n\\n # Populate Classifier object to be returned\\n clf = Classifier()\\n clf.b = self.get_b\\n clf.alphas = np.asarray(self.alphas + self.etas)\\n clf.xs = x\\n clf.ys = y\\n clf.kern = prob.xkernel\\n return clf\",\n \"def maximize_loglik_fcmnl_MPEC(model_params: Union[CupidParams, CupidParamsCSHeteroxy, CupidParamsFcmnl],\\n x_init: np.ndarray,\\n lower: Optional[np.ndarray] = None,\\n upper: Optional[np.ndarray] = None,\\n checkgrad: Optional[bool] = False,\\n verbose: Optional[bool] = False,\\n fixed_vars: Optional[List[int]] = None,\\n fixed_vals: Optional[List[float]] = None,\\n options: Optional[Dict] = {'iprint': 1}) -> Tuple[float, np.ndarray, int]:\\n n_paramsU = x_init.size\\n bases_surplus = model_params.bases_surplus\\n ncat_men, ncat_women, n_bases = bases_surplus.shape\\n n_pars_b_men, n_pars_b_women = model_params.n_pars_b_men, model_params.n_pars_b_women\\n n_pars_b = n_pars_b_men + n_pars_b_women\\n n_thetas = n_pars_b + n_bases\\n\\n try:\\n kc = KN_new()\\n except:\\n bs_error_abort(\\\"Failed to find a valid Knitro license.\\\")\\n\\n KN_add_vars(kc, n_paramsU)\\n\\n # bounds, if any\\n if lower is None:\\n # not necessary since infinite\\n KN_set_var_lobnds(kc, xLoBnds=np.full(n_paramsU, -KN_INFINITY))\\n else:\\n KN_set_var_lobnds(kc, xLoBnds=lower)\\n if upper is None:\\n KN_set_var_upbnds(kc, xUpBnds=np.full(n_paramsU, KN_INFINITY))\\n else:\\n KN_set_var_upbnds(kc, xUpBnds=upper)\\n\\n n_prod_categories = ncat_men * ncat_women\\n # Add the constraints and set the rhs and coefficients\\n n_cons = n_prod_categories\\n KN_add_cons(kc, n_cons)\\n KN_set_con_eqbnds(kc, cEqBnds=[0.0] * n_cons)\\n\\n # Define an initial point. If not set, Knitro will generate one.\\n KN_set_var_primal_init_values(kc, xInitVals=x_init)\\n\\n if fixed_vars is not None:\\n assert fixed_vals is not None\\n KN_set_var_fxbnds(kc, fixed_vars, fixed_vals)\\n\\n cb = KN_add_eval_callback(kc, evalObj=True,\\n indexCons=np.arange(n_prod_categories),\\n funcCallback=log_likelihood_fcmnl_MPEC)\\n\\n KN_set_cb_user_params(kc, cb, model_params)\\n\\n # c(x,y) has derivatives in thetas, U(x,t), U(z,y)\\n # dc(x,y)/dU(x,y) may only appear once\\n n_args_jac = n_thetas + ncat_men + ncat_women - 1\\n n_jac = n_cons * n_args_jac\\n cbjacIndexCons = np.repeat(np.arange(n_prod_categories), n_args_jac)\\n cbjacIndexVars = np.zeros(n_prod_categories * n_args_jac, int)\\n i = 0\\n for iman in range(ncat_men):\\n for iwoman in range(ncat_women):\\n # derivatives in thetas\\n cbjacIndexVars[i:(i+n_thetas)] = np.arange(n_thetas)\\n # derivatives in [iman, jwoman]\\n cbjacIndexVars[(i + n_thetas):(i+n_thetas+ncat_women)] = \\\\\\n n_thetas + iman*ncat_women + np.arange(ncat_women)\\n # derivatives in [jman, iwoman] except [iman, iwoman]\\n list_men = list(range(ncat_men))\\n del list_men[iman]\\n cbjacIndexVars[(i + n_thetas + ncat_women):(i+n_args_jac)] = \\\\\\n n_thetas + iwoman + ncat_women*np.array(list_men, int)\\n i += n_args_jac\\n\\n print(cbjacIndexCons.shape)\\n print(cbjacIndexVars.shape)\\n\\n KN_set_cb_grad(kc, cb, objGradIndexVars=KN_DENSE,\\n jacIndexCons=cbjacIndexCons, jacIndexVars=cbjacIndexVars,\\n gradCallback=grad_log_likelihood_fcmnl_MPEC)\\n\\n KN_set_int_param(kc, KN_PARAM_OUTLEV, KN_OUTLEV_ALL)\\n\\n if checkgrad:\\n # Perform a derivative check.\\n KN_set_int_param(kc, KN_PARAM_DERIVCHECK, KN_DERIVCHECK_ALL)\\n\\n # Solve the problem.\\n nStatus = KN_solve(kc)\\n\\n loglik_val, estimates = print_optimization_results_MPEC(kc)\\n\\n print_stars()\\n print(f\\\" Value of log-likelihood: {loglik_val: > 8.3f}\\\\n\\\")\\n print()\\n\\n return loglik_val, np.array(estimates), nStatus\",\n \"def make_female_3D_model\\\\\\n (TABLE_info, m1_female_crvs, m2_female_left_crvs, m2_female_right_crvs,\\\\\\n m3_female_left_crvs, m3_female_right_crvs, m4_female_crvs):\\n \\\"\\\"\\\"\\n 1 Get t_m from TABLE_info\\n \\\"\\\"\\\"\\n width = TABLE_info[0]\\n height = TABLE_info[1]\\n t_m = TABLE_info[2]\\n\\n \\\"\\\"\\\"\\n 2 Get crvs from list.\\n \\\"\\\"\\\"\\n # m1\\n m1_female_upper_crv = m1_female_crvs[0]\\n m1_female_middle_crv = m1_female_crvs[1]\\n m1_female_lower_crv = m1_female_crvs[2]\\n\\n # m2\\n m2_female_left_upper_crv = m2_female_left_crvs[0]\\n m2_female_left_middle_crv = m2_female_left_crvs[1]\\n m2_female_left_lower_crv = m2_female_left_crvs[2]\\n\\n m2_female_right_upper_crv = m2_female_right_crvs[0]\\n m2_female_right_middle_crv = m2_female_right_crvs[1]\\n m2_female_right_lower_crv = m2_female_right_crvs[2]\\n\\n # m3\\n m3_female_left_upper_crv = m3_female_left_crvs[0]\\n m3_female_left_middle_crv = m3_female_left_crvs[1]\\n m3_female_left_lower_crv = m3_female_left_crvs[2]\\n\\n m3_female_right_upper_crv = m3_female_right_crvs[0]\\n m3_female_right_middle_crv = m3_female_right_crvs[1]\\n m3_female_right_lower_crv = m3_female_right_crvs[2]\\n\\n # m4\\n m4_female_upper_crv = m4_female_crvs[0]\\n m4_female_middle_crv = m4_female_crvs[1]\\n m4_female_lower_crv = m4_female_crvs[2]\\n\\n \\\"\\\"\\\"\\n 3 Make 3D.\\n \\\"\\\"\\\"\\n # path\\n start = (0, 0, 0)\\n end = (0, 0, t_m)\\n path = rs.AddLine(start, end)\\n\\n # m1\\n m1_female_upper_model = rs.ExtrudeCurve(m1_female_upper_crv, path)\\n m1_female_middle_model = rs.ExtrudeCurve(m1_female_middle_crv, path)\\n m1_female_lower_model = rs.ExtrudeCurve(m1_female_lower_crv, path)\\n\\n rs.CapPlanarHoles(m1_female_upper_model)\\n rs.CapPlanarHoles(m1_female_middle_model)\\n rs.CapPlanarHoles(m1_female_lower_model)\\n\\n # m2 left\\n m2_female_left_upper_model = rs.ExtrudeCurve(m2_female_left_upper_crv, path)\\n m2_female_left_middle_model = rs.ExtrudeCurve(m2_female_left_middle_crv, path)\\n m2_female_left_lower_model = rs.ExtrudeCurve(m2_female_left_lower_crv, path)\\n\\n rs.CapPlanarHoles(m2_female_left_upper_model)\\n rs.CapPlanarHoles(m2_female_left_middle_model)\\n rs.CapPlanarHoles(m2_female_left_lower_model)\\n\\n # m2 right\\n m2_female_right_upper_model = rs.ExtrudeCurve(m2_female_right_upper_crv, path)\\n m2_female_right_middle_model = rs.ExtrudeCurve(m2_female_right_middle_crv, path)\\n m2_female_right_lower_model = rs.ExtrudeCurve(m2_female_right_lower_crv, path)\\n\\n rs.CapPlanarHoles(m2_female_right_upper_model)\\n rs.CapPlanarHoles(m2_female_right_middle_model)\\n rs.CapPlanarHoles(m2_female_right_lower_model)\\n\\n # m3 left\\n m3_female_left_upper_model = rs.ExtrudeCurve(m3_female_left_upper_crv, path)\\n m3_female_left_middle_model = rs.ExtrudeCurve(m3_female_left_middle_crv, path)\\n m3_female_left_lower_model = rs.ExtrudeCurve(m3_female_left_lower_crv, path)\\n\\n rs.CapPlanarHoles(m3_female_left_upper_model)\\n rs.CapPlanarHoles(m3_female_left_middle_model)\\n rs.CapPlanarHoles(m3_female_left_lower_model)\\n\\n # m3 right\\n m3_female_right_upper_model = rs.ExtrudeCurve(m3_female_right_upper_crv, path)\\n m3_female_right_middle_model = rs.ExtrudeCurve(m3_female_right_middle_crv, path)\\n m3_female_right_lower_model = rs.ExtrudeCurve(m3_female_right_lower_crv, path)\\n\\n rs.CapPlanarHoles(m3_female_right_upper_model)\\n rs.CapPlanarHoles(m3_female_right_middle_model)\\n rs.CapPlanarHoles(m3_female_right_lower_model)\\n\\n # m4\\n m4_female_upper_model = rs.ExtrudeCurve(m4_female_upper_crv, path)\\n m4_female_middle_model = rs.ExtrudeCurve(m4_female_middle_crv, path)\\n m4_female_lower_model = rs.ExtrudeCurve(m4_female_lower_crv, path)\\n\\n rs.CapPlanarHoles(m4_female_upper_model)\\n rs.CapPlanarHoles(m4_female_middle_model)\\n rs.CapPlanarHoles(m4_female_lower_model)\\n\\n female_upper_models =\\\\\\n [m1_female_upper_model, m2_female_left_upper_model, m2_female_right_upper_model,\\\\\\n m3_female_left_upper_model, m3_female_right_upper_model, m4_female_upper_model]\\n\\n female_middle_models =\\\\\\n [m1_female_middle_model, m2_female_left_middle_model, m2_female_right_middle_model,\\\\\\n m3_female_left_middle_model, m3_female_right_middle_model, m4_female_middle_model]\\n\\n female_lower_models =\\\\\\n [m1_female_lower_model, m2_female_left_lower_model, m2_female_right_lower_model,\\\\\\n m3_female_left_lower_model, m3_female_right_lower_model, m4_female_lower_model]\\n\\n # move objects\\n trans_upper = (0, 0, 2 * t_m)\\n trans_middle = (0, 0, t_m)\\n rs.MoveObjects(female_upper_models, trans_upper)\\n rs.MoveObjects(female_middle_models, trans_middle)\\n\\n\\n # deploy models\\n O = (0, 0, 0)\\n angle = 90\\n rs.RotateObjects(female_upper_models, O, angle, None, False)\\n rs.RotateObjects(female_middle_models, O, angle, None, False)\\n rs.RotateObjects(female_lower_models, O, angle, None, False)\\n\\n axis = (1, 0, 0)\\n rs.RotateObjects(female_upper_models, O, angle, axis, False)\\n rs.RotateObjects(female_middle_models, O, angle, axis, False)\\n rs.RotateObjects(female_lower_models, O, angle, axis, False)\\n\\n rs.RotateObjects(female_upper_models, O, angle, None, False)\\n rs.RotateObjects(female_middle_models, O, angle, None, False)\\n rs.RotateObjects(female_lower_models, O, angle, None, False)\\n\\n trans = (-2 * width - 3 * t_m / 2, width / 2 - 3 * t_m / 2, 0)\\n rs.MoveObjects(female_upper_models, trans)\\n rs.MoveObjects(female_middle_models, trans)\\n rs.MoveObjects(female_lower_models, trans)\\n\\n rs.DeleteObject(path)\\n\\n female_models = [female_upper_models, female_middle_models, female_lower_models]\",\n \"def factor_mat(all_dat, f_num, iterations, regularization):\\n\\n\\t# get # of users and # of items\\n\\t[u_num, i_num] = all_dat.shape\\n\\n\\t# init user factors and item factors with random values\\n\\tu_fac = np.matrix(np.random.rand(u_num, f_num))\\t# MxF\\n\\ti_fac = np.matrix(np.random.rand(i_num, f_num))\\t# NxF\\n\\n\\t# calculate the preference matrix\\n\\tpreference = cal_preference(all_dat)\\n\\n\\t# calculate the confidence matrix\\n\\tconfidence = cal_confidence(all_dat)\\n\\t\\n\\t# recalculate the user factors and item factors using the alternating least square method\\n\\tfor itr in range(iterations):\\n\\t\\tu_fac = alternate_ls(u_num, i_fac, preference, confidence, regularization)\\n\\t\\t#print itr, \\\"u_fac\\\"\\n\\t\\ti_fac = alternate_ls(i_num, u_fac, preference.T, confidence.T, regularization)\\n\\t\\t#print itr, \\\"i_fac\\\"\\n\\t\\n\\t# save the output\\n\\tdf = pd.DataFrame(u_fac)\\n\\tdf.to_csv(\\\"tmp/u_fac.tmp\\\", index=False, header=False, sep='\\\\t', encoding='utf-8')\\n\\tdf = pd.DataFrame(i_fac.T)\\n\\tdf.to_csv(\\\"tmp/i_fac.tmp\\\", index=False, header=False, sep='\\\\t', encoding='utf-8')\\n\\n\\t# an MxF user factor matrix and an FxN item factor matrix\\n\\treturn [u_fac, i_fac.T]\",\n \"def initParams(self):\\n sizes = [self.inputDim]+self.layerSizes+[self.outputDim]\\n scales = [np.sqrt(6)/np.sqrt(n+m) for n,m in zip(sizes[:-1],sizes[1:])]\\n self.stack = [[np.random.rand(m,n)*2*s-s,np.zeros((m,1))] \\\\\\n for n,m,s in zip(sizes[:-1],sizes[1:],scales)]\\n self.hActs_M = [cm.empty((s,self.maxBatch)) for s in sizes]\\n\\n if self.train:\\n # Now assuming that all layers are the same size\\n self.grad = [[cm.empty(w.shape),cm.empty(b.shape)] for w,b in self.stack]\\n self.deltasC_M = cm.empty((self.outputDim,self.maxBatch))\\n self.deltasOut_M = cm.empty((sizes[1],self.maxBatch)) \\n self.deltasIn_M = cm.empty((sizes[1],self.maxBatch)) \\n self.tmpGrad_M = cm.empty((self.layerSize,self.maxBatch))\\n \\n # Allocate memory once here and reuse\\n # Store probs\\n self.probs_M = cm.empty((self.outputDim,self.maxBatch))\\n # Store col max\\n self.rowVec_M = cm.empty((1,self.maxBatch))\\n \\n self.stack = [[cm.CUDAMatrix(w),cm.CUDAMatrix(b)]\\n for w,b in self.stack]\\n\\n if self.temporalLayer > 0:\\n # dummy bias used for temporal layer\\n dummy = cm.empty((1,1))\\n dummy.assign(0.0)\\n\\n scale = np.sqrt(6)/np.sqrt(self.layerSize*2)\\n wtf = cm.CUDAMatrix(2*scale*np.random.rand(self.layerSize,\\n self.layerSize)-scale)\\n wtb = cm.CUDAMatrix(2*scale*np.random.rand(self.layerSize,\\n self.layerSize)-scale)\\n self.stack.append([wtf,dummy])\\n self.stack.append([wtb,dummy])\\n\\n # forward and backward activations for temporal layer\\n self.hActsFor_M = cm.empty((self.layerSize,self.maxBatch))\\n self.hActsBack_M = cm.empty((self.layerSize,self.maxBatch))\\n\\n if self.train:\\n dwtf = cm.empty(wtf.shape)\\n self.grad.append([dwtf,dummy])\\n dwtb = cm.empty(wtb.shape)\\n self.grad.append([dwtb,dummy])\\n\\n self.tmpGradBack_M = cm.empty((self.layerSize,self.maxBatch))\\n self.deltasFor_M = cm.empty((self.layerSize,self.maxBatch))\\n self.deltasBack_M = cm.empty((self.layerSize,self.maxBatch))\",\n \"def u_init(self):\\n\\n u0 = self.params.u0\\n N = self.params.nparts\\n\\n u = self.dtype_u(((3, N), self.init[1], self.init[2]))\\n\\n if u0[2][0] != 1 or u0[3][0] != 1:\\n raise ProblemError('so far only q = m = 1 is implemented')\\n\\n # set first particle to u0\\n u.pos[0, 0] = u0[0][0]\\n u.pos[1, 0] = u0[0][1]\\n u.pos[2, 0] = u0[0][2]\\n u.vel[0, 0] = u0[1][0]\\n u.vel[1, 0] = u0[1][1]\\n u.vel[2, 0] = u0[1][2]\\n\\n u.q[0] = u0[2][0]\\n u.m[0] = u0[3][0]\\n\\n # initialize random seed\\n np.random.seed(N)\\n\\n comx = u.pos[0, 0]\\n comy = u.pos[1, 0]\\n comz = u.pos[2, 0]\\n\\n for n in range(1, N):\\n # draw 3 random variables in [-1,1] to shift positions\\n r = np.random.random_sample(3) - 1\\n u.pos[0, n] = r[0] + u0[0][0]\\n u.pos[1, n] = r[1] + u0[0][1]\\n u.pos[2, n] = r[2] + u0[0][2]\\n\\n # draw 3 random variables in [-5,5] to shift velocities\\n r = np.random.random_sample(3) - 5\\n u.vel[0, n] = r[0] + u0[1][0]\\n u.vel[1, n] = r[1] + u0[1][1]\\n u.vel[2, n] = r[2] + u0[1][2]\\n\\n u.q[n] = u0[2][0]\\n u.m[n] = u0[3][0]\\n\\n # gather positions to check center\\n comx += u.pos[0, n]\\n comy += u.pos[1, n]\\n comz += u.pos[2, n]\\n\\n # print('Center of positions:',comx/N,comy/N,comz/N)\\n\\n return u\",\n \"def prediction(prediction_file_name, clusters_list, svd_use_flag):\\n \\n coords = misc_functions.getWindowCoords()\\n \\n test_users = range(coords[0], coords[2] + 1) \\n test_items = range(coords[1], coords[3] + 1)\\n \\n #print \\\"len(test_users) = \\\", len(test_users)\\n #print \\\"len(test_items) = \\\", len(test_items)\\n #print \\\"test_items = \\\", test_items\\n \\n # this matrix to be written as result finally\\n #misc_functions.step()\\n prediction_matrix = zeros((len(test_users), len(test_items)), dtype = float)\\n \\n training_matrix = scipy.io.mmio.mmread(\\\"history.mtx\\\").tocsr()\\n \\n item_X_meta_matrix = scipy.io.mmio.mmread(\\\"../../../well_done/items-metas_global.mtx\\\").toarray()\\n \\n # getting meta matrices for corresponding using metas\\n meta_ctr = 0\\n meta_matrices = []\\n for meta in METAS_TO_USE:\\n if svd_use_flag:\\n meta_matrice_file_name = \\\"users-\\\" + METAS_TO_USE[meta] + \\\".svd.mtx\\\"\\n else:\\n meta_matrice_file_name = \\\"users-\\\" + METAS_TO_USE[meta] + \\\".mtx\\\"\\n exec(\\\"meta_matrices.append(scipy.io.mmio.mmread(\\\\\\\"\\\" + meta_matrice_file_name + \\\"\\\\\\\").toarray())\\\")\\n\\n #user_counter = 0\\n #for user in test_users:\\n for cur_cluster in clusters_list:\\n \\n #print \\\"cur_cluster[0] = \\\", cur_cluster[0]\\n user = int (cur_cluster[0].split(\\\"\\\\t\\\")[1])\\n #print \\\"user #\\\", user\\n \\n #user_metas = {} - changed to list because of problem with dimension\\n user_metas = []\\n \\n values = zeros((len(METAS_TO_USE), len(test_items)), dtype = float)\\n meta_ctr = 0\\n for meta in METAS_TO_USE:\\n \\n #print \\\" meta_matrices = \\\", meta_matrices\\n #print \\\" meta_matrices[meta_ctr] = \\\", meta_matrices[meta_ctr]\\n user_vector = meta_matrices[meta_ctr][user]\\n #print \\\" user_vector = \\\", user_vector\\n #print \\\" len(user_metas) = \\\", len(user_metas)\\n #print \\\" meta_ctr = \\\", meta_ctr\\n #print \\\"meta = \\\", meta\\n #misc_functions.step()\\n \\n # normalizing counts of visited metas to use them as weights later\\n if max(user_vector) != 0:\\n user_metas.append(1.0 * user_vector / max(user_vector))\\n else:\\n user_metas.append(zeros((len(user_vector), ), dtype = float))\\n #print \\\" user_metas[meta_ctr] = \\\", user_metas[meta_ctr]\\n #print \\\" user_metas[meta_ctr].shape = \\\", user_metas[meta_ctr].shape\\n \\n #for item in test_items:\\n for cluster in cur_cluster[1 : ]:\\n start_cluster_item = int(cluster.split(\\\"\\\\t\\\")[0])\\n stop_cluster_item = int(cluster.split(\\\"\\\\t\\\")[2])\\n \\n cluster_items = range(start_cluster_item, stop_cluster_item + 1)\\n \\n for item in cluster_items:\\n meta_value = item_X_meta_matrix[item, meta]\\n \\n # PRICE\\n if meta == 8:\\n meta_value = priceToPriceCat(meta_value)\\n \\n # CITY HEURISTIC\\n if meta == 11:\\n if user_metas[meta_ctr][meta_value - 1] < CITY_TRESHOLD:\\n values[:, item - coords[1]] *= CITY_COEF\\n \\\"\\\"\\\"\\n # DAYTIME\\n if meta == 17:\\n meta_value = dayTime(meta_value)\\n \\\"\\\"\\\"\\n \\n #print \\\" meta_value = \\\", meta_value\\n #print \\\" item = \\\", item\\n #step()\\n values[meta_ctr][item - coords[1]] = (user_metas[meta_ctr])[meta_value - 1]\\n \\n \\\"\\\"\\\"HEURISTICS \\\"\\\"\\\"\\n \\n \\n \\n \\n \\n \\\"\\\"\\\"\\\\\\\\ HEURISTICS \\\"\\\"\\\"\\n\\n meta_ctr += 1\\n #print \\\"values[:, 0:10] = \\\", values[:, 0:10]\\n prediction_vector = numpy.sum(META_WEIGHTS * values, axis = 0)\\n #print \\\"prediction_vector[0:10] = \\\", prediction_vector[0:10]\\n #print \\\"sum(prediction_vector) = \\\", sum(prediction_vector)\\n prediction_matrix[user - coords[0]] = prediction_vector\\n \\n #step()\\n \\n# ===== END OF MAIN CYCLE ===== \\n\\n result_matrix = scipy.sparse.csr_matrix(prediction_matrix)\\n scipy.io.mmio.mmwrite(prediction_file_name, result_matrix, field = 'real', precision = 5)\",\n \"def main():\\n logger.info(\\\"=> creating model ...\\\")\\n logger.info(\\\"Classes: %s\\\", cfg.classes)\\n\\n value_scale = 255\\n mean = [0.485, 0.456, 0.406]\\n mean = [item * value_scale for item in mean]\\n std = [0.229, 0.224, 0.225]\\n std = [item * value_scale for item in std]\\n gray_folder = os.path.join(cfg.result_path, 'gray')\\n color_folder = os.path.join(cfg.result_path, 'color')\\n\\n test_transform = pt_transform.Compose([pt_transform.Normalize(mean=mean, std=std, is_train=False)])\\n\\n if cfg.data_root[-1] == \\\"/\\\":\\n val_list = cfg.data_root + cfg.val_list\\n else:\\n val_list = cfg.data_root + '/' + cfg.val_list\\n\\n test_data = pt_dataset.SemData(\\n split='val', data_root=cfg.data_root,\\n data_list=val_list,\\n transform=test_transform)\\n\\n test_loader = ds.GeneratorDataset(test_data, column_names=[\\\"data\\\", \\\"label\\\"],\\n shuffle=False)\\n test_loader.batch(1)\\n colors = numpy.loadtxt(cfg.color_txt).astype('uint8')\\n\\n from src.model import cpnet\\n\\n CPNet = cpnet.CPNet(\\n prior_channels=256,\\n proir__size=60,\\n am_kernel_size=11,\\n pretrained=True,\\n pretrained_path=cfg.pretrain_path,\\n deep_base=True\\n )\\n\\n ms_checkpoint = load_checkpoint(cfg.ckpt)\\n load_param_into_net(CPNet, ms_checkpoint, strict_load=True)\\n CPNet.set_train(False)\\n test(test_loader, test_data.data_list, CPNet, cfg.classes, mean, std, cfg.base_size, cfg.test_h,\\n cfg.test_w, cfg.scales, gray_folder, color_folder, colors)\\n if cfg.split != 'test':\\n cal_acc(test_data.data_list, gray_folder, cfg.classes)\",\n \"def _like3(init_par, alpha, delta, plx_obs, mualpha_obs, mudelta_obs, sigma_obs, ccoef, i):\\r\\n\\r\\n\\tplx_mod, v, sigma_v = init_par[i], init_par[-4:-1], init_par[-1] \\r\\n\\tp, q, r = normalTriad(alpha, delta)\\r\\n\\tmualpha_mod = np.dot(np.transpose(p),v)*plx_mod/_A\\r\\n\\tmudelta_mod = np.dot(np.transpose(q),v)*plx_mod/_A\\r\\n \\t\\r\\n\\tsigma_plx, sigma_mualpha, sigma_mudelta = sigma_obs\\r\\n\\tr_plx_muRa, r_plx_muDec, r_muRa_muDec = ccoef[0], ccoef[1], ccoef[2] \\r\\n\\t\\r\\n\\tC = np.zeros((3,3),dtype=np.float64)\\r\\n\\tC[0,0],C[1,1],C[2,2] = sigma_plx**2.,sigma_mualpha**2., sigma_mudelta**2.\\r\\n\\tC[0,1], C[0,2] = r_plx_muRa*sigma_plx*sigma_mualpha, r_plx_muDec*sigma_plx*sigma_mudelta\\r\\n\\tC[1,0], C[1,2] = r_plx_muRa*sigma_plx*sigma_mualpha, r_muRa_muDec*sigma_mualpha*sigma_mudelta\\r\\n\\tC[2,0], C[2,1] = r_plx_muDec*sigma_plx*sigma_mudelta, r_muRa_muDec*sigma_mualpha*sigma_mudelta\\r\\n\\r\\n\\tE = np.zeros((3,3),dtype=np.float64)\\r\\n\\tE[1,1],E[2,2] = (sigma_v**2.)*(plx_mod/_A)**2., (sigma_v**2.)*(plx_mod/_A)**2.\\r\\n\\t\\r\\n\\tD = np.add(E,C)\\r\\n\\tdetD = det(D) \\r\\n\\tinvD = inv(D)\\r\\n\\t\\t\\r\\n\\ta_c = np.array([plx_obs - plx_mod, mualpha_obs - mualpha_mod, mudelta_obs-mudelta_mod])\\r\\n\\tg_func = row_matrix_col(a_c, a_c, invD)\\r\\n\\t\\r\\n\\t\\r\\n\\treturn detD, g_func\",\n \"def run(self, C, p0 = None):\\n global algorithm \\n algorithm = AdaptiveMM(self.g, C, p0 = p0, lambda0 = 2000)\\n solve()\",\n \"def c3d(self):\\n model = Sequential()\\n # 1st layer group\\n model.add(Conv3D(64, 3, 3, 3, activation='relu',\\n border_mode='same', name='conv1',\\n subsample=(1, 1, 1),\\n input_shape=self.input_shape))\\n model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),\\n border_mode='valid', name='pool1'))\\n # 2nd layer group\\n model.add(Conv3D(128, 3, 3, 3, activation='relu',\\n border_mode='same', name='conv2',\\n subsample=(1, 1, 1)))\\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\\n border_mode='valid', name='pool2'))\\n # 3rd layer group\\n model.add(Conv3D(256, 3, 3, 3, activation='relu',\\n border_mode='same', name='conv3a',\\n subsample=(1, 1, 1)))\\n model.add(Conv3D(256, 3, 3, 3, activation='relu',\\n border_mode='same', name='conv3b',\\n subsample=(1, 1, 1)))\\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\\n border_mode='valid', name='pool3'))\\n # 4th layer group\\n model.add(Conv3D(512, 3, 3, 3, activation='relu',\\n border_mode='same', name='conv4a',\\n subsample=(1, 1, 1)))\\n model.add(Conv3D(512, 3, 3, 3, activation='relu',\\n border_mode='same', name='conv4b',\\n subsample=(1, 1, 1)))\\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\\n border_mode='valid', name='pool4'))\\n\\n # 5th layer group\\n model.add(Conv3D(512, 3, 3, 3, activation='relu',\\n border_mode='same', name='conv5a',\\n subsample=(1, 1, 1)))\\n model.add(Conv3D(512, 3, 3, 3, activation='relu',\\n border_mode='same', name='conv5b',\\n subsample=(1, 1, 1)))\\n model.add(ZeroPadding3D(padding=(0, 1, 1)))\\n # model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\\n # border_mode='valid', name='pool5', dim_ordering=\\\"tf\\\"))\\n model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),\\n border_mode='same', name='pool5', dim_ordering=\\\"tf\\\"))\\n model.add(Flatten())\\n\\n # FC layers group\\n model.add(Dense(4096, activation='relu', name='fc6'))\\n model.add(Dropout(0.5))\\n model.add(Dense(4096, activation='relu', name='fc7'))\\n model.add(Dropout(0.5))\\n model.add(Dense(self.nb_classes, activation='softmax'))\\n for layer in model.layers:\\n print(layer.output_shape)\\n return model\",\n \"def create_model(num_vars, num_categs, hidden_dims, actfn=None):\\n num_outputs = max(1, num_categs)\\n num_inputs = num_vars\\n actfn = get_activation_function(actfn)\\n\\n mask = InputMask(None)\\n if num_categs > 0:\\n pre_layers = EmbedLayer(num_vars=num_vars,\\n num_categs=num_categs,\\n hidden_dim=hidden_dims[0],\\n input_mask=mask,\\n sparse_embeds=(num_vars >= 50))\\n num_inputs = pre_layers.hidden_dim\\n pre_layers = [pre_layers, actfn()]\\n else:\\n pre_layers = mask\\n\\n mlps = MultivarMLP(input_dims=num_inputs,\\n hidden_dims=hidden_dims,\\n output_dims=num_outputs,\\n extra_dims=[num_vars],\\n actfn=actfn,\\n pre_layers=pre_layers)\\n return mlps\",\n \"def CvM(self, using, dx=0.0001):\\n pits = np.array(self.PIT(using=using,dx=dx))\\n cvm_result = skgof.cvm_test(pits, stats.uniform())\\n return cvm_result.statistic, cvm_result.pvalue\",\n \"def inital_probabilities(self,fc,ec):\\n matrix = np.empty(ec*fc)\\n matrix = matrix.reshape(ec,fc)\\n matrix.fill(1/fc)\\n return matrix # repreents probability - p(e|f)\",\n \"def fixedZMPchi2(self, pars):\\n\\t\\tif not self.hasZMP and self.nZero > 0:\\n\\t\\t\\traise RuntimeError(\\\"No zero mode parameters set\\\")\\n\\t\\tif pars is not None:\\n\\t\\t\\tself.setShapeParameters(pars)\\n\\t\\ta,b,c = self.getOwnTheoryABC()\\n\\t\\tA = np.zeros((2*self.nFunc, 2*self.nFunc))\\n\\t\\tB = np.zeros((2*self.nFunc))\\n\\t\\tC = c\\n\\t\\tfor i in range(2*self.nZero):\\n\\t\\t\\tC += b[i]*self.zeroModeParameters[i]\\n\\t\\t\\tfor j in range(2*self.nZero):\\n\\t\\t\\t\\tC += self.zeroModeParameters[i]*self.zeroModeParameters[j]*a[i,j]\\n\\t\\tfor i in range(2*self.nFunc):\\n\\t\\t\\tB[i] += b[2*self.nZero+i]\\n\\t\\t\\tfor j in range(2*self.nZero):\\n\\t\\t\\t\\tB[i] += (a[2*self.nZero+i,j]+a[j,2*self.nZero+i])*self.zeroModeParameters[j]\\n\\t\\t\\tfor j in range(2*self.nFunc):\\n\\t\\t\\t\\tA[i,j] += a[2*self.nZero + i, 2*self.nZero+j]\\n\\t\\tif self.ownPinv:\\n\\t\\t\\tcouplings = -np.dot(B, utils.pinv(np.transpose(A) + A, numLim = self.numLim))\\n\\t\\telse:\\n\\t\\t\\tcouplings = -np.dot(B, la.pinv(np.transpose(A) + A))\\n\\t\\treturn np.dot(couplings, np.dot(A,couplings)) + np.dot(B,couplings) + C\",\n \"def do_PMC_ABC(self, n, real_data, prior, draws, num_keep, abc=None, criterion = 0.1\\n , show=False, oneD='both',analytic_posterior = None, param_array = None):\\n\\n if abc is None:\\n # Start from scratch\\n print (\\\"Starting PMC from scratch\\\")\\n restart = True\\n Gaussprior = priors.TruncatedGaussian(prior[\\\"mean\\\"],prior[\\\"variance\\\"]\\n ,prior[\\\"lower\\\"],prior[\\\"upper\\\"])\\n\\n abc = ABC.ABC(real_data = real_data, prior = Gaussprior, sess=n.sess\\n , get_compressor=n.get_compressor, simulator=generate_data_ABC, seed=None\\n , simulator_args=None, dictionary = self.validation_dictionary)\\n\\n else: # Use the abc object from the do_ABC() function\\n print (\\\"Starting PMC from previous ABC or PMC result\\\")\\n restart = False\\n \\n all_epsilon = abc.PMC(draws = draws, posterior = num_keep\\n , criterion = criterion, at_once = True, save_sims = None, MLE = True) \\n\\n # plot output samples and histogram of approximate posterior\\n def plot_samples_oneD():\\n \\n fig, ax = plt.subplots(2, 1, sharex = 'col', figsize = (10, 10))\\n plt.subplots_adjust(hspace = 0)\\n ax[0].scatter(abc.PMC_dict[\\\"parameters\\\"][:] , abc.PMC_dict[\\\"summaries\\\"][:]\\n , s = 1, label = \\\"Accepted samples\\\", alpha=0.5)\\n ax[0].axhline(abc.summary[0], color = 'black', linestyle = 'dashed', label = \\\"Summary of observed data\\\")\\n ax[0].set_ylabel('Network output', labelpad = 0)\\n # ax[0].set_ylim([np.min(s_[:]), np.max(s_[:])])\\n ax[0].set_xlim([prior[\\\"lower\\\"][0], prior[\\\"upper\\\"][0]])\\n \\n ax[1].hist(abc.PMC_dict[\\\"parameters\\\"], np.linspace(prior[\\\"lower\\\"][0]\\n , prior[\\\"upper\\\"][0], 100), histtype = u'step', density = True\\n , linewidth = 1.5, label = \\\"PMC posterior\\\", color = 'k')\\n\\n ax[1].set_xlabel('$\\\\\\\\theta_1 = \\\\Omega_M$ ')\\n ax[1].set_ylabel('$\\\\\\\\mathcal{P}(\\\\\\\\theta1|{\\\\\\\\bf d})$')\\n ax[1].set_yticks([])\\n\\n if analytic_posterior is not None:\\n ax[1].plot(param_array, analytic_posterior, linewidth = 1.5, color = 'C2'\\n , label = \\\"Analytic posterior\\\")\\n\\n # Theta-fid\\n ax[1].axvline(theta_fid[0], linestyle = \\\"dashed\\\", label = \\\"$\\\\\\\\theta_{fid}$\\\")\\n\\n ax[1].legend(frameon=False)\\n\\n # fig.suptitle(\\\"Only showing 1st network output summary out of %i \\\\n Full network output on real data: %s\\\"%(s_.shape[1],str(summary_)))\\n fig.suptitle(\\\"Results of PMC\\\")\\n\\n plt.savefig(f'{self.figuredir}PMC_ABC_{self.modelversion}_1D.png')\\n if show: plt.show()\\n plt.close()\\n\\n # plot output samples and histogram of the accepted samples\\n def plot_samples_twoD():\\n sys.exit('There is only 1 dimension, cannot make corner plot')\\n hist_kwargs = {} # add kwargs to give to matplotlib hist funct\\n fig, ax = plt.subplots(2, 2, figsize = (10, 10))\\n fig = corner.corner(theta_, fig=fig, truths = theta_fid\\n , labels=['$\\\\\\\\theta_1$ (variance)']\\n , plot_contours=True, range=prior, hist_kwargs=hist_kwargs)\\n fig.suptitle(\\\"Approximate posterior after PMC ABC, num_keep = %i\\\"%num_keep)\\n \\n plt.savefig(f'{self.figuredir}PMC_ABC_{self.modelversion}_2D.png')\\n if show: plt.show()\\n plt.close()\\n\\n # Plot epsilon vs iterations\\n def plot_epsilon():\\n fig, ax = plt.subplots()\\n ax.plot(all_epsilon,color='k',label='$\\\\epsilon$ values')\\n plt.xlabel('Iteration')\\n plt.ylabel('$\\\\epsilon$')\\n plt.legend()\\n \\n plt.savefig(f'{self.figuredir}PMC_ABC_{self.modelversion}_epsilon.png')\\n if show: plt.show()\\n plt.close()\\n\\n if oneD == 'both':\\n plot_samples_oneD()\\n plot_samples_twoD()\\n elif type(oneD) == bool:\\n if oneD:\\n plot_samples_oneD()\\n else:\\n plot_samples_twoD()\\n else: \\n raise ValueError('Allowed values for oneD are \\\"both\\\", True or False')\\n\\n plot_epsilon()\\n\\n return abc\",\n \"def semi_analytical_marginal_probj(param_inst, cash_tt, solu_dict,\\n cur_col_prefix_space='_'):\\n\\n choice_set_list = param_inst.model_option['choice_set_list']\\n choice_names_use = param_inst.model_option['choice_names_full_use']\\n choice_names_use = param_inst.model_option['choice_names_use']\\n\\n each_j_prob = solu_dict['each_j_prob']\\n ktp_opti_allJ = solu_dict['ktp_opti_allJ']\\n btp_opti_allJ = solu_dict['btp_opti_allJ']\\n consumption_opti_allJ = solu_dict['consumption_opti_allJ']\\n\\n # btp_fb_opti_allJ = solu_dict['btp_fb_opti_allJ']\\n # btp_ib_opti_allJ = solu_dict['btp_ib_opti_allJ']\\n # btp_fs_opti_allJ = solu_dict['btp_fs_opti_allJ']\\n # btp_il_opti_allJ = solu_dict['btp_il_opti_allJ']\\n\\n trans_prob_list = []\\n simu_output_pd_allj = 0\\n\\n for ctr, choicej in enumerate(choice_set_list):\\n\\n cur_col_prefix = choice_names_use[ctr]\\n logger.info('ctr,choicej,cur_col_prefix:\\\\n%s,%s,%s',\\n str(ctr), str(choicej), str(cur_col_prefix))\\n\\n btp_opti = btp_opti_allJ[:, ctr]\\n ktp_opti = ktp_opti_allJ[:, ctr]\\n prob_cur = each_j_prob[:, ctr]\\n consumption_opti = consumption_opti_allJ[:, ctr]\\n\\n '''\\n Get columns at centered interpolating grid points for:\\n 'cash_grid_centered'\\n 'marginal_dist'\\n 'btp_opti_grid',\\n 'ktp_opti_grid',\\n 'consumption_opti_grid' \\n '''\\n logger.info('Solve, P(COH|j), P(COH|COH,j), ctr:%s, name:%s', str(ctr), cur_col_prefix)\\n simu_output_pd_curj, trans_prob_curj = condianaly.semi_analytical_marginal(\\n param_inst,\\n cash_tt, ktp_opti, btp_opti, consumption_opti,\\n each_j_prob=prob_cur,\\n trans_prob_only=True,\\n cur_col_prefix=cur_col_prefix + cur_col_prefix_space)\\n\\n '''Store Choice J Transition Prob'''\\n trans_prob_list.append(trans_prob_curj)\\n\\n '''Update Column Names'''\\n if (ctr == 0):\\n simu_output_pd_allj = simu_output_pd_curj\\n else:\\n '''Cumulate'''\\n simu_output_pd_allj = pd.concat([simu_output_pd_allj,\\n simu_output_pd_curj], axis=1)\\n\\n \\\"\\\"\\\"\\n D. Add columns for each j of J for fbibfsil\\n \\\"\\\"\\\"\\n # D1. Get Columns from just created panda files, j specific columns\\n steady_var_suffixes_dict = hardstring.get_steady_var_suffixes()\\n btp_opti_grid_allJ_cols = [col for col in simu_output_pd_allj.columns if\\n cur_col_prefix_space + steady_var_suffixes_dict['btp_opti_grid'] in col]\\n ktp_opti_grid_allJ_cols = [col for col in simu_output_pd_allj.columns if\\n cur_col_prefix_space + steady_var_suffixes_dict['ktp_opti_grid'] in col]\\n\\n # D2. Matrix from columns\\n btp_opti_grid_allJ = simu_output_pd_allj[btp_opti_grid_allJ_cols].to_numpy()\\n ktp_opti_grid_allJ = simu_output_pd_allj[ktp_opti_grid_allJ_cols].to_numpy()\\n\\n # D3. Get fb ib fs il specific matrixes\\n btp_fb_opti_allJ, btp_ib_opti_allJ, btp_fs_opti_allJ, btp_il_opti_allJ = \\\\\\n fbibfsis.genfibs_btpstack(choice_set_list, btp_opti_grid_allJ, ktp_opti_grid_allJ, param_inst)\\n\\n # D4. Add to simu_output_pd_allj panda\\n fb_ib_fs_il_steady_var_key_list = ['btp_fb_opti_grid', 'btp_ib_opti_grid',\\n 'btp_fs_opti_grid', 'btp_il_opti_grid']\\n varnames_list = [choice_names_use[ctr] + cur_col_prefix_space + steady_var_suffixes_dict[fbibfsil_stdykey]\\n for fbibfsil_stdykey in fb_ib_fs_il_steady_var_key_list\\n for ctr, choicej in enumerate(choice_set_list)]\\n\\n # D5. Additional Panda columns with fb ib fs il information\\n varnames = \\\",\\\".join(map(str, varnames_list))\\n varmat = np.column_stack((btp_fb_opti_allJ, btp_ib_opti_allJ, btp_fs_opti_allJ, btp_il_opti_allJ))\\n simu_output_pd_allj_fbibfsil = proj_sys_sup.debug_panda(varnames, varmat, export_panda=False, log=False)\\n\\n # D6. Concatenate together, join more columns together. \\n simu_output_pd_allj = pd.concat([simu_output_pd_allj, simu_output_pd_allj_fbibfsil], axis=1)\\n\\n \\\"\\\"\\\"\\n E0. Grid Column\\n \\\"\\\"\\\"\\n cash_grid_centered_cols = [col for col in simu_output_pd_allj.columns\\n if steady_var_suffixes_dict['cash_grid_centered'] in col]\\n simu_output_pd_allj['cash_grid_centered'] = simu_output_pd_allj[cash_grid_centered_cols[0]]\\n\\n \\\"\\\"\\\"\\n E1. Adjust Probabilities due to Interpolation issue over J choices\\n \\\"\\\"\\\"\\n logger.info('simu_output_pd_allj.columns:\\\\n%s', simu_output_pd_allj.columns)\\n prob_cols = [col for col in simu_output_pd_allj.columns if 'probJ_opti_grid' in col]\\n logger.info('prob_cols:\\\\n%s', prob_cols)\\n probJ_matrix = simu_output_pd_allj[prob_cols].to_numpy()\\n logger.info('probJ_matrix:\\\\n%s', probJ_matrix)\\n\\n '''These are actually not needed, perfect symmatery'''\\n probJ_matrix_rowsum = np.reshape(np.sum(probJ_matrix, axis=1), (-1, 1))\\n logger.info('probJ_matrix_rowsum:\\\\n%s', probJ_matrix_rowsum)\\n probJ_matrix_rescale_sum1 = probJ_matrix / probJ_matrix_rowsum\\n logger.info('probJ_matrix_rescale_sum1:\\\\n%s', probJ_matrix_rescale_sum1)\\n\\n \\\"\\\"\\\"\\n E2. Overall Conditional probabilities\\n \\\"\\\"\\\"\\n trans_prob_wgtJ = 0\\n trans_prob_dict_allj = {}\\n for ctr, choicej in enumerate(choice_set_list):\\n '''E2a. Transition Probability current j'''\\n trans_prob_curj = trans_prob_list[ctr]\\n\\n '''E2b. Choice Probability over J'''\\n prob_opti_grid = probJ_matrix_rescale_sum1[:, ctr]\\n\\n '''E2c. Update current column with reweighted sum to 1 choice J prob'''\\n simu_output_pd_allj[prob_cols[ctr]] = prob_opti_grid\\n\\n '''E2d. Weighted Discrete Transition Probability'''\\n trans_prob_curj_wgted = trans_prob_curj * np.reshape(prob_opti_grid, (-1, 1))\\n logger.debug('trans_prob_curj:\\\\n%s', trans_prob_curj)\\n logger.debug('prob_opti_grid:\\\\n%s', prob_opti_grid)\\n logger.debug('trans_prob_curj_wgted:\\\\n%s', trans_prob_curj_wgted)\\n\\n '''E2e. Update Column Names'''\\n trans_prob_dict_allj[choicej] = trans_prob_curj\\n if (ctr == 0):\\n trans_prob_wgtJ = trans_prob_curj_wgted\\n else:\\n '''Cumulate'''\\n trans_prob_wgtJ = trans_prob_wgtJ + trans_prob_curj_wgted\\n\\n logger.info('trans_prob_wgtJ:\\\\n%s', trans_prob_wgtJ)\\n logger.info('np.sum(trans_prob_wgtJ):\\\\n%s', np.sum(trans_prob_wgtJ, axis=1))\\n\\n return simu_output_pd_allj, trans_prob_wgtJ, trans_prob_dict_allj\",\n \"def generate_prediction_matrix(list_of_models, list_of_tfidf, X_test):\\n n = len(X_test)\\n m = len(list_of_models)\\n \\n prediction_matrix = np.ones((n,m))\\n \\n for i, model in enumerate(list_of_models):\\n print(f\\\"Generating predictions for model: {i+1}\\\")\\n model = list_of_models[i]\\n tfidf = list_of_tfidf[i]\\n try:\\n X_test_tfidf = tfidf.transform(X_test)\\n predictions = model.predict_proba(X_test_tfidf)[:,1]\\n except Exception as e:\\n print(str(e))\\n print(predictions)\\n prediction_matrix[:,i] *= predictions\\n \\n return prediction_matrix\",\n \"def _pmatrix(kn_u, kn_d, thickness):\\n p = np.zeros((kn_u.size, 4, 4), np.complex128)\\n\\n p0 = np.exp(complex(0, 1) * kn_u * thickness)\\n p1 = np.exp(complex(0, 1) * kn_d * thickness)\\n\\n p[:, 0, 0] = 1 / p0\\n p[:, 1, 1] = p0\\n p[:, 2, 2] = 1 / p1\\n p[:, 3, 3] = p1\\n\\n return p\",\n \"def create_petsc_matrix(sizes, mtx_prealloc=None, comm=None):\\n if comm is None:\\n comm = PETSc.COMM_WORLD\\n\\n pmtx = PETSc.Mat()\\n pmtx.create(comm)\\n pmtx.setType('aij')\\n\\n pmtx.setSizes((sizes, sizes))\\n\\n if mtx_prealloc is not None:\\n pmtx.setPreallocationCSR((mtx_prealloc.indptr, mtx_prealloc.indices))\\n\\n pmtx.setUp()\\n\\n return pmtx\",\n \"def fit(self, Cui):\\n\\n _check_open_blas()\\n\\n users, items = Cui.shape\\n\\n self.user_vectors = np.random.normal(size=(users, self.num_factors))\\\\\\n .astype(self.dtype)\\n\\n self.item_vectors = np.random.normal(size=(items, self.num_factors))\\\\\\n .astype(self.dtype)\\n\\n '''\\n use_native 是否使用原生的算法（这个原生和自定义的区别还有待观察）\\n '''\\n self.solver = implicit.als.least_squares if self.use_native else least_squares\\n\\n self.fit_partial(Cui)\",\n \"def fit_cmle_mixt(copula, data, opti_method='SLSQP'):\\n psd_obs = pseudo_obs(data)\\n\\n def log_likelihood(parameters):\\n if copula.dim == 2:\\n w1, param1, param2 = parameters\\n params = [w1, param1, param2]\\n else: # dim = 3\\n w1, w2, w3, param1, param2, param3 = parameters\\n params = [w1, w2, w3, param1, param2, param3]\\n logl = -sum([ np.log(copula.get_pdf(psd_obs[0][i],psd_obs[1][i],params)) for i in range(0,len(psd_obs[0]))])\\n return logl\\n\\n def con(parameters):\\n w1, w2, w3, param1, param2, param3 = parameters\\n return 1 - w1 - w2 - w3\\n\\n if copula.dim == 2:\\n results = minimize(log_likelihood, copula.parameters_start, method=opti_method, bounds=copula.bounds_param) #options={'maxiter': 300})#.x[0]\\n else:\\n cons = {'type':'eq', 'fun': con}\\n results = minimize(log_likelihood, copula.parameters_start, method=opti_method, bounds=copula.bounds_param, constraints=cons) #options={'maxiter': 300})#.x[0]\\n\\n\\n print(\\\"method:\\\", opti_method, \\\"- success:\\\", results.success, \\\":\\\", results.message)\\n if results.success == True:\\n return (results.x, -results.fun)\\n\\n print(\\\"optimization failed\\\")\\n return None\",\n \"def make_critic(input_dim=model_config[\\\"data_dimension\\\"]):\\n model = Sequential(name=\\\"critic\\\")\\n model.add(Reshape((input_dim+(1,)), input_shape=input_dim))\\n\\n model.add(Conv3D(64, (5, 5, 5), padding='same'))\\n model.add(LeakyReLU())\\n\\n model.add(Conv3D(128, (5, 5, 5), kernel_initializer='he_normal', strides=2))\\n model.add(LeakyReLU())\\n\\n model.add(Conv3D(256, (5, 5, 5), kernel_initializer='he_normal', padding='same', strides=2))\\n model.add(LeakyReLU())\\n\\n model.add(Conv3D(512, (5, 5, 5), kernel_initializer='he_normal', padding='same'))\\n model.add(LeakyReLU())\\n\\n model.add(Flatten())\\n\\n model.add(Dense(1, kernel_initializer='he_normal'))\\n return model\",\n \"def SGHMC(gradU, p, eps=0.1, L=5, M=1, C=3, V=4):\\n \\n # randomly generate new auxiliary variable (momentum)\\n r = np.random.randn()\\n B = 0.5 * V * eps\\n for i in range(L):\\n r = r - gradU(p)*eps - r*C*eps + np.random.randn()*np.sqrt(2*(C-B)*eps)\\n p += r/M* eps\\n return p\",\n \"def p_mx_c(pm,px,py,pyx_c,pym_c,beta):\\n \\n pmx_c = np.zeros((pm.size,px.size)) # P(M|X) matrix to be returned\\n for mi in range(pm.size):\\n for xi in range(px.size):\\n pmx_c[mi,xi] = pm[mi] * np.exp(-beta * entropy(pyx_c[:,xi], pym_c[:,mi], base=2))\\n z = pmx_c.sum(axis=0)\\n pmx_c /= z #Normalize\\n \\n \\t\\n return pmx_c, z\",\n \"def transform_pc3d(pcl_c3d, Ts, seq_n, K_cur, batch_n):\\n\\n ## need to transform: flat.uvb, flat.feature['xyz'], flat.feature['normal']\\n ## no need to transform grid features\\n \\n assert batch_n % seq_n == 0 # mode==0\\n n_group = batch_n // seq_n\\n\\n ## get relative pose\\n T, R, t, target_id = relative_T(Ts, seq_n, batch_n)\\n\\n ## get accumulative length\\n nb = pcl_c3d.flat.nb\\n acc_b = []\\n acc = 0\\n acc_b.append( acc )\\n for ib in range(batch_n):\\n acc = acc + nb[ib]\\n acc_b.append( acc )\\n\\n ## process flat features\\n flat_xyz = pcl_c3d.flat.feature['xyz'] # 1*C*NB\\n flat_normal = pcl_c3d.flat.feature['normal']\\n trans_normal_list = []\\n trans_xyz_list = []\\n uvb_list = []\\n new_nb = []\\n for ib in range(batch_n):\\n ## xyz\\n trans_xyz = torch.matmul(R[ib], flat_xyz[:, :, acc_b[ib]:acc_b[ib+1]]) + t[ib]\\n mask_positive = trans_xyz[0, 2, :] > 0\\n trans_xyz = trans_xyz[:, :, mask_positive]\\n trans_xyz_list.append(trans_xyz)\\n new_nb.append(trans_xyz.shape[2])\\n\\n ## normal\\n trans_normal = torch.matmul(R[ib], flat_normal[:, :, acc_b[ib]:acc_b[ib+1]])\\n trans_normal = trans_normal[:, :, mask_positive]\\n trans_normal_list.append(trans_normal)\\n\\n ## project to uv, add b\\n uvb = torch.matmul(K_cur[ib], trans_xyz)\\n uvb[:, :2] = uvb[:, :2] / uvb[:, [2]] #- 1 , commented because in dataset_read.py there is a K_mat2py() function converting K from matlab to python coordinate\\n uvb[:, 2, :] = target_id[ib]\\n uvb_list.append(uvb)\\n\\n ## construct the new object\\n tr_pcl_c3d = PCL_C3D_Flat()\\n tr_pcl_c3d.feature['xyz'] = torch.cat(trans_xyz_list, dim=2)\\n tr_pcl_c3d.feature['normal'] = torch.cat(trans_normal_list, dim=2)\\n tr_pcl_c3d.uvb = torch.cat(uvb_list, dim=2)\\n tr_pcl_c3d.nb = new_nb\\n\\n for feat_key in pcl_c3d.flat.feature:\\n if feat_key not in ['xyz', 'normal']:\\n tr_pcl_c3d.feature[feat_key] = pcl_c3d.flat.feature[feat_key]\\n\\n return tr_pcl_c3d\",\n \"def part3c_0():\\n xs = exampleInput\\n N = 10000\\n\\n difference = 0.0\\n for ys, estimatedProb in submission.computeGibbsProbabilities( simpleCRF, \\n submission.getCRFBlocks,\\n submission.chooseGibbsCRF,\\n xs, N ).iteritems():\\n trueProb = nerUtils.computeProbability( simpleCRF, xs, ys )\\n difference = abs( trueProb - estimatedProb )\\n grader.requireIsLessThan( 5e-2, difference )\",\n \"def get_model():\\n model = ecole.scip.Model.from_file(str(DATA_DIR / \\\"bppc8-02.mps\\\"))\\n model.disable_cuts()\\n model.disable_presolve()\\n model.set_param(\\\"randomization/permuteconss\\\", True)\\n model.set_param(\\\"randomization/permutevars\\\", True)\\n model.set_param(\\\"randomization/permutationseed\\\", 784)\\n model.set_param(\\\"randomization/randomseedshift\\\", 784)\\n model.set_param(\\\"randomization/lpseed\\\", 784)\\n return model\",\n \"def solve(P, M, N, C, items, constraints):\\n Items = []\\n weight_dict = dict()\\n cost_dict = dict()\\n earn_dict = dict()\\n class_dict = dict()\\n \\n for i in items:\\n Items.append(i[0])\\n weight_dict[i[0]] = i[2]\\n cost_dict[i[0]] = i[3]\\n earn_dict[i[0]] = i[4] - i[3]\\n class_dict[i[0]] = i[1]\\n \\n Class_dict = defaultdict(list)\\n for key, value in sorted(class_dict.iteritems()):\\n Class_dict[value].append(key)\\n \\n # Create the 'prob' variable to contain the problem data\\n prob = LpProblem(\\\"The PICKITEMS Problem\\\", LpMaximize)\\n # Two dictionary called 'x_vars' and 'y_vars' are created to contain the referenced Variables\\n x_vars = LpVariable.dicts(\\\"\\\",Items,0,1,cat=LpInteger)\\n y_vars = LpVariable.dicts(\\\"#\\\",range(N),0,1,cat=LpInteger)\\n \\n # The objection function is added to 'prob' first\\n prob += lpSum([earn_dict[i]*x_vars[i] for i in Items]), \\\"Total money we can earn in this file\\\"\\n \\n # Constraints are added to 'prob'\\n prob += lpSum([weight_dict[i]*x_vars[i] for i in Items]) <= P, \\\"WeightsRequirement\\\"\\n prob += lpSum([cost_dict[i]*x_vars[i] for i in Items]) <= M, \\\"CostRequirement\\\"\\n # the relations(constraint) between class and item (x and y)\\n for num in range(C):\\n for i in Class_dict[num]:\\n prob += LpConstraint(x_vars[i]-y_vars[num] <= 0), \\\"\\\"\\n # the relations for class constraints\\n for constraint in constraints:\\n prob += lpSum([y_vars[i] for i in constraint]) <= 1, \\\"\\\"\\n # The problem is solved using PuLP's choice of Solver\\n prob.solve()\\n \\n items_chosen = []\\n for v in prob.variables():\\n if v.varValue == 1 and v.name[0]!='#':\\n items_chosen.append(v.name[1:])\\n return items_chosen\",\n \"def p_m(pmx_c,px):\\n pm = np.zeros(pmx_c.shape[0])\\n for mi in range(pm.size):\\n for xi in range(px.size):\\n pm[mi] += pmx_c[mi,xi]*px[xi]\\n return pm\",\n \"def run_pca(df, cols=None): \\n import numpy as np\\n import pandas as pd\\n import matplotlib.pyplot as plt\\n from mpl_toolkits.mplot3d import Axes3D\\n from sklearn.preprocessing import StandardScaler\\n from sklearn.decomposition import PCA\\n import mpld3\\n\\n # Define and markers to use for different categories\\n groups_dict = {(u'D', 0):('Germany, unregulated', 'g', 'o'),\\n (u'N', 0):('Norway, unregulated', 'b', 'o'),\\n (u'D', 1):('Germany, regulated', 'g', '^'),\\n (u'N', 1):('Norway, regulated', 'b', '^')}\\n \\n # Extract cols of interest\\n cats = df[['country', 'regulated']]\\n\\n if cols:\\n df = df[cols].astype(float)\\n\\n # Standardise the feature data\\n feat_std = StandardScaler().fit_transform(df)\\n\\n # Setup PCA. Initially, choose to keep ALL components\\n pca = PCA()\\n\\n # Fit model\\n pca.fit(feat_std)\\n\\n # Get explained variances (in %)\\n var_exp = 100*pca.explained_variance_ratio_\\n cum_exp = np.cumsum(var_exp)\\n\\n # Get eigenvalues\\n cov_mat = np.cov(feat_std.T)\\n eig_vals, eig_vecs = np.linalg.eig(cov_mat)\\n\\n # Get number of EVs > 1 (Kaiser-Guttman criterion)\\n # and print summary\\n n_kgc = (eig_vals > 1).sum()\\n print 'Variance explained by first %s PCs (%%):\\\\n' % n_kgc\\n print var_exp[:n_kgc]\\n print '\\\\nTotal: %.2f%%' % var_exp[:n_kgc].sum()\\n \\n # Plot\\n fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\\n \\n # Explained variance\\n axes[0].bar(range(1, len(var_exp)+1), var_exp, \\n align='center', label='Individual components')\\n axes[0].plot(range(1, len(cum_exp)+1), cum_exp, \\n 'r-o', label='Cumulative')\\n axes[0].set_xlabel('Principal component')\\n axes[0].set_ylabel('Variance explained (%)')\\n axes[0].legend(loc='center right')\\n \\n # Eigenvalues\\n axes[1].plot(range(1, len(eig_vals)+1), np.sort(eig_vals)[::-1], \\n 'r-o', label='Eigenvalues')\\n axes[1].axhline(1, c='k', ls='-', label='Kaiser-Guttman threshold')\\n axes[1].set_xlabel('Principal component')\\n axes[1].set_ylabel('Eigenvalue')\\n axes[1].legend(loc='upper right') \\n \\n # PC loadings\\n loads = pd.DataFrame(data=pca.components_, \\n columns=df.columns,\\n index=range(1, pca.components_.shape[0]+1)).T\\n\\n # Project into 2 and 3 components\\n fig = plt.figure(figsize=(16, 6))\\n \\n # Plot 2 components\\n ax = fig.add_subplot(1, 2, 1)\\n \\n # Refit the PCA, this time specifying 2 components\\n # and transforming the result\\n feat_reduced = PCA(n_components=2).fit_transform(feat_std)\\n \\n # Build df \\n data = pd.DataFrame({'PC1':feat_reduced[:, 0],\\n 'PC2':feat_reduced[:, 1],\\n 'country':cats['country'],\\n 'regulated':cats['regulated']}) \\n\\n groups = data.groupby(['country', 'regulated'])\\n \\n # Plot\\n for name, group in groups:\\n ax.scatter(group['PC1'], group['PC2'], s=60,\\n label=groups_dict[name][0],\\n c=groups_dict[name][1],\\n marker=groups_dict[name][2])\\n \\n ax.set_xlabel('First principal component')\\n ax.set_ylabel('Second principal component')\\n ax.set_title('First two PCA directions')\\n \\n # Plot 3 components\\n ax = fig.add_subplot(1, 2, 2, projection='3d', \\n elev=-150, azim=135)\\n\\n # Refit the PCA, this time specifying 3 components\\n # and transforming the result\\n feat_reduced = PCA(n_components=3).fit_transform(feat_std)\\n\\n # Build df with colours\\n data = pd.DataFrame({'PC1':feat_reduced[:, 0],\\n 'PC2':feat_reduced[:, 1],\\n 'PC3':feat_reduced[:, 2],\\n 'country':cats['country'],\\n 'regulated':cats['regulated']}) \\n \\n groups = data.groupby(['country', 'regulated'])\\n \\n # Plot\\n for name, group in groups:\\n ax.scatter(group['PC1'], group['PC2'], group['PC3'],\\n label=groups_dict[name][0],\\n c=groups_dict[name][1],\\n marker=groups_dict[name][2],\\n s=60)\\n \\n ax.set_title('First three PCA directions')\\n ax.set_xlabel('First principal component')\\n ax.set_ylabel('Second principal component')\\n ax.set_zlabel('Third principal component')\\n ax.legend(bbox_to_anchor=(0.15, -0.1), frameon=True)\\n plt.show()\\n\\n return loads\",\n \"def MPE(cpts, obs):\\r\\n s = z3.Optimize()\\r\\n # we store our variables in a dictionary because it makes retrieval easier\\r\\n var = {}\\r\\n for variable in cpts:\\r\\n var[variable[0]] = (Bool(variable[0]))\\r\\n # we use this loop to make our hard assertions\\r\\n for observations in obs:\\r\\n if observations != []:\\r\\n if observations[1] == '0':\\r\\n s.add(Not(var[observations[0]]))\\r\\n else:\\r\\n s.add(var[observations[0]])\\r\\n # call our helper function to add all of our soft assertions\\r\\n for cpt in cpts:\\r\\n weighted_clause(cpt, var, s)\\r\\n s.check()\\r\\n return s.model()\",\n \"def create_vision_transformer_model_3d(input_image_size,\\n number_of_classification_labels=1000,\\n mode=\\\"classification\\\",\\n patch_size=6,\\n number_of_transformer_layers=8,\\n transformer_units=[128, 64],\\n projection_dimension=64,\\n number_of_attention_heads=4,\\n mlp_head_units=[2048, 1024],\\n dropout_rate=0.5):\\n\\n inputs = Input(shape=input_image_size)\\n\\n patches = ExtractPatches3D(patch_size)(inputs)\\n number_of_patches = ((input_image_size[0] * input_image_size[1] * input_image_size[2]) // (patch_size ** 3))\\n encoded_patches = EncodePatches(number_of_patches,\\n projection_dimension)(patches)\\n\\n for _ in range(number_of_transformer_layers):\\n\\n x1 = LayerNormalization(epsilon=1e-6)(encoded_patches)\\n\\n attention_output = MultiHeadAttention(num_heads=number_of_attention_heads,\\n key_dim=projection_dimension,\\n dropout=dropout_rate/5.0)(x1, x1)\\n x2 = Add()([attention_output, encoded_patches])\\n x3 = LayerNormalization(epsilon=1e-6)(x2)\\n x3 = multilayer_perceptron(x3,\\n hidden_units=transformer_units,\\n dropout_rate=dropout_rate/5.0)\\n encoded_patches = Add()([x3, x2])\\n\\n representation = LayerNormalization(epsilon=1e-6)(encoded_patches)\\n representation = Flatten()(representation)\\n representation = Dropout(dropout_rate)(representation)\\n\\n features = multilayer_perceptron(representation,\\n hidden_units=mlp_head_units,\\n dropout_rate=dropout_rate)\\n\\n layer_activation = ''\\n if mode == 'classification':\\n layer_activation = 'softmax'\\n elif mode == 'regression':\\n layer_activation = 'linear'\\n elif mode == 'sigmoid':\\n layer_activation = 'sigmoid'\\n else:\\n raise ValueError('mode must be either `classification` or `regression`.')\\n\\n outputs = Dense(number_of_classification_labels,\\n activation=layer_activation)(features)\\n\\n model = Model(inputs=inputs, outputs=outputs)\\n\\n return model\",\n \"def taucmm1_ncmm3(n):\\n # Données issues de Kukharaskii : Solid States Communications 13, 1761 (1973) : Plasmon-phonon coupling in GaAs\\n wp_cmm1 = np.array([156, 193, 202, 357, 692, 748, 802])\\n tau_cmm1 = np.array([75, 84, 86, 88, 95, 102, 105])\\n wp_n = convert_wpcmm1_dop(wp_cmm1)\\n p = np.poly1d(np.polyfit(wp_n,tau_cmm1,3))\\n return p(n)\",\n \"def get_mc_pred(output,eng,frame,nImg):\\n preds_2d = [];\\n preds_3d = [];\\n \\n c,s = ut.calc_cent_scale(frame);\\n \\n center = matlab.double([list(c)],(1,2));\\n scale = matlab.double([s],(1,1));\\n \\n for i in tqdm(range(0,nImg)):\\n preds_2d.append(eng.transformMPII(output[\\\"W_final\\\"][2*i:2*(i+1)],center,scale,matlab.double([64,64],(1,2)),1));\\n preds_3d.append(output[\\\"S_final\\\"][3*i:3*i + 3]);\\n \\n print(\\\"Converting estimates to Python format...\\\");\\n preds_2d = np.array(preds_2d);\\n preds_3d = np.array(preds_3d);\\n \\n preds_2d = preds_2d.swapaxes(1, 2);\\n preds_3d = preds_3d.swapaxes(1, 2);\\n \\n return preds_2d, preds_3d;\",\n \"def create_model(\\n self,\\n return_simulations: bool = False,\\n return_rdatas: bool = False,\\n ) -> Callable[[Union[Sequence, Mapping]], Mapping]:\\n # parameter ids to consider\\n x_free_ids = self.petab_problem.get_x_ids(free=True, fixed=False)\\n\\n # fixed parameters\\n x_fixed_ids = self.petab_problem.get_x_ids(\\n free=False, fixed=True)\\n x_fixed_vals = self.petab_problem.get_x_nominal(\\n scaled=True, free=False, fixed=True)\\n\\n # extract variables for improved pickling\\n petab_problem = self.petab_problem\\n amici_model = self.amici_model\\n amici_solver = self.amici_solver\\n prior_scales = self.prior_scales\\n scaled_scales = self.scaled_scales\\n\\n if set(prior_scales.keys()) != set(x_free_ids):\\n # this should not happen\\n raise AssertionError(\\\"Parameter id mismatch\\\")\\n\\n # no gradients for pyabc\\n amici_solver.setSensitivityOrder(0)\\n\\n def model(par: Union[Sequence, Mapping]) -> Mapping:\\n \\\"\\\"\\\"The model function.\\n\\n Note: The parameters are assumed to be passed on prior scale.\\n \\\"\\\"\\\"\\n # copy since we add fixed parameters\\n par = copy.deepcopy(par)\\n\\n # convenience to allow calling model not only with dicts\\n if not isinstance(par, Mapping):\\n par = {key: val for key, val in zip(x_free_ids, par)}\\n\\n # add fixed parameters\\n for key, val in zip(x_fixed_ids, x_fixed_vals):\\n par[key] = val\\n\\n # scale parameters whose priors are not on scale\\n for key in prior_scales.keys():\\n par[key] = rescale(\\n val=par[key],\\n origin_scale=prior_scales,\\n target_scale=scaled_scales,\\n )\\n\\n # simulate model\\n sim = simulate_petab(\\n petab_problem=petab_problem,\\n amici_model=amici_model,\\n solver=amici_solver,\\n problem_parameters=par,\\n scaled_parameters=True)\\n\\n # return values of interest\\n ret = {'llh': sim[LLH]}\\n if return_simulations:\\n for i_rdata, rdata in enumerate(sim[RDATAS]):\\n ret[f'y_{i_rdata}'] = rdata['y']\\n if return_rdatas:\\n ret[RDATAS] = sim[RDATAS]\\n\\n return ret\\n\\n return model\",\n \"def simulate_qubit_pairs_3D_lattice(cbrt_n, m):\\n # Initialize some variables\\n n = np.power(cbrt_n, 3)\\n x_max = 20\\n d = np.arange(0, x_max + 1)\\n k_matrix = np.zeros((m, x_max + 1))\\n\\n # Run the simulation a total of m times\\n for i in range(m):\\n start = time.time()\\n\\n sim = chp_py.CHP_Simulation(n)\\n sims = []\\n # Add a sim for depth 0\\n sims.append(chp_py.CHP_Simulation(n, sim.state))\\n\\n for j in range(x_max):\\n gates = utils.get_lattice_gates((cbrt_n, cbrt_n, cbrt_n), j)\\n sim.apply_gates(gates)\\n sims.append(chp_py.CHP_Simulation(n, sim.state))\\n\\n # This computes the collision_probability at different\\n # depths in parallel\\n k_matrix[i, :] = utils.sims_to_collision_probabilty(sims)\\n\\n end = time.time()\\n print(\\\"Time elapsed = {:0.2f}, for sample = {}\\\".format(end - start, i))\\n\\n # Store everything in data\\n data = {\\\"d\\\": d,\\n \\\"k_matrix\\\": k_matrix,\\n \\\"n\\\": n,\\n \\\"m\\\": m}\\n\\n return data\",\n \"def runSoftmaxOnMNIST_PCA_Cubic():\\n print(\\\"[INFO] Running soft max on MNIST data with PCA 10 and Cubic Kernel...\\\")\\n\\n tempParameter = 1.0\\n print(\\\"[INFO] Using tempParameter of:\\\", tempParameter)\\n\\n ### LOAD DATA ###\\n trainX, trainY, testX, testY = getMNISTData()\\n print(\\\"[INFO] Loaded in data...\\\")\\n\\n ### Find the 10-dimensional PCA representation of the training and test set ###\\n n_components = 10\\n pcs = principalComponents(trainX)\\n train_pca10 = projectOntoPC(trainX, pcs, n_components)\\n test_pca10 = projectOntoPC(testX, pcs, n_components)\\n print(\\\"[INFO] Got PCA features...\\\")\\n\\n ### CONVERT TO CUBIC REPRESENTATION ###\\n train_cube = cubicFeatures(train_pca10)\\n test_cube = cubicFeatures(test_pca10)\\n print(\\\"[INFO] Got cubic features...\\\")\\n\\n ### TRAIN THE MODEL ###\\n start = time.time()\\n theta, costFunctionHistory = softmaxRegression(train_cube, trainY, tempParameter, alpha=0.3, lambdaFactor=1.0e-4, k=10, numIterations=150)\\n finish = time.time()\\n print(\\\"[INFO] Finished training in %f secs\\\" % (finish-start))\\n\\n ### PLOTTING ###\\n plotCostFunctionOverTime(costFunctionHistory)\\n\\n ### COMPUTE ERROR ###\\n err = computeTestError(test_cube, testY, theta, tempParameter)\\n print(\\\" ### TEST ERROR RESULTS ###\\\")\\n print(\\\"Test Error (PCA10 w/ Cubic Features):\\\", err)\\n\\n return err\",\n \"def marcovNuc (i = random.choice(stateSpace), step = 100):\\n # matrix of transition probabilities\\n #matrix = [[0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25]] \\n matrix = [[0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1], [0.4, 0.3, 0.2, 0.1]] \\n step += 1 # add one to the range because we remove it at the end\\n sims = [] # List to hold the results of the Marcov chain\\n sims.append(i) # append the seed value to the sims list\\n for x in range(step):\\n \\n if sims[-1] == 'A':\\n w = np.random.random() # Random number generator\\n # the next set of if statements determine where the random number \\n # sits on the number line of probabilities\\n if matrix[0][0] > w:\\n sims.append('A')\\n elif matrix[0][1] + matrix[0][0] > w:\\n sims.append('C')\\n elif matrix[0][2] + matrix[0][1] + matrix[0][0] > w:\\n sims.append('G')\\n else:\\n sims.append('T')\\n elif sims[-1] == 'C':\\n x = np.random.random()\\n if matrix[1][0] > x:\\n sims.append('A')\\n elif matrix[1][1] + matrix[1][0] > x:\\n sims.append('C')\\n elif matrix[1][2] + matrix[1][1] + matrix[1][0] > x:\\n sims.append('G')\\n else:\\n sims.append('T')\\n \\n elif sims[-1] == 'G':\\n y = np.random.random()\\n if matrix[2][0] > y:\\n sims.append('A')\\n elif matrix[2][1] + matrix[2][0] > y:\\n sims.append('C')\\n elif matrix[2][2] + matrix[2][1] + matrix[2][0] > y:\\n sims.append('G')\\n else:\\n sims.append('T')\\n\\n else:\\n z = np.random.random()\\n if matrix[3][0] > z:\\n sims.append('A')\\n elif matrix[3][1] + matrix[3][0] > z:\\n sims.append('C')\\n elif matrix[3][2] + matrix[3][1] + matrix[3][0] > z:\\n sims.append('G')\\n else:\\n sims.append('T')\\n\\n return sims[1:-1] # remove the initial value (the seed)\",\n \"def fit_FITC(self, Kmm, Kmn, Knn_diag):\\r\\n num_inducing = Kmm.shape[0]\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Prior approximation parameters:\\r\\n q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)\\r\\n Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn\\r\\n \\\"\\\"\\\"\\r\\n Lm = jitchol(Kmm)\\r\\n Lmi = chol_inv(Lm)\\r\\n Kmmi = np.dot(Lmi.T,Lmi)\\r\\n P0 = Kmn.T\\r\\n KmnKnm = np.dot(P0.T, P0)\\r\\n KmmiKmn = np.dot(Kmmi,P0.T)\\r\\n Qnn_diag = np.sum(P0.T*KmmiKmn,-2)\\r\\n Diag0 = Knn_diag - Qnn_diag\\r\\n R0 = jitchol(Kmmi).T\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Posterior approximation: q(f|y) = N(f| mu, Sigma)\\r\\n Sigma = Diag + P*R.T*R*P.T + K\\r\\n mu = w + P*Gamma\\r\\n \\\"\\\"\\\"\\r\\n self.w = np.zeros(self.N)\\r\\n self.Gamma = np.zeros(num_inducing)\\r\\n mu = np.zeros(self.N)\\r\\n P = P0.copy()\\r\\n R = R0.copy()\\r\\n Diag = Diag0.copy()\\r\\n Sigma_diag = Knn_diag\\r\\n RPT0 = np.dot(R0,P0.T)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Initial values - Cavity distribution parameters:\\r\\n q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}\\r\\n sigma_ = 1./tau_\\r\\n mu_ = v_/tau_\\r\\n \\\"\\\"\\\"\\r\\n self.tau_ = np.empty(self.N,dtype=float)\\r\\n self.v_ = np.empty(self.N,dtype=float)\\r\\n\\r\\n #Initial values - Marginal moments\\r\\n z = np.empty(self.N,dtype=float)\\r\\n self.Z_hat = np.empty(self.N,dtype=float)\\r\\n phi = np.empty(self.N,dtype=float)\\r\\n mu_hat = np.empty(self.N,dtype=float)\\r\\n sigma2_hat = np.empty(self.N,dtype=float)\\r\\n\\r\\n #Approximation\\r\\n epsilon_np1 = 1\\r\\n epsilon_np2 = 1\\r\\n \\tself.iterations = 0\\r\\n self.np1 = [self.tau_tilde.copy()]\\r\\n self.np2 = [self.v_tilde.copy()]\\r\\n while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:\\r\\n update_order = np.random.permutation(self.N)\\r\\n for i in update_order:\\r\\n #Cavity distribution parameters\\r\\n self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]\\r\\n self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]\\r\\n #Marginal moments\\r\\n self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model_list[self.index[i]].moments_match(self._transf_data[i],self.tau_[i],self.v_[i])\\r\\n #Site parameters update\\r\\n Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])\\r\\n Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])\\r\\n self.tau_tilde[i] += Delta_tau\\r\\n self.v_tilde[i] += Delta_v\\r\\n #Posterior distribution parameters update\\r\\n dtd1 = Delta_tau*Diag[i] + 1.\\r\\n dii = Diag[i]\\r\\n Diag[i] = dii - (Delta_tau * dii**2.)/dtd1\\r\\n pi_ = P[i,:].reshape(1,num_inducing)\\r\\n P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_\\r\\n Rp_i = np.dot(R,pi_.T)\\r\\n RTR = np.dot(R.T,np.dot(np.eye(num_inducing) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))\\r\\n R = jitchol(RTR).T\\r\\n self.w[i] += (Delta_v - Delta_tau*self.w[i])*dii/dtd1\\r\\n self.Gamma += (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)\\r\\n RPT = np.dot(R,P.T)\\r\\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\\r\\n mu = self.w + np.dot(P,self.Gamma)\\r\\n self.iterations += 1\\r\\n #Sigma recomptutation with Cholesky decompositon\\r\\n Iplus_Dprod_i = 1./(1.+ Diag0 * self.tau_tilde)\\r\\n Diag = Diag0 * Iplus_Dprod_i\\r\\n P = Iplus_Dprod_i[:,None] * P0\\r\\n safe_diag = np.where(Diag0 < self.tau_tilde, self.tau_tilde/(1.+Diag0*self.tau_tilde), (1. - Iplus_Dprod_i)/Diag0)\\r\\n L = jitchol(np.eye(num_inducing) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))\\r\\n R,info = dtrtrs(L,R0,lower=1)\\r\\n RPT = np.dot(R,P.T)\\r\\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\\r\\n self.w = Diag * self.v_tilde\\r\\n self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))\\r\\n mu = self.w + np.dot(P,self.Gamma)\\r\\n epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N\\r\\n epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N\\r\\n self.np1.append(self.tau_tilde.copy())\\r\\n self.np2.append(self.v_tilde.copy())\\r\\n\\r\\n return self._compute_GP_variables()\",\n \"def initialize_model(self, config_param_vals = None):\\n self._is_initialized = True\\n\\n self.fmu.instantiate()\\n self.fmu.reset()\\n self.fmu.setupExperiment(startTime=self.start_time)\\n if config_param_vals is not None:\\n self._apply_config(config_param_vals)\\n self.fmu.enterInitializationMode()\\n self.fmu.exitInitializationMode()\\n\\n return\",\n \"def Nmatrix(init_par, alpha, delta, obs, sigma_obs, ccoef, N):\\n\\tparallax, v, sigma_v = init_par[:-4], init_par[-4:-1], init_par[-1] \\n\\tplx_obs, mualpha_obs, mudelta_obs = obs[:, 0], obs[:, 1], obs[:, 2]\\n\\n\\tp, q, r = normalTriad(alpha, delta)\\n\\tmualpha_mod = np.dot(np.transpose(p),v)*parallax/_A\\n\\tmudelta_mod = np.dot(np.transpose(q),v)*parallax/_A\\n\\t\\n\\tplx_mod, mualpha_mod, mudelta_mod = parallax, mualpha_mod, mudelta_mod\\n\\tsigma_plx, sigma_mualpha, sigma_mudelta = np.transpose(sigma_obs)\\n\\ta,like, expo, detD = np.ones(N),np.ones(N),np.ones(N), np.ones(N) \\n\\tC = np.zeros((3,3,N),dtype=np.float64)\\n\\tC[0,0,:],C[1,1,:],C[2,2,:] = sigma_plx**2.,sigma_mualpha**2., sigma_mudelta**2.\\n\\tcorr_coefficient_plx_mualpha, corr_coefficient_plx_mudelta, corr_coefficient_mualpha_mudelta = np.zeros(N), np.zeros(N), np.zeros(N)\\n\\tcorr_coefficient_plx_mualpha[:], corr_coefficient_plx_mudelta[:], corr_coefficient_mualpha_mudelta[:] = ccoef[:, 0], ccoef[:, 1], ccoef[:, 2] \\n\\t\\n\\tC[0,1,:], C[0,2,:] = corr_coefficient_plx_mualpha*sigma_plx*sigma_mualpha, corr_coefficient_plx_mudelta*sigma_plx*sigma_mudelta\\n\\tC[1,0,:], C[1,2,:] = corr_coefficient_plx_mualpha*sigma_plx*sigma_mualpha, corr_coefficient_mualpha_mudelta*sigma_mualpha*sigma_mudelta\\n\\tC[2,0,:], C[2,1,:] = corr_coefficient_plx_mudelta*sigma_plx*sigma_mudelta, corr_coefficient_mualpha_mudelta*sigma_mualpha*sigma_mudelta\\n\\tE = np.zeros((3,3,N),dtype=np.float64)\\n\\tE[1,1,:],E[2,2,:] = (sigma_v**2.)*(parallax/_A)**2., (sigma_v**2.)*(parallax/_A)**2.\\n\\tD,invD = np.zeros((3,3,N),dtype=np.float64),np.zeros((3,3,N),dtype=np.float64)\\n\\tD = np.add(E,C)\\n\\tfor i in range(N):\\n\\t\\tdetD[i] = matrix_det(D[:,:,i]) \\n\\t\\tinvD[:,:,i] = matrix_inv(D[:,:,i])\\n\\t\\t\\n\\ta_c = np.ones((3,N))\\n\\ta_c = [plx_obs - plx_mod, mualpha_obs - mualpha_mod, mudelta_obs-mudelta_mod]\\n\\t\\n\\t\\n\\n\\t\\n\\tcprime_pi, cprime_vx, cprime_vy, cprime_vz, = np.ones((3,N)), np.ones((3,N)), \\\\\\n\\t\\t\\t\\t\\t\\t\\tnp.ones((3,N)), np.ones((3,N)), \\n\\tcprime_pi[0,:] = 1.\\n\\tcprime_pi[1,:] = np.dot(np.transpose(p),v)/_A\\n\\tcprime_pi[2,:] = np.dot(np.transpose(q),v)/_A\\n\\t\\n\\tcprime_vx[0,:] = 0.\\n\\tcprime_vx[1,:] = -np.sin(alpha)*plx_mod/_A \\n\\tcprime_vx[2,:] = -np.sin(delta)*np.cos(alpha)*plx_mod/_A\\n\\n\\t\\n\\tcprime_vy[0,:] = 0.\\n\\tcprime_vy[1,:] = np.cos(alpha)*plx_mod/_A \\n\\tcprime_vy[2,:] = -np.sin(delta)*np.sin(alpha)*plx_mod/_A\\n\\n\\tcprime_vz[0,:] = 0.\\n\\tcprime_vz[1,:] = 0. \\n\\tcprime_vz[2,:] = np.cos(delta)*plx_mod/_A\\n\\n\\tdlnd_dpi, dlnd_dsigmav = np.zeros(N), np.zeros(N)\\n\\tde_dpi, de_dsigmav = np.zeros(N), np.zeros(N)\\n\\t\\n\\n\\t### See formula A.5 \\n\\tde_dpi[:] = ((sigma_v/_A)**2.)*2.*plx_mod[:]\\n\\tde_dsigmav[:] = ((plx_mod[:]/_A)**2.)*2.*sigma_v\\n\\t\\n\\tdlnd_dpi[:] = (invD[1,1,:] + invD[2,2,:])*de_dpi[:] \\n\\tdlnd_dsigmav[:] = (invD[1,1,:] + invD[2,2,:])*de_dsigmav[:]\\n\\t\\n\\t\\n\\t\\n\\t### See formula A.7\\n\\thess = np.zeros((N+4, N+4))\\n\\n\\thess_diag_pi, hess_diag_pi_1, hess_diag_pi_2 = np.zeros(N), np.zeros(N), np.zeros(N)\\n\\thess_diag_pi_1[:] = invD[0, 0, :]*cprime_pi[0, :]*cprime_pi[0, :] + invD[0, 1, :]*cprime_pi[0, :]*cprime_pi[1, :] + invD[0, 2, :]*cprime_pi[0, :]*cprime_pi[2, :] + \\\\\\n\\t\\t\\t invD[1, 0, :]*cprime_pi[1, :]*cprime_pi[0, :] + invD[1, 1, :]*cprime_pi[1, :]*cprime_pi[1, :] + invD[1, 2, :]*cprime_pi[1, :]*cprime_pi[2, :] + \\\\\\n\\t\\t \\t invD[2, 0, :]*cprime_pi[2, :]*cprime_pi[0, :] + invD[2, 1, :]*cprime_pi[2, :]*cprime_pi[1, :] + invD[2, 2, :]*cprime_pi[2, :]*cprime_pi[2, :]\\t\\n\\t\\n\\t\\n\\t#hess_diag_pi_2[:] = np.sum(0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dpi[:]) ### Check if it's with or without sum: without!\\n\\t# So correct formula is below.\\n\\thess_diag_pi_2[:] = (0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dpi[:])\\n\\thess_diag_pi[:] = hess_diag_pi_1[:] + hess_diag_pi_2[:]\\t\\n\\n\\t\\n\\thess_diag_vx, hess_diag_vy, hess_diag_vz, hess_diag_sigmav = np.zeros(N), np.zeros(N), np.zeros(N), np.zeros(N)\\n\\thess_pi_vx, hess_pi_vy, hess_pi_vz, hess_pi_sigmav = np.zeros(N), np.zeros(N), np.zeros(N), np.zeros(N)\\n\\thess_diag_vxi, hess_diag_vyi, hess_diag_vzi = np.zeros(N), np.zeros(N), np.zeros(N)\\n\\t\\n\\thess_diag_vxi[:] = invD[0, 0, :]*cprime_vx[0, :]*cprime_vx[0, :] + invD[0, 1, :]*cprime_vx[0, :]*cprime_vx[1, :] + invD[0, 2, :]*cprime_vx[0, :]*cprime_vx[2, :] + \\\\\\n\\t\\t\\t invD[1, 0, :]*cprime_vx[1, :]*cprime_vx[0, :] + invD[1, 1, :]*cprime_vx[1, :]*cprime_vx[1, :] + invD[1, 2, :]*cprime_vx[1, :]*cprime_vx[2, :] + \\\\\\n\\t\\t\\t invD[2, 0, :]*cprime_vx[2, :]*cprime_vx[0, :] + invD[2, 1, :]*cprime_vx[2, :]*cprime_vx[1, :] + invD[2, 2, :]*cprime_vx[2, :]*cprime_vx[2, :] \\t\\t\\n\\t\\n\\thess_diag_vyi[:] = invD[0, 0, :]*cprime_vy[0, :]*cprime_vy[0, :] + invD[0, 1, :]*cprime_vy[0, :]*cprime_vy[1, :] + invD[0, 2, :]*cprime_vy[0, :]*cprime_vy[2, :] +\\\\\\n\\t\\t\\t invD[1, 0, :]*cprime_vy[1, :]*cprime_vy[0, :] + invD[1, 1, :]*cprime_vy[1, :]*cprime_vy[1, :] + invD[1, 2, :]*cprime_vy[1, :]*cprime_vy[2, :] +\\\\\\n\\t\\t\\t invD[2, 0, :]*cprime_vy[2, :]*cprime_vy[0, :] + invD[2, 1, :]*cprime_vy[2, :]*cprime_vy[1, :] + invD[2, 2, :]*cprime_vy[2, :]*cprime_vy[2, :] \\t\\n\\n\\n\\thess_diag_vzi[:] = invD[0, 0, :]*cprime_vz[0, :]*cprime_vz[0, :] + invD[0, 1, :]*cprime_vz[0, :]*cprime_vz[1, :] + invD[0, 2, :]*cprime_vz[0, :]*cprime_vz[2, :] +\\\\\\n\\t\\t\\t invD[1, 0, :]*cprime_vz[1, :]*cprime_vz[0, :] + invD[1, 1, :]*cprime_vz[1, :]*cprime_vz[1, :] + invD[1, 2, :]*cprime_vz[1, :]*cprime_vz[2, :] +\\\\\\n\\t\\t\\t invD[2, 0, :]*cprime_vz[2, :]*cprime_vz[0, :] + invD[2, 1, :]*cprime_vz[2, :]*cprime_vz[1, :] + invD[2, 2, :]*cprime_vz[2, :]*cprime_vz[2, :] \\t\\t\\n\\t\\n\\n\\thess_pi_vx[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vx[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vx[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vx[2, :] +\\\\\\n\\t\\t\\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vx[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vx[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vx[2, :] +\\\\\\n\\t\\t\\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vx[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vx[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vx[2, :] \\n\\n\\thess_pi_vy[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vy[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vy[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vy[2, :] +\\\\\\n\\t\\t\\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vy[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vy[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vy[2, :] +\\\\\\n\\t\\t\\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vy[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vy[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vy[2, :] \\n\\n\\thess_pi_vz[:] = invD[0, 0, :]*cprime_pi[0,:]*cprime_vz[0, :] + invD[0, 1, :]*cprime_pi[0,:]*cprime_vz[1, :] + invD[0, 2, :]*cprime_pi[0,:]*cprime_vz[2, :] +\\\\\\n\\t\\t\\tinvD[1, 0, :]*cprime_pi[1,:]*cprime_vz[0, :] + invD[1, 1, :]*cprime_pi[1,:]*cprime_vz[1, :] + invD[1, 2, :]*cprime_pi[1,:]*cprime_vz[2, :] +\\\\\\n\\t\\t\\tinvD[2, 0, :]*cprime_pi[2,:]*cprime_vz[0, :] + invD[2, 1, :]*cprime_pi[2,:]*cprime_vz[1, :] + invD[2, 2, :]*cprime_pi[2,:]*cprime_vz[2, :] \\n\\n\\t\\t\\t\\t\\t\\t\\n\\thess_diag_vx = np.sum(hess_diag_vxi)\\n\\thess_diag_vy = np.sum(hess_diag_vyi)\\n\\thess_diag_vz = np.sum(hess_diag_vzi)\\t\\n\\t\\n\\thess_diag_sigmav = np.sum(0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dsigmav[:]*de_dsigmav[:])\\n\\thess_pi_sigmav[:] = 0.5*(invD[1, 1, :]**2. + 2.*invD[1, 2, :]**2. + invD[2, 2, :]**2.)*de_dpi[:]*de_dsigmav[:] \\n\\n\\thess_diag = np.concatenate((hess_diag_pi, np.array([hess_diag_vx, hess_diag_vy, hess_diag_vz, hess_diag_sigmav])))\\n\\t\\n\\tfor i in range(N+4):\\n\\t\\thess[i, i] = hess_diag[i]\\n\\t\\t\\n\\t\\n\\tfor j in range(N):\\n\\t\\t\\thess[j, -4] = hess_pi_vx[j]\\n\\t\\t\\thess[j, -3] = hess_pi_vy[j]\\n\\t\\t\\thess[j, -2] = hess_pi_vz[j]\\n\\t\\t\\thess[j, -1] = hess_pi_sigmav[j]\\n\\t\\t\\thess[-4, j] = hess_pi_vx[j]\\n\\t\\t\\thess[-3, j] = hess_pi_vy[j] \\n\\t\\t\\thess[-2, j] = hess_pi_vz[j]\\n\\t\\t\\thess[-1, j] = hess_pi_sigmav[j]\\n\\t\\t\\t\\n\\n\\t\\n\\t\\n\\tpart_12, part_13, part_23 = np.zeros(N),np.zeros(N),np.zeros(N)\\n\\tfor ia in range(3):\\n\\t\\tfor ib in range(3):\\n\\t\\t\\tpart_12[:] += invD[ia, ib, :]*cprime_vx[ia, :]*cprime_vy[ib, :] \\n\\t\\t\\tpart_13[:] += invD[ia, ib, :]*cprime_vx[ia, :]*cprime_vz[ib, :] \\n\\t\\t\\tpart_23[:] += invD[ia, ib, :]*cprime_vy[ia, :]*cprime_vz[ib, :] \\t\\t\\t\\t\\n\\n\\thess[-4, -3] = np.sum(part_12)\\n\\thess[-3, -4] = hess[-4, -3]\\n\\t\\n\\thess[-4, -2] = np.sum(part_13)\\n\\thess[-2, -4] = hess[-4, -2]\\n\\n\\thess[-3, -2] = np.sum(part_23)\\n\\thess[-2, -3] = hess[-3, -2]\\n\\n\\t#### I am returning here the matrix Njk, which is defined as -E(H),\\n\\t#### where H is the hessian of the likelihood: therefore to obtain the real hessian, one\\n\\t#### should multiply this by '-1' (see function below.)\\n\\treturn hess ### See eq. 18\",\n \"def setup_milp(model, target, remove_blocked=False, exclude_reaction_ids=set()):\\n original_model = model\\n model = model.copy()\\n model.objective=target\\n for i in heterologous_reactions.keys(): # turns off each heterologous reaction in order to get the only the native metabolic network.\\n model.reactions.get_by_id(i).lower_bound=0\\n model.reactions.get_by_id(i).upper_bound=0\\n\\n for r in model.reactions:\\n if r.id.find('MetaCyc')>-1:\\n r.type='heterologous'\\n else:\\n r.type='native'\\n\\n # Set the solver to Gurobi for the fastest result. Set to CPLEX if Gurobi is not available.\\n if \\\"gurobi\\\" in cobra.util.solver.solvers.keys():\\n logger.info(\\\"Changing solver to Gurobi and tweaking some parameters.\\\")\\n if \\\"gurobi_interface\\\" not in model.solver.interface.__name__:\\n model.solver = \\\"gurobi\\\"\\n # The tolerances are set to the minimum value. This gives maximum precision.\\n problem = model.solver.problem\\n problem.params.NodeMethod = 1 # primal simplex node relaxation\\n problem.params.FeasibilityTol = 1e-9 #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\\n problem.params.OptimalityTol = 1e-3 #how sure the solver has to be about this optimum being really the best it has.\\n problem.params.IntFeasTol = 1e-9 #If a value is set to an integer, how much may it still vary? > set smallest possible\\n problem.params.MIPgapAbs = 1e-9\\n problem.params.MIPgap = 1e-9\\n problem.params.Threads=1 #In order to reduce memory usage (increased calculation time)\\n problem.params.TimeLimit = 200 # Use max 200 seconds when called, return best solution after that\\n problem.params.PoolSearchMode = 1 #0 for only finding the optimum, 1 for finding more solutions (but no quality guaranteed), 2 for finding the n best possible solutions\\n problem.params.PoolSolutions = 10 # Number of solutions kept when finding the optimal solution\\n problem.params.PoolGap = 0.9 # only store solutions within 90% of the optimal objective value\\n\\n elif \\\"cplex\\\" in cobra.util.solver.solvers.keys():\\n logger.warning(\\\"Changing solver to CPLEX, as Gurobi is not available. This may cause a big slowdown and limit options afterwards.\\\")\\n if \\\"cplex_interface\\\" not in model.solver.interface.__name__:\\n model.solver = \\\"cplex\\\"\\n # The tolerances are set to the minimum value. This gives maximum precision.\\n problem = model.solver.problem\\n problem.parameters.mip.strategy.startalgorithm.set(1) # primal simplex node relaxation\\n problem.parameters.simplex.tolerances.feasibility.set(1e-9) #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\\n problem.parameters.simplex.tolerances.optimality.set(1e-3) #possibly fine with 1e-3, try if allowed. Is how sure the solver has to be about this optimum being really the best it has.\\n problem.parameters.mip.tolerances.integrality.set(1e-9) #If a value is set to an integer, how much may it still vary? > set smallest possible\\n problem.parameters.mip.tolerances.absmipgap.set(1e-9)\\n problem.parameters.mip.tolerances.mipgap.set(1e-9)\\n problem.parameters.mip.pool.relgap.set(0.9) # For populate: find all solutions within 10% of the optimum for relgap = 0.1\\n problem.parameters.timelimit.set(200) # Use max 200 seconds for solving\\n problem.parameters.mip.limits.populate.set(20) # Find max 20 solutions (=default)\\n\\n else:\\n logger.warning(\\\"You are trying to run 'Hamlet Hot Rig' with %s. This might not end well.\\\" %\\n model.solver.interface.__name__.split(\\\".\\\")[-1])\\n pass\\n # Remove reactions that are blocked: no flux through these reactions possible. This will reduce the search space for the solver, if not done already.\\n if remove_blocked:\\n blocked_reactions = cameo.flux_analysis.analysis.find_blocked_reactions(model)\\n model.remove_reactions(blocked_reactions)\\n\\n # Make dual\\n model_with = model.copy() # This variable looks unnecessary, but is kept out of fear of messing stuff up\\n model_with.optimize()\\n dual_problem = convert_linear_problem_to_dual(model_with.solver)\\n logger.debug(\\\"Dual problem successfully created\\\")\\n\\n # Combine primal and dual\\n primal_problem = model.solver\\n\\n for var in dual_problem.variables: # All variables in the dual are copied to the primal\\n var = primal_problem.interface.Variable.clone(var)\\n primal_problem.add(var)\\n for const in dual_problem.constraints: # All constraints in the dual are copied to the primal\\n const = primal_problem.interface.Constraint.clone(const, model=primal_problem)\\n primal_problem.add(const)\\n logger.debug(\\\"Dual and primal combined\\\")\\n\\n dual_problem.optimize()\\n\\n # Dictionaries to hold the binary control variables:\\n heterologous_y_vars = {} # 1 for 'knockin', 0 for inactive\\n medium_y_vars = {} # 1 for medium addition (up to -10), 0 for no addition\\n\\n # Now the fun stuff\\n constrained_dual_vars = set()\\n\\n # For the knockins and medium additions:\\n for reaction in [r for r in model.reactions if r.type == \\\"heterologous\\\"]:\\n # Add constraint variables\\n interface = model.solver.interface\\n y_var = interface.Variable(\\\"y_\\\" + reaction.id, type=\\\"binary\\\")\\n\\n # Constrain the primal: flux through reactions maximum within (-1000, 1000), or smaller boundaries defined before\\n model.solver.add(interface.Constraint(reaction.flux_expression - 1000 * y_var, ub=0, name=\\\"primal_y_const_\\\"+reaction.id+\\\"_ub\\\"))\\n model.solver.add(interface.Constraint(reaction.flux_expression + 1000 * y_var, lb=0, name=\\\"primal_y_const_\\\"+reaction.id+\\\"_lb\\\"))\\n\\n # Constrain the dual\\n constrained_vars = []\\n\\n if reaction.upper_bound != 0:\\n dual_forward_ub = model.solver.variables[\\\"dual_\\\" + reaction.forward_variable.name + \\\"_ub\\\"]\\n model.solver.add(interface.Constraint(dual_forward_ub - 1000 * (1 - y_var), ub=0))\\n constrained_vars.append(dual_forward_ub)\\n if reaction.lower_bound != 0:\\n dual_reverse_ub = model.solver.variables[\\\"dual_\\\" + reaction.reverse_variable.name + \\\"_ub\\\"]\\n model.solver.add(interface.Constraint(dual_reverse_ub - 1000 * (1 - y_var), ub=0))\\n constrained_vars.append(dual_reverse_ub)\\n constrained_dual_vars.update(constrained_vars)\\n\\n # Add y variable to the corresponding modifications dictionary\\n heterologous_y_vars[y_var] = reaction\\n\\n logger.debug(\\\"Control variables created\\\")\\n\\n # Add number of heterologous switch contraint constraint\\n heterologous_turn_on = model.solver.interface.Constraint(\\n optlang.symbolics.Add(*heterologous_y_vars), lb=0, ub=0, name=\\\"heterologous_reaction_constraint\\\"\\n )\\n model.solver.add(heterologous_turn_on)\\n\\n # Set the objective\\n primal_objective = model.solver.objective\\n dual_objective = interface.Objective.clone(\\n dual_problem.objective, model=model.solver\\n )\\n\\n switch_objective=interface.Objective(heterologous_turn_on.expression, direction='min')\\n\\n full_objective = interface.Objective(primal_objective.expression-dual_objective.expression, direction=\\\"max\\\")\\n model.objective = full_objective\\n\\n return model,primal_objective,dual_objective,full_objective,switch_objective\",\n \"def HM_taucmm1_ncmm3(n):\\n \\n #wp = np.array([1.5e18, 3e18])\\n #tau_cmm1 = np.array([59, 70])\\n #p = np.poly1d(np.polyfit(wp,tau_cmm1,1))\\n \\n #return p(n)\\n return 70\",\n \"def mcmc_fit(mc_model, mcmc_parameters):\\n mcmc = pymc.MCMC(mc_model)\\n mcmc.use_step_method(pymc.AdaptiveMetropolis, mcmc.theta_i)\\n print('running MCMC')\\n mcmc.sample(mcmc_parameters['points'], mcmc_parameters['burn'], mcmc_parameters['steps'],\\n progress_bar=True)\\n print('finished MCMC')\\n return mcmc\",\n \"def mc3(**kwargs):\\n return _video_resnet('mc3',\\n block=BasicBlock,\\n conv_makers=[Conv3DSimple] * 2 + [Conv3DNoTemporal] * 2,\\n layers=[NUM_LAYER, NUM_LAYER, NUM_LAYER, NUM_LAYER],\\n stem=BasicStem, **kwargs)\",\n \"def example():\\n num_user, num_item, ratings = build_ticket()\\n \\n # suffle_data\\n np.random.shuffle(ratings)\\n\\n # split data to training & validation\\n train_pct = 0.9\\n train_size = int(train_pct * len(ratings))\\n train = ratings[:train_size]\\n validation = ratings[train_size:]\\n\\n # params\\n num_feature = 5\\n bmf_model = BayesianMatrixFactorization(\\n num_user, num_item, num_feature, train, validation, max_rating=1, min_rating=0, ratingsMatirx=ratings)\\n\\n start_time = time.clock()\\n bmf_model.estimate(10)\\n end_time = time.clock()\\n \\n mat = np.matrix(bmf_model.item_features)\\n with open('../data/ticket/item_feature', 'w') as f:\\n for line in mat:\\n np.savetxt(f, line, fmt='%.5f')\\n\\n print \\\"time spend = %.3f\\\" % (end_time - start_time)\\n\\n return bmf_model\",\n \"def _construct_mvn(self,\\n loc_shape,\\n diag_shape,\\n update_shape,\\n dtype=np.float32):\\n if loc_shape is not None:\\n loc = np.random.normal(size=loc_shape).astype(dtype)\\n else:\\n loc = None\\n cov_diag_factor = np.random.uniform(\\n low=1., high=2., size=diag_shape).astype(dtype)\\n cov_perturb_factor = np.random.normal(\\n size=update_shape).astype(dtype)\\n return mvdplrc.MultivariateNormalDiagPlusLowRankCovariance(\\n loc=loc,\\n cov_diag_factor=cov_diag_factor,\\n cov_perturb_factor=cov_perturb_factor)\",\n \"def proposal_rule(cov, mean, npix):\\n npix = int(npix)\\n \\n params = np.random.multivariate_normal(mean, cov)\\n # check if parameters are in right domain\\n \\n params[:npix] = test_params(params[:npix], mean[:npix],\\\\\\n cov[:npix,:npix], crit='Rpp')\\n params[npix:-1] = test_params(params[npix:-1], mean[npix:-1],\\\\\\n cov[npix:-1,npix:-1], crit='Pb')\\n params[-1] = test_params(params[-1], mean[-1], cov[-1,-1], crit='psib')\\n \\n #print(params) \\n return(params)\",\n \"def get_element(self, PP=None, prec=None, maxD=None, maxC=None, cusp_form=False, ef=True, mp=1E-8, M0_set=None, Y_set=None,\\n SetCs={}, use_mpmath=False, constant_term=\\\"pp\\\", use_temporary_file_storage=False, return_matrix=False):\\n minprec = mp\\n if PP is None:\\n PP = self.smallest_pp() # Use the smallest discriminant possible\\n if prec is None and maxD is None and maxC is None:\\n raise ValueError(\\\"Need either desired precision, number of discriminants or number of coefficients!\\\")\\n if maxD is not None:\\n # Need the correct M0 to use for getting this many coefficients.\\n prec = None\\n M0_set = ceil(maxD / self.rank()) # should be approximately good\\n for m in range(1, maxD * self.rank() + 1):\\n min_d = maxD\\n # We need to make sure that all discriminants below maxD are accounted for\\n for r in self.index_set():\\n D = D_from_rn(self.multiplier(), (r, m))\\n if D < min_d:\\n min_d = D\\n if min_d >= maxD:\\n M0_set = m\\n break\\n elif maxC is not None:\\n prec = None\\n M0_set = maxC\\n P0 = self.get_principal_parts(PP)\\n if len(P0) > 1:\\n raise NotImplementedError(\\\"More than one principal part is not implemented yet\\\")\\n P = P0[0]\\n if self._verbose > 0:\\n print(\\\"P={0}\\\".format(P))\\n print(f\\\"prec={prec}\\\")\\n print(\\\"M0_set={0}\\\".format(M0_set))\\n F = VVHarmonicWeakMaassFormElement(self, P)\\n if self._verbose > 0:\\n sys.stdout.flush()\\n if M0_set:\\n mpmath.mp.dps = 53 # Do the estimate in low precision\\n Y = mpmath.mpf(0.75)\\n M = M0_set\\n [er1, er2] = F.get_error_estimates(Y, M0_set)\\n prec = max(er1, er2)\\n if self._verbose > 0:\\n print(f\\\"prec={prec}\\\")\\n elif prec is not None:\\n [Y, M] = self.get_Y_and_M(P, self._weight, prec)\\n else:\\n raise ValueError(\\\"Could not decide number of coefficients to compute from input!\\\")\\n if self._verbose > 0:\\n print(f\\\"prec={prec}\\\")\\n print(f\\\"Y={Y}\\\")\\n print(f\\\"M={M}\\\")\\n print(f\\\"minprec={minprec}\\\")\\n # if minprec is not None and prec > minprec:\\n # prec=minprec\\n # [Y,M]=self.get_Y_and_M(P,self._weight,minprec)\\n Q = M + 10\\n if Y_set and Y_set > 0 and Y_set < 0.866:\\n Y = Y_set\\n if self._verbose > 1:\\n print(\\\"prec=\\\", prec)\\n print(\\\"Y=\\\", Y)\\n print(\\\"M=\\\", M)\\n print(\\\"Q=\\\", Q)\\n dold = mpmath.mp.dps\\n mpmath.mp.dps = max(self._dprec, prec + 10)\\n if self._verbose > 0:\\n print(\\\"using {0} digits\\\".format(mpmath.mp.dps))\\n print(\\\"P={0}\\\".format(P))\\n print(\\\"ef={0}\\\".format(ef))\\n if ef:\\n if use_mpmath:\\n Y = mpmath.mp.mpf(Y)\\n W = vv_harmonic_wmwf_setupV_ef(self, P, Y, M, Q, mpmath.mp.mpf(self._weight))\\n else:\\n RF = RealField(self._prec)\\n Y = RF(Y)\\n mpmath.mp.dps = self._setupV_prec\\n P = {'+': P}\\n W = vv_harmonic_wmwf_setupV_mpc2(self, P, Y, M, Q)\\n mpmath.mp.dps = self._prec\\n else:\\n W = vv_harmonic_wmwf_setupV(self, P, Y, M, Q, self._weight, self._sym_type, verbose=self._verbose)\\n W['space'] = self\\n W['PP'] = P\\n if return_matrix:\\n return W\\n if use_temporary_file_storage:\\n s = \\\"tmpWN\\\" + str(self.multiplier().weil_module().N()) + \\\"-\\\"\\n dirv = [\\\"/tmp\\\", \\\".\\\"]\\n try:\\n for dirs in dirv:\\n try:\\n st = os.stat(dirs)\\n [f, tmpfilename] = tempfile.mkstemp(prefix=s, dir=dirs, suffix='.sobj')\\n os.close(f)\\n raise StopIteration()\\n except OSError:\\n continue\\n except StopIteration:\\n pass\\n if self._verbose > 0:\\n print(\\\"tmpfilename={0}\\\".format(tmpfilename))\\n try:\\n save(W, tmpfilename)\\n except MemoryError:\\n print(\\\"Could not save to file!\\\")\\n pass\\n N = self.set_norm(P=PP, C=SetCs)\\n if self._verbose > 0:\\n print(\\\"N = {0}\\\".format(N))\\n print(\\\"V.parent={0}\\\".format(W['V'].parent()))\\n if use_mpmath:\\n C = solve_system_for_vv_harmonic_weak_Maass_waveforms(W, N, deb=False)\\n else:\\n C = solve_system_for_vv_harmonic_weak_Maass_waveforms_new(self, W, N)\\n if self._verbose > 0:\\n print(\\\"C001={0}\\\".format(C[0][0][1]))\\n CC = dict()\\n # we need to shift the indices of C to the (semi-)correct indices\\n # i.e. for efficiency we use integers instead of rationals\\n F = list()\\n for j in C.keys():\\n CC[j] = dict()\\n for k in C[j].keys():\\n CC[j][self.multiplier().weil_module()._D_as_integers[k]] = C[j][k]\\n F.append(VVHarmonicWeakMaassFormElement(self, P, CC[j], prec))\\n mpmath.mp.dps = dold\\n if(len(F) == 1):\\n return F[0]\\n else:\\n return F\",\n \"def train(self, prob: SvmProblem):\\n # Define variables\\n self.prob = prob\\n C = self.prob.C\\n gamma = self.prob.gamma\\n\\n # Define the inputs to CVXOPT - See Appendix G.2\\n P1 = (self.prob.xi_xj * self.prob.yi_yj) + gamma * self.prob.xstari_xstarj\\n P2 = gamma * self.prob.xstari_xstarj\\n P11 = np.hstack((P1, P2))\\n P22 = np.hstack((P2, P2))\\n P = np.vstack((P11, P22))\\n q = np.hstack((np.repeat(-1, self.prob.num), np.zeros(self.prob.num)))\\n negative_eye = -np.eye(self.prob.num, dtype='d')\\n zeros = np.zeros((self.prob.num, self.prob.num))\\n g1 = np.hstack((negative_eye, zeros))\\n g2 = np.hstack((zeros, negative_eye))\\n G = np.vstack((g1, g2))\\n h1 = np.zeros((self.prob.num, 1))\\n h2 = np.repeat(C, self.prob.num).reshape(-1, 1)\\n h = np.vstack((h1, h2))\\n A1 = np.hstack((self.prob.Y, np.zeros(self.prob.num)))\\n A2 = np.repeat(-1, 2 * self.prob.num)\\n A = np.vstack((A1, A2))\\n b = np.zeros(2)\\n b = b.reshape(-1, 1)\\n\\n P = matrix(P, tc='d')\\n q = matrix(q, tc='d')\\n G = matrix(G, tc='d')\\n h = matrix(h, tc='d')\\n A = matrix(A, tc='d')\\n b = matrix(b, tc='d')\\n\\n # Solve optimization problem using CVXOPT\\n solvers.options['show_progress'] = False\\n sol = solvers.qp(P, q, G, h, A, b)\\n alphas_deltas = np.array(sol['x'])\\n self.alphas = np.asarray(alphas_deltas[:self.prob.num])\\n self.deltas = alphas_deltas[self.prob.num:]\\n\\n # Populate Classifier object to be returned\\n clf = Classifier()\\n clf.b = self.getB()\\n clf.alphas = self.alphas\\n clf.xs = self.prob.X\\n clf.ys = self.prob.Y\\n clf.kern = self.prob.xkernel\\n return clf\",\n \"def mcmc(data, uncert=None, func=None, indparams=[],\\n params=None, pmin=None, pmax=None, stepsize=None,\\n prior=None, priorlow=None, priorup=None,\\n numit=10, nchains=10, walk='demc', wlike=False,\\n leastsq=True, chisqscale=False, grtest=True, burnin=0,\\n thinning=1, plots=False, savefile=None, savemodel=None,\\n comm=None, resume=False, log=None, rms=False):\\n\\n # Import the model function:\\n if type(func) in [list, tuple, np.ndarray]:\\n if func[0] != 'hack':\\n if len(func) == 3:\\n sys.path.append(func[2])\\n exec('from %s import %s as func'%(func[1], func[0]))\\n elif not callable(func):\\n mu.error(\\\"'func' must be either, a callable, or an iterable (list, \\\"\\n \\\"tuple, or ndarray) of strings with the model function, file, \\\"\\n \\\"and path names.\\\", log)\\n\\n if np.ndim(params) == 1: # Force it to be 2D (one for each chain)\\n params = np.atleast_2d(params)\\n nparams = len(params[0]) # Number of model params\\n ndata = len(data) # Number of data values\\n # Set default uncertainties:\\n if uncert is None:\\n uncert = np.ones(ndata)\\n # Set default boundaries:\\n if pmin is None:\\n pmin = np.zeros(nparams) - np.inf\\n if pmax is None:\\n pmax = np.zeros(nparams) + np.inf\\n # Set default stepsize:\\n if stepsize is None:\\n stepsize = 0.1 * np.abs(params[0])\\n # Set prior parameter indices:\\n if (prior is None) or (priorup is None) or (priorlow is None):\\n prior = priorup = priorlow = np.zeros(nparams) # Zero arrays\\n iprior = np.where(priorlow != 0)[0]\\n ilog = np.where(priorlow < 0)[0]\\n\\n nfree = np.sum(stepsize > 0) # Number of free parameters\\n chainlen = int(np.ceil(numit/nchains)) # Number of iterations per chain\\n ifree = np.where(stepsize > 0)[0] # Free parameter indices\\n ishare = np.where(stepsize < 0)[0] # Shared parameter indices\\n # Number of model parameters (excluding wavelet parameters):\\n if wlike:\\n mpars = nparams - 3\\n else:\\n mpars = nparams\\n\\n # Intermediate steps to run GR test and print progress report:\\n intsteps = chainlen / 10\\n\\n # Allocate arrays with variables:\\n numaccept = np.zeros(nchains) # Number of accepted proposal jumps\\n outbounds = np.zeros((nchains, nfree), np.int) # Out of bounds proposals\\n allparams = np.zeros((nchains, nfree, chainlen)) # Parameter's record\\n if savemodel is not None:\\n allmodel = np.zeros((nchains, ndata, chainlen)) # Fit model\\n\\n if resume:\\n oldparams = np.load(savefile)\\n nold = np.shape(oldparams)[2] # Number of old-run iterations\\n allparams = np.dstack((oldparams, allparams))\\n if savemodel is not None:\\n allmodel = np.dstack((np.load(savemodel), allmodel))\\n # Set params to the last-iteration state of the previous run:\\n params = np.repeat(params, nchains, 0)\\n params[:,ifree] = oldparams[:,:,-1]\\n else:\\n nold = 0\\n\\n # Set MPI flag:\\n mpi = comm is not None\\n\\n if mpi:\\n from mpi4py import MPI\\n # Send sizes info to other processes:\\n array1 = np.asarray([mpars, chainlen], np.int)\\n mu.comm_bcast(comm, array1, MPI.INT)\\n\\n # DEMC parameters:\\n gamma = 2.4 / np.sqrt(2*nfree)\\n gamma2 = 0.001 # Jump scale factor of support distribution\\n\\n # Least-squares minimization:\\n if leastsq:\\n fitargs = (params[0], func, data, uncert, indparams, stepsize, pmin, pmax,\\n prior, priorlow, priorup)\\n fitchisq, dummy = mf.modelfit(params[0,ifree], args=fitargs)\\n fitbestp = np.copy(params[0, ifree])\\n mu.msg(1, \\\"Least-squares best-fitting parameters: \\\\n{:s}\\\\n\\\\n\\\".\\n format(str(fitbestp)), log)\\n\\n # Replicate to make one set for each chain: (nchains, nparams):\\n if np.shape(params)[0] != nchains:\\n params = np.repeat(params, nchains, 0)\\n # Start chains with an initial jump:\\n for p in ifree:\\n # For each free param, use a normal distribution: \\n params[1:, p] = np.random.normal(params[0, p], stepsize[p], nchains-1)\\n # Stay within pmin and pmax boundaries:\\n params[np.where(params[:, p] < pmin[p]), p] = pmin[p]\\n params[np.where(params[:, p] > pmax[p]), p] = pmax[p]\\n \\n # Update shared parameters:\\n for s in ishare:\\n params[:, s] = params[:, -int(stepsize[s])-1]\\n\\n # Calculate chi-squared for model using current params:\\n models = np.zeros((nchains, ndata))\\n if mpi:\\n # Scatter (send) parameters to func:\\n mu.comm_scatter(comm, params[:,0:mpars].flatten(), MPI.DOUBLE)\\n # Gather (receive) evaluated models:\\n mpimodels = np.zeros(nchains*ndata, np.double)\\n mu.comm_gather(comm, mpimodels)\\n # Store them in models variable:\\n models = np.reshape(mpimodels, (nchains, ndata))\\n else:\\n for c in np.arange(nchains):\\n fargs = [params[c, 0:mpars]] + indparams # List of function's arguments\\n models[c] = func(*fargs)\\n\\n # Calculate chi-squared for each chain:\\n currchisq = np.zeros(nchains)\\n c2 = np.zeros(nchains) # No-Jeffrey's chisq\\n for c in np.arange(nchains):\\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\\n currchisq[c], c2[c] = dwt.wlikelihood(params[c, mpars:], models[c]-data,\\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n else:\\n currchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n\\n # Scale data-uncertainties such that reduced chisq = 1:\\n if chisqscale:\\n chifactor = np.sqrt(np.amin(currchisq)/(ndata-nfree))\\n uncert *= chifactor\\n # Re-calculate chisq with the new uncertainties:\\n for c in np.arange(nchains):\\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\\n currchisq[c], c2[c] = dwt.wlikelihood(params[c,mpars:], models[c]-data,\\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n else:\\n currchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n if leastsq:\\n fitchisq = currchisq[0]\\n\\n # Get lowest chi-square and best fitting parameters:\\n bestchisq = np.amin(c2)\\n bestp = np.copy(params[np.argmin(c2)])\\n bestmodel = np.copy(models[np.argmin(c2)])\\n\\n if savemodel is not None:\\n allmodel[:,:,0] = models\\n\\n # Set up the random walks:\\n if walk == \\\"mrw\\\":\\n # Generate proposal jumps from Normal Distribution for MRW:\\n mstep = np.random.normal(0, stepsize[ifree], (chainlen, nchains, nfree))\\n elif walk == \\\"demc\\\":\\n # Support random distribution:\\n support = np.random.normal(0, stepsize[ifree], (chainlen, nchains, nfree))\\n # Generate indices for the chains such r[c] != c:\\n r1 = np.random.randint(0, nchains-1, (nchains, chainlen))\\n r2 = np.random.randint(0, nchains-1, (nchains, chainlen))\\n for c in np.arange(nchains):\\n r1[c][np.where(r1[c]==c)] = nchains-1\\n r2[c][np.where(r2[c]==c)] = nchains-1\\n\\n # Uniform random distribution for the Metropolis acceptance rule:\\n unif = np.random.uniform(0, 1, (chainlen, nchains))\\n\\n # Proposed iteration parameters and chi-square (per chain):\\n nextp = np.copy(params) # Proposed parameters\\n nextchisq = np.zeros(nchains) # Chi square of nextp \\n\\n # Start loop:\\n mu.msg(1, \\\"Start MCMC chains ({:s})\\\".format(time.ctime()), log)\\n for i in np.arange(chainlen):\\n # Proposal jump:\\n if walk == \\\"mrw\\\":\\n jump = mstep[i]\\n elif walk == \\\"demc\\\":\\n jump = (gamma * (params[r1[:,i]]-params[r2[:,i]])[:,ifree] +\\n gamma2 * support[i] )\\n # Propose next point:\\n nextp[:,ifree] = params[:,ifree] + jump\\n\\n # Check it's within boundaries: \\n outpars = np.asarray(((nextp < pmin) | (nextp > pmax))[:,ifree])\\n outflag = np.any(outpars, axis=1)\\n outbounds += ((nextp < pmin) | (nextp > pmax))[:,ifree]\\n for p in ifree:\\n nextp[np.where(nextp[:, p] < pmin[p]), p] = pmin[p]\\n nextp[np.where(nextp[:, p] > pmax[p]), p] = pmax[p]\\n\\n # Update shared parameters:\\n for s in ishare:\\n nextp[:, s] = nextp[:, -int(stepsize[s])-1]\\n\\n # Evaluate the models for the proposed parameters:\\n if mpi:\\n mu.comm_scatter(comm, nextp[:,0:mpars].flatten(), MPI.DOUBLE)\\n mu.comm_gather(comm, mpimodels)\\n models = np.reshape(mpimodels, (nchains, ndata))\\n else:\\n for c in np.where(~outflag)[0]:\\n fargs = [nextp[c, 0:mpars]] + indparams # List of function's arguments\\n models[c] = func(*fargs)\\n\\n # Calculate chisq:\\n for c in np.where(~outflag)[0]:\\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\\n nextchisq[c], c2[c] = dwt.wlikelihood(nextp[c,mpars:], models[c]-data,\\n (nextp[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n else:\\n nextchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\\n (nextp[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n\\n # Reject out-of-bound jumps:\\n nextchisq[np.where(outflag)] = np.inf\\n # Evaluate which steps are accepted and update values:\\n accept = np.exp(0.5 * (currchisq - nextchisq))\\n accepted = accept >= unif[i]\\n if i >= burnin:\\n numaccept += accepted\\n # Update params and chi square:\\n params [accepted] = nextp [accepted]\\n currchisq[accepted] = nextchisq[accepted]\\n\\n # Check lowest chi-square:\\n if np.amin(c2) < bestchisq:\\n bestp = np.copy(params[np.argmin(c2)])\\n bestmodel = np.copy(models[np.argmin(c2)])\\n bestchisq = np.amin(c2)\\n\\n # Store current iteration values:\\n allparams[:,:,i+nold] = params[:, ifree]\\n if savemodel is not None:\\n models[~accepted] = allmodel[~accepted,:,i+nold-1]\\n allmodel[:,:,i+nold] = models\\n \\n # Print intermediate info:\\n if ((i+1) % intsteps == 0) and (i > 0):\\n mu.progressbar((i+1.0)/chainlen, log)\\n mu.msg(1, \\\"Out-of-bound Trials:\\\\n {:s}\\\".\\n format(np.sum(outbounds, axis=0)), log)\\n mu.msg(1, \\\"Best Parameters: (chisq={:.4f})\\\\n{:s}\\\".\\n format(bestchisq, str(bestp)), log)\\n\\n # Gelman-Rubin statistic:\\n if grtest and (i+nold) > burnin:\\n psrf = gr.convergetest(allparams[:, :, burnin:i+nold+1:thinning])\\n mu.msg(1, \\\"Gelman-Rubin statistic for free parameters:\\\\n{:s}\\\".\\n format(psrf), log)\\n if np.all(psrf < 1.01):\\n mu.msg(1, \\\"All parameters have converged to within 1% of unity.\\\", log)\\n # Save current results:\\n if savefile is not None:\\n np.save(savefile, allparams[:,:,0:i+nold])\\n if savemodel is not None:\\n np.save(savemodel, allmodel[:,:,0:i+nold])\\n\\n # Stack together the chains:\\n allstack = allparams[0, :, burnin:]\\n for c in np.arange(1, nchains):\\n allstack = np.hstack((allstack, allparams[c, :, burnin:]))\\n # And the models:\\n if savemodel is not None:\\n modelstack = allmodel[0,:,burnin:]\\n for c in np.arange(1, nchains):\\n modelstack = np.hstack((modelstack, allmodel[c, :, burnin:]))\\n\\n # Print out Summary:\\n mu.msg(1, \\\"\\\\nFin, MCMC Summary:\\\\n------------------\\\", log)\\n\\n nsample = (chainlen-burnin)*nchains # This sample\\n ntotal = (nold+chainlen-burnin)*nchains\\n BIC = bestchisq + nfree*np.log(ndata)\\n redchisq = bestchisq/(ndata-nfree)\\n sdr = np.std(bestmodel-data)\\n\\n fmtlen = len(str(ntotal))\\n mu.msg(1, \\\"Burned in iterations per chain: {:{}d}\\\".\\n format(burnin, fmtlen), log, 1)\\n mu.msg(1, \\\"Number of iterations per chain: {:{}d}\\\".\\n format(chainlen, fmtlen), log, 1)\\n mu.msg(1, \\\"MCMC sample size: {:{}d}\\\".\\n format(nsample, fmtlen), log, 1)\\n mu.msg(resume, \\\"Total MCMC sample size: {:{}d}\\\".\\n format(ntotal, fmtlen), log, 1)\\n mu.msg(1, \\\"Acceptance rate: {:.2f}%\\\\n \\\".\\n format(np.sum(numaccept)*100.0/nsample), log, 1)\\n\\n meanp = np.mean(allstack, axis=1) # Parameters mean\\n uncertp = np.std(allstack, axis=1) # Parameter standard deviation\\n mu.msg(1, \\\"Best-fit params Uncertainties Signal/Noise Sample \\\"\\n \\\"Mean\\\", log, 1)\\n for i in np.arange(nfree):\\n mu.msg(1, \\\"{: 15.7e} {: 15.7e} {:12.2f} {: 15.7e}\\\".\\n format(bestp[ifree][i], uncertp[i],\\n np.abs(bestp[ifree][i])/uncertp[i], meanp[i]), log, 1)\\n\\n if leastsq and np.any(np.abs((bestp[ifree]-fitbestp)/fitbestp) > 1e-08):\\n np.set_printoptions(precision=8)\\n mu.warning(\\\"MCMC found a better fit than the minimizer:\\\\n\\\"\\n \\\" MCMC best-fitting parameters: (chisq={:.8g})\\\\n {:s}\\\\n\\\"\\n \\\" Minimizer best-fitting parameters: (chisq={:.8g})\\\\n\\\"\\n \\\" {:s}\\\".format(bestchisq, str(bestp[ifree]), \\n fitchisq, str(fitbestp)), log)\\n\\n fmtl = len(\\\"%.4f\\\"%BIC) # Length of string formatting\\n mu.msg(1, \\\" \\\", log)\\n if chisqscale:\\n mu.msg(1, \\\"sqrt(reduced chi-squared) factor: {:{}.4f}\\\".\\n format(chifactor, fmtl), log, 1)\\n mu.msg(1, \\\"Best-parameter's chi-squared: {:{}.4f}\\\".\\n format(bestchisq, fmtl), log, 1)\\n mu.msg(1, \\\"Bayesian Information Criterion: {:{}.4f}\\\".\\n format(BIC, fmtl), log, 1)\\n mu.msg(1, \\\"Reduced chi-squared: {:{}.4f}\\\".\\n format(redchisq, fmtl), log, 1)\\n mu.msg(1, \\\"Standard deviation of residuals: {:.6g}\\\\n\\\".format(sdr), log, 1)\\n\\n\\n if rms:\\n rms, rmse, stderr, bs = ta.binrms(bestmodel-data)\\n\\n if plots:\\n print(\\\"Plotting figures ...\\\")\\n # Extract filename from savefile:\\n if savefile is not None:\\n if savefile.rfind(\\\".\\\") == -1:\\n fname = savefile[savefile.rfind(\\\"/\\\")+1:] # Cut out file extention.\\n else:\\n fname = savefile[savefile.rfind(\\\"/\\\")+1:savefile.rfind(\\\".\\\")]\\n else:\\n fname = \\\"MCMC\\\"\\n # Trace plot:\\n mp.trace(allstack, thinning=thinning, savefile=fname+\\\"_trace.png\\\",\\n sep=np.size(allstack[0])/nchains)\\n # Pairwise posteriors:\\n mp.pairwise(allstack, thinning=thinning, savefile=fname+\\\"_pairwise.png\\\")\\n # Histograms:\\n mp.histogram(allstack, thinning=thinning, savefile=fname+\\\"_posterior.png\\\")\\n # RMS vs bin size:\\n if rms:\\n mp.RMS(bs, rms, stderr, rmse, binstep=len(bs)/500+1,\\n savefile=fname+\\\"_RMS.png\\\")\\n if indparams != [] and np.size(indparams[0]) == ndata:\\n mp.modelfit(data, uncert, indparams[0], bestmodel,\\n savefile=fname+\\\"_model.png\\\")\\n\\n # Save definitive results:\\n if savefile is not None:\\n np.save(savefile, allparams)\\n if savemodel is not None:\\n np.save(savemodel, allmodel)\\n\\n return allstack, bestp\",\n \"def generate_C_for_m(t, spherical=False, sigma_m=1.0, eigen_th= 0.01):\\n if not spherical:\\n n_u= generate_n_u(t)\\n tmp= np.random.rand(len(n_u), len(n_u))\\n matrix= (tmp + tmp.T)/2.0\\n eigv, eigw= np.linalg.eigh(matrix)\\n eigv[eigv < eigen_th]= eigen_th\\n eigv= eigv*(sigma_m*sigma_m)\\n matrix= np.dot(np.dot(eigw.T, np.diag(eigv)), eigw)\\n return matrix\\n else:\\n n_u= generate_n_u(t)\\n matrix= np.eye(len(n_u))*(sigma_m*sigma_m)\\n return matrix\",\n \"def create_confusion_matrix(model, X_test, y_test, test_pred, title):\\n # Confusion matrix\\n class_names = [\\\"Skipped\\\", \\\"Not Skipped\\\"]\\n print(\\\"Confusion Matrix: %s\\\" % title)\\n print(confusion_matrix(y_test, test_pred))\\n\\n disp = plot_confusion_matrix(\\n model,\\n X_test,\\n y_test,\\n display_labels=class_names,\\n cmap=plt.cm.Blues,\\n normalize=\\\"true\\\",\\n )\\n disp.ax_.set_title(\\\"Confusion Matrix %s (Normalized)\\\" % title)\\n\\n print(\\\"Confusion Matrix %s (Normalized)\\\" % title)\\n print(disp.confusion_matrix)\\n\\n plt.show()\",\n \"def example_wbf_3d_2_models(iou_thr=0.55, draw_image=True):\\n\\n boxes_list = [\\n [\\n [0.00, 0.51, 0.41, 0.81, 0.91, 0.78],\\n [0.10, 0.31, 0.45, 0.71, 0.61, 0.85],\\n [0.01, 0.32, 0.55, 0.83, 0.93, 0.95],\\n [0.02, 0.53, 0.11, 0.11, 0.94, 0.55],\\n [0.03, 0.24, 0.34, 0.12, 0.35, 0.67],\\n ],\\n [\\n [0.04, 0.56, 0.36, 0.84, 0.92, 0.82],\\n [0.12, 0.33, 0.46, 0.72, 0.64, 0.75],\\n [0.38, 0.66, 0.55, 0.79, 0.95, 0.90],\\n [0.08, 0.49, 0.15, 0.21, 0.89, 0.67],\\n ],\\n ]\\n scores_list = [\\n [\\n 0.9,\\n 0.8,\\n 0.2,\\n 0.4,\\n 0.7,\\n ],\\n [\\n 0.5,\\n 0.8,\\n 0.7,\\n 0.3,\\n ]\\n ]\\n labels_list = [\\n [\\n 0,\\n 1,\\n 0,\\n 1,\\n 1,\\n ],\\n [\\n 1,\\n 1,\\n 1,\\n 0,\\n ]\\n ]\\n weights = [2, 1]\\n if draw_image:\\n show_boxes(boxes_list, scores_list, labels_list)\\n\\n boxes, scores, labels = weighted_boxes_fusion_3d(boxes_list, scores_list, labels_list, weights=weights, iou_thr=iou_thr, skip_box_thr=0.0)\\n\\n if draw_image:\\n show_boxes([boxes], [scores], [labels.astype(np.int32)])\\n\\n print(len(boxes))\\n print(boxes)\",\n \"def create_mtl_pt_model(model_args):\\n global_model = torch.load(model_args['global_model_fn'])\\n model = MTL_MIMIC_Model(model_args[\\\"input_dim\\\"],\\n model_args[\\\"n_layers\\\"],\\n model_args[\\\"units\\\"],\\n model_args[\\\"num_dense_shared_layers\\\"],\\n model_args[\\\"dense_shared_layer_size\\\"],\\n model_args[\\\"n_multi_layers\\\"],\\n model_args[\\\"multi_units\\\"],\\n model_args[\\\"output_dim\\\"],\\n model_args[\\\"tasks\\\"],\\n lstm = global_model.lstm,\\n shared = global_model.rest[:-2],\\n )\\n return model\",\n \"def setUp(self):\\n # Simple QP problem\\n sp.random.seed(4)\\n\\n self.n = 30\\n self.m = 30\\n self.P = sparse.csc_matrix((self.n, self.n))\\n self.q = np.zeros(self.n)\\n self.A = sparse.random(self.m, self.n, density=1.0, format='csc')\\n self.u = np.random.rand(self.m)\\n self.l = self.u\\n self.opts = {'verbose': False,\\n 'eps_abs': 1e-06,\\n 'eps_rel': 1e-06,\\n 'scaling': True,\\n 'alpha': 1.6,\\n 'max_iter': 5000,\\n 'polish': False,\\n 'warm_start': True,\\n 'polish_refine_iter': 4}\\n self.model = osqp.OSQP()\\n self.model.setup(P=self.P, q=self.q, A=self.A, l=self.l, u=self.u,\\n **self.opts)\",\n \"def generation_model(self, z, c): # P(x|z, c)\\n z_c = torch.cat((z, c), dim=1)\\n x_hat = self.decoder(z_c)\\n return x_hat\",\n \"def generation_model(self, z, c): # P(x|z, c)\\n z_c = torch.cat((z, c), dim=1)\\n x_hat = self.decoder(z_c)\\n return x_hat\",\n \"def fit_FITC(self, Kmm, Kmn, Knn_diag, epsilon=1e-3,power_ep=[1.,1.]):\\r\\n self.epsilon = epsilon\\r\\n self.eta, self.delta = power_ep\\r\\n\\r\\n num_inducing = Kmm.shape[0]\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Prior approximation parameters:\\r\\n q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)\\r\\n Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn\\r\\n \\\"\\\"\\\"\\r\\n Lm = jitchol(Kmm)\\r\\n Lmi = chol_inv(Lm)\\r\\n Kmmi = np.dot(Lmi.T,Lmi)\\r\\n P0 = Kmn.T\\r\\n KmnKnm = np.dot(P0.T, P0)\\r\\n KmmiKmn = np.dot(Kmmi,P0.T)\\r\\n Qnn_diag = np.sum(P0.T*KmmiKmn,-2)\\r\\n Diag0 = Knn_diag - Qnn_diag\\r\\n R0 = jitchol(Kmmi).T\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Posterior approximation: q(f|y) = N(f| mu, Sigma)\\r\\n Sigma = Diag + P*R.T*R*P.T + K\\r\\n mu = w + P*Gamma\\r\\n \\\"\\\"\\\"\\r\\n self.w = np.zeros(self.num_data)\\r\\n self.Gamma = np.zeros(num_inducing)\\r\\n mu = np.zeros(self.num_data)\\r\\n P = P0.copy()\\r\\n R = R0.copy()\\r\\n Diag = Diag0.copy()\\r\\n Sigma_diag = Knn_diag\\r\\n RPT0 = np.dot(R0,P0.T)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Initial values - Cavity distribution parameters:\\r\\n q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}\\r\\n sigma_ = 1./tau_\\r\\n mu_ = v_/tau_\\r\\n \\\"\\\"\\\"\\r\\n self.tau_ = np.empty(self.num_data,dtype=float)\\r\\n self.v_ = np.empty(self.num_data,dtype=float)\\r\\n\\r\\n #Initial values - Marginal moments\\r\\n z = np.empty(self.num_data,dtype=float)\\r\\n self.Z_hat = np.empty(self.num_data,dtype=float)\\r\\n phi = np.empty(self.num_data,dtype=float)\\r\\n mu_hat = np.empty(self.num_data,dtype=float)\\r\\n sigma2_hat = np.empty(self.num_data,dtype=float)\\r\\n\\r\\n #Approximation\\r\\n epsilon_np1 = 1\\r\\n epsilon_np2 = 1\\r\\n \\tself.iterations = 0\\r\\n self.np1 = [self.tau_tilde.copy()]\\r\\n self.np2 = [self.v_tilde.copy()]\\r\\n while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:\\r\\n update_order = np.random.permutation(self.num_data)\\r\\n for i in update_order:\\r\\n #Cavity distribution parameters\\r\\n self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]\\r\\n self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]\\r\\n #Marginal moments\\r\\n self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self.data[i],self.tau_[i],self.v_[i])\\r\\n #Site parameters update\\r\\n Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])\\r\\n Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])\\r\\n self.tau_tilde[i] += Delta_tau\\r\\n self.v_tilde[i] += Delta_v\\r\\n #Posterior distribution parameters update\\r\\n dtd1 = Delta_tau*Diag[i] + 1.\\r\\n dii = Diag[i]\\r\\n Diag[i] = dii - (Delta_tau * dii**2.)/dtd1\\r\\n pi_ = P[i,:].reshape(1,num_inducing)\\r\\n P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_\\r\\n Rp_i = np.dot(R,pi_.T)\\r\\n RTR = np.dot(R.T,np.dot(np.eye(num_inducing) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))\\r\\n R = jitchol(RTR).T\\r\\n self.w[i] += (Delta_v - Delta_tau*self.w[i])*dii/dtd1\\r\\n self.Gamma += (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)\\r\\n RPT = np.dot(R,P.T)\\r\\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\\r\\n mu = self.w + np.dot(P,self.Gamma)\\r\\n self.iterations += 1\\r\\n #Sigma recomptutation with Cholesky decompositon\\r\\n Iplus_Dprod_i = 1./(1.+ Diag0 * self.tau_tilde)\\r\\n Diag = Diag0 * Iplus_Dprod_i\\r\\n P = Iplus_Dprod_i[:,None] * P0\\r\\n safe_diag = np.where(Diag0 < self.tau_tilde, self.tau_tilde/(1.+Diag0*self.tau_tilde), (1. - Iplus_Dprod_i)/Diag0)\\r\\n L = jitchol(np.eye(num_inducing) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))\\r\\n R,info = dtrtrs(L,R0,lower=1)\\r\\n RPT = np.dot(R,P.T)\\r\\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\\r\\n self.w = Diag * self.v_tilde\\r\\n self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))\\r\\n mu = self.w + np.dot(P,self.Gamma)\\r\\n epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.num_data\\r\\n epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.num_data\\r\\n self.np1.append(self.tau_tilde.copy())\\r\\n self.np2.append(self.v_tilde.copy())\\r\\n\\r\\n return self._compute_GP_variables()\",\n \"def crescent_data(model_type='Full', num_inducing=10, seed=default_seed, kernel=None, optimize=True, plot=True):\\r\\n data = GPy.util.datasets.crescent_data(seed=seed)\\r\\n Y = data['Y']\\r\\n Y[Y.flatten()==-1] = 0\\r\\n\\r\\n if model_type == 'Full':\\r\\n m = GPy.models.GPClassification(data['X'], Y, kernel=kernel)\\r\\n\\r\\n elif model_type == 'DTC':\\r\\n m = GPy.models.SparseGPClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)\\r\\n m['.*len'] = 10.\\r\\n\\r\\n elif model_type == 'FITC':\\r\\n m = GPy.models.FITCClassification(data['X'], Y, kernel=kernel, num_inducing=num_inducing)\\r\\n m['.*len'] = 3.\\r\\n\\r\\n if optimize:\\r\\n m.pseudo_EM()\\r\\n\\r\\n if plot:\\r\\n m.plot()\\r\\n\\r\\n print m\\r\\n return m\",\n \"def build(obs_space: Box, action_space: Box, spec: Spec) -> MLPModel:\\n model = MLPModel(obs_space, action_space, spec.network)\\n model.initialize_parameters(spec.initializer)\\n if spec.residual:\\n model = ResidualStochasticModel(model)\\n return model\",\n \"def create_p3_feature(C3, C4, C5, feature_size=256):\\n P5 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C5_reduced')(C5)\\n P5_upsampled = layers.UpsampleLike(name='P5_upsampled')([P5, C4])\\n\\n # add P5 elementwise to C4\\n P4 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C4_reduced')(C4)\\n P4 = keras.layers.Add(name='P4_merged')([P5_upsampled, P4])\\n P4_upsampled = layers.UpsampleLike(name='P4_upsampled')([P4, C3])\\n\\n # add P4 elementwise to C3\\n P3 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C3_reduced')(C3)\\n P3 = keras.layers.Add(name='P3_merged')([P4_upsampled, P3])\\n P3 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='P3')(P3)\\n\\n return P3\",\n \"def getMCMC(self,x,y,priors={},datamodel=None):\\n import pymc\\n from operator import isSequenceType\\n from inspect import getargspec\\n from types import MethodType\\n\\n if set(priors.keys()) != set(self.params):\\n raise ValueError(\\\"input priors don't match function params\\\")\\n\\n d={}\\n for k,v in priors.iteritems():\\n if isinstance(v,pymc.StochasticBase):\\n d[k]=v\\n elif isSequenceType(v):\\n if len(v) == 2:\\n d[k]=pymc.distributions.Uniform(k,v[0],v[1])\\n else:\\n raise ValueError(\\\"couldn't understand sequence \\\"+str(v) )\\n elif v > 0:\\n d[k] = pymc.distributions.Normal(k,self.pardict[k],1.0/v/v)\\n elif v == 0:\\n d[k] = pymc.distributions.Poisson(k,self.pardict[k])\\n else:\\n raise ValueError(\\\"couldn't interpret prior \\\"+str(v))\\n\\n funcdict=dict(d)\\n if type(self.f) is MethodType:\\n xstr = getargspec(self.f)[0][1]\\n else:\\n assert callable(self.f),'object function is not a callable'\\n xstr = getargspec(self.f)[0][0]\\n funcdict[xstr]=x\\n funcdet=pymc.Deterministic(name='f',eval=self.f,parents=funcdict,doc=\\\"FunctionModel1D function\\\")\\n d['f'] = funcdet\\n\\n if type(datamodel) is tuple:\\n distobj,dataarg,kwargs=datamodel\\n if 'name' not in kwargs:\\n kwargs['name'] = 'data'\\n\\n kwargs[dataarg] = funcdet\\n kwargs['observed'] = True\\n kwargs['value'] = y\\n\\n datamodel = distobj(**kwargs)\\n\\n d[datamodel.__name__] = datamodel\\n\\n #now if any stochastics are in the kwargs, assume they are part of the model\\n for k, v in kwargs.iteritems():\\n if isinstance(v, pymc.StochasticBase):\\n d[k] = v\\n else:\\n if datamodel is None:\\n sig = np.std(y)\\n else:\\n sig = np.array(datamodel)\\n d['data'] = pymc.distributions.Normal('data',mu=funcdet,tau=1/sig/sig,observed=True,value=y)\\n\\n return pymc.MCMC(d)\",\n \"def build_mlp(input_, config):\\n current_input = input_\\n print(current_input)\\n for i in range(len(config['fcc_layers']) - 1):\\n current_input = tf.keras.layers.Dense(\\n units=config['fcc_layers'][i], activation='tanh',\\n name='fcc_layer_{}'.format(i + 1))(current_input)\\n current_input = tf.keras.layers.Dropout(\\n rate=config['dropout'], name='dropout_{}'.format(i + 1))(current_input)\\n cascade_embedding_layer = tf.keras.layers.Dense(\\n units=config['fcc_layers'][-1], activation='tanh',\\n name='cascade_embedding_layer')(current_input)\\n cascade_embedding_layer_do = tf.keras.layers.Dropout(\\n rate=config['dropout'],\\n name='cascade_embedding_dropout')(cascade_embedding_layer)\\n prediction_layer = tf.keras.layers.Dense(\\n units=1, activation='sigmoid',\\n name='prediction_layer')(cascade_embedding_layer_do)\\n return cascade_embedding_layer, prediction_layer\",\n \"def make_mlp_model():\\n return snt.Sequential([\\n snt.nets.MLP([LATENT_SIZE] * NUM_LAYERS, activate_final=True),\\n snt.LayerNorm()\\n ])\",\n \"def __init__(self, input_size: int, output_size: int, old_cols: List[AlphaModule] = None, hidden_size=768,\\n ptm=\\\"bert\\\", feature_size=768, require_proto=False, tokenizer=None, prob_l=-1) -> None:\\n super(PTMClassifier_PNN, self).__init__()\\n \\n if old_cols is None:\\n old_cols = []\\n \\n self.old_cols = []\\n \\n self.output_size = output_size\\n self.hidden_size = hidden_size # default\\n self.feature_size = feature_size\\n \\n self.fc1 = nn.Linear(self.hidden_size, 100)\\n self.classifier = nn.Linear(100, self.output_size)\\n \\n if len(old_cols) > 0:\\n self.old_fc1s = ListModule()\\n self.base_1 = nn.Sequential(\\n nn.Linear(100 * len(old_cols), 100),\\n nn.ReLU(),\\n )\\n self.base_2 = nn.Sequential(\\n nn.Linear(100 * len(old_cols), 100),\\n nn.ReLU(),\\n )\\n \\n self.adaptor1 = nn.Sequential(AlphaModule(100 * len(old_cols)),\\n self.base_1)\\n self.adaptor2 = nn.Sequential(AlphaModule(100 * len(old_cols)),\\n self.base_2)\\n \\n for old_col in old_cols:\\n self.old_fc1s.append(\\n nn.Sequential(nn.Linear(self.hidden_size, 100), nn.ReLU()))\\n self.old_fc1s[-1][0].load_state_dict(old_col.fc1.state_dict())\\n \\n # ptm\\n self.ptm = ptm.lower()\\n assert self.ptm in supported_ptm\\n ptm_ = import_from(\\\"transformers\\\", supported_ptm[self.ptm][0] + \\\"Model\\\")\\n \\n self.encoder = ptm_.from_pretrained(supported_ptm[self.ptm][1], output_hidden_states=True)\\n if tokenizer is not None:\\n self.encoder.resize_token_embeddings(len(tokenizer))\\n self.prob_l = prob_l\\n \\n self.require_proto = require_proto\\n \\n # todo : modify net into one network\\n if self.require_proto:\\n # prototype-based classification\\n self.net = nn.Sequential(\\n self.fc1\\n )\\n self.net_ = nn.Sequential(\\n self.encoder,\\n self.fc1,\\n self.encoder_adaptor,\\n nn.ReLU(),\\n )\\n else:\\n self.classifier = nn.Linear(self.feature_size, self.output_size, bias=True)\\n self.net = nn.Sequential(\\n self.classifier\\n )\\n self.net_ = nn.Sequential(\\n self.encoder,\\n self.classifier\\n )\\n \\n self.reset_parameters()\",\n \"def MILP_1(self,xData,uData):\\n print \\\"\\\\n\\\",\\\"*\\\"*80,\\\"\\\\n\\\",\\\"MILP 1: Parameter Estimation\\\\n\\\",\\\"*\\\"*80 \\n model=Model(\\\"parameters\\\")\\n outflow={}\\n d={}\\n bigM=500\\n Q_out={}\\n Q_in={}\\n N=max(l[1] for l in xData.keys())\\n print \\\"x Data size is\\\",N\\n N=max(l[1] for l in uData.keys())\\n print \\\"u Data size is\\\",N\\n for l in self.links:\\n l.d=model.addVar(lb=0,ub=200,obj=0*l.type==\\\"road\\\") \\n for t in range(1,N):\\n d[l,t]=model.addVar(lb=0,ub=200,obj=1)\\n for k in l.outgoing:\\n outflow[l,k,t]=model.addVar(lb=0,ub=200)\\n self.c[l,k]=model.addVar(lb=20,ub=200)\\n self.beta[l,k]=model.addVar(lb=0.2,ub=0.8)\\n self.alpha[l,k]=model.addVar(lb=0,ub=1)\\n self.M[l,k]=model.addVar(lb=0,ub=200)\\n d[\\\"outflow-1\\\",l,k,t]=model.addVar(vtype=GRB.BINARY) \\n d[\\\"outflow-2\\\",l,k,t]=model.addVar(vtype=GRB.BINARY) \\n model.update()\\n for t in range(1,N):\\n for l in self.links:\\n if True:\\n Q_out[l,t]=LinExpr()\\n Q_in[l,t]=LinExpr()\\n Q_out[l,t].addConstant(0)\\n Q_in[l,t].addConstant(0)\\n for k in l.outgoing:\\n model.addConstr(outflow[l,k,t]<=self.beta[l,k]*uData[l,t]*xData[l,t])\\n model.addConstr(outflow[l,k,t]<=self.M[l,k])\\n model.addConstr(outflow[l,k,t]<=self.c[l,k]-self.alpha[l,k]*xData[k,t])\\n model.addConstr(outflow[l,k,t]>=self.beta[l,k]*uData[l,t]*xData[l,t]+bigM*d[\\\"outflow-1\\\",l,k,t]-bigM)\\n model.addConstr(outflow[l,k,t]>=self.M[l,k]+bigM*d[\\\"outflow-2\\\",l,k,t]-bigM)\\n model.addConstr(outflow[l,k,t]>=self.c[l,k]-self.alpha[l,k]*xData[k,t]-bigM*d[\\\"outflow-1\\\",l,k,t]-bigM*d[\\\"outflow-2\\\",l,k,t])\\n Q_out[l,t].add(outflow[l,k,t])\\n for k in l.incoming:\\n Q_in[l,t].add(outflow[k,l,t])\\n if l.type==\\\"road\\\":\\n model.addConstr(xData[l,t+1]<=xData[l,t]- Q_out[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival) \\n else:\\n model.addConstr(xData[l,t+1]<=xData[l,t]- uData[l,t]*xData[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival)\\n for l in self.links:\\n sum=LinExpr()\\n for k in l.outgoing:\\n sum.add(self.beta[l,k])\\n model.addConstr(sum>=0)\\n \\n# J=QuadExpr()\\n# for l in self.links:\\n# for t in range(1,N):\\n# if l.type==\\\"road\\\":\\n# J.add(d[l,t]*d[l,t])\\n# model.setObjective(J)\\n model.optimize()\\n for l in self.links:\\n l.d=l.d.X\\n for k in l.outgoing:\\n self.beta[l,k]=self.beta[l,k].X\\n self.c[l,k]=self.c[l,k].X\\n self.alpha[l,k]=self.alpha[l,k].X\\n self.M[l,k]=self.M[l,k].X\\n for l in self.links:\\n for t in range(1,N):\\n l.d=max(d[l,t].X,l.d)\\n \\n \\n\\n \\n if True:\\n for t in range(1,N):\\n print \\\"*\\\"*80,\\\"time=\\\",t\\n for l in self.links:\\n print \\\"\\\\n\\\",l,\\\"x is\\\",xData[l,t],\\\"u is\\\",uData[l,t],\\\"x+ is\\\",xData[l,t+1]\\n for k in l.outgoing:\\n print k,\\\"beta:\\\",self.beta[l,k],\\\"outflow\\\",outflow[l,k,t].X\",\n \"def __init__(\\n self,\\n n_words: int,\\n n_topics: int = 3,\\n prior_mu_loc: Union[None, tf.Variable, tf.Tensor] = None,\\n prior_mu_scale_diag: Union[None, tf.Variable, tf.Tensor] = None,\\n m_mu: Union[None, tf.Variable] = None,\\n sigma_mu: Union[None, tf.Variable] = None,\\n prior_ell_loc: Union[None, tf.Variable, tf.Tensor] = None,\\n prior_ell_scale_diag: Union[None, tf.Variable, tf.Tensor] = None,\\n m_ell: Union[None, tf.Variable] = None,\\n sigma_ell: Union[None, tf.Variable] = None,\\n topics_words_logits: Union[None, tf.Variable, tf.Tensor] = None,\\n layer_sizes: List[Union[int, str]] = (300, 300, 300),\\n activation: str = \\\"relu\\\",\\n dtype: Union[np.dtype, tf.DType, str] = np.float64,\\n jitter: float = 1e-5,\\n validate_args: bool = False,\\n allow_nan_stats: bool = False,\\n name: str = \\\"CTMMuSigma\\\",\\n ):\\n super(CTMMuSigma, self).__init__(\\n n_topics=n_topics,\\n n_words=n_words,\\n topics_words_logits=topics_words_logits,\\n layer_sizes=layer_sizes,\\n activation=activation,\\n jitter=jitter,\\n dtype=dtype,\\n validate_args=validate_args,\\n allow_nan_stats=allow_nan_stats,\\n name=name,\\n )\\n\\n del self.prior_eta # unused\\n\\n dof = n_topics * (n_topics + 1) // 2\\n self.bijector_ell = tfb.FillScaleTriL(diag_shift=tf.cast(1e-5, dtype))\\n\\n if prior_mu_loc is None:\\n prior_mu_loc = np.random.standard_normal([1, n_topics]).astype(dtype=dtype)\\n\\n if prior_mu_scale_diag is None:\\n prior_mu_scale_diag = tf.zeros([1, n_topics], dtype=dtype) + 0.01\\n\\n self.prior_mu = tfd.MultivariateNormalDiag(\\n prior_mu_loc,\\n scale_diag=prior_mu_scale_diag,\\n validate_args=validate_args,\\n allow_nan_stats=allow_nan_stats,\\n name=\\\"prior_mu\\\",\\n )\\n\\n if m_mu is None:\\n m_mu = np.random.standard_normal([1, n_topics]).astype(dtype=dtype)\\n m_mu = tf.Variable(m_mu, name=\\\"m_mu\\\")\\n\\n if sigma_mu is None:\\n sigma_mu = tf.zeros([1, n_topics], dtype=dtype) + 0.01\\n sigma_mu = tfp.util.TransformedVariable(\\n sigma_mu,\\n bijector=tfb.Chain([tfb.Shift(tf.cast(1e-3, dtype)), tfb.Softplus()]),\\n name=\\\"sigma_mu\\\",\\n )\\n self.surrogate_posterior_mu = tfd.MultivariateNormalDiag(\\n m_mu,\\n scale_diag=sigma_mu,\\n validate_args=validate_args,\\n allow_nan_stats=allow_nan_stats,\\n name=\\\"posterior_mu\\\",\\n )\\n\\n if prior_ell_loc is None:\\n prior_ell_loc = np.random.standard_normal([1, dof]).astype(dtype=dtype)\\n\\n if prior_ell_scale_diag is None:\\n prior_ell_scale_diag = tf.zeros([1, dof], dtype=dtype) + 0.01\\n\\n self.prior_ell = tfd.MultivariateNormalDiag(\\n prior_ell_loc,\\n scale_diag=prior_ell_scale_diag,\\n validate_args=validate_args,\\n allow_nan_stats=allow_nan_stats,\\n name=\\\"prior_ell\\\",\\n )\\n\\n if m_ell is None:\\n m_ell = np.random.standard_normal([1, dof]).astype(dtype=dtype)\\n m_ell = tf.Variable(m_ell, name=\\\"m_ell\\\")\\n\\n if sigma_ell is None:\\n sigma_ell = tf.zeros([1, dof], dtype=dtype) + 0.01\\n sigma_ell = tfp.util.TransformedVariable(\\n sigma_ell,\\n bijector=tfb.Chain([tfb.Shift(tf.cast(1e-3, dtype)), tfb.Softplus()]),\\n name=\\\"sigma_ell\\\",\\n )\\n self.surrogate_posterior_ell = tfd.MultivariateNormalDiag(\\n m_ell,\\n scale_diag=sigma_ell,\\n validate_args=validate_args,\\n allow_nan_stats=allow_nan_stats,\\n name=\\\"posterior_ell\\\",\\n )\",\n \"def template_model():\\n model_type = 'continuous' # either 'discrete' or 'continuous'\\n model = do_mpc.model.Model(model_type)\\n\\n # Model variables:\\n var1 = model.set_variable(var_type='_x', var_name='var1')\\n var2 = model.set_variable(var_type='_x', var_name='var2')\\n\\n state = vertcat(var1,var2)\\n state_dot = model.set_variable(var_type='_x', var_name='state_dot', shape=(2.1))\\n\\n input1 = model.set_variable(var_type='_u', var_name='input1')\\n\\n\\n # Parameters:\\n # define Parameters\\n\\n model.set_rhs('var1',state_dot[0])\\n model.set_rhs('var2',state_dot[1])\\n\\n state_dot_rhs = vertcat(\\n # rhs1,\\n # rhs2)\\n model.set_rhs('state_dot',state_dot_rhs)\\n\\n model.setup()\\n\\n return model\",\n \"def init_model(args: argparse.Namespace, device) -> Tuple[MultivarMLP, AdjacencyMatrix]:\\n model = create_model(num_vars=args.num_variables, \\n num_categs=args.max_categories, \\n hidden_dims=args.hidden_dims, \\n actfn='leakyrelu')\\n \\n \\n if args.data_parallel:\\n device = torch.device(\\\"cuda:0\\\")\\n print(\\\"Data parallel activated. Using %i GPUs\\\" % torch.cuda.device_count())\\n model = nn.DataParallel(model)\\n else:\\n device = torch.device(\\\"cpu\\\")\\n \\n adj_matrix = AdjacencyMatrix(args.num_variables, device)\\n return model, adj_matrix\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56138206","0.5586942","0.55433214","0.5351772","0.529997","0.52595276","0.5223859","0.5215091","0.52088547","0.5177972","0.51590854","0.50957006","0.5061981","0.5017527","0.49781993","0.49613118","0.49568832","0.4943264","0.4940687","0.49263063","0.49221906","0.49001944","0.48897865","0.48821157","0.48529017","0.484719","0.48457196","0.48449627","0.484397","0.48433566","0.48407552","0.48275974","0.48249593","0.4812254","0.48058066","0.4803045","0.47998214","0.479786","0.47770655","0.47751758","0.4769907","0.47584185","0.47528368","0.47524956","0.4730727","0.47283858","0.47208983","0.47016177","0.4700538","0.47001374","0.46991882","0.46830988","0.4666639","0.46615368","0.46612167","0.46541017","0.46539512","0.4652109","0.46422723","0.46331862","0.46222386","0.46213853","0.46116504","0.46052435","0.46044952","0.460034","0.45964527","0.45909867","0.45669886","0.45668063","0.45645094","0.45569018","0.4549083","0.45400542","0.45340464","0.45310608","0.45273453","0.45171964","0.45169753","0.45168072","0.45161444","0.45110774","0.45085278","0.45072338","0.45024952","0.4501332","0.44985983","0.44985983","0.44984502","0.44975638","0.44890723","0.44869113","0.44806924","0.44790882","0.44771937","0.4476075","0.44749904","0.44734398","0.44709614","0.44683042"],"string":"[\n \"0.56138206\",\n \"0.5586942\",\n \"0.55433214\",\n \"0.5351772\",\n \"0.529997\",\n \"0.52595276\",\n \"0.5223859\",\n \"0.5215091\",\n \"0.52088547\",\n \"0.5177972\",\n \"0.51590854\",\n \"0.50957006\",\n \"0.5061981\",\n \"0.5017527\",\n \"0.49781993\",\n \"0.49613118\",\n \"0.49568832\",\n \"0.4943264\",\n \"0.4940687\",\n \"0.49263063\",\n \"0.49221906\",\n \"0.49001944\",\n \"0.48897865\",\n \"0.48821157\",\n \"0.48529017\",\n \"0.484719\",\n \"0.48457196\",\n \"0.48449627\",\n \"0.484397\",\n \"0.48433566\",\n \"0.48407552\",\n \"0.48275974\",\n \"0.48249593\",\n \"0.4812254\",\n \"0.48058066\",\n \"0.4803045\",\n \"0.47998214\",\n \"0.479786\",\n \"0.47770655\",\n \"0.47751758\",\n \"0.4769907\",\n \"0.47584185\",\n \"0.47528368\",\n \"0.47524956\",\n \"0.4730727\",\n \"0.47283858\",\n \"0.47208983\",\n \"0.47016177\",\n \"0.4700538\",\n \"0.47001374\",\n \"0.46991882\",\n \"0.46830988\",\n \"0.4666639\",\n \"0.46615368\",\n \"0.46612167\",\n \"0.46541017\",\n \"0.46539512\",\n \"0.4652109\",\n \"0.46422723\",\n \"0.46331862\",\n \"0.46222386\",\n \"0.46213853\",\n \"0.46116504\",\n \"0.46052435\",\n \"0.46044952\",\n \"0.460034\",\n \"0.45964527\",\n \"0.45909867\",\n \"0.45669886\",\n \"0.45668063\",\n \"0.45645094\",\n \"0.45569018\",\n \"0.4549083\",\n \"0.45400542\",\n \"0.45340464\",\n \"0.45310608\",\n \"0.45273453\",\n \"0.45171964\",\n \"0.45169753\",\n \"0.45168072\",\n \"0.45161444\",\n \"0.45110774\",\n \"0.45085278\",\n \"0.45072338\",\n \"0.45024952\",\n \"0.4501332\",\n \"0.44985983\",\n \"0.44985983\",\n \"0.44984502\",\n \"0.44975638\",\n \"0.44890723\",\n \"0.44869113\",\n \"0.44806924\",\n \"0.44790882\",\n \"0.44771937\",\n \"0.4476075\",\n \"0.44749904\",\n \"0.44734398\",\n \"0.44709614\",\n \"0.44683042\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6167692"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77980,"cells":{"query":{"kind":"string","value":"Build the original BPMF model, which we cannot sample from due to current limitations in pymc3's implementation of the Wishart distribution."},"document":{"kind":"string","value":"def build_bpmf_model(train, alpha=2, dim=10, std=0.01):\n n, m = train.shape\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\n\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n logging.info('building the BPMF model')\n with pm.Model() as bpmf:\n # Specify user feature matrix\n lambda_u = pm.Wishart(\n 'lambda_u', n=dim, V=np.eye(dim), shape=(dim, dim),\n testval=np.random.randn(dim, dim) * std)\n mu_u = pm.Normal(\n 'mu_u', mu=0, tau=beta_0 * lambda_u, shape=dim,\n testval=np.random.randn(dim) * std)\n U = pm.MvNormal(\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\n testval=np.random.randn(n, dim) * std)\n\n # Specify item feature matrix\n lambda_v = pm.Wishart(\n 'lambda_v', n=dim, V=np.eye(dim), shape=(dim, dim),\n testval=np.random.randn(dim, dim) * std)\n mu_v = pm.Normal(\n 'mu_v', mu=0, tau=beta_0 * lambda_v, shape=dim,\n testval=np.random.randn(dim) * std)\n V = pm.MvNormal(\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\n testval=np.random.randn(m, dim) * std)\n\n # Specify rating likelihood function\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\n observed=train)\n\n logging.info('done building the BPMF model')\n return bpmf"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def build_mod_bpmf_model(train, alpha=2, dim=10, std=0.01):\n n, m = train.shape\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\n\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n # We will use separate priors for sigma and correlation matrix.\n # In order to convert the upper triangular correlation values to a\n # complete correlation matrix, we need to construct an index matrix:\n n_elem = dim * (dim - 1) / 2\n tri_index = np.zeros([dim, dim], dtype=int)\n tri_index[np.triu_indices(dim, k=1)] = np.arange(n_elem)\n tri_index[np.triu_indices(dim, k=1)[::-1]] = np.arange(n_elem)\n\n logging.info('building the BPMF model')\n with pm.Model() as bpmf:\n # Specify user feature matrix\n sigma_u = pm.Uniform('sigma_u', shape=dim)\n corr_triangle_u = pm.LKJCorr(\n 'corr_u', n=1, p=dim,\n testval=np.random.randn(n_elem) * std)\n\n corr_matrix_u = corr_triangle_u[tri_index]\n corr_matrix_u = t.fill_diagonal(corr_matrix_u, 1)\n cov_matrix_u = t.diag(sigma_u).dot(corr_matrix_u.dot(t.diag(sigma_u)))\n lambda_u = t.nlinalg.matrix_inverse(cov_matrix_u)\n\n mu_u = pm.Normal(\n 'mu_u', mu=0, tau=beta_0 * t.diag(lambda_u), shape=dim,\n testval=np.random.randn(dim) * std)\n U = pm.MvNormal(\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\n testval=np.random.randn(n, dim) * std)\n\n # Specify item feature matrix\n sigma_v = pm.Uniform('sigma_v', shape=dim)\n corr_triangle_v = pm.LKJCorr(\n 'corr_v', n=1, p=dim,\n testval=np.random.randn(n_elem) * std)\n\n corr_matrix_v = corr_triangle_v[tri_index]\n corr_matrix_v = t.fill_diagonal(corr_matrix_v, 1)\n cov_matrix_v = t.diag(sigma_v).dot(corr_matrix_v.dot(t.diag(sigma_v)))\n lambda_v = t.nlinalg.matrix_inverse(cov_matrix_v)\n\n mu_v = pm.Normal(\n 'mu_v', mu=0, tau=beta_0 * t.diag(lambda_v), shape=dim,\n testval=np.random.randn(dim) * std)\n V = pm.MvNormal(\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\n testval=np.random.randn(m, dim) * std)\n\n # Specify rating likelihood function\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\n observed=train)\n\n logging.info('done building the BPMF model')\n return bpmf","def build_pmf_model(train, alpha=2, dim=10, std=0.01):\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n # Low precision reflects uncertainty; prevents overfitting.\n # We use point estimates from the data to intialize.\n # Set to mean variance across users and items.\n alpha_u = 1 / train.var(axis=1).mean()\n alpha_v = 1 / train.var(axis=0).mean()\n\n logging.info('building the PMF model')\n n, m = train.shape\n with pm.Model() as pmf:\n U = pm.MvNormal(\n 'U', mu=0, tau=alpha_u * np.eye(dim),\n shape=(n, dim), testval=np.random.randn(n, dim) * std)\n V = pm.MvNormal(\n 'V', mu=0, tau=alpha_v * np.eye(dim),\n shape=(m, dim), testval=np.random.randn(m, dim) * std)\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones(train.shape),\n observed=train)\n\n logging.info('done building PMF model')\n return pmf","def create_model(self, gwas_gen, gwas_phen):\n n_ind, n_snps = gwas_gen.eval().shape\n with pm.Model() as phenotype_model:\n beta_med = pm.Normal('beta_med',\n mu=self.vars['coef_mean'],\n sd=self.vars['coef_sd'],\n shape=(1, n_snps))\n \n mediator = pm.dot(beta_med, gwas_gen.T)\n intercept = pm.Normal('intercept', mu=0, sd=1)\n alpha = pm.Normal('alpha', mu=0, sd=1)\n phenotype_sigma = pm.HalfCauchy('phenotype_sigma',\n beta=self.vars['p_sigma_beta'])\n \n\n # Model Selection\n p = np.array([0.5, 0.5])\n mediator_model = pm.Bernoulli('mediator_model', p[1])\n\n # Model 1\n phenotype_mu_null = intercept\n\n # Model 2\n phenotype_mu_mediator = intercept + alpha * mediator\n\n phen = pm.DensityDist('phen',\n lambda value: pm.switch(mediator_model, \n pm.Normal.dist(mu=phenotype_mu_mediator, sd=phenotype_sigma).logp(value), \n pm.Normal.dist(mu=phenotype_mu_null, sd=phenotype_sigma).logp(value)\n ),\n observed=gwas_phen)\n self.steps = [pm.BinaryGibbsMetropolis(vars=[mediator_model]),\n pm.Metropolis()]\n\n \n if self.variational and self.mb:\n self.minibatch_RVs = [phen]\n self.minibatch_tensors = [gwas_gen, gwas_phen]\n return phenotype_model","def build(self):\n\n self.W_AA = self.init([self.n_atom_input_feat, self.n_hidden_AA])\n self.b_AA = model_ops.zeros(shape=[\n self.n_hidden_AA,\n ])\n\n self.W_PA = self.init([self.n_pair_input_feat, self.n_hidden_PA])\n self.b_PA = model_ops.zeros(shape=[\n self.n_hidden_PA,\n ])\n\n self.W_A = self.init([self.n_hidden_A, self.n_atom_output_feat])\n self.b_A = model_ops.zeros(shape=[\n self.n_atom_output_feat,\n ])\n\n self.trainable_weights = [\n self.W_AA, self.b_AA, self.W_PA, self.b_PA, self.W_A, self.b_A\n ]\n if self.update_pair:\n self.W_AP = self.init([self.n_atom_input_feat * 2, self.n_hidden_AP])\n self.b_AP = model_ops.zeros(shape=[\n self.n_hidden_AP,\n ])\n\n self.W_PP = self.init([self.n_pair_input_feat, self.n_hidden_PP])\n self.b_PP = model_ops.zeros(shape=[\n self.n_hidden_PP,\n ])\n\n self.W_P = self.init([self.n_hidden_P, self.n_pair_output_feat])\n self.b_P = model_ops.zeros(shape=[\n self.n_pair_output_feat,\n ])\n\n self.trainable_weights.extend(\n [self.W_AP, self.b_AP, self.W_PP, self.b_PP, self.W_P, self.b_P])","def model_setup(self):\n self.DNN = DgganMLP(self.settings.hidden_size)\n self.D = DgganMLP(self.settings.hidden_size)\n self.G = Generator(self.settings.hidden_size)","def model_setup(self):\n self.DNN = SganMLP(self.settings.number_of_bins)\n self.D = SganMLP(self.settings.number_of_bins)\n self.G = Generator()","def get_model():\n model = ecole.scip.Model.from_file(str(DATA_DIR / \"bppc8-02.mps\"))\n model.disable_cuts()\n model.disable_presolve()\n model.set_param(\"randomization/permuteconss\", True)\n model.set_param(\"randomization/permutevars\", True)\n model.set_param(\"randomization/permutationseed\", 784)\n model.set_param(\"randomization/randomseedshift\", 784)\n model.set_param(\"randomization/lpseed\", 784)\n return model","def buildP(self):\r\n\r\n print 'Building P ...'\r\n\r\n #---Building p(y|x)---#\r\n pygx_params_mlp = MLP(activations=self.hyper['pygx_activs'],\r\n dims=self.hyper['pygx_dims'],\r\n weights_init=self.hyper['pygx_W_init'],\r\n biases_init=Constant(0))\r\n\r\n pygx_params = pygx_params_mlp.apply(self.x.reshape((self.x.shape[0]*self.x.shape[1],self.x.shape[2])))\r\n pygx_params = pygx_params.reshape((self.x.shape[0],self.x.shape[1],2*self.hyper['y_dim']))\r\n pygx_params_mlp.initialize()\r\n\r\n # self.pygx_mu.shape == (minibatch size, L_x , num of dimension of y)\r\n self.pygx_mu = pygx_params[:,:,:self.hyper['y_dim']]\r\n\r\n # self.pygx_var.shape == (minibatch size, L_x, num of dimension of y)\r\n self.pygx_var = T.exp( pygx_params[:,:,self.hyper['y_dim']:] )\r\n\r\n\r\n #---Building graph for the density of p(y|x)---#\r\n little_num = 10**(-32)\r\n inside_exp = -T.sum((self.y.dimshuffle(0,'x',1) - self.pygx_mu)**2/(2*self.pygx_var), axis=2)\r\n norm_cst = (2*np.pi)**(-self.hyper['y_dim']/2.)*T.exp(T.sum(T.log(self.pygx_var), axis=2))**(-1/2.)\r\n\r\n # shape == (minibatch size, # of x samples)\r\n pygx = norm_cst*T.exp(inside_exp)\r\n\r\n # shape == (minibatch size, # of x samples)\r\n self.log_pygx = T.log(pygx + little_num)\r\n\r\n #---Building NN for p(x|z=j,w) for all j---#\r\n pxgzw_mus = [None]*self.hyper['num_clust']\r\n pxgzw_vars = [None]*self.hyper['num_clust']\r\n pxgzw = [None]*self.hyper['num_clust']\r\n\r\n for j in range(self.hyper['num_clust']):\r\n\r\n pxgzw_params_mlp = MLP(activations=self.hyper['pxgzw_activs'][j],\r\n dims=self.hyper['pxgzw_dims'][j],\r\n weights_init=self.hyper['pxgzw_W_init'],\r\n biases_init=Constant(0))\r\n\r\n pxgzw_params = pxgzw_params_mlp.apply(self.w.reshape((self.w.shape[0]*self.w.shape[1],self.w.shape[2])))\r\n pxgzw_params = pxgzw_params.reshape((self.w.shape[0],self.w.shape[1], 2*self.hyper['x_dim']))\r\n pxgzw_params_mlp.initialize()\r\n\r\n # pxgzw_mus[j].shape == (minibatch size, L_w , num of dimension of x)\r\n pxgzw_mus[j] = pxgzw_params[:,:,:self.hyper['x_dim']]\r\n\r\n # pxgzw_vars[j].shape == (minibatch size, L_w, num of dimension of x)\r\n pxgzw_vars[j] = T.exp( pxgzw_params[:,:,self.hyper['x_dim']:] )\r\n\r\n #---Building graph for the density of p(x|z=j,w)---#\r\n little_num = 10**(-32)\r\n inside_exp = -T.sum((self.x.dimshuffle(0,'x',1,2) - pxgzw_mus[j].dimshuffle(0,1,'x',2))**2/(2*pxgzw_vars[j].dimshuffle(0,1,'x',2)), axis=3)\r\n norm_cst = (2*np.pi)**(-self.hyper['x_dim']/2.)*T.exp(T.sum(T.log(pxgzw_vars[j]), axis=2))**(-1/2.)\r\n\r\n # shape == (minibatch size, # of w samples (L_w), # of x samples (L_x))\r\n pxgzw[j] = norm_cst.dimshuffle(0,1,'x')*T.exp(inside_exp)\r\n\r\n\r\n # shape is (minibatch size, L_w , # of clusters , num of dimension of x)\r\n self.pxgzw_mus = T.concatenate([mu.dimshuffle(0,1,'x',2) for mu in pxgzw_mus], axis=2)\r\n # shape is (minibatch size, L_w , # of clusters , num of dimension of x)\r\n self.pxgzw_vars = T.concatenate([var.dimshuffle(0,1,'x',2) for var in pxgzw_vars], axis=2)\r\n\r\n # self.pxgzw.shape == (minibatch size, L_w, L_x, num_clust)\r\n self.pxgzw = T.concatenate([density.dimshuffle(0,1,2,'x') for density in pxgzw], axis=3)\r\n self.log_pxgzw = T.log(self.pxgzw + little_num)\r\n\r\n #---Building the p(z=j|x,w) posterior for all j---#\r\n # self.log_pzgxw.shape == (minibatch size, L_w, L_x, num_clust)\r\n self.log_pzgxw = T.log(self.pxgzw + little_num) -T.log(T.sum(self.pxgzw + little_num, axis=3).dimshuffle(0,1,2,'x'))","def build(obs_space: Box, action_space: Box, spec: Spec) -> MLPModel:\n model = MLPModel(obs_space, action_space, spec.network)\n model.initialize_parameters(spec.initializer)\n if spec.residual:\n model = ResidualStochasticModel(model)\n return model","def build_model(self, X: pd.DataFrame, y: pd.DataFrame = None) -> pm.Model:\n idx = X.index\n \n if y is None:\n y = pd.Series(0, index=idx)\n elif self.oversample: # only if y is given\n n_pos = (y == 1).sum()\n n_neg = (y == 0).sum()\n to_add = int(np.ceil(n_neg/n_pos) - 1)\n # print(n_pos, n_neg, to_add)\n if to_add > 4:\n to_add = 4\n for i in range(to_add):\n idx = idx.append(y[y==1].index)\n X = X.loc[idx]\n y = y.loc[idx]\n \n A = X[self.v_known + self.v_oob_bio]\n B_vals = X[self.v_fuzzy]\n B_mask = (B_vals == -1).astype(int)\n C_raw = X[self.v_float_adm + self.v_float_bio]\n # C_scaled = (C_raw - self.C_mean_) / self.C_std_ \n C_scaled = np.log1p(C_raw/self.C_mean_)\n C_scaled[~np.isfinite(C_scaled)] = np.nan\n C_vals = C_scaled.fillna(0)\n C_mask = C_scaled.isnull().astype(int)\n \n coords = {\"idx\": idx, \"a\": A.columns, \"b\": B_vals.columns, \"c\": C_vals.columns}\n with pm.Model(coords=coords) as m:\n pm.Data(\"A\", A, dims=[\"idx\", \"a\"])\n pm.Data(\"B_vals\", B_vals, dims=[\"idx\", \"b\"])\n pm.Data(\"B_mask\", B_mask, dims=[\"idx\", \"b\"])\n pm.Data(\"C_vals\", C_vals, dims=[\"idx\", \"c\"])\n pm.Data(\"C_mask\", C_mask, dims=[\"idx\", \"c\"])\n pm.Data(\"y\", y, dims=[\"idx\"])\n\n pm.Normal(\"avg\", mu=0, sd=1)\n\n pm.Beta(\"h_a_incl\", alpha=1, beta=4)\n pm.Normal(\"a_coef_raw\", mu=0, sd=1, dims=[\"a\"])\n pm.Bernoulli(\"a_incl\", p=m[\"h_a_incl\"], dims=[\"a\"])\n pm.Deterministic(\"a_coef\", m['a_coef_raw'] * m['a_incl'], dims=[\"a\"])\n \n pm.Normal(\"b_vals_coef\", mu=0, sd=1, dims=[\"b\"])\n pm.Normal(\"b_mask_coef_raw\", mu=0, sd=1, dims=[\"b\"])\n pm.Beta(\"h_b_mask_incl\", alpha=1, beta=4)\n pm.Bernoulli(\"b_mask_incl\", p=m[\"h_b_mask_incl\"], dims=[\"b\"])\n pm.Deterministic(\"b_mask_coef\", m['b_mask_coef_raw'] * m['b_mask_incl'], dims=[\"b\"])\n \n pm.Normal(\"c_vals_coef\", mu=0, sd=1, dims=[\"c\"])\n pm.Normal(\"c_mask_coef_raw\", mu=0, sd=1, dims=[\"c\"])\n pm.Beta(\"h_c_mask_incl\", alpha=1, beta=4)\n pm.Bernoulli(\"c_mask_incl\", p=m[\"h_c_mask_incl\"], dims=[\"c\"])\n pm.Deterministic(\"c_mask_coef\", m['c_mask_coef_raw'] * m['c_mask_incl'], dims=[\"c\"])\n unprob = pm.Deterministic(\n \"logit\",\n m['avg']\n + tt.dot(m[\"A\"], m[\"a_coef\"])\n + tt.dot(m[\"B_vals\"] * (1 - m['B_mask']), m[\"b_vals_coef\"])\n + tt.dot(m[\"B_mask\"], m[\"b_mask_coef\"])\n + tt.dot(m[\"C_vals\"] * (1 - m['C_mask']), m[\"c_vals_coef\"])\n + tt.dot(m[\"C_mask\"], m[\"c_mask_coef\"])\n )\n pm.Bernoulli(\"y_pred\", p = tt.nnet.sigmoid(unprob), dims=['idx'], observed=m['y'])\n\n m.graph = pm.model_to_graphviz()\n\n return m","def build_model(self):\n \n # initalizing generators\n self.g12 = G12(conv_dim=self.numGenFilter, domainA_channels = self.domainA_channels, domainB_channels = self.domainB_channels)\n self.g21 = G21(conv_dim=self.numGenFilter, domainA_channels = self.domainA_channels, domainB_channels = self.domainB_channels)\n \n # initializing discriminators\n self.d1 = D1(conv_dim=self.numDiscFilter, domainA_channels = self.domainA_channels, use_labels=self.use_labels)\n self.d2 = D2(conv_dim=self.numDiscFilter, domainB_channels = self.domainB_channels, use_labels=self.use_labels)\n \n\n g_params = list(self.g12.parameters()) + list(self.g21.parameters())\n d_params = list(self.d1.parameters()) + list(self.d2.parameters())\n \n self.g_optimizer = optim.Adam(g_params, self.lr, [self.beta1, self.beta2])\n self.d_optimizer = optim.Adam(d_params, self.lr, [self.beta1, self.beta2])\n \n if torch.cuda.is_available():\n self.g12.cuda()\n self.g21.cuda()\n self.d1.cuda()\n self.d2.cuda()","def copy(self):\n model_copy = BayesianModel()\n model_copy.add_nodes_from(self.nodes())\n model_copy.add_edges_from(self.edges())\n if self.cpds:\n model_copy.add_cpds(*[cpd.copy() for cpd in self.cpds])\n return model_copy","def __init__(self, \n num_vars, \n num_hidden,\n training_inputs = None,\n algorithm = None,\n algorithm_dict = None,\n batch_size = None,\n use_momentum = None,\n W0= None, \n b0= None, \n bhid0 = None,\n zero_diag = True,\n symmetric = True,\n report_p_tilda =False,\n learn_biases = True,\n test_mode= False,\n training = True):\n \n self.num_vars = num_vars\n \n self.num_hidden = num_hidden\n \n self.batch_size = batch_size\n \n self.zero_diag = zero_diag\n \n self.algorithm = algorithm\n \n self.num_samples = 0\n \n self.num_u_gibbs = 0\n \n self.gibbs_steps = 0\n \n self.resample = False\n \n self.uniform = False\n \n self.mixture = False\n \n self.mix_params = []\n \n self.m_params = []\n \n self.mf_steps = 0\n \n self.alpha = 0\n \n self.learn_biases = learn_biases\n \n if isinstance(algorithm_dict, dict):\n \n for param in algorithm_dict.keys():\n \n if param == 'resample':\n \n self.resample = algorithm_dict[param]\n \n if param == 'mf_steps':\n \n self.mf_steps = algorithm_dict[param]\n \n if param == \"gibbs_steps\":\n \n self.gibbs_steps = algorithm_dict[param]\n \n if param == \"num_samples\":\n \n self.num_samples = algorithm_dict[param]\n \n if param == \"num_u_gibbs\":\n \n self.num_u_gibbs = algorithm_dict[param]\n \n if param == \"uniform\":\n \n self.uniform = algorithm_dict[param] \n \n if param == \"mixture\":\n \n self.mixture = algorithm_dict[param] \n \n if param == \"mix_params\":\n \n self.mix_params = algorithm_dict[param] \n \n if param == \"alpha\" and algorithm_dict[param] != None:\n #### alpha defines transition rate from\n #### uniform to mean-field distribution\n self.alpha = algorithm_dict[param] \n \n self.m_params = (1-self.alpha)*0.5*np.ones([1,self.num_vars])+\\\n self.alpha*np.mean(training_inputs,0)\n \n self.use_momentum = use_momentum\n \n self.report_p_tilda = report_p_tilda\n \n self.side = int(np.sqrt(self.num_vars))\n \n self.np_rand_gen = np.random.RandomState(1234)\n \n self.theano_rand_gen =\\\n theano.sandbox.rng_mrg.MRG_RandomStreams(self.np_rand_gen.randint(2**30))\n \n #self.theano_rand_gen =\\\n #T.shared_randomstreams.RandomStreams(self.np_rand_gen.randint(2**30))\n \n theano.config.exception_verbosity = 'high'\n \n self.node_indices = \\\n theano.shared(np.arange(self.num_vars), name=\"node_indices\")\n \n self.x = T.matrix('x')\n \n self.x_tilda = T.matrix('x_tilda')\n \n self.sampler_theta = T.matrix('sampler_theta')\n \n self.symmetric = symmetric\n \n if training:\n \n if self.num_hidden ==0:\n \n self.num_x2 = self.num_vars\n \n elif self.num_hidden > 0 :\n \n self.num_x2 = self.num_hidden\n \n self.updates = OrderedDict()\n \n self.N_train = training_inputs.shape[0]\n \n self.train_inputs = theano.shared(np.asarray(training_inputs,\n dtype=theano.config.floatX),\n borrow= True)\n \n self.learning_rate = T.dscalar('learning_rate')\n \n if self.mixture:\n \n print(\"Importance distribution was specified as mixture\"+\\\n \" of Bernoulli products\")\n \n if self.mix_params == []:\n print(\"Error: parameters defining mixture means were\"+\\\n \" not provided\")\n sys.exit()\n \n self.set_mixture_means(inputs = training_inputs)\n \n if use_momentum:\n \n print(\"Will add momentum term to gradient computations\")\n \n self.momentum = T.dscalar('learning_rate')\n \n self.grad_vec = {}\n \n self.grad_vec['W'] = theano.shared(np.zeros([self.num_vars, self.num_x2],\n dtype = theano.config.floatX), name = 'W_momentum', borrow = True)\n \n if self.num_hidden > 0:\n \n self.grad_vec['bhid'] = theano.shared(np.zeros([self.num_x2],\n dtype = theano.config.floatX), name = 'b_momentum', borrow = True)\n \n self.grad_vec['b'] = theano.shared(np.zeros([self.num_vars],\n dtype = theano.config.floatX), name = 'b_momentum', borrow = True)\n \n if test_mode:\n \n b_init =self.np_rand_gen.uniform(0,1, num_vars)\n \n W_init =self.np_rand_gen.uniform(0,1, size = (num_vars, num_vars))\n \n # also tested ones\n # b_init = np.ones(num_vars)\n \n # W_init = np.ones([num_vars, num_vars])\n \n self.b_init= np.asarray(b_init, dtype = theano.config.floatX)\n \n self.W_init= np.asarray(W_init, dtype = theano.config.floatX)\n \n self.b = theano.shared(self.b_init, name='b', borrow = False)\n \n self.W = theano.shared(self.W_init, name='W', borrow = False)\n \n print(\"Initialized with test mode\")\n \n else:\n \n if W0 is None:\n \n if self.num_hidden > 0:\n \n W0_init =\\\n self.np_rand_gen.uniform(\n -4*np.sqrt(6.0/(self.num_vars+self.num_hidden)),\\\n 4*np.sqrt(6.0 /(self.num_vars + self.num_hidden)), \n size = (num_vars, self.num_hidden)\n )\n \n W0 = np.asarray(W0_init, dtype = theano.config.floatX) \n \n if self.num_hidden == 0:\n \n # different W initializations: \n \n # W0_init =\\\n # self.np_rand_gen.uniform(-np.sqrt(3.0/(num_vars)),\\\n # np.sqrt(3.0 / (num_vars)), size = (num_vars, num_vars))\n \n # W0_init =\\\n # self.np_rand_gen.uniform(-0.00000001,\\\n # 0.00000001, size = (num_vars, num_vars))\n \n W0_init = 0.00000001*\\\n self.np_rand_gen.normal(size = (num_vars, self.num_x2)) \n \n W0 = np.asarray(W0_init, dtype = theano.config.floatX)\n \n if self.symmetric:\n \n W0 = (W0 + np.transpose(W0))/2.0\n \n if self.zero_diag:\n \n W0 = W0 - np.diag(np.diag(W0))\n \n self.W = theano.shared(value= W0, name='W', borrow=True)\n \n if self.num_hidden == 0:\n \n test_W = self.W.get_value() \n \n assert sum(np.diag(test_W)) == 0.0\n \n assert (test_W == np.transpose(test_W)).all() == True\n \n else:\n print(\"W is initialized with provided array\")\n self.W = theano.shared(value= W0, name='W', borrow=True)\n \n if b0 is None:\n \n bias_init = np.zeros(num_vars, dtype = theano.config.floatX)\n \n self.b = theano.shared(value= bias_init, name='b', borrow=True)\n \n else:\n print(\"b vector is initialized with provided vector\")\n self.b = theano.shared(value= b0, name='b', borrow=True)\n \n if bhid0 is None and self.num_hidden > 0:\n \n hbias_init = np.zeros(self.num_hidden, dtype = theano.config.floatX)\n \n self.bhid = theano.shared(value= hbias_init, name='bhid', borrow=True)\n \n elif (bhid0 is not None) and (self.num_hidden > 0):\n print(\"bhid vector is initialized with provided vector\") \n self.bhid = theano.shared(value= bhid0, name='bhid', borrow=True)\n \n self.theta = [self.W, self.b]\n \n if self.num_hidden > 0 :\n \n self.theta.append(self.bhid)\n \n self.train_set = set(range(self.N_train))\n \n self.minibatch_set = T.ivector('minibatch_set')\n \n self.sample_set = T.ivector('sample_set')\n \n if \"CD\" in self.algorithm and self.num_hidden ==0:\n \n self.x_gibbs= theano.shared(np.ones([self.batch_size,self.num_vars],\n dtype=theano.config.floatX),\n borrow = True, name= \"x_gibbs\")\n \n if \"CD\" in self.algorithm and self.num_hidden > 0:\n \n self.persistent_gibbs =\\\n theano.shared(np.ones([self.batch_size,self.num_hidden],\n dtype=theano.config.floatX),\n borrow = True, \n name= \"persistent_gibbs\")\n \n if \"CSS\" in self.algorithm and self.mf_steps > 0:\n \n init_mf_vis = self.np_rand_gen.uniform(0, \n 1, \n size =(self.num_vars,1))\n \n init_mf_vis = np.asarray(init_mf_vis, dtype = theano.config.floatX)\n \n self.mf_vis_p = theano.shared(init_mf_vis, \n name= \"mf_vis_p\", \n borrow= True)\n \n if self.num_hidden > 0:\n \n init_mf_hid = \\\n self.np_rand_gen.uniform(0, 1, size =(self.num_hidden,1))\n \n init_mf_hid = np.asarray(init_mf_hid, \n dtype = theano.config.floatX)\n \n self.mf_hid_p = theano.shared(init_mf_hid, \n name= \"mf_hid_p\", \n borrow= True)\n \n elif \"CSS\" in self.algorithm and self.gibbs_steps > 0: \n \n if self.num_hidden ==0: \n self.x_gibbs= theano.shared(np.ones([self.batch_size,self.num_vars],\n dtype=theano.config.floatX),\n borrow = True, name= \"x_gibbs\")","def build_model(self):\n self.g12 = G12(conv_dim=self.g_conv_dim)\n init_weights(self.g12, init_type='normal')\n self.g21 = G21(conv_dim=self.g_conv_dim)\n init_weights(self.g21, init_type='normal')\n self.d1 = D1(conv_dim=self.d_conv_dim, use_labels=self.use_labels)\n init_weights(self.d1, init_type='normal')\n self.d2 = D2(conv_dim=self.d_conv_dim, use_labels=self.use_labels)\n init_weights(self.d2, init_type='normal')\n self.dreid = DSiamese(class_count=self.num_classes_market)\n\n g_params = list(self.g12.parameters()) + list(self.g21.parameters())\n d_params = list(self.d1.parameters()) + list(self.d2.parameters())\n dr_params = list(self.dreid.parameters())\n\n self.g_optimizer = optim.Adam(g_params, self.lr, [self.beta1, self.beta2])\n self.d_optimizer = optim.Adam(d_params, self.lr, [self.beta1, self.beta2])\n self.dr_optimizer = optim.Adam(dr_params, self.lr, [self.beta1, self.beta2])\n\n if torch.cuda.is_available():\n self.g12.cuda()\n self.g21.cuda()\n self.d1.cuda()\n self.d2.cuda()\n self.dreid.cuda()","def create_model(self, gwas_gen, gwas_phen):\n n_ind, n_snps = gwas_gen.eval().shape\n with pm.Model() as phenotype_model:\n beta_med = pm.Normal('beta_med',\n mu=self.vars['coef_mean'],\n sd=self.vars['coef_sd'],\n shape=(1, n_snps))\n phenotype_expression_mu = pm.dot(beta_med, gwas_gen.T)\n intercept = pm.Normal('intercept', mu=0, sd=1)\n alpha = pm.Normal('alpha', mu=0, sd=1)\n phenotype_mu = intercept + alpha * phenotype_expression_mu\n if self.logistic:\n p = tinvlogit(phenotype_mu)\n phen = pm.Bernoulli('phen', p=p, observed=gwas_phen)\n else:\n phenotype_sigma = pm.HalfCauchy('phenotype_sigma',\n beta=self.vars['p_sigma_beta'])\n phen = pm.Normal('phen',\n mu=phenotype_mu,\n sd=phenotype_sigma,\n observed=gwas_phen)\n if self.variational and self.mb:\n self.minibatch_RVs = [phen]\n self.minibatch_tensors = [gwas_gen, gwas_phen]\n return phenotype_model","def buildQ(self):\r\n\r\n print 'Building Q ...'\r\n\r\n self.y = T.matrix('y')\r\n\r\n mlp = MLP(activations=self.hyper['q_activs'],\r\n dims=self.hyper['q_dims'],\r\n weights_init=self.hyper['q_W_init'],\r\n biases_init=Constant(0))\r\n\r\n q_parameters = mlp.apply(self.y)\r\n mlp.initialize()\r\n\r\n # self.qxgy_mu.shape == (minibatch size, num of dimension of x)\r\n self.qxgy_mu = q_parameters[:,:self.hyper['x_dim']]\r\n\r\n # self.qxgy_var.shape == (minibatch size, num of dimension of x)\r\n self.qxgy_var = T.exp( q_parameters[:,self.hyper['x_dim']:2*self.hyper['x_dim']] )\r\n\r\n # self.qwgy_mu.shape == (minibatch size, num of dimension of w)\r\n self.qwgy_mu = q_parameters[:,2*self.hyper['x_dim']:2*self.hyper['x_dim']+self.hyper['w_dim']]\r\n\r\n # self.qwgy_var.shape == (minibatch size, num of dimension of w)\r\n self.qwgy_var = T.exp( q_parameters[:,2*self.hyper['x_dim']+self.hyper['w_dim']:] )\r\n\r\n\r\n #---Will be useful to compute samples from q(x|y)---#\r\n #self.eps_x.shape == (minibatch size, # of x samples , # of dimension of x)\r\n self.eps_x = self.srng.normal((self.qxgy_mu.shape[0] ,self.hyper['L_x'] ,self.hyper['x_dim']))\r\n\r\n #self.x corresponds roughly to the function g(\\epsilon,y) (see reparametrization trick in Kingma 2014)\r\n #self.x.shape == (minibatch size, # of x samples , # of dimension of x)\r\n self.x = self.qxgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qxgy_var).dimshuffle(0,'x',1)*self.eps_x\r\n\r\n #---Will be useful to compute samples from q(w|y)---#\r\n #self.eps_w.shape == (minibatch size, # of w samples , # of dimension of w)\r\n self.eps_w = self.srng.normal((self.qwgy_mu.shape[0] ,self.hyper['L_w'] ,self.hyper['w_dim']))\r\n\r\n #self.w corresponds roughly to the function g(\\epsilon,y) (see reparametrization trick in Kingma 2014)\r\n #self.w.shape == (minibatch size, # of w samples , # of dimension of w)\r\n self.w = self.qwgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qwgy_var).dimshuffle(0,'x',1)*self.eps_w\r\n\r\n\r\n #---Building the log density q(x|y)---#\r\n little_num = 10**(-32)\r\n inside_exp = -T.sum((self.x - self.qxgy_mu.dimshuffle(0,'x',1))**2/(2*self.qxgy_var.dimshuffle(0,'x',1)), axis=2)\r\n norm_cst = (2*np.pi)**(-self.hyper['x_dim']/2.)*T.exp(T.sum(T.log(self.qxgy_var), axis=1))**(-1/2.)\r\n\r\n # shape == (minibatch size, # of x samples)\r\n qxgy = norm_cst.dimshuffle(0,'x')*T.exp(inside_exp)\r\n\r\n # shape == (minibatch size, # of x samples)\r\n self.log_qxgy = T.log(qxgy + little_num)","def prepare_bpe():\r\n bpe = spm.SentencePieceProcessor()\r\n bpe.load(os.path.join(Params.dataset_path, Params.bpe_model))\r\n return bpe","def build_model():","def build_model(self) -> nn.Module:\n pass","def copy(self):\n model = LBM(\n n_row_clusters=self.n_row_clusters,\n n_column_clusters=self.n_column_clusters,\n max_iter=self.max_iter,\n n_init=self.n_init,\n n_init_total_run=self.n_init_total_run,\n n_iter_early_stop=self.nb_iter_early_stop,\n rtol=self.rtol,\n atol=self.atol,\n verbosity=self.verbosity,\n use_gpu=self.use_gpu,\n gpu_index=self.gpu_index,\n )\n model._nb_rows = self._nb_rows\n model._nb_cols = self._nb_cols\n model.loglikelihood_ = self.loglikelihood_\n model._np = self._np\n model._cupyx = self._cupyx\n model.trained_successfully_ = self.trained_successfully_\n model.pi_ = copy.copy(self.pi_)\n model.alpha_1_ = copy.copy(self.alpha_1_)\n model.alpha_2_ = copy.copy(self.alpha_2_)\n model.tau_1_ = copy.copy(self.tau_1_)\n model.tau_2_ = copy.copy(self.tau_2_)\n return model","def _init_from_newform_(self, g, PP, C, prec):\n k = g.weight()\n N = g.level()\n if(k % 4 == 0):\n ep = g.atkin_lehner_eigenvalue()\n else:\n ep = -g.atkin_lehner_eigenvalue()\n kappa = mpmath.mpf(3 - k) / mp2\n if(ep == -1):\n WR = WeilRepDiscriminantForm(N, False)\n elif ep == 1:\n WR = WeilRepDiscriminantForm(N, True)\n else:\n raise ValueError(\" Sign of functional equation must be 1 or -1! Got:{0}\".format(ep))\n # print \"kappa=\",kappa\n # print \"WR=\",WR\n M = VVHarmonicWeakMaassForms(WR, kappa, prec)\n # print \"M=\",M\n self._space = M\n self.prec = prec\n self.coeff = dict()\n # We want a Harmonic weak maass form corresponding to the form g\n # that means we need to avoid any other cuspforms as well as\n # theta series...\n # If there are no oldforms we are happy\n if dimension_new_cusp_forms(N, k) == dimension_cusp_forms(N, k):\n # and to avoid theta series we need to avoid square discriminants\n # in the principal part\n if M._is_dual_rep:\n nset = [0, -1]\n else:\n nset = [-1]\n try:\n for n in nset:\n for r in WR.D(): # )_as_integers:\n D = M.D_from_rn((r, n))\n if not is_square(D):\n PP = {(WR.D[r], n): 1}\n self._principal_part = PP\n # print \"PP=\",PP,\"is ok!\"\n raise StopIteration()\n except StopIteration:\n pass\n # if(C is not None and C>0):","def BinaryPmf(p):\n pmf = thinkbayes2.Pmf()\n pmf.Set(1, p)\n pmf.Set(0, 1-p)\n return pmf","def build_model(self) -> DM:\n model = DM()\n model[self.modelroot] = content = DM()\n\n content['potential'] = DM()\n content['potential']['key'] = self.potential_key\n content['potential']['id'] = self.potential_id\n content['implementation'] = DM()\n content['implementation']['key'] = self.potential_LAMMPS_key\n content['implementation']['id'] = self.potential_LAMMPS_id\n\n for subset in self.subsets:\n subset.build_model(content)\n\n self._set_model(model)\n return model","def generate(self):\r\n \r\n components = []\r\n components.append(nn.Linear(self.n_features,self.hidden_sizes[0]))\r\n self._activation(components,self.activation)\r\n self._dropout(components,self.dropout)\r\n \r\n for i in range(1,len(self.hidden_sizes)):\r\n components.append(nn.Linear(self.hidden_sizes[i-1],self.hidden_sizes[i]))\r\n self._activation(components,self.activation)\r\n self._dropout(components,self.dropout)\r\n \r\n components.append(nn.Linear(self.hidden_sizes[-1],self.n_classes))\r\n\r\n mlp = nn.Sequential(*components)\r\n \r\n num_params = sum(p.numel() for p in mlp.parameters() if p.requires_grad)\r\n print(\"Created MLP with \"+str(num_params)+\" learnable params\")\r\n \r\n return mlp","def build(self):\n\n self.W = self.init([self.n_atom_input_feat, self.n_output])\n self.b = model_ops.zeros(shape=[\n self.n_output,\n ])\n\n self.trainable_weights = self.W + self.b","def build_model(self):\n self.G = Generator(self.g_conv_dim)\n self.D = Discriminator(self.d_conv_dim, self.c_dim)\n self.generator = Generator(self.g_conv_dim).train(False)\n\n self.G = nn.DataParallel(self.G)\n self.D = nn.DataParallel(self.D)\n\n # For Adam (Unofficial)\n # self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2])\n # self.d_optimizer = torch.optim.Adam(self.D.parameters(), self.d_lr, [self.beta1, self.beta2])\n\n # For RMSprop(Official)\n self.g_optimizer = torch.optim.RMSprop(self.G.parameters(), lr=0.0001)\n self.d_optimizer = torch.optim.RMSprop(self.D.parameters(), lr=0.0001)\n\n self.accumulate(self.generator, self.G.module, 0)\n # self.print_network(self.G, 'G')\n # self.print_network(self.D, 'D')\n \n self.G.to(self.device)\n self.D.to(self.device)\n self.generator.to(self.device)\n\n # weight init\n self.G.apply(self.weights_init)\n self.D.apply(self.weights_init)\n self.generator.apply(self.weights_init)","def build_model(self, constructor, args):\n dims = {'en': 300, 'es': 50}\n dists = {'en': 'Normal',\n 'es': 'Normal',}\n z_dim = args.model_args.get('z_dim', 64)\n h_dim = args.model_args.get('h_dim', 64)\n n_layers = args.model_args.get('n_layers', 3)\n gauss_out = (args.model != 'MultiDKS') \n encoders = {'en': models.common.DeepGaussianMLP(dims['en'], z_dim, h_dim, n_layers),\n 'es': models.common.DeepGaussianMLP(dims['es'], z_dim, h_dim, n_layers)}\n decoders = {'en': models.common.DeepGaussianMLP(z_dim, dims['en'], h_dim, n_layers),\n 'es': models.common.DeepGaussianMLP(z_dim, dims['es'], h_dim, n_layers)}\n custom_mods = [m for m in ['en', 'es'] if m in args.modalities]\n model = constructor(args.modalities,\n dims=(dims[m] for m in args.modalities),\n dists=[dists[m] for m in args.modalities],\n encoders={m: encoders[m] for m in custom_mods},\n decoders={m: decoders[m] for m in custom_mods},\n z_dim=z_dim, h_dim=h_dim,\n device=args.device, **args.model_args)\n return model","def generate_limittedmodel():\r\n print('Loading model')\r\n model = KeyedVectors.load_word2vec_format(BIN_NAME, binary=True)\r\n print('Model loaded!')\r\n\r\n print('Loading dot products')\r\n dp = np.load(DP_NAME)\r\n print('Dot products loaded')\r\n\r\n print('Filtering vocab')\r\n for name, vocab in list(model.vocab.items()):\r\n if dp[vocab.index] < MAX_DEGREE:\r\n del model.vocab[name]\r\n\r\n il = list(model.vocab.items())\r\n print('Sorting vocab')\r\n il.sort(key=lambda x: x[1].index)\r\n\r\n # Find the indexes of the words that are being kept\r\n print('Generating indexes')\r\n indexes = []\r\n for i in range(0, len(il)):\r\n name, vocab = il[i]\r\n indexes.append(vocab.index)\r\n model.vocab[name].index = i\r\n\r\n print('Modifying model weights')\r\n model.syn0 = model.syn0[indexes]\r\n\r\n print('Saving file')\r\n model.save_word2vec_format(SAVE_NAME, binary=True)","def _build_appx_elbo(self):\n\n # MC estimate of contribution from edges\n # obvious choice: uniformly sample terms... but resulting estimator is super high variance\n # edges_sample = np.copy(self.edge_idx_d[np.random.choice(self.edge_idx_d.shape[0], 3000, replace=False)]).astype(np.int32)\n # so instead use p-sampling... although it's unclear whether this really represents a major improvement\n e = self.edge_vals_d.shape[0]\n p_inc = np.sqrt(5000. / e) #use about 5000 edges for MC est\n edges_sample = item_p_sample(user_p_sample(self.edge_idx_d, p_inc)[0], p_inc)[0].astype(np.int32)\n\n # clip by value because of numerical issues\n p_edge_samples = tf.clip_by_value(self._edge_prob_samples(edges_sample), 1e-15, 1.)\n\n # reduce_mean is MC estimate over params of model, reduce_sum is summing cont from p-samp\n edge_llhd_est = 1. / p_inc**2 * tf.reduce_sum(tf.reduce_mean(tf.log(p_edge_samples), axis=0))\n\n # log(1-p_ij) = -lambda_ij, so:\n tot_lam_sum = tf.reduce_sum(self.i_tot_mass_m*self.u_tot_mass_m) # includes contribution from edges as well as non-edges\n # subtract off edge contribution:\n user_params = tf.gather(self.q_gam.mean() * self.q_theta.mean(), self.edge_idx[:,0])\n item_params = tf.gather(self.q_omega.mean() * self.q_beta.mean(), self.edge_idx[:,1])\n edges_lam_sum = tf.reduce_sum(user_params * item_params)\n nonedge_llhd_term = -(tot_lam_sum - edges_lam_sum)\n\n # hopefully lower variance than direct MC est\n #\\sum_edges log(p_ij) = -\\sum_edges lam_ij + \\sum_ij log(p_ij / (1-p_ij))\n\n # note: the reduce mean here averages over both the sampled params in p_edge_samples, and over the random choice of edges\n # edge_llhd_est = -edges_lam_sum + e*tf.reduce_mean(tf.reduce_mean(tf.log(p_edge_samples / (1. - p_edge_samples)), axis=0))\n\n self.appx_elbo = [edge_llhd_est, nonedge_llhd_term]","def setup_milp(model, target, remove_blocked=False, exclude_reaction_ids=set()):\n original_model = model\n model = model.copy()\n model.objective=target\n for i in heterologous_reactions.keys(): # turns off each heterologous reaction in order to get the only the native metabolic network.\n model.reactions.get_by_id(i).lower_bound=0\n model.reactions.get_by_id(i).upper_bound=0\n\n for r in model.reactions:\n if r.id.find('MetaCyc')>-1:\n r.type='heterologous'\n else:\n r.type='native'\n\n # Set the solver to Gurobi for the fastest result. Set to CPLEX if Gurobi is not available.\n if \"gurobi\" in cobra.util.solver.solvers.keys():\n logger.info(\"Changing solver to Gurobi and tweaking some parameters.\")\n if \"gurobi_interface\" not in model.solver.interface.__name__:\n model.solver = \"gurobi\"\n # The tolerances are set to the minimum value. This gives maximum precision.\n problem = model.solver.problem\n problem.params.NodeMethod = 1 # primal simplex node relaxation\n problem.params.FeasibilityTol = 1e-9 #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\n problem.params.OptimalityTol = 1e-3 #how sure the solver has to be about this optimum being really the best it has.\n problem.params.IntFeasTol = 1e-9 #If a value is set to an integer, how much may it still vary? > set smallest possible\n problem.params.MIPgapAbs = 1e-9\n problem.params.MIPgap = 1e-9\n problem.params.Threads=1 #In order to reduce memory usage (increased calculation time)\n problem.params.TimeLimit = 200 # Use max 200 seconds when called, return best solution after that\n problem.params.PoolSearchMode = 1 #0 for only finding the optimum, 1 for finding more solutions (but no quality guaranteed), 2 for finding the n best possible solutions\n problem.params.PoolSolutions = 10 # Number of solutions kept when finding the optimal solution\n problem.params.PoolGap = 0.9 # only store solutions within 90% of the optimal objective value\n\n elif \"cplex\" in cobra.util.solver.solvers.keys():\n logger.warning(\"Changing solver to CPLEX, as Gurobi is not available. This may cause a big slowdown and limit options afterwards.\")\n if \"cplex_interface\" not in model.solver.interface.__name__:\n model.solver = \"cplex\"\n # The tolerances are set to the minimum value. This gives maximum precision.\n problem = model.solver.problem\n problem.parameters.mip.strategy.startalgorithm.set(1) # primal simplex node relaxation\n problem.parameters.simplex.tolerances.feasibility.set(1e-9) #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\n problem.parameters.simplex.tolerances.optimality.set(1e-3) #possibly fine with 1e-3, try if allowed. Is how sure the solver has to be about this optimum being really the best it has.\n problem.parameters.mip.tolerances.integrality.set(1e-9) #If a value is set to an integer, how much may it still vary? > set smallest possible\n problem.parameters.mip.tolerances.absmipgap.set(1e-9)\n problem.parameters.mip.tolerances.mipgap.set(1e-9)\n problem.parameters.mip.pool.relgap.set(0.9) # For populate: find all solutions within 10% of the optimum for relgap = 0.1\n problem.parameters.timelimit.set(200) # Use max 200 seconds for solving\n problem.parameters.mip.limits.populate.set(20) # Find max 20 solutions (=default)\n\n else:\n logger.warning(\"You are trying to run 'Hamlet Hot Rig' with %s. This might not end well.\" %\n model.solver.interface.__name__.split(\".\")[-1])\n pass\n # Remove reactions that are blocked: no flux through these reactions possible. This will reduce the search space for the solver, if not done already.\n if remove_blocked:\n blocked_reactions = cameo.flux_analysis.analysis.find_blocked_reactions(model)\n model.remove_reactions(blocked_reactions)\n\n # Make dual\n model_with = model.copy() # This variable looks unnecessary, but is kept out of fear of messing stuff up\n model_with.optimize()\n dual_problem = convert_linear_problem_to_dual(model_with.solver)\n logger.debug(\"Dual problem successfully created\")\n\n # Combine primal and dual\n primal_problem = model.solver\n\n for var in dual_problem.variables: # All variables in the dual are copied to the primal\n var = primal_problem.interface.Variable.clone(var)\n primal_problem.add(var)\n for const in dual_problem.constraints: # All constraints in the dual are copied to the primal\n const = primal_problem.interface.Constraint.clone(const, model=primal_problem)\n primal_problem.add(const)\n logger.debug(\"Dual and primal combined\")\n\n dual_problem.optimize()\n\n # Dictionaries to hold the binary control variables:\n heterologous_y_vars = {} # 1 for 'knockin', 0 for inactive\n medium_y_vars = {} # 1 for medium addition (up to -10), 0 for no addition\n\n # Now the fun stuff\n constrained_dual_vars = set()\n\n # For the knockins and medium additions:\n for reaction in [r for r in model.reactions if r.type == \"heterologous\"]:\n # Add constraint variables\n interface = model.solver.interface\n y_var = interface.Variable(\"y_\" + reaction.id, type=\"binary\")\n\n # Constrain the primal: flux through reactions maximum within (-1000, 1000), or smaller boundaries defined before\n model.solver.add(interface.Constraint(reaction.flux_expression - 1000 * y_var, ub=0, name=\"primal_y_const_\"+reaction.id+\"_ub\"))\n model.solver.add(interface.Constraint(reaction.flux_expression + 1000 * y_var, lb=0, name=\"primal_y_const_\"+reaction.id+\"_lb\"))\n\n # Constrain the dual\n constrained_vars = []\n\n if reaction.upper_bound != 0:\n dual_forward_ub = model.solver.variables[\"dual_\" + reaction.forward_variable.name + \"_ub\"]\n model.solver.add(interface.Constraint(dual_forward_ub - 1000 * (1 - y_var), ub=0))\n constrained_vars.append(dual_forward_ub)\n if reaction.lower_bound != 0:\n dual_reverse_ub = model.solver.variables[\"dual_\" + reaction.reverse_variable.name + \"_ub\"]\n model.solver.add(interface.Constraint(dual_reverse_ub - 1000 * (1 - y_var), ub=0))\n constrained_vars.append(dual_reverse_ub)\n constrained_dual_vars.update(constrained_vars)\n\n # Add y variable to the corresponding modifications dictionary\n heterologous_y_vars[y_var] = reaction\n\n logger.debug(\"Control variables created\")\n\n # Add number of heterologous switch contraint constraint\n heterologous_turn_on = model.solver.interface.Constraint(\n optlang.symbolics.Add(*heterologous_y_vars), lb=0, ub=0, name=\"heterologous_reaction_constraint\"\n )\n model.solver.add(heterologous_turn_on)\n\n # Set the objective\n primal_objective = model.solver.objective\n dual_objective = interface.Objective.clone(\n dual_problem.objective, model=model.solver\n )\n\n switch_objective=interface.Objective(heterologous_turn_on.expression, direction='min')\n\n full_objective = interface.Objective(primal_objective.expression-dual_objective.expression, direction=\"max\")\n model.objective = full_objective\n\n return model,primal_objective,dual_objective,full_objective,switch_objective","def build_mlp(input_, config):\n current_input = input_\n print(current_input)\n for i in range(len(config['fcc_layers']) - 1):\n current_input = tf.keras.layers.Dense(\n units=config['fcc_layers'][i], activation='tanh',\n name='fcc_layer_{}'.format(i + 1))(current_input)\n current_input = tf.keras.layers.Dropout(\n rate=config['dropout'], name='dropout_{}'.format(i + 1))(current_input)\n cascade_embedding_layer = tf.keras.layers.Dense(\n units=config['fcc_layers'][-1], activation='tanh',\n name='cascade_embedding_layer')(current_input)\n cascade_embedding_layer_do = tf.keras.layers.Dropout(\n rate=config['dropout'],\n name='cascade_embedding_dropout')(cascade_embedding_layer)\n prediction_layer = tf.keras.layers.Dense(\n units=1, activation='sigmoid',\n name='prediction_layer')(cascade_embedding_layer_do)\n return cascade_embedding_layer, prediction_layer","def construct_model():\n import lbann\n\n # Layer graph\n input = lbann.Input(target_mode='N/A', name='inp_data')\n # data is 64*64*4 images + 15 scalar + 5 param\n #inp_slice = lbann.Slice(input, axis=0, slice_points=\"0 16399 16404\",name='inp_slice')\n inp_slice = lbann.Slice(input, axis=0, slice_points=str_list([0,args.ydim,args.ydim+5]),name='inp_slice')\n gt_y = lbann.Identity(inp_slice,name='gt_y')\n gt_x = lbann.Identity(inp_slice, name='gt_x') #param not used\n\n zero = lbann.Constant(value=0.0,num_neurons='1',name='zero')\n one = lbann.Constant(value=1.0,num_neurons='1',name='one')\n\n z_dim = 20 #Latent space dim\n\n z = lbann.Gaussian(mean=0.0,stdev=1.0, neuron_dims=\"20\")\n model = macc_models.MACCWAE(args.zdim,args.ydim,cf=args.mcf,use_CNN=args.useCNN)\n d1_real, d1_fake, d_adv, pred_y = model(z,gt_y)\n\n d1_real_bce = lbann.SigmoidBinaryCrossEntropy([d1_real,one],name='d1_real_bce')\n d1_fake_bce = lbann.SigmoidBinaryCrossEntropy([d1_fake,zero],name='d1_fake_bce')\n d_adv_bce = lbann.SigmoidBinaryCrossEntropy([d_adv,one],name='d_adv_bce')\n img_loss = lbann.MeanSquaredError([pred_y,gt_y])\n rec_error = lbann.L2Norm2(lbann.WeightedSum([pred_y,gt_y], scaling_factors=\"1 -1\"))\n\n layers = list(lbann.traverse_layer_graph(input))\n # Setup objective function\n weights = set()\n src_layers = []\n dst_layers = []\n for l in layers:\n if(l.weights and \"disc0\" in l.name and \"instance1\" in l.name):\n src_layers.append(l.name)\n #freeze weights in disc2\n if(l.weights and \"disc1\" in l.name):\n dst_layers.append(l.name)\n for idx in range(len(l.weights)):\n l.weights[idx].optimizer = lbann.NoOptimizer()\n weights.update(l.weights)\n l2_reg = lbann.L2WeightRegularization(weights=weights, scale=1e-4)\n d_adv_bce = lbann.LayerTerm(d_adv_bce,scale=0.01)\n obj = lbann.ObjectiveFunction([d1_real_bce,d1_fake_bce,d_adv_bce,img_loss,rec_error,l2_reg])\n # Initialize check metric callback\n metrics = [lbann.Metric(img_loss, name='recon_error')]\n #pred_y = macc_models.MACCWAE.pred_y_name\n callbacks = [lbann.CallbackPrint(),\n lbann.CallbackTimer(),\n lbann.CallbackSaveModel(dir=args.dump_models),\n lbann.CallbackReplaceWeights(source_layers=list2str(src_layers),\n destination_layers=list2str(dst_layers),\n batch_interval=2)]\n\n if(args.ltfb_batch_interval > 0) :\n callbacks.append(lbann.CallbackLTFB(batch_interval=args.ltfb_batch_interval,metric='recon_error',\n low_score_wins=True,\n exchange_hyperparameters=True))\n\n # Construct model\n return lbann.Model(args.num_epochs,\n serialize_io=True,\n weights=weights,\n layers=layers,\n metrics=metrics,\n objective_function=obj,\n callbacks=callbacks)","def _child_build_new_model(self):\n self._build_new_gp()","def _build_model(self):\n\n with tf.variable_scope(self.name):\n # adds placeholders, data_normalization and data_noise if desired. Also adds a placeholder for dropout probability\n self.layer_in_x, self.layer_in_y = self._build_input_layers()\n\n # create core multi-layer perceptron\n mlp_output_dim = 2 * self.ndim_y * self.n_centers + self.n_centers\n core_network = MLP(\n name=\"core_network\",\n input_layer=self.layer_in_x,\n output_dim=mlp_output_dim,\n hidden_sizes=self.hidden_sizes,\n hidden_nonlinearity=self.hidden_nonlinearity,\n output_nonlinearity=None,\n weight_normalization=self.weight_normalization,\n dropout_ph=self.dropout_ph if self.dropout else None\n )\n\n core_output_layer = core_network.output_layer\n\n # slice output of MLP into three equally sized parts for loc, scale and mixture weights\n slice_layer_locs = L.SliceLayer(core_output_layer, indices=slice(0, self.ndim_y * self.n_centers), axis=-1)\n slice_layer_scales = L.SliceLayer(core_output_layer, indices=slice(self.ndim_y * self.n_centers, 2 * self.ndim_y * self.n_centers), axis=-1)\n slice_layer_weights = L.SliceLayer(core_output_layer, indices=slice(2 * self.ndim_y * self.n_centers, mlp_output_dim), axis=-1)\n\n # locations mixture components\n self.reshape_layer_locs = L.ReshapeLayer(slice_layer_locs, (-1, self.n_centers, self.ndim_y))\n self.locs = L.get_output(self.reshape_layer_locs)\n\n # scales of the mixture components\n reshape_layer_scales = L.ReshapeLayer(slice_layer_scales, (-1, self.n_centers, self.ndim_y))\n self.softplus_layer_scales = L.NonlinearityLayer(reshape_layer_scales, nonlinearity=tf.nn.softplus)\n self.scales = L.get_output(self.softplus_layer_scales)\n\n # weights of the mixture components\n self.logits = L.get_output(slice_layer_weights)\n self.softmax_layer_weights = L.NonlinearityLayer(slice_layer_weights, nonlinearity=tf.nn.softmax)\n self.weights = L.get_output(self.softmax_layer_weights)\n\n # # put mixture components together\n self.y_input = L.get_output(self.layer_in_y)\n self.cat = cat = Categorical(logits=self.logits)\n self.components = components = [MultivariateNormalDiag(loc=loc, scale_diag=scale) for loc, scale\n in zip(tf.unstack(self.locs, axis=1), tf.unstack( self.scales, axis=1))]\n self.mixture = mixture = Mixture(cat=cat, components=components, value=tf.zeros_like(self.y_input))\n\n # regularization\n self._add_softmax_entropy_regularization()\n self._add_l1_l2_regularization(core_network)\n\n # tensor to store samples\n self.samples = mixture.sample() #TODO either use it or remove it\n\n # tensor to compute probabilities\n if self.data_normalization:\n self.pdf_ = mixture.prob(self.y_input) / tf.reduce_prod(self.std_y_sym)\n self.log_pdf_ = mixture.log_prob(self.y_input) - tf.reduce_sum(tf.log(self.std_y_sym))\n else:\n self.pdf_ = mixture.prob(self.y_input)\n self.log_pdf_ = mixture.log_prob(self.y_input)\n\n # symbolic tensors for getting the unnormalized mixture components\n if self.data_normalization:\n self.scales_unnormalized = self.scales * self.std_y_sym\n self.locs_unnormalized = self.locs * self.std_y_sym + self.mean_y_sym\n else:\n self.scales_unnormalized = self.scales\n self.locs_unnormalized = self.locs\n\n # initialize LayersPowered --> provides functions for serializing tf models\n LayersPowered.__init__(self, [self.softmax_layer_weights, self.softplus_layer_scales, self.reshape_layer_locs,\n self.layer_in_y])","def create_model_and_dataflow_for_inference(builder, conf, inputs):\r\n\r\n conformer_encoder = conformer_builder.ConformerEncoder(builder,\r\n input_dim=conf.mel_bands,\r\n sequence_length=conf.max_spectrogram_length,\r\n encoder_dim=conf.encoder_dim,\r\n attention_heads=conf.attention_heads,\r\n encoder_layers_per_stage=conf.encoder_layers_per_stage,\r\n dropout_rate=conf.dropout_rate,\r\n cnn_module_kernel=conf.kernel_size,\r\n subsampling_factor=conf.subsampling_factor,\r\n dtype=conf.precision)\r\n\r\n conformer_decoder = conformer_builder.ConformerDecoder(builder,\r\n encoder_dim=conf.encoder_dim,\r\n num_symbols=conf.num_symbols,\r\n for_inference=True,\r\n dtype=conf.precision)\r\n\r\n encoder_output = conformer_encoder(inputs[\"mel_spec_input\"])\r\n\r\n with builder.virtualGraph(conf.num_pipeline_stages - 1):\r\n probs_output = conformer_decoder(encoder_output)\r\n\r\n anchor_types_dict = {\r\n probs_output: popart.AnchorReturnType(\"ALL\"),\r\n }\r\n\r\n proto = builder.getModelProto()\r\n dataflow = popart.DataFlow(conf.batches_per_step, anchor_types_dict)\r\n\r\n return proto, probs_output, dataflow","def make_mlp_model():\n return snt.Sequential([\n snt.nets.MLP([LATENT_SIZE] * NUM_LAYERS, activate_final=True),\n snt.LayerNorm()\n ])","def _create_generate_input(self):\n self.keep_prob = 1.","def MILP_1(self,xData,uData):\n print \"\\n\",\"*\"*80,\"\\n\",\"MILP 1: Parameter Estimation\\n\",\"*\"*80 \n model=Model(\"parameters\")\n outflow={}\n d={}\n bigM=500\n Q_out={}\n Q_in={}\n N=max(l[1] for l in xData.keys())\n print \"x Data size is\",N\n N=max(l[1] for l in uData.keys())\n print \"u Data size is\",N\n for l in self.links:\n l.d=model.addVar(lb=0,ub=200,obj=0*l.type==\"road\") \n for t in range(1,N):\n d[l,t]=model.addVar(lb=0,ub=200,obj=1)\n for k in l.outgoing:\n outflow[l,k,t]=model.addVar(lb=0,ub=200)\n self.c[l,k]=model.addVar(lb=20,ub=200)\n self.beta[l,k]=model.addVar(lb=0.2,ub=0.8)\n self.alpha[l,k]=model.addVar(lb=0,ub=1)\n self.M[l,k]=model.addVar(lb=0,ub=200)\n d[\"outflow-1\",l,k,t]=model.addVar(vtype=GRB.BINARY) \n d[\"outflow-2\",l,k,t]=model.addVar(vtype=GRB.BINARY) \n model.update()\n for t in range(1,N):\n for l in self.links:\n if True:\n Q_out[l,t]=LinExpr()\n Q_in[l,t]=LinExpr()\n Q_out[l,t].addConstant(0)\n Q_in[l,t].addConstant(0)\n for k in l.outgoing:\n model.addConstr(outflow[l,k,t]<=self.beta[l,k]*uData[l,t]*xData[l,t])\n model.addConstr(outflow[l,k,t]<=self.M[l,k])\n model.addConstr(outflow[l,k,t]<=self.c[l,k]-self.alpha[l,k]*xData[k,t])\n model.addConstr(outflow[l,k,t]>=self.beta[l,k]*uData[l,t]*xData[l,t]+bigM*d[\"outflow-1\",l,k,t]-bigM)\n model.addConstr(outflow[l,k,t]>=self.M[l,k]+bigM*d[\"outflow-2\",l,k,t]-bigM)\n model.addConstr(outflow[l,k,t]>=self.c[l,k]-self.alpha[l,k]*xData[k,t]-bigM*d[\"outflow-1\",l,k,t]-bigM*d[\"outflow-2\",l,k,t])\n Q_out[l,t].add(outflow[l,k,t])\n for k in l.incoming:\n Q_in[l,t].add(outflow[k,l,t])\n if l.type==\"road\":\n model.addConstr(xData[l,t+1]<=xData[l,t]- Q_out[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival) \n else:\n model.addConstr(xData[l,t+1]<=xData[l,t]- uData[l,t]*xData[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival)\n for l in self.links:\n sum=LinExpr()\n for k in l.outgoing:\n sum.add(self.beta[l,k])\n model.addConstr(sum>=0)\n \n# J=QuadExpr()\n# for l in self.links:\n# for t in range(1,N):\n# if l.type==\"road\":\n# J.add(d[l,t]*d[l,t])\n# model.setObjective(J)\n model.optimize()\n for l in self.links:\n l.d=l.d.X\n for k in l.outgoing:\n self.beta[l,k]=self.beta[l,k].X\n self.c[l,k]=self.c[l,k].X\n self.alpha[l,k]=self.alpha[l,k].X\n self.M[l,k]=self.M[l,k].X\n for l in self.links:\n for t in range(1,N):\n l.d=max(d[l,t].X,l.d)\n \n \n\n \n if True:\n for t in range(1,N):\n print \"*\"*80,\"time=\",t\n for l in self.links:\n print \"\\n\",l,\"x is\",xData[l,t],\"u is\",uData[l,t],\"x+ is\",xData[l,t+1]\n for k in l.outgoing:\n print k,\"beta:\",self.beta[l,k],\"outflow\",outflow[l,k,t].X","def CreateMdp(self):\n self.states = {\"\":0}\n self.actions = {}\n self.rewards = []\n stateNodes = filter(lambda x: x.type not in [\"ActionNode\",\"MotorNode\"],self.nodes.values())\n actionNodes = filter(lambda x: x.type is \"ActionNode\",self.nodes.values())\n for i in (range(1,len(stateNodes)+1)):\n self.states[stateNodes[i-1].name] = i\n for i in (range(0,len(actionNodes))):\n self.actions[i] = actionNodes[i].name\n if(len(self.states) < 2 or len(self.actions) < 1):\n print(\"Error: States and actions must be non-empty\")\n # Transition probabilities: equal probabilities\n self.P = []\n for i in (range(0,len(self.actions))):\n b = []\n for j in (range(0,len(self.states))):\n a = []\n for k in (range(0,len(self.states))):\n a.append(1.0/len(self.states))\n b.append(a)\n self.P.append(b)\n\n # Rewards\n self.R = []\n for i in (range(0,len(self.actions))):\n b = []\n for j in (range(0,len(self.states))):\n a = []\n for k in (range(0,len(self.states))):\n a.append(actionNodes[i].reward)\n b.append(a)\n self.R.append(b)\n\n print(\"Number of states: \" + str(len(self.states)))\n print(\"Number of actions: \" + str(len(self.actions)))\n self.vi = mdptoolbox.mdp.QLearning(np.array(self.P), np.array(self.R), 0.5)\n self.vi.run()\n print(self.vi.Q)","def generate_beam(env, dec, decoder_args, beam_size, length_penalty, early_stopping, max_len=100):\n\n # check inputs\n trg, enc_src, trg_mask, src_mask = decoder_args\n src_enc = enc_src\n src_len = enc_src \n\n #assert src_enc.size(0) == src_len.size(0)\n assert beam_size >= 1\n\n # batch size / number of words\n bs = len(src_enc)\n n_words = env.n_words\n breakpoint()\n\n # expand to beam size the source latent representations / source lengths\n src_enc = src_enc.unsqueeze(1).expand((bs, beam_size) + src_enc.shape[1:]).contiguous().view((bs * beam_size,) + src_enc.shape[1:])\n #src_len = src_len.unsqueeze(1).expand(bs, beam_size).contiguous().view(-1)\n\n # generated sentences (batch with beam current hypotheses)\n #generated = src_len.new(max_len, bs * beam_size) # upcoming output\n #generated.fill_(env.pad_index) # fill upcoming ouput with <PAD>\n #generated[0].fill_(env.eos_index) # we use <EOS> for <BOS> everywhere\n\n # generated hypotheses\n generated_hyps = [BeamHypotheses(beam_size, max_len, length_penalty, early_stopping) for _ in range(bs)]\n\n # positions\n positions = src_len.new(max_len).long()\n #positions = torch.arange(max_len, out=positions).unsqueeze(1).expand_as(generated)\n\n # scores for each sentence in the beam\n beam_scores = src_enc.new(bs, beam_size).fill_(0)\n beam_scores[:, 1:] = -1e9\n beam_scores = beam_scores.view(-1)\n\n # current position\n cur_len = 1\n\n # cache compute states\n cache = {'slen': 0}\n\n # done sentences\n done = [False for _ in range(bs)]\n breakpoint()\n while cur_len < max_len:\n dec(trg[:,:-1], enc_src, trg_mask, src_mask)\n # compute word scores\n tensor = decoder(\n x=generated[:cur_len],\n lengths=src_len.new(bs * beam_size).fill_(cur_len),\n positions=positions[:cur_len],\n causal=True,\n src_enc=src_enc,\n src_len=src_len,\n cache=cache\n )\n assert tensor.size() == (1, bs * beam_size, env.dim)\n tensor = tensor.data[-1, :, :] # (bs * beam_size, dim)\n scores = env.proj(tensor) # (bs * beam_size, n_words)\n scores = F.log_softmax(scores, dim=-1) # (bs * beam_size, n_words)\n assert scores.size() == (bs * beam_size, n_words)\n\n # select next words with scores\n _scores = scores + beam_scores[:, None].expand_as(scores) # (bs * beam_size, n_words)\n _scores = _scores.view(bs, beam_size * n_words) # (bs, beam_size * n_words)\n\n next_scores, next_words = torch.topk(_scores, 2 * beam_size, dim=1, largest=True, sorted=True)\n assert next_scores.size() == next_words.size() == (bs, 2 * beam_size)\n\n # next batch beam content\n # list of (bs * beam_size) tuple(next hypothesis score, next word, current position in the batch)\n next_batch_beam = []\n\n # for each sentence\n for sent_id in range(bs):\n\n # if we are done with this sentence\n done[sent_id] = done[sent_id] or generated_hyps[sent_id].is_done(next_scores[sent_id].max().item())\n if done[sent_id]:\n next_batch_beam.extend([(0, env.pad_index, 0)] * beam_size) # pad the batch\n continue\n\n # next sentence beam content\n next_sent_beam = []\n\n # next words for this sentence\n for idx, value in zip(next_words[sent_id], next_scores[sent_id]):\n\n # get beam and word IDs\n beam_id = idx // n_words\n word_id = idx % n_words\n\n # end of sentence, or next word\n if word_id == env.eos_index or cur_len + 1 == max_len:\n generated_hyps[sent_id].add(generated[:cur_len, sent_id * beam_size + beam_id].clone().cpu(), value.item())\n else:\n next_sent_beam.append((value, word_id, sent_id * beam_size + beam_id))\n\n # the beam for next step is full\n if len(next_sent_beam) == beam_size:\n break\n\n # update next beam content\n assert len(next_sent_beam) == 0 if cur_len + 1 == max_len else beam_size\n if len(next_sent_beam) == 0:\n next_sent_beam = [(0, env.pad_index, 0)] * beam_size # pad the batch\n next_batch_beam.extend(next_sent_beam)\n assert len(next_batch_beam) == beam_size * (sent_id + 1)\n\n # sanity check / prepare next batch\n assert len(next_batch_beam) == bs * beam_size\n beam_scores = beam_scores.new([x[0] for x in next_batch_beam])\n beam_words = generated.new([x[1] for x in next_batch_beam])\n beam_idx = src_len.new([x[2] for x in next_batch_beam])\n\n # re-order batch and internal states\n generated = generated[:, beam_idx]\n generated[cur_len] = beam_words\n for k in cache.keys():\n if k != 'slen':\n cache[k] = (cache[k][0][beam_idx], cache[k][1][beam_idx])\n\n # update current length\n cur_len = cur_len + 1\n\n # stop when we are done with each sentence\n if all(done):\n break\n\n # def get_coeffs(s):\n # roots = [int(s[i + 2]) for i, c in enumerate(s) if c == 'x']\n # poly = np.poly1d(roots, r=True)\n # coeffs = list(poly.coefficients.astype(np.int64))\n # return [c % 10 for c in coeffs], coeffs\n\n # visualize hypotheses\n # print([len(x) for x in generated_hyps], cur_len)\n # globals().update( locals() );\n # !import code; code.interact(local=vars())\n # for ii in range(bs):\n # for ss, ww in sorted(generated_hyps[ii].hyp, key=lambda x: x[0], reverse=True):\n # hh = \" \".join(self.id2word[x] for x in ww.tolist())\n # print(f\"{ss:+.4f} {hh}\")\n # # cc = get_coeffs(hh[4:])\n # # print(f\"{ss:+.4f} {hh} || {cc[0]} || {cc[1]}\")\n # print(\"\")\n\n # select the best hypotheses\n tgt_len = src_len.new(bs)\n best = []\n\n for i, hypotheses in enumerate(generated_hyps):\n best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1]\n tgt_len[i] = len(best_hyp) + 1 # +1 for the <EOS> symbol\n best.append(best_hyp)\n\n # generate target batch\n decoded = src_len.new(tgt_len.max().item(), bs).fill_(env.pad_index)\n for i, hypo in enumerate(best):\n decoded[:tgt_len[i] - 1, i] = hypo\n decoded[tgt_len[i] - 1, i] = env.eos_index\n\n # sanity check\n assert (decoded == env.eos_index).sum() == 2 * bs\n\n return decoded, tgt_len, generated_hyps","def create_model():\n # Get list of all syllables: [\"<s>\", \"AH\", \"</s>\", \"<s>\", \"T\", ...]\n syllabifier = Syllabifier()\n all_syllables = syllabifier.all_syllables()\n\n # Count conditional probabilties of phoneme tuples\n tcf = TrigramCollocationFinder.from_words(all_syllables)\n bcf = BigramCollocationFinder.from_words(all_syllables)\n tri_dict = dict(sorted(tcf.ngram_fd.items(), key=lambda t: (-t[1], t[0])))\n bi_dict = dict(sorted(bcf.ngram_fd.items(), key=lambda t: (-t[1], t[0])))\n\n # Create dictionary to count cond prob all phoneme tuples\n accepted_phonemes = [i[0] for i in cmudict.phones()]\n accepted_phonemes.append('<s>')\n accepted_phonemes.append('</s>')\n phoneme_tups = [p for p in itertools.product(accepted_phonemes, repeat=3)]\n cond_probs_dict = dict([(char, 0) for char in phoneme_tups])\n\n for t in tri_dict:\n p1, p2, p3 = t[0], t[1], t[2]\n tri_count = tri_dict[t]\n bi_count = bi_dict[(p1, p2)]\n if bi_count > 1:\n cond_prob = tri_count * 1.0 / bi_count\n else:\n cond_prob = 0.0\n cond_probs_dict[(p1, p2, p3)] = cond_prob\n\n pickle.dump(cond_probs_dict, open(COND_PROBS_PATH, \"wb\"))\n return","def build_model(self) -> DM:\n model = DM()\n model[self.modelroot] = content = DM()\n\n content['key'] = self.key\n content['id'] = self.id\n content['system-family'] = self.family\n for cp in self.parameters:\n content.append('calculation-parameter', DM(cp))\n\n self._set_model(model)\n return model","def _create_inference_model(pl_module):\n new_model = get_model(pl_module.hparams)\n new_model.normalization_metrics = AnomalyScoreDistribution().cpu()\n new_model.load_state_dict(pl_module.state_dict())\n return new_model","def _getBMat(self):\n\n \"\"\"B matrix is just a mass matrix, can be easily assembled\n through fenics. However, the ordering in Fenics is not the\n mesh ordering. So we build a temp matrix and then use the\n vertex to dof map to get the right ordering interms of our\n mesh nodes.\n \"\"\"\n\n # create function space of order 1. For KL, we only restrict\n # to first order spaces.\n V = FunctionSpace(self._mesh, \"CG\", 1)\n # Define basis and bilinear form\n u = TrialFunction(V)\n v = TestFunction(V)\n a = u * v * dx\n # assemble in a temp matrix\n B_temp = assemble(a)\n\n # create petsc matrix B\n B = PETSc.Mat().create()\n B.setType('aij')\n B.setSizes(self.domain.getNodes(), self.domain.getNodes())\n B.setUp()\n\n # store the value in a a temp array B_ij\n B_ij = B_temp.array()\n\n # get the vertex to dof map\n v_to_d_map = vertex_to_dof_map(V)\n\n print '---------------------------'\n print '---------------------------'\n print ' Building Mass Matrix '\n print '---------------------------'\n print '---------------------------'\n for node_i in range(0, self.domain.getNodes()):\n for node_j in range(node_i, self.domain.getNodes()):\n B_ij_nodes = B_ij[v_to_d_map[node_i], v_to_d_map[node_j]]\n if B_ij_nodes > 0:\n B.setValue(node_i, node_j, B_ij_nodes)\n B.setValue(node_j, node_i, B_ij_nodes)\n\n B.assemblyBegin()\n B.assemblyEnd()\n print '---------------------------'\n print '---------------------------'\n print ' Finished Mass Matrix '\n print '---------------------------'\n print '---------------------------'\n return B","def get_model_with_properties():\n \n m = ConcreteModel()\n\n # ------------------------------------------------------------------\n # Data\n # ------------------------------------------------------------------\n\n m.np = 25 # Number of possible tays\n m.c = 4 # Number of components\n m.lc = 1 # Light component\n m.hc = 4 # Heavy component\n\n #### Constant parameters\n m.Rgas = 8.314 # Ideal gas constant in J/mol K\n m.Tref = 298.15 # Reference temperature in K\n\n #### Product specifications\n m.xspec_lc = 0.99 # Final liquid composition for methanol (1)\n m.xspec_hc = 0.99 # Fnal liquid composition for butanol (4)\n m.xspec_inter2 = 0.99 # Final liquid composition for ethanol (2)\n m.xspec_inter3 = 0.99 # Final liquid composition for propanol (3)\n m.Ddes = 50 # Final flowrate in distillate in mol/s\n m.Bdes = 50 # Final flowrate in bottoms in mol/s\n m.Sdes = 50 # Final flowrate in side product streams in mol/s\n\n # #### Known initial values\n m.Fi = m.Ddes + m.Bdes + 2 * m.Sdes # Side feed flowrate in mol/s\n m.Vi = 400 # Initial value for vapor flowrate in mol/s\n m.Li = 400 # Initial value for liquid flowrate in mol/s\n\n m.Tf = 358 # Side feed temperature in K\n\n m.Preb = 1.2 # Reboiler pressure in bar\n m.Pbot = 1.12 # Bottom-most tray pressure in bar\n m.Ptop = 1.08 # Top-most tray pressure in bar\n m.Pcon = 1.05 # Condenser pressure in bar\n m.Pf = 1.02\n\n m.rr0 = 0.893 # Internal reflux ratio initial value\n m.bu0 = 0.871 # Internal reflux ratio initial value\n\n\n #### Scaling factors\n m.Hscale = 1e3 \n m.Qscale = 1e-3 \n\n \n #### Constants for the calculation of liquid heat capacity\n m.cpc = {} # Constant 1 for liquid heat capacity \n m.cpc2 = {} # Constant 2 for liquid heat capacity \n m.cpc[1] = m.Rgas \n m.cpc[2] = 1\n m.cpc2['A', 1] = 1 / 100\n m.cpc2['B', 1] = 1 / 1e4\n m.cpc2['A', 2] = 1\n m.cpc2['B', 2] = 1\n\n\n # ------------------------------------------------------------------\n # Physical Properties\n #\n # Notation:\n # MW ........................ molecular weight in g/gmol\n # TB ........................ boiling point temperature in K\n # TC ........................ critical temperature in K\n # PC ........................ critical pressure in bar\n # w ........................ acentric factor\n # lden ...................... liquid density g/m3,\n # dHvap ..................... heat of vaporization in J/mol.\n # vpA, vpB, vpC, and vpD .... vapor pressure constants\n # cpA, cpB, cpC, and cpD .... heat capacity constants J/mol:\n # 1 for liq and 2 for vapor phase\n #\n # Reference A: R.C. Reid, J.M. Prausnitz and B.E. Poling,\n # \"The Properties of gases and liquids\", 1987 and 2004 Eds.\n #\n # ------------------------------------------------------------------\n\n m.prop = {} # Properties of components:\n cpL = {} # Ruczika-D method for liquid heat capacity calculation\n # (Reference A, page 6.20)\n sumA = {}\n sumB = {}\n sumC = {}\n cpL['a', 'C(H3)(C)'] = 4.19845\n cpL['b', 'C(H3)(C)'] = -0.312709\n cpL['c', 'C(H3)(C)'] = 0.178609\n cpL['a', 'C(H2)(C2)'] = 2.7345\n cpL['b', 'C(H2)(C2)'] = 0.122732\n cpL['c', 'C(H2)(C2)'] = -0.123482\n cpL['a', 'C(H2)(C)(O)'] = 0.517007\n cpL['b', 'C(H2)(C)(O)'] = 1.26631\n cpL['c', 'C(H2)(C)(O)'] = -0.0939713\n cpL['a', 'O(H)(C)'] = 16.1555\n cpL['b', 'O(H)(C)'] = -11.938\n cpL['c', 'O(H)(C)'] = 2.85117\n cpL['a', 'C(H3)(O)'] = 3.70344\n cpL['b', 'C(H3)(O)'] = -1.12884\n cpL['c', 'C(H3)(O)'] = 0.51239\n sumA[1] = (cpL['a', 'C(H3)(O)']\n + cpL['a', 'O(H)(C)']) \n sumB[1] = (cpL['b', 'C(H3)(O)']\n + cpL['b', 'O(H)(C)'])\n sumC[1] = (cpL['c', 'C(H3)(O)']\n + cpL['c', 'O(H)(C)'])\n sumA[2] = (cpL['a', 'C(H3)(C)']\n + cpL['a', 'C(H2)(C)(O)']\n + cpL['a', 'O(H)(C)'])\n sumB[2] = (cpL['b', 'C(H3)(C)']\n + cpL['b', 'C(H2)(C)(O)']\n + cpL['b', 'O(H)(C)'])\n sumC[2] = (cpL['c', 'C(H3)(C)']\n + cpL['c', 'C(H2)(C)(O)']\n + cpL['c', 'O(H)(C)'])\n sumA[3] = (cpL['a', 'C(H3)(C)']\n + cpL['a', 'C(H2)(C2)']\n + cpL['a', 'C(H2)(C)(O)']\n + cpL['a', 'O(H)(C)'])\n sumB[3] = (cpL['b', 'C(H3)(C)']\n + cpL['b', 'C(H2)(C2)']\n + cpL['b', 'C(H2)(C)(O)']\n + cpL['b', 'O(H)(C)'])\n sumC[3] = (cpL['c', 'C(H3)(C)']\n + cpL['c', 'C(H2)(C2)']\n + cpL['c', 'C(H2)(C)(O)']\n + cpL['c', 'O(H)(C)'])\n sumA[4] = (cpL['a', 'C(H3)(C)']\n + 2 * cpL['a', 'C(H2)(C2)']\n + cpL['a', 'C(H2)(C)(O)']\n + cpL['a', 'O(H)(C)'])\n sumB[4] = (cpL['b', 'C(H3)(C)']\n + 2 * cpL['b', 'C(H2)(C2)']\n + cpL['b', 'C(H2)(C)(O)']\n + cpL['b', 'O(H)(C)'])\n sumC[4] = (cpL['c', 'C(H3)(C)']\n + 2 * cpL['c', 'C(H2)(C2)']\n + cpL['c', 'C(H2)(C)(O)']\n + cpL['c', 'O(H)(C)'])\n\n ## Methanol: component 1\n m.prop[1, 'MW'] = 32.042\n m.prop[1, 'TB'] = 337.7\n m.prop[1, 'TC'] = 512.6\n m.prop[1, 'PC'] = 80.9\n m.prop[1, 'w'] = 0.556\n m.prop[1, 'lden'] = 792e3\n m.prop[1, 'dHvap'] = 38.376e3\n m.prop[1, 'vpA'] = -8.54796\n m.prop[1, 'vpB'] = 0.76982\n m.prop[1, 'vpC'] = -3.10850\n m.prop[1, 'vpD'] = 1.54481\n m.prop[1, 'cpA', 1] = sumA[1]\n m.prop[1, 'cpB', 1] = sumB[1]\n m.prop[1, 'cpC', 1] = sumC[1]\n m.prop[1, 'cpD', 1] = 0\n m.prop[1, 'cpA', 2] = 2.115e1\n m.prop[1, 'cpB', 2] = 7.092e-2\n m.prop[1, 'cpC', 2] = 2.587e-5\n m.prop[1, 'cpD', 2] = -2.852e-8\n\n\n ## Ethanol: component 2\n m.prop[2, 'MW'] = 46.069\n m.prop[2, 'TB'] = 351.4\n m.prop[2, 'TC'] = 513.9\n m.prop[2, 'PC'] = 61.4\n m.prop[2, 'w'] = 0.644\n m.prop[2, 'lden'] = 789.3e3\n m.prop[2, 'dHvap'] = 42.698e3\n m.prop[2, 'vpA'] = -8.51838\n m.prop[2, 'vpB'] = 0.34163\n m.prop[2, 'vpC'] = -5.73683\n m.prop[2, 'vpD'] = 8.32581\n m.prop[2, 'cpA', 1] = sumA[2]\n m.prop[2, 'cpB', 1] = sumB[2]\n m.prop[2, 'cpC', 1] = sumC[2]\n m.prop[2, 'cpD', 1] = 0\n m.prop[2, 'cpA', 2] = 9.014\n m.prop[2, 'cpB', 2] = 2.141e-1\n m.prop[2, 'cpC', 2] = -8.390e-5\n m.prop[2, 'cpD', 2] = 1.373e-9\n\n\n ## Propanol: component 3\n m.prop[3, 'MW'] = 60.096\n m.prop[3, 'TB'] = 370.3\n m.prop[3, 'TC'] = 536.8\n m.prop[3, 'PC'] = 51.7\n m.prop[3, 'w'] = 0.623\n m.prop[3, 'lden'] = 804e3\n m.prop[3, 'dHvap'] = 47.763e3\n m.prop[3, 'vpA'] = -8.05594\n m.prop[3, 'vpB'] = 4.25183e-2\n m.prop[3, 'vpC'] = -7.51296\n m.prop[3, 'vpD'] = 6.89004\n m.prop[3, 'cpA', 1] = sumA[3]\n m.prop[3, 'cpB', 1] = sumB[3]\n m.prop[3, 'cpC', 1] = sumC[3]\n m.prop[3, 'cpD', 1] = 0\n m.prop[3, 'cpA', 2] = 2.47\n m.prop[3, 'cpB', 2] = 3.325e-1\n m.prop[3, 'cpC', 2] = -1.855e-4\n m.prop[3, 'cpD', 2] = 4.296e-8\n\n\n ## Butanol: component 4\n m.prop[4, 'MW'] = 74.123\n m.prop[4, 'TB'] = 390.9\n m.prop[4, 'TC'] = 563.1\n m.prop[4, 'PC'] = 44.2\n m.prop[4, 'w'] = 0.593\n m.prop[4, 'lden'] = 810e3\n m.prop[4, 'dHvap'] = 52.607e3\n m.prop[4, 'vpA'] = -8.00756\n m.prop[4, 'vpB'] = 0.53783\n m.prop[4, 'vpC'] = -9.34240\n m.prop[4, 'vpD'] = 6.68692\n m.prop[4, 'cpA', 1] = sumA[4]\n m.prop[4, 'cpB', 1] = sumB[4]\n m.prop[4, 'cpC', 1] = sumC[4]\n m.prop[4, 'cpD', 1] = 0\n m.prop[4, 'cpA', 2] = 3.266\n m.prop[4, 'cpB', 2] = 4.18e-1\n m.prop[4, 'cpC', 2] = -2.242e-4\n m.prop[4, 'cpD', 2] = 4.685e-8\n\n\n return m","def __init__(\n self,\n repeats=2,\n window_sizes=[512, 1024, 2048, 4096],\n pqmf_params=[\n [1, None, None, None],\n [2, 62, 0.26700, 9.0],\n [4, 62, 0.14200, 9.0],\n [8, 62, 0.07949, 9.0],\n ],\n discriminator_params={\n \"out_channels\": 1,\n \"kernel_sizes\": [5, 3],\n \"channels\": 16,\n \"max_downsample_channels\": 512,\n \"bias\": True,\n \"downsample_scales\": [4, 4, 4, 1],\n \"nonlinear_activation\": \"LeakyReLU\",\n \"nonlinear_activation_params\": {\"negative_slope\": 0.2},\n \"pad\": \"ReflectionPad1d\",\n \"pad_params\": {},\n },\n use_weight_norm=True,\n ):\n super().__init__()\n\n # window size check\n assert len(window_sizes) == len(pqmf_params)\n sizes = [ws // p[0] for ws, p in zip(window_sizes, pqmf_params)]\n assert len(window_sizes) == sum([sizes[0] == size for size in sizes])\n\n self.repeats = repeats\n self.window_sizes = window_sizes\n self.pqmfs = torch.nn.ModuleList()\n self.discriminators = torch.nn.ModuleList()\n for pqmf_param in pqmf_params:\n d_params = copy.deepcopy(discriminator_params)\n d_params[\"in_channels\"] = pqmf_param[0]\n if pqmf_param[0] == 1:\n self.pqmfs += [torch.nn.Identity()]\n else:\n self.pqmfs += [PQMF(*pqmf_param)]\n self.discriminators += [BaseDiscriminator(**d_params)]\n\n # apply weight norm\n if use_weight_norm:\n self.apply_weight_norm()\n\n # reset parameters\n self.reset_parameters()","def solve(self):\n start = timer()\n # encode into milp\n me = MILPEncoder(MILPSolver.prob,\n MILPSolver.params.logger.LOGFILE, \n MILPSolver.params.INTRA_DEP_CONSTRS,\n MILPSolver.params.INTER_DEP_CONSTRS)\n if MILPSolver.lp == True:\n gmodel = me.lp_encode()\n else:\n gmodel = me.encode()\n # Set gurobi parameters\n pgo = 1 if MILPSolver.params.PRINT_GUROBI_OUTPUT == True else 0\n gmodel.setParam('OUTPUT_FLAG', pgo)\n tl = MILPSolver.params.TIMEOUT\n if tl != -1 : gmodel.setParam('TIME_LIMIT', tl)\n if not MILPSolver.params.DEFAULT_CUTS: \n MILPSolver.disable_default_cuts(gmodel)\n gmodel._vars = gmodel.getVars()\n # set callback cuts \n MILPSolver.id_form = IdealFormulation(MILPSolver.prob,\n gmodel, \n MILPSolver.params.IDEAL_FREQ,\n MILPSolver.params.logger.LOGFILE)\n MILPSolver.dep_cuts = DepCuts(MILPSolver.prob,\n gmodel,\n MILPSolver.params.DEP_FREQ,\n MILPSolver.params.INTRA_DEP_CUTS,\n MILPSolver.params.INTER_DEP_CUTS,\n MILPSolver.sip_params,\n MILPSolver.params.logger.LOGFILE)\n # Optimise\n if MILPSolver.params.callback_enabled() and MILPSolver.lp == False:\n gmodel.optimize(MILPSolver._callback)\n else:\n gmodel.optimize()\n\n runtime = timer() - start\n cex = None \n if MILPSolver.status == SolveResult.BRANCH_THRESHOLD:\n result = SolveResult.BRANCH_THRESHOLD\n elif gmodel.status == GRB.OPTIMAL:\n cex_shape = MILPSolver.prob.spec.input_layer.input_shape\n cex = np.zeros(cex_shape)\n for i in itertools.product(*[range(j) for j in cex_shape]):\n cex[i] = MILPSolver.prob.spec.input_layer.out_vars[i].x\n result = SolveResult.UNSATISFIED\n elif gmodel.status == GRB.TIME_LIMIT:\n result = SolveResult.TIMEOUT\n elif gmodel.status == GRB.INTERRUPTED:\n result = SolveResult.INTERRUPTED\n elif gmodel.status == GRB.INFEASIBLE or gmodel.status == GRB.INF_OR_UNBD:\n result = SolveResult.SATISFIED\n else:\n result = SolveResult.UNKNOWN\n \n # MILPSolver.logger.info('Verification problem {} solved, '\n # 'LP: {}, '\n # 'time: {:.2f}, '\n # 'result: {}.'\n # .format(MILPSolver.prob.id,\n # MILPSolver.lp,\n # runtime,\n # result.value))\n \n return SolveReport(result, runtime, cex)","def setup_b_instance(self,norm,add_ps_mask=True):\n inst_tag = self.tag + '_'+str(self.flux_array_ebin)\n b = bsm.bayesian_scan_NPTF(tag=inst_tag,nside=self.nside,work_dir='/tmp/'+self.tag+'/',psf_dir=psf_dir,nlive=700)\n # Input the data, using the external data if provided\n if self.use_external_data:\n b.load_external_data(self.f1.CTB_en_bins,[self.external_data[self.flux_array_ebin]],self.f1.CTB_exposure_maps)\n else:\n b.load_external_data(self.f1.CTB_en_bins,self.f1.CTB_count_maps,self.f1.CTB_exposure_maps)\n\n if add_ps_mask:\n b.make_mask_total(band_mask_range = [-self.band_mask,self.band_mask],mask_ring = False,ps_mask_array = self.f1.ps_mask_array)\n else:\n b.make_mask_total(band_mask_range = [-self.band_mask,self.band_mask],mask_ring = False)\n\n b.add_new_template(self.f1.template_dict)\n b.rebin_external_data(1)\n\n b.add_poiss_model('ps_model','$A_{ps}$',[0.0,3.0],False)\n b.add_poiss_model('p7','$A_{p7}$',[0.0,2.0],False)\n b.add_poiss_model('bubs','$A_{bubs}$',[0.0,2.0],False)\n b.add_poiss_model('iso','$A_{iso}$',[0.0,3.0],False)\n # Add in a fixed J_map template\n b.add_fixed_templates({'J_map':[norm*self.J_map_arr[self.flux_array_ebin]/np.mean(self.J_map_arr[self.flux_array_ebin])]})\n\n b.initiate_poissonian_edep()\n return b","def policy_gamble (self):\n\t\tidx = self.idx \t\t\t\t# internal time index of state\n\t\tprobs = self.probs\t\t\t# prob of reward for an action\n\t\tbeta = self.beta\t\t\t# inverse temp \n\n\t\t# softmax\n\t\tAct = beta*self.Q[idx]\n\t\tp = 1./(1. + np.exp(-Act))\t# probability of gamble\n\t\tself.SM[idx] = p\n\n\t\t# decide whether to take gamble based on p\n\t\trnd = np.random.random_sample()\n\t\tif rnd < p:\n\t\t\tC = 1\t# gamble\n\t\telse:\n\t\t\tC = 0\t# no gamble\n\t\tself.C[idx] = C\n\n\t\t# no gamble\n\t\tif C == 0:\t\n\t\t\treward = 0\t\t # gamble reward encoded relative to reward\n\t\t\tself.R[idx] = -1 # rewarded sure thing, coded as -1\n\t\t\tself.PE[idx] = 0 # no PE, get the thing you expected\n\t\t# gamble\n\t\telse:\n\t\t\t# decide whether a reward is delivered\n\t\t\treward = np.random.binomial(size=1, n=1, p=probs)[0]\n\t\t\tself.R[idx] = reward # indicator that reward was received\n\t\t\tif reward == 0:\n\t\t\t\treward = self.l_mag\n\t\t\telse:\n\t\t\t\treward = self.r_mag\n\t\t\tself.PE[idx] = reward - self.Q[idx]","def _create_model(n_bins=10, alpha=0.1, tol=0.1, contamination=0.1):\n n_bins = int(n_bins)\n\n hbos = HBOS(\n n_bins=n_bins,\n alpha=alpha,\n tol=tol,\n contamination=contamination\n )\n\n print('Created Model: {}'.format(hbos))\n\n return hbos","def example():\n num_user, num_item, ratings = build_ticket()\n \n # suffle_data\n np.random.shuffle(ratings)\n\n # split data to training & validation\n train_pct = 0.9\n train_size = int(train_pct * len(ratings))\n train = ratings[:train_size]\n validation = ratings[train_size:]\n\n # params\n num_feature = 5\n bmf_model = BayesianMatrixFactorization(\n num_user, num_item, num_feature, train, validation, max_rating=1, min_rating=0, ratingsMatirx=ratings)\n\n start_time = time.clock()\n bmf_model.estimate(10)\n end_time = time.clock()\n \n mat = np.matrix(bmf_model.item_features)\n with open('../data/ticket/item_feature', 'w') as f:\n for line in mat:\n np.savetxt(f, line, fmt='%.5f')\n\n print \"time spend = %.3f\" % (end_time - start_time)\n\n return bmf_model","def __init__(self, p_control = 0, p_treatment = 0, n_control = 0, n_treatment = 0, power = None, alpha = 0.05):\n self.p_control = p_control\n self.p_treatment = p_treatment\n\n self.n_control = n_control\n self.n_treatment = n_treatment\n\n self.var_control = 1 * p_control * (1 - p_control)\n self.var_treatment = 1 * p_treatment * (1 - p_treatment)\n\n self.norm_null = None\n self.norm_alt = None\n\n self.binom_null = None\n self.binom_alt = None\n\n self.binom_control = None\n self.binom_treatment = None\n\n self.confidence_control = None\n self.confidence_treatment = None\n\n if n_control > 0 and n_treatment > 0 and p_control > 0 and p_treatment > 0:\n control = self.p_control * self.n_control\n treatment = self.p_treatment * self.n_treatment\n sample = self.n_control + self.n_treatment\n\n self.p_sample = (control + treatment) / sample\n else:\n self.p_sample = None\n\n if power == 1:\n print('Sample size approaches infinity as power approaches 1, so 1 is an invalid power vlaue. Changing power to 0.99.')\n self.power = 0.99\n elif power == 0:\n print('Sample size is undefined at power of 0, so 0 is an invalid power value. Changing power to 0.01.')\n self.power = 0.01\n else:\n self.power = power\n\n self.alpha = alpha\n self.p_value = None","def naiveBayesMixture(train_set, train_labels, dev_set, bigram_lambda,unigram_smoothing_parameter, bigram_smoothing_parameter, pos_prior):\n\n # TODO: Write your code here\n # return predicted labels of development set\n\n # counters for Training Phase\n ham = Counter()\n ham_bi = Counter()\n spam = Counter()\n spam_bi = Counter()\n\n for string, label in zip(train_set, train_labels):\n for i in range(len(string)):\n word = string[i]\n if i != len(string)-1:\n word_bi = string[i] + ' ' + string[i+1]\n if label == 1:\n ham_bi.update({word_bi:1})\n else:\n spam_bi.update({word_bi:1})\n if label == 1:\n ham.update({word:1})\n else:\n spam.update({word:1})\n\n ham_len = 0\n for w in ham:\n ham_len += ham[w]\n spam_len = 0\n for w in spam:\n spam_len += spam[w]\n \n hambi_len = 0\n for w in ham_bi:\n hambi_len += ham_bi[w]\n spambi_len = 0\n for w in spam_bi:\n spambi_len += spam_bi[w]\n\n # labels for Development Phase\n dev_labels = []\n # dicts for P(word|ham) and P(word|spam)\n p_ham = {}\n p_spam = {}\n p_hambi = {}\n p_spambi = {}\n\n # develop likelihoods based on dev_set\n for word in ham:\n numerator = ham[word] + unigram_smoothing_parameter\n denominator = ham_len + unigram_smoothing_parameter*(len(ham))\n p_ham[word] = numerator / denominator\n for word in spam:\n numerator = spam[word] + unigram_smoothing_parameter\n denominator = spam_len + unigram_smoothing_parameter*(len(spam))\n p_spam[word] = numerator / denominator\n\n for word_bi in ham_bi:\n numerator = ham_bi[word_bi] + bigram_smoothing_parameter\n denominator = hambi_len + bigram_smoothing_parameter*(len(ham_bi))\n p_hambi[word_bi] = numerator / denominator\n for word_bi in spam_bi:\n numerator = spam_bi[word_bi] + bigram_smoothing_parameter\n denominator = spambi_len + bigram_smoothing_parameter*(len(spam_bi))\n p_spambi[word_bi] = numerator / denominator\n \n numerator = unigram_smoothing_parameter\n denominator = ham_len + unigram_smoothing_parameter*(len(ham))\n p_ham_zero = numerator / denominator\n denominator = spam_len + unigram_smoothing_parameter*(len(spam))\n p_spam_zero = numerator / denominator\n\n numerator = bigram_smoothing_parameter\n denominator = hambi_len + bigram_smoothing_parameter*(len(ham_bi))\n p_hambi_zero = numerator / denominator\n denominator = spambi_len + bigram_smoothing_parameter*(len(spam_bi))\n p_spambi_zero = numerator / denominator\n\n for string in dev_set:\n p_words_ham = math.log(pos_prior)\n p_words_spam = math.log(1 - pos_prior)\n\n p_words_hambi = math.log(pos_prior)\n p_words_spambi = math.log(1 - pos_prior)\n \n for i in range(len(string)):\n word = string[i]\n if word in p_ham:\n p_words_ham += math.log(p_ham[word])\n else:\n p_words_ham += math.log(p_ham_zero)\n if word in p_spam:\n p_words_spam += math.log(p_spam[word])\n else:\n p_words_spam += math.log(p_spam_zero)\n\n if i != len(string)-1:\n word_bi = string[i] + ' ' + string[i+1]\n if word_bi in p_hambi:\n p_words_hambi += math.log(p_hambi[word_bi])\n else:\n p_words_hambi += math.log(p_hambi_zero)\n if word_bi in p_spambi:\n p_words_spambi += math.log(p_spambi[word_bi])\n else:\n p_words_spambi += math.log(p_spambi_zero)\n\n p_ham_mix = p_words_ham*(1-bigram_lambda) + p_words_hambi*bigram_lambda\n p_spam_mix = p_words_spam*(1-bigram_lambda) + p_words_spambi*bigram_lambda\n\n dev_labels.append(p_ham_mix >= p_spam_mix)\n\n return dev_labels","def make_model(data):\n commodities, arcs, capacity, variable_cost, nodes, demand = \\\n data.commodities, data.arcs.size, data.capacity, \\\n data.variable_cost, data.nodes, np.amax(data.demand, axis=0)\n origins, destinations = data.origins, data.destinations\n fixed_cost = data.fixed_cost[0, :]\n\n flow = np.empty(shape=(commodities, arcs), dtype=object)\n arc_open = np.empty(shape=arcs, dtype=object)\n capacities = np.empty(shape=arcs, dtype=object)\n arc_origins, arc_destinations = get_2d_index(data.arcs, nodes)\n\n\n model = grb.Model('MCND')\n model.params.threads = 1\n model.params.LogFile = \"\"\n lazy_cons = []\n\n # Variables\n for arc in xrange(arcs):\n i, j = arc_origins[arc], arc_destinations[arc]\n arc_open[arc] = model.addVar(\n vtype=grb.GRB.BINARY, obj=fixed_cost[arc],\n name='open_arc{}-{}'.format(i, j))\n for h in xrange(commodities):\n flow[h, arc] = model.addVar(\n obj=variable_cost[arc] * data.demand[0, h],\n lb=0., ub=1.,\n vtype=grb.GRB.CONTINUOUS,\n name='flow{}.{},{}'.format(h, i, j))\n model._arc_open = arc_open\n model._flow = flow\n model.update()\n\n # Constraints\n for arc in xrange(arcs):\n for h in xrange(commodities):\n lazy_cons.append(model.addConstr(flow[h, arc] <= arc_open[arc]))\n i, j = arc_origins[arc], arc_destinations[arc]\n capacities[arc] = model.addConstr(grb.quicksum(grb.LinExpr(\n data.demand[0, h] * flow[h, arc]) for h in xrange(\n commodities)) <= capacity[arc] * arc_open[arc],\n name='cap_{}-{}'.format(i, j))\n for h in xrange(commodities):\n for n in xrange(nodes):\n rhs = 0.\n if n == origins[h]:\n rhs = 1.\n if n == destinations[h]:\n rhs = -1.\n in_arcs = get_2d_index(data.arcs, nodes)[1] == n + 1\n out_arcs = get_2d_index(data.arcs, nodes)[0] == n + 1\n lhs = grb.quicksum(flow[h, out_arcs]) - grb.quicksum(\n flow[h, in_arcs])\n model.addConstr(\n lhs=lhs, rhs=rhs, sense=grb.GRB.EQUAL,\n name='demand_n{}c{}'.format(n, h))\n\n model._capacities = capacities\n model.setParam('OutputFlag', 0)\n model.params.TimeLimit = 1000.\n model.params.MIPGap = 0.01\n model.params.Threads = 1\n model.update()\n for con in lazy_cons:\n con.Lazy = 3\n # model.write(str(model.ModelName) + '.lp')\n return model","def MakePmfTest(shelf, **options):\n #print(options)\n label = options.pop('label', '')\n xs, ds = Render(shelf,**options)\n #print()\n #print(xs)\n #print(ds)\n return thinkbayes2.Pmf(dict(zip(xs, ds)), label=label)","def build_model(self, reset_graph=False):\n\n if self.data is None:\n raise Exception('a dataset must be specified')\n\n if self.adv and (self.classifier is None):\n raise Exception('A classifier must be specified to train in adversarial mode')\n\n if reset_graph:\n tf.compat.v1.reset_default_graph()\n\n self.dataset_is_tf = self.data.is_tf\n\n if self.adv:\n self.classifier.load_model()\n\n shape_x, shape_y = self.data.shape()\n number_of_samples = shape_x[0]\n shape_x[0] = None\n\n z_dims = [None] + self.z_dim\n\n shape_y[0] = None\n\n if self.dataset_is_tf:\n if self.use_mask:\n self.X_real, self.labels, _, self.mask = self.data.next_batch()\n probs = self.classifier.predict(self.X_real)[:, :, :, 4]\n labels_masked = tf.cast(self.labels * self.mask, tf.float32)\n pm = tf.cast(tf.expand_dims(probs, -1) * labels_masked, tf.float32)\n self.classifier_proba = tf.cast(\n 1.0 - (tf.reduce_sum(input_tensor=pm, axis=[1, 2, 3]) / tf.reduce_sum(input_tensor=labels_masked,\n axis=[1, 2, 3])), tf.float32)\n self.mask.set_shape(shape_y)\n elif self.use_labels:\n self.X_real, self.labels, _, _ = self.data.next_batch()\n self.mask = self.labels\n probs = self.classifier.predict(self.X_real)[:, :, :, 4]\n pm = tf.cast(tf.expand_dims(probs, -1) * self.labels, tf.float32)\n self.classifier_proba = tf.cast(\n 1.0 - (tf.reduce_sum(input_tensor=pm, axis=[1, 2, 3]) / tf.reduce_sum(input_tensor=self.labels,\n axis=[1, 2, 3])), tf.float32)\n else:\n self.X_real, self.labels, self.classifier_proba = self.data.next_batch()\n\n self.X_real.set_shape(shape_x)\n self.labels.set_shape(shape_y)\n\n if self.use_ceil and self.ceil_value is not None:\n self.classifier_proba = tf.minimum(self.classifier_proba, self.ceil_value)\n\n self.classifier_proba = tf.nn.softmax(self.classifier_proba * self.temperature)\n self.classifier_proba = tf.expand_dims(self.classifier_proba, -1)\n\n else:\n self.X_real = tf.compat.v1.placeholder(\"float\", shape_x, name=\"X_real\")\n self.labels = tf.compat.v1.placeholder(\"float\", shape_y, name=\"labels\")\n self.classifier_proba = tf.compat.v1.placeholder(\"float\", [None, 1], name=\"classifier_proba\")\n self.adv_map = tf.compat.v1.placeholder(\"float\", shape_y, name=\"adv_map\")\n\n self.z = tf.compat.v1.placeholder(\"float\", z_dims, name=\"z_input\")\n self.is_training = tf.compat.v1.placeholder(tf.bool, shape=())\n if self.use_clf:\n self.label_used = tf.constant(self.label_used, dtype='float')\n\n # train with real and classifier\n\n if self.use_labels:\n self.pred_real = self.discriminator(self.X_real, self.labels, reuse=False)\n elif self.use_mask:\n self.pred_real = self.discriminator(self.X_real, self.mask, reuse=False)\n else:\n self.pred_real = self.discriminator(self.X_real, reuse=False)\n\n if self.adv:\n self.pred_real *= self.classifier_proba\n\n if self.batch_reg:\n self.pred_real *= self.batch_size / number_of_samples\n\n # train with fake\n if self.use_labels:\n self.X_fake = self.generator(self.X_real, self.labels, self.z, train=self.is_training)\n elif self.use_mask:\n self.X_fake = self.generator(self.X_real, self.mask, self.z, train=self.is_training)\n else:\n self.X_fake = self.generator(self.z, train=self.is_training)\n\n self.X_fake = tf.identity(self.X_fake, name=\"X_fake\")\n\n shape_x[0] = -1\n\n self.X_fake = tf.reshape(self.X_fake, shape_x)\n\n if self.use_labels:\n self.pred_fake = self.discriminator(self.X_fake, self.labels)\n elif self.use_mask:\n self.pred_fake = self.discriminator(self.X_fake, self.mask)\n else:\n self.pred_fake = self.discriminator(self.X_fake)\n\n if self.adv and not self.use_softmax:\n self.pred_fake *= 1 / number_of_samples\n\n self.g_loss = - tf.reduce_mean(input_tensor=self.pred_fake)\n\n if self.adv and not self.use_softmax:\n self.d_loss = - (tf.reduce_sum(input_tensor=self.pred_real) - tf.reduce_sum(input_tensor=self.pred_fake))\n elif self.adv:\n self.d_loss = - (tf.reduce_sum(input_tensor=self.pred_real) - tf.reduce_mean(input_tensor=self.pred_fake))\n else:\n self.d_loss = - (tf.reduce_mean(input_tensor=self.pred_real) - tf.reduce_mean(input_tensor=self.pred_fake))\n\n if self.reg_losses:\n self.d_loss = self.reg_term * self.d_loss\n self.g_loss = self.reg_term * self.g_loss\n\n if self.use_recon:\n if self.use_mask:\n x_fake_masked = (1 - self.mask) * self.X_fake\n x_real_masked = (1 - self.mask) * self.X_real\n elif self.use_labels:\n x_fake_masked = (1 - self.labels) * self.X_fake\n x_real_masked = (1 - self.labels) * self.X_real\n else:\n raise Exception('something wrong')\n\n self.g_loss += self.recon_beta * (tf.reduce_mean(\n input_tensor=tf.reduce_sum(input_tensor=tf.square(x_fake_masked - x_real_masked),\n axis=np.arange(1, len(x_fake_masked.shape)))))\n\n if self.use_clf:\n y_t = self.mask * self.label_used\n preds = self.classifier.predict(self.X_fake)\n preds = preds * self.mask\n preds = 1 - preds\n self.loss_clf = self.clf_alpha * tf.reduce_mean(\n input_tensor=-tf.reduce_sum(input_tensor=y_t * tf.math.log(tf.clip_by_value(preds, 1e-10, 1.0)),\n axis=[1]))\n self.g_loss += self.loss_clf\n\n self.neg_loss = - self.d_loss\n\n if self.use_labels:\n labels = self.labels\n elif self.use_mask:\n labels = self.mask\n else:\n labels = None\n\n if self.loss == 'gp':\n self.d_loss += losses.gp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\n\n if self.loss == 'lp':\n self.d_loss += losses.lp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\n\n if self.loss == 'ct':\n self.d_loss += losses.lp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\n self.d_loss += losses.ct_loss(self.X_real, self.discriminator, labels=labels)\n\n d_vars = [var for var in tf.compat.v1.trainable_variables() if \"discriminator\" in var.name]\n g_vars = [var for var in tf.compat.v1.trainable_variables() if \"generator\" in var.name]\n\n if self.accumulate_grd:\n # initialized with 0s\n\n self.accum_vars_D = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False) for tv in d_vars]\n self.accumulation_counter_D = tf.Variable(0.0, trainable=False)\n\n self.accum_vars_G = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False) for tv in g_vars]\n self.accumulation_counter_G = tf.Variable(0.0, trainable=False)\n\n self.D_sum = tf.compat.v1.summary.scalar('D_loss', self.d_loss)\n self.G_sum = tf.compat.v1.summary.scalar('G_loss', self.g_loss)\n\n with tf.control_dependencies(tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.UPDATE_OPS)):\n\n if self.accumulate_grd:\n\n gvs_g = self.optimizer_G.compute_gradients(self.g_loss, g_vars)\n gvs_d = self.optimizer_D.compute_gradients(self.d_loss, d_vars)\n\n self.accum_ops_G = [accumulator.assign_add(grad) for (accumulator, (grad, var)) in\n zip(self.accum_vars_G, gvs_g)]\n self.accum_ops_G.append(self.accumulation_counter_G.assign_add(1.0))\n\n self.accum_ops_D = [accumulator.assign_add(grad) for (accumulator, (grad, var)) in\n zip(self.accum_vars_D, gvs_d)]\n self.accum_ops_D.append(self.accumulation_counter_D.assign_add(1.0))\n\n self.G_step = self.optimizer_G.apply_gradients(\n [(accumulator / self.accumulation_counter_G, var) for (accumulator, (grad, var)) in\n zip(self.accum_vars_G, gvs_g)])\n\n self.D_step = self.optimizer_D.apply_gradients(\n [(accumulator / self.accumulation_counter_D, var) for (accumulator, (grad, var)) in\n zip(self.accum_vars_D, gvs_d)])\n\n self.zero_ops_G = [accumulator.assign(tf.zeros_like(tv)) for (accumulator, tv) in\n zip(self.accum_vars_G, g_vars)]\n self.zero_ops_D = [accumulator.assign(tf.zeros_like(tv)) for (accumulator, tv) in\n zip(self.accum_vars_D, d_vars)]\n\n self.zero_ops_G.append(self.accumulation_counter_G.assign(0.0))\n self.zero_ops_D.append(self.accumulation_counter_D.assign(0.0))\n\n else:\n self.G_step = self.optimizer_G.minimize(self.g_loss, var_list=g_vars)\n self.D_step = self.optimizer_D.minimize(self.d_loss, var_list=d_vars)\n\n if self.loss == 'clip':\n self.D_clipping = [w.assign(tf.clip_by_value(w, -self.clip_value, self.clip_value)) for w in d_vars]\n\n self.saver = tf.compat.v1.train.Saver()\n self.built = True","def __init__(self, ebunch=None):\n super(BayesianModel, self).__init__()\n if ebunch:\n self.add_edges_from(ebunch)\n self.cpds = []\n self.cardinalities = self.get_cardinality()\n self.probs = dict()","def __init_finalaf(self, i,h1,classes):\n self.params['W'+i]=np.random.randn(h1,classes)*self.weight_scale\n self.params['b'+i]=np.zeros(classes)","def build_B_block(self):\n\n N = self.N # number of MPC steps\n row_list = [] # reocrd the every row in B_hat\n \n first_block = self.B\n zero = Variable(torch.zeros(self.num_input, self.num_output*(N-1)))\n zero = self.vari_gpu(zero)\n row= torch.cat([first_block, zero],1)\n row_list.append(row)\n \n for i in range(1, N):\n first_block = self.A.mm(first_block)\n row = torch.cat([first_block, row[:,:self.num_output*(N-1)]],1)\n row_list.append(row) \n \n return torch.cat(row_list,0)","def create_model(instance):\n name, nitems, sets = instance\n model = grb.Model(name)\n\n # One variable for each set. Also remember which sets cover each item.\n covered_by = [[] for i in range(nitems)]\n vars = []\n for i, set in enumerate(sets):\n cost, covers = set\n vars.append(model.addVar(obj=cost, vtype=grb.GRB.BINARY, name=\"s_{0}\".format(i)))\n\n for item in covers:\n covered_by[item].append(vars[i])\n model.update()\n\n # Constraint: Each item covered at least once.\n for item in range(nitems):\n model.addConstr(grb.quicksum(covered_by[item]) >= 1)\n\n # We want to minimize. Objective coefficients already fixed during variable creation.\n model.setAttr(\"ModelSense\", grb.GRB.MINIMIZE)\n\n # Tuning parameters derived from sc_330_0\n model.read(\"mip.prm\")\n\n model.setParam(\"Threads\", 3)\n model.setParam(\"MIPGap\", 0.001) # 0.1% usually suffices\n\n return model, vars","def mlp_init(d0, d1, d2):\n W1 = 0.01*np.random.randn(d0, d1)\n b1 = np.zeros(d1)\n W2 = 0.01*np.random.randn(d1, d2)\n b2 = np.zeros(d2)\n return (W1, b1, W2, b2)","def init_P_PHM_GIVEN_PHI():\n global P_PHM_GIVEN_PHI\n for i in INTERFACE_LEVEL_ACTIONS: # ui\n P_PHM_GIVEN_PHI[i] = collections.OrderedDict()\n for j in INTERFACE_LEVEL_ACTIONS: # um\n if i == j:\n # try to weight the true command more for realistic purposes. Can be offset by using a high UM_GIVEN_UI_NOISE\n P_PHM_GIVEN_PHI[i][j] = 1.0\n else:\n # P_PHM_GIVEN_PHI[i][j] = np.random.random()*UM_GIVEN_UI_NOISE#IF UM_GIVEN_UI_NOISE is 0, then the p(um|ui) is a deterministic mapping\n P_PHM_GIVEN_PHI[i][j] = 0.0\n\n delta_dist = np.array(P_PHM_GIVEN_PHI[i].values())\n uniform_dist = (1.0 / len(INTERFACE_LEVEL_ACTIONS)) * np.ones(len(INTERFACE_LEVEL_ACTIONS))\n blended_dist = (1 - PHM_GIVEN_PHI_NOISE) * delta_dist + PHM_GIVEN_PHI_NOISE * uniform_dist # np.array\n for index, j in enumerate(INTERFACE_LEVEL_ACTIONS):\n P_PHM_GIVEN_PHI[i][j] = blended_dist[index]","def _generate_prior(self, text_lengths, feats_lengths,\n w=1) -> paddle.Tensor:\n B = len(text_lengths)\n T_text = text_lengths.max()\n T_feats = feats_lengths.max()\n\n bb_prior = paddle.full((B, T_feats, T_text), fill_value=-np.inf)\n for bidx in range(B):\n T = feats_lengths[bidx].item()\n N = text_lengths[bidx].item()\n\n key = str(T) + ',' + str(N)\n if self.cache_prior and key in self._cache:\n prob = self._cache[key]\n else:\n alpha = w * np.arange(1, T + 1, dtype=float) # (T,)\n beta = w * np.array([T - t + 1 for t in alpha])\n k = np.arange(N)\n batched_k = k[..., None] # (N,1)\n prob = betabinom.pmf(batched_k, N, alpha, beta) # (N,T)\n\n # store cache\n if self.cache_prior and key not in self._cache:\n self._cache[key] = prob\n\n prob = paddle.to_tensor(\n prob, place=text_lengths.place, dtype=\"float32\").transpose(\n (1, 0)) # -> (T,N)\n bb_prior[bidx, :T, :N] = prob\n\n return bb_prior","def build_bigram_model(data):\n\n bigrams,unigrams = get_counts(data)\n model = {}\n for bigram in bigrams:\n #unigram count of first member of bigram\n uni_count=unigrams[bigram[0]]\n bi_count=bigrams[bigram]\n\n #probability is bigram count divided by unigram count\n model[bigram]=(bi_count/float(uni_count))\n return model","def compute_probabilities():\n global total_spam_words, total_ham_words\n total_words = total_spam_words+total_ham_words\n unique_words = len(all_dict)\n print(\"Training Set Description: \")\n len_ham = len(ham_file_list)\n len_spam = len(spam_file_list)\n print(\"SPAM EMAILS: \",len_spam)\n print(\"HAM EMAILS: \",len_ham)\n print(\"Total words: \",total_words)\n print(\"Training...\")\n \n spam_probability = math.log((len_spam)/(len_spam+len_ham))\n ham_probability = math.log((len_ham)/(len_spam+len_ham))\n \n \n \n output_file = open(\"nbmodel.txt\", \"w+\", encoding=\"latin-1\")\n output_file.write(\"model_params \"+str(spam_probability)+\" \"+str(ham_probability)+\"\\n\")\n \n nbmodel = {}\n nbmodel[\"model_params\"] = (spam_probability,ham_probability)\n for word in all_dict.keys():\n spam_count = 1\n if word in spam_dict:\n spam_count+= spam_dict[word]\n \n word_spam_probability = math.log(spam_count / (total_spam_words+unique_words))\n \n ham_count = 1\n if word in ham_dict:\n ham_count+= ham_dict[word]\n \n word_ham_probability = math.log(ham_count / (total_ham_words+unique_words))\n \n output_file.write(word+\" \"+str(word_spam_probability)+\" \"+str(word_ham_probability)+\"\\n\")\n nbmodel[word] = (word_spam_probability, word_ham_probability) \n \n print(\"nbmodel.txt generated successfully...\")\n print(\"SPAM Probability: \",spam_probability)\n print(\"HAM Probability: \",ham_probability)\n output_file.close()","def create(window_size=3,\r\n input_dimension=9,\r\n output_vocabsize=8,\r\n n_quadratic_filters=2,\r\n token_representation_size=5,\r\n concatenated_representation_size=7,\r\n lr=0.01,\r\n seed=123,\r\n noise_level=0.2,\r\n qfilter_relscale=0.1,\r\n compile_mode=None):\r\n activation_function = T.tanh\r\n\r\n architecture = ConvolutionalMLP( \\\r\n window_size=window_size,\r\n n_quadratic_filters=n_quadratic_filters,\r\n activation_function=activation_function,\r\n reconstruction_cost_function=quadratic,\r\n tie_weights=False\r\n )\r\n\r\n backup = config.warn.sum_div_dimshuffle_bug\r\n config.warn.sum_div_dimshuffle_bug = False\r\n try:\r\n model = architecture.make(input_size=input_dimension,\r\n input_representation_size=token_representation_size, hidden_representation_size=concatenated_representation_size, output_size=output_vocabsize, lr=lr, seed=seed, noise_level=noise_level, qfilter_relscale=qfilter_relscale, mode=compile_mode)\r\n finally:\r\n config.warn.sum_div_dimshuffle_bug = backup\r\n return model","def generate_initial_embs(emb_type):\n def _get_emb_avg(g, lang):\n \"\"\"Compute the embedding of g as the average of its word embeddings\n :param g: the input genre\n :param lang: language\n :return: the embedding and if all words of this genre are known\n \"\"\"\n emb = np.zeros(emb_dims[lang])\n known_words_count = 0\n words = g.split()\n for w in words:\n if w in models[lang]:\n emb += models[lang][w]\n known_words_count += 1\n emb /= len(words)\n return emb, known_words_count > 0\n\n def _get_emb_wavg(g, lang, a=0.001):\n \"\"\"Compute the embeddings of g with a sentence embedding algorithm (average weighted by the word estimated frequencies)\n :param g: the input genre\n :param lang: language\n :param a: a model hyper-parameter (see Arora et al. in the paper)\n :return: the embedding and if all words of this genre are known\n \"\"\"\n emb = np.zeros(emb_dims[lang])\n known_words_count = 0\n words = g.split()\n for w in words:\n if w in models[lang]:\n emb += a / (a + word_freqs[lang][w]) * models[lang][w]\n known_words_count += 1\n emb /= len(words)\n return emb, known_words_count > 0\n\n def _remove_pc(df_embs, npc=1):\n \"\"\"Remove the pc (see Arora at el. in the paper)\n :param df_embs: the input embeddings\n :return: the normalized embeddings\n \"\"\"\n pc = _compute_pc(df_embs, npc)\n if npc == 1:\n df_embs_out = df_embs - df_embs.dot(pc.transpose()) * pc\n else:\n df_embs_out = df_embs - df_embs.dot(pc.transpose()).dot(pc)\n return df_embs_out\n\n def _compute_pc(df_embs, npc=1):\n \"\"\"Compute the pc (see Arora at el. in the paper)\n :param df_embs: the input embeddings\n :return: the principal component\n \"\"\"\n svd = TruncatedSVD(n_components=npc, n_iter=7, random_state=0)\n svd.fit(df_embs)\n return svd.components_\n\n embs = {}\n known = {}\n for g in G.nodes:\n lang = g[:2]\n norm_g = TagManager.normalize_tag_wtokenization(g, tries[lang], prefixed=True)\n if emb_type == 'avg':\n embs[g], known[g] = _get_emb_avg(norm_g, lang)\n else:\n embs[g], known[g] = _get_emb_wavg(norm_g, lang)\n\n embs = pd.DataFrame(embs).T # the embeddings are columns\n if emb_type == 'sif': # the algorithm imposes a normalization\n norm_embs = _remove_pc(embs.to_numpy())\n embs = pd.DataFrame(norm_embs, columns=embs.columns, index=embs.index)\n return embs, known","def build(self, input_shape):\n data_shape, cond_shape = input_shape\n self.z_dim = cond_shape[1] # z_dim\n self.channels = data_shape[-1]\n self.time_frames = data_shape[2]\n if self.type_gammas_betas == \"simple\":\n shape_gammas_betas = [1, self.z_dim, 1]\n if self.type_gammas_betas == \"complex\":\n shape_gammas_betas = [self.channels, self.z_dim, 1]\n if self.in_freq:\n shape_gammas_betas[-1] = data_shape[1]\n\n self.gammas = self.add_weight(\n shape=shape_gammas_betas,\n trainable=True,\n name=\"gammas\",\n initializer=\"random_normal\",\n )\n self.betas = self.add_weight(\n shape=shape_gammas_betas,\n trainable=True,\n name=\"betas\",\n initializer=\"random_normal\",\n )","def _model(self):\n common_scale = self.edp_par['common_scale'].value\n model = self.F_trans() * self.F_cont()\n # get F(h=1,k=0), which is used for normalization \n # common_scale is a common scaling factor => F(h=1,k=0) = 100*common_scale\n F_10 = model[(self.h==1)&(self.k==0)]\n model = model / np.absolute(F_10) * 100 * common_scale\n return model","def test_class():\n riskfree = .03\n maturity = 30/365\n moneyness = np.linspace(-.04, .04, 10)\n premium = np.ones_like(moneyness) * .05\n call = True\n data = {'riskfree': riskfree, 'maturity': maturity,\n 'moneyness': moneyness, 'call': call, 'premium': premium}\n\n sigma = .13\n bsm = BSmodel(sigma, data)\n\n weights = [.63]\n means = [-.01, .09]\n stds = [.16, .05]\n param = weights + means + stds\n mbs = MBSmodel(param, data)\n\n param_a, param_p, param_c = 4, 1.5, -.05\n gb2 = GB2model([param_a, param_p, param_c], data)\n print(gb2.get_pnames())\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.density(moneyness), label=model.get_name())\n plt.legend()\n plt.show()\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.premium(), label=model.get_name())\n plt.legend()\n plt.show()\n\n plt.figure()\n for model in [bsm, mbs, gb2]:\n plt.plot(moneyness, model.impvol(), label=model.get_name())\n plt.legend()\n plt.show()\n\n print('BS objective function = %.4f' % bsm.objective(sigma))\n print('GB2 objective function = %.4f'\n % gb2.objective([param_a, param_p, param_c]))","def build_model(cls, args, task):\n global PAD_IDX, EOS_IDX\n # make sure all arguments are present in older models\n w2v_lm_architecture2(args)\n\n if not hasattr(args, \"max_source_positions\"):\n args.max_source_positions = 2048\n if not hasattr(args, \"max_target_positions\"):\n args.max_target_positions = 2048\n\n tgt_dict = task.target_dictionary\n PAD_IDX = tgt_dict.pad()\n EOS_IDX = tgt_dict.eos()\n\n encoder = cls.build_encoder(args)\n assigner = cls.build_assigner(args, encoder.d)\n lm = cls.build_lm(args, task)\n\n return cls(args, encoder, assigner, lm)","def build_model(cls, args):\n base_architecture(args) \n return StyleGANGeneratorPretrain(args)","def __init__(self, in_features, out_features):\n \n ########################\n # PUT YOUR CODE HERE #\n #######################\n self.params = {'weight': np.random.normal(loc = 0, scale=0.0001, size=(out_features,in_features)),\\\n 'bias': np.zeros((1, out_features))}\n \n self.grads = {'weight': np.zeros((out_features,in_features)),\\\n 'bias': np.zeros((1, out_features))}\n ########################\n # END OF YOUR CODE #\n #######################","def __init__(self):\n super(MLE, self).__init__()\n\n # Metadata\n self.appliance = None\n self.stats = []\n self.units = None\n self.resistive = False\n self.thDelta = 0\n self.thLikelihood = 0\n self.sample_period = None\n self.sampling_method = None\n # FEATURES:\n self.onpower = {'name': 'gmm', 'model': mixture.GMM(n_components=1)}\n self.offpower = {'name': 'gmm', 'model': mixture.GMM(n_components=1)}\n self.duration = {'name': 'poisson', 'model': poisson(0)}\n\n # Trainings:\n self.onpower_train = pd.DataFrame(columns=['onpower'])\n self.offpower_train = pd.DataFrame(columns=['offpower'])\n self.duration_train = pd.DataFrame(columns=['duration'])\n\n # Constrains\n self.powerNoise = 0 # Background noise in the main\n self.powerPair = 0 # Max diff between onpower and offpower\n self.timeWindow = 0 # To avoid high computation","def bayesian_model(y, x, data=None, correlation=False, family=\"Normal\", robust = True, samples = 1000, plot_posterior=True, plot_regression=True, plot_samples = \"default\", print_summary=True, alpha = 0.05):\n\n print(\"Starting Bayesian estimation...\")\n from theano import config\n config.warn.sum_div_dimshuffle_bug = False\n import pymc3\n\n\n\n y_name = \"y\"\n x_name = \"x\"\n\n if isinstance(y,str):\n y_name = y\n try:\n y = data[y]\n except:\n pass\n if isinstance(x,str):\n x_name = x\n try:\n x = data[x]\n except:\n pass\n\n\n if correlation == True:\n #Center and scale\n x = x - np.mean(x)\n x = x / np.std(x)\n\n y = y - np.mean(y)\n y = y / np.std(y)\n\n\n\n\n data = {y_name:y,\n x_name:x}\n formula = y_name + ' ~ ' + x_name\n\n\n\n\n\n\n\n\n\n\n with pymc3.Model() as model: # model specifications in PyMC3 are wrapped in a with-statement\n if family == \"Normal\":\n family = pymc3.glm.families.Normal()\n if robust == True:\n family = pymc3.glm.families.StudentT()\n\n pymc3.glm.glm(formula, data, family=family)\n start = pymc3.find_MAP()\n step = pymc3.NUTS(scaling=start) # Instantiate MCMC sampling algorithm\n trace = pymc3.sample(samples, step, progressbar=True) # draw 2000 posterior samples using NUTS sampling\n\n# trace = trace[int(samples/4):]\n #PLOT POSTERIOR DISTRIBUTION\n if plot_posterior == True:\n pymc3.traceplot(trace)\n\n #PLOT LINES\n if plot_regression == True:\n plot_data= []\n plot_data.append(go.Scatter(x=x,\n y=y,\n mode = 'markers'))\n\n\n\n if plot_samples == \"default\":\n if len(trace) > 100:\n plot_samples = 100\n samples_range = np.random.randint(0, len(trace), plot_samples)\n else:\n plot_samples = samples\n samples_range = range(len(trace))\n else:\n samples_range = np.random.randint(0, len(trace), plot_samples)\n\n\n\n for i in samples_range:\n print(i)\n plot_data.append(go.Scatter(x=x,\n y=trace[i]['Intercept'] + trace[i]['x'] * x,\n mode = 'lines',\n opacity=0.25,\n line = {\"color\":\"grey\",\n \"width\":5}))\n layout = go.Layout(showlegend=False)\n figure = go.Figure(data = plot_data,layout=layout)\n py.plot(figure)\n\n if print_summary == True:\n print(pymc3.summary(trace,alpha=alpha))\n return(trace)","def __init__(self, in_features, out_features):\n \n ########################\n # PUT YOUR CODE HERE #\n #######################\n\n\n self.params = {'weight': 0.0001 * np.random.randn(out_features, in_features), 'bias': np.zeros((out_features, 1))}\n self.grads = {'weight': np.zeros((out_features, in_features)), 'bias': np.zeros((out_features, 1))}\n\n\n\n ########################\n # END OF YOUR CODE #\n #######################","def __init__(self, params):\n super().__init__(params)\n p = self.params\n assert p.input_dim, f'input_dim is {p.input_dim}'\n assert p.hidden_dim, f'hidden_dim is {p.hidden_dim}'\n assert p.num_heads > 0, f'num_heads is {p.num_heads}'\n # if proj_tpl does not have dim_per_head set, set it\n if p.proj_tpl.dim_per_head == 0:\n p.proj_tpl.dim_per_head = self.dim_per_head\n\n if p.device_mesh is not None:\n assert p.weight_split_dims_mapping is not None\n assert p.activation_split_dims_mapping is not None\n\n if isinstance(p.weight_split_dims_mapping, dict):\n qkv_weight_split_dims_mapping = p.weight_split_dims_mapping['qkv']\n post_weight_split_dims_mapping = p.weight_split_dims_mapping['post']\n else:\n qkv_weight_split_dims_mapping = p.weight_split_dims_mapping\n post_weight_split_dims_mapping = p.weight_split_dims_mapping\n\n def ProjectInput(input_dim):\n return p.proj_tpl.Copy().Set(\n input_dim=input_dim,\n num_heads=p.num_heads,\n use_bias=p.use_bias,\n device_mesh=p.device_mesh,\n weight_split_dims_mapping=qkv_weight_split_dims_mapping,\n make_output_proj_no_op=False)\n\n def ProjectInputOneStep(input_dim):\n return p.proj_tpl.Copy().Set(\n input_dim=input_dim,\n num_heads=p.num_heads,\n dim_per_head=self.dim_per_head * 3,\n use_bias=p.use_bias,\n device_mesh=p.device_mesh,\n weight_split_dims_mapping=qkv_weight_split_dims_mapping,\n make_output_proj_no_op=False,\n )\n\n if isinstance(p.input_dim, dict):\n key_input_dim = p.input_dim['key']\n value_input_dim = p.input_dim['value']\n query_input_dim = p.input_dim['query']\n assert key_input_dim, f'key_input_dim is {key_input_dim}'\n assert query_input_dim, f'query_input_dim is {query_input_dim}'\n else:\n key_input_dim = p.input_dim\n value_input_dim = p.input_dim\n query_input_dim = p.input_dim\n\n if p.enable_value_proj and p.enable_qkv_proj_in_onestep:\n self.CreateChild('qkv', ProjectInputOneStep(key_input_dim))\n else:\n self.CreateChild('key', ProjectInput(key_input_dim))\n self.CreateChild('query', ProjectInput(query_input_dim))\n if p.enable_value_proj:\n assert value_input_dim, f'value_input_dim is {value_input_dim}'\n self.CreateChild('value', ProjectInput(value_input_dim))\n if p.enable_query_scale and p.enable_per_dim_scale:\n self.CreateChild(\n 'per_dim_scale',\n PerDimScaleLayer.Params().Set(dim=p.proj_tpl.dim_per_head))\n self.CreateChild('atten_dropout',\n p.dropout_tpl.Set(keep_prob=1.0 - p.atten_dropout_prob))\n # Setting is_output_projection=True to set the projection direction\n # from hidden dim to input dim. Output projection follows query_input_dim.\n self.CreateChild(\n 'post',\n p.proj_tpl.Copy().Set(\n input_dim=p.output_dim or query_input_dim,\n num_heads=p.num_heads,\n is_output_projection=True,\n use_bias=p.use_bias,\n device_mesh=p.device_mesh,\n weight_split_dims_mapping=post_weight_split_dims_mapping))\n\n if p.rope_tpl:\n assert issubclass(p.rope_tpl.cls, layers.RotaryPositionalEmbeddingLayer)\n rope_p = p.rope_tpl.Copy()\n if rope_p.embedding_dim == 0:\n rope_p.embedding_dim = self.dim_per_head\n self.CreateChild('rope', rope_p)\n\n if p.attn_add_memory:\n assert p.memory_tpl is not None\n self.CreateChild(\n 'lsh_mem',\n p.memory_tpl.Copy().Set(\n input_dim=self.dim_per_head,\n output_dim=self.dim_per_head,\n name='attn_lsh_mem'))\n if p.use_scale_invariant_atten:\n assert not (p.enable_scaling_code_motion or p.atten_extra_logit)","def __init__(self, input_dim, hidden_dim, output_dim, epsilon):\n \n self.W1 = np.random.randn(input_dim, hidden_dim) / np.sqrt(input_dim)\n self.W2 = np.random.randn(hidden_dim,output_dim) / np.sqrt(hidden_dim)\n self.b1 = np.zeros((1, hidden_dim))\n self.b2 = np.zeros((1, output_dim))\n self.epsilon = epsilon","def create_model(self):\n try:\n self.model = PPO2.load(self.save_path)\n self.model.set_env(self.env)\n print(\"Loading of the latest model successful!\")\n except:\n print(\"Creating new model...\")\n self.model = PPO2(CnnPolicy, self.env, verbose=1)","def beam_search_with_heuristics(model, inputs, beam_size, max_steps, from_cond=True):\n orig_inputs = inputs['orig_inputs'][0]\n #inference_state, next_choices = model.inference(inputs, orig_inputs.db)\n inference_state, next_choices = model(inputs, db=orig_inputs.db, is_train=False)\n beam = [Hypothesis4Filtering(inference_state, next_choices)]\n\n cached_finished_seqs = [] # cache filtered trajectories\n beam_prefix = beam\n while True:\n # search prefixes with beam search\n prefixes2fill_from = []\n for step in range(max_steps):\n if len(prefixes2fill_from) >= beam_size:\n break\n\n candidates = []\n for hyp in beam_prefix:\n if hyp.inference_state.cur_item.state == hyp.inference_state.State.CHILDREN_APPLY \\\n and hyp.inference_state.cur_item.node_type == \"from\":\n # 仅差 from 未填充，单独保存，后续逻辑专门处理\n prefixes2fill_from.append(hyp)\n else:\n candidates += [(hyp, choice, choice_score.numpy().item(), hyp.score + choice_score.numpy().item())\n for choice, choice_score in hyp.next_choices]\n # 排序并限定 beam_size 个候选（减去待填充 from 的候选）\n candidates.sort(key=operator.itemgetter(3), reverse=True)\n candidates = candidates[:beam_size - len(prefixes2fill_from)]\n\n # Create the new hypotheses from the expansions\n beam_prefix = []\n for hyp, choice, choice_score, cum_score in candidates:\n inference_state = hyp.inference_state.clone()\n\n # cache column choice\n column_history = hyp.column_history[:]\n if hyp.inference_state.cur_item.state == hyp.inference_state.State.POINTER_APPLY and \\\n hyp.inference_state.cur_item.node_type == \"column\":\n column_history = column_history + [choice]\n\n # beam 内的每个候选执行一次 step()，获取下一步 choices\n next_choices = inference_state.step(choice)\n assert next_choices is not None\n beam_prefix.append(\n Hypothesis4Filtering(inference_state, next_choices, cum_score,\n hyp.choice_history + [choice],\n hyp.score_history + [choice_score],\n column_history))\n\n prefixes2fill_from.sort(key=operator.attrgetter('score'), reverse=True)\n # assert len(prefixes) == beam_size\n\n # emuerating \n beam_from = prefixes2fill_from\n max_size = 6\n unfiltered_finished = []\n prefixes_unfinished = []\n for step in range(max_steps):\n if len(unfiltered_finished) + len(prefixes_unfinished) > max_size:\n break\n\n candidates = []\n for hyp in beam_from:\n if step > 0 and hyp.inference_state.cur_item.state == hyp.inference_state.State.CHILDREN_APPLY \\\n and hyp.inference_state.cur_item.node_type == \"from\":\n prefixes_unfinished.append(hyp)\n else:\n candidates += [(hyp, choice, choice_score.numpy().item(),\n hyp.score + choice_score.numpy().item())\n for choice, choice_score in hyp.next_choices]\n candidates.sort(key=operator.itemgetter(3), reverse=True)\n candidates = candidates[:max_size - len(prefixes_unfinished)]\n\n beam_from = []\n for hyp, choice, choice_score, cum_score in candidates:\n inference_state = hyp.inference_state.clone()\n\n # cache table choice\n table_history = hyp.table_history[:]\n key_column_history = hyp.key_column_history[:]\n if hyp.inference_state.cur_item.state == hyp.inference_state.State.POINTER_APPLY:\n if hyp.inference_state.cur_item.node_type == \"table\":\n table_history = table_history + [choice]\n elif hyp.inference_state.cur_item.node_type == \"column\":\n key_column_history = key_column_history + [choice]\n\n next_choices = inference_state.step(choice)\n if next_choices is None:\n unfiltered_finished.append(Hypothesis4Filtering(\n inference_state,\n None,\n cum_score,\n hyp.choice_history + [choice],\n hyp.score_history + [choice_score],\n hyp.column_history, table_history,\n key_column_history))\n else:\n beam_from.append(\n Hypothesis4Filtering(inference_state, next_choices, cum_score,\n hyp.choice_history + [choice],\n hyp.score_history + [choice_score],\n hyp.column_history, table_history,\n key_column_history))\n # [END] for step in range(max_steps)\n\n unfiltered_finished.sort(key=operator.attrgetter('score'), reverse=True)\n\n # filtering\n filtered_finished = []\n for hyp in unfiltered_finished:\n mentioned_column_ids = set(hyp.column_history)\n mentioned_key_column_ids = set(hyp.key_column_history)\n mentioned_table_ids = set(hyp.table_history)\n\n # duplicate tables\n if len(mentioned_table_ids) != len(hyp.table_history):\n continue\n\n # the foreign key should be correctly used\n # NOTE: the new version does not predict conditions in FROM clause anymore\n if from_cond:\n covered_tables = set()\n must_include_key_columns = set()\n candidate_table_ids = sorted(mentioned_table_ids)\n start_table_id = candidate_table_ids[0]\n for table_id in candidate_table_ids[1:]:\n if table_id in covered_tables:\n continue\n try:\n path = nx.shortest_path(\n orig_inputs.db.foreign_key_graph, source=start_table_id, target=table_id)\n except (nx.NetworkXNoPath, nx.NodeNotFound):\n covered_tables.add(table_id)\n continue\n\n for source_table_id, target_table_id in zip(path, path[1:]):\n if target_table_id in covered_tables:\n continue\n if target_table_id not in mentioned_table_ids:\n continue\n col1, col2 = orig_inputs.db.foreign_key_graph[source_table_id][target_table_id]['columns']\n must_include_key_columns.add(col1)\n must_include_key_columns.add(col2)\n if not must_include_key_columns == mentioned_key_column_ids:\n continue\n\n # tables whose columns are mentioned should also exist\n must_table_ids = set()\n for col in mentioned_column_ids:\n tab_ = orig_inputs.db.columns[col].table\n if tab_ is not None:\n must_table_ids.add(tab_.id)\n if not must_table_ids.issubset(mentioned_table_ids):\n continue\n\n filtered_finished.append(hyp)\n\n filtered_finished.sort(key=operator.attrgetter('score'), reverse=True)\n # filtered.sort(key=lambda x: x.score / len(x.choice_history), reverse=True)\n prefixes_unfinished.sort(key=operator.attrgetter('score'), reverse=True)\n # new_prefixes.sort(key=lambda x: x.score / len(x.choice_history), reverse=True)\n\n prefixes_, filtered_ = merge_beams(prefixes_unfinished, filtered_finished, beam_size)\n\n if filtered_:\n cached_finished_seqs = cached_finished_seqs + filtered_\n cached_finished_seqs.sort(key=operator.attrgetter('score'), reverse=True)\n\n if prefixes_ and len(prefixes_[0].choice_history) < 200:\n beam_prefix = prefixes_\n for hyp in beam_prefix:\n hyp.table_history = []\n hyp.column_history = []\n hyp.key_column_history = []\n elif cached_finished_seqs:\n return cached_finished_seqs[:beam_size]\n else:\n return unfiltered_finished[:beam_size]","def _create_model(self):\n config = {\n \"input_features\": self.input_features,\n \"output_features\": self.output_features,\n \"combiner\": {\"type\": \"concat\", \"output_size\": 14},\n TRAINER: {\"epochs\": 2, BATCH_SIZE: 128},\n }\n return LudwigModel(config, logging_level=logging.WARN)","def fit(self, X, max_iter=30, epsilon=10e-1, rand_seed = False):\r\n \r\n if rand_seed:\r\n np.random.seed(rand_seed)\r\n \r\n # train the HMM model using the Baum-Welch algorithm\r\n # a specific instance of the expectation-maximization algorithm\r\n\r\n N = len(X)\r\n D = X[0].shape[1] # assume each x is organized (T, D)\r\n\r\n self.pi = np.ones(self.M) / self.M # initial state distribution\r\n self.A = self.random_normalised(self.M, self.M) # state transition matrix\r\n self.R = np.ones((self.M, self.K)) / self.K # mixture proportions\r\n print(\"initial A:\", self.A)\r\n print(\"initial R:\", self.R)\r\n self.mu = np.zeros((self.M, self.K, D))\r\n for i in range(self.M):\r\n for k in range(self.K):\r\n random_idx = np.random.choice(N)\r\n x = X[random_idx]\r\n random_time_idx = np.random.choice(len(x))\r\n self.mu[i,k] = x[random_time_idx]\r\n self.sigma = np.zeros((self.M, self.K, D, D))\r\n for j in range(self.M):\r\n for k in range(self.K):\r\n self.sigma[j,k] = np.eye(D)\r\n\r\n self.costs = []\r\n for epoch in trange(max_iter):\r\n alphas = []\r\n betas = []\r\n gammas = []\r\n Bs = []\r\n # components = []\r\n P = np.zeros(N)\r\n\r\n for n in range(N):\r\n x = X[n]\r\n T = len(x)\r\n\r\n # calculate B so we can lookup when updating alpha and beta\r\n B = np.zeros((self.M, T))\r\n component = np.zeros((self.M, self.K, T)) # we'll need these later\r\n for j in range(self.M):\r\n for t in range(T):\r\n for k in range(self.K):\r\n p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k])\r\n component[j,k,t] = p\r\n B[j,t] += p\r\n Bs.append(B)\r\n\r\n alpha = np.zeros((T, self.M))\r\n alpha[0] = self.pi*B[:,0]\r\n for t in range(1, T):\r\n alpha[t] = alpha[t-1].dot(self.A) * B[:,t]\r\n P[n] = alpha[-1].sum()\r\n assert(P[n] <= 1)\r\n alphas.append(alpha)\r\n\r\n beta = np.zeros((T, self.M))\r\n beta[-1] = 1\r\n for t in range(T - 2, -1, -1):\r\n beta[t] = self.A.dot(B[:,t+1] * beta[t+1])\r\n betas.append(beta)\r\n\r\n # update for Gaussians\r\n gamma = np.zeros((T, self.M, self.K))\r\n for t in range(T):\r\n alphabeta = (alphas[n][t,:] * betas[n][t,:]).sum()\r\n for j in range(self.M):\r\n factor = alphas[n][t,j] * betas[n][t,j] / alphabeta\r\n # mixture_j = component[j,:,t].sum()\r\n for k in range(self.K):\r\n gamma[t,j,k] = factor * component[j,k,t] / B[j,t]\r\n gammas.append(gamma)\r\n\r\n cost = np.log(P).sum()\r\n self.costs.append(cost)\r\n\r\n # now re-estimate pi, A, R, mu, sigma\r\n self.pi = np.array([(alphas[n][0] * betas[n][0])/P[n] for n in range(N)]).sum() / N\r\n\r\n a_den = np.zeros((self.M, 1))\r\n a_num = 0\r\n r_num = np.zeros((self.M, self.K))\r\n r_den = np.zeros(self.M)\r\n mu_num = np.zeros((self.M, self.K, D))\r\n sigma_num = np.zeros((self.M, self.K, D, D))\r\n for n in range(N):\r\n x = X[n]\r\n T = len(x)\r\n B = Bs[n]\r\n gamma = gammas[n]\r\n\r\n # denominator for A\r\n a_den += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T / P[n]\r\n\r\n # numerator for A\r\n a_num_n = np.zeros((self.M, self.M))\r\n for i in range(self.M):\r\n for j in range(self.M):\r\n for t in range(T-1):\r\n a_num_n[i,j] += alphas[n][t,i] * self.A[i,j] * B[j,t+1] * betas[n][t+1,j]\r\n a_num += a_num_n / P[n]\r\n\r\n\r\n # update mixture components\r\n r_num_n = np.zeros((self.M, self.K))\r\n r_den_n = np.zeros(self.M)\r\n for j in range(self.M):\r\n for k in range(self.K):\r\n for t in range(T):\r\n r_num_n[j,k] += gamma[t,j,k]\r\n r_den_n[j] += gamma[t,j,k]\r\n r_num += r_num_n / P[n]\r\n r_den += r_den_n / P[n]\r\n\r\n mu_num_n = np.zeros((self.M, self.K, D))\r\n sigma_num_n = np.zeros((self.M, self.K, D, D))\r\n for j in range(self.M):\r\n for k in range(self.K):\r\n for t in range(T):\r\n # update means\r\n mu_num_n[j,k] += gamma[t,j,k] * x[t]\r\n\r\n # update covariances\r\n sigma_num_n[j,k] += gamma[t,j,k] * np.outer(x[t] - self.mu[j,k], x[t] - self.mu[j,k])\r\n mu_num += mu_num_n / P[n]\r\n sigma_num += sigma_num_n / P[n]\r\n\r\n self.A = a_num / a_den\r\n assert(np.all(self.A <= 1))\r\n\r\n # update R, mu, sigma\r\n for j in range(self.M):\r\n for k in range(self.K):\r\n self.R[j,k] = r_num[j,k] / r_den[j]\r\n self.mu[j,k] = mu_num[j,k] / r_num[j,k]\r\n self.sigma[j,k] = sigma_num[j,k] / r_num[j,k]\r\n\r\n print(\"A:\", self.A)\r\n print(\"mu:\", self.mu)\r\n print(\"sigma:\", self.sigma)\r\n print(\"R:\", self.R)\r\n print(\"pi:\", self.pi)","def _get_model_vectorized(self, bdf_filename):\n ext = os.path.splitext(bdf_filename)[1].lower()\n punch = False\n if ext == '.pch':\n punch = True\n\n self.model_type = 'nastran'\n log = self.log\n if ext == '.op2':\n from pyNastran.dev.bdf_vectorized2.op2_geom_vectorized import (\n OP2Geom as OP2Geom_)\n model = OP2Geom_(make_geom=True, debug=False, log=log,\n debug_file=None)\n model.clear_results()\n model.read_op2(op2_filename=bdf_filename)\n else: # read the bdf/punch\n from pyNastran.dev.bdf_vectorized2.bdf_vectorized import BDF as BDF_\n model = BDF_(log=log, debug=True)\n\n # static_elements.bdf\n #skip_cards = [\n #'CELAS1', 'CELAS2', 'CELAS3', 'CELAS4', 'PLOTEL', 'PARAM',\n #'CDAMP1', 'CDAMP2', 'CDAMP3', 'CDAMP4', 'CVISC',\n #'TABLEM1', 'TABLEM2', 'TABLEM3', 'TABLEM4',\n #'TABLED1', 'TABLED2', 'TABLED3', 'TABLED4',\n #'PLOAD', 'PLOAD1', 'PLOAD2', 'FORCE', 'PLOAD4', 'LOAD',\n #'SPCADD', 'MAT1', 'PSHEAR', 'PSHELL', 'PTUBE', 'PDAMP',\n #'SPC1', 'CONM2', 'PELAST', 'PBEND', 'PBEAM', 'PCOMP', 'PCOMPG', 'PBAR', 'PSOLID',\n #'PBUSH1D',\n #'EPOINT',\n #'CQUADR', 'CTRIAR', 'SPOINT', 'PROD', 'PELAS', 'PVISC',\n #'CQUAD8', 'CTRIA6',\n #]\n #model.disable_cards(skip_cards)\n model.read_bdf(bdf_filename,\n punch=punch, xref=False,\n validate=True)\n #print(list(key for key in model.card_count.keys() if key not in skip_cards))\n\n #xref_loads = False\n #xref_aero = len(model.caeros) > 0\n #model.cross_reference(\n #xref=True,\n #xref_nodes=True,\n #xref_elements=False,\n #xref_nodes_with_elements=False,\n #xref_properties=True,\n #xref_masses=True,\n #xref_materials=False,\n #xref_loads=xref_loads,\n #xref_constraints=False,\n #xref_optimization=False,\n #xref_aero=False,\n #xref_sets=False,\n #)\n return model","def build_model(self):\n input_pencil = tf.keras.Input((128,128,3))\n # generator's output\n gen_image = self.gan_generator.model(input_pencil)\n # generator's output\n x = self.gan_discriminator.model([input_pencil,gen_image])\n model = tf.keras.Model(input_pencil,[x,gen_image])\n # compiling the model\n model.compile(loss=['hinge', 'mae'], optimizer = self.optimizer,loss_weights=[1,100], metrics=['accuracy'])\n self.model = model","def build_model_docplex(resite, params: Dict):\n\n from docplex.mp.model import Model\n from resite.models.docplex_utils import minimize_deployed_capacity, capacity_bigger_than_existing, \\\n generation_bigger_than_load_proportion, create_generation_y_dict\n\n data = resite.data_dict\n load = data[\"load\"].values\n regions = resite.regions\n tech_points_tuples = list(resite.tech_points_tuples)\n time_slices = define_time_slices(params[\"time_resolution\"], resite.timestamps)\n\n model = Model()\n\n # - Parameters - #\n load_perc_per_region = dict(zip(regions, params[\"perc_per_region\"]))\n\n # - Variables - #\n # Portion of capacity at each location for each technology\n model.y = model.continuous_var_dict(keys=tech_points_tuples, lb=0., ub=1.,\n name=lambda k: 'y_%s_%s_%s' % (k[0], k[1], k[2]))\n # Create generation dictionary for building speed up\n generation_potential_df = data[\"cap_factor_df\"] * data[\"cap_potential_ds\"]\n region_generation_y_dict = \\\n create_generation_y_dict(model, regions, resite.tech_points_regions_ds, generation_potential_df)\n\n # - Constraints - #\n # Impose a certain percentage of the load to be covered over each time slice\n generation_bigger_than_load_proportion(model, region_generation_y_dict, load, regions, time_slices,\n load_perc_per_region)\n # Percentage of capacity installed must be bigger than existing percentage\n existing_cap_percentage_ds = data[\"existing_cap_ds\"].divide(data[\"cap_potential_ds\"])\n capacity_bigger_than_existing(model, existing_cap_percentage_ds, tech_points_tuples)\n\n # - Objective - #\n # Minimize the capacity that is deployed\n minimize_deployed_capacity(model, data[\"cap_potential_ds\"])\n\n resite.instance = model","def BMA_Predictive_Likelihood(df_train, df_test, iterations, burnin, p_1,\n l_share):\n\n # number of individual forecasts and number of periods\n K = df_test.shape[1]\n T = df_train.shape[0]\n\n # g-prior parameter\n c = float(K**3)\n\n # separate real value and individual forecasts\n y = df_train.iloc[:, 0]\n F = df_train.iloc[:, 1:]\n\n # separate training and the hold-out sample\n m_share = 1 - l_share\n y_star = y.iloc[:int(np.round(m_share*T))]\n y_tilde = y.iloc[int(np.round(m_share*T)):]\n F_star = F.iloc[:int(np.round(m_share*T)), :]\n F_tilde = F.iloc[int(np.round(m_share*T)):, :]\n\n # initializations\n models = np.full((iterations, K), fill_value=False, dtype=bool)\n theta_hats = []\n predictive_lik = np.full(iterations, fill_value=np.nan, dtype=float)\n\n # RJ-MCMC\n # initial state = null model\n # predictive likelihood for the initial state\n y_star_bar = np.mean(y_star)\n S_right = sum((y_star - y_star_bar)**2)/(c+1)\n Z_star = np.insert(F_star.iloc[:, models[0, :]].values, 0, 1, axis=1)\n Z_star_t = np.transpose(Z_star)\n Z_tilde = np.insert(F_tilde.iloc[:, models[0, :]].values, 0, 1, axis=1)\n Z_tilde_t = np.transpose(Z_tilde)\n theta_hat = np.dot(\n np.linalg.inv(np.dot(np.transpose(Z_star), Z_star)),\n np.dot(np.transpose(Z_star), y_star)\n )\n theta_hats += [theta_hat]\n gamma = (c/(c+1)) * theta_hat\n SSR = sum((y_star - np.dot(Z_star, theta_hat))**2)\n S_left = (c/(c+1))*SSR\n S = S_left + S_right\n\n # scale to first decimal place for numerical reasons\n # find scaling order\n S_rescaled = S\n scaling_factor = 1\n while (S_rescaled < 0.1):\n S_rescaled *= 10\n scaling_factor *= 10\n # rescale S (prevent overflow)\n S = S*scaling_factor\n\n lambda_star = ((c+1) / c) * np.dot(Z_star_t, Z_star)\n lambda_star_inv = np.linalg.inv(lambda_star)\n m_len = y_star.shape[0]\n l_len = y_tilde.shape[0]\n epsilon = y_tilde - np.dot(Z_tilde, gamma)\n epsilon_t = np.transpose(epsilon)\n mid = np.linalg.inv(\n np.eye(l_len) + np.dot(np.dot(Z_tilde, lambda_star_inv), Z_tilde_t)\n )\n term_1 = S**(m_len/2)\n term_2 = (np.linalg.det(lambda_star) / np.linalg.det(\n lambda_star + np.dot(Z_tilde_t, Z_tilde)))**(1/2)\n term_3 = (S + np.dot(np.dot(epsilon_t, mid), epsilon))**(-T/2)\n predictive_lik[0] = term_1 * term_2 * term_3\n\n # initialization of the proposal model\n m_star = np.full(K, fill_value=True, dtype=bool)\n\n # main iteration loop\n for i in range(0, iterations-1):\n\n # decide which move to perform\n epsilon_move = np.random.random()\n\n # move 1\n if epsilon_move <= p_1 or sum(models[0]) == 0:\n\n # drawn one of the forecasts from the model\n drawn_f_index = np.random.randint(0, K)\n\n # if it is in the model, drop it, if not, add it\n np.copyto(m_star, models[i, :])\n m_star[drawn_f_index] = ~models[i, drawn_f_index]\n\n # if there are too many variables (d.f. = 0), the change is revert\n if sum(m_star) > (m_len-2):\n m_star[drawn_f_index] = models[i, drawn_f_index]\n\n # move 2\n else:\n\n # drawn one of the forecasts from the model and one outside\n index_in = np.arange(K)[models[i, :]]\n index_out = np.arange(K)[~models[i, :]]\n drawn_f_index_in = np.random.choice(index_in)\n drawn_f_index_out = np.random.choice(index_out)\n\n # swap these two variables\n np.copyto(m_star, models[i, :])\n m_star[drawn_f_index_in] = ~m_star[drawn_f_index_in]\n m_star[drawn_f_index_out] = ~m_star[drawn_f_index_out]\n\n # calculate the probability of acceptance\n Z_star = np.insert(F_star.iloc[:, m_star].values, 0, 1, axis=1)\n Z_star_t = np.transpose(Z_star)\n Z_tilde = np.insert(F_tilde.iloc[:, m_star].values, 0, 1, axis=1)\n Z_tilde_t = np.transpose(Z_tilde)\n theta_hat = np.dot(\n np.linalg.inv(np.dot(np.transpose(Z_star), Z_star)),\n np.dot(np.transpose(Z_star), y_star)\n )\n gamma = (c/(c+1)) * theta_hat\n SSR = sum((y_star - np.dot(Z_star, theta_hat))**2)\n S_left = (c/(c+1))*SSR\n S = S_left + S_right\n\n # rescale S (prevent overflow)\n S = S*scaling_factor\n\n lambda_star = ((c+1) / c) * np.dot(Z_star_t, Z_star)\n lambda_star_inv = np.linalg.inv(lambda_star)\n epsilon = y_tilde - np.dot(Z_tilde, gamma)\n epsilon_t = np.transpose(epsilon)\n mid = np.linalg.inv(\n np.eye(l_len) + np.dot(np.dot(Z_tilde, lambda_star_inv), Z_tilde_t)\n )\n term_1 = S**(m_len/2)\n term_2 = (np.linalg.det(lambda_star) / np.linalg.det(\n lambda_star + np.dot(Z_tilde_t, Z_tilde)))**(1/2)\n term_3 = (S + np.dot(np.dot(epsilon_t, mid), epsilon))**(-T/2)\n predictive_lik_m_star = term_1 * term_2 * term_3\n alpha = min(1, predictive_lik_m_star/predictive_lik[i])\n\n # if accepted, move to m_star, else retain the same model\n epsilon_accept = np.random.random()\n\n if epsilon_accept <= alpha:\n np.copyto(models[i+1, :], m_star)\n predictive_lik[i+1] = predictive_lik_m_star\n theta_hats += [theta_hat]\n else:\n np.copyto(models[i+1, :], models[i, :])\n predictive_lik[i+1] = predictive_lik[i]\n theta_hats += [theta_hats[-1]]\n\n # discard the burnin draws\n models = models[burnin:, :]\n predictive_lik = predictive_lik[burnin:]\n theta_hats = theta_hats[burnin:]\n\n # unique models visited by the chain\n models_uni, index_uni = np.unique(models, axis=0, return_index=True)\n predictive_lik_uni = predictive_lik[index_uni]\n # theta_hats_uni = [theta_hats[i] for i in index_uni]\n theta_hats_uni = list(np.array(theta_hats)[index_uni])\n\n # model posterior probabilities\n predictive_lik_sum = sum(predictive_lik_uni)\n posterior_prob = predictive_lik_uni / predictive_lik_sum\n\n # generate prediction for testing set\n model_fcts = np.full(\n (df_test.shape[0], len(theta_hats_uni)),\n np.nan,\n dtype=float\n )\n\n # forecast from different models\n for j in range(model_fcts.shape[1]):\n\n model_fcts[:, j] = np.dot(\n np.insert(df_test.iloc[:, models_uni[j]].values, 0, 1, axis=1),\n theta_hats_uni[j]\n )\n\n pred = np.dot(model_fcts, posterior_prob)\n\n df_pred = pd.DataFrame(\n {\"BMA (Predictive Likelihood)\": pred},\n index=df_test.index)\n\n return df_pred","def build(self, input_fields, base_models, vocab_target):\n encoder_outputs = []\n encdec_bridges = []\n decoders = []\n target_modalities = []\n # prepare for decoding of each model\n for index, model in enumerate(base_models):\n with tf.variable_scope(\n GlobalNames.ENSEMBLE_VARNAME_PREFIX + str(index)):\n with tf.variable_scope(model.name):\n input_modality, target_modality = model._create_modalities()\n encoder = model._create_encoder()\n encoder_output = model._encode(\n encoder=encoder, input_modality=input_modality,\n input_fields=input_fields)\n bridge = model._create_bridge(encoder_output)\n decoder = model._create_decoder()\n vs_name = tf.get_variable_scope().name\n decoder.name = os.path.join(vs_name, decoder.name)\n target_modality.name = os.path.join(vs_name, target_modality.name)\n encoder_outputs.append(encoder_output)\n encdec_bridges.append(bridge)\n decoders.append(decoder)\n target_modalities.append(target_modality)\n\n helper = BeamFeedback(\n vocab=vocab_target,\n batch_size=tf.shape(input_fields[GlobalNames.PH_FEATURE_IDS_NAME])[0],\n maximum_labels_length=self._maximum_labels_length,\n beam_size=self._beam_size,\n alpha=self._length_penalty,\n ensemble_weight=self.get_ensemble_weights(len(base_models)))\n infer_status = dynamic_ensemble_decode(\n decoders=decoders,\n encoder_outputs=encoder_outputs,\n bridges=encdec_bridges,\n target_modalities=target_modalities,\n helper=helper)\n predict_out = dict()\n predict_out[\"predicted_ids\"] = infer_status.predicted_ids\n predict_out[\"sequence_lengths\"] = infer_status.lengths\n predict_out[\"beam_ids\"] = infer_status.beam_ids\n predict_out[\"log_probs\"] = infer_status.log_probs\n return predict_out","def mcmc(self,BHB):\n\n ndim = self.sampndim\n nwalkers = self.sampnwalk\n #startpoint = self.theta_ml\n startpoint = self.theta_guess\n pos = [startpoint + 1e-4*np.random.randn(ndim) for i in range(nwalkers)]\n\n sampler = emcee.EnsembleSampler(nwalkers, ndim, lnlikelihood.lnprob,\n args=(self.transinfo, self.lamlim1, self.lamlim2,\n self.wave_b, self.flux_b, self.err_b,\n self.wave_r, self.flux_r, self.err_r,\n self.velres))\n\n # Clear and run the production chain.\n print(\"Running MCMC...\")\n sampler.run_mcmc(pos, self.nsteps, rstate0=np.random.get_state())\n print(\"Done.\")\n\n pl.clf()\n fig, axes = pl.subplots(ndim, 1, sharex=True, figsize=(8, 9))\n\n for ind in range(0,ndim):\n \n axes[ind].plot(sampler.chain[:, :, ind].T, color=\"k\", alpha=0.4)\n axes[ind].yaxis.set_major_locator(MaxNLocator(5))\n axes[ind].axhline(startpoint[ind], color=\"#888888\", lw=2)\n # axes[ind].set_ylabel(\"$m$\")\n\n\n fig.tight_layout(h_pad=0.0)\n fig.savefig(\"line-time_\"+BHB+\"_\"+self.transname+\"_\"+str(self.lamlim1)+\".png\") # Edited for manual runs\n\n\n burnin = self.burnin\n samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))\n self.samples = samples\n\n # Compute the quantiles.\n theta_mcmc = list(map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),\n zip(*np.percentile(samples, [16, 50, 84],\n axis=0))))\n \n \n self.theta_percentiles = theta_mcmc\n print(\"\"\"MCMC result:\"\"\") \n for ind in range(0,ndim):\n print(\"\"\" par {0} = {1[0]} +{1[1]} -{1[2]}\"\"\".format(ind, theta_mcmc[ind]))","def __init__(self, n_trials, v0, n_options, probs, r_mag, l_mag, params, gamble, \\\n\t\tUCB=False, UCB_samplemean=False, gamma=0, anneal=False):\n\n\t\tsz = n_trials\n\t\tself.n_options = n_options\t\t\t\t\t# number of choices\n\t\tself.Q = np.zeros((sz+1,n_options)) +\tv0\t# critic\n\t\tself.alpha, self.beta = params\t\t\t\t# learning rate, softmax\n\t\tself.SM = np.zeros((sz, n_options))\t\t# Softmax values\n\t\tself.rho = np.zeros(sz)\t\t\t\t\t\t# DA at choice\n\t\tself.C = np.zeros(sz,dtype=np.int)\t\t\t# choice\n\t\tself.R = np.zeros(sz,dtype=np.int)\t\t\t# indicator variable of reward\n\t\tself.probs = probs\t\t\t\t\t\t\t# prob of reward for choice\n\t\tself.r_mag = r_mag\t\t\t\t\t\t\t# magnitude of reward for choice\n\t\tself.l_mag = l_mag\t\t\t\t\t\t\t# magnitude of loss for choice\n\t\tself.PE = np.zeros(sz)\t\t\t\t\t\t# choice PE\n\t\tself.idx = 0\t\t\t\t\t\t\t\t# idx of trial in state \n\t\tself.gamble = gamble\t\t\t\t\t\t# gamble paradigm?\n\t\tself.UCB = UCB\t\t\t\t\t\t\t\t# Use UCB for policy?\t\n\t\tself.UCB_samplemean = UCB_samplemean\t\t# Use sample mean instead of Q value\n\t\tself.gamma = gamma\t\t\t\t\t\t\t# Exploration parameter\t\n\t\tself.N = np.zeros((sz+1, n_options))\t\t# number of times action selected\n\t\tself.R_by_a = np.zeros((sz+1, n_options))\t# number of times rewarded for action\n\t\tself.Acts = np.zeros((sz+1,n_options))\t\t# act values","def custom_model():\n\t# initialize the model\n\t# load weights from path\n\t# returns model\n\tmodel = mlp.get_training_model()\n\tmodel.load_state_dict(torch.load(\"model_wt.pth\"))\n\treturn model","def __init__(self,\n good_lfp_data,\n # preprocessed_data,\n sampling_frequency=1000,\n n_shuffles=500,\n wanted_window=[1500, 4000],\n alpha=0.05,\n multitaper_time_halfbandwidth_product=1,\n multitaper_time_window_duration=0.3,\n multitaper_time_window_step=0.05,\n preprocess=True,\n ):\n #self.preprocessed_data = preprocessed_data\n #self.input_data = preprocessed_data.T[wanted_window[0]:wanted_window[1]]\n self.preprocess_flag = preprocess\n self.good_lfp_data = good_lfp_data\n self.sampling_frequency = sampling_frequency\n self.n_shuffles = n_shuffles\n self.wanted_window = wanted_window\n self.alpha = alpha\n self.multitaper_time_halfbandwidth_product = \\\n multitaper_time_halfbandwidth_product\n self.multitaper_time_window_duration = multitaper_time_window_duration\n self.multitaper_time_window_step = multitaper_time_window_step","def build_model_gurobipy(resite, params: Dict):\n\n from gurobipy import Model\n from resite.models.gurobipy_utils import minimize_deployed_capacity, capacity_bigger_than_existing, \\\n generation_bigger_than_load_proportion, create_generation_y_dict\n\n data = resite.data_dict\n load = data[\"load\"].values\n regions = resite.regions\n tech_points_tuples = list(resite.tech_points_tuples)\n time_slices = define_time_slices(params[\"time_resolution\"], resite.timestamps)\n\n model = Model()\n\n # - Parameters - #\n load_perc_per_region = dict(zip(regions, params[\"perc_per_region\"]))\n\n # - Variables - #\n # Portion of capacity at each location for each technology\n y = model.addVars(tech_points_tuples, lb=0., ub=1., name=lambda k: 'y_%s_%s_%s' % (k[0], k[1], k[2]))\n # Create generation dictionary for building speed up\n generation_potential_df = data[\"cap_factor_df\"] * data[\"cap_potential_ds\"]\n region_generation_y_dict = \\\n create_generation_y_dict(y, regions, resite.tech_points_regions_ds, generation_potential_df)\n\n # - Constraints - #\n # Impose a certain percentage of the load to be covered over each time slice\n generation_bigger_than_load_proportion(model, region_generation_y_dict, load, regions, time_slices,\n load_perc_per_region)\n # Percentage of capacity installed must be bigger than existing percentage\n existing_cap_percentage_ds = data[\"existing_cap_ds\"].divide(data[\"cap_potential_ds\"])\n capacity_bigger_than_existing(model, y, existing_cap_percentage_ds, tech_points_tuples)\n\n # - Objective - #\n # Minimize the capacity that is deployed\n obj = minimize_deployed_capacity(model, y, data[\"cap_potential_ds\"])\n\n resite.instance = model\n resite.y = y\n resite.obj = obj","def __init__(self, data, params):\n super(VBPR, self).__init__(data, params)\n self.embed_k = self.params.embed_k\n self.embed_d = self.params.embed_d\n self.learning_rate = self.params.lr\n\n self.directory_parameters = f'batch_{self.params.batch_size}' \\\n f'-D_{self.params.embed_d}' \\\n f'-K_{self.params.embed_k}' \\\n f'-lr_{self.params.lr}' \\\n f'-reg_{self.params.reg}'\n\n self.process_cnn_visual_features()\n\n # Initialize Model Parameters\n self.Bp = tf.Variable(\n self.initializer(shape=[self.dim_cnn_features, 1]), name='Bp', dtype=tf.float32)\n self.Tu = tf.Variable(\n self.initializer(shape=[self.num_users, self.embed_d]),\n name='Tu', dtype=tf.float32) # (users, low_embedding_size)\n self.F = tf.Variable(\n self.cnn_features,\n name='F', dtype=tf.float32, trainable=False)\n self.E = tf.Variable(\n self.initializer(shape=[self.dim_cnn_features, self.embed_d]),\n name='E', dtype=tf.float32) # (items, low_embedding_size)\n\n self.optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)\n self.saver_ckpt = tf.train.Checkpoint(optimizer=self.optimizer, model=self)","def _build_model(self, model):\n model = model(self.state_dim, n_actions=self.n_actions)\n model.compile(loss=self._huber_loss,\n optimizer=optimizers.Adam(lr=self.learning_rate))\n return model","def build_model(keep_prob):\n model=Sequential()\n #normalization\n model.add(Lambda(lambda x: x/127.5-1.0, input_shape=glob_image_shape))\n #convolutional layers\n model.add(Conv2D(24, 5, 5, activation='elu', subsample=(2, 2)))\n model.add(Conv2D(36, 5, 5, activation='elu', subsample=(2, 2)))\n model.add(Conv2D(48, 5, 5, activation='elu', subsample=(2, 2)))\n model.add(Conv2D(64, 3, 3, activation='elu'))\n model.add(Conv2D(64, 3, 3, activation='elu'))\n #drop out to prevent over fitting\n model.add(Dropout(keep_prob))\n model.add(Flatten())\n #fully connected layers\n model.add(Dense(100, activation='elu'))\n model.add(Dense(50, activation='elu'))\n model.add(Dense(10, activation='elu'))\n model.add(Dense(1))\n return model","def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):\n\n self.params = {}\n self.params['W1'] = weight_init_std * \\\n np.random.randn(input_size, hidden_size)\n self.params['b1'] = np.zeros(hidden_size)\n self.params['W2'] = weight_init_std * \\\n np.random.randn(hidden_size, output_size)\n self.params['b2'] = np.zeros(output_size)","def build_model(self):\n raise NotImplementedError","def setup_fixed_model():\n out = {}\n out['Q'] = Q\n out['X'] = X\n out['y'] = y\n\n return out","def fit(self, h_init):\n M = h_init.shape[0]\n\n cn_states = self.create_cn_states(M, 2, self.max_copy_number, self.max_copy_number_diff)\n cn_states = np.array([cn_states] * self.N)\n cn_states[:, :, 0, :] = self.normal_copies[:, np.newaxis, :]\n\n # Remap cn states\n cn_states = cn_states[self.seg_rev_remap, :, :, :]\n\n brk_states = self.create_brk_states(M, self.max_copy_number, self.max_copy_number_diff)\n\n self.model = remixt.bpmodel.RemixtModel(\n M,\n self.N1,\n self.num_breakpoints,\n self.normal_contamination,\n cn_states,\n brk_states,\n h_init,\n self.l1,\n self.x1[:, 2],\n self.x1[:, 0:2],\n self.is_telomere,\n self.breakpoint_idx,\n self.breakpoint_orient,\n self.transition_log_prob,\n self.divergence_weight,\n )\n\n self.model.total_likelihood_mask = self._total_likelihood_mask.astype(int)\n self.model.allele_likelihood_mask = self._allele_likelihood_mask.astype(int)\n\n if self.breakpoint_init is not None:\n p_breakpoint = np.ones((self.model.self.num_breakpoints, self.model.num_brk_states))\n brk_states = np.array(self.model.brk_states)\n\n for k, bp in enumerate(self.breakpoints):\n cn = self.breakpoint_init[bp]\n\n for s in range(self.model.num_brk_states):\n if np.all(cn == brk_states[s]):\n p_breakpoint[k, s] = 1000.\n\n p_breakpoint /= np.sum(p_breakpoint, axis=-1)[:, np.newaxis]\n\n self.model.p_breakpoint = p_breakpoint\n\n self.model.transition_model = self.transition_model\n\n if self.prev_elbo is None:\n self.prev_elbo = self.model.calculate_elbo()\n\n for i in range(self.num_em_iter):\n for j in range(self.num_update_iter):\n self.variational_update()\n\n if self.do_h_update:\n self.em_update_h()\n\n self.em_update_params()\n\n elbo = self.model.calculate_elbo()\n\n self.prev_elbo_diff = elbo - self.prev_elbo\n self.prev_elbo = elbo\n\n print ('[{}] completed iteration {}'.format(_gettime(), i))\n print ('[{}] elbo: {:.10f}'.format(_gettime(), self.prev_elbo))\n print ('[{}] elbo diff: {:.10f}'.format(_gettime(), self.prev_elbo_diff))\n print ('[{}] h = {}'.format(_gettime(), np.asarray(self.model.h)))\n for name, value in self.get_likelihood_param_values().items():\n print ('[{}] {} = {}'.format(_gettime(), name, value))","def build_model_from_inputs(self):\n if self.term_list is None:\n # no supplied token list -- use vocabulary of the training dataset\n # self.term_list = self.vocabulary\n # info(\"Setting bag dimension to {} from input vocabulary.\".format(len(self.term_list)))\n # will generate the vocabulary from the input\n pass\n info(f\"Building {self.name} model\")\n bagger = None\n if self.config.max_terms is not None:\n bagger = Bag(vocabulary=self.term_list, weighting=self.base_name, ngram_range=self.ngram_range, max_terms=self.config.max_terms)\n else:\n bagger = Bag(vocabulary=self.term_list, weighting=self.base_name, ngram_range=self.ngram_range)\n\n train_idx = self.indices.get_train_instances()\n texts = Text.get_strings(self.text.data.get_slice(train_idx))\n bagger.map_collection(texts, fit=True, transform=False)\n self.term_list = bagger.get_vocabulary()\n\n self.dimension = len(self.term_list)\n self.config.dimension = self.dimension"],"string":"[\n \"def build_mod_bpmf_model(train, alpha=2, dim=10, std=0.01):\\n n, m = train.shape\\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\\n\\n # Mean value imputation on training data.\\n train = train.copy()\\n nan_mask = np.isnan(train)\\n train[nan_mask] = train[~nan_mask].mean()\\n\\n # We will use separate priors for sigma and correlation matrix.\\n # In order to convert the upper triangular correlation values to a\\n # complete correlation matrix, we need to construct an index matrix:\\n n_elem = dim * (dim - 1) / 2\\n tri_index = np.zeros([dim, dim], dtype=int)\\n tri_index[np.triu_indices(dim, k=1)] = np.arange(n_elem)\\n tri_index[np.triu_indices(dim, k=1)[::-1]] = np.arange(n_elem)\\n\\n logging.info('building the BPMF model')\\n with pm.Model() as bpmf:\\n # Specify user feature matrix\\n sigma_u = pm.Uniform('sigma_u', shape=dim)\\n corr_triangle_u = pm.LKJCorr(\\n 'corr_u', n=1, p=dim,\\n testval=np.random.randn(n_elem) * std)\\n\\n corr_matrix_u = corr_triangle_u[tri_index]\\n corr_matrix_u = t.fill_diagonal(corr_matrix_u, 1)\\n cov_matrix_u = t.diag(sigma_u).dot(corr_matrix_u.dot(t.diag(sigma_u)))\\n lambda_u = t.nlinalg.matrix_inverse(cov_matrix_u)\\n\\n mu_u = pm.Normal(\\n 'mu_u', mu=0, tau=beta_0 * t.diag(lambda_u), shape=dim,\\n testval=np.random.randn(dim) * std)\\n U = pm.MvNormal(\\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\\n testval=np.random.randn(n, dim) * std)\\n\\n # Specify item feature matrix\\n sigma_v = pm.Uniform('sigma_v', shape=dim)\\n corr_triangle_v = pm.LKJCorr(\\n 'corr_v', n=1, p=dim,\\n testval=np.random.randn(n_elem) * std)\\n\\n corr_matrix_v = corr_triangle_v[tri_index]\\n corr_matrix_v = t.fill_diagonal(corr_matrix_v, 1)\\n cov_matrix_v = t.diag(sigma_v).dot(corr_matrix_v.dot(t.diag(sigma_v)))\\n lambda_v = t.nlinalg.matrix_inverse(cov_matrix_v)\\n\\n mu_v = pm.Normal(\\n 'mu_v', mu=0, tau=beta_0 * t.diag(lambda_v), shape=dim,\\n testval=np.random.randn(dim) * std)\\n V = pm.MvNormal(\\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\\n testval=np.random.randn(m, dim) * std)\\n\\n # Specify rating likelihood function\\n R = pm.Normal(\\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\\n observed=train)\\n\\n logging.info('done building the BPMF model')\\n return bpmf\",\n \"def build_pmf_model(train, alpha=2, dim=10, std=0.01):\\n # Mean value imputation on training data.\\n train = train.copy()\\n nan_mask = np.isnan(train)\\n train[nan_mask] = train[~nan_mask].mean()\\n\\n # Low precision reflects uncertainty; prevents overfitting.\\n # We use point estimates from the data to intialize.\\n # Set to mean variance across users and items.\\n alpha_u = 1 / train.var(axis=1).mean()\\n alpha_v = 1 / train.var(axis=0).mean()\\n\\n logging.info('building the PMF model')\\n n, m = train.shape\\n with pm.Model() as pmf:\\n U = pm.MvNormal(\\n 'U', mu=0, tau=alpha_u * np.eye(dim),\\n shape=(n, dim), testval=np.random.randn(n, dim) * std)\\n V = pm.MvNormal(\\n 'V', mu=0, tau=alpha_v * np.eye(dim),\\n shape=(m, dim), testval=np.random.randn(m, dim) * std)\\n R = pm.Normal(\\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones(train.shape),\\n observed=train)\\n\\n logging.info('done building PMF model')\\n return pmf\",\n \"def create_model(self, gwas_gen, gwas_phen):\\n n_ind, n_snps = gwas_gen.eval().shape\\n with pm.Model() as phenotype_model:\\n beta_med = pm.Normal('beta_med',\\n mu=self.vars['coef_mean'],\\n sd=self.vars['coef_sd'],\\n shape=(1, n_snps))\\n \\n mediator = pm.dot(beta_med, gwas_gen.T)\\n intercept = pm.Normal('intercept', mu=0, sd=1)\\n alpha = pm.Normal('alpha', mu=0, sd=1)\\n phenotype_sigma = pm.HalfCauchy('phenotype_sigma',\\n beta=self.vars['p_sigma_beta'])\\n \\n\\n # Model Selection\\n p = np.array([0.5, 0.5])\\n mediator_model = pm.Bernoulli('mediator_model', p[1])\\n\\n # Model 1\\n phenotype_mu_null = intercept\\n\\n # Model 2\\n phenotype_mu_mediator = intercept + alpha * mediator\\n\\n phen = pm.DensityDist('phen',\\n lambda value: pm.switch(mediator_model, \\n pm.Normal.dist(mu=phenotype_mu_mediator, sd=phenotype_sigma).logp(value), \\n pm.Normal.dist(mu=phenotype_mu_null, sd=phenotype_sigma).logp(value)\\n ),\\n observed=gwas_phen)\\n self.steps = [pm.BinaryGibbsMetropolis(vars=[mediator_model]),\\n pm.Metropolis()]\\n\\n \\n if self.variational and self.mb:\\n self.minibatch_RVs = [phen]\\n self.minibatch_tensors = [gwas_gen, gwas_phen]\\n return phenotype_model\",\n \"def build(self):\\n\\n self.W_AA = self.init([self.n_atom_input_feat, self.n_hidden_AA])\\n self.b_AA = model_ops.zeros(shape=[\\n self.n_hidden_AA,\\n ])\\n\\n self.W_PA = self.init([self.n_pair_input_feat, self.n_hidden_PA])\\n self.b_PA = model_ops.zeros(shape=[\\n self.n_hidden_PA,\\n ])\\n\\n self.W_A = self.init([self.n_hidden_A, self.n_atom_output_feat])\\n self.b_A = model_ops.zeros(shape=[\\n self.n_atom_output_feat,\\n ])\\n\\n self.trainable_weights = [\\n self.W_AA, self.b_AA, self.W_PA, self.b_PA, self.W_A, self.b_A\\n ]\\n if self.update_pair:\\n self.W_AP = self.init([self.n_atom_input_feat * 2, self.n_hidden_AP])\\n self.b_AP = model_ops.zeros(shape=[\\n self.n_hidden_AP,\\n ])\\n\\n self.W_PP = self.init([self.n_pair_input_feat, self.n_hidden_PP])\\n self.b_PP = model_ops.zeros(shape=[\\n self.n_hidden_PP,\\n ])\\n\\n self.W_P = self.init([self.n_hidden_P, self.n_pair_output_feat])\\n self.b_P = model_ops.zeros(shape=[\\n self.n_pair_output_feat,\\n ])\\n\\n self.trainable_weights.extend(\\n [self.W_AP, self.b_AP, self.W_PP, self.b_PP, self.W_P, self.b_P])\",\n \"def model_setup(self):\\n self.DNN = DgganMLP(self.settings.hidden_size)\\n self.D = DgganMLP(self.settings.hidden_size)\\n self.G = Generator(self.settings.hidden_size)\",\n \"def model_setup(self):\\n self.DNN = SganMLP(self.settings.number_of_bins)\\n self.D = SganMLP(self.settings.number_of_bins)\\n self.G = Generator()\",\n \"def get_model():\\n model = ecole.scip.Model.from_file(str(DATA_DIR / \\\"bppc8-02.mps\\\"))\\n model.disable_cuts()\\n model.disable_presolve()\\n model.set_param(\\\"randomization/permuteconss\\\", True)\\n model.set_param(\\\"randomization/permutevars\\\", True)\\n model.set_param(\\\"randomization/permutationseed\\\", 784)\\n model.set_param(\\\"randomization/randomseedshift\\\", 784)\\n model.set_param(\\\"randomization/lpseed\\\", 784)\\n return model\",\n \"def buildP(self):\\r\\n\\r\\n print 'Building P ...'\\r\\n\\r\\n #---Building p(y|x)---#\\r\\n pygx_params_mlp = MLP(activations=self.hyper['pygx_activs'],\\r\\n dims=self.hyper['pygx_dims'],\\r\\n weights_init=self.hyper['pygx_W_init'],\\r\\n biases_init=Constant(0))\\r\\n\\r\\n pygx_params = pygx_params_mlp.apply(self.x.reshape((self.x.shape[0]*self.x.shape[1],self.x.shape[2])))\\r\\n pygx_params = pygx_params.reshape((self.x.shape[0],self.x.shape[1],2*self.hyper['y_dim']))\\r\\n pygx_params_mlp.initialize()\\r\\n\\r\\n # self.pygx_mu.shape == (minibatch size, L_x , num of dimension of y)\\r\\n self.pygx_mu = pygx_params[:,:,:self.hyper['y_dim']]\\r\\n\\r\\n # self.pygx_var.shape == (minibatch size, L_x, num of dimension of y)\\r\\n self.pygx_var = T.exp( pygx_params[:,:,self.hyper['y_dim']:] )\\r\\n\\r\\n\\r\\n #---Building graph for the density of p(y|x)---#\\r\\n little_num = 10**(-32)\\r\\n inside_exp = -T.sum((self.y.dimshuffle(0,'x',1) - self.pygx_mu)**2/(2*self.pygx_var), axis=2)\\r\\n norm_cst = (2*np.pi)**(-self.hyper['y_dim']/2.)*T.exp(T.sum(T.log(self.pygx_var), axis=2))**(-1/2.)\\r\\n\\r\\n # shape == (minibatch size, # of x samples)\\r\\n pygx = norm_cst*T.exp(inside_exp)\\r\\n\\r\\n # shape == (minibatch size, # of x samples)\\r\\n self.log_pygx = T.log(pygx + little_num)\\r\\n\\r\\n #---Building NN for p(x|z=j,w) for all j---#\\r\\n pxgzw_mus = [None]*self.hyper['num_clust']\\r\\n pxgzw_vars = [None]*self.hyper['num_clust']\\r\\n pxgzw = [None]*self.hyper['num_clust']\\r\\n\\r\\n for j in range(self.hyper['num_clust']):\\r\\n\\r\\n pxgzw_params_mlp = MLP(activations=self.hyper['pxgzw_activs'][j],\\r\\n dims=self.hyper['pxgzw_dims'][j],\\r\\n weights_init=self.hyper['pxgzw_W_init'],\\r\\n biases_init=Constant(0))\\r\\n\\r\\n pxgzw_params = pxgzw_params_mlp.apply(self.w.reshape((self.w.shape[0]*self.w.shape[1],self.w.shape[2])))\\r\\n pxgzw_params = pxgzw_params.reshape((self.w.shape[0],self.w.shape[1], 2*self.hyper['x_dim']))\\r\\n pxgzw_params_mlp.initialize()\\r\\n\\r\\n # pxgzw_mus[j].shape == (minibatch size, L_w , num of dimension of x)\\r\\n pxgzw_mus[j] = pxgzw_params[:,:,:self.hyper['x_dim']]\\r\\n\\r\\n # pxgzw_vars[j].shape == (minibatch size, L_w, num of dimension of x)\\r\\n pxgzw_vars[j] = T.exp( pxgzw_params[:,:,self.hyper['x_dim']:] )\\r\\n\\r\\n #---Building graph for the density of p(x|z=j,w)---#\\r\\n little_num = 10**(-32)\\r\\n inside_exp = -T.sum((self.x.dimshuffle(0,'x',1,2) - pxgzw_mus[j].dimshuffle(0,1,'x',2))**2/(2*pxgzw_vars[j].dimshuffle(0,1,'x',2)), axis=3)\\r\\n norm_cst = (2*np.pi)**(-self.hyper['x_dim']/2.)*T.exp(T.sum(T.log(pxgzw_vars[j]), axis=2))**(-1/2.)\\r\\n\\r\\n # shape == (minibatch size, # of w samples (L_w), # of x samples (L_x))\\r\\n pxgzw[j] = norm_cst.dimshuffle(0,1,'x')*T.exp(inside_exp)\\r\\n\\r\\n\\r\\n # shape is (minibatch size, L_w , # of clusters , num of dimension of x)\\r\\n self.pxgzw_mus = T.concatenate([mu.dimshuffle(0,1,'x',2) for mu in pxgzw_mus], axis=2)\\r\\n # shape is (minibatch size, L_w , # of clusters , num of dimension of x)\\r\\n self.pxgzw_vars = T.concatenate([var.dimshuffle(0,1,'x',2) for var in pxgzw_vars], axis=2)\\r\\n\\r\\n # self.pxgzw.shape == (minibatch size, L_w, L_x, num_clust)\\r\\n self.pxgzw = T.concatenate([density.dimshuffle(0,1,2,'x') for density in pxgzw], axis=3)\\r\\n self.log_pxgzw = T.log(self.pxgzw + little_num)\\r\\n\\r\\n #---Building the p(z=j|x,w) posterior for all j---#\\r\\n # self.log_pzgxw.shape == (minibatch size, L_w, L_x, num_clust)\\r\\n self.log_pzgxw = T.log(self.pxgzw + little_num) -T.log(T.sum(self.pxgzw + little_num, axis=3).dimshuffle(0,1,2,'x'))\",\n \"def build(obs_space: Box, action_space: Box, spec: Spec) -> MLPModel:\\n model = MLPModel(obs_space, action_space, spec.network)\\n model.initialize_parameters(spec.initializer)\\n if spec.residual:\\n model = ResidualStochasticModel(model)\\n return model\",\n \"def build_model(self, X: pd.DataFrame, y: pd.DataFrame = None) -> pm.Model:\\n idx = X.index\\n \\n if y is None:\\n y = pd.Series(0, index=idx)\\n elif self.oversample: # only if y is given\\n n_pos = (y == 1).sum()\\n n_neg = (y == 0).sum()\\n to_add = int(np.ceil(n_neg/n_pos) - 1)\\n # print(n_pos, n_neg, to_add)\\n if to_add > 4:\\n to_add = 4\\n for i in range(to_add):\\n idx = idx.append(y[y==1].index)\\n X = X.loc[idx]\\n y = y.loc[idx]\\n \\n A = X[self.v_known + self.v_oob_bio]\\n B_vals = X[self.v_fuzzy]\\n B_mask = (B_vals == -1).astype(int)\\n C_raw = X[self.v_float_adm + self.v_float_bio]\\n # C_scaled = (C_raw - self.C_mean_) / self.C_std_ \\n C_scaled = np.log1p(C_raw/self.C_mean_)\\n C_scaled[~np.isfinite(C_scaled)] = np.nan\\n C_vals = C_scaled.fillna(0)\\n C_mask = C_scaled.isnull().astype(int)\\n \\n coords = {\\\"idx\\\": idx, \\\"a\\\": A.columns, \\\"b\\\": B_vals.columns, \\\"c\\\": C_vals.columns}\\n with pm.Model(coords=coords) as m:\\n pm.Data(\\\"A\\\", A, dims=[\\\"idx\\\", \\\"a\\\"])\\n pm.Data(\\\"B_vals\\\", B_vals, dims=[\\\"idx\\\", \\\"b\\\"])\\n pm.Data(\\\"B_mask\\\", B_mask, dims=[\\\"idx\\\", \\\"b\\\"])\\n pm.Data(\\\"C_vals\\\", C_vals, dims=[\\\"idx\\\", \\\"c\\\"])\\n pm.Data(\\\"C_mask\\\", C_mask, dims=[\\\"idx\\\", \\\"c\\\"])\\n pm.Data(\\\"y\\\", y, dims=[\\\"idx\\\"])\\n\\n pm.Normal(\\\"avg\\\", mu=0, sd=1)\\n\\n pm.Beta(\\\"h_a_incl\\\", alpha=1, beta=4)\\n pm.Normal(\\\"a_coef_raw\\\", mu=0, sd=1, dims=[\\\"a\\\"])\\n pm.Bernoulli(\\\"a_incl\\\", p=m[\\\"h_a_incl\\\"], dims=[\\\"a\\\"])\\n pm.Deterministic(\\\"a_coef\\\", m['a_coef_raw'] * m['a_incl'], dims=[\\\"a\\\"])\\n \\n pm.Normal(\\\"b_vals_coef\\\", mu=0, sd=1, dims=[\\\"b\\\"])\\n pm.Normal(\\\"b_mask_coef_raw\\\", mu=0, sd=1, dims=[\\\"b\\\"])\\n pm.Beta(\\\"h_b_mask_incl\\\", alpha=1, beta=4)\\n pm.Bernoulli(\\\"b_mask_incl\\\", p=m[\\\"h_b_mask_incl\\\"], dims=[\\\"b\\\"])\\n pm.Deterministic(\\\"b_mask_coef\\\", m['b_mask_coef_raw'] * m['b_mask_incl'], dims=[\\\"b\\\"])\\n \\n pm.Normal(\\\"c_vals_coef\\\", mu=0, sd=1, dims=[\\\"c\\\"])\\n pm.Normal(\\\"c_mask_coef_raw\\\", mu=0, sd=1, dims=[\\\"c\\\"])\\n pm.Beta(\\\"h_c_mask_incl\\\", alpha=1, beta=4)\\n pm.Bernoulli(\\\"c_mask_incl\\\", p=m[\\\"h_c_mask_incl\\\"], dims=[\\\"c\\\"])\\n pm.Deterministic(\\\"c_mask_coef\\\", m['c_mask_coef_raw'] * m['c_mask_incl'], dims=[\\\"c\\\"])\\n unprob = pm.Deterministic(\\n \\\"logit\\\",\\n m['avg']\\n + tt.dot(m[\\\"A\\\"], m[\\\"a_coef\\\"])\\n + tt.dot(m[\\\"B_vals\\\"] * (1 - m['B_mask']), m[\\\"b_vals_coef\\\"])\\n + tt.dot(m[\\\"B_mask\\\"], m[\\\"b_mask_coef\\\"])\\n + tt.dot(m[\\\"C_vals\\\"] * (1 - m['C_mask']), m[\\\"c_vals_coef\\\"])\\n + tt.dot(m[\\\"C_mask\\\"], m[\\\"c_mask_coef\\\"])\\n )\\n pm.Bernoulli(\\\"y_pred\\\", p = tt.nnet.sigmoid(unprob), dims=['idx'], observed=m['y'])\\n\\n m.graph = pm.model_to_graphviz()\\n\\n return m\",\n \"def build_model(self):\\n \\n # initalizing generators\\n self.g12 = G12(conv_dim=self.numGenFilter, domainA_channels = self.domainA_channels, domainB_channels = self.domainB_channels)\\n self.g21 = G21(conv_dim=self.numGenFilter, domainA_channels = self.domainA_channels, domainB_channels = self.domainB_channels)\\n \\n # initializing discriminators\\n self.d1 = D1(conv_dim=self.numDiscFilter, domainA_channels = self.domainA_channels, use_labels=self.use_labels)\\n self.d2 = D2(conv_dim=self.numDiscFilter, domainB_channels = self.domainB_channels, use_labels=self.use_labels)\\n \\n\\n g_params = list(self.g12.parameters()) + list(self.g21.parameters())\\n d_params = list(self.d1.parameters()) + list(self.d2.parameters())\\n \\n self.g_optimizer = optim.Adam(g_params, self.lr, [self.beta1, self.beta2])\\n self.d_optimizer = optim.Adam(d_params, self.lr, [self.beta1, self.beta2])\\n \\n if torch.cuda.is_available():\\n self.g12.cuda()\\n self.g21.cuda()\\n self.d1.cuda()\\n self.d2.cuda()\",\n \"def copy(self):\\n model_copy = BayesianModel()\\n model_copy.add_nodes_from(self.nodes())\\n model_copy.add_edges_from(self.edges())\\n if self.cpds:\\n model_copy.add_cpds(*[cpd.copy() for cpd in self.cpds])\\n return model_copy\",\n \"def __init__(self, \\n num_vars, \\n num_hidden,\\n training_inputs = None,\\n algorithm = None,\\n algorithm_dict = None,\\n batch_size = None,\\n use_momentum = None,\\n W0= None, \\n b0= None, \\n bhid0 = None,\\n zero_diag = True,\\n symmetric = True,\\n report_p_tilda =False,\\n learn_biases = True,\\n test_mode= False,\\n training = True):\\n \\n self.num_vars = num_vars\\n \\n self.num_hidden = num_hidden\\n \\n self.batch_size = batch_size\\n \\n self.zero_diag = zero_diag\\n \\n self.algorithm = algorithm\\n \\n self.num_samples = 0\\n \\n self.num_u_gibbs = 0\\n \\n self.gibbs_steps = 0\\n \\n self.resample = False\\n \\n self.uniform = False\\n \\n self.mixture = False\\n \\n self.mix_params = []\\n \\n self.m_params = []\\n \\n self.mf_steps = 0\\n \\n self.alpha = 0\\n \\n self.learn_biases = learn_biases\\n \\n if isinstance(algorithm_dict, dict):\\n \\n for param in algorithm_dict.keys():\\n \\n if param == 'resample':\\n \\n self.resample = algorithm_dict[param]\\n \\n if param == 'mf_steps':\\n \\n self.mf_steps = algorithm_dict[param]\\n \\n if param == \\\"gibbs_steps\\\":\\n \\n self.gibbs_steps = algorithm_dict[param]\\n \\n if param == \\\"num_samples\\\":\\n \\n self.num_samples = algorithm_dict[param]\\n \\n if param == \\\"num_u_gibbs\\\":\\n \\n self.num_u_gibbs = algorithm_dict[param]\\n \\n if param == \\\"uniform\\\":\\n \\n self.uniform = algorithm_dict[param] \\n \\n if param == \\\"mixture\\\":\\n \\n self.mixture = algorithm_dict[param] \\n \\n if param == \\\"mix_params\\\":\\n \\n self.mix_params = algorithm_dict[param] \\n \\n if param == \\\"alpha\\\" and algorithm_dict[param] != None:\\n #### alpha defines transition rate from\\n #### uniform to mean-field distribution\\n self.alpha = algorithm_dict[param] \\n \\n self.m_params = (1-self.alpha)*0.5*np.ones([1,self.num_vars])+\\\\\\n self.alpha*np.mean(training_inputs,0)\\n \\n self.use_momentum = use_momentum\\n \\n self.report_p_tilda = report_p_tilda\\n \\n self.side = int(np.sqrt(self.num_vars))\\n \\n self.np_rand_gen = np.random.RandomState(1234)\\n \\n self.theano_rand_gen =\\\\\\n theano.sandbox.rng_mrg.MRG_RandomStreams(self.np_rand_gen.randint(2**30))\\n \\n #self.theano_rand_gen =\\\\\\n #T.shared_randomstreams.RandomStreams(self.np_rand_gen.randint(2**30))\\n \\n theano.config.exception_verbosity = 'high'\\n \\n self.node_indices = \\\\\\n theano.shared(np.arange(self.num_vars), name=\\\"node_indices\\\")\\n \\n self.x = T.matrix('x')\\n \\n self.x_tilda = T.matrix('x_tilda')\\n \\n self.sampler_theta = T.matrix('sampler_theta')\\n \\n self.symmetric = symmetric\\n \\n if training:\\n \\n if self.num_hidden ==0:\\n \\n self.num_x2 = self.num_vars\\n \\n elif self.num_hidden > 0 :\\n \\n self.num_x2 = self.num_hidden\\n \\n self.updates = OrderedDict()\\n \\n self.N_train = training_inputs.shape[0]\\n \\n self.train_inputs = theano.shared(np.asarray(training_inputs,\\n dtype=theano.config.floatX),\\n borrow= True)\\n \\n self.learning_rate = T.dscalar('learning_rate')\\n \\n if self.mixture:\\n \\n print(\\\"Importance distribution was specified as mixture\\\"+\\\\\\n \\\" of Bernoulli products\\\")\\n \\n if self.mix_params == []:\\n print(\\\"Error: parameters defining mixture means were\\\"+\\\\\\n \\\" not provided\\\")\\n sys.exit()\\n \\n self.set_mixture_means(inputs = training_inputs)\\n \\n if use_momentum:\\n \\n print(\\\"Will add momentum term to gradient computations\\\")\\n \\n self.momentum = T.dscalar('learning_rate')\\n \\n self.grad_vec = {}\\n \\n self.grad_vec['W'] = theano.shared(np.zeros([self.num_vars, self.num_x2],\\n dtype = theano.config.floatX), name = 'W_momentum', borrow = True)\\n \\n if self.num_hidden > 0:\\n \\n self.grad_vec['bhid'] = theano.shared(np.zeros([self.num_x2],\\n dtype = theano.config.floatX), name = 'b_momentum', borrow = True)\\n \\n self.grad_vec['b'] = theano.shared(np.zeros([self.num_vars],\\n dtype = theano.config.floatX), name = 'b_momentum', borrow = True)\\n \\n if test_mode:\\n \\n b_init =self.np_rand_gen.uniform(0,1, num_vars)\\n \\n W_init =self.np_rand_gen.uniform(0,1, size = (num_vars, num_vars))\\n \\n # also tested ones\\n # b_init = np.ones(num_vars)\\n \\n # W_init = np.ones([num_vars, num_vars])\\n \\n self.b_init= np.asarray(b_init, dtype = theano.config.floatX)\\n \\n self.W_init= np.asarray(W_init, dtype = theano.config.floatX)\\n \\n self.b = theano.shared(self.b_init, name='b', borrow = False)\\n \\n self.W = theano.shared(self.W_init, name='W', borrow = False)\\n \\n print(\\\"Initialized with test mode\\\")\\n \\n else:\\n \\n if W0 is None:\\n \\n if self.num_hidden > 0:\\n \\n W0_init =\\\\\\n self.np_rand_gen.uniform(\\n -4*np.sqrt(6.0/(self.num_vars+self.num_hidden)),\\\\\\n 4*np.sqrt(6.0 /(self.num_vars + self.num_hidden)), \\n size = (num_vars, self.num_hidden)\\n )\\n \\n W0 = np.asarray(W0_init, dtype = theano.config.floatX) \\n \\n if self.num_hidden == 0:\\n \\n # different W initializations: \\n \\n # W0_init =\\\\\\n # self.np_rand_gen.uniform(-np.sqrt(3.0/(num_vars)),\\\\\\n # np.sqrt(3.0 / (num_vars)), size = (num_vars, num_vars))\\n \\n # W0_init =\\\\\\n # self.np_rand_gen.uniform(-0.00000001,\\\\\\n # 0.00000001, size = (num_vars, num_vars))\\n \\n W0_init = 0.00000001*\\\\\\n self.np_rand_gen.normal(size = (num_vars, self.num_x2)) \\n \\n W0 = np.asarray(W0_init, dtype = theano.config.floatX)\\n \\n if self.symmetric:\\n \\n W0 = (W0 + np.transpose(W0))/2.0\\n \\n if self.zero_diag:\\n \\n W0 = W0 - np.diag(np.diag(W0))\\n \\n self.W = theano.shared(value= W0, name='W', borrow=True)\\n \\n if self.num_hidden == 0:\\n \\n test_W = self.W.get_value() \\n \\n assert sum(np.diag(test_W)) == 0.0\\n \\n assert (test_W == np.transpose(test_W)).all() == True\\n \\n else:\\n print(\\\"W is initialized with provided array\\\")\\n self.W = theano.shared(value= W0, name='W', borrow=True)\\n \\n if b0 is None:\\n \\n bias_init = np.zeros(num_vars, dtype = theano.config.floatX)\\n \\n self.b = theano.shared(value= bias_init, name='b', borrow=True)\\n \\n else:\\n print(\\\"b vector is initialized with provided vector\\\")\\n self.b = theano.shared(value= b0, name='b', borrow=True)\\n \\n if bhid0 is None and self.num_hidden > 0:\\n \\n hbias_init = np.zeros(self.num_hidden, dtype = theano.config.floatX)\\n \\n self.bhid = theano.shared(value= hbias_init, name='bhid', borrow=True)\\n \\n elif (bhid0 is not None) and (self.num_hidden > 0):\\n print(\\\"bhid vector is initialized with provided vector\\\") \\n self.bhid = theano.shared(value= bhid0, name='bhid', borrow=True)\\n \\n self.theta = [self.W, self.b]\\n \\n if self.num_hidden > 0 :\\n \\n self.theta.append(self.bhid)\\n \\n self.train_set = set(range(self.N_train))\\n \\n self.minibatch_set = T.ivector('minibatch_set')\\n \\n self.sample_set = T.ivector('sample_set')\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden ==0:\\n \\n self.x_gibbs= theano.shared(np.ones([self.batch_size,self.num_vars],\\n dtype=theano.config.floatX),\\n borrow = True, name= \\\"x_gibbs\\\")\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden > 0:\\n \\n self.persistent_gibbs =\\\\\\n theano.shared(np.ones([self.batch_size,self.num_hidden],\\n dtype=theano.config.floatX),\\n borrow = True, \\n name= \\\"persistent_gibbs\\\")\\n \\n if \\\"CSS\\\" in self.algorithm and self.mf_steps > 0:\\n \\n init_mf_vis = self.np_rand_gen.uniform(0, \\n 1, \\n size =(self.num_vars,1))\\n \\n init_mf_vis = np.asarray(init_mf_vis, dtype = theano.config.floatX)\\n \\n self.mf_vis_p = theano.shared(init_mf_vis, \\n name= \\\"mf_vis_p\\\", \\n borrow= True)\\n \\n if self.num_hidden > 0:\\n \\n init_mf_hid = \\\\\\n self.np_rand_gen.uniform(0, 1, size =(self.num_hidden,1))\\n \\n init_mf_hid = np.asarray(init_mf_hid, \\n dtype = theano.config.floatX)\\n \\n self.mf_hid_p = theano.shared(init_mf_hid, \\n name= \\\"mf_hid_p\\\", \\n borrow= True)\\n \\n elif \\\"CSS\\\" in self.algorithm and self.gibbs_steps > 0: \\n \\n if self.num_hidden ==0: \\n self.x_gibbs= theano.shared(np.ones([self.batch_size,self.num_vars],\\n dtype=theano.config.floatX),\\n borrow = True, name= \\\"x_gibbs\\\")\",\n \"def build_model(self):\\n self.g12 = G12(conv_dim=self.g_conv_dim)\\n init_weights(self.g12, init_type='normal')\\n self.g21 = G21(conv_dim=self.g_conv_dim)\\n init_weights(self.g21, init_type='normal')\\n self.d1 = D1(conv_dim=self.d_conv_dim, use_labels=self.use_labels)\\n init_weights(self.d1, init_type='normal')\\n self.d2 = D2(conv_dim=self.d_conv_dim, use_labels=self.use_labels)\\n init_weights(self.d2, init_type='normal')\\n self.dreid = DSiamese(class_count=self.num_classes_market)\\n\\n g_params = list(self.g12.parameters()) + list(self.g21.parameters())\\n d_params = list(self.d1.parameters()) + list(self.d2.parameters())\\n dr_params = list(self.dreid.parameters())\\n\\n self.g_optimizer = optim.Adam(g_params, self.lr, [self.beta1, self.beta2])\\n self.d_optimizer = optim.Adam(d_params, self.lr, [self.beta1, self.beta2])\\n self.dr_optimizer = optim.Adam(dr_params, self.lr, [self.beta1, self.beta2])\\n\\n if torch.cuda.is_available():\\n self.g12.cuda()\\n self.g21.cuda()\\n self.d1.cuda()\\n self.d2.cuda()\\n self.dreid.cuda()\",\n \"def create_model(self, gwas_gen, gwas_phen):\\n n_ind, n_snps = gwas_gen.eval().shape\\n with pm.Model() as phenotype_model:\\n beta_med = pm.Normal('beta_med',\\n mu=self.vars['coef_mean'],\\n sd=self.vars['coef_sd'],\\n shape=(1, n_snps))\\n phenotype_expression_mu = pm.dot(beta_med, gwas_gen.T)\\n intercept = pm.Normal('intercept', mu=0, sd=1)\\n alpha = pm.Normal('alpha', mu=0, sd=1)\\n phenotype_mu = intercept + alpha * phenotype_expression_mu\\n if self.logistic:\\n p = tinvlogit(phenotype_mu)\\n phen = pm.Bernoulli('phen', p=p, observed=gwas_phen)\\n else:\\n phenotype_sigma = pm.HalfCauchy('phenotype_sigma',\\n beta=self.vars['p_sigma_beta'])\\n phen = pm.Normal('phen',\\n mu=phenotype_mu,\\n sd=phenotype_sigma,\\n observed=gwas_phen)\\n if self.variational and self.mb:\\n self.minibatch_RVs = [phen]\\n self.minibatch_tensors = [gwas_gen, gwas_phen]\\n return phenotype_model\",\n \"def buildQ(self):\\r\\n\\r\\n print 'Building Q ...'\\r\\n\\r\\n self.y = T.matrix('y')\\r\\n\\r\\n mlp = MLP(activations=self.hyper['q_activs'],\\r\\n dims=self.hyper['q_dims'],\\r\\n weights_init=self.hyper['q_W_init'],\\r\\n biases_init=Constant(0))\\r\\n\\r\\n q_parameters = mlp.apply(self.y)\\r\\n mlp.initialize()\\r\\n\\r\\n # self.qxgy_mu.shape == (minibatch size, num of dimension of x)\\r\\n self.qxgy_mu = q_parameters[:,:self.hyper['x_dim']]\\r\\n\\r\\n # self.qxgy_var.shape == (minibatch size, num of dimension of x)\\r\\n self.qxgy_var = T.exp( q_parameters[:,self.hyper['x_dim']:2*self.hyper['x_dim']] )\\r\\n\\r\\n # self.qwgy_mu.shape == (minibatch size, num of dimension of w)\\r\\n self.qwgy_mu = q_parameters[:,2*self.hyper['x_dim']:2*self.hyper['x_dim']+self.hyper['w_dim']]\\r\\n\\r\\n # self.qwgy_var.shape == (minibatch size, num of dimension of w)\\r\\n self.qwgy_var = T.exp( q_parameters[:,2*self.hyper['x_dim']+self.hyper['w_dim']:] )\\r\\n\\r\\n\\r\\n #---Will be useful to compute samples from q(x|y)---#\\r\\n #self.eps_x.shape == (minibatch size, # of x samples , # of dimension of x)\\r\\n self.eps_x = self.srng.normal((self.qxgy_mu.shape[0] ,self.hyper['L_x'] ,self.hyper['x_dim']))\\r\\n\\r\\n #self.x corresponds roughly to the function g(\\\\epsilon,y) (see reparametrization trick in Kingma 2014)\\r\\n #self.x.shape == (minibatch size, # of x samples , # of dimension of x)\\r\\n self.x = self.qxgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qxgy_var).dimshuffle(0,'x',1)*self.eps_x\\r\\n\\r\\n #---Will be useful to compute samples from q(w|y)---#\\r\\n #self.eps_w.shape == (minibatch size, # of w samples , # of dimension of w)\\r\\n self.eps_w = self.srng.normal((self.qwgy_mu.shape[0] ,self.hyper['L_w'] ,self.hyper['w_dim']))\\r\\n\\r\\n #self.w corresponds roughly to the function g(\\\\epsilon,y) (see reparametrization trick in Kingma 2014)\\r\\n #self.w.shape == (minibatch size, # of w samples , # of dimension of w)\\r\\n self.w = self.qwgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qwgy_var).dimshuffle(0,'x',1)*self.eps_w\\r\\n\\r\\n\\r\\n #---Building the log density q(x|y)---#\\r\\n little_num = 10**(-32)\\r\\n inside_exp = -T.sum((self.x - self.qxgy_mu.dimshuffle(0,'x',1))**2/(2*self.qxgy_var.dimshuffle(0,'x',1)), axis=2)\\r\\n norm_cst = (2*np.pi)**(-self.hyper['x_dim']/2.)*T.exp(T.sum(T.log(self.qxgy_var), axis=1))**(-1/2.)\\r\\n\\r\\n # shape == (minibatch size, # of x samples)\\r\\n qxgy = norm_cst.dimshuffle(0,'x')*T.exp(inside_exp)\\r\\n\\r\\n # shape == (minibatch size, # of x samples)\\r\\n self.log_qxgy = T.log(qxgy + little_num)\",\n \"def prepare_bpe():\\r\\n bpe = spm.SentencePieceProcessor()\\r\\n bpe.load(os.path.join(Params.dataset_path, Params.bpe_model))\\r\\n return bpe\",\n \"def build_model():\",\n \"def build_model(self) -> nn.Module:\\n pass\",\n \"def copy(self):\\n model = LBM(\\n n_row_clusters=self.n_row_clusters,\\n n_column_clusters=self.n_column_clusters,\\n max_iter=self.max_iter,\\n n_init=self.n_init,\\n n_init_total_run=self.n_init_total_run,\\n n_iter_early_stop=self.nb_iter_early_stop,\\n rtol=self.rtol,\\n atol=self.atol,\\n verbosity=self.verbosity,\\n use_gpu=self.use_gpu,\\n gpu_index=self.gpu_index,\\n )\\n model._nb_rows = self._nb_rows\\n model._nb_cols = self._nb_cols\\n model.loglikelihood_ = self.loglikelihood_\\n model._np = self._np\\n model._cupyx = self._cupyx\\n model.trained_successfully_ = self.trained_successfully_\\n model.pi_ = copy.copy(self.pi_)\\n model.alpha_1_ = copy.copy(self.alpha_1_)\\n model.alpha_2_ = copy.copy(self.alpha_2_)\\n model.tau_1_ = copy.copy(self.tau_1_)\\n model.tau_2_ = copy.copy(self.tau_2_)\\n return model\",\n \"def _init_from_newform_(self, g, PP, C, prec):\\n k = g.weight()\\n N = g.level()\\n if(k % 4 == 0):\\n ep = g.atkin_lehner_eigenvalue()\\n else:\\n ep = -g.atkin_lehner_eigenvalue()\\n kappa = mpmath.mpf(3 - k) / mp2\\n if(ep == -1):\\n WR = WeilRepDiscriminantForm(N, False)\\n elif ep == 1:\\n WR = WeilRepDiscriminantForm(N, True)\\n else:\\n raise ValueError(\\\" Sign of functional equation must be 1 or -1! Got:{0}\\\".format(ep))\\n # print \\\"kappa=\\\",kappa\\n # print \\\"WR=\\\",WR\\n M = VVHarmonicWeakMaassForms(WR, kappa, prec)\\n # print \\\"M=\\\",M\\n self._space = M\\n self.prec = prec\\n self.coeff = dict()\\n # We want a Harmonic weak maass form corresponding to the form g\\n # that means we need to avoid any other cuspforms as well as\\n # theta series...\\n # If there are no oldforms we are happy\\n if dimension_new_cusp_forms(N, k) == dimension_cusp_forms(N, k):\\n # and to avoid theta series we need to avoid square discriminants\\n # in the principal part\\n if M._is_dual_rep:\\n nset = [0, -1]\\n else:\\n nset = [-1]\\n try:\\n for n in nset:\\n for r in WR.D(): # )_as_integers:\\n D = M.D_from_rn((r, n))\\n if not is_square(D):\\n PP = {(WR.D[r], n): 1}\\n self._principal_part = PP\\n # print \\\"PP=\\\",PP,\\\"is ok!\\\"\\n raise StopIteration()\\n except StopIteration:\\n pass\\n # if(C is not None and C>0):\",\n \"def BinaryPmf(p):\\n pmf = thinkbayes2.Pmf()\\n pmf.Set(1, p)\\n pmf.Set(0, 1-p)\\n return pmf\",\n \"def build_model(self) -> DM:\\n model = DM()\\n model[self.modelroot] = content = DM()\\n\\n content['potential'] = DM()\\n content['potential']['key'] = self.potential_key\\n content['potential']['id'] = self.potential_id\\n content['implementation'] = DM()\\n content['implementation']['key'] = self.potential_LAMMPS_key\\n content['implementation']['id'] = self.potential_LAMMPS_id\\n\\n for subset in self.subsets:\\n subset.build_model(content)\\n\\n self._set_model(model)\\n return model\",\n \"def generate(self):\\r\\n \\r\\n components = []\\r\\n components.append(nn.Linear(self.n_features,self.hidden_sizes[0]))\\r\\n self._activation(components,self.activation)\\r\\n self._dropout(components,self.dropout)\\r\\n \\r\\n for i in range(1,len(self.hidden_sizes)):\\r\\n components.append(nn.Linear(self.hidden_sizes[i-1],self.hidden_sizes[i]))\\r\\n self._activation(components,self.activation)\\r\\n self._dropout(components,self.dropout)\\r\\n \\r\\n components.append(nn.Linear(self.hidden_sizes[-1],self.n_classes))\\r\\n\\r\\n mlp = nn.Sequential(*components)\\r\\n \\r\\n num_params = sum(p.numel() for p in mlp.parameters() if p.requires_grad)\\r\\n print(\\\"Created MLP with \\\"+str(num_params)+\\\" learnable params\\\")\\r\\n \\r\\n return mlp\",\n \"def build(self):\\n\\n self.W = self.init([self.n_atom_input_feat, self.n_output])\\n self.b = model_ops.zeros(shape=[\\n self.n_output,\\n ])\\n\\n self.trainable_weights = self.W + self.b\",\n \"def build_model(self):\\n self.G = Generator(self.g_conv_dim)\\n self.D = Discriminator(self.d_conv_dim, self.c_dim)\\n self.generator = Generator(self.g_conv_dim).train(False)\\n\\n self.G = nn.DataParallel(self.G)\\n self.D = nn.DataParallel(self.D)\\n\\n # For Adam (Unofficial)\\n # self.g_optimizer = torch.optim.Adam(self.G.parameters(), self.g_lr, [self.beta1, self.beta2])\\n # self.d_optimizer = torch.optim.Adam(self.D.parameters(), self.d_lr, [self.beta1, self.beta2])\\n\\n # For RMSprop(Official)\\n self.g_optimizer = torch.optim.RMSprop(self.G.parameters(), lr=0.0001)\\n self.d_optimizer = torch.optim.RMSprop(self.D.parameters(), lr=0.0001)\\n\\n self.accumulate(self.generator, self.G.module, 0)\\n # self.print_network(self.G, 'G')\\n # self.print_network(self.D, 'D')\\n \\n self.G.to(self.device)\\n self.D.to(self.device)\\n self.generator.to(self.device)\\n\\n # weight init\\n self.G.apply(self.weights_init)\\n self.D.apply(self.weights_init)\\n self.generator.apply(self.weights_init)\",\n \"def build_model(self, constructor, args):\\n dims = {'en': 300, 'es': 50}\\n dists = {'en': 'Normal',\\n 'es': 'Normal',}\\n z_dim = args.model_args.get('z_dim', 64)\\n h_dim = args.model_args.get('h_dim', 64)\\n n_layers = args.model_args.get('n_layers', 3)\\n gauss_out = (args.model != 'MultiDKS') \\n encoders = {'en': models.common.DeepGaussianMLP(dims['en'], z_dim, h_dim, n_layers),\\n 'es': models.common.DeepGaussianMLP(dims['es'], z_dim, h_dim, n_layers)}\\n decoders = {'en': models.common.DeepGaussianMLP(z_dim, dims['en'], h_dim, n_layers),\\n 'es': models.common.DeepGaussianMLP(z_dim, dims['es'], h_dim, n_layers)}\\n custom_mods = [m for m in ['en', 'es'] if m in args.modalities]\\n model = constructor(args.modalities,\\n dims=(dims[m] for m in args.modalities),\\n dists=[dists[m] for m in args.modalities],\\n encoders={m: encoders[m] for m in custom_mods},\\n decoders={m: decoders[m] for m in custom_mods},\\n z_dim=z_dim, h_dim=h_dim,\\n device=args.device, **args.model_args)\\n return model\",\n \"def generate_limittedmodel():\\r\\n print('Loading model')\\r\\n model = KeyedVectors.load_word2vec_format(BIN_NAME, binary=True)\\r\\n print('Model loaded!')\\r\\n\\r\\n print('Loading dot products')\\r\\n dp = np.load(DP_NAME)\\r\\n print('Dot products loaded')\\r\\n\\r\\n print('Filtering vocab')\\r\\n for name, vocab in list(model.vocab.items()):\\r\\n if dp[vocab.index] < MAX_DEGREE:\\r\\n del model.vocab[name]\\r\\n\\r\\n il = list(model.vocab.items())\\r\\n print('Sorting vocab')\\r\\n il.sort(key=lambda x: x[1].index)\\r\\n\\r\\n # Find the indexes of the words that are being kept\\r\\n print('Generating indexes')\\r\\n indexes = []\\r\\n for i in range(0, len(il)):\\r\\n name, vocab = il[i]\\r\\n indexes.append(vocab.index)\\r\\n model.vocab[name].index = i\\r\\n\\r\\n print('Modifying model weights')\\r\\n model.syn0 = model.syn0[indexes]\\r\\n\\r\\n print('Saving file')\\r\\n model.save_word2vec_format(SAVE_NAME, binary=True)\",\n \"def _build_appx_elbo(self):\\n\\n # MC estimate of contribution from edges\\n # obvious choice: uniformly sample terms... but resulting estimator is super high variance\\n # edges_sample = np.copy(self.edge_idx_d[np.random.choice(self.edge_idx_d.shape[0], 3000, replace=False)]).astype(np.int32)\\n # so instead use p-sampling... although it's unclear whether this really represents a major improvement\\n e = self.edge_vals_d.shape[0]\\n p_inc = np.sqrt(5000. / e) #use about 5000 edges for MC est\\n edges_sample = item_p_sample(user_p_sample(self.edge_idx_d, p_inc)[0], p_inc)[0].astype(np.int32)\\n\\n # clip by value because of numerical issues\\n p_edge_samples = tf.clip_by_value(self._edge_prob_samples(edges_sample), 1e-15, 1.)\\n\\n # reduce_mean is MC estimate over params of model, reduce_sum is summing cont from p-samp\\n edge_llhd_est = 1. / p_inc**2 * tf.reduce_sum(tf.reduce_mean(tf.log(p_edge_samples), axis=0))\\n\\n # log(1-p_ij) = -lambda_ij, so:\\n tot_lam_sum = tf.reduce_sum(self.i_tot_mass_m*self.u_tot_mass_m) # includes contribution from edges as well as non-edges\\n # subtract off edge contribution:\\n user_params = tf.gather(self.q_gam.mean() * self.q_theta.mean(), self.edge_idx[:,0])\\n item_params = tf.gather(self.q_omega.mean() * self.q_beta.mean(), self.edge_idx[:,1])\\n edges_lam_sum = tf.reduce_sum(user_params * item_params)\\n nonedge_llhd_term = -(tot_lam_sum - edges_lam_sum)\\n\\n # hopefully lower variance than direct MC est\\n #\\\\sum_edges log(p_ij) = -\\\\sum_edges lam_ij + \\\\sum_ij log(p_ij / (1-p_ij))\\n\\n # note: the reduce mean here averages over both the sampled params in p_edge_samples, and over the random choice of edges\\n # edge_llhd_est = -edges_lam_sum + e*tf.reduce_mean(tf.reduce_mean(tf.log(p_edge_samples / (1. - p_edge_samples)), axis=0))\\n\\n self.appx_elbo = [edge_llhd_est, nonedge_llhd_term]\",\n \"def setup_milp(model, target, remove_blocked=False, exclude_reaction_ids=set()):\\n original_model = model\\n model = model.copy()\\n model.objective=target\\n for i in heterologous_reactions.keys(): # turns off each heterologous reaction in order to get the only the native metabolic network.\\n model.reactions.get_by_id(i).lower_bound=0\\n model.reactions.get_by_id(i).upper_bound=0\\n\\n for r in model.reactions:\\n if r.id.find('MetaCyc')>-1:\\n r.type='heterologous'\\n else:\\n r.type='native'\\n\\n # Set the solver to Gurobi for the fastest result. Set to CPLEX if Gurobi is not available.\\n if \\\"gurobi\\\" in cobra.util.solver.solvers.keys():\\n logger.info(\\\"Changing solver to Gurobi and tweaking some parameters.\\\")\\n if \\\"gurobi_interface\\\" not in model.solver.interface.__name__:\\n model.solver = \\\"gurobi\\\"\\n # The tolerances are set to the minimum value. This gives maximum precision.\\n problem = model.solver.problem\\n problem.params.NodeMethod = 1 # primal simplex node relaxation\\n problem.params.FeasibilityTol = 1e-9 #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\\n problem.params.OptimalityTol = 1e-3 #how sure the solver has to be about this optimum being really the best it has.\\n problem.params.IntFeasTol = 1e-9 #If a value is set to an integer, how much may it still vary? > set smallest possible\\n problem.params.MIPgapAbs = 1e-9\\n problem.params.MIPgap = 1e-9\\n problem.params.Threads=1 #In order to reduce memory usage (increased calculation time)\\n problem.params.TimeLimit = 200 # Use max 200 seconds when called, return best solution after that\\n problem.params.PoolSearchMode = 1 #0 for only finding the optimum, 1 for finding more solutions (but no quality guaranteed), 2 for finding the n best possible solutions\\n problem.params.PoolSolutions = 10 # Number of solutions kept when finding the optimal solution\\n problem.params.PoolGap = 0.9 # only store solutions within 90% of the optimal objective value\\n\\n elif \\\"cplex\\\" in cobra.util.solver.solvers.keys():\\n logger.warning(\\\"Changing solver to CPLEX, as Gurobi is not available. This may cause a big slowdown and limit options afterwards.\\\")\\n if \\\"cplex_interface\\\" not in model.solver.interface.__name__:\\n model.solver = \\\"cplex\\\"\\n # The tolerances are set to the minimum value. This gives maximum precision.\\n problem = model.solver.problem\\n problem.parameters.mip.strategy.startalgorithm.set(1) # primal simplex node relaxation\\n problem.parameters.simplex.tolerances.feasibility.set(1e-9) #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\\n problem.parameters.simplex.tolerances.optimality.set(1e-3) #possibly fine with 1e-3, try if allowed. Is how sure the solver has to be about this optimum being really the best it has.\\n problem.parameters.mip.tolerances.integrality.set(1e-9) #If a value is set to an integer, how much may it still vary? > set smallest possible\\n problem.parameters.mip.tolerances.absmipgap.set(1e-9)\\n problem.parameters.mip.tolerances.mipgap.set(1e-9)\\n problem.parameters.mip.pool.relgap.set(0.9) # For populate: find all solutions within 10% of the optimum for relgap = 0.1\\n problem.parameters.timelimit.set(200) # Use max 200 seconds for solving\\n problem.parameters.mip.limits.populate.set(20) # Find max 20 solutions (=default)\\n\\n else:\\n logger.warning(\\\"You are trying to run 'Hamlet Hot Rig' with %s. This might not end well.\\\" %\\n model.solver.interface.__name__.split(\\\".\\\")[-1])\\n pass\\n # Remove reactions that are blocked: no flux through these reactions possible. This will reduce the search space for the solver, if not done already.\\n if remove_blocked:\\n blocked_reactions = cameo.flux_analysis.analysis.find_blocked_reactions(model)\\n model.remove_reactions(blocked_reactions)\\n\\n # Make dual\\n model_with = model.copy() # This variable looks unnecessary, but is kept out of fear of messing stuff up\\n model_with.optimize()\\n dual_problem = convert_linear_problem_to_dual(model_with.solver)\\n logger.debug(\\\"Dual problem successfully created\\\")\\n\\n # Combine primal and dual\\n primal_problem = model.solver\\n\\n for var in dual_problem.variables: # All variables in the dual are copied to the primal\\n var = primal_problem.interface.Variable.clone(var)\\n primal_problem.add(var)\\n for const in dual_problem.constraints: # All constraints in the dual are copied to the primal\\n const = primal_problem.interface.Constraint.clone(const, model=primal_problem)\\n primal_problem.add(const)\\n logger.debug(\\\"Dual and primal combined\\\")\\n\\n dual_problem.optimize()\\n\\n # Dictionaries to hold the binary control variables:\\n heterologous_y_vars = {} # 1 for 'knockin', 0 for inactive\\n medium_y_vars = {} # 1 for medium addition (up to -10), 0 for no addition\\n\\n # Now the fun stuff\\n constrained_dual_vars = set()\\n\\n # For the knockins and medium additions:\\n for reaction in [r for r in model.reactions if r.type == \\\"heterologous\\\"]:\\n # Add constraint variables\\n interface = model.solver.interface\\n y_var = interface.Variable(\\\"y_\\\" + reaction.id, type=\\\"binary\\\")\\n\\n # Constrain the primal: flux through reactions maximum within (-1000, 1000), or smaller boundaries defined before\\n model.solver.add(interface.Constraint(reaction.flux_expression - 1000 * y_var, ub=0, name=\\\"primal_y_const_\\\"+reaction.id+\\\"_ub\\\"))\\n model.solver.add(interface.Constraint(reaction.flux_expression + 1000 * y_var, lb=0, name=\\\"primal_y_const_\\\"+reaction.id+\\\"_lb\\\"))\\n\\n # Constrain the dual\\n constrained_vars = []\\n\\n if reaction.upper_bound != 0:\\n dual_forward_ub = model.solver.variables[\\\"dual_\\\" + reaction.forward_variable.name + \\\"_ub\\\"]\\n model.solver.add(interface.Constraint(dual_forward_ub - 1000 * (1 - y_var), ub=0))\\n constrained_vars.append(dual_forward_ub)\\n if reaction.lower_bound != 0:\\n dual_reverse_ub = model.solver.variables[\\\"dual_\\\" + reaction.reverse_variable.name + \\\"_ub\\\"]\\n model.solver.add(interface.Constraint(dual_reverse_ub - 1000 * (1 - y_var), ub=0))\\n constrained_vars.append(dual_reverse_ub)\\n constrained_dual_vars.update(constrained_vars)\\n\\n # Add y variable to the corresponding modifications dictionary\\n heterologous_y_vars[y_var] = reaction\\n\\n logger.debug(\\\"Control variables created\\\")\\n\\n # Add number of heterologous switch contraint constraint\\n heterologous_turn_on = model.solver.interface.Constraint(\\n optlang.symbolics.Add(*heterologous_y_vars), lb=0, ub=0, name=\\\"heterologous_reaction_constraint\\\"\\n )\\n model.solver.add(heterologous_turn_on)\\n\\n # Set the objective\\n primal_objective = model.solver.objective\\n dual_objective = interface.Objective.clone(\\n dual_problem.objective, model=model.solver\\n )\\n\\n switch_objective=interface.Objective(heterologous_turn_on.expression, direction='min')\\n\\n full_objective = interface.Objective(primal_objective.expression-dual_objective.expression, direction=\\\"max\\\")\\n model.objective = full_objective\\n\\n return model,primal_objective,dual_objective,full_objective,switch_objective\",\n \"def build_mlp(input_, config):\\n current_input = input_\\n print(current_input)\\n for i in range(len(config['fcc_layers']) - 1):\\n current_input = tf.keras.layers.Dense(\\n units=config['fcc_layers'][i], activation='tanh',\\n name='fcc_layer_{}'.format(i + 1))(current_input)\\n current_input = tf.keras.layers.Dropout(\\n rate=config['dropout'], name='dropout_{}'.format(i + 1))(current_input)\\n cascade_embedding_layer = tf.keras.layers.Dense(\\n units=config['fcc_layers'][-1], activation='tanh',\\n name='cascade_embedding_layer')(current_input)\\n cascade_embedding_layer_do = tf.keras.layers.Dropout(\\n rate=config['dropout'],\\n name='cascade_embedding_dropout')(cascade_embedding_layer)\\n prediction_layer = tf.keras.layers.Dense(\\n units=1, activation='sigmoid',\\n name='prediction_layer')(cascade_embedding_layer_do)\\n return cascade_embedding_layer, prediction_layer\",\n \"def construct_model():\\n import lbann\\n\\n # Layer graph\\n input = lbann.Input(target_mode='N/A', name='inp_data')\\n # data is 64*64*4 images + 15 scalar + 5 param\\n #inp_slice = lbann.Slice(input, axis=0, slice_points=\\\"0 16399 16404\\\",name='inp_slice')\\n inp_slice = lbann.Slice(input, axis=0, slice_points=str_list([0,args.ydim,args.ydim+5]),name='inp_slice')\\n gt_y = lbann.Identity(inp_slice,name='gt_y')\\n gt_x = lbann.Identity(inp_slice, name='gt_x') #param not used\\n\\n zero = lbann.Constant(value=0.0,num_neurons='1',name='zero')\\n one = lbann.Constant(value=1.0,num_neurons='1',name='one')\\n\\n z_dim = 20 #Latent space dim\\n\\n z = lbann.Gaussian(mean=0.0,stdev=1.0, neuron_dims=\\\"20\\\")\\n model = macc_models.MACCWAE(args.zdim,args.ydim,cf=args.mcf,use_CNN=args.useCNN)\\n d1_real, d1_fake, d_adv, pred_y = model(z,gt_y)\\n\\n d1_real_bce = lbann.SigmoidBinaryCrossEntropy([d1_real,one],name='d1_real_bce')\\n d1_fake_bce = lbann.SigmoidBinaryCrossEntropy([d1_fake,zero],name='d1_fake_bce')\\n d_adv_bce = lbann.SigmoidBinaryCrossEntropy([d_adv,one],name='d_adv_bce')\\n img_loss = lbann.MeanSquaredError([pred_y,gt_y])\\n rec_error = lbann.L2Norm2(lbann.WeightedSum([pred_y,gt_y], scaling_factors=\\\"1 -1\\\"))\\n\\n layers = list(lbann.traverse_layer_graph(input))\\n # Setup objective function\\n weights = set()\\n src_layers = []\\n dst_layers = []\\n for l in layers:\\n if(l.weights and \\\"disc0\\\" in l.name and \\\"instance1\\\" in l.name):\\n src_layers.append(l.name)\\n #freeze weights in disc2\\n if(l.weights and \\\"disc1\\\" in l.name):\\n dst_layers.append(l.name)\\n for idx in range(len(l.weights)):\\n l.weights[idx].optimizer = lbann.NoOptimizer()\\n weights.update(l.weights)\\n l2_reg = lbann.L2WeightRegularization(weights=weights, scale=1e-4)\\n d_adv_bce = lbann.LayerTerm(d_adv_bce,scale=0.01)\\n obj = lbann.ObjectiveFunction([d1_real_bce,d1_fake_bce,d_adv_bce,img_loss,rec_error,l2_reg])\\n # Initialize check metric callback\\n metrics = [lbann.Metric(img_loss, name='recon_error')]\\n #pred_y = macc_models.MACCWAE.pred_y_name\\n callbacks = [lbann.CallbackPrint(),\\n lbann.CallbackTimer(),\\n lbann.CallbackSaveModel(dir=args.dump_models),\\n lbann.CallbackReplaceWeights(source_layers=list2str(src_layers),\\n destination_layers=list2str(dst_layers),\\n batch_interval=2)]\\n\\n if(args.ltfb_batch_interval > 0) :\\n callbacks.append(lbann.CallbackLTFB(batch_interval=args.ltfb_batch_interval,metric='recon_error',\\n low_score_wins=True,\\n exchange_hyperparameters=True))\\n\\n # Construct model\\n return lbann.Model(args.num_epochs,\\n serialize_io=True,\\n weights=weights,\\n layers=layers,\\n metrics=metrics,\\n objective_function=obj,\\n callbacks=callbacks)\",\n \"def _child_build_new_model(self):\\n self._build_new_gp()\",\n \"def _build_model(self):\\n\\n with tf.variable_scope(self.name):\\n # adds placeholders, data_normalization and data_noise if desired. Also adds a placeholder for dropout probability\\n self.layer_in_x, self.layer_in_y = self._build_input_layers()\\n\\n # create core multi-layer perceptron\\n mlp_output_dim = 2 * self.ndim_y * self.n_centers + self.n_centers\\n core_network = MLP(\\n name=\\\"core_network\\\",\\n input_layer=self.layer_in_x,\\n output_dim=mlp_output_dim,\\n hidden_sizes=self.hidden_sizes,\\n hidden_nonlinearity=self.hidden_nonlinearity,\\n output_nonlinearity=None,\\n weight_normalization=self.weight_normalization,\\n dropout_ph=self.dropout_ph if self.dropout else None\\n )\\n\\n core_output_layer = core_network.output_layer\\n\\n # slice output of MLP into three equally sized parts for loc, scale and mixture weights\\n slice_layer_locs = L.SliceLayer(core_output_layer, indices=slice(0, self.ndim_y * self.n_centers), axis=-1)\\n slice_layer_scales = L.SliceLayer(core_output_layer, indices=slice(self.ndim_y * self.n_centers, 2 * self.ndim_y * self.n_centers), axis=-1)\\n slice_layer_weights = L.SliceLayer(core_output_layer, indices=slice(2 * self.ndim_y * self.n_centers, mlp_output_dim), axis=-1)\\n\\n # locations mixture components\\n self.reshape_layer_locs = L.ReshapeLayer(slice_layer_locs, (-1, self.n_centers, self.ndim_y))\\n self.locs = L.get_output(self.reshape_layer_locs)\\n\\n # scales of the mixture components\\n reshape_layer_scales = L.ReshapeLayer(slice_layer_scales, (-1, self.n_centers, self.ndim_y))\\n self.softplus_layer_scales = L.NonlinearityLayer(reshape_layer_scales, nonlinearity=tf.nn.softplus)\\n self.scales = L.get_output(self.softplus_layer_scales)\\n\\n # weights of the mixture components\\n self.logits = L.get_output(slice_layer_weights)\\n self.softmax_layer_weights = L.NonlinearityLayer(slice_layer_weights, nonlinearity=tf.nn.softmax)\\n self.weights = L.get_output(self.softmax_layer_weights)\\n\\n # # put mixture components together\\n self.y_input = L.get_output(self.layer_in_y)\\n self.cat = cat = Categorical(logits=self.logits)\\n self.components = components = [MultivariateNormalDiag(loc=loc, scale_diag=scale) for loc, scale\\n in zip(tf.unstack(self.locs, axis=1), tf.unstack( self.scales, axis=1))]\\n self.mixture = mixture = Mixture(cat=cat, components=components, value=tf.zeros_like(self.y_input))\\n\\n # regularization\\n self._add_softmax_entropy_regularization()\\n self._add_l1_l2_regularization(core_network)\\n\\n # tensor to store samples\\n self.samples = mixture.sample() #TODO either use it or remove it\\n\\n # tensor to compute probabilities\\n if self.data_normalization:\\n self.pdf_ = mixture.prob(self.y_input) / tf.reduce_prod(self.std_y_sym)\\n self.log_pdf_ = mixture.log_prob(self.y_input) - tf.reduce_sum(tf.log(self.std_y_sym))\\n else:\\n self.pdf_ = mixture.prob(self.y_input)\\n self.log_pdf_ = mixture.log_prob(self.y_input)\\n\\n # symbolic tensors for getting the unnormalized mixture components\\n if self.data_normalization:\\n self.scales_unnormalized = self.scales * self.std_y_sym\\n self.locs_unnormalized = self.locs * self.std_y_sym + self.mean_y_sym\\n else:\\n self.scales_unnormalized = self.scales\\n self.locs_unnormalized = self.locs\\n\\n # initialize LayersPowered --> provides functions for serializing tf models\\n LayersPowered.__init__(self, [self.softmax_layer_weights, self.softplus_layer_scales, self.reshape_layer_locs,\\n self.layer_in_y])\",\n \"def create_model_and_dataflow_for_inference(builder, conf, inputs):\\r\\n\\r\\n conformer_encoder = conformer_builder.ConformerEncoder(builder,\\r\\n input_dim=conf.mel_bands,\\r\\n sequence_length=conf.max_spectrogram_length,\\r\\n encoder_dim=conf.encoder_dim,\\r\\n attention_heads=conf.attention_heads,\\r\\n encoder_layers_per_stage=conf.encoder_layers_per_stage,\\r\\n dropout_rate=conf.dropout_rate,\\r\\n cnn_module_kernel=conf.kernel_size,\\r\\n subsampling_factor=conf.subsampling_factor,\\r\\n dtype=conf.precision)\\r\\n\\r\\n conformer_decoder = conformer_builder.ConformerDecoder(builder,\\r\\n encoder_dim=conf.encoder_dim,\\r\\n num_symbols=conf.num_symbols,\\r\\n for_inference=True,\\r\\n dtype=conf.precision)\\r\\n\\r\\n encoder_output = conformer_encoder(inputs[\\\"mel_spec_input\\\"])\\r\\n\\r\\n with builder.virtualGraph(conf.num_pipeline_stages - 1):\\r\\n probs_output = conformer_decoder(encoder_output)\\r\\n\\r\\n anchor_types_dict = {\\r\\n probs_output: popart.AnchorReturnType(\\\"ALL\\\"),\\r\\n }\\r\\n\\r\\n proto = builder.getModelProto()\\r\\n dataflow = popart.DataFlow(conf.batches_per_step, anchor_types_dict)\\r\\n\\r\\n return proto, probs_output, dataflow\",\n \"def make_mlp_model():\\n return snt.Sequential([\\n snt.nets.MLP([LATENT_SIZE] * NUM_LAYERS, activate_final=True),\\n snt.LayerNorm()\\n ])\",\n \"def _create_generate_input(self):\\n self.keep_prob = 1.\",\n \"def MILP_1(self,xData,uData):\\n print \\\"\\\\n\\\",\\\"*\\\"*80,\\\"\\\\n\\\",\\\"MILP 1: Parameter Estimation\\\\n\\\",\\\"*\\\"*80 \\n model=Model(\\\"parameters\\\")\\n outflow={}\\n d={}\\n bigM=500\\n Q_out={}\\n Q_in={}\\n N=max(l[1] for l in xData.keys())\\n print \\\"x Data size is\\\",N\\n N=max(l[1] for l in uData.keys())\\n print \\\"u Data size is\\\",N\\n for l in self.links:\\n l.d=model.addVar(lb=0,ub=200,obj=0*l.type==\\\"road\\\") \\n for t in range(1,N):\\n d[l,t]=model.addVar(lb=0,ub=200,obj=1)\\n for k in l.outgoing:\\n outflow[l,k,t]=model.addVar(lb=0,ub=200)\\n self.c[l,k]=model.addVar(lb=20,ub=200)\\n self.beta[l,k]=model.addVar(lb=0.2,ub=0.8)\\n self.alpha[l,k]=model.addVar(lb=0,ub=1)\\n self.M[l,k]=model.addVar(lb=0,ub=200)\\n d[\\\"outflow-1\\\",l,k,t]=model.addVar(vtype=GRB.BINARY) \\n d[\\\"outflow-2\\\",l,k,t]=model.addVar(vtype=GRB.BINARY) \\n model.update()\\n for t in range(1,N):\\n for l in self.links:\\n if True:\\n Q_out[l,t]=LinExpr()\\n Q_in[l,t]=LinExpr()\\n Q_out[l,t].addConstant(0)\\n Q_in[l,t].addConstant(0)\\n for k in l.outgoing:\\n model.addConstr(outflow[l,k,t]<=self.beta[l,k]*uData[l,t]*xData[l,t])\\n model.addConstr(outflow[l,k,t]<=self.M[l,k])\\n model.addConstr(outflow[l,k,t]<=self.c[l,k]-self.alpha[l,k]*xData[k,t])\\n model.addConstr(outflow[l,k,t]>=self.beta[l,k]*uData[l,t]*xData[l,t]+bigM*d[\\\"outflow-1\\\",l,k,t]-bigM)\\n model.addConstr(outflow[l,k,t]>=self.M[l,k]+bigM*d[\\\"outflow-2\\\",l,k,t]-bigM)\\n model.addConstr(outflow[l,k,t]>=self.c[l,k]-self.alpha[l,k]*xData[k,t]-bigM*d[\\\"outflow-1\\\",l,k,t]-bigM*d[\\\"outflow-2\\\",l,k,t])\\n Q_out[l,t].add(outflow[l,k,t])\\n for k in l.incoming:\\n Q_in[l,t].add(outflow[k,l,t])\\n if l.type==\\\"road\\\":\\n model.addConstr(xData[l,t+1]<=xData[l,t]- Q_out[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival) \\n else:\\n model.addConstr(xData[l,t+1]<=xData[l,t]- uData[l,t]*xData[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival)\\n for l in self.links:\\n sum=LinExpr()\\n for k in l.outgoing:\\n sum.add(self.beta[l,k])\\n model.addConstr(sum>=0)\\n \\n# J=QuadExpr()\\n# for l in self.links:\\n# for t in range(1,N):\\n# if l.type==\\\"road\\\":\\n# J.add(d[l,t]*d[l,t])\\n# model.setObjective(J)\\n model.optimize()\\n for l in self.links:\\n l.d=l.d.X\\n for k in l.outgoing:\\n self.beta[l,k]=self.beta[l,k].X\\n self.c[l,k]=self.c[l,k].X\\n self.alpha[l,k]=self.alpha[l,k].X\\n self.M[l,k]=self.M[l,k].X\\n for l in self.links:\\n for t in range(1,N):\\n l.d=max(d[l,t].X,l.d)\\n \\n \\n\\n \\n if True:\\n for t in range(1,N):\\n print \\\"*\\\"*80,\\\"time=\\\",t\\n for l in self.links:\\n print \\\"\\\\n\\\",l,\\\"x is\\\",xData[l,t],\\\"u is\\\",uData[l,t],\\\"x+ is\\\",xData[l,t+1]\\n for k in l.outgoing:\\n print k,\\\"beta:\\\",self.beta[l,k],\\\"outflow\\\",outflow[l,k,t].X\",\n \"def CreateMdp(self):\\n self.states = {\\\"\\\":0}\\n self.actions = {}\\n self.rewards = []\\n stateNodes = filter(lambda x: x.type not in [\\\"ActionNode\\\",\\\"MotorNode\\\"],self.nodes.values())\\n actionNodes = filter(lambda x: x.type is \\\"ActionNode\\\",self.nodes.values())\\n for i in (range(1,len(stateNodes)+1)):\\n self.states[stateNodes[i-1].name] = i\\n for i in (range(0,len(actionNodes))):\\n self.actions[i] = actionNodes[i].name\\n if(len(self.states) < 2 or len(self.actions) < 1):\\n print(\\\"Error: States and actions must be non-empty\\\")\\n # Transition probabilities: equal probabilities\\n self.P = []\\n for i in (range(0,len(self.actions))):\\n b = []\\n for j in (range(0,len(self.states))):\\n a = []\\n for k in (range(0,len(self.states))):\\n a.append(1.0/len(self.states))\\n b.append(a)\\n self.P.append(b)\\n\\n # Rewards\\n self.R = []\\n for i in (range(0,len(self.actions))):\\n b = []\\n for j in (range(0,len(self.states))):\\n a = []\\n for k in (range(0,len(self.states))):\\n a.append(actionNodes[i].reward)\\n b.append(a)\\n self.R.append(b)\\n\\n print(\\\"Number of states: \\\" + str(len(self.states)))\\n print(\\\"Number of actions: \\\" + str(len(self.actions)))\\n self.vi = mdptoolbox.mdp.QLearning(np.array(self.P), np.array(self.R), 0.5)\\n self.vi.run()\\n print(self.vi.Q)\",\n \"def generate_beam(env, dec, decoder_args, beam_size, length_penalty, early_stopping, max_len=100):\\n\\n # check inputs\\n trg, enc_src, trg_mask, src_mask = decoder_args\\n src_enc = enc_src\\n src_len = enc_src \\n\\n #assert src_enc.size(0) == src_len.size(0)\\n assert beam_size >= 1\\n\\n # batch size / number of words\\n bs = len(src_enc)\\n n_words = env.n_words\\n breakpoint()\\n\\n # expand to beam size the source latent representations / source lengths\\n src_enc = src_enc.unsqueeze(1).expand((bs, beam_size) + src_enc.shape[1:]).contiguous().view((bs * beam_size,) + src_enc.shape[1:])\\n #src_len = src_len.unsqueeze(1).expand(bs, beam_size).contiguous().view(-1)\\n\\n # generated sentences (batch with beam current hypotheses)\\n #generated = src_len.new(max_len, bs * beam_size) # upcoming output\\n #generated.fill_(env.pad_index) # fill upcoming ouput with <PAD>\\n #generated[0].fill_(env.eos_index) # we use <EOS> for <BOS> everywhere\\n\\n # generated hypotheses\\n generated_hyps = [BeamHypotheses(beam_size, max_len, length_penalty, early_stopping) for _ in range(bs)]\\n\\n # positions\\n positions = src_len.new(max_len).long()\\n #positions = torch.arange(max_len, out=positions).unsqueeze(1).expand_as(generated)\\n\\n # scores for each sentence in the beam\\n beam_scores = src_enc.new(bs, beam_size).fill_(0)\\n beam_scores[:, 1:] = -1e9\\n beam_scores = beam_scores.view(-1)\\n\\n # current position\\n cur_len = 1\\n\\n # cache compute states\\n cache = {'slen': 0}\\n\\n # done sentences\\n done = [False for _ in range(bs)]\\n breakpoint()\\n while cur_len < max_len:\\n dec(trg[:,:-1], enc_src, trg_mask, src_mask)\\n # compute word scores\\n tensor = decoder(\\n x=generated[:cur_len],\\n lengths=src_len.new(bs * beam_size).fill_(cur_len),\\n positions=positions[:cur_len],\\n causal=True,\\n src_enc=src_enc,\\n src_len=src_len,\\n cache=cache\\n )\\n assert tensor.size() == (1, bs * beam_size, env.dim)\\n tensor = tensor.data[-1, :, :] # (bs * beam_size, dim)\\n scores = env.proj(tensor) # (bs * beam_size, n_words)\\n scores = F.log_softmax(scores, dim=-1) # (bs * beam_size, n_words)\\n assert scores.size() == (bs * beam_size, n_words)\\n\\n # select next words with scores\\n _scores = scores + beam_scores[:, None].expand_as(scores) # (bs * beam_size, n_words)\\n _scores = _scores.view(bs, beam_size * n_words) # (bs, beam_size * n_words)\\n\\n next_scores, next_words = torch.topk(_scores, 2 * beam_size, dim=1, largest=True, sorted=True)\\n assert next_scores.size() == next_words.size() == (bs, 2 * beam_size)\\n\\n # next batch beam content\\n # list of (bs * beam_size) tuple(next hypothesis score, next word, current position in the batch)\\n next_batch_beam = []\\n\\n # for each sentence\\n for sent_id in range(bs):\\n\\n # if we are done with this sentence\\n done[sent_id] = done[sent_id] or generated_hyps[sent_id].is_done(next_scores[sent_id].max().item())\\n if done[sent_id]:\\n next_batch_beam.extend([(0, env.pad_index, 0)] * beam_size) # pad the batch\\n continue\\n\\n # next sentence beam content\\n next_sent_beam = []\\n\\n # next words for this sentence\\n for idx, value in zip(next_words[sent_id], next_scores[sent_id]):\\n\\n # get beam and word IDs\\n beam_id = idx // n_words\\n word_id = idx % n_words\\n\\n # end of sentence, or next word\\n if word_id == env.eos_index or cur_len + 1 == max_len:\\n generated_hyps[sent_id].add(generated[:cur_len, sent_id * beam_size + beam_id].clone().cpu(), value.item())\\n else:\\n next_sent_beam.append((value, word_id, sent_id * beam_size + beam_id))\\n\\n # the beam for next step is full\\n if len(next_sent_beam) == beam_size:\\n break\\n\\n # update next beam content\\n assert len(next_sent_beam) == 0 if cur_len + 1 == max_len else beam_size\\n if len(next_sent_beam) == 0:\\n next_sent_beam = [(0, env.pad_index, 0)] * beam_size # pad the batch\\n next_batch_beam.extend(next_sent_beam)\\n assert len(next_batch_beam) == beam_size * (sent_id + 1)\\n\\n # sanity check / prepare next batch\\n assert len(next_batch_beam) == bs * beam_size\\n beam_scores = beam_scores.new([x[0] for x in next_batch_beam])\\n beam_words = generated.new([x[1] for x in next_batch_beam])\\n beam_idx = src_len.new([x[2] for x in next_batch_beam])\\n\\n # re-order batch and internal states\\n generated = generated[:, beam_idx]\\n generated[cur_len] = beam_words\\n for k in cache.keys():\\n if k != 'slen':\\n cache[k] = (cache[k][0][beam_idx], cache[k][1][beam_idx])\\n\\n # update current length\\n cur_len = cur_len + 1\\n\\n # stop when we are done with each sentence\\n if all(done):\\n break\\n\\n # def get_coeffs(s):\\n # roots = [int(s[i + 2]) for i, c in enumerate(s) if c == 'x']\\n # poly = np.poly1d(roots, r=True)\\n # coeffs = list(poly.coefficients.astype(np.int64))\\n # return [c % 10 for c in coeffs], coeffs\\n\\n # visualize hypotheses\\n # print([len(x) for x in generated_hyps], cur_len)\\n # globals().update( locals() );\\n # !import code; code.interact(local=vars())\\n # for ii in range(bs):\\n # for ss, ww in sorted(generated_hyps[ii].hyp, key=lambda x: x[0], reverse=True):\\n # hh = \\\" \\\".join(self.id2word[x] for x in ww.tolist())\\n # print(f\\\"{ss:+.4f} {hh}\\\")\\n # # cc = get_coeffs(hh[4:])\\n # # print(f\\\"{ss:+.4f} {hh} || {cc[0]} || {cc[1]}\\\")\\n # print(\\\"\\\")\\n\\n # select the best hypotheses\\n tgt_len = src_len.new(bs)\\n best = []\\n\\n for i, hypotheses in enumerate(generated_hyps):\\n best_hyp = max(hypotheses.hyp, key=lambda x: x[0])[1]\\n tgt_len[i] = len(best_hyp) + 1 # +1 for the <EOS> symbol\\n best.append(best_hyp)\\n\\n # generate target batch\\n decoded = src_len.new(tgt_len.max().item(), bs).fill_(env.pad_index)\\n for i, hypo in enumerate(best):\\n decoded[:tgt_len[i] - 1, i] = hypo\\n decoded[tgt_len[i] - 1, i] = env.eos_index\\n\\n # sanity check\\n assert (decoded == env.eos_index).sum() == 2 * bs\\n\\n return decoded, tgt_len, generated_hyps\",\n \"def create_model():\\n # Get list of all syllables: [\\\"<s>\\\", \\\"AH\\\", \\\"</s>\\\", \\\"<s>\\\", \\\"T\\\", ...]