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PY-18029 Move methods for building Python console protocol structures from pydevd_vars to pydevd_thrift
This commit is contained in:
Alexander Koshevoy
@@ -515,7 +515,7 @@ class BaseInterpreterInterface:
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name = attr.split("\t")[-1]
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array = pydevd_vars.eval_in_context(name, self.get_namespace(), self.get_namespace())
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# return pydevd_vars.table_like_struct_to_xml(array, name, roffset, coffset, rows, cols, format)
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return pydevd_vars.table_like_struct_to_thrift_struct(array, name, roffset, coffset, rows, cols, format)
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return pydevd_thrift.table_like_struct_to_thrift_struct(array, name, roffset, coffset, rows, cols, format)
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def evaluate(self, expression):
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# returns `DebugValue` of evaluated expression
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@@ -7,6 +7,7 @@ from _pydevd_bundle import pydevd_resolver
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from _pydevd_bundle.pydevd_constants import dict_iter_items, dict_keys, IS_PY3K, \
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BUILTINS_MODULE_NAME, MAXIMUM_VARIABLE_REPRESENTATION_SIZE, RETURN_VALUES_DICT, LOAD_VALUES_POLICY, ValuesPolicy, DEFAULT_VALUES_DICT
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from _pydevd_bundle.pydevd_extension_api import TypeResolveProvider, StrPresentationProvider
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from _pydevd_bundle.pydevd_vars import get_label, VariableError, array_default_format, MAXIMUM_ARRAY_SIZE
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from pydev_console.thrift_communication import console_thrift
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try:
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@@ -367,3 +368,246 @@ def var_to_struct(val, name, doTrim=True, additional_in_xml='', evaluate_full_va
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def var_to_str(val, doTrim=True, evaluate_full_value=True):
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struct = var_to_struct(val, '', doTrim, '', evaluate_full_value)
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return struct.value
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# from pydevd_vars.py
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def array_to_thrift_struct(array, name, roffset, coffset, rows, cols, format):
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# returns `GetArrayResponse`
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# array, xml, r, c, f = array_to_meta_xml(array, name, format)
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array, array_chunk, r, c, f = array_to_meta_thrift_struct(array, name, format)
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format = '%' + f
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if rows == -1 and cols == -1:
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rows = r
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cols = c
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rows = min(rows, MAXIMUM_ARRAY_SIZE)
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cols = min(cols, MAXIMUM_ARRAY_SIZE)
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# there is no obvious rule for slicing (at least 5 choices)
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if len(array) == 1 and (rows > 1 or cols > 1):
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array = array[0]
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if array.size > len(array):
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array = array[roffset:, coffset:]
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rows = min(rows, len(array))
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cols = min(cols, len(array[0]))
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if len(array) == 1:
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array = array[0]
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elif array.size == len(array):
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if roffset == 0 and rows == 1:
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array = array[coffset:]
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cols = min(cols, len(array))
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elif coffset == 0 and cols == 1:
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array = array[roffset:]
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rows = min(rows, len(array))
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def get_value(row, col):
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value = array
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if rows == 1 or cols == 1:
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if rows == 1 and cols == 1:
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value = array[0]
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else:
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value = array[(col if rows == 1 else row)]
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if "ndarray" in str(type(value)):
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value = value[0]
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else:
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value = array[row][col]
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return value
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# xml += array_data_to_xml(rows, cols, lambda r: (get_value(r, c) for c in range(cols)))
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array_chunk.data = array_data_to_thrift_struct(rows, cols, lambda r: (get_value(r, c) for c in range(cols)))
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return array_chunk
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def array_to_meta_thrift_struct(array, name, format):
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type = array.dtype.kind
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slice = name
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l = len(array.shape)
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# initial load, compute slice
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if format == '%':
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if l > 2:
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slice += '[0]' * (l - 2)
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for r in range(l - 2):
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array = array[0]
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if type == 'f':
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format = '.5f'
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elif type == 'i' or type == 'u':
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format = 'd'
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else:
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format = 's'
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else:
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format = format.replace('%', '')
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l = len(array.shape)
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reslice = ""
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if l > 2:
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raise Exception("%s has more than 2 dimensions." % slice)
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elif l == 1:
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# special case with 1D arrays arr[i, :] - row, but arr[:, i] - column with equal shape and ndim
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# http://stackoverflow.com/questions/16837946/numpy-a-2-rows-1-column-file-loadtxt-returns-1row-2-columns
