PatrickHaller/the-stack-python-1M · Datasets at Hugging Face

d57e20647da81b0ea97e3b6ddf0c1e7911b5db6d

4,094

py

Python

space.py

kourgeorge/life

5162722e9bd53e5e84d704852139802bf99b46ce

[ "BSD-2-Clause" ]

24

2018-12-04T13:35:54.000Z

2022-02-28T05:52:38.000Z

space.py

kourgeorge/life

5162722e9bd53e5e84d704852139802bf99b46ce

[ "BSD-2-Clause" ]

5

2018-12-09T14:41:21.000Z

2020-08-28T10:35:56.000Z

space.py

kourgeorge/life

5162722e9bd53e5e84d704852139802bf99b46ce

[ "BSD-2-Clause" ]

3

2020-04-18T03:29:19.000Z

2020-08-20T15:03:35.000Z

__author__ = 'gkour' from cell import Cell import numpy as np from itertools import chain class Space: def __init__(self, space_size): self._space_size = space_size self._grid = [] for i in range(space_size): self._grid = [[Cell((i, j)) for j in range(self._space_size)] for i in range(self._space_size)] def grid(self): return self._grid def cells(self): return list(chain.from_iterable(self._grid)) def update_sounds(self, time): for cell in self.cells(): cell.remove_sounds(time) def insert_creature(self, creature, coord): if not self.valid_coord(coord): print("Exception: bad coordinated in space.insert_creature") return None cell = self._grid[coord[0]][coord[1]] cell.insert_creature(creature) return cell def add_food(self, coord, amount): if not self.valid_coord(coord): print("Exception: bad coordinated in space.add_food") return None cell = self._grid[coord[0]][coord[1]] cell.add_food(amount) return cell def remove_creature(self, creature): x, y = creature.coord() self._grid[x][y].remove_creature(creature) def get_state_in_coord(self, coord, vision_range, races): if not self.valid_coord(coord): raise Exception("Exception: bad coordinated in space.get_state_in_coord") state_dim_size = 2 * vision_range + 1 dims = len(races) + 2 # races, food, and sound state = np.ones([dims, state_dim_size, state_dim_size]) * -1 for i in range(state_dim_size): for j in range(state_dim_size): abs_i = coord[0] - vision_range + i abs_j = coord[1] - vision_range + j if 0 <= abs_i < self._space_size and 0 <= abs_j < self._space_size: state[:, i, j] = self._grid[abs_i][abs_j].get_state_in_cell(races) return state def get_all_creatures(self): return [creature for cell in self.cells() for creature in cell.creatures()] def get_food_distribution(self): return [[self._grid[i][j].get_food() for j in range(self._space_size)] for i in range(self._space_size)] def get_creatures_distribution(self): return [[self._grid[i][j].num_creatures() for j in range(self._space_size)] for i in range(self._space_size)] def get_sounds_distribution(self): return [[len(self._grid[i][j].get_sounds()) for j in range(self._space_size)] for i in range(self._space_size)] def valid_coord(self, coord): x, y = coord return 0 <= x < self._space_size and 0 <= y < self._space_size def find_nearby_creature(self, creature): nearby_creatures = creature.cell().creatures() if len(nearby_creatures) < 2: return None others = [creat for creat in nearby_creatures if creat != creature] return np.random.permutation(others)[0] def find_nearby_creature_from_same_race(self, creature): others = self.get_nearby_creatures_from_same_race(creature) if others: return np.random.permutation(others)[0] return None def find_nearby_creature_from_different_race(self, creature): others = self.get_nearby_creatures_from_different_race(creature) if others: return np.random.permutation(others)[0] return None def get_nearby_creatures_from_same_race(self, creature): return [creat for creat in creature.cell().creatures() if creat != creature and creat.race_name() == creature.race_name()] def get_nearby_creatures_from_different_race(self, creature): return [creat for creat in creature.cell().creatures() if creat.race_name() != creature.race_name()] def __str__(self): string = '' for cell in self._grid: string = string + str(cell) + ' ' return string

37.907407

120

0.624328

65ff082c242a54d9d1ce0365fb35e00f6cca015c

45,653

py

Python

opengl/gl/raw/gl_4_3.py

SilentPenguin/OpenGL.py

dd16bf7ea2fa20a7ea489e711a5df20d604c34dc

[ "Apache-2.0" ]

1

2016-11-09T06:11:24.000Z

opengl/gl/raw/gl_4_3.py

SilentPenguin/OpenGL.py

dd16bf7ea2fa20a7ea489e711a5df20d604c34dc

[ "Apache-2.0" ]

3

2016-11-09T06:21:08.000Z

2016-11-18T15:17:22.000Z

opengl/gl/raw/gl_4_3.py

SilentPenguin/OpenGL.py

dd16bf7ea2fa20a7ea489e711a5df20d604c34dc

[ "Apache-2.0" ]

null

#BEWARE: automatically generated code #This code was generated by /generate/__main__.py from opengl.gl.raw.bindings import * NUM_SHADING_LANGUAGE_VERSIONS = 0x82E9 VERTEX_ATTRIB_ARRAY_LONG = 0x874E COMPRESSED_RGB8_ETC2 = 0x9274 COMPRESSED_SRGB8_ETC2 = 0x9275 COMPRESSED_RGB8_PUNCHTHROUGH_ALPHA1_ETC2 = 0x9276 COMPRESSED_SRGB8_PUNCHTHROUGH_ALPHA1_ETC2 = 0x9277 COMPRESSED_RGBA8_ETC2_EAC = 0x9278 COMPRESSED_SRGB8_ALPHA8_ETC2_EAC = 0x9279 COMPRESSED_R11_EAC = 0x9270 COMPRESSED_SIGNED_R11_EAC = 0x9271 COMPRESSED_RG11_EAC = 0x9272 COMPRESSED_SIGNED_RG11_EAC = 0x9273 PRIMITIVE_RESTART_FIXED_INDEX = 0x8D69 ANY_SAMPLES_PASSED_CONSERVATIVE = 0x8D6A MAX_ELEMENT_INDEX = 0x8D6B COMPUTE_SHADER = 0x91B9 MAX_COMPUTE_UNIFORM_BLOCKS = 0x91BB MAX_COMPUTE_TEXTURE_IMAGE_UNITS = 0x91BC MAX_COMPUTE_IMAGE_UNIFORMS = 0x91BD MAX_COMPUTE_SHARED_MEMORY_SIZE = 0x8262 MAX_COMPUTE_UNIFORM_COMPONENTS = 0x8263 MAX_COMPUTE_ATOMIC_COUNTER_BUFFERS = 0x8264 MAX_COMPUTE_ATOMIC_COUNTERS = 0x8265 MAX_COMBINED_COMPUTE_UNIFORM_COMPONENTS = 0x8266 MAX_COMPUTE_WORK_GROUP_INVOCATIONS = 0x90EB MAX_COMPUTE_WORK_GROUP_COUNT = 0x91BE MAX_COMPUTE_WORK_GROUP_SIZE = 0x91BF COMPUTE_WORK_GROUP_SIZE = 0x8267 UNIFORM_BLOCK_REFERENCED_BY_COMPUTE_SHADER = 0x90EC ATOMIC_COUNTER_BUFFER_REFERENCED_BY_COMPUTE_SHADER = 0x90ED DISPATCH_INDIRECT_BUFFER = 0x90EE DISPATCH_INDIRECT_BUFFER_BINDING = 0x90EF COMPUTE_SHADER_BIT = 0x00000020 DEBUG_OUTPUT_SYNCHRONOUS = 0x8242 DEBUG_NEXT_LOGGED_MESSAGE_LENGTH = 0x8243 DEBUG_CALLBACK_FUNCTION = 0x8244 DEBUG_CALLBACK_USER_PARAM = 0x8245 DEBUG_SOURCE_API = 0x8246 DEBUG_SOURCE_WINDOW_SYSTEM = 0x8247 DEBUG_SOURCE_SHADER_COMPILER = 0x8248 DEBUG_SOURCE_THIRD_PARTY = 0x8249 DEBUG_SOURCE_APPLICATION = 0x824A DEBUG_SOURCE_OTHER = 0x824B DEBUG_TYPE_ERROR = 0x824C DEBUG_TYPE_DEPRECATED_BEHAVIOR = 0x824D DEBUG_TYPE_UNDEFINED_BEHAVIOR = 0x824E DEBUG_TYPE_PORTABILITY = 0x824F DEBUG_TYPE_PERFORMANCE = 0x8250 DEBUG_TYPE_OTHER = 0x8251 MAX_DEBUG_MESSAGE_LENGTH = 0x9143 MAX_DEBUG_LOGGED_MESSAGES = 0x9144 DEBUG_LOGGED_MESSAGES = 0x9145 DEBUG_SEVERITY_HIGH = 0x9146 DEBUG_SEVERITY_MEDIUM = 0x9147 DEBUG_SEVERITY_LOW = 0x9148 DEBUG_TYPE_MARKER = 0x8268 DEBUG_TYPE_PUSH_GROUP = 0x8269 DEBUG_TYPE_POP_GROUP = 0x826A DEBUG_SEVERITY_NOTIFICATION = 0x826B MAX_DEBUG_GROUP_STACK_DEPTH = 0x826C DEBUG_GROUP_STACK_DEPTH = 0x826D BUFFER = 0x82E0 SHADER = 0x82E1 PROGRAM = 0x82E2 VERTEX_ARRAY = 0x8074 QUERY = 0x82E3 PROGRAM_PIPELINE = 0x82E4 SAMPLER = 0x82E6 MAX_LABEL_LENGTH = 0x82E8 DEBUG_OUTPUT = 0x92E0 CONTEXT_FLAG_DEBUG_BIT = 0x00000002 MAX_UNIFORM_LOCATIONS = 0x826E FRAMEBUFFER_DEFAULT_WIDTH = 0x9310 FRAMEBUFFER_DEFAULT_HEIGHT = 0x9311 FRAMEBUFFER_DEFAULT_LAYERS = 0x9312 FRAMEBUFFER_DEFAULT_SAMPLES = 0x9313 FRAMEBUFFER_DEFAULT_FIXED_SAMPLE_LOCATIONS = 0x9314 MAX_FRAMEBUFFER_WIDTH = 0x9315 MAX_FRAMEBUFFER_HEIGHT = 0x9316 MAX_FRAMEBUFFER_LAYERS = 0x9317 MAX_FRAMEBUFFER_SAMPLES = 0x9318 INTERNALFORMAT_SUPPORTED = 0x826F INTERNALFORMAT_PREFERRED = 0x8270 INTERNALFORMAT_RED_SIZE = 0x8271 INTERNALFORMAT_GREEN_SIZE = 0x8272 INTERNALFORMAT_BLUE_SIZE = 0x8273 INTERNALFORMAT_ALPHA_SIZE = 0x8274 INTERNALFORMAT_DEPTH_SIZE = 0x8275 INTERNALFORMAT_STENCIL_SIZE = 0x8276 INTERNALFORMAT_SHARED_SIZE = 0x8277 INTERNALFORMAT_RED_TYPE = 0x8278 INTERNALFORMAT_GREEN_TYPE = 0x8279 INTERNALFORMAT_BLUE_TYPE = 0x827A INTERNALFORMAT_ALPHA_TYPE = 0x827B INTERNALFORMAT_DEPTH_TYPE = 0x827C INTERNALFORMAT_STENCIL_TYPE = 0x827D MAX_WIDTH = 0x827E MAX_HEIGHT = 0x827F MAX_DEPTH = 0x8280 MAX_LAYERS = 0x8281 MAX_COMBINED_DIMENSIONS = 0x8282 COLOR_COMPONENTS = 0x8283 DEPTH_COMPONENTS = 0x8284 STENCIL_COMPONENTS = 0x8285 COLOR_RENDERABLE = 0x8286 DEPTH_RENDERABLE = 0x8287 STENCIL_RENDERABLE = 0x8288 FRAMEBUFFER_RENDERABLE = 0x8289 FRAMEBUFFER_RENDERABLE_LAYERED = 0x828A FRAMEBUFFER_BLEND = 0x828B READ_PIXELS = 0x828C READ_PIXELS_FORMAT = 0x828D READ_PIXELS_TYPE = 0x828E TEXTURE_IMAGE_FORMAT = 0x828F TEXTURE_IMAGE_TYPE = 0x8290 GET_TEXTURE_IMAGE_FORMAT = 0x8291 GET_TEXTURE_IMAGE_TYPE = 0x8292 MIPMAP = 0x8293 MANUAL_GENERATE_MIPMAP = 0x8294 AUTO_GENERATE_MIPMAP = 0x8295 COLOR_ENCODING = 0x8296 SRGB_READ = 0x8297 SRGB_WRITE = 0x8298 FILTER = 0x829A VERTEX_TEXTURE = 0x829B TESS_CONTROL_TEXTURE = 0x829C TESS_EVALUATION_TEXTURE = 0x829D GEOMETRY_TEXTURE = 0x829E FRAGMENT_TEXTURE = 0x829F COMPUTE_TEXTURE = 0x82A0 TEXTURE_SHADOW = 0x82A1 TEXTURE_GATHER = 0x82A2 TEXTURE_GATHER_SHADOW = 0x82A3 SHADER_IMAGE_LOAD = 0x82A4 SHADER_IMAGE_STORE = 0x82A5 SHADER_IMAGE_ATOMIC = 0x82A6 IMAGE_TEXEL_SIZE = 0x82A7 IMAGE_COMPATIBILITY_CLASS = 0x82A8 IMAGE_PIXEL_FORMAT = 0x82A9 IMAGE_PIXEL_TYPE = 0x82AA SIMULTANEOUS_TEXTURE_AND_DEPTH_TEST = 0x82AC SIMULTANEOUS_TEXTURE_AND_STENCIL_TEST = 0x82AD SIMULTANEOUS_TEXTURE_AND_DEPTH_WRITE = 0x82AE SIMULTANEOUS_TEXTURE_AND_STENCIL_WRITE = 0x82AF TEXTURE_COMPRESSED_BLOCK_WIDTH = 0x82B1 TEXTURE_COMPRESSED_BLOCK_HEIGHT = 0x82B2 TEXTURE_COMPRESSED_BLOCK_SIZE = 0x82B3 CLEAR_BUFFER = 0x82B4 TEXTURE_VIEW = 0x82B5 VIEW_COMPATIBILITY_CLASS = 0x82B6 FULL_SUPPORT = 0x82B7 CAVEAT_SUPPORT = 0x82B8 IMAGE_CLASS_4_X_32 = 0x82B9 IMAGE_CLASS_2_X_32 = 0x82BA IMAGE_CLASS_1_X_32 = 0x82BB IMAGE_CLASS_4_X_16 = 0x82BC IMAGE_CLASS_2_X_16 = 0x82BD IMAGE_CLASS_1_X_16 = 0x82BE IMAGE_CLASS_4_X_8 = 0x82BF IMAGE_CLASS_2_X_8 = 0x82C0 IMAGE_CLASS_1_X_8 = 0x82C1 IMAGE_CLASS_11_11_10 = 0x82C2 IMAGE_CLASS_10_10_10_2 = 0x82C3 VIEW_CLASS_128_BITS = 0x82C4 VIEW_CLASS_96_BITS = 0x82C5 VIEW_CLASS_64_BITS = 0x82C6 VIEW_CLASS_48_BITS = 0x82C7 VIEW_CLASS_32_BITS = 0x82C8 VIEW_CLASS_24_BITS = 0x82C9 VIEW_CLASS_16_BITS = 0x82CA VIEW_CLASS_8_BITS = 0x82CB VIEW_CLASS_S3TC_DXT1_RGB = 0x82CC VIEW_CLASS_S3TC_DXT1_RGBA = 0x82CD VIEW_CLASS_S3TC_DXT3_RGBA = 0x82CE VIEW_CLASS_S3TC_DXT5_RGBA = 0x82CF VIEW_CLASS_RGTC1_RED = 0x82D0 VIEW_CLASS_RGTC2_RG = 0x82D1 VIEW_CLASS_BPTC_UNORM = 0x82D2 VIEW_CLASS_BPTC_FLOAT = 0x82D3 UNIFORM = 0x92E1 UNIFORM_BLOCK = 0x92E2 PROGRAM_INPUT = 0x92E3 PROGRAM_OUTPUT = 0x92E4 BUFFER_VARIABLE = 0x92E5 SHADER_STORAGE_BLOCK = 0x92E6 VERTEX_SUBROUTINE = 0x92E8 TESS_CONTROL_SUBROUTINE = 0x92E9 TESS_EVALUATION_SUBROUTINE = 0x92EA GEOMETRY_SUBROUTINE = 0x92EB FRAGMENT_SUBROUTINE = 0x92EC COMPUTE_SUBROUTINE = 0x92ED VERTEX_SUBROUTINE_UNIFORM = 0x92EE TESS_CONTROL_SUBROUTINE_UNIFORM = 0x92EF TESS_EVALUATION_SUBROUTINE_UNIFORM = 0x92F0 GEOMETRY_SUBROUTINE_UNIFORM = 0x92F1 FRAGMENT_SUBROUTINE_UNIFORM = 0x92F2 COMPUTE_SUBROUTINE_UNIFORM = 0x92F3 TRANSFORM_FEEDBACK_VARYING = 0x92F4 ACTIVE_RESOURCES = 0x92F5 MAX_NAME_LENGTH = 0x92F6 MAX_NUM_ACTIVE_VARIABLES = 0x92F7 MAX_NUM_COMPATIBLE_SUBROUTINES = 0x92F8 NAME_LENGTH = 0x92F9 TYPE = 0x92FA ARRAY_SIZE = 0x92FB OFFSET = 0x92FC BLOCK_INDEX = 0x92FD ARRAY_STRIDE = 0x92FE MATRIX_STRIDE = 0x92FF IS_ROW_MAJOR = 0x9300 ATOMIC_COUNTER_BUFFER_INDEX = 0x9301 BUFFER_BINDING = 0x9302 BUFFER_DATA_SIZE = 0x9303 NUM_ACTIVE_VARIABLES = 0x9304 ACTIVE_VARIABLES = 0x9305 REFERENCED_BY_VERTEX_SHADER = 0x9306 REFERENCED_BY_TESS_CONTROL_SHADER = 0x9307 REFERENCED_BY_TESS_EVALUATION_SHADER = 0x9308 REFERENCED_BY_GEOMETRY_SHADER = 0x9309 REFERENCED_BY_FRAGMENT_SHADER = 0x930A REFERENCED_BY_COMPUTE_SHADER = 0x930B TOP_LEVEL_ARRAY_SIZE = 0x930C TOP_LEVEL_ARRAY_STRIDE = 0x930D LOCATION = 0x930E LOCATION_INDEX = 0x930F IS_PER_PATCH = 0x92E7 SHADER_STORAGE_BUFFER = 0x90D2 SHADER_STORAGE_BUFFER_BINDING = 0x90D3 SHADER_STORAGE_BUFFER_START = 0x90D4 SHADER_STORAGE_BUFFER_SIZE = 0x90D5 MAX_VERTEX_SHADER_STORAGE_BLOCKS = 0x90D6 MAX_GEOMETRY_SHADER_STORAGE_BLOCKS = 0x90D7 MAX_TESS_CONTROL_SHADER_STORAGE_BLOCKS = 0x90D8 MAX_TESS_EVALUATION_SHADER_STORAGE_BLOCKS = 0x90D9 MAX_FRAGMENT_SHADER_STORAGE_BLOCKS = 0x90DA MAX_COMPUTE_SHADER_STORAGE_BLOCKS = 0x90DB MAX_COMBINED_SHADER_STORAGE_BLOCKS = 0x90DC MAX_SHADER_STORAGE_BUFFER_BINDINGS = 0x90DD MAX_SHADER_STORAGE_BLOCK_SIZE = 0x90DE SHADER_STORAGE_BUFFER_OFFSET_ALIGNMENT = 0x90DF SHADER_STORAGE_BARRIER_BIT = 0x00002000 MAX_COMBINED_SHADER_OUTPUT_RESOURCES = 0x8F39 DEPTH_STENCIL_TEXTURE_MODE = 0x90EA TEXTURE_BUFFER_OFFSET = 0x919D TEXTURE_BUFFER_SIZE = 0x919E TEXTURE_BUFFER_OFFSET_ALIGNMENT = 0x919F TEXTURE_VIEW_MIN_LEVEL = 0x82DB TEXTURE_VIEW_NUM_LEVELS = 0x82DC TEXTURE_VIEW_MIN_LAYER = 0x82DD TEXTURE_VIEW_NUM_LAYERS = 0x82DE TEXTURE_IMMUTABLE_LEVELS = 0x82DF VERTEX_ATTRIB_BINDING = 0x82D4 VERTEX_ATTRIB_RELATIVE_OFFSET = 0x82D5 VERTEX_BINDING_DIVISOR = 0x82D6 VERTEX_BINDING_OFFSET = 0x82D7 VERTEX_BINDING_STRIDE = 0x82D8 MAX_VERTEX_ATTRIB_RELATIVE_OFFSET = 0x82D9 MAX_VERTEX_ATTRIB_BINDINGS = 0x82DA VERTEX_BINDING_BUFFER = 0x8F4F @accepts(t.enum, t.enum, t.enum, t.enum, t.void) @returns(t.void) @binds(dll) def clear_buffer_data(target, internalformat, format, type, data): ''' fill a buffer object's data store with a fixed value. gl.clear_buffer_data and gl.clear_named_buffer_data fill the entirety of a buffer object's data store with data from client memory. Args: target: the target to which the buffer object is bound for glclearbufferdata, which must must be one of the buffer binding targets in the following table:. internalformat: the internal format with which the data will be stored in the buffer object. format: the format of the data in memory addressed by data. type: the type of the data in memory addressed by data. data: the address of a memory location storing the data to be replicated into the buffer's data store. ''' @accepts(t.enum, t.enum, t.intptr, t.sizeiptr, t.enum, t.enum, t.void) @returns(t.void) @binds(dll) def clear_buffer_sub_data(target, internalformat, offset, size, format, type, data): ''' fill all or part of buffer object's data store with a fixed value. gl.clear_buffer_sub_data and gl.clear_named_buffer_sub_data fill a specified region of a buffer object's data store with data from client memory. Args: target: the target to which the buffer object is bound for glclearbuffersubdata, which must be one of the buffer binding targets in the following table:. internalformat: the internal format with which the data will be stored in the buffer object. offset: the offset in basic machine units into the buffer object's data store at which to start filling. size: the size in basic machine units of the range of the data store to fill. format: the format of the data in memory addressed by data. type: the type of the data in memory addressed by data. data: the address of a memory location storing the data to be replicated into the buffer's data store. ''' @accepts(t.uint, t.uint, t.uint) @returns(t.void) @binds(dll) def dispatch_compute(num_groups_x, num_groups_y, num_groups_z): ''' launch one or more compute work groups. gl.dispatch_compute launches one or more compute work groups. Each work group is processed by the active program object for the compute shader stage. While the individual shader invocations within a work group are executed as a unit, work groups are executed completely independently and in unspecified order. num_groups_x, num_groups_y and num_groups_z specify the number of local work groups that will be dispatched in the X, Y and Z dimensions, respectively. Args: num_groups_x: the number of work groups to be launched in the x dimension. num_groups_y: the number of work groups to be launched in the y dimension. num_groups_z: the number of work groups to be launched in the z dimension. ''' @accepts(t.intptr) @returns(t.void) @binds(dll) def dispatch_compute_indirect(indirect): ''' launch one or more compute work groups using parameters stored in a buffer. gl.dispatch_compute_indirect launches one or more compute work groups using parameters stored in the buffer object currently bound to the gl.DISPATCH_INDIRECT_BUFFER target. Each work group is processed by the active program object for the compute shader stage. While the individual shader invocations within a work group are executed as a unit, work groups are executed completely independently and in unspecified order. indirect contains the offset into the data store of the buffer object bound to the gl.DISPATCH_INDIRECT_BUFFER target at which the parameters are stored. Args: indirect: the offset into the buffer object currently bound to the gl_dispatch_indirect_buffer buffer target at which the dispatch parameters are stored. ''' @accepts(t.uint, t.enum, t.int, t.int, t.int, t.int, t.uint, t.enum, t.int, t.int, t.int, t.int, t.sizei, t.sizei, t.sizei) @returns(t.void) @binds(dll) def copy_image_sub_data(srcname, srctarget, srclevel, srcx, srcy, srcz, dstname, dsttarget, dstlevel, dstx, dsty, dstz, srcwidth, srcheight, srcdepth): ''' perform a raw data copy between two images. gl.copy_image_sub_data may be used to copy data from one image to another. gl.copy_image_sub_data does not perform general-purpose conversions such as scaling, resizing, blending, color-space, or format conversions. Args: srcname: the name of a texture or renderbuffer object from which to copy. srctarget: the target representing the namespace of the source name srcname. srclevel: the mipmap level to read from the source. srcx: the x coordinate of the left edge of the souce region to copy. srcy: the y coordinate of the top edge of the souce region to copy. srcz: the z coordinate of the near edge of the souce region to copy. dstname: the name of a texture or renderbuffer object to which to copy. dsttarget: the target representing the namespace of the destination name dstname. dstx: the x coordinate of the left edge of the destination region. dsty: the y coordinate of the top edge of the destination region. dstz: the z coordinate of the near edge of the destination region. srcwidth: the width of the region to be copied. srcheight: the height of the region to be copied. srcdepth: the depth of the region to be copied. ''' @accepts(t.enum, t.enum, t.int) @returns(t.void) @binds(dll) def framebuffer_parameteri(target, pname, param): ''' set a named parameter of a framebuffer object. gl.framebuffer_parameteri and gl.named_framebuffer_parameteri modify the value of the parameter named pname in the specified framebuffer object. There are no modifiable parameters of the default draw and read framebuffer, so they are not valid targets of these commands. Args: target: the target to which the framebuffer is bound for glframebufferparameteri. pname: the framebuffer parameter to be modified. param: the new value for the parameter named pname. ''' @accepts(t.enum, t.enum, POINTER(t.int)) @returns(t.void) @binds(dll) def get_framebuffer_parameteriv(target, pname, params): pass @accepts(t.enum, t.enum, t.enum, t.sizei, POINTER(t.int64)) @returns(t.void) @binds(dll) def get_internalformati64v(target, internalformat, pname, bufsize, params): pass @accepts(t.uint, t.int, t.int, t.int, t.int, t.sizei, t.sizei, t.sizei) @returns(t.void) @binds(dll) def invalidate_tex_sub_image(texture, level, xoffset, yoffset, zoffset, width, height, depth): ''' invalidate a region of a texture image. gl.invalidate_tex_sub_image invalidates all or part of a texture image. texture and level indicated which texture image is being invalidated. After this command, data in that subregion have undefined values. xoffset, yoffset, zoffset, width, height, and depth are interpreted as they are in gl.tex_sub_image3D. Args: texture: the name of a texture object a subregion of which to invalidate. level: the level of detail of the texture object within which the region resides. xoffset: the x offset of the region to be invalidated. yoffset: the y offset of the region to be invalidated. zoffset: the z offset of the region to be invalidated. width: the width of the region to be invalidated. height: the height of the region to be invalidated. depth: the depth of the region to be invalidated. ''' @accepts(t.uint, t.int) @returns(t.void) @binds(dll) def invalidate_tex_image(texture, level): ''' invalidate the entirety a texture image. Args: texture: the name of a texture object to invalidate. level: the level of detail of the texture object to invalidate. ''' @accepts(t.uint, t.intptr, t.sizeiptr) @returns(t.void) @binds(dll) def invalidate_buffer_sub_data(buffer, offset, length): ''' invalidate a region of a buffer object's data store. gl.invalidate_buffer_sub_data invalidates all or part of the content of the data store of a buffer object. After invalidation, the content of the specified range of the buffer's data store becomes undefined. The start of the range is given by offset and its size is given by length, both measured in basic machine units. Args: buffer: the name of a buffer object, a subrange of whose data store to invalidate. offset: the offset within the buffer's data store of the start of the range to be invalidated. length: the length of the range within the buffer's data store to be invalidated. ''' @accepts(t.uint) @returns(t.void) @binds(dll) def invalidate_buffer_data(buffer): ''' invalidate the content of a buffer object's data store. gl.invalidate_buffer_data invalidates all of the content of the data store of a buffer object. After invalidation, the content of the buffer's data store becomes undefined. Args: buffer: the name of a buffer object whose data store to invalidate. ''' @accepts(t.enum, t.sizei, POINTER(t.enum)) @returns(t.void) @binds(dll) def invalidate_framebuffer(target, numattachments, attachments): ''' invalidate the content of some or all of a framebuffer's attachments. gl.invalidate_framebuffer and gl.invalidate_named_framebuffer_data invalidate the entire contents of a specified set of attachments of a framebuffer. Args: target: the target to which the framebuffer object is attached for glinvalidateframebuffer. numattachments: the number of entries in the attachments array. attachments: a pointer to an array identifying the attachments to be invalidated. ''' @accepts(t.enum, t.sizei, POINTER(t.enum), t.int, t.int, t.sizei, t.sizei) @returns(t.void) @binds(dll) def invalidate_sub_framebuffer(target, numattachments, attachments, x, y, width, height): ''' invalidate the content of a region of some or all of a framebuffer's attachments. gl.invalidate_sub_framebuffer and gl.invalidate_named_framebuffer_sub_data invalidate the contents of a specified region of a specified set of attachments of a framebuffer. Args: target: the target to which the framebuffer object is attached for glinvalidatesubframebuffer. numattachments: the number of entries in the attachments array. attachments: a pointer to an array identifying the attachments to be invalidated. x: the x offset of the region to be invalidated. y: the y offset of the region to be invalidated. width: the width of the region to be invalidated. height: the height of the region to be invalidated. ''' @accepts(t.enum, t.void, t.sizei, t.sizei) @returns(t.void) @binds(dll) def multi_draw_arrays_indirect(mode, indirect, drawcount, stride): ''' render multiple sets of primitives from array data, taking parameters from memory. gl.multi_draw_arrays_indirect specifies multiple geometric primitives with very few subroutine calls. gl.multi_draw_arrays_indirect behaves similarly to a multitude of calls to gl.draw_arrays_instanced_base_instance, execept that the parameters to each call to gl.draw_arrays_instanced_base_instance are stored in an array in memory at the address given by indirect, separated by the stride, in basic machine units, specified by stride. If stride is zero, then the array is assumed to be tightly packed in memory. Args: mode: what kind of primitives to render. indirect: the address of an array of structures containing the draw parameters. drawcount: the the number of elements in the array of draw parameter structures. stride: the distance in basic machine units between elements of the draw parameter array. ''' @accepts(t.enum, t.enum, t.void, t.sizei, t.sizei) @returns(t.void) @binds(dll) def multi_draw_elements_indirect(mode, type, indirect, drawcount, stride): ''' render indexed primitives from array data, taking parameters from memory. gl.multi_draw_elements_indirect specifies multiple indexed geometric primitives with very few subroutine calls. gl.multi_draw_elements_indirect behaves similarly to a multitude of calls to gl.draw_elements_instanced_base_vertex_base_instance, execpt that the parameters to gl.draw_elements_instanced_base_vertex_base_instance are stored in an array in memory at the address given by indirect, separated by the stride, in basic machine units, specified by stride. If stride is zero, then the array is assumed to be tightly packed in memory. Args: mode: what kind of primitives to render. type: the type of data in the buffer bound to the gl_element_array_buffer binding. indirect: the address of a structure containing an array of draw parameters. drawcount: the number of elements in the array addressed by indirect. stride: the distance in basic machine units between elements of the draw parameter array. ''' @accepts(t.uint, t.enum, t.enum, POINTER(t.int)) @returns(t.void) @binds(dll) def get_program_interfaceiv(program, programinterface, pname, params): pass @accepts(t.uint, t.enum, t.char_p) @returns(t.uint) @binds(dll) def get_program_resource_index(program, programinterface, name): ''' query the index of a named resource within a program. gl.get_program_resource_index returns the unsigned integer index assigned to a resource named name in the interface type programInterface of program object program. Args: program: the name of a program object whose resources to query. programinterface: a token identifying the interface within program containing the resource named name. name: the name of the resource to query the index of. ''' @accepts(t.uint, t.enum, t.uint, t.sizei, POINTER(t.sizei), t.char_p) @returns(t.void) @binds(dll) def get_program_resource_name(program, programinterface, index, bufsize, length, name): ''' query the name of an indexed resource within a program. gl.get_program_resource_name retrieves the name string assigned to the single active resource with an index of index in the interface programInterface of program object program. index must be less than the number of entries in the active resource list for programInterface. Args: program: the name of a program object whose resources to query. programinterface: a token identifying the interface within program containing the indexed resource. index: the index of the resource within programinterface of program. bufsize: the size of the character array whose address is given by name. length: the address of a variable which will receive the length of the resource name. name: the address of a character array into which will be written the name of the resource. ''' @accepts(t.uint, t.enum, t.uint, t.sizei, POINTER(t.enum), t.sizei, POINTER(t.sizei), POINTER(t.int)) @returns(t.void) @binds(dll) def get_program_resourceiv(program, programinterface, index, propcount, props, bufsize, length, params): pass @accepts(t.uint, t.enum, t.char_p) @returns(t.int) @binds(dll) def get_program_resource_location(program, programinterface, name): ''' query the location of a named resource within a program. gl.get_program_resource_location returns the location assigned to the variable named name in interface programInterface of program object program. program must be the name of a program that has been linked successfully. programInterface must be one of gl.UNIFORM, gl.PROGRAM_INPUT, gl.PROGRAM_OUTPUT, gl.VERTEX_SUBROUTINE_UNIFORM, gl.TESS_CONTROL_SUBROUTINE_UNIFORM, gl.TESS_EVALUATION_SUBROUTINE_UNIFORM, gl.GEOMETRY_SUBROUTINE_UNIFORM, gl.FRAGMENT_SUBROUTINE_UNIFORM, gl.COMPUTE_SUBROUTINE_UNIFORM, or gl.TRANSFORM_FEEDBACK_BUFFER. Args: program: the name of a program object whose resources to query. programinterface: a token identifying the interface within program containing the resource named name. name: the name of the resource to query the location of. ''' @accepts(t.uint, t.enum, t.char_p) @returns(t.int) @binds(dll) def get_program_resource_location_index(program, programinterface, name): ''' query the fragment color index of a named variable within a program. gl.get_program_resource_location_index returns the fragment color index assigned to the variable named name in interface programInterface of program object program. program must be the name of a program that has been linked successfully. programInterface must be gl.PROGRAM_OUTPUT. Args: program: the name of a program object whose resources to query. programinterface: a token identifying the interface within program containing the resource named name. name: the name of the resource to query the location of. ''' @accepts(t.uint, t.uint, t.uint) @returns(t.void) @binds(dll) def shader_storage_block_binding(program, storageblockindex, storageblockbinding): ''' change an active shader storage block binding. gl.shader_storage_block_binding, changes the active shader storage block with an assigned index of storageBlockIndex in program object program. storageBlockIndex must be an active shader storage block index in program. storageBlockBinding must be less than the value of gl.MAX_SHADER_STORAGE_BUFFER_BINDINGS. If successful, gl.shader_storage_binding specifies that program will use the data store of the buffer object bound to the binding point storageBlockBinding to read and write the values of the buffer variables in the shader storage block identified by storageBlockIndex. Args: program: the name of the program containing the block whose binding to change. storageblockindex: the index storage block within the program. storageblockbinding: the index storage block binding to associate with the specified storage block. ''' @accepts(t.enum, t.enum, t.uint, t.intptr, t.sizeiptr) @returns(t.void) @binds(dll) def tex_buffer_range(target, internalformat, buffer, offset, size): ''' attach a range of a buffer object's data store to a buffer texture object. gl.tex_buffer_range and gl.texture_buffer_range attach a range of the data store of a specified buffer object to a specified texture object, and specify the storage format for the texture image found found in the buffer object. The texture object must be a buffer texture. Args: target: the target to which the texture object is bound for gltexbufferrange. internalformat: the internal format of the data in the store belonging to buffer. buffer: the name of the buffer object whose storage to attach to the active buffer texture. offset: the offset of the start of the range of the buffer's data store to attach. size: the size of the range of the buffer's data store to attach. ''' @accepts(t.enum, t.sizei, t.enum, t.sizei, t.sizei, t.boolean) @returns(t.void) @binds(dll) def tex_storage2_d_multisample(target, samples, internalformat, width, height, fixedsamplelocations): ''' specify storage for a two-dimensional multisample texture. Args: target: the target to which the texture object is bound for gltexstorage2dmultisample. samples: the number of samples in the texture. internalformat: the sized internal format to be used to store texture image data. width: the width of the texture, in texels. height: the height of the texture, in texels. fixedsamplelocations: whether the image will use identical sample locations and the same number of samples for all texels in the image, and the sample locations will not depend on the internal format or size of the image. ''' @accepts(t.enum, t.sizei, t.enum, t.sizei, t.sizei, t.sizei, t.boolean) @returns(t.void) @binds(dll) def tex_storage3_d_multisample(target, samples, internalformat, width, height, depth, fixedsamplelocations): ''' specify storage for a two-dimensional multisample array texture. Args: target: the target to which the texture object is bound for gltexstorage3dmultisample. samples: the number of samples in the texture. internalformat: the sized internal format to be used to store texture image data. width: the width of the texture, in texels. height: the height of the texture, in texels. depth: the depth of the texture, in layers. fixedsamplelocations: whether the image will use identical sample locations and the same number of samples for all texels in the image, and the sample locations will not depend on the internal format or size of the image. ''' @accepts(t.uint, t.enum, t.uint, t.enum, t.uint, t.uint, t.uint, t.uint) @returns(t.void) @binds(dll) def texture_view(texture, target, origtexture, internalformat, minlevel, numlevels, minlayer, numlayers): ''' initialize a texture as a data alias of another texture's data store. gl.texture_view initializes a texture object as an alias, or view of another texture object, sharing some or all of the parent texture's data store with the initialized texture. texture specifies a name previously reserved by a successful call to gl.gen_textures but that has not yet been bound or given a target. Args: texture: the texture object to be initialized as a view. target: the target to be used for the newly initialized texture. origtexture: the name of a texture object of which to make a view. minlevel: lowest level of detail of the view. numlevels: the number of levels of detail to include in the view. minlayer: the index of the first layer to include in the view. numlayers: the number of layers to include in the view. ''' @accepts(t.uint, t.uint, t.intptr, t.sizei) @returns(t.void) @binds(dll) def bind_vertex_buffer(bindingindex, buffer, offset, stride): ''' bind a buffer to a vertex buffer bind point. gl.bind_vertex_buffer and gl.vertex_array_vertex_buffer bind the buffer named buffer to the vertex buffer binding point whose index is given by bindingindex. gl.bind_vertex_buffer modifies the binding of the currently bound vertex array object, whereas gl.vertex_array_vertex_buffer allows the caller to specify ID of the vertex array object with an argument named vaobj, for which the binding should be modified. offset and stride specify the offset of the first element within the buffer and the distance between elements within the buffer, respectively, and are both measured in basic machine units. bindingindex must be less than the value of gl.MAX_VERTEX_ATTRIB_BINDINGS. Args: bindingindex: the index of the vertex buffer binding point to which to bind the buffer. buffer: the name of a buffer to bind to the vertex buffer binding point. offset: the offset of the first element of the buffer. stride: the distance between elements within the buffer. ''' @accepts(t.uint, t.int, t.enum, t.boolean, t.uint) @returns(t.void) @binds(dll) def vertex_attrib_format(attribindex, size, type, normalized, relativeoffset): ''' specify the organization of vertex arrays. gl.vertex_attrib_format, gl.vertex_attrib_i_format and gl.vertex_attrib_l_format, as well as gl.vertex_array_attrib_format, gl.vertex_array_attrib_i_format and gl.vertex_array_attrib_l_format specify the organization of data in vertex arrays. The first three calls operate on the bound vertex array object, whereas the last three ones modify the state of a vertex array object with ID vaobj. attribindex specifies the index of the generic vertex attribute array whose data layout is being described, and must be less than the value of gl.MAX_VERTEX_ATTRIBS. Args: attribindex: the generic vertex attribute array being described. size: the number of values per vertex that are stored in the array. type: the type of the data stored in the array. normalized: the distance between elements within the buffer. relativeoffset: the distance between elements within the buffer. ''' @accepts(t.uint, t.int, t.enum, t.uint) @returns(t.void) @binds(dll) def vertex_attrib_i_format(attribindex, size, type, relativeoffset): pass @accepts(t.uint, t.int, t.enum, t.uint) @returns(t.void) @binds(dll) def vertex_attrib_l_format(attribindex, size, type, relativeoffset): pass @accepts(t.uint, t.uint) @returns(t.void) @binds(dll) def vertex_attrib_binding(attribindex, bindingindex): ''' associate a vertex attribute and a vertex buffer binding for a vertex array object. gl.vertex_attrib_binding and gl.vertex_array_attrib_binding establishes an association between the generic vertex attribute of a vertex array object whose index is given by attribindex, and a vertex buffer binding whose index is given by bindingindex. For gl.vertex_attrib_binding, the vertex array object affected is that currently bound. For gl.vertex_array_attrib_binding, vaobj is the name of the vertex array object. Args: attribindex: the index of the attribute to associate with a vertex buffer binding. bindingindex: the index of the vertex buffer binding with which to associate the generic vertex attribute. ''' @accepts(t.uint, t.uint) @returns(t.void) @binds(dll) def vertex_binding_divisor(bindingindex, divisor): ''' modify the rate at which generic vertex attributes advance. gl.vertex_binding_divisor and gl.vertex_array_binding_divisor modify the rate at which generic vertex attributes advance when rendering multiple instances of primitives in a single draw command. If divisor is zero, the attributes using the buffer bound to bindingindex advance once per vertex. If divisor is non-zero, the attributes advance once per divisor instances of the set of vertices being rendered. An attribute is referred to as instanced if the corresponding divisor value is non-zero. Args: bindingindex: the index of the binding whose divisor to modify. divisor: the new value for the instance step rate to apply. ''' @accepts(t.enum, t.enum, t.enum, t.sizei, POINTER(t.uint), t.boolean) @returns(t.void) @binds(dll) def debug_message_control(source, type, severity, count, ids, enabled): ''' control the reporting of debug messages in a debug context. gl.debug_message_control controls the reporting of debug messages generated by a debug context. The parameters source, type and severity form a filter to select messages from the pool of potential messages generated by the GL. Args: source: the source of debug messages to enable or disable. type: the type of debug messages to enable or disable. severity: the severity of debug messages to enable or disable. count: the length of the array ids. ids: the address of an array of unsigned integers contianing the ids of the messages to enable or disable. enabled: a boolean flag determining whether the selected messages should be enabled or disabled. ''' @accepts(t.enum, t.enum, t.uint, t.enum, t.sizei, t.char_p) @returns(t.void) @binds(dll) def debug_message_insert(source, type, id, severity, length, buf): ''' inject an application-supplied message into the debug message queue. gl.debug_message_insert inserts a user-supplied message into the debug output queue. source specifies the source that will be used to classify the message and must be gl.DEBUG_SOURCE_APPLICATION or gl.DEBUG_SOURCE_THIRD_PARTY. All other sources are reserved for use by the GL implementation. type indicates the type of the message to be inserted and may be one of gl.DEBUG_TYPE_ERROR, gl.DEBUG_TYPE_DEPRECATED_BEHAVIOR, gl.DEBUG_TYPE_UNDEFINED_BEHAVIOR, gl.DEBUG_TYPE_PORTABILITY, gl.DEBUG_TYPE_PERFORMANCE, gl.DEBUG_TYPE_MARKER, gl.DEBUG_TYPE_PUSH_GROUP, gl.DEBUG_TYPE_POP_GROUP, or gl.DEBUG_TYPE_OTHER. Args: source: the source of the debug message to insert. type: the type of the debug message insert. id: the user-supplied identifier of the message to insert. severity: the severity of the debug messages to insert. length: the length string contained in the character array whose address is given by message. buf: the address of a character array containing the message to insert. ''' @accepts(t.DEBUGPROC, t.void) @returns(t.void) @binds(dll) def debug_message_callback(callback, userparam): ''' specify a callback to receive debugging messages from the GL. gl.debug_message_callback sets the current debug output callback function to the function whose address is given in callback. Args: callback: the address of a callback function that will be called when a debug message is generated. userparam: a user supplied pointer that will be passed on each invocation of callback. ''' @accepts(t.uint, t.sizei, POINTER(t.enum), POINTER(t.enum), POINTER(t.uint), POINTER(t.enum), POINTER(t.sizei), t.char_p) @returns(t.uint) @binds(dll) def get_debug_message_log(count, bufsize, sources, types, ids, severities, lengths, messagelog): ''' retrieve messages from the debug message log. gl.get_debug_message_log retrieves messages from the debug message log. A maximum of count messages are retrieved from the log. If sources is not None then the source of each message is written into up to count elements of the array. If types is not None then the type of each message is written into up to count elements of the array. Args: count: the number of debug messages to retrieve from the log. bufsize: the size of the buffer whose address is given by messagelog. sources: the address of an array of variables to receive the sources of the retrieved messages. types: the address of an array of variables to receive the types of the retrieved messages. ids: the address of an array of unsigned integers to receive the ids of the retrieved messages. severities: the address of an array of variables to receive the severites of the retrieved messages. lengths: the address of an array of variables to receive the lengths of the received messages. messagelog: the address of an array of characters that will receive the messages. ''' @accepts(t.enum, t.uint, t.sizei, t.char_p) @returns(t.void) @binds(dll) def push_debug_group(source, id, length, message): ''' push a named debug group into the command stream. gl.push_debug_group pushes a debug group described by the string message into the command stream. The value of id specifies the ID of messages generated. The parameter length contains the number of characters in message. If length is negative, it is implied that message contains a null terminated string. Args: source: the source of the debug message. id: the identifier of the message. length: the length of the message to be sent to the debug output stream. message: the a string containing the message to be sent to the debug output stream. ''' @accepts() @returns(t.void) @binds(dll) def pop_debug_group(): ''' pop the active debug group. ''' @accepts(t.enum, t.uint, t.sizei, t.char_p) @returns(t.void) @binds(dll) def object_label(identifier, name, length, label): ''' label a named object identified within a namespace. gl.object_label labels the object identified by name within the namespace given by identifier. identifier must be one of gl.BUFFER, gl.SHADER, gl.PROGRAM, gl.VERTEX_ARRAY, gl.QUERY, gl.PROGRAM_PIPELINE, gl.TRANSFORM_FEEDBACK, gl.SAMPLER, gl.TEXTURE, gl.RENDERBUFFER, gl.FRAMEBUFFER, to indicate the namespace containing the names of buffers, shaders, programs, vertex array objects, query objects, program pipelines, transform feedback objects, samplers, textures, renderbuffers and frame buffers, respectively. Args: identifier: the namespace from which the name of the object is allocated. name: the name of the object to label. length: the length of the label to be used for the object. label: the address of a string containing the label to assign to the object. ''' @accepts(t.enum, t.uint, t.sizei, POINTER(t.sizei), t.char_p) @returns(t.void) @binds(dll) def get_object_label(identifier, name, bufsize, length, label): ''' retrieve the label of a named object identified within a namespace. gl.get_object_label retrieves the label of the object identified by name within the namespace given by identifier. identifier must be one of gl.BUFFER, gl.SHADER, gl.PROGRAM, gl.VERTEX_ARRAY, gl.QUERY, gl.PROGRAM_PIPELINE, gl.TRANSFORM_FEEDBACK, gl.SAMPLER, gl.TEXTURE, gl.RENDERBUFFER, gl.FRAMEBUFFER, to indicate the namespace containing the names of buffers, shaders, programs, vertex array objects, query objects, program pipelines, transform feedback objects, samplers, textures, renderbuffers and frame buffers, respectively. Args: identifier: the namespace from which the name of the object is allocated. name: the name of the object whose label to retrieve. length: the address of a variable to receive the length of the object label. label: the address of a string that will receive the object label. ''' @accepts(t.void, t.sizei, t.char_p) @returns(t.void) @binds(dll) def object_ptr_label(ptr, length, label): ''' label a a sync object identified by a pointer. gl.object_ptr_label labels the sync object identified by ptr. Args: ptr: a pointer identifying a sync object. length: the length of the label to be used for the object. label: the address of a string containing the label to assign to the object. ''' @accepts(t.void, t.sizei, POINTER(t.sizei), t.char_p) @returns(t.void) @binds(dll) def get_object_ptr_label(ptr, bufsize, length, label): ''' retrieve the label of a sync object identified by a pointer. gl.get_object_ptr_label retrieves the label of the sync object identified by ptr. Args: ptr: the name of the sync object whose label to retrieve. length: the address of a variable to receive the length of the object label. label: the address of a string that will receive the object label. ''' @accepts(t.enum, t.void) @returns(t.void) @binds(dll) def get_pointerv(pname, params): ''' return the address of the specified pointer. gl.get_pointerv returns pointer information. pname indicates the pointer to be returned, and params is a pointer to a location in which to place the returned data. Args: pname: the pointer to be returned. params: returns the pointer value specified by pname. ''' DISPLAY_LIST = 0x82E7 @accepts(t.enum, t.void) @returns(t.void) @binds(dll) def get_pointerv(pname, params): ''' return the address of the specified pointer. gl.get_pointerv returns pointer information. pname indicates the pointer to be returned, and params is a pointer to a location in which to place the returned data. Args: pname: the pointer to be returned. params: returns the pointer value specified by pname. ''' STACK_UNDERFLOW = 0x0504 STACK_OVERFLOW = 0x0503