\\n syllabifier = Syllabifier()\\n all_syllables = syllabifier.all_syllables()\\n\\n # Count conditional probabilties of phoneme tuples\\n tcf = TrigramCollocationFinder.from_words(all_syllables)\\n bcf = BigramCollocationFinder.from_words(all_syllables)\\n tri_dict = dict(sorted(tcf.ngram_fd.items(), key=lambda t: (-t[1], t[0])))\\n bi_dict = dict(sorted(bcf.ngram_fd.items(), key=lambda t: (-t[1], t[0])))\\n\\n # Create dictionary to count cond prob all phoneme tuples\\n accepted_phonemes = [i[0] for i in cmudict.phones()]\\n accepted_phonemes.append('<s>')\\n accepted_phonemes.append('</s>')\\n phoneme_tups = [p for p in itertools.product(accepted_phonemes, repeat=3)]\\n cond_probs_dict = dict([(char, 0) for char in phoneme_tups])\\n\\n for t in tri_dict:\\n p1, p2, p3 = t[0], t[1], t[2]\\n tri_count = tri_dict[t]\\n bi_count = bi_dict[(p1, p2)]\\n if bi_count > 1:\\n cond_prob = tri_count * 1.0 / bi_count\\n else:\\n cond_prob = 0.0\\n cond_probs_dict[(p1, p2, p3)] = cond_prob\\n\\n pickle.dump(cond_probs_dict, open(COND_PROBS_PATH, \\\"wb\\\"))\\n return\",\n \"def build_model(self) -> DM:\\n model = DM()\\n model[self.modelroot] = content = DM()\\n\\n content['key'] = self.key\\n content['id'] = self.id\\n content['system-family'] = self.family\\n for cp in self.parameters:\\n content.append('calculation-parameter', DM(cp))\\n\\n self._set_model(model)\\n return model\",\n \"def _create_inference_model(pl_module):\\n new_model = get_model(pl_module.hparams)\\n new_model.normalization_metrics = AnomalyScoreDistribution().cpu()\\n new_model.load_state_dict(pl_module.state_dict())\\n return new_model\",\n \"def _getBMat(self):\\n\\n \\\"\\\"\\\"B matrix is just a mass matrix, can be easily assembled\\n through fenics. However, the ordering in Fenics is not the\\n mesh ordering. So we build a temp matrix and then use the\\n vertex to dof map to get the right ordering interms of our\\n mesh nodes.\\n \\\"\\\"\\\"\\n\\n # create function space of order 1. For KL, we only restrict\\n # to first order spaces.\\n V = FunctionSpace(self._mesh, \\\"CG\\\", 1)\\n # Define basis and bilinear form\\n u = TrialFunction(V)\\n v = TestFunction(V)\\n a = u * v * dx\\n # assemble in a temp matrix\\n B_temp = assemble(a)\\n\\n # create petsc matrix B\\n B = PETSc.Mat().create()\\n B.setType('aij')\\n B.setSizes(self.domain.getNodes(), self.domain.getNodes())\\n B.setUp()\\n\\n # store the value in a a temp array B_ij\\n B_ij = B_temp.array()\\n\\n # get the vertex to dof map\\n v_to_d_map = vertex_to_dof_map(V)\\n\\n print '---------------------------'\\n print '---------------------------'\\n print ' Building Mass Matrix '\\n print '---------------------------'\\n print '---------------------------'\\n for node_i in range(0, self.domain.getNodes()):\\n for node_j in range(node_i, self.domain.getNodes()):\\n B_ij_nodes = B_ij[v_to_d_map[node_i], v_to_d_map[node_j]]\\n if B_ij_nodes > 0:\\n B.setValue(node_i, node_j, B_ij_nodes)\\n B.setValue(node_j, node_i, B_ij_nodes)\\n\\n B.assemblyBegin()\\n B.assemblyEnd()\\n print '---------------------------'\\n print '---------------------------'\\n print ' Finished Mass Matrix '\\n print '---------------------------'\\n print '---------------------------'\\n return B\",\n \"def get_model_with_properties():\\n \\n m = ConcreteModel()\\n\\n # ------------------------------------------------------------------\\n # Data\\n # ------------------------------------------------------------------\\n\\n m.np = 25 # Number of possible tays\\n m.c = 4 # Number of components\\n m.lc = 1 # Light component\\n m.hc = 4 # Heavy component\\n\\n #### Constant parameters\\n m.Rgas = 8.314 # Ideal gas constant in J/mol K\\n m.Tref = 298.15 # Reference temperature in K\\n\\n #### Product specifications\\n m.xspec_lc = 0.99 # Final liquid composition for methanol (1)\\n m.xspec_hc = 0.99 # Fnal liquid composition for butanol (4)\\n m.xspec_inter2 = 0.99 # Final liquid composition for ethanol (2)\\n m.xspec_inter3 = 0.99 # Final liquid composition for propanol (3)\\n m.Ddes = 50 # Final flowrate in distillate in mol/s\\n m.Bdes = 50 # Final flowrate in bottoms in mol/s\\n m.Sdes = 50 # Final flowrate in side product streams in mol/s\\n\\n # #### Known initial values\\n m.Fi = m.Ddes + m.Bdes + 2 * m.Sdes # Side feed flowrate in mol/s\\n m.Vi = 400 # Initial value for vapor flowrate in mol/s\\n m.Li = 400 # Initial value for liquid flowrate in mol/s\\n\\n m.Tf = 358 # Side feed temperature in K\\n\\n m.Preb = 1.2 # Reboiler pressure in bar\\n m.Pbot = 1.12 # Bottom-most tray pressure in bar\\n m.Ptop = 1.08 # Top-most tray pressure in bar\\n m.Pcon = 1.05 # Condenser pressure in bar\\n m.Pf = 1.02\\n\\n m.rr0 = 0.893 # Internal reflux ratio initial value\\n m.bu0 = 0.871 # Internal reflux ratio initial value\\n\\n\\n #### Scaling factors\\n m.Hscale = 1e3 \\n m.Qscale = 1e-3 \\n\\n \\n #### Constants for the calculation of liquid heat capacity\\n m.cpc = {} # Constant 1 for liquid heat capacity \\n m.cpc2 = {} # Constant 2 for liquid heat capacity \\n m.cpc[1] = m.Rgas \\n m.cpc[2] = 1\\n m.cpc2['A', 1] = 1 / 100\\n m.cpc2['B', 1] = 1 / 1e4\\n m.cpc2['A', 2] = 1\\n m.cpc2['B', 2] = 1\\n\\n\\n # ------------------------------------------------------------------\\n # Physical Properties\\n #\\n # Notation:\\n # MW ........................ molecular weight in g/gmol\\n # TB ........................ boiling point temperature in K\\n # TC ........................ critical temperature in K\\n # PC ........................ critical pressure in bar\\n # w ........................ acentric factor\\n # lden ...................... liquid density g/m3,\\n # dHvap ..................... heat of vaporization in J/mol.\\n # vpA, vpB, vpC, and vpD .... vapor pressure constants\\n # cpA, cpB, cpC, and cpD .... heat capacity constants J/mol:\\n # 1 for liq and 2 for vapor phase\\n #\\n # Reference A: R.C. Reid, J.M. Prausnitz and B.E. Poling,\\n # \\\"The Properties of gases and liquids\\\", 1987 and 2004 Eds.\\n #\\n # ------------------------------------------------------------------\\n\\n m.prop = {} # Properties of components:\\n cpL = {} # Ruczika-D method for liquid heat capacity calculation\\n # (Reference A, page 6.20)\\n sumA = {}\\n sumB = {}\\n sumC = {}\\n cpL['a', 'C(H3)(C)'] = 4.19845\\n cpL['b', 'C(H3)(C)'] = -0.312709\\n cpL['c', 'C(H3)(C)'] = 0.178609\\n cpL['a', 'C(H2)(C2)'] = 2.7345\\n cpL['b', 'C(H2)(C2)'] = 0.122732\\n cpL['c', 'C(H2)(C2)'] = -0.123482\\n cpL['a', 'C(H2)(C)(O)'] = 0.517007\\n cpL['b', 'C(H2)(C)(O)'] = 1.26631\\n cpL['c', 'C(H2)(C)(O)'] = -0.0939713\\n cpL['a', 'O(H)(C)'] = 16.1555\\n cpL['b', 'O(H)(C)'] = -11.938\\n cpL['c', 'O(H)(C)'] = 2.85117\\n cpL['a', 'C(H3)(O)'] = 3.70344\\n cpL['b', 'C(H3)(O)'] = -1.12884\\n cpL['c', 'C(H3)(O)'] = 0.51239\\n sumA[1] = (cpL['a', 'C(H3)(O)']\\n + cpL['a', 'O(H)(C)']) \\n sumB[1] = (cpL['b', 'C(H3)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[1] = (cpL['c', 'C(H3)(O)']\\n + cpL['c', 'O(H)(C)'])\\n sumA[2] = (cpL['a', 'C(H3)(C)']\\n + cpL['a', 'C(H2)(C)(O)']\\n + cpL['a', 'O(H)(C)'])\\n sumB[2] = (cpL['b', 'C(H3)(C)']\\n + cpL['b', 'C(H2)(C)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[2] = (cpL['c', 'C(H3)(C)']\\n + cpL['c', 'C(H2)(C)(O)']\\n + cpL['c', 'O(H)(C)'])\\n sumA[3] = (cpL['a', 'C(H3)(C)']\\n + cpL['a', 'C(H2)(C2)']\\n + cpL['a', 'C(H2)(C)(O)']\\n + cpL['a', 'O(H)(C)'])\\n sumB[3] = (cpL['b', 'C(H3)(C)']\\n + cpL['b', 'C(H2)(C2)']\\n + cpL['b', 'C(H2)(C)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[3] = (cpL['c', 'C(H3)(C)']\\n + cpL['c', 'C(H2)(C2)']\\n + cpL['c', 'C(H2)(C)(O)']\\n + cpL['c', 'O(H)(C)'])\\n sumA[4] = (cpL['a', 'C(H3)(C)']\\n + 2 * cpL['a', 'C(H2)(C2)']\\n + cpL['a', 'C(H2)(C)(O)']\\n + cpL['a', 'O(H)(C)'])\\n sumB[4] = (cpL['b', 'C(H3)(C)']\\n + 2 * cpL['b', 'C(H2)(C2)']\\n + cpL['b', 'C(H2)(C)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[4] = (cpL['c', 'C(H3)(C)']\\n + 2 * cpL['c', 'C(H2)(C2)']\\n + cpL['c', 'C(H2)(C)(O)']\\n + cpL['c', 'O(H)(C)'])\\n\\n ## Methanol: component 1\\n m.prop[1, 'MW'] = 32.042\\n m.prop[1, 'TB'] = 337.7\\n m.prop[1, 'TC'] = 512.6\\n m.prop[1, 'PC'] = 80.9\\n m.prop[1, 'w'] = 0.556\\n m.prop[1, 'lden'] = 792e3\\n m.prop[1, 'dHvap'] = 38.376e3\\n m.prop[1, 'vpA'] = -8.54796\\n m.prop[1, 'vpB'] = 0.76982\\n m.prop[1, 'vpC'] = -3.10850\\n m.prop[1, 'vpD'] = 1.54481\\n m.prop[1, 'cpA', 1] = sumA[1]\\n m.prop[1, 'cpB', 1] = sumB[1]\\n m.prop[1, 'cpC', 1] = sumC[1]\\n m.prop[1, 'cpD', 1] = 0\\n m.prop[1, 'cpA', 2] = 2.115e1\\n m.prop[1, 'cpB', 2] = 7.092e-2\\n m.prop[1, 'cpC', 2] = 2.587e-5\\n m.prop[1, 'cpD', 2] = -2.852e-8\\n\\n\\n ## Ethanol: component 2\\n m.prop[2, 'MW'] = 46.069\\n m.prop[2, 'TB'] = 351.4\\n m.prop[2, 'TC'] = 513.9\\n m.prop[2, 'PC'] = 61.4\\n m.prop[2, 'w'] = 0.644\\n m.prop[2, 'lden'] = 789.3e3\\n m.prop[2, 'dHvap'] = 42.698e3\\n m.prop[2, 'vpA'] = -8.51838\\n m.prop[2, 'vpB'] = 0.34163\\n m.prop[2, 'vpC'] = -5.73683\\n m.prop[2, 'vpD'] = 8.32581\\n m.prop[2, 'cpA', 1] = sumA[2]\\n m.prop[2, 'cpB', 1] = sumB[2]\\n m.prop[2, 'cpC', 1] = sumC[2]\\n m.prop[2, 'cpD', 1] = 0\\n m.prop[2, 'cpA', 2] = 9.014\\n m.prop[2, 'cpB', 2] = 2.141e-1\\n m.prop[2, 'cpC', 2] = -8.390e-5\\n m.prop[2, 'cpD', 2] = 1.373e-9\\n\\n\\n ## Propanol: component 3\\n m.prop[3, 'MW'] = 60.096\\n m.prop[3, 'TB'] = 370.3\\n m.prop[3, 'TC'] = 536.8\\n m.prop[3, 'PC'] = 51.7\\n m.prop[3, 'w'] = 0.623\\n m.prop[3, 'lden'] = 804e3\\n m.prop[3, 'dHvap'] = 47.763e3\\n m.prop[3, 'vpA'] = -8.05594\\n m.prop[3, 'vpB'] = 4.25183e-2\\n m.prop[3, 'vpC'] = -7.51296\\n m.prop[3, 'vpD'] = 6.89004\\n m.prop[3, 'cpA', 1] = sumA[3]\\n m.prop[3, 'cpB', 1] = sumB[3]\\n m.prop[3, 'cpC', 1] = sumC[3]\\n m.prop[3, 'cpD', 1] = 0\\n m.prop[3, 'cpA', 2] = 2.47\\n m.prop[3, 'cpB', 2] = 3.325e-1\\n m.prop[3, 'cpC', 2] = -1.855e-4\\n m.prop[3, 'cpD', 2] = 4.296e-8\\n\\n\\n ## Butanol: component 4\\n m.prop[4, 'MW'] = 74.123\\n m.prop[4, 'TB'] = 390.9\\n m.prop[4, 'TC'] = 563.1\\n m.prop[4, 'PC'] = 44.2\\n m.prop[4, 'w'] = 0.593\\n m.prop[4, 'lden'] = 810e3\\n m.prop[4, 'dHvap'] = 52.607e3\\n m.prop[4, 'vpA'] = -8.00756\\n m.prop[4, 'vpB'] = 0.53783\\n m.prop[4, 'vpC'] = -9.34240\\n m.prop[4, 'vpD'] = 6.68692\\n m.prop[4, 'cpA', 1] = sumA[4]\\n m.prop[4, 'cpB', 1] = sumB[4]\\n m.prop[4, 'cpC', 1] = sumC[4]\\n m.prop[4, 'cpD', 1] = 0\\n m.prop[4, 'cpA', 2] = 3.266\\n m.prop[4, 'cpB', 2] = 4.18e-1\\n m.prop[4, 'cpC', 2] = -2.242e-4\\n m.prop[4, 'cpD', 2] = 4.685e-8\\n\\n\\n return m\",\n \"def __init__(\\n self,\\n repeats=2,\\n window_sizes=[512, 1024, 2048, 4096],\\n pqmf_params=[\\n [1, None, None, None],\\n [2, 62, 0.26700, 9.0],\\n [4, 62, 0.14200, 9.0],\\n [8, 62, 0.07949, 9.0],\\n ],\\n discriminator_params={\\n \\\"out_channels\\\": 1,\\n \\\"kernel_sizes\\\": [5, 3],\\n \\\"channels\\\": 16,\\n \\\"max_downsample_channels\\\": 512,\\n \\\"bias\\\": True,\\n \\\"downsample_scales\\\": [4, 4, 4, 1],\\n \\\"nonlinear_activation\\\": \\\"LeakyReLU\\\",\\n \\\"nonlinear_activation_params\\\": {\\\"negative_slope\\\": 0.2},\\n \\\"pad\\\": \\\"ReflectionPad1d\\\",\\n \\\"pad_params\\\": {},\\n },\\n use_weight_norm=True,\\n ):\\n super().__init__()\\n\\n # window size check\\n assert len(window_sizes) == len(pqmf_params)\\n sizes = [ws // p[0] for ws, p in zip(window_sizes, pqmf_params)]\\n assert len(window_sizes) == sum([sizes[0] == size for size in sizes])\\n\\n self.repeats = repeats\\n self.window_sizes = window_sizes\\n self.pqmfs = torch.nn.ModuleList()\\n self.discriminators = torch.nn.ModuleList()\\n for pqmf_param in pqmf_params:\\n d_params = copy.deepcopy(discriminator_params)\\n d_params[\\\"in_channels\\\"] = pqmf_param[0]\\n if pqmf_param[0] == 1:\\n self.pqmfs += [torch.nn.Identity()]\\n else:\\n self.pqmfs += [PQMF(*pqmf_param)]\\n self.discriminators += [BaseDiscriminator(**d_params)]\\n\\n # apply weight norm\\n if use_weight_norm:\\n self.apply_weight_norm()\\n\\n # reset parameters\\n self.reset_parameters()\",\n \"def solve(self):\\n start = timer()\\n # encode into milp\\n me = MILPEncoder(MILPSolver.prob,\\n MILPSolver.params.logger.LOGFILE, \\n MILPSolver.params.INTRA_DEP_CONSTRS,\\n MILPSolver.params.INTER_DEP_CONSTRS)\\n if MILPSolver.lp == True:\\n gmodel = me.lp_encode()\\n else:\\n gmodel = me.encode()\\n # Set gurobi parameters\\n pgo = 1 if MILPSolver.params.PRINT_GUROBI_OUTPUT == True else 0\\n gmodel.setParam('OUTPUT_FLAG', pgo)\\n tl = MILPSolver.params.TIMEOUT\\n if tl != -1 : gmodel.setParam('TIME_LIMIT', tl)\\n if not MILPSolver.params.DEFAULT_CUTS: \\n MILPSolver.disable_default_cuts(gmodel)\\n gmodel._vars = gmodel.getVars()\\n # set callback cuts \\n MILPSolver.id_form = IdealFormulation(MILPSolver.prob,\\n gmodel, \\n MILPSolver.params.IDEAL_FREQ,\\n MILPSolver.params.logger.LOGFILE)\\n MILPSolver.dep_cuts = DepCuts(MILPSolver.prob,\\n gmodel,\\n MILPSolver.params.DEP_FREQ,\\n MILPSolver.params.INTRA_DEP_CUTS,\\n MILPSolver.params.INTER_DEP_CUTS,\\n MILPSolver.sip_params,\\n MILPSolver.params.logger.LOGFILE)\\n # Optimise\\n if MILPSolver.params.callback_enabled() and MILPSolver.lp == False:\\n gmodel.optimize(MILPSolver._callback)\\n else:\\n gmodel.optimize()\\n\\n runtime = timer() - start\\n cex = None \\n if MILPSolver.status == SolveResult.BRANCH_THRESHOLD:\\n result = SolveResult.BRANCH_THRESHOLD\\n elif gmodel.status == GRB.OPTIMAL:\\n cex_shape = MILPSolver.prob.spec.input_layer.input_shape\\n cex = np.zeros(cex_shape)\\n for i in itertools.product(*[range(j) for j in cex_shape]):\\n cex[i] = MILPSolver.prob.spec.input_layer.out_vars[i].x\\n result = SolveResult.UNSATISFIED\\n elif gmodel.status == GRB.TIME_LIMIT:\\n result = SolveResult.TIMEOUT\\n elif gmodel.status == GRB.INTERRUPTED:\\n result = SolveResult.INTERRUPTED\\n elif gmodel.status == GRB.INFEASIBLE or gmodel.status == GRB.INF_OR_UNBD:\\n result = SolveResult.SATISFIED\\n else:\\n result = SolveResult.UNKNOWN\\n \\n # MILPSolver.logger.info('Verification problem {} solved, '\\n # 'LP: {}, '\\n # 'time: {:.2f}, '\\n # 'result: {}.'\\n # .format(MILPSolver.prob.id,\\n # MILPSolver.lp,\\n # runtime,\\n # result.value))\\n \\n return SolveReport(result, runtime, cex)\",\n \"def setup_b_instance(self,norm,add_ps_mask=True):\\n inst_tag = self.tag + '_'+str(self.flux_array_ebin)\\n b = bsm.bayesian_scan_NPTF(tag=inst_tag,nside=self.nside,work_dir='/tmp/'+self.tag+'/',psf_dir=psf_dir,nlive=700)\\n # Input the data, using the external data if provided\\n if self.use_external_data:\\n b.load_external_data(self.f1.CTB_en_bins,[self.external_data[self.flux_array_ebin]],self.f1.CTB_exposure_maps)\\n else:\\n b.load_external_data(self.f1.CTB_en_bins,self.f1.CTB_count_maps,self.f1.CTB_exposure_maps)\\n\\n if add_ps_mask:\\n b.make_mask_total(band_mask_range = [-self.band_mask,self.band_mask],mask_ring = False,ps_mask_array = self.f1.ps_mask_array)\\n else:\\n b.make_mask_total(band_mask_range = [-self.band_mask,self.band_mask],mask_ring = False)\\n\\n b.add_new_template(self.f1.template_dict)\\n b.rebin_external_data(1)\\n\\n b.add_poiss_model('ps_model','$A_{ps}$',[0.0,3.0],False)\\n b.add_poiss_model('p7','$A_{p7}$',[0.0,2.0],False)\\n b.add_poiss_model('bubs','$A_{bubs}$',[0.0,2.0],False)\\n b.add_poiss_model('iso','$A_{iso}$',[0.0,3.0],False)\\n # Add in a fixed J_map template\\n b.add_fixed_templates({'J_map':[norm*self.J_map_arr[self.flux_array_ebin]/np.mean(self.J_map_arr[self.flux_array_ebin])]})\\n\\n b.initiate_poissonian_edep()\\n return b\",\n \"def policy_gamble (self):\\n\\t\\tidx = self.idx \\t\\t\\t\\t# internal time index of state\\n\\t\\tprobs = self.probs\\t\\t\\t# prob of reward for an action\\n\\t\\tbeta = self.beta\\t\\t\\t# inverse temp \\n\\n\\t\\t# softmax\\n\\t\\tAct = beta*self.Q[idx]\\n\\t\\tp = 1./(1. + np.exp(-Act))\\t# probability of gamble\\n\\t\\tself.SM[idx] = p\\n\\n\\t\\t# decide whether to take gamble based on p\\n\\t\\trnd = np.random.random_sample()\\n\\t\\tif rnd < p:\\n\\t\\t\\tC = 1\\t# gamble\\n\\t\\telse:\\n\\t\\t\\tC = 0\\t# no gamble\\n\\t\\tself.C[idx] = C\\n\\n\\t\\t# no gamble\\n\\t\\tif C == 0:\\t\\n\\t\\t\\treward = 0\\t\\t # gamble reward encoded relative to reward\\n\\t\\t\\tself.R[idx] = -1 # rewarded sure thing, coded as -1\\n\\t\\t\\tself.PE[idx] = 0 # no PE, get the thing you expected\\n\\t\\t# gamble\\n\\t\\telse:\\n\\t\\t\\t# decide whether a reward is delivered\\n\\t\\t\\treward = np.random.binomial(size=1, n=1, p=probs)[0]\\n\\t\\t\\tself.R[idx] = reward # indicator that reward was received\\n\\t\\t\\tif reward == 0:\\n\\t\\t\\t\\treward = self.l_mag\\n\\t\\t\\telse:\\n\\t\\t\\t\\treward = self.r_mag\\n\\t\\t\\tself.PE[idx] = reward - self.Q[idx]\",\n \"def _create_model(n_bins=10, alpha=0.1, tol=0.1, contamination=0.1):\\n n_bins = int(n_bins)\\n\\n hbos = HBOS(\\n n_bins=n_bins,\\n alpha=alpha,\\n tol=tol,\\n contamination=contamination\\n )\\n\\n print('Created Model: {}'.format(hbos))\\n\\n return hbos\",\n \"def example():\\n num_user, num_item, ratings = build_ticket()\\n \\n # suffle_data\\n np.random.shuffle(ratings)\\n\\n # split data to training & validation\\n train_pct = 0.9\\n train_size = int(train_pct * len(ratings))\\n train = ratings[:train_size]\\n validation = ratings[train_size:]\\n\\n # params\\n num_feature = 5\\n bmf_model = BayesianMatrixFactorization(\\n num_user, num_item, num_feature, train, validation, max_rating=1, min_rating=0, ratingsMatirx=ratings)\\n\\n start_time = time.clock()\\n bmf_model.estimate(10)\\n end_time = time.clock()\\n \\n mat = np.matrix(bmf_model.item_features)\\n with open('../data/ticket/item_feature', 'w') as f:\\n for line in mat:\\n np.savetxt(f, line, fmt='%.5f')\\n\\n print \\\"time spend = %.3f\\\" % (end_time - start_time)\\n\\n return bmf_model\",\n \"def __init__(self, p_control = 0, p_treatment = 0, n_control = 0, n_treatment = 0, power = None, alpha = 0.05):\\n self.p_control = p_control\\n self.p_treatment = p_treatment\\n\\n self.n_control = n_control\\n self.n_treatment = n_treatment\\n\\n self.var_control = 1 * p_control * (1 - p_control)\\n self.var_treatment = 1 * p_treatment * (1 - p_treatment)\\n\\n self.norm_null = None\\n self.norm_alt = None\\n\\n self.binom_null = None\\n self.binom_alt = None\\n\\n self.binom_control = None\\n self.binom_treatment = None\\n\\n self.confidence_control = None\\n self.confidence_treatment = None\\n\\n if n_control > 0 and n_treatment > 0 and p_control > 0 and p_treatment > 0:\\n control = self.p_control * self.n_control\\n treatment = self.p_treatment * self.n_treatment\\n sample = self.n_control + self.n_treatment\\n\\n self.p_sample = (control + treatment) / sample\\n else:\\n self.p_sample = None\\n\\n if power == 1:\\n print('Sample size approaches infinity as power approaches 1, so 1 is an invalid power vlaue. Changing power to 0.99.')\\n self.power = 0.99\\n elif power == 0:\\n print('Sample size is undefined at power of 0, so 0 is an invalid power value. Changing power to 0.01.')\\n self.power = 0.01\\n else:\\n self.power = power\\n\\n self.alpha = alpha\\n self.p_value = None\",\n \"def naiveBayesMixture(train_set, train_labels, dev_set, bigram_lambda,unigram_smoothing_parameter, bigram_smoothing_parameter, pos_prior):\\n\\n # TODO: Write your code here\\n # return predicted labels of development set\\n\\n # counters for Training Phase\\n ham = Counter()\\n ham_bi = Counter()\\n spam = Counter()\\n spam_bi = Counter()\\n\\n for string, label in zip(train_set, train_labels):\\n for i in range(len(string)):\\n word = string[i]\\n if i != len(string)-1:\\n word_bi = string[i] + ' ' + string[i+1]\\n if label == 1:\\n ham_bi.update({word_bi:1})\\n else:\\n spam_bi.update({word_bi:1})\\n if label == 1:\\n ham.update({word:1})\\n else:\\n spam.update({word:1})\\n\\n ham_len = 0\\n for w in ham:\\n ham_len += ham[w]\\n spam_len = 0\\n for w in spam:\\n spam_len += spam[w]\\n \\n hambi_len = 0\\n for w in ham_bi:\\n hambi_len += ham_bi[w]\\n spambi_len = 0\\n for w in spam_bi:\\n spambi_len += spam_bi[w]\\n\\n # labels for Development Phase\\n dev_labels = []\\n # dicts for P(word|ham) and P(word|spam)\\n p_ham = {}\\n p_spam = {}\\n p_hambi = {}\\n p_spambi = {}\\n\\n # develop likelihoods based on dev_set\\n for word in ham:\\n numerator = ham[word] + unigram_smoothing_parameter\\n denominator = ham_len + unigram_smoothing_parameter*(len(ham))\\n p_ham[word] = numerator / denominator\\n for word in spam:\\n numerator = spam[word] + unigram_smoothing_parameter\\n denominator = spam_len + unigram_smoothing_parameter*(len(spam))\\n p_spam[word] = numerator / denominator\\n\\n for word_bi in ham_bi:\\n numerator = ham_bi[word_bi] + bigram_smoothing_parameter\\n denominator = hambi_len + bigram_smoothing_parameter*(len(ham_bi))\\n p_hambi[word_bi] = numerator / denominator\\n for word_bi in spam_bi:\\n numerator = spam_bi[word_bi] + bigram_smoothing_parameter\\n denominator = spambi_len + bigram_smoothing_parameter*(len(spam_bi))\\n p_spambi[word_bi] = numerator / denominator\\n \\n numerator = unigram_smoothing_parameter\\n denominator = ham_len + unigram_smoothing_parameter*(len(ham))\\n p_ham_zero = numerator / denominator\\n denominator = spam_len + unigram_smoothing_parameter*(len(spam))\\n p_spam_zero = numerator / denominator\\n\\n numerator = bigram_smoothing_parameter\\n denominator = hambi_len + bigram_smoothing_parameter*(len(ham_bi))\\n p_hambi_zero = numerator / denominator\\n denominator = spambi_len + bigram_smoothing_parameter*(len(spam_bi))\\n p_spambi_zero = numerator / denominator\\n\\n for string in dev_set:\\n p_words_ham = math.log(pos_prior)\\n p_words_spam = math.log(1 - pos_prior)\\n\\n p_words_hambi = math.log(pos_prior)\\n p_words_spambi = math.log(1 - pos_prior)\\n \\n for i in range(len(string)):\\n word = string[i]\\n if word in p_ham:\\n p_words_ham += math.log(p_ham[word])\\n else:\\n p_words_ham += math.log(p_ham_zero)\\n if word in p_spam:\\n p_words_spam += math.log(p_spam[word])\\n else:\\n p_words_spam += math.log(p_spam_zero)\\n\\n if i != len(string)-1:\\n word_bi = string[i] + ' ' + string[i+1]\\n if word_bi in p_hambi:\\n p_words_hambi += math.log(p_hambi[word_bi])\\n else:\\n p_words_hambi += math.log(p_hambi_zero)\\n if word_bi in p_spambi:\\n p_words_spambi += math.log(p_spambi[word_bi])\\n else:\\n p_words_spambi += math.log(p_spambi_zero)\\n\\n p_ham_mix = p_words_ham*(1-bigram_lambda) + p_words_hambi*bigram_lambda\\n p_spam_mix = p_words_spam*(1-bigram_lambda) + p_words_spambi*bigram_lambda\\n\\n dev_labels.append(p_ham_mix >= p_spam_mix)\\n\\n return dev_labels\",\n \"def make_model(data):\\n commodities, arcs, capacity, variable_cost, nodes, demand = \\\\\\n data.commodities, data.arcs.size, data.capacity, \\\\\\n data.variable_cost, data.nodes, np.amax(data.demand, axis=0)\\n origins, destinations = data.origins, data.destinations\\n fixed_cost = data.fixed_cost[0, :]\\n\\n flow = np.empty(shape=(commodities, arcs), dtype=object)\\n arc_open = np.empty(shape=arcs, dtype=object)\\n capacities = np.empty(shape=arcs, dtype=object)\\n arc_origins, arc_destinations = get_2d_index(data.arcs, nodes)\\n\\n\\n model = grb.Model('MCND')\\n model.params.threads = 1\\n model.params.LogFile = \\\"\\\"\\n lazy_cons = []\\n\\n # Variables\\n for arc in xrange(arcs):\\n i, j = arc_origins[arc], arc_destinations[arc]\\n arc_open[arc] = model.addVar(\\n vtype=grb.GRB.BINARY, obj=fixed_cost[arc],\\n name='open_arc{}-{}'.format(i, j))\\n for h in xrange(commodities):\\n flow[h, arc] = model.addVar(\\n obj=variable_cost[arc] * data.demand[0, h],\\n lb=0., ub=1.,\\n vtype=grb.GRB.CONTINUOUS,\\n name='flow{}.{},{}'.format(h, i, j))\\n model._arc_open = arc_open\\n model._flow = flow\\n model.update()\\n\\n # Constraints\\n for arc in xrange(arcs):\\n for h in xrange(commodities):\\n lazy_cons.append(model.addConstr(flow[h, arc] <= arc_open[arc]))\\n i, j = arc_origins[arc], arc_destinations[arc]\\n capacities[arc] = model.addConstr(grb.quicksum(grb.LinExpr(\\n data.demand[0, h] * flow[h, arc]) for h in xrange(\\n commodities)) <= capacity[arc] * arc_open[arc],\\n name='cap_{}-{}'.format(i, j))\\n for h in xrange(commodities):\\n for n in xrange(nodes):\\n rhs = 0.\\n if n == origins[h]:\\n rhs = 1.\\n if n == destinations[h]:\\n rhs = -1.\\n in_arcs = get_2d_index(data.arcs, nodes)[1] == n + 1\\n out_arcs = get_2d_index(data.arcs, nodes)[0] == n + 1\\n lhs = grb.quicksum(flow[h, out_arcs]) - grb.quicksum(\\n flow[h, in_arcs])\\n model.addConstr(\\n lhs=lhs, rhs=rhs, sense=grb.GRB.EQUAL,\\n name='demand_n{}c{}'.format(n, h))\\n\\n model._capacities = capacities\\n model.setParam('OutputFlag', 0)\\n model.params.TimeLimit = 1000.\\n model.params.MIPGap = 0.01\\n model.params.Threads = 1\\n model.update()\\n for con in lazy_cons:\\n con.Lazy = 3\\n # model.write(str(model.ModelName) + '.lp')\\n return model\",\n \"def MakePmfTest(shelf, **options):\\n #print(options)\\n label = options.pop('label', '')\\n xs, ds = Render(shelf,**options)\\n #print()\\n #print(xs)\\n #print(ds)\\n return thinkbayes2.Pmf(dict(zip(xs, ds)), label=label)\",\n \"def build_model(self, reset_graph=False):\\n\\n if self.data is None:\\n raise Exception('a dataset must be specified')\\n\\n if self.adv and (self.classifier is None):\\n raise Exception('A classifier must be specified to train in adversarial mode')\\n\\n if reset_graph:\\n tf.compat.v1.reset_default_graph()\\n\\n self.dataset_is_tf = self.data.is_tf\\n\\n if self.adv:\\n self.classifier.load_model()\\n\\n shape_x, shape_y = self.data.shape()\\n number_of_samples = shape_x[0]\\n shape_x[0] = None\\n\\n z_dims = [None] + self.z_dim\\n\\n shape_y[0] = None\\n\\n if self.dataset_is_tf:\\n if self.use_mask:\\n self.X_real, self.labels, _, self.mask = self.data.next_batch()\\n probs = self.classifier.predict(self.X_real)[:, :, :, 4]\\n labels_masked = tf.cast(self.labels * self.mask, tf.float32)\\n pm = tf.cast(tf.expand_dims(probs, -1) * labels_masked, tf.float32)\\n self.classifier_proba = tf.cast(\\n 1.0 - (tf.reduce_sum(input_tensor=pm, axis=[1, 2, 3]) / tf.reduce_sum(input_tensor=labels_masked,\\n axis=[1, 2, 3])), tf.float32)\\n self.mask.set_shape(shape_y)\\n elif self.use_labels:\\n self.X_real, self.labels, _, _ = self.data.next_batch()\\n self.mask = self.labels\\n probs = self.classifier.predict(self.X_real)[:, :, :, 4]\\n pm = tf.cast(tf.expand_dims(probs, -1) * self.labels, tf.float32)\\n self.classifier_proba = tf.cast(\\n 1.0 - (tf.reduce_sum(input_tensor=pm, axis=[1, 2, 3]) / tf.reduce_sum(input_tensor=self.labels,\\n axis=[1, 2, 3])), tf.float32)\\n else:\\n self.X_real, self.labels, self.classifier_proba = self.data.next_batch()\\n\\n self.X_real.set_shape(shape_x)\\n self.labels.set_shape(shape_y)\\n\\n if self.use_ceil and self.ceil_value is not None:\\n self.classifier_proba = tf.minimum(self.classifier_proba, self.ceil_value)\\n\\n self.classifier_proba = tf.nn.softmax(self.classifier_proba * self.temperature)\\n self.classifier_proba = tf.expand_dims(self.classifier_proba, -1)\\n\\n else:\\n self.X_real = tf.compat.v1.placeholder(\\\"float\\\", shape_x, name=\\\"X_real\\\")\\n self.labels = tf.compat.v1.placeholder(\\\"float\\\", shape_y, name=\\\"labels\\\")\\n self.classifier_proba = tf.compat.v1.placeholder(\\\"float\\\", [None, 1], name=\\\"classifier_proba\\\")\\n self.adv_map = tf.compat.v1.placeholder(\\\"float\\\", shape_y, name=\\\"adv_map\\\")\\n\\n self.z = tf.compat.v1.placeholder(\\\"float\\\", z_dims, name=\\\"z_input\\\")\\n self.is_training = tf.compat.v1.placeholder(tf.bool, shape=())\\n if self.use_clf:\\n self.label_used = tf.constant(self.label_used, dtype='float')\\n\\n # train with real and classifier\\n\\n if self.use_labels:\\n self.pred_real = self.discriminator(self.X_real, self.labels, reuse=False)\\n elif self.use_mask:\\n self.pred_real = self.discriminator(self.X_real, self.mask, reuse=False)\\n else:\\n self.pred_real = self.discriminator(self.X_real, reuse=False)\\n\\n if self.adv:\\n self.pred_real *= self.classifier_proba\\n\\n if self.batch_reg:\\n self.pred_real *= self.batch_size / number_of_samples\\n\\n # train with fake\\n if self.use_labels:\\n self.X_fake = self.generator(self.X_real, self.labels, self.z, train=self.is_training)\\n elif self.use_mask:\\n self.X_fake = self.generator(self.X_real, self.mask, self.z, train=self.is_training)\\n else:\\n self.X_fake = self.generator(self.z, train=self.is_training)\\n\\n self.X_fake = tf.identity(self.X_fake, name=\\\"X_fake\\\")\\n\\n shape_x[0] = -1\\n\\n self.X_fake = tf.reshape(self.X_fake, shape_x)\\n\\n if self.use_labels:\\n self.pred_fake = self.discriminator(self.X_fake, self.labels)\\n elif self.use_mask:\\n self.pred_fake = self.discriminator(self.X_fake, self.mask)\\n else:\\n self.pred_fake = self.discriminator(self.X_fake)\\n\\n if self.adv and not self.use_softmax:\\n self.pred_fake *= 1 / number_of_samples\\n\\n self.g_loss = - tf.reduce_mean(input_tensor=self.pred_fake)\\n\\n if self.adv and not self.use_softmax:\\n self.d_loss = - (tf.reduce_sum(input_tensor=self.pred_real) - tf.reduce_sum(input_tensor=self.pred_fake))\\n elif self.adv:\\n self.d_loss = - (tf.reduce_sum(input_tensor=self.pred_real) - tf.reduce_mean(input_tensor=self.pred_fake))\\n else:\\n self.d_loss = - (tf.reduce_mean(input_tensor=self.pred_real) - tf.reduce_mean(input_tensor=self.pred_fake))\\n\\n if self.reg_losses:\\n self.d_loss = self.reg_term * self.d_loss\\n self.g_loss = self.reg_term * self.g_loss\\n\\n if self.use_recon:\\n if self.use_mask:\\n x_fake_masked = (1 - self.mask) * self.X_fake\\n x_real_masked = (1 - self.mask) * self.X_real\\n elif self.use_labels:\\n x_fake_masked = (1 - self.labels) * self.X_fake\\n x_real_masked = (1 - self.labels) * self.X_real\\n else:\\n raise Exception('something wrong')\\n\\n self.g_loss += self.recon_beta * (tf.reduce_mean(\\n input_tensor=tf.reduce_sum(input_tensor=tf.square(x_fake_masked - x_real_masked),\\n axis=np.arange(1, len(x_fake_masked.shape)))))\\n\\n if self.use_clf:\\n y_t = self.mask * self.label_used\\n preds = self.classifier.predict(self.X_fake)\\n preds = preds * self.mask\\n preds = 1 - preds\\n self.loss_clf = self.clf_alpha * tf.reduce_mean(\\n input_tensor=-tf.reduce_sum(input_tensor=y_t * tf.math.log(tf.clip_by_value(preds, 1e-10, 1.0)),\\n axis=[1]))\\n self.g_loss += self.loss_clf\\n\\n self.neg_loss = - self.d_loss\\n\\n if self.use_labels:\\n labels = self.labels\\n elif self.use_mask:\\n labels = self.mask\\n else:\\n labels = None\\n\\n if self.loss == 'gp':\\n self.d_loss += losses.gp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\\n\\n if self.loss == 'lp':\\n self.d_loss += losses.lp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\\n\\n if self.loss == 'ct':\\n self.d_loss += losses.lp_loss(self.X_real, self.X_fake, self.discriminator, shape_x, labels=labels)\\n self.d_loss += losses.ct_loss(self.X_real, self.discriminator, labels=labels)\\n\\n d_vars = [var for var in tf.compat.v1.trainable_variables() if \\\"discriminator\\\" in var.name]\\n g_vars = [var for var in tf.compat.v1.trainable_variables() if \\\"generator\\\" in var.name]\\n\\n if self.accumulate_grd:\\n # initialized with 0s\\n\\n self.accum_vars_D = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False) for tv in d_vars]\\n self.accumulation_counter_D = tf.Variable(0.0, trainable=False)\\n\\n self.accum_vars_G = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False) for tv in g_vars]\\n self.accumulation_counter_G = tf.Variable(0.0, trainable=False)\\n\\n self.D_sum = tf.compat.v1.summary.scalar('D_loss', self.d_loss)\\n self.G_sum = tf.compat.v1.summary.scalar('G_loss', self.g_loss)\\n\\n with tf.control_dependencies(tf.compat.v1.get_collection(tf.compat.v1.GraphKeys.UPDATE_OPS)):\\n\\n if self.accumulate_grd:\\n\\n gvs_g = self.optimizer_G.compute_gradients(self.g_loss, g_vars)\\n gvs_d = self.optimizer_D.compute_gradients(self.d_loss, d_vars)\\n\\n self.accum_ops_G = [accumulator.assign_add(grad) for (accumulator, (grad, var)) in\\n zip(self.accum_vars_G, gvs_g)]\\n self.accum_ops_G.append(self.accumulation_counter_G.assign_add(1.0))\\n\\n self.accum_ops_D = [accumulator.assign_add(grad) for (accumulator, (grad, var)) in\\n zip(self.accum_vars_D, gvs_d)]\\n self.accum_ops_D.append(self.accumulation_counter_D.assign_add(1.0))\\n\\n self.G_step = self.optimizer_G.apply_gradients(\\n [(accumulator / self.accumulation_counter_G, var) for (accumulator, (grad, var)) in\\n zip(self.accum_vars_G, gvs_g)])\\n\\n self.D_step = self.optimizer_D.apply_gradients(\\n [(accumulator / self.accumulation_counter_D, var) for (accumulator, (grad, var)) in\\n zip(self.accum_vars_D, gvs_d)])\\n\\n self.zero_ops_G = [accumulator.assign(tf.zeros_like(tv)) for (accumulator, tv) in\\n zip(self.accum_vars_G, g_vars)]\\n self.zero_ops_D = [accumulator.assign(tf.zeros_like(tv)) for (accumulator, tv) in\\n zip(self.accum_vars_D, d_vars)]\\n\\n self.zero_ops_G.append(self.accumulation_counter_G.assign(0.0))\\n self.zero_ops_D.append(self.accumulation_counter_D.assign(0.0))\\n\\n else:\\n self.G_step = self.optimizer_G.minimize(self.g_loss, var_list=g_vars)\\n self.D_step = self.optimizer_D.minimize(self.d_loss, var_list=d_vars)\\n\\n if self.loss == 'clip':\\n self.D_clipping = [w.assign(tf.clip_by_value(w, -self.clip_value, self.clip_value)) for w in d_vars]\\n\\n self.saver = tf.compat.v1.train.Saver()\\n self.built = True\",\n \"def __init__(self, ebunch=None):\\n super(BayesianModel, self).__init__()\\n if ebunch:\\n self.add_edges_from(ebunch)\\n self.cpds = []\\n self.cardinalities = self.get_cardinality()\\n self.probs = dict()\",\n \"def __init_finalaf(self, i,h1,classes):\\n self.params['W'+i]=np.random.randn(h1,classes)*self.weight_scale\\n self.params['b'+i]=np.zeros(classes)\",\n \"def build_B_block(self):\\n\\n N = self.N # number of MPC steps\\n row_list = [] # reocrd the every row in B_hat\\n \\n first_block = self.B\\n zero = Variable(torch.zeros(self.num_input, self.num_output*(N-1)))\\n zero = self.vari_gpu(zero)\\n row= torch.cat([first_block, zero],1)\\n row_list.append(row)\\n \\n for i in range(1, N):\\n first_block = self.A.mm(first_block)\\n row = torch.cat([first_block, row[:,:self.num_output*(N-1)]],1)\\n row_list.append(row) \\n \\n return torch.cat(row_list,0)\",\n \"def create_model(instance):\\n name, nitems, sets = instance\\n model = grb.Model(name)\\n\\n # One variable for each set. Also remember which sets cover each item.\\n covered_by = [[] for i in range(nitems)]\\n vars = []\\n for i, set in enumerate(sets):\\n cost, covers = set\\n vars.append(model.addVar(obj=cost, vtype=grb.GRB.BINARY, name=\\\"s_{0}\\\".format(i)))\\n\\n for item in covers:\\n covered_by[item].append(vars[i])\\n model.update()\\n\\n # Constraint: Each item covered at least once.\\n for item in range(nitems):\\n model.addConstr(grb.quicksum(covered_by[item]) >= 1)\\n\\n # We want to minimize. Objective coefficients already fixed during variable creation.\\n model.setAttr(\\\"ModelSense\\\", grb.GRB.MINIMIZE)\\n\\n # Tuning parameters derived from sc_330_0\\n model.read(\\\"mip.prm\\\")\\n\\n model.setParam(\\\"Threads\\\", 3)\\n model.setParam(\\\"MIPGap\\\", 0.001) # 0.1% usually suffices\\n\\n return model, vars\",\n \"def mlp_init(d0, d1, d2):\\n W1 = 0.01*np.random.randn(d0, d1)\\n b1 = np.zeros(d1)\\n W2 = 0.01*np.random.randn(d1, d2)\\n b2 = np.zeros(d2)\\n return (W1, b1, W2, b2)\",\n \"def init_P_PHM_GIVEN_PHI():\\n global P_PHM_GIVEN_PHI\\n for i in INTERFACE_LEVEL_ACTIONS: # ui\\n P_PHM_GIVEN_PHI[i] = collections.OrderedDict()\\n for j in INTERFACE_LEVEL_ACTIONS: # um\\n if i == j:\\n # try to weight the true command more for realistic purposes. Can be offset by using a high UM_GIVEN_UI_NOISE\\n P_PHM_GIVEN_PHI[i][j] = 1.0\\n else:\\n # P_PHM_GIVEN_PHI[i][j] = np.random.random()*UM_GIVEN_UI_NOISE#IF UM_GIVEN_UI_NOISE is 0, then the p(um|ui) is a deterministic mapping\\n P_PHM_GIVEN_PHI[i][j] = 0.0\\n\\n delta_dist = np.array(P_PHM_GIVEN_PHI[i].values())\\n uniform_dist = (1.0 / len(INTERFACE_LEVEL_ACTIONS)) * np.ones(len(INTERFACE_LEVEL_ACTIONS))\\n blended_dist = (1 - PHM_GIVEN_PHI_NOISE) * delta_dist + PHM_GIVEN_PHI_NOISE * uniform_dist # np.array\\n for index, j in enumerate(INTERFACE_LEVEL_ACTIONS):\\n P_PHM_GIVEN_PHI[i][j] = blended_dist[index]\",\n \"def _generate_prior(self, text_lengths, feats_lengths,\\n w=1) -> paddle.Tensor:\\n B = len(text_lengths)\\n T_text = text_lengths.max()\\n T_feats = feats_lengths.max()\\n\\n bb_prior = paddle.full((B, T_feats, T_text), fill_value=-np.inf)\\n for bidx in range(B):\\n T = feats_lengths[bidx].item()\\n N = text_lengths[bidx].item()\\n\\n key = str(T) + ',' + str(N)\\n if self.cache_prior and key in self._cache:\\n prob = self._cache[key]\\n else:\\n alpha = w * np.arange(1, T + 1, dtype=float) # (T,)\\n beta = w * np.array([T - t + 1 for t in alpha])\\n k = np.arange(N)\\n batched_k = k[..., None] # (N,1)\\n prob = betabinom.pmf(batched_k, N, alpha, beta) # (N,T)\\n\\n # store cache\\n if self.cache_prior and key not in self._cache:\\n self._cache[key] = prob\\n\\n prob = paddle.to_tensor(\\n prob, place=text_lengths.place, dtype=\\\"float32\\\").transpose(\\n (1, 0)) # -> (T,N)\\n bb_prior[bidx, :T, :N] = prob\\n\\n return bb_prior\",\n \"def build_bigram_model(data):\\n\\n bigrams,unigrams = get_counts(data)\\n model = {}\\n for bigram in bigrams:\\n #unigram count of first member of bigram\\n uni_count=unigrams[bigram[0]]\\n bi_count=bigrams[bigram]\\n\\n #probability is bigram count divided by unigram count\\n model[bigram]=(bi_count/float(uni_count))\\n return model\",\n \"def compute_probabilities():\\n global total_spam_words, total_ham_words\\n total_words = total_spam_words+total_ham_words\\n unique_words = len(all_dict)\\n print(\\\"Training Set Description: \\\")\\n len_ham = len(ham_file_list)\\n len_spam = len(spam_file_list)\\n print(\\\"SPAM EMAILS: \\\",len_spam)\\n print(\\\"HAM EMAILS: \\\",len_ham)\\n print(\\\"Total words: \\\",total_words)\\n print(\\\"Training...\\\")\\n \\n spam_probability = math.log((len_spam)/(len_spam+len_ham))\\n ham_probability = math.log((len_ham)/(len_spam+len_ham))\\n \\n \\n \\n output_file = open(\\\"nbmodel.txt\\\", \\\"w+\\\", encoding=\\\"latin-1\\\")\\n output_file.write(\\\"model_params \\\"+str(spam_probability)+\\\" \\\"+str(ham_probability)+\\\"\\\\n\\\")\\n \\n nbmodel = {}\\n nbmodel[\\\"model_params\\\"] = (spam_probability,ham_probability)\\n for word in all_dict.keys():\\n spam_count = 1\\n if word in spam_dict:\\n spam_count+= spam_dict[word]\\n \\n word_spam_probability = math.log(spam_count / (total_spam_words+unique_words))\\n \\n ham_count = 1\\n if word in ham_dict:\\n ham_count+= ham_dict[word]\\n \\n word_ham_probability = math.log(ham_count / (total_ham_words+unique_words))\\n \\n output_file.write(word+\\\" \\\"+str(word_spam_probability)+\\\" \\\"+str(word_ham_probability)+\\\"\\\\n\\\")\\n nbmodel[word] = (word_spam_probability, word_ham_probability) \\n \\n print(\\\"nbmodel.txt generated successfully...\\\")\\n print(\\\"SPAM Probability: \\\",spam_probability)\\n print(\\\"HAM Probability: \\\",ham_probability)\\n output_file.close()\",\n \"def create(window_size=3,\\r\\n input_dimension=9,\\r\\n output_vocabsize=8,\\r\\n n_quadratic_filters=2,\\r\\n token_representation_size=5,\\r\\n concatenated_representation_size=7,\\r\\n lr=0.01,\\r\\n seed=123,\\r\\n noise_level=0.2,\\r\\n qfilter_relscale=0.1,\\r\\n compile_mode=None):\\r\\n activation_function = T.tanh\\r\\n\\r\\n architecture = ConvolutionalMLP( \\\\\\r\\n window_size=window_size,\\r\\n n_quadratic_filters=n_quadratic_filters,\\r\\n activation_function=activation_function,\\r\\n reconstruction_cost_function=quadratic,\\r\\n tie_weights=False\\r\\n )\\r\\n\\r\\n backup = config.warn.sum_div_dimshuffle_bug\\r\\n config.warn.sum_div_dimshuffle_bug = False\\r\\n try:\\r\\n model = architecture.make(input_size=input_dimension,\\r\\n input_representation_size=token_representation_size, hidden_representation_size=concatenated_representation_size, output_size=output_vocabsize, lr=lr, seed=seed, noise_level=noise_level, qfilter_relscale=qfilter_relscale, mode=compile_mode)\\r\\n finally:\\r\\n config.warn.sum_div_dimshuffle_bug = backup\\r\\n return model\",\n \"def generate_initial_embs(emb_type):\\n def _get_emb_avg(g, lang):\\n \\\"\\\"\\\"Compute the embedding of g as the average of its word embeddings\\n :param g: the input genre\\n :param lang: language\\n :return: the embedding and if all words of this genre are known\\n \\\"\\\"\\\"\\n emb = np.zeros(emb_dims[lang])\\n known_words_count = 0\\n words = g.split()\\n for w in words:\\n if w in models[lang]:\\n emb += models[lang][w]\\n known_words_count += 1\\n emb /= len(words)\\n return emb, known_words_count > 0\\n\\n def _get_emb_wavg(g, lang, a=0.001):\\n \\\"\\\"\\\"Compute the embeddings of g with a sentence embedding algorithm (average weighted by the word estimated frequencies)\\n :param g: the input genre\\n :param lang: language\\n :param a: a model hyper-parameter (see Arora et al. in the paper)\\n :return: the embedding and if all words of this genre are known\\n \\\"\\\"\\\"\\n emb = np.zeros(emb_dims[lang])\\n known_words_count = 0\\n words = g.split()\\n for w in words:\\n if w in models[lang]:\\n emb += a / (a + word_freqs[lang][w]) * models[lang][w]\\n known_words_count += 1\\n emb /= len(words)\\n return emb, known_words_count > 0\\n\\n def _remove_pc(df_embs, npc=1):\\n \\\"\\\"\\\"Remove the pc (see Arora at el. in the paper)\\n :param df_embs: the input embeddings\\n :return: the normalized embeddings\\n \\\"\\\"\\\"\\n pc = _compute_pc(df_embs, npc)\\n if npc == 1:\\n df_embs_out = df_embs - df_embs.dot(pc.transpose()) * pc\\n else:\\n df_embs_out = df_embs - df_embs.dot(pc.transpose()).dot(pc)\\n return df_embs_out\\n\\n def _compute_pc(df_embs, npc=1):\\n \\\"\\\"\\\"Compute the pc (see Arora at el. in the paper)\\n :param df_embs: the input embeddings\\n :return: the principal component\\n \\\"\\\"\\\"\\n svd = TruncatedSVD(n_components=npc, n_iter=7, random_state=0)\\n svd.fit(df_embs)\\n return svd.components_\\n\\n embs = {}\\n known = {}\\n for g in G.nodes:\\n lang = g[:2]\\n norm_g = TagManager.normalize_tag_wtokenization(g, tries[lang], prefixed=True)\\n if emb_type == 'avg':\\n embs[g], known[g] = _get_emb_avg(norm_g, lang)\\n else:\\n embs[g], known[g] = _get_emb_wavg(norm_g, lang)\\n\\n embs = pd.DataFrame(embs).T # the embeddings are columns\\n if emb_type == 'sif': # the algorithm imposes a normalization\\n norm_embs = _remove_pc(embs.to_numpy())\\n embs = pd.DataFrame(norm_embs, columns=embs.columns, index=embs.index)\\n return embs, known\",\n \"def build(self, input_shape):\\n data_shape, cond_shape = input_shape\\n self.z_dim = cond_shape[1] # z_dim\\n self.channels = data_shape[-1]\\n self.time_frames = data_shape[2]\\n if self.type_gammas_betas == \\\"simple\\\":\\n shape_gammas_betas = [1, self.z_dim, 1]\\n if self.type_gammas_betas == \\\"complex\\\":\\n shape_gammas_betas = [self.channels, self.z_dim, 1]\\n if self.in_freq:\\n shape_gammas_betas[-1] = data_shape[1]\\n\\n self.gammas = self.add_weight(\\n shape=shape_gammas_betas,\\n trainable=True,\\n name=\\\"gammas\\\",\\n initializer=\\\"random_normal\\\",\\n )\\n self.betas = self.add_weight(\\n shape=shape_gammas_betas,\\n trainable=True,\\n name=\\\"betas\\\",\\n initializer=\\\"random_normal\\\",\\n )\",\n \"def _model(self):\\n common_scale = self.edp_par['common_scale'].value\\n model = self.F_trans() * self.F_cont()\\n # get F(h=1,k=0), which is used for normalization \\n # common_scale is a common scaling factor => F(h=1,k=0) = 100*common_scale\\n F_10 = model[(self.h==1)&(self.k==0)]\\n model = model / np.absolute(F_10) * 100 * common_scale\\n return model\",\n \"def test_class():\\n riskfree = .03\\n maturity = 30/365\\n moneyness = np.linspace(-.04, .04, 10)\\n premium = np.ones_like(moneyness) * .05\\n call = True\\n data = {'riskfree': riskfree, 'maturity': maturity,\\n 'moneyness': moneyness, 'call': call, 'premium': premium}\\n\\n sigma = .13\\n bsm = BSmodel(sigma, data)\\n\\n weights = [.63]\\n means = [-.01, .09]\\n stds = [.16, .05]\\n param = weights + means + stds\\n mbs = MBSmodel(param, data)\\n\\n param_a, param_p, param_c = 4, 1.5, -.05\\n gb2 = GB2model([param_a, param_p, param_c], data)\\n print(gb2.get_pnames())\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.density(moneyness), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.premium(), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n plt.figure()\\n for model in [bsm, mbs, gb2]:\\n plt.plot(moneyness, model.impvol(), label=model.get_name())\\n plt.legend()\\n plt.show()\\n\\n print('BS objective function = %.4f' % bsm.objective(sigma))\\n print('GB2 objective function = %.4f'\\n % gb2.objective([param_a, param_p, param_c]))\",\n \"def build_model(cls, args, task):\\n global PAD_IDX, EOS_IDX\\n # make sure all arguments are present in older models\\n w2v_lm_architecture2(args)\\n\\n if not hasattr(args, \\\"max_source_positions\\\"):\\n args.max_source_positions = 2048\\n if not hasattr(args, \\\"max_target_positions\\\"):\\n args.max_target_positions = 2048\\n\\n tgt_dict = task.target_dictionary\\n PAD_IDX = tgt_dict.pad()\\n EOS_IDX = tgt_dict.eos()\\n\\n encoder = cls.build_encoder(args)\\n assigner = cls.build_assigner(args, encoder.d)\\n lm = cls.build_lm(args, task)\\n\\n return cls(args, encoder, assigner, lm)\",\n \"def build_model(cls, args):\\n base_architecture(args) \\n return StyleGANGeneratorPretrain(args)\",\n \"def __init__(self, in_features, out_features):\\n \\n ########################\\n # PUT YOUR CODE HERE #\\n #######################\\n self.params = {'weight': np.random.normal(loc = 0, scale=0.0001, size=(out_features,in_features)),\\\\\\n 'bias': np.zeros((1, out_features))}\\n \\n self.grads = {'weight': np.zeros((out_features,in_features)),\\\\\\n 'bias': np.zeros((1, out_features))}\\n ########################\\n # END OF YOUR CODE #\\n #######################\",\n \"def __init__(self):\\n super(MLE, self).__init__()\\n\\n # Metadata\\n self.appliance = None\\n self.stats = []\\n self.units = None\\n self.resistive = False\\n self.thDelta = 0\\n self.thLikelihood = 0\\n self.sample_period = None\\n self.sampling_method = None\\n # FEATURES:\\n self.onpower = {'name': 'gmm', 'model': mixture.GMM(n_components=1)}\\n self.offpower = {'name': 'gmm', 'model': mixture.GMM(n_components=1)}\\n self.duration = {'name': 'poisson', 'model': poisson(0)}\\n\\n # Trainings:\\n self.onpower_train = pd.DataFrame(columns=['onpower'])\\n self.offpower_train = pd.DataFrame(columns=['offpower'])\\n self.duration_train = pd.DataFrame(columns=['duration'])\\n\\n # Constrains\\n self.powerNoise = 0 # Background noise in the main\\n self.powerPair = 0 # Max diff between onpower and offpower\\n self.timeWindow = 0 # To avoid high computation\",\n \"def bayesian_model(y, x, data=None, correlation=False, family=\\\"Normal\\\", robust = True, samples = 1000, plot_posterior=True, plot_regression=True, plot_samples = \\\"default\\\", print_summary=True, alpha = 0.05):\\n\\n print(\\\"Starting Bayesian estimation...\\\")\\n from theano import config\\n config.warn.sum_div_dimshuffle_bug = False\\n import pymc3\\n\\n\\n\\n y_name = \\\"y\\\"\\n x_name = \\\"x\\\"\\n\\n if isinstance(y,str):\\n y_name = y\\n try:\\n y = data[y]\\n except:\\n pass\\n if isinstance(x,str):\\n x_name = x\\n try:\\n x = data[x]\\n except:\\n pass\\n\\n\\n if correlation == True:\\n #Center and scale\\n x = x - np.mean(x)\\n x = x / np.std(x)\\n\\n y = y - np.mean(y)\\n y = y / np.std(y)\\n\\n\\n\\n\\n data = {y_name:y,\\n x_name:x}\\n formula = y_name + ' ~ ' + x_name\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n with pymc3.Model() as model: # model specifications in PyMC3 are wrapped in a with-statement\\n if family == \\\"Normal\\\":\\n family = pymc3.glm.families.Normal()\\n if robust == True:\\n family = pymc3.glm.families.StudentT()\\n\\n pymc3.glm.glm(formula, data, family=family)\\n start = pymc3.find_MAP()\\n step = pymc3.NUTS(scaling=start) # Instantiate MCMC sampling algorithm\\n trace = pymc3.sample(samples, step, progressbar=True) # draw 2000 posterior samples using NUTS sampling\\n\\n# trace = trace[int(samples/4):]\\n #PLOT POSTERIOR DISTRIBUTION\\n if plot_posterior == True:\\n pymc3.traceplot(trace)\\n\\n #PLOT LINES\\n if plot_regression == True:\\n plot_data= []\\n plot_data.append(go.Scatter(x=x,\\n y=y,\\n mode = 'markers'))\\n\\n\\n\\n if plot_samples == \\\"default\\\":\\n if len(trace) > 100:\\n plot_samples = 100\\n samples_range = np.random.randint(0, len(trace), plot_samples)\\n else:\\n plot_samples = samples\\n samples_range = range(len(trace))\\n else:\\n samples_range = np.random.randint(0, len(trace), plot_samples)\\n\\n\\n\\n for i in samples_range:\\n print(i)\\n plot_data.append(go.Scatter(x=x,\\n y=trace[i]['Intercept'] + trace[i]['x'] * x,\\n mode = 'lines',\\n opacity=0.25,\\n line = {\\\"color\\\":\\\"grey\\\",\\n \\\"width\\\":5}))\\n layout = go.Layout(showlegend=False)\\n figure = go.Figure(data = plot_data,layout=layout)\\n py.plot(figure)\\n\\n if print_summary == True:\\n print(pymc3.summary(trace,alpha=alpha))\\n return(trace)\",\n \"def __init__(self, in_features, out_features):\\n \\n ########################\\n # PUT YOUR CODE HERE #\\n #######################\\n\\n\\n self.params = {'weight': 0.0001 * np.random.randn(out_features, in_features), 'bias': np.zeros((out_features, 1))}\\n self.grads = {'weight': np.zeros((out_features, in_features)), 'bias': np.zeros((out_features, 1))}\\n\\n\\n\\n ########################\\n # END OF YOUR CODE #\\n #######################\",\n \"def __init__(self, params):\\n super().__init__(params)\\n p = self.params\\n assert p.input_dim, f'input_dim is {p.input_dim}'\\n assert p.hidden_dim, f'hidden_dim is {p.hidden_dim}'\\n assert p.num_heads > 0, f'num_heads is {p.num_heads}'\\n # if proj_tpl does not have dim_per_head set, set it\\n if p.proj_tpl.dim_per_head == 0:\\n p.proj_tpl.dim_per_head = self.dim_per_head\\n\\n if p.device_mesh is not None:\\n assert p.weight_split_dims_mapping is not None\\n assert p.activation_split_dims_mapping is not None\\n\\n if isinstance(p.weight_split_dims_mapping, dict):\\n qkv_weight_split_dims_mapping = p.weight_split_dims_mapping['qkv']\\n post_weight_split_dims_mapping = p.weight_split_dims_mapping['post']\\n else:\\n qkv_weight_split_dims_mapping = p.weight_split_dims_mapping\\n post_weight_split_dims_mapping = p.weight_split_dims_mapping\\n\\n def ProjectInput(input_dim):\\n return p.proj_tpl.Copy().Set(\\n input_dim=input_dim,\\n num_heads=p.num_heads,\\n use_bias=p.use_bias,\\n device_mesh=p.device_mesh,\\n weight_split_dims_mapping=qkv_weight_split_dims_mapping,\\n make_output_proj_no_op=False)\\n\\n def ProjectInputOneStep(input_dim):\\n return p.proj_tpl.Copy().Set(\\n input_dim=input_dim,\\n num_heads=p.num_heads,\\n dim_per_head=self.dim_per_head * 3,\\n use_bias=p.use_bias,\\n device_mesh=p.device_mesh,\\n weight_split_dims_mapping=qkv_weight_split_dims_mapping,\\n make_output_proj_no_op=False,\\n )\\n\\n if isinstance(p.input_dim, dict):\\n key_input_dim = p.input_dim['key']\\n value_input_dim = p.input_dim['value']\\n query_input_dim = p.input_dim['query']\\n assert key_input_dim, f'key_input_dim is {key_input_dim}'\\n assert query_input_dim, f'query_input_dim is {query_input_dim}'\\n else:\\n key_input_dim = p.input_dim\\n value_input_dim = p.input_dim\\n query_input_dim = p.input_dim\\n\\n if p.enable_value_proj and p.enable_qkv_proj_in_onestep:\\n self.CreateChild('qkv', ProjectInputOneStep(key_input_dim))\\n else:\\n self.CreateChild('key', ProjectInput(key_input_dim))\\n self.CreateChild('query', ProjectInput(query_input_dim))\\n if p.enable_value_proj:\\n assert value_input_dim, f'value_input_dim is {value_input_dim}'\\n self.CreateChild('value', ProjectInput(value_input_dim))\\n if p.enable_query_scale and p.enable_per_dim_scale:\\n self.CreateChild(\\n 'per_dim_scale',\\n PerDimScaleLayer.Params().Set(dim=p.proj_tpl.dim_per_head))\\n self.CreateChild('atten_dropout',\\n p.dropout_tpl.Set(keep_prob=1.0 - p.atten_dropout_prob))\\n # Setting is_output_projection=True to set the projection direction\\n # from hidden dim to input dim. Output projection follows query_input_dim.\\n self.CreateChild(\\n 'post',\\n p.proj_tpl.Copy().Set(\\n input_dim=p.output_dim or query_input_dim,\\n num_heads=p.num_heads,\\n is_output_projection=True,\\n use_bias=p.use_bias,\\n device_mesh=p.device_mesh,\\n weight_split_dims_mapping=post_weight_split_dims_mapping))\\n\\n if p.rope_tpl:\\n assert issubclass(p.rope_tpl.cls, layers.RotaryPositionalEmbeddingLayer)\\n rope_p = p.rope_tpl.Copy()\\n if rope_p.embedding_dim == 0:\\n rope_p.embedding_dim = self.dim_per_head\\n self.CreateChild('rope', rope_p)\\n\\n if p.attn_add_memory:\\n assert p.memory_tpl is not None\\n self.CreateChild(\\n 'lsh_mem',\\n p.memory_tpl.Copy().Set(\\n input_dim=self.dim_per_head,\\n output_dim=self.dim_per_head,\\n name='attn_lsh_mem'))\\n if p.use_scale_invariant_atten:\\n assert not (p.enable_scaling_code_motion or p.atten_extra_logit)\",\n \"def __init__(self, input_dim, hidden_dim, output_dim, epsilon):\\n \\n self.W1 = np.random.randn(input_dim, hidden_dim) / np.sqrt(input_dim)\\n self.W2 = np.random.randn(hidden_dim,output_dim) / np.sqrt(hidden_dim)\\n self.b1 = np.zeros((1, hidden_dim))\\n self.b2 = np.zeros((1, output_dim))\\n self.epsilon = epsilon\",\n \"def create_model(self):\\n try:\\n self.model = PPO2.load(self.save_path)\\n self.model.set_env(self.env)\\n print(\\\"Loading of the latest model successful!\\\")\\n except:\\n print(\\\"Creating new model...\\\")\\n self.model = PPO2(CnnPolicy, self.env, verbose=1)\",\n \"def beam_search_with_heuristics(model, inputs, beam_size, max_steps, from_cond=True):\\n orig_inputs = inputs['orig_inputs'][0]\\n #inference_state, next_choices = model.inference(inputs, orig_inputs.db)\\n inference_state, next_choices = model(inputs, db=orig_inputs.db, is_train=False)\\n beam = [Hypothesis4Filtering(inference_state, next_choices)]\\n\\n cached_finished_seqs = [] # cache filtered trajectories\\n beam_prefix = beam\\n while True:\\n # search prefixes with beam search\\n prefixes2fill_from = []\\n for step in range(max_steps):\\n if len(prefixes2fill_from) >= beam_size:\\n break\\n\\n candidates = []\\n for hyp in beam_prefix:\\n if hyp.inference_state.cur_item.state == hyp.inference_state.State.CHILDREN_APPLY \\\\\\n and hyp.inference_state.cur_item.node_type == \\\"from\\\":\\n # 仅差 from 未填充，单独保存，后续逻辑专门处理\\n prefixes2fill_from.append(hyp)\\n else:\\n candidates += [(hyp, choice, choice_score.numpy().item(), hyp.score + choice_score.numpy().item())\\n for choice, choice_score in hyp.next_choices]\\n # 排序并限定 beam_size 个候选（减去待填充 from 的候选）\\n candidates.sort(key=operator.itemgetter(3), reverse=True)\\n candidates = candidates[:beam_size - len(prefixes2fill_from)]\\n\\n # Create the new hypotheses from the expansions\\n beam_prefix = []\\n for hyp, choice, choice_score, cum_score in candidates:\\n inference_state = hyp.inference_state.clone()\\n\\n # cache column choice\\n column_history = hyp.column_history[:]\\n if hyp.inference_state.cur_item.state == hyp.inference_state.State.POINTER_APPLY and \\\\\\n hyp.inference_state.cur_item.node_type == \\\"column\\\":\\n column_history = column_history + [choice]\\n\\n # beam 内的每个候选执行一次 step()，获取下一步 choices\\n next_choices = inference_state.step(choice)\\n assert next_choices is not None\\n beam_prefix.append(\\n Hypothesis4Filtering(inference_state, next_choices, cum_score,\\n hyp.choice_history + [choice],\\n hyp.score_history + [choice_score],\\n column_history))\\n\\n prefixes2fill_from.sort(key=operator.attrgetter('score'), reverse=True)\\n # assert len(prefixes) == beam_size\\n\\n # emuerating \\n beam_from = prefixes2fill_from\\n max_size = 6\\n unfiltered_finished = []\\n prefixes_unfinished = []\\n for step in range(max_steps):\\n if len(unfiltered_finished) + len(prefixes_unfinished) > max_size:\\n break\\n\\n candidates = []\\n for hyp in beam_from:\\n if step > 0 and hyp.inference_state.cur_item.state == hyp.inference_state.State.CHILDREN_APPLY \\\\\\n and hyp.inference_state.cur_item.node_type == \\\"from\\\":\\n prefixes_unfinished.append(hyp)\\n else:\\n candidates += [(hyp, choice, choice_score.numpy().item(),\\n hyp.score + choice_score.numpy().item())\\n for choice, choice_score in hyp.next_choices]\\n candidates.sort(key=operator.itemgetter(3), reverse=True)\\n candidates = candidates[:max_size - len(prefixes_unfinished)]\\n\\n beam_from = []\\n for hyp, choice, choice_score, cum_score in candidates:\\n inference_state = hyp.inference_state.clone()\\n\\n # cache table choice\\n table_history = hyp.table_history[:]\\n key_column_history = hyp.key_column_history[:]\\n if hyp.inference_state.cur_item.state == hyp.inference_state.State.POINTER_APPLY:\\n if hyp.inference_state.cur_item.node_type == \\\"table\\\":\\n table_history = table_history + [choice]\\n elif hyp.inference_state.cur_item.node_type == \\\"column\\\":\\n key_column_history = key_column_history + [choice]\\n\\n next_choices = inference_state.step(choice)\\n if next_choices is None:\\n unfiltered_finished.append(Hypothesis4Filtering(\\n inference_state,\\n None,\\n cum_score,\\n hyp.choice_history + [choice],\\n hyp.score_history + [choice_score],\\n hyp.column_history, table_history,\\n key_column_history))\\n else:\\n beam_from.append(\\n Hypothesis4Filtering(inference_state, next_choices, cum_score,\\n hyp.choice_history + [choice],\\n hyp.score_history + [choice_score],\\n hyp.column_history, table_history,\\n key_column_history))\\n # [END] for step in range(max_steps)\\n\\n unfiltered_finished.sort(key=operator.attrgetter('score'), reverse=True)\\n\\n # filtering\\n filtered_finished = []\\n for hyp in unfiltered_finished:\\n mentioned_column_ids = set(hyp.column_history)\\n mentioned_key_column_ids = set(hyp.key_column_history)\\n mentioned_table_ids = set(hyp.table_history)\\n\\n # duplicate tables\\n if len(mentioned_table_ids) != len(hyp.table_history):\\n continue\\n\\n # the foreign key should be correctly used\\n # NOTE: the new version does not predict conditions in FROM clause anymore\\n if from_cond:\\n covered_tables = set()\\n must_include_key_columns = set()\\n candidate_table_ids = sorted(mentioned_table_ids)\\n start_table_id = candidate_table_ids[0]\\n for table_id in candidate_table_ids[1:]:\\n if table_id in covered_tables:\\n continue\\n try:\\n path = nx.shortest_path(\\n orig_inputs.db.foreign_key_graph, source=start_table_id, target=table_id)\\n except (nx.NetworkXNoPath, nx.NodeNotFound):\\n covered_tables.add(table_id)\\n continue\\n\\n for source_table_id, target_table_id in zip(path, path[1:]):\\n if target_table_id in covered_tables:\\n continue\\n if target_table_id not in mentioned_table_ids:\\n continue\\n col1, col2 = orig_inputs.db.foreign_key_graph[source_table_id][target_table_id]['columns']\\n must_include_key_columns.add(col1)\\n must_include_key_columns.add(col2)\\n if not must_include_key_columns == mentioned_key_column_ids:\\n continue\\n\\n # tables whose columns are mentioned should also exist\\n must_table_ids = set()\\n for col in mentioned_column_ids:\\n tab_ = orig_inputs.db.columns[col].table\\n if tab_ is not None:\\n must_table_ids.add(tab_.id)\\n if not must_table_ids.issubset(mentioned_table_ids):\\n continue\\n\\n filtered_finished.append(hyp)\\n\\n filtered_finished.sort(key=operator.attrgetter('score'), reverse=True)\\n # filtered.sort(key=lambda x: x.score / len(x.choice_history), reverse=True)\\n prefixes_unfinished.sort(key=operator.attrgetter('score'), reverse=True)\\n # new_prefixes.sort(key=lambda x: x.score / len(x.choice_history), reverse=True)\\n\\n prefixes_, filtered_ = merge_beams(prefixes_unfinished, filtered_finished, beam_size)\\n\\n if filtered_:\\n cached_finished_seqs = cached_finished_seqs + filtered_\\n cached_finished_seqs.sort(key=operator.attrgetter('score'), reverse=True)\\n\\n if prefixes_ and len(prefixes_[0].choice_history) < 200:\\n beam_prefix = prefixes_\\n for hyp in beam_prefix:\\n hyp.table_history = []\\n hyp.column_history = []\\n hyp.key_column_history = []\\n elif cached_finished_seqs:\\n return cached_finished_seqs[:beam_size]\\n else:\\n return unfiltered_finished[:beam_size]\",\n \"def _create_model(self):\\n config = {\\n \\\"input_features\\\": self.input_features,\\n \\\"output_features\\\": self.output_features,\\n \\\"combiner\\\": {\\\"type\\\": \\\"concat\\\", \\\"output_size\\\": 14},\\n TRAINER: {\\\"epochs\\\": 2, BATCH_SIZE: 128},\\n }\\n return LudwigModel(config, logging_level=logging.WARN)\",\n \"def fit(self, X, max_iter=30, epsilon=10e-1, rand_seed = False):\\r\\n \\r\\n if rand_seed:\\r\\n np.random.seed(rand_seed)\\r\\n \\r\\n # train the HMM model using the Baum-Welch algorithm\\r\\n # a specific instance of the expectation-maximization algorithm\\r\\n\\r\\n N = len(X)\\r\\n D = X[0].shape[1] # assume each x is organized (T, D)\\r\\n\\r\\n self.pi = np.ones(self.M) / self.M # initial state distribution\\r\\n self.A = self.random_normalised(self.M, self.M) # state transition matrix\\r\\n self.R = np.ones((self.M, self.K)) / self.K # mixture proportions\\r\\n print(\\\"initial A:\\\", self.A)\\r\\n print(\\\"initial R:\\\", self.R)\\r\\n self.mu = np.zeros((self.M, self.K, D))\\r\\n for i in range(self.M):\\r\\n for k in range(self.K):\\r\\n random_idx = np.random.choice(N)\\r\\n x = X[random_idx]\\r\\n random_time_idx = np.random.choice(len(x))\\r\\n self.mu[i,k] = x[random_time_idx]\\r\\n self.sigma = np.zeros((self.M, self.K, D, D))\\r\\n for j in range(self.M):\\r\\n for k in range(self.K):\\r\\n self.sigma[j,k] = np.eye(D)\\r\\n\\r\\n self.costs = []\\r\\n for epoch in trange(max_iter):\\r\\n alphas = []\\r\\n betas = []\\r\\n gammas = []\\r\\n Bs = []\\r\\n # components = []\\r\\n P = np.zeros(N)\\r\\n\\r\\n for n in range(N):\\r\\n x = X[n]\\r\\n T = len(x)\\r\\n\\r\\n # calculate B so we can lookup when updating alpha and beta\\r\\n B = np.zeros((self.M, T))\\r\\n component = np.zeros((self.M, self.K, T)) # we'll need these later\\r\\n for j in range(self.M):\\r\\n for t in range(T):\\r\\n for k in range(self.K):\\r\\n p = self.R[j,k] * mvn.pdf(x[t], self.mu[j,k], self.sigma[j,k])\\r\\n component[j,k,t] = p\\r\\n B[j,t] += p\\r\\n Bs.append(B)\\r\\n\\r\\n alpha = np.zeros((T, self.M))\\r\\n alpha[0] = self.pi*B[:,0]\\r\\n for t in range(1, T):\\r\\n alpha[t] = alpha[t-1].dot(self.A) * B[:,t]\\r\\n P[n] = alpha[-1].sum()\\r\\n assert(P[n] <= 1)\\r\\n alphas.append(alpha)\\r\\n\\r\\n beta = np.zeros((T, self.M))\\r\\n beta[-1] = 1\\r\\n for t in range(T - 2, -1, -1):\\r\\n beta[t] = self.A.dot(B[:,t+1] * beta[t+1])\\r\\n betas.append(beta)\\r\\n\\r\\n # update for Gaussians\\r\\n gamma = np.zeros((T, self.M, self.K))\\r\\n for t in range(T):\\r\\n alphabeta = (alphas[n][t,:] * betas[n][t,:]).sum()\\r\\n for j in range(self.M):\\r\\n factor = alphas[n][t,j] * betas[n][t,j] / alphabeta\\r\\n # mixture_j = component[j,:,t].sum()\\r\\n for k in range(self.K):\\r\\n gamma[t,j,k] = factor * component[j,k,t] / B[j,t]\\r\\n gammas.append(gamma)\\r\\n\\r\\n cost = np.log(P).sum()\\r\\n self.costs.append(cost)\\r\\n\\r\\n # now re-estimate pi, A, R, mu, sigma\\r\\n self.pi = np.array([(alphas[n][0] * betas[n][0])/P[n] for n in range(N)]).sum() / N\\r\\n\\r\\n a_den = np.zeros((self.M, 1))\\r\\n a_num = 0\\r\\n r_num = np.zeros((self.M, self.K))\\r\\n r_den = np.zeros(self.M)\\r\\n mu_num = np.zeros((self.M, self.K, D))\\r\\n sigma_num = np.zeros((self.M, self.K, D, D))\\r\\n for n in range(N):\\r\\n x = X[n]\\r\\n T = len(x)\\r\\n B = Bs[n]\\r\\n gamma = gammas[n]\\r\\n\\r\\n # denominator for A\\r\\n a_den += (alphas[n][:-1] * betas[n][:-1]).sum(axis=0, keepdims=True).T / P[n]\\r\\n\\r\\n # numerator for A\\r\\n a_num_n = np.zeros((self.M, self.M))\\r\\n for i in range(self.M):\\r\\n for j in range(self.M):\\r\\n for t in range(T-1):\\r\\n a_num_n[i,j] += alphas[n][t,i] * self.A[i,j] * B[j,t+1] * betas[n][t+1,j]\\r\\n a_num += a_num_n / P[n]\\r\\n\\r\\n\\r\\n # update mixture components\\r\\n r_num_n = np.zeros((self.M, self.K))\\r\\n r_den_n = np.zeros(self.M)\\r\\n for j in range(self.M):\\r\\n for k in range(self.K):\\r\\n for t in range(T):\\r\\n r_num_n[j,k] += gamma[t,j,k]\\r\\n r_den_n[j] += gamma[t,j,k]\\r\\n r_num += r_num_n / P[n]\\r\\n r_den += r_den_n / P[n]\\r\\n\\r\\n mu_num_n = np.zeros((self.M, self.K, D))\\r\\n sigma_num_n = np.zeros((self.M, self.K, D, D))\\r\\n for j in range(self.M):\\r\\n for k in range(self.K):\\r\\n for t in range(T):\\r\\n # update means\\r\\n mu_num_n[j,k] += gamma[t,j,k] * x[t]\\r\\n\\r\\n # update covariances\\r\\n sigma_num_n[j,k] += gamma[t,j,k] * np.outer(x[t] - self.mu[j,k], x[t] - self.mu[j,k])\\r\\n mu_num += mu_num_n / P[n]\\r\\n sigma_num += sigma_num_n / P[n]\\r\\n\\r\\n self.A = a_num / a_den\\r\\n assert(np.all(self.A <= 1))\\r\\n\\r\\n # update R, mu, sigma\\r\\n for j in range(self.M):\\r\\n for k in range(self.K):\\r\\n self.R[j,k] = r_num[j,k] / r_den[j]\\r\\n self.mu[j,k] = mu_num[j,k] / r_num[j,k]\\r\\n self.sigma[j,k] = sigma_num[j,k] / r_num[j,k]\\r\\n\\r\\n print(\\\"A:\\\", self.A)\\r\\n print(\\\"mu:\\\", self.mu)\\r\\n print(\\\"sigma:\\\", self.sigma)\\r\\n print(\\\"R:\\\", self.R)\\r\\n print(\\\"pi:\\\", self.pi)\",\n \"def _get_model_vectorized(self, bdf_filename):\\n ext = os.path.splitext(bdf_filename)[1].lower()\\n punch = False\\n if ext == '.pch':\\n punch = True\\n\\n self.model_type = 'nastran'\\n log = self.log\\n if ext == '.op2':\\n from pyNastran.dev.bdf_vectorized2.op2_geom_vectorized import (\\n OP2Geom as OP2Geom_)\\n model = OP2Geom_(make_geom=True, debug=False, log=log,\\n debug_file=None)\\n model.clear_results()\\n model.read_op2(op2_filename=bdf_filename)\\n else: # read the bdf/punch\\n from pyNastran.dev.bdf_vectorized2.bdf_vectorized import BDF as BDF_\\n model = BDF_(log=log, debug=True)\\n\\n # static_elements.bdf\\n #skip_cards = [\\n #'CELAS1', 'CELAS2', 'CELAS3', 'CELAS4', 'PLOTEL', 'PARAM',\\n #'CDAMP1', 'CDAMP2', 'CDAMP3', 'CDAMP4', 'CVISC',\\n #'TABLEM1', 'TABLEM2', 'TABLEM3', 'TABLEM4',\\n #'TABLED1', 'TABLED2', 'TABLED3', 'TABLED4',\\n #'PLOAD', 'PLOAD1', 'PLOAD2', 'FORCE', 'PLOAD4', 'LOAD',\\n #'SPCADD', 'MAT1', 'PSHEAR', 'PSHELL', 'PTUBE', 'PDAMP',\\n #'SPC1', 'CONM2', 'PELAST', 'PBEND', 'PBEAM', 'PCOMP', 'PCOMPG', 'PBAR', 'PSOLID',\\n #'PBUSH1D',\\n #'EPOINT',\\n #'CQUADR', 'CTRIAR', 'SPOINT', 'PROD', 'PELAS', 'PVISC',\\n #'CQUAD8', 'CTRIA6',\\n #]\\n #model.disable_cards(skip_cards)\\n model.read_bdf(bdf_filename,\\n punch=punch, xref=False,\\n validate=True)\\n #print(list(key for key in model.card_count.keys() if key not in skip_cards))\\n\\n #xref_loads = False\\n #xref_aero = len(model.caeros) > 0\\n #model.cross_reference(\\n #xref=True,\\n #xref_nodes=True,\\n #xref_elements=False,\\n #xref_nodes_with_elements=False,\\n #xref_properties=True,\\n #xref_masses=True,\\n #xref_materials=False,\\n #xref_loads=xref_loads,\\n #xref_constraints=False,\\n #xref_optimization=False,\\n #xref_aero=False,\\n #xref_sets=False,\\n #)\\n return model\",\n \"def build_model(self):\\n input_pencil = tf.keras.Input((128,128,3))\\n # generator's output\\n gen_image = self.gan_generator.model(input_pencil)\\n # generator's output\\n x = self.gan_discriminator.model([input_pencil,gen_image])\\n model = tf.keras.Model(input_pencil,[x,gen_image])\\n # compiling the model\\n model.compile(loss=['hinge', 'mae'], optimizer = self.optimizer,loss_weights=[1,100], metrics=['accuracy'])\\n self.model = model\",\n \"def build_model_docplex(resite, params: Dict):\\n\\n from docplex.mp.model import Model\\n from resite.models.docplex_utils import minimize_deployed_capacity, capacity_bigger_than_existing, \\\\\\n generation_bigger_than_load_proportion, create_generation_y_dict\\n\\n data = resite.data_dict\\n load = data[\\\"load\\\"].values\\n regions = resite.regions\\n tech_points_tuples = list(resite.tech_points_tuples)\\n time_slices = define_time_slices(params[\\\"time_resolution\\\"], resite.timestamps)\\n\\n model = Model()\\n\\n # - Parameters - #\\n load_perc_per_region = dict(zip(regions, params[\\\"perc_per_region\\\"]))\\n\\n # - Variables - #\\n # Portion of capacity at each location for each technology\\n model.y = model.continuous_var_dict(keys=tech_points_tuples, lb=0., ub=1.,\\n name=lambda k: 'y_%s_%s_%s' % (k[0], k[1], k[2]))\\n # Create generation dictionary for building speed up\\n generation_potential_df = data[\\\"cap_factor_df\\\"] * data[\\\"cap_potential_ds\\\"]\\n region_generation_y_dict = \\\\\\n create_generation_y_dict(model, regions, resite.tech_points_regions_ds, generation_potential_df)\\n\\n # - Constraints - #\\n # Impose a certain percentage of the load to be covered over each time slice\\n generation_bigger_than_load_proportion(model, region_generation_y_dict, load, regions, time_slices,\\n load_perc_per_region)\\n # Percentage of capacity installed must be bigger than existing percentage\\n existing_cap_percentage_ds = data[\\\"existing_cap_ds\\\"].divide(data[\\\"cap_potential_ds\\\"])\\n capacity_bigger_than_existing(model, existing_cap_percentage_ds, tech_points_tuples)\\n\\n # - Objective - #\\n # Minimize the capacity that is deployed\\n minimize_deployed_capacity(model, data[\\\"cap_potential_ds\\\"])\\n\\n resite.instance = model\",\n \"def BMA_Predictive_Likelihood(df_train, df_test, iterations, burnin, p_1,\\n l_share):\\n\\n # number of individual forecasts and number of periods\\n K = df_test.shape[1]\\n T = df_train.shape[0]\\n\\n # g-prior parameter\\n c = float(K**3)\\n\\n # separate real value and individual forecasts\\n y = df_train.iloc[:, 0]\\n F = df_train.iloc[:, 1:]\\n\\n # separate training and the hold-out sample\\n m_share = 1 - l_share\\n y_star = y.iloc[:int(np.round(m_share*T))]\\n y_tilde = y.iloc[int(np.round(m_share*T)):]\\n F_star = F.iloc[:int(np.round(m_share*T)), :]\\n F_tilde = F.iloc[int(np.round(m_share*T)):, :]\\n\\n # initializations\\n models = np.full((iterations, K), fill_value=False, dtype=bool)\\n theta_hats = []\\n predictive_lik = np.full(iterations, fill_value=np.nan, dtype=float)\\n\\n # RJ-MCMC\\n # initial state = null model\\n # predictive likelihood for the initial state\\n y_star_bar = np.mean(y_star)\\n S_right = sum((y_star - y_star_bar)**2)/(c+1)\\n Z_star = np.insert(F_star.iloc[:, models[0, :]].values, 0, 1, axis=1)\\n Z_star_t = np.transpose(Z_star)\\n Z_tilde = np.insert(F_tilde.iloc[:, models[0, :]].values, 0, 1, axis=1)\\n Z_tilde_t = np.transpose(Z_tilde)\\n theta_hat = np.dot(\\n np.linalg.inv(np.dot(np.transpose(Z_star), Z_star)),\\n np.dot(np.transpose(Z_star), y_star)\\n )\\n theta_hats += [theta_hat]\\n gamma = (c/(c+1)) * theta_hat\\n SSR = sum((y_star - np.dot(Z_star, theta_hat))**2)\\n S_left = (c/(c+1))*SSR\\n S = S_left + S_right\\n\\n # scale to first decimal place for numerical reasons\\n # find scaling order\\n S_rescaled = S\\n scaling_factor = 1\\n while (S_rescaled < 0.1):\\n S_rescaled *= 10\\n scaling_factor *= 10\\n # rescale S (prevent overflow)\\n S = S*scaling_factor\\n\\n lambda_star = ((c+1) / c) * np.dot(Z_star_t, Z_star)\\n lambda_star_inv = np.linalg.inv(lambda_star)\\n m_len = y_star.shape[0]\\n l_len = y_tilde.shape[0]\\n epsilon = y_tilde - np.dot(Z_tilde, gamma)\\n epsilon_t = np.transpose(epsilon)\\n mid = np.linalg.inv(\\n np.eye(l_len) + np.dot(np.dot(Z_tilde, lambda_star_inv), Z_tilde_t)\\n )\\n term_1 = S**(m_len/2)\\n term_2 = (np.linalg.det(lambda_star) / np.linalg.det(\\n lambda_star + np.dot(Z_tilde_t, Z_tilde)))**(1/2)\\n term_3 = (S + np.dot(np.dot(epsilon_t, mid), epsilon))**(-T/2)\\n predictive_lik[0] = term_1 * term_2 * term_3\\n\\n # initialization of the proposal model\\n m_star = np.full(K, fill_value=True, dtype=bool)\\n\\n # main iteration loop\\n for i in range(0, iterations-1):\\n\\n # decide which move to perform\\n epsilon_move = np.random.random()\\n\\n # move 1\\n if epsilon_move <= p_1 or sum(models[0]) == 0:\\n\\n # drawn one of the forecasts from the model\\n drawn_f_index = np.random.randint(0, K)\\n\\n # if it is in the model, drop it, if not, add it\\n np.copyto(m_star, models[i, :])\\n m_star[drawn_f_index] = ~models[i, drawn_f_index]\\n\\n # if there are too many variables (d.f. = 0), the change is revert\\n if sum(m_star) > (m_len-2):\\n m_star[drawn_f_index] = models[i, drawn_f_index]\\n\\n # move 2\\n else:\\n\\n # drawn one of the forecasts from the model and one outside\\n index_in = np.arange(K)[models[i, :]]\\n index_out = np.arange(K)[~models[i, :]]\\n drawn_f_index_in = np.random.choice(index_in)\\n drawn_f_index_out = np.random.choice(index_out)\\n\\n # swap these two variables\\n np.copyto(m_star, models[i, :])\\n m_star[drawn_f_index_in] = ~m_star[drawn_f_index_in]\\n m_star[drawn_f_index_out] = ~m_star[drawn_f_index_out]\\n\\n # calculate the probability of acceptance\\n Z_star = np.insert(F_star.iloc[:, m_star].values, 0, 1, axis=1)\\n Z_star_t = np.transpose(Z_star)\\n Z_tilde = np.insert(F_tilde.iloc[:, m_star].values, 0, 1, axis=1)\\n Z_tilde_t = np.transpose(Z_tilde)\\n theta_hat = np.dot(\\n np.linalg.inv(np.dot(np.transpose(Z_star), Z_star)),\\n np.dot(np.transpose(Z_star), y_star)\\n )\\n gamma = (c/(c+1)) * theta_hat\\n SSR = sum((y_star - np.dot(Z_star, theta_hat))**2)\\n S_left = (c/(c+1))*SSR\\n S = S_left + S_right\\n\\n # rescale S (prevent overflow)\\n S = S*scaling_factor\\n\\n lambda_star = ((c+1) / c) * np.dot(Z_star_t, Z_star)\\n lambda_star_inv = np.linalg.inv(lambda_star)\\n epsilon = y_tilde - np.dot(Z_tilde, gamma)\\n epsilon_t = np.transpose(epsilon)\\n mid = np.linalg.inv(\\n np.eye(l_len) + np.dot(np.dot(Z_tilde, lambda_star_inv), Z_tilde_t)\\n )\\n term_1 = S**(m_len/2)\\n term_2 = (np.linalg.det(lambda_star) / np.linalg.det(\\n lambda_star + np.dot(Z_tilde_t, Z_tilde)))**(1/2)\\n term_3 = (S + np.dot(np.dot(epsilon_t, mid), epsilon))**(-T/2)\\n predictive_lik_m_star = term_1 * term_2 * term_3\\n alpha = min(1, predictive_lik_m_star/predictive_lik[i])\\n\\n # if accepted, move to m_star, else retain the same model\\n epsilon_accept = np.random.random()\\n\\n if epsilon_accept <= alpha:\\n np.copyto(models[i+1, :], m_star)\\n predictive_lik[i+1] = predictive_lik_m_star\\n theta_hats += [theta_hat]\\n else:\\n np.copyto(models[i+1, :], models[i, :])\\n predictive_lik[i+1] = predictive_lik[i]\\n theta_hats += [theta_hats[-1]]\\n\\n # discard the burnin draws\\n models = models[burnin:, :]\\n predictive_lik = predictive_lik[burnin:]\\n theta_hats = theta_hats[burnin:]\\n\\n # unique models visited by the chain\\n models_uni, index_uni = np.unique(models, axis=0, return_index=True)\\n predictive_lik_uni = predictive_lik[index_uni]\\n # theta_hats_uni = [theta_hats[i] for i in index_uni]\\n theta_hats_uni = list(np.array(theta_hats)[index_uni])\\n\\n # model posterior probabilities\\n predictive_lik_sum = sum(predictive_lik_uni)\\n posterior_prob = predictive_lik_uni / predictive_lik_sum\\n\\n # generate prediction for testing set\\n model_fcts = np.full(\\n (df_test.shape[0], len(theta_hats_uni)),\\n np.nan,\\n dtype=float\\n )\\n\\n # forecast from different models\\n for j in range(model_fcts.shape[1]):\\n\\n model_fcts[:, j] = np.dot(\\n np.insert(df_test.iloc[:, models_uni[j]].values, 0, 1, axis=1),\\n theta_hats_uni[j]\\n )\\n\\n pred = np.dot(model_fcts, posterior_prob)\\n\\n df_pred = pd.DataFrame(\\n {\\\"BMA (Predictive Likelihood)\\\": pred},\\n index=df_test.index)\\n\\n return df_pred\",\n \"def build(self, input_fields, base_models, vocab_target):\\n encoder_outputs = []\\n encdec_bridges = []\\n decoders = []\\n target_modalities = []\\n # prepare for decoding of each model\\n for index, model in enumerate(base_models):\\n with tf.variable_scope(\\n GlobalNames.ENSEMBLE_VARNAME_PREFIX + str(index)):\\n with tf.variable_scope(model.name):\\n input_modality, target_modality = model._create_modalities()\\n encoder = model._create_encoder()\\n encoder_output = model._encode(\\n encoder=encoder, input_modality=input_modality,\\n input_fields=input_fields)\\n bridge = model._create_bridge(encoder_output)\\n decoder = model._create_decoder()\\n vs_name = tf.get_variable_scope().name\\n decoder.name = os.path.join(vs_name, decoder.name)\\n target_modality.name = os.path.join(vs_name, target_modality.name)\\n encoder_outputs.append(encoder_output)\\n encdec_bridges.append(bridge)\\n decoders.append(decoder)\\n target_modalities.append(target_modality)\\n\\n helper = BeamFeedback(\\n vocab=vocab_target,\\n batch_size=tf.shape(input_fields[GlobalNames.PH_FEATURE_IDS_NAME])[0],\\n maximum_labels_length=self._maximum_labels_length,\\n beam_size=self._beam_size,\\n alpha=self._length_penalty,\\n ensemble_weight=self.get_ensemble_weights(len(base_models)))\\n infer_status = dynamic_ensemble_decode(\\n decoders=decoders,\\n encoder_outputs=encoder_outputs,\\n bridges=encdec_bridges,\\n target_modalities=target_modalities,\\n helper=helper)\\n predict_out = dict()\\n predict_out[\\\"predicted_ids\\\"] = infer_status.predicted_ids\\n predict_out[\\\"sequence_lengths\\\"] = infer_status.lengths\\n predict_out[\\\"beam_ids\\\"] = infer_status.beam_ids\\n predict_out[\\\"log_probs\\\"] = infer_status.log_probs\\n return predict_out\",\n \"def mcmc(self,BHB):\\n\\n ndim = self.sampndim\\n nwalkers = self.sampnwalk\\n #startpoint = self.theta_ml\\n startpoint = self.theta_guess\\n pos = [startpoint + 1e-4*np.random.randn(ndim) for i in range(nwalkers)]\\n\\n sampler = emcee.EnsembleSampler(nwalkers, ndim, lnlikelihood.lnprob,\\n args=(self.transinfo, self.lamlim1, self.lamlim2,\\n self.wave_b, self.flux_b, self.err_b,\\n self.wave_r, self.flux_r, self.err_r,\\n self.velres))\\n\\n # Clear and run the production chain.\\n print(\\\"Running MCMC...\\\")\\n sampler.run_mcmc(pos, self.nsteps, rstate0=np.random.get_state())\\n print(\\\"Done.\\\")\\n\\n pl.clf()\\n fig, axes = pl.subplots(ndim, 1, sharex=True, figsize=(8, 9))\\n\\n for ind in range(0,ndim):\\n \\n axes[ind].plot(sampler.chain[:, :, ind].T, color=\\\"k\\\", alpha=0.4)\\n axes[ind].yaxis.set_major_locator(MaxNLocator(5))\\n axes[ind].axhline(startpoint[ind], color=\\\"#888888\\\", lw=2)\\n # axes[ind].set_ylabel(\\\"$m$\\\")\\n\\n\\n fig.tight_layout(h_pad=0.0)\\n fig.savefig(\\\"line-time_\\\"+BHB+\\\"_\\\"+self.transname+\\\"_\\\"+str(self.lamlim1)+\\\".png\\\") # Edited for manual runs\\n\\n\\n burnin = self.burnin\\n samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))\\n self.samples = samples\\n\\n # Compute the quantiles.\\n theta_mcmc = list(map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),\\n zip(*np.percentile(samples, [16, 50, 84],\\n axis=0))))\\n \\n \\n self.theta_percentiles = theta_mcmc\\n print(\\\"\\\"\\\"MCMC result:\\\"\\\"\\\") \\n for ind in range(0,ndim):\\n print(\\\"\\\"\\\" par {0} = {1[0]} +{1[1]} -{1[2]}\\\"\\\"\\\".format(ind, theta_mcmc[ind]))\",\n \"def __init__(self, n_trials, v0, n_options, probs, r_mag, l_mag, params, gamble, \\\\\\n\\t\\tUCB=False, UCB_samplemean=False, gamma=0, anneal=False):\\n\\n\\t\\tsz = n_trials\\n\\t\\tself.n_options = n_options\\t\\t\\t\\t\\t# number of choices\\n\\t\\tself.Q = np.zeros((sz+1,n_options)) +\\tv0\\t# critic\\n\\t\\tself.alpha, self.beta = params\\t\\t\\t\\t# learning rate, softmax\\n\\t\\tself.SM = np.zeros((sz, n_options))\\t\\t# Softmax values\\n\\t\\tself.rho = np.zeros(sz)\\t\\t\\t\\t\\t\\t# DA at choice\\n\\t\\tself.C = np.zeros(sz,dtype=np.int)\\t\\t\\t# choice\\n\\t\\tself.R = np.zeros(sz,dtype=np.int)\\t\\t\\t# indicator variable of reward\\n\\t\\tself.probs = probs\\t\\t\\t\\t\\t\\t\\t# prob of reward for choice\\n\\t\\tself.r_mag = r_mag\\t\\t\\t\\t\\t\\t\\t# magnitude of reward for choice\\n\\t\\tself.l_mag = l_mag\\t\\t\\t\\t\\t\\t\\t# magnitude of loss for choice\\n\\t\\tself.PE = np.zeros(sz)\\t\\t\\t\\t\\t\\t# choice PE\\n\\t\\tself.idx = 0\\t\\t\\t\\t\\t\\t\\t\\t# idx of trial in state \\n\\t\\tself.gamble = gamble\\t\\t\\t\\t\\t\\t# gamble paradigm?\\n\\t\\tself.UCB = UCB\\t\\t\\t\\t\\t\\t\\t\\t# Use UCB for policy?\\t\\n\\t\\tself.UCB_samplemean = UCB_samplemean\\t\\t# Use sample mean instead of Q value\\n\\t\\tself.gamma = gamma\\t\\t\\t\\t\\t\\t\\t# Exploration parameter\\t\\n\\t\\tself.N = np.zeros((sz+1, n_options))\\t\\t# number of times action selected\\n\\t\\tself.R_by_a = np.zeros((sz+1, n_options))\\t# number of times rewarded for action\\n\\t\\tself.Acts = np.zeros((sz+1,n_options))\\t\\t# act values\",\n \"def custom_model():\\n\\t# initialize the model\\n\\t# load weights from path\\n\\t# returns model\\n\\tmodel = mlp.get_training_model()\\n\\tmodel.load_state_dict(torch.load(\\\"model_wt.pth\\\"))\\n\\treturn model\",\n \"def __init__(self,\\n good_lfp_data,\\n # preprocessed_data,\\n sampling_frequency=1000,\\n n_shuffles=500,\\n wanted_window=[1500, 4000],\\n alpha=0.05,\\n multitaper_time_halfbandwidth_product=1,\\n multitaper_time_window_duration=0.3,\\n multitaper_time_window_step=0.05,\\n preprocess=True,\\n ):\\n #self.preprocessed_data = preprocessed_data\\n #self.input_data = preprocessed_data.T[wanted_window[0]:wanted_window[1]]\\n self.preprocess_flag = preprocess\\n self.good_lfp_data = good_lfp_data\\n self.sampling_frequency = sampling_frequency\\n self.n_shuffles = n_shuffles\\n self.wanted_window = wanted_window\\n self.alpha = alpha\\n self.multitaper_time_halfbandwidth_product = \\\\\\n multitaper_time_halfbandwidth_product\\n self.multitaper_time_window_duration = multitaper_time_window_duration\\n self.multitaper_time_window_step = multitaper_time_window_step\",\n \"def build_model_gurobipy(resite, params: Dict):\\n\\n from gurobipy import Model\\n from resite.models.gurobipy_utils import minimize_deployed_capacity, capacity_bigger_than_existing, \\\\\\n generation_bigger_than_load_proportion, create_generation_y_dict\\n\\n data = resite.data_dict\\n load = data[\\\"load\\\"].values\\n regions = resite.regions\\n tech_points_tuples = list(resite.tech_points_tuples)\\n time_slices = define_time_slices(params[\\\"time_resolution\\\"], resite.timestamps)\\n\\n model = Model()\\n\\n # - Parameters - #\\n load_perc_per_region = dict(zip(regions, params[\\\"perc_per_region\\\"]))\\n\\n # - Variables - #\\n # Portion of capacity at each location for each technology\\n y = model.addVars(tech_points_tuples, lb=0., ub=1., name=lambda k: 'y_%s_%s_%s' % (k[0], k[1], k[2]))\\n # Create generation dictionary for building speed up\\n generation_potential_df = data[\\\"cap_factor_df\\\"] * data[\\\"cap_potential_ds\\\"]\\n region_generation_y_dict = \\\\\\n create_generation_y_dict(y, regions, resite.tech_points_regions_ds, generation_potential_df)\\n\\n # - Constraints - #\\n # Impose a certain percentage of the load to be covered over each time slice\\n generation_bigger_than_load_proportion(model, region_generation_y_dict, load, regions, time_slices,\\n load_perc_per_region)\\n # Percentage of capacity installed must be bigger than existing percentage\\n existing_cap_percentage_ds = data[\\\"existing_cap_ds\\\"].divide(data[\\\"cap_potential_ds\\\"])\\n capacity_bigger_than_existing(model, y, existing_cap_percentage_ds, tech_points_tuples)\\n\\n # - Objective - #\\n # Minimize the capacity that is deployed\\n obj = minimize_deployed_capacity(model, y, data[\\\"cap_potential_ds\\\"])\\n\\n resite.instance = model\\n resite.y = y\\n resite.obj = obj\",\n \"def __init__(self, data, params):\\n super(VBPR, self).__init__(data, params)\\n self.embed_k = self.params.embed_k\\n self.embed_d = self.params.embed_d\\n self.learning_rate = self.params.lr\\n\\n self.directory_parameters = f'batch_{self.params.batch_size}' \\\\\\n f'-D_{self.params.embed_d}' \\\\\\n f'-K_{self.params.embed_k}' \\\\\\n f'-lr_{self.params.lr}' \\\\\\n f'-reg_{self.params.reg}'\\n\\n self.process_cnn_visual_features()\\n\\n # Initialize Model Parameters\\n self.Bp = tf.Variable(\\n self.initializer(shape=[self.dim_cnn_features, 1]), name='Bp', dtype=tf.float32)\\n self.Tu = tf.Variable(\\n self.initializer(shape=[self.num_users, self.embed_d]),\\n name='Tu', dtype=tf.float32) # (users, low_embedding_size)\\n self.F = tf.Variable(\\n self.cnn_features,\\n name='F', dtype=tf.float32, trainable=False)\\n self.E = tf.Variable(\\n self.initializer(shape=[self.dim_cnn_features, self.embed_d]),\\n name='E', dtype=tf.float32) # (items, low_embedding_size)\\n\\n self.optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)\\n self.saver_ckpt = tf.train.Checkpoint(optimizer=self.optimizer, model=self)\",\n \"def _build_model(self, model):\\n model = model(self.state_dim, n_actions=self.n_actions)\\n model.compile(loss=self._huber_loss,\\n optimizer=optimizers.Adam(lr=self.learning_rate))\\n return model\",\n \"def build_model(keep_prob):\\n model=Sequential()\\n #normalization\\n model.add(Lambda(lambda x: x/127.5-1.0, input_shape=glob_image_shape))\\n #convolutional layers\\n model.add(Conv2D(24, 5, 5, activation='elu', subsample=(2, 2)))\\n model.add(Conv2D(36, 5, 5, activation='elu', subsample=(2, 2)))\\n model.add(Conv2D(48, 5, 5, activation='elu', subsample=(2, 2)))\\n model.add(Conv2D(64, 3, 3, activation='elu'))\\n model.add(Conv2D(64, 3, 3, activation='elu'))\\n #drop out to prevent over fitting\\n model.add(Dropout(keep_prob))\\n model.add(Flatten())\\n #fully connected layers\\n model.add(Dense(100, activation='elu'))\\n model.add(Dense(50, activation='elu'))\\n model.add(Dense(10, activation='elu'))\\n model.add(Dense(1))\\n return model\",\n \"def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):\\n\\n self.params = {}\\n self.params['W1'] = weight_init_std * \\\\\\n np.random.randn(input_size, hidden_size)\\n self.params['b1'] = np.zeros(hidden_size)\\n self.params['W2'] = weight_init_std * \\\\\\n np.random.randn(hidden_size, output_size)\\n self.params['b2'] = np.zeros(output_size)\",\n \"def build_model(self):\\n raise NotImplementedError\",\n \"def setup_fixed_model():\\n out = {}\\n out['Q'] = Q\\n out['X'] = X\\n out['y'] = y\\n\\n return out\",\n \"def fit(self, h_init):\\n M = h_init.shape[0]\\n\\n cn_states = self.create_cn_states(M, 2, self.max_copy_number, self.max_copy_number_diff)\\n cn_states = np.array([cn_states] * self.N)\\n cn_states[:, :, 0, :] = self.normal_copies[:, np.newaxis, :]\\n\\n # Remap cn states\\n cn_states = cn_states[self.seg_rev_remap, :, :, :]\\n\\n brk_states = self.create_brk_states(M, self.max_copy_number, self.max_copy_number_diff)\\n\\n self.model = remixt.bpmodel.RemixtModel(\\n M,\\n self.N1,\\n self.num_breakpoints,\\n self.normal_contamination,\\n cn_states,\\n brk_states,\\n h_init,\\n self.l1,\\n self.x1[:, 2],\\n self.x1[:, 0:2],\\n self.is_telomere,\\n self.breakpoint_idx,\\n self.breakpoint_orient,\\n self.transition_log_prob,\\n self.divergence_weight,\\n )\\n\\n self.model.total_likelihood_mask = self._total_likelihood_mask.astype(int)\\n self.model.allele_likelihood_mask = self._allele_likelihood_mask.astype(int)\\n\\n if self.breakpoint_init is not None:\\n p_breakpoint = np.ones((self.model.self.num_breakpoints, self.model.num_brk_states))\\n brk_states = np.array(self.model.brk_states)\\n\\n for k, bp in enumerate(self.breakpoints):\\n cn = self.breakpoint_init[bp]\\n\\n for s in range(self.model.num_brk_states):\\n if np.all(cn == brk_states[s]):\\n p_breakpoint[k, s] = 1000.\\n\\n p_breakpoint /= np.sum(p_breakpoint, axis=-1)[:, np.newaxis]\\n\\n self.model.p_breakpoint = p_breakpoint\\n\\n self.model.transition_model = self.transition_model\\n\\n if self.prev_elbo is None:\\n self.prev_elbo = self.model.calculate_elbo()\\n\\n for i in range(self.num_em_iter):\\n for j in range(self.num_update_iter):\\n self.variational_update()\\n\\n if self.do_h_update:\\n self.em_update_h()\\n\\n self.em_update_params()\\n\\n elbo = self.model.calculate_elbo()\\n\\n self.prev_elbo_diff = elbo - self.prev_elbo\\n self.prev_elbo = elbo\\n\\n print ('[{}] completed iteration {}'.format(_gettime(), i))\\n print ('[{}] elbo: {:.10f}'.format(_gettime(), self.prev_elbo))\\n print ('[{}] elbo diff: {:.10f}'.format(_gettime(), self.prev_elbo_diff))\\n print ('[{}] h = {}'.format(_gettime(), np.asarray(self.model.h)))\\n for name, value in self.get_likelihood_param_values().items():\\n print ('[{}] {} = {}'.format(_gettime(), name, value))\",\n \"def build_model_from_inputs(self):\\n if self.term_list is None:\\n # no supplied token list -- use vocabulary of the training dataset\\n # self.term_list = self.vocabulary\\n # info(\\\"Setting bag dimension to {} from input vocabulary.\\\".format(len(self.term_list)))\\n # will generate the vocabulary from the input\\n pass\\n info(f\\\"Building {self.name} model\\\")\\n bagger = None\\n if self.config.max_terms is not None:\\n bagger = Bag(vocabulary=self.term_list, weighting=self.base_name, ngram_range=self.ngram_range, max_terms=self.config.max_terms)\\n else:\\n bagger = Bag(vocabulary=self.term_list, weighting=self.base_name, ngram_range=self.ngram_range)\\n\\n train_idx = self.indices.get_train_instances()\\n texts = Text.get_strings(self.text.data.get_slice(train_idx))\\n bagger.map_collection(texts, fit=True, transform=False)\\n self.term_list = bagger.get_vocabulary()\\n\\n self.dimension = len(self.term_list)\\n self.config.dimension = self.dimension\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72188264","0.61532706","0.6023628","0.5833241","0.5797192","0.5724574","0.5723442","0.5719858","0.5715735","0.56933236","0.56768894","0.56603485","0.56331044","0.5607353","0.55702585","0.55379814","0.55144966","0.5503024","0.5484061","0.5476444","0.5475968","0.5469256","0.5462184","0.5444042","0.5443796","0.5430723","0.5424683","0.54180497","0.5417517","0.5411873","0.53801143","0.53786564","0.5377074","0.5366973","0.5349633","0.532572","0.5317982","0.5293187","0.5292982","0.52914405","0.528854","0.5286273","0.52820045","0.52808684","0.52644134","0.5263805","0.52533007","0.5235797","0.52234805","0.5215592","0.5215492","0.5213904","0.52137685","0.5209417","0.5206593","0.51973575","0.5184965","0.517339","0.51717013","0.5171498","0.5165691","0.51584035","0.51572895","0.5152775","0.5146977","0.5141447","0.5126792","0.5120179","0.51153314","0.51149535","0.5113032","0.51099294","0.5104844","0.5090857","0.50893945","0.5079361","0.5077733","0.5069667","0.506947","0.5065049","0.50628173","0.5054225","0.5052717","0.5050808","0.5050215","0.5049257","0.5048929","0.5048144","0.50419354","0.50373423","0.5029685","0.50294906","0.50211763","0.5018259","0.50180304","0.5017146","0.5017104","0.501465","0.5007552","0.500753"],"string":"[\n \"0.72188264\",\n \"0.61532706\",\n \"0.6023628\",\n \"0.5833241\",\n \"0.5797192\",\n \"0.5724574\",\n \"0.5723442\",\n \"0.5719858\",\n \"0.5715735\",\n \"0.56933236\",\n \"0.56768894\",\n \"0.56603485\",\n \"0.56331044\",\n \"0.5607353\",\n \"0.55702585\",\n \"0.55379814\",\n \"0.55144966\",\n \"0.5503024\",\n \"0.5484061\",\n \"0.5476444\",\n \"0.5475968\",\n \"0.5469256\",\n \"0.5462184\",\n \"0.5444042\",\n \"0.5443796\",\n \"0.5430723\",\n \"0.5424683\",\n \"0.54180497\",\n \"0.5417517\",\n \"0.5411873\",\n \"0.53801143\",\n \"0.53786564\",\n \"0.5377074\",\n \"0.5366973\",\n \"0.5349633\",\n \"0.532572\",\n \"0.5317982\",\n \"0.5293187\",\n \"0.5292982\",\n \"0.52914405\",\n \"0.528854\",\n \"0.5286273\",\n \"0.52820045\",\n \"0.52808684\",\n \"0.52644134\",\n \"0.5263805\",\n \"0.52533007\",\n \"0.5235797\",\n \"0.52234805\",\n \"0.5215592\",\n \"0.5215492\",\n \"0.5213904\",\n \"0.52137685\",\n \"0.5209417\",\n \"0.5206593\",\n \"0.51973575\",\n \"0.5184965\",\n \"0.517339\",\n \"0.51717013\",\n \"0.5171498\",\n \"0.5165691\",\n \"0.51584035\",\n \"0.51572895\",\n \"0.5152775\",\n \"0.5146977\",\n \"0.5141447\",\n \"0.5126792\",\n \"0.5120179\",\n \"0.51153314\",\n \"0.51149535\",\n \"0.5113032\",\n \"0.51099294\",\n \"0.5104844\",\n \"0.5090857\",\n \"0.50893945\",\n \"0.5079361\",\n \"0.5077733\",\n \"0.5069667\",\n \"0.506947\",\n \"0.5065049\",\n \"0.50628173\",\n \"0.5054225\",\n \"0.5052717\",\n \"0.5050808\",\n \"0.5050215\",\n \"0.5049257\",\n \"0.5048929\",\n \"0.5048144\",\n \"0.50419354\",\n \"0.50373423\",\n \"0.5029685\",\n \"0.50294906\",\n \"0.50211763\",\n \"0.5018259\",\n \"0.50180304\",\n \"0.5017146\",\n \"0.5017104\",\n \"0.501465\",\n \"0.5007552\",\n \"0.500753\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7269002"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77981,"cells":{"query":{"kind":"string","value":"Build the modified BPMF model using pymc3. The original model uses Wishart priors on the covariance matrices. Unfortunately, the Wishart distribution in pymc3 is currently not suitable for sampling. This"},"document":{"kind":"string","value":"def build_mod_bpmf_model(train, alpha=2, dim=10, std=0.01):\n n, m = train.shape\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\n\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n # We will use separate priors for sigma and correlation matrix.\n # In order to convert the upper triangular correlation values to a\n # complete correlation matrix, we need to construct an index matrix:\n n_elem = dim * (dim - 1) / 2\n tri_index = np.zeros([dim, dim], dtype=int)\n tri_index[np.triu_indices(dim, k=1)] = np.arange(n_elem)\n tri_index[np.triu_indices(dim, k=1)[::-1]] = np.arange(n_elem)\n\n logging.info('building the BPMF model')\n with pm.Model() as bpmf:\n # Specify user feature matrix\n sigma_u = pm.Uniform('sigma_u', shape=dim)\n corr_triangle_u = pm.LKJCorr(\n 'corr_u', n=1, p=dim,\n testval=np.random.randn(n_elem) * std)\n\n corr_matrix_u = corr_triangle_u[tri_index]\n corr_matrix_u = t.fill_diagonal(corr_matrix_u, 1)\n cov_matrix_u = t.diag(sigma_u).dot(corr_matrix_u.dot(t.diag(sigma_u)))\n lambda_u = t.nlinalg.matrix_inverse(cov_matrix_u)\n\n mu_u = pm.Normal(\n 'mu_u', mu=0, tau=beta_0 * t.diag(lambda_u), shape=dim,\n testval=np.random.randn(dim) * std)\n U = pm.MvNormal(\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\n testval=np.random.randn(n, dim) * std)\n\n # Specify item feature matrix\n sigma_v = pm.Uniform('sigma_v', shape=dim)\n corr_triangle_v = pm.LKJCorr(\n 'corr_v', n=1, p=dim,\n testval=np.random.randn(n_elem) * std)\n\n corr_matrix_v = corr_triangle_v[tri_index]\n corr_matrix_v = t.fill_diagonal(corr_matrix_v, 1)\n cov_matrix_v = t.diag(sigma_v).dot(corr_matrix_v.dot(t.diag(sigma_v)))\n lambda_v = t.nlinalg.matrix_inverse(cov_matrix_v)\n\n mu_v = pm.Normal(\n 'mu_v', mu=0, tau=beta_0 * t.diag(lambda_v), shape=dim,\n testval=np.random.randn(dim) * std)\n V = pm.MvNormal(\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\n testval=np.random.randn(m, dim) * std)\n\n # Specify rating likelihood function\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\n observed=train)\n\n logging.info('done building the BPMF model')\n return bpmf"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def build_bpmf_model(train, alpha=2, dim=10, std=0.01):\n n, m = train.shape\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\n\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n logging.info('building the BPMF model')\n with pm.Model() as bpmf:\n # Specify user feature matrix\n lambda_u = pm.Wishart(\n 'lambda_u', n=dim, V=np.eye(dim), shape=(dim, dim),\n testval=np.random.randn(dim, dim) * std)\n mu_u = pm.Normal(\n 'mu_u', mu=0, tau=beta_0 * lambda_u, shape=dim,\n testval=np.random.randn(dim) * std)\n U = pm.MvNormal(\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\n testval=np.random.randn(n, dim) * std)\n\n # Specify item feature matrix\n lambda_v = pm.Wishart(\n 'lambda_v', n=dim, V=np.eye(dim), shape=(dim, dim),\n testval=np.random.randn(dim, dim) * std)\n mu_v = pm.Normal(\n 'mu_v', mu=0, tau=beta_0 * lambda_v, shape=dim,\n testval=np.random.randn(dim) * std)\n V = pm.MvNormal(\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\n testval=np.random.randn(m, dim) * std)\n\n # Specify rating likelihood function\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\n observed=train)\n\n logging.info('done building the BPMF model')\n return bpmf","def bayesian_model(y, x, data=None, correlation=False, family=\"Normal\", robust = True, samples = 1000, plot_posterior=True, plot_regression=True, plot_samples = \"default\", print_summary=True, alpha = 0.05):\n\n print(\"Starting Bayesian estimation...\")\n from theano import config\n config.warn.sum_div_dimshuffle_bug = False\n import pymc3\n\n\n\n y_name = \"y\"\n x_name = \"x\"\n\n if isinstance(y,str):\n y_name = y\n try:\n y = data[y]\n except:\n pass\n if isinstance(x,str):\n x_name = x\n try:\n x = data[x]\n except:\n pass\n\n\n if correlation == True:\n #Center and scale\n x = x - np.mean(x)\n x = x / np.std(x)\n\n y = y - np.mean(y)\n y = y / np.std(y)\n\n\n\n\n data = {y_name:y,\n x_name:x}\n formula = y_name + ' ~ ' + x_name\n\n\n\n\n\n\n\n\n\n\n with pymc3.Model() as model: # model specifications in PyMC3 are wrapped in a with-statement\n if family == \"Normal\":\n family = pymc3.glm.families.Normal()\n if robust == True:\n family = pymc3.glm.families.StudentT()\n\n pymc3.glm.glm(formula, data, family=family)\n start = pymc3.find_MAP()\n step = pymc3.NUTS(scaling=start) # Instantiate MCMC sampling algorithm\n trace = pymc3.sample(samples, step, progressbar=True) # draw 2000 posterior samples using NUTS sampling\n\n# trace = trace[int(samples/4):]\n #PLOT POSTERIOR DISTRIBUTION\n if plot_posterior == True:\n pymc3.traceplot(trace)\n\n #PLOT LINES\n if plot_regression == True:\n plot_data= []\n plot_data.append(go.Scatter(x=x,\n y=y,\n mode = 'markers'))\n\n\n\n if plot_samples == \"default\":\n if len(trace) > 100:\n plot_samples = 100\n samples_range = np.random.randint(0, len(trace), plot_samples)\n else:\n plot_samples = samples\n samples_range = range(len(trace))\n else:\n samples_range = np.random.randint(0, len(trace), plot_samples)\n\n\n\n for i in samples_range:\n print(i)\n plot_data.append(go.Scatter(x=x,\n y=trace[i]['Intercept'] + trace[i]['x'] * x,\n mode = 'lines',\n opacity=0.25,\n line = {\"color\":\"grey\",\n \"width\":5}))\n layout = go.Layout(showlegend=False)\n figure = go.Figure(data = plot_data,layout=layout)\n py.plot(figure)\n\n if print_summary == True:\n print(pymc3.summary(trace,alpha=alpha))\n return(trace)","def mcmc_fit(mc_model, mcmc_parameters):\n mcmc = pymc.MCMC(mc_model)\n mcmc.use_step_method(pymc.AdaptiveMetropolis, mcmc.theta_i)\n print('running MCMC')\n mcmc.sample(mcmc_parameters['points'], mcmc_parameters['burn'], mcmc_parameters['steps'],\n progress_bar=True)\n print('finished MCMC')\n return mcmc","def fit_FITC(self, Kmm, Kmn, Knn_diag, epsilon=1e-3,power_ep=[1.,1.]):\r\n self.epsilon = epsilon\r\n self.eta, self.delta = power_ep\r\n\r\n num_inducing = Kmm.shape[0]\r\n\r\n \"\"\"\r\n Prior approximation parameters:\r\n q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)\r\n Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn\r\n \"\"\"\r\n Lm = jitchol(Kmm)\r\n Lmi = chol_inv(Lm)\r\n Kmmi = np.dot(Lmi.T,Lmi)\r\n P0 = Kmn.T\r\n KmnKnm = np.dot(P0.T, P0)\r\n KmmiKmn = np.dot(Kmmi,P0.T)\r\n Qnn_diag = np.sum(P0.T*KmmiKmn,-2)\r\n Diag0 = Knn_diag - Qnn_diag\r\n R0 = jitchol(Kmmi).T\r\n\r\n \"\"\"\r\n Posterior approximation: q(f|y) = N(f| mu, Sigma)\r\n Sigma = Diag + P*R.T*R*P.T + K\r\n mu = w + P*Gamma\r\n \"\"\"\r\n self.w = np.zeros(self.num_data)\r\n self.Gamma = np.zeros(num_inducing)\r\n mu = np.zeros(self.num_data)\r\n P = P0.copy()\r\n R = R0.copy()\r\n Diag = Diag0.copy()\r\n Sigma_diag = Knn_diag\r\n RPT0 = np.dot(R0,P0.T)\r\n\r\n \"\"\"\r\n Initial values - Cavity distribution parameters:\r\n q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}\r\n sigma_ = 1./tau_\r\n mu_ = v_/tau_\r\n \"\"\"\r\n self.tau_ = np.empty(self.num_data,dtype=float)\r\n self.v_ = np.empty(self.num_data,dtype=float)\r\n\r\n #Initial values - Marginal moments\r\n z = np.empty(self.num_data,dtype=float)\r\n self.Z_hat = np.empty(self.num_data,dtype=float)\r\n phi = np.empty(self.num_data,dtype=float)\r\n mu_hat = np.empty(self.num_data,dtype=float)\r\n sigma2_hat = np.empty(self.num_data,dtype=float)\r\n\r\n #Approximation\r\n epsilon_np1 = 1\r\n epsilon_np2 = 1\r\n \tself.iterations = 0\r\n self.np1 = [self.tau_tilde.copy()]\r\n self.np2 = [self.v_tilde.copy()]\r\n while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:\r\n update_order = np.random.permutation(self.num_data)\r\n for i in update_order:\r\n #Cavity distribution parameters\r\n self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]\r\n self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]\r\n #Marginal moments\r\n self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self.data[i],self.tau_[i],self.v_[i])\r\n #Site parameters update\r\n Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])\r\n Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])\r\n self.tau_tilde[i] += Delta_tau\r\n self.v_tilde[i] += Delta_v\r\n #Posterior distribution parameters update\r\n dtd1 = Delta_tau*Diag[i] + 1.\r\n dii = Diag[i]\r\n Diag[i] = dii - (Delta_tau * dii**2.)/dtd1\r\n pi_ = P[i,:].reshape(1,num_inducing)\r\n P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_\r\n Rp_i = np.dot(R,pi_.T)\r\n RTR = np.dot(R.T,np.dot(np.eye(num_inducing) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))\r\n R = jitchol(RTR).T\r\n self.w[i] += (Delta_v - Delta_tau*self.w[i])*dii/dtd1\r\n self.Gamma += (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)\r\n RPT = np.dot(R,P.T)\r\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\r\n mu = self.w + np.dot(P,self.Gamma)\r\n self.iterations += 1\r\n #Sigma recomptutation with Cholesky decompositon\r\n Iplus_Dprod_i = 1./(1.+ Diag0 * self.tau_tilde)\r\n Diag = Diag0 * Iplus_Dprod_i\r\n P = Iplus_Dprod_i[:,None] * P0\r\n safe_diag = np.where(Diag0 < self.tau_tilde, self.tau_tilde/(1.+Diag0*self.tau_tilde), (1. - Iplus_Dprod_i)/Diag0)\r\n L = jitchol(np.eye(num_inducing) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))\r\n R,info = dtrtrs(L,R0,lower=1)\r\n RPT = np.dot(R,P.T)\r\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\r\n self.w = Diag * self.v_tilde\r\n self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))\r\n mu = self.w + np.dot(P,self.Gamma)\r\n epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.num_data\r\n epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.num_data\r\n self.np1.append(self.tau_tilde.copy())\r\n self.np2.append(self.v_tilde.copy())\r\n\r\n return self._compute_GP_variables()","def fit_FITC(self, Kmm, Kmn, Knn_diag):\r\n num_inducing = Kmm.shape[0]\r\n\r\n \"\"\"\r\n Prior approximation parameters:\r\n q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)\r\n Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn\r\n \"\"\"\r\n Lm = jitchol(Kmm)\r\n Lmi = chol_inv(Lm)\r\n Kmmi = np.dot(Lmi.T,Lmi)\r\n P0 = Kmn.T\r\n KmnKnm = np.dot(P0.T, P0)\r\n KmmiKmn = np.dot(Kmmi,P0.T)\r\n Qnn_diag = np.sum(P0.T*KmmiKmn,-2)\r\n Diag0 = Knn_diag - Qnn_diag\r\n R0 = jitchol(Kmmi).T\r\n\r\n \"\"\"\r\n Posterior approximation: q(f|y) = N(f| mu, Sigma)\r\n Sigma = Diag + P*R.T*R*P.T + K\r\n mu = w + P*Gamma\r\n \"\"\"\r\n self.w = np.zeros(self.N)\r\n self.Gamma = np.zeros(num_inducing)\r\n mu = np.zeros(self.N)\r\n P = P0.copy()\r\n R = R0.copy()\r\n Diag = Diag0.copy()\r\n Sigma_diag = Knn_diag\r\n RPT0 = np.dot(R0,P0.T)\r\n\r\n \"\"\"\r\n Initial values - Cavity distribution parameters:\r\n q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}\r\n sigma_ = 1./tau_\r\n mu_ = v_/tau_\r\n \"\"\"\r\n self.tau_ = np.empty(self.N,dtype=float)\r\n self.v_ = np.empty(self.N,dtype=float)\r\n\r\n #Initial values - Marginal moments\r\n z = np.empty(self.N,dtype=float)\r\n self.Z_hat = np.empty(self.N,dtype=float)\r\n phi = np.empty(self.N,dtype=float)\r\n mu_hat = np.empty(self.N,dtype=float)\r\n sigma2_hat = np.empty(self.N,dtype=float)\r\n\r\n #Approximation\r\n epsilon_np1 = 1\r\n epsilon_np2 = 1\r\n \tself.iterations = 0\r\n self.np1 = [self.tau_tilde.copy()]\r\n self.np2 = [self.v_tilde.copy()]\r\n while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:\r\n update_order = np.random.permutation(self.N)\r\n for i in update_order:\r\n #Cavity distribution parameters\r\n self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]\r\n self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]\r\n #Marginal moments\r\n self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model_list[self.index[i]].moments_match(self._transf_data[i],self.tau_[i],self.v_[i])\r\n #Site parameters update\r\n Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])\r\n Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])\r\n self.tau_tilde[i] += Delta_tau\r\n self.v_tilde[i] += Delta_v\r\n #Posterior distribution parameters update\r\n dtd1 = Delta_tau*Diag[i] + 1.\r\n dii = Diag[i]\r\n Diag[i] = dii - (Delta_tau * dii**2.)/dtd1\r\n pi_ = P[i,:].reshape(1,num_inducing)\r\n P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_\r\n Rp_i = np.dot(R,pi_.T)\r\n RTR = np.dot(R.T,np.dot(np.eye(num_inducing) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))\r\n R = jitchol(RTR).T\r\n self.w[i] += (Delta_v - Delta_tau*self.w[i])*dii/dtd1\r\n self.Gamma += (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)\r\n RPT = np.dot(R,P.T)\r\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\r\n mu = self.w + np.dot(P,self.Gamma)\r\n self.iterations += 1\r\n #Sigma recomptutation with Cholesky decompositon\r\n Iplus_Dprod_i = 1./(1.+ Diag0 * self.tau_tilde)\r\n Diag = Diag0 * Iplus_Dprod_i\r\n P = Iplus_Dprod_i[:,None] * P0\r\n safe_diag = np.where(Diag0 < self.tau_tilde, self.tau_tilde/(1.+Diag0*self.tau_tilde), (1. - Iplus_Dprod_i)/Diag0)\r\n L = jitchol(np.eye(num_inducing) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))\r\n R,info = dtrtrs(L,R0,lower=1)\r\n RPT = np.dot(R,P.T)\r\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\r\n self.w = Diag * self.v_tilde\r\n self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))\r\n mu = self.w + np.dot(P,self.Gamma)\r\n epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N\r\n epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N\r\n self.np1.append(self.tau_tilde.copy())\r\n self.np2.append(self.v_tilde.copy())\r\n\r\n return self._compute_GP_variables()","def get_effect_size(self, summ, b, nmc=5000):\n m0b, v0b = self.DModel.models[0].predict(np.array([b])) \n m1b, v1b = self.DModel.models[1].predict(np.array([b]))\n \n d_mean_D = np.squeeze(m1b - m0b) # TODO: why was this swapped around?\n d_var_D = np.squeeze(v0b + v1b)\n d_std_D = np.sqrt(d_var_D)\n \n if d_mean_D < 0:\n pval = 1 - stats.norm.cdf(x=0, loc=d_mean_D, scale=d_std_D)\n else:\n pval = stats.norm.cdf(x=0, loc=d_mean_D, scale=d_std_D)\n \n xmin, xmax = (np.min([d_mean_D - 4*d_std_D, -0.1*d_std_D]), \n np.max([d_mean_D + 4*d_std_D, 0.1*d_std_D]))\n \n n = 300\n xrange = np.linspace(xmin, xmax, n)\n y = stats.norm.pdf(xrange, d_mean_D, d_std_D) \n \n samples = np.zeros((nmc))\n nspike = int(np.round(summ['pmp']['pmc']*nmc))\n samples[nspike:] = np.random.normal(loc=d_mean_D, \n scale=np.sqrt(d_var_D), \n size=(nmc-nspike))\n \n if not np.isscalar(b):\n d_bma = None\n else:\n \n if nspike==nmc:\n # BMA dominated by continuous model\n # Put all mass at xrange closest to b\n d_bma = np.zeros((n))\n xdelta = xrange[1] - xrange[0]\n ix = np.argmin((xrange-b)**2)\n d_bma[ix] = 1.0 / xdelta\n elif nspike==0:\n # BMA dominated by discontinuous model\n d_bma = y\n else:\n # BMA is a mixture\n kde_fit = stats.gaussian_kde(samples, \n bw_method='silverman')\n d_bma = kde_fit(xrange)\n \n return {'es_BMA': d_bma,\n 'es_Disc': y,\n 'es_disc_stats': (d_mean_D, d_std_D),\n 'pval': pval,\n 'es_range': xrange,\n 'f(b)': (m0b, m1b),\n 'es_transform': lambda z: z*d_std_D + d_mean_D}","def run_metropolis_PBC(self):\n\n # Initialize the posistions for each new Monte Carlo run\n positions = np.random.rand(self.num_p, self.num_d)\n # Initialize the distance matrix\n self.s.positions_distances_PBC(positions)\n # check if the wave function is zero\n while True:\n test_wavefunction = self.w.wavefunction(positions)\n if test_wavefunction**2 <= 1e-14:\n # print ('obs')\n # Initialize the posistions for each new Monte Carlo run\n positions = np.random.rand(self.num_p, self.num_d)\n # Initialize the distance matrix\n self.s.positions_distances_PBC(positions)\n else:\n break\n # Initialize sampler method for each new Monte Carlo run\n self.sam.initialize()\n\n for i in range(self.mc_cycles):\n new_positions = self.metropolis_step_PBC(positions)\n positions = new_positions\n self.sam.sample_values(positions)\n\n self.sam.average_values(self.mc_cycles)\n energy = self.sam.local_energy\n d_El = self.sam.derivative_energy\n var = self.sam.variance\n print ('w = ', self.w.wavefunction(positions))\n self.print_averages()\n return d_El, energy, var","def build_pmf_model(train, alpha=2, dim=10, std=0.01):\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n # Low precision reflects uncertainty; prevents overfitting.\n # We use point estimates from the data to intialize.\n # Set to mean variance across users and items.\n alpha_u = 1 / train.var(axis=1).mean()\n alpha_v = 1 / train.var(axis=0).mean()\n\n logging.info('building the PMF model')\n n, m = train.shape\n with pm.Model() as pmf:\n U = pm.MvNormal(\n 'U', mu=0, tau=alpha_u * np.eye(dim),\n shape=(n, dim), testval=np.random.randn(n, dim) * std)\n V = pm.MvNormal(\n 'V', mu=0, tau=alpha_v * np.eye(dim),\n shape=(m, dim), testval=np.random.randn(m, dim) * std)\n R = pm.Normal(\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones(train.shape),\n observed=train)\n\n logging.info('done building PMF model')\n return pmf","def p_mx_c(pm,px,py,pyx_c,pym_c,beta):\n \n pmx_c = np.zeros((pm.size,px.size)) # P(M|X) matrix to be returned\n for mi in range(pm.size):\n for xi in range(px.size):\n pmx_c[mi,xi] = pm[mi] * np.exp(-beta * entropy(pyx_c[:,xi], pym_c[:,mi], base=2))\n z = pmx_c.sum(axis=0)\n pmx_c /= z #Normalize\n \n \t\n return pmx_c, z","def prob3():\n #set up the matrices\n solvers.options['show_progress'] = False\n c = np.array([4., 7., 6., 8., 8., 9.])\n\n G = np.array([[1.,1.,0.,0.,0.,0.],\n [-1.,-1.,0.,0.,0.,0.],\n [0.,0.,1.,1.,0.,0.],\n [0.,0.,-1.,-1.,0.,0.],\n [0.,0.,0.,0.,1.,1.],\n [0.,0.,0.,0.,-1.,-1.],\n [1.,0.,1.,0.,1.,0.],\n [-1.,0.,-1.,0.,-1.,0.],\n [0.,1.,0.,1.,0.,1.],\n [0.,-1.,0.,-1.,0.,-1.]])\n G = np.vstack((G, -1*np.eye(6)))\n h = np.array([7,-7,2,-2,4,-4,5,-5,8,-8,0,0,0,0,0,0],dtype=\"float\")\n\n #convert the matrices\n c = matrix(c)\n G = matrix(G)\n h = matrix(h)\n #solve the matrices\n sol = solvers.lp(c, G, h)\n return np.ravel(sol['x']), sol['primal objective']","def model_test(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in, models_fit, label):\n # Generate fake data with some \"true\" parameters\n (D_vec, Ninv) = gen_data(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in)\n Ninv_sqrt = np.matrix(linalg.sqrtm(Ninv))\n (dust_params, sync_params, cmb_params) = params_in\n (dust_amp, sync_amp, cmb_amp) = amps_in\n \n # Beam model\n beam_mat = np.identity(3*len(nu))\n\n # Set-up MCMC\n dust_guess = np.array([1.6, 20.])\n sync_guess = np.array([-3.])\n cmb_guess = np.array([])\n guess = np.concatenate((dust_guess, sync_guess, cmb_guess))\n #ndim = len(dust_guess) + len(sync_guess) + len(cmb_guess)\n \n # Run MCMC sampler on this model\n t0 = time.time()\n dust_params_out, sync_params_out, cmb_params_out, samples \\\n = mcmc(guess, nu, D_vec, Ninv, beam_mat, models_fit, label)\n print \"MCMC run in %d sec.\" % (time.time() - t0)\n \n # Estimate error on recovered CMB amplitudes\n (F_fg, F_cmb, F) = F_matrix(nu, dust_params_out, sync_params_out, cmb_params_out, models_fit)\n H = F_fg.T*Ninv*F_fg\n x_mat = np.linalg.inv(F.T*beam_mat.T*Ninv*beam_mat*F)*F.T*beam_mat.T*Ninv*D_vec # Equation A3\n \n U, Lambda, VT = np.linalg.svd(Ninv_sqrt*F_fg, full_matrices=False) # Equation A14\n \n print \"-\"*30\n print \"F_cmb.T\", F_cmb.T.shape\n print \"Ninv_sqrt\", Ninv_sqrt.shape\n print \"F_cmb\", F_cmb.shape\n print \"I\", np.identity(U.shape[0]).shape\n print \"U\", U.shape\n print \"U.T\", U.T.shape\n print \"-\"*30\n \n \n \n N_eff_inv_cmb = F_cmb.T*Ninv_sqrt*(np.matrix(np.identity(U.shape[0])) - U*U.T)*Ninv_sqrt*F_cmb # Equation A16\n N_eff_cmb = np.linalg.inv(N_eff_inv_cmb)\n cmb_noise = np.array([N_eff_cmb[0,0], N_eff_cmb[1,1], N_eff_cmb[2,2]])\n\n gls_cmb = x_mat[0:3,0]\n cmb_chisq = (np.matrix(cmb_amp).T - gls_cmb).T*N_eff_inv_cmb*(np.matrix(cmb_amp).T - gls_cmb)\n \n # Output triangle plots for dust\n if label != None:\n if (models_fit[0] == 'mbb' and models_fit[1] == 'pow'):\n if (models_in[0] == 'mbb'):\n fig = corner.corner(samples, truths=[dust_params[0], dust_params[1], sync_params[0]],\n labels=[r\"$\\beta_d$\", r\"$T_d$\",r\"$\\alpha_s$\"])\n else :\n fig = corner.corner(samples, labels=[r\"$\\beta_d$\", r\"$T_d$\",r\"$\\alpha_s$\"])\n else :\n print 'Error! Not configured for this plot!'\n exit()\n fig.savefig('triangle_' + label + '.png')\n plt.close('all')\n \n # Run multinest sampler\n #multinest(nu, D_vec, Ninv, beam_mat, ndim, models_fit, label)\n \n return gls_cmb, cmb_chisq, cmb_noise","def mcmc(self,BHB):\n\n ndim = self.sampndim\n nwalkers = self.sampnwalk\n #startpoint = self.theta_ml\n startpoint = self.theta_guess\n pos = [startpoint + 1e-4*np.random.randn(ndim) for i in range(nwalkers)]\n\n sampler = emcee.EnsembleSampler(nwalkers, ndim, lnlikelihood.lnprob,\n args=(self.transinfo, self.lamlim1, self.lamlim2,\n self.wave_b, self.flux_b, self.err_b,\n self.wave_r, self.flux_r, self.err_r,\n self.velres))\n\n # Clear and run the production chain.\n print(\"Running MCMC...\")\n sampler.run_mcmc(pos, self.nsteps, rstate0=np.random.get_state())\n print(\"Done.\")\n\n pl.clf()\n fig, axes = pl.subplots(ndim, 1, sharex=True, figsize=(8, 9))\n\n for ind in range(0,ndim):\n \n axes[ind].plot(sampler.chain[:, :, ind].T, color=\"k\", alpha=0.4)\n axes[ind].yaxis.set_major_locator(MaxNLocator(5))\n axes[ind].axhline(startpoint[ind], color=\"#888888\", lw=2)\n # axes[ind].set_ylabel(\"$m$\")\n\n\n fig.tight_layout(h_pad=0.0)\n fig.savefig(\"line-time_\"+BHB+\"_\"+self.transname+\"_\"+str(self.lamlim1)+\".png\") # Edited for manual runs\n\n\n burnin = self.burnin\n samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))\n self.samples = samples\n\n # Compute the quantiles.\n theta_mcmc = list(map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),\n zip(*np.percentile(samples, [16, 50, 84],\n axis=0))))\n \n \n self.theta_percentiles = theta_mcmc\n print(\"\"\"MCMC result:\"\"\") \n for ind in range(0,ndim):\n print(\"\"\" par {0} = {1[0]} +{1[1]} -{1[2]}\"\"\".format(ind, theta_mcmc[ind]))","def emulator(pca, gp_model, params):\n # Weights prediction\n pred_weights = gp_predict(gp_model, params)\n\n # Inverse PCA (pred_weights * basis + mean)\n reconstructed = pca.inverse_transform(pred_weights)\n return reconstructed","def chisqdata_bs(Obsdata, Prior, mask):\n\n biarr = Obsdata.bispectra(mode=\"all\", count=\"min\")\n uv1 = np.hstack((biarr['u1'].reshape(-1,1), biarr['v1'].reshape(-1,1)))\n uv2 = np.hstack((biarr['u2'].reshape(-1,1), biarr['v2'].reshape(-1,1)))\n uv3 = np.hstack((biarr['u3'].reshape(-1,1), biarr['v3'].reshape(-1,1)))\n bi = biarr['bispec']\n sigma = biarr['sigmab']\n\n A3 = (ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv1, pulse=Prior.pulse, mask=mask),\n ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv2, pulse=Prior.pulse, mask=mask),\n ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv3, pulse=Prior.pulse, mask=mask)\n )\n\n return (bi, sigma, A3)","def create_pyomo_model():\n model = po.ConcreteModel()\n model.t = pod.ContinuousSet(bounds=(0, 1))\n model.tau = po.Var()\n\n model.ca = po.Var(model.t, bounds=(0, 50)) # set of concentrations in reaction mixture\n model.dca_dt = pod.DerivativeVar(model.ca, wrt=model.t)\n\n model.beta = po.Var() # model parameters\n\n def _material_balance(m, t):\n return m.dca_dt[t] / m.tau == -m.beta * m.ca[t]\n model.material_balance = po.Constraint(model.t, rule=_material_balance)\n\n simulator = pod.Simulator(model, \"casadi\")\n\n return model, simulator","def collapsed_gibbs_sample(self, X):\n K = self.K # number of topics\n M, V = X.shape\n alpha = self.alpha\n lmda = self.lmda\n topics = np.arange(0, K)\n\n #initialize everything uniformly\n # KxV dense matrix (used like beta)\n\n C = np.zeros(shape=(K, V), dtype=float)\n props = np.zeros(shape=(M, K), dtype=float)\n\n #Current state\n Ns = np.array(range(M), dtype=object)\n #Running sum\n MC_z = np.array(range(M), dtype=object)\n MC_c = np.zeros(shape=(K, V), dtype=float)\n\n for d in range(M):\n #allocate topics from prior\n word_indices = X[d, :].nonzero()[1]\n p = np.random.dirichlet(np.ones(K) * alpha)\n #TODO: Remove the p argument to get completely random topic assignments\n random_ks = np.random.choice(topics, size=len(word_indices), p=p)\n N_d = sp.coo_matrix((np.ones(len(word_indices)),\n (word_indices, random_ks)), shape=(V, K)).tolil()\n C = C + N_d.A.T\n Ns[d] = N_d\n MC_z[d] = sp.coo_matrix((V, K)).tolil()\n\n log_Xs = []\n perplexities = []\n for epoch in xrange(self.nr_em_epochs):\n print \"Epoch\", epoch\n for d in np.random.permutation(np.arange(M)):\n x = X[d]\n N_d = Ns[d]\n for v in np.nonzero(x)[1]:\n old_z_n = N_d[v, :].nonzero()[1][0]\n\n except_v = [v_prime for v_prime in np.nonzero(x)[1] if v_prime != v]\n N_d_except_v = N_d[except_v, :]\n C_except_v = np.copy(C[:, v])\n C_except_v[old_z_n] = C_except_v[old_z_n] - 1\n\n p = (np.sum(N_d_except_v.A, axis=0) + alpha) *\\\n ((C_except_v + lmda) / (np.sum(C, axis=1) -1 + V*lmda))\n p = p/np.sum(p)\n z_n = np.random.choice(topics, p = p)\n N_d[v, old_z_n] = 0\n N_d[v, z_n] = 1\n C[old_z_n, v] -= 1\n C[z_n, v] += 1\n Ns[d] = N_d\n MC_z[d] += N_d\n\n MC_c += C\n word_props = (MC_c.T / np.sum(MC_c, axis=1)).T\n log_X = 0\n for d in range(M):\n props_d = np.sum(MC_z[d].A, axis=0)\n props_d /= float(np.sum(props_d))\n props[d] = props_d\n ixw = np.nonzero(X[d, :])[1]\n log_X += np.sum(_doc_probability_from_p_of_z(props_d, word_props[:, ixw]))\n\n log_Xs.append(log_X)\n print log_X\n perplexities.append(self._perplexity(X, log_X))\n\n\n return props, word_props, log_Xs, perplexities","def BMA_Marginal_Likelihood(df_train, df_test, iterations, burnin, p_1):\n\n # number of individual forecasts and number of periods\n K = df_test.shape[1]\n T = df_train.shape[0]\n\n # g-prior parameter\n c = float(K**2)\n\n # separate real value and individual forecasts\n y = df_train.iloc[:, 0]\n F = df_train.iloc[:, 1:]\n\n # initializations\n models = np.full((iterations, K), fill_value=False, dtype=bool)\n theta_hats = []\n marginal_lik = np.full(iterations, fill_value=np.nan, dtype=float)\n\n # RJ-MCMC\n # initial state = null model\n # marginal likelihood for the initial state\n y_bar = np.mean(y)\n S_right = sum((y - y_bar)**2)/(c+1)\n\n Z = np.insert(F.iloc[:, models[0, :]].values, 0, 1, axis=1)\n theta_hat = np.dot(\n np.linalg.inv(np.dot(np.transpose(Z), Z)),\n np.dot(np.transpose(Z), y)\n )\n SSR = sum((y - np.dot(Z, theta_hat))**2)\n S_left = (c/(c+1))*SSR\n S = S_left + S_right\n\n # scale to first decimal place for numerical reasons\n # find scaling order\n S_rescaled = S\n scaling_factor = 1\n while (S_rescaled < 0.1):\n S_rescaled *= 10\n scaling_factor *= 10\n # rescale S (prevent overflow)\n S = S*scaling_factor\n\n k = sum(models[0, :])\n marginal_lik[0] = ((c+1)**(-k))*(S**(-((T-1)/2)))\n theta_hats += [theta_hat]\n\n # initialization of the proposal model\n m_star = np.full(K, fill_value=True, dtype=bool)\n\n # main iteration loop\n for i in range(0, iterations-1):\n\n # decide which move to perform\n epsilon_move = np.random.random()\n\n # move 1\n if epsilon_move <= p_1 or sum(models[0]) == 0:\n\n # drawn one of the forecasts from the model\n drawn_f_index = np.random.randint(0, K)\n\n # if it is in the model, drop it, if not, add it\n np.copyto(m_star, models[i, :])\n m_star[drawn_f_index] = ~models[i, drawn_f_index]\n\n # if there are too many variables (d.f. = 0), the change is revert\n if sum(m_star) > (T-2):\n m_star[drawn_f_index] = models[i, drawn_f_index]\n\n # move 2\n else:\n\n # drawn one of the forecasts from the model and one outside\n index_in = np.arange(K)[models[i, :]]\n index_out = np.arange(K)[~models[i, :]]\n drawn_f_index_in = np.random.choice(index_in)\n drawn_f_index_out = np.random.choice(index_out)\n\n # swap these two variables\n np.copyto(m_star, models[i, :])\n m_star[drawn_f_index_in] = ~m_star[drawn_f_index_in]\n m_star[drawn_f_index_out] = ~m_star[drawn_f_index_out]\n\n # calculate the probability of acceptance\n Z = np.insert(F.iloc[:, m_star].values, 0, 1, axis=1)\n theta_hat = np.dot(\n np.linalg.inv(np.dot(np.transpose(Z), Z)),\n np.dot(np.transpose(Z), y)\n )\n SSR = sum((y - np.dot(Z, theta_hat))**2)\n S_left = (c/(c+1))*SSR\n S = S_left + S_right\n\n # rescale S (prevent overflow)\n S = S*scaling_factor\n\n k = sum(m_star)\n marginal_lik_m_star = ((c+1)**(-k))*(S**(-((T-1)/2)))\n\n alpha = min(1, marginal_lik_m_star/marginal_lik[i])\n\n # if accepted, move to m_star, else retain the same model\n epsilon_accept = np.random.random()\n\n if epsilon_accept <= alpha:\n np.copyto(models[i+1, :], m_star)\n marginal_lik[i+1] = marginal_lik_m_star\n theta_hats += [theta_hat]\n else:\n np.copyto(models[i+1, :], models[i, :])\n marginal_lik[i+1] = marginal_lik[i]\n theta_hats += [theta_hats[-1]]\n\n # discard the burnin draws\n models = models[burnin:, :]\n marginal_lik = marginal_lik[burnin:]\n theta_hats = theta_hats[burnin:]\n\n # unique models visited by the chain\n models_uni, index_uni = np.unique(models, axis=0, return_index=True)\n marginal_lik_uni = marginal_lik[index_uni]\n # theta_hats_uni = [theta_hats[i] for i in index_uni]\n theta_hats_uni = list(np.array(theta_hats)[index_uni])\n\n # model posterior probabilities\n marginal_lik_sum = sum(marginal_lik_uni)\n posterior_prob = marginal_lik_uni / marginal_lik_sum\n\n # generate prediction for testing set\n model_fcts = np.full(\n (df_test.shape[0], len(theta_hats_uni)),\n np.nan,\n dtype=float\n )\n\n # forecast from different models\n for j in range(model_fcts.shape[1]):\n\n model_fcts[:, j] = np.dot(\n np.insert(df_test.iloc[:, models_uni[j]].values, 0, 1, axis=1),\n theta_hats_uni[j]\n )\n\n pred = np.dot(model_fcts, posterior_prob)\n\n df_pred = pd.DataFrame(\n {\"BMA (Marginal Likelihood)\": pred},\n index=df_test.index)\n\n return df_pred","def make_mp_forward_particles(cbc_path, min_part=1, max_part=None):\n # particles must have a label as a string otherwise it terminates\n # mass weighted particles for each of the models for the sfr/well(races/rivers) and rch\n\n print('reading cbc')\n # calculate the flow into each top layer cell\n indata = flopy.utils.CellBudgetFile(cbc_path)\n rch = indata.get_data(kstpkper=(0, 0), text='recharge', full3D=True)[0][0].filled(0) # take only the first row\n # take only the first row, no particles in southwertern boundary for wells\n well = indata.get_data(kstpkper=(0, 0), text='wells', full3D=True)[0][0].filled(0)\n sfr = indata.get_data(kstpkper=(0, 0), text='stream leakage', full3D=True)[0][0].filled(\n 0) # take only the first row\n well[well < 0] = 0\n sfr[sfr < 0] = 0\n flow = rch + well + sfr\n flow[smt.get_no_flow(0) != 1] = 0\n\n # generate particles (minimum of 1 per cell)\n num_parts = np.round(flow / flow[flow > 0].min() * min_part).astype(int)\n if max_part is not None:\n num_parts[num_parts > max_part] = int(max_part)\n\n # identify boundary condition types\n bd_type = smt.get_empty_model_grid() # 0=rch,1=well,2==sfr\n temp = np.concatenate((rch[np.newaxis], well[np.newaxis], sfr[np.newaxis]), axis=0).max(axis=0)\n bd_type[np.isclose(rch, temp)] = 0\n bd_type[np.isclose(well, temp)] = 1\n bd_type[np.isclose(sfr, temp)] = 2\n bd_type[(smt.get_no_flow(0) != 1) | np.isclose(temp, 0)] = -1\n\n outdata = flopy.modpath.mpsim.StartingLocationsFile.get_empty_starting_locations_data(num_parts.sum())\n outdata['label'] = 's'\n outdata['k0'] = 0 # I think that particles in flopy are 1 indexed, but 0 means active most layer\n js, iss = np.meshgrid(range(1, smt.cols + 1), range(1, smt.rows + 1)) # this is passed to the file as 1 indexed\n\n idx = bd_type.flatten() != -1\n group_dict = _part_group_cell_mapper(bd_type)\n start_idx = 0\n print('generating particles')\n for l, (num, i, j, bt) in enumerate(\n zip(num_parts.flatten()[idx], iss.flatten()[idx], js.flatten()[idx], bd_type.flatten()[idx])):\n if num == 0:\n raise ValueError('unexpected zero points')\n end_idx = start_idx + num\n # set particle starting location\n outdata['particlegroup'][start_idx:end_idx] = l + 1\n outdata['groupname'][start_idx:end_idx] = '{:03d}_{:03d}'.format(*group_dict[l + 1])\n outdata['i0'][start_idx:end_idx] = i\n outdata['j0'][start_idx:end_idx] = j\n outdata['xloc0'][start_idx:end_idx] = np.random.uniform(size=num)\n outdata['yloc0'][start_idx:end_idx] = np.random.uniform(size=num)\n outdata['zloc0'][start_idx:end_idx] = 1 if bt == 0 else np.random.uniform(size=num)\n start_idx = end_idx\n return outdata, bd_type, num_parts","def vv_harmonic_wmwf_phase2_1(M, PP, C, Ns, Is=None, prec=20, Yin=None):\n WR = M.WR\n kappa = M._weight\n D = WR.D\n Dsym = M.D # the symmetrized index set\n if len(Dsym) != len(C.keys()):\n raise ValueError(\"Got incompatible coefficient vector! indices={0}\".format(list(C.keys())))\n\n # we only use symmetrized values\n if(Is is None):\n Is = [0, len(D)]\n\n N = WR.N\n t = -1\n sym_type = mpmath.mpf(M._sym_type)\n verbose = M._verbose\n # ndig=12\n eps = mpmath.power(mpmath.mpf(10), mpmath.mpf(-prec))\n if verbose > 0:\n print(\"eps={0}\".format(eps))\n print(\"Yin={0}\".format(Yin))\n betai = dict()\n mbeta = dict()\n mbetai = dict()\n mm = dict()\n mmm = dict()\n mptwo = mpmath.mp.mpf(2)\n mpfour = mpmath.mp.mpf(4)\n twopi = mptwo * mpmath.pi()\n twopii = mpmath.mp.mpc(0, twopi)\n fourpi = mptwo * twopi\n mp0 = mpmath.mpf(0)\n weight = mpmath.mp.mpf(kappa)\n weight_over_two = weight / mpmath.mp.mpf(2)\n K0 = 0\n K1 = 0\n for (beta, m) in PP:\n # if( (not beta in D) or (not 1-beta in D)):\n if((beta not in D) and (1 - beta not in D)):\n raise Exception(\"Need beta={0} in D={1}\".format(beta, D))\n betai[beta] = D.index(beta)\n mbeta[beta] = 1 - beta\n mbetai[beta] = D.index(1 - beta)\n mm[(beta, m)] = (m + WR.Qv[betai[beta]])\n mmm[(beta, m)] = mpmath.mp.mpf(mm[(beta, m)])\n if verbose > 0:\n print(\"beta,m={0}, {1}\".format(beta, m))\n print(\"mm={0}\".format(mm[(beta, m)]))\n # print \"-beta=\",minus_beta\n # print \"mm=\",mm[(beta,m)]\n if mm[(beta, m)] > t:\n t = mm\n if abs(mm[(beta, m)]) < K0:\n K0 = abs(mm[(beta, m)])\n if abs(PP[(beta, m)]) > K1:\n K1 = abs(PP[(beta, m)])\n # One way to specify the principal part\n # is to only specify half and let the rest be decided\n # by the symmetry. If we specify the rest it must match\n if (mbeta[beta], m) in PP and (beta, m) in PP:\n test = abs(PP[(beta, m)] - sym_type * PP[(mbeta[beta], m)])\n if test > 0: # and not test.ae(mp0):\n raise ValueError(\"The principal part has not correct symmetry: type={0}, PP={1}\".format(sym_type, PP))\n else:\n pass\n abD = len(WR.D)\n\n if(Yin is None):\n Y0 = mpmath.mp.mpf(0.5)\n else:\n Y0 = mpmath.mp.mpf(Yin)\n kint = mpmath.mp.mpf(1 - weight)\n sym_type = M.get_sym_type()\n NA = Ns[0]\n NB = Ns[1]\n if(sym_type == 1):\n Dstart = int(0)\n Dfinish = int(WR.N) # 0,1,...,N\n elif(sym_type == -1):\n Dstart = int(1)\n Dfinish = int(WR.N - 1) # 1,2,...,N-1 (since -0=0 and -N=N)\n IA = int(max(Is[0], Dstart))\n IB = int(min(Is[1], Dfinish))\n Ms = int(min(C[Dstart].keys()))\n Mf = int(max(C[Dstart].keys()))\n Ml = int(Mf - Ms + 1)\n # Ms=int(min(C[D[Dstart]].keys())); Mf=int(max(C[D[Dstart]].keys())); Ml=int(Mf-Ms+1)\n if verbose > 0:\n print(\"Ms,Mf,Ml={0}, {1}, {2}\".format(Ms, Mf, Ml))\n K1 = K1 * 2 * N\n NAA = NA\n IAA = IA\n numys = 2\n # have Y=mpmath.mp.mpf(Y_in)\n # have to find suitable Q for the given Y\n if verbose > 0:\n print(\"dps={0}\".format(mpmath.mp.dps))\n ctmp = dict()\n ctmp_neg = dict()\n Cout = dict()\n for bi in range(IA, IB + 1):\n Cout[bi] = dict()\n stw = str(weight)[0:5]\n Qadd = 0\n Yfak = mpmath.mpf(0.95)\n Yvold = list(range(2))\n Xm = dict()\n Xpb = dict()\n Ypb = dict()\n Cv = dict()\n Q = dict()\n Qs = dict()\n Qf = dict()\n QQ = dict()\n for yloop in range(1000):\n Yv = [Y0, Yfak * Y0]\n for i in range(2):\n Q[i] = M.get_M(Yv[i], K0, K1, prec) + Qadd\n Qs[i] = 1 - Q[i]\n Qf[i] = Q[i]\n QQ[i] = mpmath.mp.mpf(1) / mpmath.mp.mpf(2 * Q[i])\n if verbose > 0:\n print(\"Yv[{0}]={1},{2}\".format(i, mppr(Yv[0]), mppr(Yv[1])))\n print(\"Q(Y)[{0}]={1}\".format(i, Q[i]))\n # print \"1/2Q[\",i,\"]=\",mppr(QQ[i])\n # Recall that the first Y-value is always the larger\n if verbose > 1:\n print(\"Yvold={0},{1}\".format(mppr(Yvold[0]), mppr(Yvold[1])))\n if Yv[0].ae(Yvold[1]):\n if verbose > 1:\n print(\"do not evaluate for Yv={0}\".format(Yv[0]))\n\n [Xm[0], Xpb[0], Ypb[0], Cv[0]] = [Xm[1], Xpb[1], Ypb[1], Cv[1]]\n [Xm[1], Xpb[1], Ypb[1], Cv[1]] = pullback_pts_weil_rep(WR, Q[1], Yv[1], weight, Dstart, Dfinish)\n else:\n for i in range(2):\n [Xm[i], Xpb[i], Ypb[i], Cv[i]] = pullback_pts_weil_rep(WR, Q[i], Yv[i], weight, Dstart, Dfinish)\n Yvold = Yv\n Zipb = dict()\n for yj in range(numys):\n Zipb[yj] = dict()\n for j in range(Qs[yj], Qf[yj] + 1):\n Zipb[yj][j] = mpmath.mp.mpc(-Ypb[yj][j], Xpb[yj][j])\n\n gamma_fak = dict()\n for yj in range(numys):\n for bi in range(IA, IB + 1):\n for l in range(Ms, Mf + 1):\n lr = mpmath.mp.mpf(l + WR.Qv[bi])\n if(lr < 0):\n lrtwo = lr * mptwo\n for j in range(Qs[yj], Qf[yj] + 1):\n gamma_fak[yj, bi, l, j] = mpmath.gammainc(kint, abs(lrtwo) * Ypb[yj][j]) * mpmath.mp.exp(-lr * Ypb[yj][j])\n else:\n for j in range(Qs[yj], Qf[yj] + 1):\n gamma_fak[yj, bi, l, j] = mpmath.mp.exp(-lr * Ypb[yj][j])\n if verbose > 0:\n print(\"Got pullback points!\")\n print(\"dps={0}\".format(mpmath.mp.dps))\n print(\"NAA={0}\".format(NAA))\n # If we want to do negative coefficients too we save time by computing simultaneously\n do_neg = True\n try:\n for n in range(NAA, NB + 1):\n if verbose > 0:\n print(\"n=\", n)\n for ai in range(IAA, IB + 1):\n if verbose > 0:\n print(\"ai={0}\".format(ai))\n mai = -ai % abD\n nr = mpmath.mp.mpf(n + WR.Qv[ai])\n nrtwo = mp2 * nr\n nri = mpmath.mp.mpc(0, nr)\n if(do_neg):\n nrm = mpmath.mp.mpf(-n + WR.Qv[ai])\n nrmi = mpmath.mp.mpc(0, nrm)\n nrmtwo = mp2 * nrm\n for yj in range(numys):\n Y = Yv[yj] * twopi\n summa = mp0\n summa_neg = mp0\n fak = dict()\n for j in range(Qs[yj], Qf[yj] + 1):\n fak[j] = mpmath.mp.exp(-nri * Xm[yj][j])\n if(do_neg):\n fak_neg = dict()\n for j in range(Qs[yj], Qf[yj] + 1):\n fak_neg[j] = mpmath.mp.exp(-nrmi * Xm[yj][j])\n for bi in range(IA, IB + 1):\n mbi = -bi % abD\n # print \"mbi=\",mbi\n for l in range(Ms, Mf + 1):\n if C[bi][l] == 0 or abs(C[bi][l]).ae(mp0):\n if verbose > 0:\n print(\"Skip coeff {0}, {1}\".format(bi, l))\n # continue\n lr = mpmath.mp.mpf(l + WR.Qv[bi])\n ilr = mpmath.mp.mpc(0, lr)\n Vtmp = mp0\n Vtmp_neg = mp0\n for j in range(Qs[yj], Qf[yj] + 1):\n if mbi != bi:\n ch = Cv[yj][j][ai, bi] + sym_type * Cv[yj][j][ai, mbi]\n else:\n ch = Cv[yj][j][ai, bi]\n if ch == 0 or ch.ae(mp0):\n continue\n tmp = (ch * mpmath.exp(ilr * Xpb[yj][j])) * gamma_fak[yj, bi, l, j]\n Vtmp = Vtmp + tmp * fak[j]\n if(do_neg):\n Vtmp_neg = Vtmp_neg + tmp * fak_neg[j]\n summa = summa + Vtmp * C[bi][l]\n if(do_neg):\n summa_neg = summa_neg + Vtmp_neg * C[bi][l]\n if verbose > 1:\n print(\"summa({0})={1}\".format(yj, summa))\n if(do_neg):\n print(\"summa_neg({0})={1}\".format(yj, summa_neg))\n wsumma = mp0\n wsumma_neg = mp0\n for (beta, m) in PP:\n app = mpmath.mp.mpf(PP[(beta, m)])\n lr = mpmath.mp.mpf(m + WR.Qv[betai[beta]])\n tmpsumma = mp0\n tmpsumma_neg = mp0\n for j in range(Qs[yj], Qf[yj] + 1):\n if betai[beta] != mbetai[beta]:\n ch = Cv[yj][j][ai, betai[beta]] + sym_type * Cv[yj][j][ai, mbetai[beta]]\n else:\n ch = Cv[yj][j][ai, betai[beta]]\n if ch == 0 or ch.ae(mp0):\n continue\n tmp = ch * mpmath.exp(lr * Zipb[yj][j])\n tmpsumma = tmpsumma + tmp * fak[j]\n if do_neg:\n tmpsumma_neg = tmpsumma_neg + tmp * fak_neg[j]\n wsumma = wsumma + app * tmpsumma\n if do_neg:\n wsumma_neg = wsumma_neg + app * tmpsumma_neg\n if verbose > 1:\n print(\"wsumma({0})={1}\".format(yj, wsumma))\n if(do_neg):\n print(\"wsumma_neg({0})={1}\".format(yj, wsumma_neg))\n sumtmp = (summa + wsumma) * QQ[yj]\n if ((D[ai], n) in PP) > 0:\n sum_tmp = sum_tmp - PP[(D[ai], n)] * mpmath.mp.exp(-nr * Y)\n lhs = mpmath.mp.exp(nr * Y)\n if verbose > 0:\n print(\"exp(2pinY)={0}\".format(mppr(lhs)))\n ctmp[yj] = sumtmp * lhs\n if(do_neg):\n sumtmp_neg = (summa_neg + wsumma_neg) * QQ[yj]\n if (D[ai], -n) in PP:\n sumtmp_neg = sumtmp_neg - PP[(D[ai], -n)] * mpmath.mp.exp(-nrm * Y)\n lhs = mpmath.gammainc(kint, abs(nrmtwo) * Y) * mpmath.mp.exp(-nrm * Y)\n if verbose > 0:\n print(\"Gamma(1-k,4pinY)={0}\".format(mppr(lhs)))\n ctmp_neg[yj] = sumtmp_neg / lhs\n # end for yj\n if(verbose > -1):\n print(\"C1[{0},{1}]={2}\".format(n, ai, ctmp[0].real))\n print(\"C2[{0},{1}]={2}\".format(n, ai, ctmp[1].real))\n if(do_neg):\n print(\"C1[{0},{1}]={2}\".format(-n, ai, ctmp_neg[0].real))\n print(\"C2[{0},{1}]={2}\".format(-n, ai, ctmp_neg[1].real))\n if(do_neg):\n err_pos = abs(ctmp[1] - ctmp[0])\n err_neg = abs(ctmp_neg[1] - ctmp_neg[0])\n err = err_pos # max(err_pos,err_neg)\n if(verbose > -1):\n print(\"err_pos={0}\".format(mppr(err_pos)))\n print(\"err_neg={0}\".format(mppr(err_neg)))\n else:\n err = abs(ctmp[1] - ctmp[0])\n if(verbose > -1):\n print(\"err={0}\".format(mppr(err)))\n if verbose > 0:\n if list(C.keys()).count(ai) > 0:\n if n in C[ai]:\n print(\"Cin({0},{1})={2}\".format(ai, n, C[ai][n].real))\n if -n in C[ai]:\n print(\"Cin({0},{1})={2}\".format(ai, -n, C[ai][-n].real))\n sys.stdout.flush()\n if(err > eps):\n\n # Have to modify\n if verbose > 0:\n print(\" Need to decrease Y!\")\n Y0 = Yv[0] * Yfak\n # Qadd=Qadd+10\n # Yv[0]=Y0; Yv[1]=Y0*mpmath.mp.mpf(0.95)\n NAA = n\n IAA = ai\n raise StopIteration()\n else:\n Cout[ai][n] = ctmp[1]\n if(do_neg):\n Cout[ai][-n] = ctmp_neg[1]\n if verbose > 0:\n print(\"OK! av={0}\".format((ctmp[1] + ctmp[0]) / mpmath.mpf(2)))\n # If we are in the range of the used C's we update\n # if(C.keys().count(ai)>0):\n # if(C[ai].keys().count(n)>0):\n # C[ai][n]=ctmp[1]\n # if(do_neg and C[ai].keys().count(-n)>0):\n # C[ai][-n]=ctmp_neg[1]\n # continue\n # end for ai\n # print \"n at end=\",n\n # end for n\n return Cout\n except StopIteration:\n if verbose > 0:\n print(\"Iteration stopped!\")\n continue\n raise StopIteration()","def buildP(self):\r\n\r\n print 'Building P ...'\r\n\r\n #---Building p(y|x)---#\r\n pygx_params_mlp = MLP(activations=self.hyper['pygx_activs'],\r\n dims=self.hyper['pygx_dims'],\r\n weights_init=self.hyper['pygx_W_init'],\r\n biases_init=Constant(0))\r\n\r\n pygx_params = pygx_params_mlp.apply(self.x.reshape((self.x.shape[0]*self.x.shape[1],self.x.shape[2])))\r\n pygx_params = pygx_params.reshape((self.x.shape[0],self.x.shape[1],2*self.hyper['y_dim']))\r\n pygx_params_mlp.initialize()\r\n\r\n # self.pygx_mu.shape == (minibatch size, L_x , num of dimension of y)\r\n self.pygx_mu = pygx_params[:,:,:self.hyper['y_dim']]\r\n\r\n # self.pygx_var.shape == (minibatch size, L_x, num of dimension of y)\r\n self.pygx_var = T.exp( pygx_params[:,:,self.hyper['y_dim']:] )\r\n\r\n\r\n #---Building graph for the density of p(y|x)---#\r\n little_num = 10**(-32)\r\n inside_exp = -T.sum((self.y.dimshuffle(0,'x',1) - self.pygx_mu)**2/(2*self.pygx_var), axis=2)\r\n norm_cst = (2*np.pi)**(-self.hyper['y_dim']/2.)*T.exp(T.sum(T.log(self.pygx_var), axis=2))**(-1/2.)\r\n\r\n # shape == (minibatch size, # of x samples)\r\n pygx = norm_cst*T.exp(inside_exp)\r\n\r\n # shape == (minibatch size, # of x samples)\r\n self.log_pygx = T.log(pygx + little_num)\r\n\r\n #---Building NN for p(x|z=j,w) for all j---#\r\n pxgzw_mus = [None]*self.hyper['num_clust']\r\n pxgzw_vars = [None]*self.hyper['num_clust']\r\n pxgzw = [None]*self.hyper['num_clust']\r\n\r\n for j in range(self.hyper['num_clust']):\r\n\r\n pxgzw_params_mlp = MLP(activations=self.hyper['pxgzw_activs'][j],\r\n dims=self.hyper['pxgzw_dims'][j],\r\n weights_init=self.hyper['pxgzw_W_init'],\r\n biases_init=Constant(0))\r\n\r\n pxgzw_params = pxgzw_params_mlp.apply(self.w.reshape((self.w.shape[0]*self.w.shape[1],self.w.shape[2])))\r\n pxgzw_params = pxgzw_params.reshape((self.w.shape[0],self.w.shape[1], 2*self.hyper['x_dim']))\r\n pxgzw_params_mlp.initialize()\r\n\r\n # pxgzw_mus[j].shape == (minibatch size, L_w , num of dimension of x)\r\n pxgzw_mus[j] = pxgzw_params[:,:,:self.hyper['x_dim']]\r\n\r\n # pxgzw_vars[j].shape == (minibatch size, L_w, num of dimension of x)\r\n pxgzw_vars[j] = T.exp( pxgzw_params[:,:,self.hyper['x_dim']:] )\r\n\r\n #---Building graph for the density of p(x|z=j,w)---#\r\n little_num = 10**(-32)\r\n inside_exp = -T.sum((self.x.dimshuffle(0,'x',1,2) - pxgzw_mus[j].dimshuffle(0,1,'x',2))**2/(2*pxgzw_vars[j].dimshuffle(0,1,'x',2)), axis=3)\r\n norm_cst = (2*np.pi)**(-self.hyper['x_dim']/2.)*T.exp(T.sum(T.log(pxgzw_vars[j]), axis=2))**(-1/2.)\r\n\r\n # shape == (minibatch size, # of w samples (L_w), # of x samples (L_x))\r\n pxgzw[j] = norm_cst.dimshuffle(0,1,'x')*T.exp(inside_exp)\r\n\r\n\r\n # shape is (minibatch size, L_w , # of clusters , num of dimension of x)\r\n self.pxgzw_mus = T.concatenate([mu.dimshuffle(0,1,'x',2) for mu in pxgzw_mus], axis=2)\r\n # shape is (minibatch size, L_w , # of clusters , num of dimension of x)\r\n self.pxgzw_vars = T.concatenate([var.dimshuffle(0,1,'x',2) for var in pxgzw_vars], axis=2)\r\n\r\n # self.pxgzw.shape == (minibatch size, L_w, L_x, num_clust)\r\n self.pxgzw = T.concatenate([density.dimshuffle(0,1,2,'x') for density in pxgzw], axis=3)\r\n self.log_pxgzw = T.log(self.pxgzw + little_num)\r\n\r\n #---Building the p(z=j|x,w) posterior for all j---#\r\n # self.log_pzgxw.shape == (minibatch size, L_w, L_x, num_clust)\r\n self.log_pzgxw = T.log(self.pxgzw + little_num) -T.log(T.sum(self.pxgzw + little_num, axis=3).dimshuffle(0,1,2,'x'))","def betabinom_artifact_model_probability(data,\n prior_p=betabinom_p_prior[0],\n prior_beta=betabinom_beta_prior[0],\n mutation_object=True):\n\n if mutation_object is True:\n trajectories = data.data\n else:\n trajectories = data\n\n # initialise list of samples for p integration\n int_p = []\n # integral over betabinom p parameter\n for p_sample in prior_p[0, :]:\n # initialise list of samples for beta integration\n int_beta = []\n # integrate over beta parameter\n for beta_sample in prior_beta[0, :]:\n # For each combination of p and beta parameters compute\n # lieklihood of observing a given time-series conditional\n # on initial time-point.\n alpha_sample = beta_sample*p_sample/(1-p_sample)\n # compute likelihood for each individual omiting first time point\n ind_likelihoods = []\n for ind_traj in trajectories:\n likelihood = betabinom.pmf(k=ind_traj[1:].AO,\n n=ind_traj[1:].DP,\n a=alpha_sample,\n b=beta_sample)\n ind_likelihoods.append(np.product(likelihood))\n # for each beta append the total likelihood (product individuals).\n int_beta.append(np.product(ind_likelihoods))\n # For each p, compute the likelihood marginalised over beta\n int_p.append(\n np.trapz(x=prior_beta[0, :],\n y=int_beta*prior_beta[1, :]))\n \n # marginalise likelihood over p\n mutation_prob = np.trapz(x=prior_p[0, :],\n y=int_p*prior_p[1, :])\n\n if mutation_object is True:\n # return updated model_comparison object \n data.betabinom_artifact_prob = mutation_prob\n return data\n else:\n # return marginalised likelihood.\n return mutation_prob","def p_ym_c(pm,px,py,pyx_c,pmx_c):\n pym_c = np.zeros((py.size,pm.size))\n for yi in range(py.size):\n for mi in range(pm.size):\n for xi in range(px.size):\n pym_c[yi,mi] += (1./pm[mi])*pyx_c[yi,xi]*pmx_c[mi,xi]*px[xi]\n return pym_c","def get_model():\n model = ecole.scip.Model.from_file(str(DATA_DIR / \"bppc8-02.mps\"))\n model.disable_cuts()\n model.disable_presolve()\n model.set_param(\"randomization/permuteconss\", True)\n model.set_param(\"randomization/permutevars\", True)\n model.set_param(\"randomization/permutationseed\", 784)\n model.set_param(\"randomization/randomseedshift\", 784)\n model.set_param(\"randomization/lpseed\", 784)\n return model","def run_pca(df, cols=None): \n import numpy as np\n import pandas as pd\n import matplotlib.pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n from sklearn.preprocessing import StandardScaler\n from sklearn.decomposition import PCA\n import mpld3\n\n # Define and markers to use for different categories\n groups_dict = {(u'D', 0):('Germany, unregulated', 'g', 'o'),\n (u'N', 0):('Norway, unregulated', 'b', 'o'),\n (u'D', 1):('Germany, regulated', 'g', '^'),\n (u'N', 1):('Norway, regulated', 'b', '^')}\n \n # Extract cols of interest\n cats = df[['country', 'regulated']]\n\n if cols:\n df = df[cols].astype(float)\n\n # Standardise the feature data\n feat_std = StandardScaler().fit_transform(df)\n\n # Setup PCA. Initially, choose to keep ALL components\n pca = PCA()\n\n # Fit model\n pca.fit(feat_std)\n\n # Get explained variances (in %)\n var_exp = 100*pca.explained_variance_ratio_\n cum_exp = np.cumsum(var_exp)\n\n # Get eigenvalues\n cov_mat = np.cov(feat_std.T)\n eig_vals, eig_vecs = np.linalg.eig(cov_mat)\n\n # Get number of EVs > 1 (Kaiser-Guttman criterion)\n # and print summary\n n_kgc = (eig_vals > 1).sum()\n print 'Variance explained by first %s PCs (%%):\\n' % n_kgc\n print var_exp[:n_kgc]\n print '\\nTotal: %.2f%%' % var_exp[:n_kgc].sum()\n \n # Plot\n fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\n \n # Explained variance\n axes[0].bar(range(1, len(var_exp)+1), var_exp, \n align='center', label='Individual components')\n axes[0].plot(range(1, len(cum_exp)+1), cum_exp, \n 'r-o', label='Cumulative')\n axes[0].set_xlabel('Principal component')\n axes[0].set_ylabel('Variance explained (%)')\n axes[0].legend(loc='center right')\n \n # Eigenvalues\n axes[1].plot(range(1, len(eig_vals)+1), np.sort(eig_vals)[::-1], \n 'r-o', label='Eigenvalues')\n axes[1].axhline(1, c='k', ls='-', label='Kaiser-Guttman threshold')\n axes[1].set_xlabel('Principal component')\n axes[1].set_ylabel('Eigenvalue')\n axes[1].legend(loc='upper right') \n \n # PC loadings\n loads = pd.DataFrame(data=pca.components_, \n columns=df.columns,\n index=range(1, pca.components_.shape[0]+1)).T\n\n # Project into 2 and 3 components\n fig = plt.figure(figsize=(16, 6))\n \n # Plot 2 components\n ax = fig.add_subplot(1, 2, 1)\n \n # Refit the PCA, this time specifying 2 components\n # and transforming the result\n feat_reduced = PCA(n_components=2).fit_transform(feat_std)\n \n # Build df \n data = pd.DataFrame({'PC1':feat_reduced[:, 0],\n 'PC2':feat_reduced[:, 1],\n 'country':cats['country'],\n 'regulated':cats['regulated']}) \n\n groups = data.groupby(['country', 'regulated'])\n \n # Plot\n for name, group in groups:\n ax.scatter(group['PC1'], group['PC2'], s=60,\n label=groups_dict[name][0],\n c=groups_dict[name][1],\n marker=groups_dict[name][2])\n \n ax.set_xlabel('First principal component')\n ax.set_ylabel('Second principal component')\n ax.set_title('First two PCA directions')\n \n # Plot 3 components\n ax = fig.add_subplot(1, 2, 2, projection='3d', \n elev=-150, azim=135)\n\n # Refit the PCA, this time specifying 3 components\n # and transforming the result\n feat_reduced = PCA(n_components=3).fit_transform(feat_std)\n\n # Build df with colours\n data = pd.DataFrame({'PC1':feat_reduced[:, 0],\n 'PC2':feat_reduced[:, 1],\n 'PC3':feat_reduced[:, 2],\n 'country':cats['country'],\n 'regulated':cats['regulated']}) \n \n groups = data.groupby(['country', 'regulated'])\n \n # Plot\n for name, group in groups:\n ax.scatter(group['PC1'], group['PC2'], group['PC3'],\n label=groups_dict[name][0],\n c=groups_dict[name][1],\n marker=groups_dict[name][2],\n s=60)\n \n ax.set_title('First three PCA directions')\n ax.set_xlabel('First principal component')\n ax.set_ylabel('Second principal component')\n ax.set_zlabel('Third principal component')\n ax.legend(bbox_to_anchor=(0.15, -0.1), frameon=True)\n plt.show()\n\n return loads","def jointUncertaintyMilp(mdp, oldPi, oldZC, unknownFeatStates, costOfQuery):\n m = Model()\n m.setParam('OutputFlag', False)\n\n # convert notation to previous implementation\n S = mdp.S\n A = mdp.A\n R = mdp.rFuncs\n psi = mdp.psi\n T = mdp.T\n alpha = mdp.alpha\n gamma = mdp.gamma\n\n # useful constants\n rLen = len(R)\n M = 10000 # a large number\n Sr = range(len(S))\n Ar = range(len(A))\n\n # decision variables\n x = m.addVars(len(S), len(A), lb=0, name='x')\n y = m.addVars(rLen, name='y')\n # y prime, a helper variable\n y0 = m.addVars(rLen, name='y0', lb=0)\n\n # oldPi is a mapping from state, action (in S x A) to occupancy\n # to be consistent with x, convert it to a mapping from (s, a) where s in Sr, a in Ar\n oldX = {(s, a): oldPi[S[s], A[a]] for s in Sr for a in Ar}\n\n # integer variables\n zR = m.addVars(rLen, vtype=GRB.BINARY, name='zR')\n zC = m.addVars(len(unknownFeatStates), vtype=GRB.BINARY, name='zC')\n # zCNew indicates the newly changed features by x. note that it does not need to be constrained as integers\n zCNew = m.addVars(len(unknownFeatStates), lb=0, name='zCNew')\n\n zSafe = m.addVar(vtype=GRB.BINARY, name='zSafe')\n\n V = lambda x_local, r: sum([x_local[s, a] * r(S[s], A[a]) for s in Sr for a in Ar])\n\n # (a) flow conservation constraint\n for sp in Sr:\n m.addConstr(sum(x[s, a] * ((s == sp) - gamma * T(S[s], A[a], S[sp])) for s in Sr for a in Ar) == alpha(S[sp]))\n\n # (b) is encoded in the transition function\n\n for consIdx in range(len(unknownFeatStates)):\n # (c) unknown features can be changed\n m.addConstr(M * zC[consIdx] >= sum(x[S.index(s), A.index(a)] for s in unknownFeatStates[consIdx] for a in A))\n # (d) constrain z^{new}_\\phi, note that lb of zCNew is 0\n m.addConstr(zCNew[consIdx] >= zC[consIdx] - oldZC[consIdx])\n\n # (e) constraints on y^0_r\n m.addConstr(sum(zC[idx] for idx in range(len(oldZC)) if oldZC[idx] == 1) <= sum(oldZC) - 1 + zSafe * M)\n for i in range(rLen):\n m.addConstr(y0[i] >= V(oldX, R[i]) - (1 - zSafe) * M)\n\n # (f) constraints on y_r\n for i in range(rLen):\n m.addConstr(y[i] <= V(x, R[i]) - y0[i] + (1 - zR[i]) * M)\n m.addConstr(y[i] <= 0 + zR[i] * M)\n\n # obj\n m.setObjective(sum([psi[i] * y[i] for i in xrange(rLen)])\n - sum(zC[idx] * costOfQuery for idx in range(len(unknownFeatStates))),\n GRB.MAXIMIZE)\n\n m.optimize()\n\n pi = {(S[s], A[a]): x[s, a].X for s in Sr for a in Ar}\n\n if config.VERBOSE:\n # print decision variables other than pi for debugging\n print 'oldZC', oldZC\n print 'zC', [zC[consIdx].X for consIdx in range(len(unknownFeatStates))]\n print 'y0 values', [y0[rIdx].X for rIdx in range(rLen)]\n print 'y values', [y[rIdx].X for rIdx in range(rLen)]\n\n if m.status == GRB.Status.OPTIMAL:\n # return feasible being true and the obj value, opt pi\n # .X attribute is to retrieve the value of the variable\n return pi\n else:\n # simply return infeasible\n raise Exception('milp problem optimal solution not found' + m.status)","def collapsed_theta_gibbs_sample(self, X):\n\n K = self.K # number of topics\n M, V = X.shape\n alpha = self.alpha\n lmda = self.lmda\n topics = np.arange(0, K)\n\n #initialize everything uniformly\n Beta = np.ones(shape=(K, V), dtype=float) / V\n for k in topics:\n Beta[k] = np.random.dirichlet(np.ones(V)*lmda)\n\n props = np.zeros(shape=(M, K), dtype=float)\n #Current state\n Ns = np.array(range(M), dtype=object)\n\n #Running sum\n MC_z = np.array(range(M), dtype=object)\n MC_beta = np.zeros(shape=(K, V), dtype=float)\n\n for d in range(M):\n #allocate topics randomly\n word_indices = X[d, :].nonzero()[1]\n p = np.random.dirichlet(np.ones(K) * alpha)\n #TODO: Remove the p argument to get completely random topic assignments\n random_ks = np.random.choice(topics, size=len(word_indices), p=p)\n Ns[d] = sp.coo_matrix((np.ones(len(word_indices)),\n (word_indices, random_ks)), shape=(V, K)).tolil()\n MC_z[d] = sp.coo_matrix((V, K)).tolil()\n\n log_Xs = []\n perplexities =[]\n t = 0\n for epoch in xrange(self.nr_em_epochs):\n t+=1\n C = np.zeros((K, V))\n print \"Epoch\", epoch\n for d in np.random.permutation(np.arange(M)):\n x = X[d]\n N_d = Ns[d]\n\n for v in np.nonzero(x)[1]:\n except_v = [v_prime for v_prime in np.nonzero(x)[1] if v_prime != v]\n N_d_except_v = N_d[except_v, :]\n old_z_n = N_d[v, :].nonzero()[1][0]\n p = (np.sum(N_d_except_v.A, axis=0) + alpha) * Beta[:, v]\n p = p.clip(min=0)\n p = p/np.sum(p)\n z_n = np.random.choice(topics, p = p)\n N_d[v, old_z_n] = 0\n N_d[v, z_n] = 1\n Ns[d] = N_d\n MC_z[d] += N_d\n C = C + N_d.A.T\n\n # Sample beta given all z and thetas\n for k in topics:\n c_beta = C[k, :]\n Beta[k, :] = np.random.dirichlet(c_beta + lmda)\n MC_beta += Beta\n\n log_X = 0\n Beta_hat = MC_beta / t\n for d in range(M):\n props_d = np.sum(MC_z[d].A, axis=0)\n props_d /= float(np.sum(props_d))\n props[d] = props_d\n ixw = np.nonzero(X[d, :])[1]\n log_X += np.sum(_doc_probability_from_p_of_z(props_d, Beta_hat[:, ixw]))\n\n log_Xs.append(log_X)\n print log_X\n perplexities.append(self._perplexity(X, log_X))\n\n return props, Beta_hat, log_Xs, perplexities","def _getBMat(self):\n\n \"\"\"B matrix is just a mass matrix, can be easily assembled\n through fenics. However, the ordering in Fenics is not the\n mesh ordering. So we build a temp matrix and then use the\n vertex to dof map to get the right ordering interms of our\n mesh nodes.\n \"\"\"\n\n # create function space of order 1. For KL, we only restrict\n # to first order spaces.\n V = FunctionSpace(self._mesh, \"CG\", 1)\n # Define basis and bilinear form\n u = TrialFunction(V)\n v = TestFunction(V)\n a = u * v * dx\n # assemble in a temp matrix\n B_temp = assemble(a)\n\n # create petsc matrix B\n B = PETSc.Mat().create()\n B.setType('aij')\n B.setSizes(self.domain.getNodes(), self.domain.getNodes())\n B.setUp()\n\n # store the value in a a temp array B_ij\n B_ij = B_temp.array()\n\n # get the vertex to dof map\n v_to_d_map = vertex_to_dof_map(V)\n\n print '---------------------------'\n print '---------------------------'\n print ' Building Mass Matrix '\n print '---------------------------'\n print '---------------------------'\n for node_i in range(0, self.domain.getNodes()):\n for node_j in range(node_i, self.domain.getNodes()):\n B_ij_nodes = B_ij[v_to_d_map[node_i], v_to_d_map[node_j]]\n if B_ij_nodes > 0:\n B.setValue(node_i, node_j, B_ij_nodes)\n B.setValue(node_j, node_i, B_ij_nodes)\n\n B.assemblyBegin()\n B.assemblyEnd()\n print '---------------------------'\n print '---------------------------'\n print ' Finished Mass Matrix '\n print '---------------------------'\n print '---------------------------'\n return B","def model_method(data, **prior_params):\n weight_m = np.vstack((data.weight_std, data.weight_std**2, data.weight_std**3))\n with pm.Model() as model:\n alpha = pm.Normal('alpha', mu=prior_params['alpha mu'], sd=prior_params['alpha sd'])\n beta = pm.Normal('beta', mu=prior_params['beta mu'], sd=prior_params['beta sd'], shape=3)\n sigma = pm.Uniform('sigma', lower=prior_params['sigma lower'], upper=prior_params['sigma upper'])\n mu = pm.Deterministic('mu', alpha + pm.math.dot(beta, weight_m))\n height = pm.Normal('height', mu=mu, sd=sigma, observed=data.height)\n return model","def MILP_1(self,xData,uData):\n print \"\\n\",\"*\"*80,\"\\n\",\"MILP 1: Parameter Estimation\\n\",\"*\"*80 \n model=Model(\"parameters\")\n outflow={}\n d={}\n bigM=500\n Q_out={}\n Q_in={}\n N=max(l[1] for l in xData.keys())\n print \"x Data size is\",N\n N=max(l[1] for l in uData.keys())\n print \"u Data size is\",N\n for l in self.links:\n l.d=model.addVar(lb=0,ub=200,obj=0*l.type==\"road\") \n for t in range(1,N):\n d[l,t]=model.addVar(lb=0,ub=200,obj=1)\n for k in l.outgoing:\n outflow[l,k,t]=model.addVar(lb=0,ub=200)\n self.c[l,k]=model.addVar(lb=20,ub=200)\n self.beta[l,k]=model.addVar(lb=0.2,ub=0.8)\n self.alpha[l,k]=model.addVar(lb=0,ub=1)\n self.M[l,k]=model.addVar(lb=0,ub=200)\n d[\"outflow-1\",l,k,t]=model.addVar(vtype=GRB.BINARY) \n d[\"outflow-2\",l,k,t]=model.addVar(vtype=GRB.BINARY) \n model.update()\n for t in range(1,N):\n for l in self.links:\n if True:\n Q_out[l,t]=LinExpr()\n Q_in[l,t]=LinExpr()\n Q_out[l,t].addConstant(0)\n Q_in[l,t].addConstant(0)\n for k in l.outgoing:\n model.addConstr(outflow[l,k,t]<=self.beta[l,k]*uData[l,t]*xData[l,t])\n model.addConstr(outflow[l,k,t]<=self.M[l,k])\n model.addConstr(outflow[l,k,t]<=self.c[l,k]-self.alpha[l,k]*xData[k,t])\n model.addConstr(outflow[l,k,t]>=self.beta[l,k]*uData[l,t]*xData[l,t]+bigM*d[\"outflow-1\",l,k,t]-bigM)\n model.addConstr(outflow[l,k,t]>=self.M[l,k]+bigM*d[\"outflow-2\",l,k,t]-bigM)\n model.addConstr(outflow[l,k,t]>=self.c[l,k]-self.alpha[l,k]*xData[k,t]-bigM*d[\"outflow-1\",l,k,t]-bigM*d[\"outflow-2\",l,k,t])\n Q_out[l,t].add(outflow[l,k,t])\n for k in l.incoming:\n Q_in[l,t].add(outflow[k,l,t])\n if l.type==\"road\":\n model.addConstr(xData[l,t+1]<=xData[l,t]- Q_out[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival) \n else:\n model.addConstr(xData[l,t+1]<=xData[l,t]- uData[l,t]*xData[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival)\n for l in self.links:\n sum=LinExpr()\n for k in l.outgoing:\n sum.add(self.beta[l,k])\n model.addConstr(sum>=0)\n \n# J=QuadExpr()\n# for l in self.links:\n# for t in range(1,N):\n# if l.type==\"road\":\n# J.add(d[l,t]*d[l,t])\n# model.setObjective(J)\n model.optimize()\n for l in self.links:\n l.d=l.d.X\n for k in l.outgoing:\n self.beta[l,k]=self.beta[l,k].X\n self.c[l,k]=self.c[l,k].X\n self.alpha[l,k]=self.alpha[l,k].X\n self.M[l,k]=self.M[l,k].X\n for l in self.links:\n for t in range(1,N):\n l.d=max(d[l,t].X,l.d)\n \n \n\n \n if True:\n for t in range(1,N):\n print \"*\"*80,\"time=\",t\n for l in self.links:\n print \"\\n\",l,\"x is\",xData[l,t],\"u is\",uData[l,t],\"x+ is\",xData[l,t+1]\n for k in l.outgoing:\n print k,\"beta:\",self.beta[l,k],\"outflow\",outflow[l,k,t].X","def __init__(self,X, Y, X_star, Y_star, K=50, alpha=1.,J=2,\n N_minibatch = 1000, full_cov=True, mb_upweight=True,\n prior_obs=1.):\n self.start_time = time.time()\n self.comm = MPI.COMM_WORLD\n self.P = self.comm.Get_size()\n self.rank = self.comm.Get_rank()\n self.total_J = int(J)\n self.full_cov = bool(full_cov)\n self.proposal_sizes = [j.size for j in np.array_split(xrange(self.total_J), self.P)]\n assert(len(self.proposal_sizes) == self.P)\n assert(sum(self.proposal_sizes) == self.total_J)\n self.prior_obs = prior_obs\n assert(self.prior_obs >0.)\n self.J = self.proposal_sizes[self.rank]\n self.K = np.zeros(self.J).astype(int)\n self.alpha=alpha\n self.X = X\n self.Y = np.array(Y).reshape(-1,1)\n (self.N, self.D) = self.X.shape\n self.M = 3 # num of hyperparameters, fixed to RBF kernel for now\n self.N_minibatch = N_minibatch\n self.init_hyp = np.empty((self.J,self.M))\n assert(self.N_minibatch <= self.N)\n if mb_upweight:\n self.mb_weight = float(self.N) / float(self.N_minibatch)\n else:\n self.mb_weight = 1.\n assert(self.N == self.Y.size)\n self.U = [np.random.choice(self.N,size=self.N_minibatch,replace=False)for j in xrange(self.J)]\n self.X_mean = self.X.mean(axis=0)\n self.X_sd = np.sqrt(self.X.var(axis=0))\n self.W = np.zeros(self.J)\n # everything J should be a list, K should be a dictionary.\n self.marginal_LL_k = [None]*self.J\n Z_map = map(self.partition_data,range(self.J))\n self.Z_proposal = [Z for Z,Z_count in Z_map]\n self.Z_count = [Z_count for Z,Z_count in Z_map]\n self.kernels = [None]*self.J # np.zeros().astype(object) #{j:None for j in xrange(self.J)}\n self.models = map(self.model_init,xrange(self.J))\n self.marginal_LL = np.copy(self.W)\n self.comm.barrier()","def simulate_qubit_pairs_3D_lattice(cbrt_n, m):\n # Initialize some variables\n n = np.power(cbrt_n, 3)\n x_max = 20\n d = np.arange(0, x_max + 1)\n k_matrix = np.zeros((m, x_max + 1))\n\n # Run the simulation a total of m times\n for i in range(m):\n start = time.time()\n\n sim = chp_py.CHP_Simulation(n)\n sims = []\n # Add a sim for depth 0\n sims.append(chp_py.CHP_Simulation(n, sim.state))\n\n for j in range(x_max):\n gates = utils.get_lattice_gates((cbrt_n, cbrt_n, cbrt_n), j)\n sim.apply_gates(gates)\n sims.append(chp_py.CHP_Simulation(n, sim.state))\n\n # This computes the collision_probability at different\n # depths in parallel\n k_matrix[i, :] = utils.sims_to_collision_probabilty(sims)\n\n end = time.time()\n print(\"Time elapsed = {:0.2f}, for sample = {}\".format(end - start, i))\n\n # Store everything in data\n data = {\"d\": d,\n \"k_matrix\": k_matrix,\n \"n\": n,\n \"m\": m}\n\n return data","def correction_factor(x_diff, y_diff, x_intersection, y_intersection, n_mc = 40000):\n \n # The intersection model\n x_sq = x_intersection ** 2\n x_input = np.concatenate((x_intersection, x_sq), axis = 1)\n\n #MCMC model - correction factors and shif\n x_shared = theano.shared(x_input)\n gp_mean_coeff = np.array([0, epsilon, c])\n\n gamma_alpha = 1\n gamma_beta = 10\n\n inv_gamma_alpha = 1\n inv_gamma_beta = 10\n\n with pm.Model() as gp_posteriors_model:\n #Priors\n tau_sq = pm.InverseGamma(\"tau_sq\", alpha = inv_gamma_alpha, beta = inv_gamma_beta)\n sigma_sq = pm.InverseGamma(\"sigma_sq\", alpha = 10, beta= 1)\n lamb_sq = pm.Gamma(\"lamb_sq\", alpha = gamma_alpha, beta = gamma_beta, shape = 2)\n theta = pm.Normal(\"theta\", mu= 0, sd = 1)\n\n #Shared variables for the input\n x_input_theta = tt.concatenate([x_shared, tt.tile(theta, (len(x_input), 1))], axis = 1)\n\n #GP definition\n #Mean\n mean_gp = pm.gp.mean.Linear(coeffs = gp_mean_coeff, intercept = 0)\n #Covariance\n cov_gp = tau_sq * pm.gp.cov.ExpQuad(x_input.shape[1] + 1, ls = tt.sqrt(lamb_sq) / 4, active_dims = [0,2])\n #GP\n gp_model = pm.gp.Marginal(mean_func=mean_gp, cov_func= cov_gp)\n\n #Marginal likelihoods\n y_ = gp_model.marginal_likelihood(\"y_\", X = x_input_theta, y = y_intersection, noise = tt.sqrt(sigma_sq))\n trace_priors = pm.sample(n_mc, tune = 10000, chains = 1) \n \n # The complement model\n x_sq = x_diff ** 2\n x_input = np.concatenate((x_diff, x_sq), axis = 1)\n\n #MCMC model - correction factors and shif\n x_shared = theano.shared(x_input)\n gp_mean_coeff = np.array([0, epsilon, c])\n\n gamma_alpha = 1\n gamma_beta = 10\n\n inv_gamma_alpha = 1\n inv_gamma_beta = 10\n\n with pm.Model() as pymc3_model:\n #Priors\n tau_sq = pm.InverseGamma(\"tau_sq\", alpha = inv_gamma_alpha, beta = inv_gamma_beta)\n sigma_sq = pm.InverseGamma(\"sigma_sq\", alpha = 10, beta= 1)\n lamb_sq = pm.Gamma(\"lamb_sq\", alpha = gamma_alpha, beta = gamma_beta, shape = 2)\n theta = pm.Normal(\"theta\", mu= 0, sd = 1)\n\n #Shared variables for the input\n x_input_theta = tt.concatenate([x_shared, tt.tile(theta, (len(x_input), 1))], axis = 1)\n\n #GP definition\n #Mean\n mean_gp = pm.gp.mean.Linear(coeffs = gp_mean_coeff, intercept = 0)\n #Covariance\n cov_gp = tau_sq * pm.gp.cov.ExpQuad(x_input.shape[1] + 1, ls = tt.sqrt(lamb_sq) / 4, active_dims = [0,2])\n #GP\n gp_model = pm.gp.Marginal(mean_func=mean_gp, cov_func= cov_gp)\n\n #Marginal likelihoods\n y_gp = gp_model.marginal_likelihood(\"y_\", X = x_input_theta, y = y_diff, noise = tt.sqrt(sigma_sq)) \n \n \n log_likelihood = np.empty(0)\n mc_integral = np.empty(n_mc) \n logp = y_gp.logp\n\n for i in tqdm(range(n_mc), desc = \"Log likelihood eval\"):\n log_likelihood = np.append(log_likelihood, logp(trace_priors[i]))\n \n for i in range(n_mc):\n m = max(log_likelihood[:(i + 1)])\n mc_integral[i] = (np.exp(m) * np.sum(np.exp(log_likelihood[:(i + 1)] - m))) / (i + 1) \n \n return log_likelihood, mc_integral","def meta_binary_outcome(z_t_obs, n_t_obs, z_c_obs, n_c_obs, study, n_draws=1000):\n\n with pm.Model(coords={\"study\": study}) as model:\n # Hyper-priors\n mu_rho = pm.Normal(\"mu_rho\", mu=0, sigma=10)\n sigma_rho = pm.Gamma(\"sigma_rho\", alpha=1.64, beta=0.64) # mode=1, sd=2\n\n omega_theta_c = pm.Beta(\"omega_theta_c\", alpha=1.01, beta=1.01)\n kappa_minus_two_theta_c = pm.Gamma(\n \"kappa_minus_two_theta_c\", alpha=2.618, beta=0.162\n ) # mode=10, sd=10\n kappa_theta_c = pm.Deterministic(\"kappa_theta_c\", kappa_minus_two_theta_c + 2)\n\n # Priors\n rho = pm.Normal(\"rho\", mu=mu_rho, sigma=sigma_rho, dims=\"study\")\n theta_c = pm.Beta(\n \"theta_c\",\n alpha=omega_theta_c * (kappa_theta_c - 2) + 1,\n beta=(1 - omega_theta_c) * (kappa_theta_c - 2) + 1,\n dims=\"study\",\n )\n theta_t = pm.Deterministic(\n \"theta_t\", pm.invlogit(rho + pm.logit(theta_c))\n ) # ilogit is logistic\n\n # Likelihood\n z_t = pm.Binomial(\"z_t\", n_t_obs, theta_t, observed=z_t_obs)\n z_c = pm.Binomial(\"z_c\", n_c_obs, theta_c, observed=z_c_obs)\n\n # Sample from the posterior\n idata = pm.sample(draws=n_draws, target_accept=0.90)\n\n return model, idata","def compute_mixing_coefficients_bot(self):\n [Ly,N] = self.b.shape\n z_u_w = self.grid_dict['z_u_w']\n\n v_upts = TTTW_func.v2u(self.v)\n\n self.sigma_bot = []\n self.Kv0 = np.zeros([Ly,N+1])\n self.Kt0 = np.zeros([Ly,N+1])\n for j in range(Ly):\n # turbulent velocity sclaes with buoyancy effects neglected\n ustar2 = self.r_D[j] * np.sqrt(self.u[j,0]**2 + v_upts[j,0]**2)\n wm = self.vonKar * np.sqrt(ustar2)\n ws = wm\n \n for k in range(1,N):\n k_w = k\n k_r = k - 1\n\n if k_w < self.kbl[j]: # NEED Zob\n sigma = np.min( [ ((z_u_w[j,k_w] - z_u_w[j,0] + self.Zob) / (self.hbbl[j] + self.Zob)),1.])\n if j ==1:\n self.sigma_bot.append(sigma)\n a1 = sigma - 2.\n a2 = 3. - 2.*sigma\n a3 = sigma - 1.\n\n self.Kv0[j,k_w] = wm * self.hbbl[j] * ( sigma * (1. + sigma * ( a1 + a2*self.Gm1_bot[j]+a3*self.dGm1_dS_bot[j]))) \n self.Kt0[j,k_w] = ws * self.hbbl[j] * ( sigma * (1. + sigma * ( a1 + a2*self.Gt1_bot[j]+a3*self.dGt1_dS_bot[j])))","def multiple_premia_gbm(nobs=2000):\r\n sigma = .15\r\n\r\n price = 1\r\n strike = np.exp(np.linspace(-.1, .1, nobs))\r\n riskfree, maturity = 0, 30/365\r\n moneyness = np.log(strike/price) - riskfree * maturity\r\n call = np.ones_like(moneyness).astype(bool)\r\n call[moneyness < 0] = False\r\n\r\n model = GBM(GBMParam(sigma=sigma), riskfree, maturity)\r\n premium = cosmethod(model, moneyness=moneyness, call=call)\r\n plt.plot(strike, premium)\r\n plt.show()","def construct_pdf_gmm(model, n_pdf):\n components = []\n for m, var in zip(model.means_, model.covariances_):\n components.append(multivariate_normal(m, var)) \n return np.apply_along_axis(lambda x: model.weights_.dot(np.array([d.pdf(x) for d in components])), 0, np.linspace(0,1,n_pdf))","def run(self, C, p0 = None):\n global algorithm \n algorithm = AdaptiveMM(self.g, C, p0 = p0, lambda0 = 2000)\n solve()","def setup_rand_gmm(n_components=3, n_features=10,\n clust_mean_std=2.0,\n cluster_std=1.0,\n cov_how='diag',\n weights_how='uniform',\n random_state=None):\n\n rng = check_random_state(random_state)\n\n ##########################\n # generate cluster means #\n ##########################\n\n # cluster means are generated from a spherical gaussian\n center_cov = np.eye(n_features) * clust_mean_std ** 2\n means = rng.multivariate_normal(mean=np.zeros(n_features),\n cov=center_cov,\n size=n_components)\n\n ################################\n # generate cluster covariances #\n ################################\n\n if cov_how.lower() == 'diag':\n # diagonal covariances\n covariances = np.array([np.eye(n_features) * cluster_std ** 2\n for _ in range(n_components)])\n elif cov_how.lower() == 'wishart':\n raise NotImplementedError # TODO-FEAT: implement this!\n else:\n raise ValueError(\"cov_how must be one of ['diag', 'wishart']\"\n \"not {}\".format(cov_how))\n\n ############################\n # generate cluster weights #\n ############################\n if weights_how.lower() == 'uniform':\n weights = rng.dirichlet(np.ones(n_components))\n elif weights_how.lower() == 'random':\n weights = np.ones(n_components) / n_components\n else:\n raise ValueError(\"weights_how must be one of ['random', 'uniform']\"\n \"not {}\".format(weights_how))\n\n return means, covariances, weights","def do_fitting(args):\n (patch_num, imc, indices, fitting_name, data, covariance, frequencies, model_path) = args\n logging.info(f\"Fitting MC: {imc:d}\")\n model_identifier, lnP = hoover.LogProb.load_model_from_yaml(model_path)\n lnP.data_setup(data=data, covariance=covariance, frequencies=frequencies)\n # Run the optimization.\n # Note, if using Nelder-Mead, can not write dictionary to\n # HDF5 file attribute, as simplex is too complex an object.\n theta_0 = lnP.theta_0()\n res = minimize(lnP, theta_0, args=(True), method=\"Powell\")\n outputs = {}\n for comp in lnP._components:\n outputs.update(\n {f\"{comp}\": {\"T_bar\": lnP.get_amplitude_expectation(res.x, component=comp),\n \"N_T\": lnP.get_amplitdue_covariance(res.x, component=comp),}})\n \n return patch_num, imc, indices, fitting_name, model_identifier, lnP, res, outputs","def semi_analytical_marginal_probj(param_inst, cash_tt, solu_dict,\n cur_col_prefix_space='_'):\n\n choice_set_list = param_inst.model_option['choice_set_list']\n choice_names_use = param_inst.model_option['choice_names_full_use']\n choice_names_use = param_inst.model_option['choice_names_use']\n\n each_j_prob = solu_dict['each_j_prob']\n ktp_opti_allJ = solu_dict['ktp_opti_allJ']\n btp_opti_allJ = solu_dict['btp_opti_allJ']\n consumption_opti_allJ = solu_dict['consumption_opti_allJ']\n\n # btp_fb_opti_allJ = solu_dict['btp_fb_opti_allJ']\n # btp_ib_opti_allJ = solu_dict['btp_ib_opti_allJ']\n # btp_fs_opti_allJ = solu_dict['btp_fs_opti_allJ']\n # btp_il_opti_allJ = solu_dict['btp_il_opti_allJ']\n\n trans_prob_list = []\n simu_output_pd_allj = 0\n\n for ctr, choicej in enumerate(choice_set_list):\n\n cur_col_prefix = choice_names_use[ctr]\n logger.info('ctr,choicej,cur_col_prefix:\\n%s,%s,%s',\n str(ctr), str(choicej), str(cur_col_prefix))\n\n btp_opti = btp_opti_allJ[:, ctr]\n ktp_opti = ktp_opti_allJ[:, ctr]\n prob_cur = each_j_prob[:, ctr]\n consumption_opti = consumption_opti_allJ[:, ctr]\n\n '''\n Get columns at centered interpolating grid points for:\n 'cash_grid_centered'\n 'marginal_dist'\n 'btp_opti_grid',\n 'ktp_opti_grid',\n 'consumption_opti_grid' \n '''\n logger.info('Solve, P(COH|j), P(COH|COH,j), ctr:%s, name:%s', str(ctr), cur_col_prefix)\n simu_output_pd_curj, trans_prob_curj = condianaly.semi_analytical_marginal(\n param_inst,\n cash_tt, ktp_opti, btp_opti, consumption_opti,\n each_j_prob=prob_cur,\n trans_prob_only=True,\n cur_col_prefix=cur_col_prefix + cur_col_prefix_space)\n\n '''Store Choice J Transition Prob'''\n trans_prob_list.append(trans_prob_curj)\n\n '''Update Column Names'''\n if (ctr == 0):\n simu_output_pd_allj = simu_output_pd_curj\n else:\n '''Cumulate'''\n simu_output_pd_allj = pd.concat([simu_output_pd_allj,\n simu_output_pd_curj], axis=1)\n\n \"\"\"\n D. Add columns for each j of J for fbibfsil\n \"\"\"\n # D1. Get Columns from just created panda files, j specific columns\n steady_var_suffixes_dict = hardstring.get_steady_var_suffixes()\n btp_opti_grid_allJ_cols = [col for col in simu_output_pd_allj.columns if\n cur_col_prefix_space + steady_var_suffixes_dict['btp_opti_grid'] in col]\n ktp_opti_grid_allJ_cols = [col for col in simu_output_pd_allj.columns if\n cur_col_prefix_space + steady_var_suffixes_dict['ktp_opti_grid'] in col]\n\n # D2. Matrix from columns\n btp_opti_grid_allJ = simu_output_pd_allj[btp_opti_grid_allJ_cols].to_numpy()\n ktp_opti_grid_allJ = simu_output_pd_allj[ktp_opti_grid_allJ_cols].to_numpy()\n\n # D3. Get fb ib fs il specific matrixes\n btp_fb_opti_allJ, btp_ib_opti_allJ, btp_fs_opti_allJ, btp_il_opti_allJ = \\\n fbibfsis.genfibs_btpstack(choice_set_list, btp_opti_grid_allJ, ktp_opti_grid_allJ, param_inst)\n\n # D4. Add to simu_output_pd_allj panda\n fb_ib_fs_il_steady_var_key_list = ['btp_fb_opti_grid', 'btp_ib_opti_grid',\n 'btp_fs_opti_grid', 'btp_il_opti_grid']\n varnames_list = [choice_names_use[ctr] + cur_col_prefix_space + steady_var_suffixes_dict[fbibfsil_stdykey]\n for fbibfsil_stdykey in fb_ib_fs_il_steady_var_key_list\n for ctr, choicej in enumerate(choice_set_list)]\n\n # D5. Additional Panda columns with fb ib fs il information\n varnames = \",\".join(map(str, varnames_list))\n varmat = np.column_stack((btp_fb_opti_allJ, btp_ib_opti_allJ, btp_fs_opti_allJ, btp_il_opti_allJ))\n simu_output_pd_allj_fbibfsil = proj_sys_sup.debug_panda(varnames, varmat, export_panda=False, log=False)\n\n # D6. Concatenate together, join more columns together. \n simu_output_pd_allj = pd.concat([simu_output_pd_allj, simu_output_pd_allj_fbibfsil], axis=1)\n\n \"\"\"\n E0. Grid Column\n \"\"\"\n cash_grid_centered_cols = [col for col in simu_output_pd_allj.columns\n if steady_var_suffixes_dict['cash_grid_centered'] in col]\n simu_output_pd_allj['cash_grid_centered'] = simu_output_pd_allj[cash_grid_centered_cols[0]]\n\n \"\"\"\n E1. Adjust Probabilities due to Interpolation issue over J choices\n \"\"\"\n logger.info('simu_output_pd_allj.columns:\\n%s', simu_output_pd_allj.columns)\n prob_cols = [col for col in simu_output_pd_allj.columns if 'probJ_opti_grid' in col]\n logger.info('prob_cols:\\n%s', prob_cols)\n probJ_matrix = simu_output_pd_allj[prob_cols].to_numpy()\n logger.info('probJ_matrix:\\n%s', probJ_matrix)\n\n '''These are actually not needed, perfect symmatery'''\n probJ_matrix_rowsum = np.reshape(np.sum(probJ_matrix, axis=1), (-1, 1))\n logger.info('probJ_matrix_rowsum:\\n%s', probJ_matrix_rowsum)\n probJ_matrix_rescale_sum1 = probJ_matrix / probJ_matrix_rowsum\n logger.info('probJ_matrix_rescale_sum1:\\n%s', probJ_matrix_rescale_sum1)\n\n \"\"\"\n E2. Overall Conditional probabilities\n \"\"\"\n trans_prob_wgtJ = 0\n trans_prob_dict_allj = {}\n for ctr, choicej in enumerate(choice_set_list):\n '''E2a. Transition Probability current j'''\n trans_prob_curj = trans_prob_list[ctr]\n\n '''E2b. Choice Probability over J'''\n prob_opti_grid = probJ_matrix_rescale_sum1[:, ctr]\n\n '''E2c. Update current column with reweighted sum to 1 choice J prob'''\n simu_output_pd_allj[prob_cols[ctr]] = prob_opti_grid\n\n '''E2d. Weighted Discrete Transition Probability'''\n trans_prob_curj_wgted = trans_prob_curj * np.reshape(prob_opti_grid, (-1, 1))\n logger.debug('trans_prob_curj:\\n%s', trans_prob_curj)\n logger.debug('prob_opti_grid:\\n%s', prob_opti_grid)\n logger.debug('trans_prob_curj_wgted:\\n%s', trans_prob_curj_wgted)\n\n '''E2e. Update Column Names'''\n trans_prob_dict_allj[choicej] = trans_prob_curj\n if (ctr == 0):\n trans_prob_wgtJ = trans_prob_curj_wgted\n else:\n '''Cumulate'''\n trans_prob_wgtJ = trans_prob_wgtJ + trans_prob_curj_wgted\n\n logger.info('trans_prob_wgtJ:\\n%s', trans_prob_wgtJ)\n logger.info('np.sum(trans_prob_wgtJ):\\n%s', np.sum(trans_prob_wgtJ, axis=1))\n\n return simu_output_pd_allj, trans_prob_wgtJ, trans_prob_dict_allj","def _like3(init_par, alpha, delta, plx_obs, mualpha_obs, mudelta_obs, sigma_obs, ccoef, i):\r\n\r\n\tplx_mod, v, sigma_v = init_par[i], init_par[-4:-1], init_par[-1] \r\n\tp, q, r = normalTriad(alpha, delta)\r\n\tmualpha_mod = np.dot(np.transpose(p),v)*plx_mod/_A\r\n\tmudelta_mod = np.dot(np.transpose(q),v)*plx_mod/_A\r\n \t\r\n\tsigma_plx, sigma_mualpha, sigma_mudelta = sigma_obs\r\n\tr_plx_muRa, r_plx_muDec, r_muRa_muDec = ccoef[0], ccoef[1], ccoef[2] \r\n\t\r\n\tC = np.zeros((3,3),dtype=np.float64)\r\n\tC[0,0],C[1,1],C[2,2] = sigma_plx**2.,sigma_mualpha**2., sigma_mudelta**2.\r\n\tC[0,1], C[0,2] = r_plx_muRa*sigma_plx*sigma_mualpha, r_plx_muDec*sigma_plx*sigma_mudelta\r\n\tC[1,0], C[1,2] = r_plx_muRa*sigma_plx*sigma_mualpha, r_muRa_muDec*sigma_mualpha*sigma_mudelta\r\n\tC[2,0], C[2,1] = r_plx_muDec*sigma_plx*sigma_mudelta, r_muRa_muDec*sigma_mualpha*sigma_mudelta\r\n\r\n\tE = np.zeros((3,3),dtype=np.float64)\r\n\tE[1,1],E[2,2] = (sigma_v**2.)*(plx_mod/_A)**2., (sigma_v**2.)*(plx_mod/_A)**2.\r\n\t\r\n\tD = np.add(E,C)\r\n\tdetD = det(D) \r\n\tinvD = inv(D)\r\n\t\t\r\n\ta_c = np.array([plx_obs - plx_mod, mualpha_obs - mualpha_mod, mudelta_obs-mudelta_mod])\r\n\tg_func = row_matrix_col(a_c, a_c, invD)\r\n\t\r\n\t\r\n\treturn detD, g_func","def p_m(pmx_c,px):\n pm = np.zeros(pmx_c.shape[0])\n for mi in range(pm.size):\n for xi in range(px.size):\n pm[mi] += pmx_c[mi,xi]*px[xi]\n return pm","def MPE(cpts, obs):\r\n s = z3.Optimize()\r\n # we store our variables in a dictionary because it makes retrieval easier\r\n var = {}\r\n for variable in cpts:\r\n var[variable[0]] = (Bool(variable[0]))\r\n # we use this loop to make our hard assertions\r\n for observations in obs:\r\n if observations != []:\r\n if observations[1] == '0':\r\n s.add(Not(var[observations[0]]))\r\n else:\r\n s.add(var[observations[0]])\r\n # call our helper function to add all of our soft assertions\r\n for cpt in cpts:\r\n weighted_clause(cpt, var, s)\r\n s.check()\r\n return s.model()","def setup_milp(model, target, remove_blocked=False, exclude_reaction_ids=set()):\n original_model = model\n model = model.copy()\n model.objective=target\n for i in heterologous_reactions.keys(): # turns off each heterologous reaction in order to get the only the native metabolic network.\n model.reactions.get_by_id(i).lower_bound=0\n model.reactions.get_by_id(i).upper_bound=0\n\n for r in model.reactions:\n if r.id.find('MetaCyc')>-1:\n r.type='heterologous'\n else:\n r.type='native'\n\n # Set the solver to Gurobi for the fastest result. Set to CPLEX if Gurobi is not available.\n if \"gurobi\" in cobra.util.solver.solvers.keys():\n logger.info(\"Changing solver to Gurobi and tweaking some parameters.\")\n if \"gurobi_interface\" not in model.solver.interface.__name__:\n model.solver = \"gurobi\"\n # The tolerances are set to the minimum value. This gives maximum precision.\n problem = model.solver.problem\n problem.params.NodeMethod = 1 # primal simplex node relaxation\n problem.params.FeasibilityTol = 1e-9 #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\n problem.params.OptimalityTol = 1e-3 #how sure the solver has to be about this optimum being really the best it has.\n problem.params.IntFeasTol = 1e-9 #If a value is set to an integer, how much may it still vary? > set smallest possible\n problem.params.MIPgapAbs = 1e-9\n problem.params.MIPgap = 1e-9\n problem.params.Threads=1 #In order to reduce memory usage (increased calculation time)\n problem.params.TimeLimit = 200 # Use max 200 seconds when called, return best solution after that\n problem.params.PoolSearchMode = 1 #0 for only finding the optimum, 1 for finding more solutions (but no quality guaranteed), 2 for finding the n best possible solutions\n problem.params.PoolSolutions = 10 # Number of solutions kept when finding the optimal solution\n problem.params.PoolGap = 0.9 # only store solutions within 90% of the optimal objective value\n\n elif \"cplex\" in cobra.util.solver.solvers.keys():\n logger.warning(\"Changing solver to CPLEX, as Gurobi is not available. This may cause a big slowdown and limit options afterwards.\")\n if \"cplex_interface\" not in model.solver.interface.__name__:\n model.solver = \"cplex\"\n # The tolerances are set to the minimum value. This gives maximum precision.\n problem = model.solver.problem\n problem.parameters.mip.strategy.startalgorithm.set(1) # primal simplex node relaxation\n problem.parameters.simplex.tolerances.feasibility.set(1e-9) #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\n problem.parameters.simplex.tolerances.optimality.set(1e-3) #possibly fine with 1e-3, try if allowed. Is how sure the solver has to be about this optimum being really the best it has.\n problem.parameters.mip.tolerances.integrality.set(1e-9) #If a value is set to an integer, how much may it still vary? > set smallest possible\n problem.parameters.mip.tolerances.absmipgap.set(1e-9)\n problem.parameters.mip.tolerances.mipgap.set(1e-9)\n problem.parameters.mip.pool.relgap.set(0.9) # For populate: find all solutions within 10% of the optimum for relgap = 0.1\n problem.parameters.timelimit.set(200) # Use max 200 seconds for solving\n problem.parameters.mip.limits.populate.set(20) # Find max 20 solutions (=default)\n\n else:\n logger.warning(\"You are trying to run 'Hamlet Hot Rig' with %s. This might not end well.\" %\n model.solver.interface.__name__.split(\".\")[-1])\n pass\n # Remove reactions that are blocked: no flux through these reactions possible. This will reduce the search space for the solver, if not done already.\n if remove_blocked:\n blocked_reactions = cameo.flux_analysis.analysis.find_blocked_reactions(model)\n model.remove_reactions(blocked_reactions)\n\n # Make dual\n model_with = model.copy() # This variable looks unnecessary, but is kept out of fear of messing stuff up\n model_with.optimize()\n dual_problem = convert_linear_problem_to_dual(model_with.solver)\n logger.debug(\"Dual problem successfully created\")\n\n # Combine primal and dual\n primal_problem = model.solver\n\n for var in dual_problem.variables: # All variables in the dual are copied to the primal\n var = primal_problem.interface.Variable.clone(var)\n primal_problem.add(var)\n for const in dual_problem.constraints: # All constraints in the dual are copied to the primal\n const = primal_problem.interface.Constraint.clone(const, model=primal_problem)\n primal_problem.add(const)\n logger.debug(\"Dual and primal combined\")\n\n dual_problem.optimize()\n\n # Dictionaries to hold the binary control variables:\n heterologous_y_vars = {} # 1 for 'knockin', 0 for inactive\n medium_y_vars = {} # 1 for medium addition (up to -10), 0 for no addition\n\n # Now the fun stuff\n constrained_dual_vars = set()\n\n # For the knockins and medium additions:\n for reaction in [r for r in model.reactions if r.type == \"heterologous\"]:\n # Add constraint variables\n interface = model.solver.interface\n y_var = interface.Variable(\"y_\" + reaction.id, type=\"binary\")\n\n # Constrain the primal: flux through reactions maximum within (-1000, 1000), or smaller boundaries defined before\n model.solver.add(interface.Constraint(reaction.flux_expression - 1000 * y_var, ub=0, name=\"primal_y_const_\"+reaction.id+\"_ub\"))\n model.solver.add(interface.Constraint(reaction.flux_expression + 1000 * y_var, lb=0, name=\"primal_y_const_\"+reaction.id+\"_lb\"))\n\n # Constrain the dual\n constrained_vars = []\n\n if reaction.upper_bound != 0:\n dual_forward_ub = model.solver.variables[\"dual_\" + reaction.forward_variable.name + \"_ub\"]\n model.solver.add(interface.Constraint(dual_forward_ub - 1000 * (1 - y_var), ub=0))\n constrained_vars.append(dual_forward_ub)\n if reaction.lower_bound != 0:\n dual_reverse_ub = model.solver.variables[\"dual_\" + reaction.reverse_variable.name + \"_ub\"]\n model.solver.add(interface.Constraint(dual_reverse_ub - 1000 * (1 - y_var), ub=0))\n constrained_vars.append(dual_reverse_ub)\n constrained_dual_vars.update(constrained_vars)\n\n # Add y variable to the corresponding modifications dictionary\n heterologous_y_vars[y_var] = reaction\n\n logger.debug(\"Control variables created\")\n\n # Add number of heterologous switch contraint constraint\n heterologous_turn_on = model.solver.interface.Constraint(\n optlang.symbolics.Add(*heterologous_y_vars), lb=0, ub=0, name=\"heterologous_reaction_constraint\"\n )\n model.solver.add(heterologous_turn_on)\n\n # Set the objective\n primal_objective = model.solver.objective\n dual_objective = interface.Objective.clone(\n dual_problem.objective, model=model.solver\n )\n\n switch_objective=interface.Objective(heterologous_turn_on.expression, direction='min')\n\n full_objective = interface.Objective(primal_objective.expression-dual_objective.expression, direction=\"max\")\n model.objective = full_objective\n\n return model,primal_objective,dual_objective,full_objective,switch_objective","def construct_MC_samples(self, N_MC):\n D = self.theta[\"long_cov\"]\n if self.MC_sep:\n n_samples = self.n_samples\n (U, V, y, N) = self.extracted_features[0]\n n_long_features = self.n_long_features\n phi = self.theta[\"phi\"]\n beta = np.hstack((self.theta[\"beta_0\"], self.theta[\"beta_1\"]))\n r = D.shape[0]\n Omega = np.random.multivariate_normal(np.zeros(r), np.eye(r), N_MC)\n S = np.zeros((n_samples, 2, 2 * N_MC, r))\n\n for i in range(n_samples):\n U_i, V_i, y_i, N_i = U[i], V[i], y[i], N[i]\n\n # compute Sigma_i\n Phi_i = [[1 / phi[l, 0]] * N_i[l]\n for l in range(n_long_features)]\n Sigma_i = np.diag(np.concatenate(Phi_i))\n\n # compute Omega_i\n D_inv = np.linalg.inv(D)\n A_i = np.linalg.inv(\n V_i.transpose().dot(Sigma_i).dot(V_i) + D_inv)\n # compute mu_i\n mu_i = (A_i.dot(V_i.transpose()).dot(Sigma_i).dot(\n y_i - U_i.dot(beta))).T[..., np.newaxis]\n C_i = np.linalg.cholesky(A_i)\n tmp = C_i.dot(Omega.T)\n S[i] = (mu_i + np.hstack((tmp, -tmp))).swapaxes(1, 2)\n else:\n C = np.linalg.cholesky(D)\n r = D.shape[0]\n Omega = np.random.multivariate_normal(np.zeros(r), np.eye(r), N_MC)\n b = Omega.dot(C.T)\n S = np.vstack((b, -b))\n\n return S","def make_synthetic_experiment(sample_data, model, make_gibbs_fn, explicit_ub=None):\n rng_key = random.PRNGKey(0)\n\n # Sampling parameters\n n_values = [100, 1000, 10000]\n\n # DPMM/PYMM parameters\n T = 20 # max number of component in the truncated stick breaking representation\n t = np.arange(T + 1)\n alpha = 1\n sigma = 0\n\n # Plotting parameters\n fig, (ax0, ax1) = plt.subplots(1, 2)\n\n priors = compute_PY_prior(alpha, sigma, n_values)\n for Npoints, prior in zip(n_values, priors):\n cluster_count = np.zeros(T + 1) # cluster count histogram\n upper_bound = np.zeros(T + 1)\n \n cluster_size = np.zeros(Npoints+1)\n\n # Repeat the experiment \n for _ in range(REPEATS):\n data = sample_data(rng_key, Npoints)\n z = sample_posterior(rng_key, model, data, N_SAMPLES,\n T=T, alpha=1,\n gibbs_fn=make_gibbs_fn(data) if USE_GIBBS else None,\n gibbs_sites=['z'] if USE_GIBBS else None,\n )\n\n cluster_count += compute_n_clusters_distribution(z, T)\n if explicit_ub is not None:\n upper_bound += explicit_ub(data, t, params_PY=(alpha, sigma))\n\n cluster_size += compute_cluster_size_distribution(z)\n\n cluster_count /= REPEATS\n cluster_size /= REPEATS\n\n # Plot cluster count histograms (ax0)\n ax0.plot(t, cluster_count, label=f\"N={Npoints}\")\n\n color = ax0.lines[-1].get_color()\n ax0.plot(t, prior[:T+1], label=f\"Prior N={Npoints}\", color=color, linestyle='dashed', lw=1)\n\n if explicit_ub is not None:\n upper_bound /= REPEATS\n ax0.plot(t[1:], upper_bound[1:], label=f\"Upper bound N={Npoints}\", color=color, linestyle='dotted', lw=1)\n\n # Plot cluster size histograms (ax1)\n bins = np.linspace(0, 1, 10, endpoint=True)\n frac = np.arange(0, Npoints + 1) / Npoints\n\n # TODO : use an actual histogram ?\n # Overlaying histograms doesn't really look good.\n hist, edges = np.histogram(frac, bins, density=True, weights=cluster_size)\n ax1.plot(0.5 * (edges[1:] + edges[:-1]), hist, color=color, label=f\"N={Npoints}\")\n\n ax0.axhline(y=1, color='black', linewidth=0.3, linestyle='dotted')\n ax0.set(title=r\"Number of clusters\", xlabel=\"$t$\", ylabel=r\"$P(T_n=t|X_{1:N})$\")\n ax0.legend()\n\n ax1.set(xlabel=\"Fraction of total size\", title=\"Size of clusters\")\n ax1.legend()\n\n plt.show()","def gen_fpm_poke_cube(dm, mp, dx_dm, **kwargs):\n if type(mp) is not falco.config.ModelParameters:\n raise TypeError('Input \"mp\" must be of type ModelParameters')\n real_positive_scalar(dx_dm, 'dx_dm', TypeError)\n \n if \"NOCUBE\" in kwargs and kwargs[\"NOCUBE\"]:\n flagGenCube = False\n else:\n flagGenCube = True\n\n # Define this flag if it doesn't exist in the older code for square actuator arrays only\n if not hasattr(dm, 'flag_hex_array'):\n dm.flag_hex_array = False\n\n # Set the order of operations\n XYZ = True\n if(hasattr(dm, 'flagZYX')):\n if(dm.flagZYX):\n XYZ = False\n\n # Compute sampling of the pupil. Assume that it is square.\n dm.dx_dm = dx_dm\n dm.dx = dx_dm\n\n # Default to being centered on a pixel if not specified\n if not(hasattr(dm, 'centering')):\n dm.centering = 'pixel'\n \n # Compute coordinates of original influence function\n Ninf0 = dm.inf0.shape[0] # Number of points across inf func at native res\n x_inf0 = np.linspace(-(Ninf0-1)/2., (Ninf0-1)/2., Ninf0)*dm.dx_inf0\n # True for even- or odd-sized influence function maps as long as they are\n # centered on the array.\n [Xinf0, Yinf0] = np.meshgrid(x_inf0, x_inf0)\n \n # Number of points across the DM surface at native inf func resolution\n Ndm0 = falco.util.ceil_even(Ninf0 + (dm.Nact - 1)*(dm.dm_spacing/dm.dx_inf0))\n # Number of points across the (un-rotated) DM surface at new, desired res.\n dm.NdmMin = falco.util.ceil_even(Ndm0*(dm.dx_inf0/dm.dx))+2.\n # Number of points across the array to fully contain the DM surface at new\n # desired resolution and z-rotation angle.\n dm.Ndm = int(ceil_even((abs(np.array([np.sqrt(2.)*np.cos(radians(45.-dm.zrot)),\n np.sqrt(2.)*np.sin(radians(45.-dm.zrot))])).max())*Ndm0*(dm.dx_inf0/dm.dx))+2)\n \n # Compute list of initial actuator center coordinates (in actutor widths).\n if(dm.flag_hex_array): # Hexagonal, hex-packed grid\n raise ValueError('flag_hex_array option not implemented yet.')\n# Nrings = dm.Nrings;\n# x_vec = [];\n# y_vec = [];\n# % row number (rowNum) is 1 for the center row and 2 is above it, etc.\n# % Nacross is the total number of segments across that row\n# for rowNum = 1:Nrings\n# Nacross = 2*Nrings - rowNum # Number of actuators across at that row (for hex tiling in a hex shape)\n# yval = sqrt(3)/2*(rowNum-1);\n# bx = Nrings - (rowNum+1)/2 # x offset from origin\n# \n# xs = (0:Nacross-1).' - bx # x values are 1 apart\n# ys = yval*ones(Nacross,1) # same y-value for the entire row\n# \n# if(rowNum==1)\n# x_vec = [x_vec;xs];\n# y_vec = [y_vec;ys]; \n# else\n# x_vec = [x_vec;xs;xs];\n# y_vec = [y_vec;ys;-ys] # rows +/-n have +/- y coordinates\n# end\n# end\n else: # Square grid [actuator widths]\n [dm.Xact, dm.Yact] = np.meshgrid(np.arange(dm.Nact) - \n dm.xc, np.arange(dm.Nact)-dm.yc)\n# # Use order='F' to compare the final datacube to Matlab's output.\n# # Otherwise, use C ordering for Python FALCO.\n# x_vec = dm.Xact.reshape(dm.Nact*dm.Nact,order='F')\n# y_vec = dm.Yact.reshape(dm.Nact*dm.Nact,order='F')\n x_vec = dm.Xact.reshape(dm.Nact*dm.Nact)\n y_vec = dm.Yact.reshape(dm.Nact*dm.Nact)\n \n dm.NactTotal = x_vec.shape[0] # Total number of actuators in the 2-D array\n dm.xy_cent_act = np.zeros((2, dm.NactTotal)) # Initialize\n\n # Compute the rotation matrix to apply to the influence function and \n # actuator center locations\n tlt = np.zeros(3)\n tlt[0] = radians(dm.xtilt)\n tlt[1] = radians(dm.ytilt)\n tlt[2] = radians(-dm.zrot)\n\n sa = sin(tlt[0])\n ca = cos(tlt[0])\n sb = sin(tlt[1])\n cb = cos(tlt[1])\n sg = sin(tlt[2])\n cg = cos(tlt[2])\n\n if XYZ:\n Mrot = np.array([[cb * cg, sa * sb * cg - ca * sg, ca * sb * cg + sa * sg, 0.0],\n [cb * sg, sa * sb * sg + ca * cg, ca * sb * sg - sa * cg, 0.0],\n [-sb, sa * cb, ca * cb, 0.0],\n [0.0, 0.0, 0.0, 1.0]])\n else:\n Mrot = np.array([ [cb * cg, -cb * sg, sb, 0.0],\n [ca * sg + sa * sb * cg, ca * cg - sa * sb * sg, -sa * cb, 0.0],\n [sa * sg - ca * sb * cg, sa * cg + ca * sb * sg, ca * cb, 0.0],\n [0.0, 0.0, 0.0, 1.0]])\n \n # Compute the actuator center coordinates in units of actuator spacings\n for iact in range(dm.NactTotal):\n xyzVals = np.array([x_vec[iact], y_vec[iact], 0., 1.])\n xyzValsRot = Mrot @ xyzVals\n dm.xy_cent_act[0, iact] = xyzValsRot[0].copy()\n dm.xy_cent_act[1, iact] = xyzValsRot[1].copy()\n\n N0 = dm.inf0.shape[0]\n Npad = falco.util.ceil_odd(np.sqrt(2.)*N0)\n inf0pad = np.zeros((Npad, Npad))\n inf0pad[int(np.ceil(Npad/2.)-np.floor(N0/2.)-1):int(np.ceil(Npad/2.)+np.floor(N0/2.)),\n int(np.ceil(Npad/2.)-np.floor(N0/2.)-1):int(np.ceil(Npad/2.)+np.floor(N0/2.))] = dm.inf0\n\n ydim = inf0pad.shape[0]\n xdim = inf0pad.shape[1]\n \n xd2 = np.fix(xdim / 2.) + 1\n yd2 = np.fix(ydim / 2.) + 1\n cx = np.arange(xdim) + 1. - xd2\n cy = np.arange(ydim) + 1. - yd2\n [Xs0, Ys0] = np.meshgrid(cx, cy)\n\n xsNewVec = np.zeros(xdim*xdim)\n ysNewVec = np.zeros(ydim*ydim)\n Xs0Vec = Xs0.reshape(xdim*xdim)\n Ys0Vec = Ys0.reshape(ydim*ydim)\n \n for ii in range(Xs0.size):\n xyzVals = np.array([Xs0Vec[ii], Ys0Vec[ii], 0., 1.])\n xyzValsRot = Mrot @ xyzVals\n xsNewVec[ii] = xyzValsRot[0]\n ysNewVec[ii] = xyzValsRot[1]\n \n # Calculate the interpolated DM grid at the new resolution\n # (set extrapolated values to 0.0)\n dm.infMaster = griddata((xsNewVec, ysNewVec), inf0pad.reshape(Npad*Npad),\n (Xs0, Ys0), method='cubic', fill_value=0.)\n\n # Crop down the influence function until it has no zero padding left\n infSum = np.sum(dm.infMaster)\n infDiff = 0.\n counter = 0\n while(abs(infDiff) <= 1e-7):\n counter = counter + 2\n infDiff = infSum - np.sum(abs(dm.infMaster[int(counter/2):int(-counter/2),\n int(counter/2):int(-counter/2)]))\n\n # Subtract an extra 2 to negate the extra step that overshoots.\n counter = counter - 2\n Ninf0pad = dm.infMaster.shape[0]-counter\n if counter == 0:\n infMaster2 = dm.infMaster.copy()\n else:\n # The cropped-down influence function\n infMaster2 = dm.infMaster[int(counter/2):int(-counter/2),\n int(counter/2):int(-counter/2)].copy()\n dm.infMaster = infMaster2\n \n Npad = Ninf0pad\n\n # True for even- or odd-sized influence function maps as long as they are\n # centered on the array.\n x_inf0 = np.linspace(-(Npad-1)/2, (Npad-1)/2., Npad)*dm.dx_inf0\n [Xinf0, Yinf0] = np.meshgrid(x_inf0, x_inf0)\n\n # Translate and resample the master influence function to be at each \n # actuator's location in the pixel grid \n\n # Compute the size of the postage stamps.\n # Number of points across the influence function array at the DM plane's\n # resolution. Want as even\n Nbox = falco.util.ceil_even(Ninf0pad*dm.dx_inf0/dx_dm)\n dm.Nbox = Nbox\n # Also compute their padded sizes for the angular spectrum (AS) propagation\n # between P2 and DM1 or between DM1 and DM2\n # Minimum number of points across for accurate angular spectrum propagation\n Nmin = falco.util.ceil_even(np.max(mp.sbp_centers)*np.max(np.abs(np.array(\n [mp.d_P2_dm1, mp.d_dm1_dm2, (mp.d_P2_dm1+mp.d_dm1_dm2)])))/dx_dm**2)\n # Use a larger array if the max sampling criterion for angular spectrum\n # propagation is violated\n dm.NboxAS = np.max(np.array([Nbox, Nmin]))\n\n # Pad the pupil to at least the size of the DM(s) surface(s) to allow all\n # actuators to be located outside the pupil.\n # (Same for both DMs)\n\n # Find actuator farthest from center:\n dm.r_cent_act = np.sqrt(dm.xy_cent_act[0, :]**2 + dm.xy_cent_act[1, :]**2)\n dm.rmax = np.max(np.abs(dm.r_cent_act))\n NpixPerAct = dm.dm_spacing/dx_dm\n if(dm.flag_hex_array):\n # padded 2 actuators past the last actuator center to avoid trying to\n # index outside the array\n dm.NdmPad = falco.util.ceil_even((2.*(dm.rmax+2))*NpixPerAct + 1)\n else:\n # DM surface array padded by the width of the padded influence function\n # to prevent indexing outside the array. \n # The 1/2 term is because the farthest actuator center is still half an\n # actuator away from the nominal array edge. \n dm.NdmPad = falco.util.ceil_even((dm.NboxAS + 2.0*(1 + (np.max(\n np.abs(dm.xy_cent_act.reshape(2*dm.NactTotal)))+0.5)*NpixPerAct)))\n\n # Compute coordinates (in meters) of the full DM array\n if(dm.centering == 'pixel'):\n # meters, coords for the full DM arrays. Origin is centered on a pixel\n dm.x_pupPad = np.linspace(-dm.NdmPad/2., (dm.NdmPad/2. - 1),\n dm.NdmPad)*dx_dm\n else:\n # meters, coords for the full DM arrays. Origin is interpixel centered\n dm.x_pupPad = np.linspace(-(dm.NdmPad-1)/2., (dm.NdmPad-1)/2.,\n dm.NdmPad)*dx_dm\n\n dm.y_pupPad = dm.x_pupPad\n\n # Make NboxPad-sized postage stamps for each actuator's influence function\n if(flagGenCube):\n if not dm.flag_hex_array:\n print(\" Influence function padded from %d to %d points for A.S. propagation.\" % (Nbox,dm.NboxAS))\n\n print('Computing datacube of DM influence functions... ', end='')\n\n # Find the locations of the postage stamps arrays in the larger pupilPad array\n dm.xy_cent_act_inPix = dm.xy_cent_act*(dm.dm_spacing/dx_dm) # Convert units to pupil-plane pixels\n dm.xy_cent_act_inPix = dm.xy_cent_act_inPix + 0.5 # For the half-pixel offset if pixel centered. \n dm.xy_cent_act_box = np.round(dm.xy_cent_act_inPix) # Center locations of the postage stamps (in between pixels), in actuator widths\n dm.xy_cent_act_box_inM = dm.xy_cent_act_box*dx_dm # now in meters\n dm.xy_box_lowerLeft = dm.xy_cent_act_box + (dm.NdmPad-Nbox)/2 - 0 # index of pixel in lower left of the postage stamp within the whole pupilPad array. +0 for Python, +1 for Matlab\n\n # Starting coordinates (in actuator widths) for updated master influence function.\n # This is interpixel centered, so do not translate!\n dm.x_box0 = np.linspace(-(Nbox-1)/2., (Nbox-1)/2., Nbox)*dx_dm\n [dm.Xbox0, dm.Ybox0] = np.meshgrid(dm.x_box0, dm.x_box0) # meters, interpixel-centered coordinates for the master influence function\n\n # (Allow for later) Limit the actuators used to those within 1 actuator width of the pupil\n r_cent_act_box_inM = np.sqrt(dm.xy_cent_act_box_inM[0, :]**2 + dm.xy_cent_act_box_inM[1, :]**2)\n # Compute and store all the influence functions:\n dm.inf_datacube = np.zeros((Nbox, Nbox, dm.NactTotal)) # initialize array of influence function \"postage stamps\"\n dm.act_ele = np.arange(dm.NactTotal) # Initialize as including all actuators\n\n inf_datacube = np.zeros((dm.NactTotal, Nbox, Nbox))\n\n interp_spline = RectBivariateSpline(x_inf0, x_inf0, dm.infMaster) # RectBivariateSpline is faster in 2-D than interp2d\n # Refer to https://scipython.com/book/chapter-8-scipy/examples/two-dimensional-interpolation-with-scipyinterpolaterectbivariatespline/\n\n for iact in range(dm.NactTotal):\n xbox = dm.x_box0 - (dm.xy_cent_act_inPix[0, iact]-dm.xy_cent_act_box[0, iact])*dx_dm # X = X0 -(x_true_center-x_box_center)\n ybox = dm.x_box0 - (dm.xy_cent_act_inPix[1, iact]-dm.xy_cent_act_box[1, iact])*dx_dm # Y = Y0 -(y_true_center-y_box_center)\n dm.inf_datacube[:, :, iact] = interp_spline(ybox, xbox)\n inf_datacube[iact, :, :] = interp_spline(ybox, xbox)\n\n print('done.')\n\n else:\n dm.act_ele = np.arange(dm.NactTotal)","def gibbs_sample(self, X):\n K = self.K # number of topics\n M, V = X.shape\n alpha = self.alpha\n lmda = self.lmda\n topics = np.arange(0, K)\n\n #initialize everything uniformly, sparse topics\n Beta = np.ones(shape=(K, V), dtype=float) / V\n Theta = np.ones(shape=(M, K), dtype=float)/ K\n\n #Running sum\n MC_z = np.array(range(M), dtype=object)\n MC_beta = np.zeros(shape=(K, V), dtype=float)\n MC_theta = np.zeros(shape=(M, K), dtype=float)\n\n for d in range(M):\n #allocate topics randomly -- this is really not needed in this case\n word_indices = X[d, :].nonzero()[1]\n random_ks = np.random.choice(topics, size = len(word_indices))\n Theta[d] = np.random.dirichlet(np.ones(K)*alpha)\n MC_z[d] = sp.coo_matrix((V, K), dtype=np.int8).tolil()\n for k in topics:\n Beta[k] = np.random.dirichlet(np.ones(V)*lmda)\n\n log_Xs = []\n perplexities = []\n t = 0\n for epoch in xrange(self.nr_em_epochs):\n print \"Epoch\", epoch\n t +=1\n C = np.zeros((K, V))\n for d in np.random.permutation(np.arange(M)):\n x = X[d]\n ixw = np.nonzero(x)[1]\n p_s = Beta[:, ixw].T * Theta[d, :]\n Z = [np.random.choice(topics, p=(p/np.sum(p))) for p in p_s]\n N_d = sp.coo_matrix((np.ones(len(ixw)), (ixw, Z)), shape=(V, K)).tolil()\n C = C + N_d.A.T\n # sample theta given z and beta\n c_theta = (np.sum(N_d.A, axis=0) + alpha)\n Theta[d, :] = np.random.dirichlet(c_theta)\n MC_z[d] += N_d\n\n # Sample beta given all z and thetas\n for k in topics:\n c_beta = C[k, :]\n Beta[k, :] = np.random.dirichlet(c_beta + lmda)\n\n MC_theta += Theta\n MC_beta += Beta\n\n log_X = 0\n Theta_hat = MC_theta / t\n Beta_hat = MC_beta / t\n\n for d in range(M):\n ixw = np.nonzero(X[d, :])[1]\n log_X += np.sum(_doc_probability_from_p_of_z(Theta_hat[d, :], Beta_hat[:, ixw]))\n\n log_Xs.append(log_X)\n print log_X\n perplexities.append(self._perplexity(X, log_X))\n return Theta_hat, Beta_hat, log_Xs, perplexities","def get_theory(p, dm, cosmo, hmc,\n return_separated=False,\n include_1h=True, include_2h=True,\n hm_correction=None,\n **kwargs):\n cls_out = []\n for tr, ls, bms in zip(dm.tracers, dm.ells, dm.beams):\n cl = hm_ang_power_spectrum(cosmo, hmc, ls, tr,\n include_1h=include_1h,\n include_2h=include_2h,\n hm_correction=hm_correction,\n zpts=p.get(\"mcmc\")[\"zpts\"],\n **kwargs)\n cl *= bms # multiply by beams\n\n if return_separated:\n cls_out.append(cl)\n else:\n cls_out += cl.tolist()\n\n cls_out = np.array(cls_out)\n return cls_out","def init_dwi_hmc_wf(hmc_transform, hmc_model, hmc_align_to, source_file,\n num_model_iterations=2, mem_gb=3, omp_nthreads=1, sloppy=False,\n name=\"dwi_hmc_wf\"):\n\n inputnode = pe.Node(\n niu.IdentityInterface(\n fields=['dwi_files', 'b0_indices', 'bvecs', 'bvals', 'b0_images', 'original_files',\n 't1_brain', 't1_mask', 't1_seg']),\n name='inputnode')\n\n outputnode = pe.Node(\n niu.IdentityInterface(\n fields=[\"final_template\", \"forward_transforms\", \"noise_free_dwis\",\n \"cnr_image\", \"optimization_data\", \"final_template_brain\",\n \"final_template_mask\"]),\n name='outputnode')\n\n workflow = Workflow(name=name)\n # Unbiased align the b0s\n b0_hmc_wf = init_b0_hmc_wf(align_to=hmc_align_to, transform=hmc_transform, sloppy=sloppy)\n # Tile the transforms so each non-b0 gets the transform from the nearest b0\n match_transforms = pe.Node(MatchTransforms(), name=\"match_transforms\")\n # Make a mask from the output template. It is bias-corrected, so good for masking\n # but bad for using as a b=0 for modeling.\n b0_template_mask = init_dwi_reference_wf(register_t1=True, name=\"b0_template_mask\",\n gen_report=False, source_file=source_file)\n\n workflow.connect([\n (inputnode, match_transforms, [('dwi_files', 'dwi_files'),\n ('b0_indices', 'b0_indices')]),\n (inputnode, b0_hmc_wf, [('b0_images', 'inputnode.b0_images')]),\n (b0_hmc_wf, outputnode, [('outputnode.final_template', 'final_template')]),\n (b0_hmc_wf, match_transforms, [(('outputnode.forward_transforms', _list_squeeze),\n 'transforms')]),\n (inputnode, b0_template_mask, [\n ('t1_brain', 'inputnode.t1_brain'),\n ('t1_mask', 'inputnode.t1_mask'),\n ('t1_seg', 'inputnode.t1_seg')]),\n (b0_hmc_wf, b0_template_mask, [\n ('outputnode.final_template', 'inputnode.b0_template')]),\n (b0_template_mask, outputnode, [\n ('outputnode.ref_image_brain', 'final_template_brain'),\n ('outputnode.dwi_mask', 'final_template_mask')\n ])\n ])\n\n # If we're just aligning based on the b=0 images, compute the b=0 tsnr as the cnr\n if hmc_model.lower() == \"none\":\n workflow.__postdesc__ = \"Each b>0 image was transformed based on the registration \" \\\n \" of the nearest b=0 image. \"\n\n concat_b0s = pe.Node(afni.TCat(outputtype=\"NIFTI_GZ\"), name=\"concat_b0s\")\n b0_tsnr = pe.Node(\n afni.TStat(options=' -cvarinvNOD ', outputtype='NIFTI_GZ'), name='b0_tsnr')\n workflow.connect([\n (match_transforms, outputnode, [('transforms', 'forward_transforms')]),\n (b0_hmc_wf, concat_b0s, [('outputnode.aligned_images', 'in_files')]),\n (concat_b0s, b0_tsnr, [('out_file', 'in_file')]),\n (b0_tsnr, outputnode, [('out_file', 'cnr_image')])])\n return workflow\n\n # Do model-based motion correction\n dwi_model_hmc_wf = init_dwi_model_hmc_wf(hmc_model, hmc_transform, mem_gb, omp_nthreads,\n num_iters=num_model_iterations)\n\n # Warp the modeled images into non-motion-corrected space\n uncorrect_model_images = pe.MapNode(\n ants.ApplyTransforms(invert_transform_flags=[True],\n interpolation='LanczosWindowedSinc'),\n iterfield=['input_image', 'reference_image', 'transforms'],\n name='uncorrect_model_images')\n workflow.__postdesc__ = \"Model-generated images were transformed into alignment with each \" \\\n \"b>0 image. Both slicewise and whole-brain QC measures (cross \" \\\n \"correlation and R^2) were calculated.\"\n workflow.connect([\n (b0_hmc_wf, dwi_model_hmc_wf, [\n ('outputnode.aligned_images', 'inputnode.warped_b0_images')]),\n (b0_template_mask, dwi_model_hmc_wf, [\n ('outputnode.dwi_mask', 'inputnode.warped_b0_mask')]),\n (inputnode, dwi_model_hmc_wf, [\n ('dwi_files', 'inputnode.dwi_files'),\n ('b0_indices', 'inputnode.b0_indices'),\n ('bvecs', 'inputnode.bvec_files'),\n ('bvals', 'inputnode.bval_files')]),\n (match_transforms, dwi_model_hmc_wf, [\n ('transforms', 'inputnode.initial_transforms')]),\n (dwi_model_hmc_wf, outputnode, [\n ('outputnode.hmc_transforms', 'forward_transforms'),\n ('outputnode.optimization_data', 'optimization_data'),\n ('outputnode.cnr_image', 'cnr_image')]),\n (dwi_model_hmc_wf, uncorrect_model_images, [\n ('outputnode.model_predicted_images', 'input_image'),\n ('outputnode.hmc_transforms', 'transforms')]),\n (inputnode, uncorrect_model_images, [('dwi_files', 'reference_image')]),\n (uncorrect_model_images, outputnode, [('output_image', 'noise_free_dwis')])\n ])\n datasinks = [node for node in workflow.list_node_names()\n if node.split(\".\")[-1].startswith(\"ds_\")]\n\n for ds in datasinks:\n workflow.get_node(ds).inputs.source_file = source_file\n\n return workflow","def mcmcrun(data, priors, dirname):\n def initParams(data):\n '''Initialize parameters from fitted mixed effects model in R.'''\n params = postdist.ParamsHolder()\n params.updateBeta(np.zeros([data.grp, data.l]))\n params.updateGamma(np.zeros([data.grp, data.l]))\n\n # linear\n # params.updateAlpha(priors.d3)\n # params.updateLambdaD(np.array(0.086).reshape(1, 1))\n\n # quadratic\n params.updateAlpha(np.array([45.50, -3.96, -1.41]).reshape(data.p, 1))\n params.updateLambdaD(np.array(0.047).reshape(1, 1))\n\n params.updateB(np.random.normal(0, np.sqrt(1.0/params.lambdaD),\n size = data.ntot*data.p).reshape(data.ntot, data.p))\n params.updateSigma2(np.array(priors.sigma2).reshape(1, 1))\n return params\n\n # initialize parameters\n temp_params = initParams(data)\n temp_paramsArray = temp_params.toArray(data.ntot, data.grp,\n data.p, data.l)\n with open(dirname+\"/updates.txt\", 'w') as f_handle:\n np.savetxt(f_handle,\n temp_paramsArray,\n delimiter=',')\n\n stoppingRule = fwsr.StoppingRule(temp_paramsArray, dirname, eps = 0.2)\n\n # MCMC updates\n # totSimulation = 10000\n counter = 1\n while True:\n counter += 1\n\n # update gamma\n gamma_pd = postdist.GammaPosterior(data, temp_params, priors)\n temp_params.updateGamma(gamma_pd.getUpdates())\n\n # update beta\n beta_pd = postdist.BetaPosterior(data, temp_params)\n temp_params.updateBeta(beta_pd.getUpdates())\n\n # update lambdaD\n lambdaD_pd = postdist.LambdaDPosterior(data, temp_params, priors)\n temp_params.updateLambdaD(lambdaD_pd.getUpdates())\n\n # update sigma2\n sigma2_pd = postdist.Sigma2Posterior(data, temp_params)\n temp_params.updateSigma2(sigma2_pd.getUpdates())\n\n # update alpha\n alpha_pd = postdist.AlphaPosterior(data, temp_params, priors)\n temp_params.updateAlpha(alpha_pd.getUpdates())\n\n # update b\n b_pd = postdist.BPosterior(data, temp_params)\n temp_params.updateB(b_pd.getUpdates())\n\n # # print out results with gaps\n # if counter % 10 == 0:\n # print \"==== This is the {0}th iteration ====\".format(counter)\n # # raw_input(\"Press Enter to Continue ...\")\n # print \"====beta (trt & ctrl): \\n{0}\".format(temp_params.beta)\n # print \"====alpha: \\n{0}\".format(temp_params.alpha)\n # print \"====lambdaD: \\n{0}\".format(temp_params.lambdaD)\n # print \"====sigma2: \\n{0}\".format(temp_params.sigma2)\n # if int(raw_input(\"Do you want the results of b? Press 0/1:\")):\n # print \"===b: {0} with Cov: {1}\".format(temp_params.b,\n # b_pd.cov[1])\n\n # store updates\n # params = np.hstack([params,\n # temp_params.toArray(data.ntot, data.grp, data.p, data.l)])\n\n # write to file\n # np.savetxt(dirname+\"/updates.txt\", params, delimiter=',')\n\n temp_paramsArray = temp_params.toArray(data.ntot, data.grp,\n data.p, data.l)\n with open(dirname+\"/updates.txt\", 'a') as f_handle:\n np.savetxt(f_handle,\n temp_paramsArray,\n delimiter=',')\n\n np.savetxt(dirname+\"/counter.txt\", np.array([counter]))\n\n isTerminated = stoppingRule.update(temp_paramsArray)\n if isTerminated:\n break","def ParticleFilterParams(fix_params=False):\n\n ## Particle filter parameters\n\n # Q_c will be the time continuous covariance matrix. \n #This should be the errors in the model.\n # in the form [x_cov, y_cov, z_cov, \n # vel_x_cov, vel_y_co, vel_z_cov, \n # mass_cov, \n # sigma_cov, shape_cov, brightness_cov, tau_cov]\n \n\n Q_c = [10., 2., 2., \n 150., 50., 50., \n 5., 0, 0,\n 1e-3, 1e-10, 0., 0.0001]\n\n\n print('Qc values used:', Q_c)\n\n Q_c = np.asarray([i**2 for i in Q_c])\n\n \n # Q_c_frag is used at reinitialisation if the fragmentation option is used\n \n Q_c_frag = [0., 0., 0., \n 0.02, 0.02, 0.02, \n 0.5, 0, 0,\n 2e-3, 5e-9, 0., 0.]\n\n Q_c_frag = [i**2 for i in Q_c_frag]\n\n ## P: starting uncertainty to initialise gaussian spread of particals. \n ## P2: starting uncertainty at reinitialisation if the fragmentation option is used\n ## in the form [x_cov, y_cov, z_cov, % of vel_x_cov, % of vel_y_co, % of vel_z_cov]\n P = [50., 50., 50., 250., 250., 250.]\n P2 = [50., 50., 50., 250., 250., 250.]\n\n ## Initialise state ranges\n\n\n ## shape parameter close to a rounded brick (1.8) (A for a sphere =1.21)\n A_min = 1.21\n A_max = 3.0 \n\n ## luminosity coefficient\n tau_min = 0.0001\n tau_max = 0.1\n\n ## lists of typical meteorite densities for different types. [chond, achond, stony-iron, iron, cometary]\n pm_mean = [3000, 3100, 4500, 7500, 850]\n pm_std = [420, 133, 133, 167, 117 ]\n\n ## to choose density values according to a distribution of meteorite percentages:\n particle_choices = []\n\n # this is created using lines 257-266; uncomment if needs changing.\n random_meteor_type = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 4, 4]\n\n #random_meteor_type = []\n #for i in range(80): # 80 % Chondrites\n # random_meteor_type.append(0)\n #for i in range(11): # 11 % Achondrites\n # random_meteor_type.append(1)\n #for i in range(2):\n # random_meteor_type.append(2) # 2 % Stony-Iron\n #for i in range(5):\n # random_meteor_type.append(3) # 5 % iron\n #for i in range(2):\n # random_meteor_type.append(4) # 2 % cometary\n\n ## ablation coefficeint \n #sigma_min = 0.001*1e-6\n #sigma_max = 0.5*1e-6\n\n\n #range_params = [m0_max, A_mean, A_std, pm_mean, pm_std, random_meteor_type, cd_mean, cd_std, sigma_min, sigma_max, K_min, K_max, tau_min, tau_max]\n range_params = [A_min, A_max, pm_mean, pm_std, random_meteor_type, tau_min, tau_max]\n\n if fix_params:\n \tQ_c[-4:] = [0., 0., 0., 0.]\n \tQ_c_frag[-4:] = [0., 0., 0., 0.]\n return Q_c, Q_c_frag, P, range_params","def create_model(self, gwas_gen, gwas_phen):\n n_ind, n_snps = gwas_gen.eval().shape\n with pm.Model() as phenotype_model:\n beta_med = pm.Normal('beta_med',\n mu=self.vars['coef_mean'],\n sd=self.vars['coef_sd'],\n shape=(1, n_snps))\n \n mediator = pm.dot(beta_med, gwas_gen.T)\n intercept = pm.Normal('intercept', mu=0, sd=1)\n alpha = pm.Normal('alpha', mu=0, sd=1)\n phenotype_sigma = pm.HalfCauchy('phenotype_sigma',\n beta=self.vars['p_sigma_beta'])\n \n\n # Model Selection\n p = np.array([0.5, 0.5])\n mediator_model = pm.Bernoulli('mediator_model', p[1])\n\n # Model 1\n phenotype_mu_null = intercept\n\n # Model 2\n phenotype_mu_mediator = intercept + alpha * mediator\n\n phen = pm.DensityDist('phen',\n lambda value: pm.switch(mediator_model, \n pm.Normal.dist(mu=phenotype_mu_mediator, sd=phenotype_sigma).logp(value), \n pm.Normal.dist(mu=phenotype_mu_null, sd=phenotype_sigma).logp(value)\n ),\n observed=gwas_phen)\n self.steps = [pm.BinaryGibbsMetropolis(vars=[mediator_model]),\n pm.Metropolis()]\n\n \n if self.variational and self.mb:\n self.minibatch_RVs = [phen]\n self.minibatch_tensors = [gwas_gen, gwas_phen]\n return phenotype_model","def sbm_pp_parameters(k, p, q, sigma=0):\n omega = (p-q)*np.eye(k) + q*np.ones([k, k])\n\n pert = sigma*np.random.randn(*omega.shape)\n pert = np.tril(pert) + np.tril(pert, -1).T\n omega += pert\n np.maximum(omega, 0, omega)\n\n return omega","def fit_mweight(data, C=1., threshold=1e-4, learning_rate=None):\n dataPM = toPM(data).T;\n m,n = dataPM.shape;\n if learning_rate is None: learning_rate = 1-np.sqrt(np.log(n)/m);\n\n L,h = np.zeros((n,n)), np.zeros((n,)) # initialize parameters (dense) & weights\n eye = list(range(n));\n Wp = np.ones((n,n))/(2*(n-1)); Wp[eye,eye] = 0; Wm = np.copy(Wp);\n Hp = np.ones((n,))/2; Hm = np.copy(Hp);\n\n for i,xpm in enumerate(dataPM):\n phat = 1./(1.+np.exp(2.*L.dot(xpm) + h))\n ell_H = (phat - (1.-xpm)/2.);\n ell_W = np.outer( ell_H , xpm);\n Wp *= learning_rate**(-ell_W); Wm *= learning_rate**(ell_W);\n Hp *= learning_rate**(-ell_H); Hm *= learning_rate**(ell_H);\n L = C/(Wp.sum(1)+Wm.sum(1))[:,np.newaxis]*(Wp-Wm)\n h = C/(Hp+Hm)*(Hp-Hm)\n \n L = .5*(L+L.T);\n L[np.abs(L)<threshold] = 0;\n L[eye,eye] = h;\n return Ising(L);","def proba(c_pred,m_pred,f_pred, dataset):\n p = np.zeros(10)\n if dataset == 'cifar10':\n for i in range(10):\n if i <4:\n if i <2:\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:2]))\n elif i <4:\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[2:4]))\n if i >=4:\n if i <6:\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[4:6]))\n elif i <8:\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[6:8]))\n elif i <10:\n p[i] = c_pred[1]*(m_pred[4]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[8:10]))\n else :\n for i in range(10):\n if i <5:\n if i <3:\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:3]))\n elif i <5:\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[3:5]))\n if i >=5:\n if i <8:\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[5:8]))\n elif i <10:\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[8:]))\n return(p)","def create_model():\n # Get list of all syllables: [\"<s>\", \"AH\", \"</s>\", \"<s>\", \"T\", ...]\n syllabifier = Syllabifier()\n all_syllables = syllabifier.all_syllables()\n\n # Count conditional probabilties of phoneme tuples\n tcf = TrigramCollocationFinder.from_words(all_syllables)\n bcf = BigramCollocationFinder.from_words(all_syllables)\n tri_dict = dict(sorted(tcf.ngram_fd.items(), key=lambda t: (-t[1], t[0])))\n bi_dict = dict(sorted(bcf.ngram_fd.items(), key=lambda t: (-t[1], t[0])))\n\n # Create dictionary to count cond prob all phoneme tuples\n accepted_phonemes = [i[0] for i in cmudict.phones()]\n accepted_phonemes.append('<s>')\n accepted_phonemes.append('</s>')\n phoneme_tups = [p for p in itertools.product(accepted_phonemes, repeat=3)]\n cond_probs_dict = dict([(char, 0) for char in phoneme_tups])\n\n for t in tri_dict:\n p1, p2, p3 = t[0], t[1], t[2]\n tri_count = tri_dict[t]\n bi_count = bi_dict[(p1, p2)]\n if bi_count > 1:\n cond_prob = tri_count * 1.0 / bi_count\n else:\n cond_prob = 0.0\n cond_probs_dict[(p1, p2, p3)] = cond_prob\n\n pickle.dump(cond_probs_dict, open(COND_PROBS_PATH, \"wb\"))\n return","def binom_artifact_model_probability(data,\n prior_p=binom_p_prior,\n mutation_object=True):\n\n if mutation_object is True:\n trajectories = data.data\n else:\n trajectories = data\n\n ind_likelihoods = []\n for ind_traj in trajectories:\n # initialise list of samples for p integration\n int_p = []\n # integral over betabinom p parameter\n for p_sample in prior_p[0, :]:\n # For each p compute the likelihood of observing\n # a given time-series conditional on\n # initial time-point.\n cond_p_likelihood = binom.pmf(k=ind_traj[1:].AO,\n n=ind_traj[1:].DP,\n p=p_sample)\n int_p.append(np.product(cond_p_likelihood))\n \n # compute the model probability for each individual\n marginal_ind_likelihood = np.trapz(x=prior_p[0,:], y=int_p*prior_p[1,:])\n ind_likelihoods.append(marginal_ind_likelihood)\n \n # Compute the product of likelihoods\n mutation_prob = np.product(ind_likelihoods)\n\n if mutation_object is True:\n # return updated model_comparison object \n data.binom_artifact_prob = mutation_prob\n return data\n else:\n # return marginalised likelihood.\n return mutation_prob","def getMCMC(self,x,y,priors={},datamodel=None):\n import pymc\n from operator import isSequenceType\n from inspect import getargspec\n from types import MethodType\n\n if set(priors.keys()) != set(self.params):\n raise ValueError(\"input priors don't match function params\")\n\n d={}\n for k,v in priors.iteritems():\n if isinstance(v,pymc.StochasticBase):\n d[k]=v\n elif isSequenceType(v):\n if len(v) == 2:\n d[k]=pymc.distributions.Uniform(k,v[0],v[1])\n else:\n raise ValueError(\"couldn't understand sequence \"+str(v) )\n elif v > 0:\n d[k] = pymc.distributions.Normal(k,self.pardict[k],1.0/v/v)\n elif v == 0:\n d[k] = pymc.distributions.Poisson(k,self.pardict[k])\n else:\n raise ValueError(\"couldn't interpret prior \"+str(v))\n\n funcdict=dict(d)\n if type(self.f) is MethodType:\n xstr = getargspec(self.f)[0][1]\n else:\n assert callable(self.f),'object function is not a callable'\n xstr = getargspec(self.f)[0][0]\n funcdict[xstr]=x\n funcdet=pymc.Deterministic(name='f',eval=self.f,parents=funcdict,doc=\"FunctionModel1D function\")\n d['f'] = funcdet\n\n if type(datamodel) is tuple:\n distobj,dataarg,kwargs=datamodel\n if 'name' not in kwargs:\n kwargs['name'] = 'data'\n\n kwargs[dataarg] = funcdet\n kwargs['observed'] = True\n kwargs['value'] = y\n\n datamodel = distobj(**kwargs)\n\n d[datamodel.__name__] = datamodel\n\n #now if any stochastics are in the kwargs, assume they are part of the model\n for k, v in kwargs.iteritems():\n if isinstance(v, pymc.StochasticBase):\n d[k] = v\n else:\n if datamodel is None:\n sig = np.std(y)\n else:\n sig = np.array(datamodel)\n d['data'] = pymc.distributions.Normal('data',mu=funcdet,tau=1/sig/sig,observed=True,value=y)\n\n return pymc.MCMC(d)","def pgibbs_estimator(X, data_model, crp_alpha, n_samples=100, \n n_sweeps=0, return_logps=False):\n n_data = X.shape[0]\n logps = np.zeros(n_samples)\n dpgmm = PyDPGMM(data_model, X, crp_alpha, seed=random.randrange(2**31))\n\n lambda_0 = data_model.lambda_0\n mu_0 = data_model.mu_0\n kappa_0 = data_model.kappa_0\n nu_0 = data_model.nu_0\n Z_0 = data_model.log_z\n\n for i in range(n_samples):\n qi = dpgmm.seqinit(n_sweeps)\n\n Nk = dpgmm.get_Nk()\n K = dpgmm.get_K()\n Z = dpgmm.get_Z()\n\n logp = lcrp(n_data, Nk, crp_alpha)\n\n for k in range(K):\n X_k = X[Z==k, :]\n logp += niw_ml(X_k, lambda_0, mu_0, kappa_0, nu_0, Z_0)\n\n logps[i] = logp - qi\n\n est= logsumexp(logps) - log(n_samples)\n\n if return_logps:\n return est, logps\n else:\n return est","def initParams(self):\n sizes = [self.inputDim]+self.layerSizes+[self.outputDim]\n scales = [np.sqrt(6)/np.sqrt(n+m) for n,m in zip(sizes[:-1],sizes[1:])]\n self.stack = [[np.random.rand(m,n)*2*s-s,np.zeros((m,1))] \\\n for n,m,s in zip(sizes[:-1],sizes[1:],scales)]\n self.hActs_M = [cm.empty((s,self.maxBatch)) for s in sizes]\n\n if self.train:\n # Now assuming that all layers are the same size\n self.grad = [[cm.empty(w.shape),cm.empty(b.shape)] for w,b in self.stack]\n self.deltasC_M = cm.empty((self.outputDim,self.maxBatch))\n self.deltasOut_M = cm.empty((sizes[1],self.maxBatch)) \n self.deltasIn_M = cm.empty((sizes[1],self.maxBatch)) \n self.tmpGrad_M = cm.empty((self.layerSize,self.maxBatch))\n \n # Allocate memory once here and reuse\n # Store probs\n self.probs_M = cm.empty((self.outputDim,self.maxBatch))\n # Store col max\n self.rowVec_M = cm.empty((1,self.maxBatch))\n \n self.stack = [[cm.CUDAMatrix(w),cm.CUDAMatrix(b)]\n for w,b in self.stack]\n\n if self.temporalLayer > 0:\n # dummy bias used for temporal layer\n dummy = cm.empty((1,1))\n dummy.assign(0.0)\n\n scale = np.sqrt(6)/np.sqrt(self.layerSize*2)\n wtf = cm.CUDAMatrix(2*scale*np.random.rand(self.layerSize,\n self.layerSize)-scale)\n wtb = cm.CUDAMatrix(2*scale*np.random.rand(self.layerSize,\n self.layerSize)-scale)\n self.stack.append([wtf,dummy])\n self.stack.append([wtb,dummy])\n\n # forward and backward activations for temporal layer\n self.hActsFor_M = cm.empty((self.layerSize,self.maxBatch))\n self.hActsBack_M = cm.empty((self.layerSize,self.maxBatch))\n\n if self.train:\n dwtf = cm.empty(wtf.shape)\n self.grad.append([dwtf,dummy])\n dwtb = cm.empty(wtb.shape)\n self.grad.append([dwtb,dummy])\n\n self.tmpGradBack_M = cm.empty((self.layerSize,self.maxBatch))\n self.deltasFor_M = cm.empty((self.layerSize,self.maxBatch))\n self.deltasBack_M = cm.empty((self.layerSize,self.maxBatch))","def mlp(input_dim,variance=1., weight_variance=None,bias_variance=100.,ARD=False):\r\n part = parts.mlp.MLP(input_dim,variance,weight_variance,bias_variance,ARD)\r\n return kern(input_dim, [part])","def bd_process_model_probability(data,\n fitness_prior=flat_fitness_prior,\n N_w_prior=flat_N_w_prior,\n mutation_object=True):\n\n if mutation_object is True:\n trajectories = data.data\n else:\n trajectories = data\n\n ind_likelihood = []\n for traj in trajectories:\n int_s = []\n for s in fitness_prior[0, :]:\n int_N_w = []\n for N_w in N_w_prior[0, :]:\n int_N_w.append(\n bd_process_conditional_likelihood_s_N(traj,\n s=s, N_w=N_w)\n )\n int_s.append(np.trapz(x=N_w_prior[0, :],\n y=int_N_w*N_w_prior[1, :]))\n\n marginalised_likelihood = np.trapz(x=fitness_prior[0, :],\n y=int_s*fitness_prior[1, :])\n ind_likelihood.append(marginalised_likelihood)\n \n mutation_prob = np.product(ind_likelihood)\n\n if mutation_object is True:\n # return updated model_comparison object \n data.bd_prob = mutation_prob\n return data\n else:\n # return marginalised likelihood.\n return mutation_prob","def part3c_2():\n xs = \"Werner & Co entered court today . Werner maintained that they were not guilty .\".split()\n N = 10000\n\n submission.computeGibbsProbabilities( englishCRF,\n submission.getCRFBlocks,\n submission.chooseGibbsCRF,\n xs, N )\n grader.requireIsTrue(True)","def get_model_with_properties():\n \n m = ConcreteModel()\n\n # ------------------------------------------------------------------\n # Data\n # ------------------------------------------------------------------\n\n m.np = 25 # Number of possible tays\n m.c = 4 # Number of components\n m.lc = 1 # Light component\n m.hc = 4 # Heavy component\n\n #### Constant parameters\n m.Rgas = 8.314 # Ideal gas constant in J/mol K\n m.Tref = 298.15 # Reference temperature in K\n\n #### Product specifications\n m.xspec_lc = 0.99 # Final liquid composition for methanol (1)\n m.xspec_hc = 0.99 # Fnal liquid composition for butanol (4)\n m.xspec_inter2 = 0.99 # Final liquid composition for ethanol (2)\n m.xspec_inter3 = 0.99 # Final liquid composition for propanol (3)\n m.Ddes = 50 # Final flowrate in distillate in mol/s\n m.Bdes = 50 # Final flowrate in bottoms in mol/s\n m.Sdes = 50 # Final flowrate in side product streams in mol/s\n\n # #### Known initial values\n m.Fi = m.Ddes + m.Bdes + 2 * m.Sdes # Side feed flowrate in mol/s\n m.Vi = 400 # Initial value for vapor flowrate in mol/s\n m.Li = 400 # Initial value for liquid flowrate in mol/s\n\n m.Tf = 358 # Side feed temperature in K\n\n m.Preb = 1.2 # Reboiler pressure in bar\n m.Pbot = 1.12 # Bottom-most tray pressure in bar\n m.Ptop = 1.08 # Top-most tray pressure in bar\n m.Pcon = 1.05 # Condenser pressure in bar\n m.Pf = 1.02\n\n m.rr0 = 0.893 # Internal reflux ratio initial value\n m.bu0 = 0.871 # Internal reflux ratio initial value\n\n\n #### Scaling factors\n m.Hscale = 1e3 \n m.Qscale = 1e-3 \n\n \n #### Constants for the calculation of liquid heat capacity\n m.cpc = {} # Constant 1 for liquid heat capacity \n m.cpc2 = {} # Constant 2 for liquid heat capacity \n m.cpc[1] = m.Rgas \n m.cpc[2] = 1\n m.cpc2['A', 1] = 1 / 100\n m.cpc2['B', 1] = 1 / 1e4\n m.cpc2['A', 2] = 1\n m.cpc2['B', 2] = 1\n\n\n # ------------------------------------------------------------------\n # Physical Properties\n #\n # Notation:\n # MW ........................ molecular weight in g/gmol\n # TB ........................ boiling point temperature in K\n # TC ........................ critical temperature in K\n # PC ........................ critical pressure in bar\n # w ........................ acentric factor\n # lden ...................... liquid density g/m3,\n # dHvap ..................... heat of vaporization in J/mol.\n # vpA, vpB, vpC, and vpD .... vapor pressure constants\n # cpA, cpB, cpC, and cpD .... heat capacity constants J/mol:\n # 1 for liq and 2 for vapor phase\n #\n # Reference A: R.C. Reid, J.M. Prausnitz and B.E. Poling,\n # \"The Properties of gases and liquids\", 1987 and 2004 Eds.\n #\n # ------------------------------------------------------------------\n\n m.prop = {} # Properties of components:\n cpL = {} # Ruczika-D method for liquid heat capacity calculation\n # (Reference A, page 6.20)\n sumA = {}\n sumB = {}\n sumC = {}\n cpL['a', 'C(H3)(C)'] = 4.19845\n cpL['b', 'C(H3)(C)'] = -0.312709\n cpL['c', 'C(H3)(C)'] = 0.178609\n cpL['a', 'C(H2)(C2)'] = 2.7345\n cpL['b', 'C(H2)(C2)'] = 0.122732\n cpL['c', 'C(H2)(C2)'] = -0.123482\n cpL['a', 'C(H2)(C)(O)'] = 0.517007\n cpL['b', 'C(H2)(C)(O)'] = 1.26631\n cpL['c', 'C(H2)(C)(O)'] = -0.0939713\n cpL['a', 'O(H)(C)'] = 16.1555\n cpL['b', 'O(H)(C)'] = -11.938\n cpL['c', 'O(H)(C)'] = 2.85117\n cpL['a', 'C(H3)(O)'] = 3.70344\n cpL['b', 'C(H3)(O)'] = -1.12884\n cpL['c', 'C(H3)(O)'] = 0.51239\n sumA[1] = (cpL['a', 'C(H3)(O)']\n + cpL['a', 'O(H)(C)']) \n sumB[1] = (cpL['b', 'C(H3)(O)']\n + cpL['b', 'O(H)(C)'])\n sumC[1] = (cpL['c', 'C(H3)(O)']\n + cpL['c', 'O(H)(C)'])\n sumA[2] = (cpL['a', 'C(H3)(C)']\n + cpL['a', 'C(H2)(C)(O)']\n + cpL['a', 'O(H)(C)'])\n sumB[2] = (cpL['b', 'C(H3)(C)']\n + cpL['b', 'C(H2)(C)(O)']\n + cpL['b', 'O(H)(C)'])\n sumC[2] = (cpL['c', 'C(H3)(C)']\n + cpL['c', 'C(H2)(C)(O)']\n + cpL['c', 'O(H)(C)'])\n sumA[3] = (cpL['a', 'C(H3)(C)']\n + cpL['a', 'C(H2)(C2)']\n + cpL['a', 'C(H2)(C)(O)']\n + cpL['a', 'O(H)(C)'])\n sumB[3] = (cpL['b', 'C(H3)(C)']\n + cpL['b', 'C(H2)(C2)']\n + cpL['b', 'C(H2)(C)(O)']\n + cpL['b', 'O(H)(C)'])\n sumC[3] = (cpL['c', 'C(H3)(C)']\n + cpL['c', 'C(H2)(C2)']\n + cpL['c', 'C(H2)(C)(O)']\n + cpL['c', 'O(H)(C)'])\n sumA[4] = (cpL['a', 'C(H3)(C)']\n + 2 * cpL['a', 'C(H2)(C2)']\n + cpL['a', 'C(H2)(C)(O)']\n + cpL['a', 'O(H)(C)'])\n sumB[4] = (cpL['b', 'C(H3)(C)']\n + 2 * cpL['b', 'C(H2)(C2)']\n + cpL['b', 'C(H2)(C)(O)']\n + cpL['b', 'O(H)(C)'])\n sumC[4] = (cpL['c', 'C(H3)(C)']\n + 2 * cpL['c', 'C(H2)(C2)']\n + cpL['c', 'C(H2)(C)(O)']\n + cpL['c', 'O(H)(C)'])\n\n ## Methanol: component 1\n m.prop[1, 'MW'] = 32.042\n m.prop[1, 'TB'] = 337.7\n m.prop[1, 'TC'] = 512.6\n m.prop[1, 'PC'] = 80.9\n m.prop[1, 'w'] = 0.556\n m.prop[1, 'lden'] = 792e3\n m.prop[1, 'dHvap'] = 38.376e3\n m.prop[1, 'vpA'] = -8.54796\n m.prop[1, 'vpB'] = 0.76982\n m.prop[1, 'vpC'] = -3.10850\n m.prop[1, 'vpD'] = 1.54481\n m.prop[1, 'cpA', 1] = sumA[1]\n m.prop[1, 'cpB', 1] = sumB[1]\n m.prop[1, 'cpC', 1] = sumC[1]\n m.prop[1, 'cpD', 1] = 0\n m.prop[1, 'cpA', 2] = 2.115e1\n m.prop[1, 'cpB', 2] = 7.092e-2\n m.prop[1, 'cpC', 2] = 2.587e-5\n m.prop[1, 'cpD', 2] = -2.852e-8\n\n\n ## Ethanol: component 2\n m.prop[2, 'MW'] = 46.069\n m.prop[2, 'TB'] = 351.4\n m.prop[2, 'TC'] = 513.9\n m.prop[2, 'PC'] = 61.4\n m.prop[2, 'w'] = 0.644\n m.prop[2, 'lden'] = 789.3e3\n m.prop[2, 'dHvap'] = 42.698e3\n m.prop[2, 'vpA'] = -8.51838\n m.prop[2, 'vpB'] = 0.34163\n m.prop[2, 'vpC'] = -5.73683\n m.prop[2, 'vpD'] = 8.32581\n m.prop[2, 'cpA', 1] = sumA[2]\n m.prop[2, 'cpB', 1] = sumB[2]\n m.prop[2, 'cpC', 1] = sumC[2]\n m.prop[2, 'cpD', 1] = 0\n m.prop[2, 'cpA', 2] = 9.014\n m.prop[2, 'cpB', 2] = 2.141e-1\n m.prop[2, 'cpC', 2] = -8.390e-5\n m.prop[2, 'cpD', 2] = 1.373e-9\n\n\n ## Propanol: component 3\n m.prop[3, 'MW'] = 60.096\n m.prop[3, 'TB'] = 370.3\n m.prop[3, 'TC'] = 536.8\n m.prop[3, 'PC'] = 51.7\n m.prop[3, 'w'] = 0.623\n m.prop[3, 'lden'] = 804e3\n m.prop[3, 'dHvap'] = 47.763e3\n m.prop[3, 'vpA'] = -8.05594\n m.prop[3, 'vpB'] = 4.25183e-2\n m.prop[3, 'vpC'] = -7.51296\n m.prop[3, 'vpD'] = 6.89004\n m.prop[3, 'cpA', 1] = sumA[3]\n m.prop[3, 'cpB', 1] = sumB[3]\n m.prop[3, 'cpC', 1] = sumC[3]\n m.prop[3, 'cpD', 1] = 0\n m.prop[3, 'cpA', 2] = 2.47\n m.prop[3, 'cpB', 2] = 3.325e-1\n m.prop[3, 'cpC', 2] = -1.855e-4\n m.prop[3, 'cpD', 2] = 4.296e-8\n\n\n ## Butanol: component 4\n m.prop[4, 'MW'] = 74.123\n m.prop[4, 'TB'] = 390.9\n m.prop[4, 'TC'] = 563.1\n m.prop[4, 'PC'] = 44.2\n m.prop[4, 'w'] = 0.593\n m.prop[4, 'lden'] = 810e3\n m.prop[4, 'dHvap'] = 52.607e3\n m.prop[4, 'vpA'] = -8.00756\n m.prop[4, 'vpB'] = 0.53783\n m.prop[4, 'vpC'] = -9.34240\n m.prop[4, 'vpD'] = 6.68692\n m.prop[4, 'cpA', 1] = sumA[4]\n m.prop[4, 'cpB', 1] = sumB[4]\n m.prop[4, 'cpC', 1] = sumC[4]\n m.prop[4, 'cpD', 1] = 0\n m.prop[4, 'cpA', 2] = 3.266\n m.prop[4, 'cpB', 2] = 4.18e-1\n m.prop[4, 'cpC', 2] = -2.242e-4\n m.prop[4, 'cpD', 2] = 4.685e-8\n\n\n return m","def buildQ(self):\r\n\r\n print 'Building Q ...'\r\n\r\n self.y = T.matrix('y')\r\n\r\n mlp = MLP(activations=self.hyper['q_activs'],\r\n dims=self.hyper['q_dims'],\r\n weights_init=self.hyper['q_W_init'],\r\n biases_init=Constant(0))\r\n\r\n q_parameters = mlp.apply(self.y)\r\n mlp.initialize()\r\n\r\n # self.qxgy_mu.shape == (minibatch size, num of dimension of x)\r\n self.qxgy_mu = q_parameters[:,:self.hyper['x_dim']]\r\n\r\n # self.qxgy_var.shape == (minibatch size, num of dimension of x)\r\n self.qxgy_var = T.exp( q_parameters[:,self.hyper['x_dim']:2*self.hyper['x_dim']] )\r\n\r\n # self.qwgy_mu.shape == (minibatch size, num of dimension of w)\r\n self.qwgy_mu = q_parameters[:,2*self.hyper['x_dim']:2*self.hyper['x_dim']+self.hyper['w_dim']]\r\n\r\n # self.qwgy_var.shape == (minibatch size, num of dimension of w)\r\n self.qwgy_var = T.exp( q_parameters[:,2*self.hyper['x_dim']+self.hyper['w_dim']:] )\r\n\r\n\r\n #---Will be useful to compute samples from q(x|y)---#\r\n #self.eps_x.shape == (minibatch size, # of x samples , # of dimension of x)\r\n self.eps_x = self.srng.normal((self.qxgy_mu.shape[0] ,self.hyper['L_x'] ,self.hyper['x_dim']))\r\n\r\n #self.x corresponds roughly to the function g(\\epsilon,y) (see reparametrization trick in Kingma 2014)\r\n #self.x.shape == (minibatch size, # of x samples , # of dimension of x)\r\n self.x = self.qxgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qxgy_var).dimshuffle(0,'x',1)*self.eps_x\r\n\r\n #---Will be useful to compute samples from q(w|y)---#\r\n #self.eps_w.shape == (minibatch size, # of w samples , # of dimension of w)\r\n self.eps_w = self.srng.normal((self.qwgy_mu.shape[0] ,self.hyper['L_w'] ,self.hyper['w_dim']))\r\n\r\n #self.w corresponds roughly to the function g(\\epsilon,y) (see reparametrization trick in Kingma 2014)\r\n #self.w.shape == (minibatch size, # of w samples , # of dimension of w)\r\n self.w = self.qwgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qwgy_var).dimshuffle(0,'x',1)*self.eps_w\r\n\r\n\r\n #---Building the log density q(x|y)---#\r\n little_num = 10**(-32)\r\n inside_exp = -T.sum((self.x - self.qxgy_mu.dimshuffle(0,'x',1))**2/(2*self.qxgy_var.dimshuffle(0,'x',1)), axis=2)\r\n norm_cst = (2*np.pi)**(-self.hyper['x_dim']/2.)*T.exp(T.sum(T.log(self.qxgy_var), axis=1))**(-1/2.)\r\n\r\n # shape == (minibatch size, # of x samples)\r\n qxgy = norm_cst.dimshuffle(0,'x')*T.exp(inside_exp)\r\n\r\n # shape == (minibatch size, # of x samples)\r\n self.log_qxgy = T.log(qxgy + little_num)","def run_emcee_mp(nsample=1000 ):#, pool=None):\n param_dict ={}\n \n obs_vec = np.loadtxt(\"./results/pk_CDE_FittingFormula-observed-truth.txt\")\n obs_vec=obs_vec[:,1]\n \n cov_simple = get_cov([0.32,0.1])\n print(np.linalg.cond(cov_simple))\n \n inv_cov = np.linalg.inv(cov_simple)\n previous_best = np.array([0.3, 0.09])\n \n ndim = len(previous_best)\n\n pos = previous_best + 1e-3 * np.random.randn(16, ndim)\n nwalkers, ndim = pos.shape\n\n #with Pool() as pool:\n sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability, args=(obs_vec, inv_cov))#, pool=pool)\n sampler.run_mcmc(pos, nsample, progress=True)\n\n return sampler","def proba_fm(m_pred,f_pred, dataset):\n p = np.zeros(10)\n if dataset == 'cifar10':\n for i in range(10):\n if i <4:\n if i <2:\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:2]))\n else:\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[2:4]))\n else:\n if i <6:\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[4:6]))\n elif i <8:\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[6:8]))\n else:\n p[i] = (m_pred[4])*(f_pred[i]/np.sum(f_pred[8:]))\n else :\n for i in range(10):\n if i <5:\n if i <3:\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:3]))\n else:\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[3:5]))\n else:\n if i <8:\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[5:8]))\n else:\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[8:]))\n return(p)","def sample_from_ibp(K, alpha, sigma, c):\n pp = poissonparams(K, alpha, sigma, c)\n new_nodes = np.random.poisson(pp)\n Ncols = new_nodes.sum()\n node_count = np.zeros(Ncols)\n\n # used to build sparse matrix, entries of each Zij=1\n colidx = [] \n rowidx = []\n rightmost_node = 0\n\n # for each clique\n for n in range(K):\n # revisit each previously seen node\n for k in range(rightmost_node):\n prob_repeat = (node_count[k] - sigma) / (n + c)\n r = np.random.rand()\n if r < prob_repeat:\n rowidx.append(n)\n colidx.append(k)\n node_count[k] += 1\n\n for k in range(rightmost_node, rightmost_node + new_nodes[n]):\n rowidx.append(n)\n colidx.append(k)\n node_count[k] += 1\n \n rightmost_node += new_nodes[n]\n\n # build sparse matrix\n data = np.ones(len(rowidx), int)\n shape = (K, Ncols)\n Z = csr_matrix((data, (rowidx, colidx)), shape)\n\n return Z","def _init_from_newform_(self, g, PP, C, prec):\n k = g.weight()\n N = g.level()\n if(k % 4 == 0):\n ep = g.atkin_lehner_eigenvalue()\n else:\n ep = -g.atkin_lehner_eigenvalue()\n kappa = mpmath.mpf(3 - k) / mp2\n if(ep == -1):\n WR = WeilRepDiscriminantForm(N, False)\n elif ep == 1:\n WR = WeilRepDiscriminantForm(N, True)\n else:\n raise ValueError(\" Sign of functional equation must be 1 or -1! Got:{0}\".format(ep))\n # print \"kappa=\",kappa\n # print \"WR=\",WR\n M = VVHarmonicWeakMaassForms(WR, kappa, prec)\n # print \"M=\",M\n self._space = M\n self.prec = prec\n self.coeff = dict()\n # We want a Harmonic weak maass form corresponding to the form g\n # that means we need to avoid any other cuspforms as well as\n # theta series...\n # If there are no oldforms we are happy\n if dimension_new_cusp_forms(N, k) == dimension_cusp_forms(N, k):\n # and to avoid theta series we need to avoid square discriminants\n # in the principal part\n if M._is_dual_rep:\n nset = [0, -1]\n else:\n nset = [-1]\n try:\n for n in nset:\n for r in WR.D(): # )_as_integers:\n D = M.D_from_rn((r, n))\n if not is_square(D):\n PP = {(WR.D[r], n): 1}\n self._principal_part = PP\n # print \"PP=\",PP,\"is ok!\"\n raise StopIteration()\n except StopIteration:\n pass\n # if(C is not None and C>0):","def heavy_fixCM_eigvals(NP, b, c, params):\n l = params['l']\n k = params['k']\n I3 = params['I3']\n # Here, omega_3 is just the MAGNITUDE, not signed\n w3 = np.abs(params['w3'][0])\n gn = params['Mm'] * params['g']\n\n # Check output if small system\n print 'gn = ', gn\n print 'b = ', b\n print 'c = ', c\n\n if NP == 1:\n pass\n elif NP == 2:\n matrix = -np.array([[0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0.],\n [(-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.,\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.],\n [0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3)],\n [(-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\n (-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.]\n ])\n print 'exact matrix = ', matrix\n eigvals = np.array([\n 1j * l * gn / (I3 * w3),\n -1j * l * gn / (I3 * w3),\n l * np.sqrt(gn) * np.sqrt(0j - 2. * l * k * (-1) ** (b) - gn) / (I3 * w3),\n -l * np.sqrt(gn) * np.sqrt(0j - 2. * l * k * (-1) ** (b) - gn) / (I3 * w3)\n ])\n print 'exact_eigvals are =', eigvals\n return eigvals\n elif NP == 3:\n matrix = -np.array([[0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0., 0., 0.],\n [(-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.,\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0., 0., 0.],\n [0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0.],\n [(-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\n (-1) ** (1 + c) * (-l * gn - 2. * (-1) ** (b) * l ** 2 * k) / (I3 * w3), 0., \\\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.],\n [0., 0., 0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3)],\n [0., 0., (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\n (-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.]\n ])\n print 'exact matrix = ', matrix\n\n eigvals = np.array([\n 1j * l * gn / (I3 * w3),\n # -1j*l*gn/(I3*w3),\n l * np.sqrt(gn) * np.sqrt(0j - 3. * l * k * (-1) ** (b) - gn) / (I3 * w3),\n # -l*np.sqrt(gn)*np.sqrt(0j-3.*l*k*(-1)**(b) - gn)/(I3*w3),\n l * np.sqrt(gn) * np.sqrt(0j - l * k * (-1) ** (b) - gn) / (I3 * w3),\n # -l*np.sqrt(gn)*np.sqrt(0j - l*k*(-1)**(b) - gn)/(I3*w3)\n ])\n return eigvals\n else:\n return np.array([])","def chisqdata_cphase(Obsdata, Prior, mask):\n\n clphasearr = Obsdata.c_phases(mode=\"all\", count=\"min\")\n uv1 = np.hstack((clphasearr['u1'].reshape(-1,1), clphasearr['v1'].reshape(-1,1)))\n uv2 = np.hstack((clphasearr['u2'].reshape(-1,1), clphasearr['v2'].reshape(-1,1)))\n uv3 = np.hstack((clphasearr['u3'].reshape(-1,1), clphasearr['v3'].reshape(-1,1)))\n clphase = clphasearr['cphase']\n sigma = clphasearr['sigmacp']\n\n A3 = (ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv1, pulse=Prior.pulse, mask=mask),\n ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv2, pulse=Prior.pulse, mask=mask),\n ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv3, pulse=Prior.pulse, mask=mask)\n )\n return (clphase, sigma, A3)","def part3c_0():\n xs = exampleInput\n N = 10000\n\n difference = 0.0\n for ys, estimatedProb in submission.computeGibbsProbabilities( simpleCRF, \n submission.getCRFBlocks,\n submission.chooseGibbsCRF,\n xs, N ).iteritems():\n trueProb = nerUtils.computeProbability( simpleCRF, xs, ys )\n difference = abs( trueProb - estimatedProb )\n grader.requireIsLessThan( 5e-2, difference )","def prob3():\n#raise NotImplementedError(\"Problem 3 Incomplete\")\n n = np.linspace(5000,500000,100)\n h = lambda x : x > 10\n MC_estimates = []\n for N in xrange(5000,505000,5000):\n X = np.random.gamma(9,scale=0.5,size=N)\n MC = 1./N*np.sum(h(X))\n MC_estimates.append(MC)\n MC_estimates = np.array(MC_estimates)\n MC_est = prob2()\n exact = np.array([1-stats.gamma(a=9,scale=0.5).cdf(10)]*n.shape[0])\n print n.shape,MC_estimates.shape\n plt.plot(n,abs(MC_estimates-exact),'r-',label=\"Monte Carlo\")\n plt.plot(n,abs(MC_est-exact),'b-',label=\"Importance\")\n plt.title(\"Error or Approx.\")\n plt.legend(loc=1)\n plt.show()","def milp(mdp, maxV, zeroConstraints=()):\n m = Model()\n m.setParam('OutputFlag', False)\n\n # convert notation to previous implementation\n S = mdp.S\n A = mdp.A\n R = mdp.rFuncs\n psi = mdp.psi\n T = mdp.T\n alpha = mdp.alpha\n gamma = mdp.gamma\n\n # useful constants\n rLen = len(R)\n M = 10000 # a large number\n Sr = range(len(S))\n Ar = range(len(A))\n\n # decision variables\n x = m.addVars(len(S), len(A), lb=0, name='x')\n z = m.addVars(rLen, vtype=GRB.BINARY, name='z')\n y = m.addVars(rLen, name='y')\n\n # constraints on y\n for i in range(rLen):\n m.addConstr(y[i] <= sum([x[s, a] * R[i](S[s], A[a]) for s in Sr for a in Ar]) - maxV[i] + (1 - z[i]) * M)\n m.addConstr(y[i] <= z[i] * M)\n\n # constraints on x (valid occupancy)\n for sp in Sr:\n m.addConstr(sum(x[s, a] * ((s == sp) - gamma * T(S[s], A[a], S[sp])) for s in Sr for a in Ar) == alpha(S[sp]))\n\n # == constraints\n for consIdx in range(len(zeroConstraints)):\n m.addConstr(sum(x[S.index(s), A.index(a)] for s, a in zeroConstraints[consIdx]) == 0)\n # obj\n m.setObjective(sum([psi[i] * y[i] for i in xrange(rLen)]), GRB.MAXIMIZE)\n\n m.optimize()\n\n pi = {(S[s], A[a]): x[s, a].X for s in Sr for a in Ar}\n\n if m.status == GRB.Status.OPTIMAL:\n # return feasible being true and the obj value, opt pi\n # .X attribute is to retrieve the value of the variable\n return pi\n else:\n # simply return infeasible\n raise Exception('milp problem optimal solution not found' + m.status)","def __init__(self, \n num_vars, \n num_hidden,\n training_inputs = None,\n algorithm = None,\n algorithm_dict = None,\n batch_size = None,\n use_momentum = None,\n W0= None, \n b0= None, \n bhid0 = None,\n zero_diag = True,\n symmetric = True,\n report_p_tilda =False,\n learn_biases = True,\n test_mode= False,\n training = True):\n \n self.num_vars = num_vars\n \n self.num_hidden = num_hidden\n \n self.batch_size = batch_size\n \n self.zero_diag = zero_diag\n \n self.algorithm = algorithm\n \n self.num_samples = 0\n \n self.num_u_gibbs = 0\n \n self.gibbs_steps = 0\n \n self.resample = False\n \n self.uniform = False\n \n self.mixture = False\n \n self.mix_params = []\n \n self.m_params = []\n \n self.mf_steps = 0\n \n self.alpha = 0\n \n self.learn_biases = learn_biases\n \n if isinstance(algorithm_dict, dict):\n \n for param in algorithm_dict.keys():\n \n if param == 'resample':\n \n self.resample = algorithm_dict[param]\n \n if param == 'mf_steps':\n \n self.mf_steps = algorithm_dict[param]\n \n if param == \"gibbs_steps\":\n \n self.gibbs_steps = algorithm_dict[param]\n \n if param == \"num_samples\":\n \n self.num_samples = algorithm_dict[param]\n \n if param == \"num_u_gibbs\":\n \n self.num_u_gibbs = algorithm_dict[param]\n \n if param == \"uniform\":\n \n self.uniform = algorithm_dict[param] \n \n if param == \"mixture\":\n \n self.mixture = algorithm_dict[param] \n \n if param == \"mix_params\":\n \n self.mix_params = algorithm_dict[param] \n \n if param == \"alpha\" and algorithm_dict[param] != None:\n #### alpha defines transition rate from\n #### uniform to mean-field distribution\n self.alpha = algorithm_dict[param] \n \n self.m_params = (1-self.alpha)*0.5*np.ones([1,self.num_vars])+\\\n self.alpha*np.mean(training_inputs,0)\n \n self.use_momentum = use_momentum\n \n self.report_p_tilda = report_p_tilda\n \n self.side = int(np.sqrt(self.num_vars))\n \n self.np_rand_gen = np.random.RandomState(1234)\n \n self.theano_rand_gen =\\\n theano.sandbox.rng_mrg.MRG_RandomStreams(self.np_rand_gen.randint(2**30))\n \n #self.theano_rand_gen =\\\n #T.shared_randomstreams.RandomStreams(self.np_rand_gen.randint(2**30))\n \n theano.config.exception_verbosity = 'high'\n \n self.node_indices = \\\n theano.shared(np.arange(self.num_vars), name=\"node_indices\")\n \n self.x = T.matrix('x')\n \n self.x_tilda = T.matrix('x_tilda')\n \n self.sampler_theta = T.matrix('sampler_theta')\n \n self.symmetric = symmetric\n \n if training:\n \n if self.num_hidden ==0:\n \n self.num_x2 = self.num_vars\n \n elif self.num_hidden > 0 :\n \n self.num_x2 = self.num_hidden\n \n self.updates = OrderedDict()\n \n self.N_train = training_inputs.shape[0]\n \n self.train_inputs = theano.shared(np.asarray(training_inputs,\n dtype=theano.config.floatX),\n borrow= True)\n \n self.learning_rate = T.dscalar('learning_rate')\n \n if self.mixture:\n \n print(\"Importance distribution was specified as mixture\"+\\\n \" of Bernoulli products\")\n \n if self.mix_params == []:\n print(\"Error: parameters defining mixture means were\"+\\\n \" not provided\")\n sys.exit()\n \n self.set_mixture_means(inputs = training_inputs)\n \n if use_momentum:\n \n print(\"Will add momentum term to gradient computations\")\n \n self.momentum = T.dscalar('learning_rate')\n \n self.grad_vec = {}\n \n self.grad_vec['W'] = theano.shared(np.zeros([self.num_vars, self.num_x2],\n dtype = theano.config.floatX), name = 'W_momentum', borrow = True)\n \n if self.num_hidden > 0:\n \n self.grad_vec['bhid'] = theano.shared(np.zeros([self.num_x2],\n dtype = theano.config.floatX), name = 'b_momentum', borrow = True)\n \n self.grad_vec['b'] = theano.shared(np.zeros([self.num_vars],\n dtype = theano.config.floatX), name = 'b_momentum', borrow = True)\n \n if test_mode:\n \n b_init =self.np_rand_gen.uniform(0,1, num_vars)\n \n W_init =self.np_rand_gen.uniform(0,1, size = (num_vars, num_vars))\n \n # also tested ones\n # b_init = np.ones(num_vars)\n \n # W_init = np.ones([num_vars, num_vars])\n \n self.b_init= np.asarray(b_init, dtype = theano.config.floatX)\n \n self.W_init= np.asarray(W_init, dtype = theano.config.floatX)\n \n self.b = theano.shared(self.b_init, name='b', borrow = False)\n \n self.W = theano.shared(self.W_init, name='W', borrow = False)\n \n print(\"Initialized with test mode\")\n \n else:\n \n if W0 is None:\n \n if self.num_hidden > 0:\n \n W0_init =\\\n self.np_rand_gen.uniform(\n -4*np.sqrt(6.0/(self.num_vars+self.num_hidden)),\\\n 4*np.sqrt(6.0 /(self.num_vars + self.num_hidden)), \n size = (num_vars, self.num_hidden)\n )\n \n W0 = np.asarray(W0_init, dtype = theano.config.floatX) \n \n if self.num_hidden == 0:\n \n # different W initializations: \n \n # W0_init =\\\n # self.np_rand_gen.uniform(-np.sqrt(3.0/(num_vars)),\\\n # np.sqrt(3.0 / (num_vars)), size = (num_vars, num_vars))\n \n # W0_init =\\\n # self.np_rand_gen.uniform(-0.00000001,\\\n # 0.00000001, size = (num_vars, num_vars))\n \n W0_init = 0.00000001*\\\n self.np_rand_gen.normal(size = (num_vars, self.num_x2)) \n \n W0 = np.asarray(W0_init, dtype = theano.config.floatX)\n \n if self.symmetric:\n \n W0 = (W0 + np.transpose(W0))/2.0\n \n if self.zero_diag:\n \n W0 = W0 - np.diag(np.diag(W0))\n \n self.W = theano.shared(value= W0, name='W', borrow=True)\n \n if self.num_hidden == 0:\n \n test_W = self.W.get_value() \n \n assert sum(np.diag(test_W)) == 0.0\n \n assert (test_W == np.transpose(test_W)).all() == True\n \n else:\n print(\"W is initialized with provided array\")\n self.W = theano.shared(value= W0, name='W', borrow=True)\n \n if b0 is None:\n \n bias_init = np.zeros(num_vars, dtype = theano.config.floatX)\n \n self.b = theano.shared(value= bias_init, name='b', borrow=True)\n \n else:\n print(\"b vector is initialized with provided vector\")\n self.b = theano.shared(value= b0, name='b', borrow=True)\n \n if bhid0 is None and self.num_hidden > 0:\n \n hbias_init = np.zeros(self.num_hidden, dtype = theano.config.floatX)\n \n self.bhid = theano.shared(value= hbias_init, name='bhid', borrow=True)\n \n elif (bhid0 is not None) and (self.num_hidden > 0):\n print(\"bhid vector is initialized with provided vector\") \n self.bhid = theano.shared(value= bhid0, name='bhid', borrow=True)\n \n self.theta = [self.W, self.b]\n \n if self.num_hidden > 0 :\n \n self.theta.append(self.bhid)\n \n self.train_set = set(range(self.N_train))\n \n self.minibatch_set = T.ivector('minibatch_set')\n \n self.sample_set = T.ivector('sample_set')\n \n if \"CD\" in self.algorithm and self.num_hidden ==0:\n \n self.x_gibbs= theano.shared(np.ones([self.batch_size,self.num_vars],\n dtype=theano.config.floatX),\n borrow = True, name= \"x_gibbs\")\n \n if \"CD\" in self.algorithm and self.num_hidden > 0:\n \n self.persistent_gibbs =\\\n theano.shared(np.ones([self.batch_size,self.num_hidden],\n dtype=theano.config.floatX),\n borrow = True, \n name= \"persistent_gibbs\")\n \n if \"CSS\" in self.algorithm and self.mf_steps > 0:\n \n init_mf_vis = self.np_rand_gen.uniform(0, \n 1, \n size =(self.num_vars,1))\n \n init_mf_vis = np.asarray(init_mf_vis, dtype = theano.config.floatX)\n \n self.mf_vis_p = theano.shared(init_mf_vis, \n name= \"mf_vis_p\", \n borrow= True)\n \n if self.num_hidden > 0:\n \n init_mf_hid = \\\n self.np_rand_gen.uniform(0, 1, size =(self.num_hidden,1))\n \n init_mf_hid = np.asarray(init_mf_hid, \n dtype = theano.config.floatX)\n \n self.mf_hid_p = theano.shared(init_mf_hid, \n name= \"mf_hid_p\", \n borrow= True)\n \n elif \"CSS\" in self.algorithm and self.gibbs_steps > 0: \n \n if self.num_hidden ==0: \n self.x_gibbs= theano.shared(np.ones([self.batch_size,self.num_vars],\n dtype=theano.config.floatX),\n borrow = True, name= \"x_gibbs\")","def __init__(self):\n MCMC.__init__(self)","def fisher_calc(\n self,\n qb,\n cbl,\n cls_input,\n cls_noise=None,\n cls_debias=None,\n cls_model=None,\n cond_noise=None,\n cond_criteria=None,\n likelihood=False,\n like_lmin=2,\n like_lmax=None,\n delta_beta_prior=None,\n null_first_cmb=False,\n use_precalc=True,\n windows=False,\n inv_fish=None,\n ):\n if cond_criteria is None:\n cond_criteria = np.inf\n well_cond = False\n\n pol_dim = 3 if self.pol else 1\n do_fg = \"fg_tt\" in cbl\n\n dkey = \"Dmat_obs_b\" if likelihood else \"Mmat\" if windows else \"Dmat_obs\"\n\n if getattr(self, dkey, None) is None or not use_precalc:\n Dmat_obs, Dmat_obs_b, dSdqb_mat1, Mmat, Mmat_mix = self.fisher_precalc(\n cbl,\n cls_input,\n cls_debias=cls_debias,\n likelihood=likelihood,\n windows=windows,\n )\n if use_precalc:\n self.Dmat_obs = Dmat_obs\n self.Dmat_obs_b = Dmat_obs_b\n self.dSdqb_mat1 = dSdqb_mat1\n self.Mmat = Mmat\n self.Mmat_mix = Mmat_mix\n else:\n if likelihood:\n Dmat_obs_b = self.Dmat_obs_b\n else:\n if windows:\n Mmat = self.Mmat\n Mmat_mix = self.Mmat_mix\n else:\n Dmat_obs = self.Dmat_obs\n dSdqb_mat1 = self.dSdqb_mat1\n\n delta_beta = 0.0\n if \"delta_beta\" in qb:\n delta_beta = qb[\"delta_beta\"][0]\n\n if not likelihood:\n dSdqb_mat1_freq = copy.deepcopy(dSdqb_mat1)\n\n if likelihood or windows or not cond_noise:\n well_cond = True\n cond_noise = None\n\n gmat_ell = OrderedDict()\n Dmat1 = OrderedDict()\n\n if cls_model is None:\n cls_model = self.get_model_spectra(\n qb, cbl, delta=True, cls_noise=cls_noise, cond_noise=cond_noise\n )\n\n mkeys = list(cls_model)\n for xname, (m0, m1) in self.map_pairs.items():\n # transfer function does not have crosses\n if xname not in cls_model[mkeys[0]]:\n continue\n gmat_ell[xname] = self.gmat_ell[xname]\n\n if well_cond:\n for spec in self.specs:\n # transfer function doesn't have eb/tb\n if \"total_{}\".format(spec) not in cls_model:\n continue\n Dmat1.setdefault(xname, OrderedDict())\n Dmat1[xname][spec] = cls_model[\"total_{}\".format(spec)][xname]\n\n if well_cond:\n Dmat1_mat = pt.dict_to_dmat(Dmat1)\n\n # Set up dSdqb frequency dependence\n for xname, (m0, m1) in self.map_pairs.items():\n # transfer function does not have crosses\n if xname not in cls_model[mkeys[0]]:\n continue\n\n if do_fg and not likelihood:\n # Add spectral index dependence\n # dSdqb now depends on qb (spec index) because\n # model is non-linear so cannot be precomputed.\n\n # get foreground at pivot point spectral index\n # and first derivative\n freq_scale0, freq_scale_deriv = xft.scale_dust(\n self.map_freqs[m0],\n self.map_freqs[m1],\n ref_freq=self.ref_freq,\n beta=self.beta_ref,\n deriv=True,\n )\n freq_scale = freq_scale0 + delta_beta * freq_scale_deriv\n freq_scale_ratio = freq_scale_deriv / freq_scale\n\n # scale foreground model by frequency scaling adjusted for beta\n for s1, sdat in dSdqb_mat1_freq[\"fg\"][xname].items():\n for s2, sdat2 in sdat.items():\n dSdqb_mat1_freq[\"fg\"][xname][s1][s2] *= freq_scale\n\n # build delta_beta term from frequency scaled model,\n # divide out frequeny scaling and apply derivative term\n for s1, sdat in dSdqb_mat1_freq[\"delta_beta\"][xname].items():\n sdat[s1] = cls_model[\"fg_{}\".format(s1)][xname] * freq_scale_ratio\n\n # Set up Dmat -- if it's not well conditioned, add noise to the\n # diagonal until it is.\n cond_iter = 0\n while not well_cond:\n cls_model = self.get_model_spectra(\n qb, cbl, delta=True, cls_noise=cls_noise, cond_noise=cond_noise\n )\n\n for xname, (m0, m1) in self.map_pairs.items():\n # transfer function does not have crosses\n if xname not in cls_model[mkeys[0]]:\n continue\n\n for spec in self.specs:\n # transfer function doesn't have eb/tb\n if \"total_{}\".format(spec) not in cls_model:\n continue\n Dmat1.setdefault(xname, OrderedDict())\n Dmat1[xname][spec] = cls_model[\"total_{}\".format(spec)][xname]\n\n Dmat1_mat = pt.dict_to_dmat(Dmat1)\n\n cond = np.abs(\n np.linalg.cond(Dmat1_mat[:, :, self.lmin :].swapaxes(0, -1))\n ).max()\n\n if cond > cond_criteria and cond_noise:\n cond_iter += 1\n # cond_noise iteration factor found through trial and error\n cond_noise *= 10 ** (cond_iter / 100.0)\n if cond_iter == 1:\n self.log(\n \"Condition criteria not met. \"\n \"Max Cond={:.0f}, Thresh={:.0f}\".format(cond, cond_criteria),\n \"debug\",\n )\n else:\n well_cond = True\n self.log(\n \"Condition criteria met. \"\n \"Max Cond={:.0f}, Thresh={:.0f}, Iter={:d}\".format(\n cond, cond_criteria, cond_iter\n ),\n \"debug\",\n )\n if cond_noise is not None:\n self.log(\"Cond_noise = {:.3e}\".format(cond_noise), \"debug\")\n\n # construct arrays from dictionaries\n Dmat1 = Dmat1_mat\n gmat = pt.dict_to_dmat(gmat_ell)\n if likelihood:\n Dmat_obs_b = pt.dict_to_dmat(Dmat_obs_b)\n else:\n if windows:\n Mmat = pt.dict_to_dmat(Mmat)\n if self.pol:\n Mmat_mix = pt.dict_to_dmat(Mmat_mix)\n else:\n Dmat_obs = pt.dict_to_dmat(Dmat_obs)\n dSdqb_mat1_freq = pt.dict_to_dsdqb_mat(dSdqb_mat1_freq, self.bin_def)\n\n # apply ell limits\n if likelihood:\n ell = slice(\n self.lmin if like_lmin is None else like_lmin,\n self.lmax + 1 if like_lmax is None else like_lmax + 1,\n )\n else:\n ell = slice(self.lmin, self.lmax + 1)\n Dmat1 = Dmat1[..., ell]\n if likelihood:\n Dmat_obs_b = Dmat_obs_b[..., ell]\n else:\n if windows:\n Mmat = Mmat[..., ell, :]\n if self.pol:\n Mmat_mix = Mmat_mix[..., ell, :]\n else:\n Dmat_obs = Dmat_obs[..., ell]\n dSdqb_mat1_freq = dSdqb_mat1_freq[..., ell]\n gmat = gmat[..., ell]\n\n self.Dmat1 = Dmat1\n\n lam, R = np.linalg.eigh(Dmat1.swapaxes(0, -1))\n bad = (lam <= 0).sum(axis=-1).astype(bool)\n if bad.sum():\n # exclude any ell's with ill-conditioned D matrix\n # this should happen only far from max like\n bad_idx = np.unique(np.where(bad)[0])\n bad_ells = np.arange(ell.start, ell.stop)[bad_idx]\n self.log(\n \"Found negative eigenvalues at ells {}\".format(bad_ells), \"warning\"\n )\n gmat[..., bad_idx] = 0\n inv_lam = 1.0 / lam\n Dinv = np.einsum(\"...ij,...j,...kj->...ik\", R, inv_lam, R).swapaxes(0, -1)\n\n if likelihood:\n # log(det(D)) = tr(log(D)), latter is numerically stable\n # compute log(D) by eigenvalue decomposition per ell\n log_lam = np.log(lam)\n Dlog = np.einsum(\"...ij,...j,...kj->...ik\", R, log_lam, R).swapaxes(0, -1)\n\n else:\n # compute ell-by-ell inverse\n # Dinv = np.linalg.inv(Dmat1.swapaxes(0, -1)).swapaxes(0, -1)\n\n # optimized matrix multiplication\n # there is something super weird about this whole matrix operation\n # that causes the computation of mats to take four times as long\n # if mat1 is not computed.\n eye = np.eye(len(gmat))\n mat1 = np.einsum(\"ij...,jk...->ik...\", eye, Dinv)\n mat2 = np.einsum(\"klm...,ln...->knm...\", dSdqb_mat1_freq, Dinv)\n mat = np.einsum(\"ik...,knm...->inm...\", mat1, mat2)\n\n if likelihood:\n # compute log likelihood as tr(g * (D^-1 * Dobs + log(D)))\n arg = np.einsum(\"ij...,jk...->ik...\", Dinv, Dmat_obs_b) + Dlog\n like = -np.einsum(\"iij,iij->\", gmat, arg) / 2.0\n\n # include priors in likelihood\n if \"delta_beta\" in qb and delta_beta_prior is not None:\n chi = (qb[\"delta_beta\"] - self.delta_beta_fix) / delta_beta_prior\n like -= chi ** 2 / 2.0\n\n if null_first_cmb:\n for spec in self.specs:\n stag = \"cmb_{}\".format(spec)\n if stag not in qb:\n continue\n chi = (qb[stag][0] - 1) / np.sqrt(1e-10)\n like -= chi ** 2 / 2.0\n\n return like\n\n # construct matrices for the qb and fisher terms,\n # and take the trace and sum over ell simultaneously\n if not windows:\n qb_vec = np.einsum(\"iil,ijkl,jil->k\", gmat, mat, Dmat_obs) / 2.0\n if not windows or (windows and inv_fish is None):\n fisher = np.einsum(\"iil,ijkl,jiml->km\", gmat, mat, dSdqb_mat1_freq) / 2\n\n if windows:\n if inv_fish is None:\n inv_fish = np.linalg.solve(fisher, np.eye(len(fisher)))\n\n # compute prefactors\n ells = np.arange(0, self.lmax + 1)\n norm = (2.0 * ells + 1.0) / 4.0 / np.pi\n\n # compute binning term\n arg = np.einsum(\"ij,kljm->klim\", inv_fish, mat)\n wbl = OrderedDict()\n bin_index = pt.dict_to_index(qb)\n spec_mask = pt.spec_mask(nmaps=self.num_maps)\n\n # compute window functions for each spectrum\n for k, (left, right) in bin_index.items():\n comp, spec = k.split(\"_\", 1)\n if comp not in [\"cmb\", \"fg\"]:\n continue\n\n # select bins for corresponding spectrum\n sarg = arg[:, :, left:right]\n\n # select the spectrum terms from the kernel matrix\n smat = spec_mask[spec][:, :, None, None] * Mmat\n if spec in [\"ee\", \"bb\"]:\n # add mixing terms\n mspec = \"bb\" if spec == \"ee\" else \"ee\"\n smat += spec_mask[mspec][:, :, None, None] * Mmat_mix\n\n # qb window function\n wbl1 = np.einsum(\"iil,ijkl,jilm->km\", gmat, sarg, smat) / 2.0 / norm\n\n # bin weighting, allowing for overlapping bin edges\n chi_bl = np.zeros_like(norm)\n for (l, r), w in zip(self.bin_def[k], self.bin_weights[k]):\n chi_bl[l:r] += w\n\n # check normalization\n cls_shape = self.cls_shape[\"fg\" if comp == \"fg\" else k][: len(norm)]\n self.log(\n \"{} qb window function normalization: {}\".format(\n k, np.sum(wbl1 * norm * chi_bl * cls_shape, axis=-1)\n ),\n \"debug\",\n )\n\n wbl[k] = wbl1\n\n return wbl\n\n bin_index = pt.dict_to_index(qb)\n\n if \"delta_beta\" in qb and delta_beta_prior is not None:\n # XXX need documentation for what happens here\n # for imposing the delta_beta prior\n sl = slice(*(bin_index[\"delta_beta\"]))\n d = 1.0 / delta_beta_prior ** 2\n qb_vec[sl] += d * self.delta_beta_fix\n fisher[sl, sl] += d\n\n if null_first_cmb:\n # blow up the fisher matrix for first bin\n # by implementing a tight prior\n for spec in self.specs:\n stag = \"cmb_{}\".format(spec)\n if stag in bin_index: # check for transfer function\n b0 = bin_index[stag][0]\n fisher[b0, b0] += 1e10\n qb_vec[b0] += 1e10\n\n # invert\n qb_vec = np.linalg.solve(fisher, qb_vec)\n inv_fish = np.linalg.solve(fisher, np.eye(len(qb_vec)))\n qb_vec = pt.arr_to_dict(qb_vec, qb)\n\n return qb_vec, inv_fish","def test_cov_q(self, ndlys=13):\n for d in self.d:\n d.flag_array[:] = False #ensure that there are no flags!\n d.select(times=np.unique(d.time_array)[:10], frequencies=d.freq_array[:16])\n for d_std in self.d_std:\n d_std.flag_array[:] = False\n d_std.select(times=np.unique(d_std.time_array)[:10], frequencies=d_std.freq_array[:16])\n self.ds = pspecdata.PSpecData(dsets=self.d, wgts=self.w, dsets_std=self.d_std)\n self.ds = pspecdata.PSpecData(dsets=self.d, wgts=self.w, dsets_std=self.d_std)\n Ntime = self.ds.Ntimes\n self.ds.set_Ndlys(ndlys)\n # Here is the analytic covariance matrix...\n chan_x, chan_y = np.meshgrid(range(self.ds.Nfreqs), range(self.ds.Nfreqs))\n cov_analytic = np.zeros((self.ds.spw_Ndlys, self.ds.spw_Ndlys), dtype=np.complex128)\n for alpha in range(self.ds.spw_Ndlys):\n for beta in range(self.ds.spw_Ndlys):\n cov_analytic[alpha, beta] = np.exp(-2j*np.pi*(alpha-beta)*(chan_x-chan_y)/self.ds.spw_Ndlys).sum()\n key1 = (0, 24, 38)\n key2 = (1, 25, 38)\n #print(cov_analytic)\n\n for input_data_weight in ['identity','iC', 'dayenu']:\n self.ds.set_weighting(input_data_weight)\n #check error raised\n if input_data_weight == 'dayenu':\n pytest.raises(ValueError,self.ds.R, key1)\n rpk = {'filter_centers':[0.],'filter_half_widths':[0.],'filter_factors':[0.]}\n self.ds.set_r_param(key1,rpk)\n self.ds.set_r_param(key2,rpk)\n for taper in taper_selection:\n qc = self.ds.cov_q_hat(key1,key2,model='dsets')\n self.assertTrue(np.allclose(np.array(list(qc.shape)),\n np.array([self.ds.Ntimes, self.ds.spw_Ndlys, self.ds.spw_Ndlys]), atol=1e-6))\n qc = self.ds.cov_q_hat(key1,key2,model='empirical')\n self.assertTrue(np.allclose(np.array(list(qc.shape)),\n np.array([self.ds.Ntimes, self.ds.spw_Ndlys, self.ds.spw_Ndlys]), atol=1e-6))\n\n \"\"\"\n Now test that analytic Error calculation gives Nchan^2\n \"\"\"\n self.ds.set_weighting('identity')\n qc = self.ds.cov_q_hat(key1, key2, model='dsets')\n self.assertTrue(np.allclose(qc,\n np.repeat(cov_analytic[np.newaxis, :, :], self.ds.Ntimes, axis=0), atol=1e-6))\n \"\"\"\n Test lists of keys\n \"\"\"\n self.ds.set_weighting('identity')\n qc=self.ds.cov_q_hat([key1], [key2], time_indices=[0], model='dsets')\n self.assertTrue(np.allclose(qc,\n np.repeat(cov_analytic[np.newaxis, :, :], self.ds.Ntimes, axis=0), atol=1e-6))\n self.assertRaises(ValueError, self.ds.cov_q_hat, key1, key2, time_indices=200)\n self.assertRaises(ValueError, self.ds.cov_q_hat, key1, key2, time_indices=\"watch out!\")","def holstein_mpo(n_bath, d_boson, e0, eks, vks):\n\n bN = np.diag(range(d_boson))\n bp = np.diag([np.sqrt(x) for x in range(1,d_boson)],k=-1) # -1 below diagonal\n bm = np.diag([np.sqrt(x) for x in range(1,d_boson)],k= 1) # 1 above diagonal \n Sx = np.array([[0.,1.],[1.,0.]])\n Sz = np.array([[1.,0.],[0.,-1.]])\n\n Dw = 3\n df = 2\n db = d_boson\n phys_bonds = [df] + [db]*n_bath\n\n L = n_bath + 1\n \n W = []\n\n ## MPO on spin site\n WS = np.zeros([1,df,df,self.Dw])\n WS[0,:,:,0] = e0*self.ops['SX'] # site energy\n WS[0,:,:,1] = self.ops['SZ'] # creation of fermion\n WS[0,:,:,2] = np.eye(df)\n W.append(Ws)\n\n ## MPO on bath sites\n Wb = np.zeros([self.Dw,db,db,self.Dw])\n Wb[0,:,:,0] = np.eye(db)\n Wb[1,:,:,1] = np.eye(db) # propagates SZ interaction term\n Wb[2,:,:,2] = np.eye(db)\n\n for i in range(L-1): \n Wb[1,:,:,0] = vks[i]*self.ops['b-'] + np.conj(vks[i])*self.ops['b+'] # interaction with spin\n Wb[2,:,:,0] = eks[i]*self.ops['bn'] # energy of boson \n W.append(Wb)\n \n\n WL = np.zeros([self.Dw,db,db,1])\n WL[0,:,:,0] = np.eye(db)\n WL[1,:,:,0] = vks[-1]*self.ops['b-'] + np.conj(vks[-1])*self.ops['b+'] # interaction with spin\n WL[2,:,:,0] = eks[-1]*self.ops['bn'] # energy of boson\n W.append(WL)\n\n return W","def vbmstep(self):\n for k in range(self.k):\n self.beta_k[k] = self.beta_0 + self.counts[k]\n self.m_k[k] = (1 / self.beta_k[k]) * (self.beta_0 * self.m_0 +\n self.counts[k] * self.means[k])\n\n tmp = (self.beta_0 * self.counts[k]) / (self.beta_0 + self.counts[k])\n tmp2 = (self.means[k] - self.m_0)\n tmp = np.linalg.inv(self.W_0) + self.counts[k] * self.covars[k] + tmp * tmp2 @ tmp2.T\n self.w_k[k] = np.linalg.inv(tmp)\n self.nu_k[k] = self.nu_0 + self.counts[k]\n self.alpha_k[k] = self.alpha_0[k] + self.counts[k]","def get_conditional_probs_asbf( p01, p10, v):\n\n ns,L =v.shape\n if ns != 2**L:\n raise ValueError\n #matrix of conitional probs.\n pi = np.zeros((ns,ns))\n p00 = 1-p10\n p11 = 1-p01\n for i in range(ns):\n #final state\n si = v[i,:]\n for j in range(i,ns):\n #initial state\n sj = v[j, :]\n #number of sites where 1->1 transition occurs, etc\n n11 = np.sum((si==1)*(sj==1))\n n10 = np.sum((si==1)*(sj==0))\n n01 = np.sum((si==0)*(sj==1))\n n00 = np.sum((si==0)*(sj==0))\n pi[i, j] = (p11**n11)*(p10**n10)*(p01**n01)*(p00**n00)\n pi[j,i] = (p11**n11)*(p10**n01)*(p01**n10)*(p00**n00)\n return pi","def mcmc(data, uncert=None, func=None, indparams=[],\n params=None, pmin=None, pmax=None, stepsize=None,\n prior=None, priorlow=None, priorup=None,\n numit=10, nchains=10, walk='demc', wlike=False,\n leastsq=True, chisqscale=False, grtest=True, burnin=0,\n thinning=1, plots=False, savefile=None, savemodel=None,\n comm=None, resume=False, log=None, rms=False):\n\n # Import the model function:\n if type(func) in [list, tuple, np.ndarray]:\n if func[0] != 'hack':\n if len(func) == 3:\n sys.path.append(func[2])\n exec('from %s import %s as func'%(func[1], func[0]))\n elif not callable(func):\n mu.error(\"'func' must be either, a callable, or an iterable (list, \"\n \"tuple, or ndarray) of strings with the model function, file, \"\n \"and path names.\", log)\n\n if np.ndim(params) == 1: # Force it to be 2D (one for each chain)\n params = np.atleast_2d(params)\n nparams = len(params[0]) # Number of model params\n ndata = len(data) # Number of data values\n # Set default uncertainties:\n if uncert is None:\n uncert = np.ones(ndata)\n # Set default boundaries:\n if pmin is None:\n pmin = np.zeros(nparams) - np.inf\n if pmax is None:\n pmax = np.zeros(nparams) + np.inf\n # Set default stepsize:\n if stepsize is None:\n stepsize = 0.1 * np.abs(params[0])\n # Set prior parameter indices:\n if (prior is None) or (priorup is None) or (priorlow is None):\n prior = priorup = priorlow = np.zeros(nparams) # Zero arrays\n iprior = np.where(priorlow != 0)[0]\n ilog = np.where(priorlow < 0)[0]\n\n nfree = np.sum(stepsize > 0) # Number of free parameters\n chainlen = int(np.ceil(numit/nchains)) # Number of iterations per chain\n ifree = np.where(stepsize > 0)[0] # Free parameter indices\n ishare = np.where(stepsize < 0)[0] # Shared parameter indices\n # Number of model parameters (excluding wavelet parameters):\n if wlike:\n mpars = nparams - 3\n else:\n mpars = nparams\n\n # Intermediate steps to run GR test and print progress report:\n intsteps = chainlen / 10\n\n # Allocate arrays with variables:\n numaccept = np.zeros(nchains) # Number of accepted proposal jumps\n outbounds = np.zeros((nchains, nfree), np.int) # Out of bounds proposals\n allparams = np.zeros((nchains, nfree, chainlen)) # Parameter's record\n if savemodel is not None:\n allmodel = np.zeros((nchains, ndata, chainlen)) # Fit model\n\n if resume:\n oldparams = np.load(savefile)\n nold = np.shape(oldparams)[2] # Number of old-run iterations\n allparams = np.dstack((oldparams, allparams))\n if savemodel is not None:\n allmodel = np.dstack((np.load(savemodel), allmodel))\n # Set params to the last-iteration state of the previous run:\n params = np.repeat(params, nchains, 0)\n params[:,ifree] = oldparams[:,:,-1]\n else:\n nold = 0\n\n # Set MPI flag:\n mpi = comm is not None\n\n if mpi:\n from mpi4py import MPI\n # Send sizes info to other processes:\n array1 = np.asarray([mpars, chainlen], np.int)\n mu.comm_bcast(comm, array1, MPI.INT)\n\n # DEMC parameters:\n gamma = 2.4 / np.sqrt(2*nfree)\n gamma2 = 0.001 # Jump scale factor of support distribution\n\n # Least-squares minimization:\n if leastsq:\n fitargs = (params[0], func, data, uncert, indparams, stepsize, pmin, pmax,\n prior, priorlow, priorup)\n fitchisq, dummy = mf.modelfit(params[0,ifree], args=fitargs)\n fitbestp = np.copy(params[0, ifree])\n mu.msg(1, \"Least-squares best-fitting parameters: \\n{:s}\\n\\n\".\n format(str(fitbestp)), log)\n\n # Replicate to make one set for each chain: (nchains, nparams):\n if np.shape(params)[0] != nchains:\n params = np.repeat(params, nchains, 0)\n # Start chains with an initial jump:\n for p in ifree:\n # For each free param, use a normal distribution: \n params[1:, p] = np.random.normal(params[0, p], stepsize[p], nchains-1)\n # Stay within pmin and pmax boundaries:\n params[np.where(params[:, p] < pmin[p]), p] = pmin[p]\n params[np.where(params[:, p] > pmax[p]), p] = pmax[p]\n \n # Update shared parameters:\n for s in ishare:\n params[:, s] = params[:, -int(stepsize[s])-1]\n\n # Calculate chi-squared for model using current params:\n models = np.zeros((nchains, ndata))\n if mpi:\n # Scatter (send) parameters to func:\n mu.comm_scatter(comm, params[:,0:mpars].flatten(), MPI.DOUBLE)\n # Gather (receive) evaluated models:\n mpimodels = np.zeros(nchains*ndata, np.double)\n mu.comm_gather(comm, mpimodels)\n # Store them in models variable:\n models = np.reshape(mpimodels, (nchains, ndata))\n else:\n for c in np.arange(nchains):\n fargs = [params[c, 0:mpars]] + indparams # List of function's arguments\n models[c] = func(*fargs)\n\n # Calculate chi-squared for each chain:\n currchisq = np.zeros(nchains)\n c2 = np.zeros(nchains) # No-Jeffrey's chisq\n for c in np.arange(nchains):\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\n currchisq[c], c2[c] = dwt.wlikelihood(params[c, mpars:], models[c]-data,\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n else:\n currchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n\n # Scale data-uncertainties such that reduced chisq = 1:\n if chisqscale:\n chifactor = np.sqrt(np.amin(currchisq)/(ndata-nfree))\n uncert *= chifactor\n # Re-calculate chisq with the new uncertainties:\n for c in np.arange(nchains):\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\n currchisq[c], c2[c] = dwt.wlikelihood(params[c,mpars:], models[c]-data,\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n else:\n currchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n if leastsq:\n fitchisq = currchisq[0]\n\n # Get lowest chi-square and best fitting parameters:\n bestchisq = np.amin(c2)\n bestp = np.copy(params[np.argmin(c2)])\n bestmodel = np.copy(models[np.argmin(c2)])\n\n if savemodel is not None:\n allmodel[:,:,0] = models\n\n # Set up the random walks:\n if walk == \"mrw\":\n # Generate proposal jumps from Normal Distribution for MRW:\n mstep = np.random.normal(0, stepsize[ifree], (chainlen, nchains, nfree))\n elif walk == \"demc\":\n # Support random distribution:\n support = np.random.normal(0, stepsize[ifree], (chainlen, nchains, nfree))\n # Generate indices for the chains such r[c] != c:\n r1 = np.random.randint(0, nchains-1, (nchains, chainlen))\n r2 = np.random.randint(0, nchains-1, (nchains, chainlen))\n for c in np.arange(nchains):\n r1[c][np.where(r1[c]==c)] = nchains-1\n r2[c][np.where(r2[c]==c)] = nchains-1\n\n # Uniform random distribution for the Metropolis acceptance rule:\n unif = np.random.uniform(0, 1, (chainlen, nchains))\n\n # Proposed iteration parameters and chi-square (per chain):\n nextp = np.copy(params) # Proposed parameters\n nextchisq = np.zeros(nchains) # Chi square of nextp \n\n # Start loop:\n mu.msg(1, \"Start MCMC chains ({:s})\".format(time.ctime()), log)\n for i in np.arange(chainlen):\n # Proposal jump:\n if walk == \"mrw\":\n jump = mstep[i]\n elif walk == \"demc\":\n jump = (gamma * (params[r1[:,i]]-params[r2[:,i]])[:,ifree] +\n gamma2 * support[i] )\n # Propose next point:\n nextp[:,ifree] = params[:,ifree] + jump\n\n # Check it's within boundaries: \n outpars = np.asarray(((nextp < pmin) | (nextp > pmax))[:,ifree])\n outflag = np.any(outpars, axis=1)\n outbounds += ((nextp < pmin) | (nextp > pmax))[:,ifree]\n for p in ifree:\n nextp[np.where(nextp[:, p] < pmin[p]), p] = pmin[p]\n nextp[np.where(nextp[:, p] > pmax[p]), p] = pmax[p]\n\n # Update shared parameters:\n for s in ishare:\n nextp[:, s] = nextp[:, -int(stepsize[s])-1]\n\n # Evaluate the models for the proposed parameters:\n if mpi:\n mu.comm_scatter(comm, nextp[:,0:mpars].flatten(), MPI.DOUBLE)\n mu.comm_gather(comm, mpimodels)\n models = np.reshape(mpimodels, (nchains, ndata))\n else:\n for c in np.where(~outflag)[0]:\n fargs = [nextp[c, 0:mpars]] + indparams # List of function's arguments\n models[c] = func(*fargs)\n\n # Calculate chisq:\n for c in np.where(~outflag)[0]:\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\n nextchisq[c], c2[c] = dwt.wlikelihood(nextp[c,mpars:], models[c]-data,\n (nextp[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n else:\n nextchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\n (nextp[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\n\n # Reject out-of-bound jumps:\n nextchisq[np.where(outflag)] = np.inf\n # Evaluate which steps are accepted and update values:\n accept = np.exp(0.5 * (currchisq - nextchisq))\n accepted = accept >= unif[i]\n if i >= burnin:\n numaccept += accepted\n # Update params and chi square:\n params [accepted] = nextp [accepted]\n currchisq[accepted] = nextchisq[accepted]\n\n # Check lowest chi-square:\n if np.amin(c2) < bestchisq:\n bestp = np.copy(params[np.argmin(c2)])\n bestmodel = np.copy(models[np.argmin(c2)])\n bestchisq = np.amin(c2)\n\n # Store current iteration values:\n allparams[:,:,i+nold] = params[:, ifree]\n if savemodel is not None:\n models[~accepted] = allmodel[~accepted,:,i+nold-1]\n allmodel[:,:,i+nold] = models\n \n # Print intermediate info:\n if ((i+1) % intsteps == 0) and (i > 0):\n mu.progressbar((i+1.0)/chainlen, log)\n mu.msg(1, \"Out-of-bound Trials:\\n {:s}\".\n format(np.sum(outbounds, axis=0)), log)\n mu.msg(1, \"Best Parameters: (chisq={:.4f})\\n{:s}\".\n format(bestchisq, str(bestp)), log)\n\n # Gelman-Rubin statistic:\n if grtest and (i+nold) > burnin:\n psrf = gr.convergetest(allparams[:, :, burnin:i+nold+1:thinning])\n mu.msg(1, \"Gelman-Rubin statistic for free parameters:\\n{:s}\".\n format(psrf), log)\n if np.all(psrf < 1.01):\n mu.msg(1, \"All parameters have converged to within 1% of unity.\", log)\n # Save current results:\n if savefile is not None:\n np.save(savefile, allparams[:,:,0:i+nold])\n if savemodel is not None:\n np.save(savemodel, allmodel[:,:,0:i+nold])\n\n # Stack together the chains:\n allstack = allparams[0, :, burnin:]\n for c in np.arange(1, nchains):\n allstack = np.hstack((allstack, allparams[c, :, burnin:]))\n # And the models:\n if savemodel is not None:\n modelstack = allmodel[0,:,burnin:]\n for c in np.arange(1, nchains):\n modelstack = np.hstack((modelstack, allmodel[c, :, burnin:]))\n\n # Print out Summary:\n mu.msg(1, \"\\nFin, MCMC Summary:\\n------------------\", log)\n\n nsample = (chainlen-burnin)*nchains # This sample\n ntotal = (nold+chainlen-burnin)*nchains\n BIC = bestchisq + nfree*np.log(ndata)\n redchisq = bestchisq/(ndata-nfree)\n sdr = np.std(bestmodel-data)\n\n fmtlen = len(str(ntotal))\n mu.msg(1, \"Burned in iterations per chain: {:{}d}\".\n format(burnin, fmtlen), log, 1)\n mu.msg(1, \"Number of iterations per chain: {:{}d}\".\n format(chainlen, fmtlen), log, 1)\n mu.msg(1, \"MCMC sample size: {:{}d}\".\n format(nsample, fmtlen), log, 1)\n mu.msg(resume, \"Total MCMC sample size: {:{}d}\".\n format(ntotal, fmtlen), log, 1)\n mu.msg(1, \"Acceptance rate: {:.2f}%\\n \".\n format(np.sum(numaccept)*100.0/nsample), log, 1)\n\n meanp = np.mean(allstack, axis=1) # Parameters mean\n uncertp = np.std(allstack, axis=1) # Parameter standard deviation\n mu.msg(1, \"Best-fit params Uncertainties Signal/Noise Sample \"\n \"Mean\", log, 1)\n for i in np.arange(nfree):\n mu.msg(1, \"{: 15.7e} {: 15.7e} {:12.2f} {: 15.7e}\".\n format(bestp[ifree][i], uncertp[i],\n np.abs(bestp[ifree][i])/uncertp[i], meanp[i]), log, 1)\n\n if leastsq and np.any(np.abs((bestp[ifree]-fitbestp)/fitbestp) > 1e-08):\n np.set_printoptions(precision=8)\n mu.warning(\"MCMC found a better fit than the minimizer:\\n\"\n \" MCMC best-fitting parameters: (chisq={:.8g})\\n {:s}\\n\"\n \" Minimizer best-fitting parameters: (chisq={:.8g})\\n\"\n \" {:s}\".format(bestchisq, str(bestp[ifree]), \n fitchisq, str(fitbestp)), log)\n\n fmtl = len(\"%.4f\"%BIC) # Length of string formatting\n mu.msg(1, \" \", log)\n if chisqscale:\n mu.msg(1, \"sqrt(reduced chi-squared) factor: {:{}.4f}\".\n format(chifactor, fmtl), log, 1)\n mu.msg(1, \"Best-parameter's chi-squared: {:{}.4f}\".\n format(bestchisq, fmtl), log, 1)\n mu.msg(1, \"Bayesian Information Criterion: {:{}.4f}\".\n format(BIC, fmtl), log, 1)\n mu.msg(1, \"Reduced chi-squared: {:{}.4f}\".\n format(redchisq, fmtl), log, 1)\n mu.msg(1, \"Standard deviation of residuals: {:.6g}\\n\".format(sdr), log, 1)\n\n\n if rms:\n rms, rmse, stderr, bs = ta.binrms(bestmodel-data)\n\n if plots:\n print(\"Plotting figures ...\")\n # Extract filename from savefile:\n if savefile is not None:\n if savefile.rfind(\".\") == -1:\n fname = savefile[savefile.rfind(\"/\")+1:] # Cut out file extention.\n else:\n fname = savefile[savefile.rfind(\"/\")+1:savefile.rfind(\".\")]\n else:\n fname = \"MCMC\"\n # Trace plot:\n mp.trace(allstack, thinning=thinning, savefile=fname+\"_trace.png\",\n sep=np.size(allstack[0])/nchains)\n # Pairwise posteriors:\n mp.pairwise(allstack, thinning=thinning, savefile=fname+\"_pairwise.png\")\n # Histograms:\n mp.histogram(allstack, thinning=thinning, savefile=fname+\"_posterior.png\")\n # RMS vs bin size:\n if rms:\n mp.RMS(bs, rms, stderr, rmse, binstep=len(bs)/500+1,\n savefile=fname+\"_RMS.png\")\n if indparams != [] and np.size(indparams[0]) == ndata:\n mp.modelfit(data, uncert, indparams[0], bestmodel,\n savefile=fname+\"_model.png\")\n\n # Save definitive results:\n if savefile is not None:\n np.save(savefile, allparams)\n if savemodel is not None:\n np.save(savemodel, allmodel)\n\n return allstack, bestp","def __init__(self, M, principal_part=None, C=None, prec=53, Lv=None):\n\n if(isinstance(M, type(Newforms(1, 12)[0]))):\n self._init_from_newform_(M, principal_part, C, prec)\n return\n if C is not None:\n if M.rank() != len(C.keys()):\n raise ValueError(\"Coefficient vector of wrong format! Got length={0}\".format(len(C)))\n # We inherit symmetry from the space\n self._sym_type = M._sym_type\n self._class_name = \"VVHarmonicWeakMaassFormElement\"\n AutomorphicFormElement.__init__(self, M, C=C, prec=prec, principal_part=principal_part)\n\n self._verbose = self._space._verbose\n self.maxdigs = prec # the number of digits needed to be displayed to print all digits of the coefficients\n if Lv is not None:\n self._Lv = Lv\n else:\n self._Lv = dict()\n self.Cp0 = 0\n self.Cp1 = 0\n self.Cm = 0\n # We also find the space corresponding to this space via inverse of xi_k and Shimura corr.\n t = QQ(RR(M.weight())) - QQ(1) / QQ(2)\n if(is_int(t)):\n k = QQ(3) - QQ(2) * QQ(RR(M.weight()))\n if self._verbose > 0:\n print(\"k={0}\".format(k))\n N = self.space().multiplier().weil_module().N()\n self.shim_corr = Newforms(N, k, names='a') # .new_subspace()\n else:\n self.shim_corr = None","def hidden_markov_model_probability(data,\n fitness_prior=flat_fitness_prior,\n N_w_prior=flat_N_w_prior,\n mutation_object=True):\n\n if mutation_object is True:\n trajectories = data.data\n else:\n trajectories = data\n\n ind_likelihood = []\n for traj in trajectories:\n int_s = []\n for s in fitness_prior[0, :]:\n int_N_w = []\n for N_w in N_w_prior[0, :]:\n int_N_w.append(\n hidden_markov_conditional_s_N(traj,\n fitness=s, N_w=N_w)\n )\n marginalised_N_w = np.trapz(x=N_w_prior[0, :],\n y=int_N_w*N_w_prior[1, :])\n int_s.append(marginalised_N_w)\n\n marginalised_likelihood = np.trapz(x=fitness_prior[0, :],\n y=int_s*fitness_prior[1, :])\n ind_likelihood.append(marginalised_likelihood)\n \n mutation_prob = np.product(ind_likelihood)\n\n if mutation_object is True:\n # return updated model_comparison object \n data.bd_prob = mutation_prob\n return data\n else:\n # return marginalised likelihood.\n return mutation_prob","def mcmc(guess, nu, D, Ninv, beam_mat, models_fit, label=None, nwalkers=50, burn=500, steps=1000, save=False):\n # Define starting points\n ndim = guess.size\n pos = [guess*(1.+0.1*np.random.randn(ndim)) for i in range(nwalkers)]\n \n # Run emcee sampler\n sampler = emcee.EnsembleSampler( nwalkers, ndim, lnprob, \n args=(nu, D, Ninv, beam_mat, models_fit) )\n sampler.run_mcmc(pos, burn+steps)\n samples = sampler.chain[:, burn:, :].reshape((-1, ndim))\n \n \"\"\"\n # Plot the chains, including burn-in\n fig, axes = plt.subplots(figsize=(13, ndim*3.), nrows=ndim)\n labels = [r'$\\beta$', r'$T_d$', r'$\\alpha_s$']\n\n for i, (ax, lab) in enumerate(zip(axes, labels)):\n _ = ax.plot(sampler.chain[:,:,i].T, c='k', alpha=0.2)\n ax.axhline(y=guess[i], c='r')\n ax.axvline(x=burn, c='r')\n ax.set_title(lab)\n fig.savefig('chains_' + label + '.png')\n plt.close('all')\n \"\"\"\n\n # Save chains to file\n if save:\n np.savetxt('samples.dat', samples)\n \n # Summary statistics for fitted parameters\n # Dust model\n beta_out = np.median(samples, axis=0)\n ndust = 0\n if (models_fit[0] == 'mbb'):\n ndust = 2\n dust_params = beta_out[0:2]\n else:\n raise NotImplementedError(\"Code not configured to fit HD16 models.\")\n \n # Synchrotron power law\n if (models_fit[1] == 'pow'):\n sync_params = beta_out[ndust:ndust+1]\n else:\n raise ValueError(\"Synchrotron model '%s' not recognized.\" % models_fit[1])\n \n # CMB\n cmb_params = np.array([])\n \n # Return summary statistics and samples\n return dust_params, sync_params, cmb_params, samples","def construct_model():\n import lbann\n\n # Layer graph\n input = lbann.Input(target_mode='N/A', name='inp_data')\n # data is 64*64*4 images + 15 scalar + 5 param\n #inp_slice = lbann.Slice(input, axis=0, slice_points=\"0 16399 16404\",name='inp_slice')\n inp_slice = lbann.Slice(input, axis=0, slice_points=str_list([0,args.ydim,args.ydim+5]),name='inp_slice')\n gt_y = lbann.Identity(inp_slice,name='gt_y')\n gt_x = lbann.Identity(inp_slice, name='gt_x') #param not used\n\n zero = lbann.Constant(value=0.0,num_neurons='1',name='zero')\n one = lbann.Constant(value=1.0,num_neurons='1',name='one')\n\n z_dim = 20 #Latent space dim\n\n z = lbann.Gaussian(mean=0.0,stdev=1.0, neuron_dims=\"20\")\n model = macc_models.MACCWAE(args.zdim,args.ydim,cf=args.mcf,use_CNN=args.useCNN)\n d1_real, d1_fake, d_adv, pred_y = model(z,gt_y)\n\n d1_real_bce = lbann.SigmoidBinaryCrossEntropy([d1_real,one],name='d1_real_bce')\n d1_fake_bce = lbann.SigmoidBinaryCrossEntropy([d1_fake,zero],name='d1_fake_bce')\n d_adv_bce = lbann.SigmoidBinaryCrossEntropy([d_adv,one],name='d_adv_bce')\n img_loss = lbann.MeanSquaredError([pred_y,gt_y])\n rec_error = lbann.L2Norm2(lbann.WeightedSum([pred_y,gt_y], scaling_factors=\"1 -1\"))\n\n layers = list(lbann.traverse_layer_graph(input))\n # Setup objective function\n weights = set()\n src_layers = []\n dst_layers = []\n for l in layers:\n if(l.weights and \"disc0\" in l.name and \"instance1\" in l.name):\n src_layers.append(l.name)\n #freeze weights in disc2\n if(l.weights and \"disc1\" in l.name):\n dst_layers.append(l.name)\n for idx in range(len(l.weights)):\n l.weights[idx].optimizer = lbann.NoOptimizer()\n weights.update(l.weights)\n l2_reg = lbann.L2WeightRegularization(weights=weights, scale=1e-4)\n d_adv_bce = lbann.LayerTerm(d_adv_bce,scale=0.01)\n obj = lbann.ObjectiveFunction([d1_real_bce,d1_fake_bce,d_adv_bce,img_loss,rec_error,l2_reg])\n # Initialize check metric callback\n metrics = [lbann.Metric(img_loss, name='recon_error')]\n #pred_y = macc_models.MACCWAE.pred_y_name\n callbacks = [lbann.CallbackPrint(),\n lbann.CallbackTimer(),\n lbann.CallbackSaveModel(dir=args.dump_models),\n lbann.CallbackReplaceWeights(source_layers=list2str(src_layers),\n destination_layers=list2str(dst_layers),\n batch_interval=2)]\n\n if(args.ltfb_batch_interval > 0) :\n callbacks.append(lbann.CallbackLTFB(batch_interval=args.ltfb_batch_interval,metric='recon_error',\n low_score_wins=True,\n exchange_hyperparameters=True))\n\n # Construct model\n return lbann.Model(args.num_epochs,\n serialize_io=True,\n weights=weights,\n layers=layers,\n metrics=metrics,\n objective_function=obj,\n callbacks=callbacks)","def camb_Pk(self,\n z = 0.,\n k = np.logspace(-4., 2., 1001),\n nonlinear = False,\n halofit = 'mead2020',\n var_1 = 'tot',\n var_2 = 'tot',\n share_delta_neff = True,\n **kwargs\n ):\n # Neutrino part\n #num_nu_massless – (float64) Effective number of massless neutrinos\n #num_nu_massive – (integer) Total physical (integer) number of massive neutrino species\n #nu_mass_eigenstates – (integer) Number of non-degenerate mass eigenstates\n #nu_mass_degeneracies – (float64 array) Degeneracy of each distinct eigenstate\n #nu_mass_fractions – (float64 array) Mass fraction in each distinct eigenstate\n #nu_mass_numbers – (integer array) Number of physical neutrinos per distinct eigenstate\n nu_mass_eigen = len(np.unique([mm for mm in self.M_nu])) if np.any(self.M_nu!=0.) else 0\n nu_mass_numbers = [list(self.M_nu).count(x) for x in set(list(self.M_nu))]\n nu_mass_numbers = sorted(nu_mass_numbers,reverse=True) if np.any(self.M_nu!=0.) else [0]\n # Set parameters\n cambparams = {\n 'num_nu_massive': self.massive_nu,\n 'num_nu_massless': self.massless_nu,\n 'nu_mass_eigenstates': nu_mass_eigen, \n 'nu_mass_numbers': nu_mass_numbers,\n 'nnu': self.N_eff,\n 'omnuh2': self.omega_nu_tot,\n 'ombh2': self.omega_b,\n 'omch2': self.omega_cdm+self.omega_wdm_tot,\n 'omk': self.Omega_K,\n 'H0': 100.*self.h,\n 'As': self.As,\n 'ns': self.ns,\n 'w': self.w0,\n 'wa': self.wa,\n 'TCMB': self.T_cmb,\n 'tau': self.tau,\n 'share_delta_neff':True,\n 'dark_energy_model':'DarkEnergyPPF'}\n # kwargs\n for key, value in kwargs.items():\n if not key in cambparams: cambparams[key] = value\n params = camb.set_params(**cambparams)\n\n # Redshifts\n z = np.atleast_1d(z)\n nz = len(z)\n\n # Possible components to use\n components = {'tot' : 'delta_tot',\n 'cdm' : 'delta_cdm',\n 'b' : 'delta_baryon',\n 'nu' : 'delta_nu',\n 'cb' : 'delta_nonu',\n 'gamma': 'delta_photon',\n 'v_cdm': 'v_newtonian_cdm',\n 'v_b' : 'v_newtonian_baryon',\n 'Phi' : 'Weyl'} # Weyl: (phi+psi)/2 is proportional to lensing potential\n\n # Number of points (according to logint)\n logint = 100\n npoints = int(logint*np.log10(k.max()/k.min()))\n dlogk = 2.*np.log10(k.max()/k.min())/npoints\n\n # Halofit version\n if nonlinear == True:\n params.NonLinearModel.set_params(halofit_version=halofit)\n params.NonLinear = camb.model.NonLinear_both\n\n # Computing spectra\n params.set_matter_power(redshifts=z,kmax=k.max()*10**dlogk,silent=True,k_per_logint=0,accurate_massive_neutrino_transfers=True)\n results = camb.get_results(params)\n kh, z, pkh = results.get_matter_power_spectrum(minkh = k.min()*10.**-dlogk, maxkh = k.max()*10**dlogk, npoints = npoints, var1 = components[var_1], var2 = components[var_2])\n\n # Interpolation to the required scales k's\n # I use UnivariateSpline because it makes good extrapolation\n pk = np.zeros((nz,len(np.atleast_1d(k))))\n for iz in range(nz):\n lnpower = si.InterpolatedUnivariateSpline(kh, np.log(pkh[iz]), k=3, ext=0, check_finite=False)\n pk[iz] = np.exp(lnpower(k))\n\n return k, pk","def init_P_PHM_GIVEN_PHI():\n global P_PHM_GIVEN_PHI\n for i in INTERFACE_LEVEL_ACTIONS: # ui\n P_PHM_GIVEN_PHI[i] = collections.OrderedDict()\n for j in INTERFACE_LEVEL_ACTIONS: # um\n if i == j:\n # try to weight the true command more for realistic purposes. Can be offset by using a high UM_GIVEN_UI_NOISE\n P_PHM_GIVEN_PHI[i][j] = 1.0\n else:\n # P_PHM_GIVEN_PHI[i][j] = np.random.random()*UM_GIVEN_UI_NOISE#IF UM_GIVEN_UI_NOISE is 0, then the p(um|ui) is a deterministic mapping\n P_PHM_GIVEN_PHI[i][j] = 0.0\n\n delta_dist = np.array(P_PHM_GIVEN_PHI[i].values())\n uniform_dist = (1.0 / len(INTERFACE_LEVEL_ACTIONS)) * np.ones(len(INTERFACE_LEVEL_ACTIONS))\n blended_dist = (1 - PHM_GIVEN_PHI_NOISE) * delta_dist + PHM_GIVEN_PHI_NOISE * uniform_dist # np.array\n for index, j in enumerate(INTERFACE_LEVEL_ACTIONS):\n P_PHM_GIVEN_PHI[i][j] = blended_dist[index]","def _build_precomputed_data(self):\n if self.num_sampled == 0:\n self._K_chol = numpy.array([])\n self._K_inv_y = numpy.array([])\n else:\n covariance_matrix = python_utils.build_covariance_matrix(\n self._covariance,\n self._points_sampled,\n noise_variance=self._points_sampled_noise_variance,\n )\n\n C = self._build_integrated_term_maxtrix(self._covariance, self._points_sampled)\n self._K_Inv = numpy.linalg.inv(covariance_matrix)\n self._K_C = numpy.empty((covariance_matrix.shape[0],covariance_matrix.shape[0]))\n self._K_C = numpy.multiply(C, self._K_Inv)\n self._K_chol = scipy.linalg.cho_factor(covariance_matrix, lower=True, overwrite_a=True)\n self._K_inv_y = scipy.linalg.cho_solve(self._K_chol, self._points_sampled_value)\n self._marginal_mean_mat = self._build_marginal_matrix_mean()\n self._marginal_mean_mat_gradient = self._build_marginal_matrix_mean_gradient()","def __init__(self, p_control = 0, p_treatment = 0, n_control = 0, n_treatment = 0, power = None, alpha = 0.05):\n self.p_control = p_control\n self.p_treatment = p_treatment\n\n self.n_control = n_control\n self.n_treatment = n_treatment\n\n self.var_control = 1 * p_control * (1 - p_control)\n self.var_treatment = 1 * p_treatment * (1 - p_treatment)\n\n self.norm_null = None\n self.norm_alt = None\n\n self.binom_null = None\n self.binom_alt = None\n\n self.binom_control = None\n self.binom_treatment = None\n\n self.confidence_control = None\n self.confidence_treatment = None\n\n if n_control > 0 and n_treatment > 0 and p_control > 0 and p_treatment > 0:\n control = self.p_control * self.n_control\n treatment = self.p_treatment * self.n_treatment\n sample = self.n_control + self.n_treatment\n\n self.p_sample = (control + treatment) / sample\n else:\n self.p_sample = None\n\n if power == 1:\n print('Sample size approaches infinity as power approaches 1, so 1 is an invalid power vlaue. Changing power to 0.99.')\n self.power = 0.99\n elif power == 0:\n print('Sample size is undefined at power of 0, so 0 is an invalid power value. Changing power to 0.01.')\n self.power = 0.01\n else:\n self.power = power\n\n self.alpha = alpha\n self.p_value = None","def batch_kl_sample_gmm_pmap(keys_bxsx2, z_mean_bxu, z_logvar_bxu, resps_c,\n gmm_z_mean_cxu, gmm_z_logvar_cxu, varmin):\n ndevs = 8\n B, S, _ = keys_bxsx2.shape\n keys_8xbd8xsx2 = np.reshape(keys_bxsx2, (ndevs, B // ndevs, S, 2))\n z_mean_8xbd8xu = np.reshape(z_mean_bxu, (ndevs, B // ndevs, -1))\n z_logvar_8xbd8xu = np.reshape(z_logvar_bxu, (ndevs, B // ndevs, -1))\n\n # Shard the memory, note the type that goes in vs comes out of the pmap'd lambda.\n keys_8xbd8xsx2 = pmap(lambda x : x)(keys_8xbd8xsx2)\n z_mean_8xbd8xu = pmap(lambda x: x)(z_mean_8xbd8xu)\n z_logvar_8xbd8xu = pmap(lambda x: x)(z_logvar_8xbd8xu)\n\n return batch_kl_sample_gmm_pmap_pre(keys_8xbd8xsx2, z_mean_8xbd8xu, z_logvar_8xbd8xu,\n resps_c, gmm_z_mean_cxu,\n gmm_z_logvar_cxu, varmin)","def construct_param_dict(params,K_RC,K_CP,m_P):\n ###scaling constants\n w=params['w']\n pd=params['pd'] # in 3D and 0.21 in 2D\n pv=params['pv']\n Er=params['Er'] ;Ek=params['Ek']\n ER=params['ER'];EC=params['EC'];EP=params['EP'];\n Eq1=params['Eq1'];Eq2=params['Eq2']\n\n\n #capture success function\n a = params['a']\n b = params['b']\n c = params['c']\n formC = params['formC']\n formPC = params['formPC']\n formPR = params['formPR']\n \n ###variables\n TR= params['TR'] ;TC= params['TC'];TP=params['TP'];D_R= params['D_R']; D_C= params['D_C']\n K_RP=K_RC*K_CP\n fmC=params['fmC'];thermyR=params['thermyR']\n thermyC=params['thermyC'];thermyP=params['thermyP']\n fmPR=params['fmPR']\n fmPC=params['fmPC']\n m_C = K_CP*m_P;m_R = K_RP*m_P\n ###normalization constants and boltzmann constant\n r0 = params['r0']\n k0 = params['k0'] # will depend on the productivity of the habitat\n a01 = a02 = params['a012'] # will depedend on the dimension of the habitat \n a03 = params['a03']\n d0= params['d0']\n q10 = params['q10'];q20 = params['q20'];\n v0R = params['v0R'];v0C =params['v0C'];v0P =params['v0P'];k = b_k\n hC0 = params['hC0'];hP0 = params['hP0'] \n \n #intrapopulation parameters\n q1=set_q1(q10,m_C,w,Eq1,TR,k)\n q2=set_q2(q20,m_P,w,Eq2,TC,k)\n K=set_K(k0,m_R,w,Ek,TR,k)\n r=set_r(r0,m_R,w,Er,TR,k)\n\n #interpopulation parameters\n a1=set_alfa(m_C,a01,K_RC,pv,pd,TR,TC,ER,EC,D_R,v0R,v0C,g,alfa,fmC,thermyR,thermyC,k,a,b,c,formC)\n a2=set_alfa(m_P,a02,K_RP,pv,pd,TR,TP,ER,EP,D_R,v0R,v0P,g,alfa,fmPR,thermyR,thermyP,k,a,b,c,formPR)\n a3=set_alfa(m_P,a03,K_CP,pv,pd,TC,TP,EC,EP,D_C,v0C,v0P,g,alfa,fmPC,thermyC,thermyP,k,a,b,c,formPC)\n\n t_hp = set_th(hP0,m_P,w,EP,k,TP)\n t_hc = set_th(hC0,m_C,w,EC,k,TC)\n param_dict={'q1':q1,'q2':q2,'K':K,'r':r,'a1':a1,'a2':a2,'a3':a3,'t_hp':t_hp,'t_hc':t_hc}\n \n return param_dict","def derive(args):\n\n config_file = args.setupfn\n conf_base = os.path.basename(config_file).split('.')[0]\n statfile = os.path.join(args.outputdir,\n \"{}_radvel.stat\".format(conf_base))\n status = load_status(statfile)\n\n msg = \"Multiplying mcmc chains by physical parameters for {}\".format(\n conf_base\n )\n print(msg)\n\n assert status.getboolean('mcmc', 'run'), \"Must run MCMC before making tables\"\n\n P, post = radvel.utils.initialize_posterior(config_file)\n post = radvel.posterior.load(status.get('fit', 'postfile'))\n chains = pd.read_csv(status.get('mcmc', 'chainfile'))\n\n try:\n mstar = np.random.normal(\n loc=P.stellar['mstar'], scale=P.stellar['mstar_err'],\n size=len(chains)\n )\n except AttributeError:\n print(\"Unable to calculate derived parameters, stellar parameters not defined the config file.\")\n return\n\n if (mstar <= 0.0).any():\n num_nan = np.sum(mstar <= 0.0)\n nan_perc = float(num_nan) / len(chains)\n mstar[mstar <= 0] = np.abs(mstar[mstar <= 0])\n print(\"WARNING: {} ({:.2f} %) of Msini samples are NaN. The stellar mass posterior may contain negative \\\nvalues. Interpret posterior with caution.\".format(num_nan, nan_perc))\n\n # Convert chains into synth basis\n synthchains = chains.copy()\n for par in post.params.keys():\n if not post.params[par].vary:\n synthchains[par] = post.params[par].value\n\n synthchains = post.params.basis.to_synth(synthchains)\n\n savestate = {'run': True}\n outcols = []\n for i in np.arange(1, P.nplanets + 1, 1):\n # Grab parameters from the chain\n def _has_col(key):\n cols = list(synthchains.columns)\n return cols.count('{}{}'.format(key, i)) == 1\n\n def _get_param(key):\n if _has_col(key):\n return synthchains['{}{}'.format(key, i)]\n else:\n return P.params['{}{}'.format(key, i)].value\n\n def _set_param(key, value):\n chains['{}{}'.format(key, i)] = value\n\n def _get_colname(key):\n return '{}{}'.format(key, i)\n\n per = _get_param('per')\n k = _get_param('k')\n e = _get_param('e')\n\n mpsini = radvel.utils.Msini(k, per, mstar, e, Msini_units='earth')\n _set_param('mpsini', mpsini)\n outcols.append(_get_colname('mpsini'))\n\n mtotal = mstar + (mpsini * c.M_earth.value) / c.M_sun.value # get total star plus planet mass\n a = radvel.utils.semi_major_axis(per, mtotal) # changed from mstar to mtotal\n \n _set_param('a', a)\n outcols.append(_get_colname('a'))\n\n musini = (mpsini * c.M_earth) / (mstar * c.M_sun)\n _set_param('musini', musini)\n outcols.append(_get_colname('musini'))\n\n try:\n rp = np.random.normal(\n loc=P.planet['rp{}'.format(i)],\n scale=P.planet['rp_err{}'.format(i)],\n size=len(chains)\n )\n\n _set_param('rp', rp)\n _set_param('rhop', radvel.utils.density(mpsini, rp))\n\n outcols.append(_get_colname('rhop'))\n except (AttributeError, KeyError):\n pass\n\n # Get quantiles and update posterior object to median\n # values returned by MCMC chains\n quantiles = chains.quantile([0.159, 0.5, 0.841])\n csvfn = os.path.join(args.outputdir, conf_base+'_derived_quantiles.csv')\n quantiles.to_csv(csvfn, columns=outcols)\n\n # saved derived paramters to posterior file\n postfile = os.path.join(args.outputdir,\n '{}_post_obj.pkl'.format(conf_base))\n post.derived = quantiles[outcols]\n post.writeto(postfile)\n savestate['quantfile'] = os.path.relpath(csvfn)\n\n csvfn = os.path.join(args.outputdir, conf_base+'_derived.csv.bz2')\n chains.to_csv(csvfn, columns=outcols, compression='bz2')\n savestate['chainfile'] = os.path.relpath(csvfn)\n\n print(\"Derived parameters:\", outcols)\n\n save_status(statfile, 'derive', savestate)","def compute_bce(num_bidders, max_value, value_pdf, num_samples=100, max_trials=20, random_seed=1232, solver_str='COIN'):\r\n\r\n status = 0 # whether the LP was feasible\r\n trials = 0 # counter on sampling trials\r\n random.seed(random_seed) # seed the random generator\r\n while status != 1 and trials < max_trials: # while we have not found a feasible LP\r\n\r\n prob = plp.LpProblem(\"BCE\", plp.LpMinimize) # create an LP instance\r\n\r\n # Sample a set of bid vectors to try to create a BCE with just them\r\n bid_vectors = product(range(max_value + 1), repeat=num_bidders)\r\n pool = tuple(bid_vectors)\r\n indices = random.sample(range(len(pool)), min(num_samples, len(pool)))\r\n sampled_bid_vectors = set([pool[i] for i in indices])\r\n\r\n # Variables are of the form (v,b) where v is a value in {0,...,max_value}\r\n # and b is a bid vector in the sampled set\r\n lp_var_keys = product(*[range(max_value + 1), sampled_bid_vectors])\r\n\r\n # Create the psi variables which correspond to Pr[b | v] for each (v,b)\r\n # pair\r\n psi_vars = plp.LpVariable.dicts('psi', lp_var_keys, lowBound=0)\r\n\r\n # Creating the best response constraints\r\n devs = product(\r\n *[range(max_value + 1), range(max_value + 1), range(0, num_bidders)])\r\n for (cur_bid, dev_bid, bidder_id) in devs: # for all b_i*, b_i', i\r\n if cur_bid != dev_bid: # if b_i* \\neq b_i'\r\n # Create all the terms of the form Pr[b | v] * pi(v) * (U_i' - U_i)\r\n # for all b \\in S, such that b_i = b_i*\r\n dev_terms = [\r\n deviation_term(dev_bid, bidder_id, v, bids,\r\n psi_vars[(v, bids)], value_pdf[v])\r\n for (v, bids) in product(*[range(max_value + 1), sampled_bid_vectors])\r\n if bids[bidder_id] == cur_bid]\r\n # Add these terms to create the best response constraint\r\n prob += plp.lpSum(dev_terms) <= 0, \"Dev_{}_{}_{}\".format(\r\n cur_bid, dev_bid, bidder_id)\r\n\r\n # Constraint that Pr[b | v] is a distribution for each fixed v\r\n for value in range(max_value + 1):\r\n cond_vars = [psi_vars[(value, bids)]\r\n for bids in sampled_bid_vectors]\r\n prob += plp.lpSum(cond_vars) == 1, \"Density_Psi_{}\".format(value)\r\n\r\n # Objective coefficients are random numbers based on the seed\r\n np.random.seed(random_seed)\r\n prob += plp.lpSum([np.random.standard_normal(1) *\r\n var for var in psi_vars.values()])\r\n\r\n # Solve LP\r\n prob.solve(get_solver(solver_str))\r\n\r\n # Get the status returned by the solver. 1 means success\r\n status = int(prob.status)\r\n\r\n # Increase the trial counter\r\n trials += 1\r\n\r\n if status == 1:\r\n # Once we have found a BCE, compute the marginal bid vector\r\n # distribution\r\n bid_pdf = {}\r\n for bid_vector in sampled_bid_vectors:\r\n # Compute the probability of the bid vector: sum_{v} Pr[b | v] *\r\n # pi(v)\r\n prob_mass = sum([plp.value(psi_vars[(v, bid_vector)]) * value_pdf[v]\r\n for v in range(max_value + 1)])\r\n # If mass is positive add it to the bid_pdf dictionary\r\n if prob_mass > 0:\r\n bid_pdf[bid_vector] = prob_mass\r\n\r\n return bid_pdf\r\n else:\r\n return None","def proba_middle(c_pred,m_pred,dataset):\n if dataset =='cifar10':\n p = np.zeros(5)\n for i in range(5):\n if i in [0,1]:\n p[i] = c_pred[0]*(m_pred[i]/(m_pred[0]+m_pred[1]))\n if i in [2,3,4]:\n p[i] = c_pred[1]*(m_pred[i]/(m_pred[2]+m_pred[3]++m_pred[4]))\n else :\n p = np.zeros(4)\n for i in range(4):\n if i in [0,1]:\n p[i] = c_pred[0]*(m_pred[i]/(m_pred[0]+m_pred[1]))\n if i in [2,3]:\n p[i] = c_pred[1]*(m_pred[i]/(m_pred[2]+m_pred[3]))\n return(p)","def calc_zchi2_batch(spectra, tdata, weights, flux, wflux, nz, nbasis, solve_matrices_algorithm=\"PCA\", use_gpu=False, fullprecision=True):\n zchi2 = np.zeros(nz)\n if (weights.sum() == 0):\n zchi2[:] = 9e99\n zcoeff = np.zeros((nz, nbasis))\n return (zchi2, zcoeff)\n if (use_gpu):\n global cp\n import cupy as cp\n #On the GPU, all operations are batch operations for all templates\n #in parallel.\n\n #1) batch_dot_product_sparse will compute dot products of all\n #spectra with all templates in batch and return a 3D array of\n #size (nz x ncols x nbasis).\n Tbs = batch_dot_product_sparse(spectra, tdata, nz, use_gpu)\n if (cp_memcheck):\n #Free memory on consumer grade GPUs with low resources\n mpool = cp.get_default_memory_pool()\n mpool.free_all_blocks()\n\n #2) On the GPU, M and y are computed for all templates at once\n #CUPY swapaxes is the equivalent of the transpose in CPU mode\n #and the @ matrix multiplication operator performs a dot\n #product for each template.\n\n ###!!! NOTE - there are 3 different options for calculating the\n ### M and y arrays -\n ### A) Straight CUPY, which works well on perlmutter with a\n ### runtime of 6.2s on 1 GPU and 2.0s on 4 GPUs, but is\n ### unusably slow on Volta generation GPUs (16.8s for only\n ### 10 targets on a 1660 Super).\n ### B) calc_M_y_batch, the custom CUDA kernel, which is the\n ### fastest at 2.9s on 1 GPU and 0.7s on 4 GPUs (and 0.7s\n ### for 10 targets on a 1660 Super) but is the most difficult\n ### from a maintenance perspective\n ### C) Use the calc_batch_dot_product_3d3d_gpu kernel to offload\n ### only the matrix multiplication for M (and transpose of\n ### Tbs) but use CUPY for everything else. This strikes a\n ### middle ground that is very maintainable but removes the\n ### bottleneck of the CUPY Volta issue. 5.7s on 1 GPU and\n ### 1.8s on 4 GPUs on Perlmutter; 1.6s for 10 targets on\n ### 1660 Super.\n ###!!! NOTE - uncomment the 2 lines below to run (A)\n #all_M = Tbs.swapaxes(-2, -1) @ (weights[None, :, None] * Tbs)\n #all_y = (Tbs.swapaxes(-2, -1) @ wflux)\n ###!!! NOTE - uncomment the below line to run (B)\n #(all_M, all_y) = calc_M_y_batch(Tbs, weights, wflux, nz, nbasis)\n ###!!! NOTE - uncomment the 2 lines below to run (C)\n all_M = calc_batch_dot_product_3d3d_gpu(Tbs, (weights[None, :, None] * Tbs), transpose_a=True, fullprecision=fullprecision)\n all_y = (Tbs.swapaxes(-2, -1) @ wflux)\n ###!!! NOTE - uncomment the 2 lines below to run an alternative\n ### version of (C) that does the transpose on the CPU - this seems\n ### to needlessly waste time though\n #all_M = calc_batch_dot_product_3d3d_gpu(cp.ascontiguousarray(Tbs.swapaxes(-2, -1)), (weights[None, :, None] * Tbs))\n #all_y = (Tbs.swapaxes(-2, -1) @ wflux)\n\n #3) Use new helper method solve_matrices to use appropriate method\n #for this template to solve for zcoeff in batch for all_M and all_y.\n #There is no Error thrown by cupy's version of linalg.solve so just\n #need to catch NotImplementedError.\n try:\n zcoeff = solve_matrices(all_M, all_y, solve_algorithm=solve_matrices_algorithm, use_gpu=True)\n except NotImplementedError:\n zchi2[:] = 9e99\n zcoeff = np.zeros((nz, nbasis))\n return (zchi2, zcoeff)\n\n #4) calc_batch_dot_product_3d2d will compute the dot product\n #of Tbs and zcoeff for all templates in parallel.\n #It is the same as model[i,:,:] = Tbs[i,:,:].dot(zcoeff[i,:])\n model = calc_batch_dot_product_3d2d(Tbs, zcoeff, use_gpu)\n\n #5) On the GPU, (flux-model)*(flux-model) is faster than\n #(flux-model)**2. The @ matrix multiplication operator performs\n #a dot product for each template. get() copies the data back\n #from the GPU to the numpy array allocated for zchi2.\n zchi2[:] = (((flux - model)*(flux-model)) @ weights).get()\n #Copy data from GPU to numpy arrays\n zcoeff = zcoeff.get()\n\n if (cp_memcheck):\n #Free memory on consumer grade GPUs with low resources\n del Tbs\n del all_M\n del all_y\n del model\n mpool = cp.get_default_memory_pool()\n mpool.free_all_blocks()\n else:\n zcoeff = np.zeros((nz, nbasis))\n #On the CPU, the templates are looped over and all operations\n #are performed on one template at a time.\n\n for i in range(nz):\n #1) dot_product_sparse_one will compute dot products of all\n #spectra with ONE template and return a 2D array of size\n #(ncols x nbasis)\n Tb = dot_product_sparse_one(spectra, tdata, i)\n\n #2) On the CPU, M and y are computed for each template\n M = Tb.T.dot(np.multiply(weights[:,None], Tb))\n y = Tb.T.dot(wflux)\n\n #3) Use new helper method solve_matrices to use appropriate method\n #for this template to solve for zcoeff for each M, y.\n #Catch LinAlgError and NotImplementedError\n try:\n zcoeff[i,:] = solve_matrices(M, y, solve_algorithm=solve_matrices_algorithm, use_gpu=False)\n except np.linalg.LinAlgError:\n zchi2[i] = 9e99\n continue\n except NotImplementedError:\n zchi2[i] = 9e99\n continue\n\n #4) Calculate dot products individually for each template\n model = Tb.dot(zcoeff[i,:])\n\n #5) Calculate this zchi2 element individually for each template\n zchi2[i] = np.dot( (flux - model)**2, weights )\n return (zchi2, zcoeff)","def __init__(self, N0, N1):\n #self.w = np.zeros(N);\n self.p0 = N0/(N0+N1) \n self.p1 = N1/(N0+N1)\n self.mu0 = np.zeros(N0+N1)\n self.mu1 = np.zeros(N0+N1)\n self.covariance = 0","def CBF(s_angles,s_amp,sig_n,N,M,S,L,lam,V):\n\ts = []\n\tfor i in range(S):\n\t temp = scipy.signal.square(4*np.pi*c0*np.linspace(i,L+i,L)/lam)\n\t s.append(temp)\n\n\ts = np.array(s)\n\t\n\tn = sig_n*(rd.randn(M,L)) #Gaussien centré\n\tfor i in range(len(s_amp)):\n\t\ts[i] *= s_amp[i]\n\tA = compute_A(s_angles,lam,M)\n\ty = A@s + n #On retrouve bien la linéarité\n\tSyy = y @ y.T.conj() / L\n\n\n\tangles1 = np.linspace(- np.pi/2,np.pi/2,N)\n\tPCBF = np.zeros(N, dtype = complex) #Ça signifie Puissance pour du classic beamforming \n\t\t\t\t\t\t\t\t\t\t\t# Un pic de puissance indiquera la position de notre source\n\n\tfor i in range(N):\n\t\ta_pcbf = a(angles1[i], lam,M)\n\t\tPCBF[i] = a_pcbf.T.conj() @ Syy @ a_pcbf / (np.linalg.norm(a_pcbf) ** 4)\n\n\treturn np.array(PCBF)/np.max(PCBF)","def create_problem(\n forest, bcs, props, nlevels, vol_frac=0.25, density=2600.0, iter_offset=0\n):\n\n # Characteristic length of the domain\n len0 = 10.0\n r0_frac = 0.05\n N = 20\n\n # Create the problem and filter object\n mfilter = MFilterCreator(r0_frac, N, a=len0)\n filter_type = mfilter.filter_callback\n obj = CreatorCallback(bcs, props)\n problem = TopOptUtils.createTopoProblem(\n forest, obj.creator_callback, filter_type, use_galerkin=True, nlevels=nlevels\n )\n\n # Get the assembler object we just created\n assembler = problem.getAssembler()\n\n # Set the load\n P = 1.0e3\n force = TopOptUtils.computeVertexLoad(\"pt1\", forest, assembler, [0, P, 0])\n temp = TopOptUtils.computeVertexLoad(\"pt2\", forest, assembler, [0, 0, P])\n force.axpy(1.0, temp)\n\n # Set the load cases into the topology optimization problem\n problem.setLoadCases([force])\n\n # Compute the fixed mass target\n lx = 50.0 # mm\n ly = 10.0 # mm\n lz = 10.0 # mm\n vol = lx * ly * lz\n m_fixed = vol_frac * (vol * density)\n\n # Set the mass constraint\n funcs = [functions.StructuralMass(assembler)]\n problem.addConstraints(0, funcs, [-m_fixed], [-1.0 / m_fixed])\n\n # Set the objective (scale the compliance objective)\n problem.setObjective([1.0e3])\n\n cb = OutputCallback(assembler, iter_offset=iter_offset)\n problem.setOutputCallback(cb.write_output)\n\n return problem"],"string":"[\n \"def build_bpmf_model(train, alpha=2, dim=10, std=0.01):\\n n, m = train.shape\\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\\n\\n # Mean value imputation on training data.\\n train = train.copy()\\n nan_mask = np.isnan(train)\\n train[nan_mask] = train[~nan_mask].mean()\\n\\n logging.info('building the BPMF model')\\n with pm.Model() as bpmf:\\n # Specify user feature matrix\\n lambda_u = pm.Wishart(\\n 'lambda_u', n=dim, V=np.eye(dim), shape=(dim, dim),\\n testval=np.random.randn(dim, dim) * std)\\n mu_u = pm.Normal(\\n 'mu_u', mu=0, tau=beta_0 * lambda_u, shape=dim,\\n testval=np.random.randn(dim) * std)\\n U = pm.MvNormal(\\n 'U', mu=mu_u, tau=lambda_u, shape=(n, dim),\\n testval=np.random.randn(n, dim) * std)\\n\\n # Specify item feature matrix\\n lambda_v = pm.Wishart(\\n 'lambda_v', n=dim, V=np.eye(dim), shape=(dim, dim),\\n testval=np.random.randn(dim, dim) * std)\\n mu_v = pm.Normal(\\n 'mu_v', mu=0, tau=beta_0 * lambda_v, shape=dim,\\n testval=np.random.randn(dim) * std)\\n V = pm.MvNormal(\\n 'V', mu=mu_v, tau=lambda_v, shape=(m, dim),\\n testval=np.random.randn(m, dim) * std)\\n\\n # Specify rating likelihood function\\n R = pm.Normal(\\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones((n, m)),\\n observed=train)\\n\\n logging.info('done building the BPMF model')\\n return bpmf\",\n \"def bayesian_model(y, x, data=None, correlation=False, family=\\\"Normal\\\", robust = True, samples = 1000, plot_posterior=True, plot_regression=True, plot_samples = \\\"default\\\", print_summary=True, alpha = 0.05):\\n\\n print(\\\"Starting Bayesian estimation...\\\")\\n from theano import config\\n config.warn.sum_div_dimshuffle_bug = False\\n import pymc3\\n\\n\\n\\n y_name = \\\"y\\\"\\n x_name = \\\"x\\\"\\n\\n if isinstance(y,str):\\n y_name = y\\n try:\\n y = data[y]\\n except:\\n pass\\n if isinstance(x,str):\\n x_name = x\\n try:\\n x = data[x]\\n except:\\n pass\\n\\n\\n if correlation == True:\\n #Center and scale\\n x = x - np.mean(x)\\n x = x / np.std(x)\\n\\n y = y - np.mean(y)\\n y = y / np.std(y)\\n\\n\\n\\n\\n data = {y_name:y,\\n x_name:x}\\n formula = y_name + ' ~ ' + x_name\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n with pymc3.Model() as model: # model specifications in PyMC3 are wrapped in a with-statement\\n if family == \\\"Normal\\\":\\n family = pymc3.glm.families.Normal()\\n if robust == True:\\n family = pymc3.glm.families.StudentT()\\n\\n pymc3.glm.glm(formula, data, family=family)\\n start = pymc3.find_MAP()\\n step = pymc3.NUTS(scaling=start) # Instantiate MCMC sampling algorithm\\n trace = pymc3.sample(samples, step, progressbar=True) # draw 2000 posterior samples using NUTS sampling\\n\\n# trace = trace[int(samples/4):]\\n #PLOT POSTERIOR DISTRIBUTION\\n if plot_posterior == True:\\n pymc3.traceplot(trace)\\n\\n #PLOT LINES\\n if plot_regression == True:\\n plot_data= []\\n plot_data.append(go.Scatter(x=x,\\n y=y,\\n mode = 'markers'))\\n\\n\\n\\n if plot_samples == \\\"default\\\":\\n if len(trace) > 100:\\n plot_samples = 100\\n samples_range = np.random.randint(0, len(trace), plot_samples)\\n else:\\n plot_samples = samples\\n samples_range = range(len(trace))\\n else:\\n samples_range = np.random.randint(0, len(trace), plot_samples)\\n\\n\\n\\n for i in samples_range:\\n print(i)\\n plot_data.append(go.Scatter(x=x,\\n y=trace[i]['Intercept'] + trace[i]['x'] * x,\\n mode = 'lines',\\n opacity=0.25,\\n line = {\\\"color\\\":\\\"grey\\\",\\n \\\"width\\\":5}))\\n layout = go.Layout(showlegend=False)\\n figure = go.Figure(data = plot_data,layout=layout)\\n py.plot(figure)\\n\\n if print_summary == True:\\n print(pymc3.summary(trace,alpha=alpha))\\n return(trace)\",\n \"def mcmc_fit(mc_model, mcmc_parameters):\\n mcmc = pymc.MCMC(mc_model)\\n mcmc.use_step_method(pymc.AdaptiveMetropolis, mcmc.theta_i)\\n print('running MCMC')\\n mcmc.sample(mcmc_parameters['points'], mcmc_parameters['burn'], mcmc_parameters['steps'],\\n progress_bar=True)\\n print('finished MCMC')\\n return mcmc\",\n \"def fit_FITC(self, Kmm, Kmn, Knn_diag, epsilon=1e-3,power_ep=[1.,1.]):\\r\\n self.epsilon = epsilon\\r\\n self.eta, self.delta = power_ep\\r\\n\\r\\n num_inducing = Kmm.shape[0]\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Prior approximation parameters:\\r\\n q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)\\r\\n Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn\\r\\n \\\"\\\"\\\"\\r\\n Lm = jitchol(Kmm)\\r\\n Lmi = chol_inv(Lm)\\r\\n Kmmi = np.dot(Lmi.T,Lmi)\\r\\n P0 = Kmn.T\\r\\n KmnKnm = np.dot(P0.T, P0)\\r\\n KmmiKmn = np.dot(Kmmi,P0.T)\\r\\n Qnn_diag = np.sum(P0.T*KmmiKmn,-2)\\r\\n Diag0 = Knn_diag - Qnn_diag\\r\\n R0 = jitchol(Kmmi).T\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Posterior approximation: q(f|y) = N(f| mu, Sigma)\\r\\n Sigma = Diag + P*R.T*R*P.T + K\\r\\n mu = w + P*Gamma\\r\\n \\\"\\\"\\\"\\r\\n self.w = np.zeros(self.num_data)\\r\\n self.Gamma = np.zeros(num_inducing)\\r\\n mu = np.zeros(self.num_data)\\r\\n P = P0.copy()\\r\\n R = R0.copy()\\r\\n Diag = Diag0.copy()\\r\\n Sigma_diag = Knn_diag\\r\\n RPT0 = np.dot(R0,P0.T)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Initial values - Cavity distribution parameters:\\r\\n q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}\\r\\n sigma_ = 1./tau_\\r\\n mu_ = v_/tau_\\r\\n \\\"\\\"\\\"\\r\\n self.tau_ = np.empty(self.num_data,dtype=float)\\r\\n self.v_ = np.empty(self.num_data,dtype=float)\\r\\n\\r\\n #Initial values - Marginal moments\\r\\n z = np.empty(self.num_data,dtype=float)\\r\\n self.Z_hat = np.empty(self.num_data,dtype=float)\\r\\n phi = np.empty(self.num_data,dtype=float)\\r\\n mu_hat = np.empty(self.num_data,dtype=float)\\r\\n sigma2_hat = np.empty(self.num_data,dtype=float)\\r\\n\\r\\n #Approximation\\r\\n epsilon_np1 = 1\\r\\n epsilon_np2 = 1\\r\\n \\tself.iterations = 0\\r\\n self.np1 = [self.tau_tilde.copy()]\\r\\n self.np2 = [self.v_tilde.copy()]\\r\\n while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:\\r\\n update_order = np.random.permutation(self.num_data)\\r\\n for i in update_order:\\r\\n #Cavity distribution parameters\\r\\n self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]\\r\\n self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]\\r\\n #Marginal moments\\r\\n self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model.moments_match(self.data[i],self.tau_[i],self.v_[i])\\r\\n #Site parameters update\\r\\n Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])\\r\\n Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])\\r\\n self.tau_tilde[i] += Delta_tau\\r\\n self.v_tilde[i] += Delta_v\\r\\n #Posterior distribution parameters update\\r\\n dtd1 = Delta_tau*Diag[i] + 1.\\r\\n dii = Diag[i]\\r\\n Diag[i] = dii - (Delta_tau * dii**2.)/dtd1\\r\\n pi_ = P[i,:].reshape(1,num_inducing)\\r\\n P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_\\r\\n Rp_i = np.dot(R,pi_.T)\\r\\n RTR = np.dot(R.T,np.dot(np.eye(num_inducing) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))\\r\\n R = jitchol(RTR).T\\r\\n self.w[i] += (Delta_v - Delta_tau*self.w[i])*dii/dtd1\\r\\n self.Gamma += (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)\\r\\n RPT = np.dot(R,P.T)\\r\\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\\r\\n mu = self.w + np.dot(P,self.Gamma)\\r\\n self.iterations += 1\\r\\n #Sigma recomptutation with Cholesky decompositon\\r\\n Iplus_Dprod_i = 1./(1.+ Diag0 * self.tau_tilde)\\r\\n Diag = Diag0 * Iplus_Dprod_i\\r\\n P = Iplus_Dprod_i[:,None] * P0\\r\\n safe_diag = np.where(Diag0 < self.tau_tilde, self.tau_tilde/(1.+Diag0*self.tau_tilde), (1. - Iplus_Dprod_i)/Diag0)\\r\\n L = jitchol(np.eye(num_inducing) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))\\r\\n R,info = dtrtrs(L,R0,lower=1)\\r\\n RPT = np.dot(R,P.T)\\r\\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\\r\\n self.w = Diag * self.v_tilde\\r\\n self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))\\r\\n mu = self.w + np.dot(P,self.Gamma)\\r\\n epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.num_data\\r\\n epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.num_data\\r\\n self.np1.append(self.tau_tilde.copy())\\r\\n self.np2.append(self.v_tilde.copy())\\r\\n\\r\\n return self._compute_GP_variables()\",\n \"def fit_FITC(self, Kmm, Kmn, Knn_diag):\\r\\n num_inducing = Kmm.shape[0]\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Prior approximation parameters:\\r\\n q(f|X) = int_{df}{N(f|KfuKuu_invu,diag(Kff-Qff)*N(u|0,Kuu)} = N(f|0,Sigma0)\\r\\n Sigma0 = diag(Knn-Qnn) + Qnn, Qnn = Knm*Kmmi*Kmn\\r\\n \\\"\\\"\\\"\\r\\n Lm = jitchol(Kmm)\\r\\n Lmi = chol_inv(Lm)\\r\\n Kmmi = np.dot(Lmi.T,Lmi)\\r\\n P0 = Kmn.T\\r\\n KmnKnm = np.dot(P0.T, P0)\\r\\n KmmiKmn = np.dot(Kmmi,P0.T)\\r\\n Qnn_diag = np.sum(P0.T*KmmiKmn,-2)\\r\\n Diag0 = Knn_diag - Qnn_diag\\r\\n R0 = jitchol(Kmmi).T\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Posterior approximation: q(f|y) = N(f| mu, Sigma)\\r\\n Sigma = Diag + P*R.T*R*P.T + K\\r\\n mu = w + P*Gamma\\r\\n \\\"\\\"\\\"\\r\\n self.w = np.zeros(self.N)\\r\\n self.Gamma = np.zeros(num_inducing)\\r\\n mu = np.zeros(self.N)\\r\\n P = P0.copy()\\r\\n R = R0.copy()\\r\\n Diag = Diag0.copy()\\r\\n Sigma_diag = Knn_diag\\r\\n RPT0 = np.dot(R0,P0.T)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n Initial values - Cavity distribution parameters:\\r\\n q_(g|mu_,sigma2_) = Product{q_i(g|mu_i,sigma2_i)}\\r\\n sigma_ = 1./tau_\\r\\n mu_ = v_/tau_\\r\\n \\\"\\\"\\\"\\r\\n self.tau_ = np.empty(self.N,dtype=float)\\r\\n self.v_ = np.empty(self.N,dtype=float)\\r\\n\\r\\n #Initial values - Marginal moments\\r\\n z = np.empty(self.N,dtype=float)\\r\\n self.Z_hat = np.empty(self.N,dtype=float)\\r\\n phi = np.empty(self.N,dtype=float)\\r\\n mu_hat = np.empty(self.N,dtype=float)\\r\\n sigma2_hat = np.empty(self.N,dtype=float)\\r\\n\\r\\n #Approximation\\r\\n epsilon_np1 = 1\\r\\n epsilon_np2 = 1\\r\\n \\tself.iterations = 0\\r\\n self.np1 = [self.tau_tilde.copy()]\\r\\n self.np2 = [self.v_tilde.copy()]\\r\\n while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:\\r\\n update_order = np.random.permutation(self.N)\\r\\n for i in update_order:\\r\\n #Cavity distribution parameters\\r\\n self.tau_[i] = 1./Sigma_diag[i] - self.eta*self.tau_tilde[i]\\r\\n self.v_[i] = mu[i]/Sigma_diag[i] - self.eta*self.v_tilde[i]\\r\\n #Marginal moments\\r\\n self.Z_hat[i], mu_hat[i], sigma2_hat[i] = self.noise_model_list[self.index[i]].moments_match(self._transf_data[i],self.tau_[i],self.v_[i])\\r\\n #Site parameters update\\r\\n Delta_tau = self.delta/self.eta*(1./sigma2_hat[i] - 1./Sigma_diag[i])\\r\\n Delta_v = self.delta/self.eta*(mu_hat[i]/sigma2_hat[i] - mu[i]/Sigma_diag[i])\\r\\n self.tau_tilde[i] += Delta_tau\\r\\n self.v_tilde[i] += Delta_v\\r\\n #Posterior distribution parameters update\\r\\n dtd1 = Delta_tau*Diag[i] + 1.\\r\\n dii = Diag[i]\\r\\n Diag[i] = dii - (Delta_tau * dii**2.)/dtd1\\r\\n pi_ = P[i,:].reshape(1,num_inducing)\\r\\n P[i,:] = pi_ - (Delta_tau*dii)/dtd1 * pi_\\r\\n Rp_i = np.dot(R,pi_.T)\\r\\n RTR = np.dot(R.T,np.dot(np.eye(num_inducing) - Delta_tau/(1.+Delta_tau*Sigma_diag[i]) * np.dot(Rp_i,Rp_i.T),R))\\r\\n R = jitchol(RTR).T\\r\\n self.w[i] += (Delta_v - Delta_tau*self.w[i])*dii/dtd1\\r\\n self.Gamma += (Delta_v - Delta_tau*mu[i])*np.dot(RTR,P[i,:].T)\\r\\n RPT = np.dot(R,P.T)\\r\\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\\r\\n mu = self.w + np.dot(P,self.Gamma)\\r\\n self.iterations += 1\\r\\n #Sigma recomptutation with Cholesky decompositon\\r\\n Iplus_Dprod_i = 1./(1.+ Diag0 * self.tau_tilde)\\r\\n Diag = Diag0 * Iplus_Dprod_i\\r\\n P = Iplus_Dprod_i[:,None] * P0\\r\\n safe_diag = np.where(Diag0 < self.tau_tilde, self.tau_tilde/(1.+Diag0*self.tau_tilde), (1. - Iplus_Dprod_i)/Diag0)\\r\\n L = jitchol(np.eye(num_inducing) + np.dot(RPT0,safe_diag[:,None]*RPT0.T))\\r\\n R,info = dtrtrs(L,R0,lower=1)\\r\\n RPT = np.dot(R,P.T)\\r\\n Sigma_diag = Diag + np.sum(RPT.T*RPT.T,-1)\\r\\n self.w = Diag * self.v_tilde\\r\\n self.Gamma = np.dot(R.T, np.dot(RPT,self.v_tilde))\\r\\n mu = self.w + np.dot(P,self.Gamma)\\r\\n epsilon_np1 = sum((self.tau_tilde-self.np1[-1])**2)/self.N\\r\\n epsilon_np2 = sum((self.v_tilde-self.np2[-1])**2)/self.N\\r\\n self.np1.append(self.tau_tilde.copy())\\r\\n self.np2.append(self.v_tilde.copy())\\r\\n\\r\\n return self._compute_GP_variables()\",\n \"def get_effect_size(self, summ, b, nmc=5000):\\n m0b, v0b = self.DModel.models[0].predict(np.array([b])) \\n m1b, v1b = self.DModel.models[1].predict(np.array([b]))\\n \\n d_mean_D = np.squeeze(m1b - m0b) # TODO: why was this swapped around?\\n d_var_D = np.squeeze(v0b + v1b)\\n d_std_D = np.sqrt(d_var_D)\\n \\n if d_mean_D < 0:\\n pval = 1 - stats.norm.cdf(x=0, loc=d_mean_D, scale=d_std_D)\\n else:\\n pval = stats.norm.cdf(x=0, loc=d_mean_D, scale=d_std_D)\\n \\n xmin, xmax = (np.min([d_mean_D - 4*d_std_D, -0.1*d_std_D]), \\n np.max([d_mean_D + 4*d_std_D, 0.1*d_std_D]))\\n \\n n = 300\\n xrange = np.linspace(xmin, xmax, n)\\n y = stats.norm.pdf(xrange, d_mean_D, d_std_D) \\n \\n samples = np.zeros((nmc))\\n nspike = int(np.round(summ['pmp']['pmc']*nmc))\\n samples[nspike:] = np.random.normal(loc=d_mean_D, \\n scale=np.sqrt(d_var_D), \\n size=(nmc-nspike))\\n \\n if not np.isscalar(b):\\n d_bma = None\\n else:\\n \\n if nspike==nmc:\\n # BMA dominated by continuous model\\n # Put all mass at xrange closest to b\\n d_bma = np.zeros((n))\\n xdelta = xrange[1] - xrange[0]\\n ix = np.argmin((xrange-b)**2)\\n d_bma[ix] = 1.0 / xdelta\\n elif nspike==0:\\n # BMA dominated by discontinuous model\\n d_bma = y\\n else:\\n # BMA is a mixture\\n kde_fit = stats.gaussian_kde(samples, \\n bw_method='silverman')\\n d_bma = kde_fit(xrange)\\n \\n return {'es_BMA': d_bma,\\n 'es_Disc': y,\\n 'es_disc_stats': (d_mean_D, d_std_D),\\n 'pval': pval,\\n 'es_range': xrange,\\n 'f(b)': (m0b, m1b),\\n 'es_transform': lambda z: z*d_std_D + d_mean_D}\",\n \"def run_metropolis_PBC(self):\\n\\n # Initialize the posistions for each new Monte Carlo run\\n positions = np.random.rand(self.num_p, self.num_d)\\n # Initialize the distance matrix\\n self.s.positions_distances_PBC(positions)\\n # check if the wave function is zero\\n while True:\\n test_wavefunction = self.w.wavefunction(positions)\\n if test_wavefunction**2 <= 1e-14:\\n # print ('obs')\\n # Initialize the posistions for each new Monte Carlo run\\n positions = np.random.rand(self.num_p, self.num_d)\\n # Initialize the distance matrix\\n self.s.positions_distances_PBC(positions)\\n else:\\n break\\n # Initialize sampler method for each new Monte Carlo run\\n self.sam.initialize()\\n\\n for i in range(self.mc_cycles):\\n new_positions = self.metropolis_step_PBC(positions)\\n positions = new_positions\\n self.sam.sample_values(positions)\\n\\n self.sam.average_values(self.mc_cycles)\\n energy = self.sam.local_energy\\n d_El = self.sam.derivative_energy\\n var = self.sam.variance\\n print ('w = ', self.w.wavefunction(positions))\\n self.print_averages()\\n return d_El, energy, var\",\n \"def build_pmf_model(train, alpha=2, dim=10, std=0.01):\\n # Mean value imputation on training data.\\n train = train.copy()\\n nan_mask = np.isnan(train)\\n train[nan_mask] = train[~nan_mask].mean()\\n\\n # Low precision reflects uncertainty; prevents overfitting.\\n # We use point estimates from the data to intialize.\\n # Set to mean variance across users and items.\\n alpha_u = 1 / train.var(axis=1).mean()\\n alpha_v = 1 / train.var(axis=0).mean()\\n\\n logging.info('building the PMF model')\\n n, m = train.shape\\n with pm.Model() as pmf:\\n U = pm.MvNormal(\\n 'U', mu=0, tau=alpha_u * np.eye(dim),\\n shape=(n, dim), testval=np.random.randn(n, dim) * std)\\n V = pm.MvNormal(\\n 'V', mu=0, tau=alpha_v * np.eye(dim),\\n shape=(m, dim), testval=np.random.randn(m, dim) * std)\\n R = pm.Normal(\\n 'R', mu=t.dot(U, V.T), tau=alpha * np.ones(train.shape),\\n observed=train)\\n\\n logging.info('done building PMF model')\\n return pmf\",\n \"def p_mx_c(pm,px,py,pyx_c,pym_c,beta):\\n \\n pmx_c = np.zeros((pm.size,px.size)) # P(M|X) matrix to be returned\\n for mi in range(pm.size):\\n for xi in range(px.size):\\n pmx_c[mi,xi] = pm[mi] * np.exp(-beta * entropy(pyx_c[:,xi], pym_c[:,mi], base=2))\\n z = pmx_c.sum(axis=0)\\n pmx_c /= z #Normalize\\n \\n \\t\\n return pmx_c, z\",\n \"def prob3():\\n #set up the matrices\\n solvers.options['show_progress'] = False\\n c = np.array([4., 7., 6., 8., 8., 9.])\\n\\n G = np.array([[1.,1.,0.,0.,0.,0.],\\n [-1.,-1.,0.,0.,0.,0.],\\n [0.,0.,1.,1.,0.,0.],\\n [0.,0.,-1.,-1.,0.,0.],\\n [0.,0.,0.,0.,1.,1.],\\n [0.,0.,0.,0.,-1.,-1.],\\n [1.,0.,1.,0.,1.,0.],\\n [-1.,0.,-1.,0.,-1.,0.],\\n [0.,1.,0.,1.,0.,1.],\\n [0.,-1.,0.,-1.,0.,-1.]])\\n G = np.vstack((G, -1*np.eye(6)))\\n h = np.array([7,-7,2,-2,4,-4,5,-5,8,-8,0,0,0,0,0,0],dtype=\\\"float\\\")\\n\\n #convert the matrices\\n c = matrix(c)\\n G = matrix(G)\\n h = matrix(h)\\n #solve the matrices\\n sol = solvers.lp(c, G, h)\\n return np.ravel(sol['x']), sol['primal objective']\",\n \"def model_test(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in, models_fit, label):\\n # Generate fake data with some \\\"true\\\" parameters\\n (D_vec, Ninv) = gen_data(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in)\\n Ninv_sqrt = np.matrix(linalg.sqrtm(Ninv))\\n (dust_params, sync_params, cmb_params) = params_in\\n (dust_amp, sync_amp, cmb_amp) = amps_in\\n \\n # Beam model\\n beam_mat = np.identity(3*len(nu))\\n\\n # Set-up MCMC\\n dust_guess = np.array([1.6, 20.])\\n sync_guess = np.array([-3.])\\n cmb_guess = np.array([])\\n guess = np.concatenate((dust_guess, sync_guess, cmb_guess))\\n #ndim = len(dust_guess) + len(sync_guess) + len(cmb_guess)\\n \\n # Run MCMC sampler on this model\\n t0 = time.time()\\n dust_params_out, sync_params_out, cmb_params_out, samples \\\\\\n = mcmc(guess, nu, D_vec, Ninv, beam_mat, models_fit, label)\\n print \\\"MCMC run in %d sec.\\\" % (time.time() - t0)\\n \\n # Estimate error on recovered CMB amplitudes\\n (F_fg, F_cmb, F) = F_matrix(nu, dust_params_out, sync_params_out, cmb_params_out, models_fit)\\n H = F_fg.T*Ninv*F_fg\\n x_mat = np.linalg.inv(F.T*beam_mat.T*Ninv*beam_mat*F)*F.T*beam_mat.T*Ninv*D_vec # Equation A3\\n \\n U, Lambda, VT = np.linalg.svd(Ninv_sqrt*F_fg, full_matrices=False) # Equation A14\\n \\n print \\\"-\\\"*30\\n print \\\"F_cmb.T\\\", F_cmb.T.shape\\n print \\\"Ninv_sqrt\\\", Ninv_sqrt.shape\\n print \\\"F_cmb\\\", F_cmb.shape\\n print \\\"I\\\", np.identity(U.shape[0]).shape\\n print \\\"U\\\", U.shape\\n print \\\"U.T\\\", U.T.shape\\n print \\\"-\\\"*30\\n \\n \\n \\n N_eff_inv_cmb = F_cmb.T*Ninv_sqrt*(np.matrix(np.identity(U.shape[0])) - U*U.T)*Ninv_sqrt*F_cmb # Equation A16\\n N_eff_cmb = np.linalg.inv(N_eff_inv_cmb)\\n cmb_noise = np.array([N_eff_cmb[0,0], N_eff_cmb[1,1], N_eff_cmb[2,2]])\\n\\n gls_cmb = x_mat[0:3,0]\\n cmb_chisq = (np.matrix(cmb_amp).T - gls_cmb).T*N_eff_inv_cmb*(np.matrix(cmb_amp).T - gls_cmb)\\n \\n # Output triangle plots for dust\\n if label != None:\\n if (models_fit[0] == 'mbb' and models_fit[1] == 'pow'):\\n if (models_in[0] == 'mbb'):\\n fig = corner.corner(samples, truths=[dust_params[0], dust_params[1], sync_params[0]],\\n labels=[r\\\"$\\\\beta_d$\\\", r\\\"$T_d$\\\",r\\\"$\\\\alpha_s$\\\"])\\n else :\\n fig = corner.corner(samples, labels=[r\\\"$\\\\beta_d$\\\", r\\\"$T_d$\\\",r\\\"$\\\\alpha_s$\\\"])\\n else :\\n print 'Error! Not configured for this plot!'\\n exit()\\n fig.savefig('triangle_' + label + '.png')\\n plt.close('all')\\n \\n # Run multinest sampler\\n #multinest(nu, D_vec, Ninv, beam_mat, ndim, models_fit, label)\\n \\n return gls_cmb, cmb_chisq, cmb_noise\",\n \"def mcmc(self,BHB):\\n\\n ndim = self.sampndim\\n nwalkers = self.sampnwalk\\n #startpoint = self.theta_ml\\n startpoint = self.theta_guess\\n pos = [startpoint + 1e-4*np.random.randn(ndim) for i in range(nwalkers)]\\n\\n sampler = emcee.EnsembleSampler(nwalkers, ndim, lnlikelihood.lnprob,\\n args=(self.transinfo, self.lamlim1, self.lamlim2,\\n self.wave_b, self.flux_b, self.err_b,\\n self.wave_r, self.flux_r, self.err_r,\\n self.velres))\\n\\n # Clear and run the production chain.\\n print(\\\"Running MCMC...\\\")\\n sampler.run_mcmc(pos, self.nsteps, rstate0=np.random.get_state())\\n print(\\\"Done.\\\")\\n\\n pl.clf()\\n fig, axes = pl.subplots(ndim, 1, sharex=True, figsize=(8, 9))\\n\\n for ind in range(0,ndim):\\n \\n axes[ind].plot(sampler.chain[:, :, ind].T, color=\\\"k\\\", alpha=0.4)\\n axes[ind].yaxis.set_major_locator(MaxNLocator(5))\\n axes[ind].axhline(startpoint[ind], color=\\\"#888888\\\", lw=2)\\n # axes[ind].set_ylabel(\\\"$m$\\\")\\n\\n\\n fig.tight_layout(h_pad=0.0)\\n fig.savefig(\\\"line-time_\\\"+BHB+\\\"_\\\"+self.transname+\\\"_\\\"+str(self.lamlim1)+\\\".png\\\") # Edited for manual runs\\n\\n\\n burnin = self.burnin\\n samples = sampler.chain[:, burnin:, :].reshape((-1, ndim))\\n self.samples = samples\\n\\n # Compute the quantiles.\\n theta_mcmc = list(map(lambda v: (v[1], v[2]-v[1], v[1]-v[0]),\\n zip(*np.percentile(samples, [16, 50, 84],\\n axis=0))))\\n \\n \\n self.theta_percentiles = theta_mcmc\\n print(\\\"\\\"\\\"MCMC result:\\\"\\\"\\\") \\n for ind in range(0,ndim):\\n print(\\\"\\\"\\\" par {0} = {1[0]} +{1[1]} -{1[2]}\\\"\\\"\\\".format(ind, theta_mcmc[ind]))\",\n \"def emulator(pca, gp_model, params):\\n # Weights prediction\\n pred_weights = gp_predict(gp_model, params)\\n\\n # Inverse PCA (pred_weights * basis + mean)\\n reconstructed = pca.inverse_transform(pred_weights)\\n return reconstructed\",\n \"def chisqdata_bs(Obsdata, Prior, mask):\\n\\n biarr = Obsdata.bispectra(mode=\\\"all\\\", count=\\\"min\\\")\\n uv1 = np.hstack((biarr['u1'].reshape(-1,1), biarr['v1'].reshape(-1,1)))\\n uv2 = np.hstack((biarr['u2'].reshape(-1,1), biarr['v2'].reshape(-1,1)))\\n uv3 = np.hstack((biarr['u3'].reshape(-1,1), biarr['v3'].reshape(-1,1)))\\n bi = biarr['bispec']\\n sigma = biarr['sigmab']\\n\\n A3 = (ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv1, pulse=Prior.pulse, mask=mask),\\n ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv2, pulse=Prior.pulse, mask=mask),\\n ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv3, pulse=Prior.pulse, mask=mask)\\n )\\n\\n return (bi, sigma, A3)\",\n \"def create_pyomo_model():\\n model = po.ConcreteModel()\\n model.t = pod.ContinuousSet(bounds=(0, 1))\\n model.tau = po.Var()\\n\\n model.ca = po.Var(model.t, bounds=(0, 50)) # set of concentrations in reaction mixture\\n model.dca_dt = pod.DerivativeVar(model.ca, wrt=model.t)\\n\\n model.beta = po.Var() # model parameters\\n\\n def _material_balance(m, t):\\n return m.dca_dt[t] / m.tau == -m.beta * m.ca[t]\\n model.material_balance = po.Constraint(model.t, rule=_material_balance)\\n\\n simulator = pod.Simulator(model, \\\"casadi\\\")\\n\\n return model, simulator\",\n \"def collapsed_gibbs_sample(self, X):\\n K = self.K # number of topics\\n M, V = X.shape\\n alpha = self.alpha\\n lmda = self.lmda\\n topics = np.arange(0, K)\\n\\n #initialize everything uniformly\\n # KxV dense matrix (used like beta)\\n\\n C = np.zeros(shape=(K, V), dtype=float)\\n props = np.zeros(shape=(M, K), dtype=float)\\n\\n #Current state\\n Ns = np.array(range(M), dtype=object)\\n #Running sum\\n MC_z = np.array(range(M), dtype=object)\\n MC_c = np.zeros(shape=(K, V), dtype=float)\\n\\n for d in range(M):\\n #allocate topics from prior\\n word_indices = X[d, :].nonzero()[1]\\n p = np.random.dirichlet(np.ones(K) * alpha)\\n #TODO: Remove the p argument to get completely random topic assignments\\n random_ks = np.random.choice(topics, size=len(word_indices), p=p)\\n N_d = sp.coo_matrix((np.ones(len(word_indices)),\\n (word_indices, random_ks)), shape=(V, K)).tolil()\\n C = C + N_d.A.T\\n Ns[d] = N_d\\n MC_z[d] = sp.coo_matrix((V, K)).tolil()\\n\\n log_Xs = []\\n perplexities = []\\n for epoch in xrange(self.nr_em_epochs):\\n print \\\"Epoch\\\", epoch\\n for d in np.random.permutation(np.arange(M)):\\n x = X[d]\\n N_d = Ns[d]\\n for v in np.nonzero(x)[1]:\\n old_z_n = N_d[v, :].nonzero()[1][0]\\n\\n except_v = [v_prime for v_prime in np.nonzero(x)[1] if v_prime != v]\\n N_d_except_v = N_d[except_v, :]\\n C_except_v = np.copy(C[:, v])\\n C_except_v[old_z_n] = C_except_v[old_z_n] - 1\\n\\n p = (np.sum(N_d_except_v.A, axis=0) + alpha) *\\\\\\n ((C_except_v + lmda) / (np.sum(C, axis=1) -1 + V*lmda))\\n p = p/np.sum(p)\\n z_n = np.random.choice(topics, p = p)\\n N_d[v, old_z_n] = 0\\n N_d[v, z_n] = 1\\n C[old_z_n, v] -= 1\\n C[z_n, v] += 1\\n Ns[d] = N_d\\n MC_z[d] += N_d\\n\\n MC_c += C\\n word_props = (MC_c.T / np.sum(MC_c, axis=1)).T\\n log_X = 0\\n for d in range(M):\\n props_d = np.sum(MC_z[d].A, axis=0)\\n props_d /= float(np.sum(props_d))\\n props[d] = props_d\\n ixw = np.nonzero(X[d, :])[1]\\n log_X += np.sum(_doc_probability_from_p_of_z(props_d, word_props[:, ixw]))\\n\\n log_Xs.append(log_X)\\n print log_X\\n perplexities.append(self._perplexity(X, log_X))\\n\\n\\n return props, word_props, log_Xs, perplexities\",\n \"def BMA_Marginal_Likelihood(df_train, df_test, iterations, burnin, p_1):\\n\\n # number of individual forecasts and number of periods\\n K = df_test.shape[1]\\n T = df_train.shape[0]\\n\\n # g-prior parameter\\n c = float(K**2)\\n\\n # separate real value and individual forecasts\\n y = df_train.iloc[:, 0]\\n F = df_train.iloc[:, 1:]\\n\\n # initializations\\n models = np.full((iterations, K), fill_value=False, dtype=bool)\\n theta_hats = []\\n marginal_lik = np.full(iterations, fill_value=np.nan, dtype=float)\\n\\n # RJ-MCMC\\n # initial state = null model\\n # marginal likelihood for the initial state\\n y_bar = np.mean(y)\\n S_right = sum((y - y_bar)**2)/(c+1)\\n\\n Z = np.insert(F.iloc[:, models[0, :]].values, 0, 1, axis=1)\\n theta_hat = np.dot(\\n np.linalg.inv(np.dot(np.transpose(Z), Z)),\\n np.dot(np.transpose(Z), y)\\n )\\n SSR = sum((y - np.dot(Z, theta_hat))**2)\\n S_left = (c/(c+1))*SSR\\n S = S_left + S_right\\n\\n # scale to first decimal place for numerical reasons\\n # find scaling order\\n S_rescaled = S\\n scaling_factor = 1\\n while (S_rescaled < 0.1):\\n S_rescaled *= 10\\n scaling_factor *= 10\\n # rescale S (prevent overflow)\\n S = S*scaling_factor\\n\\n k = sum(models[0, :])\\n marginal_lik[0] = ((c+1)**(-k))*(S**(-((T-1)/2)))\\n theta_hats += [theta_hat]\\n\\n # initialization of the proposal model\\n m_star = np.full(K, fill_value=True, dtype=bool)\\n\\n # main iteration loop\\n for i in range(0, iterations-1):\\n\\n # decide which move to perform\\n epsilon_move = np.random.random()\\n\\n # move 1\\n if epsilon_move <= p_1 or sum(models[0]) == 0:\\n\\n # drawn one of the forecasts from the model\\n drawn_f_index = np.random.randint(0, K)\\n\\n # if it is in the model, drop it, if not, add it\\n np.copyto(m_star, models[i, :])\\n m_star[drawn_f_index] = ~models[i, drawn_f_index]\\n\\n # if there are too many variables (d.f. = 0), the change is revert\\n if sum(m_star) > (T-2):\\n m_star[drawn_f_index] = models[i, drawn_f_index]\\n\\n # move 2\\n else:\\n\\n # drawn one of the forecasts from the model and one outside\\n index_in = np.arange(K)[models[i, :]]\\n index_out = np.arange(K)[~models[i, :]]\\n drawn_f_index_in = np.random.choice(index_in)\\n drawn_f_index_out = np.random.choice(index_out)\\n\\n # swap these two variables\\n np.copyto(m_star, models[i, :])\\n m_star[drawn_f_index_in] = ~m_star[drawn_f_index_in]\\n m_star[drawn_f_index_out] = ~m_star[drawn_f_index_out]\\n\\n # calculate the probability of acceptance\\n Z = np.insert(F.iloc[:, m_star].values, 0, 1, axis=1)\\n theta_hat = np.dot(\\n np.linalg.inv(np.dot(np.transpose(Z), Z)),\\n np.dot(np.transpose(Z), y)\\n )\\n SSR = sum((y - np.dot(Z, theta_hat))**2)\\n S_left = (c/(c+1))*SSR\\n S = S_left + S_right\\n\\n # rescale S (prevent overflow)\\n S = S*scaling_factor\\n\\n k = sum(m_star)\\n marginal_lik_m_star = ((c+1)**(-k))*(S**(-((T-1)/2)))\\n\\n alpha = min(1, marginal_lik_m_star/marginal_lik[i])\\n\\n # if accepted, move to m_star, else retain the same model\\n epsilon_accept = np.random.random()\\n\\n if epsilon_accept <= alpha:\\n np.copyto(models[i+1, :], m_star)\\n marginal_lik[i+1] = marginal_lik_m_star\\n theta_hats += [theta_hat]\\n else:\\n np.copyto(models[i+1, :], models[i, :])\\n marginal_lik[i+1] = marginal_lik[i]\\n theta_hats += [theta_hats[-1]]\\n\\n # discard the burnin draws\\n models = models[burnin:, :]\\n marginal_lik = marginal_lik[burnin:]\\n theta_hats = theta_hats[burnin:]\\n\\n # unique models visited by the chain\\n models_uni, index_uni = np.unique(models, axis=0, return_index=True)\\n marginal_lik_uni = marginal_lik[index_uni]\\n # theta_hats_uni = [theta_hats[i] for i in index_uni]\\n theta_hats_uni = list(np.array(theta_hats)[index_uni])\\n\\n # model posterior probabilities\\n marginal_lik_sum = sum(marginal_lik_uni)\\n posterior_prob = marginal_lik_uni / marginal_lik_sum\\n\\n # generate prediction for testing set\\n model_fcts = np.full(\\n (df_test.shape[0], len(theta_hats_uni)),\\n np.nan,\\n dtype=float\\n )\\n\\n # forecast from different models\\n for j in range(model_fcts.shape[1]):\\n\\n model_fcts[:, j] = np.dot(\\n np.insert(df_test.iloc[:, models_uni[j]].values, 0, 1, axis=1),\\n theta_hats_uni[j]\\n )\\n\\n pred = np.dot(model_fcts, posterior_prob)\\n\\n df_pred = pd.DataFrame(\\n {\\\"BMA (Marginal Likelihood)\\\": pred},\\n index=df_test.index)\\n\\n return df_pred\",\n \"def make_mp_forward_particles(cbc_path, min_part=1, max_part=None):\\n # particles must have a label as a string otherwise it terminates\\n # mass weighted particles for each of the models for the sfr/well(races/rivers) and rch\\n\\n print('reading cbc')\\n # calculate the flow into each top layer cell\\n indata = flopy.utils.CellBudgetFile(cbc_path)\\n rch = indata.get_data(kstpkper=(0, 0), text='recharge', full3D=True)[0][0].filled(0) # take only the first row\\n # take only the first row, no particles in southwertern boundary for wells\\n well = indata.get_data(kstpkper=(0, 0), text='wells', full3D=True)[0][0].filled(0)\\n sfr = indata.get_data(kstpkper=(0, 0), text='stream leakage', full3D=True)[0][0].filled(\\n 0) # take only the first row\\n well[well < 0] = 0\\n sfr[sfr < 0] = 0\\n flow = rch + well + sfr\\n flow[smt.get_no_flow(0) != 1] = 0\\n\\n # generate particles (minimum of 1 per cell)\\n num_parts = np.round(flow / flow[flow > 0].min() * min_part).astype(int)\\n if max_part is not None:\\n num_parts[num_parts > max_part] = int(max_part)\\n\\n # identify boundary condition types\\n bd_type = smt.get_empty_model_grid() # 0=rch,1=well,2==sfr\\n temp = np.concatenate((rch[np.newaxis], well[np.newaxis], sfr[np.newaxis]), axis=0).max(axis=0)\\n bd_type[np.isclose(rch, temp)] = 0\\n bd_type[np.isclose(well, temp)] = 1\\n bd_type[np.isclose(sfr, temp)] = 2\\n bd_type[(smt.get_no_flow(0) != 1) | np.isclose(temp, 0)] = -1\\n\\n outdata = flopy.modpath.mpsim.StartingLocationsFile.get_empty_starting_locations_data(num_parts.sum())\\n outdata['label'] = 's'\\n outdata['k0'] = 0 # I think that particles in flopy are 1 indexed, but 0 means active most layer\\n js, iss = np.meshgrid(range(1, smt.cols + 1), range(1, smt.rows + 1)) # this is passed to the file as 1 indexed\\n\\n idx = bd_type.flatten() != -1\\n group_dict = _part_group_cell_mapper(bd_type)\\n start_idx = 0\\n print('generating particles')\\n for l, (num, i, j, bt) in enumerate(\\n zip(num_parts.flatten()[idx], iss.flatten()[idx], js.flatten()[idx], bd_type.flatten()[idx])):\\n if num == 0:\\n raise ValueError('unexpected zero points')\\n end_idx = start_idx + num\\n # set particle starting location\\n outdata['particlegroup'][start_idx:end_idx] = l + 1\\n outdata['groupname'][start_idx:end_idx] = '{:03d}_{:03d}'.format(*group_dict[l + 1])\\n outdata['i0'][start_idx:end_idx] = i\\n outdata['j0'][start_idx:end_idx] = j\\n outdata['xloc0'][start_idx:end_idx] = np.random.uniform(size=num)\\n outdata['yloc0'][start_idx:end_idx] = np.random.uniform(size=num)\\n outdata['zloc0'][start_idx:end_idx] = 1 if bt == 0 else np.random.uniform(size=num)\\n start_idx = end_idx\\n return outdata, bd_type, num_parts\",\n \"def vv_harmonic_wmwf_phase2_1(M, PP, C, Ns, Is=None, prec=20, Yin=None):\\n WR = M.WR\\n kappa = M._weight\\n D = WR.D\\n Dsym = M.D # the symmetrized index set\\n if len(Dsym) != len(C.keys()):\\n raise ValueError(\\\"Got incompatible coefficient vector! indices={0}\\\".format(list(C.keys())))\\n\\n # we only use symmetrized values\\n if(Is is None):\\n Is = [0, len(D)]\\n\\n N = WR.N\\n t = -1\\n sym_type = mpmath.mpf(M._sym_type)\\n verbose = M._verbose\\n # ndig=12\\n eps = mpmath.power(mpmath.mpf(10), mpmath.mpf(-prec))\\n if verbose > 0:\\n print(\\\"eps={0}\\\".format(eps))\\n print(\\\"Yin={0}\\\".format(Yin))\\n betai = dict()\\n mbeta = dict()\\n mbetai = dict()\\n mm = dict()\\n mmm = dict()\\n mptwo = mpmath.mp.mpf(2)\\n mpfour = mpmath.mp.mpf(4)\\n twopi = mptwo * mpmath.pi()\\n twopii = mpmath.mp.mpc(0, twopi)\\n fourpi = mptwo * twopi\\n mp0 = mpmath.mpf(0)\\n weight = mpmath.mp.mpf(kappa)\\n weight_over_two = weight / mpmath.mp.mpf(2)\\n K0 = 0\\n K1 = 0\\n for (beta, m) in PP:\\n # if( (not beta in D) or (not 1-beta in D)):\\n if((beta not in D) and (1 - beta not in D)):\\n raise Exception(\\\"Need beta={0} in D={1}\\\".format(beta, D))\\n betai[beta] = D.index(beta)\\n mbeta[beta] = 1 - beta\\n mbetai[beta] = D.index(1 - beta)\\n mm[(beta, m)] = (m + WR.Qv[betai[beta]])\\n mmm[(beta, m)] = mpmath.mp.mpf(mm[(beta, m)])\\n if verbose > 0:\\n print(\\\"beta,m={0}, {1}\\\".format(beta, m))\\n print(\\\"mm={0}\\\".format(mm[(beta, m)]))\\n # print \\\"-beta=\\\",minus_beta\\n # print \\\"mm=\\\",mm[(beta,m)]\\n if mm[(beta, m)] > t:\\n t = mm\\n if abs(mm[(beta, m)]) < K0:\\n K0 = abs(mm[(beta, m)])\\n if abs(PP[(beta, m)]) > K1:\\n K1 = abs(PP[(beta, m)])\\n # One way to specify the principal part\\n # is to only specify half and let the rest be decided\\n # by the symmetry. If we specify the rest it must match\\n if (mbeta[beta], m) in PP and (beta, m) in PP:\\n test = abs(PP[(beta, m)] - sym_type * PP[(mbeta[beta], m)])\\n if test > 0: # and not test.ae(mp0):\\n raise ValueError(\\\"The principal part has not correct symmetry: type={0}, PP={1}\\\".format(sym_type, PP))\\n else:\\n pass\\n abD = len(WR.D)\\n\\n if(Yin is None):\\n Y0 = mpmath.mp.mpf(0.5)\\n else:\\n Y0 = mpmath.mp.mpf(Yin)\\n kint = mpmath.mp.mpf(1 - weight)\\n sym_type = M.get_sym_type()\\n NA = Ns[0]\\n NB = Ns[1]\\n if(sym_type == 1):\\n Dstart = int(0)\\n Dfinish = int(WR.N) # 0,1,...,N\\n elif(sym_type == -1):\\n Dstart = int(1)\\n Dfinish = int(WR.N - 1) # 1,2,...,N-1 (since -0=0 and -N=N)\\n IA = int(max(Is[0], Dstart))\\n IB = int(min(Is[1], Dfinish))\\n Ms = int(min(C[Dstart].keys()))\\n Mf = int(max(C[Dstart].keys()))\\n Ml = int(Mf - Ms + 1)\\n # Ms=int(min(C[D[Dstart]].keys())); Mf=int(max(C[D[Dstart]].keys())); Ml=int(Mf-Ms+1)\\n if verbose > 0:\\n print(\\\"Ms,Mf,Ml={0}, {1}, {2}\\\".format(Ms, Mf, Ml))\\n K1 = K1 * 2 * N\\n NAA = NA\\n IAA = IA\\n numys = 2\\n # have Y=mpmath.mp.mpf(Y_in)\\n # have to find suitable Q for the given Y\\n if verbose > 0:\\n print(\\\"dps={0}\\\".format(mpmath.mp.dps))\\n ctmp = dict()\\n ctmp_neg = dict()\\n Cout = dict()\\n for bi in range(IA, IB + 1):\\n Cout[bi] = dict()\\n stw = str(weight)[0:5]\\n Qadd = 0\\n Yfak = mpmath.mpf(0.95)\\n Yvold = list(range(2))\\n Xm = dict()\\n Xpb = dict()\\n Ypb = dict()\\n Cv = dict()\\n Q = dict()\\n Qs = dict()\\n Qf = dict()\\n QQ = dict()\\n for yloop in range(1000):\\n Yv = [Y0, Yfak * Y0]\\n for i in range(2):\\n Q[i] = M.get_M(Yv[i], K0, K1, prec) + Qadd\\n Qs[i] = 1 - Q[i]\\n Qf[i] = Q[i]\\n QQ[i] = mpmath.mp.mpf(1) / mpmath.mp.mpf(2 * Q[i])\\n if verbose > 0:\\n print(\\\"Yv[{0}]={1},{2}\\\".format(i, mppr(Yv[0]), mppr(Yv[1])))\\n print(\\\"Q(Y)[{0}]={1}\\\".format(i, Q[i]))\\n # print \\\"1/2Q[\\\",i,\\\"]=\\\",mppr(QQ[i])\\n # Recall that the first Y-value is always the larger\\n if verbose > 1:\\n print(\\\"Yvold={0},{1}\\\".format(mppr(Yvold[0]), mppr(Yvold[1])))\\n if Yv[0].ae(Yvold[1]):\\n if verbose > 1:\\n print(\\\"do not evaluate for Yv={0}\\\".format(Yv[0]))\\n\\n [Xm[0], Xpb[0], Ypb[0], Cv[0]] = [Xm[1], Xpb[1], Ypb[1], Cv[1]]\\n [Xm[1], Xpb[1], Ypb[1], Cv[1]] = pullback_pts_weil_rep(WR, Q[1], Yv[1], weight, Dstart, Dfinish)\\n else:\\n for i in range(2):\\n [Xm[i], Xpb[i], Ypb[i], Cv[i]] = pullback_pts_weil_rep(WR, Q[i], Yv[i], weight, Dstart, Dfinish)\\n Yvold = Yv\\n Zipb = dict()\\n for yj in range(numys):\\n Zipb[yj] = dict()\\n for j in range(Qs[yj], Qf[yj] + 1):\\n Zipb[yj][j] = mpmath.mp.mpc(-Ypb[yj][j], Xpb[yj][j])\\n\\n gamma_fak = dict()\\n for yj in range(numys):\\n for bi in range(IA, IB + 1):\\n for l in range(Ms, Mf + 1):\\n lr = mpmath.mp.mpf(l + WR.Qv[bi])\\n if(lr < 0):\\n lrtwo = lr * mptwo\\n for j in range(Qs[yj], Qf[yj] + 1):\\n gamma_fak[yj, bi, l, j] = mpmath.gammainc(kint, abs(lrtwo) * Ypb[yj][j]) * mpmath.mp.exp(-lr * Ypb[yj][j])\\n else:\\n for j in range(Qs[yj], Qf[yj] + 1):\\n gamma_fak[yj, bi, l, j] = mpmath.mp.exp(-lr * Ypb[yj][j])\\n if verbose > 0:\\n print(\\\"Got pullback points!