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# explanation: http://stackoverflow.com/questions/15165170/how-do-i-maintain-row-column-orientation-of-vectors-in-numpy?rq=1
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# we use kind of a hack - get information about memory from C_CONTIGUOUS
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is_row = array.flags['C_CONTIGUOUS']
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if is_row:
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rows = 1
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cols = len(array)
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if cols < len(array):
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reslice = '[0:%s]' % (cols)
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array = array[0:cols]
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else:
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cols = 1
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rows = len(array)
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if rows < len(array):
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reslice = '[0:%s]' % (rows)
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array = array[0:rows]
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elif l == 2:
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rows = array.shape[-2]
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cols = array.shape[-1]
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if cols < array.shape[-1] or rows < array.shape[-2]:
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reslice = '[0:%s, 0:%s]' % (rows, cols)
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array = array[0:rows, 0:cols]
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# avoid slice duplication
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if not slice.endswith(reslice):
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slice += reslice
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bounds = (0, 0)
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if type in "biufc":
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bounds = (array.min(), array.max())
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# return array, slice_to_xml(slice, rows, cols, format, type, bounds), rows, cols, format
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array_chunk = console_thrift.GetArrayResponse()
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array_chunk.slice = slice
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array_chunk.rows = rows
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array_chunk.cols = cols
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array_chunk.format = format
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array_chunk.type = type
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array_chunk.bounds = bounds
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# return array, slice_to_xml(slice, rows, cols, format, type, bounds), rows, cols, format
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return array, array_chunk, rows, cols, format
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def dataframe_to_thrift_struct(df, name, roffset, coffset, rows, cols, format):
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"""
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:type df: pandas.core.frame.DataFrame
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:type name: str
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:type coffset: int
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:type roffset: int
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:type rows: int
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:type cols: int
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:type format: str
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"""
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dim = len(df.axes)
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num_rows = df.shape[0]
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num_cols = df.shape[1] if dim > 1 else 1
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# xml = slice_to_xml(name, num_rows, num_cols, "", "", (0, 0))
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array_chunk = console_thrift.GetArrayResponse()
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array_chunk.slice = name
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array_chunk.rows = num_rows
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array_chunk.cols = num_cols
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array_chunk.format = ""
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array_chunk.type = ""
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array_chunk.bounds = (0, 0)
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if (rows, cols) == (-1, -1):
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rows, cols = num_rows, num_cols
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rows = min(rows, MAXIMUM_ARRAY_SIZE)
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cols = min(cols, MAXIMUM_ARRAY_SIZE, num_cols)
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# need to precompute column bounds here before slicing!
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col_bounds = [None] * cols
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dtypes = [None] * cols
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if dim > 1:
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for col in range(cols):
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dtype = df.dtypes.iloc[coffset + col].kind
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dtypes[col] = dtype
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if dtype in "biufc":
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cvalues = df.iloc[:, coffset + col]
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bounds = (cvalues.min(), cvalues.max())
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else:
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bounds = (0, 0)
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col_bounds[col] = bounds
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else:
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dtype = df.dtype.kind
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dtypes[0] = dtype
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col_bounds[0] = (df.min(), df.max()) if dtype in "biufc" else (0, 0)
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df = df.iloc[roffset: roffset + rows, coffset: coffset + cols] if dim > 1 else df.iloc[roffset: roffset + rows]
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rows = df.shape[0]
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cols = df.shape[1] if dim > 1 else 1
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format = format.replace('%', '')
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def col_to_format(c):
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return format if dtypes[c] == 'f' and format else array_default_format(dtypes[c])
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# xml += header_data_to_xml(rows, cols, dtypes, col_bounds, col_to_format, df, dim)
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array_chunk.headers = header_data_to_thrift_struct(rows, cols, dtypes, col_bounds, col_to_format, df, dim)
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# xml += array_data_to_xml(rows, cols, lambda r: (("%" + col_to_format(c)) % (df.iat[r, c] if dim > 1 else df.iat[r])
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# for c in range(cols)))
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array_chunk.data = array_data_to_thrift_struct(rows, cols, lambda r: (("%" + col_to_format(c)) % (df.iat[r, c] if dim > 1 else df.iat[r])
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for c in range(cols)))
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# return xml
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return array_chunk
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def array_data_to_thrift_struct(rows, cols, get_row):