39.732811

151

0.7412

8a7d81f9fd3f30534398ff05abd7412a6f78b709

4,035

py

Python

MarkReport/MarkReport.py

dedukun/MarkReport

2d92c87a69db5868d14b7a59e815b9ee72d439f9

[ "MIT" ]

null

MarkReport/MarkReport.py

dedukun/MarkReport

2d92c87a69db5868d14b7a59e815b9ee72d439f9

[ "MIT" ]

null

MarkReport/MarkReport.py

dedukun/MarkReport

2d92c87a69db5868d14b7a59e815b9ee72d439f9

[ "MIT" ]

null

#!/usr/bin/env python3 # Command line flags import os import glob import re import pyinotify import subprocess from sys import stdout, stderr from time import time, sleep from tempfile import gettempdir from distutils.dir_util import copy_tree from shutil import copyfile from weasyprint import HTML import argparse parser = argparse.ArgumentParser( description='Converts Markdown to elegant PDF reports') parser.add_argument('--basic', dest='basic', action='store_true', help='Do not enrich HTML with LaTeX and syntax highlighting (faster builds)') parser.add_argument('--watch', dest='watch', action='store_true', help='Watch the current folder for changes and rebuild automatically') parser.add_argument('--quiet', dest='quiet', action='store_true', help='Do not output any information') parser.add_argument("--timeout", type=int, default=2, help='Page generation timeout') parser.add_argument("--base-html", type=str, default="", help='The path to the base HTML file') parser.set_defaults(watch=False) args = parser.parse_args() # Check directory ok = False for file in os.listdir("."): if file.endswith(".md"): ok = True break if not ok: stderr.write("No markdown file found in the current folder") exit(1) if args.base_html != "": if not os.path.isfile(args.base_html): stderr.write("The given base HTML file doesn't exist") exit(1) script_path = os.path.dirname(os.path.realpath(__file__)) # Temp dir timestamp = str(int(time())) tmp_dir = gettempdir() + "/" + timestamp + "_md-report/" os.makedirs(tmp_dir, exist_ok=True) # Headless browser if not args.basic: from selenium import webdriver from selenium.webdriver.firefox.options import Options from selenium.webdriver.common.desired_capabilities import DesiredCapabilities options = Options() options.headless = True options.log.level = "trace" d = DesiredCapabilities.FIREFOX d['loggingPrefs'] = {'browser': 'ALL'} driver = webdriver.Firefox(options=options, capabilities=d) driver.set_page_load_timeout(args.timeout) prev_compile_time = 0 def recompile(notifier): if notifier is not None and (notifier.maskname != "IN_MODIFY" or notifier.pathname.endswith(".pdf")): return global prev_compile_time if time() - prev_compile_time < 1: return prev_compile_time = time() if not args.quiet: stdout.write("\rBuilding the PDF file...") stdout.flush() files = glob.glob(tmp_dir + '/*.md') for f in files: os.remove(f) if args.base_html == "": copyfile(script_path + "/base.html", tmp_dir + "/base.html") else: copyfile(args.base_html, tmp_dir + "/base.html") if not os.path.islink(tmp_dir + "/src"): os.symlink(script_path + "/src", tmp_dir + "/src") copy_tree(".", tmp_dir) # Markdown parsing subprocess.check_output(script_path + "/md-parsing " + tmp_dir, shell=True).decode('utf-8') html_file_name = tmp_dir + "output.html" # Interpret JS code if not args.basic: driver.get("file:///" + html_file_name) sleep(2) elem = driver.find_element_by_xpath("//*") interpreted_html = elem.get_attribute("outerHTML") with open(html_file_name, "w") as html_out_file: html_out_file.write(interpreted_html) # Create final PDF file pdf = HTML(html_file_name).write_pdf() f = open("output.pdf", 'wb') f.write(pdf) if not args.quiet: stdout.write("\rDone. ") stdout.flush() recompile(None) if not args.watch: if not args.basic: driver.quit() exit(0) watch_manager = pyinotify.WatchManager() event_notifier = pyinotify.Notifier(watch_manager, recompile) watch_manager.add_watch(os.path.abspath("."), pyinotify.ALL_EVENTS, rec=True) event_notifier.loop() if not args.basic: driver.quit()

27.827586

105

0.662949

efca9e6f2e6b4e27221c8a6c5a0028b7a586d7e2

16,097

py

Python

sdk/python/pulumi_aws/ec2/ami.py

johnktims/pulumi-aws

c838bc79043f5376c66fc66275a1e012edd3ab7d

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_aws/ec2/ami.py

johnktims/pulumi-aws

c838bc79043f5376c66fc66275a1e012edd3ab7d

[ "ECL-2.0", "Apache-2.0" ]

null

sdk/python/pulumi_aws/ec2/ami.py

johnktims/pulumi-aws

c838bc79043f5376c66fc66275a1e012edd3ab7d

[ "ECL-2.0", "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import json import warnings import pulumi import pulumi.runtime from typing import Union from .. import utilities, tables class Ami(pulumi.CustomResource): architecture: pulumi.Output[str] """ Machine architecture for created instances. Defaults to "x86_64". """ description: pulumi.Output[str] """ A longer, human-readable description for the AMI. """ ebs_block_devices: pulumi.Output[list] """ Nested block describing an EBS block device that should be attached to created instances. The structure of this block is described below. * `deleteOnTermination` (`bool`) - Boolean controlling whether the EBS volumes created to support each created instance will be deleted once that instance is terminated. * `device_name` (`str`) - The path at which the device is exposed to created instances. * `encrypted` (`bool`) - Boolean controlling whether the created EBS volumes will be encrypted. Can't be used with `snapshot_id`. * `iops` (`float`) - Number of I/O operations per second the created volumes will support. * `snapshot_id` (`str`) - The id of an EBS snapshot that will be used to initialize the created EBS volumes. If set, the `volume_size` attribute must be at least as large as the referenced snapshot. * `volume_size` (`float`) - The size of created volumes in GiB. If `snapshot_id` is set and `volume_size` is omitted then the volume will have the same size as the selected snapshot. * `volumeType` (`str`) - The type of EBS volume to create. Can be one of "standard" (the default), "io1" or "gp2". """ ena_support: pulumi.Output[bool] """ Specifies whether enhanced networking with ENA is enabled. Defaults to `false`. """ ephemeral_block_devices: pulumi.Output[list] """ Nested block describing an ephemeral block device that should be attached to created instances. The structure of this block is described below. * `device_name` (`str`) - The path at which the device is exposed to created instances. * `virtualName` (`str`) - A name for the ephemeral device, of the form "ephemeralN" where *N* is a volume number starting from zero. """ image_location: pulumi.Output[str] """ Path to an S3 object containing an image manifest, e.g. created by the `ec2-upload-bundle` command in the EC2 command line tools. """ kernel_id: pulumi.Output[str] """ The id of the kernel image (AKI) that will be used as the paravirtual kernel in created instances. """ manage_ebs_snapshots: pulumi.Output[bool] name: pulumi.Output[str] """ A region-unique name for the AMI. """ ramdisk_id: pulumi.Output[str] """ The id of an initrd image (ARI) that will be used when booting the created instances. """ root_device_name: pulumi.Output[str] """ The name of the root device (for example, `/dev/sda1`, or `/dev/xvda`). """ root_snapshot_id: pulumi.Output[str] """ The Snapshot ID for the root volume (for EBS-backed AMIs) """ sriov_net_support: pulumi.Output[str] """ When set to "simple" (the default), enables enhanced networking for created instances. No other value is supported at this time. """ tags: pulumi.Output[dict] """ A mapping of tags to assign to the resource. """ virtualization_type: pulumi.Output[str] """ Keyword to choose what virtualization mode created instances will use. Can be either "paravirtual" (the default) or "hvm". The choice of virtualization type changes the set of further arguments that are required, as described below. """ def __init__(__self__, resource_name, opts=None, architecture=None, description=None, ebs_block_devices=None, ena_support=None, ephemeral_block_devices=None, image_location=None, kernel_id=None, name=None, ramdisk_id=None, root_device_name=None, sriov_net_support=None, tags=None, virtualization_type=None, __props__=None, __name__=None, __opts__=None): """ The AMI resource allows the creation and management of a completely-custom *Amazon Machine Image* (AMI). If you just want to duplicate an existing AMI, possibly copying it to another region, it's better to use `ec2.AmiCopy` instead. If you just want to share an existing AMI with another AWS account, it's better to use `ec2.AmiLaunchPermission` instead. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] architecture: Machine architecture for created instances. Defaults to "x86_64". :param pulumi.Input[str] description: A longer, human-readable description for the AMI. :param pulumi.Input[list] ebs_block_devices: Nested block describing an EBS block device that should be attached to created instances. The structure of this block is described below. :param pulumi.Input[bool] ena_support: Specifies whether enhanced networking with ENA is enabled. Defaults to `false`. :param pulumi.Input[list] ephemeral_block_devices: Nested block describing an ephemeral block device that should be attached to created instances. The structure of this block is described below. :param pulumi.Input[str] image_location: Path to an S3 object containing an image manifest, e.g. created by the `ec2-upload-bundle` command in the EC2 command line tools. :param pulumi.Input[str] kernel_id: The id of the kernel image (AKI) that will be used as the paravirtual kernel in created instances. :param pulumi.Input[str] name: A region-unique name for the AMI. :param pulumi.Input[str] ramdisk_id: The id of an initrd image (ARI) that will be used when booting the created instances. :param pulumi.Input[str] root_device_name: The name of the root device (for example, `/dev/sda1`, or `/dev/xvda`). :param pulumi.Input[str] sriov_net_support: When set to "simple" (the default), enables enhanced networking for created instances. No other value is supported at this time. :param pulumi.Input[dict] tags: A mapping of tags to assign to the resource. :param pulumi.Input[str] virtualization_type: Keyword to choose what virtualization mode created instances will use. Can be either "paravirtual" (the default) or "hvm". The choice of virtualization type changes the set of further arguments that are required, as described below. The **ebs_block_devices** object supports the following: * `deleteOnTermination` (`pulumi.Input[bool]`) - Boolean controlling whether the EBS volumes created to support each created instance will be deleted once that instance is terminated. * `device_name` (`pulumi.Input[str]`) - The path at which the device is exposed to created instances. * `encrypted` (`pulumi.Input[bool]`) - Boolean controlling whether the created EBS volumes will be encrypted. Can't be used with `snapshot_id`. * `iops` (`pulumi.Input[float]`) - Number of I/O operations per second the created volumes will support. * `snapshot_id` (`pulumi.Input[str]`) - The id of an EBS snapshot that will be used to initialize the created EBS volumes. If set, the `volume_size` attribute must be at least as large as the referenced snapshot. * `volume_size` (`pulumi.Input[float]`) - The size of created volumes in GiB. If `snapshot_id` is set and `volume_size` is omitted then the volume will have the same size as the selected snapshot. * `volumeType` (`pulumi.Input[str]`) - The type of EBS volume to create. Can be one of "standard" (the default), "io1" or "gp2". The **ephemeral_block_devices** object supports the following: * `device_name` (`pulumi.Input[str]`) - The path at which the device is exposed to created instances. * `virtualName` (`pulumi.Input[str]`) - A name for the ephemeral device, of the form "ephemeralN" where *N* is a volume number starting from zero. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['architecture'] = architecture __props__['description'] = description __props__['ebs_block_devices'] = ebs_block_devices __props__['ena_support'] = ena_support __props__['ephemeral_block_devices'] = ephemeral_block_devices __props__['image_location'] = image_location __props__['kernel_id'] = kernel_id __props__['name'] = name __props__['ramdisk_id'] = ramdisk_id __props__['root_device_name'] = root_device_name __props__['sriov_net_support'] = sriov_net_support __props__['tags'] = tags __props__['virtualization_type'] = virtualization_type __props__['manage_ebs_snapshots'] = None __props__['root_snapshot_id'] = None super(Ami, __self__).__init__( 'aws:ec2/ami:Ami', resource_name, __props__, opts) @staticmethod def get(resource_name, id, opts=None, architecture=None, description=None, ebs_block_devices=None, ena_support=None, ephemeral_block_devices=None, image_location=None, kernel_id=None, manage_ebs_snapshots=None, name=None, ramdisk_id=None, root_device_name=None, root_snapshot_id=None, sriov_net_support=None, tags=None, virtualization_type=None): """ Get an existing Ami resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param str id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] architecture: Machine architecture for created instances. Defaults to "x86_64". :param pulumi.Input[str] description: A longer, human-readable description for the AMI. :param pulumi.Input[list] ebs_block_devices: Nested block describing an EBS block device that should be attached to created instances. The structure of this block is described below. :param pulumi.Input[bool] ena_support: Specifies whether enhanced networking with ENA is enabled. Defaults to `false`. :param pulumi.Input[list] ephemeral_block_devices: Nested block describing an ephemeral block device that should be attached to created instances. The structure of this block is described below. :param pulumi.Input[str] image_location: Path to an S3 object containing an image manifest, e.g. created by the `ec2-upload-bundle` command in the EC2 command line tools. :param pulumi.Input[str] kernel_id: The id of the kernel image (AKI) that will be used as the paravirtual kernel in created instances. :param pulumi.Input[str] name: A region-unique name for the AMI. :param pulumi.Input[str] ramdisk_id: The id of an initrd image (ARI) that will be used when booting the created instances. :param pulumi.Input[str] root_device_name: The name of the root device (for example, `/dev/sda1`, or `/dev/xvda`). :param pulumi.Input[str] root_snapshot_id: The Snapshot ID for the root volume (for EBS-backed AMIs) :param pulumi.Input[str] sriov_net_support: When set to "simple" (the default), enables enhanced networking for created instances. No other value is supported at this time. :param pulumi.Input[dict] tags: A mapping of tags to assign to the resource. :param pulumi.Input[str] virtualization_type: Keyword to choose what virtualization mode created instances will use. Can be either "paravirtual" (the default) or "hvm". The choice of virtualization type changes the set of further arguments that are required, as described below. The **ebs_block_devices** object supports the following: * `deleteOnTermination` (`pulumi.Input[bool]`) - Boolean controlling whether the EBS volumes created to support each created instance will be deleted once that instance is terminated. * `device_name` (`pulumi.Input[str]`) - The path at which the device is exposed to created instances. * `encrypted` (`pulumi.Input[bool]`) - Boolean controlling whether the created EBS volumes will be encrypted. Can't be used with `snapshot_id`. * `iops` (`pulumi.Input[float]`) - Number of I/O operations per second the created volumes will support. * `snapshot_id` (`pulumi.Input[str]`) - The id of an EBS snapshot that will be used to initialize the created EBS volumes. If set, the `volume_size` attribute must be at least as large as the referenced snapshot. * `volume_size` (`pulumi.Input[float]`) - The size of created volumes in GiB. If `snapshot_id` is set and `volume_size` is omitted then the volume will have the same size as the selected snapshot. * `volumeType` (`pulumi.Input[str]`) - The type of EBS volume to create. Can be one of "standard" (the default), "io1" or "gp2". The **ephemeral_block_devices** object supports the following: * `device_name` (`pulumi.Input[str]`) - The path at which the device is exposed to created instances. * `virtualName` (`pulumi.Input[str]`) - A name for the ephemeral device, of the form "ephemeralN" where *N* is a volume number starting from zero. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["architecture"] = architecture __props__["description"] = description __props__["ebs_block_devices"] = ebs_block_devices __props__["ena_support"] = ena_support __props__["ephemeral_block_devices"] = ephemeral_block_devices __props__["image_location"] = image_location __props__["kernel_id"] = kernel_id __props__["manage_ebs_snapshots"] = manage_ebs_snapshots __props__["name"] = name __props__["ramdisk_id"] = ramdisk_id __props__["root_device_name"] = root_device_name __props__["root_snapshot_id"] = root_snapshot_id __props__["sriov_net_support"] = sriov_net_support __props__["tags"] = tags __props__["virtualization_type"] = virtualization_type return Ami(resource_name, opts=opts, __props__=__props__) def translate_output_property(self, prop): return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

58.322464

357

0.68373

3dbf1841410630bd6fac85dc89f71eeb66d73349

11,039

py

Python

lemur/tests/conf.py

dck25/lemur

32dda00f9fb4b72091bb3f4e7be1d262ea5fd857

[ "Apache-2.0" ]

1,656

2015-09-20T03:12:28.000Z

2022-03-29T18:00:54.000Z

lemur/tests/conf.py

dck25/lemur

32dda00f9fb4b72091bb3f4e7be1d262ea5fd857

[ "Apache-2.0" ]

3,017

2015-09-18T23:15:24.000Z

2022-03-30T22:40:02.000Z

lemur/tests/conf.py

hosseinsh/lemur

fbf50b365cb0f7a0e9cae31dec1b853b958c45bb

[ "Apache-2.0" ]

401

2015-09-18T23:02:18.000Z

2022-02-20T16:13:14.000Z

# This is just Python which means you can inherit and tweak settings import base64 import os import random import string _basedir = os.path.abspath(os.path.dirname(__file__)) # generate random secrets for unittest def get_random_secret(length): secret_key = ''.join(random.choice(string.ascii_uppercase) for x in range(round(length / 4))) secret_key = secret_key + ''.join(random.choice("~!@#$%^&*()_+") for x in range(round(length / 4))) secret_key = secret_key + ''.join(random.choice(string.ascii_lowercase) for x in range(round(length / 4))) return secret_key + ''.join(random.choice(string.digits) for x in range(round(length / 4))) THREADS_PER_PAGE = 8 # General # These will need to be set to `True` if you are developing locally CORS = False debug = False TESTING = True # this is the secret key used by flask session management (utf8 encoded) SECRET_KEY = get_random_secret(length=32).encode('utf8') # You should consider storing these separately from your config (should be URL-safe) LEMUR_TOKEN_SECRET = "test" LEMUR_ENCRYPTION_KEYS = base64.urlsafe_b64encode(get_random_secret(length=32).encode('utf8')) # this is the secret used to generate oauth state tokens OAUTH_STATE_TOKEN_SECRET = base64.b64encode(get_random_secret(32).encode('utf8')) OAUTH_STATE_TOKEN_STALE_TOLERANCE_SECONDS = 15 # List of domain regular expressions that non-admin users can issue LEMUR_ALLOWED_DOMAINS = [ r"^[a-zA-Z0-9-]+\.example\.com$", r"^[a-zA-Z0-9-]+\.example\.org$", r"^example\d+\.long\.com$", ] # Mail Server # Lemur currently only supports SES for sending email, this address # needs to be verified LEMUR_EMAIL = "lemur@example.com" LEMUR_SECURITY_TEAM_EMAIL = ["security@example.com"] LEMUR_HOSTNAME = "lemur.example.com" # Logging LOG_LEVEL = "DEBUG" LOG_FILE = "lemur.log" LEMUR_DEFAULT_COUNTRY = "US" LEMUR_DEFAULT_STATE = "California" LEMUR_DEFAULT_LOCATION = "Los Gatos" LEMUR_DEFAULT_ORGANIZATION = "Example, Inc." LEMUR_DEFAULT_ORGANIZATIONAL_UNIT = "Example" LEMUR_ALLOW_WEEKEND_EXPIRATION = False # needed for test_certificates LEMUR_PORTS_FOR_DEPLOYED_CERTIFICATE_CHECK = [443, 65521, 65522, 65523, 65524] # needed for test_messaging LEMUR_REISSUE_NOTIFICATION_EXCLUDED_DESTINATIONS = ['excluded-destination'] # Database # modify this if you are not using a local database. Do not use any development or production DBs, # as Unit Tests drop the whole schema, recreate and again drop everything at the end SQLALCHEMY_DATABASE_URI = os.getenv( "SQLALCHEMY_DATABASE_URI", "postgresql://lemur:lemur@localhost:5432/lemur" ) SQLALCHEMY_TRACK_MODIFICATIONS = False # AWS LEMUR_INSTANCE_PROFILE = "Lemur" # Issuers # These will be dependent on which 3rd party that Lemur is # configured to use. # CLOUDCA_URL = '' # CLOUDCA_PEM_PATH = '' # CLOUDCA_BUNDLE = '' # number of years to issue if not specified # CLOUDCA_DEFAULT_VALIDITY = 2 DIGICERT_URL = "mock://www.digicert.com" DIGICERT_ORDER_TYPE = "ssl_plus" DIGICERT_API_KEY = "api-key" DIGICERT_ORG_ID = 111111 DIGICERT_ROOT = "ROOT" DIGICERT_CIS_URL = "mock://www.digicert.com" DIGICERT_CIS_PROFILE_NAMES = {"sha2-rsa-ecc-root": "ssl_plus"} DIGICERT_CIS_API_KEY = "api-key" DIGICERT_CIS_ROOTS = {"root": "ROOT"} VERISIGN_URL = "http://example.com" VERISIGN_PEM_PATH = "~/" VERISIGN_FIRST_NAME = "Jim" VERISIGN_LAST_NAME = "Bob" VERSIGN_EMAIL = "jim@example.com" ACME_AWS_ACCOUNT_NUMBER = "11111111111" ACME_PRIVATE_KEY = """ -----BEGIN RSA PRIVATE KEY----- MIIJJwIBAAKCAgEA0+jySNCc1i73LwDZEuIdSkZgRYQ4ZQVIioVf38RUhDElxy51 4gdWZwp8/TDpQ8cVXMj6QhdRpTVLluOz71hdvBAjxXTISRCRlItzizTgBD9CLXRh vPLIMPvAJH7JZxp9xW5oVYUcHBveQJ5tQvnP7RgPykejl7DPKm/SGKYealnoGPcP U9ipz2xXlVlx7ZKivLbaijh2kD/QE9pC//CnP31g3QFCsxOTLAWtICz5VbvaWuTT whqFs5cT3kKYAW/ccPcty573AX/9Y/UZ4+B3wxXY3/6GYPMcINRuu/7Srs3twlNu udoTNdM9SztWMYUzz1SMYad9v9LLGTrv+5Tog4YsqMFxyKrBBBz8/bf1lKwyfAW+ okvVe+1bUY8iSDuDx1O0iMyHe5w8lxsoTy91ujjr1cQDyJR70TKQpeBmfNtBVnW+ D8E6Xw2yCuL9XTyBApldzQ/J1ObPd1Hv+yzhEx4VD9QOmQPn7doiapTDYfW51o1O Mo+zuZgsclhePvzqN4/6VYXZnPE68uqx982u0W82tCorRUtzfFoO0plNRCjmV7cw 0fp0ie3VczUOH9gj4emmdQd1tVA/Esuh3XnzZ2ANwohtPytn+I3MX0Q+5k7AcRlt AyI80x8CSiDStI6pj3BlPJgma9G8u7r3E2aqW6qXCexElTCaH2t8A7JWI80CAwEA AQKCAgBDXLyQGwiQKXPYFDvs/cXz03VNA9/tdQV/SzCT8FQxhXIN5B4DEPQNY08i KUctjX6j9RtgoQsKKmvx9kY/omaBntvQK/RzDXpJrx62tMM1dmpyCpn7N24d7BlD QK6DQO+UMCmobdzmrpEzF2mCLelD5C84zRca5FCmm888mKn4gsX+EaNksu4gCr+4 sSs/KyriNHo6EALYjgB2Hx7HP1fbHd8JwhnS1TkmeFN1c/Z6o3GhDTancEjqMu9U 6vRpGIcJvflnzguVBXumJ8boInXPpQVBBybucLmTUhQ1XKbafInFCUKcf881gAXv AVi/+yjiEm1hqZ2WucpoJc0du1NBz/MP+/MxHGQ/5eaEMIz5X2QcXzQ4xn5ym0sk Hy0SmH3v/9by1GkK5eH/RTV/8bmtb8Qt0+auLQ6/ummFDjPw866Or4FdL3tx2gug fONjaZqypee+EmlLG1UmMejjCblmh0bymAHnFkf7tAJsLGd8I00PQiObEqaqd03o xiYUvrbDpCHah4gB7Uv3AgrHVTbcHsEWmXuNDooD0sSXCFMf3cA81M8vGfkypqi/ ixxZtxtdTU5oCFwI9zEjnQvdA1IZMUAmz8vLwn/fKgENek9PAV3voQr1c0ctZPvy S/k7HgJt+2Wj7Pqb4mwPgxeYVSBEM7ygOq6Gdisyhi8DP0A2fQKCAQEA6iIrSqQM pVDqhQsk9Cc0b4kdsG/EM66M7ND5Q2GLiPPFrR59Hm7ViG6h2DhwqSnSRigiO+TN jIuvD/O0kbmCUZSar19iKPiJipENN+AX3MBm1cS5Oxp6jgY+3jj4KgDQPYmL49fJ CojnmLKjrAPoUi4f/7s4O1rEAghXPrf5/9coaRPORiNi+bZK0bReJwf1GE/9CPqs FiZrQNz+/w/1MwFisG6+g0/58fp9j9r6l8JXETjpyO5F+8W8bg8M4V7aoYt5Ec2X +BG6Gq06Tvm2UssYa6iEVNSKF39ssBzKKALi4we/fcfwjq4bCTKMCjV0Tp3zY/FG 1VyDtMGKrlPnOwKCAQEA57Nw+qdh2wbihz1uKffcoDoW6Q3Ws0mu8ml+UvBn48Ur 41PKrvIb8lhVY7ZiF2/iRyodua9ztE4zvgGs7UqyHaSYHR+3mWeOAE2Hb/XiNVgu JVupTXLpx3y7d9FxvrU/27KUxhJgcbVpIGRiMn5dmY2S86EYKX1ObjZKmwvFc6+n 1YWgtI2+VOKe5+0ttig6CqzL9qJLZfL6QeAy0yTp/Wz+G1c06XTL87QNeU7CXN00 rB7I4n1Xn422rZnE64MOsARVChyE2fUC9syfimoryR9yIL2xor9QdjL2tK6ziyPq WgedY4bDjZLM5KbcHcRng0j5WCJV+pX9Hh1c4n5AlwKCAQAxjun68p56n5YEc0dv Jp1CvpM6NW4iQmAyAEnCqXMPmgnNixaQyoUIS+KWEdxG8kM/9l7IrrWTej2j8sHV 1p5vBjV3yYjNg04ZtnpFyXlDkLYzqWBL0l7+kPPdtdFRkrqBTAwAPjyfrjrXZ3id gHY8bub3CnnsllnG1F0jOW4BaVl0ZGzVC8h3cs6DdNo5CMYoT0YQEH88cQVixWR0 OLx9/10UW1yYDuWpAoxxVriURt6HFrTlgwntMP2hji37xkggyZTm3827BIWP//rH nLOq8rJIl3LrQdG5B4/J904TCglcZNdzmE6i5Nd0Ku7ZelcUDPrnvLpxjxORvyXL oJbhAoIBAD7QV9WsIQxG7oypa7828foCJYni9Yy/cg1H6jZD9HY8UuybH7yT6F2n 8uZIYIloDJksYsifNyfvd3mQbLgb4vPEVnS2z4hoGYgdfJUuvLeng0MfeWOEvroV J6GRB1wjOP+vh0O3YawR+UEN1c1Iksl5JxijWLCOxv97+nfUFiCJw19QjcPFFY9f rKLFmvniJ/IS7GydjQFDgPLw+/Zf8IuCy9TPrImJ32zfKDP11R1l3sy2v9EfF+0q dxbTNB6A9i9jzUYjeyS3lqkfyjS1Gc+5lbAonQq5APA6WsWbAxO6leL4Y4PC2ir8 XE20qsHrKADgfLCXBmYb2XYbkb3ZalsCggEAfOuB9/eLMSmtney3vDdZNF8fvEad DF+8ss8yITNQQuC0nGdXioRuvSyejOxtjHplMT5GXsgLp1vAujDQmGTv/jK+EXsU cRe4df5/EbRiUOyx/ZBepttB1meTnsH6cGPN0JnmTMQHQvanL3jjtjrC13408ONK 1yK2S4xJjKYFLT86SjKvV6g5k49ntLYk59nviqHl8bYzAVMoEjb62Z+hERwd/2hx omsEEjDt4qVqGvSyy+V/1EhqGPzm9ri3zapnorf69rscuXYYsMBZ8M6AtSio4ldB LjCRNS1lR6/mV8AqUNR9Kn2NLQyJ76yDoEVLulKZqGUsC9STN4oGJLUeFw== -----END RSA PRIVATE KEY----- """ ACME_ROOT = """ -----BEGIN CERTIFICATE----- MIIFjTCCA3WgAwIBAgIRANOxciY0IzLc9AUoUSrsnGowDQYJKoZIhvcNAQELBQAw TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh cmNoIEdyb3VwMRUwEwYDVQQDEwxJU1JHIFJvb3QgWDEwHhcNMTYxMDA2MTU0MzU1 WhcNMjExMDA2MTU0MzU1WjBKMQswCQYDVQQGEwJVUzEWMBQGA1UEChMNTGV0J3Mg RW5jcnlwdDEjMCEGA1UEAxMaTGV0J3MgRW5jcnlwdCBBdXRob3JpdHkgWDMwggEi MA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQCc0wzwWuUuR7dyXTeDs2hjMOrX NSYZJeG9vjXxcJIvt7hLQQWrqZ41CFjssSrEaIcLo+N15Obzp2JxunmBYB/XkZqf 89B4Z3HIaQ6Vkc/+5pnpYDxIzH7KTXcSJJ1HG1rrueweNwAcnKx7pwXqzkrrvUHl Npi5y/1tPJZo3yMqQpAMhnRnyH+lmrhSYRQTP2XpgofL2/oOVvaGifOFP5eGr7Dc Gu9rDZUWfcQroGWymQQ2dYBrrErzG5BJeC+ilk8qICUpBMZ0wNAxzY8xOJUWuqgz uEPxsR/DMH+ieTETPS02+OP88jNquTkxxa/EjQ0dZBYzqvqEKbbUC8DYfcOTAgMB AAGjggFnMIIBYzAOBgNVHQ8BAf8EBAMCAYYwEgYDVR0TAQH/BAgwBgEB/wIBADBU BgNVHSAETTBLMAgGBmeBDAECATA/BgsrBgEEAYLfEwEBATAwMC4GCCsGAQUFBwIB FiJodHRwOi8vY3BzLnJvb3QteDEubGV0c2VuY3J5cHQub3JnMB0GA1UdDgQWBBSo SmpjBH3duubRObemRWXv86jsoTAzBgNVHR8ELDAqMCigJqAkhiJodHRwOi8vY3Js LnJvb3QteDEubGV0c2VuY3J5cHQub3JnMHIGCCsGAQUFBwEBBGYwZDAwBggrBgEF BQcwAYYkaHR0cDovL29jc3Aucm9vdC14MS5sZXRzZW5jcnlwdC5vcmcvMDAGCCsG AQUFBzAChiRodHRwOi8vY2VydC5yb290LXgxLmxldHNlbmNyeXB0Lm9yZy8wHwYD VR0jBBgwFoAUebRZ5nu25eQBc4AIiMgaWPbpm24wDQYJKoZIhvcNAQELBQADggIB ABnPdSA0LTqmRf/Q1eaM2jLonG4bQdEnqOJQ8nCqxOeTRrToEKtwT++36gTSlBGx A/5dut82jJQ2jxN8RI8L9QFXrWi4xXnA2EqA10yjHiR6H9cj6MFiOnb5In1eWsRM UM2v3e9tNsCAgBukPHAg1lQh07rvFKm/Bz9BCjaxorALINUfZ9DD64j2igLIxle2 DPxW8dI/F2loHMjXZjqG8RkqZUdoxtID5+90FgsGIfkMpqgRS05f4zPbCEHqCXl1 eO5HyELTgcVlLXXQDgAWnRzut1hFJeczY1tjQQno6f6s+nMydLN26WuU4s3UYvOu OsUxRlJu7TSRHqDC3lSE5XggVkzdaPkuKGQbGpny+01/47hfXXNB7HntWNZ6N2Vw p7G6OfY+YQrZwIaQmhrIqJZuigsrbe3W+gdn5ykE9+Ky0VgVUsfxo52mwFYs1JKY 2PGDuWx8M6DlS6qQkvHaRUo0FMd8TsSlbF0/v965qGFKhSDeQoMpYnwcmQilRh/0 ayLThlHLN81gSkJjVrPI0Y8xCVPB4twb1PFUd2fPM3sA1tJ83sZ5v8vgFv2yofKR PB0t6JzUA81mSqM3kxl5e+IZwhYAyO0OTg3/fs8HqGTNKd9BqoUwSRBzp06JMg5b rUCGwbCUDI0mxadJ3Bz4WxR6fyNpBK2yAinWEsikxqEt -----END CERTIFICATE----- """ ACME_URL = "https://acme-v01.api.letsencrypt.org" ACME_EMAIL = "jim@example.com" ACME_TEL = "4088675309" ACME_DIRECTORY_URL = "https://acme-v01.api.letsencrypt.org" ACME_DISABLE_AUTORESOLVE = True ACME_PREFERRED_ISSUER = "R3" LDAP_AUTH = True LDAP_BIND_URI = "ldap://localhost" LDAP_BASE_DN = "dc=example,dc=com" LDAP_EMAIL_DOMAIN = "example.com" LDAP_REQUIRED_GROUP = "Lemur Access" LDAP_DEFAULT_ROLE = "role1" ALLOW_CERT_DELETION = True ENTRUST_API_CERT = "api-cert" ENTRUST_API_KEY = get_random_secret(32) ENTRUST_API_USER = "user" ENTRUST_API_PASS = get_random_secret(32) ENTRUST_URL = "https://api.entrust.net/enterprise/v2" ENTRUST_ROOT = """ -----BEGIN CERTIFICATE----- MIIEPjCCAyagAwIBAgIESlOMKDANBgkqhkiG9w0BAQsFADCBvjELMAkGA1UEBhMC VVMxFjAUBgNVBAoTDUVudHJ1c3QsIEluYy4xKDAmBgNVBAsTH1NlZSB3d3cuZW50 cnVzdC5uZXQvbGVnYWwtdGVybXMxOTA3BgNVBAsTMChjKSAyMDA5IEVudHJ1c3Qs IEluYy4gLSBmb3IgYXV0aG9yaXplZCB1c2Ugb25seTEyMDAGA1UEAxMpRW50cnVz dCBSb290IENlcnRpZmljYXRpb24gQXV0aG9yaXR5IC0gRzIwHhcNMDkwNzA3MTcy NTU0WhcNMzAxMjA3MTc1NTU0WjCBvjELMAkGA1UEBhMCVVMxFjAUBgNVBAoTDUVu dHJ1c3QsIEluYy4xKDAmBgNVBAsTH1NlZSB3d3cuZW50cnVzdC5uZXQvbGVnYWwt dGVybXMxOTA3BgNVBAsTMChjKSAyMDA5IEVudHJ1c3QsIEluYy4gLSBmb3IgYXV0 aG9yaXplZCB1c2Ugb25seTEyMDAGA1UEAxMpRW50cnVzdCBSb290IENlcnRpZmlj YXRpb24gQXV0aG9yaXR5IC0gRzIwggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEK AoIBAQC6hLZy254Ma+KZ6TABp3bqMriVQRrJ2mFOWHLP/vaCeb9zYQYKpSfYs1/T RU4cctZOMvJyig/3gxnQaoCAAEUesMfnmr8SVycco2gvCoe9amsOXmXzHHfV1IWN cCG0szLni6LVhjkCsbjSR87kyUnEO6fe+1R9V77w6G7CebI6C1XiUJgWMhNcL3hW wcKUs/Ja5CeanyTXxuzQmyWC48zCxEXFjJd6BmsqEZ+pCm5IO2/b1BEZQvePB7/1 U1+cPvQXLOZprE4yTGJ36rfo5bs0vBmLrpxR57d+tVOxMyLlbc9wPBr64ptntoP0 jaWvYkxN4FisZDQSA/i2jZRjJKRxAgMBAAGjQjBAMA4GA1UdDwEB/wQEAwIBBjAP BgNVHRMBAf8EBTADAQH/MB0GA1UdDgQWBBRqciZ60B7vfec7aVHUbI2fkBJmqzAN BgkqhkiG9w0BAQsFAAOCAQEAeZ8dlsa2eT8ijYfThwMEYGprmi5ZiXMRrEPR9RP/ jTkrwPK9T3CMqS/qF8QLVJ7UG5aYMzyorWKiAHarWWluBh1+xLlEjZivEtRh2woZ Rkfz6/djwUAFQKXSt/S1mja/qYh2iARVBCuch38aNzx+LaUa2NSJXsq9rD1s2G2v 1fN2D807iDginWyTmsQ9v4IbZT+mD12q/OWyFcq1rca8PdCE6OoGcrBNOTJ4vz4R nAuknZoh8/CbCzB428Hch0P+vGOaysXCHMnHjf87ElgI5rY97HosTvuDls4MPGmH VHOkc8KT/1EQrBVUAdj8BbGJoX90g5pJ19xOe4pIb4tF9g== -----END CERTIFICATE----- """ ENTRUST_NAME = "lemur" ENTRUST_EMAIL = "lemur@example.com" ENTRUST_PHONE = "123456" ENTRUST_ISSUING = "" ENTRUST_PRODUCT_ENTRUST = "ADVANTAGE_SSL"