\\\")\\n print(\\\"dps={0}\\\".format(mpmath.mp.dps))\\n print(\\\"NAA={0}\\\".format(NAA))\\n # If we want to do negative coefficients too we save time by computing simultaneously\\n do_neg = True\\n try:\\n for n in range(NAA, NB + 1):\\n if verbose > 0:\\n print(\\\"n=\\\", n)\\n for ai in range(IAA, IB + 1):\\n if verbose > 0:\\n print(\\\"ai={0}\\\".format(ai))\\n mai = -ai % abD\\n nr = mpmath.mp.mpf(n + WR.Qv[ai])\\n nrtwo = mp2 * nr\\n nri = mpmath.mp.mpc(0, nr)\\n if(do_neg):\\n nrm = mpmath.mp.mpf(-n + WR.Qv[ai])\\n nrmi = mpmath.mp.mpc(0, nrm)\\n nrmtwo = mp2 * nrm\\n for yj in range(numys):\\n Y = Yv[yj] * twopi\\n summa = mp0\\n summa_neg = mp0\\n fak = dict()\\n for j in range(Qs[yj], Qf[yj] + 1):\\n fak[j] = mpmath.mp.exp(-nri * Xm[yj][j])\\n if(do_neg):\\n fak_neg = dict()\\n for j in range(Qs[yj], Qf[yj] + 1):\\n fak_neg[j] = mpmath.mp.exp(-nrmi * Xm[yj][j])\\n for bi in range(IA, IB + 1):\\n mbi = -bi % abD\\n # print \\\"mbi=\\\",mbi\\n for l in range(Ms, Mf + 1):\\n if C[bi][l] == 0 or abs(C[bi][l]).ae(mp0):\\n if verbose > 0:\\n print(\\\"Skip coeff {0}, {1}\\\".format(bi, l))\\n # continue\\n lr = mpmath.mp.mpf(l + WR.Qv[bi])\\n ilr = mpmath.mp.mpc(0, lr)\\n Vtmp = mp0\\n Vtmp_neg = mp0\\n for j in range(Qs[yj], Qf[yj] + 1):\\n if mbi != bi:\\n ch = Cv[yj][j][ai, bi] + sym_type * Cv[yj][j][ai, mbi]\\n else:\\n ch = Cv[yj][j][ai, bi]\\n if ch == 0 or ch.ae(mp0):\\n continue\\n tmp = (ch * mpmath.exp(ilr * Xpb[yj][j])) * gamma_fak[yj, bi, l, j]\\n Vtmp = Vtmp + tmp * fak[j]\\n if(do_neg):\\n Vtmp_neg = Vtmp_neg + tmp * fak_neg[j]\\n summa = summa + Vtmp * C[bi][l]\\n if(do_neg):\\n summa_neg = summa_neg + Vtmp_neg * C[bi][l]\\n if verbose > 1:\\n print(\\\"summa({0})={1}\\\".format(yj, summa))\\n if(do_neg):\\n print(\\\"summa_neg({0})={1}\\\".format(yj, summa_neg))\\n wsumma = mp0\\n wsumma_neg = mp0\\n for (beta, m) in PP:\\n app = mpmath.mp.mpf(PP[(beta, m)])\\n lr = mpmath.mp.mpf(m + WR.Qv[betai[beta]])\\n tmpsumma = mp0\\n tmpsumma_neg = mp0\\n for j in range(Qs[yj], Qf[yj] + 1):\\n if betai[beta] != mbetai[beta]:\\n ch = Cv[yj][j][ai, betai[beta]] + sym_type * Cv[yj][j][ai, mbetai[beta]]\\n else:\\n ch = Cv[yj][j][ai, betai[beta]]\\n if ch == 0 or ch.ae(mp0):\\n continue\\n tmp = ch * mpmath.exp(lr * Zipb[yj][j])\\n tmpsumma = tmpsumma + tmp * fak[j]\\n if do_neg:\\n tmpsumma_neg = tmpsumma_neg + tmp * fak_neg[j]\\n wsumma = wsumma + app * tmpsumma\\n if do_neg:\\n wsumma_neg = wsumma_neg + app * tmpsumma_neg\\n if verbose > 1:\\n print(\\\"wsumma({0})={1}\\\".format(yj, wsumma))\\n if(do_neg):\\n print(\\\"wsumma_neg({0})={1}\\\".format(yj, wsumma_neg))\\n sumtmp = (summa + wsumma) * QQ[yj]\\n if ((D[ai], n) in PP) > 0:\\n sum_tmp = sum_tmp - PP[(D[ai], n)] * mpmath.mp.exp(-nr * Y)\\n lhs = mpmath.mp.exp(nr * Y)\\n if verbose > 0:\\n print(\\\"exp(2pinY)={0}\\\".format(mppr(lhs)))\\n ctmp[yj] = sumtmp * lhs\\n if(do_neg):\\n sumtmp_neg = (summa_neg + wsumma_neg) * QQ[yj]\\n if (D[ai], -n) in PP:\\n sumtmp_neg = sumtmp_neg - PP[(D[ai], -n)] * mpmath.mp.exp(-nrm * Y)\\n lhs = mpmath.gammainc(kint, abs(nrmtwo) * Y) * mpmath.mp.exp(-nrm * Y)\\n if verbose > 0:\\n print(\\\"Gamma(1-k,4pinY)={0}\\\".format(mppr(lhs)))\\n ctmp_neg[yj] = sumtmp_neg / lhs\\n # end for yj\\n if(verbose > -1):\\n print(\\\"C1[{0},{1}]={2}\\\".format(n, ai, ctmp[0].real))\\n print(\\\"C2[{0},{1}]={2}\\\".format(n, ai, ctmp[1].real))\\n if(do_neg):\\n print(\\\"C1[{0},{1}]={2}\\\".format(-n, ai, ctmp_neg[0].real))\\n print(\\\"C2[{0},{1}]={2}\\\".format(-n, ai, ctmp_neg[1].real))\\n if(do_neg):\\n err_pos = abs(ctmp[1] - ctmp[0])\\n err_neg = abs(ctmp_neg[1] - ctmp_neg[0])\\n err = err_pos # max(err_pos,err_neg)\\n if(verbose > -1):\\n print(\\\"err_pos={0}\\\".format(mppr(err_pos)))\\n print(\\\"err_neg={0}\\\".format(mppr(err_neg)))\\n else:\\n err = abs(ctmp[1] - ctmp[0])\\n if(verbose > -1):\\n print(\\\"err={0}\\\".format(mppr(err)))\\n if verbose > 0:\\n if list(C.keys()).count(ai) > 0:\\n if n in C[ai]:\\n print(\\\"Cin({0},{1})={2}\\\".format(ai, n, C[ai][n].real))\\n if -n in C[ai]:\\n print(\\\"Cin({0},{1})={2}\\\".format(ai, -n, C[ai][-n].real))\\n sys.stdout.flush()\\n if(err > eps):\\n\\n # Have to modify\\n if verbose > 0:\\n print(\\\" Need to decrease Y!\\\")\\n Y0 = Yv[0] * Yfak\\n # Qadd=Qadd+10\\n # Yv[0]=Y0; Yv[1]=Y0*mpmath.mp.mpf(0.95)\\n NAA = n\\n IAA = ai\\n raise StopIteration()\\n else:\\n Cout[ai][n] = ctmp[1]\\n if(do_neg):\\n Cout[ai][-n] = ctmp_neg[1]\\n if verbose > 0:\\n print(\\\"OK! av={0}\\\".format((ctmp[1] + ctmp[0]) / mpmath.mpf(2)))\\n # If we are in the range of the used C's we update\\n # if(C.keys().count(ai)>0):\\n # if(C[ai].keys().count(n)>0):\\n # C[ai][n]=ctmp[1]\\n # if(do_neg and C[ai].keys().count(-n)>0):\\n # C[ai][-n]=ctmp_neg[1]\\n # continue\\n # end for ai\\n # print \\\"n at end=\\\",n\\n # end for n\\n return Cout\\n except StopIteration:\\n if verbose > 0:\\n print(\\\"Iteration stopped!\\\")\\n continue\\n raise StopIteration()\",\n \"def buildP(self):\\r\\n\\r\\n print 'Building P ...'\\r\\n\\r\\n #---Building p(y|x)---#\\r\\n pygx_params_mlp = MLP(activations=self.hyper['pygx_activs'],\\r\\n dims=self.hyper['pygx_dims'],\\r\\n weights_init=self.hyper['pygx_W_init'],\\r\\n biases_init=Constant(0))\\r\\n\\r\\n pygx_params = pygx_params_mlp.apply(self.x.reshape((self.x.shape[0]*self.x.shape[1],self.x.shape[2])))\\r\\n pygx_params = pygx_params.reshape((self.x.shape[0],self.x.shape[1],2*self.hyper['y_dim']))\\r\\n pygx_params_mlp.initialize()\\r\\n\\r\\n # self.pygx_mu.shape == (minibatch size, L_x , num of dimension of y)\\r\\n self.pygx_mu = pygx_params[:,:,:self.hyper['y_dim']]\\r\\n\\r\\n # self.pygx_var.shape == (minibatch size, L_x, num of dimension of y)\\r\\n self.pygx_var = T.exp( pygx_params[:,:,self.hyper['y_dim']:] )\\r\\n\\r\\n\\r\\n #---Building graph for the density of p(y|x)---#\\r\\n little_num = 10**(-32)\\r\\n inside_exp = -T.sum((self.y.dimshuffle(0,'x',1) - self.pygx_mu)**2/(2*self.pygx_var), axis=2)\\r\\n norm_cst = (2*np.pi)**(-self.hyper['y_dim']/2.)*T.exp(T.sum(T.log(self.pygx_var), axis=2))**(-1/2.)\\r\\n\\r\\n # shape == (minibatch size, # of x samples)\\r\\n pygx = norm_cst*T.exp(inside_exp)\\r\\n\\r\\n # shape == (minibatch size, # of x samples)\\r\\n self.log_pygx = T.log(pygx + little_num)\\r\\n\\r\\n #---Building NN for p(x|z=j,w) for all j---#\\r\\n pxgzw_mus = [None]*self.hyper['num_clust']\\r\\n pxgzw_vars = [None]*self.hyper['num_clust']\\r\\n pxgzw = [None]*self.hyper['num_clust']\\r\\n\\r\\n for j in range(self.hyper['num_clust']):\\r\\n\\r\\n pxgzw_params_mlp = MLP(activations=self.hyper['pxgzw_activs'][j],\\r\\n dims=self.hyper['pxgzw_dims'][j],\\r\\n weights_init=self.hyper['pxgzw_W_init'],\\r\\n biases_init=Constant(0))\\r\\n\\r\\n pxgzw_params = pxgzw_params_mlp.apply(self.w.reshape((self.w.shape[0]*self.w.shape[1],self.w.shape[2])))\\r\\n pxgzw_params = pxgzw_params.reshape((self.w.shape[0],self.w.shape[1], 2*self.hyper['x_dim']))\\r\\n pxgzw_params_mlp.initialize()\\r\\n\\r\\n # pxgzw_mus[j].shape == (minibatch size, L_w , num of dimension of x)\\r\\n pxgzw_mus[j] = pxgzw_params[:,:,:self.hyper['x_dim']]\\r\\n\\r\\n # pxgzw_vars[j].shape == (minibatch size, L_w, num of dimension of x)\\r\\n pxgzw_vars[j] = T.exp( pxgzw_params[:,:,self.hyper['x_dim']:] )\\r\\n\\r\\n #---Building graph for the density of p(x|z=j,w)---#\\r\\n little_num = 10**(-32)\\r\\n inside_exp = -T.sum((self.x.dimshuffle(0,'x',1,2) - pxgzw_mus[j].dimshuffle(0,1,'x',2))**2/(2*pxgzw_vars[j].dimshuffle(0,1,'x',2)), axis=3)\\r\\n norm_cst = (2*np.pi)**(-self.hyper['x_dim']/2.)*T.exp(T.sum(T.log(pxgzw_vars[j]), axis=2))**(-1/2.)\\r\\n\\r\\n # shape == (minibatch size, # of w samples (L_w), # of x samples (L_x))\\r\\n pxgzw[j] = norm_cst.dimshuffle(0,1,'x')*T.exp(inside_exp)\\r\\n\\r\\n\\r\\n # shape is (minibatch size, L_w , # of clusters , num of dimension of x)\\r\\n self.pxgzw_mus = T.concatenate([mu.dimshuffle(0,1,'x',2) for mu in pxgzw_mus], axis=2)\\r\\n # shape is (minibatch size, L_w , # of clusters , num of dimension of x)\\r\\n self.pxgzw_vars = T.concatenate([var.dimshuffle(0,1,'x',2) for var in pxgzw_vars], axis=2)\\r\\n\\r\\n # self.pxgzw.shape == (minibatch size, L_w, L_x, num_clust)\\r\\n self.pxgzw = T.concatenate([density.dimshuffle(0,1,2,'x') for density in pxgzw], axis=3)\\r\\n self.log_pxgzw = T.log(self.pxgzw + little_num)\\r\\n\\r\\n #---Building the p(z=j|x,w) posterior for all j---#\\r\\n # self.log_pzgxw.shape == (minibatch size, L_w, L_x, num_clust)\\r\\n self.log_pzgxw = T.log(self.pxgzw + little_num) -T.log(T.sum(self.pxgzw + little_num, axis=3).dimshuffle(0,1,2,'x'))\",\n \"def betabinom_artifact_model_probability(data,\\n prior_p=betabinom_p_prior[0],\\n prior_beta=betabinom_beta_prior[0],\\n mutation_object=True):\\n\\n if mutation_object is True:\\n trajectories = data.data\\n else:\\n trajectories = data\\n\\n # initialise list of samples for p integration\\n int_p = []\\n # integral over betabinom p parameter\\n for p_sample in prior_p[0, :]:\\n # initialise list of samples for beta integration\\n int_beta = []\\n # integrate over beta parameter\\n for beta_sample in prior_beta[0, :]:\\n # For each combination of p and beta parameters compute\\n # lieklihood of observing a given time-series conditional\\n # on initial time-point.\\n alpha_sample = beta_sample*p_sample/(1-p_sample)\\n # compute likelihood for each individual omiting first time point\\n ind_likelihoods = []\\n for ind_traj in trajectories:\\n likelihood = betabinom.pmf(k=ind_traj[1:].AO,\\n n=ind_traj[1:].DP,\\n a=alpha_sample,\\n b=beta_sample)\\n ind_likelihoods.append(np.product(likelihood))\\n # for each beta append the total likelihood (product individuals).\\n int_beta.append(np.product(ind_likelihoods))\\n # For each p, compute the likelihood marginalised over beta\\n int_p.append(\\n np.trapz(x=prior_beta[0, :],\\n y=int_beta*prior_beta[1, :]))\\n \\n # marginalise likelihood over p\\n mutation_prob = np.trapz(x=prior_p[0, :],\\n y=int_p*prior_p[1, :])\\n\\n if mutation_object is True:\\n # return updated model_comparison object \\n data.betabinom_artifact_prob = mutation_prob\\n return data\\n else:\\n # return marginalised likelihood.\\n return mutation_prob\",\n \"def p_ym_c(pm,px,py,pyx_c,pmx_c):\\n pym_c = np.zeros((py.size,pm.size))\\n for yi in range(py.size):\\n for mi in range(pm.size):\\n for xi in range(px.size):\\n pym_c[yi,mi] += (1./pm[mi])*pyx_c[yi,xi]*pmx_c[mi,xi]*px[xi]\\n return pym_c\",\n \"def get_model():\\n model = ecole.scip.Model.from_file(str(DATA_DIR / \\\"bppc8-02.mps\\\"))\\n model.disable_cuts()\\n model.disable_presolve()\\n model.set_param(\\\"randomization/permuteconss\\\", True)\\n model.set_param(\\\"randomization/permutevars\\\", True)\\n model.set_param(\\\"randomization/permutationseed\\\", 784)\\n model.set_param(\\\"randomization/randomseedshift\\\", 784)\\n model.set_param(\\\"randomization/lpseed\\\", 784)\\n return model\",\n \"def run_pca(df, cols=None): \\n import numpy as np\\n import pandas as pd\\n import matplotlib.pyplot as plt\\n from mpl_toolkits.mplot3d import Axes3D\\n from sklearn.preprocessing import StandardScaler\\n from sklearn.decomposition import PCA\\n import mpld3\\n\\n # Define and markers to use for different categories\\n groups_dict = {(u'D', 0):('Germany, unregulated', 'g', 'o'),\\n (u'N', 0):('Norway, unregulated', 'b', 'o'),\\n (u'D', 1):('Germany, regulated', 'g', '^'),\\n (u'N', 1):('Norway, regulated', 'b', '^')}\\n \\n # Extract cols of interest\\n cats = df[['country', 'regulated']]\\n\\n if cols:\\n df = df[cols].astype(float)\\n\\n # Standardise the feature data\\n feat_std = StandardScaler().fit_transform(df)\\n\\n # Setup PCA. Initially, choose to keep ALL components\\n pca = PCA()\\n\\n # Fit model\\n pca.fit(feat_std)\\n\\n # Get explained variances (in %)\\n var_exp = 100*pca.explained_variance_ratio_\\n cum_exp = np.cumsum(var_exp)\\n\\n # Get eigenvalues\\n cov_mat = np.cov(feat_std.T)\\n eig_vals, eig_vecs = np.linalg.eig(cov_mat)\\n\\n # Get number of EVs > 1 (Kaiser-Guttman criterion)\\n # and print summary\\n n_kgc = (eig_vals > 1).sum()\\n print 'Variance explained by first %s PCs (%%):\\\\n' % n_kgc\\n print var_exp[:n_kgc]\\n print '\\\\nTotal: %.2f%%' % var_exp[:n_kgc].sum()\\n \\n # Plot\\n fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(16, 6))\\n \\n # Explained variance\\n axes[0].bar(range(1, len(var_exp)+1), var_exp, \\n align='center', label='Individual components')\\n axes[0].plot(range(1, len(cum_exp)+1), cum_exp, \\n 'r-o', label='Cumulative')\\n axes[0].set_xlabel('Principal component')\\n axes[0].set_ylabel('Variance explained (%)')\\n axes[0].legend(loc='center right')\\n \\n # Eigenvalues\\n axes[1].plot(range(1, len(eig_vals)+1), np.sort(eig_vals)[::-1], \\n 'r-o', label='Eigenvalues')\\n axes[1].axhline(1, c='k', ls='-', label='Kaiser-Guttman threshold')\\n axes[1].set_xlabel('Principal component')\\n axes[1].set_ylabel('Eigenvalue')\\n axes[1].legend(loc='upper right') \\n \\n # PC loadings\\n loads = pd.DataFrame(data=pca.components_, \\n columns=df.columns,\\n index=range(1, pca.components_.shape[0]+1)).T\\n\\n # Project into 2 and 3 components\\n fig = plt.figure(figsize=(16, 6))\\n \\n # Plot 2 components\\n ax = fig.add_subplot(1, 2, 1)\\n \\n # Refit the PCA, this time specifying 2 components\\n # and transforming the result\\n feat_reduced = PCA(n_components=2).fit_transform(feat_std)\\n \\n # Build df \\n data = pd.DataFrame({'PC1':feat_reduced[:, 0],\\n 'PC2':feat_reduced[:, 1],\\n 'country':cats['country'],\\n 'regulated':cats['regulated']}) \\n\\n groups = data.groupby(['country', 'regulated'])\\n \\n # Plot\\n for name, group in groups:\\n ax.scatter(group['PC1'], group['PC2'], s=60,\\n label=groups_dict[name][0],\\n c=groups_dict[name][1],\\n marker=groups_dict[name][2])\\n \\n ax.set_xlabel('First principal component')\\n ax.set_ylabel('Second principal component')\\n ax.set_title('First two PCA directions')\\n \\n # Plot 3 components\\n ax = fig.add_subplot(1, 2, 2, projection='3d', \\n elev=-150, azim=135)\\n\\n # Refit the PCA, this time specifying 3 components\\n # and transforming the result\\n feat_reduced = PCA(n_components=3).fit_transform(feat_std)\\n\\n # Build df with colours\\n data = pd.DataFrame({'PC1':feat_reduced[:, 0],\\n 'PC2':feat_reduced[:, 1],\\n 'PC3':feat_reduced[:, 2],\\n 'country':cats['country'],\\n 'regulated':cats['regulated']}) \\n \\n groups = data.groupby(['country', 'regulated'])\\n \\n # Plot\\n for name, group in groups:\\n ax.scatter(group['PC1'], group['PC2'], group['PC3'],\\n label=groups_dict[name][0],\\n c=groups_dict[name][1],\\n marker=groups_dict[name][2],\\n s=60)\\n \\n ax.set_title('First three PCA directions')\\n ax.set_xlabel('First principal component')\\n ax.set_ylabel('Second principal component')\\n ax.set_zlabel('Third principal component')\\n ax.legend(bbox_to_anchor=(0.15, -0.1), frameon=True)\\n plt.show()\\n\\n return loads\",\n \"def jointUncertaintyMilp(mdp, oldPi, oldZC, unknownFeatStates, costOfQuery):\\n m = Model()\\n m.setParam('OutputFlag', False)\\n\\n # convert notation to previous implementation\\n S = mdp.S\\n A = mdp.A\\n R = mdp.rFuncs\\n psi = mdp.psi\\n T = mdp.T\\n alpha = mdp.alpha\\n gamma = mdp.gamma\\n\\n # useful constants\\n rLen = len(R)\\n M = 10000 # a large number\\n Sr = range(len(S))\\n Ar = range(len(A))\\n\\n # decision variables\\n x = m.addVars(len(S), len(A), lb=0, name='x')\\n y = m.addVars(rLen, name='y')\\n # y prime, a helper variable\\n y0 = m.addVars(rLen, name='y0', lb=0)\\n\\n # oldPi is a mapping from state, action (in S x A) to occupancy\\n # to be consistent with x, convert it to a mapping from (s, a) where s in Sr, a in Ar\\n oldX = {(s, a): oldPi[S[s], A[a]] for s in Sr for a in Ar}\\n\\n # integer variables\\n zR = m.addVars(rLen, vtype=GRB.BINARY, name='zR')\\n zC = m.addVars(len(unknownFeatStates), vtype=GRB.BINARY, name='zC')\\n # zCNew indicates the newly changed features by x. note that it does not need to be constrained as integers\\n zCNew = m.addVars(len(unknownFeatStates), lb=0, name='zCNew')\\n\\n zSafe = m.addVar(vtype=GRB.BINARY, name='zSafe')\\n\\n V = lambda x_local, r: sum([x_local[s, a] * r(S[s], A[a]) for s in Sr for a in Ar])\\n\\n # (a) flow conservation constraint\\n for sp in Sr:\\n m.addConstr(sum(x[s, a] * ((s == sp) - gamma * T(S[s], A[a], S[sp])) for s in Sr for a in Ar) == alpha(S[sp]))\\n\\n # (b) is encoded in the transition function\\n\\n for consIdx in range(len(unknownFeatStates)):\\n # (c) unknown features can be changed\\n m.addConstr(M * zC[consIdx] >= sum(x[S.index(s), A.index(a)] for s in unknownFeatStates[consIdx] for a in A))\\n # (d) constrain z^{new}_\\\\phi, note that lb of zCNew is 0\\n m.addConstr(zCNew[consIdx] >= zC[consIdx] - oldZC[consIdx])\\n\\n # (e) constraints on y^0_r\\n m.addConstr(sum(zC[idx] for idx in range(len(oldZC)) if oldZC[idx] == 1) <= sum(oldZC) - 1 + zSafe * M)\\n for i in range(rLen):\\n m.addConstr(y0[i] >= V(oldX, R[i]) - (1 - zSafe) * M)\\n\\n # (f) constraints on y_r\\n for i in range(rLen):\\n m.addConstr(y[i] <= V(x, R[i]) - y0[i] + (1 - zR[i]) * M)\\n m.addConstr(y[i] <= 0 + zR[i] * M)\\n\\n # obj\\n m.setObjective(sum([psi[i] * y[i] for i in xrange(rLen)])\\n - sum(zC[idx] * costOfQuery for idx in range(len(unknownFeatStates))),\\n GRB.MAXIMIZE)\\n\\n m.optimize()\\n\\n pi = {(S[s], A[a]): x[s, a].X for s in Sr for a in Ar}\\n\\n if config.VERBOSE:\\n # print decision variables other than pi for debugging\\n print 'oldZC', oldZC\\n print 'zC', [zC[consIdx].X for consIdx in range(len(unknownFeatStates))]\\n print 'y0 values', [y0[rIdx].X for rIdx in range(rLen)]\\n print 'y values', [y[rIdx].X for rIdx in range(rLen)]\\n\\n if m.status == GRB.Status.OPTIMAL:\\n # return feasible being true and the obj value, opt pi\\n # .X attribute is to retrieve the value of the variable\\n return pi\\n else:\\n # simply return infeasible\\n raise Exception('milp problem optimal solution not found' + m.status)\",\n \"def collapsed_theta_gibbs_sample(self, X):\\n\\n K = self.K # number of topics\\n M, V = X.shape\\n alpha = self.alpha\\n lmda = self.lmda\\n topics = np.arange(0, K)\\n\\n #initialize everything uniformly\\n Beta = np.ones(shape=(K, V), dtype=float) / V\\n for k in topics:\\n Beta[k] = np.random.dirichlet(np.ones(V)*lmda)\\n\\n props = np.zeros(shape=(M, K), dtype=float)\\n #Current state\\n Ns = np.array(range(M), dtype=object)\\n\\n #Running sum\\n MC_z = np.array(range(M), dtype=object)\\n MC_beta = np.zeros(shape=(K, V), dtype=float)\\n\\n for d in range(M):\\n #allocate topics randomly\\n word_indices = X[d, :].nonzero()[1]\\n p = np.random.dirichlet(np.ones(K) * alpha)\\n #TODO: Remove the p argument to get completely random topic assignments\\n random_ks = np.random.choice(topics, size=len(word_indices), p=p)\\n Ns[d] = sp.coo_matrix((np.ones(len(word_indices)),\\n (word_indices, random_ks)), shape=(V, K)).tolil()\\n MC_z[d] = sp.coo_matrix((V, K)).tolil()\\n\\n log_Xs = []\\n perplexities =[]\\n t = 0\\n for epoch in xrange(self.nr_em_epochs):\\n t+=1\\n C = np.zeros((K, V))\\n print \\\"Epoch\\\", epoch\\n for d in np.random.permutation(np.arange(M)):\\n x = X[d]\\n N_d = Ns[d]\\n\\n for v in np.nonzero(x)[1]:\\n except_v = [v_prime for v_prime in np.nonzero(x)[1] if v_prime != v]\\n N_d_except_v = N_d[except_v, :]\\n old_z_n = N_d[v, :].nonzero()[1][0]\\n p = (np.sum(N_d_except_v.A, axis=0) + alpha) * Beta[:, v]\\n p = p.clip(min=0)\\n p = p/np.sum(p)\\n z_n = np.random.choice(topics, p = p)\\n N_d[v, old_z_n] = 0\\n N_d[v, z_n] = 1\\n Ns[d] = N_d\\n MC_z[d] += N_d\\n C = C + N_d.A.T\\n\\n # Sample beta given all z and thetas\\n for k in topics:\\n c_beta = C[k, :]\\n Beta[k, :] = np.random.dirichlet(c_beta + lmda)\\n MC_beta += Beta\\n\\n log_X = 0\\n Beta_hat = MC_beta / t\\n for d in range(M):\\n props_d = np.sum(MC_z[d].A, axis=0)\\n props_d /= float(np.sum(props_d))\\n props[d] = props_d\\n ixw = np.nonzero(X[d, :])[1]\\n log_X += np.sum(_doc_probability_from_p_of_z(props_d, Beta_hat[:, ixw]))\\n\\n log_Xs.append(log_X)\\n print log_X\\n perplexities.append(self._perplexity(X, log_X))\\n\\n return props, Beta_hat, log_Xs, perplexities\",\n \"def _getBMat(self):\\n\\n \\\"\\\"\\\"B matrix is just a mass matrix, can be easily assembled\\n through fenics. However, the ordering in Fenics is not the\\n mesh ordering. So we build a temp matrix and then use the\\n vertex to dof map to get the right ordering interms of our\\n mesh nodes.\\n \\\"\\\"\\\"\\n\\n # create function space of order 1. For KL, we only restrict\\n # to first order spaces.\\n V = FunctionSpace(self._mesh, \\\"CG\\\", 1)\\n # Define basis and bilinear form\\n u = TrialFunction(V)\\n v = TestFunction(V)\\n a = u * v * dx\\n # assemble in a temp matrix\\n B_temp = assemble(a)\\n\\n # create petsc matrix B\\n B = PETSc.Mat().create()\\n B.setType('aij')\\n B.setSizes(self.domain.getNodes(), self.domain.getNodes())\\n B.setUp()\\n\\n # store the value in a a temp array B_ij\\n B_ij = B_temp.array()\\n\\n # get the vertex to dof map\\n v_to_d_map = vertex_to_dof_map(V)\\n\\n print '---------------------------'\\n print '---------------------------'\\n print ' Building Mass Matrix '\\n print '---------------------------'\\n print '---------------------------'\\n for node_i in range(0, self.domain.getNodes()):\\n for node_j in range(node_i, self.domain.getNodes()):\\n B_ij_nodes = B_ij[v_to_d_map[node_i], v_to_d_map[node_j]]\\n if B_ij_nodes > 0:\\n B.setValue(node_i, node_j, B_ij_nodes)\\n B.setValue(node_j, node_i, B_ij_nodes)\\n\\n B.assemblyBegin()\\n B.assemblyEnd()\\n print '---------------------------'\\n print '---------------------------'\\n print ' Finished Mass Matrix '\\n print '---------------------------'\\n print '---------------------------'\\n return B\",\n \"def model_method(data, **prior_params):\\n weight_m = np.vstack((data.weight_std, data.weight_std**2, data.weight_std**3))\\n with pm.Model() as model:\\n alpha = pm.Normal('alpha', mu=prior_params['alpha mu'], sd=prior_params['alpha sd'])\\n beta = pm.Normal('beta', mu=prior_params['beta mu'], sd=prior_params['beta sd'], shape=3)\\n sigma = pm.Uniform('sigma', lower=prior_params['sigma lower'], upper=prior_params['sigma upper'])\\n mu = pm.Deterministic('mu', alpha + pm.math.dot(beta, weight_m))\\n height = pm.Normal('height', mu=mu, sd=sigma, observed=data.height)\\n return model\",\n \"def MILP_1(self,xData,uData):\\n print \\\"\\\\n\\\",\\\"*\\\"*80,\\\"\\\\n\\\",\\\"MILP 1: Parameter Estimation\\\\n\\\",\\\"*\\\"*80 \\n model=Model(\\\"parameters\\\")\\n outflow={}\\n d={}\\n bigM=500\\n Q_out={}\\n Q_in={}\\n N=max(l[1] for l in xData.keys())\\n print \\\"x Data size is\\\",N\\n N=max(l[1] for l in uData.keys())\\n print \\\"u Data size is\\\",N\\n for l in self.links:\\n l.d=model.addVar(lb=0,ub=200,obj=0*l.type==\\\"road\\\") \\n for t in range(1,N):\\n d[l,t]=model.addVar(lb=0,ub=200,obj=1)\\n for k in l.outgoing:\\n outflow[l,k,t]=model.addVar(lb=0,ub=200)\\n self.c[l,k]=model.addVar(lb=20,ub=200)\\n self.beta[l,k]=model.addVar(lb=0.2,ub=0.8)\\n self.alpha[l,k]=model.addVar(lb=0,ub=1)\\n self.M[l,k]=model.addVar(lb=0,ub=200)\\n d[\\\"outflow-1\\\",l,k,t]=model.addVar(vtype=GRB.BINARY) \\n d[\\\"outflow-2\\\",l,k,t]=model.addVar(vtype=GRB.BINARY) \\n model.update()\\n for t in range(1,N):\\n for l in self.links:\\n if True:\\n Q_out[l,t]=LinExpr()\\n Q_in[l,t]=LinExpr()\\n Q_out[l,t].addConstant(0)\\n Q_in[l,t].addConstant(0)\\n for k in l.outgoing:\\n model.addConstr(outflow[l,k,t]<=self.beta[l,k]*uData[l,t]*xData[l,t])\\n model.addConstr(outflow[l,k,t]<=self.M[l,k])\\n model.addConstr(outflow[l,k,t]<=self.c[l,k]-self.alpha[l,k]*xData[k,t])\\n model.addConstr(outflow[l,k,t]>=self.beta[l,k]*uData[l,t]*xData[l,t]+bigM*d[\\\"outflow-1\\\",l,k,t]-bigM)\\n model.addConstr(outflow[l,k,t]>=self.M[l,k]+bigM*d[\\\"outflow-2\\\",l,k,t]-bigM)\\n model.addConstr(outflow[l,k,t]>=self.c[l,k]-self.alpha[l,k]*xData[k,t]-bigM*d[\\\"outflow-1\\\",l,k,t]-bigM*d[\\\"outflow-2\\\",l,k,t])\\n Q_out[l,t].add(outflow[l,k,t])\\n for k in l.incoming:\\n Q_in[l,t].add(outflow[k,l,t])\\n if l.type==\\\"road\\\":\\n model.addConstr(xData[l,t+1]<=xData[l,t]- Q_out[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival) \\n else:\\n model.addConstr(xData[l,t+1]<=xData[l,t]- uData[l,t]*xData[l,t] + Q_in[l,t] + d[l,t] + l.lambda_arrival)\\n for l in self.links:\\n sum=LinExpr()\\n for k in l.outgoing:\\n sum.add(self.beta[l,k])\\n model.addConstr(sum>=0)\\n \\n# J=QuadExpr()\\n# for l in self.links:\\n# for t in range(1,N):\\n# if l.type==\\\"road\\\":\\n# J.add(d[l,t]*d[l,t])\\n# model.setObjective(J)\\n model.optimize()\\n for l in self.links:\\n l.d=l.d.X\\n for k in l.outgoing:\\n self.beta[l,k]=self.beta[l,k].X\\n self.c[l,k]=self.c[l,k].X\\n self.alpha[l,k]=self.alpha[l,k].X\\n self.M[l,k]=self.M[l,k].X\\n for l in self.links:\\n for t in range(1,N):\\n l.d=max(d[l,t].X,l.d)\\n \\n \\n\\n \\n if True:\\n for t in range(1,N):\\n print \\\"*\\\"*80,\\\"time=\\\",t\\n for l in self.links:\\n print \\\"\\\\n\\\",l,\\\"x is\\\",xData[l,t],\\\"u is\\\",uData[l,t],\\\"x+ is\\\",xData[l,t+1]\\n for k in l.outgoing:\\n print k,\\\"beta:\\\",self.beta[l,k],\\\"outflow\\\",outflow[l,k,t].X\",\n \"def __init__(self,X, Y, X_star, Y_star, K=50, alpha=1.,J=2,\\n N_minibatch = 1000, full_cov=True, mb_upweight=True,\\n prior_obs=1.):\\n self.start_time = time.time()\\n self.comm = MPI.COMM_WORLD\\n self.P = self.comm.Get_size()\\n self.rank = self.comm.Get_rank()\\n self.total_J = int(J)\\n self.full_cov = bool(full_cov)\\n self.proposal_sizes = [j.size for j in np.array_split(xrange(self.total_J), self.P)]\\n assert(len(self.proposal_sizes) == self.P)\\n assert(sum(self.proposal_sizes) == self.total_J)\\n self.prior_obs = prior_obs\\n assert(self.prior_obs >0.)\\n self.J = self.proposal_sizes[self.rank]\\n self.K = np.zeros(self.J).astype(int)\\n self.alpha=alpha\\n self.X = X\\n self.Y = np.array(Y).reshape(-1,1)\\n (self.N, self.D) = self.X.shape\\n self.M = 3 # num of hyperparameters, fixed to RBF kernel for now\\n self.N_minibatch = N_minibatch\\n self.init_hyp = np.empty((self.J,self.M))\\n assert(self.N_minibatch <= self.N)\\n if mb_upweight:\\n self.mb_weight = float(self.N) / float(self.N_minibatch)\\n else:\\n self.mb_weight = 1.\\n assert(self.N == self.Y.size)\\n self.U = [np.random.choice(self.N,size=self.N_minibatch,replace=False)for j in xrange(self.J)]\\n self.X_mean = self.X.mean(axis=0)\\n self.X_sd = np.sqrt(self.X.var(axis=0))\\n self.W = np.zeros(self.J)\\n # everything J should be a list, K should be a dictionary.\\n self.marginal_LL_k = [None]*self.J\\n Z_map = map(self.partition_data,range(self.J))\\n self.Z_proposal = [Z for Z,Z_count in Z_map]\\n self.Z_count = [Z_count for Z,Z_count in Z_map]\\n self.kernels = [None]*self.J # np.zeros().astype(object) #{j:None for j in xrange(self.J)}\\n self.models = map(self.model_init,xrange(self.J))\\n self.marginal_LL = np.copy(self.W)\\n self.comm.barrier()\",\n \"def simulate_qubit_pairs_3D_lattice(cbrt_n, m):\\n # Initialize some variables\\n n = np.power(cbrt_n, 3)\\n x_max = 20\\n d = np.arange(0, x_max + 1)\\n k_matrix = np.zeros((m, x_max + 1))\\n\\n # Run the simulation a total of m times\\n for i in range(m):\\n start = time.time()\\n\\n sim = chp_py.CHP_Simulation(n)\\n sims = []\\n # Add a sim for depth 0\\n sims.append(chp_py.CHP_Simulation(n, sim.state))\\n\\n for j in range(x_max):\\n gates = utils.get_lattice_gates((cbrt_n, cbrt_n, cbrt_n), j)\\n sim.apply_gates(gates)\\n sims.append(chp_py.CHP_Simulation(n, sim.state))\\n\\n # This computes the collision_probability at different\\n # depths in parallel\\n k_matrix[i, :] = utils.sims_to_collision_probabilty(sims)\\n\\n end = time.time()\\n print(\\\"Time elapsed = {:0.2f}, for sample = {}\\\".format(end - start, i))\\n\\n # Store everything in data\\n data = {\\\"d\\\": d,\\n \\\"k_matrix\\\": k_matrix,\\n \\\"n\\\": n,\\n \\\"m\\\": m}\\n\\n return data\",\n \"def correction_factor(x_diff, y_diff, x_intersection, y_intersection, n_mc = 40000):\\n \\n # The intersection model\\n x_sq = x_intersection ** 2\\n x_input = np.concatenate((x_intersection, x_sq), axis = 1)\\n\\n #MCMC model - correction factors and shif\\n x_shared = theano.shared(x_input)\\n gp_mean_coeff = np.array([0, epsilon, c])\\n\\n gamma_alpha = 1\\n gamma_beta = 10\\n\\n inv_gamma_alpha = 1\\n inv_gamma_beta = 10\\n\\n with pm.Model() as gp_posteriors_model:\\n #Priors\\n tau_sq = pm.InverseGamma(\\\"tau_sq\\\", alpha = inv_gamma_alpha, beta = inv_gamma_beta)\\n sigma_sq = pm.InverseGamma(\\\"sigma_sq\\\", alpha = 10, beta= 1)\\n lamb_sq = pm.Gamma(\\\"lamb_sq\\\", alpha = gamma_alpha, beta = gamma_beta, shape = 2)\\n theta = pm.Normal(\\\"theta\\\", mu= 0, sd = 1)\\n\\n #Shared variables for the input\\n x_input_theta = tt.concatenate([x_shared, tt.tile(theta, (len(x_input), 1))], axis = 1)\\n\\n #GP definition\\n #Mean\\n mean_gp = pm.gp.mean.Linear(coeffs = gp_mean_coeff, intercept = 0)\\n #Covariance\\n cov_gp = tau_sq * pm.gp.cov.ExpQuad(x_input.shape[1] + 1, ls = tt.sqrt(lamb_sq) / 4, active_dims = [0,2])\\n #GP\\n gp_model = pm.gp.Marginal(mean_func=mean_gp, cov_func= cov_gp)\\n\\n #Marginal likelihoods\\n y_ = gp_model.marginal_likelihood(\\\"y_\\\", X = x_input_theta, y = y_intersection, noise = tt.sqrt(sigma_sq))\\n trace_priors = pm.sample(n_mc, tune = 10000, chains = 1) \\n \\n # The complement model\\n x_sq = x_diff ** 2\\n x_input = np.concatenate((x_diff, x_sq), axis = 1)\\n\\n #MCMC model - correction factors and shif\\n x_shared = theano.shared(x_input)\\n gp_mean_coeff = np.array([0, epsilon, c])\\n\\n gamma_alpha = 1\\n gamma_beta = 10\\n\\n inv_gamma_alpha = 1\\n inv_gamma_beta = 10\\n\\n with pm.Model() as pymc3_model:\\n #Priors\\n tau_sq = pm.InverseGamma(\\\"tau_sq\\\", alpha = inv_gamma_alpha, beta = inv_gamma_beta)\\n sigma_sq = pm.InverseGamma(\\\"sigma_sq\\\", alpha = 10, beta= 1)\\n lamb_sq = pm.Gamma(\\\"lamb_sq\\\", alpha = gamma_alpha, beta = gamma_beta, shape = 2)\\n theta = pm.Normal(\\\"theta\\\", mu= 0, sd = 1)\\n\\n #Shared variables for the input\\n x_input_theta = tt.concatenate([x_shared, tt.tile(theta, (len(x_input), 1))], axis = 1)\\n\\n #GP definition\\n #Mean\\n mean_gp = pm.gp.mean.Linear(coeffs = gp_mean_coeff, intercept = 0)\\n #Covariance\\n cov_gp = tau_sq * pm.gp.cov.ExpQuad(x_input.shape[1] + 1, ls = tt.sqrt(lamb_sq) / 4, active_dims = [0,2])\\n #GP\\n gp_model = pm.gp.Marginal(mean_func=mean_gp, cov_func= cov_gp)\\n\\n #Marginal likelihoods\\n y_gp = gp_model.marginal_likelihood(\\\"y_\\\", X = x_input_theta, y = y_diff, noise = tt.sqrt(sigma_sq)) \\n \\n \\n log_likelihood = np.empty(0)\\n mc_integral = np.empty(n_mc) \\n logp = y_gp.logp\\n\\n for i in tqdm(range(n_mc), desc = \\\"Log likelihood eval\\\"):\\n log_likelihood = np.append(log_likelihood, logp(trace_priors[i]))\\n \\n for i in range(n_mc):\\n m = max(log_likelihood[:(i + 1)])\\n mc_integral[i] = (np.exp(m) * np.sum(np.exp(log_likelihood[:(i + 1)] - m))) / (i + 1) \\n \\n return log_likelihood, mc_integral\",\n \"def meta_binary_outcome(z_t_obs, n_t_obs, z_c_obs, n_c_obs, study, n_draws=1000):\\n\\n with pm.Model(coords={\\\"study\\\": study}) as model:\\n # Hyper-priors\\n mu_rho = pm.Normal(\\\"mu_rho\\\", mu=0, sigma=10)\\n sigma_rho = pm.Gamma(\\\"sigma_rho\\\", alpha=1.64, beta=0.64) # mode=1, sd=2\\n\\n omega_theta_c = pm.Beta(\\\"omega_theta_c\\\", alpha=1.01, beta=1.01)\\n kappa_minus_two_theta_c = pm.Gamma(\\n \\\"kappa_minus_two_theta_c\\\", alpha=2.618, beta=0.162\\n ) # mode=10, sd=10\\n kappa_theta_c = pm.Deterministic(\\\"kappa_theta_c\\\", kappa_minus_two_theta_c + 2)\\n\\n # Priors\\n rho = pm.Normal(\\\"rho\\\", mu=mu_rho, sigma=sigma_rho, dims=\\\"study\\\")\\n theta_c = pm.Beta(\\n \\\"theta_c\\\",\\n alpha=omega_theta_c * (kappa_theta_c - 2) + 1,\\n beta=(1 - omega_theta_c) * (kappa_theta_c - 2) + 1,\\n dims=\\\"study\\\",\\n )\\n theta_t = pm.Deterministic(\\n \\\"theta_t\\\", pm.invlogit(rho + pm.logit(theta_c))\\n ) # ilogit is logistic\\n\\n # Likelihood\\n z_t = pm.Binomial(\\\"z_t\\\", n_t_obs, theta_t, observed=z_t_obs)\\n z_c = pm.Binomial(\\\"z_c\\\", n_c_obs, theta_c, observed=z_c_obs)\\n\\n # Sample from the posterior\\n idata = pm.sample(draws=n_draws, target_accept=0.90)\\n\\n return model, idata\",\n \"def compute_mixing_coefficients_bot(self):\\n [Ly,N] = self.b.shape\\n z_u_w = self.grid_dict['z_u_w']\\n\\n v_upts = TTTW_func.v2u(self.v)\\n\\n self.sigma_bot = []\\n self.Kv0 = np.zeros([Ly,N+1])\\n self.Kt0 = np.zeros([Ly,N+1])\\n for j in range(Ly):\\n # turbulent velocity sclaes with buoyancy effects neglected\\n ustar2 = self.r_D[j] * np.sqrt(self.u[j,0]**2 + v_upts[j,0]**2)\\n wm = self.vonKar * np.sqrt(ustar2)\\n ws = wm\\n \\n for k in range(1,N):\\n k_w = k\\n k_r = k - 1\\n\\n if k_w < self.kbl[j]: # NEED Zob\\n sigma = np.min( [ ((z_u_w[j,k_w] - z_u_w[j,0] + self.Zob) / (self.hbbl[j] + self.Zob)),1.])\\n if j ==1:\\n self.sigma_bot.append(sigma)\\n a1 = sigma - 2.\\n a2 = 3. - 2.*sigma\\n a3 = sigma - 1.\\n\\n self.Kv0[j,k_w] = wm * self.hbbl[j] * ( sigma * (1. + sigma * ( a1 + a2*self.Gm1_bot[j]+a3*self.dGm1_dS_bot[j]))) \\n self.Kt0[j,k_w] = ws * self.hbbl[j] * ( sigma * (1. + sigma * ( a1 + a2*self.Gt1_bot[j]+a3*self.dGt1_dS_bot[j])))\",\n \"def multiple_premia_gbm(nobs=2000):\\r\\n sigma = .15\\r\\n\\r\\n price = 1\\r\\n strike = np.exp(np.linspace(-.1, .1, nobs))\\r\\n riskfree, maturity = 0, 30/365\\r\\n moneyness = np.log(strike/price) - riskfree * maturity\\r\\n call = np.ones_like(moneyness).astype(bool)\\r\\n call[moneyness < 0] = False\\r\\n\\r\\n model = GBM(GBMParam(sigma=sigma), riskfree, maturity)\\r\\n premium = cosmethod(model, moneyness=moneyness, call=call)\\r\\n plt.plot(strike, premium)\\r\\n plt.show()\",\n \"def construct_pdf_gmm(model, n_pdf):\\n components = []\\n for m, var in zip(model.means_, model.covariances_):\\n components.append(multivariate_normal(m, var)) \\n return np.apply_along_axis(lambda x: model.weights_.dot(np.array([d.pdf(x) for d in components])), 0, np.linspace(0,1,n_pdf))\",\n \"def run(self, C, p0 = None):\\n global algorithm \\n algorithm = AdaptiveMM(self.g, C, p0 = p0, lambda0 = 2000)\\n solve()\",\n \"def setup_rand_gmm(n_components=3, n_features=10,\\n clust_mean_std=2.0,\\n cluster_std=1.0,\\n cov_how='diag',\\n weights_how='uniform',\\n random_state=None):\\n\\n rng = check_random_state(random_state)\\n\\n ##########################\\n # generate cluster means #\\n ##########################\\n\\n # cluster means are generated from a spherical gaussian\\n center_cov = np.eye(n_features) * clust_mean_std ** 2\\n means = rng.multivariate_normal(mean=np.zeros(n_features),\\n cov=center_cov,\\n size=n_components)\\n\\n ################################\\n # generate cluster covariances #\\n ################################\\n\\n if cov_how.lower() == 'diag':\\n # diagonal covariances\\n covariances = np.array([np.eye(n_features) * cluster_std ** 2\\n for _ in range(n_components)])\\n elif cov_how.lower() == 'wishart':\\n raise NotImplementedError # TODO-FEAT: implement this!\\n else:\\n raise ValueError(\\\"cov_how must be one of ['diag', 'wishart']\\\"\\n \\\"not {}\\\".format(cov_how))\\n\\n ############################\\n # generate cluster weights #\\n ############################\\n if weights_how.lower() == 'uniform':\\n weights = rng.dirichlet(np.ones(n_components))\\n elif weights_how.lower() == 'random':\\n weights = np.ones(n_components) / n_components\\n else:\\n raise ValueError(\\\"weights_how must be one of ['random', 'uniform']\\\"\\n \\\"not {}\\\".format(weights_how))\\n\\n return means, covariances, weights\",\n \"def do_fitting(args):\\n (patch_num, imc, indices, fitting_name, data, covariance, frequencies, model_path) = args\\n logging.info(f\\\"Fitting MC: {imc:d}\\\")\\n model_identifier, lnP = hoover.LogProb.load_model_from_yaml(model_path)\\n lnP.data_setup(data=data, covariance=covariance, frequencies=frequencies)\\n # Run the optimization.\\n # Note, if using Nelder-Mead, can not write dictionary to\\n # HDF5 file attribute, as simplex is too complex an object.\\n theta_0 = lnP.theta_0()\\n res = minimize(lnP, theta_0, args=(True), method=\\\"Powell\\\")\\n outputs = {}\\n for comp in lnP._components:\\n outputs.update(\\n {f\\\"{comp}\\\": {\\\"T_bar\\\": lnP.get_amplitude_expectation(res.x, component=comp),\\n \\\"N_T\\\": lnP.get_amplitdue_covariance(res.x, component=comp),}})\\n \\n return patch_num, imc, indices, fitting_name, model_identifier, lnP, res, outputs\",\n \"def semi_analytical_marginal_probj(param_inst, cash_tt, solu_dict,\\n cur_col_prefix_space='_'):\\n\\n choice_set_list = param_inst.model_option['choice_set_list']\\n choice_names_use = param_inst.model_option['choice_names_full_use']\\n choice_names_use = param_inst.model_option['choice_names_use']\\n\\n each_j_prob = solu_dict['each_j_prob']\\n ktp_opti_allJ = solu_dict['ktp_opti_allJ']\\n btp_opti_allJ = solu_dict['btp_opti_allJ']\\n consumption_opti_allJ = solu_dict['consumption_opti_allJ']\\n\\n # btp_fb_opti_allJ = solu_dict['btp_fb_opti_allJ']\\n # btp_ib_opti_allJ = solu_dict['btp_ib_opti_allJ']\\n # btp_fs_opti_allJ = solu_dict['btp_fs_opti_allJ']\\n # btp_il_opti_allJ = solu_dict['btp_il_opti_allJ']\\n\\n trans_prob_list = []\\n simu_output_pd_allj = 0\\n\\n for ctr, choicej in enumerate(choice_set_list):\\n\\n cur_col_prefix = choice_names_use[ctr]\\n logger.info('ctr,choicej,cur_col_prefix:\\\\n%s,%s,%s',\\n str(ctr), str(choicej), str(cur_col_prefix))\\n\\n btp_opti = btp_opti_allJ[:, ctr]\\n ktp_opti = ktp_opti_allJ[:, ctr]\\n prob_cur = each_j_prob[:, ctr]\\n consumption_opti = consumption_opti_allJ[:, ctr]\\n\\n '''\\n Get columns at centered interpolating grid points for:\\n 'cash_grid_centered'\\n 'marginal_dist'\\n 'btp_opti_grid',\\n 'ktp_opti_grid',\\n 'consumption_opti_grid' \\n '''\\n logger.info('Solve, P(COH|j), P(COH|COH,j), ctr:%s, name:%s', str(ctr), cur_col_prefix)\\n simu_output_pd_curj, trans_prob_curj = condianaly.semi_analytical_marginal(\\n param_inst,\\n cash_tt, ktp_opti, btp_opti, consumption_opti,\\n each_j_prob=prob_cur,\\n trans_prob_only=True,\\n cur_col_prefix=cur_col_prefix + cur_col_prefix_space)\\n\\n '''Store Choice J Transition Prob'''\\n trans_prob_list.append(trans_prob_curj)\\n\\n '''Update Column Names'''\\n if (ctr == 0):\\n simu_output_pd_allj = simu_output_pd_curj\\n else:\\n '''Cumulate'''\\n simu_output_pd_allj = pd.concat([simu_output_pd_allj,\\n simu_output_pd_curj], axis=1)\\n\\n \\\"\\\"\\\"\\n D. Add columns for each j of J for fbibfsil\\n \\\"\\\"\\\"\\n # D1. Get Columns from just created panda files, j specific columns\\n steady_var_suffixes_dict = hardstring.get_steady_var_suffixes()\\n btp_opti_grid_allJ_cols = [col for col in simu_output_pd_allj.columns if\\n cur_col_prefix_space + steady_var_suffixes_dict['btp_opti_grid'] in col]\\n ktp_opti_grid_allJ_cols = [col for col in simu_output_pd_allj.columns if\\n cur_col_prefix_space + steady_var_suffixes_dict['ktp_opti_grid'] in col]\\n\\n # D2. Matrix from columns\\n btp_opti_grid_allJ = simu_output_pd_allj[btp_opti_grid_allJ_cols].to_numpy()\\n ktp_opti_grid_allJ = simu_output_pd_allj[ktp_opti_grid_allJ_cols].to_numpy()\\n\\n # D3. Get fb ib fs il specific matrixes\\n btp_fb_opti_allJ, btp_ib_opti_allJ, btp_fs_opti_allJ, btp_il_opti_allJ = \\\\\\n fbibfsis.genfibs_btpstack(choice_set_list, btp_opti_grid_allJ, ktp_opti_grid_allJ, param_inst)\\n\\n # D4. Add to simu_output_pd_allj panda\\n fb_ib_fs_il_steady_var_key_list = ['btp_fb_opti_grid', 'btp_ib_opti_grid',\\n 'btp_fs_opti_grid', 'btp_il_opti_grid']\\n varnames_list = [choice_names_use[ctr] + cur_col_prefix_space + steady_var_suffixes_dict[fbibfsil_stdykey]\\n for fbibfsil_stdykey in fb_ib_fs_il_steady_var_key_list\\n for ctr, choicej in enumerate(choice_set_list)]\\n\\n # D5. Additional Panda columns with fb ib fs il information\\n varnames = \\\",\\\".join(map(str, varnames_list))\\n varmat = np.column_stack((btp_fb_opti_allJ, btp_ib_opti_allJ, btp_fs_opti_allJ, btp_il_opti_allJ))\\n simu_output_pd_allj_fbibfsil = proj_sys_sup.debug_panda(varnames, varmat, export_panda=False, log=False)\\n\\n # D6. Concatenate together, join more columns together. \\n simu_output_pd_allj = pd.concat([simu_output_pd_allj, simu_output_pd_allj_fbibfsil], axis=1)\\n\\n \\\"\\\"\\\"\\n E0. Grid Column\\n \\\"\\\"\\\"\\n cash_grid_centered_cols = [col for col in simu_output_pd_allj.columns\\n if steady_var_suffixes_dict['cash_grid_centered'] in col]\\n simu_output_pd_allj['cash_grid_centered'] = simu_output_pd_allj[cash_grid_centered_cols[0]]\\n\\n \\\"\\\"\\\"\\n E1. Adjust Probabilities due to Interpolation issue over J choices\\n \\\"\\\"\\\"\\n logger.info('simu_output_pd_allj.columns:\\\\n%s', simu_output_pd_allj.columns)\\n prob_cols = [col for col in simu_output_pd_allj.columns if 'probJ_opti_grid' in col]\\n logger.info('prob_cols:\\\\n%s', prob_cols)\\n probJ_matrix = simu_output_pd_allj[prob_cols].to_numpy()\\n logger.info('probJ_matrix:\\\\n%s', probJ_matrix)\\n\\n '''These are actually not needed, perfect symmatery'''\\n probJ_matrix_rowsum = np.reshape(np.sum(probJ_matrix, axis=1), (-1, 1))\\n logger.info('probJ_matrix_rowsum:\\\\n%s', probJ_matrix_rowsum)\\n probJ_matrix_rescale_sum1 = probJ_matrix / probJ_matrix_rowsum\\n logger.info('probJ_matrix_rescale_sum1:\\\\n%s', probJ_matrix_rescale_sum1)\\n\\n \\\"\\\"\\\"\\n E2. Overall Conditional probabilities\\n \\\"\\\"\\\"\\n trans_prob_wgtJ = 0\\n trans_prob_dict_allj = {}\\n for ctr, choicej in enumerate(choice_set_list):\\n '''E2a. Transition Probability current j'''\\n trans_prob_curj = trans_prob_list[ctr]\\n\\n '''E2b. Choice Probability over J'''\\n prob_opti_grid = probJ_matrix_rescale_sum1[:, ctr]\\n\\n '''E2c. Update current column with reweighted sum to 1 choice J prob'''\\n simu_output_pd_allj[prob_cols[ctr]] = prob_opti_grid\\n\\n '''E2d. Weighted Discrete Transition Probability'''\\n trans_prob_curj_wgted = trans_prob_curj * np.reshape(prob_opti_grid, (-1, 1))\\n logger.debug('trans_prob_curj:\\\\n%s', trans_prob_curj)\\n logger.debug('prob_opti_grid:\\\\n%s', prob_opti_grid)\\n logger.debug('trans_prob_curj_wgted:\\\\n%s', trans_prob_curj_wgted)\\n\\n '''E2e. Update Column Names'''\\n trans_prob_dict_allj[choicej] = trans_prob_curj\\n if (ctr == 0):\\n trans_prob_wgtJ = trans_prob_curj_wgted\\n else:\\n '''Cumulate'''\\n trans_prob_wgtJ = trans_prob_wgtJ + trans_prob_curj_wgted\\n\\n logger.info('trans_prob_wgtJ:\\\\n%s', trans_prob_wgtJ)\\n logger.info('np.sum(trans_prob_wgtJ):\\\\n%s', np.sum(trans_prob_wgtJ, axis=1))\\n\\n return simu_output_pd_allj, trans_prob_wgtJ, trans_prob_dict_allj\",\n \"def _like3(init_par, alpha, delta, plx_obs, mualpha_obs, mudelta_obs, sigma_obs, ccoef, i):\\r\\n\\r\\n\\tplx_mod, v, sigma_v = init_par[i], init_par[-4:-1], init_par[-1] \\r\\n\\tp, q, r = normalTriad(alpha, delta)\\r\\n\\tmualpha_mod = np.dot(np.transpose(p),v)*plx_mod/_A\\r\\n\\tmudelta_mod = np.dot(np.transpose(q),v)*plx_mod/_A\\r\\n \\t\\r\\n\\tsigma_plx, sigma_mualpha, sigma_mudelta = sigma_obs\\r\\n\\tr_plx_muRa, r_plx_muDec, r_muRa_muDec = ccoef[0], ccoef[1], ccoef[2] \\r\\n\\t\\r\\n\\tC = np.zeros((3,3),dtype=np.float64)\\r\\n\\tC[0,0],C[1,1],C[2,2] = sigma_plx**2.,sigma_mualpha**2., sigma_mudelta**2.\\r\\n\\tC[0,1], C[0,2] = r_plx_muRa*sigma_plx*sigma_mualpha, r_plx_muDec*sigma_plx*sigma_mudelta\\r\\n\\tC[1,0], C[1,2] = r_plx_muRa*sigma_plx*sigma_mualpha, r_muRa_muDec*sigma_mualpha*sigma_mudelta\\r\\n\\tC[2,0], C[2,1] = r_plx_muDec*sigma_plx*sigma_mudelta, r_muRa_muDec*sigma_mualpha*sigma_mudelta\\r\\n\\r\\n\\tE = np.zeros((3,3),dtype=np.float64)\\r\\n\\tE[1,1],E[2,2] = (sigma_v**2.)*(plx_mod/_A)**2., (sigma_v**2.)*(plx_mod/_A)**2.\\r\\n\\t\\r\\n\\tD = np.add(E,C)\\r\\n\\tdetD = det(D) \\r\\n\\tinvD = inv(D)\\r\\n\\t\\t\\r\\n\\ta_c = np.array([plx_obs - plx_mod, mualpha_obs - mualpha_mod, mudelta_obs-mudelta_mod])\\r\\n\\tg_func = row_matrix_col(a_c, a_c, invD)\\r\\n\\t\\r\\n\\t\\r\\n\\treturn detD, g_func\",\n \"def p_m(pmx_c,px):\\n pm = np.zeros(pmx_c.shape[0])\\n for mi in range(pm.size):\\n for xi in range(px.size):\\n pm[mi] += pmx_c[mi,xi]*px[xi]\\n return pm\",\n \"def MPE(cpts, obs):\\r\\n s = z3.Optimize()\\r\\n # we store our variables in a dictionary because it makes retrieval easier\\r\\n var = {}\\r\\n for variable in cpts:\\r\\n var[variable[0]] = (Bool(variable[0]))\\r\\n # we use this loop to make our hard assertions\\r\\n for observations in obs:\\r\\n if observations != []:\\r\\n if observations[1] == '0':\\r\\n s.add(Not(var[observations[0]]))\\r\\n else:\\r\\n s.add(var[observations[0]])\\r\\n # call our helper function to add all of our soft assertions\\r\\n for cpt in cpts:\\r\\n weighted_clause(cpt, var, s)\\r\\n s.check()\\r\\n return s.model()\",\n \"def setup_milp(model, target, remove_blocked=False, exclude_reaction_ids=set()):\\n original_model = model\\n model = model.copy()\\n model.objective=target\\n for i in heterologous_reactions.keys(): # turns off each heterologous reaction in order to get the only the native metabolic network.\\n model.reactions.get_by_id(i).lower_bound=0\\n model.reactions.get_by_id(i).upper_bound=0\\n\\n for r in model.reactions:\\n if r.id.find('MetaCyc')>-1:\\n r.type='heterologous'\\n else:\\n r.type='native'\\n\\n # Set the solver to Gurobi for the fastest result. Set to CPLEX if Gurobi is not available.\\n if \\\"gurobi\\\" in cobra.util.solver.solvers.keys():\\n logger.info(\\\"Changing solver to Gurobi and tweaking some parameters.\\\")\\n if \\\"gurobi_interface\\\" not in model.solver.interface.__name__:\\n model.solver = \\\"gurobi\\\"\\n # The tolerances are set to the minimum value. This gives maximum precision.\\n problem = model.solver.problem\\n problem.params.NodeMethod = 1 # primal simplex node relaxation\\n problem.params.FeasibilityTol = 1e-9 #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\\n problem.params.OptimalityTol = 1e-3 #how sure the solver has to be about this optimum being really the best it has.\\n problem.params.IntFeasTol = 1e-9 #If a value is set to an integer, how much may it still vary? > set smallest possible\\n problem.params.MIPgapAbs = 1e-9\\n problem.params.MIPgap = 1e-9\\n problem.params.Threads=1 #In order to reduce memory usage (increased calculation time)\\n problem.params.TimeLimit = 200 # Use max 200 seconds when called, return best solution after that\\n problem.params.PoolSearchMode = 1 #0 for only finding the optimum, 1 for finding more solutions (but no quality guaranteed), 2 for finding the n best possible solutions\\n problem.params.PoolSolutions = 10 # Number of solutions kept when finding the optimal solution\\n problem.params.PoolGap = 0.9 # only store solutions within 90% of the optimal objective value\\n\\n elif \\\"cplex\\\" in cobra.util.solver.solvers.keys():\\n logger.warning(\\\"Changing solver to CPLEX, as Gurobi is not available. This may cause a big slowdown and limit options afterwards.\\\")\\n if \\\"cplex_interface\\\" not in model.solver.interface.__name__:\\n model.solver = \\\"cplex\\\"\\n # The tolerances are set to the minimum value. This gives maximum precision.\\n problem = model.solver.problem\\n problem.parameters.mip.strategy.startalgorithm.set(1) # primal simplex node relaxation\\n problem.parameters.simplex.tolerances.feasibility.set(1e-9) #If a flux limited to 0 by a constraint, which range around it is still considered the same as 0 > set smallest possible\\n problem.parameters.simplex.tolerances.optimality.set(1e-3) #possibly fine with 1e-3, try if allowed. Is how sure the solver has to be about this optimum being really the best it has.\\n problem.parameters.mip.tolerances.integrality.set(1e-9) #If a value is set to an integer, how much may it still vary? > set smallest possible\\n problem.parameters.mip.tolerances.absmipgap.set(1e-9)\\n problem.parameters.mip.tolerances.mipgap.set(1e-9)\\n problem.parameters.mip.pool.relgap.set(0.9) # For populate: find all solutions within 10% of the optimum for relgap = 0.1\\n problem.parameters.timelimit.set(200) # Use max 200 seconds for solving\\n problem.parameters.mip.limits.populate.set(20) # Find max 20 solutions (=default)\\n\\n else:\\n logger.warning(\\\"You are trying to run 'Hamlet Hot Rig' with %s. This might not end well.\\\" %\\n model.solver.interface.__name__.split(\\\".\\\")[-1])\\n pass\\n # Remove reactions that are blocked: no flux through these reactions possible. This will reduce the search space for the solver, if not done already.\\n if remove_blocked:\\n blocked_reactions = cameo.flux_analysis.analysis.find_blocked_reactions(model)\\n model.remove_reactions(blocked_reactions)\\n\\n # Make dual\\n model_with = model.copy() # This variable looks unnecessary, but is kept out of fear of messing stuff up\\n model_with.optimize()\\n dual_problem = convert_linear_problem_to_dual(model_with.solver)\\n logger.debug(\\\"Dual problem successfully created\\\")\\n\\n # Combine primal and dual\\n primal_problem = model.solver\\n\\n for var in dual_problem.variables: # All variables in the dual are copied to the primal\\n var = primal_problem.interface.Variable.clone(var)\\n primal_problem.add(var)\\n for const in dual_problem.constraints: # All constraints in the dual are copied to the primal\\n const = primal_problem.interface.Constraint.clone(const, model=primal_problem)\\n primal_problem.add(const)\\n logger.debug(\\\"Dual and primal combined\\\")\\n\\n dual_problem.optimize()\\n\\n # Dictionaries to hold the binary control variables:\\n heterologous_y_vars = {} # 1 for 'knockin', 0 for inactive\\n medium_y_vars = {} # 1 for medium addition (up to -10), 0 for no addition\\n\\n # Now the fun stuff\\n constrained_dual_vars = set()\\n\\n # For the knockins and medium additions:\\n for reaction in [r for r in model.reactions if r.type == \\\"heterologous\\\"]:\\n # Add constraint variables\\n interface = model.solver.interface\\n y_var = interface.Variable(\\\"y_\\\" + reaction.id, type=\\\"binary\\\")\\n\\n # Constrain the primal: flux through reactions maximum within (-1000, 1000), or smaller boundaries defined before\\n model.solver.add(interface.Constraint(reaction.flux_expression - 1000 * y_var, ub=0, name=\\\"primal_y_const_\\\"+reaction.id+\\\"_ub\\\"))\\n model.solver.add(interface.Constraint(reaction.flux_expression + 1000 * y_var, lb=0, name=\\\"primal_y_const_\\\"+reaction.id+\\\"_lb\\\"))\\n\\n # Constrain the dual\\n constrained_vars = []\\n\\n if reaction.upper_bound != 0:\\n dual_forward_ub = model.solver.variables[\\\"dual_\\\" + reaction.forward_variable.name + \\\"_ub\\\"]\\n model.solver.add(interface.Constraint(dual_forward_ub - 1000 * (1 - y_var), ub=0))\\n constrained_vars.append(dual_forward_ub)\\n if reaction.lower_bound != 0:\\n dual_reverse_ub = model.solver.variables[\\\"dual_\\\" + reaction.reverse_variable.name + \\\"_ub\\\"]\\n model.solver.add(interface.Constraint(dual_reverse_ub - 1000 * (1 - y_var), ub=0))\\n constrained_vars.append(dual_reverse_ub)\\n constrained_dual_vars.update(constrained_vars)\\n\\n # Add y variable to the corresponding modifications dictionary\\n heterologous_y_vars[y_var] = reaction\\n\\n logger.debug(\\\"Control variables created\\\")\\n\\n # Add number of heterologous switch contraint constraint\\n heterologous_turn_on = model.solver.interface.Constraint(\\n optlang.symbolics.Add(*heterologous_y_vars), lb=0, ub=0, name=\\\"heterologous_reaction_constraint\\\"\\n )\\n model.solver.add(heterologous_turn_on)\\n\\n # Set the objective\\n primal_objective = model.solver.objective\\n dual_objective = interface.Objective.clone(\\n dual_problem.objective, model=model.solver\\n )\\n\\n switch_objective=interface.Objective(heterologous_turn_on.expression, direction='min')\\n\\n full_objective = interface.Objective(primal_objective.expression-dual_objective.expression, direction=\\\"max\\\")\\n model.objective = full_objective\\n\\n return model,primal_objective,dual_objective,full_objective,switch_objective\",\n \"def construct_MC_samples(self, N_MC):\\n D = self.theta[\\\"long_cov\\\"]\\n if self.MC_sep:\\n n_samples = self.n_samples\\n (U, V, y, N) = self.extracted_features[0]\\n n_long_features = self.n_long_features\\n phi = self.theta[\\\"phi\\\"]\\n beta = np.hstack((self.theta[\\\"beta_0\\\"], self.theta[\\\"beta_1\\\"]))\\n r = D.shape[0]\\n Omega = np.random.multivariate_normal(np.zeros(r), np.eye(r), N_MC)\\n S = np.zeros((n_samples, 2, 2 * N_MC, r))\\n\\n for i in range(n_samples):\\n U_i, V_i, y_i, N_i = U[i], V[i], y[i], N[i]\\n\\n # compute Sigma_i\\n Phi_i = [[1 / phi[l, 0]] * N_i[l]\\n for l in range(n_long_features)]\\n Sigma_i = np.diag(np.concatenate(Phi_i))\\n\\n # compute Omega_i\\n D_inv = np.linalg.inv(D)\\n A_i = np.linalg.inv(\\n V_i.transpose().dot(Sigma_i).dot(V_i) + D_inv)\\n # compute mu_i\\n mu_i = (A_i.dot(V_i.transpose()).dot(Sigma_i).dot(\\n y_i - U_i.dot(beta))).T[..., np.newaxis]\\n C_i = np.linalg.cholesky(A_i)\\n tmp = C_i.dot(Omega.T)\\n S[i] = (mu_i + np.hstack((tmp, -tmp))).swapaxes(1, 2)\\n else:\\n C = np.linalg.cholesky(D)\\n r = D.shape[0]\\n Omega = np.random.multivariate_normal(np.zeros(r), np.eye(r), N_MC)\\n b = Omega.dot(C.T)\\n S = np.vstack((b, -b))\\n\\n return S\",\n \"def make_synthetic_experiment(sample_data, model, make_gibbs_fn, explicit_ub=None):\\n rng_key = random.PRNGKey(0)\\n\\n # Sampling parameters\\n n_values = [100, 1000, 10000]\\n\\n # DPMM/PYMM parameters\\n T = 20 # max number of component in the truncated stick breaking representation\\n t = np.arange(T + 1)\\n alpha = 1\\n sigma = 0\\n\\n # Plotting parameters\\n fig, (ax0, ax1) = plt.subplots(1, 2)\\n\\n priors = compute_PY_prior(alpha, sigma, n_values)\\n for Npoints, prior in zip(n_values, priors):\\n cluster_count = np.zeros(T + 1) # cluster count histogram\\n upper_bound = np.zeros(T + 1)\\n \\n cluster_size = np.zeros(Npoints+1)\\n\\n # Repeat the experiment \\n for _ in range(REPEATS):\\n data = sample_data(rng_key, Npoints)\\n z = sample_posterior(rng_key, model, data, N_SAMPLES,\\n T=T, alpha=1,\\n gibbs_fn=make_gibbs_fn(data) if USE_GIBBS else None,\\n gibbs_sites=['z'] if USE_GIBBS else None,\\n )\\n\\n cluster_count += compute_n_clusters_distribution(z, T)\\n if explicit_ub is not None:\\n upper_bound += explicit_ub(data, t, params_PY=(alpha, sigma))\\n\\n cluster_size += compute_cluster_size_distribution(z)\\n\\n cluster_count /= REPEATS\\n cluster_size /= REPEATS\\n\\n # Plot cluster count histograms (ax0)\\n ax0.plot(t, cluster_count, label=f\\\"N={Npoints}\\\")\\n\\n color = ax0.lines[-1].get_color()\\n ax0.plot(t, prior[:T+1], label=f\\\"Prior N={Npoints}\\\", color=color, linestyle='dashed', lw=1)\\n\\n if explicit_ub is not None:\\n upper_bound /= REPEATS\\n ax0.plot(t[1:], upper_bound[1:], label=f\\\"Upper bound N={Npoints}\\\", color=color, linestyle='dotted', lw=1)\\n\\n # Plot cluster size histograms (ax1)\\n bins = np.linspace(0, 1, 10, endpoint=True)\\n frac = np.arange(0, Npoints + 1) / Npoints\\n\\n # TODO : use an actual histogram ?\\n # Overlaying histograms doesn't really look good.\\n hist, edges = np.histogram(frac, bins, density=True, weights=cluster_size)\\n ax1.plot(0.5 * (edges[1:] + edges[:-1]), hist, color=color, label=f\\\"N={Npoints}\\\")\\n\\n ax0.axhline(y=1, color='black', linewidth=0.3, linestyle='dotted')\\n ax0.set(title=r\\\"Number of clusters\\\", xlabel=\\\"$t$\\\", ylabel=r\\\"$P(T_n=t|X_{1:N})$\\\")\\n ax0.legend()\\n\\n ax1.set(xlabel=\\\"Fraction of total size\\\", title=\\\"Size of clusters\\\")\\n ax1.legend()\\n\\n plt.show()\",\n \"def gen_fpm_poke_cube(dm, mp, dx_dm, **kwargs):\\n if type(mp) is not falco.config.ModelParameters:\\n raise TypeError('Input \\\"mp\\\" must be of type ModelParameters')\\n real_positive_scalar(dx_dm, 'dx_dm', TypeError)\\n \\n if \\\"NOCUBE\\\" in kwargs and kwargs[\\\"NOCUBE\\\"]:\\n flagGenCube = False\\n else:\\n flagGenCube = True\\n\\n # Define this flag if it doesn't exist in the older code for square actuator arrays only\\n if not hasattr(dm, 'flag_hex_array'):\\n dm.flag_hex_array = False\\n\\n # Set the order of operations\\n XYZ = True\\n if(hasattr(dm, 'flagZYX')):\\n if(dm.flagZYX):\\n XYZ = False\\n\\n # Compute sampling of the pupil. Assume that it is square.\\n dm.dx_dm = dx_dm\\n dm.dx = dx_dm\\n\\n # Default to being centered on a pixel if not specified\\n if not(hasattr(dm, 'centering')):\\n dm.centering = 'pixel'\\n \\n # Compute coordinates of original influence function\\n Ninf0 = dm.inf0.shape[0] # Number of points across inf func at native res\\n x_inf0 = np.linspace(-(Ninf0-1)/2., (Ninf0-1)/2., Ninf0)*dm.dx_inf0\\n # True for even- or odd-sized influence function maps as long as they are\\n # centered on the array.\\n [Xinf0, Yinf0] = np.meshgrid(x_inf0, x_inf0)\\n \\n # Number of points across the DM surface at native inf func resolution\\n Ndm0 = falco.util.ceil_even(Ninf0 + (dm.Nact - 1)*(dm.dm_spacing/dm.dx_inf0))\\n # Number of points across the (un-rotated) DM surface at new, desired res.\\n dm.NdmMin = falco.util.ceil_even(Ndm0*(dm.dx_inf0/dm.dx))+2.\\n # Number of points across the array to fully contain the DM surface at new\\n # desired resolution and z-rotation angle.\\n dm.Ndm = int(ceil_even((abs(np.array([np.sqrt(2.)*np.cos(radians(45.-dm.zrot)),\\n np.sqrt(2.)*np.sin(radians(45.-dm.zrot))])).max())*Ndm0*(dm.dx_inf0/dm.dx))+2)\\n \\n # Compute list of initial actuator center coordinates (in actutor widths).\\n if(dm.flag_hex_array): # Hexagonal, hex-packed grid\\n raise ValueError('flag_hex_array option not implemented yet.')\\n# Nrings = dm.Nrings;\\n# x_vec = [];\\n# y_vec = [];\\n# % row number (rowNum) is 1 for the center row and 2 is above it, etc.\\n# % Nacross is the total number of segments across that row\\n# for rowNum = 1:Nrings\\n# Nacross = 2*Nrings - rowNum # Number of actuators across at that row (for hex tiling in a hex shape)\\n# yval = sqrt(3)/2*(rowNum-1);\\n# bx = Nrings - (rowNum+1)/2 # x offset from origin\\n# \\n# xs = (0:Nacross-1).' - bx # x values are 1 apart\\n# ys = yval*ones(Nacross,1) # same y-value for the entire row\\n# \\n# if(rowNum==1)\\n# x_vec = [x_vec;xs];\\n# y_vec = [y_vec;ys]; \\n# else\\n# x_vec = [x_vec;xs;xs];\\n# y_vec = [y_vec;ys;-ys] # rows +/-n have +/- y coordinates\\n# end\\n# end\\n else: # Square grid [actuator widths]\\n [dm.Xact, dm.Yact] = np.meshgrid(np.arange(dm.Nact) - \\n dm.xc, np.arange(dm.Nact)-dm.yc)\\n# # Use order='F' to compare the final datacube to Matlab's output.\\n# # Otherwise, use C ordering for Python FALCO.\\n# x_vec = dm.Xact.reshape(dm.Nact*dm.Nact,order='F')\\n# y_vec = dm.Yact.reshape(dm.Nact*dm.Nact,order='F')\\n x_vec = dm.Xact.reshape(dm.Nact*dm.Nact)\\n y_vec = dm.Yact.reshape(dm.Nact*dm.Nact)\\n \\n dm.NactTotal = x_vec.shape[0] # Total number of actuators in the 2-D array\\n dm.xy_cent_act = np.zeros((2, dm.NactTotal)) # Initialize\\n\\n # Compute the rotation matrix to apply to the influence function and \\n # actuator center locations\\n tlt = np.zeros(3)\\n tlt[0] = radians(dm.xtilt)\\n tlt[1] = radians(dm.ytilt)\\n tlt[2] = radians(-dm.zrot)\\n\\n sa = sin(tlt[0])\\n ca = cos(tlt[0])\\n sb = sin(tlt[1])\\n cb = cos(tlt[1])\\n sg = sin(tlt[2])\\n cg = cos(tlt[2])\\n\\n if XYZ:\\n Mrot = np.array([[cb * cg, sa * sb * cg - ca * sg, ca * sb * cg + sa * sg, 0.0],\\n [cb * sg, sa * sb * sg + ca * cg, ca * sb * sg - sa * cg, 0.0],\\n [-sb, sa * cb, ca * cb, 0.0],\\n [0.0, 0.0, 0.0, 1.0]])\\n else:\\n Mrot = np.array([ [cb * cg, -cb * sg, sb, 0.0],\\n [ca * sg + sa * sb * cg, ca * cg - sa * sb * sg, -sa * cb, 0.0],\\n [sa * sg - ca * sb * cg, sa * cg + ca * sb * sg, ca * cb, 0.0],\\n [0.0, 0.0, 0.0, 1.0]])\\n \\n # Compute the actuator center coordinates in units of actuator spacings\\n for iact in range(dm.NactTotal):\\n xyzVals = np.array([x_vec[iact], y_vec[iact], 0., 1.])\\n xyzValsRot = Mrot @ xyzVals\\n dm.xy_cent_act[0, iact] = xyzValsRot[0].copy()\\n dm.xy_cent_act[1, iact] = xyzValsRot[1].copy()\\n\\n N0 = dm.inf0.shape[0]\\n Npad = falco.util.ceil_odd(np.sqrt(2.)*N0)\\n inf0pad = np.zeros((Npad, Npad))\\n inf0pad[int(np.ceil(Npad/2.)-np.floor(N0/2.)-1):int(np.ceil(Npad/2.)+np.floor(N0/2.)),\\n int(np.ceil(Npad/2.)-np.floor(N0/2.)-1):int(np.ceil(Npad/2.)+np.floor(N0/2.))] = dm.inf0\\n\\n ydim = inf0pad.shape[0]\\n xdim = inf0pad.shape[1]\\n \\n xd2 = np.fix(xdim / 2.) + 1\\n yd2 = np.fix(ydim / 2.) + 1\\n cx = np.arange(xdim) + 1. - xd2\\n cy = np.arange(ydim) + 1. - yd2\\n [Xs0, Ys0] = np.meshgrid(cx, cy)\\n\\n xsNewVec = np.zeros(xdim*xdim)\\n ysNewVec = np.zeros(ydim*ydim)\\n Xs0Vec = Xs0.reshape(xdim*xdim)\\n Ys0Vec = Ys0.reshape(ydim*ydim)\\n \\n for ii in range(Xs0.size):\\n xyzVals = np.array([Xs0Vec[ii], Ys0Vec[ii], 0., 1.])\\n xyzValsRot = Mrot @ xyzVals\\n xsNewVec[ii] = xyzValsRot[0]\\n ysNewVec[ii] = xyzValsRot[1]\\n \\n # Calculate the interpolated DM grid at the new resolution\\n # (set extrapolated values to 0.0)\\n dm.infMaster = griddata((xsNewVec, ysNewVec), inf0pad.reshape(Npad*Npad),\\n (Xs0, Ys0), method='cubic', fill_value=0.)\\n\\n # Crop down the influence function until it has no zero padding left\\n infSum = np.sum(dm.infMaster)\\n infDiff = 0.\\n counter = 0\\n while(abs(infDiff) <= 1e-7):\\n counter = counter + 2\\n infDiff = infSum - np.sum(abs(dm.infMaster[int(counter/2):int(-counter/2),\\n int(counter/2):int(-counter/2)]))\\n\\n # Subtract an extra 2 to negate the extra step that overshoots.\\n counter = counter - 2\\n Ninf0pad = dm.infMaster.shape[0]-counter\\n if counter == 0:\\n infMaster2 = dm.infMaster.copy()\\n else:\\n # The cropped-down influence function\\n infMaster2 = dm.infMaster[int(counter/2):int(-counter/2),\\n int(counter/2):int(-counter/2)].copy()\\n dm.infMaster = infMaster2\\n \\n Npad = Ninf0pad\\n\\n # True for even- or odd-sized influence function maps as long as they are\\n # centered on the array.\\n x_inf0 = np.linspace(-(Npad-1)/2, (Npad-1)/2., Npad)*dm.dx_inf0\\n [Xinf0, Yinf0] = np.meshgrid(x_inf0, x_inf0)\\n\\n # Translate and resample the master influence function to be at each \\n # actuator's location in the pixel grid \\n\\n # Compute the size of the postage stamps.\\n # Number of points across the influence function array at the DM plane's\\n # resolution. Want as even\\n Nbox = falco.util.ceil_even(Ninf0pad*dm.dx_inf0/dx_dm)\\n dm.Nbox = Nbox\\n # Also compute their padded sizes for the angular spectrum (AS) propagation\\n # between P2 and DM1 or between DM1 and DM2\\n # Minimum number of points across for accurate angular spectrum propagation\\n Nmin = falco.util.ceil_even(np.max(mp.sbp_centers)*np.max(np.abs(np.array(\\n [mp.d_P2_dm1, mp.d_dm1_dm2, (mp.d_P2_dm1+mp.d_dm1_dm2)])))/dx_dm**2)\\n # Use a larger array if the max sampling criterion for angular spectrum\\n # propagation is violated\\n dm.NboxAS = np.max(np.array([Nbox, Nmin]))\\n\\n # Pad the pupil to at least the size of the DM(s) surface(s) to allow all\\n # actuators to be located outside the pupil.\\n # (Same for both DMs)\\n\\n # Find actuator farthest from center:\\n dm.r_cent_act = np.sqrt(dm.xy_cent_act[0, :]**2 + dm.xy_cent_act[1, :]**2)\\n dm.rmax = np.max(np.abs(dm.r_cent_act))\\n NpixPerAct = dm.dm_spacing/dx_dm\\n if(dm.flag_hex_array):\\n # padded 2 actuators past the last actuator center to avoid trying to\\n # index outside the array\\n dm.NdmPad = falco.util.ceil_even((2.*(dm.rmax+2))*NpixPerAct + 1)\\n else:\\n # DM surface array padded by the width of the padded influence function\\n # to prevent indexing outside the array. \\n # The 1/2 term is because the farthest actuator center is still half an\\n # actuator away from the nominal array edge. \\n dm.NdmPad = falco.util.ceil_even((dm.NboxAS + 2.0*(1 + (np.max(\\n np.abs(dm.xy_cent_act.reshape(2*dm.NactTotal)))+0.5)*NpixPerAct)))\\n\\n # Compute coordinates (in meters) of the full DM array\\n if(dm.centering == 'pixel'):\\n # meters, coords for the full DM arrays. Origin is centered on a pixel\\n dm.x_pupPad = np.linspace(-dm.NdmPad/2., (dm.NdmPad/2. - 1),\\n dm.NdmPad)*dx_dm\\n else:\\n # meters, coords for the full DM arrays. Origin is interpixel centered\\n dm.x_pupPad = np.linspace(-(dm.NdmPad-1)/2., (dm.NdmPad-1)/2.,\\n dm.NdmPad)*dx_dm\\n\\n dm.y_pupPad = dm.x_pupPad\\n\\n # Make NboxPad-sized postage stamps for each actuator's influence function\\n if(flagGenCube):\\n if not dm.flag_hex_array:\\n print(\\\" Influence function padded from %d to %d points for A.S. propagation.\\\" % (Nbox,dm.NboxAS))\\n\\n print('Computing datacube of DM influence functions... ', end='')\\n\\n # Find the locations of the postage stamps arrays in the larger pupilPad array\\n dm.xy_cent_act_inPix = dm.xy_cent_act*(dm.dm_spacing/dx_dm) # Convert units to pupil-plane pixels\\n dm.xy_cent_act_inPix = dm.xy_cent_act_inPix + 0.5 # For the half-pixel offset if pixel centered. \\n dm.xy_cent_act_box = np.round(dm.xy_cent_act_inPix) # Center locations of the postage stamps (in between pixels), in actuator widths\\n dm.xy_cent_act_box_inM = dm.xy_cent_act_box*dx_dm # now in meters\\n dm.xy_box_lowerLeft = dm.xy_cent_act_box + (dm.NdmPad-Nbox)/2 - 0 # index of pixel in lower left of the postage stamp within the whole pupilPad array. +0 for Python, +1 for Matlab\\n\\n # Starting coordinates (in actuator widths) for updated master influence function.\\n # This is interpixel centered, so do not translate!\\n dm.x_box0 = np.linspace(-(Nbox-1)/2., (Nbox-1)/2., Nbox)*dx_dm\\n [dm.Xbox0, dm.Ybox0] = np.meshgrid(dm.x_box0, dm.x_box0) # meters, interpixel-centered coordinates for the master influence function\\n\\n # (Allow for later) Limit the actuators used to those within 1 actuator width of the pupil\\n r_cent_act_box_inM = np.sqrt(dm.xy_cent_act_box_inM[0, :]**2 + dm.xy_cent_act_box_inM[1, :]**2)\\n # Compute and store all the influence functions:\\n dm.inf_datacube = np.zeros((Nbox, Nbox, dm.NactTotal)) # initialize array of influence function \\\"postage stamps\\\"\\n dm.act_ele = np.arange(dm.NactTotal) # Initialize as including all actuators\\n\\n inf_datacube = np.zeros((dm.NactTotal, Nbox, Nbox))\\n\\n interp_spline = RectBivariateSpline(x_inf0, x_inf0, dm.infMaster) # RectBivariateSpline is faster in 2-D than interp2d\\n # Refer to https://scipython.com/book/chapter-8-scipy/examples/two-dimensional-interpolation-with-scipyinterpolaterectbivariatespline/\\n\\n for iact in range(dm.NactTotal):\\n xbox = dm.x_box0 - (dm.xy_cent_act_inPix[0, iact]-dm.xy_cent_act_box[0, iact])*dx_dm # X = X0 -(x_true_center-x_box_center)\\n ybox = dm.x_box0 - (dm.xy_cent_act_inPix[1, iact]-dm.xy_cent_act_box[1, iact])*dx_dm # Y = Y0 -(y_true_center-y_box_center)\\n dm.inf_datacube[:, :, iact] = interp_spline(ybox, xbox)\\n inf_datacube[iact, :, :] = interp_spline(ybox, xbox)\\n\\n print('done.')\\n\\n else:\\n dm.act_ele = np.arange(dm.NactTotal)\",\n \"def gibbs_sample(self, X):\\n K = self.K # number of topics\\n M, V = X.shape\\n alpha = self.alpha\\n lmda = self.lmda\\n topics = np.arange(0, K)\\n\\n #initialize everything uniformly, sparse topics\\n Beta = np.ones(shape=(K, V), dtype=float) / V\\n Theta = np.ones(shape=(M, K), dtype=float)/ K\\n\\n #Running sum\\n MC_z = np.array(range(M), dtype=object)\\n MC_beta = np.zeros(shape=(K, V), dtype=float)\\n MC_theta = np.zeros(shape=(M, K), dtype=float)\\n\\n for d in range(M):\\n #allocate topics randomly -- this is really not needed in this case\\n word_indices = X[d, :].nonzero()[1]\\n random_ks = np.random.choice(topics, size = len(word_indices))\\n Theta[d] = np.random.dirichlet(np.ones(K)*alpha)\\n MC_z[d] = sp.coo_matrix((V, K), dtype=np.int8).tolil()\\n for k in topics:\\n Beta[k] = np.random.dirichlet(np.ones(V)*lmda)\\n\\n log_Xs = []\\n perplexities = []\\n t = 0\\n for epoch in xrange(self.nr_em_epochs):\\n print \\\"Epoch\\\", epoch\\n t +=1\\n C = np.zeros((K, V))\\n for d in np.random.permutation(np.arange(M)):\\n x = X[d]\\n ixw = np.nonzero(x)[1]\\n p_s = Beta[:, ixw].T * Theta[d, :]\\n Z = [np.random.choice(topics, p=(p/np.sum(p))) for p in p_s]\\n N_d = sp.coo_matrix((np.ones(len(ixw)), (ixw, Z)), shape=(V, K)).tolil()\\n C = C + N_d.A.T\\n # sample theta given z and beta\\n c_theta = (np.sum(N_d.A, axis=0) + alpha)\\n Theta[d, :] = np.random.dirichlet(c_theta)\\n MC_z[d] += N_d\\n\\n # Sample beta given all z and thetas\\n for k in topics:\\n c_beta = C[k, :]\\n Beta[k, :] = np.random.dirichlet(c_beta + lmda)\\n\\n MC_theta += Theta\\n MC_beta += Beta\\n\\n log_X = 0\\n Theta_hat = MC_theta / t\\n Beta_hat = MC_beta / t\\n\\n for d in range(M):\\n ixw = np.nonzero(X[d, :])[1]\\n log_X += np.sum(_doc_probability_from_p_of_z(Theta_hat[d, :], Beta_hat[:, ixw]))\\n\\n log_Xs.append(log_X)\\n print log_X\\n perplexities.append(self._perplexity(X, log_X))\\n return Theta_hat, Beta_hat, log_Xs, perplexities\",\n \"def get_theory(p, dm, cosmo, hmc,\\n return_separated=False,\\n include_1h=True, include_2h=True,\\n hm_correction=None,\\n **kwargs):\\n cls_out = []\\n for tr, ls, bms in zip(dm.tracers, dm.ells, dm.beams):\\n cl = hm_ang_power_spectrum(cosmo, hmc, ls, tr,\\n include_1h=include_1h,\\n include_2h=include_2h,\\n hm_correction=hm_correction,\\n zpts=p.get(\\\"mcmc\\\")[\\\"zpts\\\"],\\n **kwargs)\\n cl *= bms # multiply by beams\\n\\n if return_separated:\\n cls_out.append(cl)\\n else:\\n cls_out += cl.tolist()\\n\\n cls_out = np.array(cls_out)\\n return cls_out\",\n \"def init_dwi_hmc_wf(hmc_transform, hmc_model, hmc_align_to, source_file,\\n num_model_iterations=2, mem_gb=3, omp_nthreads=1, sloppy=False,\\n name=\\\"dwi_hmc_wf\\\"):\\n\\n inputnode = pe.Node(\\n niu.IdentityInterface(\\n fields=['dwi_files', 'b0_indices', 'bvecs', 'bvals', 'b0_images', 'original_files',\\n 't1_brain', 't1_mask', 't1_seg']),\\n name='inputnode')\\n\\n outputnode = pe.Node(\\n niu.IdentityInterface(\\n fields=[\\\"final_template\\\", \\\"forward_transforms\\\", \\\"noise_free_dwis\\\",\\n \\\"cnr_image\\\", \\\"optimization_data\\\", \\\"final_template_brain\\\",\\n \\\"final_template_mask\\\"]),\\n name='outputnode')\\n\\n workflow = Workflow(name=name)\\n # Unbiased align the b0s\\n b0_hmc_wf = init_b0_hmc_wf(align_to=hmc_align_to, transform=hmc_transform, sloppy=sloppy)\\n # Tile the transforms so each non-b0 gets the transform from the nearest b0\\n match_transforms = pe.Node(MatchTransforms(), name=\\\"match_transforms\\\")\\n # Make a mask from the output template. It is bias-corrected, so good for masking\\n # but bad for using as a b=0 for modeling.\\n b0_template_mask = init_dwi_reference_wf(register_t1=True, name=\\\"b0_template_mask\\\",\\n gen_report=False, source_file=source_file)\\n\\n workflow.connect([\\n (inputnode, match_transforms, [('dwi_files', 'dwi_files'),\\n ('b0_indices', 'b0_indices')]),\\n (inputnode, b0_hmc_wf, [('b0_images', 'inputnode.b0_images')]),\\n (b0_hmc_wf, outputnode, [('outputnode.final_template', 'final_template')]),\\n (b0_hmc_wf, match_transforms, [(('outputnode.forward_transforms', _list_squeeze),\\n 'transforms')]),\\n (inputnode, b0_template_mask, [\\n ('t1_brain', 'inputnode.t1_brain'),\\n ('t1_mask', 'inputnode.t1_mask'),\\n ('t1_seg', 'inputnode.t1_seg')]),\\n (b0_hmc_wf, b0_template_mask, [\\n ('outputnode.final_template', 'inputnode.b0_template')]),\\n (b0_template_mask, outputnode, [\\n ('outputnode.ref_image_brain', 'final_template_brain'),\\n ('outputnode.dwi_mask', 'final_template_mask')\\n ])\\n ])\\n\\n # If we're just aligning based on the b=0 images, compute the b=0 tsnr as the cnr\\n if hmc_model.lower() == \\\"none\\\":\\n workflow.__postdesc__ = \\\"Each b>0 image was transformed based on the registration \\\" \\\\\\n \\\" of the nearest b=0 image. \\\"\\n\\n concat_b0s = pe.Node(afni.TCat(outputtype=\\\"NIFTI_GZ\\\"), name=\\\"concat_b0s\\\")\\n b0_tsnr = pe.Node(\\n afni.TStat(options=' -cvarinvNOD ', outputtype='NIFTI_GZ'), name='b0_tsnr')\\n workflow.connect([\\n (match_transforms, outputnode, [('transforms', 'forward_transforms')]),\\n (b0_hmc_wf, concat_b0s, [('outputnode.aligned_images', 'in_files')]),\\n (concat_b0s, b0_tsnr, [('out_file', 'in_file')]),\\n (b0_tsnr, outputnode, [('out_file', 'cnr_image')])])\\n return workflow\\n\\n # Do model-based motion correction\\n dwi_model_hmc_wf = init_dwi_model_hmc_wf(hmc_model, hmc_transform, mem_gb, omp_nthreads,\\n num_iters=num_model_iterations)\\n\\n # Warp the modeled images into non-motion-corrected space\\n uncorrect_model_images = pe.MapNode(\\n ants.ApplyTransforms(invert_transform_flags=[True],\\n interpolation='LanczosWindowedSinc'),\\n iterfield=['input_image', 'reference_image', 'transforms'],\\n name='uncorrect_model_images')\\n workflow.__postdesc__ = \\\"Model-generated images were transformed into alignment with each \\\" \\\\\\n \\\"b>0 image. Both slicewise and whole-brain QC measures (cross \\\" \\\\\\n \\\"correlation and R^2) were calculated.\\\"\\n workflow.connect([\\n (b0_hmc_wf, dwi_model_hmc_wf, [\\n ('outputnode.aligned_images', 'inputnode.warped_b0_images')]),\\n (b0_template_mask, dwi_model_hmc_wf, [\\n ('outputnode.dwi_mask', 'inputnode.warped_b0_mask')]),\\n (inputnode, dwi_model_hmc_wf, [\\n ('dwi_files', 'inputnode.dwi_files'),\\n ('b0_indices', 'inputnode.b0_indices'),\\n ('bvecs', 'inputnode.bvec_files'),\\n ('bvals', 'inputnode.bval_files')]),\\n (match_transforms, dwi_model_hmc_wf, [\\n ('transforms', 'inputnode.initial_transforms')]),\\n (dwi_model_hmc_wf, outputnode, [\\n ('outputnode.hmc_transforms', 'forward_transforms'),\\n ('outputnode.optimization_data', 'optimization_data'),\\n ('outputnode.cnr_image', 'cnr_image')]),\\n (dwi_model_hmc_wf, uncorrect_model_images, [\\n ('outputnode.model_predicted_images', 'input_image'),\\n ('outputnode.hmc_transforms', 'transforms')]),\\n (inputnode, uncorrect_model_images, [('dwi_files', 'reference_image')]),\\n (uncorrect_model_images, outputnode, [('output_image', 'noise_free_dwis')])\\n ])\\n datasinks = [node for node in workflow.list_node_names()\\n if node.split(\\\".\\\")[-1].startswith(\\\"ds_\\\")]\\n\\n for ds in datasinks:\\n workflow.get_node(ds).inputs.source_file = source_file\\n\\n return workflow\",\n \"def mcmcrun(data, priors, dirname):\\n def initParams(data):\\n '''Initialize parameters from fitted mixed effects model in R.'''\\n params = postdist.ParamsHolder()\\n params.updateBeta(np.zeros([data.grp, data.l]))\\n params.updateGamma(np.zeros([data.grp, data.l]))\\n\\n # linear\\n # params.updateAlpha(priors.d3)\\n # params.updateLambdaD(np.array(0.086).reshape(1, 1))\\n\\n # quadratic\\n params.updateAlpha(np.array([45.50, -3.96, -1.41]).reshape(data.p, 1))\\n params.updateLambdaD(np.array(0.047).reshape(1, 1))\\n\\n params.updateB(np.random.normal(0, np.sqrt(1.0/params.lambdaD),\\n size = data.ntot*data.p).reshape(data.ntot, data.p))\\n params.updateSigma2(np.array(priors.sigma2).reshape(1, 1))\\n return params\\n\\n # initialize parameters\\n temp_params = initParams(data)\\n temp_paramsArray = temp_params.toArray(data.ntot, data.grp,\\n data.p, data.l)\\n with open(dirname+\\\"/updates.txt\\\", 'w') as f_handle:\\n np.savetxt(f_handle,\\n temp_paramsArray,\\n delimiter=',')\\n\\n stoppingRule = fwsr.StoppingRule(temp_paramsArray, dirname, eps = 0.2)\\n\\n # MCMC updates\\n # totSimulation = 10000\\n counter = 1\\n while True:\\n counter += 1\\n\\n # update gamma\\n gamma_pd = postdist.GammaPosterior(data, temp_params, priors)\\n temp_params.updateGamma(gamma_pd.getUpdates())\\n\\n # update beta\\n beta_pd = postdist.BetaPosterior(data, temp_params)\\n temp_params.updateBeta(beta_pd.getUpdates())\\n\\n # update lambdaD\\n lambdaD_pd = postdist.LambdaDPosterior(data, temp_params, priors)\\n temp_params.updateLambdaD(lambdaD_pd.getUpdates())\\n\\n # update sigma2\\n sigma2_pd = postdist.Sigma2Posterior(data, temp_params)\\n temp_params.updateSigma2(sigma2_pd.getUpdates())\\n\\n # update alpha\\n alpha_pd = postdist.AlphaPosterior(data, temp_params, priors)\\n temp_params.updateAlpha(alpha_pd.getUpdates())\\n\\n # update b\\n b_pd = postdist.BPosterior(data, temp_params)\\n temp_params.updateB(b_pd.getUpdates())\\n\\n # # print out results with gaps\\n # if counter % 10 == 0:\\n # print \\\"==== This is the {0}th iteration ====\\\".format(counter)\\n # # raw_input(\\\"Press Enter to Continue ...\\\")\\n # print \\\"====beta (trt & ctrl): \\\\n{0}\\\".format(temp_params.beta)\\n # print \\\"====alpha: \\\\n{0}\\\".format(temp_params.alpha)\\n # print \\\"====lambdaD: \\\\n{0}\\\".format(temp_params.lambdaD)\\n # print \\\"====sigma2: \\\\n{0}\\\".format(temp_params.sigma2)\\n # if int(raw_input(\\\"Do you want the results of b? Press 0/1:\\\")):\\n # print \\\"===b: {0} with Cov: {1}\\\".format(temp_params.b,\\n # b_pd.cov[1])\\n\\n # store updates\\n # params = np.hstack([params,\\n # temp_params.toArray(data.ntot, data.grp, data.p, data.l)])\\n\\n # write to file\\n # np.savetxt(dirname+\\\"/updates.txt\\\", params, delimiter=',')\\n\\n temp_paramsArray = temp_params.toArray(data.ntot, data.grp,\\n data.p, data.l)\\n with open(dirname+\\\"/updates.txt\\\", 'a') as f_handle:\\n np.savetxt(f_handle,\\n temp_paramsArray,\\n delimiter=',')\\n\\n np.savetxt(dirname+\\\"/counter.txt\\\", np.array([counter]))\\n\\n isTerminated = stoppingRule.update(temp_paramsArray)\\n if isTerminated:\\n break\",\n \"def ParticleFilterParams(fix_params=False):\\n\\n ## Particle filter parameters\\n\\n # Q_c will be the time continuous covariance matrix. \\n #This should be the errors in the model.\\n # in the form [x_cov, y_cov, z_cov, \\n # vel_x_cov, vel_y_co, vel_z_cov, \\n # mass_cov, \\n # sigma_cov, shape_cov, brightness_cov, tau_cov]\\n \\n\\n Q_c = [10., 2., 2., \\n 150., 50., 50., \\n 5., 0, 0,\\n 1e-3, 1e-10, 0., 0.0001]\\n\\n\\n print('Qc values used:', Q_c)\\n\\n Q_c = np.asarray([i**2 for i in Q_c])\\n\\n \\n # Q_c_frag is used at reinitialisation if the fragmentation option is used\\n \\n Q_c_frag = [0., 0., 0., \\n 0.02, 0.02, 0.02, \\n 0.5, 0, 0,\\n 2e-3, 5e-9, 0., 0.]\\n\\n Q_c_frag = [i**2 for i in Q_c_frag]\\n\\n ## P: starting uncertainty to initialise gaussian spread of particals. \\n ## P2: starting uncertainty at reinitialisation if the fragmentation option is used\\n ## in the form [x_cov, y_cov, z_cov, % of vel_x_cov, % of vel_y_co, % of vel_z_cov]\\n P = [50., 50., 50., 250., 250., 250.]\\n P2 = [50., 50., 50., 250., 250., 250.]\\n\\n ## Initialise state ranges\\n\\n\\n ## shape parameter close to a rounded brick (1.8) (A for a sphere =1.21)\\n A_min = 1.21\\n A_max = 3.0 \\n\\n ## luminosity coefficient\\n tau_min = 0.0001\\n tau_max = 0.1\\n\\n ## lists of typical meteorite densities for different types. [chond, achond, stony-iron, iron, cometary]\\n pm_mean = [3000, 3100, 4500, 7500, 850]\\n pm_std = [420, 133, 133, 167, 117 ]\\n\\n ## to choose density values according to a distribution of meteorite percentages:\\n particle_choices = []\\n\\n # this is created using lines 257-266; uncomment if needs changing.\\n random_meteor_type = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 3, 4, 4]\\n\\n #random_meteor_type = []\\n #for i in range(80): # 80 % Chondrites\\n # random_meteor_type.append(0)\\n #for i in range(11): # 11 % Achondrites\\n # random_meteor_type.append(1)\\n #for i in range(2):\\n # random_meteor_type.append(2) # 2 % Stony-Iron\\n #for i in range(5):\\n # random_meteor_type.append(3) # 5 % iron\\n #for i in range(2):\\n # random_meteor_type.append(4) # 2 % cometary\\n\\n ## ablation coefficeint \\n #sigma_min = 0.001*1e-6\\n #sigma_max = 0.5*1e-6\\n\\n\\n #range_params = [m0_max, A_mean, A_std, pm_mean, pm_std, random_meteor_type, cd_mean, cd_std, sigma_min, sigma_max, K_min, K_max, tau_min, tau_max]\\n range_params = [A_min, A_max, pm_mean, pm_std, random_meteor_type, tau_min, tau_max]\\n\\n if fix_params:\\n \\tQ_c[-4:] = [0., 0., 0., 0.]\\n \\tQ_c_frag[-4:] = [0., 0., 0., 0.]\\n return Q_c, Q_c_frag, P, range_params\",\n \"def create_model(self, gwas_gen, gwas_phen):\\n n_ind, n_snps = gwas_gen.eval().shape\\n with pm.Model() as phenotype_model:\\n beta_med = pm.Normal('beta_med',\\n mu=self.vars['coef_mean'],\\n sd=self.vars['coef_sd'],\\n shape=(1, n_snps))\\n \\n mediator = pm.dot(beta_med, gwas_gen.T)\\n intercept = pm.Normal('intercept', mu=0, sd=1)\\n alpha = pm.Normal('alpha', mu=0, sd=1)\\n phenotype_sigma = pm.HalfCauchy('phenotype_sigma',\\n beta=self.vars['p_sigma_beta'])\\n \\n\\n # Model Selection\\n p = np.array([0.5, 0.5])\\n mediator_model = pm.Bernoulli('mediator_model', p[1])\\n\\n # Model 1\\n phenotype_mu_null = intercept\\n\\n # Model 2\\n phenotype_mu_mediator = intercept + alpha * mediator\\n\\n phen = pm.DensityDist('phen',\\n lambda value: pm.switch(mediator_model, \\n pm.Normal.dist(mu=phenotype_mu_mediator, sd=phenotype_sigma).logp(value), \\n pm.Normal.dist(mu=phenotype_mu_null, sd=phenotype_sigma).logp(value)\\n ),\\n observed=gwas_phen)\\n self.steps = [pm.BinaryGibbsMetropolis(vars=[mediator_model]),\\n pm.Metropolis()]\\n\\n \\n if self.variational and self.mb:\\n self.minibatch_RVs = [phen]\\n self.minibatch_tensors = [gwas_gen, gwas_phen]\\n return phenotype_model\",\n \"def sbm_pp_parameters(k, p, q, sigma=0):\\n omega = (p-q)*np.eye(k) + q*np.ones([k, k])\\n\\n pert = sigma*np.random.randn(*omega.shape)\\n pert = np.tril(pert) + np.tril(pert, -1).T\\n omega += pert\\n np.maximum(omega, 0, omega)\\n\\n return omega\",\n \"def fit_mweight(data, C=1., threshold=1e-4, learning_rate=None):\\n dataPM = toPM(data).T;\\n m,n = dataPM.shape;\\n if learning_rate is None: learning_rate = 1-np.sqrt(np.log(n)/m);\\n\\n L,h = np.zeros((n,n)), np.zeros((n,)) # initialize parameters (dense) & weights\\n eye = list(range(n));\\n Wp = np.ones((n,n))/(2*(n-1)); Wp[eye,eye] = 0; Wm = np.copy(Wp);\\n Hp = np.ones((n,))/2; Hm = np.copy(Hp);\\n\\n for i,xpm in enumerate(dataPM):\\n phat = 1./(1.+np.exp(2.*L.dot(xpm) + h))\\n ell_H = (phat - (1.-xpm)/2.);\\n ell_W = np.outer( ell_H , xpm);\\n Wp *= learning_rate**(-ell_W); Wm *= learning_rate**(ell_W);\\n Hp *= learning_rate**(-ell_H); Hm *= learning_rate**(ell_H);\\n L = C/(Wp.sum(1)+Wm.sum(1))[:,np.newaxis]*(Wp-Wm)\\n h = C/(Hp+Hm)*(Hp-Hm)\\n \\n L = .5*(L+L.T);\\n L[np.abs(L)<threshold] = 0;\\n L[eye,eye] = h;\\n return Ising(L);\",\n \"def proba(c_pred,m_pred,f_pred, dataset):\\n p = np.zeros(10)\\n if dataset == 'cifar10':\\n for i in range(10):\\n if i <4:\\n if i <2:\\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:2]))\\n elif i <4:\\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[2:4]))\\n if i >=4:\\n if i <6:\\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[4:6]))\\n elif i <8:\\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[6:8]))\\n elif i <10:\\n p[i] = c_pred[1]*(m_pred[4]/(m_pred[2]+m_pred[3]+m_pred[4]))*(f_pred[i]/np.sum(f_pred[8:10]))\\n else :\\n for i in range(10):\\n if i <5:\\n if i <3:\\n p[i] = c_pred[0]*(m_pred[0]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[0:3]))\\n elif i <5:\\n p[i] = c_pred[0]*(m_pred[1]/(m_pred[0]+m_pred[1]))*(f_pred[i]/np.sum(f_pred[3:5]))\\n if i >=5:\\n if i <8:\\n p[i] = c_pred[1]*(m_pred[2]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[5:8]))\\n elif i <10:\\n p[i] = c_pred[1]*(m_pred[3]/(m_pred[2]+m_pred[3]))*(f_pred[i]/np.sum(f_pred[8:]))\\n return(p)\",\n \"def create_model():\\n # Get list of all syllables: [\\\"<s>\\\", \\\"AH\\\", \\\"</s>\\\", \\\"<s>\\\", \\\"T\\\", ...]\\n syllabifier = Syllabifier()\\n all_syllables = syllabifier.all_syllables()\\n\\n # Count conditional probabilties of phoneme tuples\\n tcf = TrigramCollocationFinder.from_words(all_syllables)\\n bcf = BigramCollocationFinder.from_words(all_syllables)\\n tri_dict = dict(sorted(tcf.ngram_fd.items(), key=lambda t: (-t[1], t[0])))\\n bi_dict = dict(sorted(bcf.ngram_fd.items(), key=lambda t: (-t[1], t[0])))\\n\\n # Create dictionary to count cond prob all phoneme tuples\\n accepted_phonemes = [i[0] for i in cmudict.phones()]\\n accepted_phonemes.append('<s>')\\n accepted_phonemes.append('</s>')\\n phoneme_tups = [p for p in itertools.product(accepted_phonemes, repeat=3)]\\n cond_probs_dict = dict([(char, 0) for char in phoneme_tups])\\n\\n for t in tri_dict:\\n p1, p2, p3 = t[0], t[1], t[2]\\n tri_count = tri_dict[t]\\n bi_count = bi_dict[(p1, p2)]\\n if bi_count > 1:\\n cond_prob = tri_count * 1.0 / bi_count\\n else:\\n cond_prob = 0.0\\n cond_probs_dict[(p1, p2, p3)] = cond_prob\\n\\n pickle.dump(cond_probs_dict, open(COND_PROBS_PATH, \\\"wb\\\"))\\n return\",\n \"def binom_artifact_model_probability(data,\\n prior_p=binom_p_prior,\\n mutation_object=True):\\n\\n if mutation_object is True:\\n trajectories = data.data\\n else:\\n trajectories = data\\n\\n ind_likelihoods = []\\n for ind_traj in trajectories:\\n # initialise list of samples for p integration\\n int_p = []\\n # integral over betabinom p parameter\\n for p_sample in prior_p[0, :]:\\n # For each p compute the likelihood of observing\\n # a given time-series conditional on\\n # initial time-point.\\n cond_p_likelihood = binom.pmf(k=ind_traj[1:].AO,\\n n=ind_traj[1:].DP,\\n p=p_sample)\\n int_p.append(np.product(cond_p_likelihood))\\n \\n # compute the model probability for each individual\\n marginal_ind_likelihood = np.trapz(x=prior_p[0,:], y=int_p*prior_p[1,:])\\n ind_likelihoods.append(marginal_ind_likelihood)\\n \\n # Compute the product of likelihoods\\n mutation_prob = np.product(ind_likelihoods)\\n\\n if mutation_object is True:\\n # return updated model_comparison object \\n data.binom_artifact_prob = mutation_prob\\n return data\\n else:\\n # return marginalised likelihood.\\n return mutation_prob\",\n \"def getMCMC(self,x,y,priors={},datamodel=None):\\n import pymc\\n from operator import isSequenceType\\n from inspect import getargspec\\n from types import MethodType\\n\\n if set(priors.keys()) != set(self.params):\\n raise ValueError(\\\"input priors don't match function params\\\")\\n\\n d={}\\n for k,v in priors.iteritems():\\n if isinstance(v,pymc.StochasticBase):\\n d[k]=v\\n elif isSequenceType(v):\\n if len(v) == 2:\\n d[k]=pymc.distributions.Uniform(k,v[0],v[1])\\n else:\\n raise ValueError(\\\"couldn't understand sequence \\\"+str(v) )\\n elif v > 0:\\n d[k] = pymc.distributions.Normal(k,self.pardict[k],1.0/v/v)\\n elif v == 0:\\n d[k] = pymc.distributions.Poisson(k,self.pardict[k])\\n else:\\n raise ValueError(\\\"couldn't interpret prior \\\"+str(v))\\n\\n funcdict=dict(d)\\n if type(self.f) is MethodType:\\n xstr = getargspec(self.f)[0][1]\\n else:\\n assert callable(self.f),'object function is not a callable'\\n xstr = getargspec(self.f)[0][0]\\n funcdict[xstr]=x\\n funcdet=pymc.Deterministic(name='f',eval=self.f,parents=funcdict,doc=\\\"FunctionModel1D function\\\")\\n d['f'] = funcdet\\n\\n if type(datamodel) is tuple:\\n distobj,dataarg,kwargs=datamodel\\n if 'name' not in kwargs:\\n kwargs['name'] = 'data'\\n\\n kwargs[dataarg] = funcdet\\n kwargs['observed'] = True\\n kwargs['value'] = y\\n\\n datamodel = distobj(**kwargs)\\n\\n d[datamodel.__name__] = datamodel\\n\\n #now if any stochastics are in the kwargs, assume they are part of the model\\n for k, v in kwargs.iteritems():\\n if isinstance(v, pymc.StochasticBase):\\n d[k] = v\\n else:\\n if datamodel is None:\\n sig = np.std(y)\\n else:\\n sig = np.array(datamodel)\\n d['data'] = pymc.distributions.Normal('data',mu=funcdet,tau=1/sig/sig,observed=True,value=y)\\n\\n return pymc.MCMC(d)\",\n \"def pgibbs_estimator(X, data_model, crp_alpha, n_samples=100, \\n n_sweeps=0, return_logps=False):\\n n_data = X.shape[0]\\n logps = np.zeros(n_samples)\\n dpgmm = PyDPGMM(data_model, X, crp_alpha, seed=random.randrange(2**31))\\n\\n lambda_0 = data_model.lambda_0\\n mu_0 = data_model.mu_0\\n kappa_0 = data_model.kappa_0\\n nu_0 = data_model.nu_0\\n Z_0 = data_model.log_z\\n\\n for i in range(n_samples):\\n qi = dpgmm.seqinit(n_sweeps)\\n\\n Nk = dpgmm.get_Nk()\\n K = dpgmm.get_K()\\n Z = dpgmm.get_Z()\\n\\n logp = lcrp(n_data, Nk, crp_alpha)\\n\\n for k in range(K):\\n X_k = X[Z==k, :]\\n logp += niw_ml(X_k, lambda_0, mu_0, kappa_0, nu_0, Z_0)\\n\\n logps[i] = logp - qi\\n\\n est= logsumexp(logps) - log(n_samples)\\n\\n if return_logps:\\n return est, logps\\n else:\\n return est\",\n \"def initParams(self):\\n sizes = [self.inputDim]+self.layerSizes+[self.outputDim]\\n scales = [np.sqrt(6)/np.sqrt(n+m) for n,m in zip(sizes[:-1],sizes[1:])]\\n self.stack = [[np.random.rand(m,n)*2*s-s,np.zeros((m,1))] \\\\\\n for n,m,s in zip(sizes[:-1],sizes[1:],scales)]\\n self.hActs_M = [cm.empty((s,self.maxBatch)) for s in sizes]\\n\\n if self.train:\\n # Now assuming that all layers are the same size\\n self.grad = [[cm.empty(w.shape),cm.empty(b.shape)] for w,b in self.stack]\\n self.deltasC_M = cm.empty((self.outputDim,self.maxBatch))\\n self.deltasOut_M = cm.empty((sizes[1],self.maxBatch)) \\n self.deltasIn_M = cm.empty((sizes[1],self.maxBatch)) \\n self.tmpGrad_M = cm.empty((self.layerSize,self.maxBatch))\\n \\n # Allocate memory once here and reuse\\n # Store probs\\n self.probs_M = cm.empty((self.outputDim,self.maxBatch))\\n # Store col max\\n self.rowVec_M = cm.empty((1,self.maxBatch))\\n \\n self.stack = [[cm.CUDAMatrix(w),cm.CUDAMatrix(b)]\\n for w,b in self.stack]\\n\\n if self.temporalLayer > 0:\\n # dummy bias used for temporal layer\\n dummy = cm.empty((1,1))\\n dummy.assign(0.0)\\n\\n scale = np.sqrt(6)/np.sqrt(self.layerSize*2)\\n wtf = cm.CUDAMatrix(2*scale*np.random.rand(self.layerSize,\\n self.layerSize)-scale)\\n wtb = cm.CUDAMatrix(2*scale*np.random.rand(self.layerSize,\\n self.layerSize)-scale)\\n self.stack.append([wtf,dummy])\\n self.stack.append([wtb,dummy])\\n\\n # forward and backward activations for temporal layer\\n self.hActsFor_M = cm.empty((self.layerSize,self.maxBatch))\\n self.hActsBack_M = cm.empty((self.layerSize,self.maxBatch))\\n\\n if self.train:\\n dwtf = cm.empty(wtf.shape)\\n self.grad.append([dwtf,dummy])\\n dwtb = cm.empty(wtb.shape)\\n self.grad.append([dwtb,dummy])\\n\\n self.tmpGradBack_M = cm.empty((self.layerSize,self.maxBatch))\\n self.deltasFor_M = cm.empty((self.layerSize,self.maxBatch))\\n self.deltasBack_M = cm.empty((self.layerSize,self.maxBatch))\",\n \"def mlp(input_dim,variance=1., weight_variance=None,bias_variance=100.,ARD=False):\\r\\n part = parts.mlp.MLP(input_dim,variance,weight_variance,bias_variance,ARD)\\r\\n return kern(input_dim, [part])\",\n \"def bd_process_model_probability(data,\\n fitness_prior=flat_fitness_prior,\\n N_w_prior=flat_N_w_prior,\\n mutation_object=True):\\n\\n if mutation_object is True:\\n trajectories = data.data\\n else:\\n trajectories = data\\n\\n ind_likelihood = []\\n for traj in trajectories:\\n int_s = []\\n for s in fitness_prior[0, :]:\\n int_N_w = []\\n for N_w in N_w_prior[0, :]:\\n int_N_w.append(\\n bd_process_conditional_likelihood_s_N(traj,\\n s=s, N_w=N_w)\\n )\\n int_s.append(np.trapz(x=N_w_prior[0, :],\\n y=int_N_w*N_w_prior[1, :]))\\n\\n marginalised_likelihood = np.trapz(x=fitness_prior[0, :],\\n y=int_s*fitness_prior[1, :])\\n ind_likelihood.append(marginalised_likelihood)\\n \\n mutation_prob = np.product(ind_likelihood)\\n\\n if mutation_object is True:\\n # return updated model_comparison object \\n data.bd_prob = mutation_prob\\n return data\\n else:\\n # return marginalised likelihood.\\n return mutation_prob\",\n \"def part3c_2():\\n xs = \\\"Werner & Co entered court today . Werner maintained that they were not guilty .\\\".split()\\n N = 10000\\n\\n submission.computeGibbsProbabilities( englishCRF,\\n submission.getCRFBlocks,\\n submission.chooseGibbsCRF,\\n xs, N )\\n grader.requireIsTrue(True)\",\n \"def get_model_with_properties():\\n \\n m = ConcreteModel()\\n\\n # ------------------------------------------------------------------\\n # Data\\n # ------------------------------------------------------------------\\n\\n m.np = 25 # Number of possible tays\\n m.c = 4 # Number of components\\n m.lc = 1 # Light component\\n m.hc = 4 # Heavy component\\n\\n #### Constant parameters\\n m.Rgas = 8.314 # Ideal gas constant in J/mol K\\n m.Tref = 298.15 # Reference temperature in K\\n\\n #### Product specifications\\n m.xspec_lc = 0.99 # Final liquid composition for methanol (1)\\n m.xspec_hc = 0.99 # Fnal liquid composition for butanol (4)\\n m.xspec_inter2 = 0.99 # Final liquid composition for ethanol (2)\\n m.xspec_inter3 = 0.99 # Final liquid composition for propanol (3)\\n m.Ddes = 50 # Final flowrate in distillate in mol/s\\n m.Bdes = 50 # Final flowrate in bottoms in mol/s\\n m.Sdes = 50 # Final flowrate in side product streams in mol/s\\n\\n # #### Known initial values\\n m.Fi = m.Ddes + m.Bdes + 2 * m.Sdes # Side feed flowrate in mol/s\\n m.Vi = 400 # Initial value for vapor flowrate in mol/s\\n m.Li = 400 # Initial value for liquid flowrate in mol/s\\n\\n m.Tf = 358 # Side feed temperature in K\\n\\n m.Preb = 1.2 # Reboiler pressure in bar\\n m.Pbot = 1.12 # Bottom-most tray pressure in bar\\n m.Ptop = 1.08 # Top-most tray pressure in bar\\n m.Pcon = 1.05 # Condenser pressure in bar\\n m.Pf = 1.02\\n\\n m.rr0 = 0.893 # Internal reflux ratio initial value\\n m.bu0 = 0.871 # Internal reflux ratio initial value\\n\\n\\n #### Scaling factors\\n m.Hscale = 1e3 \\n m.Qscale = 1e-3 \\n\\n \\n #### Constants for the calculation of liquid heat capacity\\n m.cpc = {} # Constant 1 for liquid heat capacity \\n m.cpc2 = {} # Constant 2 for liquid heat capacity \\n m.cpc[1] = m.Rgas \\n m.cpc[2] = 1\\n m.cpc2['A', 1] = 1 / 100\\n m.cpc2['B', 1] = 1 / 1e4\\n m.cpc2['A', 2] = 1\\n m.cpc2['B', 2] = 1\\n\\n\\n # ------------------------------------------------------------------\\n # Physical Properties\\n #\\n # Notation:\\n # MW ........................ molecular weight in g/gmol\\n # TB ........................ boiling point temperature in K\\n # TC ........................ critical temperature in K\\n # PC ........................ critical pressure in bar\\n # w ........................ acentric factor\\n # lden ...................... liquid density g/m3,\\n # dHvap ..................... heat of vaporization in J/mol.\\n # vpA, vpB, vpC, and vpD .... vapor pressure constants\\n # cpA, cpB, cpC, and cpD .... heat capacity constants J/mol:\\n # 1 for liq and 2 for vapor phase\\n #\\n # Reference A: R.C. Reid, J.M. Prausnitz and B.E. Poling,\\n # \\\"The Properties of gases and liquids\\\", 1987 and 2004 Eds.\\n #\\n # ------------------------------------------------------------------\\n\\n m.prop = {} # Properties of components:\\n cpL = {} # Ruczika-D method for liquid heat capacity calculation\\n # (Reference A, page 6.20)\\n sumA = {}\\n sumB = {}\\n sumC = {}\\n cpL['a', 'C(H3)(C)'] = 4.19845\\n cpL['b', 'C(H3)(C)'] = -0.312709\\n cpL['c', 'C(H3)(C)'] = 0.178609\\n cpL['a', 'C(H2)(C2)'] = 2.7345\\n cpL['b', 'C(H2)(C2)'] = 0.122732\\n cpL['c', 'C(H2)(C2)'] = -0.123482\\n cpL['a', 'C(H2)(C)(O)'] = 0.517007\\n cpL['b', 'C(H2)(C)(O)'] = 1.26631\\n cpL['c', 'C(H2)(C)(O)'] = -0.0939713\\n cpL['a', 'O(H)(C)'] = 16.1555\\n cpL['b', 'O(H)(C)'] = -11.938\\n cpL['c', 'O(H)(C)'] = 2.85117\\n cpL['a', 'C(H3)(O)'] = 3.70344\\n cpL['b', 'C(H3)(O)'] = -1.12884\\n cpL['c', 'C(H3)(O)'] = 0.51239\\n sumA[1] = (cpL['a', 'C(H3)(O)']\\n + cpL['a', 'O(H)(C)']) \\n sumB[1] = (cpL['b', 'C(H3)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[1] = (cpL['c', 'C(H3)(O)']\\n + cpL['c', 'O(H)(C)'])\\n sumA[2] = (cpL['a', 'C(H3)(C)']\\n + cpL['a', 'C(H2)(C)(O)']\\n + cpL['a', 'O(H)(C)'])\\n sumB[2] = (cpL['b', 'C(H3)(C)']\\n + cpL['b', 'C(H2)(C)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[2] = (cpL['c', 'C(H3)(C)']\\n + cpL['c', 'C(H2)(C)(O)']\\n + cpL['c', 'O(H)(C)'])\\n sumA[3] = (cpL['a', 'C(H3)(C)']\\n + cpL['a', 'C(H2)(C2)']\\n + cpL['a', 'C(H2)(C)(O)']\\n + cpL['a', 'O(H)(C)'])\\n sumB[3] = (cpL['b', 'C(H3)(C)']\\n + cpL['b', 'C(H2)(C2)']\\n + cpL['b', 'C(H2)(C)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[3] = (cpL['c', 'C(H3)(C)']\\n + cpL['c', 'C(H2)(C2)']\\n + cpL['c', 'C(H2)(C)(O)']\\n + cpL['c', 'O(H)(C)'])\\n sumA[4] = (cpL['a', 'C(H3)(C)']\\n + 2 * cpL['a', 'C(H2)(C2)']\\n + cpL['a', 'C(H2)(C)(O)']\\n + cpL['a', 'O(H)(C)'])\\n sumB[4] = (cpL['b', 'C(H3)(C)']\\n + 2 * cpL['b', 'C(H2)(C2)']\\n + cpL['b', 'C(H2)(C)(O)']\\n + cpL['b', 'O(H)(C)'])\\n sumC[4] = (cpL['c', 'C(H3)(C)']\\n + 2 * cpL['c', 'C(H2)(C2)']\\n + cpL['c', 'C(H2)(C)(O)']\\n + cpL['c', 'O(H)(C)'])\\n\\n ## Methanol: component 1\\n m.prop[1, 'MW'] = 32.042\\n m.prop[1, 'TB'] = 337.7\\n m.prop[1, 'TC'] = 512.6\\n m.prop[1, 'PC'] = 80.9\\n m.prop[1, 'w'] = 0.556\\n m.prop[1, 'lden'] = 792e3\\n m.prop[1, 'dHvap'] = 38.376e3\\n m.prop[1, 'vpA'] = -8.54796\\n m.prop[1, 'vpB'] = 0.76982\\n m.prop[1, 'vpC'] = -3.10850\\n m.prop[1, 'vpD'] = 1.54481\\n m.prop[1, 'cpA', 1] = sumA[1]\\n m.prop[1, 'cpB', 1] = sumB[1]\\n m.prop[1, 'cpC', 1] = sumC[1]\\n m.prop[1, 'cpD', 1] = 0\\n m.prop[1, 'cpA', 2] = 2.115e1\\n m.prop[1, 'cpB', 2] = 7.092e-2\\n m.prop[1, 'cpC', 2] = 2.587e-5\\n m.prop[1, 'cpD', 2] = -2.852e-8\\n\\n\\n ## Ethanol: component 2\\n m.prop[2, 'MW'] = 46.069\\n m.prop[2, 'TB'] = 351.4\\n m.prop[2, 'TC'] = 513.9\\n m.prop[2, 'PC'] = 61.4\\n m.prop[2, 'w'] = 0.644\\n m.prop[2, 'lden'] = 789.3e3\\n m.prop[2, 'dHvap'] = 42.698e3\\n m.prop[2, 'vpA'] = -8.51838\\n m.prop[2, 'vpB'] = 0.34163\\n m.prop[2, 'vpC'] = -5.73683\\n m.prop[2, 'vpD'] = 8.32581\\n m.prop[2, 'cpA', 1] = sumA[2]\\n m.prop[2, 'cpB', 1] = sumB[2]\\n m.prop[2, 'cpC', 1] = sumC[2]\\n m.prop[2, 'cpD', 1] = 0\\n m.prop[2, 'cpA', 2] = 9.014\\n m.prop[2, 'cpB', 2] = 2.141e-1\\n m.prop[2, 'cpC', 2] = -8.390e-5\\n m.prop[2, 'cpD', 2] = 1.373e-9\\n\\n\\n ## Propanol: component 3\\n m.prop[3, 'MW'] = 60.096\\n m.prop[3, 'TB'] = 370.3\\n m.prop[3, 'TC'] = 536.8\\n m.prop[3, 'PC'] = 51.7\\n m.prop[3, 'w'] = 0.623\\n m.prop[3, 'lden'] = 804e3\\n m.prop[3, 'dHvap'] = 47.763e3\\n m.prop[3, 'vpA'] = -8.05594\\n m.prop[3, 'vpB'] = 4.25183e-2\\n m.prop[3, 'vpC'] = -7.51296\\n m.prop[3, 'vpD'] = 6.89004\\n m.prop[3, 'cpA', 1] = sumA[3]\\n m.prop[3, 'cpB', 1] = sumB[3]\\n m.prop[3, 'cpC', 1] = sumC[3]\\n m.prop[3, 'cpD', 1] = 0\\n m.prop[3, 'cpA', 2] = 2.47\\n m.prop[3, 'cpB', 2] = 3.325e-1\\n m.prop[3, 'cpC', 2] = -1.855e-4\\n m.prop[3, 'cpD', 2] = 4.296e-8\\n\\n\\n ## Butanol: component 4\\n m.prop[4, 'MW'] = 74.123\\n m.prop[4, 'TB'] = 390.9\\n m.prop[4, 'TC'] = 563.1\\n m.prop[4, 'PC'] = 44.2\\n m.prop[4, 'w'] = 0.593\\n m.prop[4, 'lden'] = 810e3\\n m.prop[4, 'dHvap'] = 52.607e3\\n m.prop[4, 'vpA'] = -8.00756\\n m.prop[4, 'vpB'] = 0.53783\\n m.prop[4, 'vpC'] = -9.34240\\n m.prop[4, 'vpD'] = 6.68692\\n m.prop[4, 'cpA', 1] = sumA[4]\\n m.prop[4, 'cpB', 1] = sumB[4]\\n m.prop[4, 'cpC', 1] = sumC[4]\\n m.prop[4, 'cpD', 1] = 0\\n m.prop[4, 'cpA', 2] = 3.266\\n m.prop[4, 'cpB', 2] = 4.18e-1\\n m.prop[4, 'cpC', 2] = -2.242e-4\\n m.prop[4, 'cpD', 2] = 4.685e-8\\n\\n\\n return m\",\n \"def buildQ(self):\\r\\n\\r\\n print 'Building Q ...'\\r\\n\\r\\n self.y = T.matrix('y')\\r\\n\\r\\n mlp = MLP(activations=self.hyper['q_activs'],\\r\\n dims=self.hyper['q_dims'],\\r\\n weights_init=self.hyper['q_W_init'],\\r\\n biases_init=Constant(0))\\r\\n\\r\\n q_parameters = mlp.apply(self.y)\\r\\n mlp.initialize()\\r\\n\\r\\n # self.qxgy_mu.shape == (minibatch size, num of dimension of x)\\r\\n self.qxgy_mu = q_parameters[:,:self.hyper['x_dim']]\\r\\n\\r\\n # self.qxgy_var.shape == (minibatch size, num of dimension of x)\\r\\n self.qxgy_var = T.exp( q_parameters[:,self.hyper['x_dim']:2*self.hyper['x_dim']] )\\r\\n\\r\\n # self.qwgy_mu.shape == (minibatch size, num of dimension of w)\\r\\n self.qwgy_mu = q_parameters[:,2*self.hyper['x_dim']:2*self.hyper['x_dim']+self.hyper['w_dim']]\\r\\n\\r\\n # self.qwgy_var.shape == (minibatch size, num of dimension of w)\\r\\n self.qwgy_var = T.exp( q_parameters[:,2*self.hyper['x_dim']+self.hyper['w_dim']:] )\\r\\n\\r\\n\\r\\n #---Will be useful to compute samples from q(x|y)---#\\r\\n #self.eps_x.shape == (minibatch size, # of x samples , # of dimension of x)\\r\\n self.eps_x = self.srng.normal((self.qxgy_mu.shape[0] ,self.hyper['L_x'] ,self.hyper['x_dim']))\\r\\n\\r\\n #self.x corresponds roughly to the function g(\\\\epsilon,y) (see reparametrization trick in Kingma 2014)\\r\\n #self.x.shape == (minibatch size, # of x samples , # of dimension of x)\\r\\n self.x = self.qxgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qxgy_var).dimshuffle(0,'x',1)*self.eps_x\\r\\n\\r\\n #---Will be useful to compute samples from q(w|y)---#\\r\\n #self.eps_w.shape == (minibatch size, # of w samples , # of dimension of w)\\r\\n self.eps_w = self.srng.normal((self.qwgy_mu.shape[0] ,self.hyper['L_w'] ,self.hyper['w_dim']))\\r\\n\\r\\n #self.w corresponds roughly to the function g(\\\\epsilon,y) (see reparametrization trick in Kingma 2014)\\r\\n #self.w.shape == (minibatch size, # of w samples , # of dimension of w)\\r\\n self.w = self.qwgy_mu.dimshuffle(0,'x',1) + T.sqrt(self.qwgy_var).dimshuffle(0,'x',1)*self.eps_w\\r\\n\\r\\n\\r\\n #---Building the log density q(x|y)---#\\r\\n little_num = 10**(-32)\\r\\n inside_exp = -T.sum((self.x - self.qxgy_mu.dimshuffle(0,'x',1))**2/(2*self.qxgy_var.dimshuffle(0,'x',1)), axis=2)\\r\\n norm_cst = (2*np.pi)**(-self.hyper['x_dim']/2.)*T.exp(T.sum(T.log(self.qxgy_var), axis=1))**(-1/2.)\\r\\n\\r\\n # shape == (minibatch size, # of x samples)\\r\\n qxgy = norm_cst.dimshuffle(0,'x')*T.exp(inside_exp)\\r\\n\\r\\n # shape == (minibatch size, # of x samples)\\r\\n self.log_qxgy = T.log(qxgy + little_num)\",\n \"def run_emcee_mp(nsample=1000 ):#, pool=None):\\n param_dict ={}\\n \\n obs_vec = np.loadtxt(\\\"./results/pk_CDE_FittingFormula-observed-truth.txt\\\")\\n obs_vec=obs_vec[:,1]\\n \\n cov_simple = get_cov([0.32,0.1])\\n print(np.linalg.cond(cov_simple))\\n \\n inv_cov = np.linalg.inv(cov_simple)\\n previous_best = np.array([0.3, 0.09])\\n \\n ndim = len(previous_best)\\n\\n pos = previous_best + 1e-3 * np.random.randn(16, ndim)\\n nwalkers, ndim = pos.shape\\n\\n #with Pool() as pool:\\n sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability, args=(obs_vec, inv_cov))#, pool=pool)\\n sampler.run_mcmc(pos, nsample, progress=True)\\n\\n return sampler\",\n \"def proba_fm(m_pred,f_pred, dataset):\\n p = np.zeros(10)\\n if dataset == 'cifar10':\\n for i in range(10):\\n if i <4:\\n if i <2:\\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:2]))\\n else:\\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[2:4]))\\n else:\\n if i <6:\\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[4:6]))\\n elif i <8:\\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[6:8]))\\n else:\\n p[i] = (m_pred[4])*(f_pred[i]/np.sum(f_pred[8:]))\\n else :\\n for i in range(10):\\n if i <5:\\n if i <3:\\n p[i] = (m_pred[0])*(f_pred[i]/np.sum(f_pred[0:3]))\\n else:\\n p[i] = (m_pred[1])*(f_pred[i]/np.sum(f_pred[3:5]))\\n else:\\n if i <8:\\n p[i] = (m_pred[2])*(f_pred[i]/np.sum(f_pred[5:8]))\\n else:\\n p[i] = (m_pred[3])*(f_pred[i]/np.sum(f_pred[8:]))\\n return(p)\",\n \"def sample_from_ibp(K, alpha, sigma, c):\\n pp = poissonparams(K, alpha, sigma, c)\\n new_nodes = np.random.poisson(pp)\\n Ncols = new_nodes.sum()\\n node_count = np.zeros(Ncols)\\n\\n # used to build sparse matrix, entries of each Zij=1\\n colidx = [] \\n rowidx = []\\n rightmost_node = 0\\n\\n # for each clique\\n for n in range(K):\\n # revisit each previously seen node\\n for k in range(rightmost_node):\\n prob_repeat = (node_count[k] - sigma) / (n + c)\\n r = np.random.rand()\\n if r < prob_repeat:\\n rowidx.append(n)\\n colidx.append(k)\\n node_count[k] += 1\\n\\n for k in range(rightmost_node, rightmost_node + new_nodes[n]):\\n rowidx.append(n)\\n colidx.append(k)\\n node_count[k] += 1\\n \\n rightmost_node += new_nodes[n]\\n\\n # build sparse matrix\\n data = np.ones(len(rowidx), int)\\n shape = (K, Ncols)\\n Z = csr_matrix((data, (rowidx, colidx)), shape)\\n\\n return Z\",\n \"def _init_from_newform_(self, g, PP, C, prec):\\n k = g.weight()\\n N = g.level()\\n if(k % 4 == 0):\\n ep = g.atkin_lehner_eigenvalue()\\n else:\\n ep = -g.atkin_lehner_eigenvalue()\\n kappa = mpmath.mpf(3 - k) / mp2\\n if(ep == -1):\\n WR = WeilRepDiscriminantForm(N, False)\\n elif ep == 1:\\n WR = WeilRepDiscriminantForm(N, True)\\n else:\\n raise ValueError(\\\" Sign of functional equation must be 1 or -1! Got:{0}\\\".format(ep))\\n # print \\\"kappa=\\\",kappa\\n # print \\\"WR=\\\",WR\\n M = VVHarmonicWeakMaassForms(WR, kappa, prec)\\n # print \\\"M=\\\",M\\n self._space = M\\n self.prec = prec\\n self.coeff = dict()\\n # We want a Harmonic weak maass form corresponding to the form g\\n # that means we need to avoid any other cuspforms as well as\\n # theta series...\\n # If there are no oldforms we are happy\\n if dimension_new_cusp_forms(N, k) == dimension_cusp_forms(N, k):\\n # and to avoid theta series we need to avoid square discriminants\\n # in the principal part\\n if M._is_dual_rep:\\n nset = [0, -1]\\n else:\\n nset = [-1]\\n try:\\n for n in nset:\\n for r in WR.D(): # )_as_integers:\\n D = M.D_from_rn((r, n))\\n if not is_square(D):\\n PP = {(WR.D[r], n): 1}\\n self._principal_part = PP\\n # print \\\"PP=\\\",PP,\\\"is ok!\\\"\\n raise StopIteration()\\n except StopIteration:\\n pass\\n # if(C is not None and C>0):\",\n \"def heavy_fixCM_eigvals(NP, b, c, params):\\n l = params['l']\\n k = params['k']\\n I3 = params['I3']\\n # Here, omega_3 is just the MAGNITUDE, not signed\\n w3 = np.abs(params['w3'][0])\\n gn = params['Mm'] * params['g']\\n\\n # Check output if small system\\n print 'gn = ', gn\\n print 'b = ', b\\n print 'c = ', c\\n\\n if NP == 1:\\n pass\\n elif NP == 2:\\n matrix = -np.array([[0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0.],\\n [(-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.,\\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.],\\n [0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3)],\\n [(-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\\n (-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.]\\n ])\\n print 'exact matrix = ', matrix\\n eigvals = np.array([\\n 1j * l * gn / (I3 * w3),\\n -1j * l * gn / (I3 * w3),\\n l * np.sqrt(gn) * np.sqrt(0j - 2. * l * k * (-1) ** (b) - gn) / (I3 * w3),\\n -l * np.sqrt(gn) * np.sqrt(0j - 2. * l * k * (-1) ** (b) - gn) / (I3 * w3)\\n ])\\n print 'exact_eigvals are =', eigvals\\n return eigvals\\n elif NP == 3:\\n matrix = -np.array([[0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0., 0., 0.],\\n [(-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.,\\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0., 0., 0.],\\n [0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3), 0., 0.],\\n [(-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\\n (-1) ** (1 + c) * (-l * gn - 2. * (-1) ** (b) * l ** 2 * k) / (I3 * w3), 0., \\\\\\n (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.],\\n [0., 0., 0., 0., 0., (-1) ** (1 + c) * l * gn / (I3 * w3)],\\n [0., 0., (-1) ** (1 + b + c) * l ** 2 * k / (I3 * w3), 0.,\\n (-1) ** (1 + c) * (-l * gn + (-1) ** (1 + b) * l ** 2 * k) / (I3 * w3), 0.]\\n ])\\n print 'exact matrix = ', matrix\\n\\n eigvals = np.array([\\n 1j * l * gn / (I3 * w3),\\n # -1j*l*gn/(I3*w3),\\n l * np.sqrt(gn) * np.sqrt(0j - 3. * l * k * (-1) ** (b) - gn) / (I3 * w3),\\n # -l*np.sqrt(gn)*np.sqrt(0j-3.*l*k*(-1)**(b) - gn)/(I3*w3),\\n l * np.sqrt(gn) * np.sqrt(0j - l * k * (-1) ** (b) - gn) / (I3 * w3),\\n # -l*np.sqrt(gn)*np.sqrt(0j - l*k*(-1)**(b) - gn)/(I3*w3)\\n ])\\n return eigvals\\n else:\\n return np.array([])\",\n \"def chisqdata_cphase(Obsdata, Prior, mask):\\n\\n clphasearr = Obsdata.c_phases(mode=\\\"all\\\", count=\\\"min\\\")\\n uv1 = np.hstack((clphasearr['u1'].reshape(-1,1), clphasearr['v1'].reshape(-1,1)))\\n uv2 = np.hstack((clphasearr['u2'].reshape(-1,1), clphasearr['v2'].reshape(-1,1)))\\n uv3 = np.hstack((clphasearr['u3'].reshape(-1,1), clphasearr['v3'].reshape(-1,1)))\\n clphase = clphasearr['cphase']\\n sigma = clphasearr['sigmacp']\\n\\n A3 = (ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv1, pulse=Prior.pulse, mask=mask),\\n ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv2, pulse=Prior.pulse, mask=mask),\\n ftmatrix(Prior.psize, Prior.xdim, Prior.ydim, uv3, pulse=Prior.pulse, mask=mask)\\n )\\n return (clphase, sigma, A3)\",\n \"def part3c_0():\\n xs = exampleInput\\n N = 10000\\n\\n difference = 0.0\\n for ys, estimatedProb in submission.computeGibbsProbabilities( simpleCRF, \\n submission.getCRFBlocks,\\n submission.chooseGibbsCRF,\\n xs, N ).iteritems():\\n trueProb = nerUtils.computeProbability( simpleCRF, xs, ys )\\n difference = abs( trueProb - estimatedProb )\\n grader.requireIsLessThan( 5e-2, difference )\",\n \"def prob3():\\n#raise NotImplementedError(\\\"Problem 3 Incomplete\\\")\\n n = np.linspace(5000,500000,100)\\n h = lambda x : x > 10\\n MC_estimates = []\\n for N in xrange(5000,505000,5000):\\n X = np.random.gamma(9,scale=0.5,size=N)\\n MC = 1./N*np.sum(h(X))\\n MC_estimates.append(MC)\\n MC_estimates = np.array(MC_estimates)\\n MC_est = prob2()\\n exact = np.array([1-stats.gamma(a=9,scale=0.5).cdf(10)]*n.shape[0])\\n print n.shape,MC_estimates.shape\\n plt.plot(n,abs(MC_estimates-exact),'r-',label=\\\"Monte Carlo\\\")\\n plt.plot(n,abs(MC_est-exact),'b-',label=\\\"Importance\\\")\\n plt.title(\\\"Error or Approx.\\\")\\n plt.legend(loc=1)\\n plt.show()\",\n \"def milp(mdp, maxV, zeroConstraints=()):\\n m = Model()\\n m.setParam('OutputFlag', False)\\n\\n # convert notation to previous implementation\\n S = mdp.S\\n A = mdp.A\\n R = mdp.rFuncs\\n psi = mdp.psi\\n T = mdp.T\\n alpha = mdp.alpha\\n gamma = mdp.gamma\\n\\n # useful constants\\n rLen = len(R)\\n M = 10000 # a large number\\n Sr = range(len(S))\\n Ar = range(len(A))\\n\\n # decision variables\\n x = m.addVars(len(S), len(A), lb=0, name='x')\\n z = m.addVars(rLen, vtype=GRB.BINARY, name='z')\\n y = m.addVars(rLen, name='y')\\n\\n # constraints on y\\n for i in range(rLen):\\n m.addConstr(y[i] <= sum([x[s, a] * R[i](S[s], A[a]) for s in Sr for a in Ar]) - maxV[i] + (1 - z[i]) * M)\\n m.addConstr(y[i] <= z[i] * M)\\n\\n # constraints on x (valid occupancy)\\n for sp in Sr:\\n m.addConstr(sum(x[s, a] * ((s == sp) - gamma * T(S[s], A[a], S[sp])) for s in Sr for a in Ar) == alpha(S[sp]))\\n\\n # == constraints\\n for consIdx in range(len(zeroConstraints)):\\n m.addConstr(sum(x[S.index(s), A.index(a)] for s, a in zeroConstraints[consIdx]) == 0)\\n # obj\\n m.setObjective(sum([psi[i] * y[i] for i in xrange(rLen)]), GRB.MAXIMIZE)\\n\\n m.optimize()\\n\\n pi = {(S[s], A[a]): x[s, a].X for s in Sr for a in Ar}\\n\\n if m.status == GRB.Status.OPTIMAL:\\n # return feasible being true and the obj value, opt pi\\n # .X attribute is to retrieve the value of the variable\\n return pi\\n else:\\n # simply return infeasible\\n raise Exception('milp problem optimal solution not found' + m.status)\",\n \"def __init__(self, \\n num_vars, \\n num_hidden,\\n training_inputs = None,\\n algorithm = None,\\n algorithm_dict = None,\\n batch_size = None,\\n use_momentum = None,\\n W0= None, \\n b0= None, \\n bhid0 = None,\\n zero_diag = True,\\n symmetric = True,\\n report_p_tilda =False,\\n learn_biases = True,\\n test_mode= False,\\n training = True):\\n \\n self.num_vars = num_vars\\n \\n self.num_hidden = num_hidden\\n \\n self.batch_size = batch_size\\n \\n self.zero_diag = zero_diag\\n \\n self.algorithm = algorithm\\n \\n self.num_samples = 0\\n \\n self.num_u_gibbs = 0\\n \\n self.gibbs_steps = 0\\n \\n self.resample = False\\n \\n self.uniform = False\\n \\n self.mixture = False\\n \\n self.mix_params = []\\n \\n self.m_params = []\\n \\n self.mf_steps = 0\\n \\n self.alpha = 0\\n \\n self.learn_biases = learn_biases\\n \\n if isinstance(algorithm_dict, dict):\\n \\n for param in algorithm_dict.keys():\\n \\n if param == 'resample':\\n \\n self.resample = algorithm_dict[param]\\n \\n if param == 'mf_steps':\\n \\n self.mf_steps = algorithm_dict[param]\\n \\n if param == \\\"gibbs_steps\\\":\\n \\n self.gibbs_steps = algorithm_dict[param]\\n \\n if param == \\\"num_samples\\\":\\n \\n self.num_samples = algorithm_dict[param]\\n \\n if param == \\\"num_u_gibbs\\\":\\n \\n self.num_u_gibbs = algorithm_dict[param]\\n \\n if param == \\\"uniform\\\":\\n \\n self.uniform = algorithm_dict[param] \\n \\n if param == \\\"mixture\\\":\\n \\n self.mixture = algorithm_dict[param] \\n \\n if param == \\\"mix_params\\\":\\n \\n self.mix_params = algorithm_dict[param] \\n \\n if param == \\\"alpha\\\" and algorithm_dict[param] != None:\\n #### alpha defines transition rate from\\n #### uniform to mean-field distribution\\n self.alpha = algorithm_dict[param] \\n \\n self.m_params = (1-self.alpha)*0.5*np.ones([1,self.num_vars])+\\\\\\n self.alpha*np.mean(training_inputs,0)\\n \\n self.use_momentum = use_momentum\\n \\n self.report_p_tilda = report_p_tilda\\n \\n self.side = int(np.sqrt(self.num_vars))\\n \\n self.np_rand_gen = np.random.RandomState(1234)\\n \\n self.theano_rand_gen =\\\\\\n theano.sandbox.rng_mrg.MRG_RandomStreams(self.np_rand_gen.randint(2**30))\\n \\n #self.theano_rand_gen =\\\\\\n #T.shared_randomstreams.RandomStreams(self.np_rand_gen.randint(2**30))\\n \\n theano.config.exception_verbosity = 'high'\\n \\n self.node_indices = \\\\\\n theano.shared(np.arange(self.num_vars), name=\\\"node_indices\\\")\\n \\n self.x = T.matrix('x')\\n \\n self.x_tilda = T.matrix('x_tilda')\\n \\n self.sampler_theta = T.matrix('sampler_theta')\\n \\n self.symmetric = symmetric\\n \\n if training:\\n \\n if self.num_hidden ==0:\\n \\n self.num_x2 = self.num_vars\\n \\n elif self.num_hidden > 0 :\\n \\n self.num_x2 = self.num_hidden\\n \\n self.updates = OrderedDict()\\n \\n self.N_train = training_inputs.shape[0]\\n \\n self.train_inputs = theano.shared(np.asarray(training_inputs,\\n dtype=theano.config.floatX),\\n borrow= True)\\n \\n self.learning_rate = T.dscalar('learning_rate')\\n \\n if self.mixture:\\n \\n print(\\\"Importance distribution was specified as mixture\\\"+\\\\\\n \\\" of Bernoulli products\\\")\\n \\n if self.mix_params == []:\\n print(\\\"Error: parameters defining mixture means were\\\"+\\\\\\n \\\" not provided\\\")\\n sys.exit()\\n \\n self.set_mixture_means(inputs = training_inputs)\\n \\n if use_momentum:\\n \\n print(\\\"Will add momentum term to gradient computations\\\")\\n \\n self.momentum = T.dscalar('learning_rate')\\n \\n self.grad_vec = {}\\n \\n self.grad_vec['W'] = theano.shared(np.zeros([self.num_vars, self.num_x2],\\n dtype = theano.config.floatX), name = 'W_momentum', borrow = True)\\n \\n if self.num_hidden > 0:\\n \\n self.grad_vec['bhid'] = theano.shared(np.zeros([self.num_x2],\\n dtype = theano.config.floatX), name = 'b_momentum', borrow = True)\\n \\n self.grad_vec['b'] = theano.shared(np.zeros([self.num_vars],\\n dtype = theano.config.floatX), name = 'b_momentum', borrow = True)\\n \\n if test_mode:\\n \\n b_init =self.np_rand_gen.uniform(0,1, num_vars)\\n \\n W_init =self.np_rand_gen.uniform(0,1, size = (num_vars, num_vars))\\n \\n # also tested ones\\n # b_init = np.ones(num_vars)\\n \\n # W_init = np.ones([num_vars, num_vars])\\n \\n self.b_init= np.asarray(b_init, dtype = theano.config.floatX)\\n \\n self.W_init= np.asarray(W_init, dtype = theano.config.floatX)\\n \\n self.b = theano.shared(self.b_init, name='b', borrow = False)\\n \\n self.W = theano.shared(self.W_init, name='W', borrow = False)\\n \\n print(\\\"Initialized with test mode\\\")\\n \\n else:\\n \\n if W0 is None:\\n \\n if self.num_hidden > 0:\\n \\n W0_init =\\\\\\n self.np_rand_gen.uniform(\\n -4*np.sqrt(6.0/(self.num_vars+self.num_hidden)),\\\\\\n 4*np.sqrt(6.0 /(self.num_vars + self.num_hidden)), \\n size = (num_vars, self.num_hidden)\\n )\\n \\n W0 = np.asarray(W0_init, dtype = theano.config.floatX) \\n \\n if self.num_hidden == 0:\\n \\n # different W initializations: \\n \\n # W0_init =\\\\\\n # self.np_rand_gen.uniform(-np.sqrt(3.0/(num_vars)),\\\\\\n # np.sqrt(3.0 / (num_vars)), size = (num_vars, num_vars))\\n \\n # W0_init =\\\\\\n # self.np_rand_gen.uniform(-0.00000001,\\\\\\n # 0.00000001, size = (num_vars, num_vars))\\n \\n W0_init = 0.00000001*\\\\\\n self.np_rand_gen.normal(size = (num_vars, self.num_x2)) \\n \\n W0 = np.asarray(W0_init, dtype = theano.config.floatX)\\n \\n if self.symmetric:\\n \\n W0 = (W0 + np.transpose(W0))/2.0\\n \\n if self.zero_diag:\\n \\n W0 = W0 - np.diag(np.diag(W0))\\n \\n self.W = theano.shared(value= W0, name='W', borrow=True)\\n \\n if self.num_hidden == 0:\\n \\n test_W = self.W.get_value() \\n \\n assert sum(np.diag(test_W)) == 0.0\\n \\n assert (test_W == np.transpose(test_W)).all() == True\\n \\n else:\\n print(\\\"W is initialized with provided array\\\")\\n self.W = theano.shared(value= W0, name='W', borrow=True)\\n \\n if b0 is None:\\n \\n bias_init = np.zeros(num_vars, dtype = theano.config.floatX)\\n \\n self.b = theano.shared(value= bias_init, name='b', borrow=True)\\n \\n else:\\n print(\\\"b vector is initialized with provided vector\\\")\\n self.b = theano.shared(value= b0, name='b', borrow=True)\\n \\n if bhid0 is None and self.num_hidden > 0:\\n \\n hbias_init = np.zeros(self.num_hidden, dtype = theano.config.floatX)\\n \\n self.bhid = theano.shared(value= hbias_init, name='bhid', borrow=True)\\n \\n elif (bhid0 is not None) and (self.num_hidden > 0):\\n print(\\\"bhid vector is initialized with provided vector\\\") \\n self.bhid = theano.shared(value= bhid0, name='bhid', borrow=True)\\n \\n self.theta = [self.W, self.b]\\n \\n if self.num_hidden > 0 :\\n \\n self.theta.append(self.bhid)\\n \\n self.train_set = set(range(self.N_train))\\n \\n self.minibatch_set = T.ivector('minibatch_set')\\n \\n self.sample_set = T.ivector('sample_set')\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden ==0:\\n \\n self.x_gibbs= theano.shared(np.ones([self.batch_size,self.num_vars],\\n dtype=theano.config.floatX),\\n borrow = True, name= \\\"x_gibbs\\\")\\n \\n if \\\"CD\\\" in self.algorithm and self.num_hidden > 0:\\n \\n self.persistent_gibbs =\\\\\\n theano.shared(np.ones([self.batch_size,self.num_hidden],\\n dtype=theano.config.floatX),\\n borrow = True, \\n name= \\\"persistent_gibbs\\\")\\n \\n if \\\"CSS\\\" in self.algorithm and self.mf_steps > 0:\\n \\n init_mf_vis = self.np_rand_gen.uniform(0, \\n 1, \\n size =(self.num_vars,1))\\n \\n init_mf_vis = np.asarray(init_mf_vis, dtype = theano.config.floatX)\\n \\n self.mf_vis_p = theano.shared(init_mf_vis, \\n name= \\\"mf_vis_p\\\", \\n borrow= True)\\n \\n if self.num_hidden > 0:\\n \\n init_mf_hid = \\\\\\n self.np_rand_gen.uniform(0, 1, size =(self.num_hidden,1))\\n \\n init_mf_hid = np.asarray(init_mf_hid, \\n dtype = theano.config.floatX)\\n \\n self.mf_hid_p = theano.shared(init_mf_hid, \\n name= \\\"mf_hid_p\\\", \\n borrow= True)\\n \\n elif \\\"CSS\\\" in self.algorithm and self.gibbs_steps > 0: \\n \\n if self.num_hidden ==0: \\n self.x_gibbs= theano.shared(np.ones([self.batch_size,self.num_vars],\\n dtype=theano.config.floatX),\\n borrow = True, name= \\\"x_gibbs\\\")\",\n \"def __init__(self):\\n MCMC.__init__(self)\",\n \"def fisher_calc(\\n self,\\n qb,\\n cbl,\\n cls_input,\\n cls_noise=None,\\n cls_debias=None,\\n cls_model=None,\\n cond_noise=None,\\n cond_criteria=None,\\n likelihood=False,\\n like_lmin=2,\\n like_lmax=None,\\n delta_beta_prior=None,\\n null_first_cmb=False,\\n use_precalc=True,\\n windows=False,\\n inv_fish=None,\\n ):\\n if cond_criteria is None:\\n cond_criteria = np.inf\\n well_cond = False\\n\\n pol_dim = 3 if self.pol else 1\\n do_fg = \\\"fg_tt\\\" in cbl\\n\\n dkey = \\\"Dmat_obs_b\\\" if likelihood else \\\"Mmat\\\" if windows else \\\"Dmat_obs\\\"\\n\\n if getattr(self, dkey, None) is None or not use_precalc:\\n Dmat_obs, Dmat_obs_b, dSdqb_mat1, Mmat, Mmat_mix = self.fisher_precalc(\\n cbl,\\n cls_input,\\n cls_debias=cls_debias,\\n likelihood=likelihood,\\n windows=windows,\\n )\\n if use_precalc:\\n self.Dmat_obs = Dmat_obs\\n self.Dmat_obs_b = Dmat_obs_b\\n self.dSdqb_mat1 = dSdqb_mat1\\n self.Mmat = Mmat\\n self.Mmat_mix = Mmat_mix\\n else:\\n if likelihood:\\n Dmat_obs_b = self.Dmat_obs_b\\n else:\\n if windows:\\n Mmat = self.Mmat\\n Mmat_mix = self.Mmat_mix\\n else:\\n Dmat_obs = self.Dmat_obs\\n dSdqb_mat1 = self.dSdqb_mat1\\n\\n delta_beta = 0.0\\n if \\\"delta_beta\\\" in qb:\\n delta_beta = qb[\\\"delta_beta\\\"][0]\\n\\n if not likelihood:\\n dSdqb_mat1_freq = copy.deepcopy(dSdqb_mat1)\\n\\n if likelihood or windows or not cond_noise:\\n well_cond = True\\n cond_noise = None\\n\\n gmat_ell = OrderedDict()\\n Dmat1 = OrderedDict()\\n\\n if cls_model is None:\\n cls_model = self.get_model_spectra(\\n qb, cbl, delta=True, cls_noise=cls_noise, cond_noise=cond_noise\\n )\\n\\n mkeys = list(cls_model)\\n for xname, (m0, m1) in self.map_pairs.items():\\n # transfer function does not have crosses\\n if xname not in cls_model[mkeys[0]]:\\n continue\\n gmat_ell[xname] = self.gmat_ell[xname]\\n\\n if well_cond:\\n for spec in self.specs:\\n # transfer function doesn't have eb/tb\\n if \\\"total_{}\\\".format(spec) not in cls_model:\\n continue\\n Dmat1.setdefault(xname, OrderedDict())\\n Dmat1[xname][spec] = cls_model[\\\"total_{}\\\".format(spec)][xname]\\n\\n if well_cond:\\n Dmat1_mat = pt.dict_to_dmat(Dmat1)\\n\\n # Set up dSdqb frequency dependence\\n for xname, (m0, m1) in self.map_pairs.items():\\n # transfer function does not have crosses\\n if xname not in cls_model[mkeys[0]]:\\n continue\\n\\n if do_fg and not likelihood:\\n # Add spectral index dependence\\n # dSdqb now depends on qb (spec index) because\\n # model is non-linear so cannot be precomputed.\\n\\n # get foreground at pivot point spectral index\\n # and first derivative\\n freq_scale0, freq_scale_deriv = xft.scale_dust(\\n self.map_freqs[m0],\\n self.map_freqs[m1],\\n ref_freq=self.ref_freq,\\n beta=self.beta_ref,\\n deriv=True,\\n )\\n freq_scale = freq_scale0 + delta_beta * freq_scale_deriv\\n freq_scale_ratio = freq_scale_deriv / freq_scale\\n\\n # scale foreground model by frequency scaling adjusted for beta\\n for s1, sdat in dSdqb_mat1_freq[\\\"fg\\\"][xname].items():\\n for s2, sdat2 in sdat.items():\\n dSdqb_mat1_freq[\\\"fg\\\"][xname][s1][s2] *= freq_scale\\n\\n # build delta_beta term from frequency scaled model,\\n # divide out frequeny scaling and apply derivative term\\n for s1, sdat in dSdqb_mat1_freq[\\\"delta_beta\\\"][xname].items():\\n sdat[s1] = cls_model[\\\"fg_{}\\\".format(s1)][xname] * freq_scale_ratio\\n\\n # Set up Dmat -- if it's not well conditioned, add noise to the\\n # diagonal until it is.\\n cond_iter = 0\\n while not well_cond:\\n cls_model = self.get_model_spectra(\\n qb, cbl, delta=True, cls_noise=cls_noise, cond_noise=cond_noise\\n )\\n\\n for xname, (m0, m1) in self.map_pairs.items():\\n # transfer function does not have crosses\\n if xname not in cls_model[mkeys[0]]:\\n continue\\n\\n for spec in self.specs:\\n # transfer function doesn't have eb/tb\\n if \\\"total_{}\\\".format(spec) not in cls_model:\\n continue\\n Dmat1.setdefault(xname, OrderedDict())\\n Dmat1[xname][spec] = cls_model[\\\"total_{}\\\".format(spec)][xname]\\n\\n Dmat1_mat = pt.dict_to_dmat(Dmat1)\\n\\n cond = np.abs(\\n np.linalg.cond(Dmat1_mat[:, :, self.lmin :].swapaxes(0, -1))\\n ).max()\\n\\n if cond > cond_criteria and cond_noise:\\n cond_iter += 1\\n # cond_noise iteration factor found through trial and error\\n cond_noise *= 10 ** (cond_iter / 100.0)\\n if cond_iter == 1:\\n self.log(\\n \\\"Condition criteria not met. \\\"\\n \\\"Max Cond={:.0f}, Thresh={:.0f}\\\".format(cond, cond_criteria),\\n \\\"debug\\\",\\n )\\n else:\\n well_cond = True\\n self.log(\\n \\\"Condition criteria met. \\\"\\n \\\"Max Cond={:.0f}, Thresh={:.0f}, Iter={:d}\\\".format(\\n cond, cond_criteria, cond_iter\\n ),\\n \\\"debug\\\",\\n )\\n if cond_noise is not None:\\n self.log(\\\"Cond_noise = {:.3e}\\\".format(cond_noise), \\\"debug\\\")\\n\\n # construct arrays from dictionaries\\n Dmat1 = Dmat1_mat\\n gmat = pt.dict_to_dmat(gmat_ell)\\n if likelihood:\\n Dmat_obs_b = pt.dict_to_dmat(Dmat_obs_b)\\n else:\\n if windows:\\n Mmat = pt.dict_to_dmat(Mmat)\\n if self.pol:\\n Mmat_mix = pt.dict_to_dmat(Mmat_mix)\\n else:\\n Dmat_obs = pt.dict_to_dmat(Dmat_obs)\\n dSdqb_mat1_freq = pt.dict_to_dsdqb_mat(dSdqb_mat1_freq, self.bin_def)\\n\\n # apply ell limits\\n if likelihood:\\n ell = slice(\\n self.lmin if like_lmin is None else like_lmin,\\n self.lmax + 1 if like_lmax is None else like_lmax + 1,\\n )\\n else:\\n ell = slice(self.lmin, self.lmax + 1)\\n Dmat1 = Dmat1[..., ell]\\n if likelihood:\\n Dmat_obs_b = Dmat_obs_b[..., ell]\\n else:\\n if windows:\\n Mmat = Mmat[..., ell, :]\\n if self.pol:\\n Mmat_mix = Mmat_mix[..., ell, :]\\n else:\\n Dmat_obs = Dmat_obs[..., ell]\\n dSdqb_mat1_freq = dSdqb_mat1_freq[..., ell]\\n gmat = gmat[..., ell]\\n\\n self.Dmat1 = Dmat1\\n\\n lam, R = np.linalg.eigh(Dmat1.swapaxes(0, -1))\\n bad = (lam <= 0).sum(axis=-1).astype(bool)\\n if bad.sum():\\n # exclude any ell's with ill-conditioned D matrix\\n # this should happen only far from max like\\n bad_idx = np.unique(np.where(bad)[0])\\n bad_ells = np.arange(ell.start, ell.stop)[bad_idx]\\n self.log(\\n \\\"Found negative eigenvalues at ells {}\\\".format(bad_ells), \\\"warning\\\"\\n )\\n gmat[..., bad_idx] = 0\\n inv_lam = 1.0 / lam\\n Dinv = np.einsum(\\\"...ij,...j,...kj->...ik\\\", R, inv_lam, R).swapaxes(0, -1)\\n\\n if likelihood:\\n # log(det(D)) = tr(log(D)), latter is numerically stable\\n # compute log(D) by eigenvalue decomposition per ell\\n log_lam = np.log(lam)\\n Dlog = np.einsum(\\\"...ij,...j,...kj->...ik\\\", R, log_lam, R).swapaxes(0, -1)\\n\\n else:\\n # compute ell-by-ell inverse\\n # Dinv = np.linalg.inv(Dmat1.swapaxes(0, -1)).swapaxes(0, -1)\\n\\n # optimized matrix multiplication\\n # there is something super weird about this whole matrix operation\\n # that causes the computation of mats to take four times as long\\n # if mat1 is not computed.\\n eye = np.eye(len(gmat))\\n mat1 = np.einsum(\\\"ij...,jk...->ik...\\\", eye, Dinv)\\n mat2 = np.einsum(\\\"klm...,ln...->knm...\\\", dSdqb_mat1_freq, Dinv)\\n mat = np.einsum(\\\"ik...,knm...->inm...\\\", mat1, mat2)\\n\\n if likelihood:\\n # compute log likelihood as tr(g * (D^-1 * Dobs + log(D)))\\n arg = np.einsum(\\\"ij...,jk...->ik...\\\", Dinv, Dmat_obs_b) + Dlog\\n like = -np.einsum(\\\"iij,iij->\\\", gmat, arg) / 2.0\\n\\n # include priors in likelihood\\n if \\\"delta_beta\\\" in qb and delta_beta_prior is not None:\\n chi = (qb[\\\"delta_beta\\\"] - self.delta_beta_fix) / delta_beta_prior\\n like -= chi ** 2 / 2.0\\n\\n if null_first_cmb:\\n for spec in self.specs:\\n stag = \\\"cmb_{}\\\".format(spec)\\n if stag not in qb:\\n continue\\n chi = (qb[stag][0] - 1) / np.sqrt(1e-10)\\n like -= chi ** 2 / 2.0\\n\\n return like\\n\\n # construct matrices for the qb and fisher terms,\\n # and take the trace and sum over ell simultaneously\\n if not windows:\\n qb_vec = np.einsum(\\\"iil,ijkl,jil->k\\\", gmat, mat, Dmat_obs) / 2.0\\n if not windows or (windows and inv_fish is None):\\n fisher = np.einsum(\\\"iil,ijkl,jiml->km\\\", gmat, mat, dSdqb_mat1_freq) / 2\\n\\n if windows:\\n if inv_fish is None:\\n inv_fish = np.linalg.solve(fisher, np.eye(len(fisher)))\\n\\n # compute prefactors\\n ells = np.arange(0, self.lmax + 1)\\n norm = (2.0 * ells + 1.0) / 4.0 / np.pi\\n\\n # compute binning term\\n arg = np.einsum(\\\"ij,kljm->klim\\\", inv_fish, mat)\\n wbl = OrderedDict()\\n bin_index = pt.dict_to_index(qb)\\n spec_mask = pt.spec_mask(nmaps=self.num_maps)\\n\\n # compute window functions for each spectrum\\n for k, (left, right) in bin_index.items():\\n comp, spec = k.split(\\\"_\\\", 1)\\n if comp not in [\\\"cmb\\\", \\\"fg\\\"]:\\n continue\\n\\n # select bins for corresponding spectrum\\n sarg = arg[:, :, left:right]\\n\\n # select the spectrum terms from the kernel matrix\\n smat = spec_mask[spec][:, :, None, None] * Mmat\\n if spec in [\\\"ee\\\", \\\"bb\\\"]:\\n # add mixing terms\\n mspec = \\\"bb\\\" if spec == \\\"ee\\\" else \\\"ee\\\"\\n smat += spec_mask[mspec][:, :, None, None] * Mmat_mix\\n\\n # qb window function\\n wbl1 = np.einsum(\\\"iil,ijkl,jilm->km\\\", gmat, sarg, smat) / 2.0 / norm\\n\\n # bin weighting, allowing for overlapping bin edges\\n chi_bl = np.zeros_like(norm)\\n for (l, r), w in zip(self.bin_def[k], self.bin_weights[k]):\\n chi_bl[l:r] += w\\n\\n # check normalization\\n cls_shape = self.cls_shape[\\\"fg\\\" if comp == \\\"fg\\\" else k][: len(norm)]\\n self.log(\\n \\\"{} qb window function normalization: {}\\\".format(\\n k, np.sum(wbl1 * norm * chi_bl * cls_shape, axis=-1)\\n ),\\n \\\"debug\\\",\\n )\\n\\n wbl[k] = wbl1\\n\\n return wbl\\n\\n bin_index = pt.dict_to_index(qb)\\n\\n if \\\"delta_beta\\\" in qb and delta_beta_prior is not None:\\n # XXX need documentation for what happens here\\n # for imposing the delta_beta prior\\n sl = slice(*(bin_index[\\\"delta_beta\\\"]))\\n d = 1.0 / delta_beta_prior ** 2\\n qb_vec[sl] += d * self.delta_beta_fix\\n fisher[sl, sl] += d\\n\\n if null_first_cmb:\\n # blow up the fisher matrix for first bin\\n # by implementing a tight prior\\n for spec in self.specs:\\n stag = \\\"cmb_{}\\\".format(spec)\\n if stag in bin_index: # check for transfer function\\n b0 = bin_index[stag][0]\\n fisher[b0, b0] += 1e10\\n qb_vec[b0] += 1e10\\n\\n # invert\\n qb_vec = np.linalg.solve(fisher, qb_vec)\\n inv_fish = np.linalg.solve(fisher, np.eye(len(qb_vec)))\\n qb_vec = pt.arr_to_dict(qb_vec, qb)\\n\\n return qb_vec, inv_fish\",\n \"def test_cov_q(self, ndlys=13):\\n for d in self.d:\\n d.flag_array[:] = False #ensure that there are no flags!\\n d.select(times=np.unique(d.time_array)[:10], frequencies=d.freq_array[:16])\\n for d_std in self.d_std:\\n d_std.flag_array[:] = False\\n d_std.select(times=np.unique(d_std.time_array)[:10], frequencies=d_std.freq_array[:16])\\n self.ds = pspecdata.PSpecData(dsets=self.d, wgts=self.w, dsets_std=self.d_std)\\n self.ds = pspecdata.PSpecData(dsets=self.d, wgts=self.w, dsets_std=self.d_std)\\n Ntime = self.ds.Ntimes\\n self.ds.set_Ndlys(ndlys)\\n # Here is the analytic covariance matrix...\\n chan_x, chan_y = np.meshgrid(range(self.ds.Nfreqs), range(self.ds.Nfreqs))\\n cov_analytic = np.zeros((self.ds.spw_Ndlys, self.ds.spw_Ndlys), dtype=np.complex128)\\n for alpha in range(self.ds.spw_Ndlys):\\n for beta in range(self.ds.spw_Ndlys):\\n cov_analytic[alpha, beta] = np.exp(-2j*np.pi*(alpha-beta)*(chan_x-chan_y)/self.ds.spw_Ndlys).sum()\\n key1 = (0, 24, 38)\\n key2 = (1, 25, 38)\\n #print(cov_analytic)\\n\\n for input_data_weight in ['identity','iC', 'dayenu']:\\n self.ds.set_weighting(input_data_weight)\\n #check error raised\\n if input_data_weight == 'dayenu':\\n pytest.raises(ValueError,self.ds.R, key1)\\n rpk = {'filter_centers':[0.],'filter_half_widths':[0.],'filter_factors':[0.]}\\n self.ds.set_r_param(key1,rpk)\\n self.ds.set_r_param(key2,rpk)\\n for taper in taper_selection:\\n qc = self.ds.cov_q_hat(key1,key2,model='dsets')\\n self.assertTrue(np.allclose(np.array(list(qc.shape)),\\n np.array([self.ds.Ntimes, self.ds.spw_Ndlys, self.ds.spw_Ndlys]), atol=1e-6))\\n qc = self.ds.cov_q_hat(key1,key2,model='empirical')\\n self.assertTrue(np.allclose(np.array(list(qc.shape)),\\n np.array([self.ds.Ntimes, self.ds.spw_Ndlys, self.ds.spw_Ndlys]), atol=1e-6))\\n\\n \\\"\\\"\\\"\\n Now test that analytic Error calculation gives Nchan^2\\n \\\"\\\"\\\"\\n self.ds.set_weighting('identity')\\n qc = self.ds.cov_q_hat(key1, key2, model='dsets')\\n self.assertTrue(np.allclose(qc,\\n np.repeat(cov_analytic[np.newaxis, :, :], self.ds.Ntimes, axis=0), atol=1e-6))\\n \\\"\\\"\\\"\\n Test lists of keys\\n \\\"\\\"\\\"\\n self.ds.set_weighting('identity')\\n qc=self.ds.cov_q_hat([key1], [key2], time_indices=[0], model='dsets')\\n self.assertTrue(np.allclose(qc,\\n np.repeat(cov_analytic[np.newaxis, :, :], self.ds.Ntimes, axis=0), atol=1e-6))\\n self.assertRaises(ValueError, self.ds.cov_q_hat, key1, key2, time_indices=200)\\n self.assertRaises(ValueError, self.ds.cov_q_hat, key1, key2, time_indices=\\\"watch out!\\\")\",\n \"def holstein_mpo(n_bath, d_boson, e0, eks, vks):\\n\\n bN = np.diag(range(d_boson))\\n bp = np.diag([np.sqrt(x) for x in range(1,d_boson)],k=-1) # -1 below diagonal\\n bm = np.diag([np.sqrt(x) for x in range(1,d_boson)],k= 1) # 1 above diagonal \\n Sx = np.array([[0.,1.],[1.,0.]])\\n Sz = np.array([[1.,0.],[0.,-1.]])\\n\\n Dw = 3\\n df = 2\\n db = d_boson\\n phys_bonds = [df] + [db]*n_bath\\n\\n L = n_bath + 1\\n \\n W = []\\n\\n ## MPO on spin site\\n WS = np.zeros([1,df,df,self.Dw])\\n WS[0,:,:,0] = e0*self.ops['SX'] # site energy\\n WS[0,:,:,1] = self.ops['SZ'] # creation of fermion\\n WS[0,:,:,2] = np.eye(df)\\n W.append(Ws)\\n\\n ## MPO on bath sites\\n Wb = np.zeros([self.Dw,db,db,self.Dw])\\n Wb[0,:,:,0] = np.eye(db)\\n Wb[1,:,:,1] = np.eye(db) # propagates SZ interaction term\\n Wb[2,:,:,2] = np.eye(db)\\n\\n for i in range(L-1): \\n Wb[1,:,:,0] = vks[i]*self.ops['b-'] + np.conj(vks[i])*self.ops['b+'] # interaction with spin\\n Wb[2,:,:,0] = eks[i]*self.ops['bn'] # energy of boson \\n W.append(Wb)\\n \\n\\n WL = np.zeros([self.Dw,db,db,1])\\n WL[0,:,:,0] = np.eye(db)\\n WL[1,:,:,0] = vks[-1]*self.ops['b-'] + np.conj(vks[-1])*self.ops['b+'] # interaction with spin\\n WL[2,:,:,0] = eks[-1]*self.ops['bn'] # energy of boson\\n W.append(WL)\\n\\n return W\",\n \"def vbmstep(self):\\n for k in range(self.k):\\n self.beta_k[k] = self.beta_0 + self.counts[k]\\n self.m_k[k] = (1 / self.beta_k[k]) * (self.beta_0 * self.m_0 +\\n self.counts[k] * self.means[k])\\n\\n tmp = (self.beta_0 * self.counts[k]) / (self.beta_0 + self.counts[k])\\n tmp2 = (self.means[k] - self.m_0)\\n tmp = np.linalg.inv(self.W_0) + self.counts[k] * self.covars[k] + tmp * tmp2 @ tmp2.T\\n self.w_k[k] = np.linalg.inv(tmp)\\n self.nu_k[k] = self.nu_0 + self.counts[k]\\n self.alpha_k[k] = self.alpha_0[k] + self.counts[k]\",\n \"def get_conditional_probs_asbf( p01, p10, v):\\n\\n ns,L =v.shape\\n if ns != 2**L:\\n raise ValueError\\n #matrix of conitional probs.\\n pi = np.zeros((ns,ns))\\n p00 = 1-p10\\n p11 = 1-p01\\n for i in range(ns):\\n #final state\\n si = v[i,:]\\n for j in range(i,ns):\\n #initial state\\n sj = v[j, :]\\n #number of sites where 1->1 transition occurs, etc\\n n11 = np.sum((si==1)*(sj==1))\\n n10 = np.sum((si==1)*(sj==0))\\n n01 = np.sum((si==0)*(sj==1))\\n n00 = np.sum((si==0)*(sj==0))\\n pi[i, j] = (p11**n11)*(p10**n10)*(p01**n01)*(p00**n00)\\n pi[j,i] = (p11**n11)*(p10**n01)*(p01**n10)*(p00**n00)\\n return pi\",\n \"def mcmc(data, uncert=None, func=None, indparams=[],\\n params=None, pmin=None, pmax=None, stepsize=None,\\n prior=None, priorlow=None, priorup=None,\\n numit=10, nchains=10, walk='demc', wlike=False,\\n leastsq=True, chisqscale=False, grtest=True, burnin=0,\\n thinning=1, plots=False, savefile=None, savemodel=None,\\n comm=None, resume=False, log=None, rms=False):\\n\\n # Import the model function:\\n if type(func) in [list, tuple, np.ndarray]:\\n if func[0] != 'hack':\\n if len(func) == 3:\\n sys.path.append(func[2])\\n exec('from %s import %s as func'%(func[1], func[0]))\\n elif not callable(func):\\n mu.error(\\\"'func' must be either, a callable, or an iterable (list, \\\"\\n \\\"tuple, or ndarray) of strings with the model function, file, \\\"\\n \\\"and path names.\\\", log)\\n\\n if np.ndim(params) == 1: # Force it to be 2D (one for each chain)\\n params = np.atleast_2d(params)\\n nparams = len(params[0]) # Number of model params\\n ndata = len(data) # Number of data values\\n # Set default uncertainties:\\n if uncert is None:\\n uncert = np.ones(ndata)\\n # Set default boundaries:\\n if pmin is None:\\n pmin = np.zeros(nparams) - np.inf\\n if pmax is None:\\n pmax = np.zeros(nparams) + np.inf\\n # Set default stepsize:\\n if stepsize is None:\\n stepsize = 0.1 * np.abs(params[0])\\n # Set prior parameter indices:\\n if (prior is None) or (priorup is None) or (priorlow is None):\\n prior = priorup = priorlow = np.zeros(nparams) # Zero arrays\\n iprior = np.where(priorlow != 0)[0]\\n ilog = np.where(priorlow < 0)[0]\\n\\n nfree = np.sum(stepsize > 0) # Number of free parameters\\n chainlen = int(np.ceil(numit/nchains)) # Number of iterations per chain\\n ifree = np.where(stepsize > 0)[0] # Free parameter indices\\n ishare = np.where(stepsize < 0)[0] # Shared parameter indices\\n # Number of model parameters (excluding wavelet parameters):\\n if wlike:\\n mpars = nparams - 3\\n else:\\n mpars = nparams\\n\\n # Intermediate steps to run GR test and print progress report:\\n intsteps = chainlen / 10\\n\\n # Allocate arrays with variables:\\n numaccept = np.zeros(nchains) # Number of accepted proposal jumps\\n outbounds = np.zeros((nchains, nfree), np.int) # Out of bounds proposals\\n allparams = np.zeros((nchains, nfree, chainlen)) # Parameter's record\\n if savemodel is not None:\\n allmodel = np.zeros((nchains, ndata, chainlen)) # Fit model\\n\\n if resume:\\n oldparams = np.load(savefile)\\n nold = np.shape(oldparams)[2] # Number of old-run iterations\\n allparams = np.dstack((oldparams, allparams))\\n if savemodel is not None:\\n allmodel = np.dstack((np.load(savemodel), allmodel))\\n # Set params to the last-iteration state of the previous run:\\n params = np.repeat(params, nchains, 0)\\n params[:,ifree] = oldparams[:,:,-1]\\n else:\\n nold = 0\\n\\n # Set MPI flag:\\n mpi = comm is not None\\n\\n if mpi:\\n from mpi4py import MPI\\n # Send sizes info to other processes:\\n array1 = np.asarray([mpars, chainlen], np.int)\\n mu.comm_bcast(comm, array1, MPI.INT)\\n\\n # DEMC parameters:\\n gamma = 2.4 / np.sqrt(2*nfree)\\n gamma2 = 0.001 # Jump scale factor of support distribution\\n\\n # Least-squares minimization:\\n if leastsq:\\n fitargs = (params[0], func, data, uncert, indparams, stepsize, pmin, pmax,\\n prior, priorlow, priorup)\\n fitchisq, dummy = mf.modelfit(params[0,ifree], args=fitargs)\\n fitbestp = np.copy(params[0, ifree])\\n mu.msg(1, \\\"Least-squares best-fitting parameters: \\\\n{:s}\\\\n\\\\n\\\".\\n format(str(fitbestp)), log)\\n\\n # Replicate to make one set for each chain: (nchains, nparams):\\n if np.shape(params)[0] != nchains:\\n params = np.repeat(params, nchains, 0)\\n # Start chains with an initial jump:\\n for p in ifree:\\n # For each free param, use a normal distribution: \\n params[1:, p] = np.random.normal(params[0, p], stepsize[p], nchains-1)\\n # Stay within pmin and pmax boundaries:\\n params[np.where(params[:, p] < pmin[p]), p] = pmin[p]\\n params[np.where(params[:, p] > pmax[p]), p] = pmax[p]\\n \\n # Update shared parameters:\\n for s in ishare:\\n params[:, s] = params[:, -int(stepsize[s])-1]\\n\\n # Calculate chi-squared for model using current params:\\n models = np.zeros((nchains, ndata))\\n if mpi:\\n # Scatter (send) parameters to func:\\n mu.comm_scatter(comm, params[:,0:mpars].flatten(), MPI.DOUBLE)\\n # Gather (receive) evaluated models:\\n mpimodels = np.zeros(nchains*ndata, np.double)\\n mu.comm_gather(comm, mpimodels)\\n # Store them in models variable:\\n models = np.reshape(mpimodels, (nchains, ndata))\\n else:\\n for c in np.arange(nchains):\\n fargs = [params[c, 0:mpars]] + indparams # List of function's arguments\\n models[c] = func(*fargs)\\n\\n # Calculate chi-squared for each chain:\\n currchisq = np.zeros(nchains)\\n c2 = np.zeros(nchains) # No-Jeffrey's chisq\\n for c in np.arange(nchains):\\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\\n currchisq[c], c2[c] = dwt.wlikelihood(params[c, mpars:], models[c]-data,\\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n else:\\n currchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n\\n # Scale data-uncertainties such that reduced chisq = 1:\\n if chisqscale:\\n chifactor = np.sqrt(np.amin(currchisq)/(ndata-nfree))\\n uncert *= chifactor\\n # Re-calculate chisq with the new uncertainties:\\n for c in np.arange(nchains):\\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\\n currchisq[c], c2[c] = dwt.wlikelihood(params[c,mpars:], models[c]-data,\\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n else:\\n currchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\\n (params[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n if leastsq:\\n fitchisq = currchisq[0]\\n\\n # Get lowest chi-square and best fitting parameters:\\n bestchisq = np.amin(c2)\\n bestp = np.copy(params[np.argmin(c2)])\\n bestmodel = np.copy(models[np.argmin(c2)])\\n\\n if savemodel is not None:\\n allmodel[:,:,0] = models\\n\\n # Set up the random walks:\\n if walk == \\\"mrw\\\":\\n # Generate proposal jumps from Normal Distribution for MRW:\\n mstep = np.random.normal(0, stepsize[ifree], (chainlen, nchains, nfree))\\n elif walk == \\\"demc\\\":\\n # Support random distribution:\\n support = np.random.normal(0, stepsize[ifree], (chainlen, nchains, nfree))\\n # Generate indices for the chains such r[c] != c:\\n r1 = np.random.randint(0, nchains-1, (nchains, chainlen))\\n r2 = np.random.randint(0, nchains-1, (nchains, chainlen))\\n for c in np.arange(nchains):\\n r1[c][np.where(r1[c]==c)] = nchains-1\\n r2[c][np.where(r2[c]==c)] = nchains-1\\n\\n # Uniform random distribution for the Metropolis acceptance rule:\\n unif = np.random.uniform(0, 1, (chainlen, nchains))\\n\\n # Proposed iteration parameters and chi-square (per chain):\\n nextp = np.copy(params) # Proposed parameters\\n nextchisq = np.zeros(nchains) # Chi square of nextp \\n\\n # Start loop:\\n mu.msg(1, \\\"Start MCMC chains ({:s})\\\".format(time.ctime()), log)\\n for i in np.arange(chainlen):\\n # Proposal jump:\\n if walk == \\\"mrw\\\":\\n jump = mstep[i]\\n elif walk == \\\"demc\\\":\\n jump = (gamma * (params[r1[:,i]]-params[r2[:,i]])[:,ifree] +\\n gamma2 * support[i] )\\n # Propose next point:\\n nextp[:,ifree] = params[:,ifree] + jump\\n\\n # Check it's within boundaries: \\n outpars = np.asarray(((nextp < pmin) | (nextp > pmax))[:,ifree])\\n outflag = np.any(outpars, axis=1)\\n outbounds += ((nextp < pmin) | (nextp > pmax))[:,ifree]\\n for p in ifree:\\n nextp[np.where(nextp[:, p] < pmin[p]), p] = pmin[p]\\n nextp[np.where(nextp[:, p] > pmax[p]), p] = pmax[p]\\n\\n # Update shared parameters:\\n for s in ishare:\\n nextp[:, s] = nextp[:, -int(stepsize[s])-1]\\n\\n # Evaluate the models for the proposed parameters:\\n if mpi:\\n mu.comm_scatter(comm, nextp[:,0:mpars].flatten(), MPI.DOUBLE)\\n mu.comm_gather(comm, mpimodels)\\n models = np.reshape(mpimodels, (nchains, ndata))\\n else:\\n for c in np.where(~outflag)[0]:\\n fargs = [nextp[c, 0:mpars]] + indparams # List of function's arguments\\n models[c] = func(*fargs)\\n\\n # Calculate chisq:\\n for c in np.where(~outflag)[0]:\\n if wlike: # Wavelet-based likelihood (chi-squared, actually)\\n nextchisq[c], c2[c] = dwt.wlikelihood(nextp[c,mpars:], models[c]-data,\\n (nextp[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n else:\\n nextchisq[c], c2[c] = cs.chisq(models[c], data, uncert,\\n (nextp[c]-prior)[iprior], priorlow[iprior], priorlow[iprior])\\n\\n # Reject out-of-bound jumps:\\n nextchisq[np.where(outflag)] = np.inf\\n # Evaluate which steps are accepted and update values:\\n accept = np.exp(0.5 * (currchisq - nextchisq))\\n accepted = accept >= unif[i]\\n if i >= burnin:\\n numaccept += accepted\\n # Update params and chi square:\\n params [accepted] = nextp [accepted]\\n currchisq[accepted] = nextchisq[accepted]\\n\\n # Check lowest chi-square:\\n if np.amin(c2) < bestchisq:\\n bestp = np.copy(params[np.argmin(c2)])\\n bestmodel = np.copy(models[np.argmin(c2)])\\n bestchisq = np.amin(c2)\\n\\n # Store current iteration values:\\n allparams[:,:,i+nold] = params[:, ifree]\\n if savemodel is not None:\\n models[~accepted] = allmodel[~accepted,:,i+nold-1]\\n allmodel[:,:,i+nold] = models\\n \\n # Print intermediate info:\\n if ((i+1) % intsteps == 0) and (i > 0):\\n mu.progressbar((i+1.0)/chainlen, log)\\n mu.msg(1, \\\"Out-of-bound Trials:\\\\n {:s}\\\".\\n format(np.sum(outbounds, axis=0)), log)\\n mu.msg(1, \\\"Best Parameters: (chisq={:.4f})\\\\n{:s}\\\".\\n format(bestchisq, str(bestp)), log)\\n\\n # Gelman-Rubin statistic:\\n if grtest and (i+nold) > burnin:\\n psrf = gr.convergetest(allparams[:, :, burnin:i+nold+1:thinning])\\n mu.msg(1, \\\"Gelman-Rubin statistic for free parameters:\\\\n{:s}\\\".\\n format(psrf), log)\\n if np.all(psrf < 1.01):\\n mu.msg(1, \\\"All parameters have converged to within 1% of unity.\\\", log)\\n # Save current results:\\n if savefile is not None:\\n np.save(savefile, allparams[:,:,0:i+nold])\\n if savemodel is not None:\\n np.save(savemodel, allmodel[:,:,0:i+nold])\\n\\n # Stack together the chains:\\n allstack = allparams[0, :, burnin:]\\n for c in np.arange(1, nchains):\\n allstack = np.hstack((allstack, allparams[c, :, burnin:]))\\n # And the models:\\n if savemodel is not None:\\n modelstack = allmodel[0,:,burnin:]\\n for c in np.arange(1, nchains):\\n modelstack = np.hstack((modelstack, allmodel[c, :, burnin:]))\\n\\n # Print out Summary:\\n mu.msg(1, \\\"\\\\nFin, MCMC Summary:\\\\n------------------\\\", log)\\n\\n nsample = (chainlen-burnin)*nchains # This sample\\n ntotal = (nold+chainlen-burnin)*nchains\\n BIC = bestchisq + nfree*np.log(ndata)\\n redchisq = bestchisq/(ndata-nfree)\\n sdr = np.std(bestmodel-data)\\n\\n fmtlen = len(str(ntotal))\\n mu.msg(1, \\\"Burned in iterations per chain: {:{}d}\\\".\\n format(burnin, fmtlen), log, 1)\\n mu.msg(1, \\\"Number of iterations per chain: {:{}d}\\\".\\n format(chainlen, fmtlen), log, 1)\\n mu.msg(1, \\\"MCMC sample size: {:{}d}\\\".\\n format(nsample, fmtlen), log, 1)\\n mu.msg(resume, \\\"Total MCMC sample size: {:{}d}\\\".\\n format(ntotal, fmtlen), log, 1)\\n mu.msg(1, \\\"Acceptance rate: {:.2f}%\\\\n \\\".\\n format(np.sum(numaccept)*100.0/nsample), log, 1)\\n\\n meanp = np.mean(allstack, axis=1) # Parameters mean\\n uncertp = np.std(allstack, axis=1) # Parameter standard deviation\\n mu.msg(1, \\\"Best-fit params Uncertainties Signal/Noise Sample \\\"\\n \\\"Mean\\\", log, 1)\\n for i in np.arange(nfree):\\n mu.msg(1, \\\"{: 15.7e} {: 15.7e} {:12.2f} {: 15.7e}\\\".\\n format(bestp[ifree][i], uncertp[i],\\n np.abs(bestp[ifree][i])/uncertp[i], meanp[i]), log, 1)\\n\\n if leastsq and np.any(np.abs((bestp[ifree]-fitbestp)/fitbestp) > 1e-08):\\n np.set_printoptions(precision=8)\\n mu.warning(\\\"MCMC found a better fit than the minimizer:\\\\n\\\"\\n \\\" MCMC best-fitting parameters: (chisq={:.8g})\\\\n {:s}\\\\n\\\"\\n \\\" Minimizer best-fitting parameters: (chisq={:.8g})\\\\n\\\"\\n \\\" {:s}\\\".format(bestchisq, str(bestp[ifree]), \\n fitchisq, str(fitbestp)), log)\\n\\n fmtl = len(\\\"%.4f\\\"%BIC) # Length of string formatting\\n mu.msg(1, \\\" \\\", log)\\n if chisqscale:\\n mu.msg(1, \\\"sqrt(reduced chi-squared) factor: {:{}.4f}\\\".\\n format(chifactor, fmtl), log, 1)\\n mu.msg(1, \\\"Best-parameter's chi-squared: {:{}.4f}\\\".\\n format(bestchisq, fmtl), log, 1)\\n mu.msg(1, \\\"Bayesian Information Criterion: {:{}.4f}\\\".\\n format(BIC, fmtl), log, 1)\\n mu.msg(1, \\\"Reduced chi-squared: {:{}.4f}\\\".\\n format(redchisq, fmtl), log, 1)\\n mu.msg(1, \\\"Standard deviation of residuals: {:.6g}\\\\n\\\".format(sdr), log, 1)\\n\\n\\n if rms:\\n rms, rmse, stderr, bs = ta.binrms(bestmodel-data)\\n\\n if plots:\\n print(\\\"Plotting figures ...\\\")\\n # Extract filename from savefile:\\n if savefile is not None:\\n if savefile.rfind(\\\".\\\") == -1:\\n fname = savefile[savefile.rfind(\\\"/\\\")+1:] # Cut out file extention.\\n else:\\n fname = savefile[savefile.rfind(\\\"/\\\")+1:savefile.rfind(\\\".\\\")]\\n else:\\n fname = \\\"MCMC\\\"\\n # Trace plot:\\n mp.trace(allstack, thinning=thinning, savefile=fname+\\\"_trace.png\\\",\\n sep=np.size(allstack[0])/nchains)\\n # Pairwise posteriors:\\n mp.pairwise(allstack, thinning=thinning, savefile=fname+\\\"_pairwise.png\\\")\\n # Histograms:\\n mp.histogram(allstack, thinning=thinning, savefile=fname+\\\"_posterior.png\\\")\\n # RMS vs bin size:\\n if rms:\\n mp.RMS(bs, rms, stderr, rmse, binstep=len(bs)/500+1,\\n savefile=fname+\\\"_RMS.png\\\")\\n if indparams != [] and np.size(indparams[0]) == ndata:\\n mp.modelfit(data, uncert, indparams[0], bestmodel,\\n savefile=fname+\\\"_model.png\\\")\\n\\n # Save definitive results:\\n if savefile is not None:\\n np.save(savefile, allparams)\\n if savemodel is not None:\\n np.save(savemodel, allmodel)\\n\\n return allstack, bestp\",\n \"def __init__(self, M, principal_part=None, C=None, prec=53, Lv=None):\\n\\n if(isinstance(M, type(Newforms(1, 12)[0]))):\\n self._init_from_newform_(M, principal_part, C, prec)\\n return\\n if C is not None:\\n if M.rank() != len(C.keys()):\\n raise ValueError(\\\"Coefficient vector of wrong format! Got length={0}\\\".format(len(C)))\\n # We inherit symmetry from the space\\n self._sym_type = M._sym_type\\n self._class_name = \\\"VVHarmonicWeakMaassFormElement\\\"\\n AutomorphicFormElement.__init__(self, M, C=C, prec=prec, principal_part=principal_part)\\n\\n self._verbose = self._space._verbose\\n self.maxdigs = prec # the number of digits needed to be displayed to print all digits of the coefficients\\n if Lv is not None:\\n self._Lv = Lv\\n else:\\n self._Lv = dict()\\n self.Cp0 = 0\\n self.Cp1 = 0\\n self.Cm = 0\\n # We also find the space corresponding to this space via inverse of xi_k and Shimura corr.\\n t = QQ(RR(M.weight())) - QQ(1) / QQ(2)\\n if(is_int(t)):\\n k = QQ(3) - QQ(2) * QQ(RR(M.weight()))\\n if self._verbose > 0:\\n print(\\\"k={0}\\\".format(k))\\n N = self.space().multiplier().weil_module().N()\\n self.shim_corr = Newforms(N, k, names='a') # .new_subspace()\\n else:\\n self.shim_corr = None\",\n \"def hidden_markov_model_probability(data,\\n fitness_prior=flat_fitness_prior,\\n N_w_prior=flat_N_w_prior,\\n mutation_object=True):\\n\\n if mutation_object is True:\\n trajectories = data.data\\n else:\\n trajectories = data\\n\\n ind_likelihood = []\\n for traj in trajectories:\\n int_s = []\\n for s in fitness_prior[0, :]:\\n int_N_w = []\\n for N_w in N_w_prior[0, :]:\\n int_N_w.append(\\n hidden_markov_conditional_s_N(traj,\\n fitness=s, N_w=N_w)\\n )\\n marginalised_N_w = np.trapz(x=N_w_prior[0, :],\\n y=int_N_w*N_w_prior[1, :])\\n int_s.append(marginalised_N_w)\\n\\n marginalised_likelihood = np.trapz(x=fitness_prior[0, :],\\n y=int_s*fitness_prior[1, :])\\n ind_likelihood.append(marginalised_likelihood)\\n \\n mutation_prob = np.product(ind_likelihood)\\n\\n if mutation_object is True:\\n # return updated model_comparison object \\n data.bd_prob = mutation_prob\\n return data\\n else:\\n # return marginalised likelihood.\\n return mutation_prob\",\n \"def mcmc(guess, nu, D, Ninv, beam_mat, models_fit, label=None, nwalkers=50, burn=500, steps=1000, save=False):\\n # Define starting points\\n ndim = guess.size\\n pos = [guess*(1.+0.1*np.random.randn(ndim)) for i in range(nwalkers)]\\n \\n # Run emcee sampler\\n sampler = emcee.EnsembleSampler( nwalkers, ndim, lnprob, \\n args=(nu, D, Ninv, beam_mat, models_fit) )\\n sampler.run_mcmc(pos, burn+steps)\\n samples = sampler.chain[:, burn:, :].reshape((-1, ndim))\\n \\n \\\"\\\"\\\"\\n # Plot the chains, including burn-in\\n fig, axes = plt.subplots(figsize=(13, ndim*3.), nrows=ndim)\\n labels = [r'$\\\\beta$', r'$T_d$', r'$\\\\alpha_s$']\\n\\n for i, (ax, lab) in enumerate(zip(axes, labels)):\\n _ = ax.plot(sampler.chain[:,:,i].T, c='k', alpha=0.2)\\n ax.axhline(y=guess[i], c='r')\\n ax.axvline(x=burn, c='r')\\n ax.set_title(lab)\\n fig.savefig('chains_' + label + '.png')\\n plt.close('all')\\n \\\"\\\"\\\"\\n\\n # Save chains to file\\n if save:\\n np.savetxt('samples.dat', samples)\\n \\n # Summary statistics for fitted parameters\\n # Dust model\\n beta_out = np.median(samples, axis=0)\\n ndust = 0\\n if (models_fit[0] == 'mbb'):\\n ndust = 2\\n dust_params = beta_out[0:2]\\n else:\\n raise NotImplementedError(\\\"Code not configured to fit HD16 models.\\\")\\n \\n # Synchrotron power law\\n if (models_fit[1] == 'pow'):\\n sync_params = beta_out[ndust:ndust+1]\\n else:\\n raise ValueError(\\\"Synchrotron model '%s' not recognized.\\\" % models_fit[1])\\n \\n # CMB\\n cmb_params = np.array([])\\n \\n # Return summary statistics and samples\\n return dust_params, sync_params, cmb_params, samples\",\n \"def construct_model():\\n import lbann\\n\\n # Layer graph\\n input = lbann.Input(target_mode='N/A', name='inp_data')\\n # data is 64*64*4 images + 15 scalar + 5 param\\n #inp_slice = lbann.Slice(input, axis=0, slice_points=\\\"0 16399 16404\\\",name='inp_slice')\\n inp_slice = lbann.Slice(input, axis=0, slice_points=str_list([0,args.ydim,args.ydim+5]),name='inp_slice')\\n gt_y = lbann.Identity(inp_slice,name='gt_y')\\n gt_x = lbann.Identity(inp_slice, name='gt_x') #param not used\\n\\n zero = lbann.Constant(value=0.0,num_neurons='1',name='zero')\\n one = lbann.Constant(value=1.0,num_neurons='1',name='one')\\n\\n z_dim = 20 #Latent space dim\\n\\n z = lbann.Gaussian(mean=0.0,stdev=1.0, neuron_dims=\\\"20\\\")\\n model = macc_models.MACCWAE(args.zdim,args.ydim,cf=args.mcf,use_CNN=args.useCNN)\\n d1_real, d1_fake, d_adv, pred_y = model(z,gt_y)\\n\\n d1_real_bce = lbann.SigmoidBinaryCrossEntropy([d1_real,one],name='d1_real_bce')\\n d1_fake_bce = lbann.SigmoidBinaryCrossEntropy([d1_fake,zero],name='d1_fake_bce')\\n d_adv_bce = lbann.SigmoidBinaryCrossEntropy([d_adv,one],name='d_adv_bce')\\n img_loss = lbann.MeanSquaredError([pred_y,gt_y])\\n rec_error = lbann.L2Norm2(lbann.WeightedSum([pred_y,gt_y], scaling_factors=\\\"1 -1\\\"))\\n\\n layers = list(lbann.traverse_layer_graph(input))\\n # Setup objective function\\n weights = set()\\n src_layers = []\\n dst_layers = []\\n for l in layers:\\n if(l.weights and \\\"disc0\\\" in l.name and \\\"instance1\\\" in l.name):\\n src_layers.append(l.name)\\n #freeze weights in disc2\\n if(l.weights and \\\"disc1\\\" in l.name):\\n dst_layers.append(l.name)\\n for idx in range(len(l.weights)):\\n l.weights[idx].optimizer = lbann.NoOptimizer()\\n weights.update(l.weights)\\n l2_reg = lbann.L2WeightRegularization(weights=weights, scale=1e-4)\\n d_adv_bce = lbann.LayerTerm(d_adv_bce,scale=0.01)\\n obj = lbann.ObjectiveFunction([d1_real_bce,d1_fake_bce,d_adv_bce,img_loss,rec_error,l2_reg])\\n # Initialize check metric callback\\n metrics = [lbann.Metric(img_loss, name='recon_error')]\\n #pred_y = macc_models.MACCWAE.pred_y_name\\n callbacks = [lbann.CallbackPrint(),\\n lbann.CallbackTimer(),\\n lbann.CallbackSaveModel(dir=args.dump_models),\\n lbann.CallbackReplaceWeights(source_layers=list2str(src_layers),\\n destination_layers=list2str(dst_layers),\\n batch_interval=2)]\\n\\n if(args.ltfb_batch_interval > 0) :\\n callbacks.append(lbann.CallbackLTFB(batch_interval=args.ltfb_batch_interval,metric='recon_error',\\n low_score_wins=True,\\n exchange_hyperparameters=True))\\n\\n # Construct model\\n return lbann.Model(args.num_epochs,\\n serialize_io=True,\\n weights=weights,\\n layers=layers,\\n metrics=metrics,\\n objective_function=obj,\\n callbacks=callbacks)\",\n \"def camb_Pk(self,\\n z = 0.,\\n k = np.logspace(-4., 2., 1001),\\n nonlinear = False,\\n halofit = 'mead2020',\\n var_1 = 'tot',\\n var_2 = 'tot',\\n share_delta_neff = True,\\n **kwargs\\n ):\\n # Neutrino part\\n #num_nu_massless – (float64) Effective number of massless neutrinos\\n #num_nu_massive – (integer) Total physical (integer) number of massive neutrino species\\n #nu_mass_eigenstates – (integer) Number of non-degenerate mass eigenstates\\n #nu_mass_degeneracies – (float64 array) Degeneracy of each distinct eigenstate\\n #nu_mass_fractions – (float64 array) Mass fraction in each distinct eigenstate\\n #nu_mass_numbers – (integer array) Number of physical neutrinos per distinct eigenstate\\n nu_mass_eigen = len(np.unique([mm for mm in self.M_nu])) if np.any(self.M_nu!=0.) else 0\\n nu_mass_numbers = [list(self.M_nu).count(x) for x in set(list(self.M_nu))]\\n nu_mass_numbers = sorted(nu_mass_numbers,reverse=True) if np.any(self.M_nu!=0.) else [0]\\n # Set parameters\\n cambparams = {\\n 'num_nu_massive': self.massive_nu,\\n 'num_nu_massless': self.massless_nu,\\n 'nu_mass_eigenstates': nu_mass_eigen, \\n 'nu_mass_numbers': nu_mass_numbers,\\n 'nnu': self.N_eff,\\n 'omnuh2': self.omega_nu_tot,\\n 'ombh2': self.omega_b,\\n 'omch2': self.omega_cdm+self.omega_wdm_tot,\\n 'omk': self.Omega_K,\\n 'H0': 100.*self.h,\\n 'As': self.As,\\n 'ns': self.ns,\\n 'w': self.w0,\\n 'wa': self.wa,\\n 'TCMB': self.T_cmb,\\n 'tau': self.tau,\\n 'share_delta_neff':True,\\n 'dark_energy_model':'DarkEnergyPPF'}\\n # kwargs\\n for key, value in kwargs.items():\\n if not key in cambparams: cambparams[key] = value\\n params = camb.set_params(**cambparams)\\n\\n # Redshifts\\n z = np.atleast_1d(z)\\n nz = len(z)\\n\\n # Possible components to use\\n components = {'tot' : 'delta_tot',\\n 'cdm' : 'delta_cdm',\\n 'b' : 'delta_baryon',\\n 'nu' : 'delta_nu',\\n 'cb' : 'delta_nonu',\\n 'gamma': 'delta_photon',\\n 'v_cdm': 'v_newtonian_cdm',\\n 'v_b' : 'v_newtonian_baryon',\\n 'Phi' : 'Weyl'} # Weyl: (phi+psi)/2 is proportional to lensing potential\\n\\n # Number of points (according to logint)\\n logint = 100\\n npoints = int(logint*np.log10(k.max()/k.min()))\\n dlogk = 2.*np.log10(k.max()/k.min())/npoints\\n\\n # Halofit version\\n if nonlinear == True:\\n params.NonLinearModel.set_params(halofit_version=halofit)\\n params.NonLinear = camb.model.NonLinear_both\\n\\n # Computing spectra\\n params.set_matter_power(redshifts=z,kmax=k.max()*10**dlogk,silent=True,k_per_logint=0,accurate_massive_neutrino_transfers=True)\\n results = camb.get_results(params)\\n kh, z, pkh = results.get_matter_power_spectrum(minkh = k.min()*10.**-dlogk, maxkh = k.max()*10**dlogk, npoints = npoints, var1 = components[var_1], var2 = components[var_2])\\n\\n # Interpolation to the required scales k's\\n # I use UnivariateSpline because it makes good extrapolation\\n pk = np.zeros((nz,len(np.atleast_1d(k))))\\n for iz in range(nz):\\n lnpower = si.InterpolatedUnivariateSpline(kh, np.log(pkh[iz]), k=3, ext=0, check_finite=False)\\n pk[iz] = np.exp(lnpower(k))\\n\\n return k, pk\",\n \"def init_P_PHM_GIVEN_PHI():\\n global P_PHM_GIVEN_PHI\\n for i in INTERFACE_LEVEL_ACTIONS: # ui\\n P_PHM_GIVEN_PHI[i] = collections.OrderedDict()\\n for j in INTERFACE_LEVEL_ACTIONS: # um\\n if i == j:\\n # try to weight the true command more for realistic purposes. Can be offset by using a high UM_GIVEN_UI_NOISE\\n P_PHM_GIVEN_PHI[i][j] = 1.0\\n else:\\n # P_PHM_GIVEN_PHI[i][j] = np.random.random()*UM_GIVEN_UI_NOISE#IF UM_GIVEN_UI_NOISE is 0, then the p(um|ui) is a deterministic mapping\\n P_PHM_GIVEN_PHI[i][j] = 0.0\\n\\n delta_dist = np.array(P_PHM_GIVEN_PHI[i].values())\\n uniform_dist = (1.0 / len(INTERFACE_LEVEL_ACTIONS)) * np.ones(len(INTERFACE_LEVEL_ACTIONS))\\n blended_dist = (1 - PHM_GIVEN_PHI_NOISE) * delta_dist + PHM_GIVEN_PHI_NOISE * uniform_dist # np.array\\n for index, j in enumerate(INTERFACE_LEVEL_ACTIONS):\\n P_PHM_GIVEN_PHI[i][j] = blended_dist[index]\",\n \"def _build_precomputed_data(self):\\n if self.num_sampled == 0:\\n self._K_chol = numpy.array([])\\n self._K_inv_y = numpy.array([])\\n else:\\n covariance_matrix = python_utils.build_covariance_matrix(\\n self._covariance,\\n self._points_sampled,\\n noise_variance=self._points_sampled_noise_variance,\\n )\\n\\n C = self._build_integrated_term_maxtrix(self._covariance, self._points_sampled)\\n self._K_Inv = numpy.linalg.inv(covariance_matrix)\\n self._K_C = numpy.empty((covariance_matrix.shape[0],covariance_matrix.shape[0]))\\n self._K_C = numpy.multiply(C, self._K_Inv)\\n self._K_chol = scipy.linalg.cho_factor(covariance_matrix, lower=True, overwrite_a=True)\\n self._K_inv_y = scipy.linalg.cho_solve(self._K_chol, self._points_sampled_value)\\n self._marginal_mean_mat = self._build_marginal_matrix_mean()\\n self._marginal_mean_mat_gradient = self._build_marginal_matrix_mean_gradient()\",\n \"def __init__(self, p_control = 0, p_treatment = 0, n_control = 0, n_treatment = 0, power = None, alpha = 0.05):\\n self.p_control = p_control\\n self.p_treatment = p_treatment\\n\\n self.n_control = n_control\\n self.n_treatment = n_treatment\\n\\n self.var_control = 1 * p_control * (1 - p_control)\\n self.var_treatment = 1 * p_treatment * (1 - p_treatment)\\n\\n self.norm_null = None\\n self.norm_alt = None\\n\\n self.binom_null = None\\n self.binom_alt = None\\n\\n self.binom_control = None\\n self.binom_treatment = None\\n\\n self.confidence_control = None\\n self.confidence_treatment = None\\n\\n if n_control > 0 and n_treatment > 0 and p_control > 0 and p_treatment > 0:\\n control = self.p_control * self.n_control\\n treatment = self.p_treatment * self.n_treatment\\n sample = self.n_control + self.n_treatment\\n\\n self.p_sample = (control + treatment) / sample\\n else:\\n self.p_sample = None\\n\\n if power == 1:\\n print('Sample size approaches infinity as power approaches 1, so 1 is an invalid power vlaue. Changing power to 0.99.')\\n self.power = 0.99\\n elif power == 0:\\n print('Sample size is undefined at power of 0, so 0 is an invalid power value. Changing power to 0.01.')\\n self.power = 0.01\\n else:\\n self.power = power\\n\\n self.alpha = alpha\\n self.p_value = None\",\n \"def batch_kl_sample_gmm_pmap(keys_bxsx2, z_mean_bxu, z_logvar_bxu, resps_c,\\n gmm_z_mean_cxu, gmm_z_logvar_cxu, varmin):\\n ndevs = 8\\n B, S, _ = keys_bxsx2.shape\\n keys_8xbd8xsx2 = np.reshape(keys_bxsx2, (ndevs, B // ndevs, S, 2))\\n z_mean_8xbd8xu = np.reshape(z_mean_bxu, (ndevs, B // ndevs, -1))\\n z_logvar_8xbd8xu = np.reshape(z_logvar_bxu, (ndevs, B // ndevs, -1))\\n\\n # Shard the memory, note the type that goes in vs comes out of the pmap'd lambda.\\n keys_8xbd8xsx2 = pmap(lambda x : x)(keys_8xbd8xsx2)\\n z_mean_8xbd8xu = pmap(lambda x: x)(z_mean_8xbd8xu)\\n z_logvar_8xbd8xu = pmap(lambda x: x)(z_logvar_8xbd8xu)\\n\\n return batch_kl_sample_gmm_pmap_pre(keys_8xbd8xsx2, z_mean_8xbd8xu, z_logvar_8xbd8xu,\\n resps_c, gmm_z_mean_cxu,\\n gmm_z_logvar_cxu, varmin)\",\n \"def construct_param_dict(params,K_RC,K_CP,m_P):\\n ###scaling constants\\n w=params['w']\\n pd=params['pd'] # in 3D and 0.21 in 2D\\n pv=params['pv']\\n Er=params['Er'] ;Ek=params['Ek']\\n ER=params['ER'];EC=params['EC'];EP=params['EP'];\\n Eq1=params['Eq1'];Eq2=params['Eq2']\\n\\n\\n #capture success function\\n a = params['a']\\n b = params['b']\\n c = params['c']\\n formC = params['formC']\\n formPC = params['formPC']\\n formPR = params['formPR']\\n \\n ###variables\\n TR= params['TR'] ;TC= params['TC'];TP=params['TP'];D_R= params['D_R']; D_C= params['D_C']\\n K_RP=K_RC*K_CP\\n fmC=params['fmC'];thermyR=params['thermyR']\\n thermyC=params['thermyC'];thermyP=params['thermyP']\\n fmPR=params['fmPR']\\n fmPC=params['fmPC']\\n m_C = K_CP*m_P;m_R = K_RP*m_P\\n ###normalization constants and boltzmann constant\\n r0 = params['r0']\\n k0 = params['k0'] # will depend on the productivity of the habitat\\n a01 = a02 = params['a012'] # will depedend on the dimension of the habitat \\n a03 = params['a03']\\n d0= params['d0']\\n q10 = params['q10'];q20 = params['q20'];\\n v0R = params['v0R'];v0C =params['v0C'];v0P =params['v0P'];k = b_k\\n hC0 = params['hC0'];hP0 = params['hP0'] \\n \\n #intrapopulation parameters\\n q1=set_q1(q10,m_C,w,Eq1,TR,k)\\n q2=set_q2(q20,m_P,w,Eq2,TC,k)\\n K=set_K(k0,m_R,w,Ek,TR,k)\\n r=set_r(r0,m_R,w,Er,TR,k)\\n\\n #interpopulation parameters\\n a1=set_alfa(m_C,a01,K_RC,pv,pd,TR,TC,ER,EC,D_R,v0R,v0C,g,alfa,fmC,thermyR,thermyC,k,a,b,c,formC)\\n a2=set_alfa(m_P,a02,K_RP,pv,pd,TR,TP,ER,EP,D_R,v0R,v0P,g,alfa,fmPR,thermyR,thermyP,k,a,b,c,formPR)\\n a3=set_alfa(m_P,a03,K_CP,pv,pd,TC,TP,EC,EP,D_C,v0C,v0P,g,alfa,fmPC,thermyC,thermyP,k,a,b,c,formPC)\\n\\n t_hp = set_th(hP0,m_P,w,EP,k,TP)\\n t_hc = set_th(hC0,m_C,w,EC,k,TC)\\n param_dict={'q1':q1,'q2':q2,'K':K,'r':r,'a1':a1,'a2':a2,'a3':a3,'t_hp':t_hp,'t_hc':t_hc}\\n \\n return param_dict\",\n \"def derive(args):\\n\\n config_file = args.setupfn\\n conf_base = os.path.basename(config_file).split('.')[0]\\n statfile = os.path.join(args.outputdir,\\n \\\"{}_radvel.stat\\\".format(conf_base))\\n status = load_status(statfile)\\n\\n msg = \\\"Multiplying mcmc chains by physical parameters for {}\\\".format(\\n conf_base\\n )\\n print(msg)\\n\\n assert status.getboolean('mcmc', 'run'), \\\"Must run MCMC before making tables\\\"\\n\\n P, post = radvel.utils.initialize_posterior(config_file)\\n post = radvel.posterior.load(status.get('fit', 'postfile'))\\n chains = pd.read_csv(status.get('mcmc', 'chainfile'))\\n\\n try:\\n mstar = np.random.normal(\\n loc=P.stellar['mstar'], scale=P.stellar['mstar_err'],\\n size=len(chains)\\n )\\n except AttributeError:\\n print(\\\"Unable to calculate derived parameters, stellar parameters not defined the config file.\\\")\\n return\\n\\n if (mstar <= 0.0).any():\\n num_nan = np.sum(mstar <= 0.0)\\n nan_perc = float(num_nan) / len(chains)\\n mstar[mstar <= 0] = np.abs(mstar[mstar <= 0])\\n print(\\\"WARNING: {} ({:.2f} %) of Msini samples are NaN. The stellar mass posterior may contain negative \\\\\\nvalues. Interpret posterior with caution.\\\".format(num_nan, nan_perc))\\n\\n # Convert chains into synth basis\\n synthchains = chains.copy()\\n for par in post.params.keys():\\n if not post.params[par].vary:\\n synthchains[par] = post.params[par].value\\n\\n synthchains = post.params.basis.to_synth(synthchains)\\n\\n savestate = {'run': True}\\n outcols = []\\n for i in np.arange(1, P.nplanets + 1, 1):\\n # Grab parameters from the chain\\n def _has_col(key):\\n cols = list(synthchains.columns)\\n return cols.count('{}{}'.format(key, i)) == 1\\n\\n def _get_param(key):\\n if _has_col(key):\\n return synthchains['{}{}'.format(key, i)]\\n else:\\n return P.params['{}{}'.format(key, i)].value\\n\\n def _set_param(key, value):\\n chains['{}{}'.format(key, i)] = value\\n\\n def _get_colname(key):\\n return '{}{}'.format(key, i)\\n\\n per = _get_param('per')\\n k = _get_param('k')\\n e = _get_param('e')\\n\\n mpsini = radvel.utils.Msini(k, per, mstar, e, Msini_units='earth')\\n _set_param('mpsini', mpsini)\\n outcols.append(_get_colname('mpsini'))\\n\\n mtotal = mstar + (mpsini * c.M_earth.value) / c.M_sun.value # get total star plus planet mass\\n a = radvel.utils.semi_major_axis(per, mtotal) # changed from mstar to mtotal\\n \\n _set_param('a', a)\\n outcols.append(_get_colname('a'))\\n\\n musini = (mpsini * c.M_earth) / (mstar * c.M_sun)\\n _set_param('musini', musini)\\n outcols.append(_get_colname('musini'))\\n\\n try:\\n rp = np.random.normal(\\n loc=P.planet['rp{}'.format(i)],\\n scale=P.planet['rp_err{}'.format(i)],\\n size=len(chains)\\n )\\n\\n _set_param('rp', rp)\\n _set_param('rhop', radvel.utils.density(mpsini, rp))\\n\\n outcols.append(_get_colname('rhop'))\\n except (AttributeError, KeyError):\\n pass\\n\\n # Get quantiles and update posterior object to median\\n # values returned by MCMC chains\\n quantiles = chains.quantile([0.159, 0.5, 0.841])\\n csvfn = os.path.join(args.outputdir, conf_base+'_derived_quantiles.csv')\\n quantiles.to_csv(csvfn, columns=outcols)\\n\\n # saved derived paramters to posterior file\\n postfile = os.path.join(args.outputdir,\\n '{}_post_obj.pkl'.format(conf_base))\\n post.derived = quantiles[outcols]\\n post.writeto(postfile)\\n savestate['quantfile'] = os.path.relpath(csvfn)\\n\\n csvfn = os.path.join(args.outputdir, conf_base+'_derived.csv.bz2')\\n chains.to_csv(csvfn, columns=outcols, compression='bz2')\\n savestate['chainfile'] = os.path.relpath(csvfn)\\n\\n print(\\\"Derived parameters:\\\", outcols)\\n\\n save_status(statfile, 'derive', savestate)\",\n \"def compute_bce(num_bidders, max_value, value_pdf, num_samples=100, max_trials=20, random_seed=1232, solver_str='COIN'):\\r\\n\\r\\n status = 0 # whether the LP was feasible\\r\\n trials = 0 # counter on sampling trials\\r\\n random.seed(random_seed) # seed the random generator\\r\\n while status != 1 and trials < max_trials: # while we have not found a feasible LP\\r\\n\\r\\n prob = plp.LpProblem(\\\"BCE\\\", plp.LpMinimize) # create an LP instance\\r\\n\\r\\n # Sample a set of bid vectors to try to create a BCE with just them\\r\\n bid_vectors = product(range(max_value + 1), repeat=num_bidders)\\r\\n pool = tuple(bid_vectors)\\r\\n indices = random.sample(range(len(pool)), min(num_samples, len(pool)))\\r\\n sampled_bid_vectors = set([pool[i] for i in indices])\\r\\n\\r\\n # Variables are of the form (v,b) where v is a value in {0,...,max_value}\\r\\n # and b is a bid vector in the sampled set\\r\\n lp_var_keys = product(*[range(max_value + 1), sampled_bid_vectors])\\r\\n\\r\\n # Create the psi variables which correspond to Pr[b | v] for each (v,b)\\r\\n # pair\\r\\n psi_vars = plp.LpVariable.dicts('psi', lp_var_keys, lowBound=0)\\r\\n\\r\\n # Creating the best response constraints\\r\\n devs = product(\\r\\n *[range(max_value + 1), range(max_value + 1), range(0, num_bidders)])\\r\\n for (cur_bid, dev_bid, bidder_id) in devs: # for all b_i*, b_i', i\\r\\n if cur_bid != dev_bid: # if b_i* \\\\neq b_i'\\r\\n # Create all the terms of the form Pr[b | v] * pi(v) * (U_i' - U_i)\\r\\n # for all b \\\\in S, such that b_i = b_i*\\r\\n dev_terms = [\\r\\n deviation_term(dev_bid, bidder_id, v, bids,\\r\\n psi_vars[(v, bids)], value_pdf[v])\\r\\n for (v, bids) in product(*[range(max_value + 1), sampled_bid_vectors])\\r\\n if bids[bidder_id] == cur_bid]\\r\\n # Add these terms to create the best response constraint\\r\\n prob += plp.lpSum(dev_terms) <= 0, \\\"Dev_{}_{}_{}\\\".format(\\r\\n cur_bid, dev_bid, bidder_id)\\r\\n\\r\\n # Constraint that Pr[b | v] is a distribution for each fixed v\\r\\n for value in range(max_value + 1):\\r\\n cond_vars = [psi_vars[(value, bids)]\\r\\n for bids in sampled_bid_vectors]\\r\\n prob += plp.lpSum(cond_vars) == 1, \\\"Density_Psi_{}\\\".format(value)\\r\\n\\r\\n # Objective coefficients are random numbers based on the seed\\r\\n np.random.seed(random_seed)\\r\\n prob += plp.lpSum([np.random.standard_normal(1) *\\r\\n var for var in psi_vars.values()])\\r\\n\\r\\n # Solve LP\\r\\n prob.solve(get_solver(solver_str))\\r\\n\\r\\n # Get the status returned by the solver. 1 means success\\r\\n status = int(prob.status)\\r\\n\\r\\n # Increase the trial counter\\r\\n trials += 1\\r\\n\\r\\n if status == 1:\\r\\n # Once we have found a BCE, compute the marginal bid vector\\r\\n # distribution\\r\\n bid_pdf = {}\\r\\n for bid_vector in sampled_bid_vectors:\\r\\n # Compute the probability of the bid vector: sum_{v} Pr[b | v] *\\r\\n # pi(v)\\r\\n prob_mass = sum([plp.value(psi_vars[(v, bid_vector)]) * value_pdf[v]\\r\\n for v in range(max_value + 1)])\\r\\n # If mass is positive add it to the bid_pdf dictionary\\r\\n if prob_mass > 0:\\r\\n bid_pdf[bid_vector] = prob_mass\\r\\n\\r\\n return bid_pdf\\r\\n else:\\r\\n return None\",\n \"def proba_middle(c_pred,m_pred,dataset):\\n if dataset =='cifar10':\\n p = np.zeros(5)\\n for i in range(5):\\n if i in [0,1]:\\n p[i] = c_pred[0]*(m_pred[i]/(m_pred[0]+m_pred[1]))\\n if i in [2,3,4]:\\n p[i] = c_pred[1]*(m_pred[i]/(m_pred[2]+m_pred[3]++m_pred[4]))\\n else :\\n p = np.zeros(4)\\n for i in range(4):\\n if i in [0,1]:\\n p[i] = c_pred[0]*(m_pred[i]/(m_pred[0]+m_pred[1]))\\n if i in [2,3]:\\n p[i] = c_pred[1]*(m_pred[i]/(m_pred[2]+m_pred[3]))\\n return(p)\",\n \"def calc_zchi2_batch(spectra, tdata, weights, flux, wflux, nz, nbasis, solve_matrices_algorithm=\\\"PCA\\\", use_gpu=False, fullprecision=True):\\n zchi2 = np.zeros(nz)\\n if (weights.sum() == 0):\\n zchi2[:] = 9e99\\n zcoeff = np.zeros((nz, nbasis))\\n return (zchi2, zcoeff)\\n if (use_gpu):\\n global cp\\n import cupy as cp\\n #On the GPU, all operations are batch operations for all templates\\n #in parallel.\\n\\n #1) batch_dot_product_sparse will compute dot products of all\\n #spectra with all templates in batch and return a 3D array of\\n #size (nz x ncols x nbasis).\\n Tbs = batch_dot_product_sparse(spectra, tdata, nz, use_gpu)\\n if (cp_memcheck):\\n #Free memory on consumer grade GPUs with low resources\\n mpool = cp.get_default_memory_pool()\\n mpool.free_all_blocks()\\n\\n #2) On the GPU, M and y are computed for all templates at once\\n #CUPY swapaxes is the equivalent of the transpose in CPU mode\\n #and the @ matrix multiplication operator performs a dot\\n #product for each template.\\n\\n ###!!! NOTE - there are 3 different options for calculating the\\n ### M and y arrays -\\n ### A) Straight CUPY, which works well on perlmutter with a\\n ### runtime of 6.2s on 1 GPU and 2.0s on 4 GPUs, but is\\n ### unusably slow on Volta generation GPUs (16.8s for only\\n ### 10 targets on a 1660 Super).\\n ### B) calc_M_y_batch, the custom CUDA kernel, which is the\\n ### fastest at 2.9s on 1 GPU and 0.7s on 4 GPUs (and 0.7s\\n ### for 10 targets on a 1660 Super) but is the most difficult\\n ### from a maintenance perspective\\n ### C) Use the calc_batch_dot_product_3d3d_gpu kernel to offload\\n ### only the matrix multiplication for M (and transpose of\\n ### Tbs) but use CUPY for everything else. This strikes a\\n ### middle ground that is very maintainable but removes the\\n ### bottleneck of the CUPY Volta issue. 5.7s on 1 GPU and\\n ### 1.8s on 4 GPUs on Perlmutter; 1.6s for 10 targets on\\n ### 1660 Super.\\n ###!!! NOTE - uncomment the 2 lines below to run (A)\\n #all_M = Tbs.swapaxes(-2, -1) @ (weights[None, :, None] * Tbs)\\n #all_y = (Tbs.swapaxes(-2, -1) @ wflux)\\n ###!!! NOTE - uncomment the below line to run (B)\\n #(all_M, all_y) = calc_M_y_batch(Tbs, weights, wflux, nz, nbasis)\\n ###!!! NOTE - uncomment the 2 lines below to run (C)\\n all_M = calc_batch_dot_product_3d3d_gpu(Tbs, (weights[None, :, None] * Tbs), transpose_a=True, fullprecision=fullprecision)\\n all_y = (Tbs.swapaxes(-2, -1) @ wflux)\\n ###!!! NOTE - uncomment the 2 lines below to run an alternative\\n ### version of (C) that does the transpose on the CPU - this seems\\n ### to needlessly waste time though\\n #all_M = calc_batch_dot_product_3d3d_gpu(cp.ascontiguousarray(Tbs.swapaxes(-2, -1)), (weights[None, :, None] * Tbs))\\n #all_y = (Tbs.swapaxes(-2, -1) @ wflux)\\n\\n #3) Use new helper method solve_matrices to use appropriate method\\n #for this template to solve for zcoeff in batch for all_M and all_y.\\n #There is no Error thrown by cupy's version of linalg.solve so just\\n #need to catch NotImplementedError.\\n try:\\n zcoeff = solve_matrices(all_M, all_y, solve_algorithm=solve_matrices_algorithm, use_gpu=True)\\n except NotImplementedError:\\n zchi2[:] = 9e99\\n zcoeff = np.zeros((nz, nbasis))\\n return (zchi2, zcoeff)\\n\\n #4) calc_batch_dot_product_3d2d will compute the dot product\\n #of Tbs and zcoeff for all templates in parallel.\\n #It is the same as model[i,:,:] = Tbs[i,:,:].dot(zcoeff[i,:])\\n model = calc_batch_dot_product_3d2d(Tbs, zcoeff, use_gpu)\\n\\n #5) On the GPU, (flux-model)*(flux-model) is faster than\\n #(flux-model)**2. The @ matrix multiplication operator performs\\n #a dot product for each template. get() copies the data back\\n #from the GPU to the numpy array allocated for zchi2.\\n zchi2[:] = (((flux - model)*(flux-model)) @ weights).get()\\n #Copy data from GPU to numpy arrays\\n zcoeff = zcoeff.get()\\n\\n if (cp_memcheck):\\n #Free memory on consumer grade GPUs with low resources\\n del Tbs\\n del all_M\\n del all_y\\n del model\\n mpool = cp.get_default_memory_pool()\\n mpool.free_all_blocks()\\n else:\\n zcoeff = np.zeros((nz, nbasis))\\n #On the CPU, the templates are looped over and all operations\\n #are performed on one template at a time.\\n\\n for i in range(nz):\\n #1) dot_product_sparse_one will compute dot products of all\\n #spectra with ONE template and return a 2D array of size\\n #(ncols x nbasis)\\n Tb = dot_product_sparse_one(spectra, tdata, i)\\n\\n #2) On the CPU, M and y are computed for each template\\n M = Tb.T.dot(np.multiply(weights[:,None], Tb))\\n y = Tb.T.dot(wflux)\\n\\n #3) Use new helper method solve_matrices to use appropriate method\\n #for this template to solve for zcoeff for each M, y.\\n #Catch LinAlgError and NotImplementedError\\n try:\\n zcoeff[i,:] = solve_matrices(M, y, solve_algorithm=solve_matrices_algorithm, use_gpu=False)\\n except np.linalg.LinAlgError:\\n zchi2[i] = 9e99\\n continue\\n except NotImplementedError:\\n zchi2[i] = 9e99\\n continue\\n\\n #4) Calculate dot products individually for each template\\n model = Tb.dot(zcoeff[i,:])\\n\\n #5) Calculate this zchi2 element individually for each template\\n zchi2[i] = np.dot( (flux - model)**2, weights )\\n return (zchi2, zcoeff)\",\n \"def __init__(self, N0, N1):\\n #self.w = np.zeros(N);\\n self.p0 = N0/(N0+N1) \\n self.p1 = N1/(N0+N1)\\n self.mu0 = np.zeros(N0+N1)\\n self.mu1 = np.zeros(N0+N1)\\n self.covariance = 0\",\n \"def CBF(s_angles,s_amp,sig_n,N,M,S,L,lam,V):\\n\\ts = []\\n\\tfor i in range(S):\\n\\t temp = scipy.signal.square(4*np.pi*c0*np.linspace(i,L+i,L)/lam)\\n\\t s.append(temp)\\n\\n\\ts = np.array(s)\\n\\t\\n\\tn = sig_n*(rd.randn(M,L)) #Gaussien centré\\n\\tfor i in range(len(s_amp)):\\n\\t\\ts[i] *= s_amp[i]\\n\\tA = compute_A(s_angles,lam,M)\\n\\ty = A@s + n #On retrouve bien la linéarité\\n\\tSyy = y @ y.T.conj() / L\\n\\n\\n\\tangles1 = np.linspace(- np.pi/2,np.pi/2,N)\\n\\tPCBF = np.zeros(N, dtype = complex) #Ça signifie Puissance pour du classic beamforming \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# Un pic de puissance indiquera la position de notre source\\n\\n\\tfor i in range(N):\\n\\t\\ta_pcbf = a(angles1[i], lam,M)\\n\\t\\tPCBF[i] = a_pcbf.T.conj() @ Syy @ a_pcbf / (np.linalg.norm(a_pcbf) ** 4)\\n\\n\\treturn np.array(PCBF)/np.max(PCBF)\",\n \"def create_problem(\\n forest, bcs, props, nlevels, vol_frac=0.25, density=2600.0, iter_offset=0\\n):\\n\\n # Characteristic length of the domain\\n len0 = 10.0\\n r0_frac = 0.05\\n N = 20\\n\\n # Create the problem and filter object\\n mfilter = MFilterCreator(r0_frac, N, a=len0)\\n filter_type = mfilter.filter_callback\\n obj = CreatorCallback(bcs, props)\\n problem = TopOptUtils.createTopoProblem(\\n forest, obj.creator_callback, filter_type, use_galerkin=True, nlevels=nlevels\\n )\\n\\n # Get the assembler object we just created\\n assembler = problem.getAssembler()\\n\\n # Set the load\\n P = 1.0e3\\n force = TopOptUtils.computeVertexLoad(\\\"pt1\\\", forest, assembler, [0, P, 0])\\n temp = TopOptUtils.computeVertexLoad(\\\"pt2\\\", forest, assembler, [0, 0, P])\\n force.axpy(1.0, temp)\\n\\n # Set the load cases into the topology optimization problem\\n problem.setLoadCases([force])\\n\\n # Compute the fixed mass target\\n lx = 50.0 # mm\\n ly = 10.0 # mm\\n lz = 10.0 # mm\\n vol = lx * ly * lz\\n m_fixed = vol_frac * (vol * density)\\n\\n # Set the mass constraint\\n funcs = [functions.StructuralMass(assembler)]\\n problem.addConstraints(0, funcs, [-m_fixed], [-1.0 / m_fixed])\\n\\n # Set the objective (scale the compliance objective)\\n problem.setObjective([1.0e3])\\n\\n cb = OutputCallback(assembler, iter_offset=iter_offset)\\n problem.setOutputCallback(cb.write_output)\\n\\n return problem\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6512337","0.60092825","0.567834","0.5658303","0.55903035","0.5580925","0.5573696","0.5532462","0.55020326","0.5474026","0.54726166","0.54643524","0.54269475","0.5414196","0.5403196","0.5379074","0.537768","0.53350365","0.5313411","0.52971154","0.5286176","0.52517796","0.5244757","0.5226844","0.5217662","0.51979196","0.5181515","0.5178035","0.51753724","0.5164982","0.51624215","0.5158675","0.5150065","0.5144171","0.51312786","0.5117574","0.5112566","0.5110663","0.5106372","0.51021373","0.50960225","0.5095394","0.50851613","0.50698555","0.5067987","0.5062188","0.50606644","0.50485116","0.504491","0.50407374","0.5027308","0.5026455","0.5019238","0.5016785","0.5010903","0.5010758","0.50099695","0.5008576","0.49961352","0.49923405","0.49896377","0.49869728","0.49854016","0.49838078","0.49814573","0.49701488","0.49674794","0.4964985","0.49570835","0.49557406","0.49520797","0.49390504","0.49372485","0.49336395","0.4933213","0.49323425","0.49315184","0.4923137","0.49175304","0.49174964","0.49162683","0.4912093","0.49107772","0.4900306","0.48884082","0.4886348","0.48861203","0.48734266","0.4859913","0.48484364","0.48484027","0.48474517","0.4845876","0.4844266","0.4842886","0.48408884","0.4831019","0.4829654","0.4816573","0.4815992"],"string":"[\n \"0.6512337\",\n \"0.60092825\",\n \"0.567834\",\n \"0.5658303\",\n \"0.55903035\",\n \"0.5580925\",\n \"0.5573696\",\n \"0.5532462\",\n \"0.55020326\",\n \"0.5474026\",\n \"0.54726166\",\n \"0.54643524\",\n \"0.54269475\",\n \"0.5414196\",\n \"0.5403196\",\n \"0.5379074\",\n \"0.537768\",\n \"0.53350365\",\n \"0.5313411\",\n \"0.52971154\",\n \"0.5286176\",\n \"0.52517796\",\n \"0.5244757\",\n \"0.5226844\",\n \"0.5217662\",\n \"0.51979196\",\n \"0.5181515\",\n \"0.5178035\",\n \"0.51753724\",\n \"0.5164982\",\n \"0.51624215\",\n \"0.5158675\",\n \"0.5150065\",\n \"0.5144171\",\n \"0.51312786\",\n \"0.5117574\",\n \"0.5112566\",\n \"0.5110663\",\n \"0.5106372\",\n \"0.51021373\",\n \"0.50960225\",\n \"0.5095394\",\n \"0.50851613\",\n \"0.50698555\",\n \"0.5067987\",\n \"0.5062188\",\n \"0.50606644\",\n \"0.50485116\",\n \"0.504491\",\n \"0.50407374\",\n \"0.5027308\",\n \"0.5026455\",\n \"0.5019238\",\n \"0.5016785\",\n \"0.5010903\",\n \"0.5010758\",\n \"0.50099695\",\n \"0.5008576\",\n \"0.49961352\",\n \"0.49923405\",\n \"0.49896377\",\n \"0.49869728\",\n \"0.49854016\",\n \"0.49838078\",\n \"0.49814573\",\n \"0.49701488\",\n \"0.49674794\",\n \"0.4964985\",\n \"0.49570835\",\n \"0.49557406\",\n \"0.49520797\",\n \"0.49390504\",\n \"0.49372485\",\n \"0.49336395\",\n \"0.4933213\",\n \"0.49323425\",\n \"0.49315184\",\n \"0.4923137\",\n \"0.49175304\",\n \"0.49174964\",\n \"0.49162683\",\n \"0.4912093\",\n \"0.49107772\",\n \"0.4900306\",\n \"0.48884082\",\n \"0.4886348\",\n \"0.48861203\",\n \"0.48734266\",\n \"0.4859913\",\n \"0.48484364\",\n \"0.48484027\",\n \"0.48474517\",\n \"0.4845876\",\n \"0.4844266\",\n \"0.4842886\",\n \"0.48408884\",\n \"0.4831019\",\n \"0.4829654\",\n \"0.4816573\",\n \"0.4815992\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6656812"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77982,"cells":{"query":{"kind":"string","value":"Check if a line is header."},"document":{"kind":"string","value":"def _is_header(line):\n line = line.strip()\n if line.startswith('#') or line.startswith('track') or line.startswith(\n 'browser'): # BED header\n return True\n else:\n return False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def is_header(line):\n return line[0] == '>'","def check_header(line):\n if line[:7] == '###### ':\n line = '<h6>' + line[7:] + '</h6>'\n elif line[:6] == '##### ':\n line = '<h5>' + line[6:] + '</h5>'\n elif line[:5] == '#### ':\n line = '<h4>' + line[5:] + '</h4>'\n elif line[:4] == '### ':\n line = '<h3>' + line[4:] + '</h3>'\n elif line[:3] == '## ':\n line = '<h2>' + line[3:] + '</h2>'\n elif line[:2] == '# ':\n line = '<h1>' + line[2:] + '</h1>'\n else:\n return False, ''\n\n return True, line","def is_header(fields):\n if len(fields) < 11:\n return None\n # Test a column which should usually be a number in data lines and never a number in header lines.\n try:\n float(fields[8])\n return False\n except ValueError:\n pass\n first_field = fields[0]\n # An explicitly commented line is a header.\n if first_field.startswith('#'):\n return True\n # The first field in a header is usually these two (and never these in data lines).\n if first_field.lower() == 'sample' or first_field.lower() == 'family':\n return True\n # Fallback 1: There should never be a number in a header line. If we find one, it's a data line.\n for field in fields:\n try:\n float(field)\n return False\n except ValueError:\n pass\n # Fallback 2: Just test whether any of the known labels is in the line.\n for label in LABELS:\n if label in fields:\n return True\n for label in LABELS:\n if label.lower() in fields:\n return True","def is_header(line, max_level):\n if max_level:\n return len(re.findall(\"^#{{1,{0}}} .+\".format(max_level), line)) > 0\n else:\n return len(re.findall(\"^#+ .+\", line)) > 0","def test_is_fasta_header(self):\r\n\r\n is_fasta_header = False\r\n\r\n with open(full_file_name, \"r\") as in_file:\r\n for line in in_file:\r\n is_fasta_header = mfau.is_header_line(line)\r\n\r\n # only testing the first line\r\n break\r\n\r\n self.assertEqual(is_fasta_header, True)","def has_header():\n header_content = (\"\\n\".join(CURRENT_BUFFER[:7])).lower()\n return sum(1 for keyword in KEYWORDS if header_content.find(keyword.lower()) != -1) >= 2","def has_header_row(self, strdata):\n debug = False\n comma_dec_sep_ok = True\n if debug: print(strdata)\n if len(strdata) < 2: ## a header row needs a following row to be a header\n return False\n row1_types = [lib.get_val_type(val, comma_dec_sep_ok) \n for val in strdata[0]]\n row2_types = [lib.get_val_type(val, comma_dec_sep_ok) \n for val in strdata[1]]\n str_type = mg.VAL_STRING\n empty_type = mg.VAL_EMPTY_STRING\n non_str_types = [mg.VAL_DATE, mg.VAL_NUMERIC]\n return importer.has_header_row(\n row1_types, row2_types, str_type, empty_type, non_str_types)","def check_header(self, entry):\n if entry not in self.metadata:\n raise SyntaxError(\"Header entry must be described in the metadata lines. Entry: %s is not in metadata.\" % entry)","def has_headers(self):\n for column in self.columns:\n if column.header:\n return True\n return False","def is_header(filename):\n header_extensions = {'h'}\n return os.path.splitext(filename)[1][1:] in header_extensions","def is_header_part(cell: str) -> bool:\n pattern = '|'.join([\n rf'(?:(?:three|3|six|6|nine|9|twelve|12)\\s+months?(?:\\s+periods?)?|quarters?|year|ytd)(?!ly)',\n rf'\\b(?:{MONTH})\\b',\n rf'^(?:end(?:ed|ing))?(?:20)\\s*[0-2]\\s*[0-9]{FOOTNOTE}$',\n rf'^\\d{1, 2}/\\d{1, 2}/\\d{2, 4}{FOOTNOTE}$',\n rf'^q[1-4](?:\\s*\\(\\w+\\))?{FOOTNOTE}$',\n rf'^[1-4]q(?:tr)?(?:\\d{2, 4})?',\n rf'as\\s+(?:reported|adjusted)',\n rf'year-?\\s*to-?\\s*date',\n rf'^year-$',\n rf'^to-date$',\n rf'full\\s+year',\n rf'^(?:28|29|30|31){FOOTNOTE}$',\n rf'^(?:month|quarter|year)s?{FOOTNOTE}$',\n rf'^(?:three|six|nine|twelve){FOOTNOTE}$',\n rf'^(?:operating|reported|baseline|percent|%|end(?:ed|ing)){FOOTNOTE}$',\n ORDINAL,\n rf'^(?:(?:20)\\s*[0-2]\\s*[0-9]\\*\\s*)?{UNAUDITED_EXACT}$'\n ])\n prepped = str(cell).lower().strip()\n match = re.search(allow_space_between_letters(pattern), prepped)\n return match is not None or parse_fiscal_period(cell) is not None","def is_header(file_name):\n return str(file_name).endswith('.h')","def split_head(line, is_head=lambda line: line.startswith('>')):\n if is_head(line):\n return True\n else:\n return False","def has_header_row(filepath, skiprows=[0]):\n if skiprows:\n skiprows=max(skiprows)\n else:\n skiprows = 0\n with open(filepath) as f:\n reader = csv.reader(f)\n\n data = list(reader)[skiprows:skiprows+10] # don't look beyond line 10\n \n def isnumber(item):\n try:\n float(item)\n return True\n except:\n return False\n \n first_line = data[0]\n second_line = data[1]\n \n first_line = [isnumber(item) for item in first_line]\n second_line = [isnumber(item) for item in second_line]\n \n\n if not first_line == second_line:\n return 0\n else:\n return None","def has_supported_header_hormat(cls, csv_reader):\n return csv_reader.fieldnames == cls.INGFormatHeader","def check_csv_headers(csvfile, headers):\n\n with open(csvfile, 'rb') as f:\n csv_header = f.readline()\n\n # Check the lower ones\n if headers[1][0] not in csv_header.lower():\n return False\n\n return True","def _row_or_col_is_header(s_count, v_count):\n if s_count == 1 and v_count == 1:\n return False\n else:\n return (s_count + 1) / (v_count + s_count + 1) >= 2. / 3.","def check_header(self, name, value):\r\n if value in self.headers.get(name, ''):\r\n return True\r\n return False","def check_header(self, name, value):\r\n if value in self.headers.get(name, ''):\r\n return True\r\n return False","def isDataLine(line):\n if len(line) > 1:\n return line[0] != \"#\"\n return False","def isDataLine(line):\n if len(line) > 1:\n return line[0] != \"#\"\n return False","def _header_transformer(self, lines):\n needle = b'--%s\\n' % self.boundary\n in_header = False\n for line in lines:\n if line == needle:\n in_header = True\n if in_header:\n assert line[-1] == b'\\n'\n line = line[:-1] + b'\\r\\n'\n if line == b'\\r\\n':\n in_header = False\n yield line","def verifyHeader(self, headers):\n for head in headers:\n if(hex(head[0]) == VID and hex(head[1]) == PID):\n return True\n return False","def _validate_header(self, header_row):\n\n self.logger.info(\"Validating header row.\")\n \n # assume value.\n is_valid = True\n\n # check if @header_row is perfect.\n required_keys = tuple(self.required_headers.keys())\n if sorted(header_row) == sorted(required_keys):\n self.logger.info(\"Header is valid.\")\n return is_valid\n else:\n self.logger.error(\"Header is invalid.\")\n is_valid = False\n\n # report on any missing header fields.\n missing_headers = [header for header in self.required_headers if header not in\n header_row]\n if len(missing_headers) != 0:\n self.logger.warning(\"Missing required fields: {}\".format(missing_headers))\n \n # report on any duplicate fields.\n duplicate_headers = [header for header in header_row if header_row.count(header) != 1]\n if len(duplicate_headers) != 0:\n self.logger.warning(\"Found duplicate fields: {}\".format(set(duplicate_headers)))\n\n # report on any extra fields.\n extra_headers = [header for header in header_row if header not in \n self.required_headers]\n if len(extra_headers) != 0:\n self.logger.warning(\"Found extra fields: {}\".format(extra_headers))\n \n return is_valid","def check_headerRow(self, expected, found):\n\n # spreadsheets must have either a barcode field or a object ID field, but both are not required\n header1 = 'barcode'\n header2 = ('object identifier\\n(edit heading to specify type' +\n ' - e.g. barcode)')\n expected = self.remove_annoying(header1, header2, expected, found)\n\n missing = []\n\n for header in expected:\n if header not in found:\n missing.append(header)\n\n if missing:\n self.raise_excelerror(\"Missing required value- {0}.\"\n .format(missing))\n\n return True","def test_line(line):\r\n if not line.strip():\r\n return False # if the last line is blank\r\n if line.startswith(\"#\"):\r\n return False # comment line\r\n if line.startswith(\" #\"):\r\n return False # comment line\r\n return line","def test_first_line_amiramesh(self):\n self.assertEqual(self.header.designation.filetype, 'AmiraMesh')","def validate_header_keyword(self):\n\n valid = False\n if self.headers[0].upper() == \"NAME\":\n valid = True\n if self.headers[0] != \"NAME\":\n msg = f'File keyword \"NAME\" provided as {self.headers[0]}'\n self.store_validation_issue(\"warn\", msg, \"format:cap:name\")\n else:\n msg = \"Malformed file header row, missing NAME keyword. (Case Sensitive)\"\n self.store_validation_issue(\"error\", msg, \"format:cap:name\")\n return valid","def is_line(self):\n return True","def is_line(self):\n return True","def check_valid_csv_header(self, row):\n obj = re.match(re.compile('^Year\\,Month\\,.'),\n ','.join(row))\n if not obj:\n raise Exception(\"Invalid Headers must be `Year` `Month` Check Sample file\")","def check_header(filename: str, *, is_newly_created: bool = False) -> None:\n lines = get_header_lines(filename)\n check_header_present(filename, lines)\n is_py3_file = all(\"from __future__\" not in line for line in lines)\n if is_py3_file:\n lines = lines[:EXPECTED_NUM_PY3_LINES]\n copyright_line_index = 0\n expected_header = EXPECTED_HEADER_PY3\n else:\n copyright_line_index = 1\n expected_header = EXPECTED_HEADER_PY2\n check_copyright_year(\n filename, copyright_line=lines[copyright_line_index], is_newly_created=is_newly_created\n )\n check_matches_header(\n filename, lines, expected_header=expected_header, copyright_line_index=copyright_line_index\n )","def isccp4iheader(self, text):\n #\n # CCP4i header lines look like:\n # #CCP4I VERSION CCP4Interface 1.4.1\n # #CCP4I SCRIPT LOG refmac5\n # #CCP4I DATE 25 Oct 2005 15:19:22\n #\n if self.isccp4itail(text):\n # Reject tail elements\n return \"\"\n header = self.compile(\"isccp4iheader\", r\"#CCP4I (.*)\").search(text)\n result = \"\"\n if header:\n result = header.group(0)\n return result","def is_header_content(response, key, value):\n try:\n if response.headers[key].lower() == value:\n return True\n else:\n return False\n except:\n return False","def is_line(self):\n return False","def test_header_row(self):\n header_row = self.view_class().header_row\n if not header_row:\n return\n\n response = self.view_class().get()\n # Some formatting needs to be done so that the header row\n # is compliant with the CSV dialect - all fields need\n # to be quoted.\n quoted_header_row = '\"{}\"'.format('\",\"'.join(header_row))\n self.assertContains(response, quoted_header_row)","def check_for_header(filename):\n header = {}\n start_id = -1\n with open(filename, \"r\") as f:\n start = re.compile(r\"\\bSTART|start\\b\")\n # if the file has the keyword start, extract header\n if bool(start.search(f.read())):\n f.seek(0) # set the cursor back to the beginning\n lines = f.readlines()\n for i, line in enumerate(lines):\n if start.match(line):\n start_id = i # the line number where start is used (divides header and body)\n break\n args = line.split()\n args.insert(0, \"\") # check_for_commands only handles the second argument (first is usually res_id)\n header['DEFAULT'] = check_for_commands(args, 1, 2)\n\n return header, start_id","def is_horizontal(line:tuple)->bool:\n return line[0][1] == line[1][1]","def is_line(self): \n return False","def is_header(file):\n return not file.is_dir() and not file.name == 'module.modulemap.in' and file.name != 'libcxx.imp'","def is_chunk_header(buf, offset):\n if len(buf) < offset + 0x2C:\n # our accesses below will overflow\n return False\n\n magic = struct.unpack_from(\"<7s\", buf, offset)[0]\n if magic != EVTX_HEADER_MAGIC:\n return False\n\n size = struct.unpack_from(\"<I\", buf, offset + 0x28)[0]\n if not (MIN_CHUNK_HEADER_SIZE <= size <= MAX_CHUNK_HEADER_SIZE):\n return False\n\n if len(buf) <= offset + size:\n # the chunk overruns the buffer end\n return False\n\n try:\n chunk = Evtx.Evtx.ChunkHeader(buf, offset)\n except:\n logger.debug('failed to parse chunk header', exc_info=True)\n return False\n\n if len(buf) < offset + CHUNK_SIZE:\n return False\n\n if chunk.calculate_header_checksum() != chunk.header_checksum():\n return False\n\n if chunk.calculate_data_checksum() != chunk.data_checksum():\n return False\n\n return True","def test_header(self):\n frame_with_header = self.context.frame.import_csv(\n self.dataset, schema=self.schema, header=True)\n frame_without_header = self.context.frame.import_csv(self.dataset,\n schema=self.schema, header=False)\n\n # the frame with the header should have one less row\n # because it should have skipped the first line\n self.assertEqual(len(frame_with_header.take(frame_with_header.count()).data),\n len(frame_without_header.take(frame_without_header.count()).data) - 1)\n # comparing the content of the frame with header and without\n # they should have the same rows with the only differnce being the\n # frame with the header should not have the first row\n for index in xrange(0, frame_with_header.count()):\n self.assertEqual(str(frame_with_header.take(frame_with_header.count()).data[index]),\n str(frame_without_header.take(frame_without_header.count()).data[index + 1]))","def is_header_or_footer(self, tag):\n return ((tag.name == \"text\") and tag.has_attr(\"top\")\n and ((int(tag[\"top\"]) <= self.HEADER_END_OFFSET) \n or (int(tag[\"top\"]) > self.FOOTER_START_OFFSET)))","def isHereDoc(self, lineData, column):\n return self._getTextType(lineData, column) == 'h'","def assert_header(self):\r\n\r\n if self.length > self.owner.settings[SETTINGS_MAX_FRAME_SIZE]:\r\n raise netius.ParserError(\r\n \"Headers are greater than SETTINGS_MAX_FRAME_SIZE\",\r\n stream = self.stream,\r\n error_code = FRAME_SIZE_ERROR\r\n )\r\n if self.last_type in (HEADERS, CONTINUATION) and not\\\r\n self.last_end_headers and not self.last_stream == self.stream:\r\n raise netius.ParserError(\r\n \"Cannot send frame from a different stream in middle of headers\",\r\n error_code = PROTOCOL_ERROR\r\n )","def test_check_header(self):\r\n\r\n # Default header, should not generate any errors/warnings\r\n header = ['SampleID', 'BarcodeSequence', 'LinkerPrimerSequence',\r\n 'Description']\r\n errors = []\r\n warnings = []\r\n\r\n errors, warnings = check_header(header,\r\n errors,\r\n warnings,\r\n sample_id_ix=0,\r\n desc_ix=3,\r\n bc_ix=1,\r\n linker_primer_ix=2,\r\n added_demultiplex_field=None)\r\n\r\n expected_errors = []\r\n expected_warnings = []\r\n\r\n self.assertEqual(errors, expected_errors)\r\n self.assertEqual(warnings, expected_warnings)","def process_header(line, new_ids=None):\n # extraneous headers\n if line.startswith('##') and not any(\n line.startswith('##' + header_type)\n for header_type in ('INFO', 'FILTER', 'FORMAT', 'ALT', 'contig')\n ):\n return None\n\n # non-whitelisted annotations\n match = re.match(r'##INFO=<ID=([^,]+)', line)\n if match:\n info_name = match.group(1)\n if info_name not in WHITELISTED_ANNOTATIONS:\n return None\n\n if line.startswith('#CHROM') and new_ids is not None:\n fields = line.strip().split('\\t')[:9] # fixed headers\n fields.extend(new_ids)\n line = '\\t'.join(fields) + '\\n'\n\n return line","def _parse_header(self, line):\n if self._regex_helper.search_compiled(W._re_header, line):\n if not self.headers:\n for value in re.findall(W._re_header, line):\n self.headers.append(value[0])\n raise ParsingDone\n else:\n # Dictionary which is going to be appended to the returned list\n ret = dict()\n # List of entries\n _entries = list()\n # List of values in WHAT entry\n _what_entry = list()\n for value in re.findall(W._re_header, line):\n _entries.append(value[0])\n for what_index in range(len(self.headers) - 1, len(_entries)):\n _what_entry.append(_entries[what_index])\n _what_entry_string = ' '.join(_what_entry)\n for index in range(len(self.headers)):\n if index < len(self.headers) - 1:\n ret.update({self.headers[index]: _entries[index]})\n else:\n ret.update({self.headers[index]: _what_entry_string})\n self.current_ret['RESULT'].append(ret)\n raise ParsingDone","def process_headers(fin, fout, fixed_headers):\n filtered_headers = set(item[0] for item in fixed_headers)\n filtered_headers.add(\"SAMPLE\")\n expected_values = {\n name: value for name, asserted, value in fixed_headers if asserted\n }\n errors = False\n for raw_line in fin:\n if raw_line.startswith('##'):\n # TODO: This will break if the metadata header is bad.\n name, value = raw_line[2:].rstrip().split('=', 1)\n if name in filtered_headers:\n if name in expected_values:\n if value != expected_values[name]:\n errors = True\n # TODO: propper logging\n sys.stderr.write(\n 'tcga-vcf-reheader: mismatch {}={}\\n'.format(\n name, value\n )\n )\n else: # Just some other header...\n fout.write(raw_line)\n else:\n break\n fout.write(raw_line) # raw_line should now be the data header line.\n return errors","def check_headerEntries(self, expected, found):\n\n # spreadsheets must have either a barcode field or a object ID field, but both are not required\n header1 = ('original master', 'object', 'barcode')\n header2 = ('original master', 'object',\n 'object identifier\\n(edit heading to specify type ' +\n '- e.g. barcode)')\n expected = self.remove_annoying(header1, header2, expected, found)\n\n bad_entries = []\n\n for header in expected:\n if header not in found:\n bad_entries.append(header)\n\n if bad_entries:\n self.raise_excelerror(\"Incorrect header entry for {0}.\"\n .format(bad_entries))\n return True","def _check_duplicate_headers(self, docstring: PetscDocStringImpl) -> None:\n for heading, where in self.seen_headers.items():\n if len(where) <= 1:\n continue\n\n lasti = len(where) - 1\n src_list = []\n nbefore = 2\n nafter = 0\n prev_line_begin = 0\n for i, loc in enumerate(where):\n startline = loc.start.line\n if i:\n nbefore = startline - prev_line_begin - 1\n if i == lasti:\n nafter = 2\n src_list.append(loc.formatted(num_before_context=nbefore, num_after_context=nafter, trim=False))\n prev_line_begin = startline\n mess = \"Multiple '{}' subheadings. Much like Highlanders, there can only be one:\\n{}\".format(\n self.transform(self.name), '\\n'.join(src_list)\n )\n docstring.add_diagnostic(\n Diagnostic(Diagnostic.Kind.ERROR, self.diags.section_header_unique, mess, self.extent.start)\n )\n return","def __parseHeader(self):\n line = self.__nextLine()\n while line.startswith(\"c\"):\n self.__log(\"adding comment %s\" % line)\n self.__commentList.append(line[2:])\n line = self.__nextLine()\n parts = line.split()\n if len(parts) == 4 and parts[0] == \"p\" and parts[1] == \"cnf\":\n self.__nrVar = int(parts[2])\n self.__nrClause = int(parts[3])\n assert(self.__nrVar > 0 and self.__nrClause > 0)\n return True\n else:\n return False","def isfile(line):\n return line and (line.strip()[:3] == \"FHS\" or isbatch(line))","def header(line: str, type_=1):\n if type_ == 1:\n \"\"\"\n =========================\n This is an example header\n =========================\n \"\"\"\n eq = \"\".join(['=' for _ in range(len(line))])\n para = f'{eq}\\n{line}\\n{eq}'\n elif type_ == 2:\n \"\"\"\n -------------------------\n This is an example header\n -------------------------\n \"\"\"\n eq = \"\".join(['-' for _ in range(len(line))])\n para = f'{eq}\\n{line}\\n{eq}'\n else:\n raise ValueError('Invalid type_')\n\n print(para)","def check_header(header,\r\n errors,\r\n warnings,\r\n sample_id_ix,\r\n desc_ix,\r\n bc_ix,\r\n linker_primer_ix,\r\n added_demultiplex_field=None):\r\n\r\n # Check for duplicates, append to errors if found\r\n errors = check_header_dups(header, errors)\r\n\r\n # Check for valid characters\r\n warnings = check_header_chars(header, warnings)\r\n\r\n # Check for required header fields\r\n errors = check_header_required_fields(header, errors, sample_id_ix,\r\n desc_ix, bc_ix, linker_primer_ix, added_demultiplex_field)\r\n\r\n return errors, warnings","def check_horizontal_rule(line):\n if line.count('-') >= 3 and contains_only_char(line, '-'):\n return True, '<hr></hr>'\n if line.count('*') >= 3 and contains_only_char(line, '*'):\n return True, '<hr></hr>'\n if line.count('_') >= 3 and contains_only_char(line, '_'):\n return True, '<hr></hr>'\n return False, ''","def test_csv_reader_header_fields(process_data):\n data = process_data(file_name_or_type='clean_map.csv')\n header_fields = list(data[0].keys())\n assert header_fields == [\n 'Country',\n 'City',\n 'State_Or_Province',\n 'Lat',\n 'Long',\n 'Altitude'\n ]","def is_valid_header(headers: Dict[str, Any]) -> bool:\n for name, value in headers.items():\n if not utils.is_latin_1_encodable(value):\n return False\n if utils.has_invalid_characters(name, value):\n return False\n return True","def _is_start(self, line):\n if re.match(\".*\\:\\s*\\(groupid\", line):\n return True","def header_statement(stmt, accel):\n if not isinstance(stmt, CommentNode): None\n if not stmt.value.startswith('#$'): None\n\n header = stmt.value[2:].lstrip()\n if not directive.startswith('header'): None\n\n return stmt.value","def issummary_begin(self, line):\n #\n # Summary start lines look like:\n # <B><FONT COLOR=\"#FF0000\"><!--SUMMARY_BEGIN-->\n #\n # Set up regular expression for SUMMARY_BEGIN lines\n summary = self.compile(\"issummary_begin\", r\"<\\!--SUMMARY_BEGIN-->\").search(line)\n if summary:\n return True\n return False","def has_bad_headers(self):\n\n headers = [self.sender, self.reply_to] + self.recipients\n for header in headers:\n if _has_newline(header):\n return True\n\n if self.subject:\n if _has_newline(self.subject):\n for linenum, line in enumerate(self.subject.split('\\r\\n')):\n if not line:\n return True\n if linenum > 0 and line[0] not in '\\t ':\n return True\n if _has_newline(line):\n return True\n if len(line.strip()) == 0:\n return True\n return False","def header_len(self):\n if self.num_lines_header is None:\n Nheader = 0\n with self._compression_safe_file_opener(self.input_fname, \"r\") as f:\n for i, l in enumerate(f):\n if (l[0 : len(self.header_char)] == self.header_char) or (\n l == \"\\n\"\n ):\n Nheader += 1\n else:\n break\n\n return Nheader\n else:\n return self.num_lines_header","def has_line( self, description ):\n return find_line( self.get_lines(), description ) != -1","def has_line( self, description ):\n return find_line( self.get_lines(), description ) != -1","def test_check_header_missing_fields(self):\r\n\r\n # Default header, should not generate any errors/warnings\r\n header = ['AAA', 'XXX', 'YYY',\r\n 'ZZZ']\r\n errors = []\r\n warnings = []\r\n\r\n errors, warnings = check_header(header,\r\n errors,\r\n warnings,\r\n sample_id_ix=0,\r\n desc_ix=3,\r\n bc_ix=1,\r\n linker_primer_ix=2,\r\n added_demultiplex_field=None)\r\n\r\n expected_errors = [\r\n 'Found header field AAA, expected field SampleID\\t0,0',\r\n 'Found header field XXX, expected field BarcodeSequence\\t0,1',\r\n 'Found header field YYY, expected field LinkerPrimerSequence\\t0,2',\r\n 'Found header field ZZZ, last field should be Description\\t0,3']\r\n expected_warnings = []\r\n\r\n self.assertEqual(errors, expected_errors)\r\n self.assertEqual(warnings, expected_warnings)","def isTable(line):\n # Make the string easier to parse.\n line_stripped = lineNormalise(line)\n\n # Return value.\n ret = False\n\n # If the line starts with the word table, we have a table definition!\n if line_stripped.startswith('table'):\n ret = True\n\n # Tell the horrible truth that this code could not find a table.\n return ret","def parse_header(self, out):\n self.headers = {}\n for h in out.split(\"\\r\\n\\r\\n\", 1)[0].split(\"\\r\\n\"):\n x = h.split(\":\")\n self.headers[x[0]] = \":\".join(x[1:]).lstrip()\n return True","def check_headers(self, headers):\n h = headers.values()[0]\n\n if 'DT' in PAR:\n if h.dt != PAR.DT:\n h.dt = PAR.DT\n\n if 'NT' in PAR:\n if h.nt != PAR.NT:\n print 'Warning: h.nt != PAR.NT'\n\n if 'NREC' in PAR:\n if h.nr != PAR.NREC:\n print 'Warning: h.nr != PAR.NREC'\n\n return h","def hasNextLine(self) -> bool:\n raise NotImplementedError","def test_partial_header(self):\n stream_handle = open(os.path.join(RESOURCE_PATH,\n 'part_header_SNA_SNA.txt'), MODE_ASCII_READ)\n\n self.create_parser(stream_handle)\n\n # get A-E, also metadata\n particles = self.parser.get_records(6)\n\n # check all the values against expected results.\n self.assert_particles(particles, 'short_SNA_telem_part.yml', RESOURCE_PATH)\n\n # confirm no exceptions occurred\n self.assertEqual(self.exception_callback_value, [])\n stream_handle.close()","def is_section(line: str) -> bool:\n return len(line) > 0 and (line[0] == '[' and line[len(line) - 1] == ']')","def validate_against_header_count(self):\n valid = False\n len_headers = len(\n [header for header in self.headers if \"Unnamed\" not in header]\n )\n len_annot_type = len(\n [\n annot_type\n for annot_type in self.annot_types\n if \"Unnamed\" not in annot_type\n ]\n )\n if not len_headers == len_annot_type:\n msg = (\n f\"Header mismatch: {len_annot_type} TYPE declarations \"\n f\"for {len_headers} column headers\"\n )\n self.store_validation_issue(\"error\", msg, \"format:cap:count\")\n else:\n valid = True\n return valid","def is_same_header(self, block_header):\n if len(self.variable_strings) > 0:\n if len(self.variable_strings) != len(\n block_header.variable_strings\n ):\n return False\n else:\n for sitem, oitem in zip(\n self.variable_strings, block_header.variable_strings\n ):\n if sitem != oitem:\n return False\n return True\n elif (\n len(self.data_items) > 0 and len(block_header.variable_strings) > 0\n ):\n typ_obj = (\n self.data_items[0].structure.data_item_structures[0].type_obj\n )\n if typ_obj == int or typ_obj == float:\n return bool(\n self.variable_strings[0]\n == block_header.variable_strings[0]\n )\n else:\n return True\n elif len(self.data_items) == len(block_header.variable_strings):\n return True\n return False","def validate_header(self, reply):\n # check message is from my agg to me\n check_equal(reply.header.sender, self.aggregator_uuid, self.logger)\n check_equal(reply.header.recipient, self.common_name, self.logger)\n\n # check that the federation id matches\n check_equal(reply.header.federation_id, self.federation_uuid, self.logger)\n\n # check that we agree on single_col_cert_common_name\n check_equal(reply.header.single_col_cert_common_name, self.single_col_cert_common_name, self.logger)","def _read_header(self, valid_sections={}):\n linenum = self._linenum\n\n # It's possible that we'll be at the end of the file, with some blank\n # lines, or hand-editing (or bad diff generation) has led to some\n # blank lines before a header. We'll iterate through any blank lines\n # until we reach content or an End of File.\n while True:\n header, eof = self._read_until(b'\\n')\n\n if eof:\n return None\n\n if header.strip():\n break\n\n if self._file_newlines is None:\n # Given that we read up until a '\\n', one of these are guaranteed\n # to match.\n if header.endswith(b'\\r\\n'):\n self._file_newlines = b'\\r\\n'\n else:\n assert header.endswith(b'\\n')\n\n self._file_newlines = b'\\n'\n\n assert header.endswith(self._file_newlines)\n header = header[:-len(self._file_newlines)]\n\n m = self._HEADER_RE.match(header)\n\n if not m:\n raise DiffXParseError(\n 'Unexpected or improperly formatted header: %r' % header,\n linenum=linenum)\n\n # Validate the level and section ID.\n section_id = m.group('section_id').decode('ascii')\n\n if section_id not in valid_sections:\n raise DiffXParseError(\n 'Unknown or unexpected section ID \"%(section_id)s\". '\n 'Expected one of: %(valid_sections)s'\n % {\n 'section_id': section_id,\n 'valid_sections': ', '.join(\n '\"%s\"' % _valid_section\n for _valid_section in sorted(valid_sections)\n ),\n },\n linenum=linenum)\n\n section_type = m.group('section_type')\n level = len(m.group('level'))\n\n # Parse the options out of the header.\n options_str = m.group('options')\n options = {}\n\n if options_str:\n # Options should be present.\n #\n # As this is a reference implementation, this will be strict with\n # the format. There should be exactly one space between the\n # \"#<id>:\" and the options, one space between each comma-separated\n # pair, and each key and value are expected to match a specific set\n # of characters.\n for option_pair in options_str.split(b', '):\n option_key, option_value = option_pair.split(b'=', 1)\n\n if not self._HEADER_OPTION_KEY_RE.match(option_key):\n raise DiffXParseError(\n 'Header option key \"%s\" contains invalid characters'\n % option_key.decode('ascii'),\n linenum=linenum,\n column=header.index(option_pair))\n\n if not self._HEADER_OPTION_VALUE_RE.match(option_value):\n raise DiffXParseError(\n 'Header option value \"%(value)s\" for key \"%(key)s\" '\n 'contains invalid characters'\n % {\n 'key': option_key.decode('ascii'),\n 'value': option_value.decode('ascii'),\n },\n linenum=linenum,\n column=header.index(option_pair) + len(option_key) + 1)\n\n # These should safely decode, since we've validated the\n # characters above.\n option_key = option_key.decode('ascii')\n option_value = option_value.decode('ascii')\n\n # Convert the value to an integer, if it's a number.\n try:\n option_value = int(option_value)\n except ValueError:\n pass\n\n options[option_key] = option_value\n\n self._linenum += 1\n\n return {\n 'level': level,\n 'line': linenum,\n 'options': options,\n 'section': section_id,\n 'type': section_type.decode('ascii'),\n }","def beginning_of_line():\n app = get_app()\n before_cursor = app.current_buffer.document.current_line_before_cursor\n\n return bool(\n len(before_cursor) == 0 and not app.current_buffer.document.on_first_line\n )","def check_line(self):\n if not self.hosts and not self.line:\n self.msg(\"There is no line here. You can create one with +line/createline.\")\n return\n return True","def line_exists():\n global _current_line\n return _current_line is not None","def _parse_header(self):\n # read the first bytes from the file\n header = self._stream_handle.read(HEADER_BYTES)\n match = HEADER_MATCHER.match(header)\n if not match:\n raise SampleException(\"File header does not match the header regex\")\n\n # update the state to show we have read the header\n self._increment_state(HEADER_BYTES)","def _is_blank_line(self):\n pattern = re.compile(r\"^(\\s)*$\")\n return pattern.search(self._line)","def test_headers(self):\n msg = self.shortDescription()\n self.assertTrue(False, msg=msg)\n pass","def loadHeaderSamplesFile(self, line):\n try:\n line.decode('ascii')\n except UnicodeDecodeError as err:\n raise\n tokens = line.rstrip(\"\\n\").split(\"\\t\")\n if len(tokens) < 12:\n msg = \"header should have at least 12 tab-separated columns\"\n raise ValueError(msg)\n for idx,tok in enumerate(tokens):\n if tok in self.samplesCol2idx:\n self.samplesCol2idx[tok] = idx\n for samplesCol,idx in self.samplesCol2idx.items():\n if idx is None:\n msg = \"column '%s' not found in samples file\" % samplesCol\n raise ValueError(msg)","def _isLine(self):\n return (self.width == 0 and self.height > 1) or (self.height == 0 and self.width > 1)","def _isLine(self):\n return (self.width == 0 and self.height > 1) or (self.height == 0 and self.width > 1)","def validate_unique_header(self):\n valid = False\n unique_headers = set(self.headers)\n if len(unique_headers) == len(self.headers):\n valid = True\n else:\n seen_headers = set()\n duplicate_headers = set()\n for x in self.headers:\n if x in seen_headers or seen_headers.add(x):\n duplicate_headers.add(x)\n msg = f\"Duplicated header names are not allowed: {duplicate_headers}\"\n log_exception(Annotations.dev_logger, Annotations.user_logger, msg)\n self.store_validation_issue(\"error\", msg, \"format:cap:unique\")\n valid = False\n if any(\"Unnamed\" in s for s in list(unique_headers)):\n msg = \"Headers cannot contain empty values\"\n log_exception(Annotations.dev_logger, Annotations.user_logger, msg)\n self.store_validation_issue(\"error\", msg, \"format:cap:no-empty\")\n valid = False\n return valid","def _validate_heading(self, heading_parts: List[str]):\n # Validate heading row.\n assert len(heading_parts) >= 4\n assert \"oxygen\" in heading_parts\n assert \"pulse\" in heading_parts\n assert \"blood_pressure_systolic\" in heading_parts\n assert \"blood_pressure_diastolic\" in heading_parts","def header_len(fname,header_char='#'):\n Nheader = 0\n with open(fname) as f:\n for i, l in enumerate(f):\n if ( (l[0:len(header_char)]==header_char) or (l==\"\\n\") ):\n Nheader += 1\n else:\n break\n\n return Nheader","def check_horizontal(line):\n\tline = line.rstrip('\\n')\n\tif line.rstrip() != line:\n\t\traise StyleError(\"Line has trailing white-space\")\n\tif '\\t' in line.lstrip('\\t'):\n\t\traise StyleError(\"Tabs may only be used for indentation\")","def is_casava_v180_or_later(header_line):\r\n assert header_line.startswith('@'),\\\r\n \"Non-header line passed as input. Header must start with '@'.\"\r\n fields = header_line.split(':')\r\n if len(fields) == 10 and fields[7] in 'YN':\r\n return True\r\n return False","def is_headline(node):\n pass","def is_git_diff_header(diff_header):\n return any(l.startswith('diff --git') for l in diff_header.splitlines())","def parse_header(self):","def identifyTableEntry(line):\n matches = re.findall('<td>', line)\n if len(matches) > 0:\n return True","def _read_header(self, stream):\n return","def assertTableHeaders(self, output_lines, field_names):\n table = self.parser.table(output_lines)\n headers = table['headers']\n for field in field_names:\n self.assertIn(field, headers)","def test_fasta_get_headers(self):\r\n\r\n header_records = mfau.get_record_headers(full_file_name)\r\n\r\n if debug:\r\n for header_record in header_records:\r\n print header_record.strip()\r\n\r\n self.assertGreaterEqual(len(header_records), 0)","def is_sorted_queryname(header):\n\tif(\"HD\" in header):\n\t\tif(\"SO\" in header[\"HD\"]):\n\t\t\tif(header[\"HD\"][\"SO\"] == \"queryname\"):\n\t\t\t\treturn True\n\treturn False","def _is_comment_line(self):\n pattern = re.compile(r\"^(\\s)*(//)+\")\n return pattern.search(self._line)","def _check_headers(cursor, headers):\n all_columns = set(chain.from_iterable(_columns(cursor, table) for table in DATA_TABLES))\n for header in headers:\n if header not in all_columns:\n raise ValueError('column {} not recognized'.format(header))"],"string":"[\n \"def is_header(line):\\n return line[0] == '>'\",\n \"def check_header(line):\\n if line[:7] == '###### ':\\n line = '<h6>' + line[7:] + '</h6>'\\n elif line[:6] == '##### ':\\n line = '<h5>' + line[6:] + '</h5>'\\n elif line[:5] == '#### ':\\n line = '<h4>' + line[5:] + '</h4>'\\n elif line[:4] == '### ':\\n line = '<h3>' + line[4:] + '</h3>'\\n elif line[:3] == '## ':\\n line = '<h2>' + line[3:] + '</h2>'\\n elif line[:2] == '# ':\\n line = '<h1>' + line[2:] + '</h1>'\\n else:\\n return False, ''\\n\\n return True, line\",\n \"def is_header(fields):\\n if len(fields) < 11:\\n return None\\n # Test a column which should usually be a number in data lines and never a number in header lines.\\n try:\\n float(fields[8])\\n return False\\n except ValueError:\\n pass\\n first_field = fields[0]\\n # An explicitly commented line is a header.\\n if first_field.startswith('#'):\\n return True\\n # The first field in a header is usually these two (and never these in data lines).\\n if first_field.lower() == 'sample' or first_field.lower() == 'family':\\n return True\\n # Fallback 1: There should never be a number in a header line. If we find one, it's a data line.\\n for field in fields:\\n try:\\n float(field)\\n return False\\n except ValueError:\\n pass\\n # Fallback 2: Just test whether any of the known labels is in the line.\\n for label in LABELS:\\n if label in fields:\\n return True\\n for label in LABELS:\\n if label.lower() in fields:\\n return True\",\n \"def is_header(line, max_level):\\n if max_level:\\n return len(re.findall(\\\"^#{{1,{0}}} .+\\\".format(max_level), line)) > 0\\n else:\\n return len(re.findall(\\\"^#+ .+\\\", line)) > 0\",\n \"def test_is_fasta_header(self):\\r\\n\\r\\n is_fasta_header = False\\r\\n\\r\\n with open(full_file_name, \\\"r\\\") as in_file:\\r\\n for line in in_file:\\r\\n is_fasta_header = mfau.is_header_line(line)\\r\\n\\r\\n # only testing the first line\\r\\n break\\r\\n\\r\\n self.assertEqual(is_fasta_header, True)\",\n \"def has_header():\\n header_content = (\\\"\\\\n\\\".join(CURRENT_BUFFER[:7])).lower()\\n return sum(1 for keyword in KEYWORDS if header_content.find(keyword.lower()) != -1) >= 2\",\n \"def has_header_row(self, strdata):\\n debug = False\\n comma_dec_sep_ok = True\\n if debug: print(strdata)\\n if len(strdata) < 2: ## a header row needs a following row to be a header\\n return False\\n row1_types = [lib.get_val_type(val, comma_dec_sep_ok) \\n for val in strdata[0]]\\n row2_types = [lib.get_val_type(val, comma_dec_sep_ok) \\n for val in strdata[1]]\\n str_type = mg.VAL_STRING\\n empty_type = mg.VAL_EMPTY_STRING\\n non_str_types = [mg.VAL_DATE, mg.VAL_NUMERIC]\\n return importer.has_header_row(\\n row1_types, row2_types, str_type, empty_type, non_str_types)\",\n \"def check_header(self, entry):\\n if entry not in self.metadata:\\n raise SyntaxError(\\\"Header entry must be described in the metadata lines. Entry: %s is not in metadata.\\\" % entry)\",\n \"def has_headers(self):\\n for column in self.columns:\\n if column.header:\\n return True\\n return False\",\n \"def is_header(filename):\\n header_extensions = {'h'}\\n return os.path.splitext(filename)[1][1:] in header_extensions\",\n \"def is_header_part(cell: str) -> bool:\\n pattern = '|'.join([\\n rf'(?:(?:three|3|six|6|nine|9|twelve|12)\\\\s+months?(?:\\\\s+periods?)?|quarters?|year|ytd)(?!ly)',\\n rf'\\\\b(?:{MONTH})\\\\b',\\n rf'^(?:end(?:ed|ing))?(?:20)\\\\s*[0-2]\\\\s*[0-9]{FOOTNOTE}$',\\n rf'^\\\\d{1, 2}/\\\\d{1, 2}/\\\\d{2, 4}{FOOTNOTE}$',\\n rf'^q[1-4](?:\\\\s*\\\\(\\\\w+\\\\))?{FOOTNOTE}$',\\n rf'^[1-4]q(?:tr)?(?:\\\\d{2, 4})?',\\n rf'as\\\\s+(?:reported|adjusted)',\\n rf'year-?\\\\s*to-?\\\\s*date',\\n rf'^year-$',\\n rf'^to-date$',\\n rf'full\\\\s+year',\\n rf'^(?:28|29|30|31){FOOTNOTE}$',\\n rf'^(?:month|quarter|year)s?{FOOTNOTE}$',\\n rf'^(?:three|six|nine|twelve){FOOTNOTE}$',\\n rf'^(?:operating|reported|baseline|percent|%|end(?:ed|ing)){FOOTNOTE}$',\\n ORDINAL,\\n rf'^(?:(?:20)\\\\s*[0-2]\\\\s*[0-9]\\\\*\\\\s*)?{UNAUDITED_EXACT}$'\\n ])\\n prepped = str(cell).lower().strip()\\n match = re.search(allow_space_between_letters(pattern), prepped)\\n return match is not None or parse_fiscal_period(cell) is not None\",\n \"def is_header(file_name):\\n return str(file_name).endswith('.h')\",\n \"def split_head(line, is_head=lambda line: line.startswith('>')):\\n if is_head(line):\\n return True\\n else:\\n return False\",\n \"def has_header_row(filepath, skiprows=[0]):\\n if skiprows:\\n skiprows=max(skiprows)\\n else:\\n skiprows = 0\\n with open(filepath) as f:\\n reader = csv.reader(f)\\n\\n data = list(reader)[skiprows:skiprows+10] # don't look beyond line 10\\n \\n def isnumber(item):\\n try:\\n float(item)\\n return True\\n except:\\n return False\\n \\n first_line = data[0]\\n second_line = data[1]\\n \\n first_line = [isnumber(item) for item in first_line]\\n second_line = [isnumber(item) for item in second_line]\\n \\n\\n if not first_line == second_line:\\n return 0\\n else:\\n return None\",\n \"def has_supported_header_hormat(cls, csv_reader):\\n return csv_reader.fieldnames == cls.INGFormatHeader\",\n \"def check_csv_headers(csvfile, headers):\\n\\n with open(csvfile, 'rb') as f:\\n csv_header = f.readline()\\n\\n # Check the lower ones\\n if headers[1][0] not in csv_header.lower():\\n return False\\n\\n return True\",\n \"def _row_or_col_is_header(s_count, v_count):\\n if s_count == 1 and v_count == 1:\\n return False\\n else:\\n return (s_count + 1) / (v_count + s_count + 1) >= 2. / 3.\",\n \"def check_header(self, name, value):\\r\\n if value in self.headers.get(name, ''):\\r\\n return True\\r\\n return False\",\n \"def check_header(self, name, value):\\r\\n if value in self.headers.get(name, ''):\\r\\n return True\\r\\n return False\",\n \"def isDataLine(line):\\n if len(line) > 1:\\n return line[0] != \\\"#\\\"\\n return False\",\n \"def isDataLine(line):\\n if len(line) > 1:\\n return line[0] != \\\"#\\\"\\n return False\",\n \"def _header_transformer(self, lines):\\n needle = b'--%s\\\\n' % self.boundary\\n in_header = False\\n for line in lines:\\n if line == needle:\\n in_header = True\\n if in_header:\\n assert line[-1] == b'\\\\n'\\n line = line[:-1] + b'\\\\r\\\\n'\\n if line == b'\\\\r\\\\n':\\n in_header = False\\n yield line\",\n \"def verifyHeader(self, headers):\\n for head in headers:\\n if(hex(head[0]) == VID and hex(head[1]) == PID):\\n return True\\n return False\",\n \"def _validate_header(self, header_row):\\n\\n self.logger.info(\\\"Validating header row.\\\")\\n \\n # assume value.\\n is_valid = True\\n\\n # check if @header_row is perfect.\\n required_keys = tuple(self.required_headers.keys())\\n if sorted(header_row) == sorted(required_keys):\\n self.logger.info(\\\"Header is valid.\\\")\\n return is_valid\\n else:\\n self.logger.error(\\\"Header is invalid.\\\")\\n is_valid = False\\n\\n # report on any missing header fields.\\n missing_headers = [header for header in self.required_headers if header not in\\n header_row]\\n if len(missing_headers) != 0:\\n self.logger.warning(\\\"Missing required fields: {}\\\".format(missing_headers))\\n \\n # report on any duplicate fields.\\n duplicate_headers = [header for header in header_row if header_row.count(header) != 1]\\n if len(duplicate_headers) != 0:\\n self.logger.warning(\\\"Found duplicate fields: {}\\\".format(set(duplicate_headers)))\\n\\n # report on any extra fields.\\n extra_headers = [header for header in header_row if header not in \\n self.required_headers]\\n if len(extra_headers) != 0:\\n self.logger.warning(\\\"Found extra fields: {}\\\".format(extra_headers))\\n \\n return is_valid\",\n \"def check_headerRow(self, expected, found):\\n\\n # spreadsheets must have either a barcode field or a object ID field, but both are not required\\n header1 = 'barcode'\\n header2 = ('object identifier\\\\n(edit heading to specify type' +\\n ' - e.g. barcode)')\\n expected = self.remove_annoying(header1, header2, expected, found)\\n\\n missing = []\\n\\n for header in expected:\\n if header not in found:\\n missing.append(header)\\n\\n if missing:\\n self.raise_excelerror(\\\"Missing required value- {0}.\\\"\\n .format(missing))\\n\\n return True\",\n \"def test_line(line):\\r\\n if not line.strip():\\r\\n return False # if the last line is blank\\r\\n if line.startswith(\\\"#\\\"):\\r\\n return False # comment line\\r\\n if line.startswith(\\\" #\\\"):\\r\\n return False # comment line\\r\\n return line\",\n \"def test_first_line_amiramesh(self):\\n self.assertEqual(self.header.designation.filetype, 'AmiraMesh')\",\n \"def validate_header_keyword(self):\\n\\n valid = False\\n if self.headers[0].upper() == \\\"NAME\\\":\\n valid = True\\n if self.headers[0] != \\\"NAME\\\":\\n msg = f'File keyword \\\"NAME\\\" provided as {self.headers[0]}'\\n self.store_validation_issue(\\\"warn\\\", msg, \\\"format:cap:name\\\")\\n else:\\n msg = \\\"Malformed file header row, missing NAME keyword. (Case Sensitive)\\\"\\n self.store_validation_issue(\\\"error\\\", msg, \\\"format:cap:name\\\")\\n return valid\",\n \"def is_line(self):\\n return True\",\n \"def is_line(self):\\n return True\",\n \"def check_valid_csv_header(self, row):\\n obj = re.match(re.compile('^Year\\\\,Month\\\\,.'),\\n ','.join(row))\\n if not obj:\\n raise Exception(\\\"Invalid Headers must be `Year` `Month` Check Sample file\\\")\",\n \"def check_header(filename: str, *, is_newly_created: bool = False) -> None:\\n lines = get_header_lines(filename)\\n check_header_present(filename, lines)\\n is_py3_file = all(\\\"from __future__\\\" not in line for line in lines)\\n if is_py3_file:\\n lines = lines[:EXPECTED_NUM_PY3_LINES]\\n copyright_line_index = 0\\n expected_header = EXPECTED_HEADER_PY3\\n else:\\n copyright_line_index = 1\\n expected_header = EXPECTED_HEADER_PY2\\n check_copyright_year(\\n filename, copyright_line=lines[copyright_line_index], is_newly_created=is_newly_created\\n )\\n check_matches_header(\\n filename, lines, expected_header=expected_header, copyright_line_index=copyright_line_index\\n )\",\n \"def isccp4iheader(self, text):\\n #\\n # CCP4i header lines look like:\\n # #CCP4I VERSION CCP4Interface 1.4.1\\n # #CCP4I SCRIPT LOG refmac5\\n # #CCP4I DATE 25 Oct 2005 15:19:22\\n #\\n if self.isccp4itail(text):\\n # Reject tail elements\\n return \\\"\\\"\\n header = self.compile(\\\"isccp4iheader\\\", r\\\"#CCP4I (.*)\\\").search(text)\\n result = \\\"\\\"\\n if header:\\n result = header.group(0)\\n return result\",\n \"def is_header_content(response, key, value):\\n try:\\n if response.headers[key].lower() == value:\\n return True\\n else:\\n return False\\n except:\\n return False\",\n \"def is_line(self):\\n return False\",\n \"def test_header_row(self):\\n header_row = self.view_class().header_row\\n if not header_row:\\n return\\n\\n response = self.view_class().get()\\n # Some formatting needs to be done so that the header row\\n # is compliant with the CSV dialect - all fields need\\n # to be quoted.\\n quoted_header_row = '\\\"{}\\\"'.format('\\\",\\\"'.join(header_row))\\n self.assertContains(response, quoted_header_row)\",\n \"def check_for_header(filename):\\n header = {}\\n start_id = -1\\n with open(filename, \\\"r\\\") as f:\\n start = re.compile(r\\\"\\\\bSTART|start\\\\b\\\")\\n # if the file has the keyword start, extract header\\n if bool(start.search(f.read())):\\n f.seek(0) # set the cursor back to the beginning\\n lines = f.readlines()\\n for i, line in enumerate(lines):\\n if start.match(line):\\n start_id = i # the line number where start is used (divides header and body)\\n break\\n args = line.split()\\n args.insert(0, \\\"\\\") # check_for_commands only handles the second argument (first is usually res_id)\\n header['DEFAULT'] = check_for_commands(args, 1, 2)\\n\\n return header, start_id\",\n \"def is_horizontal(line:tuple)->bool:\\n return line[0][1] == line[1][1]\",\n \"def is_line(self): \\n return False\",\n \"def is_header(file):\\n return not file.is_dir() and not file.name == 'module.modulemap.in' and file.name != 'libcxx.imp'\",\n \"def is_chunk_header(buf, offset):\\n if len(buf) < offset + 0x2C:\\n # our accesses below will overflow\\n return False\\n\\n magic = struct.unpack_from(\\\"<7s\\\", buf, offset)[0]\\n if magic != EVTX_HEADER_MAGIC:\\n return False\\n\\n size = struct.unpack_from(\\\"<I\\\", buf, offset + 0x28)[0]\\n if not (MIN_CHUNK_HEADER_SIZE <= size <= MAX_CHUNK_HEADER_SIZE):\\n return False\\n\\n if len(buf) <= offset + size:\\n # the chunk overruns the buffer end\\n return False\\n\\n try:\\n chunk = Evtx.Evtx.ChunkHeader(buf, offset)\\n except:\\n logger.debug('failed to parse chunk header', exc_info=True)\\n return False\\n\\n if len(buf) < offset + CHUNK_SIZE:\\n return False\\n\\n if chunk.calculate_header_checksum() != chunk.header_checksum():\\n return False\\n\\n if chunk.calculate_data_checksum() != chunk.data_checksum():\\n return False\\n\\n return True\",\n \"def test_header(self):\\n frame_with_header = self.context.frame.import_csv(\\n self.dataset, schema=self.schema, header=True)\\n frame_without_header = self.context.frame.import_csv(self.dataset,\\n schema=self.schema, header=False)\\n\\n # the frame with the header should have one less row\\n # because it should have skipped the first line\\n self.assertEqual(len(frame_with_header.take(frame_with_header.count()).data),\\n len(frame_without_header.take(frame_without_header.count()).data) - 1)\\n # comparing the content of the frame with header and without\\n # they should have the same rows with the only differnce being the\\n # frame with the header should not have the first row\\n for index in xrange(0, frame_with_header.count()):\\n self.assertEqual(str(frame_with_header.take(frame_with_header.count()).data[index]),\\n str(frame_without_header.take(frame_without_header.count()).data[index + 1]))\",\n \"def is_header_or_footer(self, tag):\\n return ((tag.name == \\\"text\\\") and tag.has_attr(\\\"top\\\")\\n and ((int(tag[\\\"top\\\"]) <= self.HEADER_END_OFFSET) \\n or (int(tag[\\\"top\\\"]) > self.FOOTER_START_OFFSET)))\",\n \"def isHereDoc(self, lineData, column):\\n return self._getTextType(lineData, column) == 'h'\",\n \"def assert_header(self):\\r\\n\\r\\n if self.length > self.owner.settings[SETTINGS_MAX_FRAME_SIZE]:\\r\\n raise netius.ParserError(\\r\\n \\\"Headers are greater than SETTINGS_MAX_FRAME_SIZE\\\",\\r\\n stream = self.stream,\\r\\n error_code = FRAME_SIZE_ERROR\\r\\n )\\r\\n if self.last_type in (HEADERS, CONTINUATION) and not\\\\\\r\\n self.last_end_headers and not self.last_stream == self.stream:\\r\\n raise netius.ParserError(\\r\\n \\\"Cannot send frame from a different stream in middle of headers\\\",\\r\\n error_code = PROTOCOL_ERROR\\r\\n )\",\n \"def test_check_header(self):\\r\\n\\r\\n # Default header, should not generate any errors/warnings\\r\\n header = ['SampleID', 'BarcodeSequence', 'LinkerPrimerSequence',\\r\\n 'Description']\\r\\n errors = []\\r\\n warnings = []\\r\\n\\r\\n errors, warnings = check_header(header,\\r\\n errors,\\r\\n warnings,\\r\\n sample_id_ix=0,\\r\\n desc_ix=3,\\r\\n bc_ix=1,\\r\\n linker_primer_ix=2,\\r\\n added_demultiplex_field=None)\\r\\n\\r\\n expected_errors = []\\r\\n expected_warnings = []\\r\\n\\r\\n self.assertEqual(errors, expected_errors)\\r\\n self.assertEqual(warnings, expected_warnings)\",\n \"def process_header(line, new_ids=None):\\n # extraneous headers\\n if line.startswith('##') and not any(\\n line.startswith('##' + header_type)\\n for header_type in ('INFO', 'FILTER', 'FORMAT', 'ALT', 'contig')\\n ):\\n return None\\n\\n # non-whitelisted annotations\\n match = re.match(r'##INFO=<ID=([^,]+)', line)\\n if match:\\n info_name = match.group(1)\\n if info_name not in WHITELISTED_ANNOTATIONS:\\n return None\\n\\n if line.startswith('#CHROM') and new_ids is not None:\\n fields = line.strip().split('\\\\t')[:9] # fixed headers\\n fields.extend(new_ids)\\n line = '\\\\t'.join(fields) + '\\\\n'\\n\\n return line\",\n \"def _parse_header(self, line):\\n if self._regex_helper.search_compiled(W._re_header, line):\\n if not self.headers:\\n for value in re.findall(W._re_header, line):\\n self.headers.append(value[0])\\n raise ParsingDone\\n else:\\n # Dictionary which is going to be appended to the returned list\\n ret = dict()\\n # List of entries\\n _entries = list()\\n # List of values in WHAT entry\\n _what_entry = list()\\n for value in re.findall(W._re_header, line):\\n _entries.append(value[0])\\n for what_index in range(len(self.headers) - 1, len(_entries)):\\n _what_entry.append(_entries[what_index])\\n _what_entry_string = ' '.join(_what_entry)\\n for index in range(len(self.headers)):\\n if index < len(self.headers) - 1:\\n ret.update({self.headers[index]: _entries[index]})\\n else:\\n ret.update({self.headers[index]: _what_entry_string})\\n self.current_ret['RESULT'].append(ret)\\n raise ParsingDone\",\n \"def process_headers(fin, fout, fixed_headers):\\n filtered_headers = set(item[0] for item in fixed_headers)\\n filtered_headers.add(\\\"SAMPLE\\\")\\n expected_values = {\\n name: value for name, asserted, value in fixed_headers if asserted\\n }\\n errors = False\\n for raw_line in fin:\\n if raw_line.startswith('##'):\\n # TODO: This will break if the metadata header is bad.\\n name, value = raw_line[2:].rstrip().split('=', 1)\\n if name in filtered_headers:\\n if name in expected_values:\\n if value != expected_values[name]:\\n errors = True\\n # TODO: propper logging\\n sys.stderr.write(\\n 'tcga-vcf-reheader: mismatch {}={}\\\\n'.format(\\n name, value\\n )\\n )\\n else: # Just some other header...\\n fout.write(raw_line)\\n else:\\n break\\n fout.write(raw_line) # raw_line should now be the data header line.\\n return errors\",\n \"def check_headerEntries(self, expected, found):\\n\\n # spreadsheets must have either a barcode field or a object ID field, but both are not required\\n header1 = ('original master', 'object', 'barcode')\\n header2 = ('original master', 'object',\\n 'object identifier\\\\n(edit heading to specify type ' +\\n '- e.g. barcode)')\\n expected = self.remove_annoying(header1, header2, expected, found)\\n\\n bad_entries = []\\n\\n for header in expected:\\n if header not in found:\\n bad_entries.append(header)\\n\\n if bad_entries:\\n self.raise_excelerror(\\\"Incorrect header entry for {0}.\\\"\\n .format(bad_entries))\\n return True\",\n \"def _check_duplicate_headers(self, docstring: PetscDocStringImpl) -> None:\\n for heading, where in self.seen_headers.items():\\n if len(where) <= 1:\\n continue\\n\\n lasti = len(where) - 1\\n src_list = []\\n nbefore = 2\\n nafter = 0\\n prev_line_begin = 0\\n for i, loc in enumerate(where):\\n startline = loc.start.line\\n if i:\\n nbefore = startline - prev_line_begin - 1\\n if i == lasti:\\n nafter = 2\\n src_list.append(loc.formatted(num_before_context=nbefore, num_after_context=nafter, trim=False))\\n prev_line_begin = startline\\n mess = \\\"Multiple '{}' subheadings. Much like Highlanders, there can only be one:\\\\n{}\\\".format(\\n self.transform(self.name), '\\\\n'.join(src_list)\\n )\\n docstring.add_diagnostic(\\n Diagnostic(Diagnostic.Kind.ERROR, self.diags.section_header_unique, mess, self.extent.start)\\n )\\n return\",\n \"def __parseHeader(self):\\n line = self.__nextLine()\\n while line.startswith(\\\"c\\\"):\\n self.__log(\\\"adding comment %s\\\" % line)\\n self.__commentList.append(line[2:])\\n line = self.__nextLine()\\n parts = line.split()\\n if len(parts) == 4 and parts[0] == \\\"p\\\" and parts[1] == \\\"cnf\\\":\\n self.__nrVar = int(parts[2])\\n self.__nrClause = int(parts[3])\\n assert(self.__nrVar > 0 and self.__nrClause > 0)\\n return True\\n else:\\n return False\",\n \"def isfile(line):\\n return line and (line.strip()[:3] == \\\"FHS\\\" or isbatch(line))\",\n \"def header(line: str, type_=1):\\n if type_ == 1:\\n \\\"\\\"\\\"\\n =========================\\n This is an example header\\n =========================\\n \\\"\\\"\\\"\\n eq = \\\"\\\".join(['=' for _ in range(len(line))])\\n para = f'{eq}\\\\n{line}\\\\n{eq}'\\n elif type_ == 2:\\n \\\"\\\"\\\"\\n -------------------------\\n This is an example header\\n -------------------------\\n \\\"\\\"\\\"\\n eq = \\\"\\\".join(['-' for _ in range(len(line))])\\n para = f'{eq}\\\\n{line}\\\\n{eq}'\\n else:\\n raise ValueError('Invalid type_')\\n\\n print(para)\",\n \"def check_header(header,\\r\\n errors,\\r\\n warnings,\\r\\n sample_id_ix,\\r\\n desc_ix,\\r\\n bc_ix,\\r\\n linker_primer_ix,\\r\\n added_demultiplex_field=None):\\r\\n\\r\\n # Check for duplicates, append to errors if found\\r\\n errors = check_header_dups(header, errors)\\r\\n\\r\\n # Check for valid characters\\r\\n warnings = check_header_chars(header, warnings)\\r\\n\\r\\n # Check for required header fields\\r\\n errors = check_header_required_fields(header, errors, sample_id_ix,\\r\\n desc_ix, bc_ix, linker_primer_ix, added_demultiplex_field)\\r\\n\\r\\n return errors, warnings\",\n \"def check_horizontal_rule(line):\\n if line.count('-') >= 3 and contains_only_char(line, '-'):\\n return True, '<hr></hr>'\\n if line.count('*') >= 3 and contains_only_char(line, '*'):\\n return True, '<hr></hr>'\\n if line.count('_') >= 3 and contains_only_char(line, '_'):\\n return True, '<hr></hr>'\\n return False, ''\",\n \"def test_csv_reader_header_fields(process_data):\\n data = process_data(file_name_or_type='clean_map.csv')\\n header_fields = list(data[0].keys())\\n assert header_fields == [\\n 'Country',\\n 'City',\\n 'State_Or_Province',\\n 'Lat',\\n 'Long',\\n 'Altitude'\\n ]\",\n \"def is_valid_header(headers: Dict[str, Any]) -> bool:\\n for name, value in headers.items():\\n if not utils.is_latin_1_encodable(value):\\n return False\\n if utils.has_invalid_characters(name, value):\\n return False\\n return True\",\n \"def _is_start(self, line):\\n if re.match(\\\".*\\\\:\\\\s*\\\\(groupid\\\", line):\\n return True\",\n \"def header_statement(stmt, accel):\\n if not isinstance(stmt, CommentNode): None\\n if not stmt.value.startswith('#$'): None\\n\\n header = stmt.value[2:].lstrip()\\n if not directive.startswith('header'): None\\n\\n return stmt.value\",\n \"def issummary_begin(self, line):\\n #\\n # Summary start lines look like:\\n # <B><FONT COLOR=\\\"#FF0000\\\"><!--SUMMARY_BEGIN-->\\n #\\n # Set up regular expression for SUMMARY_BEGIN lines\\n summary = self.compile(\\\"issummary_begin\\\", r\\\"<\\\\!--SUMMARY_BEGIN-->\\\").search(line)\\n if summary:\\n return True\\n return False\",\n \"def has_bad_headers(self):\\n\\n headers = [self.sender, self.reply_to] + self.recipients\\n for header in headers:\\n if _has_newline(header):\\n return True\\n\\n if self.subject:\\n if _has_newline(self.subject):\\n for linenum, line in enumerate(self.subject.split('\\\\r\\\\n')):\\n if not line:\\n return True\\n if linenum > 0 and line[0] not in '\\\\t ':\\n return True\\n if _has_newline(line):\\n return True\\n if len(line.strip()) == 0:\\n return True\\n return False\",\n \"def header_len(self):\\n if self.num_lines_header is None:\\n Nheader = 0\\n with self._compression_safe_file_opener(self.input_fname, \\\"r\\\") as f:\\n for i, l in enumerate(f):\\n if (l[0 : len(self.header_char)] == self.header_char) or (\\n l == \\\"\\\\n\\\"\\n ):\\n Nheader += 1\\n else:\\n break\\n\\n return Nheader\\n else:\\n return self.num_lines_header\",\n \"def has_line( self, description ):\\n return find_line( self.get_lines(), description ) != -1\",\n \"def has_line( self, description ):\\n return find_line( self.get_lines(), description ) != -1\",\n \"def test_check_header_missing_fields(self):\\r\\n\\r\\n # Default header, should not generate any errors/warnings\\r\\n header = ['AAA', 'XXX', 'YYY',\\r\\n 'ZZZ']\\r\\n errors = []\\r\\n warnings = []\\r\\n\\r\\n errors, warnings = check_header(header,\\r\\n errors,\\r\\n warnings,\\r\\n sample_id_ix=0,\\r\\n desc_ix=3,\\r\\n bc_ix=1,\\r\\n linker_primer_ix=2,\\r\\n added_demultiplex_field=None)\\r\\n\\r\\n expected_errors = [\\r\\n 'Found header field AAA, expected field SampleID\\\\t0,0',\\r\\n 'Found header field XXX, expected field BarcodeSequence\\\\t0,1',\\r\\n 'Found header field YYY, expected field LinkerPrimerSequence\\\\t0,2',\\r\\n 'Found header field ZZZ, last field should be Description\\\\t0,3']\\r\\n expected_warnings = []\\r\\n\\r\\n self.assertEqual(errors, expected_errors)\\r\\n self.assertEqual(warnings, expected_warnings)\",\n \"def isTable(line):\\n # Make the string easier to parse.\\n line_stripped = lineNormalise(line)\\n\\n # Return value.\\n ret = False\\n\\n # If the line starts with the word table, we have a table definition!\\n if line_stripped.startswith('table'):\\n ret = True\\n\\n # Tell the horrible truth that this code could not find a table.\\n return ret\",\n \"def parse_header(self, out):\\n self.headers = {}\\n for h in out.split(\\\"\\\\r\\\\n\\\\r\\\\n\\\", 1)[0].split(\\\"\\\\r\\\\n\\\"):\\n x = h.split(\\\":\\\")\\n self.headers[x[0]] = \\\":\\\".join(x[1:]).lstrip()\\n return True\",\n \"def check_headers(self, headers):\\n h = headers.values()[0]\\n\\n if 'DT' in PAR:\\n if h.dt != PAR.DT:\\n h.dt = PAR.DT\\n\\n if 'NT' in PAR:\\n if h.nt != PAR.NT:\\n print 'Warning: h.nt != PAR.NT'\\n\\n if 'NREC' in PAR:\\n if h.nr != PAR.NREC:\\n print 'Warning: h.nr != PAR.NREC'\\n\\n return h\",\n \"def hasNextLine(self) -> bool:\\n raise NotImplementedError\",\n \"def test_partial_header(self):\\n stream_handle = open(os.path.join(RESOURCE_PATH,\\n 'part_header_SNA_SNA.txt'), MODE_ASCII_READ)\\n\\n self.create_parser(stream_handle)\\n\\n # get A-E, also metadata\\n particles = self.parser.get_records(6)\\n\\n # check all the values against expected results.\\n self.assert_particles(particles, 'short_SNA_telem_part.yml', RESOURCE_PATH)\\n\\n # confirm no exceptions occurred\\n self.assertEqual(self.exception_callback_value, [])\\n stream_handle.close()\",\n \"def is_section(line: str) -> bool:\\n return len(line) > 0 and (line[0] == '[' and line[len(line) - 1] == ']')\",\n \"def validate_against_header_count(self):\\n valid = False\\n len_headers = len(\\n [header for header in self.headers if \\\"Unnamed\\\" not in header]\\n )\\n len_annot_type = len(\\n [\\n annot_type\\n for annot_type in self.annot_types\\n if \\\"Unnamed\\\" not in annot_type\\n ]\\n )\\n if not len_headers == len_annot_type:\\n msg = (\\n f\\\"Header mismatch: {len_annot_type} TYPE declarations \\\"\\n f\\\"for {len_headers} column headers\\\"\\n )\\n self.store_validation_issue(\\\"error\\\", msg, \\\"format:cap:count\\\")\\n else:\\n valid = True\\n return valid\",\n \"def is_same_header(self, block_header):\\n if len(self.variable_strings) > 0:\\n if len(self.variable_strings) != len(\\n block_header.variable_strings\\n ):\\n return False\\n else:\\n for sitem, oitem in zip(\\n self.variable_strings, block_header.variable_strings\\n ):\\n if sitem != oitem:\\n return False\\n return True\\n elif (\\n len(self.data_items) > 0 and len(block_header.variable_strings) > 0\\n ):\\n typ_obj = (\\n self.data_items[0].structure.data_item_structures[0].type_obj\\n )\\n if typ_obj == int or typ_obj == float:\\n return bool(\\n self.variable_strings[0]\\n == block_header.variable_strings[0]\\n )\\n else:\\n return True\\n elif len(self.data_items) == len(block_header.variable_strings):\\n return True\\n return False\",\n \"def validate_header(self, reply):\\n # check message is from my agg to me\\n check_equal(reply.header.sender, self.aggregator_uuid, self.logger)\\n check_equal(reply.header.recipient, self.common_name, self.logger)\\n\\n # check that the federation id matches\\n check_equal(reply.header.federation_id, self.federation_uuid, self.logger)\\n\\n # check that we agree on single_col_cert_common_name\\n check_equal(reply.header.single_col_cert_common_name, self.single_col_cert_common_name, self.logger)\",\n \"def _read_header(self, valid_sections={}):\\n linenum = self._linenum\\n\\n # It's possible that we'll be at the end of the file, with some blank\\n # lines, or hand-editing (or bad diff generation) has led to some\\n # blank lines before a header. We'll iterate through any blank lines\\n # until we reach content or an End of File.\\n while True:\\n header, eof = self._read_until(b'\\\\n')\\n\\n if eof:\\n return None\\n\\n if header.strip():\\n break\\n\\n if self._file_newlines is None:\\n # Given that we read up until a '\\\\n', one of these are guaranteed\\n # to match.\\n if header.endswith(b'\\\\r\\\\n'):\\n self._file_newlines = b'\\\\r\\\\n'\\n else:\\n assert header.endswith(b'\\\\n')\\n\\n self._file_newlines = b'\\\\n'\\n\\n assert header.endswith(self._file_newlines)\\n header = header[:-len(self._file_newlines)]\\n\\n m = self._HEADER_RE.match(header)\\n\\n if not m:\\n raise DiffXParseError(\\n 'Unexpected or improperly formatted header: %r' % header,\\n linenum=linenum)\\n\\n # Validate the level and section ID.\\n section_id = m.group('section_id').decode('ascii')\\n\\n if section_id not in valid_sections:\\n raise DiffXParseError(\\n 'Unknown or unexpected section ID \\\"%(section_id)s\\\". '\\n 'Expected one of: %(valid_sections)s'\\n % {\\n 'section_id': section_id,\\n 'valid_sections': ', '.join(\\n '\\\"%s\\\"' % _valid_section\\n for _valid_section in sorted(valid_sections)\\n ),\\n },\\n linenum=linenum)\\n\\n section_type = m.group('section_type')\\n level = len(m.group('level'))\\n\\n # Parse the options out of the header.\\n options_str = m.group('options')\\n options = {}\\n\\n if options_str:\\n # Options should be present.\\n #\\n # As this is a reference implementation, this will be strict with\\n # the format. There should be exactly one space between the\\n # \\\"#<id>:\\\" and the options, one space between each comma-separated\\n # pair, and each key and value are expected to match a specific set\\n # of characters.\\n for option_pair in options_str.split(b', '):\\n option_key, option_value = option_pair.split(b'=', 1)\\n\\n if not self._HEADER_OPTION_KEY_RE.match(option_key):\\n raise DiffXParseError(\\n 'Header option key \\\"%s\\\" contains invalid characters'\\n % option_key.decode('ascii'),\\n linenum=linenum,\\n column=header.index(option_pair))\\n\\n if not self._HEADER_OPTION_VALUE_RE.match(option_value):\\n raise DiffXParseError(\\n 'Header option value \\\"%(value)s\\\" for key \\\"%(key)s\\\" '\\n 'contains invalid characters'\\n % {\\n 'key': option_key.decode('ascii'),\\n 'value': option_value.decode('ascii'),\\n },\\n linenum=linenum,\\n column=header.index(option_pair) + len(option_key) + 1)\\n\\n # These should safely decode, since we've validated the\\n # characters above.\\n option_key = option_key.decode('ascii')\\n option_value = option_value.decode('ascii')\\n\\n # Convert the value to an integer, if it's a number.\\n try:\\n option_value = int(option_value)\\n except ValueError:\\n pass\\n\\n options[option_key] = option_value\\n\\n self._linenum += 1\\n\\n return {\\n 'level': level,\\n 'line': linenum,\\n 'options': options,\\n 'section': section_id,\\n 'type': section_type.decode('ascii'),\\n }\",\n \"def beginning_of_line():\\n app = get_app()\\n before_cursor = app.current_buffer.document.current_line_before_cursor\\n\\n return bool(\\n len(before_cursor) == 0 and not app.current_buffer.document.on_first_line\\n )\",\n \"def check_line(self):\\n if not self.hosts and not self.line:\\n self.msg(\\\"There is no line here. You can create one with +line/createline.\\\")\\n return\\n return True\",\n \"def line_exists():\\n global _current_line\\n return _current_line is not None\",\n \"def _parse_header(self):\\n # read the first bytes from the file\\n header = self._stream_handle.read(HEADER_BYTES)\\n match = HEADER_MATCHER.match(header)\\n if not match:\\n raise SampleException(\\\"File header does not match the header regex\\\")\\n\\n # update the state to show we have read the header\\n self._increment_state(HEADER_BYTES)\",\n \"def _is_blank_line(self):\\n pattern = re.compile(r\\\"^(\\\\s)*$\\\")\\n return pattern.search(self._line)\",\n \"def test_headers(self):\\n msg = self.shortDescription()\\n self.assertTrue(False, msg=msg)\\n pass\",\n \"def loadHeaderSamplesFile(self, line):\\n try:\\n line.decode('ascii')\\n except UnicodeDecodeError as err:\\n raise\\n tokens = line.rstrip(\\\"\\\\n\\\").split(\\\"\\\\t\\\")\\n if len(tokens) < 12:\\n msg = \\\"header should have at least 12 tab-separated columns\\\"\\n raise ValueError(msg)\\n for idx,tok in enumerate(tokens):\\n if tok in self.samplesCol2idx:\\n self.samplesCol2idx[tok] = idx\\n for samplesCol,idx in self.samplesCol2idx.items():\\n if idx is None:\\n msg = \\\"column '%s' not found in samples file\\\" % samplesCol\\n raise ValueError(msg)\",\n \"def _isLine(self):\\n return (self.width == 0 and self.height > 1) or (self.height == 0 and self.width > 1)\",\n \"def _isLine(self):\\n return (self.width == 0 and self.height > 1) or (self.height == 0 and self.width > 1)\",\n \"def validate_unique_header(self):\\n valid = False\\n unique_headers = set(self.headers)\\n if len(unique_headers) == len(self.headers):\\n valid = True\\n else:\\n seen_headers = set()\\n duplicate_headers = set()\\n for x in self.headers:\\n if x in seen_headers or seen_headers.add(x):\\n duplicate_headers.add(x)\\n msg = f\\\"Duplicated header names are not allowed: {duplicate_headers}\\\"\\n log_exception(Annotations.dev_logger, Annotations.user_logger, msg)\\n self.store_validation_issue(\\\"error\\\", msg, \\\"format:cap:unique\\\")\\n valid = False\\n if any(\\\"Unnamed\\\" in s for s in list(unique_headers)):\\n msg = \\\"Headers cannot contain empty values\\\"\\n log_exception(Annotations.dev_logger, Annotations.user_logger, msg)\\n self.store_validation_issue(\\\"error\\\", msg, \\\"format:cap:no-empty\\\")\\n valid = False\\n return valid\",\n \"def _validate_heading(self, heading_parts: List[str]):\\n # Validate heading row.\\n assert len(heading_parts) >= 4\\n assert \\\"oxygen\\\" in heading_parts\\n assert \\\"pulse\\\" in heading_parts\\n assert \\\"blood_pressure_systolic\\\" in heading_parts\\n assert \\\"blood_pressure_diastolic\\\" in heading_parts\",\n \"def header_len(fname,header_char='#'):\\n Nheader = 0\\n with open(fname) as f:\\n for i, l in enumerate(f):\\n if ( (l[0:len(header_char)]==header_char) or (l==\\\"\\\\n\\\") ):\\n Nheader += 1\\n else:\\n break\\n\\n return Nheader\",\n \"def check_horizontal(line):\\n\\tline = line.rstrip('\\\\n')\\n\\tif line.rstrip() != line:\\n\\t\\traise StyleError(\\\"Line has trailing white-space\\\")\\n\\tif '\\\\t' in line.lstrip('\\\\t'):\\n\\t\\traise StyleError(\\\"Tabs may only be used for indentation\\\")\",\n \"def is_casava_v180_or_later(header_line):\\r\\n assert header_line.startswith('@'),\\\\\\r\\n \\\"Non-header line passed as input. Header must start with '@'.\\\"\\r\\n fields = header_line.split(':')\\r\\n if len(fields) == 10 and fields[7] in 'YN':\\r\\n return True\\r\\n return False\",\n \"def is_headline(node):\\n pass\",\n \"def is_git_diff_header(diff_header):\\n return any(l.startswith('diff --git') for l in diff_header.splitlines())\",\n \"def parse_header(self):\",\n \"def identifyTableEntry(line):\\n matches = re.findall('<td>', line)\\n if len(matches) > 0:\\n return True\",\n \"def _read_header(self, stream):\\n return\",\n \"def assertTableHeaders(self, output_lines, field_names):\\n table = self.parser.table(output_lines)\\n headers = table['headers']\\n for field in field_names:\\n self.assertIn(field, headers)\",\n \"def test_fasta_get_headers(self):\\r\\n\\r\\n header_records = mfau.get_record_headers(full_file_name)\\r\\n\\r\\n if debug:\\r\\n for header_record in header_records:\\r\\n print header_record.strip()\\r\\n\\r\\n self.assertGreaterEqual(len(header_records), 0)\",\n \"def is_sorted_queryname(header):\\n\\tif(\\\"HD\\\" in header):\\n\\t\\tif(\\\"SO\\\" in header[\\\"HD\\\"]):\\n\\t\\t\\tif(header[\\\"HD\\\"][\\\"SO\\\"] == \\\"queryname\\\"):\\n\\t\\t\\t\\treturn True\\n\\treturn False\",\n \"def _is_comment_line(self):\\n pattern = re.compile(r\\\"^(\\\\s)*(//)+\\\")\\n return pattern.search(self._line)\",\n \"def _check_headers(cursor, headers):\\n all_columns = set(chain.from_iterable(_columns(cursor, table) for table in DATA_TABLES))\\n for header in headers:\\n if header not in all_columns:\\n raise ValueError('column {} not recognized'.format(header))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.90293974","0.79356164","0.7800237","0.7654996","0.7427677","0.7177651","0.71695125","0.7159226","0.7146845","0.7069064","0.70204705","0.70001435","0.69541633","0.6851527","0.68049","0.66462415","0.66202724","0.6611603","0.6611603","0.64868945","0.64868945","0.6454544","0.645367","0.6450973","0.6436246","0.64266235","0.63892406","0.63811505","0.63704157","0.63704157","0.63617474","0.6318067","0.63142216","0.63011026","0.6297134","0.6294357","0.62547433","0.62438744","0.62225074","0.62216586","0.6205822","0.6198009","0.6172425","0.6163413","0.61374694","0.61293864","0.60968137","0.6070001","0.60613906","0.6049675","0.6013428","0.59891135","0.5964819","0.5961798","0.59598285","0.594353","0.59422106","0.59386617","0.593638","0.5929476","0.59149843","0.59088","0.5897934","0.58978367","0.58978367","0.58928466","0.58660823","0.5855233","0.5851811","0.5840922","0.582926","0.5825662","0.5824777","0.5817899","0.58071303","0.57975","0.5794567","0.57888275","0.57874215","0.57864076","0.576887","0.5765431","0.5764348","0.57621175","0.57621175","0.5755046","0.5752693","0.57523364","0.5738573","0.5726782","0.56899303","0.5689105","0.56874907","0.56856346","0.56810737","0.56801766","0.5668952","0.5664655","0.5663928","0.5651647"],"string":"[\n \"0.90293974\",\n \"0.79356164\",\n \"0.7800237\",\n \"0.7654996\",\n \"0.7427677\",\n \"0.7177651\",\n \"0.71695125\",\n \"0.7159226\",\n \"0.7146845\",\n \"0.7069064\",\n \"0.70204705\",\n \"0.70001435\",\n \"0.69541633\",\n \"0.6851527\",\n \"0.68049\",\n \"0.66462415\",\n \"0.66202724\",\n \"0.6611603\",\n \"0.6611603\",\n \"0.64868945\",\n \"0.64868945\",\n \"0.6454544\",\n \"0.645367\",\n \"0.6450973\",\n \"0.6436246\",\n \"0.64266235\",\n \"0.63892406\",\n \"0.63811505\",\n \"0.63704157\",\n \"0.63704157\",\n \"0.63617474\",\n \"0.6318067\",\n \"0.63142216\",\n \"0.63011026\",\n \"0.6297134\",\n \"0.6294357\",\n \"0.62547433\",\n \"0.62438744\",\n \"0.62225074\",\n \"0.62216586\",\n \"0.6205822\",\n \"0.6198009\",\n \"0.6172425\",\n \"0.6163413\",\n \"0.61374694\",\n \"0.61293864\",\n \"0.60968137\",\n \"0.6070001\",\n \"0.60613906\",\n \"0.6049675\",\n \"0.6013428\",\n \"0.59891135\",\n \"0.5964819\",\n \"0.5961798\",\n \"0.59598285\",\n \"0.594353\",\n \"0.59422106\",\n \"0.59386617\",\n \"0.593638\",\n \"0.5929476\",\n \"0.59149843\",\n \"0.59088\",\n \"0.5897934\",\n \"0.58978367\",\n \"0.58978367\",\n \"0.58928466\",\n \"0.58660823\",\n \"0.5855233\",\n \"0.5851811\",\n \"0.5840922\",\n \"0.582926\",\n \"0.5825662\",\n \"0.5824777\",\n \"0.5817899\",\n \"0.58071303\",\n \"0.57975\",\n \"0.5794567\",\n \"0.57888275\",\n \"0.57874215\",\n \"0.57864076\",\n \"0.576887\",\n \"0.5765431\",\n \"0.5764348\",\n \"0.57621175\",\n \"0.57621175\",\n \"0.5755046\",\n \"0.5752693\",\n \"0.57523364\",\n \"0.5738573\",\n \"0.5726782\",\n \"0.56899303\",\n \"0.5689105\",\n \"0.56874907\",\n \"0.56856346\",\n \"0.56810737\",\n \"0.56801766\",\n \"0.5668952\",\n \"0.5664655\",\n \"0.5663928\",\n \"0.5651647\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8483625"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":77983,"cells":{"query":{"kind":"string","value":"Parse lines to get reads from the input read file."},"document":{"kind":"string","value":"def parse(self, *args, **kwargs):\n if self.is_gzipped:\n fin = gzip.open(self.path, 'rb')\n else:\n fin = open(self.path, 'r')\n line_num = 0\n expect_header = True\n for line in fin:\n line_num += 1\n line = line.strip()\n if not line: # skip empty lines\n continue\n if expect_header:\n if self._is_header(line):\n logger.debug(\n f\"Detected header at line {line_num}: {line!r}\")\n continue\n else:\n expect_header = False\n try:\n chrom, pos = self._parse_line(line, *args, **kwargs)\n if chrom is not None:\n yield chrom, pos\n except (IndexError, ValueError, TypeError):\n fin.close()\n raise FileFormatError(format=self.format, line_num=line_num,\n line=line)\n fin.close()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __read_lines__(self):\r\n fd = open(self.input_file, \"r\")\r\n lines = fd.readlines()\r\n fd.close()\r\n return lines","def process_lines(self, lines):\n line_index = 0\n n_lines = len(lines)\n while line_index < n_lines:\n if lines[line_index].startswith(\"HIERARCHY\"):\n line_index = self._read_skeleton(lines, line_index, n_lines)\n if lines[line_index].startswith(\"MOTION\"):\n self._read_frametime(lines, line_index+2)\n line_index = self._read_frames(lines, line_index+3, n_lines)\n else:\n line_index += 1","def parse(self, filehandle):\n l = filehandle.readline()\n if l.split()[0] != '##maf':\n return\n else:\n self.setpar(l.split()[1:])\n\n l=filehandle.readline()\n while l:\n la = l.split()\n## print la\n if(len(la)==0 or la[0]=='#'):\n## print \"skipping\"\n 1\n elif(la[0]=='a'):\n## print \"reading alignment\"\n self.readalign(la[1:], filehandle)\n else:\n## print \"end of records\"\n return\n\n l=filehandle.readline()","def _parse_ach_file(self, contents):\n file_length = len(contents)\n\n for index in range(0, file_length, self.LINE_LENGTH):\n line = contents[index:index + self.LINE_LENGTH]\n\n if line.startswith('1'):\n self._read_header(line)\n elif line.startswith('5'):\n self._read_batch_header(line)\n elif line.startswith('6'):\n self._read_entry_detail(line)\n elif line.startswith('7'):\n self._read_addenda_record(line)\n elif line.startswith('8'):\n self._read_batch_control_record(line)\n elif line.startswith('9'):\n if line == '9' * 94:\n continue\n self._read_file_control_record(line)","def parse(self):\n for line in self.lines:\n self.read_line(line)\n return self.assembler_lines","def read_lines(self):\n with open(self.pdb_file) as infile:\n self.lines = infile.readlines()","def parse_file():\n\tfile_lines = []\n\n\t## For each line in the file, if it's not empty, store it\n\tfor line in fileinput.input():\n\t\tif len(line) > 1:\n\t\t\tfile_lines.append(line.strip())\n\t\n\trun_algorithms(file_lines)","def parse(self, f):\n \n for line in f:\n self.parse_line(line)","def parse_lines(lines):\n for line in lines:\n yield Record(line)","def extract_lines(infile):\n with open(infile, 'r') as src:\n return read_on(get_line, src)","def read_input(self):\n\n with open(self.input_file, 'r') as file:\n\n data = defaultdict(list)\n for line in file:\n # Skip empty lines, meta-data and comments\n if len(line) < 2 or line[0] == '#':\n continue\n\n # Make Region object of current line\n f = Region(line)\n data[f.rid].append(f)\n\n return data","def read_records(self, input_path, offset_range_tracker):\n start_offset = offset_range_tracker.start_position()\n with tf.io.gfile.GFile(input_path, 'r') as f:\n f.seek(start_offset)\n # Read lines in and proceed once a `+` is in the 3rd position.\n record = collections.deque([], 4)\n while True:\n line = f.readline()\n if not line:\n # End of file reached\n break\n record.append(line.strip())\n if len(record) == 4 and record[0].startswith('@') and record[2] == '+':\n # Once a '+' is in position 3 a full record exists in the record.\n read_name = record[0]\n sequence = record[1]\n qual = record[3]\n self.fastq_records_counter.inc()\n yield (read_name, sequence, qual)\n record.clear()","def readlines(self):\n lines = []\n while True:\n line = self.readline()\n if line is None:\n return lines\n lines.append(line)","def read_input():\n f = open(INPUT_FILE, 'r')\n \n number_of_lines = int(f.readline())\n for i in range(number_of_lines):\n yield [i+1, f.readline().split()]\n f.close()","def readData(self):\n self._setupArrays()\n\n with open(self.filename) as fh:\n datalines = fh.readlines()[self.NLHEAD:]\n\n datalines = self._checkForBlankLines(datalines)\n\n # Set up loop over unbounded indpendent variable\n m = 0 # Unbounded independent variable mark \n while len(datalines) > 0:\n datalines = self._readData1(datalines, m)\n datalines = self._readData2(datalines, m)\n m = m + 1","def readlines(self):\n return [line for line in self]","def _read(cls, input_file, quotechar=None):\n with open(input_file, \"r\", encoding='utf-8') as f:\n lines = []\n for line in f:\n lines.append(line.strip())\n return lines","def read():\n for line in sys.stdin:\n if line == \"\\n\":\n return\n log_elements.append(log_element(line))","def parse(self, filename):\n infile = file(filename)\n for line in infile:\n self.parseLine(line)","def parse_file(self):\n with open(self.file_name, 'r', errors='ignore') as log_file:\n for line in log_file:\n self.process_line(line)","def input_file_parser(cls):\n \n # Loop through the file and store lines in an appropriate list that is passed to other class functions\n with open(cls.infile_name,'r') as infile:\n for line in infile: # Loop through the whole file\n if '$molecule' in line: # Search for a section header\n for line in infile: # Enter second loop over the lines in the section\n if '$end' in line: # If you find $end, stop loop as the section is finished\n break\n else: # Otherwise add the line to a list\n cls.molecule_lines.append(line.strip())\n if '$connection' in line: # Continue for other sections...\n for line in infile:\n if '$end' in line:\n break\n else:\n cls.connector_lines.append(line.strip())\n if '$options' in line: # Continue for other sections...\n for line in infile:\n if '$end' in line:\n break\n else:\n cls.options_lines.append(line.strip())\n\n return None","def read_lines_from_file(fname):\n return []","def parse_lines(filename):\n line_counter = 0\n with open(filename, 'r') as rf:\n for line_txt in rf:\n try:\n d = json.loads(line_txt)\n tup = (\n d['attributed_to'],\n int(d['date_time'][8:10]),\n d.get('used_first_time_today', False),\n d.get('first_utm_source', 'unknown') \n )\n except:\n print('Error parsing line_txt:', line_txt)\n line_counter += 1\n if line_counter % 10 ** 6 == 0:\n print('read %dM lines' % (line_counter // 10 ** 6))\n yield tup # yield: https://stackoverflow.com/a/231855","def parse(self):\n\t\tfirst = None\n\t\tf = open(self.input_file)\n\t\tfor line in f.readlines():\n\t\t\tif line.startswith(\"#\"):\n\t\t\t\tcontinue\n\t\t\ttry:\n\t\t\t\tflow,t,sequence,size = line.split()\n\t\t\texcept:\n\t\t\t\tcontinue\n\t\t\t# append data to a list of tuples\n\t\t\tflow = int(flow)\n\t\t\tt = float(t)\n\t\t\tsequence = int(sequence)\n\t\t\tif size == \"x\":\n\t\t\t\tcontinue\n\t\t\tsize = int(size)\n\t\t\tif not size == 0:\n\t\t\t\tif flow == 1:\n\t\t\t\t\tself.data1.append((t,sequence,size))\n\t\t\t\telif flow == 2:\n\t\t\t\t\tself.data2.append((t,sequence,size))\n\t\t\t\telif flow == 3:\n\t\t\t\t\tself.data3.append((t, sequence, size))\n\t\t\t\telif flow == 4:\n\t\t\t\t\tself.data4.append((t, sequence, size))\n\t\t\t\telif flow == 5:\n\t\t\t\t\tself.data5.append((t, sequence, size))\n\t\t\t\telse:\n\t\t\t\t\tprint \"Erroneous data: \",flow, t, sequence, size\n\t\t\t# Keep track of the minimum and maximum time seen\n\t\t\tif not self.min_time or t < self.min_time:\n\t\t\t\tself.min_time = t\n\t\t\tif not self.max_time or t > self.max_time:\n\t\t\t\tself.max_time = t\n\n\t\t\t# print len(self.data1),len(self.data2),len(self.data3),len(self.data4),len(self.data5)","def _read(self, file_name):\n f = open(file_name)\n lines = f.readlines()\n begin = 0\n end = 0\n while end < len(lines):\n op = ''\n for l in lines[begin:]:\n end += 1\n op = l.split()[0]\n if op in operations:\n self.operations.append(op)\n break\n if op == '=push':\n nfa = Automaton(lines[begin:end - 1])\n self.aut_to_push.append(nfa)\n begin = end\n f.close()","def parse_mapping_file(lines, strip_quotes=True, suppress_stripping=False):\r\n if hasattr(lines, \"upper\"):\r\n # Try opening if a string was passed\r\n try:\r\n lines = open(lines, 'U')\r\n except IOError:\r\n raise QiimeParseError(\"A string was passed that doesn't refer \"\r\n \"to an accessible filepath.\")\r\n\r\n if strip_quotes:\r\n if suppress_stripping:\r\n # remove quotes but not spaces\r\n strip_f = lambda x: x.replace('\"', '')\r\n else:\r\n # remove quotes and spaces\r\n strip_f = lambda x: x.replace('\"', '').strip()\r\n else:\r\n if suppress_stripping:\r\n # don't remove quotes or spaces\r\n strip_f = lambda x: x\r\n else:\r\n # remove spaces but not quotes\r\n strip_f = lambda x: x.strip()\r\n\r\n # Create lists to store the results\r\n mapping_data = []\r\n header = []\r\n comments = []\r\n\r\n # Begin iterating over lines\r\n for line in lines:\r\n line = strip_f(line)\r\n if not line or (suppress_stripping and not line.strip()):\r\n # skip blank lines when not stripping lines\r\n continue\r\n\r\n if line.startswith('#'):\r\n line = line[1:]\r\n if not header:\r\n header = line.strip().split('\\t')\r\n else:\r\n comments.append(line)\r\n else:\r\n # Will add empty string to empty fields\r\n tmp_line = map(strip_f, line.split('\\t'))\r\n if len(tmp_line) < len(header):\r\n tmp_line.extend([''] * (len(header) - len(tmp_line)))\r\n mapping_data.append(tmp_line)\r\n if not header:\r\n raise QiimeParseError(\"No header line was found in mapping file.\")\r\n if not mapping_data:\r\n raise QiimeParseError(\"No data found in mapping file.\")\r\n\r\n return mapping_data, header, comments","def record_finder(lines):\n for line in lines:\n if not line.strip():\n continue\n if line.startswith(\"@\"): #separate each block of reads by @\n try:\n yield curr\n except:\n pass\n curr = []\n curr.append(line.strip())\n else:\n curr.append(line.strip())\n if curr:\n yield curr #Sandra et al. 2019","def parse(self, lines):\n # convert file to string deleting end of line charcters\n citations_string = self.prepare_file(lines)\n # extract the entries from the string\n entries = list(self.find_entries(citations_string))\n entries.append(len(citations_string))\n # parse each entry to generate a citation\n for idx, jdx in zip(entries[:-1], entries[1:]):\n self.parse_entry(citations_string[idx:jdx])\n return self.force_field.citations","def parse_input():\n with open(\"input_07.txt\", \"r\") as f:\n for line in f:\n Bag(line)","def readlines(self, cr=1):\n mode = \"r\"\n\n if not cr:\n content = self.read(mode)\n return content.split(\"\\n\")\n else:\n f = self.open(mode)\n try:\n return f.readlines()\n finally:\n f.close()","def readSequences(lines):\n seqs = []\n label = None\n seq_lines = []\n for line in lines:\n line = line.strip() # strip off white space\n if not line: # skip empty lines\n continue\n if line.startswith(';'): # ignore comment lines\n continue\n # check for start of next sequence:\n if line.startswith('>'): # label line\n # first, store the previous sequence if we had one:\n if seq_lines:\n seqs.append(Sequence(label, ''.join(seq_lines)))\n seq_lines = []\n # get the label (name) for the next sequence\n label = line[1:].strip()\n else:\n # collect all lines with sequence information for this sequence:\n seq_lines.append(line)\n # take care of the last sequence in the file\n seqs.append(Sequence(label, ''.join(seq_lines)))\n return seqs","def readlines(self):\n return list(self.iterlines())","def line_parser(path):\n lines = []\n with open(path, 'r') as input:\n lines = [line.rstrip().split(',') for line in input]\n lines = [\n [[float(x1), float(y1)],\n [float(x2), float(y2)]] \n for x1, y1, x2, y2 in lines]\n return lines","def readlines(self):\n return [\"\"] + self.get(\"1.0\", END).split(\"\\n\")[:-1]","def _read_input_file(self):\n pass","def parse_lines(self, start_line=0, end_line=False):\n if end_line is False: end_line = len(self.file_ltxt)\n\n lines = self.file_ltxt\n self.E_str = \"parse_lines\"\n self.line_num = start_line\n\n # Loop over lines and parse\n while self.line_num < end_line:\n line = lines[self.line_num].strip()\n\n if line == \"echo\": print(\"\")\n\n # Parse any variables\n elif self.line_declarations['variable'](line):\n self.parse_variable_line(line)\n\n # Parse any file loading commands\n elif self.line_declarations['load'](line):\n self.parse_load_cmd(line)\n\n # Parse any file loading commands\n elif self.line_declarations['plot'](line):\n self.parse_plot_cmd(line)\n\n # Parse any file loading commands\n elif self.line_declarations['write'](line):\n self.parse_write_cmd(line)\n\n # Parse any math commands\n elif self.line_declarations['math'](line):\n self.parse_math_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['echo'](line):\n self.parse_echo_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['calc'](line):\n self.parse_calc_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['set'](line):\n self.parse_set_cmd(line)\n\n # Parse any shell commands\n elif self.line_declarations['shell'](line):\n self.parse_shell_cmd()\n\n # Parse any for loop commands\n elif self.line_declarations['for'](line):\n self.parse_for_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['script'](line):\n self.parse_script_cmd(line)\n\n elif self.line_declarations['inline_code'](line):\n getattr(self, f\"parse_{line.split()[0]}_cmd\")(line)\n\n elif self.line_declarations['if'](line):\n self.parse_if_cmd(line)\n\n # elif self.line_declarations['splice'](line):\n # self.parse_splice_cmd(line)\n\n elif self.line_declarations['glue'](line):\n self.parse_glue_cmd(line)\n\n elif self.line_declarations['exit'](line):\n print(\"\\n\\nStopped Code -exit was called.\")\n raise SystemExit\n\n # The end of control statements\n elif '}' in line:\n pass\n\n # Print a warning about unknown line\n else:\n self.print_warning(f\"I don't understand a line: '{line}'\")\n\n self.line_num += 1","def readInput(in_file_name):\n in_file = open(in_file_name, 'r')\n positions = []\n samples = []\n M = []; P = [];\n MC = []; PC = [];\n while True:\n line = in_file.readline()\n if not line: break\n if line[0] == '#': continue #skip comment\n line = line.rstrip('\\n').split('\\t')\n \n #genomic positions and allele support in plasma samples\n positions.append(int(line[0]))\n samples.append(tuple(map(int, line[1:5])))\n \n #maternal and paternal alleles\n M.append(tuple(line[5:7]))\n MC.append(tuple(map(float, line[7:9])))\n \n P.append(tuple(line[9:11]))\n PC.append(tuple(map(float, line[11:13]))) \n \n in_file.close()\n return positions, samples, M, P, MC, PC","def parse_input(part=1):\n with open(\"input_23.txt\") as f:\n return [int(x) for x in f.readline().strip()]","def lineReadfile(filename):\n#\t\"input:filename output=readlines() \"\n\tf = open(filename)\n\tlist1 =[]\n\twhile 1:\n\t\ts = f.readline()\n\t\tif s==\"\":\n\t\t\tbreak\n\t\ts=string.replace(s,\"\\n\",\"\")\n\t\tif s==\"\":\n\t\t\tcontinue\n\t\tlist1.append(s)\n\tf.close()\n\treturn list1","def read_input(input_file):\n \n logging.info(\"reading file {0}...this may take a while\".format(input_file))\n with gzip.open(input_file, 'rb') as f:\n for i, line in enumerate(f):\n \n if (i % 10000 == 0):\n logging.info(\"read {0} reviews\".format(i))\n # do some pre-processing and return list of words for each review\n # text\n yield gensim.utils.simple_preprocess(line)","def read_transcript_data(fn):\n\n def _read_lines(fn):\n # NC_000007.13\tRefSeq\tcDNA_match\t50344265\t50344518\t254\t+\t.\tID=aln58042;Target=NM_001220765.2 1 254 +;gap_count=0;identity=0.0691326;idty=1;num_ident=428;num_mismatch=0;pct_coverage=6.91326;pct_identity_gap=100;pct_identity_ungap=100;score=254\n # NC_000002.11 RefSeq cDNA_match 179671939 179672150 212 - . ID=ed951d46-194c-477a-a480-4bc64530c5ba;Target=NM_001267550.2 1 212 +;gap_count=0;identity=0.999991;idty=1;num_ident=109223;num_mismatch=1;pct_coverage=100;pct_identity_gap=99.9991;pct_identity_ungap=99.9991\n line_re = re.compile(\n \"(?P<ref_ac>\\S+)\\s+(?P<origin>\\S+)\\s+(?P<match_type>\\S+)\\s+\"\n \"(?P<g_start>\\d+)\\s+(?P<g_end>\\d+)\\s+(?P<score>\\S+)\\s+\"\n \"(?P<strand>[-+])\\s+\\.\\s+ID=(?P<aln>[^;]+);Target=(?P<tx_ac>\\S+)\"\n \"\\s+(?P<tx_start>\\d+)\\s+(?P<tx_end>\\d+).+?\"\n \"pct_coverage=(?P<pct_coverage>[^;]+);\"\n \"pct_identity_gap=(?P<pct_identity_gap>[^;]+);\"\n \"pct_identity_ungap=(?P<pct_identity_ungap>[^;]+)\"\n )\n fh = io.open(fn, \"rb\")\n while fh.peek(1)[0] == \"#\":\n fh.readline()\n while fh.peek(3)[0:3] != \"###\":\n line = fh.readline()\n try:\n yield line_re.match(line).groupdict()\n except AttributeError:\n raise Exception(\"Failed at\", line)\n raise StopIteration\n def _key(e):\n return (e[\"tx_ac\"], not e[\"ref_ac\"].startswith(\"NC_\"), e[\"ref_ac\"], e[\"aln\"])\n return itertools.groupby(sorted(_read_lines(fn), key=_key), key=_key)","def parse_lines(self, lines):\n assert isinstance(lines, Iterable)\n\n for line in lines:\n name, values = self.parse_line(line)\n self.add(name, values)","def process_lines(line_queue, result_queue, sim_info, analysis_info, args, merged_reads):\n\t\n\twhile True:\n\t\tline = line_queue.get()\n\t\t\n\t\t# if we're done, stop\n\t\tif line is None:\n\t\t\tresult_queue.put(None)\n\t\t\tbreak\n\t\t# skip header lines\n\t\telif line[0] == '@':\n\t\t\tcontinue\n\t\t# process this line\n\t\telse:\n\t\t\tresult = score_read(line, sim_info, analysis_info, args, merged_reads)\n\t\t\tresult_queue.put(result)","def read (self, path):\n\n\t\tself.data = []\n\t\t# print \"*** path: %s***\" % path\n\t\tdir, filename = os.path.split (path)\n\t\troot, ext = os.path.splitext (filename)\n\t\t# encoding = 'ISO-8859-1' # utf-8\n\t\ts = codecs.open(path,'r', self.encoding).read()\n\t\t## s = unicode(f.read(),'utf-8')\n\t\ts = self.preprocess (s)\n\t\tlines = split (s, self.linesep)\n\t\tschema = self.splitline(lines[0])\n\n\t\t## print \"** %s **\" % os.path.splitext(filename)[0]\n\t\tif self.verbose:\n\t\t\tprint \"read %d lines from %s\" % (len(lines), path)\n\n\t\tfor i in range(1,len(lines)):\n\t\t\tif not lines[i].strip(): \n\t\t\t\t# print 'skipping line (%d)' % i\n\t\t\t\tcontinue\n\t\t\tfields = self.splitline(lines[i])\n\t\t\titem = self.entry_class (fields, schema)\n\t\t\tif self.accept (item):\n\t\t\t\tself.add (item)\n\n\t\tself.schema = schema\n\t\t# self.data.sort (lastNameCmp)","def read():\n try:\n #Open and parse input files.\n nodeFile = open(sys.argv[1], 'r')\n edgeFile = open(sys.argv[2], 'r')\n\t\n parse_nodes(nodeFile)\n parse_edges(edgeFile)\n nodeFile.close()\n\tedgeFile.close()\n\treturn \n except:\n print 'problem parsing input'\n #Put here some more information - usage...","def read_line(self, *types):\n words = self.in_file.readline().strip().split()\n if len(words) == 1:\n return types[0](words[0])\n return [types[min(i, len(types) - 1)](words[i])\n for i in range(len(words))]","def readAllfromFile(self):\n with open(self._fname, 'r') as f:\n lines = f.readlines()\n readList = []\n for line in lines:\n line = line.strip()\n if len(line) > 1:\n gra = self._readGrafromLine(line)\n readList.append(gra)\n f.close()\n return readList","def parse_file(self, parse_all=False, file=None):\n if not parse_all:\n input()\n for line in sys.stdin:\n self.parse_and_add(line)","def read_input(input_file):\n\n logging.info(\"reading file {0}...this may take a while\".format(input_file))\n with gzip.open(input_file, 'rb') as f:\n for i, line in enumerate(f):\n\n if i % 10000 == 0:\n logging.info(\"read {0} reviews\".format(i))\n # do some pre-processing and return list of words for each review\n # text\n yield gensim.utils.simple_preprocess(line)","def readrules(self, fomalines):\n for lineno, l in enumerate(fomalines):\n if 'define' in l or 'def ' in l:\n rulecom = l.split(' #')\n r = re.findall(\"(defi?n?e?)\\s+(\\S+)\\s+([^;]+)\", rulecom[0])\n if len(r[0]) != 3:\n print \"Syntax error on line %i\" % lineno\n (_, rulename, rule) = r[0]\n if len(rulecom) > 1:\n commentline = rulecom[1].strip()\n else:\n commentline = ''\n self.rule_add(rulename, rule, commentline)\n if 'chain' in l:\n l = l.replace(';','')\n chain = re.findall('chain\\s+(.*)', l)\n rc = chain[0].replace(' ','').split(',')\n self.rc = rc","def _parse(self, infile):\n raise NotImplementedError()","def parse_input():\r\n endpoints_file = open(\"input.txt\", \"r\")\r\n lines = endpoints_file.readlines()\r\n line_number = 0\r\n endpoints = []\r\n for line in lines:\r\n line = line.strip()\r\n if line_number == 0:\r\n period = int(line)\r\n elif line_number == 1:\r\n max_attempts = int(line)\r\n else:\r\n endpoints.append(line)\r\n line_number += 1\r\n endpoints_file.close()\r\n return endpoints, period, max_attempts","def read_data(cls, input_file, quotechar=None):\r\n if 'pkl' in str(input_file):\r\n lines = load_pickle(input_file)\r\n else:\r\n lines = input_file\r\n return lines","def read_input():\n splits = []\n with open('solutions/day5/input.txt') as f:\n for line in f:\n splits.append(line.strip('\\n'))\n return splits","def read_file(fname):\n recs=[]\n logger.info('Start read file %s', fname)\n with open(fname) as inf:\n for line in inf:\n recs.append(line.strip().split())\n logger.info('End reading with recnumber %d', len(recs))\n return recs","def read_input_file(path: str):\n with open(path, \"r\") as f:\n return f.readlines()","def parse(self, infile):\r\n raise NotImplementedError()","def parse_fastq (rec_lines):\n data = []\n data.append(rec_lines[0][1:])\n data.append(rec_lines[1])\n data.append(rec_lines[3])\n return data","def job_reader(path) -> Generator[ParsedActionType, None, None]:\n try:\n with open(path, \"r\") as f:\n parser = Parser()\n for line in f:\n result = parser.process_line(line)\n if result is not None:\n yield result\n except IOError as err:\n print(\"Error opening/reading from file '{0}': {1}\"\n .format(err.filename, err.strerror))","def read_data(input_file):\n\n def process_line(labels, words):\n l = ' '.join([label for label in labels if len(label) > 0])\n w = ' '.join([word for word in words if len(word) > 0])\n lines.append((l, w))\n words = []\n labels = []\n return words, labels, lines\n\n rf = open(input_file, 'r')\n lines = [];\n words = [];\n labels = []\n for line in rf:\n word = line.strip().split(' ')[0]\n label = line.strip().split(' ')[-1]\n # here we dont do \"DOCSTART\" check\n\n if len(line.strip()) == 0: # and words[-1] == '.'\n words, labels, lines = process_line(labels, words)\n words.append(word)\n labels.append(label)\n rf.close()\n return lines","def read_data(cls, input_file,quotechar = None):\n if 'pkl' in str(input_file):\n lines = load_pickle(input_file)\n else:\n lines = input_file\n return lines","async def readlines(self, hint=-1):\n if 'b' in self._mode:\n raise APIException(\n \"readline on a binary file is not permitted: {}\".format(\n self._uri)\n )\n # read the entire file in and decode it\n lines = await self.read().decode().split(\"\\n\")\n return lines","def readlines(self) -> list[bytes] | None:","def read_input_file(path):\n file_input = os.path.join(path, 'INPUT')\n f = open(file_input, 'r')\n lines = f.read().replace('\\n', ':')\n f.close()\n lines = ' '.join(lines.split()) #remove whitespace\n lines = re.split(':', lines.replace(': ', ':'))\n return lines","def read_records(self, input_path, offset_range_tracker):\n start_offset = offset_range_tracker.start_position()\n with tf.io.gfile.GFile(input_path, 'r') as f:\n f.seek(start_offset)\n # Read the first key\n line = f.readline()\n key = line.strip('>\\n')\n seq = ''\n while True:\n line = f.readline()\n if line.startswith('>'):\n # New record reached.\n yield (key, seq)\n self.fasta_records_counter.inc()\n key = line.strip('>\\n')\n seq = ''\n else:\n seq += line.strip()\n\n if not line:\n # End of file reached; Yield and break from loop\n yield (key, seq)\n self.fasta_records_counter.inc()\n break","def _readline_ins(self):\n if self._ins_filehandle is None:\n if not os.path.exists(self._ins_filename):\n raise Exception(\n \"instruction file '{0}' not found\".format(self._ins_filename)\n )\n self._ins_filehandle = open(self._ins_filename, \"r\")\n line = self._ins_filehandle.readline()\n self._ins_linecount += 1\n if line == \"\":\n return None\n self._last_line = line\n # check for spaces in between the markers - this gets ugly\n line = line.lower()\n if self._marker is not None and self._marker in line:\n\n # def find_all(a_str, sub):\n # start = 0\n # while True:\n # start = a_str.find(sub, start)\n # if start == -1:\n # return\n # yield start\n # start += len(sub)\n # poss speedup using regex\n midx = [m.start() for m in re.finditer(re.escape(self._marker), line)]\n # midx = list(find_all(line, self._marker))\n midx.append(len(line))\n first = line[: midx[0]].strip()\n tokens = []\n if len(first) > 0:\n # tokens.append(first)\n tokens.extend([f.strip() for f in first.split()])\n for idx in range(1, len(midx) - 1, 2):\n mstr = line[midx[idx - 1] : midx[idx] + 1]\n ostr = line[midx[idx] + 1 : midx[idx + 1]]\n tokens.append(mstr)\n tokens.extend(ostr.split())\n else:\n tokens = line.strip().split()\n return tokens","def __parse(self):\n lines = self.file.readlines()\n for i in range(0, len(lines)):\n line = lines[i]\n tokens = line.split()\n if tokens[0] == \"#start\":\n trial_name = tokens[1]\n trial = Trial(trial_name)\n self.trials[trial_name] = trial\n elif tokens[0] == \"#end\":\n continue\n else:\n date_str = tokens[0] + \" \" + tokens[1]\n date = datetime.strptime(date_str, \"%m/%d/%y %H:%M:%S\")\n sound_file = line[18:-1].strip()\n event = Event(date, sound_file, 0)\n trial.addevent(event)","def read_input(self, infile):\n self.infile = open(infile, 'r')\n input_list = None\n for l in [line.strip('\\n') for line in self.infile]:\n input_list = l.split(' ')\n \n pid = 0\n for i in range(0, len(input_list), 2):\n self.processes.append(Process(pid, int(input_list[i]), int(input_list[i+1])))\n pid += 1\n self.process_count = len(self.processes)\n self.infile.close()\n return","def read(infile):\n if isinstance(infile, str):\n infile = open(infile)\n\n with infile:\n while True:\n cmt = infile.readline().strip()\n seq = infile.readline().strip()\n plus = infile.readline().strip()\n qual = infile.readline().strip()\n\n if not cmt:\n break\n if not cmt.startswith('@') or plus != '+':\n raise ValueError('fastq file <{}> is corrupted'.format(infile.path))\n yield SRecord(cmt=cmt[1:], seq=seq, qual=qual)","def read_input():\n # Use with to make sure the file will be closed after the block executed\n with open('snapshot_input.txt') as f:\n # Split the line at line breaks\n x = f.read().splitlines()\n # Get the data of restructuring, three positive integers N , C , and D\n # Use generator expression for time and space efficiency\n restructuring_info = (i.split() for i in x if len(i.split())==3)\n # Get the data of single machine, four integers D, P, R and G\n machine_info = (i.split() for i in x if len(i.split())!=3)\n # Get the length of restructuring data\n length = sum(1 for i in x if len(i.split())==3)\n\n return restructuring_info, machine_info, length","def get_input():\n # return TEST_INPUT.split('\\n')\n with open(INPUT_FILE) as f:\n return f.readlines()","def _fetch_data_lines(self) -> list:\n with open(self.full_path, \"rb\") as file:\n all_lines = file.readlines()\n return self._screen_invalid_lines(all_lines)","def read_text(self, text):\n if isinstance(text, (str, unicode)):\n lines = text.split('\\n')\n else:\n lines = text\n\n for line in lines:\n l = line.strip()\n\n if line == ST_POS0:\n self._state = ST_POS0\n elif line == ST_TRNS:\n self._state = ST_TRNS\n elif line == ST_POS0:\n self._state = ST_POS0\n else:\n self._parse_line(line)","def read(file):\n\n blocks = ['bus', 'load', 'fshunt', 'gen', 'branch', 'transf', 'area',\n 'twotermdc', 'vscdc', 'impedcorr', 'mtdc', 'msline', 'zone',\n 'interarea', 'owner', 'facts', 'swshunt', 'gne', 'Q']\n nol = [1, 1, 1, 1, 1, 4, 1,\n 0, 0, 0, 0, 0, 1,\n 0, 1, 0, 0, 0, 0]\n rawd = re.compile('rawd\\d\\d')\n\n retval = True\n version = 0\n b = 0 # current block index\n raw = {}\n for item in blocks:\n raw[item] = []\n\n data = []\n mdata = [] # multi-line data\n mline = 0 # line counter for multi-line models\n\n # parse file into raw with to_number conversions\n fid = open(file, 'r')\n for num, line in enumerate(fid.readlines()):\n line = line.strip()\n if num == 0: # get basemva and frequency\n data = line.split('/')[0]\n data = data.split(',')\n\n mva = float(data[1])\n freq = float(data[5])\n version = int(data[2])\n\n if not version:\n version = int(rawd.search(line).group(0).strip('rawd'))\n if version < 32 or version > 33:\n logging.warning('RAW file version is not 32 or 33. Error may occur.')\n continue\n elif num == 1: # store the case info line\n logging.info(line)\n continue\n elif num == 2:\n continue\n elif num >= 3:\n if line[0:2] == '0 ' or line[0:3] == ' 0 ': # end of block\n b += 1\n continue\n elif line[0] is 'Q': # end of file\n break\n data = line.split(',')\n\n data = [to_number(item) for item in data]\n mdata.append(data)\n mline += 1\n if mline == nol[b]:\n if nol[b] == 1:\n mdata = mdata[0]\n raw[blocks[b]].append(mdata)\n mdata = []\n mline = 0\n fid.close()\n\n # add device elements params and add to PSAT formatted dictionary\n\n for data in raw['bus']:\n \"\"\"version 32:\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10\n ID, NAME, BasekV, Type, Area Zone Owner Va, Vm, latitude longitude\n \"\"\"\n idx = data[0]\n ty = data[3]\n angle = data[8]\n try:\n lat = data[9]\n except:\n # logging.warning('<No Coordinates in .raw file>')\n param = {'idx': idx,\n 'name': data[1],\n 'Vn': data[2],\n 'type': data[3],\n 'area': data[4],\n 'voltage': data[7],\n 'region': data[5],\n 'owner': data[6],\n 'angle': angle,\n }\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5]]\n else:\n param = {'idx': idx,\n 'name': data[1],\n 'Vn': data[2],\n 'type': data[3],\n 'area': data[4],\n 'voltage': data[7],\n 'region': data[5],\n 'owner': data[6],\n 'angle': angle,\n 'latitude': data[9],\n 'longitude': data[10]\n }\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5], data[9], data[10]]\n Settings.Bus.append(psatlist)\n Settings.BusNames.append(data[1])\n # Add BusSTORE Dictionary For Later Reference\n Settings.BusStore[idx] = param\n\n xcoord = [34.560040, 34.938385, 34.360040, 40.5152473, 40.3142473, 36.527401, 36.857401, 36.687401, 36.856401,\n 40.487041, 36.903901, 36.702901, 35.832561, 33.386047, 33.185047, 37.105571, 37.104154, 33.706718,\n 37.103549, 36.703539, 37.103559, 36.703549, 36.033561, 35.631561, 36.032561, 35.732561, 36.525401,\n 36.857401, 49.869314, 50.969314, 51.979314, 52.481674, 54.973192, 56.276212, 41.734596, 34.551015,\n 34.652015, 34.537507, 34.587507, 34.157904, 33.714453, 33.762453, 39.548160, 39.496160, 34.313143,\n 34.545782, 34.380686, 34.111686, 34.137762, 34.118650, 34.158650, 33.918650, 33.718650, 34.018650,\n 34.018650, 34.018650, 34.018650, 34.018650, 34.312456, 34.315456, 34.243600, 34.566258, 34.565258,\n 46.064672, 46.565672, 45.514571, 45.606833, 45.806833, 44.890000, 45.596416, 45.295416, 45.891161,\n 47.954899, 46.511440, 45.913936, 45.713936, 46.669335, 47.954899, 47.624154, 43.784730, 44.482350,\n 42.006860, 42.934919, 42.731919, 43.013135, 44.068350, 43.558350, 42.438350, 42.938350, 44.068350,\n 43.558350, 43.048350, 42.638350, 44.068350, 43.558350, 43.048350, 42.638350, 43.620189, 39.120428,\n 40.398031, 35.216200, 35.215200, 36.202099, 39.777745, 39.539598, 37.052929, 35.403217, 35.352217,\n 36.807243, 39.567450, 40.807689, 40.806689, 41.008689, 39.555494, 37.954721, 38.406721, 38.906721,\n 38.656721]\n ycoord = [-109.277313, -110.303798, -109.777313, -107.546455, -107.546455, -108.325669, -108.654569, -108.486669,\n -108.325669, -107.185575, -111.390408, -111.390408, -111.448566, -112.860397, -112.659397, -108.243555,\n -108.441191, -112.322033, -111.590816, -111.190816, -111.190816, -111.590806, -111.648566, -111.248566,\n -111.249566, -111.647566, -108.655669, -108.323669, -122.150895, -122.150895, -122.150895, -121.61684,\n -121.924221, -122.21370, -108.790427, -117.568105, -117.538105, -118.607375, -118.658375, -118.280282,\n -118.146319, -118.096319, -112.52797, -112.72797, -118.690631, -118.389938, -118.478496, -118.478496,\n -118.299917, -118.095428, -118.095428, -118.095428, -118.095428, -118.195428, -118.395428, -117.995428,\n -117.795428, -117.995428, -118.481217, -118.891217, -118.391667, -117.166428, -117.368428, -106.60906,\n -106.80906, -122.681289, -121.114785, -122.113785, -123.29000, -121.312202, -121.114202, -106.612578,\n -118.997945, -112.88531, -120.692286, -120.693974, -119.571501, -120.997945, -122.219492, -118.77463,\n -121.019484, -121.316546, -114.419206, -114.419206, -120.956476, -120.79484, -120.93484, -121.216546,\n -121.156546, -121.215484, -121.135484, -121.255484, -121.175484, -121.013484, -120.733484, -121.053484,\n -120.973484, -118.865882, -122.073631, -122.263453, -120.847567, -120.900567, -120.129849, -122.142965,\n -122.262993, -121.021929, -119.450452, -119.450452, -121.779037, -122.276225, -122.135718, -121.935718,\n -121.935718, -121.24000, -121.18379, -121.10879, -121.27379, -121.23979]\n\n #for idx, line in enumerate(Settings.Bus):\n # line.extend([xcoord[idx], ycoord[idx]])\n\n maxV = 1.1\n minV = 0.9\n maxQ = 1\n minQ = 0\n convimp = 0\n status = 1\n loss = 1\n\n for data in raw['load']:\n \"\"\"version 32:\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11\n Bus, Id, Status, Area, Zone, PL(MW), QL (MW), IP, IQ, YP, YQ, OWNER\n \"\"\"\n\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n voltage = Settings.BusStore[busidx]['voltage']\n param = {'bus': busidx,\n 'Vn': vn,\n 'Sn': mva,\n 'p': (data[5] + data[7] * voltage + data[9] * voltage ** 2) / mva,\n 'q': (data[6] + data[8] * voltage - data[10] * voltage ** 2) / mva,\n 'owner': data[11],\n 'type': Settings.BusStore[busidx]['type'],\n 'voltage': voltage\n }\n\n psatlist = [busidx, mva, vn, param['p'], param['q'], maxV, minV, convimp, status]\n Settings.PQ.append(psatlist)\n \"\"\"CONFIRM THAT OTHER BUSES HAVE 0 P and 0 Q which are not added\"\"\"\n\n for data in raw['fshunt']:\n \"\"\"\n 0, 1, 2, 3, 4\n Bus, name, Status, g (MW), b (Mvar)\n \"\"\"\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n param = {'bus': busidx,\n 'Vn': vn,\n 'status': data[2],\n 'Sn': mva,\n 'g': data[3] / mva,\n 'b': data[4] / mva,\n }\n\n psatlist = [busidx, mva, vn, freq, param['g'], param['b'], param['status']]\n Settings.Shunt.append(psatlist)\n\n gen_idx = 0\n type = 6\n\n for data in raw['gen']:\n \"\"\"\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11, 12, 13, 14, 15, 16,17,18,19\n I,ID,PG,QG,QT,QB,VS,IREG,MBASE,ZR,ZX,RT,XT,GTAP,STAT,RMPCT,PT,PB,O1,F1\n \"\"\"\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n gen_mva = data[8]\n gen_idx += 1\n status = data[14]\n leak = 0\n param = {'Sn': gen_mva,\n 'Vn': vn,\n 'u': status,\n 'idx': gen_idx,\n 'bus': busidx,\n 'pg': status * data[2] / mva,\n 'qg': status * data[3] / mva,\n 'qmax': data[4] / mva,\n 'qmin': data[5] / mva,\n 'v0': data[6],\n 'ra': data[9], # ra armature resistance\n 'xs': data[10], # xs synchronous reactance\n 'pmax': data[16] / mva,\n 'pmin': data[17] / mva,\n }\n\n if Settings.BusStore[busidx]['type'] == 3: #Check Bus Type for Slack\n refangle = 0\n refBus = 1\n PGuess = 1\n swlist = [busidx, gen_mva, vn, param['v0'], refangle, param['qmax'], param['qmin'],\n maxV, minV, PGuess, loss, refBus, status]\n SW = swlist\n Settings.SW.append(swlist)\n Settings.SWStore[busidx] = param\n Settings.SynStore[busidx] = param\n continue\n\n if busidx not in Settings.BusStore.keys():\n \"\"\" Need data from .dyr file. Create initial list, then append data from .dyr\"\"\"\n else:\n # psatlist = [busidx, gen_mva, vn, freq, type, leak, param['ra'],param['xs']]\n # Syn.append(psatlist)\n Settings.SynStore[busidx] = param\n pvlist = [busidx, gen_mva, vn, param['pg'], Settings.BusStore[busidx]['voltage'],\n param['qmax'], param['qmin'], maxV, minV, loss, status]\n Settings.PV.append(pvlist)\n\n\n for data in raw['branch']:\n \"\"\"\n I,J,ID,R,X,B,RATEA,RATEB,RATEC,GI,BI,GJ,BJ,ST,LEN,O1,F1,...,O4,F4\n \"\"\"\n param = {'bus1': data[0],\n 'bus2': data[1],\n 'id' : data[2],\n 'r': data[3],\n 'x': data[4],\n 'b': data[5],\n 'rate_a': data[6],\n 'rate_b': data[7],\n 'rate_c': data[8],\n 'Vn': Settings.BusStore[data[0]]['Vn'],\n 'Vn2': Settings.BusStore[data[1]]['Vn'],\n 'length': data[14],\n 'Ilim': EMPTY,\n 'Plim': EMPTY,\n 'Slim': EMPTY,\n 'status': data[13]\n }\n\n psatlist = [param['bus1'], param['bus2'], param['rate_c'], param['Vn'], freq, EMPTY,\n param['length'], param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\n param['Slim'], param['status']]\n Settings.Lineij.append([data[0], data[1], data[2]])\n Settings.Lineji.append([data[1], data[0], data[2]])\n Settings.LineOrd[param['bus1']].append(psatlist)\n Settings.branches += 1\n Settings.linecount += 1\n Settings.LineBusMatij[param['bus2']].append(Settings.branches)\n Settings.LineBusMatji[param['bus1']].append(Settings.branches)\n\n for data in raw['transf']:\n \"\"\"\n I,J,K,CKT,CW,CZ,CM,MAG1,MAG2,NMETR,'NAME',STAT,O1,F1,...,O4,F4\n R1-2,X1-2,SBASE1-2\n WINDV1,NOMV1,ANG1,RATA1,RATB1,RATC1,COD1,CONT1,RMA1,RMI1,VMA1,VMI1,NTP1,TAB1,CR1,CX1\n WINDV2,NOMV2\n \"\"\"\n if len(data[1]) < 5:\n ty = 2\n else:\n ty = 3\n if ty == 3:\n continue\n # raise NotImplementedError('Three-winding transformer not implemented')\n\n tap = data[2][0]\n phi = data[2][2]\n\n if tap == 1 and phi == 0:\n trasf = False\n else:\n trasf = True\n param = {'trasf': trasf,\n 'bus1': data[0][0],\n 'bus2': data[0][1],\n 'u': data[0][11],\n 'b': data[0][8],\n 'r': data[1][0],\n 'x': data[1][1],\n 'tap': tap,\n 'phi': phi,\n 'rate_a': data[2][3],\n 'Vn': Settings.BusStore[busidx]['Vn'],\n 'Vn2': Settings.BusStore[busidx]['Vn'],\n # 'length': data[?][?], FIND CORRECT INDEX\n 'Ilim': EMPTY,\n 'Plim': EMPTY,\n 'Slim': EMPTY,\n }\n psatlist = [param['bus1'], param['bus2'], param['rate_a'], param['Vn'], freq, EMPTY,\n EMPTY, param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\n param['Slim'], param['u']]\n\n Settings.LineOrd[param['bus1']].append(psatlist)\n Settings.linecount += 1\n Settings.transformers += 1\n # ADD Line Data(All Branch Types) to Sys Param Dict after .dyr Transformer Data Added\n # Re-Order Line Data for correct sequence\n for key in Settings.LineOrd:\n for item in Settings.LineOrd[key]:\n Settings.Line.append(item)\n\n for data in raw['area']:\n Settings.Areas.append(data[4])\n\n for data in raw['zone']:\n Settings.Regions.append(data[1])\n\n return retval","def read_input():\n return Path(__file__).with_name('input.txt').read_text().splitlines()","def __read_data__(self):\n with open(self.file, 'r') as data:\n sentence = []\n tags = []\n for line in data:\n terms = line.rstrip().split(WHITESPACE)\n for term in terms:\n word_tag = tuple(term.split(TAGCHAR))\n word = word_tag[0]\n tag = word_tag[1]\n self.word_tag_dict[word_tag] += 1\n self.tag_dict[tag] += 1\n self.__add_to_word_dict__(word, tag)\n if self.isNumberWord(word):\n self.numbers += 1\n if word[0].isupper() and len(sentence) > 0:\n self.cap_no_start += 1\n sentence.append(word)\n tags.append(tag)\n if tag == ENDOFSENTENCE:\n self.sentences.append(tuple(sentence))\n self.tags.append(tuple(tags))\n sentence = []\n tags = []","def read_lines(fp: TextIO) -> Iterator[str]:\n while line := fp.readline().strip():\n yield line","def readlines(path):\n with open(path, \"r\") as f:\n return [line.strip() for line in f.readlines()]","def parse_data(self, fileinput):\n with open(fileinput, 'rb') as fh:\n for line in fh:\n try:\n name, address, phone = line.strip().split(\",\")\n self.data.append(Document(name, address, phone))\n except Exception, ex:\n raise SerialException(\": Failed to parse input line %s: %s\" % (line, ex))\n return","def read_linelog():","def _read(self, openf=None, stepfilter=None):\n itemstack = []\n current = None\n result = {}\n xkeys = None\n timeskip = False\n laststep = False\n\n if openf is None:\n f = open(self.filepath)\n else:\n f = openf\n\n line = 'start'\n while line != '':\n lastpos = f.tell()\n line = f.readline()\n if line == '':\n continue\n \n if itemstack is not None and len(itemstack) > 0: \n cast = itemstack.pop()\n raw = line.split()\n values = [t(r) for t, r in zip(cast, raw)]\n if len(values) == 1:\n values = values[0]\n\n if current == \"time\":\n if stepfilter is not None and values not in stepfilter:\n timeskip = True\n elif (self.index is not None and values != self.index):\n if values > self.index:\n if openf is None:\n return {}\n else:\n timeskip = True\n laststep = True\n else:\n timeskip = True\n elif self.index is None:\n self.index = values\n else:\n timeskip = False\n \n if len(itemstack) == 0 and current not in result:\n result[current] = values\n else:\n if current not in result:\n result[current] = []\n result[current].append(values)\n continue\n elif itemstack is None and current == \"atoms\":\n if \"ITEM\" in line:\n current = None\n if openf is not None:\n f.seek(lastpos)\n break\n else:\n #E.g. line: 1 4 -65.9625 1.54915 1.46824 5 30.976 \n vals = line.split()\n sid, atype = tuple(map(int, vals[0:2]))\n result[\"type\"].append(atype)\n result[\"id\"].append(sid)\n x, y, z = tuple(map(float, vals[2:5]))\n result[\"xyz\"].append((x, y, z))\n if len(vals) > 5 and xkeys is not None:\n for ikey, v in enumerate(vals[5:]):\n result[xkeys[ikey]].append(eval(v))\n continue # pragma: no cover\n \n if \"ITEM: TIMESTEP\" in line:\n if laststep:\n f.seek(lastpos)\n break\n itemstack.append((int,))\n current = \"time\"\n timeskip = False\n elif not timeskip:\n if \"ITEM: NUMBER OF ATOMS\" in line:\n itemstack.append((int,))\n current = \"natoms\"\n elif \"ITEM: BOX BOUNDS\" in line:\n period = line.strip().split(\"BOX BOUNDS\")\n if len(period) == 2 and period[1] != '':\n result[\"periodic\"] = period[1].strip().split()\n else:\n result[\"periodic\"] = (\"ss\", \"ss\" ,\"ss\")\n \n\t\t # Changes by JPRIEDS to accommodate triclinic boxes\n\t\t # Written 170719\n\t\t if len(result[\"periodic\"]) == 6:\n\t\t\titemstack.extend([(float, float, float)]*3)\n\t\t\tcurrent = \"box\"\n\t\t\tresult[\"periodic\"] = result[\"periodic\"][3:]\n\t\t elif len(result[\"periodic\"]) == 3:\n\t\t\titemstack.extend([(float, float)]*3)\n\t\t\tcurrent = \"box\"\n\t\t else:\n emsg = \"Could not classify periodic bounds: {}\"\n raise ValueError(emsg.format(result[\"periodic\"]))\n elif \"ITEM: ATOMS\" in line:\n itemstack = None\n current = \"atoms\"\n result[\"type\"] = []\n result[\"id\"] = []\n result[\"xyz\"] = []\n \n #The first two headings in the line have \"ITEM: ATOMS\", the\n #rest are usuall id, type, x, y, z, rest...\n headings = line.split()\n extras = len(headings) > 7\n if extras:\n xkeys = []\n xheadings = headings[7:]\n for xhead in xheadings:\n key = \"atom:{}\".format(xhead)\n result[key] = []\n xkeys.append(key)\n \n if openf is None:\n #Close the file since we opened it.\n f.close()\n \n return result","def parse_file(self, file_path) -> list:\n data = []\n with open(file_path, 'rb') as f:\n lines = pickle.load(f)\n for line in lines:\n input, output = line\n if input.strip() == \"\" or output.strip() == \"\":\n continue\n input_len = len(input.split())\n output_len = len(output.split())\n if input_len > 50 or output_len > 50:\n continue\n data_item = Text2TextDataItem(input_text=input, output_text=output, tokenizer=self.tokenizer,\n share_vocab=self.share_vocab)\n data.append(data_item)\n return data","def _read(self, file_path: str) -> Iterator[Instance]:\n with open(file_path) as f:\n for line in f:\n pairs = line.split()\n words, tags = zip(*(pair.split(\"###\") for pair in pairs))\n yield self.text_to_instance([Token(word) for word in words], tags)","def readlines(self, n = None):\n\t\tif not self._input: raise PlumberExceptions.PipeTypeException(self)\n\t\tresult = []\n\t\twhile n == None or n > 0:\n\t\t\tline = self.readline()\n\t\t\tif not line: return result\n\t\t\tresult.append(line)\n\t\t\tn -= 1\n\t\treturn result","def read(read_file) -> list:\n result = []\n try:\n with open(read_file) as file:\n for lines in file:\n line = decode(lines.strip(\"\"))\n result.append(extract_information(line))\n global header\n header = result[0:2]\n result = result[3:]\n for word in result:\n if \"None\" in word[0:3]:\n raise InvalidPrincessException(\"Invalid princess!\")\n continue\n return result\n except FileNotFoundError:\n raise Exception(\"File not found!\")","def process_line(line):\n\n name_comp_list = []\n givenname_comp_list = []\n surname_comp_list = []\n geocode_comp_list = []\n locality_comp_list = []\n date1_comp_list = []\n date2_comp_list = []\n\n # Split the line into the basic fields - - - - - - - - - - - - - - - - - - -\n #\n if (config.in_file_type in ['CSV','CSVQ','TAB','TABQ']):\n # Comma or tabulator separated\n try:\n line_list = config.line_parser.parse(line)\n except:\n log_message('CSV line parsing failed with inout: '+line,'err')\n\n if (len(line_list) < config.input_len):\n log_message('Input line does not contain enough fields,' +\\\n 'fill up with empty fields','warn')\n while (len(line_list) < config.input_len):\n line_list.append('')\n\n config.curr_line_list = line_list # Save current line list\n\n # Extract fields into different component lists - - - - - - - - - - - - - -\n #\n if (config.input_component['name'] != []): # Extract name fields\n for i in config.input_component['name']:\n name_comp_list.append(line_list[i])\n\n else: # Extract givenname and surname into separate components - - - - - -\n if (config.input_component['givenname'] != []): # Extract g-name fields\n for i in config.input_component['givenname']:\n givenname_comp_list.append(line_list[i])\n\n if (config.input_component['surname'] != []): # Extract surname fields\n for i in config.input_component['surname']:\n surname_comp_list.append(line_list[i])\n\n if (config.input_component['geocode'] != []): # Extract geocode fields\n for i in config.input_component['geocode']:\n geocode_comp_list.append(line_list[i])\n\n if (config.input_component['locality'] != []): # Extract locality fields\n for i in config.input_component['locality']:\n locality_comp_list.append(line_list[i])\n\n if (config.input_component['date1'] != []): # Extract date1 fields\n for i in config.input_component['date1']:\n date1_comp_list.append(line_list[i])\n\n if (config.input_component['date2'] != []): # Extract date2 fields\n for i in config.input_component['date2']:\n date2_comp_list.append(line_list[i])\n\n elif (config.in_file_type == 'COL'): # Column based input file - - - - - - -\n\n if (len(line) < config.input_len):\n log_message('Input line is not long enough, fill up with spaces','warn')\n line += ' '*(config.input_len-len(line))\n\n if (config.input_component['name'] != []): # Extract name fields\n for (col_start,length) in config.input_component['name']:\n name_comp_list.append(line[col_start,col_start+length])\n\n else: # Extract givenname and surname into separate components - - - - - -\n if (config.input_component['givenname'] != []): # Extract g-name fields\n for (col_start,length) in config.input_component['givenname']:\n givenname_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['surname'] != []): # Extract surname fields\n for (col_start,length) in config.input_component['surname']:\n surname_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['geocode'] != []): # Extract geocode fields\n for (col_start,length) in config.input_component['geocode']:\n geocode_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['locality'] != []): # Extract locality fields\n for (col_start,length) in config.input_component['locality']:\n locality_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['date1'] != []): # Extract date1 fields\n for (col_start,length) in config.input_component['date1']:\n date1_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['date2'] != []): # Extract date2 fields\n for (col_start,length) in config.input_component['date2']:\n date2_comp_list.append(line[col_start,col_start+length])\n\n # elif (config.in_file_type == 'SQL'): # - - - - - - - - - - - - - - - - - -\n\n ################################\n # Add later: SQL database access\n ################################\n\n msg = [' Component basic field lists:', \\\n ' Name: '+str(name_comp_list), \\\n ' Given name: '+str(givenname_comp_list), \\\n ' Surname: '+str(surname_comp_list), \\\n ' Geocode: '+str(geocode_comp_list), \\\n ' Locality: '+str(locality_comp_list), \\\n ' Date1: '+str(date1_comp_list), \\\n ' Date2: '+str(date2_comp_list)]\n log_message(msg,'v2')\n\n name_comp = ''\n givenname_comp = ''\n surname_comp = ''\n geocode_comp = ''\n locality_comp = ''\n date1_comp = ''\n date2_comp = ''\n\n # Now clean and then concatenate component lists into strings - - - - - - - -\n #\n if (name_comp_list != []): # Name component\n name_comp = name_comp_list[0] # Start with first field in list\n\n for f in name_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['name'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['name'] == 1):\n sep = check_field_spill(name_comp, f)\n\n name_comp = name_comp+sep+f # Append separator and field\n\n if (givenname_comp_list != []): # Givenname component - - - - - - - - - - -\n givenname_comp = givenname_comp_list[0] # Start with first field in list\n\n for f in givenname_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['givenname'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['givenname'] == 1):\n sep = check_field_spill(givenname_comp, f)\n\n givenname_comp = givenname_comp+sep+f # Append separator and field\n\n if (surname_comp_list != []): # Surname component - - - - - - - - - - - - -\n surname_comp = surname_comp_list[0] # Start with first field in list\n\n for f in surname_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['surname'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['surname'] == 1):\n sep = check_field_spill(surname_comp, f)\n\n surname_comp = surname_comp+sep+f # Append separator and field\n\n if (geocode_comp_list != []): # Geocode component - - - - - - - - - - - - -\n geocode_comp = geocode_comp_list[0] # Start with first field in list\n\n for f in geocode_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['geocode'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['geocode'] == 1):\n sep = check_field_spill(geocode_comp, f)\n\n geocode_comp = geocode_comp+sep+f # Append separator and field\n\n if (locality_comp_list != []): # Locality component - - - - - - - - - - - -\n locality_comp = locality_comp_list[0] # Start with first field in list\n\n for f in locality_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['locality'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['locality'] == 1):\n sep = check_field_spill(locality_comp, f)\n\n locality_comp = locality_comp+sep+f # Append separator and field\n\n if (date1_comp_list != []): # Date1 component - - - - - - - - - - - - - - -\n date1_comp = date1_comp_list[0] # Start with first field in list\n\n for f in date1_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['date1'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['date1'] == 1):\n if (date1_comp[-1] != ' ') and (f[0] != ' '):\n tmp_list0 = date1_comp.split()\n tmp_list1 = f.split()\n check_word = tmp_list0[-1]+tmp_list1[0]\n\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\n 'sep','oct','nov','dec','january','february','march','april', \\\n 'may','june','july','august','september','october','november', \\\n 'december']):\n\n sep = '' # Set separator to no space\n msg = ' Correct date1 word spilling: \"'+date1_comp+'\",\"'+f+'\"'\n log_message(msg,'v1')\n\n date1_comp = date1_comp+sep+f # Append separator and field\n\n if (date2_comp_list != []): # Date2 component - - - - - - - - - - - - - - -\n date2_comp = date2_comp_list[0] # Start with first field in list\n\n for f in date2_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['date2'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['date2'] == 1):\n if (date2_comp[-1] != ' ') and (f[0] != ' '):\n tmp_list0 = date1_comp.split()\n tmp_list1 = f.split()\n check_word = tmp_list0[-1]+tmp_list1[0]\n\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\n 'sep','oct','nov','dec','january','february','march','april', \\\n 'may','june','july','august','september','october','november', \\\n 'december']):\n\n sep = '' # Set separator to no space\n msg = ' Correct date1 word spilling: \"'+date1_comp+'\",\"'+f+'\"'\n log_message(msg,'v1')\n\n date2_comp = date2_comp+sep+f # Append separator and field\n\n # Check if name component is given or givenname and surname separately - - -\n #\n if (config.input_component['givenname'] != []) or \\\n (config.input_component['surname'] != []):\n name_comp = [givenname_comp, surname_comp]\n\n msg = [' Components:', \\\n ' Name: \"'+str(name_comp)+'\"', \\\n ' Geocode: \"'+geocode_comp+'\"', \\\n ' Locality: \"'+locality_comp+'\"', \\\n ' Date1: \"'+date1_comp+'\"', \\\n ' Date2: \"'+date2_comp+'\"']\n log_message(msg,'v1')\n\n return [name_comp, geocode_comp, locality_comp, date1_comp, date2_comp]","def read_lines(filename):\n with file(filename) as f:\n for line in f:\n _line = line.strip()\n if _line:\n yield _line","def read_file(inp_fn):\n lines = [line.strip().split(\",\")\n for line in open(inp_fn)\n if not (line.startswith(\"#\"))]\n return [(int(line[0]), year_record({\"male\": int(line[-3]),\n \"female\": int(line[-2]),\n \"unknown\": int(line[-1])},\n None, None))\n for line in lines[1:]]","def parse_file(self):\n # read the first line in the file\n line = self._stream_handle.readline()\n\n while line:\n # check for a data line or a dcl logger line we specifically ignore\n data_match = DATA_LINE_MATCHER.match(line)\n ignore_match = IGNORE_LINE_MATCHER.match(line)\n\n if data_match:\n # found a data line, extract this particle\n # DCL controller timestamp is the port_timestamp\n dcl_controller_timestamp = data_match.groups()[DCL_TIMESTAMP_GROUP]\n port_timestamp = dcl_time_to_ntp(dcl_controller_timestamp)\n\n particle = self._extract_sample(self.particle_class,\n None,\n data_match,\n port_timestamp=port_timestamp,\n preferred_ts=DataParticleKey.PORT_TIMESTAMP)\n\n self._record_buffer.append(particle)\n\n elif not ignore_match:\n # we found a line with an unknown format, call an exception\n error_message = 'Found line with unknown format %s' % line\n log.warn(error_message)\n self._exception_callback(SampleException(error_message))\n\n # read the next line\n line = self._stream_handle.readline()","def read(filename, maximum):\n raws = []\n with open(filename, \"r\") as f:\n for _, line in zip(range(maximum), f):\n raw = parse_entry(line)\n raws.append(raw)\n return raws","def parse(self):\r\n # open the file\r\n # try to find the first line with Time =\r\n # send the file to a recursive residualParser\r\n try:\r\n with open(self.filepath, 'rb') as f:\r\n for line in f:\r\n if line.startswith('Time ='):\r\n self.timestep = self.__getTime(line)\r\n self.__residuals[self.timestep] = {}\r\n self.__parseResiduals(f)\r\n except Exception as e:\r\n raise 'Failed to parse {}:\\n\\t{}'.format(self.filepath, e)","def read(self, start_line, num_lines, timeout=0):\n # emulate timeout by sleeping timeout time\n if timeout > 0:\n read_timeout = float(timeout) / 1000\n #logging.debug(\"sleep \" + str(read_timeout) + \" seconds\")\n time.sleep(read_timeout)\n\n output = []\n attributes = []\n\n # get output\n for i in range(start_line, start_line + num_lines + 1):\n output.append(self.shm_output.read(self.columns, i * self.columns))\n if not CONQUE_FAST_MODE:\n attributes.append(self.shm_attributes.read(self.columns, i * self.columns))\n\n return (output, attributes)","def _read_file(self, current_path):\n # type: (str)->None\n\n if current_path in self._parsed_files:\n return\n self._parsed_files.add(current_path)\n\n with open(current_path, \"r\") as interfaces:\n # When the first non-comment line is parsed, header\n # comments have been read in.\n header_parsed = False\n # Loop through the interfaces file.\n for line in interfaces:\n # 1. Identify the clauses by analyzing the first\n # word of each line.\n # 2. Go to the next line if the current line is a comment.\n # line = line.strip().replace(\"\\n\", \"\")\n if not line:\n pass\n elif line.strip().startswith(\"#\") is True:\n if not header_parsed:\n self._header_comments += line\n else:\n # Header comments can no longer\n # be parsed in when the first interfaces\n # line is parsed in.\n header_parsed = True\n self._parse_iface(line, current_path)\n # Ignore blank lines.\n if not line.isspace():\n self._parse_details(line)\n self._read_auto(line)\n self._read_hotplug(line)\n\n # Is there some file to source ?\n source_path = self._read_sourced_path(line)\n if source_path:\n self._read_files(source_path)\n\n # TODO: lots of directives are completly ignored\n # and would be deleted","def parse_input(filename):\n with open(filename, 'r') as input_file:\n return [i.rstrip() for i in input_file]","def read_file(self, filename):\n with open(filename, 'r') as file:\n for line in file:\n l = line.strip()\n\n if l == ST_POS0:\n self._state = ST_POS0\n elif l == ST_TRNS:\n self._state = ST_TRNS\n elif l == ST_POS1:\n self._state = ST_POS1\n else:\n self._parse_line(l)\n self._state = None","def _read_log(self):\n\n line_regex = compile(r\"\\[I\\]\\s*\\(\\d+ms\\)[^\\d]+(?P<counter>\\d+)\"\n r\"[^\\d]+(?P<timestamp>\\d+(\\.\\d+)?)[^\\d]+\"\n r\"(?P<acceleration>\\d+);\")\n values = []\n with open(self.filepath) as file:\n for line in file:\n match = line_regex.match(line)\n if match:\n values.append({\n 'counter':\n int(match['counter']),\n 'timestamp':\n int(float(match['timestamp']) * 1000),\n 'acceleration':\n int(match['acceleration'])\n })\n\n self.values = values","def read(self):\n global ALIVE\n line = sys.stdin.readline()\n if line:\n self.datalines.append(line.rstrip())\n else:\n ALIVE = False","def load_input(self, file_name):\n with open(file_name, \"r\") as in_file:\n self.all_lines = [line.rstrip('\\n') for line in in_file]","def _parse(self):\n with open(_join(self.man_dir, self.man_fn)) as fp:\n lines = fp.readlines()\n \n desc_indxs = []\n for i, L in enumerate(lines):\n if \"#landuse\" in L or \" # landuse\" in L:\n desc_indxs.append(i-1)\n desc_indxs.append(i-2)\n desc_indxs.append(i-3)\n \n lines = [L[:L.find('#')].strip() for L in lines]\n lines = [L for i, L in enumerate(lines) if len(L) > 0 or i in desc_indxs]\n\n del desc_indxs\n \n self.datver = lines.pop(0)\n self.nofe = int(lines.pop(0))\n self.sim_years = int(lines.pop(0))\n \n # Read Plant Growth Section\n self.plants = PlantLoops(lines, self)\n\n # Read Operation Section\n self.ops = OpLoops(lines, self)\n \n # Read Initial Condition Section\n self.inis = IniLoops(lines, self)\n \n # Read Surface Effects Section\n self.surfs = SurfLoops(lines, self)\n \n # Read Contour Section\n self.contours = ContourLoops(lines, self)\n \n # Read Drainage Section\n self.drains = DrainLoops(lines, self)\n \n # Read Yearly Section\n self.years = YearLoops(lines, self)\n \n # Read Management Section \n self.man = ManagementLoop(lines, self)","def readSequenceLengths(lines):\n sequenceLengths = {} # an OrderedDict might be nice\n isSearching = True\n for line in lines:\n if line.isspace(): isSearching = True\n elif isSearching:\n w = line.split()\n if w[0] == \"s\":\n seqName, seqSize = w[1], w[5]\n sequenceLengths[seqName] = seqSize\n isSearching = False\n return sequenceLengths","def read_in_file():\n\t# Declare variables\n\treads = []\n\n\t# Get command line arguments\n\targuments = sys.argv\n\targuments_length = len(arguments)\n\n\t# Read file is the first argument\n\tread_file_name = arguments[1]\n\n\t# Process read file \n\tread_file = open(read_file_name, 'r')\n\tfor line in read_file:\n\t\tread_info = line.split()\n\t\tread_string = read_info[2].replace('\\'', '')\n\t\tnew_read = GenerativeRead(read_string, [], read_info[5], read_info[3], None, [], read_info[0], read_info[1], read_info[4]) \n\t\treads.append(new_read)\n\tread_file.close()\n\n\t# Repeat regions file in the second argument\n\trepeat_file_name = arguments[2]\n\n\t# Process repeat file\n\trepeat_file = open(repeat_file_name, 'r')\n\talignments = [[]]\n\talignment_index = -1\n\tprevious_line = ''\n\n\n\tfor line in repeat_file:\n\t\talignment_info = line.split()\n\n\t\t# This consists of a tuple of alignment string, alignment start position and alignment chromosome\n\t\t#new_align = alignment_info[2], alignment_info[4], alignment_info[3]\n\n\t\tnew_align = Alignment(alignment_info[2], None, alignment_info[4], alignment_info[3])\n\n\t\tif previous_line != alignment_info[0]:\n\t\t\t# It is not a repeat\n\t\t\talignment_index = alignment_index + 1\n\t\t\talignments.append([])\n\t\t\tprevious_line = alignment_info[0]\n\n\t\talignments[alignment_index].append(new_align)\n\n\trepeat_file.close()\n\n\t# Associate each read with the other alignments\n\tfor read in reads:\n\t\t# Find the other alignments\n\t\tpos = read.get_position()\n\t\tfound = False\n\t\tfound_index = -1\n\n\t\tfor a_index, alignment_lists in enumerate(alignments):\n\t\t\t# find matching alignments\n\t\t\t# TODO: Don't add alignment already have\n\t\t\t# TODO: Make functional with filter\n\t\t\tfor align in alignment_lists:\n\t\t\t\tif align.get_position() == pos:\n\t\t\t\t\tfound = True\n\t\t\t\t\tfound_index = a_index\n\t\t\t\t\tbreak\n\n\t\t\tif found is True:\n\t\t\t\tbreak\n\n\t\tif found is True:\n\t\t\tfor new_align in alignments[found_index]:\n\t\t\t\tread.add_alignment(new_align)\n\t\t\t\n\n\n\t# SNP files are the remaining ones\n\tsnp_file_names = [arguments[file_id] for file_id in range(3, arguments_length) ]\n\n\t# Process SNP files\n\tfor file_name in snp_file_names:\n\t\tsnp_file = open(file_name, 'r')\n\n\t\tfor line in snp_file:\n\t\t\tsnp_info = line.split()\n\t\t\tsnps = snp_info[3].split('/')\n\t\t\tsnp_pos = int(float(snp_info[2]))\n\n\t\t\t# Ignore alleles that are longer than one base\n\n\t\t\t\n\t\t\tif all(len(x) < 2 for x in snps):\n\n\t\t\t\t# Iterate through reads and determine whether or not it contains this SNP\n\t\t\t\tpos_low = snp_pos - 49\n\t\t\t\n\n\t\t\t\tfor read in reads:\n\t\t\t\t\tpositions = read.get_alignment_positions()\n\n\t\t\t\t\tfor p_index, p in enumerate(positions):\n\t\t\t\t\t\tp = int(float(p))\n\t\t\t\t\t\tif p >= pos_low and p <= snp_pos:\n\t\t\t\t\t\t\t# Get index of snp\n\t\t\t\t\t\t\toffset = snp_pos - p\n\t\t\t\t\t\t\tcalls = [0, 0, 0, 0]\n\t\t\t\t\t\t\tfor snp in snps:\n\t\t\t\t\t\t\t\tcall_index = get_base_num(snp)\n\t\t\t\t\t\t\t\tcalls[call_index] = 1\n\n\t\t\t\t\t\t\t# Add the SNP to the read\n\t\t\t\t\t\t\tread.add_snp(p_index, offset, calls)\n\t\t\t\t\t\t\t\n\t\tsnp_file.close()\n\treturn reads"],"string":"[\n \"def __read_lines__(self):\\r\\n fd = open(self.input_file, \\\"r\\\")\\r\\n lines = fd.readlines()\\r\\n fd.close()\\r\\n return lines\",\n \"def process_lines(self, lines):\\n line_index = 0\\n n_lines = len(lines)\\n while line_index < n_lines:\\n if lines[line_index].startswith(\\\"HIERARCHY\\\"):\\n line_index = self._read_skeleton(lines, line_index, n_lines)\\n if lines[line_index].startswith(\\\"MOTION\\\"):\\n self._read_frametime(lines, line_index+2)\\n line_index = self._read_frames(lines, line_index+3, n_lines)\\n else:\\n line_index += 1\",\n \"def parse(self, filehandle):\\n l = filehandle.readline()\\n if l.split()[0] != '##maf':\\n return\\n else:\\n self.setpar(l.split()[1:])\\n\\n l=filehandle.readline()\\n while l:\\n la = l.split()\\n## print la\\n if(len(la)==0 or la[0]=='#'):\\n## print \\\"skipping\\\"\\n 1\\n elif(la[0]=='a'):\\n## print \\\"reading alignment\\\"\\n self.readalign(la[1:], filehandle)\\n else:\\n## print \\\"end of records\\\"\\n return\\n\\n l=filehandle.readline()\",\n \"def _parse_ach_file(self, contents):\\n file_length = len(contents)\\n\\n for index in range(0, file_length, self.LINE_LENGTH):\\n line = contents[index:index + self.LINE_LENGTH]\\n\\n if line.startswith('1'):\\n self._read_header(line)\\n elif line.startswith('5'):\\n self._read_batch_header(line)\\n elif line.startswith('6'):\\n self._read_entry_detail(line)\\n elif line.startswith('7'):\\n self._read_addenda_record(line)\\n elif line.startswith('8'):\\n self._read_batch_control_record(line)\\n elif line.startswith('9'):\\n if line == '9' * 94:\\n continue\\n self._read_file_control_record(line)\",\n \"def parse(self):\\n for line in self.lines:\\n self.read_line(line)\\n return self.assembler_lines\",\n \"def read_lines(self):\\n with open(self.pdb_file) as infile:\\n self.lines = infile.readlines()\",\n \"def parse_file():\\n\\tfile_lines = []\\n\\n\\t## For each line in the file, if it's not empty, store it\\n\\tfor line in fileinput.input():\\n\\t\\tif len(line) > 1:\\n\\t\\t\\tfile_lines.append(line.strip())\\n\\t\\n\\trun_algorithms(file_lines)\",\n \"def parse(self, f):\\n \\n for line in f:\\n self.parse_line(line)\",\n \"def parse_lines(lines):\\n for line in lines:\\n yield Record(line)\",\n \"def extract_lines(infile):\\n with open(infile, 'r') as src:\\n return read_on(get_line, src)\",\n \"def read_input(self):\\n\\n with open(self.input_file, 'r') as file:\\n\\n data = defaultdict(list)\\n for line in file:\\n # Skip empty lines, meta-data and comments\\n if len(line) < 2 or line[0] == '#':\\n continue\\n\\n # Make Region object of current line\\n f = Region(line)\\n data[f.rid].append(f)\\n\\n return data\",\n \"def read_records(self, input_path, offset_range_tracker):\\n start_offset = offset_range_tracker.start_position()\\n with tf.io.gfile.GFile(input_path, 'r') as f:\\n f.seek(start_offset)\\n # Read lines in and proceed once a `+` is in the 3rd position.\\n record = collections.deque([], 4)\\n while True:\\n line = f.readline()\\n if not line:\\n # End of file reached\\n break\\n record.append(line.strip())\\n if len(record) == 4 and record[0].startswith('@') and record[2] == '+':\\n # Once a '+' is in position 3 a full record exists in the record.\\n read_name = record[0]\\n sequence = record[1]\\n qual = record[3]\\n self.fastq_records_counter.inc()\\n yield (read_name, sequence, qual)\\n record.clear()\",\n \"def readlines(self):\\n lines = []\\n while True:\\n line = self.readline()\\n if line is None:\\n return lines\\n lines.append(line)\",\n \"def read_input():\\n f = open(INPUT_FILE, 'r')\\n \\n number_of_lines = int(f.readline())\\n for i in range(number_of_lines):\\n yield [i+1, f.readline().split()]\\n f.close()\",\n \"def readData(self):\\n self._setupArrays()\\n\\n with open(self.filename) as fh:\\n datalines = fh.readlines()[self.NLHEAD:]\\n\\n datalines = self._checkForBlankLines(datalines)\\n\\n # Set up loop over unbounded indpendent variable\\n m = 0 # Unbounded independent variable mark \\n while len(datalines) > 0:\\n datalines = self._readData1(datalines, m)\\n datalines = self._readData2(datalines, m)\\n m = m + 1\",\n \"def readlines(self):\\n return [line for line in self]\",\n \"def _read(cls, input_file, quotechar=None):\\n with open(input_file, \\\"r\\\", encoding='utf-8') as f:\\n lines = []\\n for line in f:\\n lines.append(line.strip())\\n return lines\",\n \"def read():\\n for line in sys.stdin:\\n if line == \\\"\\\\n\\\":\\n return\\n log_elements.append(log_element(line))\",\n \"def parse(self, filename):\\n infile = file(filename)\\n for line in infile:\\n self.parseLine(line)\",\n \"def parse_file(self):\\n with open(self.file_name, 'r', errors='ignore') as log_file:\\n for line in log_file:\\n self.process_line(line)\",\n \"def input_file_parser(cls):\\n \\n # Loop through the file and store lines in an appropriate list that is passed to other class functions\\n with open(cls.infile_name,'r') as infile:\\n for line in infile: # Loop through the whole file\\n if '$molecule' in line: # Search for a section header\\n for line in infile: # Enter second loop over the lines in the section\\n if '$end' in line: # If you find $end, stop loop as the section is finished\\n break\\n else: # Otherwise add the line to a list\\n cls.molecule_lines.append(line.strip())\\n if '$connection' in line: # Continue for other sections...\\n for line in infile:\\n if '$end' in line:\\n break\\n else:\\n cls.connector_lines.append(line.strip())\\n if '$options' in line: # Continue for other sections...\\n for line in infile:\\n if '$end' in line:\\n break\\n else:\\n cls.options_lines.append(line.strip())\\n\\n return None\",\n \"def read_lines_from_file(fname):\\n return []\",\n \"def parse_lines(filename):\\n line_counter = 0\\n with open(filename, 'r') as rf:\\n for line_txt in rf:\\n try:\\n d = json.loads(line_txt)\\n tup = (\\n d['attributed_to'],\\n int(d['date_time'][8:10]),\\n d.get('used_first_time_today', False),\\n d.get('first_utm_source', 'unknown') \\n )\\n except:\\n print('Error parsing line_txt:', line_txt)\\n line_counter += 1\\n if line_counter % 10 ** 6 == 0:\\n print('read %dM lines' % (line_counter // 10 ** 6))\\n yield tup # yield: https://stackoverflow.com/a/231855\",\n \"def parse(self):\\n\\t\\tfirst = None\\n\\t\\tf = open(self.input_file)\\n\\t\\tfor line in f.readlines():\\n\\t\\t\\tif line.startswith(\\\"#\\\"):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tflow,t,sequence,size = line.split()\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\t# append data to a list of tuples\\n\\t\\t\\tflow = int(flow)\\n\\t\\t\\tt = float(t)\\n\\t\\t\\tsequence = int(sequence)\\n\\t\\t\\tif size == \\\"x\\\":\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tsize = int(size)\\n\\t\\t\\tif not size == 0:\\n\\t\\t\\t\\tif flow == 1:\\n\\t\\t\\t\\t\\tself.data1.append((t,sequence,size))\\n\\t\\t\\t\\telif flow == 2:\\n\\t\\t\\t\\t\\tself.data2.append((t,sequence,size))\\n\\t\\t\\t\\telif flow == 3:\\n\\t\\t\\t\\t\\tself.data3.append((t, sequence, size))\\n\\t\\t\\t\\telif flow == 4:\\n\\t\\t\\t\\t\\tself.data4.append((t, sequence, size))\\n\\t\\t\\t\\telif flow == 5:\\n\\t\\t\\t\\t\\tself.data5.append((t, sequence, size))\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprint \\\"Erroneous data: \\\",flow, t, sequence, size\\n\\t\\t\\t# Keep track of the minimum and maximum time seen\\n\\t\\t\\tif not self.min_time or t < self.min_time:\\n\\t\\t\\t\\tself.min_time = t\\n\\t\\t\\tif not self.max_time or t > self.max_time:\\n\\t\\t\\t\\tself.max_time = t\\n\\n\\t\\t\\t# print len(self.data1),len(self.data2),len(self.data3),len(self.data4),len(self.data5)\",\n \"def _read(self, file_name):\\n f = open(file_name)\\n lines = f.readlines()\\n begin = 0\\n end = 0\\n while end < len(lines):\\n op = ''\\n for l in lines[begin:]:\\n end += 1\\n op = l.split()[0]\\n if op in operations:\\n self.operations.append(op)\\n break\\n if op == '=push':\\n nfa = Automaton(lines[begin:end - 1])\\n self.aut_to_push.append(nfa)\\n begin = end\\n f.close()\",\n \"def parse_mapping_file(lines, strip_quotes=True, suppress_stripping=False):\\r\\n if hasattr(lines, \\\"upper\\\"):\\r\\n # Try opening if a string was passed\\r\\n try:\\r\\n lines = open(lines, 'U')\\r\\n except IOError:\\r\\n raise QiimeParseError(\\\"A string was passed that doesn't refer \\\"\\r\\n \\\"to an accessible filepath.\\\")\\r\\n\\r\\n if strip_quotes:\\r\\n if suppress_stripping:\\r\\n # remove quotes but not spaces\\r\\n strip_f = lambda x: x.replace('\\\"', '')\\r\\n else:\\r\\n # remove quotes and spaces\\r\\n strip_f = lambda x: x.replace('\\\"', '').strip()\\r\\n else:\\r\\n if suppress_stripping:\\r\\n # don't remove quotes or spaces\\r\\n strip_f = lambda x: x\\r\\n else:\\r\\n # remove spaces but not quotes\\r\\n strip_f = lambda x: x.strip()\\r\\n\\r\\n # Create lists to store the results\\r\\n mapping_data = []\\r\\n header = []\\r\\n comments = []\\r\\n\\r\\n # Begin iterating over lines\\r\\n for line in lines:\\r\\n line = strip_f(line)\\r\\n if not line or (suppress_stripping and not line.strip()):\\r\\n # skip blank lines when not stripping lines\\r\\n continue\\r\\n\\r\\n if line.startswith('#'):\\r\\n line = line[1:]\\r\\n if not header:\\r\\n header = line.strip().split('\\\\t')\\r\\n else:\\r\\n comments.append(line)\\r\\n else:\\r\\n # Will add empty string to empty fields\\r\\n tmp_line = map(strip_f, line.split('\\\\t'))\\r\\n if len(tmp_line) < len(header):\\r\\n tmp_line.extend([''] * (len(header) - len(tmp_line)))\\r\\n mapping_data.append(tmp_line)\\r\\n if not header:\\r\\n raise QiimeParseError(\\\"No header line was found in mapping file.\\\")\\r\\n if not mapping_data:\\r\\n raise QiimeParseError(\\\"No data found in mapping file.