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array_data = console_thrift.ArrayData()
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# xml = "<arraydata rows=\"%s\" cols=\"%s\"/>\n" % (rows, cols)
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array_data.rows = rows
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array_data.cols = cols
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# `ArrayData.data`
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data = []
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for row in range(rows):
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# xml += "<row index=\"%s\"/>\n" % to_string(row)
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# for value in get_row(row):
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# xml += var_to_xml(value, '')
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data.append([var_to_str(value) for value in get_row(row)])
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array_data.data = data
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# return xml
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return array_data
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def header_data_to_thrift_struct(rows, cols, dtypes, col_bounds, col_to_format, df, dim):
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# xml = "<headerdata rows=\"%s\" cols=\"%s\">\n" % (rows, cols)
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array_headers = console_thrift.ArrayHeaders()
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col_headers = []
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for col in range(cols):
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col_label = get_label(df.axes[1].values[col]) if dim > 1 else str(col)
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bounds = col_bounds[col]
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col_format = "%" + col_to_format(col)
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col_header = console_thrift.ColHeader()
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# col_header.index = col
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col_header.label = col_label
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col_header.type = dtypes[col]
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col_header.format = col_to_format(col)
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col_header.max = col_format % bounds[1]
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col_header.min = col_format % bounds[0]
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col_headers.append(col_header)
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row_headers = []
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for row in range(rows):
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row_header = console_thrift.RowHeader()
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row_header.index = row
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row_header.label = get_label(df.axes[0].values[row])
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row_headers.append(row_header)
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# xml += "<rowheader index=\"%s\" label = \"%s\"/>\n" % (str(row), get_label(df.axes[0].values[row]))
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# xml += "</headerdata>\n"
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array_headers.colHeaders = col_headers
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array_headers.rowHeaders = row_headers
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# return xml
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return array_headers
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TYPE_TO_THRIFT_STRUCT_CONVERTERS = {"ndarray": array_to_thrift_struct, "DataFrame": dataframe_to_thrift_struct, "Series": dataframe_to_thrift_struct}
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def table_like_struct_to_thrift_struct(array, name, roffset, coffset, rows, cols, format):
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# returns `GetArrayResponse`
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_, type_name, _ = get_type(array)
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if type_name in TYPE_TO_THRIFT_STRUCT_CONVERTERS:
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return TYPE_TO_THRIFT_STRUCT_CONVERTERS[type_name](array, name, roffset, coffset, rows, cols, format)
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else:
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raise VariableError("type %s not supported" % type_name)
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@@ -6,9 +6,7 @@ import pickle
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from _pydev_imps._pydev_saved_modules import thread
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from _pydevd_bundle.pydevd_constants import get_frame, get_thread_id, xrange
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from _pydevd_bundle.pydevd_custom_frames import get_custom_frame
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from _pydevd_bundle.pydevd_thrift import var_to_str
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from _pydevd_bundle.pydevd_xml import ExceptionOnEvaluate, get_type, var_to_xml
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from pydev_console.thrift_communication import console_thrift
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try:
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from StringIO import StringIO
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@@ -645,243 +643,3 @@ def table_like_struct_to_xml(array, name, roffset, coffset, rows, cols, format):
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else:
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raise VariableError("type %s not supported" % type_name)
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def array_to_thrift_struct(array, name, roffset, coffset, rows, cols, format):
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# returns `GetArrayResponse`
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# array, xml, r, c, f = array_to_meta_xml(array, name, format)
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array, array_chunk, r, c, f = array_to_meta_thrift_struct(array, name, format)
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format = '%' + f
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if rows == -1 and cols == -1:
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rows = r
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cols = c
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rows = min(rows, MAXIMUM_ARRAY_SIZE)
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cols = min(cols, MAXIMUM_ARRAY_SIZE)
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# there is no obvious rule for slicing (at least 5 choices)
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if len(array) == 1 and (rows > 1 or cols > 1):
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array = array[0]
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if array.size > len(array):
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array = array[roffset:, coffset:]
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rows = min(rows, len(array))
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cols = min(cols, len(array[0]))
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if len(array) == 1:
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array = array[0]
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elif array.size == len(array):
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if roffset == 0 and rows == 1:
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array = array[coffset:]
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cols = min(cols, len(array))
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elif coffset == 0 and cols == 1:
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array = array[roffset:]
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rows = min(rows, len(array))