41.973384

110

0.875079

94ebc71450e564b8f4a69951afee87f0f3a93673

405

py

Python

experiments/fdtd-2d/tmp_files/8010.py

LoopTilingBenchmark/benchmark

52a3d2e70216552a498fd91de02a2fa9cb62122c

[ "BSD-2-Clause" ]

null

experiments/fdtd-2d/tmp_files/8010.py

LoopTilingBenchmark/benchmark

52a3d2e70216552a498fd91de02a2fa9cb62122c

[ "BSD-2-Clause" ]

null

experiments/fdtd-2d/tmp_files/8010.py

LoopTilingBenchmark/benchmark

52a3d2e70216552a498fd91de02a2fa9cb62122c

[ "BSD-2-Clause" ]

null

from chill import * source('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/polybench/polybench-code/stencils/fdtd-2d/kernel.c') destination('/uufs/chpc.utah.edu/common/home/u1142914/lib/ytopt_vinu/experiments/fdtd-2d/tmp_files/8010.c') procedure('kernel_fdtd_2d') loop(0) known(' nx > 1 ') known(' ny > 1 ') tile(1,2,8,2) tile(1,4,8,4) tile(2,2,8,2) tile(2,4,8,4) tile(3,2,8,2) tile(3,4,8,4)

22.5

116

0.716049

f72d894c5dd643cc66f3cf18f2330569c6a1b5c9

6,672

py

Python

bindings/python/ensmallen_graph/datasets/string/pantoearwandensis.py

caufieldjh/ensmallen_graph

14e98b1cdbc73193a84a913d7d4f2b2b3eb2c43a

[ "MIT" ]

null

bindings/python/ensmallen_graph/datasets/string/pantoearwandensis.py

caufieldjh/ensmallen_graph

14e98b1cdbc73193a84a913d7d4f2b2b3eb2c43a

[ "MIT" ]

null

bindings/python/ensmallen_graph/datasets/string/pantoearwandensis.py

caufieldjh/ensmallen_graph

14e98b1cdbc73193a84a913d7d4f2b2b3eb2c43a

[ "MIT" ]

null

""" This file offers the methods to automatically retrieve the graph Pantoea rwandensis. The graph is automatically retrieved from the STRING repository. Report --------------------- At the time of rendering these methods (please see datetime below), the graph had the following characteristics: Datetime: 2021-02-02 22:19:45.558653 The undirected graph Pantoea rwandensis has 3765 nodes and 306976 weighted edges, of which none are self-loops. The graph is dense as it has a density of 0.04332 and has 12 connected components, where the component with most nodes has 3741 nodes and the component with the least nodes has 2 nodes. The graph median node degree is 128, the mean node degree is 163.07, and the node degree mode is 1. The top 5 most central nodes are 1076550.LH22_12305 (degree 1310), 1076550.LH22_16485 (degree 1299), 1076550.LH22_02530 (degree 1292), 1076550.LH22_19995 (degree 1166) and 1076550.LH22_07950 (degree 1066). References --------------------- Please cite the following if you use the data: @article{szklarczyk2019string, title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets}, author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others}, journal={Nucleic acids research}, volume={47}, number={D1}, pages={D607--D613}, year={2019}, publisher={Oxford University Press} } Usage example ---------------------- The usage of this graph is relatively straightforward: .. code:: python # First import the function to retrieve the graph from the datasets from ensmallen_graph.datasets.string import PantoeaRwandensis # Then load the graph graph = PantoeaRwandensis() # Finally, you can do anything with it, for instance, compute its report: print(graph) # If you need to run a link prediction task with validation, # you can split the graph using a connected holdout as follows: train_graph, validation_graph = graph.connected_holdout( # You can use an 80/20 split the holdout, for example. train_size=0.8, # The random state is used to reproduce the holdout. random_state=42, # Wether to show a loading bar. verbose=True ) # Remember that, if you need, you can enable the memory-time trade-offs: train_graph.enable( vector_sources=True, vector_destinations=True, vector_outbounds=True ) # Consider using the methods made available in the Embiggen package # to run graph embedding or link prediction tasks. """ from typing import Dict from ..automatic_graph_retrieval import AutomaticallyRetrievedGraph from ...ensmallen_graph import EnsmallenGraph # pylint: disable=import-error def PantoeaRwandensis( directed: bool = False, verbose: int = 2, cache_path: str = "graphs/string", **additional_graph_kwargs: Dict ) -> EnsmallenGraph: """Return new instance of the Pantoea rwandensis graph. The graph is automatically retrieved from the STRING repository. Parameters ------------------- directed: bool = False, Wether to load the graph as directed or undirected. By default false. verbose: int = 2, Wether to show loading bars during the retrieval and building of the graph. cache_path: str = "graphs", Where to store the downloaded graphs. additional_graph_kwargs: Dict, Additional graph kwargs. Returns ----------------------- Instace of Pantoea rwandensis graph. Report --------------------- At the time of rendering these methods (please see datetime below), the graph had the following characteristics: Datetime: 2021-02-02 22:19:45.558653 The undirected graph Pantoea rwandensis has 3765 nodes and 306976 weighted edges, of which none are self-loops. The graph is dense as it has a density of 0.04332 and has 12 connected components, where the component with most nodes has 3741 nodes and the component with the least nodes has 2 nodes. The graph median node degree is 128, the mean node degree is 163.07, and the node degree mode is 1. The top 5 most central nodes are 1076550.LH22_12305 (degree 1310), 1076550.LH22_16485 (degree 1299), 1076550.LH22_02530 (degree 1292), 1076550.LH22_19995 (degree 1166) and 1076550.LH22_07950 (degree 1066). References --------------------- Please cite the following if you use the data: @article{szklarczyk2019string, title={STRING v11: protein--protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets}, author={Szklarczyk, Damian and Gable, Annika L and Lyon, David and Junge, Alexander and Wyder, Stefan and Huerta-Cepas, Jaime and Simonovic, Milan and Doncheva, Nadezhda T and Morris, John H and Bork, Peer and others}, journal={Nucleic acids research}, volume={47}, number={D1}, pages={D607--D613}, year={2019}, publisher={Oxford University Press} } Usage example ---------------------- The usage of this graph is relatively straightforward: .. code:: python # First import the function to retrieve the graph from the datasets from ensmallen_graph.datasets.string import PantoeaRwandensis # Then load the graph graph = PantoeaRwandensis() # Finally, you can do anything with it, for instance, compute its report: print(graph) # If you need to run a link prediction task with validation, # you can split the graph using a connected holdout as follows: train_graph, validation_graph = graph.connected_holdout( # You can use an 80/20 split the holdout, for example. train_size=0.8, # The random state is used to reproduce the holdout. random_state=42, # Wether to show a loading bar. verbose=True ) # Remember that, if you need, you can enable the memory-time trade-offs: train_graph.enable( vector_sources=True, vector_destinations=True, vector_outbounds=True ) # Consider using the methods made available in the Embiggen package # to run graph embedding or link prediction tasks. """ return AutomaticallyRetrievedGraph( graph_name="PantoeaRwandensis", dataset="string", directed=directed, verbose=verbose, cache_path=cache_path, additional_graph_kwargs=additional_graph_kwargs )()

34.931937

223

0.702938

f46add89112802971419bce983a111ed3621f685

1,297

py

Python

test/cpython/test_xmllib.py

aisk/pyston

ac69cfef0621dbc8901175e84fa2b5cb5781a646

[ "BSD-2-Clause", "Apache-2.0" ]

1

2020-02-06T14:28:45.000Z

test/cpython/test_xmllib.py

aisk/pyston

ac69cfef0621dbc8901175e84fa2b5cb5781a646

[ "BSD-2-Clause", "Apache-2.0" ]

null

test/cpython/test_xmllib.py

aisk/pyston

ac69cfef0621dbc8901175e84fa2b5cb5781a646

[ "BSD-2-Clause", "Apache-2.0" ]

1

2020-02-06T14:29:00.000Z

'''Test module to thest the xmllib module. Sjoerd Mullender ''' testdoc = """\ <?xml version="1.0" encoding="UTF-8" standalone='yes' ?>  <?processing instructions are allowed in the same places as comments ?> <!DOCTYPE greeting [ <!ELEMENT greeting (#PCDATA)> ]> <greeting>Hello, world!</greeting> """ nsdoc = "<foo xmlns='URI' attr='val'/>" from test import test_support import unittest # Silence Py3k warning xmllib = test_support.import_module('xmllib', deprecated=True) class XMLParserTestCase(unittest.TestCase): def test_simple(self): parser = xmllib.XMLParser() for c in testdoc: parser.feed(c) parser.close() def test_default_namespace(self): class H(xmllib.XMLParser): def unknown_starttag(self, name, attr): self.name, self.attr = name, attr h=H() h.feed(nsdoc) h.close() # The default namespace applies to elements... self.assertEqual(h.name, "URI foo") # but not to attributes self.assertEqual(h.attr, {'attr':'val'}) def test_main(): test_support.run_unittest(XMLParserTestCase) if __name__ == "__main__": test_main()

26.469388

71

0.643793

1849d6bbbc33afae0f34ffbeba3eb895383fbf85

1,040

py

Python

web/judge/migrations/0004_auto_20160225_2110.py

jc-hiroto/esoe-oop-judge-github

76e9ba90001d6d3eeee64e9209c50586c5aa766b

[ "MIT" ]

1

2020-03-15T05:47:32.000Z

web/judge/migrations/0004_auto_20160225_2110.py

jc-hiroto/esoe-oop-judge-github

76e9ba90001d6d3eeee64e9209c50586c5aa766b

[ "MIT" ]

3

2020-03-15T06:17:12.000Z

2021-06-10T22:39:28.000Z

web/judge/migrations/0004_auto_20160225_2110.py

jc-hiroto/esoe-oop-judge-github

76e9ba90001d6d3eeee64e9209c50586c5aa766b

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Generated by Django 1.9.2 on 2016-02-25 21:10 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('judge', '0003_auto_20160223_2222'), ] operations = [ migrations.AlterField( model_name='submission', name='running_time', field=models.IntegerField(default=0), ), migrations.AlterField( model_name='submission', name='score', field=models.IntegerField(db_index=True, default=0), ), migrations.AlterField( model_name='submission', name='status', field=models.CharField(choices=[('SU', 'Submitting'), ('SE', 'Submission Error'), ('JU', 'Judging'), ('AC', 'Accepted'), ('PA', 'Partially Accepted'), ('TL', 'Time Limit Exceeded'), ('ML', 'Memory Limit Exceeded'), ('RE', 'Runtime Error'), ('CE', 'Compile Error')], default='SU', max_length=2), ), ]

33.548387

306

0.584615

70e24749170e918510099ad864529e0ceda16cae

8,194

py

Python

vega/trainer/callbacks/metrics_evaluator.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

null

vega/trainer/callbacks/metrics_evaluator.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

null

vega/trainer/callbacks/metrics_evaluator.py

jie311/vega

1bba6100ead802697e691403b951e6652a99ccae

[ "MIT" ]

null

# -*- coding:utf-8 -*- # Copyright (C) 2020. Huawei Technologies Co., Ltd. All rights reserved. # This program is free software; you can redistribute it and/or modify # it under the terms of the MIT License. # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # MIT License for more details. """ProgressLogger call defination.""" import vega from copy import deepcopy from .callback import Callback from vega.common import ClassFactory, ClassType @ClassFactory.register(ClassType.CALLBACK) class MetricsEvaluator(Callback): """Callback that shows the progress of evaluating metrics.""" def __init__(self): """Initialize MetricsEvaluator callback.""" super(MetricsEvaluator, self).__init__() self.priority = 230 def before_train(self, logs=None): """Be called before the training process.""" self.do_validation = self.params.get('do_validation', False) self.cur_loss = None self.loss_avg = None self.cur_train_perfs = None self.best_train_perfs = None self.cur_valid_perfs = None self.best_valid_perfs = None self.best_valid_changed = False self.summary_perfs = None self.perfs_cmp_mode = self.trainer.config.perfs_cmp_mode self.perfs_cmp_key = self.trainer.config.perfs_cmp_key # get_train_metric_after_epoch: detector or no need to get train_metrics after epoch self.get_train_metric_after_epoch = self.trainer.config.get_train_metric_after_epoch def before_epoch(self, epoch, logs=None): """Be called before each epoach.""" self.train_metrics = self.trainer.train_metrics self.valid_metrics = self.trainer.valid_metrics self.counted_steps = 0 self.total_loss = 0 if self.train_metrics is not None: self.train_metrics.reset() if self.do_validation and self.valid_metrics is not None: self.valid_metrics.reset() def before_train_step(self, batch_index, logs=None): """Be called before a batch training.""" self.train_batch = logs['train_batch'] def after_train_step(self, batch_index, logs=None): """Be called after each train batch.""" if isinstance(self.train_batch, list) and isinstance(self.train_batch[0], dict): input, target = self.train_batch, None else: input, target = self.train_batch if isinstance(logs['lr'], list): self.lr = logs['lr'][0] else: self.lr = logs['lr'] if self.trainer.config.is_detection_trainer: self.cur_loss = logs['loss'] self.loss_avg = self.cur_loss else: if isinstance(input, dict): batch_size = 1 elif isinstance(input, list): batch_size = len(input) else: batch_size = input.size(0) self.cur_loss = logs['loss'] self.loss_avg = self._average_loss(batch_size, self.cur_loss) logs.update({'cur_loss': self.cur_loss, 'loss_avg': self.loss_avg, 'lr': self.lr}) def before_valid_step(self, batch_index, logs=None): """Be called before a batch validation.""" self.valid_batch = logs['valid_batch'] def after_valid_step(self, batch_index, logs=None): """Be called after each batch of validation.""" if self.do_validation and self.valid_metrics is not None: if isinstance(self.valid_batch, list) and isinstance(self.valid_batch[0], dict): target = self.valid_batch else: target = self.valid_batch[1] output = logs['valid_batch_output'] self.valid_metrics(output, target) def after_valid(self, logs=None): """Be called after validation.""" if self.do_validation and self.valid_metrics is not None: # Get the summary of valid metrics metrics_results = self.valid_metrics.results if vega.is_torch_backend() and self.trainer.distributed: for key, value in metrics_results.items(): metrics_results[key] = self.trainer._metric_average(value, key) if 'loss' in metrics_results: metrics_results.pop('loss') if 'global_step' in metrics_results: metrics_results.pop('global_step') self.cur_valid_perfs = metrics_results logs.update({'cur_valid_perfs': self.cur_valid_perfs}) # update best valid perfs based on current valid valid_perfs if self.best_valid_perfs is None: self.best_valid_changed = True self.best_valid_perfs = self.cur_valid_perfs else: self.best_valid_changed = self._update_best_perfs(self.cur_valid_perfs, self.best_valid_perfs) logs.update({'cur_valid_perfs': self.cur_valid_perfs, 'best_valid_perfs': self.best_valid_perfs, 'best_valid_perfs_changed': self.best_valid_changed}) def after_epoch(self, epoch, logs=None): """Be called after each epoch.""" self.summary_perfs = logs.get('summary_perfs', {}) self.summary_perfs.update({'loss_avg': self.loss_avg}) if self.train_metrics is not None and self.get_train_metric_after_epoch: # Get the summary of train metrics metrics_results = self.train_metrics.results self.cur_train_perfs = metrics_results # update best train perfs based on current train perfs if self.best_train_perfs is None: self.best_train_perfs = deepcopy(self.cur_train_perfs) else: self._update_best_perfs(self.cur_train_perfs, self.best_train_perfs) self.summary_perfs.update({'cur_train_perfs': self.cur_train_perfs, 'best_train_perfs': self.best_train_perfs}) if self.do_validation and self.valid_metrics is not None: self.summary_perfs.update({'cur_valid_perfs': self.cur_valid_perfs, 'best_valid_perfs': self.best_valid_perfs, 'best_valid_perfs_changed': self.best_valid_changed}) logs.update({'summary_perfs': self.summary_perfs}) def after_train(self, logs=None): """Be called before training.""" self.after_epoch(self.trainer.epochs, logs) def _update_best_perfs(self, cur_perfs, best_perfs): best_changed = False if self.perfs_cmp_key is None: # Select the first kye as default for comparsion self.perfs_cmp_key = list(cur_perfs.keys())[0] # Get the values for comparsion based on key if isinstance(best_perfs[self.perfs_cmp_key], list): best_val = best_perfs[self.perfs_cmp_key][0] cur_val = cur_perfs[self.perfs_cmp_key][0] else: best_val = best_perfs[self.perfs_cmp_key] cur_val = cur_perfs[self.perfs_cmp_key] # Store the perfs after comparison based on mode if self.perfs_cmp_mode is None: self.perfs_cmp_mode = self.valid_metrics.objectives.get(self.perfs_cmp_key) if self.perfs_cmp_mode == 'MAX': if cur_val > best_val: best_perfs.update(deepcopy(cur_perfs)) best_changed = True elif self.perfs_cmp_mode == 'MIN': if cur_val < best_val: best_perfs.update(deepcopy(cur_perfs)) best_changed = True else: best_perfs.update(deepcopy(cur_perfs)) best_changed = True return best_changed def _average_loss(self, batch_size, cur_loss): self.counted_steps += batch_size self.total_loss += cur_loss * batch_size averaged_loss = self.total_loss / self.counted_steps return averaged_loss

45.270718

92

0.631316

de46ca6dd5ff959d02ea01656a89eb1105dd5065

2,193

py

Python

pcbs/kicad_db/kicad_StepUp_tools/kicadStepUpMod/Init.py

Roboy/bldc_testbed

a52860d40627feb2c4b40d96fb973122e41c464f

[ "BSD-3-Clause" ]

2

2019-11-27T03:54:12.000Z

2020-07-19T09:02:06.000Z

pcbs/kicad_db/kicad_StepUp_tools/kicadStepUpMod/Init.py

Roboy/bldc_testbed

a52860d40627feb2c4b40d96fb973122e41c464f

[ "BSD-3-Clause" ]

null

pcbs/kicad_db/kicad_StepUp_tools/kicadStepUpMod/Init.py

Roboy/bldc_testbed

a52860d40627feb2c4b40d96fb973122e41c464f

[ "BSD-3-Clause" ]

2

2020-09-30T13:40:09.000Z

2020-10-14T21:35:23.000Z

# Idf init module # (c) 2003 Juergen Riegel # # Gathering all the information to start FreeCAD # This is the second one of three init scripts, the third one # runs when the gui is up #*************************************************************************** #* (c) Juergen Riegel (juergen.riegel@web.de) 2002 * #* * #* This file is part of the FreeCAD CAx development system. * #* * #* This program is free software; you can redistribute it and/or modify * #* it under the terms of the GNU Lesser General Public License (LGPL) * #* as published by the Free Software Foundation; either version 2 of * #* the License, or (at your option) any later version. * #* for detail see the LICENCE text file. * #* * #* FreeCAD is distributed in the hope that it will be useful, * #* but WITHOUT ANY WARRANTY; without even the implied warranty of * #* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * #* GNU Lesser General Public License for more details. * #* * #* You should have received a copy of the GNU Library General Public * #* License along with FreeCAD; if not, write to the Free Software * #* Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 * #* USA * #* * #* Juergen Riegel 2002 * #***************************************************************************/ # two options for IDF added by Milos Koutny (12-Feb-2010) FreeCAD.addImportType("Kicad pcb board/mod File Type (*.kicad_pcb *.emn *.kicad_mod)","kicadStepUptools") #FreeCAD.addImportType("IDF emp File Type (*.emp)","Import_Emp")

57.710526

106

0.45098

480e0eeb14fec032c8baf838bf916ca87ca12ac0

1,082

py

Python

setup.py

ohahlev/ahlev-django-location

7d6060ab7b21509f53790f5863b596f2b95c286a

[ "BSD-3-Clause" ]

null

setup.py

ohahlev/ahlev-django-location

7d6060ab7b21509f53790f5863b596f2b95c286a

[ "BSD-3-Clause" ]

null

setup.py

ohahlev/ahlev-django-location

7d6060ab7b21509f53790f5863b596f2b95c286a

[ "BSD-3-Clause" ]

null

# -*- coding: utf-8 -*- from setuptools import setup, find_packages import location setup( name='ahlev-django-location', version=location.__version__, description='location app using django framework', long_description='location app using django framework', long_description_content_type='text/x-rst', author='ahlev', author_email='ohahlev@gmail.com', include_package_data=True, url='https://github.com/ohahlev/ahlev-django-location/tree/%s' % location.__version__, packages=find_packages(), install_requires=[ 'django-tinymce', ], classifiers=[ 'Development Status :: 4 - Beta', 'Environment :: Web Environment', 'Intended Audience :: Developers', 'License :: OSI Approved :: BSD License', 'Operating System :: OS Independent', 'Programming Language :: Python', ], zip_safe=False, ) # Usage of setup.py: # $> python setup.py register # registering package on PYPI # $> python setup.py build sdist upload # build, make source dist and upload to PYPI

32.787879

90

0.666359

74571d6bb03c639a683afd4b035d536ff41ec503

3,929

py

Python

bitmovin_api_sdk/models/ad_analytics_greater_than_or_equal_filter.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

11

2019-07-03T10:41:16.000Z

2022-02-25T21:48:06.000Z

bitmovin_api_sdk/models/ad_analytics_greater_than_or_equal_filter.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

8

2019-11-23T00:01:25.000Z

2021-04-29T12:30:31.000Z

bitmovin_api_sdk/models/ad_analytics_greater_than_or_equal_filter.py

jaythecaesarean/bitmovin-api-sdk-python

48166511fcb9082041c552ace55a9b66cc59b794

[ "MIT" ]

13

2020-01-02T14:58:18.000Z

2022-03-26T12:10:30.000Z

# coding: utf-8 from enum import Enum from six import string_types, iteritems from bitmovin_api_sdk.common.poscheck import poscheck_model from bitmovin_api_sdk.models.ad_analytics_abstract_filter import AdAnalyticsAbstractFilter from bitmovin_api_sdk.models.ad_analytics_attribute import AdAnalyticsAttribute import pprint import six class AdAnalyticsGreaterThanOrEqualFilter(AdAnalyticsAbstractFilter): @poscheck_model def __init__(self, name=None, value=None): # type: (AdAnalyticsAttribute, object) -> None super(AdAnalyticsGreaterThanOrEqualFilter, self).__init__(name=name) self._value = None self.discriminator = None if value is not None: self.value = value @property def openapi_types(self): types = {} if hasattr(super(AdAnalyticsGreaterThanOrEqualFilter, self), 'openapi_types'): types = getattr(super(AdAnalyticsGreaterThanOrEqualFilter, self), 'openapi_types') types.update({ 'value': 'object' }) return types @property def attribute_map(self): attributes = {} if hasattr(super(AdAnalyticsGreaterThanOrEqualFilter, self), 'attribute_map'): attributes = getattr(super(AdAnalyticsGreaterThanOrEqualFilter, self), 'attribute_map') attributes.update({ 'value': 'value' }) return attributes @property def value(self): # type: () -> object """Gets the value of this AdAnalyticsGreaterThanOrEqualFilter. :return: The value of this AdAnalyticsGreaterThanOrEqualFilter. :rtype: object """ return self._value @value.setter def value(self, value): # type: (object) -> None """Sets the value of this AdAnalyticsGreaterThanOrEqualFilter. :param value: The value of this AdAnalyticsGreaterThanOrEqualFilter. :type: object """ if value is not None: if not isinstance(value, object): raise TypeError("Invalid type for `value`, type has to be `object`") self._value = value def to_dict(self): """Returns the model properties as a dict""" result = {} if hasattr(super(AdAnalyticsGreaterThanOrEqualFilter, self), "to_dict"): result = super(AdAnalyticsGreaterThanOrEqualFilter, self).to_dict() for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if value is None: continue if isinstance(value, list): if len(value) == 0: continue result[self.attribute_map.get(attr)] = [y.value if isinstance(y, Enum) else y for y in [x.to_dict() if hasattr(x, "to_dict") else x for x in value]] elif hasattr(value, "to_dict"): result[self.attribute_map.get(attr)] = value.to_dict() elif isinstance(value, Enum): result[self.attribute_map.get(attr)] = value.value elif isinstance(value, dict): result[self.attribute_map.get(attr)] = {k: (v.to_dict() if hasattr(v, "to_dict") else v) for (k, v) in value.items()} else: result[self.attribute_map.get(attr)] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, AdAnalyticsGreaterThanOrEqualFilter): return False return self.__dict__ == other.__dict__ def __ne__(self, other): """Returns true if both objects are not equal""" return not self == other

32.471074

164

0.62255

738d8af85050022696ea7bea03e265239930ec89

2,561

py

Python

hprof/_special_cases.py

SonyMobile/py-hyprof

932f559640da450c39966c35072464ce4b003d1a

[ "MIT" ]

11

2020-04-09T05:58:20.000Z

2022-02-27T16:59:19.000Z

hprof/_special_cases.py

SonyMobile/py-hyprof

932f559640da450c39966c35072464ce4b003d1a

[ "MIT" ]

null

hprof/_special_cases.py

SonyMobile/py-hyprof

932f559640da450c39966c35072464ce4b003d1a

[ "MIT" ]

3

2020-04-15T07:10:16.000Z

2021-12-09T08:47:32.000Z

# Copyright (C) 2020 Sony Mobile Communications Inc. # Licensed under the LICENSE. ''' This module contains code for handling some Java classes in specific ways, e.g. making str() return the actual text of a java.lang.String. ''' import codecs def _jstr_to_str(self): ''' get the string contents of a java.lang.String ''' data = getattr(self, 'value', None) if data is not None: # OpenJDK has a 'coder' attribute that tells us the encoding: # https://github.com/openjdk/jdk/blob/6c9d6507/src/java.base/share/classes/java/lang/String.java#L163 coder = getattr(self, 'coder', None) if len(data) == 0: return '' elif isinstance(data[0], bytes) or isinstance(data[0], int): # Could be ART/Android 'compressed' ascii bytes, or OpenJDK bytes static_latin1 = getattr(self, 'LATIN1', None) static_utf16 = getattr(self, 'UTF16', None) if coder is None: # could be Android/ART 'compressed' bytes_encoding = 'ascii' elif coder == static_latin1: bytes_encoding = 'latin-1' elif coder == static_utf16: # big- or little-endian? May depend on the machine the hprof came from. # Let's guess little-endian. bytes_encoding = 'utf-16-le' else: raise ValueError('unknown string class encoding') return bytes(b&0xff for b in data).decode(bytes_encoding) elif coder is None and isinstance(data[0], str): # Looks like ART/Android 'uncompressed' utf16 chars. # char arrays may have surrogate pairs that should be merged into # real unicode characters. The simplest solution is to flatten them # to bytes, then decode them properly. joined = ''.join(data) flattened = codecs.encode(joined, 'utf-16-be', 'surrogatepass') return flattened.decode('utf-16-be') # alright, let the wrapper handle it. raise TypeError('unknown string class layout') def _wrap_with_fallback(old, new): def fallback_wrapper(*args, **kwargs): ''' calls the replacement function; if it fails, calls the original. ''' try: return new(*args, **kwargs) except Exception: # pylint: disable=broad-except if old is None: raise return old(*args, **kwargs) return fallback_wrapper def add(hprof_file, clsname, method_name, func): ''' add a special function onto a class. ''' for heap in hprof_file.heaps: for cls in heap.classes.get(clsname, ()): old = getattr(cls, method_name, None) wrapper = _wrap_with_fallback(old, func) setattr(cls, method_name, wrapper) def setup_builtins(hf): ''' setup all special case builtins. ''' add(hf, 'java.lang.String', '__str__', _jstr_to_str)

34.146667

103

0.705974

ce38b23f0def48d6b313dbf6dc93e8db0579e153

5,470

py

Python

train_cnn.py

damaha/iasi-atmosphere

e54ef3f0ca57afdaff43a6c6915c7cabbd430ee4

[ "MIT" ]

null

train_cnn.py

damaha/iasi-atmosphere

e54ef3f0ca57afdaff43a6c6915c7cabbd430ee4

[ "MIT" ]

null

train_cnn.py

damaha/iasi-atmosphere

e54ef3f0ca57afdaff43a6c6915c7cabbd430ee4

[ "MIT" ]

null

import json, os import numpy as np import pandas as pd from data_load import get_datafiles from generators import iasi_generator from models import * from keras.callbacks import ModelCheckpoint, TensorBoard, CSVLogger, ReduceLROnPlateau from keras.utils import multi_gpu_model from keras.models import model_from_json from tensorflow.python.client import device_lib def get_available_gpus(): local_device_protos = device_lib.list_local_devices() return [x.name for x in local_device_protos if x.device_type == 'GPU'] ### Parameters ### params = {"ModelName" : "models/model_firsttry", "ModelType" : simple_model_1, "n_comp" : 128, "f_spec" : 5, "batchsize" : 16, "epochs" : 52, "period_weights" : 10, "seed" : 18374, "outputs" : np.arange(47, 137).astype(int), "path" : ".../IASI/data_v3/", "train_code" : open("train_cnn.py", "r").read(), "model_code" : open("models.py", "r").read(), "generator_code" : open("generators.py", "r").read()} np.random.seed(params['seed']) ### Split data and make generators ### train_files, valid_files = get_datafiles(params["path"]) X_shape = np.load(valid_files[0][0]).shape Y_shape = np.load(valid_files[0][1]).shape indexs = [None, params["outputs"]] dc = json.load(open("scaling_coeffs.json")) train_generator = iasi_generator(train_files, batch_size=params['batchsize'], selected_channels=indexs, norm_coeffs=[dc['mean'], dc['variance']]) valid_generator = iasi_generator(valid_files, batch_size=params['batchsize'], selected_channels=indexs, norm_coeffs=[dc['mean'], dc['variance']]) model = params["ModelType"](X_shape, params["outputs"].size, n_comp=params["n_comp"], f_spec=params["n_comp"]) if os.path.isfile(params["ModelName"]+"_config.json") and os.path.isfile(params["ModelName"]+".h5"): if os.path.isfile(params["ModelName"]+"_history.json"): json_hist = json.load(open(params["ModelName"]+"_history.json","r")) else: json_hist = {'loss':[],'val_loss':[]} json_str = json.load(open(params["ModelName"]+"_config.json","r")) model = model_from_json(json_str) model.load_weights(params["ModelName"]+".h5") log = np.genfromtxt(params["ModelName"]+".log", delimiter=",",skip_header=1) e_init = int(log[-1,0] // params["period_weights"] * params["period_weights"]) model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse']) print("Continuing training process from epoch %d" % e_init) else: model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse']) json_string = model.to_json() json.dump(json_string, open(params["ModelName"]+"_config.json", "w")) model_object = model e_init = 0 json_hist = {'loss':[],'val_loss':[]} nb_gpus = len(get_available_gpus()) if nb_gpus > 1: m_model = multi_gpu_model(model, gpus=nb_gpus) m_model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mse']) model_object = m_model else: model_object = model import time start = time.time() history = model_object.fit_generator(train_generator, callbacks=[ModelCheckpoint(params["ModelName"].split('/')[-1]+".{epoch:02d}.h5", monitor='loss', verbose=1, period=params["period_weights"], save_weights_only=True), # TensorBoard(log_dir='tmp/logs/'), CSVLogger(params["ModelName"]+'.log'), ReduceLROnPlateau(monitor='loss', factor=0.2, patience=5, min_lr=0.0001, min_delta=0.001)], validation_data=valid_generator, epochs=params["epochs"], max_queue_size=5, verbose=2, initial_epoch=e_init) seconds = time.time()-start m, s = divmod(seconds, 60) h, m = divmod(m, 60) d, h = divmod(h, 24) print('It took %d:%d:%02d:%02d to train.' % (d, h, m, s)) model.save_weights(params["ModelName"]+".h5") import socket dct = history.history dct['loss'] = json_hist['loss']+dct['loss'] dct['val_loss'] = json_hist['val_loss']+dct['val_loss'] dct["number_of_gpus"] = nb_gpus dct["hostname"] = socket.gethostname() dct["training_files"] = str(train_files) dct["test_files"] = str(valid_files) dct["training_time"] = '%d:%d:%02d:%02d' % (d, h, m, s) dct.update(params) class NpEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.integer): return int(obj) elif isinstance(obj, np.floating): return float(obj) elif isinstance(obj, np.ndarray): return obj.tolist() else: return super(NpEncoder, self).default(obj) json.dump(dct, open(params["ModelName"]+"_history.json", 'w'), cls=NpEncoder)

41.755725

145

0.568556

b443357f110ebd075f6ea689480bb84b32219729

3,733

py

Python

utils/scripts/c64/C64Charset.py

xahmol/8bit-Unity

b4f3bee00e012ca1755afba550a5270dce0a1054

[ "BSD-2-Clause" ]

42

2018-12-12T01:00:59.000Z

2022-03-27T07:32:29.000Z

utils/scripts/c64/C64Charset.py

xahmol/8bit-Unity

b4f3bee00e012ca1755afba550a5270dce0a1054

[ "BSD-2-Clause" ]

13

2020-11-06T13:50:45.000Z

2022-01-25T07:17:37.000Z

utils/scripts/c64/C64Charset.py

xahmol/8bit-Unity

b4f3bee00e012ca1755afba550a5270dce0a1054

[ "BSD-2-Clause" ]

8

2020-11-14T04:30:26.000Z

2021-01-16T17:55:19.000Z

""" * Copyright (c) 2018 Anthony Beaucamp. * * This software is provided 'as-is', without any express or implied warranty. * In no event will the authors be held liable for any damages arising from * the use of this software. * * Permission is granted to anyone to use this software for any purpose, * including commercial applications, and to alter it and redistribute it * freely, subject to the following restrictions: * * 1. The origin of this software must not be misrepresented * you must not * claim that you wrote the original software. If you use this software in a * product, an acknowledgment in the product documentation would be * appreciated but is not required. * * 2. Altered source versions must be plainly marked as such, and must not * be misrepresented as being the original software. * * 3. This notice may not be removed or altered from any distribution. * * 4. The names of this software and/or it's copyright holders may not be * used to endorse or promote products derived from this software without * specific prior written permission. """ import io, os, sys, csv from PIL import Image charFile = sys.argv[1] output = sys.argv[2] try: sharedColors = [int(n) for n in sys.argv[3].split(',')] except: sharedColors = [] flagFile = charFile.replace('-c64.png', '.csv') ############################# # Read char and font bitmaps charImg = Image.open(charFile) charRaw = list(charImg.getdata()) print "Charset size: {%i,%i}; Colors: %i" % (charImg.size[0], charImg.size[1], max(charRaw)) ######################### # Allocate shared colors if sharedColors == []: distrib = [0] * 16 for row in range(0, charImg.size[1], 8): for col in range(0, charImg.size[0], 4): # Collect block data block = [] for j in range(0, 8): index = (row+j)*charImg.size[0]+col for i in range(0, 4): block.append(charRaw[index+i]) for i in range(0, 16): if i in block: distrib[i] += 1 popular = sorted(range(len(distrib)), key=distrib.__getitem__)[13:16] order = sorted(range(len(popular)), key=popular.__getitem__) sharedColors = [popular[i] for i in order] colData = [chr(c) for c in sharedColors] ############################ # Rearrange into 4*8 blocks charBlocks = [] attrData = [chr(0x0f)] * 128 for row in range(0, charImg.size[1], 8): for col in range(0, charImg.size[0], 4): for j in range(0, 8): for i in range(0, 4): color = charRaw[(row+j)*charImg.size[0]+col+i] if color in sharedColors: charBlocks.append(sharedColors.index(color)) else: attrData[row*4+col/4] = chr(color%8+8) charBlocks.append(3) ############################################ # Convert char and font data to C64 format charData = [chr(0)] * (128*8) for i in range(0, len(charBlocks), 4): charData[i/4] = chr((charBlocks[i+0]<<6) + (charBlocks[i+1]<<4) + (charBlocks[i+2]<<2) + (charBlocks[i+3]<<0)) ####################### # Read character flags flagData = [chr(0)] * 128 with open(flagFile) as csvfile: i = 0 rows = csv.reader(csvfile, delimiter=',') for row in rows: for elt in row: flagData[i] = chr(int(elt)) i += 1 ############################ # Write output binary file f2 = io.open(output, 'wb') f2.write(''.join(colData)) f2.write(''.join(charData)) f2.write(''.join(attrData)) f2.write(''.join(flagData)) f2.close()