\\\")\\r\\n\\r\\n return mapping_data, header, comments\",\n \"def record_finder(lines):\\n for line in lines:\\n if not line.strip():\\n continue\\n if line.startswith(\\\"@\\\"): #separate each block of reads by @\\n try:\\n yield curr\\n except:\\n pass\\n curr = []\\n curr.append(line.strip())\\n else:\\n curr.append(line.strip())\\n if curr:\\n yield curr #Sandra et al. 2019\",\n \"def parse(self, lines):\\n # convert file to string deleting end of line charcters\\n citations_string = self.prepare_file(lines)\\n # extract the entries from the string\\n entries = list(self.find_entries(citations_string))\\n entries.append(len(citations_string))\\n # parse each entry to generate a citation\\n for idx, jdx in zip(entries[:-1], entries[1:]):\\n self.parse_entry(citations_string[idx:jdx])\\n return self.force_field.citations\",\n \"def parse_input():\\n with open(\\\"input_07.txt\\\", \\\"r\\\") as f:\\n for line in f:\\n Bag(line)\",\n \"def readlines(self, cr=1):\\n mode = \\\"r\\\"\\n\\n if not cr:\\n content = self.read(mode)\\n return content.split(\\\"\\\\n\\\")\\n else:\\n f = self.open(mode)\\n try:\\n return f.readlines()\\n finally:\\n f.close()\",\n \"def readSequences(lines):\\n seqs = []\\n label = None\\n seq_lines = []\\n for line in lines:\\n line = line.strip() # strip off white space\\n if not line: # skip empty lines\\n continue\\n if line.startswith(';'): # ignore comment lines\\n continue\\n # check for start of next sequence:\\n if line.startswith('>'): # label line\\n # first, store the previous sequence if we had one:\\n if seq_lines:\\n seqs.append(Sequence(label, ''.join(seq_lines)))\\n seq_lines = []\\n # get the label (name) for the next sequence\\n label = line[1:].strip()\\n else:\\n # collect all lines with sequence information for this sequence:\\n seq_lines.append(line)\\n # take care of the last sequence in the file\\n seqs.append(Sequence(label, ''.join(seq_lines)))\\n return seqs\",\n \"def readlines(self):\\n return list(self.iterlines())\",\n \"def line_parser(path):\\n lines = []\\n with open(path, 'r') as input:\\n lines = [line.rstrip().split(',') for line in input]\\n lines = [\\n [[float(x1), float(y1)],\\n [float(x2), float(y2)]] \\n for x1, y1, x2, y2 in lines]\\n return lines\",\n \"def readlines(self):\\n return [\\\"\\\"] + self.get(\\\"1.0\\\", END).split(\\\"\\\\n\\\")[:-1]\",\n \"def _read_input_file(self):\\n pass\",\n \"def parse_lines(self, start_line=0, end_line=False):\\n if end_line is False: end_line = len(self.file_ltxt)\\n\\n lines = self.file_ltxt\\n self.E_str = \\\"parse_lines\\\"\\n self.line_num = start_line\\n\\n # Loop over lines and parse\\n while self.line_num < end_line:\\n line = lines[self.line_num].strip()\\n\\n if line == \\\"echo\\\": print(\\\"\\\")\\n\\n # Parse any variables\\n elif self.line_declarations['variable'](line):\\n self.parse_variable_line(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['load'](line):\\n self.parse_load_cmd(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['plot'](line):\\n self.parse_plot_cmd(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['write'](line):\\n self.parse_write_cmd(line)\\n\\n # Parse any math commands\\n elif self.line_declarations['math'](line):\\n self.parse_math_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['echo'](line):\\n self.parse_echo_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['calc'](line):\\n self.parse_calc_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['set'](line):\\n self.parse_set_cmd(line)\\n\\n # Parse any shell commands\\n elif self.line_declarations['shell'](line):\\n self.parse_shell_cmd()\\n\\n # Parse any for loop commands\\n elif self.line_declarations['for'](line):\\n self.parse_for_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['script'](line):\\n self.parse_script_cmd(line)\\n\\n elif self.line_declarations['inline_code'](line):\\n getattr(self, f\\\"parse_{line.split()[0]}_cmd\\\")(line)\\n\\n elif self.line_declarations['if'](line):\\n self.parse_if_cmd(line)\\n\\n # elif self.line_declarations['splice'](line):\\n # self.parse_splice_cmd(line)\\n\\n elif self.line_declarations['glue'](line):\\n self.parse_glue_cmd(line)\\n\\n elif self.line_declarations['exit'](line):\\n print(\\\"\\\\n\\\\nStopped Code -exit was called.\\\")\\n raise SystemExit\\n\\n # The end of control statements\\n elif '}' in line:\\n pass\\n\\n # Print a warning about unknown line\\n else:\\n self.print_warning(f\\\"I don't understand a line: '{line}'\\\")\\n\\n self.line_num += 1\",\n \"def readInput(in_file_name):\\n in_file = open(in_file_name, 'r')\\n positions = []\\n samples = []\\n M = []; P = [];\\n MC = []; PC = [];\\n while True:\\n line = in_file.readline()\\n if not line: break\\n if line[0] == '#': continue #skip comment\\n line = line.rstrip('\\\\n').split('\\\\t')\\n \\n #genomic positions and allele support in plasma samples\\n positions.append(int(line[0]))\\n samples.append(tuple(map(int, line[1:5])))\\n \\n #maternal and paternal alleles\\n M.append(tuple(line[5:7]))\\n MC.append(tuple(map(float, line[7:9])))\\n \\n P.append(tuple(line[9:11]))\\n PC.append(tuple(map(float, line[11:13]))) \\n \\n in_file.close()\\n return positions, samples, M, P, MC, PC\",\n \"def parse_input(part=1):\\n with open(\\\"input_23.txt\\\") as f:\\n return [int(x) for x in f.readline().strip()]\",\n \"def lineReadfile(filename):\\n#\\t\\\"input:filename output=readlines() \\\"\\n\\tf = open(filename)\\n\\tlist1 =[]\\n\\twhile 1:\\n\\t\\ts = f.readline()\\n\\t\\tif s==\\\"\\\":\\n\\t\\t\\tbreak\\n\\t\\ts=string.replace(s,\\\"\\\\n\\\",\\\"\\\")\\n\\t\\tif s==\\\"\\\":\\n\\t\\t\\tcontinue\\n\\t\\tlist1.append(s)\\n\\tf.close()\\n\\treturn list1\",\n \"def read_input(input_file):\\n \\n logging.info(\\\"reading file {0}...this may take a while\\\".format(input_file))\\n with gzip.open(input_file, 'rb') as f:\\n for i, line in enumerate(f):\\n \\n if (i % 10000 == 0):\\n logging.info(\\\"read {0} reviews\\\".format(i))\\n # do some pre-processing and return list of words for each review\\n # text\\n yield gensim.utils.simple_preprocess(line)\",\n \"def read_transcript_data(fn):\\n\\n def _read_lines(fn):\\n # NC_000007.13\\tRefSeq\\tcDNA_match\\t50344265\\t50344518\\t254\\t+\\t.\\tID=aln58042;Target=NM_001220765.2 1 254 +;gap_count=0;identity=0.0691326;idty=1;num_ident=428;num_mismatch=0;pct_coverage=6.91326;pct_identity_gap=100;pct_identity_ungap=100;score=254\\n # NC_000002.11 RefSeq cDNA_match 179671939 179672150 212 - . ID=ed951d46-194c-477a-a480-4bc64530c5ba;Target=NM_001267550.2 1 212 +;gap_count=0;identity=0.999991;idty=1;num_ident=109223;num_mismatch=1;pct_coverage=100;pct_identity_gap=99.9991;pct_identity_ungap=99.9991\\n line_re = re.compile(\\n \\\"(?P<ref_ac>\\\\S+)\\\\s+(?P<origin>\\\\S+)\\\\s+(?P<match_type>\\\\S+)\\\\s+\\\"\\n \\\"(?P<g_start>\\\\d+)\\\\s+(?P<g_end>\\\\d+)\\\\s+(?P<score>\\\\S+)\\\\s+\\\"\\n \\\"(?P<strand>[-+])\\\\s+\\\\.\\\\s+ID=(?P<aln>[^;]+);Target=(?P<tx_ac>\\\\S+)\\\"\\n \\\"\\\\s+(?P<tx_start>\\\\d+)\\\\s+(?P<tx_end>\\\\d+).+?\\\"\\n \\\"pct_coverage=(?P<pct_coverage>[^;]+);\\\"\\n \\\"pct_identity_gap=(?P<pct_identity_gap>[^;]+);\\\"\\n \\\"pct_identity_ungap=(?P<pct_identity_ungap>[^;]+)\\\"\\n )\\n fh = io.open(fn, \\\"rb\\\")\\n while fh.peek(1)[0] == \\\"#\\\":\\n fh.readline()\\n while fh.peek(3)[0:3] != \\\"###\\\":\\n line = fh.readline()\\n try:\\n yield line_re.match(line).groupdict()\\n except AttributeError:\\n raise Exception(\\\"Failed at\\\", line)\\n raise StopIteration\\n def _key(e):\\n return (e[\\\"tx_ac\\\"], not e[\\\"ref_ac\\\"].startswith(\\\"NC_\\\"), e[\\\"ref_ac\\\"], e[\\\"aln\\\"])\\n return itertools.groupby(sorted(_read_lines(fn), key=_key), key=_key)\",\n \"def parse_lines(self, lines):\\n assert isinstance(lines, Iterable)\\n\\n for line in lines:\\n name, values = self.parse_line(line)\\n self.add(name, values)\",\n \"def process_lines(line_queue, result_queue, sim_info, analysis_info, args, merged_reads):\\n\\t\\n\\twhile True:\\n\\t\\tline = line_queue.get()\\n\\t\\t\\n\\t\\t# if we're done, stop\\n\\t\\tif line is None:\\n\\t\\t\\tresult_queue.put(None)\\n\\t\\t\\tbreak\\n\\t\\t# skip header lines\\n\\t\\telif line[0] == '@':\\n\\t\\t\\tcontinue\\n\\t\\t# process this line\\n\\t\\telse:\\n\\t\\t\\tresult = score_read(line, sim_info, analysis_info, args, merged_reads)\\n\\t\\t\\tresult_queue.put(result)\",\n \"def read (self, path):\\n\\n\\t\\tself.data = []\\n\\t\\t# print \\\"*** path: %s***\\\" % path\\n\\t\\tdir, filename = os.path.split (path)\\n\\t\\troot, ext = os.path.splitext (filename)\\n\\t\\t# encoding = 'ISO-8859-1' # utf-8\\n\\t\\ts = codecs.open(path,'r', self.encoding).read()\\n\\t\\t## s = unicode(f.read(),'utf-8')\\n\\t\\ts = self.preprocess (s)\\n\\t\\tlines = split (s, self.linesep)\\n\\t\\tschema = self.splitline(lines[0])\\n\\n\\t\\t## print \\\"** %s **\\\" % os.path.splitext(filename)[0]\\n\\t\\tif self.verbose:\\n\\t\\t\\tprint \\\"read %d lines from %s\\\" % (len(lines), path)\\n\\n\\t\\tfor i in range(1,len(lines)):\\n\\t\\t\\tif not lines[i].strip(): \\n\\t\\t\\t\\t# print 'skipping line (%d)' % i\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tfields = self.splitline(lines[i])\\n\\t\\t\\titem = self.entry_class (fields, schema)\\n\\t\\t\\tif self.accept (item):\\n\\t\\t\\t\\tself.add (item)\\n\\n\\t\\tself.schema = schema\\n\\t\\t# self.data.sort (lastNameCmp)\",\n \"def read():\\n try:\\n #Open and parse input files.\\n nodeFile = open(sys.argv[1], 'r')\\n edgeFile = open(sys.argv[2], 'r')\\n\\t\\n parse_nodes(nodeFile)\\n parse_edges(edgeFile)\\n nodeFile.close()\\n\\tedgeFile.close()\\n\\treturn \\n except:\\n print 'problem parsing input'\\n #Put here some more information - usage...\",\n \"def read_line(self, *types):\\n words = self.in_file.readline().strip().split()\\n if len(words) == 1:\\n return types[0](words[0])\\n return [types[min(i, len(types) - 1)](words[i])\\n for i in range(len(words))]\",\n \"def readAllfromFile(self):\\n with open(self._fname, 'r') as f:\\n lines = f.readlines()\\n readList = []\\n for line in lines:\\n line = line.strip()\\n if len(line) > 1:\\n gra = self._readGrafromLine(line)\\n readList.append(gra)\\n f.close()\\n return readList\",\n \"def parse_file(self, parse_all=False, file=None):\\n if not parse_all:\\n input()\\n for line in sys.stdin:\\n self.parse_and_add(line)\",\n \"def read_input(input_file):\\n\\n logging.info(\\\"reading file {0}...this may take a while\\\".format(input_file))\\n with gzip.open(input_file, 'rb') as f:\\n for i, line in enumerate(f):\\n\\n if i % 10000 == 0:\\n logging.info(\\\"read {0} reviews\\\".format(i))\\n # do some pre-processing and return list of words for each review\\n # text\\n yield gensim.utils.simple_preprocess(line)\",\n \"def readrules(self, fomalines):\\n for lineno, l in enumerate(fomalines):\\n if 'define' in l or 'def ' in l:\\n rulecom = l.split(' #')\\n r = re.findall(\\\"(defi?n?e?)\\\\s+(\\\\S+)\\\\s+([^;]+)\\\", rulecom[0])\\n if len(r[0]) != 3:\\n print \\\"Syntax error on line %i\\\" % lineno\\n (_, rulename, rule) = r[0]\\n if len(rulecom) > 1:\\n commentline = rulecom[1].strip()\\n else:\\n commentline = ''\\n self.rule_add(rulename, rule, commentline)\\n if 'chain' in l:\\n l = l.replace(';','')\\n chain = re.findall('chain\\\\s+(.*)', l)\\n rc = chain[0].replace(' ','').split(',')\\n self.rc = rc\",\n \"def _parse(self, infile):\\n raise NotImplementedError()\",\n \"def parse_input():\\r\\n endpoints_file = open(\\\"input.txt\\\", \\\"r\\\")\\r\\n lines = endpoints_file.readlines()\\r\\n line_number = 0\\r\\n endpoints = []\\r\\n for line in lines:\\r\\n line = line.strip()\\r\\n if line_number == 0:\\r\\n period = int(line)\\r\\n elif line_number == 1:\\r\\n max_attempts = int(line)\\r\\n else:\\r\\n endpoints.append(line)\\r\\n line_number += 1\\r\\n endpoints_file.close()\\r\\n return endpoints, period, max_attempts\",\n \"def read_data(cls, input_file, quotechar=None):\\r\\n if 'pkl' in str(input_file):\\r\\n lines = load_pickle(input_file)\\r\\n else:\\r\\n lines = input_file\\r\\n return lines\",\n \"def read_input():\\n splits = []\\n with open('solutions/day5/input.txt') as f:\\n for line in f:\\n splits.append(line.strip('\\\\n'))\\n return splits\",\n \"def read_file(fname):\\n recs=[]\\n logger.info('Start read file %s', fname)\\n with open(fname) as inf:\\n for line in inf:\\n recs.append(line.strip().split())\\n logger.info('End reading with recnumber %d', len(recs))\\n return recs\",\n \"def read_input_file(path: str):\\n with open(path, \\\"r\\\") as f:\\n return f.readlines()\",\n \"def parse(self, infile):\\r\\n raise NotImplementedError()\",\n \"def parse_fastq (rec_lines):\\n data = []\\n data.append(rec_lines[0][1:])\\n data.append(rec_lines[1])\\n data.append(rec_lines[3])\\n return data\",\n \"def job_reader(path) -> Generator[ParsedActionType, None, None]:\\n try:\\n with open(path, \\\"r\\\") as f:\\n parser = Parser()\\n for line in f:\\n result = parser.process_line(line)\\n if result is not None:\\n yield result\\n except IOError as err:\\n print(\\\"Error opening/reading from file '{0}': {1}\\\"\\n .format(err.filename, err.strerror))\",\n \"def read_data(input_file):\\n\\n def process_line(labels, words):\\n l = ' '.join([label for label in labels if len(label) > 0])\\n w = ' '.join([word for word in words if len(word) > 0])\\n lines.append((l, w))\\n words = []\\n labels = []\\n return words, labels, lines\\n\\n rf = open(input_file, 'r')\\n lines = [];\\n words = [];\\n labels = []\\n for line in rf:\\n word = line.strip().split(' ')[0]\\n label = line.strip().split(' ')[-1]\\n # here we dont do \\\"DOCSTART\\\" check\\n\\n if len(line.strip()) == 0: # and words[-1] == '.'\\n words, labels, lines = process_line(labels, words)\\n words.append(word)\\n labels.append(label)\\n rf.close()\\n return lines\",\n \"def read_data(cls, input_file,quotechar = None):\\n if 'pkl' in str(input_file):\\n lines = load_pickle(input_file)\\n else:\\n lines = input_file\\n return lines\",\n \"async def readlines(self, hint=-1):\\n if 'b' in self._mode:\\n raise APIException(\\n \\\"readline on a binary file is not permitted: {}\\\".format(\\n self._uri)\\n )\\n # read the entire file in and decode it\\n lines = await self.read().decode().split(\\\"\\\\n\\\")\\n return lines\",\n \"def readlines(self) -> list[bytes] | None:\",\n \"def read_input_file(path):\\n file_input = os.path.join(path, 'INPUT')\\n f = open(file_input, 'r')\\n lines = f.read().replace('\\\\n', ':')\\n f.close()\\n lines = ' '.join(lines.split()) #remove whitespace\\n lines = re.split(':', lines.replace(': ', ':'))\\n return lines\",\n \"def read_records(self, input_path, offset_range_tracker):\\n start_offset = offset_range_tracker.start_position()\\n with tf.io.gfile.GFile(input_path, 'r') as f:\\n f.seek(start_offset)\\n # Read the first key\\n line = f.readline()\\n key = line.strip('>\\\\n')\\n seq = ''\\n while True:\\n line = f.readline()\\n if line.startswith('>'):\\n # New record reached.\\n yield (key, seq)\\n self.fasta_records_counter.inc()\\n key = line.strip('>\\\\n')\\n seq = ''\\n else:\\n seq += line.strip()\\n\\n if not line:\\n # End of file reached; Yield and break from loop\\n yield (key, seq)\\n self.fasta_records_counter.inc()\\n break\",\n \"def _readline_ins(self):\\n if self._ins_filehandle is None:\\n if not os.path.exists(self._ins_filename):\\n raise Exception(\\n \\\"instruction file '{0}' not found\\\".format(self._ins_filename)\\n )\\n self._ins_filehandle = open(self._ins_filename, \\\"r\\\")\\n line = self._ins_filehandle.readline()\\n self._ins_linecount += 1\\n if line == \\\"\\\":\\n return None\\n self._last_line = line\\n # check for spaces in between the markers - this gets ugly\\n line = line.lower()\\n if self._marker is not None and self._marker in line:\\n\\n # def find_all(a_str, sub):\\n # start = 0\\n # while True:\\n # start = a_str.find(sub, start)\\n # if start == -1:\\n # return\\n # yield start\\n # start += len(sub)\\n # poss speedup using regex\\n midx = [m.start() for m in re.finditer(re.escape(self._marker), line)]\\n # midx = list(find_all(line, self._marker))\\n midx.append(len(line))\\n first = line[: midx[0]].strip()\\n tokens = []\\n if len(first) > 0:\\n # tokens.append(first)\\n tokens.extend([f.strip() for f in first.split()])\\n for idx in range(1, len(midx) - 1, 2):\\n mstr = line[midx[idx - 1] : midx[idx] + 1]\\n ostr = line[midx[idx] + 1 : midx[idx + 1]]\\n tokens.append(mstr)\\n tokens.extend(ostr.split())\\n else:\\n tokens = line.strip().split()\\n return tokens\",\n \"def __parse(self):\\n lines = self.file.readlines()\\n for i in range(0, len(lines)):\\n line = lines[i]\\n tokens = line.split()\\n if tokens[0] == \\\"#start\\\":\\n trial_name = tokens[1]\\n trial = Trial(trial_name)\\n self.trials[trial_name] = trial\\n elif tokens[0] == \\\"#end\\\":\\n continue\\n else:\\n date_str = tokens[0] + \\\" \\\" + tokens[1]\\n date = datetime.strptime(date_str, \\\"%m/%d/%y %H:%M:%S\\\")\\n sound_file = line[18:-1].strip()\\n event = Event(date, sound_file, 0)\\n trial.addevent(event)\",\n \"def read_input(self, infile):\\n self.infile = open(infile, 'r')\\n input_list = None\\n for l in [line.strip('\\\\n') for line in self.infile]:\\n input_list = l.split(' ')\\n \\n pid = 0\\n for i in range(0, len(input_list), 2):\\n self.processes.append(Process(pid, int(input_list[i]), int(input_list[i+1])))\\n pid += 1\\n self.process_count = len(self.processes)\\n self.infile.close()\\n return\",\n \"def read(infile):\\n if isinstance(infile, str):\\n infile = open(infile)\\n\\n with infile:\\n while True:\\n cmt = infile.readline().strip()\\n seq = infile.readline().strip()\\n plus = infile.readline().strip()\\n qual = infile.readline().strip()\\n\\n if not cmt:\\n break\\n if not cmt.startswith('@') or plus != '+':\\n raise ValueError('fastq file <{}> is corrupted'.format(infile.path))\\n yield SRecord(cmt=cmt[1:], seq=seq, qual=qual)\",\n \"def read_input():\\n # Use with to make sure the file will be closed after the block executed\\n with open('snapshot_input.txt') as f:\\n # Split the line at line breaks\\n x = f.read().splitlines()\\n # Get the data of restructuring, three positive integers N , C , and D\\n # Use generator expression for time and space efficiency\\n restructuring_info = (i.split() for i in x if len(i.split())==3)\\n # Get the data of single machine, four integers D, P, R and G\\n machine_info = (i.split() for i in x if len(i.split())!=3)\\n # Get the length of restructuring data\\n length = sum(1 for i in x if len(i.split())==3)\\n\\n return restructuring_info, machine_info, length\",\n \"def get_input():\\n # return TEST_INPUT.split('\\\\n')\\n with open(INPUT_FILE) as f:\\n return f.readlines()\",\n \"def _fetch_data_lines(self) -> list:\\n with open(self.full_path, \\\"rb\\\") as file:\\n all_lines = file.readlines()\\n return self._screen_invalid_lines(all_lines)\",\n \"def read_text(self, text):\\n if isinstance(text, (str, unicode)):\\n lines = text.split('\\\\n')\\n else:\\n lines = text\\n\\n for line in lines:\\n l = line.strip()\\n\\n if line == ST_POS0:\\n self._state = ST_POS0\\n elif line == ST_TRNS:\\n self._state = ST_TRNS\\n elif line == ST_POS0:\\n self._state = ST_POS0\\n else:\\n self._parse_line(line)\",\n \"def read(file):\\n\\n blocks = ['bus', 'load', 'fshunt', 'gen', 'branch', 'transf', 'area',\\n 'twotermdc', 'vscdc', 'impedcorr', 'mtdc', 'msline', 'zone',\\n 'interarea', 'owner', 'facts', 'swshunt', 'gne', 'Q']\\n nol = [1, 1, 1, 1, 1, 4, 1,\\n 0, 0, 0, 0, 0, 1,\\n 0, 1, 0, 0, 0, 0]\\n rawd = re.compile('rawd\\\\d\\\\d')\\n\\n retval = True\\n version = 0\\n b = 0 # current block index\\n raw = {}\\n for item in blocks:\\n raw[item] = []\\n\\n data = []\\n mdata = [] # multi-line data\\n mline = 0 # line counter for multi-line models\\n\\n # parse file into raw with to_number conversions\\n fid = open(file, 'r')\\n for num, line in enumerate(fid.readlines()):\\n line = line.strip()\\n if num == 0: # get basemva and frequency\\n data = line.split('/')[0]\\n data = data.split(',')\\n\\n mva = float(data[1])\\n freq = float(data[5])\\n version = int(data[2])\\n\\n if not version:\\n version = int(rawd.search(line).group(0).strip('rawd'))\\n if version < 32 or version > 33:\\n logging.warning('RAW file version is not 32 or 33. Error may occur.')\\n continue\\n elif num == 1: # store the case info line\\n logging.info(line)\\n continue\\n elif num == 2:\\n continue\\n elif num >= 3:\\n if line[0:2] == '0 ' or line[0:3] == ' 0 ': # end of block\\n b += 1\\n continue\\n elif line[0] is 'Q': # end of file\\n break\\n data = line.split(',')\\n\\n data = [to_number(item) for item in data]\\n mdata.append(data)\\n mline += 1\\n if mline == nol[b]:\\n if nol[b] == 1:\\n mdata = mdata[0]\\n raw[blocks[b]].append(mdata)\\n mdata = []\\n mline = 0\\n fid.close()\\n\\n # add device elements params and add to PSAT formatted dictionary\\n\\n for data in raw['bus']:\\n \\\"\\\"\\\"version 32:\\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10\\n ID, NAME, BasekV, Type, Area Zone Owner Va, Vm, latitude longitude\\n \\\"\\\"\\\"\\n idx = data[0]\\n ty = data[3]\\n angle = data[8]\\n try:\\n lat = data[9]\\n except:\\n # logging.warning('<No Coordinates in .raw file>')\\n param = {'idx': idx,\\n 'name': data[1],\\n 'Vn': data[2],\\n 'type': data[3],\\n 'area': data[4],\\n 'voltage': data[7],\\n 'region': data[5],\\n 'owner': data[6],\\n 'angle': angle,\\n }\\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5]]\\n else:\\n param = {'idx': idx,\\n 'name': data[1],\\n 'Vn': data[2],\\n 'type': data[3],\\n 'area': data[4],\\n 'voltage': data[7],\\n 'region': data[5],\\n 'owner': data[6],\\n 'angle': angle,\\n 'latitude': data[9],\\n 'longitude': data[10]\\n }\\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5], data[9], data[10]]\\n Settings.Bus.append(psatlist)\\n Settings.BusNames.append(data[1])\\n # Add BusSTORE Dictionary For Later Reference\\n Settings.BusStore[idx] = param\\n\\n xcoord = [34.560040, 34.938385, 34.360040, 40.5152473, 40.3142473, 36.527401, 36.857401, 36.687401, 36.856401,\\n 40.487041, 36.903901, 36.702901, 35.832561, 33.386047, 33.185047, 37.105571, 37.104154, 33.706718,\\n 37.103549, 36.703539, 37.103559, 36.703549, 36.033561, 35.631561, 36.032561, 35.732561, 36.525401,\\n 36.857401, 49.869314, 50.969314, 51.979314, 52.481674, 54.973192, 56.276212, 41.734596, 34.551015,\\n 34.652015, 34.537507, 34.587507, 34.157904, 33.714453, 33.762453, 39.548160, 39.496160, 34.313143,\\n 34.545782, 34.380686, 34.111686, 34.137762, 34.118650, 34.158650, 33.918650, 33.718650, 34.018650,\\n 34.018650, 34.018650, 34.018650, 34.018650, 34.312456, 34.315456, 34.243600, 34.566258, 34.565258,\\n 46.064672, 46.565672, 45.514571, 45.606833, 45.806833, 44.890000, 45.596416, 45.295416, 45.891161,\\n 47.954899, 46.511440, 45.913936, 45.713936, 46.669335, 47.954899, 47.624154, 43.784730, 44.482350,\\n 42.006860, 42.934919, 42.731919, 43.013135, 44.068350, 43.558350, 42.438350, 42.938350, 44.068350,\\n 43.558350, 43.048350, 42.638350, 44.068350, 43.558350, 43.048350, 42.638350, 43.620189, 39.120428,\\n 40.398031, 35.216200, 35.215200, 36.202099, 39.777745, 39.539598, 37.052929, 35.403217, 35.352217,\\n 36.807243, 39.567450, 40.807689, 40.806689, 41.008689, 39.555494, 37.954721, 38.406721, 38.906721,\\n 38.656721]\\n ycoord = [-109.277313, -110.303798, -109.777313, -107.546455, -107.546455, -108.325669, -108.654569, -108.486669,\\n -108.325669, -107.185575, -111.390408, -111.390408, -111.448566, -112.860397, -112.659397, -108.243555,\\n -108.441191, -112.322033, -111.590816, -111.190816, -111.190816, -111.590806, -111.648566, -111.248566,\\n -111.249566, -111.647566, -108.655669, -108.323669, -122.150895, -122.150895, -122.150895, -121.61684,\\n -121.924221, -122.21370, -108.790427, -117.568105, -117.538105, -118.607375, -118.658375, -118.280282,\\n -118.146319, -118.096319, -112.52797, -112.72797, -118.690631, -118.389938, -118.478496, -118.478496,\\n -118.299917, -118.095428, -118.095428, -118.095428, -118.095428, -118.195428, -118.395428, -117.995428,\\n -117.795428, -117.995428, -118.481217, -118.891217, -118.391667, -117.166428, -117.368428, -106.60906,\\n -106.80906, -122.681289, -121.114785, -122.113785, -123.29000, -121.312202, -121.114202, -106.612578,\\n -118.997945, -112.88531, -120.692286, -120.693974, -119.571501, -120.997945, -122.219492, -118.77463,\\n -121.019484, -121.316546, -114.419206, -114.419206, -120.956476, -120.79484, -120.93484, -121.216546,\\n -121.156546, -121.215484, -121.135484, -121.255484, -121.175484, -121.013484, -120.733484, -121.053484,\\n -120.973484, -118.865882, -122.073631, -122.263453, -120.847567, -120.900567, -120.129849, -122.142965,\\n -122.262993, -121.021929, -119.450452, -119.450452, -121.779037, -122.276225, -122.135718, -121.935718,\\n -121.935718, -121.24000, -121.18379, -121.10879, -121.27379, -121.23979]\\n\\n #for idx, line in enumerate(Settings.Bus):\\n # line.extend([xcoord[idx], ycoord[idx]])\\n\\n maxV = 1.1\\n minV = 0.9\\n maxQ = 1\\n minQ = 0\\n convimp = 0\\n status = 1\\n loss = 1\\n\\n for data in raw['load']:\\n \\\"\\\"\\\"version 32:\\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11\\n Bus, Id, Status, Area, Zone, PL(MW), QL (MW), IP, IQ, YP, YQ, OWNER\\n \\\"\\\"\\\"\\n\\n busidx = data[0]\\n vn = Settings.BusStore[busidx]['Vn']\\n voltage = Settings.BusStore[busidx]['voltage']\\n param = {'bus': busidx,\\n 'Vn': vn,\\n 'Sn': mva,\\n 'p': (data[5] + data[7] * voltage + data[9] * voltage ** 2) / mva,\\n 'q': (data[6] + data[8] * voltage - data[10] * voltage ** 2) / mva,\\n 'owner': data[11],\\n 'type': Settings.BusStore[busidx]['type'],\\n 'voltage': voltage\\n }\\n\\n psatlist = [busidx, mva, vn, param['p'], param['q'], maxV, minV, convimp, status]\\n Settings.PQ.append(psatlist)\\n \\\"\\\"\\\"CONFIRM THAT OTHER BUSES HAVE 0 P and 0 Q which are not added\\\"\\\"\\\"\\n\\n for data in raw['fshunt']:\\n \\\"\\\"\\\"\\n 0, 1, 2, 3, 4\\n Bus, name, Status, g (MW), b (Mvar)\\n \\\"\\\"\\\"\\n busidx = data[0]\\n vn = Settings.BusStore[busidx]['Vn']\\n param = {'bus': busidx,\\n 'Vn': vn,\\n 'status': data[2],\\n 'Sn': mva,\\n 'g': data[3] / mva,\\n 'b': data[4] / mva,\\n }\\n\\n psatlist = [busidx, mva, vn, freq, param['g'], param['b'], param['status']]\\n Settings.Shunt.append(psatlist)\\n\\n gen_idx = 0\\n type = 6\\n\\n for data in raw['gen']:\\n \\\"\\\"\\\"\\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11, 12, 13, 14, 15, 16,17,18,19\\n I,ID,PG,QG,QT,QB,VS,IREG,MBASE,ZR,ZX,RT,XT,GTAP,STAT,RMPCT,PT,PB,O1,F1\\n \\\"\\\"\\\"\\n busidx = data[0]\\n vn = Settings.BusStore[busidx]['Vn']\\n gen_mva = data[8]\\n gen_idx += 1\\n status = data[14]\\n leak = 0\\n param = {'Sn': gen_mva,\\n 'Vn': vn,\\n 'u': status,\\n 'idx': gen_idx,\\n 'bus': busidx,\\n 'pg': status * data[2] / mva,\\n 'qg': status * data[3] / mva,\\n 'qmax': data[4] / mva,\\n 'qmin': data[5] / mva,\\n 'v0': data[6],\\n 'ra': data[9], # ra armature resistance\\n 'xs': data[10], # xs synchronous reactance\\n 'pmax': data[16] / mva,\\n 'pmin': data[17] / mva,\\n }\\n\\n if Settings.BusStore[busidx]['type'] == 3: #Check Bus Type for Slack\\n refangle = 0\\n refBus = 1\\n PGuess = 1\\n swlist = [busidx, gen_mva, vn, param['v0'], refangle, param['qmax'], param['qmin'],\\n maxV, minV, PGuess, loss, refBus, status]\\n SW = swlist\\n Settings.SW.append(swlist)\\n Settings.SWStore[busidx] = param\\n Settings.SynStore[busidx] = param\\n continue\\n\\n if busidx not in Settings.BusStore.keys():\\n \\\"\\\"\\\" Need data from .dyr file. Create initial list, then append data from .dyr\\\"\\\"\\\"\\n else:\\n # psatlist = [busidx, gen_mva, vn, freq, type, leak, param['ra'],param['xs']]\\n # Syn.append(psatlist)\\n Settings.SynStore[busidx] = param\\n pvlist = [busidx, gen_mva, vn, param['pg'], Settings.BusStore[busidx]['voltage'],\\n param['qmax'], param['qmin'], maxV, minV, loss, status]\\n Settings.PV.append(pvlist)\\n\\n\\n for data in raw['branch']:\\n \\\"\\\"\\\"\\n I,J,ID,R,X,B,RATEA,RATEB,RATEC,GI,BI,GJ,BJ,ST,LEN,O1,F1,...,O4,F4\\n \\\"\\\"\\\"\\n param = {'bus1': data[0],\\n 'bus2': data[1],\\n 'id' : data[2],\\n 'r': data[3],\\n 'x': data[4],\\n 'b': data[5],\\n 'rate_a': data[6],\\n 'rate_b': data[7],\\n 'rate_c': data[8],\\n 'Vn': Settings.BusStore[data[0]]['Vn'],\\n 'Vn2': Settings.BusStore[data[1]]['Vn'],\\n 'length': data[14],\\n 'Ilim': EMPTY,\\n 'Plim': EMPTY,\\n 'Slim': EMPTY,\\n 'status': data[13]\\n }\\n\\n psatlist = [param['bus1'], param['bus2'], param['rate_c'], param['Vn'], freq, EMPTY,\\n param['length'], param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\\n param['Slim'], param['status']]\\n Settings.Lineij.append([data[0], data[1], data[2]])\\n Settings.Lineji.append([data[1], data[0], data[2]])\\n Settings.LineOrd[param['bus1']].append(psatlist)\\n Settings.branches += 1\\n Settings.linecount += 1\\n Settings.LineBusMatij[param['bus2']].append(Settings.branches)\\n Settings.LineBusMatji[param['bus1']].append(Settings.branches)\\n\\n for data in raw['transf']:\\n \\\"\\\"\\\"\\n I,J,K,CKT,CW,CZ,CM,MAG1,MAG2,NMETR,'NAME',STAT,O1,F1,...,O4,F4\\n R1-2,X1-2,SBASE1-2\\n WINDV1,NOMV1,ANG1,RATA1,RATB1,RATC1,COD1,CONT1,RMA1,RMI1,VMA1,VMI1,NTP1,TAB1,CR1,CX1\\n WINDV2,NOMV2\\n \\\"\\\"\\\"\\n if len(data[1]) < 5:\\n ty = 2\\n else:\\n ty = 3\\n if ty == 3:\\n continue\\n # raise NotImplementedError('Three-winding transformer not implemented')\\n\\n tap = data[2][0]\\n phi = data[2][2]\\n\\n if tap == 1 and phi == 0:\\n trasf = False\\n else:\\n trasf = True\\n param = {'trasf': trasf,\\n 'bus1': data[0][0],\\n 'bus2': data[0][1],\\n 'u': data[0][11],\\n 'b': data[0][8],\\n 'r': data[1][0],\\n 'x': data[1][1],\\n 'tap': tap,\\n 'phi': phi,\\n 'rate_a': data[2][3],\\n 'Vn': Settings.BusStore[busidx]['Vn'],\\n 'Vn2': Settings.BusStore[busidx]['Vn'],\\n # 'length': data[?][?], FIND CORRECT INDEX\\n 'Ilim': EMPTY,\\n 'Plim': EMPTY,\\n 'Slim': EMPTY,\\n }\\n psatlist = [param['bus1'], param['bus2'], param['rate_a'], param['Vn'], freq, EMPTY,\\n EMPTY, param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\\n param['Slim'], param['u']]\\n\\n Settings.LineOrd[param['bus1']].append(psatlist)\\n Settings.linecount += 1\\n Settings.transformers += 1\\n # ADD Line Data(All Branch Types) to Sys Param Dict after .dyr Transformer Data Added\\n # Re-Order Line Data for correct sequence\\n for key in Settings.LineOrd:\\n for item in Settings.LineOrd[key]:\\n Settings.Line.append(item)\\n\\n for data in raw['area']:\\n Settings.Areas.append(data[4])\\n\\n for data in raw['zone']:\\n Settings.Regions.append(data[1])\\n\\n return retval\",\n \"def read_input():\\n return Path(__file__).with_name('input.txt').read_text().splitlines()\",\n \"def __read_data__(self):\\n with open(self.file, 'r') as data:\\n sentence = []\\n tags = []\\n for line in data:\\n terms = line.rstrip().split(WHITESPACE)\\n for term in terms:\\n word_tag = tuple(term.split(TAGCHAR))\\n word = word_tag[0]\\n tag = word_tag[1]\\n self.word_tag_dict[word_tag] += 1\\n self.tag_dict[tag] += 1\\n self.__add_to_word_dict__(word, tag)\\n if self.isNumberWord(word):\\n self.numbers += 1\\n if word[0].isupper() and len(sentence) > 0:\\n self.cap_no_start += 1\\n sentence.append(word)\\n tags.append(tag)\\n if tag == ENDOFSENTENCE:\\n self.sentences.append(tuple(sentence))\\n self.tags.append(tuple(tags))\\n sentence = []\\n tags = []\",\n \"def read_lines(fp: TextIO) -> Iterator[str]:\\n while line := fp.readline().strip():\\n yield line\",\n \"def readlines(path):\\n with open(path, \\\"r\\\") as f:\\n return [line.strip() for line in f.readlines()]\",\n \"def parse_data(self, fileinput):\\n with open(fileinput, 'rb') as fh:\\n for line in fh:\\n try:\\n name, address, phone = line.strip().split(\\\",\\\")\\n self.data.append(Document(name, address, phone))\\n except Exception, ex:\\n raise SerialException(\\\": Failed to parse input line %s: %s\\\" % (line, ex))\\n return\",\n \"def read_linelog():\",\n \"def _read(self, openf=None, stepfilter=None):\\n itemstack = []\\n current = None\\n result = {}\\n xkeys = None\\n timeskip = False\\n laststep = False\\n\\n if openf is None:\\n f = open(self.filepath)\\n else:\\n f = openf\\n\\n line = 'start'\\n while line != '':\\n lastpos = f.tell()\\n line = f.readline()\\n if line == '':\\n continue\\n \\n if itemstack is not None and len(itemstack) > 0: \\n cast = itemstack.pop()\\n raw = line.split()\\n values = [t(r) for t, r in zip(cast, raw)]\\n if len(values) == 1:\\n values = values[0]\\n\\n if current == \\\"time\\\":\\n if stepfilter is not None and values not in stepfilter:\\n timeskip = True\\n elif (self.index is not None and values != self.index):\\n if values > self.index:\\n if openf is None:\\n return {}\\n else:\\n timeskip = True\\n laststep = True\\n else:\\n timeskip = True\\n elif self.index is None:\\n self.index = values\\n else:\\n timeskip = False\\n \\n if len(itemstack) == 0 and current not in result:\\n result[current] = values\\n else:\\n if current not in result:\\n result[current] = []\\n result[current].append(values)\\n continue\\n elif itemstack is None and current == \\\"atoms\\\":\\n if \\\"ITEM\\\" in line:\\n current = None\\n if openf is not None:\\n f.seek(lastpos)\\n break\\n else:\\n #E.g. line: 1 4 -65.9625 1.54915 1.46824 5 30.976 \\n vals = line.split()\\n sid, atype = tuple(map(int, vals[0:2]))\\n result[\\\"type\\\"].append(atype)\\n result[\\\"id\\\"].append(sid)\\n x, y, z = tuple(map(float, vals[2:5]))\\n result[\\\"xyz\\\"].append((x, y, z))\\n if len(vals) > 5 and xkeys is not None:\\n for ikey, v in enumerate(vals[5:]):\\n result[xkeys[ikey]].append(eval(v))\\n continue # pragma: no cover\\n \\n if \\\"ITEM: TIMESTEP\\\" in line:\\n if laststep:\\n f.seek(lastpos)\\n break\\n itemstack.append((int,))\\n current = \\\"time\\\"\\n timeskip = False\\n elif not timeskip:\\n if \\\"ITEM: NUMBER OF ATOMS\\\" in line:\\n itemstack.append((int,))\\n current = \\\"natoms\\\"\\n elif \\\"ITEM: BOX BOUNDS\\\" in line:\\n period = line.strip().split(\\\"BOX BOUNDS\\\")\\n if len(period) == 2 and period[1] != '':\\n result[\\\"periodic\\\"] = period[1].strip().split()\\n else:\\n result[\\\"periodic\\\"] = (\\\"ss\\\", \\\"ss\\\" ,\\\"ss\\\")\\n \\n\\t\\t # Changes by JPRIEDS to accommodate triclinic boxes\\n\\t\\t # Written 170719\\n\\t\\t if len(result[\\\"periodic\\\"]) == 6:\\n\\t\\t\\titemstack.extend([(float, float, float)]*3)\\n\\t\\t\\tcurrent = \\\"box\\\"\\n\\t\\t\\tresult[\\\"periodic\\\"] = result[\\\"periodic\\\"][3:]\\n\\t\\t elif len(result[\\\"periodic\\\"]) == 3:\\n\\t\\t\\titemstack.extend([(float, float)]*3)\\n\\t\\t\\tcurrent = \\\"box\\\"\\n\\t\\t else:\\n emsg = \\\"Could not classify periodic bounds: {}\\\"\\n raise ValueError(emsg.format(result[\\\"periodic\\\"]))\\n elif \\\"ITEM: ATOMS\\\" in line:\\n itemstack = None\\n current = \\\"atoms\\\"\\n result[\\\"type\\\"] = []\\n result[\\\"id\\\"] = []\\n result[\\\"xyz\\\"] = []\\n \\n #The first two headings in the line have \\\"ITEM: ATOMS\\\", the\\n #rest are usuall id, type, x, y, z, rest...\\n headings = line.split()\\n extras = len(headings) > 7\\n if extras:\\n xkeys = []\\n xheadings = headings[7:]\\n for xhead in xheadings:\\n key = \\\"atom:{}\\\".format(xhead)\\n result[key] = []\\n xkeys.append(key)\\n \\n if openf is None:\\n #Close the file since we opened it.\\n f.close()\\n \\n return result\",\n \"def parse_file(self, file_path) -> list:\\n data = []\\n with open(file_path, 'rb') as f:\\n lines = pickle.load(f)\\n for line in lines:\\n input, output = line\\n if input.strip() == \\\"\\\" or output.strip() == \\\"\\\":\\n continue\\n input_len = len(input.split())\\n output_len = len(output.split())\\n if input_len > 50 or output_len > 50:\\n continue\\n data_item = Text2TextDataItem(input_text=input, output_text=output, tokenizer=self.tokenizer,\\n share_vocab=self.share_vocab)\\n data.append(data_item)\\n return data\",\n \"def _read(self, file_path: str) -> Iterator[Instance]:\\n with open(file_path) as f:\\n for line in f:\\n pairs = line.split()\\n words, tags = zip(*(pair.split(\\\"###\\\") for pair in pairs))\\n yield self.text_to_instance([Token(word) for word in words], tags)\",\n \"def readlines(self, n = None):\\n\\t\\tif not self._input: raise PlumberExceptions.PipeTypeException(self)\\n\\t\\tresult = []\\n\\t\\twhile n == None or n > 0:\\n\\t\\t\\tline = self.readline()\\n\\t\\t\\tif not line: return result\\n\\t\\t\\tresult.append(line)\\n\\t\\t\\tn -= 1\\n\\t\\treturn result\",\n \"def read(read_file) -> list:\\n result = []\\n try:\\n with open(read_file) as file:\\n for lines in file:\\n line = decode(lines.strip(\\\"\\\"))\\n result.append(extract_information(line))\\n global header\\n header = result[0:2]\\n result = result[3:]\\n for word in result:\\n if \\\"None\\\" in word[0:3]:\\n raise InvalidPrincessException(\\\"Invalid princess!\\\")\\n continue\\n return result\\n except FileNotFoundError:\\n raise Exception(\\\"File not found!\\\")\",\n \"def process_line(line):\\n\\n name_comp_list = []\\n givenname_comp_list = []\\n surname_comp_list = []\\n geocode_comp_list = []\\n locality_comp_list = []\\n date1_comp_list = []\\n date2_comp_list = []\\n\\n # Split the line into the basic fields - - - - - - - - - - - - - - - - - - -\\n #\\n if (config.in_file_type in ['CSV','CSVQ','TAB','TABQ']):\\n # Comma or tabulator separated\\n try:\\n line_list = config.line_parser.parse(line)\\n except:\\n log_message('CSV line parsing failed with inout: '+line,'err')\\n\\n if (len(line_list) < config.input_len):\\n log_message('Input line does not contain enough fields,' +\\\\\\n 'fill up with empty fields','warn')\\n while (len(line_list) < config.input_len):\\n line_list.append('')\\n\\n config.curr_line_list = line_list # Save current line list\\n\\n # Extract fields into different component lists - - - - - - - - - - - - - -\\n #\\n if (config.input_component['name'] != []): # Extract name fields\\n for i in config.input_component['name']:\\n name_comp_list.append(line_list[i])\\n\\n else: # Extract givenname and surname into separate components - - - - - -\\n if (config.input_component['givenname'] != []): # Extract g-name fields\\n for i in config.input_component['givenname']:\\n givenname_comp_list.append(line_list[i])\\n\\n if (config.input_component['surname'] != []): # Extract surname fields\\n for i in config.input_component['surname']:\\n surname_comp_list.append(line_list[i])\\n\\n if (config.input_component['geocode'] != []): # Extract geocode fields\\n for i in config.input_component['geocode']:\\n geocode_comp_list.append(line_list[i])\\n\\n if (config.input_component['locality'] != []): # Extract locality fields\\n for i in config.input_component['locality']:\\n locality_comp_list.append(line_list[i])\\n\\n if (config.input_component['date1'] != []): # Extract date1 fields\\n for i in config.input_component['date1']:\\n date1_comp_list.append(line_list[i])\\n\\n if (config.input_component['date2'] != []): # Extract date2 fields\\n for i in config.input_component['date2']:\\n date2_comp_list.append(line_list[i])\\n\\n elif (config.in_file_type == 'COL'): # Column based input file - - - - - - -\\n\\n if (len(line) < config.input_len):\\n log_message('Input line is not long enough, fill up with spaces','warn')\\n line += ' '*(config.input_len-len(line))\\n\\n if (config.input_component['name'] != []): # Extract name fields\\n for (col_start,length) in config.input_component['name']:\\n name_comp_list.append(line[col_start,col_start+length])\\n\\n else: # Extract givenname and surname into separate components - - - - - -\\n if (config.input_component['givenname'] != []): # Extract g-name fields\\n for (col_start,length) in config.input_component['givenname']:\\n givenname_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['surname'] != []): # Extract surname fields\\n for (col_start,length) in config.input_component['surname']:\\n surname_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['geocode'] != []): # Extract geocode fields\\n for (col_start,length) in config.input_component['geocode']:\\n geocode_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['locality'] != []): # Extract locality fields\\n for (col_start,length) in config.input_component['locality']:\\n locality_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['date1'] != []): # Extract date1 fields\\n for (col_start,length) in config.input_component['date1']:\\n date1_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['date2'] != []): # Extract date2 fields\\n for (col_start,length) in config.input_component['date2']:\\n date2_comp_list.append(line[col_start,col_start+length])\\n\\n # elif (config.in_file_type == 'SQL'): # - - - - - - - - - - - - - - - - - -\\n\\n ################################\\n # Add later: SQL database access\\n ################################\\n\\n msg = [' Component basic field lists:', \\\\\\n ' Name: '+str(name_comp_list), \\\\\\n ' Given name: '+str(givenname_comp_list), \\\\\\n ' Surname: '+str(surname_comp_list), \\\\\\n ' Geocode: '+str(geocode_comp_list), \\\\\\n ' Locality: '+str(locality_comp_list), \\\\\\n ' Date1: '+str(date1_comp_list), \\\\\\n ' Date2: '+str(date2_comp_list)]\\n log_message(msg,'v2')\\n\\n name_comp = ''\\n givenname_comp = ''\\n surname_comp = ''\\n geocode_comp = ''\\n locality_comp = ''\\n date1_comp = ''\\n date2_comp = ''\\n\\n # Now clean and then concatenate component lists into strings - - - - - - - -\\n #\\n if (name_comp_list != []): # Name component\\n name_comp = name_comp_list[0] # Start with first field in list\\n\\n for f in name_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['name'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['name'] == 1):\\n sep = check_field_spill(name_comp, f)\\n\\n name_comp = name_comp+sep+f # Append separator and field\\n\\n if (givenname_comp_list != []): # Givenname component - - - - - - - - - - -\\n givenname_comp = givenname_comp_list[0] # Start with first field in list\\n\\n for f in givenname_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['givenname'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['givenname'] == 1):\\n sep = check_field_spill(givenname_comp, f)\\n\\n givenname_comp = givenname_comp+sep+f # Append separator and field\\n\\n if (surname_comp_list != []): # Surname component - - - - - - - - - - - - -\\n surname_comp = surname_comp_list[0] # Start with first field in list\\n\\n for f in surname_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['surname'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['surname'] == 1):\\n sep = check_field_spill(surname_comp, f)\\n\\n surname_comp = surname_comp+sep+f # Append separator and field\\n\\n if (geocode_comp_list != []): # Geocode component - - - - - - - - - - - - -\\n geocode_comp = geocode_comp_list[0] # Start with first field in list\\n\\n for f in geocode_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['geocode'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['geocode'] == 1):\\n sep = check_field_spill(geocode_comp, f)\\n\\n geocode_comp = geocode_comp+sep+f # Append separator and field\\n\\n if (locality_comp_list != []): # Locality component - - - - - - - - - - - -\\n locality_comp = locality_comp_list[0] # Start with first field in list\\n\\n for f in locality_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['locality'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['locality'] == 1):\\n sep = check_field_spill(locality_comp, f)\\n\\n locality_comp = locality_comp+sep+f # Append separator and field\\n\\n if (date1_comp_list != []): # Date1 component - - - - - - - - - - - - - - -\\n date1_comp = date1_comp_list[0] # Start with first field in list\\n\\n for f in date1_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['date1'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['date1'] == 1):\\n if (date1_comp[-1] != ' ') and (f[0] != ' '):\\n tmp_list0 = date1_comp.split()\\n tmp_list1 = f.split()\\n check_word = tmp_list0[-1]+tmp_list1[0]\\n\\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\\\\n 'sep','oct','nov','dec','january','february','march','april', \\\\\\n 'may','june','july','august','september','october','november', \\\\\\n 'december']):\\n\\n sep = '' # Set separator to no space\\n msg = ' Correct date1 word spilling: \\\"'+date1_comp+'\\\",\\\"'+f+'\\\"'\\n log_message(msg,'v1')\\n\\n date1_comp = date1_comp+sep+f # Append separator and field\\n\\n if (date2_comp_list != []): # Date2 component - - - - - - - - - - - - - - -\\n date2_comp = date2_comp_list[0] # Start with first field in list\\n\\n for f in date2_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['date2'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['date2'] == 1):\\n if (date2_comp[-1] != ' ') and (f[0] != ' '):\\n tmp_list0 = date1_comp.split()\\n tmp_list1 = f.split()\\n check_word = tmp_list0[-1]+tmp_list1[0]\\n\\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\\\\n 'sep','oct','nov','dec','january','february','march','april', \\\\\\n 'may','june','july','august','september','october','november', \\\\\\n 'december']):\\n\\n sep = '' # Set separator to no space\\n msg = ' Correct date1 word spilling: \\\"'+date1_comp+'\\\",\\\"'+f+'\\\"'\\n log_message(msg,'v1')\\n\\n date2_comp = date2_comp+sep+f # Append separator and field\\n\\n # Check if name component is given or givenname and surname separately - - -\\n #\\n if (config.input_component['givenname'] != []) or \\\\\\n (config.input_component['surname'] != []):\\n name_comp = [givenname_comp, surname_comp]\\n\\n msg = [' Components:', \\\\\\n ' Name: \\\"'+str(name_comp)+'\\\"', \\\\\\n ' Geocode: \\\"'+geocode_comp+'\\\"', \\\\\\n ' Locality: \\\"'+locality_comp+'\\\"', \\\\\\n ' Date1: \\\"'+date1_comp+'\\\"', \\\\\\n ' Date2: \\\"'+date2_comp+'\\\"']\\n log_message(msg,'v1')\\n\\n return [name_comp, geocode_comp, locality_comp, date1_comp, date2_comp]\",\n \"def read_lines(filename):\\n with file(filename) as f:\\n for line in f:\\n _line = line.strip()\\n if _line:\\n yield _line\",\n \"def read_file(inp_fn):\\n lines = [line.strip().split(\\\",\\\")\\n for line in open(inp_fn)\\n if not (line.startswith(\\\"#\\\"))]\\n return [(int(line[0]), year_record({\\\"male\\\": int(line[-3]),\\n \\\"female\\\": int(line[-2]),\\n \\\"unknown\\\": int(line[-1])},\\n None, None))\\n for line in lines[1:]]\",\n \"def parse_file(self):\\n # read the first line in the file\\n line = self._stream_handle.readline()\\n\\n while line:\\n # check for a data line or a dcl logger line we specifically ignore\\n data_match = DATA_LINE_MATCHER.match(line)\\n ignore_match = IGNORE_LINE_MATCHER.match(line)\\n\\n if data_match:\\n # found a data line, extract this particle\\n # DCL controller timestamp is the port_timestamp\\n dcl_controller_timestamp = data_match.groups()[DCL_TIMESTAMP_GROUP]\\n port_timestamp = dcl_time_to_ntp(dcl_controller_timestamp)\\n\\n particle = self._extract_sample(self.particle_class,\\n None,\\n data_match,\\n port_timestamp=port_timestamp,\\n preferred_ts=DataParticleKey.PORT_TIMESTAMP)\\n\\n self._record_buffer.append(particle)\\n\\n elif not ignore_match:\\n # we found a line with an unknown format, call an exception\\n error_message = 'Found line with unknown format %s' % line\\n log.warn(error_message)\\n self._exception_callback(SampleException(error_message))\\n\\n # read the next line\\n line = self._stream_handle.readline()\",\n \"def read(filename, maximum):\\n raws = []\\n with open(filename, \\\"r\\\") as f:\\n for _, line in zip(range(maximum), f):\\n raw = parse_entry(line)\\n raws.append(raw)\\n return raws\",\n \"def parse(self):\\r\\n # open the file\\r\\n # try to find the first line with Time =\\r\\n # send the file to a recursive residualParser\\r\\n try:\\r\\n with open(self.filepath, 'rb') as f:\\r\\n for line in f:\\r\\n if line.startswith('Time ='):\\r\\n self.timestep = self.__getTime(line)\\r\\n self.__residuals[self.timestep] = {}\\r\\n self.__parseResiduals(f)\\r\\n except Exception as e:\\r\\n raise 'Failed to parse {}:\\\\n\\\\t{}'.format(self.filepath, e)\",\n \"def read(self, start_line, num_lines, timeout=0):\\n # emulate timeout by sleeping timeout time\\n if timeout > 0:\\n read_timeout = float(timeout) / 1000\\n #logging.debug(\\\"sleep \\\" + str(read_timeout) + \\\" seconds\\\")\\n time.sleep(read_timeout)\\n\\n output = []\\n attributes = []\\n\\n # get output\\n for i in range(start_line, start_line + num_lines + 1):\\n output.append(self.shm_output.read(self.columns, i * self.columns))\\n if not CONQUE_FAST_MODE:\\n attributes.append(self.shm_attributes.read(self.columns, i * self.columns))\\n\\n return (output, attributes)\",\n \"def _read_file(self, current_path):\\n # type: (str)->None\\n\\n if current_path in self._parsed_files:\\n return\\n self._parsed_files.add(current_path)\\n\\n with open(current_path, \\\"r\\\") as interfaces:\\n # When the first non-comment line is parsed, header\\n # comments have been read in.\\n header_parsed = False\\n # Loop through the interfaces file.\\n for line in interfaces:\\n # 1. Identify the clauses by analyzing the first\\n # word of each line.\\n # 2. Go to the next line if the current line is a comment.\\n # line = line.strip().replace(\\\"\\\\n\\\", \\\"\\\")\\n if not line:\\n pass\\n elif line.strip().startswith(\\\"#\\\") is True:\\n if not header_parsed:\\n self._header_comments += line\\n else:\\n # Header comments can no longer\\n # be parsed in when the first interfaces\\n # line is parsed in.\\n header_parsed = True\\n self._parse_iface(line, current_path)\\n # Ignore blank lines.\\n if not line.isspace():\\n self._parse_details(line)\\n self._read_auto(line)\\n self._read_hotplug(line)\\n\\n # Is there some file to source ?\\n source_path = self._read_sourced_path(line)\\n if source_path:\\n self._read_files(source_path)\\n\\n # TODO: lots of directives are completly ignored\\n # and would be deleted\",\n \"def parse_input(filename):\\n with open(filename, 'r') as input_file:\\n return [i.rstrip() for i in input_file]\",\n \"def read_file(self, filename):\\n with open(filename, 'r') as file:\\n for line in file:\\n l = line.strip()\\n\\n if l == ST_POS0:\\n self._state = ST_POS0\\n elif l == ST_TRNS:\\n self._state = ST_TRNS\\n elif l == ST_POS1:\\n self._state = ST_POS1\\n else:\\n self._parse_line(l)\\n self._state = None\",\n \"def _read_log(self):\\n\\n line_regex = compile(r\\\"\\\\[I\\\\]\\\\s*\\\\(\\\\d+ms\\\\)[^\\\\d]+(?P<counter>\\\\d+)\\\"\\n r\\\"[^\\\\d]+(?P<timestamp>\\\\d+(\\\\.\\\\d+)?)[^\\\\d]+\\\"\\n r\\\"(?P<acceleration>\\\\d+);\\\")\\n values = []\\n with open(self.filepath) as file:\\n for line in file:\\n match = line_regex.match(line)\\n if match:\\n values.append({\\n 'counter':\\n int(match['counter']),\\n 'timestamp':\\n int(float(match['timestamp']) * 1000),\\n 'acceleration':\\n int(match['acceleration'])\\n })\\n\\n self.values = values\",\n \"def read(self):\\n global ALIVE\\n line = sys.stdin.readline()\\n if line:\\n self.datalines.append(line.rstrip())\\n else:\\n ALIVE = False\",\n \"def load_input(self, file_name):\\n with open(file_name, \\\"r\\\") as in_file:\\n self.all_lines = [line.rstrip('\\\\n') for line in in_file]\",\n \"def _parse(self):\\n with open(_join(self.man_dir, self.man_fn)) as fp:\\n lines = fp.readlines()\\n \\n desc_indxs = []\\n for i, L in enumerate(lines):\\n if \\\"#landuse\\\" in L or \\\" # landuse\\\" in L:\\n desc_indxs.append(i-1)\\n desc_indxs.append(i-2)\\n desc_indxs.append(i-3)\\n \\n lines = [L[:L.find('#')].strip() for L in lines]\\n lines = [L for i, L in enumerate(lines) if len(L) > 0 or i in desc_indxs]\\n\\n del desc_indxs\\n \\n self.datver = lines.pop(0)\\n self.nofe = int(lines.pop(0))\\n self.sim_years = int(lines.pop(0))\\n \\n # Read Plant Growth Section\\n self.plants = PlantLoops(lines, self)\\n\\n # Read Operation Section\\n self.ops = OpLoops(lines, self)\\n \\n # Read Initial Condition Section\\n self.inis = IniLoops(lines, self)\\n \\n # Read Surface Effects Section\\n self.surfs = SurfLoops(lines, self)\\n \\n # Read Contour Section\\n self.contours = ContourLoops(lines, self)\\n \\n # Read Drainage Section\\n self.drains = DrainLoops(lines, self)\\n \\n # Read Yearly Section\\n self.years = YearLoops(lines, self)\\n \\n # Read Management Section \\n self.man = ManagementLoop(lines, self)\",\n \"def readSequenceLengths(lines):\\n sequenceLengths = {} # an OrderedDict might be nice\\n isSearching = True\\n for line in lines:\\n if line.isspace(): isSearching = True\\n elif isSearching:\\n w = line.split()\\n if w[0] == \\\"s\\\":\\n seqName, seqSize = w[1], w[5]\\n sequenceLengths[seqName] = seqSize\\n isSearching = False\\n return sequenceLengths\",\n \"def read_in_file():\\n\\t# Declare variables\\n\\treads = []\\n\\n\\t# Get command line arguments\\n\\targuments = sys.argv\\n\\targuments_length = len(arguments)\\n\\n\\t# Read file is the first argument\\n\\tread_file_name = arguments[1]\\n\\n\\t# Process read file \\n\\tread_file = open(read_file_name, 'r')\\n\\tfor line in read_file:\\n\\t\\tread_info = line.split()\\n\\t\\tread_string = read_info[2].replace('\\\\'', '')\\n\\t\\tnew_read = GenerativeRead(read_string, [], read_info[5], read_info[3], None, [], read_info[0], read_info[1], read_info[4]) \\n\\t\\treads.append(new_read)\\n\\tread_file.close()\\n\\n\\t# Repeat regions file in the second argument\\n\\trepeat_file_name = arguments[2]\\n\\n\\t# Process repeat file\\n\\trepeat_file = open(repeat_file_name, 'r')\\n\\talignments = [[]]\\n\\talignment_index = -1\\n\\tprevious_line = ''\\n\\n\\n\\tfor line in repeat_file:\\n\\t\\talignment_info = line.split()\\n\\n\\t\\t# This consists of a tuple of alignment string, alignment start position and alignment chromosome\\n\\t\\t#new_align = alignment_info[2], alignment_info[4], alignment_info[3]\\n\\n\\t\\tnew_align = Alignment(alignment_info[2], None, alignment_info[4], alignment_info[3])\\n\\n\\t\\tif previous_line != alignment_info[0]:\\n\\t\\t\\t# It is not a repeat\\n\\t\\t\\talignment_index = alignment_index + 1\\n\\t\\t\\talignments.append([])\\n\\t\\t\\tprevious_line = alignment_info[0]\\n\\n\\t\\talignments[alignment_index].append(new_align)\\n\\n\\trepeat_file.close()\\n\\n\\t# Associate each read with the other alignments\\n\\tfor read in reads:\\n\\t\\t# Find the other alignments\\n\\t\\tpos = read.get_position()\\n\\t\\tfound = False\\n\\t\\tfound_index = -1\\n\\n\\t\\tfor a_index, alignment_lists in enumerate(alignments):\\n\\t\\t\\t# find matching alignments\\n\\t\\t\\t# TODO: Don't add alignment already have\\n\\t\\t\\t# TODO: Make functional with filter\\n\\t\\t\\tfor align in alignment_lists:\\n\\t\\t\\t\\tif align.get_position() == pos:\\n\\t\\t\\t\\t\\tfound = True\\n\\t\\t\\t\\t\\tfound_index = a_index\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t\\tif found is True:\\n\\t\\t\\t\\tbreak\\n\\n\\t\\tif found is True:\\n\\t\\t\\tfor new_align in alignments[found_index]:\\n\\t\\t\\t\\tread.add_alignment(new_align)\\n\\t\\t\\t\\n\\n\\n\\t# SNP files are the remaining ones\\n\\tsnp_file_names = [arguments[file_id] for file_id in range(3, arguments_length) ]\\n\\n\\t# Process SNP files\\n\\tfor file_name in snp_file_names:\\n\\t\\tsnp_file = open(file_name, 'r')\\n\\n\\t\\tfor line in snp_file:\\n\\t\\t\\tsnp_info = line.split()\\n\\t\\t\\tsnps = snp_info[3].split('/')\\n\\t\\t\\tsnp_pos = int(float(snp_info[2]))\\n\\n\\t\\t\\t# Ignore alleles that are longer than one base\\n\\n\\t\\t\\t\\n\\t\\t\\tif all(len(x) < 2 for x in snps):\\n\\n\\t\\t\\t\\t# Iterate through reads and determine whether or not it contains this SNP\\n\\t\\t\\t\\tpos_low = snp_pos - 49\\n\\t\\t\\t\\n\\n\\t\\t\\t\\tfor read in reads:\\n\\t\\t\\t\\t\\tpositions = read.get_alignment_positions()\\n\\n\\t\\t\\t\\t\\tfor p_index, p in enumerate(positions):\\n\\t\\t\\t\\t\\t\\tp = int(float(p))\\n\\t\\t\\t\\t\\t\\tif p >= pos_low and p <= snp_pos:\\n\\t\\t\\t\\t\\t\\t\\t# Get index of snp\\n\\t\\t\\t\\t\\t\\t\\toffset = snp_pos - p\\n\\t\\t\\t\\t\\t\\t\\tcalls = [0, 0, 0, 0]\\n\\t\\t\\t\\t\\t\\t\\tfor snp in snps:\\n\\t\\t\\t\\t\\t\\t\\t\\tcall_index = get_base_num(snp)\\n\\t\\t\\t\\t\\t\\t\\t\\tcalls[call_index] = 1\\n\\n\\t\\t\\t\\t\\t\\t\\t# Add the SNP to the read\\n\\t\\t\\t\\t\\t\\t\\tread.add_snp(p_index, offset, calls)\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\tsnp_file.close()\\n\\treturn reads\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69258976","0.66003734","0.65865076","0.65514225","0.65241826","0.6469301","0.6401494","0.63737977","0.6328333","0.62591934","0.6223086","0.6206942","0.6202654","0.61415637","0.6081667","0.60471404","0.6029287","0.60218334","0.6015924","0.5982434","0.5980215","0.59731436","0.59390455","0.5904927","0.58820075","0.5865649","0.5861693","0.58583885","0.5836758","0.58355856","0.5835179","0.58326274","0.5816773","0.5811248","0.5798579","0.57956207","0.5794222","0.579349","0.579105","0.5781028","0.5778163","0.57749045","0.5765243","0.576407","0.57626003","0.57618475","0.57613456","0.5745054","0.574182","0.57372063","0.5734676","0.5727549","0.57261807","0.57242954","0.5724271","0.5719516","0.571481","0.57089597","0.56908494","0.5683099","0.5674996","0.5674703","0.5666601","0.56601816","0.56527233","0.56344855","0.56306297","0.5629145","0.562863","0.56251055","0.56250197","0.5619039","0.56184757","0.561551","0.5613401","0.5609681","0.560074","0.5596674","0.55954224","0.55922735","0.5591201","0.5581913","0.5581853","0.55776256","0.55735224","0.5573298","0.55729985","0.5571045","0.55653477","0.55592394","0.5553845","0.55501044","0.55489326","0.55421394","0.55416673","0.55246776","0.55240834","0.5516079","0.5511115","0.55077904","0.5506929"],"string":"[\n \"0.69258976\",\n \"0.66003734\",\n \"0.65865076\",\n \"0.65514225\",\n \"0.65241826\",\n \"0.6469301\",\n \"0.6401494\",\n \"0.63737977\",\n \"0.6328333\",\n \"0.62591934\",\n \"0.6223086\",\n \"0.6206942\",\n \"0.6202654\",\n \"0.61415637\",\n \"0.6081667\",\n \"0.60471404\",\n \"0.6029287\",\n \"0.60218334\",\n \"0.6015924\",\n \"0.5982434\",\n \"0.5980215\",\n \"0.59731436\",\n \"0.59390455\",\n \"0.5904927\",\n \"0.58820075\",\n \"0.5865649\",\n \"0.5861693\",\n \"0.58583885\",\n \"0.5836758\",\n \"0.58355856\",\n \"0.5835179\",\n \"0.58326274\",\n \"0.5816773\",\n \"0.5811248\",\n \"0.5798579\",\n \"0.57956207\",\n \"0.5794222\",\n \"0.579349\",\n \"0.579105\",\n \"0.5781028\",\n \"0.5778163\",\n \"0.57749045\",\n \"0.5765243\",\n \"0.576407\",\n \"0.57626003\",\n \"0.57618475\",\n \"0.57613456\",\n \"0.5745054\",\n \"0.574182\",\n \"0.57372063\",\n \"0.5734676\",\n \"0.5727549\",\n \"0.57261807\",\n \"0.57242954\",\n \"0.5724271\",\n \"0.5719516\",\n \"0.571481\",\n \"0.57089597\",\n \"0.56908494\",\n \"0.5683099\",\n \"0.5674996\",\n \"0.5674703\",\n \"0.5666601\",\n \"0.56601816\",\n \"0.56527233\",\n \"0.56344855\",\n \"0.56306297\",\n \"0.5629145\",\n \"0.562863\",\n \"0.56251055\",\n \"0.56250197\",\n \"0.5619039\",\n \"0.56184757\",\n \"0.561551\",\n \"0.5613401\",\n \"0.5609681\",\n \"0.560074\",\n \"0.5596674\",\n \"0.55954224\",\n \"0.55922735\",\n \"0.5591201\",\n \"0.5581913\",\n \"0.5581853\",\n \"0.55776256\",\n \"0.55735224\",\n \"0.5573298\",\n \"0.55729985\",\n \"0.5571045\",\n \"0.55653477\",\n \"0.55592394\",\n \"0.5553845\",\n \"0.55501044\",\n \"0.55489326\",\n \"0.55421394\",\n \"0.55416673\",\n \"0.55246776\",\n \"0.55240834\",\n \"0.5516079\",\n \"0.5511115\",\n \"0.55077904\",\n \"0.5506929\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77984,"cells":{"query":{"kind":"string","value":"Parse lines to get reads from the input read file."},"document":{"kind":"string","value":"def parse(self, paired=False, shift=100):\n for read in self.handle:\n if read.is_unmapped or read.is_qcfail or read.is_secondary \\\n or read.is_supplementary:\n continue\n if paired:\n if not read.is_paired:\n logger.debug(\n f\"Skipped single-end read: {read.to_string()!r}\")\n continue\n if read.is_read1 and read.is_proper_pair \\\n and not read.mate_is_unmapped:\n chrom1 = read.reference_name\n start1 = read.reference_start\n end1 = read.reference_end\n chrom2 = read.next_reference_name\n if read.is_reverse:\n start = end1 + read.template_length\n end = end1\n else:\n start = start1\n end = start1 + read.template_length\n if read.template_length == 0:\n logger.debug(\n f\"Detected read with TLEN=0: {read.to_string()!r}\")\n if chrom1 == chrom2:\n yield chrom1, (start + end) // 2\n else:\n continue\n else:\n continue\n else:\n if read.is_paired:\n logger.debug(\n f\"Skipped paired-end read: {read.to_string()!r}\")\n continue\n if read.is_unmapped:\n continue\n else:\n chrom = read.reference_name\n start = read.reference_start\n end = read.reference_end\n if read.is_reverse:\n pos = end - shift\n else:\n pos = start + shift\n yield chrom, pos\n self.handle.close()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __read_lines__(self):\r\n fd = open(self.input_file, \"r\")\r\n lines = fd.readlines()\r\n fd.close()\r\n return lines","def process_lines(self, lines):\n line_index = 0\n n_lines = len(lines)\n while line_index < n_lines:\n if lines[line_index].startswith(\"HIERARCHY\"):\n line_index = self._read_skeleton(lines, line_index, n_lines)\n if lines[line_index].startswith(\"MOTION\"):\n self._read_frametime(lines, line_index+2)\n line_index = self._read_frames(lines, line_index+3, n_lines)\n else:\n line_index += 1","def parse(self, filehandle):\n l = filehandle.readline()\n if l.split()[0] != '##maf':\n return\n else:\n self.setpar(l.split()[1:])\n\n l=filehandle.readline()\n while l:\n la = l.split()\n## print la\n if(len(la)==0 or la[0]=='#'):\n## print \"skipping\"\n 1\n elif(la[0]=='a'):\n## print \"reading alignment\"\n self.readalign(la[1:], filehandle)\n else:\n## print \"end of records\"\n return\n\n l=filehandle.readline()","def _parse_ach_file(self, contents):\n file_length = len(contents)\n\n for index in range(0, file_length, self.LINE_LENGTH):\n line = contents[index:index + self.LINE_LENGTH]\n\n if line.startswith('1'):\n self._read_header(line)\n elif line.startswith('5'):\n self._read_batch_header(line)\n elif line.startswith('6'):\n self._read_entry_detail(line)\n elif line.startswith('7'):\n self._read_addenda_record(line)\n elif line.startswith('8'):\n self._read_batch_control_record(line)\n elif line.startswith('9'):\n if line == '9' * 94:\n continue\n self._read_file_control_record(line)","def parse(self):\n for line in self.lines:\n self.read_line(line)\n return self.assembler_lines","def read_lines(self):\n with open(self.pdb_file) as infile:\n self.lines = infile.readlines()","def parse_file():\n\tfile_lines = []\n\n\t## For each line in the file, if it's not empty, store it\n\tfor line in fileinput.input():\n\t\tif len(line) > 1:\n\t\t\tfile_lines.append(line.strip())\n\t\n\trun_algorithms(file_lines)","def parse(self, f):\n \n for line in f:\n self.parse_line(line)","def parse_lines(lines):\n for line in lines:\n yield Record(line)","def extract_lines(infile):\n with open(infile, 'r') as src:\n return read_on(get_line, src)","def read_input(self):\n\n with open(self.input_file, 'r') as file:\n\n data = defaultdict(list)\n for line in file:\n # Skip empty lines, meta-data and comments\n if len(line) < 2 or line[0] == '#':\n continue\n\n # Make Region object of current line\n f = Region(line)\n data[f.rid].append(f)\n\n return data","def read_records(self, input_path, offset_range_tracker):\n start_offset = offset_range_tracker.start_position()\n with tf.io.gfile.GFile(input_path, 'r') as f:\n f.seek(start_offset)\n # Read lines in and proceed once a `+` is in the 3rd position.\n record = collections.deque([], 4)\n while True:\n line = f.readline()\n if not line:\n # End of file reached\n break\n record.append(line.strip())\n if len(record) == 4 and record[0].startswith('@') and record[2] == '+':\n # Once a '+' is in position 3 a full record exists in the record.\n read_name = record[0]\n sequence = record[1]\n qual = record[3]\n self.fastq_records_counter.inc()\n yield (read_name, sequence, qual)\n record.clear()","def readlines(self):\n lines = []\n while True:\n line = self.readline()\n if line is None:\n return lines\n lines.append(line)","def read_input():\n f = open(INPUT_FILE, 'r')\n \n number_of_lines = int(f.readline())\n for i in range(number_of_lines):\n yield [i+1, f.readline().split()]\n f.close()","def readData(self):\n self._setupArrays()\n\n with open(self.filename) as fh:\n datalines = fh.readlines()[self.NLHEAD:]\n\n datalines = self._checkForBlankLines(datalines)\n\n # Set up loop over unbounded indpendent variable\n m = 0 # Unbounded independent variable mark \n while len(datalines) > 0:\n datalines = self._readData1(datalines, m)\n datalines = self._readData2(datalines, m)\n m = m + 1","def readlines(self):\n return [line for line in self]","def _read(cls, input_file, quotechar=None):\n with open(input_file, \"r\", encoding='utf-8') as f:\n lines = []\n for line in f:\n lines.append(line.strip())\n return lines","def read():\n for line in sys.stdin:\n if line == \"\\n\":\n return\n log_elements.append(log_element(line))","def parse(self, filename):\n infile = file(filename)\n for line in infile:\n self.parseLine(line)","def parse_file(self):\n with open(self.file_name, 'r', errors='ignore') as log_file:\n for line in log_file:\n self.process_line(line)","def input_file_parser(cls):\n \n # Loop through the file and store lines in an appropriate list that is passed to other class functions\n with open(cls.infile_name,'r') as infile:\n for line in infile: # Loop through the whole file\n if '$molecule' in line: # Search for a section header\n for line in infile: # Enter second loop over the lines in the section\n if '$end' in line: # If you find $end, stop loop as the section is finished\n break\n else: # Otherwise add the line to a list\n cls.molecule_lines.append(line.strip())\n if '$connection' in line: # Continue for other sections...\n for line in infile:\n if '$end' in line:\n break\n else:\n cls.connector_lines.append(line.strip())\n if '$options' in line: # Continue for other sections...\n for line in infile:\n if '$end' in line:\n break\n else:\n cls.options_lines.append(line.strip())\n\n return None","def read_lines_from_file(fname):\n return []","def parse_lines(filename):\n line_counter = 0\n with open(filename, 'r') as rf:\n for line_txt in rf:\n try:\n d = json.loads(line_txt)\n tup = (\n d['attributed_to'],\n int(d['date_time'][8:10]),\n d.get('used_first_time_today', False),\n d.get('first_utm_source', 'unknown') \n )\n except:\n print('Error parsing line_txt:', line_txt)\n line_counter += 1\n if line_counter % 10 ** 6 == 0:\n print('read %dM lines' % (line_counter // 10 ** 6))\n yield tup # yield: https://stackoverflow.com/a/231855","def parse(self):\n\t\tfirst = None\n\t\tf = open(self.input_file)\n\t\tfor line in f.readlines():\n\t\t\tif line.startswith(\"#\"):\n\t\t\t\tcontinue\n\t\t\ttry:\n\t\t\t\tflow,t,sequence,size = line.split()\n\t\t\texcept:\n\t\t\t\tcontinue\n\t\t\t# append data to a list of tuples\n\t\t\tflow = int(flow)\n\t\t\tt = float(t)\n\t\t\tsequence = int(sequence)\n\t\t\tif size == \"x\":\n\t\t\t\tcontinue\n\t\t\tsize = int(size)\n\t\t\tif not size == 0:\n\t\t\t\tif flow == 1:\n\t\t\t\t\tself.data1.append((t,sequence,size))\n\t\t\t\telif flow == 2:\n\t\t\t\t\tself.data2.append((t,sequence,size))\n\t\t\t\telif flow == 3:\n\t\t\t\t\tself.data3.append((t, sequence, size))\n\t\t\t\telif flow == 4:\n\t\t\t\t\tself.data4.append((t, sequence, size))\n\t\t\t\telif flow == 5:\n\t\t\t\t\tself.data5.append((t, sequence, size))\n\t\t\t\telse:\n\t\t\t\t\tprint \"Erroneous data: \",flow, t, sequence, size\n\t\t\t# Keep track of the minimum and maximum time seen\n\t\t\tif not self.min_time or t < self.min_time:\n\t\t\t\tself.min_time = t\n\t\t\tif not self.max_time or t > self.max_time:\n\t\t\t\tself.max_time = t\n\n\t\t\t# print len(self.data1),len(self.data2),len(self.data3),len(self.data4),len(self.data5)","def _read(self, file_name):\n f = open(file_name)\n lines = f.readlines()\n begin = 0\n end = 0\n while end < len(lines):\n op = ''\n for l in lines[begin:]:\n end += 1\n op = l.split()[0]\n if op in operations:\n self.operations.append(op)\n break\n if op == '=push':\n nfa = Automaton(lines[begin:end - 1])\n self.aut_to_push.append(nfa)\n begin = end\n f.close()","def parse_mapping_file(lines, strip_quotes=True, suppress_stripping=False):\r\n if hasattr(lines, \"upper\"):\r\n # Try opening if a string was passed\r\n try:\r\n lines = open(lines, 'U')\r\n except IOError:\r\n raise QiimeParseError(\"A string was passed that doesn't refer \"\r\n \"to an accessible filepath.\")\r\n\r\n if strip_quotes:\r\n if suppress_stripping:\r\n # remove quotes but not spaces\r\n strip_f = lambda x: x.replace('\"', '')\r\n else:\r\n # remove quotes and spaces\r\n strip_f = lambda x: x.replace('\"', '').strip()\r\n else:\r\n if suppress_stripping:\r\n # don't remove quotes or spaces\r\n strip_f = lambda x: x\r\n else:\r\n # remove spaces but not quotes\r\n strip_f = lambda x: x.strip()\r\n\r\n # Create lists to store the results\r\n mapping_data = []\r\n header = []\r\n comments = []\r\n\r\n # Begin iterating over lines\r\n for line in lines:\r\n line = strip_f(line)\r\n if not line or (suppress_stripping and not line.strip()):\r\n # skip blank lines when not stripping lines\r\n continue\r\n\r\n if line.startswith('#'):\r\n line = line[1:]\r\n if not header:\r\n header = line.strip().split('\\t')\r\n else:\r\n comments.append(line)\r\n else:\r\n # Will add empty string to empty fields\r\n tmp_line = map(strip_f, line.split('\\t'))\r\n if len(tmp_line) < len(header):\r\n tmp_line.extend([''] * (len(header) - len(tmp_line)))\r\n mapping_data.append(tmp_line)\r\n if not header:\r\n raise QiimeParseError(\"No header line was found in mapping file.\")\r\n if not mapping_data:\r\n raise QiimeParseError(\"No data found in mapping file.\")\r\n\r\n return mapping_data, header, comments","def record_finder(lines):\n for line in lines:\n if not line.strip():\n continue\n if line.startswith(\"@\"): #separate each block of reads by @\n try:\n yield curr\n except:\n pass\n curr = []\n curr.append(line.strip())\n else:\n curr.append(line.strip())\n if curr:\n yield curr #Sandra et al. 2019","def parse(self, lines):\n # convert file to string deleting end of line charcters\n citations_string = self.prepare_file(lines)\n # extract the entries from the string\n entries = list(self.find_entries(citations_string))\n entries.append(len(citations_string))\n # parse each entry to generate a citation\n for idx, jdx in zip(entries[:-1], entries[1:]):\n self.parse_entry(citations_string[idx:jdx])\n return self.force_field.citations","def parse_input():\n with open(\"input_07.txt\", \"r\") as f:\n for line in f:\n Bag(line)","def readSequences(lines):\n seqs = []\n label = None\n seq_lines = []\n for line in lines:\n line = line.strip() # strip off white space\n if not line: # skip empty lines\n continue\n if line.startswith(';'): # ignore comment lines\n continue\n # check for start of next sequence:\n if line.startswith('>'): # label line\n # first, store the previous sequence if we had one:\n if seq_lines:\n seqs.append(Sequence(label, ''.join(seq_lines)))\n seq_lines = []\n # get the label (name) for the next sequence\n label = line[1:].strip()\n else:\n # collect all lines with sequence information for this sequence:\n seq_lines.append(line)\n # take care of the last sequence in the file\n seqs.append(Sequence(label, ''.join(seq_lines)))\n return seqs","def readlines(self, cr=1):\n mode = \"r\"\n\n if not cr:\n content = self.read(mode)\n return content.split(\"\\n\")\n else:\n f = self.open(mode)\n try:\n return f.readlines()\n finally:\n f.close()","def readlines(self):\n return list(self.iterlines())","def line_parser(path):\n lines = []\n with open(path, 'r') as input:\n lines = [line.rstrip().split(',') for line in input]\n lines = [\n [[float(x1), float(y1)],\n [float(x2), float(y2)]] \n for x1, y1, x2, y2 in lines]\n return lines","def readlines(self):\n return [\"\"] + self.get(\"1.0\", END).split(\"\\n\")[:-1]","def _read_input_file(self):\n pass","def readInput(in_file_name):\n in_file = open(in_file_name, 'r')\n positions = []\n samples = []\n M = []; P = [];\n MC = []; PC = [];\n while True:\n line = in_file.readline()\n if not line: break\n if line[0] == '#': continue #skip comment\n line = line.rstrip('\\n').split('\\t')\n \n #genomic positions and allele support in plasma samples\n positions.append(int(line[0]))\n samples.append(tuple(map(int, line[1:5])))\n \n #maternal and paternal alleles\n M.append(tuple(line[5:7]))\n MC.append(tuple(map(float, line[7:9])))\n \n P.append(tuple(line[9:11]))\n PC.append(tuple(map(float, line[11:13]))) \n \n in_file.close()\n return positions, samples, M, P, MC, PC","def parse_lines(self, start_line=0, end_line=False):\n if end_line is False: end_line = len(self.file_ltxt)\n\n lines = self.file_ltxt\n self.E_str = \"parse_lines\"\n self.line_num = start_line\n\n # Loop over lines and parse\n while self.line_num < end_line:\n line = lines[self.line_num].strip()\n\n if line == \"echo\": print(\"\")\n\n # Parse any variables\n elif self.line_declarations['variable'](line):\n self.parse_variable_line(line)\n\n # Parse any file loading commands\n elif self.line_declarations['load'](line):\n self.parse_load_cmd(line)\n\n # Parse any file loading commands\n elif self.line_declarations['plot'](line):\n self.parse_plot_cmd(line)\n\n # Parse any file loading commands\n elif self.line_declarations['write'](line):\n self.parse_write_cmd(line)\n\n # Parse any math commands\n elif self.line_declarations['math'](line):\n self.parse_math_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['echo'](line):\n self.parse_echo_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['calc'](line):\n self.parse_calc_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['set'](line):\n self.parse_set_cmd(line)\n\n # Parse any shell commands\n elif self.line_declarations['shell'](line):\n self.parse_shell_cmd()\n\n # Parse any for loop commands\n elif self.line_declarations['for'](line):\n self.parse_for_cmd(line)\n\n # Parse any echo commands\n elif self.line_declarations['script'](line):\n self.parse_script_cmd(line)\n\n elif self.line_declarations['inline_code'](line):\n getattr(self, f\"parse_{line.split()[0]}_cmd\")(line)\n\n elif self.line_declarations['if'](line):\n self.parse_if_cmd(line)\n\n # elif self.line_declarations['splice'](line):\n # self.parse_splice_cmd(line)\n\n elif self.line_declarations['glue'](line):\n self.parse_glue_cmd(line)\n\n elif self.line_declarations['exit'](line):\n print(\"\\n\\nStopped Code -exit was called.\")\n raise SystemExit\n\n # The end of control statements\n elif '}' in line:\n pass\n\n # Print a warning about unknown line\n else:\n self.print_warning(f\"I don't understand a line: '{line}'\")\n\n self.line_num += 1","def parse_input(part=1):\n with open(\"input_23.txt\") as f:\n return [int(x) for x in f.readline().strip()]","def lineReadfile(filename):\n#\t\"input:filename output=readlines() \"\n\tf = open(filename)\n\tlist1 =[]\n\twhile 1:\n\t\ts = f.readline()\n\t\tif s==\"\":\n\t\t\tbreak\n\t\ts=string.replace(s,\"\\n\",\"\")\n\t\tif s==\"\":\n\t\t\tcontinue\n\t\tlist1.append(s)\n\tf.close()\n\treturn list1","def read_input(input_file):\n \n logging.info(\"reading file {0}...this may take a while\".format(input_file))\n with gzip.open(input_file, 'rb') as f:\n for i, line in enumerate(f):\n \n if (i % 10000 == 0):\n logging.info(\"read {0} reviews\".format(i))\n # do some pre-processing and return list of words for each review\n # text\n yield gensim.utils.simple_preprocess(line)","def read_transcript_data(fn):\n\n def _read_lines(fn):\n # NC_000007.13\tRefSeq\tcDNA_match\t50344265\t50344518\t254\t+\t.\tID=aln58042;Target=NM_001220765.2 1 254 +;gap_count=0;identity=0.0691326;idty=1;num_ident=428;num_mismatch=0;pct_coverage=6.91326;pct_identity_gap=100;pct_identity_ungap=100;score=254\n # NC_000002.11 RefSeq cDNA_match 179671939 179672150 212 - . ID=ed951d46-194c-477a-a480-4bc64530c5ba;Target=NM_001267550.2 1 212 +;gap_count=0;identity=0.999991;idty=1;num_ident=109223;num_mismatch=1;pct_coverage=100;pct_identity_gap=99.9991;pct_identity_ungap=99.9991\n line_re = re.compile(\n \"(?P<ref_ac>\\S+)\\s+(?P<origin>\\S+)\\s+(?P<match_type>\\S+)\\s+\"\n \"(?P<g_start>\\d+)\\s+(?P<g_end>\\d+)\\s+(?P<score>\\S+)\\s+\"\n \"(?P<strand>[-+])\\s+\\.\\s+ID=(?P<aln>[^;]+);Target=(?P<tx_ac>\\S+)\"\n \"\\s+(?P<tx_start>\\d+)\\s+(?P<tx_end>\\d+).+?\"\n \"pct_coverage=(?P<pct_coverage>[^;]+);\"\n \"pct_identity_gap=(?P<pct_identity_gap>[^;]+);\"\n \"pct_identity_ungap=(?P<pct_identity_ungap>[^;]+)\"\n )\n fh = io.open(fn, \"rb\")\n while fh.peek(1)[0] == \"#\":\n fh.readline()\n while fh.peek(3)[0:3] != \"###\":\n line = fh.readline()\n try:\n yield line_re.match(line).groupdict()\n except AttributeError:\n raise Exception(\"Failed at\", line)\n raise StopIteration\n def _key(e):\n return (e[\"tx_ac\"], not e[\"ref_ac\"].startswith(\"NC_\"), e[\"ref_ac\"], e[\"aln\"])\n return itertools.groupby(sorted(_read_lines(fn), key=_key), key=_key)","def parse_lines(self, lines):\n assert isinstance(lines, Iterable)\n\n for line in lines:\n name, values = self.parse_line(line)\n self.add(name, values)","def process_lines(line_queue, result_queue, sim_info, analysis_info, args, merged_reads):\n\t\n\twhile True:\n\t\tline = line_queue.get()\n\t\t\n\t\t# if we're done, stop\n\t\tif line is None:\n\t\t\tresult_queue.put(None)\n\t\t\tbreak\n\t\t# skip header lines\n\t\telif line[0] == '@':\n\t\t\tcontinue\n\t\t# process this line\n\t\telse:\n\t\t\tresult = score_read(line, sim_info, analysis_info, args, merged_reads)\n\t\t\tresult_queue.put(result)","def read():\n try:\n #Open and parse input files.\n nodeFile = open(sys.argv[1], 'r')\n edgeFile = open(sys.argv[2], 'r')\n\t\n parse_nodes(nodeFile)\n parse_edges(edgeFile)\n nodeFile.close()\n\tedgeFile.close()\n\treturn \n except:\n print 'problem parsing input'\n #Put here some more information - usage...","def read (self, path):\n\n\t\tself.data = []\n\t\t# print \"*** path: %s***\" % path\n\t\tdir, filename = os.path.split (path)\n\t\troot, ext = os.path.splitext (filename)\n\t\t# encoding = 'ISO-8859-1' # utf-8\n\t\ts = codecs.open(path,'r', self.encoding).read()\n\t\t## s = unicode(f.read(),'utf-8')\n\t\ts = self.preprocess (s)\n\t\tlines = split (s, self.linesep)\n\t\tschema = self.splitline(lines[0])\n\n\t\t## print \"** %s **\" % os.path.splitext(filename)[0]\n\t\tif self.verbose:\n\t\t\tprint \"read %d lines from %s\" % (len(lines), path)\n\n\t\tfor i in range(1,len(lines)):\n\t\t\tif not lines[i].strip(): \n\t\t\t\t# print 'skipping line (%d)' % i\n\t\t\t\tcontinue\n\t\t\tfields = self.splitline(lines[i])\n\t\t\titem = self.entry_class (fields, schema)\n\t\t\tif self.accept (item):\n\t\t\t\tself.add (item)\n\n\t\tself.schema = schema\n\t\t# self.data.sort (lastNameCmp)","def read_line(self, *types):\n words = self.in_file.readline().strip().split()\n if len(words) == 1:\n return types[0](words[0])\n return [types[min(i, len(types) - 1)](words[i])\n for i in range(len(words))]","def readAllfromFile(self):\n with open(self._fname, 'r') as f:\n lines = f.readlines()\n readList = []\n for line in lines:\n line = line.strip()\n if len(line) > 1:\n gra = self._readGrafromLine(line)\n readList.append(gra)\n f.close()\n return readList","def parse_file(self, parse_all=False, file=None):\n if not parse_all:\n input()\n for line in sys.stdin:\n self.parse_and_add(line)","def read_input(input_file):\n\n logging.info(\"reading file {0}...this may take a while\".format(input_file))\n with gzip.open(input_file, 'rb') as f:\n for i, line in enumerate(f):\n\n if i % 10000 == 0:\n logging.info(\"read {0} reviews\".format(i))\n # do some pre-processing and return list of words for each review\n # text\n yield gensim.utils.simple_preprocess(line)","def readrules(self, fomalines):\n for lineno, l in enumerate(fomalines):\n if 'define' in l or 'def ' in l:\n rulecom = l.split(' #')\n r = re.findall(\"(defi?n?e?)\\s+(\\S+)\\s+([^;]+)\", rulecom[0])\n if len(r[0]) != 3:\n print \"Syntax error on line %i\" % lineno\n (_, rulename, rule) = r[0]\n if len(rulecom) > 1:\n commentline = rulecom[1].strip()\n else:\n commentline = ''\n self.rule_add(rulename, rule, commentline)\n if 'chain' in l:\n l = l.replace(';','')\n chain = re.findall('chain\\s+(.*)', l)\n rc = chain[0].replace(' ','').split(',')\n self.rc = rc","def _parse(self, infile):\n raise NotImplementedError()","def parse_input():\r\n endpoints_file = open(\"input.txt\", \"r\")\r\n lines = endpoints_file.readlines()\r\n line_number = 0\r\n endpoints = []\r\n for line in lines:\r\n line = line.strip()\r\n if line_number == 0:\r\n period = int(line)\r\n elif line_number == 1:\r\n max_attempts = int(line)\r\n else:\r\n endpoints.append(line)\r\n line_number += 1\r\n endpoints_file.close()\r\n return endpoints, period, max_attempts","def read_input():\n splits = []\n with open('solutions/day5/input.txt') as f:\n for line in f:\n splits.append(line.strip('\\n'))\n return splits","def read_data(cls, input_file, quotechar=None):\r\n if 'pkl' in str(input_file):\r\n lines = load_pickle(input_file)\r\n else:\r\n lines = input_file\r\n return lines","def read_file(fname):\n recs=[]\n logger.info('Start read file %s', fname)\n with open(fname) as inf:\n for line in inf:\n recs.append(line.strip().split())\n logger.info('End reading with recnumber %d', len(recs))\n return recs","def read_input_file(path: str):\n with open(path, \"r\") as f:\n return f.readlines()","def parse(self, infile):\r\n raise NotImplementedError()","def parse_fastq (rec_lines):\n data = []\n data.append(rec_lines[0][1:])\n data.append(rec_lines[1])\n data.append(rec_lines[3])\n return data","def job_reader(path) -> Generator[ParsedActionType, None, None]:\n try:\n with open(path, \"r\") as f:\n parser = Parser()\n for line in f:\n result = parser.process_line(line)\n if result is not None:\n yield result\n except IOError as err:\n print(\"Error opening/reading from file '{0}': {1}\"\n .format(err.filename, err.strerror))","def read_data(input_file):\n\n def process_line(labels, words):\n l = ' '.join([label for label in labels if len(label) > 0])\n w = ' '.join([word for word in words if len(word) > 0])\n lines.append((l, w))\n words = []\n labels = []\n return words, labels, lines\n\n rf = open(input_file, 'r')\n lines = [];\n words = [];\n labels = []\n for line in rf:\n word = line.strip().split(' ')[0]\n label = line.strip().split(' ')[-1]\n # here we dont do \"DOCSTART\" check\n\n if len(line.strip()) == 0: # and words[-1] == '.'\n words, labels, lines = process_line(labels, words)\n words.append(word)\n labels.append(label)\n rf.close()\n return lines","def read_data(cls, input_file,quotechar = None):\n if 'pkl' in str(input_file):\n lines = load_pickle(input_file)\n else:\n lines = input_file\n return lines","async def readlines(self, hint=-1):\n if 'b' in self._mode:\n raise APIException(\n \"readline on a binary file is not permitted: {}\".format(\n self._uri)\n )\n # read the entire file in and decode it\n lines = await self.read().decode().split(\"\\n\")\n return lines","def readlines(self) -> list[bytes] | None:","def read_input_file(path):\n file_input = os.path.join(path, 'INPUT')\n f = open(file_input, 'r')\n lines = f.read().replace('\\n', ':')\n f.close()\n lines = ' '.join(lines.split()) #remove whitespace\n lines = re.split(':', lines.replace(': ', ':'))\n return lines","def read_records(self, input_path, offset_range_tracker):\n start_offset = offset_range_tracker.start_position()\n with tf.io.gfile.GFile(input_path, 'r') as f:\n f.seek(start_offset)\n # Read the first key\n line = f.readline()\n key = line.strip('>\\n')\n seq = ''\n while True:\n line = f.readline()\n if line.startswith('>'):\n # New record reached.\n yield (key, seq)\n self.fasta_records_counter.inc()\n key = line.strip('>\\n')\n seq = ''\n else:\n seq += line.strip()\n\n if not line:\n # End of file reached; Yield and break from loop\n yield (key, seq)\n self.fasta_records_counter.inc()\n break","def _readline_ins(self):\n if self._ins_filehandle is None:\n if not os.path.exists(self._ins_filename):\n raise Exception(\n \"instruction file '{0}' not found\".format(self._ins_filename)\n )\n self._ins_filehandle = open(self._ins_filename, \"r\")\n line = self._ins_filehandle.readline()\n self._ins_linecount += 1\n if line == \"\":\n return None\n self._last_line = line\n # check for spaces in between the markers - this gets ugly\n line = line.lower()\n if self._marker is not None and self._marker in line:\n\n # def find_all(a_str, sub):\n # start = 0\n # while True:\n # start = a_str.find(sub, start)\n # if start == -1:\n # return\n # yield start\n # start += len(sub)\n # poss speedup using regex\n midx = [m.start() for m in re.finditer(re.escape(self._marker), line)]\n # midx = list(find_all(line, self._marker))\n midx.append(len(line))\n first = line[: midx[0]].strip()\n tokens = []\n if len(first) > 0:\n # tokens.append(first)\n tokens.extend([f.strip() for f in first.split()])\n for idx in range(1, len(midx) - 1, 2):\n mstr = line[midx[idx - 1] : midx[idx] + 1]\n ostr = line[midx[idx] + 1 : midx[idx + 1]]\n tokens.append(mstr)\n tokens.extend(ostr.split())\n else:\n tokens = line.strip().split()\n return tokens","def __parse(self):\n lines = self.file.readlines()\n for i in range(0, len(lines)):\n line = lines[i]\n tokens = line.split()\n if tokens[0] == \"#start\":\n trial_name = tokens[1]\n trial = Trial(trial_name)\n self.trials[trial_name] = trial\n elif tokens[0] == \"#end\":\n continue\n else:\n date_str = tokens[0] + \" \" + tokens[1]\n date = datetime.strptime(date_str, \"%m/%d/%y %H:%M:%S\")\n sound_file = line[18:-1].strip()\n event = Event(date, sound_file, 0)\n trial.addevent(event)","def read_input(self, infile):\n self.infile = open(infile, 'r')\n input_list = None\n for l in [line.strip('\\n') for line in self.infile]:\n input_list = l.split(' ')\n \n pid = 0\n for i in range(0, len(input_list), 2):\n self.processes.append(Process(pid, int(input_list[i]), int(input_list[i+1])))\n pid += 1\n self.process_count = len(self.processes)\n self.infile.close()\n return","def read_input():\n # Use with to make sure the file will be closed after the block executed\n with open('snapshot_input.txt') as f:\n # Split the line at line breaks\n x = f.read().splitlines()\n # Get the data of restructuring, three positive integers N , C , and D\n # Use generator expression for time and space efficiency\n restructuring_info = (i.split() for i in x if len(i.split())==3)\n # Get the data of single machine, four integers D, P, R and G\n machine_info = (i.split() for i in x if len(i.split())!=3)\n # Get the length of restructuring data\n length = sum(1 for i in x if len(i.split())==3)\n\n return restructuring_info, machine_info, length","def read(infile):\n if isinstance(infile, str):\n infile = open(infile)\n\n with infile:\n while True:\n cmt = infile.readline().strip()\n seq = infile.readline().strip()\n plus = infile.readline().strip()\n qual = infile.readline().strip()\n\n if not cmt:\n break\n if not cmt.startswith('@') or plus != '+':\n raise ValueError('fastq file <{}> is corrupted'.format(infile.path))\n yield SRecord(cmt=cmt[1:], seq=seq, qual=qual)","def get_input():\n # return TEST_INPUT.split('\\n')\n with open(INPUT_FILE) as f:\n return f.readlines()","def read_text(self, text):\n if isinstance(text, (str, unicode)):\n lines = text.split('\\n')\n else:\n lines = text\n\n for line in lines:\n l = line.strip()\n\n if line == ST_POS0:\n self._state = ST_POS0\n elif line == ST_TRNS:\n self._state = ST_TRNS\n elif line == ST_POS0:\n self._state = ST_POS0\n else:\n self._parse_line(line)","def _fetch_data_lines(self) -> list:\n with open(self.full_path, \"rb\") as file:\n all_lines = file.readlines()\n return self._screen_invalid_lines(all_lines)","def read(file):\n\n blocks = ['bus', 'load', 'fshunt', 'gen', 'branch', 'transf', 'area',\n 'twotermdc', 'vscdc', 'impedcorr', 'mtdc', 'msline', 'zone',\n 'interarea', 'owner', 'facts', 'swshunt', 'gne', 'Q']\n nol = [1, 1, 1, 1, 1, 4, 1,\n 0, 0, 0, 0, 0, 1,\n 0, 1, 0, 0, 0, 0]\n rawd = re.compile('rawd\\d\\d')\n\n retval = True\n version = 0\n b = 0 # current block index\n raw = {}\n for item in blocks:\n raw[item] = []\n\n data = []\n mdata = [] # multi-line data\n mline = 0 # line counter for multi-line models\n\n # parse file into raw with to_number conversions\n fid = open(file, 'r')\n for num, line in enumerate(fid.readlines()):\n line = line.strip()\n if num == 0: # get basemva and frequency\n data = line.split('/')[0]\n data = data.split(',')\n\n mva = float(data[1])\n freq = float(data[5])\n version = int(data[2])\n\n if not version:\n version = int(rawd.search(line).group(0).strip('rawd'))\n if version < 32 or version > 33:\n logging.warning('RAW file version is not 32 or 33. Error may occur.')\n continue\n elif num == 1: # store the case info line\n logging.info(line)\n continue\n elif num == 2:\n continue\n elif num >= 3:\n if line[0:2] == '0 ' or line[0:3] == ' 0 ': # end of block\n b += 1\n continue\n elif line[0] is 'Q': # end of file\n break\n data = line.split(',')\n\n data = [to_number(item) for item in data]\n mdata.append(data)\n mline += 1\n if mline == nol[b]:\n if nol[b] == 1:\n mdata = mdata[0]\n raw[blocks[b]].append(mdata)\n mdata = []\n mline = 0\n fid.close()\n\n # add device elements params and add to PSAT formatted dictionary\n\n for data in raw['bus']:\n \"\"\"version 32:\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10\n ID, NAME, BasekV, Type, Area Zone Owner Va, Vm, latitude longitude\n \"\"\"\n idx = data[0]\n ty = data[3]\n angle = data[8]\n try:\n lat = data[9]\n except:\n # logging.warning('<No Coordinates in .raw file>')\n param = {'idx': idx,\n 'name': data[1],\n 'Vn': data[2],\n 'type': data[3],\n 'area': data[4],\n 'voltage': data[7],\n 'region': data[5],\n 'owner': data[6],\n 'angle': angle,\n }\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5]]\n else:\n param = {'idx': idx,\n 'name': data[1],\n 'Vn': data[2],\n 'type': data[3],\n 'area': data[4],\n 'voltage': data[7],\n 'region': data[5],\n 'owner': data[6],\n 'angle': angle,\n 'latitude': data[9],\n 'longitude': data[10]\n }\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5], data[9], data[10]]\n Settings.Bus.append(psatlist)\n Settings.BusNames.append(data[1])\n # Add BusSTORE Dictionary For Later Reference\n Settings.BusStore[idx] = param\n\n xcoord = [34.560040, 34.938385, 34.360040, 40.5152473, 40.3142473, 36.527401, 36.857401, 36.687401, 36.856401,\n 40.487041, 36.903901, 36.702901, 35.832561, 33.386047, 33.185047, 37.105571, 37.104154, 33.706718,\n 37.103549, 36.703539, 37.103559, 36.703549, 36.033561, 35.631561, 36.032561, 35.732561, 36.525401,\n 36.857401, 49.869314, 50.969314, 51.979314, 52.481674, 54.973192, 56.276212, 41.734596, 34.551015,\n 34.652015, 34.537507, 34.587507, 34.157904, 33.714453, 33.762453, 39.548160, 39.496160, 34.313143,\n 34.545782, 34.380686, 34.111686, 34.137762, 34.118650, 34.158650, 33.918650, 33.718650, 34.018650,\n 34.018650, 34.018650, 34.018650, 34.018650, 34.312456, 34.315456, 34.243600, 34.566258, 34.565258,\n 46.064672, 46.565672, 45.514571, 45.606833, 45.806833, 44.890000, 45.596416, 45.295416, 45.891161,\n 47.954899, 46.511440, 45.913936, 45.713936, 46.669335, 47.954899, 47.624154, 43.784730, 44.482350,\n 42.006860, 42.934919, 42.731919, 43.013135, 44.068350, 43.558350, 42.438350, 42.938350, 44.068350,\n 43.558350, 43.048350, 42.638350, 44.068350, 43.558350, 43.048350, 42.638350, 43.620189, 39.120428,\n 40.398031, 35.216200, 35.215200, 36.202099, 39.777745, 39.539598, 37.052929, 35.403217, 35.352217,\n 36.807243, 39.567450, 40.807689, 40.806689, 41.008689, 39.555494, 37.954721, 38.406721, 38.906721,\n 38.656721]\n ycoord = [-109.277313, -110.303798, -109.777313, -107.546455, -107.546455, -108.325669, -108.654569, -108.486669,\n -108.325669, -107.185575, -111.390408, -111.390408, -111.448566, -112.860397, -112.659397, -108.243555,\n -108.441191, -112.322033, -111.590816, -111.190816, -111.190816, -111.590806, -111.648566, -111.248566,\n -111.249566, -111.647566, -108.655669, -108.323669, -122.150895, -122.150895, -122.150895, -121.61684,\n -121.924221, -122.21370, -108.790427, -117.568105, -117.538105, -118.607375, -118.658375, -118.280282,\n -118.146319, -118.096319, -112.52797, -112.72797, -118.690631, -118.389938, -118.478496, -118.478496,\n -118.299917, -118.095428, -118.095428, -118.095428, -118.095428, -118.195428, -118.395428, -117.995428,\n -117.795428, -117.995428, -118.481217, -118.891217, -118.391667, -117.166428, -117.368428, -106.60906,\n -106.80906, -122.681289, -121.114785, -122.113785, -123.29000, -121.312202, -121.114202, -106.612578,\n -118.997945, -112.88531, -120.692286, -120.693974, -119.571501, -120.997945, -122.219492, -118.77463,\n -121.019484, -121.316546, -114.419206, -114.419206, -120.956476, -120.79484, -120.93484, -121.216546,\n -121.156546, -121.215484, -121.135484, -121.255484, -121.175484, -121.013484, -120.733484, -121.053484,\n -120.973484, -118.865882, -122.073631, -122.263453, -120.847567, -120.900567, -120.129849, -122.142965,\n -122.262993, -121.021929, -119.450452, -119.450452, -121.779037, -122.276225, -122.135718, -121.935718,\n -121.935718, -121.24000, -121.18379, -121.10879, -121.27379, -121.23979]\n\n #for idx, line in enumerate(Settings.Bus):\n # line.extend([xcoord[idx], ycoord[idx]])\n\n maxV = 1.1\n minV = 0.9\n maxQ = 1\n minQ = 0\n convimp = 0\n status = 1\n loss = 1\n\n for data in raw['load']:\n \"\"\"version 32:\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11\n Bus, Id, Status, Area, Zone, PL(MW), QL (MW), IP, IQ, YP, YQ, OWNER\n \"\"\"\n\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n voltage = Settings.BusStore[busidx]['voltage']\n param = {'bus': busidx,\n 'Vn': vn,\n 'Sn': mva,\n 'p': (data[5] + data[7] * voltage + data[9] * voltage ** 2) / mva,\n 'q': (data[6] + data[8] * voltage - data[10] * voltage ** 2) / mva,\n 'owner': data[11],\n 'type': Settings.BusStore[busidx]['type'],\n 'voltage': voltage\n }\n\n psatlist = [busidx, mva, vn, param['p'], param['q'], maxV, minV, convimp, status]\n Settings.PQ.append(psatlist)\n \"\"\"CONFIRM THAT OTHER BUSES HAVE 0 P and 0 Q which are not added\"\"\"\n\n for data in raw['fshunt']:\n \"\"\"\n 0, 1, 2, 3, 4\n Bus, name, Status, g (MW), b (Mvar)\n \"\"\"\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n param = {'bus': busidx,\n 'Vn': vn,\n 'status': data[2],\n 'Sn': mva,\n 'g': data[3] / mva,\n 'b': data[4] / mva,\n }\n\n psatlist = [busidx, mva, vn, freq, param['g'], param['b'], param['status']]\n Settings.Shunt.append(psatlist)\n\n gen_idx = 0\n type = 6\n\n for data in raw['gen']:\n \"\"\"\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11, 12, 13, 14, 15, 16,17,18,19\n I,ID,PG,QG,QT,QB,VS,IREG,MBASE,ZR,ZX,RT,XT,GTAP,STAT,RMPCT,PT,PB,O1,F1\n \"\"\"\n busidx = data[0]\n vn = Settings.BusStore[busidx]['Vn']\n gen_mva = data[8]\n gen_idx += 1\n status = data[14]\n leak = 0\n param = {'Sn': gen_mva,\n 'Vn': vn,\n 'u': status,\n 'idx': gen_idx,\n 'bus': busidx,\n 'pg': status * data[2] / mva,\n 'qg': status * data[3] / mva,\n 'qmax': data[4] / mva,\n 'qmin': data[5] / mva,\n 'v0': data[6],\n 'ra': data[9], # ra armature resistance\n 'xs': data[10], # xs synchronous reactance\n 'pmax': data[16] / mva,\n 'pmin': data[17] / mva,\n }\n\n if Settings.BusStore[busidx]['type'] == 3: #Check Bus Type for Slack\n refangle = 0\n refBus = 1\n PGuess = 1\n swlist = [busidx, gen_mva, vn, param['v0'], refangle, param['qmax'], param['qmin'],\n maxV, minV, PGuess, loss, refBus, status]\n SW = swlist\n Settings.SW.append(swlist)\n Settings.SWStore[busidx] = param\n Settings.SynStore[busidx] = param\n continue\n\n if busidx not in Settings.BusStore.keys():\n \"\"\" Need data from .dyr file. Create initial list, then append data from .dyr\"\"\"\n else:\n # psatlist = [busidx, gen_mva, vn, freq, type, leak, param['ra'],param['xs']]\n # Syn.append(psatlist)\n Settings.SynStore[busidx] = param\n pvlist = [busidx, gen_mva, vn, param['pg'], Settings.BusStore[busidx]['voltage'],\n param['qmax'], param['qmin'], maxV, minV, loss, status]\n Settings.PV.append(pvlist)\n\n\n for data in raw['branch']:\n \"\"\"\n I,J,ID,R,X,B,RATEA,RATEB,RATEC,GI,BI,GJ,BJ,ST,LEN,O1,F1,...,O4,F4\n \"\"\"\n param = {'bus1': data[0],\n 'bus2': data[1],\n 'id' : data[2],\n 'r': data[3],\n 'x': data[4],\n 'b': data[5],\n 'rate_a': data[6],\n 'rate_b': data[7],\n 'rate_c': data[8],\n 'Vn': Settings.BusStore[data[0]]['Vn'],\n 'Vn2': Settings.BusStore[data[1]]['Vn'],\n 'length': data[14],\n 'Ilim': EMPTY,\n 'Plim': EMPTY,\n 'Slim': EMPTY,\n 'status': data[13]\n }\n\n psatlist = [param['bus1'], param['bus2'], param['rate_c'], param['Vn'], freq, EMPTY,\n param['length'], param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\n param['Slim'], param['status']]\n Settings.Lineij.append([data[0], data[1], data[2]])\n Settings.Lineji.append([data[1], data[0], data[2]])\n Settings.LineOrd[param['bus1']].append(psatlist)\n Settings.branches += 1\n Settings.linecount += 1\n Settings.LineBusMatij[param['bus2']].append(Settings.branches)\n Settings.LineBusMatji[param['bus1']].append(Settings.branches)\n\n for data in raw['transf']:\n \"\"\"\n I,J,K,CKT,CW,CZ,CM,MAG1,MAG2,NMETR,'NAME',STAT,O1,F1,...,O4,F4\n R1-2,X1-2,SBASE1-2\n WINDV1,NOMV1,ANG1,RATA1,RATB1,RATC1,COD1,CONT1,RMA1,RMI1,VMA1,VMI1,NTP1,TAB1,CR1,CX1\n WINDV2,NOMV2\n \"\"\"\n if len(data[1]) < 5:\n ty = 2\n else:\n ty = 3\n if ty == 3:\n continue\n # raise NotImplementedError('Three-winding transformer not implemented')\n\n tap = data[2][0]\n phi = data[2][2]\n\n if tap == 1 and phi == 0:\n trasf = False\n else:\n trasf = True\n param = {'trasf': trasf,\n 'bus1': data[0][0],\n 'bus2': data[0][1],\n 'u': data[0][11],\n 'b': data[0][8],\n 'r': data[1][0],\n 'x': data[1][1],\n 'tap': tap,\n 'phi': phi,\n 'rate_a': data[2][3],\n 'Vn': Settings.BusStore[busidx]['Vn'],\n 'Vn2': Settings.BusStore[busidx]['Vn'],\n # 'length': data[?][?], FIND CORRECT INDEX\n 'Ilim': EMPTY,\n 'Plim': EMPTY,\n 'Slim': EMPTY,\n }\n psatlist = [param['bus1'], param['bus2'], param['rate_a'], param['Vn'], freq, EMPTY,\n EMPTY, param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\n param['Slim'], param['u']]\n\n Settings.LineOrd[param['bus1']].append(psatlist)\n Settings.linecount += 1\n Settings.transformers += 1\n # ADD Line Data(All Branch Types) to Sys Param Dict after .dyr Transformer Data Added\n # Re-Order Line Data for correct sequence\n for key in Settings.LineOrd:\n for item in Settings.LineOrd[key]:\n Settings.Line.append(item)\n\n for data in raw['area']:\n Settings.Areas.append(data[4])\n\n for data in raw['zone']:\n Settings.Regions.append(data[1])\n\n return retval","def read_input():\n return Path(__file__).with_name('input.txt').read_text().splitlines()","def __read_data__(self):\n with open(self.file, 'r') as data:\n sentence = []\n tags = []\n for line in data:\n terms = line.rstrip().split(WHITESPACE)\n for term in terms:\n word_tag = tuple(term.split(TAGCHAR))\n word = word_tag[0]\n tag = word_tag[1]\n self.word_tag_dict[word_tag] += 1\n self.tag_dict[tag] += 1\n self.__add_to_word_dict__(word, tag)\n if self.isNumberWord(word):\n self.numbers += 1\n if word[0].isupper() and len(sentence) > 0:\n self.cap_no_start += 1\n sentence.append(word)\n tags.append(tag)\n if tag == ENDOFSENTENCE:\n self.sentences.append(tuple(sentence))\n self.tags.append(tuple(tags))\n sentence = []\n tags = []","def read_lines(fp: TextIO) -> Iterator[str]:\n while line := fp.readline().strip():\n yield line","def readlines(path):\n with open(path, \"r\") as f:\n return [line.strip() for line in f.readlines()]","def parse_data(self, fileinput):\n with open(fileinput, 'rb') as fh:\n for line in fh:\n try:\n name, address, phone = line.strip().split(\",\")\n self.data.append(Document(name, address, phone))\n except Exception, ex:\n raise SerialException(\": Failed to parse input line %s: %s\" % (line, ex))\n return","def read_linelog():","def _read(self, openf=None, stepfilter=None):\n itemstack = []\n current = None\n result = {}\n xkeys = None\n timeskip = False\n laststep = False\n\n if openf is None:\n f = open(self.filepath)\n else:\n f = openf\n\n line = 'start'\n while line != '':\n lastpos = f.tell()\n line = f.readline()\n if line == '':\n continue\n \n if itemstack is not None and len(itemstack) > 0: \n cast = itemstack.pop()\n raw = line.split()\n values = [t(r) for t, r in zip(cast, raw)]\n if len(values) == 1:\n values = values[0]\n\n if current == \"time\":\n if stepfilter is not None and values not in stepfilter:\n timeskip = True\n elif (self.index is not None and values != self.index):\n if values > self.index:\n if openf is None:\n return {}\n else:\n timeskip = True\n laststep = True\n else:\n timeskip = True\n elif self.index is None:\n self.index = values\n else:\n timeskip = False\n \n if len(itemstack) == 0 and current not in result:\n result[current] = values\n else:\n if current not in result:\n result[current] = []\n result[current].append(values)\n continue\n elif itemstack is None and current == \"atoms\":\n if \"ITEM\" in line:\n current = None\n if openf is not None:\n f.seek(lastpos)\n break\n else:\n #E.g. line: 1 4 -65.9625 1.54915 1.46824 5 30.976 \n vals = line.split()\n sid, atype = tuple(map(int, vals[0:2]))\n result[\"type\"].append(atype)\n result[\"id\"].append(sid)\n x, y, z = tuple(map(float, vals[2:5]))\n result[\"xyz\"].append((x, y, z))\n if len(vals) > 5 and xkeys is not None:\n for ikey, v in enumerate(vals[5:]):\n result[xkeys[ikey]].append(eval(v))\n continue # pragma: no cover\n \n if \"ITEM: TIMESTEP\" in line:\n if laststep:\n f.seek(lastpos)\n break\n itemstack.append((int,))\n current = \"time\"\n timeskip = False\n elif not timeskip:\n if \"ITEM: NUMBER OF ATOMS\" in line:\n itemstack.append((int,))\n current = \"natoms\"\n elif \"ITEM: BOX BOUNDS\" in line:\n period = line.strip().split(\"BOX BOUNDS\")\n if len(period) == 2 and period[1] != '':\n result[\"periodic\"] = period[1].strip().split()\n else:\n result[\"periodic\"] = (\"ss\", \"ss\" ,\"ss\")\n \n\t\t # Changes by JPRIEDS to accommodate triclinic boxes\n\t\t # Written 170719\n\t\t if len(result[\"periodic\"]) == 6:\n\t\t\titemstack.extend([(float, float, float)]*3)\n\t\t\tcurrent = \"box\"\n\t\t\tresult[\"periodic\"] = result[\"periodic\"][3:]\n\t\t elif len(result[\"periodic\"]) == 3:\n\t\t\titemstack.extend([(float, float)]*3)\n\t\t\tcurrent = \"box\"\n\t\t else:\n emsg = \"Could not classify periodic bounds: {}\"\n raise ValueError(emsg.format(result[\"periodic\"]))\n elif \"ITEM: ATOMS\" in line:\n itemstack = None\n current = \"atoms\"\n result[\"type\"] = []\n result[\"id\"] = []\n result[\"xyz\"] = []\n \n #The first two headings in the line have \"ITEM: ATOMS\", the\n #rest are usuall id, type, x, y, z, rest...\n headings = line.split()\n extras = len(headings) > 7\n if extras:\n xkeys = []\n xheadings = headings[7:]\n for xhead in xheadings:\n key = \"atom:{}\".format(xhead)\n result[key] = []\n xkeys.append(key)\n \n if openf is None:\n #Close the file since we opened it.\n f.close()\n \n return result","def _read(self, file_path: str) -> Iterator[Instance]:\n with open(file_path) as f:\n for line in f:\n pairs = line.split()\n words, tags = zip(*(pair.split(\"###\") for pair in pairs))\n yield self.text_to_instance([Token(word) for word in words], tags)","def parse_file(self, file_path) -> list:\n data = []\n with open(file_path, 'rb') as f:\n lines = pickle.load(f)\n for line in lines:\n input, output = line\n if input.strip() == \"\" or output.strip() == \"\":\n continue\n input_len = len(input.split())\n output_len = len(output.split())\n if input_len > 50 or output_len > 50:\n continue\n data_item = Text2TextDataItem(input_text=input, output_text=output, tokenizer=self.tokenizer,\n share_vocab=self.share_vocab)\n data.append(data_item)\n return data","def process_line(line):\n\n name_comp_list = []\n givenname_comp_list = []\n surname_comp_list = []\n geocode_comp_list = []\n locality_comp_list = []\n date1_comp_list = []\n date2_comp_list = []\n\n # Split the line into the basic fields - - - - - - - - - - - - - - - - - - -\n #\n if (config.in_file_type in ['CSV','CSVQ','TAB','TABQ']):\n # Comma or tabulator separated\n try:\n line_list = config.line_parser.parse(line)\n except:\n log_message('CSV line parsing failed with inout: '+line,'err')\n\n if (len(line_list) < config.input_len):\n log_message('Input line does not contain enough fields,' +\\\n 'fill up with empty fields','warn')\n while (len(line_list) < config.input_len):\n line_list.append('')\n\n config.curr_line_list = line_list # Save current line list\n\n # Extract fields into different component lists - - - - - - - - - - - - - -\n #\n if (config.input_component['name'] != []): # Extract name fields\n for i in config.input_component['name']:\n name_comp_list.append(line_list[i])\n\n else: # Extract givenname and surname into separate components - - - - - -\n if (config.input_component['givenname'] != []): # Extract g-name fields\n for i in config.input_component['givenname']:\n givenname_comp_list.append(line_list[i])\n\n if (config.input_component['surname'] != []): # Extract surname fields\n for i in config.input_component['surname']:\n surname_comp_list.append(line_list[i])\n\n if (config.input_component['geocode'] != []): # Extract geocode fields\n for i in config.input_component['geocode']:\n geocode_comp_list.append(line_list[i])\n\n if (config.input_component['locality'] != []): # Extract locality fields\n for i in config.input_component['locality']:\n locality_comp_list.append(line_list[i])\n\n if (config.input_component['date1'] != []): # Extract date1 fields\n for i in config.input_component['date1']:\n date1_comp_list.append(line_list[i])\n\n if (config.input_component['date2'] != []): # Extract date2 fields\n for i in config.input_component['date2']:\n date2_comp_list.append(line_list[i])\n\n elif (config.in_file_type == 'COL'): # Column based input file - - - - - - -\n\n if (len(line) < config.input_len):\n log_message('Input line is not long enough, fill up with spaces','warn')\n line += ' '*(config.input_len-len(line))\n\n if (config.input_component['name'] != []): # Extract name fields\n for (col_start,length) in config.input_component['name']:\n name_comp_list.append(line[col_start,col_start+length])\n\n else: # Extract givenname and surname into separate components - - - - - -\n if (config.input_component['givenname'] != []): # Extract g-name fields\n for (col_start,length) in config.input_component['givenname']:\n givenname_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['surname'] != []): # Extract surname fields\n for (col_start,length) in config.input_component['surname']:\n surname_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['geocode'] != []): # Extract geocode fields\n for (col_start,length) in config.input_component['geocode']:\n geocode_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['locality'] != []): # Extract locality fields\n for (col_start,length) in config.input_component['locality']:\n locality_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['date1'] != []): # Extract date1 fields\n for (col_start,length) in config.input_component['date1']:\n date1_comp_list.append(line[col_start,col_start+length])\n\n if (config.input_component['date2'] != []): # Extract date2 fields\n for (col_start,length) in config.input_component['date2']:\n date2_comp_list.append(line[col_start,col_start+length])\n\n # elif (config.in_file_type == 'SQL'): # - - - - - - - - - - - - - - - - - -\n\n ################################\n # Add later: SQL database access\n ################################\n\n msg = [' Component basic field lists:', \\\n ' Name: '+str(name_comp_list), \\\n ' Given name: '+str(givenname_comp_list), \\\n ' Surname: '+str(surname_comp_list), \\\n ' Geocode: '+str(geocode_comp_list), \\\n ' Locality: '+str(locality_comp_list), \\\n ' Date1: '+str(date1_comp_list), \\\n ' Date2: '+str(date2_comp_list)]\n log_message(msg,'v2')\n\n name_comp = ''\n givenname_comp = ''\n surname_comp = ''\n geocode_comp = ''\n locality_comp = ''\n date1_comp = ''\n date2_comp = ''\n\n # Now clean and then concatenate component lists into strings - - - - - - - -\n #\n if (name_comp_list != []): # Name component\n name_comp = name_comp_list[0] # Start with first field in list\n\n for f in name_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['name'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['name'] == 1):\n sep = check_field_spill(name_comp, f)\n\n name_comp = name_comp+sep+f # Append separator and field\n\n if (givenname_comp_list != []): # Givenname component - - - - - - - - - - -\n givenname_comp = givenname_comp_list[0] # Start with first field in list\n\n for f in givenname_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['givenname'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['givenname'] == 1):\n sep = check_field_spill(givenname_comp, f)\n\n givenname_comp = givenname_comp+sep+f # Append separator and field\n\n if (surname_comp_list != []): # Surname component - - - - - - - - - - - - -\n surname_comp = surname_comp_list[0] # Start with first field in list\n\n for f in surname_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['surname'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['surname'] == 1):\n sep = check_field_spill(surname_comp, f)\n\n surname_comp = surname_comp+sep+f # Append separator and field\n\n if (geocode_comp_list != []): # Geocode component - - - - - - - - - - - - -\n geocode_comp = geocode_comp_list[0] # Start with first field in list\n\n for f in geocode_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['geocode'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['geocode'] == 1):\n sep = check_field_spill(geocode_comp, f)\n\n geocode_comp = geocode_comp+sep+f # Append separator and field\n\n if (locality_comp_list != []): # Locality component - - - - - - - - - - - -\n locality_comp = locality_comp_list[0] # Start with first field in list\n\n for f in locality_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['locality'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['locality'] == 1):\n sep = check_field_spill(locality_comp, f)\n\n locality_comp = locality_comp+sep+f # Append separator and field\n\n if (date1_comp_list != []): # Date1 component - - - - - - - - - - - - - - -\n date1_comp = date1_comp_list[0] # Start with first field in list\n\n for f in date1_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['date1'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['date1'] == 1):\n if (date1_comp[-1] != ' ') and (f[0] != ' '):\n tmp_list0 = date1_comp.split()\n tmp_list1 = f.split()\n check_word = tmp_list0[-1]+tmp_list1[0]\n\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\n 'sep','oct','nov','dec','january','february','march','april', \\\n 'may','june','july','august','september','october','november', \\\n 'december']):\n\n sep = '' # Set separator to no space\n msg = ' Correct date1 word spilling: \"'+date1_comp+'\",\"'+f+'\"'\n log_message(msg,'v1')\n\n date1_comp = date1_comp+sep+f # Append separator and field\n\n if (date2_comp_list != []): # Date2 component - - - - - - - - - - - - - - -\n date2_comp = date2_comp_list[0] # Start with first field in list\n\n for f in date2_comp_list[1:]: # Loop over following fields (if any)\n if (f != ''):\n if (config.input_space_sep['date2'] == 1):\n sep = ' ' # Set separator to space between fields\n else:\n sep = '' # No space between fields\n\n # Check field spilling only if space separator is set to ' ' \n #\n if (sep == ' ') and (config.input_check_spilling['date2'] == 1):\n if (date2_comp[-1] != ' ') and (f[0] != ' '):\n tmp_list0 = date1_comp.split()\n tmp_list1 = f.split()\n check_word = tmp_list0[-1]+tmp_list1[0]\n\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\n 'sep','oct','nov','dec','january','february','march','april', \\\n 'may','june','july','august','september','october','november', \\\n 'december']):\n\n sep = '' # Set separator to no space\n msg = ' Correct date1 word spilling: \"'+date1_comp+'\",\"'+f+'\"'\n log_message(msg,'v1')\n\n date2_comp = date2_comp+sep+f # Append separator and field\n\n # Check if name component is given or givenname and surname separately - - -\n #\n if (config.input_component['givenname'] != []) or \\\n (config.input_component['surname'] != []):\n name_comp = [givenname_comp, surname_comp]\n\n msg = [' Components:', \\\n ' Name: \"'+str(name_comp)+'\"', \\\n ' Geocode: \"'+geocode_comp+'\"', \\\n ' Locality: \"'+locality_comp+'\"', \\\n ' Date1: \"'+date1_comp+'\"', \\\n ' Date2: \"'+date2_comp+'\"']\n log_message(msg,'v1')\n\n return [name_comp, geocode_comp, locality_comp, date1_comp, date2_comp]","def readlines(self, n = None):\n\t\tif not self._input: raise PlumberExceptions.PipeTypeException(self)\n\t\tresult = []\n\t\twhile n == None or n > 0:\n\t\t\tline = self.readline()\n\t\t\tif not line: return result\n\t\t\tresult.append(line)\n\t\t\tn -= 1\n\t\treturn result","def read(read_file) -> list:\n result = []\n try:\n with open(read_file) as file:\n for lines in file:\n line = decode(lines.strip(\"\"))\n result.append(extract_information(line))\n global header\n header = result[0:2]\n result = result[3:]\n for word in result:\n if \"None\" in word[0:3]:\n raise InvalidPrincessException(\"Invalid princess!\")\n continue\n return result\n except FileNotFoundError:\n raise Exception(\"File not found!\")","def read_file(inp_fn):\n lines = [line.strip().split(\",\")\n for line in open(inp_fn)\n if not (line.startswith(\"#\"))]\n return [(int(line[0]), year_record({\"male\": int(line[-3]),\n \"female\": int(line[-2]),\n \"unknown\": int(line[-1])},\n None, None))\n for line in lines[1:]]","def read_lines(filename):\n with file(filename) as f:\n for line in f:\n _line = line.strip()\n if _line:\n yield _line","def parse_file(self):\n # read the first line in the file\n line = self._stream_handle.readline()\n\n while line:\n # check for a data line or a dcl logger line we specifically ignore\n data_match = DATA_LINE_MATCHER.match(line)\n ignore_match = IGNORE_LINE_MATCHER.match(line)\n\n if data_match:\n # found a data line, extract this particle\n # DCL controller timestamp is the port_timestamp\n dcl_controller_timestamp = data_match.groups()[DCL_TIMESTAMP_GROUP]\n port_timestamp = dcl_time_to_ntp(dcl_controller_timestamp)\n\n particle = self._extract_sample(self.particle_class,\n None,\n data_match,\n port_timestamp=port_timestamp,\n preferred_ts=DataParticleKey.PORT_TIMESTAMP)\n\n self._record_buffer.append(particle)\n\n elif not ignore_match:\n # we found a line with an unknown format, call an exception\n error_message = 'Found line with unknown format %s' % line\n log.warn(error_message)\n self._exception_callback(SampleException(error_message))\n\n # read the next line\n line = self._stream_handle.readline()","def read(filename, maximum):\n raws = []\n with open(filename, \"r\") as f:\n for _, line in zip(range(maximum), f):\n raw = parse_entry(line)\n raws.append(raw)\n return raws","def parse(self):\r\n # open the file\r\n # try to find the first line with Time =\r\n # send the file to a recursive residualParser\r\n try:\r\n with open(self.filepath, 'rb') as f:\r\n for line in f:\r\n if line.startswith('Time ='):\r\n self.timestep = self.__getTime(line)\r\n self.__residuals[self.timestep] = {}\r\n self.__parseResiduals(f)\r\n except Exception as e:\r\n raise 'Failed to parse {}:\\n\\t{}'.format(self.filepath, e)","def _read_file(self, current_path):\n # type: (str)->None\n\n if current_path in self._parsed_files:\n return\n self._parsed_files.add(current_path)\n\n with open(current_path, \"r\") as interfaces:\n # When the first non-comment line is parsed, header\n # comments have been read in.\n header_parsed = False\n # Loop through the interfaces file.\n for line in interfaces:\n # 1. Identify the clauses by analyzing the first\n # word of each line.\n # 2. Go to the next line if the current line is a comment.\n # line = line.strip().replace(\"\\n\", \"\")\n if not line:\n pass\n elif line.strip().startswith(\"#\") is True:\n if not header_parsed:\n self._header_comments += line\n else:\n # Header comments can no longer\n # be parsed in when the first interfaces\n # line is parsed in.\n header_parsed = True\n self._parse_iface(line, current_path)\n # Ignore blank lines.\n if not line.isspace():\n self._parse_details(line)\n self._read_auto(line)\n self._read_hotplug(line)\n\n # Is there some file to source ?\n source_path = self._read_sourced_path(line)\n if source_path:\n self._read_files(source_path)\n\n # TODO: lots of directives are completly ignored\n # and would be deleted","def read(self, start_line, num_lines, timeout=0):\n # emulate timeout by sleeping timeout time\n if timeout > 0:\n read_timeout = float(timeout) / 1000\n #logging.debug(\"sleep \" + str(read_timeout) + \" seconds\")\n time.sleep(read_timeout)\n\n output = []\n attributes = []\n\n # get output\n for i in range(start_line, start_line + num_lines + 1):\n output.append(self.shm_output.read(self.columns, i * self.columns))\n if not CONQUE_FAST_MODE:\n attributes.append(self.shm_attributes.read(self.columns, i * self.columns))\n\n return (output, attributes)","def parse_input(filename):\n with open(filename, 'r') as input_file:\n return [i.rstrip() for i in input_file]","def read_file(self, filename):\n with open(filename, 'r') as file:\n for line in file:\n l = line.strip()\n\n if l == ST_POS0:\n self._state = ST_POS0\n elif l == ST_TRNS:\n self._state = ST_TRNS\n elif l == ST_POS1:\n self._state = ST_POS1\n else:\n self._parse_line(l)\n self._state = None","def _read_log(self):\n\n line_regex = compile(r\"\\[I\\]\\s*\\(\\d+ms\\)[^\\d]+(?P<counter>\\d+)\"\n r\"[^\\d]+(?P<timestamp>\\d+(\\.\\d+)?)[^\\d]+\"\n r\"(?P<acceleration>\\d+);\")\n values = []\n with open(self.filepath) as file:\n for line in file:\n match = line_regex.match(line)\n if match:\n values.append({\n 'counter':\n int(match['counter']),\n 'timestamp':\n int(float(match['timestamp']) * 1000),\n 'acceleration':\n int(match['acceleration'])\n })\n\n self.values = values","def read(self):\n global ALIVE\n line = sys.stdin.readline()\n if line:\n self.datalines.append(line.rstrip())\n else:\n ALIVE = False","def load_input(self, file_name):\n with open(file_name, \"r\") as in_file:\n self.all_lines = [line.rstrip('\\n') for line in in_file]","def _parse(self):\n with open(_join(self.man_dir, self.man_fn)) as fp:\n lines = fp.readlines()\n \n desc_indxs = []\n for i, L in enumerate(lines):\n if \"#landuse\" in L or \" # landuse\" in L:\n desc_indxs.append(i-1)\n desc_indxs.append(i-2)\n desc_indxs.append(i-3)\n \n lines = [L[:L.find('#')].strip() for L in lines]\n lines = [L for i, L in enumerate(lines) if len(L) > 0 or i in desc_indxs]\n\n del desc_indxs\n \n self.datver = lines.pop(0)\n self.nofe = int(lines.pop(0))\n self.sim_years = int(lines.pop(0))\n \n # Read Plant Growth Section\n self.plants = PlantLoops(lines, self)\n\n # Read Operation Section\n self.ops = OpLoops(lines, self)\n \n # Read Initial Condition Section\n self.inis = IniLoops(lines, self)\n \n # Read Surface Effects Section\n self.surfs = SurfLoops(lines, self)\n \n # Read Contour Section\n self.contours = ContourLoops(lines, self)\n \n # Read Drainage Section\n self.drains = DrainLoops(lines, self)\n \n # Read Yearly Section\n self.years = YearLoops(lines, self)\n \n # Read Management Section \n self.man = ManagementLoop(lines, self)","def read_in_file():\n\t# Declare variables\n\treads = []\n\n\t# Get command line arguments\n\targuments = sys.argv\n\targuments_length = len(arguments)\n\n\t# Read file is the first argument\n\tread_file_name = arguments[1]\n\n\t# Process read file \n\tread_file = open(read_file_name, 'r')\n\tfor line in read_file:\n\t\tread_info = line.split()\n\t\tread_string = read_info[2].replace('\\'', '')\n\t\tnew_read = GenerativeRead(read_string, [], read_info[5], read_info[3], None, [], read_info[0], read_info[1], read_info[4]) \n\t\treads.append(new_read)\n\tread_file.close()\n\n\t# Repeat regions file in the second argument\n\trepeat_file_name = arguments[2]\n\n\t# Process repeat file\n\trepeat_file = open(repeat_file_name, 'r')\n\talignments = [[]]\n\talignment_index = -1\n\tprevious_line = ''\n\n\n\tfor line in repeat_file:\n\t\talignment_info = line.split()\n\n\t\t# This consists of a tuple of alignment string, alignment start position and alignment chromosome\n\t\t#new_align = alignment_info[2], alignment_info[4], alignment_info[3]\n\n\t\tnew_align = Alignment(alignment_info[2], None, alignment_info[4], alignment_info[3])\n\n\t\tif previous_line != alignment_info[0]:\n\t\t\t# It is not a repeat\n\t\t\talignment_index = alignment_index + 1\n\t\t\talignments.append([])\n\t\t\tprevious_line = alignment_info[0]\n\n\t\talignments[alignment_index].append(new_align)\n\n\trepeat_file.close()\n\n\t# Associate each read with the other alignments\n\tfor read in reads:\n\t\t# Find the other alignments\n\t\tpos = read.get_position()\n\t\tfound = False\n\t\tfound_index = -1\n\n\t\tfor a_index, alignment_lists in enumerate(alignments):\n\t\t\t# find matching alignments\n\t\t\t# TODO: Don't add alignment already have\n\t\t\t# TODO: Make functional with filter\n\t\t\tfor align in alignment_lists:\n\t\t\t\tif align.get_position() == pos:\n\t\t\t\t\tfound = True\n\t\t\t\t\tfound_index = a_index\n\t\t\t\t\tbreak\n\n\t\t\tif found is True:\n\t\t\t\tbreak\n\n\t\tif found is True:\n\t\t\tfor new_align in alignments[found_index]:\n\t\t\t\tread.add_alignment(new_align)\n\t\t\t\n\n\n\t# SNP files are the remaining ones\n\tsnp_file_names = [arguments[file_id] for file_id in range(3, arguments_length) ]\n\n\t# Process SNP files\n\tfor file_name in snp_file_names:\n\t\tsnp_file = open(file_name, 'r')\n\n\t\tfor line in snp_file:\n\t\t\tsnp_info = line.split()\n\t\t\tsnps = snp_info[3].split('/')\n\t\t\tsnp_pos = int(float(snp_info[2]))\n\n\t\t\t# Ignore alleles that are longer than one base\n\n\t\t\t\n\t\t\tif all(len(x) < 2 for x in snps):\n\n\t\t\t\t# Iterate through reads and determine whether or not it contains this SNP\n\t\t\t\tpos_low = snp_pos - 49\n\t\t\t\n\n\t\t\t\tfor read in reads:\n\t\t\t\t\tpositions = read.get_alignment_positions()\n\n\t\t\t\t\tfor p_index, p in enumerate(positions):\n\t\t\t\t\t\tp = int(float(p))\n\t\t\t\t\t\tif p >= pos_low and p <= snp_pos:\n\t\t\t\t\t\t\t# Get index of snp\n\t\t\t\t\t\t\toffset = snp_pos - p\n\t\t\t\t\t\t\tcalls = [0, 0, 0, 0]\n\t\t\t\t\t\t\tfor snp in snps:\n\t\t\t\t\t\t\t\tcall_index = get_base_num(snp)\n\t\t\t\t\t\t\t\tcalls[call_index] = 1\n\n\t\t\t\t\t\t\t# Add the SNP to the read\n\t\t\t\t\t\t\tread.add_snp(p_index, offset, calls)\n\t\t\t\t\t\t\t\n\t\tsnp_file.close()\n\treturn reads","def readSequenceLengths(lines):\n sequenceLengths = {} # an OrderedDict might be nice\n isSearching = True\n for line in lines:\n if line.isspace(): isSearching = True\n elif isSearching:\n w = line.split()\n if w[0] == \"s\":\n seqName, seqSize = w[1], w[5]\n sequenceLengths[seqName] = seqSize\n isSearching = False\n return sequenceLengths"],"string":"[\n \"def __read_lines__(self):\\r\\n fd = open(self.input_file, \\\"r\\\")\\r\\n lines = fd.readlines()\\r\\n fd.close()\\r\\n return lines\",\n \"def process_lines(self, lines):\\n line_index = 0\\n n_lines = len(lines)\\n while line_index < n_lines:\\n if lines[line_index].startswith(\\\"HIERARCHY\\\"):\\n line_index = self._read_skeleton(lines, line_index, n_lines)\\n if lines[line_index].startswith(\\\"MOTION\\\"):\\n self._read_frametime(lines, line_index+2)\\n line_index = self._read_frames(lines, line_index+3, n_lines)\\n else:\\n line_index += 1\",\n \"def parse(self, filehandle):\\n l = filehandle.readline()\\n if l.split()[0] != '##maf':\\n return\\n else:\\n self.setpar(l.split()[1:])\\n\\n l=filehandle.readline()\\n while l:\\n la = l.split()\\n## print la\\n if(len(la)==0 or la[0]=='#'):\\n## print \\\"skipping\\\"\\n 1\\n elif(la[0]=='a'):\\n## print \\\"reading alignment\\\"\\n self.readalign(la[1:], filehandle)\\n else:\\n## print \\\"end of records\\\"\\n return\\n\\n l=filehandle.readline()\",\n \"def _parse_ach_file(self, contents):\\n file_length = len(contents)\\n\\n for index in range(0, file_length, self.LINE_LENGTH):\\n line = contents[index:index + self.LINE_LENGTH]\\n\\n if line.startswith('1'):\\n self._read_header(line)\\n elif line.startswith('5'):\\n self._read_batch_header(line)\\n elif line.startswith('6'):\\n self._read_entry_detail(line)\\n elif line.startswith('7'):\\n self._read_addenda_record(line)\\n elif line.startswith('8'):\\n self._read_batch_control_record(line)\\n elif line.startswith('9'):\\n if line == '9' * 94:\\n continue\\n self._read_file_control_record(line)\",\n \"def parse(self):\\n for line in self.lines:\\n self.read_line(line)\\n return self.assembler_lines\",\n \"def read_lines(self):\\n with open(self.pdb_file) as infile:\\n self.lines = infile.readlines()\",\n \"def parse_file():\\n\\tfile_lines = []\\n\\n\\t## For each line in the file, if it's not empty, store it\\n\\tfor line in fileinput.input():\\n\\t\\tif len(line) > 1:\\n\\t\\t\\tfile_lines.append(line.strip())\\n\\t\\n\\trun_algorithms(file_lines)\",\n \"def parse(self, f):\\n \\n for line in f:\\n self.parse_line(line)\",\n \"def parse_lines(lines):\\n for line in lines:\\n yield Record(line)\",\n \"def extract_lines(infile):\\n with open(infile, 'r') as src:\\n return read_on(get_line, src)\",\n \"def read_input(self):\\n\\n with open(self.input_file, 'r') as file:\\n\\n data = defaultdict(list)\\n for line in file:\\n # Skip empty lines, meta-data and comments\\n if len(line) < 2 or line[0] == '#':\\n continue\\n\\n # Make Region object of current line\\n f = Region(line)\\n data[f.rid].append(f)\\n\\n return data\",\n \"def read_records(self, input_path, offset_range_tracker):\\n start_offset = offset_range_tracker.start_position()\\n with tf.io.gfile.GFile(input_path, 'r') as f:\\n f.seek(start_offset)\\n # Read lines in and proceed once a `+` is in the 3rd position.\\n record = collections.deque([], 4)\\n while True:\\n line = f.readline()\\n if not line:\\n # End of file reached\\n break\\n record.append(line.strip())\\n if len(record) == 4 and record[0].startswith('@') and record[2] == '+':\\n # Once a '+' is in position 3 a full record exists in the record.\\n read_name = record[0]\\n sequence = record[1]\\n qual = record[3]\\n self.fastq_records_counter.inc()\\n yield (read_name, sequence, qual)\\n record.clear()\",\n \"def readlines(self):\\n lines = []\\n while True:\\n line = self.readline()\\n if line is None:\\n return lines\\n lines.append(line)\",\n \"def read_input():\\n f = open(INPUT_FILE, 'r')\\n \\n number_of_lines = int(f.readline())\\n for i in range(number_of_lines):\\n yield [i+1, f.readline().split()]\\n f.close()\",\n \"def readData(self):\\n self._setupArrays()\\n\\n with open(self.filename) as fh:\\n datalines = fh.readlines()[self.NLHEAD:]\\n\\n datalines = self._checkForBlankLines(datalines)\\n\\n # Set up loop over unbounded indpendent variable\\n m = 0 # Unbounded independent variable mark \\n while len(datalines) > 0:\\n datalines = self._readData1(datalines, m)\\n datalines = self._readData2(datalines, m)\\n m = m + 1\",\n \"def readlines(self):\\n return [line for line in self]\",\n \"def _read(cls, input_file, quotechar=None):\\n with open(input_file, \\\"r\\\", encoding='utf-8') as f:\\n lines = []\\n for line in f:\\n lines.append(line.strip())\\n return lines\",\n \"def read():\\n for line in sys.stdin:\\n if line == \\\"\\\\n\\\":\\n return\\n log_elements.append(log_element(line))\",\n \"def parse(self, filename):\\n infile = file(filename)\\n for line in infile:\\n self.parseLine(line)\",\n \"def parse_file(self):\\n with open(self.file_name, 'r', errors='ignore') as log_file:\\n for line in log_file:\\n self.process_line(line)\",\n \"def input_file_parser(cls):\\n \\n # Loop through the file and store lines in an appropriate list that is passed to other class functions\\n with open(cls.infile_name,'r') as infile:\\n for line in infile: # Loop through the whole file\\n if '$molecule' in line: # Search for a section header\\n for line in infile: # Enter second loop over the lines in the section\\n if '$end' in line: # If you find $end, stop loop as the section is finished\\n break\\n else: # Otherwise add the line to a list\\n cls.molecule_lines.append(line.strip())\\n if '$connection' in line: # Continue for other sections...\\n for line in infile:\\n if '$end' in line:\\n break\\n else:\\n cls.connector_lines.append(line.strip())\\n if '$options' in line: # Continue for other sections...\\n for line in infile:\\n if '$end' in line:\\n break\\n else:\\n cls.options_lines.append(line.strip())\\n\\n return None\",\n \"def read_lines_from_file(fname):\\n return []\",\n \"def parse_lines(filename):\\n line_counter = 0\\n with open(filename, 'r') as rf:\\n for line_txt in rf:\\n try:\\n d = json.loads(line_txt)\\n tup = (\\n d['attributed_to'],\\n int(d['date_time'][8:10]),\\n d.get('used_first_time_today', False),\\n d.get('first_utm_source', 'unknown') \\n )\\n except:\\n print('Error parsing line_txt:', line_txt)\\n line_counter += 1\\n if line_counter % 10 ** 6 == 0:\\n print('read %dM lines' % (line_counter // 10 ** 6))\\n yield tup # yield: https://stackoverflow.com/a/231855\",\n \"def parse(self):\\n\\t\\tfirst = None\\n\\t\\tf = open(self.input_file)\\n\\t\\tfor line in f.readlines():\\n\\t\\t\\tif line.startswith(\\\"#\\\"):\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tflow,t,sequence,size = line.split()\\n\\t\\t\\texcept:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\t# append data to a list of tuples\\n\\t\\t\\tflow = int(flow)\\n\\t\\t\\tt = float(t)\\n\\t\\t\\tsequence = int(sequence)\\n\\t\\t\\tif size == \\\"x\\\":\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tsize = int(size)\\n\\t\\t\\tif not size == 0:\\n\\t\\t\\t\\tif flow == 1:\\n\\t\\t\\t\\t\\tself.data1.append((t,sequence,size))\\n\\t\\t\\t\\telif flow == 2:\\n\\t\\t\\t\\t\\tself.data2.append((t,sequence,size))\\n\\t\\t\\t\\telif flow == 3:\\n\\t\\t\\t\\t\\tself.data3.append((t, sequence, size))\\n\\t\\t\\t\\telif flow == 4:\\n\\t\\t\\t\\t\\tself.data4.append((t, sequence, size))\\n\\t\\t\\t\\telif flow == 5:\\n\\t\\t\\t\\t\\tself.data5.append((t, sequence, size))\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprint \\\"Erroneous data: \\\",flow, t, sequence, size\\n\\t\\t\\t# Keep track of the minimum and maximum time seen\\n\\t\\t\\tif not self.min_time or t < self.min_time:\\n\\t\\t\\t\\tself.min_time = t\\n\\t\\t\\tif not self.max_time or t > self.max_time:\\n\\t\\t\\t\\tself.max_time = t\\n\\n\\t\\t\\t# print len(self.data1),len(self.data2),len(self.data3),len(self.data4),len(self.data5)\",\n \"def _read(self, file_name):\\n f = open(file_name)\\n lines = f.readlines()\\n begin = 0\\n end = 0\\n while end < len(lines):\\n op = ''\\n for l in lines[begin:]:\\n end += 1\\n op = l.split()[0]\\n if op in operations:\\n self.operations.append(op)\\n break\\n if op == '=push':\\n nfa = Automaton(lines[begin:end - 1])\\n self.aut_to_push.append(nfa)\\n begin = end\\n f.close()\",\n \"def parse_mapping_file(lines, strip_quotes=True, suppress_stripping=False):\\r\\n if hasattr(lines, \\\"upper\\\"):\\r\\n # Try opening if a string was passed\\r\\n try:\\r\\n lines = open(lines, 'U')\\r\\n except IOError:\\r\\n raise QiimeParseError(\\\"A string was passed that doesn't refer \\\"\\r\\n \\\"to an accessible filepath.\\\")\\r\\n\\r\\n if strip_quotes:\\r\\n if suppress_stripping:\\r\\n # remove quotes but not spaces\\r\\n strip_f = lambda x: x.replace('\\\"', '')\\r\\n else:\\r\\n # remove quotes and spaces\\r\\n strip_f = lambda x: x.replace('\\\"', '').strip()\\r\\n else:\\r\\n if suppress_stripping:\\r\\n # don't remove quotes or spaces\\r\\n strip_f = lambda x: x\\r\\n else:\\r\\n # remove spaces but not quotes\\r\\n strip_f = lambda x: x.strip()\\r\\n\\r\\n # Create lists to store the results\\r\\n mapping_data = []\\r\\n header = []\\r\\n comments = []\\r\\n\\r\\n # Begin iterating over lines\\r\\n for line in lines:\\r\\n line = strip_f(line)\\r\\n if not line or (suppress_stripping and not line.strip()):\\r\\n # skip blank lines when not stripping lines\\r\\n continue\\r\\n\\r\\n if line.startswith('#'):\\r\\n line = line[1:]\\r\\n if not header:\\r\\n header = line.strip().split('\\\\t')\\r\\n else:\\r\\n comments.append(line)\\r\\n else:\\r\\n # Will add empty string to empty fields\\r\\n tmp_line = map(strip_f, line.split('\\\\t'))\\r\\n if len(tmp_line) < len(header):\\r\\n tmp_line.extend([''] * (len(header) - len(tmp_line)))\\r\\n mapping_data.append(tmp_line)\\r\\n if not header:\\r\\n raise QiimeParseError(\\\"No header line was found in mapping file.\\\")\\r\\n if not mapping_data:\\r\\n raise QiimeParseError(\\\"No data found in mapping file.\\\")\\r\\n\\r\\n return mapping_data, header, comments\",\n \"def record_finder(lines):\\n for line in lines:\\n if not line.strip():\\n continue\\n if line.startswith(\\\"@\\\"): #separate each block of reads by @\\n try:\\n yield curr\\n except:\\n pass\\n curr = []\\n curr.append(line.strip())\\n else:\\n curr.append(line.strip())\\n if curr:\\n yield curr #Sandra et al. 2019\",\n \"def parse(self, lines):\\n # convert file to string deleting end of line charcters\\n citations_string = self.prepare_file(lines)\\n # extract the entries from the string\\n entries = list(self.find_entries(citations_string))\\n entries.append(len(citations_string))\\n # parse each entry to generate a citation\\n for idx, jdx in zip(entries[:-1], entries[1:]):\\n self.parse_entry(citations_string[idx:jdx])\\n return self.force_field.citations\",\n \"def parse_input():\\n with open(\\\"input_07.txt\\\", \\\"r\\\") as f:\\n for line in f:\\n Bag(line)\",\n \"def readSequences(lines):\\n seqs = []\\n label = None\\n seq_lines = []\\n for line in lines:\\n line = line.strip() # strip off white space\\n if not line: # skip empty lines\\n continue\\n if line.startswith(';'): # ignore comment lines\\n continue\\n # check for start of next sequence:\\n if line.startswith('>'): # label line\\n # first, store the previous sequence if we had one:\\n if seq_lines:\\n seqs.append(Sequence(label, ''.join(seq_lines)))\\n seq_lines = []\\n # get the label (name) for the next sequence\\n label = line[1:].strip()\\n else:\\n # collect all lines with sequence information for this sequence:\\n seq_lines.append(line)\\n # take care of the last sequence in the file\\n seqs.append(Sequence(label, ''.join(seq_lines)))\\n return seqs\",\n \"def readlines(self, cr=1):\\n mode = \\\"r\\\"\\n\\n if not cr:\\n content = self.read(mode)\\n return content.split(\\\"\\\\n\\\")\\n else:\\n f = self.open(mode)\\n try:\\n return f.readlines()\\n finally:\\n f.close()\",\n \"def readlines(self):\\n return list(self.iterlines())\",\n \"def line_parser(path):\\n lines = []\\n with open(path, 'r') as input:\\n lines = [line.rstrip().split(',') for line in input]\\n lines = [\\n [[float(x1), float(y1)],\\n [float(x2), float(y2)]] \\n for x1, y1, x2, y2 in lines]\\n return lines\",\n \"def readlines(self):\\n return [\\\"\\\"] + self.get(\\\"1.0\\\", END).split(\\\"\\\\n\\\")[:-1]\",\n \"def _read_input_file(self):\\n pass\",\n \"def readInput(in_file_name):\\n in_file = open(in_file_name, 'r')\\n positions = []\\n samples = []\\n M = []; P = [];\\n MC = []; PC = [];\\n while True:\\n line = in_file.readline()\\n if not line: break\\n if line[0] == '#': continue #skip comment\\n line = line.rstrip('\\\\n').split('\\\\t')\\n \\n #genomic positions and allele support in plasma samples\\n positions.append(int(line[0]))\\n samples.append(tuple(map(int, line[1:5])))\\n \\n #maternal and paternal alleles\\n M.append(tuple(line[5:7]))\\n MC.append(tuple(map(float, line[7:9])))\\n \\n P.append(tuple(line[9:11]))\\n PC.append(tuple(map(float, line[11:13]))) \\n \\n in_file.close()\\n return positions, samples, M, P, MC, PC\",\n \"def parse_lines(self, start_line=0, end_line=False):\\n if end_line is False: end_line = len(self.file_ltxt)\\n\\n lines = self.file_ltxt\\n self.E_str = \\\"parse_lines\\\"\\n self.line_num = start_line\\n\\n # Loop over lines and parse\\n while self.line_num < end_line:\\n line = lines[self.line_num].strip()\\n\\n if line == \\\"echo\\\": print(\\\"\\\")\\n\\n # Parse any variables\\n elif self.line_declarations['variable'](line):\\n self.parse_variable_line(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['load'](line):\\n self.parse_load_cmd(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['plot'](line):\\n self.parse_plot_cmd(line)\\n\\n # Parse any file loading commands\\n elif self.line_declarations['write'](line):\\n self.parse_write_cmd(line)\\n\\n # Parse any math commands\\n elif self.line_declarations['math'](line):\\n self.parse_math_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['echo'](line):\\n self.parse_echo_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['calc'](line):\\n self.parse_calc_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['set'](line):\\n self.parse_set_cmd(line)\\n\\n # Parse any shell commands\\n elif self.line_declarations['shell'](line):\\n self.parse_shell_cmd()\\n\\n # Parse any for loop commands\\n elif self.line_declarations['for'](line):\\n self.parse_for_cmd(line)\\n\\n # Parse any echo commands\\n elif self.line_declarations['script'](line):\\n self.parse_script_cmd(line)\\n\\n elif self.line_declarations['inline_code'](line):\\n getattr(self, f\\\"parse_{line.split()[0]}_cmd\\\")(line)\\n\\n elif self.line_declarations['if'](line):\\n self.parse_if_cmd(line)\\n\\n # elif self.line_declarations['splice'](line):\\n # self.parse_splice_cmd(line)\\n\\n elif self.line_declarations['glue'](line):\\n self.parse_glue_cmd(line)\\n\\n elif self.line_declarations['exit'](line):\\n print(\\\"\\\\n\\\\nStopped Code -exit was called.\\\")\\n raise SystemExit\\n\\n # The end of control statements\\n elif '}' in line:\\n pass\\n\\n # Print a warning about unknown line\\n else:\\n self.print_warning(f\\\"I don't understand a line: '{line}'\\\")\\n\\n self.line_num += 1\",\n \"def parse_input(part=1):\\n with open(\\\"input_23.txt\\\") as f:\\n return [int(x) for x in f.readline().strip()]\",\n \"def lineReadfile(filename):\\n#\\t\\\"input:filename output=readlines() \\\"\\n\\tf = open(filename)\\n\\tlist1 =[]\\n\\twhile 1:\\n\\t\\ts = f.readline()\\n\\t\\tif s==\\\"\\\":\\n\\t\\t\\tbreak\\n\\t\\ts=string.replace(s,\\\"\\\\n\\\",\\\"\\\")\\n\\t\\tif s==\\\"\\\":\\n\\t\\t\\tcontinue\\n\\t\\tlist1.append(s)\\n\\tf.close()\\n\\treturn list1\",\n \"def read_input(input_file):\\n \\n logging.info(\\\"reading file {0}...this may take a while\\\".format(input_file))\\n with gzip.open(input_file, 'rb') as f:\\n for i, line in enumerate(f):\\n \\n if (i % 10000 == 0):\\n logging.info(\\\"read {0} reviews\\\".format(i))\\n # do some pre-processing and return list of words for each review\\n # text\\n yield gensim.utils.simple_preprocess(line)\",\n \"def read_transcript_data(fn):\\n\\n def _read_lines(fn):\\n # NC_000007.13\\tRefSeq\\tcDNA_match\\t50344265\\t50344518\\t254\\t+\\t.\\tID=aln58042;Target=NM_001220765.2 1 254 +;gap_count=0;identity=0.0691326;idty=1;num_ident=428;num_mismatch=0;pct_coverage=6.91326;pct_identity_gap=100;pct_identity_ungap=100;score=254\\n # NC_000002.11 RefSeq cDNA_match 179671939 179672150 212 - . ID=ed951d46-194c-477a-a480-4bc64530c5ba;Target=NM_001267550.2 1 212 +;gap_count=0;identity=0.999991;idty=1;num_ident=109223;num_mismatch=1;pct_coverage=100;pct_identity_gap=99.9991;pct_identity_ungap=99.9991\\n line_re = re.compile(\\n \\\"(?P<ref_ac>\\\\S+)\\\\s+(?P<origin>\\\\S+)\\\\s+(?P<match_type>\\\\S+)\\\\s+\\\"\\n \\\"(?P<g_start>\\\\d+)\\\\s+(?P<g_end>\\\\d+)\\\\s+(?P<score>\\\\S+)\\\\s+\\\"\\n \\\"(?P<strand>[-+])\\\\s+\\\\.\\\\s+ID=(?P<aln>[^;]+);Target=(?P<tx_ac>\\\\S+)\\\"\\n \\\"\\\\s+(?P<tx_start>\\\\d+)\\\\s+(?P<tx_end>\\\\d+).+?\\\"\\n \\\"pct_coverage=(?P<pct_coverage>[^;]+);\\\"\\n \\\"pct_identity_gap=(?P<pct_identity_gap>[^;]+);\\\"\\n \\\"pct_identity_ungap=(?P<pct_identity_ungap>[^;]+)\\\"\\n )\\n fh = io.open(fn, \\\"rb\\\")\\n while fh.peek(1)[0] == \\\"#\\\":\\n fh.readline()\\n while fh.peek(3)[0:3] != \\\"###\\\":\\n line = fh.readline()\\n try:\\n yield line_re.match(line).groupdict()\\n except AttributeError:\\n raise Exception(\\\"Failed at\\\", line)\\n raise StopIteration\\n def _key(e):\\n return (e[\\\"tx_ac\\\"], not e[\\\"ref_ac\\\"].startswith(\\\"NC_\\\"), e[\\\"ref_ac\\\"], e[\\\"aln\\\"])\\n return itertools.groupby(sorted(_read_lines(fn), key=_key), key=_key)\",\n \"def parse_lines(self, lines):\\n assert isinstance(lines, Iterable)\\n\\n for line in lines:\\n name, values = self.parse_line(line)\\n self.add(name, values)\",\n \"def process_lines(line_queue, result_queue, sim_info, analysis_info, args, merged_reads):\\n\\t\\n\\twhile True:\\n\\t\\tline = line_queue.get()\\n\\t\\t\\n\\t\\t# if we're done, stop\\n\\t\\tif line is None:\\n\\t\\t\\tresult_queue.put(None)\\n\\t\\t\\tbreak\\n\\t\\t# skip header lines\\n\\t\\telif line[0] == '@':\\n\\t\\t\\tcontinue\\n\\t\\t# process this line\\n\\t\\telse:\\n\\t\\t\\tresult = score_read(line, sim_info, analysis_info, args, merged_reads)\\n\\t\\t\\tresult_queue.put(result)\",\n \"def read():\\n try:\\n #Open and parse input files.\\n nodeFile = open(sys.argv[1], 'r')\\n edgeFile = open(sys.argv[2], 'r')\\n\\t\\n parse_nodes(nodeFile)\\n parse_edges(edgeFile)\\n nodeFile.close()\\n\\tedgeFile.close()\\n\\treturn \\n except:\\n print 'problem parsing input'\\n #Put here some more information - usage...\",\n \"def read (self, path):\\n\\n\\t\\tself.data = []\\n\\t\\t# print \\\"*** path: %s***\\\" % path\\n\\t\\tdir, filename = os.path.split (path)\\n\\t\\troot, ext = os.path.splitext (filename)\\n\\t\\t# encoding = 'ISO-8859-1' # utf-8\\n\\t\\ts = codecs.open(path,'r', self.encoding).read()\\n\\t\\t## s = unicode(f.read(),'utf-8')\\n\\t\\ts = self.preprocess (s)\\n\\t\\tlines = split (s, self.linesep)\\n\\t\\tschema = self.splitline(lines[0])\\n\\n\\t\\t## print \\\"** %s **\\\" % os.path.splitext(filename)[0]\\n\\t\\tif self.verbose:\\n\\t\\t\\tprint \\\"read %d lines from %s\\\" % (len(lines), path)\\n\\n\\t\\tfor i in range(1,len(lines)):\\n\\t\\t\\tif not lines[i].strip(): \\n\\t\\t\\t\\t# print 'skipping line (%d)' % i\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\tfields = self.splitline(lines[i])\\n\\t\\t\\titem = self.entry_class (fields, schema)\\n\\t\\t\\tif self.accept (item):\\n\\t\\t\\t\\tself.add (item)\\n\\n\\t\\tself.schema = schema\\n\\t\\t# self.data.sort (lastNameCmp)\",\n \"def read_line(self, *types):\\n words = self.in_file.readline().strip().split()\\n if len(words) == 1:\\n return types[0](words[0])\\n return [types[min(i, len(types) - 1)](words[i])\\n for i in range(len(words))]\",\n \"def readAllfromFile(self):\\n with open(self._fname, 'r') as f:\\n lines = f.readlines()\\n readList = []\\n for line in lines:\\n line = line.strip()\\n if len(line) > 1:\\n gra = self._readGrafromLine(line)\\n readList.append(gra)\\n f.close()\\n return readList\",\n \"def parse_file(self, parse_all=False, file=None):\\n if not parse_all:\\n input()\\n for line in sys.stdin:\\n self.parse_and_add(line)\",\n \"def read_input(input_file):\\n\\n logging.info(\\\"reading file {0}...this may take a while\\\".format(input_file))\\n with gzip.open(input_file, 'rb') as f:\\n for i, line in enumerate(f):\\n\\n if i % 10000 == 0:\\n logging.info(\\\"read {0} reviews\\\".format(i))\\n # do some pre-processing and return list of words for each review\\n # text\\n yield gensim.utils.simple_preprocess(line)\",\n \"def readrules(self, fomalines):\\n for lineno, l in enumerate(fomalines):\\n if 'define' in l or 'def ' in l:\\n rulecom = l.split(' #')\\n r = re.findall(\\\"(defi?n?e?)\\\\s+(\\\\S+)\\\\s+([^;]+)\\\", rulecom[0])\\n if len(r[0]) != 3:\\n print \\\"Syntax error on line %i\\\" % lineno\\n (_, rulename, rule) = r[0]\\n if len(rulecom) > 1:\\n commentline = rulecom[1].strip()\\n else:\\n commentline = ''\\n self.rule_add(rulename, rule, commentline)\\n if 'chain' in l:\\n l = l.replace(';','')\\n chain = re.findall('chain\\\\s+(.*)', l)\\n rc = chain[0].replace(' ','').split(',')\\n self.rc = rc\",\n \"def _parse(self, infile):\\n raise NotImplementedError()\",\n \"def parse_input():\\r\\n endpoints_file = open(\\\"input.txt\\\", \\\"r\\\")\\r\\n lines = endpoints_file.readlines()\\r\\n line_number = 0\\r\\n endpoints = []\\r\\n for line in lines:\\r\\n line = line.strip()\\r\\n if line_number == 0:\\r\\n period = int(line)\\r\\n elif line_number == 1:\\r\\n max_attempts = int(line)\\r\\n else:\\r\\n endpoints.append(line)\\r\\n line_number += 1\\r\\n endpoints_file.close()\\r\\n return endpoints, period, max_attempts\",\n \"def read_input():\\n splits = []\\n with open('solutions/day5/input.txt') as f:\\n for line in f:\\n splits.append(line.strip('\\\\n'))\\n return splits\",\n \"def read_data(cls, input_file, quotechar=None):\\r\\n if 'pkl' in str(input_file):\\r\\n lines = load_pickle(input_file)\\r\\n else:\\r\\n lines = input_file\\r\\n return lines\",\n \"def read_file(fname):\\n recs=[]\\n logger.info('Start read file %s', fname)\\n with open(fname) as inf:\\n for line in inf:\\n recs.append(line.strip().split())\\n logger.info('End reading with recnumber %d', len(recs))\\n return recs\",\n \"def read_input_file(path: str):\\n with open(path, \\\"r\\\") as f:\\n return f.readlines()\",\n \"def parse(self, infile):\\r\\n raise NotImplementedError()\",\n \"def parse_fastq (rec_lines):\\n data = []\\n data.append(rec_lines[0][1:])\\n data.append(rec_lines[1])\\n data.append(rec_lines[3])\\n return data\",\n \"def job_reader(path) -> Generator[ParsedActionType, None, None]:\\n try:\\n with open(path, \\\"r\\\") as f:\\n parser = Parser()\\n for line in f:\\n result = parser.process_line(line)\\n if result is not None:\\n yield result\\n except IOError as err:\\n print(\\\"Error opening/reading from file '{0}': {1}\\\"\\n .format(err.filename, err.strerror))\",\n \"def read_data(input_file):\\n\\n def process_line(labels, words):\\n l = ' '.join([label for label in labels if len(label) > 0])\\n w = ' '.join([word for word in words if len(word) > 0])\\n lines.append((l, w))\\n words = []\\n labels = []\\n return words, labels, lines\\n\\n rf = open(input_file, 'r')\\n lines = [];\\n words = [];\\n labels = []\\n for line in rf:\\n word = line.strip().split(' ')[0]\\n label = line.strip().split(' ')[-1]\\n # here we dont do \\\"DOCSTART\\\" check\\n\\n if len(line.strip()) == 0: # and words[-1] == '.'\\n words, labels, lines = process_line(labels, words)\\n words.append(word)\\n labels.append(label)\\n rf.close()\\n return lines\",\n \"def read_data(cls, input_file,quotechar = None):\\n if 'pkl' in str(input_file):\\n lines = load_pickle(input_file)\\n else:\\n lines = input_file\\n return lines\",\n \"async def readlines(self, hint=-1):\\n if 'b' in self._mode:\\n raise APIException(\\n \\\"readline on a binary file is not permitted: {}\\\".format(\\n self._uri)\\n )\\n # read the entire file in and decode it\\n lines = await self.read().decode().split(\\\"\\\\n\\\")\\n return lines\",\n \"def readlines(self) -> list[bytes] | None:\",\n \"def read_input_file(path):\\n file_input = os.path.join(path, 'INPUT')\\n f = open(file_input, 'r')\\n lines = f.read().replace('\\\\n', ':')\\n f.close()\\n lines = ' '.join(lines.split()) #remove whitespace\\n lines = re.split(':', lines.replace(': ', ':'))\\n return lines\",\n \"def read_records(self, input_path, offset_range_tracker):\\n start_offset = offset_range_tracker.start_position()\\n with tf.io.gfile.GFile(input_path, 'r') as f:\\n f.seek(start_offset)\\n # Read the first key\\n line = f.readline()\\n key = line.strip('>\\\\n')\\n seq = ''\\n while True:\\n line = f.readline()\\n if line.startswith('>'):\\n # New record reached.\\n yield (key, seq)\\n self.fasta_records_counter.inc()\\n key = line.strip('>\\\\n')\\n seq = ''\\n else:\\n seq += line.strip()\\n\\n if not line:\\n # End of file reached; Yield and break from loop\\n yield (key, seq)\\n self.fasta_records_counter.inc()\\n break\",\n \"def _readline_ins(self):\\n if self._ins_filehandle is None:\\n if not os.path.exists(self._ins_filename):\\n raise Exception(\\n \\\"instruction file '{0}' not found\\\".format(self._ins_filename)\\n )\\n self._ins_filehandle = open(self._ins_filename, \\\"r\\\")\\n line = self._ins_filehandle.readline()\\n self._ins_linecount += 1\\n if line == \\\"\\\":\\n return None\\n self._last_line = line\\n # check for spaces in between the markers - this gets ugly\\n line = line.lower()\\n if self._marker is not None and self._marker in line:\\n\\n # def find_all(a_str, sub):\\n # start = 0\\n # while True:\\n # start = a_str.find(sub, start)\\n # if start == -1:\\n # return\\n # yield start\\n # start += len(sub)\\n # poss speedup using regex\\n midx = [m.start() for m in re.finditer(re.escape(self._marker), line)]\\n # midx = list(find_all(line, self._marker))\\n midx.append(len(line))\\n first = line[: midx[0]].strip()\\n tokens = []\\n if len(first) > 0:\\n # tokens.append(first)\\n tokens.extend([f.strip() for f in first.split()])\\n for idx in range(1, len(midx) - 1, 2):\\n mstr = line[midx[idx - 1] : midx[idx] + 1]\\n ostr = line[midx[idx] + 1 : midx[idx + 1]]\\n tokens.append(mstr)\\n tokens.extend(ostr.split())\\n else:\\n tokens = line.strip().split()\\n return tokens\",\n \"def __parse(self):\\n lines = self.file.readlines()\\n for i in range(0, len(lines)):\\n line = lines[i]\\n tokens = line.split()\\n if tokens[0] == \\\"#start\\\":\\n trial_name = tokens[1]\\n trial = Trial(trial_name)\\n self.trials[trial_name] = trial\\n elif tokens[0] == \\\"#end\\\":\\n continue\\n else:\\n date_str = tokens[0] + \\\" \\\" + tokens[1]\\n date = datetime.strptime(date_str, \\\"%m/%d/%y %H:%M:%S\\\")\\n sound_file = line[18:-1].strip()\\n event = Event(date, sound_file, 0)\\n trial.addevent(event)\",\n \"def read_input(self, infile):\\n self.infile = open(infile, 'r')\\n input_list = None\\n for l in [line.strip('\\\\n') for line in self.infile]:\\n input_list = l.split(' ')\\n \\n pid = 0\\n for i in range(0, len(input_list), 2):\\n self.processes.append(Process(pid, int(input_list[i]), int(input_list[i+1])))\\n pid += 1\\n self.process_count = len(self.processes)\\n self.infile.close()\\n return\",\n \"def read_input():\\n # Use with to make sure the file will be closed after the block executed\\n with open('snapshot_input.txt') as f:\\n # Split the line at line breaks\\n x = f.read().splitlines()\\n # Get the data of restructuring, three positive integers N , C , and D\\n # Use generator expression for time and space efficiency\\n restructuring_info = (i.split() for i in x if len(i.split())==3)\\n # Get the data of single machine, four integers D, P, R and G\\n machine_info = (i.split() for i in x if len(i.split())!=3)\\n # Get the length of restructuring data\\n length = sum(1 for i in x if len(i.split())==3)\\n\\n return restructuring_info, machine_info, length\",\n \"def read(infile):\\n if isinstance(infile, str):\\n infile = open(infile)\\n\\n with infile:\\n while True:\\n cmt = infile.readline().strip()\\n seq = infile.readline().strip()\\n plus = infile.readline().strip()\\n qual = infile.readline().strip()\\n\\n if not cmt:\\n break\\n if not cmt.startswith('@') or plus != '+':\\n raise ValueError('fastq file <{}> is corrupted'.format(infile.path))\\n yield SRecord(cmt=cmt[1:], seq=seq, qual=qual)\",\n \"def get_input():\\n # return TEST_INPUT.split('\\\\n')\\n with open(INPUT_FILE) as f:\\n return f.readlines()\",\n \"def read_text(self, text):\\n if isinstance(text, (str, unicode)):\\n lines = text.split('\\\\n')\\n else:\\n lines = text\\n\\n for line in lines:\\n l = line.strip()\\n\\n if line == ST_POS0:\\n self._state = ST_POS0\\n elif line == ST_TRNS:\\n self._state = ST_TRNS\\n elif line == ST_POS0:\\n self._state = ST_POS0\\n else:\\n self._parse_line(line)\",\n \"def _fetch_data_lines(self) -> list:\\n with open(self.full_path, \\\"rb\\\") as file:\\n all_lines = file.readlines()\\n return self._screen_invalid_lines(all_lines)\",\n \"def read(file):\\n\\n blocks = ['bus', 'load', 'fshunt', 'gen', 'branch', 'transf', 'area',\\n 'twotermdc', 'vscdc', 'impedcorr', 'mtdc', 'msline', 'zone',\\n 'interarea', 'owner', 'facts', 'swshunt', 'gne', 'Q']\\n nol = [1, 1, 1, 1, 1, 4, 1,\\n 0, 0, 0, 0, 0, 1,\\n 0, 1, 0, 0, 0, 0]\\n rawd = re.compile('rawd\\\\d\\\\d')\\n\\n retval = True\\n version = 0\\n b = 0 # current block index\\n raw = {}\\n for item in blocks:\\n raw[item] = []\\n\\n data = []\\n mdata = [] # multi-line data\\n mline = 0 # line counter for multi-line models\\n\\n # parse file into raw with to_number conversions\\n fid = open(file, 'r')\\n for num, line in enumerate(fid.readlines()):\\n line = line.strip()\\n if num == 0: # get basemva and frequency\\n data = line.split('/')[0]\\n data = data.split(',')\\n\\n mva = float(data[1])\\n freq = float(data[5])\\n version = int(data[2])\\n\\n if not version:\\n version = int(rawd.search(line).group(0).strip('rawd'))\\n if version < 32 or version > 33:\\n logging.warning('RAW file version is not 32 or 33. Error may occur.')\\n continue\\n elif num == 1: # store the case info line\\n logging.info(line)\\n continue\\n elif num == 2:\\n continue\\n elif num >= 3:\\n if line[0:2] == '0 ' or line[0:3] == ' 0 ': # end of block\\n b += 1\\n continue\\n elif line[0] is 'Q': # end of file\\n break\\n data = line.split(',')\\n\\n data = [to_number(item) for item in data]\\n mdata.append(data)\\n mline += 1\\n if mline == nol[b]:\\n if nol[b] == 1:\\n mdata = mdata[0]\\n raw[blocks[b]].append(mdata)\\n mdata = []\\n mline = 0\\n fid.close()\\n\\n # add device elements params and add to PSAT formatted dictionary\\n\\n for data in raw['bus']:\\n \\\"\\\"\\\"version 32:\\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10\\n ID, NAME, BasekV, Type, Area Zone Owner Va, Vm, latitude longitude\\n \\\"\\\"\\\"\\n idx = data[0]\\n ty = data[3]\\n angle = data[8]\\n try:\\n lat = data[9]\\n except:\\n # logging.warning('<No Coordinates in .raw file>')\\n param = {'idx': idx,\\n 'name': data[1],\\n 'Vn': data[2],\\n 'type': data[3],\\n 'area': data[4],\\n 'voltage': data[7],\\n 'region': data[5],\\n 'owner': data[6],\\n 'angle': angle,\\n }\\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5]]\\n else:\\n param = {'idx': idx,\\n 'name': data[1],\\n 'Vn': data[2],\\n 'type': data[3],\\n 'area': data[4],\\n 'voltage': data[7],\\n 'region': data[5],\\n 'owner': data[6],\\n 'angle': angle,\\n 'latitude': data[9],\\n 'longitude': data[10]\\n }\\n psatlist = [data[0], data[2], data[7], angle, data[4], data[5], data[9], data[10]]\\n Settings.Bus.append(psatlist)\\n Settings.BusNames.append(data[1])\\n # Add BusSTORE Dictionary For Later Reference\\n Settings.BusStore[idx] = param\\n\\n xcoord = [34.560040, 34.938385, 34.360040, 40.5152473, 40.3142473, 36.527401, 36.857401, 36.687401, 36.856401,\\n 40.487041, 36.903901, 36.702901, 35.832561, 33.386047, 33.185047, 37.105571, 37.104154, 33.706718,\\n 37.103549, 36.703539, 37.103559, 36.703549, 36.033561, 35.631561, 36.032561, 35.732561, 36.525401,\\n 36.857401, 49.869314, 50.969314, 51.979314, 52.481674, 54.973192, 56.276212, 41.734596, 34.551015,\\n 34.652015, 34.537507, 34.587507, 34.157904, 33.714453, 33.762453, 39.548160, 39.496160, 34.313143,\\n 34.545782, 34.380686, 34.111686, 34.137762, 34.118650, 34.158650, 33.918650, 33.718650, 34.018650,\\n 34.018650, 34.018650, 34.018650, 34.018650, 34.312456, 34.315456, 34.243600, 34.566258, 34.565258,\\n 46.064672, 46.565672, 45.514571, 45.606833, 45.806833, 44.890000, 45.596416, 45.295416, 45.891161,\\n 47.954899, 46.511440, 45.913936, 45.713936, 46.669335, 47.954899, 47.624154, 43.784730, 44.482350,\\n 42.006860, 42.934919, 42.731919, 43.013135, 44.068350, 43.558350, 42.438350, 42.938350, 44.068350,\\n 43.558350, 43.048350, 42.638350, 44.068350, 43.558350, 43.048350, 42.638350, 43.620189, 39.120428,\\n 40.398031, 35.216200, 35.215200, 36.202099, 39.777745, 39.539598, 37.052929, 35.403217, 35.352217,\\n 36.807243, 39.567450, 40.807689, 40.806689, 41.008689, 39.555494, 37.954721, 38.406721, 38.906721,\\n 38.656721]\\n ycoord = [-109.277313, -110.303798, -109.777313, -107.546455, -107.546455, -108.325669, -108.654569, -108.486669,\\n -108.325669, -107.185575, -111.390408, -111.390408, -111.448566, -112.860397, -112.659397, -108.243555,\\n -108.441191, -112.322033, -111.590816, -111.190816, -111.190816, -111.590806, -111.648566, -111.248566,\\n -111.249566, -111.647566, -108.655669, -108.323669, -122.150895, -122.150895, -122.150895, -121.61684,\\n -121.924221, -122.21370, -108.790427, -117.568105, -117.538105, -118.607375, -118.658375, -118.280282,\\n -118.146319, -118.096319, -112.52797, -112.72797, -118.690631, -118.389938, -118.478496, -118.478496,\\n -118.299917, -118.095428, -118.095428, -118.095428, -118.095428, -118.195428, -118.395428, -117.995428,\\n -117.795428, -117.995428, -118.481217, -118.891217, -118.391667, -117.166428, -117.368428, -106.60906,\\n -106.80906, -122.681289, -121.114785, -122.113785, -123.29000, -121.312202, -121.114202, -106.612578,\\n -118.997945, -112.88531, -120.692286, -120.693974, -119.571501, -120.997945, -122.219492, -118.77463,\\n -121.019484, -121.316546, -114.419206, -114.419206, -120.956476, -120.79484, -120.93484, -121.216546,\\n -121.156546, -121.215484, -121.135484, -121.255484, -121.175484, -121.013484, -120.733484, -121.053484,\\n -120.973484, -118.865882, -122.073631, -122.263453, -120.847567, -120.900567, -120.129849, -122.142965,\\n -122.262993, -121.021929, -119.450452, -119.450452, -121.779037, -122.276225, -122.135718, -121.935718,\\n -121.935718, -121.24000, -121.18379, -121.10879, -121.27379, -121.23979]\\n\\n #for idx, line in enumerate(Settings.Bus):\\n # line.extend([xcoord[idx], ycoord[idx]])\\n\\n maxV = 1.1\\n minV = 0.9\\n maxQ = 1\\n minQ = 0\\n convimp = 0\\n status = 1\\n loss = 1\\n\\n for data in raw['load']:\\n \\\"\\\"\\\"version 32:\\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11\\n Bus, Id, Status, Area, Zone, PL(MW), QL (MW), IP, IQ, YP, YQ, OWNER\\n \\\"\\\"\\\"\\n\\n busidx = data[0]\\n vn = Settings.BusStore[busidx]['Vn']\\n voltage = Settings.BusStore[busidx]['voltage']\\n param = {'bus': busidx,\\n 'Vn': vn,\\n 'Sn': mva,\\n 'p': (data[5] + data[7] * voltage + data[9] * voltage ** 2) / mva,\\n 'q': (data[6] + data[8] * voltage - data[10] * voltage ** 2) / mva,\\n 'owner': data[11],\\n 'type': Settings.BusStore[busidx]['type'],\\n 'voltage': voltage\\n }\\n\\n psatlist = [busidx, mva, vn, param['p'], param['q'], maxV, minV, convimp, status]\\n Settings.PQ.append(psatlist)\\n \\\"\\\"\\\"CONFIRM THAT OTHER BUSES HAVE 0 P and 0 Q which are not added\\\"\\\"\\\"\\n\\n for data in raw['fshunt']:\\n \\\"\\\"\\\"\\n 0, 1, 2, 3, 4\\n Bus, name, Status, g (MW), b (Mvar)\\n \\\"\\\"\\\"\\n busidx = data[0]\\n vn = Settings.BusStore[busidx]['Vn']\\n param = {'bus': busidx,\\n 'Vn': vn,\\n 'status': data[2],\\n 'Sn': mva,\\n 'g': data[3] / mva,\\n 'b': data[4] / mva,\\n }\\n\\n psatlist = [busidx, mva, vn, freq, param['g'], param['b'], param['status']]\\n Settings.Shunt.append(psatlist)\\n\\n gen_idx = 0\\n type = 6\\n\\n for data in raw['gen']:\\n \\\"\\\"\\\"\\n 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11, 12, 13, 14, 15, 16,17,18,19\\n I,ID,PG,QG,QT,QB,VS,IREG,MBASE,ZR,ZX,RT,XT,GTAP,STAT,RMPCT,PT,PB,O1,F1\\n \\\"\\\"\\\"\\n busidx = data[0]\\n vn = Settings.BusStore[busidx]['Vn']\\n gen_mva = data[8]\\n gen_idx += 1\\n status = data[14]\\n leak = 0\\n param = {'Sn': gen_mva,\\n 'Vn': vn,\\n 'u': status,\\n 'idx': gen_idx,\\n 'bus': busidx,\\n 'pg': status * data[2] / mva,\\n 'qg': status * data[3] / mva,\\n 'qmax': data[4] / mva,\\n 'qmin': data[5] / mva,\\n 'v0': data[6],\\n 'ra': data[9], # ra armature resistance\\n 'xs': data[10], # xs synchronous reactance\\n 'pmax': data[16] / mva,\\n 'pmin': data[17] / mva,\\n }\\n\\n if Settings.BusStore[busidx]['type'] == 3: #Check Bus Type for Slack\\n refangle = 0\\n refBus = 1\\n PGuess = 1\\n swlist = [busidx, gen_mva, vn, param['v0'], refangle, param['qmax'], param['qmin'],\\n maxV, minV, PGuess, loss, refBus, status]\\n SW = swlist\\n Settings.SW.append(swlist)\\n Settings.SWStore[busidx] = param\\n Settings.SynStore[busidx] = param\\n continue\\n\\n if busidx not in Settings.BusStore.keys():\\n \\\"\\\"\\\" Need data from .dyr file. Create initial list, then append data from .dyr\\\"\\\"\\\"\\n else:\\n # psatlist = [busidx, gen_mva, vn, freq, type, leak, param['ra'],param['xs']]\\n # Syn.append(psatlist)\\n Settings.SynStore[busidx] = param\\n pvlist = [busidx, gen_mva, vn, param['pg'], Settings.BusStore[busidx]['voltage'],\\n param['qmax'], param['qmin'], maxV, minV, loss, status]\\n Settings.PV.append(pvlist)\\n\\n\\n for data in raw['branch']:\\n \\\"\\\"\\\"\\n I,J,ID,R,X,B,RATEA,RATEB,RATEC,GI,BI,GJ,BJ,ST,LEN,O1,F1,...,O4,F4\\n \\\"\\\"\\\"\\n param = {'bus1': data[0],\\n 'bus2': data[1],\\n 'id' : data[2],\\n 'r': data[3],\\n 'x': data[4],\\n 'b': data[5],\\n 'rate_a': data[6],\\n 'rate_b': data[7],\\n 'rate_c': data[8],\\n 'Vn': Settings.BusStore[data[0]]['Vn'],\\n 'Vn2': Settings.BusStore[data[1]]['Vn'],\\n 'length': data[14],\\n 'Ilim': EMPTY,\\n 'Plim': EMPTY,\\n 'Slim': EMPTY,\\n 'status': data[13]\\n }\\n\\n psatlist = [param['bus1'], param['bus2'], param['rate_c'], param['Vn'], freq, EMPTY,\\n param['length'], param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\\n param['Slim'], param['status']]\\n Settings.Lineij.append([data[0], data[1], data[2]])\\n Settings.Lineji.append([data[1], data[0], data[2]])\\n Settings.LineOrd[param['bus1']].append(psatlist)\\n Settings.branches += 1\\n Settings.linecount += 1\\n Settings.LineBusMatij[param['bus2']].append(Settings.branches)\\n Settings.LineBusMatji[param['bus1']].append(Settings.branches)\\n\\n for data in raw['transf']:\\n \\\"\\\"\\\"\\n I,J,K,CKT,CW,CZ,CM,MAG1,MAG2,NMETR,'NAME',STAT,O1,F1,...,O4,F4\\n R1-2,X1-2,SBASE1-2\\n WINDV1,NOMV1,ANG1,RATA1,RATB1,RATC1,COD1,CONT1,RMA1,RMI1,VMA1,VMI1,NTP1,TAB1,CR1,CX1\\n WINDV2,NOMV2\\n \\\"\\\"\\\"\\n if len(data[1]) < 5:\\n ty = 2\\n else:\\n ty = 3\\n if ty == 3:\\n continue\\n # raise NotImplementedError('Three-winding transformer not implemented')\\n\\n tap = data[2][0]\\n phi = data[2][2]\\n\\n if tap == 1 and phi == 0:\\n trasf = False\\n else:\\n trasf = True\\n param = {'trasf': trasf,\\n 'bus1': data[0][0],\\n 'bus2': data[0][1],\\n 'u': data[0][11],\\n 'b': data[0][8],\\n 'r': data[1][0],\\n 'x': data[1][1],\\n 'tap': tap,\\n 'phi': phi,\\n 'rate_a': data[2][3],\\n 'Vn': Settings.BusStore[busidx]['Vn'],\\n 'Vn2': Settings.BusStore[busidx]['Vn'],\\n # 'length': data[?][?], FIND CORRECT INDEX\\n 'Ilim': EMPTY,\\n 'Plim': EMPTY,\\n 'Slim': EMPTY,\\n }\\n psatlist = [param['bus1'], param['bus2'], param['rate_a'], param['Vn'], freq, EMPTY,\\n EMPTY, param['r'], param['x'], param['b'], param['Ilim'], param['Plim'], EMPTY, EMPTY,\\n param['Slim'], param['u']]\\n\\n Settings.LineOrd[param['bus1']].append(psatlist)\\n Settings.linecount += 1\\n Settings.transformers += 1\\n # ADD Line Data(All Branch Types) to Sys Param Dict after .dyr Transformer Data Added\\n # Re-Order Line Data for correct sequence\\n for key in Settings.LineOrd:\\n for item in Settings.LineOrd[key]:\\n Settings.Line.append(item)\\n\\n for data in raw['area']:\\n Settings.Areas.append(data[4])\\n\\n for data in raw['zone']:\\n Settings.Regions.append(data[1])\\n\\n return retval\",\n \"def read_input():\\n return Path(__file__).with_name('input.txt').read_text().splitlines()\",\n \"def __read_data__(self):\\n with open(self.file, 'r') as data:\\n sentence = []\\n tags = []\\n for line in data:\\n terms = line.rstrip().split(WHITESPACE)\\n for term in terms:\\n word_tag = tuple(term.split(TAGCHAR))\\n word = word_tag[0]\\n tag = word_tag[1]\\n self.word_tag_dict[word_tag] += 1\\n self.tag_dict[tag] += 1\\n self.__add_to_word_dict__(word, tag)\\n if self.isNumberWord(word):\\n self.numbers += 1\\n if word[0].isupper() and len(sentence) > 0:\\n self.cap_no_start += 1\\n sentence.append(word)\\n tags.append(tag)\\n if tag == ENDOFSENTENCE:\\n self.sentences.append(tuple(sentence))\\n self.tags.append(tuple(tags))\\n sentence = []\\n tags = []\",\n \"def read_lines(fp: TextIO) -> Iterator[str]:\\n while line := fp.readline().strip():\\n yield line\",\n \"def readlines(path):\\n with open(path, \\\"r\\\") as f:\\n return [line.strip() for line in f.readlines()]\",\n \"def parse_data(self, fileinput):\\n with open(fileinput, 'rb') as fh:\\n for line in fh:\\n try:\\n name, address, phone = line.strip().split(\\\",\\\")\\n self.data.append(Document(name, address, phone))\\n except Exception, ex:\\n raise SerialException(\\\": Failed to parse input line %s: %s\\\" % (line, ex))\\n return\",\n \"def read_linelog():\",\n \"def _read(self, openf=None, stepfilter=None):\\n itemstack = []\\n current = None\\n result = {}\\n xkeys = None\\n timeskip = False\\n laststep = False\\n\\n if openf is None:\\n f = open(self.filepath)\\n else:\\n f = openf\\n\\n line = 'start'\\n while line != '':\\n lastpos = f.tell()\\n line = f.readline()\\n if line == '':\\n continue\\n \\n if itemstack is not None and len(itemstack) > 0: \\n cast = itemstack.pop()\\n raw = line.split()\\n values = [t(r) for t, r in zip(cast, raw)]\\n if len(values) == 1:\\n values = values[0]\\n\\n if current == \\\"time\\\":\\n if stepfilter is not None and values not in stepfilter:\\n timeskip = True\\n elif (self.index is not None and values != self.index):\\n if values > self.index:\\n if openf is None:\\n return {}\\n else:\\n timeskip = True\\n laststep = True\\n else:\\n timeskip = True\\n elif self.index is None:\\n self.index = values\\n else:\\n timeskip = False\\n \\n if len(itemstack) == 0 and current not in result:\\n result[current] = values\\n else:\\n if current not in result:\\n result[current] = []\\n result[current].append(values)\\n continue\\n elif itemstack is None and current == \\\"atoms\\\":\\n if \\\"ITEM\\\" in line:\\n current = None\\n if openf is not None:\\n f.seek(lastpos)\\n break\\n else:\\n #E.g. line: 1 4 -65.9625 1.54915 1.46824 5 30.976 \\n vals = line.split()\\n sid, atype = tuple(map(int, vals[0:2]))\\n result[\\\"type\\\"].append(atype)\\n result[\\\"id\\\"].append(sid)\\n x, y, z = tuple(map(float, vals[2:5]))\\n result[\\\"xyz\\\"].append((x, y, z))\\n if len(vals) > 5 and xkeys is not None:\\n for ikey, v in enumerate(vals[5:]):\\n result[xkeys[ikey]].append(eval(v))\\n continue # pragma: no cover\\n \\n if \\\"ITEM: TIMESTEP\\\" in line:\\n if laststep:\\n f.seek(lastpos)\\n break\\n itemstack.append((int,))\\n current = \\\"time\\\"\\n timeskip = False\\n elif not timeskip:\\n if \\\"ITEM: NUMBER OF ATOMS\\\" in line:\\n itemstack.append((int,))\\n current = \\\"natoms\\\"\\n elif \\\"ITEM: BOX BOUNDS\\\" in line:\\n period = line.strip().split(\\\"BOX BOUNDS\\\")\\n if len(period) == 2 and period[1] != '':\\n result[\\\"periodic\\\"] = period[1].strip().split()\\n else:\\n result[\\\"periodic\\\"] = (\\\"ss\\\", \\\"ss\\\" ,\\\"ss\\\")\\n \\n\\t\\t # Changes by JPRIEDS to accommodate triclinic boxes\\n\\t\\t # Written 170719\\n\\t\\t if len(result[\\\"periodic\\\"]) == 6:\\n\\t\\t\\titemstack.extend([(float, float, float)]*3)\\n\\t\\t\\tcurrent = \\\"box\\\"\\n\\t\\t\\tresult[\\\"periodic\\\"] = result[\\\"periodic\\\"][3:]\\n\\t\\t elif len(result[\\\"periodic\\\"]) == 3:\\n\\t\\t\\titemstack.extend([(float, float)]*3)\\n\\t\\t\\tcurrent = \\\"box\\\"\\n\\t\\t else:\\n emsg = \\\"Could not classify periodic bounds: {}\\\"\\n raise ValueError(emsg.format(result[\\\"periodic\\\"]))\\n elif \\\"ITEM: ATOMS\\\" in line:\\n itemstack = None\\n current = \\\"atoms\\\"\\n result[\\\"type\\\"] = []\\n result[\\\"id\\\"] = []\\n result[\\\"xyz\\\"] = []\\n \\n #The first two headings in the line have \\\"ITEM: ATOMS\\\", the\\n #rest are usuall id, type, x, y, z, rest...\\n headings = line.split()\\n extras = len(headings) > 7\\n if extras:\\n xkeys = []\\n xheadings = headings[7:]\\n for xhead in xheadings:\\n key = \\\"atom:{}\\\".format(xhead)\\n result[key] = []\\n xkeys.append(key)\\n \\n if openf is None:\\n #Close the file since we opened it.\\n f.close()\\n \\n return result\",\n \"def _read(self, file_path: str) -> Iterator[Instance]:\\n with open(file_path) as f:\\n for line in f:\\n pairs = line.split()\\n words, tags = zip(*(pair.split(\\\"###\\\") for pair in pairs))\\n yield self.text_to_instance([Token(word) for word in words], tags)\",\n \"def parse_file(self, file_path) -> list:\\n data = []\\n with open(file_path, 'rb') as f:\\n lines = pickle.load(f)\\n for line in lines:\\n input, output = line\\n if input.strip() == \\\"\\\" or output.strip() == \\\"\\\":\\n continue\\n input_len = len(input.split())\\n output_len = len(output.split())\\n if input_len > 50 or output_len > 50:\\n continue\\n data_item = Text2TextDataItem(input_text=input, output_text=output, tokenizer=self.tokenizer,\\n share_vocab=self.share_vocab)\\n data.append(data_item)\\n return data\",\n \"def process_line(line):\\n\\n name_comp_list = []\\n givenname_comp_list = []\\n surname_comp_list = []\\n geocode_comp_list = []\\n locality_comp_list = []\\n date1_comp_list = []\\n date2_comp_list = []\\n\\n # Split the line into the basic fields - - - - - - - - - - - - - - - - - - -\\n #\\n if (config.in_file_type in ['CSV','CSVQ','TAB','TABQ']):\\n # Comma or tabulator separated\\n try:\\n line_list = config.line_parser.parse(line)\\n except:\\n log_message('CSV line parsing failed with inout: '+line,'err')\\n\\n if (len(line_list) < config.input_len):\\n log_message('Input line does not contain enough fields,' +\\\\\\n 'fill up with empty fields','warn')\\n while (len(line_list) < config.input_len):\\n line_list.append('')\\n\\n config.curr_line_list = line_list # Save current line list\\n\\n # Extract fields into different component lists - - - - - - - - - - - - - -\\n #\\n if (config.input_component['name'] != []): # Extract name fields\\n for i in config.input_component['name']:\\n name_comp_list.append(line_list[i])\\n\\n else: # Extract givenname and surname into separate components - - - - - -\\n if (config.input_component['givenname'] != []): # Extract g-name fields\\n for i in config.input_component['givenname']:\\n givenname_comp_list.append(line_list[i])\\n\\n if (config.input_component['surname'] != []): # Extract surname fields\\n for i in config.input_component['surname']:\\n surname_comp_list.append(line_list[i])\\n\\n if (config.input_component['geocode'] != []): # Extract geocode fields\\n for i in config.input_component['geocode']:\\n geocode_comp_list.append(line_list[i])\\n\\n if (config.input_component['locality'] != []): # Extract locality fields\\n for i in config.input_component['locality']:\\n locality_comp_list.append(line_list[i])\\n\\n if (config.input_component['date1'] != []): # Extract date1 fields\\n for i in config.input_component['date1']:\\n date1_comp_list.append(line_list[i])\\n\\n if (config.input_component['date2'] != []): # Extract date2 fields\\n for i in config.input_component['date2']:\\n date2_comp_list.append(line_list[i])\\n\\n elif (config.in_file_type == 'COL'): # Column based input file - - - - - - -\\n\\n if (len(line) < config.input_len):\\n log_message('Input line is not long enough, fill up with spaces','warn')\\n line += ' '*(config.input_len-len(line))\\n\\n if (config.input_component['name'] != []): # Extract name fields\\n for (col_start,length) in config.input_component['name']:\\n name_comp_list.append(line[col_start,col_start+length])\\n\\n else: # Extract givenname and surname into separate components - - - - - -\\n if (config.input_component['givenname'] != []): # Extract g-name fields\\n for (col_start,length) in config.input_component['givenname']:\\n givenname_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['surname'] != []): # Extract surname fields\\n for (col_start,length) in config.input_component['surname']:\\n surname_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['geocode'] != []): # Extract geocode fields\\n for (col_start,length) in config.input_component['geocode']:\\n geocode_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['locality'] != []): # Extract locality fields\\n for (col_start,length) in config.input_component['locality']:\\n locality_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['date1'] != []): # Extract date1 fields\\n for (col_start,length) in config.input_component['date1']:\\n date1_comp_list.append(line[col_start,col_start+length])\\n\\n if (config.input_component['date2'] != []): # Extract date2 fields\\n for (col_start,length) in config.input_component['date2']:\\n date2_comp_list.append(line[col_start,col_start+length])\\n\\n # elif (config.in_file_type == 'SQL'): # - - - - - - - - - - - - - - - - - -\\n\\n ################################\\n # Add later: SQL database access\\n ################################\\n\\n msg = [' Component basic field lists:', \\\\\\n ' Name: '+str(name_comp_list), \\\\\\n ' Given name: '+str(givenname_comp_list), \\\\\\n ' Surname: '+str(surname_comp_list), \\\\\\n ' Geocode: '+str(geocode_comp_list), \\\\\\n ' Locality: '+str(locality_comp_list), \\\\\\n ' Date1: '+str(date1_comp_list), \\\\\\n ' Date2: '+str(date2_comp_list)]\\n log_message(msg,'v2')\\n\\n name_comp = ''\\n givenname_comp = ''\\n surname_comp = ''\\n geocode_comp = ''\\n locality_comp = ''\\n date1_comp = ''\\n date2_comp = ''\\n\\n # Now clean and then concatenate component lists into strings - - - - - - - -\\n #\\n if (name_comp_list != []): # Name component\\n name_comp = name_comp_list[0] # Start with first field in list\\n\\n for f in name_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['name'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['name'] == 1):\\n sep = check_field_spill(name_comp, f)\\n\\n name_comp = name_comp+sep+f # Append separator and field\\n\\n if (givenname_comp_list != []): # Givenname component - - - - - - - - - - -\\n givenname_comp = givenname_comp_list[0] # Start with first field in list\\n\\n for f in givenname_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['givenname'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['givenname'] == 1):\\n sep = check_field_spill(givenname_comp, f)\\n\\n givenname_comp = givenname_comp+sep+f # Append separator and field\\n\\n if (surname_comp_list != []): # Surname component - - - - - - - - - - - - -\\n surname_comp = surname_comp_list[0] # Start with first field in list\\n\\n for f in surname_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['surname'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['surname'] == 1):\\n sep = check_field_spill(surname_comp, f)\\n\\n surname_comp = surname_comp+sep+f # Append separator and field\\n\\n if (geocode_comp_list != []): # Geocode component - - - - - - - - - - - - -\\n geocode_comp = geocode_comp_list[0] # Start with first field in list\\n\\n for f in geocode_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['geocode'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['geocode'] == 1):\\n sep = check_field_spill(geocode_comp, f)\\n\\n geocode_comp = geocode_comp+sep+f # Append separator and field\\n\\n if (locality_comp_list != []): # Locality component - - - - - - - - - - - -\\n locality_comp = locality_comp_list[0] # Start with first field in list\\n\\n for f in locality_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['locality'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['locality'] == 1):\\n sep = check_field_spill(locality_comp, f)\\n\\n locality_comp = locality_comp+sep+f # Append separator and field\\n\\n if (date1_comp_list != []): # Date1 component - - - - - - - - - - - - - - -\\n date1_comp = date1_comp_list[0] # Start with first field in list\\n\\n for f in date1_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['date1'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['date1'] == 1):\\n if (date1_comp[-1] != ' ') and (f[0] != ' '):\\n tmp_list0 = date1_comp.split()\\n tmp_list1 = f.split()\\n check_word = tmp_list0[-1]+tmp_list1[0]\\n\\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\\\\n 'sep','oct','nov','dec','january','february','march','april', \\\\\\n 'may','june','july','august','september','october','november', \\\\\\n 'december']):\\n\\n sep = '' # Set separator to no space\\n msg = ' Correct date1 word spilling: \\\"'+date1_comp+'\\\",\\\"'+f+'\\\"'\\n log_message(msg,'v1')\\n\\n date1_comp = date1_comp+sep+f # Append separator and field\\n\\n if (date2_comp_list != []): # Date2 component - - - - - - - - - - - - - - -\\n date2_comp = date2_comp_list[0] # Start with first field in list\\n\\n for f in date2_comp_list[1:]: # Loop over following fields (if any)\\n if (f != ''):\\n if (config.input_space_sep['date2'] == 1):\\n sep = ' ' # Set separator to space between fields\\n else:\\n sep = '' # No space between fields\\n\\n # Check field spilling only if space separator is set to ' ' \\n #\\n if (sep == ' ') and (config.input_check_spilling['date2'] == 1):\\n if (date2_comp[-1] != ' ') and (f[0] != ' '):\\n tmp_list0 = date1_comp.split()\\n tmp_list1 = f.split()\\n check_word = tmp_list0[-1]+tmp_list1[0]\\n\\n if (check_word in ['jan','feb','mar','apr','may','jun','jul','aug', \\\\\\n 'sep','oct','nov','dec','january','february','march','april', \\\\\\n 'may','june','july','august','september','october','november', \\\\\\n 'december']):\\n\\n sep = '' # Set separator to no space\\n msg = ' Correct date1 word spilling: \\\"'+date1_comp+'\\\",\\\"'+f+'\\\"'\\n log_message(msg,'v1')\\n\\n date2_comp = date2_comp+sep+f # Append separator and field\\n\\n # Check if name component is given or givenname and surname separately - - -\\n #\\n if (config.input_component['givenname'] != []) or \\\\\\n (config.input_component['surname'] != []):\\n name_comp = [givenname_comp, surname_comp]\\n\\n msg = [' Components:', \\\\\\n ' Name: \\\"'+str(name_comp)+'\\\"', \\\\\\n ' Geocode: \\\"'+geocode_comp+'\\\"', \\\\\\n ' Locality: \\\"'+locality_comp+'\\\"', \\\\\\n ' Date1: \\\"'+date1_comp+'\\\"', \\\\\\n ' Date2: \\\"'+date2_comp+'\\\"']\\n log_message(msg,'v1')\\n\\n return [name_comp, geocode_comp, locality_comp, date1_comp, date2_comp]\",\n \"def readlines(self, n = None):\\n\\t\\tif not self._input: raise PlumberExceptions.PipeTypeException(self)\\n\\t\\tresult = []\\n\\t\\twhile n == None or n > 0:\\n\\t\\t\\tline = self.readline()\\n\\t\\t\\tif not line: return result\\n\\t\\t\\tresult.append(line)\\n\\t\\t\\tn -= 1\\n\\t\\treturn result\",\n \"def read(read_file) -> list:\\n result = []\\n try:\\n with open(read_file) as file:\\n for lines in file:\\n line = decode(lines.strip(\\\"\\\"))\\n result.append(extract_information(line))\\n global header\\n header = result[0:2]\\n result = result[3:]\\n for word in result:\\n if \\\"None\\\" in word[0:3]:\\n raise InvalidPrincessException(\\\"Invalid princess!\\\")\\n continue\\n return result\\n except FileNotFoundError:\\n raise Exception(\\\"File not found!\\\")\",\n \"def read_file(inp_fn):\\n lines = [line.strip().split(\\\",\\\")\\n for line in open(inp_fn)\\n if not (line.startswith(\\\"#\\\"))]\\n return [(int(line[0]), year_record({\\\"male\\\": int(line[-3]),\\n \\\"female\\\": int(line[-2]),\\n \\\"unknown\\\": int(line[-1])},\\n None, None))\\n for line in lines[1:]]\",\n \"def read_lines(filename):\\n with file(filename) as f:\\n for line in f:\\n _line = line.strip()\\n if _line:\\n yield _line\",\n \"def parse_file(self):\\n # read the first line in the file\\n line = self._stream_handle.readline()\\n\\n while line:\\n # check for a data line or a dcl logger line we specifically ignore\\n data_match = DATA_LINE_MATCHER.match(line)\\n ignore_match = IGNORE_LINE_MATCHER.match(line)\\n\\n if data_match:\\n # found a data line, extract this particle\\n # DCL controller timestamp is the port_timestamp\\n dcl_controller_timestamp = data_match.groups()[DCL_TIMESTAMP_GROUP]\\n port_timestamp = dcl_time_to_ntp(dcl_controller_timestamp)\\n\\n particle = self._extract_sample(self.particle_class,\\n None,\\n data_match,\\n port_timestamp=port_timestamp,\\n preferred_ts=DataParticleKey.PORT_TIMESTAMP)\\n\\n self._record_buffer.append(particle)\\n\\n elif not ignore_match:\\n # we found a line with an unknown format, call an exception\\n error_message = 'Found line with unknown format %s' % line\\n log.warn(error_message)\\n self._exception_callback(SampleException(error_message))\\n\\n # read the next line\\n line = self._stream_handle.readline()\",\n \"def read(filename, maximum):\\n raws = []\\n with open(filename, \\\"r\\\") as f:\\n for _, line in zip(range(maximum), f):\\n raw = parse_entry(line)\\n raws.append(raw)\\n return raws\",\n \"def parse(self):\\r\\n # open the file\\r\\n # try to find the first line with Time =\\r\\n # send the file to a recursive residualParser\\r\\n try:\\r\\n with open(self.filepath, 'rb') as f:\\r\\n for line in f:\\r\\n if line.startswith('Time ='):\\r\\n self.timestep = self.__getTime(line)\\r\\n self.__residuals[self.timestep] = {}\\r\\n self.__parseResiduals(f)\\r\\n except Exception as e:\\r\\n raise 'Failed to parse {}:\\\\n\\\\t{}'.format(self.filepath, e)\",\n \"def _read_file(self, current_path):\\n # type: (str)->None\\n\\n if current_path in self._parsed_files:\\n return\\n self._parsed_files.add(current_path)\\n\\n with open(current_path, \\\"r\\\") as interfaces:\\n # When the first non-comment line is parsed, header\\n # comments have been read in.\\n header_parsed = False\\n # Loop through the interfaces file.\\n for line in interfaces:\\n # 1. Identify the clauses by analyzing the first\\n # word of each line.\\n # 2. Go to the next line if the current line is a comment.\\n # line = line.strip().replace(\\\"\\\\n\\\", \\\"\\\")\\n if not line:\\n pass\\n elif line.strip().startswith(\\\"#\\\") is True:\\n if not header_parsed:\\n self._header_comments += line\\n else:\\n # Header comments can no longer\\n # be parsed in when the first interfaces\\n # line is parsed in.\\n header_parsed = True\\n self._parse_iface(line, current_path)\\n # Ignore blank lines.\\n if not line.isspace():\\n self._parse_details(line)\\n self._read_auto(line)\\n self._read_hotplug(line)\\n\\n # Is there some file to source ?\\n source_path = self._read_sourced_path(line)\\n if source_path:\\n self._read_files(source_path)\\n\\n # TODO: lots of directives are completly ignored\\n # and would be deleted\",\n \"def read(self, start_line, num_lines, timeout=0):\\n # emulate timeout by sleeping timeout time\\n if timeout > 0:\\n read_timeout = float(timeout) / 1000\\n #logging.debug(\\\"sleep \\\" + str(read_timeout) + \\\" seconds\\\")\\n time.sleep(read_timeout)\\n\\n output = []\\n attributes = []\\n\\n # get output\\n for i in range(start_line, start_line + num_lines + 1):\\n output.append(self.shm_output.read(self.columns, i * self.columns))\\n if not CONQUE_FAST_MODE:\\n attributes.append(self.shm_attributes.read(self.columns, i * self.columns))\\n\\n return (output, attributes)\",\n \"def parse_input(filename):\\n with open(filename, 'r') as input_file:\\n return [i.rstrip() for i in input_file]\",\n \"def read_file(self, filename):\\n with open(filename, 'r') as file:\\n for line in file:\\n l = line.strip()\\n\\n if l == ST_POS0:\\n self._state = ST_POS0\\n elif l == ST_TRNS:\\n self._state = ST_TRNS\\n elif l == ST_POS1:\\n self._state = ST_POS1\\n else:\\n self._parse_line(l)\\n self._state = None\",\n \"def _read_log(self):\\n\\n line_regex = compile(r\\\"\\\\[I\\\\]\\\\s*\\\\(\\\\d+ms\\\\)[^\\\\d]+(?P<counter>\\\\d+)\\\"\\n r\\\"[^\\\\d]+(?P<timestamp>\\\\d+(\\\\.\\\\d+)?)[^\\\\d]+\\\"\\n r\\\"(?P<acceleration>\\\\d+);\\\")\\n values = []\\n with open(self.filepath) as file:\\n for line in file:\\n match = line_regex.match(line)\\n if match:\\n values.append({\\n 'counter':\\n int(match['counter']),\\n 'timestamp':\\n int(float(match['timestamp']) * 1000),\\n 'acceleration':\\n int(match['acceleration'])\\n })\\n\\n self.values = values\",\n \"def read(self):\\n global ALIVE\\n line = sys.stdin.readline()\\n if line:\\n self.datalines.append(line.rstrip())\\n else:\\n ALIVE = False\",\n \"def load_input(self, file_name):\\n with open(file_name, \\\"r\\\") as in_file:\\n self.all_lines = [line.rstrip('\\\\n') for line in in_file]\",\n \"def _parse(self):\\n with open(_join(self.man_dir, self.man_fn)) as fp:\\n lines = fp.readlines()\\n \\n desc_indxs = []\\n for i, L in enumerate(lines):\\n if \\\"#landuse\\\" in L or \\\" # landuse\\\" in L:\\n desc_indxs.append(i-1)\\n desc_indxs.append(i-2)\\n desc_indxs.append(i-3)\\n \\n lines = [L[:L.find('#')].strip() for L in lines]\\n lines = [L for i, L in enumerate(lines) if len(L) > 0 or i in desc_indxs]\\n\\n del desc_indxs\\n \\n self.datver = lines.pop(0)\\n self.nofe = int(lines.pop(0))\\n self.sim_years = int(lines.pop(0))\\n \\n # Read Plant Growth Section\\n self.plants = PlantLoops(lines, self)\\n\\n # Read Operation Section\\n self.ops = OpLoops(lines, self)\\n \\n # Read Initial Condition Section\\n self.inis = IniLoops(lines, self)\\n \\n # Read Surface Effects Section\\n self.surfs = SurfLoops(lines, self)\\n \\n # Read Contour Section\\n self.contours = ContourLoops(lines, self)\\n \\n # Read Drainage Section\\n self.drains = DrainLoops(lines, self)\\n \\n # Read Yearly Section\\n self.years = YearLoops(lines, self)\\n \\n # Read Management Section \\n self.man = ManagementLoop(lines, self)\",\n \"def read_in_file():\\n\\t# Declare variables\\n\\treads = []\\n\\n\\t# Get command line arguments\\n\\targuments = sys.argv\\n\\targuments_length = len(arguments)\\n\\n\\t# Read file is the first argument\\n\\tread_file_name = arguments[1]\\n\\n\\t# Process read file \\n\\tread_file = open(read_file_name, 'r')\\n\\tfor line in read_file:\\n\\t\\tread_info = line.split()\\n\\t\\tread_string = read_info[2].replace('\\\\'', '')\\n\\t\\tnew_read = GenerativeRead(read_string, [], read_info[5], read_info[3], None, [], read_info[0], read_info[1], read_info[4]) \\n\\t\\treads.append(new_read)\\n\\tread_file.close()\\n\\n\\t# Repeat regions file in the second argument\\n\\trepeat_file_name = arguments[2]\\n\\n\\t# Process repeat file\\n\\trepeat_file = open(repeat_file_name, 'r')\\n\\talignments = [[]]\\n\\talignment_index = -1\\n\\tprevious_line = ''\\n\\n\\n\\tfor line in repeat_file:\\n\\t\\talignment_info = line.split()\\n\\n\\t\\t# This consists of a tuple of alignment string, alignment start position and alignment chromosome\\n\\t\\t#new_align = alignment_info[2], alignment_info[4], alignment_info[3]\\n\\n\\t\\tnew_align = Alignment(alignment_info[2], None, alignment_info[4], alignment_info[3])\\n\\n\\t\\tif previous_line != alignment_info[0]:\\n\\t\\t\\t# It is not a repeat\\n\\t\\t\\talignment_index = alignment_index + 1\\n\\t\\t\\talignments.append([])\\n\\t\\t\\tprevious_line = alignment_info[0]\\n\\n\\t\\talignments[alignment_index].append(new_align)\\n\\n\\trepeat_file.close()\\n\\n\\t# Associate each read with the other alignments\\n\\tfor read in reads:\\n\\t\\t# Find the other alignments\\n\\t\\tpos = read.get_position()\\n\\t\\tfound = False\\n\\t\\tfound_index = -1\\n\\n\\t\\tfor a_index, alignment_lists in enumerate(alignments):\\n\\t\\t\\t# find matching alignments\\n\\t\\t\\t# TODO: Don't add alignment already have\\n\\t\\t\\t# TODO: Make functional with filter\\n\\t\\t\\tfor align in alignment_lists:\\n\\t\\t\\t\\tif align.get_position() == pos:\\n\\t\\t\\t\\t\\tfound = True\\n\\t\\t\\t\\t\\tfound_index = a_index\\n\\t\\t\\t\\t\\tbreak\\n\\n\\t\\t\\tif found is True:\\n\\t\\t\\t\\tbreak\\n\\n\\t\\tif found is True:\\n\\t\\t\\tfor new_align in alignments[found_index]:\\n\\t\\t\\t\\tread.add_alignment(new_align)\\n\\t\\t\\t\\n\\n\\n\\t# SNP files are the remaining ones\\n\\tsnp_file_names = [arguments[file_id] for file_id in range(3, arguments_length) ]\\n\\n\\t# Process SNP files\\n\\tfor file_name in snp_file_names:\\n\\t\\tsnp_file = open(file_name, 'r')\\n\\n\\t\\tfor line in snp_file:\\n\\t\\t\\tsnp_info = line.split()\\n\\t\\t\\tsnps = snp_info[3].split('/')\\n\\t\\t\\tsnp_pos = int(float(snp_info[2]))\\n\\n\\t\\t\\t# Ignore alleles that are longer than one base\\n\\n\\t\\t\\t\\n\\t\\t\\tif all(len(x) < 2 for x in snps):\\n\\n\\t\\t\\t\\t# Iterate through reads and determine whether or not it contains this SNP\\n\\t\\t\\t\\tpos_low = snp_pos - 49\\n\\t\\t\\t\\n\\n\\t\\t\\t\\tfor read in reads:\\n\\t\\t\\t\\t\\tpositions = read.get_alignment_positions()\\n\\n\\t\\t\\t\\t\\tfor p_index, p in enumerate(positions):\\n\\t\\t\\t\\t\\t\\tp = int(float(p))\\n\\t\\t\\t\\t\\t\\tif p >= pos_low and p <= snp_pos:\\n\\t\\t\\t\\t\\t\\t\\t# Get index of snp\\n\\t\\t\\t\\t\\t\\t\\toffset = snp_pos - p\\n\\t\\t\\t\\t\\t\\t\\tcalls = [0, 0, 0, 0]\\n\\t\\t\\t\\t\\t\\t\\tfor snp in snps:\\n\\t\\t\\t\\t\\t\\t\\t\\tcall_index = get_base_num(snp)\\n\\t\\t\\t\\t\\t\\t\\t\\tcalls[call_index] = 1\\n\\n\\t\\t\\t\\t\\t\\t\\t# Add the SNP to the read\\n\\t\\t\\t\\t\\t\\t\\tread.add_snp(p_index, offset, calls)\\n\\t\\t\\t\\t\\t\\t\\t\\n\\t\\tsnp_file.close()\\n\\treturn reads\",\n \"def readSequenceLengths(lines):\\n sequenceLengths = {} # an OrderedDict might be nice\\n isSearching = True\\n for line in lines:\\n if line.isspace(): isSearching = True\\n elif isSearching:\\n w = line.split()\\n if w[0] == \\\"s\\\":\\n seqName, seqSize = w[1], w[5]\\n sequenceLengths[seqName] = seqSize\\n isSearching = False\\n return sequenceLengths\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69234943","0.66012603","0.65864605","0.65512294","0.6521317","0.6467308","0.6402224","0.63724","0.6326894","0.6258525","0.62227803","0.6207747","0.6200397","0.61411375","0.60814875","0.604438","0.6026843","0.602283","0.60144675","0.5981471","0.5980653","0.59719604","0.5938189","0.5905897","0.5882756","0.5866031","0.5862231","0.5857319","0.5838323","0.5834593","0.58321786","0.58299327","0.5817428","0.58102995","0.57997775","0.57961375","0.57948077","0.5794428","0.5789978","0.5782371","0.5778817","0.5773591","0.5766494","0.5765637","0.57651293","0.57634807","0.5761204","0.57443637","0.5742957","0.57382005","0.57355857","0.5729332","0.5725365","0.5724756","0.5724048","0.57199496","0.5715885","0.5709588","0.5691492","0.5683179","0.56740665","0.5672446","0.56643045","0.56604755","0.5653024","0.5634405","0.5631229","0.56289387","0.5627923","0.56278527","0.5625285","0.5618343","0.561771","0.5617128","0.56145984","0.5610403","0.55983853","0.55957115","0.5594497","0.5594059","0.5592478","0.5580783","0.5580589","0.5575278","0.5575024","0.5574572","0.5573103","0.5570496","0.55649215","0.55593604","0.55542994","0.55492175","0.55485547","0.55422956","0.5541657","0.5524773","0.5523645","0.5514962","0.5512795","0.5509331","0.55084836"],"string":"[\n \"0.69234943\",\n \"0.66012603\",\n \"0.65864605\",\n \"0.65512294\",\n \"0.6521317\",\n \"0.6467308\",\n \"0.6402224\",\n \"0.63724\",\n \"0.6326894\",\n \"0.6258525\",\n \"0.62227803\",\n \"0.6207747\",\n \"0.6200397\",\n \"0.61411375\",\n \"0.60814875\",\n \"0.604438\",\n \"0.6026843\",\n \"0.602283\",\n \"0.60144675\",\n \"0.5981471\",\n \"0.5980653\",\n \"0.59719604\",\n \"0.5938189\",\n \"0.5905897\",\n \"0.5882756\",\n \"0.5866031\",\n \"0.5862231\",\n \"0.5857319\",\n \"0.5838323\",\n \"0.5834593\",\n \"0.58321786\",\n \"0.58299327\",\n \"0.5817428\",\n \"0.58102995\",\n \"0.57997775\",\n \"0.57961375\",\n \"0.57948077\",\n \"0.5794428\",\n \"0.5789978\",\n \"0.5782371\",\n \"0.5778817\",\n \"0.5773591\",\n \"0.5766494\",\n \"0.5765637\",\n \"0.57651293\",\n \"0.57634807\",\n \"0.5761204\",\n \"0.57443637\",\n \"0.5742957\",\n \"0.57382005\",\n \"0.57355857\",\n \"0.5729332\",\n \"0.5725365\",\n \"0.5724756\",\n \"0.5724048\",\n \"0.57199496\",\n \"0.5715885\",\n \"0.5709588\",\n \"0.5691492\",\n \"0.5683179\",\n \"0.56740665\",\n \"0.5672446\",\n \"0.56643045\",\n \"0.56604755\",\n \"0.5653024\",\n \"0.5634405\",\n \"0.5631229\",\n \"0.56289387\",\n \"0.5627923\",\n \"0.56278527\",\n \"0.5625285\",\n \"0.5618343\",\n \"0.561771\",\n \"0.5617128\",\n \"0.56145984\",\n \"0.5610403\",\n \"0.55983853\",\n \"0.55957115\",\n \"0.5594497\",\n \"0.5594059\",\n \"0.5592478\",\n \"0.5580783\",\n \"0.5580589\",\n \"0.5575278\",\n \"0.5575024\",\n \"0.5574572\",\n \"0.5573103\",\n \"0.5570496\",\n \"0.55649215\",\n \"0.55593604\",\n \"0.55542994\",\n \"0.55492175\",\n \"0.55485547\",\n \"0.55422956\",\n \"0.5541657\",\n \"0.5524773\",\n \"0.5523645\",\n \"0.5514962\",\n \"0.5512795\",\n \"0.5509331\",\n \"0.55084836\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77985,"cells":{"query":{"kind":"string","value":"Get proper read parser for the given format."},"document":{"kind":"string","value":"def get_read_parser(format):\n format = format.lower()\n if format == 'bed':\n return BedReadParser\n elif format == 'bedpe':\n return BedPeReadParser\n elif format == 'sam':\n return SamReadParser\n elif format == 'bam':\n return BamReadParser\n else:\n raise ValueError(f\"unknown read file format: {format!r}\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_parser(filepath, cfg):\n if not os.path.isfile(filepath):\n LOG.error('File not found: %s', filepath)\n return\n valid_parsers = importer.get_parsers(filepath, cfg)\n if not valid_parsers:\n LOG.error('No parsers found for file: %s', filepath)\n return\n\n if len(valid_parsers) > 1:\n while True:\n print('More than one valid parser found. '\n 'Please select which one to use:')\n for idx, vp in enumerate(valid_parsers):\n print('[{}] {}'.format(idx, vp.__name__))\n inp = input()\n try:\n parser = valid_parsers[inp]\n break\n except (IndexError, TypeError):\n print('Invalid input. Please select the parser number.')\n else:\n parser = valid_parsers[0]\n\n return parser","def get_parser(self):\n if self.vendor and self.platform and self.version:\n cls = self.profile.get_profile().get_parser(\n self.vendor.code, self.platform.name, self.version.version\n )\n if cls:\n return get_handler(cls)(self)\n return get_handler(\"noc.cm.parsers.base.BaseParser\")(self)","def read(source, format=None):\n # Lazy imports to not globber the namespace.\n from prov.model import ProvDocument\n\n from prov.serializers import Registry\n Registry.load_serializers()\n serializers = Registry.serializers.keys()\n\n if format:\n return ProvDocument.deserialize(source=source, format=format.lower())\n\n for format in serializers:\n try:\n return ProvDocument.deserialize(source=source, format=format)\n except:\n pass\n else:\n raise TypeError(\"Could not read from the source. To get a proper \"\n \"error message, specify the format with the 'format' \"\n \"parameter.\")","def get_first_available_parser():\n if sys.platform == 'cli':\n try:\n from bridge.parser.bridge_dotnet import Parser\n return Parser\n except ImportError:\n pass\n elif sys.platform[:4] == 'java':\n try:\n from bridge.parser.bridge_java import Parser\n return Parser\n except ImportError:\n pass\n \n from bridge.parser.bridge_default import Parser\n \n return Parser","def get_parser(project, layoutdir):\n for parser_class in PARSERS:\n parser = parser_class(project, layoutdir)\n if parser.can_load():\n return parser\n raise ValueError(\"No loader available for '{0}'.\".format(project))","def read(fname, fmt=None):\n if not fmt:\n fmt = fname.split(\".\")[-1]\n\n if fmt in ['yml', 'yaml']:\n return _storage_read(fname, yaml.safe_load)\n elif fmt == \"json\":\n return _storage_read(fname, json.load)\n elif fmt == \"pickle\":\n return _storage_read(fname, pickle.load, 'rb')\n else:\n raise Exception()","def read(filename, file_format=None, **kwargs):\n if not isinstance(filename, str):\n raise TypeError()\n if not (file_format is None or file_format in {\"tough\", \"json\"}):\n raise ValueError()\n\n fmt = (\n file_format\n if file_format\n else filetype_from_filename(filename, _extension_to_filetype)\n )\n fmt = fmt if fmt else \"tough\"\n\n return _reader_map[fmt](filename, **kwargs)","def _get_parser(self, language: str):\n parser = None\n if language:\n parser = self.parsers.get(language)\n\n if not parser:\n self.log.warning(f\"Content parser for {language} is not available.\")\n return parser","def deserialize(self, content, format):\r\n desired_format = None\r\n\r\n format = format.split(';')[0]\r\n\r\n for short_format, long_format in self.content_types.items():\r\n if format == long_format:\r\n if hasattr(self, \"from_%s\" % short_format):\r\n desired_format = short_format\r\n break\r\n\r\n if desired_format is None:\r\n raise UnsupportedDeserializationFormat(\"The format indicated '%s' had no available deserialization method. Please check your ``formats`` and ``content_types`` on your Serializer.\" % format)\r\n\r\n try:\r\n deserialized = getattr(self, \"from_%s\" % desired_format)(content)\r\n except UnsupportedDeserializationFormat, e:\r\n raise\r\n except Exception, e:\r\n raise BadRequest()\r\n\r\n return deserialized","def get_parser_for_file_type(file_type):\n parser = file_type.upper()\n if file_type not in SUPPORTED_PARSERS:\n parser = 'XML'\n return parser","def _get_parser(current_parser=None):\n if not current_parser:\n current_parser = configparser.ConfigParser()\n current_parser.read(PRAW_FILE_PATH)\n return current_parser","def get_parser_for_uri(uri):\n path = uri_to_path(uri)\n parser = get_extension(path)\n\n if parser not in SUPPORTED_PARSERS:\n parser = 'XML'\n\n return parser","def getParser(config, verbose, zenity):\n\tif verbose: print \"Creating parser object\"\n\ttry: parser = SafeConfigParser()\n\texcept:\n\t\tif zenity: \n\t\t\tos.system(\"zenity --error --text='Error: Failed to create parser'\")\n\t\tprint \"Failed to create parser object.\"\n\t\treturn None\n\tparser.optionxform=str\t\t\t\t# Maintain case sensitivity\n\ttry: parser.read(config)\n\texcept:\n\t\tif zenity:\n\t\t\tos.system(\"zenity --error --text='Error: Failed to read config'\")\n\t\tprint \"Failed to read from configuration file.\"\n\t\treturn None\t\n\treturn parser","def getParser(self):\n return self._parser","def parse(self, parser):\n with self.reading:\n chunks = [self.read_buffer.dequeue() or (yield self.base.read(self.bufsize))]\n try:\n while True:\n tupe, result = parser.__parser__()(chunks[-1], False)\n if tupe & ParserResult.DONE:\n value, chunk, _ = result\n del chunks[:]\n self.read_buffer.enqueue(chunk)\n do_return(value)\n elif tupe & ParserResult.PARTIAL:\n parser = result\n chunks.append((yield self.base.read(self.bufsize)))\n else:\n raise ParserError(result)\n except BrokenPipeError:\n # try to terminate parser with last chunk\n tupe, result = parser.__parser__()(b'', True)\n if tupe & ParserResult.DONE:\n value, chunk, _ = result\n del chunks[:]\n self.read_buffer.enqueue(chunk)\n do_return(value)\n raise\n finally:\n for chunk in chunks:\n self.read_buffer.enqueue(chunk)","def _get_format(self, token):\n if token in self._formats:\n return self._formats[token]\n\n if self._style is None:\n result = self._get_format_from_document(token, self._document)\n else:\n result = self._get_format_from_style(token, self._style)\n\n self._formats[token] = result\n return result","def _ReadSerializerStream(self):\n stream_name = 'serializer.txt'\n if not self._HasStream(stream_name):\n return\n\n serialization_format = self._ReadStream(stream_name)\n if serialization_format != definitions.SERIALIZER_FORMAT_JSON:\n raise ValueError(\n 'Unsupported stored serialization format: {0:s}'.format(\n serialization_format))\n\n return serialization_format","def getFormatManager(self) -> ghidra.app.util.viewer.format.FormatManager:\n ...","def get_for(self, file=None, format=''):\n if isinstance(file, DirectoryStream):\n # directory 'stream'\n return (self._names['dir'],)\n if format:\n try:\n converter = (self._names[format],)\n except KeyError:\n raise ValueError(\n f'Format specifier `{format}` not recognised'\n )\n else:\n converter = self.identify(file)\n if not converter:\n if not file or file.mode == 'w' or maybe_text(file):\n format = self._default_text\n else:\n format = self._default_binary\n if file and format:\n if Path(file.name).suffix:\n level = logging.WARNING\n else:\n level = logging.DEBUG\n logging.log(\n level,\n f'Could not infer format from filename `{file.name}`. '\n f'Falling back to default `{format}` format'\n )\n try:\n converter = (self._names[format],)\n except KeyError:\n pass\n return converter","def read_file(self,filename):\n\n if (config.mode_format == \"simple\"): return self.read_file_simple(filename)\n if (config.mode_format == \"agsm\"): return self.read_file_agsm(filename)\n sys.exit(\"ERROR: unrecognised format \\\"\"+config.mode_format+\"\\\".\\n\" \\\n +\" Please choose another value for mode_format in AIMS_configure.py\")","def read_f(self, fmt, offset=None):\n\n if offset is not None:\n self.seek(offset)\n\n # place commas before and after each instance of S or Z\n for special in ['S', 'Z']:\n fmt = fmt.replace(special, ',' + special + ',')\n\n # split S and Z into isolated strings\n fmt = fmt.split(',')\n\n # construct a tuple of unpacked data\n data = ()\n for subfmt in fmt:\n if subfmt == 'S':\n data += (self.read_string(),)\n elif subfmt == 'Z':\n data += (self.read_bool(),)\n else:\n data += self.read_and_unpack(subfmt)\n\n if len(data) == 1:\n return data[0]\n else:\n return data","def read(self, fmt):\n fmt = '>' + fmt\n if not PY3:\n fmt = fmt.encode('utf-8')\n return struct.unpack(fmt, self.bin.read(format_byte_size(fmt)))","def __parse(self) -> object:\r\n char = self.data[self.idx: self.idx + 1]\r\n if char in [b'1', b'2', b'3', b'4', b'5', b'6', b'7', b'8', b'9', b'0']:\r\n str_len = int(self.__read_to(b':'))\r\n return self.__read(str_len)\r\n elif char == b'i':\r\n self.idx += 1\r\n return int(self.__read_to(b'e'))\r\n elif char == b'd':\r\n return self.__parse_dict()\r\n elif char == b'l':\r\n return self.__parse_list()\r\n elif char == b'':\r\n raise DecodingError('Unexpected End of File at index position of {0}.'.format(str(self.idx)))\r\n else:\r\n raise DecodingError('Invalid token character ({0}) at position {1}.'.format(str(char), str(self.idx)))","def get_source(self, format: str) -> Source:\n if format in SOURCE_MAP:\n s = SOURCE_MAP[format]\n return s(self)\n else:\n raise TypeError(f\"{format} in an unrecognized format\")","def _parse(self, fmtstr):\n def _match_brace(string, start_pos, pair='[]'):\n \"\"\"Pairing brackets (used internally in _parse method)\"\"\"\n depth = 1\n if string[start_pos] != pair[0]:\n return None\n for index, char in enumerate(string[start_pos + 1:]):\n if char == pair[0]:\n depth += 1\n elif char == pair[1]:\n depth -= 1\n if depth == 0:\n return start_pos + index + 1\n return None\n\n #----------------------------------------------------------------------\n\n t_fmt = self.__class__._T_FMT\n t_prefix = self.__class__._T_PREFIX\n\n ptr = 0\n # it seems that field id 0 is invalid\n field_id = 1\n length = len(fmtstr)\n parsed_list = []\n\n while ptr < length:\n parsed = {}\n m_prefix = t_prefix.match(fmtstr[ptr:])\n if m_prefix:\n ptr += _get_length_of_match(m_prefix)\n parsed['prefix'] = m_prefix.group(1)\n\n # check if we have a nested structure\n if m_prefix.group(2):\n brace_offset = _match_brace(fmtstr, ptr - 1)\n\n # bracket not match\n if not brace_offset:\n raise BadFormatString(\n 'Unmatched brace on position {0}'.format(ptr)\n )\n parsed['field_id'] = field_id\n parsed['field_type'] = 'a'\n parsed['subcontent'] = self._parse(\n fmtstr[ptr:brace_offset]\n )\n ptr = brace_offset + 1\n field_id += 1\n\n parsed_list.append(parsed)\n continue\n m_fmt = t_fmt.match(fmtstr[ptr:])\n if m_fmt:\n ptr += _get_length_of_match(m_fmt)\n\n # fmt is an alias\n if m_fmt.group(2):\n parsed['field_type'] = self.__class__\\\n .FIELD_ALIAS[m_fmt.group(2)]\n # fmt is an actual field type\n elif m_fmt.group(1):\n parsed['field_type'] = m_fmt.group(1)\n\n # save field id\n parsed['field_id'] = field_id\n\n # check for type clones (e.g. `v3')\n if m_fmt.group(3):\n parsed['repeat'] = int(m_fmt.group(3))\n field_id += int(m_fmt.group(3))\n else:\n parsed['repeat'] = 1\n field_id += 1\n\n parsed_list.append(parsed)\n\n else:\n raise BadFormatString(\n 'Invalid token on position {0}'.format(ptr)\n )\n\n # all set\n return parsed_list","def parser_formatter(format_class, **kwargs):\n try:\n return lambda prog: format_class(prog, **kwargs)\n except TypeError:\n return format_class","def parser_formatter(format_class, **kwargs):\n try:\n return lambda prog: format_class(prog, **kwargs)\n except TypeError:\n return format_class","def read(self, sync_map_format, input_file_path, parameters=None):\n if sync_map_format is None:\n self.log_exc(u\"Sync map format is None\", None, True, ValueError)\n if sync_map_format not in SyncMapFormat.CODE_TO_CLASS:\n self.log_exc(u\"Sync map format '%s' is not allowed\" % (sync_map_format), None, True, ValueError)\n if not gf.file_can_be_read(input_file_path):\n self.log_exc(u\"Cannot read sync map file '%s'. Wrong permissions?\" % (input_file_path), None, True, OSError)\n\n self.log([u\"Input format: '%s'\", sync_map_format])\n self.log([u\"Input path: '%s'\", input_file_path])\n self.log([u\"Input parameters: '%s'\", parameters])\n\n reader = (SyncMapFormat.CODE_TO_CLASS[sync_map_format])(\n variant=sync_map_format,\n parameters=parameters,\n rconf=self.rconf,\n logger=self.logger\n )\n\n # open file for reading\n self.log(u\"Reading input file...\")\n with io.open(input_file_path, \"r\", encoding=\"utf-8\") as input_file:\n input_text = input_file.read()\n reader.parse(input_text=input_text, syncmap=self)\n self.log(u\"Reading input file... done\")\n\n # overwrite language if requested\n language = gf.safe_get(parameters, gc.PPN_SYNCMAP_LANGUAGE, None)\n if language is not None:\n self.log([u\"Overwriting language to '%s'\", language])\n for fragment in self.fragments:\n fragment.text_fragment.language = language","def get_reader(fn):\n if is_bed(fn):\n return BedReader(fn)\n elif is_vcf(fn):\n return VcfReader(fn)\n else:\n raise ValueError(\"Could not get reader for %s\" % fn)","def _get_reader(self, filepath: str):\n file_extension = os.path.splitext(filepath)[-1]\n\n self._validate_file(filepath)\n\n if file_extension == \".ipynb\":\n return NotebookReader(filepath)\n elif file_extension in [\".py\", \".r\"]:\n return FileReader(filepath)\n else:\n raise ValueError(f\"File type {file_extension} is not supported.\")","def parser ( self ):\n return self._parser","def read(ftype, inDir, inSuffix, startTime, endTime):\n\n\tif ftype == 'ryan': return readRyan(inDir, inSuffix, startTime, endTime)\t\n\telif ftype == 'segmotion': return readSegmotion(inDir, inSuffix, startTime, endTime)\n\telif ftype == 'probsevere': return readProbSevere(inDir, inSuffix, startTime, endTime)","def from_devfile(devfile, file_format=None, raiseErr=True, **kwargs):\n from mmdev import parsers\n if file_format is None:\n file_format = os.path.splitext(devfile)[1][1:]\n try:\n parsercls = parsers.PARSERS[file_format]\n except KeyError:\n raise KeyError(\"File extension '%s' not recognized\" % file_format)\n\n return parsercls(devfile, raiseErr=raiseErr, **kwargs)","def load(resource_url, format='auto'):\n\n if format=='auto':\n format = os.path.splitext(resource_url)[-1].strip('.')\n\n if format=='pickle':\n resource_val = pickle.load(find(resource_url))\n elif format=='raw':\n resource_val = find(resource_url).open()\n else:\n assert format not in FORMATS\n raise ValueError('Unknown format type: %s' % format)\n\n return resource_val","def parser(self):\n return self._parser","def parse(self, format='json'):\n if format not in SERIALIZE_FORMATS:\n raise ValueError('Incorrect serialize format.')\n\n return _drafter.parse_blueprint_to(self.content, format)","def from_file(file_name, format='') -> Union['flare.struc.Structure',\n List['flare.struc.Structure']]:\n\n try:\n with open(file_name, 'r') as _:\n pass\n except:\n raise FileNotFoundError\n\n if 'xyz' in file_name or 'xyz' in format.lower():\n raise NotImplementedError\n\n if 'json' in format.lower() or '.json' in file_name:\n # Assumed format is one FLARE structure per line,\n # or one line with many FLARE structures\n with open(file_name, 'r') as f:\n thelines = f.readlines()\n\n non_empty_lines = [loads(line) for line in thelines if\n len(line) > 2]\n\n structures = [Structure.from_dict(struc_dict) for struc_dict in\n non_empty_lines]\n\n if len(structures) == 1:\n return structures[0]\n else:\n return structures\n\n is_poscar = 'POSCAR' in file_name or 'CONTCAR' in file_name or 'vasp' in format.lower()\n if is_poscar and _pmg_present:\n pmg_structure = pmgvaspio.Poscar.from_file(file_name).structure\n return Structure.from_pmg_structure(pmg_structure)\n elif is_poscar and not _pmg_present:\n raise ImportError(\"Pymatgen not imported; \" \\\n \"functionality requires pymatgen.\")","def __parser__(self):\n return self","def read_meth(filename, name, window, smoothen=5):\n file_type = file_sniffer(filename)\n logging.info(\"File is of type {}\".format(file_type))\n try:\n if file_type.startswith(\"nanopolish\"):\n return parse_nanopolish(filename, file_type, name, window, smoothen=smoothen)\n elif file_type == \"nanocompore\":\n return parse_nanocompore(filename, name, window)\n elif file_type == \"ont-cram\":\n return parse_ont_cram(filename, name, window)\n except Exception:\n sys.stderr.write(\"\\n\\n\\nInput file {} not recognized!\\n\".format(filename))\n sys.stderr.write(\"\\n\\n\\nDetailed error:\\n\")\n raise","def set_parser(self, output_format):\n self.output_parser = output_parsers.get(output_format, lambda x:x)","def _runParser(self):\n with open(self.var('filePath')) as f:\n return json.load(f)","def GetParserObjectByName(cls, parser_name):\n parser_class = cls._parser_classes.get(parser_name, None)\n if not parser_class:\n return\n return parser_class()","def get_parser_from_config(base_config, attributes, default_parser):\n # check all the places `parser` might be set\n # highest precedence is base_config['attributes']['parser'] - this is if\n # `com.scalyr.config.log.attributes.parser is set as a label\n if \"attributes\" in base_config and \"parser\" in base_config[\"attributes\"]:\n return base_config[\"attributes\"][\"parser\"]\n\n # next precedence is base_config['parser'] - this is if\n # `com.scalyr.config.log.parser` is set as a label\n if \"parser\" in base_config:\n return base_config[\"parser\"]\n\n # lowest precedence is attributes['parser'] - this is if\n # `parser` is a label and labels are being uploaded as attributes\n # and the `parser` label passes the attribute filters\n if \"parser\" in attributes:\n return attributes[\"parser\"]\n\n # if we are here, then we found nothing so return the default\n return default_parser","def get_parser(self):\n parser = SafeConfigParser({'channels': []})\n exists = parser.read(self.config_file_path)\n if not exists:\n self.create_default_config(parser)\n\n return parser","def read_and_unpack(self, fmt):\n try:\n return unpack(\n self.byte_order + fmt,\n self.read(calcsize(self.byte_order + fmt)))\n except Exception as e:\n if e.args[0].startswith('unpack requires a buffer of'):\n raise EOFError(e)\n else:\n raise","def parserFactory(intLanguageName, debugMode):\r\n #if text.getDebug() != debugMode:\r\n # text.setDebugRecurs(debugMode)\r\n\r\n return THE_PARSER","def _get_reader_class(basename: str) -> Type[BaseEEGReader]:\n if basename.endswith(\".h5\"):\n return RamulatorHDF5Reader\n elif basename.endswith((\".bdf\", \".mff\", \".raw\")):\n return ScalpEEGReader\n elif basename.endswith(\".npy\"):\n return NumpyEEGReader\n else:\n return SplitEEGReader","def select_parser():\n\n try:\n select_texttools_parser()\n except ImportError:\n select_python_parser()","def create_parser():\n pass","def __init__(self, file_format, location):\n Reader.__init__(self, file_format, location)","def parser_dispatch(lang=\"cpp\"):\n parser_cls = {\n \"cpp\": cpp.Parser,\n \"c\": c.Parser,\n }.get(lang)\n return parser_cls","def get_parser(self):\n config_parser = ConfigParser.ConfigParser()\n config_parser.read(self.get_config)\n return config_parser","def get_parser():\n _program_name = Path(__file__).stem\n example = f''' Example: >> {_program_name} sample.odb\\n '''\n parser = ArgumentParser(description=__doc__.split('..')[0], # Don't include module author part of doc string\n formatter_class=ArgumentDefaultsHelpFormatter, epilog=example, prog=_program_name)\n parser.add_argument(nargs=1,\n dest='input_file',\n type=str,\n help='odb or odbreport file for extracting data',\n metavar='sample.odb')\n parser.add_argument('-o', '--output-file',\n dest='output_file',\n type=str,\n help='file for printing output',\n metavar='sample.h5')\n parser.add_argument('-f', '--output-file-type',\n dest='output_type',\n choices=['yaml', 'json', 'h5'],\n type=str,\n default='h5',\n help='Type of file in which to store output data',\n metavar='h5')\n parser.add_argument('-r', '--odb-report-args',\n dest='odb_report_args',\n type=str,\n help='Arguments to give to the odbreport command. Require the ``option=value`` interface style.',\n metavar='\"step=step1 results\"')\n parser.add_argument('-a', '--abaqus-command',\n dest='abaqus_command',\n type=str,\n default=_settings._default_abaqus_command,\n help='Abaqus command to use',\n metavar='/path/to/abaqus')\n parser.add_argument('-d', '--delete-report-file',\n action=\"store_true\",\n dest='delete_report_file',\n default=False,\n help='Delete after parsing the file created by the odbreport command')\n parser.add_argument('-v', '--verbose',\n action=\"store_true\",\n dest='verbose',\n default=False,\n help='Print all messages')\n return parser","def _read(self):\n try:\n self.json_dict[\"jobs file format\"] == 6\n except (KeyError, AssertionError):\n print('Warning: %s \"jobs file format\" should be 6' % self.filepath)\n #raise JobFileFormatError\n # substitute config keys:\n if self.json_dict:\n for key in [\"JSONfileToBeEdited\",\n \"<PLAYER.JSON>\",\n \"<CONTROLLER.JSON>\",\n \"skip keys with # in them\",\n \"rFactor escape slash\"]:\n if key in self.json_dict:\n self.config[key] = self.json_dict[key]\n return self.json_dict, self.config","def read(self, format=None, epsg=None):\n if not format:\n format = self.default_output\n if self.data is None and self.features:\n if type(self.features) == str:\n self.features = json.loads(self.features)\n features = self.features\n\n if 'type' in features and features['type'] == 'FeatureCollection':\n self.data = geopandas.GeoDataFrame.from_features(\n self.features['features'])\n else:\n self.data = geopandas.GeoDataFrame.from_features(features)\n if not self.data.crs:\n if hasattr(self, 'crs'):\n self.data.crs = self.crs\n else:\n self.get_epsg()\n\n return self.transform_data(outformat=format, epsg=epsg)","def parse(self): # noqa: F811\n # If we have a file name, we need to read that in.\n if self.url and self.url != _PLACEHOLDER_URL:\n from .util.url import fetch_url\n encoding = self.options.get('encoding', None)\n self.specification = fetch_url(self.url, encoding = encoding)\n\n # If we have a spec string, try to parse it.\n if self._spec_string:\n from .util.formats import parse_spec\n self.specification = parse_spec(self._spec_string, self.url)\n\n # Perform some sanitization in lenient mode.\n if not self.options.get('strict', True):\n from .util import stringify_keys\n self.specification = stringify_keys(self.specification)\n\n # If we have a parsed spec, convert it to JSON. Then we can validate\n # the JSON. At this point, we *require* a parsed specification to exist,\n # so we might as well assert.\n assert self.specification, 'No specification parsed, cannot validate!'\n\n self._validate()","def __parse(self, to_parse):\n path = Path(to_parse)\n if not path.exists():\n raise FileNotFoundError(f\"Configuration file {path.absolute()} not found.\")\n else:\n with path.open() as file:\n data = json.load(file)\n\n if \"name\" not in data:\n raise AssertionError(f\"Missing fundamental parameter: name\")\n Validator(\n [\n (data[\"name\"], str),\n (data[\"include\"] if \"include\" in data else {}, dict),\n (data[\"modules\"] if \"modules\" in data else [], list),\n ]\n )\n # Input parsing\n if \"include\" in data:\n name = data[\"name\"]\n data = self.validate_include(data[\"include\"])\n data[\"name\"] = name\n elif \"modules\" not in data:\n raise AssertionError(\"No modules neither includes are defined.\")\n\n if \"args\" in data:\n Validator().dict(data[\"args\"])\n self.__get_modules(data)","def parse(self):\n return Parse(constants=self.parse_defines(),\n enums=self.parse_enums(),\n structs=self.parse_structs(),\n fundecls=self.parse_functions())","def _get_format(self, request):\n\n # Derive a list of 'formats.Format' instances from the list of formats these views support.\n supported_formats = [formats.find(format) for format in self.supported_formats]\n\n # Determine format by extension...\n if '.' in request.path:\n extension = request.path.split('.')[-1]\n\n try:\n format = formats.find_by_extension(extension)\n except formats.UnknownFormat:\n return None\n\n if format in supported_formats:\n return format\n else:\n return None\n\n # Determine format by HTTP Accept header...\n if 'HTTP_ACCEPT' in request.META:\n content_types = parse_http_accept_header(request.META['HTTP_ACCEPT'])\n\n # Only consider 'accept' headers with a single format in an attempt to play nice\n # with browsers that ask for formats they really should not want.\n if len(content_types) == 1:\n content_type = content_types[0]\n\n # If the request has no preference as to the format of its response, prefer the\n # first of the view's supported formats.\n if content_type == '*/*':\n return supported_formats[0]\n\n try:\n format = formats.find_by_content_type(content_type)\n except formats.UnknownFormat:\n return None\n\n if format in supported_formats:\n return format\n else:\n return None\n\n # If no format is given by either extension or header, default to the format given in\n # RESPITE_DEFAULT_FORMAT (given, of course, that it's supported by the view).\n if DEFAULT_FORMAT:\n format = formats.find(DEFAULT_FORMAT)\n\n if format in supported_formats:\n return format\n else:\n return None","def reader(name, version=None, mimetype=None):\n\treturn _data_processor('read', name, version, mimetype)","def parse_data_format_from_path(path):\n data_format = None\n if os.path.isfile(path):\n data_format = os.path.splitext(path)[1].lower()\n else:\n # TODO: Add support for more data formats\n pass\n\n if data_format is None:\n return generate_response(warning='The path %s is not understandable.' % path)\n # elif data_format not in supported_formats:\n # return generate_response(warning='The data format %s is currently not supported.' % data_format)\n return generate_response(result=data_format)","def get_spec_file(*, path:str, format:str) -> dict:\n ext ={'json': json,\n 'yaml': yaml}\n try:\n with open(path, 'r') as f:\n return ext[format].load(f)\n except Exception as e:\n logger.error(f'file could not be loaded {path}')\n raise","def select_python_parser():\n\n global selected_parser\n import cvs2svn_rcsparse.default\n selected_parser = cvs2svn_rcsparse.default.Parser","def load_with_some_format(filename, formats):\n # Try to load file with formats that match its extension in format order\n data = None\n extension = os.path.splitext(filename)[1][1:]\n\n for format in formats:\n if extension in format.filenameExtensions:\n try:\n data = format.load(filename)\n break\n except InvalidFileFormatException:\n pass\n\n # If load by extension failed, try to load files in any format independently of their extension\n if not data:\n for format in formats:\n try:\n data = format.load(filename)\n break\n except InvalidFileFormatException:\n pass\n return data","def __init__(self, parser):\n if parser == \"csv\":\n self._parser = CSVParser()\n elif parser == \"static\":\n self._parser = StaticParser()\n else:\n raise NotImplementedError","def __new__(cls, format):\n self = super(SF_Pattern, cls).__new__(cls)\n\n if isinstance(format, bytes):\n uni_str = format.decode('ISO-8859-1') # decode to unicode\n trans_str = translate(uni_str) # translate only works with unicode\n re_fmt = trans_str.encode('ISO-8859-1') # encode back to bytes\n self._spec = _gbspec\n else:\n re_fmt = translate(format)\n self._spec = _gspec\n\n self._format = format\n self._re = cre = re.compile(re_fmt)\n\n if cre.groupindex and len(cre.groupindex) != cre.groups:\n raise RuntimeError('cannot mix mapped and unmapped specifiers')\n elif not cre.groupindex:\n self._retfunc = self._return_tuple\n self._type = tuple\n else:\n self._retfunc = self._return_dict\n self._type = dict\n\n self._casts = self._get_types()\n\n return self","def read(self, f):\n return self.parse(f.read())","def get_parser(prog=None, usage=None, description=None, epilog=None):\n try:\n formatter = getattr(sys.modules[__name__], USAGE_FORMAT.capitalize() + 'HelpFormatter')\n except AttributeError as err:\n raise InternalError(\"Invalid USAGE_FORMAT: %s\" % USAGE_FORMAT) from err\n return MutableArgumentGroupParser(prog=prog,\n usage=usage,\n description=description,\n epilog=epilog,\n formatter_class=formatter)","def _get_parserobj(self, option_list):\n if '--version' in self.parselines[0]:\n if 'optparse' == self.parser_type:\n parser = OptionParser(version=\"dummy\")\n else:\n parser = ArgumentParser(\n version='dummy',\n formatter_class=RawDescriptionHelpFormatter)\n else:\n if 'optparse' == self.parser_type:\n parser = OptionParser()\n else:\n parser = ArgumentParser(\n formatter_class=RawDescriptionHelpFormatter)\n for opt in option_list:\n if opt['short'] and self.parser_type is 'optparse':\n parser.add_option(opt['short'], opt['long'],\n metavar=opt['metavar'],\n help=opt['help'].strip())\n elif not opt['short'] and self.parser_type is 'optparse':\n parser.add_option(opt['long'],\n metavar=opt['metavar'],\n help=opt['help'].strip())\n elif opt['short'] and self.parser_type is 'argparse':\n parser.add_argument(opt['short'], opt['long'],\n metavar=opt['metavar'],\n help=opt['help'].strip())\n elif not opt['short'] and self.parser_type is 'argparse':\n parser.add_argument(opt['long'],\n metavar=opt['metavar'],\n help=opt['help'].strip())\n else:\n raise InvalidParserTypeError(\"Invalid paresr type.\")\n return parser","def parse(self):\n raise NotImplementedError(\"Parse not specified!\")","def parse(program):\n return read_from_tokens(tokenize(program))","def _load_parser(self, grammar: str, protocol: Protocol) -> None:\n self.parser = parsley.makeGrammar(grammar, {\n 'punctuation': string.punctuation,\n 'ascii_uppercase': string.ascii_uppercase,\n 'ascii_lowercase': string.ascii_lowercase,\n 'itertools': itertools,\n\n 'Art': Art,\n 'ArtField': ArtField,\n 'Field': Field,\n 'RelLoc': RelLoc,\n 'Names': Names,\n\n 'protocol': protocol,\n 'Boolean': Boolean,\n 'Size': Size,\n 'ArgumentExpression': ArgumentExpression,\n 'MethodInvocationExpression': MethodInvocationExpression,\n 'ConstantExpression': ConstantExpression,\n 'FieldAccessExpression': FieldAccessExpression,\n 'ThisExpression': ThisExpression,\n 'IfElseExpression': IfElseExpression,\n })","def create_parser(lang=None, parser_class=None, lemmas=False, corefs=False,\n beta='repair', normalize=True, post_process=True):\n if not lang and not parser_class:\n raise RuntimeError(\n 'Either \"lang\" or \"parser_class\" must be specified.')\n\n if parser_class:\n package = None\n if parser_class[0] == '.':\n parser_class = parser_class[1:]\n package = '.'\n path_parts = parser_class.split('.')\n module_name = '.'.join(path_parts[:-1])\n class_name = path_parts[-1]\n class_obj = getattr(import_module(module_name, package=package),\n class_name)\n parser = class_obj(lemmas=lemmas, corefs=corefs, beta=beta,\n normalize=normalize, post_process=post_process)\n if lang and parser.lang != lang:\n raise RuntimeError(\n 'Specified language and parser class do not match.')\n return parser\n elif lang == 'en':\n return ParserEN(lemmas=lemmas, corefs=corefs, beta=beta,\n normalize=normalize, post_process=post_process)\n else:\n raise RuntimeError('Unknown parser: {}'.format(lang))","def _get_config_parsser(config_file):\n config_parser = Parser()\n # read() return a list of file name that read success\n open_success_file = config_parser.read(config_file)\n if len(open_success_file) == 0:\n return 1, \"can not open file: \" + config_file\n else:\n return 0, config_parser","def find_parser(optimize=1):\n for p in PARSERS:\n if not p.parsing:\n print('REUSE PARSER')\n return p\n print('NEW PARSER')\n return Parser(optimize=optimize)","def _parse(self, args):\n parser = self._create_parser()\n return parser.parse(args)","def get_parser():\n\n parser = parser.ArgumentParser()\n return parser","def get_reader(self) -> SpectrumReader:\n # Get the reader class\n reader_class = self.get_reader_class()\n\n # Get the common options between ourselves and the reader class\n common_options = self.get_common_options(reader_class)\n\n # Get the options sub-list corresponding to those options\n common_option_sub_list = self.get_options_sub_list(common_options)\n\n return reader_class(common_option_sub_list)","def test_default_read():\n # If new data formats are added to preprocess, they need to be tested\n tested_data_formats = [\"ASCII\", \"SU\", \"SAC\"]\n\n preprocess = Default()\n assert(set(tested_data_formats) ==\n set(preprocess._obs_acceptable_data_formats))\n\n st1 = preprocess.read(os.path.join(TEST_DATA, \"AA.S0001.BXY.semd\"),\n data_format=\"ascii\")\n\n st2 = preprocess.read(os.path.join(TEST_DATA, \"Uy_file_single_d.su\"),\n data_format=\"su\")\n\n st3 = preprocess.read(os.path.join(TEST_DATA, \"AA.S0001.BXY.sac\"),\n data_format=\"sac\")\n\n assert(st1[0].stats.npts == st2[0].stats.npts)\n assert(st3[0].stats.npts == st2[0].stats.npts)","def _read(self, preset_type):\n logger.debug('read presets for %s', self._device.name)\n with self._file_open_rlock(preset_type) as f:\n f.seek(0)\n return yaml.full_load(f) or {}","def peak_file_parser(infile):\n # Open file and make sure supported\n pfile = open_zipped(infile)\n file_parsers = {'bed': bed_file}\n file_parser = None\n if pfile.name == '<stdin>':\n file_parser = bed_file\n else:\n for n in file_parsers:\n if in_name(pfile, n):\n file_parser = file_parsers[n]\n if not file_parser:\n nm = pfile.name\n pfile.close()\n raise Exception('Unknown file type: {}'.format(nm))\n\n # Return the iterator\n return file_parser(pfile)","def load(filename, fmt=Format.json, encoding=\"utf-8\"):\n if fmt == Format.json:\n with io.open(filename, mode=\"r\", encoding=encoding) as fp:\n return json.load(fp)\n elif fmt == Format.json_gz:\n with gzip.open(filename, mode=\"rb\") as fp:\n return json.loads(fp.read().decode(encoding))\n else:\n raise errors.UnsupportedFormat(fmt)","def find_by_name(name):\n for format in FORMATS:\n if name == format.name:\n return format\n\n raise UnknownFormat('No format found with name \"%s\"' % name)","def read_design(designfile):\r\n designtype = None\r\n if re.search(r\"\\.adm$\", designfile, flags=re.I) is not None:\r\n designtype = XMLDesign\r\n elif re.search(r\"\\.xml$\", designfile, flags=re.I) is not None:\r\n designtype = XMLDesign\r\n elif re.search(r\"\\.json$\", designfile, flags=re.I) is not None:\r\n designtype = JSONDesign\r\n design = designtype(designfile)\r\n return design","def read_parse_file(params):\n\tparam_names = []\n\tparam_options = []\n\tif not os.path.isfile(params.parse_file):\n\t\tprint(\"parse file does not exist! ({})\".format(params.parse_file))\n\t\tsys.exit(NO_PARSE)\n\twith open(params.parse_file, 'r') as pf:\n\t\t# first line should be iteration regex\n\t\tsetattr(params, 'iteration_regex', re.compile(pf.readline().strip()))\n\t\tfor line in pf:\n\t\t\tparam_desc = line.split(';')\n\t\t\tparam_names.append(param_desc[0])\n\t\t\tparam_options.append(param_desc[1])\n\n\treturn param_names,param_options","def getConverter( format ):\n\n data = set(format.split(\"-\"))\n\n if \"one\" in data:\n if \"forward\" in data:\n if \"closed\" in data:\n return __one_forward_closed \n else:\n return __one_forward_open\n else:\n if \"closed\" in data:\n return __one_both_closed\n else:\n return __one_both_open\n else:\n if \"forward\" in data:\n if \"closed\" in data:\n return __zero_forward_closed\n else:\n return __zero_forward_open\n else:\n if \"closed\" in data:\n return __zero_both_closed\n else:\n return __zero_both_open","def parse(self):\n logger=self.logger\n tokenizer=Tokenizer()\n self.scope=produtil.testing.parsetree.Scope()\n self.override(self.scope)\n self.parser=Parser(self.run_mode,logger,self.verbose)\n self.parser.requested_platform_name=self.platform_name\n morevars=self.make_vars()\n with open(self.inloc,'rt') as fileobj:\n self.parse_result=self.parser.parse(\n TokenizeFile(tokenizer,fileobj,self.inloc,1),self.scope,\n unique_id=self.unique_id,morevars=morevars)","def get_parsers(self):\n return tuple([getattr(self, '_{}'.format(i)) for i in self.parsers_available])","def parse(self):\n raise NotImplementedError","def parse(cls, raw, style_cls):\n return cls(raw, style_cls)","def read(self, format=None, epsg=None):\n if not format:\n format = self.default_output\n if self.ext not in formats.VECTOR:\n raise UnsupportedFormatException(\n \"Only the following vector formats are supported: {}\".format(\n ','.join(formats.VECTOR)\n )\n )\n if self.data is None:\n self.data = geopandas.read_file(self.uri)\n if self.filters:\n self.filter_data()\n return self.transform_data(format, epsg)","def read(self, *args):\n return _libsbml.ASTBasePlugin_read(self, *args)","def parse(self, stream, media_type=None, parser_context=None):\n encoding = parser_context.get('encoding', settings.DEFAULT_CHARSET)\n decoded_stream = codecs.getreader(encoding)(stream)\n return decoded_stream","def get_format_table(self):\n try:\n with open(self._config.values['format'], 'r') as f:\n return f.read()\n except:\n return None","def select_parser(self, request, resource):\n\n # 1. get from resource\n if resource.mapper:\n return resource.mapper\n # 2. get from content type\n mapper_name = self._get_name_from_content_type(request)\n if mapper_name:\n return self._get_mapper(mapper_name)\n # 3. get from url\n mapper_name = self._get_name_from_url(request)\n if mapper_name:\n return self._get_mapper(mapper_name)\n # 4. use resource's default\n if resource.default_mapper:\n return resource.default_mapper\n # 5. use manager's default\n return self._get_default_mapper()","def load(cls, path, format=None):\n path_was_string = isinstance(path, basestring)\n if path_was_string:\n (folder, filename) = os.path.split(path)\n (name, extension) = os.path.splitext(filename)\n if format is None:\n plugin = kurt.plugin.Kurt.get_plugin(extension=extension)\n if not plugin:\n raise UnknownFormat(extension)\n fp = open(path, \"rb\")\n else:\n fp = path\n assert format, \"Format is required\"\n plugin = kurt.plugin.Kurt.get_plugin(format)\n\n if not plugin:\n raise ValueError, \"Unknown format %r\" % format\n\n project = plugin.load(fp)\n if path_was_string:\n fp.close()\n project.convert(plugin)\n if isinstance(path, basestring):\n project.path = path\n if not project.name:\n project.name = name\n return project","def get_parser(subparsers, parent=None):\n parser = subparsers.add_parser(\n \"flow\",\n description=\"Invoke ML on MCU flow\",\n parents=[parent] if parent else [],\n add_help=(parent is None),\n )\n parser.set_defaults(func=handle)\n add_common_options(parser)\n add_context_options(parser)\n add_flow_options(parser)\n subparsers = parser.add_subparsers(dest=\"subcommand2\") # this line changed\n load.get_parser(subparsers)\n tune.get_parser(subparsers)\n build.get_parser(subparsers)\n compile_.get_parser(subparsers)\n run.get_parser(subparsers)","def _parse(self, infile):\n raise NotImplementedError()","def reader_factory(mode: str = \"json\") -> Callable:\n module_dict = dict(name=f\".{mode}.reader\", package=\"pubsub\")\n module = import_module(**module_dict)\n class_constructor = getattr(module, \"Reader\")\n\n return class_constructor","def _read_format(instream):\n format = int(le.uint32.read_from(instream))\n if format & PCF_BYTE_MASK:\n base = be\n else:\n base = le\n return format, base"],"string":"[\n \"def _get_parser(filepath, cfg):\\n if not os.path.isfile(filepath):\\n LOG.error('File not found: %s', filepath)\\n return\\n valid_parsers = importer.get_parsers(filepath, cfg)\\n if not valid_parsers:\\n LOG.error('No parsers found for file: %s', filepath)\\n return\\n\\n if len(valid_parsers) > 1:\\n while True:\\n print('More than one valid parser found. '\\n 'Please select which one to use:')\\n for idx, vp in enumerate(valid_parsers):\\n print('[{}] {}'.format(idx, vp.__name__))\\n inp = input()\\n try:\\n parser = valid_parsers[inp]\\n break\\n except (IndexError, TypeError):\\n print('Invalid input. Please select the parser number.')\\n else:\\n parser = valid_parsers[0]\\n\\n return parser\",\n \"def get_parser(self):\\n if self.vendor and self.platform and self.version:\\n cls = self.profile.get_profile().get_parser(\\n self.vendor.code, self.platform.name, self.version.version\\n )\\n if cls:\\n return get_handler(cls)(self)\\n return get_handler(\\\"noc.cm.parsers.base.BaseParser\\\")(self)\",\n \"def read(source, format=None):\\n # Lazy imports to not globber the namespace.\\n from prov.model import ProvDocument\\n\\n from prov.serializers import Registry\\n Registry.load_serializers()\\n serializers = Registry.serializers.keys()\\n\\n if format:\\n return ProvDocument.deserialize(source=source, format=format.lower())\\n\\n for format in serializers:\\n try:\\n return ProvDocument.deserialize(source=source, format=format)\\n except:\\n pass\\n else:\\n raise TypeError(\\\"Could not read from the source. To get a proper \\\"\\n \\\"error message, specify the format with the 'format' \\\"\\n \\\"parameter.\\\")\",\n \"def get_first_available_parser():\\n if sys.platform == 'cli':\\n try:\\n from bridge.parser.bridge_dotnet import Parser\\n return Parser\\n except ImportError:\\n pass\\n elif sys.platform[:4] == 'java':\\n try:\\n from bridge.parser.bridge_java import Parser\\n return Parser\\n except ImportError:\\n pass\\n \\n from bridge.parser.bridge_default import Parser\\n \\n return Parser\",\n \"def get_parser(project, layoutdir):\\n for parser_class in PARSERS:\\n parser = parser_class(project, layoutdir)\\n if parser.can_load():\\n return parser\\n raise ValueError(\\\"No loader available for '{0}'.\\\".format(project))\",\n \"def read(fname, fmt=None):\\n if not fmt:\\n fmt = fname.split(\\\".\\\")[-1]\\n\\n if fmt in ['yml', 'yaml']:\\n return _storage_read(fname, yaml.safe_load)\\n elif fmt == \\\"json\\\":\\n return _storage_read(fname, json.load)\\n elif fmt == \\\"pickle\\\":\\n return _storage_read(fname, pickle.load, 'rb')\\n else:\\n raise Exception()\",\n \"def read(filename, file_format=None, **kwargs):\\n if not isinstance(filename, str):\\n raise TypeError()\\n if not (file_format is None or file_format in {\\\"tough\\\", \\\"json\\\"}):\\n raise ValueError()\\n\\n fmt = (\\n file_format\\n if file_format\\n else filetype_from_filename(filename, _extension_to_filetype)\\n )\\n fmt = fmt if fmt else \\\"tough\\\"\\n\\n return _reader_map[fmt](filename, **kwargs)\",\n \"def _get_parser(self, language: str):\\n parser = None\\n if language:\\n parser = self.parsers.get(language)\\n\\n if not parser:\\n self.log.warning(f\\\"Content parser for {language} is not available.\\\")\\n return parser\",\n \"def deserialize(self, content, format):\\r\\n desired_format = None\\r\\n\\r\\n format = format.split(';')[0]\\r\\n\\r\\n for short_format, long_format in self.content_types.items():\\r\\n if format == long_format:\\r\\n if hasattr(self, \\\"from_%s\\\" % short_format):\\r\\n desired_format = short_format\\r\\n break\\r\\n\\r\\n if desired_format is None:\\r\\n raise UnsupportedDeserializationFormat(\\\"The format indicated '%s' had no available deserialization method. Please check your ``formats`` and ``content_types`` on your Serializer.\\\" % format)\\r\\n\\r\\n try:\\r\\n deserialized = getattr(self, \\\"from_%s\\\" % desired_format)(content)\\r\\n except UnsupportedDeserializationFormat, e:\\r\\n raise\\r\\n except Exception, e:\\r\\n raise BadRequest()\\r\\n\\r\\n return deserialized\",\n \"def get_parser_for_file_type(file_type):\\n parser = file_type.upper()\\n if file_type not in SUPPORTED_PARSERS:\\n parser = 'XML'\\n return parser\",\n \"def _get_parser(current_parser=None):\\n if not current_parser:\\n current_parser = configparser.ConfigParser()\\n current_parser.read(PRAW_FILE_PATH)\\n return current_parser\",\n \"def get_parser_for_uri(uri):\\n path = uri_to_path(uri)\\n parser = get_extension(path)\\n\\n if parser not in SUPPORTED_PARSERS:\\n parser = 'XML'\\n\\n return parser\",\n \"def getParser(config, verbose, zenity):\\n\\tif verbose: print \\\"Creating parser object\\\"\\n\\ttry: parser = SafeConfigParser()\\n\\texcept:\\n\\t\\tif zenity: \\n\\t\\t\\tos.system(\\\"zenity --error --text='Error: Failed to create parser'\\\")\\n\\t\\tprint \\\"Failed to create parser object.\\\"\\n\\t\\treturn None\\n\\tparser.optionxform=str\\t\\t\\t\\t# Maintain case sensitivity\\n\\ttry: parser.read(config)\\n\\texcept:\\n\\t\\tif zenity:\\n\\t\\t\\tos.system(\\\"zenity --error --text='Error: Failed to read config'\\\")\\n\\t\\tprint \\\"Failed to read from configuration file.\\\"\\n\\t\\treturn None\\t\\n\\treturn parser\",\n \"def getParser(self):\\n return self._parser\",\n \"def parse(self, parser):\\n with self.reading:\\n chunks = [self.read_buffer.dequeue() or (yield self.base.read(self.bufsize))]\\n try:\\n while True:\\n tupe, result = parser.__parser__()(chunks[-1], False)\\n if tupe & ParserResult.DONE:\\n value, chunk, _ = result\\n del chunks[:]\\n self.read_buffer.enqueue(chunk)\\n do_return(value)\\n elif tupe & ParserResult.PARTIAL:\\n parser = result\\n chunks.append((yield self.base.read(self.bufsize)))\\n else:\\n raise ParserError(result)\\n except BrokenPipeError:\\n # try to terminate parser with last chunk\\n tupe, result = parser.__parser__()(b'', True)\\n if tupe & ParserResult.DONE:\\n value, chunk, _ = result\\n del chunks[:]\\n self.read_buffer.enqueue(chunk)\\n do_return(value)\\n raise\\n finally:\\n for chunk in chunks:\\n self.read_buffer.enqueue(chunk)\",\n \"def _get_format(self, token):\\n if token in self._formats:\\n return self._formats[token]\\n\\n if self._style is None:\\n result = self._get_format_from_document(token, self._document)\\n else:\\n result = self._get_format_from_style(token, self._style)\\n\\n self._formats[token] = result\\n return result\",\n \"def _ReadSerializerStream(self):\\n stream_name = 'serializer.txt'\\n if not self._HasStream(stream_name):\\n return\\n\\n serialization_format = self._ReadStream(stream_name)\\n if serialization_format != definitions.SERIALIZER_FORMAT_JSON:\\n raise ValueError(\\n 'Unsupported stored serialization format: {0:s}'.format(\\n serialization_format))\\n\\n return serialization_format\",\n \"def getFormatManager(self) -> ghidra.app.util.viewer.format.FormatManager:\\n ...\",\n \"def get_for(self, file=None, format=''):\\n if isinstance(file, DirectoryStream):\\n # directory 'stream'\\n return (self._names['dir'],)\\n if format:\\n try:\\n converter = (self._names[format],)\\n except KeyError:\\n raise ValueError(\\n f'Format specifier `{format}` not recognised'\\n )\\n else:\\n converter = self.identify(file)\\n if not converter:\\n if not file or file.mode == 'w' or maybe_text(file):\\n format = self._default_text\\n else:\\n format = self._default_binary\\n if file and format:\\n if Path(file.name).suffix:\\n level = logging.WARNING\\n else:\\n level = logging.DEBUG\\n logging.log(\\n level,\\n f'Could not infer format from filename `{file.name}`. '\\n f'Falling back to default `{format}` format'\\n )\\n try:\\n converter = (self._names[format],)\\n except KeyError:\\n pass\\n return converter\",\n \"def read_file(self,filename):\\n\\n if (config.mode_format == \\\"simple\\\"): return self.read_file_simple(filename)\\n if (config.mode_format == \\\"agsm\\\"): return self.read_file_agsm(filename)\\n sys.exit(\\\"ERROR: unrecognised format \\\\\\\"\\\"+config.mode_format+\\\"\\\\\\\".\\\\n\\\" \\\\\\n +\\\" Please choose another value for mode_format in AIMS_configure.py\\\")\",\n \"def read_f(self, fmt, offset=None):\\n\\n if offset is not None:\\n self.seek(offset)\\n\\n # place commas before and after each instance of S or Z\\n for special in ['S', 'Z']:\\n fmt = fmt.replace(special, ',' + special + ',')\\n\\n # split S and Z into isolated strings\\n fmt = fmt.split(',')\\n\\n # construct a tuple of unpacked data\\n data = ()\\n for subfmt in fmt:\\n if subfmt == 'S':\\n data += (self.read_string(),)\\n elif subfmt == 'Z':\\n data += (self.read_bool(),)\\n else:\\n data += self.read_and_unpack(subfmt)\\n\\n if len(data) == 1:\\n return data[0]\\n else:\\n return data\",\n \"def read(self, fmt):\\n fmt = '>' + fmt\\n if not PY3:\\n fmt = fmt.encode('utf-8')\\n return struct.unpack(fmt, self.bin.read(format_byte_size(fmt)))\",\n \"def __parse(self) -> object:\\r\\n char = self.data[self.idx: self.idx + 1]\\r\\n if char in [b'1', b'2', b'3', b'4', b'5', b'6', b'7', b'8', b'9', b'0']:\\r\\n str_len = int(self.__read_to(b':'))\\r\\n return self.__read(str_len)\\r\\n elif char == b'i':\\r\\n self.idx += 1\\r\\n return int(self.__read_to(b'e'))\\r\\n elif char == b'd':\\r\\n return self.__parse_dict()\\r\\n elif char == b'l':\\r\\n return self.__parse_list()\\r\\n elif char == b'':\\r\\n raise DecodingError('Unexpected End of File at index position of {0}.'.format(str(self.idx)))\\r\\n else:\\r\\n raise DecodingError('Invalid token character ({0}) at position {1}.'.format(str(char), str(self.idx)))\",\n \"def get_source(self, format: str) -> Source:\\n if format in SOURCE_MAP:\\n s = SOURCE_MAP[format]\\n return s(self)\\n else:\\n raise TypeError(f\\\"{format} in an unrecognized format\\\")\",\n \"def _parse(self, fmtstr):\\n def _match_brace(string, start_pos, pair='[]'):\\n \\\"\\\"\\\"Pairing brackets (used internally in _parse method)\\\"\\\"\\\"\\n depth = 1\\n if string[start_pos] != pair[0]:\\n return None\\n for index, char in enumerate(string[start_pos + 1:]):\\n if char == pair[0]:\\n depth += 1\\n elif char == pair[1]:\\n depth -= 1\\n if depth == 0:\\n return start_pos + index + 1\\n return None\\n\\n #----------------------------------------------------------------------\\n\\n t_fmt = self.__class__._T_FMT\\n t_prefix = self.__class__._T_PREFIX\\n\\n ptr = 0\\n # it seems that field id 0 is invalid\\n field_id = 1\\n length = len(fmtstr)\\n parsed_list = []\\n\\n while ptr < length:\\n parsed = {}\\n m_prefix = t_prefix.match(fmtstr[ptr:])\\n if m_prefix:\\n ptr += _get_length_of_match(m_prefix)\\n parsed['prefix'] = m_prefix.group(1)\\n\\n # check if we have a nested structure\\n if m_prefix.group(2):\\n brace_offset = _match_brace(fmtstr, ptr - 1)\\n\\n # bracket not match\\n if not brace_offset:\\n raise BadFormatString(\\n 'Unmatched brace on position {0}'.format(ptr)\\n )\\n parsed['field_id'] = field_id\\n parsed['field_type'] = 'a'\\n parsed['subcontent'] = self._parse(\\n fmtstr[ptr:brace_offset]\\n )\\n ptr = brace_offset + 1\\n field_id += 1\\n\\n parsed_list.append(parsed)\\n continue\\n m_fmt = t_fmt.match(fmtstr[ptr:])\\n if m_fmt:\\n ptr += _get_length_of_match(m_fmt)\\n\\n # fmt is an alias\\n if m_fmt.group(2):\\n parsed['field_type'] = self.__class__\\\\\\n .FIELD_ALIAS[m_fmt.group(2)]\\n # fmt is an actual field type\\n elif m_fmt.group(1):\\n parsed['field_type'] = m_fmt.group(1)\\n\\n # save field id\\n parsed['field_id'] = field_id\\n\\n # check for type clones (e.g. `v3')\\n if m_fmt.group(3):\\n parsed['repeat'] = int(m_fmt.group(3))\\n field_id += int(m_fmt.group(3))\\n else:\\n parsed['repeat'] = 1\\n field_id += 1\\n\\n parsed_list.append(parsed)\\n\\n else:\\n raise BadFormatString(\\n 'Invalid token on position {0}'.format(ptr)\\n )\\n\\n # all set\\n return parsed_list\",\n \"def parser_formatter(format_class, **kwargs):\\n try:\\n return lambda prog: format_class(prog, **kwargs)\\n except TypeError:\\n return format_class\",\n \"def parser_formatter(format_class, **kwargs):\\n try:\\n return lambda prog: format_class(prog, **kwargs)\\n except TypeError:\\n return format_class\",\n \"def read(self, sync_map_format, input_file_path, parameters=None):\\n if sync_map_format is None:\\n self.log_exc(u\\\"Sync map format is None\\\", None, True, ValueError)\\n if sync_map_format not in SyncMapFormat.CODE_TO_CLASS:\\n self.log_exc(u\\\"Sync map format '%s' is not allowed\\\" % (sync_map_format), None, True, ValueError)\\n if not gf.file_can_be_read(input_file_path):\\n self.log_exc(u\\\"Cannot read sync map file '%s'. Wrong permissions?\\\" % (input_file_path), None, True, OSError)\\n\\n self.log([u\\\"Input format: '%s'\\\", sync_map_format])\\n self.log([u\\\"Input path: '%s'\\\", input_file_path])\\n self.log([u\\\"Input parameters: '%s'\\\", parameters])\\n\\n reader = (SyncMapFormat.CODE_TO_CLASS[sync_map_format])(\\n variant=sync_map_format,\\n parameters=parameters,\\n rconf=self.rconf,\\n logger=self.logger\\n )\\n\\n # open file for reading\\n self.log(u\\\"Reading input file...\\\")\\n with io.open(input_file_path, \\\"r\\\", encoding=\\\"utf-8\\\") as input_file:\\n input_text = input_file.read()\\n reader.parse(input_text=input_text, syncmap=self)\\n self.log(u\\\"Reading input file... done\\\")\\n\\n # overwrite language if requested\\n language = gf.safe_get(parameters, gc.PPN_SYNCMAP_LANGUAGE, None)\\n if language is not None:\\n self.log([u\\\"Overwriting language to '%s'\\\", language])\\n for fragment in self.fragments:\\n fragment.text_fragment.language = language\",\n \"def get_reader(fn):\\n if is_bed(fn):\\n return BedReader(fn)\\n elif is_vcf(fn):\\n return VcfReader(fn)\\n else:\\n raise ValueError(\\\"Could not get reader for %s\\\" % fn)\",\n \"def _get_reader(self, filepath: str):\\n file_extension = os.path.splitext(filepath)[-1]\\n\\n self._validate_file(filepath)\\n\\n if file_extension == \\\".ipynb\\\":\\n return NotebookReader(filepath)\\n elif file_extension in [\\\".py\\\", \\\".r\\\"]:\\n return FileReader(filepath)\\n else:\\n raise ValueError(f\\\"File type {file_extension} is not supported.\\\")\",\n \"def parser ( self ):\\n return self._parser\",\n \"def read(ftype, inDir, inSuffix, startTime, endTime):\\n\\n\\tif ftype == 'ryan': return readRyan(inDir, inSuffix, startTime, endTime)\\t\\n\\telif ftype == 'segmotion': return readSegmotion(inDir, inSuffix, startTime, endTime)\\n\\telif ftype == 'probsevere': return readProbSevere(inDir, inSuffix, startTime, endTime)\",\n \"def from_devfile(devfile, file_format=None, raiseErr=True, **kwargs):\\n from mmdev import parsers\\n if file_format is None:\\n file_format = os.path.splitext(devfile)[1][1:]\\n try:\\n parsercls = parsers.PARSERS[file_format]\\n except KeyError:\\n raise KeyError(\\\"File extension '%s' not recognized\\\" % file_format)\\n\\n return parsercls(devfile, raiseErr=raiseErr, **kwargs)\",\n \"def load(resource_url, format='auto'):\\n\\n if format=='auto':\\n format = os.path.splitext(resource_url)[-1].strip('.')\\n\\n if format=='pickle':\\n resource_val = pickle.load(find(resource_url))\\n elif format=='raw':\\n resource_val = find(resource_url).open()\\n else:\\n assert format not in FORMATS\\n raise ValueError('Unknown format type: %s' % format)\\n\\n return resource_val\",\n \"def parser(self):\\n return self._parser\",\n \"def parse(self, format='json'):\\n if format not in SERIALIZE_FORMATS:\\n raise ValueError('Incorrect serialize format.')\\n\\n return _drafter.parse_blueprint_to(self.content, format)\",\n \"def from_file(file_name, format='') -> Union['flare.struc.Structure',\\n List['flare.struc.Structure']]:\\n\\n try:\\n with open(file_name, 'r') as _:\\n pass\\n except:\\n raise FileNotFoundError\\n\\n if 'xyz' in file_name or 'xyz' in format.lower():\\n raise NotImplementedError\\n\\n if 'json' in format.lower() or '.json' in file_name:\\n # Assumed format is one FLARE structure per line,\\n # or one line with many FLARE structures\\n with open(file_name, 'r') as f:\\n thelines = f.readlines()\\n\\n non_empty_lines = [loads(line) for line in thelines if\\n len(line) > 2]\\n\\n structures = [Structure.from_dict(struc_dict) for struc_dict in\\n non_empty_lines]\\n\\n if len(structures) == 1:\\n return structures[0]\\n else:\\n return structures\\n\\n is_poscar = 'POSCAR' in file_name or 'CONTCAR' in file_name or 'vasp' in format.lower()\\n if is_poscar and _pmg_present:\\n pmg_structure = pmgvaspio.Poscar.from_file(file_name).structure\\n return Structure.from_pmg_structure(pmg_structure)\\n elif is_poscar and not _pmg_present:\\n raise ImportError(\\\"Pymatgen not imported; \\\" \\\\\\n \\\"functionality requires pymatgen.\\\")\",\n \"def __parser__(self):\\n return self\",\n \"def read_meth(filename, name, window, smoothen=5):\\n file_type = file_sniffer(filename)\\n logging.info(\\\"File is of type {}\\\".format(file_type))\\n try:\\n if file_type.startswith(\\\"nanopolish\\\"):\\n return parse_nanopolish(filename, file_type, name, window, smoothen=smoothen)\\n elif file_type == \\\"nanocompore\\\":\\n return parse_nanocompore(filename, name, window)\\n elif file_type == \\\"ont-cram\\\":\\n return parse_ont_cram(filename, name, window)\\n except Exception:\\n sys.stderr.write(\\\"\\\\n\\\\n\\\\nInput file {} not recognized!\\\\n\\\".format(filename))\\n sys.stderr.write(\\\"\\\\n\\\\n\\\\nDetailed error:\\\\n\\\")\\n raise\",\n \"def set_parser(self, output_format):\\n self.output_parser = output_parsers.get(output_format, lambda x:x)\",\n \"def _runParser(self):\\n with open(self.var('filePath')) as f:\\n return json.load(f)\",\n \"def GetParserObjectByName(cls, parser_name):\\n parser_class = cls._parser_classes.get(parser_name, None)\\n if not parser_class:\\n return\\n return parser_class()\",\n \"def get_parser_from_config(base_config, attributes, default_parser):\\n # check all the places `parser` might be set\\n # highest precedence is base_config['attributes']['parser'] - this is if\\n # `com.scalyr.config.log.attributes.parser is set as a label\\n if \\\"attributes\\\" in base_config and \\\"parser\\\" in base_config[\\\"attributes\\\"]:\\n return base_config[\\\"attributes\\\"][\\\"parser\\\"]\\n\\n # next precedence is base_config['parser'] - this is if\\n # `com.scalyr.config.log.parser` is set as a label\\n if \\\"parser\\\" in base_config:\\n return base_config[\\\"parser\\\"]\\n\\n # lowest precedence is attributes['parser'] - this is if\\n # `parser` is a label and labels are being uploaded as attributes\\n # and the `parser` label passes the attribute filters\\n if \\\"parser\\\" in attributes:\\n return attributes[\\\"parser\\\"]\\n\\n # if we are here, then we found nothing so return the default\\n return default_parser\",\n \"def get_parser(self):\\n parser = SafeConfigParser({'channels': []})\\n exists = parser.read(self.config_file_path)\\n if not exists:\\n self.create_default_config(parser)\\n\\n return parser\",\n \"def read_and_unpack(self, fmt):\\n try:\\n return unpack(\\n self.byte_order + fmt,\\n self.read(calcsize(self.byte_order + fmt)))\\n except Exception as e:\\n if e.args[0].startswith('unpack requires a buffer of'):\\n raise EOFError(e)\\n else:\\n raise\",\n \"def parserFactory(intLanguageName, debugMode):\\r\\n #if text.getDebug() != debugMode:\\r\\n # text.setDebugRecurs(debugMode)\\r\\n\\r\\n return THE_PARSER\",\n \"def _get_reader_class(basename: str) -> Type[BaseEEGReader]:\\n if basename.endswith(\\\".h5\\\"):\\n return RamulatorHDF5Reader\\n elif basename.endswith((\\\".bdf\\\", \\\".mff\\\", \\\".raw\\\")):\\n return ScalpEEGReader\\n elif basename.endswith(\\\".npy\\\"):\\n return NumpyEEGReader\\n else:\\n return SplitEEGReader\",\n \"def select_parser():\\n\\n try:\\n select_texttools_parser()\\n except ImportError:\\n select_python_parser()\",\n \"def create_parser():\\n pass\",\n \"def __init__(self, file_format, location):\\n Reader.__init__(self, file_format, location)\",\n \"def parser_dispatch(lang=\\\"cpp\\\"):\\n parser_cls = {\\n \\\"cpp\\\": cpp.Parser,\\n \\\"c\\\": c.Parser,\\n }.get(lang)\\n return parser_cls\",\n \"def get_parser(self):\\n config_parser = ConfigParser.ConfigParser()\\n config_parser.read(self.get_config)\\n return config_parser\",\n \"def get_parser():\\n _program_name = Path(__file__).stem\\n example = f''' Example: >> {_program_name} sample.odb\\\\n '''\\n parser = ArgumentParser(description=__doc__.split('..')[0], # Don't include module author part of doc string\\n formatter_class=ArgumentDefaultsHelpFormatter, epilog=example, prog=_program_name)\\n parser.add_argument(nargs=1,\\n dest='input_file',\\n type=str,\\n help='odb or odbreport file for extracting data',\\n metavar='sample.odb')\\n parser.add_argument('-o', '--output-file',\\n dest='output_file',\\n type=str,\\n help='file for printing output',\\n metavar='sample.h5')\\n parser.add_argument('-f', '--output-file-type',\\n dest='output_type',\\n choices=['yaml', 'json', 'h5'],\\n type=str,\\n default='h5',\\n help='Type of file in which to store output data',\\n metavar='h5')\\n parser.add_argument('-r', '--odb-report-args',\\n dest='odb_report_args',\\n type=str,\\n help='Arguments to give to the odbreport command. Require the ``option=value`` interface style.',\\n metavar='\\\"step=step1 results\\\"')\\n parser.add_argument('-a', '--abaqus-command',\\n dest='abaqus_command',\\n type=str,\\n default=_settings._default_abaqus_command,\\n help='Abaqus command to use',\\n metavar='/path/to/abaqus')\\n parser.add_argument('-d', '--delete-report-file',\\n action=\\\"store_true\\\",\\n dest='delete_report_file',\\n default=False,\\n help='Delete after parsing the file created by the odbreport command')\\n parser.add_argument('-v', '--verbose',\\n action=\\\"store_true\\\",\\n dest='verbose',\\n default=False,\\n help='Print all messages')\\n return parser\",\n \"def _read(self):\\n try:\\n self.json_dict[\\\"jobs file format\\\"] == 6\\n except (KeyError, AssertionError):\\n print('Warning: %s \\\"jobs file format\\\" should be 6' % self.filepath)\\n #raise JobFileFormatError\\n # substitute config keys:\\n if self.json_dict:\\n for key in [\\\"JSONfileToBeEdited\\\",\\n \\\"<PLAYER.JSON>\\\",\\n \\\"<CONTROLLER.JSON>\\\",\\n \\\"skip keys with # in them\\\",\\n \\\"rFactor escape slash\\\"]:\\n if key in self.json_dict:\\n self.config[key] = self.json_dict[key]\\n return self.json_dict, self.config\",\n \"def read(self, format=None, epsg=None):\\n if not format:\\n format = self.default_output\\n if self.data is None and self.features:\\n if type(self.features) == str:\\n self.features = json.loads(self.features)\\n features = self.features\\n\\n if 'type' in features and features['type'] == 'FeatureCollection':\\n self.data = geopandas.GeoDataFrame.from_features(\\n self.features['features'])\\n else:\\n self.data = geopandas.GeoDataFrame.from_features(features)\\n if not self.data.crs:\\n if hasattr(self, 'crs'):\\n self.data.crs = self.crs\\n else:\\n self.get_epsg()\\n\\n return self.transform_data(outformat=format, epsg=epsg)\",\n \"def parse(self): # noqa: F811\\n # If we have a file name, we need to read that in.\\n if self.url and self.url != _PLACEHOLDER_URL:\\n from .util.url import fetch_url\\n encoding = self.options.get('encoding', None)\\n self.specification = fetch_url(self.url, encoding = encoding)\\n\\n # If we have a spec string, try to parse it.\\n if self._spec_string:\\n from .util.formats import parse_spec\\n self.specification = parse_spec(self._spec_string, self.url)\\n\\n # Perform some sanitization in lenient mode.\\n if not self.options.get('strict', True):\\n from .util import stringify_keys\\n self.specification = stringify_keys(self.specification)\\n\\n # If we have a parsed spec, convert it to JSON. Then we can validate\\n # the JSON. At this point, we *require* a parsed specification to exist,\\n # so we might as well assert.\\n assert self.specification, 'No specification parsed, cannot validate!'\\n\\n self._validate()\",\n \"def __parse(self, to_parse):\\n path = Path(to_parse)\\n if not path.exists():\\n raise FileNotFoundError(f\\\"Configuration file {path.absolute()} not found.\\\")\\n else:\\n with path.open() as file:\\n data = json.load(file)\\n\\n if \\\"name\\\" not in data:\\n raise AssertionError(f\\\"Missing fundamental parameter: name\\\")\\n Validator(\\n [\\n (data[\\\"name\\\"], str),\\n (data[\\\"include\\\"] if \\\"include\\\" in data else {}, dict),\\n (data[\\\"modules\\\"] if \\\"modules\\\" in data else [], list),\\n ]\\n )\\n # Input parsing\\n if \\\"include\\\" in data:\\n name = data[\\\"name\\\"]\\n data = self.validate_include(data[\\\"include\\\"])\\n data[\\\"name\\\"] = name\\n elif \\\"modules\\\" not in data:\\n raise AssertionError(\\\"No modules neither includes are defined.\\\")\\n\\n if \\\"args\\\" in data:\\n Validator().dict(data[\\\"args\\\"])\\n self.__get_modules(data)\",\n \"def parse(self):\\n return Parse(constants=self.parse_defines(),\\n enums=self.parse_enums(),\\n structs=self.parse_structs(),\\n fundecls=self.parse_functions())\",\n \"def _get_format(self, request):\\n\\n # Derive a list of 'formats.Format' instances from the list of formats these views support.\\n supported_formats = [formats.find(format) for format in self.supported_formats]\\n\\n # Determine format by extension...\\n if '.' in request.path:\\n extension = request.path.split('.')[-1]\\n\\n try:\\n format = formats.find_by_extension(extension)\\n except formats.UnknownFormat:\\n return None\\n\\n if format in supported_formats:\\n return format\\n else:\\n return None\\n\\n # Determine format by HTTP Accept header...\\n if 'HTTP_ACCEPT' in request.META:\\n content_types = parse_http_accept_header(request.META['HTTP_ACCEPT'])\\n\\n # Only consider 'accept' headers with a single format in an attempt to play nice\\n # with browsers that ask for formats they really should not want.\\n if len(content_types) == 1:\\n content_type = content_types[0]\\n\\n # If the request has no preference as to the format of its response, prefer the\\n # first of the view's supported formats.\\n if content_type == '*/*':\\n return supported_formats[0]\\n\\n try:\\n format = formats.find_by_content_type(content_type)\\n except formats.UnknownFormat:\\n return None\\n\\n if format in supported_formats:\\n return format\\n else:\\n return None\\n\\n # If no format is given by either extension or header, default to the format given in\\n # RESPITE_DEFAULT_FORMAT (given, of course, that it's supported by the view).\\n if DEFAULT_FORMAT:\\n format = formats.find(DEFAULT_FORMAT)\\n\\n if format in supported_formats:\\n return format\\n else:\\n return None\",\n \"def reader(name, version=None, mimetype=None):\\n\\treturn _data_processor('read', name, version, mimetype)\",\n \"def parse_data_format_from_path(path):\\n data_format = None\\n if os.path.isfile(path):\\n data_format = os.path.splitext(path)[1].lower()\\n else:\\n # TODO: Add support for more data formats\\n pass\\n\\n if data_format is None:\\n return generate_response(warning='The path %s is not understandable.' % path)\\n # elif data_format not in supported_formats:\\n # return generate_response(warning='The data format %s is currently not supported.' % data_format)\\n return generate_response(result=data_format)\",\n \"def get_spec_file(*, path:str, format:str) -> dict:\\n ext ={'json': json,\\n 'yaml': yaml}\\n try:\\n with open(path, 'r') as f:\\n return ext[format].load(f)\\n except Exception as e:\\n logger.error(f'file could not be loaded {path}')\\n raise\",\n \"def select_python_parser():\\n\\n global selected_parser\\n import cvs2svn_rcsparse.default\\n selected_parser = cvs2svn_rcsparse.default.Parser\",\n \"def load_with_some_format(filename, formats):\\n # Try to load file with formats that match its extension in format order\\n data = None\\n extension = os.path.splitext(filename)[1][1:]\\n\\n for format in formats:\\n if extension in format.filenameExtensions:\\n try:\\n data = format.load(filename)\\n break\\n except InvalidFileFormatException:\\n pass\\n\\n # If load by extension failed, try to load files in any format independently of their extension\\n if not data:\\n for format in formats:\\n try:\\n data = format.load(filename)\\n break\\n except InvalidFileFormatException:\\n pass\\n return data\",\n \"def __init__(self, parser):\\n if parser == \\\"csv\\\":\\n self._parser = CSVParser()\\n elif parser == \\\"static\\\":\\n self._parser = StaticParser()\\n else:\\n raise NotImplementedError\",\n \"def __new__(cls, format):\\n self = super(SF_Pattern, cls).__new__(cls)\\n\\n if isinstance(format, bytes):\\n uni_str = format.decode('ISO-8859-1') # decode to unicode\\n trans_str = translate(uni_str) # translate only works with unicode\\n re_fmt = trans_str.encode('ISO-8859-1') # encode back to bytes\\n self._spec = _gbspec\\n else:\\n re_fmt = translate(format)\\n self._spec = _gspec\\n\\n self._format = format\\n self._re = cre = re.compile(re_fmt)\\n\\n if cre.groupindex and len(cre.groupindex) != cre.groups:\\n raise RuntimeError('cannot mix mapped and unmapped specifiers')\\n elif not cre.groupindex:\\n self._retfunc = self._return_tuple\\n self._type = tuple\\n else:\\n self._retfunc = self._return_dict\\n self._type = dict\\n\\n self._casts = self._get_types()\\n\\n return self\",\n \"def read(self, f):\\n return self.parse(f.read())\",\n \"def get_parser(prog=None, usage=None, description=None, epilog=None):\\n try:\\n formatter = getattr(sys.modules[__name__], USAGE_FORMAT.capitalize() + 'HelpFormatter')\\n except AttributeError as err:\\n raise InternalError(\\\"Invalid USAGE_FORMAT: %s\\\" % USAGE_FORMAT) from err\\n return MutableArgumentGroupParser(prog=prog,\\n usage=usage,\\n description=description,\\n epilog=epilog,\\n formatter_class=formatter)\",\n \"def _get_parserobj(self, option_list):\\n if '--version' in self.parselines[0]:\\n if 'optparse' == self.parser_type:\\n parser = OptionParser(version=\\\"dummy\\\")\\n else:\\n parser = ArgumentParser(\\n version='dummy',\\n formatter_class=RawDescriptionHelpFormatter)\\n else:\\n if 'optparse' == self.parser_type:\\n parser = OptionParser()\\n else:\\n parser = ArgumentParser(\\n formatter_class=RawDescriptionHelpFormatter)\\n for opt in option_list:\\n if opt['short'] and self.parser_type is 'optparse':\\n parser.add_option(opt['short'], opt['long'],\\n metavar=opt['metavar'],\\n help=opt['help'].strip())\\n elif not opt['short'] and self.parser_type is 'optparse':\\n parser.add_option(opt['long'],\\n metavar=opt['metavar'],\\n help=opt['help'].strip())\\n elif opt['short'] and self.parser_type is 'argparse':\\n parser.add_argument(opt['short'], opt['long'],\\n metavar=opt['metavar'],\\n help=opt['help'].strip())\\n elif not opt['short'] and self.parser_type is 'argparse':\\n parser.add_argument(opt['long'],\\n metavar=opt['metavar'],\\n help=opt['help'].strip())\\n else:\\n raise InvalidParserTypeError(\\\"Invalid paresr type.\\\")\\n return parser\",\n \"def parse(self):\\n raise NotImplementedError(\\\"Parse not specified!\\\")\",\n \"def parse(program):\\n return read_from_tokens(tokenize(program))\",\n \"def _load_parser(self, grammar: str, protocol: Protocol) -> None:\\n self.parser = parsley.makeGrammar(grammar, {\\n 'punctuation': string.punctuation,\\n 'ascii_uppercase': string.ascii_uppercase,\\n 'ascii_lowercase': string.ascii_lowercase,\\n 'itertools': itertools,\\n\\n 'Art': Art,\\n 'ArtField': ArtField,\\n 'Field': Field,\\n 'RelLoc': RelLoc,\\n 'Names': Names,\\n\\n 'protocol': protocol,\\n 'Boolean': Boolean,\\n 'Size': Size,\\n 'ArgumentExpression': ArgumentExpression,\\n 'MethodInvocationExpression': MethodInvocationExpression,\\n 'ConstantExpression': ConstantExpression,\\n 'FieldAccessExpression': FieldAccessExpression,\\n 'ThisExpression': ThisExpression,\\n 'IfElseExpression': IfElseExpression,\\n })\",\n \"def create_parser(lang=None, parser_class=None, lemmas=False, corefs=False,\\n beta='repair', normalize=True, post_process=True):\\n if not lang and not parser_class:\\n raise RuntimeError(\\n 'Either \\\"lang\\\" or \\\"parser_class\\\" must be specified.')\\n\\n if parser_class:\\n package = None\\n if parser_class[0] == '.':\\n parser_class = parser_class[1:]\\n package = '.'\\n path_parts = parser_class.split('.')\\n module_name = '.'.join(path_parts[:-1])\\n class_name = path_parts[-1]\\n class_obj = getattr(import_module(module_name, package=package),\\n class_name)\\n parser = class_obj(lemmas=lemmas, corefs=corefs, beta=beta,\\n normalize=normalize, post_process=post_process)\\n if lang and parser.lang != lang:\\n raise RuntimeError(\\n 'Specified language and parser class do not match.')\\n return parser\\n elif lang == 'en':\\n return ParserEN(lemmas=lemmas, corefs=corefs, beta=beta,\\n normalize=normalize, post_process=post_process)\\n else:\\n raise RuntimeError('Unknown parser: {}'.format(lang))\",\n \"def _get_config_parsser(config_file):\\n config_parser = Parser()\\n # read() return a list of file name that read success\\n open_success_file = config_parser.read(config_file)\\n if len(open_success_file) == 0:\\n return 1, \\\"can not open file: \\\" + config_file\\n else:\\n return 0, config_parser\",\n \"def find_parser(optimize=1):\\n for p in PARSERS:\\n if not p.parsing:\\n print('REUSE PARSER')\\n return p\\n print('NEW PARSER')\\n return Parser(optimize=optimize)\",\n \"def _parse(self, args):\\n parser = self._create_parser()\\n return parser.parse(args)\",\n \"def get_parser():\\n\\n parser = parser.ArgumentParser()\\n return parser\",\n \"def get_reader(self) -> SpectrumReader:\\n # Get the reader class\\n reader_class = self.get_reader_class()\\n\\n # Get the common options between ourselves and the reader class\\n common_options = self.get_common_options(reader_class)\\n\\n # Get the options sub-list corresponding to those options\\n common_option_sub_list = self.get_options_sub_list(common_options)\\n\\n return reader_class(common_option_sub_list)\",\n \"def test_default_read():\\n # If new data formats are added to preprocess, they need to be tested\\n tested_data_formats = [\\\"ASCII\\\", \\\"SU\\\", \\\"SAC\\\"]\\n\\n preprocess = Default()\\n assert(set(tested_data_formats) ==\\n set(preprocess._obs_acceptable_data_formats))\\n\\n st1 = preprocess.read(os.path.join(TEST_DATA, \\\"AA.S0001.BXY.semd\\\"),\\n data_format=\\\"ascii\\\")\\n\\n st2 = preprocess.read(os.path.join(TEST_DATA, \\\"Uy_file_single_d.su\\\"),\\n data_format=\\\"su\\\")\\n\\n st3 = preprocess.read(os.path.join(TEST_DATA, \\\"AA.S0001.BXY.sac\\\"),\\n data_format=\\\"sac\\\")\\n\\n assert(st1[0].stats.npts == st2[0].stats.npts)\\n assert(st3[0].stats.npts == st2[0].stats.npts)\",\n \"def _read(self, preset_type):\\n logger.debug('read presets for %s', self._device.name)\\n with self._file_open_rlock(preset_type) as f:\\n f.seek(0)\\n return yaml.full_load(f) or {}\",\n \"def peak_file_parser(infile):\\n # Open file and make sure supported\\n pfile = open_zipped(infile)\\n file_parsers = {'bed': bed_file}\\n file_parser = None\\n if pfile.name == '<stdin>':\\n file_parser = bed_file\\n else:\\n for n in file_parsers:\\n if in_name(pfile, n):\\n file_parser = file_parsers[n]\\n if not file_parser:\\n nm = pfile.name\\n pfile.close()\\n raise Exception('Unknown file type: {}'.format(nm))\\n\\n # Return the iterator\\n return file_parser(pfile)\",\n \"def load(filename, fmt=Format.json, encoding=\\\"utf-8\\\"):\\n if fmt == Format.json:\\n with io.open(filename, mode=\\\"r\\\", encoding=encoding) as fp:\\n return json.load(fp)\\n elif fmt == Format.json_gz:\\n with gzip.open(filename, mode=\\\"rb\\\") as fp:\\n return json.loads(fp.read().decode(encoding))\\n else:\\n raise errors.UnsupportedFormat(fmt)\",\n \"def find_by_name(name):\\n for format in FORMATS:\\n if name == format.name:\\n return format\\n\\n raise UnknownFormat('No format found with name \\\"%s\\\"' % name)\",\n \"def read_design(designfile):\\r\\n designtype = None\\r\\n if re.search(r\\\"\\\\.adm$\\\", designfile, flags=re.I) is not None:\\r\\n designtype = XMLDesign\\r\\n elif re.search(r\\\"\\\\.xml$\\\", designfile, flags=re.I) is not None:\\r\\n designtype = XMLDesign\\r\\n elif re.search(r\\\"\\\\.json$\\\", designfile, flags=re.I) is not None:\\r\\n designtype = JSONDesign\\r\\n design = designtype(designfile)\\r\\n return design\",\n \"def read_parse_file(params):\\n\\tparam_names = []\\n\\tparam_options = []\\n\\tif not os.path.isfile(params.parse_file):\\n\\t\\tprint(\\\"parse file does not exist! ({})\\\".format(params.parse_file))\\n\\t\\tsys.exit(NO_PARSE)\\n\\twith open(params.parse_file, 'r') as pf:\\n\\t\\t# first line should be iteration regex\\n\\t\\tsetattr(params, 'iteration_regex', re.compile(pf.readline().strip()))\\n\\t\\tfor line in pf:\\n\\t\\t\\tparam_desc = line.split(';')\\n\\t\\t\\tparam_names.append(param_desc[0])\\n\\t\\t\\tparam_options.append(param_desc[1])\\n\\n\\treturn param_names,param_options\",\n \"def getConverter( format ):\\n\\n data = set(format.split(\\\"-\\\"))\\n\\n if \\\"one\\\" in data:\\n if \\\"forward\\\" in data:\\n if \\\"closed\\\" in data:\\n return __one_forward_closed \\n else:\\n return __one_forward_open\\n else:\\n if \\\"closed\\\" in data:\\n return __one_both_closed\\n else:\\n return __one_both_open\\n else:\\n if \\\"forward\\\" in data:\\n if \\\"closed\\\" in data:\\n return __zero_forward_closed\\n else:\\n return __zero_forward_open\\n else:\\n if \\\"closed\\\" in data:\\n return __zero_both_closed\\n else:\\n return __zero_both_open\",\n \"def parse(self):\\n logger=self.logger\\n tokenizer=Tokenizer()\\n self.scope=produtil.testing.parsetree.Scope()\\n self.override(self.scope)\\n self.parser=Parser(self.run_mode,logger,self.verbose)\\n self.parser.requested_platform_name=self.platform_name\\n morevars=self.make_vars()\\n with open(self.inloc,'rt') as fileobj:\\n self.parse_result=self.parser.parse(\\n TokenizeFile(tokenizer,fileobj,self.inloc,1),self.scope,\\n unique_id=self.unique_id,morevars=morevars)\",\n \"def get_parsers(self):\\n return tuple([getattr(self, '_{}'.format(i)) for i in self.parsers_available])\",\n \"def parse(self):\\n raise NotImplementedError\",\n \"def parse(cls, raw, style_cls):\\n return cls(raw, style_cls)\",\n \"def read(self, format=None, epsg=None):\\n if not format:\\n format = self.default_output\\n if self.ext not in formats.VECTOR:\\n raise UnsupportedFormatException(\\n \\\"Only the following vector formats are supported: {}\\\".format(\\n ','.join(formats.VECTOR)\\n )\\n )\\n if self.data is None:\\n self.data = geopandas.read_file(self.uri)\\n if self.filters:\\n self.filter_data()\\n return self.transform_data(format, epsg)\",\n \"def read(self, *args):\\n return _libsbml.ASTBasePlugin_read(self, *args)\",\n \"def parse(self, stream, media_type=None, parser_context=None):\\n encoding = parser_context.get('encoding', settings.DEFAULT_CHARSET)\\n decoded_stream = codecs.getreader(encoding)(stream)\\n return decoded_stream\",\n \"def get_format_table(self):\\n try:\\n with open(self._config.values['format'], 'r') as f:\\n return f.read()\\n except:\\n return None\",\n \"def select_parser(self, request, resource):\\n\\n # 1. get from resource\\n if resource.mapper:\\n return resource.mapper\\n # 2. get from content type\\n mapper_name = self._get_name_from_content_type(request)\\n if mapper_name:\\n return self._get_mapper(mapper_name)\\n # 3. get from url\\n mapper_name = self._get_name_from_url(request)\\n if mapper_name:\\n return self._get_mapper(mapper_name)\\n # 4. use resource's default\\n if resource.default_mapper:\\n return resource.default_mapper\\n # 5. use manager's default\\n return self._get_default_mapper()\",\n \"def load(cls, path, format=None):\\n path_was_string = isinstance(path, basestring)\\n if path_was_string:\\n (folder, filename) = os.path.split(path)\\n (name, extension) = os.path.splitext(filename)\\n if format is None:\\n plugin = kurt.plugin.Kurt.get_plugin(extension=extension)\\n if not plugin:\\n raise UnknownFormat(extension)\\n fp = open(path, \\\"rb\\\")\\n else:\\n fp = path\\n assert format, \\\"Format is required\\\"\\n plugin = kurt.plugin.Kurt.get_plugin(format)\\n\\n if not plugin:\\n raise ValueError, \\\"Unknown format %r\\\" % format\\n\\n project = plugin.load(fp)\\n if path_was_string:\\n fp.close()\\n project.convert(plugin)\\n if isinstance(path, basestring):\\n project.path = path\\n if not project.name:\\n project.name = name\\n return project\",\n \"def get_parser(subparsers, parent=None):\\n parser = subparsers.add_parser(\\n \\\"flow\\\",\\n description=\\\"Invoke ML on MCU flow\\\",\\n parents=[parent] if parent else [],\\n add_help=(parent is None),\\n )\\n parser.set_defaults(func=handle)\\n add_common_options(parser)\\n add_context_options(parser)\\n add_flow_options(parser)\\n subparsers = parser.add_subparsers(dest=\\\"subcommand2\\\") # this line changed\\n load.get_parser(subparsers)\\n tune.get_parser(subparsers)\\n build.get_parser(subparsers)\\n compile_.get_parser(subparsers)\\n run.get_parser(subparsers)\",\n \"def _parse(self, infile):\\n raise NotImplementedError()\",\n \"def reader_factory(mode: str = \\\"json\\\") -> Callable:\\n module_dict = dict(name=f\\\".{mode}.reader\\\", package=\\\"pubsub\\\")\\n module = import_module(**module_dict)\\n class_constructor = getattr(module, \\\"Reader\\\")\\n\\n return class_constructor\",\n \"def _read_format(instream):\\n format = int(le.uint32.read_from(instream))\\n if format & PCF_BYTE_MASK:\\n base = be\\n else:\\n base = le\\n return format, base\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6131928","0.60937136","0.6045996","0.58665407","0.58590126","0.5809873","0.58025086","0.57651305","0.5758732","0.57091504","0.5667895","0.5651484","0.56421953","0.5586466","0.55685693","0.5534864","0.5503993","0.54882073","0.54529166","0.54084545","0.5385271","0.5347651","0.534626","0.52727383","0.52503484","0.5246822","0.5246822","0.52161264","0.5215623","0.52113044","0.52098995","0.5197878","0.51975036","0.5196198","0.51928115","0.51927584","0.51907724","0.5188857","0.5186639","0.5184655","0.51697165","0.5168382","0.5167854","0.51661634","0.51506066","0.5144126","0.51305914","0.512901","0.5109223","0.50917","0.5071882","0.50689507","0.5061219","0.50568205","0.50463","0.50340664","0.5028728","0.5022192","0.50157106","0.50153005","0.5011174","0.50087076","0.5001331","0.49883032","0.4983566","0.49662474","0.49599084","0.49512336","0.49501258","0.49471578","0.49426845","0.49421197","0.49342725","0.49331093","0.49264613","0.49253166","0.4918985","0.49172413","0.49149606","0.4914598","0.49110273","0.49105257","0.4908033","0.49035066","0.48992026","0.48736733","0.4872987","0.48721567","0.4870505","0.48695463","0.48688695","0.48590314","0.48564085","0.48524043","0.48508728","0.48462927","0.4845014","0.4844443","0.48425636","0.4835524"],"string":"[\n \"0.6131928\",\n \"0.60937136\",\n \"0.6045996\",\n \"0.58665407\",\n \"0.58590126\",\n \"0.5809873\",\n \"0.58025086\",\n \"0.57651305\",\n \"0.5758732\",\n \"0.57091504\",\n \"0.5667895\",\n \"0.5651484\",\n \"0.56421953\",\n \"0.5586466\",\n \"0.55685693\",\n \"0.5534864\",\n \"0.5503993\",\n \"0.54882073\",\n \"0.54529166\",\n \"0.54084545\",\n \"0.5385271\",\n \"0.5347651\",\n \"0.534626\",\n \"0.52727383\",\n \"0.52503484\",\n \"0.5246822\",\n \"0.5246822\",\n \"0.52161264\",\n \"0.5215623\",\n \"0.52113044\",\n \"0.52098995\",\n \"0.5197878\",\n \"0.51975036\",\n \"0.5196198\",\n \"0.51928115\",\n \"0.51927584\",\n \"0.51907724\",\n \"0.5188857\",\n \"0.5186639\",\n \"0.5184655\",\n \"0.51697165\",\n \"0.5168382\",\n \"0.5167854\",\n \"0.51661634\",\n \"0.51506066\",\n \"0.5144126\",\n \"0.51305914\",\n \"0.512901\",\n \"0.5109223\",\n \"0.50917\",\n \"0.5071882\",\n \"0.50689507\",\n \"0.5061219\",\n \"0.50568205\",\n \"0.50463\",\n \"0.50340664\",\n \"0.5028728\",\n \"0.5022192\",\n \"0.50157106\",\n \"0.50153005\",\n \"0.5011174\",\n \"0.50087076\",\n \"0.5001331\",\n \"0.49883032\",\n \"0.4983566\",\n \"0.49662474\",\n \"0.49599084\",\n \"0.49512336\",\n \"0.49501258\",\n \"0.49471578\",\n \"0.49426845\",\n \"0.49421197\",\n \"0.49342725\",\n \"0.49331093\",\n \"0.49264613\",\n \"0.49253166\",\n \"0.4918985\",\n \"0.49172413\",\n \"0.49149606\",\n \"0.4914598\",\n \"0.49110273\",\n \"0.49105257\",\n \"0.4908033\",\n \"0.49035066\",\n \"0.48992026\",\n \"0.48736733\",\n \"0.4872987\",\n \"0.48721567\",\n \"0.4870505\",\n \"0.48695463\",\n \"0.48688695\",\n \"0.48590314\",\n \"0.48564085\",\n \"0.48524043\",\n \"0.48508728\",\n \"0.48462927\",\n \"0.4845014\",\n \"0.4844443\",\n \"0.48425636\",\n \"0.4835524\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.88633776"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77986,"cells":{"query":{"kind":"string","value":"Test a recursive merge of two status dictionaries"},"document":{"kind":"string","value":"def test_recursive_update():\n\n test = Status.update_dict({'generation': TEST_1_ATTRS_1},\n {'generation': TEST_1_ATTRS_2})\n\n assert test['generation']['run_id'] == TEST_1_ATTRS_1['run_id']\n assert test['generation']['job_status'] == TEST_1_ATTRS_2['job_status']"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_recursive_merge(self):\n dict_1 = {\n 'key': {\n 'deep_key_1': 'original_value_1',\n 'deep_key_2': 'original_value_2'\n }\n }\n dict_2 = {\n 'key': {\n 'deep_key_2': 'new_value_2',\n 'deep_key_3': 'new_value_3'\n }\n }\n\n result = deep_dict_merge(dict_1, dict_2)\n\n assert dict_1 == {\n 'key': {\n 'deep_key_1': 'original_value_1',\n 'deep_key_2': 'original_value_2'\n }\n }\n assert dict_2 == {\n 'key': {\n 'deep_key_2': 'new_value_2',\n 'deep_key_3': 'new_value_3'\n }\n }\n assert result == {\n 'key': {\n 'deep_key_1': 'original_value_1',\n 'deep_key_2': 'new_value_2',\n 'deep_key_3': 'new_value_3'\n }\n }","def _helm_merge(a, b):\n if not (isinstance(b, dict) and isinstance(a, dict)):\n # if either one is not a dict,\n # there's no merging to do: use 'b'\n return b\n for key, value in b.items():\n if key in a:\n a[key] = _helm_merge(a[key], value)\n else:\n a[key] = value\n return a","def test_single_level(self):\n dict_1 = {\n 'key_1': 'original_value_1',\n 'key_2': 'original_value_2'\n }\n dict_2 = {\n 'key_2': 'new_value_2',\n 'key_3': 'new_value_3'\n }\n\n result = deep_dict_merge(dict_1, dict_2)\n\n assert dict_1 == {\n 'key_1': 'original_value_1',\n 'key_2': 'original_value_2'\n }\n assert dict_2 == {\n 'key_2': 'new_value_2',\n 'key_3': 'new_value_3'\n }\n assert result == {\n 'key_1': 'original_value_1',\n 'key_2': 'new_value_2',\n 'key_3': 'new_value_3',\n }","def merge(a, b, path=None, update=True):\n # print(\"\\nMerging: a=\" + str(a) + \" b=\" + str(b) + \" path=\" + str(path) )\n if path is None:\n path = []\n for key in b:\n if key in a:\n if isinstance(a[key], dict) and isinstance(b[key], dict):\n merge(a[key], b[key], path + [str(key)])\n elif a[key] == b[key]:\n pass # same leaf value\n elif isinstance(a[key], list) and isinstance(b[key], list):\n for idx, val in enumerate(b[key]):\n a[key][idx] = merge(a[key][idx],\n b[key][idx],\n path + [str(key), str(idx)],\n update=update)\n elif update:\n a[key] = b[key]\n else:\n raise Exception('Conflict at %s' %\n '.'.join(path + [str(key)]))\n else:\n a[key] = b[key]\n return a","def test_merge_list_traditional(self):\n mdict = copy.deepcopy(self.dict1)\n mdict[\"A\"] = [\"B\", \"b\"]\n ret = dictupdate.merge_list(copy.deepcopy(self.dict1), {\"A\": \"b\"})\n self.assertEqual(mdict, ret)","def test_merge_aggregate_traditional(self):\n mdict = copy.deepcopy(self.dict1)\n mdict[\"A\"] = \"b\"\n ret = dictupdate.merge_overwrite(copy.deepcopy(self.dict1), {\"A\": \"b\"})\n self.assertEqual(mdict, ret)","def _merge(x, y):\n for key in x:\n if key in y:\n x[key] = _merge(x[key], y[key])\n y[key] = None\n for key in y:\n if y[key] is not None:\n x[key] = y[key]\n return x","def dictmerge(x, y, path=None, overwrite=False, extend=False):\n if path is None:\n path = []\n for key in y:\n if key in x:\n if isinstance(x[key], (dict, MutableMapping)) and isinstance(\n y[key], (dict, MutableMapping)\n ):\n dictmerge(\n x[key],\n y[key],\n path + [str(key)],\n overwrite=overwrite,\n extend=extend,\n )\n elif x[key] == y[key]:\n pass # same leaf value\n else:\n if not overwrite:\n raise Exception(\"Conflict at %s\" % \".\".join(path + [str(key)]))\n if isinstance(x[key], list) and isinstance(y[key], list) and extend:\n x[key].extend(y[key])\n else:\n x[key] = y[key]\n else:\n x[key] = y[key]\n return x","def merge(self, a, b, path=None):\n if path is None: path = []\n for key in b:\n if key in a:\n if isinstance(a[key], dict) and isinstance(b[key], dict):\n if key == 'attributes':\n self.merge_attribute_defs(b, a)\n else:\n self.merge(a[key], b[key], path + [str(key)])\n elif a[key] == b[key]:\n pass # same leaf value\n else:\n # raise Exception('Conflict at %s' % '.'.join(path + [str(key)]))\n self.append_or_replace(a,b,key, '/'.join(path + [str(key)]));\n else:\n a[key] = b[key]\n return a","def merge_dict(a: dict, b: dict, path=None) -> dict:\n if path is None:\n path = []\n for key in b:\n if key in a:\n if isinstance(a[key], dict) and isinstance(b[key], dict):\n merge_dict(a[key], b[key], path + [str(key)])\n elif a[key] == b[key]:\n pass # same leaf value\n else:\n a[key] = b[key]\n else:\n a[key] = b[key]\n return a","def _merge_obj(result, obj, pointer=''): # changed code\n if not isinstance(result, dict):\n result = {}\n\n if not isinstance(obj, dict):\n return obj\n\n for key, value in obj.items():\n if isinstance(value, dict):\n target = result.get(key)\n if isinstance(target, dict):\n _merge_obj(target, value, pointer=f'{pointer}/{key}') # changed code\n continue\n result[key] = {}\n _merge_obj(result[key], value, pointer=f'{pointer}/{key}') # changed code\n continue\n\n # new code\n if key in result:\n pointer_and_key = f'{pointer}/{key}'\n # Exceptions.\n if (value is None and pointer_and_key == '/definitions/Milestone/properties/documents/deprecated' and\n repo_name in ('ocds_milestone_documents_extension', 'public-private-partnerships')):\n warnings.warn(f're-adds {pointer}')\n elif (value == [] and pointer_and_key == '/required' and\n repo_name == 'ocds_pagination_extension'):\n warnings.warn(f'empties {pointer_and_key}')\n else:\n if is_profile:\n message = ' - check for repeats across extension_versions.json, dependencies, testDependencies'\n else:\n message = ''\n raise Exception(f'unexpectedly overwrites {pointer_and_key}{message}')\n\n if value is None:\n result.pop(key, None)\n continue\n result[key] = value\n return result","def variant_add(v1: dict, v2: dict) -> Dict[str, Any]:\n left = set(v1.keys()).difference(v2.keys())\n right = set(v2.keys()).difference(v1.keys())\n joint = set(v1.keys()) & set(v2.keys())\n\n # deal with __migrator: ordering\n if \"__migrator\" in v2:\n ordering = v2[\"__migrator\"].get(\"ordering\", {})\n operation = v2[\"__migrator\"].get(\"operation\")\n # handle special operations\n if operation:\n return VARIANT_OP[operation](v1, v2)\n else:\n ordering = {}\n\n # special keys\n if \"__migrator\" in right:\n right.remove(\"__migrator\")\n\n # special keys in joint\n special_variants = {}\n if \"pin_run_as_build\" in joint:\n # For run_as_build we enforce the migrator's pin\n # TODO: should this just be a normal ordering merge, favoring more exact pins?\n joint.remove(\"pin_run_as_build\")\n special_variants[\"pin_run_as_build\"] = {\n **v1[\"pin_run_as_build\"],\n **v2[\"pin_run_as_build\"],\n }\n\n if \"zip_keys\" in joint:\n # zip_keys is a bit weird to join on as we don't have a particularly good way of identifying\n # a block. Longer term having these be named blocks would make life WAY simpler\n # That does require changes to conda-build itself though\n #\n # A zip_keys block is deemed mergeable if zkₛ,ᵢ ⊂ zkₘ,ᵢ\n zk_out = []\n zk_l = {frozenset(e) for e in v1[\"zip_keys\"]}\n zk_r = {frozenset(e) for e in v2[\"zip_keys\"]}\n\n for zk_r_i in sorted(zk_r, key=lambda x: -len(x)):\n for zk_l_i in sorted(zk_l, key=lambda x: -len(x)):\n # Merge the longest common zk first\n if zk_l_i.issubset(zk_r_i):\n zk_l.remove(zk_l_i)\n zk_r.remove(zk_r_i)\n zk_out.append(zk_r_i)\n break\n else:\n # Nothing to do\n pass\n\n zk_out.extend(zk_l)\n zk_out.extend(zk_r)\n zk_out = sorted(\n [sorted(zk) for zk in zk_out], key=lambda x: (len(x), str(x))\n )\n\n joint.remove(\"zip_keys\")\n special_variants[\"zip_keys\"] = zk_out\n\n joint_variant = {}\n for k in joint:\n v_left, v_right = ensure_list(v1[k]), ensure_list(v2[k])\n joint_variant[k] = variant_key_add(\n k, v_left, v_right, ordering=ordering.get(k, None)\n )\n\n out = {\n **toolz.keyfilter(lambda k: k in left, v1),\n **toolz.keyfilter(lambda k: k in right, v2),\n **joint_variant,\n **special_variants,\n }\n\n return out","def dict_merge(a, b, path=None):\n\n if path is None: path = []\n for key in b:\n if key in a:\n if isinstance(a[key], dict) and isinstance(b[key], dict):\n dict_merge(a[key], b[key], path + [str(key)])\n else:\n a[key] = b[key]\n return a","def test_merge_dict(self):\n cd = ConfigDict.from_dict({\n 'a': 1,\n 'b': {\n 'c': 2,\n 'd': 3,\n 'e': {'h': 4},\n 'f': [5,6],\n 'g': [7,8]\n }\n })\n\n cd.merge_dict({\n 'a': 11,\n 'z': 99,\n 'b': {\n 'c': 22,\n 'e': {'y': 999},\n 'g': [77,88],\n }\n })\n\n self.assertEquals(cd.a,11)\n self.assertEquals(cd.b.c, 22)\n self.assertEquals(cd.b.d, 3)\n self.assertEquals(cd.b.e.h, 4)\n self.assertEquals(cd.b.e.y, 999)\n self.assertEquals(cd.b.f, [5,6])\n self.assertEquals(cd.b.g, [77, 88])\n self.assertEquals(cd.z,99)","def conditional_merge(a_dict, b_dict):\n # Go through each key in b_dict\n for k in b_dict:\n if isinstance(b_dict[k], dict):\n if k not in a_dict:\n # It's a new section. Initialize it...\n a_dict[k] = {}\n # ... and transfer over the section comments, if available\n try:\n a_dict.comments[k] = b_dict.comments[k]\n except AttributeError:\n pass\n conditional_merge(a_dict[k], b_dict[k])\n elif k not in a_dict:\n # It's a scalar. Transfer over the value...\n a_dict[k] = b_dict[k]\n # ... then its comments, if available:\n try:\n a_dict.comments[k] = b_dict.comments[k]\n except AttributeError:\n pass","def merge(a, b):\n if isinstance(a, CONFIG_VALID) \\\n and isinstance(b, CONFIG_VALID):\n # dict update\n if isinstance(a, dict) and isinstance(b, dict):\n a.update(b)\n return a\n # list update\n _a = list(a)\n for x in list(b):\n if x not in _a:\n _a.append(x)\n return _a\n if a and b:\n raise Exception(\"Cannot merge\")\n raise NotImplementedError","def _merge_two(self, obj1, obj2):\r\n for uniq_ident in obj2.keys():\r\n if (uniq_ident not in obj1) \\\r\n or (obj1[uniq_ident]['modified'] \\\r\n < obj2[uniq_ident]['modified']):\r\n obj1[uniq_ident] = obj2[uniq_ident]\r\n\r\n return obj1 # self._dict_to_list(obj1)\r","def test_merge_dicts(self):\n dict_A = {\"key1\": \"value1\", \"key2\": \"value2\"}\n dict_B = {\"key1\": \"value1\"}\n\n expected_log = [\n \"WARNING:blinkpy.helpers.util:Duplicates found during merge: ['key1']. \"\n \"Renaming is recommended.\"\n ]\n\n with self.assertLogs(level=\"DEBUG\") as merge_log:\n merge_dicts(dict_A, dict_B)\n self.assertListEqual(merge_log.output, expected_log)","def test_merge_list_append(self):\n mdict = copy.deepcopy(self.dict1)\n mdict[\"A\"] = [\"B\", \"b\", \"c\"]\n\n # Prepare a modified copy of dict1 that has a list as a val for the key of 'A'\n mdict1 = copy.deepcopy(self.dict1)\n mdict1[\"A\"] = [\"B\"]\n ret = dictupdate.merge_list(mdict1, {\"A\": [\"b\", \"c\"]})\n self.assertEqual(\n {\"A\": [[\"B\"], [\"b\", \"c\"]], \"C\": {\"D\": \"E\", \"F\": {\"I\": \"J\", \"G\": \"H\"}}}, ret\n )","def test_merge_overwrite_traditional(self):\n mdict = copy.deepcopy(self.dict1)\n mdict[\"A\"] = \"b\"\n ret = dictupdate.merge_overwrite(copy.deepcopy(self.dict1), {\"A\": \"b\"})\n self.assertEqual(mdict, ret)","def _merge_dicts(dict1, dict2, path=None):\n if path is None:\n path = []\n for key, value2 in six.iteritems(dict2):\n if key not in dict1:\n dict1[key] = value2\n continue\n value1 = dict1[key]\n if value1 == value2:\n continue\n elif isinstance(value1, dict) and isinstance(value2, dict):\n _merge_dicts(dict1[key], dict2[key], path=path+[key])\n elif isinstance(value1, (set, frozenset)) and isinstance(value2, (set, frozenset, list)):\n dict1[key] = value1 | frozenset(value2)\n else:\n raise TypeError(\"Cannot merge {} with {} at {}\".format(type(value1), type(value2), '/'.join(path)))\n return dict1","def _merge_a_into_b(a, b):\n for k, v in a.items():\n # a must specify keys that are in b\n # if k not in b:\n # raise KeyError('{} is not a valid config key'.format(k))\n\n # recursively merge dicts\n if isinstance(v, dict):\n if not b.get(k, False):\n b[k] = v\n else:\n try:\n _merge_a_into_b(a[k], b[k])\n except:\n print(\"Error under config key: {}\".format(k))\n raise\n else:\n b[k] = v","def deep_merge(a, b, path=None, append=False):\n for key in b:\n if key in a:\n if isinstance(a[key], dict) and isinstance(b[key], dict):\n deep_merge(a[key], b[key], (path or []) + [str(key)], append)\n continue\n is_lists = isinstance(a[key], list) and isinstance(b[key], list)\n if is_lists and append:\n a[key] += b[key]\n else:\n a[key] = b[key]\n else:\n a[key] = b[key]\n return a","def recursiveMergeDict(aDict, bDict):\n aDictItems = None\n if crifanLib.crifanSystem.isPython2(): # is python 2\n aDictItems = aDict.iteritems()\n else: # is python 3\n aDictItems = aDict.items()\n\n for aKey, aValue in aDictItems:\n # print(\"------ [%s]=%s\" % (aKey, aValue))\n if aKey not in bDict:\n bDict[aKey] = aValue\n else:\n bValue = bDict[aKey]\n # print(\"aValue=%s\" % aValue)\n # print(\"bValue=%s\" % bValue)\n if isinstance(aValue, dict):\n recursiveMergeDict(aValue, bValue)\n elif isinstance(aValue, list):\n aValueListLen = len(aValue)\n bValueListLen = len(bValue)\n bValueListMaxIdx = bValueListLen - 1\n for aListIdx in range(aValueListLen):\n # print(\"---[%d]\" % aListIdx)\n aListItem = aValue[aListIdx]\n # print(\"aListItem=%s\" % aListItem)\n if aListIdx <= bValueListMaxIdx:\n bListItem = bValue[aListIdx]\n # print(\"bListItem=%s\" % bListItem)\n recursiveMergeDict(aListItem, bListItem)\n else:\n # print(\"bDict=%s\" % bDict)\n # print(\"aKey=%s\" % aKey)\n # print(\"aListItem=%s\" % aListItem)\n bDict[aKey].append(aListItem)\n\n return bDict","def deep_merge(a, b):\n assert quacks_like_dict(a), quacks_like_dict(b)\n dst = a.copy()\n\n stack = [(dst, b)]\n while stack:\n current_dst, current_src = stack.pop()\n for key in current_src:\n if key not in current_dst:\n current_dst[key] = current_src[key]\n else:\n if (quacks_like_dict(current_src[key])\n and quacks_like_dict(current_dst[key])):\n stack.append((current_dst[key], current_src[key]))\n else:\n current_dst[key] = current_src[key]\n return dst","def json_flat_diff(a, b):\r\n\r\n res_a = {}\r\n res_b = {}\r\n for key in set(a.keys()).union(set(b.keys())):\r\n a_value = a.get(key)\r\n b_value = b.get(key)\r\n if a_value != b_value:\r\n res_a[key] = a_value\r\n res_b[key] = b_value\r\n # Mind the parentheses below lest you return ({}, None) if res_a is None.\r\n return (res_a, res_b) if res_a != {} else None","def _merge_dicts(d1, d2):\n for key, value in d2.items():\n if key in d1:\n if isinstance(d1[key], dict) and isinstance(value, dict):\n _merge_dicts(d1[key], value)\n continue\n d1[key] = value","def deep_merge(source, dest):\n for key, value in source.iteritems():\n if key in dest:\n if isinstance(value, dict) and isinstance(dest[key], dict):\n deep_merge(value, dest[key])\n continue\n elif isinstance(value, list) and isinstance(dest[key], list):\n for item in value:\n if item not in dest[key]:\n dest[key].append(item)\n continue\n dest[key] = value","def __merge_configs(cls, a, b, path=None):\n\n # This function is modeled after:\n # https://stackoverflow.com/questions/7204805/dictionaries-of-dictionaries-merge\n\n if path is None: path = []\n\n for key in b:\n if key in a:\n if isinstance(a[key], dict) and isinstance(b[key], dict):\n cls.__merge_configs(a[key], b[key], path + [str(key)])\n elif isinstance(a[key], list) and isinstance(b[key], list):\n # Easy enough to merge lists of non-dict items...\n try:\n al = set(a[key])\n bl = set(b[key])\n bl.update(al)\n a[key] = list(bl)\n except (TypeError, AttributeError) as e:\n log.warning('problem merging lists when merging configurations'\n '(are there dict args in one at path %s?):'\n '\\n%s\\n%s\\nKeeping the first one due to error %s'\n % (path, a[key], b[key], e))\n else:\n # Same key, but different value type. This is difficult to\n # handle so we just keep the value of the first one.\n pass\n else:\n a[key] = b[key]\n return a","def _merge_a_into_b(a, b):\n if type(a) is not edict:\n return\n\n for k, v in a.items():\n # recursively merge dicts\n if type(v) is edict:\n try:\n b[k] = edict()\n _merge_a_into_b(a[k], b[k])\n except:\n print('Error under config key: {}'.format(k))\n raise\n else:\n b[k] = v","def merge_dict_recursive(target, src):\r\n for k in src.keys():\r\n if ((k in target and isinstance(target[k], dict) and\r\n isinstance(src[k], collections.Mapping))):\r\n merge_dict_recursive(target[k], src[k])\r\n else:\r\n target[k] = src[k]","def merge(a: dict, b: dict) -> dict:\n return __merge(a, b)","def merge_config(a, b):\n for key, b_value in b.items():\n if not isinstance(b_value, dict):\n a[key] = b_value\n else:\n a_value = a.get(key)\n if a_value is not None and isinstance(a_value, dict):\n merge_config(a_value, b_value)\n else:\n a[key] = b_value\n return a","def deep_merge(d1, d2):\n for k, v in d1.copy().items():\n if k in d2:\n if all(isinstance(e, MutableMapping) for e in (v, d2[k])):\n d2[k] = ConfigManager.deep_merge(v, d2[k])\n\n if k == \"*\":\n for _k, _v in d2.items():\n if all(isinstance(e, MutableMapping) for e in (v, d2[_k])):\n d2[_k] = ConfigManager.deep_merge(v, d2[_k])\n del d1[k]\n d3 = d1.copy()\n d3.update(d2)\n return d3","def merge_dictionaries(d1, d2):\n\n if d2 is None: \n return\n\n for k, v in d2.items():\n if k not in d1:\n d1[k] = dict()\n if isinstance(v, dict):\n merge_dictionaries(d1[k], v)\n else:\n d1[k] = v","def test_merge_two_two_same():\n run_merge([1, 3], [1, 3], [1, 1, 3, 3])","def merge_dict(target, addition):\n for key in addition:\n if key in target and isinstance(target[key], dict) \\\n and isinstance(addition[key], dict):\n merge_dict(target[key], addition[key])\n else:\n target[key] = addition[key]","def unify_walk(d1, d2, U):\r\n for (k1, v1) in d1.items():\r\n if d2.has_key(k1):\r\n U = unify_walk(v1, d2[k1], U)\r\n if U is False:\r\n return False\r\n return U","def merge_dicts(a, b, path=None):\n if path is None:\n path = []\n for key in b:\n if key in a:\n if isinstance(a[key], dict) and isinstance(b[key], dict):\n merge_dicts(a[key], b[key], path + [str(key)])\n elif type(a) is type(b):\n a[key] = b[key]\n else:\n raise Exception('Conflicting types at %s'\n % '.'.join(path + [str(key)]))\n else:\n a[key] = b[key]\n return a","def merge_dicts(base, changes):\n for k, v in changes.items():\n if isinstance(v, dict):\n merge_dicts(base.setdefault(k, {}), v)\n else:\n base.setdefault(k, v)","def merge_trees(data, new_data):\n for key, val in new_data.items():\n if isinstance(val, dict):\n if key not in data:\n data[key] = new_data[key]\n else:\n merge_trees(data[key], new_data[key])\n\n else:\n if key not in data:\n data[key] = val\n else:\n data[key] = data[key] + val","def _iterativediff(t1, t2, subdir):\n if t1 is None:\n t1 = {}\n if t2 is None:\n t2 = {}\n\n for e1 in t1:\n realname = subdir + pycompat.fsencode(e1.name)\n\n if e1.type == pygit2.GIT_OBJ_TREE:\n try:\n e2 = t2[e1.name]\n if e2.type != pygit2.GIT_OBJ_TREE:\n e2 = None\n except KeyError:\n e2 = None\n\n stack.append((realname + b'/', e1, e2))\n else:\n n1, fl1 = self.find(realname)\n\n try:\n e2 = t2[e1.name]\n n2, fl2 = other.find(realname)\n except KeyError:\n e2 = None\n n2, fl2 = (None, b'')\n\n if e2 is not None and e2.type == pygit2.GIT_OBJ_TREE:\n stack.append((realname + b'/', None, e2))\n\n if not match(realname):\n continue\n\n if n1 != n2 or fl1 != fl2:\n result[realname] = ((n1, fl1), (n2, fl2))\n elif clean:\n result[realname] = None\n\n for e2 in t2:\n if e2.name in t1:\n continue\n\n realname = subdir + pycompat.fsencode(e2.name)\n\n if e2.type == pygit2.GIT_OBJ_TREE:\n stack.append((realname + b'/', None, e2))\n elif match(realname):\n n2, fl2 = other.find(realname)\n result[realname] = ((None, b''), (n2, fl2))","def _merge_data(self, d1, d2):\n if not d1 and not d2:\n if isinstance(d1, dict) or isinstance(d2, dict):\n return {}\n return []\n if not d2:\n return d1\n if not d1:\n return d2\n if isinstance(d1, list) and isinstance(d2, list):\n return list(set(d1 + d2))\n if isinstance(d1, list) and not isinstance(d2, dict):\n if d2 in d1:\n return d1\n return d1 + make_list(d2)\n if isinstance(d2, list) and not isinstance(d1, dict):\n if d1 in d2:\n return d2\n return d2 + make_list(d1)\n if not isinstance(d1, dict) and not isinstance(d2, dict):\n if d1 == d2:\n return make_list(d1)\n else:\n return [d1, d2]\n\n res = d1\n for key2, val2 in d2.items():\n if key2 in res:\n res[key2] = self._merge_data(val2, res[key2])\n else:\n res[key2] = val2\n return res","def recursively_update_with(a, b):\n\n for key in b:\n if key in a:\n if isinstance(a[key], dict) and isinstance(b[key], dict):\n recursively_update_with(a[key], b[key])\n else:\n a[key] = b[key]\n else:\n a[key] = b[key]","def combine_dict(self, dict2):\n # iterate through smaller data set\n # base_set will be the larger set and is used for updating\n if len(self.content[\"values\"]) > len(dict2[\"values\"]):\n large_set = self.content[\"values\"]\n small_set = dict2[\"values\"]\n base_set = self.content\n else:\n small_set = self.content[\"values\"]\n large_set = dict2[\"values\"]\n base_set = dict2\n\n subset = {}\n for key in small_set.keys():\n # determine wether to compare keys\n if key in large_set:\n updated_l = large_set[key][\"updated_at\"]\n updated_s = small_set[key][\"updated_at\"]\n if updated_l == 'NULL':\n if updated_s != 'NULL':\n # update to not NULL set\n # if both updated_at are NULL, things\n # are ambiguos. We could defer to created_at\n # but for simplicity we will default to\n # the values in the larger set\n subset[key] = small_set[key]\n else:\n if updated_s == 'NULL':\n # update to not NULL set\n subset[key] = large_set[key]\n else:\n if updated_l > updated_s:\n subset[key] = large_set[key]\n else:\n subset[key] =small_set[key]\n else:\n subset[key] = small_set[key]\n base_set[\"values\"].update(subset)\n new_obj = BackupData()\n new_obj.load_from_dict(base_set)\n return new_obj","def compare_dicts(dict1, dict2, dict1_name=\"d1\", dict2_name=\"d2\", path=\"\"):\n # Setup paths to track key exploration. The path parameter is used to allow\n # recursive comparisions and track what's being compared.\n result = True\n for key in dict1.keys():\n dict1_path = \"{}{}[{}]\".format(dict1_name, path, key)\n dict2_path = \"{}{}[{}]\".format(dict2_name, path, key)\n if key not in dict2.keys():\n log.debug(\"%s not a valid key in %s.\", dict1_path, dict2_path)\n result = False\n elif isinstance(dict1[key], dict) and isinstance(dict2[key], dict):\n log.debug(\n \"%s and %s contain dictionary. Evaluating.\", dict1_path,\n dict2_path\n )\n result = compare_dicts(\n dict1[key], dict2[key], dict1_name, dict2_name,\n path=\"[{}]\".format(key)\n )\n elif isinstance(dict1[key], list) and isinstance(dict2[key], list):\n log.debug(\n \"%s and %s key '%s' contains list. Validating dict1 items \"\n \"exist in dict2.\", dict1_path, dict2_path, key\n )\n if not all([bool(item in dict2[key]) for item in dict1[key]]):\n log.debug(\n \"Mismatch: %s value is '%s' while %s value is '%s'.\",\n dict1_path, dict1[key], dict2_path, dict2[key]\n )\n result = False\n # Hack for NetBox v2.6.7 requiring integers for some values\n elif key in [\"status\", \"type\"]:\n if dict1[key] != dict2[key][\"value\"]:\n log.debug(\n \"Mismatch: %s value is '%s' while %s value is '%s'.\",\n dict1_path, dict1[key], dict2_path, dict2[key][\"value\"]\n )\n result = False\n elif dict1[key] != dict2[key]:\n log.debug(\n \"Mismatch: %s value is '%s' while %s value is '%s'.\",\n dict1_path, dict1[key], dict2_path, dict2[key]\n )\n # Allow the modification of device sites by ignoring the value\n if \"site\" in path and key == \"name\":\n log.debug(\"Site mismatch is allowed. Moving on.\")\n else:\n result = False\n if result:\n log.debug(\"%s and %s values match.\", dict1_path, dict2_path)\n else:\n log.debug(\"%s and %s values do not match.\", dict1_path, dict2_path)\n return result\n log.debug(\"Final dictionary compare result: %s\", result)\n return result","def merge_dicts(a, b):\n if not isinstance(b, dict):\n return b\n\n result = a\n for k, v in b.items():\n if k in result and isinstance(result[k], dict):\n result[k] = merge_dicts(result[k], v)\n else:\n result[k] = v\n return result","def merge(): #Status: WIP\r\n pass","def dict_merge( dict_1, dict_2 ):\n\n if dict_1 is None:\n if dict_2 is None:\n # or return None\n return_val = None\n else:\n # or\n return_val = dict_2\n\n else:\n if dict_2 is None:\n # or return\n return_val = dict_2\n else:\n return_val = { **dict_1 , **dict_2}\n\n\n print( f\"dict_merg return_value = >return_val<\")\n return return_val","def mergeDict(old: dict, new: dict, layer=1) -> dict:\n \n from collections import Mapping\n changed = False\n for key, val in new.items():\n # print(\"{} ({})\".format(key, type(old.get(key))))\n if not key in old:\n print(\"{}Adding new value {}\".format(' ' * layer, key))\n changed = True\n old[key] = val\n elif issubclass(type(old[key]), Mapping) and issubclass(type(val), Mapping):\n print(\"{}Merging dict {}\".format(' ' * layer, key))\n changed = changed or mergeDict(old[key], val, layer + 1)\n\n return changed","def merge_two_dicts(x, y):\n\tz = x.copy()\n\tz.update(y)\n\treturn z","def merge(one, two, overwrite=False, typecheck=True):\n if one is two:\n return\n\n if typecheck and not same_type(one, two):\n raise ValueError('Type mismatch')\n\n for (key, value) in two.items():\n if key not in one:\n one[key] = value\n\n if typecheck and not same_type(one[key], value):\n raise ValueError('Type mismatch')\n if isinstance(value, dict):\n merge(one[key], two[key], overwrite, typecheck)\n elif not overwrite:\n continue\n else:\n one[key] = two[key]","def json_merge_patch(servicedef, source, with_):\n\n if isinstance(source, list) or isinstance(with_, list):\n return with_\n\n if not isinstance(source, dict):\n raise TypeError('source must be a dict, got %s' % (type(source)))\n\n if not isinstance(with_, dict):\n raise TypeError('with_ must be a dict, got %s' % (type(with_)))\n\n isdebug() and logger.debug('JSON merge:\\nsource = %s\\nwith = %s' %\n (json.dumps(source, indent=2),\n json.dumps(with_, indent=2)))\n\n if ( '$ref' in source and\n '$ref' in with_ and\n source == with_):\n # If merging 2 $refs and they have the same target, nothing\n # to do ... but avoid infinite recusion!\n return source\n\n # Need to make a copy of source, as this is going to be modified\n # Only make a shallow copy here - only the shallow properties\n # are modified in this call. Recursively deeper calls will make\n # deeper copies as needed.\n source = _eval_shallow(servicedef, source, need_copy=True)\n\n # with_ is only used in a readonly fashion, so no need to copy\n with_ = _eval_shallow(servicedef, with_, need_copy=False)\n\n for key, value in with_.items():\n if value is None:\n # Remove the key if present in the source\n if key in source:\n del source[key]\n elif ( isinstance(value, dict) and\n key in source and\n isinstance(source[key], dict)):\n # If this key is a dict in both source and with_, recurse\n source[key] = json_merge_patch(servicedef, source[key], value)\n else:\n # Otherwise update the source for this key. This may add the\n # key to source if it was not already present\n source[key] = value\n\n isdebug() and logger.debug('JSON merge result:\\n%s' %\n (json.dumps(source, indent=2)))\n\n return source","def test_merge_json(self):\n cd = ConfigDict.from_dict({\n 'a': 1,\n 'b': {\n 'c': 2,\n 'd': 3,\n 'e': {'h': 4},\n 'f': [5,6],\n 'g': [7,8]\n }\n })\n\n cd.merge_json(\"\"\"{\n \"a\": 11,\n \"z\": 99, /* here is a comment */\n \"b\": {\n \"c\": 22,\n \"e\": {\"y\": 999},\n // another sort of comment\n \"g\": [77,88]\n }\n /*\n this might be a rather long\n type of comment that spans several lines...\n */\n }\"\"\")\n\n self.assertEquals(cd.a,11)\n self.assertEquals(cd.b.c, 22)\n self.assertEquals(cd.b.d, 3)\n self.assertEquals(cd.b.e.h, 4)\n self.assertEquals(cd.b.e.y, 999)\n self.assertEquals(cd.b.f, [5,6])\n self.assertEquals(cd.b.g, [77, 88])\n self.assertEquals(cd.z,99)","def merge_two_dicts(x, y):\r\n z = x.copy()\r\n z.update(y)\r\n return z","def merge(first: Dict[Any, Any], second: Dict[Any, Any]) -> Dict[Any, Any]:\n if not isinstance(second, dict):\n return second\n result = deepcopy(first)\n for key, value in second.items():\n if key in result and isinstance(result[key], dict):\n result[key] = merge(result[key], value)\n else:\n result[key] = deepcopy(value)\n return result","def test_merge_overwrite_missing_source_key(self):\n mdict = copy.deepcopy(self.dict1)\n mdict[\"D\"] = \"new\"\n ret = dictupdate.merge_overwrite(copy.deepcopy(self.dict1), {\"D\": \"new\"})\n self.assertEqual(mdict, ret)","def merge_dicts(dict1, dict2, custom_merge_func=None):\n # type: (Any, Any, Optional[Callable[[str, Any, Any, Any], Any]]) -> Any\n if not isinstance(dict1, dict) or not isinstance(dict2, dict):\n return dict2\n for k in dict2:\n if k in dict1:\n res = None\n if custom_merge_func:\n res = custom_merge_func(k, dict1[k], dict2[k], _not_set)\n dict1[k] = merge_dicts(dict1[k], dict2[k], custom_merge_func) if res is _not_set else res\n else:\n dict1[k] = dict2[k]\n return dict1","def mergedicts(dict1, dict2):\n for k in set(dict1.keys()).union(dict2.keys()):\n if k in dict1 and k in dict2:\n if isinstance(dict1[k], dict) and isinstance(dict2[k], dict):\n yield (k, dict(mergedicts(dict1[k], dict2[k])))\n else:\n # If one of the values is not a dict, you can't continue merging it.\n # Value from second dict overrides one in first and we move on.\n yield (k, dict2[k])\n # Alternatively, replace this with exception raiser to alert you of value conflicts\n elif k in dict1:\n yield (k, dict1[k])\n else:\n yield (k, dict2[k])","def dict_match(left, right, res=None):\n if res is None:\n res = [True, ]\n if res[0] == False:\n return False\n for k in right.keys():\n if (k in left):\n if (isinstance(left[k], dict) and isinstance(right[k], dict)):\n dict_match(left[k], right[k], res=res)\n else:\n res[0] = res[0] and left[k] == right[k]\n if res[0] == False:\n break\n return res[0]","def merge_dicts(a, b):\n new_dict = defaultdict(list)\n add_to_dict(a, new_dict)\n add_to_dict(b, new_dict)\n return new_dict","def merge(target_config, other_config):\n for key, value in other_config.items():\n if key not in target_config or not isinstance(value, dict):\n target_config[key] = value\n else:\n merge(target_config[key], other_config[key])","def recursive_update(d1, d2):\n\n for k in d1.keys():\n if k not in d2:\n continue\n\n if isinstance(d1[k], dict) and isinstance(d2[k], dict):\n d1[k] = recursive_update(d1[k], d2[k])\n else:\n d1[k] = d2[k]\n\n for k in d2.keys():\n if k not in d1:\n d1[k] = d2[k]\n\n return d1","def merge_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def merge_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def merge_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def _merge(self, other: dict):\n self._storage = dict_merge(self._storage, other)","def merge_two_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def merge_two_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def merge_two_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def merge_two_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def merge_two_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def merge_two_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def merge_two_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def merge_two_dicts(x, y):\n z = x.copy()\n z.update(y)\n return z","def merge_dicts(src, patch):\n for key in patch:\n if key in src:\n if isinstance(src[key], dict) and isinstance(patch[key], dict):\n merge_dicts(src[key], patch[key])\n else:\n src[key] = merge_values(src[key], patch[key])\n else:\n src[key] = patch[key]\n\n return src","def _merge(old, new):\n from collections.abc import Mapping\n if new is None:\n return\n for key in new:\n if isinstance(new[key], Mapping):\n if isinstance(old.get(key), Mapping):\n yield (old[key], new[key])\n else:\n old[key] = dict(new[key])\n else:\n old[key] = new[key]","def recursive_update(d1, d2):\n\n for key, value in d2.items():\n if key in d1 and isinstance(d1[key], dict) and isinstance(value, dict):\n recursive_update(d1[key], value)\n else:\n d1[key] = value","def test_merge_two_two():\n run_merge([1, 3], [2, 4], [1, 2, 3, 4])","def test_merge_one():\n run_merge([2], [1], [1, 2])","def merge_two_dicts(self, x, y):\n z = x.copy()\n z.update(y)\n return z","def test_merge_merges_two_pairs():\n L = [1, 3, 5]\n R = [2, 4, 6]\n assert merge(L, R) == [1, 2, 3, 4, 5, 6]","def test_merge_empty():\n run_merge([], [], [])","def canBeMergedWith(self, other):","def test_dict_merge_immutable():\n x1 = {'one': 1, 'two': 2}\n x1_cop = x1.copy()\n ir.dict_merge(x1, {'three': 3, 'two': None})\n assert x1 == x1_cop\n ir.dict_merge({'ten': 10, 'one': '1'}, x1)\n assert x1 == x1_cop","def merge_dicts(self, source, target):\n\n source_keys = set(source.keys())\n target_keys = set(target.keys())\n\n # added\n new = source_keys - target_keys\n for key in new:\n target[key] = source[key]\n\n # removed\n removed = target_keys - source_keys\n\n # changed\n common = target_keys.intersection(source_keys)\n\n diff = set()\n better = 0\n for key in common:\n if not self.ignore_fuzzy:\n fuzzy_source = 'fuzzy' in source[key].flags or \\\n source[key].obsolete\n fuzzy_target = 'fuzzy' in target[key].flags or \\\n target[key].obsolete\n else:\n fuzzy_source = fuzzy_target = False\n\n # sources that are not fuzzy/obsolete and which are of better\n # quality than the existing target keys (empty or fuzzy/obsolete)\n # are taken as non-conflicting but summed as changed\n better_singular = source[key].msgstr and \\\n (not target[key].msgstr or fuzzy_target)\n better_plural = source[key].msgstr_plural and \\\n (not target[key].msgstr_plural or fuzzy_target)\n if not fuzzy_source and (better_singular or better_plural):\n target[key] = source[key]\n better += 1\n elif target[key].msgstr != source[key].msgstr or \\\n target[key].msgstr_plural != source[key].msgstr_plural:\n diff.add(key)\n\n if len(diff) > 0:\n if self.merge_conflict:\n target = None\n elif self.merge_overwrite:\n for key in diff:\n te = target[key]\n se = source[key]\n te.msgstr = se.msgstr\n te.msgid_plural = se.msgid_plural\n te.msgstr_plural = se.msgstr_plural\n te.flags = se.flags\n elif self.merge_skip:\n pass # nothing has to be done\n else:\n self.log(\"We have a bug.\")\n\n return (target, len(new), len(diff) + better, len(removed))","def test_merge_word_dicts():\n ref_dict = {\"accordeon\": [\"acordeon\"],\n \"accoster\": [\"accostage\", \"acostage\"]}\n\n new_dict = {\"accordeon\": [\"accordeons\"],\n \"accostage\": [\"accostages\"],\n \"accumulateur\": [\"acumulateur\", \"acc\"]}\n\n word_dict.merge_word_dicts(ref_dict, new_dict)\n\n expected_dict = {\"accordeon\": [\"acordeon\", \"accordeons\"],\n \"accoster\": [\"accostage\", \"acostage\", \"accostages\"],\n \"accumulateur\": [\"acumulateur\", \"acc\"]}\n\n for correct_word in ref_dict:\n assert sorted(list(set(ref_dict[correct_word]))) == sorted(list(set(expected_dict[correct_word])))","def test_match_ordered(self):\n first = dict(\n a=1,\n b=2,\n )\n\n second = OrderedDict(\n b=2,\n a=1,\n )\n\n check_keys_match_recursive(first, second, [])","def merge_dicts(source, destination):\n for key, value in source.items():\n if isinstance(value, dict):\n node = destination.setdefault(key, {})\n merge_dicts(value, node)\n else:\n destination[key] = value\n\n return destination","def build_ast(first: Dict[str, Any], second: Dict[str, Any]) -> Dict[str, Any]:\n first_keys = first.keys()\n second_keys = second.keys()\n\n add_keys = second_keys - first_keys\n remove_keys = first_keys - second_keys\n common_keys = first_keys & second_keys\n\n added = {\n key: {TYPE: ADDED, VALUE: second[key]}\n for key in add_keys\n }\n removed = {\n key: {TYPE: REMOVED, VALUE: first[key]}\n for key in remove_keys\n }\n\n common = {}\n for key in common_keys:\n first_item = first[key]\n second_item = second[key]\n if first_item == second_item:\n common[key] = {\n TYPE: UNCHANGED,\n VALUE: second_item,\n }\n elif isinstance(first_item, dict) and isinstance(second_item, dict):\n common[key] = {\n TYPE: PARENT,\n CHILDREN: build_ast(first_item, second_item),\n }\n else:\n common[key] = {\n TYPE: CHANGED,\n VALUE: second_item,\n OLD_VALUE: first_item,\n }\n\n return {**common, **added, **removed}","def _do_merge(ext, exts_other):\n for ext_other in exts_other:\n if not ext.is_duplicate(ext_other):\n return False\n return True","def test_merge_person_2(self):\r\n result = merge('op_same', 'Person')\r\n self.assertEqual(result,\r\n {'Related Organizations': '<ul></ul>',\r\n 'Kind': u'Dude',\r\n 'Alternate Names': u'Nickname',\r\n 'Related Crises': '<ul></ul>',\r\n 'Maps': '<ul class=\"unstyled\"><li><iframe width=\"425\" height=\"350\" frameborder=\"0\" scrolling=\"no\" marginheight=\"0\" marginwidth=\"0\" src=\"https://maps.google.com/maps?q=New York, New York, USA&output=embed\"></iframe></li><br /></ul>',\r\n 'Citations': '<ul></ul>',\r\n 'External Links': '<ul></ul>',\r\n 'Location': 'New York, New York, USA<br />',\r\n 'Social': '<ul class=\"unstyled\"></ul>',\r\n 'Images': '<ul class=\"thumbnails\"></ul>',\r\n 'Name': u'Nickname',\r\n 'Description': u'Dude abides'}\r\n )","def _dict_merge(merge_dict, into_dict):\n for k, v in merge_dict.items():\n if k not in into_dict:\n into_dict[k] = v\n continue\n\n current_val = into_dict[k]\n\n if isinstance(v, dict) and isinstance(current_val, dict):\n _dict_merge(v, current_val)\n continue\n\n # otherwise we just overwrite\n into_dict[k] = v","def merge(a, b):\n a = sort_place(a)\n b = sort_place(b)\n ai = 0\n bi = 0\n d = None\n wina = []\n winb = []\n bk = {}\n conflict = False\n ret = []\n while True:\n try:\n x = a[ai]\n except:\n x = {None: None}\n try:\n y = b[bi]\n except:\n y = {None: None}\n\n xk = list(x)[0]\n yk = list(y)[0]\n xv = x[xk]\n yv = y[yk]\n\n # end\n if xk is None and yk is None and not conflict:\n break\n\n # no conflict\n if not conflict:\n if xk == yk:\n # both keys and values are same (match)\n if xv == yv:\n # current val counter = value or ++\n if None in [xv, d] or d < xv:\n d = xv\n ret.append({xk: d})\n if d is not None:\n d += 1\n # keys are same but values differ\n else:\n # val counter = max value\n d = max(filter(None, [xv,yv,d]))\n ret.append({xk: d})\n d += 1\n ai += 1\n bi += 1\n # differing keys. conflict! or one of the lists ran out\n else:\n conflict = True\n # set up windows\n wina = [xk]\n winb = [yk]\n # and store key's values\n bk = {xk: xv, yk: yv}\n ai += 1\n bi += 1\n # there was a conflict in the past\n else:\n # if there is an item, put it in the window\n if xk is not None:\n wina.append(xk)\n ai += 1\n if yk is not None:\n winb.append(yk)\n bi += 1\n\n # avoiding None since xk/yk can be None, \n # but really they shouldn't be here. check later\n found = False\n\n foundv = None\n\n # see if key in opposite window\n if xk in winb:\n found = xk\n foundv = xv\n if yk in wina:\n found = yk\n foundv = yv\n # does this overwrite anything?\n if not found:\n bk[xk] = xv\n bk[yk] = yv\n \n out = xk is None and yk is None and not found\n\n if found is not False or out:\n # crop windows up until found value\n posa = []\n posb = []\n for p, w in [(posa, wina), (posb, winb)]:\n for k in w:\n if k == found:\n break\n p.append(k) \n wina = wina[len(posa)+1:]\n winb = winb[len(posb)+1:]\n # sort cropped windows based on 1st value\n posa, posb = sorted([posa,posb], key=lambda i: '' if len(i)==0 else i[0])\n # add them to the return\n FOUND_ONE = {found: foundv}\n for p in [*posa, *posb, FOUND_ONE]:\n if p is FOUND_ONE:\n p = found\n pv = foundv\n if out:\n break\n else:\n pv = bk[p]\n\n if None in [pv, d] or d < pv:\n d = pv\n ret.append({p: d})\n if d is not None:\n d += 1\n # backtrack to resume point\n ai -= len(wina)\n bi -= len(winb)\n wina = []\n winb = []\n conflict = False\n \n \n else:\n pass\n \n\n # # same\n # if list(x) == list(y):\n # if xv is None and yv is None:\n # ret.append({xk: d})\n # if d is not None:\n # d+=1\n # else:\n # if d is not None:\n # ret.append({xk: d})\n # d+=1\n # continue\n # not_nones = [i for i in [xv,yv] if i is not None]\n # if len(not_nones) == 1:\n return ret","def merge(incoming={}, output={}, overwrite=False):\n\t_output = output.copy()\n\tfor _key, _value in incoming.items(): # loop through each key/value pair\n\t\tif (_key in _output) and isinstance(_value, dict): # detect when we need to recurse\n\t\t\t_output[_key] = merge(_value, _output[_key]) # recurse\n\t\telse: # _key is not in output\n\t\t\tif _key in _output and overwrite == False: # we check if it already exists, and if we care\n\t\t\t\tcontinue # don't overwrite existing values unless overwrite is 'True'\n\t\t\t_output[_key] = _value # add key/value pair\n\n\treturn _output # give back the merged dict","def merge_results(res1, res2):\n empty = []\n keys = set(res1).union(res2)\n return dict((k, res1.get(k, empty) + res2.get(k, empty)) for k in keys)","def dict_merge(dest, upd, recursive_update=True, merge_lists=False):\n if (not isinstance(dest, collections.Mapping)) \\\n or (not isinstance(upd, collections.Mapping)):\n raise TypeError('Cannot update using non-dict types in dictupdate.update()')\n updkeys = list(upd.keys())\n if not set(list(dest.keys())) & set(updkeys):\n recursive_update = False\n if recursive_update:\n for key in updkeys:\n val = upd[key]\n try:\n dest_subkey = dest.get(key, None)\n except AttributeError:\n dest_subkey = None\n if isinstance(dest_subkey, collections.Mapping) \\\n and isinstance(val, collections.Mapping):\n ret = dict_merge(dest_subkey, val, merge_lists=merge_lists)\n dest[key] = ret\n elif isinstance(dest_subkey, list) \\\n and isinstance(val, list):\n if merge_lists:\n merged = copy.deepcopy(dest_subkey)\n merged.extend([x for x in val if x not in merged])\n dest[key] = merged\n else:\n dest[key] = upd[key]\n else:\n dest[key] = upd[key]\n return dest\n else:\n try:\n for k in upd:\n dest[k] = upd[k]\n except AttributeError:\n # this mapping is not a dict\n for k in upd:\n dest[k] = upd[k]\n return dest","def merge(dict1: dict, dict2: dict):\n if dict1 is None or dict2 is None:\n return None\n keys1 = dict1.keys()\n keys2 = dict2.keys()\n if keys1 & keys2 != set():\n raise Exception(\"Non linear patterns not supported\")\n dict_r = {k: dict1[k] for k in keys1}\n dict_r.update({k: dict2[k] for k in keys2})\n return dict_r","def merge(source, destination):\n for key, value in source.items():\n if isinstance(value, dict):\n # get node or create one\n node = destination.setdefault(key, {})\n merge(value, node)\n else:\n destination[key] = value\n\n return destination","def test_contain(fs1,fs2):\n if test_leaf(fs1) == False or test_leaf(fs2) == False:\n raise ValueError('Two arguments must be leaf nodes.')\n key_1 = fs1.keys()\n key_2 = fs2.keys()\n new_key_1 = []\n new_key_2 = []\n for i in key_1:\n if i in key_2:\n new_key_1.append(i)\n for i in key_2:\n if i in key_1:\n new_key_2.append(i)\n len_1 = len(key_1)\n len_2 = len(key_2)\n new_len_1 = len(new_key_1)\n new_len_2 = len(new_key_2)\n # Now new_len_1 and new_len_2 are the same keys in both fs, or both []\n # means no same keys\n #print new_key_1\n #print new_key_2\n if new_len_1 == 0 and new_len_2 == 0:\n return None\n elif len_1 != new_len_1 and len_2 != new_len_2:\n ret = FeatStruct()\n for i in new_key_1:\n ret[i] = fs1[i]\n return ret\n elif len_1 == new_len_1 and len_2 != new_len_2:\n return 1 # len_1 not changed, it is contained in len_2\n elif len_1 != new_len_1 and len_2 == new_len_2:\n return -1 # len_2 contained in len_1\n else:\n return 0 # Neigher has changed, so they are equal"],"string":"[\n \"def test_recursive_merge(self):\\n dict_1 = {\\n 'key': {\\n 'deep_key_1': 'original_value_1',\\n 'deep_key_2': 'original_value_2'\\n }\\n }\\n dict_2 = {\\n 'key': {\\n 'deep_key_2': 'new_value_2',\\n 'deep_key_3': 'new_value_3'\\n }\\n }\\n\\n result = deep_dict_merge(dict_1, dict_2)\\n\\n assert dict_1 == {\\n 'key': {\\n 'deep_key_1': 'original_value_1',\\n 'deep_key_2': 'original_value_2'\\n }\\n }\\n assert dict_2 == {\\n 'key': {\\n 'deep_key_2': 'new_value_2',\\n 'deep_key_3': 'new_value_3'\\n }\\n }\\n assert result == {\\n 'key': {\\n 'deep_key_1': 'original_value_1',\\n 'deep_key_2': 'new_value_2',\\n 'deep_key_3': 'new_value_3'\\n }\\n }\",\n \"def _helm_merge(a, b):\\n if not (isinstance(b, dict) and isinstance(a, dict)):\\n # if either one is not a dict,\\n # there's no merging to do: use 'b'\\n return b\\n for key, value in b.items():\\n if key in a:\\n a[key] = _helm_merge(a[key], value)\\n else:\\n a[key] = value\\n return a\",\n \"def test_single_level(self):\\n dict_1 = {\\n 'key_1': 'original_value_1',\\n 'key_2': 'original_value_2'\\n }\\n dict_2 = {\\n 'key_2': 'new_value_2',\\n 'key_3': 'new_value_3'\\n }\\n\\n result = deep_dict_merge(dict_1, dict_2)\\n\\n assert dict_1 == {\\n 'key_1': 'original_value_1',\\n 'key_2': 'original_value_2'\\n }\\n assert dict_2 == {\\n 'key_2': 'new_value_2',\\n 'key_3': 'new_value_3'\\n }\\n assert result == {\\n 'key_1': 'original_value_1',\\n 'key_2': 'new_value_2',\\n 'key_3': 'new_value_3',\\n }\",\n \"def merge(a, b, path=None, update=True):\\n # print(\\\"\\\\nMerging: a=\\\" + str(a) + \\\" b=\\\" + str(b) + \\\" path=\\\" + str(path) )\\n if path is None:\\n path = []\\n for key in b:\\n if key in a:\\n if isinstance(a[key], dict) and isinstance(b[key], dict):\\n merge(a[key], b[key], path + [str(key)])\\n elif a[key] == b[key]:\\n pass # same leaf value\\n elif isinstance(a[key], list) and isinstance(b[key], list):\\n for idx, val in enumerate(b[key]):\\n a[key][idx] = merge(a[key][idx],\\n b[key][idx],\\n path + [str(key), str(idx)],\\n update=update)\\n elif update:\\n a[key] = b[key]\\n else:\\n raise Exception('Conflict at %s' %\\n '.'.join(path + [str(key)]))\\n else:\\n a[key] = b[key]\\n return a\",\n \"def test_merge_list_traditional(self):\\n mdict = copy.deepcopy(self.dict1)\\n mdict[\\\"A\\\"] = [\\\"B\\\", \\\"b\\\"]\\n ret = dictupdate.merge_list(copy.deepcopy(self.dict1), {\\\"A\\\": \\\"b\\\"})\\n self.assertEqual(mdict, ret)\",\n \"def test_merge_aggregate_traditional(self):\\n mdict = copy.deepcopy(self.dict1)\\n mdict[\\\"A\\\"] = \\\"b\\\"\\n ret = dictupdate.merge_overwrite(copy.deepcopy(self.dict1), {\\\"A\\\": \\\"b\\\"})\\n self.assertEqual(mdict, ret)\",\n \"def _merge(x, y):\\n for key in x:\\n if key in y:\\n x[key] = _merge(x[key], y[key])\\n y[key] = None\\n for key in y:\\n if y[key] is not None:\\n x[key] = y[key]\\n return x\",\n \"def dictmerge(x, y, path=None, overwrite=False, extend=False):\\n if path is None:\\n path = []\\n for key in y:\\n if key in x:\\n if isinstance(x[key], (dict, MutableMapping)) and isinstance(\\n y[key], (dict, MutableMapping)\\n ):\\n dictmerge(\\n x[key],\\n y[key],\\n path + [str(key)],\\n overwrite=overwrite,\\n extend=extend,\\n )\\n elif x[key] == y[key]:\\n pass # same leaf value\\n else:\\n if not overwrite:\\n raise Exception(\\\"Conflict at %s\\\" % \\\".\\\".join(path + [str(key)]))\\n if isinstance(x[key], list) and isinstance(y[key], list) and extend:\\n x[key].extend(y[key])\\n else:\\n x[key] = y[key]\\n else:\\n x[key] = y[key]\\n return x\",\n \"def merge(self, a, b, path=None):\\n if path is None: path = []\\n for key in b:\\n if key in a:\\n if isinstance(a[key], dict) and isinstance(b[key], dict):\\n if key == 'attributes':\\n self.merge_attribute_defs(b, a)\\n else:\\n self.merge(a[key], b[key], path + [str(key)])\\n elif a[key] == b[key]:\\n pass # same leaf value\\n else:\\n # raise Exception('Conflict at %s' % '.'.join(path + [str(key)]))\\n self.append_or_replace(a,b,key, '/'.join(path + [str(key)]));\\n else:\\n a[key] = b[key]\\n return a\",\n \"def merge_dict(a: dict, b: dict, path=None) -> dict:\\n if path is None:\\n path = []\\n for key in b:\\n if key in a:\\n if isinstance(a[key], dict) and isinstance(b[key], dict):\\n merge_dict(a[key], b[key], path + [str(key)])\\n elif a[key] == b[key]:\\n pass # same leaf value\\n else:\\n a[key] = b[key]\\n else:\\n a[key] = b[key]\\n return a\",\n \"def _merge_obj(result, obj, pointer=''): # changed code\\n if not isinstance(result, dict):\\n result = {}\\n\\n if not isinstance(obj, dict):\\n return obj\\n\\n for key, value in obj.items():\\n if isinstance(value, dict):\\n target = result.get(key)\\n if isinstance(target, dict):\\n _merge_obj(target, value, pointer=f'{pointer}/{key}') # changed code\\n continue\\n result[key] = {}\\n _merge_obj(result[key], value, pointer=f'{pointer}/{key}') # changed code\\n continue\\n\\n # new code\\n if key in result:\\n pointer_and_key = f'{pointer}/{key}'\\n # Exceptions.\\n if (value is None and pointer_and_key == '/definitions/Milestone/properties/documents/deprecated' and\\n repo_name in ('ocds_milestone_documents_extension', 'public-private-partnerships')):\\n warnings.warn(f're-adds {pointer}')\\n elif (value == [] and pointer_and_key == '/required' and\\n repo_name == 'ocds_pagination_extension'):\\n warnings.warn(f'empties {pointer_and_key}')\\n else:\\n if is_profile:\\n message = ' - check for repeats across extension_versions.json, dependencies, testDependencies'\\n else:\\n message = ''\\n raise Exception(f'unexpectedly overwrites {pointer_and_key}{message}')\\n\\n if value is None:\\n result.pop(key, None)\\n continue\\n result[key] = value\\n return result\",\n \"def variant_add(v1: dict, v2: dict) -> Dict[str, Any]:\\n left = set(v1.keys()).difference(v2.keys())\\n right = set(v2.keys()).difference(v1.keys())\\n joint = set(v1.keys()) & set(v2.keys())\\n\\n # deal with __migrator: ordering\\n if \\\"__migrator\\\" in v2:\\n ordering = v2[\\\"__migrator\\\"].get(\\\"ordering\\\", {})\\n operation = v2[\\\"__migrator\\\"].get(\\\"operation\\\")\\n # handle special operations\\n if operation:\\n return VARIANT_OP[operation](v1, v2)\\n else:\\n ordering = {}\\n\\n # special keys\\n if \\\"__migrator\\\" in right:\\n right.remove(\\\"__migrator\\\")\\n\\n # special keys in joint\\n special_variants = {}\\n if \\\"pin_run_as_build\\\" in joint:\\n # For run_as_build we enforce the migrator's pin\\n # TODO: should this just be a normal ordering merge, favoring more exact pins?\\n joint.remove(\\\"pin_run_as_build\\\")\\n special_variants[\\\"pin_run_as_build\\\"] = {\\n **v1[\\\"pin_run_as_build\\\"],\\n **v2[\\\"pin_run_as_build\\\"],\\n }\\n\\n if \\\"zip_keys\\\" in joint:\\n # zip_keys is a bit weird to join on as we don't have a particularly good way of identifying\\n # a block. Longer term having these be named blocks would make life WAY simpler\\n # That does require changes to conda-build itself though\\n #\\n # A zip_keys block is deemed mergeable if zkₛ,ᵢ ⊂ zkₘ,ᵢ\\n zk_out = []\\n zk_l = {frozenset(e) for e in v1[\\\"zip_keys\\\"]}\\n zk_r = {frozenset(e) for e in v2[\\\"zip_keys\\\"]}\\n\\n for zk_r_i in sorted(zk_r, key=lambda x: -len(x)):\\n for zk_l_i in sorted(zk_l, key=lambda x: -len(x)):\\n # Merge the longest common zk first\\n if zk_l_i.issubset(zk_r_i):\\n zk_l.remove(zk_l_i)\\n zk_r.remove(zk_r_i)\\n zk_out.append(zk_r_i)\\n break\\n else:\\n # Nothing to do\\n pass\\n\\n zk_out.extend(zk_l)\\n zk_out.extend(zk_r)\\n zk_out = sorted(\\n [sorted(zk) for zk in zk_out], key=lambda x: (len(x), str(x))\\n )\\n\\n joint.remove(\\\"zip_keys\\\")\\n special_variants[\\\"zip_keys\\\"] = zk_out\\n\\n joint_variant = {}\\n for k in joint:\\n v_left, v_right = ensure_list(v1[k]), ensure_list(v2[k])\\n joint_variant[k] = variant_key_add(\\n k, v_left, v_right, ordering=ordering.get(k, None)\\n )\\n\\n out = {\\n **toolz.keyfilter(lambda k: k in left, v1),\\n **toolz.keyfilter(lambda k: k in right, v2),\\n **joint_variant,\\n **special_variants,\\n }\\n\\n return out\",\n \"def dict_merge(a, b, path=None):\\n\\n if path is None: path = []\\n for key in b:\\n if key in a:\\n if isinstance(a[key], dict) and isinstance(b[key], dict):\\n dict_merge(a[key], b[key], path + [str(key)])\\n else:\\n a[key] = b[key]\\n return a\",\n \"def test_merge_dict(self):\\n cd = ConfigDict.from_dict({\\n 'a': 1,\\n 'b': {\\n 'c': 2,\\n 'd': 3,\\n 'e': {'h': 4},\\n 'f': [5,6],\\n 'g': [7,8]\\n }\\n })\\n\\n cd.merge_dict({\\n 'a': 11,\\n 'z': 99,\\n 'b': {\\n 'c': 22,\\n 'e': {'y': 999},\\n 'g': [77,88],\\n }\\n })\\n\\n self.assertEquals(cd.a,11)\\n self.assertEquals(cd.b.c, 22)\\n self.assertEquals(cd.b.d, 3)\\n self.assertEquals(cd.b.e.h, 4)\\n self.assertEquals(cd.b.e.y, 999)\\n self.assertEquals(cd.b.f, [5,6])\\n self.assertEquals(cd.b.g, [77, 88])\\n self.assertEquals(cd.z,99)\",\n \"def conditional_merge(a_dict, b_dict):\\n # Go through each key in b_dict\\n for k in b_dict:\\n if isinstance(b_dict[k], dict):\\n if k not in a_dict:\\n # It's a new section. Initialize it...\\n a_dict[k] = {}\\n # ... and transfer over the section comments, if available\\n try:\\n a_dict.comments[k] = b_dict.comments[k]\\n except AttributeError:\\n pass\\n conditional_merge(a_dict[k], b_dict[k])\\n elif k not in a_dict:\\n # It's a scalar. Transfer over the value...\\n a_dict[k] = b_dict[k]\\n # ... then its comments, if available:\\n try:\\n a_dict.comments[k] = b_dict.comments[k]\\n except AttributeError:\\n pass\",\n \"def merge(a, b):\\n if isinstance(a, CONFIG_VALID) \\\\\\n and isinstance(b, CONFIG_VALID):\\n # dict update\\n if isinstance(a, dict) and isinstance(b, dict):\\n a.update(b)\\n return a\\n # list update\\n _a = list(a)\\n for x in list(b):\\n if x not in _a:\\n _a.append(x)\\n return _a\\n if a and b:\\n raise Exception(\\\"Cannot merge\\\")\\n raise NotImplementedError\",\n \"def _merge_two(self, obj1, obj2):\\r\\n for uniq_ident in obj2.keys():\\r\\n if (uniq_ident not in obj1) \\\\\\r\\n or (obj1[uniq_ident]['modified'] \\\\\\r\\n < obj2[uniq_ident]['modified']):\\r\\n obj1[uniq_ident] = obj2[uniq_ident]\\r\\n\\r\\n return obj1 # self._dict_to_list(obj1)\\r\",\n \"def test_merge_dicts(self):\\n dict_A = {\\\"key1\\\": \\\"value1\\\", \\\"key2\\\": \\\"value2\\\"}\\n dict_B = {\\\"key1\\\": \\\"value1\\\"}\\n\\n expected_log = [\\n \\\"WARNING:blinkpy.helpers.util:Duplicates found during merge: ['key1']. \\\"\\n \\\"Renaming is recommended.\\\"\\n ]\\n\\n with self.assertLogs(level=\\\"DEBUG\\\") as merge_log:\\n merge_dicts(dict_A, dict_B)\\n self.assertListEqual(merge_log.output, expected_log)\",\n \"def test_merge_list_append(self):\\n mdict = copy.deepcopy(self.dict1)\\n mdict[\\\"A\\\"] = [\\\"B\\\", \\\"b\\\", \\\"c\\\"]\\n\\n # Prepare a modified copy of dict1 that has a list as a val for the key of 'A'\\n mdict1 = copy.deepcopy(self.dict1)\\n mdict1[\\\"A\\\"] = [\\\"B\\\"]\\n ret = dictupdate.merge_list(mdict1, {\\\"A\\\": [\\\"b\\\", \\\"c\\\"]})\\n self.assertEqual(\\n {\\\"A\\\": [[\\\"B\\\"], [\\\"b\\\", \\\"c\\\"]], \\\"C\\\": {\\\"D\\\": \\\"E\\\", \\\"F\\\": {\\\"I\\\": \\\"J\\\", \\\"G\\\": \\\"H\\\"}}}, ret\\n )\",\n \"def test_merge_overwrite_traditional(self):\\n mdict = copy.deepcopy(self.dict1)\\n mdict[\\\"A\\\"] = \\\"b\\\"\\n ret = dictupdate.merge_overwrite(copy.deepcopy(self.dict1), {\\\"A\\\": \\\"b\\\"})\\n self.assertEqual(mdict, ret)\",\n \"def _merge_dicts(dict1, dict2, path=None):\\n if path is None:\\n path = []\\n for key, value2 in six.iteritems(dict2):\\n if key not in dict1:\\n dict1[key] = value2\\n continue\\n value1 = dict1[key]\\n if value1 == value2:\\n continue\\n elif isinstance(value1, dict) and isinstance(value2, dict):\\n _merge_dicts(dict1[key], dict2[key], path=path+[key])\\n elif isinstance(value1, (set, frozenset)) and isinstance(value2, (set, frozenset, list)):\\n dict1[key] = value1 | frozenset(value2)\\n else:\\n raise TypeError(\\\"Cannot merge {} with {} at {}\\\".format(type(value1), type(value2), '/'.join(path)))\\n return dict1\",\n \"def _merge_a_into_b(a, b):\\n for k, v in a.items():\\n # a must specify keys that are in b\\n # if k not in b:\\n # raise KeyError('{} is not a valid config key'.format(k))\\n\\n # recursively merge dicts\\n if isinstance(v, dict):\\n if not b.get(k, False):\\n b[k] = v\\n else:\\n try:\\n _merge_a_into_b(a[k], b[k])\\n except:\\n print(\\\"Error under config key: {}\\\".format(k))\\n raise\\n else:\\n b[k] = v\",\n \"def deep_merge(a, b, path=None, append=False):\\n for key in b:\\n if key in a:\\n if isinstance(a[key], dict) and isinstance(b[key], dict):\\n deep_merge(a[key], b[key], (path or []) + [str(key)], append)\\n continue\\n is_lists = isinstance(a[key], list) and isinstance(b[key], list)\\n if is_lists and append:\\n a[key] += b[key]\\n else:\\n a[key] = b[key]\\n else:\\n a[key] = b[key]\\n return a\",\n \"def recursiveMergeDict(aDict, bDict):\\n aDictItems = None\\n if crifanLib.crifanSystem.isPython2(): # is python 2\\n aDictItems = aDict.iteritems()\\n else: # is python 3\\n aDictItems = aDict.items()\\n\\n for aKey, aValue in aDictItems:\\n # print(\\\"------ [%s]=%s\\\" % (aKey, aValue))\\n if aKey not in bDict:\\n bDict[aKey] = aValue\\n else:\\n bValue = bDict[aKey]\\n # print(\\\"aValue=%s\\\" % aValue)\\n # print(\\\"bValue=%s\\\" % bValue)\\n if isinstance(aValue, dict):\\n recursiveMergeDict(aValue, bValue)\\n elif isinstance(aValue, list):\\n aValueListLen = len(aValue)\\n bValueListLen = len(bValue)\\n bValueListMaxIdx = bValueListLen - 1\\n for aListIdx in range(aValueListLen):\\n # print(\\\"---[%d]\\\" % aListIdx)\\n aListItem = aValue[aListIdx]\\n # print(\\\"aListItem=%s\\\" % aListItem)\\n if aListIdx <= bValueListMaxIdx:\\n bListItem = bValue[aListIdx]\\n # print(\\\"bListItem=%s\\\" % bListItem)\\n recursiveMergeDict(aListItem, bListItem)\\n else:\\n # print(\\\"bDict=%s\\\" % bDict)\\n # print(\\\"aKey=%s\\\" % aKey)\\n # print(\\\"aListItem=%s\\\" % aListItem)\\n bDict[aKey].append(aListItem)\\n\\n return bDict\",\n \"def deep_merge(a, b):\\n assert quacks_like_dict(a), quacks_like_dict(b)\\n dst = a.copy()\\n\\n stack = [(dst, b)]\\n while stack:\\n current_dst, current_src = stack.pop()\\n for key in current_src:\\n if key not in current_dst:\\n current_dst[key] = current_src[key]\\n else:\\n if (quacks_like_dict(current_src[key])\\n and quacks_like_dict(current_dst[key])):\\n stack.append((current_dst[key], current_src[key]))\\n else:\\n current_dst[key] = current_src[key]\\n return dst\",\n \"def json_flat_diff(a, b):\\r\\n\\r\\n res_a = {}\\r\\n res_b = {}\\r\\n for key in set(a.keys()).union(set(b.keys())):\\r\\n a_value = a.get(key)\\r\\n b_value = b.get(key)\\r\\n if a_value != b_value:\\r\\n res_a[key] = a_value\\r\\n res_b[key] = b_value\\r\\n # Mind the parentheses below lest you return ({}, None) if res_a is None.