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def get_value(row, col):
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value = array
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if rows == 1 or cols == 1:
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if rows == 1 and cols == 1:
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value = array[0]
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else:
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value = array[(col if rows == 1 else row)]
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if "ndarray" in str(type(value)):
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value = value[0]
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else:
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value = array[row][col]
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return value
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# xml += array_data_to_xml(rows, cols, lambda r: (get_value(r, c) for c in range(cols)))
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array_chunk.data = array_data_to_thrift_struct(rows, cols, lambda r: (get_value(r, c) for c in range(cols)))
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return array_chunk
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def array_to_meta_thrift_struct(array, name, format):
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type = array.dtype.kind
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slice = name
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l = len(array.shape)
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# initial load, compute slice
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if format == '%':
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if l > 2:
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slice += '[0]' * (l - 2)
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for r in range(l - 2):
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array = array[0]
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if type == 'f':
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format = '.5f'
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elif type == 'i' or type == 'u':
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format = 'd'
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else:
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format = 's'
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else:
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format = format.replace('%', '')
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l = len(array.shape)
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reslice = ""
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if l > 2:
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raise Exception("%s has more than 2 dimensions." % slice)
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elif l == 1:
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# special case with 1D arrays arr[i, :] - row, but arr[:, i] - column with equal shape and ndim
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# http://stackoverflow.com/questions/16837946/numpy-a-2-rows-1-column-file-loadtxt-returns-1row-2-columns
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# explanation: http://stackoverflow.com/questions/15165170/how-do-i-maintain-row-column-orientation-of-vectors-in-numpy?rq=1
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# we use kind of a hack - get information about memory from C_CONTIGUOUS
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is_row = array.flags['C_CONTIGUOUS']
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if is_row:
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rows = 1
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cols = len(array)
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if cols < len(array):
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reslice = '[0:%s]' % (cols)
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array = array[0:cols]
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else:
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cols = 1
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rows = len(array)
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if rows < len(array):
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reslice = '[0:%s]' % (rows)
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array = array[0:rows]
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elif l == 2:
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rows = array.shape[-2]
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cols = array.shape[-1]
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if cols < array.shape[-1] or rows < array.shape[-2]:
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reslice = '[0:%s, 0:%s]' % (rows, cols)
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array = array[0:rows, 0:cols]
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# avoid slice duplication
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if not slice.endswith(reslice):
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slice += reslice
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bounds = (0, 0)
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if type in "biufc":
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bounds = (array.min(), array.max())
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# return array, slice_to_xml(slice, rows, cols, format, type, bounds), rows, cols, format
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array_chunk = console_thrift.GetArrayResponse()
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array_chunk.slice = slice
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array_chunk.rows = rows
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array_chunk.cols = cols
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array_chunk.format = format
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array_chunk.type = type
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array_chunk.bounds = bounds
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# return array, slice_to_xml(slice, rows, cols, format, type, bounds), rows, cols, format
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return array, array_chunk, rows, cols, format
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def dataframe_to_thrift_struct(df, name, roffset, coffset, rows, cols, format):
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"""
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||||
:type df: pandas.core.frame.DataFrame
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||||
:type name: str
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||||
:type coffset: int
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||||
:type roffset: int
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||||
:type rows: int
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||||
:type cols: int
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||||
:type format: str
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||||
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||||
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"""
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||||
dim = len(df.axes)
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||||
num_rows = df.shape[0]
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||||
num_cols = df.shape[1] if dim > 1 else 1
|
||||
# xml = slice_to_xml(name, num_rows, num_cols, "", "", (0, 0))
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||||
array_chunk = console_thrift.GetArrayResponse()
|
||||
array_chunk.slice = name
|
||||
array_chunk.rows = num_rows
|
||||
array_chunk.cols = num_cols
|
||||
array_chunk.format = ""
|
||||
array_chunk.type = ""
|
||||
array_chunk.bounds = (0, 0)
|
||||
|
||||
if (rows, cols) == (-1, -1):
|
||||
rows, cols = num_rows, num_cols
|
||||
|
||||
rows = min(rows, MAXIMUM_ARRAY_SIZE)
|
||||
cols = min(cols, MAXIMUM_ARRAY_SIZE, num_cols)