35.552381

115

0.574873

5854eb450a0117586b28899963e92f6b24d51f1d

7,956

py

Python

app/grandchallenge/core/views.py

kant/grand-challenge.org

608266ae3376448fc56c3bb4e34138ab81a45e2a

[ "Apache-2.0" ]

null

app/grandchallenge/core/views.py

kant/grand-challenge.org

608266ae3376448fc56c3bb4e34138ab81a45e2a

[ "Apache-2.0" ]

null

app/grandchallenge/core/views.py

kant/grand-challenge.org

608266ae3376448fc56c3bb4e34138ab81a45e2a

[ "Apache-2.0" ]

null

from django.conf import settings from django.core.files.storage import DefaultStorage from django.http import Http404 from django.shortcuts import render from django.template import Template, TemplateSyntaxError, RequestContext from django.utils._os import safe_join from grandchallenge.challenges.models import Challenge from grandchallenge.subdomains.utils import reverse from grandchallenge.pages.models import Page, ErrorPage def site(request): site = request.challenge pages = site.page_set.all() if len(pages) == 0: currentpage = ErrorPage( challenge=site, title="no_pages_found", html="No pages found for this site. Please log in and add some pages.", ) else: currentpage = pages[0] currentpage = getRenderedPageIfAllowed(currentpage, request) return render( request, "page.html", {"site": site, "currentpage": currentpage, "pages": pages}, ) def renderTags(request, p, recursecount=0): """ render page contents using django template system This makes it possible to use tags like '{% dataset %}' in page content. If a rendered tag results in another tag, this can be rendered recursively as long as recurse limit is not exceeded. """ recurselimit = 2 try: t = Template("{% load grandchallenge_tags %}" + p.html) except TemplateSyntaxError as e: # when page contents cannot be rendered, just display raw contents and include error message on page errormsg = ( '<span class="pageError"> Error rendering template: %s </span>' % e ) pagecontents = p.html + errormsg return pagecontents # pass page to context here to be able to render tags based on which page does the rendering context = RequestContext(request, {"currentpage": p}) pagecontents = t.render(context) if ( "{%" in pagecontents or "{{" in pagecontents ): # if rendered tags results in another tag, try to render this as well if recursecount < recurselimit: p2 = copy_page(p) p2.html = pagecontents return renderTags(request, p2, recursecount + 1) else: # when page contents cannot be rendered, just display raw contents and include error message on page errormsg = ( '<span class="pageError"> Error rendering template: rendering recursed further than' + str(recurselimit) + " </span>" ) pagecontents = p.html + errormsg return pagecontents def permissionMessage(request, p): if request.user.is_authenticated: msg = """ <div class="system_message"> <h2> Restricted page</h2> <p>This page can only be viewed by participants of this project to view this page please make sure of the following:</p> <ul> <li>First, log in to {} by using the 'Sign in' button at the top right.</li> <li>Second, you need to join / register with the specific project you are interested in as a participant. The link to do this is provided by the project organizers on the project website.</li> </ul> <div> """.format( settings.MAIN_PROJECT_NAME ) title = p.title else: msg = ( "The page '" + p.title + "' can only be viewed by registered users. Please sign in to view this page." ) title = p.title return ErrorPage(challenge=request.challenge, title=title, html=msg) # TODO: could a decorator be better then all these ..IfAllowed pages? def getRenderedPageIfAllowed(page_or_page_title, request): """ check permissions and render tags in page. If string title is given page is looked for return nice message if not allowed to view""" if isinstance(page_or_page_title, bytes): page_or_page_title = page_or_page_title.decode() if isinstance(page_or_page_title, str): page_title = page_or_page_title try: p = request.challenge.page_set.get(title__iexact=page_title) except Page.DoesNotExist: raise Http404 else: p = page_or_page_title if p.can_be_viewed_by(request.user): p.html = renderTags(request, p) currentpage = p else: currentpage = permissionMessage(request, p) return currentpage def get_data_folder_path(challenge_short_name): """ Returns physical base path to the root of the folder where all files for this project are kept """ return safe_join(settings.MEDIA_ROOT, challenge_short_name) def get_dirnames(path): """ Get all directory names in path as list of strings Raises: OSError if directory can not be found """ storage = DefaultStorage() dirnames = storage.listdir(path)[0] dirnames.sort() return dirnames def comicmain(request, page_title=""): """ show content as main page item. Loads pages from the main project """ challenge_short_name = settings.MAIN_PROJECT_NAME try: site = Challenge.objects.get(short_name__iexact=challenge_short_name) except Challenge.DoesNotExist: link = reverse("challenges:create") link = link + "?short_name=%s" % challenge_short_name link_html = create_HTML_a( link, "Create project '%s'" % challenge_short_name ) html = """I'm trying to show the first page for main project '%s' here, but '%s' does not exist. %s.""" % ( challenge_short_name, challenge_short_name, link_html, ) page = create_temp_page(title="no_pages_found", html=html) return render( request, "temppage.html", {"site": page.challenge, "currentpage": page}, ) pages = site.page_set.all() if len(pages) == 0: link = reverse( "pages:list", kwargs={"challenge_short_name": challenge_short_name} ) link_html = create_HTML_a(link, "admin interface") html = """I'm trying to show the first page for main project '%s' here, but '%s' contains no pages. Please add some in the %s.""" % ( challenge_short_name, challenge_short_name, link_html, ) page = create_temp_page(title="no_pages_found", html=html) return render( request, "temppage.html", {"site": page.challenge, "currentpage": page}, ) if page_title: pages = [p for p in pages if p.title.lower() == page_title.lower()] if len(pages) != 1: raise ValueError( f"{len(pages)} pages with title {page_title} were found for {site}" ) page = pages[0] page.html = renderTags(request, page) return render(request, "page.html", {"currentpage": page}) # ======================================== not called directly from urls.py == def create_HTML_a(link_url, link_text): return '<a href="' + link_url + '">' + link_text + "</a>" def create_HTML_a_img(link_url, image_url): """ create a linked image """ img = '<img src="' + image_url + '">' linked_image = create_HTML_a(link_url, img) return linked_image def copy_page(page): return Page(challenge=page.challenge, title=page.title, html=page.html) def create_temp_page(title="temp_page", html=""): """ Create a quick mockup page which you can show, without needing to read anything from database """ site = Challenge() # any page requires a site, create on the fly here. site.short_name = "Temp" site.name = "Temporary page" site.skin = "" return Page(challenge=site, title=title, html=html)

34

138

0.620161

6658ed3ef1023a8e42b737c37eff5491771e23b9

1,081

py

Python

Curso_Em_Video_Python/ex036.py

ThallesTorres/Curso_Em_Video_Python

95ffbff5a03f11fee41df746604dfe435f385a3b

[ "MIT" ]

null

Curso_Em_Video_Python/ex036.py

ThallesTorres/Curso_Em_Video_Python

95ffbff5a03f11fee41df746604dfe435f385a3b

[ "MIT" ]

null

Curso_Em_Video_Python/ex036.py

ThallesTorres/Curso_Em_Video_Python

95ffbff5a03f11fee41df746604dfe435f385a3b

[ "MIT" ]

null

# Ex: 036 - Escreva um progrma para aprovar o empréstimo bancário para a compra # de uma casa. O programa vai perguntar o valor da casa, o salário do comprador # e em quantos anos ele vai pagar. Calcule o valor da prestação mensal, dabendo # que eka não pode exceder 30% do salário ou então o empréstimo será negado. print(''' -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- --Seja bem-vindo! --Exercício 036 -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- ''') print('-=-Preencha os Dados-=- ') valor_casa = int(input('Valor da Casa: R$')) salario = int(input('Salário do Comprador: R$')) anos = int(input('Em quantos Anos: ')) meses = anos * 12 prestacao = valor_casa / meses print('-=-Dados Finais-=-') print(f'Valor da Casa: R${valor_casa} \nAnos: {anos} \nMeses: {meses} \nPrestações: R${prestacao:.2f}') if prestacao >= salario * 30 / 100: print('Empréstimo Não Aprovado!!)') else: print('Empréstimo Aprovado!!') print(''' -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- --Obrigado pelo uso! --Desenvolvido por Thalles Torres -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-''')

34.870968

104

0.580944

c3432f85181dd70d2d23e9283aa3fe38f031b3ac

290

py

Python

CS Practical/A21.py

adityaruplaha/adityaruplaha-school-projects

b19d06f356fafdbe4189bade59d4a4d1ffec567f

[ "MIT" ]

null

CS Practical/A21.py

adityaruplaha/adityaruplaha-school-projects

b19d06f356fafdbe4189bade59d4a4d1ffec567f

[ "MIT" ]

null

CS Practical/A21.py

adityaruplaha/adityaruplaha-school-projects

b19d06f356fafdbe4189bade59d4a4d1ffec567f

[ "MIT" ]

null

# Maximum of 3 no.s & sum of no equal no.s print("Enter 3 no.s:") a = float(input()) b = float(input()) c = float(input()) m = a S = 0 L = [a, b, c] for e in L: if e > m: m = e if L.count(e) == 1: S += e print("Maximum:", m) print("Sum of non-equal numbers:", S)

15.263158

42

0.506897

ea2dc0bab98cb4b1c1866b9525684e070969166c

9,138

py

Python

audio_to_midi/converter.py

surajpaib/audio-to-midi

a58c23b8b5b1083e9d993a1b666258d22c861986

[ "MIT" ]

null

audio_to_midi/converter.py

surajpaib/audio-to-midi

a58c23b8b5b1083e9d993a1b666258d22c861986

[ "MIT" ]

null

audio_to_midi/converter.py

surajpaib/audio-to-midi

a58c23b8b5b1083e9d993a1b666258d22c861986

[ "MIT" ]

1

2021-01-25T14:54:06.000Z

import cmath import numpy import math from audio_to_midi import midi_writer, notes class Converter(object): """ An object which takes in a list of audio samples and transforms them into midi data which is written out to a .mid file. """ def __init__( self, samples=None, samplerate=None, channels=0, time_window=None, activation_level=None, condense=None, single_note=None, outfile=None, progress_callback=None, ): """ FFT object constructor. samples is a list of raw audio samples. rate is the sampling frequency of the audio samples. time_window is the interval (in ms) over which to compute the fft's. activation_level is the volume cutoff percentage for frequencies. outfile is the MIDI file to be written to. """ self.samples = samples self.channels = channels self.length = len(samples) self.samplerate = samplerate self.time_window = time_window self.activation_level = activation_level self.condense = condense self.single_note = single_note self.outfile = outfile self.progress_callback = progress_callback self.notes = notes.generate() self.bpm = int((60 * 1000) / self.time_window) # Get the number of samples per time_window self.step_size = self.time_window_to_step_size( self.time_window, self.samplerate ) def time_window_to_step_size(self, time_window, rate): """ time_window is the time in ms over which to compute fft's. rate is the audio sampling rate in samples/sec. Transforms the time window into an index step size and returns the result. """ # rate/1000(samples/ms) * time_window(ms) = step_size(samples) rate_per_ms = rate / 1000 step_size = rate_per_ms * time_window return int(step_size) def reduce_freqs(self, freqs): """ freqs is a list of amplitudes produced by fft_to_frequencies(). Reduces the list of frequencies to a list of notes and their respective volumes by determining what note each frequency is closest to. It then reduces the list of amplitudes for each note to a single amplitude by summing them together. """ reduced_freqs = {} for freq in freqs: for key, val in self.notes.items(): key = key - 7 # Find the freq's equivalence class, adding the amplitudes. if val[0] <= freq[0] <= val[2]: if key in reduced_freqs.keys(): prev = reduced_freqs[key] prev += freq[1] reduced_freqs.update({key: prev}) else: reduced_freqs.update({key: freq[1]}) return reduced_freqs def freqs_to_midi(self, freq_list, channel): """ freq_list is a list of frequencies with normalized amplitudes. Takes a list of notes and transforms the amplitude to a midi volume as well as adding track and channel info. """ activation_level = self.activation_level / 100.0 midi_list = [] for freqs in freq_list: midi_notes = {} for key, val in freqs.items(): if val >= activation_level: # The key is the midi note. midi_notes.update( { key: { "track": 0, "channel": channel, "volume": int(127 * val), "duration": 1, } } ) if self.single_note: max_note = None index = None for note, info in midi_notes.items(): if max_note == None: max_note = midi_notes[note] elif info["volume"] > max_note["volume"]: max_note = midi_notes[note] index = note if max_note == None: midi_notes = {} else: midi_notes = {index: max_note} midi_list.append(midi_notes) return midi_list def fft_to_frequencies(self, amplitudes): """ amplitudes is a list of amplitudes produced by the fft. Takes a list of amplitudes and transforms it into a list of midi notes by passing the list through reduce_freqs() and freqs_to_midi(). """ size = len(amplitudes) freqs = [] # Determine the true amplitudes for i in range(size // 2): re = amplitudes[i].real im = amplitudes[i].imag amplitude = math.sqrt(re ** 2 + im ** 2) # Determine the frequency in Hz freq = i * float(self.samplerate) / size if freq > 20000.0: break else: freqs.append([freq, amplitude]) # Transform the frequency info into midi compatible data. return self.reduce_freqs(freqs[1:]) def normalize_freqs(self, freq_list): """ freq list is a list of dicts containing all of the frequency data from the wav file. Normalizes the amplitudes of every frequency in every time step. """ max_amplitude = 0.0 for freqs in freq_list: for key, freq in freqs.items(): if freq > max_amplitude: max_amplitude = freq for freqs in freq_list: for key, amplitude in freqs.items(): new_amplitude = amplitude / max_amplitude freqs.update({key: new_amplitude}) return freq_list def condense_midi_notes(self, midi_list): """ midi_list is a list of dicts containing midi compatible data. Combines consecutive notes accross time steps, using the maximum volume seen in the list as the resulting note's volume. """ for i in range(len(midi_list)): if i < len(midi_list) - 1: cur_midi = midi_list[i] for note, info in cur_midi.items(): j = i + 1 while j < len(midi_list) - 1: next_midi = midi_list[j] if note in next_midi.keys(): if next_midi[note]["volume"] > cur_midi[note]["volume"]: new_volume = next_midi[note]["volume"] else: new_volume = cur_midi[note]["volume"] info.update( {"duration": info["duration"] + 1, "volume": new_volume} ) cur_midi.update({note: info}) next_midi.pop(note, None) else: break j += 1 return midi_list def convert(self): """ Performs the fft for each time step and uses fft_to_frequencies to transform the result into midi compatible data. This data is then passed to a midi file writer to be written out. """ steps = int(len(self.samples) / self.step_size) writer = midi_writer.MidiWriter(self.outfile, self.time_window, self.bpm) freqs = [] samples = [] for i in range(self.channels): samples.append([s[i] for s in self.samples]) self.samples = samples current = 0 total = self.channels * steps for channel in range(self.channels): freqs = [] writer.reset_time() for i in range(steps): current += 1 if self.progress_callback: self.progress_callback(current, total) if i < steps - 1: amplitudes = numpy.fft.fft( self.samples[channel][ self.step_size * i : (self.step_size * i + self.step_size) ] ) else: amplitudes = numpy.fft.fft( self.samples[channel][self.step_size * i :] ) freqs.append(self.fft_to_frequencies(amplitudes)) freqs = self.normalize_freqs(freqs) if self.condense: midi_list = self.condense_midi_notes(self.freqs_to_midi(freqs, channel)) else: midi_list = self.freqs_to_midi(freqs, channel) writer.add_notes(midi_list) writer.write_file()

33.472527

88

0.513789

3a1c276ca55878250d87fff12000e5c1f3562b56

4,711

py

Python

calc.py

dokalanyi/Pascal-Interpreter

1719eadc289cac28a1bb742e5990f0bd2e8efce0

[ "MIT" ]

null

calc.py

dokalanyi/Pascal-Interpreter

1719eadc289cac28a1bb742e5990f0bd2e8efce0

[ "MIT" ]

null

calc.py

dokalanyi/Pascal-Interpreter

1719eadc289cac28a1bb742e5990f0bd2e8efce0

[ "MIT" ]

null

# Token types # # EOF (end-of-file) token is used to indicate that # there is no more input left for lexical analysis INTEGER, PLUS, MINUS, MUL, DIV, EOF = 'INTEGER', 'PLUS', 'MINUS', 'MUL', 'DIV', 'EOF' class Token(object): def __init__(self, type, value): # token type: INTEGER, PLUS, or EOF self.type = type # token value: 0, 1, 2. 3, 4, 5, 6, 7, 8, 9, '+', or None self.value = value def __str__(self): """String representation of the class instance. Examples: Token(INTEGER, 3) Token(PLUS '+') """ return 'Token({type}, {value})'.format( type=self.type, value=repr(self.value) ) def __repr__(self): return self.__str__() class Interpreter(object): def __init__(self, text): # client string input, e.g. "3+5" self.text = text # self.pos is an index into self.text self.pos = 0 # current token instance self.current_token = None def error(self): raise Exception('Error parsing input') def get_next_token(self): """Lexical analyzer (also known as scanner or tokenizer) This method is responsible for breaking a sentence apart into tokens. One token at a time. """ text = self.text # is self.pos index past the end of the self.text ? # if so, then return EOF token because there is no more # input left to convert into tokens if self.pos > len(text) - 1: return Token(EOF, None) # get a character at the position self.pos and decide # what token to create based on the single character current_char = text[self.pos] #eliminate white space while current_char == ' ': self.pos += 1 current_char = text[self.pos] # if the character is a digit then convert it to # integer, create an INTEGER token, increment self.pos # index to point to the next character after the digit, # and return the INTEGER token if current_char.isdigit(): token = Token(INTEGER, int(current_char)) self.pos += 1 return token if current_char == '+': token = Token(PLUS, current_char) self.pos += 1 return token if current_char == '*': token = Token(MUL, current_char) self.pos += 1 return token if current_char == '/': token = Token(DIV, current_char) self.pos += 1 return token if current_char == '-': token = Token(MINUS, current_char) self.pos += 1 return token self.error() def eat(self, token_type): # compare the current token type with the passed token # type and if they match then "eat" the current token # and assign the next token to the self.current_token, # otherwise raise an exception. if self.current_token.type == token_type: self.current_token = self.get_next_token() else: self.error() def expr(self): """expr -> INTEGER PLUS INTEGER""" # set current token to the first token taken from the input self.current_token = self.get_next_token() # we expect the current token to be a single-digit integer left = '' while self.current_token.type != PLUS and self.current_token.type != EOF and self.current_token.type != MINUS and self.current_token.type != DIV and self.current_token.type != MUL: left = left + str(self.current_token.value) self.eat(INTEGER) # we expect the current token to be a '+' token op = self.current_token if self.current_token.type == PLUS: self.eat(PLUS) elif self.current_token.type == MINUS: self.eat(MINUS) elif self.current_token.type == MUL: self.eat(MUL) elif self.current_token.type == DIV: self.eat(DIV) # we expect the current token to be a single-digit integer right = '' while self.current_token.type != PLUS and self.current_token.type != EOF and self.current_token.type != MINUS and self.current_token.type != DIV and self.current_token.type != MUL: right = right + str(self.current_token.value) self.eat(INTEGER) # after the above call the self.current_token is set to # EOF token # at this point INTEGER PLUS INTEGER sequence of tokens # has been successfully found and the method can just # return the result of adding two integers, thus # effectively interpreting client input result = self.doOp(op, left, right) return result def doOp(self, op, left, right): if op.type == PLUS: result = int(left) + int(right) elif op.type == MINUS: result = int(left) - int(right) elif op.type == MUL: result = int(left) * int(right) elif op.type == DIV: result = int(left) / int(right) return result def main(): while True: try: # To run under Python3 replace 'raw_input' call # with 'input' text = raw_input('calc> ') except EOFError: break if not text: continue interpreter = Interpreter(text) result = interpreter.expr() print(result) if __name__ == '__main__': main()

28.041667

182

0.683082

939c36ccf6618151f365d70182964ee06d8ac61e

729

py

Python

recursive_optimizer/__init__.py

yick2232/google-research

99021ebda945e232abdcc592f2cea1375b3c84f7

[ "Apache-2.0" ]

11

2020-01-29T07:25:04.000Z

2022-03-05T16:01:21.000Z

recursive_optimizer/__init__.py

RubensZimbres/google-research

562c7c6ef959cb3cb382b1b660ccc45e8f5289c4

[ "Apache-2.0" ]

13

2020-01-28T22:19:53.000Z

2022-02-10T00:39:26.000Z

recursive_optimizer/__init__.py

RubensZimbres/google-research

562c7c6ef959cb3cb382b1b660ccc45e8f5289c4

[ "Apache-2.0" ]

2

2019-12-07T19:01:03.000Z

2020-03-19T16:53:04.000Z

# coding=utf-8 # Copyright 2019 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Nothing to do. Existence of this file marks the directory as # importable package; required by Python 3.2 and earlier.

40.5

74

0.76406

ce518a4cbeb47b5c82d369ead45612b3497b8a6f

855

py

Python

riberry/app/backends/impl/pool/tasks/external_task_receiver.py

srafehi/riberry

2ffa48945264177c6cef88512c1bc80ca4bf1d5e

[ "MIT" ]

2

2019-12-09T10:24:36.000Z

2019-12-09T10:26:56.000Z

riberry/app/backends/impl/pool/tasks/external_task_receiver.py

srafehi/riberry

2ffa48945264177c6cef88512c1bc80ca4bf1d5e

[ "MIT" ]

2

2018-06-11T11:34:28.000Z

2018-08-22T12:00:19.000Z

riberry/app/backends/impl/pool/tasks/external_task_receiver.py

srafehi/riberry

2ffa48945264177c6cef88512c1bc80ca4bf1d5e

[ "MIT" ]

null

from typing import List import riberry from riberry.app import current_context as ctx from ..task_queue import TaskQueue def ready_external_tasks() -> List[riberry.model.job.JobExecutionExternalTask]: return riberry.model.conn.query( riberry.model.job.JobExecutionExternalTask ).filter_by( status='READY', ).join(riberry.model.job.JobExecution).filter_by( status='ACTIVE', ).join(riberry.model.job.Job).filter_by( instance=ctx.current.riberry_app_instance, ).all() def queue_receiver_tasks(queue: TaskQueue): with queue.lock: if not queue.limit_reached(): with riberry.model.conn: external_tasks = ready_external_tasks() while external_tasks and not queue.limit_reached(): queue.submit_receiver_task(external_tasks.pop())

31.666667

79

0.690058

ad6fff3a54b188437bd925df50e52c3fa72b81d0

1,837

py

Python

projects/data_generation/synthetic_multi_view_facial_image_generation/algorithm/Rotate_and_Render/models/networks/sync_batchnorm/scatter_gather.py

ESOGU-SRLAB/opendr

f2eb5a6d7a070d3534d470987c3abc69eec53905

[ "Apache-2.0" ]

217

2020-04-10T16:39:36.000Z

2022-03-30T15:39:04.000Z

projects/data_generation/synthetic_multi_view_facial_image_generation/algorithm/Rotate_and_Render/models/networks/sync_batchnorm/scatter_gather.py

ESOGU-SRLAB/opendr

f2eb5a6d7a070d3534d470987c3abc69eec53905

[ "Apache-2.0" ]

46

2021-12-16T13:23:04.000Z

2022-03-30T11:36:29.000Z

projects/data_generation/synthetic_multi_view_facial_image_generation/algorithm/Rotate_and_Render/models/networks/sync_batchnorm/scatter_gather.py

ESOGU-SRLAB/opendr

f2eb5a6d7a070d3534d470987c3abc69eec53905

[ "Apache-2.0" ]

29

2021-12-16T09:26:13.000Z

2022-03-29T15:19:18.000Z

import torch from torch.nn.parallel._functions import Scatter def scatter(inputs, target_gpus, dim=0, chunk_size=None): r""" Slices tensors into approximately equal chunks and distributes them across given GPUs. Duplicates references to objects that are not tensors. """ def scatter_map(obj): if isinstance(obj, torch.Tensor): return Scatter.apply(target_gpus, chunk_size, dim, obj) if isinstance(obj, tuple) and len(obj) > 0: return list(zip(*map(scatter_map, obj))) if isinstance(obj, list) and len(obj) > 0: return list(map(list, zip(*map(scatter_map, obj)))) if isinstance(obj, dict) and len(obj) > 0: return list(map(type(obj), zip(*map(scatter_map, obj.items())))) return [obj for targets in target_gpus] # After scatter_map is called, a scatter_map cell will exist. This cell # has a reference to the actual function scatter_map, which has references # to a closure that has a reference to the scatter_map cell (because the # fn is recursive). To avoid this reference cycle, we set the function to # None, clearing the cell try: res = scatter_map(inputs) finally: scatter_map = None return res def scatter_kwargs(inputs, kwargs, target_gpus, dim=0, chunk_size=None): r"""Scatter with support for kwargs dictionary""" inputs = scatter(inputs, target_gpus, dim, chunk_size) if inputs else [] kwargs = scatter(kwargs, target_gpus, dim, chunk_size) if kwargs else [] if len(inputs) < len(kwargs): inputs.extend([() for _ in range(len(kwargs) - len(inputs))]) elif len(kwargs) < len(inputs): kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))]) inputs = tuple(inputs) kwargs = tuple(kwargs) return inputs, kwargs

40.822222

78

0.666848

476b947c306085d748a829bf44be6a10610b06f6

938

py

Python

examples/redis/cache.py

PeterPZhang/open-box

987f91edf0f502d678459f7cc50070cae34accd9

[ "MIT" ]

null

examples/redis/cache.py

PeterPZhang/open-box

987f91edf0f502d678459f7cc50070cae34accd9

[ "MIT" ]

null

examples/redis/cache.py

PeterPZhang/open-box

987f91edf0f502d678459f7cc50070cae34accd9

[ "MIT" ]

null

from open_box.redis import get_redis_client from open_box.redis.cache import WrapperCache startup_nodes = [ {'host': '<host1>', 'port': '<port1>'}, {'host': '<host2>', 'port': '<port2>'}, {'host': '<host3>', 'port': '<port3>'}, ] rc = get_redis_client(startup_nodes) cache = WrapperCache(rc) def cache_context_usage(**kwargs): with cache.CacheContext(key_prefix='good', timeout=5, **kwargs) as ctx: if ctx.val: print('[from cache]{}: {}'.format(ctx.key, ctx.val)) else: ctx.val = 100 print('[to cache]{}: {}'.format(ctx.key, ctx.val)) @cache.CacheDecorator(key_prefix='hnf', timeout=5) def cache_decorator_usage(a, b=3, *args, **kwargs): return {'a': a, 'b': b, 'args': args, 'kwargs': kwargs} # return 1 # return datetime.now() if __name__ == '__main__': cache_context_usage(name='hnf', age=28) print(cache_decorator_usage('lx', 299, age=28))

30.258065

75

0.615139

705e5e75732ca7e8ec91e23fa2351c83b1eb9234

19,885

py

Python

tensorflow/python/keras/layers/dense_attention.py

bubblebooy/tensorflow

585de3969452255a1f8cd6d295b87a2e2d8298e9

[ "Apache-2.0" ]

null

tensorflow/python/keras/layers/dense_attention.py

bubblebooy/tensorflow

585de3969452255a1f8cd6d295b87a2e2d8298e9

[ "Apache-2.0" ]

null

tensorflow/python/keras/layers/dense_attention.py

bubblebooy/tensorflow

585de3969452255a1f8cd6d295b87a2e2d8298e9

[ "Apache-2.0" ]

null

# Copyright 2019 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Attention layers that can be used in sequence DNN/CNN models. This file follows the terminology of https://arxiv.org/abs/1706.03762 Figure 2. Attention is formed by three tensors: Query, Key and Value. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape from tensorflow.python.keras import backend as K from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.utils import tf_utils from tensorflow.python.ops import array_ops from tensorflow.python.ops import init_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn from tensorflow.python.util.tf_export import keras_export class BaseDenseAttention(Layer): """Base Attention class for Dense networks. This class is suitable for Dense or CNN networks, and not for RNN networks. Implementations of attention mechanisms should inherit from this class, and reuse the `apply_attention_scores()` method. Args: causal: Boolean. Set to `True` for decoder self-attention. Adds a mask such that position `i` cannot attend to positions `j > i`. This prevents the flow of information from the future towards the past. dropout: Float between 0 and 1. Fraction of the units to drop for the attention scores. Call Arguments: inputs: List of the following tensors: * query: Query `Tensor` of shape `[batch_size, Tq, dim]`. * value: Value `Tensor` of shape `[batch_size, Tv, dim]`. * key: Optional key `Tensor` of shape `[batch_size, Tv, dim]`. If not given, will use `value` for both `key` and `value`, which is the most common case. mask: List of the following tensors: * query_mask: A boolean mask `Tensor` of shape `[batch_size, Tq]`. If given, the output will be zero at the positions where `mask==False`. * value_mask: A boolean mask `Tensor` of shape `[batch_size, Tv]`. If given, will apply the mask such that values at positions where `mask==False` do not contribute to the result. training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (no dropout). Output shape: Attention outputs of shape `[batch_size, Tq, dim]`. """ def __init__(self, causal=False, dropout=0.0, **kwargs): super(BaseDenseAttention, self).__init__(**kwargs) self.causal = causal self.dropout = dropout self.supports_masking = True def _calculate_scores(self, query, key): """Calculates attention scores. Args: query: Query tensor of shape `[batch_size, Tq, dim]`. key: Key tensor of shape `[batch_size, Tv, dim]`. Returns: Tensor of shape `[batch_size, Tq, Tv]`. """ return NotImplementedError def _apply_scores(self, scores, value, scores_mask=None, training=None): """Applies attention scores to the given value tensor. To use this method in your attention layer, follow the steps: * Use `query` tensor of shape `[batch_size, Tq]` and `key` tensor of shape `[batch_size, Tv]` to calculate the attention `scores`. * Pass `scores` and `value` tensors to this method. The method applies `scores_mask`, calculates `attention_distribution = softmax(scores)`, then returns `matmul(attention_distribution, value). * Apply `query_mask` and return the result. Args: scores: Scores float tensor of shape `[batch_size, Tq, Tv]`. value: Value tensor of shape `[batch_size, Tv, dim]`. scores_mask: A boolean mask `Tensor` of shape `[batch_size, 1, Tv]` or `[batch_size, Tq, Tv]`. If given, scores at positions where `scores_mask==False` do not contribute to the result. It must contain at least one `True` value in each line along the last dimension. training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (no dropout). Returns: Tensor of shape `[batch_size, Tq, dim]`. """ if scores_mask is not None: padding_mask = math_ops.logical_not(scores_mask) # Bias so padding positions do not contribute to attention distribution. scores -= 1.e9 * math_ops.cast(padding_mask, dtype=K.floatx()) if training is None: training = K.learning_phase() weights = nn.softmax(scores) def dropped_weights(): return nn.dropout(weights, rate=self.dropout) weights = tf_utils.smart_cond( training, dropped_weights, lambda: array_ops.identity(weights)) return math_ops.matmul(weights, value) # TODO(b/125916026): Consider exposing a __call__ method with named args. def call(self, inputs, mask=None, training=None): self._validate_call_args(inputs=inputs, mask=mask) q = inputs[0] v = inputs[1] k = inputs[2] if len(inputs) > 2 else v q_mask = mask[0] if mask else None v_mask = mask[1] if mask else None scores = self._calculate_scores(query=q, key=k) if v_mask is not None: # Mask of shape [batch_size, 1, Tv]. v_mask = array_ops.expand_dims(v_mask, axis=-2) if self.causal: # Creates a lower triangular mask, so position i cannot attend to # positions j>i. This prevents the flow of information from the future # into the past. scores_shape = array_ops.shape(scores) # causal_mask_shape = [1, Tq, Tv]. causal_mask_shape = array_ops.concat( [array_ops.ones_like(scores_shape[:-2]), scores_shape[-2:]], axis=0) causal_mask = _lower_triangular_mask(causal_mask_shape) else: causal_mask = None scores_mask = _merge_masks(v_mask, causal_mask) result = self._apply_scores( scores=scores, value=v, scores_mask=scores_mask, training=training) if q_mask is not None: # Mask of shape [batch_size, Tq, 1]. q_mask = array_ops.expand_dims(q_mask, axis=-1) result *= math_ops.cast(q_mask, dtype=result.dtype) return result def compute_mask(self, inputs, mask=None): self._validate_call_args(inputs=inputs, mask=mask) if mask: q_mask = mask[0] if q_mask is None: return None return ops.convert_to_tensor_v2(q_mask) return None def _validate_call_args(self, inputs, mask): """Validates arguments of the call method.""" class_name = self.__class__.__name__ if not isinstance(inputs, list): raise ValueError( '{} layer must be called on a list of inputs, namely [query, value] ' 'or [query, value, key].'.format(class_name)) if len(inputs) < 2 or len(inputs) > 3: raise ValueError( '{} layer accepts inputs list of length 2 or 3, ' 'namely [query, value] or [query, value, key]. ' 'Given length: {}'.format(class_name, len(inputs))) if mask: if not isinstance(mask, list): raise ValueError( '{} layer mask must be a list, ' 'namely [query_mask, value_mask].'.format(class_name)) if len(mask) < 2 or len(mask) > 3: raise ValueError( '{} layer mask must be a list of length 2 or 3, namely [query_mask, ' 'value_mask] or [query_mask, value_mask, key_mask]. Given length: {}'.format(class_name, len(mask))) def get_config(self): config = { 'causal': self.causal, 'dropout': self.dropout, } base_config = super(BaseDenseAttention, self).get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export('keras.layers.Attention') class Attention(BaseDenseAttention): """Dot-product attention layer, a.k.a. Luong-style attention. Inputs are `query` tensor of shape `[batch_size, Tq, dim]`, `value` tensor of shape `[batch_size, Tv, dim]` and `key` tensor of shape `[batch_size, Tv, dim]`. The calculation follows the steps: 1. Calculate scores with shape `[batch_size, Tq, Tv]` as a `query`-`key` dot product: `scores = tf.matmul(query, key, transpose_b=True)`. 2. Use scores to calculate a distribution with shape `[batch_size, Tq, Tv]`: `distribution = tf.nn.softmax(scores)`. 3. Use `distribution` to create a linear combination of `value` with shape `[batch_size, Tq, dim]`: `return tf.matmul(distribution, value)`. Args: use_scale: If `True`, will create a scalar variable to scale the attention scores. causal: Boolean. Set to `True` for decoder self-attention. Adds a mask such that position `i` cannot attend to positions `j > i`. This prevents the flow of information from the future towards the past. dropout: Float between 0 and 1. Fraction of the units to drop for the attention scores. Call Arguments: inputs: List of the following tensors: * query: Query `Tensor` of shape `[batch_size, Tq, dim]`. * value: Value `Tensor` of shape `[batch_size, Tv, dim]`. * key: Optional key `Tensor` of shape `[batch_size, Tv, dim]`. If not given, will use `value` for both `key` and `value`, which is the most common case. mask: List of the following tensors: * query_mask: A boolean mask `Tensor` of shape `[batch_size, Tq]`. If given, the output will be zero at the positions where `mask==False`. * value_mask: A boolean mask `Tensor` of shape `[batch_size, Tv]`. If given, will apply the mask such that values at positions where `mask==False` do not contribute to the result. training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (no dropout). Output shape: Attention outputs of shape `[batch_size, Tq, dim]`. The meaning of `query`, `value` and `key` depend on the application. In the case of text similarity, for example, `query` is the sequence embeddings of the first piece of text and `value` is the sequence embeddings of the second piece of text. `key` is usually the same tensor as `value`. Here is a code example for using `Attention` in a CNN+Attention network: ```python # Variable-length int sequences. query_input = tf.keras.Input(shape=(None,), dtype='int32') value_input = tf.keras.Input(shape=(None,), dtype='int32') # Embedding lookup. token_embedding = tf.keras.layers.Embedding(max_tokens, dimension) # Query embeddings of shape [batch_size, Tq, dimension]. query_embeddings = token_embedding(query_input) # Value embeddings of shape [batch_size, Tv, dimension]. value_embeddings = token_embedding(value_input) # CNN layer. cnn_layer = tf.keras.layers.Conv1D( filters=100, kernel_size=4, # Use 'same' padding so outputs have the same shape as inputs. padding='same') # Query encoding of shape [batch_size, Tq, filters]. query_seq_encoding = cnn_layer(query_embeddings) # Value encoding of shape [batch_size, Tv, filters]. value_seq_encoding = cnn_layer(value_embeddings) # Query-value attention of shape [batch_size, Tq, filters]. query_value_attention_seq = tf.keras.layers.Attention()( [query_seq_encoding, value_seq_encoding]) # Reduce over the sequence axis to produce encodings of shape # [batch_size, filters]. query_encoding = tf.keras.layers.GlobalAveragePooling1D()( query_seq_encoding) query_value_attention = tf.keras.layers.GlobalAveragePooling1D()( query_value_attention_seq) # Concatenate query and document encodings to produce a DNN input layer. input_layer = tf.keras.layers.Concatenate()( [query_encoding, query_value_attention]) # Add DNN layers, and create Model. # ... ``` """ def __init__(self, use_scale=False, **kwargs): super(Attention, self).__init__(**kwargs) self.use_scale = use_scale def build(self, input_shape): """Creates scale variable if use_scale==True.""" if self.use_scale: self.scale = self.add_weight( name='scale', shape=(), initializer=init_ops.ones_initializer(), dtype=self.dtype, trainable=True) else: self.scale = None super(Attention, self).build(input_shape) def _calculate_scores(self, query, key): """Calculates attention scores as a query-key dot product. Args: query: Query tensor of shape `[batch_size, Tq, dim]`. key: Key tensor of shape `[batch_size, Tv, dim]`. Returns: Tensor of shape `[batch_size, Tq, Tv]`. """ scores = math_ops.matmul(query, key, transpose_b=True) if self.scale is not None: scores *= self.scale return scores def get_config(self): config = {'use_scale': self.use_scale} base_config = super(Attention, self).get_config() return dict(list(base_config.items()) + list(config.items())) @keras_export('keras.layers.AdditiveAttention') class AdditiveAttention(BaseDenseAttention): """Additive attention layer, a.k.a. Bahdanau-style attention. Inputs are `query` tensor of shape `[batch_size, Tq, dim]`, `value` tensor of shape `[batch_size, Tv, dim]` and `key` tensor of shape `[batch_size, Tv, dim]`. The calculation follows the steps: 1. Reshape `query` and `value` into shapes `[batch_size, Tq, 1, dim]` and `[batch_size, 1, Tv, dim]` respectively. 2. Calculate scores with shape `[batch_size, Tq, Tv]` as a non-linear sum: `scores = tf.reduce_sum(tf.tanh(query + value), axis=-1)` 3. Use scores to calculate a distribution with shape `[batch_size, Tq, Tv]`: `distribution = tf.nn.softmax(scores)`. 4. Use `distribution` to create a linear combination of `value` with shape `batch_size, Tq, dim]`: `return tf.matmul(distribution, value)`. Args: use_scale: If `True`, will create a variable to scale the attention scores. causal: Boolean. Set to `True` for decoder self-attention. Adds a mask such that position `i` cannot attend to positions `j > i`. This prevents the flow of information from the future towards the past. dropout: Float between 0 and 1. Fraction of the units to drop for the attention scores. Call Arguments: inputs: List of the following tensors: * query: Query `Tensor` of shape `[batch_size, Tq, dim]`. * value: Value `Tensor` of shape `[batch_size, Tv, dim]`. * key: Optional key `Tensor` of shape `[batch_size, Tv, dim]`. If not given, will use `value` for both `key` and `value`, which is the most common case. mask: List of the following tensors: * query_mask: A boolean mask `Tensor` of shape `[batch_size, Tq]`. If given, the output will be zero at the positions where `mask==False`. * value_mask: A boolean mask `Tensor` of shape `[batch_size, Tv]`. If given, will apply the mask such that values at positions where `mask==False` do not contribute to the result. training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (no dropout). Output shape: Attention outputs of shape `[batch_size, Tq, dim]`. The meaning of `query`, `value` and `key` depend on the application. In the case of text similarity, for example, `query` is the sequence embeddings of the first piece of text and `value` is the sequence embeddings of the second piece of text. `key` is usually the same tensor as `value`. Here is a code example for using `AdditiveAttention` in a CNN+Attention network: ```python # Variable-length int sequences. query_input = tf.keras.Input(shape=(None,), dtype='int32') value_input = tf.keras.Input(shape=(None,), dtype='int32') # Embedding lookup. token_embedding = tf.keras.layers.Embedding(max_tokens, dimension) # Query embeddings of shape [batch_size, Tq, dimension]. query_embeddings = token_embedding(query_input) # Value embeddings of shape [batch_size, Tv, dimension]. value_embeddings = token_embedding(value_input) # CNN layer. cnn_layer = tf.keras.layers.Conv1D( filters=100, kernel_size=4, # Use 'same' padding so outputs have the same shape as inputs. padding='same') # Query encoding of shape [batch_size, Tq, filters]. query_seq_encoding = cnn_layer(query_embeddings) # Value encoding of shape [batch_size, Tv, filters]. value_seq_encoding = cnn_layer(value_embeddings) # Query-value attention of shape [batch_size, Tq, filters]. query_value_attention_seq = tf.keras.layers.AdditiveAttention()( [query_seq_encoding, value_seq_encoding]) # Reduce over the sequence axis to produce encodings of shape # [batch_size, filters]. query_encoding = tf.keras.layers.GlobalAveragePooling1D()( query_seq_encoding) query_value_attention = tf.keras.layers.GlobalAveragePooling1D()( query_value_attention_seq) # Concatenate query and document encodings to produce a DNN input layer. input_layer = tf.keras.layers.Concatenate()( [query_encoding, query_value_attention]) # Add DNN layers, and create Model. # ... ``` """ def __init__(self, use_scale=True, **kwargs): super(AdditiveAttention, self).__init__(**kwargs) self.use_scale = use_scale def build(self, input_shape): v_shape = tensor_shape.TensorShape(input_shape[1]) dim = v_shape[-1] if isinstance(dim, tensor_shape.Dimension): dim = dim.value if self.use_scale: self.scale = self.add_weight( name='scale', shape=[dim], initializer=init_ops.glorot_uniform_initializer(), dtype=self.dtype, trainable=True) else: self.scale = None super(AdditiveAttention, self).build(input_shape) def _calculate_scores(self, query, key): """Calculates attention scores as a nonlinear sum of query and key. Args: query: Query tensor of shape `[batch_size, Tq, dim]`. key: Key tensor of shape `[batch_size, Tv, dim]`. Returns: Tensor of shape `[batch_size, Tq, Tv]`. """ # Reshape tensors to enable broadcasting. # Reshape into [batch_size, Tq, 1, dim]. q_reshaped = array_ops.expand_dims(query, axis=-2) # Reshape into [batch_size, 1, Tv, dim]. k_reshaped = array_ops.expand_dims(key, axis=-3) if self.use_scale: scale = self.scale else: scale = 1. return math_ops.reduce_sum( scale * math_ops.tanh(q_reshaped + k_reshaped), axis=-1) def get_config(self): config = {'use_scale': self.use_scale} base_config = super(AdditiveAttention, self).get_config() return dict(list(base_config.items()) + list(config.items())) def _lower_triangular_mask(shape): """Creates a lower-triangular boolean mask over the last 2 dimensions.""" row_index = math_ops.cumsum( array_ops.ones(shape=shape, dtype=dtypes.int32), axis=-2) col_index = math_ops.cumsum( array_ops.ones(shape=shape, dtype=dtypes.int32), axis=-1) return math_ops.greater_equal(row_index, col_index) def _merge_masks(x, y): if x is None: return y if y is None: return x return math_ops.logical_and(x, y)