\\r\\n return (res_a, res_b) if res_a != {} else None\",\n \"def _merge_dicts(d1, d2):\\n for key, value in d2.items():\\n if key in d1:\\n if isinstance(d1[key], dict) and isinstance(value, dict):\\n _merge_dicts(d1[key], value)\\n continue\\n d1[key] = value\",\n \"def deep_merge(source, dest):\\n for key, value in source.iteritems():\\n if key in dest:\\n if isinstance(value, dict) and isinstance(dest[key], dict):\\n deep_merge(value, dest[key])\\n continue\\n elif isinstance(value, list) and isinstance(dest[key], list):\\n for item in value:\\n if item not in dest[key]:\\n dest[key].append(item)\\n continue\\n dest[key] = value\",\n \"def __merge_configs(cls, a, b, path=None):\\n\\n # This function is modeled after:\\n # https://stackoverflow.com/questions/7204805/dictionaries-of-dictionaries-merge\\n\\n if path is None: path = []\\n\\n for key in b:\\n if key in a:\\n if isinstance(a[key], dict) and isinstance(b[key], dict):\\n cls.__merge_configs(a[key], b[key], path + [str(key)])\\n elif isinstance(a[key], list) and isinstance(b[key], list):\\n # Easy enough to merge lists of non-dict items...\\n try:\\n al = set(a[key])\\n bl = set(b[key])\\n bl.update(al)\\n a[key] = list(bl)\\n except (TypeError, AttributeError) as e:\\n log.warning('problem merging lists when merging configurations'\\n '(are there dict args in one at path %s?):'\\n '\\\\n%s\\\\n%s\\\\nKeeping the first one due to error %s'\\n % (path, a[key], b[key], e))\\n else:\\n # Same key, but different value type. This is difficult to\\n # handle so we just keep the value of the first one.\\n pass\\n else:\\n a[key] = b[key]\\n return a\",\n \"def _merge_a_into_b(a, b):\\n if type(a) is not edict:\\n return\\n\\n for k, v in a.items():\\n # recursively merge dicts\\n if type(v) is edict:\\n try:\\n b[k] = edict()\\n _merge_a_into_b(a[k], b[k])\\n except:\\n print('Error under config key: {}'.format(k))\\n raise\\n else:\\n b[k] = v\",\n \"def merge_dict_recursive(target, src):\\r\\n for k in src.keys():\\r\\n if ((k in target and isinstance(target[k], dict) and\\r\\n isinstance(src[k], collections.Mapping))):\\r\\n merge_dict_recursive(target[k], src[k])\\r\\n else:\\r\\n target[k] = src[k]\",\n \"def merge(a: dict, b: dict) -> dict:\\n return __merge(a, b)\",\n \"def merge_config(a, b):\\n for key, b_value in b.items():\\n if not isinstance(b_value, dict):\\n a[key] = b_value\\n else:\\n a_value = a.get(key)\\n if a_value is not None and isinstance(a_value, dict):\\n merge_config(a_value, b_value)\\n else:\\n a[key] = b_value\\n return a\",\n \"def deep_merge(d1, d2):\\n for k, v in d1.copy().items():\\n if k in d2:\\n if all(isinstance(e, MutableMapping) for e in (v, d2[k])):\\n d2[k] = ConfigManager.deep_merge(v, d2[k])\\n\\n if k == \\\"*\\\":\\n for _k, _v in d2.items():\\n if all(isinstance(e, MutableMapping) for e in (v, d2[_k])):\\n d2[_k] = ConfigManager.deep_merge(v, d2[_k])\\n del d1[k]\\n d3 = d1.copy()\\n d3.update(d2)\\n return d3\",\n \"def merge_dictionaries(d1, d2):\\n\\n if d2 is None: \\n return\\n\\n for k, v in d2.items():\\n if k not in d1:\\n d1[k] = dict()\\n if isinstance(v, dict):\\n merge_dictionaries(d1[k], v)\\n else:\\n d1[k] = v\",\n \"def test_merge_two_two_same():\\n run_merge([1, 3], [1, 3], [1, 1, 3, 3])\",\n \"def merge_dict(target, addition):\\n for key in addition:\\n if key in target and isinstance(target[key], dict) \\\\\\n and isinstance(addition[key], dict):\\n merge_dict(target[key], addition[key])\\n else:\\n target[key] = addition[key]\",\n \"def unify_walk(d1, d2, U):\\r\\n for (k1, v1) in d1.items():\\r\\n if d2.has_key(k1):\\r\\n U = unify_walk(v1, d2[k1], U)\\r\\n if U is False:\\r\\n return False\\r\\n return U\",\n \"def merge_dicts(a, b, path=None):\\n if path is None:\\n path = []\\n for key in b:\\n if key in a:\\n if isinstance(a[key], dict) and isinstance(b[key], dict):\\n merge_dicts(a[key], b[key], path + [str(key)])\\n elif type(a) is type(b):\\n a[key] = b[key]\\n else:\\n raise Exception('Conflicting types at %s'\\n % '.'.join(path + [str(key)]))\\n else:\\n a[key] = b[key]\\n return a\",\n \"def merge_dicts(base, changes):\\n for k, v in changes.items():\\n if isinstance(v, dict):\\n merge_dicts(base.setdefault(k, {}), v)\\n else:\\n base.setdefault(k, v)\",\n \"def merge_trees(data, new_data):\\n for key, val in new_data.items():\\n if isinstance(val, dict):\\n if key not in data:\\n data[key] = new_data[key]\\n else:\\n merge_trees(data[key], new_data[key])\\n\\n else:\\n if key not in data:\\n data[key] = val\\n else:\\n data[key] = data[key] + val\",\n \"def _iterativediff(t1, t2, subdir):\\n if t1 is None:\\n t1 = {}\\n if t2 is None:\\n t2 = {}\\n\\n for e1 in t1:\\n realname = subdir + pycompat.fsencode(e1.name)\\n\\n if e1.type == pygit2.GIT_OBJ_TREE:\\n try:\\n e2 = t2[e1.name]\\n if e2.type != pygit2.GIT_OBJ_TREE:\\n e2 = None\\n except KeyError:\\n e2 = None\\n\\n stack.append((realname + b'/', e1, e2))\\n else:\\n n1, fl1 = self.find(realname)\\n\\n try:\\n e2 = t2[e1.name]\\n n2, fl2 = other.find(realname)\\n except KeyError:\\n e2 = None\\n n2, fl2 = (None, b'')\\n\\n if e2 is not None and e2.type == pygit2.GIT_OBJ_TREE:\\n stack.append((realname + b'/', None, e2))\\n\\n if not match(realname):\\n continue\\n\\n if n1 != n2 or fl1 != fl2:\\n result[realname] = ((n1, fl1), (n2, fl2))\\n elif clean:\\n result[realname] = None\\n\\n for e2 in t2:\\n if e2.name in t1:\\n continue\\n\\n realname = subdir + pycompat.fsencode(e2.name)\\n\\n if e2.type == pygit2.GIT_OBJ_TREE:\\n stack.append((realname + b'/', None, e2))\\n elif match(realname):\\n n2, fl2 = other.find(realname)\\n result[realname] = ((None, b''), (n2, fl2))\",\n \"def _merge_data(self, d1, d2):\\n if not d1 and not d2:\\n if isinstance(d1, dict) or isinstance(d2, dict):\\n return {}\\n return []\\n if not d2:\\n return d1\\n if not d1:\\n return d2\\n if isinstance(d1, list) and isinstance(d2, list):\\n return list(set(d1 + d2))\\n if isinstance(d1, list) and not isinstance(d2, dict):\\n if d2 in d1:\\n return d1\\n return d1 + make_list(d2)\\n if isinstance(d2, list) and not isinstance(d1, dict):\\n if d1 in d2:\\n return d2\\n return d2 + make_list(d1)\\n if not isinstance(d1, dict) and not isinstance(d2, dict):\\n if d1 == d2:\\n return make_list(d1)\\n else:\\n return [d1, d2]\\n\\n res = d1\\n for key2, val2 in d2.items():\\n if key2 in res:\\n res[key2] = self._merge_data(val2, res[key2])\\n else:\\n res[key2] = val2\\n return res\",\n \"def recursively_update_with(a, b):\\n\\n for key in b:\\n if key in a:\\n if isinstance(a[key], dict) and isinstance(b[key], dict):\\n recursively_update_with(a[key], b[key])\\n else:\\n a[key] = b[key]\\n else:\\n a[key] = b[key]\",\n \"def combine_dict(self, dict2):\\n # iterate through smaller data set\\n # base_set will be the larger set and is used for updating\\n if len(self.content[\\\"values\\\"]) > len(dict2[\\\"values\\\"]):\\n large_set = self.content[\\\"values\\\"]\\n small_set = dict2[\\\"values\\\"]\\n base_set = self.content\\n else:\\n small_set = self.content[\\\"values\\\"]\\n large_set = dict2[\\\"values\\\"]\\n base_set = dict2\\n\\n subset = {}\\n for key in small_set.keys():\\n # determine wether to compare keys\\n if key in large_set:\\n updated_l = large_set[key][\\\"updated_at\\\"]\\n updated_s = small_set[key][\\\"updated_at\\\"]\\n if updated_l == 'NULL':\\n if updated_s != 'NULL':\\n # update to not NULL set\\n # if both updated_at are NULL, things\\n # are ambiguos. We could defer to created_at\\n # but for simplicity we will default to\\n # the values in the larger set\\n subset[key] = small_set[key]\\n else:\\n if updated_s == 'NULL':\\n # update to not NULL set\\n subset[key] = large_set[key]\\n else:\\n if updated_l > updated_s:\\n subset[key] = large_set[key]\\n else:\\n subset[key] =small_set[key]\\n else:\\n subset[key] = small_set[key]\\n base_set[\\\"values\\\"].update(subset)\\n new_obj = BackupData()\\n new_obj.load_from_dict(base_set)\\n return new_obj\",\n \"def compare_dicts(dict1, dict2, dict1_name=\\\"d1\\\", dict2_name=\\\"d2\\\", path=\\\"\\\"):\\n # Setup paths to track key exploration. The path parameter is used to allow\\n # recursive comparisions and track what's being compared.\\n result = True\\n for key in dict1.keys():\\n dict1_path = \\\"{}{}[{}]\\\".format(dict1_name, path, key)\\n dict2_path = \\\"{}{}[{}]\\\".format(dict2_name, path, key)\\n if key not in dict2.keys():\\n log.debug(\\\"%s not a valid key in %s.\\\", dict1_path, dict2_path)\\n result = False\\n elif isinstance(dict1[key], dict) and isinstance(dict2[key], dict):\\n log.debug(\\n \\\"%s and %s contain dictionary. Evaluating.\\\", dict1_path,\\n dict2_path\\n )\\n result = compare_dicts(\\n dict1[key], dict2[key], dict1_name, dict2_name,\\n path=\\\"[{}]\\\".format(key)\\n )\\n elif isinstance(dict1[key], list) and isinstance(dict2[key], list):\\n log.debug(\\n \\\"%s and %s key '%s' contains list. Validating dict1 items \\\"\\n \\\"exist in dict2.\\\", dict1_path, dict2_path, key\\n )\\n if not all([bool(item in dict2[key]) for item in dict1[key]]):\\n log.debug(\\n \\\"Mismatch: %s value is '%s' while %s value is '%s'.\\\",\\n dict1_path, dict1[key], dict2_path, dict2[key]\\n )\\n result = False\\n # Hack for NetBox v2.6.7 requiring integers for some values\\n elif key in [\\\"status\\\", \\\"type\\\"]:\\n if dict1[key] != dict2[key][\\\"value\\\"]:\\n log.debug(\\n \\\"Mismatch: %s value is '%s' while %s value is '%s'.\\\",\\n dict1_path, dict1[key], dict2_path, dict2[key][\\\"value\\\"]\\n )\\n result = False\\n elif dict1[key] != dict2[key]:\\n log.debug(\\n \\\"Mismatch: %s value is '%s' while %s value is '%s'.\\\",\\n dict1_path, dict1[key], dict2_path, dict2[key]\\n )\\n # Allow the modification of device sites by ignoring the value\\n if \\\"site\\\" in path and key == \\\"name\\\":\\n log.debug(\\\"Site mismatch is allowed. Moving on.\\\")\\n else:\\n result = False\\n if result:\\n log.debug(\\\"%s and %s values match.\\\", dict1_path, dict2_path)\\n else:\\n log.debug(\\\"%s and %s values do not match.\\\", dict1_path, dict2_path)\\n return result\\n log.debug(\\\"Final dictionary compare result: %s\\\", result)\\n return result\",\n \"def merge_dicts(a, b):\\n if not isinstance(b, dict):\\n return b\\n\\n result = a\\n for k, v in b.items():\\n if k in result and isinstance(result[k], dict):\\n result[k] = merge_dicts(result[k], v)\\n else:\\n result[k] = v\\n return result\",\n \"def merge(): #Status: WIP\\r\\n pass\",\n \"def dict_merge( dict_1, dict_2 ):\\n\\n if dict_1 is None:\\n if dict_2 is None:\\n # or return None\\n return_val = None\\n else:\\n # or\\n return_val = dict_2\\n\\n else:\\n if dict_2 is None:\\n # or return\\n return_val = dict_2\\n else:\\n return_val = { **dict_1 , **dict_2}\\n\\n\\n print( f\\\"dict_merg return_value = >return_val<\\\")\\n return return_val\",\n \"def mergeDict(old: dict, new: dict, layer=1) -> dict:\\n \\n from collections import Mapping\\n changed = False\\n for key, val in new.items():\\n # print(\\\"{} ({})\\\".format(key, type(old.get(key))))\\n if not key in old:\\n print(\\\"{}Adding new value {}\\\".format(' ' * layer, key))\\n changed = True\\n old[key] = val\\n elif issubclass(type(old[key]), Mapping) and issubclass(type(val), Mapping):\\n print(\\\"{}Merging dict {}\\\".format(' ' * layer, key))\\n changed = changed or mergeDict(old[key], val, layer + 1)\\n\\n return changed\",\n \"def merge_two_dicts(x, y):\\n\\tz = x.copy()\\n\\tz.update(y)\\n\\treturn z\",\n \"def merge(one, two, overwrite=False, typecheck=True):\\n if one is two:\\n return\\n\\n if typecheck and not same_type(one, two):\\n raise ValueError('Type mismatch')\\n\\n for (key, value) in two.items():\\n if key not in one:\\n one[key] = value\\n\\n if typecheck and not same_type(one[key], value):\\n raise ValueError('Type mismatch')\\n if isinstance(value, dict):\\n merge(one[key], two[key], overwrite, typecheck)\\n elif not overwrite:\\n continue\\n else:\\n one[key] = two[key]\",\n \"def json_merge_patch(servicedef, source, with_):\\n\\n if isinstance(source, list) or isinstance(with_, list):\\n return with_\\n\\n if not isinstance(source, dict):\\n raise TypeError('source must be a dict, got %s' % (type(source)))\\n\\n if not isinstance(with_, dict):\\n raise TypeError('with_ must be a dict, got %s' % (type(with_)))\\n\\n isdebug() and logger.debug('JSON merge:\\\\nsource = %s\\\\nwith = %s' %\\n (json.dumps(source, indent=2),\\n json.dumps(with_, indent=2)))\\n\\n if ( '$ref' in source and\\n '$ref' in with_ and\\n source == with_):\\n # If merging 2 $refs and they have the same target, nothing\\n # to do ... but avoid infinite recusion!\\n return source\\n\\n # Need to make a copy of source, as this is going to be modified\\n # Only make a shallow copy here - only the shallow properties\\n # are modified in this call. Recursively deeper calls will make\\n # deeper copies as needed.\\n source = _eval_shallow(servicedef, source, need_copy=True)\\n\\n # with_ is only used in a readonly fashion, so no need to copy\\n with_ = _eval_shallow(servicedef, with_, need_copy=False)\\n\\n for key, value in with_.items():\\n if value is None:\\n # Remove the key if present in the source\\n if key in source:\\n del source[key]\\n elif ( isinstance(value, dict) and\\n key in source and\\n isinstance(source[key], dict)):\\n # If this key is a dict in both source and with_, recurse\\n source[key] = json_merge_patch(servicedef, source[key], value)\\n else:\\n # Otherwise update the source for this key. This may add the\\n # key to source if it was not already present\\n source[key] = value\\n\\n isdebug() and logger.debug('JSON merge result:\\\\n%s' %\\n (json.dumps(source, indent=2)))\\n\\n return source\",\n \"def test_merge_json(self):\\n cd = ConfigDict.from_dict({\\n 'a': 1,\\n 'b': {\\n 'c': 2,\\n 'd': 3,\\n 'e': {'h': 4},\\n 'f': [5,6],\\n 'g': [7,8]\\n }\\n })\\n\\n cd.merge_json(\\\"\\\"\\\"{\\n \\\"a\\\": 11,\\n \\\"z\\\": 99, /* here is a comment */\\n \\\"b\\\": {\\n \\\"c\\\": 22,\\n \\\"e\\\": {\\\"y\\\": 999},\\n // another sort of comment\\n \\\"g\\\": [77,88]\\n }\\n /*\\n this might be a rather long\\n type of comment that spans several lines...\\n */\\n }\\\"\\\"\\\")\\n\\n self.assertEquals(cd.a,11)\\n self.assertEquals(cd.b.c, 22)\\n self.assertEquals(cd.b.d, 3)\\n self.assertEquals(cd.b.e.h, 4)\\n self.assertEquals(cd.b.e.y, 999)\\n self.assertEquals(cd.b.f, [5,6])\\n self.assertEquals(cd.b.g, [77, 88])\\n self.assertEquals(cd.z,99)\",\n \"def merge_two_dicts(x, y):\\r\\n z = x.copy()\\r\\n z.update(y)\\r\\n return z\",\n \"def merge(first: Dict[Any, Any], second: Dict[Any, Any]) -> Dict[Any, Any]:\\n if not isinstance(second, dict):\\n return second\\n result = deepcopy(first)\\n for key, value in second.items():\\n if key in result and isinstance(result[key], dict):\\n result[key] = merge(result[key], value)\\n else:\\n result[key] = deepcopy(value)\\n return result\",\n \"def test_merge_overwrite_missing_source_key(self):\\n mdict = copy.deepcopy(self.dict1)\\n mdict[\\\"D\\\"] = \\\"new\\\"\\n ret = dictupdate.merge_overwrite(copy.deepcopy(self.dict1), {\\\"D\\\": \\\"new\\\"})\\n self.assertEqual(mdict, ret)\",\n \"def merge_dicts(dict1, dict2, custom_merge_func=None):\\n # type: (Any, Any, Optional[Callable[[str, Any, Any, Any], Any]]) -> Any\\n if not isinstance(dict1, dict) or not isinstance(dict2, dict):\\n return dict2\\n for k in dict2:\\n if k in dict1:\\n res = None\\n if custom_merge_func:\\n res = custom_merge_func(k, dict1[k], dict2[k], _not_set)\\n dict1[k] = merge_dicts(dict1[k], dict2[k], custom_merge_func) if res is _not_set else res\\n else:\\n dict1[k] = dict2[k]\\n return dict1\",\n \"def mergedicts(dict1, dict2):\\n for k in set(dict1.keys()).union(dict2.keys()):\\n if k in dict1 and k in dict2:\\n if isinstance(dict1[k], dict) and isinstance(dict2[k], dict):\\n yield (k, dict(mergedicts(dict1[k], dict2[k])))\\n else:\\n # If one of the values is not a dict, you can't continue merging it.\\n # Value from second dict overrides one in first and we move on.\\n yield (k, dict2[k])\\n # Alternatively, replace this with exception raiser to alert you of value conflicts\\n elif k in dict1:\\n yield (k, dict1[k])\\n else:\\n yield (k, dict2[k])\",\n \"def dict_match(left, right, res=None):\\n if res is None:\\n res = [True, ]\\n if res[0] == False:\\n return False\\n for k in right.keys():\\n if (k in left):\\n if (isinstance(left[k], dict) and isinstance(right[k], dict)):\\n dict_match(left[k], right[k], res=res)\\n else:\\n res[0] = res[0] and left[k] == right[k]\\n if res[0] == False:\\n break\\n return res[0]\",\n \"def merge_dicts(a, b):\\n new_dict = defaultdict(list)\\n add_to_dict(a, new_dict)\\n add_to_dict(b, new_dict)\\n return new_dict\",\n \"def merge(target_config, other_config):\\n for key, value in other_config.items():\\n if key not in target_config or not isinstance(value, dict):\\n target_config[key] = value\\n else:\\n merge(target_config[key], other_config[key])\",\n \"def recursive_update(d1, d2):\\n\\n for k in d1.keys():\\n if k not in d2:\\n continue\\n\\n if isinstance(d1[k], dict) and isinstance(d2[k], dict):\\n d1[k] = recursive_update(d1[k], d2[k])\\n else:\\n d1[k] = d2[k]\\n\\n for k in d2.keys():\\n if k not in d1:\\n d1[k] = d2[k]\\n\\n return d1\",\n \"def merge_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def merge_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def merge_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def _merge(self, other: dict):\\n self._storage = dict_merge(self._storage, other)\",\n \"def merge_two_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def merge_two_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def merge_two_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def merge_two_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def merge_two_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def merge_two_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def merge_two_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def merge_two_dicts(x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def merge_dicts(src, patch):\\n for key in patch:\\n if key in src:\\n if isinstance(src[key], dict) and isinstance(patch[key], dict):\\n merge_dicts(src[key], patch[key])\\n else:\\n src[key] = merge_values(src[key], patch[key])\\n else:\\n src[key] = patch[key]\\n\\n return src\",\n \"def _merge(old, new):\\n from collections.abc import Mapping\\n if new is None:\\n return\\n for key in new:\\n if isinstance(new[key], Mapping):\\n if isinstance(old.get(key), Mapping):\\n yield (old[key], new[key])\\n else:\\n old[key] = dict(new[key])\\n else:\\n old[key] = new[key]\",\n \"def recursive_update(d1, d2):\\n\\n for key, value in d2.items():\\n if key in d1 and isinstance(d1[key], dict) and isinstance(value, dict):\\n recursive_update(d1[key], value)\\n else:\\n d1[key] = value\",\n \"def test_merge_two_two():\\n run_merge([1, 3], [2, 4], [1, 2, 3, 4])\",\n \"def test_merge_one():\\n run_merge([2], [1], [1, 2])\",\n \"def merge_two_dicts(self, x, y):\\n z = x.copy()\\n z.update(y)\\n return z\",\n \"def test_merge_merges_two_pairs():\\n L = [1, 3, 5]\\n R = [2, 4, 6]\\n assert merge(L, R) == [1, 2, 3, 4, 5, 6]\",\n \"def test_merge_empty():\\n run_merge([], [], [])\",\n \"def canBeMergedWith(self, other):\",\n \"def test_dict_merge_immutable():\\n x1 = {'one': 1, 'two': 2}\\n x1_cop = x1.copy()\\n ir.dict_merge(x1, {'three': 3, 'two': None})\\n assert x1 == x1_cop\\n ir.dict_merge({'ten': 10, 'one': '1'}, x1)\\n assert x1 == x1_cop\",\n \"def merge_dicts(self, source, target):\\n\\n source_keys = set(source.keys())\\n target_keys = set(target.keys())\\n\\n # added\\n new = source_keys - target_keys\\n for key in new:\\n target[key] = source[key]\\n\\n # removed\\n removed = target_keys - source_keys\\n\\n # changed\\n common = target_keys.intersection(source_keys)\\n\\n diff = set()\\n better = 0\\n for key in common:\\n if not self.ignore_fuzzy:\\n fuzzy_source = 'fuzzy' in source[key].flags or \\\\\\n source[key].obsolete\\n fuzzy_target = 'fuzzy' in target[key].flags or \\\\\\n target[key].obsolete\\n else:\\n fuzzy_source = fuzzy_target = False\\n\\n # sources that are not fuzzy/obsolete and which are of better\\n # quality than the existing target keys (empty or fuzzy/obsolete)\\n # are taken as non-conflicting but summed as changed\\n better_singular = source[key].msgstr and \\\\\\n (not target[key].msgstr or fuzzy_target)\\n better_plural = source[key].msgstr_plural and \\\\\\n (not target[key].msgstr_plural or fuzzy_target)\\n if not fuzzy_source and (better_singular or better_plural):\\n target[key] = source[key]\\n better += 1\\n elif target[key].msgstr != source[key].msgstr or \\\\\\n target[key].msgstr_plural != source[key].msgstr_plural:\\n diff.add(key)\\n\\n if len(diff) > 0:\\n if self.merge_conflict:\\n target = None\\n elif self.merge_overwrite:\\n for key in diff:\\n te = target[key]\\n se = source[key]\\n te.msgstr = se.msgstr\\n te.msgid_plural = se.msgid_plural\\n te.msgstr_plural = se.msgstr_plural\\n te.flags = se.flags\\n elif self.merge_skip:\\n pass # nothing has to be done\\n else:\\n self.log(\\\"We have a bug.\\\")\\n\\n return (target, len(new), len(diff) + better, len(removed))\",\n \"def test_merge_word_dicts():\\n ref_dict = {\\\"accordeon\\\": [\\\"acordeon\\\"],\\n \\\"accoster\\\": [\\\"accostage\\\", \\\"acostage\\\"]}\\n\\n new_dict = {\\\"accordeon\\\": [\\\"accordeons\\\"],\\n \\\"accostage\\\": [\\\"accostages\\\"],\\n \\\"accumulateur\\\": [\\\"acumulateur\\\", \\\"acc\\\"]}\\n\\n word_dict.merge_word_dicts(ref_dict, new_dict)\\n\\n expected_dict = {\\\"accordeon\\\": [\\\"acordeon\\\", \\\"accordeons\\\"],\\n \\\"accoster\\\": [\\\"accostage\\\", \\\"acostage\\\", \\\"accostages\\\"],\\n \\\"accumulateur\\\": [\\\"acumulateur\\\", \\\"acc\\\"]}\\n\\n for correct_word in ref_dict:\\n assert sorted(list(set(ref_dict[correct_word]))) == sorted(list(set(expected_dict[correct_word])))\",\n \"def test_match_ordered(self):\\n first = dict(\\n a=1,\\n b=2,\\n )\\n\\n second = OrderedDict(\\n b=2,\\n a=1,\\n )\\n\\n check_keys_match_recursive(first, second, [])\",\n \"def merge_dicts(source, destination):\\n for key, value in source.items():\\n if isinstance(value, dict):\\n node = destination.setdefault(key, {})\\n merge_dicts(value, node)\\n else:\\n destination[key] = value\\n\\n return destination\",\n \"def build_ast(first: Dict[str, Any], second: Dict[str, Any]) -> Dict[str, Any]:\\n first_keys = first.keys()\\n second_keys = second.keys()\\n\\n add_keys = second_keys - first_keys\\n remove_keys = first_keys - second_keys\\n common_keys = first_keys & second_keys\\n\\n added = {\\n key: {TYPE: ADDED, VALUE: second[key]}\\n for key in add_keys\\n }\\n removed = {\\n key: {TYPE: REMOVED, VALUE: first[key]}\\n for key in remove_keys\\n }\\n\\n common = {}\\n for key in common_keys:\\n first_item = first[key]\\n second_item = second[key]\\n if first_item == second_item:\\n common[key] = {\\n TYPE: UNCHANGED,\\n VALUE: second_item,\\n }\\n elif isinstance(first_item, dict) and isinstance(second_item, dict):\\n common[key] = {\\n TYPE: PARENT,\\n CHILDREN: build_ast(first_item, second_item),\\n }\\n else:\\n common[key] = {\\n TYPE: CHANGED,\\n VALUE: second_item,\\n OLD_VALUE: first_item,\\n }\\n\\n return {**common, **added, **removed}\",\n \"def _do_merge(ext, exts_other):\\n for ext_other in exts_other:\\n if not ext.is_duplicate(ext_other):\\n return False\\n return True\",\n \"def test_merge_person_2(self):\\r\\n result = merge('op_same', 'Person')\\r\\n self.assertEqual(result,\\r\\n {'Related Organizations': '<ul></ul>',\\r\\n 'Kind': u'Dude',\\r\\n 'Alternate Names': u'Nickname',\\r\\n 'Related Crises': '<ul></ul>',\\r\\n 'Maps': '<ul class=\\\"unstyled\\\"><li><iframe width=\\\"425\\\" height=\\\"350\\\" frameborder=\\\"0\\\" scrolling=\\\"no\\\" marginheight=\\\"0\\\" marginwidth=\\\"0\\\" src=\\\"https://maps.google.com/maps?q=New York, New York, USA&output=embed\\\"></iframe></li><br /></ul>',\\r\\n 'Citations': '<ul></ul>',\\r\\n 'External Links': '<ul></ul>',\\r\\n 'Location': 'New York, New York, USA<br />',\\r\\n 'Social': '<ul class=\\\"unstyled\\\"></ul>',\\r\\n 'Images': '<ul class=\\\"thumbnails\\\"></ul>',\\r\\n 'Name': u'Nickname',\\r\\n 'Description': u'Dude abides'}\\r\\n )\",\n \"def _dict_merge(merge_dict, into_dict):\\n for k, v in merge_dict.items():\\n if k not in into_dict:\\n into_dict[k] = v\\n continue\\n\\n current_val = into_dict[k]\\n\\n if isinstance(v, dict) and isinstance(current_val, dict):\\n _dict_merge(v, current_val)\\n continue\\n\\n # otherwise we just overwrite\\n into_dict[k] = v\",\n \"def merge(a, b):\\n a = sort_place(a)\\n b = sort_place(b)\\n ai = 0\\n bi = 0\\n d = None\\n wina = []\\n winb = []\\n bk = {}\\n conflict = False\\n ret = []\\n while True:\\n try:\\n x = a[ai]\\n except:\\n x = {None: None}\\n try:\\n y = b[bi]\\n except:\\n y = {None: None}\\n\\n xk = list(x)[0]\\n yk = list(y)[0]\\n xv = x[xk]\\n yv = y[yk]\\n\\n # end\\n if xk is None and yk is None and not conflict:\\n break\\n\\n # no conflict\\n if not conflict:\\n if xk == yk:\\n # both keys and values are same (match)\\n if xv == yv:\\n # current val counter = value or ++\\n if None in [xv, d] or d < xv:\\n d = xv\\n ret.append({xk: d})\\n if d is not None:\\n d += 1\\n # keys are same but values differ\\n else:\\n # val counter = max value\\n d = max(filter(None, [xv,yv,d]))\\n ret.append({xk: d})\\n d += 1\\n ai += 1\\n bi += 1\\n # differing keys. conflict! or one of the lists ran out\\n else:\\n conflict = True\\n # set up windows\\n wina = [xk]\\n winb = [yk]\\n # and store key's values\\n bk = {xk: xv, yk: yv}\\n ai += 1\\n bi += 1\\n # there was a conflict in the past\\n else:\\n # if there is an item, put it in the window\\n if xk is not None:\\n wina.append(xk)\\n ai += 1\\n if yk is not None:\\n winb.append(yk)\\n bi += 1\\n\\n # avoiding None since xk/yk can be None, \\n # but really they shouldn't be here. check later\\n found = False\\n\\n foundv = None\\n\\n # see if key in opposite window\\n if xk in winb:\\n found = xk\\n foundv = xv\\n if yk in wina:\\n found = yk\\n foundv = yv\\n # does this overwrite anything?\\n if not found:\\n bk[xk] = xv\\n bk[yk] = yv\\n \\n out = xk is None and yk is None and not found\\n\\n if found is not False or out:\\n # crop windows up until found value\\n posa = []\\n posb = []\\n for p, w in [(posa, wina), (posb, winb)]:\\n for k in w:\\n if k == found:\\n break\\n p.append(k) \\n wina = wina[len(posa)+1:]\\n winb = winb[len(posb)+1:]\\n # sort cropped windows based on 1st value\\n posa, posb = sorted([posa,posb], key=lambda i: '' if len(i)==0 else i[0])\\n # add them to the return\\n FOUND_ONE = {found: foundv}\\n for p in [*posa, *posb, FOUND_ONE]:\\n if p is FOUND_ONE:\\n p = found\\n pv = foundv\\n if out:\\n break\\n else:\\n pv = bk[p]\\n\\n if None in [pv, d] or d < pv:\\n d = pv\\n ret.append({p: d})\\n if d is not None:\\n d += 1\\n # backtrack to resume point\\n ai -= len(wina)\\n bi -= len(winb)\\n wina = []\\n winb = []\\n conflict = False\\n \\n \\n else:\\n pass\\n \\n\\n # # same\\n # if list(x) == list(y):\\n # if xv is None and yv is None:\\n # ret.append({xk: d})\\n # if d is not None:\\n # d+=1\\n # else:\\n # if d is not None:\\n # ret.append({xk: d})\\n # d+=1\\n # continue\\n # not_nones = [i for i in [xv,yv] if i is not None]\\n # if len(not_nones) == 1:\\n return ret\",\n \"def merge(incoming={}, output={}, overwrite=False):\\n\\t_output = output.copy()\\n\\tfor _key, _value in incoming.items(): # loop through each key/value pair\\n\\t\\tif (_key in _output) and isinstance(_value, dict): # detect when we need to recurse\\n\\t\\t\\t_output[_key] = merge(_value, _output[_key]) # recurse\\n\\t\\telse: # _key is not in output\\n\\t\\t\\tif _key in _output and overwrite == False: # we check if it already exists, and if we care\\n\\t\\t\\t\\tcontinue # don't overwrite existing values unless overwrite is 'True'\\n\\t\\t\\t_output[_key] = _value # add key/value pair\\n\\n\\treturn _output # give back the merged dict\",\n \"def merge_results(res1, res2):\\n empty = []\\n keys = set(res1).union(res2)\\n return dict((k, res1.get(k, empty) + res2.get(k, empty)) for k in keys)\",\n \"def dict_merge(dest, upd, recursive_update=True, merge_lists=False):\\n if (not isinstance(dest, collections.Mapping)) \\\\\\n or (not isinstance(upd, collections.Mapping)):\\n raise TypeError('Cannot update using non-dict types in dictupdate.update()')\\n updkeys = list(upd.keys())\\n if not set(list(dest.keys())) & set(updkeys):\\n recursive_update = False\\n if recursive_update:\\n for key in updkeys:\\n val = upd[key]\\n try:\\n dest_subkey = dest.get(key, None)\\n except AttributeError:\\n dest_subkey = None\\n if isinstance(dest_subkey, collections.Mapping) \\\\\\n and isinstance(val, collections.Mapping):\\n ret = dict_merge(dest_subkey, val, merge_lists=merge_lists)\\n dest[key] = ret\\n elif isinstance(dest_subkey, list) \\\\\\n and isinstance(val, list):\\n if merge_lists:\\n merged = copy.deepcopy(dest_subkey)\\n merged.extend([x for x in val if x not in merged])\\n dest[key] = merged\\n else:\\n dest[key] = upd[key]\\n else:\\n dest[key] = upd[key]\\n return dest\\n else:\\n try:\\n for k in upd:\\n dest[k] = upd[k]\\n except AttributeError:\\n # this mapping is not a dict\\n for k in upd:\\n dest[k] = upd[k]\\n return dest\",\n \"def merge(dict1: dict, dict2: dict):\\n if dict1 is None or dict2 is None:\\n return None\\n keys1 = dict1.keys()\\n keys2 = dict2.keys()\\n if keys1 & keys2 != set():\\n raise Exception(\\\"Non linear patterns not supported\\\")\\n dict_r = {k: dict1[k] for k in keys1}\\n dict_r.update({k: dict2[k] for k in keys2})\\n return dict_r\",\n \"def merge(source, destination):\\n for key, value in source.items():\\n if isinstance(value, dict):\\n # get node or create one\\n node = destination.setdefault(key, {})\\n merge(value, node)\\n else:\\n destination[key] = value\\n\\n return destination\",\n \"def test_contain(fs1,fs2):\\n if test_leaf(fs1) == False or test_leaf(fs2) == False:\\n raise ValueError('Two arguments must be leaf nodes.')\\n key_1 = fs1.keys()\\n key_2 = fs2.keys()\\n new_key_1 = []\\n new_key_2 = []\\n for i in key_1:\\n if i in key_2:\\n new_key_1.append(i)\\n for i in key_2:\\n if i in key_1:\\n new_key_2.append(i)\\n len_1 = len(key_1)\\n len_2 = len(key_2)\\n new_len_1 = len(new_key_1)\\n new_len_2 = len(new_key_2)\\n # Now new_len_1 and new_len_2 are the same keys in both fs, or both []\\n # means no same keys\\n #print new_key_1\\n #print new_key_2\\n if new_len_1 == 0 and new_len_2 == 0:\\n return None\\n elif len_1 != new_len_1 and len_2 != new_len_2:\\n ret = FeatStruct()\\n for i in new_key_1:\\n ret[i] = fs1[i]\\n return ret\\n elif len_1 == new_len_1 and len_2 != new_len_2:\\n return 1 # len_1 not changed, it is contained in len_2\\n elif len_1 != new_len_1 and len_2 == new_len_2:\\n return -1 # len_2 contained in len_1\\n else:\\n return 0 # Neigher has changed, so they are equal\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70699894","0.68547225","0.68238133","0.66119456","0.65711254","0.65020204","0.6499048","0.6461705","0.6442443","0.6428743","0.6409568","0.63924336","0.6374389","0.6356342","0.6348538","0.6343374","0.6339143","0.6316803","0.62815475","0.62786376","0.62719953","0.6268792","0.6240714","0.62120324","0.62008053","0.6191231","0.6183825","0.61566615","0.612764","0.61219925","0.6110099","0.6100288","0.60984623","0.6081836","0.60530376","0.6044654","0.60341567","0.6033258","0.60243","0.59960175","0.59828395","0.59754455","0.59690803","0.59669244","0.596345","0.59555554","0.594885","0.59396577","0.59278077","0.59058255","0.58641547","0.5847726","0.5845762","0.5842072","0.5841243","0.58282053","0.5820292","0.57947546","0.57887733","0.5785497","0.57784975","0.5774914","0.5749929","0.57396764","0.57396764","0.57396764","0.5736844","0.5736209","0.5736209","0.5736209","0.5736209","0.5736209","0.5736209","0.5736209","0.5736209","0.5729788","0.57228255","0.57159084","0.57053006","0.5698905","0.5687988","0.56863886","0.56859696","0.5676286","0.5674628","0.5672704","0.5672096","0.5668794","0.5665879","0.56606865","0.5656856","0.56546295","0.5649192","0.56486565","0.56476057","0.5645669","0.5636185","0.5634199","0.56334025","0.5625165"],"string":"[\n \"0.70699894\",\n \"0.68547225\",\n \"0.68238133\",\n \"0.66119456\",\n \"0.65711254\",\n \"0.65020204\",\n \"0.6499048\",\n \"0.6461705\",\n \"0.6442443\",\n \"0.6428743\",\n \"0.6409568\",\n \"0.63924336\",\n \"0.6374389\",\n \"0.6356342\",\n \"0.6348538\",\n \"0.6343374\",\n \"0.6339143\",\n \"0.6316803\",\n \"0.62815475\",\n \"0.62786376\",\n \"0.62719953\",\n \"0.6268792\",\n \"0.6240714\",\n \"0.62120324\",\n \"0.62008053\",\n \"0.6191231\",\n \"0.6183825\",\n \"0.61566615\",\n \"0.612764\",\n \"0.61219925\",\n \"0.6110099\",\n \"0.6100288\",\n \"0.60984623\",\n \"0.6081836\",\n \"0.60530376\",\n \"0.6044654\",\n \"0.60341567\",\n \"0.6033258\",\n \"0.60243\",\n \"0.59960175\",\n \"0.59828395\",\n \"0.59754455\",\n \"0.59690803\",\n \"0.59669244\",\n \"0.596345\",\n \"0.59555554\",\n \"0.594885\",\n \"0.59396577\",\n \"0.59278077\",\n \"0.59058255\",\n \"0.58641547\",\n \"0.5847726\",\n \"0.5845762\",\n \"0.5842072\",\n \"0.5841243\",\n \"0.58282053\",\n \"0.5820292\",\n \"0.57947546\",\n \"0.57887733\",\n \"0.5785497\",\n \"0.57784975\",\n \"0.5774914\",\n \"0.5749929\",\n \"0.57396764\",\n \"0.57396764\",\n \"0.57396764\",\n \"0.5736844\",\n \"0.5736209\",\n \"0.5736209\",\n \"0.5736209\",\n \"0.5736209\",\n \"0.5736209\",\n \"0.5736209\",\n \"0.5736209\",\n \"0.5736209\",\n \"0.5729788\",\n \"0.57228255\",\n \"0.57159084\",\n \"0.57053006\",\n \"0.5698905\",\n \"0.5687988\",\n \"0.56863886\",\n \"0.56859696\",\n \"0.5676286\",\n \"0.5674628\",\n \"0.5672704\",\n \"0.5672096\",\n \"0.5668794\",\n \"0.5665879\",\n \"0.56606865\",\n \"0.5656856\",\n \"0.56546295\",\n \"0.5649192\",\n \"0.56486565\",\n \"0.56476057\",\n \"0.5645669\",\n \"0.5636185\",\n \"0.5634199\",\n \"0.56334025\",\n \"0.5625165\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64845693"},"document_rank":{"kind":"string","value":"7"}}},{"rowIdx":77987,"cells":{"query":{"kind":"string","value":"Test file creation and collection"},"document":{"kind":"string","value":"def test_file_collection():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.make_job_file(STATUS_DIR, 'generation', 'test1', TEST_1_ATTRS_1)\n Status.make_job_file(STATUS_DIR, 'generation', 'test2', TEST_2_ATTRS_1)\n\n Status.update(STATUS_DIR)\n with open(os.path.join(STATUS_DIR, 'rev_status.json'), 'r') as f:\n data = json.load(f)\n assert str(TEST_1_ATTRS_1) in str(data)\n assert str(TEST_2_ATTRS_1) in str(data)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_file_creation(data, logging_file_name):\n create_instance(data, logging_file_name)\n log_file_name = create_file_path(logging_file_name)\n print(log_file_name)\n if data is None or len(data) == 0:\n assert not os.path.exists(log_file_name)\n else:\n assert os.path.exists(log_file_name)","def test_create_files(self):\n\n testdir = \"test_output\"\n test_submission = Submission()\n self.addCleanup(os.remove, \"submission.tar.gz\")\n self.addCleanup(shutil.rmtree, testdir)\n\n test_submission.create_files(testdir)\n\n self.doCleanups()","def test_get_file_object(self):\n pass","def test_create(self):\n path = self.tmp_py()\n # Creating a file that doesn't exist should succeed\n self.cls.create(path)\n self.assertTrue(os.path.exists(path))\n # Created file should be a valid script (If not, raises an error)\n self.cls.verify(path)\n # Can't create it again: it already exists\n self.assertRaises(exceptions.PathFoundError,self.cls.create,path)","def test_create_file(self):\n def validate_in(*args, **kwargs):\n # Validate that the element is built properly\n wrap = args[0]\n\n self.assertEqual('chk', wrap._get_val_str(vf._FILE_CHKSUM))\n self.assertEqual(50, wrap.expected_file_size)\n self.assertEqual('f_name', wrap.file_name)\n self.assertEqual('application/octet-stream',\n wrap.internet_media_type)\n self.assertEqual('f_type', wrap.enum_type)\n self.assertEqual('v_uuid', wrap.vios_uuid)\n self.assertEqual('tdev_uuid', wrap.tdev_udid)\n ret = adp.Response('reqmethod', 'reqpath', 'status', 'reason', {})\n ret.entry = ewrap.EntryWrapper._bld(self.adpt, tag='File').entry\n return ret\n self.adpt.create.side_effect = validate_in\n\n ts._create_file(self.adpt, 'f_name', 'f_type', 'v_uuid', 'chk', 50,\n 'tdev_uuid')\n self.assertTrue(self.adpt.create.called)","def test_CRUD_ProjectFiles_Successfully(self):\n\n real_file_code = get_content('BasicTemplateAlgorithm.py')\n second_real_file_code = get_content('BasicTemplateForexAlgorithm.py')\n\n fakeFile = {\"name\":\"Hello.py\", \"code\": \"Hello World!\"}\n realFile = {\"name\":\"main.py\", \"code\": real_file_code}\n secondRealFile = {\"name\":\"lol.py\", \"code\": second_real_file_code}\n\n # Create a new project and make sure there are no files\n project = self.api.create_project(\"Test project - \", \"Py\")\n self.assertTrue(project['success'])\n self.assertTrue(project['projects'][0]['projectId'] > 0)\n\n # Add random file\n randomAdd = self.api.add_project_file(project['projects'][0]['projectId'], fakeFile[\"name\"], fakeFile[\"code\"])\n self.assertTrue(randomAdd['success'])\n self.assertTrue(randomAdd['files'][0]['content'] == fakeFile['code'])\n self.assertTrue(randomAdd['files'][0]['name'] == fakeFile['name'])\n\n # Update names of file\n updatedName = self.api.update_project_filename(project['projects'][0]['projectId'], randomAdd['files'][0]['name'], realFile['name'])\n self.assertTrue(updatedName['success'])\n\n # Replace content of file\n updateContents = self.api.update_project_file_content(project['projects'][0]['projectId'], realFile[\"name\"], realFile['code'])\n self.assertTrue(updateContents['success'])\n\n # Read single file\n readFile = self.api.read_project_file(project['projects'][0]['projectId'], realFile['name'])\n self.assertTrue(readFile['success'])\n self.assertTrue(readFile['files'][0]['content'] == realFile['code'])\n self.assertTrue(readFile['files'][0]['name'] == realFile['name'])\n\n # Add a second file\n secondFile = self.api.add_project_file(project['projects'][0]['projectId'], secondRealFile['name'], secondRealFile['code'])\n self.assertTrue(secondFile['success'])\n self.assertTrue(secondFile['files'][0]['content'] == secondRealFile['code'])\n self.assertTrue(secondFile['files'][0]['name'] == secondRealFile['name'])\n\n # Read multiple files\n readFiles = self.api.read_project_files(project['projects'][0]['projectId'])\n self.assertTrue(readFiles['success'])\n self.assertTrue(len(readFiles['files']) == 2)\n\n # Delete the second file\n deleteFile = self.api.delete_project_file(project['projects'][0]['projectId'], secondRealFile['name'])\n self.assertTrue(deleteFile['success'])\n\n # Read files\n readFilesAgain = self.api.read_project_files(project['projects'][0]['projectId'])\n self.assertTrue(readFilesAgain['success'])\n self.assertTrue(len(readFilesAgain['files']) == 1)\n self.assertTrue(readFilesAgain['files'][0]['name'] == realFile['name'])\n\n # Delete the project\n deleteProject = self.api.delete_project(project['projects'][0]['projectId'])\n self.assertTrue(deleteProject['success'])","def test_upload_file(self):\n pass","def test_fileOperations(self): \n from DIRAC import gConfig\n testSE = 'testSE'\n #rssClient = ResourceStatusClient()\n #result = rssClient.getStorageElementsList( 'Read' )\n #result = gConfig.getSections( '/Resources/StorageElements' )\n #if result['OK'] and result['Value']:\n # testSE = result['Value'][0]\n #if result['Ok']:\n # testSE = result['Value'][ 0 ] \n \n result = self.fc.addFile( { testFile: { 'PFN': 'testfile', \n 'SE': testSE , \n 'Size':0, \n 'GUID':0, \n 'Checksum':'0' } } )\n self.assert_( result['OK'] )\n if gConfig.getValue( '/Resources/StorageElements/%s/AccessProtocol.1/Host' % testSE, '' ):\n result = self.fc.getReplicas( testFile )\n self.assert_( result['OK'] )\n self.assert_( testFile in result['Value']['Successful'] )","def test_file_field():","def test_upload_file1(self):\n pass","def test_create1(self):\n fname = TempfileManager.create_tempfile()\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertTrue(fname.startswith('tmp'))","def test_create_content(self):\n url = reverse('content-list')\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\n content_file.write(b\"The contents of the temporary file.\\n\")\n content_file.seek(0)\n data = {\n 'name': 'Content File',\n 'description': 'File 1',\n 'content_file': content_file,\n 'updated_time': date.today(),\n 'creators': [],\n 'coverage': '',\n 'subjects': [],\n 'keywords': [],\n 'workareas': [],\n 'language': '',\n 'cataloger': ''\n }\n response = self.client.post(url, data, format='multipart')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)","def test_record_create_files(db, location):\n record = CernSearchRecord.create({\"title\": \"test\"})\n\n assert 0 == len(record.files)\n assert 0 == len(record.files_content)\n\n file_content = b\"Hello world!\"\n record.files[\"hello.txt\"] = BytesIO(file_content)\n db.session.commit()\n\n record = CernSearchRecord.get_record(record.id)\n\n assert record[\"_bucket\"] == record.bucket_id\n assert record[\"_files\"]\n\n file_1 = record.files[\"hello.txt\"]\n assert \"hello.txt\" == file_1[\"key\"]\n assert 1 == len(record.files)\n assert 1 == len(record[\"_files\"])\n assert 0 == len(record.files_content)\n\n storage = file_1.obj.file.storage() # type: FileStorage\n fp = storage.open(mode=READ_MODE_BINARY)\n\n try:\n assert file_content == fp.read()\n finally:\n fp.close()","def tests_ti_file_create(self):\n indicator_data = {\n 'md5': uuid.uuid4().hex.upper(),\n 'owner': self.owner,\n 'rating': randint(0, 5),\n }\n ti = self.ti.file(**indicator_data)\n r = ti.create()\n\n # assert response\n assert r.status_code == 201\n\n # retrieve indicator for asserts\n ti = self.ti.file(**indicator_data)\n r = ti.single()\n response_data = r.json()\n ti_data = response_data.get('data', {}).get(ti.api_entity)\n\n # validate response data\n assert r.status_code == 200\n assert response_data.get('status') == 'Success'\n\n # validate ti data\n assert ti_data.get('confidence') == indicator_data.get('confidence')\n assert ti_data.get(ti.api_entity) == indicator_data.get(ti.api_entity)\n assert ti_data.get('rating') == indicator_data.get('rating')\n\n # cleanup indicator\n r = ti.delete()\n assert r.status_code == 200","def test_create4(self):\n TempfileManager.sequential_files(2)\n fname = TempfileManager.create_tempfile()\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertEqual(fname, 'tmp2')\n #\n TempfileManager.unique_files()\n fname = TempfileManager.create_tempfile()\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 2)\n fname = os.path.basename(fname)\n self.assertNotEqual(fname, 'tmp3')\n self.assertTrue(fname.startswith('tmp'))","def test_add_remove_files(self):\n filepath = os.path.join(\n os.getcwd(), self.hiarc_util.TEST_FILE_PATH, 'Test.txt')\n f1 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n f2 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n f3 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n\n c1 = self.hiarc_collections.create_collection(\n self.hiarc_util.create_collection())\n\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f1.key), c1.key)\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f2.key), c1.key)\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f3.key), c1.key)\n\n files = self.hiarc_collections.get_collection_files(c1.key)\n assert len(files) == 3\n\n self.hiarc_collections.remove_file_from_collection(c1.key, f3.key)\n files = self.hiarc_collections.get_collection_files(c1.key)\n assert len(files) == 2\n\n for f in [f1.key, f2.key, f3.key]:\n self.hiarc_files.delete_file(f)\n self.assertRaises(hiarc.rest.ApiException,\n self.hiarc_files.get_file, f)","def test_create_run(self):\n pass","def test_save_to_file(self):\n self.assertFalse(os.path.exists(\"file.json\"))","def test_get_file_content(self):\n pass","def test_get_items(self):\n filepath = os.path.join(\n os.getcwd(), self.hiarc_util.TEST_FILE_PATH, 'Test.txt')\n c1 = self.hiarc_collections.create_collection(\n self.hiarc_util.create_collection())\n c2 = self.hiarc_collections.create_collection(\n self.hiarc_util.create_collection())\n c3 = self.hiarc_collections.create_collection(\n self.hiarc_util.create_collection())\n c4 = self.hiarc_collections.create_collection(\n self.hiarc_util.create_collection())\n c5 = self.hiarc_collections.create_collection(\n self.hiarc_util.create_collection())\n c6 = self.hiarc_collections.create_collection(\n self.hiarc_util.create_collection())\n\n self.hiarc_collections.add_child_to_collection(c1.key, c2.key)\n self.hiarc_collections.add_child_to_collection(c1.key, c3.key)\n self.hiarc_collections.add_child_to_collection(c1.key, c4.key)\n self.hiarc_collections.add_child_to_collection(c2.key, c5.key)\n self.hiarc_collections.add_child_to_collection(c4.key, c6.key)\n\n f1 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n f2 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n f3 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n f4 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n f5 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f1.key), c1.key)\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f2.key), c1.key)\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f3.key), c1.key)\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f4.key), c2.key)\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f5.key), c3.key)\n\n items = self.hiarc_collections.get_collection_items(c1.key)\n children = items.child_collections\n assert len(children) == 3\n assert next((c for c in children if self.hiarc_util.compare_entity_to_entity(\n c, c2)), None) is not None\n assert next((c for c in children if self.hiarc_util.compare_entity_to_entity(\n c, c3)), None) is not None\n assert next((c for c in children if self.hiarc_util.compare_entity_to_entity(\n c, c4)), None) is not None\n assert next((c for c in children if self.hiarc_util.compare_entity_to_entity(\n c, c5)), None) is None\n assert next((c for c in children if self.hiarc_util.compare_entity_to_entity(\n c, c6)), None) is None\n\n files = items.files\n assert len(files) == 3\n assert next((f for f in files if self.hiarc_util.compare_entity_to_entity(\n f, f1)), None) is not None\n assert next((f for f in files if self.hiarc_util.compare_entity_to_entity(\n f, f2)), None) is not None\n assert next((f for f in files if self.hiarc_util.compare_entity_to_entity(\n f, f3)), None) is not None\n assert next((f for f in files if self.hiarc_util.compare_entity_to_entity(\n f, f4)), None) is None\n assert next((f for f in files if self.hiarc_util.compare_entity_to_entity(\n f, f5)), None) is None\n\n for f in [f1.key, f2.key, f3.key, f4.key, f5.key]:\n self.hiarc_files.delete_file(f)\n self.assertRaises(hiarc.rest.ApiException,\n self.hiarc_files.get_file, f)","def test_to_file(self):\n fd, fp = mkstemp()\n close(fd)\n st = SampleTemplate.create(self.metadata, self.new_study)\n st.to_file(fp)\n self._clean_up_files.append(fp)\n with open(fp, 'U') as f:\n obs = f.read()\n self.assertEqual(obs, EXP_SAMPLE_TEMPLATE)\n\n fd, fp = mkstemp()\n close(fd)\n st.to_file(fp, {'2.Sample1', '2.Sample3'})\n self._clean_up_files.append(fp)\n\n with open(fp, 'U') as f:\n obs = f.read()\n self.assertEqual(obs, EXP_SAMPLE_TEMPLATE_FEWER_SAMPLES)","def test_load(api):\n # upload file to file.io servers\n uploaded_file = api.upload(\n tag='test_file',\n expiry='1d',\n path='tests/test_file.txt'\n )\n\n # check that instance of FileIO has these fields\n assert uploaded_file.link\n assert uploaded_file.key\n assert uploaded_file.tag\n assert uploaded_file.path\n\n # check that the uploaded file was added to uploaded files list\n assert api.show_uploads()\n\n # check that our list is not empty\n assert api.file_obj_list\n\n # export the file in json format\n api.export('tests/exported.json')\n\n # check that exported file exists\n assert path.isfile('tests/exported.json')\n\n # set it to empty list\n api.file_obj_list = []\n\n # load the file in json format\n api.load('tests/exported.json')\n\n # remove the file\n remove('tests/exported.json')\n\n # check that the uploaded file was added to uploaded files list\n assert api.show_uploads()\n\n # check that our list is not empty\n assert api.file_obj_list\n\n # export the file in pkl format\n api.export('tests/exported.pkl')\n\n # set it to empty list\n api.file_obj_list = []\n\n # load the file in pkl format\n api.load('tests/exported.pkl')\n\n # remove exported.pkl file\n remove('tests/exported.pkl')\n\n # check that the uploaded file was added to uploaded files list\n assert api.show_uploads()\n\n # check that our list is not empty\n assert api.file_obj_list","def test_file(self):\n browser = self.layer.get_web_browser(smi_settings)\n\n image = test_filename('test.txt')\n browser.login(self.username, self.username)\n self.assertEqual(browser.open('/root/edit'), 200)\n browser.macros.create(\n 'Silva File', id='file', title='Text File', file=image)\n self.assertEqual(\n browser.inspect.folder_listing, ['index', 'file'])\n\n # The user should by the last author on the content and container.\n self.assertEqual(\n self.root.sec_get_last_author_info().userid(),\n self.username)\n self.assertEqual(\n self.root.file.sec_get_last_author_info().userid(),\n self.username)\n\n # Visit the edit page\n self.assertEqual(\n browser.inspect.folder_listing['file'].click(),\n 200)\n self.assertEqual(browser.url, '/root/file/edit/tab_edit')\n self.assertEqual(browser.inspect.breadcrumbs, ['root', 'Text File'])\n browser.inspect.breadcrumbs['root'].click()\n browser.macros.delete('file')","def tests_ti_file_create_2(self):\n metadata = {\n 'size': 84504,\n 'sha256': '33af46377c0e52ca19aea233b3afb64505b32fac2231ec7a8a6795812fae0d10',\n 'md5': 'a9ba66af82897dadb82e3e89c70ae7ac',\n 'sha1': '19d08af69fe15af22ba81f045e31230150d4bdad',\n }\n file_indicator = self.ti.file(**metadata)\n file_indicator.delete()\n\n assert file_indicator.data['sha1'] == metadata['sha1']\n response = file_indicator.create()\n assert response.ok\n unique_id = ':'.join([metadata[x] for x in ['sha256', 'sha1', 'md5']])\n file_indicator = self.ti.file(unique_id=unique_id, **metadata)\n\n assert file_indicator.data['sha256'] == metadata['sha256']\n assert file_indicator.data['sha1'] == metadata['sha1']\n\n response = file_indicator.update()\n assert response.ok\n file_indicator.delete()","def test_create1a(self):\n fname = TempfileManager.create_tempfile(dir=tempdir)\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertTrue(fname.startswith('tmp'))","def test_add_and_read_file(self, tmpdir, fs):\n with fs.add_file('my-dir', 'my-file') as f:\n f.write('hello')\n assert fs.read('my-dir', 'my-file') == 'hello'","def create_mock_files(self,\n project_id=\"DEV-test\",\n count=3,\n prefix=\"mock_data_file\",\n file_format=\"dcm\",\n outdir=\".\",\n msg = \"This is a mock data file for testing purposes. Delete me!\",\n write_tsv = True\n ):\n prog,proj = project_id.split(\"-\")\n authz = [\"/programs/{}/projects/{}\".format(prog,proj)]\n acl = [prog,proj]\n\n mfiles = {'file_name':[],'md5sum':[],\"file_size\":[],\"object_id\":[],\"storage_urls\":[],\"acl\":[],\"authz\":[]}\n for i in range(count):\n file_name = \"{}_{}.{}\".format(prefix,i+1,file_format)\n object_id = str(uuid.uuid4())\n mfiles['file_name'].append(file_name)\n mfiles['object_id'].append(object_id)\n mfiles['authz'].append(authz)\n mfiles['acl'].append(acl)\n\n\n output = \"{}/{}\".format(outdir,file_name)\n os.system(\"touch {}\".format(output))\n file_msg =\"{} File {} of {}. {} with object_id {}.\".format(msg,i+1,count,file_name,object_id)\n cmd = 'echo \"{}\" > {}'.format(file_msg,file_name)\n os.system(cmd)\n\n with open(output, 'rb') as file_to_check:\n file_contents = file_to_check.read()\n #cmd = \"!md5 mock_data_file_{}.{}\".format(i+1,file_format))\n md5 = hashlib.md5(file_contents).hexdigest() #check in shell: !md5 mock_data_file_3.dcm\n\n mfiles['md5sum'].append(md5)\n mfiles['file_size'].append(os.stat(output).st_size)\n urls=\"s3://this-is-a-fake-url-for:{}\".format(file_name)\n mfiles['storage_urls'].append([urls])\n\n return mfiles","def test_create_files(self):\n opts = mock.Mock()\n opts.repo = self.repo\n opts.source = 'src'\n opts.version = '0.0.1'\n opts.version_file = None\n opts.test_mode = False\n opts.templates = ['include steve/*']\n opts.history_file = 'HISTORY.md'\n opts.package = 'unittests'\n opts.desc = \"DESCRIPTION\"\n opts.org = \"ORG\"\n opts.develop = 'develop'\n opts.requirements = 'requirements.txt'\n opts.test_requirements = 'test-requirements.txt'\n opts.pypi_package_name = None\n opts.python = None\n opts.create_version_file = False\n opts.gitignore_url = \"GIT_IGNORE_URL\"\n opts.add_gitignore = False\n create_files(opts)\n\n dir_list = os.listdir(self.repo)\n self.failUnless('cirrus.conf' in dir_list)\n self.failUnless('HISTORY.md' in dir_list)\n self.failUnless('MANIFEST.in' in dir_list)\n self.failUnless('setup.py' in dir_list)\n\n cirrus_conf = os.path.join(self.repo, 'cirrus.conf')\n config = ConfigParser.RawConfigParser()\n config.read(cirrus_conf)\n self.assertEqual(config.get('package', 'name'), opts.package)\n self.assertEqual(config.get('package', 'version'), opts.version)\n\n history = os.path.join(self.repo, 'HISTORY.md')\n with open(history, 'r') as handle:\n self.failUnless('CIRRUS_HISTORY_SENTINEL' in handle.read())\n\n manifest = os.path.join(self.repo, 'MANIFEST.in')\n with open(manifest, 'r') as handle:\n content = handle.read()\n self.failUnless('include requirements.txt' in content)\n self.failUnless('include cirrus.conf' in content)\n self.failUnless('include steve/*' in content)\n\n version = os.path.join(self.repo, 'src', 'unittests', '__init__.py')\n with open(version, 'r') as handle:\n self.failUnless(opts.version in handle.read())","def test_get_files_for_user(self):\n filepath = os.path.join(\n os.getcwd(), self.hiarc_util.TEST_FILE_PATH, 'Test.txt')\n\n u1 = self.hiarc_users.create_user(self.hiarc_util.create_user())\n c1 = self.hiarc_collections.create_collection(\n self.hiarc_util.create_collection())\n\n f1 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n f2 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n f3 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f1.key), c1.key)\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f2.key), c1.key)\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f3.key), c1.key)\n\n self.assertRaises(hiarc.rest.ApiException,\n self.hiarc_collections.get_collection_files, c1.key, x_hiarc_user_key=u1.key)\n\n self.hiarc_collections.add_user_to_collection(\n hiarc.AddUserToCollectionRequest(u1.key, hiarc.AccessLevel.READ_ONLY), c1.key)\n\n files = self.hiarc_collections.get_collection_files(\n c1.key, x_hiarc_user_key=u1.key)\n assert len(files) == 3\n\n ut = self.hiarc_token.create_user_token(\n hiarc.CreateUserTokenRequest(key=u1.key))\n lc = hiarc.CollectionApi(hiarc.ApiClient(\n configuration=self.hiarc_util.init_hiarc_client_jwt_token(ut.bearer_token)))\n files = lc.get_collection_files(c1.key)\n assert len(files) == 3\n\n # reset ApiClient to Admin User\n hiarc.ApiClient(\n configuration=self.hiarc_util.init_hiarc_config_admin())\n\n for f in [f1.key, f2.key, f3.key]:\n self.hiarc_files.delete_file(f)\n self.assertRaises(hiarc.rest.ApiException,\n self.hiarc_files.get_file, f)","def create(self):\n self.create_file()","def test_create2(self):\n fname = TempfileManager.create_tempfile(prefix='foo')\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertTrue(fname.startswith('foo'))","def test_file_has_details(self):\n file_content = bytes(\n \"\"\"\n 1001 | Eggs | 12\\n\n 1002 | Bread | 1\\n\n 1003 | Butter | 1\\n\n 1004 | Milk | 1\\n\n 1005 | Corn | 2\n \"\"\", encoding='UTF-8'\n )\n\n test_file = SimpleUploadedFile(\n \"sample.txt\", file_content, content_type=\"text/txt\")\n \n new_file = File.objects.create(\n file=test_file,\n rows=5,\n items='Eggs, Bread, Butter, Milk, Corn'\n )\n\n file_details = File.objects.get(id=new_file.id)\n self.assertEqual(file_details.file.name, new_file.file.name)\n self.assertEqual(file_details.rows, new_file.rows)\n self.assertEqual(file_details.items, new_file.items)","def test_upload(self):\n fake_file_name = 'fake_file_name'\n\n backend = self.test_init_valid()\n backend.upload(fake_file_name)\n\n backend.vault.concurrent_create_archive_from_file.assert_called_once_with(filename=fake_file_name, description='')","def test_delete_with_file(self):\n c1 = self.hiarc_collections.create_collection(\n self.hiarc_util.create_collection())\n\n filepath = os.path.join(\n os.getcwd(), self.hiarc_util.TEST_FILE_PATH, 'Test.txt')\n f1 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n\n self.hiarc_collections.add_file_to_collection(\n hiarc.AddFileToCollectionRequest(f1.key), c1.key)\n\n self.hiarc_collections.delete_collection(c1.key)\n self.assertRaises(hiarc.rest.ApiException,\n self.hiarc_collections.get_collection, c1.key)\n\n r = self.hiarc_files.get_file(f1.key)\n assert f1 == r\n\n self.hiarc_files.delete_file(f1.key)\n self.assertRaises(hiarc.rest.ApiException,\n self.hiarc_files.get_file, f1.key)","def testFileset(self):\n testCollection = CouchCollection(database=self.testInit.couchDbName,\n url=self.testInit.couchUrl,\n name=\"Thunderstruck\")\n testFileset = CouchFileset(database=self.testInit.couchDbName,\n url=self.testInit.couchUrl,\n name=\"TestFileset\")\n testCollection.addFileset(testFileset)\n\n testFiles = {}\n for i in range(5):\n lfn = makeUUID()\n testFile = File(lfn=lfn, size=random.randint(1024, 4096),\n events=random.randint(1024, 4096))\n testFiles[lfn] = testFile\n testFileset.add([testFile])\n\n for file in testFileset.fileset().files:\n self.assertTrue(file[\"lfn\"] in testFiles,\n \"Error: File missing.\")\n self.assertEqual(file[\"events\"], testFiles[file[\"lfn\"]][\"events\"],\n \"Error: Wrong number of events.\")\n self.assertEqual(file[\"size\"], testFiles[file[\"lfn\"]][\"size\"],\n \"Error: Wrong file size.\")\n return","def test_file_analyzer(self):\r\n file_analyzer = FileAnalyzer(\"C:\\\\Users\\\\Himan\\\\Desktop\\\\Semester 2\\\\SSW 810\\\\HW\\\\Assignment 8\")\r\n self.assertEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 1, 'function': 5, 'line': 100, 'char': 4472}, \\\r\n 'HW08_Test_Himanshu.py': {'class': 1, 'function': 3, 'line': 38, 'char': 1861}})\r\n self.assertNotEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 0, 'function': 5, 'line': 46, 'char': 1931}})\r\n\r\n self.assertNotEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 1, 'function': 5, 'line': 100}}) # testing less fields\r\n\r\n with self.assertRaises(FileNotFoundError): # raises exception error\r\n FileAnalyzer(\"C:\\\\Users\\\\Himan\\\\Desktop\\\\Semester 2\\\\SSW 810\\\\HW\\\\Assignment 10\").files_summary","def __create_test_file(self):\n self.test_file = os.path.join(os.path.dirname(self.server_path), \"data\")\n with open(self.test_file, \"ab+\") as f:\n n_blocks = int(self.args.size) // self.max_block_size\n for i in range(n_blocks):\n f.write(bytearray(os.urandom(self.max_block_size)))\n remaining = int(self.args.size) % self.max_block_size\n if remaining > 0:\n f.write(bytearray(os.urandom(remaining)))\n self.assertEqual(int(self.args.size), os.path.getsize(self.test_file))","def test_add_remove_files_as_user(self):\n u1 = self.hiarc_users.create_user(self.hiarc_util.create_user())\n\n filepath = os.path.join(\n os.getcwd(), self.hiarc_util.TEST_FILE_PATH, 'Test.txt')\n f1 = self.hiarc_files.create_file(\n self.hiarc_util.create_file(), filepath)\n\n c1 = self.hiarc_collections.create_collection(\n self.hiarc_util.create_collection())\n\n aftcr = hiarc.AddFileToCollectionRequest(f1.key)\n self.assertRaises(hiarc.rest.ApiException, self.hiarc_collections.add_file_to_collection,\n aftcr, c1.key, x_hiarc_user_key=u1.key)\n\n self.hiarc_collections.add_user_to_collection(\n hiarc.AddUserToCollectionRequest(u1.key, hiarc.AccessLevel.READ_ONLY), c1.key)\n self.assertRaises(hiarc.rest.ApiException, self.hiarc_collections.add_file_to_collection,\n aftcr, c1.key, x_hiarc_user_key=u1.key)\n\n u2 = self.hiarc_users.create_user(self.hiarc_util.create_user())\n self.hiarc_collections.add_user_to_collection(\n hiarc.AddUserToCollectionRequest(u2.key, hiarc.AccessLevel.READ_WRITE), c1.key)\n self.hiarc_collections.add_file_to_collection(\n aftcr, c1.key, x_hiarc_user_key=u2.key)\n\n files = self.hiarc_collections.get_collection_files(c1.key)\n assert len(files) == 1\n\n u3 = self.hiarc_users.create_user(self.hiarc_util.create_user())\n self.assertRaises(hiarc.rest.ApiException, self.hiarc_collections.remove_file_from_collection,\n c1.key, f1.key, x_hiarc_user_key=u3.key)\n self.assertRaises(hiarc.rest.ApiException, self.hiarc_collections.remove_file_from_collection,\n c1.key, f1.key, x_hiarc_user_key=u1.key)\n self.hiarc_collections.remove_file_from_collection(\n c1.key, f1.key, x_hiarc_user_key=u2.key)\n\n files = self.hiarc_collections.get_collection_files(c1.key)\n assert len(files) == 0\n\n self.hiarc_files.delete_file(f1.key)\n self.assertRaises(hiarc.rest.ApiException,\n self.hiarc_files.get_file, f1.key)","def test_identify_contents_1(self):\n Path(self.base_dir, \"new_dir\").mkdir()\n Path(self.base_dir, \"file1.txt\").touch()\n Path(self.base_dir, \".DS_Store\").touch()\n list_of_items = basic.identify_contents(self.base_dir, kind=\"file\")\n exp_num_items = 2\n self.assertEqual(len(list_of_items), exp_num_items)","def test_files(host, f):\n assert host.file(f).exists","def test_add1(self):\n try:\n TempfileManager.add_tempfile(tempdir + 'add1')\n self.fail(\"Expected IOError because file 'add1' does not exist\")\n except IOError:\n pass","def test_upload_dir_contents_one_file(self):\n self._test_upload_dir_contents(filenames=['file1'])","def test_make_file():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.make_job_file(STATUS_DIR, 'generation', 'test1', TEST_1_ATTRS_1)\n status = Status.retrieve_job_status(STATUS_DIR, 'generation', 'test1')\n msg = 'Failed, status is \"{}\"'.format(status)\n assert status == 'R', msg","def test_write_file():\n filename = 'test'\n content = 'hello!'\n\n write_file(content, filename)\n assert read_file(filename) == 'hello!'","def test_create_files(self):\n fitting_report.create_prob_group(result=self.result,\n support_pages_dir=self.dir.name,\n options=self.options)\n self.assertTrue(os.path.exists(self.result.fitting_report_link))","def test_create(self):\n pass","def test_newFile(self):\n self.write(\"service1.json\", [{\"host\": \"host1\", \"port\": 123},\n {\"host\": \"host2\", \"port\": 124}])\n self.write(\"service2.json\", [])\n self.pump()\n self.assertNodesEqual(\n knownNodes(self.disco, \"service1\", \"staging\"),\n [self.node(\"service1\", \"host1\", 123),\n self.node(\"service1\", \"host2\", 124)])","def test_005_write_file(self):\n __test = chess_storage.ChessStorage()\n __test_data = list(range(consts.TEST_LIST_LENGHT))\n __dir_game_saves = os.path.dirname(__file__)\n __dir_game_saves = os.path.join(__dir_game_saves, 'games')\n __dir_game_saves = os.path.join(__dir_game_saves, consts.TEST_FILENAME)\n # pylint: disable = protected-access\n __save_test = __test._ChessStorage__write_file(__dir_game_saves, __test_data)\n # pylint: enable = protected-access\n self.assertEqual(__save_test, consts.ERROR_CODES[\"SUCCESSFULL\"])","def test_fs_instance(self):\n b1 = BaseModel()\n models.storage.save()\n self.assertEqual(os.path.exists('file.json'), True)","def setUp(self):\r\n\r\n fd, self.sample_fasta_fp = mkstemp(prefix=\"sample_fasta_\",\r\n suffix=\".fna\")\r\n close(fd)\r\n seq_file = open(self.sample_fasta_fp, 'w')\r\n seq_file.write(sample_fasta_file)\r\n seq_file.close()\r\n\r\n fd, self.sample_fasta_invalid_fp = mkstemp(prefix=\"sample_fasta_\",\r\n suffix=\".fna\")\r\n close(fd)\r\n seq_file = open(self.sample_fasta_invalid_fp, 'w')\r\n seq_file.write(sample_fasta_file_invalid)\r\n seq_file.close()\r\n\r\n fd, self.sample_mapping_fp = mkstemp(prefix=\"sample_mapping_\",\r\n suffix=\".txt\")\r\n close(fd)\r\n map_file = open(self.sample_mapping_fp, \"w\")\r\n map_file.write(sample_mapping_file)\r\n map_file.close()\r\n\r\n fd, self.sample_tree_3tips_fp = mkstemp(\r\n prefix=\"sample_tree3tips_\",\r\n suffix=\".tre\")\r\n close(fd)\r\n tree_file = open(self.sample_tree_3tips_fp, \"w\")\r\n tree_file.write(sample_tree_file_3tips)\r\n tree_file.close()\r\n\r\n fd, self.sample_tree_5tips_fp = mkstemp(\r\n prefix=\"sample_tree3tips_\",\r\n suffix=\".tre\")\r\n close(fd)\r\n tree_file = open(self.sample_tree_5tips_fp, \"w\")\r\n tree_file.write(sample_tree_file_5tips)\r\n tree_file.close()\r\n\r\n fd, self.sample_mapping_file_errors_fp =\\\r\n mkstemp(prefix=\"error_mapping_\", suffix=\".txt\")\r\n close(fd)\r\n map_file = open(self.sample_mapping_file_errors_fp, \"w\")\r\n map_file.write(sample_mapping_file_errors)\r\n map_file.close()\r\n\r\n self._files_to_remove = [self.sample_fasta_fp,\r\n self.sample_fasta_invalid_fp, self.sample_mapping_fp,\r\n self.sample_tree_3tips_fp, self.sample_tree_5tips_fp,\r\n self.sample_mapping_file_errors_fp]\r\n\r\n self.output_dir =\\\r\n mkdtemp(prefix=\"validate_demultiplexed_fasta_\",\r\n suffix=\"/\")","def create_file(path):\n command = ['touch', TEST_FILE]\n file_operation(path, command)","def test_create_collection(self):\n pass","def test_cannot_write_file(self):\n self.api.write_data('/some-fake/path/to-create-file/', 'some-string')","def test_write_file(self):\n test = Server()\n test.cur_dir = os.getcwd()\n inputs = [['write_file', 'test_file1.txt', 'Hello world'],\n ['write_file', 'test_file2.txt', 'Hello world'],\n ['write_file', 'test_file1.txt']]\n response = ['written successfully',\n 'file created and written successfully',\n 'contents erased successfully']\n res = []\n for val in inputs:\n res.append(test.write_file(val))\n self.assertListEqual(res, response)","def test_create_file_via_ansible_playbook(self):\n param = {\n \"task_file\": \"kalliope/neurons/ansible_playbook/tests/test_ansible_playbook_neuron.yml\"\n }\n\n Ansible_playbook(**param)\n\n self.assertTrue(os.path.isfile(self.test_file))\n\n if os.path.exists(self.test_file):\n os.remove(self.test_file)","def test_upload_file(self):\n\n uploadFile = os.path.join(testdatadir, \"upload.data\")\n r = gracedb.writeFile(eventId, uploadFile)\n self.assertEqual(r.status, 201) # CREATED\n r_content = r.json()\n link = r_content['permalink']\n\n self.assertEqual(\n open(uploadFile, 'r').read(),\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\n )\n\n self.assertEqual(\n open(uploadFile, 'r').read(),\n gracedb.get(link).read()\n )\n\n # Re-upload slightly different file.\n uploadFile2 = os.path.join(testdatadir, \"upload2.data\")\n r = gracedb.writeFile(\n eventId,\n filename=\"upload.data\",\n filecontents=open(uploadFile2, 'r'))\n self.assertEqual(r.status, 201) # CREATED\n r_content = r.json()\n link2 = r_content['permalink']\n\n self.assertEqual(\n open(uploadFile2, 'r').read(),\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\n )\n\n self.assertEqual(\n open(uploadFile2, 'r').read(),\n gracedb.get(link2).read()\n )\n\n self.assertNotEqual(link, link2)","def test_create_document(self):\n self.client.force_login(user=Customer.objects.get(id=self.dummy.customer[0]).user)\n url = reverse('document-list')\n\n data = {\n 'file':open('Murphy.txt'),\n 'type':2,\n 'res':self.res_id,\n 'obsolete': True,\n 'description':'Sth',\n }\n response = self.client.post(url,data,format='multipart')\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n document = Document.objects.get(id=json.loads(response.content)['id'])\n self.assertEqual(document.res_id,self.res_id)\n self.client.logout()","def test_create3(self):\n fname = TempfileManager.create_tempfile(suffix='bar')\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertTrue(fname.endswith('bar'))","def setUp(self):\n self.setUpPyfakefs()\n\n self.fs.create_file(f\"{BASE_PATH}/_info.yml\", contents=CATEGORY_INFO)\n self.fs.create_file(f\"{BASE_PATH}/root.md\", contents=MARKDOWN_WITH_METADATA)\n self.fs.create_file(\n f\"{BASE_PATH}/root_without_metadata.md\", contents=MARKDOWN_WITHOUT_METADATA\n )\n self.fs.create_file(f\"{BASE_PATH}/not_a_page.md/_info.yml\", contents=CATEGORY_INFO)\n self.fs.create_file(f\"{BASE_PATH}/category/_info.yml\", contents=CATEGORY_INFO)\n self.fs.create_file(\n f\"{BASE_PATH}/category/with_metadata.md\", contents=MARKDOWN_WITH_METADATA\n )\n self.fs.create_file(f\"{BASE_PATH}/category/subcategory/_info.yml\", contents=CATEGORY_INFO)\n self.fs.create_file(\n f\"{BASE_PATH}/category/subcategory/with_metadata.md\", contents=MARKDOWN_WITH_METADATA\n )\n self.fs.create_file(\n f\"{BASE_PATH}/category/subcategory/without_metadata.md\",\n contents=MARKDOWN_WITHOUT_METADATA\n )\n\n temp = f\"{BASE_PATH}/tmp\" # noqa: S108\n self.fs.create_file(f\"{temp}/_info.yml\", contents=CATEGORY_INFO)\n self.fs.create_file(f\"{temp}.md\", contents=MARKDOWN_WITH_METADATA)\n self.fs.create_file(f\"{temp}/category/_info.yml\", contents=CATEGORY_INFO)\n self.fs.create_dir(f\"{temp}/category/subcategory_without_info\")","def test_add_parsed_files(self):\n file_name = 'a_test_file'\n Historical_ROAs_Parser()._add_parsed_files([file_name])\n with Historical_ROAs_Parsed_Table() as t:\n sql = f\"SELECT * FROM {t.name} WHERE file = '{file_name}'\"\n assert len(t.execute(sql)) == 1\n sql = f\"DELETE FROM {t.name} WHERE file = '{file_name}'\"\n t.execute(sql)","def _test(self, file_name):\n data = bob.io.base.load(file_name)\n assert (_data == data).all()","def test_create_part(self):\n pass","def test_with_new_file(self):\n repository = self.create_repository(tool_name='Test')\n review_request = self.create_review_request(\n repository=repository,\n submitter=self.user,\n publish=True)\n diffset = self.create_diffset(review_request)\n filediff = self.create_filediff(diffset,\n source_revision=PRE_CREATION)\n\n rsp = self.api_get(\n get_original_file_url(review_request, diffset, filediff),\n expected_status=404)\n self.assertEqual(rsp['stat'], 'fail')\n self.assertEqual(rsp['err']['code'], DOES_NOT_EXIST.code)","def setUp(self):\n self._file = 'test.g3'\n write_example_file(self._file)","def test_add_1(self):\n contents = testdata.get_words()\n d = testdata.create_dir()\n ts = {\n \"foo.txt\": [contents],\n }\n ds = d.add(ts)\n path = ds[0]\n self.assertTrue(os.path.isfile(path), \"{} does not exist\".format(path))\n self.assertEqual(contents, path.read_text())","def test_successful_file(self):\n\n url = '/%s/jobs/%i/input_files/' % (self.api, self.job.id)\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n results = result['results']\n self.assertEqual(len(results), 2)\n for result in results:\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])\n self.assertIn('file_name', result)\n self.assertIn('workspace', result)\n self.assertIn('media_type', result)\n self.assertIn('file_type', result)\n self.assertIn('file_size', result)\n self.assertIn('file_path', result)\n self.assertIn('is_deleted', result)\n self.assertIn('url', result)\n self.assertIn('created', result)\n self.assertIn('deleted', result)\n self.assertIn('data_started', result)\n self.assertIn('data_ended', result)\n self.assertIn('source_started', result)\n self.assertIn('source_ended', result)\n self.assertIn('last_modified', result)\n self.assertIn('geometry', result)\n self.assertIn('center_point', result)\n self.assertIn('countries', result)\n self.assertIn('job_type', result)\n self.assertIn('job', result)\n self.assertIn('job_exe', result)\n self.assertIn('job_output', result)\n self.assertIn('recipe_type', result)\n self.assertIn('recipe', result)\n self.assertIn('recipe_node', result)\n self.assertIn('batch', result)\n self.assertFalse(result['is_superseded'])\n self.assertIn('superseded', result)","def test_loading_document(self):","def test_files_always_created():\n c = TestClient()\n c.save({\"dep/conanfile.py\": GenConanfile(\"dep\", \"0.1\"),\n \"consumer/conanfile.txt\": \"[requires]\\ndep/0.1\"})\n c.run(\"create dep\")\n c.run(\"install consumer -g VirtualBuildEnv -g VirtualRunEnv -of=.\")\n ext = \"bat\" if platform.system() == \"Windows\" else \"sh\"\n\n arch = c.get_default_host_profile().settings['arch']\n assert os.path.isfile(os.path.join(c.current_folder, f\"conanbuild.{ext}\"))\n assert os.path.isfile(os.path.join(c.current_folder, f\"conanrun.{ext}\"))\n assert os.path.isfile(os.path.join(c.current_folder, f\"conanbuildenv-release-{arch}.{ext}\"))\n assert os.path.isfile(os.path.join(c.current_folder, f\"conanbuildenv-release-{arch}.{ext}\"))","def test_create_files_with_python(self):\n opts = mock.Mock()\n opts.repo = self.repo\n opts.create_version_file = True\n opts.source = 'src'\n opts.version = '0.0.1'\n opts.version_file = None\n opts.org = \"ORG\"\n opts.desc = \"DESCRIPTION\"\n opts.templates = []\n opts.test_mode = False\n opts.history_file = 'HISTORY.md'\n opts.package = 'unittests'\n opts.develop = 'develop'\n opts.requirements = 'requirements.txt'\n opts.pypi_package_name = 'pypi.package.unittest'\n opts.python = 'python3'\n opts.gitignore_url = \"GIT_IGNORE_URL\"\n opts.add_gitignore = False\n opts.test_requirements = 'test-requirements.txt'\n version = os.path.join(self.repo, 'src', 'unittests', '__init__.py')\n os.system('rm -f {}'.format(version))\n create_files(opts)\n\n dir_list = os.listdir(self.repo)\n self.failUnless('cirrus.conf' in dir_list)\n self.failUnless('HISTORY.md' in dir_list)\n self.failUnless('MANIFEST.in' in dir_list)\n self.failUnless('setup.py' in dir_list)\n\n cirrus_conf = os.path.join(self.repo, 'cirrus.conf')\n config = ConfigParser.RawConfigParser()\n config.read(cirrus_conf)\n self.assertEqual(config.get('package', 'name'), opts.pypi_package_name)\n self.assertEqual(config.get('package', 'version'), opts.version)\n self.assertEqual(config.get('build', 'python'), 'python3')","def setUp(self):\n # Generates directory names\n self.tempdir = tempfile.mkdtemp()\n self.subdir = os.path.join(self.tempdir, \"dir\")\n self.emptydir = os.path.join(self.tempdir, \"empty\")\n # Populates directories\n os.makedirs(self.subdir)\n os.makedirs(self.emptydir)\n # Populates files\n self.root_fcount = 3\n self.nest_fcount = 5\n for i in range(0, self.root_fcount):\n with open(os.path.join(self.tempdir, \"%i.txt\" % i), \"w+\") as f:\n f.write(\"Test.\")\n for i in range(0, self.nest_fcount):\n with open(os.path.join(self.subdir, \"%i.txt\" % i), \"w+\") as f:\n f.write(\"Test.\")\n self.filename = os.path.join(self.subdir, \"nontxt.mp3\")\n with open(self.filename, \"w+\") as f:\n f.write(\"Test.\")","def test_file_generation(self):\n # For file generation submission, call generate route for D1 and check results\n post_json = {\"submission_id\": self.generation_submission_id, \"file_type\": \"D1\",\n \"start\": \"01/02/2016\", \"end\": \"02/03/2016\"}\n response = self.app.post_json(\"/v1/generate_file/\", post_json, headers={\"x-session-id\": self.session_id})\n\n self.assertEqual(response.status_code, 200)\n json = response.json\n\n # use_aws is true when the PR unit tests run so the date range specified returns no results.\n # checking is in place for \"failed\" until use_aws is flipped to false\n self.assertIn(json[\"status\"], [\"failed\", \"waiting\", \"finished\"])\n self.assertEqual(json[\"file_type\"], \"D1\")\n self.assertIn(\"url\", json)\n self.assertEqual(json[\"start\"], \"01/02/2016\")\n self.assertEqual(json[\"end\"], \"02/03/2016\")\n\n # this is to accommodate for checking for the \"failed\" status\n self.assertIn(json[\"message\"], [\"\", \"D1 data unavailable for the specified date range\"])\n\n # Then call check generation route for D2, E and F and check results\n post_json = {\"submission_id\": self.generation_submission_id, \"file_type\": \"E\"}\n response = self.app.post_json(\"/v1/check_generation_status/\", post_json,\n headers={\"x-session-id\": self.session_id})\n\n self.assertEqual(response.status_code, 200)\n json = response.json\n self.assertEqual(json[\"status\"], \"finished\")\n self.assertEqual(json[\"file_type\"], \"E\")\n self.assertEqual(json[\"url\"], \"#\")\n self.assertEqual(json[\"message\"], \"\")\n\n post_json = {\"submission_id\": self.generation_submission_id, \"file_type\": \"D2\"}\n response = self.app.post_json(\"/v1/check_generation_status/\", post_json,\n headers={\"x-session-id\": self.session_id})\n\n self.assertEqual(response.status_code, 200)\n json = response.json\n self.assertEqual(json[\"status\"], \"failed\")\n self.assertEqual(json[\"file_type\"], \"D2\")\n self.assertEqual(json[\"url\"], \"#\")\n self.assertEqual(json[\"message\"], \"Generated file had file-level errors\")\n\n post_json = {\"submission_id\": self.generation_submission_id, \"file_type\": \"F\"}\n response = self.app.post_json(\"/v1/check_generation_status/\", post_json,\n headers={\"x-session-id\": self.session_id})\n\n self.assertEqual(response.status_code, 200)\n json = response.json\n self.assertEqual(json[\"status\"], \"failed\")\n self.assertEqual(json[\"file_type\"], \"F\")\n self.assertEqual(json[\"url\"], \"#\")\n self.assertEqual(json[\"message\"], \"File was invalid\")\n\n # Test permission error\n self.login_user()\n post_json = {\"submission_id\": self.generation_submission_id, \"file_type\": \"D1\",\n \"start\": \"01/02/2016\", \"end\": \"02/03/2016\"}\n response = self.app.post_json(\"/v1/generate_file/\", post_json,\n headers={\"x-session-id\": self.session_id}, expect_errors=True)\n\n self.assertEqual(response.status_code, 403)\n json = response.json\n self.assertEqual(json[\"status\"], \"failed\")\n self.assertEqual(json[\"file_type\"], \"D1\")\n self.assertEqual(json[\"url\"], \"#\")\n self.assertEqual(json[\"start\"], \"\")\n self.assertEqual(json[\"end\"], \"\")\n self.assertEqual(json[\"message\"], \"User does not have permission to view that submission\")","def test_create_from_pear(self):\n pass","def test_add_model_file(self):\n with open(TEST_AVATAR_PATH, 'rb') as avatar:\n self.user.avatar_tmp = File(avatar)\n self.user.save()\n\n with DataArchive(self.user, DATA_DOWNLOADS_WORKING_DIR) as archive:\n file_path = archive.add_model_file(self.user.avatar_tmp)\n\n self.assertTrue(os.path.isfile(file_path))\n \n data_dir_path = str(archive.data_dir_path)\n self.assertTrue(str(file_path).startswith(data_dir_path))","def setUp(self):\n super().setUp()\n self.file_path = 'file.json'","def file_factory(test_workspace):\n\n return FileCreator(test_workspace)","def test_memory_created(self):\n with TemporaryDirectory(dir=\"./\") as cur_dir:\n \"\"\" Test some memory name\"\"\"\n filepath, memory_file = self.create_config(memory=\"\", corpora=DEFAULT_CORPUS, cur_dir=cur_dir)\n self._dispatch(\n train=0.8,\n test=0.1,\n dev=0.1,\n config=filepath,\n output_dir=p.join(cur_dir, \"output\")\n )\n self.assertTrue(p.isfile(memory_file+\".csv\"), \"A new file should have been created\")","def test_fileinuse (self):\t\t\n\t\t# open a file:\n\t\ttestfilepath = os.path.join (self.testfolder, \"fileinuse.txt\")\n\t\tf = open(testfilepath,\"a\") #opens file with name of \"test.txt\"\n\t\tf.write(\"This file is now opened and i'm writting on it \\n\")\n\t\tself.assertEqual (MD.fileinuse(testfilepath), True) # Checks a file that it is beign written.\n\t\tself.assertEqual (MD.folderinuse(self.testfolder), True) # Checks if any file inside the folder is beign used.\n\t\tf.close()\n\t\tself.assertEqual (MD.fileinuse(testfilepath), False) # Cheks a file that it is closed.\n\t\tself.assertEqual (MD.folderinuse(self.testfolder), False)","def test_upload(self):\n with self.client:\n file = dict(\n file=(BytesIO(b'my file contents'), \"foto.jpg\"),\n )\n response = self.client.post('/upload',\n content_type='multipart/form-data',\n data=file)\n self.assertEqual(status.HTTP_200_OK, response.status_code)\n self.assertIn('foto.jpg', os.listdir(PHOTOS_SAVE_PATH))\n self.assertIn('foto.jpg', [photo.filename for photo in Photo.query.all()])","def test_writer_with_file():\n outputfile = \"testfile.txt\"\n GCMT(write=outputfile)\n assert os.path.exists(outputfile)\n os.remove(outputfile)","def test_observable_file_add(self):\n with tempfile.NamedTemporaryFile('wb', delete=False) as f:\n f.write(b\"content\")\n filename = f.name\n fileinfo = self.api.observable_file_add(filename, ['file_tag'])\n os.remove(filename)\n # SHA256 of \"content\"\n expected_filename = \"FILE:ed7002b439e9ac845f22357d822bac1444730fbdb6016d3ec9432297b9ec9f73\"\n self.assertEqual(fileinfo[0]['value'], expected_filename)\n tags = [t['name'] for t in fileinfo[0]['tags']]\n self.assertEqual(tags, ['file_tag'])","def test_load_file(self):\n self.assertTrue(os.path.exists(MEDIA_ROOT+\"/pl_test1_\"+self.loader.version))\n self.assertTrue(os.path.exists(MEDIA_ROOT+\"/pl_test2_\"+self.loader.version))","def test_content_file(self):\n\n url=[\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\",\n \"http://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data\",\n \"http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\"]\n cwd=os.getcwd()\n list_of_files=requester.batch_url_to_csv(url, fnames=[\"m1\", \"m2\",\"m3\"])\n total_rows=0\n reader_list=[]\n for j in range(len(list_of_files)):\n reader=csv.DictReader(list_of_files[j])\n for rows in reader:\n total_rows+=1\n reader_list.append(total_rows)\n\n unique=set((reader_list))\n if len(unique)!=len(reader_list):\n with self.assertRaises(AssertionError):\n requester.batch_url_to_csv(url,fnames=['m1','m2','m3'])","def test_simple_creation():\n # Get model file\n create.main(\"mlp\", \"10:12:8\", \"model_test.tar\")","def test_init_flowgram_file(self):\r\n fh, tmp_filename = init_flowgram_file(n=100, l=400)\r\n self.assert_(exists(tmp_filename))\r\n self.tmp_filename = tmp_filename\r\n fh.close()\r\n result_file_content = list(open(tmp_filename))\r\n\r\n self.assertEqual(result_file_content, [\"100 400\\n\"])","def setUp(self):\n self.junk_file = '/tmp/asdfoowersdfuixlwles'","def test_api_create_unknown_file(api):\n api.create_article(\n {\"title\": \"A Title\"},\n {\"key1\": \"value1\"},\n {'test.zip': 'FFFDASFAFADADFA'},\n )\n req_call = requests.request\n req_kw = req_call.call_args[1]\n data_lines = req_kw['data'].split(b'\\r\\n')\n # Unknown file type is skipped\n assert len(data_lines) == 11","def test_create_already_exists():\n current_folder = os.path.dirname(os.path.realpath(__file__))\n misc_folder = os.path.join(current_folder, \"misc\")\n model_file = os.path.join(misc_folder, \"model.tar\")\n create.main(\"mlp\", \"10:12:8\", model_file)\n # TODO: Check if error was logged","def test_file_io(self):\n temp_directory = tempfile.mkdtemp()\n filename = os.path.join(temp_directory, \"test.h5\")\n\n points = np.array([\n [0.1, 0.1, 0.1],\n [1.1, 2.1, 3.1],\n [1.3, 2.2, 3.4]])\n voxel_size = 0.5\n min_corner = Vector3f(-1, -2, -3)\n vg = VoxelGrid(voxel_size, min_corner=min_corner, points=points)\n\n # test writing\n vg.save(filename)\n self.assertTrue(os.path.isfile(filename))\n\n # test reading\n vg2 = VoxelGrid.from_file(filename)\n self.assertAlmostEquals(voxel_size, vg2.voxel_size)\n np.testing.assert_array_almost_equal(vg.min_corner, vg2.min_corner)\n shutil.rmtree(temp_directory)","def test_file_error(self):\n my_reader = DataSetReader()\n covid_list = CovidCase.objects.all()\n\n with self.assertRaises(IOError):\n my_reader.writeFile(covid_list, \"Not_A_File.csv\")","def test_create_image(self):\n pass","def create_test(self, test_case, file_name):\n with open(os.path.join(self.tests, file_name), 'w+') as f:\n f.write(test_case)","def assertCreatesFile(self, filename, nonzero=True):\n self.assertFalse(os.path.exists(filename), \"file already exists: {}\".format(filename))\n yield\n self.assertTrue(os.path.exists(filename), \"file was not created: {}\".format(filename))\n if nonzero:\n self.assertFilesizeNonZero(filename)","def test_identify_contents_2(self):\n Path(self.base_dir, \"new_dir\").mkdir()\n Path(self.base_dir, \"file1.txt\").touch()\n Path(self.base_dir, \".DS_Store\").touch()\n ignore_set = set([\".DS_Store\"])\n list_of_items = basic.identify_contents(self.base_dir, kind=\"file\",\n ignore_set=ignore_set)\n exp_num_items = 1\n self.assertEqual(len(list_of_items), exp_num_items)","def create_test_set(self):\n test_files = os.listdir(self.image_folder_path)\n test_files = sorted_alphanumeric(test_files)\n delete_files(self.root_name, \"/VOC2021/ImageSets/Main\")\n write_txt(\"test.txt\", self.txt_path, test_files)","def test_save_method(self):\n\n models.storage.save()\n self.assertTrue(os.path.exists('file.json'))","def test_get_file_accessors(self):\n pass","def setUp(self):\n\n try:\n os.mkdir(self.pipeline_folder)\n except FileExistsError:\n pass\n\n with open(self.pipeline_spec_file, 'w+') as stream:\n json.dump(self.pipeline_spec, stream)\n\n with open(self.pipeline_source_file, 'w+') as stream:\n json.dump(self.source_description, stream)\n\n self.source = Source(folder=self.pipeline_folder)","def test_to_file(self):\n fd, fp = mkstemp()\n close(fd)\n pt = PrepTemplate.create(self.metadata, self.new_raw_data,\n self.test_study, self.data_type)\n pt.to_file(fp)\n self._clean_up_files.append(fp)\n with open(fp, 'U') as f:\n obs = f.read()\n self.assertEqual(obs, EXP_PREP_TEMPLATE)","def setUp(self):\n if os.path.exists('file.json'):\n os.remove(\"file.json\")","def testGetFileset(self):\n assert self.dummyFileSet.name == self.dummySubscription['fileset'].name, \\\n 'Couldn\\'t add Fileset to Subscription - name does not match'\n\n for x in self.dummyFileSet.listNewFiles():\n assert x in self.dummySubscription['fileset'].newfiles, \\\n 'Couldn\\'t add Fileset to Subscription - newFiles Set does not match'\n\n assert self.dummyFileSet.getFiles(type='set') == \\\n self.dummySubscription['fileset'].getFiles(type='set'), \\\n 'Couldn\\'t add Fileset to Subscription - %s Set does not match' % x"],"string":"[\n \"def test_file_creation(data, logging_file_name):\\n create_instance(data, logging_file_name)\\n log_file_name = create_file_path(logging_file_name)\\n print(log_file_name)\\n if data is None or len(data) == 0:\\n assert not os.path.exists(log_file_name)\\n else:\\n assert os.path.exists(log_file_name)\",\n \"def test_create_files(self):\\n\\n testdir = \\\"test_output\\\"\\n test_submission = Submission()\\n self.addCleanup(os.remove, \\\"submission.tar.gz\\\")\\n self.addCleanup(shutil.rmtree, testdir)\\n\\n test_submission.create_files(testdir)\\n\\n self.doCleanups()\",\n \"def test_get_file_object(self):\\n pass\",\n \"def test_create(self):\\n path = self.tmp_py()\\n # Creating a file that doesn't exist should succeed\\n self.cls.create(path)\\n self.assertTrue(os.path.exists(path))\\n # Created file should be a valid script (If not, raises an error)\\n self.cls.verify(path)\\n # Can't create it again: it already exists\\n self.assertRaises(exceptions.PathFoundError,self.cls.create,path)\",\n \"def test_create_file(self):\\n def validate_in(*args, **kwargs):\\n # Validate that the element is built properly\\n wrap = args[0]\\n\\n self.assertEqual('chk', wrap._get_val_str(vf._FILE_CHKSUM))\\n self.assertEqual(50, wrap.expected_file_size)\\n self.assertEqual('f_name', wrap.file_name)\\n self.assertEqual('application/octet-stream',\\n wrap.internet_media_type)\\n self.assertEqual('f_type', wrap.enum_type)\\n self.assertEqual('v_uuid', wrap.vios_uuid)\\n self.assertEqual('tdev_uuid', wrap.tdev_udid)\\n ret = adp.Response('reqmethod', 'reqpath', 'status', 'reason', {})\\n ret.entry = ewrap.EntryWrapper._bld(self.adpt, tag='File').entry\\n return ret\\n self.adpt.create.side_effect = validate_in\\n\\n ts._create_file(self.adpt, 'f_name', 'f_type', 'v_uuid', 'chk', 50,\\n 'tdev_uuid')\\n self.assertTrue(self.adpt.create.called)\",\n \"def test_CRUD_ProjectFiles_Successfully(self):\\n\\n real_file_code = get_content('BasicTemplateAlgorithm.py')\\n second_real_file_code = get_content('BasicTemplateForexAlgorithm.py')\\n\\n fakeFile = {\\\"name\\\":\\\"Hello.py\\\", \\\"code\\\": \\\"Hello World!\\\"}\\n realFile = {\\\"name\\\":\\\"main.py\\\", \\\"code\\\": real_file_code}\\n secondRealFile = {\\\"name\\\":\\\"lol.py\\\", \\\"code\\\": second_real_file_code}\\n\\n # Create a new project and make sure there are no files\\n project = self.api.create_project(\\\"Test project - \\\", \\\"Py\\\")\\n self.assertTrue(project['success'])\\n self.assertTrue(project['projects'][0]['projectId'] > 0)\\n\\n # Add random file\\n randomAdd = self.api.add_project_file(project['projects'][0]['projectId'], fakeFile[\\\"name\\\"], fakeFile[\\\"code\\\"])\\n self.assertTrue(randomAdd['success'])\\n self.assertTrue(randomAdd['files'][0]['content'] == fakeFile['code'])\\n self.assertTrue(randomAdd['files'][0]['name'] == fakeFile['name'])\\n\\n # Update names of file\\n updatedName = self.api.update_project_filename(project['projects'][0]['projectId'], randomAdd['files'][0]['name'], realFile['name'])\\n self.assertTrue(updatedName['success'])\\n\\n # Replace content of file\\n updateContents = self.api.update_project_file_content(project['projects'][0]['projectId'], realFile[\\\"name\\\"], realFile['code'])\\n self.assertTrue(updateContents['success'])\\n\\n # Read single file\\n readFile = self.api.read_project_file(project['projects'][0]['projectId'], realFile['name'])\\n self.assertTrue(readFile['success'])\\n self.assertTrue(readFile['files'][0]['content'] == realFile['code'])\\n self.assertTrue(readFile['files'][0]['name'] == realFile['name'])\\n\\n # Add a second file\\n secondFile = self.api.add_project_file(project['projects'][0]['projectId'], secondRealFile['name'], secondRealFile['code'])\\n self.assertTrue(secondFile['success'])\\n self.assertTrue(secondFile['files'][0]['content'] == secondRealFile['code'])\\n self.assertTrue(secondFile['files'][0]['name'] == secondRealFile['name'])\\n\\n # Read multiple files\\n readFiles = self.api.read_project_files(project['projects'][0]['projectId'])\\n self.assertTrue(readFiles['success'])\\n self.assertTrue(len(readFiles['files']) == 2)\\n\\n # Delete the second file\\n deleteFile = self.api.delete_project_file(project['projects'][0]['projectId'], secondRealFile['name'])\\n self.assertTrue(deleteFile['success'])\\n\\n # Read files\\n readFilesAgain = self.api.read_project_files(project['projects'][0]['projectId'])\\n self.assertTrue(readFilesAgain['success'])\\n self.assertTrue(len(readFilesAgain['files']) == 1)\\n self.assertTrue(readFilesAgain['files'][0]['name'] == realFile['name'])\\n\\n # Delete the project\\n deleteProject = self.api.delete_project(project['projects'][0]['projectId'])\\n self.assertTrue(deleteProject['success'])\",\n \"def test_upload_file(self):\\n pass\",\n \"def test_fileOperations(self): \\n from DIRAC import gConfig\\n testSE = 'testSE'\\n #rssClient = ResourceStatusClient()\\n #result = rssClient.getStorageElementsList( 'Read' )\\n #result = gConfig.getSections( '/Resources/StorageElements' )\\n #if result['OK'] and result['Value']:\\n # testSE = result['Value'][0]\\n #if result['Ok']:\\n # testSE = result['Value'][ 0 ] \\n \\n result = self.fc.addFile( { testFile: { 'PFN': 'testfile', \\n 'SE': testSE , \\n 'Size':0, \\n 'GUID':0, \\n 'Checksum':'0' } } )\\n self.assert_( result['OK'] )\\n if gConfig.getValue( '/Resources/StorageElements/%s/AccessProtocol.1/Host' % testSE, '' ):\\n result = self.fc.getReplicas( testFile )\\n self.assert_( result['OK'] )\\n self.assert_( testFile in result['Value']['Successful'] )\",\n \"def test_file_field():\",\n \"def test_upload_file1(self):\\n pass\",\n \"def test_create1(self):\\n fname = TempfileManager.create_tempfile()\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertTrue(fname.startswith('tmp'))\",\n \"def test_create_content(self):\\n url = reverse('content-list')\\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\\n content_file.write(b\\\"The contents of the temporary file.\\\\n\\\")\\n content_file.seek(0)\\n data = {\\n 'name': 'Content File',\\n 'description': 'File 1',\\n 'content_file': content_file,\\n 'updated_time': date.today(),\\n 'creators': [],\\n 'coverage': '',\\n 'subjects': [],\\n 'keywords': [],\\n 'workareas': [],\\n 'language': '',\\n 'cataloger': ''\\n }\\n response = self.client.post(url, data, format='multipart')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\",\n \"def test_record_create_files(db, location):\\n record = CernSearchRecord.create({\\\"title\\\": \\\"test\\\"})\\n\\n assert 0 == len(record.files)\\n assert 0 == len(record.files_content)\\n\\n file_content = b\\\"Hello world!\\\"\\n record.files[\\\"hello.txt\\\"] = BytesIO(file_content)\\n db.session.commit()\\n\\n record = CernSearchRecord.get_record(record.id)\\n\\n assert record[\\\"_bucket\\\"] == record.bucket_id\\n assert record[\\\"_files\\\"]\\n\\n file_1 = record.files[\\\"hello.txt\\\"]\\n assert \\\"hello.txt\\\" == file_1[\\\"key\\\"]\\n assert 1 == len(record.files)\\n assert 1 == len(record[\\\"_files\\\"])\\n assert 0 == len(record.files_content)\\n\\n storage = file_1.obj.file.storage() # type: FileStorage\\n fp = storage.open(mode=READ_MODE_BINARY)\\n\\n try:\\n assert file_content == fp.read()\\n finally:\\n fp.close()\",\n \"def tests_ti_file_create(self):\\n indicator_data = {\\n 'md5': uuid.uuid4().hex.upper(),\\n 'owner': self.owner,\\n 'rating': randint(0, 5),\\n }\\n ti = self.ti.file(**indicator_data)\\n r = ti.create()\\n\\n # assert response\\n assert r.status_code == 201\\n\\n # retrieve indicator for asserts\\n ti = self.ti.file(**indicator_data)\\n r = ti.single()\\n response_data = r.json()\\n ti_data = response_data.get('data', {}).get(ti.api_entity)\\n\\n # validate response data\\n assert r.status_code == 200\\n assert response_data.get('status') == 'Success'\\n\\n # validate ti data\\n assert ti_data.get('confidence') == indicator_data.get('confidence')\\n assert ti_data.get(ti.api_entity) == indicator_data.get(ti.api_entity)\\n assert ti_data.get('rating') == indicator_data.get('rating')\\n\\n # cleanup indicator\\n r = ti.delete()\\n assert r.status_code == 200\",\n \"def test_create4(self):\\n TempfileManager.sequential_files(2)\\n fname = TempfileManager.create_tempfile()\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertEqual(fname, 'tmp2')\\n #\\n TempfileManager.unique_files()\\n fname = TempfileManager.create_tempfile()\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 2)\\n fname = os.path.basename(fname)\\n self.assertNotEqual(fname, 'tmp3')\\n self.assertTrue(fname.startswith('tmp'))\",\n \"def test_add_remove_files(self):\\n filepath = os.path.join(\\n os.getcwd(), self.hiarc_util.TEST_FILE_PATH, 'Test.txt')\\n f1 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n f2 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n f3 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n\\n c1 = self.hiarc_collections.create_collection(\\n self.hiarc_util.create_collection())\\n\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f1.key), c1.key)\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f2.key), c1.key)\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f3.key), c1.key)\\n\\n files = self.hiarc_collections.get_collection_files(c1.key)\\n assert len(files) == 3\\n\\n self.hiarc_collections.remove_file_from_collection(c1.key, f3.key)\\n files = self.hiarc_collections.get_collection_files(c1.key)\\n assert len(files) == 2\\n\\n for f in [f1.key, f2.key, f3.key]:\\n self.hiarc_files.delete_file(f)\\n self.assertRaises(hiarc.rest.ApiException,\\n self.hiarc_files.get_file, f)\",\n \"def test_create_run(self):\\n pass\",\n \"def test_save_to_file(self):\\n self.assertFalse(os.path.exists(\\\"file.json\\\"))\",\n \"def test_get_file_content(self):\\n pass\",\n \"def test_get_items(self):\\n filepath = os.path.join(\\n os.getcwd(), self.hiarc_util.TEST_FILE_PATH, 'Test.txt')\\n c1 = self.hiarc_collections.create_collection(\\n self.hiarc_util.create_collection())\\n c2 = self.hiarc_collections.create_collection(\\n self.hiarc_util.create_collection())\\n c3 = self.hiarc_collections.create_collection(\\n self.hiarc_util.create_collection())\\n c4 = self.hiarc_collections.create_collection(\\n self.hiarc_util.create_collection())\\n c5 = self.hiarc_collections.create_collection(\\n self.hiarc_util.create_collection())\\n c6 = self.hiarc_collections.create_collection(\\n self.hiarc_util.create_collection())\\n\\n self.hiarc_collections.add_child_to_collection(c1.key, c2.key)\\n self.hiarc_collections.add_child_to_collection(c1.key, c3.key)\\n self.hiarc_collections.add_child_to_collection(c1.key, c4.key)\\n self.hiarc_collections.add_child_to_collection(c2.key, c5.key)\\n self.hiarc_collections.add_child_to_collection(c4.key, c6.key)\\n\\n f1 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n f2 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n f3 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n f4 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n f5 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f1.key), c1.key)\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f2.key), c1.key)\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f3.key), c1.key)\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f4.key), c2.key)\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f5.key), c3.key)\\n\\n items = self.hiarc_collections.get_collection_items(c1.key)\\n children = items.child_collections\\n assert len(children) == 3\\n assert next((c for c in children if self.hiarc_util.compare_entity_to_entity(\\n c, c2)), None) is not None\\n assert next((c for c in children if self.hiarc_util.compare_entity_to_entity(\\n c, c3)), None) is not None\\n assert next((c for c in children if self.hiarc_util.compare_entity_to_entity(\\n c, c4)), None) is not None\\n assert next((c for c in children if self.hiarc_util.compare_entity_to_entity(\\n c, c5)), None) is None\\n assert next((c for c in children if self.hiarc_util.compare_entity_to_entity(\\n c, c6)), None) is None\\n\\n files = items.files\\n assert len(files) == 3\\n assert next((f for f in files if self.hiarc_util.compare_entity_to_entity(\\n f, f1)), None) is not None\\n assert next((f for f in files if self.hiarc_util.compare_entity_to_entity(\\n f, f2)), None) is not None\\n assert next((f for f in files if self.hiarc_util.compare_entity_to_entity(\\n f, f3)), None) is not None\\n assert next((f for f in files if self.hiarc_util.compare_entity_to_entity(\\n f, f4)), None) is None\\n assert next((f for f in files if self.hiarc_util.compare_entity_to_entity(\\n f, f5)), None) is None\\n\\n for f in [f1.key, f2.key, f3.key, f4.key, f5.key]:\\n self.hiarc_files.delete_file(f)\\n self.assertRaises(hiarc.rest.ApiException,\\n self.hiarc_files.get_file, f)\",\n \"def test_to_file(self):\\n fd, fp = mkstemp()\\n close(fd)\\n st = SampleTemplate.create(self.metadata, self.new_study)\\n st.to_file(fp)\\n self._clean_up_files.append(fp)\\n with open(fp, 'U') as f:\\n obs = f.read()\\n self.assertEqual(obs, EXP_SAMPLE_TEMPLATE)\\n\\n fd, fp = mkstemp()\\n close(fd)\\n st.to_file(fp, {'2.Sample1', '2.Sample3'})\\n self._clean_up_files.append(fp)\\n\\n with open(fp, 'U') as f:\\n obs = f.read()\\n self.assertEqual(obs, EXP_SAMPLE_TEMPLATE_FEWER_SAMPLES)\",\n \"def test_load(api):\\n # upload file to file.io servers\\n uploaded_file = api.upload(\\n tag='test_file',\\n expiry='1d',\\n path='tests/test_file.txt'\\n )\\n\\n # check that instance of FileIO has these fields\\n assert uploaded_file.link\\n assert uploaded_file.key\\n assert uploaded_file.tag\\n assert uploaded_file.path\\n\\n # check that the uploaded file was added to uploaded files list\\n assert api.show_uploads()\\n\\n # check that our list is not empty\\n assert api.file_obj_list\\n\\n # export the file in json format\\n api.export('tests/exported.json')\\n\\n # check that exported file exists\\n assert path.isfile('tests/exported.json')\\n\\n # set it to empty list\\n api.file_obj_list = []\\n\\n # load the file in json format\\n api.load('tests/exported.json')\\n\\n # remove the file\\n remove('tests/exported.json')\\n\\n # check that the uploaded file was added to uploaded files list\\n assert api.show_uploads()\\n\\n # check that our list is not empty\\n assert api.file_obj_list\\n\\n # export the file in pkl format\\n api.export('tests/exported.pkl')\\n\\n # set it to empty list\\n api.file_obj_list = []\\n\\n # load the file in pkl format\\n api.load('tests/exported.pkl')\\n\\n # remove exported.pkl file\\n remove('tests/exported.pkl')\\n\\n # check that the uploaded file was added to uploaded files list\\n assert api.show_uploads()\\n\\n # check that our list is not empty\\n assert api.file_obj_list\",\n \"def test_file(self):\\n browser = self.layer.get_web_browser(smi_settings)\\n\\n image = test_filename('test.txt')\\n browser.login(self.username, self.username)\\n self.assertEqual(browser.open('/root/edit'), 200)\\n browser.macros.create(\\n 'Silva File', id='file', title='Text File', file=image)\\n self.assertEqual(\\n browser.inspect.folder_listing, ['index', 'file'])\\n\\n # The user should by the last author on the content and container.\\n self.assertEqual(\\n self.root.sec_get_last_author_info().userid(),\\n self.username)\\n self.assertEqual(\\n self.root.file.sec_get_last_author_info().userid(),\\n self.username)\\n\\n # Visit the edit page\\n self.assertEqual(\\n browser.inspect.folder_listing['file'].click(),\\n 200)\\n self.assertEqual(browser.url, '/root/file/edit/tab_edit')\\n self.assertEqual(browser.inspect.breadcrumbs, ['root', 'Text File'])\\n browser.inspect.breadcrumbs['root'].click()\\n browser.macros.delete('file')\",\n \"def tests_ti_file_create_2(self):\\n metadata = {\\n 'size': 84504,\\n 'sha256': '33af46377c0e52ca19aea233b3afb64505b32fac2231ec7a8a6795812fae0d10',\\n 'md5': 'a9ba66af82897dadb82e3e89c70ae7ac',\\n 'sha1': '19d08af69fe15af22ba81f045e31230150d4bdad',\\n }\\n file_indicator = self.ti.file(**metadata)\\n file_indicator.delete()\\n\\n assert file_indicator.data['sha1'] == metadata['sha1']\\n response = file_indicator.create()\\n assert response.ok\\n unique_id = ':'.join([metadata[x] for x in ['sha256', 'sha1', 'md5']])\\n file_indicator = self.ti.file(unique_id=unique_id, **metadata)\\n\\n assert file_indicator.data['sha256'] == metadata['sha256']\\n assert file_indicator.data['sha1'] == metadata['sha1']\\n\\n response = file_indicator.update()\\n assert response.ok\\n file_indicator.delete()\",\n \"def test_create1a(self):\\n fname = TempfileManager.create_tempfile(dir=tempdir)\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertTrue(fname.startswith('tmp'))\",\n \"def test_add_and_read_file(self, tmpdir, fs):\\n with fs.add_file('my-dir', 'my-file') as f:\\n f.write('hello')\\n assert fs.read('my-dir', 'my-file') == 'hello'\",\n \"def create_mock_files(self,\\n project_id=\\\"DEV-test\\\",\\n count=3,\\n prefix=\\\"mock_data_file\\\",\\n file_format=\\\"dcm\\\",\\n outdir=\\\".\\\",\\n msg = \\\"This is a mock data file for testing purposes. Delete me!\\\",\\n write_tsv = True\\n ):\\n prog,proj = project_id.split(\\\"-\\\")\\n authz = [\\\"/programs/{}/projects/{}\\\".format(prog,proj)]\\n acl = [prog,proj]\\n\\n mfiles = {'file_name':[],'md5sum':[],\\\"file_size\\\":[],\\\"object_id\\\":[],\\\"storage_urls\\\":[],\\\"acl\\\":[],\\\"authz\\\":[]}\\n for i in range(count):\\n file_name = \\\"{}_{}.{}\\\".format(prefix,i+1,file_format)\\n object_id = str(uuid.uuid4())\\n mfiles['file_name'].append(file_name)\\n mfiles['object_id'].append(object_id)\\n mfiles['authz'].append(authz)\\n mfiles['acl'].append(acl)\\n\\n\\n output = \\\"{}/{}\\\".format(outdir,file_name)\\n os.system(\\\"touch {}\\\".format(output))\\n file_msg =\\\"{} File {} of {}. {} with object_id {}.\\\".format(msg,i+1,count,file_name,object_id)\\n cmd = 'echo \\\"{}\\\" > {}'.format(file_msg,file_name)\\n os.system(cmd)\\n\\n with open(output, 'rb') as file_to_check:\\n file_contents = file_to_check.read()\\n #cmd = \\\"!md5 mock_data_file_{}.{}\\\".format(i+1,file_format))\\n md5 = hashlib.md5(file_contents).hexdigest() #check in shell: !md5 mock_data_file_3.dcm\\n\\n mfiles['md5sum'].append(md5)\\n mfiles['file_size'].append(os.stat(output).st_size)\\n urls=\\\"s3://this-is-a-fake-url-for:{}\\\".format(file_name)\\n mfiles['storage_urls'].append([urls])\\n\\n return mfiles\",\n \"def test_create_files(self):\\n opts = mock.Mock()\\n opts.repo = self.repo\\n opts.source = 'src'\\n opts.version = '0.0.1'\\n opts.version_file = None\\n opts.test_mode = False\\n opts.templates = ['include steve/*']\\n opts.history_file = 'HISTORY.md'\\n opts.package = 'unittests'\\n opts.desc = \\\"DESCRIPTION\\\"\\n opts.org = \\\"ORG\\\"\\n opts.develop = 'develop'\\n opts.requirements = 'requirements.txt'\\n opts.test_requirements = 'test-requirements.txt'\\n opts.pypi_package_name = None\\n opts.python = None\\n opts.create_version_file = False\\n opts.gitignore_url = \\\"GIT_IGNORE_URL\\\"\\n opts.add_gitignore = False\\n create_files(opts)\\n\\n dir_list = os.listdir(self.repo)\\n self.failUnless('cirrus.conf' in dir_list)\\n self.failUnless('HISTORY.md' in dir_list)\\n self.failUnless('MANIFEST.in' in dir_list)\\n self.failUnless('setup.py' in dir_list)\\n\\n cirrus_conf = os.path.join(self.repo, 'cirrus.conf')\\n config = ConfigParser.RawConfigParser()\\n config.read(cirrus_conf)\\n self.assertEqual(config.get('package', 'name'), opts.package)\\n self.assertEqual(config.get('package', 'version'), opts.version)\\n\\n history = os.path.join(self.repo, 'HISTORY.md')\\n with open(history, 'r') as handle:\\n self.failUnless('CIRRUS_HISTORY_SENTINEL' in handle.read())\\n\\n manifest = os.path.join(self.repo, 'MANIFEST.in')\\n with open(manifest, 'r') as handle:\\n content = handle.read()\\n self.failUnless('include requirements.txt' in content)\\n self.failUnless('include cirrus.conf' in content)\\n self.failUnless('include steve/*' in content)\\n\\n version = os.path.join(self.repo, 'src', 'unittests', '__init__.py')\\n with open(version, 'r') as handle:\\n self.failUnless(opts.version in handle.read())\",\n \"def test_get_files_for_user(self):\\n filepath = os.path.join(\\n os.getcwd(), self.hiarc_util.TEST_FILE_PATH, 'Test.txt')\\n\\n u1 = self.hiarc_users.create_user(self.hiarc_util.create_user())\\n c1 = self.hiarc_collections.create_collection(\\n self.hiarc_util.create_collection())\\n\\n f1 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n f2 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n f3 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f1.key), c1.key)\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f2.key), c1.key)\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f3.key), c1.key)\\n\\n self.assertRaises(hiarc.rest.ApiException,\\n self.hiarc_collections.get_collection_files, c1.key, x_hiarc_user_key=u1.key)\\n\\n self.hiarc_collections.add_user_to_collection(\\n hiarc.AddUserToCollectionRequest(u1.key, hiarc.AccessLevel.READ_ONLY), c1.key)\\n\\n files = self.hiarc_collections.get_collection_files(\\n c1.key, x_hiarc_user_key=u1.key)\\n assert len(files) == 3\\n\\n ut = self.hiarc_token.create_user_token(\\n hiarc.CreateUserTokenRequest(key=u1.key))\\n lc = hiarc.CollectionApi(hiarc.ApiClient(\\n configuration=self.hiarc_util.init_hiarc_client_jwt_token(ut.bearer_token)))\\n files = lc.get_collection_files(c1.key)\\n assert len(files) == 3\\n\\n # reset ApiClient to Admin User\\n hiarc.ApiClient(\\n configuration=self.hiarc_util.init_hiarc_config_admin())\\n\\n for f in [f1.key, f2.key, f3.key]:\\n self.hiarc_files.delete_file(f)\\n self.assertRaises(hiarc.rest.ApiException,\\n self.hiarc_files.get_file, f)\",\n \"def create(self):\\n self.create_file()\",\n \"def test_create2(self):\\n fname = TempfileManager.create_tempfile(prefix='foo')\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertTrue(fname.startswith('foo'))\",\n \"def test_file_has_details(self):\\n file_content = bytes(\\n \\\"\\\"\\\"\\n 1001 | Eggs | 12\\\\n\\n 1002 | Bread | 1\\\\n\\n 1003 | Butter | 1\\\\n\\n 1004 | Milk | 1\\\\n\\n 1005 | Corn | 2\\n \\\"\\\"\\\", encoding='UTF-8'\\n )\\n\\n test_file = SimpleUploadedFile(\\n \\\"sample.txt\\\", file_content, content_type=\\\"text/txt\\\")\\n \\n new_file = File.objects.create(\\n file=test_file,\\n rows=5,\\n items='Eggs, Bread, Butter, Milk, Corn'\\n )\\n\\n file_details = File.objects.get(id=new_file.id)\\n self.assertEqual(file_details.file.name, new_file.file.name)\\n self.assertEqual(file_details.rows, new_file.rows)\\n self.assertEqual(file_details.items, new_file.items)\",\n \"def test_upload(self):\\n fake_file_name = 'fake_file_name'\\n\\n backend = self.test_init_valid()\\n backend.upload(fake_file_name)\\n\\n backend.vault.concurrent_create_archive_from_file.assert_called_once_with(filename=fake_file_name, description='')\",\n \"def test_delete_with_file(self):\\n c1 = self.hiarc_collections.create_collection(\\n self.hiarc_util.create_collection())\\n\\n filepath = os.path.join(\\n os.getcwd(), self.hiarc_util.TEST_FILE_PATH, 'Test.txt')\\n f1 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n\\n self.hiarc_collections.add_file_to_collection(\\n hiarc.AddFileToCollectionRequest(f1.key), c1.key)\\n\\n self.hiarc_collections.delete_collection(c1.key)\\n self.assertRaises(hiarc.rest.ApiException,\\n self.hiarc_collections.get_collection, c1.key)\\n\\n r = self.hiarc_files.get_file(f1.key)\\n assert f1 == r\\n\\n self.hiarc_files.delete_file(f1.key)\\n self.assertRaises(hiarc.rest.ApiException,\\n self.hiarc_files.get_file, f1.key)\",\n \"def testFileset(self):\\n testCollection = CouchCollection(database=self.testInit.couchDbName,\\n url=self.testInit.couchUrl,\\n name=\\\"Thunderstruck\\\")\\n testFileset = CouchFileset(database=self.testInit.couchDbName,\\n url=self.testInit.couchUrl,\\n name=\\\"TestFileset\\\")\\n testCollection.addFileset(testFileset)\\n\\n testFiles = {}\\n for i in range(5):\\n lfn = makeUUID()\\n testFile = File(lfn=lfn, size=random.randint(1024, 4096),\\n events=random.randint(1024, 4096))\\n testFiles[lfn] = testFile\\n testFileset.add([testFile])\\n\\n for file in testFileset.fileset().files:\\n self.assertTrue(file[\\\"lfn\\\"] in testFiles,\\n \\\"Error: File missing.\\\")\\n self.assertEqual(file[\\\"events\\\"], testFiles[file[\\\"lfn\\\"]][\\\"events\\\"],\\n \\\"Error: Wrong number of events.\\\")\\n self.assertEqual(file[\\\"size\\\"], testFiles[file[\\\"lfn\\\"]][\\\"size\\\"],\\n \\\"Error: Wrong file size.\\\")\\n return\",\n \"def test_file_analyzer(self):\\r\\n file_analyzer = FileAnalyzer(\\\"C:\\\\\\\\Users\\\\\\\\Himan\\\\\\\\Desktop\\\\\\\\Semester 2\\\\\\\\SSW 810\\\\\\\\HW\\\\\\\\Assignment 8\\\")\\r\\n self.assertEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 1, 'function': 5, 'line': 100, 'char': 4472}, \\\\\\r\\n 'HW08_Test_Himanshu.py': {'class': 1, 'function': 3, 'line': 38, 'char': 1861}})\\r\\n self.assertNotEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 0, 'function': 5, 'line': 46, 'char': 1931}})\\r\\n\\r\\n self.assertNotEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 1, 'function': 5, 'line': 100}}) # testing less fields\\r\\n\\r\\n with self.assertRaises(FileNotFoundError): # raises exception error\\r\\n FileAnalyzer(\\\"C:\\\\\\\\Users\\\\\\\\Himan\\\\\\\\Desktop\\\\\\\\Semester 2\\\\\\\\SSW 810\\\\\\\\HW\\\\\\\\Assignment 10\\\").files_summary\",\n \"def __create_test_file(self):\\n self.test_file = os.path.join(os.path.dirname(self.server_path), \\\"data\\\")\\n with open(self.test_file, \\\"ab+\\\") as f:\\n n_blocks = int(self.args.size) // self.max_block_size\\n for i in range(n_blocks):\\n f.write(bytearray(os.urandom(self.max_block_size)))\\n remaining = int(self.args.size) % self.max_block_size\\n if remaining > 0:\\n f.write(bytearray(os.urandom(remaining)))\\n self.assertEqual(int(self.args.size), os.path.getsize(self.test_file))\",\n \"def test_add_remove_files_as_user(self):\\n u1 = self.hiarc_users.create_user(self.hiarc_util.create_user())\\n\\n filepath = os.path.join(\\n os.getcwd(), self.hiarc_util.TEST_FILE_PATH, 'Test.txt')\\n f1 = self.hiarc_files.create_file(\\n self.hiarc_util.create_file(), filepath)\\n\\n c1 = self.hiarc_collections.create_collection(\\n self.hiarc_util.create_collection())\\n\\n aftcr = hiarc.AddFileToCollectionRequest(f1.key)\\n self.assertRaises(hiarc.rest.ApiException, self.hiarc_collections.add_file_to_collection,\\n aftcr, c1.key, x_hiarc_user_key=u1.key)\\n\\n self.hiarc_collections.add_user_to_collection(\\n hiarc.AddUserToCollectionRequest(u1.key, hiarc.AccessLevel.READ_ONLY), c1.key)\\n self.assertRaises(hiarc.rest.ApiException, self.hiarc_collections.add_file_to_collection,\\n aftcr, c1.key, x_hiarc_user_key=u1.key)\\n\\n u2 = self.hiarc_users.create_user(self.hiarc_util.create_user())\\n self.hiarc_collections.add_user_to_collection(\\n hiarc.AddUserToCollectionRequest(u2.key, hiarc.AccessLevel.READ_WRITE), c1.key)\\n self.hiarc_collections.add_file_to_collection(\\n aftcr, c1.key, x_hiarc_user_key=u2.key)\\n\\n files = self.hiarc_collections.get_collection_files(c1.key)\\n assert len(files) == 1\\n\\n u3 = self.hiarc_users.create_user(self.hiarc_util.create_user())\\n self.assertRaises(hiarc.rest.ApiException, self.hiarc_collections.remove_file_from_collection,\\n c1.key, f1.key, x_hiarc_user_key=u3.key)\\n self.assertRaises(hiarc.rest.ApiException, self.hiarc_collections.remove_file_from_collection,\\n c1.key, f1.key, x_hiarc_user_key=u1.key)\\n self.hiarc_collections.remove_file_from_collection(\\n c1.key, f1.key, x_hiarc_user_key=u2.key)\\n\\n files = self.hiarc_collections.get_collection_files(c1.key)\\n assert len(files) == 0\\n\\n self.hiarc_files.delete_file(f1.key)\\n self.assertRaises(hiarc.rest.ApiException,\\n self.hiarc_files.get_file, f1.key)\",\n \"def test_identify_contents_1(self):\\n Path(self.base_dir, \\\"new_dir\\\").mkdir()\\n Path(self.base_dir, \\\"file1.txt\\\").touch()\\n Path(self.base_dir, \\\".DS_Store\\\").touch()\\n list_of_items = basic.identify_contents(self.base_dir, kind=\\\"file\\\")\\n exp_num_items = 2\\n self.assertEqual(len(list_of_items), exp_num_items)\",\n \"def test_files(host, f):\\n assert host.file(f).exists\",\n \"def test_add1(self):\\n try:\\n TempfileManager.add_tempfile(tempdir + 'add1')\\n self.fail(\\\"Expected IOError because file 'add1' does not exist\\\")\\n except IOError:\\n pass\",\n \"def test_upload_dir_contents_one_file(self):\\n self._test_upload_dir_contents(filenames=['file1'])\",\n \"def test_make_file():\\n with tempfile.TemporaryDirectory() as STATUS_DIR:\\n Status.make_job_file(STATUS_DIR, 'generation', 'test1', TEST_1_ATTRS_1)\\n status = Status.retrieve_job_status(STATUS_DIR, 'generation', 'test1')\\n msg = 'Failed, status is \\\"{}\\\"'.format(status)\\n assert status == 'R', msg\",\n \"def test_write_file():\\n filename = 'test'\\n content = 'hello!'\\n\\n write_file(content, filename)\\n assert read_file(filename) == 'hello!'\",\n \"def test_create_files(self):\\n fitting_report.create_prob_group(result=self.result,\\n support_pages_dir=self.dir.name,\\n options=self.options)\\n self.assertTrue(os.path.exists(self.result.fitting_report_link))\",\n \"def test_create(self):\\n pass\",\n \"def test_newFile(self):\\n self.write(\\\"service1.json\\\", [{\\\"host\\\": \\\"host1\\\", \\\"port\\\": 123},\\n {\\\"host\\\": \\\"host2\\\", \\\"port\\\": 124}])\\n self.write(\\\"service2.json\\\", [])\\n self.pump()\\n self.assertNodesEqual(\\n knownNodes(self.disco, \\\"service1\\\", \\\"staging\\\"),\\n [self.node(\\\"service1\\\", \\\"host1\\\", 123),\\n self.node(\\\"service1\\\", \\\"host2\\\", 124)])\",\n \"def test_005_write_file(self):\\n __test = chess_storage.ChessStorage()\\n __test_data = list(range(consts.TEST_LIST_LENGHT))\\n __dir_game_saves = os.path.dirname(__file__)\\n __dir_game_saves = os.path.join(__dir_game_saves, 'games')\\n __dir_game_saves = os.path.join(__dir_game_saves, consts.TEST_FILENAME)\\n # pylint: disable = protected-access\\n __save_test = __test._ChessStorage__write_file(__dir_game_saves, __test_data)\\n # pylint: enable = protected-access\\n self.assertEqual(__save_test, consts.ERROR_CODES[\\\"SUCCESSFULL\\\"])\",\n \"def test_fs_instance(self):\\n b1 = BaseModel()\\n models.storage.save()\\n self.assertEqual(os.path.exists('file.json'), True)\",\n \"def setUp(self):\\r\\n\\r\\n fd, self.sample_fasta_fp = mkstemp(prefix=\\\"sample_fasta_\\\",\\r\\n suffix=\\\".fna\\\")\\r\\n close(fd)\\r\\n seq_file = open(self.sample_fasta_fp, 'w')\\r\\n seq_file.write(sample_fasta_file)\\r\\n seq_file.close()\\r\\n\\r\\n fd, self.sample_fasta_invalid_fp = mkstemp(prefix=\\\"sample_fasta_\\\",\\r\\n suffix=\\\".fna\\\")\\r\\n close(fd)\\r\\n seq_file = open(self.sample_fasta_invalid_fp, 'w')\\r\\n seq_file.write(sample_fasta_file_invalid)\\r\\n seq_file.close()\\r\\n\\r\\n fd, self.sample_mapping_fp = mkstemp(prefix=\\\"sample_mapping_\\\",\\r\\n suffix=\\\".txt\\\")\\r\\n close(fd)\\r\\n map_file = open(self.sample_mapping_fp, \\\"w\\\")\\r\\n map_file.write(sample_mapping_file)\\r\\n map_file.close()\\r\\n\\r\\n fd, self.sample_tree_3tips_fp = mkstemp(\\r\\n prefix=\\\"sample_tree3tips_\\\",\\r\\n suffix=\\\".tre\\\")\\r\\n close(fd)\\r\\n tree_file = open(self.sample_tree_3tips_fp, \\\"w\\\")\\r\\n tree_file.write(sample_tree_file_3tips)\\r\\n tree_file.close()\\r\\n\\r\\n fd, self.sample_tree_5tips_fp = mkstemp(\\r\\n prefix=\\\"sample_tree3tips_\\\",\\r\\n suffix=\\\".tre\\\")\\r\\n close(fd)\\r\\n tree_file = open(self.sample_tree_5tips_fp, \\\"w\\\")\\r\\n tree_file.write(sample_tree_file_5tips)\\r\\n tree_file.close()\\r\\n\\r\\n fd, self.sample_mapping_file_errors_fp =\\\\\\r\\n mkstemp(prefix=\\\"error_mapping_\\\", suffix=\\\".txt\\\")\\r\\n close(fd)\\r\\n map_file = open(self.sample_mapping_file_errors_fp, \\\"w\\\")\\r\\n map_file.write(sample_mapping_file_errors)\\r\\n map_file.close()\\r\\n\\r\\n self._files_to_remove = [self.sample_fasta_fp,\\r\\n self.sample_fasta_invalid_fp, self.sample_mapping_fp,\\r\\n self.sample_tree_3tips_fp, self.sample_tree_5tips_fp,\\r\\n self.sample_mapping_file_errors_fp]\\r\\n\\r\\n self.output_dir =\\\\\\r\\n mkdtemp(prefix=\\\"validate_demultiplexed_fasta_\\\",\\r\\n suffix=\\\"/\\\")\",\n \"def create_file(path):\\n command = ['touch', TEST_FILE]\\n file_operation(path, command)\",\n \"def test_create_collection(self):\\n pass\",\n \"def test_cannot_write_file(self):\\n self.api.write_data('/some-fake/path/to-create-file/', 'some-string')\",\n \"def test_write_file(self):\\n test = Server()\\n test.cur_dir = os.getcwd()\\n inputs = [['write_file', 'test_file1.txt', 'Hello world'],\\n ['write_file', 'test_file2.txt', 'Hello world'],\\n ['write_file', 'test_file1.txt']]\\n response = ['written successfully',\\n 'file created and written successfully',\\n 'contents erased successfully']\\n res = []\\n for val in inputs:\\n res.append(test.write_file(val))\\n self.assertListEqual(res, response)\",\n \"def test_create_file_via_ansible_playbook(self):\\n param = {\\n \\\"task_file\\\": \\\"kalliope/neurons/ansible_playbook/tests/test_ansible_playbook_neuron.yml\\\"\\n }\\n\\n Ansible_playbook(**param)\\n\\n self.assertTrue(os.path.isfile(self.test_file))\\n\\n if os.path.exists(self.test_file):\\n os.remove(self.test_file)\",\n \"def test_upload_file(self):\\n\\n uploadFile = os.path.join(testdatadir, \\\"upload.data\\\")\\n r = gracedb.writeFile(eventId, uploadFile)\\n self.assertEqual(r.status, 201) # CREATED\\n r_content = r.json()\\n link = r_content['permalink']\\n\\n self.assertEqual(\\n open(uploadFile, 'r').read(),\\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\\n )\\n\\n self.assertEqual(\\n open(uploadFile, 'r').read(),\\n gracedb.get(link).read()\\n )\\n\\n # Re-upload slightly different file.\\n uploadFile2 = os.path.join(testdatadir, \\\"upload2.data\\\")\\n r = gracedb.writeFile(\\n eventId,\\n filename=\\\"upload.data\\\",\\n filecontents=open(uploadFile2, 'r'))\\n self.assertEqual(r.status, 201) # CREATED\\n r_content = r.json()\\n link2 = r_content['permalink']\\n\\n self.assertEqual(\\n open(uploadFile2, 'r').read(),\\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\\n )\\n\\n self.assertEqual(\\n open(uploadFile2, 'r').read(),\\n gracedb.get(link2).read()\\n )\\n\\n self.assertNotEqual(link, link2)\",\n \"def test_create_document(self):\\n self.client.force_login(user=Customer.objects.get(id=self.dummy.customer[0]).user)\\n url = reverse('document-list')\\n\\n data = {\\n 'file':open('Murphy.txt'),\\n 'type':2,\\n 'res':self.res_id,\\n 'obsolete': True,\\n 'description':'Sth',\\n }\\n response = self.client.post(url,data,format='multipart')\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n document = Document.objects.get(id=json.loads(response.content)['id'])\\n self.assertEqual(document.res_id,self.res_id)\\n self.client.logout()\",\n \"def test_create3(self):\\n fname = TempfileManager.create_tempfile(suffix='bar')\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertTrue(fname.endswith('bar'))\",\n \"def setUp(self):\\n self.setUpPyfakefs()\\n\\n self.fs.create_file(f\\\"{BASE_PATH}/_info.yml\\\", contents=CATEGORY_INFO)\\n self.fs.create_file(f\\\"{BASE_PATH}/root.md\\\", contents=MARKDOWN_WITH_METADATA)\\n self.fs.create_file(\\n f\\\"{BASE_PATH}/root_without_metadata.md\\\", contents=MARKDOWN_WITHOUT_METADATA\\n )\\n self.fs.create_file(f\\\"{BASE_PATH}/not_a_page.md/_info.yml\\\", contents=CATEGORY_INFO)\\n self.fs.create_file(f\\\"{BASE_PATH}/category/_info.yml\\\", contents=CATEGORY_INFO)\\n self.fs.create_file(\\n f\\\"{BASE_PATH}/category/with_metadata.md\\\", contents=MARKDOWN_WITH_METADATA\\n )\\n self.fs.create_file(f\\\"{BASE_PATH}/category/subcategory/_info.yml\\\", contents=CATEGORY_INFO)\\n self.fs.create_file(\\n f\\\"{BASE_PATH}/category/subcategory/with_metadata.md\\\", contents=MARKDOWN_WITH_METADATA\\n )\\n self.fs.create_file(\\n f\\\"{BASE_PATH}/category/subcategory/without_metadata.md\\\",\\n contents=MARKDOWN_WITHOUT_METADATA\\n )\\n\\n temp = f\\\"{BASE_PATH}/tmp\\\" # noqa: S108\\n self.fs.create_file(f\\\"{temp}/_info.yml\\\", contents=CATEGORY_INFO)\\n self.fs.create_file(f\\\"{temp}.md\\\", contents=MARKDOWN_WITH_METADATA)\\n self.fs.create_file(f\\\"{temp}/category/_info.yml\\\", contents=CATEGORY_INFO)\\n self.fs.create_dir(f\\\"{temp}/category/subcategory_without_info\\\")\",\n \"def test_add_parsed_files(self):\\n file_name = 'a_test_file'\\n Historical_ROAs_Parser()._add_parsed_files([file_name])\\n with Historical_ROAs_Parsed_Table() as t:\\n sql = f\\\"SELECT * FROM {t.name} WHERE file = '{file_name}'\\\"\\n assert len(t.execute(sql)) == 1\\n sql = f\\\"DELETE FROM {t.name} WHERE file = '{file_name}'\\\"\\n t.execute(sql)\",\n \"def _test(self, file_name):\\n data = bob.io.base.load(file_name)\\n assert (_data == data).all()\",\n \"def test_create_part(self):\\n pass\",\n \"def test_with_new_file(self):\\n repository = self.create_repository(tool_name='Test')\\n review_request = self.create_review_request(\\n repository=repository,\\n submitter=self.user,\\n publish=True)\\n diffset = self.create_diffset(review_request)\\n filediff = self.create_filediff(diffset,\\n source_revision=PRE_CREATION)\\n\\n rsp = self.api_get(\\n get_original_file_url(review_request, diffset, filediff),\\n expected_status=404)\\n self.assertEqual(rsp['stat'], 'fail')\\n self.assertEqual(rsp['err']['code'], DOES_NOT_EXIST.code)\",\n \"def setUp(self):\\n self._file = 'test.g3'\\n write_example_file(self._file)\",\n \"def test_add_1(self):\\n contents = testdata.get_words()\\n d = testdata.create_dir()\\n ts = {\\n \\\"foo.txt\\\": [contents],\\n }\\n ds = d.add(ts)\\n path = ds[0]\\n self.assertTrue(os.path.isfile(path), \\\"{} does not exist\\\".format(path))\\n self.assertEqual(contents, path.read_text())\",\n \"def test_successful_file(self):\\n\\n url = '/%s/jobs/%i/input_files/' % (self.api, self.job.id)\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n results = result['results']\\n self.assertEqual(len(results), 2)\\n for result in results:\\n self.assertTrue(result['id'] in [self.file3.id, self.file4.id])\\n self.assertIn('file_name', result)\\n self.assertIn('workspace', result)\\n self.assertIn('media_type', result)\\n self.assertIn('file_type', result)\\n self.assertIn('file_size', result)\\n self.assertIn('file_path', result)\\n self.assertIn('is_deleted', result)\\n self.assertIn('url', result)\\n self.assertIn('created', result)\\n self.assertIn('deleted', result)\\n self.assertIn('data_started', result)\\n self.assertIn('data_ended', result)\\n self.assertIn('source_started', result)\\n self.assertIn('source_ended', result)\\n self.assertIn('last_modified', result)\\n self.assertIn('geometry', result)\\n self.assertIn('center_point', result)\\n self.assertIn('countries', result)\\n self.assertIn('job_type', result)\\n self.assertIn('job', result)\\n self.assertIn('job_exe', result)\\n self.assertIn('job_output', result)\\n self.assertIn('recipe_type', result)\\n self.assertIn('recipe', result)\\n self.assertIn('recipe_node', result)\\n self.assertIn('batch', result)\\n self.assertFalse(result['is_superseded'])\\n self.assertIn('superseded', result)\",\n \"def test_loading_document(self):\",\n \"def test_files_always_created():\\n c = TestClient()\\n c.save({\\\"dep/conanfile.py\\\": GenConanfile(\\\"dep\\\", \\\"0.1\\\"),\\n \\\"consumer/conanfile.txt\\\": \\\"[requires]\\\\ndep/0.1\\\"})\\n c.run(\\\"create dep\\\")\\n c.run(\\\"install consumer -g VirtualBuildEnv -g VirtualRunEnv -of=.\\\")\\n ext = \\\"bat\\\" if platform.system() == \\\"Windows\\\" else \\\"sh\\\"\\n\\n arch = c.get_default_host_profile().settings['arch']\\n assert os.path.isfile(os.path.join(c.current_folder, f\\\"conanbuild.{ext}\\\"))\\n assert os.path.isfile(os.path.join(c.current_folder, f\\\"conanrun.{ext}\\\"))\\n assert os.path.isfile(os.path.join(c.current_folder, f\\\"conanbuildenv-release-{arch}.{ext}\\\"))\\n assert os.path.isfile(os.path.join(c.current_folder, f\\\"conanbuildenv-release-{arch}.{ext}\\\"))\",\n \"def test_create_files_with_python(self):\\n opts = mock.Mock()\\n opts.repo = self.repo\\n opts.create_version_file = True\\n opts.source = 'src'\\n opts.version = '0.0.1'\\n opts.version_file = None\\n opts.org = \\\"ORG\\\"\\n opts.desc = \\\"DESCRIPTION\\\"\\n opts.templates = []\\n opts.test_mode = False\\n opts.history_file = 'HISTORY.md'\\n opts.package = 'unittests'\\n opts.develop = 'develop'\\n opts.requirements = 'requirements.txt'\\n opts.pypi_package_name = 'pypi.package.unittest'\\n opts.python = 'python3'\\n opts.gitignore_url = \\\"GIT_IGNORE_URL\\\"\\n opts.add_gitignore = False\\n opts.test_requirements = 'test-requirements.txt'\\n version = os.path.join(self.repo, 'src', 'unittests', '__init__.py')\\n os.system('rm -f {}'.format(version))\\n create_files(opts)\\n\\n dir_list = os.listdir(self.repo)\\n self.failUnless('cirrus.conf' in dir_list)\\n self.failUnless('HISTORY.md' in dir_list)\\n self.failUnless('MANIFEST.in' in dir_list)\\n self.failUnless('setup.py' in dir_list)\\n\\n cirrus_conf = os.path.join(self.repo, 'cirrus.conf')\\n config = ConfigParser.RawConfigParser()\\n config.read(cirrus_conf)\\n self.assertEqual(config.get('package', 'name'), opts.pypi_package_name)\\n self.assertEqual(config.get('package', 'version'), opts.version)\\n self.assertEqual(config.get('build', 'python'), 'python3')\",\n \"def setUp(self):\\n # Generates directory names\\n self.tempdir = tempfile.mkdtemp()\\n self.subdir = os.path.join(self.tempdir, \\\"dir\\\")\\n self.emptydir = os.path.join(self.tempdir, \\\"empty\\\")\\n # Populates directories\\n os.makedirs(self.subdir)\\n os.makedirs(self.emptydir)\\n # Populates files\\n self.root_fcount = 3\\n self.nest_fcount = 5\\n for i in range(0, self.root_fcount):\\n with open(os.path.join(self.tempdir, \\\"%i.txt\\\" % i), \\\"w+\\\") as f:\\n f.write(\\\"Test.\\\")\\n for i in range(0, self.nest_fcount):\\n with open(os.path.join(self.subdir, \\\"%i.txt\\\" % i), \\\"w+\\\") as f:\\n f.write(\\\"Test.\\\")\\n self.filename = os.path.join(self.subdir, \\\"nontxt.mp3\\\")\\n with open(self.filename, \\\"w+\\\") as f:\\n f.write(\\\"Test.\\\")\",\n \"def test_file_generation(self):\\n # For file generation submission, call generate route for D1 and check results\\n post_json = {\\\"submission_id\\\": self.generation_submission_id, \\\"file_type\\\": \\\"D1\\\",\\n \\\"start\\\": \\\"01/02/2016\\\", \\\"end\\\": \\\"02/03/2016\\\"}\\n response = self.app.post_json(\\\"/v1/generate_file/\\\", post_json, headers={\\\"x-session-id\\\": self.session_id})\\n\\n self.assertEqual(response.status_code, 200)\\n json = response.json\\n\\n # use_aws is true when the PR unit tests run so the date range specified returns no results.\\n # checking is in place for \\\"failed\\\" until use_aws is flipped to false\\n self.assertIn(json[\\\"status\\\"], [\\\"failed\\\", \\\"waiting\\\", \\\"finished\\\"])\\n self.assertEqual(json[\\\"file_type\\\"], \\\"D1\\\")\\n self.assertIn(\\\"url\\\", json)\\n self.assertEqual(json[\\\"start\\\"], \\\"01/02/2016\\\")\\n self.assertEqual(json[\\\"end\\\"], \\\"02/03/2016\\\")\\n\\n # this is to accommodate for checking for the \\\"failed\\\" status\\n self.assertIn(json[\\\"message\\\"], [\\\"\\\", \\\"D1 data unavailable for the specified date range\\\"])\\n\\n # Then call check generation route for D2, E and F and check results\\n post_json = {\\\"submission_id\\\": self.generation_submission_id, \\\"file_type\\\": \\\"E\\\"}\\n response = self.app.post_json(\\\"/v1/check_generation_status/\\\", post_json,\\n headers={\\\"x-session-id\\\": self.session_id})\\n\\n self.assertEqual(response.status_code, 200)\\n json = response.json\\n self.assertEqual(json[\\\"status\\\"], \\\"finished\\\")\\n self.assertEqual(json[\\\"file_type\\\"], \\\"E\\\")\\n self.assertEqual(json[\\\"url\\\"], \\\"#\\\")\\n self.assertEqual(json[\\\"message\\\"], \\\"\\\")\\n\\n post_json = {\\\"submission_id\\\": self.generation_submission_id, \\\"file_type\\\": \\\"D2\\\"}\\n response = self.app.post_json(\\\"/v1/check_generation_status/\\\", post_json,\\n headers={\\\"x-session-id\\\": self.session_id})\\n\\n self.assertEqual(response.status_code, 200)\\n json = response.json\\n self.assertEqual(json[\\\"status\\\"], \\\"failed\\\")\\n self.assertEqual(json[\\\"file_type\\\"], \\\"D2\\\")\\n self.assertEqual(json[\\\"url\\\"], \\\"#\\\")\\n self.assertEqual(json[\\\"message\\\"], \\\"Generated file had file-level errors\\\")\\n\\n post_json = {\\\"submission_id\\\": self.generation_submission_id, \\\"file_type\\\": \\\"F\\\"}\\n response = self.app.post_json(\\\"/v1/check_generation_status/\\\", post_json,\\n headers={\\\"x-session-id\\\": self.session_id})\\n\\n self.assertEqual(response.status_code, 200)\\n json = response.json\\n self.assertEqual(json[\\\"status\\\"], \\\"failed\\\")\\n self.assertEqual(json[\\\"file_type\\\"], \\\"F\\\")\\n self.assertEqual(json[\\\"url\\\"], \\\"#\\\")\\n self.assertEqual(json[\\\"message\\\"], \\\"File was invalid\\\")\\n\\n # Test permission error\\n self.login_user()\\n post_json = {\\\"submission_id\\\": self.generation_submission_id, \\\"file_type\\\": \\\"D1\\\",\\n \\\"start\\\": \\\"01/02/2016\\\", \\\"end\\\": \\\"02/03/2016\\\"}\\n response = self.app.post_json(\\\"/v1/generate_file/\\\", post_json,\\n headers={\\\"x-session-id\\\": self.session_id}, expect_errors=True)\\n\\n self.assertEqual(response.status_code, 403)\\n json = response.json\\n self.assertEqual(json[\\\"status\\\"], \\\"failed\\\")\\n self.assertEqual(json[\\\"file_type\\\"], \\\"D1\\\")\\n self.assertEqual(json[\\\"url\\\"], \\\"#\\\")\\n self.assertEqual(json[\\\"start\\\"], \\\"\\\")\\n self.assertEqual(json[\\\"end\\\"], \\\"\\\")\\n self.assertEqual(json[\\\"message\\\"], \\\"User does not have permission to view that submission\\\")\",\n \"def test_create_from_pear(self):\\n pass\",\n \"def test_add_model_file(self):\\n with open(TEST_AVATAR_PATH, 'rb') as avatar:\\n self.user.avatar_tmp = File(avatar)\\n self.user.save()\\n\\n with DataArchive(self.user, DATA_DOWNLOADS_WORKING_DIR) as archive:\\n file_path = archive.add_model_file(self.user.avatar_tmp)\\n\\n self.assertTrue(os.path.isfile(file_path))\\n \\n data_dir_path = str(archive.data_dir_path)\\n self.assertTrue(str(file_path).startswith(data_dir_path))\",\n \"def setUp(self):\\n super().setUp()\\n self.file_path = 'file.json'\",\n \"def file_factory(test_workspace):\\n\\n return FileCreator(test_workspace)\",\n \"def test_memory_created(self):\\n with TemporaryDirectory(dir=\\\"./\\\") as cur_dir:\\n \\\"\\\"\\\" Test some memory name\\\"\\\"\\\"\\n filepath, memory_file = self.create_config(memory=\\\"\\\", corpora=DEFAULT_CORPUS, cur_dir=cur_dir)\\n self._dispatch(\\n train=0.8,\\n test=0.1,\\n dev=0.1,\\n config=filepath,\\n output_dir=p.join(cur_dir, \\\"output\\\")\\n )\\n self.assertTrue(p.isfile(memory_file+\\\".csv\\\"), \\\"A new file should have been created\\\")\",\n \"def test_fileinuse (self):\\t\\t\\n\\t\\t# open a file:\\n\\t\\ttestfilepath = os.path.join (self.testfolder, \\\"fileinuse.txt\\\")\\n\\t\\tf = open(testfilepath,\\\"a\\\") #opens file with name of \\\"test.txt\\\"\\n\\t\\tf.write(\\\"This file is now opened and i'm writting on it \\\\n\\\")\\n\\t\\tself.assertEqual (MD.fileinuse(testfilepath), True) # Checks a file that it is beign written.\\n\\t\\tself.assertEqual (MD.folderinuse(self.testfolder), True) # Checks if any file inside the folder is beign used.\\n\\t\\tf.close()\\n\\t\\tself.assertEqual (MD.fileinuse(testfilepath), False) # Cheks a file that it is closed.\\n\\t\\tself.assertEqual (MD.folderinuse(self.testfolder), False)\",\n \"def test_upload(self):\\n with self.client:\\n file = dict(\\n file=(BytesIO(b'my file contents'), \\\"foto.jpg\\\"),\\n )\\n response = self.client.post('/upload',\\n content_type='multipart/form-data',\\n data=file)\\n self.assertEqual(status.HTTP_200_OK, response.status_code)\\n self.assertIn('foto.jpg', os.listdir(PHOTOS_SAVE_PATH))\\n self.assertIn('foto.jpg', [photo.filename for photo in Photo.query.all()])\",\n \"def test_writer_with_file():\\n outputfile = \\\"testfile.txt\\\"\\n GCMT(write=outputfile)\\n assert os.path.exists(outputfile)\\n os.remove(outputfile)\",\n \"def test_observable_file_add(self):\\n with tempfile.NamedTemporaryFile('wb', delete=False) as f:\\n f.write(b\\\"content\\\")\\n filename = f.name\\n fileinfo = self.api.observable_file_add(filename, ['file_tag'])\\n os.remove(filename)\\n # SHA256 of \\\"content\\\"\\n expected_filename = \\\"FILE:ed7002b439e9ac845f22357d822bac1444730fbdb6016d3ec9432297b9ec9f73\\\"\\n self.assertEqual(fileinfo[0]['value'], expected_filename)\\n tags = [t['name'] for t in fileinfo[0]['tags']]\\n self.assertEqual(tags, ['file_tag'])\",\n \"def test_load_file(self):\\n self.assertTrue(os.path.exists(MEDIA_ROOT+\\\"/pl_test1_\\\"+self.loader.version))\\n self.assertTrue(os.path.exists(MEDIA_ROOT+\\\"/pl_test2_\\\"+self.loader.version))\",\n \"def test_content_file(self):\\n\\n url=[\\\"http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\\\",\\n \\\"http://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data\\\",\\n \\\"http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data\\\"]\\n cwd=os.getcwd()\\n list_of_files=requester.batch_url_to_csv(url, fnames=[\\\"m1\\\", \\\"m2\\\",\\\"m3\\\"])\\n total_rows=0\\n reader_list=[]\\n for j in range(len(list_of_files)):\\n reader=csv.DictReader(list_of_files[j])\\n for rows in reader:\\n total_rows+=1\\n reader_list.append(total_rows)\\n\\n unique=set((reader_list))\\n if len(unique)!=len(reader_list):\\n with self.assertRaises(AssertionError):\\n requester.batch_url_to_csv(url,fnames=['m1','m2','m3'])\",\n \"def test_simple_creation():\\n # Get model file\\n create.main(\\\"mlp\\\", \\\"10:12:8\\\", \\\"model_test.tar\\\")\",\n \"def test_init_flowgram_file(self):\\r\\n fh, tmp_filename = init_flowgram_file(n=100, l=400)\\r\\n self.assert_(exists(tmp_filename))\\r\\n self.tmp_filename = tmp_filename\\r\\n fh.close()\\r\\n result_file_content = list(open(tmp_filename))\\r\\n\\r\\n self.assertEqual(result_file_content, [\\\"100 400\\\\n\\\"])\",\n \"def setUp(self):\\n self.junk_file = '/tmp/asdfoowersdfuixlwles'\",\n \"def test_api_create_unknown_file(api):\\n api.create_article(\\n {\\\"title\\\": \\\"A Title\\\"},\\n {\\\"key1\\\": \\\"value1\\\"},\\n {'test.zip': 'FFFDASFAFADADFA'},\\n )\\n req_call = requests.request\\n req_kw = req_call.call_args[1]\\n data_lines = req_kw['data'].split(b'\\\\r\\\\n')\\n # Unknown file type is skipped\\n assert len(data_lines) == 11\",\n \"def test_create_already_exists():\\n current_folder = os.path.dirname(os.path.realpath(__file__))\\n misc_folder = os.path.join(current_folder, \\\"misc\\\")\\n model_file = os.path.join(misc_folder, \\\"model.tar\\\")\\n create.main(\\\"mlp\\\", \\\"10:12:8\\\", model_file)\\n # TODO: Check if error was logged\",\n \"def test_file_io(self):\\n temp_directory = tempfile.mkdtemp()\\n filename = os.path.join(temp_directory, \\\"test.h5\\\")\\n\\n points = np.array([\\n [0.1, 0.1, 0.1],\\n [1.1, 2.1, 3.1],\\n [1.3, 2.2, 3.4]])\\n voxel_size = 0.5\\n min_corner = Vector3f(-1, -2, -3)\\n vg = VoxelGrid(voxel_size, min_corner=min_corner, points=points)\\n\\n # test writing\\n vg.save(filename)\\n self.assertTrue(os.path.isfile(filename))\\n\\n # test reading\\n vg2 = VoxelGrid.from_file(filename)\\n self.assertAlmostEquals(voxel_size, vg2.voxel_size)\\n np.testing.assert_array_almost_equal(vg.min_corner, vg2.min_corner)\\n shutil.rmtree(temp_directory)\",\n \"def test_file_error(self):\\n my_reader = DataSetReader()\\n covid_list = CovidCase.objects.all()\\n\\n with self.assertRaises(IOError):\\n my_reader.writeFile(covid_list, \\\"Not_A_File.csv\\\")\",\n \"def test_create_image(self):\\n pass\",\n \"def create_test(self, test_case, file_name):\\n with open(os.path.join(self.tests, file_name), 'w+') as f:\\n f.write(test_case)\",\n \"def assertCreatesFile(self, filename, nonzero=True):\\n self.assertFalse(os.path.exists(filename), \\\"file already exists: {}\\\".format(filename))\\n yield\\n self.assertTrue(os.path.exists(filename), \\\"file was not created: {}\\\".format(filename))\\n if nonzero:\\n self.assertFilesizeNonZero(filename)\",\n \"def test_identify_contents_2(self):\\n Path(self.base_dir, \\\"new_dir\\\").mkdir()\\n Path(self.base_dir, \\\"file1.txt\\\").touch()\\n Path(self.base_dir, \\\".DS_Store\\\").touch()\\n ignore_set = set([\\\".DS_Store\\\"])\\n list_of_items = basic.identify_contents(self.base_dir, kind=\\\"file\\\",\\n ignore_set=ignore_set)\\n exp_num_items = 1\\n self.assertEqual(len(list_of_items), exp_num_items)\",\n \"def create_test_set(self):\\n test_files = os.listdir(self.image_folder_path)\\n test_files = sorted_alphanumeric(test_files)\\n delete_files(self.root_name, \\\"/VOC2021/ImageSets/Main\\\")\\n write_txt(\\\"test.txt\\\", self.txt_path, test_files)\",\n \"def test_save_method(self):\\n\\n models.storage.save()\\n self.assertTrue(os.path.exists('file.json'))\",\n \"def test_get_file_accessors(self):\\n pass\",\n \"def setUp(self):\\n\\n try:\\n os.mkdir(self.pipeline_folder)\\n except FileExistsError:\\n pass\\n\\n with open(self.pipeline_spec_file, 'w+') as stream:\\n json.dump(self.pipeline_spec, stream)\\n\\n with open(self.pipeline_source_file, 'w+') as stream:\\n json.dump(self.source_description, stream)\\n\\n self.source = Source(folder=self.pipeline_folder)\",\n \"def test_to_file(self):\\n fd, fp = mkstemp()\\n close(fd)\\n pt = PrepTemplate.create(self.metadata, self.new_raw_data,\\n self.test_study, self.data_type)\\n pt.to_file(fp)\\n self._clean_up_files.append(fp)\\n with open(fp, 'U') as f:\\n obs = f.read()\\n self.assertEqual(obs, EXP_PREP_TEMPLATE)\",\n \"def setUp(self):\\n if os.path.exists('file.json'):\\n os.remove(\\\"file.json\\\")\",\n \"def testGetFileset(self):\\n assert self.dummyFileSet.name == self.dummySubscription['fileset'].name, \\\\\\n 'Couldn\\\\'t add Fileset to Subscription - name does not match'\\n\\n for x in self.dummyFileSet.listNewFiles():\\n assert x in self.dummySubscription['fileset'].newfiles, \\\\\\n 'Couldn\\\\'t add Fileset to Subscription - newFiles Set does not match'\\n\\n assert self.dummyFileSet.getFiles(type='set') == \\\\\\n self.dummySubscription['fileset'].getFiles(type='set'), \\\\\\n 'Couldn\\\\'t add Fileset to Subscription - %s Set does not match' % x\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7461448","0.73518395","0.73045737","0.7251613","0.7166898","0.71258205","0.70516205","0.7050503","0.70366","0.7008296","0.69309276","0.6856841","0.68465465","0.6802331","0.6774128","0.6764452","0.6762398","0.6749557","0.6735404","0.6731379","0.6729802","0.67196846","0.6681531","0.6681264","0.66736716","0.6657472","0.6654161","0.6644215","0.663958","0.662065","0.66199946","0.6616215","0.66137177","0.6610926","0.66045296","0.6595294","0.65773183","0.65762275","0.6570739","0.6570267","0.6557824","0.6499325","0.64988947","0.6496245","0.64821535","0.6471721","0.6469912","0.64678174","0.64518386","0.6445043","0.6443636","0.6433777","0.64322704","0.6423869","0.6423088","0.6421693","0.6418493","0.64116246","0.64101917","0.64091754","0.6405112","0.6405096","0.64037925","0.6403038","0.64023274","0.639731","0.6395235","0.6390053","0.6383431","0.6365916","0.6363182","0.6358711","0.6357196","0.6356727","0.6350582","0.6345585","0.6345168","0.63439566","0.63400126","0.63351476","0.6334959","0.6325752","0.63209367","0.6318105","0.63150316","0.630878","0.629828","0.6297025","0.62960476","0.62920415","0.6290469","0.62887466","0.6287097","0.62751186","0.6273864","0.625532","0.6251687","0.62455225","0.62440735","0.62405884"],"string":"[\n \"0.7461448\",\n \"0.73518395\",\n \"0.73045737\",\n \"0.7251613\",\n \"0.7166898\",\n \"0.71258205\",\n \"0.70516205\",\n \"0.7050503\",\n \"0.70366\",\n \"0.7008296\",\n \"0.69309276\",\n \"0.6856841\",\n \"0.68465465\",\n \"0.6802331\",\n \"0.6774128\",\n \"0.6764452\",\n \"0.6762398\",\n \"0.6749557\",\n \"0.6735404\",\n \"0.6731379\",\n \"0.6729802\",\n \"0.67196846\",\n \"0.6681531\",\n \"0.6681264\",\n \"0.66736716\",\n \"0.6657472\",\n \"0.6654161\",\n \"0.6644215\",\n \"0.663958\",\n \"0.662065\",\n \"0.66199946\",\n \"0.6616215\",\n \"0.66137177\",\n \"0.6610926\",\n \"0.66045296\",\n \"0.6595294\",\n \"0.65773183\",\n \"0.65762275\",\n \"0.6570739\",\n \"0.6570267\",\n \"0.6557824\",\n \"0.6499325\",\n \"0.64988947\",\n \"0.6496245\",\n \"0.64821535\",\n \"0.6471721\",\n \"0.6469912\",\n \"0.64678174\",\n \"0.64518386\",\n \"0.6445043\",\n \"0.6443636\",\n \"0.6433777\",\n \"0.64322704\",\n \"0.6423869\",\n \"0.6423088\",\n \"0.6421693\",\n \"0.6418493\",\n \"0.64116246\",\n \"0.64101917\",\n \"0.64091754\",\n \"0.6405112\",\n \"0.6405096\",\n \"0.64037925\",\n \"0.6403038\",\n \"0.64023274\",\n \"0.639731\",\n \"0.6395235\",\n \"0.6390053\",\n \"0.6383431\",\n \"0.6365916\",\n \"0.6363182\",\n \"0.6358711\",\n \"0.6357196\",\n \"0.6356727\",\n \"0.6350582\",\n \"0.6345585\",\n \"0.6345168\",\n \"0.63439566\",\n \"0.63400126\",\n \"0.63351476\",\n \"0.6334959\",\n \"0.6325752\",\n \"0.63209367\",\n \"0.6318105\",\n \"0.63150316\",\n \"0.630878\",\n \"0.629828\",\n \"0.6297025\",\n \"0.62960476\",\n \"0.62920415\",\n \"0.6290469\",\n \"0.62887466\",\n \"0.6287097\",\n \"0.62751186\",\n \"0.6273864\",\n \"0.625532\",\n \"0.6251687\",\n \"0.62455225\",\n \"0.62440735\",\n \"0.62405884\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7302167"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":77988,"cells":{"query":{"kind":"string","value":"Test file creation and reading"},"document":{"kind":"string","value":"def test_make_file():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.make_job_file(STATUS_DIR, 'generation', 'test1', TEST_1_ATTRS_1)\n status = Status.retrieve_job_status(STATUS_DIR, 'generation', 'test1')\n msg = 'Failed, status is \"{}\"'.format(status)\n assert status == 'R', msg"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_file_creation(data, logging_file_name):\n create_instance(data, logging_file_name)\n log_file_name = create_file_path(logging_file_name)\n print(log_file_name)\n if data is None or len(data) == 0:\n assert not os.path.exists(log_file_name)\n else:\n assert os.path.exists(log_file_name)","def test_create(self):\n path = self.tmp_py()\n # Creating a file that doesn't exist should succeed\n self.cls.create(path)\n self.assertTrue(os.path.exists(path))\n # Created file should be a valid script (If not, raises an error)\n self.cls.verify(path)\n # Can't create it again: it already exists\n self.assertRaises(exceptions.PathFoundError,self.cls.create,path)","def test_write_file():\n filename = 'test'\n content = 'hello!'\n\n write_file(content, filename)\n assert read_file(filename) == 'hello!'","def test_read_file():\n filename = 'sample'\n assert read_file(filename) == 'hello!\\n'","def test_get_infile(self):\r\n pass # not practically testable, but obvious file I/O\r","def test_read_file_from_disk(self):\r\n actual_data = read_file_from_disk(self.test_file1)\r\n self.assertEqual(self.test_file1_data, actual_data)","def test_save_to_file(self):\n self.assertFalse(os.path.exists(\"file.json\"))","def test_init_flowgram_file(self):\r\n fh, tmp_filename = init_flowgram_file(n=100, l=400)\r\n self.assert_(exists(tmp_filename))\r\n self.tmp_filename = tmp_filename\r\n fh.close()\r\n result_file_content = list(open(tmp_filename))\r\n\r\n self.assertEqual(result_file_content, [\"100 400\\n\"])","def tests_ti_file_create_2(self):\n metadata = {\n 'size': 84504,\n 'sha256': '33af46377c0e52ca19aea233b3afb64505b32fac2231ec7a8a6795812fae0d10',\n 'md5': 'a9ba66af82897dadb82e3e89c70ae7ac',\n 'sha1': '19d08af69fe15af22ba81f045e31230150d4bdad',\n }\n file_indicator = self.ti.file(**metadata)\n file_indicator.delete()\n\n assert file_indicator.data['sha1'] == metadata['sha1']\n response = file_indicator.create()\n assert response.ok\n unique_id = ':'.join([metadata[x] for x in ['sha256', 'sha1', 'md5']])\n file_indicator = self.ti.file(unique_id=unique_id, **metadata)\n\n assert file_indicator.data['sha256'] == metadata['sha256']\n assert file_indicator.data['sha1'] == metadata['sha1']\n\n response = file_indicator.update()\n assert response.ok\n file_indicator.delete()","def test_create1(self):\n fname = TempfileManager.create_tempfile()\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertTrue(fname.startswith('tmp'))","def test_to_file(self):\n fd, fp = mkstemp()\n close(fd)\n st = SampleTemplate.create(self.metadata, self.new_study)\n st.to_file(fp)\n self._clean_up_files.append(fp)\n with open(fp, 'U') as f:\n obs = f.read()\n self.assertEqual(obs, EXP_SAMPLE_TEMPLATE)\n\n fd, fp = mkstemp()\n close(fd)\n st.to_file(fp, {'2.Sample1', '2.Sample3'})\n self._clean_up_files.append(fp)\n\n with open(fp, 'U') as f:\n obs = f.read()\n self.assertEqual(obs, EXP_SAMPLE_TEMPLATE_FEWER_SAMPLES)","def tests_ti_file_create(self):\n indicator_data = {\n 'md5': uuid.uuid4().hex.upper(),\n 'owner': self.owner,\n 'rating': randint(0, 5),\n }\n ti = self.ti.file(**indicator_data)\n r = ti.create()\n\n # assert response\n assert r.status_code == 201\n\n # retrieve indicator for asserts\n ti = self.ti.file(**indicator_data)\n r = ti.single()\n response_data = r.json()\n ti_data = response_data.get('data', {}).get(ti.api_entity)\n\n # validate response data\n assert r.status_code == 200\n assert response_data.get('status') == 'Success'\n\n # validate ti data\n assert ti_data.get('confidence') == indicator_data.get('confidence')\n assert ti_data.get(ti.api_entity) == indicator_data.get(ti.api_entity)\n assert ti_data.get('rating') == indicator_data.get('rating')\n\n # cleanup indicator\n r = ti.delete()\n assert r.status_code == 200","def test_get_file_content(self):\n pass","def test_get_file_object(self):\n pass","def test_nonfile(self):\n self.assertEqual(None,readfiles.read_file(\"tests.txt))","def test_create_from_file(self):\n # TODO: Expand test to both openeye and rdkit toolkits\n filename = get_data_file_path(\"molecules/toluene.mol2\")\n\n molecule1 = Molecule(filename, allow_undefined_stereo=True)\n with open(filename, \"r\") as infile:\n molecule2 = Molecule(\n infile, file_format=\"MOL2\", allow_undefined_stereo=True\n )\n assert molecule1 == molecule2\n\n import gzip\n\n with gzip.GzipFile(filename + \".gz\", \"r\") as infile:\n molecule3 = Molecule(\n infile, file_format=\"MOL2\", allow_undefined_stereo=True\n )\n assert molecule3 == molecule1\n\n # Ensure that attempting to initialize a single Molecule from a file\n # containing multiple molecules raises a ValueError\n with pytest.raises(ValueError) as exc_info:\n filename = get_data_file_path(\"molecules/zinc-subset-tripos.mol2.gz\")\n molecule = Molecule(filename, allow_undefined_stereo=True)","def test_add_and_read_file(self, tmpdir, fs):\n with fs.add_file('my-dir', 'my-file') as f:\n f.write('hello')\n assert fs.read('my-dir', 'my-file') == 'hello'","def test_readfile(self):\n fname = os.path.join(self.datadir, 'monol_testA_E3-50_rebin4_gti') + \\\n HEN_FILE_EXTENSION\n command = \"{0}\".format(fname)\n\n hen.io.main(command.split())","def test_create_file(self):\n def validate_in(*args, **kwargs):\n # Validate that the element is built properly\n wrap = args[0]\n\n self.assertEqual('chk', wrap._get_val_str(vf._FILE_CHKSUM))\n self.assertEqual(50, wrap.expected_file_size)\n self.assertEqual('f_name', wrap.file_name)\n self.assertEqual('application/octet-stream',\n wrap.internet_media_type)\n self.assertEqual('f_type', wrap.enum_type)\n self.assertEqual('v_uuid', wrap.vios_uuid)\n self.assertEqual('tdev_uuid', wrap.tdev_udid)\n ret = adp.Response('reqmethod', 'reqpath', 'status', 'reason', {})\n ret.entry = ewrap.EntryWrapper._bld(self.adpt, tag='File').entry\n return ret\n self.adpt.create.side_effect = validate_in\n\n ts._create_file(self.adpt, 'f_name', 'f_type', 'v_uuid', 'chk', 50,\n 'tdev_uuid')\n self.assertTrue(self.adpt.create.called)","def testReadFile(self):\n content = archive_parser.Archive.GLOBAL_SIG\n file_name = 'test_file'\n content += file_name + ' ' * (archive_parser.Archive.FILE_ID_LENGTH -\n len(file_name))\n content += ' ' * archive_parser.Archive.FILE_TIMESTAMP_LENGTH\n content += ' ' * archive_parser.Archive.OWNER_ID_LENGTH\n content += ' ' * archive_parser.Archive.GROUP_ID_LENGTH\n content += ' ' * archive_parser.Archive.FILE_MODE_LENGTH\n\n message = 'test file contents'\n message_size = str(len(message))\n content += message_size + ' ' * (archive_parser.Archive.CONTENT_SIZE_LENGTH -\n len(message_size))\n content += archive_parser.Archive.END_TAG\n content += message\n archive = archive_parser.Archive(content)\n archive.Parse()\n self.assertIn(file_name, archive.files)\n self.assertEquals(archive.files[file_name], message)","def test_create_from_file(self):\n # TODO: Expand test to both openeye and rdkit toolkits\n filename = get_data_file_path(\"molecules/toluene.mol2\")\n\n molecule1 = Molecule(filename, allow_undefined_stereo=True)\n with open(filename, \"r\") as infile:\n molecule2 = Molecule(\n infile, file_format=\"MOL2\", allow_undefined_stereo=True\n )\n assert molecule1 == molecule2\n\n import gzip\n\n with gzip.GzipFile(filename + \".gz\", \"r\") as infile:\n molecule3 = Molecule(\n infile, file_format=\"MOL2\", allow_undefined_stereo=True\n )\n assert molecule3 == molecule1\n\n # Ensure that attempting to initialize a single Molecule from a file\n # containing multiple molecules raises a ValueError\n filename = get_data_file_path(\"molecules/butane_multi.sdf\")\n\n with pytest.raises(\n ValueError,\n match=\"Specified file or file-like.*exactly one molecule\",\n ):\n Molecule(filename, allow_undefined_stereo=True)","def create_file(path):\n command = ['touch', TEST_FILE]\n file_operation(path, command)","def test_create2(self):\n fname = TempfileManager.create_tempfile(prefix='foo')\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertTrue(fname.startswith('foo'))","def _test(self, file_name):\n data = bob.io.base.load(file_name)\n assert (_data == data).all()","def testRetrieveStateFile(self):\n # Test call was succesful\n self.assertEqual(resource_manager.RetrieveResourceState(), {})\n\n # Test file was created\n self.assertTrue(os.path.exists(config.RESOURCE_FILE))\n\n # Test bad resource file\n with open(config.RESOURCE_FILE, 'w') as fh:\n fh.write(\"blah\")\n fh.close()\n self.assertRaises(TurbiniaException, resource_manager.RetrieveResourceState)\n os.remove(config.RESOURCE_FILE)","def test_file_collection():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.make_job_file(STATUS_DIR, 'generation', 'test1', TEST_1_ATTRS_1)\n Status.make_job_file(STATUS_DIR, 'generation', 'test2', TEST_2_ATTRS_1)\n\n Status.update(STATUS_DIR)\n with open(os.path.join(STATUS_DIR, 'rev_status.json'), 'r') as f:\n data = json.load(f)\n assert str(TEST_1_ATTRS_1) in str(data)\n assert str(TEST_2_ATTRS_1) in str(data)","def __create_test_file(self):\n self.test_file = os.path.join(os.path.dirname(self.server_path), \"data\")\n with open(self.test_file, \"ab+\") as f:\n n_blocks = int(self.args.size) // self.max_block_size\n for i in range(n_blocks):\n f.write(bytearray(os.urandom(self.max_block_size)))\n remaining = int(self.args.size) % self.max_block_size\n if remaining > 0:\n f.write(bytearray(os.urandom(remaining)))\n self.assertEqual(int(self.args.size), os.path.getsize(self.test_file))","def test_from_file_fail(self):\n with TemporaryDirectory() as tmp:\n fp = os.path.join(tmp, \"test.txt\")\n open(fp, 'a').close()\n assert os.path.exists(fp)\n with self.assertRaises(TypeError):\n BaseDataClass.from_file(fp)","def test_cannot_write_file(self):\n self.api.write_data('/some-fake/path/to-create-file/', 'some-string')","def test_error(file_path):\n assert check_file(file_path), \"Training file is not generated\"","def test_file_exists(self):\n self.assertTrue(os.path.exists(\"file.json\"))","def test_file(self):\n browser = self.layer.get_web_browser(smi_settings)\n\n image = test_filename('test.txt')\n browser.login(self.username, self.username)\n self.assertEqual(browser.open('/root/edit'), 200)\n browser.macros.create(\n 'Silva File', id='file', title='Text File', file=image)\n self.assertEqual(\n browser.inspect.folder_listing, ['index', 'file'])\n\n # The user should by the last author on the content and container.\n self.assertEqual(\n self.root.sec_get_last_author_info().userid(),\n self.username)\n self.assertEqual(\n self.root.file.sec_get_last_author_info().userid(),\n self.username)\n\n # Visit the edit page\n self.assertEqual(\n browser.inspect.folder_listing['file'].click(),\n 200)\n self.assertEqual(browser.url, '/root/file/edit/tab_edit')\n self.assertEqual(browser.inspect.breadcrumbs, ['root', 'Text File'])\n browser.inspect.breadcrumbs['root'].click()\n browser.macros.delete('file')","def test_create1a(self):\n fname = TempfileManager.create_tempfile(dir=tempdir)\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertTrue(fname.startswith('tmp'))","def test_file(tmpdir):\n file_path = tmpdir / 'test.txt'\n file_path = file_path.write_binary(b'This is some test data!')\n return file_path","def test_file_field():","def test_custom_file_exists() -> None:\n with tempfile.TemporaryDirectory() as tmpdirname:\n file_location = os.path.join(tmpdirname, \"foo.txt\")\n with open(file_location, \"wb\") as f:\n f.write(b\"foo\")\n\n nonexistent_file_location = os.path.join(tmpdirname, \"bar.txt\")\n\n # Confirm that the file initially exists\n assert os.path.exists(file_location)\n\n # Get an absolute path for an existing file and a nonexistent file\n absolute_file_location = os.path.abspath(file_location)\n non_existent_absolute_file_location = os.path.abspath(nonexistent_file_location)\n\n # Create InputFile instances\n input_file = PyArrowFileIO().new_input(location=f\"{absolute_file_location}\")\n non_existent_input_file = PyArrowFileIO().new_input(location=f\"{non_existent_absolute_file_location}\")\n\n # Test opening and reading the file\n assert input_file.exists()\n assert not non_existent_input_file.exists()\n\n # Create OutputFile instances\n file = PyArrowFileIO().new_output(location=f\"{absolute_file_location}\")\n non_existent_file = PyArrowFileIO().new_output(location=f\"{non_existent_absolute_file_location}\")\n\n # Test opening and reading the file\n assert file.exists()\n assert not non_existent_file.exists()","def test_fileinuse (self):\t\t\n\t\t# open a file:\n\t\ttestfilepath = os.path.join (self.testfolder, \"fileinuse.txt\")\n\t\tf = open(testfilepath,\"a\") #opens file with name of \"test.txt\"\n\t\tf.write(\"This file is now opened and i'm writting on it \\n\")\n\t\tself.assertEqual (MD.fileinuse(testfilepath), True) # Checks a file that it is beign written.\n\t\tself.assertEqual (MD.folderinuse(self.testfolder), True) # Checks if any file inside the folder is beign used.\n\t\tf.close()\n\t\tself.assertEqual (MD.fileinuse(testfilepath), False) # Cheks a file that it is closed.\n\t\tself.assertEqual (MD.folderinuse(self.testfolder), False)","def test_make_fna(self):\r\n fna_fp = os.path.join(self.sff_dir, 'test.fna')\r\n fna_gz_fp = os.path.join(self.gz_sff_dir, 'test_gz.fna')\r\n make_fna(self.sff_fp, fna_fp)\r\n make_fna(self.sff_gz_fp, fna_gz_fp)\r\n self.assertEqual(open(fna_fp).read(), fna_txt)\r\n self.assertEqual(open(fna_gz_fp).read(), fna_txt)","def test_empty_file(self):\n\n temp = tempfile.NamedTemporaryFile()\n temp.flush()\n self.assertRaises(MalformedFileError, NBTFile, temp.name)","def test_make_flow_txt(self):\r\n flow_fp = os.path.join(self.sff_dir, 'test.txt')\r\n flow_gz_fp = os.path.join(self.gz_sff_dir, 'test_gz.txt')\r\n make_flow_txt(self.sff_fp, flow_fp)\r\n make_flow_txt(self.sff_gz_fp, flow_gz_fp)\r\n self.assertEqual(open(flow_fp).read(), flow_txt)\r\n self.assertEqual(open(flow_gz_fp).read(), flow_txt)","def test_create4(self):\n TempfileManager.sequential_files(2)\n fname = TempfileManager.create_tempfile()\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertEqual(fname, 'tmp2')\n #\n TempfileManager.unique_files()\n fname = TempfileManager.create_tempfile()\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 2)\n fname = os.path.basename(fname)\n self.assertNotEqual(fname, 'tmp3')\n self.assertTrue(fname.startswith('tmp'))","def test_file_io(self):\n temp_directory = tempfile.mkdtemp()\n filename = os.path.join(temp_directory, \"test.h5\")\n\n points = np.array([\n [0.1, 0.1, 0.1],\n [1.1, 2.1, 3.1],\n [1.3, 2.2, 3.4]])\n voxel_size = 0.5\n min_corner = Vector3f(-1, -2, -3)\n vg = VoxelGrid(voxel_size, min_corner=min_corner, points=points)\n\n # test writing\n vg.save(filename)\n self.assertTrue(os.path.isfile(filename))\n\n # test reading\n vg2 = VoxelGrid.from_file(filename)\n self.assertAlmostEquals(voxel_size, vg2.voxel_size)\n np.testing.assert_array_almost_equal(vg.min_corner, vg2.min_corner)\n shutil.rmtree(temp_directory)","def test_CRUD_ProjectFiles_Successfully(self):\n\n real_file_code = get_content('BasicTemplateAlgorithm.py')\n second_real_file_code = get_content('BasicTemplateForexAlgorithm.py')\n\n fakeFile = {\"name\":\"Hello.py\", \"code\": \"Hello World!\"}\n realFile = {\"name\":\"main.py\", \"code\": real_file_code}\n secondRealFile = {\"name\":\"lol.py\", \"code\": second_real_file_code}\n\n # Create a new project and make sure there are no files\n project = self.api.create_project(\"Test project - \", \"Py\")\n self.assertTrue(project['success'])\n self.assertTrue(project['projects'][0]['projectId'] > 0)\n\n # Add random file\n randomAdd = self.api.add_project_file(project['projects'][0]['projectId'], fakeFile[\"name\"], fakeFile[\"code\"])\n self.assertTrue(randomAdd['success'])\n self.assertTrue(randomAdd['files'][0]['content'] == fakeFile['code'])\n self.assertTrue(randomAdd['files'][0]['name'] == fakeFile['name'])\n\n # Update names of file\n updatedName = self.api.update_project_filename(project['projects'][0]['projectId'], randomAdd['files'][0]['name'], realFile['name'])\n self.assertTrue(updatedName['success'])\n\n # Replace content of file\n updateContents = self.api.update_project_file_content(project['projects'][0]['projectId'], realFile[\"name\"], realFile['code'])\n self.assertTrue(updateContents['success'])\n\n # Read single file\n readFile = self.api.read_project_file(project['projects'][0]['projectId'], realFile['name'])\n self.assertTrue(readFile['success'])\n self.assertTrue(readFile['files'][0]['content'] == realFile['code'])\n self.assertTrue(readFile['files'][0]['name'] == realFile['name'])\n\n # Add a second file\n secondFile = self.api.add_project_file(project['projects'][0]['projectId'], secondRealFile['name'], secondRealFile['code'])\n self.assertTrue(secondFile['success'])\n self.assertTrue(secondFile['files'][0]['content'] == secondRealFile['code'])\n self.assertTrue(secondFile['files'][0]['name'] == secondRealFile['name'])\n\n # Read multiple files\n readFiles = self.api.read_project_files(project['projects'][0]['projectId'])\n self.assertTrue(readFiles['success'])\n self.assertTrue(len(readFiles['files']) == 2)\n\n # Delete the second file\n deleteFile = self.api.delete_project_file(project['projects'][0]['projectId'], secondRealFile['name'])\n self.assertTrue(deleteFile['success'])\n\n # Read files\n readFilesAgain = self.api.read_project_files(project['projects'][0]['projectId'])\n self.assertTrue(readFilesAgain['success'])\n self.assertTrue(len(readFilesAgain['files']) == 1)\n self.assertTrue(readFilesAgain['files'][0]['name'] == realFile['name'])\n\n # Delete the project\n deleteProject = self.api.delete_project(project['projects'][0]['projectId'])\n self.assertTrue(deleteProject['success'])","def test_create_content(self):\n url = reverse('content-list')\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\n content_file.write(b\"The contents of the temporary file.\\n\")\n content_file.seek(0)\n data = {\n 'name': 'Content File',\n 'description': 'File 1',\n 'content_file': content_file,\n 'updated_time': date.today(),\n 'creators': [],\n 'coverage': '',\n 'subjects': [],\n 'keywords': [],\n 'workareas': [],\n 'language': '',\n 'cataloger': ''\n }\n response = self.client.post(url, data, format='multipart')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)","def test_main(data, tmp_path):\n\n main(data, tmp_path)\n\n FILES = (\n \"gd32f888x(0-1)xx-pinctrl.h\",\n \"gd32f888x(2-3)xx-pinctrl.h\",\n \"gd32f888y(0-1)xx-pinctrl.h\",\n \"gd32f999x(0-1)xx-pinctrl.h\",\n \"gd32f999x(2-3)xx-pinctrl.h\",\n \"gd32f999y(0-1)xx-pinctrl.h\",\n )\n\n for file in FILES:\n ref_file = data / file\n gen_file = tmp_path / file\n\n assert gen_file.exists()\n\n with open(ref_file) as ref, open(gen_file) as gen:\n assert ref.read() == gen.read()","def generate_test_txt(name, path):\n with open(path + '/test.txt', 'a') as file:\n file.write('data/test/' + name + '\\n')","def test_005_write_file(self):\n __test = chess_storage.ChessStorage()\n __test_data = list(range(consts.TEST_LIST_LENGHT))\n __dir_game_saves = os.path.dirname(__file__)\n __dir_game_saves = os.path.join(__dir_game_saves, 'games')\n __dir_game_saves = os.path.join(__dir_game_saves, consts.TEST_FILENAME)\n # pylint: disable = protected-access\n __save_test = __test._ChessStorage__write_file(__dir_game_saves, __test_data)\n # pylint: enable = protected-access\n self.assertEqual(__save_test, consts.ERROR_CODES[\"SUCCESSFULL\"])","def testExampleFileGeneration(ref):\n outdir = ref.tmp_dir\n outpath = os.path.join(outdir, 'file_result.html')\n generate_file(outpath)\n ref.assertTextFileCorrect(outpath, 'file_result.html',\n ignore_substrings=['Copyright', 'Version'])","def test_record_create_files(db, location):\n record = CernSearchRecord.create({\"title\": \"test\"})\n\n assert 0 == len(record.files)\n assert 0 == len(record.files_content)\n\n file_content = b\"Hello world!\"\n record.files[\"hello.txt\"] = BytesIO(file_content)\n db.session.commit()\n\n record = CernSearchRecord.get_record(record.id)\n\n assert record[\"_bucket\"] == record.bucket_id\n assert record[\"_files\"]\n\n file_1 = record.files[\"hello.txt\"]\n assert \"hello.txt\" == file_1[\"key\"]\n assert 1 == len(record.files)\n assert 1 == len(record[\"_files\"])\n assert 0 == len(record.files_content)\n\n storage = file_1.obj.file.storage() # type: FileStorage\n fp = storage.open(mode=READ_MODE_BINARY)\n\n try:\n assert file_content == fp.read()\n finally:\n fp.close()","def test_read_file(self):\n restart_path = os.path.join(arc_path, 'arc', 'testing', 'restart(H,H2O2,N2H3,CH3CO2).yml')\n input_dict = read_file(restart_path)\n self.assertIsInstance(input_dict, dict)\n self.assertTrue('reactions' in input_dict)\n self.assertTrue('freq_level' in input_dict)\n self.assertTrue('use_bac' in input_dict)\n self.assertTrue('ts_guess_level' in input_dict)\n self.assertTrue('running_jobs' in input_dict)\n\n with self.assertRaises(InputError):\n read_file('nopath')","def test_writer_with_file():\n outputfile = \"testfile.txt\"\n GCMT(write=outputfile)\n assert os.path.exists(outputfile)\n os.remove(outputfile)","def test_create3(self):\n fname = TempfileManager.create_tempfile(suffix='bar')\n OUTPUT = open(fname, 'w')\n OUTPUT.write('tempfile\\n')\n OUTPUT.close()\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\n fname = os.path.basename(fname)\n self.assertTrue(fname.endswith('bar'))","def test_to_file(self):\n fd, fp = mkstemp()\n close(fd)\n pt = PrepTemplate.create(self.metadata, self.new_raw_data,\n self.test_study, self.data_type)\n pt.to_file(fp)\n self._clean_up_files.append(fp)\n with open(fp, 'U') as f:\n obs = f.read()\n self.assertEqual(obs, EXP_PREP_TEMPLATE)","def test_add1(self):\n try:\n TempfileManager.add_tempfile(tempdir + 'add1')\n self.fail(\"Expected IOError because file 'add1' does not exist\")\n except IOError:\n pass","def test_basic():\n f = Level3File(get_test_data('nids/Level3_FFC_N0Q_20140407_1805.nids', as_file_obj=False))\n assert f.metadata['prod_time'].replace(second=0) == datetime(2014, 4, 7, 18, 5)\n assert f.metadata['vol_time'].replace(second=0) == datetime(2014, 4, 7, 18, 5)\n assert f.metadata['msg_time'].replace(second=0) == datetime(2014, 4, 7, 18, 6)\n assert f.filename == get_test_data('nids/Level3_FFC_N0Q_20140407_1805.nids',\n as_file_obj=False)\n\n # At this point, really just want to make sure that __str__ is able to run and produce\n # something not empty, the format is still up for grabs.\n assert str(f)","def test_upload_file(self):\n\n uploadFile = os.path.join(testdatadir, \"upload.data\")\n r = gracedb.writeFile(eventId, uploadFile)\n self.assertEqual(r.status, 201) # CREATED\n r_content = r.json()\n link = r_content['permalink']\n\n self.assertEqual(\n open(uploadFile, 'r').read(),\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\n )\n\n self.assertEqual(\n open(uploadFile, 'r').read(),\n gracedb.get(link).read()\n )\n\n # Re-upload slightly different file.\n uploadFile2 = os.path.join(testdatadir, \"upload2.data\")\n r = gracedb.writeFile(\n eventId,\n filename=\"upload.data\",\n filecontents=open(uploadFile2, 'r'))\n self.assertEqual(r.status, 201) # CREATED\n r_content = r.json()\n link2 = r_content['permalink']\n\n self.assertEqual(\n open(uploadFile2, 'r').read(),\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\n )\n\n self.assertEqual(\n open(uploadFile2, 'r').read(),\n gracedb.get(link2).read()\n )\n\n self.assertNotEqual(link, link2)","def test_file_has_details(self):\n file_content = bytes(\n \"\"\"\n 1001 | Eggs | 12\\n\n 1002 | Bread | 1\\n\n 1003 | Butter | 1\\n\n 1004 | Milk | 1\\n\n 1005 | Corn | 2\n \"\"\", encoding='UTF-8'\n )\n\n test_file = SimpleUploadedFile(\n \"sample.txt\", file_content, content_type=\"text/txt\")\n \n new_file = File.objects.create(\n file=test_file,\n rows=5,\n items='Eggs, Bread, Butter, Milk, Corn'\n )\n\n file_details = File.objects.get(id=new_file.id)\n self.assertEqual(file_details.file.name, new_file.file.name)\n self.assertEqual(file_details.rows, new_file.rows)\n self.assertEqual(file_details.items, new_file.items)","def test_make_file_read_only():\n\n filename = os.path.join(tempfile.gettempdir(), \"jade-test-file.txt\")\n if os.path.exists(filename):\n os.chmod(filename, stat.S_IWRITE)\n os.remove(filename)\n\n with open(filename, \"w\") as f:\n f.write(\"Hello World\")\n\n prev_mode = os.stat(filename)\n make_file_read_only(filename)\n # Disabling because it doesn't work on Windows.\n # s = os.stat(filename)\n # assert s.st_mode != prev_mode\n # assert s.st_mode == 33060\n\n if os.path.exists(filename):\n os.chmod(filename, stat.S_IWRITE)\n os.remove(filename)","def test_with_new_file(self):\n repository = self.create_repository(tool_name='Test')\n review_request = self.create_review_request(\n repository=repository,\n submitter=self.user,\n publish=True)\n diffset = self.create_diffset(review_request)\n filediff = self.create_filediff(diffset,\n source_revision=PRE_CREATION)\n\n rsp = self.api_get(\n get_original_file_url(review_request, diffset, filediff),\n expected_status=404)\n self.assertEqual(rsp['stat'], 'fail')\n self.assertEqual(rsp['err']['code'], DOES_NOT_EXIST.code)","def test_fileOperations(self): \n from DIRAC import gConfig\n testSE = 'testSE'\n #rssClient = ResourceStatusClient()\n #result = rssClient.getStorageElementsList( 'Read' )\n #result = gConfig.getSections( '/Resources/StorageElements' )\n #if result['OK'] and result['Value']:\n # testSE = result['Value'][0]\n #if result['Ok']:\n # testSE = result['Value'][ 0 ] \n \n result = self.fc.addFile( { testFile: { 'PFN': 'testfile', \n 'SE': testSE , \n 'Size':0, \n 'GUID':0, \n 'Checksum':'0' } } )\n self.assert_( result['OK'] )\n if gConfig.getValue( '/Resources/StorageElements/%s/AccessProtocol.1/Host' % testSE, '' ):\n result = self.fc.getReplicas( testFile )\n self.assert_( result['OK'] )\n self.assert_( testFile in result['Value']['Successful'] )","def test_with_file(self, file_path):\n result = self.run(file_path=file_path)\n return self._handle_test_result(result)","def test_file_error(self):\n my_reader = DataSetReader()\n covid_list = CovidCase.objects.all()\n\n with self.assertRaises(IOError):\n my_reader.writeFile(covid_list, \"Not_A_File.csv\")","def test_create_files(self):\n opts = mock.Mock()\n opts.repo = self.repo\n opts.source = 'src'\n opts.version = '0.0.1'\n opts.version_file = None\n opts.test_mode = False\n opts.templates = ['include steve/*']\n opts.history_file = 'HISTORY.md'\n opts.package = 'unittests'\n opts.desc = \"DESCRIPTION\"\n opts.org = \"ORG\"\n opts.develop = 'develop'\n opts.requirements = 'requirements.txt'\n opts.test_requirements = 'test-requirements.txt'\n opts.pypi_package_name = None\n opts.python = None\n opts.create_version_file = False\n opts.gitignore_url = \"GIT_IGNORE_URL\"\n opts.add_gitignore = False\n create_files(opts)\n\n dir_list = os.listdir(self.repo)\n self.failUnless('cirrus.conf' in dir_list)\n self.failUnless('HISTORY.md' in dir_list)\n self.failUnless('MANIFEST.in' in dir_list)\n self.failUnless('setup.py' in dir_list)\n\n cirrus_conf = os.path.join(self.repo, 'cirrus.conf')\n config = ConfigParser.RawConfigParser()\n config.read(cirrus_conf)\n self.assertEqual(config.get('package', 'name'), opts.package)\n self.assertEqual(config.get('package', 'version'), opts.version)\n\n history = os.path.join(self.repo, 'HISTORY.md')\n with open(history, 'r') as handle:\n self.failUnless('CIRRUS_HISTORY_SENTINEL' in handle.read())\n\n manifest = os.path.join(self.repo, 'MANIFEST.in')\n with open(manifest, 'r') as handle:\n content = handle.read()\n self.failUnless('include requirements.txt' in content)\n self.failUnless('include cirrus.conf' in content)\n self.failUnless('include steve/*' in content)\n\n version = os.path.join(self.repo, 'src', 'unittests', '__init__.py')\n with open(version, 'r') as handle:\n self.failUnless(opts.version in handle.read())","def test_file_reader(self) -> None:\n result = [['123', 'Jin He', 'Computer Science'],\n ['234', 'Nanda Koka', 'Software Engineering'],\n ['345', 'Benji Cai', 'Software Engineering']]\n # file have header\n self.assertTrue(\n list(file_reader('C:/Users/Nidhi/Desktop/SEM3/810/HW08/student_majors.txt', 3, '|', True)) == result)\n # file without header\n self.assertFalse(\n list(file_reader('C:/Users/Nidhi/Desktop/SEM3/810/HW08/student_majors.txt', 3, '|')) == result)\n # More than 3 datafield\n with self.assertRaises(ValueError):\n list(file_reader(\n 'C:/Users/Nidhi/Desktop/SEM3/810/HW08/student_majors.txt', 4, '|', True))\n # file not found\n with self.assertRaises(FileNotFoundError):\n list(file_reader('abc.txt', 3, '|', True))","def test_read_delete(self):\n\n expected = \"Hello, World! This has been written by Fun Ilrys.\"\n File(\"hi\").write(expected)\n actual = File(\"hi\").read()\n\n self.assertEqual(expected, actual)\n\n expected = False\n File(\"hi\").delete()\n actual = PyFunceble.path.isfile(\"hi\")\n\n self.assertEqual(expected, actual)","def test_GFD_export_create_file(self):\n filepath = '1.txt'\n gfd = flow_processing_input.GroundFlowData()\n gfd.detector_flow_data = createGFDDataset(1).dataset\n gfd.export_to_file(filepath)\n # Check if file was created at filepath\n self.assertTrue(os.path.exists(filepath))\n os.remove(filepath)","def test_files(host, f):\n assert host.file(f).exists","def create(self):\n self.create_file()","def test_text_file_by_path(self):\n self.command.package = self.input_ovf\n self.command.file_path = \"sample_cfg.txt\"\n self.command.run()\n self.command.finished()\n self.check_diff(\"\"\"\n <ovf:File ovf:href=\"input.iso\" ovf:id=\"file2\" ovf:size=\"{iso_size}\" />\n- <ovf:File ovf:href=\"sample_cfg.txt\" ovf:id=\"textfile\" \\\novf:size=\"{cfg_size}\" />\n </ovf:References>\n\"\"\".format(iso_size=self.FILE_SIZE['input.iso'],\n cfg_size=self.FILE_SIZE['sample_cfg.txt']))\n self.assertFalse(os.path.exists(os.path.join(self.temp_dir,\n \"sample_cfg.txt\")),\n \"deleted file should not be exported\")","def assertCreatesFile(self, filename, nonzero=True):\n self.assertFalse(os.path.exists(filename), \"file already exists: {}\".format(filename))\n yield\n self.assertTrue(os.path.exists(filename), \"file was not created: {}\".format(filename))\n if nonzero:\n self.assertFilesizeNonZero(filename)","def test_invalidFile(self):\n self.assertRaises(cesmEnvLib.checkFile(\"blah\", \"write\"))","def test_valid_file_name(self):\n Base.save_to_file([self.r0, self.s1])\n self.assertTrue(path.exists('Base.json'))","def test_read_from_file():\n reading_file = d.read_code_from_file()\n assert type(reading_file) == list\n assert len(reading_file) == 7\n assert \"\\n\" not in d.read_code_from_file()","def test_create_files_with_python(self):\n opts = mock.Mock()\n opts.repo = self.repo\n opts.create_version_file = True\n opts.source = 'src'\n opts.version = '0.0.1'\n opts.version_file = None\n opts.org = \"ORG\"\n opts.desc = \"DESCRIPTION\"\n opts.templates = []\n opts.test_mode = False\n opts.history_file = 'HISTORY.md'\n opts.package = 'unittests'\n opts.develop = 'develop'\n opts.requirements = 'requirements.txt'\n opts.pypi_package_name = 'pypi.package.unittest'\n opts.python = 'python3'\n opts.gitignore_url = \"GIT_IGNORE_URL\"\n opts.add_gitignore = False\n opts.test_requirements = 'test-requirements.txt'\n version = os.path.join(self.repo, 'src', 'unittests', '__init__.py')\n os.system('rm -f {}'.format(version))\n create_files(opts)\n\n dir_list = os.listdir(self.repo)\n self.failUnless('cirrus.conf' in dir_list)\n self.failUnless('HISTORY.md' in dir_list)\n self.failUnless('MANIFEST.in' in dir_list)\n self.failUnless('setup.py' in dir_list)\n\n cirrus_conf = os.path.join(self.repo, 'cirrus.conf')\n config = ConfigParser.RawConfigParser()\n config.read(cirrus_conf)\n self.assertEqual(config.get('package', 'name'), opts.pypi_package_name)\n self.assertEqual(config.get('package', 'version'), opts.version)\n self.assertEqual(config.get('build', 'python'), 'python3')","def test_bad_file(self):\n with tempfile.NamedTemporaryFile(suffix='.json') as test_file:\n with open(test_file.name, 'wt') as test_file_handle:\n test_file_handle.write(\"garbage garbage garbage\\n\")\n\n with self.assertRaises(ConfigFileError):\n engine = Engine(test_file.name, self.api_token)","def test_load(api):\n # upload file to file.io servers\n uploaded_file = api.upload(\n tag='test_file',\n expiry='1d',\n path='tests/test_file.txt'\n )\n\n # check that instance of FileIO has these fields\n assert uploaded_file.link\n assert uploaded_file.key\n assert uploaded_file.tag\n assert uploaded_file.path\n\n # check that the uploaded file was added to uploaded files list\n assert api.show_uploads()\n\n # check that our list is not empty\n assert api.file_obj_list\n\n # export the file in json format\n api.export('tests/exported.json')\n\n # check that exported file exists\n assert path.isfile('tests/exported.json')\n\n # set it to empty list\n api.file_obj_list = []\n\n # load the file in json format\n api.load('tests/exported.json')\n\n # remove the file\n remove('tests/exported.json')\n\n # check that the uploaded file was added to uploaded files list\n assert api.show_uploads()\n\n # check that our list is not empty\n assert api.file_obj_list\n\n # export the file in pkl format\n api.export('tests/exported.pkl')\n\n # set it to empty list\n api.file_obj_list = []\n\n # load the file in pkl format\n api.load('tests/exported.pkl')\n\n # remove exported.pkl file\n remove('tests/exported.pkl')\n\n # check that the uploaded file was added to uploaded files list\n assert api.show_uploads()\n\n # check that our list is not empty\n assert api.file_obj_list","def test_create_files(self):\n\n testdir = \"test_output\"\n test_submission = Submission()\n self.addCleanup(os.remove, \"submission.tar.gz\")\n self.addCleanup(shutil.rmtree, testdir)\n\n test_submission.create_files(testdir)\n\n self.doCleanups()","def test_read_file():\n assert read_file('test_read_file.txt') == \"\"\"ABCDEFGHIJKLMNOPQRSTUVWXYZ?\nabcdefghijklmnopqrstuvwxyz.\n\"\"\"","def test_memory_name_form_file(self):\n with TemporaryDirectory(dir=\"./\") as cur_dir:\n \"\"\" Test some memory name\"\"\"\n filepath, memory_file = self.create_config(memory=\"$file$\", corpora=DEFAULT_CORPUS, cur_dir=cur_dir)\n self._dispatch(\n train=0.8,\n test=0.1,\n dev=0.1,\n config=filepath,\n output_dir=p.join(cur_dir, \"output\")\n )\n\n name = p.splitext(p.basename(filepath))[0] # [0] form splitext is everything but .xml\n self.assertTrue(p.isfile(memory_file.replace(\"$file$\", name)+\".csv\"),\n \"A new file should have been created using the config file name\")","def test_create_csv(self):\n\n # absolute path to xml file to parse\n xml_file = os.path.join(self.xmlfilepath, \"DLTINS_20210117_01of01.xml\")\n\n # absolute path to the csv file to create\n csv_file = os.path.join(self.csvfile, \"DLTINS_20210117_01of01.csv\")\n\n # Test for correct data\n self.assertEqual(create_csv(xml_file, self.csvfile), csv_file)\n\n # Test for incorrect input xml file\n self.assertEqual(create_csv(\"somerandomfile\", self.csvfile), None)\n\n # Test for incorrect path to write csv to\n self.assertEqual(create_csv(xml_file, r\"D:\\kqcA CK j \"), None)","def test_binary_contents_twice(file, tmp_path):\n name = tmp_path / \"1px.gif\"\n\n # First run state ensures file is created\n ret = file.managed(name=str(name), contents=BINARY_FILE)\n assert ret.result is True\n\n # Second run of state ensures file is in correct state\n ret = file.managed(name=str(name), contents=BINARY_FILE)\n assert ret.result is True","def test_read_file_to_list():\n new_file = \"Hello,World\"\n data = race.read_file_to_list()\n assert type(data) == list\n for x in data:\n assert type(x) == dict\n assert len(data) == 33\n with pytest.raises(FileNotFoundError):\n Race(new_file).read_file_to_list()","def test_open_read(self, remote_mock_dir):\n\n file_path = posixpath.join(remote_mock_dir, \"test.txt\")\n with HdfsHook() as hook:\n with hook.open(file_path) as file_:\n content = file_.read()\n assert content == b\"Test file\\n\"","def test_to_yaml(self):\n file_to_read = \"this_yaml_is_a_ghost.yaml\"\n\n expected = False\n actual = PyFunceble.path.isfile(file_to_read)\n\n self.assertEqual(expected, actual)\n\n to_write = {\"hello\": [\"This is PyFunceble!\", \"Uhh!\"], \"world\": \"Fun Ilrys\"}\n\n expected = \"\"\"hello: [This is PyFunceble!, Uhh!]\nworld: Fun Ilrys\n\"\"\"\n\n Dict(to_write).to_yaml(file_to_read)\n\n actual = File(file_to_read).read()\n self.assertEqual(expected, actual)\n\n File(file_to_read).delete()\n\n expected = False\n actual = PyFunceble.path.isfile(file_to_read)\n\n self.assertEqual(expected, actual)","def create_test(self, test_case, file_name):\n with open(os.path.join(self.tests, file_name), 'w+') as f:\n f.write(test_case)","def test_upload_file(self):\n pass","def test_create_file_via_ansible_playbook(self):\n param = {\n \"task_file\": \"kalliope/neurons/ansible_playbook/tests/test_ansible_playbook_neuron.yml\"\n }\n\n Ansible_playbook(**param)\n\n self.assertTrue(os.path.isfile(self.test_file))\n\n if os.path.exists(self.test_file):\n os.remove(self.test_file)","def test_kyc_get_file(self):\n pass","def test_read_from_file():\n from scraper import read_from_file\n assert read_from_file(TEST_FILE) == (TEST_CONTENT, 'utf-8')","def test_file_read():\n expected = [\"scorevideo LOG\\n\", \"File: log.mat\"]\n with open(TEST_RES + \"/file_read.txt\", 'r') as file:\n actual = file.readlines()\n assert expected == actual","def test_file_exists(self):\n with TemporaryDirectory() as tmp:\n # define path to file\n fp = os.path.join(tmp, \"asdf.txt\")\n\n # write atomically to file\n with atomic_write(fp, \"w\") as f:\n f.write(\"asdf\")\n\n # ensure file exists\n assert os.path.exists(fp)\n\n # ensure atomic_write to same file raises an error as it already exists\n try:\n with atomic_write(fp, \"w\") as f:\n f.write(\"asdf\")\n except FileExistsError as e:\n self.assertIsInstance(e, FileExistsError)","def test_newFile(self):\n self.write(\"service1.json\", [{\"host\": \"host1\", \"port\": 123},\n {\"host\": \"host2\", \"port\": 124}])\n self.write(\"service2.json\", [])\n self.pump()\n self.assertNodesEqual(\n knownNodes(self.disco, \"service1\", \"staging\"),\n [self.node(\"service1\", \"host1\", 123),\n self.node(\"service1\", \"host2\", 124)])","def file_factory(test_workspace):\n\n return FileCreator(test_workspace)","def create_mock_files(self,\n project_id=\"DEV-test\",\n count=3,\n prefix=\"mock_data_file\",\n file_format=\"dcm\",\n outdir=\".\",\n msg = \"This is a mock data file for testing purposes. Delete me!\",\n write_tsv = True\n ):\n prog,proj = project_id.split(\"-\")\n authz = [\"/programs/{}/projects/{}\".format(prog,proj)]\n acl = [prog,proj]\n\n mfiles = {'file_name':[],'md5sum':[],\"file_size\":[],\"object_id\":[],\"storage_urls\":[],\"acl\":[],\"authz\":[]}\n for i in range(count):\n file_name = \"{}_{}.{}\".format(prefix,i+1,file_format)\n object_id = str(uuid.uuid4())\n mfiles['file_name'].append(file_name)\n mfiles['object_id'].append(object_id)\n mfiles['authz'].append(authz)\n mfiles['acl'].append(acl)\n\n\n output = \"{}/{}\".format(outdir,file_name)\n os.system(\"touch {}\".format(output))\n file_msg =\"{} File {} of {}. {} with object_id {}.\".format(msg,i+1,count,file_name,object_id)\n cmd = 'echo \"{}\" > {}'.format(file_msg,file_name)\n os.system(cmd)\n\n with open(output, 'rb') as file_to_check:\n file_contents = file_to_check.read()\n #cmd = \"!md5 mock_data_file_{}.{}\".format(i+1,file_format))\n md5 = hashlib.md5(file_contents).hexdigest() #check in shell: !md5 mock_data_file_3.dcm\n\n mfiles['md5sum'].append(md5)\n mfiles['file_size'].append(os.stat(output).st_size)\n urls=\"s3://this-is-a-fake-url-for:{}\".format(file_name)\n mfiles['storage_urls'].append([urls])\n\n return mfiles","def test_default_creation_2():\n actual = os.path.join('.', 'test_files', 'rc_test_default.input')\n times = list(range(0, 30, 5))\n params = {\"names\": ['V'],\n \"values\": [\n [1],\n [0],\n [-1],\n [0],\n [1]\n ]\n }\n input_creator = InputCreator(None, times, params)\n f_out = input_creator.default_creation_2()\n with open(actual) as f_actual:\n actual_content = f_actual.read()\n\n content = f_out.getvalue()\n\n assert_equal(content, actual_content)","def test_write_to_file(self):\n test_filename = \"test_write.cf\"\n cf1 = ConfigFile()\n cf1[\"key1\"] = \"val1\"\n cf1[\"key2\"] = \"val2\"\n cf1[\"key3\"] = \"val3\"\n cf1.write_to_file(filename=test_filename)\n\n cf2 = ConfigFile()\n cf2.load_from_file(filename=test_filename)\n\n self.assertTrue(cf1 == cf2)","def make_test_file(path: Path, name: str = None):\n if not name:\n name = \"test.txt\"\n\n path.mkdir(parents=True, exist_ok=True)\n test_file = path.joinpath(name)\n test_file.touch()\n mtime = (datetime.today() - timedelta(2)).timestamp()\n os.utime(test_file, (mtime, mtime))\n return test_file","def test_simple_config_file(self):\n # create the config file:\n test_name = \"unit_test_test_1\"\n K = \"'key'\"\n fam = 3\n W = 5\n D = 8\n num_circuits = 1\n num_inputs = 1\n config_file_text = \"\\n\".join([\"test_type = RANDOM\",\n \" = \".join([\"K\", str(K)]),\n \" = \".join([\"fam\", str(fam)]),\n \" = \".join([\"W\", str(W)]),\n \" = \".join([\"D\", str(D)]),\n \" = \".join([\"num_circuits\",\n str(num_circuits)]),\n \" = \".join([\"num_inputs\",\n str(num_inputs)]),\n \" = \".join([\"generate\", \"True\"])])\n config_file = StringIO.StringIO(config_file_text)\n # create the parser/generator:\n fho = tfho.test_file_handle_object()\n pag = gen.parser_and_generator(test_name, config_file, fho)\n pag.parse_and_generate()\n # retrieve the test file and check that it is correct:\n test_file = fho.get_file(os.path.join(test_name, \"test.txt\"))\n test_file_text = test_file.getvalue()\n expected_test_file_text = \"\\n\".join(\n [\"KEY\",\n os.path.join(\"stealth\", test_name, \"key\", str(1)),\n \"CIRCUIT\",\n os.path.join(\"stealth\", test_name, \"circuit\", str(1)),\n \"INPUT\",\n os.path.join(\"stealth\", test_name, \"input\", str(1)),\n \"\"])\n self.assertEqual(expected_test_file_text, test_file_text)\n # retrieve the key file and check that it is correct:\n key_file = fho.get_file(os.path.join(test_name, \"key\", \"1\"))\n key_file_text = key_file.getvalue()\n self.assertEqual(K, key_file_text)\n # retrieve the input and check that it is correct:\n input_file = fho.get_file(os.path.join(test_name, \"input\", \"1\"))\n input_file_text = input_file.getvalue()\n # check that input text begins and ends with a bracket:\n self.assertEqual(\"[\", input_file_text[0])\n self.assertEqual(\"]\", input_file_text[-1])\n # check that all bits are 0 or 1:\n for bit in input_file_text[1:-1]:\n self.assertTrue((bit == '0') or (bit == '1'))\n # retrieve the circuit and check that it begins with the correct header:\n circuit_file = fho.get_file(os.path.join(test_name, \"circuit\", \"1\"))\n circuit_file_text = circuit_file.getvalue()\n circuit_header = circuit_file_text.split(\"\\n\")[0]\n (W_string, D_string, fam_string) = circuit_header.split(\",\")\n W_value = int(W_string.split(\"=\")[-1])\n D_value = float(D_string.split(\"=\")[-1])\n fam_value = int(fam_string.split(\"=\")[-1])\n self.assertEqual(W, W_value)\n self.assertEqual(D, D_value)\n self.assertEqual(fam, fam_value)","def test_api_create_unknown_file(api):\n api.create_article(\n {\"title\": \"A Title\"},\n {\"key1\": \"value1\"},\n {'test.zip': 'FFFDASFAFADADFA'},\n )\n req_call = requests.request\n req_kw = req_call.call_args[1]\n data_lines = req_kw['data'].split(b'\\r\\n')\n # Unknown file type is skipped\n assert len(data_lines) == 11","def test_file_analyzer(self):\r\n file_analyzer = FileAnalyzer(\"C:\\\\Users\\\\Himan\\\\Desktop\\\\Semester 2\\\\SSW 810\\\\HW\\\\Assignment 8\")\r\n self.assertEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 1, 'function': 5, 'line': 100, 'char': 4472}, \\\r\n 'HW08_Test_Himanshu.py': {'class': 1, 'function': 3, 'line': 38, 'char': 1861}})\r\n self.assertNotEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 0, 'function': 5, 'line': 46, 'char': 1931}})\r\n\r\n self.assertNotEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 1, 'function': 5, 'line': 100}}) # testing less fields\r\n\r\n with self.assertRaises(FileNotFoundError): # raises exception error\r\n FileAnalyzer(\"C:\\\\Users\\\\Himan\\\\Desktop\\\\Semester 2\\\\SSW 810\\\\HW\\\\Assignment 10\").files_summary"],"string":"[\n \"def test_file_creation(data, logging_file_name):\\n create_instance(data, logging_file_name)\\n log_file_name = create_file_path(logging_file_name)\\n print(log_file_name)\\n if data is None or len(data) == 0:\\n assert not os.path.exists(log_file_name)\\n else:\\n assert os.path.exists(log_file_name)\",\n \"def test_create(self):\\n path = self.tmp_py()\\n # Creating a file that doesn't exist should succeed\\n self.cls.create(path)\\n self.assertTrue(os.path.exists(path))\\n # Created file should be a valid script (If not, raises an error)\\n self.cls.verify(path)\\n # Can't create it again: it already exists\\n self.assertRaises(exceptions.PathFoundError,self.cls.create,path)\",\n \"def test_write_file():\\n filename = 'test'\\n content = 'hello!'\\n\\n write_file(content, filename)\\n assert read_file(filename) == 'hello!'\",\n \"def test_read_file():\\n filename = 'sample'\\n assert read_file(filename) == 'hello!\\\\n'\",\n \"def test_get_infile(self):\\r\\n pass # not practically testable, but obvious file I/O\\r\",\n \"def test_read_file_from_disk(self):\\r\\n actual_data = read_file_from_disk(self.test_file1)\\r\\n self.assertEqual(self.test_file1_data, actual_data)\",\n \"def test_save_to_file(self):\\n self.assertFalse(os.path.exists(\\\"file.json\\\"))\",\n \"def test_init_flowgram_file(self):\\r\\n fh, tmp_filename = init_flowgram_file(n=100, l=400)\\r\\n self.assert_(exists(tmp_filename))\\r\\n self.tmp_filename = tmp_filename\\r\\n fh.close()\\r\\n result_file_content = list(open(tmp_filename))\\r\\n\\r\\n self.assertEqual(result_file_content, [\\\"100 400\\\\n\\\"])\",\n \"def tests_ti_file_create_2(self):\\n metadata = {\\n 'size': 84504,\\n 'sha256': '33af46377c0e52ca19aea233b3afb64505b32fac2231ec7a8a6795812fae0d10',\\n 'md5': 'a9ba66af82897dadb82e3e89c70ae7ac',\\n 'sha1': '19d08af69fe15af22ba81f045e31230150d4bdad',\\n }\\n file_indicator = self.ti.file(**metadata)\\n file_indicator.delete()\\n\\n assert file_indicator.data['sha1'] == metadata['sha1']\\n response = file_indicator.create()\\n assert response.ok\\n unique_id = ':'.join([metadata[x] for x in ['sha256', 'sha1', 'md5']])\\n file_indicator = self.ti.file(unique_id=unique_id, **metadata)\\n\\n assert file_indicator.data['sha256'] == metadata['sha256']\\n assert file_indicator.data['sha1'] == metadata['sha1']\\n\\n response = file_indicator.update()\\n assert response.ok\\n file_indicator.delete()\",\n \"def test_create1(self):\\n fname = TempfileManager.create_tempfile()\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertTrue(fname.startswith('tmp'))\",\n \"def test_to_file(self):\\n fd, fp = mkstemp()\\n close(fd)\\n st = SampleTemplate.create(self.metadata, self.new_study)\\n st.to_file(fp)\\n self._clean_up_files.append(fp)\\n with open(fp, 'U') as f:\\n obs = f.read()\\n self.assertEqual(obs, EXP_SAMPLE_TEMPLATE)\\n\\n fd, fp = mkstemp()\\n close(fd)\\n st.to_file(fp, {'2.Sample1', '2.Sample3'})\\n self._clean_up_files.append(fp)\\n\\n with open(fp, 'U') as f:\\n obs = f.read()\\n self.assertEqual(obs, EXP_SAMPLE_TEMPLATE_FEWER_SAMPLES)\",\n \"def tests_ti_file_create(self):\\n indicator_data = {\\n 'md5': uuid.uuid4().hex.upper(),\\n 'owner': self.owner,\\n 'rating': randint(0, 5),\\n }\\n ti = self.ti.file(**indicator_data)\\n r = ti.create()\\n\\n # assert response\\n assert r.status_code == 201\\n\\n # retrieve indicator for asserts\\n ti = self.ti.file(**indicator_data)\\n r = ti.single()\\n response_data = r.json()\\n ti_data = response_data.get('data', {}).get(ti.api_entity)\\n\\n # validate response data\\n assert r.status_code == 200\\n assert response_data.get('status') == 'Success'\\n\\n # validate ti data\\n assert ti_data.get('confidence') == indicator_data.get('confidence')\\n assert ti_data.get(ti.api_entity) == indicator_data.get(ti.api_entity)\\n assert ti_data.get('rating') == indicator_data.get('rating')\\n\\n # cleanup indicator\\n r = ti.delete()\\n assert r.status_code == 200\",\n \"def test_get_file_content(self):\\n pass\",\n \"def test_get_file_object(self):\\n pass\",\n \"def test_nonfile(self):\\n self.assertEqual(None,readfiles.read_file(\\\"tests.txt))\",\n \"def test_create_from_file(self):\\n # TODO: Expand test to both openeye and rdkit toolkits\\n filename = get_data_file_path(\\\"molecules/toluene.mol2\\\")\\n\\n molecule1 = Molecule(filename, allow_undefined_stereo=True)\\n with open(filename, \\\"r\\\") as infile:\\n molecule2 = Molecule(\\n infile, file_format=\\\"MOL2\\\", allow_undefined_stereo=True\\n )\\n assert molecule1 == molecule2\\n\\n import gzip\\n\\n with gzip.GzipFile(filename + \\\".gz\\\", \\\"r\\\") as infile:\\n molecule3 = Molecule(\\n infile, file_format=\\\"MOL2\\\", allow_undefined_stereo=True\\n )\\n assert molecule3 == molecule1\\n\\n # Ensure that attempting to initialize a single Molecule from a file\\n # containing multiple molecules raises a ValueError\\n with pytest.raises(ValueError) as exc_info:\\n filename = get_data_file_path(\\\"molecules/zinc-subset-tripos.mol2.gz\\\")\\n molecule = Molecule(filename, allow_undefined_stereo=True)\",\n \"def test_add_and_read_file(self, tmpdir, fs):\\n with fs.add_file('my-dir', 'my-file') as f:\\n f.write('hello')\\n assert fs.read('my-dir', 'my-file') == 'hello'\",\n \"def test_readfile(self):\\n fname = os.path.join(self.datadir, 'monol_testA_E3-50_rebin4_gti') + \\\\\\n HEN_FILE_EXTENSION\\n command = \\\"{0}\\\".format(fname)\\n\\n hen.io.main(command.split())\",\n \"def test_create_file(self):\\n def validate_in(*args, **kwargs):\\n # Validate that the element is built properly\\n wrap = args[0]\\n\\n self.assertEqual('chk', wrap._get_val_str(vf._FILE_CHKSUM))\\n self.assertEqual(50, wrap.expected_file_size)\\n self.assertEqual('f_name', wrap.file_name)\\n self.assertEqual('application/octet-stream',\\n wrap.internet_media_type)\\n self.assertEqual('f_type', wrap.enum_type)\\n self.assertEqual('v_uuid', wrap.vios_uuid)\\n self.assertEqual('tdev_uuid', wrap.tdev_udid)\\n ret = adp.Response('reqmethod', 'reqpath', 'status', 'reason', {})\\n ret.entry = ewrap.EntryWrapper._bld(self.adpt, tag='File').entry\\n return ret\\n self.adpt.create.side_effect = validate_in\\n\\n ts._create_file(self.adpt, 'f_name', 'f_type', 'v_uuid', 'chk', 50,\\n 'tdev_uuid')\\n self.assertTrue(self.adpt.create.called)\",\n \"def testReadFile(self):\\n content = archive_parser.Archive.GLOBAL_SIG\\n file_name = 'test_file'\\n content += file_name + ' ' * (archive_parser.Archive.FILE_ID_LENGTH -\\n len(file_name))\\n content += ' ' * archive_parser.Archive.FILE_TIMESTAMP_LENGTH\\n content += ' ' * archive_parser.Archive.OWNER_ID_LENGTH\\n content += ' ' * archive_parser.Archive.GROUP_ID_LENGTH\\n content += ' ' * archive_parser.Archive.FILE_MODE_LENGTH\\n\\n message = 'test file contents'\\n message_size = str(len(message))\\n content += message_size + ' ' * (archive_parser.Archive.CONTENT_SIZE_LENGTH -\\n len(message_size))\\n content += archive_parser.Archive.END_TAG\\n content += message\\n archive = archive_parser.Archive(content)\\n archive.Parse()\\n self.assertIn(file_name, archive.files)\\n self.assertEquals(archive.files[file_name], message)\",\n \"def test_create_from_file(self):\\n # TODO: Expand test to both openeye and rdkit toolkits\\n filename = get_data_file_path(\\\"molecules/toluene.mol2\\\")\\n\\n molecule1 = Molecule(filename, allow_undefined_stereo=True)\\n with open(filename, \\\"r\\\") as infile:\\n molecule2 = Molecule(\\n infile, file_format=\\\"MOL2\\\", allow_undefined_stereo=True\\n )\\n assert molecule1 == molecule2\\n\\n import gzip\\n\\n with gzip.GzipFile(filename + \\\".gz\\\", \\\"r\\\") as infile:\\n molecule3 = Molecule(\\n infile, file_format=\\\"MOL2\\\", allow_undefined_stereo=True\\n )\\n assert molecule3 == molecule1\\n\\n # Ensure that attempting to initialize a single Molecule from a file\\n # containing multiple molecules raises a ValueError\\n filename = get_data_file_path(\\\"molecules/butane_multi.sdf\\\")\\n\\n with pytest.raises(\\n ValueError,\\n match=\\\"Specified file or file-like.*exactly one molecule\\\",\\n ):\\n Molecule(filename, allow_undefined_stereo=True)\",\n \"def create_file(path):\\n command = ['touch', TEST_FILE]\\n file_operation(path, command)\",\n \"def test_create2(self):\\n fname = TempfileManager.create_tempfile(prefix='foo')\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertTrue(fname.startswith('foo'))\",\n \"def _test(self, file_name):\\n data = bob.io.base.load(file_name)\\n assert (_data == data).all()\",\n \"def testRetrieveStateFile(self):\\n # Test call was succesful\\n self.assertEqual(resource_manager.RetrieveResourceState(), {})\\n\\n # Test file was created\\n self.assertTrue(os.path.exists(config.RESOURCE_FILE))\\n\\n # Test bad resource file\\n with open(config.RESOURCE_FILE, 'w') as fh:\\n fh.write(\\\"blah\\\")\\n fh.close()\\n self.assertRaises(TurbiniaException, resource_manager.RetrieveResourceState)\\n os.remove(config.RESOURCE_FILE)\",\n \"def test_file_collection():\\n with tempfile.TemporaryDirectory() as STATUS_DIR:\\n Status.make_job_file(STATUS_DIR, 'generation', 'test1', TEST_1_ATTRS_1)\\n Status.make_job_file(STATUS_DIR, 'generation', 'test2', TEST_2_ATTRS_1)\\n\\n Status.update(STATUS_DIR)\\n with open(os.path.join(STATUS_DIR, 'rev_status.json'), 'r') as f:\\n data = json.load(f)\\n assert str(TEST_1_ATTRS_1) in str(data)\\n assert str(TEST_2_ATTRS_1) in str(data)\",\n \"def __create_test_file(self):\\n self.test_file = os.path.join(os.path.dirname(self.server_path), \\\"data\\\")\\n with open(self.test_file, \\\"ab+\\\") as f:\\n n_blocks = int(self.args.size) // self.max_block_size\\n for i in range(n_blocks):\\n f.write(bytearray(os.urandom(self.max_block_size)))\\n remaining = int(self.args.size) % self.max_block_size\\n if remaining > 0:\\n f.write(bytearray(os.urandom(remaining)))\\n self.assertEqual(int(self.args.size), os.path.getsize(self.test_file))\",\n \"def test_from_file_fail(self):\\n with TemporaryDirectory() as tmp:\\n fp = os.path.join(tmp, \\\"test.txt\\\")\\n open(fp, 'a').close()\\n assert os.path.exists(fp)\\n with self.assertRaises(TypeError):\\n BaseDataClass.from_file(fp)\",\n \"def test_cannot_write_file(self):\\n self.api.write_data('/some-fake/path/to-create-file/', 'some-string')\",\n \"def test_error(file_path):\\n assert check_file(file_path), \\\"Training file is not generated\\\"\",\n \"def test_file_exists(self):\\n self.assertTrue(os.path.exists(\\\"file.json\\\"))\",\n \"def test_file(self):\\n browser = self.layer.get_web_browser(smi_settings)\\n\\n image = test_filename('test.txt')\\n browser.login(self.username, self.username)\\n self.assertEqual(browser.open('/root/edit'), 200)\\n browser.macros.create(\\n 'Silva File', id='file', title='Text File', file=image)\\n self.assertEqual(\\n browser.inspect.folder_listing, ['index', 'file'])\\n\\n # The user should by the last author on the content and container.\\n self.assertEqual(\\n self.root.sec_get_last_author_info().userid(),\\n self.username)\\n self.assertEqual(\\n self.root.file.sec_get_last_author_info().userid(),\\n self.username)\\n\\n # Visit the edit page\\n self.assertEqual(\\n browser.inspect.folder_listing['file'].click(),\\n 200)\\n self.assertEqual(browser.url, '/root/file/edit/tab_edit')\\n self.assertEqual(browser.inspect.breadcrumbs, ['root', 'Text File'])\\n browser.inspect.breadcrumbs['root'].click()\\n browser.macros.delete('file')\",\n \"def test_create1a(self):\\n fname = TempfileManager.create_tempfile(dir=tempdir)\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertTrue(fname.startswith('tmp'))\",\n \"def test_file(tmpdir):\\n file_path = tmpdir / 'test.txt'\\n file_path = file_path.write_binary(b'This is some test data!')\\n return file_path\",\n \"def test_file_field():\",\n \"def test_custom_file_exists() -> None:\\n with tempfile.TemporaryDirectory() as tmpdirname:\\n file_location = os.path.join(tmpdirname, \\\"foo.txt\\\")\\n with open(file_location, \\\"wb\\\") as f:\\n f.write(b\\\"foo\\\")\\n\\n nonexistent_file_location = os.path.join(tmpdirname, \\\"bar.txt\\\")\\n\\n # Confirm that the file initially exists\\n assert os.path.exists(file_location)\\n\\n # Get an absolute path for an existing file and a nonexistent file\\n absolute_file_location = os.path.abspath(file_location)\\n non_existent_absolute_file_location = os.path.abspath(nonexistent_file_location)\\n\\n # Create InputFile instances\\n input_file = PyArrowFileIO().new_input(location=f\\\"{absolute_file_location}\\\")\\n non_existent_input_file = PyArrowFileIO().new_input(location=f\\\"{non_existent_absolute_file_location}\\\")\\n\\n # Test opening and reading the file\\n assert input_file.exists()\\n assert not non_existent_input_file.exists()\\n\\n # Create OutputFile instances\\n file = PyArrowFileIO().new_output(location=f\\\"{absolute_file_location}\\\")\\n non_existent_file = PyArrowFileIO().new_output(location=f\\\"{non_existent_absolute_file_location}\\\")\\n\\n # Test opening and reading the file\\n assert file.exists()\\n assert not non_existent_file.exists()\",\n \"def test_fileinuse (self):\\t\\t\\n\\t\\t# open a file:\\n\\t\\ttestfilepath = os.path.join (self.testfolder, \\\"fileinuse.txt\\\")\\n\\t\\tf = open(testfilepath,\\\"a\\\") #opens file with name of \\\"test.txt\\\"\\n\\t\\tf.write(\\\"This file is now opened and i'm writting on it \\\\n\\\")\\n\\t\\tself.assertEqual (MD.fileinuse(testfilepath), True) # Checks a file that it is beign written.\\n\\t\\tself.assertEqual (MD.folderinuse(self.testfolder), True) # Checks if any file inside the folder is beign used.\\n\\t\\tf.close()\\n\\t\\tself.assertEqual (MD.fileinuse(testfilepath), False) # Cheks a file that it is closed.\\n\\t\\tself.assertEqual (MD.folderinuse(self.testfolder), False)\",\n \"def test_make_fna(self):\\r\\n fna_fp = os.path.join(self.sff_dir, 'test.fna')\\r\\n fna_gz_fp = os.path.join(self.gz_sff_dir, 'test_gz.fna')\\r\\n make_fna(self.sff_fp, fna_fp)\\r\\n make_fna(self.sff_gz_fp, fna_gz_fp)\\r\\n self.assertEqual(open(fna_fp).read(), fna_txt)\\r\\n self.assertEqual(open(fna_gz_fp).read(), fna_txt)\",\n \"def test_empty_file(self):\\n\\n temp = tempfile.NamedTemporaryFile()\\n temp.flush()\\n self.assertRaises(MalformedFileError, NBTFile, temp.name)\",\n \"def test_make_flow_txt(self):\\r\\n flow_fp = os.path.join(self.sff_dir, 'test.txt')\\r\\n flow_gz_fp = os.path.join(self.gz_sff_dir, 'test_gz.txt')\\r\\n make_flow_txt(self.sff_fp, flow_fp)\\r\\n make_flow_txt(self.sff_gz_fp, flow_gz_fp)\\r\\n self.assertEqual(open(flow_fp).read(), flow_txt)\\r\\n self.assertEqual(open(flow_gz_fp).read(), flow_txt)\",\n \"def test_create4(self):\\n TempfileManager.sequential_files(2)\\n fname = TempfileManager.create_tempfile()\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertEqual(fname, 'tmp2')\\n #\\n TempfileManager.unique_files()\\n fname = TempfileManager.create_tempfile()\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 2)\\n fname = os.path.basename(fname)\\n self.assertNotEqual(fname, 'tmp3')\\n self.assertTrue(fname.startswith('tmp'))\",\n \"def test_file_io(self):\\n temp_directory = tempfile.mkdtemp()\\n filename = os.path.join(temp_directory, \\\"test.h5\\\")\\n\\n points = np.array([\\n [0.1, 0.1, 0.1],\\n [1.1, 2.1, 3.1],\\n [1.3, 2.2, 3.4]])\\n voxel_size = 0.5\\n min_corner = Vector3f(-1, -2, -3)\\n vg = VoxelGrid(voxel_size, min_corner=min_corner, points=points)\\n\\n # test writing\\n vg.save(filename)\\n self.assertTrue(os.path.isfile(filename))\\n\\n # test reading\\n vg2 = VoxelGrid.from_file(filename)\\n self.assertAlmostEquals(voxel_size, vg2.voxel_size)\\n np.testing.assert_array_almost_equal(vg.min_corner, vg2.min_corner)\\n shutil.rmtree(temp_directory)\",\n \"def test_CRUD_ProjectFiles_Successfully(self):\\n\\n real_file_code = get_content('BasicTemplateAlgorithm.py')\\n second_real_file_code = get_content('BasicTemplateForexAlgorithm.py')\\n\\n fakeFile = {\\\"name\\\":\\\"Hello.py\\\", \\\"code\\\": \\\"Hello World!\\\"}\\n realFile = {\\\"name\\\":\\\"main.py\\\", \\\"code\\\": real_file_code}\\n secondRealFile = {\\\"name\\\":\\\"lol.py\\\", \\\"code\\\": second_real_file_code}\\n\\n # Create a new project and make sure there are no files\\n project = self.api.create_project(\\\"Test project - \\\", \\\"Py\\\")\\n self.assertTrue(project['success'])\\n self.assertTrue(project['projects'][0]['projectId'] > 0)\\n\\n # Add random file\\n randomAdd = self.api.add_project_file(project['projects'][0]['projectId'], fakeFile[\\\"name\\\"], fakeFile[\\\"code\\\"])\\n self.assertTrue(randomAdd['success'])\\n self.assertTrue(randomAdd['files'][0]['content'] == fakeFile['code'])\\n self.assertTrue(randomAdd['files'][0]['name'] == fakeFile['name'])\\n\\n # Update names of file\\n updatedName = self.api.update_project_filename(project['projects'][0]['projectId'], randomAdd['files'][0]['name'], realFile['name'])\\n self.assertTrue(updatedName['success'])\\n\\n # Replace content of file\\n updateContents = self.api.update_project_file_content(project['projects'][0]['projectId'], realFile[\\\"name\\\"], realFile['code'])\\n self.assertTrue(updateContents['success'])\\n\\n # Read single file\\n readFile = self.api.read_project_file(project['projects'][0]['projectId'], realFile['name'])\\n self.assertTrue(readFile['success'])\\n self.assertTrue(readFile['files'][0]['content'] == realFile['code'])\\n self.assertTrue(readFile['files'][0]['name'] == realFile['name'])\\n\\n # Add a second file\\n secondFile = self.api.add_project_file(project['projects'][0]['projectId'], secondRealFile['name'], secondRealFile['code'])\\n self.assertTrue(secondFile['success'])\\n self.assertTrue(secondFile['files'][0]['content'] == secondRealFile['code'])\\n self.assertTrue(secondFile['files'][0]['name'] == secondRealFile['name'])\\n\\n # Read multiple files\\n readFiles = self.api.read_project_files(project['projects'][0]['projectId'])\\n self.assertTrue(readFiles['success'])\\n self.assertTrue(len(readFiles['files']) == 2)\\n\\n # Delete the second file\\n deleteFile = self.api.delete_project_file(project['projects'][0]['projectId'], secondRealFile['name'])\\n self.assertTrue(deleteFile['success'])\\n\\n # Read files\\n readFilesAgain = self.api.read_project_files(project['projects'][0]['projectId'])\\n self.assertTrue(readFilesAgain['success'])\\n self.assertTrue(len(readFilesAgain['files']) == 1)\\n self.assertTrue(readFilesAgain['files'][0]['name'] == realFile['name'])\\n\\n # Delete the project\\n deleteProject = self.api.delete_project(project['projects'][0]['projectId'])\\n self.assertTrue(deleteProject['success'])\",\n \"def test_create_content(self):\\n url = reverse('content-list')\\n with tempfile.NamedTemporaryFile(suffix='.txt') as content_file:\\n content_file.write(b\\\"The contents of the temporary file.\\\\n\\\")\\n content_file.seek(0)\\n data = {\\n 'name': 'Content File',\\n 'description': 'File 1',\\n 'content_file': content_file,\\n 'updated_time': date.today(),\\n 'creators': [],\\n 'coverage': '',\\n 'subjects': [],\\n 'keywords': [],\\n 'workareas': [],\\n 'language': '',\\n 'cataloger': ''\\n }\\n response = self.client.post(url, data, format='multipart')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED)\",\n \"def test_main(data, tmp_path):\\n\\n main(data, tmp_path)\\n\\n FILES = (\\n \\\"gd32f888x(0-1)xx-pinctrl.h\\\",\\n \\\"gd32f888x(2-3)xx-pinctrl.h\\\",\\n \\\"gd32f888y(0-1)xx-pinctrl.h\\\",\\n \\\"gd32f999x(0-1)xx-pinctrl.h\\\",\\n \\\"gd32f999x(2-3)xx-pinctrl.h\\\",\\n \\\"gd32f999y(0-1)xx-pinctrl.h\\\",\\n )\\n\\n for file in FILES:\\n ref_file = data / file\\n gen_file = tmp_path / file\\n\\n assert gen_file.exists()\\n\\n with open(ref_file) as ref, open(gen_file) as gen:\\n assert ref.read() == gen.read()\",\n \"def generate_test_txt(name, path):\\n with open(path + '/test.txt', 'a') as file:\\n file.write('data/test/' + name + '\\\\n')\",\n \"def test_005_write_file(self):\\n __test = chess_storage.ChessStorage()\\n __test_data = list(range(consts.TEST_LIST_LENGHT))\\n __dir_game_saves = os.path.dirname(__file__)\\n __dir_game_saves = os.path.join(__dir_game_saves, 'games')\\n __dir_game_saves = os.path.join(__dir_game_saves, consts.TEST_FILENAME)\\n # pylint: disable = protected-access\\n __save_test = __test._ChessStorage__write_file(__dir_game_saves, __test_data)\\n # pylint: enable = protected-access\\n self.assertEqual(__save_test, consts.ERROR_CODES[\\\"SUCCESSFULL\\\"])\",\n \"def testExampleFileGeneration(ref):\\n outdir = ref.tmp_dir\\n outpath = os.path.join(outdir, 'file_result.html')\\n generate_file(outpath)\\n ref.assertTextFileCorrect(outpath, 'file_result.html',\\n ignore_substrings=['Copyright', 'Version'])\",\n \"def test_record_create_files(db, location):\\n record = CernSearchRecord.create({\\\"title\\\": \\\"test\\\"})\\n\\n assert 0 == len(record.files)\\n assert 0 == len(record.files_content)\\n\\n file_content = b\\\"Hello world!\\\"\\n record.files[\\\"hello.txt\\\"] = BytesIO(file_content)\\n db.session.commit()\\n\\n record = CernSearchRecord.get_record(record.id)\\n\\n assert record[\\\"_bucket\\\"] == record.bucket_id\\n assert record[\\\"_files\\\"]\\n\\n file_1 = record.files[\\\"hello.txt\\\"]\\n assert \\\"hello.txt\\\" == file_1[\\\"key\\\"]\\n assert 1 == len(record.files)\\n assert 1 == len(record[\\\"_files\\\"])\\n assert 0 == len(record.files_content)\\n\\n storage = file_1.obj.file.storage() # type: FileStorage\\n fp = storage.open(mode=READ_MODE_BINARY)\\n\\n try:\\n assert file_content == fp.read()\\n finally:\\n fp.close()\",\n \"def test_read_file(self):\\n restart_path = os.path.join(arc_path, 'arc', 'testing', 'restart(H,H2O2,N2H3,CH3CO2).yml')\\n input_dict = read_file(restart_path)\\n self.assertIsInstance(input_dict, dict)\\n self.assertTrue('reactions' in input_dict)\\n self.assertTrue('freq_level' in input_dict)\\n self.assertTrue('use_bac' in input_dict)\\n self.assertTrue('ts_guess_level' in input_dict)\\n self.assertTrue('running_jobs' in input_dict)\\n\\n with self.assertRaises(InputError):\\n read_file('nopath')\",\n \"def test_writer_with_file():\\n outputfile = \\\"testfile.txt\\\"\\n GCMT(write=outputfile)\\n assert os.path.exists(outputfile)\\n os.remove(outputfile)\",\n \"def test_create3(self):\\n fname = TempfileManager.create_tempfile(suffix='bar')\\n OUTPUT = open(fname, 'w')\\n OUTPUT.write('tempfile\\\\n')\\n OUTPUT.close()\\n self.assertEqual(len(list(glob.glob(tempdir + '*'))), 1)\\n fname = os.path.basename(fname)\\n self.assertTrue(fname.endswith('bar'))\",\n \"def test_to_file(self):\\n fd, fp = mkstemp()\\n close(fd)\\n pt = PrepTemplate.create(self.metadata, self.new_raw_data,\\n self.test_study, self.data_type)\\n pt.to_file(fp)\\n self._clean_up_files.append(fp)\\n with open(fp, 'U') as f:\\n obs = f.read()\\n self.assertEqual(obs, EXP_PREP_TEMPLATE)\",\n \"def test_add1(self):\\n try:\\n TempfileManager.add_tempfile(tempdir + 'add1')\\n self.fail(\\\"Expected IOError because file 'add1' does not exist\\\")\\n except IOError:\\n pass\",\n \"def test_basic():\\n f = Level3File(get_test_data('nids/Level3_FFC_N0Q_20140407_1805.nids', as_file_obj=False))\\n assert f.metadata['prod_time'].replace(second=0) == datetime(2014, 4, 7, 18, 5)\\n assert f.metadata['vol_time'].replace(second=0) == datetime(2014, 4, 7, 18, 5)\\n assert f.metadata['msg_time'].replace(second=0) == datetime(2014, 4, 7, 18, 6)\\n assert f.filename == get_test_data('nids/Level3_FFC_N0Q_20140407_1805.nids',\\n as_file_obj=False)\\n\\n # At this point, really just want to make sure that __str__ is able to run and produce\\n # something not empty, the format is still up for grabs.\\n assert str(f)\",\n \"def test_upload_file(self):\\n\\n uploadFile = os.path.join(testdatadir, \\\"upload.data\\\")\\n r = gracedb.writeFile(eventId, uploadFile)\\n self.assertEqual(r.status, 201) # CREATED\\n r_content = r.json()\\n link = r_content['permalink']\\n\\n self.assertEqual(\\n open(uploadFile, 'r').read(),\\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\\n )\\n\\n self.assertEqual(\\n open(uploadFile, 'r').read(),\\n gracedb.get(link).read()\\n )\\n\\n # Re-upload slightly different file.\\n uploadFile2 = os.path.join(testdatadir, \\\"upload2.data\\\")\\n r = gracedb.writeFile(\\n eventId,\\n filename=\\\"upload.data\\\",\\n filecontents=open(uploadFile2, 'r'))\\n self.assertEqual(r.status, 201) # CREATED\\n r_content = r.json()\\n link2 = r_content['permalink']\\n\\n self.assertEqual(\\n open(uploadFile2, 'r').read(),\\n gracedb.get(gracedb.files(eventId).json()['upload.data']).read()\\n )\\n\\n self.assertEqual(\\n open(uploadFile2, 'r').read(),\\n gracedb.get(link2).read()\\n )\\n\\n self.assertNotEqual(link, link2)\",\n \"def test_file_has_details(self):\\n file_content = bytes(\\n \\\"\\\"\\\"\\n 1001 | Eggs | 12\\\\n\\n 1002 | Bread | 1\\\\n\\n 1003 | Butter | 1\\\\n\\n 1004 | Milk | 1\\\\n\\n 1005 | Corn | 2\\n \\\"\\\"\\\", encoding='UTF-8'\\n )\\n\\n test_file = SimpleUploadedFile(\\n \\\"sample.txt\\\", file_content, content_type=\\\"text/txt\\\")\\n \\n new_file = File.objects.create(\\n file=test_file,\\n rows=5,\\n items='Eggs, Bread, Butter, Milk, Corn'\\n )\\n\\n file_details = File.objects.get(id=new_file.id)\\n self.assertEqual(file_details.file.name, new_file.file.name)\\n self.assertEqual(file_details.rows, new_file.rows)\\n self.assertEqual(file_details.items, new_file.items)\",\n \"def test_make_file_read_only():\\n\\n filename = os.path.join(tempfile.gettempdir(), \\\"jade-test-file.txt\\\")\\n if os.path.exists(filename):\\n os.chmod(filename, stat.S_IWRITE)\\n os.remove(filename)\\n\\n with open(filename, \\\"w\\\") as f:\\n f.write(\\\"Hello World\\\")\\n\\n prev_mode = os.stat(filename)\\n make_file_read_only(filename)\\n # Disabling because it doesn't work on Windows.\\n # s = os.stat(filename)\\n # assert s.st_mode != prev_mode\\n # assert s.st_mode == 33060\\n\\n if os.path.exists(filename):\\n os.chmod(filename, stat.S_IWRITE)\\n os.remove(filename)\",\n \"def test_with_new_file(self):\\n repository = self.create_repository(tool_name='Test')\\n review_request = self.create_review_request(\\n repository=repository,\\n submitter=self.user,\\n publish=True)\\n diffset = self.create_diffset(review_request)\\n filediff = self.create_filediff(diffset,\\n source_revision=PRE_CREATION)\\n\\n rsp = self.api_get(\\n get_original_file_url(review_request, diffset, filediff),\\n expected_status=404)\\n self.assertEqual(rsp['stat'], 'fail')\\n self.assertEqual(rsp['err']['code'], DOES_NOT_EXIST.code)\",\n \"def test_fileOperations(self): \\n from DIRAC import gConfig\\n testSE = 'testSE'\\n #rssClient = ResourceStatusClient()\\n #result = rssClient.getStorageElementsList( 'Read' )\\n #result = gConfig.getSections( '/Resources/StorageElements' )\\n #if result['OK'] and result['Value']:\\n # testSE = result['Value'][0]\\n #if result['Ok']:\\n # testSE = result['Value'][ 0 ] \\n \\n result = self.fc.addFile( { testFile: { 'PFN': 'testfile', \\n 'SE': testSE , \\n 'Size':0, \\n 'GUID':0, \\n 'Checksum':'0' } } )\\n self.assert_( result['OK'] )\\n if gConfig.getValue( '/Resources/StorageElements/%s/AccessProtocol.1/Host' % testSE, '' ):\\n result = self.fc.getReplicas( testFile )\\n self.assert_( result['OK'] )\\n self.assert_( testFile in result['Value']['Successful'] )\",\n \"def test_with_file(self, file_path):\\n result = self.run(file_path=file_path)\\n return self._handle_test_result(result)\",\n \"def test_file_error(self):\\n my_reader = DataSetReader()\\n covid_list = CovidCase.objects.all()\\n\\n with self.assertRaises(IOError):\\n my_reader.writeFile(covid_list, \\\"Not_A_File.csv\\\")\",\n \"def test_create_files(self):\\n opts = mock.Mock()\\n opts.repo = self.repo\\n opts.source = 'src'\\n opts.version = '0.0.1'\\n opts.version_file = None\\n opts.test_mode = False\\n opts.templates = ['include steve/*']\\n opts.history_file = 'HISTORY.md'\\n opts.package = 'unittests'\\n opts.desc = \\\"DESCRIPTION\\\"\\n opts.org = \\\"ORG\\\"\\n opts.develop = 'develop'\\n opts.requirements = 'requirements.txt'\\n opts.test_requirements = 'test-requirements.txt'\\n opts.pypi_package_name = None\\n opts.python = None\\n opts.create_version_file = False\\n opts.gitignore_url = \\\"GIT_IGNORE_URL\\\"\\n opts.add_gitignore = False\\n create_files(opts)\\n\\n dir_list = os.listdir(self.repo)\\n self.failUnless('cirrus.conf' in dir_list)\\n self.failUnless('HISTORY.md' in dir_list)\\n self.failUnless('MANIFEST.in' in dir_list)\\n self.failUnless('setup.py' in dir_list)\\n\\n cirrus_conf = os.path.join(self.repo, 'cirrus.conf')\\n config = ConfigParser.RawConfigParser()\\n config.read(cirrus_conf)\\n self.assertEqual(config.get('package', 'name'), opts.package)\\n self.assertEqual(config.get('package', 'version'), opts.version)\\n\\n history = os.path.join(self.repo, 'HISTORY.md')\\n with open(history, 'r') as handle:\\n self.failUnless('CIRRUS_HISTORY_SENTINEL' in handle.read())\\n\\n manifest = os.path.join(self.repo, 'MANIFEST.in')\\n with open(manifest, 'r') as handle:\\n content = handle.read()\\n self.failUnless('include requirements.txt' in content)\\n self.failUnless('include cirrus.conf' in content)\\n self.failUnless('include steve/*' in content)\\n\\n version = os.path.join(self.repo, 'src', 'unittests', '__init__.py')\\n with open(version, 'r') as handle:\\n self.failUnless(opts.version in handle.read())\",\n \"def test_file_reader(self) -> None:\\n result = [['123', 'Jin He', 'Computer Science'],\\n ['234', 'Nanda Koka', 'Software Engineering'],\\n ['345', 'Benji Cai', 'Software Engineering']]\\n # file have header\\n self.assertTrue(\\n list(file_reader('C:/Users/Nidhi/Desktop/SEM3/810/HW08/student_majors.txt', 3, '|', True)) == result)\\n # file without header\\n self.assertFalse(\\n list(file_reader('C:/Users/Nidhi/Desktop/SEM3/810/HW08/student_majors.txt', 3, '|')) == result)\\n # More than 3 datafield\\n with self.assertRaises(ValueError):\\n list(file_reader(\\n 'C:/Users/Nidhi/Desktop/SEM3/810/HW08/student_majors.txt', 4, '|', True))\\n # file not found\\n with self.assertRaises(FileNotFoundError):\\n list(file_reader('abc.txt', 3, '|', True))\",\n \"def test_read_delete(self):\\n\\n expected = \\\"Hello, World! This has been written by Fun Ilrys.\\\"\\n File(\\\"hi\\\").write(expected)\\n actual = File(\\\"hi\\\").read()\\n\\n self.assertEqual(expected, actual)\\n\\n expected = False\\n File(\\\"hi\\\").delete()\\n actual = PyFunceble.path.isfile(\\\"hi\\\")\\n\\n self.assertEqual(expected, actual)\",\n \"def test_GFD_export_create_file(self):\\n filepath = '1.txt'\\n gfd = flow_processing_input.GroundFlowData()\\n gfd.detector_flow_data = createGFDDataset(1).dataset\\n gfd.export_to_file(filepath)\\n # Check if file was created at filepath\\n self.assertTrue(os.path.exists(filepath))\\n os.remove(filepath)\",\n \"def test_files(host, f):\\n assert host.file(f).exists\",\n \"def create(self):\\n self.create_file()\",\n \"def test_text_file_by_path(self):\\n self.command.package = self.input_ovf\\n self.command.file_path = \\\"sample_cfg.txt\\\"\\n self.command.run()\\n self.command.finished()\\n self.check_diff(\\\"\\\"\\\"\\n <ovf:File ovf:href=\\\"input.iso\\\" ovf:id=\\\"file2\\\" ovf:size=\\\"{iso_size}\\\" />\\n- <ovf:File ovf:href=\\\"sample_cfg.txt\\\" ovf:id=\\\"textfile\\\" \\\\\\novf:size=\\\"{cfg_size}\\\" />\\n </ovf:References>\\n\\\"\\\"\\\".format(iso_size=self.FILE_SIZE['input.iso'],\\n cfg_size=self.FILE_SIZE['sample_cfg.txt']))\\n self.assertFalse(os.path.exists(os.path.join(self.temp_dir,\\n \\\"sample_cfg.txt\\\")),\\n \\\"deleted file should not be exported\\\")\",\n \"def assertCreatesFile(self, filename, nonzero=True):\\n self.assertFalse(os.path.exists(filename), \\\"file already exists: {}\\\".format(filename))\\n yield\\n self.assertTrue(os.path.exists(filename), \\\"file was not created: {}\\\".format(filename))\\n if nonzero:\\n self.assertFilesizeNonZero(filename)\",\n \"def test_invalidFile(self):\\n self.assertRaises(cesmEnvLib.checkFile(\\\"blah\\\", \\\"write\\\"))\",\n \"def test_valid_file_name(self):\\n Base.save_to_file([self.r0, self.s1])\\n self.assertTrue(path.exists('Base.json'))\",\n \"def test_read_from_file():\\n reading_file = d.read_code_from_file()\\n assert type(reading_file) == list\\n assert len(reading_file) == 7\\n assert \\\"\\\\n\\\" not in d.read_code_from_file()\",\n \"def test_create_files_with_python(self):\\n opts = mock.Mock()\\n opts.repo = self.repo\\n opts.create_version_file = True\\n opts.source = 'src'\\n opts.version = '0.0.1'\\n opts.version_file = None\\n opts.org = \\\"ORG\\\"\\n opts.desc = \\\"DESCRIPTION\\\"\\n opts.templates = []\\n opts.test_mode = False\\n opts.history_file = 'HISTORY.md'\\n opts.package = 'unittests'\\n opts.develop = 'develop'\\n opts.requirements = 'requirements.txt'\\n opts.pypi_package_name = 'pypi.package.unittest'\\n opts.python = 'python3'\\n opts.gitignore_url = \\\"GIT_IGNORE_URL\\\"\\n opts.add_gitignore = False\\n opts.test_requirements = 'test-requirements.txt'\\n version = os.path.join(self.repo, 'src', 'unittests', '__init__.py')\\n os.system('rm -f {}'.format(version))\\n create_files(opts)\\n\\n dir_list = os.listdir(self.repo)\\n self.failUnless('cirrus.conf' in dir_list)\\n self.failUnless('HISTORY.md' in dir_list)\\n self.failUnless('MANIFEST.in' in dir_list)\\n self.failUnless('setup.py' in dir_list)\\n\\n cirrus_conf = os.path.join(self.repo, 'cirrus.conf')\\n config = ConfigParser.RawConfigParser()\\n config.read(cirrus_conf)\\n self.assertEqual(config.get('package', 'name'), opts.pypi_package_name)\\n self.assertEqual(config.get('package', 'version'), opts.version)\\n self.assertEqual(config.get('build', 'python'), 'python3')\",\n \"def test_bad_file(self):\\n with tempfile.NamedTemporaryFile(suffix='.json') as test_file:\\n with open(test_file.name, 'wt') as test_file_handle:\\n test_file_handle.write(\\\"garbage garbage garbage\\\\n\\\")\\n\\n with self.assertRaises(ConfigFileError):\\n engine = Engine(test_file.name, self.api_token)\",\n \"def test_load(api):\\n # upload file to file.io servers\\n uploaded_file = api.upload(\\n tag='test_file',\\n expiry='1d',\\n path='tests/test_file.txt'\\n )\\n\\n # check that instance of FileIO has these fields\\n assert uploaded_file.link\\n assert uploaded_file.key\\n assert uploaded_file.tag\\n assert uploaded_file.path\\n\\n # check that the uploaded file was added to uploaded files list\\n assert api.show_uploads()\\n\\n # check that our list is not empty\\n assert api.file_obj_list\\n\\n # export the file in json format\\n api.export('tests/exported.json')\\n\\n # check that exported file exists\\n assert path.isfile('tests/exported.json')\\n\\n # set it to empty list\\n api.file_obj_list = []\\n\\n # load the file in json format\\n api.load('tests/exported.json')\\n\\n # remove the file\\n remove('tests/exported.json')\\n\\n # check that the uploaded file was added to uploaded files list\\n assert api.show_uploads()\\n\\n # check that our list is not empty\\n assert api.file_obj_list\\n\\n # export the file in pkl format\\n api.export('tests/exported.pkl')\\n\\n # set it to empty list\\n api.file_obj_list = []\\n\\n # load the file in pkl format\\n api.load('tests/exported.pkl')\\n\\n # remove exported.pkl file\\n remove('tests/exported.pkl')\\n\\n # check that the uploaded file was added to uploaded files list\\n assert api.show_uploads()\\n\\n # check that our list is not empty\\n assert api.file_obj_list\",\n \"def test_create_files(self):\\n\\n testdir = \\\"test_output\\\"\\n test_submission = Submission()\\n self.addCleanup(os.remove, \\\"submission.tar.gz\\\")\\n self.addCleanup(shutil.rmtree, testdir)\\n\\n test_submission.create_files(testdir)\\n\\n self.doCleanups()\",\n \"def test_read_file():\\n assert read_file('test_read_file.txt') == \\\"\\\"\\\"ABCDEFGHIJKLMNOPQRSTUVWXYZ?\\nabcdefghijklmnopqrstuvwxyz.\\n\\\"\\\"\\\"\",\n \"def test_memory_name_form_file(self):\\n with TemporaryDirectory(dir=\\\"./\\\") as cur_dir:\\n \\\"\\\"\\\" Test some memory name\\\"\\\"\\\"\\n filepath, memory_file = self.create_config(memory=\\\"$file$\\\", corpora=DEFAULT_CORPUS, cur_dir=cur_dir)\\n self._dispatch(\\n train=0.8,\\n test=0.1,\\n dev=0.1,\\n config=filepath,\\n output_dir=p.join(cur_dir, \\\"output\\\")\\n )\\n\\n name = p.splitext(p.basename(filepath))[0] # [0] form splitext is everything but .xml\\n self.assertTrue(p.isfile(memory_file.replace(\\\"$file$\\\", name)+\\\".csv\\\"),\\n \\\"A new file should have been created using the config file name\\\")\",\n \"def test_create_csv(self):\\n\\n # absolute path to xml file to parse\\n xml_file = os.path.join(self.xmlfilepath, \\\"DLTINS_20210117_01of01.xml\\\")\\n\\n # absolute path to the csv file to create\\n csv_file = os.path.join(self.csvfile, \\\"DLTINS_20210117_01of01.csv\\\")\\n\\n # Test for correct data\\n self.assertEqual(create_csv(xml_file, self.csvfile), csv_file)\\n\\n # Test for incorrect input xml file\\n self.assertEqual(create_csv(\\\"somerandomfile\\\", self.csvfile), None)\\n\\n # Test for incorrect path to write csv to\\n self.assertEqual(create_csv(xml_file, r\\\"D:\\\\kqcA CK j \\\"), None)\",\n \"def test_binary_contents_twice(file, tmp_path):\\n name = tmp_path / \\\"1px.gif\\\"\\n\\n # First run state ensures file is created\\n ret = file.managed(name=str(name), contents=BINARY_FILE)\\n assert ret.result is True\\n\\n # Second run of state ensures file is in correct state\\n ret = file.managed(name=str(name), contents=BINARY_FILE)\\n assert ret.result is True\",\n \"def test_read_file_to_list():\\n new_file = \\\"Hello,World\\\"\\n data = race.read_file_to_list()\\n assert type(data) == list\\n for x in data:\\n assert type(x) == dict\\n assert len(data) == 33\\n with pytest.raises(FileNotFoundError):\\n Race(new_file).read_file_to_list()\",\n \"def test_open_read(self, remote_mock_dir):\\n\\n file_path = posixpath.join(remote_mock_dir, \\\"test.txt\\\")\\n with HdfsHook() as hook:\\n with hook.open(file_path) as file_:\\n content = file_.read()\\n assert content == b\\\"Test file\\\\n\\\"\",\n \"def test_to_yaml(self):\\n file_to_read = \\\"this_yaml_is_a_ghost.yaml\\\"\\n\\n expected = False\\n actual = PyFunceble.path.isfile(file_to_read)\\n\\n self.assertEqual(expected, actual)\\n\\n to_write = {\\\"hello\\\": [\\\"This is PyFunceble!\\\", \\\"Uhh!\\\"], \\\"world\\\": \\\"Fun Ilrys\\\"}\\n\\n expected = \\\"\\\"\\\"hello: [This is PyFunceble!, Uhh!]\\nworld: Fun Ilrys\\n\\\"\\\"\\\"\\n\\n Dict(to_write).to_yaml(file_to_read)\\n\\n actual = File(file_to_read).read()\\n self.assertEqual(expected, actual)\\n\\n File(file_to_read).delete()\\n\\n expected = False\\n actual = PyFunceble.path.isfile(file_to_read)\\n\\n self.assertEqual(expected, actual)\",\n \"def create_test(self, test_case, file_name):\\n with open(os.path.join(self.tests, file_name), 'w+') as f:\\n f.write(test_case)\",\n \"def test_upload_file(self):\\n pass\",\n \"def test_create_file_via_ansible_playbook(self):\\n param = {\\n \\\"task_file\\\": \\\"kalliope/neurons/ansible_playbook/tests/test_ansible_playbook_neuron.yml\\\"\\n }\\n\\n Ansible_playbook(**param)\\n\\n self.assertTrue(os.path.isfile(self.test_file))\\n\\n if os.path.exists(self.test_file):\\n os.remove(self.test_file)\",\n \"def test_kyc_get_file(self):\\n pass\",\n \"def test_read_from_file():\\n from scraper import read_from_file\\n assert read_from_file(TEST_FILE) == (TEST_CONTENT, 'utf-8')\",\n \"def test_file_read():\\n expected = [\\\"scorevideo LOG\\\\n\\\", \\\"File: log.mat\\\"]\\n with open(TEST_RES + \\\"/file_read.txt\\\", 'r') as file:\\n actual = file.readlines()\\n assert expected == actual\",\n \"def test_file_exists(self):\\n with TemporaryDirectory() as tmp:\\n # define path to file\\n fp = os.path.join(tmp, \\\"asdf.txt\\\")\\n\\n # write atomically to file\\n with atomic_write(fp, \\\"w\\\") as f:\\n f.write(\\\"asdf\\\")\\n\\n # ensure file exists\\n assert os.path.exists(fp)\\n\\n # ensure atomic_write to same file raises an error as it already exists\\n try:\\n with atomic_write(fp, \\\"w\\\") as f:\\n f.write(\\\"asdf\\\")\\n except FileExistsError as e:\\n self.assertIsInstance(e, FileExistsError)\",\n \"def test_newFile(self):\\n self.write(\\\"service1.json\\\", [{\\\"host\\\": \\\"host1\\\", \\\"port\\\": 123},\\n {\\\"host\\\": \\\"host2\\\", \\\"port\\\": 124}])\\n self.write(\\\"service2.json\\\", [])\\n self.pump()\\n self.assertNodesEqual(\\n knownNodes(self.disco, \\\"service1\\\", \\\"staging\\\"),\\n [self.node(\\\"service1\\\", \\\"host1\\\", 123),\\n self.node(\\\"service1\\\", \\\"host2\\\", 124)])\",\n \"def file_factory(test_workspace):\\n\\n return FileCreator(test_workspace)\",\n \"def create_mock_files(self,\\n project_id=\\\"DEV-test\\\",\\n count=3,\\n prefix=\\\"mock_data_file\\\",\\n file_format=\\\"dcm\\\",\\n outdir=\\\".\\\",\\n msg = \\\"This is a mock data file for testing purposes. Delete me!\\\",\\n write_tsv = True\\n ):\\n prog,proj = project_id.split(\\\"-\\\")\\n authz = [\\\"/programs/{}/projects/{}\\\".format(prog,proj)]\\n acl = [prog,proj]\\n\\n mfiles = {'file_name':[],'md5sum':[],\\\"file_size\\\":[],\\\"object_id\\\":[],\\\"storage_urls\\\":[],\\\"acl\\\":[],\\\"authz\\\":[]}\\n for i in range(count):\\n file_name = \\\"{}_{}.{}\\\".format(prefix,i+1,file_format)\\n object_id = str(uuid.uuid4())\\n mfiles['file_name'].append(file_name)\\n mfiles['object_id'].append(object_id)\\n mfiles['authz'].append(authz)\\n mfiles['acl'].append(acl)\\n\\n\\n output = \\\"{}/{}\\\".format(outdir,file_name)\\n os.system(\\\"touch {}\\\".format(output))\\n file_msg =\\\"{} File {} of {}. {} with object_id {}.\\\".format(msg,i+1,count,file_name,object_id)\\n cmd = 'echo \\\"{}\\\" > {}'.format(file_msg,file_name)\\n os.system(cmd)\\n\\n with open(output, 'rb') as file_to_check:\\n file_contents = file_to_check.read()\\n #cmd = \\\"!md5 mock_data_file_{}.{}\\\".format(i+1,file_format))\\n md5 = hashlib.md5(file_contents).hexdigest() #check in shell: !md5 mock_data_file_3.dcm\\n\\n mfiles['md5sum'].append(md5)\\n mfiles['file_size'].append(os.stat(output).st_size)\\n urls=\\\"s3://this-is-a-fake-url-for:{}\\\".format(file_name)\\n mfiles['storage_urls'].append([urls])\\n\\n return mfiles\",\n \"def test_default_creation_2():\\n actual = os.path.join('.', 'test_files', 'rc_test_default.input')\\n times = list(range(0, 30, 5))\\n params = {\\\"names\\\": ['V'],\\n \\\"values\\\": [\\n [1],\\n [0],\\n [-1],\\n [0],\\n [1]\\n ]\\n }\\n input_creator = InputCreator(None, times, params)\\n f_out = input_creator.default_creation_2()\\n with open(actual) as f_actual:\\n actual_content = f_actual.read()\\n\\n content = f_out.getvalue()\\n\\n assert_equal(content, actual_content)\",\n \"def test_write_to_file(self):\\n test_filename = \\\"test_write.cf\\\"\\n cf1 = ConfigFile()\\n cf1[\\\"key1\\\"] = \\\"val1\\\"\\n cf1[\\\"key2\\\"] = \\\"val2\\\"\\n cf1[\\\"key3\\\"] = \\\"val3\\\"\\n cf1.write_to_file(filename=test_filename)\\n\\n cf2 = ConfigFile()\\n cf2.load_from_file(filename=test_filename)\\n\\n self.assertTrue(cf1 == cf2)\",\n \"def make_test_file(path: Path, name: str = None):\\n if not name:\\n name = \\\"test.txt\\\"\\n\\n path.mkdir(parents=True, exist_ok=True)\\n test_file = path.joinpath(name)\\n test_file.touch()\\n mtime = (datetime.today() - timedelta(2)).timestamp()\\n os.utime(test_file, (mtime, mtime))\\n return test_file\",\n \"def test_simple_config_file(self):\\n # create the config file:\\n test_name = \\\"unit_test_test_1\\\"\\n K = \\\"'key'\\\"\\n fam = 3\\n W = 5\\n D = 8\\n num_circuits = 1\\n num_inputs = 1\\n config_file_text = \\\"\\\\n\\\".join([\\\"test_type = RANDOM\\\",\\n \\\" = \\\".join([\\\"K\\\", str(K)]),\\n \\\" = \\\".join([\\\"fam\\\", str(fam)]),\\n \\\" = \\\".join([\\\"W\\\", str(W)]),\\n \\\" = \\\".join([\\\"D\\\", str(D)]),\\n \\\" = \\\".join([\\\"num_circuits\\\",\\n str(num_circuits)]),\\n \\\" = \\\".join([\\\"num_inputs\\\",\\n str(num_inputs)]),\\n \\\" = \\\".join([\\\"generate\\\", \\\"True\\\"])])\\n config_file = StringIO.StringIO(config_file_text)\\n # create the parser/generator:\\n fho = tfho.test_file_handle_object()\\n pag = gen.parser_and_generator(test_name, config_file, fho)\\n pag.parse_and_generate()\\n # retrieve the test file and check that it is correct:\\n test_file = fho.get_file(os.path.join(test_name, \\\"test.txt\\\"))\\n test_file_text = test_file.getvalue()\\n expected_test_file_text = \\\"\\\\n\\\".join(\\n [\\\"KEY\\\",\\n os.path.join(\\\"stealth\\\", test_name, \\\"key\\\", str(1)),\\n \\\"CIRCUIT\\\",\\n os.path.join(\\\"stealth\\\", test_name, \\\"circuit\\\", str(1)),\\n \\\"INPUT\\\",\\n os.path.join(\\\"stealth\\\", test_name, \\\"input\\\", str(1)),\\n \\\"\\\"])\\n self.assertEqual(expected_test_file_text, test_file_text)\\n # retrieve the key file and check that it is correct:\\n key_file = fho.get_file(os.path.join(test_name, \\\"key\\\", \\\"1\\\"))\\n key_file_text = key_file.getvalue()\\n self.assertEqual(K, key_file_text)\\n # retrieve the input and check that it is correct:\\n input_file = fho.get_file(os.path.join(test_name, \\\"input\\\", \\\"1\\\"))\\n input_file_text = input_file.getvalue()\\n # check that input text begins and ends with a bracket:\\n self.assertEqual(\\\"[\\\", input_file_text[0])\\n self.assertEqual(\\\"]\\\", input_file_text[-1])\\n # check that all bits are 0 or 1:\\n for bit in input_file_text[1:-1]:\\n self.assertTrue((bit == '0') or (bit == '1'))\\n # retrieve the circuit and check that it begins with the correct header:\\n circuit_file = fho.get_file(os.path.join(test_name, \\\"circuit\\\", \\\"1\\\"))\\n circuit_file_text = circuit_file.getvalue()\\n circuit_header = circuit_file_text.split(\\\"\\\\n\\\")[0]\\n (W_string, D_string, fam_string) = circuit_header.split(\\\",\\\")\\n W_value = int(W_string.split(\\\"=\\\")[-1])\\n D_value = float(D_string.split(\\\"=\\\")[-1])\\n fam_value = int(fam_string.split(\\\"=\\\")[-1])\\n self.assertEqual(W, W_value)\\n self.assertEqual(D, D_value)\\n self.assertEqual(fam, fam_value)\",\n \"def test_api_create_unknown_file(api):\\n api.create_article(\\n {\\\"title\\\": \\\"A Title\\\"},\\n {\\\"key1\\\": \\\"value1\\\"},\\n {'test.zip': 'FFFDASFAFADADFA'},\\n )\\n req_call = requests.request\\n req_kw = req_call.call_args[1]\\n data_lines = req_kw['data'].split(b'\\\\r\\\\n')\\n # Unknown file type is skipped\\n assert len(data_lines) == 11\",\n \"def test_file_analyzer(self):\\r\\n file_analyzer = FileAnalyzer(\\\"C:\\\\\\\\Users\\\\\\\\Himan\\\\\\\\Desktop\\\\\\\\Semester 2\\\\\\\\SSW 810\\\\\\\\HW\\\\\\\\Assignment 8\\\")\\r\\n self.assertEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 1, 'function': 5, 'line': 100, 'char': 4472}, \\\\\\r\\n 'HW08_Test_Himanshu.py': {'class': 1, 'function': 3, 'line': 38, 'char': 1861}})\\r\\n self.assertNotEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 0, 'function': 5, 'line': 46, 'char': 1931}})\\r\\n\\r\\n self.assertNotEqual(file_analyzer.files_summary, {'HW08_Himanshu.py': {'class': 1, 'function': 5, 'line': 100}}) # testing less fields\\r\\n\\r\\n with self.assertRaises(FileNotFoundError): # raises exception error\\r\\n FileAnalyzer(\\\"C:\\\\\\\\Users\\\\\\\\Himan\\\\\\\\Desktop\\\\\\\\Semester 2\\\\\\\\SSW 810\\\\\\\\HW\\\\\\\\Assignment 10\\\").files_summary\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7360315","0.72867465","0.7264493","0.7150804","0.7088082","0.7009468","0.69931275","0.6988977","0.69652444","0.6931245","0.6907508","0.68741745","0.6857578","0.6853722","0.6774158","0.6766216","0.6752629","0.67525613","0.6718231","0.6716792","0.6703876","0.6694229","0.66938156","0.66923344","0.6686302","0.66394234","0.66358566","0.6617437","0.65868545","0.6586124","0.6585102","0.6567274","0.65564626","0.65395314","0.65388924","0.6528017","0.65217054","0.648666","0.6482857","0.6447869","0.64473414","0.6442938","0.64282185","0.64258254","0.6413809","0.63985384","0.63960886","0.63816035","0.6367155","0.636604","0.63659686","0.6359981","0.63529134","0.6346977","0.63469046","0.63397056","0.6338651","0.63214767","0.6321218","0.63156796","0.63132685","0.63106835","0.63098556","0.630628","0.6301481","0.62965304","0.62944907","0.628896","0.6263419","0.6261442","0.62582934","0.62581795","0.6255796","0.6253735","0.6225613","0.6224858","0.6220934","0.6212582","0.6211937","0.6207368","0.620501","0.62002","0.61975217","0.61934316","0.61919093","0.6189989","0.6186331","0.61823756","0.6177854","0.61769086","0.6174959","0.6170176","0.6165425","0.61643744","0.6162862","0.61619264","0.61593676","0.61565596","0.6153067","0.6150806"],"string":"[\n \"0.7360315\",\n \"0.72867465\",\n \"0.7264493\",\n \"0.7150804\",\n \"0.7088082\",\n \"0.7009468\",\n \"0.69931275\",\n \"0.6988977\",\n \"0.69652444\",\n \"0.6931245\",\n \"0.6907508\",\n \"0.68741745\",\n \"0.6857578\",\n \"0.6853722\",\n \"0.6774158\",\n \"0.6766216\",\n \"0.6752629\",\n \"0.67525613\",\n \"0.6718231\",\n \"0.6716792\",\n \"0.6703876\",\n \"0.6694229\",\n \"0.66938156\",\n \"0.66923344\",\n \"0.6686302\",\n \"0.66394234\",\n \"0.66358566\",\n \"0.6617437\",\n \"0.65868545\",\n \"0.6586124\",\n \"0.6585102\",\n \"0.6567274\",\n \"0.65564626\",\n \"0.65395314\",\n \"0.65388924\",\n \"0.6528017\",\n \"0.65217054\",\n \"0.648666\",\n \"0.6482857\",\n \"0.6447869\",\n \"0.64473414\",\n \"0.6442938\",\n \"0.64282185\",\n \"0.64258254\",\n \"0.6413809\",\n \"0.63985384\",\n \"0.63960886\",\n \"0.63816035\",\n \"0.6367155\",\n \"0.636604\",\n \"0.63659686\",\n \"0.6359981\",\n \"0.63529134\",\n \"0.6346977\",\n \"0.63469046\",\n \"0.63397056\",\n \"0.6338651\",\n \"0.63214767\",\n \"0.6321218\",\n \"0.63156796\",\n \"0.63132685\",\n \"0.63106835\",\n \"0.63098556\",\n \"0.630628\",\n \"0.6301481\",\n \"0.62965304\",\n \"0.62944907\",\n \"0.628896\",\n \"0.6263419\",\n \"0.6261442\",\n \"0.62582934\",\n \"0.62581795\",\n \"0.6255796\",\n \"0.6253735\",\n \"0.6225613\",\n \"0.6224858\",\n \"0.6220934\",\n \"0.6212582\",\n \"0.6211937\",\n \"0.6207368\",\n \"0.620501\",\n \"0.62002\",\n \"0.61975217\",\n \"0.61934316\",\n \"0.61919093\",\n \"0.6189989\",\n \"0.6186331\",\n \"0.61823756\",\n \"0.6177854\",\n \"0.61769086\",\n \"0.6174959\",\n \"0.6170176\",\n \"0.6165425\",\n \"0.61643744\",\n \"0.6162862\",\n \"0.61619264\",\n \"0.61593676\",\n \"0.61565596\",\n \"0.6153067\",\n \"0.6150806\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6928599"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":77989,"cells":{"query":{"kind":"string","value":"Test job addition and exist check"},"document":{"kind":"string","value":"def test_job_exists():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'job_status': 'submitted'})\n exists = Status.job_exists(STATUS_DIR, 'test1')\n assert exists"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_job_addition():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1')\n status1 = Status(STATUS_DIR).data['generation']['test1']['job_status']\n\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'job_status': 'finished',\n 'additional': 'test'})\n status2 = Status(STATUS_DIR).data['generation']['test1']['job_status']\n\n assert status2 == status1","def test_matching_jobs_existing(self):\n self.assertEquals(\n self.query_api.get_matching_jobs(\n \"try\", \"146071751b1e\",\n 'Linux x86-64 try build'), json.loads(JOBS_SCHEDULE))","def job_exists(self, job):\n\n with open(os.devnull, 'w') as devnull:\n result = subprocess.call(self.cli + [PlatformJenkinsJavaCLI.GET_JOB, job.name], stdout=devnull)\n\n return result == 0","def check_job_exists( job_list, analysis_group_id, reprocess_config_id):\n for job in job_list:\n struct = JSONMessage.unserialize(job.input_message)\n\n if( int( struct.analysis_group_id ) == int( analysis_group_id ) and \\\n int( struct.reprocess_config_id ) == int( reprocess_config_id ) ):\n return 1\n return 0","def test_fake_job( self ):\n hda = MockHda( job=UNDEFINED_JOB )\n self.history.active_datasets.append( hda )\n job_dict, warnings = extract.summarize( trans=self.trans )\n assert not warnings\n assert len( job_dict ) == 1\n fake_job = job_dict.keys()[ 0 ]\n assert fake_job.id.startswith( \"fake_\" )\n datasets = job_dict.values()[ 0 ]\n assert datasets == [ ( None, hda ) ]","def _check_for_finished_job(self):\n raise NotImplementedError","def test_jobs_successful(self):\n\n workspace = storage_test_utils.create_workspace()\n file1 = storage_test_utils.create_file()\n data_dict = {\n 'version': '1.0',\n 'input_data': [{\n 'name': 'INPUT_IMAGE',\n 'file_id': file1.id\n }],\n 'output_data': [{\n 'name': 'output_file_pngs',\n 'workspace_id': workspace.id\n }]}\n\n secret_configuration = {\n 'version': '6',\n 'priority': 50,\n 'output_workspaces': {'default': storage_test_utils.create_workspace().name},\n 'settings': {\n 'DB_HOST': 'som.host.name',\n 'DB_PASS': 'secret_password'\n }\n }\n\n seed_job_type = job_test_utils.create_seed_job_type(configuration=secret_configuration)\n seed_job = job_test_utils.create_job(job_type=seed_job_type, status='RUNNING', input=data_dict)\n\n url = '/%s/jobs/%d/' % (self.api, seed_job.id)\n response = self.client.generic('GET', url)\n result = json.loads(response.content)\n\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n self.assertEqual(result['configuration']['priority'],50)\n self.assertNotIn('DB_PASS', result['configuration']['settings'])","def test_submit_jobs(self):\r\n\r\n submit_jobs([self.command], prefix=\"test_job\")\r\n # Try and wait ten times, could be made nicer with alarm()\r\n for i in range(10):\r\n if exists(self.tmp_result_file):\r\n observed_text = \"\".join(list(open(self.tmp_result_file)))\r\n self.assertEqual(observed_text, \"hello\\n\")\r\n return\r\n else:\r\n sleep(10)\r\n # if we get here we failed\r\n self.fail(\"The test job apparently never finished.\\n\"\r\n + \"check the jobs error log and check the queue status\\n.\")","def check_jobs(self):\n # New/aborted jobs\n try:\n jobs = self.sm.get_job('%', phase = 'QUEUED')\n for job in jobs:\n self._launch_job(Job(job['job']))\n res = self.sm.get_aborted_jobs()\n aborts = [x['identifier'] for x in res]\n # Completed jobs\n for t in self.threads:\n if t.isDone() or t.name in aborts:\n self.threads.remove(t)\n # Set job status to COMPLETED\n job = Job(self.sm.get_job(t.name)[0]['job'])\n if t._Future__excpt == None:\n job.set_phase('COMPLETED')\n if t._Future__result != None:\n job.set_results(t._Future__result) \n status = True\n else:\n job.set_phase('ERROR')\n job.set_error_summary(str(t._Future__excpt[1]).replace(\"'\", \"\"))\n status = False\n job.set_end_time(datetime.utcnow().isoformat())\n self.sm.update_job(job = job, completed = status)\n except Exception, e:\n print \"Error:\", e","def test_create_job(self):\n engine = Engine(self.config_file, self.api_token)\n\n engine.create_job()\n\n assert engine.ingest_job_id == 23","def test_get_refresh_job_status(self):\n pass","def test_valid_n_jobs(n_jobs: Any) -> None:\n check_n_jobs(n_jobs)","def test_job_replacement():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'job_status': 'submitted'})\n\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'addition': 'test',\n 'job_status': 'finished'},\n replace=True)\n\n status = Status(STATUS_DIR).data['generation']['test1']['job_status']\n addition = Status(STATUS_DIR).data['generation']['test1']['addition']\n assert status == 'finished'\n assert addition == 'test'","def test_successful_job(self):\n\n successful_job = json.loads(TREEHERDER_JOB % (\"success\", \"completed\"))\n self.assertEquals(self.query_api.get_job_status(successful_job), SUCCESS)","def test_successful(self):\n\n url = '/%s/jobs/' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n for entry in result['results']:\n expected = None\n if entry['id'] == self.job1.id:\n expected = self.job1\n elif entry['id'] == self.job2.id:\n expected = self.job2\n elif entry['id'] == self.job3.id:\n expected = self.job3\n else:\n self.fail('Found unexpected result: %s' % entry['id'])\n self.assertEqual(entry['job_type']['name'], expected.job_type.name)\n self.assertEqual(entry['job_type_rev']['job_type']['id'], expected.job_type.id)\n self.assertEqual(entry['is_superseded'], expected.is_superseded)","def test_check_opt_crashed(self):\n self.assertEqual(check_opt(self.jobset2.job), 'ocrashed')","def test_check_job_dependencies_not_finished(database):\n sess = database.session\n sub = SubmissionFactory(submission_id=1)\n job = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['waiting'],\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'])\n sess.add_all([sub, job])\n sess.commit()\n\n with pytest.raises(ValueError):\n check_job_dependencies(job.job_id)","def check_sp_job(self,\n label: str,\n job: 'JobAdapter',\n ):\n if 'mrci' in self.sp_level.method and job.level is not None and 'mrci' not in job.level.method:\n # This is a CCSD job ran before MRCI. Spawn MRCI\n self.run_sp_job(label)\n elif job.job_status[1]['status'] == 'done':\n self.post_sp_actions(label,\n sp_path=os.path.join(job.local_path_to_output_file),\n level=job.level,\n )\n # Update restart dictionary and save the yaml restart file:\n self.save_restart_dict()\n if self.species_dict[label].number_of_atoms == 1:\n # save the geometry from the sp job for monoatomic species for which no opt/freq jobs will be spawned\n self.output[label]['paths']['geo'] = job.local_path_to_output_file\n else:\n self.troubleshoot_ess(label=label,\n job=job,\n level_of_theory=job.level,\n )","def test_start_already_running(self, mock_add_job, mock_get_job):\n mock_get_job.return_value = MagicMock()\n\n result = self.aggregator.start(self.node_id)\n\n self.assertFalse(result)\n self.assertFalse(mock_add_job.called)","def test_job_pass(self):\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \"extras/tests/dummy_jobs\")):\n\n module = \"test_pass\"\n name = \"TestPass\"\n job_class = get_job(f\"local/{module}/{name}\")\n\n job_result = JobResult.objects.create(\n name=job_class.class_path,\n obj_type=self.job_content_type,\n user=None,\n job_id=uuid.uuid4(),\n )\n\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\n job_result.refresh_from_db()\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)","def test_local_job(self):\r\n exp = \"abc; echo $? > xyz\"\r\n obs = local_job('abc', 'xyz', 'notused', 'notused')\r\n self.assertEqual(obs, exp)","def testA_StraightThrough(self):\n # Do pre-submit job check\n nRunning = getCondorRunningJobs()\n self.assertEqual(nRunning, 0, \"User currently has %i running jobs. Test will not continue\" % (nRunning))\n\n myThread = threading.currentThread()\n workload = self.createTestWorkload()\n config = self.getConfig()\n\n\n name = 'WMAgent_Test1'\n site = self.sites[0]\n nSubs = 5\n nFiles = 10\n workloadPath = os.path.join(self.testDir, 'workloadTest',\n 'TestWorkload', 'WMSandbox',\n 'WMWorkload.pkl')\n\n # Create a collection of files\n self.createFileCollection(name = name, nSubs = nSubs,\n nFiles = nFiles,\n workflowURL = workloadPath,\n site = site)\n\n\n\n ############################################################\n # Test the JobCreator\n\n\n config.Agent.componentName = 'JobCreator'\n testJobCreator = JobCreatorPoller(config = config)\n\n testJobCreator.algorithm()\n time.sleep(5)\n\n\n # Did all jobs get created?\n getJobsAction = self.daoFactory(classname = \"Jobs.GetAllJobs\")\n result = getJobsAction.execute(state = 'Created', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs*nFiles)\n\n\n # Count database objects\n result = myThread.dbi.processData('SELECT * FROM wmbs_sub_files_acquired')[0].fetchall()\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n # Find the test directory\n testDirectory = os.path.join(self.testDir, 'TestWorkload', 'ReReco')\n self.assertTrue('JobCollection_1_0' in os.listdir(testDirectory))\n self.assertTrue(len(os.listdir(testDirectory)) <= 20)\n\n groupDirectory = os.path.join(testDirectory, 'JobCollection_1_0')\n\n # First job should be in here\n self.assertTrue('job_1' in os.listdir(groupDirectory))\n jobFile = os.path.join(groupDirectory, 'job_1', 'job.pkl')\n self.assertTrue(os.path.isfile(jobFile))\n with open(jobFile, 'rb') as f:\n job = pickle.load(f)\n\n\n self.assertEqual(job['workflow'], name)\n self.assertEqual(len(job['input_files']), 1)\n self.assertEqual(os.path.basename(job['sandbox']), 'TestWorkload-Sandbox.tar.bz2')\n\n\n\n\n\n\n\n\n\n\n ###############################################################\n # Now test the JobSubmitter\n\n config.Agent.componentName = 'JobSubmitter'\n testJobSubmitter = JobSubmitterPoller(config = config)\n\n\n testJobSubmitter.algorithm()\n\n\n # Check that jobs are in the right state\n result = getJobsAction.execute(state = 'Created', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Executing', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n\n # Check assigned locations\n getLocationAction = self.daoFactory(classname = \"Jobs.GetLocation\")\n for id in result:\n loc = getLocationAction.execute(jobid = id)\n self.assertEqual(loc, [[site]])\n\n\n # Check to make sure we have running jobs\n nRunning = getCondorRunningJobs()\n self.assertEqual(nRunning, nFiles * nSubs)\n\n\n #################################################################\n # Now the JobTracker\n\n\n config.Agent.componentName = 'JobTracker'\n testJobTracker = JobTrackerPoller(config = config)\n testJobTracker.setup()\n\n testJobTracker.algorithm()\n\n # Running the algo without removing the jobs should do nothing\n result = getJobsAction.execute(state = 'Executing', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n condorRM()\n time.sleep(1)\n\n # All jobs gone?\n nRunning = getCondorRunningJobs()\n self.assertEqual(nRunning, 0)\n\n\n testJobTracker.algorithm()\n time.sleep(5)\n\n # Running the algo without removing the jobs should do nothing\n result = getJobsAction.execute(state = 'Executing', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Complete', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n\n\n #################################################################\n # Now the JobAccountant\n\n # First you need to load all jobs\n\n\n self.getFWJRAction = self.daoFactory(classname = \"Jobs.GetFWJRByState\")\n completeJobs = self.getFWJRAction.execute(state = \"complete\")\n\n\n # Create reports for all jobs\n self.createReports(jobs = completeJobs, retryCount = 0)\n\n\n\n\n\n\n config.Agent.componentName = 'JobAccountant'\n testJobAccountant = JobAccountantPoller(config = config)\n testJobAccountant.setup()\n\n\n # It should do something with the jobs\n testJobAccountant.algorithm()\n\n\n # All the jobs should be done now\n result = getJobsAction.execute(state = 'Complete', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Success', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n\n #######################################################################\n # Now the JobArchiver\n\n\n config.Agent.componentName = 'JobArchiver'\n testJobArchiver = JobArchiverPoller(config = config)\n\n\n testJobArchiver.algorithm()\n\n # All the jobs should be cleaned up\n result = getJobsAction.execute(state = 'Success', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Cleanout', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n logDir = os.path.join(self.testDir, 'logs')\n\n for job in completeJobs:\n self.assertFalse(os.path.exists(job['fwjr_path']))\n jobFolder = 'JobCluster_%i' \\\n % (int(job['id']/config.JobArchiver.numberOfJobsToCluster))\n jobPath = os.path.join(logDir, jobFolder, 'Job_%i.tar' %(job['id']))\n self.assertTrue(os.path.isfile(jobPath))\n self.assertTrue(os.path.getsize(jobPath) > 0)\n\n\n\n\n ###########################################################################\n # Now the TaskAchiver\n\n\n config.Agent.componentName = 'TaskArchiver'\n testTaskArchiver = TaskArchiverPoller(config = config)\n\n\n testTaskArchiver.algorithm()\n\n\n result = getJobsAction.execute(state = 'Cleanout', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n\n\n for jdict in completeJobs:\n job = Job(id = jdict['id'])\n self.assertFalse(job.exists())\n\n\n\n\n\n if os.path.isdir('testDir'):\n shutil.rmtree('testDir')\n shutil.copytree('%s' %self.testDir, os.path.join(os.getcwd(), 'testDir'))\n\n\n\n\n return","def job_exists(self, job_id):\n\n return True if self.get_status(job_id) else False","def test_get_job(self):\n response = self.client.open(\n '/v1/job/{id}'.format(id='id_example'),\n method='GET',\n content_type='application/json')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","def test_running_job(self):\n\n running_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"running\"))\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def jobHealthy(self, count):\n job = self.tester.submission_result.job\n for idx in range(count - 1):\n if (job.health == 'healthy'):\n return True\n print(\"health check fail : %d\" % idx )\n time.sleep(1)\n job.refresh()\n self.assertEqual('healthy', job.health)\n return False","def test_post_pay_job_does_not_exist(self):\n freelancer_id = self.create_object('api/freelancers',\n self.valid_freelancers[0])\n\n payment = {'freelancer_id': freelancer_id,\n 'job_id': -100, # Non existing job id\n 'amount': 300}\n\n response = requests.post(self.BASE_API + self.ENDPOINT,\n data=json.dumps(payment))\n\n self.assertEqual(400, response.status_code,\n msg=(f'Able to receive a non existing '\n f'job in a transaction'\n f'Response code: {response.status_code}'))","def test_pending_job(self):\n\n pending_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"pending\"))\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def test_status(self):\n\n url = '/%s/jobs/?status=RUNNING' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 1)\n self.assertEqual(result['results'][0]['job_type']['id'], self.job1.job_type.id)","def test_check_opt_complete(self):\n self.assertEqual(check_opt(self.jobset3.job), 'completed')","def test_make_jobs(self):\r\n # no commands should make no jobs files\r\n self.assertEqual(make_jobs([], \"test\", self.queue), [])\r\n\r\n # one job file should be created\r\n filenames = make_jobs([self.command], \"test_qsub\", self.queue)\r\n self.assertTrue(len(filenames) == 1)\r\n observed_text = list(open(filenames[0]))\r\n\r\n self.assertEqual(\"\".join(observed_text),\r\n QSUB_TEXT % (\"72:00:00\", 1, 1, self.queue,\r\n \"test_qsub\", \"oe\",\r\n self.command))","def test_creating_a_job(created_job, bulk_request):\n assert created_job.job == 'THEJOBID'\n assert created_job.job_url == 'https://salesforce/services/async/34.0/job/THEJOBID'\n assert created_job.pending_batches == []\n assert created_job.is_open\n\n bulk_request.assert_called_once_with(\n 'post',\n 'https://salesforce/services/async/34.0/job',\n data=XMLMatcher('''<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n <jobInfo xmlns=\"http://www.force.com/2009/06/asyncapi/dataload\">\n <operation>update</operation>\n <object>Lead</object>\n <contentType>CSV</contentType>\n </jobInfo>\n ''')\n )","def maybe_commit(job):","def test_can_create_job(self):\n\t\tself.job.save()\n\t\tjob_instance = Job.objects.get(pk=1)\n\t\tself.assertEqual(\n\t\t\tjob_instance.created_by,\n\t\t\tself.created_by,\n\t\t\t\"created_by fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.organisation,\n\t\t\tself.organisation,\n\t\t\t\"organisation fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.category,\n\t\t\tself.category,\n\t\t\t\"category fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.title,\n\t\t\tself.title,\n\t\t\t\"title fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.description,\n\t\t\tself.description,\n\t\t\t\"description fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.allow_comments,\n\t\t\tself.allow_comments,\n\t\t\t\"allow_comments fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.start_accepting_applications_at,\n\t\t\tself.start_accepting_applications_at,\n\t\t\t\"start_accepting_applications_at fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.stop_accepting_applications_at,\n\t\t\tself.stop_accepting_applications_at,\n\t\t\t\"stop_accepting_applications_at fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.employment_term,\n\t\t\tself.employment_term,\n\t\t\t\"employment_term fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.seniority_level,\n\t\t\tself.seniority_level,\n\t\t\t\"seniority_level fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.location,\n\t\t\tself.location,\n\t\t\t\"location fields don't match.\"\n\t\t)","def test_post_job(self):\n body = UnitTesterJobCreateReq()\n response = self.client.open(\n '/v1/job',\n method='POST',\n data=json.dumps(body),\n content_type='application/json')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","def test_ready_only_job_pass(self):\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \"extras/tests/dummy_jobs\")):\n\n module = \"test_read_only_pass\"\n name = \"TestReadOnlyPass\"\n job_class = get_job(f\"local/{module}/{name}\")\n\n job_result = JobResult.objects.create(\n name=job_class.class_path,\n obj_type=self.job_content_type,\n user=None,\n job_id=uuid.uuid4(),\n )\n\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\n job_result.refresh_from_db()\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)\n self.assertEqual(Site.objects.count(), 0) # Ensure DB transaction was aborted","def test_upload_orchestration_no_batches(new_job):\n with mock.patch.object(new_job, 'create') as create, \\\n mock.patch.object(new_job, 'add_batch') as add_batch, \\\n mock.patch.object(new_job, 'abort') as abort, \\\n mock.patch.object(new_job, 'close') as close, \\\n mock.patch.object(new_job, 'wait') as wait:\n\n new_job.upload(['Id', 'Description'], [])\n\n create.assert_called_once_with()\n assert add_batch.call_count == 0\n abort.assert_called_once_with()\n assert close.call_count == 0\n assert wait.call_count == 0","def CheckIfJobFinished(jobid, numseq, to_email, g_params): # {{{\n bsname = \"job_final_process\"\n path_result = os.path.join(g_params['path_static'], 'result')\n rstdir = os.path.join(path_result, jobid)\n gen_logfile = g_params['gen_logfile']\n gen_errfile = g_params['gen_errfile']\n name_server = g_params['name_server']\n g_params['jobid'] = jobid\n g_params['numseq'] = numseq\n g_params['to_email'] = to_email\n jsonfile = os.path.join(rstdir, f\"{bsname}.json\")\n myfunc.WriteFile(json.dumps(g_params, sort_keys=True), jsonfile, \"w\")\n binpath_script = os.path.join(g_params['webserver_root'], \"env\", \"bin\")\n\n finished_idx_file = \"%s/finished_seqindex.txt\"%(rstdir)\n failed_idx_file = \"%s/failed_seqindex.txt\"%(rstdir)\n py_scriptfile = os.path.join(binpath_script, f\"{bsname}.py\")\n finished_idx_list = []\n failed_idx_list = []\n if os.path.exists(finished_idx_file):\n finished_idx_list = list(set(myfunc.ReadIDList(finished_idx_file)))\n if os.path.exists(failed_idx_file):\n failed_idx_list = list(set(myfunc.ReadIDList(failed_idx_file)))\n\n lockname = f\"{bsname}.lock\"\n lock_file = os.path.join(g_params['path_result'], g_params['jobid'],\n lockname)\n\n num_processed = len(finished_idx_list)+len(failed_idx_list)\n if num_processed >= numseq: # finished\n if ('THRESHOLD_NUMSEQ_CHECK_IF_JOB_FINISH' in g_params\n and numseq <= g_params['THRESHOLD_NUMSEQ_CHECK_IF_JOB_FINISH']):\n cmd = [\"python\", py_scriptfile, \"-i\", jsonfile]\n (isSubmitSuccess, t_runtime) = webcom.RunCmd(cmd, gen_logfile, gen_errfile)\n elif not os.path.exists(lock_file):\n bash_scriptfile = f\"{rstdir}/{bsname},{name_server},{jobid}.sh\"\n code_str_list = []\n code_str_list.append(\"#!/bin/bash\")\n cmdline = f\"python {py_scriptfile} -i {jsonfile}\"\n code_str_list.append(cmdline)\n code = \"\\n\".join(code_str_list)\n myfunc.WriteFile(code, bash_scriptfile, mode=\"w\", isFlush=True)\n os.chmod(bash_scriptfile, 0o755)\n os.chdir(rstdir)\n cmd = ['sbatch', bash_scriptfile]\n cmdline = \" \".join(cmd)\n verbose = False\n if 'DEBUG' in g_params and g_params['DEBUG']:\n verbose = True\n webcom.loginfo(\"Run cmdline: %s\"%(cmdline), gen_logfile)\n (isSubmitSuccess, t_runtime) = webcom.RunCmd(cmd, gen_logfile, gen_errfile, verbose)\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"isSubmitSuccess: %s\"%(str(isSubmitSuccess)), gen_logfile)","def examine_job(self):\n if self.data is None:\n print(\"Could not download job id\", self.job_id)\n return\n self.duration = self.read_data(\"duration\")\n self.between_commands = self.read_data(\"between_commands\")\n\n print(\"---\")\n print(\"test_id: {}\".format(self.job_id))\n print(\"duration:\")\n Job.print_results(self.duration)\n print(\"between_commands:\")\n Job.print_results(self.between_commands)\n print(\"\")","def test_successful(self):\n\n url = '/%s/job-types/status/' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 4)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)","def mark(self, job, status='succeeded'):\n pass","def _check_analyzed_job(self, job, container):\n self.log(u\"Checking the Job object generated from container\")\n\n self.log(u\"Checking that the Job is not None\")\n if job is None:\n self._failed(u\"Unable to create a Job from the container.\")\n return\n\n self.log(u\"Checking that the Job has at least one Task\")\n if len(job) == 0:\n self._failed(u\"Unable to create at least one Task from the container.\")\n return\n\n if self.rconf[RuntimeConfiguration.JOB_MAX_TASKS] > 0:\n self.log(u\"Checking that the Job does not have too many Tasks\")\n if len(job) > self.rconf[RuntimeConfiguration.JOB_MAX_TASKS]:\n self._failed(u\"The Job has %d Tasks, more than the maximum allowed (%d).\" % (\n len(job),\n self.rconf[RuntimeConfiguration.JOB_MAX_TASKS]\n ))\n return\n\n self.log(u\"Checking that each Task text file is well formed\")\n for task in job.tasks:\n self.log([u\"Checking Task text file '%s'\", task.text_file_path])\n text_file_bstring = container.read_entry(task.text_file_path)\n if (text_file_bstring is None) or (len(text_file_bstring) == 0):\n self._failed(u\"Text file '%s' is empty\" % task.text_file_path)\n return\n self._check_utf8_encoding(text_file_bstring)\n if not self.result.passed:\n self._failed(u\"Text file '%s' is not encoded in UTF-8\" % task.text_file_path)\n return\n self._check_not_empty(text_file_bstring)\n if not self.result.passed:\n self._failed(u\"Text file '%s' is empty\" % task.text_file_path)\n return\n self.log([u\"Checking Task text file '%s': passed\", task.text_file_path])\n self.log(u\"Checking each Task text file is well formed: passed\")","def _validate_jobs(\n self,\n check_nlst_warn: bool = False\n ):\n counter = 0\n for job in self.jobs:\n counter += 1\n print(job.job_id)\n if counter == 0:\n ignore_restarts = False\n else:\n ignore_restarts = True\n\n check_input_files(\n hrldas_namelist=job.hrldas_namelist,\n hydro_namelist=job.hydro_namelist,\n sim_dir=os.getcwd(),\n ignore_restarts=ignore_restarts,\n check_nlst_warn=check_nlst_warn\n )","def test_jobset(self):\n self.assertEqual(self.jobset.indir, 'indir')","def test_call_first_time(self, query_repo_url, get_credentials, valid_revision, get):\n self.assertEquals(\n self.query_api._get_all_jobs(\"try\", \"146071751b1e\"),\n json.loads(JOBS_SCHEDULE))\n\n assert get.call_count == 1\n\n # Test that this fills our caches\n self.assertEquals(\n query_jobs.JOBS_CACHE[(\"try\", \"146071751b1e\")],\n json.loads(JOBS_SCHEDULE))","def test_successful_on_get(self):\n\n url = '/%s/jobs/' % self.api\n\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)","def in_queue(self):\n if self.get_db('jobid') is None:\n log.debug('jobid not found for calculation.')\n return False\n else:\n # get the jobid\n jobid = self.get_db('jobid')\n # see if jobid is in queue\n _, jobids_in_queue, _ = getstatusoutput('qselect',\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n\n if str(jobid) in jobids_in_queue.split('\\n'):\n # get details on specific jobid in case it is complete\n status, output, err = getstatusoutput(['qstat', jobid],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n if status == 0:\n lines = output.split('\\n')\n fields = lines[2].split()\n job_status = fields[4]\n if job_status == 'C':\n return False\n else:\n return True\n else:\n return False","def test_006_add(self):\n HEADING()\n db = self.db\n\n count = 5\n\n db.connect()\n\n db.delete_jobs()\n\n for id in range(0,count):\n job = db.insert(\"job\" + str(id))\n\n assert len(db) == count","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def test_getJobState(self):\n self.assertRaises(JobNotExistsError, self.foo.getJobState)\n self.foo.setTorqueJobId('asd')\n self.assertEqual(self.foo.getJobState(), 'NEW')","def test_job_fail(self):\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \"extras/tests/dummy_jobs\")):\n\n module = \"test_fail\"\n name = \"TestFail\"\n job_class = get_job(f\"local/{module}/{name}\")\n job_result = JobResult.objects.create(\n name=job_class.class_path,\n obj_type=self.job_content_type,\n user=None,\n job_id=uuid.uuid4(),\n )\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\n job_result.refresh_from_db()\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_ERRORED)","def isFinished(self):\r\n try:\r\n output = Popen(\"qstat | grep \"+self.jobId, shell=True, stdout=PIPE, stderr=PIPE).communicate()[0]\r\n if self.jobId in output:\r\n if output.split()[4] == \"Eqw\":\r\n #If the job fails, print a warning, and wait a minute so the user can check why the job fails,\r\n #before resubmitting the job.\r\n logging.warning(\"job \" + output.split()[2] + \" failed to run, resubmitting in one minute\")\r\n time.sleep(60)\r\n output = Popen(\"qdel \"+self.jobId, shell=True, stdout=PIPE, stderr=PIPE).communicate()[0]\r\n self.submit()\r\n return False\r\n else:\r\n logging.info(\"job with ID: \" + self.jobId + \" is finished.\")\r\n return True\r\n \r\n except ValueError:\r\n logging.info(\"Error: waiting for not submitted job...\")","def test_add_seed_job_type(self):\n\n url = '/%s/job-types/' % self.api\n manifest = copy.deepcopy(job_test_utils.COMPLETE_MANIFEST)\n manifest['job']['name'] = 'my-new-job'\n\n json_data = {\n 'icon_code': 'BEEF',\n 'is_published': True,\n 'docker_image': 'my-new-job-1.0.0-seed:1.0.0',\n 'manifest': manifest,\n 'configuration': self.configuration\n }\n\n good_setting = {\n 'DB_HOST': 'scale'\n }\n\n response = self.client.generic('POST', url, json.dumps(json_data), 'application/json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED, response.content)\n self.assertTrue('/%s/job-types/my-new-job/1.0.0/' % self.api in response['location'])\n\n job_type = JobType.objects.filter(name='my-new-job').first()\n\n results = json.loads(response.content)\n self.assertEqual(results['id'], job_type.id)\n self.assertEqual(results['version'], job_type.version)\n self.assertEqual(results['title'], job_type.get_title())\n self.assertEqual(results['revision_num'], job_type.revision_num)\n self.assertEqual(results['revision_num'], 1)\n self.assertIsNone(results['max_scheduled'])\n self.assertEqual(results['configuration']['settings'], good_setting)","def test_enqueue(self):\n dbpool = buildConnectionPool(self, jobSchema + schemaText)\n yield self._enqueue(dbpool, 1, 2)\n\n # Make sure we have one JOB and one DUMMY_WORK_ITEM\n @transactionally(dbpool.connection)\n def checkJob(txn):\n return JobItem.all(txn)\n\n jobs = yield checkJob\n self.assertTrue(len(jobs) == 1)\n self.assertTrue(jobs[0].workType == \"DUMMY_WORK_ITEM\")\n self.assertTrue(jobs[0].assigned is None)\n self.assertEqual(jobs[0].isAssigned, 0)\n\n @transactionally(dbpool.connection)\n def checkWork(txn):\n return DummyWorkItem.all(txn)\n\n work = yield checkWork\n self.assertTrue(len(work) == 1)\n self.assertTrue(work[0].jobID == jobs[0].jobID)","def test_cachedjob_waits_for_new_job_pre_existing_job(uncached_job):\n\n # Setup\n cached_job = uncached_job\n initial_job_key = cached_job.current_job_id\n\n # Execute\n cached_job.wait_for_new_job(sleep=2)\n final_job_key = cached_job.current_job_id\n\n # Verify\n assert initial_job_key == None\n assert initial_job_key != final_job_key\n assert final_job_key == JOB_ID","def testJobEmpty(databases):\n\n def checkEmpty(config):\n assert config.getParameter('') == None\n assert len(config.getInputs()) == 0\n assert len(config.getInputsGrouped()) == 0\n assert len(config.getTasks()) == 0\n assert len(config.getTaskSequence()) == 0\n\n config = DataGenerator(databases).createConfiguration()\n runWithReload(databases, config, checkEmpty)","def test_create_ingest_job(self):\n ingest_mgmr = IngestManager()\n ingest_mgmr.validate_config_file(self.example_config_data)\n ingest_mgmr.validate_properties()\n ingest_mgmr.owner = self.user.pk\n job = ingest_mgmr.create_ingest_job()\n assert (job.id is not None)","def test_wrong_job(self):\n job = MockJob()\n container_name = 'container_identifier'\n self._mock_open.return_value.__enter__.return_value.readline. \\\n return_value = container_name\n\n # return an error trying to read file with container name\n self._mock_open.return_value.__enter__.side_effect = [\n PermissionError,\n FileNotFoundError,\n ]\n\n self.assertNotEqual(job.pid, 0)\n # non-zero pid, no container name is found -> refer to ForkSpawner\n validation_result = self._container_spawner.validate(job)\n self._mock_open.assert_called()\n self._mock_fork.assert_called()\n self.assertEqual(validation_result,\n spawner.PROCESS_RUNNING)\n # same check, a different exception\n self.assertEqual(self._container_spawner.validate(job),\n spawner.PROCESS_RUNNING)","def test_successful_empty(self):\n\n url = '/%s/jobs/%i/' % (self.api, self.job.id)\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(result['max_tries'], 3)\n self.assertEqual(result['job_type']['name'], self.job.job_type.name)\n self.assertEqual(result['job_type_rev']['job_type']['name'], self.job.job_type.name)\n\n if self.recipe:\n self.assertEqual(result['recipe']['recipe_type']['name'], self.recipe.recipe_type.name)\n else:\n self.assertEqual(len(result['recipe']), 0)","def test_job_delete_non_existing(self):\n self.assertRaises(exceptions.NotFoundException,\n self.client.jobs.delete, 'does-not-exist')","def isJobRunning ( self ):\n #cmd = \"qstat \" + str(self.jobid)\n \n #magicString='Unknown Job Id' ### magicString _might_ need to be changed if Torque version changes\n #(output, error) = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()\n\n if self.ofile_exists(): #output.find(magicString) >=0 or redhawkStatsRe.search(output):\n self.status = \"finished\"\n return False\n \n\n return True","def checkjob(sid, jid):\n with slycat.web.server.remote.get_session(sid) as session:\n return session.checkjob(jid)","def test_running_job(self):\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def test_batch(self):\n batch = batch_test_utils.create_batch()\n self.job1.batch_id = batch.id\n self.job1.save()\n\n url = '/%s/jobs/?batch_id=%d' % (self.api, batch.id)\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 1)\n self.assertEqual(result['results'][0]['id'], self.job1.id)","def test_job_id(self):\n\n url = '/%s/jobs/?job_id=%s' % (self.api, self.job1.id)\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 1)\n self.assertEqual(result['results'][0]['id'], self.job1.id)","def testJobKilling(self):\n change = ChangeState(self.config, \"changestate_t\")\n\n locationAction = self.daoFactory(classname=\"Locations.New\")\n locationAction.execute(\"site1\", pnn=\"T2_CH_CERN\")\n\n testWorkflow = Workflow(spec=self.specUrl, owner=\"Steve\",\n name=\"wf001\", task=self.taskName)\n testWorkflow.create()\n testFileset = Fileset(name=\"TestFileset\")\n testFileset.create()\n\n for i in range(4):\n newFile = File(lfn=\"File%s\" % i, locations=set([\"T2_CH_CERN\"]))\n newFile.create()\n testFileset.addFile(newFile)\n\n testFileset.commit()\n testSubscription = Subscription(fileset=testFileset,\n workflow=testWorkflow,\n split_algo=\"FileBased\")\n testSubscription.create()\n\n splitter = SplitterFactory()\n jobFactory = splitter(package=\"WMCore.WMBS\",\n subscription=testSubscription)\n jobGroup = jobFactory(files_per_job=1)[0]\n\n assert len(jobGroup.jobs) == 4, \\\n \"Error: Splitting should have created four jobs.\"\n\n testJobA = jobGroup.jobs[0]\n testJobA[\"user\"] = \"sfoulkes\"\n testJobA[\"group\"] = \"DMWM\"\n testJobA[\"taskType\"] = \"Processing\"\n testJobB = jobGroup.jobs[1]\n testJobB[\"user\"] = \"sfoulkes\"\n testJobB[\"group\"] = \"DMWM\"\n testJobB[\"taskType\"] = \"Processing\"\n testJobC = jobGroup.jobs[2]\n testJobC[\"user\"] = \"sfoulkes\"\n testJobC[\"group\"] = \"DMWM\"\n testJobC[\"taskType\"] = \"Processing\"\n testJobD = jobGroup.jobs[3]\n testJobD[\"user\"] = \"sfoulkes\"\n testJobD[\"group\"] = \"DMWM\"\n testJobD[\"taskType\"] = \"Processing\"\n\n change.persist([testJobA], \"created\", \"new\")\n change.persist([testJobB], \"jobfailed\", \"executing\")\n change.persist([testJobC, testJobD], \"executing\", \"created\")\n\n change.persist([testJobA], \"killed\", \"created\")\n change.persist([testJobB], \"killed\", \"jobfailed\")\n change.persist([testJobC, testJobD], \"killed\", \"executing\")\n\n for job in [testJobA, testJobB, testJobC, testJobD]:\n job.load()\n self.assertEqual(job['retry_count'], 99999)\n self.assertEqual(job['state'], 'killed')\n\n return","def test_successful(self):\n\n url = '/%s/job-types/running/' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 1)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job.job_type.name)\n self.assertEqual(result['results'][0]['count'], 1)\n self.assertIsNotNone(result['results'][0]['longest_running'])","def check_job_status_by_id(job_id):\n print('=' * 40)\n print('check_status_by_job_id', job_id)\n print('=' * 40)\n\n it_worked = check_job_status(job)\n if it_worked:\n return ok_resp(job)\n\n user_msg = ('PreprocessJob still in process: %s') % (job_id)\n return err_resp(user_msg)","def test_cachedjob_job_key(uncached_job):\n\n # Setup\n c_job = uncached_job\n\n # Execute\n c_job.wait_for_new_job(sleep=2)\n\n # Verify\n assert c_job.current_job_id == JOB_ID","def test_matching_jobs_invalid(self):\n self.assertEquals(\n self.query_api.get_matching_jobs(\n \"try\", \"146071751b1e\",\n 'Invalid buildername'), [])","def testJobOneTask(databases):\n\n class CustomGenerator(DataGenerator):\n numTasks = 1\n numInputs = [ 0 ]\n numOutputs = [ 0 ]\n\n gen = CustomGenerator(databases)\n gen.createDefinitions()\n config = gen.createConfiguration()\n\n def checkOne(config):\n taskName = gen.tasks[0]\n #assert config.getProduct('') is None\n assert len(config.getInputs()) == 0\n assert len(config.getInputsGrouped()) == 0\n assert len(config.getTasks()) == 1\n task, = config.getTasks()\n assert task is not None\n assert task.getName() == taskName\n assert len(config.getTaskSequence()) == 1\n\n runWithReload(databases, config, checkOne)","def test_raises_on_missing_job(self):\n from furious.async import Async\n from furious.errors import NotInContextError\n from furious.processors import run_job\n\n work = Async(\"nothere\")\n work._options.pop('job')\n assert 'job' not in work._options\n\n self.assertRaises(NotInContextError, run_job)","def test_updateJobState(self):\n self.assertRaises(JobNotExistsError, self.foo.updateJobState, 'asd')\n self.foo.setTorqueJobId('asd')\n self.assertEqual(self.foo.getJobState(), 'NEW')","def test_job_delete_completed_job(self):\n test_app = self._create_app()\n class_path = \"spark.jobserver.VeryShortDoubleJob\"\n job = self.client.jobs.create(test_app, class_path,\n ctx=self._get_functional_context())\n time.sleep(3)\n self._wait_till_job_is_done(job)\n self.assertRaises(exceptions.NotFoundException,\n self.client.jobs.delete, job.jobId)","def _add_job_data(\n self,\n job: Job,\n ) -> Tuple[str, bool]:\n jid = job.job_id()\n try:\n job_result = job.result()\n self._add_result_data(job_result, jid)\n LOG.debug(\"Job data added [Job ID: %s]\", jid)\n # sets the endtime to be the time the last successful job was added\n self.end_datetime = datetime.now()\n return jid, True\n except Exception as ex: # pylint: disable=broad-except\n # Handle cancelled jobs\n status = job.status()\n if status == JobStatus.CANCELLED:\n LOG.warning(\"Job was cancelled before completion [Job ID: %s]\", jid)\n return jid, False\n if status == JobStatus.ERROR:\n LOG.error(\n \"Job data not added for errored job [Job ID: %s]\\nError message: %s\",\n jid,\n job.error_message(),\n )\n return jid, False\n LOG.warning(\"Adding data from job failed [Job ID: %s]\", job.job_id())\n raise ex","async def test_haos(coresys: CoreSys):\n\n class TestClass:\n \"\"\"Test class.\"\"\"\n\n def __init__(self, coresys: CoreSys):\n \"\"\"Initialize the test class.\"\"\"\n self.coresys = coresys\n\n @Job(conditions=[JobCondition.HAOS])\n async def execute(self):\n \"\"\"Execute the class method.\"\"\"\n return True\n\n test = TestClass(coresys)\n coresys.hassos._available = True\n assert await test.execute()\n\n coresys.hassos._available = False\n assert not await test.execute()","def work(self, job):\n pass","def check_slurm_job_submission(expected_name):\n cmd = ['scontrol', 'show', 'job']\n job_id = 0\n found_job = False\n while True:\n while True:\n try:\n out = Popen(cmd, stdout=PIPE, stderr=PIPE).communicate()[0]\n break\n except:\n sleep(1)\n out = out.split('\\n')\n if 'error' in out[0]:\n sleep(1)\n msg = 'Error checking job status for {0}'.format(expected_name)\n logging.warning(msg)\n continue\n for line in out:\n for word in line.split():\n if 'JobId' in word:\n index = word.find('=') + 1\n job_id = int(word[index:])\n # continue\n if 'Name' in word:\n index = word.find('=') + 1\n if word[index:] == expected_name:\n found_job = True\n\n if found_job and job_id != 0:\n return found_job, job_id\n sleep(1)\n return found_job, job_id","def _check_phase(self, jobid):\n\n self._existing_tables()\n\n time.sleep(1)\n\n if jobid not in self.job_dict.keys():\n log.error(\"Job not present in job dictionary.\")\n return\n\n else:\n phase = self.job_dict[str(jobid)]\n return phase","def test_successful_job(self, _is_coalesced):\n successful_job = json.loads(BASE_JSON % (SUCCESS, 1433166610, 1, 1433166609))[0]\n self.assertEquals(self.query_api.get_job_status(successful_job), SUCCESS)","def check(args, session: Session = NEW_SESSION) -> None:\n if args.allow_multiple and not args.limit > 1:\n raise SystemExit(\"To use option --allow-multiple, you must set the limit to a value greater than 1.\")\n if args.hostname and args.local:\n raise SystemExit(\"You can't use --hostname and --local at the same time\")\n\n query = select(Job).where(Job.state == JobState.RUNNING).order_by(Job.latest_heartbeat.desc())\n if args.job_type:\n query = query.where(Job.job_type == args.job_type)\n if args.hostname:\n query = query.where(Job.hostname == args.hostname)\n if args.local:\n query = query.where(Job.hostname == get_hostname())\n if args.limit > 0:\n query = query.limit(args.limit)\n\n alive_jobs: list[Job] = [job for job in session.scalars(query) if job.is_alive()]\n\n count_alive_jobs = len(alive_jobs)\n if count_alive_jobs == 0:\n raise SystemExit(\"No alive jobs found.\")\n if count_alive_jobs > 1 and not args.allow_multiple:\n raise SystemExit(f\"Found {count_alive_jobs} alive jobs. Expected only one.\")\n if count_alive_jobs == 1:\n print(\"Found one alive job.\")\n else:\n print(f\"Found {count_alive_jobs} alive jobs.\")","def job_status(bot, update, args, job_queue, chat_data):\n if len(args) == 0:\n update.message.reply_text('No parameter provided')\n return\n\n job_name = args[0]\n if job_name not in settings.JOBS:\n update.message.reply_text(\n 'Sorry {0} is not a valid job'.format(job_name))\n return\n\n job = find_job(job_name, job_queue)\n\n if not job:\n update.message.reply_text('{0} job is not running'.format(job_name))\n return\n\n update.message.reply_text('{0} job is running'.format(job_name))","def test_successful(self):\n\n url = '/%s/job-types/pending/' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 1)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job.job_type.name)\n self.assertEqual(result['results'][0]['count'], 1)\n self.assertIsNotNone(result['results'][0]['longest_pending'])","def on_job_update(_job):\n nonlocal job\n job = _job\n # Asserts that job is either pending or canceled.\n assert job.state in ['PENDING', 'CANCELED'], (\n 'job that canceled immediately after submission has wrong '\n 'state `%s`' % job.state\n )","def test_cluster_jobs_script(self):\r\n\r\n qiime_config = load_qiime_config()\r\n submit_script = qiime_config['cluster_jobs_fp']\r\n\r\n if (submit_script):\r\n full_path = which(submit_script)\r\n if full_path:\r\n submit_script = full_path\r\n self.assertTrue(exists(submit_script),\r\n \"cluster_jobs_fp is not set to a valid path in qiime config: %s\" % submit_script)\r\n # check if executable\r\n self.assertTrue(access(submit_script, X_OK),\r\n \"cluster_jobs_fp is not executable: %s\" % submit_script)\r\n else:\r\n # Can't run in parallel, but not a critical error\r\n pass","def test_dies_if_no_job(self):\n from furious.async import Async\n from furious.context._execution import _ExecutionContext\n from furious.processors import run_job\n\n work = Async(\"dir\", kwargs={'something': None})\n work._options.pop('job')\n assert 'job' not in work._options\n\n with _ExecutionContext(work):\n self.assertRaises(Exception, run_job)","def test_successful(self, mock_create, mock_msg_mgr):\n\n json_data = {\n \"input\" : {\n 'version': '6',\n 'files': {'input_a': [self.source_file.id]},\n 'json': {}\n },\n \"job_type_id\" : self.job_type1.pk\n }\n\n url = '/%s/jobs/' % self.api\n response = self.client.generic('POST', url, json.dumps(json_data), 'application/json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED, response.content)\n\n result = json.loads(response.content)\n\n #Response should be new v6 job detail response\n self.assertEqual(result['execution'], None)\n self.assertEqual(result['max_tries'], 3)\n self.assertTrue('/%s/jobs/' % self.api in response['location'])\n mock_create.assert_called_once()","def add_job_state(self, job):","def check_job_status(job):\n assert isinstance(job, PreprocessJob),\\\n 'job must be a PreprocessJob'\n\n if job.is_finished():\n return True\n\n return True\n \"\"\"\n ye_task = AsyncResult(job.task_id,\n app=preprocess_csv_file)\n\n if ye_task.state == 'SUCCESS':\n\n if ye_task.result['success']:\n\n preprocess_data = ContentFile(json.dumps(ye_task.result['data']))\n\n new_name = 'preprocess_%s.json' % get_alphanumeric_lowercase(8)\n job.metadata_file.save(new_name,\n preprocess_data)\n job.set_state_success()\n\n job.user_message = 'Task completed! Preprocess is available'\n job.save()\n\n else:\n # Didn't work so well\n job.set_state_failure(ye_task.result['message'])\n job.save()\n\n ye_task.forget()\n return True\n\n elif ye_task.state == STATE_FAILURE:\n job.set_state_failure('ye_task failed....')\n job.save()\n ye_task.forget()\n return True\n\n return False\n \"\"\"","def _check_jobs_submitted(status, module):\n\n submitted = False\n if module in status.data:\n jobs = status.data[module]\n for job in jobs.keys():\n if job != 'pipeline_index':\n submitted = True\n break\n return submitted","def test_check_job_dependencies_ready(mock_sqs_queue, database):\n # Mock so it always returns the mock queue for the test\n mock_sqs_queue.return_value = SQSMockQueue\n sess = database.session\n sub = SubmissionFactory(submission_id=1)\n job = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['finished'],\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'],\n number_of_errors=0)\n job_2 = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['waiting'],\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'])\n sess.add_all([sub, job, job_2])\n sess.commit()\n\n # Job 1 finished, it is a prerequisite for job 2 (waiting) but it has errors\n job_dep = JobDependency(job_id=job_2.job_id, prerequisite_id=job.job_id)\n sess.add(job_dep)\n sess.commit()\n\n check_job_dependencies(job.job_id)\n\n assert job_2.job_status_id == JOB_STATUS_DICT['ready']","async def test_misusage(my_job):\n\n # Disable some pylint checks, they are redundant here in the test.\n # pylint: disable=missing-docstring,unused-variable,function-redefined\n\n # Check that namesake job managers are not allowed.\n class MyNamesakeJobManager(jobmanager.JobManager):\n pass\n with pytest.raises(AssertionError,\n message='Successfully made two job managers with the '\n 'same names!'):\n class MyNamesakeJobManager(jobmanager.JobManager):\n pass\n\n # Check that it is impossible to register two job functions with the\n # same name inside a single job manager, but it is possible in the\n # different job managers.\n class MyJobManager1(jobmanager.JobManager):\n pass\n\n class MyJobManager2(jobmanager.JobManager):\n pass\n\n @MyJobManager1.job()\n def my_namesake_job():\n pass\n\n @MyJobManager2.job()\n def my_namesake_job():\n pass\n\n with pytest.raises(AssertionError,\n message='Successfully registered two job function with '\n 'the same name!'):\n @MyJobManager1.job()\n def my_namesake_job():\n pass\n\n # Check that it is impossible to submit a job with an identifier\n # which is already used by some other job.\n\n # Submit 2 jobs with the same identifier and check that second\n # submission raises an exception.\n job_id = uuid.uuid4()\n\n def on_job_submit(job):\n job.id = job_id\n return job\n my_job.set_on_submit(on_job_submit)\n\n # Submit a job #1.\n await my_job.job(mustfail=False)\n\n with pytest.raises(django.db.utils.IntegrityError,\n message='Successfully submitted a job with the '\n 'identifier that has already been used for '\n 'another job!'):\n # Submit a job #2.\n await my_job.job(mustfail=False)\n\n # Process ASGI messages and wait for the job to finish.\n await my_job.process_jobs()","def remove_job_if_exists(self, name: str, context: CallbackContext) -> bool:\n current_jobs = context.job_queue.get_jobs_by_name(name)\n if not current_jobs:\n return False\n for job in current_jobs:\n job.schedule_removal()\n return True","def remove_job_if_exists(self, name: str, context: CallbackContext) -> bool:\n current_jobs = context.job_queue.get_jobs_by_name(name)\n if not current_jobs:\n return False\n for job in current_jobs:\n job.schedule_removal()\n return True","def test_py_job_create(self):\n test_app = self._create_py_app()\n class_path = \"example_jobs.word_count.WordCountSparkSessionJob\"\n conf = \"input.strings = ['a', 'b', 'a', 'b']\"\n job = self._create_job(test_app, class_path, conf,\n ctx=self._get_functional_py_context())\n time.sleep(3)\n self.assertTrue(len(job.jobId) > 0)\n self.assertTrue(job.status == \"STARTED\")\n self._wait_till_job_is_done(job)","def _addjob(self, job: Job):\n if self.domain is not None and self.model is not None:\n job = copy.deepcopy(job)\n job._add_hydro_namelist(self.base_hydro_namelist)\n job._add_hrldas_namelist(self.base_hrldas_namelist)\n\n self.jobs.append(job)\n else:\n raise AttributeError('Can not add a job to a simulation without a model and a domain')","def remove_job_if_exists(name: str, context: ContextTypes.DEFAULT_TYPE) -> bool:\n current_jobs = context.job_queue.get_jobs_by_name(name)\n if not current_jobs:\n return False\n for job in current_jobs:\n job.schedule_removal()\n return True","def verify_job(cls, auth_key, job_id):\n key = ObjectId(job_id)\n user_id = ObjectId(auth_key)\n db = cls.mongo_cli.get_database(collection=Job.collection_name)\n if db.count({\"_id\": key, \"user_id\": user_id}) > 0:\n return True\n return False","def test_reenqueue_job1(client):\n with client.application.app_context():\n pytest.skip(\"Not implemented\")","def remove_job_if_exists(name, context):\n current_jobs = context.job_queue.get_jobs_by_name(name)\n if not current_jobs:\n return False\n for job in current_jobs:\n job.schedule_removal()\n return True"],"string":"[\n \"def test_job_addition():\\n with tempfile.TemporaryDirectory() as STATUS_DIR:\\n Status.add_job(STATUS_DIR, 'generation', 'test1')\\n status1 = Status(STATUS_DIR).data['generation']['test1']['job_status']\\n\\n Status.add_job(STATUS_DIR, 'generation', 'test1',\\n job_attrs={'job_status': 'finished',\\n 'additional': 'test'})\\n status2 = Status(STATUS_DIR).data['generation']['test1']['job_status']\\n\\n assert status2 == status1\",\n \"def test_matching_jobs_existing(self):\\n self.assertEquals(\\n self.query_api.get_matching_jobs(\\n \\\"try\\\", \\\"146071751b1e\\\",\\n 'Linux x86-64 try build'), json.loads(JOBS_SCHEDULE))\",\n \"def job_exists(self, job):\\n\\n with open(os.devnull, 'w') as devnull:\\n result = subprocess.call(self.cli + [PlatformJenkinsJavaCLI.GET_JOB, job.name], stdout=devnull)\\n\\n return result == 0\",\n \"def check_job_exists( job_list, analysis_group_id, reprocess_config_id):\\n for job in job_list:\\n struct = JSONMessage.unserialize(job.input_message)\\n\\n if( int( struct.analysis_group_id ) == int( analysis_group_id ) and \\\\\\n int( struct.reprocess_config_id ) == int( reprocess_config_id ) ):\\n return 1\\n return 0\",\n \"def test_fake_job( self ):\\n hda = MockHda( job=UNDEFINED_JOB )\\n self.history.active_datasets.append( hda )\\n job_dict, warnings = extract.summarize( trans=self.trans )\\n assert not warnings\\n assert len( job_dict ) == 1\\n fake_job = job_dict.keys()[ 0 ]\\n assert fake_job.id.startswith( \\\"fake_\\\" )\\n datasets = job_dict.values()[ 0 ]\\n assert datasets == [ ( None, hda ) ]\",\n \"def _check_for_finished_job(self):\\n raise NotImplementedError\",\n \"def test_jobs_successful(self):\\n\\n workspace = storage_test_utils.create_workspace()\\n file1 = storage_test_utils.create_file()\\n data_dict = {\\n 'version': '1.0',\\n 'input_data': [{\\n 'name': 'INPUT_IMAGE',\\n 'file_id': file1.id\\n }],\\n 'output_data': [{\\n 'name': 'output_file_pngs',\\n 'workspace_id': workspace.id\\n }]}\\n\\n secret_configuration = {\\n 'version': '6',\\n 'priority': 50,\\n 'output_workspaces': {'default': storage_test_utils.create_workspace().name},\\n 'settings': {\\n 'DB_HOST': 'som.host.name',\\n 'DB_PASS': 'secret_password'\\n }\\n }\\n\\n seed_job_type = job_test_utils.create_seed_job_type(configuration=secret_configuration)\\n seed_job = job_test_utils.create_job(job_type=seed_job_type, status='RUNNING', input=data_dict)\\n\\n url = '/%s/jobs/%d/' % (self.api, seed_job.id)\\n response = self.client.generic('GET', url)\\n result = json.loads(response.content)\\n\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n self.assertEqual(result['configuration']['priority'],50)\\n self.assertNotIn('DB_PASS', result['configuration']['settings'])\",\n \"def test_submit_jobs(self):\\r\\n\\r\\n submit_jobs([self.command], prefix=\\\"test_job\\\")\\r\\n # Try and wait ten times, could be made nicer with alarm()\\r\\n for i in range(10):\\r\\n if exists(self.tmp_result_file):\\r\\n observed_text = \\\"\\\".join(list(open(self.tmp_result_file)))\\r\\n self.assertEqual(observed_text, \\\"hello\\\\n\\\")\\r\\n return\\r\\n else:\\r\\n sleep(10)\\r\\n # if we get here we failed\\r\\n self.fail(\\\"The test job apparently never finished.\\\\n\\\"\\r\\n + \\\"check the jobs error log and check the queue status\\\\n.\\\")\",\n \"def check_jobs(self):\\n # New/aborted jobs\\n try:\\n jobs = self.sm.get_job('%', phase = 'QUEUED')\\n for job in jobs:\\n self._launch_job(Job(job['job']))\\n res = self.sm.get_aborted_jobs()\\n aborts = [x['identifier'] for x in res]\\n # Completed jobs\\n for t in self.threads:\\n if t.isDone() or t.name in aborts:\\n self.threads.remove(t)\\n # Set job status to COMPLETED\\n job = Job(self.sm.get_job(t.name)[0]['job'])\\n if t._Future__excpt == None:\\n job.set_phase('COMPLETED')\\n if t._Future__result != None:\\n job.set_results(t._Future__result) \\n status = True\\n else:\\n job.set_phase('ERROR')\\n job.set_error_summary(str(t._Future__excpt[1]).replace(\\\"'\\\", \\\"\\\"))\\n status = False\\n job.set_end_time(datetime.utcnow().isoformat())\\n self.sm.update_job(job = job, completed = status)\\n except Exception, e:\\n print \\\"Error:\\\", e\",\n \"def test_create_job(self):\\n engine = Engine(self.config_file, self.api_token)\\n\\n engine.create_job()\\n\\n assert engine.ingest_job_id == 23\",\n \"def test_get_refresh_job_status(self):\\n pass\",\n \"def test_valid_n_jobs(n_jobs: Any) -> None:\\n check_n_jobs(n_jobs)\",\n \"def test_job_replacement():\\n with tempfile.TemporaryDirectory() as STATUS_DIR:\\n Status.add_job(STATUS_DIR, 'generation', 'test1',\\n job_attrs={'job_status': 'submitted'})\\n\\n Status.add_job(STATUS_DIR, 'generation', 'test1',\\n job_attrs={'addition': 'test',\\n 'job_status': 'finished'},\\n replace=True)\\n\\n status = Status(STATUS_DIR).data['generation']['test1']['job_status']\\n addition = Status(STATUS_DIR).data['generation']['test1']['addition']\\n assert status == 'finished'\\n assert addition == 'test'\",\n \"def test_successful_job(self):\\n\\n successful_job = json.loads(TREEHERDER_JOB % (\\\"success\\\", \\\"completed\\\"))\\n self.assertEquals(self.query_api.get_job_status(successful_job), SUCCESS)\",\n \"def test_successful(self):\\n\\n url = '/%s/jobs/' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n for entry in result['results']:\\n expected = None\\n if entry['id'] == self.job1.id:\\n expected = self.job1\\n elif entry['id'] == self.job2.id:\\n expected = self.job2\\n elif entry['id'] == self.job3.id:\\n expected = self.job3\\n else:\\n self.fail('Found unexpected result: %s' % entry['id'])\\n self.assertEqual(entry['job_type']['name'], expected.job_type.name)\\n self.assertEqual(entry['job_type_rev']['job_type']['id'], expected.job_type.id)\\n self.assertEqual(entry['is_superseded'], expected.is_superseded)\",\n \"def test_check_opt_crashed(self):\\n self.assertEqual(check_opt(self.jobset2.job), 'ocrashed')\",\n \"def test_check_job_dependencies_not_finished(database):\\n sess = database.session\\n sub = SubmissionFactory(submission_id=1)\\n job = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['waiting'],\\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'])\\n sess.add_all([sub, job])\\n sess.commit()\\n\\n with pytest.raises(ValueError):\\n check_job_dependencies(job.job_id)\",\n \"def check_sp_job(self,\\n label: str,\\n job: 'JobAdapter',\\n ):\\n if 'mrci' in self.sp_level.method and job.level is not None and 'mrci' not in job.level.method:\\n # This is a CCSD job ran before MRCI. Spawn MRCI\\n self.run_sp_job(label)\\n elif job.job_status[1]['status'] == 'done':\\n self.post_sp_actions(label,\\n sp_path=os.path.join(job.local_path_to_output_file),\\n level=job.level,\\n )\\n # Update restart dictionary and save the yaml restart file:\\n self.save_restart_dict()\\n if self.species_dict[label].number_of_atoms == 1:\\n # save the geometry from the sp job for monoatomic species for which no opt/freq jobs will be spawned\\n self.output[label]['paths']['geo'] = job.local_path_to_output_file\\n else:\\n self.troubleshoot_ess(label=label,\\n job=job,\\n level_of_theory=job.level,\\n )\",\n \"def test_start_already_running(self, mock_add_job, mock_get_job):\\n mock_get_job.return_value = MagicMock()\\n\\n result = self.aggregator.start(self.node_id)\\n\\n self.assertFalse(result)\\n self.assertFalse(mock_add_job.called)\",\n \"def test_job_pass(self):\\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \\\"extras/tests/dummy_jobs\\\")):\\n\\n module = \\\"test_pass\\\"\\n name = \\\"TestPass\\\"\\n job_class = get_job(f\\\"local/{module}/{name}\\\")\\n\\n job_result = JobResult.objects.create(\\n name=job_class.class_path,\\n obj_type=self.job_content_type,\\n user=None,\\n job_id=uuid.uuid4(),\\n )\\n\\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\\n job_result.refresh_from_db()\\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)\",\n \"def test_local_job(self):\\r\\n exp = \\\"abc; echo $? > xyz\\\"\\r\\n obs = local_job('abc', 'xyz', 'notused', 'notused')\\r\\n self.assertEqual(obs, exp)\",\n \"def testA_StraightThrough(self):\\n # Do pre-submit job check\\n nRunning = getCondorRunningJobs()\\n self.assertEqual(nRunning, 0, \\\"User currently has %i running jobs. Test will not continue\\\" % (nRunning))\\n\\n myThread = threading.currentThread()\\n workload = self.createTestWorkload()\\n config = self.getConfig()\\n\\n\\n name = 'WMAgent_Test1'\\n site = self.sites[0]\\n nSubs = 5\\n nFiles = 10\\n workloadPath = os.path.join(self.testDir, 'workloadTest',\\n 'TestWorkload', 'WMSandbox',\\n 'WMWorkload.pkl')\\n\\n # Create a collection of files\\n self.createFileCollection(name = name, nSubs = nSubs,\\n nFiles = nFiles,\\n workflowURL = workloadPath,\\n site = site)\\n\\n\\n\\n ############################################################\\n # Test the JobCreator\\n\\n\\n config.Agent.componentName = 'JobCreator'\\n testJobCreator = JobCreatorPoller(config = config)\\n\\n testJobCreator.algorithm()\\n time.sleep(5)\\n\\n\\n # Did all jobs get created?\\n getJobsAction = self.daoFactory(classname = \\\"Jobs.GetAllJobs\\\")\\n result = getJobsAction.execute(state = 'Created', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs*nFiles)\\n\\n\\n # Count database objects\\n result = myThread.dbi.processData('SELECT * FROM wmbs_sub_files_acquired')[0].fetchall()\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n # Find the test directory\\n testDirectory = os.path.join(self.testDir, 'TestWorkload', 'ReReco')\\n self.assertTrue('JobCollection_1_0' in os.listdir(testDirectory))\\n self.assertTrue(len(os.listdir(testDirectory)) <= 20)\\n\\n groupDirectory = os.path.join(testDirectory, 'JobCollection_1_0')\\n\\n # First job should be in here\\n self.assertTrue('job_1' in os.listdir(groupDirectory))\\n jobFile = os.path.join(groupDirectory, 'job_1', 'job.pkl')\\n self.assertTrue(os.path.isfile(jobFile))\\n with open(jobFile, 'rb') as f:\\n job = pickle.load(f)\\n\\n\\n self.assertEqual(job['workflow'], name)\\n self.assertEqual(len(job['input_files']), 1)\\n self.assertEqual(os.path.basename(job['sandbox']), 'TestWorkload-Sandbox.tar.bz2')\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n ###############################################################\\n # Now test the JobSubmitter\\n\\n config.Agent.componentName = 'JobSubmitter'\\n testJobSubmitter = JobSubmitterPoller(config = config)\\n\\n\\n testJobSubmitter.algorithm()\\n\\n\\n # Check that jobs are in the right state\\n result = getJobsAction.execute(state = 'Created', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Executing', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n\\n # Check assigned locations\\n getLocationAction = self.daoFactory(classname = \\\"Jobs.GetLocation\\\")\\n for id in result:\\n loc = getLocationAction.execute(jobid = id)\\n self.assertEqual(loc, [[site]])\\n\\n\\n # Check to make sure we have running jobs\\n nRunning = getCondorRunningJobs()\\n self.assertEqual(nRunning, nFiles * nSubs)\\n\\n\\n #################################################################\\n # Now the JobTracker\\n\\n\\n config.Agent.componentName = 'JobTracker'\\n testJobTracker = JobTrackerPoller(config = config)\\n testJobTracker.setup()\\n\\n testJobTracker.algorithm()\\n\\n # Running the algo without removing the jobs should do nothing\\n result = getJobsAction.execute(state = 'Executing', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n condorRM()\\n time.sleep(1)\\n\\n # All jobs gone?\\n nRunning = getCondorRunningJobs()\\n self.assertEqual(nRunning, 0)\\n\\n\\n testJobTracker.algorithm()\\n time.sleep(5)\\n\\n # Running the algo without removing the jobs should do nothing\\n result = getJobsAction.execute(state = 'Executing', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Complete', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n\\n\\n #################################################################\\n # Now the JobAccountant\\n\\n # First you need to load all jobs\\n\\n\\n self.getFWJRAction = self.daoFactory(classname = \\\"Jobs.GetFWJRByState\\\")\\n completeJobs = self.getFWJRAction.execute(state = \\\"complete\\\")\\n\\n\\n # Create reports for all jobs\\n self.createReports(jobs = completeJobs, retryCount = 0)\\n\\n\\n\\n\\n\\n\\n config.Agent.componentName = 'JobAccountant'\\n testJobAccountant = JobAccountantPoller(config = config)\\n testJobAccountant.setup()\\n\\n\\n # It should do something with the jobs\\n testJobAccountant.algorithm()\\n\\n\\n # All the jobs should be done now\\n result = getJobsAction.execute(state = 'Complete', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Success', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n\\n #######################################################################\\n # Now the JobArchiver\\n\\n\\n config.Agent.componentName = 'JobArchiver'\\n testJobArchiver = JobArchiverPoller(config = config)\\n\\n\\n testJobArchiver.algorithm()\\n\\n # All the jobs should be cleaned up\\n result = getJobsAction.execute(state = 'Success', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Cleanout', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n logDir = os.path.join(self.testDir, 'logs')\\n\\n for job in completeJobs:\\n self.assertFalse(os.path.exists(job['fwjr_path']))\\n jobFolder = 'JobCluster_%i' \\\\\\n % (int(job['id']/config.JobArchiver.numberOfJobsToCluster))\\n jobPath = os.path.join(logDir, jobFolder, 'Job_%i.tar' %(job['id']))\\n self.assertTrue(os.path.isfile(jobPath))\\n self.assertTrue(os.path.getsize(jobPath) > 0)\\n\\n\\n\\n\\n ###########################################################################\\n # Now the TaskAchiver\\n\\n\\n config.Agent.componentName = 'TaskArchiver'\\n testTaskArchiver = TaskArchiverPoller(config = config)\\n\\n\\n testTaskArchiver.algorithm()\\n\\n\\n result = getJobsAction.execute(state = 'Cleanout', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n\\n\\n for jdict in completeJobs:\\n job = Job(id = jdict['id'])\\n self.assertFalse(job.exists())\\n\\n\\n\\n\\n\\n if os.path.isdir('testDir'):\\n shutil.rmtree('testDir')\\n shutil.copytree('%s' %self.testDir, os.path.join(os.getcwd(), 'testDir'))\\n\\n\\n\\n\\n return\",\n \"def job_exists(self, job_id):\\n\\n return True if self.get_status(job_id) else False\",\n \"def test_get_job(self):\\n response = self.client.open(\\n '/v1/job/{id}'.format(id='id_example'),\\n method='GET',\\n content_type='application/json')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"def test_running_job(self):\\n\\n running_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"running\\\"))\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def jobHealthy(self, count):\\n job = self.tester.submission_result.job\\n for idx in range(count - 1):\\n if (job.health == 'healthy'):\\n return True\\n print(\\\"health check fail : %d\\\" % idx )\\n time.sleep(1)\\n job.refresh()\\n self.assertEqual('healthy', job.health)\\n return False\",\n \"def test_post_pay_job_does_not_exist(self):\\n freelancer_id = self.create_object('api/freelancers',\\n self.valid_freelancers[0])\\n\\n payment = {'freelancer_id': freelancer_id,\\n 'job_id': -100, # Non existing job id\\n 'amount': 300}\\n\\n response = requests.post(self.BASE_API + self.ENDPOINT,\\n data=json.dumps(payment))\\n\\n self.assertEqual(400, response.status_code,\\n msg=(f'Able to receive a non existing '\\n f'job in a transaction'\\n f'Response code: {response.status_code}'))\",\n \"def test_pending_job(self):\\n\\n pending_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"pending\\\"))\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def test_status(self):\\n\\n url = '/%s/jobs/?status=RUNNING' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 1)\\n self.assertEqual(result['results'][0]['job_type']['id'], self.job1.job_type.id)\",\n \"def test_check_opt_complete(self):\\n self.assertEqual(check_opt(self.jobset3.job), 'completed')\",\n \"def test_make_jobs(self):\\r\\n # no commands should make no jobs files\\r\\n self.assertEqual(make_jobs([], \\\"test\\\", self.queue), [])\\r\\n\\r\\n # one job file should be created\\r\\n filenames = make_jobs([self.command], \\\"test_qsub\\\", self.queue)\\r\\n self.assertTrue(len(filenames) == 1)\\r\\n observed_text = list(open(filenames[0]))\\r\\n\\r\\n self.assertEqual(\\\"\\\".join(observed_text),\\r\\n QSUB_TEXT % (\\\"72:00:00\\\", 1, 1, self.queue,\\r\\n \\\"test_qsub\\\", \\\"oe\\\",\\r\\n self.command))\",\n \"def test_creating_a_job(created_job, bulk_request):\\n assert created_job.job == 'THEJOBID'\\n assert created_job.job_url == 'https://salesforce/services/async/34.0/job/THEJOBID'\\n assert created_job.pending_batches == []\\n assert created_job.is_open\\n\\n bulk_request.assert_called_once_with(\\n 'post',\\n 'https://salesforce/services/async/34.0/job',\\n data=XMLMatcher('''<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>\\n <jobInfo xmlns=\\\"http://www.force.com/2009/06/asyncapi/dataload\\\">\\n <operation>update</operation>\\n <object>Lead</object>\\n <contentType>CSV</contentType>\\n </jobInfo>\\n ''')\\n )\",\n \"def maybe_commit(job):\",\n \"def test_can_create_job(self):\\n\\t\\tself.job.save()\\n\\t\\tjob_instance = Job.objects.get(pk=1)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.created_by,\\n\\t\\t\\tself.created_by,\\n\\t\\t\\t\\\"created_by fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.organisation,\\n\\t\\t\\tself.organisation,\\n\\t\\t\\t\\\"organisation fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.category,\\n\\t\\t\\tself.category,\\n\\t\\t\\t\\\"category fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.title,\\n\\t\\t\\tself.title,\\n\\t\\t\\t\\\"title fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.description,\\n\\t\\t\\tself.description,\\n\\t\\t\\t\\\"description fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.allow_comments,\\n\\t\\t\\tself.allow_comments,\\n\\t\\t\\t\\\"allow_comments fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.start_accepting_applications_at,\\n\\t\\t\\tself.start_accepting_applications_at,\\n\\t\\t\\t\\\"start_accepting_applications_at fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.stop_accepting_applications_at,\\n\\t\\t\\tself.stop_accepting_applications_at,\\n\\t\\t\\t\\\"stop_accepting_applications_at fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.employment_term,\\n\\t\\t\\tself.employment_term,\\n\\t\\t\\t\\\"employment_term fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.seniority_level,\\n\\t\\t\\tself.seniority_level,\\n\\t\\t\\t\\\"seniority_level fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.location,\\n\\t\\t\\tself.location,\\n\\t\\t\\t\\\"location fields don't match.\\\"\\n\\t\\t)\",\n \"def test_post_job(self):\\n body = UnitTesterJobCreateReq()\\n response = self.client.open(\\n '/v1/job',\\n method='POST',\\n data=json.dumps(body),\\n content_type='application/json')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"def test_ready_only_job_pass(self):\\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \\\"extras/tests/dummy_jobs\\\")):\\n\\n module = \\\"test_read_only_pass\\\"\\n name = \\\"TestReadOnlyPass\\\"\\n job_class = get_job(f\\\"local/{module}/{name}\\\")\\n\\n job_result = JobResult.objects.create(\\n name=job_class.class_path,\\n obj_type=self.job_content_type,\\n user=None,\\n job_id=uuid.uuid4(),\\n )\\n\\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\\n job_result.refresh_from_db()\\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)\\n self.assertEqual(Site.objects.count(), 0) # Ensure DB transaction was aborted\",\n \"def test_upload_orchestration_no_batches(new_job):\\n with mock.patch.object(new_job, 'create') as create, \\\\\\n mock.patch.object(new_job, 'add_batch') as add_batch, \\\\\\n mock.patch.object(new_job, 'abort') as abort, \\\\\\n mock.patch.object(new_job, 'close') as close, \\\\\\n mock.patch.object(new_job, 'wait') as wait:\\n\\n new_job.upload(['Id', 'Description'], [])\\n\\n create.assert_called_once_with()\\n assert add_batch.call_count == 0\\n abort.assert_called_once_with()\\n assert close.call_count == 0\\n assert wait.call_count == 0\",\n \"def CheckIfJobFinished(jobid, numseq, to_email, g_params): # {{{\\n bsname = \\\"job_final_process\\\"\\n path_result = os.path.join(g_params['path_static'], 'result')\\n rstdir = os.path.join(path_result, jobid)\\n gen_logfile = g_params['gen_logfile']\\n gen_errfile = g_params['gen_errfile']\\n name_server = g_params['name_server']\\n g_params['jobid'] = jobid\\n g_params['numseq'] = numseq\\n g_params['to_email'] = to_email\\n jsonfile = os.path.join(rstdir, f\\\"{bsname}.json\\\")\\n myfunc.WriteFile(json.dumps(g_params, sort_keys=True), jsonfile, \\\"w\\\")\\n binpath_script = os.path.join(g_params['webserver_root'], \\\"env\\\", \\\"bin\\\")\\n\\n finished_idx_file = \\\"%s/finished_seqindex.txt\\\"%(rstdir)\\n failed_idx_file = \\\"%s/failed_seqindex.txt\\\"%(rstdir)\\n py_scriptfile = os.path.join(binpath_script, f\\\"{bsname}.py\\\")\\n finished_idx_list = []\\n failed_idx_list = []\\n if os.path.exists(finished_idx_file):\\n finished_idx_list = list(set(myfunc.ReadIDList(finished_idx_file)))\\n if os.path.exists(failed_idx_file):\\n failed_idx_list = list(set(myfunc.ReadIDList(failed_idx_file)))\\n\\n lockname = f\\\"{bsname}.lock\\\"\\n lock_file = os.path.join(g_params['path_result'], g_params['jobid'],\\n lockname)\\n\\n num_processed = len(finished_idx_list)+len(failed_idx_list)\\n if num_processed >= numseq: # finished\\n if ('THRESHOLD_NUMSEQ_CHECK_IF_JOB_FINISH' in g_params\\n and numseq <= g_params['THRESHOLD_NUMSEQ_CHECK_IF_JOB_FINISH']):\\n cmd = [\\\"python\\\", py_scriptfile, \\\"-i\\\", jsonfile]\\n (isSubmitSuccess, t_runtime) = webcom.RunCmd(cmd, gen_logfile, gen_errfile)\\n elif not os.path.exists(lock_file):\\n bash_scriptfile = f\\\"{rstdir}/{bsname},{name_server},{jobid}.sh\\\"\\n code_str_list = []\\n code_str_list.append(\\\"#!/bin/bash\\\")\\n cmdline = f\\\"python {py_scriptfile} -i {jsonfile}\\\"\\n code_str_list.append(cmdline)\\n code = \\\"\\\\n\\\".join(code_str_list)\\n myfunc.WriteFile(code, bash_scriptfile, mode=\\\"w\\\", isFlush=True)\\n os.chmod(bash_scriptfile, 0o755)\\n os.chdir(rstdir)\\n cmd = ['sbatch', bash_scriptfile]\\n cmdline = \\\" \\\".join(cmd)\\n verbose = False\\n if 'DEBUG' in g_params and g_params['DEBUG']:\\n verbose = True\\n webcom.loginfo(\\\"Run cmdline: %s\\\"%(cmdline), gen_logfile)\\n (isSubmitSuccess, t_runtime) = webcom.RunCmd(cmd, gen_logfile, gen_errfile, verbose)\\n if 'DEBUG' in g_params and g_params['DEBUG']:\\n webcom.loginfo(\\\"isSubmitSuccess: %s\\\"%(str(isSubmitSuccess)), gen_logfile)\",\n \"def examine_job(self):\\n if self.data is None:\\n print(\\\"Could not download job id\\\", self.job_id)\\n return\\n self.duration = self.read_data(\\\"duration\\\")\\n self.between_commands = self.read_data(\\\"between_commands\\\")\\n\\n print(\\\"---\\\")\\n print(\\\"test_id: {}\\\".format(self.job_id))\\n print(\\\"duration:\\\")\\n Job.print_results(self.duration)\\n print(\\\"between_commands:\\\")\\n Job.print_results(self.between_commands)\\n print(\\\"\\\")\",\n \"def test_successful(self):\\n\\n url = '/%s/job-types/status/' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 4)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)\",\n \"def mark(self, job, status='succeeded'):\\n pass\",\n \"def _check_analyzed_job(self, job, container):\\n self.log(u\\\"Checking the Job object generated from container\\\")\\n\\n self.log(u\\\"Checking that the Job is not None\\\")\\n if job is None:\\n self._failed(u\\\"Unable to create a Job from the container.\\\")\\n return\\n\\n self.log(u\\\"Checking that the Job has at least one Task\\\")\\n if len(job) == 0:\\n self._failed(u\\\"Unable to create at least one Task from the container.\\\")\\n return\\n\\n if self.rconf[RuntimeConfiguration.JOB_MAX_TASKS] > 0:\\n self.log(u\\\"Checking that the Job does not have too many Tasks\\\")\\n if len(job) > self.rconf[RuntimeConfiguration.JOB_MAX_TASKS]:\\n self._failed(u\\\"The Job has %d Tasks, more than the maximum allowed (%d).\\\" % (\\n len(job),\\n self.rconf[RuntimeConfiguration.JOB_MAX_TASKS]\\n ))\\n return\\n\\n self.log(u\\\"Checking that each Task text file is well formed\\\")\\n for task in job.tasks:\\n self.log([u\\\"Checking Task text file '%s'\\\", task.text_file_path])\\n text_file_bstring = container.read_entry(task.text_file_path)\\n if (text_file_bstring is None) or (len(text_file_bstring) == 0):\\n self._failed(u\\\"Text file '%s' is empty\\\" % task.text_file_path)\\n return\\n self._check_utf8_encoding(text_file_bstring)\\n if not self.result.passed:\\n self._failed(u\\\"Text file '%s' is not encoded in UTF-8\\\" % task.text_file_path)\\n return\\n self._check_not_empty(text_file_bstring)\\n if not self.result.passed:\\n self._failed(u\\\"Text file '%s' is empty\\\" % task.text_file_path)\\n return\\n self.log([u\\\"Checking Task text file '%s': passed\\\", task.text_file_path])\\n self.log(u\\\"Checking each Task text file is well formed: passed\\\")\",\n \"def _validate_jobs(\\n self,\\n check_nlst_warn: bool = False\\n ):\\n counter = 0\\n for job in self.jobs:\\n counter += 1\\n print(job.job_id)\\n if counter == 0:\\n ignore_restarts = False\\n else:\\n ignore_restarts = True\\n\\n check_input_files(\\n hrldas_namelist=job.hrldas_namelist,\\n hydro_namelist=job.hydro_namelist,\\n sim_dir=os.getcwd(),\\n ignore_restarts=ignore_restarts,\\n check_nlst_warn=check_nlst_warn\\n )\",\n \"def test_jobset(self):\\n self.assertEqual(self.jobset.indir, 'indir')\",\n \"def test_call_first_time(self, query_repo_url, get_credentials, valid_revision, get):\\n self.assertEquals(\\n self.query_api._get_all_jobs(\\\"try\\\", \\\"146071751b1e\\\"),\\n json.loads(JOBS_SCHEDULE))\\n\\n assert get.call_count == 1\\n\\n # Test that this fills our caches\\n self.assertEquals(\\n query_jobs.JOBS_CACHE[(\\\"try\\\", \\\"146071751b1e\\\")],\\n json.loads(JOBS_SCHEDULE))\",\n \"def test_successful_on_get(self):\\n\\n url = '/%s/jobs/' % self.api\\n\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\",\n \"def in_queue(self):\\n if self.get_db('jobid') is None:\\n log.debug('jobid not found for calculation.')\\n return False\\n else:\\n # get the jobid\\n jobid = self.get_db('jobid')\\n # see if jobid is in queue\\n _, jobids_in_queue, _ = getstatusoutput('qselect',\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n\\n if str(jobid) in jobids_in_queue.split('\\\\n'):\\n # get details on specific jobid in case it is complete\\n status, output, err = getstatusoutput(['qstat', jobid],\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n if status == 0:\\n lines = output.split('\\\\n')\\n fields = lines[2].split()\\n job_status = fields[4]\\n if job_status == 'C':\\n return False\\n else:\\n return True\\n else:\\n return False\",\n \"def test_006_add(self):\\n HEADING()\\n db = self.db\\n\\n count = 5\\n\\n db.connect()\\n\\n db.delete_jobs()\\n\\n for id in range(0,count):\\n job = db.insert(\\\"job\\\" + str(id))\\n\\n assert len(db) == count\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def test_getJobState(self):\\n self.assertRaises(JobNotExistsError, self.foo.getJobState)\\n self.foo.setTorqueJobId('asd')\\n self.assertEqual(self.foo.getJobState(), 'NEW')\",\n \"def test_job_fail(self):\\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \\\"extras/tests/dummy_jobs\\\")):\\n\\n module = \\\"test_fail\\\"\\n name = \\\"TestFail\\\"\\n job_class = get_job(f\\\"local/{module}/{name}\\\")\\n job_result = JobResult.objects.create(\\n name=job_class.class_path,\\n obj_type=self.job_content_type,\\n user=None,\\n job_id=uuid.uuid4(),\\n )\\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\\n job_result.refresh_from_db()\\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_ERRORED)\",\n \"def isFinished(self):\\r\\n try:\\r\\n output = Popen(\\\"qstat | grep \\\"+self.jobId, shell=True, stdout=PIPE, stderr=PIPE).communicate()[0]\\r\\n if self.jobId in output:\\r\\n if output.split()[4] == \\\"Eqw\\\":\\r\\n #If the job fails, print a warning, and wait a minute so the user can check why the job fails,\\r\\n #before resubmitting the job.\\r\\n logging.warning(\\\"job \\\" + output.split()[2] + \\\" failed to run, resubmitting in one minute\\\")\\r\\n time.sleep(60)\\r\\n output = Popen(\\\"qdel \\\"+self.jobId, shell=True, stdout=PIPE, stderr=PIPE).communicate()[0]\\r\\n self.submit()\\r\\n return False\\r\\n else:\\r\\n logging.info(\\\"job with ID: \\\" + self.jobId + \\\" is finished.\\\")\\r\\n return True\\r\\n \\r\\n except ValueError:\\r\\n logging.info(\\\"Error: waiting for not submitted job...\\\")\",\n \"def test_add_seed_job_type(self):\\n\\n url = '/%s/job-types/' % self.api\\n manifest = copy.deepcopy(job_test_utils.COMPLETE_MANIFEST)\\n manifest['job']['name'] = 'my-new-job'\\n\\n json_data = {\\n 'icon_code': 'BEEF',\\n 'is_published': True,\\n 'docker_image': 'my-new-job-1.0.0-seed:1.0.0',\\n 'manifest': manifest,\\n 'configuration': self.configuration\\n }\\n\\n good_setting = {\\n 'DB_HOST': 'scale'\\n }\\n\\n response = self.client.generic('POST', url, json.dumps(json_data), 'application/json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED, response.content)\\n self.assertTrue('/%s/job-types/my-new-job/1.0.0/' % self.api in response['location'])\\n\\n job_type = JobType.objects.filter(name='my-new-job').first()\\n\\n results = json.loads(response.content)\\n self.assertEqual(results['id'], job_type.id)\\n self.assertEqual(results['version'], job_type.version)\\n self.assertEqual(results['title'], job_type.get_title())\\n self.assertEqual(results['revision_num'], job_type.revision_num)\\n self.assertEqual(results['revision_num'], 1)\\n self.assertIsNone(results['max_scheduled'])\\n self.assertEqual(results['configuration']['settings'], good_setting)\",\n \"def test_enqueue(self):\\n dbpool = buildConnectionPool(self, jobSchema + schemaText)\\n yield self._enqueue(dbpool, 1, 2)\\n\\n # Make sure we have one JOB and one DUMMY_WORK_ITEM\\n @transactionally(dbpool.connection)\\n def checkJob(txn):\\n return JobItem.all(txn)\\n\\n jobs = yield checkJob\\n self.assertTrue(len(jobs) == 1)\\n self.assertTrue(jobs[0].workType == \\\"DUMMY_WORK_ITEM\\\")\\n self.assertTrue(jobs[0].assigned is None)\\n self.assertEqual(jobs[0].isAssigned, 0)\\n\\n @transactionally(dbpool.connection)\\n def checkWork(txn):\\n return DummyWorkItem.all(txn)\\n\\n work = yield checkWork\\n self.assertTrue(len(work) == 1)\\n self.assertTrue(work[0].jobID == jobs[0].jobID)\",\n \"def test_cachedjob_waits_for_new_job_pre_existing_job(uncached_job):\\n\\n # Setup\\n cached_job = uncached_job\\n initial_job_key = cached_job.current_job_id\\n\\n # Execute\\n cached_job.wait_for_new_job(sleep=2)\\n final_job_key = cached_job.current_job_id\\n\\n # Verify\\n assert initial_job_key == None\\n assert initial_job_key != final_job_key\\n assert final_job_key == JOB_ID\",\n \"def testJobEmpty(databases):\\n\\n def checkEmpty(config):\\n assert config.getParameter('') == None\\n assert len(config.getInputs()) == 0\\n assert len(config.getInputsGrouped()) == 0\\n assert len(config.getTasks()) == 0\\n assert len(config.getTaskSequence()) == 0\\n\\n config = DataGenerator(databases).createConfiguration()\\n runWithReload(databases, config, checkEmpty)\",\n \"def test_create_ingest_job(self):\\n ingest_mgmr = IngestManager()\\n ingest_mgmr.validate_config_file(self.example_config_data)\\n ingest_mgmr.validate_properties()\\n ingest_mgmr.owner = self.user.pk\\n job = ingest_mgmr.create_ingest_job()\\n assert (job.id is not None)\",\n \"def test_wrong_job(self):\\n job = MockJob()\\n container_name = 'container_identifier'\\n self._mock_open.return_value.__enter__.return_value.readline. \\\\\\n return_value = container_name\\n\\n # return an error trying to read file with container name\\n self._mock_open.return_value.__enter__.side_effect = [\\n PermissionError,\\n FileNotFoundError,\\n ]\\n\\n self.assertNotEqual(job.pid, 0)\\n # non-zero pid, no container name is found -> refer to ForkSpawner\\n validation_result = self._container_spawner.validate(job)\\n self._mock_open.assert_called()\\n self._mock_fork.assert_called()\\n self.assertEqual(validation_result,\\n spawner.PROCESS_RUNNING)\\n # same check, a different exception\\n self.assertEqual(self._container_spawner.validate(job),\\n spawner.PROCESS_RUNNING)\",\n \"def test_successful_empty(self):\\n\\n url = '/%s/jobs/%i/' % (self.api, self.job.id)\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(result['max_tries'], 3)\\n self.assertEqual(result['job_type']['name'], self.job.job_type.name)\\n self.assertEqual(result['job_type_rev']['job_type']['name'], self.job.job_type.name)\\n\\n if self.recipe:\\n self.assertEqual(result['recipe']['recipe_type']['name'], self.recipe.recipe_type.name)\\n else:\\n self.assertEqual(len(result['recipe']), 0)\",\n \"def test_job_delete_non_existing(self):\\n self.assertRaises(exceptions.NotFoundException,\\n self.client.jobs.delete, 'does-not-exist')\",\n \"def isJobRunning ( self ):\\n #cmd = \\\"qstat \\\" + str(self.jobid)\\n \\n #magicString='Unknown Job Id' ### magicString _might_ need to be changed if Torque version changes\\n #(output, error) = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()\\n\\n if self.ofile_exists(): #output.find(magicString) >=0 or redhawkStatsRe.search(output):\\n self.status = \\\"finished\\\"\\n return False\\n \\n\\n return True\",\n \"def checkjob(sid, jid):\\n with slycat.web.server.remote.get_session(sid) as session:\\n return session.checkjob(jid)\",\n \"def test_running_job(self):\\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def test_batch(self):\\n batch = batch_test_utils.create_batch()\\n self.job1.batch_id = batch.id\\n self.job1.save()\\n\\n url = '/%s/jobs/?batch_id=%d' % (self.api, batch.id)\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 1)\\n self.assertEqual(result['results'][0]['id'], self.job1.id)\",\n \"def test_job_id(self):\\n\\n url = '/%s/jobs/?job_id=%s' % (self.api, self.job1.id)\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 1)\\n self.assertEqual(result['results'][0]['id'], self.job1.id)\",\n \"def testJobKilling(self):\\n change = ChangeState(self.config, \\\"changestate_t\\\")\\n\\n locationAction = self.daoFactory(classname=\\\"Locations.New\\\")\\n locationAction.execute(\\\"site1\\\", pnn=\\\"T2_CH_CERN\\\")\\n\\n testWorkflow = Workflow(spec=self.specUrl, owner=\\\"Steve\\\",\\n name=\\\"wf001\\\", task=self.taskName)\\n testWorkflow.create()\\n testFileset = Fileset(name=\\\"TestFileset\\\")\\n testFileset.create()\\n\\n for i in range(4):\\n newFile = File(lfn=\\\"File%s\\\" % i, locations=set([\\\"T2_CH_CERN\\\"]))\\n newFile.create()\\n testFileset.addFile(newFile)\\n\\n testFileset.commit()\\n testSubscription = Subscription(fileset=testFileset,\\n workflow=testWorkflow,\\n split_algo=\\\"FileBased\\\")\\n testSubscription.create()\\n\\n splitter = SplitterFactory()\\n jobFactory = splitter(package=\\\"WMCore.WMBS\\\",\\n subscription=testSubscription)\\n jobGroup = jobFactory(files_per_job=1)[0]\\n\\n assert len(jobGroup.jobs) == 4, \\\\\\n \\\"Error: Splitting should have created four jobs.\\\"\\n\\n testJobA = jobGroup.jobs[0]\\n testJobA[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobA[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobA[\\\"taskType\\\"] = \\\"Processing\\\"\\n testJobB = jobGroup.jobs[1]\\n testJobB[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobB[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobB[\\\"taskType\\\"] = \\\"Processing\\\"\\n testJobC = jobGroup.jobs[2]\\n testJobC[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobC[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobC[\\\"taskType\\\"] = \\\"Processing\\\"\\n testJobD = jobGroup.jobs[3]\\n testJobD[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobD[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobD[\\\"taskType\\\"] = \\\"Processing\\\"\\n\\n change.persist([testJobA], \\\"created\\\", \\\"new\\\")\\n change.persist([testJobB], \\\"jobfailed\\\", \\\"executing\\\")\\n change.persist([testJobC, testJobD], \\\"executing\\\", \\\"created\\\")\\n\\n change.persist([testJobA], \\\"killed\\\", \\\"created\\\")\\n change.persist([testJobB], \\\"killed\\\", \\\"jobfailed\\\")\\n change.persist([testJobC, testJobD], \\\"killed\\\", \\\"executing\\\")\\n\\n for job in [testJobA, testJobB, testJobC, testJobD]:\\n job.load()\\n self.assertEqual(job['retry_count'], 99999)\\n self.assertEqual(job['state'], 'killed')\\n\\n return\",\n \"def test_successful(self):\\n\\n url = '/%s/job-types/running/' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 1)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job.job_type.name)\\n self.assertEqual(result['results'][0]['count'], 1)\\n self.assertIsNotNone(result['results'][0]['longest_running'])\",\n \"def check_job_status_by_id(job_id):\\n print('=' * 40)\\n print('check_status_by_job_id', job_id)\\n print('=' * 40)\\n\\n it_worked = check_job_status(job)\\n if it_worked:\\n return ok_resp(job)\\n\\n user_msg = ('PreprocessJob still in process: %s') % (job_id)\\n return err_resp(user_msg)\",\n \"def test_cachedjob_job_key(uncached_job):\\n\\n # Setup\\n c_job = uncached_job\\n\\n # Execute\\n c_job.wait_for_new_job(sleep=2)\\n\\n # Verify\\n assert c_job.current_job_id == JOB_ID\",\n \"def test_matching_jobs_invalid(self):\\n self.assertEquals(\\n self.query_api.get_matching_jobs(\\n \\\"try\\\", \\\"146071751b1e\\\",\\n 'Invalid buildername'), [])\",\n \"def testJobOneTask(databases):\\n\\n class CustomGenerator(DataGenerator):\\n numTasks = 1\\n numInputs = [ 0 ]\\n numOutputs = [ 0 ]\\n\\n gen = CustomGenerator(databases)\\n gen.createDefinitions()\\n config = gen.createConfiguration()\\n\\n def checkOne(config):\\n taskName = gen.tasks[0]\\n #assert config.getProduct('') is None\\n assert len(config.getInputs()) == 0\\n assert len(config.getInputsGrouped()) == 0\\n assert len(config.getTasks()) == 1\\n task, = config.getTasks()\\n assert task is not None\\n assert task.getName() == taskName\\n assert len(config.getTaskSequence()) == 1\\n\\n runWithReload(databases, config, checkOne)\",\n \"def test_raises_on_missing_job(self):\\n from furious.async import Async\\n from furious.errors import NotInContextError\\n from furious.processors import run_job\\n\\n work = Async(\\\"nothere\\\")\\n work._options.pop('job')\\n assert 'job' not in work._options\\n\\n self.assertRaises(NotInContextError, run_job)\",\n \"def test_updateJobState(self):\\n self.assertRaises(JobNotExistsError, self.foo.updateJobState, 'asd')\\n self.foo.setTorqueJobId('asd')\\n self.assertEqual(self.foo.getJobState(), 'NEW')\",\n \"def test_job_delete_completed_job(self):\\n test_app = self._create_app()\\n class_path = \\\"spark.jobserver.VeryShortDoubleJob\\\"\\n job = self.client.jobs.create(test_app, class_path,\\n ctx=self._get_functional_context())\\n time.sleep(3)\\n self._wait_till_job_is_done(job)\\n self.assertRaises(exceptions.NotFoundException,\\n self.client.jobs.delete, job.jobId)\",\n \"def _add_job_data(\\n self,\\n job: Job,\\n ) -> Tuple[str, bool]:\\n jid = job.job_id()\\n try:\\n job_result = job.result()\\n self._add_result_data(job_result, jid)\\n LOG.debug(\\\"Job data added [Job ID: %s]\\\", jid)\\n # sets the endtime to be the time the last successful job was added\\n self.end_datetime = datetime.now()\\n return jid, True\\n except Exception as ex: # pylint: disable=broad-except\\n # Handle cancelled jobs\\n status = job.status()\\n if status == JobStatus.CANCELLED:\\n LOG.warning(\\\"Job was cancelled before completion [Job ID: %s]\\\", jid)\\n return jid, False\\n if status == JobStatus.ERROR:\\n LOG.error(\\n \\\"Job data not added for errored job [Job ID: %s]\\\\nError message: %s\\\",\\n jid,\\n job.error_message(),\\n )\\n return jid, False\\n LOG.warning(\\\"Adding data from job failed [Job ID: %s]\\\", job.job_id())\\n raise ex\",\n \"async def test_haos(coresys: CoreSys):\\n\\n class TestClass:\\n \\\"\\\"\\\"Test class.\\\"\\\"\\\"\\n\\n def __init__(self, coresys: CoreSys):\\n \\\"\\\"\\\"Initialize the test class.\\\"\\\"\\\"\\n self.coresys = coresys\\n\\n @Job(conditions=[JobCondition.HAOS])\\n async def execute(self):\\n \\\"\\\"\\\"Execute the class method.\\\"\\\"\\\"\\n return True\\n\\n test = TestClass(coresys)\\n coresys.hassos._available = True\\n assert await test.execute()\\n\\n coresys.hassos._available = False\\n assert not await test.execute()\",\n \"def work(self, job):\\n pass\",\n \"def check_slurm_job_submission(expected_name):\\n cmd = ['scontrol', 'show', 'job']\\n job_id = 0\\n found_job = False\\n while True:\\n while True:\\n try:\\n out = Popen(cmd, stdout=PIPE, stderr=PIPE).communicate()[0]\\n break\\n except:\\n sleep(1)\\n out = out.split('\\\\n')\\n if 'error' in out[0]:\\n sleep(1)\\n msg = 'Error checking job status for {0}'.format(expected_name)\\n logging.warning(msg)\\n continue\\n for line in out:\\n for word in line.split():\\n if 'JobId' in word:\\n index = word.find('=') + 1\\n job_id = int(word[index:])\\n # continue\\n if 'Name' in word:\\n index = word.find('=') + 1\\n if word[index:] == expected_name:\\n found_job = True\\n\\n if found_job and job_id != 0:\\n return found_job, job_id\\n sleep(1)\\n return found_job, job_id\",\n \"def _check_phase(self, jobid):\\n\\n self._existing_tables()\\n\\n time.sleep(1)\\n\\n if jobid not in self.job_dict.keys():\\n log.error(\\\"Job not present in job dictionary.\\\")\\n return\\n\\n else:\\n phase = self.job_dict[str(jobid)]\\n return phase\",\n \"def test_successful_job(self, _is_coalesced):\\n successful_job = json.loads(BASE_JSON % (SUCCESS, 1433166610, 1, 1433166609))[0]\\n self.assertEquals(self.query_api.get_job_status(successful_job), SUCCESS)\",\n \"def check(args, session: Session = NEW_SESSION) -> None:\\n if args.allow_multiple and not args.limit > 1:\\n raise SystemExit(\\\"To use option --allow-multiple, you must set the limit to a value greater than 1.\\\")\\n if args.hostname and args.local:\\n raise SystemExit(\\\"You can't use --hostname and --local at the same time\\\")\\n\\n query = select(Job).where(Job.state == JobState.RUNNING).order_by(Job.latest_heartbeat.desc())\\n if args.job_type:\\n query = query.where(Job.job_type == args.job_type)\\n if args.hostname:\\n query = query.where(Job.hostname == args.hostname)\\n if args.local:\\n query = query.where(Job.hostname == get_hostname())\\n if args.limit > 0:\\n query = query.limit(args.limit)\\n\\n alive_jobs: list[Job] = [job for job in session.scalars(query) if job.is_alive()]\\n\\n count_alive_jobs = len(alive_jobs)\\n if count_alive_jobs == 0:\\n raise SystemExit(\\\"No alive jobs found.\\\")\\n if count_alive_jobs > 1 and not args.allow_multiple:\\n raise SystemExit(f\\\"Found {count_alive_jobs} alive jobs. Expected only one.\\\")\\n if count_alive_jobs == 1:\\n print(\\\"Found one alive job.