|
||||
# need to precompute column bounds here before slicing!
|
||||
col_bounds = [None] * cols
|
||||
dtypes = [None] * cols
|
||||
if dim > 1:
|
||||
for col in range(cols):
|
||||
dtype = df.dtypes.iloc[coffset + col].kind
|
||||
dtypes[col] = dtype
|
||||
if dtype in "biufc":
|
||||
cvalues = df.iloc[:, coffset + col]
|
||||
bounds = (cvalues.min(), cvalues.max())
|
||||
else:
|
||||
bounds = (0, 0)
|
||||
col_bounds[col] = bounds
|
||||
else:
|
||||
dtype = df.dtype.kind
|
||||
dtypes[0] = dtype
|
||||
col_bounds[0] = (df.min(), df.max()) if dtype in "biufc" else (0, 0)
|
||||
|
||||
df = df.iloc[roffset: roffset + rows, coffset: coffset + cols] if dim > 1 else df.iloc[roffset: roffset + rows]
|
||||
rows = df.shape[0]
|
||||
cols = df.shape[1] if dim > 1 else 1
|
||||
format = format.replace('%', '')
|
||||
|
||||
def col_to_format(c):
|
||||
return format if dtypes[c] == 'f' and format else array_default_format(dtypes[c])
|
||||
|
||||
# xml += header_data_to_xml(rows, cols, dtypes, col_bounds, col_to_format, df, dim)
|
||||
array_chunk.headers = header_data_to_thrift_struct(rows, cols, dtypes, col_bounds, col_to_format, df, dim)
|
||||
# xml += array_data_to_xml(rows, cols, lambda r: (("%" + col_to_format(c)) % (df.iat[r, c] if dim > 1 else df.iat[r])
|
||||
# for c in range(cols)))
|
||||
array_chunk.data = array_data_to_thrift_struct(rows, cols, lambda r: (("%" + col_to_format(c)) % (df.iat[r, c] if dim > 1 else df.iat[r])
|
||||
for c in range(cols)))
|
||||
# return xml
|
||||
return array_chunk
|
||||
|
||||
|
||||
def array_data_to_thrift_struct(rows, cols, get_row):
|
||||
array_data = console_thrift.ArrayData()
|
||||
# xml = "<arraydata rows=\"%s\" cols=\"%s\"/>\n" % (rows, cols)
|
||||
array_data.rows = rows
|
||||
array_data.cols = cols
|
||||
# `ArrayData.data`
|
||||
data = []
|
||||
for row in range(rows):
|
||||
# xml += "<row index=\"%s\"/>\n" % to_string(row)
|
||||
# for value in get_row(row):
|
||||
# xml += var_to_xml(value, '')
|
||||
data.append([var_to_str(value) for value in get_row(row)])
|
||||
|
||||
array_data.data = data
|
||||
# return xml
|
||||
return array_data
|
||||
|
||||
|
||||
def header_data_to_thrift_struct(rows, cols, dtypes, col_bounds, col_to_format, df, dim):
|
||||
# xml = "<headerdata rows=\"%s\" cols=\"%s\">\n" % (rows, cols)
|
||||
array_headers = console_thrift.ArrayHeaders()
|
||||
col_headers = []
|
||||
for col in range(cols):
|
||||
col_label = get_label(df.axes[1].values[col]) if dim > 1 else str(col)
|
||||
bounds = col_bounds[col]
|
||||
col_format = "%" + col_to_format(col)
|
||||
col_header = console_thrift.ColHeader()
|
||||
# col_header.index = col
|
||||
col_header.label = col_label
|
||||
col_header.type = dtypes[col]
|
||||
col_header.format = col_to_format(col)
|
||||
col_header.max = col_format % bounds[1]
|
||||
col_header.min = col_format % bounds[0]
|
||||
col_headers.append(col_header)
|
||||
row_headers = []
|
||||
for row in range(rows):
|
||||
row_header = console_thrift.RowHeader()
|
||||
row_header.index = row
|
||||
row_header.label = get_label(df.axes[0].values[row])
|
||||
row_headers.append(row_header)
|
||||
# xml += "<rowheader index=\"%s\" label = \"%s\"/>\n" % (str(row), get_label(df.axes[0].values[row]))
|
||||
# xml += "</headerdata>\n"
|
||||
array_headers.colHeaders = col_headers
|
||||
array_headers.rowHeaders = row_headers
|
||||
# return xml
|
||||
return array_headers
|
||||
|
||||
|
||||
TYPE_TO_THRIFT_STRUCT_CONVERTERS = {"ndarray": array_to_thrift_struct, "DataFrame": dataframe_to_thrift_struct, "Series": dataframe_to_thrift_struct}
|
||||
|
||||
|
||||
def table_like_struct_to_thrift_struct(array, name, roffset, coffset, rows, cols, format):
|
||||
# returns `GetArrayResponse`
|
||||
_, type_name, _ = get_type(array)
|
||||
if type_name in TYPE_TO_THRIFT_STRUCT_CONVERTERS:
|
||||
return TYPE_TO_THRIFT_STRUCT_CONVERTERS[type_name](array, name, roffset, coffset, rows, cols, format)
|
||||
else:
|
||||
raise VariableError("type %s not supported" % type_name)
|
||||
|
||||
Reference in New Issue
Block a user