39.454365

112

0.687956

15044c9ff40d8ee69d3e66a461c632f30038d67f

42,592

py

Python

nion/ui/DrawingContext.py

dhauge/nionui

7b86b70f9a3048b071684bb841963e02b492fdac

[ "Apache-2.0" ]

null

nion/ui/DrawingContext.py

dhauge/nionui

7b86b70f9a3048b071684bb841963e02b492fdac

[ "Apache-2.0" ]

null

nion/ui/DrawingContext.py

dhauge/nionui

7b86b70f9a3048b071684bb841963e02b492fdac

[ "Apache-2.0" ]

null

""" DrawingContext module contains classes related to drawing context. DrawingContexts are able to be handled directly by the UI system or produce javascript or svg to do the drawing. """ # standard libraries import base64 import collections from contextlib import contextmanager import copy import io import logging import math import re import struct import sys import time import threading import typing import xml.sax.saxutils # third party libraries import imageio import numpy # local libraries # None # pylint: disable=star-args def get_rgba_view_from_rgba_data(rgba_data): return rgba_data.view(numpy.uint8).reshape(rgba_data.shape + (4,)) def get_rgba_data_from_rgba(rgba_image): return rgba_image.view(numpy.uint32).reshape(rgba_image.shape[:-1]) def get_byte_view(rgba_image): return rgba_image.view(numpy.uint8).reshape(rgba_image.shape + (-1, )) def get_red_view(rgba_image, byteorder=None): if byteorder is None: byteorder = sys.byteorder bytes = get_byte_view(rgba_image) assert bytes.shape[-1] == 4 if byteorder == 'little': return bytes[..., 2] # strip A off BGRA else: return bytes[..., 1] # strip A off ARGB def get_green_view(rgba_image, byteorder=None): if byteorder is None: byteorder = sys.byteorder bytes = get_byte_view(rgba_image) assert bytes.shape[-1] == 4 if byteorder == 'little': return bytes[..., 1] # strip A off BGRA else: return bytes[..., 2] # strip A off ARGB def get_blue_view(rgba_image, byteorder=None): if byteorder is None: byteorder = sys.byteorder bytes = get_byte_view(rgba_image) assert bytes.shape[-1] == 4 if byteorder == 'little': return bytes[..., 0] # strip A off BGRA else: return bytes[..., 3] # strip A off ARGB def get_alpha_view(rgba_image, byteorder=None): if byteorder is None: byteorder = sys.byteorder bytes = get_byte_view(rgba_image) assert bytes.shape[-1] == 4 if byteorder == 'little': return bytes[..., 3] # A of BGRA else: return bytes[..., 0] # A of ARGB class DrawingContext: """ Path commands (begin_path, close_path, move_to, line_to, etc.) should not be intermixed with transform commands (translate, scale, rotate). """ # TODO: stroke_fill # TODO: circle __image_id = 0 __image_id_lock = threading.RLock() def __init__(self): self.commands = [] self.binary_commands = bytearray() self.save_count = 0 self.images = dict() def copy_from(self, drawing_context): assert self.save_count == 0 assert drawing_context.save_count == 0 self.commands = drawing_context.commands self.binary_commands = drawing_context.binary_commands self.images = drawing_context.images def add(self, drawing_context): self.commands.extend(drawing_context.commands) self.binary_commands.extend(drawing_context.binary_commands) self.images.update(drawing_context.images) def clear(self): self.commands = [] self.binary_commands = [] self.save_count = 0 self.images = dict() def to_js(self): js = "" for command in self.commands: command_id = command[0] command_args = command[1:] if command_id == "save": js += "ctx.save();" elif command_id == "restore": js += "ctx.restore();" elif command_id == "beginPath": js += "ctx.beginPath();" elif command_id == "closePath": js += "ctx.closePath();" elif command_id == "clip": js += "ctx.beginPath();" js += "ctx.rect({0}, {1}, {2}, {3});".format(*command_args) js += "ctx.clip();" elif command_id == "translate": js += "ctx.translate({0}, {1});".format(*command_args) elif command_id == "scale": js += "ctx.scale({0}, {1});".format(*command_args) elif command_id == "rotate": js += "ctx.rotate({0});".format(*command_args) elif command_id == "moveTo": js += "ctx.moveTo({0}, {1});".format(*command_args) elif command_id == "lineTo": js += "ctx.lineTo({0}, {1});".format(*command_args) elif command_id == "rect": js += "ctx.rect({0}, {1}, {2}, {3});".format(*command_args) elif command_id == "arc": x, y, r, sa, ea, ac = command_args js += "ctx.arc({0}, {1}, {2}, {3}, {4}, {5});".format(x, y, r, sa, ea, "true" if ac else "false") elif command_id == "arcTo": x1, y1, x2, y2, r = command_args js += "ctx.arcTo({0}, {1}, {2}, {3}, {4});".format(x1, y1, x2, y2, r) elif command_id == "cubicTo": x1, y1, x2, y2, x, y = command_args js += "ctx.bezierCurveTo({0}, {1}, {2}, {3}, {4}, {5});".format(x1, y1, x2, y2, x, y) elif command_id == "quadraticTo": x1, y1, x, y = command_args js += "ctx.quadraticCurveTo({0}, {1}, {2}, {3});".format(x1, y1, x, y) elif command_id == "image": w, h, image, image_id, a, b, c, d = command_args js += "ctx.rect({0}, {1}, {2}, {3});".format(a, b, c, d) elif command_id == "data": w, h, data, data_id, a, b, c, d, low, high, color_table = command_args js += "ctx.rect({0}, {1}, {2}, {3});".format(a, b, c, d) elif command_id == "stroke": js += "ctx.stroke();" elif command_id == "sleep": pass # used for performance testing elif command_id == "fill": js += "ctx.fill();" elif command_id == "fillText": text, x, y, max_width = command_args js += "ctx.fillText('{0}', {1}, {2}{3});".format(xml.sax.saxutils.escape(text), x, y, ", {0}".format(max_width) if max_width else "") elif command_id == "fillStyleGradient": command_var = command_args[0] js += "ctx.fillStyle = {0};".format("grad" + str(command_var)) elif command_id == "fillStyle": js += "ctx.fillStyle = '{0}';".format(*command_args) elif command_id == "font": js += "ctx.font = '{0}';".format(*command_args) elif command_id == "textAlign": js += "ctx.textAlign = '{0}';".format(*command_args) elif command_id == "textBaseline": js += "ctx.textBaseline = '{0}';".format(*command_args) elif command_id == "strokeStyle": js += "ctx.strokeStyle = '{0}';".format(*command_args) elif command_id == "lineWidth": js += "ctx.lineWidth = {0};".format(*command_args) elif command_id == "lineDash": js += "ctx.lineDash = {0};".format(*command_args) elif command_id == "lineCap": js += "ctx.lineCap = '{0}';".format(*command_args) elif command_id == "lineJoin": js += "ctx.lineJoin = '{0}';".format(*command_args) elif command_id == "gradient": command_var, width, height, x1, y1, x2, y2 = command_args # pylint: disable=invalid-name js_var = "grad" + str(command_var) js += "var {0} = ctx.createLinearGradient({1}, {2}, {3}, {4});".format(js_var, x1, y1, x2 - x1, y2 - y1) elif command_id == "colorStop": command_var, x, color = command_args js_var = "grad" + str(command_var) js += "{0}.addColorStop({1}, '{2}');".format(js_var, x, color) return js def to_svg(self, size, viewbox): svg = "" defs = "" path = "" next_clip_id = 1 transform = list() closers = list() fill_style = None fill_opacity = 1.0 stroke_style = None stroke_opacity = 1.0 line_cap = "square" line_join = "bevel" line_width = 1.0 line_dash = None text_anchor = "start" text_baseline = "alphabetic" font_style = None font_weight = None font_size = None font_unit = None font_family = None contexts = collections.deque() gradient_start = None gradient_stops = list() # make a SVG 1.1 compatible color, opacity tuple def parse_color(color_str: str) -> typing.Tuple[str, float]: color_str = ''.join(color_str.split()) if color_str.startswith("rgba"): c = re.split("rgba$(\d+),(\d+),(\d+),([\d.]+)$", color_str) return f"rgb({c[1]}, {c[2]}, {c[3]})", float(c[4]) return color_str, 1.0 for command in self.commands: command_id = command[0] #logging.debug(command_id) command_args = command[1:] if command_id == "save": context = dict() context["path"] = path context["transform"] = copy.deepcopy(transform) context["fill_style"] = fill_style context["fill_opacity"] = fill_opacity context["stroke_style"] = stroke_style context["stroke_opacity"] = stroke_opacity context["line_cap"] = line_cap context["line_join"] = line_join context["line_width"] = line_width context["line_dash"] = line_dash context["font_style"] = font_style context["font_weight"] = font_weight context["font_size"] = font_size context["font_unit"] = font_unit context["font_family"] = font_family context["text_anchor"] = text_anchor context["text_baseline"] = text_baseline context["closers"] = copy.deepcopy(closers) closers = list() contexts.append(context) elif command_id == "restore": svg += "".join(closers) context = contexts.pop() path = context["path"] transform = context["transform"] fill_style = context["fill_style"] fill_opacity = context["fill_opacity"] font_style = context["font_style"] font_weight = context["font_weight"] font_size = context["font_size"] font_unit = context["font_unit"] font_family = context["font_family"] text_anchor = context["text_anchor"] text_baseline = context["text_baseline"] stroke_style = context["stroke_style"] stroke_opacity = context["stroke_opacity"] line_cap = context["line_cap"] line_join = context["line_join"] line_width = context["line_width"] line_dash = context["line_dash"] closers = context["closers"] elif command_id == "beginPath": path = "" elif command_id == "closePath": path += " Z" elif command_id == "moveTo": path += " M {0} {1}".format(*command_args) elif command_id == "lineTo": path += " L {0} {1}".format(*command_args) elif command_id == "rect": x, y, w, h = command_args path += " M {0} {1}".format(x, y) path += " L {0} {1}".format(x + w, y) path += " L {0} {1}".format(x + w, y + h) path += " L {0} {1}".format(x, y + h) path += " Z" elif command_id == "arc": x, y, r, sa, ea, ac = command_args # js += "ctx.arc({0}, {1}, {2}, {3}, {4}, {5});".format(x, y, r, sa, ea, "true" if ac else "false") elif command_id == "arcTo": x1, y1, x2, y2, r = command_args # js += "ctx.arcTo({0}, {1}, {2}, {3}, {4});".format(x1, y1, x2, y2, r) elif command_id == "cubicTo": path += " C {0} {1}, {2} {3}, {4} {5}".format(*command_args) elif command_id == "quadraticTo": path += " Q {0} {1}, {2} {3}".format(*command_args) elif command_id == "clip": x, y, w, h = command_args clip_id = "clip" + str(next_clip_id) next_clip_id += 1 transform_str = " transform='{0}'".format(" ".join(transform)) if len(transform) > 0 else "" defs_format_str = "<clipPath id='{0}'><rect x='{1}' y='{2}' width='{3}' height='{4}'{5} /></clipPath>" defs += defs_format_str.format(clip_id, x, y, w, h, transform_str) svg += "<g style='clip-path: url(#{0});'>".format(clip_id) closers.append("</g>") elif command_id == "translate": transform.append("translate({0},{1})".format(*command_args)) elif command_id == "scale": transform.append("scale({0},{1})".format(*command_args)) elif command_id == "rotate": transform.append("rotate({0})".format(*command_args)) elif command_id == "image": w, h, image, image_id, a, b, c, d = command_args png_file = io.BytesIO() rgba_data = get_rgba_view_from_rgba_data(image) # image compression is time consuming. pass parameters to make this step as fast as possible. # see nionswift-642. imageio.imwrite(png_file, rgba_data[..., (2,1,0,3)], "png", optimize=False, compress_level=1) png_encoded = base64.b64encode(png_file.getvalue()).decode('utf=8') transform_str = " transform='{0}'".format(" ".join(transform)) if len(transform) > 0 else "" svg_format_str = "<image x='{0}' y='{1}' width='{2}' height='{3}' xlink:href='data:image/png;base64,{4}'{5} />" svg += svg_format_str.format(a, b, c, d, png_encoded, transform_str) elif command_id == "data": w, h, data, data_id, a, b, c, d, low, high, color_table, color_table_image_id = command_args m = 255.0 / (high - low) if high != low else 1 image = numpy.empty(data.shape, numpy.uint32) if color_table is not None: adj_color_table = numpy.empty(color_table.shape, numpy.uint32) # ordering of color_table is BGRA # ordering of adj_color_table is RGBA get_byte_view(adj_color_table)[:, 0] = get_byte_view(color_table)[:, 2] get_byte_view(adj_color_table)[:, 1] = get_byte_view(color_table)[:, 1] get_byte_view(adj_color_table)[:, 2] = get_byte_view(color_table)[:, 0] get_byte_view(adj_color_table)[:, 3] = get_byte_view(color_table)[:, 3] clipped_array = numpy.clip((m * (data - low)).astype(int), 0, 255).astype(numpy.uint8) image[:] = adj_color_table[clipped_array] else: clipped_array = numpy.clip(data, low, high) numpy.subtract(clipped_array, low, out=clipped_array) numpy.multiply(clipped_array, m, out=clipped_array) get_red_view(image)[:] = clipped_array get_green_view(image)[:] = clipped_array get_blue_view(image)[:] = clipped_array get_alpha_view(image)[:] = 255 png_file = io.BytesIO() # image compression is time consuming. pass parameters to make this step as fast as possible. # see nionswift-642. imageio.imwrite(png_file, get_rgba_view_from_rgba_data(image), "png", optimize=False, compress_level=1) png_encoded = base64.b64encode(png_file.getvalue()).decode('utf=8') transform_str = " transform='{0}'".format(" ".join(transform)) if len(transform) > 0 else "" svg_format_str = "<image x='{0}' y='{1}' width='{2}' height='{3}' xlink:href='data:image/png;base64,{4}'{5} />" svg += svg_format_str.format(a, b, c, d, png_encoded, transform_str) elif command_id == "stroke": if stroke_style is not None: transform_str = " transform='{0}'".format(" ".join(transform)) if len(transform) > 0 else "" dash_str = " stroke-dasharray='{0}, {1}'".format(line_dash, line_dash) if line_dash else "" svg += f"<path d='{path}' fill='none' stroke='{stroke_style}' stroke-opacity='{stroke_opacity}' stroke-width='{line_width}' stroke-linejoin='{line_join}' stroke-linecap='{line_cap}'{dash_str}{transform_str} />" elif command_id == "sleep": pass # used for performance testing elif command_id == "fill": if fill_style is not None: transform_str = " transform='{0}'".format(" ".join(transform)) if len(transform) > 0 else "" svg += f"<path d='{path}' fill='{fill_style}' fill-opacity='{fill_opacity}' stroke='none'{transform_str} />" elif command_id == "fillText": text, x, y, max_width = command_args transform_str = " transform='{0}'".format(" ".join(transform)) if len(transform) > 0 else "" font_str = "" if font_style: font_str += " font-style='{0}'".format(font_style) if font_weight: font_str += " font-weight='{0}'".format(font_weight) if font_size: font_str += " font-size='{0}{1}'".format(font_size, font_unit) if font_family: font_str += " font-family='{0}'".format(font_family) if fill_style: font_str += " fill='{0}'".format(fill_style) if fill_opacity < 1.0: font_str += " fill-opacity='{0}'".format(fill_opacity) svg_format_str = "<text x='{0}' y='{1}' text-anchor='{3}' alignment-baseline='{4}'{5}{6}>{2}</text>" svg += svg_format_str.format(x, y, xml.sax.saxutils.escape(text), text_anchor, text_baseline, font_str, transform_str) elif command_id == "fillStyleGradient": command_var = command_args[0] defs += gradient_start + "".join(gradient_stops) + "</linearGradient>" fill_style = "url(#{0})".format("grad" + str(command_var)) elif command_id == "fillStyle": fill_style, fill_opacity = parse_color(command_args[0]) elif command_id == "font": font_style = None font_weight = None font_size = None font_unit = None font_family = None for font_part in [s for s in command_args[0].split(" ") if s]: if font_part == "italic": font_style = "italic" elif font_part == "bold": font_weight = "bold" elif font_part.endswith("px") and int(font_part[0:-2]) > 0: font_size = int(font_part[0:-2]) font_unit = "px" elif font_part.endswith("pt") and int(font_part[0:-2]) > 0: font_size = int(font_part[0:-2]) font_unit = "pt" else: font_family = font_part elif command_id == "textAlign": text_anchors = {"start": "start", "end": "end", "left": "start", "center": "middle", "right": "end"} text_anchor = text_anchors.get(command_args[0], "start") elif command_id == "textBaseline": text_baselines = {"top": "hanging", "hanging": "hanging", "middle": "middle", "alphabetic": "alphabetic", "ideaographic": "ideaographic", "bottom": "bottom"} text_baseline = text_baselines.get(command_args[0], "alphabetic") elif command_id == "strokeStyle": stroke_style, stroke_opacity = parse_color(command_args[0]) elif command_id == "lineWidth": line_width = command_args[0] elif command_id == "lineDash": line_dash = command_args[0] elif command_id == "lineCap": line_caps = {"square": "square", "round": "round", "butt": "butt"} line_cap = line_caps.get(command_args[0], "square") elif command_id == "lineJoin": line_joins = {"round": "round", "miter": "miter", "bevel": "bevel"} line_join = line_joins.get(command_args[0], "bevel") elif command_id == "gradient": # assumes that gradient will be used immediately after being # declared and stops being defined. this is currently enforced by # the way the commands are generated in drawing context. command_var, w, h, x1, y1, x2, y2 = command_args grad_id = "grad" + str(command_var) gradient_start = "<linearGradient id='{0}' x1='{1}' y1='{2}' x2='{3}' y2='{4}'>".format(grad_id, float(x1 / w), float(y1 / h), float(x2 / w), float(y2 / h)) elif command_id == "colorStop": command_var, x, color = command_args gradient_stops.append("<stop offset='{0}%' stop-color='{1}' />".format(int(x * 100), color)) else: logging.debug("Unknown command %s", command) xmlns = "xmlns='http://www.w3.org/2000/svg' xmlns:xlink='http://www.w3.org/1999/xlink'" viewbox_str = "{0} {1} {2} {3}".format(viewbox.left, viewbox.top, viewbox.width, viewbox.height) result = "<svg version='1.1' baseProfile='full' width='{0}' height='{1}' viewBox='{2}' {3}>".format(size.width, size.height, viewbox_str, xmlns) result += "<defs>" + defs + "</defs>" result += svg result += "</svg>" return result @contextmanager def saver(self): self.save() try: yield finally: self.restore() def save(self): self.commands.append(("save", )) self.binary_commands.extend(b"save") self.save_count += 1 def restore(self): self.commands.append(("restore", )) self.binary_commands.extend(b"rest") self.save_count -= 1 def begin_layer(self, layer_id: int, layer_seed: int, a, b, c, d) -> None: self.commands.append(("begin_layer", int(layer_id), int(layer_seed), float(a), float(b), float(c), float(d))) self.binary_commands.extend(struct.pack("4siiffff", b"bgly", int(layer_id), int(layer_seed), float(a), float(b), float(c), float(d))) def end_layer(self, layer_id: int, layer_seed: int, a, b, c, d) -> None: self.commands.append(("end_layer", int(layer_id), int(layer_seed), float(a), float(b), float(c), float(d))) self.binary_commands.extend(struct.pack("4siiffff", b"enly", int(layer_id), int(layer_seed), float(a), float(b), float(c), float(d))) def begin_path(self): self.commands.append(("beginPath", )) self.binary_commands.extend(b"bpth") def close_path(self): self.commands.append(("closePath", )) self.binary_commands.extend(b"cpth") def clip_rect(self, a, b, c, d): self.commands.append(("clip", float(a), float(b), float(c), float(d))) self.binary_commands.extend(struct.pack("4sffff", b"clip", float(a), float(b), float(c), float(d))) def translate(self, x, y): self.commands.append(("translate", float(x), float(y))) self.binary_commands.extend(struct.pack("4sff", b"tran", float(x), float(y))) def scale(self, x, y): self.commands.append(("scale", float(x), float(y))) self.binary_commands.extend(struct.pack("4sff", b"scal", float(x), float(y))) def rotate(self, radians): self.commands.append(("rotate", math.degrees(float(radians)))) self.binary_commands.extend(struct.pack("4sf", b"rota", math.degrees(float(radians)))) def move_to(self, x, y): self.commands.append(("moveTo", float(x), float(y))) self.binary_commands.extend(struct.pack("4sff", b"move", float(x), float(y))) def line_to(self, x, y): self.commands.append(("lineTo", float(x), float(y))) self.binary_commands.extend(struct.pack("4sff", b"line", float(x), float(y))) def rect(self, l, t, w, h): self.commands.append(("rect", float(l), float(t), float(w), float(h))) self.binary_commands.extend(struct.pack("4sffff", b"rect", float(l), float(t), float(w), float(h))) def round_rect(self, x, y, w, h, r): self.move_to(x + r, y) self.arc_to(x + w, y, x + w, y + r, r) self.arc_to(x + w, y + h, x + w - r, y + h, r) self.arc_to(x, y + h, x, y + h - r, r) self.arc_to(x, y, x + r, y, r) self.close_path() def arc(self, x, y, r, sa, ea, ac=False): self.commands.append(("arc", float(x), float(y), float(r), float(sa), float(ea), bool(ac))) self.binary_commands.extend(struct.pack("4sfffffi", b"arc ", float(x), float(y), float(r), float(sa), float(ea), bool(ac))) def arc_to(self, x1, y1, x2, y2, r): self.commands.append(("arcTo", float(x1), float(y1), float(x2), float(y2), float(r))) self.binary_commands.extend(struct.pack("4sfffff", b"arct", float(x1), float(y1), float(x2), float(y2), float(r))) def bezier_curve_to(self, x1, y1, x2, y2, x, y): self.commands.append(("cubicTo", float(x1), float(y1), float(x2), float(y2), float(x), float(y))) self.binary_commands.extend(struct.pack("4sffffff", b"cubc", float(x1), float(y1), float(x2), float(y2), float(x), float(y))) def quadratic_curve_to(self, x1, y1, x, y): self.commands.append(("quadraticTo", float(x1), float(y1), float(x), float(y))) self.binary_commands.extend(struct.pack("4sffff", b"quad", float(x1), float(y1), float(x), float(y))) def draw_image(self, img, x, y, width, height): # img should be rgba pack, uint32 assert img.dtype == numpy.uint32 with DrawingContext.__image_id_lock: DrawingContext.__image_id += 1 image_id = DrawingContext.__image_id self.commands.append( ("image", img.shape[1], img.shape[0], img, int(image_id), float(x), float(y), float(width), float(height))) self.images[str(image_id)] = img self.binary_commands.extend(struct.pack("4siiiffff", b"imag", img.shape[1], img.shape[0], int(image_id), float(x), float(y), float(width), float(height))) def draw_data(self, img, x, y, width, height, low, high, color_map_data): # img should be float assert img.dtype == numpy.float32 with DrawingContext.__image_id_lock: DrawingContext.__image_id += 1 image_id = DrawingContext.__image_id if color_map_data is not None: DrawingContext.__image_id += 1 color_map_image_id = DrawingContext.__image_id else: color_map_image_id = 0 self.images[str(image_id)] = img if color_map_data is not None: self.images[str(color_map_image_id)] = color_map_data self.commands.append( ("data", img.shape[1], img.shape[0], img, int(image_id), float(x), float(y), float(width), float(height), float(low), float(high), color_map_data, int(color_map_image_id))) self.binary_commands.extend(struct.pack("4siiiffffffi", b"data", img.shape[1], img.shape[0], int(image_id), float(x), float(y), float(width), float(height), float(low), float(high), int(color_map_image_id))) def stroke(self): self.commands.append(("stroke", )) self.binary_commands.extend(b"strk") def sleep(self, duration): self.commands.append(("sleep", float(duration))) self.binary_commands.extend(struct.pack("4sf", b"slep", float(duration))) def mark_latency(self): self.commands.append(("latency", time.perf_counter())) self.binary_commands.extend(struct.pack("<4sd", b"latn", time.perf_counter())) def message(self, text): self.commands.append(("message", text)) text_encoded = text.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}s0i".format(len(text_encoded)), b"mesg", len(text_encoded), text_encoded)) def timestamp(self, timestamp): self.commands.append(("timestamp", timestamp)) timestamp_encoded = timestamp.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}s0i".format(len(timestamp_encoded)), b"time", len(timestamp_encoded), timestamp_encoded)) def fill(self): self.commands.append(("fill", )) self.binary_commands.extend(b"fill") def fill_text(self, text, x, y, max_width=None): text = str(text) if text is not None else str() self.commands.append(("fillText", text, float(x), float(y), float(max_width) if max_width else 0)) text_encoded = text.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}sfff".format(len(text_encoded)), b"text", len(text_encoded), text_encoded, float(x), float(y), float(max_width) if max_width else 0)) @property def fill_style(self): raise NotImplementedError() @fill_style.setter def fill_style(self, a): a = a or "rgba(0, 0, 0, 0.0)" if isinstance(a, DrawingContext.LinearGradient): self.commands.extend(a.commands) self.commands.append(("fillStyleGradient", int(a.command_var))) self.binary_commands.extend(a.binary_commands) self.binary_commands.extend(struct.pack("4si", b"flsg", int(a.command_var))) else: self.commands.append(("fillStyle", str(a))) a_encoded = a.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}s0i".format(len(a_encoded)), b"flst", len(a_encoded), a_encoded)) @property def font(self): raise NotImplementedError() @font.setter def font(self, a): """ Set the text font. Supports 'normal', 'bold', 'italic', size specific as '14px', and font-family. """ self.commands.append(("font", str(a))) a_encoded = a.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}s0i".format(len(a_encoded)), b"font", len(a_encoded), a_encoded)) def __get_text_align(self): raise NotImplementedError() def __set_text_align(self, a): """ Set text alignment. Valid values are 'start', 'end', 'left', 'center', 'right'. Default is 'start'. Default is 'start'. """ self.commands.append(("textAlign", str(a))) a_encoded = a.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}s0i".format(len(a_encoded)), b"algn", len(a_encoded), a_encoded)) text_align = property(__get_text_align, __set_text_align) def __get_text_baseline(self): raise NotImplementedError() def __set_text_baseline(self, a): """ Set the text baseline. Valid values are 'top', 'hanging', 'middle', 'alphabetic', 'ideographic', and 'bottom'. Default is 'alphabetic'. """ self.commands.append(("textBaseline", str(a))) a_encoded = a.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}s0i".format(len(a_encoded)), b"tbas", len(a_encoded), a_encoded)) text_baseline = property(__get_text_baseline, __set_text_baseline) def __get_stroke_style(self): raise NotImplementedError() def __set_stroke_style(self, a): a = a or "rgba(0, 0, 0, 0.0)" self.commands.append(("strokeStyle", str(a))) a_encoded = a.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}s0i".format(len(a_encoded)), b"stst", len(a_encoded), a_encoded)) stroke_style = property(__get_stroke_style, __set_stroke_style) def __get_line_width(self): raise NotImplementedError() def __set_line_width(self, a): self.commands.append(("lineWidth", float(a))) self.binary_commands.extend(struct.pack("4sf", b"linw", float(a))) line_width = property(__get_line_width, __set_line_width) def __get_line_dash(self): raise NotImplementedError() def __set_line_dash(self, a): """ Set the line dash. Takes a single value with the length of the dash. """ self.commands.append(("lineDash", float(a))) self.binary_commands.extend(struct.pack("4sf", b"ldsh", float(a))) line_dash = property(__get_line_dash, __set_line_dash) def __get_line_cap(self): raise NotImplementedError() def __set_line_cap(self, a): """ Set the line join. Valid values are 'square', 'round', 'butt'. Default is 'square'. """ self.commands.append(("lineCap", str(a))) a_encoded = a.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}s0i".format(len(a_encoded)), b"lcap", len(a_encoded), a_encoded)) line_cap = property(__get_line_cap, __set_line_cap) def __get_line_join(self): raise NotImplementedError() def __set_line_join(self, a): """ Set the line join. Valid values are 'round', 'miter', 'bevel'. Default is 'bevel'. """ self.commands.append(("lineJoin", str(a))) a_encoded = a.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}s0i".format(len(a_encoded)), b"lnjn", len(a_encoded), a_encoded)) line_join = property(__get_line_join, __set_line_join) class LinearGradient: next = 1 def __init__(self, width, height, x1, y1, x2, y2): # pylint: disable=invalid-name self.commands = [] self.binary_commands = [] self.command_var = DrawingContext.LinearGradient.next self.commands.append(("gradient", self.command_var, float(width), float(height), float(x1), float(y1), float(x2), float(y2))) self.binary_commands.extend(struct.pack("4siffffff", b"grad", self.command_var, float(width), float(height), float(x1), float(y1), float(x2), float(y2))) DrawingContext.LinearGradient.next += 1 def add_color_stop(self, x, color): self.commands.append(("colorStop", self.command_var, float(x), str(color))) color_encoded = color.encode("utf-8") self.binary_commands.extend(struct.pack("4sifi{}s0i".format(len(color_encoded)), b"grcs", self.command_var, float(x), len(color_encoded), color_encoded)) def create_linear_gradient(self, width, height, x1, y1, x2, y2): # pylint: disable=invalid-name gradient = DrawingContext.LinearGradient(width, height, x1, y1, x2, y2) return gradient def statistics(self, stat_id): self.commands.append(("statistics", str(stat_id))) stat_id_encoded = stat_id.encode("utf-8") self.binary_commands.extend(struct.pack("4si{}s0i".format(len(stat_id_encoded)), b"stat", len(stat_id_encoded), stat_id_encoded)) # https://www.w3.org/TR/SVG11/types.html#ColorKeywords # processed with: """ import re with open("colors.txt", "r") as f: while True: color_line = f.readline() if not color_line: break color_line = color_line.strip() rgb_line = f.readline().strip().replace(" ", "") c = re.split("rgb$(\d+),(\d+),(\d+)$", rgb_line) print(f"\t\"{color_line}\": \"#{int(c[1]):02x}{int(c[2]):02x}{int(c[3]):02x}\",") """ svg_color_map = { "aliceblue": "#f0f8ff", "antiquewhite": "#faebd7", "aqua": "#00ffff", "aquamarine": "#7fffd4", "azure": "#f0ffff", "beige": "#f5f5dc", "bisque": "#ffe4c4", "black": "#000000", "blanchedalmond": "#ffebcd", "blue": "#0000ff", "blueviolet": "#8a2be2", "brown": "#a52a2a", "burlywood": "#deb887", "cadetblue": "#5f9ea0", "chartreuse": "#7fff00", "chocolate": "#d2691e", "coral": "#ff7f50", "cornflowerblue": "#6495ed", "cornsilk": "#fff8dc", "crimson": "#dc143c", "cyan": "#00ffff", "darkblue": "#00008b", "darkcyan": "#008b8b", "darkgoldenrod": "#b8860b", "darkgray": "#a9a9a9", "darkgreen": "#006400", "darkgrey": "#a9a9a9", "darkkhaki": "#bdb76b", "darkmagenta": "#8b008b", "darkolivegreen": "#556b2f", "darkorange": "#ff8c00", "darkorchid": "#9932cc", "darkred": "#8b0000", "darksalmon": "#e9967a", "darkseagreen": "#8fbc8f", "darkslateblue": "#483d8b", "darkslategray": "#2f4f4f", "darkslategrey": "#2f4f4f", "darkturquoise": "#00ced1", "darkviolet": "#9400d3", "deeppink": "#ff1493", "deepskyblue": "#00bfff", "dimgray": "#696969", "dimgrey": "#696969", "dodgerblue": "#1e90ff", "firebrick": "#b22222", "floralwhite": "#fffaf0", "forestgreen": "#228b22", "fuchsia": "#ff00ff", "gainsboro": "#dcdcdc", "ghostwhite": "#f8f8ff", "gold": "#ffd700", "goldenrod": "#daa520", "gray": "#808080", "grey": "#808080", "green": "#008000", "greenyellow": "#adff2f", "honeydew": "#f0fff0", "hotpink": "#ff69b4", "indianred": "#cd5c5c", "indigo": "#4b0082", "ivory": "#fffff0", "khaki": "#f0e68c", "lavender": "#e6e6fa", "lavenderblush": "#fff0f5", "lawngreen": "#7cfc00", "lemonchiffon": "#fffacd", "lightblue": "#add8e6", "lightcoral": "#f08080", "lightcyan": "#e0ffff", "lightgoldenrodyellow": "#fafad2", "lightgray": "#d3d3d3", "lightgreen": "#90ee90", "lightgrey": "#d3d3d3", "lightpink": "#ffb6c1", "lightsalmon": "#ffa07a", "lightseagreen": "#20b2aa", "lightskyblue": "#87cefa", "lightslategray": "#778899", "lightslategrey": "#778899", "lightsteelblue": "#b0c4de", "lightyellow": "#ffffe0", "lime": "#00ff00", "limegreen": "#32cd32", "linen": "#faf0e6", "magenta": "#ff00ff", "maroon": "#800000", "mediumaquamarine": "#66cdaa", "mediumblue": "#0000cd", "mediumorchid": "#ba55d3", "mediumpurple": "#9370db", "mediumseagreen": "#3cb371", "mediumslateblue": "#7b68ee", "mediumspringgreen": "#00fa9a", "mediumturquoise": "#48d1cc", "mediumvioletred": "#c71585", "midnightblue": "#191970", "mintcream": "#f5fffa", "mistyrose": "#ffe4e1", "moccasin": "#ffe4b5", "navajowhite": "#ffdead", "navy": "#000080", "oldlace": "#fdf5e6", "olive": "#808000", "olivedrab": "#6b8e23", "orange": "#ffa500", "orangered": "#ff4500", "orchid": "#da70d6", "palegoldenrod": "#eee8aa", "palegreen": "#98fb98", "paleturquoise": "#afeeee", "palevioletred": "#db7093", "papayawhip": "#ffefd5", "peachpuff": "#ffdab9", "peru": "#cd853f", "pink": "#ffc0cb", "plum": "#dda0dd", "powderblue": "#b0e0e6", "purple": "#800080", "red": "#ff0000", "rosybrown": "#bc8f8f", "royalblue": "#4169e1", "saddlebrown": "#8b4513", "salmon": "#fa8072", "sandybrown": "#f4a460", "seagreen": "#2e8b57", "seashell": "#fff5ee", "sienna": "#a0522d", "silver": "#c0c0c0", "skyblue": "#87ceeb", "slateblue": "#6a5acd", "slategray": "#708090", "slategrey": "#708090", "snow": "#fffafa", "springgreen": "#00ff7f", "steelblue": "#4682b4", "tan": "#d2b48c", "teal": "#008080", "thistle": "#d8bfd8", "tomato": "#ff6347", "turquoise": "#40e0d0", "violet": "#ee82ee", "wheat": "#f5deb3", "white": "#ffffff", "whitesmoke": "#f5f5f5", "yellow": "#ffff00", "yellowgreen": "#9acd32", } svg_color_reverse_map = {v: k for k, v in svg_color_map.items()} def color_without_alpha(color: typing.Optional[str]) -> typing.Optional[str]: if not color: return None color = color.strip().replace(" ", "") c = re.split("rgba$(\d+),(\d+),(\d+),([\d.]+)$", color) if len(c) > 1: return f"#{int(c[1]):02x}{int(c[2]):02x}{int(c[3]):02x}" c = re.split("rgb$(\d+),(\d+),(\d+)$", color) if len(c) > 1: return f"#{int(c[1]):02x}{int(c[2]):02x}{int(c[3]):02x}" if color.startswith("#"): if len(color) == 9: return f"#{color.lower()[3:]}" if len(color) == 7: return color.lower() return color def named_color_without_alpha(color: str) -> typing.Optional[str]: color_with_alpha = color_without_alpha(color) return svg_color_reverse_map.get(color_with_alpha, color_with_alpha) if color_with_alpha else None def hex_color(color: str) -> typing.Optional[str]: if not color: return None color = color.strip().replace(" ", "") c = re.split("rgba$(\d+),(\d+),(\d+),([\d.]+)$", color) if len(c) > 1: return f"#{int(255 * float(c[4])):02x}{int(c[1]):02x}{int(c[2]):02x}{int(c[3]):02x}" c = re.split("rgb$(\d+),(\d+),(\d+)$", color) if len(c) > 1: return f"#{int(c[1]):02x}{int(c[2]):02x}{int(c[3]):02x}" if color.startswith("#"): if len(color) == 9 or len(color) == 7: return color.lower() return svg_color_map.get(color, color)

43.684103

230

0.551183

e47554772e4cd7fe02f1fc31e4e1459b249a801f

5,134

py

Python

build_tools/precommit_checker.py

gabbar538/phani538

dff4c48ede8cc813596a9660784fea181b787ee0

[ "BSD-3-Clause" ]

5

2017-06-19T02:03:11.000Z

2019-05-13T06:27:57.000Z

build_tools/precommit_checker.py

gabbar538/phani538

dff4c48ede8cc813596a9660784fea181b787ee0

[ "BSD-3-Clause" ]

60

2016-03-19T16:01:27.000Z

2016-06-23T16:26:10.000Z

build_tools/precommit_checker.py

gabbar538/phani538

dff4c48ede8cc813596a9660784fea181b787ee0

[ "BSD-3-Clause" ]

5

2017-06-14T08:06:46.000Z

2019-05-13T06:28:28.000Z

#!/usr/local/fbcode/gcc-4.8.1-glibc-2.17-fb/bin/python2.7 from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import argparse import commands import subprocess import sys import re import os import time # # Simple logger # class Log: LOG_FILE = "/tmp/precommit-check.log" def __init__(self): self.filename = Log.LOG_FILE self.f = open(self.filename, 'w+', 0) def caption(self, str): line = "\n##### %s #####\n" % str if self.f: self.f.write("%s \n" % line) else: print(line) def error(self, str): data = "\n\n##### ERROR ##### %s" % str if self.f: self.f.write("%s \n" % data) else: print(data) def log(self, str): if self.f: self.f.write("%s \n" % str) else: print(str) # # Shell Environment # class Env(object): def __init__(self, tests): self.tests = tests self.log = Log() def shell(self, cmd, path=os.getcwd()): if path: os.chdir(path) self.log.log("==== shell session ===========================") self.log.log("%s> %s" % (path, cmd)) status = subprocess.call("cd %s; %s" % (path, cmd), shell=True, stdout=self.log.f, stderr=self.log.f) self.log.log("status = %s" % status) self.log.log("============================================== \n\n") return status def GetOutput(self, cmd, path=os.getcwd()): if path: os.chdir(path) self.log.log("==== shell session ===========================") self.log.log("%s> %s" % (path, cmd)) status, out = commands.getstatusoutput(cmd) self.log.log("status = %s" % status) self.log.log("out = %s" % out) self.log.log("============================================== \n\n") return status, out # # Pre-commit checker # class PreCommitChecker(Env): def __init__(self, tests): Env.__init__(self, tests) # # Get commands for a given job from the determinator file # def get_commands(self, test): status, out = self.GetOutput( "build_tools/rocksdb-lego-determinator %s" % test, ".") return status, out # # Run a specific CI job # def run_test(self, test): self.log.caption("Running test %s locally" % test) # get commands for the CI job determinator status, cmds = self.get_commands(test) if status != 0: self.log.error("Error getting commands for test %s" % test) return False # Parse the JSON to extract the commands to run cmds = re.findall("'shell':'([^\']*)'", cmds) if len(cmds) == 0: self.log.log("No commands found") return False # Run commands for cmd in cmds: # Replace J=<..> with the local environment variable if "J" in os.environ: cmd = cmd.replace("J=1", "J=%s" % os.environ["J"]) cmd = cmd.replace("make ", "make -j%s " % os.environ["J"]) # Run the command status = self.shell(cmd, ".") if status != 0: self.log.error("Error running command %s for test %s" % (cmd, test)) return False return True # # Run specified CI jobs # def run_tests(self): if not self.tests: self.log.error("Invalid args. Please provide tests") return False self.print_separator() self.print_row("TEST", "RESULT") self.print_separator() for test in self.tests: start_time = time.time() self.print_test(test) result = self.run_test(test) elapsed_min = (time.time() - start_time) / 60 if not result: self.log.error("Error running test %s" % test) self.print_result("FAIL (%dm)" % elapsed_min) return False self.print_result("PASS (%dm)" % elapsed_min) self.print_separator() return True # # Print a line # def print_separator(self): print("".ljust(60, "-")) # # Print two colums # def print_row(self, c0, c1): print("%s%s" % (c0.ljust(40), c1.ljust(20))) def print_test(self, test): print(test.ljust(40), end="") sys.stdout.flush() def print_result(self, result): print(result.ljust(20)) # # Main # parser = argparse.ArgumentParser(description='RocksDB pre-commit checker.') # <test ....> parser.add_argument('test', nargs='+', help='CI test(s) to run. e.g: unit punit asan tsan ubsan') print("Please follow log %s" % Log.LOG_FILE) args = parser.parse_args() checker = PreCommitChecker(args.test) if not checker.run_tests(): print("Error running tests. Please check log file %s" % Log.LOG_FILE) sys.exit(1) sys.exit(0)