\\\")\\n else:\\n print(f\\\"Found {count_alive_jobs} alive jobs.\\\")\",\n \"def job_status(bot, update, args, job_queue, chat_data):\\n if len(args) == 0:\\n update.message.reply_text('No parameter provided')\\n return\\n\\n job_name = args[0]\\n if job_name not in settings.JOBS:\\n update.message.reply_text(\\n 'Sorry {0} is not a valid job'.format(job_name))\\n return\\n\\n job = find_job(job_name, job_queue)\\n\\n if not job:\\n update.message.reply_text('{0} job is not running'.format(job_name))\\n return\\n\\n update.message.reply_text('{0} job is running'.format(job_name))\",\n \"def test_successful(self):\\n\\n url = '/%s/job-types/pending/' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 1)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job.job_type.name)\\n self.assertEqual(result['results'][0]['count'], 1)\\n self.assertIsNotNone(result['results'][0]['longest_pending'])\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\\n # Asserts that job is either pending or canceled.\\n assert job.state in ['PENDING', 'CANCELED'], (\\n 'job that canceled immediately after submission has wrong '\\n 'state `%s`' % job.state\\n )\",\n \"def test_cluster_jobs_script(self):\\r\\n\\r\\n qiime_config = load_qiime_config()\\r\\n submit_script = qiime_config['cluster_jobs_fp']\\r\\n\\r\\n if (submit_script):\\r\\n full_path = which(submit_script)\\r\\n if full_path:\\r\\n submit_script = full_path\\r\\n self.assertTrue(exists(submit_script),\\r\\n \\\"cluster_jobs_fp is not set to a valid path in qiime config: %s\\\" % submit_script)\\r\\n # check if executable\\r\\n self.assertTrue(access(submit_script, X_OK),\\r\\n \\\"cluster_jobs_fp is not executable: %s\\\" % submit_script)\\r\\n else:\\r\\n # Can't run in parallel, but not a critical error\\r\\n pass\",\n \"def test_dies_if_no_job(self):\\n from furious.async import Async\\n from furious.context._execution import _ExecutionContext\\n from furious.processors import run_job\\n\\n work = Async(\\\"dir\\\", kwargs={'something': None})\\n work._options.pop('job')\\n assert 'job' not in work._options\\n\\n with _ExecutionContext(work):\\n self.assertRaises(Exception, run_job)\",\n \"def test_successful(self, mock_create, mock_msg_mgr):\\n\\n json_data = {\\n \\\"input\\\" : {\\n 'version': '6',\\n 'files': {'input_a': [self.source_file.id]},\\n 'json': {}\\n },\\n \\\"job_type_id\\\" : self.job_type1.pk\\n }\\n\\n url = '/%s/jobs/' % self.api\\n response = self.client.generic('POST', url, json.dumps(json_data), 'application/json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED, response.content)\\n\\n result = json.loads(response.content)\\n\\n #Response should be new v6 job detail response\\n self.assertEqual(result['execution'], None)\\n self.assertEqual(result['max_tries'], 3)\\n self.assertTrue('/%s/jobs/' % self.api in response['location'])\\n mock_create.assert_called_once()\",\n \"def add_job_state(self, job):\",\n \"def check_job_status(job):\\n assert isinstance(job, PreprocessJob),\\\\\\n 'job must be a PreprocessJob'\\n\\n if job.is_finished():\\n return True\\n\\n return True\\n \\\"\\\"\\\"\\n ye_task = AsyncResult(job.task_id,\\n app=preprocess_csv_file)\\n\\n if ye_task.state == 'SUCCESS':\\n\\n if ye_task.result['success']:\\n\\n preprocess_data = ContentFile(json.dumps(ye_task.result['data']))\\n\\n new_name = 'preprocess_%s.json' % get_alphanumeric_lowercase(8)\\n job.metadata_file.save(new_name,\\n preprocess_data)\\n job.set_state_success()\\n\\n job.user_message = 'Task completed! Preprocess is available'\\n job.save()\\n\\n else:\\n # Didn't work so well\\n job.set_state_failure(ye_task.result['message'])\\n job.save()\\n\\n ye_task.forget()\\n return True\\n\\n elif ye_task.state == STATE_FAILURE:\\n job.set_state_failure('ye_task failed....')\\n job.save()\\n ye_task.forget()\\n return True\\n\\n return False\\n \\\"\\\"\\\"\",\n \"def _check_jobs_submitted(status, module):\\n\\n submitted = False\\n if module in status.data:\\n jobs = status.data[module]\\n for job in jobs.keys():\\n if job != 'pipeline_index':\\n submitted = True\\n break\\n return submitted\",\n \"def test_check_job_dependencies_ready(mock_sqs_queue, database):\\n # Mock so it always returns the mock queue for the test\\n mock_sqs_queue.return_value = SQSMockQueue\\n sess = database.session\\n sub = SubmissionFactory(submission_id=1)\\n job = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['finished'],\\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'],\\n number_of_errors=0)\\n job_2 = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['waiting'],\\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'])\\n sess.add_all([sub, job, job_2])\\n sess.commit()\\n\\n # Job 1 finished, it is a prerequisite for job 2 (waiting) but it has errors\\n job_dep = JobDependency(job_id=job_2.job_id, prerequisite_id=job.job_id)\\n sess.add(job_dep)\\n sess.commit()\\n\\n check_job_dependencies(job.job_id)\\n\\n assert job_2.job_status_id == JOB_STATUS_DICT['ready']\",\n \"async def test_misusage(my_job):\\n\\n # Disable some pylint checks, they are redundant here in the test.\\n # pylint: disable=missing-docstring,unused-variable,function-redefined\\n\\n # Check that namesake job managers are not allowed.\\n class MyNamesakeJobManager(jobmanager.JobManager):\\n pass\\n with pytest.raises(AssertionError,\\n message='Successfully made two job managers with the '\\n 'same names!'):\\n class MyNamesakeJobManager(jobmanager.JobManager):\\n pass\\n\\n # Check that it is impossible to register two job functions with the\\n # same name inside a single job manager, but it is possible in the\\n # different job managers.\\n class MyJobManager1(jobmanager.JobManager):\\n pass\\n\\n class MyJobManager2(jobmanager.JobManager):\\n pass\\n\\n @MyJobManager1.job()\\n def my_namesake_job():\\n pass\\n\\n @MyJobManager2.job()\\n def my_namesake_job():\\n pass\\n\\n with pytest.raises(AssertionError,\\n message='Successfully registered two job function with '\\n 'the same name!'):\\n @MyJobManager1.job()\\n def my_namesake_job():\\n pass\\n\\n # Check that it is impossible to submit a job with an identifier\\n # which is already used by some other job.\\n\\n # Submit 2 jobs with the same identifier and check that second\\n # submission raises an exception.\\n job_id = uuid.uuid4()\\n\\n def on_job_submit(job):\\n job.id = job_id\\n return job\\n my_job.set_on_submit(on_job_submit)\\n\\n # Submit a job #1.\\n await my_job.job(mustfail=False)\\n\\n with pytest.raises(django.db.utils.IntegrityError,\\n message='Successfully submitted a job with the '\\n 'identifier that has already been used for '\\n 'another job!'):\\n # Submit a job #2.\\n await my_job.job(mustfail=False)\\n\\n # Process ASGI messages and wait for the job to finish.\\n await my_job.process_jobs()\",\n \"def remove_job_if_exists(self, name: str, context: CallbackContext) -> bool:\\n current_jobs = context.job_queue.get_jobs_by_name(name)\\n if not current_jobs:\\n return False\\n for job in current_jobs:\\n job.schedule_removal()\\n return True\",\n \"def remove_job_if_exists(self, name: str, context: CallbackContext) -> bool:\\n current_jobs = context.job_queue.get_jobs_by_name(name)\\n if not current_jobs:\\n return False\\n for job in current_jobs:\\n job.schedule_removal()\\n return True\",\n \"def test_py_job_create(self):\\n test_app = self._create_py_app()\\n class_path = \\\"example_jobs.word_count.WordCountSparkSessionJob\\\"\\n conf = \\\"input.strings = ['a', 'b', 'a', 'b']\\\"\\n job = self._create_job(test_app, class_path, conf,\\n ctx=self._get_functional_py_context())\\n time.sleep(3)\\n self.assertTrue(len(job.jobId) > 0)\\n self.assertTrue(job.status == \\\"STARTED\\\")\\n self._wait_till_job_is_done(job)\",\n \"def _addjob(self, job: Job):\\n if self.domain is not None and self.model is not None:\\n job = copy.deepcopy(job)\\n job._add_hydro_namelist(self.base_hydro_namelist)\\n job._add_hrldas_namelist(self.base_hrldas_namelist)\\n\\n self.jobs.append(job)\\n else:\\n raise AttributeError('Can not add a job to a simulation without a model and a domain')\",\n \"def remove_job_if_exists(name: str, context: ContextTypes.DEFAULT_TYPE) -> bool:\\n current_jobs = context.job_queue.get_jobs_by_name(name)\\n if not current_jobs:\\n return False\\n for job in current_jobs:\\n job.schedule_removal()\\n return True\",\n \"def verify_job(cls, auth_key, job_id):\\n key = ObjectId(job_id)\\n user_id = ObjectId(auth_key)\\n db = cls.mongo_cli.get_database(collection=Job.collection_name)\\n if db.count({\\\"_id\\\": key, \\\"user_id\\\": user_id}) > 0:\\n return True\\n return False\",\n \"def test_reenqueue_job1(client):\\n with client.application.app_context():\\n pytest.skip(\\\"Not implemented\\\")\",\n \"def remove_job_if_exists(name, context):\\n current_jobs = context.job_queue.get_jobs_by_name(name)\\n if not current_jobs:\\n return False\\n for job in current_jobs:\\n job.schedule_removal()\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72872704","0.7204852","0.68101156","0.66714615","0.6622515","0.66078657","0.6596242","0.65822595","0.6559245","0.64998597","0.64640236","0.6440778","0.64162385","0.6409369","0.63889015","0.6380754","0.63741344","0.6320242","0.62653357","0.62551504","0.62322813","0.6228221","0.6226369","0.6221608","0.6215835","0.62041056","0.6203264","0.62021214","0.6187554","0.61869454","0.61638486","0.6162798","0.6130696","0.61254966","0.612323","0.6095473","0.6088956","0.60881186","0.6062315","0.60604304","0.6054386","0.60522425","0.6049385","0.60353947","0.6035326","0.60233104","0.60228974","0.6009568","0.60042113","0.6000244","0.59951925","0.5993628","0.59889996","0.5981596","0.59815043","0.59803516","0.597096","0.59693176","0.59687674","0.59680927","0.5967281","0.59664696","0.59634614","0.595968","0.59537685","0.5952734","0.5950344","0.5949809","0.5945777","0.5943704","0.5932243","0.59297574","0.5918614","0.59173065","0.5905163","0.5902224","0.58957857","0.5890949","0.5890141","0.58875406","0.5886111","0.5883973","0.58767","0.587352","0.5871707","0.58692837","0.5862224","0.5858321","0.5853005","0.58474255","0.5843099","0.5840996","0.5833719","0.5833719","0.5833594","0.5825569","0.58248883","0.5822632","0.5820943","0.58201164"],"string":"[\n \"0.72872704\",\n \"0.7204852\",\n \"0.68101156\",\n \"0.66714615\",\n \"0.6622515\",\n \"0.66078657\",\n \"0.6596242\",\n \"0.65822595\",\n \"0.6559245\",\n \"0.64998597\",\n \"0.64640236\",\n \"0.6440778\",\n \"0.64162385\",\n \"0.6409369\",\n \"0.63889015\",\n \"0.6380754\",\n \"0.63741344\",\n \"0.6320242\",\n \"0.62653357\",\n \"0.62551504\",\n \"0.62322813\",\n \"0.6228221\",\n \"0.6226369\",\n \"0.6221608\",\n \"0.6215835\",\n \"0.62041056\",\n \"0.6203264\",\n \"0.62021214\",\n \"0.6187554\",\n \"0.61869454\",\n \"0.61638486\",\n \"0.6162798\",\n \"0.6130696\",\n \"0.61254966\",\n \"0.612323\",\n \"0.6095473\",\n \"0.6088956\",\n \"0.60881186\",\n \"0.6062315\",\n \"0.60604304\",\n \"0.6054386\",\n \"0.60522425\",\n \"0.6049385\",\n \"0.60353947\",\n \"0.6035326\",\n \"0.60233104\",\n \"0.60228974\",\n \"0.6009568\",\n \"0.60042113\",\n \"0.6000244\",\n \"0.59951925\",\n \"0.5993628\",\n \"0.59889996\",\n \"0.5981596\",\n \"0.59815043\",\n \"0.59803516\",\n \"0.597096\",\n \"0.59693176\",\n \"0.59687674\",\n \"0.59680927\",\n \"0.5967281\",\n \"0.59664696\",\n \"0.59634614\",\n \"0.595968\",\n \"0.59537685\",\n \"0.5952734\",\n \"0.5950344\",\n \"0.5949809\",\n \"0.5945777\",\n \"0.5943704\",\n \"0.5932243\",\n \"0.59297574\",\n \"0.5918614\",\n \"0.59173065\",\n \"0.5905163\",\n \"0.5902224\",\n \"0.58957857\",\n \"0.5890949\",\n \"0.5890141\",\n \"0.58875406\",\n \"0.5886111\",\n \"0.5883973\",\n \"0.58767\",\n \"0.587352\",\n \"0.5871707\",\n \"0.58692837\",\n \"0.5862224\",\n \"0.5858321\",\n \"0.5853005\",\n \"0.58474255\",\n \"0.5843099\",\n \"0.5840996\",\n \"0.5833719\",\n \"0.5833719\",\n \"0.5833594\",\n \"0.5825569\",\n \"0.58248883\",\n \"0.5822632\",\n \"0.5820943\",\n \"0.58201164\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7668623"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77990,"cells":{"query":{"kind":"string","value":"Test job addition and exist check"},"document":{"kind":"string","value":"def test_job_addition():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1')\n status1 = Status(STATUS_DIR).data['generation']['test1']['job_status']\n\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'job_status': 'finished',\n 'additional': 'test'})\n status2 = Status(STATUS_DIR).data['generation']['test1']['job_status']\n\n assert status2 == status1"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_job_exists():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'job_status': 'submitted'})\n exists = Status.job_exists(STATUS_DIR, 'test1')\n assert exists","def test_matching_jobs_existing(self):\n self.assertEquals(\n self.query_api.get_matching_jobs(\n \"try\", \"146071751b1e\",\n 'Linux x86-64 try build'), json.loads(JOBS_SCHEDULE))","def job_exists(self, job):\n\n with open(os.devnull, 'w') as devnull:\n result = subprocess.call(self.cli + [PlatformJenkinsJavaCLI.GET_JOB, job.name], stdout=devnull)\n\n return result == 0","def check_job_exists( job_list, analysis_group_id, reprocess_config_id):\n for job in job_list:\n struct = JSONMessage.unserialize(job.input_message)\n\n if( int( struct.analysis_group_id ) == int( analysis_group_id ) and \\\n int( struct.reprocess_config_id ) == int( reprocess_config_id ) ):\n return 1\n return 0","def test_fake_job( self ):\n hda = MockHda( job=UNDEFINED_JOB )\n self.history.active_datasets.append( hda )\n job_dict, warnings = extract.summarize( trans=self.trans )\n assert not warnings\n assert len( job_dict ) == 1\n fake_job = job_dict.keys()[ 0 ]\n assert fake_job.id.startswith( \"fake_\" )\n datasets = job_dict.values()[ 0 ]\n assert datasets == [ ( None, hda ) ]","def _check_for_finished_job(self):\n raise NotImplementedError","def test_jobs_successful(self):\n\n workspace = storage_test_utils.create_workspace()\n file1 = storage_test_utils.create_file()\n data_dict = {\n 'version': '1.0',\n 'input_data': [{\n 'name': 'INPUT_IMAGE',\n 'file_id': file1.id\n }],\n 'output_data': [{\n 'name': 'output_file_pngs',\n 'workspace_id': workspace.id\n }]}\n\n secret_configuration = {\n 'version': '6',\n 'priority': 50,\n 'output_workspaces': {'default': storage_test_utils.create_workspace().name},\n 'settings': {\n 'DB_HOST': 'som.host.name',\n 'DB_PASS': 'secret_password'\n }\n }\n\n seed_job_type = job_test_utils.create_seed_job_type(configuration=secret_configuration)\n seed_job = job_test_utils.create_job(job_type=seed_job_type, status='RUNNING', input=data_dict)\n\n url = '/%s/jobs/%d/' % (self.api, seed_job.id)\n response = self.client.generic('GET', url)\n result = json.loads(response.content)\n\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n self.assertEqual(result['configuration']['priority'],50)\n self.assertNotIn('DB_PASS', result['configuration']['settings'])","def test_submit_jobs(self):\r\n\r\n submit_jobs([self.command], prefix=\"test_job\")\r\n # Try and wait ten times, could be made nicer with alarm()\r\n for i in range(10):\r\n if exists(self.tmp_result_file):\r\n observed_text = \"\".join(list(open(self.tmp_result_file)))\r\n self.assertEqual(observed_text, \"hello\\n\")\r\n return\r\n else:\r\n sleep(10)\r\n # if we get here we failed\r\n self.fail(\"The test job apparently never finished.\\n\"\r\n + \"check the jobs error log and check the queue status\\n.\")","def check_jobs(self):\n # New/aborted jobs\n try:\n jobs = self.sm.get_job('%', phase = 'QUEUED')\n for job in jobs:\n self._launch_job(Job(job['job']))\n res = self.sm.get_aborted_jobs()\n aborts = [x['identifier'] for x in res]\n # Completed jobs\n for t in self.threads:\n if t.isDone() or t.name in aborts:\n self.threads.remove(t)\n # Set job status to COMPLETED\n job = Job(self.sm.get_job(t.name)[0]['job'])\n if t._Future__excpt == None:\n job.set_phase('COMPLETED')\n if t._Future__result != None:\n job.set_results(t._Future__result) \n status = True\n else:\n job.set_phase('ERROR')\n job.set_error_summary(str(t._Future__excpt[1]).replace(\"'\", \"\"))\n status = False\n job.set_end_time(datetime.utcnow().isoformat())\n self.sm.update_job(job = job, completed = status)\n except Exception, e:\n print \"Error:\", e","def test_create_job(self):\n engine = Engine(self.config_file, self.api_token)\n\n engine.create_job()\n\n assert engine.ingest_job_id == 23","def test_get_refresh_job_status(self):\n pass","def test_valid_n_jobs(n_jobs: Any) -> None:\n check_n_jobs(n_jobs)","def test_job_replacement():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'job_status': 'submitted'})\n\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'addition': 'test',\n 'job_status': 'finished'},\n replace=True)\n\n status = Status(STATUS_DIR).data['generation']['test1']['job_status']\n addition = Status(STATUS_DIR).data['generation']['test1']['addition']\n assert status == 'finished'\n assert addition == 'test'","def test_successful_job(self):\n\n successful_job = json.loads(TREEHERDER_JOB % (\"success\", \"completed\"))\n self.assertEquals(self.query_api.get_job_status(successful_job), SUCCESS)","def test_successful(self):\n\n url = '/%s/jobs/' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n for entry in result['results']:\n expected = None\n if entry['id'] == self.job1.id:\n expected = self.job1\n elif entry['id'] == self.job2.id:\n expected = self.job2\n elif entry['id'] == self.job3.id:\n expected = self.job3\n else:\n self.fail('Found unexpected result: %s' % entry['id'])\n self.assertEqual(entry['job_type']['name'], expected.job_type.name)\n self.assertEqual(entry['job_type_rev']['job_type']['id'], expected.job_type.id)\n self.assertEqual(entry['is_superseded'], expected.is_superseded)","def test_check_opt_crashed(self):\n self.assertEqual(check_opt(self.jobset2.job), 'ocrashed')","def test_check_job_dependencies_not_finished(database):\n sess = database.session\n sub = SubmissionFactory(submission_id=1)\n job = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['waiting'],\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'])\n sess.add_all([sub, job])\n sess.commit()\n\n with pytest.raises(ValueError):\n check_job_dependencies(job.job_id)","def check_sp_job(self,\n label: str,\n job: 'JobAdapter',\n ):\n if 'mrci' in self.sp_level.method and job.level is not None and 'mrci' not in job.level.method:\n # This is a CCSD job ran before MRCI. Spawn MRCI\n self.run_sp_job(label)\n elif job.job_status[1]['status'] == 'done':\n self.post_sp_actions(label,\n sp_path=os.path.join(job.local_path_to_output_file),\n level=job.level,\n )\n # Update restart dictionary and save the yaml restart file:\n self.save_restart_dict()\n if self.species_dict[label].number_of_atoms == 1:\n # save the geometry from the sp job for monoatomic species for which no opt/freq jobs will be spawned\n self.output[label]['paths']['geo'] = job.local_path_to_output_file\n else:\n self.troubleshoot_ess(label=label,\n job=job,\n level_of_theory=job.level,\n )","def test_start_already_running(self, mock_add_job, mock_get_job):\n mock_get_job.return_value = MagicMock()\n\n result = self.aggregator.start(self.node_id)\n\n self.assertFalse(result)\n self.assertFalse(mock_add_job.called)","def test_job_pass(self):\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \"extras/tests/dummy_jobs\")):\n\n module = \"test_pass\"\n name = \"TestPass\"\n job_class = get_job(f\"local/{module}/{name}\")\n\n job_result = JobResult.objects.create(\n name=job_class.class_path,\n obj_type=self.job_content_type,\n user=None,\n job_id=uuid.uuid4(),\n )\n\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\n job_result.refresh_from_db()\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)","def test_local_job(self):\r\n exp = \"abc; echo $? > xyz\"\r\n obs = local_job('abc', 'xyz', 'notused', 'notused')\r\n self.assertEqual(obs, exp)","def testA_StraightThrough(self):\n # Do pre-submit job check\n nRunning = getCondorRunningJobs()\n self.assertEqual(nRunning, 0, \"User currently has %i running jobs. Test will not continue\" % (nRunning))\n\n myThread = threading.currentThread()\n workload = self.createTestWorkload()\n config = self.getConfig()\n\n\n name = 'WMAgent_Test1'\n site = self.sites[0]\n nSubs = 5\n nFiles = 10\n workloadPath = os.path.join(self.testDir, 'workloadTest',\n 'TestWorkload', 'WMSandbox',\n 'WMWorkload.pkl')\n\n # Create a collection of files\n self.createFileCollection(name = name, nSubs = nSubs,\n nFiles = nFiles,\n workflowURL = workloadPath,\n site = site)\n\n\n\n ############################################################\n # Test the JobCreator\n\n\n config.Agent.componentName = 'JobCreator'\n testJobCreator = JobCreatorPoller(config = config)\n\n testJobCreator.algorithm()\n time.sleep(5)\n\n\n # Did all jobs get created?\n getJobsAction = self.daoFactory(classname = \"Jobs.GetAllJobs\")\n result = getJobsAction.execute(state = 'Created', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs*nFiles)\n\n\n # Count database objects\n result = myThread.dbi.processData('SELECT * FROM wmbs_sub_files_acquired')[0].fetchall()\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n # Find the test directory\n testDirectory = os.path.join(self.testDir, 'TestWorkload', 'ReReco')\n self.assertTrue('JobCollection_1_0' in os.listdir(testDirectory))\n self.assertTrue(len(os.listdir(testDirectory)) <= 20)\n\n groupDirectory = os.path.join(testDirectory, 'JobCollection_1_0')\n\n # First job should be in here\n self.assertTrue('job_1' in os.listdir(groupDirectory))\n jobFile = os.path.join(groupDirectory, 'job_1', 'job.pkl')\n self.assertTrue(os.path.isfile(jobFile))\n with open(jobFile, 'rb') as f:\n job = pickle.load(f)\n\n\n self.assertEqual(job['workflow'], name)\n self.assertEqual(len(job['input_files']), 1)\n self.assertEqual(os.path.basename(job['sandbox']), 'TestWorkload-Sandbox.tar.bz2')\n\n\n\n\n\n\n\n\n\n\n ###############################################################\n # Now test the JobSubmitter\n\n config.Agent.componentName = 'JobSubmitter'\n testJobSubmitter = JobSubmitterPoller(config = config)\n\n\n testJobSubmitter.algorithm()\n\n\n # Check that jobs are in the right state\n result = getJobsAction.execute(state = 'Created', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Executing', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n\n # Check assigned locations\n getLocationAction = self.daoFactory(classname = \"Jobs.GetLocation\")\n for id in result:\n loc = getLocationAction.execute(jobid = id)\n self.assertEqual(loc, [[site]])\n\n\n # Check to make sure we have running jobs\n nRunning = getCondorRunningJobs()\n self.assertEqual(nRunning, nFiles * nSubs)\n\n\n #################################################################\n # Now the JobTracker\n\n\n config.Agent.componentName = 'JobTracker'\n testJobTracker = JobTrackerPoller(config = config)\n testJobTracker.setup()\n\n testJobTracker.algorithm()\n\n # Running the algo without removing the jobs should do nothing\n result = getJobsAction.execute(state = 'Executing', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n condorRM()\n time.sleep(1)\n\n # All jobs gone?\n nRunning = getCondorRunningJobs()\n self.assertEqual(nRunning, 0)\n\n\n testJobTracker.algorithm()\n time.sleep(5)\n\n # Running the algo without removing the jobs should do nothing\n result = getJobsAction.execute(state = 'Executing', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Complete', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n\n\n #################################################################\n # Now the JobAccountant\n\n # First you need to load all jobs\n\n\n self.getFWJRAction = self.daoFactory(classname = \"Jobs.GetFWJRByState\")\n completeJobs = self.getFWJRAction.execute(state = \"complete\")\n\n\n # Create reports for all jobs\n self.createReports(jobs = completeJobs, retryCount = 0)\n\n\n\n\n\n\n config.Agent.componentName = 'JobAccountant'\n testJobAccountant = JobAccountantPoller(config = config)\n testJobAccountant.setup()\n\n\n # It should do something with the jobs\n testJobAccountant.algorithm()\n\n\n # All the jobs should be done now\n result = getJobsAction.execute(state = 'Complete', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Success', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n\n #######################################################################\n # Now the JobArchiver\n\n\n config.Agent.componentName = 'JobArchiver'\n testJobArchiver = JobArchiverPoller(config = config)\n\n\n testJobArchiver.algorithm()\n\n # All the jobs should be cleaned up\n result = getJobsAction.execute(state = 'Success', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Cleanout', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n logDir = os.path.join(self.testDir, 'logs')\n\n for job in completeJobs:\n self.assertFalse(os.path.exists(job['fwjr_path']))\n jobFolder = 'JobCluster_%i' \\\n % (int(job['id']/config.JobArchiver.numberOfJobsToCluster))\n jobPath = os.path.join(logDir, jobFolder, 'Job_%i.tar' %(job['id']))\n self.assertTrue(os.path.isfile(jobPath))\n self.assertTrue(os.path.getsize(jobPath) > 0)\n\n\n\n\n ###########################################################################\n # Now the TaskAchiver\n\n\n config.Agent.componentName = 'TaskArchiver'\n testTaskArchiver = TaskArchiverPoller(config = config)\n\n\n testTaskArchiver.algorithm()\n\n\n result = getJobsAction.execute(state = 'Cleanout', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n\n\n for jdict in completeJobs:\n job = Job(id = jdict['id'])\n self.assertFalse(job.exists())\n\n\n\n\n\n if os.path.isdir('testDir'):\n shutil.rmtree('testDir')\n shutil.copytree('%s' %self.testDir, os.path.join(os.getcwd(), 'testDir'))\n\n\n\n\n return","def job_exists(self, job_id):\n\n return True if self.get_status(job_id) else False","def test_get_job(self):\n response = self.client.open(\n '/v1/job/{id}'.format(id='id_example'),\n method='GET',\n content_type='application/json')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","def test_running_job(self):\n\n running_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"running\"))\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def jobHealthy(self, count):\n job = self.tester.submission_result.job\n for idx in range(count - 1):\n if (job.health == 'healthy'):\n return True\n print(\"health check fail : %d\" % idx )\n time.sleep(1)\n job.refresh()\n self.assertEqual('healthy', job.health)\n return False","def test_post_pay_job_does_not_exist(self):\n freelancer_id = self.create_object('api/freelancers',\n self.valid_freelancers[0])\n\n payment = {'freelancer_id': freelancer_id,\n 'job_id': -100, # Non existing job id\n 'amount': 300}\n\n response = requests.post(self.BASE_API + self.ENDPOINT,\n data=json.dumps(payment))\n\n self.assertEqual(400, response.status_code,\n msg=(f'Able to receive a non existing '\n f'job in a transaction'\n f'Response code: {response.status_code}'))","def test_pending_job(self):\n\n pending_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"pending\"))\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def test_status(self):\n\n url = '/%s/jobs/?status=RUNNING' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 1)\n self.assertEqual(result['results'][0]['job_type']['id'], self.job1.job_type.id)","def test_check_opt_complete(self):\n self.assertEqual(check_opt(self.jobset3.job), 'completed')","def test_make_jobs(self):\r\n # no commands should make no jobs files\r\n self.assertEqual(make_jobs([], \"test\", self.queue), [])\r\n\r\n # one job file should be created\r\n filenames = make_jobs([self.command], \"test_qsub\", self.queue)\r\n self.assertTrue(len(filenames) == 1)\r\n observed_text = list(open(filenames[0]))\r\n\r\n self.assertEqual(\"\".join(observed_text),\r\n QSUB_TEXT % (\"72:00:00\", 1, 1, self.queue,\r\n \"test_qsub\", \"oe\",\r\n self.command))","def test_creating_a_job(created_job, bulk_request):\n assert created_job.job == 'THEJOBID'\n assert created_job.job_url == 'https://salesforce/services/async/34.0/job/THEJOBID'\n assert created_job.pending_batches == []\n assert created_job.is_open\n\n bulk_request.assert_called_once_with(\n 'post',\n 'https://salesforce/services/async/34.0/job',\n data=XMLMatcher('''<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n <jobInfo xmlns=\"http://www.force.com/2009/06/asyncapi/dataload\">\n <operation>update</operation>\n <object>Lead</object>\n <contentType>CSV</contentType>\n </jobInfo>\n ''')\n )","def maybe_commit(job):","def test_can_create_job(self):\n\t\tself.job.save()\n\t\tjob_instance = Job.objects.get(pk=1)\n\t\tself.assertEqual(\n\t\t\tjob_instance.created_by,\n\t\t\tself.created_by,\n\t\t\t\"created_by fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.organisation,\n\t\t\tself.organisation,\n\t\t\t\"organisation fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.category,\n\t\t\tself.category,\n\t\t\t\"category fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.title,\n\t\t\tself.title,\n\t\t\t\"title fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.description,\n\t\t\tself.description,\n\t\t\t\"description fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.allow_comments,\n\t\t\tself.allow_comments,\n\t\t\t\"allow_comments fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.start_accepting_applications_at,\n\t\t\tself.start_accepting_applications_at,\n\t\t\t\"start_accepting_applications_at fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.stop_accepting_applications_at,\n\t\t\tself.stop_accepting_applications_at,\n\t\t\t\"stop_accepting_applications_at fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.employment_term,\n\t\t\tself.employment_term,\n\t\t\t\"employment_term fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.seniority_level,\n\t\t\tself.seniority_level,\n\t\t\t\"seniority_level fields don't match.\"\n\t\t)\n\t\tself.assertEqual(\n\t\t\tjob_instance.location,\n\t\t\tself.location,\n\t\t\t\"location fields don't match.\"\n\t\t)","def test_post_job(self):\n body = UnitTesterJobCreateReq()\n response = self.client.open(\n '/v1/job',\n method='POST',\n data=json.dumps(body),\n content_type='application/json')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","def test_ready_only_job_pass(self):\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \"extras/tests/dummy_jobs\")):\n\n module = \"test_read_only_pass\"\n name = \"TestReadOnlyPass\"\n job_class = get_job(f\"local/{module}/{name}\")\n\n job_result = JobResult.objects.create(\n name=job_class.class_path,\n obj_type=self.job_content_type,\n user=None,\n job_id=uuid.uuid4(),\n )\n\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\n job_result.refresh_from_db()\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)\n self.assertEqual(Site.objects.count(), 0) # Ensure DB transaction was aborted","def test_upload_orchestration_no_batches(new_job):\n with mock.patch.object(new_job, 'create') as create, \\\n mock.patch.object(new_job, 'add_batch') as add_batch, \\\n mock.patch.object(new_job, 'abort') as abort, \\\n mock.patch.object(new_job, 'close') as close, \\\n mock.patch.object(new_job, 'wait') as wait:\n\n new_job.upload(['Id', 'Description'], [])\n\n create.assert_called_once_with()\n assert add_batch.call_count == 0\n abort.assert_called_once_with()\n assert close.call_count == 0\n assert wait.call_count == 0","def CheckIfJobFinished(jobid, numseq, to_email, g_params): # {{{\n bsname = \"job_final_process\"\n path_result = os.path.join(g_params['path_static'], 'result')\n rstdir = os.path.join(path_result, jobid)\n gen_logfile = g_params['gen_logfile']\n gen_errfile = g_params['gen_errfile']\n name_server = g_params['name_server']\n g_params['jobid'] = jobid\n g_params['numseq'] = numseq\n g_params['to_email'] = to_email\n jsonfile = os.path.join(rstdir, f\"{bsname}.json\")\n myfunc.WriteFile(json.dumps(g_params, sort_keys=True), jsonfile, \"w\")\n binpath_script = os.path.join(g_params['webserver_root'], \"env\", \"bin\")\n\n finished_idx_file = \"%s/finished_seqindex.txt\"%(rstdir)\n failed_idx_file = \"%s/failed_seqindex.txt\"%(rstdir)\n py_scriptfile = os.path.join(binpath_script, f\"{bsname}.py\")\n finished_idx_list = []\n failed_idx_list = []\n if os.path.exists(finished_idx_file):\n finished_idx_list = list(set(myfunc.ReadIDList(finished_idx_file)))\n if os.path.exists(failed_idx_file):\n failed_idx_list = list(set(myfunc.ReadIDList(failed_idx_file)))\n\n lockname = f\"{bsname}.lock\"\n lock_file = os.path.join(g_params['path_result'], g_params['jobid'],\n lockname)\n\n num_processed = len(finished_idx_list)+len(failed_idx_list)\n if num_processed >= numseq: # finished\n if ('THRESHOLD_NUMSEQ_CHECK_IF_JOB_FINISH' in g_params\n and numseq <= g_params['THRESHOLD_NUMSEQ_CHECK_IF_JOB_FINISH']):\n cmd = [\"python\", py_scriptfile, \"-i\", jsonfile]\n (isSubmitSuccess, t_runtime) = webcom.RunCmd(cmd, gen_logfile, gen_errfile)\n elif not os.path.exists(lock_file):\n bash_scriptfile = f\"{rstdir}/{bsname},{name_server},{jobid}.sh\"\n code_str_list = []\n code_str_list.append(\"#!/bin/bash\")\n cmdline = f\"python {py_scriptfile} -i {jsonfile}\"\n code_str_list.append(cmdline)\n code = \"\\n\".join(code_str_list)\n myfunc.WriteFile(code, bash_scriptfile, mode=\"w\", isFlush=True)\n os.chmod(bash_scriptfile, 0o755)\n os.chdir(rstdir)\n cmd = ['sbatch', bash_scriptfile]\n cmdline = \" \".join(cmd)\n verbose = False\n if 'DEBUG' in g_params and g_params['DEBUG']:\n verbose = True\n webcom.loginfo(\"Run cmdline: %s\"%(cmdline), gen_logfile)\n (isSubmitSuccess, t_runtime) = webcom.RunCmd(cmd, gen_logfile, gen_errfile, verbose)\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"isSubmitSuccess: %s\"%(str(isSubmitSuccess)), gen_logfile)","def examine_job(self):\n if self.data is None:\n print(\"Could not download job id\", self.job_id)\n return\n self.duration = self.read_data(\"duration\")\n self.between_commands = self.read_data(\"between_commands\")\n\n print(\"---\")\n print(\"test_id: {}\".format(self.job_id))\n print(\"duration:\")\n Job.print_results(self.duration)\n print(\"between_commands:\")\n Job.print_results(self.between_commands)\n print(\"\")","def test_successful(self):\n\n url = '/%s/job-types/status/' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 4)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)","def mark(self, job, status='succeeded'):\n pass","def _check_analyzed_job(self, job, container):\n self.log(u\"Checking the Job object generated from container\")\n\n self.log(u\"Checking that the Job is not None\")\n if job is None:\n self._failed(u\"Unable to create a Job from the container.\")\n return\n\n self.log(u\"Checking that the Job has at least one Task\")\n if len(job) == 0:\n self._failed(u\"Unable to create at least one Task from the container.\")\n return\n\n if self.rconf[RuntimeConfiguration.JOB_MAX_TASKS] > 0:\n self.log(u\"Checking that the Job does not have too many Tasks\")\n if len(job) > self.rconf[RuntimeConfiguration.JOB_MAX_TASKS]:\n self._failed(u\"The Job has %d Tasks, more than the maximum allowed (%d).\" % (\n len(job),\n self.rconf[RuntimeConfiguration.JOB_MAX_TASKS]\n ))\n return\n\n self.log(u\"Checking that each Task text file is well formed\")\n for task in job.tasks:\n self.log([u\"Checking Task text file '%s'\", task.text_file_path])\n text_file_bstring = container.read_entry(task.text_file_path)\n if (text_file_bstring is None) or (len(text_file_bstring) == 0):\n self._failed(u\"Text file '%s' is empty\" % task.text_file_path)\n return\n self._check_utf8_encoding(text_file_bstring)\n if not self.result.passed:\n self._failed(u\"Text file '%s' is not encoded in UTF-8\" % task.text_file_path)\n return\n self._check_not_empty(text_file_bstring)\n if not self.result.passed:\n self._failed(u\"Text file '%s' is empty\" % task.text_file_path)\n return\n self.log([u\"Checking Task text file '%s': passed\", task.text_file_path])\n self.log(u\"Checking each Task text file is well formed: passed\")","def _validate_jobs(\n self,\n check_nlst_warn: bool = False\n ):\n counter = 0\n for job in self.jobs:\n counter += 1\n print(job.job_id)\n if counter == 0:\n ignore_restarts = False\n else:\n ignore_restarts = True\n\n check_input_files(\n hrldas_namelist=job.hrldas_namelist,\n hydro_namelist=job.hydro_namelist,\n sim_dir=os.getcwd(),\n ignore_restarts=ignore_restarts,\n check_nlst_warn=check_nlst_warn\n )","def test_jobset(self):\n self.assertEqual(self.jobset.indir, 'indir')","def test_call_first_time(self, query_repo_url, get_credentials, valid_revision, get):\n self.assertEquals(\n self.query_api._get_all_jobs(\"try\", \"146071751b1e\"),\n json.loads(JOBS_SCHEDULE))\n\n assert get.call_count == 1\n\n # Test that this fills our caches\n self.assertEquals(\n query_jobs.JOBS_CACHE[(\"try\", \"146071751b1e\")],\n json.loads(JOBS_SCHEDULE))","def test_successful_on_get(self):\n\n url = '/%s/jobs/' % self.api\n\n response = self.client.get(url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)","def in_queue(self):\n if self.get_db('jobid') is None:\n log.debug('jobid not found for calculation.')\n return False\n else:\n # get the jobid\n jobid = self.get_db('jobid')\n # see if jobid is in queue\n _, jobids_in_queue, _ = getstatusoutput('qselect',\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n\n if str(jobid) in jobids_in_queue.split('\\n'):\n # get details on specific jobid in case it is complete\n status, output, err = getstatusoutput(['qstat', jobid],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n if status == 0:\n lines = output.split('\\n')\n fields = lines[2].split()\n job_status = fields[4]\n if job_status == 'C':\n return False\n else:\n return True\n else:\n return False","def test_006_add(self):\n HEADING()\n db = self.db\n\n count = 5\n\n db.connect()\n\n db.delete_jobs()\n\n for id in range(0,count):\n job = db.insert(\"job\" + str(id))\n\n assert len(db) == count","def test_ensure_not_ts_pass(self):\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')","def test_getJobState(self):\n self.assertRaises(JobNotExistsError, self.foo.getJobState)\n self.foo.setTorqueJobId('asd')\n self.assertEqual(self.foo.getJobState(), 'NEW')","def test_job_fail(self):\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \"extras/tests/dummy_jobs\")):\n\n module = \"test_fail\"\n name = \"TestFail\"\n job_class = get_job(f\"local/{module}/{name}\")\n job_result = JobResult.objects.create(\n name=job_class.class_path,\n obj_type=self.job_content_type,\n user=None,\n job_id=uuid.uuid4(),\n )\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\n job_result.refresh_from_db()\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_ERRORED)","def isFinished(self):\r\n try:\r\n output = Popen(\"qstat | grep \"+self.jobId, shell=True, stdout=PIPE, stderr=PIPE).communicate()[0]\r\n if self.jobId in output:\r\n if output.split()[4] == \"Eqw\":\r\n #If the job fails, print a warning, and wait a minute so the user can check why the job fails,\r\n #before resubmitting the job.\r\n logging.warning(\"job \" + output.split()[2] + \" failed to run, resubmitting in one minute\")\r\n time.sleep(60)\r\n output = Popen(\"qdel \"+self.jobId, shell=True, stdout=PIPE, stderr=PIPE).communicate()[0]\r\n self.submit()\r\n return False\r\n else:\r\n logging.info(\"job with ID: \" + self.jobId + \" is finished.\")\r\n return True\r\n \r\n except ValueError:\r\n logging.info(\"Error: waiting for not submitted job...\")","def test_add_seed_job_type(self):\n\n url = '/%s/job-types/' % self.api\n manifest = copy.deepcopy(job_test_utils.COMPLETE_MANIFEST)\n manifest['job']['name'] = 'my-new-job'\n\n json_data = {\n 'icon_code': 'BEEF',\n 'is_published': True,\n 'docker_image': 'my-new-job-1.0.0-seed:1.0.0',\n 'manifest': manifest,\n 'configuration': self.configuration\n }\n\n good_setting = {\n 'DB_HOST': 'scale'\n }\n\n response = self.client.generic('POST', url, json.dumps(json_data), 'application/json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED, response.content)\n self.assertTrue('/%s/job-types/my-new-job/1.0.0/' % self.api in response['location'])\n\n job_type = JobType.objects.filter(name='my-new-job').first()\n\n results = json.loads(response.content)\n self.assertEqual(results['id'], job_type.id)\n self.assertEqual(results['version'], job_type.version)\n self.assertEqual(results['title'], job_type.get_title())\n self.assertEqual(results['revision_num'], job_type.revision_num)\n self.assertEqual(results['revision_num'], 1)\n self.assertIsNone(results['max_scheduled'])\n self.assertEqual(results['configuration']['settings'], good_setting)","def test_enqueue(self):\n dbpool = buildConnectionPool(self, jobSchema + schemaText)\n yield self._enqueue(dbpool, 1, 2)\n\n # Make sure we have one JOB and one DUMMY_WORK_ITEM\n @transactionally(dbpool.connection)\n def checkJob(txn):\n return JobItem.all(txn)\n\n jobs = yield checkJob\n self.assertTrue(len(jobs) == 1)\n self.assertTrue(jobs[0].workType == \"DUMMY_WORK_ITEM\")\n self.assertTrue(jobs[0].assigned is None)\n self.assertEqual(jobs[0].isAssigned, 0)\n\n @transactionally(dbpool.connection)\n def checkWork(txn):\n return DummyWorkItem.all(txn)\n\n work = yield checkWork\n self.assertTrue(len(work) == 1)\n self.assertTrue(work[0].jobID == jobs[0].jobID)","def test_cachedjob_waits_for_new_job_pre_existing_job(uncached_job):\n\n # Setup\n cached_job = uncached_job\n initial_job_key = cached_job.current_job_id\n\n # Execute\n cached_job.wait_for_new_job(sleep=2)\n final_job_key = cached_job.current_job_id\n\n # Verify\n assert initial_job_key == None\n assert initial_job_key != final_job_key\n assert final_job_key == JOB_ID","def testJobEmpty(databases):\n\n def checkEmpty(config):\n assert config.getParameter('') == None\n assert len(config.getInputs()) == 0\n assert len(config.getInputsGrouped()) == 0\n assert len(config.getTasks()) == 0\n assert len(config.getTaskSequence()) == 0\n\n config = DataGenerator(databases).createConfiguration()\n runWithReload(databases, config, checkEmpty)","def test_create_ingest_job(self):\n ingest_mgmr = IngestManager()\n ingest_mgmr.validate_config_file(self.example_config_data)\n ingest_mgmr.validate_properties()\n ingest_mgmr.owner = self.user.pk\n job = ingest_mgmr.create_ingest_job()\n assert (job.id is not None)","def test_wrong_job(self):\n job = MockJob()\n container_name = 'container_identifier'\n self._mock_open.return_value.__enter__.return_value.readline. \\\n return_value = container_name\n\n # return an error trying to read file with container name\n self._mock_open.return_value.__enter__.side_effect = [\n PermissionError,\n FileNotFoundError,\n ]\n\n self.assertNotEqual(job.pid, 0)\n # non-zero pid, no container name is found -> refer to ForkSpawner\n validation_result = self._container_spawner.validate(job)\n self._mock_open.assert_called()\n self._mock_fork.assert_called()\n self.assertEqual(validation_result,\n spawner.PROCESS_RUNNING)\n # same check, a different exception\n self.assertEqual(self._container_spawner.validate(job),\n spawner.PROCESS_RUNNING)","def test_successful_empty(self):\n\n url = '/%s/jobs/%i/' % (self.api, self.job.id)\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(result['max_tries'], 3)\n self.assertEqual(result['job_type']['name'], self.job.job_type.name)\n self.assertEqual(result['job_type_rev']['job_type']['name'], self.job.job_type.name)\n\n if self.recipe:\n self.assertEqual(result['recipe']['recipe_type']['name'], self.recipe.recipe_type.name)\n else:\n self.assertEqual(len(result['recipe']), 0)","def test_job_delete_non_existing(self):\n self.assertRaises(exceptions.NotFoundException,\n self.client.jobs.delete, 'does-not-exist')","def isJobRunning ( self ):\n #cmd = \"qstat \" + str(self.jobid)\n \n #magicString='Unknown Job Id' ### magicString _might_ need to be changed if Torque version changes\n #(output, error) = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()\n\n if self.ofile_exists(): #output.find(magicString) >=0 or redhawkStatsRe.search(output):\n self.status = \"finished\"\n return False\n \n\n return True","def checkjob(sid, jid):\n with slycat.web.server.remote.get_session(sid) as session:\n return session.checkjob(jid)","def test_running_job(self):\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)","def test_batch(self):\n batch = batch_test_utils.create_batch()\n self.job1.batch_id = batch.id\n self.job1.save()\n\n url = '/%s/jobs/?batch_id=%d' % (self.api, batch.id)\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 1)\n self.assertEqual(result['results'][0]['id'], self.job1.id)","def test_job_id(self):\n\n url = '/%s/jobs/?job_id=%s' % (self.api, self.job1.id)\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 1)\n self.assertEqual(result['results'][0]['id'], self.job1.id)","def testJobKilling(self):\n change = ChangeState(self.config, \"changestate_t\")\n\n locationAction = self.daoFactory(classname=\"Locations.New\")\n locationAction.execute(\"site1\", pnn=\"T2_CH_CERN\")\n\n testWorkflow = Workflow(spec=self.specUrl, owner=\"Steve\",\n name=\"wf001\", task=self.taskName)\n testWorkflow.create()\n testFileset = Fileset(name=\"TestFileset\")\n testFileset.create()\n\n for i in range(4):\n newFile = File(lfn=\"File%s\" % i, locations=set([\"T2_CH_CERN\"]))\n newFile.create()\n testFileset.addFile(newFile)\n\n testFileset.commit()\n testSubscription = Subscription(fileset=testFileset,\n workflow=testWorkflow,\n split_algo=\"FileBased\")\n testSubscription.create()\n\n splitter = SplitterFactory()\n jobFactory = splitter(package=\"WMCore.WMBS\",\n subscription=testSubscription)\n jobGroup = jobFactory(files_per_job=1)[0]\n\n assert len(jobGroup.jobs) == 4, \\\n \"Error: Splitting should have created four jobs.\"\n\n testJobA = jobGroup.jobs[0]\n testJobA[\"user\"] = \"sfoulkes\"\n testJobA[\"group\"] = \"DMWM\"\n testJobA[\"taskType\"] = \"Processing\"\n testJobB = jobGroup.jobs[1]\n testJobB[\"user\"] = \"sfoulkes\"\n testJobB[\"group\"] = \"DMWM\"\n testJobB[\"taskType\"] = \"Processing\"\n testJobC = jobGroup.jobs[2]\n testJobC[\"user\"] = \"sfoulkes\"\n testJobC[\"group\"] = \"DMWM\"\n testJobC[\"taskType\"] = \"Processing\"\n testJobD = jobGroup.jobs[3]\n testJobD[\"user\"] = \"sfoulkes\"\n testJobD[\"group\"] = \"DMWM\"\n testJobD[\"taskType\"] = \"Processing\"\n\n change.persist([testJobA], \"created\", \"new\")\n change.persist([testJobB], \"jobfailed\", \"executing\")\n change.persist([testJobC, testJobD], \"executing\", \"created\")\n\n change.persist([testJobA], \"killed\", \"created\")\n change.persist([testJobB], \"killed\", \"jobfailed\")\n change.persist([testJobC, testJobD], \"killed\", \"executing\")\n\n for job in [testJobA, testJobB, testJobC, testJobD]:\n job.load()\n self.assertEqual(job['retry_count'], 99999)\n self.assertEqual(job['state'], 'killed')\n\n return","def test_successful(self):\n\n url = '/%s/job-types/running/' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 1)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job.job_type.name)\n self.assertEqual(result['results'][0]['count'], 1)\n self.assertIsNotNone(result['results'][0]['longest_running'])","def check_job_status_by_id(job_id):\n print('=' * 40)\n print('check_status_by_job_id', job_id)\n print('=' * 40)\n\n it_worked = check_job_status(job)\n if it_worked:\n return ok_resp(job)\n\n user_msg = ('PreprocessJob still in process: %s') % (job_id)\n return err_resp(user_msg)","def test_cachedjob_job_key(uncached_job):\n\n # Setup\n c_job = uncached_job\n\n # Execute\n c_job.wait_for_new_job(sleep=2)\n\n # Verify\n assert c_job.current_job_id == JOB_ID","def test_matching_jobs_invalid(self):\n self.assertEquals(\n self.query_api.get_matching_jobs(\n \"try\", \"146071751b1e\",\n 'Invalid buildername'), [])","def testJobOneTask(databases):\n\n class CustomGenerator(DataGenerator):\n numTasks = 1\n numInputs = [ 0 ]\n numOutputs = [ 0 ]\n\n gen = CustomGenerator(databases)\n gen.createDefinitions()\n config = gen.createConfiguration()\n\n def checkOne(config):\n taskName = gen.tasks[0]\n #assert config.getProduct('') is None\n assert len(config.getInputs()) == 0\n assert len(config.getInputsGrouped()) == 0\n assert len(config.getTasks()) == 1\n task, = config.getTasks()\n assert task is not None\n assert task.getName() == taskName\n assert len(config.getTaskSequence()) == 1\n\n runWithReload(databases, config, checkOne)","def test_raises_on_missing_job(self):\n from furious.async import Async\n from furious.errors import NotInContextError\n from furious.processors import run_job\n\n work = Async(\"nothere\")\n work._options.pop('job')\n assert 'job' not in work._options\n\n self.assertRaises(NotInContextError, run_job)","def test_updateJobState(self):\n self.assertRaises(JobNotExistsError, self.foo.updateJobState, 'asd')\n self.foo.setTorqueJobId('asd')\n self.assertEqual(self.foo.getJobState(), 'NEW')","def test_job_delete_completed_job(self):\n test_app = self._create_app()\n class_path = \"spark.jobserver.VeryShortDoubleJob\"\n job = self.client.jobs.create(test_app, class_path,\n ctx=self._get_functional_context())\n time.sleep(3)\n self._wait_till_job_is_done(job)\n self.assertRaises(exceptions.NotFoundException,\n self.client.jobs.delete, job.jobId)","def _add_job_data(\n self,\n job: Job,\n ) -> Tuple[str, bool]:\n jid = job.job_id()\n try:\n job_result = job.result()\n self._add_result_data(job_result, jid)\n LOG.debug(\"Job data added [Job ID: %s]\", jid)\n # sets the endtime to be the time the last successful job was added\n self.end_datetime = datetime.now()\n return jid, True\n except Exception as ex: # pylint: disable=broad-except\n # Handle cancelled jobs\n status = job.status()\n if status == JobStatus.CANCELLED:\n LOG.warning(\"Job was cancelled before completion [Job ID: %s]\", jid)\n return jid, False\n if status == JobStatus.ERROR:\n LOG.error(\n \"Job data not added for errored job [Job ID: %s]\\nError message: %s\",\n jid,\n job.error_message(),\n )\n return jid, False\n LOG.warning(\"Adding data from job failed [Job ID: %s]\", job.job_id())\n raise ex","async def test_haos(coresys: CoreSys):\n\n class TestClass:\n \"\"\"Test class.\"\"\"\n\n def __init__(self, coresys: CoreSys):\n \"\"\"Initialize the test class.\"\"\"\n self.coresys = coresys\n\n @Job(conditions=[JobCondition.HAOS])\n async def execute(self):\n \"\"\"Execute the class method.\"\"\"\n return True\n\n test = TestClass(coresys)\n coresys.hassos._available = True\n assert await test.execute()\n\n coresys.hassos._available = False\n assert not await test.execute()","def work(self, job):\n pass","def check_slurm_job_submission(expected_name):\n cmd = ['scontrol', 'show', 'job']\n job_id = 0\n found_job = False\n while True:\n while True:\n try:\n out = Popen(cmd, stdout=PIPE, stderr=PIPE).communicate()[0]\n break\n except:\n sleep(1)\n out = out.split('\\n')\n if 'error' in out[0]:\n sleep(1)\n msg = 'Error checking job status for {0}'.format(expected_name)\n logging.warning(msg)\n continue\n for line in out:\n for word in line.split():\n if 'JobId' in word:\n index = word.find('=') + 1\n job_id = int(word[index:])\n # continue\n if 'Name' in word:\n index = word.find('=') + 1\n if word[index:] == expected_name:\n found_job = True\n\n if found_job and job_id != 0:\n return found_job, job_id\n sleep(1)\n return found_job, job_id","def _check_phase(self, jobid):\n\n self._existing_tables()\n\n time.sleep(1)\n\n if jobid not in self.job_dict.keys():\n log.error(\"Job not present in job dictionary.\")\n return\n\n else:\n phase = self.job_dict[str(jobid)]\n return phase","def test_successful_job(self, _is_coalesced):\n successful_job = json.loads(BASE_JSON % (SUCCESS, 1433166610, 1, 1433166609))[0]\n self.assertEquals(self.query_api.get_job_status(successful_job), SUCCESS)","def check(args, session: Session = NEW_SESSION) -> None:\n if args.allow_multiple and not args.limit > 1:\n raise SystemExit(\"To use option --allow-multiple, you must set the limit to a value greater than 1.\")\n if args.hostname and args.local:\n raise SystemExit(\"You can't use --hostname and --local at the same time\")\n\n query = select(Job).where(Job.state == JobState.RUNNING).order_by(Job.latest_heartbeat.desc())\n if args.job_type:\n query = query.where(Job.job_type == args.job_type)\n if args.hostname:\n query = query.where(Job.hostname == args.hostname)\n if args.local:\n query = query.where(Job.hostname == get_hostname())\n if args.limit > 0:\n query = query.limit(args.limit)\n\n alive_jobs: list[Job] = [job for job in session.scalars(query) if job.is_alive()]\n\n count_alive_jobs = len(alive_jobs)\n if count_alive_jobs == 0:\n raise SystemExit(\"No alive jobs found.\")\n if count_alive_jobs > 1 and not args.allow_multiple:\n raise SystemExit(f\"Found {count_alive_jobs} alive jobs. Expected only one.\")\n if count_alive_jobs == 1:\n print(\"Found one alive job.\")\n else:\n print(f\"Found {count_alive_jobs} alive jobs.\")","def job_status(bot, update, args, job_queue, chat_data):\n if len(args) == 0:\n update.message.reply_text('No parameter provided')\n return\n\n job_name = args[0]\n if job_name not in settings.JOBS:\n update.message.reply_text(\n 'Sorry {0} is not a valid job'.format(job_name))\n return\n\n job = find_job(job_name, job_queue)\n\n if not job:\n update.message.reply_text('{0} job is not running'.format(job_name))\n return\n\n update.message.reply_text('{0} job is running'.format(job_name))","def test_successful(self):\n\n url = '/%s/job-types/pending/' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 1)\n self.assertEqual(result['results'][0]['job_type']['name'], self.job.job_type.name)\n self.assertEqual(result['results'][0]['count'], 1)\n self.assertIsNotNone(result['results'][0]['longest_pending'])","def on_job_update(_job):\n nonlocal job\n job = _job\n # Asserts that job is either pending or canceled.\n assert job.state in ['PENDING', 'CANCELED'], (\n 'job that canceled immediately after submission has wrong '\n 'state `%s`' % job.state\n )","def test_cluster_jobs_script(self):\r\n\r\n qiime_config = load_qiime_config()\r\n submit_script = qiime_config['cluster_jobs_fp']\r\n\r\n if (submit_script):\r\n full_path = which(submit_script)\r\n if full_path:\r\n submit_script = full_path\r\n self.assertTrue(exists(submit_script),\r\n \"cluster_jobs_fp is not set to a valid path in qiime config: %s\" % submit_script)\r\n # check if executable\r\n self.assertTrue(access(submit_script, X_OK),\r\n \"cluster_jobs_fp is not executable: %s\" % submit_script)\r\n else:\r\n # Can't run in parallel, but not a critical error\r\n pass","def test_dies_if_no_job(self):\n from furious.async import Async\n from furious.context._execution import _ExecutionContext\n from furious.processors import run_job\n\n work = Async(\"dir\", kwargs={'something': None})\n work._options.pop('job')\n assert 'job' not in work._options\n\n with _ExecutionContext(work):\n self.assertRaises(Exception, run_job)","def test_successful(self, mock_create, mock_msg_mgr):\n\n json_data = {\n \"input\" : {\n 'version': '6',\n 'files': {'input_a': [self.source_file.id]},\n 'json': {}\n },\n \"job_type_id\" : self.job_type1.pk\n }\n\n url = '/%s/jobs/' % self.api\n response = self.client.generic('POST', url, json.dumps(json_data), 'application/json')\n self.assertEqual(response.status_code, status.HTTP_201_CREATED, response.content)\n\n result = json.loads(response.content)\n\n #Response should be new v6 job detail response\n self.assertEqual(result['execution'], None)\n self.assertEqual(result['max_tries'], 3)\n self.assertTrue('/%s/jobs/' % self.api in response['location'])\n mock_create.assert_called_once()","def add_job_state(self, job):","def check_job_status(job):\n assert isinstance(job, PreprocessJob),\\\n 'job must be a PreprocessJob'\n\n if job.is_finished():\n return True\n\n return True\n \"\"\"\n ye_task = AsyncResult(job.task_id,\n app=preprocess_csv_file)\n\n if ye_task.state == 'SUCCESS':\n\n if ye_task.result['success']:\n\n preprocess_data = ContentFile(json.dumps(ye_task.result['data']))\n\n new_name = 'preprocess_%s.json' % get_alphanumeric_lowercase(8)\n job.metadata_file.save(new_name,\n preprocess_data)\n job.set_state_success()\n\n job.user_message = 'Task completed! Preprocess is available'\n job.save()\n\n else:\n # Didn't work so well\n job.set_state_failure(ye_task.result['message'])\n job.save()\n\n ye_task.forget()\n return True\n\n elif ye_task.state == STATE_FAILURE:\n job.set_state_failure('ye_task failed....')\n job.save()\n ye_task.forget()\n return True\n\n return False\n \"\"\"","def _check_jobs_submitted(status, module):\n\n submitted = False\n if module in status.data:\n jobs = status.data[module]\n for job in jobs.keys():\n if job != 'pipeline_index':\n submitted = True\n break\n return submitted","def test_check_job_dependencies_ready(mock_sqs_queue, database):\n # Mock so it always returns the mock queue for the test\n mock_sqs_queue.return_value = SQSMockQueue\n sess = database.session\n sub = SubmissionFactory(submission_id=1)\n job = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['finished'],\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'],\n number_of_errors=0)\n job_2 = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['waiting'],\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'])\n sess.add_all([sub, job, job_2])\n sess.commit()\n\n # Job 1 finished, it is a prerequisite for job 2 (waiting) but it has errors\n job_dep = JobDependency(job_id=job_2.job_id, prerequisite_id=job.job_id)\n sess.add(job_dep)\n sess.commit()\n\n check_job_dependencies(job.job_id)\n\n assert job_2.job_status_id == JOB_STATUS_DICT['ready']","async def test_misusage(my_job):\n\n # Disable some pylint checks, they are redundant here in the test.\n # pylint: disable=missing-docstring,unused-variable,function-redefined\n\n # Check that namesake job managers are not allowed.\n class MyNamesakeJobManager(jobmanager.JobManager):\n pass\n with pytest.raises(AssertionError,\n message='Successfully made two job managers with the '\n 'same names!'):\n class MyNamesakeJobManager(jobmanager.JobManager):\n pass\n\n # Check that it is impossible to register two job functions with the\n # same name inside a single job manager, but it is possible in the\n # different job managers.\n class MyJobManager1(jobmanager.JobManager):\n pass\n\n class MyJobManager2(jobmanager.JobManager):\n pass\n\n @MyJobManager1.job()\n def my_namesake_job():\n pass\n\n @MyJobManager2.job()\n def my_namesake_job():\n pass\n\n with pytest.raises(AssertionError,\n message='Successfully registered two job function with '\n 'the same name!'):\n @MyJobManager1.job()\n def my_namesake_job():\n pass\n\n # Check that it is impossible to submit a job with an identifier\n # which is already used by some other job.\n\n # Submit 2 jobs with the same identifier and check that second\n # submission raises an exception.\n job_id = uuid.uuid4()\n\n def on_job_submit(job):\n job.id = job_id\n return job\n my_job.set_on_submit(on_job_submit)\n\n # Submit a job #1.\n await my_job.job(mustfail=False)\n\n with pytest.raises(django.db.utils.IntegrityError,\n message='Successfully submitted a job with the '\n 'identifier that has already been used for '\n 'another job!'):\n # Submit a job #2.\n await my_job.job(mustfail=False)\n\n # Process ASGI messages and wait for the job to finish.\n await my_job.process_jobs()","def remove_job_if_exists(self, name: str, context: CallbackContext) -> bool:\n current_jobs = context.job_queue.get_jobs_by_name(name)\n if not current_jobs:\n return False\n for job in current_jobs:\n job.schedule_removal()\n return True","def remove_job_if_exists(self, name: str, context: CallbackContext) -> bool:\n current_jobs = context.job_queue.get_jobs_by_name(name)\n if not current_jobs:\n return False\n for job in current_jobs:\n job.schedule_removal()\n return True","def test_py_job_create(self):\n test_app = self._create_py_app()\n class_path = \"example_jobs.word_count.WordCountSparkSessionJob\"\n conf = \"input.strings = ['a', 'b', 'a', 'b']\"\n job = self._create_job(test_app, class_path, conf,\n ctx=self._get_functional_py_context())\n time.sleep(3)\n self.assertTrue(len(job.jobId) > 0)\n self.assertTrue(job.status == \"STARTED\")\n self._wait_till_job_is_done(job)","def _addjob(self, job: Job):\n if self.domain is not None and self.model is not None:\n job = copy.deepcopy(job)\n job._add_hydro_namelist(self.base_hydro_namelist)\n job._add_hrldas_namelist(self.base_hrldas_namelist)\n\n self.jobs.append(job)\n else:\n raise AttributeError('Can not add a job to a simulation without a model and a domain')","def remove_job_if_exists(name: str, context: ContextTypes.DEFAULT_TYPE) -> bool:\n current_jobs = context.job_queue.get_jobs_by_name(name)\n if not current_jobs:\n return False\n for job in current_jobs:\n job.schedule_removal()\n return True","def verify_job(cls, auth_key, job_id):\n key = ObjectId(job_id)\n user_id = ObjectId(auth_key)\n db = cls.mongo_cli.get_database(collection=Job.collection_name)\n if db.count({\"_id\": key, \"user_id\": user_id}) > 0:\n return True\n return False","def test_reenqueue_job1(client):\n with client.application.app_context():\n pytest.skip(\"Not implemented\")","def remove_job_if_exists(name, context):\n current_jobs = context.job_queue.get_jobs_by_name(name)\n if not current_jobs:\n return False\n for job in current_jobs:\n job.schedule_removal()\n return True"],"string":"[\n \"def test_job_exists():\\n with tempfile.TemporaryDirectory() as STATUS_DIR:\\n Status.add_job(STATUS_DIR, 'generation', 'test1',\\n job_attrs={'job_status': 'submitted'})\\n exists = Status.job_exists(STATUS_DIR, 'test1')\\n assert exists\",\n \"def test_matching_jobs_existing(self):\\n self.assertEquals(\\n self.query_api.get_matching_jobs(\\n \\\"try\\\", \\\"146071751b1e\\\",\\n 'Linux x86-64 try build'), json.loads(JOBS_SCHEDULE))\",\n \"def job_exists(self, job):\\n\\n with open(os.devnull, 'w') as devnull:\\n result = subprocess.call(self.cli + [PlatformJenkinsJavaCLI.GET_JOB, job.name], stdout=devnull)\\n\\n return result == 0\",\n \"def check_job_exists( job_list, analysis_group_id, reprocess_config_id):\\n for job in job_list:\\n struct = JSONMessage.unserialize(job.input_message)\\n\\n if( int( struct.analysis_group_id ) == int( analysis_group_id ) and \\\\\\n int( struct.reprocess_config_id ) == int( reprocess_config_id ) ):\\n return 1\\n return 0\",\n \"def test_fake_job( self ):\\n hda = MockHda( job=UNDEFINED_JOB )\\n self.history.active_datasets.append( hda )\\n job_dict, warnings = extract.summarize( trans=self.trans )\\n assert not warnings\\n assert len( job_dict ) == 1\\n fake_job = job_dict.keys()[ 0 ]\\n assert fake_job.id.startswith( \\\"fake_\\\" )\\n datasets = job_dict.values()[ 0 ]\\n assert datasets == [ ( None, hda ) ]\",\n \"def _check_for_finished_job(self):\\n raise NotImplementedError\",\n \"def test_jobs_successful(self):\\n\\n workspace = storage_test_utils.create_workspace()\\n file1 = storage_test_utils.create_file()\\n data_dict = {\\n 'version': '1.0',\\n 'input_data': [{\\n 'name': 'INPUT_IMAGE',\\n 'file_id': file1.id\\n }],\\n 'output_data': [{\\n 'name': 'output_file_pngs',\\n 'workspace_id': workspace.id\\n }]}\\n\\n secret_configuration = {\\n 'version': '6',\\n 'priority': 50,\\n 'output_workspaces': {'default': storage_test_utils.create_workspace().name},\\n 'settings': {\\n 'DB_HOST': 'som.host.name',\\n 'DB_PASS': 'secret_password'\\n }\\n }\\n\\n seed_job_type = job_test_utils.create_seed_job_type(configuration=secret_configuration)\\n seed_job = job_test_utils.create_job(job_type=seed_job_type, status='RUNNING', input=data_dict)\\n\\n url = '/%s/jobs/%d/' % (self.api, seed_job.id)\\n response = self.client.generic('GET', url)\\n result = json.loads(response.content)\\n\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n self.assertEqual(result['configuration']['priority'],50)\\n self.assertNotIn('DB_PASS', result['configuration']['settings'])\",\n \"def test_submit_jobs(self):\\r\\n\\r\\n submit_jobs([self.command], prefix=\\\"test_job\\\")\\r\\n # Try and wait ten times, could be made nicer with alarm()\\r\\n for i in range(10):\\r\\n if exists(self.tmp_result_file):\\r\\n observed_text = \\\"\\\".join(list(open(self.tmp_result_file)))\\r\\n self.assertEqual(observed_text, \\\"hello\\\\n\\\")\\r\\n return\\r\\n else:\\r\\n sleep(10)\\r\\n # if we get here we failed\\r\\n self.fail(\\\"The test job apparently never finished.\\\\n\\\"\\r\\n + \\\"check the jobs error log and check the queue status\\\\n.\\\")\",\n \"def check_jobs(self):\\n # New/aborted jobs\\n try:\\n jobs = self.sm.get_job('%', phase = 'QUEUED')\\n for job in jobs:\\n self._launch_job(Job(job['job']))\\n res = self.sm.get_aborted_jobs()\\n aborts = [x['identifier'] for x in res]\\n # Completed jobs\\n for t in self.threads:\\n if t.isDone() or t.name in aborts:\\n self.threads.remove(t)\\n # Set job status to COMPLETED\\n job = Job(self.sm.get_job(t.name)[0]['job'])\\n if t._Future__excpt == None:\\n job.set_phase('COMPLETED')\\n if t._Future__result != None:\\n job.set_results(t._Future__result) \\n status = True\\n else:\\n job.set_phase('ERROR')\\n job.set_error_summary(str(t._Future__excpt[1]).replace(\\\"'\\\", \\\"\\\"))\\n status = False\\n job.set_end_time(datetime.utcnow().isoformat())\\n self.sm.update_job(job = job, completed = status)\\n except Exception, e:\\n print \\\"Error:\\\", e\",\n \"def test_create_job(self):\\n engine = Engine(self.config_file, self.api_token)\\n\\n engine.create_job()\\n\\n assert engine.ingest_job_id == 23\",\n \"def test_get_refresh_job_status(self):\\n pass\",\n \"def test_valid_n_jobs(n_jobs: Any) -> None:\\n check_n_jobs(n_jobs)\",\n \"def test_job_replacement():\\n with tempfile.TemporaryDirectory() as STATUS_DIR:\\n Status.add_job(STATUS_DIR, 'generation', 'test1',\\n job_attrs={'job_status': 'submitted'})\\n\\n Status.add_job(STATUS_DIR, 'generation', 'test1',\\n job_attrs={'addition': 'test',\\n 'job_status': 'finished'},\\n replace=True)\\n\\n status = Status(STATUS_DIR).data['generation']['test1']['job_status']\\n addition = Status(STATUS_DIR).data['generation']['test1']['addition']\\n assert status == 'finished'\\n assert addition == 'test'\",\n \"def test_successful_job(self):\\n\\n successful_job = json.loads(TREEHERDER_JOB % (\\\"success\\\", \\\"completed\\\"))\\n self.assertEquals(self.query_api.get_job_status(successful_job), SUCCESS)\",\n \"def test_successful(self):\\n\\n url = '/%s/jobs/' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n for entry in result['results']:\\n expected = None\\n if entry['id'] == self.job1.id:\\n expected = self.job1\\n elif entry['id'] == self.job2.id:\\n expected = self.job2\\n elif entry['id'] == self.job3.id:\\n expected = self.job3\\n else:\\n self.fail('Found unexpected result: %s' % entry['id'])\\n self.assertEqual(entry['job_type']['name'], expected.job_type.name)\\n self.assertEqual(entry['job_type_rev']['job_type']['id'], expected.job_type.id)\\n self.assertEqual(entry['is_superseded'], expected.is_superseded)\",\n \"def test_check_opt_crashed(self):\\n self.assertEqual(check_opt(self.jobset2.job), 'ocrashed')\",\n \"def test_check_job_dependencies_not_finished(database):\\n sess = database.session\\n sub = SubmissionFactory(submission_id=1)\\n job = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['waiting'],\\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'])\\n sess.add_all([sub, job])\\n sess.commit()\\n\\n with pytest.raises(ValueError):\\n check_job_dependencies(job.job_id)\",\n \"def check_sp_job(self,\\n label: str,\\n job: 'JobAdapter',\\n ):\\n if 'mrci' in self.sp_level.method and job.level is not None and 'mrci' not in job.level.method:\\n # This is a CCSD job ran before MRCI. Spawn MRCI\\n self.run_sp_job(label)\\n elif job.job_status[1]['status'] == 'done':\\n self.post_sp_actions(label,\\n sp_path=os.path.join(job.local_path_to_output_file),\\n level=job.level,\\n )\\n # Update restart dictionary and save the yaml restart file:\\n self.save_restart_dict()\\n if self.species_dict[label].number_of_atoms == 1:\\n # save the geometry from the sp job for monoatomic species for which no opt/freq jobs will be spawned\\n self.output[label]['paths']['geo'] = job.local_path_to_output_file\\n else:\\n self.troubleshoot_ess(label=label,\\n job=job,\\n level_of_theory=job.level,\\n )\",\n \"def test_start_already_running(self, mock_add_job, mock_get_job):\\n mock_get_job.return_value = MagicMock()\\n\\n result = self.aggregator.start(self.node_id)\\n\\n self.assertFalse(result)\\n self.assertFalse(mock_add_job.called)\",\n \"def test_job_pass(self):\\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \\\"extras/tests/dummy_jobs\\\")):\\n\\n module = \\\"test_pass\\\"\\n name = \\\"TestPass\\\"\\n job_class = get_job(f\\\"local/{module}/{name}\\\")\\n\\n job_result = JobResult.objects.create(\\n name=job_class.class_path,\\n obj_type=self.job_content_type,\\n user=None,\\n job_id=uuid.uuid4(),\\n )\\n\\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\\n job_result.refresh_from_db()\\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)\",\n \"def test_local_job(self):\\r\\n exp = \\\"abc; echo $? > xyz\\\"\\r\\n obs = local_job('abc', 'xyz', 'notused', 'notused')\\r\\n self.assertEqual(obs, exp)\",\n \"def testA_StraightThrough(self):\\n # Do pre-submit job check\\n nRunning = getCondorRunningJobs()\\n self.assertEqual(nRunning, 0, \\\"User currently has %i running jobs. Test will not continue\\\" % (nRunning))\\n\\n myThread = threading.currentThread()\\n workload = self.createTestWorkload()\\n config = self.getConfig()\\n\\n\\n name = 'WMAgent_Test1'\\n site = self.sites[0]\\n nSubs = 5\\n nFiles = 10\\n workloadPath = os.path.join(self.testDir, 'workloadTest',\\n 'TestWorkload', 'WMSandbox',\\n 'WMWorkload.pkl')\\n\\n # Create a collection of files\\n self.createFileCollection(name = name, nSubs = nSubs,\\n nFiles = nFiles,\\n workflowURL = workloadPath,\\n site = site)\\n\\n\\n\\n ############################################################\\n # Test the JobCreator\\n\\n\\n config.Agent.componentName = 'JobCreator'\\n testJobCreator = JobCreatorPoller(config = config)\\n\\n testJobCreator.algorithm()\\n time.sleep(5)\\n\\n\\n # Did all jobs get created?\\n getJobsAction = self.daoFactory(classname = \\\"Jobs.GetAllJobs\\\")\\n result = getJobsAction.execute(state = 'Created', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs*nFiles)\\n\\n\\n # Count database objects\\n result = myThread.dbi.processData('SELECT * FROM wmbs_sub_files_acquired')[0].fetchall()\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n # Find the test directory\\n testDirectory = os.path.join(self.testDir, 'TestWorkload', 'ReReco')\\n self.assertTrue('JobCollection_1_0' in os.listdir(testDirectory))\\n self.assertTrue(len(os.listdir(testDirectory)) <= 20)\\n\\n groupDirectory = os.path.join(testDirectory, 'JobCollection_1_0')\\n\\n # First job should be in here\\n self.assertTrue('job_1' in os.listdir(groupDirectory))\\n jobFile = os.path.join(groupDirectory, 'job_1', 'job.pkl')\\n self.assertTrue(os.path.isfile(jobFile))\\n with open(jobFile, 'rb') as f:\\n job = pickle.load(f)\\n\\n\\n self.assertEqual(job['workflow'], name)\\n self.assertEqual(len(job['input_files']), 1)\\n self.assertEqual(os.path.basename(job['sandbox']), 'TestWorkload-Sandbox.tar.bz2')\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n ###############################################################\\n # Now test the JobSubmitter\\n\\n config.Agent.componentName = 'JobSubmitter'\\n testJobSubmitter = JobSubmitterPoller(config = config)\\n\\n\\n testJobSubmitter.algorithm()\\n\\n\\n # Check that jobs are in the right state\\n result = getJobsAction.execute(state = 'Created', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Executing', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n\\n # Check assigned locations\\n getLocationAction = self.daoFactory(classname = \\\"Jobs.GetLocation\\\")\\n for id in result:\\n loc = getLocationAction.execute(jobid = id)\\n self.assertEqual(loc, [[site]])\\n\\n\\n # Check to make sure we have running jobs\\n nRunning = getCondorRunningJobs()\\n self.assertEqual(nRunning, nFiles * nSubs)\\n\\n\\n #################################################################\\n # Now the JobTracker\\n\\n\\n config.Agent.componentName = 'JobTracker'\\n testJobTracker = JobTrackerPoller(config = config)\\n testJobTracker.setup()\\n\\n testJobTracker.algorithm()\\n\\n # Running the algo without removing the jobs should do nothing\\n result = getJobsAction.execute(state = 'Executing', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n condorRM()\\n time.sleep(1)\\n\\n # All jobs gone?\\n nRunning = getCondorRunningJobs()\\n self.assertEqual(nRunning, 0)\\n\\n\\n testJobTracker.algorithm()\\n time.sleep(5)\\n\\n # Running the algo without removing the jobs should do nothing\\n result = getJobsAction.execute(state = 'Executing', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Complete', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n\\n\\n #################################################################\\n # Now the JobAccountant\\n\\n # First you need to load all jobs\\n\\n\\n self.getFWJRAction = self.daoFactory(classname = \\\"Jobs.GetFWJRByState\\\")\\n completeJobs = self.getFWJRAction.execute(state = \\\"complete\\\")\\n\\n\\n # Create reports for all jobs\\n self.createReports(jobs = completeJobs, retryCount = 0)\\n\\n\\n\\n\\n\\n\\n config.Agent.componentName = 'JobAccountant'\\n testJobAccountant = JobAccountantPoller(config = config)\\n testJobAccountant.setup()\\n\\n\\n # It should do something with the jobs\\n testJobAccountant.algorithm()\\n\\n\\n # All the jobs should be done now\\n result = getJobsAction.execute(state = 'Complete', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Success', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n\\n #######################################################################\\n # Now the JobArchiver\\n\\n\\n config.Agent.componentName = 'JobArchiver'\\n testJobArchiver = JobArchiverPoller(config = config)\\n\\n\\n testJobArchiver.algorithm()\\n\\n # All the jobs should be cleaned up\\n result = getJobsAction.execute(state = 'Success', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Cleanout', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n logDir = os.path.join(self.testDir, 'logs')\\n\\n for job in completeJobs:\\n self.assertFalse(os.path.exists(job['fwjr_path']))\\n jobFolder = 'JobCluster_%i' \\\\\\n % (int(job['id']/config.JobArchiver.numberOfJobsToCluster))\\n jobPath = os.path.join(logDir, jobFolder, 'Job_%i.tar' %(job['id']))\\n self.assertTrue(os.path.isfile(jobPath))\\n self.assertTrue(os.path.getsize(jobPath) > 0)\\n\\n\\n\\n\\n ###########################################################################\\n # Now the TaskAchiver\\n\\n\\n config.Agent.componentName = 'TaskArchiver'\\n testTaskArchiver = TaskArchiverPoller(config = config)\\n\\n\\n testTaskArchiver.algorithm()\\n\\n\\n result = getJobsAction.execute(state = 'Cleanout', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n\\n\\n for jdict in completeJobs:\\n job = Job(id = jdict['id'])\\n self.assertFalse(job.exists())\\n\\n\\n\\n\\n\\n if os.path.isdir('testDir'):\\n shutil.rmtree('testDir')\\n shutil.copytree('%s' %self.testDir, os.path.join(os.getcwd(), 'testDir'))\\n\\n\\n\\n\\n return\",\n \"def job_exists(self, job_id):\\n\\n return True if self.get_status(job_id) else False\",\n \"def test_get_job(self):\\n response = self.client.open(\\n '/v1/job/{id}'.format(id='id_example'),\\n method='GET',\\n content_type='application/json')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"def test_running_job(self):\\n\\n running_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"running\\\"))\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def jobHealthy(self, count):\\n job = self.tester.submission_result.job\\n for idx in range(count - 1):\\n if (job.health == 'healthy'):\\n return True\\n print(\\\"health check fail : %d\\\" % idx )\\n time.sleep(1)\\n job.refresh()\\n self.assertEqual('healthy', job.health)\\n return False\",\n \"def test_post_pay_job_does_not_exist(self):\\n freelancer_id = self.create_object('api/freelancers',\\n self.valid_freelancers[0])\\n\\n payment = {'freelancer_id': freelancer_id,\\n 'job_id': -100, # Non existing job id\\n 'amount': 300}\\n\\n response = requests.post(self.BASE_API + self.ENDPOINT,\\n data=json.dumps(payment))\\n\\n self.assertEqual(400, response.status_code,\\n msg=(f'Able to receive a non existing '\\n f'job in a transaction'\\n f'Response code: {response.status_code}'))\",\n \"def test_pending_job(self):\\n\\n pending_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"pending\\\"))\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def test_status(self):\\n\\n url = '/%s/jobs/?status=RUNNING' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 1)\\n self.assertEqual(result['results'][0]['job_type']['id'], self.job1.job_type.id)\",\n \"def test_check_opt_complete(self):\\n self.assertEqual(check_opt(self.jobset3.job), 'completed')\",\n \"def test_make_jobs(self):\\r\\n # no commands should make no jobs files\\r\\n self.assertEqual(make_jobs([], \\\"test\\\", self.queue), [])\\r\\n\\r\\n # one job file should be created\\r\\n filenames = make_jobs([self.command], \\\"test_qsub\\\", self.queue)\\r\\n self.assertTrue(len(filenames) == 1)\\r\\n observed_text = list(open(filenames[0]))\\r\\n\\r\\n self.assertEqual(\\\"\\\".join(observed_text),\\r\\n QSUB_TEXT % (\\\"72:00:00\\\", 1, 1, self.queue,\\r\\n \\\"test_qsub\\\", \\\"oe\\\",\\r\\n self.command))\",\n \"def test_creating_a_job(created_job, bulk_request):\\n assert created_job.job == 'THEJOBID'\\n assert created_job.job_url == 'https://salesforce/services/async/34.0/job/THEJOBID'\\n assert created_job.pending_batches == []\\n assert created_job.is_open\\n\\n bulk_request.assert_called_once_with(\\n 'post',\\n 'https://salesforce/services/async/34.0/job',\\n data=XMLMatcher('''<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>\\n <jobInfo xmlns=\\\"http://www.force.com/2009/06/asyncapi/dataload\\\">\\n <operation>update</operation>\\n <object>Lead</object>\\n <contentType>CSV</contentType>\\n </jobInfo>\\n ''')\\n )\",\n \"def maybe_commit(job):\",\n \"def test_can_create_job(self):\\n\\t\\tself.job.save()\\n\\t\\tjob_instance = Job.objects.get(pk=1)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.created_by,\\n\\t\\t\\tself.created_by,\\n\\t\\t\\t\\\"created_by fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.organisation,\\n\\t\\t\\tself.organisation,\\n\\t\\t\\t\\\"organisation fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.category,\\n\\t\\t\\tself.category,\\n\\t\\t\\t\\\"category fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.title,\\n\\t\\t\\tself.title,\\n\\t\\t\\t\\\"title fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.description,\\n\\t\\t\\tself.description,\\n\\t\\t\\t\\\"description fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.allow_comments,\\n\\t\\t\\tself.allow_comments,\\n\\t\\t\\t\\\"allow_comments fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.start_accepting_applications_at,\\n\\t\\t\\tself.start_accepting_applications_at,\\n\\t\\t\\t\\\"start_accepting_applications_at fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.stop_accepting_applications_at,\\n\\t\\t\\tself.stop_accepting_applications_at,\\n\\t\\t\\t\\\"stop_accepting_applications_at fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.employment_term,\\n\\t\\t\\tself.employment_term,\\n\\t\\t\\t\\\"employment_term fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.seniority_level,\\n\\t\\t\\tself.seniority_level,\\n\\t\\t\\t\\\"seniority_level fields don't match.\\\"\\n\\t\\t)\\n\\t\\tself.assertEqual(\\n\\t\\t\\tjob_instance.location,\\n\\t\\t\\tself.location,\\n\\t\\t\\t\\\"location fields don't match.\\\"\\n\\t\\t)\",\n \"def test_post_job(self):\\n body = UnitTesterJobCreateReq()\\n response = self.client.open(\\n '/v1/job',\\n method='POST',\\n data=json.dumps(body),\\n content_type='application/json')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"def test_ready_only_job_pass(self):\\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \\\"extras/tests/dummy_jobs\\\")):\\n\\n module = \\\"test_read_only_pass\\\"\\n name = \\\"TestReadOnlyPass\\\"\\n job_class = get_job(f\\\"local/{module}/{name}\\\")\\n\\n job_result = JobResult.objects.create(\\n name=job_class.class_path,\\n obj_type=self.job_content_type,\\n user=None,\\n job_id=uuid.uuid4(),\\n )\\n\\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\\n job_result.refresh_from_db()\\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)\\n self.assertEqual(Site.objects.count(), 0) # Ensure DB transaction was aborted\",\n \"def test_upload_orchestration_no_batches(new_job):\\n with mock.patch.object(new_job, 'create') as create, \\\\\\n mock.patch.object(new_job, 'add_batch') as add_batch, \\\\\\n mock.patch.object(new_job, 'abort') as abort, \\\\\\n mock.patch.object(new_job, 'close') as close, \\\\\\n mock.patch.object(new_job, 'wait') as wait:\\n\\n new_job.upload(['Id', 'Description'], [])\\n\\n create.assert_called_once_with()\\n assert add_batch.call_count == 0\\n abort.assert_called_once_with()\\n assert close.call_count == 0\\n assert wait.call_count == 0\",\n \"def CheckIfJobFinished(jobid, numseq, to_email, g_params): # {{{\\n bsname = \\\"job_final_process\\\"\\n path_result = os.path.join(g_params['path_static'], 'result')\\n rstdir = os.path.join(path_result, jobid)\\n gen_logfile = g_params['gen_logfile']\\n gen_errfile = g_params['gen_errfile']\\n name_server = g_params['name_server']\\n g_params['jobid'] = jobid\\n g_params['numseq'] = numseq\\n g_params['to_email'] = to_email\\n jsonfile = os.path.join(rstdir, f\\\"{bsname}.json\\\")\\n myfunc.WriteFile(json.dumps(g_params, sort_keys=True), jsonfile, \\\"w\\\")\\n binpath_script = os.path.join(g_params['webserver_root'], \\\"env\\\", \\\"bin\\\")\\n\\n finished_idx_file = \\\"%s/finished_seqindex.txt\\\"%(rstdir)\\n failed_idx_file = \\\"%s/failed_seqindex.txt\\\"%(rstdir)\\n py_scriptfile = os.path.join(binpath_script, f\\\"{bsname}.py\\\")\\n finished_idx_list = []\\n failed_idx_list = []\\n if os.path.exists(finished_idx_file):\\n finished_idx_list = list(set(myfunc.ReadIDList(finished_idx_file)))\\n if os.path.exists(failed_idx_file):\\n failed_idx_list = list(set(myfunc.ReadIDList(failed_idx_file)))\\n\\n lockname = f\\\"{bsname}.lock\\\"\\n lock_file = os.path.join(g_params['path_result'], g_params['jobid'],\\n lockname)\\n\\n num_processed = len(finished_idx_list)+len(failed_idx_list)\\n if num_processed >= numseq: # finished\\n if ('THRESHOLD_NUMSEQ_CHECK_IF_JOB_FINISH' in g_params\\n and numseq <= g_params['THRESHOLD_NUMSEQ_CHECK_IF_JOB_FINISH']):\\n cmd = [\\\"python\\\", py_scriptfile, \\\"-i\\\", jsonfile]\\n (isSubmitSuccess, t_runtime) = webcom.RunCmd(cmd, gen_logfile, gen_errfile)\\n elif not os.path.exists(lock_file):\\n bash_scriptfile = f\\\"{rstdir}/{bsname},{name_server},{jobid}.sh\\\"\\n code_str_list = []\\n code_str_list.append(\\\"#!/bin/bash\\\")\\n cmdline = f\\\"python {py_scriptfile} -i {jsonfile}\\\"\\n code_str_list.append(cmdline)\\n code = \\\"\\\\n\\\".join(code_str_list)\\n myfunc.WriteFile(code, bash_scriptfile, mode=\\\"w\\\", isFlush=True)\\n os.chmod(bash_scriptfile, 0o755)\\n os.chdir(rstdir)\\n cmd = ['sbatch', bash_scriptfile]\\n cmdline = \\\" \\\".join(cmd)\\n verbose = False\\n if 'DEBUG' in g_params and g_params['DEBUG']:\\n verbose = True\\n webcom.loginfo(\\\"Run cmdline: %s\\\"%(cmdline), gen_logfile)\\n (isSubmitSuccess, t_runtime) = webcom.RunCmd(cmd, gen_logfile, gen_errfile, verbose)\\n if 'DEBUG' in g_params and g_params['DEBUG']:\\n webcom.loginfo(\\\"isSubmitSuccess: %s\\\"%(str(isSubmitSuccess)), gen_logfile)\",\n \"def examine_job(self):\\n if self.data is None:\\n print(\\\"Could not download job id\\\", self.job_id)\\n return\\n self.duration = self.read_data(\\\"duration\\\")\\n self.between_commands = self.read_data(\\\"between_commands\\\")\\n\\n print(\\\"---\\\")\\n print(\\\"test_id: {}\\\".format(self.job_id))\\n print(\\\"duration:\\\")\\n Job.print_results(self.duration)\\n print(\\\"between_commands:\\\")\\n Job.print_results(self.between_commands)\\n print(\\\"\\\")\",\n \"def test_successful(self):\\n\\n url = '/%s/job-types/status/' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 4)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job1.job_type.name)\\n self.assertEqual(result['results'][0]['job_counts'][0]['count'], 1)\",\n \"def mark(self, job, status='succeeded'):\\n pass\",\n \"def _check_analyzed_job(self, job, container):\\n self.log(u\\\"Checking the Job object generated from container\\\")\\n\\n self.log(u\\\"Checking that the Job is not None\\\")\\n if job is None:\\n self._failed(u\\\"Unable to create a Job from the container.\\\")\\n return\\n\\n self.log(u\\\"Checking that the Job has at least one Task\\\")\\n if len(job) == 0:\\n self._failed(u\\\"Unable to create at least one Task from the container.\\\")\\n return\\n\\n if self.rconf[RuntimeConfiguration.JOB_MAX_TASKS] > 0:\\n self.log(u\\\"Checking that the Job does not have too many Tasks\\\")\\n if len(job) > self.rconf[RuntimeConfiguration.JOB_MAX_TASKS]:\\n self._failed(u\\\"The Job has %d Tasks, more than the maximum allowed (%d).\\\" % (\\n len(job),\\n self.rconf[RuntimeConfiguration.JOB_MAX_TASKS]\\n ))\\n return\\n\\n self.log(u\\\"Checking that each Task text file is well formed\\\")\\n for task in job.tasks:\\n self.log([u\\\"Checking Task text file '%s'\\\", task.text_file_path])\\n text_file_bstring = container.read_entry(task.text_file_path)\\n if (text_file_bstring is None) or (len(text_file_bstring) == 0):\\n self._failed(u\\\"Text file '%s' is empty\\\" % task.text_file_path)\\n return\\n self._check_utf8_encoding(text_file_bstring)\\n if not self.result.passed:\\n self._failed(u\\\"Text file '%s' is not encoded in UTF-8\\\" % task.text_file_path)\\n return\\n self._check_not_empty(text_file_bstring)\\n if not self.result.passed:\\n self._failed(u\\\"Text file '%s' is empty\\\" % task.text_file_path)\\n return\\n self.log([u\\\"Checking Task text file '%s': passed\\\", task.text_file_path])\\n self.log(u\\\"Checking each Task text file is well formed: passed\\\")\",\n \"def _validate_jobs(\\n self,\\n check_nlst_warn: bool = False\\n ):\\n counter = 0\\n for job in self.jobs:\\n counter += 1\\n print(job.job_id)\\n if counter == 0:\\n ignore_restarts = False\\n else:\\n ignore_restarts = True\\n\\n check_input_files(\\n hrldas_namelist=job.hrldas_namelist,\\n hydro_namelist=job.hydro_namelist,\\n sim_dir=os.getcwd(),\\n ignore_restarts=ignore_restarts,\\n check_nlst_warn=check_nlst_warn\\n )\",\n \"def test_jobset(self):\\n self.assertEqual(self.jobset.indir, 'indir')\",\n \"def test_call_first_time(self, query_repo_url, get_credentials, valid_revision, get):\\n self.assertEquals(\\n self.query_api._get_all_jobs(\\\"try\\\", \\\"146071751b1e\\\"),\\n json.loads(JOBS_SCHEDULE))\\n\\n assert get.call_count == 1\\n\\n # Test that this fills our caches\\n self.assertEquals(\\n query_jobs.JOBS_CACHE[(\\\"try\\\", \\\"146071751b1e\\\")],\\n json.loads(JOBS_SCHEDULE))\",\n \"def test_successful_on_get(self):\\n\\n url = '/%s/jobs/' % self.api\\n\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\",\n \"def in_queue(self):\\n if self.get_db('jobid') is None:\\n log.debug('jobid not found for calculation.')\\n return False\\n else:\\n # get the jobid\\n jobid = self.get_db('jobid')\\n # see if jobid is in queue\\n _, jobids_in_queue, _ = getstatusoutput('qselect',\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n\\n if str(jobid) in jobids_in_queue.split('\\\\n'):\\n # get details on specific jobid in case it is complete\\n status, output, err = getstatusoutput(['qstat', jobid],\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n if status == 0:\\n lines = output.split('\\\\n')\\n fields = lines[2].split()\\n job_status = fields[4]\\n if job_status == 'C':\\n return False\\n else:\\n return True\\n else:\\n return False\",\n \"def test_006_add(self):\\n HEADING()\\n db = self.db\\n\\n count = 5\\n\\n db.connect()\\n\\n db.delete_jobs()\\n\\n for id in range(0,count):\\n job = db.insert(\\\"job\\\" + str(id))\\n\\n assert len(db) == count\",\n \"def test_ensure_not_ts_pass(self):\\n self.assertEqual(ensure_not_ts(self.jobset1), 'completed')\",\n \"def test_getJobState(self):\\n self.assertRaises(JobNotExistsError, self.foo.getJobState)\\n self.foo.setTorqueJobId('asd')\\n self.assertEqual(self.foo.getJobState(), 'NEW')\",\n \"def test_job_fail(self):\\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \\\"extras/tests/dummy_jobs\\\")):\\n\\n module = \\\"test_fail\\\"\\n name = \\\"TestFail\\\"\\n job_class = get_job(f\\\"local/{module}/{name}\\\")\\n job_result = JobResult.objects.create(\\n name=job_class.class_path,\\n obj_type=self.job_content_type,\\n user=None,\\n job_id=uuid.uuid4(),\\n )\\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\\n job_result.refresh_from_db()\\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_ERRORED)\",\n \"def isFinished(self):\\r\\n try:\\r\\n output = Popen(\\\"qstat | grep \\\"+self.jobId, shell=True, stdout=PIPE, stderr=PIPE).communicate()[0]\\r\\n if self.jobId in output:\\r\\n if output.split()[4] == \\\"Eqw\\\":\\r\\n #If the job fails, print a warning, and wait a minute so the user can check why the job fails,\\r\\n #before resubmitting the job.\\r\\n logging.warning(\\\"job \\\" + output.split()[2] + \\\" failed to run, resubmitting in one minute\\\")\\r\\n time.sleep(60)\\r\\n output = Popen(\\\"qdel \\\"+self.jobId, shell=True, stdout=PIPE, stderr=PIPE).communicate()[0]\\r\\n self.submit()\\r\\n return False\\r\\n else:\\r\\n logging.info(\\\"job with ID: \\\" + self.jobId + \\\" is finished.\\\")\\r\\n return True\\r\\n \\r\\n except ValueError:\\r\\n logging.info(\\\"Error: waiting for not submitted job...\\\")\",\n \"def test_add_seed_job_type(self):\\n\\n url = '/%s/job-types/' % self.api\\n manifest = copy.deepcopy(job_test_utils.COMPLETE_MANIFEST)\\n manifest['job']['name'] = 'my-new-job'\\n\\n json_data = {\\n 'icon_code': 'BEEF',\\n 'is_published': True,\\n 'docker_image': 'my-new-job-1.0.0-seed:1.0.0',\\n 'manifest': manifest,\\n 'configuration': self.configuration\\n }\\n\\n good_setting = {\\n 'DB_HOST': 'scale'\\n }\\n\\n response = self.client.generic('POST', url, json.dumps(json_data), 'application/json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED, response.content)\\n self.assertTrue('/%s/job-types/my-new-job/1.0.0/' % self.api in response['location'])\\n\\n job_type = JobType.objects.filter(name='my-new-job').first()\\n\\n results = json.loads(response.content)\\n self.assertEqual(results['id'], job_type.id)\\n self.assertEqual(results['version'], job_type.version)\\n self.assertEqual(results['title'], job_type.get_title())\\n self.assertEqual(results['revision_num'], job_type.revision_num)\\n self.assertEqual(results['revision_num'], 1)\\n self.assertIsNone(results['max_scheduled'])\\n self.assertEqual(results['configuration']['settings'], good_setting)\",\n \"def test_enqueue(self):\\n dbpool = buildConnectionPool(self, jobSchema + schemaText)\\n yield self._enqueue(dbpool, 1, 2)\\n\\n # Make sure we have one JOB and one DUMMY_WORK_ITEM\\n @transactionally(dbpool.connection)\\n def checkJob(txn):\\n return JobItem.all(txn)\\n\\n jobs = yield checkJob\\n self.assertTrue(len(jobs) == 1)\\n self.assertTrue(jobs[0].workType == \\\"DUMMY_WORK_ITEM\\\")\\n self.assertTrue(jobs[0].assigned is None)\\n self.assertEqual(jobs[0].isAssigned, 0)\\n\\n @transactionally(dbpool.connection)\\n def checkWork(txn):\\n return DummyWorkItem.all(txn)\\n\\n work = yield checkWork\\n self.assertTrue(len(work) == 1)\\n self.assertTrue(work[0].jobID == jobs[0].jobID)\",\n \"def test_cachedjob_waits_for_new_job_pre_existing_job(uncached_job):\\n\\n # Setup\\n cached_job = uncached_job\\n initial_job_key = cached_job.current_job_id\\n\\n # Execute\\n cached_job.wait_for_new_job(sleep=2)\\n final_job_key = cached_job.current_job_id\\n\\n # Verify\\n assert initial_job_key == None\\n assert initial_job_key != final_job_key\\n assert final_job_key == JOB_ID\",\n \"def testJobEmpty(databases):\\n\\n def checkEmpty(config):\\n assert config.getParameter('') == None\\n assert len(config.getInputs()) == 0\\n assert len(config.getInputsGrouped()) == 0\\n assert len(config.getTasks()) == 0\\n assert len(config.getTaskSequence()) == 0\\n\\n config = DataGenerator(databases).createConfiguration()\\n runWithReload(databases, config, checkEmpty)\",\n \"def test_create_ingest_job(self):\\n ingest_mgmr = IngestManager()\\n ingest_mgmr.validate_config_file(self.example_config_data)\\n ingest_mgmr.validate_properties()\\n ingest_mgmr.owner = self.user.pk\\n job = ingest_mgmr.create_ingest_job()\\n assert (job.id is not None)\",\n \"def test_wrong_job(self):\\n job = MockJob()\\n container_name = 'container_identifier'\\n self._mock_open.return_value.__enter__.return_value.readline. \\\\\\n return_value = container_name\\n\\n # return an error trying to read file with container name\\n self._mock_open.return_value.__enter__.side_effect = [\\n PermissionError,\\n FileNotFoundError,\\n ]\\n\\n self.assertNotEqual(job.pid, 0)\\n # non-zero pid, no container name is found -> refer to ForkSpawner\\n validation_result = self._container_spawner.validate(job)\\n self._mock_open.assert_called()\\n self._mock_fork.assert_called()\\n self.assertEqual(validation_result,\\n spawner.PROCESS_RUNNING)\\n # same check, a different exception\\n self.assertEqual(self._container_spawner.validate(job),\\n spawner.PROCESS_RUNNING)\",\n \"def test_successful_empty(self):\\n\\n url = '/%s/jobs/%i/' % (self.api, self.job.id)\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(result['max_tries'], 3)\\n self.assertEqual(result['job_type']['name'], self.job.job_type.name)\\n self.assertEqual(result['job_type_rev']['job_type']['name'], self.job.job_type.name)\\n\\n if self.recipe:\\n self.assertEqual(result['recipe']['recipe_type']['name'], self.recipe.recipe_type.name)\\n else:\\n self.assertEqual(len(result['recipe']), 0)\",\n \"def test_job_delete_non_existing(self):\\n self.assertRaises(exceptions.NotFoundException,\\n self.client.jobs.delete, 'does-not-exist')\",\n \"def isJobRunning ( self ):\\n #cmd = \\\"qstat \\\" + str(self.jobid)\\n \\n #magicString='Unknown Job Id' ### magicString _might_ need to be changed if Torque version changes\\n #(output, error) = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE).communicate()\\n\\n if self.ofile_exists(): #output.find(magicString) >=0 or redhawkStatsRe.search(output):\\n self.status = \\\"finished\\\"\\n return False\\n \\n\\n return True\",\n \"def checkjob(sid, jid):\\n with slycat.web.server.remote.get_session(sid) as session:\\n return session.checkjob(jid)\",\n \"def test_running_job(self):\\n running_job = json.loads(BASE_JSON % ('null', 'null', 0, 'null'))[0]\\n self.assertEquals(self.query_api.get_job_status(running_job), RUNNING)\",\n \"def test_batch(self):\\n batch = batch_test_utils.create_batch()\\n self.job1.batch_id = batch.id\\n self.job1.save()\\n\\n url = '/%s/jobs/?batch_id=%d' % (self.api, batch.id)\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 1)\\n self.assertEqual(result['results'][0]['id'], self.job1.id)\",\n \"def test_job_id(self):\\n\\n url = '/%s/jobs/?job_id=%s' % (self.api, self.job1.id)\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 1)\\n self.assertEqual(result['results'][0]['id'], self.job1.id)\",\n \"def testJobKilling(self):\\n change = ChangeState(self.config, \\\"changestate_t\\\")\\n\\n locationAction = self.daoFactory(classname=\\\"Locations.New\\\")\\n locationAction.execute(\\\"site1\\\", pnn=\\\"T2_CH_CERN\\\")\\n\\n testWorkflow = Workflow(spec=self.specUrl, owner=\\\"Steve\\\",\\n name=\\\"wf001\\\", task=self.taskName)\\n testWorkflow.create()\\n testFileset = Fileset(name=\\\"TestFileset\\\")\\n testFileset.create()\\n\\n for i in range(4):\\n newFile = File(lfn=\\\"File%s\\\" % i, locations=set([\\\"T2_CH_CERN\\\"]))\\n newFile.create()\\n testFileset.addFile(newFile)\\n\\n testFileset.commit()\\n testSubscription = Subscription(fileset=testFileset,\\n workflow=testWorkflow,\\n split_algo=\\\"FileBased\\\")\\n testSubscription.create()\\n\\n splitter = SplitterFactory()\\n jobFactory = splitter(package=\\\"WMCore.WMBS\\\",\\n subscription=testSubscription)\\n jobGroup = jobFactory(files_per_job=1)[0]\\n\\n assert len(jobGroup.jobs) == 4, \\\\\\n \\\"Error: Splitting should have created four jobs.\\\"\\n\\n testJobA = jobGroup.jobs[0]\\n testJobA[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobA[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobA[\\\"taskType\\\"] = \\\"Processing\\\"\\n testJobB = jobGroup.jobs[1]\\n testJobB[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobB[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobB[\\\"taskType\\\"] = \\\"Processing\\\"\\n testJobC = jobGroup.jobs[2]\\n testJobC[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobC[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobC[\\\"taskType\\\"] = \\\"Processing\\\"\\n testJobD = jobGroup.jobs[3]\\n testJobD[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobD[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobD[\\\"taskType\\\"] = \\\"Processing\\\"\\n\\n change.persist([testJobA], \\\"created\\\", \\\"new\\\")\\n change.persist([testJobB], \\\"jobfailed\\\", \\\"executing\\\")\\n change.persist([testJobC, testJobD], \\\"executing\\\", \\\"created\\\")\\n\\n change.persist([testJobA], \\\"killed\\\", \\\"created\\\")\\n change.persist([testJobB], \\\"killed\\\", \\\"jobfailed\\\")\\n change.persist([testJobC, testJobD], \\\"killed\\\", \\\"executing\\\")\\n\\n for job in [testJobA, testJobB, testJobC, testJobD]:\\n job.load()\\n self.assertEqual(job['retry_count'], 99999)\\n self.assertEqual(job['state'], 'killed')\\n\\n return\",\n \"def test_successful(self):\\n\\n url = '/%s/job-types/running/' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 1)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job.job_type.name)\\n self.assertEqual(result['results'][0]['count'], 1)\\n self.assertIsNotNone(result['results'][0]['longest_running'])\",\n \"def check_job_status_by_id(job_id):\\n print('=' * 40)\\n print('check_status_by_job_id', job_id)\\n print('=' * 40)\\n\\n it_worked = check_job_status(job)\\n if it_worked:\\n return ok_resp(job)\\n\\n user_msg = ('PreprocessJob still in process: %s') % (job_id)\\n return err_resp(user_msg)\",\n \"def test_cachedjob_job_key(uncached_job):\\n\\n # Setup\\n c_job = uncached_job\\n\\n # Execute\\n c_job.wait_for_new_job(sleep=2)\\n\\n # Verify\\n assert c_job.current_job_id == JOB_ID\",\n \"def test_matching_jobs_invalid(self):\\n self.assertEquals(\\n self.query_api.get_matching_jobs(\\n \\\"try\\\", \\\"146071751b1e\\\",\\n 'Invalid buildername'), [])\",\n \"def testJobOneTask(databases):\\n\\n class CustomGenerator(DataGenerator):\\n numTasks = 1\\n numInputs = [ 0 ]\\n numOutputs = [ 0 ]\\n\\n gen = CustomGenerator(databases)\\n gen.createDefinitions()\\n config = gen.createConfiguration()\\n\\n def checkOne(config):\\n taskName = gen.tasks[0]\\n #assert config.getProduct('') is None\\n assert len(config.getInputs()) == 0\\n assert len(config.getInputsGrouped()) == 0\\n assert len(config.getTasks()) == 1\\n task, = config.getTasks()\\n assert task is not None\\n assert task.getName() == taskName\\n assert len(config.getTaskSequence()) == 1\\n\\n runWithReload(databases, config, checkOne)\",\n \"def test_raises_on_missing_job(self):\\n from furious.async import Async\\n from furious.errors import NotInContextError\\n from furious.processors import run_job\\n\\n work = Async(\\\"nothere\\\")\\n work._options.pop('job')\\n assert 'job' not in work._options\\n\\n self.assertRaises(NotInContextError, run_job)\",\n \"def test_updateJobState(self):\\n self.assertRaises(JobNotExistsError, self.foo.updateJobState, 'asd')\\n self.foo.setTorqueJobId('asd')\\n self.assertEqual(self.foo.getJobState(), 'NEW')\",\n \"def test_job_delete_completed_job(self):\\n test_app = self._create_app()\\n class_path = \\\"spark.jobserver.VeryShortDoubleJob\\\"\\n job = self.client.jobs.create(test_app, class_path,\\n ctx=self._get_functional_context())\\n time.sleep(3)\\n self._wait_till_job_is_done(job)\\n self.assertRaises(exceptions.NotFoundException,\\n self.client.jobs.delete, job.jobId)\",\n \"def _add_job_data(\\n self,\\n job: Job,\\n ) -> Tuple[str, bool]:\\n jid = job.job_id()\\n try:\\n job_result = job.result()\\n self._add_result_data(job_result, jid)\\n LOG.debug(\\\"Job data added [Job ID: %s]\\\", jid)\\n # sets the endtime to be the time the last successful job was added\\n self.end_datetime = datetime.now()\\n return jid, True\\n except Exception as ex: # pylint: disable=broad-except\\n # Handle cancelled jobs\\n status = job.status()\\n if status == JobStatus.CANCELLED:\\n LOG.warning(\\\"Job was cancelled before completion [Job ID: %s]\\\", jid)\\n return jid, False\\n if status == JobStatus.ERROR:\\n LOG.error(\\n \\\"Job data not added for errored job [Job ID: %s]\\\\nError message: %s\\\",\\n jid,\\n job.error_message(),\\n )\\n return jid, False\\n LOG.warning(\\\"Adding data from job failed [Job ID: %s]\\\", job.job_id())\\n raise ex\",\n \"async def test_haos(coresys: CoreSys):\\n\\n class TestClass:\\n \\\"\\\"\\\"Test class.\\\"\\\"\\\"\\n\\n def __init__(self, coresys: CoreSys):\\n \\\"\\\"\\\"Initialize the test class.\\\"\\\"\\\"\\n self.coresys = coresys\\n\\n @Job(conditions=[JobCondition.HAOS])\\n async def execute(self):\\n \\\"\\\"\\\"Execute the class method.\\\"\\\"\\\"\\n return True\\n\\n test = TestClass(coresys)\\n coresys.hassos._available = True\\n assert await test.execute()\\n\\n coresys.hassos._available = False\\n assert not await test.execute()\",\n \"def work(self, job):\\n pass\",\n \"def check_slurm_job_submission(expected_name):\\n cmd = ['scontrol', 'show', 'job']\\n job_id = 0\\n found_job = False\\n while True:\\n while True:\\n try:\\n out = Popen(cmd, stdout=PIPE, stderr=PIPE).communicate()[0]\\n break\\n except:\\n sleep(1)\\n out = out.split('\\\\n')\\n if 'error' in out[0]:\\n sleep(1)\\n msg = 'Error checking job status for {0}'.format(expected_name)\\n logging.warning(msg)\\n continue\\n for line in out:\\n for word in line.split():\\n if 'JobId' in word:\\n index = word.find('=') + 1\\n job_id = int(word[index:])\\n # continue\\n if 'Name' in word:\\n index = word.find('=') + 1\\n if word[index:] == expected_name:\\n found_job = True\\n\\n if found_job and job_id != 0:\\n return found_job, job_id\\n sleep(1)\\n return found_job, job_id\",\n \"def _check_phase(self, jobid):\\n\\n self._existing_tables()\\n\\n time.sleep(1)\\n\\n if jobid not in self.job_dict.keys():\\n log.error(\\\"Job not present in job dictionary.\\\")\\n return\\n\\n else:\\n phase = self.job_dict[str(jobid)]\\n return phase\",\n \"def test_successful_job(self, _is_coalesced):\\n successful_job = json.loads(BASE_JSON % (SUCCESS, 1433166610, 1, 1433166609))[0]\\n self.assertEquals(self.query_api.get_job_status(successful_job), SUCCESS)\",\n \"def check(args, session: Session = NEW_SESSION) -> None:\\n if args.allow_multiple and not args.limit > 1:\\n raise SystemExit(\\\"To use option --allow-multiple, you must set the limit to a value greater than 1.\\\")\\n if args.hostname and args.local:\\n raise SystemExit(\\\"You can't use --hostname and --local at the same time\\\")\\n\\n query = select(Job).where(Job.state == JobState.RUNNING).order_by(Job.latest_heartbeat.desc())\\n if args.job_type:\\n query = query.where(Job.job_type == args.job_type)\\n if args.hostname:\\n query = query.where(Job.hostname == args.hostname)\\n if args.local:\\n query = query.where(Job.hostname == get_hostname())\\n if args.limit > 0:\\n query = query.limit(args.limit)\\n\\n alive_jobs: list[Job] = [job for job in session.scalars(query) if job.is_alive()]\\n\\n count_alive_jobs = len(alive_jobs)\\n if count_alive_jobs == 0:\\n raise SystemExit(\\\"No alive jobs found.\\\")\\n if count_alive_jobs > 1 and not args.allow_multiple:\\n raise SystemExit(f\\\"Found {count_alive_jobs} alive jobs. Expected only one.\\\")\\n if count_alive_jobs == 1:\\n print(\\\"Found one alive job.\\\")\\n else:\\n print(f\\\"Found {count_alive_jobs} alive jobs.\\\")\",\n \"def job_status(bot, update, args, job_queue, chat_data):\\n if len(args) == 0:\\n update.message.reply_text('No parameter provided')\\n return\\n\\n job_name = args[0]\\n if job_name not in settings.JOBS:\\n update.message.reply_text(\\n 'Sorry {0} is not a valid job'.format(job_name))\\n return\\n\\n job = find_job(job_name, job_queue)\\n\\n if not job:\\n update.message.reply_text('{0} job is not running'.format(job_name))\\n return\\n\\n update.message.reply_text('{0} job is running'.format(job_name))\",\n \"def test_successful(self):\\n\\n url = '/%s/job-types/pending/' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 1)\\n self.assertEqual(result['results'][0]['job_type']['name'], self.job.job_type.name)\\n self.assertEqual(result['results'][0]['count'], 1)\\n self.assertIsNotNone(result['results'][0]['longest_pending'])\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\\n # Asserts that job is either pending or canceled.\\n assert job.state in ['PENDING', 'CANCELED'], (\\n 'job that canceled immediately after submission has wrong '\\n 'state `%s`' % job.state\\n )\",\n \"def test_cluster_jobs_script(self):\\r\\n\\r\\n qiime_config = load_qiime_config()\\r\\n submit_script = qiime_config['cluster_jobs_fp']\\r\\n\\r\\n if (submit_script):\\r\\n full_path = which(submit_script)\\r\\n if full_path:\\r\\n submit_script = full_path\\r\\n self.assertTrue(exists(submit_script),\\r\\n \\\"cluster_jobs_fp is not set to a valid path in qiime config: %s\\\" % submit_script)\\r\\n # check if executable\\r\\n self.assertTrue(access(submit_script, X_OK),\\r\\n \\\"cluster_jobs_fp is not executable: %s\\\" % submit_script)\\r\\n else:\\r\\n # Can't run in parallel, but not a critical error\\r\\n pass\",\n \"def test_dies_if_no_job(self):\\n from furious.async import Async\\n from furious.context._execution import _ExecutionContext\\n from furious.processors import run_job\\n\\n work = Async(\\\"dir\\\", kwargs={'something': None})\\n work._options.pop('job')\\n assert 'job' not in work._options\\n\\n with _ExecutionContext(work):\\n self.assertRaises(Exception, run_job)\",\n \"def test_successful(self, mock_create, mock_msg_mgr):\\n\\n json_data = {\\n \\\"input\\\" : {\\n 'version': '6',\\n 'files': {'input_a': [self.source_file.id]},\\n 'json': {}\\n },\\n \\\"job_type_id\\\" : self.job_type1.pk\\n }\\n\\n url = '/%s/jobs/' % self.api\\n response = self.client.generic('POST', url, json.dumps(json_data), 'application/json')\\n self.assertEqual(response.status_code, status.HTTP_201_CREATED, response.content)\\n\\n result = json.loads(response.content)\\n\\n #Response should be new v6 job detail response\\n self.assertEqual(result['execution'], None)\\n self.assertEqual(result['max_tries'], 3)\\n self.assertTrue('/%s/jobs/' % self.api in response['location'])\\n mock_create.assert_called_once()\",\n \"def add_job_state(self, job):\",\n \"def check_job_status(job):\\n assert isinstance(job, PreprocessJob),\\\\\\n 'job must be a PreprocessJob'\\n\\n if job.is_finished():\\n return True\\n\\n return True\\n \\\"\\\"\\\"\\n ye_task = AsyncResult(job.task_id,\\n app=preprocess_csv_file)\\n\\n if ye_task.state == 'SUCCESS':\\n\\n if ye_task.result['success']:\\n\\n preprocess_data = ContentFile(json.dumps(ye_task.result['data']))\\n\\n new_name = 'preprocess_%s.json' % get_alphanumeric_lowercase(8)\\n job.metadata_file.save(new_name,\\n preprocess_data)\\n job.set_state_success()\\n\\n job.user_message = 'Task completed! Preprocess is available'\\n job.save()\\n\\n else:\\n # Didn't work so well\\n job.set_state_failure(ye_task.result['message'])\\n job.save()\\n\\n ye_task.forget()\\n return True\\n\\n elif ye_task.state == STATE_FAILURE:\\n job.set_state_failure('ye_task failed....')\\n job.save()\\n ye_task.forget()\\n return True\\n\\n return False\\n \\\"\\\"\\\"\",\n \"def _check_jobs_submitted(status, module):\\n\\n submitted = False\\n if module in status.data:\\n jobs = status.data[module]\\n for job in jobs.keys():\\n if job != 'pipeline_index':\\n submitted = True\\n break\\n return submitted\",\n \"def test_check_job_dependencies_ready(mock_sqs_queue, database):\\n # Mock so it always returns the mock queue for the test\\n mock_sqs_queue.return_value = SQSMockQueue\\n sess = database.session\\n sub = SubmissionFactory(submission_id=1)\\n job = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['finished'],\\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'],\\n number_of_errors=0)\\n job_2 = JobFactory(submission_id=sub.submission_id, job_status_id=JOB_STATUS_DICT['waiting'],\\n job_type_id=JOB_TYPE_DICT['csv_record_validation'], file_type_id=FILE_TYPE_DICT['award'])\\n sess.add_all([sub, job, job_2])\\n sess.commit()\\n\\n # Job 1 finished, it is a prerequisite for job 2 (waiting) but it has errors\\n job_dep = JobDependency(job_id=job_2.job_id, prerequisite_id=job.job_id)\\n sess.add(job_dep)\\n sess.commit()\\n\\n check_job_dependencies(job.job_id)\\n\\n assert job_2.job_status_id == JOB_STATUS_DICT['ready']\",\n \"async def test_misusage(my_job):\\n\\n # Disable some pylint checks, they are redundant here in the test.\\n # pylint: disable=missing-docstring,unused-variable,function-redefined\\n\\n # Check that namesake job managers are not allowed.\\n class MyNamesakeJobManager(jobmanager.JobManager):\\n pass\\n with pytest.raises(AssertionError,\\n message='Successfully made two job managers with the '\\n 'same names!'):\\n class MyNamesakeJobManager(jobmanager.JobManager):\\n pass\\n\\n # Check that it is impossible to register two job functions with the\\n # same name inside a single job manager, but it is possible in the\\n # different job managers.\\n class MyJobManager1(jobmanager.JobManager):\\n pass\\n\\n class MyJobManager2(jobmanager.JobManager):\\n pass\\n\\n @MyJobManager1.job()\\n def my_namesake_job():\\n pass\\n\\n @MyJobManager2.job()\\n def my_namesake_job():\\n pass\\n\\n with pytest.raises(AssertionError,\\n message='Successfully registered two job function with '\\n 'the same name!'):\\n @MyJobManager1.job()\\n def my_namesake_job():\\n pass\\n\\n # Check that it is impossible to submit a job with an identifier\\n # which is already used by some other job.\\n\\n # Submit 2 jobs with the same identifier and check that second\\n # submission raises an exception.\\n job_id = uuid.uuid4()\\n\\n def on_job_submit(job):\\n job.id = job_id\\n return job\\n my_job.set_on_submit(on_job_submit)\\n\\n # Submit a job #1.\\n await my_job.job(mustfail=False)\\n\\n with pytest.raises(django.db.utils.IntegrityError,\\n message='Successfully submitted a job with the '\\n 'identifier that has already been used for '\\n 'another job!'):\\n # Submit a job #2.\\n await my_job.job(mustfail=False)\\n\\n # Process ASGI messages and wait for the job to finish.\\n await my_job.process_jobs()\",\n \"def remove_job_if_exists(self, name: str, context: CallbackContext) -> bool:\\n current_jobs = context.job_queue.get_jobs_by_name(name)\\n if not current_jobs:\\n return False\\n for job in current_jobs:\\n job.schedule_removal()\\n return True\",\n \"def remove_job_if_exists(self, name: str, context: CallbackContext) -> bool:\\n current_jobs = context.job_queue.get_jobs_by_name(name)\\n if not current_jobs:\\n return False\\n for job in current_jobs:\\n job.schedule_removal()\\n return True\",\n \"def test_py_job_create(self):\\n test_app = self._create_py_app()\\n class_path = \\\"example_jobs.word_count.WordCountSparkSessionJob\\\"\\n conf = \\\"input.strings = ['a', 'b', 'a', 'b']\\\"\\n job = self._create_job(test_app, class_path, conf,\\n ctx=self._get_functional_py_context())\\n time.sleep(3)\\n self.assertTrue(len(job.jobId) > 0)\\n self.assertTrue(job.status == \\\"STARTED\\\")\\n self._wait_till_job_is_done(job)\",\n \"def _addjob(self, job: Job):\\n if self.domain is not None and self.model is not None:\\n job = copy.deepcopy(job)\\n job._add_hydro_namelist(self.base_hydro_namelist)\\n job._add_hrldas_namelist(self.base_hrldas_namelist)\\n\\n self.jobs.append(job)\\n else:\\n raise AttributeError('Can not add a job to a simulation without a model and a domain')\",\n \"def remove_job_if_exists(name: str, context: ContextTypes.DEFAULT_TYPE) -> bool:\\n current_jobs = context.job_queue.get_jobs_by_name(name)\\n if not current_jobs:\\n return False\\n for job in current_jobs:\\n job.schedule_removal()\\n return True\",\n \"def verify_job(cls, auth_key, job_id):\\n key = ObjectId(job_id)\\n user_id = ObjectId(auth_key)\\n db = cls.mongo_cli.get_database(collection=Job.collection_name)\\n if db.count({\\\"_id\\\": key, \\\"user_id\\\": user_id}) > 0:\\n return True\\n return False\",\n \"def test_reenqueue_job1(client):\\n with client.application.app_context():\\n pytest.skip(\\\"Not implemented\\\")\",\n \"def remove_job_if_exists(name, context):\\n current_jobs = context.job_queue.get_jobs_by_name(name)\\n if not current_jobs:\\n return False\\n for job in current_jobs:\\n job.schedule_removal()\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7668623","0.7204852","0.68101156","0.66714615","0.6622515","0.66078657","0.6596242","0.65822595","0.6559245","0.64998597","0.64640236","0.6440778","0.64162385","0.6409369","0.63889015","0.6380754","0.63741344","0.6320242","0.62653357","0.62551504","0.62322813","0.6228221","0.6226369","0.6221608","0.6215835","0.62041056","0.6203264","0.62021214","0.6187554","0.61869454","0.61638486","0.6162798","0.6130696","0.61254966","0.612323","0.6095473","0.6088956","0.60881186","0.6062315","0.60604304","0.6054386","0.60522425","0.6049385","0.60353947","0.6035326","0.60233104","0.60228974","0.6009568","0.60042113","0.6000244","0.59951925","0.5993628","0.59889996","0.5981596","0.59815043","0.59803516","0.597096","0.59693176","0.59687674","0.59680927","0.5967281","0.59664696","0.59634614","0.595968","0.59537685","0.5952734","0.5950344","0.5949809","0.5945777","0.5943704","0.5932243","0.59297574","0.5918614","0.59173065","0.5905163","0.5902224","0.58957857","0.5890949","0.5890141","0.58875406","0.5886111","0.5883973","0.58767","0.587352","0.5871707","0.58692837","0.5862224","0.5858321","0.5853005","0.58474255","0.5843099","0.5840996","0.5833719","0.5833719","0.5833594","0.5825569","0.58248883","0.5822632","0.5820943","0.58201164"],"string":"[\n \"0.7668623\",\n \"0.7204852\",\n \"0.68101156\",\n \"0.66714615\",\n \"0.6622515\",\n \"0.66078657\",\n \"0.6596242\",\n \"0.65822595\",\n \"0.6559245\",\n \"0.64998597\",\n \"0.64640236\",\n \"0.6440778\",\n \"0.64162385\",\n \"0.6409369\",\n \"0.63889015\",\n \"0.6380754\",\n \"0.63741344\",\n \"0.6320242\",\n \"0.62653357\",\n \"0.62551504\",\n \"0.62322813\",\n \"0.6228221\",\n \"0.6226369\",\n \"0.6221608\",\n \"0.6215835\",\n \"0.62041056\",\n \"0.6203264\",\n \"0.62021214\",\n \"0.6187554\",\n \"0.61869454\",\n \"0.61638486\",\n \"0.6162798\",\n \"0.6130696\",\n \"0.61254966\",\n \"0.612323\",\n \"0.6095473\",\n \"0.6088956\",\n \"0.60881186\",\n \"0.6062315\",\n \"0.60604304\",\n \"0.6054386\",\n \"0.60522425\",\n \"0.6049385\",\n \"0.60353947\",\n \"0.6035326\",\n \"0.60233104\",\n \"0.60228974\",\n \"0.6009568\",\n \"0.60042113\",\n \"0.6000244\",\n \"0.59951925\",\n \"0.5993628\",\n \"0.59889996\",\n \"0.5981596\",\n \"0.59815043\",\n \"0.59803516\",\n \"0.597096\",\n \"0.59693176\",\n \"0.59687674\",\n \"0.59680927\",\n \"0.5967281\",\n \"0.59664696\",\n \"0.59634614\",\n \"0.595968\",\n \"0.59537685\",\n \"0.5952734\",\n \"0.5950344\",\n \"0.5949809\",\n \"0.5945777\",\n \"0.5943704\",\n \"0.5932243\",\n \"0.59297574\",\n \"0.5918614\",\n \"0.59173065\",\n \"0.5905163\",\n \"0.5902224\",\n \"0.58957857\",\n \"0.5890949\",\n \"0.5890141\",\n \"0.58875406\",\n \"0.5886111\",\n \"0.5883973\",\n \"0.58767\",\n \"0.587352\",\n \"0.5871707\",\n \"0.58692837\",\n \"0.5862224\",\n \"0.5858321\",\n \"0.5853005\",\n \"0.58474255\",\n \"0.5843099\",\n \"0.5840996\",\n \"0.5833719\",\n \"0.5833719\",\n \"0.5833594\",\n \"0.5825569\",\n \"0.58248883\",\n \"0.5822632\",\n \"0.5820943\",\n \"0.58201164\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72872704"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":77991,"cells":{"query":{"kind":"string","value":"Test job addition and replacement"},"document":{"kind":"string","value":"def test_job_replacement():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'job_status': 'submitted'})\n\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'addition': 'test',\n 'job_status': 'finished'},\n replace=True)\n\n status = Status(STATUS_DIR).data['generation']['test1']['job_status']\n addition = Status(STATUS_DIR).data['generation']['test1']['addition']\n assert status == 'finished'\n assert addition == 'test'"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_job_addition():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1')\n status1 = Status(STATUS_DIR).data['generation']['test1']['job_status']\n\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'job_status': 'finished',\n 'additional': 'test'})\n status2 = Status(STATUS_DIR).data['generation']['test1']['job_status']\n\n assert status2 == status1","def test_submit_jobs(self):\r\n\r\n submit_jobs([self.command], prefix=\"test_job\")\r\n # Try and wait ten times, could be made nicer with alarm()\r\n for i in range(10):\r\n if exists(self.tmp_result_file):\r\n observed_text = \"\".join(list(open(self.tmp_result_file)))\r\n self.assertEqual(observed_text, \"hello\\n\")\r\n return\r\n else:\r\n sleep(10)\r\n # if we get here we failed\r\n self.fail(\"The test job apparently never finished.\\n\"\r\n + \"check the jobs error log and check the queue status\\n.\")","def test_local_job(self):\r\n exp = \"abc; echo $? > xyz\"\r\n obs = local_job('abc', 'xyz', 'notused', 'notused')\r\n self.assertEqual(obs, exp)","def work(self, job):\n pass","def testJobPostponedInspection(databases):\n\n class CustomGenerator(DataGenerator):\n pass\n gen = CustomGenerator(databases)\n\n image = gen.createProduct('image')\n buildFw = gen.createFramework('build', [], [ image ])\n testFw = gen.createFramework('test', [ image ], [])\n buildTask = gen.createTask('build', buildFw)\n testTask1 = gen.createTask('test1', testFw)\n testTask2 = gen.createTask('test2', testFw)\n testTask3 = gen.createTask('test3', testFw)\n\n tr = gen.createTaskRunner(name='tr_build',\n capabilities=['build', 'test'])\n\n def simulate(config):\n sanityCheck(gen, config)\n\n job, = config.createJobs(gen.owner)\n # TODO: The toXML functionality should probably be tested\n # in a separate test case.\n\n # Verify execution:\n # Successfully complete first build task.\n task = job.assignTask(databases.resourceDB[tr])\n assert task is not None\n assert task.getName() == buildTask\n taskDone(job, buildTask)\n assert job.result == ResultCode.OK\n assert job.getFinalResult() == None\n # Successfully complete first test task, without result.\n task = job.assignTask(databases.resourceDB[tr])\n assert (task is not None) is not None\n assert task.getName() == testTask1\n taskDone(job, testTask1, ResultCode.INSPECT)\n assert not job.isExecutionFinished()\n assert not job.hasFinalResult()\n assert job.result == ResultCode.INSPECT\n assert job.getFinalResult() is None\n # Successfully complete second test task, with result.\n task = job.assignTask(databases.resourceDB[tr])\n assert task is not None\n assert task.getName() == testTask2\n taskDone(job, testTask2, ResultCode.OK)\n assert not job.isExecutionFinished()\n assert not job.hasFinalResult()\n assert job.result == ResultCode.INSPECT\n assert job.getFinalResult() is None\n # Successfully complete third test task, without result.\n task = job.assignTask(databases.resourceDB[tr])\n assert task is not None\n assert task.getName() == testTask3\n taskDone(job, testTask3, ResultCode.INSPECT)\n assert job.isExecutionFinished()\n assert not job.hasFinalResult()\n assert job.result == ResultCode.INSPECT\n assert job.getFinalResult() is None\n # Attempt to set invalid inspection result.\n with raises(ValueError):\n job.inspectDone(testTask1, ResultCode.CANCELLED, 'invalid')\n # Complete inspection of first task.\n job.inspectDone(testTask1, ResultCode.WARNING, 'inspect 1')\n assert job.isExecutionFinished()\n assert not job.hasFinalResult()\n assert job.result == ResultCode.INSPECT\n assert job.getFinalResult() is None\n # Attempt to change inspection result.\n with raises(IllegalStateError):\n job.inspectDone(testTask1, ResultCode.OK, 'invalid')\n # Complete inspection of third task.\n job.inspectDone(testTask3, ResultCode.OK, 'inspect 3')\n assert job.isExecutionFinished()\n assert job.hasFinalResult()\n assert job.result == ResultCode.WARNING\n assert job.getFinalResult() == ResultCode.WARNING\n\n runWithReload(databases, gen.createConfiguration(), simulate)","def test_008_modify(self):\n HEADING()\n db = self.db\n\n db.connect()\n\n job = {\"job_name\": \"modifyme\", \"input_filename\":\"file1\"}\n\n db.add(job)\n\n originalFilename = self.db.find_jobs(\"job_name\", \"modifyme\")[0][\"input_filename\"]\n\n job = {\"job_name\": \"modifyme\", \"input_filename\":\"file2\"}\n\n db.modify(job)\n\n newFilename = self.db.find_jobs(\"job_name\", \"modifyme\")[0][\"input_filename\"]\n\n assert(originalFilename != newFilename)","def testA_StraightThrough(self):\n # Do pre-submit job check\n nRunning = getCondorRunningJobs()\n self.assertEqual(nRunning, 0, \"User currently has %i running jobs. Test will not continue\" % (nRunning))\n\n myThread = threading.currentThread()\n workload = self.createTestWorkload()\n config = self.getConfig()\n\n\n name = 'WMAgent_Test1'\n site = self.sites[0]\n nSubs = 5\n nFiles = 10\n workloadPath = os.path.join(self.testDir, 'workloadTest',\n 'TestWorkload', 'WMSandbox',\n 'WMWorkload.pkl')\n\n # Create a collection of files\n self.createFileCollection(name = name, nSubs = nSubs,\n nFiles = nFiles,\n workflowURL = workloadPath,\n site = site)\n\n\n\n ############################################################\n # Test the JobCreator\n\n\n config.Agent.componentName = 'JobCreator'\n testJobCreator = JobCreatorPoller(config = config)\n\n testJobCreator.algorithm()\n time.sleep(5)\n\n\n # Did all jobs get created?\n getJobsAction = self.daoFactory(classname = \"Jobs.GetAllJobs\")\n result = getJobsAction.execute(state = 'Created', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs*nFiles)\n\n\n # Count database objects\n result = myThread.dbi.processData('SELECT * FROM wmbs_sub_files_acquired')[0].fetchall()\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n # Find the test directory\n testDirectory = os.path.join(self.testDir, 'TestWorkload', 'ReReco')\n self.assertTrue('JobCollection_1_0' in os.listdir(testDirectory))\n self.assertTrue(len(os.listdir(testDirectory)) <= 20)\n\n groupDirectory = os.path.join(testDirectory, 'JobCollection_1_0')\n\n # First job should be in here\n self.assertTrue('job_1' in os.listdir(groupDirectory))\n jobFile = os.path.join(groupDirectory, 'job_1', 'job.pkl')\n self.assertTrue(os.path.isfile(jobFile))\n with open(jobFile, 'rb') as f:\n job = pickle.load(f)\n\n\n self.assertEqual(job['workflow'], name)\n self.assertEqual(len(job['input_files']), 1)\n self.assertEqual(os.path.basename(job['sandbox']), 'TestWorkload-Sandbox.tar.bz2')\n\n\n\n\n\n\n\n\n\n\n ###############################################################\n # Now test the JobSubmitter\n\n config.Agent.componentName = 'JobSubmitter'\n testJobSubmitter = JobSubmitterPoller(config = config)\n\n\n testJobSubmitter.algorithm()\n\n\n # Check that jobs are in the right state\n result = getJobsAction.execute(state = 'Created', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Executing', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n\n # Check assigned locations\n getLocationAction = self.daoFactory(classname = \"Jobs.GetLocation\")\n for id in result:\n loc = getLocationAction.execute(jobid = id)\n self.assertEqual(loc, [[site]])\n\n\n # Check to make sure we have running jobs\n nRunning = getCondorRunningJobs()\n self.assertEqual(nRunning, nFiles * nSubs)\n\n\n #################################################################\n # Now the JobTracker\n\n\n config.Agent.componentName = 'JobTracker'\n testJobTracker = JobTrackerPoller(config = config)\n testJobTracker.setup()\n\n testJobTracker.algorithm()\n\n # Running the algo without removing the jobs should do nothing\n result = getJobsAction.execute(state = 'Executing', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n condorRM()\n time.sleep(1)\n\n # All jobs gone?\n nRunning = getCondorRunningJobs()\n self.assertEqual(nRunning, 0)\n\n\n testJobTracker.algorithm()\n time.sleep(5)\n\n # Running the algo without removing the jobs should do nothing\n result = getJobsAction.execute(state = 'Executing', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Complete', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n\n\n #################################################################\n # Now the JobAccountant\n\n # First you need to load all jobs\n\n\n self.getFWJRAction = self.daoFactory(classname = \"Jobs.GetFWJRByState\")\n completeJobs = self.getFWJRAction.execute(state = \"complete\")\n\n\n # Create reports for all jobs\n self.createReports(jobs = completeJobs, retryCount = 0)\n\n\n\n\n\n\n config.Agent.componentName = 'JobAccountant'\n testJobAccountant = JobAccountantPoller(config = config)\n testJobAccountant.setup()\n\n\n # It should do something with the jobs\n testJobAccountant.algorithm()\n\n\n # All the jobs should be done now\n result = getJobsAction.execute(state = 'Complete', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Success', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n\n #######################################################################\n # Now the JobArchiver\n\n\n config.Agent.componentName = 'JobArchiver'\n testJobArchiver = JobArchiverPoller(config = config)\n\n\n testJobArchiver.algorithm()\n\n # All the jobs should be cleaned up\n result = getJobsAction.execute(state = 'Success', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n result = getJobsAction.execute(state = 'Cleanout', jobType = \"Processing\")\n self.assertEqual(len(result), nSubs * nFiles)\n\n\n logDir = os.path.join(self.testDir, 'logs')\n\n for job in completeJobs:\n self.assertFalse(os.path.exists(job['fwjr_path']))\n jobFolder = 'JobCluster_%i' \\\n % (int(job['id']/config.JobArchiver.numberOfJobsToCluster))\n jobPath = os.path.join(logDir, jobFolder, 'Job_%i.tar' %(job['id']))\n self.assertTrue(os.path.isfile(jobPath))\n self.assertTrue(os.path.getsize(jobPath) > 0)\n\n\n\n\n ###########################################################################\n # Now the TaskAchiver\n\n\n config.Agent.componentName = 'TaskArchiver'\n testTaskArchiver = TaskArchiverPoller(config = config)\n\n\n testTaskArchiver.algorithm()\n\n\n result = getJobsAction.execute(state = 'Cleanout', jobType = \"Processing\")\n self.assertEqual(len(result), 0)\n\n\n for jdict in completeJobs:\n job = Job(id = jdict['id'])\n self.assertFalse(job.exists())\n\n\n\n\n\n if os.path.isdir('testDir'):\n shutil.rmtree('testDir')\n shutil.copytree('%s' %self.testDir, os.path.join(os.getcwd(), 'testDir'))\n\n\n\n\n return","def test_create_replacement_jobstep(self, get_test_stats, get_job_parameters,\n create_jenkins_job_from_params, fetch_artifact):\n fetch_artifact.return_value.content = json.dumps({\n 'phase': 'Test',\n 'cmd': 'py.test --junit=junit.xml {test_names}',\n 'tests': [\n 'foo/bar.py',\n 'foo/baz.py',\n 'foo.bar.test_biz',\n 'foo.bar.test_buz',\n ],\n })\n create_jenkins_job_from_params.return_value = {\n 'job_name': 'foo-bar',\n 'build_no': 23,\n }\n\n get_job_parameters.return_value = {'PARAM': '44'}\n\n get_test_stats.return_value = {\n ('foo', 'bar'): 50,\n ('foo', 'baz'): 15,\n ('foo', 'bar', 'test_biz'): 10,\n ('foo', 'bar', 'test_buz'): 200,\n }, 68\n\n project = self.create_project()\n build = self.create_build(project)\n job = self.create_job(build, data={\n 'job_name': 'server',\n 'build_no': '35',\n })\n phase = self.create_jobphase(job)\n step = self.create_jobstep(phase, data={\n 'item_id': 13,\n 'job_name': 'server',\n })\n\n artifact = self.create_artifact(\n step=step,\n name='tests.json',\n data={'fileName': 'tests.json'},\n )\n\n buildstep = self.get_buildstep()\n with mock.patch.object(JobPlan, 'get_build_step_for_job') as get_build_step_for_job:\n get_build_step_for_job.return_value = (None, buildstep)\n buildstep.fetch_artifact(artifact)\n\n phase2 = JobPhase.query.filter(\n JobPhase.job_id == job.id,\n JobPhase.id != phase.id,\n ).first()\n assert phase2, \"phase wasn't created\"\n\n new_steps = sorted(phase2.current_steps, key=lambda x: x.data['weight'], reverse=True)\n\n failstep = new_steps[0]\n failstep.result = Result.infra_failed\n failstep.status = Status.finished\n db.session.add(failstep)\n db.session.commit()\n\n replacement_step = buildstep.create_replacement_jobstep(failstep)\n # new jobstep should still be part of same job/phase\n assert replacement_step.job == job\n assert replacement_step.phase == phase2\n # make sure .steps actually includes the new jobstep\n assert len(phase2.steps) == 3\n # make sure replacement id is correctly set\n assert failstep.replacement_id == replacement_step.id\n\n # Now perform same tests as test_job_expansion, making sure that the\n # replacement step still satisfies all the correct attributes\n new_steps = sorted(phase2.current_steps, key=lambda x: x.data['weight'], reverse=True)\n assert new_steps[0] == replacement_step\n\n assert len(new_steps) == 2\n assert new_steps[0].data['expanded'] is True\n assert new_steps[0].data['build_no'] == 23\n assert new_steps[0].data['job_name'] == 'foo-bar'\n assert new_steps[0].data['tests'] == ['foo.bar.test_buz']\n assert new_steps[0].data['path'] == ''\n assert new_steps[0].data['cmd'] == 'py.test --junit=junit.xml {test_names}'\n assert new_steps[0].data['weight'] == 201\n\n assert new_steps[1].data['expanded'] is True\n assert new_steps[1].data['build_no'] == 23\n assert new_steps[1].data['job_name'] == 'foo-bar'\n assert new_steps[1].data['tests'] == [\n 'foo/bar.py',\n 'foo/baz.py',\n 'foo.bar.test_biz',\n ]\n assert new_steps[1].data['path'] == ''\n assert new_steps[1].data['cmd'] == 'py.test --junit=junit.xml {test_names}'\n assert new_steps[1].data['weight'] == 78\n\n fetch_artifact.assert_called_once_with(artifact.step, artifact.data)\n get_job_parameters.assert_any_call(\n job,\n changes_bid=new_steps[0].id.hex,\n script='py.test --junit=junit.xml foo.bar.test_buz',\n path='',\n setup_script='',\n teardown_script='',\n )\n create_jenkins_job_from_params.assert_any_call(\n job_name='foo-bar',\n changes_bid=new_steps[0].id.hex,\n is_diff=False,\n params=[{'name': 'PARAM', 'value': '44'}],\n )\n get_job_parameters.assert_any_call(\n job,\n changes_bid=new_steps[1].id.hex,\n script='py.test --junit=junit.xml foo/bar.py foo/baz.py foo.bar.test_biz',\n path='',\n setup_script='',\n teardown_script='',\n )\n create_jenkins_job_from_params.assert_any_call(\n job_name='foo-bar',\n changes_bid=new_steps[1].id.hex,\n is_diff=False,\n params=[{'name': 'PARAM', 'value': '44'}],\n )\n\n # If fetch_artifact() is called again with different weights so\n # that it divvies up the tests differently, does a broken\n # double-shard build result?\n\n get_test_stats.return_value = {\n ('foo', 'bar'): 50,\n ('foo', 'baz'): 15,\n ('foo', 'bar', 'test_biz'): 10,\n ('foo', 'bar', 'test_buz'): 55,\n }, 68\n\n buildstep.fetch_artifact(artifact)\n\n all_steps = phase2.current_steps\n assert len(all_steps) == 2","def testJobTwiceProduct(databases):\n\n class CustomGenerator(DataGenerator):\n pass\n gen = CustomGenerator(databases)\n\n image = gen.createProduct('image')\n buildFw = gen.createFramework('build', [], [ image ])\n testFw = gen.createFramework('test', [ image ], [])\n buildTask1 = gen.createTask('build1', buildFw)\n buildTask2 = gen.createTask('build2', buildFw)\n testTask = gen.createTask('test', testFw)\n\n buildTR = gen.createTaskRunner(name='tr_build', capabilities=['build'])\n testTR = gen.createTaskRunner(name='tr_test', capabilities=['test'])\n\n def simulate(config):\n sanityCheck(gen, config)\n\n # TODO: Is it OK that Config.createJob does not put record into\n # job DB? If so, document.\n job, = config.createJobs(gen.owner)\n # Note: Disabled to save time.\n # TODO: The toXML functionality should probably be tested\n # in a separate test case.\n #self.jobDB.add(job)\n\n # Verify execution:\n # Successfully complete first build task.\n task = job.assignTask(databases.resourceDB[buildTR])\n assert task is not None\n assert task.getName().startswith('build')\n taskDone(job, task.getName())\n # Start test task.\n task = job.assignTask(databases.resourceDB[testTR])\n assert task is not None\n assert task.getName() == testTask\n # Complete second build task, but make it fail.\n task = job.assignTask(databases.resourceDB[buildTR])\n assert task is not None\n assert task.getName().startswith('build')\n taskDone(job, task.getName(), ResultCode.ERROR)\n # Successfully complete test task.\n taskDone(job, testTask)\n assert job.isExecutionFinished()\n assert job.hasFinalResult()\n\n runWithReload(databases, gen.createConfiguration(), simulate)","def test_upload_orchestration(new_job):\n with mock.patch.object(new_job, 'create') as create, \\\n mock.patch.object(new_job, 'add_batch') as add_batch, \\\n mock.patch.object(new_job, 'abort') as abort, \\\n mock.patch.object(new_job, 'close') as close, \\\n mock.patch.object(new_job, 'wait') as wait:\n\n def add_a_batch_for_real(*args, **kwargs):\n new_job.pending_batches = new_job.pending_batches or []\n new_job.pending_batches.append('FAKE ONE')\n add_batch.side_effect = add_a_batch_for_real\n\n new_job.upload(['Id', 'Description'], [[1, 2], [3, 4]])\n\n create.assert_called_once_with()\n add_batch.assert_called_once_with(['Id', 'Description'], [[1, 2], [3, 4]])\n assert abort.call_count == 0\n close.assert_called_once_with()\n wait.assert_called_once_with()","def testJobKilling(self):\n change = ChangeState(self.config, \"changestate_t\")\n\n locationAction = self.daoFactory(classname=\"Locations.New\")\n locationAction.execute(\"site1\", pnn=\"T2_CH_CERN\")\n\n testWorkflow = Workflow(spec=self.specUrl, owner=\"Steve\",\n name=\"wf001\", task=self.taskName)\n testWorkflow.create()\n testFileset = Fileset(name=\"TestFileset\")\n testFileset.create()\n\n for i in range(4):\n newFile = File(lfn=\"File%s\" % i, locations=set([\"T2_CH_CERN\"]))\n newFile.create()\n testFileset.addFile(newFile)\n\n testFileset.commit()\n testSubscription = Subscription(fileset=testFileset,\n workflow=testWorkflow,\n split_algo=\"FileBased\")\n testSubscription.create()\n\n splitter = SplitterFactory()\n jobFactory = splitter(package=\"WMCore.WMBS\",\n subscription=testSubscription)\n jobGroup = jobFactory(files_per_job=1)[0]\n\n assert len(jobGroup.jobs) == 4, \\\n \"Error: Splitting should have created four jobs.\"\n\n testJobA = jobGroup.jobs[0]\n testJobA[\"user\"] = \"sfoulkes\"\n testJobA[\"group\"] = \"DMWM\"\n testJobA[\"taskType\"] = \"Processing\"\n testJobB = jobGroup.jobs[1]\n testJobB[\"user\"] = \"sfoulkes\"\n testJobB[\"group\"] = \"DMWM\"\n testJobB[\"taskType\"] = \"Processing\"\n testJobC = jobGroup.jobs[2]\n testJobC[\"user\"] = \"sfoulkes\"\n testJobC[\"group\"] = \"DMWM\"\n testJobC[\"taskType\"] = \"Processing\"\n testJobD = jobGroup.jobs[3]\n testJobD[\"user\"] = \"sfoulkes\"\n testJobD[\"group\"] = \"DMWM\"\n testJobD[\"taskType\"] = \"Processing\"\n\n change.persist([testJobA], \"created\", \"new\")\n change.persist([testJobB], \"jobfailed\", \"executing\")\n change.persist([testJobC, testJobD], \"executing\", \"created\")\n\n change.persist([testJobA], \"killed\", \"created\")\n change.persist([testJobB], \"killed\", \"jobfailed\")\n change.persist([testJobC, testJobD], \"killed\", \"executing\")\n\n for job in [testJobA, testJobB, testJobC, testJobD]:\n job.load()\n self.assertEqual(job['retry_count'], 99999)\n self.assertEqual(job['state'], 'killed')\n\n return","def testJobCombinedProduct(databases):\n\n class CustomGenerator(DataGenerator):\n pass\n gen = CustomGenerator(databases)\n\n image = gen.createProduct('image', False, True)\n buildFw = gen.createFramework('build', [], [ image ])\n testFw = gen.createFramework('test', [ image ], [])\n buildTask1 = gen.createTask('build1', buildFw)\n buildTask2 = gen.createTask('build2', buildFw)\n testTask = gen.createTask('test', testFw)\n\n buildTR = gen.createTaskRunner(name='tr_build', capabilities=['build'])\n testTR = gen.createTaskRunner(name='tr_test', capabilities=['test'])\n\n def simulate(config):\n sanityCheck(gen, config)\n\n job, = config.createJobs(gen.owner)\n # TODO: The toXML functionality should probably be tested\n # in a separate test case.\n\n # Verify execution:\n # Successfully complete first build task.\n task = job.assignTask(databases.resourceDB[buildTR])\n assert task is not None\n assert task.getName().startswith('build')\n taskDone(job, task.getName())\n # Try to start test task (should fail).\n task = job.assignTask(databases.resourceDB[testTR])\n assert task is None\n # Complete second build task, but make it fail.\n task = job.assignTask(databases.resourceDB[buildTR])\n assert task is not None\n assert task.getName().startswith('build')\n taskDone(job, task.getName(), ResultCode.ERROR)\n # Try to start test task (should succeed).\n task = job.assignTask(databases.resourceDB[testTR])\n assert task is not None\n assert task.getName() == testTask\n # Successfully complete test task.\n taskDone(job, testTask)\n assert job.isExecutionFinished()\n assert job.hasFinalResult()\n # Check that locators have been stored separately.\n producers = set()\n for taskId, locator in job.getProduct(image).getProducers():\n assert taskId.startswith('build')\n assert locator == locatorForTask(taskId)\n\n runWithReload(databases, gen.createConfiguration(), simulate)","def test_update(executable):\n from tempfile import mkdtemp\n from shutil import rmtree\n from pylada.jobfolder.jobfolder import JobFolder\n from pylada.process.jobfolder import JobFolderProcess\n from pylada import default_comm\n from functional import Functional\n\n root = JobFolder()\n for n in xrange(3):\n job = root / str(n)\n job.functional = Functional(executable, [n])\n job.params['sleep'] = 1\n supp = JobFolder()\n for n in xrange(3, 6):\n job = supp / str(n)\n job.functional = Functional(executable, [n])\n job.params['sleep'] = 1\n\n comm = default_comm.copy()\n comm['n'] = 4\n\n dir = mkdtemp()\n try: \n program = JobFolderProcess(root, nbpools=2, outdir=dir, keepalive=True)\n assert program.keepalive \n\n # compute current jobs.\n program.start(comm)\n program.wait()\n assert hasattr(program, '_comm')\n\n # compute second set of updated jobs\n program.update(supp)\n program.wait()\n\n finally:\n try: rmtree(dir)\n except: pass\n\n # check with deleteold=True\n dir = mkdtemp()\n try: \n program = JobFolderProcess(root, nbpools=2, outdir=dir, keepalive=True)\n assert program.keepalive \n\n # compute current jobs.\n program.start(comm)\n program.wait()\n assert hasattr(program, '_comm')\n\n # compute second set of updated jobs\n program.update(supp, deleteold=True)\n assert hasattr(program, '_comm')\n program.wait()\n\n finally:\n try: rmtree(dir)\n except: pass","def test_jobs_successful(self):\n\n workspace = storage_test_utils.create_workspace()\n file1 = storage_test_utils.create_file()\n data_dict = {\n 'version': '1.0',\n 'input_data': [{\n 'name': 'INPUT_IMAGE',\n 'file_id': file1.id\n }],\n 'output_data': [{\n 'name': 'output_file_pngs',\n 'workspace_id': workspace.id\n }]}\n\n secret_configuration = {\n 'version': '6',\n 'priority': 50,\n 'output_workspaces': {'default': storage_test_utils.create_workspace().name},\n 'settings': {\n 'DB_HOST': 'som.host.name',\n 'DB_PASS': 'secret_password'\n }\n }\n\n seed_job_type = job_test_utils.create_seed_job_type(configuration=secret_configuration)\n seed_job = job_test_utils.create_job(job_type=seed_job_type, status='RUNNING', input=data_dict)\n\n url = '/%s/jobs/%d/' % (self.api, seed_job.id)\n response = self.client.generic('GET', url)\n result = json.loads(response.content)\n\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n self.assertEqual(result['configuration']['priority'],50)\n self.assertNotIn('DB_PASS', result['configuration']['settings'])","def add_job_state(self, job):","def test_job_pass(self):\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \"extras/tests/dummy_jobs\")):\n\n module = \"test_pass\"\n name = \"TestPass\"\n job_class = get_job(f\"local/{module}/{name}\")\n\n job_result = JobResult.objects.create(\n name=job_class.class_path,\n obj_type=self.job_content_type,\n user=None,\n job_id=uuid.uuid4(),\n )\n\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\n job_result.refresh_from_db()\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)","def test_results_workers(self, affiliate_items):\n success_count = 0\n updater = mock.Mock()\n\n few_workers = BatchJob(affiliate_items, updater, workers=1)\n for result in few_workers.run():\n success_count += int(not result.is_error)\n\n many_workers = BatchJob(affiliate_items, updater, workers=4)\n for result in many_workers.run():\n success_count += int(not result.is_error)\n\n assert success_count == 8\n assert updater.call_count == 8","def test_get_refresh_job_status(self):\n pass","def test_run_job1(client):\n with client.application.app_context():\n app = client.application\n app.redis.flushall()\n\n task_id = str(uuid4())\n t = Task.create_task(task_id)\n j = t.create_job()\n job_id = j.job_id\n t.save()\n\n exec_mock = MagicMock()\n exec_mock.validate_max_running_executions.return_value = True\n client.application.executor = exec_mock\n\n queue = Queue(\"jobs\", is_async=False, connection=client.application.redis)\n result = queue.enqueue(job_mod.run_job, t.task_id, job_id, \"image\", \"command\")\n\n worker = SimpleWorker([queue], connection=queue.connection)\n worker.work(burst=True)\n\n t.reload()\n expect(t.jobs).to_length(1)\n\n job = t.jobs[0]\n expect(job.executions).to_length(1)\n\n execution = job.executions[0]\n expect(execution.image).to_equal(\"image\")\n expect(execution.command).to_equal(\"command\")\n\n hash_key = f\"rq:job:{result.id}\"\n\n res = app.redis.exists(hash_key)\n expect(res).to_be_true()\n\n res = app.redis.hget(hash_key, \"status\")\n expect(res).to_equal(\"finished\")\n\n res = app.redis.hexists(hash_key, \"data\")\n expect(res).to_be_true()\n\n keys = app.redis.keys()\n next_job_id = [\n key\n\n for key in keys\n\n if key.decode(\"utf-8\").startswith(\"rq:job\")\n and not key.decode(\"utf-8\").endswith(result.id)\n ]\n expect(next_job_id).to_length(1)\n next_job_id = next_job_id[0]\n\n res = app.redis.exists(next_job_id)\n expect(res).to_be_true()\n\n res = app.redis.hget(next_job_id, \"status\")\n expect(res).to_equal(\"queued\")\n\n res = app.redis.hexists(next_job_id, \"data\")\n expect(res).to_be_true()\n\n res = app.redis.hget(next_job_id, \"origin\")\n expect(res).to_equal(\"monitor\")\n\n res = app.redis.hget(next_job_id, \"description\")\n expect(res).to_equal(\n f\"fastlane.worker.job.monitor_job('{task_id}', '{job_id}', '{execution.execution_id}')\"\n )\n\n res = app.redis.hget(next_job_id, \"timeout\")\n expect(res).to_equal(\"-1\")\n\n t.reload()\n expect(t.jobs[0].executions[0].status).to_equal(JobExecution.Status.running)","def test_valid_n_jobs(n_jobs: Any) -> None:\n check_n_jobs(n_jobs)","def test_update_with_fail(executable):\n from tempfile import mkdtemp\n from shutil import rmtree\n from pylada.jobfolder.jobfolder import JobFolder\n from pylada.process.jobfolder import JobFolderProcess\n from pylada.process import Fail\n from pylada import default_comm\n from functional import Functional\n\n root = JobFolder()\n for n in xrange(3):\n job = root / str(n)\n job.functional = Functional(executable, [n])\n job.params['sleep'] = 1\n root['1'].functional.order = 666\n root['1'].sleep = None\n supp = JobFolder()\n for n in xrange(3, 6):\n job = supp / str(n)\n job.functional = Functional(executable, [n])\n job.params['sleep'] = 1\n supp['5'].sleep = 0\n supp['5'].functional.order = 666\n\n comm = default_comm.copy()\n comm['n'] = 4\n\n dir = mkdtemp()\n try: \n program = JobFolderProcess(root, nbpools=2, outdir=dir, keepalive=True)\n\n # compute current jobs.\n program.start(comm)\n try: program.wait()\n except Fail: pass\n else: raise Exception()\n assert len(program.errors) == 1\n\n # compute second set of updated jobs\n program.update(supp)\n try: program.wait()\n except Fail: pass\n else: raise Exception()\n assert len(program.errors) == 2\n program.errors.clear()\n\n\n finally:\n try: rmtree(dir)\n except: pass","def test_make_jobs(self):\r\n # no commands should make no jobs files\r\n self.assertEqual(make_jobs([], \"test\", self.queue), [])\r\n\r\n # one job file should be created\r\n filenames = make_jobs([self.command], \"test_qsub\", self.queue)\r\n self.assertTrue(len(filenames) == 1)\r\n observed_text = list(open(filenames[0]))\r\n\r\n self.assertEqual(\"\".join(observed_text),\r\n QSUB_TEXT % (\"72:00:00\", 1, 1, self.queue,\r\n \"test_qsub\", \"oe\",\r\n self.command))","def test_fake_job( self ):\n hda = MockHda( job=UNDEFINED_JOB )\n self.history.active_datasets.append( hda )\n job_dict, warnings = extract.summarize( trans=self.trans )\n assert not warnings\n assert len( job_dict ) == 1\n fake_job = job_dict.keys()[ 0 ]\n assert fake_job.id.startswith( \"fake_\" )\n datasets = job_dict.values()[ 0 ]\n assert datasets == [ ( None, hda ) ]","def test_torque_job(self):\r\n exp = 'echo \"abc; echo $? > xyz\" | qsub -k oe -N MOTU -q queue'\r\n obs = torque_job('abc', 'xyz', '123', 'queue')\r\n self.assertEqual(obs, exp)","def testJobOneTask(databases):\n\n class CustomGenerator(DataGenerator):\n numTasks = 1\n numInputs = [ 0 ]\n numOutputs = [ 0 ]\n\n gen = CustomGenerator(databases)\n gen.createDefinitions()\n config = gen.createConfiguration()\n\n def checkOne(config):\n taskName = gen.tasks[0]\n #assert config.getProduct('') is None\n assert len(config.getInputs()) == 0\n assert len(config.getInputsGrouped()) == 0\n assert len(config.getTasks()) == 1\n task, = config.getTasks()\n assert task is not None\n assert task.getName() == taskName\n assert len(config.getTaskSequence()) == 1\n\n runWithReload(databases, config, checkOne)","def test_matching_jobs_existing(self):\n self.assertEquals(\n self.query_api.get_matching_jobs(\n \"try\", \"146071751b1e\",\n 'Linux x86-64 try build'), json.loads(JOBS_SCHEDULE))","def run_job(job, interrupt_if_necessary):","def update_job_state(self, job):","def on_job_update(_job):\n nonlocal job\n job = _job\n # Asserts that job is either pending or canceled.\n assert job.state in ['PENDING', 'CANCELED'], (\n 'job that canceled immediately after submission has wrong '\n 'state `%s`' % job.state\n )","def test_reenqueue_job1(client):\n with client.application.app_context():\n pytest.skip(\"Not implemented\")","def test_job_exists():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'job_status': 'submitted'})\n exists = Status.job_exists(STATUS_DIR, 'test1')\n assert exists","def test_updateJobState(self):\n self.assertRaises(JobNotExistsError, self.foo.updateJobState, 'asd')\n self.foo.setTorqueJobId('asd')\n self.assertEqual(self.foo.getJobState(), 'NEW')","def test_override_job(self):\n test_container = TestContainer()\n job_1 = ThreadJob(name=\"test_job\",\n function=run_me,\n callback=test_container.on_job_done,\n args=[\"job_1\"])\n job_2 = ThreadJob(name=\"test_job\",\n function=run_me,\n callback=test_container.on_job_done,\n args=[\"job_2\"])\n job_3 = ThreadJob(name=\"test_job\",\n function=run_me,\n callback=test_container.on_job_done,\n args=[\"job_3\"])\n pool = ThreadPool()\n pool.new_job(job_1)\n pool.new_job(job_2)\n pool.new_job(job_3)\n test_container.wait_until_got_number_of_callbacks(3)\n self.assertEqual(len(test_container.futures), 3)\n # Here is what happens. job_1 runs so cannot be cancelled by job_2, so\n # job_1 keeps running while job_2 is added to the queue. Then we add\n # job_3, which cancels job_2, which immediately calls it's callback,\n # thus the first future in the result is from job_2. Then job_1\n # eventually finishes. Then job_3 starts and finishes.\n self.assertTrue(test_container.futures[0].cancelled())\n self.assertFalse(test_container.futures[1].cancelled())\n self.assertEqual(test_container.futures[1].result(), \"job_1\")\n self.assertFalse(test_container.futures[2].cancelled())\n self.assertEqual(test_container.futures[2].result(), \"job_3\")","def maybe_commit(job):","def _addjob(self, job: Job):\n self._job = copy.deepcopy(job)\n self._job.restart = True","def test_job_expansion(self, get_test_stats, get_job_parameters,\n create_jenkins_job_from_params, fetch_artifact):\n fetch_artifact.return_value.content = json.dumps({\n 'phase': 'Test',\n 'cmd': 'py.test --junit=junit.xml {test_names}',\n 'tests': [\n 'foo/bar.py',\n 'foo/baz.py',\n 'foo.bar.test_biz',\n 'foo.bar.test_buz',\n ],\n })\n create_jenkins_job_from_params.return_value = {\n 'job_name': 'foo-bar',\n 'build_no': 23,\n }\n\n get_job_parameters.return_value = {'PARAM': '44'}\n\n get_test_stats.return_value = {\n ('foo', 'bar'): 50,\n ('foo', 'baz'): 15,\n ('foo', 'bar', 'test_biz'): 10,\n ('foo', 'bar', 'test_buz'): 200,\n }, 68\n\n project = self.create_project()\n build = self.create_build(project)\n job = self.create_job(build, data={\n 'job_name': 'server',\n 'build_no': '35',\n })\n phase = self.create_jobphase(job)\n step = self.create_jobstep(phase, data={\n 'item_id': 13,\n 'job_name': 'server',\n })\n\n artifact = self.create_artifact(\n step=step,\n name='tests.json',\n data={'fileName': 'tests.json'},\n )\n\n buildstep = self.get_buildstep()\n with mock.patch.object(JobPlan, 'get_build_step_for_job') as get_build_step_for_job:\n get_build_step_for_job.return_value = (None, buildstep)\n buildstep.fetch_artifact(artifact)\n\n phase2 = JobPhase.query.filter(\n JobPhase.job_id == job.id,\n JobPhase.id != phase.id,\n ).first()\n\n assert phase2, \"phase wasn't created\"\n assert phase2.label == 'Test'\n assert phase2.status == Status.queued\n\n new_steps = sorted(phase2.current_steps, key=lambda x: x.data['weight'], reverse=True)\n\n assert len(new_steps) == 2\n assert new_steps[0].data['expanded'] is True\n assert new_steps[0].data['build_no'] == 23\n assert new_steps[0].data['job_name'] == 'foo-bar'\n assert new_steps[0].data['tests'] == ['foo.bar.test_buz']\n assert new_steps[0].data['path'] == ''\n assert new_steps[0].data['cmd'] == 'py.test --junit=junit.xml {test_names}'\n assert new_steps[0].data['weight'] == 201\n\n assert new_steps[1].data['expanded'] is True\n assert new_steps[1].data['build_no'] == 23\n assert new_steps[1].data['job_name'] == 'foo-bar'\n assert new_steps[1].data['tests'] == [\n 'foo/bar.py',\n 'foo/baz.py',\n 'foo.bar.test_biz',\n ]\n assert new_steps[1].data['path'] == ''\n assert new_steps[1].data['cmd'] == 'py.test --junit=junit.xml {test_names}'\n assert new_steps[1].data['weight'] == 78\n\n fetch_artifact.assert_called_once_with(artifact.step, artifact.data)\n get_job_parameters.assert_any_call(\n job,\n changes_bid=new_steps[0].id.hex,\n script='py.test --junit=junit.xml foo.bar.test_buz',\n path='',\n setup_script='',\n teardown_script='',\n )\n create_jenkins_job_from_params.assert_any_call(\n job_name='foo-bar',\n changes_bid=new_steps[0].id.hex,\n is_diff=False,\n params=[{'name': 'PARAM', 'value': '44'}],\n )\n get_job_parameters.assert_any_call(\n job,\n changes_bid=new_steps[1].id.hex,\n script='py.test --junit=junit.xml foo/bar.py foo/baz.py foo.bar.test_biz',\n path='',\n setup_script='',\n teardown_script='',\n )\n create_jenkins_job_from_params.assert_any_call(\n job_name='foo-bar',\n changes_bid=new_steps[1].id.hex,\n is_diff=False,\n params=[{'name': 'PARAM', 'value': '44'}],\n )\n\n # If fetch_artifact() is called again with different weights so\n # that it divvies up the tests differently, does a broken\n # double-shard build result?\n\n get_test_stats.return_value = {\n ('foo', 'bar'): 50,\n ('foo', 'baz'): 15,\n ('foo', 'bar', 'test_biz'): 10,\n ('foo', 'bar', 'test_buz'): 55,\n }, 68\n\n buildstep.fetch_artifact(artifact)\n\n all_steps = phase2.current_steps\n assert len(all_steps) == 2","def test_creating_a_job(created_job, bulk_request):\n assert created_job.job == 'THEJOBID'\n assert created_job.job_url == 'https://salesforce/services/async/34.0/job/THEJOBID'\n assert created_job.pending_batches == []\n assert created_job.is_open\n\n bulk_request.assert_called_once_with(\n 'post',\n 'https://salesforce/services/async/34.0/job',\n data=XMLMatcher('''<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n <jobInfo xmlns=\"http://www.force.com/2009/06/asyncapi/dataload\">\n <operation>update</operation>\n <object>Lead</object>\n <contentType>CSV</contentType>\n </jobInfo>\n ''')\n )","def mark(self, job, status='succeeded'):\n pass","def testJobTRSetRandomRun(databases):\n\n class CustomGenerator(DataGenerator):\n chanceChainProduct = 0.4\n numTaskRunners = 5\n chanceTRFramework = 0.7\n chanceTRAllowedForJob = 0.7\n chanceTRAllowedForTask = 0.5\n chanceTRSetOverride = 0.4\n\n def frameworksForTaskRunner(self):\n return [\n framework for framework in self.frameworks\n if self.rnd.random() < self.chanceTRFramework\n ]\n\n def createConfiguration(self):\n def randomTRSet(chance):\n return (\n tr for tr in self.taskRunners\n if self.rnd.random() < chance\n )\n config = DataGenerator.createConfiguration(self)\n config._setRunners(randomTRSet(self.chanceTRAllowedForJob))\n for task in config.getTasks():\n if self.rnd.random() < self.chanceTRSetOverride:\n task._setRunners(\n randomTRSet(self.chanceTRAllowedForTask)\n )\n config._notify()\n return config\n\n def checkResults(gen, job):\n\n def checkExecutionFinishedTask(task):\n assert task.isDone()\n taskRunners = task.getRunners() or job.getRunners()\n runnerId = task['runner']\n if taskRunners:\n assert runnerId in taskRunners\n trCaps = databases.resourceDB[runnerId].capabilities\n for cap in task.getNeededCaps():\n assert cap in trCaps\n\n def allInputsReady(task):\n for input in task.getInputs():\n if not job.getProduct(input).isAvailable():\n return False\n return True\n\n def checkTaskRunners(task, onlyThis = None):\n if onlyThis is not None:\n taskRunners = [onlyThis]\n else:\n taskRunners = task.getRunners() or job.getRunners()\n for runnerId in taskRunners:\n # Target is not checked here, because DataGenerator uses\n # the same target for the job and all the task runners.\n assert not databases.resourceDB[runnerId].capabilities \\\n >= task.getNeededCaps()\n\n def checkNotDone(tasksNotDone, noTasksDone, runnerId):\n #assert noTasksDone\n if runnerId is None:\n assert noTasksDone\n else:\n assert len(tasksNotDone) != 0\n for task in tasksNotDone:\n if allInputsReady(task):\n assert runnerId not in \\\n (task.getRunners() or job.getRunners())\n\n for item in job.getTaskGroupSequence():\n if isinstance(item, TaskGroup):\n runnerId = item.getRunnerId()\n neededCaps = item.getNeededCaps()\n noTasksDone = True\n tasksNotDone = []\n taskRunners = None\n for task in item.getChildren():\n runners = task.getRunners() or job.getRunners()\n if runners:\n if taskRunners is None:\n taskRunners = set(runners)\n else:\n taskRunners &= runners\n if task.isExecutionFinished():\n checkExecutionFinishedTask(task)\n assert task['runner'] == runnerId\n noTasksDone = False\n else:\n tasksNotDone.append(task)\n if taskRunners is None:\n assert len(tasksNotDone) == 0\n elif taskRunners:\n if runnerId in taskRunners:\n for task in tasksNotDone:\n if allInputsReady(task):\n checkTaskRunners(task, runnerId)\n else:\n checkNotDone(tasksNotDone, noTasksDone, runnerId)\n else:\n checkNotDone(tasksNotDone, noTasksDone, runnerId)\n else:\n task = item # item is a task\n if task.isExecutionFinished():\n checkExecutionFinishedTask(task)\n elif allInputsReady(task):\n checkTaskRunners(task)\n\n seed = 123456789\n rnd = random.Random(seed)\n runs = 10\n randomRuns(databases, runs, rnd, CustomGenerator, checkResults)","def test_successful_job(self):\n\n successful_job = json.loads(TREEHERDER_JOB % (\"success\", \"completed\"))\n self.assertEquals(self.query_api.get_job_status(successful_job), SUCCESS)","async def test_misusage(my_job):\n\n # Disable some pylint checks, they are redundant here in the test.\n # pylint: disable=missing-docstring,unused-variable,function-redefined\n\n # Check that namesake job managers are not allowed.\n class MyNamesakeJobManager(jobmanager.JobManager):\n pass\n with pytest.raises(AssertionError,\n message='Successfully made two job managers with the '\n 'same names!'):\n class MyNamesakeJobManager(jobmanager.JobManager):\n pass\n\n # Check that it is impossible to register two job functions with the\n # same name inside a single job manager, but it is possible in the\n # different job managers.\n class MyJobManager1(jobmanager.JobManager):\n pass\n\n class MyJobManager2(jobmanager.JobManager):\n pass\n\n @MyJobManager1.job()\n def my_namesake_job():\n pass\n\n @MyJobManager2.job()\n def my_namesake_job():\n pass\n\n with pytest.raises(AssertionError,\n message='Successfully registered two job function with '\n 'the same name!'):\n @MyJobManager1.job()\n def my_namesake_job():\n pass\n\n # Check that it is impossible to submit a job with an identifier\n # which is already used by some other job.\n\n # Submit 2 jobs with the same identifier and check that second\n # submission raises an exception.\n job_id = uuid.uuid4()\n\n def on_job_submit(job):\n job.id = job_id\n return job\n my_job.set_on_submit(on_job_submit)\n\n # Submit a job #1.\n await my_job.job(mustfail=False)\n\n with pytest.raises(django.db.utils.IntegrityError,\n message='Successfully submitted a job with the '\n 'identifier that has already been used for '\n 'another job!'):\n # Submit a job #2.\n await my_job.job(mustfail=False)\n\n # Process ASGI messages and wait for the job to finish.\n await my_job.process_jobs()","def on_job_update(_job):\n nonlocal job\n job = _job\n # Asserts that job is either pending or canceled.\n assert job.state in ['PENDING', 'CANCELED'], (\n 'Job that canceled immediately after submission has wrong '\n 'state `{job.state}`!'\n )","def test_check_opt_crashed(self):\n self.assertEqual(check_opt(self.jobset2.job), 'ocrashed')","def test_results(self, affiliate_items):\n processed_count = 0\n error_count = 0\n\n updater = mock.Mock()\n batch_job = BatchJob(affiliate_items, updater)\n\n for result in batch_job.run():\n processed_count += 1\n error_count += int(result.is_error)\n\n assert updater.call_count == 4\n assert processed_count == 4\n assert error_count == 0","def test_reattaching_job(self, rj_update):\n\n running_job = reattach_job('test-reattach-job')\n\n rj_update.assert_called_once_with(info=None)","def on_job_update(_job):\n nonlocal job\n job = _job\n # Asserts that job is either pending or canceled.\n assert job.state in ['PENDING', 'CANCELED'], (\n 'Job that canceled immediately after submission has wrong '\n f'state `{job.state}`!')","def on_job_update(_job):\n nonlocal job\n job = _job\n # Asserts that job is either pending or canceled.\n assert job.state in ['PENDING', 'CANCELED'], (\n 'Job that canceled immediately after submission has wrong '\n f'state `{job.state}`!')","def on_job_update(_job):\n nonlocal job\n job = _job\n # Asserts that job is either pending or canceled.\n assert job.state in ['PENDING', 'CANCELED'], (\n 'Job that canceled immediately after submission has wrong '\n f'state `{job.state}`!')","def test_create_job(self):\n engine = Engine(self.config_file, self.api_token)\n\n engine.create_job()\n\n assert engine.ingest_job_id == 23","def test_batch(self):\n pass","def test_upload_orchestration_no_batches(new_job):\n with mock.patch.object(new_job, 'create') as create, \\\n mock.patch.object(new_job, 'add_batch') as add_batch, \\\n mock.patch.object(new_job, 'abort') as abort, \\\n mock.patch.object(new_job, 'close') as close, \\\n mock.patch.object(new_job, 'wait') as wait:\n\n new_job.upload(['Id', 'Description'], [])\n\n create.assert_called_once_with()\n assert add_batch.call_count == 0\n abort.assert_called_once_with()\n assert close.call_count == 0\n assert wait.call_count == 0","def test_run_jobs(self):\n qm = QMCalculator(software='mopac',\n method='pm3',\n fileStore=self.fileStore,\n onlyCyclics=True,\n maxRadicalNumber=0,\n )\n output_directory = os.path.join(qm.settings.fileStore, '..', '..')\n qm.set_default_output_directory(output_directory)\n\n spc1 = Species().from_smiles('c1ccccc1')\n spc2 = Species().from_smiles('CC1C=CC=CC=1')\n spc_list = [spc1, spc2]\n\n qm.run_jobs(spc_list, procnum=1)","async def test_concurrent_jobs(my_job):\n\n # The log of job status changes, grouped by job id cause jobs run\n # asynchronously.\n job_event_log = {}\n\n def on_job_update(job):\n \"\"\"The callback to log events into `job_event_log`.\"\"\"\n assert isinstance(job.id, uuid.UUID), 'Job id is not UUID!'\n job_log = job_event_log.setdefault(job.id, [])\n job_log.append(job.state)\n\n my_job.set_on_update(on_job_update)\n\n def on_job_submit(job):\n assert isinstance(job.id, uuid.UUID), 'Job id is not UUID!'\n job.id = job.kwds.pop('job_id')\n return job\n\n my_job.set_on_submit(on_job_submit)\n\n # Submit 3 jobs (with fancy identifiers) to check that they are processed\n # in the order of submission.\n # Submit a job #1.\n job1_id = '11111111-1111-1111-1111-111111111111'\n await my_job.job(mustfail=False, job_id=job1_id)\n\n # Submit a job #2.\n job2_id = '{22222222-2222-2222-2222-222222222222}'\n await my_job.job(mustfail=False, job_id=job2_id)\n\n # Submit a job #3.\n job3_id = uuid.UUID('33333333-3333-3333-3333-333333333333')\n await my_job.job(mustfail=False, job_id=job3_id)\n\n # Process ASGI messages and wait for the jobs to finish.\n await my_job.process_jobs(jobs=3)\n\n expected_log = {\n uuid.UUID('11111111-1111-1111-1111-111111111111'):\n ['PENDING', 'WORKING', 'DONE'],\n uuid.UUID('22222222-2222-2222-2222-222222222222'):\n ['PENDING', 'WORKING', 'DONE'],\n uuid.UUID('33333333-3333-3333-3333-333333333333'):\n ['PENDING', 'WORKING', 'DONE'],\n }\n\n assert job_event_log == expected_log, (\n 'Log of concurrent jobs status changes are different from '\n 'the expected one!')","def test_job_save_label_update(self, lookup_users_mock, elasticsearch_update_mock):\n from bilbyui.models import BilbyJob, Label\n\n label1 = Label.objects.create(name=\"label 1\", description=\"my label 1\", protected=True)\n\n job = BilbyJob.objects.create(\n user_id=self.user.id,\n name=\"Test1\",\n description=\"first job\",\n job_controller_id=2,\n private=False,\n ini_string=create_test_ini_string({\"detectors\": \"['H1']\"}),\n )\n\n job.labels.add(label1)\n\n label1.name = \"label 2\"\n label1.save()\n\n # Update should have been called three times\n self.assertEqual(elasticsearch_update_mock.call_count, 3)\n\n self.assertDictEqual(\n elasticsearch_update_mock.mock_calls[-1].kwargs[\"doc\"], generate_elastic_doc(job, self.user)\n )","def test_aggregation_different_jobs(self):\n job1 = DarshanIngestedJob(label=\"jobA\", file_details={})\n job2 = DarshanIngestedJob(label=\"jobB\", file_details={})\n\n self.assertRaises(AssertionError, lambda: job1.aggregate(job2))","def createjob(args):\n ncell = args.ncell\n nmg = args.nmg\n nsi = args.nsi\n nvac = args.nvac\n a0 = args.a0\n temp = args.temp\n nseeds = args.nseeds\n seeds = args.seeds\n nsteps = args.nsteps\n foldername_append = args.foldername_append\n pot = args.pot\n submit = args.submit\n submitdebug = args.submitdebug\n submittime_hours = args.submittime_hours\n test = args.test\n testfiles = args.testfiles\n nodes = args.nodes\n verbose = args.verbose\n\n\n ### check if ase runner/quippy/lammpps-data formats are known\n ase_formats = mu.ase_get_known_formats_class(verbose=True)\n ase_formats.check_if_default_formats_known(copy_and_adapt_formatspy_anyhow=False)\n\n # definex ffsocket inet/unix\n if nodes == 1:\n ffsocket = \"unix\"\n elif nodes > 1:\n ffsocket = \"inet\"\n else:\n sys.exit(\"Number of nodes has to be positive!\")\n\n\n # define ntasks, neval\n lmp_par = 2 # = OMP_NUM_THREADS\n ntasks = cores = nodes * 28\n ipi_inst = 4 # for sure best on fidis\n neval = ipi_inst*2 # was alwasy better, for ompi and impi\n\n ##### get the seed(s).\n if type(seeds) == bool:\n seeds = random.sample(range(1, 999999), nseeds)\n print('seeds',seeds)\n if test == True:\n nseeds = 1\n seeds = [1]\n print('seeds',seeds)\n nseeds = len(seeds)\n\n ##### a few checks\n scripts = mu.scripts()\n mypot = mu.mypot(pot)\n if submit is True or submitdebug is True:\n hostcheck = os.environ[\"myhost\"]\n if hostcheck == \"\":\n sys.exit('host unknown 87')\n\n\n ##### here only chck if the potential can be set up. (in.lmp)\n ##### the same command is then executed for every kmc folder\n ace = mu.ase_calculate_ene(pot=pot,\n potpath=False,\n units='eV',geopt=False,kmc=True,verbose=verbose)\n ace.pot_get_and_ase_lmp_cmd(kmc=True,temp=temp,nsteps=nsteps,ffsocket=ffsocket)\n\n ##### if test\n if test == True:\n nsteps = 50\n\n file_ipi_input_runner = scripts + \"/i-pi-mc_scripts/input-runner.xml\"\n\n\n ####################################\n # get directory\n ####################################\n if verbose:\n print(\"get directory\")\n pcsi = nsi/ncell**3.*100\n pcmg = nmg/ncell**3.*100\n pcvac = nvac/ncell**3.*100\n if args.cubic == True:\n pc = \"cubic\"\n else:\n pc = \"primitive\"\n directory = str(ncell)+\"x\"+str(ncell)+\"x\"+str(ncell)+\"_\"+pc+\"_\"+pot+\"_\"+\\\n str(temp)+\"K_\"+\\\n str(nvac)+\"Vac_\"+str(nmg)+\"Mg_\"+str(nsi)+\"Si__\"+\\\n str(round(pcvac,3))+\"pctVac_\"+str(round(pcmg,3))+\"pctMg_\"+str(round(pcsi,3))+\"pctSi\"\n if foldername_append != \"\":\n directory = directory+\"_\"+foldername_append\n\n ###############################################\n # make the structure\n ###############################################\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell,nsi,nmg,nvac,a0,create_fake_vacancy = True,cubic=args.cubic)\n atomsc = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell,nsi,nmg,nvac,a0,cubic=args.cubic)\n\n # make the atomic structure\n # this was to play ... not necessary now?\n if False:\n nndist = a0/np.sqrt(2.)\n\n from ase.io import read as ase_read\n from ase.io import write as ase_write\n\n ###############################################\n # get the amount of 1NN in a relly large cell\n ###############################################\n atomsc_fakevac_i = ase_read('dataxx.extxyz3',index=\":\",format='extxyz') # works, cell ist not changed\n #atomsc_fakevac_i = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=10,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n #nn = mu.ase_get_neighborlist(atomsc_fakevac_i,atomnr=0,cutoff=3.,skin=0.1)\n #print(\"nn\",nn,'len',len(nn))\n #nn = mu.ase_get_neighborlist(atomsc_fakevac_i,atomnr=0,cutoff=8.5,skin=0.1)\n #print(\"nn\",nn,'len',len(nn))\n #sys.exit()\n\n print(len(atomsc_fakevac_i),type(atomsc_fakevac_i))\n\n for idx,i in enumerate(atomsc_fakevac_i):\n print('aa',atomsc_fakevac_i[idx].positions[0])\n #print('aa',i.positions[0])\n print('ipi')\n atomsc_fakevac_i = ase_read('dataxx.ipi2',index=\":\",format='ipi') # works, cell ist not changed\n print(len(atomsc_fakevac_i),type(atomsc_fakevac_i))\n for idx,i in enumerate(atomsc_fakevac_i):\n print('aa',atomsc_fakevac_i[idx].positions[0])\n #print('aa',i.positions[0])\n print('quippy')\n atomsc_fakevac_i = ase_read('dataxx.quippy.xyz2',index=\":\",format='quippy') # works, cell ist not changed\n\n\n\n filename = '../sim.xyz'\n filename = '../simulation.pos_0.xyz'\n mu.count_amount_1NN_around_vacancies(filename,cutoffa=nndist,cutoffb=a0,skin=0.1,format='ipi')\n sys.exit()\n\n def mysave_quippy_xyz(atomsc_fakevac,text=False):\n if type(text) == bool:\n sys.exit('define text')\n atomsc_fakevac.write('data.quippy.xyz',format='quippy',append=True)\n #atomsc_fakevac.write('data.xyz',format=\"extxyz\",append=True)\n atomsc_fakevac.write('data'+text+'.quippy.xyz',format='quippy',append=True)\n #atomsc_fakevac.write('data'+text+'.xyz',format=\"extxyz\",append=True)\n return\n\n # create Al with single vacancy\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\n #print('from ....',(atomsc_fakevac.positions)[0])\n #for i in NN_1_indices:\n # print((atomsc_fakevac.positions)[i])\n print('NN_1_indices (orig ):',NN_1_indices)\n print('NN_2_indices (orig ):',NN_2_indices)\n #sys.exit()\n atomsc_fakevac.write('dataxx.quippy.xyz',format='quippy',append=True)\n atomsc_fakevac.write('dataxx.poscar',format='vasp',append=True)\n atomsc_fakevac.write('dataxx.ipi',format='ipi',append=True) # works, currently so implemented that it canges cell\n atomsc_fakevac.write('dataxx.xyz',format='xyz',append=True)\n atomsc_fakevac.write('dataxx.extxyz',format='extxyz',append=True)\n atomsc_fakevac.write('dataxx.lammps-data',format='lammps-data',append=True)\n atomsc_fakevac.write('dataxx.lammps-runner',format='lammps-runner',append=True)\n\n atomsc_fakevac_a = ase_read('dataxx.extxyz',format='extxyz') # works, cell ist not changed\n atomsc_fakevac_a.write('dataxx.extxyz2',format='extxyz',append=True) # works, cell is not changed\n\n atomsc_fakevac_b = ase_read('dataxx.xyz',format='xyz') # not working # but this should work\n atomsc_fakevac_b.write('dataxx.xyz2',format='xyz',append=True) # this is working\n\n atomsc_fakevac_c = ase_read('dataxx.ipi',format='ipi') # works, currently so implemented that it canges cell\n #print('ipi cell',atomsc_fakevac_c.get_cell())\n\n atomsc_fakevac_c.write('dataxx.ipi2',format='ipi',append=True) # works, just writes the cell it gests.\n atomsc_fakevac_c.write('dataxx.ipi2_poscar',format='vasp',append=True) # works, just writes the cell it gests.\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_c,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\n print('NN_1_indices (ipi ):',NN_1_indices)\n print('NN_2_indices (ipi ):',NN_2_indices)\n #print('from ....',(atomsc_fakevac_c.positions)[0])\n #for i in NN_1_indices:\n # print((atomsc_fakevac_c.positions)[i])\n\n atomsc_fakevac_cc = ase_read('dataxx.ipi2_poscar',format='vasp') # works, currently so implemented that it canges cell\n atomsc_fakevac_cc.write('dataxx.ipi2_poscar2',format='vasp',append=True)\n atomsc_fakevac_cc.write('dataxx.ipi2_poscar2_ipi',format='ipi',append=True) # works, just writes the cell it gests.\n #print('ipi cell2 (ext):',atomsc_fakevac_cc.get_cell())\n #print()\n #print('now quippy')\n atomsc_fakevac_d = ase_read('dataxx.quippy.xyz',format='quippy')\n #print('quippy cell (ext)',atomsc_fakevac_d.get_cell())\n atomsc_fakevac_d.write('dataxx.quippy.xyz2',format='quippy',append=True)\n atomsc_fakevac_d.write('dataxx.quippy.xyz2_extxyz',format='extxyz',append=True)\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_d,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\n print('NN_1_indices (quippy):',NN_1_indices)\n print('NN_2_indices (quippy):',NN_2_indices)\n #print('from ....',(atomsc_fakevac_d.positions)[0])\n #for i in NN_1_indices:\n # print((atomsc_fakevac_d.positions)[i])\n path = \"/home/glensk/kmc/run_michele/Si6Mg6V1.1_/simulation.pos_libatom_2struct.xyz\"\n atomsc_fakevac_e = ase_read(path,format='quippy')\n\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_e,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\n print('NN_1_indices (kmc ):',NN_1_indices)\n print('NN_2_indices (kmc ):',NN_2_indices)\n sys.exit()\n\n NN_1_indices = mu.ase_get_neighborlist(atomsc_fakevac,atomnr=0,cutoff=nndist,skin=0.1)\n NN_1_2_indices_tmp = mu.ase_get_neighborlist(atomsc_fakevac,atomnr=0,cutoff=a0,skin=0.1)\n print('NN_1_indices :',NN_1_indices)\n NN_2_indices = np.sort(np.array(mu.diff(NN_1_2_indices_tmp,NN_1_indices)))\n print('NN_2_indices :',NN_2_indices)\n NN_1_2_indices = np.concatenate((NN_1_indices, NN_2_indices ))\n print('NN_1_2_indices:',NN_1_2_indices)\n\n\n # fill only 1NN (with one species)\n for i in [ 'Mg', 'Si' ]:\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n mysave_quippy_xyz(atomsc_fakevac,text=\"1NN\")\n for ii in NN_1_indices:\n atomsc_fakevac[ii].symbol = i\n mysave_quippy_xyz(atomsc_fakevac,text=\"1NN\")\n\n # fill only 2NN (with one species)\n for i in [ 'Mg', 'Si' ]:\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n mysave_quippy_xyz(atomsc_fakevac,text=\"2NN\")\n for ii in NN_2_indices:\n atomsc_fakevac[ii].symbol = i\n mysave_quippy_xyz(atomsc_fakevac,text=\"2NN\")\n\n # fill 1NN and 2NN (with one species)\n for i in [ 'Mg', 'Si' ]:\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n mysave_quippy_xyz(atomsc_fakevac,text=\"1and2NN\")\n for ii in NN_1_2_indices:\n atomsc_fakevac[ii].symbol = i\n mysave_quippy_xyz(atomsc_fakevac,text=\"1and2NN\")\n\n # dif compositions in 1NN shell\n filling = [ 2,4,6,8,10]\n for fi in filling:\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n mysave_quippy_xyz(atomsc_fakevac,text=\"1NN_diffcomp\")\n for idx,ii in enumerate(NN_1_indices):\n if idx < fi: ch = \"Mg\"\n else: ch = \"Si\"\n atomsc_fakevac[ii].symbol = ch\n mysave_quippy_xyz(atomsc_fakevac,text=\"1NN_diffcomp\")\n\n\n sys.exit()\n\n #mu.ase_get_known_formats(show=True, add_missing_formats=False, copy_formats=False, verbose=False,show_formatspy=True)\n for i in [ 'Mg', 'Si' ]:\n for ii in [ 0,1,2,3,4,5]:\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=i+'_'+str(ii))\n\n\n sys.exit()\n\n\n # show the input variables\n print('--------------------------- check the input --------------------------------')\n print('JOBS (nseeds) ',nseeds,'(defined by -nseeds / or -seeds)')\n print('seeds ',seeds)\n print('nsteps ',nsteps)\n print()\n print('ncell ',ncell,\"(\",atomsc.get_number_of_atoms(),\"atoms )\")\n print('nsi ',nsi, \"(\",pcsi,\"at%)\")\n print('nmg ',nmg,\"(\",pcmg,\"at%)\")\n print('nvac ',nvac,\"(\",pcvac,\"at%)\")\n print('a0 ',a0,\"angstrom\")\n print('temp ',temp,\"K\")\n print()\n print('mypot.pot ',mypot.pot)\n print('mypot.potpath ',mypot.potpath)\n print()\n print('directory ',directory)\n print('submit ',submit)\n print('submitdebug ',submitdebug)\n print()\n print('nodes ',nodes)\n print('ffsocket ',ffsocket)\n #print('python ver ',sys.version_info[0])\n #print()\n print('--------------------------- check the input --------------------------------')\n if submit == True or submitdebug == True:\n mu.get_from_prompt_Yy_orexit(\"Are the ine input variables ok? [y]es: \")\n\n # make the directory\n if os.path.isdir(directory):\n mu.get_from_prompt_Yy_orexit(\"This main directory exists already, shall I add jobs? [y]es: \")\n mu.mkdir(directory)\n\n # create README.md\n IPI_COMMAND = os.environ[\"IPI_COMMAND\"]\n LAMMPS_COMMAND = os.environ[\"LAMMPS_COMMAND\"]\n mu.create_READMEtxt(directory,add=[\"# to start manually (1): python \"+IPI_COMMAND+\" input-runner.xml\",\"# to start manually (2):\"+LAMMPS_COMMAND+\" < in.lmp\"])\n\n for seed in seeds:\n\n # make jobdirectory\n jobdir = directory+'/seed'+str(seed)\n print('jobdir',jobdir)\n if os.path.exists(jobdir):\n sys.exit(\"jobdirectory \"+str(jobdir)+\" already exists!\")\n mu.mkdir(jobdir)\n\n # get data.lmp and data.ipi\n atomsc.write(jobdir+'/data.runnerformat.lmp',format='lammps-runner')\n atomsc_fakevac.write(jobdir+'/data.ipi',format='ipi')\n atomsc_fakevac.write(jobdir+'/data.extxyz',format='extxyz')\n #atomsc_fakevac.write(jobdir+'/data_fakevac.ipi',format='ipi')\n\n if testfiles == True:\n atomsc.write(jobdir+'/data.lmp',format='lammps-data')\n atomsc.write(jobdir+'/data.POSCAR',format='vasp')\n atomsc.write(jobdir+'/data.xyz',format='xyz')\n atomsc.write(jobdir+'/data.extxyz',format='extxyz')\n atomsc.write(jobdir+'/data.espresso-in',format='espresso-in')\n\n # create in.lmp\n ace = mu.ase_calculate_ene(pot=pot,potpath=mypot.potpath,\n units='eV',geopt=False,kmc=True,verbose=verbose)\n address = socket.gethostname()+\"_\"+os.path.basename(jobdir)\n print('address',address)\n ace.pot_get_and_ase_lmp_cmd(kmc=True,temp=temp,nsteps=nsteps,ffsocket=ffsocket,address=address)\n mu.lammps_write_inputfile(folder=jobdir,filename='in.lmp',positions='data.runnerformat.lmp',ace=ace)\n\n # create input-runner.xml (should be made without copying)\n mu.create_ipi_kmc_inputfile(jobdir,filename=\"input-runner.xml\",nsteps=nsteps,stride=100,seed=seed,a0=a0,ncell=ncell,nsi=nsi,nmg=nmg,nvac=nvac,neval=neval,temp=temp,nodes=nodes,address=address,testrun=test,cubic=args.cubic)\n\n # create submit-ipi-kmc.sh (should be made without copying)\n mu.create_submitskript_ipi_kmc(jobdir+\"/submit-ipi-kmc.sh\",nodes,ntasks,\n lmp_par=lmp_par,\n ipi_inst=ipi_inst,\n ffsocket=ffsocket,\n submittime_hours=submittime_hours,\n SBATCH=True)\n\n # create osubmit-ipi-kmc.sh (should be made without copying)\n mu.create_submitskript_ipi_kmc(jobdir+\"/osubmit-ipi-kmc.sh\",nodes,ntasks,\n lmp_par=lmp_par,\n ipi_inst=ipi_inst,\n ffsocket=ffsocket,\n submittime_hours=submittime_hours,\n SBATCH=False)\n\n # submit the job (execute either this or submit-ipi-kmc.sh_all3, not both)\n #mu.submitjob(submit=submit,submitdebug=submitdebug,jobdir=jobdir,submitskript=\"submit-ipi-kmc.sh\")\n\n # get submit-ipi-kmc.sh_all3 (should be made without copying)\n if nseeds == 3:\n mu.create_submitskript_ipi_kmc(directory+\"/submit-ipi-kmc.sh_all3\",nodes,ntasks,\n lmp_par=lmp_par,\n ipi_inst=ipi_inst,\n ffsocket=ffsocket,\n submittime_hours=submittime_hours,\n SBATCH=True,\n LOOPFOLDER=True)\n\n # submit the job (execute either this or submit-ipi-kmc.sh_all3, not both)\n #mu.submitjob(submit=submit,submitdebug=submitdebug,jobdir=directory,submitskript=\"submit-ipi-kmc.sh_all3\")\n if submit == True:\n mu.submitjob(submit_to_que=True,submit_to_debug_que=False,jobdir=directory,submitskript=\"submit-ipi-kmc.sh_all3\")\n\n\n print('done')\n return","def test_run_ended(self):","def test_update9(self):\n pass","def store(self, job, result):\n pass","def test_job_complete(self):\r\n t = mergeorder(['A', 'B', 'C', 'D', 'E'], 'foo')\r\n self.assertFalse(job_complete(t))\r\n self.assertFalse(job_complete(t.Children[0]))\r\n self.assertFalse(job_complete(t.Children[1].Children[1]))\r\n\r\n self.assertRaises(JobError, job_complete, t.Children[0].Children[0])\r\n\r\n f = 'test_parallel_merge_otus_JOB_COMPLETE_TEST.poll'\r\n self.assertFalse(os.path.exists(f))\r\n\r\n testf = open(f, 'w')\r\n testf.write('0\\n')\r\n testf.close()\r\n t.PollPath = f\r\n t.StartTime = 10\r\n\r\n self.assertTrue(job_complete(t))\r\n self.assertNotEqual(t.EndTime, None)\r\n self.assertNotEqual(t.TotalTime, None)\r\n\r\n testf = open(f, 'w')\r\n testf.write('1\\n')\r\n testf.close()\r\n\r\n self.assertRaises(JobError, job_complete, t)\r\n t.Processed = False\r\n self.assertRaises(JobError, job_complete, t)\r\n\r\n os.remove(f)","async def test_free_space(coresys: CoreSys):\n\n class TestClass:\n \"\"\"Test class.\"\"\"\n\n def __init__(self, coresys: CoreSys):\n \"\"\"Initialize the test class.\"\"\"\n self.coresys = coresys\n\n @Job(conditions=[JobCondition.FREE_SPACE])\n async def execute(self):\n \"\"\"Execute the class method.\"\"\"\n return True\n\n test = TestClass(coresys)\n with patch(\"shutil.disk_usage\", return_value=(42, 42, (1024.0 ** 3))):\n assert await test.execute()\n\n with patch(\"shutil.disk_usage\", return_value=(42, 42, (512.0 ** 3))):\n assert not await test.execute()","def test_post_job(self):\n body = UnitTesterJobCreateReq()\n response = self.client.open(\n '/v1/job',\n method='POST',\n data=json.dumps(body),\n content_type='application/json')\n self.assert200(response,\n 'Response body is : ' + response.data.decode('utf-8'))","def test_edit_seed_job_type_and_update(self, mock_create, mock_msg_mgr):\n\n url = '/%s/job-types/' % self.api\n manifest = copy.deepcopy(job_test_utils.MINIMUM_MANIFEST)\n manifest['job']['packageVersion'] = '1.0.1'\n\n json_data = {\n 'icon_code': 'BEEF',\n 'is_published': True,\n 'max_scheduled': 1,\n 'is_active': False,\n 'docker_image': 'my-job-1.0.0-seed:1.0.1',\n 'manifest': manifest,\n 'configuration': self.configuration,\n 'auto_update': True\n }\n\n response = self.client.generic('POST', url, json.dumps(json_data), 'application/json')\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n results = json.loads(response.content)\n self.assertTrue(results['is_valid'])\n self.assertEqual(len(results['warnings']), 1)\n self.assertEqual(results['warnings'][0]['name'], 'DEPRECATED_RECIPES')\n\n job_type = JobType.objects.filter(name='my-minimum-job', version='1.0.0').first()\n\n results = json.loads(response.content)\n self.assertTrue(results['is_valid'])\n self.assertEqual(len(results['warnings']), 1)\n self.assertEqual(results['warnings'][0]['name'], 'DEPRECATED_RECIPES')\n \n self.assertEqual(True, job_type.is_published)\n self.assertFalse(job_type.is_active)\n self.assertEqual(job_type.docker_image, 'my-job-1.0.0-seed:1.0.1')\n self.assertEqual(2, job_type.revision_num)\n\n mock_create.assert_called_with(self.recipe_type1.id, job_type.id)","def test_job_fail(self):\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \"extras/tests/dummy_jobs\")):\n\n module = \"test_fail\"\n name = \"TestFail\"\n job_class = get_job(f\"local/{module}/{name}\")\n job_result = JobResult.objects.create(\n name=job_class.class_path,\n obj_type=self.job_content_type,\n user=None,\n job_id=uuid.uuid4(),\n )\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\n job_result.refresh_from_db()\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_ERRORED)","def processjob(self, job):\n self.model.add_documents(job)\n self.jobsdone += 1\n if SAVE_DEBUG and self.jobsdone % SAVE_DEBUG == 0:\n fname = os.path.join(tempfile.gettempdir(), 'lsi_worker.pkl')\n self.model.save(fname)","def test_successful(self):\n\n url = '/%s/jobs/' % self.api\n response = self.client.generic('GET', url)\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\n\n result = json.loads(response.content)\n self.assertEqual(len(result['results']), 3)\n for entry in result['results']:\n expected = None\n if entry['id'] == self.job1.id:\n expected = self.job1\n elif entry['id'] == self.job2.id:\n expected = self.job2\n elif entry['id'] == self.job3.id:\n expected = self.job3\n else:\n self.fail('Found unexpected result: %s' % entry['id'])\n self.assertEqual(entry['job_type']['name'], expected.job_type.name)\n self.assertEqual(entry['job_type_rev']['job_type']['id'], expected.job_type.id)\n self.assertEqual(entry['is_superseded'], expected.is_superseded)","def test_workerConnectionPoolPerformJob(self):\n clock = Clock()\n peerPool = ControllerQueue(clock, None)\n factory = peerPool.workerListenerFactory()\n\n def peer():\n p = factory.buildProtocol(None)\n t = StringTransport()\n p.makeConnection(t)\n return p, t\n\n worker1, _ignore_trans1 = peer()\n worker2, _ignore_trans2 = peer()\n\n # Ask the worker to do something.\n worker1.performJob(JobDescriptor(1, 1, \"ABC\"))\n self.assertEquals(worker1.currentLoad, 1)\n self.assertEquals(worker2.currentLoad, 0)\n\n # Now ask the pool to do something\n peerPool.workerPool.performJob(JobDescriptor(2, 1, \"ABC\"))\n self.assertEquals(worker1.currentLoad, 1)\n self.assertEquals(worker2.currentLoad, 1)","def test_multiple_commands_at_same_time(self):","def add_job(self, job):\n self.job_list.append(job)\n self.num_jobs += 1","def jobs(\n ctx: typer.Context,\n op_id: str = typer.Argument(\n ...,\n autocompletion=completion_op_id,\n callback=check_for_op_id,\n help=\"A valid op-id. e.g. get_markets_prices\",\n ),\n param_string: Optional[str] = typer.Option(\n None,\n \"--param-string\",\n \"-p\",\n help=\"Optional. Full or partial parameters as a json encoded dictionary string. \"\n \"Keys must be valid parameters for selected op_id.\",\n ),\n default_params: bool = typer.Option(\n False,\n \"-d\",\n \"--default-params\",\n help=\"Include all parameters that are required, or have default values. \"\n \"Missing values will be 'NOTSET'.\",\n ),\n callback_path: Optional[Path] = typer.Option(\n None,\n \"-c\",\n \"--callbacks\",\n help=\"Optional. Path to custom callbacks to be used. \",\n ),\n file_name: str = typer.Option(\n \"created-jobs/${esi_job_op_id}-${esi_job_uid}\",\n \"-n\",\n \"--file-name\",\n help=(\n \"File name for the new job, must be unique if multiple jobs. \"\n \"Can include directories, \"\n \"and the file type suffix will be added based on --format-id.\"\n ),\n ),\n data_path: Optional[Path] = typer.Option(\n None,\n \"--data-file\",\n \"-i\",\n help=(\n \"Optional. Path to json, csv, or yaml file with full or partial parameters. \"\n \"Must result in a list of dicts.\"\n ),\n ),\n format_id: FormatChoices = typer.Option(\n FormatChoices.json,\n \"-f\",\n \"--format-id\",\n show_choices=True,\n help=\"Output file format.\",\n ),\n path_out: Path = typer.Argument(\n \"./tmp\",\n help=\"Parent path for saving the new jobs, will be prepended to --file-name.\",\n ),\n):\n operation_manifest: OperationManifest = ctx.obj[\"operation_manifest\"]\n # path_out = optional_object(path_out, Path, \".\")\n if path_out.is_file:\n typer.BadParameter(\"path_out must not be a file.\")\n file_data: Optional[List[Dict]] = get_params_from_file(data_path)\n parameters: Dict = decode_param_string(param_string)\n if callback_path is None:\n callback_collection = default_callback_collection()\n else:\n callback_collection = load_callbacks(callback_path)\n jobs_: List[EsiJob] = []\n try:\n op_info = operation_manifest.op_info(op_id)\n if not file_data:\n job = op_info.create_job(\n parameters,\n callback_collection,\n include_default_params=default_params,\n # only_required_default_params=False,\n # allow_notset=False,\n )\n jobs_.append(job)\n else:\n for params in file_data:\n params.update(parameters)\n job = op_info.create_job(\n params,\n callback_collection,\n include_default_params=default_params,\n # only_required_default_params=False,\n # allow_notset=False,\n )\n jobs_.append(job)\n except Exception as ex:\n raise typer.BadParameter(\n f\"Exception creating job. {ex.__class__.__name__}: {ex}\"\n )\n for job in jobs_:\n file_path = resolve_job_file_path(job, file_name, path_out)\n try:\n save_path = job.serialize_file(file_path, format_id)\n except Exception as ex:\n raise typer.BadParameter(\n f\"Error saving job to {save_path}. {ex.__class__.__name__}, {ex}\"\n )\n logger.info(\"Saved job %s at %s\", job.uid, file_path)\n typer.echo(f\"{len(jobs_)} jobs saved to {path_out}\")\n report_finished_task(ctx)","def test_update_case(self):\n pass","def test_create_batch_job(self, affiliate_items):\n with mock.patch('chiton.rack.affiliates.bulk.BatchJob') as batch_job:\n bulk_update_affiliate_item_details(affiliate_items)\n\n call_args = batch_job.call_args[0]\n assert call_args[0].count() == 4\n assert call_args[1] == update_affiliate_item_details","def testJobSummary(self):\n change = ChangeState(self.config, \"changestate_t\")\n\n locationAction = self.daoFactory(classname=\"Locations.New\")\n locationAction.execute(\"site1\", pnn=\"T2_CH_CERN\")\n\n testWorkflow = Workflow(spec=self.specUrl, owner=\"Steve\",\n name=\"wf001\", task=self.taskName)\n testWorkflow.create()\n testFileset = Fileset(name=\"TestFileset\")\n testFileset.create()\n\n testFile = File(lfn=\"SomeLFNC\", locations=set([\"T2_CH_CERN\"]))\n testFile.create()\n testFileset.addFile(testFile)\n testFileset.commit()\n\n testSubscription = Subscription(fileset=testFileset,\n workflow=testWorkflow)\n testSubscription.create()\n\n splitter = SplitterFactory()\n jobFactory = splitter(package=\"WMCore.WMBS\",\n subscription=testSubscription)\n jobGroup = jobFactory(files_per_job=1)[0]\n\n assert len(jobGroup.jobs) == 1, \\\n \"Error: Splitting should have created one job.\"\n\n testJobA = jobGroup.jobs[0]\n testJobA[\"user\"] = \"cinquo\"\n testJobA[\"group\"] = \"DMWM\"\n testJobA[\"taskType\"] = \"Production\"\n\n change.propagate([testJobA], 'created', 'new')\n myReport = Report()\n reportPath = os.path.join(getTestBase(),\n \"WMCore_t/JobStateMachine_t/Report.pkl\")\n myReport.unpersist(reportPath)\n\n change.propagate([testJobA], 'executing', 'created')\n testJobA[\"fwjr\"] = myReport\n change.propagate([testJobA], 'jobfailed', 'executing')\n\n changeStateDB = self.couchServer.connectDatabase(dbname=self.config.JobStateMachine.jobSummaryDBName)\n allDocs = changeStateDB.document(\"_all_docs\")\n\n self.assertEqual(len(allDocs[\"rows\"]), 2,\n \"Error: Wrong number of documents\")\n\n fwjrDoc = {'state': None}\n for resultRow in allDocs[\"rows\"]:\n if resultRow[\"id\"] != \"_design/WMStats\":\n fwjrDoc = changeStateDB.document(resultRow[\"id\"])\n break\n\n self.assertEqual(fwjrDoc['state'], 'jobfailed',\n \"Error: summary doesn't have the expected job state\")\n\n del testJobA[\"fwjr\"]\n\n change.propagate([testJobA], 'jobcooloff', 'jobfailed', updatesummary=True)\n return","def test_006_add(self):\n HEADING()\n db = self.db\n\n count = 5\n\n db.connect()\n\n db.delete_jobs()\n\n for id in range(0,count):\n job = db.insert(\"job\" + str(id))\n\n assert len(db) == count","async def test_job_done(my_job):\n\n # Set up callback to get notifications when job state changes.\n job = None\n job_update_counter = 0\n\n def on_job_update(_job):\n \"\"\"The callback updates `job` and `job_update_counter`.\"\"\"\n nonlocal job, job_update_counter\n job = _job\n job_update_counter += 1\n # Assert that simple job is reported as cancelable only when it\n # is in the `PENDING` or `CANCELING` state.\n if job.state in ['PENDING', 'CANCELING']:\n assert job.is_cancelable, ( # pylint: disable=no-member\n 'Job is not cancelable when it must be cancelable!')\n else:\n assert not job.is_cancelable, ( # pylint: disable=no-member\n 'Job is cancelable when it must not be cancelable!')\n\n my_job.set_on_update(on_job_update)\n\n # Submit a job which must finish OK.\n new_job = await my_job.job(mustfail=False)\n\n # Check job instance of new job (returned from job function).\n assert new_job.is_cancelable, ( # pylint: disable=no-member\n 'Job instance states that just submitted job is not cancelable!')\n assert new_job.state == 'PENDING'\n assert new_job.id is not None\n assert new_job.started is None\n assert new_job.args == []\n assert new_job.kwds == {'mustfail': False}\n assert new_job.message == ''\n assert new_job.payload == {}\n assert new_job.readiness is None\n\n # Check current job state right after job is submitted.\n assert job.state == 'PENDING', ('Just submitted job has wrong state '\n f'`{job.state}`!')\n\n # Process ASGI messages and wait for the job to finish.\n await my_job.process_jobs()\n\n # Check job state when job is done.\n assert job.state == 'DONE', f'Finished job has wrong state `{job.state}`!'\n\n # Check that job update callback has been called three times:\n # 1. job is submitted\n # 2. job switches to the working state\n # 3. job finishes\n assert job_update_counter == 3, 'Job updated wrong number of times!'","def test_update_goal(self):\n pass","def test_basic_execution(self):","def execute(self, job):\n raise NotImplementedError","def test_update_scenario(self):\n pass","def on_job_update(_job):\n nonlocal job\n job = _job","def on_job_update(_job):\n nonlocal job\n job = _job","def on_job_update(_job):\n nonlocal job\n job = _job","def on_job_update(_job):\n nonlocal job\n job = _job","def add(self, job):\r\n self.jobs.put(job)","def testDuplicateJobReports(self):\n change = ChangeState(self.config, \"changestate_t\")\n\n locationAction = self.daoFactory(classname=\"Locations.New\")\n locationAction.execute(\"site1\", pnn=\"T2_CH_CERN\")\n\n testWorkflow = Workflow(spec=self.specUrl, owner=\"Steve\",\n name=\"wf001\", task=self.taskName)\n testWorkflow.create()\n testFileset = Fileset(name=\"TestFileset\")\n testFileset.create()\n\n testFile = File(lfn=\"SomeLFNC\", locations=set([\"T2_CH_CERN\"]))\n testFile.create()\n testFileset.addFile(testFile)\n testFileset.commit()\n\n testSubscription = Subscription(fileset=testFileset,\n workflow=testWorkflow)\n testSubscription.create()\n\n splitter = SplitterFactory()\n jobFactory = splitter(package=\"WMCore.WMBS\",\n subscription=testSubscription)\n jobGroup = jobFactory(files_per_job=1)[0]\n\n assert len(jobGroup.jobs) == 1, \\\n \"Error: Splitting should have created one job.\"\n\n testJobA = jobGroup.jobs[0]\n testJobA[\"user\"] = \"sfoulkes\"\n testJobA[\"group\"] = \"DMWM\"\n testJobA[\"taskType\"] = \"Processing\"\n\n change.propagate([testJobA], 'created', 'new')\n myReport = Report()\n reportPath = os.path.join(getTestBase(),\n \"WMCore_t/JobStateMachine_t/Report.pkl\")\n myReport.unpersist(reportPath)\n testJobA[\"fwjr\"] = myReport\n\n change.propagate([testJobA], 'executing', 'created')\n change.propagate([testJobA], 'executing', 'created')\n\n changeStateDB = self.couchServer.connectDatabase(dbname=\"changestate_t/fwjrs\")\n allDocs = changeStateDB.document(\"_all_docs\")\n\n self.assertEqual(len(allDocs[\"rows\"]), 2,\n \"Error: Wrong number of documents\")\n\n for resultRow in allDocs[\"rows\"]:\n if resultRow[\"id\"] != \"_design/FWJRDump\":\n changeStateDB.document(resultRow[\"id\"])\n break\n\n return","def test_check_opt_complete(self):\n self.assertEqual(check_opt(self.jobset3.job), 'completed')","def test(executable):\n from tempfile import mkdtemp\n from os.path import join\n from shutil import rmtree\n from numpy import all, arange, abs, array\n from pylada.jobfolder.jobfolder import JobFolder\n from pylada.jobfolder.massextract import MassExtract\n from pylada.jobfolder import save\n from pylada.process.jobfolder import JobFolderProcess\n from pylada.process import Fail, AlreadyStarted, NotStarted\n from pylada import default_comm\n from functional import Functional\n\n root = JobFolder()\n for n in xrange(8):\n job = root / str(n)\n job.functional = Functional(executable, [n])\n job.params['sleep'] = 1\n\n comm = default_comm.copy()\n comm['n'] = 4\n\n dir = mkdtemp()\n try: \n program = JobFolderProcess(root, nbpools=2, outdir=dir)\n assert program.nbjobsleft > 0\n # program not started. should fail.\n try: program.poll()\n except NotStarted: pass\n else: raise Exception()\n try: program.wait()\n except NotStarted: pass\n else: raise Exception()\n\n # now starting for real.\n program.start(comm)\n assert len(program.process) == 2\n # Should not be possible to start twice.\n try: program.start(comm)\n except AlreadyStarted: pass\n else: raise Exception()\n while not program.poll(): continue\n assert program.nbjobsleft == 0\n save(root, join(dir, 'dict.dict'), overwrite=True)\n extract = MassExtract(join(dir, 'dict.dict'))\n assert all(extract.success.itervalues())\n order = array(extract.order.values()).flatten()\n assert all(arange(8) - order == 0)\n pi = array(extract.pi.values()).flatten()\n assert all(abs(pi - array([0.0, 3.2, 3.162353, 3.150849,\n 3.146801, 3.144926, 3.143907, 3.143293]))\\\n < 1e-5 )\n error = array(extract.error.values()).flatten()\n assert all(abs(error - array([3.141593, 0.05840735, 0.02076029, 0.009256556,\n 0.005207865, 0.00333321, 0.002314774, 0.001700664]))\\\n < 1e-5 )\n assert all(n['n'] == comm['n'] for n in extract.comm)\n # restart\n assert program.poll()\n assert len(program.process) == 0\n program.start(comm)\n assert len(program.process) == 0\n assert program.poll()\n finally:\n try: rmtree(dir)\n except: pass\n\n try: \n job = root / str(666)\n job.functional = Functional(executable, [666])\n program = JobFolderProcess(root, nbpools=2, outdir=dir)\n assert program.nbjobsleft > 0\n program.start(comm)\n program.wait()\n assert program.nbjobsleft == 0\n except Fail as r: \n assert len(program.errors.keys()) == 1\n assert '666' in program.errors\n assert len(program._finished) == 8\n else: raise Exception\n finally:\n try: rmtree(dir)\n except: pass\n try: \n job.functional.order = [667]\n program = JobFolderProcess(root, nbpools=2, outdir=dir)\n assert program.nbjobsleft > 0\n program.start(comm)\n program.wait()\n assert program.nbjobsleft == 0\n finally:\n try: rmtree(dir)\n except: pass","def test_pending_job(self):\n\n pending_job = json.loads(TREEHERDER_JOB % (\"unknown\", \"pending\"))\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)","def test_change_provisioned_throughput_usual_case():","def modify_job(filename, batch_name, node_choice, ppn_list, qe_switch, atoms):\n\tfin = open(filename, \"r\"); file = fin.read(); fin.close()\n\t# fin_head = open(\"/home/twchang/bin/others/job.sh-mod\", 'r').read()\n\tfin_head = Job.job_head\n\tnode_str = \"+\".join([\"node{:02d}:ppn={}\".format(node_choice[i], ppn_list[i]) for i in range(len(ppn_list))])\n\ttail = re.search(r\".*(\\s*-np.*)\", file, re.S).group(1).strip()\n\ttail = re.sub(r\"\\s*>>\\s*out\", r\"\", tail)\n\tif qe_switch:\n\t\ttail = re.sub(r\"(NPROCS\\s*).*(/bin/)\", r\"\\g<1>/data2/twchang/opt/q-e-qe-6.4.1\\g<2>\", tail, re.S)\n\telse: # vasp mode\n\t\ttail = re.sub(r\"(NPROCS\\s*).*(/bin/).*(\\n)\", r\"\\g<1>/home/twchang\\g<2>vasp_noncol \\g<3>\", tail, re.S)\n\tfile = fin_head + \" \" + tail\n\tfile = re.sub(r\"(#PBS\\s+-N\\s+).*\", r\"\\g<1>{}\".format(batch_name), file) # if batch_name != \"\" else file # modify batch_name\n\tfile = re.sub(r\"(#PBS\\s+-l\\s+nodes=).*\", r\"\\g<1>{}\".format(node_str), file) # modify ppn\n\tfile = re.sub(r\"(-np\\s+\\$NPROCS\\s+.*\\s+<).*?(\\..*>\\s+).*?(\\..*)\", r\"\\g<1>{}\\g<2>{}\\g<3>\".format(atoms, atoms), file) if atoms else file\n\tfile = re.sub(r\"/data2/twchang/q-e-qe-6\\.1\\.0/bin\", r\"/data2/twchang/opt/q-e-qe-6.4.1/bin\", file)# if qe_switch else file\n\tfout = open(filename, \"w\"); fout.write(file)","def test_replace():\n t_before = Task('finish book', 'brian', False)\n t_after = t_before._replace(id=10,done=True)\n t_expected = Task('finish book', 'brian', True, 10)\n assert t_after == t_expected","def test_start_already_running(self, mock_add_job, mock_get_job):\n mock_get_job.return_value = MagicMock()\n\n result = self.aggregator.start(self.node_id)\n\n self.assertFalse(result)\n self.assertFalse(mock_add_job.called)","def run(self):\n\t\tself.endpoint = self.from_reference.coord\t# the endpoint of every job is the thing ordering this job\n\n\t\tif to_do == \"carry\":\t# carry sth. from A to B\n\t\t\tpass\n\t\telif to_do == \"grade\":\t# grade an area for a building\n\t\t\tunit = self.searchUnit(self.endpoint, \"grader\")\n\t\t\t\n\t\telif to_do == \"build\":\t# build a building\n\t\t\tunit = self.searchUnit(self.endpoint, \"builder\")\n\t\t#TODO: if no settler fits to the building to be seized, one settler has to learn the job\n\t\telif to_do == \"learn a job\":\t# learn a job like lumberjack, butcher ... also for the soldier training\n\t\t\tpass\n\t\telif to_do == \"seize a building\":\t# civil and also military buildings\n\t\t\tpass","def _addjob(self, job: Job):\n if self.domain is not None and self.model is not None:\n job = copy.deepcopy(job)\n job._add_hydro_namelist(self.base_hydro_namelist)\n job._add_hrldas_namelist(self.base_hrldas_namelist)\n\n self.jobs.append(job)\n else:\n raise AttributeError('Can not add a job to a simulation without a model and a domain')","def standard_job_set(msg):\n\n run_num = msg['run']\n jobs = [[], [], [], [], []]\n new_dep = {'time': None, 'md5': None}\n\n # Add ROME jobs first\n cmd_prefix = \"./midanalyzer.exe -b -i romeConfig.xml -r \"\n cmd_suffix = \" -m offline -p 0 -q\"\n rome_dir = offline_dir + '/rome-processors'\n\n job = {}\n job['meta'] = datadir + '/shim/.crunchd_metadata.json'\n job['cmd'] = cmd_prefix + str(run_num) + cmd_suffix\n job['clean'] = 'rm histos*.root run*.root'\n\n job['name'] = 'single-laser'\n job['dir'] = rome_dir + '/single-laser'\n job['deps'] = {}\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\n jobs[0].append(job)\n\n job = copy.copy(job)\n job['name'] = 'double-laser'\n job['dir'] = rome_dir + '/double-laser'\n job['deps'] = {}\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\n jobs[0].append(job)\n\n job = copy.copy(job)\n job['name'] = 'capacitec'\n job['dir'] = rome_dir + '/capacitec'\n job['deps'] = {}\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\n jobs[0].append(job)\n\n job = copy.copy(job)\n job['name'] = 'shim-platform'\n job['dir'] = rome_dir + '/shim-platform'\n job['deps'] = {}\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\n jobs[0].append(job)\n\n job = copy.copy(job)\n job['name'] = 'metrolab'\n job['dir'] = rome_dir + '/metrolab'\n job['deps'] = {}\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\n jobs[0].append(job)\n\n job = copy.copy(job)\n job['name'] = 'mscb-cart'\n job['dir'] = rome_dir + '/mscb-cart'\n job['deps'] = {}\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\n jobs[0].append(job)\n\n job = copy.copy(job)\n job['name'] = 'mscb-ring'\n job['dir'] = rome_dir + '/mscb-ring'\n job['deps'] = {}\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\n jobs[0].append(job)\n\n job = copy.copy(job)\n job['name'] = 'tilt-sensor'\n job['dir'] = rome_dir + '/tilt-sensor'\n job['deps'] = {}\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\n jobs[0].append(job)\n\n job = copy.copy(job)\n job['name'] = 'hall-probe'\n job['dir'] = rome_dir + '/hall-probe'\n job['deps'] = {}\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\n jobs[0].append(job)\n\n # Make sure run attributes are extracted.\n job = {}\n job['name'] = 'extract_run_attr'\n job['dir'] = offline_dir + '/crunchers'\n job['cmd'] = 'python scripts/extract_run_attr.py %i' % run_num\n job['clean'] = None\n job['meta'] = datadir + '/crunched/.crunchd_metadata.json'\n job['deps'] = {}\n jobs[0].append(job)\n\n # Now the data bundling job.\n job = {}\n job['name'] = 'make-shim-dataset'\n job['cmd'] = 'bin/make_shim_dataset %i' % run_num\n job['clean'] = None\n job['dir'] = offline_dir + '/crunchers'\n job['meta'] = datadir + '/shim/.crunchd_metadata.json'\n job['deps'] = {}\n job['deps'][offline_dir + '/crunchers/bin/make_shim_dataset'] = new_dep\n job['deps']['data/shim/*%05i.root' % run_num] = new_dep\n jobs[1].append(job)\n\n # Finally apply fixes.\n # job = {}\n # job['name'] = 'fix-probe-remap'\n # job['dir'] = offline_dir\n # job['cmd'] = 'bin/fix_run_probe_map '\n # job['cmd'] += 'data/crunched/run_%05i.root ' % run_num\n # job['cmd'] += 'data/crunched/ %i' % run_num\n # job['clean'] = None\n # job['meta'] = datadir + '/crunched/.crunchd_metadata.json'\n # job['deps'] = {}\n # job['deps'][offline_dir + '/bin/recrunch_fids'] = new_dep\n # job['deps'][datadir + '/shim/run_%05i.root' % run_num] = new_dep\n # jobs[2].append(job)\n\n # Automatically generate extracted dataset\n job = {}\n job['name'] = 'extraction'\n job['dir'] = offline_dir + '/crunchers'\n job['cmd'] = 'bin/make_extracted_dataset '\n job['cmd'] += 'data/crunched/run_%05i.root' % run_num\n job['clean'] = None\n job['meta'] = datadir + '/extracted/.crunchd_metadata.json'\n job['deps'] = {}\n job['deps'][offline_dir + '/crunchers/bin/make_extracted_dataset'] = new_dep\n job['deps'][datadir + '/crunched/run_%05i.root' % run_num] = new_dep\n jobs[3].append(job)\n\n return jobs","def test_nextjob(self):\n\n dbpool = buildConnectionPool(self, jobSchema + schemaText)\n now = datetime.datetime.utcnow()\n\n # Empty job queue\n @inlineCallbacks\n def _next(txn, priority=WORK_PRIORITY_LOW):\n job = yield JobItem.nextjob(txn, now, priority, 1)\n if job is not None:\n work = yield job.workItem()\n else:\n work = None\n returnValue((job, work))\n job, work = yield inTransaction(dbpool.connection, _next)\n self.assertTrue(job is None)\n self.assertTrue(work is None)\n\n # Unassigned job with future notBefore not returned\n yield self._enqueue(dbpool, 1, 1, now + datetime.timedelta(days=1))\n job, work = yield inTransaction(dbpool.connection, _next)\n self.assertTrue(job is None)\n self.assertTrue(work is None)\n\n # Unassigned job with past notBefore returned\n yield self._enqueue(dbpool, 2, 1, now + datetime.timedelta(days=-1), priority=WORK_PRIORITY_HIGH)\n job, work = yield inTransaction(dbpool.connection, _next)\n self.assertTrue(job is not None)\n self.assertTrue(work.a == 2)\n assignID = job.jobID\n\n # Assigned job with past notBefore not returned\n @inlineCallbacks\n def assignJob(txn, when=None):\n assignee = yield JobItem.load(txn, assignID)\n yield assignee.assign(now if when is None else when, ControllerQueue.queueOverdueTimeout)\n yield inTransaction(dbpool.connection, assignJob)\n job, work = yield inTransaction(dbpool.connection, _next)\n self.assertTrue(job is None)\n self.assertTrue(work is None)\n\n # Unassigned, paused job with past notBefore not returned\n yield self._enqueue(dbpool, 3, 1, now + datetime.timedelta(days=-1), priority=WORK_PRIORITY_HIGH)\n @inlineCallbacks\n def pauseJob(txn, pause=True):\n works = yield DummyWorkItem.all(txn)\n for work in works:\n if work.a == 3:\n job = yield JobItem.load(txn, work.jobID)\n yield job.pauseIt(pause)\n yield inTransaction(dbpool.connection, pauseJob)\n job, work = yield inTransaction(dbpool.connection, _next)\n self.assertTrue(job is None)\n self.assertTrue(work is None)\n\n # Unassigned, paused then unpaused job with past notBefore is returned\n yield inTransaction(dbpool.connection, pauseJob, pause=False)\n job, work = yield inTransaction(dbpool.connection, _next)\n self.assertTrue(job is not None)\n self.assertTrue(work.a == 3)\n @inlineCallbacks\n def deleteJob(txn, jobID):\n job = yield JobItem.load(txn, jobID)\n yield job.delete()\n yield inTransaction(dbpool.connection, deleteJob, jobID=job.jobID)\n\n # Unassigned low priority job with past notBefore not returned if high priority required\n yield self._enqueue(dbpool, 4, 1, now + datetime.timedelta(days=-1))\n job, work = yield inTransaction(dbpool.connection, _next, priority=WORK_PRIORITY_HIGH)\n self.assertTrue(job is None)\n self.assertTrue(work is None)\n\n # Unassigned low priority job with past notBefore not returned if medium priority required\n yield self._enqueue(dbpool, 5, 1, now + datetime.timedelta(days=-1))\n job, work = yield inTransaction(dbpool.connection, _next, priority=WORK_PRIORITY_MEDIUM)\n self.assertTrue(job is None)\n self.assertTrue(work is None)\n\n # Assigned job with past notBefore, but overdue is not returned\n yield inTransaction(dbpool.connection, assignJob, when=now + datetime.timedelta(days=-1))\n job, work = yield inTransaction(dbpool.connection, _next, priority=WORK_PRIORITY_HIGH)\n self.assertTrue(job is None)\n self.assertTrue(work is None)","def test_normal_user_trying_to_update_job(self):\n normal_user = self.user_login.become_normal_user()\n job = Job.objects.create(name='somejob',\n workflow_version=self.workflow_version,\n job_order={},\n user=normal_user,\n share_group=self.share_group,\n job_settings=self.vm_job_settings,\n job_flavor=self.job_flavor,\n )\n url = reverse('v2-admin_job-list') + '{}/'.format(job.id)\n response = self.client.put(url, format='json',\n data={\n 'state': Job.JOB_STATE_FINISHED,\n 'step': Job.JOB_STEP_RUNNING,\n })\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)","def test_py_job_result(self):\n test_app = self._create_py_app()\n class_path = \"example_jobs.word_count.WordCountSparkSessionJob\"\n conf = \"input.strings = ['a', 'b', 'a', 'b']\"\n job = self._create_job(test_app, class_path, conf,\n ctx=self._get_functional_py_context())\n time.sleep(3)\n self._wait_till_job_is_done(job)\n job = self.client.jobs.get(job.jobId)\n self.assertEqual(\"FINISHED\", job.status)\n self.assertEqual({\"'a'\": 2, \"'b'\": 2}, job.result)","def test_getJobState(self):\n self.assertRaises(JobNotExistsError, self.foo.getJobState)\n self.foo.setTorqueJobId('asd')\n self.assertEqual(self.foo.getJobState(), 'NEW')","async def test_running(coresys: CoreSys):\n\n class TestClass:\n \"\"\"Test class.\"\"\"\n\n def __init__(self, coresys: CoreSys):\n \"\"\"Initialize the test class.\"\"\"\n self.coresys = coresys\n\n @Job(conditions=[JobCondition.RUNNING])\n async def execute(self):\n \"\"\"Execute the class method.\"\"\"\n return True\n\n test = TestClass(coresys)\n\n coresys.core.state = CoreState.RUNNING\n assert await test.execute()\n\n coresys.core.state = CoreState.FREEZE\n assert not await test.execute()"],"string":"[\n \"def test_job_addition():\\n with tempfile.TemporaryDirectory() as STATUS_DIR:\\n Status.add_job(STATUS_DIR, 'generation', 'test1')\\n status1 = Status(STATUS_DIR).data['generation']['test1']['job_status']\\n\\n Status.add_job(STATUS_DIR, 'generation', 'test1',\\n job_attrs={'job_status': 'finished',\\n 'additional': 'test'})\\n status2 = Status(STATUS_DIR).data['generation']['test1']['job_status']\\n\\n assert status2 == status1\",\n \"def test_submit_jobs(self):\\r\\n\\r\\n submit_jobs([self.command], prefix=\\\"test_job\\\")\\r\\n # Try and wait ten times, could be made nicer with alarm()\\r\\n for i in range(10):\\r\\n if exists(self.tmp_result_file):\\r\\n observed_text = \\\"\\\".join(list(open(self.tmp_result_file)))\\r\\n self.assertEqual(observed_text, \\\"hello\\\\n\\\")\\r\\n return\\r\\n else:\\r\\n sleep(10)\\r\\n # if we get here we failed\\r\\n self.fail(\\\"The test job apparently never finished.\\\\n\\\"\\r\\n + \\\"check the jobs error log and check the queue status\\\\n.\\\")\",\n \"def test_local_job(self):\\r\\n exp = \\\"abc; echo $? > xyz\\\"\\r\\n obs = local_job('abc', 'xyz', 'notused', 'notused')\\r\\n self.assertEqual(obs, exp)\",\n \"def work(self, job):\\n pass\",\n \"def testJobPostponedInspection(databases):\\n\\n class CustomGenerator(DataGenerator):\\n pass\\n gen = CustomGenerator(databases)\\n\\n image = gen.createProduct('image')\\n buildFw = gen.createFramework('build', [], [ image ])\\n testFw = gen.createFramework('test', [ image ], [])\\n buildTask = gen.createTask('build', buildFw)\\n testTask1 = gen.createTask('test1', testFw)\\n testTask2 = gen.createTask('test2', testFw)\\n testTask3 = gen.createTask('test3', testFw)\\n\\n tr = gen.createTaskRunner(name='tr_build',\\n capabilities=['build', 'test'])\\n\\n def simulate(config):\\n sanityCheck(gen, config)\\n\\n job, = config.createJobs(gen.owner)\\n # TODO: The toXML functionality should probably be tested\\n # in a separate test case.\\n\\n # Verify execution:\\n # Successfully complete first build task.\\n task = job.assignTask(databases.resourceDB[tr])\\n assert task is not None\\n assert task.getName() == buildTask\\n taskDone(job, buildTask)\\n assert job.result == ResultCode.OK\\n assert job.getFinalResult() == None\\n # Successfully complete first test task, without result.\\n task = job.assignTask(databases.resourceDB[tr])\\n assert (task is not None) is not None\\n assert task.getName() == testTask1\\n taskDone(job, testTask1, ResultCode.INSPECT)\\n assert not job.isExecutionFinished()\\n assert not job.hasFinalResult()\\n assert job.result == ResultCode.INSPECT\\n assert job.getFinalResult() is None\\n # Successfully complete second test task, with result.\\n task = job.assignTask(databases.resourceDB[tr])\\n assert task is not None\\n assert task.getName() == testTask2\\n taskDone(job, testTask2, ResultCode.OK)\\n assert not job.isExecutionFinished()\\n assert not job.hasFinalResult()\\n assert job.result == ResultCode.INSPECT\\n assert job.getFinalResult() is None\\n # Successfully complete third test task, without result.\\n task = job.assignTask(databases.resourceDB[tr])\\n assert task is not None\\n assert task.getName() == testTask3\\n taskDone(job, testTask3, ResultCode.INSPECT)\\n assert job.isExecutionFinished()\\n assert not job.hasFinalResult()\\n assert job.result == ResultCode.INSPECT\\n assert job.getFinalResult() is None\\n # Attempt to set invalid inspection result.\\n with raises(ValueError):\\n job.inspectDone(testTask1, ResultCode.CANCELLED, 'invalid')\\n # Complete inspection of first task.\\n job.inspectDone(testTask1, ResultCode.WARNING, 'inspect 1')\\n assert job.isExecutionFinished()\\n assert not job.hasFinalResult()\\n assert job.result == ResultCode.INSPECT\\n assert job.getFinalResult() is None\\n # Attempt to change inspection result.\\n with raises(IllegalStateError):\\n job.inspectDone(testTask1, ResultCode.OK, 'invalid')\\n # Complete inspection of third task.\\n job.inspectDone(testTask3, ResultCode.OK, 'inspect 3')\\n assert job.isExecutionFinished()\\n assert job.hasFinalResult()\\n assert job.result == ResultCode.WARNING\\n assert job.getFinalResult() == ResultCode.WARNING\\n\\n runWithReload(databases, gen.createConfiguration(), simulate)\",\n \"def test_008_modify(self):\\n HEADING()\\n db = self.db\\n\\n db.connect()\\n\\n job = {\\\"job_name\\\": \\\"modifyme\\\", \\\"input_filename\\\":\\\"file1\\\"}\\n\\n db.add(job)\\n\\n originalFilename = self.db.find_jobs(\\\"job_name\\\", \\\"modifyme\\\")[0][\\\"input_filename\\\"]\\n\\n job = {\\\"job_name\\\": \\\"modifyme\\\", \\\"input_filename\\\":\\\"file2\\\"}\\n\\n db.modify(job)\\n\\n newFilename = self.db.find_jobs(\\\"job_name\\\", \\\"modifyme\\\")[0][\\\"input_filename\\\"]\\n\\n assert(originalFilename != newFilename)\",\n \"def testA_StraightThrough(self):\\n # Do pre-submit job check\\n nRunning = getCondorRunningJobs()\\n self.assertEqual(nRunning, 0, \\\"User currently has %i running jobs. Test will not continue\\\" % (nRunning))\\n\\n myThread = threading.currentThread()\\n workload = self.createTestWorkload()\\n config = self.getConfig()\\n\\n\\n name = 'WMAgent_Test1'\\n site = self.sites[0]\\n nSubs = 5\\n nFiles = 10\\n workloadPath = os.path.join(self.testDir, 'workloadTest',\\n 'TestWorkload', 'WMSandbox',\\n 'WMWorkload.pkl')\\n\\n # Create a collection of files\\n self.createFileCollection(name = name, nSubs = nSubs,\\n nFiles = nFiles,\\n workflowURL = workloadPath,\\n site = site)\\n\\n\\n\\n ############################################################\\n # Test the JobCreator\\n\\n\\n config.Agent.componentName = 'JobCreator'\\n testJobCreator = JobCreatorPoller(config = config)\\n\\n testJobCreator.algorithm()\\n time.sleep(5)\\n\\n\\n # Did all jobs get created?\\n getJobsAction = self.daoFactory(classname = \\\"Jobs.GetAllJobs\\\")\\n result = getJobsAction.execute(state = 'Created', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs*nFiles)\\n\\n\\n # Count database objects\\n result = myThread.dbi.processData('SELECT * FROM wmbs_sub_files_acquired')[0].fetchall()\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n # Find the test directory\\n testDirectory = os.path.join(self.testDir, 'TestWorkload', 'ReReco')\\n self.assertTrue('JobCollection_1_0' in os.listdir(testDirectory))\\n self.assertTrue(len(os.listdir(testDirectory)) <= 20)\\n\\n groupDirectory = os.path.join(testDirectory, 'JobCollection_1_0')\\n\\n # First job should be in here\\n self.assertTrue('job_1' in os.listdir(groupDirectory))\\n jobFile = os.path.join(groupDirectory, 'job_1', 'job.pkl')\\n self.assertTrue(os.path.isfile(jobFile))\\n with open(jobFile, 'rb') as f:\\n job = pickle.load(f)\\n\\n\\n self.assertEqual(job['workflow'], name)\\n self.assertEqual(len(job['input_files']), 1)\\n self.assertEqual(os.path.basename(job['sandbox']), 'TestWorkload-Sandbox.tar.bz2')\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n ###############################################################\\n # Now test the JobSubmitter\\n\\n config.Agent.componentName = 'JobSubmitter'\\n testJobSubmitter = JobSubmitterPoller(config = config)\\n\\n\\n testJobSubmitter.algorithm()\\n\\n\\n # Check that jobs are in the right state\\n result = getJobsAction.execute(state = 'Created', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Executing', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n\\n # Check assigned locations\\n getLocationAction = self.daoFactory(classname = \\\"Jobs.GetLocation\\\")\\n for id in result:\\n loc = getLocationAction.execute(jobid = id)\\n self.assertEqual(loc, [[site]])\\n\\n\\n # Check to make sure we have running jobs\\n nRunning = getCondorRunningJobs()\\n self.assertEqual(nRunning, nFiles * nSubs)\\n\\n\\n #################################################################\\n # Now the JobTracker\\n\\n\\n config.Agent.componentName = 'JobTracker'\\n testJobTracker = JobTrackerPoller(config = config)\\n testJobTracker.setup()\\n\\n testJobTracker.algorithm()\\n\\n # Running the algo without removing the jobs should do nothing\\n result = getJobsAction.execute(state = 'Executing', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n condorRM()\\n time.sleep(1)\\n\\n # All jobs gone?\\n nRunning = getCondorRunningJobs()\\n self.assertEqual(nRunning, 0)\\n\\n\\n testJobTracker.algorithm()\\n time.sleep(5)\\n\\n # Running the algo without removing the jobs should do nothing\\n result = getJobsAction.execute(state = 'Executing', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Complete', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n\\n\\n #################################################################\\n # Now the JobAccountant\\n\\n # First you need to load all jobs\\n\\n\\n self.getFWJRAction = self.daoFactory(classname = \\\"Jobs.GetFWJRByState\\\")\\n completeJobs = self.getFWJRAction.execute(state = \\\"complete\\\")\\n\\n\\n # Create reports for all jobs\\n self.createReports(jobs = completeJobs, retryCount = 0)\\n\\n\\n\\n\\n\\n\\n config.Agent.componentName = 'JobAccountant'\\n testJobAccountant = JobAccountantPoller(config = config)\\n testJobAccountant.setup()\\n\\n\\n # It should do something with the jobs\\n testJobAccountant.algorithm()\\n\\n\\n # All the jobs should be done now\\n result = getJobsAction.execute(state = 'Complete', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Success', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n\\n #######################################################################\\n # Now the JobArchiver\\n\\n\\n config.Agent.componentName = 'JobArchiver'\\n testJobArchiver = JobArchiverPoller(config = config)\\n\\n\\n testJobArchiver.algorithm()\\n\\n # All the jobs should be cleaned up\\n result = getJobsAction.execute(state = 'Success', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n result = getJobsAction.execute(state = 'Cleanout', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), nSubs * nFiles)\\n\\n\\n logDir = os.path.join(self.testDir, 'logs')\\n\\n for job in completeJobs:\\n self.assertFalse(os.path.exists(job['fwjr_path']))\\n jobFolder = 'JobCluster_%i' \\\\\\n % (int(job['id']/config.JobArchiver.numberOfJobsToCluster))\\n jobPath = os.path.join(logDir, jobFolder, 'Job_%i.tar' %(job['id']))\\n self.assertTrue(os.path.isfile(jobPath))\\n self.assertTrue(os.path.getsize(jobPath) > 0)\\n\\n\\n\\n\\n ###########################################################################\\n # Now the TaskAchiver\\n\\n\\n config.Agent.componentName = 'TaskArchiver'\\n testTaskArchiver = TaskArchiverPoller(config = config)\\n\\n\\n testTaskArchiver.algorithm()\\n\\n\\n result = getJobsAction.execute(state = 'Cleanout', jobType = \\\"Processing\\\")\\n self.assertEqual(len(result), 0)\\n\\n\\n for jdict in completeJobs:\\n job = Job(id = jdict['id'])\\n self.assertFalse(job.exists())\\n\\n\\n\\n\\n\\n if os.path.isdir('testDir'):\\n shutil.rmtree('testDir')\\n shutil.copytree('%s' %self.testDir, os.path.join(os.getcwd(), 'testDir'))\\n\\n\\n\\n\\n return\",\n \"def test_create_replacement_jobstep(self, get_test_stats, get_job_parameters,\\n create_jenkins_job_from_params, fetch_artifact):\\n fetch_artifact.return_value.content = json.dumps({\\n 'phase': 'Test',\\n 'cmd': 'py.test --junit=junit.xml {test_names}',\\n 'tests': [\\n 'foo/bar.py',\\n 'foo/baz.py',\\n 'foo.bar.test_biz',\\n 'foo.bar.test_buz',\\n ],\\n })\\n create_jenkins_job_from_params.return_value = {\\n 'job_name': 'foo-bar',\\n 'build_no': 23,\\n }\\n\\n get_job_parameters.return_value = {'PARAM': '44'}\\n\\n get_test_stats.return_value = {\\n ('foo', 'bar'): 50,\\n ('foo', 'baz'): 15,\\n ('foo', 'bar', 'test_biz'): 10,\\n ('foo', 'bar', 'test_buz'): 200,\\n }, 68\\n\\n project = self.create_project()\\n build = self.create_build(project)\\n job = self.create_job(build, data={\\n 'job_name': 'server',\\n 'build_no': '35',\\n })\\n phase = self.create_jobphase(job)\\n step = self.create_jobstep(phase, data={\\n 'item_id': 13,\\n 'job_name': 'server',\\n })\\n\\n artifact = self.create_artifact(\\n step=step,\\n name='tests.json',\\n data={'fileName': 'tests.json'},\\n )\\n\\n buildstep = self.get_buildstep()\\n with mock.patch.object(JobPlan, 'get_build_step_for_job') as get_build_step_for_job:\\n get_build_step_for_job.return_value = (None, buildstep)\\n buildstep.fetch_artifact(artifact)\\n\\n phase2 = JobPhase.query.filter(\\n JobPhase.job_id == job.id,\\n JobPhase.id != phase.id,\\n ).first()\\n assert phase2, \\\"phase wasn't created\\\"\\n\\n new_steps = sorted(phase2.current_steps, key=lambda x: x.data['weight'], reverse=True)\\n\\n failstep = new_steps[0]\\n failstep.result = Result.infra_failed\\n failstep.status = Status.finished\\n db.session.add(failstep)\\n db.session.commit()\\n\\n replacement_step = buildstep.create_replacement_jobstep(failstep)\\n # new jobstep should still be part of same job/phase\\n assert replacement_step.job == job\\n assert replacement_step.phase == phase2\\n # make sure .steps actually includes the new jobstep\\n assert len(phase2.steps) == 3\\n # make sure replacement id is correctly set\\n assert failstep.replacement_id == replacement_step.id\\n\\n # Now perform same tests as test_job_expansion, making sure that the\\n # replacement step still satisfies all the correct attributes\\n new_steps = sorted(phase2.current_steps, key=lambda x: x.data['weight'], reverse=True)\\n assert new_steps[0] == replacement_step\\n\\n assert len(new_steps) == 2\\n assert new_steps[0].data['expanded'] is True\\n assert new_steps[0].data['build_no'] == 23\\n assert new_steps[0].data['job_name'] == 'foo-bar'\\n assert new_steps[0].data['tests'] == ['foo.bar.test_buz']\\n assert new_steps[0].data['path'] == ''\\n assert new_steps[0].data['cmd'] == 'py.test --junit=junit.xml {test_names}'\\n assert new_steps[0].data['weight'] == 201\\n\\n assert new_steps[1].data['expanded'] is True\\n assert new_steps[1].data['build_no'] == 23\\n assert new_steps[1].data['job_name'] == 'foo-bar'\\n assert new_steps[1].data['tests'] == [\\n 'foo/bar.py',\\n 'foo/baz.py',\\n 'foo.bar.test_biz',\\n ]\\n assert new_steps[1].data['path'] == ''\\n assert new_steps[1].data['cmd'] == 'py.test --junit=junit.xml {test_names}'\\n assert new_steps[1].data['weight'] == 78\\n\\n fetch_artifact.assert_called_once_with(artifact.step, artifact.data)\\n get_job_parameters.assert_any_call(\\n job,\\n changes_bid=new_steps[0].id.hex,\\n script='py.test --junit=junit.xml foo.bar.test_buz',\\n path='',\\n setup_script='',\\n teardown_script='',\\n )\\n create_jenkins_job_from_params.assert_any_call(\\n job_name='foo-bar',\\n changes_bid=new_steps[0].id.hex,\\n is_diff=False,\\n params=[{'name': 'PARAM', 'value': '44'}],\\n )\\n get_job_parameters.assert_any_call(\\n job,\\n changes_bid=new_steps[1].id.hex,\\n script='py.test --junit=junit.xml foo/bar.py foo/baz.py foo.bar.test_biz',\\n path='',\\n setup_script='',\\n teardown_script='',\\n )\\n create_jenkins_job_from_params.assert_any_call(\\n job_name='foo-bar',\\n changes_bid=new_steps[1].id.hex,\\n is_diff=False,\\n params=[{'name': 'PARAM', 'value': '44'}],\\n )\\n\\n # If fetch_artifact() is called again with different weights so\\n # that it divvies up the tests differently, does a broken\\n # double-shard build result?\\n\\n get_test_stats.return_value = {\\n ('foo', 'bar'): 50,\\n ('foo', 'baz'): 15,\\n ('foo', 'bar', 'test_biz'): 10,\\n ('foo', 'bar', 'test_buz'): 55,\\n }, 68\\n\\n buildstep.fetch_artifact(artifact)\\n\\n all_steps = phase2.current_steps\\n assert len(all_steps) == 2\",\n \"def testJobTwiceProduct(databases):\\n\\n class CustomGenerator(DataGenerator):\\n pass\\n gen = CustomGenerator(databases)\\n\\n image = gen.createProduct('image')\\n buildFw = gen.createFramework('build', [], [ image ])\\n testFw = gen.createFramework('test', [ image ], [])\\n buildTask1 = gen.createTask('build1', buildFw)\\n buildTask2 = gen.createTask('build2', buildFw)\\n testTask = gen.createTask('test', testFw)\\n\\n buildTR = gen.createTaskRunner(name='tr_build', capabilities=['build'])\\n testTR = gen.createTaskRunner(name='tr_test', capabilities=['test'])\\n\\n def simulate(config):\\n sanityCheck(gen, config)\\n\\n # TODO: Is it OK that Config.createJob does not put record into\\n # job DB? If so, document.\\n job, = config.createJobs(gen.owner)\\n # Note: Disabled to save time.\\n # TODO: The toXML functionality should probably be tested\\n # in a separate test case.\\n #self.jobDB.add(job)\\n\\n # Verify execution:\\n # Successfully complete first build task.\\n task = job.assignTask(databases.resourceDB[buildTR])\\n assert task is not None\\n assert task.getName().startswith('build')\\n taskDone(job, task.getName())\\n # Start test task.\\n task = job.assignTask(databases.resourceDB[testTR])\\n assert task is not None\\n assert task.getName() == testTask\\n # Complete second build task, but make it fail.\\n task = job.assignTask(databases.resourceDB[buildTR])\\n assert task is not None\\n assert task.getName().startswith('build')\\n taskDone(job, task.getName(), ResultCode.ERROR)\\n # Successfully complete test task.\\n taskDone(job, testTask)\\n assert job.isExecutionFinished()\\n assert job.hasFinalResult()\\n\\n runWithReload(databases, gen.createConfiguration(), simulate)\",\n \"def test_upload_orchestration(new_job):\\n with mock.patch.object(new_job, 'create') as create, \\\\\\n mock.patch.object(new_job, 'add_batch') as add_batch, \\\\\\n mock.patch.object(new_job, 'abort') as abort, \\\\\\n mock.patch.object(new_job, 'close') as close, \\\\\\n mock.patch.object(new_job, 'wait') as wait:\\n\\n def add_a_batch_for_real(*args, **kwargs):\\n new_job.pending_batches = new_job.pending_batches or []\\n new_job.pending_batches.append('FAKE ONE')\\n add_batch.side_effect = add_a_batch_for_real\\n\\n new_job.upload(['Id', 'Description'], [[1, 2], [3, 4]])\\n\\n create.assert_called_once_with()\\n add_batch.assert_called_once_with(['Id', 'Description'], [[1, 2], [3, 4]])\\n assert abort.call_count == 0\\n close.assert_called_once_with()\\n wait.assert_called_once_with()\",\n \"def testJobKilling(self):\\n change = ChangeState(self.config, \\\"changestate_t\\\")\\n\\n locationAction = self.daoFactory(classname=\\\"Locations.New\\\")\\n locationAction.execute(\\\"site1\\\", pnn=\\\"T2_CH_CERN\\\")\\n\\n testWorkflow = Workflow(spec=self.specUrl, owner=\\\"Steve\\\",\\n name=\\\"wf001\\\", task=self.taskName)\\n testWorkflow.create()\\n testFileset = Fileset(name=\\\"TestFileset\\\")\\n testFileset.create()\\n\\n for i in range(4):\\n newFile = File(lfn=\\\"File%s\\\" % i, locations=set([\\\"T2_CH_CERN\\\"]))\\n newFile.create()\\n testFileset.addFile(newFile)\\n\\n testFileset.commit()\\n testSubscription = Subscription(fileset=testFileset,\\n workflow=testWorkflow,\\n split_algo=\\\"FileBased\\\")\\n testSubscription.create()\\n\\n splitter = SplitterFactory()\\n jobFactory = splitter(package=\\\"WMCore.WMBS\\\",\\n subscription=testSubscription)\\n jobGroup = jobFactory(files_per_job=1)[0]\\n\\n assert len(jobGroup.jobs) == 4, \\\\\\n \\\"Error: Splitting should have created four jobs.\\\"\\n\\n testJobA = jobGroup.jobs[0]\\n testJobA[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobA[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobA[\\\"taskType\\\"] = \\\"Processing\\\"\\n testJobB = jobGroup.jobs[1]\\n testJobB[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobB[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobB[\\\"taskType\\\"] = \\\"Processing\\\"\\n testJobC = jobGroup.jobs[2]\\n testJobC[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobC[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobC[\\\"taskType\\\"] = \\\"Processing\\\"\\n testJobD = jobGroup.jobs[3]\\n testJobD[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobD[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobD[\\\"taskType\\\"] = \\\"Processing\\\"\\n\\n change.persist([testJobA], \\\"created\\\", \\\"new\\\")\\n change.persist([testJobB], \\\"jobfailed\\\", \\\"executing\\\")\\n change.persist([testJobC, testJobD], \\\"executing\\\", \\\"created\\\")\\n\\n change.persist([testJobA], \\\"killed\\\", \\\"created\\\")\\n change.persist([testJobB], \\\"killed\\\", \\\"jobfailed\\\")\\n change.persist([testJobC, testJobD], \\\"killed\\\", \\\"executing\\\")\\n\\n for job in [testJobA, testJobB, testJobC, testJobD]:\\n job.load()\\n self.assertEqual(job['retry_count'], 99999)\\n self.assertEqual(job['state'], 'killed')\\n\\n return\",\n \"def testJobCombinedProduct(databases):\\n\\n class CustomGenerator(DataGenerator):\\n pass\\n gen = CustomGenerator(databases)\\n\\n image = gen.createProduct('image', False, True)\\n buildFw = gen.createFramework('build', [], [ image ])\\n testFw = gen.createFramework('test', [ image ], [])\\n buildTask1 = gen.createTask('build1', buildFw)\\n buildTask2 = gen.createTask('build2', buildFw)\\n testTask = gen.createTask('test', testFw)\\n\\n buildTR = gen.createTaskRunner(name='tr_build', capabilities=['build'])\\n testTR = gen.createTaskRunner(name='tr_test', capabilities=['test'])\\n\\n def simulate(config):\\n sanityCheck(gen, config)\\n\\n job, = config.createJobs(gen.owner)\\n # TODO: The toXML functionality should probably be tested\\n # in a separate test case.\\n\\n # Verify execution:\\n # Successfully complete first build task.\\n task = job.assignTask(databases.resourceDB[buildTR])\\n assert task is not None\\n assert task.getName().startswith('build')\\n taskDone(job, task.getName())\\n # Try to start test task (should fail).\\n task = job.assignTask(databases.resourceDB[testTR])\\n assert task is None\\n # Complete second build task, but make it fail.\\n task = job.assignTask(databases.resourceDB[buildTR])\\n assert task is not None\\n assert task.getName().startswith('build')\\n taskDone(job, task.getName(), ResultCode.ERROR)\\n # Try to start test task (should succeed).\\n task = job.assignTask(databases.resourceDB[testTR])\\n assert task is not None\\n assert task.getName() == testTask\\n # Successfully complete test task.\\n taskDone(job, testTask)\\n assert job.isExecutionFinished()\\n assert job.hasFinalResult()\\n # Check that locators have been stored separately.\\n producers = set()\\n for taskId, locator in job.getProduct(image).getProducers():\\n assert taskId.startswith('build')\\n assert locator == locatorForTask(taskId)\\n\\n runWithReload(databases, gen.createConfiguration(), simulate)\",\n \"def test_update(executable):\\n from tempfile import mkdtemp\\n from shutil import rmtree\\n from pylada.jobfolder.jobfolder import JobFolder\\n from pylada.process.jobfolder import JobFolderProcess\\n from pylada import default_comm\\n from functional import Functional\\n\\n root = JobFolder()\\n for n in xrange(3):\\n job = root / str(n)\\n job.functional = Functional(executable, [n])\\n job.params['sleep'] = 1\\n supp = JobFolder()\\n for n in xrange(3, 6):\\n job = supp / str(n)\\n job.functional = Functional(executable, [n])\\n job.params['sleep'] = 1\\n\\n comm = default_comm.copy()\\n comm['n'] = 4\\n\\n dir = mkdtemp()\\n try: \\n program = JobFolderProcess(root, nbpools=2, outdir=dir, keepalive=True)\\n assert program.keepalive \\n\\n # compute current jobs.\\n program.start(comm)\\n program.wait()\\n assert hasattr(program, '_comm')\\n\\n # compute second set of updated jobs\\n program.update(supp)\\n program.wait()\\n\\n finally:\\n try: rmtree(dir)\\n except: pass\\n\\n # check with deleteold=True\\n dir = mkdtemp()\\n try: \\n program = JobFolderProcess(root, nbpools=2, outdir=dir, keepalive=True)\\n assert program.keepalive \\n\\n # compute current jobs.\\n program.start(comm)\\n program.wait()\\n assert hasattr(program, '_comm')\\n\\n # compute second set of updated jobs\\n program.update(supp, deleteold=True)\\n assert hasattr(program, '_comm')\\n program.wait()\\n\\n finally:\\n try: rmtree(dir)\\n except: pass\",\n \"def test_jobs_successful(self):\\n\\n workspace = storage_test_utils.create_workspace()\\n file1 = storage_test_utils.create_file()\\n data_dict = {\\n 'version': '1.0',\\n 'input_data': [{\\n 'name': 'INPUT_IMAGE',\\n 'file_id': file1.id\\n }],\\n 'output_data': [{\\n 'name': 'output_file_pngs',\\n 'workspace_id': workspace.id\\n }]}\\n\\n secret_configuration = {\\n 'version': '6',\\n 'priority': 50,\\n 'output_workspaces': {'default': storage_test_utils.create_workspace().name},\\n 'settings': {\\n 'DB_HOST': 'som.host.name',\\n 'DB_PASS': 'secret_password'\\n }\\n }\\n\\n seed_job_type = job_test_utils.create_seed_job_type(configuration=secret_configuration)\\n seed_job = job_test_utils.create_job(job_type=seed_job_type, status='RUNNING', input=data_dict)\\n\\n url = '/%s/jobs/%d/' % (self.api, seed_job.id)\\n response = self.client.generic('GET', url)\\n result = json.loads(response.content)\\n\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n self.assertEqual(result['configuration']['priority'],50)\\n self.assertNotIn('DB_PASS', result['configuration']['settings'])\",\n \"def add_job_state(self, job):\",\n \"def test_job_pass(self):\\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \\\"extras/tests/dummy_jobs\\\")):\\n\\n module = \\\"test_pass\\\"\\n name = \\\"TestPass\\\"\\n job_class = get_job(f\\\"local/{module}/{name}\\\")\\n\\n job_result = JobResult.objects.create(\\n name=job_class.class_path,\\n obj_type=self.job_content_type,\\n user=None,\\n job_id=uuid.uuid4(),\\n )\\n\\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\\n job_result.refresh_from_db()\\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_COMPLETED)\",\n \"def test_results_workers(self, affiliate_items):\\n success_count = 0\\n updater = mock.Mock()\\n\\n few_workers = BatchJob(affiliate_items, updater, workers=1)\\n for result in few_workers.run():\\n success_count += int(not result.is_error)\\n\\n many_workers = BatchJob(affiliate_items, updater, workers=4)\\n for result in many_workers.run():\\n success_count += int(not result.is_error)\\n\\n assert success_count == 8\\n assert updater.call_count == 8\",\n \"def test_get_refresh_job_status(self):\\n pass\",\n \"def test_run_job1(client):\\n with client.application.app_context():\\n app = client.application\\n app.redis.flushall()\\n\\n task_id = str(uuid4())\\n t = Task.create_task(task_id)\\n j = t.create_job()\\n job_id = j.job_id\\n t.save()\\n\\n exec_mock = MagicMock()\\n exec_mock.validate_max_running_executions.return_value = True\\n client.application.executor = exec_mock\\n\\n queue = Queue(\\\"jobs\\\", is_async=False, connection=client.application.redis)\\n result = queue.enqueue(job_mod.run_job, t.task_id, job_id, \\\"image\\\", \\\"command\\\")\\n\\n worker = SimpleWorker([queue], connection=queue.connection)\\n worker.work(burst=True)\\n\\n t.reload()\\n expect(t.jobs).to_length(1)\\n\\n job = t.jobs[0]\\n expect(job.executions).to_length(1)\\n\\n execution = job.executions[0]\\n expect(execution.image).to_equal(\\\"image\\\")\\n expect(execution.command).to_equal(\\\"command\\\")\\n\\n hash_key = f\\\"rq:job:{result.id}\\\"\\n\\n res = app.redis.exists(hash_key)\\n expect(res).to_be_true()\\n\\n res = app.redis.hget(hash_key, \\\"status\\\")\\n expect(res).to_equal(\\\"finished\\\")\\n\\n res = app.redis.hexists(hash_key, \\\"data\\\")\\n expect(res).to_be_true()\\n\\n keys = app.redis.keys()\\n next_job_id = [\\n key\\n\\n for key in keys\\n\\n if key.decode(\\\"utf-8\\\").startswith(\\\"rq:job\\\")\\n and not key.decode(\\\"utf-8\\\").endswith(result.id)\\n ]\\n expect(next_job_id).to_length(1)\\n next_job_id = next_job_id[0]\\n\\n res = app.redis.exists(next_job_id)\\n expect(res).to_be_true()\\n\\n res = app.redis.hget(next_job_id, \\\"status\\\")\\n expect(res).to_equal(\\\"queued\\\")\\n\\n res = app.redis.hexists(next_job_id, \\\"data\\\")\\n expect(res).to_be_true()\\n\\n res = app.redis.hget(next_job_id, \\\"origin\\\")\\n expect(res).to_equal(\\\"monitor\\\")\\n\\n res = app.redis.hget(next_job_id, \\\"description\\\")\\n expect(res).to_equal(\\n f\\\"fastlane.worker.job.monitor_job('{task_id}', '{job_id}', '{execution.execution_id}')\\\"\\n )\\n\\n res = app.redis.hget(next_job_id, \\\"timeout\\\")\\n expect(res).to_equal(\\\"-1\\\")\\n\\n t.reload()\\n expect(t.jobs[0].executions[0].status).to_equal(JobExecution.Status.running)\",\n \"def test_valid_n_jobs(n_jobs: Any) -> None:\\n check_n_jobs(n_jobs)\",\n \"def test_update_with_fail(executable):\\n from tempfile import mkdtemp\\n from shutil import rmtree\\n from pylada.jobfolder.jobfolder import JobFolder\\n from pylada.process.jobfolder import JobFolderProcess\\n from pylada.process import Fail\\n from pylada import default_comm\\n from functional import Functional\\n\\n root = JobFolder()\\n for n in xrange(3):\\n job = root / str(n)\\n job.functional = Functional(executable, [n])\\n job.params['sleep'] = 1\\n root['1'].functional.order = 666\\n root['1'].sleep = None\\n supp = JobFolder()\\n for n in xrange(3, 6):\\n job = supp / str(n)\\n job.functional = Functional(executable, [n])\\n job.params['sleep'] = 1\\n supp['5'].sleep = 0\\n supp['5'].functional.order = 666\\n\\n comm = default_comm.copy()\\n comm['n'] = 4\\n\\n dir = mkdtemp()\\n try: \\n program = JobFolderProcess(root, nbpools=2, outdir=dir, keepalive=True)\\n\\n # compute current jobs.\\n program.start(comm)\\n try: program.wait()\\n except Fail: pass\\n else: raise Exception()\\n assert len(program.errors) == 1\\n\\n # compute second set of updated jobs\\n program.update(supp)\\n try: program.wait()\\n except Fail: pass\\n else: raise Exception()\\n assert len(program.errors) == 2\\n program.errors.clear()\\n\\n\\n finally:\\n try: rmtree(dir)\\n except: pass\",\n \"def test_make_jobs(self):\\r\\n # no commands should make no jobs files\\r\\n self.assertEqual(make_jobs([], \\\"test\\\", self.queue), [])\\r\\n\\r\\n # one job file should be created\\r\\n filenames = make_jobs([self.command], \\\"test_qsub\\\", self.queue)\\r\\n self.assertTrue(len(filenames) == 1)\\r\\n observed_text = list(open(filenames[0]))\\r\\n\\r\\n self.assertEqual(\\\"\\\".join(observed_text),\\r\\n QSUB_TEXT % (\\\"72:00:00\\\", 1, 1, self.queue,\\r\\n \\\"test_qsub\\\", \\\"oe\\\",\\r\\n self.command))\",\n \"def test_fake_job( self ):\\n hda = MockHda( job=UNDEFINED_JOB )\\n self.history.active_datasets.append( hda )\\n job_dict, warnings = extract.summarize( trans=self.trans )\\n assert not warnings\\n assert len( job_dict ) == 1\\n fake_job = job_dict.keys()[ 0 ]\\n assert fake_job.id.startswith( \\\"fake_\\\" )\\n datasets = job_dict.values()[ 0 ]\\n assert datasets == [ ( None, hda ) ]\",\n \"def test_torque_job(self):\\r\\n exp = 'echo \\\"abc; echo $? > xyz\\\" | qsub -k oe -N MOTU -q queue'\\r\\n obs = torque_job('abc', 'xyz', '123', 'queue')\\r\\n self.assertEqual(obs, exp)\",\n \"def testJobOneTask(databases):\\n\\n class CustomGenerator(DataGenerator):\\n numTasks = 1\\n numInputs = [ 0 ]\\n numOutputs = [ 0 ]\\n\\n gen = CustomGenerator(databases)\\n gen.createDefinitions()\\n config = gen.createConfiguration()\\n\\n def checkOne(config):\\n taskName = gen.tasks[0]\\n #assert config.getProduct('') is None\\n assert len(config.getInputs()) == 0\\n assert len(config.getInputsGrouped()) == 0\\n assert len(config.getTasks()) == 1\\n task, = config.getTasks()\\n assert task is not None\\n assert task.getName() == taskName\\n assert len(config.getTaskSequence()) == 1\\n\\n runWithReload(databases, config, checkOne)\",\n \"def test_matching_jobs_existing(self):\\n self.assertEquals(\\n self.query_api.get_matching_jobs(\\n \\\"try\\\", \\\"146071751b1e\\\",\\n 'Linux x86-64 try build'), json.loads(JOBS_SCHEDULE))\",\n \"def run_job(job, interrupt_if_necessary):\",\n \"def update_job_state(self, job):\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\\n # Asserts that job is either pending or canceled.\\n assert job.state in ['PENDING', 'CANCELED'], (\\n 'job that canceled immediately after submission has wrong '\\n 'state `%s`' % job.state\\n )\",\n \"def test_reenqueue_job1(client):\\n with client.application.app_context():\\n pytest.skip(\\\"Not implemented\\\")\",\n \"def test_job_exists():\\n with tempfile.TemporaryDirectory() as STATUS_DIR:\\n Status.add_job(STATUS_DIR, 'generation', 'test1',\\n job_attrs={'job_status': 'submitted'})\\n exists = Status.job_exists(STATUS_DIR, 'test1')\\n assert exists\",\n \"def test_updateJobState(self):\\n self.assertRaises(JobNotExistsError, self.foo.updateJobState, 'asd')\\n self.foo.setTorqueJobId('asd')\\n self.assertEqual(self.foo.getJobState(), 'NEW')\",\n \"def test_override_job(self):\\n test_container = TestContainer()\\n job_1 = ThreadJob(name=\\\"test_job\\\",\\n function=run_me,\\n callback=test_container.on_job_done,\\n args=[\\\"job_1\\\"])\\n job_2 = ThreadJob(name=\\\"test_job\\\",\\n function=run_me,\\n callback=test_container.on_job_done,\\n args=[\\\"job_2\\\"])\\n job_3 = ThreadJob(name=\\\"test_job\\\",\\n function=run_me,\\n callback=test_container.on_job_done,\\n args=[\\\"job_3\\\"])\\n pool = ThreadPool()\\n pool.new_job(job_1)\\n pool.new_job(job_2)\\n pool.new_job(job_3)\\n test_container.wait_until_got_number_of_callbacks(3)\\n self.assertEqual(len(test_container.futures), 3)\\n # Here is what happens. job_1 runs so cannot be cancelled by job_2, so\\n # job_1 keeps running while job_2 is added to the queue. Then we add\\n # job_3, which cancels job_2, which immediately calls it's callback,\\n # thus the first future in the result is from job_2. Then job_1\\n # eventually finishes. Then job_3 starts and finishes.\\n self.assertTrue(test_container.futures[0].cancelled())\\n self.assertFalse(test_container.futures[1].cancelled())\\n self.assertEqual(test_container.futures[1].result(), \\\"job_1\\\")\\n self.assertFalse(test_container.futures[2].cancelled())\\n self.assertEqual(test_container.futures[2].result(), \\\"job_3\\\")\",\n \"def maybe_commit(job):\",\n \"def _addjob(self, job: Job):\\n self._job = copy.deepcopy(job)\\n self._job.restart = True\",\n \"def test_job_expansion(self, get_test_stats, get_job_parameters,\\n create_jenkins_job_from_params, fetch_artifact):\\n fetch_artifact.return_value.content = json.dumps({\\n 'phase': 'Test',\\n 'cmd': 'py.test --junit=junit.xml {test_names}',\\n 'tests': [\\n 'foo/bar.py',\\n 'foo/baz.py',\\n 'foo.bar.test_biz',\\n 'foo.bar.test_buz',\\n ],\\n })\\n create_jenkins_job_from_params.return_value = {\\n 'job_name': 'foo-bar',\\n 'build_no': 23,\\n }\\n\\n get_job_parameters.return_value = {'PARAM': '44'}\\n\\n get_test_stats.return_value = {\\n ('foo', 'bar'): 50,\\n ('foo', 'baz'): 15,\\n ('foo', 'bar', 'test_biz'): 10,\\n ('foo', 'bar', 'test_buz'): 200,\\n }, 68\\n\\n project = self.create_project()\\n build = self.create_build(project)\\n job = self.create_job(build, data={\\n 'job_name': 'server',\\n 'build_no': '35',\\n })\\n phase = self.create_jobphase(job)\\n step = self.create_jobstep(phase, data={\\n 'item_id': 13,\\n 'job_name': 'server',\\n })\\n\\n artifact = self.create_artifact(\\n step=step,\\n name='tests.json',\\n data={'fileName': 'tests.json'},\\n )\\n\\n buildstep = self.get_buildstep()\\n with mock.patch.object(JobPlan, 'get_build_step_for_job') as get_build_step_for_job:\\n get_build_step_for_job.return_value = (None, buildstep)\\n buildstep.fetch_artifact(artifact)\\n\\n phase2 = JobPhase.query.filter(\\n JobPhase.job_id == job.id,\\n JobPhase.id != phase.id,\\n ).first()\\n\\n assert phase2, \\\"phase wasn't created\\\"\\n assert phase2.label == 'Test'\\n assert phase2.status == Status.queued\\n\\n new_steps = sorted(phase2.current_steps, key=lambda x: x.data['weight'], reverse=True)\\n\\n assert len(new_steps) == 2\\n assert new_steps[0].data['expanded'] is True\\n assert new_steps[0].data['build_no'] == 23\\n assert new_steps[0].data['job_name'] == 'foo-bar'\\n assert new_steps[0].data['tests'] == ['foo.bar.test_buz']\\n assert new_steps[0].data['path'] == ''\\n assert new_steps[0].data['cmd'] == 'py.test --junit=junit.xml {test_names}'\\n assert new_steps[0].data['weight'] == 201\\n\\n assert new_steps[1].data['expanded'] is True\\n assert new_steps[1].data['build_no'] == 23\\n assert new_steps[1].data['job_name'] == 'foo-bar'\\n assert new_steps[1].data['tests'] == [\\n 'foo/bar.py',\\n 'foo/baz.py',\\n 'foo.bar.test_biz',\\n ]\\n assert new_steps[1].data['path'] == ''\\n assert new_steps[1].data['cmd'] == 'py.test --junit=junit.xml {test_names}'\\n assert new_steps[1].data['weight'] == 78\\n\\n fetch_artifact.assert_called_once_with(artifact.step, artifact.data)\\n get_job_parameters.assert_any_call(\\n job,\\n changes_bid=new_steps[0].id.hex,\\n script='py.test --junit=junit.xml foo.bar.test_buz',\\n path='',\\n setup_script='',\\n teardown_script='',\\n )\\n create_jenkins_job_from_params.assert_any_call(\\n job_name='foo-bar',\\n changes_bid=new_steps[0].id.hex,\\n is_diff=False,\\n params=[{'name': 'PARAM', 'value': '44'}],\\n )\\n get_job_parameters.assert_any_call(\\n job,\\n changes_bid=new_steps[1].id.hex,\\n script='py.test --junit=junit.xml foo/bar.py foo/baz.py foo.bar.test_biz',\\n path='',\\n setup_script='',\\n teardown_script='',\\n )\\n create_jenkins_job_from_params.assert_any_call(\\n job_name='foo-bar',\\n changes_bid=new_steps[1].id.hex,\\n is_diff=False,\\n params=[{'name': 'PARAM', 'value': '44'}],\\n )\\n\\n # If fetch_artifact() is called again with different weights so\\n # that it divvies up the tests differently, does a broken\\n # double-shard build result?\\n\\n get_test_stats.return_value = {\\n ('foo', 'bar'): 50,\\n ('foo', 'baz'): 15,\\n ('foo', 'bar', 'test_biz'): 10,\\n ('foo', 'bar', 'test_buz'): 55,\\n }, 68\\n\\n buildstep.fetch_artifact(artifact)\\n\\n all_steps = phase2.current_steps\\n assert len(all_steps) == 2\",\n \"def test_creating_a_job(created_job, bulk_request):\\n assert created_job.job == 'THEJOBID'\\n assert created_job.job_url == 'https://salesforce/services/async/34.0/job/THEJOBID'\\n assert created_job.pending_batches == []\\n assert created_job.is_open\\n\\n bulk_request.assert_called_once_with(\\n 'post',\\n 'https://salesforce/services/async/34.0/job',\\n data=XMLMatcher('''<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>\\n <jobInfo xmlns=\\\"http://www.force.com/2009/06/asyncapi/dataload\\\">\\n <operation>update</operation>\\n <object>Lead</object>\\n <contentType>CSV</contentType>\\n </jobInfo>\\n ''')\\n )\",\n \"def mark(self, job, status='succeeded'):\\n pass\",\n \"def testJobTRSetRandomRun(databases):\\n\\n class CustomGenerator(DataGenerator):\\n chanceChainProduct = 0.4\\n numTaskRunners = 5\\n chanceTRFramework = 0.7\\n chanceTRAllowedForJob = 0.7\\n chanceTRAllowedForTask = 0.5\\n chanceTRSetOverride = 0.4\\n\\n def frameworksForTaskRunner(self):\\n return [\\n framework for framework in self.frameworks\\n if self.rnd.random() < self.chanceTRFramework\\n ]\\n\\n def createConfiguration(self):\\n def randomTRSet(chance):\\n return (\\n tr for tr in self.taskRunners\\n if self.rnd.random() < chance\\n )\\n config = DataGenerator.createConfiguration(self)\\n config._setRunners(randomTRSet(self.chanceTRAllowedForJob))\\n for task in config.getTasks():\\n if self.rnd.random() < self.chanceTRSetOverride:\\n task._setRunners(\\n randomTRSet(self.chanceTRAllowedForTask)\\n )\\n config._notify()\\n return config\\n\\n def checkResults(gen, job):\\n\\n def checkExecutionFinishedTask(task):\\n assert task.isDone()\\n taskRunners = task.getRunners() or job.getRunners()\\n runnerId = task['runner']\\n if taskRunners:\\n assert runnerId in taskRunners\\n trCaps = databases.resourceDB[runnerId].capabilities\\n for cap in task.getNeededCaps():\\n assert cap in trCaps\\n\\n def allInputsReady(task):\\n for input in task.getInputs():\\n if not job.getProduct(input).isAvailable():\\n return False\\n return True\\n\\n def checkTaskRunners(task, onlyThis = None):\\n if onlyThis is not None:\\n taskRunners = [onlyThis]\\n else:\\n taskRunners = task.getRunners() or job.getRunners()\\n for runnerId in taskRunners:\\n # Target is not checked here, because DataGenerator uses\\n # the same target for the job and all the task runners.\\n assert not databases.resourceDB[runnerId].capabilities \\\\\\n >= task.getNeededCaps()\\n\\n def checkNotDone(tasksNotDone, noTasksDone, runnerId):\\n #assert noTasksDone\\n if runnerId is None:\\n assert noTasksDone\\n else:\\n assert len(tasksNotDone) != 0\\n for task in tasksNotDone:\\n if allInputsReady(task):\\n assert runnerId not in \\\\\\n (task.getRunners() or job.getRunners())\\n\\n for item in job.getTaskGroupSequence():\\n if isinstance(item, TaskGroup):\\n runnerId = item.getRunnerId()\\n neededCaps = item.getNeededCaps()\\n noTasksDone = True\\n tasksNotDone = []\\n taskRunners = None\\n for task in item.getChildren():\\n runners = task.getRunners() or job.getRunners()\\n if runners:\\n if taskRunners is None:\\n taskRunners = set(runners)\\n else:\\n taskRunners &= runners\\n if task.isExecutionFinished():\\n checkExecutionFinishedTask(task)\\n assert task['runner'] == runnerId\\n noTasksDone = False\\n else:\\n tasksNotDone.append(task)\\n if taskRunners is None:\\n assert len(tasksNotDone) == 0\\n elif taskRunners:\\n if runnerId in taskRunners:\\n for task in tasksNotDone:\\n if allInputsReady(task):\\n checkTaskRunners(task, runnerId)\\n else:\\n checkNotDone(tasksNotDone, noTasksDone, runnerId)\\n else:\\n checkNotDone(tasksNotDone, noTasksDone, runnerId)\\n else:\\n task = item # item is a task\\n if task.isExecutionFinished():\\n checkExecutionFinishedTask(task)\\n elif allInputsReady(task):\\n checkTaskRunners(task)\\n\\n seed = 123456789\\n rnd = random.Random(seed)\\n runs = 10\\n randomRuns(databases, runs, rnd, CustomGenerator, checkResults)\",\n \"def test_successful_job(self):\\n\\n successful_job = json.loads(TREEHERDER_JOB % (\\\"success\\\", \\\"completed\\\"))\\n self.assertEquals(self.query_api.get_job_status(successful_job), SUCCESS)\",\n \"async def test_misusage(my_job):\\n\\n # Disable some pylint checks, they are redundant here in the test.\\n # pylint: disable=missing-docstring,unused-variable,function-redefined\\n\\n # Check that namesake job managers are not allowed.\\n class MyNamesakeJobManager(jobmanager.JobManager):\\n pass\\n with pytest.raises(AssertionError,\\n message='Successfully made two job managers with the '\\n 'same names!'):\\n class MyNamesakeJobManager(jobmanager.JobManager):\\n pass\\n\\n # Check that it is impossible to register two job functions with the\\n # same name inside a single job manager, but it is possible in the\\n # different job managers.\\n class MyJobManager1(jobmanager.JobManager):\\n pass\\n\\n class MyJobManager2(jobmanager.JobManager):\\n pass\\n\\n @MyJobManager1.job()\\n def my_namesake_job():\\n pass\\n\\n @MyJobManager2.job()\\n def my_namesake_job():\\n pass\\n\\n with pytest.raises(AssertionError,\\n message='Successfully registered two job function with '\\n 'the same name!'):\\n @MyJobManager1.job()\\n def my_namesake_job():\\n pass\\n\\n # Check that it is impossible to submit a job with an identifier\\n # which is already used by some other job.\\n\\n # Submit 2 jobs with the same identifier and check that second\\n # submission raises an exception.\\n job_id = uuid.uuid4()\\n\\n def on_job_submit(job):\\n job.id = job_id\\n return job\\n my_job.set_on_submit(on_job_submit)\\n\\n # Submit a job #1.\\n await my_job.job(mustfail=False)\\n\\n with pytest.raises(django.db.utils.IntegrityError,\\n message='Successfully submitted a job with the '\\n 'identifier that has already been used for '\\n 'another job!'):\\n # Submit a job #2.\\n await my_job.job(mustfail=False)\\n\\n # Process ASGI messages and wait for the job to finish.\\n await my_job.process_jobs()\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\\n # Asserts that job is either pending or canceled.\\n assert job.state in ['PENDING', 'CANCELED'], (\\n 'Job that canceled immediately after submission has wrong '\\n 'state `{job.state}`!'\\n )\",\n \"def test_check_opt_crashed(self):\\n self.assertEqual(check_opt(self.jobset2.job), 'ocrashed')\",\n \"def test_results(self, affiliate_items):\\n processed_count = 0\\n error_count = 0\\n\\n updater = mock.Mock()\\n batch_job = BatchJob(affiliate_items, updater)\\n\\n for result in batch_job.run():\\n processed_count += 1\\n error_count += int(result.is_error)\\n\\n assert updater.call_count == 4\\n assert processed_count == 4\\n assert error_count == 0\",\n \"def test_reattaching_job(self, rj_update):\\n\\n running_job = reattach_job('test-reattach-job')\\n\\n rj_update.assert_called_once_with(info=None)\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\\n # Asserts that job is either pending or canceled.\\n assert job.state in ['PENDING', 'CANCELED'], (\\n 'Job that canceled immediately after submission has wrong '\\n f'state `{job.state}`!')\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\\n # Asserts that job is either pending or canceled.\\n assert job.state in ['PENDING', 'CANCELED'], (\\n 'Job that canceled immediately after submission has wrong '\\n f'state `{job.state}`!')\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\\n # Asserts that job is either pending or canceled.\\n assert job.state in ['PENDING', 'CANCELED'], (\\n 'Job that canceled immediately after submission has wrong '\\n f'state `{job.state}`!')\",\n \"def test_create_job(self):\\n engine = Engine(self.config_file, self.api_token)\\n\\n engine.create_job()\\n\\n assert engine.ingest_job_id == 23\",\n \"def test_batch(self):\\n pass\",\n \"def test_upload_orchestration_no_batches(new_job):\\n with mock.patch.object(new_job, 'create') as create, \\\\\\n mock.patch.object(new_job, 'add_batch') as add_batch, \\\\\\n mock.patch.object(new_job, 'abort') as abort, \\\\\\n mock.patch.object(new_job, 'close') as close, \\\\\\n mock.patch.object(new_job, 'wait') as wait:\\n\\n new_job.upload(['Id', 'Description'], [])\\n\\n create.assert_called_once_with()\\n assert add_batch.call_count == 0\\n abort.assert_called_once_with()\\n assert close.call_count == 0\\n assert wait.call_count == 0\",\n \"def test_run_jobs(self):\\n qm = QMCalculator(software='mopac',\\n method='pm3',\\n fileStore=self.fileStore,\\n onlyCyclics=True,\\n maxRadicalNumber=0,\\n )\\n output_directory = os.path.join(qm.settings.fileStore, '..', '..')\\n qm.set_default_output_directory(output_directory)\\n\\n spc1 = Species().from_smiles('c1ccccc1')\\n spc2 = Species().from_smiles('CC1C=CC=CC=1')\\n spc_list = [spc1, spc2]\\n\\n qm.run_jobs(spc_list, procnum=1)\",\n \"async def test_concurrent_jobs(my_job):\\n\\n # The log of job status changes, grouped by job id cause jobs run\\n # asynchronously.\\n job_event_log = {}\\n\\n def on_job_update(job):\\n \\\"\\\"\\\"The callback to log events into `job_event_log`.\\\"\\\"\\\"\\n assert isinstance(job.id, uuid.UUID), 'Job id is not UUID!'\\n job_log = job_event_log.setdefault(job.id, [])\\n job_log.append(job.state)\\n\\n my_job.set_on_update(on_job_update)\\n\\n def on_job_submit(job):\\n assert isinstance(job.id, uuid.UUID), 'Job id is not UUID!'\\n job.id = job.kwds.pop('job_id')\\n return job\\n\\n my_job.set_on_submit(on_job_submit)\\n\\n # Submit 3 jobs (with fancy identifiers) to check that they are processed\\n # in the order of submission.\\n # Submit a job #1.\\n job1_id = '11111111-1111-1111-1111-111111111111'\\n await my_job.job(mustfail=False, job_id=job1_id)\\n\\n # Submit a job #2.\\n job2_id = '{22222222-2222-2222-2222-222222222222}'\\n await my_job.job(mustfail=False, job_id=job2_id)\\n\\n # Submit a job #3.\\n job3_id = uuid.UUID('33333333-3333-3333-3333-333333333333')\\n await my_job.job(mustfail=False, job_id=job3_id)\\n\\n # Process ASGI messages and wait for the jobs to finish.\\n await my_job.process_jobs(jobs=3)\\n\\n expected_log = {\\n uuid.UUID('11111111-1111-1111-1111-111111111111'):\\n ['PENDING', 'WORKING', 'DONE'],\\n uuid.UUID('22222222-2222-2222-2222-222222222222'):\\n ['PENDING', 'WORKING', 'DONE'],\\n uuid.UUID('33333333-3333-3333-3333-333333333333'):\\n ['PENDING', 'WORKING', 'DONE'],\\n }\\n\\n assert job_event_log == expected_log, (\\n 'Log of concurrent jobs status changes are different from '\\n 'the expected one!')\",\n \"def test_job_save_label_update(self, lookup_users_mock, elasticsearch_update_mock):\\n from bilbyui.models import BilbyJob, Label\\n\\n label1 = Label.objects.create(name=\\\"label 1\\\", description=\\\"my label 1\\\", protected=True)\\n\\n job = BilbyJob.objects.create(\\n user_id=self.user.id,\\n name=\\\"Test1\\\",\\n description=\\\"first job\\\",\\n job_controller_id=2,\\n private=False,\\n ini_string=create_test_ini_string({\\\"detectors\\\": \\\"['H1']\\\"}),\\n )\\n\\n job.labels.add(label1)\\n\\n label1.name = \\\"label 2\\\"\\n label1.save()\\n\\n # Update should have been called three times\\n self.assertEqual(elasticsearch_update_mock.call_count, 3)\\n\\n self.assertDictEqual(\\n elasticsearch_update_mock.mock_calls[-1].kwargs[\\\"doc\\\"], generate_elastic_doc(job, self.user)\\n )\",\n \"def test_aggregation_different_jobs(self):\\n job1 = DarshanIngestedJob(label=\\\"jobA\\\", file_details={})\\n job2 = DarshanIngestedJob(label=\\\"jobB\\\", file_details={})\\n\\n self.assertRaises(AssertionError, lambda: job1.aggregate(job2))\",\n \"def createjob(args):\\n ncell = args.ncell\\n nmg = args.nmg\\n nsi = args.nsi\\n nvac = args.nvac\\n a0 = args.a0\\n temp = args.temp\\n nseeds = args.nseeds\\n seeds = args.seeds\\n nsteps = args.nsteps\\n foldername_append = args.foldername_append\\n pot = args.pot\\n submit = args.submit\\n submitdebug = args.submitdebug\\n submittime_hours = args.submittime_hours\\n test = args.test\\n testfiles = args.testfiles\\n nodes = args.nodes\\n verbose = args.verbose\\n\\n\\n ### check if ase runner/quippy/lammpps-data formats are known\\n ase_formats = mu.ase_get_known_formats_class(verbose=True)\\n ase_formats.check_if_default_formats_known(copy_and_adapt_formatspy_anyhow=False)\\n\\n # definex ffsocket inet/unix\\n if nodes == 1:\\n ffsocket = \\\"unix\\\"\\n elif nodes > 1:\\n ffsocket = \\\"inet\\\"\\n else:\\n sys.exit(\\\"Number of nodes has to be positive!\\\")\\n\\n\\n # define ntasks, neval\\n lmp_par = 2 # = OMP_NUM_THREADS\\n ntasks = cores = nodes * 28\\n ipi_inst = 4 # for sure best on fidis\\n neval = ipi_inst*2 # was alwasy better, for ompi and impi\\n\\n ##### get the seed(s).\\n if type(seeds) == bool:\\n seeds = random.sample(range(1, 999999), nseeds)\\n print('seeds',seeds)\\n if test == True:\\n nseeds = 1\\n seeds = [1]\\n print('seeds',seeds)\\n nseeds = len(seeds)\\n\\n ##### a few checks\\n scripts = mu.scripts()\\n mypot = mu.mypot(pot)\\n if submit is True or submitdebug is True:\\n hostcheck = os.environ[\\\"myhost\\\"]\\n if hostcheck == \\\"\\\":\\n sys.exit('host unknown 87')\\n\\n\\n ##### here only chck if the potential can be set up. (in.lmp)\\n ##### the same command is then executed for every kmc folder\\n ace = mu.ase_calculate_ene(pot=pot,\\n potpath=False,\\n units='eV',geopt=False,kmc=True,verbose=verbose)\\n ace.pot_get_and_ase_lmp_cmd(kmc=True,temp=temp,nsteps=nsteps,ffsocket=ffsocket)\\n\\n ##### if test\\n if test == True:\\n nsteps = 50\\n\\n file_ipi_input_runner = scripts + \\\"/i-pi-mc_scripts/input-runner.xml\\\"\\n\\n\\n ####################################\\n # get directory\\n ####################################\\n if verbose:\\n print(\\\"get directory\\\")\\n pcsi = nsi/ncell**3.*100\\n pcmg = nmg/ncell**3.*100\\n pcvac = nvac/ncell**3.*100\\n if args.cubic == True:\\n pc = \\\"cubic\\\"\\n else:\\n pc = \\\"primitive\\\"\\n directory = str(ncell)+\\\"x\\\"+str(ncell)+\\\"x\\\"+str(ncell)+\\\"_\\\"+pc+\\\"_\\\"+pot+\\\"_\\\"+\\\\\\n str(temp)+\\\"K_\\\"+\\\\\\n str(nvac)+\\\"Vac_\\\"+str(nmg)+\\\"Mg_\\\"+str(nsi)+\\\"Si__\\\"+\\\\\\n str(round(pcvac,3))+\\\"pctVac_\\\"+str(round(pcmg,3))+\\\"pctMg_\\\"+str(round(pcsi,3))+\\\"pctSi\\\"\\n if foldername_append != \\\"\\\":\\n directory = directory+\\\"_\\\"+foldername_append\\n\\n ###############################################\\n # make the structure\\n ###############################################\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell,nsi,nmg,nvac,a0,create_fake_vacancy = True,cubic=args.cubic)\\n atomsc = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell,nsi,nmg,nvac,a0,cubic=args.cubic)\\n\\n # make the atomic structure\\n # this was to play ... not necessary now?\\n if False:\\n nndist = a0/np.sqrt(2.)\\n\\n from ase.io import read as ase_read\\n from ase.io import write as ase_write\\n\\n ###############################################\\n # get the amount of 1NN in a relly large cell\\n ###############################################\\n atomsc_fakevac_i = ase_read('dataxx.extxyz3',index=\\\":\\\",format='extxyz') # works, cell ist not changed\\n #atomsc_fakevac_i = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=10,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n #nn = mu.ase_get_neighborlist(atomsc_fakevac_i,atomnr=0,cutoff=3.,skin=0.1)\\n #print(\\\"nn\\\",nn,'len',len(nn))\\n #nn = mu.ase_get_neighborlist(atomsc_fakevac_i,atomnr=0,cutoff=8.5,skin=0.1)\\n #print(\\\"nn\\\",nn,'len',len(nn))\\n #sys.exit()\\n\\n print(len(atomsc_fakevac_i),type(atomsc_fakevac_i))\\n\\n for idx,i in enumerate(atomsc_fakevac_i):\\n print('aa',atomsc_fakevac_i[idx].positions[0])\\n #print('aa',i.positions[0])\\n print('ipi')\\n atomsc_fakevac_i = ase_read('dataxx.ipi2',index=\\\":\\\",format='ipi') # works, cell ist not changed\\n print(len(atomsc_fakevac_i),type(atomsc_fakevac_i))\\n for idx,i in enumerate(atomsc_fakevac_i):\\n print('aa',atomsc_fakevac_i[idx].positions[0])\\n #print('aa',i.positions[0])\\n print('quippy')\\n atomsc_fakevac_i = ase_read('dataxx.quippy.xyz2',index=\\\":\\\",format='quippy') # works, cell ist not changed\\n\\n\\n\\n filename = '../sim.xyz'\\n filename = '../simulation.pos_0.xyz'\\n mu.count_amount_1NN_around_vacancies(filename,cutoffa=nndist,cutoffb=a0,skin=0.1,format='ipi')\\n sys.exit()\\n\\n def mysave_quippy_xyz(atomsc_fakevac,text=False):\\n if type(text) == bool:\\n sys.exit('define text')\\n atomsc_fakevac.write('data.quippy.xyz',format='quippy',append=True)\\n #atomsc_fakevac.write('data.xyz',format=\\\"extxyz\\\",append=True)\\n atomsc_fakevac.write('data'+text+'.quippy.xyz',format='quippy',append=True)\\n #atomsc_fakevac.write('data'+text+'.xyz',format=\\\"extxyz\\\",append=True)\\n return\\n\\n # create Al with single vacancy\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\\n #print('from ....',(atomsc_fakevac.positions)[0])\\n #for i in NN_1_indices:\\n # print((atomsc_fakevac.positions)[i])\\n print('NN_1_indices (orig ):',NN_1_indices)\\n print('NN_2_indices (orig ):',NN_2_indices)\\n #sys.exit()\\n atomsc_fakevac.write('dataxx.quippy.xyz',format='quippy',append=True)\\n atomsc_fakevac.write('dataxx.poscar',format='vasp',append=True)\\n atomsc_fakevac.write('dataxx.ipi',format='ipi',append=True) # works, currently so implemented that it canges cell\\n atomsc_fakevac.write('dataxx.xyz',format='xyz',append=True)\\n atomsc_fakevac.write('dataxx.extxyz',format='extxyz',append=True)\\n atomsc_fakevac.write('dataxx.lammps-data',format='lammps-data',append=True)\\n atomsc_fakevac.write('dataxx.lammps-runner',format='lammps-runner',append=True)\\n\\n atomsc_fakevac_a = ase_read('dataxx.extxyz',format='extxyz') # works, cell ist not changed\\n atomsc_fakevac_a.write('dataxx.extxyz2',format='extxyz',append=True) # works, cell is not changed\\n\\n atomsc_fakevac_b = ase_read('dataxx.xyz',format='xyz') # not working # but this should work\\n atomsc_fakevac_b.write('dataxx.xyz2',format='xyz',append=True) # this is working\\n\\n atomsc_fakevac_c = ase_read('dataxx.ipi',format='ipi') # works, currently so implemented that it canges cell\\n #print('ipi cell',atomsc_fakevac_c.get_cell())\\n\\n atomsc_fakevac_c.write('dataxx.ipi2',format='ipi',append=True) # works, just writes the cell it gests.\\n atomsc_fakevac_c.write('dataxx.ipi2_poscar',format='vasp',append=True) # works, just writes the cell it gests.\\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_c,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\\n print('NN_1_indices (ipi ):',NN_1_indices)\\n print('NN_2_indices (ipi ):',NN_2_indices)\\n #print('from ....',(atomsc_fakevac_c.positions)[0])\\n #for i in NN_1_indices:\\n # print((atomsc_fakevac_c.positions)[i])\\n\\n atomsc_fakevac_cc = ase_read('dataxx.ipi2_poscar',format='vasp') # works, currently so implemented that it canges cell\\n atomsc_fakevac_cc.write('dataxx.ipi2_poscar2',format='vasp',append=True)\\n atomsc_fakevac_cc.write('dataxx.ipi2_poscar2_ipi',format='ipi',append=True) # works, just writes the cell it gests.\\n #print('ipi cell2 (ext):',atomsc_fakevac_cc.get_cell())\\n #print()\\n #print('now quippy')\\n atomsc_fakevac_d = ase_read('dataxx.quippy.xyz',format='quippy')\\n #print('quippy cell (ext)',atomsc_fakevac_d.get_cell())\\n atomsc_fakevac_d.write('dataxx.quippy.xyz2',format='quippy',append=True)\\n atomsc_fakevac_d.write('dataxx.quippy.xyz2_extxyz',format='extxyz',append=True)\\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_d,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\\n print('NN_1_indices (quippy):',NN_1_indices)\\n print('NN_2_indices (quippy):',NN_2_indices)\\n #print('from ....',(atomsc_fakevac_d.positions)[0])\\n #for i in NN_1_indices:\\n # print((atomsc_fakevac_d.positions)[i])\\n path = \\\"/home/glensk/kmc/run_michele/Si6Mg6V1.1_/simulation.pos_libatom_2struct.xyz\\\"\\n atomsc_fakevac_e = ase_read(path,format='quippy')\\n\\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_e,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\\n print('NN_1_indices (kmc ):',NN_1_indices)\\n print('NN_2_indices (kmc ):',NN_2_indices)\\n sys.exit()\\n\\n NN_1_indices = mu.ase_get_neighborlist(atomsc_fakevac,atomnr=0,cutoff=nndist,skin=0.1)\\n NN_1_2_indices_tmp = mu.ase_get_neighborlist(atomsc_fakevac,atomnr=0,cutoff=a0,skin=0.1)\\n print('NN_1_indices :',NN_1_indices)\\n NN_2_indices = np.sort(np.array(mu.diff(NN_1_2_indices_tmp,NN_1_indices)))\\n print('NN_2_indices :',NN_2_indices)\\n NN_1_2_indices = np.concatenate((NN_1_indices, NN_2_indices ))\\n print('NN_1_2_indices:',NN_1_2_indices)\\n\\n\\n # fill only 1NN (with one species)\\n for i in [ 'Mg', 'Si' ]:\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1NN\\\")\\n for ii in NN_1_indices:\\n atomsc_fakevac[ii].symbol = i\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1NN\\\")\\n\\n # fill only 2NN (with one species)\\n for i in [ 'Mg', 'Si' ]:\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"2NN\\\")\\n for ii in NN_2_indices:\\n atomsc_fakevac[ii].symbol = i\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"2NN\\\")\\n\\n # fill 1NN and 2NN (with one species)\\n for i in [ 'Mg', 'Si' ]:\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1and2NN\\\")\\n for ii in NN_1_2_indices:\\n atomsc_fakevac[ii].symbol = i\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1and2NN\\\")\\n\\n # dif compositions in 1NN shell\\n filling = [ 2,4,6,8,10]\\n for fi in filling:\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1NN_diffcomp\\\")\\n for idx,ii in enumerate(NN_1_indices):\\n if idx < fi: ch = \\\"Mg\\\"\\n else: ch = \\\"Si\\\"\\n atomsc_fakevac[ii].symbol = ch\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1NN_diffcomp\\\")\\n\\n\\n sys.exit()\\n\\n #mu.ase_get_known_formats(show=True, add_missing_formats=False, copy_formats=False, verbose=False,show_formatspy=True)\\n for i in [ 'Mg', 'Si' ]:\\n for ii in [ 0,1,2,3,4,5]:\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=i+'_'+str(ii))\\n\\n\\n sys.exit()\\n\\n\\n # show the input variables\\n print('--------------------------- check the input --------------------------------')\\n print('JOBS (nseeds) ',nseeds,'(defined by -nseeds / or -seeds)')\\n print('seeds ',seeds)\\n print('nsteps ',nsteps)\\n print()\\n print('ncell ',ncell,\\\"(\\\",atomsc.get_number_of_atoms(),\\\"atoms )\\\")\\n print('nsi ',nsi, \\\"(\\\",pcsi,\\\"at%)\\\")\\n print('nmg ',nmg,\\\"(\\\",pcmg,\\\"at%)\\\")\\n print('nvac ',nvac,\\\"(\\\",pcvac,\\\"at%)\\\")\\n print('a0 ',a0,\\\"angstrom\\\")\\n print('temp ',temp,\\\"K\\\")\\n print()\\n print('mypot.pot ',mypot.pot)\\n print('mypot.potpath ',mypot.potpath)\\n print()\\n print('directory ',directory)\\n print('submit ',submit)\\n print('submitdebug ',submitdebug)\\n print()\\n print('nodes ',nodes)\\n print('ffsocket ',ffsocket)\\n #print('python ver ',sys.version_info[0])\\n #print()\\n print('--------------------------- check the input --------------------------------')\\n if submit == True or submitdebug == True:\\n mu.get_from_prompt_Yy_orexit(\\\"Are the ine input variables ok? [y]es: \\\")\\n\\n # make the directory\\n if os.path.isdir(directory):\\n mu.get_from_prompt_Yy_orexit(\\\"This main directory exists already, shall I add jobs? [y]es: \\\")\\n mu.mkdir(directory)\\n\\n # create README.md\\n IPI_COMMAND = os.environ[\\\"IPI_COMMAND\\\"]\\n LAMMPS_COMMAND = os.environ[\\\"LAMMPS_COMMAND\\\"]\\n mu.create_READMEtxt(directory,add=[\\\"# to start manually (1): python \\\"+IPI_COMMAND+\\\" input-runner.xml\\\",\\\"# to start manually (2):\\\"+LAMMPS_COMMAND+\\\" < in.lmp\\\"])\\n\\n for seed in seeds:\\n\\n # make jobdirectory\\n jobdir = directory+'/seed'+str(seed)\\n print('jobdir',jobdir)\\n if os.path.exists(jobdir):\\n sys.exit(\\\"jobdirectory \\\"+str(jobdir)+\\\" already exists!\\\")\\n mu.mkdir(jobdir)\\n\\n # get data.lmp and data.ipi\\n atomsc.write(jobdir+'/data.runnerformat.lmp',format='lammps-runner')\\n atomsc_fakevac.write(jobdir+'/data.ipi',format='ipi')\\n atomsc_fakevac.write(jobdir+'/data.extxyz',format='extxyz')\\n #atomsc_fakevac.write(jobdir+'/data_fakevac.ipi',format='ipi')\\n\\n if testfiles == True:\\n atomsc.write(jobdir+'/data.lmp',format='lammps-data')\\n atomsc.write(jobdir+'/data.POSCAR',format='vasp')\\n atomsc.write(jobdir+'/data.xyz',format='xyz')\\n atomsc.write(jobdir+'/data.extxyz',format='extxyz')\\n atomsc.write(jobdir+'/data.espresso-in',format='espresso-in')\\n\\n # create in.lmp\\n ace = mu.ase_calculate_ene(pot=pot,potpath=mypot.potpath,\\n units='eV',geopt=False,kmc=True,verbose=verbose)\\n address = socket.gethostname()+\\\"_\\\"+os.path.basename(jobdir)\\n print('address',address)\\n ace.pot_get_and_ase_lmp_cmd(kmc=True,temp=temp,nsteps=nsteps,ffsocket=ffsocket,address=address)\\n mu.lammps_write_inputfile(folder=jobdir,filename='in.lmp',positions='data.runnerformat.lmp',ace=ace)\\n\\n # create input-runner.xml (should be made without copying)\\n mu.create_ipi_kmc_inputfile(jobdir,filename=\\\"input-runner.xml\\\",nsteps=nsteps,stride=100,seed=seed,a0=a0,ncell=ncell,nsi=nsi,nmg=nmg,nvac=nvac,neval=neval,temp=temp,nodes=nodes,address=address,testrun=test,cubic=args.cubic)\\n\\n # create submit-ipi-kmc.sh (should be made without copying)\\n mu.create_submitskript_ipi_kmc(jobdir+\\\"/submit-ipi-kmc.sh\\\",nodes,ntasks,\\n lmp_par=lmp_par,\\n ipi_inst=ipi_inst,\\n ffsocket=ffsocket,\\n submittime_hours=submittime_hours,\\n SBATCH=True)\\n\\n # create osubmit-ipi-kmc.sh (should be made without copying)\\n mu.create_submitskript_ipi_kmc(jobdir+\\\"/osubmit-ipi-kmc.sh\\\",nodes,ntasks,\\n lmp_par=lmp_par,\\n ipi_inst=ipi_inst,\\n ffsocket=ffsocket,\\n submittime_hours=submittime_hours,\\n SBATCH=False)\\n\\n # submit the job (execute either this or submit-ipi-kmc.sh_all3, not both)\\n #mu.submitjob(submit=submit,submitdebug=submitdebug,jobdir=jobdir,submitskript=\\\"submit-ipi-kmc.sh\\\")\\n\\n # get submit-ipi-kmc.sh_all3 (should be made without copying)\\n if nseeds == 3:\\n mu.create_submitskript_ipi_kmc(directory+\\\"/submit-ipi-kmc.sh_all3\\\",nodes,ntasks,\\n lmp_par=lmp_par,\\n ipi_inst=ipi_inst,\\n ffsocket=ffsocket,\\n submittime_hours=submittime_hours,\\n SBATCH=True,\\n LOOPFOLDER=True)\\n\\n # submit the job (execute either this or submit-ipi-kmc.sh_all3, not both)\\n #mu.submitjob(submit=submit,submitdebug=submitdebug,jobdir=directory,submitskript=\\\"submit-ipi-kmc.sh_all3\\\")\\n if submit == True:\\n mu.submitjob(submit_to_que=True,submit_to_debug_que=False,jobdir=directory,submitskript=\\\"submit-ipi-kmc.sh_all3\\\")\\n\\n\\n print('done')\\n return\",\n \"def test_run_ended(self):\",\n \"def test_update9(self):\\n pass\",\n \"def store(self, job, result):\\n pass\",\n \"def test_job_complete(self):\\r\\n t = mergeorder(['A', 'B', 'C', 'D', 'E'], 'foo')\\r\\n self.assertFalse(job_complete(t))\\r\\n self.assertFalse(job_complete(t.Children[0]))\\r\\n self.assertFalse(job_complete(t.Children[1].Children[1]))\\r\\n\\r\\n self.assertRaises(JobError, job_complete, t.Children[0].Children[0])\\r\\n\\r\\n f = 'test_parallel_merge_otus_JOB_COMPLETE_TEST.poll'\\r\\n self.assertFalse(os.path.exists(f))\\r\\n\\r\\n testf = open(f, 'w')\\r\\n testf.write('0\\\\n')\\r\\n testf.close()\\r\\n t.PollPath = f\\r\\n t.StartTime = 10\\r\\n\\r\\n self.assertTrue(job_complete(t))\\r\\n self.assertNotEqual(t.EndTime, None)\\r\\n self.assertNotEqual(t.TotalTime, None)\\r\\n\\r\\n testf = open(f, 'w')\\r\\n testf.write('1\\\\n')\\r\\n testf.close()\\r\\n\\r\\n self.assertRaises(JobError, job_complete, t)\\r\\n t.Processed = False\\r\\n self.assertRaises(JobError, job_complete, t)\\r\\n\\r\\n os.remove(f)\",\n \"async def test_free_space(coresys: CoreSys):\\n\\n class TestClass:\\n \\\"\\\"\\\"Test class.\\\"\\\"\\\"\\n\\n def __init__(self, coresys: CoreSys):\\n \\\"\\\"\\\"Initialize the test class.\\\"\\\"\\\"\\n self.coresys = coresys\\n\\n @Job(conditions=[JobCondition.FREE_SPACE])\\n async def execute(self):\\n \\\"\\\"\\\"Execute the class method.\\\"\\\"\\\"\\n return True\\n\\n test = TestClass(coresys)\\n with patch(\\\"shutil.disk_usage\\\", return_value=(42, 42, (1024.0 ** 3))):\\n assert await test.execute()\\n\\n with patch(\\\"shutil.disk_usage\\\", return_value=(42, 42, (512.0 ** 3))):\\n assert not await test.execute()\",\n \"def test_post_job(self):\\n body = UnitTesterJobCreateReq()\\n response = self.client.open(\\n '/v1/job',\\n method='POST',\\n data=json.dumps(body),\\n content_type='application/json')\\n self.assert200(response,\\n 'Response body is : ' + response.data.decode('utf-8'))\",\n \"def test_edit_seed_job_type_and_update(self, mock_create, mock_msg_mgr):\\n\\n url = '/%s/job-types/' % self.api\\n manifest = copy.deepcopy(job_test_utils.MINIMUM_MANIFEST)\\n manifest['job']['packageVersion'] = '1.0.1'\\n\\n json_data = {\\n 'icon_code': 'BEEF',\\n 'is_published': True,\\n 'max_scheduled': 1,\\n 'is_active': False,\\n 'docker_image': 'my-job-1.0.0-seed:1.0.1',\\n 'manifest': manifest,\\n 'configuration': self.configuration,\\n 'auto_update': True\\n }\\n\\n response = self.client.generic('POST', url, json.dumps(json_data), 'application/json')\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n results = json.loads(response.content)\\n self.assertTrue(results['is_valid'])\\n self.assertEqual(len(results['warnings']), 1)\\n self.assertEqual(results['warnings'][0]['name'], 'DEPRECATED_RECIPES')\\n\\n job_type = JobType.objects.filter(name='my-minimum-job', version='1.0.0').first()\\n\\n results = json.loads(response.content)\\n self.assertTrue(results['is_valid'])\\n self.assertEqual(len(results['warnings']), 1)\\n self.assertEqual(results['warnings'][0]['name'], 'DEPRECATED_RECIPES')\\n \\n self.assertEqual(True, job_type.is_published)\\n self.assertFalse(job_type.is_active)\\n self.assertEqual(job_type.docker_image, 'my-job-1.0.0-seed:1.0.1')\\n self.assertEqual(2, job_type.revision_num)\\n\\n mock_create.assert_called_with(self.recipe_type1.id, job_type.id)\",\n \"def test_job_fail(self):\\n with self.settings(JOBS_ROOT=os.path.join(settings.BASE_DIR, \\\"extras/tests/dummy_jobs\\\")):\\n\\n module = \\\"test_fail\\\"\\n name = \\\"TestFail\\\"\\n job_class = get_job(f\\\"local/{module}/{name}\\\")\\n job_result = JobResult.objects.create(\\n name=job_class.class_path,\\n obj_type=self.job_content_type,\\n user=None,\\n job_id=uuid.uuid4(),\\n )\\n run_job(data={}, request=None, commit=False, job_result_pk=job_result.pk)\\n job_result.refresh_from_db()\\n self.assertEqual(job_result.status, JobResultStatusChoices.STATUS_ERRORED)\",\n \"def processjob(self, job):\\n self.model.add_documents(job)\\n self.jobsdone += 1\\n if SAVE_DEBUG and self.jobsdone % SAVE_DEBUG == 0:\\n fname = os.path.join(tempfile.gettempdir(), 'lsi_worker.pkl')\\n self.model.save(fname)\",\n \"def test_successful(self):\\n\\n url = '/%s/jobs/' % self.api\\n response = self.client.generic('GET', url)\\n self.assertEqual(response.status_code, status.HTTP_200_OK, response.content)\\n\\n result = json.loads(response.content)\\n self.assertEqual(len(result['results']), 3)\\n for entry in result['results']:\\n expected = None\\n if entry['id'] == self.job1.id:\\n expected = self.job1\\n elif entry['id'] == self.job2.id:\\n expected = self.job2\\n elif entry['id'] == self.job3.id:\\n expected = self.job3\\n else:\\n self.fail('Found unexpected result: %s' % entry['id'])\\n self.assertEqual(entry['job_type']['name'], expected.job_type.name)\\n self.assertEqual(entry['job_type_rev']['job_type']['id'], expected.job_type.id)\\n self.assertEqual(entry['is_superseded'], expected.is_superseded)\",\n \"def test_workerConnectionPoolPerformJob(self):\\n clock = Clock()\\n peerPool = ControllerQueue(clock, None)\\n factory = peerPool.workerListenerFactory()\\n\\n def peer():\\n p = factory.buildProtocol(None)\\n t = StringTransport()\\n p.makeConnection(t)\\n return p, t\\n\\n worker1, _ignore_trans1 = peer()\\n worker2, _ignore_trans2 = peer()\\n\\n # Ask the worker to do something.\\n worker1.performJob(JobDescriptor(1, 1, \\\"ABC\\\"))\\n self.assertEquals(worker1.currentLoad, 1)\\n self.assertEquals(worker2.currentLoad, 0)\\n\\n # Now ask the pool to do something\\n peerPool.workerPool.performJob(JobDescriptor(2, 1, \\\"ABC\\\"))\\n self.assertEquals(worker1.currentLoad, 1)\\n self.assertEquals(worker2.currentLoad, 1)\",\n \"def test_multiple_commands_at_same_time(self):\",\n \"def add_job(self, job):\\n self.job_list.append(job)\\n self.num_jobs += 1\",\n \"def jobs(\\n ctx: typer.Context,\\n op_id: str = typer.Argument(\\n ...,\\n autocompletion=completion_op_id,\\n callback=check_for_op_id,\\n help=\\\"A valid op-id. e.g. get_markets_prices\\\",\\n ),\\n param_string: Optional[str] = typer.Option(\\n None,\\n \\\"--param-string\\\",\\n \\\"-p\\\",\\n help=\\\"Optional. Full or partial parameters as a json encoded dictionary string. \\\"\\n \\\"Keys must be valid parameters for selected op_id.\\\",\\n ),\\n default_params: bool = typer.Option(\\n False,\\n \\\"-d\\\",\\n \\\"--default-params\\\",\\n help=\\\"Include all parameters that are required, or have default values. \\\"\\n \\\"Missing values will be 'NOTSET'.\\\",\\n ),\\n callback_path: Optional[Path] = typer.Option(\\n None,\\n \\\"-c\\\",\\n \\\"--callbacks\\\",\\n help=\\\"Optional. Path to custom callbacks to be used. \\\",\\n ),\\n file_name: str = typer.Option(\\n \\\"created-jobs/${esi_job_op_id}-${esi_job_uid}\\\",\\n \\\"-n\\\",\\n \\\"--file-name\\\",\\n help=(\\n \\\"File name for the new job, must be unique if multiple jobs. \\\"\\n \\\"Can include directories, \\\"\\n \\\"and the file type suffix will be added based on --format-id.\\\"\\n ),\\n ),\\n data_path: Optional[Path] = typer.Option(\\n None,\\n \\\"--data-file\\\",\\n \\\"-i\\\",\\n help=(\\n \\\"Optional. Path to json, csv, or yaml file with full or partial parameters. \\\"\\n \\\"Must result in a list of dicts.\\\"\\n ),\\n ),\\n format_id: FormatChoices = typer.Option(\\n FormatChoices.json,\\n \\\"-f\\\",\\n \\\"--format-id\\\",\\n show_choices=True,\\n help=\\\"Output file format.\\\",\\n ),\\n path_out: Path = typer.Argument(\\n \\\"./tmp\\\",\\n help=\\\"Parent path for saving the new jobs, will be prepended to --file-name.\\\",\\n ),\\n):\\n operation_manifest: OperationManifest = ctx.obj[\\\"operation_manifest\\\"]\\n # path_out = optional_object(path_out, Path, \\\".\\\")\\n if path_out.is_file:\\n typer.BadParameter(\\\"path_out must not be a file.\\\")\\n file_data: Optional[List[Dict]] = get_params_from_file(data_path)\\n parameters: Dict = decode_param_string(param_string)\\n if callback_path is None:\\n callback_collection = default_callback_collection()\\n else:\\n callback_collection = load_callbacks(callback_path)\\n jobs_: List[EsiJob] = []\\n try:\\n op_info = operation_manifest.op_info(op_id)\\n if not file_data:\\n job = op_info.create_job(\\n parameters,\\n callback_collection,\\n include_default_params=default_params,\\n # only_required_default_params=False,\\n # allow_notset=False,\\n )\\n jobs_.append(job)\\n else:\\n for params in file_data:\\n params.update(parameters)\\n job = op_info.create_job(\\n params,\\n callback_collection,\\n include_default_params=default_params,\\n # only_required_default_params=False,\\n # allow_notset=False,\\n )\\n jobs_.append(job)\\n except Exception as ex:\\n raise typer.BadParameter(\\n f\\\"Exception creating job. {ex.__class__.__name__}: {ex}\\\"\\n )\\n for job in jobs_:\\n file_path = resolve_job_file_path(job, file_name, path_out)\\n try:\\n save_path = job.serialize_file(file_path, format_id)\\n except Exception as ex:\\n raise typer.BadParameter(\\n f\\\"Error saving job to {save_path}. {ex.__class__.__name__}, {ex}\\\"\\n )\\n logger.info(\\\"Saved job %s at %s\\\", job.uid, file_path)\\n typer.echo(f\\\"{len(jobs_)} jobs saved to {path_out}\\\")\\n report_finished_task(ctx)\",\n \"def test_update_case(self):\\n pass\",\n \"def test_create_batch_job(self, affiliate_items):\\n with mock.patch('chiton.rack.affiliates.bulk.BatchJob') as batch_job:\\n bulk_update_affiliate_item_details(affiliate_items)\\n\\n call_args = batch_job.call_args[0]\\n assert call_args[0].count() == 4\\n assert call_args[1] == update_affiliate_item_details\",\n \"def testJobSummary(self):\\n change = ChangeState(self.config, \\\"changestate_t\\\")\\n\\n locationAction = self.daoFactory(classname=\\\"Locations.New\\\")\\n locationAction.execute(\\\"site1\\\", pnn=\\\"T2_CH_CERN\\\")\\n\\n testWorkflow = Workflow(spec=self.specUrl, owner=\\\"Steve\\\",\\n name=\\\"wf001\\\", task=self.taskName)\\n testWorkflow.create()\\n testFileset = Fileset(name=\\\"TestFileset\\\")\\n testFileset.create()\\n\\n testFile = File(lfn=\\\"SomeLFNC\\\", locations=set([\\\"T2_CH_CERN\\\"]))\\n testFile.create()\\n testFileset.addFile(testFile)\\n testFileset.commit()\\n\\n testSubscription = Subscription(fileset=testFileset,\\n workflow=testWorkflow)\\n testSubscription.create()\\n\\n splitter = SplitterFactory()\\n jobFactory = splitter(package=\\\"WMCore.WMBS\\\",\\n subscription=testSubscription)\\n jobGroup = jobFactory(files_per_job=1)[0]\\n\\n assert len(jobGroup.jobs) == 1, \\\\\\n \\\"Error: Splitting should have created one job.\\\"\\n\\n testJobA = jobGroup.jobs[0]\\n testJobA[\\\"user\\\"] = \\\"cinquo\\\"\\n testJobA[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobA[\\\"taskType\\\"] = \\\"Production\\\"\\n\\n change.propagate([testJobA], 'created', 'new')\\n myReport = Report()\\n reportPath = os.path.join(getTestBase(),\\n \\\"WMCore_t/JobStateMachine_t/Report.pkl\\\")\\n myReport.unpersist(reportPath)\\n\\n change.propagate([testJobA], 'executing', 'created')\\n testJobA[\\\"fwjr\\\"] = myReport\\n change.propagate([testJobA], 'jobfailed', 'executing')\\n\\n changeStateDB = self.couchServer.connectDatabase(dbname=self.config.JobStateMachine.jobSummaryDBName)\\n allDocs = changeStateDB.document(\\\"_all_docs\\\")\\n\\n self.assertEqual(len(allDocs[\\\"rows\\\"]), 2,\\n \\\"Error: Wrong number of documents\\\")\\n\\n fwjrDoc = {'state': None}\\n for resultRow in allDocs[\\\"rows\\\"]:\\n if resultRow[\\\"id\\\"] != \\\"_design/WMStats\\\":\\n fwjrDoc = changeStateDB.document(resultRow[\\\"id\\\"])\\n break\\n\\n self.assertEqual(fwjrDoc['state'], 'jobfailed',\\n \\\"Error: summary doesn't have the expected job state\\\")\\n\\n del testJobA[\\\"fwjr\\\"]\\n\\n change.propagate([testJobA], 'jobcooloff', 'jobfailed', updatesummary=True)\\n return\",\n \"def test_006_add(self):\\n HEADING()\\n db = self.db\\n\\n count = 5\\n\\n db.connect()\\n\\n db.delete_jobs()\\n\\n for id in range(0,count):\\n job = db.insert(\\\"job\\\" + str(id))\\n\\n assert len(db) == count\",\n \"async def test_job_done(my_job):\\n\\n # Set up callback to get notifications when job state changes.\\n job = None\\n job_update_counter = 0\\n\\n def on_job_update(_job):\\n \\\"\\\"\\\"The callback updates `job` and `job_update_counter`.\\\"\\\"\\\"\\n nonlocal job, job_update_counter\\n job = _job\\n job_update_counter += 1\\n # Assert that simple job is reported as cancelable only when it\\n # is in the `PENDING` or `CANCELING` state.\\n if job.state in ['PENDING', 'CANCELING']:\\n assert job.is_cancelable, ( # pylint: disable=no-member\\n 'Job is not cancelable when it must be cancelable!')\\n else:\\n assert not job.is_cancelable, ( # pylint: disable=no-member\\n 'Job is cancelable when it must not be cancelable!')\\n\\n my_job.set_on_update(on_job_update)\\n\\n # Submit a job which must finish OK.\\n new_job = await my_job.job(mustfail=False)\\n\\n # Check job instance of new job (returned from job function).\\n assert new_job.is_cancelable, ( # pylint: disable=no-member\\n 'Job instance states that just submitted job is not cancelable!')\\n assert new_job.state == 'PENDING'\\n assert new_job.id is not None\\n assert new_job.started is None\\n assert new_job.args == []\\n assert new_job.kwds == {'mustfail': False}\\n assert new_job.message == ''\\n assert new_job.payload == {}\\n assert new_job.readiness is None\\n\\n # Check current job state right after job is submitted.\\n assert job.state == 'PENDING', ('Just submitted job has wrong state '\\n f'`{job.state}`!')\\n\\n # Process ASGI messages and wait for the job to finish.\\n await my_job.process_jobs()\\n\\n # Check job state when job is done.\\n assert job.state == 'DONE', f'Finished job has wrong state `{job.state}`!'\\n\\n # Check that job update callback has been called three times:\\n # 1. job is submitted\\n # 2. job switches to the working state\\n # 3. job finishes\\n assert job_update_counter == 3, 'Job updated wrong number of times!'\",\n \"def test_update_goal(self):\\n pass\",\n \"def test_basic_execution(self):\",\n \"def execute(self, job):\\n raise NotImplementedError\",\n \"def test_update_scenario(self):\\n pass\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\",\n \"def on_job_update(_job):\\n nonlocal job\\n job = _job\",\n \"def add(self, job):\\r\\n self.jobs.put(job)\",\n \"def testDuplicateJobReports(self):\\n change = ChangeState(self.config, \\\"changestate_t\\\")\\n\\n locationAction = self.daoFactory(classname=\\\"Locations.New\\\")\\n locationAction.execute(\\\"site1\\\", pnn=\\\"T2_CH_CERN\\\")\\n\\n testWorkflow = Workflow(spec=self.specUrl, owner=\\\"Steve\\\",\\n name=\\\"wf001\\\", task=self.taskName)\\n testWorkflow.create()\\n testFileset = Fileset(name=\\\"TestFileset\\\")\\n testFileset.create()\\n\\n testFile = File(lfn=\\\"SomeLFNC\\\", locations=set([\\\"T2_CH_CERN\\\"]))\\n testFile.create()\\n testFileset.addFile(testFile)\\n testFileset.commit()\\n\\n testSubscription = Subscription(fileset=testFileset,\\n workflow=testWorkflow)\\n testSubscription.create()\\n\\n splitter = SplitterFactory()\\n jobFactory = splitter(package=\\\"WMCore.WMBS\\\",\\n subscription=testSubscription)\\n jobGroup = jobFactory(files_per_job=1)[0]\\n\\n assert len(jobGroup.jobs) == 1, \\\\\\n \\\"Error: Splitting should have created one job.\\\"\\n\\n testJobA = jobGroup.jobs[0]\\n testJobA[\\\"user\\\"] = \\\"sfoulkes\\\"\\n testJobA[\\\"group\\\"] = \\\"DMWM\\\"\\n testJobA[\\\"taskType\\\"] = \\\"Processing\\\"\\n\\n change.propagate([testJobA], 'created', 'new')\\n myReport = Report()\\n reportPath = os.path.join(getTestBase(),\\n \\\"WMCore_t/JobStateMachine_t/Report.pkl\\\")\\n myReport.unpersist(reportPath)\\n testJobA[\\\"fwjr\\\"] = myReport\\n\\n change.propagate([testJobA], 'executing', 'created')\\n change.propagate([testJobA], 'executing', 'created')\\n\\n changeStateDB = self.couchServer.connectDatabase(dbname=\\\"changestate_t/fwjrs\\\")\\n allDocs = changeStateDB.document(\\\"_all_docs\\\")\\n\\n self.assertEqual(len(allDocs[\\\"rows\\\"]), 2,\\n \\\"Error: Wrong number of documents\\\")\\n\\n for resultRow in allDocs[\\\"rows\\\"]:\\n if resultRow[\\\"id\\\"] != \\\"_design/FWJRDump\\\":\\n changeStateDB.document(resultRow[\\\"id\\\"])\\n break\\n\\n return\",\n \"def test_check_opt_complete(self):\\n self.assertEqual(check_opt(self.jobset3.job), 'completed')\",\n \"def test(executable):\\n from tempfile import mkdtemp\\n from os.path import join\\n from shutil import rmtree\\n from numpy import all, arange, abs, array\\n from pylada.jobfolder.jobfolder import JobFolder\\n from pylada.jobfolder.massextract import MassExtract\\n from pylada.jobfolder import save\\n from pylada.process.jobfolder import JobFolderProcess\\n from pylada.process import Fail, AlreadyStarted, NotStarted\\n from pylada import default_comm\\n from functional import Functional\\n\\n root = JobFolder()\\n for n in xrange(8):\\n job = root / str(n)\\n job.functional = Functional(executable, [n])\\n job.params['sleep'] = 1\\n\\n comm = default_comm.copy()\\n comm['n'] = 4\\n\\n dir = mkdtemp()\\n try: \\n program = JobFolderProcess(root, nbpools=2, outdir=dir)\\n assert program.nbjobsleft > 0\\n # program not started. should fail.\\n try: program.poll()\\n except NotStarted: pass\\n else: raise Exception()\\n try: program.wait()\\n except NotStarted: pass\\n else: raise Exception()\\n\\n # now starting for real.\\n program.start(comm)\\n assert len(program.process) == 2\\n # Should not be possible to start twice.\\n try: program.start(comm)\\n except AlreadyStarted: pass\\n else: raise Exception()\\n while not program.poll(): continue\\n assert program.nbjobsleft == 0\\n save(root, join(dir, 'dict.dict'), overwrite=True)\\n extract = MassExtract(join(dir, 'dict.dict'))\\n assert all(extract.success.itervalues())\\n order = array(extract.order.values()).flatten()\\n assert all(arange(8) - order == 0)\\n pi = array(extract.pi.values()).flatten()\\n assert all(abs(pi - array([0.0, 3.2, 3.162353, 3.150849,\\n 3.146801, 3.144926, 3.143907, 3.143293]))\\\\\\n < 1e-5 )\\n error = array(extract.error.values()).flatten()\\n assert all(abs(error - array([3.141593, 0.05840735, 0.02076029, 0.009256556,\\n 0.005207865, 0.00333321, 0.002314774, 0.001700664]))\\\\\\n < 1e-5 )\\n assert all(n['n'] == comm['n'] for n in extract.comm)\\n # restart\\n assert program.poll()\\n assert len(program.process) == 0\\n program.start(comm)\\n assert len(program.process) == 0\\n assert program.poll()\\n finally:\\n try: rmtree(dir)\\n except: pass\\n\\n try: \\n job = root / str(666)\\n job.functional = Functional(executable, [666])\\n program = JobFolderProcess(root, nbpools=2, outdir=dir)\\n assert program.nbjobsleft > 0\\n program.start(comm)\\n program.wait()\\n assert program.nbjobsleft == 0\\n except Fail as r: \\n assert len(program.errors.keys()) == 1\\n assert '666' in program.errors\\n assert len(program._finished) == 8\\n else: raise Exception\\n finally:\\n try: rmtree(dir)\\n except: pass\\n try: \\n job.functional.order = [667]\\n program = JobFolderProcess(root, nbpools=2, outdir=dir)\\n assert program.nbjobsleft > 0\\n program.start(comm)\\n program.wait()\\n assert program.nbjobsleft == 0\\n finally:\\n try: rmtree(dir)\\n except: pass\",\n \"def test_pending_job(self):\\n\\n pending_job = json.loads(TREEHERDER_JOB % (\\\"unknown\\\", \\\"pending\\\"))\\n self.assertEquals(self.query_api.get_job_status(pending_job), PENDING)\",\n \"def test_change_provisioned_throughput_usual_case():\",\n \"def modify_job(filename, batch_name, node_choice, ppn_list, qe_switch, atoms):\\n\\tfin = open(filename, \\\"r\\\"); file = fin.read(); fin.close()\\n\\t# fin_head = open(\\\"/home/twchang/bin/others/job.sh-mod\\\", 'r').read()\\n\\tfin_head = Job.job_head\\n\\tnode_str = \\\"+\\\".join([\\\"node{:02d}:ppn={}\\\".format(node_choice[i], ppn_list[i]) for i in range(len(ppn_list))])\\n\\ttail = re.search(r\\\".*(\\\\s*-np.*)\\\", file, re.S).group(1).strip()\\n\\ttail = re.sub(r\\\"\\\\s*>>\\\\s*out\\\", r\\\"\\\", tail)\\n\\tif qe_switch:\\n\\t\\ttail = re.sub(r\\\"(NPROCS\\\\s*).*(/bin/)\\\", r\\\"\\\\g<1>/data2/twchang/opt/q-e-qe-6.4.1\\\\g<2>\\\", tail, re.S)\\n\\telse: # vasp mode\\n\\t\\ttail = re.sub(r\\\"(NPROCS\\\\s*).*(/bin/).*(\\\\n)\\\", r\\\"\\\\g<1>/home/twchang\\\\g<2>vasp_noncol \\\\g<3>\\\", tail, re.S)\\n\\tfile = fin_head + \\\" \\\" + tail\\n\\tfile = re.sub(r\\\"(#PBS\\\\s+-N\\\\s+).*\\\", r\\\"\\\\g<1>{}\\\".format(batch_name), file) # if batch_name != \\\"\\\" else file # modify batch_name\\n\\tfile = re.sub(r\\\"(#PBS\\\\s+-l\\\\s+nodes=).*\\\", r\\\"\\\\g<1>{}\\\".format(node_str), file) # modify ppn\\n\\tfile = re.sub(r\\\"(-np\\\\s+\\\\$NPROCS\\\\s+.*\\\\s+<).*?(\\\\..*>\\\\s+).*?(\\\\..*)\\\", r\\\"\\\\g<1>{}\\\\g<2>{}\\\\g<3>\\\".format(atoms, atoms), file) if atoms else file\\n\\tfile = re.sub(r\\\"/data2/twchang/q-e-qe-6\\\\.1\\\\.0/bin\\\", r\\\"/data2/twchang/opt/q-e-qe-6.4.1/bin\\\", file)# if qe_switch else file\\n\\tfout = open(filename, \\\"w\\\"); fout.write(file)\",\n \"def test_replace():\\n t_before = Task('finish book', 'brian', False)\\n t_after = t_before._replace(id=10,done=True)\\n t_expected = Task('finish book', 'brian', True, 10)\\n assert t_after == t_expected\",\n \"def test_start_already_running(self, mock_add_job, mock_get_job):\\n mock_get_job.return_value = MagicMock()\\n\\n result = self.aggregator.start(self.node_id)\\n\\n self.assertFalse(result)\\n self.assertFalse(mock_add_job.called)\",\n \"def run(self):\\n\\t\\tself.endpoint = self.from_reference.coord\\t# the endpoint of every job is the thing ordering this job\\n\\n\\t\\tif to_do == \\\"carry\\\":\\t# carry sth. from A to B\\n\\t\\t\\tpass\\n\\t\\telif to_do == \\\"grade\\\":\\t# grade an area for a building\\n\\t\\t\\tunit = self.searchUnit(self.endpoint, \\\"grader\\\")\\n\\t\\t\\t\\n\\t\\telif to_do == \\\"build\\\":\\t# build a building\\n\\t\\t\\tunit = self.searchUnit(self.endpoint, \\\"builder\\\")\\n\\t\\t#TODO: if no settler fits to the building to be seized, one settler has to learn the job\\n\\t\\telif to_do == \\\"learn a job\\\":\\t# learn a job like lumberjack, butcher ... also for the soldier training\\n\\t\\t\\tpass\\n\\t\\telif to_do == \\\"seize a building\\\":\\t# civil and also military buildings\\n\\t\\t\\tpass\",\n \"def _addjob(self, job: Job):\\n if self.domain is not None and self.model is not None:\\n job = copy.deepcopy(job)\\n job._add_hydro_namelist(self.base_hydro_namelist)\\n job._add_hrldas_namelist(self.base_hrldas_namelist)\\n\\n self.jobs.append(job)\\n else:\\n raise AttributeError('Can not add a job to a simulation without a model and a domain')\",\n \"def standard_job_set(msg):\\n\\n run_num = msg['run']\\n jobs = [[], [], [], [], []]\\n new_dep = {'time': None, 'md5': None}\\n\\n # Add ROME jobs first\\n cmd_prefix = \\\"./midanalyzer.exe -b -i romeConfig.xml -r \\\"\\n cmd_suffix = \\\" -m offline -p 0 -q\\\"\\n rome_dir = offline_dir + '/rome-processors'\\n\\n job = {}\\n job['meta'] = datadir + '/shim/.crunchd_metadata.json'\\n job['cmd'] = cmd_prefix + str(run_num) + cmd_suffix\\n job['clean'] = 'rm histos*.root run*.root'\\n\\n job['name'] = 'single-laser'\\n job['dir'] = rome_dir + '/single-laser'\\n job['deps'] = {}\\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\\n jobs[0].append(job)\\n\\n job = copy.copy(job)\\n job['name'] = 'double-laser'\\n job['dir'] = rome_dir + '/double-laser'\\n job['deps'] = {}\\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\\n jobs[0].append(job)\\n\\n job = copy.copy(job)\\n job['name'] = 'capacitec'\\n job['dir'] = rome_dir + '/capacitec'\\n job['deps'] = {}\\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\\n jobs[0].append(job)\\n\\n job = copy.copy(job)\\n job['name'] = 'shim-platform'\\n job['dir'] = rome_dir + '/shim-platform'\\n job['deps'] = {}\\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\\n jobs[0].append(job)\\n\\n job = copy.copy(job)\\n job['name'] = 'metrolab'\\n job['dir'] = rome_dir + '/metrolab'\\n job['deps'] = {}\\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\\n jobs[0].append(job)\\n\\n job = copy.copy(job)\\n job['name'] = 'mscb-cart'\\n job['dir'] = rome_dir + '/mscb-cart'\\n job['deps'] = {}\\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\\n jobs[0].append(job)\\n\\n job = copy.copy(job)\\n job['name'] = 'mscb-ring'\\n job['dir'] = rome_dir + '/mscb-ring'\\n job['deps'] = {}\\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\\n jobs[0].append(job)\\n\\n job = copy.copy(job)\\n job['name'] = 'tilt-sensor'\\n job['dir'] = rome_dir + '/tilt-sensor'\\n job['deps'] = {}\\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\\n jobs[0].append(job)\\n\\n job = copy.copy(job)\\n job['name'] = 'hall-probe'\\n job['dir'] = rome_dir + '/hall-probe'\\n job['deps'] = {}\\n job['deps'][job['dir'] + '/midanalyzer.exe'] = new_dep\\n jobs[0].append(job)\\n\\n # Make sure run attributes are extracted.\\n job = {}\\n job['name'] = 'extract_run_attr'\\n job['dir'] = offline_dir + '/crunchers'\\n job['cmd'] = 'python scripts/extract_run_attr.py %i' % run_num\\n job['clean'] = None\\n job['meta'] = datadir + '/crunched/.crunchd_metadata.json'\\n job['deps'] = {}\\n jobs[0].append(job)\\n\\n # Now the data bundling job.\\n job = {}\\n job['name'] = 'make-shim-dataset'\\n job['cmd'] = 'bin/make_shim_dataset %i' % run_num\\n job['clean'] = None\\n job['dir'] = offline_dir + '/crunchers'\\n job['meta'] = datadir + '/shim/.crunchd_metadata.json'\\n job['deps'] = {}\\n job['deps'][offline_dir + '/crunchers/bin/make_shim_dataset'] = new_dep\\n job['deps']['data/shim/*%05i.root' % run_num] = new_dep\\n jobs[1].append(job)\\n\\n # Finally apply fixes.\\n # job = {}\\n # job['name'] = 'fix-probe-remap'\\n # job['dir'] = offline_dir\\n # job['cmd'] = 'bin/fix_run_probe_map '\\n # job['cmd'] += 'data/crunched/run_%05i.root ' % run_num\\n # job['cmd'] += 'data/crunched/ %i' % run_num\\n # job['clean'] = None\\n # job['meta'] = datadir + '/crunched/.crunchd_metadata.json'\\n # job['deps'] = {}\\n # job['deps'][offline_dir + '/bin/recrunch_fids'] = new_dep\\n # job['deps'][datadir + '/shim/run_%05i.root' % run_num] = new_dep\\n # jobs[2].append(job)\\n\\n # Automatically generate extracted dataset\\n job = {}\\n job['name'] = 'extraction'\\n job['dir'] = offline_dir + '/crunchers'\\n job['cmd'] = 'bin/make_extracted_dataset '\\n job['cmd'] += 'data/crunched/run_%05i.root' % run_num\\n job['clean'] = None\\n job['meta'] = datadir + '/extracted/.crunchd_metadata.json'\\n job['deps'] = {}\\n job['deps'][offline_dir + '/crunchers/bin/make_extracted_dataset'] = new_dep\\n job['deps'][datadir + '/crunched/run_%05i.root' % run_num] = new_dep\\n jobs[3].append(job)\\n\\n return jobs\",\n \"def test_nextjob(self):\\n\\n dbpool = buildConnectionPool(self, jobSchema + schemaText)\\n now = datetime.datetime.utcnow()\\n\\n # Empty job queue\\n @inlineCallbacks\\n def _next(txn, priority=WORK_PRIORITY_LOW):\\n job = yield JobItem.nextjob(txn, now, priority, 1)\\n if job is not None:\\n work = yield job.workItem()\\n else:\\n work = None\\n returnValue((job, work))\\n job, work = yield inTransaction(dbpool.connection, _next)\\n self.assertTrue(job is None)\\n self.assertTrue(work is None)\\n\\n # Unassigned job with future notBefore not returned\\n yield self._enqueue(dbpool, 1, 1, now + datetime.timedelta(days=1))\\n job, work = yield inTransaction(dbpool.connection, _next)\\n self.assertTrue(job is None)\\n self.assertTrue(work is None)\\n\\n # Unassigned job with past notBefore returned\\n yield self._enqueue(dbpool, 2, 1, now + datetime.timedelta(days=-1), priority=WORK_PRIORITY_HIGH)\\n job, work = yield inTransaction(dbpool.connection, _next)\\n self.assertTrue(job is not None)\\n self.assertTrue(work.a == 2)\\n assignID = job.jobID\\n\\n # Assigned job with past notBefore not returned\\n @inlineCallbacks\\n def assignJob(txn, when=None):\\n assignee = yield JobItem.load(txn, assignID)\\n yield assignee.assign(now if when is None else when, ControllerQueue.queueOverdueTimeout)\\n yield inTransaction(dbpool.connection, assignJob)\\n job, work = yield inTransaction(dbpool.connection, _next)\\n self.assertTrue(job is None)\\n self.assertTrue(work is None)\\n\\n # Unassigned, paused job with past notBefore not returned\\n yield self._enqueue(dbpool, 3, 1, now + datetime.timedelta(days=-1), priority=WORK_PRIORITY_HIGH)\\n @inlineCallbacks\\n def pauseJob(txn, pause=True):\\n works = yield DummyWorkItem.all(txn)\\n for work in works:\\n if work.a == 3:\\n job = yield JobItem.load(txn, work.jobID)\\n yield job.pauseIt(pause)\\n yield inTransaction(dbpool.connection, pauseJob)\\n job, work = yield inTransaction(dbpool.connection, _next)\\n self.assertTrue(job is None)\\n self.assertTrue(work is None)\\n\\n # Unassigned, paused then unpaused job with past notBefore is returned\\n yield inTransaction(dbpool.connection, pauseJob, pause=False)\\n job, work = yield inTransaction(dbpool.connection, _next)\\n self.assertTrue(job is not None)\\n self.assertTrue(work.a == 3)\\n @inlineCallbacks\\n def deleteJob(txn, jobID):\\n job = yield JobItem.load(txn, jobID)\\n yield job.delete()\\n yield inTransaction(dbpool.connection, deleteJob, jobID=job.jobID)\\n\\n # Unassigned low priority job with past notBefore not returned if high priority required\\n yield self._enqueue(dbpool, 4, 1, now + datetime.timedelta(days=-1))\\n job, work = yield inTransaction(dbpool.connection, _next, priority=WORK_PRIORITY_HIGH)\\n self.assertTrue(job is None)\\n self.assertTrue(work is None)\\n\\n # Unassigned low priority job with past notBefore not returned if medium priority required\\n yield self._enqueue(dbpool, 5, 1, now + datetime.timedelta(days=-1))\\n job, work = yield inTransaction(dbpool.connection, _next, priority=WORK_PRIORITY_MEDIUM)\\n self.assertTrue(job is None)\\n self.assertTrue(work is None)\\n\\n # Assigned job with past notBefore, but overdue is not returned\\n yield inTransaction(dbpool.connection, assignJob, when=now + datetime.timedelta(days=-1))\\n job, work = yield inTransaction(dbpool.connection, _next, priority=WORK_PRIORITY_HIGH)\\n self.assertTrue(job is None)\\n self.assertTrue(work is None)\",\n \"def test_normal_user_trying_to_update_job(self):\\n normal_user = self.user_login.become_normal_user()\\n job = Job.objects.create(name='somejob',\\n workflow_version=self.workflow_version,\\n job_order={},\\n user=normal_user,\\n share_group=self.share_group,\\n job_settings=self.vm_job_settings,\\n job_flavor=self.job_flavor,\\n )\\n url = reverse('v2-admin_job-list') + '{}/'.format(job.id)\\n response = self.client.put(url, format='json',\\n data={\\n 'state': Job.JOB_STATE_FINISHED,\\n 'step': Job.JOB_STEP_RUNNING,\\n })\\n self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\",\n \"def test_py_job_result(self):\\n test_app = self._create_py_app()\\n class_path = \\\"example_jobs.word_count.WordCountSparkSessionJob\\\"\\n conf = \\\"input.strings = ['a', 'b', 'a', 'b']\\\"\\n job = self._create_job(test_app, class_path, conf,\\n ctx=self._get_functional_py_context())\\n time.sleep(3)\\n self._wait_till_job_is_done(job)\\n job = self.client.jobs.get(job.jobId)\\n self.assertEqual(\\\"FINISHED\\\", job.status)\\n self.assertEqual({\\\"'a'\\\": 2, \\\"'b'\\\": 2}, job.result)\",\n \"def test_getJobState(self):\\n self.assertRaises(JobNotExistsError, self.foo.getJobState)\\n self.foo.setTorqueJobId('asd')\\n self.assertEqual(self.foo.getJobState(), 'NEW')\",\n \"async def test_running(coresys: CoreSys):\\n\\n class TestClass:\\n \\\"\\\"\\\"Test class.\\\"\\\"\\\"\\n\\n def __init__(self, coresys: CoreSys):\\n \\\"\\\"\\\"Initialize the test class.\\\"\\\"\\\"\\n self.coresys = coresys\\n\\n @Job(conditions=[JobCondition.RUNNING])\\n async def execute(self):\\n \\\"\\\"\\\"Execute the class method.\\\"\\\"\\\"\\n return True\\n\\n test = TestClass(coresys)\\n\\n coresys.core.state = CoreState.RUNNING\\n assert await test.execute()\\n\\n coresys.core.state = CoreState.FREEZE\\n assert not await test.execute()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.726686","0.6905744","0.6609184","0.6590907","0.64921033","0.6491962","0.6479421","0.6470252","0.6433977","0.63717645","0.6351452","0.63509756","0.62832236","0.62299746","0.6220371","0.62189835","0.6209426","0.620375","0.6172135","0.61520845","0.61494327","0.6145255","0.61177015","0.6106582","0.6100851","0.60984117","0.6074632","0.6071954","0.60511726","0.6048984","0.6042688","0.60283303","0.60282475","0.60103005","0.60046345","0.5998812","0.5993723","0.5974443","0.5966907","0.5961108","0.5959756","0.5957996","0.5936749","0.59339726","0.59268385","0.5922994","0.5922994","0.5922994","0.5922229","0.5920241","0.59173006","0.59135276","0.583843","0.58357126","0.5834235","0.58310133","0.5824624","0.5811345","0.5809928","0.57899183","0.57865494","0.57860434","0.57843083","0.5780244","0.5774009","0.57661676","0.576566","0.5762299","0.5754269","0.5752315","0.5750423","0.5749622","0.5735861","0.5721037","0.5710502","0.5704822","0.57021785","0.57003766","0.5699509","0.56983924","0.56983924","0.56983924","0.56983924","0.5696271","0.569537","0.56924075","0.56888497","0.5688593","0.56842387","0.5681437","0.568018","0.5679522","0.5678426","0.567796","0.56760454","0.5674234","0.5669387","0.56665504","0.5658818","0.565297"],"string":"[\n \"0.726686\",\n \"0.6905744\",\n \"0.6609184\",\n \"0.6590907\",\n \"0.64921033\",\n \"0.6491962\",\n \"0.6479421\",\n \"0.6470252\",\n \"0.6433977\",\n \"0.63717645\",\n \"0.6351452\",\n \"0.63509756\",\n \"0.62832236\",\n \"0.62299746\",\n \"0.6220371\",\n \"0.62189835\",\n \"0.6209426\",\n \"0.620375\",\n \"0.6172135\",\n \"0.61520845\",\n \"0.61494327\",\n \"0.6145255\",\n \"0.61177015\",\n \"0.6106582\",\n \"0.6100851\",\n \"0.60984117\",\n \"0.6074632\",\n \"0.6071954\",\n \"0.60511726\",\n \"0.6048984\",\n \"0.6042688\",\n \"0.60283303\",\n \"0.60282475\",\n \"0.60103005\",\n \"0.60046345\",\n \"0.5998812\",\n \"0.5993723\",\n \"0.5974443\",\n \"0.5966907\",\n \"0.5961108\",\n \"0.5959756\",\n \"0.5957996\",\n \"0.5936749\",\n \"0.59339726\",\n \"0.59268385\",\n \"0.5922994\",\n \"0.5922994\",\n \"0.5922994\",\n \"0.5922229\",\n \"0.5920241\",\n \"0.59173006\",\n \"0.59135276\",\n \"0.583843\",\n \"0.58357126\",\n \"0.5834235\",\n \"0.58310133\",\n \"0.5824624\",\n \"0.5811345\",\n \"0.5809928\",\n \"0.57899183\",\n \"0.57865494\",\n \"0.57860434\",\n \"0.57843083\",\n \"0.5780244\",\n \"0.5774009\",\n \"0.57661676\",\n \"0.576566\",\n \"0.5762299\",\n \"0.5754269\",\n \"0.5752315\",\n \"0.5750423\",\n \"0.5749622\",\n \"0.5735861\",\n \"0.5721037\",\n \"0.5710502\",\n \"0.5704822\",\n \"0.57021785\",\n \"0.57003766\",\n \"0.5699509\",\n \"0.56983924\",\n \"0.56983924\",\n \"0.56983924\",\n \"0.56983924\",\n \"0.5696271\",\n \"0.569537\",\n \"0.56924075\",\n \"0.56888497\",\n \"0.5688593\",\n \"0.56842387\",\n \"0.5681437\",\n \"0.568018\",\n \"0.5679522\",\n \"0.5678426\",\n \"0.567796\",\n \"0.56760454\",\n \"0.5674234\",\n \"0.5669387\",\n \"0.56665504\",\n \"0.5658818\",\n \"0.565297\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7602344"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77992,"cells":{"query":{"kind":"string","value":"Execute module as pytest with detailed summary report."},"document":{"kind":"string","value":"def execute_pytest(capture='all', flags='-rapP'):\n\n fname = os.path.basename(__file__)\n pytest.main(['-q', '--show-capture={}'.format(capture), fname, flags])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def run(run_type, module, config):\n print(\" -----------------------------------------------------------------\")\n print(\" Beginning \" + run_type.lower() + \" test suite \")\n print(\" -----------------------------------------------------------------\")\n print(\"\")\n summary = run_quiet(run_type, module, config)\n print(\" -----------------------------------------------------------------\")\n print(\" \" + run_type.capitalize() + \" test suite complete \")\n print(\" -----------------------------------------------------------------\")\n print(\"\")\n return summary","def main():\n run_test_summary1a()\n run_test_summary1c()\n run_test_summary1c()","def task_test():\n return {\n \"actions\": [[\"pytest\", \"--cov=mad_gui\", \"--cov-config=.coveragerc\", \"-vv\"]],\n \"verbosity\": 2,\n }","def test_using_summary_only(checker, capsys):\n phmdoctest.main.generate_using(config_file=Path(\"tests/generate_summary.toml\"))\n summary = \"phmdoctest- tests/generate_summary.toml generated 12 pytest files\"\n checker(summary, capsys.readouterr().out)","def test_basic_usage(self):\n self._test_reports_helper({}, [\"report.txt\"])","def test(self):\n cmd = TaskCreator.get_pytest() + \" \"\n options = ('-vv '\n '-n auto '\n '--ignore=tests/experiments/ '\n '--html=build/tests/html/tests.html '\n '--junitxml=build/tests/xml/tests.xml '\n '--cov ' + self.project_name_sc + ' '\n '--cov-report term '\n '--cov-report html:build/tests/coverage/html '\n '--cov-report xml:build/tests/coverage/xml/coverage.xml ')\n return {\n \"actions\": [cmd + options],\n \"verbosity\": 2\n }","def pytest(context):\n exec_cmd = \"pytest\"\n run_cmd(context, exec_cmd)","def pytest_terminal_summary(self, terminalreporter, exitstatus):\n # pylint: disable=unused-argument\n terminalreporter.section(\"Test Information\")\n for test, info in self._info.items():\n for datum in info:\n terminalreporter.write(\"{}: {}\\n\".format(test, datum))","def test(self):\n for arch, python in self.python:\n self.run(f\"{python} -m pytest\")","def runTests(self):\n \n pass","def test_basic_execution(self):","def test_func(verbose: bool) -> None:\n click.echo(verbose)","def test_module_summary(self):\n with sphinx_build('pyexample'):\n with open('_build/text/docfx_yaml/example.example.yml') as yml_file:\n data = yaml.safe_load(yml_file)\n for item in data['items']:\n if item['uid'] == 'example.example':\n self.assertEqual(\n item['summary'],\n 'Example module\\n\\nThis is a description'\n )\n break\n else:\n self.fail('Module not found')","def main() -> int:\n parser = argparse.ArgumentParser()\n parser.add_argument('--dir-metadata',\n type=pathlib.Path, required=True)\n\n args = parser.parse_args()\n\n with LockedMetadata(args.dir_metadata, __file__) as md:\n summary_dict = {}\n passing_tests = []\n failing_tests = []\n for f in md.tests_pickle_files:\n try:\n trr = TestRunResult.construct_from_pickle(f)\n summary_dict[f\"{trr.testname}.{trr.seed}\"] = \\\n ('PASS' if trr.passed else\n 'FAILED' + (\" {T}\" if (trr.failure_mode == Failure_Modes.TIMEOUT) else \"\"))\n if trr.passed:\n passing_tests.append(trr)\n else:\n failing_tests.append(trr)\n except RuntimeError as e:\n failing_tests.append(\n TestRunResult(\n name='broken_test',\n failure_message=str(e)\n ))\n\n md.regr_log = md.dir_run/'regr.log'\n md.regr_log_junit = md.dir_run/'regr_junit.xml'\n md.regr_log_junit_merged = md.dir_run/'regr_junit_merged.xml'\n\n # Write results as junit_xml\n with open(md.regr_log_junit,\n 'w',\n encoding='UTF-8') as junit_xml,\\\n open(md.regr_log_junit_merged,\n 'w',\n encoding='UTF-8') as junit_merged_xml:\n output_run_results_junit_xml(passing_tests, failing_tests,\n junit_xml,\n junit_merged_xml)\n\n with open(md.regr_log, 'w', encoding='UTF-8') as outfile:\n # Write results as regr.log (custom logfile format)\n output_results_text(passing_tests, failing_tests, summary_dict,\n outfile)\n\n test_summary_dict = create_test_summary_dict(passing_tests +\n failing_tests)\n\n cov_summary_dict = {}\n if md.simulator == \"xlm\":\n cov_summary_dict = create_cov_summary_dict(md)\n else:\n print(\"Warning: Not generating coverage summary, unsupported \" \\\n f\"simulator {md.simulator}\")\n\n html_report_filename = md.dir_run/'report.html'\n with open(html_report_filename, 'w') as outfile:\n output_results_html(md, passing_tests + failing_tests,\n test_summary_dict, cov_summary_dict, outfile)\n\n json_report_filename = md.dir_run/'report.json'\n with open(json_report_filename, 'w') as json_report_file:\n output_results_dvsim_json(md, test_summary_dict, cov_summary_dict,\n json_report_file)\n\n svg_summary_filename = md.dir_run/'summary.svg'\n with open(svg_summary_filename, 'w') as svg_summary_file:\n output_results_svg(test_summary_dict, cov_summary_dict,\n svg_summary_file)\n\n # Print a summary line to the terminal\n print(gen_summary_line(passing_tests, failing_tests))\n\n # Succeed if no tests failed\n return 1 if failing_tests else 0","def main():\n run_test_all()","def get_test_summary(repo:MLRepo):\n pass","def test_basic(testdir):\n testdir.makepyfile(\"\"\"import sys\"\"\")\n result = testdir.runpytest('--pylint')\n assert 'Missing module docstring' in result.stdout.str()\n assert 'Unused import sys' in result.stdout.str()\n assert 'Final newline missing' in result.stdout.str()\n assert 'passed' not in result.stdout.str()","def test_main():\n # Setup\n # Exercise\n # Verify","def get_description():\n return \"run tests according to config\"","def test_summary(args, path):\n log = os.path.join(path, \"kstest.log.json\")\n with open(log) as f:\n data = json.load(f)\n scenario = data[0].get(\"scenario\", None)\n if not scenario:\n raise RuntimeError(\"No scenario found in %s\" % log)\n\n # The json log filename needs to be in the form of <scenario>-<YYYY-MM-DD>.json\n datename = f\"{scenario}-1990-01-01\"\n shutil.copy(log, datename+\".json\")\n datenames = [datename]\n all_logs = {datename: path}\n\n report = summary(args, (d+\".json\" for d in datenames), all_logs)\n if args.output:\n with open(args.output, \"w\") as f:\n f.write(report)\n else:\n print(report)","def runtest(self):","def main():\n # ============================================================================\n\n working_folder = os.path.dirname(os.path.realpath(__file__))\n os.chdir(working_folder)\n\n description_string =\\\n \"A set of python tests to fetch data from Open Weather Map\"\n epilog_string = (\"These tests should be run regularly to ensure that the \"\n \"API server is healthy\")\n\n parser = argparse.ArgumentParser(\n prog='OWM-API-test',\n description=description_string,\n epilog=epilog_string)\n\n parser.add_argument(\n \"--tb\",\n action='store',\n default='line',\n nargs='?',\n choices=['auto', 'long', 'short', 'no', 'line', 'native'],\n help='Set the traceback level for pytest',\n dest='traceback')\n\n parser.add_argument(\n \"-v\", \"--verbose\",\n action='store_true',\n help='Increase output verbosity',\n dest='verbose')\n\n parser.add_argument(\n \"-q\", \"--quiet\",\n action='store_true',\n help='Reduce output verbosity')\n\n args = parser.parse_args()\n\n # ============================================================================\n # construct pytest commands\n pytest_command = []\n if args.verbose:\n # we double increase verbosity to make it actually verbose\n pytest_command.extend([\"-v\", \"-v\"])\n # this only overwrites traceback argument if left at default\n if args.traceback == \"line\":\n args.traceback = \"long\"\n\n if args.quiet: # this overwrites and tb argument given\n args.traceback = \"no\"\n\n pytest_command.extend([\"--tb\", args.traceback])\n\n # ============================================================================\n\n printing_functions.test_declaration(\"Running Tests...\")\n run_local_tests(pytest_command)","def run_tests():\n test_command = \"pytest -s \" + os.path.join(root_path, \"cases\", \"test_cases.py::TestCases::test_cases\") + \" --html=\" + os.path.join(root_path, \"reports\", \"qa_testing_report.html\")\n\n subprocess.run(test_command, shell=True)","def test_execution_profiling(self):\n self._test_reports_helper({\"--profile-execution\": \"\"}, [\"report.txt\"])","def spec_tests():\n pass","def test(session) -> None:\n session.install(\".[test]\")\n session.run(\"pytest\", \"-n\", \"auto\", *session.posargs)","def test_all():\n for descr, args in INPUT_PARAMS.iteritems():\n func = run_summarize_h5lmt\n func.description = 'bin/summarize_h5lmt.py ' + descr\n yield func, args","def test_module(self):\n pass","def tests():","def test():\n with lcd(BASEDIR):\n local('virtenv/bin/coverage run runtests.py -v2')\n local('virtenv/bin/coverage report -m')","def runalltests():\n doctest.testmod()","def tests():\n api.local('nosetests')","def pytest_runtest_makereport(item):\n global itr\n pytest_html = item.config.pluginmanager.getplugin('html')\n outcome = yield\n report = outcome.get_result()\n extra = getattr(report, 'extra', [])\n report.description = str(item.function.__doc__)\n report.function = item.function.__doc__\n report_directory = os.path.dirname(item.config.option.htmlpath)\n\n if report.when == 'call' or report.when == \"setup\":\n extra.append(pytest_html.extras.url(MyConfigFiles.PCN_AppURL))\n xfail = hasattr(report, 'wasxfail')\n #print(\"Xfaile details::\", xfail)\n if (report.skipped and xfail) or (report.failed and not xfail): # or report.outcome:\n #print(\"Report . Node ID::\", report.nodeid)\n file_name = report.nodeid.replace(\"QA/TestCases/\", '\\\\')\n file_name = file_name.replace(\"::\", \"_\") + \".png\"\n _capture_screenshot(file_name)\n extra.append(pytest_html.extras.html('<div>Log description</div>'))\n if file_name:\n html = '<div><img src=\"%s\" alt=\"screenshot\" style=\"width:304px;height:228px;\" ' \\\n 'onclick=\"window.open(this.src)\" align=\"right\"/></div>' % file_name\n extra.append(pytest_html.extras.html(html))\n print(\"Inside IF--HTML\", file_name)\n # movfiletodir(file_name)\n report.extra = extra","def main():\n # Disable *.pyc files\n sys.dont_write_bytecode = True\n\n # Add \"..\" to module search path\n cur_dir = os.path.dirname(os.path.realpath(__file__))\n top_dir = os.path.abspath(os.path.join(cur_dir, os.pardir))\n sys.path.append(top_dir)\n\n # Parse command line arguments\n parser = argparse.ArgumentParser(description=__doc__)\n parser.add_argument(\"-v\", \"--verbose\", action=\"count\", default=0,\n help=\"verbosity level, use: [-v | -vv | -vvv]\")\n parser.add_argument(\"-s\", \"--start-directory\", default=None,\n help=\"directory to start discovery\")\n parser.add_argument(\"-p\", \"--pattern\", default=\"test*.py\",\n help=\"pattern to match test files ('test*.py' default)\")\n parser.add_argument(\"test\", nargs=\"*\",\n help=\"test specs (e.g. module.TestCase.test_func)\")\n args = parser.parse_args()\n\n if not args.start_directory:\n args.start_directory = cur_dir\n\n if args.verbose > 2:\n logging.basicConfig(level=logging.DEBUG, format=\"DEBUG: %(message)s\")\n\n loader = unittest.TestLoader()\n if args.test:\n # Add particular tests\n for test in args.test:\n suite = unittest.TestSuite()\n suite.addTests(loader.loadTestsFromName(test))\n else:\n # Find all tests\n suite = loader.discover(args.start_directory, args.pattern)\n\n runner = unittest.TextTestRunner(verbosity=args.verbose)\n result = runner.run(suite)\n return result.wasSuccessful()","def summarize(self):\n \n print self._num_tests, \"tests ran with\", len(self._failed_tests), \"failures:\", sorted(list(self._failed_tests))\n\n self._num_tests = 0\n self._failed_tests = set()","def __main() :\n launchTests()","def run(self):\n cmd = 'coverage run setup.py test && coverage report -m'\n check_call(cmd, shell=True)","def test_script(self) -> None:\n main()","def _test():\n import doctest\n doctest.testmod(verbose=1)","def test_single_test_case():\n pass","def print_tests_results(self):\n\n for test in self.test_report:\n for detail in test:\n print detail + ': ', test[detail]","def run_all_tests(self) -> None:\n self.run_trt_precision_tests()\n logging.info(\"Check analysis result at: %s\", self._output_dir)","def describe_tests(remit, sourcelist):\n print('* run tests with \"%s\"' % remit)\n print('* tests to run: ')\n for line in pretty_list(sourcelist, 7):\n print(' ' + line)\n print('* writers to test: ')\n for line in pretty_list(spec.TEST['writer'], 7):\n print(' ' + line)\n print('* options to test: ')\n for line in pretty_list(spec.TEST['pandoc_options'], 7):\n print(' ' + line)\n print('* blacklisted: ')\n for config in spec.BLACKLIST:\n command = make_command(remit=remit,\n source='TEST',\n writer=config['writer'],\n pandoc_options=config['pandoc_options'],\n extension=config['extension'])\n print(' ' + ' '.join(command))\n print(' (' + config['comment'] + ')')","def describe():","def module_test(mod=None):\r\n func_list = [item[1] for item in mod.__dict__.items() if isinstance(item[0],str) and item[0].startswith('test')]\r\n sys.stderr.write(\"\\n\".join([func.__name__ + \": \" + (\"PASS\" if func() else \"FAIL\") for func in func_list] + [ func.__doc__ for func in func_list if func.__doc__ is not None]))\r\n sys.stderr.write(\"\\n\")","def test_basic_plugin(pytester, otel_service):\n pytester.makepyfile(\n common_code\n + \"\"\"\ndef test_basic():\n time.sleep(5)\n pass\n\"\"\")\n assertTest(pytester, \"test_basic\", \"passed\", STATUS_CODE_OK, \"passed\", STATUS_CODE_OK)","def run_test_summary1a():\n print()\n print('--------------------------------------------------')\n print('Testing the summary1a function:')\n print('--------------------------------------------------')\n\n format_string = ' summary1a( {} )'\n test_results = [0, 0] # Number of tests passed, failed.\n\n # Test 1:\n expected = 4\n sequence = (20, 23, 29, 30, 33, 29, 100, 2, 4)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 2:\n expected = 4\n sequence = (23, 29, 30, 33, 29, 100, 2)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 3:\n expected = 3\n sequence = (20, 29, 30, 33, 29, 100, 100, 99, 40, 30, 30, 2)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 4:\n expected = 3\n sequence = (29, 29, 30, 33, 29, 100, 100, 99, 40, 30, 30)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 5:\n expected = 2\n sequence = (30, 33, 29, 17, 100, 99, 40, 30, 30)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 6:\n expected = 1\n sequence = (30, 33, 13, 100, 99, 40, 30, 30)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 7:\n expected = 0\n sequence = (30, 33, 4, 10, 21, 100, 99, 40, 30, 30)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 8:\n expected = 3\n sequence = (5, 3, 3)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 9:\n expected = 2\n sequence = (5, 3)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 10:\n expected = 1\n sequence = (5,)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 11:\n expected = 0\n sequence = ()\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1a(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n print_summary_of_test_results(test_results)","def pytest_runtest_logreport(report):\n\n report_test_status(logger, report)","def create_test_summary(args, TEST_RESULTS):\n logging.error(\"Creating test summary report...\")\n\n try:\n test_summary = \"Performance Metrics of {APP} Application Tested from this PR\\n\".format(APP=args.bundle_id)\n test_summary += \"---------------------------------------------------------------\\n\"\n\n for element in TEST_RESULTS:\n if element != LAUNCHES:\n test_summary += \"> {KEY}: {VALUE}\".format(KEY=element, VALUE=TEST_RESULTS[element])\n if element == INSTALL_LAUNCH_DURATION:\n if int(TEST_RESULTS[INSTALL_LAUNCH_DURATION]) > args.duration_limit:\n test_summary += \"ms :x:\\n\"\n else:\n test_summary += \"ms :white_check_mark:\\n\"\n\n if element == INSTALL_MEMORY_USAGE:\n if int(TEST_RESULTS[INSTALL_MEMORY_USAGE]) > args.memory_limit:\n test_summary += \"MB :x:\\n\"\n else:\n test_summary += \"MB :white_check_mark:\\n\"\n\n if element == APP_SIZE:\n if int(TEST_RESULTS[APP_SIZE]) > args.size_limit:\n test_summary += \"MB :x:\\n\"\n else:\n test_summary += \"MB :white_check_mark:\\n\"\n test_summary += \"---------------------------------------------------------------\\n\"\n\n for element in TEST_RESULTS[LAUNCHES]:\n test_summary += \"> DEVICE: {DEVICE} | LAUNCH TYPE: {LAUNCH_TYPE} | \".format(DEVICE=element[DEVICE], LAUNCH_TYPE=element[LAUNCH_TYPE])\n test_summary += \"DURATION: {DURATION}ms \".format(DURATION=element[LAUNCH_DURATION])\n if int(element[LAUNCH_DURATION]) > args.duration_limit:\n test_summary += \" :x: | \"\n else:\n test_summary += \" :white_check_mark: | \"\n\n test_summary += \"MEMORY USAGE: {MEMORY_USAGE}MB \".format(MEMORY_USAGE=element[MEMORY_USAGE])\n if int(element[MEMORY_USAGE]) > args.memory_limit:\n test_summary += \" :x:\\n\"\n else:\n test_summary += \" :white_check_mark:\\n\"\n test_summary += \"----------------------------------------------------\\n\"\n\n except Exception as e:\n logging.error(\"Creating test summary failed with error '{ERROR}'\".format(ERROR=e))\n return None\n\n logging.info(test_summary)\n return test_summary","def _test():\n import doctest\n return doctest.testmod(verbose=True)","def _test():\n import doctest\n return doctest.testmod(verbose=True)","def run_tests(args, applog):\n try:\n additional_args = []\n additional_args.extend([\"--pyargs\", \"dent_os_testbed.test.test_suite\", \"--strict-markers\"])\n additional_args.append(\"--durations=0\")\n if args.stdout:\n additional_args.append(\"--capture=tee-sys\")\n\n suite_groups = args.suite_groups if args.suite_groups else PYTEST_SUITE_GROUPS.keys()\n for sg_name in suite_groups:\n sg = PYTEST_SUITE_GROUPS[sg_name]\n pytest_args = []\n if not sg:\n continue\n pytest._current_suite = sg_name\n pytest_args.append(\"-m\")\n markers_string = sg[0] + \"\".join([(\" or %s\" % suite) for suite in sg[1:]])\n pytest_args.append(markers_string)\n pytest_args.append(\"--html=%s/report_%s.html\" % (LOGDIR, sg_name))\n pytest_args.append(\"--junitxml=%s/junit_%s.xml\" % (LOGDIR, sg_name))\n pytest_args.append(\"--self-contained-html\")\n if args.suite_tests:\n pytest_args.append(\"-k\")\n pytest_args.append(args.suite_tests)\n input_args = additional_args + pytest_args\n applog.info(\"Triggering pytest with args : %s\" % input_args)\n pytest.main(input_args)\n except Exception as e:\n applog.exception(\"Error running tests\", exc_info=e)\n raise","def test_rr_summary(results):\n # pylint: disable=unidiomatic-typecheck\n test_result = results.summary()\n assert type(test_result).__name__ == \"Summary\"\n assert type(test_result.tables) == list\n assert len(test_result.tables) == 3\n assert len(test_result.extra_txt) > 0","def test():\n import unittest\n\n tests = unittest.TestLoader().discover(\"tests\")\n unittest.TextTestRunner(verbosity=2).run(tests)","def run_tests(self):\n raise NotImplementedError","def test():\n report = JUnitReport()\n url = \"http://\"+env.roledefs['admin'][0]\n test_url(report, \"Verify dataset exists and contains 'tag:lagrummet.se,2009:rinfo'\", \"admin.dataset\",\n \"%(url)s/sys/dataset/rdf.rdf\" % venv(), \"tag:lagrummet.se,2009:rinfo\")\n test_url(report, \"Verify current.atom exists and contains 'RInfo Base Data'\", \"admin.current\",\n \"%(url)s/feed/current.atom\" % venv(), \"RInfo Base Data\")\n test_url(report, \"Verify files index appears in root url\", \"admin.index\", url, \"Index\" % venv())\n if not report.empty():\n file_name = \"%(projectroot)s/testreport/admin_test_report.log\" % venv()\n report.create_report(file_name)\n print \"Created report '%s'\" % file_name","def debugging_tests():\n logging.warning(\"Running debugging tests...\")\n pass","def tests(context):\n black(context)\n isort(context)\n flake8(context)\n pylint(context)\n yamllint(context)\n pydocstyle(context)\n bandit(context)\n pytest(context)\n\n print(\"All tests have passed!\")","def test_arrange_test_result_one_module(self):\n pass_1 = self._create_test_result(status=test_runner_base.PASSED_STATUS)\n pass_2 = self._create_test_result(status=test_runner_base.PASSED_STATUS)\n pass_3 = self._create_test_result(status=test_runner_base.PASSED_STATUS)\n fail_1 = self._create_test_result(status=test_runner_base.FAILED_STATUS)\n fail_2 = self._create_test_result(status=test_runner_base.FAILED_STATUS)\n ignore_1 = self._create_test_result(status=test_runner_base.IGNORED_STATUS)\n reporter_1 = result_reporter.ResultReporter()\n reporter_1.all_test_results.extend([pass_1, pass_2, pass_3])\n reporter_2 = result_reporter.ResultReporter()\n reporter_2.all_test_results.extend([fail_1, fail_2, ignore_1])\n info_dict = {}\n aei.AtestExecutionInfo._arrange_test_result(info_dict, [reporter_1, reporter_2])\n expect_summary = {aei._STATUS_IGNORED_KEY : 1,\n aei._STATUS_FAILED_KEY : 2,\n aei._STATUS_PASSED_KEY : 3}\n self.assertEqual(expect_summary, info_dict[aei._TOTAL_SUMMARY_KEY])","def run_tests(params):\n default_test_params = [\n \"./tests\",\n \"-vv\",\n \"--json-report\",\n \"--key-file\",\n DEFAULT_KEY_FILE.name\n ]\n\n with DEBUG_LOG_FILE.open(\"a\") as debug_out:\n with redirect_stdout(debug_out):\n with redirect_stderr(sys.stdout):\n return pytest.main(default_test_params + params)","def summary(self, **kwargs):\n raise ValueError(\"This function is not available in lazy results evaluation as it would \"\n \"require all pairwise tests to be performed.\")","def run_tests(self, test_labels):\n import pytest\n\n argv = []\n if self.verbosity == 0:\n argv.append('--quiet')\n if self.verbosity == 2:\n argv.append('--verbose')\n if self.verbosity == 3:\n argv.append('-vv')\n if self.failfast:\n argv.append('--exitfirst')\n if self.keepdb:\n argv.append('--reuse-db')\n\n argv.extend(test_labels)\n return pytest.main(argv)","def test_example_runs(self):\n run_example(\n verbose=False,\n testapp=self.testapp,\n )","def do_test(self):\n\t\tshutit_global.shutit_global_object.yield_to_draw()\n\t\tif not self.build['dotest']:\n\t\t\tself.log('Tests configured off, not running',level=logging.DEBUG)\n\t\t\treturn\n\t\t# Test in reverse order\n\t\tself.log('PHASE: test', level=logging.DEBUG)\n\t\tself.stop_all()\n\t\tself.start_all()\n\t\tfor module_id in self.module_ids(rev=True):\n\t\t\t# Only test if it's installed.\n\t\t\tif self.is_installed(self.shutit_map[module_id]):\n\t\t\t\tself.log('RUNNING TEST ON: ' + module_id, level=logging.DEBUG)\n\t\t\t\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\n\t\t\t\tif not self.shutit_map[module_id].test(self):\n\t\t\t\t\tself.fail(module_id + ' failed on test', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\n\t\t\t\tself.logout(echo=False)","def test():\n loader = unittest.TestLoader()\n suite = loader.discover(os.path.dirname(__file__))\n runner = unittest.TextTestRunner()\n runner.run(suite)","def run_test(self):\n raise NotImplementedError","def unittest(core):\n\n # local import because nose uses pkg_resources that is very slow to import\n import nose.core\n\n config = core.config\n\n application_name = config.get(APPLICATION_NAME)\n application_package = config.get(APPLICATION_ROOT)\n\n extension_names = config.get(EXTENSION_NAMES)\n tests = config.get(TESTS)\n report_enabled = config.get(REPORT_ENABLED)\n report_file = config.get(REPORT_FILE)\n coverage_enabled = core.config.get(COVERAGE_ENABLED)\n verbose = core.config.get(DETAILS)\n exclude_systests = core.config.get(EXCLUDE_SYSTESTS)\n\n matching_packages = packages.find_packages(\n extension_names, core.extension_manager, application_name, application_package)\n command = create_nose_command(\n matching_packages, verbose, tests, report_enabled, report_file, exclude_systests)\n\n if coverage_enabled:\n coverage_file = core.config.get(COVERAGE_FILE)\n xml_enabled = core.config.get(COVERAGE_XML_ENABLED)\n xml_file = core.config.get(COVERAGE_XML_FILE)\n with coverage.coverage(matching_packages, sys.stdout, coverage_file, xml_enabled, xml_file):\n result = nose.core.run(argv=command)\n else:\n result = nose.core.run(argv=command)\n\n return 0 if result else 1","def testmodule():\n import doctest\n import sys\n thismodule = sys.modules[__name__]\n return doctest.testmod(m=thismodule)","def testmodule():\n import doctest\n import sys\n thismodule = sys.modules[__name__]\n return doctest.testmod(m=thismodule)","def run(self):\n if self.verbose:\n print(f'Running {self.name} tests...')\n\n # try running setup if there is one\n if self.setup:\n self.