25.798995

78

0.52688

dcd4e49bc12865f676c32b8621a702c29c0cda1a

6,579

py

Python

Server/src/virtualenv/Lib/site-packages/pip/basecommand.py

ppyordanov/HCI_4_Future_Cities

4dc7dc59acccf30357bde66524c2d64c29908de8

[ "MIT" ]

null

Server/src/virtualenv/Lib/site-packages/pip/basecommand.py

ppyordanov/HCI_4_Future_Cities

4dc7dc59acccf30357bde66524c2d64c29908de8

[ "MIT" ]

null

Server/src/virtualenv/Lib/site-packages/pip/basecommand.py

ppyordanov/HCI_4_Future_Cities

4dc7dc59acccf30357bde66524c2d64c29908de8

[ "MIT" ]

null

"""Base Command class, and related routines""" import os import sys import tempfile import traceback import time import optparse from pip import cmdoptions from pip.locations import running_under_virtualenv from pip.log import logger from pip.download import PipSession from pip.exceptions import (BadCommand, InstallationError, UninstallationError, CommandError, PreviousBuildDirError) from pip.backwardcompat import StringIO from pip.baseparser import ConfigOptionParser, UpdatingDefaultsHelpFormatter from pip.status_codes import (SUCCESS, ERROR, UNKNOWN_ERROR, VIRTUALENV_NOT_FOUND, PREVIOUS_BUILD_DIR_ERROR) from pip.util import get_prog __all__ = ['Command'] class Command(object): name = None usage = None hidden = False def __init__(self): parser_kw = { 'usage': self.usage, 'prog': '%s %s' % (get_prog(), self.name), 'formatter': UpdatingDefaultsHelpFormatter(), 'add_help_option': False, 'name': self.name, 'description': self.__doc__, } self.parser = ConfigOptionParser(**parser_kw) # Commands should add options to this option group optgroup_name = '%s Options' % self.name.capitalize() self.cmd_opts = optparse.OptionGroup(self.parser, optgroup_name) # Add the general options gen_opts = cmdoptions.make_option_group(cmdoptions.general_group, self.parser) self.parser.add_option_group(gen_opts) def _build_session(self, options): session = PipSession() # Handle custom ca-bundles from the user if options.cert: session.verify = options.cert # Handle timeouts if options.timeout: session.timeout = options.timeout # Handle configured proxies if options.proxy: session.proxies = { "http": options.proxy, "https": options.proxy, } # Determine if we can prompt the user for authentication or not session.auth.prompting = not options.no_input return session def setup_logging(self): pass def parse_args(self, args): # factored out for testability return self.parser.parse_args(args) def main(self, args): options, args = self.parse_args(args) level = 1 # Notify level += options.verbose level -= options.quiet level = logger.level_for_integer(4 - level) complete_log = [] logger.add_consumers( (level, sys.stdout), (logger.DEBUG, complete_log.append), ) if options.log_explicit_levels: logger.explicit_levels = True self.setup_logging() # TODO: try to get these passing down from the command? # without resorting to os.environ to hold these. if options.no_input: os.environ['PIP_NO_INPUT'] = '1' if options.exists_action: os.environ['PIP_EXISTS_ACTION'] = ' '.join(options.exists_action) if options.require_venv: # If a venv is required check if it can really be found if not running_under_virtualenv(): logger.fatal('Could not find an activated virtualenv (required).') sys.exit(VIRTUALENV_NOT_FOUND) if options.log: log_fp = open_logfile(options.log, 'a') logger.add_consumers((logger.DEBUG, log_fp)) else: log_fp = None exit = SUCCESS store_log = False try: status = self.run(options, args) # FIXME: all commands should return an exit status # and when it is done, isinstance is not needed anymore if isinstance(status, int): exit = status except PreviousBuildDirError: e = sys.exc_info()[1] logger.fatal(str(e)) logger.info('Exception information:\n%s' % format_exc()) store_log = True exit = PREVIOUS_BUILD_DIR_ERROR except (InstallationError, UninstallationError): e = sys.exc_info()[1] logger.fatal(str(e)) logger.info('Exception information:\n%s' % format_exc()) store_log = True exit = ERROR except BadCommand: e = sys.exc_info()[1] logger.fatal(str(e)) logger.info('Exception information:\n%s' % format_exc()) store_log = True exit = ERROR except CommandError: e = sys.exc_info()[1] logger.fatal('ERROR: %s' % e) logger.info('Exception information:\n%s' % format_exc()) exit = ERROR except KeyboardInterrupt: logger.fatal('Operation cancelled by user') logger.info('Exception information:\n%s' % format_exc()) store_log = True exit = ERROR except: logger.fatal('Exception:\n%s' % format_exc()) store_log = True exit = UNKNOWN_ERROR if store_log: log_file_fn = options.log_file text = '\n'.join(complete_log) try: log_file_fp = open_logfile(log_file_fn, 'w') except IOError: temp = tempfile.NamedTemporaryFile(delete=False) log_file_fn = temp.name log_file_fp = open_logfile(log_file_fn, 'w') logger.fatal('Storing debug log for failure in %s' % log_file_fn) log_file_fp.write(text) log_file_fp.close() if log_fp is not None: log_fp.close() return exit def format_exc(exc_info=None): if exc_info is None: exc_info = sys.exc_info() out = StringIO() traceback.print_exception(*exc_info, **dict(file=out)) return out.getvalue() def open_logfile(filename, mode='a'): """Open the named log file in append mode. If the file already exists, a separator will also be printed to the file to separate past activity from current activity. """ filename = os.path.expanduser(filename) filename = os.path.abspath(filename) dirname = os.path.dirname(filename) if not os.path.exists(dirname): os.makedirs(dirname) exists = os.path.exists(filename) log_fp = open(filename, mode) if exists: log_fp.write('%s\n' % ('-' * 60)) log_fp.write('%s run on %s\n' % (sys.argv[0], time.strftime('%c'))) return log_fp

32.569307

86

0.599483

13b62d4511f4cc83a8c14532cd3a71cb9b3d69a0

3,720

py

Python

tfx/components/example_gen/custom_executors/avro_component_test.py

rtg0795/tfx

63c31b719896eef645df3850d0e6b946e44cd059

[ "Apache-2.0" ]

1

2022-03-29T23:06:54.000Z

tfx/components/example_gen/custom_executors/avro_component_test.py

rtg0795/tfx

63c31b719896eef645df3850d0e6b946e44cd059

[ "Apache-2.0" ]

null

tfx/components/example_gen/custom_executors/avro_component_test.py

rtg0795/tfx

63c31b719896eef645df3850d0e6b946e44cd059

[ "Apache-2.0" ]

null

# Copyright 2019 Google LLC. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tests for using avro_executor with example_gen component.""" import os from unittest import mock import tensorflow as tf from tfx.components.example_gen.component import FileBasedExampleGen from tfx.components.example_gen.custom_executors import avro_executor from tfx.dsl.components.base import executor_spec from tfx.dsl.io import fileio from tfx.orchestration import data_types from tfx.orchestration import metadata from tfx.orchestration import publisher from tfx.orchestration.launcher import in_process_component_launcher from tfx.proto import example_gen_pb2 from tfx.utils import name_utils from ml_metadata.proto import metadata_store_pb2 class ExampleGenComponentWithAvroExecutorTest(tf.test.TestCase): def setUp(self): super().setUp() # Create input_base. input_data_dir = os.path.join( os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'testdata') self.avro_dir_path = os.path.join(input_data_dir, 'external') # Create input_config. self.input_config = example_gen_pb2.Input(splits=[ example_gen_pb2.Input.Split(name='avro', pattern='avro/*.avro'), ]) # Create output_config. self.output_config = example_gen_pb2.Output( split_config=example_gen_pb2.SplitConfig(splits=[ example_gen_pb2.SplitConfig.Split(name='train', hash_buckets=2), example_gen_pb2.SplitConfig.Split(name='eval', hash_buckets=1) ])) @mock.patch.object(publisher, 'Publisher') def testRun(self, mock_publisher): mock_publisher.return_value.publish_execution.return_value = {} example_gen = FileBasedExampleGen( custom_executor_spec=executor_spec.ExecutorClassSpec( avro_executor.Executor), input_base=self.avro_dir_path, input_config=self.input_config, output_config=self.output_config).with_id('AvroExampleGen') output_data_dir = os.path.join( os.environ.get('TEST_UNDECLARED_OUTPUTS_DIR', self.get_temp_dir()), self._testMethodName) pipeline_root = os.path.join(output_data_dir, 'Test') fileio.makedirs(pipeline_root) pipeline_info = data_types.PipelineInfo( pipeline_name='Test', pipeline_root=pipeline_root, run_id='123') driver_args = data_types.DriverArgs(enable_cache=True) connection_config = metadata_store_pb2.ConnectionConfig() connection_config.sqlite.SetInParent() metadata_connection = metadata.Metadata(connection_config) launcher = in_process_component_launcher.InProcessComponentLauncher.create( component=example_gen, pipeline_info=pipeline_info, driver_args=driver_args, metadata_connection=metadata_connection, beam_pipeline_args=[], additional_pipeline_args={}) self.assertEqual( launcher._component_info.component_type, name_utils.get_full_name(FileBasedExampleGen)) launcher.launch() mock_publisher.return_value.publish_execution.assert_called_once() # Check output paths. self.assertTrue(fileio.exists(os.path.join(pipeline_root, example_gen.id))) if __name__ == '__main__': tf.test.main()

37.2

80

0.755914

edda9bbdebf1499633c9f39e9c2f8d54767542a8

3,164

py

Python

supergnova.py

YiliangTracyZhang/SUPERGNOVA

a7f40c23a3e1a0ed7fefd5bed9ac55b8414a9e0f

[ "MIT" ]

15

2020-05-11T01:10:47.000Z

2022-01-24T07:53:20.000Z

supergnova.py

YiliangTracyZhang/SUPERGNOVA

a7f40c23a3e1a0ed7fefd5bed9ac55b8414a9e0f

[ "MIT" ]

6

2020-05-11T00:22:01.000Z

2021-09-08T12:32:09.000Z

supergnova.py

YiliangTracyZhang/SUPERGNOVA

a7f40c23a3e1a0ed7fefd5bed9ac55b8414a9e0f

[ "MIT" ]

1

2020-09-07T15:26:42.000Z

#!/usr/bin/env python ''' Local genetic correlation estimation SUPERGNOVA Created on 2020-5-4 Happy birthday PKU! @author: Yiliang ''' import argparse, os.path, sys from subprocess import call import multiprocessing import pandas as pd import numpy as np from prep import prep from ldsc_thin import ldscore from heritability import heritability from pheno import pheno from calculate import calculate try: x = pd.DataFrame({'A': [1, 2, 3]}) x.drop_duplicates(subset='A') except TypeError: raise ImportError('SUPERGNOVA requires pandas version > 0.15.2') pd.set_option('display.max_rows', 500) pd.set_option('display.max_columns', 500) pd.set_option('display.width', 1000) pd.set_option('precision', 4) pd.set_option('max_colwidth',1000) np.set_printoptions(linewidth=1000) np.set_printoptions(precision=4) # returns whether the parent directory of path exists def parent_dir_exists(path): return os.path.exists(os.path.abspath(os.path.join(path, os.pardir))) def pipeline(args): pd.options.mode.chained_assignment = None # Sanity check args if not parent_dir_exists(args.out): raise ValueError('--out flag points to an invalid path.') print('Preparing files for analysis...') gwas_snps, bed, N1, N2 = prep(args.bfile, args.partition, args.sumstats1, args.sumstats2, args.N1, args.N2) print('Calculating LD scores...') ld_scores = ldscore(args.bfile, gwas_snps) gwas_snps = gwas_snps[gwas_snps['SNP'].isin(ld_scores['SNP'])] print('{} SNPs included in our analysis...'.format(len(gwas_snps))) print('Calculating heritability...') h_1, h_2 = heritability(gwas_snps, ld_scores, N1, N2) print('The genome-wide heritability of the first trait is {}.\nThe genome-wide heritability of the second trait is {}.'.format(h_1, h_2)) print('Calculating phenotypic correlation...') pheno_corr, pheno_corr_var = pheno(gwas_snps, ld_scores, N1, N2, h_1, h_2) print('Calculating local genetic covariance...') out = calculate(args.bfile, bed, args.thread, gwas_snps, ld_scores, N1, N2, pheno_corr, pheno_corr_var) out.to_csv(args.out, sep=' ', na_rep='NA', index=False) parser = argparse.ArgumentParser() parser.add_argument('sumstats1', help='The first sumstats file.') parser.add_argument('sumstats2', help='The second sumstats file.') parser.add_argument('--bfile', required=True, type=str, help='Prefix for Plink .bed/.bim/.fam file.') parser.add_argument('--partition', required=True, type=str, help='Genome partition file in bed format') parser.add_argument('--N1', type=int, help='N of the sumstats1 file. If not provided, this value will be inferred ' 'from the sumstats1 arg.') parser.add_argument('--N2', type=int, help='N of the sumstats2 file. If not provided, this value will be inferred ' 'from the sumstats2 arg.') parser.add_argument('--out', required=True, type=str, help='Location to output results.') parser.add_argument('--thread', default= multiprocessing.cpu_count(), type=int, help='Thread numbers used for calculation. Default = CPU numbers.') if __name__ == '__main__': pipeline(parser.parse_args())

33.305263

141

0.724399

017c03371cdbd061e19d38b5bb000475f49a7cad

21

py

Python

aliyun-python-sdk-nas/aliyunsdknas/__init__.py

DataDog/aliyun-openapi-python-sdk

5cbee29bce6416dd62f61f0c3786b1af6ea0d84f

[ "Apache-2.0" ]

1

2021-07-27T13:23:27.000Z

norminette/version.py

N0ich/norminette

a19fe6b057591ef3faca469c62d3454242ce3d77

[ "MIT" ]

null

norminette/version.py

N0ich/norminette

a19fe6b057591ef3faca469c62d3454242ce3d77

[ "MIT" ]

1

2021-02-23T11:27:54.000Z

__version__ = "3.1.2"

21

0.666667

643380b5db062df9515193b5a0b4d2baa6f64478

10,223

py

Python

auto_changelog/repository.py

stdtom/auto-changelog

1e2d26671569555a3d03e70e897d6f12ad61a108

[ "MIT" ]

null

auto_changelog/repository.py

stdtom/auto-changelog

1e2d26671569555a3d03e70e897d6f12ad61a108

[ "MIT" ]

7

2020-10-03T21:03:51.000Z

2020-10-20T18:34:49.000Z

auto_changelog/repository.py

stdtom/auto-changelog

1e2d26671569555a3d03e70e897d6f12ad61a108

[ "MIT" ]

null

import logging import re from datetime import date from hashlib import sha256 from typing import Dict, List, Tuple, Any, Optional from urllib.parse import urljoin from git import Repo, Commit, TagReference from auto_changelog.domain_model import RepositoryInterface, Changelog, default_tag_pattern class GitRepository(RepositoryInterface): def __init__( self, repository_path, latest_version: Optional[str] = None, skip_unreleased: bool = True, tag_prefix: str = "", tag_pattern: Optional[str] = None, ): self.repository = Repo(repository_path) self.tag_prefix = tag_prefix self.tag_pattern = tag_pattern self.commit_tags_index = self._init_commit_tags_index(self.repository, self.tag_prefix, self.tag_pattern) # in case of defined latest version, unreleased is used as latest release self._skip_unreleased = skip_unreleased and not bool(latest_version) self._latest_version = latest_version or None def generate_changelog( self, title: str = "Changelog", description: str = "", remote: str = "origin", issue_pattern: Optional[str] = None, issue_url: Optional[str] = None, diff_url: Optional[str] = None, starting_commit: str = "", stopping_commit: str = "HEAD", ) -> Changelog: locallogger = logging.getLogger("repository.generate_changelog") issue_url = issue_url or self._issue_from_git_remote_url(remote) diff_url = diff_url or self._diff_from_git_remote_url(remote) changelog = Changelog(title, description, issue_pattern, issue_url, self.tag_prefix, self.tag_pattern) if self._repository_is_empty(): locallogger.info("Repository is empty.") return changelog iter_rev = self._get_iter_rev(starting_commit, stopping_commit) commits = self.repository.iter_commits(iter_rev) # Some thoughts here # First we need to check if all commits are "released". If not, we have to create our special "Unreleased" # release. Then we simply iter over all commits, assign them to current release or create new if we find it. first_commit = True skip = self._skip_unreleased locallogger.debug("Start iterating commits") for commit in commits: sha = commit.hexsha[0:7] locallogger.debug("Found commit {}".format(sha)) if skip and commit not in self.commit_tags_index: locallogger.debug("Skipping unreleased commit " + sha) continue else: skip = False if first_commit and commit not in self.commit_tags_index: # if no last version specified by the user => consider HEAD if not self._latest_version: locallogger.debug("Adding release 'unreleased'") changelog.add_release("Unreleased", "HEAD", date.today(), sha256()) else: locallogger.debug("Adding release '{}'".format(self._latest_version)) changelog.add_release(self._latest_version, self._latest_version, date.today(), sha256()) first_commit = False if commit in self.commit_tags_index: attributes = self._extract_release_args(commit, self.commit_tags_index[commit]) locallogger.debug("Adding release '{}' with attributes {}".format(attributes[0], attributes)) changelog.add_release(*attributes) attributes = self._extract_note_args(commit) locallogger.debug("Adding commit {} with attributes {}".format(sha, attributes)) changelog.add_note(*attributes) # create the compare url for each release releases = changelog.releases # we are using len(changelog.releases) - 1 because there is not compare url for the oldest version if diff_url is not None: # if links are off for release_index in reversed(range(len(changelog.releases) - 1)): releases[release_index].set_compare_url(diff_url, releases[release_index + 1].title) return changelog def _issue_from_git_remote_url(self, remote: str) -> Optional[str]: """ Creates issue url with {id} format key """ try: url = self._remote_url(remote) return url + "/issues/{id}" except ValueError as e: logging.error("%s. Turning off issue links.", e) return None def _diff_from_git_remote_url(self, remote: str): try: url = self._remote_url(remote) return urljoin(url + "/", "compare/{previous}...{current}") except ValueError as e: logging.error("%s. Turning off compare url links.", e) return None def _remote_url(self, remote: str) -> str: """ Extract remote url from remote url """ url = self._get_git_url(remote=remote) url = GitRepository._sanitize_remote_url(url) return url @staticmethod def _sanitize_remote_url(remote: str) -> str: # 'git@github.com:Michael-F-Bryan/auto-changelog.git' -> 'https://github.com/Michael-F-Bryan/auto-changelog' # 'https://github.com/Michael-F-Bryan/auto-changelog.git' -> 'https://github.com/Michael-F-Bryan/auto-changelog' return re.sub(r"^(https|git|ssh)(:\/\/|@)(.*@)?([^\/:]+)[\/:]([^\/:]+)\/(.+).git$", r"https://\4/\5/\6", remote) # This part is hard to mock, separate method is nice approach how to overcome this problem def _get_git_url(self, remote: str) -> str: remote_config = self.repository.remote(name=remote).config_reader # remote url can be in one of this three options # Test is the option exits before access it, otherwise the program crashes if remote_config.has_option("url"): return remote_config.get("url") elif remote_config.has_option("pushurl"): return remote_config.get("pushurl") elif remote_config.has_option("pullurl"): return remote_config.get("pullurl") else: return "" def _get_iter_rev(self, starting_commit: str, stopping_commit: str): if starting_commit: c = self.repository.commit(starting_commit) if not c.parents: # starting_commit is initial commit, # treat as default starting_commit = "" else: # iter_commits iters from the first rev to the second rev, # but not contains the second rev. # Here we set the second rev to its previous one then the # second rev would be included. starting_commit = "{}~1".format(starting_commit) iter_rev = "{0}...{1}".format(stopping_commit, starting_commit) if starting_commit else stopping_commit return iter_rev def _repository_is_empty(self): return not bool(self.repository.references) @staticmethod def _init_commit_tags_index( repo: Repo, tag_prefix: str, tag_pattern: Optional[str] = None ) -> Dict[Commit, List[TagReference]]: """ Create reverse index """ reverse_tag_index = {} semver_regex = default_tag_pattern for tagref in repo.tags: tag_name = tagref.name commit = tagref.commit consider_tag = False # consider & remove the prefix if we found one if tag_name.startswith(tag_prefix): tag_name = tag_name.replace(tag_prefix, "") # if user specified a tag pattern => consider it if tag_pattern is not None: if re.fullmatch(tag_pattern, tag_name): consider_tag = True # no tag pattern specified by user => check semver semantic elif re.fullmatch(semver_regex, tag_name): consider_tag = True # good format of the tag => consider it if consider_tag: if commit not in reverse_tag_index: reverse_tag_index[commit] = [] reverse_tag_index[commit].append(tagref) return reverse_tag_index @staticmethod def _extract_release_args(commit, tags) -> Tuple[str, str, Any, Any]: """ Extracts arguments for release """ title = ", ".join(map(lambda tag: "{}".format(tag.name), tags)) date_ = commit.authored_datetime.date() sha = commit.hexsha # TODO parse message, be carefull about commit message and tags message return title, title, date_, sha @staticmethod def _extract_note_args(commit) -> Tuple[str, str, str, str, str, str]: """ Extracts arguments for release Note from commit """ sha = commit.hexsha message = commit.message type_, scope, description, body, footer = GitRepository._parse_conventional_commit(message) return sha, type_, description, scope, body, footer @staticmethod def _parse_conventional_commit(message: str) -> Tuple[str, str, str, str, str]: type_ = scope = description = body_footer = body = footer = "" # TODO this is less restrictive version of re. I have somewhere more restrictive one, maybe as option? match = re.match(r"^(\w+)($\w+$)?!?: (.*)(\n\n[\w\W]+)?$", message) if match: type_, scope, description, body_footer = match.groups(default="") else: locallogger = logging.getLogger("repository._parse_conventional_commit") locallogger.debug("Commit message did not match expected pattern: {}".format(message)) if scope: scope = scope[1:-1] if body_footer: bf_match = re.match(r"^(\n\n[\w\W]+?)?(\n\n([a-zA-Z-]+|BREAKING[- ]CHANGE)(: | #)[\w\W]+)$", body_footer) if bf_match: result = bf_match.groups(default="") body = result[0][2:] footer = result[1][2:] else: body = body_footer[2:] return type_, scope, description, body, footer

44.641921

120

0.618605

b3f29f81a1d42fbd0483bc103b184160732e777e

4,195

py

Python

evaluation_t5.py

nunziati/open-question-answering-newsqa

85c6acf43163e6e0de8975b5047ddfa3b09f03d1

[ "MIT" ]

2

2022-03-15T17:48:25.000Z

2022-03-15T17:48:54.000Z

evaluation_t5.py

nunziati/open-question-answering-newsqa

85c6acf43163e6e0de8975b5047ddfa3b09f03d1

[ "MIT" ]

null

evaluation_t5.py

nunziati/open-question-answering-newsqa

85c6acf43163e6e0de8975b5047ddfa3b09f03d1

[ "MIT" ]

null

from __future__ import print_function from tqdm import tqdm import torch from datasets import load_dataset from transformers import T5ForConditionalGeneration, T5Tokenizer from torch.utils.data import DataLoader import argparse from MyDataset import Dataset import MyDataset import argparse dataset_instruction = { "duorc": { "parser": MyDataset.DatasetMap.duorc, "test_set": "test" }, "squad": { "parser": MyDataset.DatasetMap.squad, "test_set": "validation" } } def parse_command_line_arguments(): parser = argparse.ArgumentParser( description='CLI for evaluating T5 T2T model') parser.add_argument('--t5_model', type=str, default="results/t5-base/checkpoint-31", help="What type of T5 model do you want use?") parser.add_argument('--dataset', type=str, default='duorc-SelfRC', help="Dataset to be used, if more level provided for the dataset use the '-' token, e.g. duorc-SelfRC") parser.add_argument('--batch_size', type=int, default=16, help='mini-batch size (default: 16)') parser.add_argument('--workers', type=int, default=10, help='number of working units used to load the data (default: 10)') parser.add_argument('--device', default='cuda', type=str, help='device to be used for computations (in {cpu, cuda:0, cuda:1, ...}, default: cpu)') parser.add_argument('--max_input_length', type=int, default=512, help='Maximum lenght of input text, (default: 512, maximum admitted: 512)') parser.add_argument('--seed', type=int, default=7, help='Seed for random initialization (default: 7)') parsed_arguments = parser.parse_args() return parsed_arguments if __name__ == '__main__': args = parse_command_line_arguments() for k, v in args.__dict__.items(): print(k + '=' + str(v)) dataset_info = args.dataset.split("-") name = dataset_info[0] _data = None if len(dataset_info) == 1: _data = load_dataset(name) else: _data = load_dataset(name, dataset_info[1]) model = T5ForConditionalGeneration.from_pretrained(args.t5_model) tokenizer = T5Tokenizer.from_pretrained(args.t5_model) _test_set = Dataset(_data[dataset_instruction[name]["test_set"]], tokenizer, parser=dataset_instruction[name]["parser"]) my_testset_dataloader = DataLoader(_test_set, batch_size=args.batch_size, num_workers=args.workers, collate_fn=lambda data: _test_set.pack_minibatch(data)) device = args.device model.to(device) model.eval() with torch.no_grad(): model_predictions_encoded = [] target_encoded = [] for contexts, questions, answers in tqdm(my_testset_dataloader): inputs = list(map(lambda tuple: f"question: {tuple[0]} context:{tuple[1]}", zip( questions, contexts))) encoded_inputs = tokenizer( inputs, padding="longest", max_length=args.max_input_length, truncation=True, return_tensors="pt", ) encoded_targets = tokenizer( answers, padding="longest", max_length=args.max_input_length, truncation=True, return_tensors="pt", ) encoded_inputs, attention_mask = encoded_inputs.input_ids, encoded_inputs.attention_mask encoded_targets = encoded_targets.input_ids encoded_inputs = encoded_inputs.to(device) encoded_targets = encoded_targets.to(device) attention_mask = attention_mask.to(device) model_predictions = model.generate( input_ids=encoded_inputs, attention_mask=attention_mask) model_predictions_encoded += model_predictions.tolist() target_encoded += encoded_targets.tolist() f1, exact_match = _test_set.evaluate( target_encoded, model_predictions_encoded) print(f"\t F1 = {f1:.2f}, EM = {exact_match:.2f}")

37.455357

159

0.633135

8232b249ef72b53ec3ca5cc4fdc42ad6b50c9826

24,143

py

Python

cocoeval.py

gcardosoy/detectron2

a5e17ef84c1c511007670f886df631c074809726

[ "Apache-2.0" ]

null

cocoeval.py

gcardosoy/detectron2

a5e17ef84c1c511007670f886df631c074809726

[ "Apache-2.0" ]

null

cocoeval.py

gcardosoy/detectron2

a5e17ef84c1c511007670f886df631c074809726

[ "Apache-2.0" ]

null

__author__ = 'tsungyi' import numpy as np import datetime import time from collections import defaultdict from . import mask as maskUtils import copy class COCOeval: # Interface for evaluating detection on the Microsoft COCO dataset. # # The usage for CocoEval is as follows: # cocoGt=..., cocoDt=... # load dataset and results # E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object # E.params.recThrs = ...; # set parameters as desired # E.evaluate(); # run per image evaluation # E.accumulate(); # accumulate per image results # E.summarize(); # display summary metrics of results # For example usage see evalDemo.m and http://mscoco.org/. # # The evaluation parameters are as follows (defaults in brackets): # imgIds - [all] N img ids to use for evaluation # catIds - [all] K cat ids to use for evaluation # iouThrs - [.5:.05:.95] T=10 IoU thresholds for evaluation # recThrs - [0:.01:1] R=101 recall thresholds for evaluation # areaRng - [...] A=4 object area ranges for evaluation # maxDets - [1 10 100] M=3 thresholds on max detections per image # iouType - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints' # iouType replaced the now DEPRECATED useSegm parameter. # useCats - [1] if true use category labels for evaluation # Note: if useCats=0 category labels are ignored as in proposal scoring. # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified. # # evaluate(): evaluates detections on every image and every category and # concats the results into the "evalImgs" with fields: # dtIds - [1xD] id for each of the D detections (dt) # gtIds - [1xG] id for each of the G ground truths (gt) # dtMatches - [TxD] matching gt id at each IoU or 0 # gtMatches - [TxG] matching dt id at each IoU or 0 # dtScores - [1xD] confidence of each dt # gtIgnore - [1xG] ignore flag for each gt # dtIgnore - [TxD] ignore flag for each dt at each IoU # # accumulate(): accumulates the per-image, per-category evaluation # results in "evalImgs" into the dictionary "eval" with fields: # params - parameters used for evaluation # date - date evaluation was performed # counts - [T,R,K,A,M] parameter dimensions (see above) # precision - [TxRxKxAxM] precision for every evaluation setting # recall - [TxKxAxM] max recall for every evaluation setting # Note: precision and recall==-1 for settings with no gt objects. # # See also coco, mask, pycocoDemo, pycocoEvalDemo # # Microsoft COCO Toolbox. version 2.0 # Data, paper, and tutorials available at: http://mscoco.org/ # Code written by Piotr Dollar and Tsung-Yi Lin, 2015. # Licensed under the Simplified BSD License [see coco/license.txt] def __init__(self, cocoGt=None, cocoDt=None, iouType='segm'): ''' Initialize CocoEval using coco APIs for gt and dt :param cocoGt: coco object with ground truth annotations :param cocoDt: coco object with detection results :return: None ''' if not iouType: print('iouType not specified. use default iouType segm') self.cocoGt = cocoGt # ground truth COCO API self.cocoDt = cocoDt # detections COCO API self.evalImgs = defaultdict(list) # per-image per-category evaluation results [KxAxI] elements self.eval = {} # accumulated evaluation results self._gts = defaultdict(list) # gt for evaluation self._dts = defaultdict(list) # dt for evaluation self.params = Params(iouType=iouType) # parameters self._paramsEval = {} # parameters for evaluation self.stats = [] # result summarization self.ious = {} # ious between all gts and dts if not cocoGt is None: self.params.imgIds = sorted(cocoGt.getImgIds()) self.params.catIds = sorted(cocoGt.getCatIds()) def _prepare(self): ''' Prepare ._gts and ._dts for evaluation based on params :return: None ''' def _toMask(anns, coco): # modify ann['segmentation'] by reference for ann in anns: rle = coco.annToRLE(ann) ann['segmentation'] = rle p = self.params if p.useCats: gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)) else: gts=self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds)) dts=self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds)) # convert ground truth to mask if iouType == 'segm' if p.iouType == 'segm': _toMask(gts, self.cocoGt) _toMask(dts, self.cocoDt) # set ignore flag for gt in gts: gt['ignore'] = gt['ignore'] if 'ignore' in gt else 0 gt['ignore'] = 'iscrowd' in gt and gt['iscrowd'] if p.iouType == 'keypoints': gt['ignore'] = (gt['num_keypoints'] == 0) or gt['ignore'] self._gts = defaultdict(list) # gt for evaluation self._dts = defaultdict(list) # dt for evaluation for gt in gts: self._gts[gt['image_id'], gt['category_id']].append(gt) for dt in dts: self._dts[dt['image_id'], dt['category_id']].append(dt) self.evalImgs = defaultdict(list) # per-image per-category evaluation results self.eval = {} # accumulated evaluation results def evaluate(self): ''' Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None ''' tic = time.time() print('Running per image evaluation...') p = self.params # add backward compatibility if useSegm is specified in params if not p.useSegm is None: p.iouType = 'segm' if p.useSegm == 1 else 'bbox' print('useSegm (deprecated) is not None. Running {} evaluation'.format(p.iouType)) print('Evaluate annotation type *{}*'.format(p.iouType)) p.imgIds = list(np.unique(p.imgIds)) if p.useCats: p.catIds = list(np.unique(p.catIds)) p.maxDets = sorted(p.maxDets) self.params=p self._prepare() # loop through images, area range, max detection number catIds = p.catIds if p.useCats else [-1] if p.iouType == 'segm' or p.iouType == 'bbox': computeIoU = self.computeIoU elif p.iouType == 'keypoints': computeIoU = self.computeOks self.ious = {(imgId, catId): computeIoU(imgId, catId) \ for imgId in p.imgIds for catId in catIds} evaluateImg = self.evaluateImg maxDet = p.maxDets[-1] self.evalImgs = [evaluateImg(imgId, catId, areaRng, maxDet) for catId in catIds for areaRng in p.areaRng for imgId in p.imgIds ] self._paramsEval = copy.deepcopy(self.params) toc = time.time() print('DONE (t={:0.2f}s).'.format(toc-tic)) def computeIoU(self, imgId, catId): p = self.params if p.useCats: gt = self._gts[imgId,catId] dt = self._dts[imgId,catId] else: gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]] dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]] if len(gt) == 0 and len(dt) ==0: return [] inds = np.argsort([-d['score'] for d in dt], kind='mergesort') dt = [dt[i] for i in inds] if len(dt) > p.maxDets[-1]: dt=dt[0:p.maxDets[-1]] if p.iouType == 'segm': g = [g['segmentation'] for g in gt] d = [d['segmentation'] for d in dt] elif p.iouType == 'bbox': g = [g['bbox'] for g in gt] d = [d['bbox'] for d in dt] else: raise Exception('unknown iouType for iou computation') # compute iou between each dt and gt region iscrowd = [int(o['iscrowd']) for o in gt] ious = maskUtils.iou(d,g,iscrowd) return ious def computeOks(self, imgId, catId): p = self.params # dimention here should be Nxm gts = self._gts[imgId, catId] dts = self._dts[imgId, catId] inds = np.argsort([-d['score'] for d in dts], kind='mergesort') dts = [dts[i] for i in inds] if len(dts) > p.maxDets[-1]: dts = dts[0:p.maxDets[-1]] # if len(gts) == 0 and len(dts) == 0: if len(gts) == 0 or len(dts) == 0: return [] ious = np.zeros((len(dts), len(gts))) sigmas = p.kpt_oks_sigmas vars = (sigmas * 2)**2 k = len(sigmas) # compute oks between each detection and ground truth object for j, gt in enumerate(gts): # create bounds for ignore regions(double the gt bbox) g = np.array(gt['keypoints']) xg = g[0::3]; yg = g[1::3]; vg = g[2::3] k1 = np.count_nonzero(vg > 0) bb = gt['bbox'] x0 = bb[0] - bb[2]; x1 = bb[0] + bb[2] * 2 y0 = bb[1] - bb[3]; y1 = bb[1] + bb[3] * 2 for i, dt in enumerate(dts): d = np.array(dt['keypoints']) xd = d[0::3]; yd = d[1::3] if k1>0: # measure the per-keypoint distance if keypoints visible dx = xd - xg dy = yd - yg else: # measure minimum distance to keypoints in (x0,y0) & (x1,y1) z = np.zeros((k)) dx = np.max((z, x0-xd),axis=0)+np.max((z, xd-x1),axis=0) dy = np.max((z, y0-yd),axis=0)+np.max((z, yd-y1),axis=0) e = (dx**2 + dy**2) / vars / (gt['area']+np.spacing(1)) / 2 if k1 > 0: e=e[vg > 0] ious[i, j] = np.sum(np.exp(-e)) / e.shape[0] return ious def evaluateImg(self, imgId, catId, aRng, maxDet): ''' perform evaluation for single category and image :return: dict (single image results) ''' p = self.params if p.useCats: gt = self._gts[imgId,catId] dt = self._dts[imgId,catId] else: gt = [_ for cId in p.catIds for _ in self._gts[imgId,cId]] dt = [_ for cId in p.catIds for _ in self._dts[imgId,cId]] if len(gt) == 0 and len(dt) ==0: return None for g in gt: if g['ignore'] or (g['area']<aRng[0] or g['area']>aRng[1]): g['_ignore'] = 1 else: g['_ignore'] = 0 # sort dt highest score first, sort gt ignore last gtind = np.argsort([g['_ignore'] for g in gt], kind='mergesort') gt = [gt[i] for i in gtind] dtind = np.argsort([-d['score'] for d in dt], kind='mergesort') dt = [dt[i] for i in dtind[0:maxDet]] iscrowd = [int(o['iscrowd']) for o in gt] # load computed ious ious = self.ious[imgId, catId][:, gtind] if len(self.ious[imgId, catId]) > 0 else self.ious[imgId, catId] T = len(p.iouThrs) G = len(gt) D = len(dt) gtm = np.zeros((T,G)) dtm = np.zeros((T,D)) gtIg = np.array([g['_ignore'] for g in gt]) dtIg = np.zeros((T,D)) if not len(ious)==0: for tind, t in enumerate(p.iouThrs): for dind, d in enumerate(dt): # information about best match so far (m=-1 -> unmatched) iou = min([t,1-1e-10]) m = -1 for gind, g in enumerate(gt): # if this gt already matched, and not a crowd, continue if gtm[tind,gind]>0 and not iscrowd[gind]: continue # if dt matched to reg gt, and on ignore gt, stop if m>-1 and gtIg[m]==0 and gtIg[gind]==1: break # continue to next gt unless better match made if ious[dind,gind] < iou: continue # if match successful and best so far, store appropriately iou=ious[dind,gind] m=gind # if match made store id of match for both dt and gt if m ==-1: continue dtIg[tind,dind] = gtIg[m] dtm[tind,dind] = gt[m]['id'] gtm[tind,m] = d['id'] # set unmatched detections outside of area range to ignore a = np.array([d['area']<aRng[0] or d['area']>aRng[1] for d in dt]).reshape((1, len(dt))) dtIg = np.logical_or(dtIg, np.logical_and(dtm==0, np.repeat(a,T,0))) # store results for given image and category return { 'image_id': imgId, 'category_id': catId, 'aRng': aRng, 'maxDet': maxDet, 'dtIds': [d['id'] for d in dt], 'gtIds': [g['id'] for g in gt], 'dtMatches': dtm, 'gtMatches': gtm, 'dtScores': [d['score'] for d in dt], 'gtIgnore': gtIg, 'dtIgnore': dtIg, } def accumulate(self, p = None): ''' Accumulate per image evaluation results and store the result in self.eval :param p: input params for evaluation :return: None ''' print('Accumulating evaluation results...') tic = time.time() if not self.evalImgs: print('Please run evaluate() first') # allows input customized parameters if p is None: p = self.params p.catIds = p.catIds if p.useCats == 1 else [-1] T = len(p.iouThrs) R = len(p.recThrs) K = len(p.catIds) if p.useCats else 1 A = len(p.areaRng) M = len(p.maxDets) precision = -np.ones((T,R,K,A,M)) # -1 for the precision of absent categories recall = -np.ones((T,K,A,M)) scores = -np.ones((T,R,K,A,M)) # create dictionary for future indexing _pe = self._paramsEval catIds = _pe.catIds if _pe.useCats else [-1] setK = set(catIds) setA = set(map(tuple, _pe.areaRng)) setM = set(_pe.maxDets) setI = set(_pe.imgIds) # get inds to evaluate k_list = [n for n, k in enumerate(p.catIds) if k in setK] m_list = [m for n, m in enumerate(p.maxDets) if m in setM] a_list = [n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA] i_list = [n for n, i in enumerate(p.imgIds) if i in setI] I0 = len(_pe.imgIds) A0 = len(_pe.areaRng) # retrieve E at each category, area range, and max number of detections for k, k0 in enumerate(k_list): Nk = k0*A0*I0 for a, a0 in enumerate(a_list): Na = a0*I0 for m, maxDet in enumerate(m_list): E = [self.evalImgs[Nk + Na + i] for i in i_list] E = [e for e in E if not e is None] if len(E) == 0: continue dtScores = np.concatenate([e['dtScores'][0:maxDet] for e in E]) # different sorting method generates slightly different results. # mergesort is used to be consistent as Matlab implementation. inds = np.argsort(-dtScores, kind='mergesort') dtScoresSorted = dtScores[inds] dtm = np.concatenate([e['dtMatches'][:,0:maxDet] for e in E], axis=1)[:,inds] dtIg = np.concatenate([e['dtIgnore'][:,0:maxDet] for e in E], axis=1)[:,inds] gtIg = np.concatenate([e['gtIgnore'] for e in E]) npig = np.count_nonzero(gtIg==0 ) if npig == 0: continue tps = np.logical_and( dtm, np.logical_not(dtIg) ) fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg) ) tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float) fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float) for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)): tp = np.array(tp) fp = np.array(fp) nd = len(tp) rc = tp / npig pr = tp / (fp+tp+np.spacing(1)) q = np.zeros((R,)) ss = np.zeros((R,)) if nd: recall[t,k,a,m] = rc[-1] else: recall[t,k,a,m] = 0 # numpy is slow without cython optimization for accessing elements # use python array gets significant speed improvement pr = pr.tolist(); q = q.tolist() for i in range(nd-1, 0, -1): if pr[i] > pr[i-1]: pr[i-1] = pr[i] inds = np.searchsorted(rc, p.recThrs, side='left') try: for ri, pi in enumerate(inds): q[ri] = pr[pi] ss[ri] = dtScoresSorted[pi] except: pass precision[t,:,k,a,m] = np.array(q) scores[t,:,k,a,m] = np.array(ss) self.eval = { 'params': p, 'counts': [T, R, K, A, M], 'date': datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), 'precision': precision, 'recall': recall, 'scores': scores, } toc = time.time() print('DONE (t={:0.2f}s).'.format( toc-tic)) def summarize(self): ''' Compute and display summary metrics for evaluation results. Note this functin can *only* be applied on the default parameter setting ''' def _summarize( ap=1, iouThr=None, areaRng='all', maxDets=100 ): p = self.params iStr = ' {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}' titleStr = 'Average Precision' if ap == 1 else 'Average Recall' typeStr = '(AP)' if ap==1 else '(AR)' iouStr = '{:0.2f}:{:0.2f}'.format(p.iouThrs[0], p.iouThrs[-1]) \ if iouThr is None else '{:0.2f}'.format(iouThr) aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng] mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets] if ap == 1: # dimension of precision: [TxRxKxAxM] s = self.eval['precision'] # IoU if iouThr is not None: t = np.where(iouThr == p.iouThrs)[0] s = s[t] s = s[:,:,:,aind,mind] else: # dimension of recall: [TxKxAxM] s = self.eval['recall'] if iouThr is not None: t = np.where(iouThr == p.iouThrs)[0] s = s[t] s = s[:,:,aind,mind] if len(s[s>-1])==0: mean_s = -1 else: mean_s = np.mean(s[s>-1]) print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s)) return mean_s def _summarizeDets(): stats = np.zeros((12,)) stats[0] = _summarize(1) stats[1] = _summarize(1, iouThr=.5, maxDets=self.params.maxDets[2]) stats[2] = _summarize(1, iouThr=.75, maxDets=self.params.maxDets[2]) stats[3] = _summarize(1, areaRng='small', maxDets=self.params.maxDets[2]) stats[4] = _summarize(1, areaRng='medium', maxDets=self.params.maxDets[2]) stats[5] = _summarize(1, areaRng='large', maxDets=self.params.maxDets[2]) stats[6] = _summarize(0, maxDets=self.params.maxDets[0]) stats[7] = _summarize(0, maxDets=self.params.maxDets[1]) stats[8] = _summarize(0, maxDets=self.params.maxDets[2]) stats[9] = _summarize(0, areaRng='small', maxDets=self.params.maxDets[2]) stats[10] = _summarize(0, areaRng='medium', maxDets=self.params.maxDets[2]) stats[11] = _summarize(0, areaRng='large', maxDets=self.params.maxDets[2]) return stats def _summarizeKps(): stats = np.zeros((10,)) stats[0] = _summarize(1, maxDets=20) stats[1] = _summarize(1, maxDets=20, iouThr=.5) stats[2] = _summarize(1, maxDets=20, iouThr=.75) stats[3] = _summarize(1, maxDets=20, areaRng='medium') stats[4] = _summarize(1, maxDets=20, areaRng='large') stats[5] = _summarize(0, maxDets=20) stats[6] = _summarize(0, maxDets=20, iouThr=.5) stats[7] = _summarize(0, maxDets=20, iouThr=.75) stats[8] = _summarize(0, maxDets=20, areaRng='medium') stats[9] = _summarize(0, maxDets=20, areaRng='large') return stats if not self.eval: raise Exception('Please run accumulate() first') iouType = self.params.iouType if iouType == 'segm' or iouType == 'bbox': summarize = _summarizeDets elif iouType == 'keypoints': summarize = _summarizeKps self.stats = summarize() def __str__(self): self.summarize() class Params: ''' Params for coco evaluation api ''' def setDetParams(self): self.imgIds = [] self.catIds = [] # np.arange causes trouble. the data point on arange is slightly larger than the true value self.iouThrs = np.linspace(.5, 0.75, int(np.round((0.75 - .5) / .05)) + 1, endpoint=True) self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01)) + 1, endpoint=True) self.maxDets = [1, 10, 100] self.areaRng = [[0 ** 2, 1e5 ** 2], [0 ** 2, 32 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] self.areaRngLbl = ['all', 'small', 'medium', 'large'] self.useCats = 1 def setKpParams(self): self.imgIds = [] self.catIds = [] # np.arange causes trouble. the data point on arange is slightly larger than the true value self.iouThrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True) self.recThrs = np.linspace(.0, 1.00, int(np.round((1.00 - .0) / .01)) + 1, endpoint=True) self.maxDets = [20] self.areaRng = [[0 ** 2, 1e5 ** 2], [32 ** 2, 96 ** 2], [96 ** 2, 1e5 ** 2]] self.areaRngLbl = ['all', 'medium', 'large'] self.useCats = 1 self.kpt_oks_sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62,.62, 1.07, 1.07, .87, .87, .89, .89])/10.0 def __init__(self, iouType='segm'): if iouType == 'segm' or iouType == 'bbox': self.setDetParams() elif iouType == 'keypoints': self.setKpParams() else: raise Exception('iouType not supported') self.iouType = iouType # useSegm is deprecated self.useSegm = None