__process_setup()\n\n final_report = [None] * len(self.tests)\n\n for test_in, test_out in sorted(self.tests.items()):\n # increment total num of tests\n self.total += 1\n\n if self.verbose:\n print(f'#{self.total}')\n\n # evaluate test input w/ setup vars, if any\n try:\n inp = eval(test_in, self.vars)\n except Exception as err:\n print(f'Issue during evaluation of test input: {err}')\n final_report[self.total - 1] = 'input eval error'\n if self.verbose:\n print(f'Test input was: {test_in}')\n print('Vars from execution: {}'.format({k : v for k, v in self.vars.items() if k != '__builtins__'}))\n continue\n\n \n # checking if function input has more than one arg\n if type(inp) in (list, tuple):\n try:\n student_out = self.student_function(*inp)\n except Exception as err:\n print(f'Issue while running student code: {err}')\n final_report[self.total - 1] = f'student code error: {err}; input: {inp}; func_name: {self.name}'\n if self.verbose:\n print(f'Function being run was: {self.name}')\n print(f'Inputs were: {inp}')\n continue\n else:\n try:\n student_out = self.student_function(inp)\n except Exception as err:\n print(f'Issue while running student code: {err}')\n final_report[self.total - 1] = f'student code error: {err}; input: {inp}; func_name: {self.name}'\n if self.verbose:\n print(f'Function being run was: {self.name}')\n print(f'Input was: {inp}')\n continue\n\n # ans alias for ease of answer checking\n self.vars['ans'] = student_out\n\n if self.schema:\n format_vals = eval(test_out, self.vars)\n results, maybe_failed_schema = self.__process_schema(format_vals)\n if all(results):\n self.correct += 1\n final_report[self.total - 1] = 'PASSED'\n else:\n # failed at least one of the tests\n failed_str = \" and \".join([\", \".join(maybe_failed_schema[:-1]),maybe_failed_schema[-1]] if len(maybe_failed_schema) > 2 else maybe_failed_schema)\n final_report[self.total - 1] = f'FAILED; failed following assertion(s): {failed_str}'\n else:\n expected_ans = eval(test_out, self.vars)\n if student_out == expected_ans:\n self.correct += 1\n final_report[self.total - 1] = 'PASSED'\n else:\n # failed the only test\n final_report[self.total - 1] = f'FAILED; got {repr(student_out)} but expected {repr(expected_ans)}'\n\n # run callback function, if there is one\n if self.callback:\n if self.verbose:\n print('Running callback...')\n print('call back is:', self.callback)\n\n # once done, put the final report on the queue\n self.queue.put((self.student_username, self.name, f'{self.correct}/{self.total}', final_report))","def test():\n nose.run()","def run(context, path=\"\"):\n common.success(f\"Tests {path} running \")\n return start.run_python(\n context,\n f\"-m pytest {path}\"\n )","def run_test_summary1b():\n print()\n print('--------------------------------------------------')\n print('Testing the summary1b function:')\n print('--------------------------------------------------')\n\n format_string = ' summary1b( {} )'\n test_results = [0, 0] # Number of tests passed, failed.\n\n # Test 1:\n expected = 83\n sequence = (20, 23, 29, 30, 33, 29, 100, 2, 4)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 2:\n expected = 83\n sequence = (23, 29, 30, 33, 29, 100, 2)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 3:\n expected = 60\n sequence = (20, 29, 30, 33, 29, 100, 100, 99, 40, 30, 30, 2)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 4:\n expected = 87\n sequence = (29, 29, 30, 33, 29, 100, 100, 99, 40, 30, 30)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 5:\n expected = 46\n sequence = (30, 33, 29, 17, 100, 99, 40, 30, 30)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 6:\n expected = 13\n sequence = (30, 33, 13, 100, 99, 40, 30, 30)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 7:\n expected = 0\n sequence = (30, 33, 4, 10, 21, 100, 99, 40, 30, 30)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 8:\n expected = 11\n sequence = (5, 3, 3)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 9:\n expected = 8\n sequence = (5, 3)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 10:\n expected = 5\n sequence = (5,)\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n # Test 11:\n expected = 0\n sequence = ()\n print_expected_result_of_test([sequence], expected, test_results,\n format_string)\n actual = summary1b(sequence)\n print_actual_result_of_test(expected, actual, test_results)\n\n print_summary_of_test_results(test_results)","def summary(app):\n click.echo(get_summary(app))","def runTest(self):\n self.setUp()\n self.test_ProstateReporting1()","def print_summary(self):\n #outcomes = self.get_outcomes()\n #passes = 'Passes: %i' % sum(1 for outcome in outcomes if outcome == Result.PASS)\n #untested = 'Untested: %i' % sum(1 for outcome in outcomes if outcome == Result.UNTESTED)\n #errors = 'Errors: %i' % sum(1 for outcome in outcomes if outcome == Result.ERROR)\n #fails = 'Fails: %i' % sum(1 for outcome in outcomes if outcome == Result.FAIL)\n print('')\n print ('Passes: %i' % self.get_pass_count())\n print ('Fails: %i' % self.get_fail_count())\n print ('Errors: %i' % self.get_error_count())\n print ('Untested: %i' % self.get_untested_count())\n print ('Skipped: %i' % self.get_skipped_count())","def test_func(debug: bool) -> None:\n click.echo(debug)","def test():\n import unittest\n tests = unittest.TestLoader().discover(tests)\n unittest.TextTestRunner(verbosity=2).run(tests)","def test_help_ok(self):\n from trac import __version__\n\n test_name = sys._getframe().f_code.co_name\n d = {'version': __version__,\n 'date_format_hint': get_date_format_hint()}\n expected_results = self.expected_results[test_name] % d\n rv, output = self._execute('help')\n self.assertEqual(0, rv)\n self.assertEqual(expected_results, output)","def test():\n import unittest\n tests = unittest.TestLoader().discover('tests')\n unittest.TextTestRunner(verbosity=2).run(tests)","def test(self):\n return self._test(result_count=1, failure_amount=1)","def test(self):\n return self._test(result_count=1, failure_amount=1)","def main():\n # add all new test suites per test module here\n suite_date = test_date.suite()\n suite_ng = test_ng.suite()\n suite_page = test_page.suite()\n suite_container = test_container.suite()\n\n # add the suite to be tested here\n alltests = unittest.TestSuite((suite_date,\n suite_ng,\n suite_page,\n suite_container))\n\n # run the suite\n runner = unittest.TextTestRunner()\n runner.run(alltests)","def test():\r\n import unittest\r\n tests=unittest.TestLoader().discover('tests')\r\n unittest.TextTestRunner(verbosity=2).run(tests)","def test_passed():\n pass","def print_results(self):\n for test_cases in self._tests:\n for test_case in test_cases:\n print('{} ...ok'.format(test_case.get_name()))\n return 0","def test():\r\n import unittest\r\n tests = unittest.TestLoader().discover('tests')\r\n unittest.TextTestRunner(verbosity=2).run(tests)","def test():\r\n import unittest\r\n tests = unittest.TestLoader().discover('tests')\r\n unittest.TextTestRunner(verbosity=2).run(tests)","def task_test():\n return {\n 'actions': ['py.test tests/'],\n }","def run_tests():\n parser = ArgumentParser()\n parser.add_argument('name',nargs='?',default=None,help=\"Suite or test name\")\n parser.add_argument('-b','--bin-dir',help=\"Directory where Firebird binaries tools are\")\n parser.add_argument('-d','--db-dir',help=\"Directory to use for test databases\")\n parser.add_argument('--archive',action='store_true',help=\"Save last run results to archive\")\n parser.add_argument('--rerun',action='store_true',help=\"Run only tests that don't PASSed in last run\")\n parser.add_argument('--untested',action='store_true',help=\"Run only tests that were UNTESTED in last run\")\n parser.add_argument('-v','--verbose',action='store_true',help=\"Be more verbose\")\n parser.add_argument('--verbosity',type=int,choices=[0,1,2],default=1,help=\"Set verbosity; --verbosity=2 is the same as -v\")\n parser.add_argument('-q','--quiet',action='store_true',help=\"Be less verbose\")\n parser.add_argument('-x','--xunit',action='store_true',help=\"Provides test results also in the standard XUnit XML format\")\n parser.add_argument('-e','--expect',type=str,metavar=\"FILENAME\",help=\"Test results file to be used as expeted outcomes\")\n if rpyc_available:\n parser.add_argument('--remote',action='store_true',help=\"Connect to remote fbtest server\")\n\n parser.add_argument('-u','--update',action='store_true',help=\"Update last run results with re-run results\")\n parser.add_argument('-w','--password',help=\"SYSDBA password\")\n parser.add_argument('-o','--host',help=\"Remote Firebird or fbtest host machine identification\")\n parser.add_argument('-p','--person',help=\"QA person name\")\n parser.add_argument('-a','--arch',help=\"Firebird architecture: SS, CS, SC, EM\")\n parser.add_argument('-s','--sequence',type=int,help=\"Run sequence number for this target\")\n parser.add_argument('-k','--skip',help=\"Suite or test name or name of file with suite/test names to skip\")\n parser.add_argument('-c','--client',help=\"Use specified Firebird client library\")\n parser.set_defaults(rerun=False,untested=False,update=False,server=False,register=False,\n remote=False,host='localhost',password='masterkey',\n sequence=1,arch='SS',person=UNKNOWN)\n\n script_runner.run_tests(parser.parse_args())","def main():\n\n parser = argparse.ArgumentParser(prog=\"run_test.py\",\n formatter_class=argparse.RawTextHelpFormatter)\n parser.add_argument('id', help=\"Id of a test\")\n args = parser.parse_args()\n\n configure_logger()\n\n test_info = TESTS.get(args.id, None)\n if not test_info:\n test_info.log.error(f'{args.id} does not exist')\n exit(ErrorCode.CRITICAL)\n os.environ['DISPLAY'] = \":0.0\"\n\n test = Test(args.id, test_info)\n result = test.run()\n\n test.log.info('#' * 80)\n if not result:\n test.log.error('TEST FAILED')\n else:\n test.log.info('TEST PASSED')\n test.log.info('#' * 80)\n exit(not result)","def test_download_deployment_run_test_report(self):\n pass","def _main():\r\n @enum.unique\r\n class Verbosity(enum.IntEnum):\r\n NOTHING = 0\r\n SIMPLE = 1\r\n DETAIL = 2\r\n\r\n CURRENT_PATH = Path('./file/report.txt')\r\n EXEC_DATETIME = datetime.today()\r\n FILE_MOD_DATETIME = datetime.fromtimestamp(CURRENT_PATH.stat().st_mtime)\r\n LOG_PATH = CURRENT_PATH.with_name(\r\n CURRENT_PATH.stem + f'_{EXEC_DATETIME.date()}.log')\r\n \r\n with open(CURRENT_PATH, mode='w') as fw:\r\n fw.write(\r\n f'[Date of script modification] {str(FILE_MOD_DATETIME)}\\n'\r\n f'[Date of this test execution] {str(EXEC_DATETIME)}\\n'\r\n '\\n')\r\n\r\n unittest.main(\r\n module=\"tests.test_login\", \r\n testRunner=unittest.TextTestRunner(\r\n stream=fw,\r\n descriptions=False,\r\n verbosity=Verbosity.DETAIL))","def test_Demo(self):\n self._run(self._example_scenarios, \"Demo\")","def run(cls): \n tests_to_run = cls.config.TESTS # A list of 5-tuple elements specifying the tests to run. See the\n # 'Test Setup' section in config.py.template for more info.\n test_group_name = \"Alchemist Tests\" # A short string identifier for this test run.\n output_dir = cls.config.OUTPUT_DIR # The output file where we write results.\n \n try:\n os.makedirs(output_dir,0o777)\n except:\n pass\n num_tests_to_run = len(tests_to_run)\n\n print(OUTPUT_DIVIDER_STRING)\n if num_tests_to_run == 1:\n print(\"Running %d test in %s\" % (num_tests_to_run, test_group_name))\n else:\n print(\"Running %d tests in %s\" % (num_tests_to_run, test_group_name))\n failed_tests = []\n\n cls.before_run_tests()\n \n spark_settings = []\n for i in cls.config.SPARK_SETTINGS:\n spark_settings.append(i.to_array()[0])\n \n output_settings = []\n for i in cls.config.OUTPUT_SETTINGS:\n output_settings.append(i.to_array()[0])\n \n main_class = \"altest.AlTest\"\n\n for meta_data, opt_sets in tests_to_run:\n print(OUTPUT_DIVIDER_STRING + '\\n')\n# print(\"Running test command: '%s' ... \" % main_class)\n \n meta = {}\n meta_pairs = [i.to_tuple() for i in meta_data]\n for mp in meta_pairs:\n meta[mp[0].replace('-', '_')] = mp[1].replace('0x20', ' ')\n \n meta_settings = []\n for i in meta_data:\n meta_settings.append(i.to_array()[0])\n \n# stdout_filename = \"%s/%s.out\" % (output_dir, meta['short_name'])\n# stderr_filename = \"%s/%s.err\" % (output_dir, meta['short_name'])\n# \n# out_file = open(output_dir + \"/\" + meta['short_name'] + \".out\", 'w')\n\n # Run a test for all combinations of the OptionSets given, then capture\n # and print the output.\n opt_set_arrays = [i.to_array() for i in opt_sets]\n for opt_list in itertools.product(*opt_set_arrays):\n\n cmd = cls.get_spark_submit_cmd(spark_settings, main_class, output_settings, meta_settings, opt_list)\n# print(\"\\nSetting env var SPARK_SUBMIT_OPTS: %s\" % java_opts_str)\n# test_env[\"SPARK_SUBMIT_OPTS\"] = java_opts_str\n print(\"Running command:\")\n print(\"%s\\n\" % cmd)\n Popen(cmd, shell=True, env=test_env).wait()\n\n try:\n src = output_dir + meta['short_name'] + '_latest/'\n src_files = os.listdir(src)\n src_file = src_files[0][:-4]\n new_dir = output_dir + src_file\n os.makedirs(new_dir)\n for file_name in src_files:\n full_file_name = os.path.join(src, file_name)\n if (os.path.isfile(full_file_name)):\n shutil.copy(full_file_name, new_dir)\n except:\n pass\n \n# result_string = cls.process_output(config, meta['short_name'], opt_list,\n# stdout_filename, stderr_filename)\n# print(OUTPUT_DIVIDER_STRING)\n# print(\"\\nResult: \" + result_string)\n# print(OUTPUT_DIVIDER_STRING)\n# if \"FAILED\" in result_string:\n# failed_tests.append(meta['short_name'])\n# \n# \n# out_file.write(result_string + \"\\n\")\n# out_file.flush()\n\n if num_tests_to_run == 1:\n print(\"Finished running %d test in %s.\" % (num_tests_to_run, test_group_name))\n else:\n print(\"Finished running %d tests in %s.\" % (num_tests_to_run, test_group_name))\n# print(\"\\nNumber of failed tests: %d, failed tests: %s\" %\n# (len(failed_tests), \",\".join(failed_tests)))\n print(OUTPUT_DIVIDER_STRING)"],"string":"[\n \"def run(run_type, module, config):\\n print(\\\" -----------------------------------------------------------------\\\")\\n print(\\\" Beginning \\\" + run_type.lower() + \\\" test suite \\\")\\n print(\\\" -----------------------------------------------------------------\\\")\\n print(\\\"\\\")\\n summary = run_quiet(run_type, module, config)\\n print(\\\" -----------------------------------------------------------------\\\")\\n print(\\\" \\\" + run_type.capitalize() + \\\" test suite complete \\\")\\n print(\\\" -----------------------------------------------------------------\\\")\\n print(\\\"\\\")\\n return summary\",\n \"def main():\\n run_test_summary1a()\\n run_test_summary1c()\\n run_test_summary1c()\",\n \"def task_test():\\n return {\\n \\\"actions\\\": [[\\\"pytest\\\", \\\"--cov=mad_gui\\\", \\\"--cov-config=.coveragerc\\\", \\\"-vv\\\"]],\\n \\\"verbosity\\\": 2,\\n }\",\n \"def test_using_summary_only(checker, capsys):\\n phmdoctest.main.generate_using(config_file=Path(\\\"tests/generate_summary.toml\\\"))\\n summary = \\\"phmdoctest- tests/generate_summary.toml generated 12 pytest files\\\"\\n checker(summary, capsys.readouterr().out)\",\n \"def test_basic_usage(self):\\n self._test_reports_helper({}, [\\\"report.txt\\\"])\",\n \"def test(self):\\n cmd = TaskCreator.get_pytest() + \\\" \\\"\\n options = ('-vv '\\n '-n auto '\\n '--ignore=tests/experiments/ '\\n '--html=build/tests/html/tests.html '\\n '--junitxml=build/tests/xml/tests.xml '\\n '--cov ' + self.project_name_sc + ' '\\n '--cov-report term '\\n '--cov-report html:build/tests/coverage/html '\\n '--cov-report xml:build/tests/coverage/xml/coverage.xml ')\\n return {\\n \\\"actions\\\": [cmd + options],\\n \\\"verbosity\\\": 2\\n }\",\n \"def pytest(context):\\n exec_cmd = \\\"pytest\\\"\\n run_cmd(context, exec_cmd)\",\n \"def pytest_terminal_summary(self, terminalreporter, exitstatus):\\n # pylint: disable=unused-argument\\n terminalreporter.section(\\\"Test Information\\\")\\n for test, info in self._info.items():\\n for datum in info:\\n terminalreporter.write(\\\"{}: {}\\\\n\\\".format(test, datum))\",\n \"def test(self):\\n for arch, python in self.python:\\n self.run(f\\\"{python} -m pytest\\\")\",\n \"def runTests(self):\\n \\n pass\",\n \"def test_basic_execution(self):\",\n \"def test_func(verbose: bool) -> None:\\n click.echo(verbose)\",\n \"def test_module_summary(self):\\n with sphinx_build('pyexample'):\\n with open('_build/text/docfx_yaml/example.example.yml') as yml_file:\\n data = yaml.safe_load(yml_file)\\n for item in data['items']:\\n if item['uid'] == 'example.example':\\n self.assertEqual(\\n item['summary'],\\n 'Example module\\\\n\\\\nThis is a description'\\n )\\n break\\n else:\\n self.fail('Module not found')\",\n \"def main() -> int:\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--dir-metadata',\\n type=pathlib.Path, required=True)\\n\\n args = parser.parse_args()\\n\\n with LockedMetadata(args.dir_metadata, __file__) as md:\\n summary_dict = {}\\n passing_tests = []\\n failing_tests = []\\n for f in md.tests_pickle_files:\\n try:\\n trr = TestRunResult.construct_from_pickle(f)\\n summary_dict[f\\\"{trr.testname}.{trr.seed}\\\"] = \\\\\\n ('PASS' if trr.passed else\\n 'FAILED' + (\\\" {T}\\\" if (trr.failure_mode == Failure_Modes.TIMEOUT) else \\\"\\\"))\\n if trr.passed:\\n passing_tests.append(trr)\\n else:\\n failing_tests.append(trr)\\n except RuntimeError as e:\\n failing_tests.append(\\n TestRunResult(\\n name='broken_test',\\n failure_message=str(e)\\n ))\\n\\n md.regr_log = md.dir_run/'regr.log'\\n md.regr_log_junit = md.dir_run/'regr_junit.xml'\\n md.regr_log_junit_merged = md.dir_run/'regr_junit_merged.xml'\\n\\n # Write results as junit_xml\\n with open(md.regr_log_junit,\\n 'w',\\n encoding='UTF-8') as junit_xml,\\\\\\n open(md.regr_log_junit_merged,\\n 'w',\\n encoding='UTF-8') as junit_merged_xml:\\n output_run_results_junit_xml(passing_tests, failing_tests,\\n junit_xml,\\n junit_merged_xml)\\n\\n with open(md.regr_log, 'w', encoding='UTF-8') as outfile:\\n # Write results as regr.log (custom logfile format)\\n output_results_text(passing_tests, failing_tests, summary_dict,\\n outfile)\\n\\n test_summary_dict = create_test_summary_dict(passing_tests +\\n failing_tests)\\n\\n cov_summary_dict = {}\\n if md.simulator == \\\"xlm\\\":\\n cov_summary_dict = create_cov_summary_dict(md)\\n else:\\n print(\\\"Warning: Not generating coverage summary, unsupported \\\" \\\\\\n f\\\"simulator {md.simulator}\\\")\\n\\n html_report_filename = md.dir_run/'report.html'\\n with open(html_report_filename, 'w') as outfile:\\n output_results_html(md, passing_tests + failing_tests,\\n test_summary_dict, cov_summary_dict, outfile)\\n\\n json_report_filename = md.dir_run/'report.json'\\n with open(json_report_filename, 'w') as json_report_file:\\n output_results_dvsim_json(md, test_summary_dict, cov_summary_dict,\\n json_report_file)\\n\\n svg_summary_filename = md.dir_run/'summary.svg'\\n with open(svg_summary_filename, 'w') as svg_summary_file:\\n output_results_svg(test_summary_dict, cov_summary_dict,\\n svg_summary_file)\\n\\n # Print a summary line to the terminal\\n print(gen_summary_line(passing_tests, failing_tests))\\n\\n # Succeed if no tests failed\\n return 1 if failing_tests else 0\",\n \"def main():\\n run_test_all()\",\n \"def get_test_summary(repo:MLRepo):\\n pass\",\n \"def test_basic(testdir):\\n testdir.makepyfile(\\\"\\\"\\\"import sys\\\"\\\"\\\")\\n result = testdir.runpytest('--pylint')\\n assert 'Missing module docstring' in result.stdout.str()\\n assert 'Unused import sys' in result.stdout.str()\\n assert 'Final newline missing' in result.stdout.str()\\n assert 'passed' not in result.stdout.str()\",\n \"def test_main():\\n # Setup\\n # Exercise\\n # Verify\",\n \"def get_description():\\n return \\\"run tests according to config\\\"\",\n \"def test_summary(args, path):\\n log = os.path.join(path, \\\"kstest.log.json\\\")\\n with open(log) as f:\\n data = json.load(f)\\n scenario = data[0].get(\\\"scenario\\\", None)\\n if not scenario:\\n raise RuntimeError(\\\"No scenario found in %s\\\" % log)\\n\\n # The json log filename needs to be in the form of <scenario>-<YYYY-MM-DD>.json\\n datename = f\\\"{scenario}-1990-01-01\\\"\\n shutil.copy(log, datename+\\\".json\\\")\\n datenames = [datename]\\n all_logs = {datename: path}\\n\\n report = summary(args, (d+\\\".json\\\" for d in datenames), all_logs)\\n if args.output:\\n with open(args.output, \\\"w\\\") as f:\\n f.write(report)\\n else:\\n print(report)\",\n \"def runtest(self):\",\n \"def main():\\n # ============================================================================\\n\\n working_folder = os.path.dirname(os.path.realpath(__file__))\\n os.chdir(working_folder)\\n\\n description_string =\\\\\\n \\\"A set of python tests to fetch data from Open Weather Map\\\"\\n epilog_string = (\\\"These tests should be run regularly to ensure that the \\\"\\n \\\"API server is healthy\\\")\\n\\n parser = argparse.ArgumentParser(\\n prog='OWM-API-test',\\n description=description_string,\\n epilog=epilog_string)\\n\\n parser.add_argument(\\n \\\"--tb\\\",\\n action='store',\\n default='line',\\n nargs='?',\\n choices=['auto', 'long', 'short', 'no', 'line', 'native'],\\n help='Set the traceback level for pytest',\\n dest='traceback')\\n\\n parser.add_argument(\\n \\\"-v\\\", \\\"--verbose\\\",\\n action='store_true',\\n help='Increase output verbosity',\\n dest='verbose')\\n\\n parser.add_argument(\\n \\\"-q\\\", \\\"--quiet\\\",\\n action='store_true',\\n help='Reduce output verbosity')\\n\\n args = parser.parse_args()\\n\\n # ============================================================================\\n # construct pytest commands\\n pytest_command = []\\n if args.verbose:\\n # we double increase verbosity to make it actually verbose\\n pytest_command.extend([\\\"-v\\\", \\\"-v\\\"])\\n # this only overwrites traceback argument if left at default\\n if args.traceback == \\\"line\\\":\\n args.traceback = \\\"long\\\"\\n\\n if args.quiet: # this overwrites and tb argument given\\n args.traceback = \\\"no\\\"\\n\\n pytest_command.extend([\\\"--tb\\\", args.traceback])\\n\\n # ============================================================================\\n\\n printing_functions.test_declaration(\\\"Running Tests...\\\")\\n run_local_tests(pytest_command)\",\n \"def run_tests():\\n test_command = \\\"pytest -s \\\" + os.path.join(root_path, \\\"cases\\\", \\\"test_cases.py::TestCases::test_cases\\\") + \\\" --html=\\\" + os.path.join(root_path, \\\"reports\\\", \\\"qa_testing_report.html\\\")\\n\\n subprocess.run(test_command, shell=True)\",\n \"def test_execution_profiling(self):\\n self._test_reports_helper({\\\"--profile-execution\\\": \\\"\\\"}, [\\\"report.txt\\\"])\",\n \"def spec_tests():\\n pass\",\n \"def test(session) -> None:\\n session.install(\\\".[test]\\\")\\n session.run(\\\"pytest\\\", \\\"-n\\\", \\\"auto\\\", *session.posargs)\",\n \"def test_all():\\n for descr, args in INPUT_PARAMS.iteritems():\\n func = run_summarize_h5lmt\\n func.description = 'bin/summarize_h5lmt.py ' + descr\\n yield func, args\",\n \"def test_module(self):\\n pass\",\n \"def tests():\",\n \"def test():\\n with lcd(BASEDIR):\\n local('virtenv/bin/coverage run runtests.py -v2')\\n local('virtenv/bin/coverage report -m')\",\n \"def runalltests():\\n doctest.testmod()\",\n \"def tests():\\n api.local('nosetests')\",\n \"def pytest_runtest_makereport(item):\\n global itr\\n pytest_html = item.config.pluginmanager.getplugin('html')\\n outcome = yield\\n report = outcome.get_result()\\n extra = getattr(report, 'extra', [])\\n report.description = str(item.function.__doc__)\\n report.function = item.function.__doc__\\n report_directory = os.path.dirname(item.config.option.htmlpath)\\n\\n if report.when == 'call' or report.when == \\\"setup\\\":\\n extra.append(pytest_html.extras.url(MyConfigFiles.PCN_AppURL))\\n xfail = hasattr(report, 'wasxfail')\\n #print(\\\"Xfaile details::\\\", xfail)\\n if (report.skipped and xfail) or (report.failed and not xfail): # or report.outcome:\\n #print(\\\"Report . Node ID::\\\", report.nodeid)\\n file_name = report.nodeid.replace(\\\"QA/TestCases/\\\", '\\\\\\\\')\\n file_name = file_name.replace(\\\"::\\\", \\\"_\\\") + \\\".png\\\"\\n _capture_screenshot(file_name)\\n extra.append(pytest_html.extras.html('<div>Log description</div>'))\\n if file_name:\\n html = '<div><img src=\\\"%s\\\" alt=\\\"screenshot\\\" style=\\\"width:304px;height:228px;\\\" ' \\\\\\n 'onclick=\\\"window.open(this.src)\\\" align=\\\"right\\\"/></div>' % file_name\\n extra.append(pytest_html.extras.html(html))\\n print(\\\"Inside IF--HTML\\\", file_name)\\n # movfiletodir(file_name)\\n report.extra = extra\",\n \"def main():\\n # Disable *.pyc files\\n sys.dont_write_bytecode = True\\n\\n # Add \\\"..\\\" to module search path\\n cur_dir = os.path.dirname(os.path.realpath(__file__))\\n top_dir = os.path.abspath(os.path.join(cur_dir, os.pardir))\\n sys.path.append(top_dir)\\n\\n # Parse command line arguments\\n parser = argparse.ArgumentParser(description=__doc__)\\n parser.add_argument(\\\"-v\\\", \\\"--verbose\\\", action=\\\"count\\\", default=0,\\n help=\\\"verbosity level, use: [-v | -vv | -vvv]\\\")\\n parser.add_argument(\\\"-s\\\", \\\"--start-directory\\\", default=None,\\n help=\\\"directory to start discovery\\\")\\n parser.add_argument(\\\"-p\\\", \\\"--pattern\\\", default=\\\"test*.py\\\",\\n help=\\\"pattern to match test files ('test*.py' default)\\\")\\n parser.add_argument(\\\"test\\\", nargs=\\\"*\\\",\\n help=\\\"test specs (e.g. module.TestCase.test_func)\\\")\\n args = parser.parse_args()\\n\\n if not args.start_directory:\\n args.start_directory = cur_dir\\n\\n if args.verbose > 2:\\n logging.basicConfig(level=logging.DEBUG, format=\\\"DEBUG: %(message)s\\\")\\n\\n loader = unittest.TestLoader()\\n if args.test:\\n # Add particular tests\\n for test in args.test:\\n suite = unittest.TestSuite()\\n suite.addTests(loader.loadTestsFromName(test))\\n else:\\n # Find all tests\\n suite = loader.discover(args.start_directory, args.pattern)\\n\\n runner = unittest.TextTestRunner(verbosity=args.verbose)\\n result = runner.run(suite)\\n return result.wasSuccessful()\",\n \"def summarize(self):\\n \\n print self._num_tests, \\\"tests ran with\\\", len(self._failed_tests), \\\"failures:\\\", sorted(list(self._failed_tests))\\n\\n self._num_tests = 0\\n self._failed_tests = set()\",\n \"def __main() :\\n launchTests()\",\n \"def run(self):\\n cmd = 'coverage run setup.py test && coverage report -m'\\n check_call(cmd, shell=True)\",\n \"def test_script(self) -> None:\\n main()\",\n \"def _test():\\n import doctest\\n doctest.testmod(verbose=1)\",\n \"def test_single_test_case():\\n pass\",\n \"def print_tests_results(self):\\n\\n for test in self.test_report:\\n for detail in test:\\n print detail + ': ', test[detail]\",\n \"def run_all_tests(self) -> None:\\n self.run_trt_precision_tests()\\n logging.info(\\\"Check analysis result at: %s\\\", self._output_dir)\",\n \"def describe_tests(remit, sourcelist):\\n print('* run tests with \\\"%s\\\"' % remit)\\n print('* tests to run: ')\\n for line in pretty_list(sourcelist, 7):\\n print(' ' + line)\\n print('* writers to test: ')\\n for line in pretty_list(spec.TEST['writer'], 7):\\n print(' ' + line)\\n print('* options to test: ')\\n for line in pretty_list(spec.TEST['pandoc_options'], 7):\\n print(' ' + line)\\n print('* blacklisted: ')\\n for config in spec.BLACKLIST:\\n command = make_command(remit=remit,\\n source='TEST',\\n writer=config['writer'],\\n pandoc_options=config['pandoc_options'],\\n extension=config['extension'])\\n print(' ' + ' '.join(command))\\n print(' (' + config['comment'] + ')')\",\n \"def describe():\",\n \"def module_test(mod=None):\\r\\n func_list = [item[1] for item in mod.__dict__.items() if isinstance(item[0],str) and item[0].startswith('test')]\\r\\n sys.stderr.write(\\\"\\\\n\\\".join([func.__name__ + \\\": \\\" + (\\\"PASS\\\" if func() else \\\"FAIL\\\") for func in func_list] + [ func.__doc__ for func in func_list if func.__doc__ is not None]))\\r\\n sys.stderr.write(\\\"\\\\n\\\")\",\n \"def test_basic_plugin(pytester, otel_service):\\n pytester.makepyfile(\\n common_code\\n + \\\"\\\"\\\"\\ndef test_basic():\\n time.sleep(5)\\n pass\\n\\\"\\\"\\\")\\n assertTest(pytester, \\\"test_basic\\\", \\\"passed\\\", STATUS_CODE_OK, \\\"passed\\\", STATUS_CODE_OK)\",\n \"def run_test_summary1a():\\n print()\\n print('--------------------------------------------------')\\n print('Testing the summary1a function:')\\n print('--------------------------------------------------')\\n\\n format_string = ' summary1a( {} )'\\n test_results = [0, 0] # Number of tests passed, failed.\\n\\n # Test 1:\\n expected = 4\\n sequence = (20, 23, 29, 30, 33, 29, 100, 2, 4)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 2:\\n expected = 4\\n sequence = (23, 29, 30, 33, 29, 100, 2)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 3:\\n expected = 3\\n sequence = (20, 29, 30, 33, 29, 100, 100, 99, 40, 30, 30, 2)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 4:\\n expected = 3\\n sequence = (29, 29, 30, 33, 29, 100, 100, 99, 40, 30, 30)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 5:\\n expected = 2\\n sequence = (30, 33, 29, 17, 100, 99, 40, 30, 30)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 6:\\n expected = 1\\n sequence = (30, 33, 13, 100, 99, 40, 30, 30)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 7:\\n expected = 0\\n sequence = (30, 33, 4, 10, 21, 100, 99, 40, 30, 30)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 8:\\n expected = 3\\n sequence = (5, 3, 3)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 9:\\n expected = 2\\n sequence = (5, 3)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 10:\\n expected = 1\\n sequence = (5,)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 11:\\n expected = 0\\n sequence = ()\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1a(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n print_summary_of_test_results(test_results)\",\n \"def pytest_runtest_logreport(report):\\n\\n report_test_status(logger, report)\",\n \"def create_test_summary(args, TEST_RESULTS):\\n logging.error(\\\"Creating test summary report...\\\")\\n\\n try:\\n test_summary = \\\"Performance Metrics of {APP} Application Tested from this PR\\\\n\\\".format(APP=args.bundle_id)\\n test_summary += \\\"---------------------------------------------------------------\\\\n\\\"\\n\\n for element in TEST_RESULTS:\\n if element != LAUNCHES:\\n test_summary += \\\"> {KEY}: {VALUE}\\\".format(KEY=element, VALUE=TEST_RESULTS[element])\\n if element == INSTALL_LAUNCH_DURATION:\\n if int(TEST_RESULTS[INSTALL_LAUNCH_DURATION]) > args.duration_limit:\\n test_summary += \\\"ms :x:\\\\n\\\"\\n else:\\n test_summary += \\\"ms :white_check_mark:\\\\n\\\"\\n\\n if element == INSTALL_MEMORY_USAGE:\\n if int(TEST_RESULTS[INSTALL_MEMORY_USAGE]) > args.memory_limit:\\n test_summary += \\\"MB :x:\\\\n\\\"\\n else:\\n test_summary += \\\"MB :white_check_mark:\\\\n\\\"\\n\\n if element == APP_SIZE:\\n if int(TEST_RESULTS[APP_SIZE]) > args.size_limit:\\n test_summary += \\\"MB :x:\\\\n\\\"\\n else:\\n test_summary += \\\"MB :white_check_mark:\\\\n\\\"\\n test_summary += \\\"---------------------------------------------------------------\\\\n\\\"\\n\\n for element in TEST_RESULTS[LAUNCHES]:\\n test_summary += \\\"> DEVICE: {DEVICE} | LAUNCH TYPE: {LAUNCH_TYPE} | \\\".format(DEVICE=element[DEVICE], LAUNCH_TYPE=element[LAUNCH_TYPE])\\n test_summary += \\\"DURATION: {DURATION}ms \\\".format(DURATION=element[LAUNCH_DURATION])\\n if int(element[LAUNCH_DURATION]) > args.duration_limit:\\n test_summary += \\\" :x: | \\\"\\n else:\\n test_summary += \\\" :white_check_mark: | \\\"\\n\\n test_summary += \\\"MEMORY USAGE: {MEMORY_USAGE}MB \\\".format(MEMORY_USAGE=element[MEMORY_USAGE])\\n if int(element[MEMORY_USAGE]) > args.memory_limit:\\n test_summary += \\\" :x:\\\\n\\\"\\n else:\\n test_summary += \\\" :white_check_mark:\\\\n\\\"\\n test_summary += \\\"----------------------------------------------------\\\\n\\\"\\n\\n except Exception as e:\\n logging.error(\\\"Creating test summary failed with error '{ERROR}'\\\".format(ERROR=e))\\n return None\\n\\n logging.info(test_summary)\\n return test_summary\",\n \"def _test():\\n import doctest\\n return doctest.testmod(verbose=True)\",\n \"def _test():\\n import doctest\\n return doctest.testmod(verbose=True)\",\n \"def run_tests(args, applog):\\n try:\\n additional_args = []\\n additional_args.extend([\\\"--pyargs\\\", \\\"dent_os_testbed.test.test_suite\\\", \\\"--strict-markers\\\"])\\n additional_args.append(\\\"--durations=0\\\")\\n if args.stdout:\\n additional_args.append(\\\"--capture=tee-sys\\\")\\n\\n suite_groups = args.suite_groups if args.suite_groups else PYTEST_SUITE_GROUPS.keys()\\n for sg_name in suite_groups:\\n sg = PYTEST_SUITE_GROUPS[sg_name]\\n pytest_args = []\\n if not sg:\\n continue\\n pytest._current_suite = sg_name\\n pytest_args.append(\\\"-m\\\")\\n markers_string = sg[0] + \\\"\\\".join([(\\\" or %s\\\" % suite) for suite in sg[1:]])\\n pytest_args.append(markers_string)\\n pytest_args.append(\\\"--html=%s/report_%s.html\\\" % (LOGDIR, sg_name))\\n pytest_args.append(\\\"--junitxml=%s/junit_%s.xml\\\" % (LOGDIR, sg_name))\\n pytest_args.append(\\\"--self-contained-html\\\")\\n if args.suite_tests:\\n pytest_args.append(\\\"-k\\\")\\n pytest_args.append(args.suite_tests)\\n input_args = additional_args + pytest_args\\n applog.info(\\\"Triggering pytest with args : %s\\\" % input_args)\\n pytest.main(input_args)\\n except Exception as e:\\n applog.exception(\\\"Error running tests\\\", exc_info=e)\\n raise\",\n \"def test_rr_summary(results):\\n # pylint: disable=unidiomatic-typecheck\\n test_result = results.summary()\\n assert type(test_result).__name__ == \\\"Summary\\\"\\n assert type(test_result.tables) == list\\n assert len(test_result.tables) == 3\\n assert len(test_result.extra_txt) > 0\",\n \"def test():\\n import unittest\\n\\n tests = unittest.TestLoader().discover(\\\"tests\\\")\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def run_tests(self):\\n raise NotImplementedError\",\n \"def test():\\n report = JUnitReport()\\n url = \\\"http://\\\"+env.roledefs['admin'][0]\\n test_url(report, \\\"Verify dataset exists and contains 'tag:lagrummet.se,2009:rinfo'\\\", \\\"admin.dataset\\\",\\n \\\"%(url)s/sys/dataset/rdf.rdf\\\" % venv(), \\\"tag:lagrummet.se,2009:rinfo\\\")\\n test_url(report, \\\"Verify current.atom exists and contains 'RInfo Base Data'\\\", \\\"admin.current\\\",\\n \\\"%(url)s/feed/current.atom\\\" % venv(), \\\"RInfo Base Data\\\")\\n test_url(report, \\\"Verify files index appears in root url\\\", \\\"admin.index\\\", url, \\\"Index\\\" % venv())\\n if not report.empty():\\n file_name = \\\"%(projectroot)s/testreport/admin_test_report.log\\\" % venv()\\n report.create_report(file_name)\\n print \\\"Created report '%s'\\\" % file_name\",\n \"def debugging_tests():\\n logging.warning(\\\"Running debugging tests...\\\")\\n pass\",\n \"def tests(context):\\n black(context)\\n isort(context)\\n flake8(context)\\n pylint(context)\\n yamllint(context)\\n pydocstyle(context)\\n bandit(context)\\n pytest(context)\\n\\n print(\\\"All tests have passed!\\\")\",\n \"def test_arrange_test_result_one_module(self):\\n pass_1 = self._create_test_result(status=test_runner_base.PASSED_STATUS)\\n pass_2 = self._create_test_result(status=test_runner_base.PASSED_STATUS)\\n pass_3 = self._create_test_result(status=test_runner_base.PASSED_STATUS)\\n fail_1 = self._create_test_result(status=test_runner_base.FAILED_STATUS)\\n fail_2 = self._create_test_result(status=test_runner_base.FAILED_STATUS)\\n ignore_1 = self._create_test_result(status=test_runner_base.IGNORED_STATUS)\\n reporter_1 = result_reporter.ResultReporter()\\n reporter_1.all_test_results.extend([pass_1, pass_2, pass_3])\\n reporter_2 = result_reporter.ResultReporter()\\n reporter_2.all_test_results.extend([fail_1, fail_2, ignore_1])\\n info_dict = {}\\n aei.AtestExecutionInfo._arrange_test_result(info_dict, [reporter_1, reporter_2])\\n expect_summary = {aei._STATUS_IGNORED_KEY : 1,\\n aei._STATUS_FAILED_KEY : 2,\\n aei._STATUS_PASSED_KEY : 3}\\n self.assertEqual(expect_summary, info_dict[aei._TOTAL_SUMMARY_KEY])\",\n \"def run_tests(params):\\n default_test_params = [\\n \\\"./tests\\\",\\n \\\"-vv\\\",\\n \\\"--json-report\\\",\\n \\\"--key-file\\\",\\n DEFAULT_KEY_FILE.name\\n ]\\n\\n with DEBUG_LOG_FILE.open(\\\"a\\\") as debug_out:\\n with redirect_stdout(debug_out):\\n with redirect_stderr(sys.stdout):\\n return pytest.main(default_test_params + params)\",\n \"def summary(self, **kwargs):\\n raise ValueError(\\\"This function is not available in lazy results evaluation as it would \\\"\\n \\\"require all pairwise tests to be performed.\\\")\",\n \"def run_tests(self, test_labels):\\n import pytest\\n\\n argv = []\\n if self.verbosity == 0:\\n argv.append('--quiet')\\n if self.verbosity == 2:\\n argv.append('--verbose')\\n if self.verbosity == 3:\\n argv.append('-vv')\\n if self.failfast:\\n argv.append('--exitfirst')\\n if self.keepdb:\\n argv.append('--reuse-db')\\n\\n argv.extend(test_labels)\\n return pytest.main(argv)\",\n \"def test_example_runs(self):\\n run_example(\\n verbose=False,\\n testapp=self.testapp,\\n )\",\n \"def do_test(self):\\n\\t\\tshutit_global.shutit_global_object.yield_to_draw()\\n\\t\\tif not self.build['dotest']:\\n\\t\\t\\tself.log('Tests configured off, not running',level=logging.DEBUG)\\n\\t\\t\\treturn\\n\\t\\t# Test in reverse order\\n\\t\\tself.log('PHASE: test', level=logging.DEBUG)\\n\\t\\tself.stop_all()\\n\\t\\tself.start_all()\\n\\t\\tfor module_id in self.module_ids(rev=True):\\n\\t\\t\\t# Only test if it's installed.\\n\\t\\t\\tif self.is_installed(self.shutit_map[module_id]):\\n\\t\\t\\t\\tself.log('RUNNING TEST ON: ' + module_id, level=logging.DEBUG)\\n\\t\\t\\t\\tself.login(prompt_prefix=module_id,command=shutit_global.shutit_global_object.bash_startup_command,echo=False)\\n\\t\\t\\t\\tif not self.shutit_map[module_id].test(self):\\n\\t\\t\\t\\t\\tself.fail(module_id + ' failed on test', shutit_pexpect_child=self.get_shutit_pexpect_session_from_id('target_child').pexpect_child) # pragma: no cover\\n\\t\\t\\t\\tself.logout(echo=False)\",\n \"def test():\\n loader = unittest.TestLoader()\\n suite = loader.discover(os.path.dirname(__file__))\\n runner = unittest.TextTestRunner()\\n runner.run(suite)\",\n \"def run_test(self):\\n raise NotImplementedError\",\n \"def unittest(core):\\n\\n # local import because nose uses pkg_resources that is very slow to import\\n import nose.core\\n\\n config = core.config\\n\\n application_name = config.get(APPLICATION_NAME)\\n application_package = config.get(APPLICATION_ROOT)\\n\\n extension_names = config.get(EXTENSION_NAMES)\\n tests = config.get(TESTS)\\n report_enabled = config.get(REPORT_ENABLED)\\n report_file = config.get(REPORT_FILE)\\n coverage_enabled = core.config.get(COVERAGE_ENABLED)\\n verbose = core.config.get(DETAILS)\\n exclude_systests = core.config.get(EXCLUDE_SYSTESTS)\\n\\n matching_packages = packages.find_packages(\\n extension_names, core.extension_manager, application_name, application_package)\\n command = create_nose_command(\\n matching_packages, verbose, tests, report_enabled, report_file, exclude_systests)\\n\\n if coverage_enabled:\\n coverage_file = core.config.get(COVERAGE_FILE)\\n xml_enabled = core.config.get(COVERAGE_XML_ENABLED)\\n xml_file = core.config.get(COVERAGE_XML_FILE)\\n with coverage.coverage(matching_packages, sys.stdout, coverage_file, xml_enabled, xml_file):\\n result = nose.core.run(argv=command)\\n else:\\n result = nose.core.run(argv=command)\\n\\n return 0 if result else 1\",\n \"def testmodule():\\n import doctest\\n import sys\\n thismodule = sys.modules[__name__]\\n return doctest.testmod(m=thismodule)\",\n \"def testmodule():\\n import doctest\\n import sys\\n thismodule = sys.modules[__name__]\\n return doctest.testmod(m=thismodule)\",\n \"def run(self):\\n if self.verbose:\\n print(f'Running {self.name} tests...')\\n\\n # try running setup if there is one\\n if self.setup:\\n self.__process_setup()\\n\\n final_report = [None] * len(self.tests)\\n\\n for test_in, test_out in sorted(self.tests.items()):\\n # increment total num of tests\\n self.total += 1\\n\\n if self.verbose:\\n print(f'#{self.total}')\\n\\n # evaluate test input w/ setup vars, if any\\n try:\\n inp = eval(test_in, self.vars)\\n except Exception as err:\\n print(f'Issue during evaluation of test input: {err}')\\n final_report[self.total - 1] = 'input eval error'\\n if self.verbose:\\n print(f'Test input was: {test_in}')\\n print('Vars from execution: {}'.format({k : v for k, v in self.vars.items() if k != '__builtins__'}))\\n continue\\n\\n \\n # checking if function input has more than one arg\\n if type(inp) in (list, tuple):\\n try:\\n student_out = self.student_function(*inp)\\n except Exception as err:\\n print(f'Issue while running student code: {err}')\\n final_report[self.total - 1] = f'student code error: {err}; input: {inp}; func_name: {self.name}'\\n if self.verbose:\\n print(f'Function being run was: {self.name}')\\n print(f'Inputs were: {inp}')\\n continue\\n else:\\n try:\\n student_out = self.student_function(inp)\\n except Exception as err:\\n print(f'Issue while running student code: {err}')\\n final_report[self.total - 1] = f'student code error: {err}; input: {inp}; func_name: {self.name}'\\n if self.verbose:\\n print(f'Function being run was: {self.name}')\\n print(f'Input was: {inp}')\\n continue\\n\\n # ans alias for ease of answer checking\\n self.vars['ans'] = student_out\\n\\n if self.schema:\\n format_vals = eval(test_out, self.vars)\\n results, maybe_failed_schema = self.__process_schema(format_vals)\\n if all(results):\\n self.correct += 1\\n final_report[self.total - 1] = 'PASSED'\\n else:\\n # failed at least one of the tests\\n failed_str = \\\" and \\\".join([\\\", \\\".join(maybe_failed_schema[:-1]),maybe_failed_schema[-1]] if len(maybe_failed_schema) > 2 else maybe_failed_schema)\\n final_report[self.total - 1] = f'FAILED; failed following assertion(s): {failed_str}'\\n else:\\n expected_ans = eval(test_out, self.vars)\\n if student_out == expected_ans:\\n self.correct += 1\\n final_report[self.total - 1] = 'PASSED'\\n else:\\n # failed the only test\\n final_report[self.total - 1] = f'FAILED; got {repr(student_out)} but expected {repr(expected_ans)}'\\n\\n # run callback function, if there is one\\n if self.callback:\\n if self.verbose:\\n print('Running callback...')\\n print('call back is:', self.callback)\\n\\n # once done, put the final report on the queue\\n self.queue.put((self.student_username, self.name, f'{self.correct}/{self.total}', final_report))\",\n \"def test():\\n nose.run()\",\n \"def run(context, path=\\\"\\\"):\\n common.success(f\\\"Tests {path} running \\\")\\n return start.run_python(\\n context,\\n f\\\"-m pytest {path}\\\"\\n )\",\n \"def run_test_summary1b():\\n print()\\n print('--------------------------------------------------')\\n print('Testing the summary1b function:')\\n print('--------------------------------------------------')\\n\\n format_string = ' summary1b( {} )'\\n test_results = [0, 0] # Number of tests passed, failed.\\n\\n # Test 1:\\n expected = 83\\n sequence = (20, 23, 29, 30, 33, 29, 100, 2, 4)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 2:\\n expected = 83\\n sequence = (23, 29, 30, 33, 29, 100, 2)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 3:\\n expected = 60\\n sequence = (20, 29, 30, 33, 29, 100, 100, 99, 40, 30, 30, 2)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 4:\\n expected = 87\\n sequence = (29, 29, 30, 33, 29, 100, 100, 99, 40, 30, 30)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 5:\\n expected = 46\\n sequence = (30, 33, 29, 17, 100, 99, 40, 30, 30)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 6:\\n expected = 13\\n sequence = (30, 33, 13, 100, 99, 40, 30, 30)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 7:\\n expected = 0\\n sequence = (30, 33, 4, 10, 21, 100, 99, 40, 30, 30)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 8:\\n expected = 11\\n sequence = (5, 3, 3)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 9:\\n expected = 8\\n sequence = (5, 3)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 10:\\n expected = 5\\n sequence = (5,)\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n # Test 11:\\n expected = 0\\n sequence = ()\\n print_expected_result_of_test([sequence], expected, test_results,\\n format_string)\\n actual = summary1b(sequence)\\n print_actual_result_of_test(expected, actual, test_results)\\n\\n print_summary_of_test_results(test_results)\",\n \"def summary(app):\\n click.echo(get_summary(app))\",\n \"def runTest(self):\\n self.setUp()\\n self.test_ProstateReporting1()\",\n \"def print_summary(self):\\n #outcomes = self.get_outcomes()\\n #passes = 'Passes: %i' % sum(1 for outcome in outcomes if outcome == Result.PASS)\\n #untested = 'Untested: %i' % sum(1 for outcome in outcomes if outcome == Result.UNTESTED)\\n #errors = 'Errors: %i' % sum(1 for outcome in outcomes if outcome == Result.ERROR)\\n #fails = 'Fails: %i' % sum(1 for outcome in outcomes if outcome == Result.FAIL)\\n print('')\\n print ('Passes: %i' % self.get_pass_count())\\n print ('Fails: %i' % self.get_fail_count())\\n print ('Errors: %i' % self.get_error_count())\\n print ('Untested: %i' % self.get_untested_count())\\n print ('Skipped: %i' % self.get_skipped_count())\",\n \"def test_func(debug: bool) -> None:\\n click.echo(debug)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover(tests)\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test_help_ok(self):\\n from trac import __version__\\n\\n test_name = sys._getframe().f_code.co_name\\n d = {'version': __version__,\\n 'date_format_hint': get_date_format_hint()}\\n expected_results = self.expected_results[test_name] % d\\n rv, output = self._execute('help')\\n self.assertEqual(0, rv)\\n self.assertEqual(expected_results, output)\",\n \"def test():\\n import unittest\\n tests = unittest.TestLoader().discover('tests')\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test(self):\\n return self._test(result_count=1, failure_amount=1)\",\n \"def test(self):\\n return self._test(result_count=1, failure_amount=1)\",\n \"def main():\\n # add all new test suites per test module here\\n suite_date = test_date.suite()\\n suite_ng = test_ng.suite()\\n suite_page = test_page.suite()\\n suite_container = test_container.suite()\\n\\n # add the suite to be tested here\\n alltests = unittest.TestSuite((suite_date,\\n suite_ng,\\n suite_page,\\n suite_container))\\n\\n # run the suite\\n runner = unittest.TextTestRunner()\\n runner.run(alltests)\",\n \"def test():\\r\\n import unittest\\r\\n tests=unittest.TestLoader().discover('tests')\\r\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test_passed():\\n pass\",\n \"def print_results(self):\\n for test_cases in self._tests:\\n for test_case in test_cases:\\n print('{} ...ok'.format(test_case.get_name()))\\n return 0\",\n \"def test():\\r\\n import unittest\\r\\n tests = unittest.TestLoader().discover('tests')\\r\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def test():\\r\\n import unittest\\r\\n tests = unittest.TestLoader().discover('tests')\\r\\n unittest.TextTestRunner(verbosity=2).run(tests)\",\n \"def task_test():\\n return {\\n 'actions': ['py.test tests/'],\\n }\",\n \"def run_tests():\\n parser = ArgumentParser()\\n parser.add_argument('name',nargs='?',default=None,help=\\\"Suite or test name\\\")\\n parser.add_argument('-b','--bin-dir',help=\\\"Directory where Firebird binaries tools are\\\")\\n parser.add_argument('-d','--db-dir',help=\\\"Directory to use for test databases\\\")\\n parser.add_argument('--archive',action='store_true',help=\\\"Save last run results to archive\\\")\\n parser.add_argument('--rerun',action='store_true',help=\\\"Run only tests that don't PASSed in last run\\\")\\n parser.add_argument('--untested',action='store_true',help=\\\"Run only tests that were UNTESTED in last run\\\")\\n parser.add_argument('-v','--verbose',action='store_true',help=\\\"Be more verbose\\\")\\n parser.add_argument('--verbosity',type=int,choices=[0,1,2],default=1,help=\\\"Set verbosity; --verbosity=2 is the same as -v\\\")\\n parser.add_argument('-q','--quiet',action='store_true',help=\\\"Be less verbose\\\")\\n parser.add_argument('-x','--xunit',action='store_true',help=\\\"Provides test results also in the standard XUnit XML format\\\")\\n parser.add_argument('-e','--expect',type=str,metavar=\\\"FILENAME\\\",help=\\\"Test results file to be used as expeted outcomes\\\")\\n if rpyc_available:\\n parser.add_argument('--remote',action='store_true',help=\\\"Connect to remote fbtest server\\\")\\n\\n parser.add_argument('-u','--update',action='store_true',help=\\\"Update last run results with re-run results\\\")\\n parser.add_argument('-w','--password',help=\\\"SYSDBA password\\\")\\n parser.add_argument('-o','--host',help=\\\"Remote Firebird or fbtest host machine identification\\\")\\n parser.add_argument('-p','--person',help=\\\"QA person name\\\")\\n parser.add_argument('-a','--arch',help=\\\"Firebird architecture: SS, CS, SC, EM\\\")\\n parser.add_argument('-s','--sequence',type=int,help=\\\"Run sequence number for this target\\\")\\n parser.add_argument('-k','--skip',help=\\\"Suite or test name or name of file with suite/test names to skip\\\")\\n parser.add_argument('-c','--client',help=\\\"Use specified Firebird client library\\\")\\n parser.set_defaults(rerun=False,untested=False,update=False,server=False,register=False,\\n remote=False,host='localhost',password='masterkey',\\n sequence=1,arch='SS',person=UNKNOWN)\\n\\n script_runner.run_tests(parser.parse_args())\",\n \"def main():\\n\\n parser = argparse.ArgumentParser(prog=\\\"run_test.py\\\",\\n formatter_class=argparse.RawTextHelpFormatter)\\n parser.add_argument('id', help=\\\"Id of a test\\\")\\n args = parser.parse_args()\\n\\n configure_logger()\\n\\n test_info = TESTS.get(args.id, None)\\n if not test_info:\\n test_info.log.error(f'{args.id} does not exist')\\n exit(ErrorCode.CRITICAL)\\n os.environ['DISPLAY'] = \\\":0.0\\\"\\n\\n test = Test(args.id, test_info)\\n result = test.run()\\n\\n test.log.info('#' * 80)\\n if not result:\\n test.log.error('TEST FAILED')\\n else:\\n test.log.info('TEST PASSED')\\n test.log.info('#' * 80)\\n exit(not result)\",\n \"def test_download_deployment_run_test_report(self):\\n pass\",\n \"def _main():\\r\\n @enum.unique\\r\\n class Verbosity(enum.IntEnum):\\r\\n NOTHING = 0\\r\\n SIMPLE = 1\\r\\n DETAIL = 2\\r\\n\\r\\n CURRENT_PATH = Path('./file/report.txt')\\r\\n EXEC_DATETIME = datetime.today()\\r\\n FILE_MOD_DATETIME = datetime.fromtimestamp(CURRENT_PATH.stat().st_mtime)\\r\\n LOG_PATH = CURRENT_PATH.with_name(\\r\\n CURRENT_PATH.stem + f'_{EXEC_DATETIME.date()}.log')\\r\\n \\r\\n with open(CURRENT_PATH, mode='w') as fw:\\r\\n fw.write(\\r\\n f'[Date of script modification] {str(FILE_MOD_DATETIME)}\\\\n'\\r\\n f'[Date of this test execution] {str(EXEC_DATETIME)}\\\\n'\\r\\n '\\\\n')\\r\\n\\r\\n unittest.main(\\r\\n module=\\\"tests.test_login\\\", \\r\\n testRunner=unittest.TextTestRunner(\\r\\n stream=fw,\\r\\n descriptions=False,\\r\\n verbosity=Verbosity.DETAIL))\",\n \"def test_Demo(self):\\n self._run(self._example_scenarios, \\\"Demo\\\")\",\n \"def run(cls): \\n tests_to_run = cls.config.TESTS # A list of 5-tuple elements specifying the tests to run. See the\\n # 'Test Setup' section in config.py.template for more info.\\n test_group_name = \\\"Alchemist Tests\\\" # A short string identifier for this test run.\\n output_dir = cls.config.OUTPUT_DIR # The output file where we write results.\\n \\n try:\\n os.makedirs(output_dir,0o777)\\n except:\\n pass\\n num_tests_to_run = len(tests_to_run)\\n\\n print(OUTPUT_DIVIDER_STRING)\\n if num_tests_to_run == 1:\\n print(\\\"Running %d test in %s\\\" % (num_tests_to_run, test_group_name))\\n else:\\n print(\\\"Running %d tests in %s\\\" % (num_tests_to_run, test_group_name))\\n failed_tests = []\\n\\n cls.before_run_tests()\\n \\n spark_settings = []\\n for i in cls.config.SPARK_SETTINGS:\\n spark_settings.append(i.to_array()[0])\\n \\n output_settings = []\\n for i in cls.config.OUTPUT_SETTINGS:\\n output_settings.append(i.to_array()[0])\\n \\n main_class = \\\"altest.AlTest\\\"\\n\\n for meta_data, opt_sets in tests_to_run:\\n print(OUTPUT_DIVIDER_STRING + '\\\\n')\\n# print(\\\"Running test command: '%s' ... \\\" % main_class)\\n \\n meta = {}\\n meta_pairs = [i.to_tuple() for i in meta_data]\\n for mp in meta_pairs:\\n meta[mp[0].replace('-', '_')] = mp[1].replace('0x20', ' ')\\n \\n meta_settings = []\\n for i in meta_data:\\n meta_settings.append(i.to_array()[0])\\n \\n# stdout_filename = \\\"%s/%s.out\\\" % (output_dir, meta['short_name'])\\n# stderr_filename = \\\"%s/%s.err\\\" % (output_dir, meta['short_name'])\\n# \\n# out_file = open(output_dir + \\\"/\\\" + meta['short_name'] + \\\".out\\\", 'w')\\n\\n # Run a test for all combinations of the OptionSets given, then capture\\n # and print the output.\\n opt_set_arrays = [i.to_array() for i in opt_sets]\\n for opt_list in itertools.product(*opt_set_arrays):\\n\\n cmd = cls.get_spark_submit_cmd(spark_settings, main_class, output_settings, meta_settings, opt_list)\\n# print(\\\"\\\\nSetting env var SPARK_SUBMIT_OPTS: %s\\\" % java_opts_str)\\n# test_env[\\\"SPARK_SUBMIT_OPTS\\\"] = java_opts_str\\n print(\\\"Running command:\\\")\\n print(\\\"%s\\\\n\\\" % cmd)\\n Popen(cmd, shell=True, env=test_env).wait()\\n\\n try:\\n src = output_dir + meta['short_name'] + '_latest/'\\n src_files = os.listdir(src)\\n src_file = src_files[0][:-4]\\n new_dir = output_dir + src_file\\n os.makedirs(new_dir)\\n for file_name in src_files:\\n full_file_name = os.path.join(src, file_name)\\n if (os.path.isfile(full_file_name)):\\n shutil.copy(full_file_name, new_dir)\\n except:\\n pass\\n \\n# result_string = cls.process_output(config, meta['short_name'], opt_list,\\n# stdout_filename, stderr_filename)\\n# print(OUTPUT_DIVIDER_STRING)\\n# print(\\\"\\\\nResult: \\\" + result_string)\\n# print(OUTPUT_DIVIDER_STRING)\\n# if \\\"FAILED\\\" in result_string:\\n# failed_tests.append(meta['short_name'])\\n# \\n# \\n# out_file.write(result_string + \\\"\\\\n\\\")\\n# out_file.flush()\\n\\n if num_tests_to_run == 1:\\n print(\\\"Finished running %d test in %s.\\\" % (num_tests_to_run, test_group_name))\\n else:\\n print(\\\"Finished running %d tests in %s.\\\" % (num_tests_to_run, test_group_name))\\n# print(\\\"\\\\nNumber of failed tests: %d, failed tests: %s\\\" %\\n# (len(failed_tests), \\\",\\\".join(failed_tests)))\\n print(OUTPUT_DIVIDER_STRING)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72504675","0.68715066","0.6861118","0.67923087","0.6693342","0.66854435","0.6595857","0.6548619","0.65412146","0.6535354","0.6469479","0.6465738","0.6464574","0.64562017","0.641981","0.64085966","0.637916","0.63596404","0.6356207","0.63495654","0.6342631","0.63028324","0.62705195","0.6265045","0.6263057","0.62458193","0.62319475","0.62218356","0.6210603","0.6202911","0.6181155","0.61775","0.6138773","0.61320853","0.6125326","0.61206234","0.6100006","0.60965174","0.6096501","0.6090873","0.6087394","0.60736656","0.6052206","0.60513294","0.604799","0.6044789","0.602149","0.60205734","0.60157907","0.6008629","0.6008629","0.6000758","0.5994564","0.598955","0.59863394","0.59859973","0.5985596","0.5979145","0.5978971","0.5974511","0.59729004","0.596579","0.5964043","0.5951186","0.59340125","0.59194565","0.5917131","0.59163207","0.59163207","0.5911023","0.5899515","0.5896503","0.5893994","0.58828974","0.58828324","0.5872708","0.5866247","0.5864868","0.5864856","0.58613455","0.5857306","0.5857306","0.5850406","0.58474624","0.58462536","0.5845753","0.5840861","0.5840861","0.5838364","0.58379847","0.58358","0.5834713","0.58315206","0.5830731","0.5827977"],"string":"[\n \"0.72504675\",\n \"0.68715066\",\n \"0.6861118\",\n \"0.67923087\",\n \"0.6693342\",\n \"0.66854435\",\n \"0.6595857\",\n \"0.6548619\",\n \"0.65412146\",\n \"0.6535354\",\n \"0.6469479\",\n \"0.6465738\",\n \"0.6464574\",\n \"0.64562017\",\n \"0.641981\",\n \"0.64085966\",\n \"0.637916\",\n \"0.63596404\",\n \"0.6356207\",\n \"0.63495654\",\n \"0.6342631\",\n \"0.63028324\",\n \"0.62705195\",\n \"0.6265045\",\n \"0.6263057\",\n \"0.62458193\",\n \"0.62319475\",\n \"0.62218356\",\n \"0.6210603\",\n \"0.6202911\",\n \"0.6181155\",\n \"0.61775\",\n \"0.6138773\",\n \"0.61320853\",\n \"0.6125326\",\n \"0.61206234\",\n \"0.6100006\",\n \"0.60965174\",\n \"0.6096501\",\n \"0.6090873\",\n \"0.6087394\",\n \"0.60736656\",\n \"0.6052206\",\n \"0.60513294\",\n \"0.604799\",\n \"0.6044789\",\n \"0.602149\",\n \"0.60205734\",\n \"0.60157907\",\n \"0.6008629\",\n \"0.6008629\",\n \"0.6000758\",\n \"0.5994564\",\n \"0.598955\",\n \"0.59863394\",\n \"0.59859973\",\n \"0.5985596\",\n \"0.5979145\",\n \"0.5978971\",\n \"0.5974511\",\n \"0.59729004\",\n \"0.596579\",\n \"0.5964043\",\n \"0.5951186\",\n \"0.59340125\",\n \"0.59194565\",\n \"0.5917131\",\n \"0.59163207\",\n \"0.59163207\",\n \"0.5911023\",\n \"0.5899515\",\n \"0.5896503\",\n \"0.5893994\",\n \"0.58828974\",\n \"0.58828324\",\n \"0.5872708\",\n \"0.5866247\",\n \"0.5864868\",\n \"0.5864856\",\n \"0.58613455\",\n \"0.5857306\",\n \"0.5857306\",\n \"0.5850406\",\n \"0.58474624\",\n \"0.58462536\",\n \"0.5845753\",\n \"0.5840861\",\n \"0.5840861\",\n \"0.5838364\",\n \"0.58379847\",\n \"0.58358\",\n \"0.5834713\",\n \"0.58315206\",\n \"0.5830731\",\n \"0.5827977\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6190488"},"document_rank":{"kind":"string","value":"35"}}},{"rowIdx":77993,"cells":{"query":{"kind":"string","value":"First, it finds the total amount of contacts for each listing_id. Second, it finds the top most contacted listings."},"document":{"kind":"string","value":"def find_top_five_most_contacted_listings_per_month(self):\n available_dates = self.contacts.quer_distinct_components(\"contact_date\", \"Contacts\")\n available_listing_ids = self.contacts.quer_distinct_components(\"listing_id\", \"Contacts\")\n \n self.ordered = {}\n for date in available_dates:\n listing = []\n month = self.contacts.quer_component_using_column(\"listing_id\", \"contact_date\", \"Contacts\", date)\n new = [month[0] for month in month]\n for listing_id in available_listing_ids:\n count = new.count(listing_id)\n listing.append([count, listing_id])\n listing = sorted(listing, key=lambda x: x[0], reverse=True)\n\n self.ordered[date] = listing[:5]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_listings(self):\n listing_ids = self.context.listing_ids\n if len(listing_ids) == 0:\n return\n listing_ids = [lid.lower() for lid in listing_ids]\n params = {\n 'limit': 0,\n 'offset': 0,\n 'lang': self.portal_state.language(),\n }\n params.update({\n 'listing_ids': listing_ids,\n })\n params = prepare_search_params(params)\n results = search(params, batching=False, context=self.context)\n if results is None or len(results) == 0:\n return\n\n # sort the results based on the listing_ids\n results = [(item['id']['value'], item) for item in results]\n results = dict(results)\n return [results.get(id) for id in listing_ids if id in results]","def _get_listings(self):\n listing_ids = self.config.get('listing_ids', [])\n if len(listing_ids) == 0:\n return\n listing_ids = [lid.lower() for lid in listing_ids]\n params = {\n 'limit': 0,\n 'offset': 0,\n 'lang': self.portal_state.language(),\n }\n params.update(self.config)\n params['listing_ids'] = listing_ids\n params = prepare_search_params(params)\n results = search(params, batching=False, context=self.context)\n if results is None or len(results) == 0:\n return\n\n # sort the results based on the listing_ids\n results = [(item['id']['value'], item) for item in results]\n results = dict(results)\n self._listings = [\n results.get(id) for id in listing_ids if id in results\n ]","def get_all(self):\n total_contacts = []\n get_count = {\n 'query': {\n 'object': 'CONTACT',\n 'select': {\n 'field': 'RECORDNO'\n },\n 'pagesize': '1'\n }\n }\n\n response = self.format_and_send_request(get_count)\n count = int(response['data']['@totalcount'])\n pagesize = 2000\n offset = 0\n for i in range(0, count, pagesize):\n data = {\n 'query': {\n 'object': 'CONTACT',\n 'select': {\n 'field': [\n 'RECORDNO',\n 'CONTACTNAME',\n 'COMPANYNAME',\n 'FIRSTNAME',\n 'LASTNAME',\n 'INITIAL',\n 'PRINTAS',\n 'TAXABLE',\n 'MAILADDRESS.ADDRESS1'\n ]\n },\n 'pagesize': pagesize,\n 'offset': offset\n }\n }\n contacts = self.format_and_send_request(data)['data']['CONTACT']\n total_contacts = total_contacts + contacts\n offset = offset + pagesize\n return total_contacts","def getTopRank(ss_def, contacts_def):\n rank_seq = {}\n contacts_true = contacts_def.keys()\n for i in range(1, len(ss_def)):\n smotif = getSmotif(ss_def[i - 1], ss_def[i])\n no_of_contacts = 0\n for j in range(smotif[0][0], smotif[0][1] + 1):\n if j in contacts_true:\n contacts = contacts_def[j]\n for contact in contacts:\n if contact in range(smotif[1][0], smotif[1][1] + 1):\n no_of_contacts += 1\n\n rank_seq[i - 1] = no_of_contacts\n top_rank = getTopSmotif(rank_seq)\n\n return top_rank, top_rank + 1","def getcontacts():\n contacts = {}\n\n try:\n #get list of contact ids\n contactids = r.smembers(\"contacts\")\n\n #for each contact id get data\n for contactid in contactids:\n contacts.update(_getcontact(str(contactid)))\n return contacts\n except:\n print \"Unexpected error:\", sys.exc_info()[0]\n raise","def pull_listings(zipcode=4581, propertyType=4, minyear = None, category = 'listings', write = False, date = True, silent = False):\n if category == 'listings':\n apiurl = \"https://api.boliga.dk/api/v2/search/results\"\n elif category == 'sold' or category == 'bbrsearch':\n apiurl = \"https://api.boliga.dk/api/v2/sold/search/results\"\n else:\n print('Nonvalid category - use \"listings\", \"sold\" or \"bbrsearch\"')\n return None\n results = []\n page = 1\n totalpages = 1\n parameters = {'sort': 'date-d',\n 'zipcodeFrom': zipcode,\n 'zipcodeTo': zipcode,\n 'street': ''}\n if category == 'bbrsearch':\n parameters['bbrsearch'] = 1\n parameters['sort'] = 'date-a'\n else:\n parameters['searchTab'] = 1\n if propertyType != None:\n parameters['propertyType'] = propertyType\n if minyear != None:\n parameters['salesDateMin'] = minyear\n write = False\n while page <= totalpages:\n parameters['page'] = page\n response = requests.get(apiurl, params=parameters)\n if response.status_code != 200:\n print(\"Page {}: status_code {} - aborting!\".format(page, response.status_code))\n break\n totalpages = response.json()['meta']['totalPages']\n if not silent:\n print(\"Page {} of {}\\n{}\".format(page, totalpages, response.url))\n if totalpages >= 3600:\n print('Totalpages {}, aborting!'.format(totalpages))\n break\n results.extend(response.json()['results'])\n page += 1\n time.sleep(1)\n if write:\n if date:\n filename = category + '/' + category + str(datetime.now().strftime('%Y%m%d')) + '.txt'\n else:\n filename = category + '.txt'\n with open(filename, 'w') as file:\n file.write(json.dumps(results))\n return results","def parse_listings(addresses, sites):\n\n for site in sites:\n for address in addresses:\n listing = api_search(address, site)\n # Process the listing\n process_listing(listing, site)","def extract_listings(self, bs_obj, timestamp):\n listings = bs_obj.find_all(\"div\", class_=\"listing\")\n for listing_element in listings:\n image_div = listing_element.find(\"div\", class_=\"tcl\")\n pricing_div = listing_element.find(\"div\", class_=\"tclr\")\n primary_div = listing_element.find(\"div\", style=\"margin-left:90px;margin-right:120px;\")\n\n item = {}\n item[\"market_name\"] = \"Alphabay\"\n item[\"listing_name\"] = primary_div.find(\"a\", class_=\"bstd\").text\n\n # Parse pricing\n price_string = pricing_div.find(\"span\", class_=\"std\").text.strip()\n\n usd_price = price_string.split(\"\\n\")[0].split(\" \")\n usd_price = usd_price[len(usd_price)-1].replace(\",\", \"\")\n\n btc_price = price_string.split(\"\\n\")[1].split(\" \")\n btc_price = btc_price[0].lstrip(\"(\")\n\n\n item[\"listing_price_usd\"] = str(float(usd_price))\n item[\"listing_price_btc\"] = str(float(btc_price))\n\n # Split the element value by \"-\" so that we can grab item number and category.\n # Text being parsed looks like: \"Item # 30361 - Botnets & Malware / Botnets & Malware\"\n item_number, item_category = tuple(value.strip() for value in primary_div.find(\"span\", class_=\"std\", style=\"line-height:12px;\").text.split(\"-\"))\n item[\"listing_item_number\"] = int(item_number.split(\"#\")[1].strip())\n item[\"listing_category\"] = item_category\n\n # Grab who is listing this item\n vendor_id = primary_div.find(\"span\", class_=\"normal\")\n if not vendor_id:\n vendor_id = primary_div.find(\"span\", class_=\"red\")\n if not vendor_id:\n raise Exception(\"Error with CSS\")\n vendor_id = vendor_id.text.split(\"-\")[2].split(\" \")\n vendor_name = vendor_id[0].strip()\n vendor_id = vendor_id[len(vendor_id) - 1].lstrip(\"(\").rstrip(\")\")\n else:\n vendor_name = primary_div.find(\"span\", class_=\"normal\").find(\"a\", class_=\"std\").text\n vendor_id = vendor_id.text\n vendor_id = vendor_id.strip().split(\"(\")[1].rstrip(\")\")\n item[\"vendor_name\"] = vendor_name\n item[\"vendor_id\"] = int(vendor_id)\n\n\n # Grab views, bids, and quantities\n meta_info = list(parent for parent in primary_div.find_all(\"span\", class_=\"std\") if \"Bid\" in parent.text)[0].text\n view_bid_info, quantity_info = tuple(meta_info.split(\"Quantity left: \"))\n item[\"listing_views\"] = int(view_bid_info.split(\"/\")[0].split(\" \")[1])\n item[\"listing_bids\"] = view_bid_info.split(\"/\")[1].strip().split(\": \")[1]\n item[\"listing_item_quantity\"] = quantity_info\n\n # Grab the listing date from the listing image\n image_date = image_div.find(\"a\", class_=\"notext\").find(\"img\", class_=\"listing\")['src']\n image_date = \"-\".join(image_date.split(\"images/uploads/\")[1].split(\"/\")[:3])\n item[\"listing_date\"] = image_date\n item[\"timestamp\"] = timestamp\n yield item","def test_amount_of_listings_many_listings(self):\n listings = steam_market.get_total_amount_of_listings(soup=get_soup_from_path(TEST_FILE_MANY_RESULTS))\n self.assertEqual(164720, listings)","def search_contact_list(self):\n\n search_db = Database()\n result = search_db.contact_search(self.name)\n if not result:\n print Fore.YELLOW + ' No such contact'\n return None\n if result > 1:\n print ' Which contact ??'\n for items in result:\n if items[2] > 1:\n print Fore.BLUE + ' %s %s %s' % ([items[0]], items[1], items[2])\n else:\n print str(items[1]), items[2]\n\n return result","def getallcontacts(self):\n feed_url = self.contacts_client.GetFeedUri(projection='full')\n total_read = 0\n while True:\n print('Retrieving contacts... (%d retrieved so far)' % total_read)\n feed = self.contacts_client.get_feed(uri=feed_url,\n auth_token=None,\n desired_class=gdata.contacts.data.ContactsFeed)\n total_read += len(feed.entry)\n for entry in feed.entry:\n yield entry\n next_link = feed.GetNextLink()\n if next_link is None:\n print('All contacts retrieved: %d total' % total_read)\n break\n feed_url = next_link.href","def listingsPage(city, stateCode=None):\n try:\n # Retrieve listings from Hoya database\n listings = list(db.listings.find({\"address\": {\"city\": city}}))\n\n # Retrieve listings from external (realtor) API\n url = os.getenv(\"API_URL\")\n\n querystring = {\n \"city\": city,\n \"limit\": \"20\",\n \"offset\": \"0\",\n \"state_code\": stateCode,\n \"sort\": \"relevance\",\n }\n\n headers = {\n \"x-rapidapi-key\": os.getenv(\"API_KEY\"),\n \"x-rapidapi-host\": os.getenv(\"API_HOST\"),\n }\n response = requests.request(\n \"GET\", url, headers=headers, params=querystring\n ).json()\n\n # For each listing in response[\"properties\"] (excluding metadata)\n # append to our listings list\n # given that our API returns inconsistent data, handle cases where keys\n # don't exist\n\n for prop in response[\"properties\"]:\n sqFootage = None\n if prop.get(\"lot_size\", None) is None:\n sqFootage = prop.get(\"building_size\", {}).get(\n \"size\", random.randint(900, 3400)\n )\n if prop.get(\"building_size\", None) is None:\n sqFootage = prop.get(\"lot_size\", {}).get(\n \"size\", random.randint(900, 3400)\n )\n\n listing = {\n \"_id\": prop.get(\"property_id\", ObjectId()),\n \"numBedrooms\": prop.get(\"beds\", random.randint(1, 5)),\n \"numBathrooms\": prop.get(\"baths\", random.randint(1, 5)),\n \"sqFootage\": sqFootage,\n \"price\": prop.get(\"price\", random.randint(230000, 800000)),\n \"address\": {\n # TODO: instead of None, pass in city, state from req.form\n \"city\": prop.get(\"address\", {}).get(\"city\", None),\n \"state\": prop.get(\"address\", {}).get(\"state\", None),\n \"zip\": prop.get(\"address\", {}).get(\"postal_code\", None),\n },\n }\n listings.append(listing)\n return render_template('listings.html', listings=listings)\n except (KeyError, ValueError):\n # Return custom 404 error page, set status code to 404\n # We use 404 here (rather than 500) because 404 means\n # \"resource not found\"\n return render_template(\"404.html\"), 404","def total_data(map_index, next_sse_index, ss_def, contacts_def):\n no_of_contacts = 0\n contacts_true = contacts_def.keys()\n start, end = ss_def[next_sse_index][3], ss_def[next_sse_index][4]\n for i in range(start, end + 1):\n if i in contacts_true:\n contacts = contacts_def[i]\n for contact in contacts:\n for index in map_index:\n tstart, tend = ss_def[index][3], ss_def[index][4]\n if contact in range(tstart, tend + 1):\n no_of_contacts += 1\n return no_of_contacts","def listing_count(self) -> int:\n return pulumi.get(self, \"listing_count\")","def get_all_contacts(self,\n hook,\n resource,\n data=None,\n headers=None,\n extra_options=None):\n all_pages = []\n total_contacts = -1\n next_token = None\n\n while len(all_pages) != total_contacts:\n if not next_token:\n result = hook.run('{}/contacts'.format(resource),\n data,\n headers,\n extra_options).json()\n else:\n result = hook.run('{}/contacts/{}'.format(resource, next_token),\n data,\n headers,\n extra_options).json()\n\n all_pages += result.get('contacts', None)\n\n total_contacts = result.get('total_contacts', None)\n\n if 'bookmark' in result:\n next_token = result.get('bookmark', None)\n\n return all_pages","def fetch_contacts(owner_account_id):\n resp = oauth.tapkey.get(f\"Owners/{owner_account_id}/Contacts?$select=id,identifier\")\n contacts = resp.json()\n return contacts","def extract_listings(self, soup, timestamp):\n\n listings = soup.find_all(\"div\", class_=\"around\")\n for listing_element in listings:\n title_div = listing_element.find(\"div\", class_=\"oTitle\")\n primary_div = listing_element.find(\"div\", class_=\"oOfferBody\")\n if not primary_div:\n self.logger.info(\"skipping entry:\" + repr(title_div.find(\"a\").text.strip()) + \"| Reason: No primary_div found.\")\n continue\n\n listing_meta_div = primary_div.find(\"td\", class_=\"oOfTextDetail\")\n vendor_div = listing_meta_div.find(\"div\", class_=\"oVendor\")\n\n item = {}\n item[\"market_name\"] = \"DreamMarket\"\n item[\"listing_name\"] = title_div.find(\"a\").text.strip()\n item[\"timestamp\"] = timestamp\n\n # starting at 1 to avoid the bitcoin icon\n item[\"listing_price_btc\"] = float(listing_meta_div.find(\"div\", class_=\"oPrice\").text.strip()[1:])\n item[\"listing_escrow\"] = primary_div.find(\"div\", class_=\"escrowBox\").text\n\n\n vendor_name = list(tag for tag in vendor_div.find_all(\"a\") if tag[\"href\"].startswith(\"./\"))[0].text.strip()\n vendor_transactions = int(vendor_div.find(\"span\", title=\"Successful transactions\").text.lstrip(\"(\").rstrip(\")\"))\n vendor_rating = vendor_div.find(\"span\", class_=\"userRating\")\n if vendor_rating:\n vendor_rating = float(vendor_rating.text.strip())\n else:\n vendor_rating = float(0)\n\n item[\"vendor_name\"] = vendor_name\n item[\"vendor_transactions\"] = vendor_transactions\n item[\"vendor_rating\"] = vendor_rating\n\n yield item","def ListAllContacts(self):\n feed = self.gd_client.GetContacts()\n self.contacts = self.CleanPhoneNumbers(self.GetContactsInfo(feed))\n return self.contacts","def get_contacts(self):\n contacts = Membership.objects.filter(entity = self, key_contact = True).order_by('importance_to_entity')\n return contacts","def _search(self, query_obj, num, after, reverse, count=0):\r\n builder = SearchBuilder(query_obj,\r\n after = after, num = num, reverse = reverse,\r\n count = count,\r\n wrap = ListingController.builder_wrapper)\r\n\r\n listing = LinkListing(builder, show_nums=True)\r\n\r\n # have to do it in two steps since total_num and timing are only\r\n # computed after fetch_more\r\n res = listing.listing()\r\n timing = time_module.time() - builder.start_time\r\n\r\n return builder.total_num, timing, res","def store_listings():\n\n scraped_listings = scrape_listings() # Used to store scraped listings\n valid_listings = [] # Used to store valid listings (after filtering)\n num_valid_listings = 1 # Counts the number of valid listings for output\n\n for listing in scraped_listings:\n # If the Listing ID of the current listing does not exist in the database...\n if session.query(DatabaseListing).filter_by(listing_id=listing.listing_id).first() is None:\n # Safe-guard distance variable (decimal)\n if listing.distance == \"n/a\":\n listing.distance = 0.0\n else:\n listing.distance = listing.distance[:-2]\n\n # Create listing object for database\n listing = DatabaseListing(\n created=listing.created,\n available=listing.available,\n listing_type=listing.listing_type,\n room_type=listing.room_type,\n address=listing.address,\n distance=listing.distance,\n sublet=listing.sublet,\n rooms=int(listing.rooms),\n features=listing.features,\n price=listing.price,\n listing_id=int(listing.listing_id),\n link=listing.link\n )\n\n # Save listing to database\n session.add(listing)\n session.commit()\n \n\n if (program_features.FILTER == True) and (filter_listings(listing) == False):\n print (\"Listing #{} does not match filter\".format(num_valid_listings))\n else:\n # Save valid listing (with ASCII encoding for Email server)\n valid_listings.append(\"\"\"Date Posted: {} | Available: {} | {} | Type: {}\n Address: {}\n Price: {} | Distance: {} | Sublet: {} | Rooms: {}\n Features: {}\n {}\"\"\".format(listing.created, listing.available,\n listing.listing_type, listing.room_type,\n listing.address, listing.price,\n (str(listing.distance) + \" km\"),\n listing.sublet, listing.rooms,\n str(listing.features.encode('utf-8')), listing.link))\n\n # Output success statement \n print(\"Listing #{} loaded\".format(num_valid_listings))\n \n # Increment valid listings counter\n num_valid_listings += 1\n \n # If the Listing ID of the current listing exists in the database...\n else:\n print(\"Listing already loaded\")\n\n return valid_listings # Return valid listings array","def Run(self):\n return self.ListAllContacts()","def test_list_referrals_by_desc_case_number(self):\n user = factories.UserFactory()\n # NB: set up a situation where case number and due date would yield different orders\n referrals = [\n factories.ReferralFactory(\n state=models.ReferralState.RECEIVED,\n post__users=[user],\n urgency_level=models.ReferralUrgency.objects.get(\n duration=timedelta(days=7)\n ),\n ),\n factories.ReferralFactory(\n state=models.ReferralState.RECEIVED,\n post__users=[user],\n urgency_level=models.ReferralUrgency.objects.get(\n duration=timedelta(days=1)\n ),\n ),\n ]\n\n self.setup_elasticsearch()\n response = self.client.get(\n f\"/api/referrallites/?user={user.id}&sort=case_number&sort_dir=desc\",\n HTTP_AUTHORIZATION=f\"Token {Token.objects.get_or_create(user=user)[0]}\",\n )\n\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response.json()[\"count\"], 2)\n self.assertEqual(response.json()[\"results\"][0][\"id\"], referrals[1].id)\n self.assertEqual(response.json()[\"results\"][1][\"id\"], referrals[0].id)","def test_amount_of_listings(self):\n listings = steam_market.get_total_amount_of_listings(soup=get_soup_from_path(TEST_FILE_NORMAL_LISTING))\n self.assertEqual(10, listings)","def _createContactListsCollections(folder):\n collections = [\n {'id': 'all_cls', 'tit': _('all_cls'), 'subj': (u'search', ), 'query': [\n {'i': 'portal_type',\n 'o': 'plone.app.querystring.operation.selection.is',\n 'v': ['contact_list']}],\n 'cond': u\"\", 'bypass': [],\n 'flds': (u'select_row', u'pretty_link', u'relative_path',\n u'review_state', u'CreationDate', u'actions'),\n 'sort': u'sortable_title', 'rev': False, 'count': False},\n ]\n _createDashboardCollections(folder, collections)","def index(request):\n # Attempt to map highest bid amount to each items' ID.\n di = {}\n for obj in Listing.objects.filter(is_active=True):\n try: \n di[obj.id] = sorted([bid.bid_amount for bid in obj.bids.all()])[-1]\n except:\n di[obj.id] = obj.starting_bid\n \n #print(di) - For Debugging \n\n # Return index template\n return render(request, \"auctions/index.html\", {\n \"listings\": Listing.objects.filter(is_active=True),\n \"di\": di\n })","def scrape_listings():\n scraped_listings = [] # Used to store apartment listings\n links = [] # Used to store links to apartment listings (seperate tag)\n\n # Download \"The Canon\" website\n URL = \"https://www.thecannon.ca/classifieds/housing\"\n headers = program_features.HEADERS\n page = requests.get(URL, headers=headers)\n\n # Parse document\n soup = BeautifulSoup(page.content, \"html.parser\")\n page_listings = list(soup.find_all('td')) # Find all listing information, and store as list\n\n # Used to find URL parameters for each apartment listing\n for link in soup.find_all(\"a\"):\n # URL Format Example: \"<a href=\"/page.php?cid=347306&id=26&t=housing\">1219 Gordon St, Guelph</a>\"\n if link.has_attr('href') and (\"t=housing\" in link.attrs['href']):\n links.append(\"https://www.thecannon.ca\" + link.attrs['href'])\n\n # Iterate list \n for i, listing in enumerate(page_listings, 1):\n # Group every 10 elements into a listing object\n if i % 10 == 0:\n index = int(i / 10) - 1 # Calculate index of link that matches the current listing \n\n # Append listing object to array\n scraped_listings.append(\n # Create listing object\n WebListing(\n page_listings[i - 10].get_text().strip(), # Date post was created\n page_listings[i - 9].get_text().strip(), # Date apartment is available\n page_listings[i - 8].get_text().strip(), # Offering type\n page_listings[i - 7].get_text().strip(), # Housing type\n page_listings[i - 6].get_text().strip(), # Address \n page_listings[i - 5].get_text().strip(), # Price\n page_listings[i - 4].get_text().strip(), # Distance \n page_listings[i - 3].get_text().strip(), # Sublet permission\n page_listings[i - 2].get_text().strip(), # Number of rooms\n page_listings[i - 1].get_text().strip(), # Features\n links[index][38:44], # Listing ID (stored in link)\n links[index] # Listing Link\n )\n )\n\n return scraped_listings # Return listings array","def listings_list():\n\n inputs = Listing.convert_inputs(request.args)\n form = ListingSearchForm(data=inputs)\n if form.validate():\n listings = Listing.find_all(inputs)\n serialized = [listing.serialize(\n isDetailed=False\n ) for listing in listings]\n return (jsonify(listings=serialized), 200)\n else:\n return (jsonify(errors=[\"Bad request\"]), 400)","def listing_messages(listing_id):\n Listing.query.get_or_404(listing_id)\n\n auth_username = get_jwt_identity()\n all_messages = Message.find_by_listing(listing_id, auth_username)\n print(\"ALL MESSAGES: \", all_messages)\n serialized = [message.serialize() for message in all_messages]\n return (jsonify(messages=serialized), 200)","def search_contact(request, **kwargs):\n limit = int(request.GET.get('limit', constants.DEFAULT_LIMIT))\n offset = int(request.GET.get('offset', constants.DEFAULT_OFFSET))\n search_term = request.GET.get('search_term')\n contact = private.Contact()\n data = contact.fetch_list(limit, offset, search_term)\n return JsonResponse({'objects': data})","def get_contacts(self, count=-1, excluded_guid=None):\n current_len = len(self._contacts)\n if current_len == 0 or count == 0:\n return []\n\n if count < 0:\n count = current_len\n else:\n count = min(count, current_len)\n\n if excluded_guid is None:\n # Get the last `count` contacts.\n contact_list = self._contacts[-count:]\n else:\n contact_list = []\n for contact in reversed(self._contacts):\n if contact.guid == excluded_guid:\n continue\n contact_list.append(contact)\n if len(contact_list) >= count:\n break\n return contact_list","def get_frequent_contacts(user, limit=5) -> typing.List[Contact]:\n contacts_frequent = (\n Contact.objects.filter(user=user)\n .annotate(count=Count(\"interactions\"))\n .order_by(\"-count\")[:limit]\n )\n return list(contacts_frequent)","def contact_info(self, sensitive=True):\n account_id = self.account_id()\n retry_count = 5\n\n req_url = self.get(\"/accounts/{}/contacts\".format(account_id))['ResultUrl']\n resp = self.get(req_url)\n tries = 0\n while 'Contacts' not in resp and tries < retry_count:\n resp = self.get(req_url)\n tries += 1\n time.sleep(1)\n contacts = resp['Contacts']\n\n contact_data = list()\n for contact in contacts:\n row_data = {\n 'ContactId': contact['Id'],\n 'Email': \"*****@****.***\" if sensitive else contact['Email'],\n 'FirstName': \"*****\" if sensitive else contact['FirstName'],\n 'LastName': \"*****\" if sensitive else contact['LastName'],\n 'Status': contact.get('Status'),\n 'MembeshipEnabled': contact.get('MembershipEnabled'),\n 'TermsOfUseAccepted': contact['TermsOfUseAccepted'],\n }\n\n if 'MembershipLevel' in contact:\n row_data['MembershipLevel'] = contact['MembershipLevel']['Name']\n\n # Map all field values into a dict for convenience\n field_values = {val['FieldName']: val['Value']\n for val in contact['FieldValues']}\n\n # Get list of authorizations\n if 'Managed Authorizations' in field_values:\n authorizations = [i['Label']\n for i in field_values['Managed Authorizations']]\n row_data['Authorizations'] = authorizations\n\n contact_data.append(row_data)\n self.__contact_df = pd.DataFrame(contact_data).set_index('ContactId')\n return self.__contact_df","def getListingLists(**kwargs):","def build_listing(self, num, after, reverse, count):\r\n self.num = num\r\n self.count = count\r\n self.after = after\r\n self.reverse = reverse\r\n\r\n if after is not None:\r\n self.robots = \"noindex,follow\"\r\n\r\n self.query_obj = self.query()\r\n self.builder_obj = self.builder()\r\n self.listing_obj = self.listing()\r\n content = self.content()\r\n res = self.render_cls(content = content,\r\n show_sidebar = self.show_sidebar,\r\n nav_menus = self.menus,\r\n title = self.title(),\r\n infotext = self.infotext,\r\n robots = self.robots,\r\n top_filter = self.top_filter,\r\n header_sub_nav = self.header_sub_nav,\r\n **self.render_params).render()\r\n return res","def count(listing):\n if 'meta' in listing and 'query_total' in listing['meta']:\n return listing['meta']['query_total']","def filter_listings(listing):\n\n MIN_PRICE = program_features.MIN_PRICE\n MAX_PRICE = program_features.MAX_PRICE\n\n MAX_DISTANCE = program_features.MAX_DISTANCE\n\n MIN_NUM_ROOMS = program_features.MIN_NUM_ROOMS\n MAX_NUM_ROOMS = program_features.MAX_NUM_ROOMS\n\n if (float(listing.price[1:4]) < MIN_PRICE) or (float(listing.price[1:4]) > MAX_PRICE):\n print (\"Listing price = {}\".format(listing.price[1:4], ))\n return False\n elif (float(listing.distance) > MAX_DISTANCE):\n print (\"Listing distance = {}\".format(float(listing.distance)))\n return False\n elif (int(listing.rooms) < MIN_NUM_ROOMS) or (int(listing.rooms) > MAX_NUM_ROOMS) :\n print (\"Listing rooms = {}\".format(int(listing.rooms)))\n return False\n else:\n return True","def getContactRoute(ss_def, contacts_def):\n control = 0 # to control the while loop until the length of all SSEs\n i = 0 # i, j are the indices of the SSE array\n j = 0\n map_route = []\n while control != len(ss_def) - 1:\n\n # [20, 64, 28, 24, 56, 28, 24, 40, 24, 24, 88] 11\n if control == 0:\n control += 1\n i, j = getTopRank(ss_def, contacts_def) # get the largest intra contact smotif\n map_route.append([i, j, 'start'])\n else:\n if i == 0:\n ti = j\n j += 1\n control += 1\n direction = 'right'\n map_route.append([ti, j, direction])\n elif j == len(ss_def) - 1: # -1 to deal with array index\n tj = i\n i -= 1\n control += 1\n direction = 'left'\n map_route.append([i, tj, direction])\n else:\n ti, tj, new_direction = nextSS(map_route, ss_def, contacts_def)\n control += 1\n map_route.append([ti, tj, new_direction])\n return map_route","def test_list_referrals_by_asc_case_number(self):\n user = factories.UserFactory()\n # NB: set up a situation where case number and due date would yield different orders\n referrals = [\n factories.ReferralFactory(\n id=4,\n state=models.ReferralState.RECEIVED,\n post__users=[user],\n urgency_level=models.ReferralUrgency.objects.get(\n duration=timedelta(days=7)\n ),\n ),\n factories.ReferralFactory(\n id=1989,\n state=models.ReferralState.RECEIVED,\n post__users=[user],\n urgency_level=models.ReferralUrgency.objects.get(\n duration=timedelta(days=1)\n ),\n ),\n ]\n\n self.setup_elasticsearch()\n response = self.client.get(\n f\"/api/referrallites/?user={user.id}&sort=case_number&sort_dir=asc\",\n HTTP_AUTHORIZATION=f\"Token {Token.objects.get_or_create(user=user)[0]}\",\n )\n\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response.json()[\"count\"], 2)\n self.assertEqual(response.json()[\"results\"][0][\"id\"], referrals[0].id)\n self.assertEqual(response.json()[\"results\"][1][\"id\"], referrals[1].id)","def getContactsData(service, groupResourceName, maxMembers):\n # get the ids of the contacts inside the specified group\n contactsIDs = service.contactGroups().get(\n resourceName=groupResourceName, \n maxMembers=maxMembers).execute()[\"memberResourceNames\"]\n\n # get data of the contacts that correspond to the ids obtained\n contactsData = service.people().getBatchGet(\n resourceNames=contactsIDs,\n personFields='names,emailAddresses').execute()[\"responses\"]\n\n # extract the names and the emailAddresses of the contacts\n namessList = [] \n mailsList = []\n for contact in contactsData:\n try:\n namessList.append(contact[\"person\"][\"names\"][0][\"displayName\"])\n except:\n raise Exception(\"All contacts must have a name associated\")\n mailsList.append(contact[\"person\"][\"emailAddresses\"][0][\"value\"])\n return namessList, mailsList","def test_get_filter_effective_contacts(self):\n data = {\"type_contact\": 1}\n response = self.client.get(reverse('contacts-filter'), data)\n # import pdb; pdb.set_trace()\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n self.assertEqual(response.data[\"count\"], 2)","def fetch_contact_messages(self, org, contact, created_after, created_before):\n pass","def getSorted(self):\n return sorted(self.contacts)","def recommend_n_listings(self, listing_id, request_filter, n=5):\n # precondition\n if n < 1:\n raise ValueError('Number of recommendations to compute must be at least 1')\n # get target listing\n target_listing = Listing_Repository().get_by_id(listing_id)\n if not target_listing:\n raise ValueError('No Listing found for given ID \"{}\"'.format(listing_id))\n # query building\n listing_query = Query \\\n .from_(self.listings) \\\n .join(self.hosts).on(self.listings.host_id == self.hosts.id)\\\n .select(\n # the relevant fields to compute row similarity (encode room_type)\n self.listings.id, self.listings.longitude, self.listings.latitude,\n MapRT(self.listings.room_type).as_('room_type'), self.listings.price,\n self.listings.min_nights, self.listings.num_of_reviews, self.listings.availability\n )\n # add filter criteria\n listing_query = Listing_Repository().add_where_clauses(listing_query, request_filter)\n # execute query\n listings_dict = self._execute_select_query(listing_query.get_sql())\n # compute recommendations and get their IDs\n rec_ids = self._compute_n_recommendations(listings_dict, target_listing, n)\n # fetch listings\n return Listing_Repository().get_all_by_id(rec_ids)","def find_duplicates_for_lead(lead, comparator_field):\n assert lead and comparator_field\n\n search_values = None\n if comparator_field == 'company':\n search_values = [lead['display_name']]\n else:\n comparator_field_plural = '{0}s'.format(comparator_field)\n search_values = [e[comparator_field]\n for c in lead['contacts']\n for e in c[comparator_field_plural]]\n\n # keep all values of the comparator field as a set for easier\n # computation of intersections below\n lead_elems = set(search_values)\n\n if not search_values:\n return []\n\n query = '{0} in ({1}) sort:date_created'.format(\n comparator_field,\n ', '.join('\"%s\"' % val.encode('utf-8') for val in search_values)\n )\n logger.debug('query = %s', query)\n\n duplicates = []\n has_more = True\n offset = 0\n while has_more:\n resp = api.get('lead', params={\n 'query': query,\n '_skip': offset,\n '_fields': 'id,display_name,status_label,contacts,opportunities'\n })\n leads = resp['data']\n logger.debug('Fetched %d of %d duplicate leads.', len(leads), resp['total_results'])\n\n # Add leads to our list of duplicates iff they are exact matches,\n # making sure to exclude the original lead from our duplicates\n for l in leads:\n if l['id'] == lead['id']:\n logger.debug('Removed lead %s from duplicate search results.', l['id'])\n continue\n\n if comparator_field == 'company':\n if lead['display_name'].strip().lower() == l['display_name'].strip().lower():\n duplicates.append(l)\n else:\n l_elems = set()\n for contact in l['contacts']:\n for elem in contact[comparator_field_plural]:\n l_elems.add(elem[comparator_field])\n\n logger.debug('%s (%s) %ss: [\"%s\"]', l['id'], l['display_name'], comparator_field, ','.join(l_elems))\n\n # if at least one of the phones/emails/etc. is a match,\n # consider this lead a duplicate\n intersection = lead_elems & l_elems\n if intersection:\n duplicates.append(l)\n\n offset += len(leads)\n has_more = resp['has_more']\n\n return duplicates","def topAuthors():\n c = db.cursor()\n c.execute(\"select name, sum(hits) as hits\\\n from authorhits group by name\\\n order by hits desc;\")\n results = c.fetchall()\n c.close()\n return results","def get_contacts_list(self):\n return [(id + 1, contact) for id, contact in enumerate(self.contact_list)]","def generate_dict_top_five(self):\n all_months = {}\n # Loops over each avilable non-duplicate date\n available_dates = self.contacts.quer_distinct_components(\"contact_date\", \"Contacts\")\n for date in available_dates:\n top_five = self.ordered[date]\n all_months[date] = {}\n\n # Loops over top five to append their components to the final dict\n for index in range(len(top_five)):\n listing_id = top_five[index][1]\n total_contacts = top_five[index][0]\n\n all_months[date].update(\n {\n index+1: {\n 'Ranking': index+1,\n 'Listing Id': listing_id,\n 'Make': self.listing.quer_component_using_column(\"make\", \"id\", \"Listings\", listing_id)[0][0],\n 'Selling Price': self.listing.quer_component_using_column(\"price\", \"id\", \"Listings\", listing_id)[0][0],\n 'Mileage': self.listing.quer_component_using_column(\"mileage\", \"id\", \"Listings\", listing_id)[0][0],\n 'Total Amount of Contacts': total_contacts\n }\n }\n )\n\n return all_months","def GetContactList(self):\n\t\tfeeds = []\n\t\tfeed = self.client.GetContacts()\n\t\tfeeds.append(feed)\n\t\tnext = feed.GetNextLink()\n\t\twhile next:\n\t\t\tfeed = self.client.GetContacts(uri=next.href)\n\t\t\tfeeds.append(feed)\n\t\t\tnext = feed.GetNextLink()\n\t\t\n\t\tcontacts = []\n\t\tfor feed in feeds:\n\t\t\tif not feed.entry:\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tfor i, entry in enumerate(feed.entry):\n\t\t\t\t\tcontacts.append(entry)\n\t\treturn contacts","def list_contacts(self):\n return self.contacts","def summarize_contacts_nodb(tac_clustered):\n rec_l = []\n for tacnum, cluster in tac_clustered.groupby('group'):\n rec = {'cluster': tacnum}\n \n # Start and stop of cluster\n rec['frame_start'] = cluster['frame'].min()\n rec['frame_stop'] = cluster['frame'].max()\n rec['duration'] = rec['frame_stop'] - rec['frame_start'] + 1\n \n # Mean tip and fol of cluster\n rec['tip_x'] = cluster['tip_x'].mean()\n rec['tip_y'] = cluster['tip_y'].mean()\n rec['fol_x'] = cluster['fol_x'].mean()\n rec['fol_y'] = cluster['fol_y'].mean()\n rec['pixlen'] = np.sqrt(\n (rec['tip_y'] - rec['fol_y']) ** 2 +\n (rec['tip_x'] - rec['fol_x']) ** 2)\n \n rec_l.append(rec)\n contacts_summary = pandas.DataFrame.from_records(rec_l).set_index('cluster')\n \n return contacts_summary","def stars_top_employee_lists(request, top_number, kind, id):\n try:\n if request.method == 'GET':\n if kind == 'category':\n top_list = Star.objects.filter(category__id=id).values(\n 'to_user__pk',\n 'to_user__username',\n 'to_user__first_name',\n 'to_user__last_name',\n 'to_user__level'\n 'to_user__avatar').annotate(num_stars=Count('to_user')).order_by('-num_stars')[:top_number]\n elif kind == 'keyword':\n top_list = Star.objects.filter(keyword__id=id).values(\n 'to_user__pk',\n 'to_user__username',\n 'to_user__first_name',\n 'to_user__last_name',\n 'to_user__level',\n 'to_user__avatar').annotate(num_stars=Count('to_user')).order_by('-num_stars')[:top_number]\n else:\n return Response(status=status.HTTP_412_PRECONDITION_FAILED)\n serializer = StarTopEmployeeLists(top_list, many=True)\n return Response(serializer.data, status=status.HTTP_200_OK)\n except Exception as e:\n raise APIException(e)","def find_top_salespeople(name_list, sales_list, n):\n top_names = []\n temp_name = [] + name_list\n temp_sales = [] + sales_list\n\n while len(top_names) < n and len(temp_sales) != 0: \n max_sales = temp_sales.index(max(temp_sales))\n num = temp_sales[max_sales]\n top_names.append(temp_name[max_sales])\n temp_name.remove(temp_name[max_sales])\n temp_sales.remove(temp_sales[max_sales])\n\n if num in temp_sales:\n max_sales2 = temp_sales.index(max(temp_sales))\n top_names.append(temp_name[max_sales2])\n temp_name.remove(temp_name[max_sales2])\n temp_sales.remove(temp_sales[max_sales2])\n\n return top_names","def get_recent_contacts(user, limit=5, timespan_days=14) -> typing.List[Contact]:\n timespan_recent = datetime.now().astimezone() - timedelta(days=timespan_days)\n contacts_recent = (\n Contact.objects.filter(interactions__was_at__gt=timespan_recent)\n .filter(user=user)\n .annotate(count=Count(\"interactions\"))\n .order_by(\"-count\")[:limit]\n )\n return list(contacts_recent)","def listing_show(listing_id):\n\n listing = Listing.query.get_or_404(listing_id)\n return (jsonify(listing=listing.serialize(isDetailed=True)), 200)","def contacts(self):\n return ContactCollection(self.request)","def refreshContacts(self):\n contact_ids = self._getAllContactIds()\n contacts = self._getContacts(contact_ids)\n\n self.contacts = [LineContact(self, contact) for contact in contacts]\n\n self.contacts.sort()","def searchNearbyHospitals(lat, lng, radius = 5000, limit_search_count = 10):\n # Initialising the GooglePlaces constructor \n google_places = GooglePlaces(GOOGLE_MAPS_API_KEY) \n \n query_result = google_places.nearby_search( \n lat_lng ={'lat': lat, 'lng': lng}, \n radius = radius, \n types =[types.TYPE_HOSPITAL]) \n \n nearby_hospitals = []\n\n for place in query_result.places[:limit_search_count]: \n hospital = dict()\n place.get_details()\n hospital['hospitalName'] = place.name\n hospital['address'] = place.formatted_address\n hospital['phoneNumber'] = place.local_phone_number \n nearby_hospitals.append(hospital)\n\n if(len(nearby_hospitals) == 0):\n nearby_hospitals = searchNearbyHospitals(lat = lat, lng = lng, radius = radius+5000)\n\n return nearby_hospitals","def get(self, request):\n listings = self.get_queryset().all().order_by(\"start_date\")\n username = None\n auth = request.user.is_authenticated\n if auth:\n username = request.user.username\n return render(request, 'listings/list.html', {'listings': listings,\n 'username': username,\n 'auth': auth})","async def get_contacts_for_contact_group(dbcon: DBConnection, contact_group_id: int) -> Iterable[object_models.Contact]:\n q = \"\"\"select\n contacts.id, contacts.name, contacts.email, contacts.phone, contacts.active\n from contact_group_contacts, contacts\n where contact_group_contacts.contact_group_id = %s\n and contact_group_contacts.contact_id = contacts.id\"\"\"\n return [object_models.Contact(*row) for row in await dbcon.fetch_all(q, (contact_group_id,))]","def main(token, customerid=None):\n\n # All availavle schedules to user\n all_schedules = [\"All the time\"]\n\n # Collects existin schedules from user's account\n cust_schedules = schedules.get_schedule(token, customerid=customerid)\n\n # Gets the names of the schedules\n for key, _value in cust_schedules.items():\n all_schedules.append(key)\n\n single_contacts = contacts.get_all(token, customerid=customerid)\n contactgroups = group_contacts.get_all(token, customerid=customerid)\n single_contacts.update(contactgroups)\n\n single_contacts = format_contacts(single_contacts)\n\n contact_info = choose_contacts(single_contacts, all_schedules)\n\n return contact_info","def update_contacts(self, contact_list):\n updated_contacts = 0\n request_list = list()\n\n # stale_contacts contains all old contacts at first, all current\n # contacts get then removed so that the remaining can get deleted\n stale_contacts = set(self.contacts)\n\n for contact in contact_list:\n c = Persona.query.get(contact[\"id\"])\n\n if c is None:\n c = Persona(id=contact[\"id\"], _stub=True)\n\n if c._stub is True:\n request_list.append(contact[\"id\"])\n\n try:\n # Old and new contact; remove from stale list\n stale_contacts.remove(c)\n except KeyError:\n # New contact\n self.contacts.append(c)\n updated_contacts += 1\n\n # Remove old contacts that are not new contacts\n for contact in stale_contacts:\n self.contacts.remove(contact)\n\n app.logger.info(\"Updated {}'s contacts: {} added, {} removed, {} requested\".format(\n self.username, updated_contacts, len(stale_contacts), len(request_list)))\n\n return request_list","def get_queryset(self):\n contact_data = Contact.objects.filter(contact_groups__in=Member.objects.filter(\n user=self.request.user).values('group_id').distinct())\n\n return contact_data","def contact_count(self, *args, **kwargs) -> Any:\n pass","def display_contact(self):\n contacts = \"\".join(str(contact) for contact in self.contact_list)\n print(contacts)","def test_get_contact_lists(self):\n url, parsed = self.prepare_urls('v1:contact_list-list', subdomain=self.company.subdomain)\n \n response = self.client.get(url, HTTP_HOST=parsed.netloc)\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\n\n self.authenticate_user()\n response = self.client.get(url, HTTP_HOST=parsed.netloc)\n self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n content = json.loads(response.content)\n self.assertEqual(len(content), self.contact_lists_count)","def _get_findings(scc, source):\n request = ListFindingsRequest(\n parent=source,\n )\n findings_response = scc.list_findings(request=request)\n findings_dict = {}\n for fr in findings_response:\n finding = fr.finding\n finding_id = finding.name.split('/')[5]\n findings_dict[finding_id] = finding\n return findings_dict","def TopCheckins(results, search_term):\n top_five = Counter([row[1] for row in results]).most_common()[:5]\n if search_term == 'total':\n search_term = 'checkin'\n (commander, count) = top_five.pop(0)\n text = 'The %s %s is %s with %s checkins.\\n' % (\n search_term.title(), FoodRank(search_term), commander, count)\n text += 'Other %s fans include: ' % search_term\n if len(top_five) > 1:\n for eater in top_five[:-1]:\n text += '%s with %s, ' % eater\n text += 'and %s with %s.' % top_five[-1]\n elif len(top_five) == 1:\n text += '%s with %s.' % top_five[0]\n else:\n text += 'No one.'\n return text","def display_by_category(request, category):\n if request.method == \"POST\":\n form = SearchForm(request.POST)\n if form.is_valid():\n products = AuctionListing.objects.filter(buyer=None).filter(\n category__iexact=category).filter(title__icontains=form.cleaned_data[\"query\"])\n else:\n products = AuctionListing.objects.filter(buyer=None).filter(\n category__iexact=category)\n else:\n products = AuctionListing.objects.filter(buyer=None).filter(\n category__iexact=category)\n form = None\n bids = []\n for product in products:\n bid = product.bids.all().aggregate(Max(\"bid\")).get(\"bid__max\")\n bids.append(bid)\n return render(request, \"auctions/index.html\", {\n \"zip_products_bids\": zip(products, bids),\n \"category\": category,\n \"form\": form,\n \"title\": \"Active Listing\",\n })","def get_listings(drug, page):\n file_number = (page-1) * 25\n html = open('%s_data/%s.html' % (drug, file_number)).read()\n soup = BeautifulSoup(html, \"lxml\")\n\n container = soup.findAll(\"div\", id=\"cat_item\")\n \n items = []\n for item in container:\n title_elem = item.find(class_=\"h2\")\n title = title_elem.text\n if \"lottery\" in title.lower():\n # ignore lottery listings because they mess up the average price\n continue\n try:\n quantity = guess_quantity(title)\n except:\n print \"failed:\", title \n \n if not quantity:\n continue\n\n url = title_elem.attrs['href']\n price = item.find(class_=\"price_big\").text[1:] # remove bitcoin glyph\n price = float(price.replace(',', ''))\n seller = item.find(\"a\", href=re.compile(\"/silkroad/user/\")).text\n thumb_e = item.find(\"img\", src=re.compile(\"^data:\"))\n thumb = (thumb_e and thumb_e.attrs['src']) or \"\"\n ships_from = [x for x in item.find(id=\"cat_item_description\").children][8]\n country = ships_from[12:]\n\n items.append({\n 'title': title,\n \"seller\": seller,\n \"price\": price,\n \"quantity\": quantity,\n \"url\": url,\n \"country\": silkroad_country_to_iso(country),\n \"thumb\": uri_to_file(thumb),\n })\n\n print \"parsed page: %s (%s.html)\" % (page, file_number)\n return items","def view_contacts(self):\n with open(self.filename, \"r\") as contactsFile:\n contacts = self.display_contact(contactsFile.readlines())\n\n if not contacts:\n return self.msgbox(\"No contacts found.\")\n\n self.msgbox(msg=\"\\n\".join(contacts), title=\"Showing All Contacts\")","def get_listings_for_city(self, city, per_month = True):\n assert(isinstance(city, str) and city in self.allcities)\n\n if city in self.us_cities:\n print(f\"City {city} is in country United States .\")\n elif city in self.global_cities:\n print(f\"City {city} is in country {self.city_to_country_map[city]}\")\n \n listing_file_path = os.path.join(config_data.AIRBNB_DATA_DIR,f\"{city}/listings.csv\")\n assert(os.path.isfile(listing_file_path)),f\"file not found at {review_file_path}!\"\n \n df = pd.read_csv(listing_file_path)\n if df.columns[0] == \"date\" and df.columns[1] ==\"n_listings\":\n pass\n else:\n print(f\"{listing_file_path} is not the correct file. Please double check. \")\n\n # preprocess the reviews\n df.date = pd.to_datetime(df.date)\n df['month'] = df.date.dt.month\n df['year'] = df.date.dt.year\n df['week'] = df.date.dt.isocalendar().week\n\n# if per_month:\n# num_listings = df\n# else:\n# num_listings = df.groupby([lambda x: x.year]).agg([sum])\n num_listings = df.set_index([\"date\"])\n num_listings = num_listings.sort_index()\n return num_listings","def sort_contacts(contacts):\n \n key_list = list(contacts.keys()) #get keys\n key_list.sort() #sort key_list\n sorted_list = [] #initialize sorted list\n for key in key_list:\n contact = (key, contacts[key][0], contacts[key][1]) #create tuple\n sorted_list += [contact] #add tuple to list\n \n return(sorted_list)","def related_reports(self):\n return Report.objects.exclude(contact_email__isnull=True).filter(contact_email__iexact=self.contact_email).order_by('status', '-create_date')[:1000]","def get(self, request):\n username = None\n user = None\n auth = request.user.is_authenticated\n if auth:\n user = request.user\n username = request.user.username\n listings = self.get_queryset().filter(guest=user)\\\n .order_by(\"start_date\")\n\n return render(request, 'listings/booked.html', {'listings': listings,\n 'username': username,\n 'auth': auth})","def show_contacts():\n data_list = queries2.contacts()[0]\n table_titles = queries2.contacts()[1]\n title = \"Contacts\"\n return render_template('pages.html', data_list=data_list, title=title, table_titles=table_titles)","def truncate_contact_list(self, LibraryID, ListID):\n list_of_contacts = self.getListContacts(LibraryID=LibraryID, ListID=ListID)\n failures = []\n if list_of_contacts:\n for contact in list_of_contacts:\n RecipientID = contact['RecipientID']\n try:\n self.removeContact(LibraryID=LibraryID, ListID=ListID, RecipientID=RecipientID)\n except Exception as e:\n # print e\n failures.append(RecipientID)\n if failures:\n return True, []\n else:\n return False, failures","def test_amount_of_listings_no_listings(self):\n listings = steam_market.get_total_amount_of_listings(soup=get_soup_from_path(TEST_FILE_NO_RESULTS))\n self.assertEqual(0, listings)","def how_many_comments(comment_queryset, count=10):\n\n if count == \"all\":\n return comment_queryset\n return comment_queryset[:int(count)]","def scrape_details(self, listings_dict, url):\n try:\n next_page_url = None\n if \"paginationNext\" in listings_dict['props']['_page']['linkTags']:\n next_page_url = listings_dict['props']['_page']['linkTags']['paginationNext']['href']\n listings = listings_dict['props']['searchData']['homes']\n for listing in listings:\n try:\n full_address = listing['location']['partialLocation']\n address, unitNum = find_unit_num(full_address)\n if address == \"Address Not Disclosed\":\n skip_listing(self.college, 'data', 'Trulia')\n continue\n full_price = listing['price']['formattedPrice']\n price_low, price_high = find_prices(full_price)\n beds = listing['bedrooms']['formattedValue']\n beds = ''.join([x for x in beds if x.isdigit()])\n if beds:\n if '-' in beds:\n beds = int(beds[:beds.find('-')])\n else:\n beds = int(beds)\n else:\n beds = None\n baths = listing['bathrooms']['formattedValue']\n baths = ''.join([x for x in baths if not x.isalpha()])\n if baths:\n if '-' in baths:\n baths = float(baths[:baths.find('-')])\n else:\n baths = float(baths)\n else:\n baths = None\n sqft = None\n if 'floorSpace' in listing and listing['floorSpace']:\n sqft = listing['floorSpace']['formattedDimension']\n sqft = int(''.join([x for x in sqft if x.isdigit()])) if sqft else None\n tags = listing['tags']\n pets = None\n for tag in tags:\n if \"PET FRIENDLY\" in tag.values():\n pets = True\n photos = listing['media']['photos']\n images = list()\n for photo in photos:\n images.append(photo['url']['small'])\n detail_link = 'https://www.trulia.com' + listing['url']\n latitude = listing['location']['coordinates']['latitude']\n longitude = listing['location']['coordinates']['longitude']\n # Build document for DB\n unit = {\n 'address': address,\n 'unitNum': unitNum,\n 'price_high': price_high,\n 'price_low': price_low,\n 'beds': beds,\n 'baths': baths,\n 'pets': pets,\n 'sqft': sqft,\n 'provider': 'Trulia',\n 'images': images,\n 'URL': detail_link,\n 'original_site': None,\n 'available': 'Now',\n 'latitude': latitude,\n 'longitude': longitude\n }\n write_to_raw_json(unit, self.college)\n\n # Print Scraping errors and write to log file\n except Exception as e:\n write_to_error_log(self.college, 'Trulia', e, link=url)\n skip_listing(self.college, 'error', 'Trulia')\n continue\n\n except Exception as e:\n write_to_error_log(self.college, 'Trulia', e, link=url)\n skip_listing(self.college, 'error', 'Trulia')\n\n return next_page_url","def display_search_info(client, listing_id):\n try:\n resp = client.get_listing_info(listing_id)\n except exceptions.ServerRequestError as e:\n if e.status_code == 404:\n logger.info(uxstring.UxString.app_does_not_exist.format(listing_id))\n return\n else:\n raise e\n result_json = resp.json()\n\n title = click.style(\"App Name : \", fg=\"blue\") + click.style(\n \"{}\".format(result_json[\"title\"]))\n created_by = click.style(\"Created By : \", fg=\"blue\") + click.style(\n \"{}\".format(result_json[\"username\"]))\n\n desc = click.style(\"Description : \", fg=\"blue\") + click.style(\n \"{}\".format(result_json[\"description\"]))\n price = click.style(\"Price Range : \", fg=\"blue\") + click.style(\n \"{} - {} Satoshis\").format(result_json[\"min_price\"], result_json[\"max_price\"])\n\n if result_json[\"rating_count\"] == 0:\n rating_str = \"Not yet rated\"\n else:\n rating_str = \"{:.1f} ({} rating\".format(result_json[\"average_rating\"],\n int(result_json[\"rating_count\"]))\n if result_json[\"rating_count\"] > 1:\n rating_str += \"s\"\n rating_str += \")\"\n rating = click.style(\"Rating : \", fg=\"blue\") + click.style(\"{}\".format(rating_str))\n\n doc_url = click.style(\"Docs URL : \", fg=\"blue\") + click.style(\n \"{}\".format(result_json[\"website_url\"]))\n app_url = click.style(\"App URL : \", fg=\"blue\") + click.style(\n \"{}\".format(result_json[\"app_url\"]))\n category = click.style(\"Category : \", fg=\"blue\") + click.style(\n \"{}\".format(result_json[\"category\"]))\n version = click.style(\"Version : \", fg=\"blue\") + click.style(\n \"{}\".format(result_json[\"version\"]))\n last_updated_str = util.format_date(result_json[\"updated\"])\n last_update = click.style(\"Last Update : \", fg=\"blue\") + click.style(\n \"{}\".format(last_updated_str))\n quick_start = click.style(\"Quick Start\\n\\n\", fg=\"blue\") + click.style(\n result_json[\"quick_buy\"])\n is_active = click.style(\"Status : \", fg=\"blue\")\n if result_json[\"is_active\"] and result_json[\"is_up\"] and result_json[\"is_healthy\"]:\n is_active += click.style(\"Active\")\n else:\n is_active += click.style(\"Inactive\")\n\n availability = click.style(\"Availability : \", fg=\"blue\") + click.style(\n \"{:.2f}%\".format(result_json[\"average_uptime\"] * 100))\n\n usage_docs = None\n if \"usage_docs\" in result_json:\n usage_docs = click.style(\"Detailed usage\\n\\n\", fg=\"blue\") + click.style(\n result_json[\"usage_docs\"])\n\n pager_components = [title, desc, created_by, price, rating, \"\\n\",\n is_active, availability, \"\\n\",\n doc_url, app_url, \"\\n\",\n category, version, last_update, \"\\n\",\n quick_start, \"\\n\"]\n\n if usage_docs:\n pager_components.append(usage_docs + \"\\n\")\n\n final_str = \"\\n\".join(pager_components)\n\n logger.info(final_str, pager=True)","def get_postings(next_posting_id):\n connection = sqlite.connect(\"postings.db\")\n cursor = connection.cursor()\n\n query = []\n query.append(\"SELECT id,raw_post FROM postings\")\n if next_posting_id != None:\n query.append(\"WHERE id > %s\" % next_posting_id)\n query.append(\"ORDER BY id\")\n query.append(\"LIMIT %s\" % POSTING_CHUNK_SIZE)\n query = \" \".join(query)\n\n cursor.execute(query)\n\n results = []\n for row in cursor:\n id,raw_post = row\n\n posting = json.loads(raw_post)\n\n results.append((id, posting))\n\n return results","def get_comments_by_country(pages, hotel, country):\n url = \"http://www.booking.com/reviewlist.es.html\"\n headers = {\n 'User-Agent': \"PostmanRuntime/7.20.1\",\n 'Accept': \"*/*\",\n 'Cache-Control': \"no-cache\",\n 'Postman-Token': \"4b4e2c78-12c0-42a7-807a-29f5f7378ae5,e75b58fb-25dd-4fdd-b97a-47650ed52d41\", # NOQA\n 'Host': \"www.booking.com\",\n 'Accept-Encoding': \"gzip, deflate\",\n 'Cookie': \"bkng=11UmFuZG9tSVYkc2RlIyh9Yaa29%2F3xUOLbca8KLfxLPeck0I1eO54zQUW2YGGgHUJ6NVSV%2BmLwJzaS5ibHX0J%2BdueF6GNDCq1X0NvEJAU9t%2FoaAC2%2FMBm39Gz0lTSWuf6zuBVIiNGAI88YDjaj4w5H8Lrv7T0Yug9jg%2FpPsONkdMVLMiYifIslIsLvFl07K%2BTKGRykCAxOsgE%3D\", # NOQA\n 'Connection': \"keep-alive\",\n 'cache-control': \"no-cache\"\n }\n\n params = {\n 'cc1': country,\n 'pagename': hotel,\n 'type': 'total',\n 'dist': str(1),\n 'rows': str(20)\n }\n\n def build_soup_comment_request(page: int, list_of_countries):\n if page == 0:\n params['offset'] = str(page)\n else:\n params['offset'] = str(page * 20)\n\n response = requests.get(url=url, params=params, headers=headers)\n comments_soup = BeautifulSoup(response.content, 'html.parser')\n span = comments_soup.select('.bui-avatar-block__flag img')\n [get_flags(item, list_of_countries) for item in span]\n\n countries_list = {}\n [build_soup_comment_request(page, countries_list) for page in range(pages)]\n return countries_list","def update_contacts(self):\n self.contacts = self.db.list_contacts()\n return self.list_contacts()","def pull_contacts(self, org, modified_after, modified_before, progress_callback=None):\n pass","def test_list_referrals_by_desc_object(self):\n user = factories.UserFactory()\n referrals = [\n factories.ReferralFactory(\n state=models.ReferralState.RECEIVED,\n object=\"First by alphabetical order\",\n post__users=[user],\n urgency_level=models.ReferralUrgency.objects.get(\n duration=timedelta(days=1)\n ),\n ),\n factories.ReferralFactory(\n state=models.ReferralState.RECEIVED,\n object=\"Second by alphabetical order\",\n post__users=[user],\n urgency_level=models.ReferralUrgency.objects.get(\n duration=timedelta(days=1)\n ),\n ),\n ]\n\n self.setup_elasticsearch()\n response = self.client.get(\n f\"/api/referrallites/?user={user.id}&sort=object.keyword&sort_dir=desc\",\n HTTP_AUTHORIZATION=f\"Token {Token.objects.get_or_create(user=user)[0]}\",\n )\n\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response.json()[\"count\"], 2)\n self.assertEqual(response.json()[\"results\"][0][\"id\"], referrals[1].id)\n self.assertEqual(response.json()[\"results\"][1][\"id\"], referrals[0].id)","def get_contacts(self):\n\n\t\treturn self.__contacts","def top_sources_female(args: Dict[str, Any]) -> List[object]:\n query = [\n {\n \"$match\": {\n \"body\": {\"$ne\": \"\"},\n \"quotesUpdated\": {\"$exists\": True},\n \"outlet\": {\"$in\": args[\"outlets\"]},\n \"publishedAt\": {\n \"$gte\": args[\"begin_date\"],\n \"$lt\": args[\"end_date\"] + timedelta(days=1),\n },\n }\n },\n {\"$project\": {\"outlet\": 1.0, \"sourcesFemale\": 1.0}},\n {\"$unwind\": {\"path\": \"$sourcesFemale\", \"preserveNullAndEmptyArrays\": False}},\n {\"$group\": {\"_id\": \"$sourcesFemale\", \"count\": {\"$sum\": 1.0}}},\n {\"$sort\": {\"count\": args[\"sort\"]}},\n {\"$limit\": args[\"limit\"]},\n ]\n return query","def listing_id(self, listing_id):\n\n self._listing_id = listing_id","def calcRestaurantList2(latlngs, cuisines, distance):\n restlist = []\n used = []\n cuisine = str(cuisines[0])\n if len(cuisines) > 1:\n cuisine = \",\".join(cuisines)\n minrating = 5.0\n worst = ''\n ratings = []\n for point in latlngs:\n yelpresults = search2(cuisine,point,distance)['businesses']\n processedyelpresults = processResults(yelpresults)\n for result in processedyelpresults:\n if (result not in used):\n if len(restlist) < 40:\n restlist.append(processedyelpresults[result])\n used.append(result)\n ratings.append(float(processedyelpresults[result]['rating']))\n if float(processedyelpresults[result]['rating']) < minrating:\n minrating = float(processedyelpresults[result]['rating'])\n worst = result\n # print (\"The worst restaurant is {0}\".format(worst))\n elif len(restlist) >= 40:\n ratings.sort()\n minrating = ratings[0]\n if float(processedyelpresults[result]['rating']) > ratings[0]:\n if worst in restlist:\n ratings.remove(minrating)\n restlist.remove(restlist.index(worst))\n # print (\"Removed {0}, which had a rating of {1}. It was in restlist\".format(worst, minrating))\n if len(restlist) <= 45:\n restlist.append(processedyelpresults[result])\n # print (\"Added {0}, which had a rating of {1}\".format(result, processedyelpresults[result]['rating']))\n else:\n minrating = float(ratings[0])\n # print (\"The minimum rating for a restaurant is {0}\".format(minrating))\n for r in restlist:\n # print (r)\n if float(r['rating']) == minrating:\n restlist.remove(r)\n # print (\"Removed {0}, which had a rating of {1}. Matched on minrating\".format(r, minrating))\n if minrating in ratings:\n ratings.remove(minrating)\n if len(restlist) <= 45:\n restlist.append(processedyelpresults[result])\n # print (\"Added {0}, which had a rating of {1}\".format(result, processedyelpresults[result]['rating']))\n\n # pprint.pprint(restlist)\n # print(used)\n\n return restlist","def contact_list(self):\n return self._contact_list","def view_shoppinglists(current_user):\n\n limit = request.args.get('limit', 10)\n q = request.args.get('q', None)\n page = int(request.args.get('page', 1))\n\n results = []\n shoplists = ShoppingList.query.filter_by(\n user_id=current_user.id)\n\n if q is not None:\n q = q.lower()\n shoplists = shoplists.filter(\n ShoppingList.name.like(\"%\" + q.strip() + \"%\"))\n # search_count = ShoppingList.query.filter_by(\n # user_id=current_user.id).filter(\n # ShoppingList.name.like(\"%\" + q.strip() + \"%\")).count()\n # # print('search: '+search_count)\n\n\n if limit:\n try:\n if int(limit):\n shoplists = shoplists.filter_by(\n user_id=current_user.id).paginate(page=page,\n per_page=int(\n limit), error_out=False).items\n\n for shoplist in shoplists:\n results.append(shoplist.json())\n\n if len(results) == 0:\n return response('failed', 'Shopping list not found', 404)\n return get_response('ShoppingLists', results, count=ShoppingList.query.filter_by(\n user_id=current_user.id).count(), page=page, limit=limit)\n\n except ValueError:\n return response('failed', 'Limit should be an integer', 400)\n\n for shoplist in shoplists.all():\n results.append(shoplist.json())\n\n if len(results) == 0:\n print(len(results))\n return response('failed', 'Shopping list not found', 404)\n return get_response('ShoppingLists', results, count=ShoppingList.query.filter_by(\n user_id=current_user.id).count, page=page, limit=limit)","def sorted(self):\n return (\n self.get_queryset().annotate(\n null_member=models.Count('member'),\n ).order_by('-null_member', 'member')\n )","def count_backbone_contacts(fuzzball_path):\n print(fuzzball_path)\n\n fuzzball = prody.loadAtoms(fuzzball_path)\n fuzzball_hv = fuzzball.getHierView()\n ligand = fuzzball_hv['X', 1]\n ligand_resname = ligand.getResname()\n\n constraints = Generate_Constraints(ligand_resname)\n\n motif_count = 0\n bb_contacts = 0\n\n for index, motif in enumerate(fuzzball_hv.iterResidues(), start=1):\n if index != 1:\n constraint_dict = constraints.determine_constraint_atoms(motif, ligand)\n if constraint_dict is False: continue\n if constraint_dict['residue']['atom_names'][0] in ['C', 'CA', 'O', 'N']:\n bb_contacts += 1\n motif_count += 1\n\n return {'motifs': motif_count,\n 'bb_contacts': bb_contacts,\n 'fuzzball': fuzzball_path}","def show_listings(offset):\n items = Item.query.filter(Item.status == \"listed\").order_by(desc(Item.date_listed)).offset(offset).limit(LIMIT).all()\n return jsonify(data=[item.serialize for item in items])\n #return render_template('primary_user_interface.html', items=items)","def get_nearest_films(films_list, number, input_location):\n output_list = []\n for film_data in films_list:\n film_dist = int(distance.distance(film_data[1], input_location).km)\n film_data.append(film_dist)\n output_list.append(film_data)\n output_list.sort(key=lambda x: x[-1])\n if len(output_list) >= int(number):\n output_list.pop()\n dist_list = [film[-1] for film in output_list]\n print(f'Closest film distance: {dist_list[0]} km.')\n print(f'Furthest film distance: {dist_list[-1]} km.')\n return output_list","def contacts(self):\n if \"contacts\" in self._prop_dict:\n return ContactsCollectionPage(self._prop_dict[\"contacts\"])\n else:\n return None","def contacts_in_route_count(self):\n subprods = SubscriptionProduct.objects.filter(route=self).distinct('subscription__contact')\n return subprods.count","def render_todays_listings(request, context, callsigns):\n context['listings_matrix'] = []\n for callsign in callsigns:\n whats_on_today_url = settings.SODOR_ENDPOINT + 'tvss/' + callsign + '/today/'\n data = requests.get(whats_on_today_url, headers={'X-PBSAUTH': settings.TVSS_KEY})\n\n if data.status_code == 200:\n jd = data.json()\n # have to loop through and covert the goofy timestamps into datetime objects\n for f in jd['feeds']:\n for l in f['listings']:\n l['start_time_obj'] = datetime.datetime.strptime(l['start_time'], \"%H%M\")\n l['callsign'] = callsign\n context['listings_matrix'].append(jd)\n return context","def top_sources_all(args: Dict[str, Any]) -> List[object]:\n query = [\n {\n \"$match\": {\n \"body\": {\"$ne\": \"\"},\n \"quotesUpdated\": {\"$exists\": True},\n \"outlet\": {\"$in\": args[\"outlets\"]},\n \"publishedAt\": {\n \"$gte\": args[\"begin_date\"],\n \"$lt\": args[\"end_date\"] + timedelta(days=1),\n },\n }\n },\n {\n \"$project\": {\n \"outlet\": 1,\n \"sourcesMale\": 1,\n \"sourcesFemale\": 1,\n \"allSources\": {\n \"$concatArrays\": [\n {\"$ifNull\": [\"$sourcesFemale\", []]},\n {\"$ifNull\": [\"$sourcesMale\", []]},\n ]\n },\n }\n },\n {\"$unwind\": {\"path\": \"$allSources\", \"preserveNullAndEmptyArrays\": False}},\n {\"$group\": {\"_id\": \"$allSources\", \"count\": {\"$sum\": 1.0}}},\n {\"$sort\": {\"count\": args[\"sort\"]}},\n {\"$limit\": args[\"limit\"]},\n ]\n return query"],"string":"[\n \"def _get_listings(self):\\n listing_ids = self.context.listing_ids\\n if len(listing_ids) == 0:\\n return\\n listing_ids = [lid.lower() for lid in listing_ids]\\n params = {\\n 'limit': 0,\\n 'offset': 0,\\n 'lang': self.portal_state.language(),\\n }\\n params.update({\\n 'listing_ids': listing_ids,\\n })\\n params = prepare_search_params(params)\\n results = search(params, batching=False, context=self.context)\\n if results is None or len(results) == 0:\\n return\\n\\n # sort the results based on the listing_ids\\n results = [(item['id']['value'], item) for item in results]\\n results = dict(results)\\n return [results.get(id) for id in listing_ids if id in results]\",\n \"def _get_listings(self):\\n listing_ids = self.config.get('listing_ids', [])\\n if len(listing_ids) == 0:\\n return\\n listing_ids = [lid.lower() for lid in listing_ids]\\n params = {\\n 'limit': 0,\\n 'offset': 0,\\n 'lang': self.portal_state.language(),\\n }\\n params.update(self.config)\\n params['listing_ids'] = listing_ids\\n params = prepare_search_params(params)\\n results = search(params, batching=False, context=self.context)\\n if results is None or len(results) == 0:\\n return\\n\\n # sort the results based on the listing_ids\\n results = [(item['id']['value'], item) for item in results]\\n results = dict(results)\\n self._listings = [\\n results.get(id) for id in listing_ids if id in results\\n ]\",\n \"def get_all(self):\\n total_contacts = []\\n get_count = {\\n 'query': {\\n 'object': 'CONTACT',\\n 'select': {\\n 'field': 'RECORDNO'\\n },\\n 'pagesize': '1'\\n }\\n }\\n\\n response = self.format_and_send_request(get_count)\\n count = int(response['data']['@totalcount'])\\n pagesize = 2000\\n offset = 0\\n for i in range(0, count, pagesize):\\n data = {\\n 'query': {\\n 'object': 'CONTACT',\\n 'select': {\\n 'field': [\\n 'RECORDNO',\\n 'CONTACTNAME',\\n 'COMPANYNAME',\\n 'FIRSTNAME',\\n 'LASTNAME',\\n 'INITIAL',\\n 'PRINTAS',\\n 'TAXABLE',\\n 'MAILADDRESS.ADDRESS1'\\n ]\\n },\\n 'pagesize': pagesize,\\n 'offset': offset\\n }\\n }\\n contacts = self.format_and_send_request(data)['data']['CONTACT']\\n total_contacts = total_contacts + contacts\\n offset = offset + pagesize\\n return total_contacts\",\n \"def getTopRank(ss_def, contacts_def):\\n rank_seq = {}\\n contacts_true = contacts_def.keys()\\n for i in range(1, len(ss_def)):\\n smotif = getSmotif(ss_def[i - 1], ss_def[i])\\n no_of_contacts = 0\\n for j in range(smotif[0][0], smotif[0][1] + 1):\\n if j in contacts_true:\\n contacts = contacts_def[j]\\n for contact in contacts:\\n if contact in range(smotif[1][0], smotif[1][1] + 1):\\n no_of_contacts += 1\\n\\n rank_seq[i - 1] = no_of_contacts\\n top_rank = getTopSmotif(rank_seq)\\n\\n return top_rank, top_rank + 1\",\n \"def getcontacts():\\n contacts = {}\\n\\n try:\\n #get list of contact ids\\n contactids = r.smembers(\\\"contacts\\\")\\n\\n #for each contact id get data\\n for contactid in contactids:\\n contacts.update(_getcontact(str(contactid)))\\n return contacts\\n except:\\n print \\\"Unexpected error:\\\", sys.exc_info()[0]\\n raise\",\n \"def pull_listings(zipcode=4581, propertyType=4, minyear = None, category = 'listings', write = False, date = True, silent = False):\\n if category == 'listings':\\n apiurl = \\\"https://api.boliga.dk/api/v2/search/results\\\"\\n elif category == 'sold' or category == 'bbrsearch':\\n apiurl = \\\"https://api.boliga.dk/api/v2/sold/search/results\\\"\\n else:\\n print('Nonvalid category - use \\\"listings\\\", \\\"sold\\\" or \\\"bbrsearch\\\"')\\n return None\\n results = []\\n page = 1\\n totalpages = 1\\n parameters = {'sort': 'date-d',\\n 'zipcodeFrom': zipcode,\\n 'zipcodeTo': zipcode,\\n 'street': ''}\\n if category == 'bbrsearch':\\n parameters['bbrsearch'] = 1\\n parameters['sort'] = 'date-a'\\n else:\\n parameters['searchTab'] = 1\\n if propertyType != None:\\n parameters['propertyType'] = propertyType\\n if minyear != None:\\n parameters['salesDateMin'] = minyear\\n write = False\\n while page <= totalpages:\\n parameters['page'] = page\\n response = requests.get(apiurl, params=parameters)\\n if response.status_code != 200:\\n print(\\\"Page {}: status_code {} - aborting!\\\".format(page, response.status_code))\\n break\\n totalpages = response.json()['meta']['totalPages']\\n if not silent:\\n print(\\\"Page {} of {}\\\\n{}\\\".format(page, totalpages, response.url))\\n if totalpages >= 3600:\\n print('Totalpages {}, aborting!'.format(totalpages))\\n break\\n results.extend(response.json()['results'])\\n page += 1\\n time.sleep(1)\\n if write:\\n if date:\\n filename = category + '/' + category + str(datetime.now().strftime('%Y%m%d')) + '.txt'\\n else:\\n filename = category + '.txt'\\n with open(filename, 'w') as file:\\n file.write(json.dumps(results))\\n return results\",\n \"def parse_listings(addresses, sites):\\n\\n for site in sites:\\n for address in addresses:\\n listing = api_search(address, site)\\n # Process the listing\\n process_listing(listing, site)\",\n \"def extract_listings(self, bs_obj, timestamp):\\n listings = bs_obj.find_all(\\\"div\\\", class_=\\\"listing\\\")\\n for listing_element in listings:\\n image_div = listing_element.find(\\\"div\\\", class_=\\\"tcl\\\")\\n pricing_div = listing_element.find(\\\"div\\\", class_=\\\"tclr\\\")\\n primary_div = listing_element.find(\\\"div\\\", style=\\\"margin-left:90px;margin-right:120px;\\\")\\n\\n item = {}\\n item[\\\"market_name\\\"] = \\\"Alphabay\\\"\\n item[\\\"listing_name\\\"] = primary_div.find(\\\"a\\\", class_=\\\"bstd\\\").text\\n\\n # Parse pricing\\n price_string = pricing_div.find(\\\"span\\\", class_=\\\"std\\\").text.strip()\\n\\n usd_price = price_string.split(\\\"\\\\n\\\")[0].split(\\\" \\\")\\n usd_price = usd_price[len(usd_price)-1].replace(\\\",\\\", \\\"\\\")\\n\\n btc_price = price_string.split(\\\"\\\\n\\\")[1].split(\\\" \\\")\\n btc_price = btc_price[0].lstrip(\\\"(\\\")\\n\\n\\n item[\\\"listing_price_usd\\\"] = str(float(usd_price))\\n item[\\\"listing_price_btc\\\"] = str(float(btc_price))\\n\\n # Split the element value by \\\"-\\\" so that we can grab item number and category.\\n # Text being parsed looks like: \\\"Item # 30361 - Botnets & Malware / Botnets & Malware\\\"\\n item_number, item_category = tuple(value.strip() for value in primary_div.find(\\\"span\\\", class_=\\\"std\\\", style=\\\"line-height:12px;\\\").text.split(\\\"-\\\"))\\n item[\\\"listing_item_number\\\"] = int(item_number.split(\\\"#\\\")[1].strip())\\n item[\\\"listing_category\\\"] = item_category\\n\\n # Grab who is listing this item\\n vendor_id = primary_div.find(\\\"span\\\", class_=\\\"normal\\\")\\n if not vendor_id:\\n vendor_id = primary_div.find(\\\"span\\\", class_=\\\"red\\\")\\n if not vendor_id:\\n raise Exception(\\\"Error with CSS\\\")\\n vendor_id = vendor_id.text.split(\\\"-\\\")[2].split(\\\" \\\")\\n vendor_name = vendor_id[0].strip()\\n vendor_id = vendor_id[len(vendor_id) - 1].lstrip(\\\"(\\\").rstrip(\\\")\\\")\\n else:\\n vendor_name = primary_div.find(\\\"span\\\", class_=\\\"normal\\\").find(\\\"a\\\", class_=\\\"std\\\").text\\n vendor_id = vendor_id.text\\n vendor_id = vendor_id.strip().split(\\\"(\\\")[1].rstrip(\\\")\\\")\\n item[\\\"vendor_name\\\"] = vendor_name\\n item[\\\"vendor_id\\\"] = int(vendor_id)\\n\\n\\n # Grab views, bids, and quantities\\n meta_info = list(parent for parent in primary_div.find_all(\\\"span\\\", class_=\\\"std\\\") if \\\"Bid\\\" in parent.text)[0].text\\n view_bid_info, quantity_info = tuple(meta_info.split(\\\"Quantity left: \\\"))\\n item[\\\"listing_views\\\"] = int(view_bid_info.split(\\\"/\\\")[0].split(\\\" \\\")[1])\\n item[\\\"listing_bids\\\"] = view_bid_info.split(\\\"/\\\")[1].strip().split(\\\": \\\")[1]\\n item[\\\"listing_item_quantity\\\"] = quantity_info\\n\\n # Grab the listing date from the listing image\\n image_date = image_div.find(\\\"a\\\", class_=\\\"notext\\\").find(\\\"img\\\", class_=\\\"listing\\\")['src']\\n image_date = \\\"-\\\".join(image_date.split(\\\"images/uploads/\\\")[1].split(\\\"/\\\")[:3])\\n item[\\\"listing_date\\\"] = image_date\\n item[\\\"timestamp\\\"] = timestamp\\n yield item\",\n \"def test_amount_of_listings_many_listings(self):\\n listings = steam_market.get_total_amount_of_listings(soup=get_soup_from_path(TEST_FILE_MANY_RESULTS))\\n self.assertEqual(164720, listings)\",\n \"def search_contact_list(self):\\n\\n search_db = Database()\\n result = search_db.contact_search(self.name)\\n if not result:\\n print Fore.YELLOW + ' No such contact'\\n return None\\n if result > 1:\\n print ' Which contact ??'\\n for items in result:\\n if items[2] > 1:\\n print Fore.BLUE + ' %s %s %s' % ([items[0]], items[1], items[2])\\n else:\\n print str(items[1]), items[2]\\n\\n return result\",\n \"def getallcontacts(self):\\n feed_url = self.contacts_client.GetFeedUri(projection='full')\\n total_read = 0\\n while True:\\n print('Retrieving contacts... (%d retrieved so far)' % total_read)\\n feed = self.contacts_client.get_feed(uri=feed_url,\\n auth_token=None,\\n desired_class=gdata.contacts.data.ContactsFeed)\\n total_read += len(feed.entry)\\n for entry in feed.entry:\\n yield entry\\n next_link = feed.GetNextLink()\\n if next_link is None:\\n print('All contacts retrieved: %d total' % total_read)\\n break\\n feed_url = next_link.href\",\n \"def listingsPage(city, stateCode=None):\\n try:\\n # Retrieve listings from Hoya database\\n listings = list(db.listings.find({\\\"address\\\": {\\\"city\\\": city}}))\\n\\n # Retrieve listings from external (realtor) API\\n url = os.getenv(\\\"API_URL\\\")\\n\\n querystring = {\\n \\\"city\\\": city,\\n \\\"limit\\\": \\\"20\\\",\\n \\\"offset\\\": \\\"0\\\",\\n \\\"state_code\\\": stateCode,\\n \\\"sort\\\": \\\"relevance\\\",\\n }\\n\\n headers = {\\n \\\"x-rapidapi-key\\\": os.getenv(\\\"API_KEY\\\"),\\n \\\"x-rapidapi-host\\\": os.getenv(\\\"API_HOST\\\"),\\n }\\n response = requests.request(\\n \\\"GET\\\", url, headers=headers, params=querystring\\n ).json()\\n\\n # For each listing in response[\\\"properties\\\"] (excluding metadata)\\n # append to our listings list\\n # given that our API returns inconsistent data, handle cases where keys\\n # don't exist\\n\\n for prop in response[\\\"properties\\\"]:\\n sqFootage = None\\n if prop.get(\\\"lot_size\\\", None) is None:\\n sqFootage = prop.get(\\\"building_size\\\", {}).get(\\n \\\"size\\\", random.randint(900, 3400)\\n )\\n if prop.get(\\\"building_size\\\", None) is None:\\n sqFootage = prop.get(\\\"lot_size\\\", {}).get(\\n \\\"size\\\", random.randint(900, 3400)\\n )\\n\\n listing = {\\n \\\"_id\\\": prop.get(\\\"property_id\\\", ObjectId()),\\n \\\"numBedrooms\\\": prop.get(\\\"beds\\\", random.randint(1, 5)),\\n \\\"numBathrooms\\\": prop.get(\\\"baths\\\", random.randint(1, 5)),\\n \\\"sqFootage\\\": sqFootage,\\n \\\"price\\\": prop.get(\\\"price\\\", random.randint(230000, 800000)),\\n \\\"address\\\": {\\n # TODO: instead of None, pass in city, state from req.form\\n \\\"city\\\": prop.get(\\\"address\\\", {}).get(\\\"city\\\", None),\\n \\\"state\\\": prop.get(\\\"address\\\", {}).get(\\\"state\\\", None),\\n \\\"zip\\\": prop.get(\\\"address\\\", {}).get(\\\"postal_code\\\", None),\\n },\\n }\\n listings.append(listing)\\n return render_template('listings.html', listings=listings)\\n except (KeyError, ValueError):\\n # Return custom 404 error page, set status code to 404\\n # We use 404 here (rather than 500) because 404 means\\n # \\\"resource not found\\\"\\n return render_template(\\\"404.html\\\"), 404\",\n \"def total_data(map_index, next_sse_index, ss_def, contacts_def):\\n no_of_contacts = 0\\n contacts_true = contacts_def.keys()\\n start, end = ss_def[next_sse_index][3], ss_def[next_sse_index][4]\\n for i in range(start, end + 1):\\n if i in contacts_true:\\n contacts = contacts_def[i]\\n for contact in contacts:\\n for index in map_index:\\n tstart, tend = ss_def[index][3], ss_def[index][4]\\n if contact in range(tstart, tend + 1):\\n no_of_contacts += 1\\n return no_of_contacts\",\n \"def listing_count(self) -> int:\\n return pulumi.get(self, \\\"listing_count\\\")\",\n \"def get_all_contacts(self,\\n hook,\\n resource,\\n data=None,\\n headers=None,\\n extra_options=None):\\n all_pages = []\\n total_contacts = -1\\n next_token = None\\n\\n while len(all_pages) != total_contacts:\\n if not next_token:\\n result = hook.run('{}/contacts'.format(resource),\\n data,\\n headers,\\n extra_options).json()\\n else:\\n result = hook.run('{}/contacts/{}'.format(resource, next_token),\\n data,\\n headers,\\n extra_options).json()\\n\\n all_pages += result.get('contacts', None)\\n\\n total_contacts = result.get('total_contacts', None)\\n\\n if 'bookmark' in result:\\n next_token = result.get('bookmark', None)\\n\\n return all_pages\",\n \"def fetch_contacts(owner_account_id):\\n resp = oauth.tapkey.get(f\\\"Owners/{owner_account_id}/Contacts?$select=id,identifier\\\")\\n contacts = resp.json()\\n return contacts\",\n \"def extract_listings(self, soup, timestamp):\\n\\n listings = soup.find_all(\\\"div\\\", class_=\\\"around\\\")\\n for listing_element in listings:\\n title_div = listing_element.find(\\\"div\\\", class_=\\\"oTitle\\\")\\n primary_div = listing_element.find(\\\"div\\\", class_=\\\"oOfferBody\\\")\\n if not primary_div:\\n self.logger.info(\\\"skipping entry:\\\" + repr(title_div.find(\\\"a\\\").text.strip()) + \\\"| Reason: No primary_div found.\\\")\\n continue\\n\\n listing_meta_div = primary_div.find(\\\"td\\\", class_=\\\"oOfTextDetail\\\")\\n vendor_div = listing_meta_div.find(\\\"div\\\", class_=\\\"oVendor\\\")\\n\\n item = {}\\n item[\\\"market_name\\\"] = \\\"DreamMarket\\\"\\n item[\\\"listing_name\\\"] = title_div.find(\\\"a\\\").text.strip()\\n item[\\\"timestamp\\\"] = timestamp\\n\\n # starting at 1 to avoid the bitcoin icon\\n item[\\\"listing_price_btc\\\"] = float(listing_meta_div.find(\\\"div\\\", class_=\\\"oPrice\\\").text.strip()[1:])\\n item[\\\"listing_escrow\\\"] = primary_div.find(\\\"div\\\", class_=\\\"escrowBox\\\").text\\n\\n\\n vendor_name = list(tag for tag in vendor_div.find_all(\\\"a\\\") if tag[\\\"href\\\"].startswith(\\\"./\\\"))[0].text.strip()\\n vendor_transactions = int(vendor_div.find(\\\"span\\\", title=\\\"Successful transactions\\\").text.lstrip(\\\"(\\\").rstrip(\\\")\\\"))\\n vendor_rating = vendor_div.find(\\\"span\\\", class_=\\\"userRating\\\")\\n if vendor_rating:\\n vendor_rating = float(vendor_rating.text.strip())\\n else:\\n vendor_rating = float(0)\\n\\n item[\\\"vendor_name\\\"] = vendor_name\\n item[\\\"vendor_transactions\\\"] = vendor_transactions\\n item[\\\"vendor_rating\\\"] = vendor_rating\\n\\n yield item\",\n \"def ListAllContacts(self):\\n feed = self.gd_client.GetContacts()\\n self.contacts = self.CleanPhoneNumbers(self.GetContactsInfo(feed))\\n return self.contacts\",\n \"def get_contacts(self):\\n contacts = Membership.objects.filter(entity = self, key_contact = True).order_by('importance_to_entity')\\n return contacts\",\n \"def _search(self, query_obj, num, after, reverse, count=0):\\r\\n builder = SearchBuilder(query_obj,\\r\\n after = after, num = num, reverse = reverse,\\r\\n count = count,\\r\\n wrap = ListingController.builder_wrapper)\\r\\n\\r\\n listing = LinkListing(builder, show_nums=True)\\r\\n\\r\\n # have to do it in two steps since total_num and timing are only\\r\\n # computed after fetch_more\\r\\n res = listing.listing()\\r\\n timing = time_module.time() - builder.start_time\\r\\n\\r\\n return builder.total_num, timing, res\",\n \"def store_listings():\\n\\n scraped_listings = scrape_listings() # Used to store scraped listings\\n valid_listings = [] # Used to store valid listings (after filtering)\\n num_valid_listings = 1 # Counts the number of valid listings for output\\n\\n for listing in scraped_listings:\\n # If the Listing ID of the current listing does not exist in the database...\\n if session.query(DatabaseListing).filter_by(listing_id=listing.listing_id).first() is None:\\n # Safe-guard distance variable (decimal)\\n if listing.distance == \\\"n/a\\\":\\n listing.distance = 0.0\\n else:\\n listing.distance = listing.distance[:-2]\\n\\n # Create listing object for database\\n listing = DatabaseListing(\\n created=listing.created,\\n available=listing.available,\\n listing_type=listing.listing_type,\\n room_type=listing.room_type,\\n address=listing.address,\\n distance=listing.distance,\\n sublet=listing.sublet,\\n rooms=int(listing.rooms),\\n features=listing.features,\\n price=listing.price,\\n listing_id=int(listing.listing_id),\\n link=listing.link\\n )\\n\\n # Save listing to database\\n session.add(listing)\\n session.commit()\\n \\n\\n if (program_features.FILTER == True) and (filter_listings(listing) == False):\\n print (\\\"Listing #{} does not match filter\\\".format(num_valid_listings))\\n else:\\n # Save valid listing (with ASCII encoding for Email server)\\n valid_listings.append(\\\"\\\"\\\"Date Posted: {} | Available: {} | {} | Type: {}\\n Address: {}\\n Price: {} | Distance: {} | Sublet: {} | Rooms: {}\\n Features: {}\\n {}\\\"\\\"\\\".format(listing.created, listing.available,\\n listing.listing_type, listing.room_type,\\n listing.address, listing.price,\\n (str(listing.distance) + \\\" km\\\"),\\n listing.sublet, listing.rooms,\\n str(listing.features.encode('utf-8')), listing.link))\\n\\n # Output success statement \\n print(\\\"Listing #{} loaded\\\".format(num_valid_listings))\\n \\n # Increment valid listings counter\\n num_valid_listings += 1\\n \\n # If the Listing ID of the current listing exists in the database...\\n else:\\n print(\\\"Listing already loaded\\\")\\n\\n return valid_listings # Return valid listings array\",\n \"def Run(self):\\n return self.ListAllContacts()\",\n \"def test_list_referrals_by_desc_case_number(self):\\n user = factories.UserFactory()\\n # NB: set up a situation where case number and due date would yield different orders\\n referrals = [\\n factories.ReferralFactory(\\n state=models.ReferralState.RECEIVED,\\n post__users=[user],\\n urgency_level=models.ReferralUrgency.objects.get(\\n duration=timedelta(days=7)\\n ),\\n ),\\n factories.ReferralFactory(\\n state=models.ReferralState.RECEIVED,\\n post__users=[user],\\n urgency_level=models.ReferralUrgency.objects.get(\\n duration=timedelta(days=1)\\n ),\\n ),\\n ]\\n\\n self.setup_elasticsearch()\\n response = self.client.get(\\n f\\\"/api/referrallites/?user={user.id}&sort=case_number&sort_dir=desc\\\",\\n HTTP_AUTHORIZATION=f\\\"Token {Token.objects.get_or_create(user=user)[0]}\\\",\\n )\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(response.json()[\\\"count\\\"], 2)\\n self.assertEqual(response.json()[\\\"results\\\"][0][\\\"id\\\"], referrals[1].id)\\n self.assertEqual(response.json()[\\\"results\\\"][1][\\\"id\\\"], referrals[0].id)\",\n \"def test_amount_of_listings(self):\\n listings = steam_market.get_total_amount_of_listings(soup=get_soup_from_path(TEST_FILE_NORMAL_LISTING))\\n self.assertEqual(10, listings)\",\n \"def _createContactListsCollections(folder):\\n collections = [\\n {'id': 'all_cls', 'tit': _('all_cls'), 'subj': (u'search', ), 'query': [\\n {'i': 'portal_type',\\n 'o': 'plone.app.querystring.operation.selection.is',\\n 'v': ['contact_list']}],\\n 'cond': u\\\"\\\", 'bypass': [],\\n 'flds': (u'select_row', u'pretty_link', u'relative_path',\\n u'review_state', u'CreationDate', u'actions'),\\n 'sort': u'sortable_title', 'rev': False, 'count': False},\\n ]\\n _createDashboardCollections(folder, collections)\",\n \"def index(request):\\n # Attempt to map highest bid amount to each items' ID.\\n di = {}\\n for obj in Listing.objects.filter(is_active=True):\\n try: \\n di[obj.id] = sorted([bid.bid_amount for bid in obj.bids.all()])[-1]\\n except:\\n di[obj.id] = obj.starting_bid\\n \\n #print(di) - For Debugging \\n\\n # Return index template\\n return render(request, \\\"auctions/index.html\\\", {\\n \\\"listings\\\": Listing.objects.filter(is_active=True),\\n \\\"di\\\": di\\n })\",\n \"def scrape_listings():\\n scraped_listings = [] # Used to store apartment listings\\n links = [] # Used to store links to apartment listings (seperate tag)\\n\\n # Download \\\"The Canon\\\" website\\n URL = \\\"https://www.thecannon.ca/classifieds/housing\\\"\\n headers = program_features.HEADERS\\n page = requests.get(URL, headers=headers)\\n\\n # Parse document\\n soup = BeautifulSoup(page.content, \\\"html.parser\\\")\\n page_listings = list(soup.find_all('td')) # Find all listing information, and store as list\\n\\n # Used to find URL parameters for each apartment listing\\n for link in soup.find_all(\\\"a\\\"):\\n # URL Format Example: \\\"<a href=\\\"/page.php?cid=347306&id=26&t=housing\\\">1219 Gordon St, Guelph</a>\\\"\\n if link.has_attr('href') and (\\\"t=housing\\\" in link.attrs['href']):\\n links.append(\\\"https://www.thecannon.ca\\\" + link.attrs['href'])\\n\\n # Iterate list \\n for i, listing in enumerate(page_listings, 1):\\n # Group every 10 elements into a listing object\\n if i % 10 == 0:\\n index = int(i / 10) - 1 # Calculate index of link that matches the current listing \\n\\n # Append listing object to array\\n scraped_listings.append(\\n # Create listing object\\n WebListing(\\n page_listings[i - 10].get_text().strip(), # Date post was created\\n page_listings[i - 9].get_text().strip(), # Date apartment is available\\n page_listings[i - 8].get_text().strip(), # Offering type\\n page_listings[i - 7].get_text().strip(), # Housing type\\n page_listings[i - 6].get_text().strip(), # Address \\n page_listings[i - 5].get_text().strip(), # Price\\n page_listings[i - 4].get_text().strip(), # Distance \\n page_listings[i - 3].get_text().strip(), # Sublet permission\\n page_listings[i - 2].get_text().strip(), # Number of rooms\\n page_listings[i - 1].get_text().strip(), # Features\\n links[index][38:44], # Listing ID (stored in link)\\n links[index] # Listing Link\\n )\\n )\\n\\n return scraped_listings # Return listings array\",\n \"def listings_list():\\n\\n inputs = Listing.convert_inputs(request.args)\\n form = ListingSearchForm(data=inputs)\\n if form.validate():\\n listings = Listing.find_all(inputs)\\n serialized = [listing.serialize(\\n isDetailed=False\\n ) for listing in listings]\\n return (jsonify(listings=serialized), 200)\\n else:\\n return (jsonify(errors=[\\\"Bad request\\\"]), 400)\",\n \"def listing_messages(listing_id):\\n Listing.query.get_or_404(listing_id)\\n\\n auth_username = get_jwt_identity()\\n all_messages = Message.find_by_listing(listing_id, auth_username)\\n print(\\\"ALL MESSAGES: \\\", all_messages)\\n serialized = [message.serialize() for message in all_messages]\\n return (jsonify(messages=serialized), 200)\",\n \"def search_contact(request, **kwargs):\\n limit = int(request.GET.get('limit', constants.DEFAULT_LIMIT))\\n offset = int(request.GET.get('offset', constants.DEFAULT_OFFSET))\\n search_term = request.GET.get('search_term')\\n contact = private.Contact()\\n data = contact.fetch_list(limit, offset, search_term)\\n return JsonResponse({'objects': data})\",\n \"def get_contacts(self, count=-1, excluded_guid=None):\\n current_len = len(self._contacts)\\n if current_len == 0 or count == 0:\\n return []\\n\\n if count < 0:\\n count = current_len\\n else:\\n count = min(count, current_len)\\n\\n if excluded_guid is None:\\n # Get the last `count` contacts.\\n contact_list = self._contacts[-count:]\\n else:\\n contact_list = []\\n for contact in reversed(self._contacts):\\n if contact.guid == excluded_guid:\\n continue\\n contact_list.append(contact)\\n if len(contact_list) >= count:\\n break\\n return contact_list\",\n \"def get_frequent_contacts(user, limit=5) -> typing.List[Contact]:\\n contacts_frequent = (\\n Contact.objects.filter(user=user)\\n .annotate(count=Count(\\\"interactions\\\"))\\n .order_by(\\\"-count\\\")[:limit]\\n )\\n return list(contacts_frequent)\",\n \"def contact_info(self, sensitive=True):\\n account_id = self.account_id()\\n retry_count = 5\\n\\n req_url = self.get(\\\"/accounts/{}/contacts\\\".format(account_id))['ResultUrl']\\n resp = self.get(req_url)\\n tries = 0\\n while 'Contacts' not in resp and tries < retry_count:\\n resp = self.get(req_url)\\n tries += 1\\n time.sleep(1)\\n contacts = resp['Contacts']\\n\\n contact_data = list()\\n for contact in contacts:\\n row_data = {\\n 'ContactId': contact['Id'],\\n 'Email': \\\"*****@****.***\\\" if sensitive else contact['Email'],\\n 'FirstName': \\\"*****\\\" if sensitive else contact['FirstName'],\\n 'LastName': \\\"*****\\\" if sensitive else contact['LastName'],\\n 'Status': contact.get('Status'),\\n 'MembeshipEnabled': contact.get('MembershipEnabled'),\\n 'TermsOfUseAccepted': contact['TermsOfUseAccepted'],\\n }\\n\\n if 'MembershipLevel' in contact:\\n row_data['MembershipLevel'] = contact['MembershipLevel']['Name']\\n\\n # Map all field values into a dict for convenience\\n field_values = {val['FieldName']: val['Value']\\n for val in contact['FieldValues']}\\n\\n # Get list of authorizations\\n if 'Managed Authorizations' in field_values:\\n authorizations = [i['Label']\\n for i in field_values['Managed Authorizations']]\\n row_data['Authorizations'] = authorizations\\n\\n contact_data.append(row_data)\\n self.__contact_df = pd.DataFrame(contact_data).set_index('ContactId')\\n return self.__contact_df\",\n \"def getListingLists(**kwargs):\",\n \"def build_listing(self, num, after, reverse, count):\\r\\n self.num = num\\r\\n self.count = count\\r\\n self.after = after\\r\\n self.reverse = reverse\\r\\n\\r\\n if after is not None:\\r\\n self.robots = \\\"noindex,follow\\\"\\r\\n\\r\\n self.query_obj = self.query()\\r\\n self.builder_obj = self.builder()\\r\\n self.listing_obj = self.listing()\\r\\n content = self.content()\\r\\n res = self.render_cls(content = content,\\r\\n show_sidebar = self.show_sidebar,\\r\\n nav_menus = self.menus,\\r\\n title = self.title(),\\r\\n infotext = self.infotext,\\r\\n robots = self.robots,\\r\\n top_filter = self.top_filter,\\r\\n header_sub_nav = self.header_sub_nav,\\r\\n **self.render_params).render()\\r\\n return res\",\n \"def count(listing):\\n if 'meta' in listing and 'query_total' in listing['meta']:\\n return listing['meta']['query_total']\",\n \"def filter_listings(listing):\\n\\n MIN_PRICE = program_features.MIN_PRICE\\n MAX_PRICE = program_features.MAX_PRICE\\n\\n MAX_DISTANCE = program_features.MAX_DISTANCE\\n\\n MIN_NUM_ROOMS = program_features.MIN_NUM_ROOMS\\n MAX_NUM_ROOMS = program_features.MAX_NUM_ROOMS\\n\\n if (float(listing.price[1:4]) < MIN_PRICE) or (float(listing.price[1:4]) > MAX_PRICE):\\n print (\\\"Listing price = {}\\\".format(listing.price[1:4], ))\\n return False\\n elif (float(listing.distance) > MAX_DISTANCE):\\n print (\\\"Listing distance = {}\\\".format(float(listing.distance)))\\n return False\\n elif (int(listing.rooms) < MIN_NUM_ROOMS) or (int(listing.rooms) > MAX_NUM_ROOMS) :\\n print (\\\"Listing rooms = {}\\\".format(int(listing.rooms)))\\n return False\\n else:\\n return True\",\n \"def getContactRoute(ss_def, contacts_def):\\n control = 0 # to control the while loop until the length of all SSEs\\n i = 0 # i, j are the indices of the SSE array\\n j = 0\\n map_route = []\\n while control != len(ss_def) - 1:\\n\\n # [20, 64, 28, 24, 56, 28, 24, 40, 24, 24, 88] 11\\n if control == 0:\\n control += 1\\n i, j = getTopRank(ss_def, contacts_def) # get the largest intra contact smotif\\n map_route.append([i, j, 'start'])\\n else:\\n if i == 0:\\n ti = j\\n j += 1\\n control += 1\\n direction = 'right'\\n map_route.append([ti, j, direction])\\n elif j == len(ss_def) - 1: # -1 to deal with array index\\n tj = i\\n i -= 1\\n control += 1\\n direction = 'left'\\n map_route.append([i, tj, direction])\\n else:\\n ti, tj, new_direction = nextSS(map_route, ss_def, contacts_def)\\n control += 1\\n map_route.append([ti, tj, new_direction])\\n return map_route\",\n \"def test_list_referrals_by_asc_case_number(self):\\n user = factories.UserFactory()\\n # NB: set up a situation where case number and due date would yield different orders\\n referrals = [\\n factories.ReferralFactory(\\n id=4,\\n state=models.ReferralState.RECEIVED,\\n post__users=[user],\\n urgency_level=models.ReferralUrgency.objects.get(\\n duration=timedelta(days=7)\\n ),\\n ),\\n factories.ReferralFactory(\\n id=1989,\\n state=models.ReferralState.RECEIVED,\\n post__users=[user],\\n urgency_level=models.ReferralUrgency.objects.get(\\n duration=timedelta(days=1)\\n ),\\n ),\\n ]\\n\\n self.setup_elasticsearch()\\n response = self.client.get(\\n f\\\"/api/referrallites/?user={user.id}&sort=case_number&sort_dir=asc\\\",\\n HTTP_AUTHORIZATION=f\\\"Token {Token.objects.get_or_create(user=user)[0]}\\\",\\n )\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(response.json()[\\\"count\\\"], 2)\\n self.assertEqual(response.json()[\\\"results\\\"][0][\\\"id\\\"], referrals[0].id)\\n self.assertEqual(response.json()[\\\"results\\\"][1][\\\"id\\\"], referrals[1].id)\",\n \"def getContactsData(service, groupResourceName, maxMembers):\\n # get the ids of the contacts inside the specified group\\n contactsIDs = service.contactGroups().get(\\n resourceName=groupResourceName, \\n maxMembers=maxMembers).execute()[\\\"memberResourceNames\\\"]\\n\\n # get data of the contacts that correspond to the ids obtained\\n contactsData = service.people().getBatchGet(\\n resourceNames=contactsIDs,\\n personFields='names,emailAddresses').execute()[\\\"responses\\\"]\\n\\n # extract the names and the emailAddresses of the contacts\\n namessList = [] \\n mailsList = []\\n for contact in contactsData:\\n try:\\n namessList.append(contact[\\\"person\\\"][\\\"names\\\"][0][\\\"displayName\\\"])\\n except:\\n raise Exception(\\\"All contacts must have a name associated\\\")\\n mailsList.append(contact[\\\"person\\\"][\\\"emailAddresses\\\"][0][\\\"value\\\"])\\n return namessList, mailsList\",\n \"def test_get_filter_effective_contacts(self):\\n data = {\\\"type_contact\\\": 1}\\n response = self.client.get(reverse('contacts-filter'), data)\\n # import pdb; pdb.set_trace()\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n self.assertEqual(response.data[\\\"count\\\"], 2)\",\n \"def fetch_contact_messages(self, org, contact, created_after, created_before):\\n pass\",\n \"def getSorted(self):\\n return sorted(self.contacts)\",\n \"def recommend_n_listings(self, listing_id, request_filter, n=5):\\n # precondition\\n if n < 1:\\n raise ValueError('Number of recommendations to compute must be at least 1')\\n # get target listing\\n target_listing = Listing_Repository().get_by_id(listing_id)\\n if not target_listing:\\n raise ValueError('No Listing found for given ID \\\"{}\\\"'.format(listing_id))\\n # query building\\n listing_query = Query \\\\\\n .from_(self.listings) \\\\\\n .join(self.hosts).on(self.listings.host_id == self.hosts.id)\\\\\\n .select(\\n # the relevant fields to compute row similarity (encode room_type)\\n self.listings.id, self.listings.longitude, self.listings.latitude,\\n MapRT(self.listings.room_type).as_('room_type'), self.listings.price,\\n self.listings.min_nights, self.listings.num_of_reviews, self.listings.availability\\n )\\n # add filter criteria\\n listing_query = Listing_Repository().add_where_clauses(listing_query, request_filter)\\n # execute query\\n listings_dict = self._execute_select_query(listing_query.get_sql())\\n # compute recommendations and get their IDs\\n rec_ids = self._compute_n_recommendations(listings_dict, target_listing, n)\\n # fetch listings\\n return Listing_Repository().get_all_by_id(rec_ids)\",\n \"def find_duplicates_for_lead(lead, comparator_field):\\n assert lead and comparator_field\\n\\n search_values = None\\n if comparator_field == 'company':\\n search_values = [lead['display_name']]\\n else:\\n comparator_field_plural = '{0}s'.format(comparator_field)\\n search_values = [e[comparator_field]\\n for c in lead['contacts']\\n for e in c[comparator_field_plural]]\\n\\n # keep all values of the comparator field as a set for easier\\n # computation of intersections below\\n lead_elems = set(search_values)\\n\\n if not search_values:\\n return []\\n\\n query = '{0} in ({1}) sort:date_created'.format(\\n comparator_field,\\n ', '.join('\\\"%s\\\"' % val.encode('utf-8') for val in search_values)\\n )\\n logger.debug('query = %s', query)\\n\\n duplicates = []\\n has_more = True\\n offset = 0\\n while has_more:\\n resp = api.get('lead', params={\\n 'query': query,\\n '_skip': offset,\\n '_fields': 'id,display_name,status_label,contacts,opportunities'\\n })\\n leads = resp['data']\\n logger.debug('Fetched %d of %d duplicate leads.', len(leads), resp['total_results'])\\n\\n # Add leads to our list of duplicates iff they are exact matches,\\n # making sure to exclude the original lead from our duplicates\\n for l in leads:\\n if l['id'] == lead['id']:\\n logger.debug('Removed lead %s from duplicate search results.', l['id'])\\n continue\\n\\n if comparator_field == 'company':\\n if lead['display_name'].strip().lower() == l['display_name'].strip().lower():\\n duplicates.append(l)\\n else:\\n l_elems = set()\\n for contact in l['contacts']:\\n for elem in contact[comparator_field_plural]:\\n l_elems.add(elem[comparator_field])\\n\\n logger.debug('%s (%s) %ss: [\\\"%s\\\"]', l['id'], l['display_name'], comparator_field, ','.join(l_elems))\\n\\n # if at least one of the phones/emails/etc. is a match,\\n # consider this lead a duplicate\\n intersection = lead_elems & l_elems\\n if intersection:\\n duplicates.append(l)\\n\\n offset += len(leads)\\n has_more = resp['has_more']\\n\\n return duplicates\",\n \"def topAuthors():\\n c = db.cursor()\\n c.execute(\\\"select name, sum(hits) as hits\\\\\\n from authorhits group by name\\\\\\n order by hits desc;\\\")\\n results = c.fetchall()\\n c.close()\\n return results\",\n \"def get_contacts_list(self):\\n return [(id + 1, contact) for id, contact in enumerate(self.contact_list)]\",\n \"def generate_dict_top_five(self):\\n all_months = {}\\n # Loops over each avilable non-duplicate date\\n available_dates = self.contacts.quer_distinct_components(\\\"contact_date\\\", \\\"Contacts\\\")\\n for date in available_dates:\\n top_five = self.ordered[date]\\n all_months[date] = {}\\n\\n # Loops over top five to append their components to the final dict\\n for index in range(len(top_five)):\\n listing_id = top_five[index][1]\\n total_contacts = top_five[index][0]\\n\\n all_months[date].update(\\n {\\n index+1: {\\n 'Ranking': index+1,\\n 'Listing Id': listing_id,\\n 'Make': self.listing.quer_component_using_column(\\\"make\\\", \\\"id\\\", \\\"Listings\\\", listing_id)[0][0],\\n 'Selling Price': self.listing.quer_component_using_column(\\\"price\\\", \\\"id\\\", \\\"Listings\\\", listing_id)[0][0],\\n 'Mileage': self.listing.quer_component_using_column(\\\"mileage\\\", \\\"id\\\", \\\"Listings\\\", listing_id)[0][0],\\n 'Total Amount of Contacts': total_contacts\\n }\\n }\\n )\\n\\n return all_months\",\n \"def GetContactList(self):\\n\\t\\tfeeds = []\\n\\t\\tfeed = self.client.GetContacts()\\n\\t\\tfeeds.append(feed)\\n\\t\\tnext = feed.GetNextLink()\\n\\t\\twhile next:\\n\\t\\t\\tfeed = self.client.GetContacts(uri=next.href)\\n\\t\\t\\tfeeds.append(feed)\\n\\t\\t\\tnext = feed.GetNextLink()\\n\\t\\t\\n\\t\\tcontacts = []\\n\\t\\tfor feed in feeds:\\n\\t\\t\\tif not feed.entry:\\n\\t\\t\\t\\tcontinue\\n\\t\\t\\telse:\\n\\t\\t\\t\\tfor i, entry in enumerate(feed.entry):\\n\\t\\t\\t\\t\\tcontacts.append(entry)\\n\\t\\treturn contacts\",\n \"def list_contacts(self):\\n return self.contacts\",\n \"def summarize_contacts_nodb(tac_clustered):\\n rec_l = []\\n for tacnum, cluster in tac_clustered.groupby('group'):\\n rec = {'cluster': tacnum}\\n \\n # Start and stop of cluster\\n rec['frame_start'] = cluster['frame'].min()\\n rec['frame_stop'] = cluster['frame'].max()\\n rec['duration'] = rec['frame_stop'] - rec['frame_start'] + 1\\n \\n # Mean tip and fol of cluster\\n rec['tip_x'] = cluster['tip_x'].mean()\\n rec['tip_y'] = cluster['tip_y'].mean()\\n rec['fol_x'] = cluster['fol_x'].mean()\\n rec['fol_y'] = cluster['fol_y'].mean()\\n rec['pixlen'] = np.sqrt(\\n (rec['tip_y'] - rec['fol_y']) ** 2 +\\n (rec['tip_x'] - rec['fol_x']) ** 2)\\n \\n rec_l.append(rec)\\n contacts_summary = pandas.DataFrame.from_records(rec_l).set_index('cluster')\\n \\n return contacts_summary\",\n \"def stars_top_employee_lists(request, top_number, kind, id):\\n try:\\n if request.method == 'GET':\\n if kind == 'category':\\n top_list = Star.objects.filter(category__id=id).values(\\n 'to_user__pk',\\n 'to_user__username',\\n 'to_user__first_name',\\n 'to_user__last_name',\\n 'to_user__level'\\n 'to_user__avatar').annotate(num_stars=Count('to_user')).order_by('-num_stars')[:top_number]\\n elif kind == 'keyword':\\n top_list = Star.objects.filter(keyword__id=id).values(\\n 'to_user__pk',\\n 'to_user__username',\\n 'to_user__first_name',\\n 'to_user__last_name',\\n 'to_user__level',\\n 'to_user__avatar').annotate(num_stars=Count('to_user')).order_by('-num_stars')[:top_number]\\n else:\\n return Response(status=status.HTTP_412_PRECONDITION_FAILED)\\n serializer = StarTopEmployeeLists(top_list, many=True)\\n return Response(serializer.data, status=status.HTTP_200_OK)\\n except Exception as e:\\n raise APIException(e)\",\n \"def find_top_salespeople(name_list, sales_list, n):\\n top_names = []\\n temp_name = [] + name_list\\n temp_sales = [] + sales_list\\n\\n while len(top_names) < n and len(temp_sales) != 0: \\n max_sales = temp_sales.index(max(temp_sales))\\n num = temp_sales[max_sales]\\n top_names.append(temp_name[max_sales])\\n temp_name.remove(temp_name[max_sales])\\n temp_sales.remove(temp_sales[max_sales])\\n\\n if num in temp_sales:\\n max_sales2 = temp_sales.index(max(temp_sales))\\n top_names.append(temp_name[max_sales2])\\n temp_name.remove(temp_name[max_sales2])\\n temp_sales.remove(temp_sales[max_sales2])\\n\\n return top_names\",\n \"def get_recent_contacts(user, limit=5, timespan_days=14) -> typing.List[Contact]:\\n timespan_recent = datetime.now().astimezone() - timedelta(days=timespan_days)\\n contacts_recent = (\\n Contact.objects.filter(interactions__was_at__gt=timespan_recent)\\n .filter(user=user)\\n .annotate(count=Count(\\\"interactions\\\"))\\n .order_by(\\\"-count\\\")[:limit]\\n )\\n return list(contacts_recent)\",\n \"def listing_show(listing_id):\\n\\n listing = Listing.query.get_or_404(listing_id)\\n return (jsonify(listing=listing.serialize(isDetailed=True)), 200)\",\n \"def contacts(self):\\n return ContactCollection(self.request)\",\n \"def refreshContacts(self):\\n contact_ids = self._getAllContactIds()\\n contacts = self._getContacts(contact_ids)\\n\\n self.contacts = [LineContact(self, contact) for contact in contacts]\\n\\n self.contacts.sort()\",\n \"def searchNearbyHospitals(lat, lng, radius = 5000, limit_search_count = 10):\\n # Initialising the GooglePlaces constructor \\n google_places = GooglePlaces(GOOGLE_MAPS_API_KEY) \\n \\n query_result = google_places.nearby_search( \\n lat_lng ={'lat': lat, 'lng': lng}, \\n radius = radius, \\n types =[types.TYPE_HOSPITAL]) \\n \\n nearby_hospitals = []\\n\\n for place in query_result.places[:limit_search_count]: \\n hospital = dict()\\n place.get_details()\\n hospital['hospitalName'] = place.name\\n hospital['address'] = place.formatted_address\\n hospital['phoneNumber'] = place.local_phone_number \\n nearby_hospitals.append(hospital)\\n\\n if(len(nearby_hospitals) == 0):\\n nearby_hospitals = searchNearbyHospitals(lat = lat, lng = lng, radius = radius+5000)\\n\\n return nearby_hospitals\",\n \"def get(self, request):\\n listings = self.get_queryset().all().order_by(\\\"start_date\\\")\\n username = None\\n auth = request.user.is_authenticated\\n if auth:\\n username = request.user.username\\n return render(request, 'listings/list.html', {'listings': listings,\\n 'username': username,\\n 'auth': auth})\",\n \"async def get_contacts_for_contact_group(dbcon: DBConnection, contact_group_id: int) -> Iterable[object_models.Contact]:\\n q = \\\"\\\"\\\"select\\n contacts.id, contacts.name, contacts.email, contacts.phone, contacts.active\\n from contact_group_contacts, contacts\\n where contact_group_contacts.contact_group_id = %s\\n and contact_group_contacts.contact_id = contacts.id\\\"\\\"\\\"\\n return [object_models.Contact(*row) for row in await dbcon.fetch_all(q, (contact_group_id,))]\",\n \"def main(token, customerid=None):\\n\\n # All availavle schedules to user\\n all_schedules = [\\\"All the time\\\"]\\n\\n # Collects existin schedules from user's account\\n cust_schedules = schedules.get_schedule(token, customerid=customerid)\\n\\n # Gets the names of the schedules\\n for key, _value in cust_schedules.items():\\n all_schedules.append(key)\\n\\n single_contacts = contacts.get_all(token, customerid=customerid)\\n contactgroups = group_contacts.get_all(token, customerid=customerid)\\n single_contacts.update(contactgroups)\\n\\n single_contacts = format_contacts(single_contacts)\\n\\n contact_info = choose_contacts(single_contacts, all_schedules)\\n\\n return contact_info\",\n \"def update_contacts(self, contact_list):\\n updated_contacts = 0\\n request_list = list()\\n\\n # stale_contacts contains all old contacts at first, all current\\n # contacts get then removed so that the remaining can get deleted\\n stale_contacts = set(self.contacts)\\n\\n for contact in contact_list:\\n c = Persona.query.get(contact[\\\"id\\\"])\\n\\n if c is None:\\n c = Persona(id=contact[\\\"id\\\"], _stub=True)\\n\\n if c._stub is True:\\n request_list.append(contact[\\\"id\\\"])\\n\\n try:\\n # Old and new contact; remove from stale list\\n stale_contacts.remove(c)\\n except KeyError:\\n # New contact\\n self.contacts.append(c)\\n updated_contacts += 1\\n\\n # Remove old contacts that are not new contacts\\n for contact in stale_contacts:\\n self.contacts.remove(contact)\\n\\n app.logger.info(\\\"Updated {}'s contacts: {} added, {} removed, {} requested\\\".format(\\n self.username, updated_contacts, len(stale_contacts), len(request_list)))\\n\\n return request_list\",\n \"def get_queryset(self):\\n contact_data = Contact.objects.filter(contact_groups__in=Member.objects.filter(\\n user=self.request.user).values('group_id').distinct())\\n\\n return contact_data\",\n \"def contact_count(self, *args, **kwargs) -> Any:\\n pass\",\n \"def display_contact(self):\\n contacts = \\\"\\\".join(str(contact) for contact in self.contact_list)\\n print(contacts)\",\n \"def test_get_contact_lists(self):\\n url, parsed = self.prepare_urls('v1:contact_list-list', subdomain=self.company.subdomain)\\n \\n response = self.client.get(url, HTTP_HOST=parsed.netloc)\\n self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)\\n\\n self.authenticate_user()\\n response = self.client.get(url, HTTP_HOST=parsed.netloc)\\n self.assertEqual(response.status_code, status.HTTP_200_OK)\\n\\n content = json.loads(response.content)\\n self.assertEqual(len(content), self.contact_lists_count)\",\n \"def _get_findings(scc, source):\\n request = ListFindingsRequest(\\n parent=source,\\n )\\n findings_response = scc.list_findings(request=request)\\n findings_dict = {}\\n for fr in findings_response:\\n finding = fr.finding\\n finding_id = finding.name.split('/')[5]\\n findings_dict[finding_id] = finding\\n return findings_dict\",\n \"def TopCheckins(results, search_term):\\n top_five = Counter([row[1] for row in results]).most_common()[:5]\\n if search_term == 'total':\\n search_term = 'checkin'\\n (commander, count) = top_five.pop(0)\\n text = 'The %s %s is %s with %s checkins.\\\\n' % (\\n search_term.title(), FoodRank(search_term), commander, count)\\n text += 'Other %s fans include: ' % search_term\\n if len(top_five) > 1:\\n for eater in top_five[:-1]:\\n text += '%s with %s, ' % eater\\n text += 'and %s with %s.' % top_five[-1]\\n elif len(top_five) == 1:\\n text += '%s with %s.' % top_five[0]\\n else:\\n text += 'No one.'\\n return text\",\n \"def display_by_category(request, category):\\n if request.method == \\\"POST\\\":\\n form = SearchForm(request.POST)\\n if form.is_valid():\\n products = AuctionListing.objects.filter(buyer=None).filter(\\n category__iexact=category).filter(title__icontains=form.cleaned_data[\\\"query\\\"])\\n else:\\n products = AuctionListing.objects.filter(buyer=None).filter(\\n category__iexact=category)\\n else:\\n products = AuctionListing.objects.filter(buyer=None).filter(\\n category__iexact=category)\\n form = None\\n bids = []\\n for product in products:\\n bid = product.bids.all().aggregate(Max(\\\"bid\\\")).get(\\\"bid__max\\\")\\n bids.append(bid)\\n return render(request, \\\"auctions/index.html\\\", {\\n \\\"zip_products_bids\\\": zip(products, bids),\\n \\\"category\\\": category,\\n \\\"form\\\": form,\\n \\\"title\\\": \\\"Active Listing\\\",\\n })\",\n \"def get_listings(drug, page):\\n file_number = (page-1) * 25\\n html = open('%s_data/%s.html' % (drug, file_number)).read()\\n soup = BeautifulSoup(html, \\\"lxml\\\")\\n\\n container = soup.findAll(\\\"div\\\", id=\\\"cat_item\\\")\\n \\n items = []\\n for item in container:\\n title_elem = item.find(class_=\\\"h2\\\")\\n title = title_elem.text\\n if \\\"lottery\\\" in title.lower():\\n # ignore lottery listings because they mess up the average price\\n continue\\n try:\\n quantity = guess_quantity(title)\\n except:\\n print \\\"failed:\\\", title \\n \\n if not quantity:\\n continue\\n\\n url = title_elem.attrs['href']\\n price = item.find(class_=\\\"price_big\\\").text[1:] # remove bitcoin glyph\\n price = float(price.replace(',', ''))\\n seller = item.find(\\\"a\\\", href=re.compile(\\\"/silkroad/user/\\\")).text\\n thumb_e = item.find(\\\"img\\\", src=re.compile(\\\"^data:\\\"))\\n thumb = (thumb_e and thumb_e.attrs['src']) or \\\"\\\"\\n ships_from = [x for x in item.find(id=\\\"cat_item_description\\\").children][8]\\n country = ships_from[12:]\\n\\n items.append({\\n 'title': title,\\n \\\"seller\\\": seller,\\n \\\"price\\\": price,\\n \\\"quantity\\\": quantity,\\n \\\"url\\\": url,\\n \\\"country\\\": silkroad_country_to_iso(country),\\n \\\"thumb\\\": uri_to_file(thumb),\\n })\\n\\n print \\\"parsed page: %s (%s.html)\\\" % (page, file_number)\\n return items\",\n \"def view_contacts(self):\\n with open(self.filename, \\\"r\\\") as contactsFile:\\n contacts = self.display_contact(contactsFile.readlines())\\n\\n if not contacts:\\n return self.msgbox(\\\"No contacts found.\\\")\\n\\n self.msgbox(msg=\\\"\\\\n\\\".join(contacts), title=\\\"Showing All Contacts\\\")\",\n \"def get_listings_for_city(self, city, per_month = True):\\n assert(isinstance(city, str) and city in self.allcities)\\n\\n if city in self.us_cities:\\n print(f\\\"City {city} is in country United States .\\\")\\n elif city in self.global_cities:\\n print(f\\\"City {city} is in country {self.city_to_country_map[city]}\\\")\\n \\n listing_file_path = os.path.join(config_data.AIRBNB_DATA_DIR,f\\\"{city}/listings.csv\\\")\\n assert(os.path.isfile(listing_file_path)),f\\\"file not found at {review_file_path}!\\\"\\n \\n df = pd.read_csv(listing_file_path)\\n if df.columns[0] == \\\"date\\\" and df.columns[1] ==\\\"n_listings\\\":\\n pass\\n else:\\n print(f\\\"{listing_file_path} is not the correct file. Please double check. \\\")\\n\\n # preprocess the reviews\\n df.date = pd.to_datetime(df.date)\\n df['month'] = df.date.dt.month\\n df['year'] = df.date.dt.year\\n df['week'] = df.date.dt.isocalendar().week\\n\\n# if per_month:\\n# num_listings = df\\n# else:\\n# num_listings = df.groupby([lambda x: x.year]).agg([sum])\\n num_listings = df.set_index([\\\"date\\\"])\\n num_listings = num_listings.sort_index()\\n return num_listings\",\n \"def sort_contacts(contacts):\\n \\n key_list = list(contacts.keys()) #get keys\\n key_list.sort() #sort key_list\\n sorted_list = [] #initialize sorted list\\n for key in key_list:\\n contact = (key, contacts[key][0], contacts[key][1]) #create tuple\\n sorted_list += [contact] #add tuple to list\\n \\n return(sorted_list)\",\n \"def related_reports(self):\\n return Report.objects.exclude(contact_email__isnull=True).filter(contact_email__iexact=self.contact_email).order_by('status', '-create_date')[:1000]\",\n \"def get(self, request):\\n username = None\\n user = None\\n auth = request.user.is_authenticated\\n if auth:\\n user = request.user\\n username = request.user.username\\n listings = self.get_queryset().filter(guest=user)\\\\\\n .order_by(\\\"start_date\\\")\\n\\n return render(request, 'listings/booked.html', {'listings': listings,\\n 'username': username,\\n 'auth': auth})\",\n \"def show_contacts():\\n data_list = queries2.contacts()[0]\\n table_titles = queries2.contacts()[1]\\n title = \\\"Contacts\\\"\\n return render_template('pages.html', data_list=data_list, title=title, table_titles=table_titles)\",\n \"def truncate_contact_list(self, LibraryID, ListID):\\n list_of_contacts = self.getListContacts(LibraryID=LibraryID, ListID=ListID)\\n failures = []\\n if list_of_contacts:\\n for contact in list_of_contacts:\\n RecipientID = contact['RecipientID']\\n try:\\n self.removeContact(LibraryID=LibraryID, ListID=ListID, RecipientID=RecipientID)\\n except Exception as e:\\n # print e\\n failures.append(RecipientID)\\n if failures:\\n return True, []\\n else:\\n return False, failures\",\n \"def test_amount_of_listings_no_listings(self):\\n listings = steam_market.get_total_amount_of_listings(soup=get_soup_from_path(TEST_FILE_NO_RESULTS))\\n self.assertEqual(0, listings)\",\n \"def how_many_comments(comment_queryset, count=10):\\n\\n if count == \\\"all\\\":\\n return comment_queryset\\n return comment_queryset[:int(count)]\",\n \"def scrape_details(self, listings_dict, url):\\n try:\\n next_page_url = None\\n if \\\"paginationNext\\\" in listings_dict['props']['_page']['linkTags']:\\n next_page_url = listings_dict['props']['_page']['linkTags']['paginationNext']['href']\\n listings = listings_dict['props']['searchData']['homes']\\n for listing in listings:\\n try:\\n full_address = listing['location']['partialLocation']\\n address, unitNum = find_unit_num(full_address)\\n if address == \\\"Address Not Disclosed\\\":\\n skip_listing(self.college, 'data', 'Trulia')\\n continue\\n full_price = listing['price']['formattedPrice']\\n price_low, price_high = find_prices(full_price)\\n beds = listing['bedrooms']['formattedValue']\\n beds = ''.join([x for x in beds if x.isdigit()])\\n if beds:\\n if '-' in beds:\\n beds = int(beds[:beds.find('-')])\\n else:\\n beds = int(beds)\\n else:\\n beds = None\\n baths = listing['bathrooms']['formattedValue']\\n baths = ''.join([x for x in baths if not x.isalpha()])\\n if baths:\\n if '-' in baths:\\n baths = float(baths[:baths.find('-')])\\n else:\\n baths = float(baths)\\n else:\\n baths = None\\n sqft = None\\n if 'floorSpace' in listing and listing['floorSpace']:\\n sqft = listing['floorSpace']['formattedDimension']\\n sqft = int(''.join([x for x in sqft if x.isdigit()])) if sqft else None\\n tags = listing['tags']\\n pets = None\\n for tag in tags:\\n if \\\"PET FRIENDLY\\\" in tag.values():\\n pets = True\\n photos = listing['media']['photos']\\n images = list()\\n for photo in photos:\\n images.append(photo['url']['small'])\\n detail_link = 'https://www.trulia.com' + listing['url']\\n latitude = listing['location']['coordinates']['latitude']\\n longitude = listing['location']['coordinates']['longitude']\\n # Build document for DB\\n unit = {\\n 'address': address,\\n 'unitNum': unitNum,\\n 'price_high': price_high,\\n 'price_low': price_low,\\n 'beds': beds,\\n 'baths': baths,\\n 'pets': pets,\\n 'sqft': sqft,\\n 'provider': 'Trulia',\\n 'images': images,\\n 'URL': detail_link,\\n 'original_site': None,\\n 'available': 'Now',\\n 'latitude': latitude,\\n 'longitude': longitude\\n }\\n write_to_raw_json(unit, self.college)\\n\\n # Print Scraping errors and write to log file\\n except Exception as e:\\n write_to_error_log(self.college, 'Trulia', e, link=url)\\n skip_listing(self.college, 'error', 'Trulia')\\n continue\\n\\n except Exception as e:\\n write_to_error_log(self.college, 'Trulia', e, link=url)\\n skip_listing(self.college, 'error', 'Trulia')\\n\\n return next_page_url\",\n \"def display_search_info(client, listing_id):\\n try:\\n resp = client.get_listing_info(listing_id)\\n except exceptions.ServerRequestError as e:\\n if e.status_code == 404:\\n logger.info(uxstring.UxString.app_does_not_exist.format(listing_id))\\n return\\n else:\\n raise e\\n result_json = resp.json()\\n\\n title = click.style(\\\"App Name : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(result_json[\\\"title\\\"]))\\n created_by = click.style(\\\"Created By : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(result_json[\\\"username\\\"]))\\n\\n desc = click.style(\\\"Description : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(result_json[\\\"description\\\"]))\\n price = click.style(\\\"Price Range : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{} - {} Satoshis\\\").format(result_json[\\\"min_price\\\"], result_json[\\\"max_price\\\"])\\n\\n if result_json[\\\"rating_count\\\"] == 0:\\n rating_str = \\\"Not yet rated\\\"\\n else:\\n rating_str = \\\"{:.1f} ({} rating\\\".format(result_json[\\\"average_rating\\\"],\\n int(result_json[\\\"rating_count\\\"]))\\n if result_json[\\\"rating_count\\\"] > 1:\\n rating_str += \\\"s\\\"\\n rating_str += \\\")\\\"\\n rating = click.style(\\\"Rating : \\\", fg=\\\"blue\\\") + click.style(\\\"{}\\\".format(rating_str))\\n\\n doc_url = click.style(\\\"Docs URL : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(result_json[\\\"website_url\\\"]))\\n app_url = click.style(\\\"App URL : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(result_json[\\\"app_url\\\"]))\\n category = click.style(\\\"Category : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(result_json[\\\"category\\\"]))\\n version = click.style(\\\"Version : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(result_json[\\\"version\\\"]))\\n last_updated_str = util.format_date(result_json[\\\"updated\\\"])\\n last_update = click.style(\\\"Last Update : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{}\\\".format(last_updated_str))\\n quick_start = click.style(\\\"Quick Start\\\\n\\\\n\\\", fg=\\\"blue\\\") + click.style(\\n result_json[\\\"quick_buy\\\"])\\n is_active = click.style(\\\"Status : \\\", fg=\\\"blue\\\")\\n if result_json[\\\"is_active\\\"] and result_json[\\\"is_up\\\"] and result_json[\\\"is_healthy\\\"]:\\n is_active += click.style(\\\"Active\\\")\\n else:\\n is_active += click.style(\\\"Inactive\\\")\\n\\n availability = click.style(\\\"Availability : \\\", fg=\\\"blue\\\") + click.style(\\n \\\"{:.2f}%\\\".format(result_json[\\\"average_uptime\\\"] * 100))\\n\\n usage_docs = None\\n if \\\"usage_docs\\\" in result_json:\\n usage_docs = click.style(\\\"Detailed usage\\\\n\\\\n\\\", fg=\\\"blue\\\") + click.style(\\n result_json[\\\"usage_docs\\\"])\\n\\n pager_components = [title, desc, created_by, price, rating, \\\"\\\\n\\\",\\n is_active, availability, \\\"\\\\n\\\",\\n doc_url, app_url, \\\"\\\\n\\\",\\n category, version, last_update, \\\"\\\\n\\\",\\n quick_start, \\\"\\\\n\\\"]\\n\\n if usage_docs:\\n pager_components.append(usage_docs + \\\"\\\\n\\\")\\n\\n final_str = \\\"\\\\n\\\".join(pager_components)\\n\\n logger.info(final_str, pager=True)\",\n \"def get_postings(next_posting_id):\\n connection = sqlite.connect(\\\"postings.db\\\")\\n cursor = connection.cursor()\\n\\n query = []\\n query.append(\\\"SELECT id,raw_post FROM postings\\\")\\n if next_posting_id != None:\\n query.append(\\\"WHERE id > %s\\\" % next_posting_id)\\n query.append(\\\"ORDER BY id\\\")\\n query.append(\\\"LIMIT %s\\\" % POSTING_CHUNK_SIZE)\\n query = \\\" \\\".join(query)\\n\\n cursor.execute(query)\\n\\n results = []\\n for row in cursor:\\n id,raw_post = row\\n\\n posting = json.loads(raw_post)\\n\\n results.append((id, posting))\\n\\n return results\",\n \"def get_comments_by_country(pages, hotel, country):\\n url = \\\"http://www.booking.com/reviewlist.es.html\\\"\\n headers = {\\n 'User-Agent': \\\"PostmanRuntime/7.20.1\\\",\\n 'Accept': \\\"*/*\\\",\\n 'Cache-Control': \\\"no-cache\\\",\\n 'Postman-Token': \\\"4b4e2c78-12c0-42a7-807a-29f5f7378ae5,e75b58fb-25dd-4fdd-b97a-47650ed52d41\\\", # NOQA\\n 'Host': \\\"www.booking.com\\\",\\n 'Accept-Encoding': \\\"gzip, deflate\\\",\\n 'Cookie': \\\"bkng=11UmFuZG9tSVYkc2RlIyh9Yaa29%2F3xUOLbca8KLfxLPeck0I1eO54zQUW2YGGgHUJ6NVSV%2BmLwJzaS5ibHX0J%2BdueF6GNDCq1X0NvEJAU9t%2FoaAC2%2FMBm39Gz0lTSWuf6zuBVIiNGAI88YDjaj4w5H8Lrv7T0Yug9jg%2FpPsONkdMVLMiYifIslIsLvFl07K%2BTKGRykCAxOsgE%3D\\\", # NOQA\\n 'Connection': \\\"keep-alive\\\",\\n 'cache-control': \\\"no-cache\\\"\\n }\\n\\n params = {\\n 'cc1': country,\\n 'pagename': hotel,\\n 'type': 'total',\\n 'dist': str(1),\\n 'rows': str(20)\\n }\\n\\n def build_soup_comment_request(page: int, list_of_countries):\\n if page == 0:\\n params['offset'] = str(page)\\n else:\\n params['offset'] = str(page * 20)\\n\\n response = requests.get(url=url, params=params, headers=headers)\\n comments_soup = BeautifulSoup(response.content, 'html.parser')\\n span = comments_soup.select('.bui-avatar-block__flag img')\\n [get_flags(item, list_of_countries) for item in span]\\n\\n countries_list = {}\\n [build_soup_comment_request(page, countries_list) for page in range(pages)]\\n return countries_list\",\n \"def update_contacts(self):\\n self.contacts = self.db.list_contacts()\\n return self.list_contacts()\",\n \"def pull_contacts(self, org, modified_after, modified_before, progress_callback=None):\\n pass\",\n \"def test_list_referrals_by_desc_object(self):\\n user = factories.UserFactory()\\n referrals = [\\n factories.ReferralFactory(\\n state=models.ReferralState.RECEIVED,\\n object=\\\"First by alphabetical order\\\",\\n post__users=[user],\\n urgency_level=models.ReferralUrgency.objects.get(\\n duration=timedelta(days=1)\\n ),\\n ),\\n factories.ReferralFactory(\\n state=models.ReferralState.RECEIVED,\\n object=\\\"Second by alphabetical order\\\",\\n post__users=[user],\\n urgency_level=models.ReferralUrgency.objects.get(\\n duration=timedelta(days=1)\\n ),\\n ),\\n ]\\n\\n self.setup_elasticsearch()\\n response = self.client.get(\\n f\\\"/api/referrallites/?user={user.id}&sort=object.keyword&sort_dir=desc\\\",\\n HTTP_AUTHORIZATION=f\\\"Token {Token.objects.get_or_create(user=user)[0]}\\\",\\n )\\n\\n self.assertEqual(response.status_code, 200)\\n self.assertEqual(response.json()[\\\"count\\\"], 2)\\n self.assertEqual(response.json()[\\\"results\\\"][0][\\\"id\\\"], referrals[1].id)\\n self.assertEqual(response.json()[\\\"results\\\"][1][\\\"id\\\"], referrals[0].id)\",\n \"def get_contacts(self):\\n\\n\\t\\treturn self.__contacts\",\n \"def top_sources_female(args: Dict[str, Any]) -> List[object]:\\n query = [\\n {\\n \\\"$match\\\": {\\n \\\"body\\\": {\\\"$ne\\\": \\\"\\\"},\\n \\\"quotesUpdated\\\": {\\\"$exists\\\": True},\\n \\\"outlet\\\": {\\\"$in\\\": args[\\\"outlets\\\"]},\\n \\\"publishedAt\\\": {\\n \\\"$gte\\\": args[\\\"begin_date\\\"],\\n \\\"$lt\\\": args[\\\"end_date\\\"] + timedelta(days=1),\\n },\\n }\\n },\\n {\\\"$project\\\": {\\\"outlet\\\": 1.0, \\\"sourcesFemale\\\": 1.0}},\\n {\\\"$unwind\\\": {\\\"path\\\": \\\"$sourcesFemale\\\", \\\"preserveNullAndEmptyArrays\\\": False}},\\n {\\\"$group\\\": {\\\"_id\\\": \\\"$sourcesFemale\\\", \\\"count\\\": {\\\"$sum\\\": 1.0}}},\\n {\\\"$sort\\\": {\\\"count\\\": args[\\\"sort\\\"]}},\\n {\\\"$limit\\\": args[\\\"limit\\\"]},\\n ]\\n return query\",\n \"def listing_id(self, listing_id):\\n\\n self._listing_id = listing_id\",\n \"def calcRestaurantList2(latlngs, cuisines, distance):\\n restlist = []\\n used = []\\n cuisine = str(cuisines[0])\\n if len(cuisines) > 1:\\n cuisine = \\\",\\\".join(cuisines)\\n minrating = 5.0\\n worst = ''\\n ratings = []\\n for point in latlngs:\\n yelpresults = search2(cuisine,point,distance)['businesses']\\n processedyelpresults = processResults(yelpresults)\\n for result in processedyelpresults:\\n if (result not in used):\\n if len(restlist) < 40:\\n restlist.append(processedyelpresults[result])\\n used.append(result)\\n ratings.append(float(processedyelpresults[result]['rating']))\\n if float(processedyelpresults[result]['rating']) < minrating:\\n minrating = float(processedyelpresults[result]['rating'])\\n worst = result\\n # print (\\\"The worst restaurant is {0}\\\".format(worst))\\n elif len(restlist) >= 40:\\n ratings.sort()\\n minrating = ratings[0]\\n if float(processedyelpresults[result]['rating']) > ratings[0]:\\n if worst in restlist:\\n ratings.remove(minrating)\\n restlist.remove(restlist.index(worst))\\n # print (\\\"Removed {0}, which had a rating of {1}. It was in restlist\\\".format(worst, minrating))\\n if len(restlist) <= 45:\\n restlist.append(processedyelpresults[result])\\n # print (\\\"Added {0}, which had a rating of {1}\\\".format(result, processedyelpresults[result]['rating']))\\n else:\\n minrating = float(ratings[0])\\n # print (\\\"The minimum rating for a restaurant is {0}\\\".format(minrating))\\n for r in restlist:\\n # print (r)\\n if float(r['rating']) == minrating:\\n restlist.remove(r)\\n # print (\\\"Removed {0}, which had a rating of {1}. Matched on minrating\\\".format(r, minrating))\\n if minrating in ratings:\\n ratings.remove(minrating)\\n if len(restlist) <= 45:\\n restlist.append(processedyelpresults[result])\\n # print (\\\"Added {0}, which had a rating of {1}\\\".format(result, processedyelpresults[result]['rating']))\\n\\n # pprint.pprint(restlist)\\n # print(used)\\n\\n return restlist\",\n \"def contact_list(self):\\n return self._contact_list\",\n \"def view_shoppinglists(current_user):\\n\\n limit = request.args.get('limit', 10)\\n q = request.args.get('q', None)\\n page = int(request.args.get('page', 1))\\n\\n results = []\\n shoplists = ShoppingList.query.filter_by(\\n user_id=current_user.id)\\n\\n if q is not None:\\n q = q.lower()\\n shoplists = shoplists.filter(\\n ShoppingList.name.like(\\\"%\\\" + q.strip() + \\\"%\\\"))\\n # search_count = ShoppingList.query.filter_by(\\n # user_id=current_user.id).filter(\\n # ShoppingList.name.like(\\\"%\\\" + q.strip() + \\\"%\\\")).count()\\n # # print('search: '+search_count)\\n\\n\\n if limit:\\n try:\\n if int(limit):\\n shoplists = shoplists.filter_by(\\n user_id=current_user.id).paginate(page=page,\\n per_page=int(\\n limit), error_out=False).items\\n\\n for shoplist in shoplists:\\n results.append(shoplist.json())\\n\\n if len(results) == 0:\\n return response('failed', 'Shopping list not found', 404)\\n return get_response('ShoppingLists', results, count=ShoppingList.query.filter_by(\\n user_id=current_user.id).count(), page=page, limit=limit)\\n\\n except ValueError:\\n return response('failed', 'Limit should be an integer', 400)\\n\\n for shoplist in shoplists.all():\\n results.append(shoplist.json())\\n\\n if len(results) == 0:\\n print(len(results))\\n return response('failed', 'Shopping list not found', 404)\\n return get_response('ShoppingLists', results, count=ShoppingList.query.filter_by(\\n user_id=current_user.id).count, page=page, limit=limit)\",\n \"def sorted(self):\\n return (\\n self.get_queryset().annotate(\\n null_member=models.Count('member'),\\n ).order_by('-null_member', 'member')\\n )\",\n \"def count_backbone_contacts(fuzzball_path):\\n print(fuzzball_path)\\n\\n fuzzball = prody.loadAtoms(fuzzball_path)\\n fuzzball_hv = fuzzball.getHierView()\\n ligand = fuzzball_hv['X', 1]\\n ligand_resname = ligand.getResname()\\n\\n constraints = Generate_Constraints(ligand_resname)\\n\\n motif_count = 0\\n bb_contacts = 0\\n\\n for index, motif in enumerate(fuzzball_hv.iterResidues(), start=1):\\n if index != 1:\\n constraint_dict = constraints.determine_constraint_atoms(motif, ligand)\\n if constraint_dict is False: continue\\n if constraint_dict['residue']['atom_names'][0] in ['C', 'CA', 'O', 'N']:\\n bb_contacts += 1\\n motif_count += 1\\n\\n return {'motifs': motif_count,\\n 'bb_contacts': bb_contacts,\\n 'fuzzball': fuzzball_path}\",\n \"def show_listings(offset):\\n items = Item.query.filter(Item.status == \\\"listed\\\").order_by(desc(Item.date_listed)).offset(offset).limit(LIMIT).all()\\n return jsonify(data=[item.serialize for item in items])\\n #return render_template('primary_user_interface.html', items=items)\",\n \"def get_nearest_films(films_list, number, input_location):\\n output_list = []\\n for film_data in films_list:\\n film_dist = int(distance.distance(film_data[1], input_location).km)\\n film_data.append(film_dist)\\n output_list.append(film_data)\\n output_list.sort(key=lambda x: x[-1])\\n if len(output_list) >= int(number):\\n output_list.pop()\\n dist_list = [film[-1] for film in output_list]\\n print(f'Closest film distance: {dist_list[0]} km.')\\n print(f'Furthest film distance: {dist_list[-1]} km.')\\n return output_list\",\n \"def contacts(self):\\n if \\\"contacts\\\" in self._prop_dict:\\n return ContactsCollectionPage(self._prop_dict[\\\"contacts\\\"])\\n else:\\n return None\",\n \"def contacts_in_route_count(self):\\n subprods = SubscriptionProduct.objects.filter(route=self).distinct('subscription__contact')\\n return subprods.count\",\n \"def render_todays_listings(request, context, callsigns):\\n context['listings_matrix'] = []\\n for callsign in callsigns:\\n whats_on_today_url = settings.SODOR_ENDPOINT + 'tvss/' + callsign + '/today/'\\n data = requests.get(whats_on_today_url, headers={'X-PBSAUTH': settings.TVSS_KEY})\\n\\n if data.status_code == 200:\\n jd = data.json()\\n # have to loop through and covert the goofy timestamps into datetime objects\\n for f in jd['feeds']:\\n for l in f['listings']:\\n l['start_time_obj'] = datetime.datetime.strptime(l['start_time'], \\\"%H%M\\\")\\n l['callsign'] = callsign\\n context['listings_matrix'].append(jd)\\n return context\",\n \"def top_sources_all(args: Dict[str, Any]) -> List[object]:\\n query = [\\n {\\n \\\"$match\\\": {\\n \\\"body\\\": {\\\"$ne\\\": \\\"\\\"},\\n \\\"quotesUpdated\\\": {\\\"$exists\\\": True},\\n \\\"outlet\\\": {\\\"$in\\\": args[\\\"outlets\\\"]},\\n \\\"publishedAt\\\": {\\n \\\"$gte\\\": args[\\\"begin_date\\\"],\\n \\\"$lt\\\": args[\\\"end_date\\\"] + timedelta(days=1),\\n },\\n }\\n },\\n {\\n \\\"$project\\\": {\\n \\\"outlet\\\": 1,\\n \\\"sourcesMale\\\": 1,\\n \\\"sourcesFemale\\\": 1,\\n \\\"allSources\\\": {\\n \\\"$concatArrays\\\": [\\n {\\\"$ifNull\\\": [\\\"$sourcesFemale\\\", []]},\\n {\\\"$ifNull\\\": [\\\"$sourcesMale\\\", []]},\\n ]\\n },\\n }\\n },\\n {\\\"$unwind\\\": {\\\"path\\\": \\\"$allSources\\\", \\\"preserveNullAndEmptyArrays\\\": False}},\\n {\\\"$group\\\": {\\\"_id\\\": \\\"$allSources\\\", \\\"count\\\": {\\\"$sum\\\": 1.0}}},\\n {\\\"$sort\\\": {\\\"count\\\": args[\\\"sort\\\"]}},\\n {\\\"$limit\\\": args[\\\"limit\\\"]},\\n ]\\n return query\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5714392","0.5504233","0.53158736","0.52735156","0.50318676","0.50244784","0.50148946","0.50043106","0.49452168","0.49347422","0.49322554","0.4921248","0.48818243","0.48799545","0.48490244","0.4818993","0.4793874","0.4769546","0.47455272","0.46993467","0.46582356","0.46567675","0.46537188","0.4637964","0.4637631","0.45986983","0.45981678","0.45979956","0.45955738","0.45929223","0.45837846","0.45769867","0.45685878","0.45673373","0.4566598","0.4563688","0.45492604","0.4527762","0.45163107","0.45128286","0.4510285","0.450446","0.4489529","0.44746587","0.44721106","0.44671708","0.44647697","0.44409773","0.44360954","0.4419768","0.44108623","0.43881312","0.43667233","0.43663308","0.4365239","0.43525183","0.4337113","0.4335431","0.43192607","0.43159705","0.43127075","0.4309155","0.43062815","0.43057188","0.43017825","0.42933607","0.42899066","0.42846397","0.4274771","0.42733246","0.42703918","0.42610413","0.42540783","0.4253749","0.42518058","0.42497215","0.4242933","0.4241677","0.42409715","0.423708","0.42355388","0.4235497","0.42278996","0.42170712","0.42060813","0.4203497","0.42032066","0.41982952","0.41976395","0.41899878","0.41769096","0.41750717","0.41734937","0.4171735","0.4171488","0.41709578","0.41704923","0.41605678","0.4155437","0.41532713"],"string":"[\n \"0.5714392\",\n \"0.5504233\",\n \"0.53158736\",\n \"0.52735156\",\n \"0.50318676\",\n \"0.50244784\",\n \"0.50148946\",\n \"0.50043106\",\n \"0.49452168\",\n \"0.49347422\",\n \"0.49322554\",\n \"0.4921248\",\n \"0.48818243\",\n \"0.48799545\",\n \"0.48490244\",\n \"0.4818993\",\n \"0.4793874\",\n \"0.4769546\",\n \"0.47455272\",\n \"0.46993467\",\n \"0.46582356\",\n \"0.46567675\",\n \"0.46537188\",\n \"0.4637964\",\n \"0.4637631\",\n \"0.45986983\",\n \"0.45981678\",\n \"0.45979956\",\n \"0.45955738\",\n \"0.45929223\",\n \"0.45837846\",\n \"0.45769867\",\n \"0.45685878\",\n \"0.45673373\",\n \"0.4566598\",\n \"0.4563688\",\n \"0.45492604\",\n \"0.4527762\",\n \"0.45163107\",\n \"0.45128286\",\n \"0.4510285\",\n \"0.450446\",\n \"0.4489529\",\n \"0.44746587\",\n \"0.44721106\",\n \"0.44671708\",\n \"0.44647697\",\n \"0.44409773\",\n \"0.44360954\",\n \"0.4419768\",\n \"0.44108623\",\n \"0.43881312\",\n \"0.43667233\",\n \"0.43663308\",\n \"0.4365239\",\n \"0.43525183\",\n \"0.4337113\",\n \"0.4335431\",\n \"0.43192607\",\n \"0.43159705\",\n \"0.43127075\",\n \"0.4309155\",\n \"0.43062815\",\n \"0.43057188\",\n \"0.43017825\",\n \"0.42933607\",\n \"0.42899066\",\n \"0.42846397\",\n \"0.4274771\",\n \"0.42733246\",\n \"0.42703918\",\n \"0.42610413\",\n \"0.42540783\",\n \"0.4253749\",\n \"0.42518058\",\n \"0.42497215\",\n \"0.4242933\",\n \"0.4241677\",\n \"0.42409715\",\n \"0.423708\",\n \"0.42355388\",\n \"0.4235497\",\n \"0.42278996\",\n \"0.42170712\",\n \"0.42060813\",\n \"0.4203497\",\n \"0.42032066\",\n \"0.41982952\",\n \"0.41976395\",\n \"0.41899878\",\n \"0.41769096\",\n \"0.41750717\",\n \"0.41734937\",\n \"0.4171735\",\n \"0.4171488\",\n \"0.41709578\",\n \"0.41704923\",\n \"0.41605678\",\n \"0.4155437\",\n \"0.41532713\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7177975"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":77994,"cells":{"query":{"kind":"string","value":"Parses the collected data into a dict"},"document":{"kind":"string","value":"def generate_dict_top_five(self):\n all_months = {}\n # Loops over each avilable non-duplicate date\n available_dates = self.contacts.quer_distinct_components(\"contact_date\", \"Contacts\")\n for date in available_dates:\n top_five = self.ordered[date]\n all_months[date] = {}\n\n # Loops over top five to append their components to the final dict\n for index in range(len(top_five)):\n listing_id = top_five[index][1]\n total_contacts = top_five[index][0]\n\n all_months[date].update(\n {\n index+1: {\n 'Ranking': index+1,\n 'Listing Id': listing_id,\n 'Make': self.listing.quer_component_using_column(\"make\", \"id\", \"Listings\", listing_id)[0][0],\n 'Selling Price': self.listing.quer_component_using_column(\"price\", \"id\", \"Listings\", listing_id)[0][0],\n 'Mileage': self.listing.quer_component_using_column(\"mileage\", \"id\", \"Listings\", listing_id)[0][0],\n 'Total Amount of Contacts': total_contacts\n }\n }\n )\n\n return all_months"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def parse_data( self ):\n self.parsed_data = dict( self.results )","def get_data(self):\n self.data = dict()\n # list to save all the attributes we are going to create\n self.attr = []\n # list to save all the groups available in the incomming input\n self.groups.extend(self.values.keys())\n # Grouping\n self.parse_data()","def get_structure(self):\n main = {}\n for line in self.load():\n match = re.match('^\\s*([A-Za-z0-9_]+)(\\((\\d+)\\))?=(.*)$', line)\n if match:\n key = match.group(1)\n index = match.group(3)\n value = match.group(4)\n if index is None:\n main[key] = self.parse_data_value(value)\n else:\n if key not in main:\n main[key] = []\n main[key].append(self.parse_data_value(value))\n #else:\n # print(line)\n return main","def extractData(self) -> Dict[str, str]:\n if self.hasScore():\n result = {}\n result[\"date\"] = self.getMatchDate()\n result[\"score\"] = self.getMatchScore()\n for team in self.TEAMS:\n result[\"team_\" + team] = self.getSquadName(team)\n\n return result\n return None","def raw_data() -> Dict:\n return {\"neighbourhood\":\"Buttes-Montmartre\",\"room_type\":\"Entire home/apt\",\"minimum_nights\":1.555,\"mois\":2,\"voyageurs\":2.5,\"chambres\":1,\"lits\":1,\"salle_de_bains\":1}","def prepare_data(data: list) -> dict:\n d = {}\n for t in data:\n d[t[0]] = read_text(t[1])\n return d","def process_data(data):\n info = {\n 'cities': [],\n 'temperatures': [],\n 'humidities': [],\n }\n cities = data['list']\n for city in cities:\n main_data = city['main']\n info['cities'].append(city['name'])\n info['temperatures'].append(main_data['temp'])\n info['humidities'].append(main_data['humidity'])\n\n return info","async def parse(self, raw: str) -> dict:","def parse_data(self):\n\t\traise NotImplementedError('%s: No parse function implemented!' % self.name)","def getParsedDic(self):\n return {}","def _read_data(self, data_record):\n data_dict = {}\n data_labels = self._header_dict['labels']\n data = data_record.strip().split()\n\n if self.num_columns != len(data):\n err_msg = \"GliderParser._read_data(): Num Of Columns NOT EQUAL to Num of Data items: \" + \\\n \"Expected Columns= %s vs Actual Data= %s\" % (self.num_columns, len(data))\n log.error(err_msg)\n raise DatasetParserException(err_msg)\n\n # extract record to dictionary\n for ii, value in enumerate(data):\n label = data_labels[ii]\n data_dict[label] = value\n\n return data_dict","def get_data(self):\n with LoggingTime(\"Fetching data took: \"):\n try:\n with open(self.__io_params.input_file_path, 'r', encoding=\"utf8\") as csvFiles:\n reader = csv.reader(csvFiles, delimiter=',', quotechar='|')\n dictionary = {}\n for data in reader:\n if len(data) > 0 and data[0]: # ignore malformed data lines\n word_base = data[0].strip().lower()\n if self.word_is_eligible(word_base) and word_base not in dictionary:\n dictionary[word_base] = ProcessingEntry(word_base)\n logging.info(\"Loaded %i search terms\" % (len(dictionary)))\n return dictionary\n except IOError as e:\n logging.error(\"IO Error has occurred. Check input file\")\n logging.error(e)\n raise\n except:\n logging.error(\"Reading data failed. Check input file format\")\n raise","def make_feed_dict(self, data):\r\n raise NotImplementedError","def extract(self, data):","def parse(self):\n\n\t\t# Open and parse the file\n\t\twith open(self.name, 'r') as fdi:\n\t\t\tfor line in fdi:\n\t\t\t\twords = [word for word in line.split(' ') if word != ' ' and word != ':']\n\t\t\t\t\n\t\t\t\t# Store the data in the hash\n\t\t\t\tself.data_hash[int(words[0], 16)] = int(words[1])\n\n\t\t# Sort the dictionary by addresses\n\t\tself.data_hash = od(sorted(self.data_hash.items(), key = lambda t : t[0]))\n\n\t\tprint 'Total Bytes :', float(sum(self.data_hash.values())) / 1024 / 1024\n\n\t\treturn","def _collect_data(self):\n data = {\n \"K\": self.K,\n \"root\": self.root\n }\n return data","def get_data(self):\n def _clean_search_hit(search_hit):\n \"\"\"\n Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema.\n\n :param search_hit:\n :return Dictionary: A dictionary with the cleaned data\n \"\"\"\n\n hit_name = search_hit.find(class_='hit-name')\n hit_url = hit_name.get('href')\n hit_id = hit_url.split('/')[-1]\n name = hit_name.get_text().split(',')[0].title().split()\n\n current_city = search_hit.find(class_='hit-location').get_text().upper()\n\n # Find all Addresses for search result.\n try:\n address = search_hit.find(class_='hit-pastAddresses').find_all(class_='hit-values')\n address = list({a.text.upper().replace('.', '') for a in address})\n except AttributeError:\n address = list()\n\n # find the address that is most likely the current main address.\n try:\n address.insert(0, address.pop(address.index(current_city)))\n except ValueError:\n address.insert(0, current_city)\n\n address = [\n {\n '@type': 'PostalAddress',\n 'addressLocality': locality.title(),\n 'addressRegion': region\n } for locality, region in [a.split(', ') for a in address]]\n\n work_location = {'@type': 'Place'}\n try:\n work_location['name'] = search_hit\\\n .find(class_='hit-work')\\\n .find(class_='hit-values')\\\n .get_text()\\\n .title()\n except AttributeError:\n work_location['name'] = ''\n\n alumni_of = {'@type': 'EducationalOrganization'}\n try:\n alumni_of['name'] = search_hit\\\n .find(class_='hit-high-school')\\\n .find(class_='hit-values')\\\n .get_text().title()\n except AttributeError:\n pass\n\n return {\n '@id': hit_id,\n '@type': 'Person',\n 'name': ' '.join(name),\n 'givenName': name[0],\n 'middleName': ' '.join(name[1:-1]),\n 'familyName': name[-1],\n 'url': hit_url,\n 'address': address,\n 'workLocation': work_location,\n 'alumniOf': alumni_of,\n }\n\n def _refine_search(search_str, options):\n \"\"\"\n Takes a list of WebElements and a search string, looks for string in the text of each WebElement, and\n press the option if found. Returns Boolean for found status\n\n :param search_str: str of the desired option.\n :param options: list of WebElements from Beautify Soup that represents all of the available options.\n :return:\n \"\"\"\n search_str = search_str.upper()\n logging.info(f'Looking for \\'{search_str}\\'')\n try:\n for option in options:\n option_text = option.text.upper()\n logging.info(f'Option Checked: {option_text}')\n if search_str in option_text:\n option.click()\n time.sleep(2)\n logging.info(f'Option Selected: {option_text}')\n return True\n else:\n return False\n except AttributeError:\n return True\n except StaleElementReferenceException as e:\n ChromeCrash(e)\n\n with self.driver(executable_path=self.DRIVER_DIR) as driver:\n driver.get(self.url)\n\n \"\"\"\n The CSS for the page doesn't show the State nor the City selector options if the page is too narrow,\n so we need to make sure the browser is open wide enough for the CSS to make those options visible. \n \"\"\"\n driver.fullscreen_window()\n\n # Refine the search by State\n address_region = self.person.get('addressRegion', '')\n address_region = STATES.get(address_region.upper(), address_region.upper())\n region_options = driver\\\n .find_element_by_class_name(\"STATE\")\\\n .find_elements_by_class_name(\"refinementList-text\")\n\n if not _refine_search(address_region, region_options):\n return False\n\n # Narrow the search by pressing a City option\n address_locality = self.person.get('addressLocality').title()\n locality_options = driver\\\n .find_element_by_class_name(\"CITY\")\\\n .find_elements_by_class_name(\"refinementList-text\")\n\n if not _refine_search(address_locality, locality_options):\n return False\n\n \"\"\"\n The Page Loads dynamically, so we need to scroll down the page to show all the search results. It needs to\n be done in steps with a pause between movements to allow for loading. \n Here it will first get the current location on the page, attempt to move down the page, and then check to\n see if the location changed.\n \"\"\"\n\n if self.auto_scroll and len(driver.find_elements_by_class_name(\"ais-InfiniteHits-item\")) > 15:\n current_height, new_height = 0, driver.execute_script(\"return document.body.scrollHeight\")\n\n while new_height != current_height:\n # Scroll down to the bottom of the page\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\n # Wait to load page\n time.sleep(SCROLL_PAUSE_TIME)\n\n # Calculate new scroll height and compare with last scroll height\n current_height, new_height = new_height, driver.execute_script(\"return document.body.scrollHeight\")\n\n page_source = driver.page_source\n page_soup = bs(page_source, 'html.parser')\n search_results = list(page_soup.find_all(class_='ais-InfiniteHits-item'))\n for i, search_result in enumerate(search_results):\n search_results[i] = _clean_search_hit(search_result)\n\n self.data_from_website = pd.DataFrame(search_results)\n self.data_from_website.set_index('@id', inplace=True)\n return True","def get_data():\n data = {}\n with open(app.config['DATA_CSV'], 'r') as csvfile:\n presence_reader = csv.reader(csvfile, delimiter=',')\n for i, row in enumerate(presence_reader):\n if len(row) != 4:\n # ignore header and footer lines\n continue\n\n try:\n user_id = int(row[0])\n date = datetime.strptime(row[1], '%Y-%m-%d').date()\n start = datetime.strptime(row[2], '%H:%M:%S').time()\n end = datetime.strptime(row[3], '%H:%M:%S').time()\n except (ValueError, TypeError):\n log.debug('Problem with line %d: ', i, exc_info=True)\n\n data.setdefault(user_id, {})[date] = {'start': start, 'end': end}\n return data","def transform_string_to_dictionary(data_text: str) -> Dict[str, float]:\n # TODO: create an empty population dictionary\n # TODO: iterate through each line of the data set\n # TODO: extract the ordered pair on this line\n # the ordered pair has the format:\n # (Date, population count in thousands of persons)\n # TODO: extract the dat and store it as a string\n # TODO: convert the population count to a float and store it\n # TODO: add the new key-value pair to the dictionary where\n # the key is the date and the population is the value\n # TODO: return the population dictionary","def parse_data(data):\n\n d = dict()\n for key in data.keys():\n d[int(key)] = parse_date_period(data[key])\n\n return d","def parse(data):\n try:\n header = decompress(data)\n version, game, save, log = parse_version(header, data)\n if version not in (Version.USERPATCH15, Version.DE, Version.HD):\n raise RuntimeError(f\"{version} not supported\")\n de = parse_de(header, version, save)\n hd = parse_hd(header, version, save)\n metadata, num_players = parse_metadata(header)\n map_ = parse_map(header, version)\n players, mod, device = parse_players(header, num_players, version, save)\n scenario = parse_scenario(header, num_players, version, save)\n lobby = parse_lobby(header, version, save)\n except (struct.error, zlib.error, AssertionError, MemoryError, ValueError) as e:\n raise RuntimeError(f\"could not parse: {e}\")\n return dict(\n version=version,\n game_version=game,\n save_version=save,\n log_version=log,\n players=players,\n map=map_,\n de=de,\n hd=hd,\n mod=de.get('dlc_ids') if de else mod,\n metadata=metadata,\n scenario=scenario,\n lobby=lobby,\n device=device\n )","def parse(data, raw=False, quiet=False):\n jc.utils.compatibility(__name__, info.compatible, quiet)\n jc.utils.input_type_check(data)\n\n raw_output = {}\n\n if jc.utils.has_data(data):\n\n for line in filter(None, data.splitlines()):\n linedata = line.split(':', maxsplit=1)\n key = linedata[0].strip().lower().replace(' ', '_').replace('.', '_')\n value = linedata[1].strip()\n raw_output[key] = value\n\n if raw:\n return raw_output\n else:\n return _process(raw_output)","def parse_data(data):\n parsed_data = {}\n for i, chunk in enumerate(re.split(r'\\n{2,}', data)):\n if i == 0:\n match = re.search(r'^(.*?) interest: (.*)\\n(.*?); (.*?)$', chunk)\n if match:\n source, query, geo, period = match.groups()\n parsed_data['info'] = {'source': source, 'query': query,\n 'geo': geo, 'period': period}\n else:\n chunk = _clean_subtable(chunk)\n rows = [row for row in csv.reader(StringIO(chunk)) if row]\n if not rows:\n continue\n label, parsed_rows = _parse_rows(rows)\n if label in parsed_data:\n parsed_data[label+'_1'] = parsed_data.pop(label)\n parsed_data[label+'_2'] = parsed_rows\n else:\n parsed_data[label] = parsed_rows\n\n return parsed_data","def data(self) -> dict[str, Any]:\n raise NotImplementedError()","def _process_info(resp: suds.sudsobject) -> dict:\n last = resp.ultimoValor\n return dict(fonte = str(resp.fonte),\n gestor = str(resp.gestorProprietario),\n freq = str(resp.periodicidadeSigla),\n nome = str(resp.nomeCompleto),\n number = int(resp.oid),\n final = dt(last.ano, last.mes, last.dia))","def _parse_input(self):\n parser = re.compile(r'(\\w+)\\D+(\\d+)\\D+(\\d+)\\D+(\\d+)')\n reindeer = {}\n for line in self.puzzle_input.splitlines():\n instruction = parser.match(line)\n if not instruction:\n continue\n name, speed, flight, rest = instruction.groups()\n reindeer[name] = Reindeer(int(speed), int(flight), int(rest))\n return reindeer","def parse_data(self):\n\n msg = self.xml['dom'].childNodes[0]\n self.data = xml_to_dicts(msg, False)\n\n # Get some metadata together\n self.id = \"%s:%s\" % (self.data['src']['name']['#cdata'], self.data['src']['id']['#cdata'])\n self.temp = self.data['tmpr']['#cdata']\n self.watts = self.data['ch1']['watts']['#cdata']\n\n # Time - CurrentCost and local\n self.date = {}\n self.date['cc'] = [ int(self.data['date'][k]['#cdata']) for k in ('dsb','hr','min','sec') ]\n self.date['now'] = localtime()","def parse(u):\n rec = {}\n\n try:\n r = requests.get(u, headers=headers)\n\n if r.status_code == 200:\n html = r.text\n soup = BeautifulSoup(html, 'lxml')\n overview_section = soup.select('.Raw-s14xcvr1-0 gXqFYO')\n full_name_section = soup.select('.sc-iwsKbI kjxnCg')\n years_of_practice_section = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\n language_section = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\n office_location_section = soup.select('.Paragraph-fqygwe-0 cojhks')\n hospital_affiliation_section = soup.select('.Paragraph-fqygwe-0 fwayNy')\n specialties = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\n education_and_medical_training_section = soup.select('.EducationAndExperience__Item-xn5fll-0 bzYYRk')\n certification_and_licensure_section = soup.select('.Paragraph-fqygwe-0 bQPwuv')\n\n if overview_section:\n overview = overview_section[0].text.replace('\"', '')\n if full_name_section:\n full_name = full_name_section[0].text\n if years_of_practice_section:\n years_of_practice = years_of_practice_section[0].text.strip().replace('\"', '')\n if language_section:\n language = language_section[0].text.strip().replace('\"', '')\n if office_location_section:\n office_location = office_location_section[0].text\n if hospital_affiliation_section:\n hospital_affiliation = hospital_affiliation_section[0].text.strip().replace('\"', '')\n if specialties_section:\n specialties = specialties_section[0].text.replace('\"', '')\n if education_and_medical_training_section:\n education_and_medical_training = education_and_medical_training_section[0].text\n if certification_and_licensure_section:\n certification_and_licensure = certification_and_licensure_section[0].text\n\n\n rec = {'overview': overview, 'full_name': full_name, 'years_of_practice': years_of_practice, 'language': language,\n 'office_location': office_location, 'hospital_affiliation': hospital_affiliation, 'specialties':specialties,\n 'education_and_medical_training': education_and_medical_training,\n 'certification_and_licensure': certification_and_licensure}\n except Exception as ex:\n print('Exception while parsing')\n print(str(ex))\n finally:\n return json.dumps(rec)","def read_item(data: DataModel) -> Dict:\n convertor = Convertor(data)\n return {'output': convertor.get_humanized_data()}","def extract_data(self):\n values = {}\n for injkey in self.data_sets.keys():\n values[injkey] = {}\n alldata = self.data_sets[injkey]\n paramkeys = alldata['params'].keys()\n for datakey in alldata.keys():\n if not datakey == 'params':\n values[injkey][datakey] = {}\n values[injkey][datakey]['metric_val'] = {}\n values[injkey][datakey]['metric_val']['vals'] = []\n for paramkey in paramkeys:\n values[injkey][datakey][paramkey] = {}\n values[injkey][datakey][paramkey]['vals'] = []\n trials = alldata[datakey]\n for trial_num in trials.keys():\n trial = trials[trial_num]\n values[injkey][datakey]['metric_val']['vals'] \\\n .append(trial['metric_val'])\n values[injkey][datakey]['metric_val']['type'] \\\n = trial['metric']\n values[injkey][datakey]['metric_val']['units'] \\\n = 'dimensionless'\n param_vals = trial['params']\n for param_name in param_vals.keys():\n val, units = self.parse_pint_string(\n pint_string=param_vals[param_name]\n )\n values[injkey][datakey][param_name]['vals'] \\\n .append(float(val))\n values[injkey][datakey][param_name]['units'] \\\n = units\n self.values = values","def metadata(self):\n self.data_as_dict = {}\n for ele in self.data:\n self.data_as_dict[ele.name] = ele.value\n return self.data_as_dict","def parse_data(self):\n data = {}\n content = self.headers.get('content-type', None)\n if content:\n ctype, pdict = parse_header(content)\n if ctype == 'application/json':\n length = int(self.headers['content-length'])\n data = json.loads(self.bytes_to_str(self.rfile.read(length)))\n return data","def process(raw):\n entry = { }\n cooked = [ ]\n\n for line in raw:\n line = line.strip()\n if len(line) == 0 or line[0]==\"#\" :\n continue\n parts = line.split(';')\n if len(parts) == 3:\n entry[\"description\"] = parts[0].strip() #adding key and values to the dict\n entry[\"long\"] = parts[1].strip()\n entry[\"lat\"] = parts[2].strip()\n cooked.append(entry) #add this dict entry into the array\n entry = { }\n continue\n else:\n raise ValueError(\"Trouble wiht line: '{}'\\n\".format(line))\n \n return cooked #returning an array of dicts","def setup_parser(self) -> Dict[str, Any]:\n\n\t# % year,doy, RH(m), Month, day, azimuth(deg),freq, satNu, LSP amp,pk2noise,UTC(hr) \n\t# 2021 9 4.888 1 9 225.3 1 2 9.51 3.23 10.08\n\t# 2021 9 5.018 1 9 181.3 1 15 7.79 2.84 15.67\n\t# 2021 9 5.123 1 9 185.4 1 16 6.27 3.01 0.68\n #----+----0----+----1----+----2----+----3----+----4----+----5----+----6----+----7\n return dict(\n skip_header=1,\n names=(\n \"year\",\n \"doy\",\n \"reflection_height\",\n \"month\",\n \"day\",\n \"azimuth\",\n \"frequency\",\n \"satellite\",\n \"amplitude\",\n \"peak2noise\",\n \"hour\",\n ),\n dtype=(\"f8\", \"f8\", \"f8\", \"f8\", \"f8\", \"f8\", \"f8\", \"f8\", \"f8\", \"f8\", \"f8\"),\n )","def parse(self):\n\n # if data has already been parsed, do nothing\n if self._data:\n return\n\n stats = {\n \"genres\": {},\n \"artists\": {},\n \"global_stats\": {\n \"songs\": 0,\n \"lines\": 0,\n \"words\": 0\n }\n }\n try:\n with open(self._filename) as file:\n objects = ijson.items(file, \"item\")\n\n # compute metrics\n for object in objects:\n\n lines = len(object[\"lyrics\"])\n words = sum([len(line.split()) for line in object[\"lyrics\"]])\n\n genre = object[\"genre\"]\n stats[\"genres\"][genre] = stats[\"genres\"].get(genre, {\"artists\": {}})\n genre_obj = stats[\"genres\"][genre]\n genre_obj[\"songs\"] = genre_obj.get(\"songs\", 0) + 1\n genre_obj[\"lines\"] = genre_obj.get(\"lines\", 0) + lines\n genre_obj[\"words\"] = genre_obj.get(\"words\", 0) + words\n genre_obj[\"is_music\"] = genre_obj.get(\"is_music\", 0)\n if object[\"is_music\"] != \"false\":\n genre_obj[\"is_music\"] += 1\n\n artist = object[\"artist\"]\n stats[\"artists\"][artist] = stats[\"artists\"].get(artist, 0) + 1\n stats[\"genres\"][genre][\"artists\"][artist] = stats[\"genres\"][genre][\"artists\"].get(artist, 0) + 1\n\n # update global stats\n stats[\"global_stats\"][\"songs\"] += 1\n stats[\"global_stats\"][\"lines\"] += lines\n stats[\"global_stats\"][\"words\"] += words\n\n # calculate averages for each genre\n for genre, genre_stats in stats[\"genres\"].items():\n genre_stats[\"avg_line_length\"] = genre_stats[\"words\"] / genre_stats[\"lines\"]\n genre_stats[\"avg_lines\"] = genre_stats[\"lines\"] / genre_stats[\"songs\"]\n genre_stats[\"avg_words\"] = genre_stats[\"words\"] / genre_stats[\"songs\"]\n\n # calculate global averages\n stats[\"global_stats\"][\"avg_line_length\"] = stats[\"global_stats\"][\"words\"] / stats[\"global_stats\"][\"lines\"]\n stats[\"global_stats\"][\"avg_lines\"] = stats[\"global_stats\"][\"lines\"] / stats[\"global_stats\"][\"songs\"]\n stats[\"global_stats\"][\"avg_words\"] = stats[\"global_stats\"][\"words\"] / stats[\"global_stats\"][\"songs\"]\n\n self._data = stats\n\n except IOError as e:\n print(\"Exception occurred: \", e)","def _build_parsed_values(self):\n\n self.final_result = []\n retrieved_data_types = set() # keep track of data type ID's unpacked from record\n\n # Get the file time from the file name\n if self._file_time:\n self.final_result.append(self._encode_value(\n AdcptMWVSParticleKey.FILE_TIME, self._file_time, str))\n else:\n self.final_result.append({DataParticleKey.VALUE_ID: AdcptMWVSParticleKey.FILE_TIME,\n DataParticleKey.VALUE: None})\n\n # Get the sequence number from the file name\n if self._sequence_number:\n self.final_result.append(self._encode_value(\n AdcptMWVSParticleKey.SEQUENCE_NUMBER, self._sequence_number, int))\n else:\n self.final_result.append({DataParticleKey.VALUE_ID: AdcptMWVSParticleKey.SEQUENCE_NUMBER,\n DataParticleKey.VALUE: None})\n\n # Get the number of data types from the Header\n num_data_types = struct.unpack_from('<B', self.raw_data, HEADER_NUM_DATA_TYPES_OFFSET)\n # Get the list of offsets from the Header\n offsets = struct.unpack_from('<%sI' % num_data_types, self.raw_data, HEADER_OFFSETS_OFFSET)\n\n # Unpack Type IDs from the offsets\n for offset in offsets:\n data_type_id, = struct.unpack_from('<h', self.raw_data, offset)\n # keep track of retrieved data types\n retrieved_data_types.add(data_type_id)\n\n # Feed the data through the corresponding encoding function and unpacking rules\n try:\n self.encoding_func_dict[data_type_id][ENCODE_FUNC](\n offset + ID_TYPE_SIZE, self.encoding_func_dict[data_type_id][UNPACK_RULES])\n except KeyError:\n log.debug(\"Skipping unsupported data type ID: %s at offset: %s\",\n data_type_id, offset)\n\n # go through the list of expected data type ID's, fill in None for missing data type ID's\n missing_data = EXPECTED_PARTICLE_IDS_SET.difference(retrieved_data_types)\n for data_type_id in missing_data:\n if data_type_id is VARIABLE_LEADER:\n # timestamp is essential for a data particle - no timestamp, no particle\n message = \"Variable Leader Data Type is required for internal timestamp, \" \\\n \"particle ignored.\"\n log.warn(message)\n raise RecoverableSampleException(message)\n\n self.final_result.extend(self.encoding_func_dict[data_type_id][ENCODE_NULL])\n\n log.trace(\"FINAL RESULT: %s\\n\", self.final_result)\n\n return self.final_result","def _get_data(self):\n raw_data = self._get_raw_data()\n if not raw_data:\n return None\n result = {}\n for line in raw_data:\n if 'tcp' in line:\n parts = line.split()\n proto = parts[0]\n local_addr = parts[3]\n state = parts[5]\n ip, port = local_addr.rsplit(':', 1)\n port = str(port)\n result[port] = 1\n if state == 'LISTEN':\n if port not in self.charts['ports']:\n self.charts['ports'].add_dimension([port, port, 'absolute'])\n return result","def parse_data( self ):\n super( PivotGraph, self ).parse_data()\n new_parsed_data = {}\n parsed_data = getattr( self, 'parsed_data', self.results )\n for pivot, data in parsed_data.items():\n new_pivot = self.parse_pivot( pivot )\n data = self.parse_datum( data )\n if data != None:\n new_parsed_data[ new_pivot ] = data\n self.parsed_data = new_parsed_data","def parse(\n data: str,\n raw: bool = False,\n quiet: bool = False\n) -> Dict:\n jc.utils.compatibility(__name__, info.compatible, quiet)\n jc.utils.input_type_check(data)\n\n raw_output: Dict = {}\n split_me = {'it_value:', 'it_interval:'}\n\n if jc.utils.has_data(data):\n\n for line in filter(None, data.splitlines()):\n\n # epoll files\n if line.startswith('tfd:'):\n line_match = re.findall(r'(?P<key>\\S+):(?:\\s+)?(?P<val>\\S+s*)', line)\n if line_match:\n raw_output.update({'epoll': {k.strip(): v.strip() for k, v in line_match}})\n continue\n\n # inotify files\n if line.startswith('inotify'):\n split_line = line[8:].split()\n raw_output['inotify'] = {}\n for item in split_line:\n k, v = item.split(':', maxsplit=1)\n raw_output['inotify'][k] = v\n continue\n\n # fanotify files\n if line.startswith('fanotify'):\n split_line = line[9:].split()\n\n if not 'fanotify' in raw_output:\n raw_output['fanotify'] = {}\n\n for item in split_line:\n k, v = item.split(':', maxsplit=1)\n raw_output['fanotify'][k] = v\n continue\n\n # timerfd files\n if line.split()[0] in split_me:\n split_line = line.replace(':', '').replace('(', '').replace(')', '').replace(',', '').split()\n raw_output[split_line[0]] = [int(x) for x in split_line[1:]]\n continue\n\n key, val = line.split(':', maxsplit=1)\n raw_output[key.strip()] = val.strip()\n continue\n\n return raw_output if raw else _process(raw_output)","def parse(self) -> Dictionary:\n self.parsed_dictionary = dictionary = Dictionary()\n state = State.pre_signature\n for lineno, line in self.line_iter:\n lineno += 1\n line = decomment_and_normalize(line)\n if line == \"\": continue\n parsed = False\n expected_lines = State.expected_lines(state)\n for t in expected_lines:\n parsed, state = t.parse_line(state, dictionary, line, lineno)\n if parsed: break\n if not parsed:\n raise DictionaryParseError(lineno, expected_lines, self.source)\n if State.is_not_final(state):\n raise DictionaryParseError(lineno + 1, expected_lines, self.source)\n try:\n del dictionary._last_article\n del dictionary._last_definition\n del dictionary._last_example\n del dictionary._last_idiom\n except AttributeError:\n pass\n return dictionary","def collection_loader(data: str) -> dict:\n tmp: dict[str, Optional[Union[int, bool, str]]] = {}\n\n for key, val in json.loads(data).items():\n if isinstance(val, (str, bool, type(None))):\n # These are stored as string but must be parsed into `Quantity`\n if key in (\"cutoff\", \"switch_width\"):\n tmp[key] = unit.Quantity(*json.loads(val).values()) # type: ignore[arg-type]\n else:\n tmp[key] = val\n elif isinstance(val, dict):\n if key == \"key_map\":\n key_map = {}\n\n for key_, val_ in val.items():\n if \"atom_indices\" in key_:\n topology_key: Union[\n TopologyKey,\n LibraryChargeTopologyKey,\n ] = TopologyKey.parse_raw(key_)\n\n else:\n topology_key = LibraryChargeTopologyKey.parse_raw(key_)\n\n potential_key = PotentialKey(**val_)\n\n key_map[topology_key] = potential_key\n\n tmp[key] = key_map # type: ignore[assignment]\n\n elif key == \"potentials\":\n potentials = {}\n\n for key_, val_ in val.items():\n potential_key = PotentialKey.parse_raw(key_)\n potential = Potential.parse_raw(json.dumps(val_))\n\n potentials[potential_key] = potential\n\n tmp[key] = potentials # type: ignore[assignment]\n\n else:\n raise NotImplementedError(f\"Cannot parse {key} in this JSON.\")\n\n return tmp","def requestData(self):\n newsRaw = {'de': self.networkManager.fetchNews('de'), 'en': self.networkManager.fetchNews('en'),\n 'fr': self.networkManager.fetchNews('fr')}\n eventsRaw = {'de': self.networkManager.fetchEvents('de'), 'en': self.networkManager.fetchEvents('en'),\n 'fr': self.networkManager.fetchEvents('fr')}\n news = {}\n events = {}\n categories = {}\n eventCategories = {}\n for language in ['de', 'en', 'fr']:\n news[language] = self.newsAndEventsParser.parseNews(newsRaw[language], language)\n events[language] = self.newsAndEventsParser.parseEvents(eventsRaw[language], language)\n categories[language] = self.newsAndEventsParser.getCategories()\n eventCategories[language] = self.newsAndEventsParser.getEventCategories()\n events['de'] += self.newsAndEventsParser.readAcademicCalendarEvents()\n return news, events, categories, eventCategories","def parse_input_file(self, input_path):\n with open(input_path) as input_json_data:\n input_data = json.load(input_json_data)\n\n self.total_outlets = input_data['machine']['outlets']['count_n']\n self.total_items_quantity = input_data['machine']['total_items_quantity']\n self.beverages = input_data['machine']['beverages']\n\n parsed_data = {\n 'total_outlets': self.total_outlets,\n 'total_items_quantity': self.total_items_quantity,\n 'beverages': self.beverages\n }\n\n return parsed_data","def _parse(file_contents):\n\n if file_contents is None or file_contents == '':\n return {}\n\n result = {}\n\n for line in file_contents.splitlines():\n # Full line comment\n if line[:1] == '#':\n continue\n\n parts = line.split('=', 1)\n\n # Not a full key-value pair.\n if len(parts) < 2:\n continue\n\n result[parts[0].strip()] = parts[1].strip()\n\n return result","def _parse_cookie_data() -> dict:\n\n with open('Data/cookies.json') as cookie_data_file:\n cookie_data_dict = json.load(cookie_data_file)\n\n # Cast the necessary data\n for cookie_type in cookie_data_dict:\n cookie_type['weight'] = float(cookie_type['weight'])\n cookie_type['target'] = CookieHuntTarget(cookie_type['target'])\n\n return cookie_data_dict","def run(self):\n return_dict = {}\n for x in self.file_list:\n return_dict[x] = self.parseFile(x)\n\n return return_dict","def parse(data: str, raw: bool =False, quiet: bool =False) -> Dict:\n jc.utils.compatibility(__name__, info.compatible, quiet)\n jc.utils.input_type_check(data)\n\n linedata = data.splitlines()\n linedata.reverse() # For popping\n result: Response = {\"screens\": [], \"unassociated_devices\": []}\n\n if jc.utils.has_data(data):\n while linedata:\n screen = _parse_screen(linedata)\n if screen:\n result[\"screens\"].append(screen)\n else:\n device = _parse_device(linedata, quiet)\n if device:\n result[\"unassociated_devices\"].append(device)\n\n if not result[\"unassociated_devices\"] and not result[\"screens\"]:\n return {}\n\n return result","def parse(self):\n result = {}\n if self.detail_statu:\n sel = Selector(text=self.driver.page_source)\n\n fact_table = sel.xpath(\n '//div[@class=\"facts-table\"]//text()').extract()\n result['facts'] = [list(i)\n for i in zip(fact_table[:: 2],\n fact_table[1:: 2])]\n\n tax_table = sel.xpath(\n '//div[@class=\"tax-values\"]//text()').extract()\n result['taxs'] = [list(i)\n for i in zip(tax_table[:: 2],\n tax_table[1:: 2])]\n\n listing_detail = sel.xpath(\n '//div[@class=\"amenities-container\"]//text()').extract()\n result['detail'] = listing_detail\n result['page_source'] = self.driver.page_source\n self.detail_statu = False\n else:\n self.log.warning(\n '---- Detail page url out of reach, use .search() first to get the detail page')\n return result","def getData(self):\n return dict(self._dump_data)","def collect(self) -> Dict:\n raise NotImplementedError()","def parse_data( self ):\n super( PivotGroupGraph, self ).parse_data()\n new_parsed_data = {}\n parsed_data = getattr( self, 'parsed_data', self.results )\n for pivot, groups in parsed_data.items():\n new_pivot = self.parse_pivot(pivot)\n if new_pivot == None:\n continue\n new_groups = {}\n new_parsed_data[ new_pivot ] = new_groups\n for group, data in groups.items():\n new_group = self.parse_group(group)\n new_datum = self.parse_datum(data)\n if new_group == None:\n continue\n new_groups[ new_group ] = new_datum\n if len(new_groups) == 0:\n del new_parsed_data[new_pivot]\n self.parsed_data = new_parsed_data","def parse_data(infile):\n blocks = re.compile(' '.join(['=' * 9] * 8))\n dashes = re.compile('^-{79}$')\n title = re.compile('^Timings for (.*)$')\n row = re.compile(' '.join(['(.{9})'] * 7) + ' (.{8,9})')\n\n lines = infile.readlines()\n\n data = co.OrderedDict()\n index = 0\n\n while index < len(lines):\n line = lines[index]\n\n if blocks.match(line):\n try:\n name = title.match(lines[index + 1]).group(1)\n except Exception:\n index += 1\n continue\n\n data[name] = {}\n\n assert dashes.match(lines[index + 2])\n\n cols = parse_row(row, lines[index + 3])\n\n assert blocks.match(lines[index + 4])\n\n get_row = parse_row(row, lines[index + 5])\n assert get_row[0] == 'get'\n\n set_row = parse_row(row, lines[index + 6])\n assert set_row[0] == 'set'\n\n delete_row = parse_row(row, lines[index + 7])\n assert delete_row[0] == 'delete'\n\n assert blocks.match(lines[index + 9])\n\n data[name]['get'] = dict(zip(cols, get_row))\n data[name]['set'] = dict(zip(cols, set_row))\n data[name]['delete'] = dict(zip(cols, delete_row))\n\n index += 10\n else:\n index += 1\n\n return data","def info() -> Dict[str, Any]:","def get_data(self):\n\n self.read_expression()\n self.read_tfs()\n self.read_metadata()\n self.set_gold_standard_and_priors()","def get_soup_general_data(soup):\n data_dict = {}\n\n name = soup.find(class_='product_title')\n if name:\n data_dict['name_of_game'] = name.h1.text\n\n pub = soup.find('li', class_='summary_detail publisher')\n if pub:\n data_dict['publisher'] = pub.a.text.strip()\n\n rel_date = soup.find('li', class_='summary_detail release_data')\n if rel_date:\n rel_date = rel_date.find('span', class_='data')\n if rel_date:\n data_dict['release_date'] = rel_date.text.strip()\n\n num_p = soup.find(\"li\", class_=\"summary_detail product_players\")\n if num_p:\n data_dict['num_players'] = num_p.find(class_=\"data\").text\n\n genres = soup.find(\"li\", class_='summary_detail product_genre')\n if genres:\n genres = genres.find_all('span', class_='data')\n data_dict['genres'] = [genre.text for genre in genres]\n\n age = soup.find(\"li\", class_=\"summary_detail product_rating\")\n if age:\n data_dict['age_rating'] = age.find('span', class_=\"data\").text\n\n return data_dict","def read_data(raw_data: list):\r\n cleaned_data = {}\r\n for data_item in raw_data:\r\n clean_data_item = pre_process_string_data(data_item)\r\n if clean_data_item is not None:\r\n cleaned_data[clean_data_item['_id']] = clean_data_item\r\n return cleaned_data","def tc_parse(data):\n digest, time, status = data.split()\n result = {\"resource\": digest, \"md5\": digest, \"timestamp\": time}\n\n if status == \"NO_DATA\":\n result[\"status\"] = \"UKNOWN\"\n else:\n result = status_from_percentage(int(status))\n\n return result","def parse_source_json(data):\n data_dict=[]\n for item in data:\n tmp_dict ={}\n tmp_dict[\"_id\"]=item[\"POIID\"]\n tmp_dict[\"POIName\"]=item[\"POIName\"]\n tmp_dict[\"Address\"]=item[\"Address\"]\n try:\n if item[\"isDining\"]=='Y':\n tmp_dict[\"isDining\"] = True\n else:\n tmp_dict[\"isDining\"] = False\n except:\n tmp_dict[\"isDining\"] = None\n tmp_dict[\"location\"] = {\"type\": \"Point\", \"coordinates\": [item[\"X\"],item[\"Y\"]]}\n \n data_dict.append(tmp_dict)\n return data_dict","def as_dict(self) -> Dict[str, Any]:\n return {\"ttl\": self.ttl, \"type\": self.type.value, \"data\": self.data}","def get_data(self):\n data = {}\n _priv = self.get('_private', [])\n\n def check_data(v):\n if isinstance(v, Yaco):\n v = v.get_data()\n elif isinstance(v, list):\n v = [check_data(x) for x in v]\n return v\n\n for k in list(self.keys()):\n if k in _priv:\n continue\n if isinstance(k, (str)) and k and k[0] == '_':\n continue\n # print self.keys()\n # print k, 'x' * 30\n data[k] = check_data(self[k])\n return data","def _parse_ddwrt_response(data_str):\n return {key: val for key, val in _DDWRT_DATA_REGEX.findall(data_str)}","def process_raw_data(self):\n \n # Get all participant names, or return straight away if no data was\n # loaded yet.\n if hasattr(self, \"raw\"):\n participants = self.raw.keys()\n participants.sort()\n else:\n self.data = None\n return\n\n # Count the number of participants.\n n = len(participants)\n \n # Find out how many questions there were.\n n_questions = 0\n for i, ppname in enumerate(participants):\n if self.raw[ppname] is None:\n continue\n if len(self.raw[ppname][\"QuestionNumber\"]) > n_questions:\n n_questions = len(self.raw[ppname][\"QuestionNumber\"])\n \n # Define some variables of interest.\n vor = []\n for i in range(n_questions):\n vor.append(\"Q%d_resp\" % (i+1))\n vor.append(\"Q%d_RT\" % (i+1))\n \n # Create a data dict for each variable of interest.\n self.data = {}\n self.data[\"ppname\"] = []\n for var in vor:\n self.data[var] = numpy.zeros(n, dtype=float) * numpy.NaN\n \n # Loop through all participants.\n for i, ppname in enumerate(participants):\n # Add the participant name.\n self.data[\"ppname\"].append(copy.deepcopy(ppname))\n # Skip empty datasets.\n if self.raw[ppname] is None:\n continue\n # Compute stuff relevant to this task.\n for j, qnr in enumerate(self.raw[ppname][\"QuestionNumber\"]):\n # Split Questionnaire 3, Q13 and Q14 into sub-questions\n if \"Q3\" in self._task_name and int(qnr) in [13,14]:\n # These questions split out into two parts: A description\n # of what each sub-part is, and a Boolean response for\n # each sub-part in the question. Example:\n # \"1_1_1_1_1_0//Television_VideogameConsole(suchas:WiiUPlayStationXboxorNintendoDS)_Tablet(likeanIPad)_Smartphone_LaptoporDesktopComputer_Noneofthese\"\n bool_resp, descr = self.raw[ppname][\"Response\"][j].split(\"//\")\n bool_resp = map(int, bool_resp.split(\"_\"))\n descr = descr.split(\"_\")\n # Store the data in the dict.\n for k, item in enumerate(descr):\n # Clean up the item name.\n if \"(\" in item:\n item = item[:item.find(\"(\")]\n var = \"Q%s_%s_resp\" % (int(qnr), item)\n # Create a new entry in the dict for this variable, if\n # one doesn't exist yet.\n if var not in self.data.keys():\n self.data[var] = numpy.zeros(n, dtype=float) * numpy.NaN\n # Store the data in the dict.\n self.data[var][i] = bool_resp[k]\n # Store response time for the whole item.\n self.data[\"Q%s_RT\" % (int(qnr))][i] = \\\n float(self.raw[ppname][\"TimeEndQuestion\"][j]) \\\n - float(self.raw[ppname][\"TimeStartQuestion\"][j])\n # All other questions are one-question one-response:\n else:\n # Store the data in the dict.\n self.data[\"Q%s_resp\" % (int(qnr))][i] = \\\n float(self.raw[ppname][\"Response\"][j])\n self.data[\"Q%s_RT\" % (int(qnr))][i] = \\\n self.raw[ppname][\"TimeEndQuestion\"][j] \\\n - self.raw[ppname][\"TimeStartQuestion\"][j]","def _read_info(self):\n my_filelines = self.file_lines\n info = dict()\n\n for i, line in enumerate(my_filelines):\n if line.startswith(\"VEHICLE\"):\n vehicle_pro_start = i + 2\n elif line.startswith(\"CUSTOMER\"):\n customer_pro_start = i + 3\n\n elif line.startswith(\"NUMBER\"):\n splited = line.split(' ')\n info[splited[0]] = 0\n info[splited[-1]] = 0\n return info, (vehicle_pro_start, customer_pro_start)","def get_data_from_bs(bs_data):\n all_raw_data = bs_data.find_all(\"properties\")\n return (to_dict(tag for tag in raw if type(tag) is Tag) for raw in all_raw_data)","def process_raw_data(self):\n \n # Define some variables of interest.\n vor = [\"n_sentences\", \"n_correct\", \"p_correct\", \"median_RT\", \\\n \"mean_RT\", \"stdev_RT\", \"scaled_stdev_RT\"]\n \n # Get all participant names, or return straight away if no data was\n # loaded yet.\n if hasattr(self, \"raw\"):\n participants = self.raw.keys()\n participants.sort()\n else:\n self.data = None\n return\n\n # Count the number of participants.\n n = len(participants)\n \n # Create a data dict for each variable of interest.\n self.data = {}\n self.data[\"ppname\"] = []\n for var in vor:\n self.data[var] = numpy.zeros(n, dtype=float) * numpy.NaN\n \n # Loop through all participants.\n for i, ppname in enumerate(participants):\n # Add the participant name.\n self.data[\"ppname\"].append(copy.deepcopy(ppname))\n # Skip empty datasets.\n if self.raw[ppname] is None:\n continue\n # Compute stuff relevant to this task.\n self.data[\"n_sentences\"][i] = len(self.raw[ppname][\"Sentence\"])\n self.data[\"n_correct\"][i] = numpy.sum(self.raw[ppname][\"correct\"])\n self.data[\"p_correct\"][i] = float(self.data[\"n_correct\"][i]) \\\n / float(self.data[\"n_sentences\"][i])\n self.data[\"median_RT\"][i] = numpy.nanmedian(self.raw[ppname][\"RT\"])\n self.data[\"mean_RT\"][i] = numpy.nanmean(self.raw[ppname][\"RT\"])\n self.data[\"stdev_RT\"][i] = numpy.nanstd(self.raw[ppname][\"RT\"])\n # Compute a scaled standard deviation of the response time, scaled to the\n # median response time to remove the correlation between the two.\n self.data[\"scaled_stdev_RT\"] = self.data[\"stdev_RT\"] / self.data[\"median_RT\"]","def _parse(self):\n\n self.specification = {}\n\n while True:\n try:\n line = self._lines.current\n if ':' in line:\n self.specification.update(self._parse_spec())\n elif line.startswith('NODE_COORD_SECTION'):\n next(self._lines)\n self.coords = self._parse_coords()\n elif line.startswith('EDGE_WEIGHT_SECTION'):\n next(self._lines)\n self.weights = self._parse_weights()\n elif line.startswith('DISPLAY_DATA_SECTION'):\n next(self._lines)\n self.display = self._parse_coords()\n else:\n break\n except StopIteration:\n break\n\n del self._lines","def read(self):\n dictionary = {}\n with open(self.path) as file:\n key_header = \"\"\n for line in file:\n entry = line.strip().split()\n if len(entry) == 0:\n continue\n if len(entry) == 1:\n key_header = entry[0]+\"_\"\n else:\n key = entry[0].strip()\n value = reduce(lambda x1, y1: x1+\" \" + y1, entry[1:])\n dictionary[key_header+key] = value\n return dictionary","def process_data(file: TextIO) -> 'Climatematch':\n climate_dict = {}\n line = file.readline()\n \n while line != '':\n username = line.strip()\n climate_dict[username] = {}\n \n line = file.readline().strip()\n climate_dict[username]['name'] = line\n line = file.readline().strip()\n climate_dict[username]['location'] = line\n \n climate_dict[username]['bio'] = ''\n line = file.readline()\n while line != 'ENDBIO\\n': \n climate_dict[username]['bio'] += line\n line = file.readline()\n \n climate_dict[username]['skills'] = []\n line = file.readline().strip()\n while line != 'ENDSKILL': \n climate_dict[username]['skills'].append(line)\n line = file.readline().strip()\n \n climate_dict[username]['interest'] = []\n line = file.readline().strip() \n while line != 'END': \n climate_dict[username]['interest'].append(line)\n line = file.readline().strip()\n line = file.readline()\n \n return climate_dict","def _parse(self):\n pass","def meta_data(self) -> Dict:\n pass","def parse(self):","def as_dict(self) -> dict[str, Any]:\n return {\n \"type\": self.type,\n \"timestamp\": self.timestamp,\n \"data\": self.data or {},\n }","def dict(self):\n return self.data","def parse(self, data):\n raise NotImplementedError","def process_tweet(tweet):\n d = {}\n d['hastags'] = [hashtag['text'] for hashtag in tweet['entities']['hashtags']]\n d['text'] = tweet['text']\n d['user'] = tweet['user']['screen_name']\n d['user_loc'] = tweet['user']['location']\n return d","def data(self) -> dict:\n raise NotImplementedError()","def handle_data(data):\n items = data.split(',')\n items.sort()\n results = {}\n i = 1\n for item in items:\n it = item.split(' ')\n results[i] = (it[0], it[1], int(it[2]))\n print str(i) + \" \" + it[0]\n i += 1\n return results","def decodemeta(data):\n d = {}\n for l in data.split('\\0'):\n if l:\n key, value = l.split(':')\n d[key] = value\n return d","def extract_metadata(self):\n if self.is_generatable_file:\n logger.debug(\"Converting collected details to dict..\")\n if self.metadata_collector:\n self.metadata = MetadataToDict(\n metadata_collector=self.metadata_collector,\n file_import=self.file_import,\n )\n self.metadata.build_integration_dict()","def get_data(self, data):\n self.data = {}\n self.data[ATTR_PM1] = data['current']['values'][0]['value']\n self.data[ATTR_PM25] = data['current']['values'][1]['value']\n self.data[ATTR_PM25_LIMIT] = data['current']['standards'][0]['limit']\n self.data[ATTR_PM25_PERCENT] = (data['current']['standards'][0]\n ['percent'])\n self.data[ATTR_PM10] = data['current']['values'][2]['value']\n self.data[ATTR_PM10_LIMIT] = data['current']['standards'][1]['limit']\n self.data[ATTR_PM10_PERCENT] = (data['current']['standards'][1]\n ['percent'])\n self.data[ATTR_PRESSURE] = data['current']['values'][3]['value']\n self.data[ATTR_HUMIDITY] = data['current']['values'][4]['value']\n self.data[ATTR_TEMPERATURE] = data['current']['values'][5]['value']\n self.data[ATTR_CAQI] = data['current']['indexes'][0]['value']\n self.data[ATTR_CAQI_LEVEL] = (data['current']['indexes'][0]\n ['level'].lower().replace('_', ' '))","def collected_data(self) -> Iterable[Dict[str, float]]:\n return self._collected_data","def ingest(in_info):\n if type(in_info) == str:\n with open(in_info) as infile:\n lines = (line.split(None) for line in infile)\n in_dict = {defn[0] : defn[1:] for defn in lines}\n else:\n in_dict = in_info\n return in_dict","def _process_dict(data):\n new_dict = {}\n for key in data.keys():\n\tnew_dict['name'] = data['printerName']\n #new_dict[key] = data[key]\n\n #FIGURE OUT AND UPDATE PRINTER STATUS\n status = BUSY_STATUS\n error_msg = \"\"\n if \"FrontPanelMessage\" in data:\n if data[\"FrontPanelMessage\"].lower() in READY_MESSAGES:\n status = READY_STATUS\n elif \"error\" in data[\"FrontPanelMessage\"].lower():\n status = ERROR_STATUS\n error_msg = \"general error\"\n \n if \"TonerStatus\" in data:\n if data[\"TonerStatus\"].find(\"2\") != -1:\n status = ERROR_STATUS\n error_msg = \"Toner Error\"\n #if len(new_dict[\"TonerStatus\"]) > 4:\n #new_dict[\"TonerStatus\"] = new_dict[\"TonerStatus\"][4:]\n\n if \"PaperStatus\" in data:\n if data[\"PaperStatus\"].find(\"2\") != -1:\n status = ERROR_STATUS\n error_msg = \"Paper Status Error\"\n elif data[\"PaperStatus\"].find(\"1\") != -1:\n status = ERROR_STATUS\n error_msg = \"Out of Paper\"\n #if len(new_dict[\"PaperStatus\"]) > 4:\n #new_dict[\"PaperStatus\"] = new_dict[\"PaperStatus\"][4:]\n\n if \"PaperJamStatus\" in data:\n if data[\"PaperJamStatus\"].find(\"1\") != -1:\n status = ERROR_STATUS\n error_msg = \"Paper Jam\"\n #if len(new_dict[\"PaperJamStatus\"]) > 4:\n #new_dict[\"PaperJamStatus\"] = new_dict[\"PaperJamStatus\"][4:]\n\n new_dict[\"status\"] = status\n new_dict[\"error_msg\"] = error_msg\n new_dict[\"location\"] = PRINTERS[new_dict[\"name\"]][0]\n new_dict[\"building_name\"] = PRINTERS[new_dict[\"name\"]][1]\n new_dict[\"latitude\"] = PRINTERS[new_dict[\"name\"]][2]\n new_dict[\"longitude\"] = PRINTERS[new_dict[\"name\"]][3]\n new_dict[\"atResidence\"] = PRINTERS[new_dict[\"name\"]][4]\n return new_dict","def to_dict(self) -> dict:","def _to_dict(self, data_list):\n data_dict = dict(pair.split('=') for pair in data_list)\n return data_dict","def parse_data(self, data):\n pids = set()\n cams = set()\n \n for info in data:\n pids.add(info[1])\n cams.add(info[2])\n return len(pids), len(cams)","def process_data(data):\n # set up an OrderedDict to hold the data with initial data set to 0\n output = OrderedDict((s, {'calls': [], 'out_count': 0, 'out_dur':\n datetime.timedelta(seconds=0), 'in_count': 0, 'in_dur':\n datetime.timedelta(seconds=0), 'total_dur':\n datetime.timedelta(seconds=0)}) for s in config.SALESPEOPLE)\n for d in data:\n # assume it's an outgoing call.\n call_type = 'outgoing'\n key = d['From']\n tofrom = '{0} {1}'.format(d['To CID Name'], d['To CID Number'])\n if not tofrom.strip():\n tofrom = d['Digits'].strip() or d['To']\n out_count, in_count = 1, 0\n dur_h, dur_m, dur_s = map(int, d['Duration'].split(':'))\n duration = datetime.timedelta(hours=dur_h, minutes=dur_m,\n seconds=dur_s)\n out_dur = duration\n in_dur = datetime.timedelta(seconds=0)\n if key not in config.SALESPEOPLE:\n # it's an incoming call if the From name isn't one of the\n # config.SALESPEOPLE. Adjust the data accordingly\n call_type = 'incoming'\n key = d['To']\n tofrom = '{0} {1}'.format(d['From CID Name'], d['From CID Number'])\n if not tofrom.strip():\n tofrom = d['To']\n out_count, in_count = 0, 1\n out_dur = datetime.timedelta(seconds=0)\n in_dur = duration\n\n # format the phone numbers\n tofrom = re.sub(r'1?(\\d{3})(\\d{3})(\\d{4})$', r'\\1-\\2-\\3', tofrom)\n\n output[key]['calls'].append({'time': d['Start Time'], 'type':\n call_type, 'duration': d['Duration'], 'tofrom': tofrom})\n output[key]['out_count'] = output[key]['out_count'] + out_count\n output[key]['out_dur'] = output[key]['out_dur'] + out_dur\n output[key]['in_count'] = output[key]['in_count'] + in_count\n output[key]['in_dur'] = output[key]['in_dur'] + in_dur\n output[key]['total_dur'] = output[key]['total_dur'] + duration\n\n return output","def get_data(self):\n return self.parsed_data","def parsing_no_hits_data(global_avg_trimmed_length):\n\n #No Hit Counter\n no_hit_counter = 0\n\n #Totally trimmed counter\n totally_trimmed_counter = 0\n\n #No hits results\n average_trimmed_no_hit_length=[]\n\n #Opening and Parsing blast_no_hits_report.txt\n no_hit_results = open('blast_no_hits_report.txt', 'r')\n for line in no_hit_results:\n data = line.split(\"\\t\")\n \n if line.startswith('SeqID'):\n continue\n else:\n average_trimmed_no_hit_length.append(float(data[4]))\n global_avg_trimmed_length.append(float(data[4]))\n \n no_hit_counter +=1\n \n if float(data[4]) == 0:\n totally_trimmed_counter +=1\n continue\n no_hit_results.close\n\n\n return {'no_hit_counter':no_hit_counter, 'totally_trimmed_counter':totally_trimmed_counter, \n 'average_trimmed_no_hit_length':average_trimmed_no_hit_length, 'global_avg_trimmed_length':global_avg_trimmed_length}","def massage_api_response(api_data):\n return_dict = defaultdict(list)\n legs = api_data['legs'][0]\n\n return_dict['start_address'].append(legs['start_address'])\n return_dict['end_address'].append(legs['end_address'])\n return_dict['distance'].append(legs['distance']['text'])\n return_dict['duration'].append(legs['duration']['text'])\n if 'duration_in_traffic' in legs:\n (return_dict['duration_in_traffic']\n .append(legs['duration_in_traffic']['text']))\n return_dict['travel_mode'].append(legs['steps'][0]['travel_mode'])\n\n for instruction in legs['steps']:\n (return_dict['instructions']\n .append(BeautifulSoup(instruction['html_instructions'],\n 'html.parser').get_text()))\n return_dict['step_distance'].append(instruction['distance'])\n return return_dict","def parse_data(self, file_data):\n if self.data:\n all_api_reference = self.data.findAll([\"h4\",\"h3\"])\n for api in all_api_reference: \n title = api.text.replace(' #', '')\n\n href = self.parse_link(file_data, api)\n\n content = self.parse_content(api)\n\n example = self.parse_example(api)\n\n section = {\n 'title': title,\n 'href': href,\n 'content': content,\n 'example': example\n }\n self.parsed_data.append(section)","def __load(self) -> Dict:\n return dict()","def _get_new_data(self, page_url, soup):\n data = {}\n data['url'] = page_url\n title = soup.find('dd', class_='lemmaWgt-lemmaTitle-title').find('h1')\n data['title'] = title.get_text()\n summary = soup.find('div', class_='lemma-summary')\n data['summary'] = summary.get_text()\n return data","def parse_data(block):\n rval = {\n 'Total' : None,\n 'Electronic' : None,\n 'Translational' : None,\n 'Rotational' : None,\n 'Vibrational' : None\n }\n for line in block.splitlines():\n if re.match(r'^\\s*Total', line):\n key = 'Total'\n elif re.match(r'^\\s*Electronic', line):\n key = 'Electronic'\n elif re.match(r'^\\s*Translational', line):\n key = 'Translational'\n elif re.match(r'^\\s*Rotational', line):\n key = 'Rotational'\n elif re.match(r'^\\s*Vibrational', line):\n key = 'Vibrational'\n else:\n key = None\n if key:\n words = line.strip().split()\n try:\n rval[key] = float(words[1])\n except ValueError:\n raise ValueError('Invalid thermodynamic format.')\n return rval","def parse(self, data):\n self._data = data\n return self._parse_headers()","def _separate_raw_data(self, raw_data):\n for key, value in raw_data.items():\n if type(value) == dict:\n self.data_dict[key] = value\n elif type(value) == list:\n self.data_list[key] = value","def parse_event(self, raw_data: str):\n event_attributes = raw_data.replace(\"\\n\", \"\").split(\",\")\n for j in range(len(event_attributes)):\n event_attributes[j] = str_to_number(event_attributes[j])\n return dict(zip(METASTOCK_7_COLUMN_KEYS, event_attributes))","def parse_api(self, soup):\n return {}","def load_data(self):\n super(MudderyObjectCreater, self).load_data()\n \n data = self.get_data_record()\n if not data:\n return\n \n # set common object's info\n self.obj_list = {}\n\n for obj in data.obj_list.split(\",\"):\n obj_key = \"\"\n number = 0\n arg = obj.split(\":\", 1)\n if len(arg) == 1:\n obj_key = arg[0]\n number = 1\n elif len(arg) >= 2:\n obj_key = arg[0]\n number = int(arg[1])\n\n self.obj_list[obj_key] = number","def parse_data(data):\n result = {}\n if data:\n params = data.split('&')\n for item in params:\n key, value = item.split('=')\n result[key] = value\n return decode_data(result)","def parse_bibtex(self, data: str) -> Dict:\n\n new_bib = [line for line in data.splitlines() if \"= ,\" not in line]\n new_bib = \"\\n\".join(new_bib)\n bib_db: bibtexparser.bibdatabase.BibDatabase = bibtexparser.loads(new_bib)\n result = dict()\n for entry in bib_db.entries:\n osti_id = entry[\"ID\"].split(\"_\")[1]\n result[osti_id] = entry\n return result"],"string":"[\n \"def parse_data( self ):\\n self.parsed_data = dict( self.results )\",\n \"def get_data(self):\\n self.data = dict()\\n # list to save all the attributes we are going to create\\n self.attr = []\\n # list to save all the groups available in the incomming input\\n self.groups.extend(self.values.keys())\\n # Grouping\\n self.parse_data()\",\n \"def get_structure(self):\\n main = {}\\n for line in self.load():\\n match = re.match('^\\\\s*([A-Za-z0-9_]+)(\\\\((\\\\d+)\\\\))?=(.*)$', line)\\n if match:\\n key = match.group(1)\\n index = match.group(3)\\n value = match.group(4)\\n if index is None:\\n main[key] = self.parse_data_value(value)\\n else:\\n if key not in main:\\n main[key] = []\\n main[key].append(self.parse_data_value(value))\\n #else:\\n # print(line)\\n return main\",\n \"def extractData(self) -> Dict[str, str]:\\n if self.hasScore():\\n result = {}\\n result[\\\"date\\\"] = self.getMatchDate()\\n result[\\\"score\\\"] = self.getMatchScore()\\n for team in self.TEAMS:\\n result[\\\"team_\\\" + team] = self.getSquadName(team)\\n\\n return result\\n return None\",\n \"def raw_data() -> Dict:\\n return {\\\"neighbourhood\\\":\\\"Buttes-Montmartre\\\",\\\"room_type\\\":\\\"Entire home/apt\\\",\\\"minimum_nights\\\":1.555,\\\"mois\\\":2,\\\"voyageurs\\\":2.5,\\\"chambres\\\":1,\\\"lits\\\":1,\\\"salle_de_bains\\\":1}\",\n \"def prepare_data(data: list) -> dict:\\n d = {}\\n for t in data:\\n d[t[0]] = read_text(t[1])\\n return d\",\n \"def process_data(data):\\n info = {\\n 'cities': [],\\n 'temperatures': [],\\n 'humidities': [],\\n }\\n cities = data['list']\\n for city in cities:\\n main_data = city['main']\\n info['cities'].append(city['name'])\\n info['temperatures'].append(main_data['temp'])\\n info['humidities'].append(main_data['humidity'])\\n\\n return info\",\n \"async def parse(self, raw: str) -> dict:\",\n \"def parse_data(self):\\n\\t\\traise NotImplementedError('%s: No parse function implemented!' % self.name)\",\n \"def getParsedDic(self):\\n return {}\",\n \"def _read_data(self, data_record):\\n data_dict = {}\\n data_labels = self._header_dict['labels']\\n data = data_record.strip().split()\\n\\n if self.num_columns != len(data):\\n err_msg = \\\"GliderParser._read_data(): Num Of Columns NOT EQUAL to Num of Data items: \\\" + \\\\\\n \\\"Expected Columns= %s vs Actual Data= %s\\\" % (self.num_columns, len(data))\\n log.error(err_msg)\\n raise DatasetParserException(err_msg)\\n\\n # extract record to dictionary\\n for ii, value in enumerate(data):\\n label = data_labels[ii]\\n data_dict[label] = value\\n\\n return data_dict\",\n \"def get_data(self):\\n with LoggingTime(\\\"Fetching data took: \\\"):\\n try:\\n with open(self.__io_params.input_file_path, 'r', encoding=\\\"utf8\\\") as csvFiles:\\n reader = csv.reader(csvFiles, delimiter=',', quotechar='|')\\n dictionary = {}\\n for data in reader:\\n if len(data) > 0 and data[0]: # ignore malformed data lines\\n word_base = data[0].strip().lower()\\n if self.word_is_eligible(word_base) and word_base not in dictionary:\\n dictionary[word_base] = ProcessingEntry(word_base)\\n logging.info(\\\"Loaded %i search terms\\\" % (len(dictionary)))\\n return dictionary\\n except IOError as e:\\n logging.error(\\\"IO Error has occurred. Check input file\\\")\\n logging.error(e)\\n raise\\n except:\\n logging.error(\\\"Reading data failed. Check input file format\\\")\\n raise\",\n \"def make_feed_dict(self, data):\\r\\n raise NotImplementedError\",\n \"def extract(self, data):\",\n \"def parse(self):\\n\\n\\t\\t# Open and parse the file\\n\\t\\twith open(self.name, 'r') as fdi:\\n\\t\\t\\tfor line in fdi:\\n\\t\\t\\t\\twords = [word for word in line.split(' ') if word != ' ' and word != ':']\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Store the data in the hash\\n\\t\\t\\t\\tself.data_hash[int(words[0], 16)] = int(words[1])\\n\\n\\t\\t# Sort the dictionary by addresses\\n\\t\\tself.data_hash = od(sorted(self.data_hash.items(), key = lambda t : t[0]))\\n\\n\\t\\tprint 'Total Bytes :', float(sum(self.data_hash.values())) / 1024 / 1024\\n\\n\\t\\treturn\",\n \"def _collect_data(self):\\n data = {\\n \\\"K\\\": self.K,\\n \\\"root\\\": self.root\\n }\\n return data\",\n \"def get_data(self):\\n def _clean_search_hit(search_hit):\\n \\\"\\\"\\\"\\n Takes in a search result hit as a BeautifySoup tag and pulls out all the data to match the desired schema.\\n\\n :param search_hit:\\n :return Dictionary: A dictionary with the cleaned data\\n \\\"\\\"\\\"\\n\\n hit_name = search_hit.find(class_='hit-name')\\n hit_url = hit_name.get('href')\\n hit_id = hit_url.split('/')[-1]\\n name = hit_name.get_text().split(',')[0].title().split()\\n\\n current_city = search_hit.find(class_='hit-location').get_text().upper()\\n\\n # Find all Addresses for search result.\\n try:\\n address = search_hit.find(class_='hit-pastAddresses').find_all(class_='hit-values')\\n address = list({a.text.upper().replace('.', '') for a in address})\\n except AttributeError:\\n address = list()\\n\\n # find the address that is most likely the current main address.\\n try:\\n address.insert(0, address.pop(address.index(current_city)))\\n except ValueError:\\n address.insert(0, current_city)\\n\\n address = [\\n {\\n '@type': 'PostalAddress',\\n 'addressLocality': locality.title(),\\n 'addressRegion': region\\n } for locality, region in [a.split(', ') for a in address]]\\n\\n work_location = {'@type': 'Place'}\\n try:\\n work_location['name'] = search_hit\\\\\\n .find(class_='hit-work')\\\\\\n .find(class_='hit-values')\\\\\\n .get_text()\\\\\\n .title()\\n except AttributeError:\\n work_location['name'] = ''\\n\\n alumni_of = {'@type': 'EducationalOrganization'}\\n try:\\n alumni_of['name'] = search_hit\\\\\\n .find(class_='hit-high-school')\\\\\\n .find(class_='hit-values')\\\\\\n .get_text().title()\\n except AttributeError:\\n pass\\n\\n return {\\n '@id': hit_id,\\n '@type': 'Person',\\n 'name': ' '.join(name),\\n 'givenName': name[0],\\n 'middleName': ' '.join(name[1:-1]),\\n 'familyName': name[-1],\\n 'url': hit_url,\\n 'address': address,\\n 'workLocation': work_location,\\n 'alumniOf': alumni_of,\\n }\\n\\n def _refine_search(search_str, options):\\n \\\"\\\"\\\"\\n Takes a list of WebElements and a search string, looks for string in the text of each WebElement, and\\n press the option if found. Returns Boolean for found status\\n\\n :param search_str: str of the desired option.\\n :param options: list of WebElements from Beautify Soup that represents all of the available options.\\n :return:\\n \\\"\\\"\\\"\\n search_str = search_str.upper()\\n logging.info(f'Looking for \\\\'{search_str}\\\\'')\\n try:\\n for option in options:\\n option_text = option.text.upper()\\n logging.info(f'Option Checked: {option_text}')\\n if search_str in option_text:\\n option.click()\\n time.sleep(2)\\n logging.info(f'Option Selected: {option_text}')\\n return True\\n else:\\n return False\\n except AttributeError:\\n return True\\n except StaleElementReferenceException as e:\\n ChromeCrash(e)\\n\\n with self.driver(executable_path=self.DRIVER_DIR) as driver:\\n driver.get(self.url)\\n\\n \\\"\\\"\\\"\\n The CSS for the page doesn't show the State nor the City selector options if the page is too narrow,\\n so we need to make sure the browser is open wide enough for the CSS to make those options visible. \\n \\\"\\\"\\\"\\n driver.fullscreen_window()\\n\\n # Refine the search by State\\n address_region = self.person.get('addressRegion', '')\\n address_region = STATES.get(address_region.upper(), address_region.upper())\\n region_options = driver\\\\\\n .find_element_by_class_name(\\\"STATE\\\")\\\\\\n .find_elements_by_class_name(\\\"refinementList-text\\\")\\n\\n if not _refine_search(address_region, region_options):\\n return False\\n\\n # Narrow the search by pressing a City option\\n address_locality = self.person.get('addressLocality').title()\\n locality_options = driver\\\\\\n .find_element_by_class_name(\\\"CITY\\\")\\\\\\n .find_elements_by_class_name(\\\"refinementList-text\\\")\\n\\n if not _refine_search(address_locality, locality_options):\\n return False\\n\\n \\\"\\\"\\\"\\n The Page Loads dynamically, so we need to scroll down the page to show all the search results. It needs to\\n be done in steps with a pause between movements to allow for loading. \\n Here it will first get the current location on the page, attempt to move down the page, and then check to\\n see if the location changed.\\n \\\"\\\"\\\"\\n\\n if self.auto_scroll and len(driver.find_elements_by_class_name(\\\"ais-InfiniteHits-item\\\")) > 15:\\n current_height, new_height = 0, driver.execute_script(\\\"return document.body.scrollHeight\\\")\\n\\n while new_height != current_height:\\n # Scroll down to the bottom of the page\\n driver.execute_script(\\\"window.scrollTo(0, document.body.scrollHeight);\\\")\\n\\n # Wait to load page\\n time.sleep(SCROLL_PAUSE_TIME)\\n\\n # Calculate new scroll height and compare with last scroll height\\n current_height, new_height = new_height, driver.execute_script(\\\"return document.body.scrollHeight\\\")\\n\\n page_source = driver.page_source\\n page_soup = bs(page_source, 'html.parser')\\n search_results = list(page_soup.find_all(class_='ais-InfiniteHits-item'))\\n for i, search_result in enumerate(search_results):\\n search_results[i] = _clean_search_hit(search_result)\\n\\n self.data_from_website = pd.DataFrame(search_results)\\n self.data_from_website.set_index('@id', inplace=True)\\n return True\",\n \"def get_data():\\n data = {}\\n with open(app.config['DATA_CSV'], 'r') as csvfile:\\n presence_reader = csv.reader(csvfile, delimiter=',')\\n for i, row in enumerate(presence_reader):\\n if len(row) != 4:\\n # ignore header and footer lines\\n continue\\n\\n try:\\n user_id = int(row[0])\\n date = datetime.strptime(row[1], '%Y-%m-%d').date()\\n start = datetime.strptime(row[2], '%H:%M:%S').time()\\n end = datetime.strptime(row[3], '%H:%M:%S').time()\\n except (ValueError, TypeError):\\n log.debug('Problem with line %d: ', i, exc_info=True)\\n\\n data.setdefault(user_id, {})[date] = {'start': start, 'end': end}\\n return data\",\n \"def transform_string_to_dictionary(data_text: str) -> Dict[str, float]:\\n # TODO: create an empty population dictionary\\n # TODO: iterate through each line of the data set\\n # TODO: extract the ordered pair on this line\\n # the ordered pair has the format:\\n # (Date, population count in thousands of persons)\\n # TODO: extract the dat and store it as a string\\n # TODO: convert the population count to a float and store it\\n # TODO: add the new key-value pair to the dictionary where\\n # the key is the date and the population is the value\\n # TODO: return the population dictionary\",\n \"def parse_data(data):\\n\\n d = dict()\\n for key in data.keys():\\n d[int(key)] = parse_date_period(data[key])\\n\\n return d\",\n \"def parse(data):\\n try:\\n header = decompress(data)\\n version, game, save, log = parse_version(header, data)\\n if version not in (Version.USERPATCH15, Version.DE, Version.HD):\\n raise RuntimeError(f\\\"{version} not supported\\\")\\n de = parse_de(header, version, save)\\n hd = parse_hd(header, version, save)\\n metadata, num_players = parse_metadata(header)\\n map_ = parse_map(header, version)\\n players, mod, device = parse_players(header, num_players, version, save)\\n scenario = parse_scenario(header, num_players, version, save)\\n lobby = parse_lobby(header, version, save)\\n except (struct.error, zlib.error, AssertionError, MemoryError, ValueError) as e:\\n raise RuntimeError(f\\\"could not parse: {e}\\\")\\n return dict(\\n version=version,\\n game_version=game,\\n save_version=save,\\n log_version=log,\\n players=players,\\n map=map_,\\n de=de,\\n hd=hd,\\n mod=de.get('dlc_ids') if de else mod,\\n metadata=metadata,\\n scenario=scenario,\\n lobby=lobby,\\n device=device\\n )\",\n \"def parse(data, raw=False, quiet=False):\\n jc.utils.compatibility(__name__, info.compatible, quiet)\\n jc.utils.input_type_check(data)\\n\\n raw_output = {}\\n\\n if jc.utils.has_data(data):\\n\\n for line in filter(None, data.splitlines()):\\n linedata = line.split(':', maxsplit=1)\\n key = linedata[0].strip().lower().replace(' ', '_').replace('.', '_')\\n value = linedata[1].strip()\\n raw_output[key] = value\\n\\n if raw:\\n return raw_output\\n else:\\n return _process(raw_output)\",\n \"def parse_data(data):\\n parsed_data = {}\\n for i, chunk in enumerate(re.split(r'\\\\n{2,}', data)):\\n if i == 0:\\n match = re.search(r'^(.*?) interest: (.*)\\\\n(.*?); (.*?)$', chunk)\\n if match:\\n source, query, geo, period = match.groups()\\n parsed_data['info'] = {'source': source, 'query': query,\\n 'geo': geo, 'period': period}\\n else:\\n chunk = _clean_subtable(chunk)\\n rows = [row for row in csv.reader(StringIO(chunk)) if row]\\n if not rows:\\n continue\\n label, parsed_rows = _parse_rows(rows)\\n if label in parsed_data:\\n parsed_data[label+'_1'] = parsed_data.pop(label)\\n parsed_data[label+'_2'] = parsed_rows\\n else:\\n parsed_data[label] = parsed_rows\\n\\n return parsed_data\",\n \"def data(self) -> dict[str, Any]:\\n raise NotImplementedError()\",\n \"def _process_info(resp: suds.sudsobject) -> dict:\\n last = resp.ultimoValor\\n return dict(fonte = str(resp.fonte),\\n gestor = str(resp.gestorProprietario),\\n freq = str(resp.periodicidadeSigla),\\n nome = str(resp.nomeCompleto),\\n number = int(resp.oid),\\n final = dt(last.ano, last.mes, last.dia))\",\n \"def _parse_input(self):\\n parser = re.compile(r'(\\\\w+)\\\\D+(\\\\d+)\\\\D+(\\\\d+)\\\\D+(\\\\d+)')\\n reindeer = {}\\n for line in self.puzzle_input.splitlines():\\n instruction = parser.match(line)\\n if not instruction:\\n continue\\n name, speed, flight, rest = instruction.groups()\\n reindeer[name] = Reindeer(int(speed), int(flight), int(rest))\\n return reindeer\",\n \"def parse_data(self):\\n\\n msg = self.xml['dom'].childNodes[0]\\n self.data = xml_to_dicts(msg, False)\\n\\n # Get some metadata together\\n self.id = \\\"%s:%s\\\" % (self.data['src']['name']['#cdata'], self.data['src']['id']['#cdata'])\\n self.temp = self.data['tmpr']['#cdata']\\n self.watts = self.data['ch1']['watts']['#cdata']\\n\\n # Time - CurrentCost and local\\n self.date = {}\\n self.date['cc'] = [ int(self.data['date'][k]['#cdata']) for k in ('dsb','hr','min','sec') ]\\n self.date['now'] = localtime()\",\n \"def parse(u):\\n rec = {}\\n\\n try:\\n r = requests.get(u, headers=headers)\\n\\n if r.status_code == 200:\\n html = r.text\\n soup = BeautifulSoup(html, 'lxml')\\n overview_section = soup.select('.Raw-s14xcvr1-0 gXqFYO')\\n full_name_section = soup.select('.sc-iwsKbI kjxnCg')\\n years_of_practice_section = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\\n language_section = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\\n office_location_section = soup.select('.Paragraph-fqygwe-0 cojhks')\\n hospital_affiliation_section = soup.select('.Paragraph-fqygwe-0 fwayNy')\\n specialties = soup.select('.DataField__Data-c3wc7f-1 gLHSHx')\\n education_and_medical_training_section = soup.select('.EducationAndExperience__Item-xn5fll-0 bzYYRk')\\n certification_and_licensure_section = soup.select('.Paragraph-fqygwe-0 bQPwuv')\\n\\n if overview_section:\\n overview = overview_section[0].text.replace('\\\"', '')\\n if full_name_section:\\n full_name = full_name_section[0].text\\n if years_of_practice_section:\\n years_of_practice = years_of_practice_section[0].text.strip().replace('\\\"', '')\\n if language_section:\\n language = language_section[0].text.strip().replace('\\\"', '')\\n if office_location_section:\\n office_location = office_location_section[0].text\\n if hospital_affiliation_section:\\n hospital_affiliation = hospital_affiliation_section[0].text.strip().replace('\\\"', '')\\n if specialties_section:\\n specialties = specialties_section[0].text.replace('\\\"', '')\\n if education_and_medical_training_section:\\n education_and_medical_training = education_and_medical_training_section[0].text\\n if certification_and_licensure_section:\\n certification_and_licensure = certification_and_licensure_section[0].text\\n\\n\\n rec = {'overview': overview, 'full_name': full_name, 'years_of_practice': years_of_practice, 'language': language,\\n 'office_location': office_location, 'hospital_affiliation': hospital_affiliation, 'specialties':specialties,\\n 'education_and_medical_training': education_and_medical_training,\\n 'certification_and_licensure': certification_and_licensure}\\n except Exception as ex:\\n print('Exception while parsing')\\n print(str(ex))\\n finally:\\n return json.dumps(rec)\",\n \"def read_item(data: DataModel) -> Dict:\\n convertor = Convertor(data)\\n return {'output': convertor.get_humanized_data()}\",\n \"def extract_data(self):\\n values = {}\\n for injkey in self.data_sets.keys():\\n values[injkey] = {}\\n alldata = self.data_sets[injkey]\\n paramkeys = alldata['params'].keys()\\n for datakey in alldata.keys():\\n if not datakey == 'params':\\n values[injkey][datakey] = {}\\n values[injkey][datakey]['metric_val'] = {}\\n values[injkey][datakey]['metric_val']['vals'] = []\\n for paramkey in paramkeys:\\n values[injkey][datakey][paramkey] = {}\\n values[injkey][datakey][paramkey]['vals'] = []\\n trials = alldata[datakey]\\n for trial_num in trials.keys():\\n trial = trials[trial_num]\\n values[injkey][datakey]['metric_val']['vals'] \\\\\\n .append(trial['metric_val'])\\n values[injkey][datakey]['metric_val']['type'] \\\\\\n = trial['metric']\\n values[injkey][datakey]['metric_val']['units'] \\\\\\n = 'dimensionless'\\n param_vals = trial['params']\\n for param_name in param_vals.keys():\\n val, units = self.parse_pint_string(\\n pint_string=param_vals[param_name]\\n )\\n values[injkey][datakey][param_name]['vals'] \\\\\\n .append(float(val))\\n values[injkey][datakey][param_name]['units'] \\\\\\n = units\\n self.values = values\",\n \"def metadata(self):\\n self.data_as_dict = {}\\n for ele in self.data:\\n self.data_as_dict[ele.name] = ele.value\\n return self.data_as_dict\",\n \"def parse_data(self):\\n data = {}\\n content = self.headers.get('content-type', None)\\n if content:\\n ctype, pdict = parse_header(content)\\n if ctype == 'application/json':\\n length = int(self.headers['content-length'])\\n data = json.loads(self.bytes_to_str(self.rfile.read(length)))\\n return data\",\n \"def process(raw):\\n entry = { }\\n cooked = [ ]\\n\\n for line in raw:\\n line = line.strip()\\n if len(line) == 0 or line[0]==\\\"#\\\" :\\n continue\\n parts = line.split(';')\\n if len(parts) == 3:\\n entry[\\\"description\\\"] = parts[0].strip() #adding key and values to the dict\\n entry[\\\"long\\\"] = parts[1].strip()\\n entry[\\\"lat\\\"] = parts[2].strip()\\n cooked.append(entry) #add this dict entry into the array\\n entry = { }\\n continue\\n else:\\n raise ValueError(\\\"Trouble wiht line: '{}'\\\\n\\\".format(line))\\n \\n return cooked #returning an array of dicts\",\n \"def setup_parser(self) -> Dict[str, Any]:\\n\\n\\t# % year,doy, RH(m), Month, day, azimuth(deg),freq, satNu, LSP amp,pk2noise,UTC(hr) \\n\\t# 2021 9 4.888 1 9 225.3 1 2 9.51 3.23 10.08\\n\\t# 2021 9 5.018 1 9 181.3 1 15 7.79 2.84 15.67\\n\\t# 2021 9 5.123 1 9 185.4 1 16 6.27 3.01 0.68\\n #----+----0----+----1----+----2----+----3----+----4----+----5----+----6----+----7\\n return dict(\\n skip_header=1,\\n names=(\\n \\\"year\\\",\\n \\\"doy\\\",\\n \\\"reflection_height\\\",\\n \\\"month\\\",\\n \\\"day\\\",\\n \\\"azimuth\\\",\\n \\\"frequency\\\",\\n \\\"satellite\\\",\\n \\\"amplitude\\\",\\n \\\"peak2noise\\\",\\n \\\"hour\\\",\\n ),\\n dtype=(\\\"f8\\\", \\\"f8\\\", \\\"f8\\\", \\\"f8\\\", \\\"f8\\\", \\\"f8\\\", \\\"f8\\\", \\\"f8\\\", \\\"f8\\\", \\\"f8\\\", \\\"f8\\\"),\\n )\",\n \"def parse(self):\\n\\n # if data has already been parsed, do nothing\\n if self._data:\\n return\\n\\n stats = {\\n \\\"genres\\\": {},\\n \\\"artists\\\": {},\\n \\\"global_stats\\\": {\\n \\\"songs\\\": 0,\\n \\\"lines\\\": 0,\\n \\\"words\\\": 0\\n }\\n }\\n try:\\n with open(self._filename) as file:\\n objects = ijson.items(file, \\\"item\\\")\\n\\n # compute metrics\\n for object in objects:\\n\\n lines = len(object[\\\"lyrics\\\"])\\n words = sum([len(line.split()) for line in object[\\\"lyrics\\\"]])\\n\\n genre = object[\\\"genre\\\"]\\n stats[\\\"genres\\\"][genre] = stats[\\\"genres\\\"].get(genre, {\\\"artists\\\": {}})\\n genre_obj = stats[\\\"genres\\\"][genre]\\n genre_obj[\\\"songs\\\"] = genre_obj.get(\\\"songs\\\", 0) + 1\\n genre_obj[\\\"lines\\\"] = genre_obj.get(\\\"lines\\\", 0) + lines\\n genre_obj[\\\"words\\\"] = genre_obj.get(\\\"words\\\", 0) + words\\n genre_obj[\\\"is_music\\\"] = genre_obj.get(\\\"is_music\\\", 0)\\n if object[\\\"is_music\\\"] != \\\"false\\\":\\n genre_obj[\\\"is_music\\\"] += 1\\n\\n artist = object[\\\"artist\\\"]\\n stats[\\\"artists\\\"][artist] = stats[\\\"artists\\\"].get(artist, 0) + 1\\n stats[\\\"genres\\\"][genre][\\\"artists\\\"][artist] = stats[\\\"genres\\\"][genre][\\\"artists\\\"].get(artist, 0) + 1\\n\\n # update global stats\\n stats[\\\"global_stats\\\"][\\\"songs\\\"] += 1\\n stats[\\\"global_stats\\\"][\\\"lines\\\"] += lines\\n stats[\\\"global_stats\\\"][\\\"words\\\"] += words\\n\\n # calculate averages for each genre\\n for genre, genre_stats in stats[\\\"genres\\\"].items():\\n genre_stats[\\\"avg_line_length\\\"] = genre_stats[\\\"words\\\"] / genre_stats[\\\"lines\\\"]\\n genre_stats[\\\"avg_lines\\\"] = genre_stats[\\\"lines\\\"] / genre_stats[\\\"songs\\\"]\\n genre_stats[\\\"avg_words\\\"] = genre_stats[\\\"words\\\"] / genre_stats[\\\"songs\\\"]\\n\\n # calculate global averages\\n stats[\\\"global_stats\\\"][\\\"avg_line_length\\\"] = stats[\\\"global_stats\\\"][\\\"words\\\"] / stats[\\\"global_stats\\\"][\\\"lines\\\"]\\n stats[\\\"global_stats\\\"][\\\"avg_lines\\\"] = stats[\\\"global_stats\\\"][\\\"lines\\\"] / stats[\\\"global_stats\\\"][\\\"songs\\\"]\\n stats[\\\"global_stats\\\"][\\\"avg_words\\\"] = stats[\\\"global_stats\\\"][\\\"words\\\"] / stats[\\\"global_stats\\\"][\\\"songs\\\"]\\n\\n self._data = stats\\n\\n except IOError as e:\\n print(\\\"Exception occurred: \\\", e)\",\n \"def _build_parsed_values(self):\\n\\n self.final_result = []\\n retrieved_data_types = set() # keep track of data type ID's unpacked from record\\n\\n # Get the file time from the file name\\n if self._file_time:\\n self.final_result.append(self._encode_value(\\n AdcptMWVSParticleKey.FILE_TIME, self._file_time, str))\\n else:\\n self.final_result.append({DataParticleKey.VALUE_ID: AdcptMWVSParticleKey.FILE_TIME,\\n DataParticleKey.VALUE: None})\\n\\n # Get the sequence number from the file name\\n if self._sequence_number:\\n self.final_result.append(self._encode_value(\\n AdcptMWVSParticleKey.SEQUENCE_NUMBER, self._sequence_number, int))\\n else:\\n self.final_result.append({DataParticleKey.VALUE_ID: AdcptMWVSParticleKey.SEQUENCE_NUMBER,\\n DataParticleKey.VALUE: None})\\n\\n # Get the number of data types from the Header\\n num_data_types = struct.unpack_from('<B', self.raw_data, HEADER_NUM_DATA_TYPES_OFFSET)\\n # Get the list of offsets from the Header\\n offsets = struct.unpack_from('<%sI' % num_data_types, self.raw_data, HEADER_OFFSETS_OFFSET)\\n\\n # Unpack Type IDs from the offsets\\n for offset in offsets:\\n data_type_id, = struct.unpack_from('<h', self.raw_data, offset)\\n # keep track of retrieved data types\\n retrieved_data_types.add(data_type_id)\\n\\n # Feed the data through the corresponding encoding function and unpacking rules\\n try:\\n self.encoding_func_dict[data_type_id][ENCODE_FUNC](\\n offset + ID_TYPE_SIZE, self.encoding_func_dict[data_type_id][UNPACK_RULES])\\n except KeyError:\\n log.debug(\\\"Skipping unsupported data type ID: %s at offset: %s\\\",\\n data_type_id, offset)\\n\\n # go through the list of expected data type ID's, fill in None for missing data type ID's\\n missing_data = EXPECTED_PARTICLE_IDS_SET.difference(retrieved_data_types)\\n for data_type_id in missing_data:\\n if data_type_id is VARIABLE_LEADER:\\n # timestamp is essential for a data particle - no timestamp, no particle\\n message = \\\"Variable Leader Data Type is required for internal timestamp, \\\" \\\\\\n \\\"particle ignored.\\\"\\n log.warn(message)\\n raise RecoverableSampleException(message)\\n\\n self.final_result.extend(self.encoding_func_dict[data_type_id][ENCODE_NULL])\\n\\n log.trace(\\\"FINAL RESULT: %s\\\\n\\\", self.final_result)\\n\\n return self.final_result\",\n \"def _get_data(self):\\n raw_data = self._get_raw_data()\\n if not raw_data:\\n return None\\n result = {}\\n for line in raw_data:\\n if 'tcp' in line:\\n parts = line.split()\\n proto = parts[0]\\n local_addr = parts[3]\\n state = parts[5]\\n ip, port = local_addr.rsplit(':', 1)\\n port = str(port)\\n result[port] = 1\\n if state == 'LISTEN':\\n if port not in self.charts['ports']:\\n self.charts['ports'].add_dimension([port, port, 'absolute'])\\n return result\",\n \"def parse_data( self ):\\n super( PivotGraph, self ).parse_data()\\n new_parsed_data = {}\\n parsed_data = getattr( self, 'parsed_data', self.results )\\n for pivot, data in parsed_data.items():\\n new_pivot = self.parse_pivot( pivot )\\n data = self.parse_datum( data )\\n if data != None:\\n new_parsed_data[ new_pivot ] = data\\n self.parsed_data = new_parsed_data\",\n \"def parse(\\n data: str,\\n raw: bool = False,\\n quiet: bool = False\\n) -> Dict:\\n jc.utils.compatibility(__name__, info.compatible, quiet)\\n jc.utils.input_type_check(data)\\n\\n raw_output: Dict = {}\\n split_me = {'it_value:', 'it_interval:'}\\n\\n if jc.utils.has_data(data):\\n\\n for line in filter(None, data.splitlines()):\\n\\n # epoll files\\n if line.startswith('tfd:'):\\n line_match = re.findall(r'(?P<key>\\\\S+):(?:\\\\s+)?(?P<val>\\\\S+s*)', line)\\n if line_match:\\n raw_output.update({'epoll': {k.strip(): v.strip() for k, v in line_match}})\\n continue\\n\\n # inotify files\\n if line.startswith('inotify'):\\n split_line = line[8:].split()\\n raw_output['inotify'] = {}\\n for item in split_line:\\n k, v = item.split(':', maxsplit=1)\\n raw_output['inotify'][k] = v\\n continue\\n\\n # fanotify files\\n if line.startswith('fanotify'):\\n split_line = line[9:].split()\\n\\n if not 'fanotify' in raw_output:\\n raw_output['fanotify'] = {}\\n\\n for item in split_line:\\n k, v = item.split(':', maxsplit=1)\\n raw_output['fanotify'][k] = v\\n continue\\n\\n # timerfd files\\n if line.split()[0] in split_me:\\n split_line = line.replace(':', '').replace('(', '').replace(')', '').replace(',', '').split()\\n raw_output[split_line[0]] = [int(x) for x in split_line[1:]]\\n continue\\n\\n key, val = line.split(':', maxsplit=1)\\n raw_output[key.strip()] = val.strip()\\n continue\\n\\n return raw_output if raw else _process(raw_output)\",\n \"def parse(self) -> Dictionary:\\n self.parsed_dictionary = dictionary = Dictionary()\\n state = State.pre_signature\\n for lineno, line in self.line_iter:\\n lineno += 1\\n line = decomment_and_normalize(line)\\n if line == \\\"\\\": continue\\n parsed = False\\n expected_lines = State.expected_lines(state)\\n for t in expected_lines:\\n parsed, state = t.parse_line(state, dictionary, line, lineno)\\n if parsed: break\\n if not parsed:\\n raise DictionaryParseError(lineno, expected_lines, self.source)\\n if State.is_not_final(state):\\n raise DictionaryParseError(lineno + 1, expected_lines, self.source)\\n try:\\n del dictionary._last_article\\n del dictionary._last_definition\\n del dictionary._last_example\\n del dictionary._last_idiom\\n except AttributeError:\\n pass\\n return dictionary\",\n \"def collection_loader(data: str) -> dict:\\n tmp: dict[str, Optional[Union[int, bool, str]]] = {}\\n\\n for key, val in json.loads(data).items():\\n if isinstance(val, (str, bool, type(None))):\\n # These are stored as string but must be parsed into `Quantity`\\n if key in (\\\"cutoff\\\", \\\"switch_width\\\"):\\n tmp[key] = unit.Quantity(*json.loads(val).values()) # type: ignore[arg-type]\\n else:\\n tmp[key] = val\\n elif isinstance(val, dict):\\n if key == \\\"key_map\\\":\\n key_map = {}\\n\\n for key_, val_ in val.items():\\n if \\\"atom_indices\\\" in key_:\\n topology_key: Union[\\n TopologyKey,\\n LibraryChargeTopologyKey,\\n ] = TopologyKey.parse_raw(key_)\\n\\n else:\\n topology_key = LibraryChargeTopologyKey.parse_raw(key_)\\n\\n potential_key = PotentialKey(**val_)\\n\\n key_map[topology_key] = potential_key\\n\\n tmp[key] = key_map # type: ignore[assignment]\\n\\n elif key == \\\"potentials\\\":\\n potentials = {}\\n\\n for key_, val_ in val.items():\\n potential_key = PotentialKey.parse_raw(key_)\\n potential = Potential.parse_raw(json.dumps(val_))\\n\\n potentials[potential_key] = potential\\n\\n tmp[key] = potentials # type: ignore[assignment]\\n\\n else:\\n raise NotImplementedError(f\\\"Cannot parse {key} in this JSON.\\\")\\n\\n return tmp\",\n \"def requestData(self):\\n newsRaw = {'de': self.networkManager.fetchNews('de'), 'en': self.networkManager.fetchNews('en'),\\n 'fr': self.networkManager.fetchNews('fr')}\\n eventsRaw = {'de': self.networkManager.fetchEvents('de'), 'en': self.networkManager.fetchEvents('en'),\\n 'fr': self.networkManager.fetchEvents('fr')}\\n news = {}\\n events = {}\\n categories = {}\\n eventCategories = {}\\n for language in ['de', 'en', 'fr']:\\n news[language] = self.newsAndEventsParser.parseNews(newsRaw[language], language)\\n events[language] = self.newsAndEventsParser.parseEvents(eventsRaw[language], language)\\n categories[language] = self.newsAndEventsParser.getCategories()\\n eventCategories[language] = self.newsAndEventsParser.getEventCategories()\\n events['de'] += self.newsAndEventsParser.readAcademicCalendarEvents()\\n return news, events, categories, eventCategories\",\n \"def parse_input_file(self, input_path):\\n with open(input_path) as input_json_data:\\n input_data = json.load(input_json_data)\\n\\n self.total_outlets = input_data['machine']['outlets']['count_n']\\n self.total_items_quantity = input_data['machine']['total_items_quantity']\\n self.beverages = input_data['machine']['beverages']\\n\\n parsed_data = {\\n 'total_outlets': self.total_outlets,\\n 'total_items_quantity': self.total_items_quantity,\\n 'beverages': self.beverages\\n }\\n\\n return parsed_data\",\n \"def _parse(file_contents):\\n\\n if file_contents is None or file_contents == '':\\n return {}\\n\\n result = {}\\n\\n for line in file_contents.splitlines():\\n # Full line comment\\n if line[:1] == '#':\\n continue\\n\\n parts = line.split('=', 1)\\n\\n # Not a full key-value pair.\\n if len(parts) < 2:\\n continue\\n\\n result[parts[0].strip()] = parts[1].strip()\\n\\n return result\",\n \"def _parse_cookie_data() -> dict:\\n\\n with open('Data/cookies.json') as cookie_data_file:\\n cookie_data_dict = json.load(cookie_data_file)\\n\\n # Cast the necessary data\\n for cookie_type in cookie_data_dict:\\n cookie_type['weight'] = float(cookie_type['weight'])\\n cookie_type['target'] = CookieHuntTarget(cookie_type['target'])\\n\\n return cookie_data_dict\",\n \"def run(self):\\n return_dict = {}\\n for x in self.file_list:\\n return_dict[x] = self.parseFile(x)\\n\\n return return_dict\",\n \"def parse(data: str, raw: bool =False, quiet: bool =False) -> Dict:\\n jc.utils.compatibility(__name__, info.compatible, quiet)\\n jc.utils.input_type_check(data)\\n\\n linedata = data.splitlines()\\n linedata.reverse() # For popping\\n result: Response = {\\\"screens\\\": [], \\\"unassociated_devices\\\": []}\\n\\n if jc.utils.has_data(data):\\n while linedata:\\n screen = _parse_screen(linedata)\\n if screen:\\n result[\\\"screens\\\"].append(screen)\\n else:\\n device = _parse_device(linedata, quiet)\\n if device:\\n result[\\\"unassociated_devices\\\"].append(device)\\n\\n if not result[\\\"unassociated_devices\\\"] and not result[\\\"screens\\\"]:\\n return {}\\n\\n return result\",\n \"def parse(self):\\n result = {}\\n if self.detail_statu:\\n sel = Selector(text=self.driver.page_source)\\n\\n fact_table = sel.xpath(\\n '//div[@class=\\\"facts-table\\\"]//text()').extract()\\n result['facts'] = [list(i)\\n for i in zip(fact_table[:: 2],\\n fact_table[1:: 2])]\\n\\n tax_table = sel.xpath(\\n '//div[@class=\\\"tax-values\\\"]//text()').extract()\\n result['taxs'] = [list(i)\\n for i in zip(tax_table[:: 2],\\n tax_table[1:: 2])]\\n\\n listing_detail = sel.xpath(\\n '//div[@class=\\\"amenities-container\\\"]//text()').extract()\\n result['detail'] = listing_detail\\n result['page_source'] = self.driver.page_source\\n self.detail_statu = False\\n else:\\n self.log.warning(\\n '---- Detail page url out of reach, use .search() first to get the detail page')\\n return result\",\n \"def getData(self):\\n return dict(self._dump_data)\",\n \"def collect(self) -> Dict:\\n raise NotImplementedError()\",\n \"def parse_data( self ):\\n super( PivotGroupGraph, self ).parse_data()\\n new_parsed_data = {}\\n parsed_data = getattr( self, 'parsed_data', self.results )\\n for pivot, groups in parsed_data.items():\\n new_pivot = self.parse_pivot(pivot)\\n if new_pivot == None:\\n continue\\n new_groups = {}\\n new_parsed_data[ new_pivot ] = new_groups\\n for group, data in groups.items():\\n new_group = self.parse_group(group)\\n new_datum = self.parse_datum(data)\\n if new_group == None:\\n continue\\n new_groups[ new_group ] = new_datum\\n if len(new_groups) == 0:\\n del new_parsed_data[new_pivot]\\n self.parsed_data = new_parsed_data\",\n \"def parse_data(infile):\\n blocks = re.compile(' '.join(['=' * 9] * 8))\\n dashes = re.compile('^-{79}$')\\n title = re.compile('^Timings for (.*)$')\\n row = re.compile(' '.join(['(.{9})'] * 7) + ' (.{8,9})')\\n\\n lines = infile.readlines()\\n\\n data = co.OrderedDict()\\n index = 0\\n\\n while index < len(lines):\\n line = lines[index]\\n\\n if blocks.match(line):\\n try:\\n name = title.match(lines[index + 1]).group(1)\\n except Exception:\\n index += 1\\n continue\\n\\n data[name] = {}\\n\\n assert dashes.match(lines[index + 2])\\n\\n cols = parse_row(row, lines[index + 3])\\n\\n assert blocks.match(lines[index + 4])\\n\\n get_row = parse_row(row, lines[index + 5])\\n assert get_row[0] == 'get'\\n\\n set_row = parse_row(row, lines[index + 6])\\n assert set_row[0] == 'set'\\n\\n delete_row = parse_row(row, lines[index + 7])\\n assert delete_row[0] == 'delete'\\n\\n assert blocks.match(lines[index + 9])\\n\\n data[name]['get'] = dict(zip(cols, get_row))\\n data[name]['set'] = dict(zip(cols, set_row))\\n data[name]['delete'] = dict(zip(cols, delete_row))\\n\\n index += 10\\n else:\\n index += 1\\n\\n return data\",\n \"def info() -> Dict[str, Any]:\",\n \"def get_data(self):\\n\\n self.read_expression()\\n self.read_tfs()\\n self.read_metadata()\\n self.set_gold_standard_and_priors()\",\n \"def get_soup_general_data(soup):\\n data_dict = {}\\n\\n name = soup.find(class_='product_title')\\n if name:\\n data_dict['name_of_game'] = name.h1.text\\n\\n pub = soup.find('li', class_='summary_detail publisher')\\n if pub:\\n data_dict['publisher'] = pub.a.text.strip()\\n\\n rel_date = soup.find('li', class_='summary_detail release_data')\\n if rel_date:\\n rel_date = rel_date.find('span', class_='data')\\n if rel_date:\\n data_dict['release_date'] = rel_date.text.strip()\\n\\n num_p = soup.find(\\\"li\\\", class_=\\\"summary_detail product_players\\\")\\n if num_p:\\n data_dict['num_players'] = num_p.find(class_=\\\"data\\\").text\\n\\n genres = soup.find(\\\"li\\\", class_='summary_detail product_genre')\\n if genres:\\n genres = genres.find_all('span', class_='data')\\n data_dict['genres'] = [genre.text for genre in genres]\\n\\n age = soup.find(\\\"li\\\", class_=\\\"summary_detail product_rating\\\")\\n if age:\\n data_dict['age_rating'] = age.find('span', class_=\\\"data\\\").text\\n\\n return data_dict\",\n \"def read_data(raw_data: list):\\r\\n cleaned_data = {}\\r\\n for data_item in raw_data:\\r\\n clean_data_item = pre_process_string_data(data_item)\\r\\n if clean_data_item is not None:\\r\\n cleaned_data[clean_data_item['_id']] = clean_data_item\\r\\n return cleaned_data\",\n \"def tc_parse(data):\\n digest, time, status = data.split()\\n result = {\\\"resource\\\": digest, \\\"md5\\\": digest, \\\"timestamp\\\": time}\\n\\n if status == \\\"NO_DATA\\\":\\n result[\\\"status\\\"] = \\\"UKNOWN\\\"\\n else:\\n result = status_from_percentage(int(status))\\n\\n return result\",\n \"def parse_source_json(data):\\n data_dict=[]\\n for item in data:\\n tmp_dict ={}\\n tmp_dict[\\\"_id\\\"]=item[\\\"POIID\\\"]\\n tmp_dict[\\\"POIName\\\"]=item[\\\"POIName\\\"]\\n tmp_dict[\\\"Address\\\"]=item[\\\"Address\\\"]\\n try:\\n if item[\\\"isDining\\\"]=='Y':\\n tmp_dict[\\\"isDining\\\"] = True\\n else:\\n tmp_dict[\\\"isDining\\\"] = False\\n except:\\n tmp_dict[\\\"isDining\\\"] = None\\n tmp_dict[\\\"location\\\"] = {\\\"type\\\": \\\"Point\\\", \\\"coordinates\\\": [item[\\\"X\\\"],item[\\\"Y\\\"]]}\\n \\n data_dict.append(tmp_dict)\\n return data_dict\",\n \"def as_dict(self) -> Dict[str, Any]:\\n return {\\\"ttl\\\": self.ttl, \\\"type\\\": self.type.value, \\\"data\\\": self.data}\",\n \"def get_data(self):\\n data = {}\\n _priv = self.get('_private', [])\\n\\n def check_data(v):\\n if isinstance(v, Yaco):\\n v = v.get_data()\\n elif isinstance(v, list):\\n v = [check_data(x) for x in v]\\n return v\\n\\n for k in list(self.keys()):\\n if k in _priv:\\n continue\\n if isinstance(k, (str)) and k and k[0] == '_':\\n continue\\n # print self.keys()\\n # print k, 'x' * 30\\n data[k] = check_data(self[k])\\n return data\",\n \"def _parse_ddwrt_response(data_str):\\n return {key: val for key, val in _DDWRT_DATA_REGEX.findall(data_str)}\",\n \"def process_raw_data(self):\\n \\n # Get all participant names, or return straight away if no data was\\n # loaded yet.\\n if hasattr(self, \\\"raw\\\"):\\n participants = self.raw.keys()\\n participants.sort()\\n else:\\n self.data = None\\n return\\n\\n # Count the number of participants.\\n n = len(participants)\\n \\n # Find out how many questions there were.\\n n_questions = 0\\n for i, ppname in enumerate(participants):\\n if self.raw[ppname] is None:\\n continue\\n if len(self.raw[ppname][\\\"QuestionNumber\\\"]) > n_questions:\\n n_questions = len(self.raw[ppname][\\\"QuestionNumber\\\"])\\n \\n # Define some variables of interest.\\n vor = []\\n for i in range(n_questions):\\n vor.append(\\\"Q%d_resp\\\" % (i+1))\\n vor.append(\\\"Q%d_RT\\\" % (i+1))\\n \\n # Create a data dict for each variable of interest.\\n self.data = {}\\n self.data[\\\"ppname\\\"] = []\\n for var in vor:\\n self.data[var] = numpy.zeros(n, dtype=float) * numpy.NaN\\n \\n # Loop through all participants.\\n for i, ppname in enumerate(participants):\\n # Add the participant name.\\n self.data[\\\"ppname\\\"].append(copy.deepcopy(ppname))\\n # Skip empty datasets.\\n if self.raw[ppname] is None:\\n continue\\n # Compute stuff relevant to this task.\\n for j, qnr in enumerate(self.raw[ppname][\\\"QuestionNumber\\\"]):\\n # Split Questionnaire 3, Q13 and Q14 into sub-questions\\n if \\\"Q3\\\" in self._task_name and int(qnr) in [13,14]:\\n # These questions split out into two parts: A description\\n # of what each sub-part is, and a Boolean response for\\n # each sub-part in the question. Example:\\n # \\\"1_1_1_1_1_0//Television_VideogameConsole(suchas:WiiUPlayStationXboxorNintendoDS)_Tablet(likeanIPad)_Smartphone_LaptoporDesktopComputer_Noneofthese\\\"\\n bool_resp, descr = self.raw[ppname][\\\"Response\\\"][j].split(\\\"//\\\")\\n bool_resp = map(int, bool_resp.split(\\\"_\\\"))\\n descr = descr.split(\\\"_\\\")\\n # Store the data in the dict.\\n for k, item in enumerate(descr):\\n # Clean up the item name.\\n if \\\"(\\\" in item:\\n item = item[:item.find(\\\"(\\\")]\\n var = \\\"Q%s_%s_resp\\\" % (int(qnr), item)\\n # Create a new entry in the dict for this variable, if\\n # one doesn't exist yet.\\n if var not in self.data.keys():\\n self.data[var] = numpy.zeros(n, dtype=float) * numpy.NaN\\n # Store the data in the dict.\\n self.data[var][i] = bool_resp[k]\\n # Store response time for the whole item.\\n self.data[\\\"Q%s_RT\\\" % (int(qnr))][i] = \\\\\\n float(self.raw[ppname][\\\"TimeEndQuestion\\\"][j]) \\\\\\n - float(self.raw[ppname][\\\"TimeStartQuestion\\\"][j])\\n # All other questions are one-question one-response:\\n else:\\n # Store the data in the dict.\\n self.data[\\\"Q%s_resp\\\" % (int(qnr))][i] = \\\\\\n float(self.raw[ppname][\\\"Response\\\"][j])\\n self.data[\\\"Q%s_RT\\\" % (int(qnr))][i] = \\\\\\n self.raw[ppname][\\\"TimeEndQuestion\\\"][j] \\\\\\n - self.raw[ppname][\\\"TimeStartQuestion\\\"][j]\",\n \"def _read_info(self):\\n my_filelines = self.file_lines\\n info = dict()\\n\\n for i, line in enumerate(my_filelines):\\n if line.startswith(\\\"VEHICLE\\\"):\\n vehicle_pro_start = i + 2\\n elif line.startswith(\\\"CUSTOMER\\\"):\\n customer_pro_start = i + 3\\n\\n elif line.startswith(\\\"NUMBER\\\"):\\n splited = line.split(' ')\\n info[splited[0]] = 0\\n info[splited[-1]] = 0\\n return info, (vehicle_pro_start, customer_pro_start)\",\n \"def get_data_from_bs(bs_data):\\n all_raw_data = bs_data.find_all(\\\"properties\\\")\\n return (to_dict(tag for tag in raw if type(tag) is Tag) for raw in all_raw_data)\",\n \"def process_raw_data(self):\\n \\n # Define some variables of interest.\\n vor = [\\\"n_sentences\\\", \\\"n_correct\\\", \\\"p_correct\\\", \\\"median_RT\\\", \\\\\\n \\\"mean_RT\\\", \\\"stdev_RT\\\", \\\"scaled_stdev_RT\\\"]\\n \\n # Get all participant names, or return straight away if no data was\\n # loaded yet.\\n if hasattr(self, \\\"raw\\\"):\\n participants = self.raw.keys()\\n participants.sort()\\n else:\\n self.data = None\\n return\\n\\n # Count the number of participants.\\n n = len(participants)\\n \\n # Create a data dict for each variable of interest.\\n self.data = {}\\n self.data[\\\"ppname\\\"] = []\\n for var in vor:\\n self.data[var] = numpy.zeros(n, dtype=float) * numpy.NaN\\n \\n # Loop through all participants.\\n for i, ppname in enumerate(participants):\\n # Add the participant name.\\n self.data[\\\"ppname\\\"].append(copy.deepcopy(ppname))\\n # Skip empty datasets.\\n if self.raw[ppname] is None:\\n continue\\n # Compute stuff relevant to this task.\\n self.data[\\\"n_sentences\\\"][i] = len(self.raw[ppname][\\\"Sentence\\\"])\\n self.data[\\\"n_correct\\\"][i] = numpy.sum(self.raw[ppname][\\\"correct\\\"])\\n self.data[\\\"p_correct\\\"][i] = float(self.data[\\\"n_correct\\\"][i]) \\\\\\n / float(self.data[\\\"n_sentences\\\"][i])\\n self.data[\\\"median_RT\\\"][i] = numpy.nanmedian(self.raw[ppname][\\\"RT\\\"])\\n self.data[\\\"mean_RT\\\"][i] = numpy.nanmean(self.raw[ppname][\\\"RT\\\"])\\n self.data[\\\"stdev_RT\\\"][i] = numpy.nanstd(self.raw[ppname][\\\"RT\\\"])\\n # Compute a scaled standard deviation of the response time, scaled to the\\n # median response time to remove the correlation between the two.\\n self.data[\\\"scaled_stdev_RT\\\"] = self.data[\\\"stdev_RT\\\"] / self.data[\\\"median_RT\\\"]\",\n \"def _parse(self):\\n\\n self.specification = {}\\n\\n while True:\\n try:\\n line = self._lines.current\\n if ':' in line:\\n self.specification.update(self._parse_spec())\\n elif line.startswith('NODE_COORD_SECTION'):\\n next(self._lines)\\n self.coords = self._parse_coords()\\n elif line.startswith('EDGE_WEIGHT_SECTION'):\\n next(self._lines)\\n self.weights = self._parse_weights()\\n elif line.startswith('DISPLAY_DATA_SECTION'):\\n next(self._lines)\\n self.display = self._parse_coords()\\n else:\\n break\\n except StopIteration:\\n break\\n\\n del self._lines\",\n \"def read(self):\\n dictionary = {}\\n with open(self.path) as file:\\n key_header = \\\"\\\"\\n for line in file:\\n entry = line.strip().split()\\n if len(entry) == 0:\\n continue\\n if len(entry) == 1:\\n key_header = entry[0]+\\\"_\\\"\\n else:\\n key = entry[0].strip()\\n value = reduce(lambda x1, y1: x1+\\\" \\\" + y1, entry[1:])\\n dictionary[key_header+key] = value\\n return dictionary\",\n \"def process_data(file: TextIO) -> 'Climatematch':\\n climate_dict = {}\\n line = file.readline()\\n \\n while line != '':\\n username = line.strip()\\n climate_dict[username] = {}\\n \\n line = file.readline().strip()\\n climate_dict[username]['name'] = line\\n line = file.readline().strip()\\n climate_dict[username]['location'] = line\\n \\n climate_dict[username]['bio'] = ''\\n line = file.readline()\\n while line != 'ENDBIO\\\\n': \\n climate_dict[username]['bio'] += line\\n line = file.readline()\\n \\n climate_dict[username]['skills'] = []\\n line = file.readline().strip()\\n while line != 'ENDSKILL': \\n climate_dict[username]['skills'].append(line)\\n line = file.readline().strip()\\n \\n climate_dict[username]['interest'] = []\\n line = file.readline().strip() \\n while line != 'END': \\n climate_dict[username]['interest'].append(line)\\n line = file.readline().strip()\\n line = file.readline()\\n \\n return climate_dict\",\n \"def _parse(self):\\n pass\",\n \"def meta_data(self) -> Dict:\\n pass\",\n \"def parse(self):\",\n \"def as_dict(self) -> dict[str, Any]:\\n return {\\n \\\"type\\\": self.type,\\n \\\"timestamp\\\": self.timestamp,\\n \\\"data\\\": self.data or {},\\n }\",\n \"def dict(self):\\n return self.data\",\n \"def parse(self, data):\\n raise NotImplementedError\",\n \"def process_tweet(tweet):\\n d = {}\\n d['hastags'] = [hashtag['text'] for hashtag in tweet['entities']['hashtags']]\\n d['text'] = tweet['text']\\n d['user'] = tweet['user']['screen_name']\\n d['user_loc'] = tweet['user']['location']\\n return d\",\n \"def data(self) -> dict:\\n raise NotImplementedError()\",\n \"def handle_data(data):\\n items = data.split(',')\\n items.sort()\\n results = {}\\n i = 1\\n for item in items:\\n it = item.split(' ')\\n results[i] = (it[0], it[1], int(it[2]))\\n print str(i) + \\\" \\\" + it[0]\\n i += 1\\n return results\",\n \"def decodemeta(data):\\n d = {}\\n for l in data.split('\\\\0'):\\n if l:\\n key, value = l.split(':')\\n d[key] = value\\n return d\",\n \"def extract_metadata(self):\\n if self.is_generatable_file:\\n logger.debug(\\\"Converting collected details to dict..\\\")\\n if self.metadata_collector:\\n self.metadata = MetadataToDict(\\n metadata_collector=self.metadata_collector,\\n file_import=self.file_import,\\n )\\n self.metadata.build_integration_dict()\",\n \"def get_data(self, data):\\n self.data = {}\\n self.data[ATTR_PM1] = data['current']['values'][0]['value']\\n self.data[ATTR_PM25] = data['current']['values'][1]['value']\\n self.data[ATTR_PM25_LIMIT] = data['current']['standards'][0]['limit']\\n self.data[ATTR_PM25_PERCENT] = (data['current']['standards'][0]\\n ['percent'])\\n self.data[ATTR_PM10] = data['current']['values'][2]['value']\\n self.data[ATTR_PM10_LIMIT] = data['current']['standards'][1]['limit']\\n self.data[ATTR_PM10_PERCENT] = (data['current']['standards'][1]\\n ['percent'])\\n self.data[ATTR_PRESSURE] = data['current']['values'][3]['value']\\n self.data[ATTR_HUMIDITY] = data['current']['values'][4]['value']\\n self.data[ATTR_TEMPERATURE] = data['current']['values'][5]['value']\\n self.data[ATTR_CAQI] = data['current']['indexes'][0]['value']\\n self.data[ATTR_CAQI_LEVEL] = (data['current']['indexes'][0]\\n ['level'].lower().replace('_', ' '))\",\n \"def collected_data(self) -> Iterable[Dict[str, float]]:\\n return self._collected_data\",\n \"def ingest(in_info):\\n if type(in_info) == str:\\n with open(in_info) as infile:\\n lines = (line.split(None) for line in infile)\\n in_dict = {defn[0] : defn[1:] for defn in lines}\\n else:\\n in_dict = in_info\\n return in_dict\",\n \"def _process_dict(data):\\n new_dict = {}\\n for key in data.keys():\\n\\tnew_dict['name'] = data['printerName']\\n #new_dict[key] = data[key]\\n\\n #FIGURE OUT AND UPDATE PRINTER STATUS\\n status = BUSY_STATUS\\n error_msg = \\\"\\\"\\n if \\\"FrontPanelMessage\\\" in data:\\n if data[\\\"FrontPanelMessage\\\"].lower() in READY_MESSAGES:\\n status = READY_STATUS\\n elif \\\"error\\\" in data[\\\"FrontPanelMessage\\\"].lower():\\n status = ERROR_STATUS\\n error_msg = \\\"general error\\\"\\n \\n if \\\"TonerStatus\\\" in data:\\n if data[\\\"TonerStatus\\\"].find(\\\"2\\\") != -1:\\n status = ERROR_STATUS\\n error_msg = \\\"Toner Error\\\"\\n #if len(new_dict[\\\"TonerStatus\\\"]) > 4:\\n #new_dict[\\\"TonerStatus\\\"] = new_dict[\\\"TonerStatus\\\"][4:]\\n\\n if \\\"PaperStatus\\\" in data:\\n if data[\\\"PaperStatus\\\"].find(\\\"2\\\") != -1:\\n status = ERROR_STATUS\\n error_msg = \\\"Paper Status Error\\\"\\n elif data[\\\"PaperStatus\\\"].find(\\\"1\\\") != -1:\\n status = ERROR_STATUS\\n error_msg = \\\"Out of Paper\\\"\\n #if len(new_dict[\\\"PaperStatus\\\"]) > 4:\\n #new_dict[\\\"PaperStatus\\\"] = new_dict[\\\"PaperStatus\\\"][4:]\\n\\n if \\\"PaperJamStatus\\\" in data:\\n if data[\\\"PaperJamStatus\\\"].find(\\\"1\\\") != -1:\\n status = ERROR_STATUS\\n error_msg = \\\"Paper Jam\\\"\\n #if len(new_dict[\\\"PaperJamStatus\\\"]) > 4:\\n #new_dict[\\\"PaperJamStatus\\\"] = new_dict[\\\"PaperJamStatus\\\"][4:]\\n\\n new_dict[\\\"status\\\"] = status\\n new_dict[\\\"error_msg\\\"] = error_msg\\n new_dict[\\\"location\\\"] = PRINTERS[new_dict[\\\"name\\\"]][0]\\n new_dict[\\\"building_name\\\"] = PRINTERS[new_dict[\\\"name\\\"]][1]\\n new_dict[\\\"latitude\\\"] = PRINTERS[new_dict[\\\"name\\\"]][2]\\n new_dict[\\\"longitude\\\"] = PRINTERS[new_dict[\\\"name\\\"]][3]\\n new_dict[\\\"atResidence\\\"] = PRINTERS[new_dict[\\\"name\\\"]][4]\\n return new_dict\",\n \"def to_dict(self) -> dict:\",\n \"def _to_dict(self, data_list):\\n data_dict = dict(pair.split('=') for pair in data_list)\\n return data_dict\",\n \"def parse_data(self, data):\\n pids = set()\\n cams = set()\\n \\n for info in data:\\n pids.add(info[1])\\n cams.add(info[2])\\n return len(pids), len(cams)\",\n \"def process_data(data):\\n # set up an OrderedDict to hold the data with initial data set to 0\\n output = OrderedDict((s, {'calls': [], 'out_count': 0, 'out_dur':\\n datetime.timedelta(seconds=0), 'in_count': 0, 'in_dur':\\n datetime.timedelta(seconds=0), 'total_dur':\\n datetime.timedelta(seconds=0)}) for s in config.SALESPEOPLE)\\n for d in data:\\n # assume it's an outgoing call.\\n call_type = 'outgoing'\\n key = d['From']\\n tofrom = '{0} {1}'.format(d['To CID Name'], d['To CID Number'])\\n if not tofrom.strip():\\n tofrom = d['Digits'].strip() or d['To']\\n out_count, in_count = 1, 0\\n dur_h, dur_m, dur_s = map(int, d['Duration'].split(':'))\\n duration = datetime.timedelta(hours=dur_h, minutes=dur_m,\\n seconds=dur_s)\\n out_dur = duration\\n in_dur = datetime.timedelta(seconds=0)\\n if key not in config.SALESPEOPLE:\\n # it's an incoming call if the From name isn't one of the\\n # config.SALESPEOPLE. Adjust the data accordingly\\n call_type = 'incoming'\\n key = d['To']\\n tofrom = '{0} {1}'.format(d['From CID Name'], d['From CID Number'])\\n if not tofrom.strip():\\n tofrom = d['To']\\n out_count, in_count = 0, 1\\n out_dur = datetime.timedelta(seconds=0)\\n in_dur = duration\\n\\n # format the phone numbers\\n tofrom = re.sub(r'1?(\\\\d{3})(\\\\d{3})(\\\\d{4})$', r'\\\\1-\\\\2-\\\\3', tofrom)\\n\\n output[key]['calls'].append({'time': d['Start Time'], 'type':\\n call_type, 'duration': d['Duration'], 'tofrom': tofrom})\\n output[key]['out_count'] = output[key]['out_count'] + out_count\\n output[key]['out_dur'] = output[key]['out_dur'] + out_dur\\n output[key]['in_count'] = output[key]['in_count'] + in_count\\n output[key]['in_dur'] = output[key]['in_dur'] + in_dur\\n output[key]['total_dur'] = output[key]['total_dur'] + duration\\n\\n return output\",\n \"def get_data(self):\\n return self.parsed_data\",\n \"def parsing_no_hits_data(global_avg_trimmed_length):\\n\\n #No Hit Counter\\n no_hit_counter = 0\\n\\n #Totally trimmed counter\\n totally_trimmed_counter = 0\\n\\n #No hits results\\n average_trimmed_no_hit_length=[]\\n\\n #Opening and Parsing blast_no_hits_report.txt\\n no_hit_results = open('blast_no_hits_report.txt', 'r')\\n for line in no_hit_results:\\n data = line.split(\\\"\\\\t\\\")\\n \\n if line.startswith('SeqID'):\\n continue\\n else:\\n average_trimmed_no_hit_length.append(float(data[4]))\\n global_avg_trimmed_length.append(float(data[4]))\\n \\n no_hit_counter +=1\\n \\n if float(data[4]) == 0:\\n totally_trimmed_counter +=1\\n continue\\n no_hit_results.close\\n\\n\\n return {'no_hit_counter':no_hit_counter, 'totally_trimmed_counter':totally_trimmed_counter, \\n 'average_trimmed_no_hit_length':average_trimmed_no_hit_length, 'global_avg_trimmed_length':global_avg_trimmed_length}\",\n \"def massage_api_response(api_data):\\n return_dict = defaultdict(list)\\n legs = api_data['legs'][0]\\n\\n return_dict['start_address'].append(legs['start_address'])\\n return_dict['end_address'].append(legs['end_address'])\\n return_dict['distance'].append(legs['distance']['text'])\\n return_dict['duration'].append(legs['duration']['text'])\\n if 'duration_in_traffic' in legs:\\n (return_dict['duration_in_traffic']\\n .append(legs['duration_in_traffic']['text']))\\n return_dict['travel_mode'].append(legs['steps'][0]['travel_mode'])\\n\\n for instruction in legs['steps']:\\n (return_dict['instructions']\\n .append(BeautifulSoup(instruction['html_instructions'],\\n 'html.parser').get_text()))\\n return_dict['step_distance'].append(instruction['distance'])\\n return return_dict\",\n \"def parse_data(self, file_data):\\n if self.data:\\n all_api_reference = self.data.findAll([\\\"h4\\\",\\\"h3\\\"])\\n for api in all_api_reference: \\n title = api.text.replace(' #', '')\\n\\n href = self.parse_link(file_data, api)\\n\\n content = self.parse_content(api)\\n\\n example = self.parse_example(api)\\n\\n section = {\\n 'title': title,\\n 'href': href,\\n 'content': content,\\n 'example': example\\n }\\n self.parsed_data.append(section)\",\n \"def __load(self) -> Dict:\\n return dict()\",\n \"def _get_new_data(self, page_url, soup):\\n data = {}\\n data['url'] = page_url\\n title = soup.find('dd', class_='lemmaWgt-lemmaTitle-title').find('h1')\\n data['title'] = title.get_text()\\n summary = soup.find('div', class_='lemma-summary')\\n data['summary'] = summary.get_text()\\n return data\",\n \"def parse_data(block):\\n rval = {\\n 'Total' : None,\\n 'Electronic' : None,\\n 'Translational' : None,\\n 'Rotational' : None,\\n 'Vibrational' : None\\n }\\n for line in block.splitlines():\\n if re.match(r'^\\\\s*Total', line):\\n key = 'Total'\\n elif re.match(r'^\\\\s*Electronic', line):\\n key = 'Electronic'\\n elif re.match(r'^\\\\s*Translational', line):\\n key = 'Translational'\\n elif re.match(r'^\\\\s*Rotational', line):\\n key = 'Rotational'\\n elif re.match(r'^\\\\s*Vibrational', line):\\n key = 'Vibrational'\\n else:\\n key = None\\n if key:\\n words = line.strip().split()\\n try:\\n rval[key] = float(words[1])\\n except ValueError:\\n raise ValueError('Invalid thermodynamic format.')\\n return rval\",\n \"def parse(self, data):\\n self._data = data\\n return self._parse_headers()\",\n \"def _separate_raw_data(self, raw_data):\\n for key, value in raw_data.items():\\n if type(value) == dict:\\n self.data_dict[key] = value\\n elif type(value) == list:\\n self.data_list[key] = value\",\n \"def parse_event(self, raw_data: str):\\n event_attributes = raw_data.replace(\\\"\\\\n\\\", \\\"\\\").split(\\\",\\\")\\n for j in range(len(event_attributes)):\\n event_attributes[j] = str_to_number(event_attributes[j])\\n return dict(zip(METASTOCK_7_COLUMN_KEYS, event_attributes))\",\n \"def parse_api(self, soup):\\n return {}\",\n \"def load_data(self):\\n super(MudderyObjectCreater, self).load_data()\\n \\n data = self.get_data_record()\\n if not data:\\n return\\n \\n # set common object's info\\n self.obj_list = {}\\n\\n for obj in data.obj_list.split(\\\",\\\"):\\n obj_key = \\\"\\\"\\n number = 0\\n arg = obj.split(\\\":\\\", 1)\\n if len(arg) == 1:\\n obj_key = arg[0]\\n number = 1\\n elif len(arg) >= 2:\\n obj_key = arg[0]\\n number = int(arg[1])\\n\\n self.obj_list[obj_key] = number\",\n \"def parse_data(data):\\n result = {}\\n if data:\\n params = data.split('&')\\n for item in params:\\n key, value = item.split('=')\\n result[key] = value\\n return decode_data(result)\",\n \"def parse_bibtex(self, data: str) -> Dict:\\n\\n new_bib = [line for line in data.splitlines() if \\\"= ,\\\" not in line]\\n new_bib = \\\"\\\\n\\\".join(new_bib)\\n bib_db: bibtexparser.bibdatabase.BibDatabase = bibtexparser.loads(new_bib)\\n result = dict()\\n for entry in bib_db.entries:\\n osti_id = entry[\\\"ID\\\"].split(\\\"_\\\")[1]\\n result[osti_id] = entry\\n return result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7335327","0.69097733","0.6876233","0.66875476","0.64781004","0.6423315","0.63635683","0.6295866","0.6179777","0.6155332","0.6152996","0.6149452","0.614944","0.61326593","0.6130262","0.61247736","0.611546","0.60994387","0.6095851","0.6092316","0.6058877","0.6057332","0.6057252","0.6049579","0.60366493","0.6011974","0.6006078","0.59955436","0.59762","0.596932","0.59475213","0.59341866","0.5928448","0.59002656","0.5891335","0.5878575","0.5876698","0.5874984","0.5869757","0.5860817","0.58453584","0.5833198","0.58315","0.58218426","0.5818262","0.581728","0.5815935","0.5794222","0.57901734","0.5785707","0.578267","0.57809216","0.5757653","0.5754654","0.575442","0.57523257","0.57448965","0.57424086","0.5740971","0.57388526","0.57380664","0.5723235","0.57211065","0.5718079","0.57125264","0.57060266","0.57053447","0.57043743","0.570093","0.5700752","0.570007","0.56994677","0.5698165","0.56883","0.56859833","0.5684747","0.56795114","0.5672523","0.56658435","0.5662955","0.5659795","0.5658853","0.5656995","0.5651191","0.5648777","0.5647329","0.56449723","0.56438047","0.56430495","0.564043","0.56292677","0.5628782","0.5628418","0.56236","0.5617291","0.561588","0.56124514","0.5611682","0.56095356","0.5609343","0.56029016"],"string":"[\n \"0.7335327\",\n \"0.69097733\",\n \"0.6876233\",\n \"0.66875476\",\n \"0.64781004\",\n \"0.6423315\",\n \"0.63635683\",\n \"0.6295866\",\n \"0.6179777\",\n \"0.6155332\",\n \"0.6152996\",\n \"0.6149452\",\n \"0.614944\",\n \"0.61326593\",\n \"0.6130262\",\n \"0.61247736\",\n \"0.611546\",\n \"0.60994387\",\n \"0.6095851\",\n \"0.6092316\",\n \"0.6058877\",\n \"0.6057332\",\n \"0.6057252\",\n \"0.6049579\",\n \"0.60366493\",\n \"0.6011974\",\n \"0.6006078\",\n \"0.59955436\",\n \"0.59762\",\n \"0.596932\",\n \"0.59475213\",\n \"0.59341866\",\n \"0.5928448\",\n \"0.59002656\",\n \"0.5891335\",\n \"0.5878575\",\n \"0.5876698\",\n \"0.5874984\",\n \"0.5869757\",\n \"0.5860817\",\n \"0.58453584\",\n \"0.5833198\",\n \"0.58315\",\n \"0.58218426\",\n \"0.5818262\",\n \"0.581728\",\n \"0.5815935\",\n \"0.5794222\",\n \"0.57901734\",\n \"0.5785707\",\n \"0.578267\",\n \"0.57809216\",\n \"0.5757653\",\n \"0.5754654\",\n \"0.575442\",\n \"0.57523257\",\n \"0.57448965\",\n \"0.57424086\",\n \"0.5740971\",\n \"0.57388526\",\n \"0.57380664\",\n \"0.5723235\",\n \"0.57211065\",\n \"0.5718079\",\n \"0.57125264\",\n \"0.57060266\",\n \"0.57053447\",\n \"0.57043743\",\n \"0.570093\",\n \"0.5700752\",\n \"0.570007\",\n \"0.56994677\",\n \"0.5698165\",\n \"0.56883\",\n \"0.56859833\",\n \"0.5684747\",\n \"0.56795114\",\n \"0.5672523\",\n \"0.56658435\",\n \"0.5662955\",\n \"0.5659795\",\n \"0.5658853\",\n \"0.5656995\",\n \"0.5651191\",\n \"0.5648777\",\n \"0.5647329\",\n \"0.56449723\",\n \"0.56438047\",\n \"0.56430495\",\n \"0.564043\",\n \"0.56292677\",\n \"0.5628782\",\n \"0.5628418\",\n \"0.56236\",\n \"0.5617291\",\n \"0.561588\",\n \"0.56124514\",\n \"0.5611682\",\n \"0.56095356\",\n \"0.5609343\",\n \"0.56029016\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77995,"cells":{"query":{"kind":"string","value":"Return a link to the image in its source Website or details about the image that help looking it up or debugging it. Override for your dataset, but pass to this function if you encounter images not in your dataset."},"document":{"kind":"string","value":"def image_reference(self, image_id):\n return \"\""},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_image_url():","def get_image_link(self):\n table = self.soup.find('table')\n image_tag = table.find('img')\n image_name = self.soup.find_all(\"b\")[1].text\n return image_tag['src'], image_name\n\n # image = td.find_all('img')\n # print(image)\n # if image is not None:\n # return urljoin(self.base_url, image['src'])","def get_detail_image(self):\n facility_data = self.get_raw_facilities_data()\n if facility_data is None:\n return None\n else:\n return facility_data['detailImageUrl']","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info[\"source\"] == \"coco\":\n return \"http://cocodataset.org/#explore?id={}\".format(info[\"id\"])\n else:\n super(CocoDataset, self).image_reference(image_id)","def image_url(self) -> str:\n return pulumi.get(self, \"image_url\")","def image(self, link, title, alt):\n if not link.startswith(('http://', 'https://')):\n source_dir = os.path.dirname(self.source_path)\n link = os.path.abspath(os.path.join(source_dir, link))\n return '<img src=\"%s\" title=\"%s\" alt=\"%s\" />' % (link, title, alt)","def image_link(self):\r\n\r\n if not self._image_link:\r\n warnings.warn(\"Seems like you are trying to pull out the image link while not having it.\", Warning, stacklevel=2)\r\n\r\n return self._image_link","def get_image_url(img):\n # element['data-src'] and element.get('data-src') doesn't work\n for k, v in img.items():\n if k == 'data-src':\n # https://t.nhentai.net/galleries/<gallerycode>/<page#>t.<extension>\n # https://i.nhentai.net/galleries/<gallerycode>/<page#>.<extension>\n return v[:8] + 'i' + v[9:32] + v[32:].replace('t.', '.', 1)","def image_url(self) -> str:\n return self._image_url","def image_url(self):\n context = aq_inner(self.context)\n obj_url = context.absolute_url()\n if hasattr(context, 'getField'):\n field = self.context.getField('image')\n if not field:\n field = context.getField(IMAGE_FIELD_NAME)\n\n if field and field.get_size(context) > 0:\n return u'%s/%s_%s' % (obj_url, field.getName(), 'thumb')\n\n return u\"%s/isaw_logo.png\" % self.portal.absolute_url()","def get_url_image(self, obj):\n return settings.IMAGE_HOST + obj.image.url","def get_url_image(self, obj):\n return settings.IMAGE_HOST + obj.image.url","def get_url_image(self, obj):\n return settings.IMAGE_HOST + obj.image.url","def image_url(self):\n return self.photo_url or GENERIC_IMAGE","def get_image_url(self):\n return self.get_extract_image_urls(is_first=True)","def image(self) -> str:\n return pulumi.get(self, \"image\")","def get_url_image(self, obj):\n return settings.SERVER_HOST + obj.image.url","def get_image_info(filename: str) -> str:\n url = \"%s?format=json&action=query&prop=imageinfo&iiprop=url&titles=File:%s\" % (WIKIDATA_URL, filename)\n # Perform request\n print_debug(\"Sending GET %s\" % url)\n response = requests.get(url)\n data = response.json()\n print_debug(\"%s -> %d\" % (url, response.status_code))\n print_debug(\"%s\" % response.text)\n return data[\"query\"][\"pages\"][\"-1\"][\"imageinfo\"][0][\"url\"]","def get_possible_thumbnail(self):\n meta = self.get_meta_data()\n print meta\n if \"og:image\" in meta:\n return meta[\"og:image\"]\n elif \"twitter:image:src\" in meta:\n return meta[\"twitter:image:src\"]\n else:\n images = self.get_image_data()\n temp_url = \"\"\n temp_width = 0\n for img in images:\n if img[\"image_width\"] > temp_width:\n temp_url = img[\"image_url\"]\n temp_width = img[\"image_width\"]\n\n return temp_url","def return_image(val, model_id, message_name, field_name, mime, sind):\n column_data_source = curdoc().get_model_by_name(sind)\n index = column_data_source.tags[0]\n url = \"http://{0}/image/\".format(_host) + \"---\".join([model_id, message_name, field_name, mime, sind, str(index)])\n return url","def get_thumbnail_url():","def show_image_ref():\n return get_image_ref()","def source_image_link(self, image_id):\n return self.image_info[image_id][\"path\"]","def source_image_link(self, image_id):\n return self.image_info[image_id][\"path\"]","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def image(self) -> Optional[str]:\n return pulumi.get(self, \"image\")","def get_image_url(article):\n get_image = article.find_all(\"img\")[0]\n return get_image.get(\"src\").replace(\"../../\", \"http://books.toscrape.com/\")","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info[\"source\"] == \"pedestrian\":\n return info[\"path\"]\n else:\n super(self.__class__, self).image_reference(image_id)","def met(r):\n image_url = r.get(\"image\")\n if image_url is None:\n if r.get(\"source\") is not None:\n image_url = r.get(\"source\").get(\"href\")\n image_name = r.get(\"name\")\n image_artist = r.get(\"Who\")\n return image_url, image_name, image_artist","def _collect_img_links(self):\n raise NotImplementedError","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info[\"source\"] == \"balloon\":\n return info[\"path\"]\n else:\n super(self.__class__, self).image_reference(image_id)","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info[\"source\"] == \"balloon\":\n return info[\"path\"]\n else:\n super(self.__class__, self).image_reference(image_id)","def image_url(self):\n return \"{}/mjpeg_read.php\".format(self.base_url)","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info['source'] == 'local':\n return info['source']\n else:\n super(self.__class__).image_reference(self, image_id)","def thumbnail_url(self):\n return None","def image(self):\n image_id = self.attrs.get('ImageID', self.attrs['Image'])\n if image_id is None:\n return None\n return self.client.images.get(image_id.split(':')[1])","def get_list_image(self):\n facility_data = self.get_raw_facilities_data()\n if facility_data is None:\n return None\n else:\n return facility_data['listImageUrl']","def image_preview(self):\r\n h = '<img src=\"%s\" alt=\"Campaign badge\"/>' % self.image.url\r\n return mark_safe(h)","def get_image_uri(self):\n return \"data:image/png;base64,\" + \\\n self.browser.get_screenshot_as_base64()","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info[\"source\"] == \"vesicle\":\n return info[\"path\"]\n else:\n super(self.__class__, self).image_reference(image_id)","def get_image(self):\n try:\n self.image = self.soup.find(class_=\"recipe-header-image\").find(\"img\").get(\"src\", \"\")\n except Exception:\n current_app.logger.error(f\"Could not extract image: {traceback.format_exc()}\")\n self.image = \"\"","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info['source'] == self.dataset_name:\n return info['id']\n else:\n super.image_reference(image_id)","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info[\"source\"].strip().lower() == 'homeobject':\n return info[\"path\"]\n else:\n super(self.__class__, self).image_reference(image_id)","def getImageTile(self, **kwargs):\n if self.hasImage():\n imgtileurl = self.getImage().absolute_url(1) + '_tile'\n portal_obj = getToolByName(self, 'portal_url').getPortalObject()\n portal_url = portal_obj.absolute_url(1)\n imgtileurl = imgtileurl.replace(portal_url, '')\n return imgtileurl\n return ''","def get_image_url(self, image_name: str, thumbnail: bool = False) -> str:\n pass","def get_image(result):\n article_id = result['id']\n id_ = article_id[14:]\n href = article_id[:14]\n\n #FIXME: not working\n image_url = \"http://www.jpress.nli.org.il/Olive/APA/NLI_heb/get/GetImage.ashx?kind=block&href=%s&id=%s&ext=.png\" %(href, id_)\n \n return image_url","def img_url_display(self):\n url = '%s=s%s' % (self.img_url, self.DISPLAY_SIZE_PX)\n if self.img_rot in Plaque.ALLOWED_ROTATIONS:\n url = \"%s-r%s\" % (url, self.img_rot)\n return url","def get_image_url(self, image, hostname):\n\n if image.image_type == 'local':\n return \"http://\" + hostname + \"/\" + settings.IMAGES_URL + \"/\" + image.deployment.campaign.short_name + \"/\" + image.deployment.short_name + \"/\" + image.image_name\n elif image.image_type == 'envirocoms':\n return \"http://\" + hostname + \"/ecoms_proxy?image=\" + image.image_path","def get_image_comic_url(session, response):\n soup = bs(response.text, 'lxml')\n for div in soup.find_all('div', class_=\"img-comic-container\"):\n for a in div.find_all('a', class_=\"img-comic-link\"):\n for img in a.find_all('img', src=True):\n return \"https:\" + img['src']","def _get_pic_link(self, tree, xpath_adr='/html/body/div[1]/div[2]/div[3]/div[1]/div[1]/div/div/img/@data-src'):\n try:\n return tree.xpath(xpath_adr)[0]\n except:\n print('WARNING: Could not scrape game card web address, check review xpath address')\n return np.nan","def visualize(self):\n return \"https://neuroglancer.bossdb.io/#!{'layers':{'image':{'source':'boss://__replace_me__'}}}\".replace(\n \"__replace_me__\",\n f\"{self.volume_provider.boss._project._base_protocol}://{self.volume_provider.boss._project._base_url}/{self.collection_name}/{self.experiment_name}/{self.channel_name}\",\n )","def getReferenceImageUrl(self, name):\n bucket = self.productSearch.bucket\n blobName = self._getReferenceImageBlobName(name)\n return bucket.blob(blobName).public_url","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info[\"source\"] == \"dsb\":\n return info[\"path\"]\n else:\n super(self.__class__, self).image_reference(image_id)","def image(self, src, title, text):\n src = escape_link(src)\n text = escape(text, quote=True)\n if title:\n title = escape(title, quote=True)\n html = '<img src=\"%s\" alt=\"%s\" title=\"%s\"' % (src, text, title)\n else:\n html = '<img src=\"%s\" alt=\"%s\"' % (src, text)\n if self.options.get('use_xhtml'):\n return '%s />' % html\n return '%s>' % html","def _get_image_url(self, image_filepath):\n return self.IMAGE_URL + image_filepath","def get_image_url(self, image_id):\n if image_id in self.image_id_to_url:\n return self.image_id_to_url[image_id]\n return None","def get_wiki_picture(self):\n url = \"https://fr.wikipedia.org/w/api.php?action=query&prop=pageimages&format=json&piprop=original&pageids={}\".format(self.page_id) \n self.page = str(self.page_id)\n self.response = requests.get(url)\n self.data = self.response.json()\n\n try:\n self.wiki_pic = (self.data['query']['pages'][self.page]['original']['source'])\n return (self.wiki_pic)\n\n except KeyError:\n self.s = requests.Session()\n url = \"https://fr.wikipedia.org/w/api.php?action=query&prop=images&format=json&piprop=original&pageids={}&imlimit=1\".format(self.page_id) \n self.response = self.s.get(url)\n self.data = self.response.json()\n self.wiki_pic_name = sxelf.data['query']['pages'][self.page]['images'][0]['title']\n self.s = requests.Session()\n url = \"https://fr.wikipedia.org/w/api.php?action=query&titles={}&prop=imageinfo&iiprop=url&format=json\".format(self.wiki_pic_name) \n self.response = self.s.get(url)\n self.data = self.response.json()\n self.wiki_pic = self.data['query']['pages']['-1']['imageinfo'][0]['url']\n return (self.wiki_pic)","def get_image_source(soup_recipe):\n image_source = soup_recipe.find(\"img\", {\"itemprop\": \"image\"})\n if not image_source:\n return None\n return image_source[\"src\"]","def get_image_url(self, image_url):\n if image_url:\n return '{0}?source={1}'.format(self.config['links']['imageProxy'], image_url)\n else:\n return None","def still_image_url(self) -> str:\n\t\treturn 'grab.jpg?oid={0}'.format(self._oid)","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info[\"source\"] == \"self_annotation\":\n return os.path.basename(info[\"path\"])\n else:\n super(CarsAndVehiclesDataset, self).image_reference(image_id)","def thumbnail(self):\n return self.get_thumbnail_url()","def get_image_data(self):\n raise NotImplementedError(str(type(self)) + 'does not'\n 'implement get_image.')","def img(self, obj):\n return mark_safe(\n f\"<img src='/{obj.image.url}' width='{obj.image.width}' height='{obj.image.height}' />\"\n )","def media_image_url(self):\n if (media_status := self._media_status()[0]) is None:\n return None\n\n images = media_status.images\n\n return images[0].url if images and images[0].url else None","def getimage(self):","def image_preview(self):\r\n h = '<img src=\"%s\" alt=\"%s\"/>' % (self.image_resized_url, self.title)\r\n return mark_safe(h)","def cover_image_url(cls, data_source, identifier, filename=None,\n scaled_size=None):\n root = cls.cover_image_root(data_source, scaled_size)\n args = [identifier.type, identifier.identifier, filename]\n args = [urllib.quote(x) for x in args]\n return root + \"%s/%s/%s\" % tuple(args)","def image(self) -> pulumi.Output[Optional[str]]:\n return pulumi.get(self, \"image\")","def getNewsIconURL(newsBrain):","def wiki_image(pagetext):\n images = [i for i in pagetext.images if i not in EXCLUDED_IMAGES]\n if len(images) > 0:\n return images[0]\n else:\n return ''","def _get_information(self):\n return [photo.attrs[\"data-src\"] for photo in self._tab.find_all(\"img\", class_=\"lazyload\")]","def image(self) -> str:\n return self._image","def image(self):\n return self.__getattr__(\"image\", _returnType=\"value\", _location=\"remote\")","def get_entry_image(uri):\n entry = get_entry(uri)\n try:\n return entry['images'][0]['files'][1]['uri'].replace('https','http')\n except KeyError:\n # No image in the entry\n return ''","def get_image(self, image_id):\n index = int(image_id)\n if index >= len(self._image_urls):\n message = \"Url index does not exist: '%i'\" % index\n return (None, message)\n url = self._image_urls[index]\n message = \"Successful URL found.\"\n return (url, message)","def process_image_url(value, field):\n if field.width:\n if not value:\n return u\"无\"\n return mark_safe(\"\"\"\n <a href=\"{0}\" target=\"_blank\"><img src=\"{0}\" width=\"{1}\" a>\n \"\"\".format(absolute_media_path(value), field.width))\n # only show url address.\n elif value:\n shorten_value = ''\n if len(value) > 20:\n shorten_value = value[0:12] + \"...\"\n return mark_safe(\"\"\"\n <a href=\"{0}\" target=\"_blank\" title=\"{0}\" >{1}</a>\n \"\"\".format(absolute_media_path(value), shorten_value if shorten_value else value))\n else:\n return \"\"","def get_url_for_min_resolution(self, min_height, min_width, image):","def get_image(self):\n return self.image","def get_image(self):\n return self.image","def get_image(self):\n return self.image"],"string":"[\n \"def get_image_url():\",\n \"def get_image_link(self):\\n table = self.soup.find('table')\\n image_tag = table.find('img')\\n image_name = self.soup.find_all(\\\"b\\\")[1].text\\n return image_tag['src'], image_name\\n\\n # image = td.find_all('img')\\n # print(image)\\n # if image is not None:\\n # return urljoin(self.base_url, image['src'])\",\n \"def get_detail_image(self):\\n facility_data = self.get_raw_facilities_data()\\n if facility_data is None:\\n return None\\n else:\\n return facility_data['detailImageUrl']\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info[\\\"source\\\"] == \\\"coco\\\":\\n return \\\"http://cocodataset.org/#explore?id={}\\\".format(info[\\\"id\\\"])\\n else:\\n super(CocoDataset, self).image_reference(image_id)\",\n \"def image_url(self) -> str:\\n return pulumi.get(self, \\\"image_url\\\")\",\n \"def image(self, link, title, alt):\\n if not link.startswith(('http://', 'https://')):\\n source_dir = os.path.dirname(self.source_path)\\n link = os.path.abspath(os.path.join(source_dir, link))\\n return '<img src=\\\"%s\\\" title=\\\"%s\\\" alt=\\\"%s\\\" />' % (link, title, alt)\",\n \"def image_link(self):\\r\\n\\r\\n if not self._image_link:\\r\\n warnings.warn(\\\"Seems like you are trying to pull out the image link while not having it.\\\", Warning, stacklevel=2)\\r\\n\\r\\n return self._image_link\",\n \"def get_image_url(img):\\n # element['data-src'] and element.get('data-src') doesn't work\\n for k, v in img.items():\\n if k == 'data-src':\\n # https://t.nhentai.net/galleries/<gallerycode>/<page#>t.<extension>\\n # https://i.nhentai.net/galleries/<gallerycode>/<page#>.<extension>\\n return v[:8] + 'i' + v[9:32] + v[32:].replace('t.', '.', 1)\",\n \"def image_url(self) -> str:\\n return self._image_url\",\n \"def image_url(self):\\n context = aq_inner(self.context)\\n obj_url = context.absolute_url()\\n if hasattr(context, 'getField'):\\n field = self.context.getField('image')\\n if not field:\\n field = context.getField(IMAGE_FIELD_NAME)\\n\\n if field and field.get_size(context) > 0:\\n return u'%s/%s_%s' % (obj_url, field.getName(), 'thumb')\\n\\n return u\\\"%s/isaw_logo.png\\\" % self.portal.absolute_url()\",\n \"def get_url_image(self, obj):\\n return settings.IMAGE_HOST + obj.image.url\",\n \"def get_url_image(self, obj):\\n return settings.IMAGE_HOST + obj.image.url\",\n \"def get_url_image(self, obj):\\n return settings.IMAGE_HOST + obj.image.url\",\n \"def image_url(self):\\n return self.photo_url or GENERIC_IMAGE\",\n \"def get_image_url(self):\\n return self.get_extract_image_urls(is_first=True)\",\n \"def image(self) -> str:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def get_url_image(self, obj):\\n return settings.SERVER_HOST + obj.image.url\",\n \"def get_image_info(filename: str) -> str:\\n url = \\\"%s?format=json&action=query&prop=imageinfo&iiprop=url&titles=File:%s\\\" % (WIKIDATA_URL, filename)\\n # Perform request\\n print_debug(\\\"Sending GET %s\\\" % url)\\n response = requests.get(url)\\n data = response.json()\\n print_debug(\\\"%s -> %d\\\" % (url, response.status_code))\\n print_debug(\\\"%s\\\" % response.text)\\n return data[\\\"query\\\"][\\\"pages\\\"][\\\"-1\\\"][\\\"imageinfo\\\"][0][\\\"url\\\"]\",\n \"def get_possible_thumbnail(self):\\n meta = self.get_meta_data()\\n print meta\\n if \\\"og:image\\\" in meta:\\n return meta[\\\"og:image\\\"]\\n elif \\\"twitter:image:src\\\" in meta:\\n return meta[\\\"twitter:image:src\\\"]\\n else:\\n images = self.get_image_data()\\n temp_url = \\\"\\\"\\n temp_width = 0\\n for img in images:\\n if img[\\\"image_width\\\"] > temp_width:\\n temp_url = img[\\\"image_url\\\"]\\n temp_width = img[\\\"image_width\\\"]\\n\\n return temp_url\",\n \"def return_image(val, model_id, message_name, field_name, mime, sind):\\n column_data_source = curdoc().get_model_by_name(sind)\\n index = column_data_source.tags[0]\\n url = \\\"http://{0}/image/\\\".format(_host) + \\\"---\\\".join([model_id, message_name, field_name, mime, sind, str(index)])\\n return url\",\n \"def get_thumbnail_url():\",\n \"def show_image_ref():\\n return get_image_ref()\",\n \"def source_image_link(self, image_id):\\n return self.image_info[image_id][\\\"path\\\"]\",\n \"def source_image_link(self, image_id):\\n return self.image_info[image_id][\\\"path\\\"]\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def image(self) -> Optional[str]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def get_image_url(article):\\n get_image = article.find_all(\\\"img\\\")[0]\\n return get_image.get(\\\"src\\\").replace(\\\"../../\\\", \\\"http://books.toscrape.com/\\\")\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info[\\\"source\\\"] == \\\"pedestrian\\\":\\n return info[\\\"path\\\"]\\n else:\\n super(self.__class__, self).image_reference(image_id)\",\n \"def met(r):\\n image_url = r.get(\\\"image\\\")\\n if image_url is None:\\n if r.get(\\\"source\\\") is not None:\\n image_url = r.get(\\\"source\\\").get(\\\"href\\\")\\n image_name = r.get(\\\"name\\\")\\n image_artist = r.get(\\\"Who\\\")\\n return image_url, image_name, image_artist\",\n \"def _collect_img_links(self):\\n raise NotImplementedError\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info[\\\"source\\\"] == \\\"balloon\\\":\\n return info[\\\"path\\\"]\\n else:\\n super(self.__class__, self).image_reference(image_id)\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info[\\\"source\\\"] == \\\"balloon\\\":\\n return info[\\\"path\\\"]\\n else:\\n super(self.__class__, self).image_reference(image_id)\",\n \"def image_url(self):\\n return \\\"{}/mjpeg_read.php\\\".format(self.base_url)\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info['source'] == 'local':\\n return info['source']\\n else:\\n super(self.__class__).image_reference(self, image_id)\",\n \"def thumbnail_url(self):\\n return None\",\n \"def image(self):\\n image_id = self.attrs.get('ImageID', self.attrs['Image'])\\n if image_id is None:\\n return None\\n return self.client.images.get(image_id.split(':')[1])\",\n \"def get_list_image(self):\\n facility_data = self.get_raw_facilities_data()\\n if facility_data is None:\\n return None\\n else:\\n return facility_data['listImageUrl']\",\n \"def image_preview(self):\\r\\n h = '<img src=\\\"%s\\\" alt=\\\"Campaign badge\\\"/>' % self.image.url\\r\\n return mark_safe(h)\",\n \"def get_image_uri(self):\\n return \\\"data:image/png;base64,\\\" + \\\\\\n self.browser.get_screenshot_as_base64()\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info[\\\"source\\\"] == \\\"vesicle\\\":\\n return info[\\\"path\\\"]\\n else:\\n super(self.__class__, self).image_reference(image_id)\",\n \"def get_image(self):\\n try:\\n self.image = self.soup.find(class_=\\\"recipe-header-image\\\").find(\\\"img\\\").get(\\\"src\\\", \\\"\\\")\\n except Exception:\\n current_app.logger.error(f\\\"Could not extract image: {traceback.format_exc()}\\\")\\n self.image = \\\"\\\"\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info['source'] == self.dataset_name:\\n return info['id']\\n else:\\n super.image_reference(image_id)\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info[\\\"source\\\"].strip().lower() == 'homeobject':\\n return info[\\\"path\\\"]\\n else:\\n super(self.__class__, self).image_reference(image_id)\",\n \"def getImageTile(self, **kwargs):\\n if self.hasImage():\\n imgtileurl = self.getImage().absolute_url(1) + '_tile'\\n portal_obj = getToolByName(self, 'portal_url').getPortalObject()\\n portal_url = portal_obj.absolute_url(1)\\n imgtileurl = imgtileurl.replace(portal_url, '')\\n return imgtileurl\\n return ''\",\n \"def get_image_url(self, image_name: str, thumbnail: bool = False) -> str:\\n pass\",\n \"def get_image(result):\\n article_id = result['id']\\n id_ = article_id[14:]\\n href = article_id[:14]\\n\\n #FIXME: not working\\n image_url = \\\"http://www.jpress.nli.org.il/Olive/APA/NLI_heb/get/GetImage.ashx?kind=block&href=%s&id=%s&ext=.png\\\" %(href, id_)\\n \\n return image_url\",\n \"def img_url_display(self):\\n url = '%s=s%s' % (self.img_url, self.DISPLAY_SIZE_PX)\\n if self.img_rot in Plaque.ALLOWED_ROTATIONS:\\n url = \\\"%s-r%s\\\" % (url, self.img_rot)\\n return url\",\n \"def get_image_url(self, image, hostname):\\n\\n if image.image_type == 'local':\\n return \\\"http://\\\" + hostname + \\\"/\\\" + settings.IMAGES_URL + \\\"/\\\" + image.deployment.campaign.short_name + \\\"/\\\" + image.deployment.short_name + \\\"/\\\" + image.image_name\\n elif image.image_type == 'envirocoms':\\n return \\\"http://\\\" + hostname + \\\"/ecoms_proxy?image=\\\" + image.image_path\",\n \"def get_image_comic_url(session, response):\\n soup = bs(response.text, 'lxml')\\n for div in soup.find_all('div', class_=\\\"img-comic-container\\\"):\\n for a in div.find_all('a', class_=\\\"img-comic-link\\\"):\\n for img in a.find_all('img', src=True):\\n return \\\"https:\\\" + img['src']\",\n \"def _get_pic_link(self, tree, xpath_adr='/html/body/div[1]/div[2]/div[3]/div[1]/div[1]/div/div/img/@data-src'):\\n try:\\n return tree.xpath(xpath_adr)[0]\\n except:\\n print('WARNING: Could not scrape game card web address, check review xpath address')\\n return np.nan\",\n \"def visualize(self):\\n return \\\"https://neuroglancer.bossdb.io/#!{'layers':{'image':{'source':'boss://__replace_me__'}}}\\\".replace(\\n \\\"__replace_me__\\\",\\n f\\\"{self.volume_provider.boss._project._base_protocol}://{self.volume_provider.boss._project._base_url}/{self.collection_name}/{self.experiment_name}/{self.channel_name}\\\",\\n )\",\n \"def getReferenceImageUrl(self, name):\\n bucket = self.productSearch.bucket\\n blobName = self._getReferenceImageBlobName(name)\\n return bucket.blob(blobName).public_url\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info[\\\"source\\\"] == \\\"dsb\\\":\\n return info[\\\"path\\\"]\\n else:\\n super(self.__class__, self).image_reference(image_id)\",\n \"def image(self, src, title, text):\\n src = escape_link(src)\\n text = escape(text, quote=True)\\n if title:\\n title = escape(title, quote=True)\\n html = '<img src=\\\"%s\\\" alt=\\\"%s\\\" title=\\\"%s\\\"' % (src, text, title)\\n else:\\n html = '<img src=\\\"%s\\\" alt=\\\"%s\\\"' % (src, text)\\n if self.options.get('use_xhtml'):\\n return '%s />' % html\\n return '%s>' % html\",\n \"def _get_image_url(self, image_filepath):\\n return self.IMAGE_URL + image_filepath\",\n \"def get_image_url(self, image_id):\\n if image_id in self.image_id_to_url:\\n return self.image_id_to_url[image_id]\\n return None\",\n \"def get_wiki_picture(self):\\n url = \\\"https://fr.wikipedia.org/w/api.php?action=query&prop=pageimages&format=json&piprop=original&pageids={}\\\".format(self.page_id) \\n self.page = str(self.page_id)\\n self.response = requests.get(url)\\n self.data = self.response.json()\\n\\n try:\\n self.wiki_pic = (self.data['query']['pages'][self.page]['original']['source'])\\n return (self.wiki_pic)\\n\\n except KeyError:\\n self.s = requests.Session()\\n url = \\\"https://fr.wikipedia.org/w/api.php?action=query&prop=images&format=json&piprop=original&pageids={}&imlimit=1\\\".format(self.page_id) \\n self.response = self.s.get(url)\\n self.data = self.response.json()\\n self.wiki_pic_name = sxelf.data['query']['pages'][self.page]['images'][0]['title']\\n self.s = requests.Session()\\n url = \\\"https://fr.wikipedia.org/w/api.php?action=query&titles={}&prop=imageinfo&iiprop=url&format=json\\\".format(self.wiki_pic_name) \\n self.response = self.s.get(url)\\n self.data = self.response.json()\\n self.wiki_pic = self.data['query']['pages']['-1']['imageinfo'][0]['url']\\n return (self.wiki_pic)\",\n \"def get_image_source(soup_recipe):\\n image_source = soup_recipe.find(\\\"img\\\", {\\\"itemprop\\\": \\\"image\\\"})\\n if not image_source:\\n return None\\n return image_source[\\\"src\\\"]\",\n \"def get_image_url(self, image_url):\\n if image_url:\\n return '{0}?source={1}'.format(self.config['links']['imageProxy'], image_url)\\n else:\\n return None\",\n \"def still_image_url(self) -> str:\\n\\t\\treturn 'grab.jpg?oid={0}'.format(self._oid)\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info[\\\"source\\\"] == \\\"self_annotation\\\":\\n return os.path.basename(info[\\\"path\\\"])\\n else:\\n super(CarsAndVehiclesDataset, self).image_reference(image_id)\",\n \"def thumbnail(self):\\n return self.get_thumbnail_url()\",\n \"def get_image_data(self):\\n raise NotImplementedError(str(type(self)) + 'does not'\\n 'implement get_image.')\",\n \"def img(self, obj):\\n return mark_safe(\\n f\\\"<img src='/{obj.image.url}' width='{obj.image.width}' height='{obj.image.height}' />\\\"\\n )\",\n \"def media_image_url(self):\\n if (media_status := self._media_status()[0]) is None:\\n return None\\n\\n images = media_status.images\\n\\n return images[0].url if images and images[0].url else None\",\n \"def getimage(self):\",\n \"def image_preview(self):\\r\\n h = '<img src=\\\"%s\\\" alt=\\\"%s\\\"/>' % (self.image_resized_url, self.title)\\r\\n return mark_safe(h)\",\n \"def cover_image_url(cls, data_source, identifier, filename=None,\\n scaled_size=None):\\n root = cls.cover_image_root(data_source, scaled_size)\\n args = [identifier.type, identifier.identifier, filename]\\n args = [urllib.quote(x) for x in args]\\n return root + \\\"%s/%s/%s\\\" % tuple(args)\",\n \"def image(self) -> pulumi.Output[Optional[str]]:\\n return pulumi.get(self, \\\"image\\\")\",\n \"def getNewsIconURL(newsBrain):\",\n \"def wiki_image(pagetext):\\n images = [i for i in pagetext.images if i not in EXCLUDED_IMAGES]\\n if len(images) > 0:\\n return images[0]\\n else:\\n return ''\",\n \"def _get_information(self):\\n return [photo.attrs[\\\"data-src\\\"] for photo in self._tab.find_all(\\\"img\\\", class_=\\\"lazyload\\\")]\",\n \"def image(self) -> str:\\n return self._image\",\n \"def image(self):\\n return self.__getattr__(\\\"image\\\", _returnType=\\\"value\\\", _location=\\\"remote\\\")\",\n \"def get_entry_image(uri):\\n entry = get_entry(uri)\\n try:\\n return entry['images'][0]['files'][1]['uri'].replace('https','http')\\n except KeyError:\\n # No image in the entry\\n return ''\",\n \"def get_image(self, image_id):\\n index = int(image_id)\\n if index >= len(self._image_urls):\\n message = \\\"Url index does not exist: '%i'\\\" % index\\n return (None, message)\\n url = self._image_urls[index]\\n message = \\\"Successful URL found.\\\"\\n return (url, message)\",\n \"def process_image_url(value, field):\\n if field.width:\\n if not value:\\n return u\\\"无\\\"\\n return mark_safe(\\\"\\\"\\\"\\n <a href=\\\"{0}\\\" target=\\\"_blank\\\"><img src=\\\"{0}\\\" width=\\\"{1}\\\" a>\\n \\\"\\\"\\\".format(absolute_media_path(value), field.width))\\n # only show url address.\\n elif value:\\n shorten_value = ''\\n if len(value) > 20:\\n shorten_value = value[0:12] + \\\"...\\\"\\n return mark_safe(\\\"\\\"\\\"\\n <a href=\\\"{0}\\\" target=\\\"_blank\\\" title=\\\"{0}\\\" >{1}</a>\\n \\\"\\\"\\\".format(absolute_media_path(value), shorten_value if shorten_value else value))\\n else:\\n return \\\"\\\"\",\n \"def get_url_for_min_resolution(self, min_height, min_width, image):\",\n \"def get_image(self):\\n return self.image\",\n \"def get_image(self):\\n return self.image\",\n \"def get_image(self):\\n return self.image\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7356831","0.7320701","0.7044893","0.69738954","0.69392264","0.6825294","0.6793268","0.6658253","0.6630269","0.658179","0.6563168","0.6563168","0.6563168","0.6556118","0.65384156","0.6535433","0.64960885","0.6488741","0.6426024","0.642439","0.6424377","0.6402457","0.6375991","0.6375991","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6364919","0.6353154","0.6331072","0.6328031","0.63130957","0.62598646","0.62598646","0.6258842","0.62570095","0.62393445","0.6225923","0.62103474","0.6204981","0.6198672","0.6194207","0.61921656","0.6178177","0.6169239","0.61664724","0.61589277","0.61511207","0.6148642","0.6144426","0.61382335","0.6136801","0.61290824","0.6126719","0.6123123","0.60862434","0.60711807","0.6064135","0.60477203","0.6046175","0.6044503","0.60426295","0.6041634","0.60381764","0.60300446","0.60264504","0.60232395","0.6019128","0.6007703","0.5999613","0.5971294","0.5969618","0.59679747","0.5966406","0.5952665","0.594992","0.59350234","0.5919932","0.59157103","0.5911997","0.59047097","0.59047097","0.59047097"],"string":"[\n \"0.7356831\",\n \"0.7320701\",\n \"0.7044893\",\n \"0.69738954\",\n \"0.69392264\",\n \"0.6825294\",\n \"0.6793268\",\n \"0.6658253\",\n \"0.6630269\",\n \"0.658179\",\n \"0.6563168\",\n \"0.6563168\",\n \"0.6563168\",\n \"0.6556118\",\n \"0.65384156\",\n \"0.6535433\",\n \"0.64960885\",\n \"0.6488741\",\n \"0.6426024\",\n \"0.642439\",\n \"0.6424377\",\n \"0.6402457\",\n \"0.6375991\",\n \"0.6375991\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6364919\",\n \"0.6353154\",\n \"0.6331072\",\n \"0.6328031\",\n \"0.63130957\",\n \"0.62598646\",\n \"0.62598646\",\n \"0.6258842\",\n \"0.62570095\",\n \"0.62393445\",\n \"0.6225923\",\n \"0.62103474\",\n \"0.6204981\",\n \"0.6198672\",\n \"0.6194207\",\n \"0.61921656\",\n \"0.6178177\",\n \"0.6169239\",\n \"0.61664724\",\n \"0.61589277\",\n \"0.61511207\",\n \"0.6148642\",\n \"0.6144426\",\n \"0.61382335\",\n \"0.6136801\",\n \"0.61290824\",\n \"0.6126719\",\n \"0.6123123\",\n \"0.60862434\",\n \"0.60711807\",\n \"0.6064135\",\n \"0.60477203\",\n \"0.6046175\",\n \"0.6044503\",\n \"0.60426295\",\n \"0.6041634\",\n \"0.60381764\",\n \"0.60300446\",\n \"0.60264504\",\n \"0.60232395\",\n \"0.6019128\",\n \"0.6007703\",\n \"0.5999613\",\n \"0.5971294\",\n \"0.5969618\",\n \"0.59679747\",\n \"0.5966406\",\n \"0.5952665\",\n \"0.594992\",\n \"0.59350234\",\n \"0.5919932\",\n \"0.59157103\",\n \"0.5911997\",\n \"0.59047097\",\n \"0.59047097\",\n \"0.59047097\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77996,"cells":{"query":{"kind":"string","value":"Prepares the Dataset class for use."},"document":{"kind":"string","value":"def prepare(self, class_map=None):\n def clean_name(name):\n \"\"\"Returns a shorter version of object names for cleaner display.\"\"\"\n return \",\".join(name.split(\",\")[:1])\n\n # Build (or rebuild) everything else from the info dicts.\n self.num_classes = len(self.class_info)\n self.class_ids = np.arange(self.num_classes)\n self.class_names = [clean_name(c[\"name\"]) for c in self.class_info]\n self.num_images = len(self.image_info)\n self._image_ids = np.arange(self.num_images)\n\n self.class_from_source_map = {\"{}.{}\".format(info['source'], info['id']): id\n for info, id in zip(self.class_info, self.class_ids)}\n\n # Map sources to class_ids they support\n self.sources = list(set([i['source'] for i in self.class_info]))\n self.source_class_ids = {}\n # Loop over datasets\n for source in self.sources:\n self.source_class_ids[source] = []\n # Find classes that belong to this dataset\n for i, info in enumerate(self.class_info):\n # Include BG class in all datasets\n if i == 0 or source == info['source']:\n self.source_class_ids[source].append(i)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def prepare(self):\n if self.opts['verbose']:\n print(\"Preparing dataset (one-time operation)...\")\n # Create paths files and load them back in\n self._build_ID_sets()\n self._create_ID_files()\n self._load_ID_files()\n if self.opts['verbose']:\n print(\"... done with preparing the dataset.\")","def prepare_dataset(fpath):\n raise NotImplementedError","def prepare_data(self):\n try:\n self.train_dataset = self.datasets['train']\n self.val_dataset = self.datasets['val']\n try:\n self.test_dataset = self.datasets['test']\n except:\n pass\n except Exception as e:\n print('Data was not succesfully prepared:', e)","def __init__(self):\n self.__dataset = None","def __init__(self):\n self.args = self._prepare_args(locals())\n self.requires_full_dataset_in_memory = False","def setUp(self):\n self.dataset = self.dataset_cls()","def _prepare_data(self):\n #TODO hardcoded values need to change\n print_info(\"Preprocessing the train data...\")\n self._place_dataset(os.path.join(self._hparams[\"temp-data\"], \"train\"),\n self.TRAIN_OUT_PATH)\n\n print_info(\"Preprocessing the test data...\")\n self._place_dataset(os.path.join(self._hparams[\"temp-data\"], \"test\"),\n self.TEST_OUT_PATH)\n\n print_info(\"Preprocessing the validation data...\")\n self._place_dataset(os.path.join(self._hparams[\"temp-data\"], \"val\"),\n self.VAL_OUT_PATH)","def __init__(self, dataset: Dataset):\n self.dataset = dataset","def __init__(self, dataset):\n self._dataset = dataset","def setUp(self):\n self.directory = tempfile.TemporaryDirectory()\n self.dataset = self.dataset_cls(cache_root=self.directory.name)","def prepare_data(self):","def _make_data(self):\n pdf_datasets_all = make_pdf_datasets(self.pdf_list, self.xlims, self.ylims, self.tlims, self.dims, 9)\n self.pdf_dataset = np.concatenate(pdf_datasets_all, axis = 0)\n self.PDE_dataset = make_PDE_dataset(self.num_collocation, self.xlims, self.ylims, self.tlims, self.dims)\n self.BC_dataset = make_BC_dataset(self.num_BC, self.xlims, self.ylims, self.tlims, self.dims)","def __prepare_dataset(dataset, verbose=True):\r\n supported_datasets = ['coco', 'voc']\r\n if isinstance(dataset, ExternalDataset):\r\n if dataset.dataset_type.lower() not in supported_datasets:\r\n raise UserWarning(\"ExternalDataset dataset_type must be one of: \", supported_datasets)\r\n\r\n dataset_root = dataset.path\r\n\r\n if verbose:\r\n print(\"Loading {} type dataset...\".format(dataset.dataset_type))\r\n\r\n if dataset.dataset_type.lower() == 'voc':\r\n from gluoncv.data import VOCDetection\r\n\r\n dataset = VOCDetection(root=dataset_root,\r\n splits=[(2007, 'trainval'), (2012, 'trainval')])\r\n\r\n elif dataset.dataset_type.lower() == 'coco':\r\n from gluoncv.data import COCODetection\r\n\r\n dataset = COCODetection(root=dataset_root,\r\n splits=['instances_train2017'])\r\n if verbose:\r\n print(\"ExternalDataset loaded.\")\r\n return dataset\r\n elif isinstance(dataset, DetectionDataset) or issubclass(type(dataset), DetectionDataset):\r\n dataset.set_image_transform(ImageToNDArrayTransform())\r\n dataset.set_target_transform(BoundingBoxListToNumpyArray())\r\n return dataset\r\n else:\r\n raise ValueError(\"Dataset type {} not supported\".format(type(dataset)))","def _prepare(self, data, train=True):\n if data is None:\n return None\n\n if hasattr(data, \"to_tfdataset\"):\n return data.to_tfdataset(train=train)\n else:\n return data","def prepare(cls):","def prep_data(dataset_config=None):\n \n\n data_struct = DataStruct(dataset_config)\n\n data_name = data_struct.name\n datasets_dir = data_struct.data_path\n out_dir = data_struct.save_path\n\n # If dataset already downloaded an unpacked, do nothing\n if os.path.isdir(out_dir):\n print('{} already downloaded, unpacked and processed.'.format(data_name))\n return\n\n # Check if download is required\n data_url = data_struct.url\n compressed_file_name = downloader(datasets_dir, data_url)\n\n # Unpack compressed dataset file\n unpacker(compressed_file_name, out_dir)\n\n # Custom preprocessing steps for data sets\n custom_preprocessor(out_dir)","def setup(self):\n in_dataset, out_dataset = self.get_datasets()","def setup(self):\n in_dataset, out_dataset = self.get_datasets()","def prepare_dataset(self, dataset_type: str) -> Dataset:\n\n logger.info(\"Creating features from dataset file at %s\", self.hparams.data_dir)\n\n if dataset_type == \"train\":\n dataset = self.processor.get_train_dataset(self.hparams.data_dir, self.hparams.train_file_name)\n elif dataset_type == \"dev\":\n dataset = self.processor.get_dev_dataset(self.hparams.data_dir, self.hparams.dev_file_name)\n elif dataset_type == \"test\":\n dataset = self.processor.get_test_dataset(self.hparams.data_dir, self.hparams.test_file_name)\n else:\n raise ValueError(f\"{dataset_type} do not support. [train|dev|test]\")\n logger.info(f\"Prepare {dataset_type} dataset (Count: {len(dataset)}) \")\n return dataset","def build_data_set(self):\n if not self.assert_data_correct():\n self.download_all_data()\n self.unpack_rename_data()\n self.split_data_characters()\n self.clean_data_fragments()\n self.create_font_data()\n if not self.assert_train_augmented():\n self.augment_train_data()\n if not self.assert_style_data_correct():\n self.download_style_data()\n self.unpack_rename_data()","def prepare_data(self):\n data = self._get_dataset(self.hparams.dataset_path)\n label_encoder = data[\"label_encoder\"]\n del data[\"label_encoder\"]\n\n click.secho(\"Building inputs and labels.\", fg=\"yellow\")\n datasets = {\n \"train\": defaultdict(list),\n \"valid\": defaultdict(list),\n \"test\": defaultdict(list),\n }\n for dataset_name, dataset in data.items():\n for sample in dataset:\n instance = self.build_input(\n self.tokenizer, sample[\"text\"], label_encoder, sample[\"label\"]\n )\n for input_name, input_array in instance.items():\n datasets[dataset_name][input_name].append(input_array)\n\n click.secho(\"Padding inputs and building tensors.\", fg=\"yellow\")\n tensor_datasets = {\"train\": [], \"valid\": [], \"test\": []}\n for dataset_name, dataset in datasets.items():\n dataset = self.pad_dataset(dataset, padding=self.tokenizer.pad_index)\n for input_name in MODEL_INPUTS:\n if input_name == \"labels\":\n tensor = torch.tensor(dataset[input_name], dtype=torch.float32)\n else:\n tensor = torch.tensor(dataset[input_name])\n tensor_datasets[dataset_name].append(tensor)\n\n self.train_dataset = TensorDataset(*tensor_datasets[\"train\"])\n self.valid_dataset = TensorDataset(*tensor_datasets[\"valid\"])\n self.test_dataset = TensorDataset(*tensor_datasets[\"test\"])\n click.secho(\n \"Train dataset (Batch, Candidates, Seq length): {}\".format(\n self.train_dataset.tensors[0].shape\n ),\n fg=\"yellow\",\n )\n click.secho(\n \"Valid dataset (Batch, Candidates, Seq length): {}\".format(\n self.valid_dataset.tensors[0].shape\n ),\n fg=\"yellow\",\n )\n click.secho(\n \"Test dataset (Batch, Candidates, Seq length): {}\".format(\n self.test_dataset.tensors[0].shape\n ),\n fg=\"yellow\",\n )","def initialise_dataset_loader(\n self, data_param=None, task_param=None, data_partitioner=None):\n raise NotImplementedError","def __init__(self, name_dataset, reduce=False):\n dataset = TUDataset(root='data/TUDataset', name=name_dataset)\n if reduce:\n new_dataset = []\n for i in tqdm(range(len(dataset))):\n aux_graph = copy.deepcopy(dataset[i])\n aux_graph.edge_index = TUDData.reduce_edges(aux_graph.edge_index)\n new_dataset.append(copy.deepcopy(aux_graph))\n dataset = WrapperSynthetic(new_dataset, dataset.num_node_features,\n dataset.num_classes, None)\n super(TUDData, self).__init__(dataset)","def setup(self):\n in_dataset, out_dataset = self.get_datasets()\n \n out_dataset[0].create_dataset(in_dataset[0])\n\n in_pData, out_pData = self.get_plugin_datasets()\n\n in_pData[0].plugin_data_setup( '',)\n out_pData[0].plugin_data_setup( 'PROJECTION','multiple')","def __init__(self, config):\n logger.info(f\"{self.__class__.__name__}: Dataset initializing ...\")\n super().__init__(config)","def prepare_process(self, dataset):\n if dataset is not None:\n pass","def __init__(self, **kwargs):\n DataLoader.__init__(self, **kwargs)","def _init_al_dataset(self):\n\n self._init_dataset()\n\n train_dataset = self.datasets['train']\n\n dataset_size = len(train_dataset)\n self.budget = math.ceil(self.budget_frac*dataset_size)\n Sampler.__init__(self, config, self.budget) # TODO: Weird place to initialise this\n\n all_indices = set(np.arange(dataset_size))\n k_initial = math.ceil(len(all_indices)*self.initial_budget_frac)\n initial_indices = random.sample(list(all_indices), k=k_initial)\n\n sampler_init = data.sampler.SubsetRandomSampler(initial_indices) # need to sample from training dataset\n\n self.labelled_dataloader = data.DataLoader(train_dataset, sampler=sampler_init, batch_size=self.batch_size, drop_last=True)\n self.val_dataloader = data.DataLoader(self.datasets['valid'], batch_size=self.batch_size, drop_last=False)\n self.test_dataloader = data.DataLoader(self.datasets['test'], batch_size=self.batch_size, drop_last=False)\n\n return all_indices, initial_indices","def setup(self):\n # TODO check if need both dataset together\n self.train_dataset = ABSADataset(data_path=self.train_path, mode=self.in_mode, task=self.task, \n tokenizer=self.tokenizer, vocab=\"bert\")\n self.vocabulary = self.train_dataset.vocabulary\n\n self.eval_dataset = ABSADataset(data_path=self.dev_path, mode=self.in_mode, task=self.task,\n tokenizer=self.tokenizer, vocab=self.vocabulary)\n #self.train_restaurant = ABSADataset(data_path=RESTAURANT_TRAIN)\n #self.eval_restaurant = ABSADataset(data_path=RESTAURANT_DEV)","def __init__(self, unencoded_dataset): \n self._unencoded_dataset = unencoded_dataset\n self._plain_train = self._get_plain_train()\n self._plain_test = self._get_plain_test()\n self._cipher_train = self._get_cipher_train()\n self._cipher_test = self._get_cipher_test()","def prepare_data_for_training(args):\n # Form the train/test splits and write them to disk\n dataset = data.Dataset(args)\n # get image classes and image counts in each class\n label_map = dataset.get_class_info()\n class_count = len(list(label_map.values()))\n # split the data and store it in log dir\n df_train, df_test = dataset.split_dataset()\n\n # perform dataset augmentations\n image_data = augment.Augmentation(args)\n # get the data gens for training and test images\n train_data_gen, _ = image_data.map_fn_train(df_train)\n test_data_gen, _ = image_data.map_fn_test(df_test)\n\n return train_data_gen, test_data_gen, df_train, df_test, class_count","def build_dataset(self):\n self.dataset = KITTIBEVDataset(self.dataset_config, self.transform)\n return self.dataset","def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.dir_A = opt.dataroot\n self.classes_A, self.class_to_idx_A = self._find_classes(self.dir_A) # find classes in '/path/to/data/trainA'\n samples_A = make_dataset(self.dir_A, self.class_to_idx_A, extensions=self.img_extension, is_valid_file=None) # samples (list): List of (sample path, class_index) tuples\n self.A_paths = [s[0] for s in samples_A]\n self.A_targets = [s[1] for s in samples_A]\n #self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))\n input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc\n self.transform = get_transform(opt, grayscale=(input_nc == 1))","def prepare(self, class_map=None):\n\n def clean_name(name):\n \"\"\"Returns a shorter version of object names for cleaner display.\"\"\"\n return \",\".join(name.split(\",\")[:1])\n\n # Build (or rebuild) everything else from the info dicts.\n self.num_classes = len(self.class_info)\n self.class_ids = np.arange(self.num_classes)\n self.class_names = [clean_name(c[\"name\"]) for c in self.class_info]\n self.num_images = len(self.image_info)\n self._image_ids = np.arange(self.num_images)\n\n # Mapping from source class and image IDs to internal IDs\n self.class_from_source_map = {\"{}.{}\".format(info['source'], info['id']): id\n for info, id in zip(self.class_info, self.class_ids)}\n self.image_from_source_map = {\"{}.{}\".format(info['source'], info['id']): id\n for info, id in zip(self.image_info, self.image_ids)}\n\n # Map sources to class_ids they support\n self.sources = list(set([i['source'] for i in self.class_info]))\n self.source_class_ids = {}\n # Loop over datasets\n for source in self.sources:\n self.source_class_ids[source] = []\n # Find classes that belong to this dataset\n for i, info in enumerate(self.class_info):\n # Include BG class in all datasets\n if i == 0 or source == info['source']:\n self.source_class_ids[source].append(i)","def setup(self):\n in_dataset, out_dataset = self.get_datasets()\n \n out_dataset[0].create_dataset(in_dataset[0])\n\n in_pData, out_pData = self.get_plugin_datasets()\n\n in_pData[0].plugin_data_setup( 'PROJECTION',multiple)\n out_pData[0].plugin_data_setup( 'PROJECTION',multiple)","def setup(self, ds):\n pass","def prep_dataset(settings):\n train_dims = settings[\"train_dims\"]\n # Open HDF store. This is usually a soft link to our filtered dataset\n input_df, target_df, const = load_from_store(settings[\"dataset_path\"], columns=train_dims)\n\n try:\n del input_df[\"nions\"] # Delete leftover artifact from dataset split\n except KeyError:\n pass\n\n target_df = drop_outliers(target_df, settings)\n target_df = drop_nans(target_df)\n\n data_df = filter_input(input_df, target_df)\n del target_df, input_df\n data_df = convert_dtype(data_df, settings)\n\n return data_df","def __init__(self, **kwargs):\n DataLoader.__init__(self, **kwargs)\n \n self._results_ = None","def prepare_data(self, config: TreeConfigParser) -> None:\n self.data = Data(config)\n self.data.prepare_input()\n self.data.prepare_output()","def setUp(self):\n self.dataset = get_test_dataset()","def init_data(dataset_config: dict):\n # train and dev will be in random order, test may be ordered according to labels\n if dataset_config[\"name\"] == \"CoLA\":\n train, dev, test, num_classes = load_cola(dataset_config)\n elif dataset_config[\"name\"] == \"AGNews\":\n train, dev, test, num_classes = load_ag_news(dataset_config)\n elif dataset_config[\"name\"] == \"DBPedia\":\n train, dev, test, num_classes = load_dbpedia(dataset_config)\n elif dataset_config[\"name\"] == \"YRF\":\n train, dev, test, num_classes = load_yrf(dataset_config)\n else:\n raise NameError(f\"Dataset {dataset_config['name']} not implemented.\")\n # etc.\n\n # shrink size if debugging\n if dataset_config[\"debug\"]:\n # choose a random subset using huggingface select function\n train = train.select(random.sample(range(len(train)), k=200))\n dev = dev.select(random.sample(range(len(dev)), k=40))\n test = test.select(random.sample(range(len(test)), k=200))\n\n # create class imbalance\n random.seed(dataset_config[\"seed\"])\n if dataset_config[\"pool_balance\"] == \"balanced\":\n pass\n elif dataset_config[\"pool_balance\"] == \"imbalanced\":\n train = train.filter(lambda example: create_imbalanced_dataset(example, dataset_config[\"imbalance_prop\"], dataset_config['imbalance_cls']))\n else:\n NameError(f\"pool_balance = {dataset_config['pool_balance']} not allowed\")\n\n if dataset_config[\"dev_balance\"] == \"balanced\":\n pass\n elif dataset_config[\"dev_balance\"] == \"imbalanced\":\n dev = dev.filter(lambda example: create_imbalanced_dataset(example, dataset_config[\"imbalance_prop\"], dataset_config['imbalance_cls']))\n else:\n NameError(f\"dev_balance = {dataset_config['dev_balance']} not allowed\")\n\n # get seed labelled pool indices (using the same seed data every time)\n random.seed(dataset_config[\"seed\"])\n if dataset_config[\"seed_balance\"] == \"balanced\":\n # this is random (will have some variance vs pool)\n indices = list(range(len(train)))\n unlabelled_pool_idx, labelled_pool_idx = split(\n indices,\n random_state=dataset_config[\"seed\"],\n test_size=dataset_config[\"seed_size\"]\n )\n elif dataset_config[\"seed_balance\"] == \"stratified\":\n # this is the same as the underlying train set (which may be unbalanced)\n indices = list(range(len(train)))\n unlabelled_pool_idx, labelled_pool_idx = split(\n indices,\n random_state=dataset_config[\"seed\"],\n test_size=dataset_config[\"seed_size\"],\n stratify=train['label']\n )\n elif dataset_config[\"seed_balance\"] == \"imbalanced\":\n # artificially sample an imbalanced seed set from the pool\n unlabelled_pool_idx, labelled_pool_idx = create_imbalanced_seed(\n train,\n num_classes,\n dataset_config[\"seed_size\"],\n dataset_config['imbalance_prop'],\n dataset_config['imbalance_cls']\n )\n else:\n raise NameError(f\"seed_balance = {dataset_config['seed_balance']} not allowed\")\n\n return train, dev, test, num_classes, labelled_pool_idx, unlabelled_pool_idx","def prepare_data(self):\n import subprocess\n # Download coco data set into dir specified by config then /data/coco\n subprocess.call([f\"{get_original_cwd()}/bin/fetch_dataset.sh\", f\"{self.dir}/data/coco\", f\"{get_original_cwd()}\"])\n # subprocess.call([f\"bin/fetch_dataset.sh\", f\"{self.dir}/data/coco\"])\n task = \"instances\" if self.instance else \"person_keypoints\"\n register_coco_instances(\"train\", {}, f\"{self.dir}/data/coco/{task}_train2014.json\",\n f\"{self.dir}/data/coco/train2014\")\n register_coco_instances(\"val\", {}, f\"{self.dir}/data/coco/{task}_minival2014.json\",\n f\"{self.dir}/data/coco/val2014\")\n register_coco_instances(\"test\", {}, f\"{self.dir}/data/coco/{task}_valminusminival2014.json\",\n f\"{self.dir}/data/coco/val2014\")","def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') # create a path '/path/to/data/trainA'\n self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') # create a path '/path/to/data/trainB'\n\n self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA'\n self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n self.A_size = len(self.A_paths) # get the size of dataset A\n self.B_size = len(self.B_paths) # get the size of dataset B\n btoA = self.opt.direction == 'BtoA'\n input_nc = self.opt.output_nc if btoA else self.opt.input_nc # get the number of channels of input image\n output_nc = self.opt.input_nc if btoA else self.opt.output_nc # get the number of channels of output image\n self.transform_A = get_transform(self.opt, grayscale=(input_nc == 1))\n self.transform_B = get_transform(self.opt, grayscale=(output_nc == 1))","def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n\n self.data_folder = os.path.join(opt.dataroot, opt.phase)\n self.image_file_names = sorted(os.listdir(self.data_folder))\n self.batch_size = opt.batch_size\n self.z_dim = opt.z_dim\n self.imsize = opt.crop_size\n\n self.transform = self.get_transform(True, True, True, opt.center_crop)","def _prepare_sets(self):\n\n ds_images, ds_labels = self._load_images_labels()\n\n ds_images_2 = ds_images.take(self.val_count)\n ds_labels_2 = ds_labels.take(self.val_count)\n ds_images_1 = ds_images.skip(self.val_count)\n ds_labels_1 = ds_labels.skip(self.val_count)\n\n ds_1 = (ds_images_1, ds_labels_1)\n ds_2 = (ds_images_2, ds_labels_2)\n\n return ds_1, ds_2","def prep_data():\n loader = DLoader()\n cap = loader.visitor_cnt\n\n pass","def prepare(self):\n\t\tif len(os.listdir(self.storagedir)):\n\t\t\traise Exception(\"There are already files in\", self.storagedir + \".\", \"Delete manually!\")\n\t\t\n\t\tself.worddict = {\"__pad__\": 0, \"__oov__\": 1}\n\t\tself.classdict = {}\n\t\tself.raw_documents, self.tokenized_documents = {}, {}\n\t\tself.X, self.Y = {}, {}\n\n\t\tfor dataset in self.DATASETS_TMP:\n\t\t\tself.get_raw_data(dataset)\n\t\t\tself.delete_empty_documents(dataset)\n\t\t\tself.tokenize_documents(dataset)\n\n\t\tself.make_classdict()\n\t\tself.make_worddict()\n\t\tself.make_embeddings()\n\t\tself.reverse_dicts()\n\n\t\tfor dataset in self.DATASETS_TMP:\n\t\t\tself.make_X(dataset)\n\t\t\tself.shuffle_dataset(dataset)\n\n\t\tif not \"dev\" in self.X:\n\t\t\tself.split_dev()\t\t\n\t\tself.make_hybrid()\n\t\tself.store()","def setup(self, stage: Union[str, None] = None) -> None:\n self.data_splits = {}\n # set up each of the dataset splits\n for key, path in self.paths.items():\n self.data_splits[key] = self.dataset_class(path)","def init_dataset(validation_dataset_name):\n transform = transforms.Compose([transforms.ToPILImage(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\n \n if validation_dataset_name == 'datasetRAP':\n # validation = 8317 images = 166 batches of 50 images + 1 batch of 17 images\n dataset_valid = loader_rapdataset_yiqiang.RAPDataset(0,False,'/storage/Datasets/Rap-PedestrianAttributeRecognition/',transform)\n labels = loader_rapdataset_yiqiang.ATTRIBUTES\n datset_attr_nbr = 92\n elif validation_dataset_name == 'datasetPETA':\n dataset_valid = loader_peta_dataset.PETADataset(False, '/storage/Datasets/PETA-PEdesTrianAttribute', transform)\n labels = loader_peta_dataset.ATTRIBUTES\n datset_attr_nbr = 104\n elif validation_dataset_name == 'datasetRAPPETA':\n dataset_valid = loader_rap_plus_peta_dataset.RAPPlusPETADataset(False, '/storage/Datasets/Rap-PedestrianAttributeRecognition/', '/storage/Datasets/PETA-PEdesTrianAttribute', transform)\n labels = [peta_label for rap_label,peta_label in loader_rap_plus_peta_dataset.ATTRIBUTES]\n datset_attr_nbr = 49\n\n print (\"Dataset valid size :\", dataset_valid.__len__())\n print (\"Dataset Attributes number :\", datset_attr_nbr)\n assert (len(labels) == datset_attr_nbr)\n\n dataloader_valid = DataLoader(dataset_valid, batch_size=Param_Batchsize, shuffle=True, num_workers=Param_Nb_Workers)\n\n return dataloader_valid, dataset_valid","def _init_train_loader(self):\n # Choose the right dataset type\n if self.config_args[\"num_members\"] > 1:\n class_dataset_wrapper = dataset_wrapper.MixMoDataset\n else:\n class_dataset_wrapper = dataset_wrapper.MSDADataset\n\n # Load augmentations\n self.traindatasetwrapper = class_dataset_wrapper(\n dataset=self.train_dataset,\n num_classes=int(self.config_args[\"data\"][\"num_classes\"]),\n num_members=self.config_args[\"num_members\"],\n dict_config=self.config_args[\"training\"][\"dataset_wrapper\"],\n properties=self.properties\n )\n\n # Build standard sampler\n _train_sampler = torch.utils.data.sampler.RandomSampler(\n data_source=self.traindatasetwrapper, ## only needed for its length\n num_samples=None,\n replacement=False,\n )\n\n # Wrap it with the repeating sampler used for multi-input models\n batch_sampler = batch_repetition_sampler.BatchRepetitionSampler(\n sampler=_train_sampler,\n batch_size=self.batch_size,\n num_members=self.config_args[\"num_members\"],\n drop_last=True,\n config_batch_sampler=self.config_args[\"training\"][\"batch_sampler\"]\n )\n\n self.train_loader = torch.utils.data.DataLoader(\n self.traindatasetwrapper,\n batch_sampler=batch_sampler,\n num_workers=self.num_workers,\n batch_size=1,\n shuffle=False,\n sampler=None,\n drop_last=False,\n pin_memory=True,\n )","def prepare_data(dataset, train_ratio=0.8, input_dim=None, seed=10):\n # Retrieve main path of project\n dirname = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\n\n # Download and store dataset at chosen location\n if dataset == 'Cora' or dataset == 'PubMed' or dataset == 'Citeseer':\n path = os.path.join(dirname, 'data')\n data = Planetoid(path, name=dataset, split='full')[0]\n data.name = dataset\n data.num_classes = (max(data.y)+1).item()\n # data.train_mask, data.val_mask, data.test_mask = split_function(data.y.numpy())\n # data = Planetoid(path, name=dataset, split='public', transform=T.NormalizeFeatures(), num_train_per_class=20, num_val=500, num_test=1000)\n\n elif dataset == 'Amazon':\n path = os.path.join(dirname, 'data', 'Amazon')\n data = Amazon(path, 'photo')[0]\n data.name = dataset\n data.num_classes = (max(data.y)+1).item()\n data.train_mask, data.val_mask, data.test_mask = split_function(\n data.y.numpy(), seed=seed)\n # Amazon: 4896 train, 1224 val, 1530 test\n \n elif dataset in ['syn1', 'syn2', 'syn4', 'syn5']: \n data = synthetic_data(\n dataset, dirname, train_ratio, input_dim)\n \n elif dataset == 'syn6':\n data = gc_data(dataset, dirname, train_ratio)\n\n elif dataset == 'Mutagenicity':\n data = gc_data(dataset, dirname, train_ratio)\n\n return data","def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.dir_A = os.path.join(opt.dataroot, opt.phase5 + 'A') # create a path '/path/to/data/trainA'\n #self.dir_B20 = os.path.join(opt.dataroot, opt.phase + 'B20') # create a path '/path/to/data/trainB1'\n\n #self.dir_B2 = os.path.join(opt.dataroot, opt.phase + 'B2') # create a path '/path/to/data/trainB2'\n self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA'\n #self.B20_paths = sorted(make_dataset(self.dir_B20, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n #self.B2_paths = sorted(make_dataset(self.dir_B2, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n self.A_size = len(self.A_paths) # get the size of dataset A\n # self.B20_size = len(self.B20_paths) # get the size of dataset B\n #self.B2_size = len(self.B2_paths)\n\n #self.dir_A50 = os.path.join(opt.dataroot, opt.phase + 'A50') # create a path '/path/to/data/trainA'\n self.dir_B50 = os.path.join(opt.dataroot, opt.phase5 + 'B50') # create a path '/path/to/data/trainB1'\n #self.dir_B2 = os.path.join(opt.dataroot, opt.phase + 'B2') # create a path '/path/to/data/trainB2'\n #self.A50_paths = sorted(make_dataset(self.dir_A50, opt.max_dataset_size)) # load images from '/path/to/data/trainA'\n self.B50_paths = sorted(make_dataset(self.dir_B50, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n #self.B2_paths = sorted(make_dataset(self.dir_B2, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n #self.A50_size = len(self.A50_paths) # get the size of dataset A\n self.B50_size = len(self.B50_paths) # get the size of dataset B\n #self.B2_size = len(self.B2_paths)\n\n self.dir_B100 = os.path.join(opt.dataroot, opt.phase5 + 'B100') # create a path '/path/to/data/trainB1'\n self.B100_paths = sorted(\n make_dataset(self.dir_B100, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n self.B100_size = len(self.B100_paths) # get the size of dataset B\n\n self.dir_B150 = os.path.join(opt.dataroot, opt.phase5 + 'B150') # create a path '/path/to/data/trainB1'\n self.B150_paths = sorted(\n make_dataset(self.dir_B150, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n self.B150_size = len(self.B150_paths) # get the size of dataset B\n\n\n\n self.dir_m0 = os.path.join(opt.dataroot, 'mask_0') # create a path '/path/to/data/trainB1'\n self.m0_paths = sorted(\n make_dataset(self.dir_m0, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n self.m0_size = len(self.m0_paths) # get the size of dataset B\n\n\n self.dir_m50 = os.path.join(opt.dataroot, 'mask_50') # create a path '/path/to/data/trainB1'\n self.m50_paths = sorted(\n make_dataset(self.dir_m50, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n self.m50_size = len(self.m50_paths) # get the size of dataset B\n\n\n\n self.dir_m100 = os.path.join(opt.dataroot, 'mask_100') # create a path '/path/to/data/trainB1'\n self.m100_paths = sorted(\n make_dataset(self.dir_m100, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n self.m100_size = len(self.m100_paths) # get the size of dataset B\n\n\n self.dir_m150 = os.path.join(opt.dataroot, 'mask_150') # create a path '/path/to/data/trainB1'\n self.m150_paths = sorted(\n make_dataset(self.dir_m150, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\n self.m150_size = len(self.m150_paths) # get the size of dataset B\n\n\n \n\n btoA = self.opt.direction == 'BtoA'\n input_nc = self.opt.output_nc if btoA else self.opt.input_nc # get the number of channels of input image\n output_nc = self.opt.input_nc if btoA else self.opt.output_nc # get the number of channels of output image\n self.transform_A = get_transform(self.opt, grayscale=(input_nc == 1))\n self.transform_B = get_transform(self.opt, grayscale=(output_nc == 1))","def build_dataset(self):\n print(\"reading data of images currently , please wait......\")\n x_train, y_train, _ = get_images(self.train_directory)\n x_test, y_test, _ = get_images(self.test_directory)\n x_train, y_train = image_subset(self.num_classes, x_train, y_train)\n x_test, y_test = image_subset(self.num_classes, x_test, y_test)\n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n self.x_train = x_train / 255\n self.x_test = x_test / 255\n self.y_train = utils.to_categorical(y_train, self.num_classes)\n self.y_test = utils.to_categorical(y_test, self.num_classes)","def prepare(self):","def _init_from_DataArrays(self, data, validate=True):\n self._data_vars = self._DataArrays_as_mapping(data)\n\n if (len(self) > 1) and validate:\n first = self[0]\n for i in range(1, len(self)):\n da = self[i]\n first._is_compatible(da, raise_error=True)\n\n self._check_all_different_ids(self._data_vars.values())\n\n self.__itemattr = []\n for key, value in self._data_vars.items():\n self._set_name_attr(key, value)\n\n self.plot = _DatasetPlotter(self)\n\n if len(self) > 0:\n self._set_spectral_attributes(self.geometry)\n\n # since Dataset is MutableMapping it has values and keys by default\n # but we delete those to avoid confusion\n # self.values = None\n self.keys = None","def setup(self, stage: Optional[str] = None) -> None:\n if self.dataset_type == \"hdf5\":\n DS = SegmentationHDF5Dataset\n else:\n DS = SegmentationFolderDataset\n\n self.trainset = DS(\n path=self._get_path(\"train\", self.dataset_type, is_mask=False),\n mask_path=self._get_path(\"train\", self.dataset_type, is_mask=True),\n img_transforms=self.img_transforms,\n inst_transforms=self.inst_transforms,\n return_sem=False,\n normalization=self.normalization,\n **self.kwargs,\n )\n\n self.validset = DS(\n path=self._get_path(\"valid\", self.dataset_type, is_mask=False),\n mask_path=self._get_path(\"valid\", self.dataset_type, is_mask=True),\n img_transforms=self.img_transforms,\n inst_transforms=self.inst_transforms,\n return_sem=False,\n normalization=self.normalization,\n **self.kwargs,\n )\n\n self.testset = DS(\n path=self._get_path(\"test\", self.dataset_type, is_mask=False),\n mask_path=self._get_path(\"test\", self.dataset_type, is_mask=True),\n img_transforms=self.img_transforms,\n inst_transforms=self.inst_transforms,\n return_sem=False,\n normalization=self.normalization,\n **self.kwargs,\n )","def setUpClass(cls):\n # print(\"SWEData.setUpClass\", flush=True)\n super(TestIonSpecificsOptions, cls).setUpClass()\n # print(cls.data.iloc[:, :7])\n # print(cls.data.columns.values)\n cls.data = cls.data.xs(\"\", axis=1, level=\"N\")","def init_dset(self, dset_name, dset_shape, dset_attrs):\n dtype = dset_attrs['dtype']\n chunks = dset_attrs['chunks']\n attrs = dset_attrs['attrs']\n name = dset_attrs.get('name', None)\n if name is not None:\n dset_name = name\n\n if chunks:\n chunks = tuple(chunks)\n\n logger.debug('Creating {} with shape: {}, dtype: {}, chunks: {}'\n .format(dset_name, dset_shape, dtype, chunks))\n ds = self._dst_h5.create_dataset(dset_name, shape=dset_shape,\n dtype=dtype, chunks=chunks)\n if attrs:\n for attr, value in attrs.items():\n if attr not in ['freq', 'start']:\n ds.attrs[attr] = value\n\n logger.info('- {} initialized'.format(dset_name))\n\n return ds","def init_locally_processed_dataset(directory, source_datasets, uuid_=None):\n md = ptype.DatasetMetadata(\n id_=uuid_,\n # Default creation time is creation of an image.\n creation_dt=datetime.datetime.utcfromtimestamp(directory.stat().st_ctime),\n lineage=ptype.LineageMetadata(\n machine=ptype.MachineMetadata(\n hostname=socket.getfqdn(),\n runtime_id=_RUNTIME_ID,\n uname=' '.join(os.uname())\n ),\n source_datasets=source_datasets\n )\n )\n _note_package_vers(md)\n return md","def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.dir_images = os.path.join(opt.dataroot, opt.phase) # get the image directory\n self.image_paths = sorted(make_dataset(self.dir_images, opt.max_dataset_size)) # get image paths\n assert(self.opt.load_size >= self.opt.crop_size) # crop_size should be smaller than the size of loaded image\n self.input_nc = self.opt.output_nc\n self.output_nc = self.opt.input_nc","def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.SR_factor = opt.SR_factor\n\n assert util_dataset.check_whether_last_dir(opt.dataroot), 'when SingleDataset, opt.dataroot:{} should be dir and contains only image files'.format(opt.dataroot)\n self.dir_A = opt.dataroot\n self.A_paths = sorted(make_images_dataset(self.dir_A, opt.max_dataset_size)) # get image paths\n\n self.input_nc = self.opt.input_nc\n self.output_nc = self.opt.output_nc","def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))\n input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc\n self.transform = get_transform(opt, grayscale=(input_nc == 1))","def setup(self):\n\n folder_name, file_name, url, md5 = self.resource\n dataset_folder = os.path.join(self.data_root, folder_name)\n if not os.path.exists(dataset_folder):\n sh_utils.download_and_extract_archive(url, dataset_folder, md5, file_name)\n\n test_transform = tv_transforms.Compose(\n [\n tv_transforms.ToTensor(),\n tv_transforms.Lambda(lambda x: x.permute(1, 2, 0)),\n ]\n )\n\n dataset_out = tv_datasets.ImageFolder(\n root=dataset_folder, transform=test_transform\n )\n self.images_only_dataset_out = sh_data_torch.IndexedTorchDataset(\n sh_data_torch.ImagesOnlyTorchDataset(dataset_out)\n )","def __init__(self, file, sdef, name, path, attrs, parent, value, dtype, compress, link_info=None):\n super(Dataset, self).__init__(file, sdef, name, path, attrs, parent, link_info)\n # print \"Creating Dataset, sdef=\"\n # pp.pprint(sdef)\n if 'attributes' in self.sdef['df']:\n self.attributes = copy.deepcopy(self.sdef['df']['attributes'])\n # del self.sdef['df']['attributes'] # if do this, no need to check for attributes in mk_dsinfo\n # print \"found attributes:\"\n # else:\n # print \"did not find attributes:\"\n # pp.pprint(self.attributes)\n # if self.sdef['df']:\n self.dsinfo = self.mk_dsinfo(value)\n self.merge_attribute_defs(self.attributes, self.dsinfo['atags'])\n # else:\n # definition empty, must be custom dataset\n # self.dsinfo = {}\n self.merge_attrs()\n if self.link_info:\n # this dataset set to link to another. Already done in Node. Nothing to do here\n pass\n else:\n # creating new dataset (normally done)\n self.link_node = None\n # compress = \"gzip\" if compress else None\n # self.h5node = self.h5parent.create_dataset(self.name, data=value,\n # dtype=dtype, compression=compress)\n #- self.file.file_pointer.create_dataset(self.full_path, data=value,\n #- dtype=dtype, compression=compress)\n self.file.create_dataset(self.full_path, data=value, dtype=dtype,\n compress=compress)\n # self.file.h5commands.append(\"create_dataset(%s, %s)\" % (self.full_path, value))\n # if dtype:\n # self.h5node = self.h5parent.create_dataset(self.name, data=value, dtype=dtype)\n # else: # should find out what default value for dtype used in h5py and use that, combine these\n # self.h5node = self.h5parent.create_dataset(self.name, data=value)\n self.set_attr_values()","def setUpClass(cls):\n cls._test_dc = DataCleaner(data = create_test_df())","def setup(self):\n in_dataset, out_dataset = self.get_datasets()\n \n out_dataset[0].create_dataset(in_dataset[0])\n out_dataset[1].create_dataset(in_dataset[1])\n\n in_pData, out_pData = self.get_plugin_datasets()\n\n in_pData[0].plugin_data_setup( 'SINOGRAM',multiple)\n out_pData[0].plugin_data_setup( 'PROJECTION','multiple')\n\n in_pData[1].plugin_data_setup( 'PROJECTION',)\n out_pData[1].plugin_data_setup( 'PROJECTION','multiple')","def InitDataset(self):\n train_txt = 'ImageSets/Main/train.txt'\n val_txt = 'ImageSets/Main/val.txt'\n annotations = \"Annotations\"\n jpegimages = \"JPEGImages\"\n images_path = train_txt if (self.is_train) else val_txt \n images_path = readTxt(os.path.join(self.path, images_path))\n images_path.pop(-1)\n # rawdata format: [path_2_image, path_2_xml]\n rawData = list()\n for each in images_path:\n xml = os.path.join(self.path, annotations, each + '.xml')\n jpeg = os.path.join(self.path, jpegimages, each + '.jpg')\n rawData.append([jpeg, xml])\n return rawData","def prepare(self):\n pass","def prepare(self):\n pass","def prepare(self):\n pass","def __init__(self, dataset_name, source_path_str=None, th=0, nprocs=1):\n\n # wherever you call from, the dataset root path\n # will be the same, \\\\|// relative to project root,\n # as marked by the -(@ @)- .kilroy flag file\n # --oOO---(_)--- OOo--\n\n base_path = mooltipath(\"datasets\")\n\n # dataset directory path\n self.path = Path(base_path, dataset_name)\n\n # database file path\n self.db_path = Path(self.path, \"database.db\")\n\n # manifest file path\n mnf_path = Path(base_path, dataset_name + \".mnf\")\n\n # samples audio files path\n self.samples_path = Path(self.path, \"samples\")\n\n # if no source path was provided, it means\n # that we want to retrieve an existing AudioDataset db\n if not source_path_str:\n # if the database file does not exist, we bailout friendly\n if not self.db_path.is_file():\n iprint(\"Dataset database file does not exist.\")\n iprint(\"Please insure that the dataset name is correct and\")\n iprint(\"that the dataset has been previously created.\")\n iprint(\"### ABORTING! ###\")\n raise FileNotFoundError(\"Dataset database not found\")\n\n # else retrieve dataset configuration from db\n self._get_config_from_db()\n else:\n # Else we have to build the dataset from its manifest.\n # we expect:\n # - the manifest file to exist,\n # - the ds directory to not exist\n # - the source_dir to exist.\n # Else exit friendly\n source_path = Path(source_path_str).absolute()\n\n if not mnf_path.is_file():\n iprint(\"Dataset manifest does not exist.\")\n iprint(\"Please insure that the dataset name is correct.\")\n iprint(\"### ABORTING! ###\")\n raise FileNotFoundError(\"Dataset manifest not found\")\n\n if not source_path.is_dir():\n iprint(f\"Source directory ({source_path}) does not exist.\")\n iprint(\"Please insure that provided path is correct.\")\n iprint(\"### ABORTING! ###\")\n raise FileNotFoundError(\"Dataset source directory not found\")\n\n if self.path.exists():\n iprint(f\"The dataset directory ({self.path}) already exists.\")\n iprint(\n \"If you really intent to CREATE this dataset, please \"\n \"erase this directory first\"\n )\n iprint(\"### ABORTING! ###\")\n raise FileExistsError(\"Dataset directory already exists\")\n\n iprint(f\">>>>> Starting Dataset {dataset_name} build\")\n # Create directory structure\n self.samples_path.mkdir(parents=True)\n\n # Read manifest\n with mnf_path.open(\"r\") as f:\n lines = f.read().split(\"\\n\")\n sample_rate = int(lines[0])\n duration = float(lines[1])\n overlap = float(lines[2])\n md5 = lines[3]\n filenames = lines[4:]\n\n # Perform sanity checks\n assert duration > 0, \"duration must be strictly positive\"\n assert overlap >= 0, \"overlap must be positive or zero\"\n assert (\n sample_rate <= 44100 and sample_rate >= 1024\n ), \"sample_rate must belong to [1024 - 44100]\"\n assert overlap < duration, \"overlap must be < duration\"\n\n # Pre Populate instance properties\n self.sample_rate = sample_rate\n self.duration = duration\n self.overlap = overlap\n self.filenames = filenames\n self.nb_samples = 0\n\n # Create database tables\n self._create_tables()\n\n # Perform actual audio chunks slicing\n self._build(source_path, th, nprocs)\n\n # Compute md5 checksum and check it agains manifest expected value\n assert self._is_valid(\n md5\n ), \"Computed MD5 checksum does not match manifest expected value.\"\n iprint(\"Checksum OK!\")\n\n # Everything went well, we are done (Yay!)\n iprint(f\">>>>> Dataset {dataset_name} successfully created.\")\n\n return","def setup_class(cls):\n self = cls()\n self.remove_files_created_during_previous_runs()\n if not os.path.exists(self.plaintext_directory):\n os.makedirs(self.plaintext_directory)\n\n if not os.path.exists(self.training_path):\n os.makedirs(self.training_path)\n\n if not os.path.exists(self.heldout_path):\n os.makedirs(self.heldout_path)\n\n prepare_data(self.paths)","def _build_datasets(self):\n self._build_datasets_sis3302()\n self._build_datasets_sis3305()","def __init__(self):\n self.model = None\n self.joined_datasets = None\n self.id_col = None\n self.val_col = None\n self.pop_col = None\n self.total_population_per_unit = None\n self.centroids_of_areal_data = None\n self.prepared_data = None\n self.unknown_area_id = None\n\n # Parameters\n self.lags = None\n self.step = None\n self.min_no_of_observations = None\n self.max_search_radius = None","def prepare(self):\n\n # step 0: load only when not loaded yet\n if TRAINING in self.data \\\n and VALIDATION in self.data:\n return\n\n # step 1: load the file names\n file_list = sorted(glob.glob(self.location+\"*.mhd\"))\n # count the number of data points\n\n # make a stratified validation set\n # note, the seed decides the validation set, but it is deterministic in the names\n random.seed(317070)\n patient_names = [self.patient_name_from_file_name(f) for f in file_list]\n validation_patients = random.sample(patient_names, int(VALIDATION_SET_SIZE*len(patient_names)))\n\n # make the static data empty\n for s in self.datasets:\n self.data[s] = []\n self.labels[s] = []\n self.names[s] = []\n\n # load the filenames and put into the right dataset\n labels_as_dict = defaultdict(list)\n\n with open(paths.LUNA_LABELS_PATH, 'rb') as csvfile:\n reader = csv.reader(csvfile, delimiter=',', quotechar='|')\n next(reader) # skip the header\n for row in reader:\n label = (float(row[1]), float(row[2]), float(row[3]), float(row[4]))\n labels_as_dict[str(row[0])].append(label)\n\n for patient_file in file_list:\n patient_name = self.patient_name_from_file_name(patient_file)\n\n if patient_name in validation_patients:\n s = VALIDATION\n else:\n s = TRAINING\n label = labels_as_dict[str(patient_name)]\n if self.only_positive and not label:\n continue\n self.data[s].append(patient_file)\n \n if self.pick_nodule:\n self.labels[s].append([random.choice(label)]) \n else:\n self.labels[s].append(label)\n \n \n self.names[s].append(patient_name)\n\n # give every patient a unique number\n last_index = -1\n for s in self.datasets:\n self.indices[s] = range(last_index+1,last_index+1+len(self.data[s]))\n if len(self.indices[s]) > 0:\n last_index = self.indices[s][-1]\n print s, len(self.indices[s]), \"samples\"","def __init__(self, subset):\n if subset not in ('background', 'evaluation'):\n raise(ValueError, 'subset must be one of (background, evaluation)')\n self.subset = subset\n\n self.df = pd.DataFrame(self.index_subset(self.subset))\n\n # Index of dataframe has direct correspondence to item in dataset\n self.df = self.df.assign(id=self.df.index.values)\n\n # Convert arbitrary class names of dataset to ordered 0-(num_speakers - 1) integers\n self.unique_characters = sorted(self.df['class_name'].unique())\n self.class_name_to_id = {self.unique_characters[i]: i for i in range(self.num_classes())}\n self.df = self.df.assign(class_id=self.df['class_name'].apply(lambda c: self.class_name_to_id[c]))\n\n # Create dicts\n self.datasetid_to_filepath = self.df.to_dict()['filepath']\n self.datasetid_to_class_id = self.df.to_dict()['class_id']\n\n # Setup transforms\n self.transform = transforms.Compose([\n transforms.CenterCrop(224),\n transforms.Resize(84),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225])\n ])","def __init__(self, dir_path, window_size,\n user_map_path, computer_map_path, auth_type_map_path, logon_type_map_path):\n logging.info(f\"Initiating Dataset instance for directory {dir_path}\")\n self.directory = dir_path\n self.filenames = [filename for filename in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, filename))]\n assert len(self.filenames) > 0\n random.shuffle(self.filenames)\n self.window_size = window_size\n self.len = self.count_len()\n self.user_map, self.user_count = util.load_mapping(user_map_path)\n self.computer_map, self.computer_count = util.load_mapping(computer_map_path)\n self.auth_type_map, self.auth_type_count = util.load_mapping(auth_type_map_path)\n self.logon_type_map, self.logon_type_count = util.load_mapping(logon_type_map_path)","def prepare_data(self) -> None:\n if (self.root).is_dir():\n logger.info(\"Found the dataset.\")\n else:\n download_and_extract(self.root, DOWNLOAD_INFO)","def _init_dataset(self, data_config, split='train'):\n assert split in {'train', 'valid'}\n\n # load datasets\n print(f'Load {split} dataset')\n if data_config['type'] == 'npy':\n dataset = MSDMelDataset(\n data_config['mel_root'], data_config[f'{split}_tids_fn'],\n data_config['label_fn'], on_mem=data_config['on_mem'],\n ignore_intersection=data_config['ignore_label_intersection'],\n transform=ToVariable())\n\n elif data_config['type'] == 'hdf':\n dataset = MSDMelHDFDataset(\n data_config['hdf_fn'], data_config[f'{split}_tids_fn'],\n data_config['label_fn'],\n ignore_intersection=data_config['ignore_label_intersection'],\n transform=ToVariable())\n\n elif data_config['type'] == 'audio':\n dataset = MSDAudioDataset(\n data_config['audio_root'], data_config[f'{split}_tids_fn'],\n data_config['tid2path_fn'], data_config['label_fn'],\n ignore_intersection=data_config['ignore_label_intersection'],\n device='cpu',\n transform=ToVariable())\n\n return dataset","def __init__(self, subset):\n if subset not in ('background', 'evaluation'):\n raise(ValueError, 'subset must be one of (background, evaluation)')\n self.subset = subset\n\n self.df = pd.DataFrame(self.index_subset(self.subset))\n\n # Index of dataframe has direct correspondence to item in dataset\n self.df = self.df.assign(id=self.df.index.values)\n\n # Convert arbitrary class names of dataset to ordered 0-(num_speakers - 1) integers\n self.unique_characters = sorted(self.df['class_name'].unique())\n self.class_name_to_id = {self.unique_characters[i]: i for i in range(self.num_classes())}\n self.df = self.df.assign(class_id=self.df['class_name'].apply(lambda c: self.class_name_to_id[c]))\n\n # Create dicts\n self.datasetid_to_filepath = self.df.to_dict()['data']\n self.datasetid_to_class_id = self.df.to_dict()['class_id']","def __init__(self, subset):\n if subset not in ('background', 'evaluation'):\n raise(ValueError, 'subset must be one of (background, evaluation)')\n self.subset = subset\n\n self.df = pd.DataFrame(self.index_subset(self.subset))\n\n # Index of dataframe has direct correspondence to item in dataset\n self.df = self.df.assign(id=self.df.index.values)\n\n # Convert arbitrary class names of dataset to ordered 0-(num_speakers - 1) integers\n self.unique_characters = sorted(self.df['class_name'].unique())\n self.class_name_to_id = {self.unique_characters[i]: i for i in range(self.num_classes())}\n self.df = self.df.assign(class_id=self.df['class_name'].apply(lambda c: self.class_name_to_id[c]))\n\n # Create dicts\n self.datasetid_to_filepath = self.df.to_dict()['data']\n self.datasetid_to_class_id = self.df.to_dict()['class_id']","def __init__(self, subset):\n if subset not in ('background', 'evaluation'):\n raise(ValueError, 'subset must be one of (background, evaluation)')\n self.subset = subset\n\n self.df = pd.DataFrame(self.index_subset(self.subset))\n\n # Index of dataframe has direct correspondence to item in dataset\n self.df = self.df.assign(id=self.df.index.values)\n\n # Convert arbitrary class names of dataset to ordered 0-(num_speakers - 1) integers\n self.unique_characters = sorted(self.df['class_name'].unique())\n self.class_name_to_id = {self.unique_characters[i]: i for i in range(self.num_classes())}\n self.df = self.df.assign(class_id=self.df['class_name'].apply(lambda c: self.class_name_to_id[c]))\n\n # Create dicts\n self.datasetid_to_filepath = self.df.to_dict()['data']\n self.datasetid_to_class_id = self.df.to_dict()['class_id']","def __init__(self, subset):\n if subset not in ('background', 'evaluation'):\n raise(ValueError, 'subset must be one of (background, evaluation)')\n self.subset = subset\n\n self.df = pd.DataFrame(self.index_subset(self.subset))\n\n # Index of dataframe has direct correspondence to item in dataset\n self.df = self.df.assign(id=self.df.index.values)\n\n # Convert arbitrary class names of dataset to ordered 0-(num_speakers - 1) integers\n self.unique_characters = sorted(self.df['class_name'].unique())\n self.class_name_to_id = {self.unique_characters[i]: i for i in range(self.num_classes())}\n self.df = self.df.assign(class_id=self.df['class_name'].apply(lambda c: self.class_name_to_id[c]))\n\n # Create dicts\n self.datasetid_to_filepath = self.df.to_dict()['filepath']\n self.datasetid_to_class_id = self.df.to_dict()['class_id']","def prepare_data():\n gennet.prepare_data('Resnet50')","def setup(self, stage: Union[str, None] = None) -> None:\n self.data_splits = {}\n # set up each of the dataset splits\n for key, path in self.paths.items():\n self.data_splits[key] = PointCloudDataset(\n self.dataset_class(path), self._point_cloud_size, self._sample_size\n )","def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n self.dir_AB = os.path.join(opt.dataroot, opt.phase) # get the image directory\n self.AB_paths = sorted(make_dataset(self.dir_AB, opt.max_dataset_size)) # get image paths\n assert(self.opt.load_size >= self.opt.crop_size) # crop_size should be smaller than the size of loaded image\n self.input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc\n self.output_nc = self.opt.input_nc if self.opt.direction == 'BtoA' else self.opt.output_nc\n\n\n\n\n basic_dir = '/home/akira/Project/simplestCNN_sparse2dense/data/32-128data'\n bin32file = os.path.join(basic_dir, \"Lidar32.bin\")\n bin128file = os.path.join(basic_dir, \"Lidar128.bin\")\n bin32pa = os.path.join(basic_dir, \"Lidar32.pa\")\n bin128pa = os.path.join(basic_dir, \"Lidar128.pa\")\n rimg32loader = ImageTensorBinLoader(bin32file, bin32pa)\n rimg128loader = ImageTensorBinLoader(bin128file, bin128pa)\n self.rimgdataset = SyncDataset((0, 500))\n self.rimgdataset.registerDataLoader(rimg32loader, \"rimg32\")\n self.rimgdataset.registerDataLoader(rimg128loader, \"rimg128\")","def build_training_data_loader(self) -> DataLoader:\n pass","def __init__(\n self,\n path,\n tier,\n embeddings=None,\n preprocessor=None,\n transform=True):\n super().__init__()\n assert os.path.isdir(path)\n assert os.path.exists(os.path.join(path, f'{tier}.csv'))\n assert tier in self.TIERS\n\n self.path = path\n self.tier = tier\n self.data = None\n self.embeddings = embeddings\n self.preprocessor = preprocessor\n self.is_transform = transform\n self._init_dataset()","def __init__(\n self,\n embedder_path: Optional[str] = None,\n dataset: Optional[np.ndarray] = None,\n metadata: Optional[List[AnyStr]] = None,\n ) -> None:\n self._embedder_path = embedder_path\n\n # Cache dataset list which can be concatenated to the single dataset. This\n # is used since users may load the data several times, if each time the data\n # are directly concatenated together, memory copy will be costed each time.\n self._cache_dataset_list = []\n\n if dataset is None:\n # Sets dataset and metadata as empty. Will load them from raw input data\n # later.\n self._dataset = np.array([])\n self._metadata = []\n else:\n # Directly sets dataset and metadata.\n self._dataset = dataset\n self._metadata = metadata","def create_dataset(dataset_type, soruce, opts): \n\n p = PreProcessor(dataset_type, opts)\n\n # If we are NOT running \"implementation.py\", we read the data from file\n if dataset_type == \"train\" or dataset_type == \"dev\" or dataset_type == \"test\":\n path_to_data = soruce\n p.read_labelled_data(path_to_data) \n # Otherwise, we read the sentence that \"implementation.py\" gave us\n elif dataset_type == \"submit\":\n submission_sentence = soruce\n p.read_test_data(submission_sentence)\n\n # Encode all the data to a list of torchTensors\n encoded_tokens, encoded_pred, encoded_tokens_pos, encoded_labels = p.encode_all_data()\n # Create SRL dataset\n dataset = SRLDataset(x=encoded_tokens, pr=encoded_pred, p=encoded_tokens_pos, y=encoded_labels)\n print(\"{} dataset size is {}\".format(dataset_type, len(dataset)))\n\n if dataset_type == \"train\" or dataset_type == \"dev\" or dataset_type == \"test\":\n return dataset\n elif dataset_type == \"submit\":\n return dataset, p.list_l_original_predicates","def _create_init(self):\n\n assert self._topology is not None, \\\n \"Topology must be given for a creation constructor\"\n\n # initialize the runs group\n runs_grp = self._h5.create_group(RUNS)\n\n # initialize the settings group\n settings_grp = self._h5.create_group(SETTINGS)\n\n # create the topology dataset\n self._h5.create_dataset(TOPOLOGY, data=self._topology)\n\n # sparse fields\n if self._sparse_fields is not None:\n\n # make a dataset for the sparse fields allowed. this requires\n # a 'special' datatype for variable length strings. This is\n # supported by HDF5 but not numpy.\n vlen_str_dt = h5py.special_dtype(vlen=str)\n\n # create the dataset with empty values for the length of the\n # sparse fields given\n sparse_fields_ds = settings_grp.create_dataset(SPARSE_FIELDS,\n (len(self._sparse_fields),),\n dtype=vlen_str_dt,\n maxshape=(None,))\n\n # set the flags\n for i, sparse_field in enumerate(self._sparse_fields):\n sparse_fields_ds[i] = sparse_field\n\n\n # field feature shapes and dtypes\n\n # initialize to the defaults, this gives values to\n # self._n_coords, and self.field_feature_dtypes, and\n # self.field_feature_shapes\n self._set_default_init_field_attributes(n_dims=self._n_dims)\n\n # save the number of dimensions and number of atoms in settings\n settings_grp.create_dataset(N_DIMS_STR, data=np.array(self._n_dims))\n settings_grp.create_dataset(N_ATOMS, data=np.array(self._n_coords))\n\n # the main rep atom idxs\n settings_grp.create_dataset(MAIN_REP_IDXS, data=self._main_rep_idxs, dtype=int)\n\n # alt_reps settings\n alt_reps_idxs_grp = settings_grp.create_group(ALT_REPS_IDXS)\n for alt_rep_name, idxs in self._alt_reps.items():\n alt_reps_idxs_grp.create_dataset(alt_rep_name, data=idxs, dtype=int)\n\n # if both feature shapes and dtypes were specified overwrite\n # (or initialize if not set by defaults) the defaults\n if (self._field_feature_shapes_kwarg is not None) and\\\n (self._field_feature_dtypes_kwarg is not None):\n\n self._field_feature_shapes.update(self._field_feature_shapes_kwarg)\n self._field_feature_dtypes.update(self._field_feature_dtypes_kwarg)\n\n # any sparse field with unspecified shape and dtype must be\n # set to None so that it will be set at runtime\n for sparse_field in self.sparse_fields:\n if (not sparse_field in self._field_feature_shapes) or \\\n (not sparse_field in self._field_feature_dtypes):\n self._field_feature_shapes[sparse_field] = None\n self._field_feature_dtypes[sparse_field] = None\n\n\n # save the field feature shapes and dtypes in the settings group\n shapes_grp = settings_grp.create_group(FIELD_FEATURE_SHAPES_STR)\n for field_path, field_shape in self._field_feature_shapes.items():\n if field_shape is None:\n # set it as a dimensionless array of NaN\n field_shape = np.array(np.nan)\n\n shapes_grp.create_dataset(field_path, data=field_shape)\n\n dtypes_grp = settings_grp.create_group(FIELD_FEATURE_DTYPES_STR)\n for field_path, field_dtype in self._field_feature_dtypes.items():\n if field_dtype is None:\n dt_str = NONE_STR\n else:\n # make a json string of the datatype that can be read\n # in again, we call np.dtype again because there is no\n # np.float.descr attribute\n dt_str = json.dumps(np.dtype(field_dtype).descr)\n\n dtypes_grp.create_dataset(field_path, data=dt_str)\n\n # initialize the units group\n unit_grp = self._h5.create_group(UNITS)\n\n # if units were not given set them all to None\n if self._units is None:\n self._units = {}\n for field_path in self._field_feature_shapes.keys():\n self._units[field_path] = None\n\n # set the units\n for field_path, unit_value in self._units.items():\n\n # ignore the field if not given\n if unit_value is None:\n continue\n\n unit_path = '{}/{}'.format(UNITS, field_path)\n\n unit_grp.create_dataset(unit_path, data=unit_value)\n\n\n # create the group for the run data records\n records_grp = settings_grp.create_group(RECORD_FIELDS)\n\n # create a dataset for the continuation run tuples\n # (continuation_run, base_run), where the first element\n # of the new run that is continuing the run in the second\n # position\n self._init_continuations()","def __init__(self, opt):\n BaseDataset.__init__(self, opt)\n\n transform_list = [ transforms.ToTensor() ]\n self.transform = transforms.Compose(transform_list)\n\n dir_self = \"./datasets/self_2020/wOBJ_processed/\" + str(opt.phase)\n self.files_self_A_rgbd = sorted(glob.glob(dir_self + '/A_rgbd' + '/*.*'))\n self.files_self_B_depth = sorted(glob.glob(dir_self + '/B_d' + '/*.*'))\n self.files_self_obj_rgb = sorted(glob.glob(dir_self + '/obj_rgb' + '/*.*'))\n tmp = int(len(self.files_self_A_rgbd) / 2)\n self.files_self_A_rgbd = self.files_self_A_rgbd[:tmp]\n self.files_self_B_depth = self.files_self_B_depth[:tmp]\n self.files_self_obj_rgb = self.files_self_obj_rgb[:tmp]\n\n dir_self = \"./datasets/self_2020/woOBJ_processed/\" + str(opt.phase)\n files_wo_A_rgbd = sorted(glob.glob(dir_self + '/A_rgbd' + '/*.*'))\n files_wo_B_depth = sorted(glob.glob(dir_self + '/B_d' + '/*.*'))\n files_wo_obj_rgb = sorted(glob.glob(dir_self + '/obj_rgb' + '/*.*'))\n tmp = int(len(files_wo_A_rgbd) / 2)\n files_wo_A_rgbd = files_wo_A_rgbd[:tmp]\n files_wo_B_depth = files_wo_B_depth[:tmp]\n files_wo_obj_rgb = files_wo_obj_rgb[:tmp]\n\n self.files_self_A_rgbd.extend(files_wo_A_rgbd)\n self.files_self_B_depth.extend(files_wo_B_depth)\n self.files_self_obj_rgb.extend(files_wo_obj_rgb)\n\n datasize = opt.max_dataset_size\n if opt.max_dataset_size is None:\n datasize = int(len(self.files_self_A_rgbd))\n\n self.files_self_A_rgbd = self.files_self_A_rgbd[:datasize]\n self.files_self_B_depth = self.files_self_B_depth[:datasize]\n self.files_self_obj_rgb = self.files_self_obj_rgb[:datasize]","def prepare_dataset(self, xs: List[str], ys: List[str], batch_size: int = None):\n\n if batch_size is None:\n batch_size = self.cM.batch_size\n\n examples = [data.Example.fromlist([x, y], self.data_fields) for x, y in zip(xs, ys)]\n\n dataset = data.Dataset(examples, fields=self.data_fields)\n\n iterator = data.BucketIterator(dataset, batch_size=batch_size, shuffle=False)\n\n return iterator","def create_dataset(opt):\n\tdata_loader = CustomDatasetDataLoader(opt)\n\tdataset = data_loader.load_data()\n\treturn dataset","def setUpClass(cls) -> None:\n cls.DATA_MODEL = cls.load_data_model()","def __init__(self):\n self.data_set = []\n self.finalized_data = LogData()","def prepare(self):\n raise NotImplemented","def __init__(self, args):\n self.train_img_file = os.path.join(args.data_dir, args.train_img_file)\n self.train_lbl_file = os.path.join(args.data_dir, args.train_lbl_file)\n self.test_img_file = os.path.join(args.data_dir, args.test_img_file)\n self.test_lbl_file = os.path.join(args.data_dir, args.test_lbl_file)\n self.batch_size = args.batch_size\n self.num_workers = args.data_workders\n self.shuffle = True\n self.dataset_name = args.dataset_name\n self.pin_memory = False #args.cuda\n\n # check dataset files exist\n files = [self.train_img_file, self.train_lbl_file,\n self.test_img_file, self.test_lbl_file]\n for file in files:\n if not os.path.isfile(file):\n msg = \"Data file not found. Please check the path \" +\\\n \"or download files using scripts/download_files.py \"\n raise IOError(msg)","def init(*args):\n global dataset\n dataset = args[0]","def setUpClass(cls):\n cls.test_file_1 = \"/tmp/test_data_loader_dummy_1.pkl\"\n cls.test_file_2 = \"/tmp/test_data_loader_dummy_2.pkl\"\n cls.in_cols = [\"file\", \"id\", \"len\", \"seq\", \"phyche\", \"pssm\", \"logits\",\n \"ss\", \"h_0\", \"h_1\", \"h_2\", \"lm_logits\"]\n cls.out_cols = [\"dataset\", \"id\", \"len\", \"position\", \"amino\",\n \"phyche\", \"pssm\", \"logits\", \"ss\", \"h_0\", \"h_1\", \"h_2\",\n \"lm_logits\"]\n\n seq = np.array([[0., 0., 1.],\n [1., 0., 0.]])\n phyche = np.array([[0., 0.], # phyche\n [1., 0.]])\n pssm = np.array([[0., 0., .8], # pssm\n [.8, 0., 0.]])\n logits = np.array([[0.1, 0., 0.9], # logits\n [0.9, 0., 0.1]])\n ss = np.array([[0., 0., 1.], # ss\n [1., 0., 0.]])\n h_0 = np.array([[0., 0., 1., 0.],\n [1., 0., 0., 0.]])\n h_1 = np.array([[0., 0., 1., 0.],\n [1., 0., 0., 0.]])\n h_2 = np.array([[0., 0., 1., 0.], # h_2\n [1., 0., 0., 0.]])\n lm_logits = np.array([[0., 0., 1.], # lm_logits\n [1., 0., 0.]])\n\n ex_1_in = (\"dummy_train.tfrecords\", # file\n \"id1\", # id\n 2, # len\n seq,\n phyche,\n pssm,\n logits,\n ss,\n h_0,\n h_1,\n h_2,\n lm_logits,\n )\n ex_1_out = [tuple([\"train\", ex_1_in[1], ex_1_in[2], j] + [ex_1_in[i][j, :] for i in range(3, len(ex_1_in))]) for j in range(2)]\n\n in_df = pd.DataFrame.from_records(data=[ex_1_in], columns=cls.in_cols)\n # write to file\n in_df.to_pickle(cls.test_file_1)\n\n cls.out_df = pd.DataFrame.from_records(data=ex_1_out, columns=cls.out_cols)"],"string":"[\n \"def prepare(self):\\n if self.opts['verbose']:\\n print(\\\"Preparing dataset (one-time operation)...\\\")\\n # Create paths files and load them back in\\n self._build_ID_sets()\\n self._create_ID_files()\\n self._load_ID_files()\\n if self.opts['verbose']:\\n print(\\\"... done with preparing the dataset.\\\")\",\n \"def prepare_dataset(fpath):\\n raise NotImplementedError\",\n \"def prepare_data(self):\\n try:\\n self.train_dataset = self.datasets['train']\\n self.val_dataset = self.datasets['val']\\n try:\\n self.test_dataset = self.datasets['test']\\n except:\\n pass\\n except Exception as e:\\n print('Data was not succesfully prepared:', e)\",\n \"def __init__(self):\\n self.__dataset = None\",\n \"def __init__(self):\\n self.args = self._prepare_args(locals())\\n self.requires_full_dataset_in_memory = False\",\n \"def setUp(self):\\n self.dataset = self.dataset_cls()\",\n \"def _prepare_data(self):\\n #TODO hardcoded values need to change\\n print_info(\\\"Preprocessing the train data...\\\")\\n self._place_dataset(os.path.join(self._hparams[\\\"temp-data\\\"], \\\"train\\\"),\\n self.TRAIN_OUT_PATH)\\n\\n print_info(\\\"Preprocessing the test data...\\\")\\n self._place_dataset(os.path.join(self._hparams[\\\"temp-data\\\"], \\\"test\\\"),\\n self.TEST_OUT_PATH)\\n\\n print_info(\\\"Preprocessing the validation data...\\\")\\n self._place_dataset(os.path.join(self._hparams[\\\"temp-data\\\"], \\\"val\\\"),\\n self.VAL_OUT_PATH)\",\n \"def __init__(self, dataset: Dataset):\\n self.dataset = dataset\",\n \"def __init__(self, dataset):\\n self._dataset = dataset\",\n \"def setUp(self):\\n self.directory = tempfile.TemporaryDirectory()\\n self.dataset = self.dataset_cls(cache_root=self.directory.name)\",\n \"def prepare_data(self):\",\n \"def _make_data(self):\\n pdf_datasets_all = make_pdf_datasets(self.pdf_list, self.xlims, self.ylims, self.tlims, self.dims, 9)\\n self.pdf_dataset = np.concatenate(pdf_datasets_all, axis = 0)\\n self.PDE_dataset = make_PDE_dataset(self.num_collocation, self.xlims, self.ylims, self.tlims, self.dims)\\n self.BC_dataset = make_BC_dataset(self.num_BC, self.xlims, self.ylims, self.tlims, self.dims)\",\n \"def __prepare_dataset(dataset, verbose=True):\\r\\n supported_datasets = ['coco', 'voc']\\r\\n if isinstance(dataset, ExternalDataset):\\r\\n if dataset.dataset_type.lower() not in supported_datasets:\\r\\n raise UserWarning(\\\"ExternalDataset dataset_type must be one of: \\\", supported_datasets)\\r\\n\\r\\n dataset_root = dataset.path\\r\\n\\r\\n if verbose:\\r\\n print(\\\"Loading {} type dataset...\\\".format(dataset.dataset_type))\\r\\n\\r\\n if dataset.dataset_type.lower() == 'voc':\\r\\n from gluoncv.data import VOCDetection\\r\\n\\r\\n dataset = VOCDetection(root=dataset_root,\\r\\n splits=[(2007, 'trainval'), (2012, 'trainval')])\\r\\n\\r\\n elif dataset.dataset_type.lower() == 'coco':\\r\\n from gluoncv.data import COCODetection\\r\\n\\r\\n dataset = COCODetection(root=dataset_root,\\r\\n splits=['instances_train2017'])\\r\\n if verbose:\\r\\n print(\\\"ExternalDataset loaded.\\\")\\r\\n return dataset\\r\\n elif isinstance(dataset, DetectionDataset) or issubclass(type(dataset), DetectionDataset):\\r\\n dataset.set_image_transform(ImageToNDArrayTransform())\\r\\n dataset.set_target_transform(BoundingBoxListToNumpyArray())\\r\\n return dataset\\r\\n else:\\r\\n raise ValueError(\\\"Dataset type {} not supported\\\".format(type(dataset)))\",\n \"def _prepare(self, data, train=True):\\n if data is None:\\n return None\\n\\n if hasattr(data, \\\"to_tfdataset\\\"):\\n return data.to_tfdataset(train=train)\\n else:\\n return data\",\n \"def prepare(cls):\",\n \"def prep_data(dataset_config=None):\\n \\n\\n data_struct = DataStruct(dataset_config)\\n\\n data_name = data_struct.name\\n datasets_dir = data_struct.data_path\\n out_dir = data_struct.save_path\\n\\n # If dataset already downloaded an unpacked, do nothing\\n if os.path.isdir(out_dir):\\n print('{} already downloaded, unpacked and processed.'.format(data_name))\\n return\\n\\n # Check if download is required\\n data_url = data_struct.url\\n compressed_file_name = downloader(datasets_dir, data_url)\\n\\n # Unpack compressed dataset file\\n unpacker(compressed_file_name, out_dir)\\n\\n # Custom preprocessing steps for data sets\\n custom_preprocessor(out_dir)\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\",\n \"def prepare_dataset(self, dataset_type: str) -> Dataset:\\n\\n logger.info(\\\"Creating features from dataset file at %s\\\", self.hparams.data_dir)\\n\\n if dataset_type == \\\"train\\\":\\n dataset = self.processor.get_train_dataset(self.hparams.data_dir, self.hparams.train_file_name)\\n elif dataset_type == \\\"dev\\\":\\n dataset = self.processor.get_dev_dataset(self.hparams.data_dir, self.hparams.dev_file_name)\\n elif dataset_type == \\\"test\\\":\\n dataset = self.processor.get_test_dataset(self.hparams.data_dir, self.hparams.test_file_name)\\n else:\\n raise ValueError(f\\\"{dataset_type} do not support. [train|dev|test]\\\")\\n logger.info(f\\\"Prepare {dataset_type} dataset (Count: {len(dataset)}) \\\")\\n return dataset\",\n \"def build_data_set(self):\\n if not self.assert_data_correct():\\n self.download_all_data()\\n self.unpack_rename_data()\\n self.split_data_characters()\\n self.clean_data_fragments()\\n self.create_font_data()\\n if not self.assert_train_augmented():\\n self.augment_train_data()\\n if not self.assert_style_data_correct():\\n self.download_style_data()\\n self.unpack_rename_data()\",\n \"def prepare_data(self):\\n data = self._get_dataset(self.hparams.dataset_path)\\n label_encoder = data[\\\"label_encoder\\\"]\\n del data[\\\"label_encoder\\\"]\\n\\n click.secho(\\\"Building inputs and labels.\\\", fg=\\\"yellow\\\")\\n datasets = {\\n \\\"train\\\": defaultdict(list),\\n \\\"valid\\\": defaultdict(list),\\n \\\"test\\\": defaultdict(list),\\n }\\n for dataset_name, dataset in data.items():\\n for sample in dataset:\\n instance = self.build_input(\\n self.tokenizer, sample[\\\"text\\\"], label_encoder, sample[\\\"label\\\"]\\n )\\n for input_name, input_array in instance.items():\\n datasets[dataset_name][input_name].append(input_array)\\n\\n click.secho(\\\"Padding inputs and building tensors.\\\", fg=\\\"yellow\\\")\\n tensor_datasets = {\\\"train\\\": [], \\\"valid\\\": [], \\\"test\\\": []}\\n for dataset_name, dataset in datasets.items():\\n dataset = self.pad_dataset(dataset, padding=self.tokenizer.pad_index)\\n for input_name in MODEL_INPUTS:\\n if input_name == \\\"labels\\\":\\n tensor = torch.tensor(dataset[input_name], dtype=torch.float32)\\n else:\\n tensor = torch.tensor(dataset[input_name])\\n tensor_datasets[dataset_name].append(tensor)\\n\\n self.train_dataset = TensorDataset(*tensor_datasets[\\\"train\\\"])\\n self.valid_dataset = TensorDataset(*tensor_datasets[\\\"valid\\\"])\\n self.test_dataset = TensorDataset(*tensor_datasets[\\\"test\\\"])\\n click.secho(\\n \\\"Train dataset (Batch, Candidates, Seq length): {}\\\".format(\\n self.train_dataset.tensors[0].shape\\n ),\\n fg=\\\"yellow\\\",\\n )\\n click.secho(\\n \\\"Valid dataset (Batch, Candidates, Seq length): {}\\\".format(\\n self.valid_dataset.tensors[0].shape\\n ),\\n fg=\\\"yellow\\\",\\n )\\n click.secho(\\n \\\"Test dataset (Batch, Candidates, Seq length): {}\\\".format(\\n self.test_dataset.tensors[0].shape\\n ),\\n fg=\\\"yellow\\\",\\n )\",\n \"def initialise_dataset_loader(\\n self, data_param=None, task_param=None, data_partitioner=None):\\n raise NotImplementedError\",\n \"def __init__(self, name_dataset, reduce=False):\\n dataset = TUDataset(root='data/TUDataset', name=name_dataset)\\n if reduce:\\n new_dataset = []\\n for i in tqdm(range(len(dataset))):\\n aux_graph = copy.deepcopy(dataset[i])\\n aux_graph.edge_index = TUDData.reduce_edges(aux_graph.edge_index)\\n new_dataset.append(copy.deepcopy(aux_graph))\\n dataset = WrapperSynthetic(new_dataset, dataset.num_node_features,\\n dataset.num_classes, None)\\n super(TUDData, self).__init__(dataset)\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\\n \\n out_dataset[0].create_dataset(in_dataset[0])\\n\\n in_pData, out_pData = self.get_plugin_datasets()\\n\\n in_pData[0].plugin_data_setup( '',)\\n out_pData[0].plugin_data_setup( 'PROJECTION','multiple')\",\n \"def __init__(self, config):\\n logger.info(f\\\"{self.__class__.__name__}: Dataset initializing ...\\\")\\n super().__init__(config)\",\n \"def prepare_process(self, dataset):\\n if dataset is not None:\\n pass\",\n \"def __init__(self, **kwargs):\\n DataLoader.__init__(self, **kwargs)\",\n \"def _init_al_dataset(self):\\n\\n self._init_dataset()\\n\\n train_dataset = self.datasets['train']\\n\\n dataset_size = len(train_dataset)\\n self.budget = math.ceil(self.budget_frac*dataset_size)\\n Sampler.__init__(self, config, self.budget) # TODO: Weird place to initialise this\\n\\n all_indices = set(np.arange(dataset_size))\\n k_initial = math.ceil(len(all_indices)*self.initial_budget_frac)\\n initial_indices = random.sample(list(all_indices), k=k_initial)\\n\\n sampler_init = data.sampler.SubsetRandomSampler(initial_indices) # need to sample from training dataset\\n\\n self.labelled_dataloader = data.DataLoader(train_dataset, sampler=sampler_init, batch_size=self.batch_size, drop_last=True)\\n self.val_dataloader = data.DataLoader(self.datasets['valid'], batch_size=self.batch_size, drop_last=False)\\n self.test_dataloader = data.DataLoader(self.datasets['test'], batch_size=self.batch_size, drop_last=False)\\n\\n return all_indices, initial_indices\",\n \"def setup(self):\\n # TODO check if need both dataset together\\n self.train_dataset = ABSADataset(data_path=self.train_path, mode=self.in_mode, task=self.task, \\n tokenizer=self.tokenizer, vocab=\\\"bert\\\")\\n self.vocabulary = self.train_dataset.vocabulary\\n\\n self.eval_dataset = ABSADataset(data_path=self.dev_path, mode=self.in_mode, task=self.task,\\n tokenizer=self.tokenizer, vocab=self.vocabulary)\\n #self.train_restaurant = ABSADataset(data_path=RESTAURANT_TRAIN)\\n #self.eval_restaurant = ABSADataset(data_path=RESTAURANT_DEV)\",\n \"def __init__(self, unencoded_dataset): \\n self._unencoded_dataset = unencoded_dataset\\n self._plain_train = self._get_plain_train()\\n self._plain_test = self._get_plain_test()\\n self._cipher_train = self._get_cipher_train()\\n self._cipher_test = self._get_cipher_test()\",\n \"def prepare_data_for_training(args):\\n # Form the train/test splits and write them to disk\\n dataset = data.Dataset(args)\\n # get image classes and image counts in each class\\n label_map = dataset.get_class_info()\\n class_count = len(list(label_map.values()))\\n # split the data and store it in log dir\\n df_train, df_test = dataset.split_dataset()\\n\\n # perform dataset augmentations\\n image_data = augment.Augmentation(args)\\n # get the data gens for training and test images\\n train_data_gen, _ = image_data.map_fn_train(df_train)\\n test_data_gen, _ = image_data.map_fn_test(df_test)\\n\\n return train_data_gen, test_data_gen, df_train, df_test, class_count\",\n \"def build_dataset(self):\\n self.dataset = KITTIBEVDataset(self.dataset_config, self.transform)\\n return self.dataset\",\n \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.dir_A = opt.dataroot\\n self.classes_A, self.class_to_idx_A = self._find_classes(self.dir_A) # find classes in '/path/to/data/trainA'\\n samples_A = make_dataset(self.dir_A, self.class_to_idx_A, extensions=self.img_extension, is_valid_file=None) # samples (list): List of (sample path, class_index) tuples\\n self.A_paths = [s[0] for s in samples_A]\\n self.A_targets = [s[1] for s in samples_A]\\n #self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))\\n input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc\\n self.transform = get_transform(opt, grayscale=(input_nc == 1))\",\n \"def prepare(self, class_map=None):\\n\\n def clean_name(name):\\n \\\"\\\"\\\"Returns a shorter version of object names for cleaner display.\\\"\\\"\\\"\\n return \\\",\\\".join(name.split(\\\",\\\")[:1])\\n\\n # Build (or rebuild) everything else from the info dicts.\\n self.num_classes = len(self.class_info)\\n self.class_ids = np.arange(self.num_classes)\\n self.class_names = [clean_name(c[\\\"name\\\"]) for c in self.class_info]\\n self.num_images = len(self.image_info)\\n self._image_ids = np.arange(self.num_images)\\n\\n # Mapping from source class and image IDs to internal IDs\\n self.class_from_source_map = {\\\"{}.{}\\\".format(info['source'], info['id']): id\\n for info, id in zip(self.class_info, self.class_ids)}\\n self.image_from_source_map = {\\\"{}.{}\\\".format(info['source'], info['id']): id\\n for info, id in zip(self.image_info, self.image_ids)}\\n\\n # Map sources to class_ids they support\\n self.sources = list(set([i['source'] for i in self.class_info]))\\n self.source_class_ids = {}\\n # Loop over datasets\\n for source in self.sources:\\n self.source_class_ids[source] = []\\n # Find classes that belong to this dataset\\n for i, info in enumerate(self.class_info):\\n # Include BG class in all datasets\\n if i == 0 or source == info['source']:\\n self.source_class_ids[source].append(i)\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\\n \\n out_dataset[0].create_dataset(in_dataset[0])\\n\\n in_pData, out_pData = self.get_plugin_datasets()\\n\\n in_pData[0].plugin_data_setup( 'PROJECTION',multiple)\\n out_pData[0].plugin_data_setup( 'PROJECTION',multiple)\",\n \"def setup(self, ds):\\n pass\",\n \"def prep_dataset(settings):\\n train_dims = settings[\\\"train_dims\\\"]\\n # Open HDF store. This is usually a soft link to our filtered dataset\\n input_df, target_df, const = load_from_store(settings[\\\"dataset_path\\\"], columns=train_dims)\\n\\n try:\\n del input_df[\\\"nions\\\"] # Delete leftover artifact from dataset split\\n except KeyError:\\n pass\\n\\n target_df = drop_outliers(target_df, settings)\\n target_df = drop_nans(target_df)\\n\\n data_df = filter_input(input_df, target_df)\\n del target_df, input_df\\n data_df = convert_dtype(data_df, settings)\\n\\n return data_df\",\n \"def __init__(self, **kwargs):\\n DataLoader.__init__(self, **kwargs)\\n \\n self._results_ = None\",\n \"def prepare_data(self, config: TreeConfigParser) -> None:\\n self.data = Data(config)\\n self.data.prepare_input()\\n self.data.prepare_output()\",\n \"def setUp(self):\\n self.dataset = get_test_dataset()\",\n \"def init_data(dataset_config: dict):\\n # train and dev will be in random order, test may be ordered according to labels\\n if dataset_config[\\\"name\\\"] == \\\"CoLA\\\":\\n train, dev, test, num_classes = load_cola(dataset_config)\\n elif dataset_config[\\\"name\\\"] == \\\"AGNews\\\":\\n train, dev, test, num_classes = load_ag_news(dataset_config)\\n elif dataset_config[\\\"name\\\"] == \\\"DBPedia\\\":\\n train, dev, test, num_classes = load_dbpedia(dataset_config)\\n elif dataset_config[\\\"name\\\"] == \\\"YRF\\\":\\n train, dev, test, num_classes = load_yrf(dataset_config)\\n else:\\n raise NameError(f\\\"Dataset {dataset_config['name']} not implemented.\\\")\\n # etc.\\n\\n # shrink size if debugging\\n if dataset_config[\\\"debug\\\"]:\\n # choose a random subset using huggingface select function\\n train = train.select(random.sample(range(len(train)), k=200))\\n dev = dev.select(random.sample(range(len(dev)), k=40))\\n test = test.select(random.sample(range(len(test)), k=200))\\n\\n # create class imbalance\\n random.seed(dataset_config[\\\"seed\\\"])\\n if dataset_config[\\\"pool_balance\\\"] == \\\"balanced\\\":\\n pass\\n elif dataset_config[\\\"pool_balance\\\"] == \\\"imbalanced\\\":\\n train = train.filter(lambda example: create_imbalanced_dataset(example, dataset_config[\\\"imbalance_prop\\\"], dataset_config['imbalance_cls']))\\n else:\\n NameError(f\\\"pool_balance = {dataset_config['pool_balance']} not allowed\\\")\\n\\n if dataset_config[\\\"dev_balance\\\"] == \\\"balanced\\\":\\n pass\\n elif dataset_config[\\\"dev_balance\\\"] == \\\"imbalanced\\\":\\n dev = dev.filter(lambda example: create_imbalanced_dataset(example, dataset_config[\\\"imbalance_prop\\\"], dataset_config['imbalance_cls']))\\n else:\\n NameError(f\\\"dev_balance = {dataset_config['dev_balance']} not allowed\\\")\\n\\n # get seed labelled pool indices (using the same seed data every time)\\n random.seed(dataset_config[\\\"seed\\\"])\\n if dataset_config[\\\"seed_balance\\\"] == \\\"balanced\\\":\\n # this is random (will have some variance vs pool)\\n indices = list(range(len(train)))\\n unlabelled_pool_idx, labelled_pool_idx = split(\\n indices,\\n random_state=dataset_config[\\\"seed\\\"],\\n test_size=dataset_config[\\\"seed_size\\\"]\\n )\\n elif dataset_config[\\\"seed_balance\\\"] == \\\"stratified\\\":\\n # this is the same as the underlying train set (which may be unbalanced)\\n indices = list(range(len(train)))\\n unlabelled_pool_idx, labelled_pool_idx = split(\\n indices,\\n random_state=dataset_config[\\\"seed\\\"],\\n test_size=dataset_config[\\\"seed_size\\\"],\\n stratify=train['label']\\n )\\n elif dataset_config[\\\"seed_balance\\\"] == \\\"imbalanced\\\":\\n # artificially sample an imbalanced seed set from the pool\\n unlabelled_pool_idx, labelled_pool_idx = create_imbalanced_seed(\\n train,\\n num_classes,\\n dataset_config[\\\"seed_size\\\"],\\n dataset_config['imbalance_prop'],\\n dataset_config['imbalance_cls']\\n )\\n else:\\n raise NameError(f\\\"seed_balance = {dataset_config['seed_balance']} not allowed\\\")\\n\\n return train, dev, test, num_classes, labelled_pool_idx, unlabelled_pool_idx\",\n \"def prepare_data(self):\\n import subprocess\\n # Download coco data set into dir specified by config then /data/coco\\n subprocess.call([f\\\"{get_original_cwd()}/bin/fetch_dataset.sh\\\", f\\\"{self.dir}/data/coco\\\", f\\\"{get_original_cwd()}\\\"])\\n # subprocess.call([f\\\"bin/fetch_dataset.sh\\\", f\\\"{self.dir}/data/coco\\\"])\\n task = \\\"instances\\\" if self.instance else \\\"person_keypoints\\\"\\n register_coco_instances(\\\"train\\\", {}, f\\\"{self.dir}/data/coco/{task}_train2014.json\\\",\\n f\\\"{self.dir}/data/coco/train2014\\\")\\n register_coco_instances(\\\"val\\\", {}, f\\\"{self.dir}/data/coco/{task}_minival2014.json\\\",\\n f\\\"{self.dir}/data/coco/val2014\\\")\\n register_coco_instances(\\\"test\\\", {}, f\\\"{self.dir}/data/coco/{task}_valminusminival2014.json\\\",\\n f\\\"{self.dir}/data/coco/val2014\\\")\",\n \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.dir_A = os.path.join(opt.dataroot, opt.phase + 'A') # create a path '/path/to/data/trainA'\\n self.dir_B = os.path.join(opt.dataroot, opt.phase + 'B') # create a path '/path/to/data/trainB'\\n\\n self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA'\\n self.B_paths = sorted(make_dataset(self.dir_B, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n self.A_size = len(self.A_paths) # get the size of dataset A\\n self.B_size = len(self.B_paths) # get the size of dataset B\\n btoA = self.opt.direction == 'BtoA'\\n input_nc = self.opt.output_nc if btoA else self.opt.input_nc # get the number of channels of input image\\n output_nc = self.opt.input_nc if btoA else self.opt.output_nc # get the number of channels of output image\\n self.transform_A = get_transform(self.opt, grayscale=(input_nc == 1))\\n self.transform_B = get_transform(self.opt, grayscale=(output_nc == 1))\",\n \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n\\n self.data_folder = os.path.join(opt.dataroot, opt.phase)\\n self.image_file_names = sorted(os.listdir(self.data_folder))\\n self.batch_size = opt.batch_size\\n self.z_dim = opt.z_dim\\n self.imsize = opt.crop_size\\n\\n self.transform = self.get_transform(True, True, True, opt.center_crop)\",\n \"def _prepare_sets(self):\\n\\n ds_images, ds_labels = self._load_images_labels()\\n\\n ds_images_2 = ds_images.take(self.val_count)\\n ds_labels_2 = ds_labels.take(self.val_count)\\n ds_images_1 = ds_images.skip(self.val_count)\\n ds_labels_1 = ds_labels.skip(self.val_count)\\n\\n ds_1 = (ds_images_1, ds_labels_1)\\n ds_2 = (ds_images_2, ds_labels_2)\\n\\n return ds_1, ds_2\",\n \"def prep_data():\\n loader = DLoader()\\n cap = loader.visitor_cnt\\n\\n pass\",\n \"def prepare(self):\\n\\t\\tif len(os.listdir(self.storagedir)):\\n\\t\\t\\traise Exception(\\\"There are already files in\\\", self.storagedir + \\\".\\\", \\\"Delete manually!\\\")\\n\\t\\t\\n\\t\\tself.worddict = {\\\"__pad__\\\": 0, \\\"__oov__\\\": 1}\\n\\t\\tself.classdict = {}\\n\\t\\tself.raw_documents, self.tokenized_documents = {}, {}\\n\\t\\tself.X, self.Y = {}, {}\\n\\n\\t\\tfor dataset in self.DATASETS_TMP:\\n\\t\\t\\tself.get_raw_data(dataset)\\n\\t\\t\\tself.delete_empty_documents(dataset)\\n\\t\\t\\tself.tokenize_documents(dataset)\\n\\n\\t\\tself.make_classdict()\\n\\t\\tself.make_worddict()\\n\\t\\tself.make_embeddings()\\n\\t\\tself.reverse_dicts()\\n\\n\\t\\tfor dataset in self.DATASETS_TMP:\\n\\t\\t\\tself.make_X(dataset)\\n\\t\\t\\tself.shuffle_dataset(dataset)\\n\\n\\t\\tif not \\\"dev\\\" in self.X:\\n\\t\\t\\tself.split_dev()\\t\\t\\n\\t\\tself.make_hybrid()\\n\\t\\tself.store()\",\n \"def setup(self, stage: Union[str, None] = None) -> None:\\n self.data_splits = {}\\n # set up each of the dataset splits\\n for key, path in self.paths.items():\\n self.data_splits[key] = self.dataset_class(path)\",\n \"def init_dataset(validation_dataset_name):\\n transform = transforms.Compose([transforms.ToPILImage(),transforms.ToTensor(),transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])\\n \\n if validation_dataset_name == 'datasetRAP':\\n # validation = 8317 images = 166 batches of 50 images + 1 batch of 17 images\\n dataset_valid = loader_rapdataset_yiqiang.RAPDataset(0,False,'/storage/Datasets/Rap-PedestrianAttributeRecognition/',transform)\\n labels = loader_rapdataset_yiqiang.ATTRIBUTES\\n datset_attr_nbr = 92\\n elif validation_dataset_name == 'datasetPETA':\\n dataset_valid = loader_peta_dataset.PETADataset(False, '/storage/Datasets/PETA-PEdesTrianAttribute', transform)\\n labels = loader_peta_dataset.ATTRIBUTES\\n datset_attr_nbr = 104\\n elif validation_dataset_name == 'datasetRAPPETA':\\n dataset_valid = loader_rap_plus_peta_dataset.RAPPlusPETADataset(False, '/storage/Datasets/Rap-PedestrianAttributeRecognition/', '/storage/Datasets/PETA-PEdesTrianAttribute', transform)\\n labels = [peta_label for rap_label,peta_label in loader_rap_plus_peta_dataset.ATTRIBUTES]\\n datset_attr_nbr = 49\\n\\n print (\\\"Dataset valid size :\\\", dataset_valid.__len__())\\n print (\\\"Dataset Attributes number :\\\", datset_attr_nbr)\\n assert (len(labels) == datset_attr_nbr)\\n\\n dataloader_valid = DataLoader(dataset_valid, batch_size=Param_Batchsize, shuffle=True, num_workers=Param_Nb_Workers)\\n\\n return dataloader_valid, dataset_valid\",\n \"def _init_train_loader(self):\\n # Choose the right dataset type\\n if self.config_args[\\\"num_members\\\"] > 1:\\n class_dataset_wrapper = dataset_wrapper.MixMoDataset\\n else:\\n class_dataset_wrapper = dataset_wrapper.MSDADataset\\n\\n # Load augmentations\\n self.traindatasetwrapper = class_dataset_wrapper(\\n dataset=self.train_dataset,\\n num_classes=int(self.config_args[\\\"data\\\"][\\\"num_classes\\\"]),\\n num_members=self.config_args[\\\"num_members\\\"],\\n dict_config=self.config_args[\\\"training\\\"][\\\"dataset_wrapper\\\"],\\n properties=self.properties\\n )\\n\\n # Build standard sampler\\n _train_sampler = torch.utils.data.sampler.RandomSampler(\\n data_source=self.traindatasetwrapper, ## only needed for its length\\n num_samples=None,\\n replacement=False,\\n )\\n\\n # Wrap it with the repeating sampler used for multi-input models\\n batch_sampler = batch_repetition_sampler.BatchRepetitionSampler(\\n sampler=_train_sampler,\\n batch_size=self.batch_size,\\n num_members=self.config_args[\\\"num_members\\\"],\\n drop_last=True,\\n config_batch_sampler=self.config_args[\\\"training\\\"][\\\"batch_sampler\\\"]\\n )\\n\\n self.train_loader = torch.utils.data.DataLoader(\\n self.traindatasetwrapper,\\n batch_sampler=batch_sampler,\\n num_workers=self.num_workers,\\n batch_size=1,\\n shuffle=False,\\n sampler=None,\\n drop_last=False,\\n pin_memory=True,\\n )\",\n \"def prepare_data(dataset, train_ratio=0.8, input_dim=None, seed=10):\\n # Retrieve main path of project\\n dirname = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\\n\\n # Download and store dataset at chosen location\\n if dataset == 'Cora' or dataset == 'PubMed' or dataset == 'Citeseer':\\n path = os.path.join(dirname, 'data')\\n data = Planetoid(path, name=dataset, split='full')[0]\\n data.name = dataset\\n data.num_classes = (max(data.y)+1).item()\\n # data.train_mask, data.val_mask, data.test_mask = split_function(data.y.numpy())\\n # data = Planetoid(path, name=dataset, split='public', transform=T.NormalizeFeatures(), num_train_per_class=20, num_val=500, num_test=1000)\\n\\n elif dataset == 'Amazon':\\n path = os.path.join(dirname, 'data', 'Amazon')\\n data = Amazon(path, 'photo')[0]\\n data.name = dataset\\n data.num_classes = (max(data.y)+1).item()\\n data.train_mask, data.val_mask, data.test_mask = split_function(\\n data.y.numpy(), seed=seed)\\n # Amazon: 4896 train, 1224 val, 1530 test\\n \\n elif dataset in ['syn1', 'syn2', 'syn4', 'syn5']: \\n data = synthetic_data(\\n dataset, dirname, train_ratio, input_dim)\\n \\n elif dataset == 'syn6':\\n data = gc_data(dataset, dirname, train_ratio)\\n\\n elif dataset == 'Mutagenicity':\\n data = gc_data(dataset, dirname, train_ratio)\\n\\n return data\",\n \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.dir_A = os.path.join(opt.dataroot, opt.phase5 + 'A') # create a path '/path/to/data/trainA'\\n #self.dir_B20 = os.path.join(opt.dataroot, opt.phase + 'B20') # create a path '/path/to/data/trainB1'\\n\\n #self.dir_B2 = os.path.join(opt.dataroot, opt.phase + 'B2') # create a path '/path/to/data/trainB2'\\n self.A_paths = sorted(make_dataset(self.dir_A, opt.max_dataset_size)) # load images from '/path/to/data/trainA'\\n #self.B20_paths = sorted(make_dataset(self.dir_B20, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n #self.B2_paths = sorted(make_dataset(self.dir_B2, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n self.A_size = len(self.A_paths) # get the size of dataset A\\n # self.B20_size = len(self.B20_paths) # get the size of dataset B\\n #self.B2_size = len(self.B2_paths)\\n\\n #self.dir_A50 = os.path.join(opt.dataroot, opt.phase + 'A50') # create a path '/path/to/data/trainA'\\n self.dir_B50 = os.path.join(opt.dataroot, opt.phase5 + 'B50') # create a path '/path/to/data/trainB1'\\n #self.dir_B2 = os.path.join(opt.dataroot, opt.phase + 'B2') # create a path '/path/to/data/trainB2'\\n #self.A50_paths = sorted(make_dataset(self.dir_A50, opt.max_dataset_size)) # load images from '/path/to/data/trainA'\\n self.B50_paths = sorted(make_dataset(self.dir_B50, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n #self.B2_paths = sorted(make_dataset(self.dir_B2, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n #self.A50_size = len(self.A50_paths) # get the size of dataset A\\n self.B50_size = len(self.B50_paths) # get the size of dataset B\\n #self.B2_size = len(self.B2_paths)\\n\\n self.dir_B100 = os.path.join(opt.dataroot, opt.phase5 + 'B100') # create a path '/path/to/data/trainB1'\\n self.B100_paths = sorted(\\n make_dataset(self.dir_B100, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n self.B100_size = len(self.B100_paths) # get the size of dataset B\\n\\n self.dir_B150 = os.path.join(opt.dataroot, opt.phase5 + 'B150') # create a path '/path/to/data/trainB1'\\n self.B150_paths = sorted(\\n make_dataset(self.dir_B150, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n self.B150_size = len(self.B150_paths) # get the size of dataset B\\n\\n\\n\\n self.dir_m0 = os.path.join(opt.dataroot, 'mask_0') # create a path '/path/to/data/trainB1'\\n self.m0_paths = sorted(\\n make_dataset(self.dir_m0, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n self.m0_size = len(self.m0_paths) # get the size of dataset B\\n\\n\\n self.dir_m50 = os.path.join(opt.dataroot, 'mask_50') # create a path '/path/to/data/trainB1'\\n self.m50_paths = sorted(\\n make_dataset(self.dir_m50, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n self.m50_size = len(self.m50_paths) # get the size of dataset B\\n\\n\\n\\n self.dir_m100 = os.path.join(opt.dataroot, 'mask_100') # create a path '/path/to/data/trainB1'\\n self.m100_paths = sorted(\\n make_dataset(self.dir_m100, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n self.m100_size = len(self.m100_paths) # get the size of dataset B\\n\\n\\n self.dir_m150 = os.path.join(opt.dataroot, 'mask_150') # create a path '/path/to/data/trainB1'\\n self.m150_paths = sorted(\\n make_dataset(self.dir_m150, opt.max_dataset_size)) # load images from '/path/to/data/trainB'\\n self.m150_size = len(self.m150_paths) # get the size of dataset B\\n\\n\\n \\n\\n btoA = self.opt.direction == 'BtoA'\\n input_nc = self.opt.output_nc if btoA else self.opt.input_nc # get the number of channels of input image\\n output_nc = self.opt.input_nc if btoA else self.opt.output_nc # get the number of channels of output image\\n self.transform_A = get_transform(self.opt, grayscale=(input_nc == 1))\\n self.transform_B = get_transform(self.opt, grayscale=(output_nc == 1))\",\n \"def build_dataset(self):\\n print(\\\"reading data of images currently , please wait......\\\")\\n x_train, y_train, _ = get_images(self.train_directory)\\n x_test, y_test, _ = get_images(self.test_directory)\\n x_train, y_train = image_subset(self.num_classes, x_train, y_train)\\n x_test, y_test = image_subset(self.num_classes, x_test, y_test)\\n x_train = x_train.astype('float32')\\n x_test = x_test.astype('float32')\\n self.x_train = x_train / 255\\n self.x_test = x_test / 255\\n self.y_train = utils.to_categorical(y_train, self.num_classes)\\n self.y_test = utils.to_categorical(y_test, self.num_classes)\",\n \"def prepare(self):\",\n \"def _init_from_DataArrays(self, data, validate=True):\\n self._data_vars = self._DataArrays_as_mapping(data)\\n\\n if (len(self) > 1) and validate:\\n first = self[0]\\n for i in range(1, len(self)):\\n da = self[i]\\n first._is_compatible(da, raise_error=True)\\n\\n self._check_all_different_ids(self._data_vars.values())\\n\\n self.__itemattr = []\\n for key, value in self._data_vars.items():\\n self._set_name_attr(key, value)\\n\\n self.plot = _DatasetPlotter(self)\\n\\n if len(self) > 0:\\n self._set_spectral_attributes(self.geometry)\\n\\n # since Dataset is MutableMapping it has values and keys by default\\n # but we delete those to avoid confusion\\n # self.values = None\\n self.keys = None\",\n \"def setup(self, stage: Optional[str] = None) -> None:\\n if self.dataset_type == \\\"hdf5\\\":\\n DS = SegmentationHDF5Dataset\\n else:\\n DS = SegmentationFolderDataset\\n\\n self.trainset = DS(\\n path=self._get_path(\\\"train\\\", self.dataset_type, is_mask=False),\\n mask_path=self._get_path(\\\"train\\\", self.dataset_type, is_mask=True),\\n img_transforms=self.img_transforms,\\n inst_transforms=self.inst_transforms,\\n return_sem=False,\\n normalization=self.normalization,\\n **self.kwargs,\\n )\\n\\n self.validset = DS(\\n path=self._get_path(\\\"valid\\\", self.dataset_type, is_mask=False),\\n mask_path=self._get_path(\\\"valid\\\", self.dataset_type, is_mask=True),\\n img_transforms=self.img_transforms,\\n inst_transforms=self.inst_transforms,\\n return_sem=False,\\n normalization=self.normalization,\\n **self.kwargs,\\n )\\n\\n self.testset = DS(\\n path=self._get_path(\\\"test\\\", self.dataset_type, is_mask=False),\\n mask_path=self._get_path(\\\"test\\\", self.dataset_type, is_mask=True),\\n img_transforms=self.img_transforms,\\n inst_transforms=self.inst_transforms,\\n return_sem=False,\\n normalization=self.normalization,\\n **self.kwargs,\\n )\",\n \"def setUpClass(cls):\\n # print(\\\"SWEData.setUpClass\\\", flush=True)\\n super(TestIonSpecificsOptions, cls).setUpClass()\\n # print(cls.data.iloc[:, :7])\\n # print(cls.data.columns.values)\\n cls.data = cls.data.xs(\\\"\\\", axis=1, level=\\\"N\\\")\",\n \"def init_dset(self, dset_name, dset_shape, dset_attrs):\\n dtype = dset_attrs['dtype']\\n chunks = dset_attrs['chunks']\\n attrs = dset_attrs['attrs']\\n name = dset_attrs.get('name', None)\\n if name is not None:\\n dset_name = name\\n\\n if chunks:\\n chunks = tuple(chunks)\\n\\n logger.debug('Creating {} with shape: {}, dtype: {}, chunks: {}'\\n .format(dset_name, dset_shape, dtype, chunks))\\n ds = self._dst_h5.create_dataset(dset_name, shape=dset_shape,\\n dtype=dtype, chunks=chunks)\\n if attrs:\\n for attr, value in attrs.items():\\n if attr not in ['freq', 'start']:\\n ds.attrs[attr] = value\\n\\n logger.info('- {} initialized'.format(dset_name))\\n\\n return ds\",\n \"def init_locally_processed_dataset(directory, source_datasets, uuid_=None):\\n md = ptype.DatasetMetadata(\\n id_=uuid_,\\n # Default creation time is creation of an image.\\n creation_dt=datetime.datetime.utcfromtimestamp(directory.stat().st_ctime),\\n lineage=ptype.LineageMetadata(\\n machine=ptype.MachineMetadata(\\n hostname=socket.getfqdn(),\\n runtime_id=_RUNTIME_ID,\\n uname=' '.join(os.uname())\\n ),\\n source_datasets=source_datasets\\n )\\n )\\n _note_package_vers(md)\\n return md\",\n \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.dir_images = os.path.join(opt.dataroot, opt.phase) # get the image directory\\n self.image_paths = sorted(make_dataset(self.dir_images, opt.max_dataset_size)) # get image paths\\n assert(self.opt.load_size >= self.opt.crop_size) # crop_size should be smaller than the size of loaded image\\n self.input_nc = self.opt.output_nc\\n self.output_nc = self.opt.input_nc\",\n \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.SR_factor = opt.SR_factor\\n\\n assert util_dataset.check_whether_last_dir(opt.dataroot), 'when SingleDataset, opt.dataroot:{} should be dir and contains only image files'.format(opt.dataroot)\\n self.dir_A = opt.dataroot\\n self.A_paths = sorted(make_images_dataset(self.dir_A, opt.max_dataset_size)) # get image paths\\n\\n self.input_nc = self.opt.input_nc\\n self.output_nc = self.opt.output_nc\",\n \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.A_paths = sorted(make_dataset(opt.dataroot, opt.max_dataset_size))\\n input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc\\n self.transform = get_transform(opt, grayscale=(input_nc == 1))\",\n \"def setup(self):\\n\\n folder_name, file_name, url, md5 = self.resource\\n dataset_folder = os.path.join(self.data_root, folder_name)\\n if not os.path.exists(dataset_folder):\\n sh_utils.download_and_extract_archive(url, dataset_folder, md5, file_name)\\n\\n test_transform = tv_transforms.Compose(\\n [\\n tv_transforms.ToTensor(),\\n tv_transforms.Lambda(lambda x: x.permute(1, 2, 0)),\\n ]\\n )\\n\\n dataset_out = tv_datasets.ImageFolder(\\n root=dataset_folder, transform=test_transform\\n )\\n self.images_only_dataset_out = sh_data_torch.IndexedTorchDataset(\\n sh_data_torch.ImagesOnlyTorchDataset(dataset_out)\\n )\",\n \"def __init__(self, file, sdef, name, path, attrs, parent, value, dtype, compress, link_info=None):\\n super(Dataset, self).__init__(file, sdef, name, path, attrs, parent, link_info)\\n # print \\\"Creating Dataset, sdef=\\\"\\n # pp.pprint(sdef)\\n if 'attributes' in self.sdef['df']:\\n self.attributes = copy.deepcopy(self.sdef['df']['attributes'])\\n # del self.sdef['df']['attributes'] # if do this, no need to check for attributes in mk_dsinfo\\n # print \\\"found attributes:\\\"\\n # else:\\n # print \\\"did not find attributes:\\\"\\n # pp.pprint(self.attributes)\\n # if self.sdef['df']:\\n self.dsinfo = self.mk_dsinfo(value)\\n self.merge_attribute_defs(self.attributes, self.dsinfo['atags'])\\n # else:\\n # definition empty, must be custom dataset\\n # self.dsinfo = {}\\n self.merge_attrs()\\n if self.link_info:\\n # this dataset set to link to another. Already done in Node. Nothing to do here\\n pass\\n else:\\n # creating new dataset (normally done)\\n self.link_node = None\\n # compress = \\\"gzip\\\" if compress else None\\n # self.h5node = self.h5parent.create_dataset(self.name, data=value,\\n # dtype=dtype, compression=compress)\\n #- self.file.file_pointer.create_dataset(self.full_path, data=value,\\n #- dtype=dtype, compression=compress)\\n self.file.create_dataset(self.full_path, data=value, dtype=dtype,\\n compress=compress)\\n # self.file.h5commands.append(\\\"create_dataset(%s, %s)\\\" % (self.full_path, value))\\n # if dtype:\\n # self.h5node = self.h5parent.create_dataset(self.name, data=value, dtype=dtype)\\n # else: # should find out what default value for dtype used in h5py and use that, combine these\\n # self.h5node = self.h5parent.create_dataset(self.name, data=value)\\n self.set_attr_values()\",\n \"def setUpClass(cls):\\n cls._test_dc = DataCleaner(data = create_test_df())\",\n \"def setup(self):\\n in_dataset, out_dataset = self.get_datasets()\\n \\n out_dataset[0].create_dataset(in_dataset[0])\\n out_dataset[1].create_dataset(in_dataset[1])\\n\\n in_pData, out_pData = self.get_plugin_datasets()\\n\\n in_pData[0].plugin_data_setup( 'SINOGRAM',multiple)\\n out_pData[0].plugin_data_setup( 'PROJECTION','multiple')\\n\\n in_pData[1].plugin_data_setup( 'PROJECTION',)\\n out_pData[1].plugin_data_setup( 'PROJECTION','multiple')\",\n \"def InitDataset(self):\\n train_txt = 'ImageSets/Main/train.txt'\\n val_txt = 'ImageSets/Main/val.txt'\\n annotations = \\\"Annotations\\\"\\n jpegimages = \\\"JPEGImages\\\"\\n images_path = train_txt if (self.is_train) else val_txt \\n images_path = readTxt(os.path.join(self.path, images_path))\\n images_path.pop(-1)\\n # rawdata format: [path_2_image, path_2_xml]\\n rawData = list()\\n for each in images_path:\\n xml = os.path.join(self.path, annotations, each + '.xml')\\n jpeg = os.path.join(self.path, jpegimages, each + '.jpg')\\n rawData.append([jpeg, xml])\\n return rawData\",\n \"def prepare(self):\\n pass\",\n \"def prepare(self):\\n pass\",\n \"def prepare(self):\\n pass\",\n \"def __init__(self, dataset_name, source_path_str=None, th=0, nprocs=1):\\n\\n # wherever you call from, the dataset root path\\n # will be the same, \\\\\\\\|// relative to project root,\\n # as marked by the -(@ @)- .kilroy flag file\\n # --oOO---(_)--- OOo--\\n\\n base_path = mooltipath(\\\"datasets\\\")\\n\\n # dataset directory path\\n self.path = Path(base_path, dataset_name)\\n\\n # database file path\\n self.db_path = Path(self.path, \\\"database.db\\\")\\n\\n # manifest file path\\n mnf_path = Path(base_path, dataset_name + \\\".mnf\\\")\\n\\n # samples audio files path\\n self.samples_path = Path(self.path, \\\"samples\\\")\\n\\n # if no source path was provided, it means\\n # that we want to retrieve an existing AudioDataset db\\n if not source_path_str:\\n # if the database file does not exist, we bailout friendly\\n if not self.db_path.is_file():\\n iprint(\\\"Dataset database file does not exist.\\\")\\n iprint(\\\"Please insure that the dataset name is correct and\\\")\\n iprint(\\\"that the dataset has been previously created.\\\")\\n iprint(\\\"### ABORTING! ###\\\")\\n raise FileNotFoundError(\\\"Dataset database not found\\\")\\n\\n # else retrieve dataset configuration from db\\n self._get_config_from_db()\\n else:\\n # Else we have to build the dataset from its manifest.\\n # we expect:\\n # - the manifest file to exist,\\n # - the ds directory to not exist\\n # - the source_dir to exist.\\n # Else exit friendly\\n source_path = Path(source_path_str).absolute()\\n\\n if not mnf_path.is_file():\\n iprint(\\\"Dataset manifest does not exist.\\\")\\n iprint(\\\"Please insure that the dataset name is correct.\\\")\\n iprint(\\\"### ABORTING! ###\\\")\\n raise FileNotFoundError(\\\"Dataset manifest not found\\\")\\n\\n if not source_path.is_dir():\\n iprint(f\\\"Source directory ({source_path}) does not exist.\\\")\\n iprint(\\\"Please insure that provided path is correct.\\\")\\n iprint(\\\"### ABORTING! ###\\\")\\n raise FileNotFoundError(\\\"Dataset source directory not found\\\")\\n\\n if self.path.exists():\\n iprint(f\\\"The dataset directory ({self.path}) already exists.\\\")\\n iprint(\\n \\\"If you really intent to CREATE this dataset, please \\\"\\n \\\"erase this directory first\\\"\\n )\\n iprint(\\\"### ABORTING! ###\\\")\\n raise FileExistsError(\\\"Dataset directory already exists\\\")\\n\\n iprint(f\\\">>>>> Starting Dataset {dataset_name} build\\\")\\n # Create directory structure\\n self.samples_path.mkdir(parents=True)\\n\\n # Read manifest\\n with mnf_path.open(\\\"r\\\") as f:\\n lines = f.read().split(\\\"\\\\n\\\")\\n sample_rate = int(lines[0])\\n duration = float(lines[1])\\n overlap = float(lines[2])\\n md5 = lines[3]\\n filenames = lines[4:]\\n\\n # Perform sanity checks\\n assert duration > 0, \\\"duration must be strictly positive\\\"\\n assert overlap >= 0, \\\"overlap must be positive or zero\\\"\\n assert (\\n sample_rate <= 44100 and sample_rate >= 1024\\n ), \\\"sample_rate must belong to [1024 - 44100]\\\"\\n assert overlap < duration, \\\"overlap must be < duration\\\"\\n\\n # Pre Populate instance properties\\n self.sample_rate = sample_rate\\n self.duration = duration\\n self.overlap = overlap\\n self.filenames = filenames\\n self.nb_samples = 0\\n\\n # Create database tables\\n self._create_tables()\\n\\n # Perform actual audio chunks slicing\\n self._build(source_path, th, nprocs)\\n\\n # Compute md5 checksum and check it agains manifest expected value\\n assert self._is_valid(\\n md5\\n ), \\\"Computed MD5 checksum does not match manifest expected value.\\\"\\n iprint(\\\"Checksum OK!\\\")\\n\\n # Everything went well, we are done (Yay!)\\n iprint(f\\\">>>>> Dataset {dataset_name} successfully created.\\\")\\n\\n return\",\n \"def setup_class(cls):\\n self = cls()\\n self.remove_files_created_during_previous_runs()\\n if not os.path.exists(self.plaintext_directory):\\n os.makedirs(self.plaintext_directory)\\n\\n if not os.path.exists(self.training_path):\\n os.makedirs(self.training_path)\\n\\n if not os.path.exists(self.heldout_path):\\n os.makedirs(self.heldout_path)\\n\\n prepare_data(self.paths)\",\n \"def _build_datasets(self):\\n self._build_datasets_sis3302()\\n self._build_datasets_sis3305()\",\n \"def __init__(self):\\n self.model = None\\n self.joined_datasets = None\\n self.id_col = None\\n self.val_col = None\\n self.pop_col = None\\n self.total_population_per_unit = None\\n self.centroids_of_areal_data = None\\n self.prepared_data = None\\n self.unknown_area_id = None\\n\\n # Parameters\\n self.lags = None\\n self.step = None\\n self.min_no_of_observations = None\\n self.max_search_radius = None\",\n \"def prepare(self):\\n\\n # step 0: load only when not loaded yet\\n if TRAINING in self.data \\\\\\n and VALIDATION in self.data:\\n return\\n\\n # step 1: load the file names\\n file_list = sorted(glob.glob(self.location+\\\"*.mhd\\\"))\\n # count the number of data points\\n\\n # make a stratified validation set\\n # note, the seed decides the validation set, but it is deterministic in the names\\n random.seed(317070)\\n patient_names = [self.patient_name_from_file_name(f) for f in file_list]\\n validation_patients = random.sample(patient_names, int(VALIDATION_SET_SIZE*len(patient_names)))\\n\\n # make the static data empty\\n for s in self.datasets:\\n self.data[s] = []\\n self.labels[s] = []\\n self.names[s] = []\\n\\n # load the filenames and put into the right dataset\\n labels_as_dict = defaultdict(list)\\n\\n with open(paths.LUNA_LABELS_PATH, 'rb') as csvfile:\\n reader = csv.reader(csvfile, delimiter=',', quotechar='|')\\n next(reader) # skip the header\\n for row in reader:\\n label = (float(row[1]), float(row[2]), float(row[3]), float(row[4]))\\n labels_as_dict[str(row[0])].append(label)\\n\\n for patient_file in file_list:\\n patient_name = self.patient_name_from_file_name(patient_file)\\n\\n if patient_name in validation_patients:\\n s = VALIDATION\\n else:\\n s = TRAINING\\n label = labels_as_dict[str(patient_name)]\\n if self.only_positive and not label:\\n continue\\n self.data[s].append(patient_file)\\n \\n if self.pick_nodule:\\n self.labels[s].append([random.choice(label)]) \\n else:\\n self.labels[s].append(label)\\n \\n \\n self.names[s].append(patient_name)\\n\\n # give every patient a unique number\\n last_index = -1\\n for s in self.datasets:\\n self.indices[s] = range(last_index+1,last_index+1+len(self.data[s]))\\n if len(self.indices[s]) > 0:\\n last_index = self.indices[s][-1]\\n print s, len(self.indices[s]), \\\"samples\\\"\",\n \"def __init__(self, subset):\\n if subset not in ('background', 'evaluation'):\\n raise(ValueError, 'subset must be one of (background, evaluation)')\\n self.subset = subset\\n\\n self.df = pd.DataFrame(self.index_subset(self.subset))\\n\\n # Index of dataframe has direct correspondence to item in dataset\\n self.df = self.df.assign(id=self.df.index.values)\\n\\n # Convert arbitrary class names of dataset to ordered 0-(num_speakers - 1) integers\\n self.unique_characters = sorted(self.df['class_name'].unique())\\n self.class_name_to_id = {self.unique_characters[i]: i for i in range(self.num_classes())}\\n self.df = self.df.assign(class_id=self.df['class_name'].apply(lambda c: self.class_name_to_id[c]))\\n\\n # Create dicts\\n self.datasetid_to_filepath = self.df.to_dict()['filepath']\\n self.datasetid_to_class_id = self.df.to_dict()['class_id']\\n\\n # Setup transforms\\n self.transform = transforms.Compose([\\n transforms.CenterCrop(224),\\n transforms.Resize(84),\\n transforms.ToTensor(),\\n transforms.Normalize(mean=[0.485, 0.456, 0.406],\\n std=[0.229, 0.224, 0.225])\\n ])\",\n \"def __init__(self, dir_path, window_size,\\n user_map_path, computer_map_path, auth_type_map_path, logon_type_map_path):\\n logging.info(f\\\"Initiating Dataset instance for directory {dir_path}\\\")\\n self.directory = dir_path\\n self.filenames = [filename for filename in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, filename))]\\n assert len(self.filenames) > 0\\n random.shuffle(self.filenames)\\n self.window_size = window_size\\n self.len = self.count_len()\\n self.user_map, self.user_count = util.load_mapping(user_map_path)\\n self.computer_map, self.computer_count = util.load_mapping(computer_map_path)\\n self.auth_type_map, self.auth_type_count = util.load_mapping(auth_type_map_path)\\n self.logon_type_map, self.logon_type_count = util.load_mapping(logon_type_map_path)\",\n \"def prepare_data(self) -> None:\\n if (self.root).is_dir():\\n logger.info(\\\"Found the dataset.\\\")\\n else:\\n download_and_extract(self.root, DOWNLOAD_INFO)\",\n \"def _init_dataset(self, data_config, split='train'):\\n assert split in {'train', 'valid'}\\n\\n # load datasets\\n print(f'Load {split} dataset')\\n if data_config['type'] == 'npy':\\n dataset = MSDMelDataset(\\n data_config['mel_root'], data_config[f'{split}_tids_fn'],\\n data_config['label_fn'], on_mem=data_config['on_mem'],\\n ignore_intersection=data_config['ignore_label_intersection'],\\n transform=ToVariable())\\n\\n elif data_config['type'] == 'hdf':\\n dataset = MSDMelHDFDataset(\\n data_config['hdf_fn'], data_config[f'{split}_tids_fn'],\\n data_config['label_fn'],\\n ignore_intersection=data_config['ignore_label_intersection'],\\n transform=ToVariable())\\n\\n elif data_config['type'] == 'audio':\\n dataset = MSDAudioDataset(\\n data_config['audio_root'], data_config[f'{split}_tids_fn'],\\n data_config['tid2path_fn'], data_config['label_fn'],\\n ignore_intersection=data_config['ignore_label_intersection'],\\n device='cpu',\\n transform=ToVariable())\\n\\n return dataset\",\n \"def __init__(self, subset):\\n if subset not in ('background', 'evaluation'):\\n raise(ValueError, 'subset must be one of (background, evaluation)')\\n self.subset = subset\\n\\n self.df = pd.DataFrame(self.index_subset(self.subset))\\n\\n # Index of dataframe has direct correspondence to item in dataset\\n self.df = self.df.assign(id=self.df.index.values)\\n\\n # Convert arbitrary class names of dataset to ordered 0-(num_speakers - 1) integers\\n self.unique_characters = sorted(self.df['class_name'].unique())\\n self.class_name_to_id = {self.unique_characters[i]: i for i in range(self.num_classes())}\\n self.df = self.df.assign(class_id=self.df['class_name'].apply(lambda c: self.class_name_to_id[c]))\\n\\n # Create dicts\\n self.datasetid_to_filepath = self.df.to_dict()['data']\\n self.datasetid_to_class_id = self.df.to_dict()['class_id']\",\n \"def __init__(self, subset):\\n if subset not in ('background', 'evaluation'):\\n raise(ValueError, 'subset must be one of (background, evaluation)')\\n self.subset = subset\\n\\n self.df = pd.DataFrame(self.index_subset(self.subset))\\n\\n # Index of dataframe has direct correspondence to item in dataset\\n self.df = self.df.assign(id=self.df.index.values)\\n\\n # Convert arbitrary class names of dataset to ordered 0-(num_speakers - 1) integers\\n self.unique_characters = sorted(self.df['class_name'].unique())\\n self.class_name_to_id = {self.unique_characters[i]: i for i in range(self.num_classes())}\\n self.df = self.df.assign(class_id=self.df['class_name'].apply(lambda c: self.class_name_to_id[c]))\\n\\n # Create dicts\\n self.datasetid_to_filepath = self.df.to_dict()['data']\\n self.datasetid_to_class_id = self.df.to_dict()['class_id']\",\n \"def __init__(self, subset):\\n if subset not in ('background', 'evaluation'):\\n raise(ValueError, 'subset must be one of (background, evaluation)')\\n self.subset = subset\\n\\n self.df = pd.DataFrame(self.index_subset(self.subset))\\n\\n # Index of dataframe has direct correspondence to item in dataset\\n self.df = self.df.assign(id=self.df.index.values)\\n\\n # Convert arbitrary class names of dataset to ordered 0-(num_speakers - 1) integers\\n self.unique_characters = sorted(self.df['class_name'].unique())\\n self.class_name_to_id = {self.unique_characters[i]: i for i in range(self.num_classes())}\\n self.df = self.df.assign(class_id=self.df['class_name'].apply(lambda c: self.class_name_to_id[c]))\\n\\n # Create dicts\\n self.datasetid_to_filepath = self.df.to_dict()['data']\\n self.datasetid_to_class_id = self.df.to_dict()['class_id']\",\n \"def __init__(self, subset):\\n if subset not in ('background', 'evaluation'):\\n raise(ValueError, 'subset must be one of (background, evaluation)')\\n self.subset = subset\\n\\n self.df = pd.DataFrame(self.index_subset(self.subset))\\n\\n # Index of dataframe has direct correspondence to item in dataset\\n self.df = self.df.assign(id=self.df.index.values)\\n\\n # Convert arbitrary class names of dataset to ordered 0-(num_speakers - 1) integers\\n self.unique_characters = sorted(self.df['class_name'].unique())\\n self.class_name_to_id = {self.unique_characters[i]: i for i in range(self.num_classes())}\\n self.df = self.df.assign(class_id=self.df['class_name'].apply(lambda c: self.class_name_to_id[c]))\\n\\n # Create dicts\\n self.datasetid_to_filepath = self.df.to_dict()['filepath']\\n self.datasetid_to_class_id = self.df.to_dict()['class_id']\",\n \"def prepare_data():\\n gennet.prepare_data('Resnet50')\",\n \"def setup(self, stage: Union[str, None] = None) -> None:\\n self.data_splits = {}\\n # set up each of the dataset splits\\n for key, path in self.paths.items():\\n self.data_splits[key] = PointCloudDataset(\\n self.dataset_class(path), self._point_cloud_size, self._sample_size\\n )\",\n \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n self.dir_AB = os.path.join(opt.dataroot, opt.phase) # get the image directory\\n self.AB_paths = sorted(make_dataset(self.dir_AB, opt.max_dataset_size)) # get image paths\\n assert(self.opt.load_size >= self.opt.crop_size) # crop_size should be smaller than the size of loaded image\\n self.input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc\\n self.output_nc = self.opt.input_nc if self.opt.direction == 'BtoA' else self.opt.output_nc\\n\\n\\n\\n\\n basic_dir = '/home/akira/Project/simplestCNN_sparse2dense/data/32-128data'\\n bin32file = os.path.join(basic_dir, \\\"Lidar32.bin\\\")\\n bin128file = os.path.join(basic_dir, \\\"Lidar128.bin\\\")\\n bin32pa = os.path.join(basic_dir, \\\"Lidar32.pa\\\")\\n bin128pa = os.path.join(basic_dir, \\\"Lidar128.pa\\\")\\n rimg32loader = ImageTensorBinLoader(bin32file, bin32pa)\\n rimg128loader = ImageTensorBinLoader(bin128file, bin128pa)\\n self.rimgdataset = SyncDataset((0, 500))\\n self.rimgdataset.registerDataLoader(rimg32loader, \\\"rimg32\\\")\\n self.rimgdataset.registerDataLoader(rimg128loader, \\\"rimg128\\\")\",\n \"def build_training_data_loader(self) -> DataLoader:\\n pass\",\n \"def __init__(\\n self,\\n path,\\n tier,\\n embeddings=None,\\n preprocessor=None,\\n transform=True):\\n super().__init__()\\n assert os.path.isdir(path)\\n assert os.path.exists(os.path.join(path, f'{tier}.csv'))\\n assert tier in self.TIERS\\n\\n self.path = path\\n self.tier = tier\\n self.data = None\\n self.embeddings = embeddings\\n self.preprocessor = preprocessor\\n self.is_transform = transform\\n self._init_dataset()\",\n \"def __init__(\\n self,\\n embedder_path: Optional[str] = None,\\n dataset: Optional[np.ndarray] = None,\\n metadata: Optional[List[AnyStr]] = None,\\n ) -> None:\\n self._embedder_path = embedder_path\\n\\n # Cache dataset list which can be concatenated to the single dataset. This\\n # is used since users may load the data several times, if each time the data\\n # are directly concatenated together, memory copy will be costed each time.\\n self._cache_dataset_list = []\\n\\n if dataset is None:\\n # Sets dataset and metadata as empty. Will load them from raw input data\\n # later.\\n self._dataset = np.array([])\\n self._metadata = []\\n else:\\n # Directly sets dataset and metadata.\\n self._dataset = dataset\\n self._metadata = metadata\",\n \"def create_dataset(dataset_type, soruce, opts): \\n\\n p = PreProcessor(dataset_type, opts)\\n\\n # If we are NOT running \\\"implementation.py\\\", we read the data from file\\n if dataset_type == \\\"train\\\" or dataset_type == \\\"dev\\\" or dataset_type == \\\"test\\\":\\n path_to_data = soruce\\n p.read_labelled_data(path_to_data) \\n # Otherwise, we read the sentence that \\\"implementation.py\\\" gave us\\n elif dataset_type == \\\"submit\\\":\\n submission_sentence = soruce\\n p.read_test_data(submission_sentence)\\n\\n # Encode all the data to a list of torchTensors\\n encoded_tokens, encoded_pred, encoded_tokens_pos, encoded_labels = p.encode_all_data()\\n # Create SRL dataset\\n dataset = SRLDataset(x=encoded_tokens, pr=encoded_pred, p=encoded_tokens_pos, y=encoded_labels)\\n print(\\\"{} dataset size is {}\\\".format(dataset_type, len(dataset)))\\n\\n if dataset_type == \\\"train\\\" or dataset_type == \\\"dev\\\" or dataset_type == \\\"test\\\":\\n return dataset\\n elif dataset_type == \\\"submit\\\":\\n return dataset, p.list_l_original_predicates\",\n \"def _create_init(self):\\n\\n assert self._topology is not None, \\\\\\n \\\"Topology must be given for a creation constructor\\\"\\n\\n # initialize the runs group\\n runs_grp = self._h5.create_group(RUNS)\\n\\n # initialize the settings group\\n settings_grp = self._h5.create_group(SETTINGS)\\n\\n # create the topology dataset\\n self._h5.create_dataset(TOPOLOGY, data=self._topology)\\n\\n # sparse fields\\n if self._sparse_fields is not None:\\n\\n # make a dataset for the sparse fields allowed. this requires\\n # a 'special' datatype for variable length strings. This is\\n # supported by HDF5 but not numpy.\\n vlen_str_dt = h5py.special_dtype(vlen=str)\\n\\n # create the dataset with empty values for the length of the\\n # sparse fields given\\n sparse_fields_ds = settings_grp.create_dataset(SPARSE_FIELDS,\\n (len(self._sparse_fields),),\\n dtype=vlen_str_dt,\\n maxshape=(None,))\\n\\n # set the flags\\n for i, sparse_field in enumerate(self._sparse_fields):\\n sparse_fields_ds[i] = sparse_field\\n\\n\\n # field feature shapes and dtypes\\n\\n # initialize to the defaults, this gives values to\\n # self._n_coords, and self.field_feature_dtypes, and\\n # self.field_feature_shapes\\n self._set_default_init_field_attributes(n_dims=self._n_dims)\\n\\n # save the number of dimensions and number of atoms in settings\\n settings_grp.create_dataset(N_DIMS_STR, data=np.array(self._n_dims))\\n settings_grp.create_dataset(N_ATOMS, data=np.array(self._n_coords))\\n\\n # the main rep atom idxs\\n settings_grp.create_dataset(MAIN_REP_IDXS, data=self._main_rep_idxs, dtype=int)\\n\\n # alt_reps settings\\n alt_reps_idxs_grp = settings_grp.create_group(ALT_REPS_IDXS)\\n for alt_rep_name, idxs in self._alt_reps.items():\\n alt_reps_idxs_grp.create_dataset(alt_rep_name, data=idxs, dtype=int)\\n\\n # if both feature shapes and dtypes were specified overwrite\\n # (or initialize if not set by defaults) the defaults\\n if (self._field_feature_shapes_kwarg is not None) and\\\\\\n (self._field_feature_dtypes_kwarg is not None):\\n\\n self._field_feature_shapes.update(self._field_feature_shapes_kwarg)\\n self._field_feature_dtypes.update(self._field_feature_dtypes_kwarg)\\n\\n # any sparse field with unspecified shape and dtype must be\\n # set to None so that it will be set at runtime\\n for sparse_field in self.sparse_fields:\\n if (not sparse_field in self._field_feature_shapes) or \\\\\\n (not sparse_field in self._field_feature_dtypes):\\n self._field_feature_shapes[sparse_field] = None\\n self._field_feature_dtypes[sparse_field] = None\\n\\n\\n # save the field feature shapes and dtypes in the settings group\\n shapes_grp = settings_grp.create_group(FIELD_FEATURE_SHAPES_STR)\\n for field_path, field_shape in self._field_feature_shapes.items():\\n if field_shape is None:\\n # set it as a dimensionless array of NaN\\n field_shape = np.array(np.nan)\\n\\n shapes_grp.create_dataset(field_path, data=field_shape)\\n\\n dtypes_grp = settings_grp.create_group(FIELD_FEATURE_DTYPES_STR)\\n for field_path, field_dtype in self._field_feature_dtypes.items():\\n if field_dtype is None:\\n dt_str = NONE_STR\\n else:\\n # make a json string of the datatype that can be read\\n # in again, we call np.dtype again because there is no\\n # np.float.descr attribute\\n dt_str = json.dumps(np.dtype(field_dtype).descr)\\n\\n dtypes_grp.create_dataset(field_path, data=dt_str)\\n\\n # initialize the units group\\n unit_grp = self._h5.create_group(UNITS)\\n\\n # if units were not given set them all to None\\n if self._units is None:\\n self._units = {}\\n for field_path in self._field_feature_shapes.keys():\\n self._units[field_path] = None\\n\\n # set the units\\n for field_path, unit_value in self._units.items():\\n\\n # ignore the field if not given\\n if unit_value is None:\\n continue\\n\\n unit_path = '{}/{}'.format(UNITS, field_path)\\n\\n unit_grp.create_dataset(unit_path, data=unit_value)\\n\\n\\n # create the group for the run data records\\n records_grp = settings_grp.create_group(RECORD_FIELDS)\\n\\n # create a dataset for the continuation run tuples\\n # (continuation_run, base_run), where the first element\\n # of the new run that is continuing the run in the second\\n # position\\n self._init_continuations()\",\n \"def __init__(self, opt):\\n BaseDataset.__init__(self, opt)\\n\\n transform_list = [ transforms.ToTensor() ]\\n self.transform = transforms.Compose(transform_list)\\n\\n dir_self = \\\"./datasets/self_2020/wOBJ_processed/\\\" + str(opt.phase)\\n self.files_self_A_rgbd = sorted(glob.glob(dir_self + '/A_rgbd' + '/*.*'))\\n self.files_self_B_depth = sorted(glob.glob(dir_self + '/B_d' + '/*.*'))\\n self.files_self_obj_rgb = sorted(glob.glob(dir_self + '/obj_rgb' + '/*.*'))\\n tmp = int(len(self.files_self_A_rgbd) / 2)\\n self.files_self_A_rgbd = self.files_self_A_rgbd[:tmp]\\n self.files_self_B_depth = self.files_self_B_depth[:tmp]\\n self.files_self_obj_rgb = self.files_self_obj_rgb[:tmp]\\n\\n dir_self = \\\"./datasets/self_2020/woOBJ_processed/\\\" + str(opt.phase)\\n files_wo_A_rgbd = sorted(glob.glob(dir_self + '/A_rgbd' + '/*.*'))\\n files_wo_B_depth = sorted(glob.glob(dir_self + '/B_d' + '/*.*'))\\n files_wo_obj_rgb = sorted(glob.glob(dir_self + '/obj_rgb' + '/*.*'))\\n tmp = int(len(files_wo_A_rgbd) / 2)\\n files_wo_A_rgbd = files_wo_A_rgbd[:tmp]\\n files_wo_B_depth = files_wo_B_depth[:tmp]\\n files_wo_obj_rgb = files_wo_obj_rgb[:tmp]\\n\\n self.files_self_A_rgbd.extend(files_wo_A_rgbd)\\n self.files_self_B_depth.extend(files_wo_B_depth)\\n self.files_self_obj_rgb.extend(files_wo_obj_rgb)\\n\\n datasize = opt.max_dataset_size\\n if opt.max_dataset_size is None:\\n datasize = int(len(self.files_self_A_rgbd))\\n\\n self.files_self_A_rgbd = self.files_self_A_rgbd[:datasize]\\n self.files_self_B_depth = self.files_self_B_depth[:datasize]\\n self.files_self_obj_rgb = self.files_self_obj_rgb[:datasize]\",\n \"def prepare_dataset(self, xs: List[str], ys: List[str], batch_size: int = None):\\n\\n if batch_size is None:\\n batch_size = self.cM.batch_size\\n\\n examples = [data.Example.fromlist([x, y], self.data_fields) for x, y in zip(xs, ys)]\\n\\n dataset = data.Dataset(examples, fields=self.data_fields)\\n\\n iterator = data.BucketIterator(dataset, batch_size=batch_size, shuffle=False)\\n\\n return iterator\",\n \"def create_dataset(opt):\\n\\tdata_loader = CustomDatasetDataLoader(opt)\\n\\tdataset = data_loader.load_data()\\n\\treturn dataset\",\n \"def setUpClass(cls) -> None:\\n cls.DATA_MODEL = cls.load_data_model()\",\n \"def __init__(self):\\n self.data_set = []\\n self.finalized_data = LogData()\",\n \"def prepare(self):\\n raise NotImplemented\",\n \"def __init__(self, args):\\n self.train_img_file = os.path.join(args.data_dir, args.train_img_file)\\n self.train_lbl_file = os.path.join(args.data_dir, args.train_lbl_file)\\n self.test_img_file = os.path.join(args.data_dir, args.test_img_file)\\n self.test_lbl_file = os.path.join(args.data_dir, args.test_lbl_file)\\n self.batch_size = args.batch_size\\n self.num_workers = args.data_workders\\n self.shuffle = True\\n self.dataset_name = args.dataset_name\\n self.pin_memory = False #args.cuda\\n\\n # check dataset files exist\\n files = [self.train_img_file, self.train_lbl_file,\\n self.test_img_file, self.test_lbl_file]\\n for file in files:\\n if not os.path.isfile(file):\\n msg = \\\"Data file not found. Please check the path \\\" +\\\\\\n \\\"or download files using scripts/download_files.py \\\"\\n raise IOError(msg)\",\n \"def init(*args):\\n global dataset\\n dataset = args[0]\",\n \"def setUpClass(cls):\\n cls.test_file_1 = \\\"/tmp/test_data_loader_dummy_1.pkl\\\"\\n cls.test_file_2 = \\\"/tmp/test_data_loader_dummy_2.pkl\\\"\\n cls.in_cols = [\\\"file\\\", \\\"id\\\", \\\"len\\\", \\\"seq\\\", \\\"phyche\\\", \\\"pssm\\\", \\\"logits\\\",\\n \\\"ss\\\", \\\"h_0\\\", \\\"h_1\\\", \\\"h_2\\\", \\\"lm_logits\\\"]\\n cls.out_cols = [\\\"dataset\\\", \\\"id\\\", \\\"len\\\", \\\"position\\\", \\\"amino\\\",\\n \\\"phyche\\\", \\\"pssm\\\", \\\"logits\\\", \\\"ss\\\", \\\"h_0\\\", \\\"h_1\\\", \\\"h_2\\\",\\n \\\"lm_logits\\\"]\\n\\n seq = np.array([[0., 0., 1.],\\n [1., 0., 0.]])\\n phyche = np.array([[0., 0.], # phyche\\n [1., 0.]])\\n pssm = np.array([[0., 0., .8], # pssm\\n [.8, 0., 0.]])\\n logits = np.array([[0.1, 0., 0.9], # logits\\n [0.9, 0., 0.1]])\\n ss = np.array([[0., 0., 1.], # ss\\n [1., 0., 0.]])\\n h_0 = np.array([[0., 0., 1., 0.],\\n [1., 0., 0., 0.]])\\n h_1 = np.array([[0., 0., 1., 0.],\\n [1., 0., 0., 0.]])\\n h_2 = np.array([[0., 0., 1., 0.], # h_2\\n [1., 0., 0., 0.]])\\n lm_logits = np.array([[0., 0., 1.], # lm_logits\\n [1., 0., 0.]])\\n\\n ex_1_in = (\\\"dummy_train.tfrecords\\\", # file\\n \\\"id1\\\", # id\\n 2, # len\\n seq,\\n phyche,\\n pssm,\\n logits,\\n ss,\\n h_0,\\n h_1,\\n h_2,\\n lm_logits,\\n )\\n ex_1_out = [tuple([\\\"train\\\", ex_1_in[1], ex_1_in[2], j] + [ex_1_in[i][j, :] for i in range(3, len(ex_1_in))]) for j in range(2)]\\n\\n in_df = pd.DataFrame.from_records(data=[ex_1_in], columns=cls.in_cols)\\n # write to file\\n in_df.to_pickle(cls.test_file_1)\\n\\n cls.out_df = pd.DataFrame.from_records(data=ex_1_out, columns=cls.out_cols)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7758656","0.7241418","0.7239436","0.7222972","0.7210236","0.72034883","0.70932466","0.7042313","0.6992051","0.694836","0.69467616","0.6880937","0.68526316","0.680469","0.67841667","0.6780072","0.67789966","0.67789966","0.6729223","0.6705986","0.66246676","0.6616101","0.6598656","0.65732265","0.65643954","0.65037817","0.6502379","0.65002894","0.6494117","0.64853","0.6472539","0.64658093","0.64648503","0.6459618","0.645439","0.64523846","0.6435447","0.64228034","0.6417391","0.63528216","0.6331403","0.63282156","0.6324698","0.6322555","0.63217396","0.63167185","0.63106585","0.6309102","0.6308503","0.6301384","0.62681764","0.6264796","0.6254109","0.62503505","0.6245075","0.6239829","0.623037","0.6228067","0.62254727","0.62146527","0.62116826","0.6210382","0.62057406","0.6203419","0.6200573","0.6190458","0.61895835","0.618716","0.618716","0.618716","0.6181416","0.6170341","0.6163564","0.61608887","0.61598897","0.6149682","0.6146483","0.6139694","0.61329305","0.6122166","0.6122166","0.6122166","0.61193323","0.6111788","0.6109334","0.6105416","0.6104199","0.60986614","0.60938156","0.6088435","0.608612","0.6077443","0.6074605","0.6063527","0.6061274","0.60562325","0.605276","0.60498005","0.60465926","0.6044792"],"string":"[\n \"0.7758656\",\n \"0.7241418\",\n \"0.7239436\",\n \"0.7222972\",\n \"0.7210236\",\n \"0.72034883\",\n \"0.70932466\",\n \"0.7042313\",\n \"0.6992051\",\n \"0.694836\",\n \"0.69467616\",\n \"0.6880937\",\n \"0.68526316\",\n \"0.680469\",\n \"0.67841667\",\n \"0.6780072\",\n \"0.67789966\",\n \"0.67789966\",\n \"0.6729223\",\n \"0.6705986\",\n \"0.66246676\",\n \"0.6616101\",\n \"0.6598656\",\n \"0.65732265\",\n \"0.65643954\",\n \"0.65037817\",\n \"0.6502379\",\n \"0.65002894\",\n \"0.6494117\",\n \"0.64853\",\n \"0.6472539\",\n \"0.64658093\",\n \"0.64648503\",\n \"0.6459618\",\n \"0.645439\",\n \"0.64523846\",\n \"0.6435447\",\n \"0.64228034\",\n \"0.6417391\",\n \"0.63528216\",\n \"0.6331403\",\n \"0.63282156\",\n \"0.6324698\",\n \"0.6322555\",\n \"0.63217396\",\n \"0.63167185\",\n \"0.63106585\",\n \"0.6309102\",\n \"0.6308503\",\n \"0.6301384\",\n \"0.62681764\",\n \"0.6264796\",\n \"0.6254109\",\n \"0.62503505\",\n \"0.6245075\",\n \"0.6239829\",\n \"0.623037\",\n \"0.6228067\",\n \"0.62254727\",\n \"0.62146527\",\n \"0.62116826\",\n \"0.6210382\",\n \"0.62057406\",\n \"0.6203419\",\n \"0.6200573\",\n \"0.6190458\",\n \"0.61895835\",\n \"0.618716\",\n \"0.618716\",\n \"0.618716\",\n \"0.6181416\",\n \"0.6170341\",\n \"0.6163564\",\n \"0.61608887\",\n \"0.61598897\",\n \"0.6149682\",\n \"0.6146483\",\n \"0.6139694\",\n \"0.61329305\",\n \"0.6122166\",\n \"0.6122166\",\n \"0.6122166\",\n \"0.61193323\",\n \"0.6111788\",\n \"0.6109334\",\n \"0.6105416\",\n \"0.6104199\",\n \"0.60986614\",\n \"0.60938156\",\n \"0.6088435\",\n \"0.608612\",\n \"0.6077443\",\n \"0.6074605\",\n \"0.6063527\",\n \"0.6061274\",\n \"0.60562325\",\n \"0.605276\",\n \"0.60498005\",\n \"0.60465926\",\n \"0.6044792\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6476941"},"document_rank":{"kind":"string","value":"30"}}},{"rowIdx":77997,"cells":{"query":{"kind":"string","value":"Returns a shorter version of object names for cleaner display."},"document":{"kind":"string","value":"def clean_name(name):\n return \",\".join(name.split(\",\")[:1])"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_names_short(self):\r\n return [p.get_name() for p in self.people]","def get_names_from_full_objects(objects):\n return [obj.name for obj in objects]","def __str__(self):\n name=''\n for prop in self.properties.itervalues():\n name+=str(prop)\n return name","def __str__(self):\n table = 'objects'.join(self.galcat.__str__().split('objects')[1:])\n return self.__repr__()+'\\n'+table","def display_name(self, obj):\n return six.text_type(obj)","def __str__(self):\n if not self.is_obf:\n if self.name == \"\":\n # Anonymous mode: just return parent, id, class.\n return str(self.parent) + \".\" + self.cls + \"-\" + str(self.id)\n else:\n # Has name: return all of the above + name.\n return str(self.parent) + \".\" + self.cls + \"-\" + str(self.id) \\\n + \"_\" + self.name\n else:\n # If we're obfuscated, just return the mangled name.\n return self.obf_name","def __str__(self):\n if self.keys():\n childStrings = [str(child) for child in self.itervalues()]\n return \"[{} {}]\".format(self.name,\" \".join(childStrings))\n else:\n return \"[{}]\".format(self.name)","def full_names(self) -> List[str]:\n self.names = [\n \".\".join(prod)\n for prod in product(*self._namespaces, self.terminals)\n ]\n return self.names","def object_name(self):\n name = self.full_name\n if self.overload and self.overload.overload_id:\n name += f'({self.overload.overload_id})'\n return name","def __str__(self):\n return \"%-10s\" % self._id + \"%-20s\" % self._name","def get_pretty_name(obj):\n if not hasattr(obj, \"__qualname__\") and not hasattr(obj, \"__name__\"):\n obj = getattr(obj, \"__class__\", obj)\n if hasattr(obj, \"__qualname__\"):\n return obj.__qualname__\n if hasattr(obj, \"__name__\"):\n return obj.__name__\n return str(obj)","def __str__(self):\n return type(self).__name__ + str(vars(self))","def __str__(self):\n return f\"{self.full_name} ({self.short_name})\"","def __str__(self):\n return f\"{self.full_name} ({self.short_name})\"","def getName(obj):","def pretty_name(self) -> str:\n try:\n return self._names_from_attrs('pretty_name')\n except AttributeError: # todo: what exception\n warnings.warn('pretty name not found in metadata, fallback to globals.py')\n if self.__short_name in globals._dataset_pretty_names.keys():\n return globals._dataset_pretty_names[self.__short_name]\n else:\n warnings.warn('pretty name also not found in globals.py, use short name')\n return self.__short_name","def __str__(self):\n return (\n f'{self.__class__.__name__}'\n f'\\n> defined by: {self._str_meta_()}'\n f'\\n> with columns: {self._str_colnames()}'\n f'\\n> {len(self)} objects'\n f'\\n{APtable.__str__(self)}'\n )","def get_printable_object_name(obj):\n try:\n if isinstance(obj, dict):\n return \"Dict ID={}: first-5-keys={}\".format(id(obj), list(obj.keys())[:10])\n else:\n return \"{}: {}\".format(type(obj), str(obj))\n except TypeError:\n return \"Foreign object (raised TypeError): {}, ID={}\".format(type(obj), id(obj))\n except ModuleNotFoundError:\n return \"Foreign object (raised ModuleNotFoundError): {}, ID={}\".format(type(obj), id(obj))","def display_name(self):\n\t\tif self.creature:\n\t\t\treturn self.creature.name_instance + \" the \" + self.object_name\n\t\t\n\t\tif self.item:\n\t\t\tif self.equipment and self.equipment.equipped:\n\t\t\t\treturn self.object_name + \" (equipped)\"\n\t\t\telse:\n\t\t\t\treturn self.object_name","def __str__(self):\n return f\"names is {self.names}, all is {self.all}, klass is {self.klass}, x is {self.x}, y is {self.y}\"","def __repr__(self) -> str:\n properties = [x for x in dir(self) if\n not x.startswith('_') and not callable(getattr(self, x))]\n property_strings = [f'{x}:{getattr(self, x, None)}' for x in properties]\n return ', '.join(property_strings)","def name_get(self):\n result = []\n for r in self:\n result.append((r.id, u\"%s %s\" % ('PO', r.name)))\n return result","def __str__(self):\n s = \"\"\n for x in self.__members:\n v = getattr(self, x)\n if s: s+=\", \"\n s += \"%s: %s\" % (x, `v`)\n return s","def __str__(self):\n return '{}({})'.format(type(self).__name__, self.__name)","def get_all_object_names(self):\n o_objects = []\n for s in [\"Non Model\", \"Solids\", \"Unclassified\", \"Sheets\", \"Lines\"]:\n o_objects += self.design.modeler.get_objects_in_group(s)\n return o_objects","def __str__(self):\r\n\r\n return 'v{} ({} objects)'.format(self.get_version(), len(self.objects))","def stringReco(obj):\n name = obj.get_name()\n name = obj._pid if (name is None) else name\n return (\"pdg: \" + name + \" E: \" + str(obj._E)\n + \" px: \" + str(obj._px) + \" py: \" + str(obj._py)\n + \" pz: \"+ str(obj._pz) + \" mass: \" + str(obj._m))","def display_allergens(self):\n return ', '.join(allergens.name for allergens in self.allergens.all()[:3])","def name(self):\n return [o.name for o in self.obs]","def short_name(self) -> str:\r\n\t\treturn f'{self.last_name} {self.first_name}'","def __str__(self):\n return \"%s(%s)\" % (self[0], \", \".join(arg.name for arg in self[1:]))","def display_name(cls):\n return cls.name.replace('_', ' ').title()","def __str__(self):\n rc = str(self.name)\n for b in self.beitrag:\n rc += str(b)\n return rc","def __str__(self):\n\n return \"%s:\\n%s\" % (self.name, self.top_terms_tuples(25))","def to_short_string(self):\n return f'{self.name} - {self.resource_type}'","def display_name(self):","def __str__(self):\n\n return \"name \\n\" + \" \" + str(self.name).center(9)","def _compact_class_repr(obj):\n dict_str_list = []\n post_repr_string = \"\"\n\n # If features are present, then shorten it.\n init_func = obj.__init__\n if _sys.version_info.major == 2:\n init_func = init_func.__func__\n\n fields = _inspect.getargspec(init_func).args\n fields = fields[1:] # remove self\n if 'features' in fields:\n fields.remove('features')\n features = obj.get(\"features\")\n if features is not None:\n post_repr_string = ' on %s feature(s)' % len(features)\n if 'excluded_features' in fields:\n fields.remove('excluded_features')\n\n # GLC transformers.\n if issubclass(obj.__class__, _Transformer):\n for attr in fields:\n dict_str_list.append(\"%s=%s\" % (attr, obj.get(attr).__repr__()))\n\n # Chains\n elif obj.__class__ == TransformerChain:\n _step_classes = list(map(lambda x: x.__class__.__name__, obj.get('steps')))\n _steps = _internal_utils.pretty_print_list(\n _step_classes, 'steps', False)\n dict_str_list.append(_steps)\n\n # For user defined transformers.\n else:\n for attr in fields:\n dict_str_list.append(\"%s=%s\" % (attr, obj.__dict__[attr]))\n\n return \"%s(%s)%s\" % (obj.__class__.__name__, \", \".join(dict_str_list),\n post_repr_string)","def __repr__(self) -> str:\n r = \", \".join(\n f\"{name}={option}\"\n for name, option in sorted(vars(self).items())\n )\n return f\"{self.__class__.__name__}({r})\"","def __str__(self):\n class_dictionary = vars(self)\n out = self.__class__.__name__\n for k,v in class_dictionary.items():\n if v > 0:\n out += ' : ' + str(k) + ' = ' + str(v)\n return out","def get_name(cls: Type[\"Attribution\"]) -> str:\n return \"\".join(\n [\n char if char.islower() or idx == 0 else \" \" + char\n for idx, char in enumerate(cls.__name__)\n ]\n )","def display_name(name, obj, local):\n prefix = '' if local else '.'\n\n if isinstance(obj, SeeError):\n suffix = '?'\n elif hasattr(obj, '__call__'):\n suffix = '()'\n else:\n suffix = ''\n\n return ''.join((prefix, name, suffix))","def __str__(self):\n return '%s' % (self.name)","def __str__(self):\n return '%s' % (self.name)","def __str__(self):\n return '%s' % (self.name)","def __str__(self):\n return '%s' % (self.name)","def __str__(self):\n return '%s' % (self.name)","def display_name(self):\n if self.name and self.address and self.name != self.address:\n return \", \".join([self.name, self.address])\n return self.name","def __str__(self):\n return str(self.__dict__['_obj'])","def longName(self):\n return self.name()","def _cls_repr(self):\n prefixes = []\n for k in self.names:\n # list only params with not default values\n if self[k].isDefault:\n continue\n prefixes.append(\"%s=%s\" % (k, self[k].value))\n return prefixes","def short_displayname(self):\n return self.get_short_displayname()","def get_temporary_name(self):\n return ('<%s>' % type(self.obj).__name__,)","def __str__(self):\n return self.make_flat()","def __repr__(self):\n props = [\n f'{type(self).__name__} ({hex(id(self))})',\n ]\n\n for attr in dir(self):\n if not attr.startswith('_') and attr[0].islower():\n name = ' '.join(attr.split('_')).capitalize() + ':'\n value = getattr(self, attr)\n if callable(value):\n continue\n if isinstance(value, str):\n value = f'\"{value}\"'\n props.append(f' {name:28s} {value}')\n\n return '\\n'.join(props)","def GenericRepr(obj, names):\n\n def ReprIndent(name, as_ref):\n return ' %s=%s' % (name, Repr(getattr(obj, name), as_ref).replace(\n '\\n', '\\n '))\n\n return '%s(\\n%s\\n)' % (obj.__class__.__name__, ',\\n'.join(\n ReprIndent(name, as_ref) for (name, as_ref) in names.items()))","def __str__(self) -> str:\n if self.name_field:\n return str(getattr(self, self.name_field))\n # noinspection PyUnresolvedReferences\n data = [\n # Collect the string representations of related objects.\n # getattr(self, fk_field.attname) and\n # fk_field.value_from_object(self) would only return the primary\n # key of the related object.\n str(getattr(self, fk_field.name))\n for fk_field in get_model_fields(\n self._meta.model, base=False, foreign=True, m2m=False\n )\n if not fk_field.null\n ]\n if len(data) < 2:\n # Cannot build a more meaningful representation than the default.\n return super().__str__()\n else:\n template = \"{}\" + \" ({})\" * (len(data) - 1)\n return template.format(*data)","def __str__(self):\n return 'str-human.%s' % self.name","def __str__(self):\n\n return '__str__ for Object'","def __str__(self):\n return '%s | %s' % (self.name_one, self.name_two)","def __str__(self):\n return self.__name","def __str__(self):\n return f\"<{full_class_name(self)} {self.name!r} @{'%x' % id(self)}>\"","def __str__(self):\n return \"{}\".format(self.name)","def __str__(self):\n return \"{}\".format(self.name)","def __str__(self):\n return \"{}\".format(self.name)","def __str__(self):\n return \"{}\".format(self.name)","def __str__(self):\n return \"{}\".format(self.name)","def __str__(self):\n return \"{}\".format(self.name)","def __str__(self):\n return \"{}\".format(self.name)","def get_short_name(self):\n\n return self.name","def extract_names(collection):\n return map(repr, collection)","def initialled_name(obj):\n initials = ''.join([name[0] for name in obj.first_names.split(' ')])\n return \"{}, {}\".format(obj.last_names, initials)","def __str__(self):\n out = \"\"\n for i, m in enumerate(self):\n out += \" \" + str(m) if i > 0 else str(m)\n out += \" \"\n return out","def get_display_names(self):\n return self.display_names","def __str__(self):\n return str(self.name)","def __str__(self):\n return str(self.name)","def __str__(self):\n return str(self.name)","def __str__(self):\n return str(self.name)","def __str__(self):\n return str(self.name)","def __str__(self):\n return str(self.name)","def __str__(self):\n return str(self.name)","def __str__(self):\n class_name_str = str(self.__class__.__name__) + \": (\"\n attributes_str = str(self.head_vertex) + \", \" + \\\n str(self.tail_vertex) + \")\"\n str_rep = class_name_str + attributes_str\n return str_rep","def long_displayname(self):\n return self.get_long_displayname()","def names_singleton(self):\r\n return self.object_name","def __str__(self):\n return str(self.obj)","def __str__(self):\n # add a dagger symbol to the class name if needed\n temp = super().__str__()\n if self.dagger:\n temp += \".H\"\n return temp","def get_descriptive_name(self):\r\n long_name = str(self.year)+' '+self.make + ' '+self.model\r\n return long_name.title()","def __str__(self):\n return f'{self.name}' # TODO","def name(self):\n # easy enough\n return \"{0.first} {0.last}\".format(self)","def get_name(self):\n return '.'.join(self.name)","def get_descriptive_name(self):\r\n long_name=str(self.year)+' '+self.make+' '+self.model\r\n return long_name.title()","def __str__(self):\n return f'{self._name} has {self._calories} calories, {self._carbohydrates}' +\\\n f'g. carbohydrates, {self._fat}g. of fat and {self._proteins}g. of proteins'","def get_descriptive_name(self):\r\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\r\n return long_name.title()","def fullname(cls): # pylint: disable=no-self-argument\n return func.concat(\n (select([Project.name]).where(Project.id == cls.project_id).as_scalar()),\n \" \",\n cls.name,\n )","def full_name(self) -> str:\n # return self.separator.join(map(lambda x: x.name, self.path()))\n return self.separator.join(map(lambda x: x.tagged_name, self.path()))","def __str__(self):\n return self._name","def print_objects(self):\n print(\"Spaces: {}\".format([s.name for s in self.spaces]))\n print(\"Characters: {}\".format([c.name for c in self.characters]))\n print(\"Items: {}\".format([i.name for i in self.items]))","def __str__(self):\n ingredient_names = [str(ingredient) for ingredient in self.ingredients]\n return ', '.join(ingredient_names)","def __str__(self):\n\t\treturn self.full_name","def get_measurement_objects_name(self):\n return self.object_name.value","def get_descriptive_name(self):\n long_name = f\"{self.make} {self.model} {self.year}\"\n \n return long_name.title()"],"string":"[\n \"def get_names_short(self):\\r\\n return [p.get_name() for p in self.people]\",\n \"def get_names_from_full_objects(objects):\\n return [obj.name for obj in objects]\",\n \"def __str__(self):\\n name=''\\n for prop in self.properties.itervalues():\\n name+=str(prop)\\n return name\",\n \"def __str__(self):\\n table = 'objects'.join(self.galcat.__str__().split('objects')[1:])\\n return self.__repr__()+'\\\\n'+table\",\n \"def display_name(self, obj):\\n return six.text_type(obj)\",\n \"def __str__(self):\\n if not self.is_obf:\\n if self.name == \\\"\\\":\\n # Anonymous mode: just return parent, id, class.\\n return str(self.parent) + \\\".\\\" + self.cls + \\\"-\\\" + str(self.id)\\n else:\\n # Has name: return all of the above + name.\\n return str(self.parent) + \\\".\\\" + self.cls + \\\"-\\\" + str(self.id) \\\\\\n + \\\"_\\\" + self.name\\n else:\\n # If we're obfuscated, just return the mangled name.\\n return self.obf_name\",\n \"def __str__(self):\\n if self.keys():\\n childStrings = [str(child) for child in self.itervalues()]\\n return \\\"[{} {}]\\\".format(self.name,\\\" \\\".join(childStrings))\\n else:\\n return \\\"[{}]\\\".format(self.name)\",\n \"def full_names(self) -> List[str]:\\n self.names = [\\n \\\".\\\".join(prod)\\n for prod in product(*self._namespaces, self.terminals)\\n ]\\n return self.names\",\n \"def object_name(self):\\n name = self.full_name\\n if self.overload and self.overload.overload_id:\\n name += f'({self.overload.overload_id})'\\n return name\",\n \"def __str__(self):\\n return \\\"%-10s\\\" % self._id + \\\"%-20s\\\" % self._name\",\n \"def get_pretty_name(obj):\\n if not hasattr(obj, \\\"__qualname__\\\") and not hasattr(obj, \\\"__name__\\\"):\\n obj = getattr(obj, \\\"__class__\\\", obj)\\n if hasattr(obj, \\\"__qualname__\\\"):\\n return obj.__qualname__\\n if hasattr(obj, \\\"__name__\\\"):\\n return obj.__name__\\n return str(obj)\",\n \"def __str__(self):\\n return type(self).__name__ + str(vars(self))\",\n \"def __str__(self):\\n return f\\\"{self.full_name} ({self.short_name})\\\"\",\n \"def __str__(self):\\n return f\\\"{self.full_name} ({self.short_name})\\\"\",\n \"def getName(obj):\",\n \"def pretty_name(self) -> str:\\n try:\\n return self._names_from_attrs('pretty_name')\\n except AttributeError: # todo: what exception\\n warnings.warn('pretty name not found in metadata, fallback to globals.py')\\n if self.__short_name in globals._dataset_pretty_names.keys():\\n return globals._dataset_pretty_names[self.__short_name]\\n else:\\n warnings.warn('pretty name also not found in globals.py, use short name')\\n return self.__short_name\",\n \"def __str__(self):\\n return (\\n f'{self.__class__.__name__}'\\n f'\\\\n> defined by: {self._str_meta_()}'\\n f'\\\\n> with columns: {self._str_colnames()}'\\n f'\\\\n> {len(self)} objects'\\n f'\\\\n{APtable.__str__(self)}'\\n )\",\n \"def get_printable_object_name(obj):\\n try:\\n if isinstance(obj, dict):\\n return \\\"Dict ID={}: first-5-keys={}\\\".format(id(obj), list(obj.keys())[:10])\\n else:\\n return \\\"{}: {}\\\".format(type(obj), str(obj))\\n except TypeError:\\n return \\\"Foreign object (raised TypeError): {}, ID={}\\\".format(type(obj), id(obj))\\n except ModuleNotFoundError:\\n return \\\"Foreign object (raised ModuleNotFoundError): {}, ID={}\\\".format(type(obj), id(obj))\",\n \"def display_name(self):\\n\\t\\tif self.creature:\\n\\t\\t\\treturn self.creature.name_instance + \\\" the \\\" + self.object_name\\n\\t\\t\\n\\t\\tif self.item:\\n\\t\\t\\tif self.equipment and self.equipment.equipped:\\n\\t\\t\\t\\treturn self.object_name + \\\" (equipped)\\\"\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn self.object_name\",\n \"def __str__(self):\\n return f\\\"names is {self.names}, all is {self.all}, klass is {self.klass}, x is {self.x}, y is {self.y}\\\"\",\n \"def __repr__(self) -> str:\\n properties = [x for x in dir(self) if\\n not x.startswith('_') and not callable(getattr(self, x))]\\n property_strings = [f'{x}:{getattr(self, x, None)}' for x in properties]\\n return ', '.join(property_strings)\",\n \"def name_get(self):\\n result = []\\n for r in self:\\n result.append((r.id, u\\\"%s %s\\\" % ('PO', r.name)))\\n return result\",\n \"def __str__(self):\\n s = \\\"\\\"\\n for x in self.__members:\\n v = getattr(self, x)\\n if s: s+=\\\", \\\"\\n s += \\\"%s: %s\\\" % (x, `v`)\\n return s\",\n \"def __str__(self):\\n return '{}({})'.format(type(self).__name__, self.__name)\",\n \"def get_all_object_names(self):\\n o_objects = []\\n for s in [\\\"Non Model\\\", \\\"Solids\\\", \\\"Unclassified\\\", \\\"Sheets\\\", \\\"Lines\\\"]:\\n o_objects += self.design.modeler.get_objects_in_group(s)\\n return o_objects\",\n \"def __str__(self):\\r\\n\\r\\n return 'v{} ({} objects)'.format(self.get_version(), len(self.objects))\",\n \"def stringReco(obj):\\n name = obj.get_name()\\n name = obj._pid if (name is None) else name\\n return (\\\"pdg: \\\" + name + \\\" E: \\\" + str(obj._E)\\n + \\\" px: \\\" + str(obj._px) + \\\" py: \\\" + str(obj._py)\\n + \\\" pz: \\\"+ str(obj._pz) + \\\" mass: \\\" + str(obj._m))\",\n \"def display_allergens(self):\\n return ', '.join(allergens.name for allergens in self.allergens.all()[:3])\",\n \"def name(self):\\n return [o.name for o in self.obs]\",\n \"def short_name(self) -> str:\\r\\n\\t\\treturn f'{self.last_name} {self.first_name}'\",\n \"def __str__(self):\\n return \\\"%s(%s)\\\" % (self[0], \\\", \\\".join(arg.name for arg in self[1:]))\",\n \"def display_name(cls):\\n return cls.name.replace('_', ' ').title()\",\n \"def __str__(self):\\n rc = str(self.name)\\n for b in self.beitrag:\\n rc += str(b)\\n return rc\",\n \"def __str__(self):\\n\\n return \\\"%s:\\\\n%s\\\" % (self.name, self.top_terms_tuples(25))\",\n \"def to_short_string(self):\\n return f'{self.name} - {self.resource_type}'\",\n \"def display_name(self):\",\n \"def __str__(self):\\n\\n return \\\"name \\\\n\\\" + \\\" \\\" + str(self.name).center(9)\",\n \"def _compact_class_repr(obj):\\n dict_str_list = []\\n post_repr_string = \\\"\\\"\\n\\n # If features are present, then shorten it.\\n init_func = obj.__init__\\n if _sys.version_info.major == 2:\\n init_func = init_func.__func__\\n\\n fields = _inspect.getargspec(init_func).args\\n fields = fields[1:] # remove self\\n if 'features' in fields:\\n fields.remove('features')\\n features = obj.get(\\\"features\\\")\\n if features is not None:\\n post_repr_string = ' on %s feature(s)' % len(features)\\n if 'excluded_features' in fields:\\n fields.remove('excluded_features')\\n\\n # GLC transformers.\\n if issubclass(obj.__class__, _Transformer):\\n for attr in fields:\\n dict_str_list.append(\\\"%s=%s\\\" % (attr, obj.get(attr).__repr__()))\\n\\n # Chains\\n elif obj.__class__ == TransformerChain:\\n _step_classes = list(map(lambda x: x.__class__.__name__, obj.get('steps')))\\n _steps = _internal_utils.pretty_print_list(\\n _step_classes, 'steps', False)\\n dict_str_list.append(_steps)\\n\\n # For user defined transformers.\\n else:\\n for attr in fields:\\n dict_str_list.append(\\\"%s=%s\\\" % (attr, obj.__dict__[attr]))\\n\\n return \\\"%s(%s)%s\\\" % (obj.__class__.__name__, \\\", \\\".join(dict_str_list),\\n post_repr_string)\",\n \"def __repr__(self) -> str:\\n r = \\\", \\\".join(\\n f\\\"{name}={option}\\\"\\n for name, option in sorted(vars(self).items())\\n )\\n return f\\\"{self.__class__.__name__}({r})\\\"\",\n \"def __str__(self):\\n class_dictionary = vars(self)\\n out = self.__class__.__name__\\n for k,v in class_dictionary.items():\\n if v > 0:\\n out += ' : ' + str(k) + ' = ' + str(v)\\n return out\",\n \"def get_name(cls: Type[\\\"Attribution\\\"]) -> str:\\n return \\\"\\\".join(\\n [\\n char if char.islower() or idx == 0 else \\\" \\\" + char\\n for idx, char in enumerate(cls.__name__)\\n ]\\n )\",\n \"def display_name(name, obj, local):\\n prefix = '' if local else '.'\\n\\n if isinstance(obj, SeeError):\\n suffix = '?'\\n elif hasattr(obj, '__call__'):\\n suffix = '()'\\n else:\\n suffix = ''\\n\\n return ''.join((prefix, name, suffix))\",\n \"def __str__(self):\\n return '%s' % (self.name)\",\n \"def __str__(self):\\n return '%s' % (self.name)\",\n \"def __str__(self):\\n return '%s' % (self.name)\",\n \"def __str__(self):\\n return '%s' % (self.name)\",\n \"def __str__(self):\\n return '%s' % (self.name)\",\n \"def display_name(self):\\n if self.name and self.address and self.name != self.address:\\n return \\\", \\\".join([self.name, self.address])\\n return self.name\",\n \"def __str__(self):\\n return str(self.__dict__['_obj'])\",\n \"def longName(self):\\n return self.name()\",\n \"def _cls_repr(self):\\n prefixes = []\\n for k in self.names:\\n # list only params with not default values\\n if self[k].isDefault:\\n continue\\n prefixes.append(\\\"%s=%s\\\" % (k, self[k].value))\\n return prefixes\",\n \"def short_displayname(self):\\n return self.get_short_displayname()\",\n \"def get_temporary_name(self):\\n return ('<%s>' % type(self.obj).__name__,)\",\n \"def __str__(self):\\n return self.make_flat()\",\n \"def __repr__(self):\\n props = [\\n f'{type(self).__name__} ({hex(id(self))})',\\n ]\\n\\n for attr in dir(self):\\n if not attr.startswith('_') and attr[0].islower():\\n name = ' '.join(attr.split('_')).capitalize() + ':'\\n value = getattr(self, attr)\\n if callable(value):\\n continue\\n if isinstance(value, str):\\n value = f'\\\"{value}\\\"'\\n props.append(f' {name:28s} {value}')\\n\\n return '\\\\n'.join(props)\",\n \"def GenericRepr(obj, names):\\n\\n def ReprIndent(name, as_ref):\\n return ' %s=%s' % (name, Repr(getattr(obj, name), as_ref).replace(\\n '\\\\n', '\\\\n '))\\n\\n return '%s(\\\\n%s\\\\n)' % (obj.__class__.__name__, ',\\\\n'.join(\\n ReprIndent(name, as_ref) for (name, as_ref) in names.items()))\",\n \"def __str__(self) -> str:\\n if self.name_field:\\n return str(getattr(self, self.name_field))\\n # noinspection PyUnresolvedReferences\\n data = [\\n # Collect the string representations of related objects.\\n # getattr(self, fk_field.attname) and\\n # fk_field.value_from_object(self) would only return the primary\\n # key of the related object.\\n str(getattr(self, fk_field.name))\\n for fk_field in get_model_fields(\\n self._meta.model, base=False, foreign=True, m2m=False\\n )\\n if not fk_field.null\\n ]\\n if len(data) < 2:\\n # Cannot build a more meaningful representation than the default.\\n return super().__str__()\\n else:\\n template = \\\"{}\\\" + \\\" ({})\\\" * (len(data) - 1)\\n return template.format(*data)\",\n \"def __str__(self):\\n return 'str-human.%s' % self.name\",\n \"def __str__(self):\\n\\n return '__str__ for Object'\",\n \"def __str__(self):\\n return '%s | %s' % (self.name_one, self.name_two)\",\n \"def __str__(self):\\n return self.__name\",\n \"def __str__(self):\\n return f\\\"<{full_class_name(self)} {self.name!r} @{'%x' % id(self)}>\\\"\",\n \"def __str__(self):\\n return \\\"{}\\\".format(self.name)\",\n \"def __str__(self):\\n return \\\"{}\\\".format(self.name)\",\n \"def __str__(self):\\n return \\\"{}\\\".format(self.name)\",\n \"def __str__(self):\\n return \\\"{}\\\".format(self.name)\",\n \"def __str__(self):\\n return \\\"{}\\\".format(self.name)\",\n \"def __str__(self):\\n return \\\"{}\\\".format(self.name)\",\n \"def __str__(self):\\n return \\\"{}\\\".format(self.name)\",\n \"def get_short_name(self):\\n\\n return self.name\",\n \"def extract_names(collection):\\n return map(repr, collection)\",\n \"def initialled_name(obj):\\n initials = ''.join([name[0] for name in obj.first_names.split(' ')])\\n return \\\"{}, {}\\\".format(obj.last_names, initials)\",\n \"def __str__(self):\\n out = \\\"\\\"\\n for i, m in enumerate(self):\\n out += \\\" \\\" + str(m) if i > 0 else str(m)\\n out += \\\" \\\"\\n return out\",\n \"def get_display_names(self):\\n return self.display_names\",\n \"def __str__(self):\\n return str(self.name)\",\n \"def __str__(self):\\n return str(self.name)\",\n \"def __str__(self):\\n return str(self.name)\",\n \"def __str__(self):\\n return str(self.name)\",\n \"def __str__(self):\\n return str(self.name)\",\n \"def __str__(self):\\n return str(self.name)\",\n \"def __str__(self):\\n return str(self.name)\",\n \"def __str__(self):\\n class_name_str = str(self.__class__.__name__) + \\\": (\\\"\\n attributes_str = str(self.head_vertex) + \\\", \\\" + \\\\\\n str(self.tail_vertex) + \\\")\\\"\\n str_rep = class_name_str + attributes_str\\n return str_rep\",\n \"def long_displayname(self):\\n return self.get_long_displayname()\",\n \"def names_singleton(self):\\r\\n return self.object_name\",\n \"def __str__(self):\\n return str(self.obj)\",\n \"def __str__(self):\\n # add a dagger symbol to the class name if needed\\n temp = super().__str__()\\n if self.dagger:\\n temp += \\\".H\\\"\\n return temp\",\n \"def get_descriptive_name(self):\\r\\n long_name = str(self.year)+' '+self.make + ' '+self.model\\r\\n return long_name.title()\",\n \"def __str__(self):\\n return f'{self.name}' # TODO\",\n \"def name(self):\\n # easy enough\\n return \\\"{0.first} {0.last}\\\".format(self)\",\n \"def get_name(self):\\n return '.'.join(self.name)\",\n \"def get_descriptive_name(self):\\r\\n long_name=str(self.year)+' '+self.make+' '+self.model\\r\\n return long_name.title()\",\n \"def __str__(self):\\n return f'{self._name} has {self._calories} calories, {self._carbohydrates}' +\\\\\\n f'g. carbohydrates, {self._fat}g. of fat and {self._proteins}g. of proteins'\",\n \"def get_descriptive_name(self):\\r\\n long_name = str(self.year) + ' ' + self.make + ' ' + self.model\\r\\n return long_name.title()\",\n \"def fullname(cls): # pylint: disable=no-self-argument\\n return func.concat(\\n (select([Project.name]).where(Project.id == cls.project_id).as_scalar()),\\n \\\" \\\",\\n cls.name,\\n )\",\n \"def full_name(self) -> str:\\n # return self.separator.join(map(lambda x: x.name, self.path()))\\n return self.separator.join(map(lambda x: x.tagged_name, self.path()))\",\n \"def __str__(self):\\n return self._name\",\n \"def print_objects(self):\\n print(\\\"Spaces: {}\\\".format([s.name for s in self.spaces]))\\n print(\\\"Characters: {}\\\".format([c.name for c in self.characters]))\\n print(\\\"Items: {}\\\".format([i.name for i in self.items]))\",\n \"def __str__(self):\\n ingredient_names = [str(ingredient) for ingredient in self.ingredients]\\n return ', '.join(ingredient_names)\",\n \"def __str__(self):\\n\\t\\treturn self.full_name\",\n \"def get_measurement_objects_name(self):\\n return self.object_name.value\",\n \"def get_descriptive_name(self):\\n long_name = f\\\"{self.make} {self.model} {self.year}\\\"\\n \\n return long_name.title()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6896629","0.68546605","0.6810425","0.6623793","0.66023505","0.6594769","0.64818907","0.6474733","0.6473779","0.64563364","0.6453014","0.64212185","0.6386153","0.6386153","0.6374827","0.6345542","0.6344488","0.6342986","0.63186526","0.63086903","0.62997484","0.6287605","0.6282785","0.6276912","0.62538815","0.62509257","0.62448007","0.62426776","0.623534","0.6233987","0.6225412","0.62229246","0.6208596","0.6205621","0.6182659","0.6165017","0.61511546","0.6149606","0.61492944","0.6141628","0.61401755","0.6125118","0.6124529","0.6124529","0.6124529","0.6124529","0.6124529","0.611666","0.61012626","0.6089391","0.60887665","0.60753447","0.60671437","0.60649955","0.6058519","0.60546887","0.605363","0.605338","0.60391736","0.6032148","0.6030801","0.6030372","0.6022937","0.6022937","0.6022937","0.6022937","0.6022937","0.6022937","0.6022937","0.60180455","0.60159224","0.60141706","0.6012786","0.6011515","0.5997032","0.5997032","0.5997032","0.5997032","0.5997032","0.5997032","0.5997032","0.59961456","0.59956443","0.59922427","0.5982907","0.59814495","0.5977438","0.5977393","0.59754086","0.5975095","0.5969954","0.59647465","0.59624946","0.5958818","0.59570885","0.59555936","0.59544295","0.5944699","0.5943297","0.5935165","0.5933499"],"string":"[\n \"0.6896629\",\n \"0.68546605\",\n \"0.6810425\",\n \"0.6623793\",\n \"0.66023505\",\n \"0.6594769\",\n \"0.64818907\",\n \"0.6474733\",\n \"0.6473779\",\n \"0.64563364\",\n \"0.6453014\",\n \"0.64212185\",\n \"0.6386153\",\n \"0.6386153\",\n \"0.6374827\",\n \"0.6345542\",\n \"0.6344488\",\n \"0.6342986\",\n \"0.63186526\",\n \"0.63086903\",\n \"0.62997484\",\n \"0.6287605\",\n \"0.6282785\",\n \"0.6276912\",\n \"0.62538815\",\n \"0.62509257\",\n \"0.62448007\",\n \"0.62426776\",\n \"0.623534\",\n \"0.6233987\",\n \"0.6225412\",\n \"0.62229246\",\n \"0.6208596\",\n \"0.6205621\",\n \"0.6182659\",\n \"0.6165017\",\n \"0.61511546\",\n \"0.6149606\",\n \"0.61492944\",\n \"0.6141628\",\n \"0.61401755\",\n \"0.6125118\",\n \"0.6124529\",\n \"0.6124529\",\n \"0.6124529\",\n \"0.6124529\",\n \"0.6124529\",\n \"0.611666\",\n \"0.61012626\",\n \"0.6089391\",\n \"0.60887665\",\n \"0.60753447\",\n \"0.60671437\",\n \"0.60649955\",\n \"0.6058519\",\n \"0.60546887\",\n \"0.605363\",\n \"0.605338\",\n \"0.60391736\",\n \"0.6032148\",\n \"0.6030801\",\n \"0.6030372\",\n \"0.6022937\",\n \"0.6022937\",\n \"0.6022937\",\n \"0.6022937\",\n \"0.6022937\",\n \"0.6022937\",\n \"0.6022937\",\n \"0.60180455\",\n \"0.60159224\",\n \"0.60141706\",\n \"0.6012786\",\n \"0.6011515\",\n \"0.5997032\",\n \"0.5997032\",\n \"0.5997032\",\n \"0.5997032\",\n \"0.5997032\",\n \"0.5997032\",\n \"0.5997032\",\n \"0.59961456\",\n \"0.59956443\",\n \"0.59922427\",\n \"0.5982907\",\n \"0.59814495\",\n \"0.5977438\",\n \"0.5977393\",\n \"0.59754086\",\n \"0.5975095\",\n \"0.5969954\",\n \"0.59647465\",\n \"0.59624946\",\n \"0.5958818\",\n \"0.59570885\",\n \"0.59555936\",\n \"0.59544295\",\n \"0.5944699\",\n \"0.5943297\",\n \"0.5935165\",\n \"0.5933499\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":77998,"cells":{"query":{"kind":"string","value":"Takes a source class ID and returns the int class ID assigned to it."},"document":{"kind":"string","value":"def map_source_class_id(self, source_class_id):\n return self.class_from_source_map[source_class_id]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_source_class_id(self, class_id, source):\n info = self.class_info[class_id]\n assert info['source'] == source\n return info['id']","def get_source_class_id(self, class_id, source):\n info = self.class_info[class_id]\n assert info['source'] == source\n return info['id']","def class_id(self):\n return self._class_id","def class_id(self) -> str:\n return self._class_id","def class_id(self) -> str:\n return self._class_id","def class_name_to_id(self, class_name: str):\n\n return self.class_to_idx[str(class_name)]","def get_catalog_identifier(source: Union[Source, int]):\n return source.catalogid if isinstance(source, Source) else int(source)","def cls_identifier(cls):\n if hasattr(cls, 'get_cls_identifier'):\n return cls.get_cls_identifier()\n return Encoder.default_identifier(cls)","def set_instance_id_from_source(self, source=None):\n self.instance_id = get_instance_id_from_source(source=source)\n return self.instance_id","def source_id(self):\n return self._source_id","def source_id(self) -> str:\n return self._source_id","def source_id(self) -> str:\n return self._source_id","def classId(self):\n return _internals.CorrelationId_classId(self)","def getID(self) -> int:\n ...","def rclass_id_lookup(cur):\n if 'rclass_id' not in _tables:\n cur.execute(\"SELECT name, rclass_id FROM rclass\")\n _tables['rclass_id'] = dict(cur)\n return _tables['rclass_id']","def get_instance_id_from_source(source=None):\n filename = getattr(source, 'name')\n instance_id = None\n try:\n offset = filename.find('-console-output.txt')\n if offset != -1:\n instance_id = filename[0:-offset]\n except Exception as e: # noqa\n pass\n return instance_id","def map_id_to_idx(self, class_ids):\n class_idx = torch.zeros(class_ids.shape, dtype=int)\n for k, v in self.id2idx.items():\n class_idx[class_ids == k] = v\n\n class_idx = class_idx.to(device)\n return class_idx","def getHNID(self, source):\n urlStart = source.find('score_') + 6\n urlEnd = source.find('\"', urlStart)\n try: \n return int(source[urlStart:urlEnd])\n except ValueError:\n return -1","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def _get_id(self):\n return self.__id","def getID():","def source_id(self) -> Optional[str]:\n return pulumi.get(self, \"source_id\")","def _get_det_id(self, source):\n match = re.match(r\"Camp\\.0:pnCCD\\.(\\d)\", source)\n number = str.zfill(match.groups()[0], 4)\n return \"pnccd_\" + number","def generate_unique_class_type_id(self): \n if Project.__next_type_id > 2_000_000_000:\n raise Exception(\"Too many classes\")\n\n Project.__next_type_id += 1\n return Project.__next_type_id","def id_to_class_name(self, identifier: int):\n\n return self.id_to_class_mapping()[identifier]","def _get_id(self, id_family: str) -> int:\n res = self._id_manager[id_family]\n self._id_manager[id_family] += 1\n return res","def get_id(self) -> int:\n return self.id","def get_id(self) -> int:\n return self.id","def _id(self):\n pass","def _get_id(self):\n return self.id","def source_identifier(self) -> str:\n return pulumi.get(self, \"source_identifier\")","def source_identifier(self) -> str:\n return pulumi.get(self, \"source_identifier\")","def key(cls, id):\n\n if isinstance(cls, type):\n cls = cls.__name__\n return str(cls) + '.' + str(id)","def get_primary_id(self):","def id(self):\n # Might also be a first 12-characters shortcut.\n return self._id","def get_id(self):\n pass","def get_id(self):\n pass","def get_id(self):\n pass","def get_id(self):\n pass","def get_id(self): # pragma: no cover\n pass","def lookup_class_idx(self,label):\r\n \r\n return self.class_labels[label]","def id(self):\n\t\treturn self.__id","def src_task_id(self):\n return struct.unpack('<H', self.pkt.payload[4:6])[0]","def get_ident():\n return -1","def newid(self, target_table):\n self.new_id[target_table] += 1\n return self.new_id[target_table]","def get_id(self):\n\n\t\treturn self.__id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def id(self) -> int:\n return self._id","def GetId(self):\n return int(self.id)","def source_id(self, source_id: str):\n\n self._source_id = source_id","def to_number(self, id):\r\n if isinstance(id, int):\r\n return id\r\n else:\r\n return self.name_to_number(id)","def get_id(type_: Dict[str, str]) -> int:\n return int(type_[f'{type_name}_id'])","def obj_id(self) -> int:\n return int(self.index.split(\"/\")[-1]) if self.index else None"],"string":"[\n \"def get_source_class_id(self, class_id, source):\\n info = self.class_info[class_id]\\n assert info['source'] == source\\n return info['id']\",\n \"def get_source_class_id(self, class_id, source):\\n info = self.class_info[class_id]\\n assert info['source'] == source\\n return info['id']\",\n \"def class_id(self):\\n return self._class_id\",\n \"def class_id(self) -> str:\\n return self._class_id\",\n \"def class_id(self) -> str:\\n return self._class_id\",\n \"def class_name_to_id(self, class_name: str):\\n\\n return self.class_to_idx[str(class_name)]\",\n \"def get_catalog_identifier(source: Union[Source, int]):\\n return source.catalogid if isinstance(source, Source) else int(source)\",\n \"def cls_identifier(cls):\\n if hasattr(cls, 'get_cls_identifier'):\\n return cls.get_cls_identifier()\\n return Encoder.default_identifier(cls)\",\n \"def set_instance_id_from_source(self, source=None):\\n self.instance_id = get_instance_id_from_source(source=source)\\n return self.instance_id\",\n \"def source_id(self):\\n return self._source_id\",\n \"def source_id(self) -> str:\\n return self._source_id\",\n \"def source_id(self) -> str:\\n return self._source_id\",\n \"def classId(self):\\n return _internals.CorrelationId_classId(self)\",\n \"def getID(self) -> int:\\n ...\",\n \"def rclass_id_lookup(cur):\\n if 'rclass_id' not in _tables:\\n cur.execute(\\\"SELECT name, rclass_id FROM rclass\\\")\\n _tables['rclass_id'] = dict(cur)\\n return _tables['rclass_id']\",\n \"def get_instance_id_from_source(source=None):\\n filename = getattr(source, 'name')\\n instance_id = None\\n try:\\n offset = filename.find('-console-output.txt')\\n if offset != -1:\\n instance_id = filename[0:-offset]\\n except Exception as e: # noqa\\n pass\\n return instance_id\",\n \"def map_id_to_idx(self, class_ids):\\n class_idx = torch.zeros(class_ids.shape, dtype=int)\\n for k, v in self.id2idx.items():\\n class_idx[class_ids == k] = v\\n\\n class_idx = class_idx.to(device)\\n return class_idx\",\n \"def getHNID(self, source):\\n urlStart = source.find('score_') + 6\\n urlEnd = source.find('\\\"', urlStart)\\n try: \\n return int(source[urlStart:urlEnd])\\n except ValueError:\\n return -1\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def _get_id(self):\\n return self.__id\",\n \"def getID():\",\n \"def source_id(self) -> Optional[str]:\\n return pulumi.get(self, \\\"source_id\\\")\",\n \"def _get_det_id(self, source):\\n match = re.match(r\\\"Camp\\\\.0:pnCCD\\\\.(\\\\d)\\\", source)\\n number = str.zfill(match.groups()[0], 4)\\n return \\\"pnccd_\\\" + number\",\n \"def generate_unique_class_type_id(self): \\n if Project.__next_type_id > 2_000_000_000:\\n raise Exception(\\\"Too many classes\\\")\\n\\n Project.__next_type_id += 1\\n return Project.__next_type_id\",\n \"def id_to_class_name(self, identifier: int):\\n\\n return self.id_to_class_mapping()[identifier]\",\n \"def _get_id(self, id_family: str) -> int:\\n res = self._id_manager[id_family]\\n self._id_manager[id_family] += 1\\n return res\",\n \"def get_id(self) -> int:\\n return self.id\",\n \"def get_id(self) -> int:\\n return self.id\",\n \"def _id(self):\\n pass\",\n \"def _get_id(self):\\n return self.id\",\n \"def source_identifier(self) -> str:\\n return pulumi.get(self, \\\"source_identifier\\\")\",\n \"def source_identifier(self) -> str:\\n return pulumi.get(self, \\\"source_identifier\\\")\",\n \"def key(cls, id):\\n\\n if isinstance(cls, type):\\n cls = cls.__name__\\n return str(cls) + '.' + str(id)\",\n \"def get_primary_id(self):\",\n \"def id(self):\\n # Might also be a first 12-characters shortcut.\\n return self._id\",\n \"def get_id(self):\\n pass\",\n \"def get_id(self):\\n pass\",\n \"def get_id(self):\\n pass\",\n \"def get_id(self):\\n pass\",\n \"def get_id(self): # pragma: no cover\\n pass\",\n \"def lookup_class_idx(self,label):\\r\\n \\r\\n return self.class_labels[label]\",\n \"def id(self):\\n\\t\\treturn self.__id\",\n \"def src_task_id(self):\\n return struct.unpack('<H', self.pkt.payload[4:6])[0]\",\n \"def get_ident():\\n return -1\",\n \"def newid(self, target_table):\\n self.new_id[target_table] += 1\\n return self.new_id[target_table]\",\n \"def get_id(self):\\n\\n\\t\\treturn self.__id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def id(self) -> int:\\n return self._id\",\n \"def GetId(self):\\n return int(self.id)\",\n \"def source_id(self, source_id: str):\\n\\n self._source_id = source_id\",\n \"def to_number(self, id):\\r\\n if isinstance(id, int):\\r\\n return id\\r\\n else:\\r\\n return self.name_to_number(id)\",\n \"def get_id(type_: Dict[str, str]) -> int:\\n return int(type_[f'{type_name}_id'])\",\n \"def obj_id(self) -> int:\\n return int(self.index.split(\\\"/\\\")[-1]) if self.index else None\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8493009","0.8493009","0.6766999","0.6760587","0.6760587","0.66088873","0.6549115","0.6302364","0.62795174","0.6275053","0.6236329","0.6236329","0.61188203","0.6107271","0.59653497","0.5949911","0.58152884","0.572042","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5719192","0.5716977","0.5707617","0.56925917","0.56247824","0.5624083","0.5616711","0.56162584","0.56162584","0.56057626","0.5589944","0.5568896","0.5568896","0.55406487","0.55396384","0.55352724","0.55264896","0.55264896","0.55264896","0.55264896","0.5497022","0.54880124","0.5469746","0.5452178","0.5449819","0.54451704","0.5438472","0.54297626","0.54297626","0.54297626","0.54297626","0.54297626","0.54297626","0.54297626","0.54297626","0.54297626","0.54297626","0.54297626","0.54297626","0.54297626","0.54297626","0.54228324","0.53816426","0.5379307","0.53781056","0.5349772"],"string":"[\n \"0.8493009\",\n \"0.8493009\",\n \"0.6766999\",\n \"0.6760587\",\n \"0.6760587\",\n \"0.66088873\",\n \"0.6549115\",\n \"0.6302364\",\n \"0.62795174\",\n \"0.6275053\",\n \"0.6236329\",\n \"0.6236329\",\n \"0.61188203\",\n \"0.6107271\",\n \"0.59653497\",\n \"0.5949911\",\n \"0.58152884\",\n \"0.572042\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5719192\",\n \"0.5716977\",\n \"0.5707617\",\n \"0.56925917\",\n \"0.56247824\",\n \"0.5624083\",\n \"0.5616711\",\n \"0.56162584\",\n \"0.56162584\",\n \"0.56057626\",\n \"0.5589944\",\n \"0.5568896\",\n \"0.5568896\",\n \"0.55406487\",\n \"0.55396384\",\n \"0.55352724\",\n \"0.55264896\",\n \"0.55264896\",\n \"0.55264896\",\n \"0.55264896\",\n \"0.5497022\",\n \"0.54880124\",\n \"0.5469746\",\n \"0.5452178\",\n \"0.5449819\",\n \"0.54451704\",\n \"0.5438472\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54297626\",\n \"0.54228324\",\n \"0.53816426\",\n \"0.5379307\",\n \"0.53781056\",\n \"0.5349772\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7406211"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":77999,"cells":{"query":{"kind":"string","value":"Map an internal class ID to the corresponding class ID in the source dataset."},"document":{"kind":"string","value":"def get_source_class_id(self, class_id, source):\n info = self.class_info[class_id]\n assert info['source'] == source\n return info['id']"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def map_source_class_id(self, source_class_id):\n return self.class_from_source_map[source_class_id]","def map_source_class_id(self, source_class_id):\n return self.class_from_source_map[source_class_id]","def map_id_to_idx(self, class_ids):\n class_idx = torch.zeros(class_ids.shape, dtype=int)\n for k, v in self.id2idx.items():\n class_idx[class_ids == k] = v\n\n class_idx = class_idx.to(device)\n return class_idx","def remap_ids(self, id_map: Dict[int, int]) -> None:","def class_name_to_id(self, class_name: str):\n\n return self.class_to_idx[str(class_name)]","def prepare(self, class_map=None):\n\n def clean_name(name):\n \"\"\"Returns a shorter version of object names for cleaner display.\"\"\"\n return \",\".join(name.split(\",\")[:1])\n\n # Build (or rebuild) everything else from the info dicts.\n self.num_classes = len(self.class_info)\n self.class_ids = np.arange(self.num_classes)\n self.class_names = [clean_name(c[\"name\"]) for c in self.class_info]\n self.num_images = len(self.image_info)\n self._image_ids = np.arange(self.num_images)\n\n # Mapping from source class and image IDs to internal IDs\n self.class_from_source_map = {\"{}.{}\".format(info['source'], info['id']): id\n for info, id in zip(self.class_info, self.class_ids)}\n self.image_from_source_map = {\"{}.{}\".format(info['source'], info['id']): id\n for info, id in zip(self.image_info, self.image_ids)}\n\n # Map sources to class_ids they support\n self.sources = list(set([i['source'] for i in self.class_info]))\n self.source_class_ids = {}\n # Loop over datasets\n for source in self.sources:\n self.source_class_ids[source] = []\n # Find classes that belong to this dataset\n for i, info in enumerate(self.class_info):\n # Include BG class in all datasets\n if i == 0 or source == info['source']:\n self.source_class_ids[source].append(i)","def prepare(self, class_map=None):\n def clean_name(name):\n \"\"\"Returns a shorter version of object names for cleaner display.\"\"\"\n return \",\".join(name.split(\",\")[:1])\n\n # Build (or rebuild) everything else from the info dicts.\n self.num_classes = len(self.class_info)\n self.class_ids = np.arange(self.num_classes)\n self.class_names = [clean_name(c[\"name\"]) for c in self.class_info]\n self.num_images = len(self.image_info)\n self._image_ids = np.arange(self.num_images)\n\n self.class_from_source_map = {\"{}.{}\".format(info['source'], info['id']): id\n for info, id in zip(self.class_info, self.class_ids)}\n\n # Map sources to class_ids they support\n self.sources = list(set([i['source'] for i in self.class_info]))\n self.source_class_ids = {}\n # Loop over datasets\n for source in self.sources:\n self.source_class_ids[source] = []\n # Find classes that belong to this dataset\n for i, info in enumerate(self.class_info):\n # Include BG class in all datasets\n if i == 0 or source == info['source']:\n self.source_class_ids[source].append(i)","def remap_ids(self, id_map: Dict[int, int]) -> None:\n super().remap_ids(id_map)\n nds = self.nodes\n for i, nd_id in enumerate(nds):\n nds[i] = id_map.get(nd_id, 0)","def map(self, datasetType, dataId):\n\n func = getattr(self, 'map_' + datasetType)\n return func(dataId)","def map_to_per_ntype(self, ids): # -> None:\n ...","def view(self) -> 'typing.Mapping[int, Dataclass]':","def map_from_parent_nid(self, layer_id, parent_nids, remap_local=...):\n ...","def map_column_to_index(self, col):\n if col in self.column_maps:\n return\n\n # First construct the map from original ids to new ones.\n ids = pd.concat((self.train[col], self.test[col])).unique()\n n = len(ids)\n idmap = dict(itertools.izip(ids, xrange(n)))\n\n # Next use the map to convert the ids in-place.\n self.train.loc[:, col] = self.train[col].apply(lambda _id: idmap[_id])\n self.test.loc[:, col] = self.test[col].apply(lambda _id: idmap[_id])\n\n # Now swap key for value in the idmap to provide a way to convert back.\n reverse_map = {val: key for key, val in idmap.iteritems()}\n self.column_maps[col] = reverse_map","def make_classdict(self):\n\t\ttarget_names = self.fetched[\"train\"].target_names\n\t\tself.classdict = {target_names[idx]: idx for idx in range(len(target_names))}","def remap(self, old_id):\n if old_id in self.mapping:\n new_id = self.mapping[old_id]\n else:\n if not self.reuse or old_id in self.blocklist:\n # We cannot reuse the old-id\n new_id = self.next_id()\n else:\n # We can reuse the old-id\n new_id = old_id\n if isinstance(old_id, int) and old_id >= self._nextid:\n self._nextid = old_id + 1\n self.mapping[old_id] = new_id\n return new_id","def map_ids(df, idname, remove=False):\n # Lookup id-mapping class in global table.\n klass = IDMAP_TASKS[idname]\n task = klass()\n\n # Shouldn't really need this here.\n if not task.complete():\n task.run()\n\n # Load idmap from file.\n with task.output().open() as f:\n idmap = pd.read_csv(f, index_col=0)\n\n # Create tmp column to store new column.\n tmpname = '_new_%s' % idname\n idmap[tmpname] = idmap.index\n\n # Ensure data types match up after merge.\n for colname in klass.ids:\n dtype = df[colname].dtype.type\n idmap[colname] = idmap[colname].values.astype(dtype)\n\n # Move values from tmp column to `idname` column and delete tmp.\n df = df.merge(idmap, how='left', on=klass.ids)\n df[idname] = df[tmpname]\n del df[tmpname]\n\n # Remove columns used to create mapping, if requeseted.\n if remove:\n for colname in klass.ids:\n del df[colname]\n\n return df","def _set_mapper(self, classification_dict):\n d = {class_code: class_index for class_index, class_code in enumerate(classification_dict.keys())}\n # Here we update the dict so that code 65 remains unchanged.\n # Indeed, 65 is reserved for noise/artefacts points, that will be deleted by transform \"DropPointsByClass\".\n d.update({65: 65})\n self.mapper = np.vectorize(lambda class_code: d.get(class_code))","def remap_ids(self, id_map: Dict[int, int]) -> None:\n super().remap_ids(id_map)\n ents = self.entities\n for i, ent_id in enumerate(ents):\n ents[i] = id_map.get(ent_id, 0)","def class_id(self, class_id):\n\n self._class_id = class_id","def map_id(id, user = True):\n\n # Reading the mapping array from models folder\n\n uq = None\n if user:\n uq = np.load(configs.USER_IDS_PATH)\n else:\n uq = np.load(configs.MOVIE_IDS_PATH)\n\n\n # Returning the new id\n if id in uq:\n return np.where(uq==id)[0][0]\n else:\n return None","def remap_ids(self, id_map: Dict[int, int]) -> None:\n super().remap_ids(id_map)\n self.puppet = id_map.get(self.puppet, 0)","def before_update(mapper, conn, target):\n if not target.id_:\n dataset = ObjectNumber.parse(target.d_id)\n target.id_ = str(PartitionNumber(dataset, target.sequence_id))","def map_column_to_index(self, col):\n if col in self.column_maps:\n return\n\n # First construct the map from original ids to new ones.\n ids = self.dataset[col].unique()\n n = len(ids)\n idmap = dict(itertools.izip(ids, xrange(n)))\n\n # Next use the map to conver the ids in-place.\n self.dataset[col] = self.dataset[col].apply(lambda _id: idmap[_id])\n\n # Now swap key for value in the idmap to provide a way to convert back.\n reverse_map = {val: key for key, val in idmap.iteritems()}\n self.column_maps[col] = reverse_map","def rclass_id_lookup(cur):\n if 'rclass_id' not in _tables:\n cur.execute(\"SELECT name, rclass_id FROM rclass\")\n _tables['rclass_id'] = dict(cur)\n return _tables['rclass_id']","def map_to_per_etype(self, ids): # -> None:\n ...","def mapping(self, release_id, grouping):\n\n helper = Known(self.config, self.session)\n classes = [g['name']['full'] for g in grouping]\n return helper.mapping(release_id, classes)","def id_to_class_name(self, identifier: int):\n\n return self.id_to_class_mapping()[identifier]","def update_class_records():\n logger.info('Fetching class ids and labels for '\n 'classes with direct instances')\n timestamp = statistics.get_current_timestamp()\n results = sparql.sparql_query(queries.QUERY_CLASSES,\n fallback=queries.QUERY_CLASSSES_FALLBACK)\n\n if 'error' in results:\n logger.error('Got no SPARQL results.')\n return\n\n updated = {}\n\n def value(binding, key):\n return binding[key]['value']\n\n for binding in results['results']['bindings']:\n uri = value(binding, 'cl')\n if not sparql.is_wikidata_entity(uri):\n continue\n\n qid = sparql.wikidata_entity_id(uri)\n label = value(binding, 'clLabel')\n record = {'l': label, }\n\n if 'c' in binding:\n record['i'] = int(value(binding, 'c'))\n updated[qid] = record\n\n logger.info('Augmenting current classes data ...')\n data = statistics.get_json_data('classes')\n merged = statistics.merge(data, updated, default_others={'i': 0})\n statistics.update_json_data('classes', merged, timestamp)","def initMaps(self):\r\n assert isinstance(self.CLASSES, (list, tuple))\r\n assert self.CLASSES[0] == \"__background__\"\r\n cls = self.CLASSES\r\n self.name_to_id = dict(zip(cls, range(len(cls))))\r\n self.id_to_name = dict(zip(range(len(cls)), cls))","def process_images_class(images_class):\n\n images_class_to_int_map = {image: i for (i, image) in enumerate(sorted(set(images_class)))}\n print('Mapping:\\n{}'.format(images_class_to_int_map))\n images_class_int = [images_class_to_int_map[i] for i in images_class]\n\n return to_categorical(images_class_int, num_classes=len(set(images_class)))","def remap_ids(self, id_map: Dict[int, int]) -> None:\n super().remap_ids(id_map)\n self.door = id_map.get(self.door, 0)","def class_id(self) -> str:\n return self._class_id","def class_id(self) -> str:\n return self._class_id","def class_id(self):\n return self._class_id","def name2Id(self, classExpression):\n out = classExpression\n name_id = self.gen_name_id()\n for k, v in name_id.items():\n out = re.sub(\"\\'\"+k+\"\\'\", v, out) # Suspect this not Pythonic. Could probably be done with a fancy map lambda combo. \n return out","def cls_identifier(cls):\n if hasattr(cls, 'get_cls_identifier'):\n return cls.get_cls_identifier()\n return Encoder.default_identifier(cls)","def get_by_id (self, id):\n clsfor_obj = self.classfor()\n clsfor_obj.from_id (id)\n return clsfor_obj\n return\n for name, obj in inspect.getmembers (clsfor_obj):\n if isinstance (obj, RField):\n key = clsfor_obj.prepare_key (name, int(id))","def mapper_id(self, structure):\n type_ = structure['type']\n mapper = self.mappers[type_]\n return id(mapper)","def _map_class(max_iou_indices, labels):\n #add on index column\n max_iou_indices = tf.stack([tf.reshape(tf.convert_to_tensor([np.arange(0, tf.shape(all_anchors)[0])]), [1, tf.shape(max_iou_indices)[0]]),\n tf.cast(tf.expand_dims(max_iou_indices, axis=0), dtype=tf.int32)], axis=0)\n max_iou_indices = tf.transpose(tf.squeeze(max_iou_indices))\n broadcasted_labels = tf.broadcast_to(labels, [tf.shape(all_anchors)[0], tf.shape(random_labels)[0]])\n anchor_classes = tf.gather_nd(broadcasted_labels, temp)\n return anchor_classes","def map_to_per_ntype(self, ids): # -> tuple[Unknown, Unknown]:\n ...","def map_to_per_ntype(self, ids): # -> tuple[Unknown, Unknown]:\n ...","def lookup_class_idx(self,label):\r\n \r\n return self.class_labels[label]","def key(cls, id):\n\n if isinstance(cls, type):\n cls = cls.__name__\n return str(cls) + '.' + str(id)","def _map_new_class_index(y, order):\n return np.array(list(map(lambda x: order.index(x), y)))","def id_index_map(self):\n result = {}\n for index, component_data in iteritems(self):\n result[id(component_data)] = index\n return result","def map_to_per_etype(self, ids): # -> tuple[Unknown, Unknown]:\n ...","def map_to_per_etype(self, ids): # -> tuple[Unknown, Unknown]:\n ...","def cellannotation2ID(self, annotation_type):\n annotations = list(self.adata.obs[annotation_type])\n annotations_set = sorted(set(annotations))\n \n mapping = {a:idx for idx,a in enumerate(annotations_set)}\n \n truth_labels = [mapping[a] for a in annotations]\n self.adata.obs['label'] = pd.Categorical(values=truth_labels)\n #18m-unannotated\n # \n return mapping","def assign_ref_id(self: _EntityT, ref_id: EntityId) -> _EntityT:\n return dataclasses.replace(self, ref_id=ref_id)","def makeIdFactory(self, dataRef):\n # With the default configuration, this IdFactory doesn't do anything, because\n # the IDs it generates are immediately overwritten by the ID from the reference\n # catalog (since that's in config.measurement.copyColumns). But we create one here anyway, to\n # allow us to revert back to the old behavior of generating new forced source IDs,\n # just by renaming the ID in config.copyColumns to \"object_id\".\n expBits = dataRef.get(self.config.coaddName + \"CoaddId_bits\")\n expId = int(dataRef.get(self.config.coaddName + \"CoaddId\"))\n return lsst.afw.table.IdFactory.makeSource(expId, 64 - expBits)","def id_to_index(self, id):\n raise NotImplementedError","def _do_mapping(self):\n pass","def image_reference(self, image_id):\n info = self.image_info[image_id]\n if info['source'] == self.dataset_name:\n return info['id']\n else:\n super.image_reference(image_id)","def before_update(mapper, conn, target):\n if isinstance(target, Column):\n raise TypeError('Got a column instead of a table')\n\n if target.id_ is None:\n dataset_id = ObjectNumber.parse(target.d_id)\n target.id_ = str(TableNumber(dataset_id, target.sequence_id))","def map_to_homo_nid(self, ids, ntype):\n ...","def map_to_homo_nid(self, ids, ntype):\n ...","def getId(self):\n if getattr(self,'id',None):\n return self.id\n name = self.name\n #--Singleton records \n if name in frozenset(('FMAP','GAME','JOUR','KLST','PCDT','REFR','SPLM','TES3')):\n return None\n #--Special records.\n elif name == 'CELL':\n reader = self.getReader()\n srName = reader.findSubRecord('NAME',name)\n srData = reader.findSubRecord('DATA',name)\n (flags,gridX,gridY) = struct.unpack('3i',record.data)\n if flags & 1:\n self.id = cstrip(srName)\n else:\n self.id = '[%d,%d]' % (gridX,gridY)\n elif name == 'INFO':\n srData = self.getReader().findSubRecord('INAM',name)\n self.id = cstrip(srData)\n elif name == 'LAND':\n srData = self.getReader().findSubRecord('INTV',name)\n self.id = '[%d,%d]' % struct.unpack('2i',srData)\n elif name == 'PGRD':\n reader = self.getReader()\n srData = reader.findSubRecord('DATA',name)\n srName = reader.findSubRecord('NAME',name)\n gridXY = struct.unpack('2i',srData[:8])\n if srData != (0,0) or not srName:\n self.id = '[%d,%d]' % gridXY\n else:\n self.id = cstrip(srName)\n elif name == 'SCPT':\n srData = self.getReader().findSubRecord('SCHD',name)\n self.id = cstrip(srData[:32])\n #--Most records: id in NAME record.\n else:\n srData = self.getReader().findSubRecord('NAME',name)\n self.id = srData and cstrip(srData)\n #--Done\n return self.id","def encode_target(df, target_column):\n df_mod = df.copy()\n targets = df_mod[target_column].unique()\n map_to_int = {name: n for n, name in enumerate(targets)}\n #print map_to_int\n df_mod[\"Target\"] = df_mod[target_column].replace(map_to_int)\n return (df_mod, targets)","def map_user_id(self, users, project_in, project_out):\n raise NotImplementedError()","def __init__(self, annoPath, classes , datasetName, setID,useDiff = True,\n convertToPerson = None, convertIdToCls = None,\n is_image_index_flattened=False, splitIndex=1):\n self._annoPath = annoPath\n self._classes = classes\n self._datasetName = datasetName\n self._setID = setID\n self.num_classes = len(classes)\n self.useDiff = useDiff\n self._classToIndex = self._create_classToIndex(classes)\n self._convertToPerson = convertToPerson\n self._convertIdToCls = convertIdToCls\n self._is_image_index_flattened = is_image_index_flattened\n self._splitIndex = 1","def get_identifier_map(self) -> None:\n id_mapping_dict = self._get_identifiers_from_kbs()\n id_mapping_dict = self._add_uniprot_identifiers(id_mapping_dict)\n id_mapping_dict = self._add_chebi_identifiers(id_mapping_dict)\n id_mapping_dict = self._add_bridge_db_identifiers(id_mapping_dict)\n\n print(\"merging similar...\")\n id_mapping_dict = pathway_utils.merge_similar(id_mapping_dict)\n\n print(\"generating local identifiers...\")\n self.forward_map, self.backward_map = self._generate_local_identifiers(id_mapping_dict)\n self.save_id_dict()","def mapFilter(self, filtername, filterclass, userid):\n # logging.info('%s, %s, %s' % (filtername, filterclass, userid))\n redis_cli = self.get_redis_cli()\n f_conf = self.config.filter_keys_conf\n rKey = f_conf[filtername].format(**{filtername:filterclass})\n if not rKey:\n raise ValueError, \"Haven't %s filter keys\" % filtername\n offset = int(userid)-stdoffset(self)\n if offset>0 and offset <= self.config.MAX_BITMAP_LENGTH:\n redis_cli.setbit(rKey, offset, 1)\n logging.debug('Save auid in redis by setbit %s %d 1' % (rKey, offset))","def create_class_indices(self) -> None:\n\n categories = list(np.loadtxt(self.path_manager.categories_file(), delimiter=\",\", dtype=str))\n\n if self.include_noise_samples and not self.multi_label_classification:\n categories.append(\"noise\")\n\n self.class_to_idx = {}\n\n for idx, class_name in enumerate(sorted(categories)):\n self.class_to_idx[class_name] = idx","def _id(self):\n pass","def convert_uid(klass, short_or_long_uid):\n if '_' in short_or_long_uid:\n return short_or_long_uid.split('_')[1]\n else:\n return klass.generate_uid(identifier=short_or_long_uid)","def _mapping(self, pdb_id, loop_type, normalizer):\n\n mapping = {}\n with self.session() as session:\n query = self.query(session, pdb_id).filter_by(type=loop_type)\n for result in query:\n unit_ids = normalizer(result.unit_ids)\n if unit_ids in mapping:\n self.logger.error(\"Loop %s duplicates %s\",\n result.loop_id, mapping[unit_ids])\n continue\n mapping[unit_ids] = result.loop_id\n return mapping","def assignClass(self):\n classes = {}\n classes['en'] = 0\n classes['nl'] = 0\n assignedClass = \"\"\n\n for record in self.data:\n if record[-1] == 'en':\n classes['en'] += 1\n elif record[-1] == 'nl':\n classes['nl'] += 1\n\n max = 0\n for key in classes.keys():\n # get max class\n if max < classes[key]:\n max = classes[key]\n assignedClass = key\n\n self.enClass = classes['en']\n self.nlClass = classes['nl']\n\n return assignedClass","def _create_labels_and_mapping(self, labels, mapping):\n numbered_classes = list(enumerate(list(labels), start=0))\n if mapping:\n new_mapping = {number: str(mapping[label]) for number, label in numbered_classes}\n else:\n new_mapping = {number: str(label) for number, label in numbered_classes}\n new_labels = [new_mapping[numbered[0]] for numbered in numbered_classes]\n\n return new_labels, new_mapping","def map_device_to_id(dev_map, device):\n for elem in dev_map:\n if device == elem['path']:\n return elem['id']","def id_for_label(self, id_):\n return id_","def id_to_local_index(self, id):\n raise NotImplementedError","def _map_predictions_to_new_cids(predictions, old_cids2new_cids):\r\n # transform predictions with new class ids: decs and probs correspond to training class ids\r\n # for plotting they should be transformed to new (inference or evaluation) class ids\r\n # e.g. output_Nclasses = 5,\r\n # training_cids2inference_cids = [-1, 1, 1, 0, -1] --> [2, 1, 1, 0, 2]\r\n # before: probs.shape: (..., 5) --> after: probs.shape: (..., 3)\r\n # for probs: using the probability of union rule: P(A ∪ B) = P(A) + P(B) - P(A ∩ B)\r\n # with P(A ∩ B) = 0 probabilities have to be summed\r\n # TODO(panos): save computations by checking if inference and training\r\n # problem defs are the same\r\n\r\n supported_keys = {\r\n 'l1_probabilities', 'rawimages', 'rawimagespaths', 'decisions', 'l1_decisions',\r\n 'l1_logits', 'l1_probabilities', 'l2_vehicle_decisions', 'l2_vehicle_logits',\r\n 'l2_vehicle_probabilities', 'l2_human_decisions', 'l2_human_logits',\r\n 'l2_human_probabilities'}\r\n # check if supported_keys is a superset of predictions.keys()\r\n assert supported_keys >= predictions.keys(), (\r\n f\"Supported keys are {sorted(supported_keys)} and predictions keys are {sorted(set(predictions.keys()))}. \"\r\n f\"Supported keys must be a superset of predictions keys for resizing predictions.\")\r\n\r\n probs, decs = itemgetter('l1_probabilities', 'decisions')(predictions)\r\n old_cids2new_cids = _replacevoids(old_cids2new_cids)\r\n ocids2ncids = tf.cast(old_cids2new_cids, tf.int32)\r\n decs = tf.gather(ocids2ncids, decs)\r\n try:\r\n probs_transposed = tf.transpose(probs, (3, 0, 1, 2))\r\n probs_transformed = tf.unsorted_segment_sum(\r\n probs_transposed, ocids2ncids, tf.reduce_max(ocids2ncids) + 1)\r\n probs = tf.transpose(probs_transformed, (1, 2, 3, 0))\r\n except:\r\n tf.logging.info('\\nl1_probabilities are not transformed to new cids.\\n')\r\n tf.logging.info('\\nOnly decisions were transformed to new cids.\\n')\r\n\r\n new_predictions = predictions\r\n new_predictions.update({'l1_probabilities': probs, 'decisions': decs})\r\n\r\n return new_predictions","def class_to_db(self):","def get_id_name_mapping_from_db(session, cls):\n\n return {obj.id: obj.name for obj in session.query(cls).all()}","def extract_mapping(self) -> DatasetMapping:\n # store fields\n fields = []\n for col in self.data.columns:\n #get field label\n label = col\n #get field type using PANDAS_TYPE (see apps.utils.utils)\n col_type = self.data[col].dtype\n field_type = PANDAS_TYPE[col_type]\n #set field\n field = FieldMapping(label=label, type=field_type)\n fields.append(field)\n self.mapping.append(label)\n return DatasetMapping(fields=fields)","def db_to_class(cls, record):\n raise NotImplementedError()","def node_mapping(self):\n ...","def get_preprocessed_seq_data(self, raw_data, cls):\n # Check that input data has unique ids\n self._check_unique_ids(raw_data)\n\n cls_id = int(self.class_name_to_class_id[cls])\n\n data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'similarity_scores']\n data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\n unique_gt_ids = []\n unique_tracker_ids = []\n num_gt_dets = 0\n num_tracker_dets = 0\n for t in range(raw_data['num_timesteps']):\n\n # Only extract relevant dets for this class for preproc and eval (cls)\n gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)\n gt_class_mask = gt_class_mask.astype(np.bool)\n gt_ids = raw_data['gt_ids'][t][gt_class_mask]\n gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]\n\n tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)\n tracker_class_mask = tracker_class_mask.astype(np.bool)\n tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]\n tracker_dets = [raw_data['tracker_dets'][t][ind] for ind in range(len(tracker_class_mask)) if\n tracker_class_mask[ind]]\n similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]\n\n # Match tracker and gt dets (with hungarian algorithm)\n unmatched_indices = np.arange(tracker_ids.shape[0])\n if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\n matching_scores = similarity_scores.copy()\n matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = -10000\n match_rows, match_cols = linear_sum_assignment(-matching_scores)\n actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\n match_cols = match_cols[actually_matched_mask]\n\n unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)\n\n # For unmatched tracker dets, remove those that are greater than 50% within a crowd ignore region.\n unmatched_tracker_dets = [tracker_dets[i] for i in range(len(tracker_dets)) if i in unmatched_indices]\n ignore_region = raw_data['gt_ignore_region'][t]\n intersection_with_ignore_region = self._calculate_mask_ious(unmatched_tracker_dets, [ignore_region],\n is_encoded=True, do_ioa=True)\n is_within_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps, axis=1)\n\n # Apply preprocessing to remove unwanted tracker dets.\n to_remove_tracker = unmatched_indices[is_within_ignore_region]\n data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\n data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\n similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\n\n # Keep all ground truth detections\n data['gt_ids'][t] = gt_ids\n data['gt_dets'][t] = gt_dets\n data['similarity_scores'][t] = similarity_scores\n\n unique_gt_ids += list(np.unique(data['gt_ids'][t]))\n unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\n num_tracker_dets += len(data['tracker_ids'][t])\n num_gt_dets += len(data['gt_ids'][t])\n\n # Re-label IDs such that there are no empty IDs\n if len(unique_gt_ids) > 0:\n unique_gt_ids = np.unique(unique_gt_ids)\n gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\n gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\n for t in range(raw_data['num_timesteps']):\n if len(data['gt_ids'][t]) > 0:\n data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\n if len(unique_tracker_ids) > 0:\n unique_tracker_ids = np.unique(unique_tracker_ids)\n tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\n tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\n for t in range(raw_data['num_timesteps']):\n if len(data['tracker_ids'][t]) > 0:\n data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\n\n # Record overview statistics.\n data['num_tracker_dets'] = num_tracker_dets\n data['num_gt_dets'] = num_gt_dets\n data['num_tracker_ids'] = len(unique_tracker_ids)\n data['num_gt_ids'] = len(unique_gt_ids)\n data['num_timesteps'] = raw_data['num_timesteps']\n data['seq'] = raw_data['seq']\n data['cls'] = cls\n\n # Ensure again that ids are unique per timestep after preproc.\n self._check_unique_ids(data, after_preproc=True)\n\n return data","def identify_class(self, cls):","def test_data_source_soaps_id_replace_post(self):\n pass","def _string_label_to_class_id_postprocessor(\n string_label, label_classes, default=-1, **unused_kwargs):\n if string_label in label_classes:\n return label_classes.index(string_label)\n else:\n return default","def separate_by_class(dataset, class_index):\n class_dictionary = {}\n for data_row in dataset:\n # determine what to use as a key\n # for the dictionary\n dict_key = data_row[class_index]\n \n # remove the class attribute from the\n # data so it doesn't screw up stats\n del data_row[class_index]\n\n if dict_key not in class_dictionary:\n class_dictionary[dict_key] = [data_row]\n else:\n class_dictionary[dict_key].append(data_row)\n\n return class_dictionary","def encode_target(df, target_column, label_map):\n df_mod = df.copy()\n targets = df_mod[target_column].unique()\n # map_to_int = {name: n for n, name in enumerate(targets)}\n df_mod[target_column] = df_mod[target_column].replace(label_map)\n\n return (df_mod, targets)","def __init__(self, mapping: Dict[int, int]):\n self.mapping = mapping","def _set_preprocessing_mapper(self, classification_preprocessing_dict):\n d = {key: value for key, value in classification_preprocessing_dict.items()}\n self.preprocessing_mapper = np.vectorize(lambda class_code: d.get(class_code, class_code))","def id_to_base_id(self, id):\n if self.xy_tiling is None and self.pc_tiling is None:\n return id\n return self.get_tile_from_path(id)[1]","def _data_id_from_label(self, label):\n for data_item in self.state.data_items:\n if data_item['name'] == label:\n return data_item['id']","def id_to_extended_id(item_id, item_class):\n id_prefix = {\n MSTrack: \"00030020\",\n MSAlbum: \"0004002c\",\n MSArtist: \"10050024\",\n MSAlbumList: \"000d006c\",\n MSPlaylist: \"0006006c\",\n MSArtistTracklist: \"100f006c\",\n MSFavorites: None, # This one is unknown\n MSCollection: None, # This one is unknown\n }\n out = id_prefix[item_class]\n if out:\n out += item_id\n return out","def map_to_homo_nid(self, ids, ntype): # -> None:\n ...","def entity_classes_to_map_over(cls):\n return [exp_models.ExplorationSnapshotMetadataModel]","def encode_label(self, label: str) -> int:\n return self.class_map[label]","def setId(self, *args):\n return _libsbml.SpeciesTypeComponentMapInProduct_setId(self, *args)","def class_and_replace(org_label, classes):\r\n # Classify and replace\r\n keys = np.unique(org_label)\r\n values = classes #0 for non veg, 1 for veg\r\n dictionary = dict(zip(keys, values))\r\n classified = replace(org_label, dictionary)\r\n \r\n # Label merged regions with unique ID\r\n labeld = label(classified)\r\n number_of_labels = np.unique(labeld)\r\n newvals = np.asarray(list(range(1,(len(number_of_labels) + 1))))\r\n keys_ID = number_of_labels\r\n values_ID = newvals\r\n dictionary_ID = dict(zip(keys_ID, values_ID))\r\n classified_ID = replace(labeld, dictionary_ID)\r\n \r\n del(labeld)\r\n \r\n return classified, classified_ID","def __init__(self, samples_per_class=10, n_classes=10, n_features=1):\n self.samples_per_class = samples_per_class\n self.n_classes = n_classes\n self.n_features = n_features\n\n # Create a dataframe to be consistent with other Datasets\n self.df = pd.DataFrame({\n 'class_id': [i % self.n_classes for i in range(len(self))]\n })\n self.df = self.df.assign(id=self.df.index.values)","def __init__(self, key):\n self.key = [int_mapping(k) for k in key]","def from_key(cls, id):\n return super().from_key(id)","def _map_data_types(dtype):\n return _data_type_map[dtype]","def update_id2idx(self):\n self._id2idx = {}\n for n, cell in enumerate(self._cell_list):\n self._id2idx[cell.id()] = n","def test_data_source_soaps_id_patch(self):\n pass"],"string":"[\n \"def map_source_class_id(self, source_class_id):\\n return self.class_from_source_map[source_class_id]\",\n \"def map_source_class_id(self, source_class_id):\\n return self.class_from_source_map[source_class_id]\",\n \"def map_id_to_idx(self, class_ids):\\n class_idx = torch.zeros(class_ids.shape, dtype=int)\\n for k, v in self.id2idx.items():\\n class_idx[class_ids == k] = v\\n\\n class_idx = class_idx.to(device)\\n return class_idx\",\n \"def remap_ids(self, id_map: Dict[int, int]) -> None:\",\n \"def class_name_to_id(self, class_name: str):\\n\\n return self.class_to_idx[str(class_name)]\",\n \"def prepare(self, class_map=None):\\n\\n def clean_name(name):\\n \\\"\\\"\\\"Returns a shorter version of object names for cleaner display.\\\"\\\"\\\"\\n return \\\",\\\".join(name.split(\\\",\\\")[:1])\\n\\n # Build (or rebuild) everything else from the info dicts.\\n self.num_classes = len(self.class_info)\\n self.class_ids = np.arange(self.num_classes)\\n self.class_names = [clean_name(c[\\\"name\\\"]) for c in self.class_info]\\n self.num_images = len(self.image_info)\\n self._image_ids = np.arange(self.num_images)\\n\\n # Mapping from source class and image IDs to internal IDs\\n self.class_from_source_map = {\\\"{}.{}\\\".format(info['source'], info['id']): id\\n for info, id in zip(self.class_info, self.class_ids)}\\n self.image_from_source_map = {\\\"{}.{}\\\".format(info['source'], info['id']): id\\n for info, id in zip(self.image_info, self.image_ids)}\\n\\n # Map sources to class_ids they support\\n self.sources = list(set([i['source'] for i in self.class_info]))\\n self.source_class_ids = {}\\n # Loop over datasets\\n for source in self.sources:\\n self.source_class_ids[source] = []\\n # Find classes that belong to this dataset\\n for i, info in enumerate(self.class_info):\\n # Include BG class in all datasets\\n if i == 0 or source == info['source']:\\n self.source_class_ids[source].append(i)\",\n \"def prepare(self, class_map=None):\\n def clean_name(name):\\n \\\"\\\"\\\"Returns a shorter version of object names for cleaner display.\\\"\\\"\\\"\\n return \\\",\\\".join(name.split(\\\",\\\")[:1])\\n\\n # Build (or rebuild) everything else from the info dicts.\\n self.num_classes = len(self.class_info)\\n self.class_ids = np.arange(self.num_classes)\\n self.class_names = [clean_name(c[\\\"name\\\"]) for c in self.class_info]\\n self.num_images = len(self.image_info)\\n self._image_ids = np.arange(self.num_images)\\n\\n self.class_from_source_map = {\\\"{}.{}\\\".format(info['source'], info['id']): id\\n for info, id in zip(self.class_info, self.class_ids)}\\n\\n # Map sources to class_ids they support\\n self.sources = list(set([i['source'] for i in self.class_info]))\\n self.source_class_ids = {}\\n # Loop over datasets\\n for source in self.sources:\\n self.source_class_ids[source] = []\\n # Find classes that belong to this dataset\\n for i, info in enumerate(self.class_info):\\n # Include BG class in all datasets\\n if i == 0 or source == info['source']:\\n self.source_class_ids[source].append(i)\",\n \"def remap_ids(self, id_map: Dict[int, int]) -> None:\\n super().remap_ids(id_map)\\n nds = self.nodes\\n for i, nd_id in enumerate(nds):\\n nds[i] = id_map.get(nd_id, 0)\",\n \"def map(self, datasetType, dataId):\\n\\n func = getattr(self, 'map_' + datasetType)\\n return func(dataId)\",\n \"def map_to_per_ntype(self, ids): # -> None:\\n ...\",\n \"def view(self) -> 'typing.Mapping[int, Dataclass]':\",\n \"def map_from_parent_nid(self, layer_id, parent_nids, remap_local=...):\\n ...\",\n \"def map_column_to_index(self, col):\\n if col in self.column_maps:\\n return\\n\\n # First construct the map from original ids to new ones.\\n ids = pd.concat((self.train[col], self.test[col])).unique()\\n n = len(ids)\\n idmap = dict(itertools.izip(ids, xrange(n)))\\n\\n # Next use the map to convert the ids in-place.\\n self.train.loc[:, col] = self.train[col].apply(lambda _id: idmap[_id])\\n self.test.loc[:, col] = self.test[col].apply(lambda _id: idmap[_id])\\n\\n # Now swap key for value in the idmap to provide a way to convert back.\\n reverse_map = {val: key for key, val in idmap.iteritems()}\\n self.column_maps[col] = reverse_map\",\n \"def make_classdict(self):\\n\\t\\ttarget_names = self.fetched[\\\"train\\\"].target_names\\n\\t\\tself.classdict = {target_names[idx]: idx for idx in range(len(target_names))}\",\n \"def remap(self, old_id):\\n if old_id in self.mapping:\\n new_id = self.mapping[old_id]\\n else:\\n if not self.reuse or old_id in self.blocklist:\\n # We cannot reuse the old-id\\n new_id = self.next_id()\\n else:\\n # We can reuse the old-id\\n new_id = old_id\\n if isinstance(old_id, int) and old_id >= self._nextid:\\n self._nextid = old_id + 1\\n self.mapping[old_id] = new_id\\n return new_id\",\n \"def map_ids(df, idname, remove=False):\\n # Lookup id-mapping class in global table.\\n klass = IDMAP_TASKS[idname]\\n task = klass()\\n\\n # Shouldn't really need this here.\\n if not task.complete():\\n task.run()\\n\\n # Load idmap from file.\\n with task.output().open() as f:\\n idmap = pd.read_csv(f, index_col=0)\\n\\n # Create tmp column to store new column.\\n tmpname = '_new_%s' % idname\\n idmap[tmpname] = idmap.index\\n\\n # Ensure data types match up after merge.\\n for colname in klass.ids:\\n dtype = df[colname].dtype.type\\n idmap[colname] = idmap[colname].values.astype(dtype)\\n\\n # Move values from tmp column to `idname` column and delete tmp.\\n df = df.merge(idmap, how='left', on=klass.ids)\\n df[idname] = df[tmpname]\\n del df[tmpname]\\n\\n # Remove columns used to create mapping, if requeseted.\\n if remove:\\n for colname in klass.ids:\\n del df[colname]\\n\\n return df\",\n \"def _set_mapper(self, classification_dict):\\n d = {class_code: class_index for class_index, class_code in enumerate(classification_dict.keys())}\\n # Here we update the dict so that code 65 remains unchanged.\\n # Indeed, 65 is reserved for noise/artefacts points, that will be deleted by transform \\\"DropPointsByClass\\\".\\n d.update({65: 65})\\n self.mapper = np.vectorize(lambda class_code: d.get(class_code))\",\n \"def remap_ids(self, id_map: Dict[int, int]) -> None:\\n super().remap_ids(id_map)\\n ents = self.entities\\n for i, ent_id in enumerate(ents):\\n ents[i] = id_map.get(ent_id, 0)\",\n \"def class_id(self, class_id):\\n\\n self._class_id = class_id\",\n \"def map_id(id, user = True):\\n\\n # Reading the mapping array from models folder\\n\\n uq = None\\n if user:\\n uq = np.load(configs.USER_IDS_PATH)\\n else:\\n uq = np.load(configs.MOVIE_IDS_PATH)\\n\\n\\n # Returning the new id\\n if id in uq:\\n return np.where(uq==id)[0][0]\\n else:\\n return None\",\n \"def remap_ids(self, id_map: Dict[int, int]) -> None:\\n super().remap_ids(id_map)\\n self.puppet = id_map.get(self.puppet, 0)\",\n \"def before_update(mapper, conn, target):\\n if not target.id_:\\n dataset = ObjectNumber.parse(target.d_id)\\n target.id_ = str(PartitionNumber(dataset, target.sequence_id))\",\n \"def map_column_to_index(self, col):\\n if col in self.column_maps:\\n return\\n\\n # First construct the map from original ids to new ones.\\n ids = self.dataset[col].unique()\\n n = len(ids)\\n idmap = dict(itertools.izip(ids, xrange(n)))\\n\\n # Next use the map to conver the ids in-place.\\n self.dataset[col] = self.dataset[col].apply(lambda _id: idmap[_id])\\n\\n # Now swap key for value in the idmap to provide a way to convert back.\\n reverse_map = {val: key for key, val in idmap.iteritems()}\\n self.column_maps[col] = reverse_map\",\n \"def rclass_id_lookup(cur):\\n if 'rclass_id' not in _tables:\\n cur.execute(\\\"SELECT name, rclass_id FROM rclass\\\")\\n _tables['rclass_id'] = dict(cur)\\n return _tables['rclass_id']\",\n \"def map_to_per_etype(self, ids): # -> None:\\n ...\",\n \"def mapping(self, release_id, grouping):\\n\\n helper = Known(self.config, self.session)\\n classes = [g['name']['full'] for g in grouping]\\n return helper.mapping(release_id, classes)\",\n \"def id_to_class_name(self, identifier: int):\\n\\n return self.id_to_class_mapping()[identifier]\",\n \"def update_class_records():\\n logger.info('Fetching class ids and labels for '\\n 'classes with direct instances')\\n timestamp = statistics.get_current_timestamp()\\n results = sparql.sparql_query(queries.QUERY_CLASSES,\\n fallback=queries.QUERY_CLASSSES_FALLBACK)\\n\\n if 'error' in results:\\n logger.error('Got no SPARQL results.')\\n return\\n\\n updated = {}\\n\\n def value(binding, key):\\n return binding[key]['value']\\n\\n for binding in results['results']['bindings']:\\n uri = value(binding, 'cl')\\n if not sparql.is_wikidata_entity(uri):\\n continue\\n\\n qid = sparql.wikidata_entity_id(uri)\\n label = value(binding, 'clLabel')\\n record = {'l': label, }\\n\\n if 'c' in binding:\\n record['i'] = int(value(binding, 'c'))\\n updated[qid] = record\\n\\n logger.info('Augmenting current classes data ...')\\n data = statistics.get_json_data('classes')\\n merged = statistics.merge(data, updated, default_others={'i': 0})\\n statistics.update_json_data('classes', merged, timestamp)\",\n \"def initMaps(self):\\r\\n assert isinstance(self.CLASSES, (list, tuple))\\r\\n assert self.CLASSES[0] == \\\"__background__\\\"\\r\\n cls = self.CLASSES\\r\\n self.name_to_id = dict(zip(cls, range(len(cls))))\\r\\n self.id_to_name = dict(zip(range(len(cls)), cls))\",\n \"def process_images_class(images_class):\\n\\n images_class_to_int_map = {image: i for (i, image) in enumerate(sorted(set(images_class)))}\\n print('Mapping:\\\\n{}'.format(images_class_to_int_map))\\n images_class_int = [images_class_to_int_map[i] for i in images_class]\\n\\n return to_categorical(images_class_int, num_classes=len(set(images_class)))\",\n \"def remap_ids(self, id_map: Dict[int, int]) -> None:\\n super().remap_ids(id_map)\\n self.door = id_map.get(self.door, 0)\",\n \"def class_id(self) -> str:\\n return self._class_id\",\n \"def class_id(self) -> str:\\n return self._class_id\",\n \"def class_id(self):\\n return self._class_id\",\n \"def name2Id(self, classExpression):\\n out = classExpression\\n name_id = self.gen_name_id()\\n for k, v in name_id.items():\\n out = re.sub(\\\"\\\\'\\\"+k+\\\"\\\\'\\\", v, out) # Suspect this not Pythonic. Could probably be done with a fancy map lambda combo. \\n return out\",\n \"def cls_identifier(cls):\\n if hasattr(cls, 'get_cls_identifier'):\\n return cls.get_cls_identifier()\\n return Encoder.default_identifier(cls)\",\n \"def get_by_id (self, id):\\n clsfor_obj = self.classfor()\\n clsfor_obj.from_id (id)\\n return clsfor_obj\\n return\\n for name, obj in inspect.getmembers (clsfor_obj):\\n if isinstance (obj, RField):\\n key = clsfor_obj.prepare_key (name, int(id))\",\n \"def mapper_id(self, structure):\\n type_ = structure['type']\\n mapper = self.mappers[type_]\\n return id(mapper)\",\n \"def _map_class(max_iou_indices, labels):\\n #add on index column\\n max_iou_indices = tf.stack([tf.reshape(tf.convert_to_tensor([np.arange(0, tf.shape(all_anchors)[0])]), [1, tf.shape(max_iou_indices)[0]]),\\n tf.cast(tf.expand_dims(max_iou_indices, axis=0), dtype=tf.int32)], axis=0)\\n max_iou_indices = tf.transpose(tf.squeeze(max_iou_indices))\\n broadcasted_labels = tf.broadcast_to(labels, [tf.shape(all_anchors)[0], tf.shape(random_labels)[0]])\\n anchor_classes = tf.gather_nd(broadcasted_labels, temp)\\n return anchor_classes\",\n \"def map_to_per_ntype(self, ids): # -> tuple[Unknown, Unknown]:\\n ...\",\n \"def map_to_per_ntype(self, ids): # -> tuple[Unknown, Unknown]:\\n ...\",\n \"def lookup_class_idx(self,label):\\r\\n \\r\\n return self.class_labels[label]\",\n \"def key(cls, id):\\n\\n if isinstance(cls, type):\\n cls = cls.__name__\\n return str(cls) + '.' + str(id)\",\n \"def _map_new_class_index(y, order):\\n return np.array(list(map(lambda x: order.index(x), y)))\",\n \"def id_index_map(self):\\n result = {}\\n for index, component_data in iteritems(self):\\n result[id(component_data)] = index\\n return result\",\n \"def map_to_per_etype(self, ids): # -> tuple[Unknown, Unknown]:\\n ...\",\n \"def map_to_per_etype(self, ids): # -> tuple[Unknown, Unknown]:\\n ...\",\n \"def cellannotation2ID(self, annotation_type):\\n annotations = list(self.adata.obs[annotation_type])\\n annotations_set = sorted(set(annotations))\\n \\n mapping = {a:idx for idx,a in enumerate(annotations_set)}\\n \\n truth_labels = [mapping[a] for a in annotations]\\n self.adata.obs['label'] = pd.Categorical(values=truth_labels)\\n #18m-unannotated\\n # \\n return mapping\",\n \"def assign_ref_id(self: _EntityT, ref_id: EntityId) -> _EntityT:\\n return dataclasses.replace(self, ref_id=ref_id)\",\n \"def makeIdFactory(self, dataRef):\\n # With the default configuration, this IdFactory doesn't do anything, because\\n # the IDs it generates are immediately overwritten by the ID from the reference\\n # catalog (since that's in config.measurement.copyColumns). But we create one here anyway, to\\n # allow us to revert back to the old behavior of generating new forced source IDs,\\n # just by renaming the ID in config.copyColumns to \\\"object_id\\\".\\n expBits = dataRef.get(self.config.coaddName + \\\"CoaddId_bits\\\")\\n expId = int(dataRef.get(self.config.coaddName + \\\"CoaddId\\\"))\\n return lsst.afw.table.IdFactory.makeSource(expId, 64 - expBits)\",\n \"def id_to_index(self, id):\\n raise NotImplementedError\",\n \"def _do_mapping(self):\\n pass\",\n \"def image_reference(self, image_id):\\n info = self.image_info[image_id]\\n if info['source'] == self.dataset_name:\\n return info['id']\\n else:\\n super.image_reference(image_id)\",\n \"def before_update(mapper, conn, target):\\n if isinstance(target, Column):\\n raise TypeError('Got a column instead of a table')\\n\\n if target.id_ is None:\\n dataset_id = ObjectNumber.parse(target.d_id)\\n target.id_ = str(TableNumber(dataset_id, target.sequence_id))\",\n \"def map_to_homo_nid(self, ids, ntype):\\n ...\",\n \"def map_to_homo_nid(self, ids, ntype):\\n ...\",\n \"def getId(self):\\n if getattr(self,'id',None):\\n return self.id\\n name = self.name\\n #--Singleton records \\n if name in frozenset(('FMAP','GAME','JOUR','KLST','PCDT','REFR','SPLM','TES3')):\\n return None\\n #--Special records.\\n elif name == 'CELL':\\n reader = self.getReader()\\n srName = reader.findSubRecord('NAME',name)\\n srData = reader.findSubRecord('DATA',name)\\n (flags,gridX,gridY) = struct.unpack('3i',record.data)\\n if flags & 1:\\n self.id = cstrip(srName)\\n else:\\n self.id = '[%d,%d]' % (gridX,gridY)\\n elif name == 'INFO':\\n srData = self.getReader().findSubRecord('INAM',name)\\n self.id = cstrip(srData)\\n elif name == 'LAND':\\n srData = self.getReader().findSubRecord('INTV',name)\\n self.id = '[%d,%d]' % struct.unpack('2i',srData)\\n elif name == 'PGRD':\\n reader = self.getReader()\\n srData = reader.findSubRecord('DATA',name)\\n srName = reader.findSubRecord('NAME',name)\\n gridXY = struct.unpack('2i',srData[:8])\\n if srData != (0,0) or not srName:\\n self.id = '[%d,%d]' % gridXY\\n else:\\n self.id = cstrip(srName)\\n elif name == 'SCPT':\\n srData = self.getReader().findSubRecord('SCHD',name)\\n self.id = cstrip(srData[:32])\\n #--Most records: id in NAME record.\\n else:\\n srData = self.getReader().findSubRecord('NAME',name)\\n self.id = srData and cstrip(srData)\\n #--Done\\n return self.id\",\n \"def encode_target(df, target_column):\\n df_mod = df.copy()\\n targets = df_mod[target_column].unique()\\n map_to_int = {name: n for n, name in enumerate(targets)}\\n #print map_to_int\\n df_mod[\\\"Target\\\"] = df_mod[target_column].replace(map_to_int)\\n return (df_mod, targets)\",\n \"def map_user_id(self, users, project_in, project_out):\\n raise NotImplementedError()\",\n \"def __init__(self, annoPath, classes , datasetName, setID,useDiff = True,\\n convertToPerson = None, convertIdToCls = None,\\n is_image_index_flattened=False, splitIndex=1):\\n self._annoPath = annoPath\\n self._classes = classes\\n self._datasetName = datasetName\\n self._setID = setID\\n self.num_classes = len(classes)\\n self.useDiff = useDiff\\n self._classToIndex = self._create_classToIndex(classes)\\n self._convertToPerson = convertToPerson\\n self._convertIdToCls = convertIdToCls\\n self._is_image_index_flattened = is_image_index_flattened\\n self._splitIndex = 1\",\n \"def get_identifier_map(self) -> None:\\n id_mapping_dict = self._get_identifiers_from_kbs()\\n id_mapping_dict = self._add_uniprot_identifiers(id_mapping_dict)\\n id_mapping_dict = self._add_chebi_identifiers(id_mapping_dict)\\n id_mapping_dict = self._add_bridge_db_identifiers(id_mapping_dict)\\n\\n print(\\\"merging similar...\\\")\\n id_mapping_dict = pathway_utils.merge_similar(id_mapping_dict)\\n\\n print(\\\"generating local identifiers...\\\")\\n self.forward_map, self.backward_map = self._generate_local_identifiers(id_mapping_dict)\\n self.save_id_dict()\",\n \"def mapFilter(self, filtername, filterclass, userid):\\n # logging.info('%s, %s, %s' % (filtername, filterclass, userid))\\n redis_cli = self.get_redis_cli()\\n f_conf = self.config.filter_keys_conf\\n rKey = f_conf[filtername].format(**{filtername:filterclass})\\n if not rKey:\\n raise ValueError, \\\"Haven't %s filter keys\\\" % filtername\\n offset = int(userid)-stdoffset(self)\\n if offset>0 and offset <= self.config.MAX_BITMAP_LENGTH:\\n redis_cli.setbit(rKey, offset, 1)\\n logging.debug('Save auid in redis by setbit %s %d 1' % (rKey, offset))\",\n \"def create_class_indices(self) -> None:\\n\\n categories = list(np.loadtxt(self.path_manager.categories_file(), delimiter=\\\",\\\", dtype=str))\\n\\n if self.include_noise_samples and not self.multi_label_classification:\\n categories.append(\\\"noise\\\")\\n\\n self.class_to_idx = {}\\n\\n for idx, class_name in enumerate(sorted(categories)):\\n self.class_to_idx[class_name] = idx\",\n \"def _id(self):\\n pass\",\n \"def convert_uid(klass, short_or_long_uid):\\n if '_' in short_or_long_uid:\\n return short_or_long_uid.split('_')[1]\\n else:\\n return klass.generate_uid(identifier=short_or_long_uid)\",\n \"def _mapping(self, pdb_id, loop_type, normalizer):\\n\\n mapping = {}\\n with self.session() as session:\\n query = self.query(session, pdb_id).filter_by(type=loop_type)\\n for result in query:\\n unit_ids = normalizer(result.unit_ids)\\n if unit_ids in mapping:\\n self.logger.error(\\\"Loop %s duplicates %s\\\",\\n result.loop_id, mapping[unit_ids])\\n continue\\n mapping[unit_ids] = result.loop_id\\n return mapping\",\n \"def assignClass(self):\\n classes = {}\\n classes['en'] = 0\\n classes['nl'] = 0\\n assignedClass = \\\"\\\"\\n\\n for record in self.data:\\n if record[-1] == 'en':\\n classes['en'] += 1\\n elif record[-1] == 'nl':\\n classes['nl'] += 1\\n\\n max = 0\\n for key in classes.keys():\\n # get max class\\n if max < classes[key]:\\n max = classes[key]\\n assignedClass = key\\n\\n self.enClass = classes['en']\\n self.nlClass = classes['nl']\\n\\n return assignedClass\",\n \"def _create_labels_and_mapping(self, labels, mapping):\\n numbered_classes = list(enumerate(list(labels), start=0))\\n if mapping:\\n new_mapping = {number: str(mapping[label]) for number, label in numbered_classes}\\n else:\\n new_mapping = {number: str(label) for number, label in numbered_classes}\\n new_labels = [new_mapping[numbered[0]] for numbered in numbered_classes]\\n\\n return new_labels, new_mapping\",\n \"def map_device_to_id(dev_map, device):\\n for elem in dev_map:\\n if device == elem['path']:\\n return elem['id']\",\n \"def id_for_label(self, id_):\\n return id_\",\n \"def id_to_local_index(self, id):\\n raise NotImplementedError\",\n \"def _map_predictions_to_new_cids(predictions, old_cids2new_cids):\\r\\n # transform predictions with new class ids: decs and probs correspond to training class ids\\r\\n # for plotting they should be transformed to new (inference or evaluation) class ids\\r\\n # e.g. output_Nclasses = 5,\\r\\n # training_cids2inference_cids = [-1, 1, 1, 0, -1] --> [2, 1, 1, 0, 2]\\r\\n # before: probs.shape: (..., 5) --> after: probs.shape: (..., 3)\\r\\n # for probs: using the probability of union rule: P(A ∪ B) = P(A) + P(B) - P(A ∩ B)\\r\\n # with P(A ∩ B) = 0 probabilities have to be summed\\r\\n # TODO(panos): save computations by checking if inference and training\\r\\n # problem defs are the same\\r\\n\\r\\n supported_keys = {\\r\\n 'l1_probabilities', 'rawimages', 'rawimagespaths', 'decisions', 'l1_decisions',\\r\\n 'l1_logits', 'l1_probabilities', 'l2_vehicle_decisions', 'l2_vehicle_logits',\\r\\n 'l2_vehicle_probabilities', 'l2_human_decisions', 'l2_human_logits',\\r\\n 'l2_human_probabilities'}\\r\\n # check if supported_keys is a superset of predictions.keys()\\r\\n assert supported_keys >= predictions.keys(), (\\r\\n f\\\"Supported keys are {sorted(supported_keys)} and predictions keys are {sorted(set(predictions.keys()))}. \\\"\\r\\n f\\\"Supported keys must be a superset of predictions keys for resizing predictions.\\\")\\r\\n\\r\\n probs, decs = itemgetter('l1_probabilities', 'decisions')(predictions)\\r\\n old_cids2new_cids = _replacevoids(old_cids2new_cids)\\r\\n ocids2ncids = tf.cast(old_cids2new_cids, tf.int32)\\r\\n decs = tf.gather(ocids2ncids, decs)\\r\\n try:\\r\\n probs_transposed = tf.transpose(probs, (3, 0, 1, 2))\\r\\n probs_transformed = tf.unsorted_segment_sum(\\r\\n probs_transposed, ocids2ncids, tf.reduce_max(ocids2ncids) + 1)\\r\\n probs = tf.transpose(probs_transformed, (1, 2, 3, 0))\\r\\n except:\\r\\n tf.logging.info('\\\\nl1_probabilities are not transformed to new cids.\\\\n')\\r\\n tf.logging.info('\\\\nOnly decisions were transformed to new cids.\\\\n')\\r\\n\\r\\n new_predictions = predictions\\r\\n new_predictions.update({'l1_probabilities': probs, 'decisions': decs})\\r\\n\\r\\n return new_predictions\",\n \"def class_to_db(self):\",\n \"def get_id_name_mapping_from_db(session, cls):\\n\\n return {obj.id: obj.name for obj in session.query(cls).all()}\",\n \"def extract_mapping(self) -> DatasetMapping:\\n # store fields\\n fields = []\\n for col in self.data.columns:\\n #get field label\\n label = col\\n #get field type using PANDAS_TYPE (see apps.utils.utils)\\n col_type = self.data[col].dtype\\n field_type = PANDAS_TYPE[col_type]\\n #set field\\n field = FieldMapping(label=label, type=field_type)\\n fields.append(field)\\n self.mapping.append(label)\\n return DatasetMapping(fields=fields)\",\n \"def db_to_class(cls, record):\\n raise NotImplementedError()\",\n \"def node_mapping(self):\\n ...\",\n \"def get_preprocessed_seq_data(self, raw_data, cls):\\n # Check that input data has unique ids\\n self._check_unique_ids(raw_data)\\n\\n cls_id = int(self.class_name_to_class_id[cls])\\n\\n data_keys = ['gt_ids', 'tracker_ids', 'gt_dets', 'tracker_dets', 'similarity_scores']\\n data = {key: [None] * raw_data['num_timesteps'] for key in data_keys}\\n unique_gt_ids = []\\n unique_tracker_ids = []\\n num_gt_dets = 0\\n num_tracker_dets = 0\\n for t in range(raw_data['num_timesteps']):\\n\\n # Only extract relevant dets for this class for preproc and eval (cls)\\n gt_class_mask = np.atleast_1d(raw_data['gt_classes'][t] == cls_id)\\n gt_class_mask = gt_class_mask.astype(np.bool)\\n gt_ids = raw_data['gt_ids'][t][gt_class_mask]\\n gt_dets = [raw_data['gt_dets'][t][ind] for ind in range(len(gt_class_mask)) if gt_class_mask[ind]]\\n\\n tracker_class_mask = np.atleast_1d(raw_data['tracker_classes'][t] == cls_id)\\n tracker_class_mask = tracker_class_mask.astype(np.bool)\\n tracker_ids = raw_data['tracker_ids'][t][tracker_class_mask]\\n tracker_dets = [raw_data['tracker_dets'][t][ind] for ind in range(len(tracker_class_mask)) if\\n tracker_class_mask[ind]]\\n similarity_scores = raw_data['similarity_scores'][t][gt_class_mask, :][:, tracker_class_mask]\\n\\n # Match tracker and gt dets (with hungarian algorithm)\\n unmatched_indices = np.arange(tracker_ids.shape[0])\\n if gt_ids.shape[0] > 0 and tracker_ids.shape[0] > 0:\\n matching_scores = similarity_scores.copy()\\n matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = -10000\\n match_rows, match_cols = linear_sum_assignment(-matching_scores)\\n actually_matched_mask = matching_scores[match_rows, match_cols] > 0 + np.finfo('float').eps\\n match_cols = match_cols[actually_matched_mask]\\n\\n unmatched_indices = np.delete(unmatched_indices, match_cols, axis=0)\\n\\n # For unmatched tracker dets, remove those that are greater than 50% within a crowd ignore region.\\n unmatched_tracker_dets = [tracker_dets[i] for i in range(len(tracker_dets)) if i in unmatched_indices]\\n ignore_region = raw_data['gt_ignore_region'][t]\\n intersection_with_ignore_region = self._calculate_mask_ious(unmatched_tracker_dets, [ignore_region],\\n is_encoded=True, do_ioa=True)\\n is_within_ignore_region = np.any(intersection_with_ignore_region > 0.5 + np.finfo('float').eps, axis=1)\\n\\n # Apply preprocessing to remove unwanted tracker dets.\\n to_remove_tracker = unmatched_indices[is_within_ignore_region]\\n data['tracker_ids'][t] = np.delete(tracker_ids, to_remove_tracker, axis=0)\\n data['tracker_dets'][t] = np.delete(tracker_dets, to_remove_tracker, axis=0)\\n similarity_scores = np.delete(similarity_scores, to_remove_tracker, axis=1)\\n\\n # Keep all ground truth detections\\n data['gt_ids'][t] = gt_ids\\n data['gt_dets'][t] = gt_dets\\n data['similarity_scores'][t] = similarity_scores\\n\\n unique_gt_ids += list(np.unique(data['gt_ids'][t]))\\n unique_tracker_ids += list(np.unique(data['tracker_ids'][t]))\\n num_tracker_dets += len(data['tracker_ids'][t])\\n num_gt_dets += len(data['gt_ids'][t])\\n\\n # Re-label IDs such that there are no empty IDs\\n if len(unique_gt_ids) > 0:\\n unique_gt_ids = np.unique(unique_gt_ids)\\n gt_id_map = np.nan * np.ones((np.max(unique_gt_ids) + 1))\\n gt_id_map[unique_gt_ids] = np.arange(len(unique_gt_ids))\\n for t in range(raw_data['num_timesteps']):\\n if len(data['gt_ids'][t]) > 0:\\n data['gt_ids'][t] = gt_id_map[data['gt_ids'][t]].astype(np.int)\\n if len(unique_tracker_ids) > 0:\\n unique_tracker_ids = np.unique(unique_tracker_ids)\\n tracker_id_map = np.nan * np.ones((np.max(unique_tracker_ids) + 1))\\n tracker_id_map[unique_tracker_ids] = np.arange(len(unique_tracker_ids))\\n for t in range(raw_data['num_timesteps']):\\n if len(data['tracker_ids'][t]) > 0:\\n data['tracker_ids'][t] = tracker_id_map[data['tracker_ids'][t]].astype(np.int)\\n\\n # Record overview statistics.\\n data['num_tracker_dets'] = num_tracker_dets\\n data['num_gt_dets'] = num_gt_dets\\n data['num_tracker_ids'] = len(unique_tracker_ids)\\n data['num_gt_ids'] = len(unique_gt_ids)\\n data['num_timesteps'] = raw_data['num_timesteps']\\n data['seq'] = raw_data['seq']\\n data['cls'] = cls\\n\\n # Ensure again that ids are unique per timestep after preproc.\\n self._check_unique_ids(data, after_preproc=True)\\n\\n return data\",\n \"def identify_class(self, cls):\",\n \"def test_data_source_soaps_id_replace_post(self):\\n pass\",\n \"def _string_label_to_class_id_postprocessor(\\n string_label, label_classes, default=-1, **unused_kwargs):\\n if string_label in label_classes:\\n return label_classes.index(string_label)\\n else:\\n return default\",\n \"def separate_by_class(dataset, class_index):\\n class_dictionary = {}\\n for data_row in dataset:\\n # determine what to use as a key\\n # for the dictionary\\n dict_key = data_row[class_index]\\n \\n # remove the class attribute from the\\n # data so it doesn't screw up stats\\n del data_row[class_index]\\n\\n if dict_key not in class_dictionary:\\n class_dictionary[dict_key] = [data_row]\\n else:\\n class_dictionary[dict_key].append(data_row)\\n\\n return class_dictionary\",\n \"def encode_target(df, target_column, label_map):\\n df_mod = df.copy()\\n targets = df_mod[target_column].unique()\\n # map_to_int = {name: n for n, name in enumerate(targets)}\\n df_mod[target_column] = df_mod[target_column].replace(label_map)\\n\\n return (df_mod, targets)\",\n \"def __init__(self, mapping: Dict[int, int]):\\n self.mapping = mapping\",\n \"def _set_preprocessing_mapper(self, classification_preprocessing_dict):\\n d = {key: value for key, value in classification_preprocessing_dict.items()}\\n self.preprocessing_mapper = np.vectorize(lambda class_code: d.get(class_code, class_code))\",\n \"def id_to_base_id(self, id):\\n if self.xy_tiling is None and self.pc_tiling is None:\\n return id\\n return self.get_tile_from_path(id)[1]\",\n \"def _data_id_from_label(self, label):\\n for data_item in self.state.data_items:\\n if data_item['name'] == label:\\n return data_item['id']\",\n \"def id_to_extended_id(item_id, item_class):\\n id_prefix = {\\n MSTrack: \\\"00030020\\\",\\n MSAlbum: \\\"0004002c\\\",\\n MSArtist: \\\"10050024\\\",\\n MSAlbumList: \\\"000d006c\\\",\\n MSPlaylist: \\\"0006006c\\\",\\n MSArtistTracklist: \\\"100f006c\\\",\\n MSFavorites: None, # This one is unknown\\n MSCollection: None, # This one is unknown\\n }\\n out = id_prefix[item_class]\\n if out:\\n out += item_id\\n return out\",\n \"def map_to_homo_nid(self, ids, ntype): # -> None:\\n ...\",\n \"def entity_classes_to_map_over(cls):\\n return [exp_models.ExplorationSnapshotMetadataModel]\",\n \"def encode_label(self, label: str) -> int:\\n return self.class_map[label]\",\n \"def setId(self, *args):\\n return _libsbml.SpeciesTypeComponentMapInProduct_setId(self, *args)\",\n \"def class_and_replace(org_label, classes):\\r\\n # Classify and replace\\r\\n keys = np.unique(org_label)\\r\\n values = classes #0 for non veg, 1 for veg\\r\\n dictionary = dict(zip(keys, values))\\r\\n classified = replace(org_label, dictionary)\\r\\n \\r\\n # Label merged regions with unique ID\\r\\n labeld = label(classified)\\r\\n number_of_labels = np.unique(labeld)\\r\\n newvals = np.asarray(list(range(1,(len(number_of_labels) + 1))))\\r\\n keys_ID = number_of_labels\\r\\n values_ID = newvals\\r\\n dictionary_ID = dict(zip(keys_ID, values_ID))\\r\\n classified_ID = replace(labeld, dictionary_ID)\\r\\n \\r\\n del(labeld)\\r\\n \\r\\n return classified, classified_ID\",\n \"def __init__(self, samples_per_class=10, n_classes=10, n_features=1):\\n self.samples_per_class = samples_per_class\\n self.n_classes = n_classes\\n self.n_features = n_features\\n\\n # Create a dataframe to be consistent with other Datasets\\n self.df = pd.DataFrame({\\n 'class_id': [i % self.n_classes for i in range(len(self))]\\n })\\n self.df = self.df.assign(id=self.df.index.values)\",\n \"def __init__(self, key):\\n self.key = [int_mapping(k) for k in key]\",\n \"def from_key(cls, id):\\n return super().from_key(id)\",\n \"def _map_data_types(dtype):\\n return _data_type_map[dtype]\",\n \"def update_id2idx(self):\\n self._id2idx = {}\\n for n, cell in enumerate(self._cell_list):\\n self._id2idx[cell.id()] = n\",\n \"def test_data_source_soaps_id_patch(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73133224","0.73133224","0.67706954","0.6197687","0.6047091","0.6020897","0.59112316","0.58618355","0.5850386","0.5751023","0.5733445","0.56539035","0.56316966","0.56235695","0.562181","0.5613915","0.5586703","0.54934883","0.5488558","0.54843575","0.5481464","0.5463257","0.5431779","0.5413206","0.5408277","0.539843","0.5387071","0.535343","0.53351474","0.53186893","0.5271466","0.5267492","0.5267492","0.52574337","0.5237681","0.5229384","0.5209345","0.5208791","0.518277","0.51816505","0.51816505","0.5173257","0.515969","0.51588595","0.5152495","0.5138949","0.5138949","0.5133254","0.51121765","0.5103286","0.51012677","0.5094001","0.507893","0.503303","0.50250334","0.50250334","0.5017679","0.50059557","0.5002554","0.49926117","0.4970756","0.49693745","0.4947105","0.49396506","0.49220538","0.49188036","0.49185482","0.49158072","0.4901841","0.489006","0.4885856","0.48833588","0.48826045","0.48777324","0.4875198","0.48709366","0.48661867","0.48608145","0.48603496","0.48595402","0.48569468","0.48508105","0.48475283","0.4834296","0.48298964","0.4823414","0.4819606","0.48152298","0.4806883","0.4805445","0.48006338","0.47974154","0.47943577","0.47931957","0.47930935","0.47887868","0.47841224","0.47806612","0.4779559"],"string":"[\n \"0.73133224\",\n \"0.73133224\",\n \"0.67706954\",\n \"0.6197687\",\n \"0.6047091\",\n \"0.6020897\",\n \"0.59112316\",\n \"0.58618355\",\n \"0.5850386\",\n \"0.5751023\",\n \"0.5733445\",\n \"0.56539035\",\n \"0.56316966\",\n \"0.56235695\",\n \"0.562181\",\n \"0.5613915\",\n \"0.5586703\",\n \"0.54934883\",\n \"0.5488558\",\n \"0.54843575\",\n \"0.5481464\",\n \"0.5463257\",\n \"0.5431779\",\n \"0.5413206\",\n \"0.5408277\",\n \"0.539843\",\n \"0.5387071\",\n \"0.535343\",\n \"0.53351474\",\n \"0.53186893\",\n \"0.5271466\",\n \"0.5267492\",\n \"0.5267492\",\n \"0.52574337\",\n \"0.5237681\",\n \"0.5229384\",\n \"0.5209345\",\n \"0.5208791\",\n \"0.518277\",\n \"0.51816505\",\n \"0.51816505\",\n \"0.5173257\",\n \"0.515969\",\n \"0.51588595\",\n \"0.5152495\",\n \"0.5138949\",\n \"0.5138949\",\n \"0.5133254\",\n \"0.51121765\",\n \"0.5103286\",\n \"0.51012677\",\n \"0.5094001\",\n \"0.507893\",\n \"0.503303\",\n \"0.50250334\",\n \"0.50250334\",\n \"0.5017679\",\n \"0.50059557\",\n \"0.5002554\",\n \"0.49926117\",\n \"0.4970756\",\n \"0.49693745\",\n \"0.4947105\",\n \"0.49396506\",\n \"0.49220538\",\n \"0.49188036\",\n \"0.49185482\",\n \"0.49158072\",\n \"0.4901841\",\n \"0.489006\",\n \"0.4885856\",\n \"0.48833588\",\n \"0.48826045\",\n \"0.48777324\",\n \"0.4875198\",\n \"0.48709366\",\n \"0.48661867\",\n \"0.48608145\",\n \"0.48603496\",\n \"0.48595402\",\n \"0.48569468\",\n \"0.48508105\",\n \"0.48475283\",\n \"0.4834296\",\n \"0.48298964\",\n \"0.4823414\",\n \"0.4819606\",\n \"0.48152298\",\n \"0.4806883\",\n \"0.4805445\",\n \"0.48006338\",\n \"0.47974154\",\n \"0.47943577\",\n \"0.47931957\",\n \"0.47930935\",\n \"0.47887868\",\n \"0.47841224\",\n \"0.47806612\",\n \"0.4779559\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6395847"},"document_rank":{"kind":"string","value":"4"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":779,"numItemsPerPage":100,"numTotalItems":95772,"offset":77900,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjM1Mjg2MCwianRpIjoiZWQxYjgzZTgtY2Q3Ni00MTY0LTk1MjItYTdiODYwZmQyY2ViIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYzNTY0NjAsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.VBGHbC0J7dECtkZjoYaiJgA108G_yzaBCEszmwwzrUunaODVYgcBz27nGVPOnfyKAM1eMIHyHOIqVaDIEoOwDA","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Get the delta X from a left direction | def test_findXCoordinateFromDirection_left(self):
actual_result = rules.findXCoordinateFromDirection(8)
expected_result = -2
self.assertEqual(actual_result, expected_result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def left_distance(self):\n return self.x",
"def pos_left(self, x=1):\n\n self.x -= x\n return self.pos(self.x, self.y)",
"def downleft(self):\n return Coord([self.x - 1, self.y + 1])",
"def get_left(self):\n return -self.l_motor.get()",
"def left(self):\n if self.pos >... | [
"0.82075435",
"0.79344296",
"0.7572694",
"0.7388498",
"0.735274",
"0.731266",
"0.731266",
"0.7269696",
"0.71689415",
"0.71689415",
"0.7132461",
"0.7108066",
"0.7085654",
"0.70678705",
"0.7056366",
"0.701981",
"0.7010041",
"0.69809663",
"0.6925751",
"0.69125366",
"0.69064295",... | 0.65682304 | 37 |
Handle a bad direction | def test_findXCoordinateFromDirection_bad(self):
self.assertRaises(ValueError,
rules.findXCoordinateFromDirection,
80) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def invalid_direction(direction):\n valid_directions = [\"forwards\", \"backwards\"]\n\n if direction not in valid_directions:\n return f\"`direction` must be one of [{', '.join(valid_directions)}]\"\n\n return False",
"def test_findDirection_bad(self):\n startCoordinate = coordinate.Coord... | [
"0.64457107",
"0.6431128",
"0.59023845",
"0.58593196",
"0.5857923",
"0.58337206",
"0.5814574",
"0.5794645",
"0.57255983",
"0.5713911",
"0.56316334",
"0.56134814",
"0.55767596",
"0.5555252",
"0.5540778",
"0.55354196",
"0.55186427",
"0.5508847",
"0.5497151",
"0.5490839",
"0.548... | 0.5892146 | 3 |
Get the delta Y from a left direction | def test_findYCoordinateFromDirection_left(self):
actual_result = rules.findYCoordinateFromDirection(7)
expected_result = 0
self.assertEqual(actual_result, expected_result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def downleft(self):\n return Coord([self.x - 1, self.y + 1])",
"def get_left(self):\n return -self.l_motor.get()",
"def pos_down(self, y=1):\n\n self.y += y\n return self.pos(self.x, self.y)",
"def upleft(self):\n return Coord([self.x - 1, self.y - 1])",
"def __get_y__(se... | [
"0.735407",
"0.7098737",
"0.69108516",
"0.6823766",
"0.6746996",
"0.6623058",
"0.6428845",
"0.6383905",
"0.6308379",
"0.63070005",
"0.6301892",
"0.6296186",
"0.62940204",
"0.6260142",
"0.6233566",
"0.6217046",
"0.6187829",
"0.61841154",
"0.6119185",
"0.6114301",
"0.61121523",... | 0.67268586 | 5 |
Get the delta Y from a right direction | def test_findYCoordinateFromDirection_up(self):
actual_result = rules.findYCoordinateFromDirection(1)
expected_result = 2
self.assertEqual(actual_result, expected_result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __get_y__(self):\n return self.Direction['y']",
"def calculate_position_y(cls, position_y, direction):\n cls.raise_exception_if_invalid_direction(direction)\n return position_y + cls.DIRECTION_MAPPER[direction][1]",
"def getBotRightVelocity(self):\n\t\tif len(self.prevPositions) < 2:\n... | [
"0.7415944",
"0.7058163",
"0.6893687",
"0.6861486",
"0.679843",
"0.67935485",
"0.67726815",
"0.6753201",
"0.6743264",
"0.6693129",
"0.6676823",
"0.6672408",
"0.6672115",
"0.6671134",
"0.66600674",
"0.6654607",
"0.6645873",
"0.6626593",
"0.66253406",
"0.6601017",
"0.65896916",... | 0.6340395 | 45 |
Get the delta Y from a left direction | def test_findYCoordinateFromDirection_down(self):
actual_result = rules.findYCoordinateFromDirection(4)
expected_result = -2
self.assertEqual(actual_result, expected_result) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def downleft(self):\n return Coord([self.x - 1, self.y + 1])",
"def get_left(self):\n return -self.l_motor.get()",
"def pos_down(self, y=1):\n\n self.y += y\n return self.pos(self.x, self.y)",
"def upleft(self):\n return Coord([self.x - 1, self.y - 1])",
"def __get_y__(se... | [
"0.735407",
"0.7098737",
"0.69108516",
"0.6823766",
"0.6746996",
"0.67268586",
"0.6623058",
"0.6428845",
"0.6383905",
"0.6308379",
"0.63070005",
"0.6301892",
"0.6296186",
"0.62940204",
"0.6260142",
"0.6233566",
"0.6217046",
"0.6187829",
"0.61841154",
"0.6119185",
"0.6114301",... | 0.59450686 | 46 |
Handle a bad direction | def test_findYCoordinateFromDirection_bad(self):
self.assertRaises(ValueError,
rules.findYCoordinateFromDirection,
80) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def invalid_direction(direction):\n valid_directions = [\"forwards\", \"backwards\"]\n\n if direction not in valid_directions:\n return f\"`direction` must be one of [{', '.join(valid_directions)}]\"\n\n return False",
"def test_findDirection_bad(self):\n startCoordinate = coordinate.Coord... | [
"0.64457107",
"0.6431128",
"0.59023845",
"0.5892146",
"0.58593196",
"0.5857923",
"0.58337206",
"0.5814574",
"0.5794645",
"0.57255983",
"0.5713911",
"0.56316334",
"0.56134814",
"0.55767596",
"0.5555252",
"0.55354196",
"0.55186427",
"0.5508847",
"0.5497151",
"0.5490839",
"0.548... | 0.5540778 | 15 |
Return a dictionary of diffstat information for a diffset. | def diffstats(diffset):
counts = diffset.get_total_line_counts()
# TODO: Take into account the non raw counts review board might
# have to display information about replacement.
return {
'insert': counts.get('raw_insert_count', 0),
'delete': counts.get('raw_delete_count', 0),
} | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def diffstat(self):\r\n url = '{0}/diffstat'.format(self.get_url())\r\n request = http.Request('GET', url)\r\n\r\n return request, parsers.parse_json",
"def diffstat(self):\n for diffstat in self._get_paged(\"diffstat\"):\n yield DiffStat(diffstat, **self._new_session_args)... | [
"0.70968556",
"0.666974",
"0.59959733",
"0.59312314",
"0.5841939",
"0.57571334",
"0.566059",
"0.5640931",
"0.563014",
"0.5592719",
"0.5575118",
"0.5567187",
"0.5483914",
"0.54734266",
"0.54181707",
"0.5389446",
"0.53859234",
"0.5384173",
"0.53651166",
"0.5353329",
"0.5309579"... | 0.8136382 | 0 |
Return a dictionary containing diffstat information. If no rev is provided, use the latest diffset. If the requesting user is the submitter, take any draft diffsets into account. | def get_diffstats(review_request, user, rev=None):
# Ensure we're working with the base review request, not a draft.
review_request = review_request.get_review_request()
if rev is None:
# If the user is the submitter we might want to use the draft diffset.
draft = review_request.get_draft(u... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def diff(self, rev=None):\r\n args = []\r\n if rev is not None:\r\n args.append(\"-r %d\" % rev)\r\n out = self._authsvn('diff', args)\r\n return out",
"def diff(self, revision=None):\n if not revision:\n revision = Revision.latest_revision(self.instance)\... | [
"0.59980303",
"0.5838497",
"0.5777238",
"0.5759859",
"0.5542309",
"0.5527837",
"0.54685724",
"0.54519284",
"0.5437927",
"0.53403556",
"0.52737635",
"0.524713",
"0.52007115",
"0.52005863",
"0.51799476",
"0.51608956",
"0.51590776",
"0.51563096",
"0.5123263",
"0.51172936",
"0.51... | 0.74918723 | 0 |
Reserve the origin attribute while getting dummies | def GetDummies(data_set, categorical_features):
reserve_name = data_set.name
reserve_trn_len = data_set.trn_len
data_set = pd.get_dummies(data_set, columns=categorical_features, drop_first=True)
data_set.name = reserve_name
data_set.trn_len = reserve_trn_len
return data_set | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clear_dummy_obj(self):\n for d in self.dummies:\n self.map.remove_node(d)\n\n self.dummies = []",
"def untie_everything(self):\r\n self.tied_indices = []",
"def __resetLocal__(self,featureVals):\n self.amITrained = False\n self._amplitudes = {}\n self._eigs ... | [
"0.5686756",
"0.5557571",
"0.5537352",
"0.55227154",
"0.54721504",
"0.54721504",
"0.54721504",
"0.5448542",
"0.53795433",
"0.5373506",
"0.52839226",
"0.5265491",
"0.5261756",
"0.52273184",
"0.517853",
"0.5169756",
"0.5163168",
"0.5160226",
"0.51585186",
"0.51465416",
"0.51300... | 0.0 | -1 |
Concatenate train set and test set, This may lead to data leakage, but we have to do that because some tricks suck as dummy code will be wrong | def ConcatDF(train_set, test_set):
df_all = pd.concat([train_set, test_set], sort=True).reset_index(drop=True)
df_all.trn_len = train_set.shape[0]
return df_all | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ConcatDF(train_set, test_set):\n return pd.concat([train_set, test_set], sort=True).reset_index(drop=True)",
"def get_training_and_testing_sets(data, Y):\r\n data = pd.concat([data, Y], axis=1)\r\n x,y=data.shape\r\n train_X_sub1=data[0:x//6]\r\n dev_X_sub1 = data[x//6:x//6 + x//12]\r\n tes... | [
"0.72702205",
"0.7119447",
"0.6611562",
"0.6522652",
"0.6515046",
"0.6469268",
"0.640398",
"0.6212674",
"0.6208587",
"0.6185535",
"0.6140687",
"0.60801977",
"0.59785014",
"0.59742194",
"0.59706175",
"0.59375256",
"0.5934598",
"0.593107",
"0.59287834",
"0.5923713",
"0.5889146"... | 0.7195508 | 1 |
Divide the data set that concatenated from train set and test set | def DivideDF(df_all):
return df_all.iloc[:df_all.trn_len], df_all.iloc[df_all.trn_len:] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __split_dataset(self):\n self.train, self.valid, _, _ = train_test_split(self.data, self.data, test_size=0.2)\n self.valid, self.test, _, _ = train_test_split(self.valid, self.valid, test_size=0.5)",
"def split_dataset(data_set, train_size, test_size):\n # Generate random indices without rep... | [
"0.73923093",
"0.7316695",
"0.73096097",
"0.7292028",
"0.7082199",
"0.70577854",
"0.699461",
"0.69900775",
"0.69663644",
"0.69636",
"0.69602627",
"0.69523257",
"0.6950047",
"0.69360214",
"0.69216895",
"0.69130886",
"0.6910069",
"0.68873096",
"0.6882777",
"0.68530935",
"0.6824... | 0.0 | -1 |
Get train and test data set. | def GetDataSet(path):
# Read csv files
df_train_set = pd.read_csv(os.path.join(path, "train.csv"))
df_test_set = pd.read_csv(os.path.join(path, "test.csv"))
# Filter
df_train_set.drop("Id", axis=1, inplace=True)
df_test_set.drop("Id", axis=1, inplace=True)
# Assign name
df_train_set.name... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_dataset(self):\n\n trainset = datasets.STL10('datasets/STL10/train/', split='train', transform=self.train_transforms,\n target_transform=None, download=True)\n valset = datasets.STL10('datasets/STL10/test/', split='test', transform=self.val_transforms,\n ... | [
"0.7832964",
"0.7789843",
"0.7632272",
"0.75655854",
"0.7256563",
"0.72525346",
"0.7169658",
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"0.7151106",
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"0.70096624",
"0.70075047",
"0.6993283",
"0.69746387",
"0.69455457",
"0.69... | 0.0 | -1 |
Simply fill nan values | def DealWithMissingValues(data_set: pd.DataFrame):
data_set.fillna(method="pad", inplace=True) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fill_nan(A):\n\tinds = np.arange(A.shape[0])\n\tgood = np.where(np.isfinite(A))\n\tA[np.isnan(A)] = np.interp(inds[np.isnan(A)], inds[good], A[good])\n\treturn A",
"def fill_nan(array):\n idx = np.arange(array.shape[0])\n good = np.where(np.isfinite(array))\n interp = interpo... | [
"0.8199574",
"0.7925708",
"0.77308667",
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"0.7597855",
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"0.69066197",
"0.6883187",
"0.68601936",
"0.681218",
"0.681136",... | 0.65897727 | 25 |
Show missing numbers if it exists | def GetMissingValues(data_set: pd.DataFrame):
# Get missing features and missing line counts
missing_features = []
missing_line_counts = []
for column in data_set.columns:
missing_line_count = data_set[column].isnull().sum()
if missing_line_count != 0:
missing_features.append... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def missing_values():\n print('Missings in the train data:', train_data.isnull().sum())",
"def task8_missing_number(num):\n check_list = list(range(1, max(num) + 1))\n result = list(set(check_list) - set(num))\n return result",
"def get_missing_test_numbers(a_dict, logged_test_numbers):\n tnum_l... | [
"0.6732465",
"0.6668786",
"0.6668368",
"0.6469321",
"0.64369404",
"0.6392679",
"0.63542736",
"0.6340361",
"0.6182563",
"0.6148259",
"0.6077315",
"0.6046442",
"0.59800965",
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"0.59599406",
"0.593957",
"0.590098",
"0.5886578",
"0.5875229",
"0.5852242",
"0.581376",
... | 0.0 | -1 |
Parses Conferences on wikicfp domain and follow links to actual conference page if link exists | def parse_wikicfp_page(self, response):
# Processing of individual CFP page within wikicfp
if re.search('cfp/servlet/event.showcfp', response.url): # Conference page
self.num_conf_crawled += 1
# Series information exists only for series crawl in wikicfp_all
response.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_links(self, from_year):\n self.links = []\n self.titles = []\n self.speakers = []\n self.dates = []\n\n r = requests.get(self.calendar_url)\n soup = BeautifulSoup(r.text, \"html.parser\")\n\n if self.verbose:\n print(\"Getting links for press con... | [
"0.6242029",
"0.5957307",
"0.5563976",
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"0.54143894",
"0.5413671",
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"0.5020961",
"0.49787804",
"0.49642515",
"0.4960764",
"0.4945461",
"0.494287... | 0.6074987 | 1 |
we use statistics of ngram to disambiguate the rhyme category, but there is still risk of mismatching and ambiguity | def get_line_rhyme(self, line):
tail_char = line[-1]
if tail_char in self._rhyme_dic:
rhyme_candis = self._rhyme_dic[tail_char]
if len(rhyme_candis) == 1:
return rhyme_candis[0]
if tail_char in self._char_rhyme_map:
bigram = line[-2] + line[-... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ngram_frequency(word):\r\n\tword = word.lower()\r\n\tword = re.sub(r'[^A-Za-z. ]','',word)\r\n\tngram_statistics = {}\r\n\tngram_categorization_model_keys = []\r\n\tngram_categorization_model_occurances = []\r\n\tres = [0 for _ in range(0,300)]\r\n\tfor ituple in ngram_categorization_model:\r\n\t\tngram_catego... | [
"0.6331422",
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"0.57811457",
"0.57735",
"0.57678723",
"0.57591045",
"0.57580376",... | 0.0 | -1 |
pick houses from self.env_set | def _pick_envs_to_load(self, split, max_envs, start_idx, in_order):
if split in ['val', 'test'] or in_order:
pruned_env_set = self.env_set[start_idx:start_idx+max_envs] # could be void if start_idx arrives end
else:
if max_envs < len(self.env_set):
env_inds = np.random.choice(len(self.env_s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def select_households(self, attr, old, new):\n index = new[0]\n school_id = self.data.iloc[index].school_id\n # List all agents that have the same school id\n same_school = self.data[self.data.school_id == school_id].index\n self.source.selected.indices = list(same_school)",
"d... | [
"0.53169477",
"0.5086531",
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"0.48321876",
"0.48022148",
"0.47826496",
"0.47820404",
"0.47569978",
"0.47... | 0.5894043 | 0 |
idx qid, house question, answer qe, ae type attr path_ix nav_ids nav_types nav_ego_feats navs of (l, 3200) float32 nav_action_inputs navs of (l, ) int64 nav_action_outputs navs of (l, ) int64 nav_ego_imgs navs of (l, 224, 224, 3) uint8 if necessary | def __getitem__(self, index):
idx = self.available_idx[index]
qn = self.questions[idx]
qid = qn['id']
house = qn['house']
attr, room_attr = self.question_to_attribute(qn)
# encode question and answer
qe = self.encoded_questions[qn['h5_id']]
ae = self.encoded_questions[qn['h5_id']]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def construct_paths(data, relation_level_words, qald=False,goldorpred='gold'):\n abstract_question = data[goldorpred]['abstract_question'].replace('<e>', 'entity1').replace('<l>', 'literal1')\n question = ei.vocabularize(nlutils.tokenize(abstract_question))\n\n \"\"\"======\"\"\"\n question_dep = []\n ... | [
"0.47627646",
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"0.44526723",
"0.44371733",
"0.4418434",
"0.44158706",
"0.440... | 0.667768 | 0 |
Run set_target_object/room before calling this function! | def spawn_agent(self, min_dist, max_dist, split):
conn_map = self.episode_house.env.house.connMap
point_cands = np.argwhere((conn_map > min_dist) & (conn_map <= max_dist) )
if point_cands.shape[0] == 0:
return None, None
point_idx = np.random.choice(point_cands.shape[0]) if split == 'train' else 0... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def target(self, target) :\n\t\ttry :\n\t\t\tself._target = target\n\t\texcept Exception as e:\n\t\t\traise e",
"def target(self, target) :\n\t\ttry :\n\t\t\tself._target = target\n\t\texcept Exception as e:\n\t\t\traise e",
"def init(self, target):\n pass",
"def setCurrentTarget(self):\n if se... | [
"0.63913363",
"0.63913363",
"0.62136173",
"0.59611523",
"0.5901944",
"0.5878155",
"0.5878058",
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"0.55148864",
"0.5501707",
"0.54905826",
"0.5456762",
"0.5454063",
"0.54427016",
"0.54412... | 0.0 | -1 |
attrs are [object_color_equal, object_size_bigger/smaller, object_dist_farther/closer, room_size_bigger/smaller] room_attrs are [inroom, xroom] | def question_to_attribute(self, qn):
attr = ''
if 'object_color' in qn['type']:
attr = 'object_color_equal'
elif 'object_size' in qn['type']:
compare_type = 'bigger' if 'bigger' in qn['question'] else 'smaller'
attr = 'object_size_%s' % compare_type
elif 'room_size' in qn['type']:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, room,\n monster_symbol='m', monster_color='white',\n monster_name='generic_monster', monster_ai='basic',\n ai_action=True, ai_action_radius=10,\n fighter_hp=1, fighter_defense=0, fighter_power=0, fighter_xp=0,\n trea... | [
"0.5633651",
"0.56281585",
"0.54987335",
"0.5497923",
"0.51746804",
"0.5157434",
"0.51292056",
"0.50903624",
"0.50852436",
"0.5079441",
"0.5076012",
"0.5069625",
"0.5069535",
"0.50191796",
"0.4992347",
"0.4981469",
"0.49468",
"0.49361178",
"0.4929939",
"0.49253476",
"0.492441... | 0.55781496 | 2 |
Wrapped datetime specific instance. | def DT (self) :
return self._body | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def datetime(self):\n return datetime(*tuple(self))",
"def __init__(self, strict=True, **kwargs):\r\n self.strict = strict\r\n super(DateTime, self).__init__(**kwargs)",
"def omniscient_datetime(*args):\n d = original_datetime(*args)\n if settings.USE_TZ:\n d = timezone.make_a... | [
"0.6891938",
"0.65516645",
"0.63991517",
"0.6353348",
"0.6125995",
"0.6077955",
"0.6050468",
"0.5954843",
"0.5946195",
"0.59021163",
"0.5884555",
"0.5871482",
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"0.5719277",
"0.57126653",
"0.56486267",
"0.5619723",
"0.5610949",
"0.56015664",
"0.5593643",
"0.5531774... | 0.0 | -1 |
Do basic tests over the gaussian function | def test_gauss(gaussian):
x_gauss, x = gaussian
assert len(x) == 25
assert x_gauss[0] == x_gauss[-1] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_gaussian():\n x0 = Parameter('x0')\n sig = Parameter('sig', positive=True)\n x = Variable('x')\n\n new = sympy.exp(-(x - x0)**2/(2*sig**2))/sympy.sqrt((2*sympy.pi*sig**2))\n assert isinstance(new, sympy.Expr)\n g = Gaussian(x, x0, sig)\n assert issubclass(g.__class__, sympy.Expr)\n ... | [
"0.71569026",
"0.71300036",
"0.69926065",
"0.689196",
"0.6883935",
"0.68262583",
"0.6825799",
"0.6679407",
"0.66321474",
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"0.6415374",
"0.64126265",
"0.6411648",
"0.6408169",
"0.6404639",
"0.63606... | 0.70641977 | 2 |
Make sure that the 1d convolution implemented provides the same result as the 2d convolution (python package implementation) | def test_gaussian_filter():
def rgb2gray(rgb):
r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
return gray
img = rgb2gray(np.array(Image.open('data/graf.png')))
gx, x = gauss_module.gauss(4)
gx = gx.reshape(1, gx.shape[0])
gy ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_same_convolution(conv1d_placeholder, spatial_onehot, output_size, width, stride):\n conv_layer = Convolution((3, output_size), lambda x: 1, strides=stride, padding=\"same\")\n output = conv_layer(conv1d_placeholder)\n output_width = output.axes.find_by_name(\"W\")[0].length\n assert output_wid... | [
"0.7460406",
"0.73170173",
"0.7062893",
"0.7062232",
"0.7018009",
"0.7001281",
"0.6940282",
"0.6900652",
"0.6690584",
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"0.66121566",
"0.65842104",
"0.65494543",
"0.653681",
"0.6512027",
"0.650163... | 0.0 | -1 |
Due to how Python manages scope, the creation of closure is wrapped into a function. The lambda cannot be created in the loop, because then all function calls will refer to the same variable!. | def _create_new_criteria_function(lm,mm):
return lambda D, i, u, j: parametrized_insertion_criteria(D, i, u, j,
lm=lm, mm=mm) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_lambda(n):\n for i in range(n):\n yield lambda : i",
"def test_lambda(n):\n return [lambda v=i: v for i in range(n)]",
"def _gen_closure(fnc,arg):\n return lambda s: fnc(arg)",
"def test_closure(a):\n def x1(b):\n\n def x4(c):\n return b\n return x... | [
"0.66687864",
"0.6453177",
"0.6441694",
"0.62486774",
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"0.6149522",
"0.613451",
"0.6026405",
"0.5967721",
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"0.5916454",
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"0.5900501",
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"0.5739094",
"0.57091045",
"0.5662242",
"0.56328",
"0.5598219",
"0.55570084",
"0.55467045"... | 0.0 | -1 |
This is the implementation of Mole and Jameson (1976) cheapest insertion algorithm. The emerging route is first initialized according to which strain criterion (insertion cost calculation method) is used, On each step an unrouted customer for which the insertion cost is lowest (between any two nodes on the emerging rou... | def mole_jameson_insertion_init(D, d, C, L=None, minimize_K=False,
strain_criterion='all' ):
callback_configurations = []
if strain_criterion=='proximity_ranking' or strain_criterion=='all':
callback_configurations.append(
( _create_new_criteria_function(lm=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def safeJourney(Alist,s,d):\n #Initialize dictionaries\n dinit = 10**6\n Edict = {} #Explored nodes\n Udict = {} #Unexplored nodes\n path = [[] for l in Alist]\n\n Alen = len(Alist) #length of Alist\n dinits = [dinit]*Alen #list of airport indexes\n Udict = dict(zip(list(range(Alen)),dinits... | [
"0.6560185",
"0.6236826",
"0.6223014",
"0.6154328",
"0.6079165",
"0.60468",
"0.5980044",
"0.5947014",
"0.5939437",
"0.5888121",
"0.58642095",
"0.58552253",
"0.58538413",
"0.58406043",
"0.57866955",
"0.57730013",
"0.57542545",
"0.5741422",
"0.57220036",
"0.57218945",
"0.569465... | 0.52982587 | 68 |
Initialize the graphics view. | def __init__(self, type, controller):
super(GUI, self).__init__()
self.controller = controller
self.layout = QtWidgets.QHBoxLayout(self)
self.leftSide = QtWidgets.QVBoxLayout(self)
self.generateWidget = Generate(self, self.controller)
self.leftSide.addWidget(self.gene... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_graphics(self):\n self.renderer = vtk.vtkRenderer()\n self.window = vtk.vtkRenderWindow()\n self.window.AddRenderer(self.renderer)\n self.renderer.SetBackground(1.0, 1.0, 1.0)\n self.window.SetSize(1000, 1000)\n\n # Create a trackball interacter to transofor... | [
"0.738486",
"0.7245235",
"0.7245235",
"0.71699125",
"0.69324094",
"0.6878057",
"0.68248576",
"0.67956203",
"0.67449474",
"0.67285466",
"0.67102",
"0.67010546",
"0.66953194",
"0.66546637",
"0.6624726",
"0.6610313",
"0.6596297",
"0.6591724",
"0.6586789",
"0.65769166",
"0.655664... | 0.0 | -1 |
this test shows that when list() is given a iterable object list will create a list and put each item in that iterable object into that created list | def testCreateFromIterable(self):
self.assertEqual(["c","h","e","k","a"],list("cheka")) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_iterlist_op_1():\n\n @ops.iterlist_op\n def f(x):\n return [4, 5, 6]\n\n result = f(iter([1, 2, 3])) # Passing in an iterator, as expected\n\n assert(isinstance(result, list)), f\"{result}\"\n assert(result == [4, 5, 6])",
"def test_listiter_op_1():\n\n @ops.listiter_op\n de... | [
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"0.6685624",
"0.65879583",
"0.65396696",
"0.65396696",
"0.6528297",
"0.65097594",
"0.6507952",
"0.6507952",
"0.639472",
"0.6357265",
"0.62581646",
"0.6256366",
"0.624183",
"0.6190519",
"0.6117395",
"0.6117395",
"0.60700786",
"0.6008047",
"0.60052884",
"0.5988655... | 0.64752835 | 9 |
this testcase proves that for list object '==' checks equality based on content checking, rather that reference checking | def testEquality(self):
list1 = [1,2]
list2 = [1,2]
# not the same object
self.assertIsNot(list1,list2)
self.assertNotEqual(id(list1),id(list2))
self.assertFalse(list1 is list2)
# but content is equal
self.assertEqual(list1,list2)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_list_identity(self):\n pass",
"def check_lists_equal( list1, list2, assertobject ):\n assertEqualsImproved( len( list1 ), len( list2 ), assertobject )\n for element in list1:\n assertobject.assertIn( element, list2 )",
"def testMutable(self):\n alist = [1,2,3]\n aref = alist\... | [
"0.71370006",
"0.7099526",
"0.6874867",
"0.6859387",
"0.6776912",
"0.6727213",
"0.6631619",
"0.65954477",
"0.6538188",
"0.6502381",
"0.6497191",
"0.6493001",
"0.6463698",
"0.6459172",
"0.64429367",
"0.6437611",
"0.6419223",
"0.6414189",
"0.63548326",
"0.6349761",
"0.6301157",... | 0.7994808 | 0 |
difference between "list.sort" and builtin "sorted" is that "list.sort" sort the list in place while builtin "sorted" return a new list sorted by specific order | def testSort(self):
numlist = [6,4.78,1.2,5]
numlist.sort()
self.assertEqual([1.2,4.78,5,6],numlist)
strlist = ["kgb","mss","cheka"]
strlist.sort()
self.assertEqual(["cheka","kgb","mss"],strlist)
# ------------ reverse sort
num... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sort_list(self,list_):\r\n list_.sort()",
"def sort_list():\n fun_list = basic_list_exception.make_list()\n fun_list.sort()\n return fun_list",
"def sorted(x) -> List:\n pass",
"def python_sort(a_list):\n \n start_time = time.time()\n\n a_list.sort()\n\n end_time = time.time(... | [
"0.7887157",
"0.73782283",
"0.73607516",
"0.7267121",
"0.72059846",
"0.71099776",
"0.7100431",
"0.70780563",
"0.70418423",
"0.70310795",
"0.69471747",
"0.6916764",
"0.6910774",
"0.69079554",
"0.68409765",
"0.6807745",
"0.6721783",
"0.6717191",
"0.6674685",
"0.6646439",
"0.663... | 0.71553606 | 5 |
element in the lsit can be changed | def testMutable(self):
alist = [1,2,3]
aref = alist
alist[1] = "stasi"
self.assertEqual([1,"stasi",3],aref) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change():",
"def updateLstAllElement(self):\n self.lstAllElement=[self.idVet, self.libelle,self.marque,self.quantite,self.prixHTVA,self.tauxTVA,self.taille,self.categorie,self.couleur,self.lstAssorti]",
"def element(self, element):\n pass",
"def changed(self):\n\t\tpass",
"def update_E(se... | [
"0.5974379",
"0.5922887",
"0.5706644",
"0.56755984",
"0.5652195",
"0.5602778",
"0.55848145",
"0.5569042",
"0.5516961",
"0.5512565",
"0.5488783",
"0.54719365",
"0.54701895",
"0.5461035",
"0.54609835",
"0.542505",
"0.54209566",
"0.5420029",
"0.5420029",
"0.5420029",
"0.5414117"... | 0.54831463 | 11 |
list is mutable type, has the 'reference' nature | def testReferenceFeature(self):
alist = [1]
aref = alist
alist.append("stasi")
self.assertEqual([1,"stasi"],aref)
self.assertIs(aref,alist) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ref_ex1():\n\n print(\"Look Alikes\")\n print(\"===========\")\n\n lst1 = [7, 3, 2]\n lst2 = [7, 3, 2]\n print(lst1, lst2)\n\n lst1[1] = -8\n print(lst1, lst2)\n\n print(\"\")\n print(\"Aliases\")\n print(\"=======\")\n\n lst3 = [1, 5, 9]\n lst4 = lst3\n print(lst3, lst4)... | [
"0.7354346",
"0.7250251",
"0.70623386",
"0.693525",
"0.6908979",
"0.6905237",
"0.68102694",
"0.6598454",
"0.65117925",
"0.64936775",
"0.6433605",
"0.64088255",
"0.6204775",
"0.6192246",
"0.61391217",
"0.6081045",
"0.6048543",
"0.60200906",
"0.59972274",
"0.5973449",
"0.597120... | 0.6211245 | 12 |
Return an operator given the path to a database. | def loadOperator(pathToDb, loadCycle, loadNode, allowMissing=False):
# `import armi` doesn't work if imported at top
from armi import cases
if not os.path.exists(pathToDb):
raise ValueError(
f"Specified database at path {pathToDb} does not exist. \n\n"
"Double check that esc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def operator(self):\n col = self.pos\n operators = [\"||\", \"&&\", \">>\", \"<<\", \"!=\", \">=\", \"<=\", \"==\", \"##\"] + \\\n [\"-\", \"+\", \"!\", \"*\", \"/\", \"|\", \"&\", \"^\", \"<\", \">\", \"?\", \":\", \"~\", \"#\", \"=\", \"%\"]\n try:\n index = self.match_... | [
"0.5361934",
"0.5345323",
"0.52730507",
"0.5133861",
"0.51303923",
"0.5125685",
"0.5095104",
"0.5086308",
"0.5076296",
"0.50663847",
"0.504918",
"0.5047888",
"0.5047888",
"0.5047888",
"0.5047888",
"0.5047888",
"0.5047888",
"0.5047888",
"0.5047888",
"0.5047888",
"0.5047888",
... | 0.5978742 | 0 |
Convert database files between different versions. | def convertDatabase(
inputDBName: str,
outputDBName: Optional[str] = None,
outputVersion: Optional[str] = None,
nodes: Optional[List[Tuple[int, int]]] = None,
):
dbIn = databaseFactory(inputDBName, permission=Permissions.READ_ONLY_FME)
if dbIn.version == outputVersion:
runLog.important(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upgradedb(self, args):\n upgrade_db(args.dbfile)",
"def _convert_rosetta_db_to_basic_db(self):\n pass",
"def migrate(ctx, start_version, end_version):\n if ctx.obj['TYPE'] == 'file':\n if ctx.obj['DATA_FILE']:\n file_path = ctx.obj['DATA_FILE']\n else:\n ... | [
"0.63407254",
"0.6322698",
"0.61341935",
"0.60159844",
"0.57008606",
"0.567188",
"0.56600946",
"0.56428075",
"0.56238115",
"0.55924803",
"0.5589563",
"0.5580526",
"0.55748826",
"0.5569592",
"0.556925",
"0.55508006",
"0.5528553",
"0.55067235",
"0.5505626",
"0.54876965",
"0.547... | 0.68520933 | 0 |
Return the underlying h5py File that provides the backing storage for a database. This is done here because HDF5 isn't an official aspect of the base Database abstraction, and thus making this part of the base Database class interface wouldn't be ideal. However, we violate this assumption when working with "auxiliary" ... | def _getH5File(db):
if isinstance(db, Database3):
return db.h5db
else:
raise TypeError("Unsupported Database type ({})!".format(type(db))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def open_db(self):\n # if it is not open, open it.\n if self.h5file.isopen is True:\n return self.h5file\n else:\n self.h5file = tb.open_file(filename=self.filepath, mode='a')\n assert self.h5file.isopen\n return self.h5file",
"def hdf5_file(self):\n ... | [
"0.63894004",
"0.6325323",
"0.5979841",
"0.5845819",
"0.5709331",
"0.5573591",
"0.55464363",
"0.5419186",
"0.5413009",
"0.5404134",
"0.5402279",
"0.5396553",
"0.5393392",
"0.53786933",
"0.53507555",
"0.5344345",
"0.5339578",
"0.5317838",
"0.5294479",
"0.5289991",
"0.52769095"... | 0.72821635 | 0 |
Parse lines from a file containing scattering data. Assume that lines without two or three numerical columns are not scattering data and are header or junk. An empty array is returned if these criteria are not met. | def parse_sas_data_line(line):
cols = line.split()
ncols = len(cols)
if ncols < 2:
data = np.array([],dtype=np.float)
else:
if ncols > 3:
ncols = 3
try:
data = np.array(cols[0:ncols], dtype=np.float)
except:
data = np.array([... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def read_data_file(input_file):\n header_lines = 0\n last_pound_pos = -1\n with open(input_file, 'r') as data_file:\n while (data_file.read(1) == '#'):\n last_pound_pos = data_file.tell()\n header = data_file.readline()\n header_lines += 1\n\n #Read the next ... | [
"0.6135837",
"0.5916294",
"0.59119976",
"0.5905361",
"0.5874234",
"0.58589786",
"0.5851976",
"0.5756723",
"0.5734717",
"0.56929773",
"0.5651191",
"0.56496507",
"0.56231004",
"0.56061757",
"0.55397755",
"0.55212134",
"0.55057025",
"0.5502649",
"0.54836565",
"0.54817307",
"0.54... | 0.584905 | 7 |
Load magnitude of scattering vector, q, and intensity, I, data from file and filter for values between q_min and q_max. | def read_sas_file(filename):
data = np.array([],dtype=np.float)
with open(filename,'r') as f:
for line in f:
# Only the first 2 or 3 columns are parsed
cols = parse_sas_data_line(line)
if cols.any():
if dat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_scatter_curve(filename, q_min, q_max):\n \n try:\n scatter_data = read_sas_file(filename)\n except:\n print(\"Unable to load \" + filename)\n print(\"Check that the file exists, and contains lines with at least two columns of numberical data\\n\")\n sys.exit(1)\n\n ... | [
"0.62943393",
"0.58814245",
"0.54279727",
"0.5416429",
"0.5340518",
"0.53365",
"0.5312966",
"0.53028476",
"0.5295134",
"0.52634937",
"0.5233531",
"0.5220195",
"0.5213372",
"0.51964384",
"0.5186909",
"0.5168423",
"0.5128792",
"0.5117724",
"0.51074344",
"0.5094335",
"0.5092197"... | 0.0 | -1 |
Load magnitude of scattering vector, q, and intensity, I, data (and potentially error in I) from file and filter for values between q_min and q_max. | def load_scatter_curve(filename, q_min, q_max):
try:
scatter_data = read_sas_file(filename)
except:
print("Unable to load " + filename)
print("Check that the file exists, and contains lines with at least two columns of numberical data\n")
sys.exit(1)
qrange_mask = (scat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadDataFile(self, filename):\n self.magneticfield = MagneticField(filename)\n self._meqfile = filename\n\n wallR, wallZ = self.magneticfield.getWall()\n\n self.rmin, self.rmax = np.amin(wallR), np.amax(wallR)\n self.zmin, self.zmax = np.amin(wallZ), np.amax(wallZ)",
"def r... | [
"0.5680521",
"0.5428513",
"0.5353534",
"0.5295121",
"0.5253234",
"0.5213317",
"0.5192037",
"0.5187126",
"0.5159289",
"0.5138375",
"0.5130577",
"0.5127485",
"0.5093703",
"0.5088959",
"0.50883",
"0.50879556",
"0.5084919",
"0.5079182",
"0.50727534",
"0.5027459",
"0.5026445",
"... | 0.6320465 | 0 |
Read in a list of scattering curve files and return a list of dictionaries which contain the file name ('file'), an array of Q and I(Q) vales ('data'), the radius of gyration ('rg') and the crosssection ('rxs1'). | def read_scatter_curves(curve_files, units, param):
curves = []
for curve_file in curve_files:
curve = {}
curve['file'] = curve_file
# Read in the scattering curve
# Modeling is performed in angstroms so convert files in nm to a
if units == 'nm':
curve['da... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_from_sc_file(axes, f, uf, true_max):\n f_combo_dict = collections.defaultdict(list)\n uf_combo_dict = collections.defaultdict(list)\n max_x = -10000\n max_y = -10000\n min_x = 10000\n min_y = 10000\n\n for fileType in [uf, f]:\n for i, item in enumerate(fileType):\n ... | [
"0.62675303",
"0.6176889",
"0.6142395",
"0.58858454",
"0.5876028",
"0.5847257",
"0.5815131",
"0.58049107",
"0.5796159",
"0.57542104",
"0.57402223",
"0.5739488",
"0.57298195",
"0.5717066",
"0.57040155",
"0.5665045",
"0.5653632",
"0.5630872",
"0.56305236",
"0.56249666",
"0.5598... | 0.72098076 | 0 |
Load yaml file containing the ranges for all analyses. | def process_qrange_file(filename):
f = open(filename, 'r')
q_ranges = yaml.load(f)
return q_ranges | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_yaml_file(self, path):\n with path.open('r') as handle:\n data = load_yaml(handle)\n\n self.set_all(**self.SCHEMA.load(data).data)",
"def load_yaml(self):\n env = self.state.document.settings.env\n relpath, abspath = env.relfn2path(directives.path(self.arguments[0]... | [
"0.63422006",
"0.57995796",
"0.5766984",
"0.574815",
"0.573764",
"0.57361096",
"0.5614416",
"0.5582548",
"0.5572529",
"0.5568093",
"0.5553858",
"0.5536593",
"0.54736644",
"0.5459305",
"0.538914",
"0.53844225",
"0.53229624",
"0.53033197",
"0.52961624",
"0.52952296",
"0.5284542... | 0.68835 | 0 |
Match two scatter curves. Get intensity, I, values from one data set at the q (scaterring vector magnitude) values present in the other. Input is two q vs I scattering curves. Output is I values from the source data set matched to the q values of the target set. | def match_scatter_curves(target_data, source_data):
# Create list of calculated I values matched to the nearest experimental q
# Remember that the arrays in python start at 0, those in Fortran at 1
last_source = len(source_data)
last_target = len(target_data)
# Initialize array to hold the calcula... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compare_curves(target_data, source_data, q_min, q_max, chi2):\n\n if chi2:\n rfactor, scale = calculate_chi2(target_data, source_data, q_min, q_max)\n else:\n rfactor, scale = calculate_rfactor(\n target_data, source_data, q_min, q_max)\n\n return rfactor, scale",
"def calcu... | [
"0.56384337",
"0.5514092",
"0.5465273",
"0.5464144",
"0.54291075",
"0.54191035",
"0.5410992",
"0.5403209",
"0.53972155",
"0.5342674",
"0.53372824",
"0.532874",
"0.5325885",
"0.5307892",
"0.5297777",
"0.5290474",
"0.52871025",
"0.5279677",
"0.52782655",
"0.5225266",
"0.5209964... | 0.7495544 | 0 |
Compute comparison metric (R factor or Chi^2). Input is two q vs I scattering curves and the min/max q values to use to compare them. The target (experimental) curve is scaled to match the source (theoretical) one. This is because the theoretical curve is based on a calculation of I/Io. Output is the R factor and the s... | def compare_curves(target_data, source_data, q_min, q_max, chi2):
if chi2:
rfactor, scale = calculate_chi2(target_data, source_data, q_min, q_max)
else:
rfactor, scale = calculate_rfactor(
target_data, source_data, q_min, q_max)
return rfactor, scale | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calculate_rfactor(target_data, source_data, q_min, q_max):\n\n matched_source_I = match_scatter_curves(target_data, source_data)\n\n # Get average I for experimental and calculated values over matched q range\n matched_no = len(matched_source_I)\n expt_avg = np.mean(target_data[0:matched_no, 1])\n ... | [
"0.68376523",
"0.6492629",
"0.55108064",
"0.5445589",
"0.5386808",
"0.5345272",
"0.53385323",
"0.52746767",
"0.52524316",
"0.52124363",
"0.51650137",
"0.5163609",
"0.51559365",
"0.5129782",
"0.5114901",
"0.51110995",
"0.50248575",
"0.50235295",
"0.5023434",
"0.5019229",
"0.50... | 0.79531777 | 0 |
Compute R factor comparing two scattering curves. Input is two q vs I scattering curves and the min/max q values to use to compare them. The target (experimental) curve is scaled to match the source (theoretical) one. This is because the theoretical curve is based on a calculation of I/Io. Output is the R factor and th... | def calculate_rfactor(target_data, source_data, q_min, q_max):
matched_source_I = match_scatter_curves(target_data, source_data)
# Get average I for experimental and calculated values over matched q range
matched_no = len(matched_source_I)
expt_avg = np.mean(target_data[0:matched_no, 1])
calc_avg ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compare_curves(target_data, source_data, q_min, q_max, chi2):\n\n if chi2:\n rfactor, scale = calculate_chi2(target_data, source_data, q_min, q_max)\n else:\n rfactor, scale = calculate_rfactor(\n target_data, source_data, q_min, q_max)\n\n return rfactor, scale",
"def calcu... | [
"0.7753233",
"0.58153874",
"0.5712545",
"0.53757167",
"0.5279661",
"0.52676696",
"0.52249604",
"0.51984113",
"0.5190254",
"0.51797056",
"0.5145067",
"0.5140006",
"0.5114124",
"0.510577",
"0.5075585",
"0.5032694",
"0.502384",
"0.5012232",
"0.49598452",
"0.4957949",
"0.4931579"... | 0.68906873 | 1 |
Compute Chi^2 comparing two scattering curves. Input is two q vs I scattering curves and the min/max q values to use to compare them. The target (experimental) curve is scaled to match the source (theoretical) one. This is because the theoretical curve is based on a calculation of I/Io. Output is the Chi^2 and the scal... | def calculate_chi2(target_data, source_data, q_min, q_max):
matched_source_I = match_scatter_curves(target_data, source_data)
# Get average I for experimental and calculated values over matched q
# range
matched_no = len(matched_source_I)
expt_avg = np.mean(target_data[0:matched_no, 1])
calc_a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compare_curves(target_data, source_data, q_min, q_max, chi2):\n\n if chi2:\n rfactor, scale = calculate_chi2(target_data, source_data, q_min, q_max)\n else:\n rfactor, scale = calculate_rfactor(\n target_data, source_data, q_min, q_max)\n\n return rfactor, scale",
"def calcu... | [
"0.72039247",
"0.5903445",
"0.5728763",
"0.5679894",
"0.56745183",
"0.563039",
"0.5551781",
"0.5550915",
"0.5501156",
"0.5463921",
"0.5455437",
"0.5452026",
"0.5440274",
"0.54046345",
"0.5401157",
"0.5397274",
"0.5318091",
"0.53127307",
"0.5302951",
"0.52710074",
"0.52679455"... | 0.7524854 | 0 |
Linear fit x vs y data. calc_type (rg, rxs1, rxs2) determines value computed from gradient and intercept in Guinier and other analyses of small angle scattering curves. | def sas_curve_fit(x, y, calc_type):
result = {}
n = len(x)
if (n == 0) or (len(y) == 0):
err = "Error: No values to fit in " + calc_type + " calculation\n"
raise Exception(err)
#result['error'] = err
else:
# Linear fit to the input x and y values
#fit_coeffs = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def linearfit_s1(x, y):\r\n x *= N.ones(1); y *= N.ones(1)\r\n mx = N.mean(x); my = N.mean(y)\r\n slope = 1.; yint = my-mx\r\n rl_slope, sd_slope, rl_yint, sd_yint, s_yint, cov = robust_linefit(x, y)\r\n \r\n return slope, sd_slope, yint, sd_yint, s_yint, cov",
"def linear_regression(self, x_da... | [
"0.64826363",
"0.6309423",
"0.6292486",
"0.6138011",
"0.6078534",
"0.59990513",
"0.59841806",
"0.5863627",
"0.5851407",
"0.58428884",
"0.5836996",
"0.5771239",
"0.5766344",
"0.5713542",
"0.56316483",
"0.5586694",
"0.55863845",
"0.55366975",
"0.5526817",
"0.5521595",
"0.55129"... | 0.7035865 | 0 |
Outputs graph of x and y to a pdf file. x and y are intended to be functions of the magnitude of scattering vector, q, and intensity, I. Values that are computed from the graph (outputs) and range of R? qfit written on graph | def graph_sas_curve(filename, x, y, title_text, x_lab, y_lab,
x_min, x_max, y_min, y_max, **kwargs):
fit_coeffs = kwargs.get('fitcoeffs', None)
outputs = kwargs.get('outputs', None)
rq_range = kwargs.get('rqrange', None)
mask = kwargs.get('mask', None)
# Plot the input x, y val... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def plot_pdf(data,b,X,outfile):\t\r\n\tme = \"LE_Plot.plot_pdf: \"\r\n\tshowplot = False\r\n\tt0 = time.time()\r\n\t## Data\r\n\tx, y = data\r\n\txmax, ymax = np.abs(x).max(), np.abs(y).max()\r\n\t## Plot pdf\r\n\tfs = 25\r\n\t# counts, xedges, yedges, im = plt.hist2d(x,y, bins=100, range=[[-2*X,+2*X],blim(b,X)], ... | [
"0.65296495",
"0.62556636",
"0.6085604",
"0.6072946",
"0.6003194",
"0.59075755",
"0.58855313",
"0.58549",
"0.5809094",
"0.5739868",
"0.56985754",
"0.56939775",
"0.5660247",
"0.5650745",
"0.5650164",
"0.56166774",
"0.55975235",
"0.55961955",
"0.5593082",
"0.558692",
"0.5572759... | 0.5387877 | 43 |
Prints out formated q and I columns to output. | def output_sas_curve(curve, filename):
if filename is not None:
output = open(filename, 'w')
else:
output = sys.stdout
for qi_pair in curve:
output.write("{0:7.4f} {1:7.4f}\n".format(qi_pair[0], qi_pair[1]))
output.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def printqtable(self):\n\t\tout = \"PRINTING QTABLE\\n\"\n\t\tfor key in self.qtable:\n\t\t\tout += \"state: \" + str(key) + \"\\n\"\n\t\t\tfor i in range(self.game.pips+1):\n\t\t\t\tout += f\"rew{i}: {self.qtable[key][i]:.3f} \"\n\t\t\tout += \"\\n\"\n\t\treturn out",
"def print_PQ(q):\n for item in q:\n ... | [
"0.70717496",
"0.7048381",
"0.6404846",
"0.6365367",
"0.6351997",
"0.6332962",
"0.61797464",
"0.61541086",
"0.6131866",
"0.6126434",
"0.6097812",
"0.60565346",
"0.60377866",
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"0.60274976",
"0.5978773",
"0.59669054",
"0.58863413",
"0.588584",
"0.5870688",
"0.58671... | 0.0 | -1 |
Calculate the Rg and Rxs1 from linear fits to the functions of the input curve values (q, I). In the case of Rg the Guinier fit is to q^2 vs ln(I), for Rxs1 it is to q^2 vs ln(Iq). | def get_curve_descriptors(curve, rg_min, rg_max, rxs1_min, rxs1_max, *args):
# Fitting is performed on:
# q^2 vs ln(I) for Rg
# q^2 vs ln(I*q) for Rxs
x = curve[:, 0] ** 2
y_rg = np.log(curve[:, 1])
y_rxs = np.log(curve[:, 1] * curve[:, 0])
# Create mask to select range of q values for Rg ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sas_curve_fit(x, y, calc_type):\n\n result = {}\n\n n = len(x)\n\n if (n == 0) or (len(y) == 0):\n err = \"Error: No values to fit in \" + calc_type + \" calculation\\n\"\n raise Exception(err)\n #result['error'] = err\n else:\n # Linear fit to the input x and y values\n... | [
"0.605578",
"0.5877867",
"0.5832726",
"0.5832662",
"0.57186884",
"0.5693924",
"0.56421226",
"0.5623981",
"0.5517047",
"0.54979545",
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"0.5453601",
"0.541977",
"0.5409155",
"0.5367099",
"0.53388757",
"0.53322685",
"0.532931",
"0.5319078",
... | 0.5903254 | 1 |
Saves dictionary in path. | def save_json_file(**kwargs):
dict_to_save = kwargs["dict_to_save"]
path = kwargs["path"]
with open(path,'wb') as fp:
json.dump(dict_to_save, fp)
return | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save(self, path='dict.pic'):\n with open(path, 'wb') as f:\n pickle.dump(self.__dictionary, f)",
"def save(self, path):\n with open(path, 'wb') as f:\n pkl.dump(self, f)",
"def save(self, path: Path, data: dict):\n self.info(data)\n os.makedirs(path.parent,... | [
"0.8156424",
"0.7739863",
"0.77196157",
"0.7709216",
"0.7681167",
"0.7499659",
"0.7499659",
"0.7499659",
"0.74758714",
"0.7360721",
"0.73478764",
"0.72891444",
"0.7285685",
"0.7285685",
"0.7285685",
"0.7253103",
"0.7243494",
"0.7231061",
"0.7181783",
"0.71751034",
"0.71315217... | 0.6719308 | 45 |
This is the method used by Predictors.ai to interact with the model. It is the only method that needs to be implemented to deploy the model on Predictors.ai | def predictors_ai_interface(self, **kwargs):
pipe_id = kwargs['pipe_id']
input_data = kwargs['input_data']
input_files_dir = kwargs['input_files_dir']
output_files_dir = kwargs['output_files_dir']
output_data = self.predict(pipe_id, input_data, input_files_d... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(self) -> None:\n self.model = self.trainer.train_model(self.model, self.data)",
"def predict_api():\n pass",
"def predict():\n\n predict_cfg = get_predict_args()\n device = get_device()\n print(device)\n\n # load checkpoint\n ckpt_path = find_ckpt_in_directory(predict_cfg.ckpt)... | [
"0.6984333",
"0.68782455",
"0.6673396",
"0.66272396",
"0.6600812",
"0.65296173",
"0.6517009",
"0.65158325",
"0.64813536",
"0.644822",
"0.6429023",
"0.64018166",
"0.6354974",
"0.63139385",
"0.6303747",
"0.62957084",
"0.6285682",
"0.6280027",
"0.627423",
"0.6257732",
"0.6257732... | 0.0 | -1 |
Create the core messagehandler. Also starts up depending threads | def __init__(self):
threading.Thread.__init__(self)
self.shutdown = False
self.name = 'domoscore'
self.rpc = rpc(self.name)
self.rpc.log_info("starting main thread")
self.rpc.handle(self.register, "register")
self.rpc.handle(self.sensorValue, "sensorValue")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init(self):\n self.dispatcher.start()\n self.replyer.start()",
"def start(self):\n\n def pubsub_thread():\n \"\"\" Call get_message in loop to fire _handler. \"\"\"\n\n while not self._stop.is_set():\n self._pubsub.get_message()\n sleep... | [
"0.6369178",
"0.63625515",
"0.633308",
"0.6186753",
"0.61862725",
"0.6167738",
"0.60974264",
"0.6088992",
"0.60875434",
"0.60620826",
"0.5952578",
"0.59207726",
"0.58730865",
"0.5850386",
"0.58470225",
"0.58376884",
"0.57883096",
"0.5787849",
"0.5764191",
"0.5751453",
"0.5750... | 0.63266504 | 3 |
RPC function to register a module | def register(self, data=None):
returnvalue = False
try:
module = Module.get_by_name(data['name'])
except DoesNotExist:
module = Module.add(name=data['name'], queue=data['queue'])
for rpc in data['rpc']:
argslist = []
if "args" ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register(self, module):\n tagvalues = \"\\n\".join([\"%s: %s\" % (attr, str(getattr(module, attr))) for attr in dir(module) if attr in ['create', 'menu', 'name', 'label'] ])\n # tagvalues = \"\\n\".join([\"%s\" % (attr) for attr in dir(module) if attr not in ['urls'] ])\n logger.debug(\"mo... | [
"0.710305",
"0.68038905",
"0.65690994",
"0.65593517",
"0.6511528",
"0.6500591",
"0.6408624",
"0.6359136",
"0.6344225",
"0.6296262",
"0.6287295",
"0.62527144",
"0.6180132",
"0.6180132",
"0.61799145",
"0.61693335",
"0.61673915",
"0.6166312",
"0.6161581",
"0.615456",
"0.6125936"... | 0.6791904 | 2 |
RPC function to send a data value to the core. It also triggers the checking of depending triggers | def sensorValue(self, key=None, value=None, timestamp=None):
try:
self.logger.debug('logging trigger value for {0} with value {1}'.format(key, value))
Sensor.get_by_id(key).add_value(value)
except Exception as e:
self.logger.warn('Something went wrong registering tri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def send(self, value):\n pass",
"def send(value):\r\n return value",
"def _send_data(self):\n pass",
"def send(self, data):",
"def send(self, data):\n pass",
"def send_data(self, data):\n # test_dict = {'ext': '1105', 'ip_address': '192.168.10.55', 'status': 'OK', 'ping': '... | [
"0.6560559",
"0.6557983",
"0.65332717",
"0.6475165",
"0.63426757",
"0.63372713",
"0.629647",
"0.6270873",
"0.6257218",
"0.61608404",
"0.61465144",
"0.61347085",
"0.6066361",
"0.60478175",
"0.6029204",
"0.6023097",
"0.6022667",
"0.6011719",
"0.5990419",
"0.59707654",
"0.596811... | 0.0 | -1 |
Start the core thread | def run(self):
self.logger.info("starting Dashi consumer")
while not self.shutdown:
self.rpc.listen() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start(self):\n self.thread.start()",
"def start(self) -> None:\n start_thread(super().start, self.__class__.__name__)",
"def start(self):\n self._thread.start()",
"def start(self):\n self._setup_thread()\n self.thread.start()",
"def start(self):\n \n sel... | [
"0.73384786",
"0.7254174",
"0.7210217",
"0.7201224",
"0.6993055",
"0.6963443",
"0.6897489",
"0.68625474",
"0.681053",
"0.68081605",
"0.6780735",
"0.67657864",
"0.6750371",
"0.6750371",
"0.6680953",
"0.6675509",
"0.6644162",
"0.6599766",
"0.6574553",
"0.65421563",
"0.65279335"... | 0.0 | -1 |
Stop the core thread | def end(self):
self.shutdown = True | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stop(self):\n self._Thread__stop()",
"def stop(self):\n debug(\"CBA4.__worker_thread.stop()\")\n self.__run = False\n #end stop()",
"def stop(self):\n\n self.stop_thread = True",
"def stop_thread(self):\n t, e = self.workers[0]\n e = e.set() # pu... | [
"0.8118453",
"0.7949373",
"0.7930008",
"0.7887663",
"0.76762074",
"0.76762074",
"0.7591198",
"0.75860834",
"0.7577884",
"0.7577884",
"0.7577884",
"0.7577884",
"0.7559412",
"0.75473344",
"0.7522022",
"0.7494112",
"0.74908835",
"0.74758166",
"0.74500954",
"0.74500954",
"0.74500... | 0.0 | -1 |
Create a api handler. This is automaticaly build from the messagehandler | def __init__(self):
threading.Thread.__init__(self)
self.shutdown = False
self.name = 'api'
self.rpc = rpc(self.name)
self.rpc.log_info("starting rpc api thread")
rpchandle = domoslog.rpchandler(self.rpc)
self.logger = logging.getLogger('api')
self.logger.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_new_handler(self, *args, **kwargs):",
"def create_handler(event, context):\n return update_endpoint(event)",
"def __create_handler():\n if not ServiceHandler.instance:\n ServiceHandler.instance = ServiceHandler()\n return ServiceHandler.instance",
"def __init__(self, common_api_handl... | [
"0.65934217",
"0.6533182",
"0.63217163",
"0.61371607",
"0.6033087",
"0.5883099",
"0.5846581",
"0.5829885",
"0.58280313",
"0.5784566",
"0.5784225",
"0.5632384",
"0.56148505",
"0.55882126",
"0.5583079",
"0.55631435",
"0.5549574",
"0.5393488",
"0.53734934",
"0.5373018",
"0.53643... | 0.0 | -1 |
RPC api call, returns a list with a tuple with module data (name, queue, Active) | def listModules(self):
modules = [(module.name,
module.queue,
module.Active) for module in self.db.getModules()]
return modules | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rpc(self) -> global___Rpc:",
"def get_queues_info() -> List[QueueInfo]:\n from src.server.oasisapi.analyses.models import AnalysisTaskStatus\n\n # setup an entry for every element in the broker (this will include\n # queues with no workers yet)\n res = [\n {\n 'name': q,\n ... | [
"0.6413622",
"0.61402905",
"0.6116551",
"0.6082819",
"0.6021262",
"0.588938",
"0.58609927",
"0.5812053",
"0.5793564",
"0.57764167",
"0.57638174",
"0.5659447",
"0.5590769",
"0.5558482",
"0.553459",
"0.55314624",
"0.55239093",
"0.54997844",
"0.54942393",
"0.5483135",
"0.5470676... | 0.5761541 | 11 |
RPC call, returns a list of tuples with sensors database (identifier, instant, activated, module name, description) | def listSensors(self, module=None):
if module:
try:
sensors = self.db.getModuleSensors(self.db.getModule(module))
except DoesNotExist:
return None
else:
sensors = self.db.getSensors()
# convert to list of tuples
return [... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sensors():\n sensor_data = query_db('SELECT * FROM sensors')\n return jsonify(results=sensor_data)",
"def GET_sensors(self):\n self.sensors.GetAll()\n self.sensors.sensors['charging-state'] = \\\n pyrobot.CHARGING_STATES[self.sensors.sensors['charging-state']]\n print simplejson.dumps(sel... | [
"0.6393391",
"0.6253768",
"0.59395397",
"0.593439",
"0.5932335",
"0.58385205",
"0.56590295",
"0.5607915",
"0.5571273",
"0.5545934",
"0.54861724",
"0.54751205",
"0.5471921",
"0.546852",
"0.54496706",
"0.5435644",
"0.53989005",
"0.53935105",
"0.53745294",
"0.53367937",
"0.53067... | 0.6304722 | 1 |
RPC api call, lists all arguments of a sensor | def listSensorArgs(self, sensor=None):
returnvalue = None
if not sensor:
pass
else:
sensor = self.db.getSensorByIdent(sensor)
returnvalue = self.db.getSensorDict(sensor)
return returnvalue | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rpc_info():",
"def rpc(self) -> global___Rpc:",
"def calls_cmd(args):\n r = requete(\"VoiceService.VoiceApplication:getCallList\")\n if r is None or not 'status' in r:\n return\n\n r = r['status']\n if len(args) == 1 and args[0] == '?':\n return print(r[0].... | [
"0.6629112",
"0.59871316",
"0.57738215",
"0.57366514",
"0.5699133",
"0.5685285",
"0.5684429",
"0.5664077",
"0.56445086",
"0.54334176",
"0.5389509",
"0.5366804",
"0.53633016",
"0.5363134",
"0.5350959",
"0.53481513",
"0.5347571",
"0.533289",
"0.5327297",
"0.53271383",
"0.531899... | 0.62036693 | 1 |
returns a list of a 2 value tuple, key, arguments. Arguments are a list of arguments, each containing a list containing the name, type, optionality and description | def listPrototypes(self, module=None):
returnvalue = None
try:
module = self.db.getModule(module)
except DoesNotExist:
returnvalue = None
else:
rpcs = self.db.getRPCs(module, 'add')
returnvalue = [(rpc.Key, [(arg.name,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getPair(self, args):\r\n return self.name, self.getValue(args)",
"def args(self) -> tuple[Basic, ...]:\n return self._args",
"def get_json_argument_list():\n list_of_arguments_to_get = [\"finish_time\", \"segmentation_training_samples\", \"patch_count_per_image\", \"learning_rate\", \"batc... | [
"0.6293299",
"0.62735635",
"0.6106884",
"0.61037457",
"0.60892504",
"0.60835505",
"0.60782856",
"0.6032734",
"0.599288",
"0.5980638",
"0.59729356",
"0.5970055",
"0.5867083",
"0.5850874",
"0.5845503",
"0.58453166",
"0.5821932",
"0.5813906",
"0.5792243",
"0.5780724",
"0.5774805... | 0.0 | -1 |
Start the api handler thread | def run(self):
self.logger.info("start consuming api calls")
while not self.shutdown:
self.rpc.listen() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def start_API():\n\n print(\"Starting OrderManager API Server.\")\n thread_ordermanager_API.start()",
"def _start(self):\n\n super(PySwitchLibApiDaemonRunner, self)._start()",
"async def start(self):",
"async def start(self):",
"def run(self):\n while self.running:\n self.han... | [
"0.74724424",
"0.69080186",
"0.6738512",
"0.6738512",
"0.6694738",
"0.66475725",
"0.6623388",
"0.65421504",
"0.64729136",
"0.64465594",
"0.64427185",
"0.64390844",
"0.642696",
"0.63928586",
"0.63877904",
"0.6363439",
"0.6363371",
"0.6354995",
"0.62945694",
"0.62651324",
"0.62... | 0.6741574 | 2 |
Parse a $$math$$ block | def parse_block_math(self, m):
self.tokens.append({
'type': 'block_math',
'text': m.group(1)
}) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_math_cmd(self, line):\n self.E_str = \"parse_math_cmd\"\n line = line.strip()\n\n new_var_name, metadata_name = self.get_variable_name(line)\n\n # Split the line by =\n words = line.split('=')\n _, maths = words\n maths = maths.replace(\"$\", \"\")\n ... | [
"0.6627344",
"0.6515901",
"0.6399946",
"0.6230076",
"0.6224127",
"0.61547863",
"0.6136919",
"0.61289555",
"0.6112584",
"0.60356355",
"0.5937152",
"0.5899705",
"0.5892373",
"0.5864457",
"0.58395207",
"0.58043087",
"0.5791234",
"0.5728444",
"0.5706931",
"0.5688357",
"0.5681846"... | 0.78205043 | 0 |
`initialize` is called only once when the model is being loaded. Implementing `initialize` function is optional. This function allows the model to intialize any state associated with this model. | def initialize(self, args):
# You must parse model_config. JSON string is not parsed here
self.model_config = json.loads(args['model_config'])
print("model_config:", self.model_config)
self.input_names = []
for input_config in self.model_config["input"]:
self.input_n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def initialize_model(self):\n pass",
"def initialize(self, model):\n pass",
"def initialize(self) -> None:\n self.model = load(self.path)",
"def init_model(self):\n pass",
"def initialize(self):\n pass",
"def _initialize(self):\n pass",
"def _initialize(self):\n ... | [
"0.7922824",
"0.7833245",
"0.7121588",
"0.7108596",
"0.70317143",
"0.7006685",
"0.7006685",
"0.7006685",
"0.69785035",
"0.69778067",
"0.696979",
"0.696979",
"0.696979",
"0.696979",
"0.696979",
"0.69580877",
"0.69351006",
"0.69351006",
"0.6934833",
"0.68810725",
"0.68810725",
... | 0.0 | -1 |
`execute` must be implemented in every Python model. `execute` function receives a list of pb_utils.InferenceRequest as the only argument. This function is called when an inference is requested for this model. Depending on the batching configuration (e.g. Dynamic Batching) used, `requests` may contain multiple requests... | def execute(self, requests):
responses = []
for request in requests:
infer_outputs = pb_utils.get_input_tensor_by_name(
request, self.input_names[0])
im_infos = pb_utils.get_input_tensor_by_name(request,
sel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ExecuteBatch(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')",
"def ExecuteBatch(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n conte... | [
"0.6375495",
"0.6375495",
"0.6048663",
"0.59890074",
"0.5898378",
"0.5888777",
"0.5833882",
"0.5833882",
"0.5737521",
"0.5663895",
"0.5434578",
"0.5345063",
"0.5344431",
"0.5314354",
"0.527206",
"0.52704257",
"0.52529854",
"0.52165484",
"0.5213345",
"0.52020395",
"0.52017784"... | 0.64383364 | 0 |
`finalize` is called only once when the model is being unloaded. Implementing `finalize` function is optional. This function allows the model to perform any necessary clean ups before exit. | def finalize(self):
print('Cleaning up...') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _finalize(self):\n for model in self.models:\n model._finalize()",
"def finalize(self):\n raise NotImplementedError('Must be implemented in subclasses.')",
"def finalize(self):\n self.classifier.finalize()",
"def finalize():\n pass",
"def finalize():\n pass",
"def fi... | [
"0.75447696",
"0.70889837",
"0.7084198",
"0.7040922",
"0.7040922",
"0.69991946",
"0.69760233",
"0.69760233",
"0.6966195",
"0.6964688",
"0.6964595",
"0.6964595",
"0.6964595",
"0.6964595",
"0.6964595",
"0.6964595",
"0.6962548",
"0.69426155",
"0.689655",
"0.689655",
"0.689655",
... | 0.68017924 | 21 |
Loads data from a CSV file located at `csv_file` | def load_data_from_csv(csv_file, users_to_i = {}, items_to_i = {}):
raw_data = []
with open(csv_file) as f:
csvreader = csv.reader(f)
# skipping first row (header)
next(csvreader)
for user, item in csvreader:
raw_data.append((user, item))
return load_data_from_arr... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def loadCSV(input_file):",
"def load(csvfile):\n return PsychoPyCSV(csvfile)",
"def get_data(self, csv_file):\n pass",
"def load_dataset_from(csv_file: str) -> pd.DataFrame:\n\n print(\">>> LOADING DATASET FROM FILE {filename}\".format(filename=csv_file))\n if not csv_file.endswith(\".csv\"):... | [
"0.7783272",
"0.7730919",
"0.7629962",
"0.7581937",
"0.7467191",
"0.74592704",
"0.7457141",
"0.7454635",
"0.7404338",
"0.7354931",
"0.7325368",
"0.7277852",
"0.7268166",
"0.7259237",
"0.7245137",
"0.7231865",
"0.72284836",
"0.7227219",
"0.7214435",
"0.7155065",
"0.7096461",
... | 0.735131 | 10 |
Generate a WMI base a specific marker Trace marker is used to identify the underlying object | def wmi_trace_marker(marker: Enum) -> Struct:
return Struct(
"version" / Int16ul,
"type" / check_enum(marker),
"flags" / Const(0xc0, Int8ul)
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\n return f\"Marker at {super().__repr__()}\"",
"def __repr__(self):\n return f\"Marker at {super().__repr__()}\"",
"def get_reflect_marker(self):\r\n return Marker((255, 255, 255), self._screen)",
"def export_base(self):\n \n return (self.typ + \" no \" + str... | [
"0.60454005",
"0.60230863",
"0.5618681",
"0.53838336",
"0.53631455",
"0.53274184",
"0.52811784",
"0.52787894",
"0.5269781",
"0.5246951",
"0.5200085",
"0.51915157",
"0.5154832",
"0.5153821",
"0.51111484",
"0.51029664",
"0.50822985",
"0.50801104",
"0.50572926",
"0.50393933",
"0... | 0.6092112 | 0 |
Makes a system call to rest_auth.py, a Python script modified from | def getrmlsthelper(self):
printtime('Downloading {} alleles'.format(self.analysistype), self.start)
# Extract the path of the current script from the full path + file name
homepath = os.path.split(os.path.abspath(__file__))[0]
# Set the path/name of the folder to contain the new alleles... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def auth():\n pass",
"def auth():\n pass",
"def main():\n\n config_file = 'auth_demo.cfg'\n config = ConfigParser.SafeConfigParser({\n 'username':'',\n })\n config.read(config_file)\n if not config.has_section('auth_demo_login'):\n config.add_section('auth... | [
"0.6345018",
"0.6345018",
"0.5996943",
"0.5966874",
"0.5905733",
"0.5859134",
"0.57976",
"0.576943",
"0.57551086",
"0.5743807",
"0.5737722",
"0.5687899",
"0.5615365",
"0.5601679",
"0.55998474",
"0.55698365",
"0.55685717",
"0.5567104",
"0.55488694",
"0.5520772",
"0.55117303",
... | 0.0 | -1 |
Return data store for agency. | def kv(self):
return self._consul.kv | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data_store(self) -> 'outputs.DataStoreInfoBaseResponse':\n return pulumi.get(self, \"data_store\")",
"def _get_store(self):\n return self._store",
"def data_store(self) -> pulumi.Input['DataStoreInfoBaseArgs']:\n return pulumi.get(self, \"data_store\")",
"def getSessionStore(self):\n... | [
"0.64798516",
"0.62940806",
"0.6177713",
"0.6065085",
"0.59708",
"0.5899037",
"0.5863626",
"0.58171207",
"0.58065635",
"0.57615834",
"0.5732366",
"0.5675867",
"0.5629987",
"0.55948126",
"0.5584164",
"0.5581087",
"0.5557894",
"0.5557404",
"0.55573314",
"0.5552282",
"0.54406166... | 0.0 | -1 |
Removes anything the inmemory pseudodatabase contains, to ensure test independence. | def reset(self):
self.jobs: Dict[int, JobRow] = {}
self.events: Dict[int, List[EventRow]] = {}
self.job_counter = count(1)
self.queries: List[Tuple[str, Dict[str, Any]]] = []
self.notify_event = None
self.notify_channels = []
self.periodic_defers: Dict[str, int] =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tearDown(self):\n with database() as db:\n db.query(\"DELETE FROM persons WHERE person_name = 'test_person_a' OR person_name = 'test_person_b'\")",
"def tearDown(self):\r\n empty_db()",
"def tearDown(self):\r\n empty_db()",
"def tearDown(self):\r\n empty_db()",
"d... | [
"0.72212243",
"0.7075657",
"0.7075657",
"0.7075657",
"0.7075657",
"0.7075657",
"0.7075657",
"0.691395",
"0.68814236",
"0.6716124",
"0.6716124",
"0.6716124",
"0.6716124",
"0.6716124",
"0.6716124",
"0.6716124",
"0.6716124",
"0.6716124",
"0.66772574",
"0.6613947",
"0.6603796",
... | 0.0 | -1 |
Calling a query will call the _ method on this class. Suffix is "run" if no result is expected, "one" if a single result, and "all" if multiple results. | def generic_execute(self, query, suffix, **arguments) -> Any:
query_name = self.reverse_queries[query]
self.queries.append((query_name, arguments))
return getattr(self, f"{query_name}_{suffix}")(**arguments) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_query(self):\n return _run_query(self.query)",
"def _run_query(self):",
"def query(self, query):",
"def query(self, **kwargs):",
"def query(self):\n pass",
"def query(self):",
"def make_query(self):",
"def __call__(self, query=None, limit=1, uid=None):\n\n # Query prefix\... | [
"0.6719964",
"0.66792995",
"0.64606917",
"0.6383278",
"0.6371573",
"0.63211226",
"0.627995",
"0.6194975",
"0.5992636",
"0.58021146",
"0.5753793",
"0.57446074",
"0.57445633",
"0.56929576",
"0.5677893",
"0.56512135",
"0.56392777",
"0.56234354",
"0.5610585",
"0.55985904",
"0.558... | 0.61180556 | 8 |
Procedurally generates a table and adds it to the XML | def _add_table(self, name, offset, rot, half_size, friction, has_legs):
# Create body for this table, and add it to worldbody
table_body = new_body(name=name, pos=offset - np.array([0, 0, half_size[2]]))
self.worldbody.append(table_body)
# Create core attributes for table geoms
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def buildTables(self, xmlfile, htmlfile):\n t = xeputils.xeptable.XEPTable()\n for xep in self.xeps:\n # Do not include XEP readme and template in the table.\n if isinstance( xep.nr, ( int, long ) ) :\n t.updateXEP(xep)\n t.writeXMLTable(xmlfile)\n t... | [
"0.74516946",
"0.72935635",
"0.7018654",
"0.70033413",
"0.68429774",
"0.68148804",
"0.66371644",
"0.65015805",
"0.6497859",
"0.6451108",
"0.62612915",
"0.61967397",
"0.6164456",
"0.6125158",
"0.6068412",
"0.6035069",
"0.5993856",
"0.59926885",
"0.5904884",
"0.58805615",
"0.58... | 0.0 | -1 |
Configures correct locations for this arena | def configure_location(self):
# Set floor correctly
self.floor.set("pos", array_to_string(self.bottom_pos)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_locations():\n STATUS['locations']['monster'][0] = generate_random_coord(STATUS['grid_size'])\n STATUS['locations']['monster'][1] = generate_random_coord(STATUS['grid_size'])\n STATUS['locations']['weapon'][0] = generate_random_coord(STATUS['grid_size'])\n STATUS['locations']['weapon'][1] = gen... | [
"0.5902864",
"0.5788083",
"0.566627",
"0.5588298",
"0.55418116",
"0.55088085",
"0.5442755",
"0.54118943",
"0.53610355",
"0.53148246",
"0.53037614",
"0.5274148",
"0.5269604",
"0.5227204",
"0.52206093",
"0.5210463",
"0.52102774",
"0.51957375",
"0.51864976",
"0.51828134",
"0.517... | 0.6191456 | 0 |
Runs any necessary postprocessing on the imported Arena model | def _postprocess_arena(self):
# Create tables
for i, (offset, rot, half_size, friction, legs) in enumerate(
zip(self.table_offsets, self.table_rots, self.table_half_sizes, self.table_frictions, self.has_legs)
):
self._add_table(
name=f"table{i}",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _postprocessor(self) :\n\t\tlogging.debug(\"nning AR post processor\")\n\t\tpass",
"def main_modeling_pipeline():\n\n\n data_df = pd.read_csv('gs://aiplatformfilipegracio2020/head_train_data.csv')\n data_df = data_df[[LABEL, 'price', 'days_on_site']]\n\n class_weights = calculate_class_weights(data_... | [
"0.63163775",
"0.583071",
"0.5803663",
"0.57914853",
"0.5732038",
"0.5630254",
"0.5630254",
"0.5630254",
"0.5630254",
"0.5630254",
"0.5598505",
"0.5596272",
"0.5550122",
"0.55418843",
"0.551331",
"0.551143",
"0.5502309",
"0.5475127",
"0.547062",
"0.54696137",
"0.5464052",
"... | 0.51183176 | 99 |
Edit created order by its primary key | def editOrder(request, pk):
order = Meal.objects.get(pk=pk)
form = OrderForm(instance=order)
if request.method == 'POST':
filledForm = OrderForm(request.POST, instance=order)
if filledForm.is_valid():
filledForm.save()
form = filledForm
note = "Your order ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_order():",
"def update_order():",
"def create_order():",
"def put_new_order(self, item: dict):\n return self.table_connection.put_item(Item=item)",
"def save(self, *args, **kwargs):\n if not self.order_number:\n self.order_number = self._generate_order_number()\n ... | [
"0.7377786",
"0.7377786",
"0.6603852",
"0.6351103",
"0.6250598",
"0.6250598",
"0.6185037",
"0.6178835",
"0.60784626",
"0.60784626",
"0.6042809",
"0.5995732",
"0.5981196",
"0.5974003",
"0.59242314",
"0.5906804",
"0.5896384",
"0.5890739",
"0.58623105",
"0.58623105",
"0.58623105... | 0.56949407 | 34 |
Determines the server's behaviour using urls. | def run_server_by_urls(getter_url, handler_url):
get_method = find_getter(getter_url)
logger.debug('start serving')
get_method(getter_url, handler_url) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_urls(self):\n base_test_url = 'http://{}:{}/'.format(TESTING_CONFIG['host'],\n TESTING_CONFIG['port'])\n self.conn._host_url == base_test_url\n self.conn.aheader_url == base_test_url + 'analysis_header'\n self.conn.atail_url == base... | [
"0.6468079",
"0.6056906",
"0.5978593",
"0.5850525",
"0.57687324",
"0.56448376",
"0.5586892",
"0.5585556",
"0.5571096",
"0.5512941",
"0.55023026",
"0.5501335",
"0.5497448",
"0.5475079",
"0.5463967",
"0.54323035",
"0.5373179",
"0.5372019",
"0.5353865",
"0.5350774",
"0.53449684"... | 0.5297544 | 27 |
Test loading the data into memory | def test_loading(self):
self.assertIsInstance(self.data.districts, list) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _load_test_data(self):\n self._save_test_data()",
"def load_data(self) -> None:",
"def load_data(self):",
"def test_002_load_data(self):\n __test = chess_storage.ChessStorage()\n __test_filename = consts.TEST_FILENAME\n __test_data = list(range(consts.TEST_LIST_LENGHT))\n ... | [
"0.76403165",
"0.75613415",
"0.74405694",
"0.73429775",
"0.698014",
"0.6953178",
"0.69166166",
"0.6904472",
"0.68588537",
"0.676182",
"0.6701181",
"0.6680089",
"0.6637727",
"0.6630223",
"0.66285294",
"0.6604002",
"0.6601542",
"0.65942174",
"0.6586814",
"0.65772706",
"0.656904... | 0.6041755 | 72 |
Test that we can retrieve actual data | def test_retrieval(self):
self.assertIsInstance(self.data.get_district_by_name(
"New Paltz Central School District"
), shapefile._ShapeRecord)
self.assertIsInstance(SocialNPHS.data.district, shapefile._ShapeRecord)
with self.assertRaises(IndexError):
self.data.ge... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_data_object_get_details(self):\n pass",
"def test_get_info(self):\n pass",
"def test_get_record(self):\n pass",
"def test_get(self):\n pass",
"def test_get_records(self):\n pass",
"def test_raw_data(self):\n self.assertEqual(self.tester.raw_data, 1)",
... | [
"0.83040386",
"0.7592085",
"0.7570853",
"0.75216335",
"0.750367",
"0.74942774",
"0.74742824",
"0.74613625",
"0.73578",
"0.7357461",
"0.7232688",
"0.72317475",
"0.71951586",
"0.71870357",
"0.71821666",
"0.7178218",
"0.71503276",
"0.7138974",
"0.7121461",
"0.7082353",
"0.705159... | 0.0 | -1 |
If input object is an ndarray it will be converted into a dict holding dtype, shape and the data, base64 encoded. | def default(self, obj):
numpy_types = (
np.bool_,
# np.bytes_, -- python `bytes` class is not json serializable
# np.complex64, -- python `complex` class is not json serializable
# np.complex128, -- python `complex` class is not json serializable
# n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def default(self, obj):\n if isinstance(obj, np.ndarray):\n if obj.flags['C_CONTIGUOUS']:\n obj_data = obj.data\n else:\n cont_obj = np.ascontiguousarray(obj)\n assert(cont_obj.flags['C_CONTIGUOUS'])\n obj_data = cont_obj.data... | [
"0.7285015",
"0.7285015",
"0.7259372",
"0.7155377",
"0.7137759",
"0.6932167",
"0.6918994",
"0.6918994",
"0.6918994",
"0.64177775",
"0.6341543",
"0.6310709",
"0.5843858",
"0.5809174",
"0.58036816",
"0.5672971",
"0.5655243",
"0.56076634",
"0.55190325",
"0.5500997",
"0.5490805",... | 0.0 | -1 |
Read dense Jester dataset and split train/test data randomly. | def read_jester_data(fname='jester-dense-subset-100x20.csv'):
logging.info('reading data')
try:
data = pd.read_csv(fname)
except IOError as err:
print str(err)
url = 'https://gist.github.com/macks22/b40ac9c685e920ad3ca2'
print 'download from: %s' % url
sys.exit(DATA_N... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_train_test_split():\n # Load all documents\n conn = sq.connect(config.DB_FILE)\n documents = pd.read_sql_query('select pubmed_id, review_id, included, title, abstract from article ', conn)\n\n # Identify unique review IDs\n review_ids = documents['review_id'].unique()\n\n # Set seed for random sampli... | [
"0.6528886",
"0.64985585",
"0.6490711",
"0.64342165",
"0.6429354",
"0.6394741",
"0.6342159",
"0.6324972",
"0.6307256",
"0.62858176",
"0.6265146",
"0.6257298",
"0.6205019",
"0.620443",
"0.6178737",
"0.6150126",
"0.611777",
"0.60921246",
"0.60828966",
"0.6066437",
"0.60641325",... | 0.74138194 | 0 |
Construct the Probabilistic Matrix Factorization model using pymc3. Note that the `testval` param for U and V initialize the model away from 0 using a small amount of Gaussian noise. | def build_pmf_model(train, alpha=2, dim=10, std=0.01):
# Mean value imputation on training data.
train = train.copy()
nan_mask = np.isnan(train)
train[nan_mask] = train[~nan_mask].mean()
# Low precision reflects uncertainty; prevents overfitting.
# We use point estimates from the data to intial... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def model_test(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in, models_fit, label):\n # Generate fake data with some \"true\" parameters\n (D_vec, Ninv) = gen_data(nu, fsigma_T, fsigma_P, models_in, amps_in, params_in)\n Ninv_sqrt = np.matrix(linalg.sqrtm(Ninv))\n (dust_params, sync_params, cmb_p... | [
"0.56138206",
"0.5586942",
"0.55433214",
"0.5351772",
"0.529997",
"0.52595276",
"0.5223859",
"0.5215091",
"0.52088547",
"0.5177972",
"0.51590854",
"0.50957006",
"0.5061981",
"0.5017527",
"0.49781993",
"0.49613118",
"0.49568832",
"0.4943264",
"0.4940687",
"0.49263063",
"0.4922... | 0.6167692 | 0 |
Build the original BPMF model, which we cannot sample from due to current limitations in pymc3's implementation of the Wishart distribution. | def build_bpmf_model(train, alpha=2, dim=10, std=0.01):
n, m = train.shape
beta_0 = 1 # scaling factor for lambdas; unclear on its use
# Mean value imputation on training data.
train = train.copy()
nan_mask = np.isnan(train)
train[nan_mask] = train[~nan_mask].mean()
logging.info('building... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_mod_bpmf_model(train, alpha=2, dim=10, std=0.01):\n n, m = train.shape\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\n\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n # We... | [
"0.72188264",
"0.61532706",
"0.6023628",
"0.5833241",
"0.5797192",
"0.5724574",
"0.5723442",
"0.5719858",
"0.5715735",
"0.56933236",
"0.56768894",
"0.56603485",
"0.56331044",
"0.5607353",
"0.55702585",
"0.55379814",
"0.55144966",
"0.5503024",
"0.5484061",
"0.5476444",
"0.5475... | 0.7269002 | 0 |
Build the modified BPMF model using pymc3. The original model uses Wishart priors on the covariance matrices. Unfortunately, the Wishart distribution in pymc3 is currently not suitable for sampling. This | def build_mod_bpmf_model(train, alpha=2, dim=10, std=0.01):
n, m = train.shape
beta_0 = 1 # scaling factor for lambdas; unclear on its use
# Mean value imputation on training data.
train = train.copy()
nan_mask = np.isnan(train)
train[nan_mask] = train[~nan_mask].mean()
# We will use sepa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_bpmf_model(train, alpha=2, dim=10, std=0.01):\n n, m = train.shape\n beta_0 = 1 # scaling factor for lambdas; unclear on its use\n\n # Mean value imputation on training data.\n train = train.copy()\n nan_mask = np.isnan(train)\n train[nan_mask] = train[~nan_mask].mean()\n\n logging.... | [
"0.6512337",
"0.60092825",
"0.567834",
"0.5658303",
"0.55903035",
"0.5580925",
"0.5573696",
"0.5532462",
"0.55020326",
"0.5474026",
"0.54726166",
"0.54643524",
"0.54269475",
"0.5414196",
"0.5403196",
"0.5379074",
"0.537768",
"0.53350365",
"0.5313411",
"0.52971154",
"0.5286176... | 0.6656812 | 0 |
Check if a line is header. | def _is_header(line):
line = line.strip()
if line.startswith('#') or line.startswith('track') or line.startswith(
'browser'): # BED header
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_header(line):\n return line[0] == '>'",
"def check_header(line):\n if line[:7] == '###### ':\n line = '<h6>' + line[7:] + '</h6>'\n elif line[:6] == '##### ':\n line = '<h5>' + line[6:] + '</h5>'\n elif line[:5] == '#### ':\n line = '<h4>' + line[5:] + '</h4>'\n elif li... | [
"0.90293974",
"0.79356164",
"0.7800237",
"0.7654996",
"0.7427677",
"0.7177651",
"0.71695125",
"0.7159226",
"0.7146845",
"0.7069064",
"0.70204705",
"0.70001435",
"0.69541633",
"0.6851527",
"0.68049",
"0.66462415",
"0.66202724",
"0.6611603",
"0.6611603",
"0.64868945",
"0.648689... | 0.8483625 | 1 |
Parse lines to get reads from the input read file. | def parse(self, *args, **kwargs):
if self.is_gzipped:
fin = gzip.open(self.path, 'rb')
else:
fin = open(self.path, 'r')
line_num = 0
expect_header = True
for line in fin:
line_num += 1
line = line.strip()
if not line: #... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __read_lines__(self):\r\n fd = open(self.input_file, \"r\")\r\n lines = fd.readlines()\r\n fd.close()\r\n return lines",
"def process_lines(self, lines):\n line_index = 0\n n_lines = len(lines)\n while line_index < n_lines:\n if lines[line_index].st... | [
"0.69258976",
"0.66003734",
"0.65865076",
"0.65514225",
"0.65241826",
"0.6469301",
"0.6401494",
"0.63737977",
"0.6328333",
"0.62591934",
"0.6223086",
"0.6206942",
"0.6202654",
"0.61415637",
"0.6081667",
"0.60471404",
"0.6029287",
"0.60218334",
"0.6015924",
"0.5982434",
"0.598... | 0.0 | -1 |
Parse lines to get reads from the input read file. | def parse(self, paired=False, shift=100):
for read in self.handle:
if read.is_unmapped or read.is_qcfail or read.is_secondary \
or read.is_supplementary:
continue
if paired:
if not read.is_paired:
logger.debug(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __read_lines__(self):\r\n fd = open(self.input_file, \"r\")\r\n lines = fd.readlines()\r\n fd.close()\r\n return lines",
"def process_lines(self, lines):\n line_index = 0\n n_lines = len(lines)\n while line_index < n_lines:\n if lines[line_index].st... | [
"0.69234943",
"0.66012603",
"0.65864605",
"0.65512294",
"0.6521317",
"0.6467308",
"0.6402224",
"0.63724",
"0.6326894",
"0.6258525",
"0.62227803",
"0.6207747",
"0.6200397",
"0.61411375",
"0.60814875",
"0.604438",
"0.6026843",
"0.602283",
"0.60144675",
"0.5981471",
"0.5980653",... | 0.0 | -1 |
Get proper read parser for the given format. | def get_read_parser(format):
format = format.lower()
if format == 'bed':
return BedReadParser
elif format == 'bedpe':
return BedPeReadParser
elif format == 'sam':
return SamReadParser
elif format == 'bam':
return BamReadParser
else:
raise ValueError(f"unkn... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_parser(filepath, cfg):\n if not os.path.isfile(filepath):\n LOG.error('File not found: %s', filepath)\n return\n valid_parsers = importer.get_parsers(filepath, cfg)\n if not valid_parsers:\n LOG.error('No parsers found for file: %s', filepath)\n return\n\n if len(va... | [
"0.6131928",
"0.60937136",
"0.6045996",
"0.58665407",
"0.58590126",
"0.5809873",
"0.58025086",
"0.57651305",
"0.5758732",
"0.57091504",
"0.5667895",
"0.5651484",
"0.56421953",
"0.5586466",
"0.55685693",
"0.5534864",
"0.5503993",
"0.54882073",
"0.54529166",
"0.54084545",
"0.53... | 0.88633776 | 0 |
Test a recursive merge of two status dictionaries | def test_recursive_update():
test = Status.update_dict({'generation': TEST_1_ATTRS_1},
{'generation': TEST_1_ATTRS_2})
assert test['generation']['run_id'] == TEST_1_ATTRS_1['run_id']
assert test['generation']['job_status'] == TEST_1_ATTRS_2['job_status'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_recursive_merge(self):\n dict_1 = {\n 'key': {\n 'deep_key_1': 'original_value_1',\n 'deep_key_2': 'original_value_2'\n }\n }\n dict_2 = {\n 'key': {\n 'deep_key_2': 'new_value_2',\n 'deep_key... | [
"0.70699894",
"0.68547225",
"0.68238133",
"0.66119456",
"0.65711254",
"0.65020204",
"0.6499048",
"0.6461705",
"0.6442443",
"0.6428743",
"0.6409568",
"0.63924336",
"0.6374389",
"0.6356342",
"0.6348538",
"0.6343374",
"0.6339143",
"0.6316803",
"0.62815475",
"0.62786376",
"0.6271... | 0.64845693 | 7 |
Test file creation and collection | def test_file_collection():
with tempfile.TemporaryDirectory() as STATUS_DIR:
Status.make_job_file(STATUS_DIR, 'generation', 'test1', TEST_1_ATTRS_1)
Status.make_job_file(STATUS_DIR, 'generation', 'test2', TEST_2_ATTRS_1)
Status.update(STATUS_DIR)
with open(os.path.join(STATUS_DIR, ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_file_creation(data, logging_file_name):\n create_instance(data, logging_file_name)\n log_file_name = create_file_path(logging_file_name)\n print(log_file_name)\n if data is None or len(data) == 0:\n assert not os.path.exists(log_file_name)\n else:\n assert os.path.exists(log_f... | [
"0.7461448",
"0.73518395",
"0.73045737",
"0.7251613",
"0.7166898",
"0.71258205",
"0.70516205",
"0.7050503",
"0.70366",
"0.7008296",
"0.69309276",
"0.6856841",
"0.68465465",
"0.6802331",
"0.6774128",
"0.6764452",
"0.6762398",
"0.6749557",
"0.6735404",
"0.6731379",
"0.6729802",... | 0.7302167 | 3 |
Test file creation and reading | def test_make_file():
with tempfile.TemporaryDirectory() as STATUS_DIR:
Status.make_job_file(STATUS_DIR, 'generation', 'test1', TEST_1_ATTRS_1)
status = Status.retrieve_job_status(STATUS_DIR, 'generation', 'test1')
msg = 'Failed, status is "{}"'.format(status)
assert status == 'R', m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_file_creation(data, logging_file_name):\n create_instance(data, logging_file_name)\n log_file_name = create_file_path(logging_file_name)\n print(log_file_name)\n if data is None or len(data) == 0:\n assert not os.path.exists(log_file_name)\n else:\n assert os.path.exists(log_f... | [
"0.7360315",
"0.72867465",
"0.7264493",
"0.7150804",
"0.7088082",
"0.7009468",
"0.69931275",
"0.6988977",
"0.69652444",
"0.6931245",
"0.6907508",
"0.68741745",
"0.6857578",
"0.6853722",
"0.6774158",
"0.6766216",
"0.6752629",
"0.67525613",
"0.6718231",
"0.6716792",
"0.6703876"... | 0.6928599 | 10 |
Test job addition and exist check | def test_job_exists():
with tempfile.TemporaryDirectory() as STATUS_DIR:
Status.add_job(STATUS_DIR, 'generation', 'test1',
job_attrs={'job_status': 'submitted'})
exists = Status.job_exists(STATUS_DIR, 'test1')
assert exists | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_job_addition():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1')\n status1 = Status(STATUS_DIR).data['generation']['test1']['job_status']\n\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={... | [
"0.72872704",
"0.7204852",
"0.68101156",
"0.66714615",
"0.6622515",
"0.66078657",
"0.6596242",
"0.65822595",
"0.6559245",
"0.64998597",
"0.64640236",
"0.6440778",
"0.64162385",
"0.6409369",
"0.63889015",
"0.6380754",
"0.63741344",
"0.6320242",
"0.62653357",
"0.62551504",
"0.6... | 0.7668623 | 0 |
Test job addition and exist check | def test_job_addition():
with tempfile.TemporaryDirectory() as STATUS_DIR:
Status.add_job(STATUS_DIR, 'generation', 'test1')
status1 = Status(STATUS_DIR).data['generation']['test1']['job_status']
Status.add_job(STATUS_DIR, 'generation', 'test1',
job_attrs={'job_status... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_job_exists():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={'job_status': 'submitted'})\n exists = Status.job_exists(STATUS_DIR, 'test1')\n assert exists",
"def test_matching_jobs_existin... | [
"0.7668623",
"0.7204852",
"0.68101156",
"0.66714615",
"0.6622515",
"0.66078657",
"0.6596242",
"0.65822595",
"0.6559245",
"0.64998597",
"0.64640236",
"0.6440778",
"0.64162385",
"0.6409369",
"0.63889015",
"0.6380754",
"0.63741344",
"0.6320242",
"0.62653357",
"0.62551504",
"0.62... | 0.72872704 | 1 |
Test job addition and replacement | def test_job_replacement():
with tempfile.TemporaryDirectory() as STATUS_DIR:
Status.add_job(STATUS_DIR, 'generation', 'test1',
job_attrs={'job_status': 'submitted'})
Status.add_job(STATUS_DIR, 'generation', 'test1',
job_attrs={'addition': 'test',
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_job_addition():\n with tempfile.TemporaryDirectory() as STATUS_DIR:\n Status.add_job(STATUS_DIR, 'generation', 'test1')\n status1 = Status(STATUS_DIR).data['generation']['test1']['job_status']\n\n Status.add_job(STATUS_DIR, 'generation', 'test1',\n job_attrs={... | [
"0.726686",
"0.6905744",
"0.6609184",
"0.6590907",
"0.64921033",
"0.6491962",
"0.6479421",
"0.6470252",
"0.6433977",
"0.63717645",
"0.6351452",
"0.63509756",
"0.62832236",
"0.62299746",
"0.6220371",
"0.62189835",
"0.6209426",
"0.620375",
"0.6172135",
"0.61520845",
"0.61494327... | 0.7602344 | 0 |
Execute module as pytest with detailed summary report. | def execute_pytest(capture='all', flags='-rapP'):
fname = os.path.basename(__file__)
pytest.main(['-q', '--show-capture={}'.format(capture), fname, flags]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run(run_type, module, config):\n print(\" -----------------------------------------------------------------\")\n print(\" Beginning \" + run_type.lower() + \" test suite \")\n print(\" -----------------------------------------------------------------\")\n print(\"\")\n summary = run_quiet(run_... | [
"0.72504675",
"0.68715066",
"0.6861118",
"0.67923087",
"0.6693342",
"0.66854435",
"0.6595857",
"0.6548619",
"0.65412146",
"0.6535354",
"0.6469479",
"0.6465738",
"0.6464574",
"0.64562017",
"0.641981",
"0.64085966",
"0.637916",
"0.63596404",
"0.6356207",
"0.63495654",
"0.634263... | 0.6190488 | 35 |
First, it finds the total amount of contacts for each listing_id. Second, it finds the top most contacted listings. | def find_top_five_most_contacted_listings_per_month(self):
available_dates = self.contacts.quer_distinct_components("contact_date", "Contacts")
available_listing_ids = self.contacts.quer_distinct_components("listing_id", "Contacts")
self.ordered = {}
for date in available_dates:... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_listings(self):\n listing_ids = self.context.listing_ids\n if len(listing_ids) == 0:\n return\n listing_ids = [lid.lower() for lid in listing_ids]\n params = {\n 'limit': 0,\n 'offset': 0,\n 'lang': self.portal_state.language(),\n ... | [
"0.5714392",
"0.5504233",
"0.53158736",
"0.52735156",
"0.50318676",
"0.50244784",
"0.50148946",
"0.50043106",
"0.49452168",
"0.49347422",
"0.49322554",
"0.4921248",
"0.48818243",
"0.48799545",
"0.48490244",
"0.4818993",
"0.4793874",
"0.4769546",
"0.47455272",
"0.46993467",
"0... | 0.7177975 | 0 |
Parses the collected data into a dict | def generate_dict_top_five(self):
all_months = {}
# Loops over each avilable non-duplicate date
available_dates = self.contacts.quer_distinct_components("contact_date", "Contacts")
for date in available_dates:
top_five = self.ordered[date]
all_months[date] = {}
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_data( self ):\n self.parsed_data = dict( self.results )",
"def get_data(self):\n self.data = dict()\n # list to save all the attributes we are going to create\n self.attr = []\n # list to save all the groups available in the incomming input\n self.groups.extend... | [
"0.7335327",
"0.69097733",
"0.6876233",
"0.66875476",
"0.64781004",
"0.6423315",
"0.63635683",
"0.6295866",
"0.6179777",
"0.6155332",
"0.6152996",
"0.6149452",
"0.614944",
"0.61326593",
"0.6130262",
"0.61247736",
"0.611546",
"0.60994387",
"0.6095851",
"0.6092316",
"0.6058877"... | 0.0 | -1 |
Return a link to the image in its source Website or details about the image that help looking it up or debugging it. Override for your dataset, but pass to this function if you encounter images not in your dataset. | def image_reference(self, image_id):
return "" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_image_url():",
"def get_image_link(self):\n table = self.soup.find('table')\n image_tag = table.find('img')\n image_name = self.soup.find_all(\"b\")[1].text\n return image_tag['src'], image_name\n\n # image = td.find_all('img')\n # print(image)\n # if imag... | [
"0.7356831",
"0.7320701",
"0.7044893",
"0.69738954",
"0.69392264",
"0.6825294",
"0.6793268",
"0.6658253",
"0.6630269",
"0.658179",
"0.6563168",
"0.6563168",
"0.6563168",
"0.6556118",
"0.65384156",
"0.6535433",
"0.64960885",
"0.6488741",
"0.6426024",
"0.642439",
"0.6424377",
... | 0.0 | -1 |
Prepares the Dataset class for use. | def prepare(self, class_map=None):
def clean_name(name):
"""Returns a shorter version of object names for cleaner display."""
return ",".join(name.split(",")[:1])
# Build (or rebuild) everything else from the info dicts.
self.num_classes = len(self.class_info)
se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prepare(self):\n if self.opts['verbose']:\n print(\"Preparing dataset (one-time operation)...\")\n # Create paths files and load them back in\n self._build_ID_sets()\n self._create_ID_files()\n self._load_ID_files()\n if self.opts['verbose']:\n pr... | [
"0.7758656",
"0.7241418",
"0.7239436",
"0.7222972",
"0.7210236",
"0.72034883",
"0.70932466",
"0.7042313",
"0.6992051",
"0.694836",
"0.69467616",
"0.6880937",
"0.68526316",
"0.680469",
"0.67841667",
"0.6780072",
"0.67789966",
"0.67789966",
"0.6729223",
"0.6705986",
"0.66246676... | 0.6476941 | 30 |
Returns a shorter version of object names for cleaner display. | def clean_name(name):
return ",".join(name.split(",")[:1]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_names_short(self):\r\n return [p.get_name() for p in self.people]",
"def get_names_from_full_objects(objects):\n return [obj.name for obj in objects]",
"def __str__(self):\n name=''\n for prop in self.properties.itervalues():\n name+=str(prop)\n return name",
... | [
"0.6896629",
"0.68546605",
"0.6810425",
"0.6623793",
"0.66023505",
"0.6594769",
"0.64818907",
"0.6474733",
"0.6473779",
"0.64563364",
"0.6453014",
"0.64212185",
"0.6386153",
"0.6386153",
"0.6374827",
"0.6345542",
"0.6344488",
"0.6342986",
"0.63186526",
"0.63086903",
"0.629974... | 0.0 | -1 |
Takes a source class ID and returns the int class ID assigned to it. | def map_source_class_id(self, source_class_id):
return self.class_from_source_map[source_class_id] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_source_class_id(self, class_id, source):\n info = self.class_info[class_id]\n assert info['source'] == source\n return info['id']",
"def get_source_class_id(self, class_id, source):\n info = self.class_info[class_id]\n assert info['source'] == source\n return inf... | [
"0.8493009",
"0.8493009",
"0.6766999",
"0.6760587",
"0.6760587",
"0.66088873",
"0.6549115",
"0.6302364",
"0.62795174",
"0.6275053",
"0.6236329",
"0.6236329",
"0.61188203",
"0.6107271",
"0.59653497",
"0.5949911",
"0.58152884",
"0.572042",
"0.5719192",
"0.5719192",
"0.5719192",... | 0.7406211 | 3 |
Map an internal class ID to the corresponding class ID in the source dataset. | def get_source_class_id(self, class_id, source):
info = self.class_info[class_id]
assert info['source'] == source
return info['id'] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def map_source_class_id(self, source_class_id):\n return self.class_from_source_map[source_class_id]",
"def map_source_class_id(self, source_class_id):\n return self.class_from_source_map[source_class_id]",
"def map_id_to_idx(self, class_ids):\n class_idx = torch.zeros(class_ids.shape, dty... | [
"0.73133224",
"0.73133224",
"0.67706954",
"0.6197687",
"0.6047091",
"0.6020897",
"0.59112316",
"0.58618355",
"0.5850386",
"0.5751023",
"0.5733445",
"0.56539035",
"0.56316966",
"0.56235695",
"0.562181",
"0.5613915",
"0.5586703",
"0.54934883",
"0.5488558",
"0.54843575",
"0.5481... | 0.6395847 | 4 |
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