45.127103

131

0.513896

0b29649b61db77da0b93b9ee22c9960e8637d3d4

135

py

Python

eks_node_migrator.py

ahrizvi/eks-node-migrator

2972c3011f81cda9c78dabb13ec6449ce70506ab

[ "Apache-2.0" ]

1

2021-11-23T22:34:23.000Z

eks_node_migrator.py

ahrizvi/eks-node-migrator

2972c3011f81cda9c78dabb13ec6449ce70506ab

[ "Apache-2.0" ]

null

eks_node_migrator.py

ahrizvi/eks-node-migrator

2972c3011f81cda9c78dabb13ec6449ce70506ab

[ "Apache-2.0" ]

null

#!/usr/bin/env python3 import sys from eksmigrator.cli import main if __name__ == "__main__": main(sys.argv[1:]) # Test CI Build

15

32

0.696296

0d1b368a39f4ff83bd9d1e4ed657f07dce6389b1

3,123

py

Python

pytar/pytar.py

douglas-archives/pytar

c53f801487cbf5e3eda02ccaf53d119b37f744a5

[ "BSD-3-Clause" ]

null

pytar/pytar.py

douglas-archives/pytar

c53f801487cbf5e3eda02ccaf53d119b37f744a5

[ "BSD-3-Clause" ]

3

2018-01-13T21:04:13.000Z

pytar/pytar.py

douglas-archives/pytar

c53f801487cbf5e3eda02ccaf53d119b37f744a5

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python # -*- coding: utf-8 -*- from __future__ import unicode_literals import os import sys import tarfile from datetime import datetime PY3 = sys.version > '3' def list_contents(tar_file): """ Listing contents from a tar file. This implementation is based on Python core code, (tarfile.list). """ members = tar_file.getmembers() output = '' for tarinfo in members: line = '' line += tarfile.filemode(tarinfo.mode) + ' ' owner = '{0}/{1}'.format( tarinfo.uname or tarinfo.uid, tarinfo.gname or tarinfo.gid ) line += owner if tarinfo.ischr() or tarinfo.isblk(): dev = '{0},{1}'.format(tarinfo.devmajor, tarinfo.devminor) line += dev.center(10) + ' ' else: line += '{0:10}'.format(tarinfo.size) + ' ' line += str(datetime.fromtimestamp(tarinfo.mtime)) + ' ' line += '{0}{1}{2}'.format( tarinfo.name if PY3 else tarinfo.name.decode('utf-8'), ('/' if tarinfo.isdir() else ''), ' ' ) if tarinfo.issym(): line += '-> {0}'.format(tarinfo.linkname) if tarinfo.islnk(): line += 'link to {0}'.format(tarinfo.linkname) output += '{0}\n'.format(line) return output or 'Nothing to output.' def pytar_extract(tar_file_name, extract_path=None, verbose=False): is_a_valid_tarfile = False messages = { 'path_does_not_exist': { 'status': 'fail', 'message': 'ERROR: This path does not exist.' }, 'is_dir': { 'status': 'fail', 'message': 'ERROR: This is a directory not a tar file.' }, 'not_a_tar_file': { 'status': 'fail', 'message': 'ERROR: This may not be a tar file or may be corrupted.' }, 'empty_tar_file': { 'status': 'fail', 'message': 'This is an empty tar file.' }, 'wrong_extract_path': { 'status': 'fail', 'message': 'ERROR: The extract path does not exist.' }, 'success': { 'status': 'success', 'message': 'Successfully extracted.', 'verbose': '' }, } if not os.path.exists(tar_file_name): return messages['path_does_not_exist'] if os.path.isdir(tar_file_name): return messages['is_dir'] if not extract_path: path = os.path.dirname(tar_file_name) else: if os.path.isdir(extract_path): path = extract_path else: return messages['wrong_extract_path'] is_a_valid_tarfile = tarfile.is_tarfile(tar_file_name) if not is_a_valid_tarfile: return messages['not_a_tar_file'] tar_file = tarfile.open(tar_file_name) members = tar_file.getmembers() if not members: return messages['empty_tar_file'] tar_file.extractall(path, members) if verbose: messages['success']['verbose'] = list_contents(tar_file) tar_file.close() return messages['success']

27.394737

79

0.557477

85b6ebf36cb629e3e337eacd878160a22f8a34fd

992

py

Python

decisions/csgo_decider.py

LavinaVRovine/hazard

e0408374dc0b76f8b9a0107f5f12cca2d4c033ef

[ "MIT" ]

1

2020-10-05T14:19:35.000Z

decisions/csgo_decider.py

LavinaVRovine/hazard

e0408374dc0b76f8b9a0107f5f12cca2d4c033ef

[ "MIT" ]

null

decisions/csgo_decider.py

LavinaVRovine/hazard

e0408374dc0b76f8b9a0107f5f12cca2d4c033ef

[ "MIT" ]

null

from decisions.decider import Decider from data.get_csgo_data import get_csgo_data from helpers.custom_exceptions import TeamNotFound, NoMatchData from data.csgo_data_creator import CSGOData class CSGODecider(Decider): def __init__(self, match_row, db_location, data_handler: CSGOData): super().__init__(match_row, db_location) self.df = get_csgo_data() self.data_handler = data_handler def decide_match_action(self, predictor) -> dict: try: match_row = self.data_handler.create_match_stats_row( self.team_1_name, self.team_2_name ) except (TeamNotFound, NoMatchData) as e: print(e) return {"team1": self.team_1_name, "team2": self.team_2_name} else: # no error match_row_df = match_row.to_frame().T.reindex( columns=predictor.training_columns ) return super().decide(predictor, match_row_df.fillna(0))

34.206897

73

0.660282

455374c0e6052ce457b53773e38d5167a64582bb

716

py

Python

seqauto/migrations/0026_auto_20210524_1225.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

5

2021-01-14T03:34:42.000Z

2022-03-07T15:34:18.000Z

seqauto/migrations/0026_auto_20210524_1225.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

551

2020-10-19T00:02:38.000Z

2022-03-30T02:18:22.000Z

seqauto/migrations/0026_auto_20210524_1225.py

SACGF/variantgrid

515195e2f03a0da3a3e5f2919d8e0431babfd9c9

[ "RSA-MD" ]

null

# Generated by Django 3.2.1 on 2021-05-24 02:55 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('seqauto', '0025_auto_20210429_1030'), ] operations = [ migrations.AddField( model_name='enrichmentkit', name='sample_variants_type', field=models.CharField(choices=[('U', 'Unknown'), ('G', 'Germline'), ('M', 'Mixed (Single Sample)'), ('S', 'Somatic only (Tumor minus normal)')], default='U', max_length=1), ), migrations.AddField( model_name='enrichmentkit', name='variant_zygosity_count', field=models.BooleanField(default=True), ), ]

29.833333

185

0.597765

0d08c8a63b54cb7c2104f06446934c62508ccdeb

386

py

Python

100DaysOfDays/Dia03/ex14.py

giselemanuel/programming-challenges

c7f37685989190b71ed7ec773f1dc407fd961cf1

[ "MIT" ]

null

100DaysOfDays/Dia03/ex14.py

giselemanuel/programming-challenges

c7f37685989190b71ed7ec773f1dc407fd961cf1

[ "MIT" ]

null

100DaysOfDays/Dia03/ex14.py

giselemanuel/programming-challenges

c7f37685989190b71ed7ec773f1dc407fd961cf1

[ "MIT" ]

null

""" Exercício Python 14: Escreva um programa que converta uma temperatura digitando em graus Celsius e converta para graus Fahrenheit. """ print("-" * 50) print(f'{"Conversor de temperatura":^50}') print("-" * 50) temperatura_c = float(input("Insira a temperatura em ºC : ")) temperatura_f = (temperatura_c * (9/5)) + 32 print(f"Temperaturas:\n{temperatura_c}ºC\n{temperatura_f}ºF")

27.571429

79

0.722798

caf0fada7d3792bba3513627bf3b45d08f6c08ff

4,170

py

Python

loihi_network.py

russelljjarvis/brian2_loihi

bb94ab014aabefca0861613ba0343c13619668f3

[ "MIT" ]

6

2021-05-03T06:20:07.000Z

2022-01-25T14:17:20.000Z

loihi_network.py

russelljjarvis/brian2_loihi

bb94ab014aabefca0861613ba0343c13619668f3

[ "MIT" ]

17

2021-04-28T08:54:23.000Z

2021-12-08T19:30:46.000Z

loihi_network.py

russelljjarvis/brian2_loihi

bb94ab014aabefca0861613ba0343c13619668f3

[ "MIT" ]

5

2021-07-11T23:15:03.000Z

2022-03-17T13:48:57.000Z

from brian2 import Network, ExplicitStateUpdater, StateUpdateMethod, defaultclock, ms import warnings class LoihiNetwork(Network): """ The LoihiNetwork extends the Network class from Brian2. Note that it is important to use the LoihiNetwork class and not the magic network. Methods ------- __init__(*objs, **kwds) Initializes the LoihiNetwork and the Network run(duration, **kwargs) Checks for problems and runs the Brian network simulation """ def __init__(self, *objs, **kwds): """ Initializes the LoihiNetwork and the Network This method registers two ExplicitStateUpdater as StateUpdateMethod. These update methods are used by Loihi to integrate diffeential equations. Further, the dt is set to 1, again to match a Loihi simulation. Afterwards the __init__() method from the Brian2 Network is called, initializing a default Brian2 network. Finally, the default schedule from Brian2 is reordered to match Loihi. All arguments are passed to the parent init method. Parameters ---------- *objs : All arguments defined by the parent class **kwds : optional All keyword arguments defined by the parent class """ # Define first order forward euler, if not already defined if ('forward_euler' not in StateUpdateMethod.stateupdaters): eq_forward_euler = ''' x_new = x + dt * f(x,t) ''' forward_euler = ExplicitStateUpdater(eq_forward_euler, stochastic='none') StateUpdateMethod.register('forward_euler', forward_euler) # Define exact state updater for the pre/post traces for learning, if not already defined if ('exact_synapse' not in StateUpdateMethod.stateupdaters): eq_exact_synapse = ''' x_0 = dt*f(x,t) x_new = int(x_0) ''' exact_synapse = ExplicitStateUpdater(eq_exact_synapse, stochastic='none') StateUpdateMethod.register('exact_synapse', exact_synapse) # Set default clock dt defaultclock.dt = 1*ms # Call super init super().__init__(*objs, **kwds) # Reorder schedule to match Loihi self.schedule = ['start', 'synapses', 'groups', 'thresholds', 'resets', 'end'] def run(self, duration, **kwargs): """ Checks for problems and runs the Brian network simulation The run method overwrites the run method from the Network class. Just before running the simulation, it checks if the most important settings are still valid. If not, a warning is shown. The user should be able to choose other settings, but should be warned that results can then vary from Loihi. Afterwards the parent run() method is called. The duration is modified, such that the LoihiNetwork run() method will only take an integer without Brian's time information (e.g. ms). All keyword arguments are passed to the parent run() method. Parameters ---------- duration : int Duration of the simulation as an integer value, no time (e.g. ms) should to be added **kwargs : optional All keyword arguments defined by the parent method Raises ------ Warning If defautlclock dt value has changed and is not set to 1ms any more Warning If the schedule has changed and is not in the Loihi-like order any more """ # Check if the user has manually changed defaultclock and print warning if (defaultclock.dt != 1*ms): warnings.warn("The defaultclock.dt is not set to 1*ms, this may cause results which deviate from Loihi.") # Check if the user has manually changed schedule and print warning if (self.schedule != ['start', 'synapses', 'groups', 'thresholds', 'resets', 'end']): warnings.warn("The schedule has changed, this may cause results which deviate from Loihi.") # Call run method from Brian Network super().run(duration*ms, **kwargs)

42.55102

117

0.647482

6d8136f7c7a113ce056948b917c046e58618c707

1,691

py

Python

config/wsgi.py

naeunpark/nomadgram

3b3f245fd627ae160ff9492806799ddf22cbdbca

[ "MIT" ]

null

config/wsgi.py

naeunpark/nomadgram

3b3f245fd627ae160ff9492806799ddf22cbdbca

[ "MIT" ]

5

2020-06-05T19:18:08.000Z

2021-09-08T00:15:11.000Z

config/wsgi.py

naeunpark/nomadgram

3b3f245fd627ae160ff9492806799ddf22cbdbca

[ "MIT" ]

null

""" WSGI config for NOMADGRAM project. This module contains the WSGI application used by Django's development server and any production WSGI deployments. It should expose a module-level variable named ``application``. Django's ``runserver`` and ``runfcgi`` commands discover this application via the ``WSGI_APPLICATION`` setting. Usually you will have the standard Django WSGI application here, but it also might make sense to replace the whole Django WSGI application with a custom one that later delegates to the Django one. For example, you could introduce WSGI middleware here, or combine a Django application with an application of another framework. """ import os import sys from django.core.wsgi import get_wsgi_application # This allows easy placement of apps within the interior # nomadgram directory. app_path = os.path.abspath(os.path.join( os.path.dirname(os.path.abspath(__file__)), os.pardir)) sys.path.append(os.path.join(app_path, 'nomadgram')) # We defer to a DJANGO_SETTINGS_MODULE already in the environment. This breaks # if running multiple sites in the same mod_wsgi process. To fix this, use # mod_wsgi daemon mode with each site in its own daemon process, or use # os.environ["DJANGO_SETTINGS_MODULE"] = "config.settings.production" os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings.production") # This application object is used by any WSGI server configured to use this # file. This includes Django's development server, if the WSGI_APPLICATION # setting points here. application = get_wsgi_application() # Apply WSGI middleware here. # from helloworld.wsgi import HelloWorldApplication # application = HelloWorldApplication(application)

41.243902

79

0.797161

fb7f9b253725ebdd1121d2d9e9879925e74f7398

789

py

Python

setup.py

kkristof200/swagger_scraper

8d63b04b89383743da823a92b472fa5078264328

[ "MIT" ]

null

setup.py

kkristof200/swagger_scraper

8d63b04b89383743da823a92b472fa5078264328

[ "MIT" ]

null

setup.py

kkristof200/swagger_scraper

8d63b04b89383743da823a92b472fa5078264328

[ "MIT" ]

null

import setuptools, os readme_path = os.path.join(os.getcwd(), "README.md") if os.path.exists(readme_path): with open(readme_path, "r") as f: long_description = f.read() else: long_description = 'swagger_scraper' setuptools.setup( name="swagger_scraper", version="0.0.1", author="Kristof", description="scrape swagger htmls into python files", long_description=long_description, long_description_content_type="text/markdown", url="https://github.com/kkristof200/swagger_scraper", packages=setuptools.find_packages(), install_requires=[], classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", ], python_requires='>=3.5', )

30.346154

57

0.675539

c98edb2dac482f9187081d0879f49a55a468c796

2,170

py

Python

docs/demos/long_callback_examples/long_callback_celery_progress_bar.py

neumann-nico/dash-labs

0bb9ef244919bf8882f97b183f0c7ab0f88762bb

[ "MIT" ]

1

2021-09-01T12:53:15.000Z

docs/demos/long_callback_examples/long_callback_celery_progress_bar.py

neumann-nico/dash-labs

0bb9ef244919bf8882f97b183f0c7ab0f88762bb

[ "MIT" ]

null

docs/demos/long_callback_examples/long_callback_celery_progress_bar.py

neumann-nico/dash-labs

0bb9ef244919bf8882f97b183f0c7ab0f88762bb

[ "MIT" ]

null

import time import dash import dash_html_components as html import dash_labs as dl from dash_labs.plugins import DiskcacheCachingCallbackManager, CeleryCallbackManager # ## Celery on RabbitMQ # from celery import Celery # celery_app = Celery(__name__, backend='rpc://', broker='pyamqp://') # long_callback_manager = CeleryCallbackManager(celery_app) # ## Celery on Redis # from celery import Celery # celery_app = Celery( # __name__, broker='redis://localhost:6379/0', backend='redis://localhost:6379/1' # ) # long_callback_manager = CeleryCallbackManager(celery_app) ## Diskcache import diskcache cache = diskcache.Cache("./cache") long_callback_manager = DiskcacheCachingCallbackManager(cache) app = dash.Dash( __name__, plugins=[ dl.plugins.FlexibleCallbacks(), dl.plugins.HiddenComponents(), dl.plugins.LongCallback(long_callback_manager), ], ) app.layout = html.Div( [ html.Div( [ html.P(id="paragraph_id", children=["Button not clicked"]), html.Progress(id="progress_bar"), ] ), html.Button(id="button_id", children="Run Job!"), html.Button(id="cancel_button_id", children="Cancel Running Job!"), ] ) @app.long_callback( output=dl.Output("paragraph_id", "children"), args=dl.Input("button_id", "n_clicks"), running=[ (dl.Output("button_id", "disabled"), True, False), (dl.Output("cancel_button_id", "disabled"), False, True), ( dl.Output("paragraph_id", "style"), {"visibility": "hidden"}, {"visibility": "visible"}, ), ( dl.Output("progress_bar", "style"), {"visibility": "visible"}, {"visibility": "hidden"}, ), ], cancel=[dl.Input("cancel_button_id", "n_clicks")], progress=dl.Output("progress_bar", ("value", "max")), ) def callback(set_progress, n_clicks): total = 10 for i in range(total): time.sleep(0.5) set_progress((str(i + 1), str(total))) return [f"Clicked {n_clicks} times"] if __name__ == "__main__": app.run_server(debug=True)

27.820513

85

0.624885

cd30e423b3ab441d827805104d7bea43b51e167a

1,317

py

Python

sdk/python/pulumi_azure_nextgen/digitaltwins/v20200301preview/__init__.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

31

2020-09-21T09:41:01.000Z

2021-02-26T13:21:59.000Z

sdk/python/pulumi_azure_nextgen/digitaltwins/v20200301preview/__init__.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

231

2020-09-21T09:38:45.000Z

2021-03-01T11:16:03.000Z

sdk/python/pulumi_azure_nextgen/digitaltwins/v20200301preview/__init__.py

pulumi/pulumi-azure-nextgen

452736b0a1cf584c2d4c04666e017af6e9b2c15c

[ "Apache-2.0" ]

4

2020-09-29T14:14:59.000Z

2021-02-10T20:38:16.000Z

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** # Export this package's modules as members: from ._enums import * from .digital_twin import * from .digital_twins_endpoint import * from .get_digital_twin import * from .get_digital_twins_endpoint import * from ._inputs import * from . import outputs def _register_module(): import pulumi from ... import _utilities class Module(pulumi.runtime.ResourceModule): _version = _utilities.get_semver_version() def version(self): return Module._version def construct(self, name: str, typ: str, urn: str) -> pulumi.Resource: if typ == "azure-nextgen:digitaltwins/v20200301preview:DigitalTwin": return DigitalTwin(name, pulumi.ResourceOptions(urn=urn)) elif typ == "azure-nextgen:digitaltwins/v20200301preview:DigitalTwinsEndpoint": return DigitalTwinsEndpoint(name, pulumi.ResourceOptions(urn=urn)) else: raise Exception(f"unknown resource type {typ}") _module_instance = Module() pulumi.runtime.register_resource_module("azure-nextgen", "digitaltwins/v20200301preview", _module_instance) _register_module()

34.657895

111

0.700835

597e4848ea3da5f71337ef3d6b9b31e263b7dc8d

6,431

py

Python

test/functional/rpc_bind.py

konjoinfinity/konjocoin

3f638857a17fc844f6ee208aac9c7aefa179f0d5

[ "MIT" ]

null

test/functional/rpc_bind.py

konjoinfinity/konjocoin

3f638857a17fc844f6ee208aac9c7aefa179f0d5

[ "MIT" ]

null

test/functional/rpc_bind.py

konjoinfinity/konjocoin

3f638857a17fc844f6ee208aac9c7aefa179f0d5

[ "MIT" ]

null

#!/usr/bin/env python3 # Copyright (c) 2014-2018 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """Test running konjocoind with the -rpcbind and -rpcallowip options.""" import sys from test_framework.netutil import all_interfaces, addr_to_hex, get_bind_addrs, test_ipv6_local from test_framework.test_framework import KonjocoinTestFramework, SkipTest from test_framework.util import assert_equal, assert_raises_rpc_error, get_rpc_proxy, rpc_port, rpc_url class RPCBindTest(KonjocoinTestFramework): def set_test_params(self): self.setup_clean_chain = True self.bind_to_localhost_only = False self.num_nodes = 1 def setup_network(self): self.add_nodes(self.num_nodes, None) def add_options(self, parser): parser.add_argument("--ipv4", action='store_true', dest="run_ipv4", help="Run ipv4 tests only", default=False) parser.add_argument("--ipv6", action='store_true', dest="run_ipv6", help="Run ipv6 tests only", default=False) parser.add_argument("--nonloopback", action='store_true', dest="run_nonloopback", help="Run non-loopback tests only", default=False) def run_bind_test(self, allow_ips, connect_to, addresses, expected): ''' Start a node with requested rpcallowip and rpcbind parameters, then try to connect, and check if the set of bound addresses matches the expected set. ''' self.log.info("Bind test for %s" % str(addresses)) expected = [(addr_to_hex(addr), port) for (addr, port) in expected] base_args = ['-disablewallet', '-nolisten'] if allow_ips: base_args += ['-rpcallowip=' + x for x in allow_ips] binds = ['-rpcbind='+addr for addr in addresses] self.nodes[0].rpchost = connect_to self.start_node(0, base_args + binds) pid = self.nodes[0].process.pid assert_equal(set(get_bind_addrs(pid)), set(expected)) self.stop_nodes() def run_allowip_test(self, allow_ips, rpchost, rpcport): ''' Start a node with rpcallow IP, and request getnetworkinfo at a non-localhost IP. ''' self.log.info("Allow IP test for %s:%d" % (rpchost, rpcport)) node_args = \ ['-disablewallet', '-nolisten'] + \ ['-rpcallowip='+x for x in allow_ips] + \ ['-rpcbind='+addr for addr in ['127.0.0.1', "%s:%d" % (rpchost, rpcport)]] # Bind to localhost as well so start_nodes doesn't hang self.nodes[0].rpchost = None self.start_nodes([node_args]) # connect to node through non-loopback interface node = get_rpc_proxy(rpc_url(self.nodes[0].datadir, 0, "%s:%d" % (rpchost, rpcport)), 0, coveragedir=self.options.coveragedir) node.getnetworkinfo() self.stop_nodes() def run_test(self): # due to OS-specific network stats queries, this test works only on Linux if sum([self.options.run_ipv4, self.options.run_ipv6, self.options.run_nonloopback]) > 1: raise AssertionError("Only one of --ipv4, --ipv6 and --nonloopback can be set") self.log.info("Check for linux") if not sys.platform.startswith('linux'): raise SkipTest("This test can only be run on linux.") self.log.info("Check for ipv6") have_ipv6 = test_ipv6_local() if not have_ipv6 and not (self.options.run_ipv4 or self.options.run_nonloopback): raise SkipTest("This test requires ipv6 support.") self.log.info("Check for non-loopback interface") self.non_loopback_ip = None for name,ip in all_interfaces(): if ip != '127.0.0.1': self.non_loopback_ip = ip break if self.non_loopback_ip is None and self.options.run_nonloopback: raise SkipTest("This test requires a non-loopback ip address.") self.defaultport = rpc_port(0) if not self.options.run_nonloopback: self._run_loopback_tests() if not self.options.run_ipv4 and not self.options.run_ipv6: self._run_nonloopback_tests() def _run_loopback_tests(self): if self.options.run_ipv4: # check only IPv4 localhost (explicit) self.run_bind_test(['127.0.0.1'], '127.0.0.1', ['127.0.0.1'], [('127.0.0.1', self.defaultport)]) # check only IPv4 localhost (explicit) with alternative port self.run_bind_test(['127.0.0.1'], '127.0.0.1:32171', ['127.0.0.1:32171'], [('127.0.0.1', 32171)]) # check only IPv4 localhost (explicit) with multiple alternative ports on same host self.run_bind_test(['127.0.0.1'], '127.0.0.1:32171', ['127.0.0.1:32171', '127.0.0.1:32172'], [('127.0.0.1', 32171), ('127.0.0.1', 32172)]) else: # check default without rpcallowip (IPv4 and IPv6 localhost) self.run_bind_test(None, '127.0.0.1', [], [('127.0.0.1', self.defaultport), ('::1', self.defaultport)]) # check default with rpcallowip (IPv4 and IPv6 localhost) self.run_bind_test(['127.0.0.1'], '127.0.0.1', [], [('127.0.0.1', self.defaultport), ('::1', self.defaultport)]) # check only IPv6 localhost (explicit) self.run_bind_test(['[::1]'], '[::1]', ['[::1]'], [('::1', self.defaultport)]) # check both IPv4 and IPv6 localhost (explicit) self.run_bind_test(['127.0.0.1'], '127.0.0.1', ['127.0.0.1', '[::1]'], [('127.0.0.1', self.defaultport), ('::1', self.defaultport)]) def _run_nonloopback_tests(self): self.log.info("Using interface %s for testing" % self.non_loopback_ip) # check only non-loopback interface self.run_bind_test([self.non_loopback_ip], self.non_loopback_ip, [self.non_loopback_ip], [(self.non_loopback_ip, self.defaultport)]) # Check that with invalid rpcallowip, we are denied self.run_allowip_test([self.non_loopback_ip], self.non_loopback_ip, self.defaultport) assert_raises_rpc_error(-342, "non-JSON HTTP response with '403 Forbidden' from server", self.run_allowip_test, ['1.1.1.1'], self.non_loopback_ip, self.defaultport) if __name__ == '__main__': RPCBindTest().main()

49.469231

172

0.633494

3dbdd1b2f561e1c2c1747342ecd61b8bc53e8f0d

572

py

Python

_Listas/inter_vetor.py

M3nin0/supreme-broccoli

186c1ea3b839ba3139f9301660dec8fbd27a162e

[ "Apache-2.0" ]

null

_Listas/inter_vetor.py

M3nin0/supreme-broccoli

186c1ea3b839ba3139f9301660dec8fbd27a162e

[ "Apache-2.0" ]

null

_Listas/inter_vetor.py

M3nin0/supreme-broccoli

186c1ea3b839ba3139f9301660dec8fbd27a162e

[ "Apache-2.0" ]

null

# Gerando um vetor intermediario com elemento de dois outros vetores vetorUM = [] vetorMED = [] vetorDOIS = [] ct = 1 while ct <= 10: elm = int(input("Insira um valor do vetor 1: ")) vetorUM.append(elm) vetorMED.append(elm) elm = int(input("Insira um valor do vetor 2: ")) vetorDOIS.append(elm) vetorMED.append(elm) ct += 1 print("Os elementos inseridos no primeiro vetor são:\n", vetorUM) print("Os elementos inseridos no segundo vetor são:\n", vetorDOIS) print("E o vetor gerado com o elementos dos dois vetores tem os seguintes elementos:\n", vetorMED)

23.833333

98

0.713287

e329aae6856bb6329cece3dc2ca1f71bfb681f8b

11,208

py

Python

sktime/forecasting/tests/test_all_forecasters.py

ltoniazzi/sktime

0ea07803115c1ec7463dde99f049b131d639f4a7

[ "BSD-3-Clause" ]

1

2021-11-02T18:56:12.000Z

sktime/forecasting/tests/test_all_forecasters.py

ltoniazzi/sktime

0ea07803115c1ec7463dde99f049b131d639f4a7

[ "BSD-3-Clause" ]

null

sktime/forecasting/tests/test_all_forecasters.py

ltoniazzi/sktime

0ea07803115c1ec7463dde99f049b131d639f4a7

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python3 -u # -*- coding: utf-8 -*- # copyright: sktime developers, BSD-3-Clause License (see LICENSE file) # test API provided through BaseForecaster __author__ = ["Markus Löning"] __all__ = [ "test_raises_not_fitted_error", "test_score", "test_predict_time_index", "test_update_predict_predicted_index", "test_update_predict_predicted_index_update_params", "test_y_multivariate_raises_error", "test_get_fitted_params", "test_predict_time_index_in_sample_full", "test_predict_pred_interval", "test_update_predict_single", "test_y_invalid_type_raises_error", "test_predict_time_index_with_X", "test_X_invalid_type_raises_error", ] import numpy as np import pandas as pd import pytest from sktime.exceptions import NotFittedError from sktime.forecasting.model_selection import SlidingWindowSplitter from sktime.forecasting.model_selection import temporal_train_test_split from sktime.forecasting.tests._config import TEST_ALPHAS from sktime.forecasting.tests._config import TEST_FHS from sktime.forecasting.tests._config import TEST_OOS_FHS from sktime.forecasting.tests._config import TEST_STEP_LENGTHS from sktime.forecasting.tests._config import TEST_WINDOW_LENGTHS from sktime.forecasting.tests._config import VALID_INDEX_FH_COMBINATIONS from sktime.performance_metrics.forecasting import ( mean_absolute_percentage_error, ) from sktime.utils import all_estimators from sktime.utils._testing.estimator_checks import _construct_instance from sktime.utils._testing.forecasting import _assert_correct_pred_time_index from sktime.utils._testing.forecasting import _get_expected_index_for_update_predict from sktime.utils._testing.forecasting import _make_fh from sktime.utils._testing.forecasting import make_forecasting_problem from sktime.utils._testing.series import _make_series from sktime.utils.validation.forecasting import check_fh # get all forecasters FORECASTERS = all_estimators(estimator_types="forecaster", return_names=False) FH0 = 1 INVALID_INPUT_TYPES = [np.empty(20), list(), tuple()] # testing data y = make_forecasting_problem() y_train, y_test = temporal_train_test_split(y, train_size=0.75) @pytest.mark.parametrize("Forecaster", FORECASTERS) def test_get_fitted_params(Forecaster): f = _construct_instance(Forecaster) f.fit(y_train, fh=FH0) try: params = f.get_fitted_params() assert isinstance(params, dict) except NotImplementedError: pass @pytest.mark.parametrize("Forecaster", FORECASTERS) def test_raises_not_fitted_error(Forecaster): # We here check extra method of the forecaster API: update and update_predict. f = _construct_instance(Forecaster) # predict is check in test suite for all estimators with pytest.raises(NotFittedError): f.update(y_test, update_params=False) with pytest.raises(NotFittedError): cv = SlidingWindowSplitter(fh=1, window_length=1, start_with_window=False) f.update_predict(y_test, cv=cv) try: with pytest.raises(NotFittedError): f.get_fitted_params() except NotImplementedError: pass @pytest.mark.parametrize("Forecaster", FORECASTERS) def test_y_multivariate_raises_error(Forecaster): # Check that multivariate y raises an appropriate error message. y = _make_series(n_columns=2) f = _construct_instance(Forecaster) with pytest.raises(ValueError, match=r"univariate"): f.fit(y, fh=FH0) @pytest.mark.parametrize("Forecaster", FORECASTERS) @pytest.mark.parametrize("y", INVALID_INPUT_TYPES) def test_y_invalid_type_raises_error(Forecaster, y): f = _construct_instance(Forecaster) with pytest.raises(TypeError, match=r"type"): f.fit(y, fh=FH0) @pytest.mark.parametrize("Forecaster", FORECASTERS) @pytest.mark.parametrize("X", INVALID_INPUT_TYPES) def test_X_invalid_type_raises_error(Forecaster, X): f = _construct_instance(Forecaster) try: with pytest.raises(TypeError, match=r"type"): f.fit(y_train, X, fh=FH0) except NotImplementedError as e: msg = str(e).lower() assert "exogenous" in msg @pytest.mark.parametrize("Forecaster", FORECASTERS) @pytest.mark.parametrize( "index_type, fh_type, is_relative", VALID_INDEX_FH_COMBINATIONS ) @pytest.mark.parametrize("steps", TEST_FHS) # fh steps def test_predict_time_index(Forecaster, index_type, fh_type, is_relative, steps): # Check that predicted time index matches forecasting horizon. y_train = make_forecasting_problem(index_type=index_type) cutoff = y_train.index[-1] fh = _make_fh(cutoff, steps, fh_type, is_relative) f = _construct_instance(Forecaster) # Some estimators may not support all time index types and fh types, hence we # need to catch NotImplementedErrors. try: f.fit(y_train, fh=fh) y_pred = f.predict() _assert_correct_pred_time_index(y_pred.index, y_train.index[-1], fh) except NotImplementedError: pass @pytest.mark.parametrize("Forecaster", FORECASTERS) @pytest.mark.parametrize( "index_type, fh_type, is_relative", VALID_INDEX_FH_COMBINATIONS ) @pytest.mark.parametrize("steps", TEST_OOS_FHS) # fh steps def test_predict_time_index_with_X(Forecaster, index_type, fh_type, is_relative, steps): # Check that predicted time index matches forecasting horizon. y, X = make_forecasting_problem(index_type=index_type, make_X=True) cutoff = y.index[len(y) // 2] fh = _make_fh(cutoff, steps, fh_type, is_relative) y_train, y_test, X_train, X_test = temporal_train_test_split(y, X, fh=fh) f = _construct_instance(Forecaster) # Some estimators may not support all time index types and fh types, hence we # need to catch NotImplementedErrors. try: f.fit(y_train, X_train, fh=fh) y_pred = f.predict(X=X_test) _assert_correct_pred_time_index(y_pred.index, y_train.index[-1], fh) except NotImplementedError: pass @pytest.mark.parametrize("Forecaster", FORECASTERS) @pytest.mark.parametrize( "index_type, fh_type, is_relative", VALID_INDEX_FH_COMBINATIONS ) def test_predict_time_index_in_sample_full( Forecaster, index_type, fh_type, is_relative ): # Check that predicted time index matched forecasting horizon for full in-sample # predictions. y_train = make_forecasting_problem(index_type=index_type) cutoff = y_train.index[-1] steps = -np.arange(len(y_train)) # full in-sample fh fh = _make_fh(cutoff, steps, fh_type, is_relative) f = _construct_instance(Forecaster) # Some estimators may not support all time index types and fh types, hence we # need to catch NotImplementedErrors. try: f.fit(y_train, fh=fh) y_pred = f.predict() _assert_correct_pred_time_index(y_pred.index, y_train.index[-1], fh) except NotImplementedError: pass def _check_pred_ints(pred_ints: list, y_train: pd.Series, y_pred: pd.Series, fh): # make iterable if isinstance(pred_ints, pd.DataFrame): pred_ints = [pred_ints] for pred_int in pred_ints: # check column naming convention assert list(pred_int.columns) == ["lower", "upper"] # check time index _assert_correct_pred_time_index(pred_int.index, y_train.index[-1], fh) # check values assert np.all(pred_int["upper"] > y_pred) assert np.all(pred_int["lower"] < y_pred) # check if errors are weakly monotonically increasing # pred_errors = y_pred - pred_int["lower"] # # assert pred_errors.is_mononotic_increasing # assert np.all( # pred_errors.values[1:].round(4) >= pred_errors.values[:-1].round(4) # ) @pytest.mark.parametrize("Forecaster", FORECASTERS) @pytest.mark.parametrize("fh", TEST_OOS_FHS) @pytest.mark.parametrize("alpha", TEST_ALPHAS) def test_predict_pred_interval(Forecaster, fh, alpha): # Check prediction intervals. f = _construct_instance(Forecaster) f.fit(y_train, fh=fh) try: y_pred, pred_ints = f.predict(return_pred_int=True, alpha=alpha) _check_pred_ints(pred_ints, y_train, y_pred, fh) except NotImplementedError: pass @pytest.mark.parametrize("Forecaster", FORECASTERS) @pytest.mark.parametrize("fh", TEST_OOS_FHS) def test_score(Forecaster, fh): # Check score method f = _construct_instance(Forecaster) f.fit(y_train, fh=fh) y_pred = f.predict() fh_idx = check_fh(fh).to_indexer() # get zero based index expected = mean_absolute_percentage_error( y_pred, y_test.iloc[fh_idx], symmetric=True ) # compare with actual score f = _construct_instance(Forecaster) f.fit(y_train, fh=fh) actual = f.score(y_test.iloc[fh_idx], fh=fh) assert actual == expected @pytest.mark.parametrize("Forecaster", FORECASTERS) @pytest.mark.parametrize("fh", TEST_OOS_FHS) @pytest.mark.parametrize("update_params", [True, False]) def test_update_predict_single(Forecaster, fh, update_params): # Check correct time index of update-predict f = _construct_instance(Forecaster) f.fit(y_train, fh=fh) y_pred = f.update_predict_single(y_test, update_params=update_params) _assert_correct_pred_time_index(y_pred.index, y_test.index[-1], fh) def _check_update_predict_predicted_index( Forecaster, fh, window_length, step_length, update_params ): y = make_forecasting_problem(all_positive=True, index_type="datetime") y_train, y_test = temporal_train_test_split(y) cv = SlidingWindowSplitter( fh, window_length=window_length, step_length=step_length, start_with_window=False, ) f = _construct_instance(Forecaster) f.fit(y_train, fh=fh) y_pred = f.update_predict(y_test, cv=cv, update_params=update_params) assert isinstance(y_pred, (pd.Series, pd.DataFrame)) if isinstance(y_pred, pd.DataFrame): assert y_pred.shape[1] > 1 expected = _get_expected_index_for_update_predict(y_test, fh, step_length) actual = y_pred.index np.testing.assert_array_equal(actual, expected) # test with update_params=False and different values for steps_length @pytest.mark.parametrize("Forecaster", FORECASTERS) @pytest.mark.parametrize("fh", TEST_OOS_FHS) @pytest.mark.parametrize("window_length", TEST_WINDOW_LENGTHS) @pytest.mark.parametrize("step_length", TEST_STEP_LENGTHS) @pytest.mark.parametrize("update_params", [False]) def test_update_predict_predicted_index( Forecaster, fh, window_length, step_length, update_params ): _check_update_predict_predicted_index( Forecaster, fh, window_length, step_length, update_params ) # test with update_params=True and step_length=1 @pytest.mark.parametrize("Forecaster", FORECASTERS) @pytest.mark.parametrize("fh", TEST_OOS_FHS) @pytest.mark.parametrize("window_length", TEST_WINDOW_LENGTHS) @pytest.mark.parametrize("step_length", [1]) @pytest.mark.parametrize("update_params", [True]) def test_update_predict_predicted_index_update_params( Forecaster, fh, window_length, step_length, update_params ): _check_update_predict_predicted_index( Forecaster, fh, window_length, step_length, update_params )

36.38961

88

0.746074

13200bc2c7a8e7f91d588fd9142a94445ea1f9d9

3,353

py

Python

ticTacToeGUI.py

yashovardhan99/Tic-Tac-Toe

62797eb622827fac5b00a990401c8d5ddeb6a637

[ "Apache-2.0" ]

1

2018-04-27T09:30:51.000Z

ticTacToeGUI.py

yashovardhan99/Tic-Tac-Toe

62797eb622827fac5b00a990401c8d5ddeb6a637

[ "Apache-2.0" ]

null

ticTacToeGUI.py

yashovardhan99/Tic-Tac-Toe

62797eb622827fac5b00a990401c8d5ddeb6a637

[ "Apache-2.0" ]

null

from tkinter import * import tkinter.messagebox from ticTacToe import getAIMove,isWinner,isSpaceFree,makeMove b = ['']*10 var = ['']*10 gameBoard = ['']*10 playerLetter='X' #Change later AILetter = 'O' playerColor='red'#Will be able to change later AIColor='blue' playerMove=False startGameCheck = False moves = 0 master=Tk() def makeGUIMove(pos,board,letter): #To make the relevant move and also update the GUI accordingly makeMove(letter,board,pos) if letter is playerLetter: b[pos].config(text=letter,disabledforeground=playerColor) else: b[pos].config(text=letter,disabledforeground=AIColor) b[pos].config(state=DISABLED) #Check if winner as well! pass def checkDraw(): global moves if moves>=9: tkinter.messagebox.showinfo(title='Tic Tac Toe',message="It's a draw!") def makeAIMove(): global moves,playerMove move = getAIMove(gameBoard,AILetter) makeGUIMove(move,gameBoard,AILetter) playerMove=True moves = moves+1 if isWinner(gameBoard,AILetter): tkinter.messagebox.showinfo(title='Tic Tac Toe',message="Oops! The AI wins!") else: checkDraw() def onClick(id): global moves if not startGameCheck: startGame() return global playerMove if playerMove and isSpaceFree(id,gameBoard): playerMove=False makeGUIMove(id,gameBoard,playerLetter) moves = moves+1 if isWinner(gameBoard,playerLetter): tkinter.messagebox.showinfo(title='Tic Tac Toe',message="You Win!") else: checkDraw() makeAIMove() #check for winner else: #Do Something maybe pass def restartGame(): global gameBoard,moves,b,var,playerMove,startGameCheck for i in range(1,10): gameBoard[i]=str(i) var[i]=Variable(value=0) b[i].config(text=str(i),state=NORMAL) playerMove=False startGameCheck=False moves=0 startGame() def __init__(): global gameBoard,master #Initial setup of game board for i in range(1,10): gameBoard[i]=str(i) var[i]=Variable(value=0) b[i] = Button(master,text=str(i),font={"arial",10,"bold"},padx=2,pady=2,overrelief=RIDGE,command= lambda id=i:onClick(id)) #b[i].pack(fill=BOTH,expand=1) if i in range(1,4): b[i].grid(row=2,column=i-1,sticky=NSEW) elif i in range(4,7): b[i].grid(row=1,column=i-4,sticky=NSEW) else: b[i].grid(row=0,column=i-7,sticky=NSEW) for i in range(3): Grid.columnconfigure(master,i,weight=1,minsize=80) Grid.rowconfigure(master,i,weight=1,minsize=80) menubar = Menu(master) menubar.add_command(label='Restart Game',command=restartGame) master.config(menu=menubar) master.title("Tic Tac Toe") startGame() #Starting here def startGame(): global moves global playerMove global startGameCheck startGameCheck=True #starts the logical part of the game #We assume right now that the player starts first and is X (RED) moves=0 current = 0 #0 for player, 1 for AI if current==0: playerMove=TRUE else: makeAIMove() playerMove=False #Calls mainloop for GUI setup. Should only be called once __init__() #Inirial setup master.mainloop()

27.710744

130

0.650164

a699ae1a93e1ae038d689ac23ff2015712a89146

9,984

py

Python

src/Final-NeuralFBProphet/optim/bayesian.py

ds-wook/Final-NeuralFBProphet

0cb44bfa0ca1ea49077e29bce4d34af2c6a4d618

[ "Apache-2.0" ]

16

2021-04-29T13:27:37.000Z

2021-12-30T00:19:00.000Z

src/Final-NeuralFBProphet/optim/bayesian.py

ds-wook/Final-NeuralFBProphet

0cb44bfa0ca1ea49077e29bce4d34af2c6a4d618

[ "Apache-2.0" ]

null

src/Final-NeuralFBProphet/optim/bayesian.py

ds-wook/Final-NeuralFBProphet

0cb44bfa0ca1ea49077e29bce4d34af2c6a4d618

[ "Apache-2.0" ]

3

2021-05-20T04:05:15.000Z

2021-05-20T04:12:40.000Z

from typing import Callable import joblib import optuna import pandas as pd from fbprophet import Prophet from optuna import Trial from optuna.samplers import TPESampler from sklearn.metrics import mean_squared_error class BayesianOptimizer: def __init__(self, objective_function: object): self.objective_function = objective_function def build_study(self, trials: int, verbose: bool = False): sampler = TPESampler(seed=42) study = optuna.create_study( study_name="TPE hyperparameter", direction="minimize", sampler=sampler, ) study.optimize(self.objective_function, n_trials=trials) if verbose: self.display_study_statistics(study) return study def display_study_statistics(study: optuna.create_study): print("Best Score:", study.best_value) print("Best trial:", study.best_trial.params) @staticmethod def save_params(study: optuna.create_study, params_name: str): params = study.best_trial.params joblib.dump(params, "../../parameters/" + params_name) @staticmethod def save_two_params(study: optuna.create_study, params_name: str): prophet_params = study.best_params prophet_params["growth"] = "logistic" prophet_params["seasonality_mode"] = "additive" prophet_params["weekly_seasonality"] = True prophet_params["daily_seasonality"] = True prophet_params["yearly_seasonality"] = False joblib.dump(prophet_params, "../../parameters/" + params_name) @staticmethod def plot_optimization_history(study: optuna.create_study) -> optuna.visualization: return optuna.visualization.plot_optimization_history(study) @staticmethod def plot_param_importances(study: optuna.create_study) -> optuna.visualization: return optuna.visualization.plot_param_importances(study) @staticmethod def plot_edf(study: optuna.create_study) -> optuna.visualization: return optuna.visualization.plot_edf(study) def ontune_prophet_objective( train: pd.DataFrame, valid: pd.Series, cap: float, floor: float ) -> Callable[[Trial], float]: def objective(trial: Trial) -> float: params = { "changepoint_range": trial.suggest_discrete_uniform( "changepoint_range", 0.8, 0.95, 0.001 ), "n_changepoints": trial.suggest_int("n_changepoints", 20, 35), "changepoint_prior_scale": trial.suggest_discrete_uniform( "changepoint_prior_scale", 0.001, 0.5, 0.001 ), "seasonality_prior_scale": trial.suggest_discrete_uniform( "seasonality_prior_scale", 1, 25, 0.5 ), "yearly_fourier": trial.suggest_int("yearly_fourier", 5, 15), "monthly_fourier": trial.suggest_int("monthly_fourier", 3, 12), "weekly_fourier": trial.suggest_int("weekly_fourier", 3, 7), "quaterly_fourier": trial.suggest_int("quaterly_fourier", 3, 10), "yearly_prior": trial.suggest_discrete_uniform("yearly_prior", 1, 25, 0.5), "monthly_prior": trial.suggest_discrete_uniform( "monthly_prior", 1, 25, 0.5 ), "weekly_prior": trial.suggest_discrete_uniform("weekly_prior", 1, 25, 0.5), "quaterly_prior": trial.suggest_discrete_uniform( "quaterly_prior", 1, 25, 0.5 ), "growth": "logistic", "seasonality_mode": "additive", "weekly_seasonality": True, "daily_seasonality": True, } # fit_model model = Prophet( changepoint_range=params["changepoint_prior_scale"], n_changepoints=params["n_changepoints"], changepoint_prior_scale=params["changepoint_prior_scale"], seasonality_prior_scale=params["seasonality_prior_scale"], yearly_seasonality=False, weekly_seasonality=True, daily_seasonality=True, growth="logistic", seasonality_mode="additive", ) model.add_seasonality( name="yearly", period=365.25, fourier_order=params["yearly_fourier"], prior_scale=params["yearly_prior"], ) model.add_seasonality( name="monthly", period=30.5, fourier_order=params["monthly_fourier"], prior_scale=params["monthly_prior"], ) model.add_seasonality( name="weekly", period=7, fourier_order=params["weekly_fourier"], prior_scale=params["weekly_prior"], ) model.add_seasonality( name="quaterly", period=365.25 / 4, fourier_order=params["quaterly_fourier"], prior_scale=params["quaterly_prior"], ) train["cap"] = cap train["floor"] = floor model.fit(train) future = model.make_future_dataframe(periods=144, freq="d") future["cap"] = cap future["floor"] = floor forecast = model.predict(future) valid_forecast = forecast.tail(7) rmse = mean_squared_error(valid.y, valid_forecast.yhat, squared=False) return rmse return objective def vcenter_prophet_objective( train: pd.DataFrame, valid: pd.Series, cap: float, floor: float ) -> Callable[[Trial], float]: def objective(trial: Trial) -> float: params = { "changepoint_range": trial.suggest_discrete_uniform( "changepoint_range", 0.8, 0.95, 0.001 ), "n_changepoints": trial.suggest_int("n_changepoints", 20, 35), "changepoint_prior_scale": trial.suggest_discrete_uniform( "changepoint_prior_scale", 0.001, 0.5, 0.001 ), "seasonality_prior_scale": trial.suggest_discrete_uniform( "seasonality_prior_scale", 1, 25, 0.5 ), "yearly_fourier": trial.suggest_int("yearly_fourier", 5, 15), "monthly_fourier": trial.suggest_int("monthly_fourier", 3, 12), "weekly_fourier": trial.suggest_int("weekly_fourier", 3, 7), "quaterly_fourier": trial.suggest_int("quaterly_fourier", 3, 10), "yearly_prior": trial.suggest_discrete_uniform("yearly_prior", 1, 25, 0.5), "monthly_prior": trial.suggest_discrete_uniform( "monthly_prior", 1, 25, 0.5 ), "weekly_prior": trial.suggest_discrete_uniform("weekly_prior", 1, 25, 0.5), "quaterly_prior": trial.suggest_discrete_uniform( "quaterly_prior", 1, 25, 0.5 ), "growth": "logistic", "seasonality_mode": "additive", "weekly_seasonality": True, "daily_seasonality": True, } # fit_model model = Prophet( changepoint_range=params["changepoint_prior_scale"], n_changepoints=params["n_changepoints"], changepoint_prior_scale=params["changepoint_prior_scale"], seasonality_prior_scale=params["seasonality_prior_scale"], yearly_seasonality=False, weekly_seasonality=True, daily_seasonality=True, growth="logistic", seasonality_mode="additive", ) model.add_seasonality( name="yearly", period=365.25, fourier_order=params["yearly_fourier"], prior_scale=params["yearly_prior"], ) model.add_seasonality( name="monthly", period=30.5, fourier_order=params["monthly_fourier"], prior_scale=params["monthly_prior"], ) model.add_seasonality( name="weekly", period=7, fourier_order=params["weekly_fourier"], prior_scale=params["weekly_prior"], ) model.add_seasonality( name="quaterly", period=365.25 / 4, fourier_order=params["quaterly_fourier"], prior_scale=params["quaterly_prior"], ) train["cap"] = cap train["floor"] = floor model.fit(train) future = model.make_future_dataframe(periods=144, freq="d") future["cap"] = cap future["floor"] = floor forecast = model.predict(future) valid_forecast = forecast.tail(7) rmse = mean_squared_error(valid.y, valid_forecast.yhat, squared=False) return rmse return objective def two_second_prophet_objective( train: pd.DataFrame, valid: pd.Series, cap: float, floor: float ) -> Callable[[Trial], float]: def objective(trial: Trial) -> float: params = { "changepoint_range": trial.suggest_discrete_uniform( "changepoint_range", 0.8, 0.95, 0.001 ), "n_changepoints": trial.suggest_int("n_changepoints", 20, 35), "changepoint_prior_scale": trial.suggest_discrete_uniform( "changepoint_prior_scale", 0.001, 0.5, 0.001 ), "seasonality_prior_scale": trial.suggest_discrete_uniform( "seasonality_prior_scale", 1, 25, 0.5 ), "growth": "logistic", "seasonality_mode": "additive", "yearly_seasonality": False, "weekly_seasonality": True, "daily_seasonality": True, } # fit_model m = Prophet(**params) train["cap"] = cap train["floor"] = floor m.fit(train) future = m.make_future_dataframe(periods=163, freq="H") future["cap"] = cap future["floor"] = floor forecast = m.predict(future) valid_forecast = forecast.tail(163) val_rmse = mean_squared_error(valid.y, valid_forecast.yhat, squared=False) return val_rmse return objective

36.977778

87

0.605869

9a1e3c63e76154b488ead642b72aca448ac44a5e

8,624

py

Python

qmcpack_reader.py

Paul-St-Young/solid_hydrogen

dd218cd431a283dc1a371a0af5696074d63b8c6c

[ "MIT" ]

2

2020-08-13T23:32:03.000Z

2021-03-28T01:14:06.000Z

qmcpack_reader.py

Paul-St-Young/solid_hydrogen

dd218cd431a283dc1a371a0af5696074d63b8c6c

[ "MIT" ]

null

qmcpack_reader.py

Paul-St-Young/solid_hydrogen

dd218cd431a283dc1a371a0af5696074d63b8c6c

[ "MIT" ]

null

#!/usr/bin/env python import os import pandas as pd def get_supertwists(qmc_out): """ read supercell twists from QMCPACK output Args: qmc_out (str): QMCPACK output, must contain "Super twist #" Return: np.array: an array of twist vectors (ntwist, ndim) """ from qharv.reel import ascii_out mm = ascii_out.read(qmc_out) idxl = ascii_out.all_lines_with_tag(mm, 'Super twist #') lines = ascii_out.all_lines_at_idx(mm ,idxl) data = [] for line in lines: text = ascii_out.lr_mark(line, '[', ']') vec = np.array(text.split(), dtype=float) data.append(vec) mat = np.array(data) return mat def epl_val_err(epl_out): """ convert epl_out to a pandas DataFrame. epl_out is expected to be an output of energy.pl from QMCPACK It simply has to have the format {name:22c}={val:17.3f} +/- {err:26.4f}. rows with forces will be recognized with 'force_prefix' Args: epl_out (str): energy.pl output filename Returns: pd.DataFrame: df contains columns ['name','val','err'] """ tab = pd.read_csv(epl_out, delimiter='=', names=['name', 'text']) tab = tab.dropna() def text2val(text): tokens = text.split('+/-') if len(tokens) != 2: raise NotImplementedError('unrecognized value '+text) val,err = map(float, tokens) return pd.Series({'val':val, 'err':err}) df = pd.concat([ tab.drop('text', axis=1), tab['text'].apply(text2val)], axis=1) return df def epldf_to_entry(df): names = [name.strip() for name in df.name.values] ymean = ['%s_mean' % name for name in names] yerror = ['%s_error' % name for name in names] names1 = np.concatenate([ymean, yerror]) means = df.val.values errs = df.err.values entry = pd.Series(np.concatenate([means, errs]), names1) return entry def get_forces(df, natom, prefix='force', ndim=3): yml = [] yel = [] for iatom in range(natom): for idim in range(ndim): col = '%s_%d_%d' % (prefix, iatom, idim) sel = df.name.apply(lambda x: col in x) y1m = df.loc[sel].val.squeeze() y1e = df.loc[sel].err.squeeze() yml.append(y1m) yel.append(y1e) return np.array(yml), np.array(yel) def sk_from_fs_out(fs_out): """ extract fluctuating S(k) from qmcfinitesize output returns: kmag,sk,vk,spk,spsk kmag: magnitude of kvectors, sk: raw fluc. S(k), vk: long-range potential after break-up spk: kmags for splined S(k), spsk: splined S(k) """ import reader bint = reader.BlockInterpreter() sfile = reader.SearchableFile(fs_out) # read raw data block_text = sfile.block_text('#SK_RAW_START#','#SK_RAW_STOP#') kmag,sk,vk = bint.matrix(block_text[block_text.find('\n')+1:]).T # read splined S(k) block_text = sfile.block_text('#SK_SPLINE_START#','#SK_SPLINE_STOP#') spk,spsk = bint.matrix(block_text[block_text.find('\n')+1:]).T return kmag,sk,vk,spk,spsk # end def # =============== complicated functions =============== import numpy as np from copy import deepcopy def read_jastrows(jas_node): """ 'jas_node' should be an xml node containing bspline jastrows put coefficients and attributes into a list of dictionaries """ if (jas_node.attrib["type"] != "Two-Body"): # works for one-body! miracle! pass#raise TypeError("input is not a two-body Jastrow xml node") elif (jas_node.attrib["function"].lower() != "bspline"): raise NotImplementedError("can only handle bspline Jastrows for now") # end if data = [] for corr in jas_node.xpath('./correlation'): coeff = corr.xpath('./coefficients')[0] entry = deepcopy( corr.attrib ) entry.update(coeff.attrib) entry['coeff'] = np.array(coeff.text.split(),dtype=float) entry['type'] = jas_node.attrib['type'] data.append(entry) # end for corr return data # end def read_jastrows from lxml import etree def extract_jastrows(qmcpack_input,json_name='jas.json',warn=True,force_refresh=False): """ given a QMCPACK input that contains linear optimization, extract all printed Jastrows and store in a local database 1. parse 'qmcpack_input' for the qmc[@metho="linear"] section 2. for each *.opt.xml, parse if it exists 3. parse each opt.xml and make local database """ failed = False subdir = os.path.dirname(qmcpack_input) target_json = os.path.join(subdir,json_name) if os.path.isfile(target_json) and (not force_refresh): if warn: print("skipping %s" % subdir) # end if return 0 # skip ths file # end if parser = etree.XMLParser(remove_blank_text=True) # get prefix xml = etree.parse(qmcpack_input,parser) proj = xml.xpath("//project")[0] prefix = proj.attrib['id'] # determine number of optimization loops all_qmc_sections = xml.xpath('.//qmc[@method="linear"]') all_iopt = 0 # track multiple 'linear' sections data = [] for qmc_section in all_qmc_sections: # for each linear optimization: # find the number of loops nopt = 1 loop = qmc_section.getparent() if loop.tag == 'loop': nopt = int(loop.attrib['max']) # end if # collect all jastrow coefficients for iopt in range(nopt): # get optimization file opt_file = prefix + ".s%s.opt.xml" % str(all_iopt).zfill(3) opt_xml = os.path.join(subdir,opt_file) if not os.path.isfile(opt_xml): if warn: print("skipping %d in %s" % (all_iopt,subdir)) # end if continue # end if # parse optimization file opt = etree.parse(opt_xml,parser) jnodes = opt.xpath('//jastrow') for jas_node in jnodes: entries = read_jastrows(jas_node) for entry in entries: entry['iopt'] = all_iopt # end for entry data.append(entry) # end for all_iopt += 1 # end for iopt # end for qmc_section if len(data) == 0: failed = True else: df = pd.DataFrame( data ) df.to_json(target_json) # end if return failed # end def extract_jastrows def extract_best_jastrow_set(opt_input,opt_json='opt_scalar.json',nequil='auto',force_refresh=False): import nexus_addon as na subdir = os.path.dirname(opt_input) # locally create jas.json extract_jastrows(opt_input,force_refresh=force_refresh) # locally create opt_scalar.json scalar_json = os.path.join(subdir,opt_json) if (not os.path.isfile(scalar_json)) or force_refresh: # initialize analyzer from qmca import QBase options = {"equilibration":nequil} QBase.options.transfer_from(options) entry = na.scalars_from_input(opt_input) pd.DataFrame(entry).to_json(scalar_json) # end if # get best jastrow set best_jas = collect_best_jastrow_set(subdir) return best_jas # end def extract_best_jastrow_set def collect_best_jastrow_set(subdir,jas_json='jas.json',opt_json='opt_scalar.json' ,rval_weight=0.75,rerr_weight=0.25): """ find best set of jastrows in 'subdir', assume files: 1. jas.json: a database of QMCPACK bspline jastrows with 'iopt' column 2. opt_scalar.json: a database of QMCPACK scalars including 'LocalEnergy_mean', 'LocalEnergy_error', 'Variance_mean', and 'Variance_error' """ from dmc_database_analyzer import div_columns jfile = os.path.join(subdir,jas_json) if not os.path.isfile(jfile): raise RuntimeError('%s not found in %s' % (jfile,subdir)) # end if ofile = os.path.join(subdir,opt_json) if not os.path.isfile(ofile): raise RuntimeError('%s not found in %s' % (ofile,subdir)) # end if jdf = pd.read_json(jfile) # jastrows sdf = pd.read_json(ofile) # scalars # same units for stddev and LocalEnergy sdf['stddev_mean'] = sdf['Variance_mean'].apply(np.sqrt) sdf['stddev_error'] = sdf['Variance_error'].apply(np.sqrt) # make ratios ratio_mean, ratio_error = div_columns(['stddev','LocalEnergy'],sdf) # take both value and error into account rv_cost = ratio_mean/ratio_mean.mean() re_cost = ratio_error/ratio_error.mean() # make a cost function cost = rv_cost*rval_weight + re_cost*rerr_weight # minimize cost function idx = np.argmin(cost) # grab winner jastrows best_jas = jdf[jdf['iopt']==idx].copy() return best_jas # end def collect_best_jastrow_set

32.666667

147

0.64019

3a79321c3f9f4274a159e46b2adb15ca78439857

5,166

py

Python

maskrcnn_benchmark/modeling/matcher.py

Sreehari-S/mask-rcnn-benchmark

b4434c39fccda80575276308da86b6e944540445

[ "MIT" ]

null

maskrcnn_benchmark/modeling/matcher.py

Sreehari-S/mask-rcnn-benchmark

b4434c39fccda80575276308da86b6e944540445

[ "MIT" ]

1

2020-02-18T12:25:48.000Z

maskrcnn_benchmark/modeling/matcher.py

Sreehari-S/mask-rcnn-benchmark

b4434c39fccda80575276308da86b6e944540445

[ "MIT" ]

null

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. import torch class Matcher(object): """ This class assigns to each predicted "element" (e.g., a box) a ground-truth element. Each predicted element will have exactly zero or one matches; each ground-truth element may be assigned to zero or more predicted elements. Matching is based on the MxN match_quality_matrix, that characterizes how well each (ground-truth, predicted)-pair match. For example, if the elements are boxes, the matrix may contain box IoU overlap values. The matcher returns a tensor of size N containing the index of the ground-truth element m that matches to prediction n. If there is no match, a negative value is returned. """ BELOW_LOW_THRESHOLD = -1 BETWEEN_THRESHOLDS = -2 def __init__(self, high_threshold, low_threshold, allow_low_quality_matches=False): """ Args: high_threshold (float): quality values greater than or equal to this value are candidate matches. low_threshold (float): a lower quality threshold used to stratify matches into three levels: 1) matches >= high_threshold 2) BETWEEN_THRESHOLDS matches in [low_threshold, high_threshold) 3) BELOW_LOW_THRESHOLD matches in [0, low_threshold) allow_low_quality_matches (bool): if True, produce additional matches for predictions that have only low-quality match candidates. See set_low_quality_matches_ for more details. """ assert low_threshold <= high_threshold self.high_threshold = high_threshold self.low_threshold = low_threshold self.allow_low_quality_matches = allow_low_quality_matches def __call__(self, match_quality_matrix): """ Args: match_quality_matrix (Tensor[float]): an MxN tensor, containing the pairwise quality between M ground-truth elements and N predicted elements. Returns: matches (Tensor[int64]): an N tensor where N[i] is a matched gt in [0, M - 1] or a negative value indicating that prediction i could not be matched. """ if match_quality_matrix.numel() == 0: # empty targets or proposals not supported during training if match_quality_matrix.shape[0] == 0: raise ValueError( "No ground-truth boxes available for one of the images " "during training") else: raise ValueError( "No proposal boxes available for one of the images " "during training") #print(match_quality_matrix) # match_quality_matrix is M (gt) x N (predicted) # Max over gt elements (dim 0) to find best gt candidate for each prediction matched_vals, matches = match_quality_matrix.max(dim=0) if self.allow_low_quality_matches: all_matches = matches.clone() # Assign candidate matches with low quality to negative (unassigned) values below_low_threshold = matched_vals < self.low_threshold between_thresholds = (matched_vals >= self.low_threshold) & ( matched_vals < self.high_threshold ) matches[below_low_threshold] = Matcher.BELOW_LOW_THRESHOLD matches[between_thresholds] = Matcher.BETWEEN_THRESHOLDS if self.allow_low_quality_matches: self.set_low_quality_matches_(matches, all_matches, match_quality_matrix) return matches def set_low_quality_matches_(self, matches, all_matches, match_quality_matrix): """ Produce additional matches for predictions that have only low-quality matches. Specifically, for each ground-truth find the set of predictions that have maximum overlap with it (including ties); for each prediction in that set, if it is unmatched, then match it to the ground-truth with which it has the highest quality value. """ # For each gt, find the prediction with which it has highest quality highest_quality_foreach_gt, _ = match_quality_matrix.max(dim=1) # Find highest quality match available, even if it is low, including ties gt_pred_pairs_of_highest_quality = torch.nonzero( match_quality_matrix == highest_quality_foreach_gt[:, None] ) # Example gt_pred_pairs_of_highest_quality: # tensor([[ 0, 39796], # [ 1, 32055], # [ 1, 32070], # [ 2, 39190], # [ 2, 40255], # [ 3, 40390], # [ 3, 41455], # [ 4, 45470], # [ 5, 45325], # [ 5, 46390]]) # Each row is a (gt index, prediction index) # Note how gt items 1, 2, 3, and 5 each have two ties pred_inds_to_update = gt_pred_pairs_of_highest_quality[:, 1] matches[pred_inds_to_update] = all_matches[pred_inds_to_update]

45.315789

88

0.634533

e1544f1fa22df9f7413b0ddbbdd89fb780379761

3,696

py

Python

venv/lib/python3.8/site-packages/django/contrib/sites/models.py

Joshua-Barawa/My-Photos

adcaea48149c6b31e9559b045709d538d0b749bc

[ "PostgreSQL", "Unlicense" ]

16

2019-08-10T12:24:06.000Z

2020-05-21T09:11:14.000Z

venv/lib/python3.8/site-packages/django/contrib/sites/models.py

Joshua-Barawa/My-Photos

adcaea48149c6b31e9559b045709d538d0b749bc

[ "PostgreSQL", "Unlicense" ]

12

2019-08-10T11:55:29.000Z

2020-05-21T04:46:30.000Z

venv/lib/python3.8/site-packages/django/contrib/sites/models.py

Joshua-Barawa/My-Photos

adcaea48149c6b31e9559b045709d538d0b749bc

[ "PostgreSQL", "Unlicense" ]

4

2022-03-12T10:17:00.000Z

2022-03-26T08:40:43.000Z

import string from django.core.exceptions import ImproperlyConfigured, ValidationError from django.db import models from django.db.models.signals import pre_delete, pre_save from django.http.request import split_domain_port from django.utils.translation import gettext_lazy as _ SITE_CACHE = {} def _simple_domain_name_validator(value): """ Validate that the given value contains no whitespaces to prevent common typos. """ checks = ((s in value) for s in string.whitespace) if any(checks): raise ValidationError( _("The domain name cannot contain any spaces or tabs."), code="invalid", ) class SiteManager(models.Manager): use_in_migrations = True def _get_site_by_id(self, site_id): if site_id not in SITE_CACHE: site = self.get(pk=site_id) SITE_CACHE[site_id] = site return SITE_CACHE[site_id] def _get_site_by_request(self, request): host = request.get_host() try: # First attempt to look up the site by host with or without port. if host not in SITE_CACHE: SITE_CACHE[host] = self.get(domain__iexact=host) return SITE_CACHE[host] except Site.DoesNotExist: # Fallback to looking up site after stripping port from the host. domain, port = split_domain_port(host) if domain not in SITE_CACHE: SITE_CACHE[domain] = self.get(domain__iexact=domain) return SITE_CACHE[domain] def get_current(self, request=None): """ Return the current Site based on the SITE_ID in the project's settings. If SITE_ID isn't defined, return the site with domain matching request.get_host(). The ``Site`` object is cached the first time it's retrieved from the database. """ from django.conf import settings if getattr(settings, "SITE_ID", ""): site_id = settings.SITE_ID return self._get_site_by_id(site_id) elif request: return self._get_site_by_request(request) raise ImproperlyConfigured( 'You\'re using the Django "sites framework" without having ' "set the SITE_ID setting. Create a site in your database and " "set the SITE_ID setting or pass a request to " "Site.objects.get_current() to fix this error." ) def clear_cache(self): """Clear the ``Site`` object cache.""" global SITE_CACHE SITE_CACHE = {} def get_by_natural_key(self, domain): return self.get(domain=domain) class Site(models.Model): domain = models.CharField( _("domain name"), max_length=100, validators=[_simple_domain_name_validator], unique=True, ) name = models.CharField(_("display name"), max_length=50) objects = SiteManager() class Meta: db_table = "django_site" verbose_name = _("site") verbose_name_plural = _("sites") ordering = ["domain"] def __str__(self): return self.domain def natural_key(self): return (self.domain,) def clear_site_cache(sender, **kwargs): """ Clear the cache (if primed) each time a site is saved or deleted. """ instance = kwargs["instance"] using = kwargs["using"] try: del SITE_CACHE[instance.pk] except KeyError: pass try: del SITE_CACHE[Site.objects.using(using).get(pk=instance.pk).domain] except (KeyError, Site.DoesNotExist): pass pre_save.connect(clear_site_cache, sender=Site) pre_delete.connect(clear_site_cache, sender=Site)

30.295082

79

0.63934

5bfb71a41a82dce4ecfd0fe6c106a7c141b25e7d

927

py

Python

nomadgram/notifications/migrations/0002_auto_20190314_1205.py

SimJunSik/nomadgram

958f16824268a3e5cbeb449f8142209d367d731c

[ "MIT" ]

null

nomadgram/notifications/migrations/0002_auto_20190314_1205.py

SimJunSik/nomadgram

958f16824268a3e5cbeb449f8142209d367d731c

[ "MIT" ]

5

2020-06-05T20:17:40.000Z

2021-09-08T01:10:06.000Z

nomadgram/notifications/migrations/0002_auto_20190314_1205.py

SimJunSik/nomadgram

958f16824268a3e5cbeb449f8142209d367d731c

[ "MIT" ]

null

# Generated by Django 2.0.13 on 2019-03-14 03:05 from django.db import migrations, models import django.db.models.deletion import django.utils.timezone class Migration(migrations.Migration): dependencies = [ ('notifications', '0001_initial'), ] operations = [ migrations.AddField( model_name='notification', name='created_at', field=models.DateTimeField(auto_now_add=True, default=django.utils.timezone.now), preserve_default=False, ), migrations.AddField( model_name='notification', name='updated_at', field=models.DateTimeField(auto_now=True), ), migrations.AlterField( model_name='notification', name='image', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE, to='images.Image'), ), ]

28.96875

123

0.621359

e0cbb66fff93925310894ca72883b9ce8076f2a8

3,455

py

Python

mayan/apps/sources/widgets.py

Dave360-crypto/mayan-edms

9cd37537461347f79ff0429e4b8b16fd2446798d

[ "Apache-2.0" ]

3

2020-02-03T11:58:51.000Z

2020-10-20T03:52:21.000Z

mayan/apps/sources/widgets.py

Dave360-crypto/mayan-edms

9cd37537461347f79ff0429e4b8b16fd2446798d

[ "Apache-2.0" ]

null

mayan/apps/sources/widgets.py

Dave360-crypto/mayan-edms

9cd37537461347f79ff0429e4b8b16fd2446798d

[ "Apache-2.0" ]

2

2020-10-24T11:10:06.000Z

2021-03-03T20:05:38.000Z

from django import forms from django.conf import settings from django.core.urlresolvers import reverse from django.utils.encoding import force_unicode from django.utils.html import strip_tags from django.utils.http import urlencode from django.utils.safestring import mark_safe from django.utils.translation import ugettext_lazy as _ from converter.literals import DEFAULT_PAGE_NUMBER, DEFAULT_ROTATION, DEFAULT_ZOOM_LEVEL from documents.conf.settings import THUMBNAIL_SIZE, PREVIEW_SIZE class FamFamRadioFieldRenderer(forms.widgets.RadioFieldRenderer): def render(self): results = [] results.append(u'<ul>\n') for w in self: if w.choice_value: famfam_template = u'<span class="famfam active famfam-%s" style="vertical-align: bottom;"></span>' % w.choice_value else: famfam_template = u'<span class="famfam active famfam-cross" style="vertical-align: bottom;"></span>' results.append(u'<li class="undecorated_list">%s%s</li>' % (famfam_template, force_unicode(w))) results.append(u'\n</ul>') return mark_safe(u'\n'.join(results)) class FamFamRadioSelect(forms.widgets.RadioSelect): renderer = FamFamRadioFieldRenderer def staging_file_thumbnail(staging_file, **kwargs): return staging_file_html_widget(staging_file, click_view='stagingfolderfile-image-view', **kwargs) def staging_file_html_widget(staging_file, click_view=None, page=DEFAULT_PAGE_NUMBER, zoom=DEFAULT_ZOOM_LEVEL, rotation=DEFAULT_ROTATION, gallery_name=None, fancybox_class='fancybox-staging', image_class='lazy-load', title=None, size=THUMBNAIL_SIZE, nolazyload=False): result = [] alt_text = _(u'staging file page image') query_dict = { 'page': page, 'zoom': zoom, 'rotation': rotation, 'size': size, } if gallery_name: gallery_template = u'rel="%s"' % gallery_name else: gallery_template = u'' query_string = urlencode(query_dict) preview_view = u'%s?%s' % (reverse('stagingfolderfile-image-view', args=[staging_file.staging_folder.pk, staging_file.encoded_filename]), query_string) plain_template = [] plain_template.append(u'<img src="%s" alt="%s" />' % (preview_view, alt_text)) result.append(u'<div class="tc" id="staging_file-%s-%d">' % (staging_file.filename, page if page else DEFAULT_PAGE_NUMBER)) if title: title_template = u'title="%s"' % strip_tags(title) else: title_template = u'' if click_view: # TODO: fix this hack query_dict['size'] = PREVIEW_SIZE query_string = urlencode(query_dict) result.append(u'<a %s class="%s" href="%s" %s>' % (gallery_template, fancybox_class, u'%s?%s' % (reverse(click_view, args=[staging_file.staging_folder.pk, staging_file.encoded_filename]), query_string), title_template)) if nolazyload: result.append(u'<img style="border: 1px solid black;" src="%s" alt="%s" />' % (preview_view, alt_text)) else: result.append(u'<img class="thin_border %s" data-original="%s" src="%simages/ajax-loader.gif" alt="%s" />' % (image_class, preview_view, settings.STATIC_URL, alt_text)) result.append(u'<noscript><img style="border: 1px solid black;" src="%s" alt="%s" /></noscript>' % (preview_view, alt_text)) if click_view: result.append(u'</a>') result.append(u'</div>') return mark_safe(u''.join(result))

40.647059

268

0.690883

4cd88cf135a6403e80af9e7ddad837dcac5fb245

9,248

py

Python

sdk/python/pulumi_azure_native/web/v20200901/web_app_backup_configuration.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/web/v20200901/web_app_backup_configuration.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

sdk/python/pulumi_azure_native/web/v20200901/web_app_backup_configuration.py

pulumi-bot/pulumi-azure-native

f7b9490b5211544318e455e5cceafe47b628e12c

[ "Apache-2.0" ]

null

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union from ... import _utilities, _tables from . import outputs from ._enums import * from ._inputs import * __all__ = ['WebAppBackupConfiguration'] class WebAppBackupConfiguration(pulumi.CustomResource): def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, backup_name: Optional[pulumi.Input[str]] = None, backup_schedule: Optional[pulumi.Input[pulumi.InputType['BackupScheduleArgs']]] = None, databases: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['DatabaseBackupSettingArgs']]]]] = None, enabled: Optional[pulumi.Input[bool]] = None, kind: Optional[pulumi.Input[str]] = None, name: Optional[pulumi.Input[str]] = None, resource_group_name: Optional[pulumi.Input[str]] = None, storage_account_url: Optional[pulumi.Input[str]] = None, __props__=None, __name__=None, __opts__=None): """ Description of a backup which will be performed. :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] backup_name: Name of the backup. :param pulumi.Input[pulumi.InputType['BackupScheduleArgs']] backup_schedule: Schedule for the backup if it is executed periodically. :param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['DatabaseBackupSettingArgs']]]] databases: Databases included in the backup. :param pulumi.Input[bool] enabled: True if the backup schedule is enabled (must be included in that case), false if the backup schedule should be disabled. :param pulumi.Input[str] kind: Kind of resource. :param pulumi.Input[str] name: Name of the app. :param pulumi.Input[str] resource_group_name: Name of the resource group to which the resource belongs. :param pulumi.Input[str] storage_account_url: SAS URL to the container. """ if __name__ is not None: warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning) resource_name = __name__ if __opts__ is not None: warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning) opts = __opts__ if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = dict() __props__['backup_name'] = backup_name __props__['backup_schedule'] = backup_schedule __props__['databases'] = databases __props__['enabled'] = enabled __props__['kind'] = kind if name is None and not opts.urn: raise TypeError("Missing required property 'name'") __props__['name'] = name if resource_group_name is None and not opts.urn: raise TypeError("Missing required property 'resource_group_name'") __props__['resource_group_name'] = resource_group_name if storage_account_url is None and not opts.urn: raise TypeError("Missing required property 'storage_account_url'") __props__['storage_account_url'] = storage_account_url __props__['system_data'] = None __props__['type'] = None alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:web/v20200901:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-native:web:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-nextgen:web:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-native:web/latest:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-nextgen:web/latest:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-native:web/v20150801:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-nextgen:web/v20150801:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-native:web/v20160801:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-nextgen:web/v20160801:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-native:web/v20180201:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-nextgen:web/v20180201:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-native:web/v20181101:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-nextgen:web/v20181101:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-native:web/v20190801:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-nextgen:web/v20190801:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-native:web/v20200601:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-nextgen:web/v20200601:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-native:web/v20201001:WebAppBackupConfiguration"), pulumi.Alias(type_="azure-nextgen:web/v20201001:WebAppBackupConfiguration")]) opts = pulumi.ResourceOptions.merge(opts, alias_opts) super(WebAppBackupConfiguration, __self__).__init__( 'azure-native:web/v20200901:WebAppBackupConfiguration', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None) -> 'WebAppBackupConfiguration': """ Get an existing WebAppBackupConfiguration resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = dict() __props__["backup_name"] = None __props__["backup_schedule"] = None __props__["databases"] = None __props__["enabled"] = None __props__["kind"] = None __props__["name"] = None __props__["storage_account_url"] = None __props__["system_data"] = None __props__["type"] = None return WebAppBackupConfiguration(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="backupName") def backup_name(self) -> pulumi.Output[Optional[str]]: """ Name of the backup. """ return pulumi.get(self, "backup_name") @property @pulumi.getter(name="backupSchedule") def backup_schedule(self) -> pulumi.Output[Optional['outputs.BackupScheduleResponse']]: """ Schedule for the backup if it is executed periodically. """ return pulumi.get(self, "backup_schedule") @property @pulumi.getter def databases(self) -> pulumi.Output[Optional[Sequence['outputs.DatabaseBackupSettingResponse']]]: """ Databases included in the backup. """ return pulumi.get(self, "databases") @property @pulumi.getter def enabled(self) -> pulumi.Output[Optional[bool]]: """ True if the backup schedule is enabled (must be included in that case), false if the backup schedule should be disabled. """ return pulumi.get(self, "enabled") @property @pulumi.getter def kind(self) -> pulumi.Output[Optional[str]]: """ Kind of resource. """ return pulumi.get(self, "kind") @property @pulumi.getter def name(self) -> pulumi.Output[str]: """ Resource Name. """ return pulumi.get(self, "name") @property @pulumi.getter(name="storageAccountUrl") def storage_account_url(self) -> pulumi.Output[str]: """ SAS URL to the container. """ return pulumi.get(self, "storage_account_url") @property @pulumi.getter(name="systemData") def system_data(self) -> pulumi.Output['outputs.SystemDataResponse']: """ The system metadata relating to this resource. """ return pulumi.get(self, "system_data") @property @pulumi.getter def type(self) -> pulumi.Output[str]: """ Resource type. """ return pulumi.get(self, "type") def translate_output_property(self, prop): return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop def translate_input_property(self, prop): return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop

48.166667

1,481

0.67074

ebf056812b0025810154ffeef59c87b2f8a28ead

5,217

py

Python

datahub/public_private_fund_members.py

Xingquan-Li/stock-1

0d8ff7ffd27e32cfcdd01d5ca217345b97ce950d

[ "BSD-3-Clause" ]

1

2022-03-15T07:41:22.000Z

datahub/public_private_fund_members.py

Xingquan-Li/stock-1

0d8ff7ffd27e32cfcdd01d5ca217345b97ce950d

[ "BSD-3-Clause" ]

null

datahub/public_private_fund_members.py

Xingquan-Li/stock-1

0d8ff7ffd27e32cfcdd01d5ca217345b97ce950d

[ "BSD-3-Clause" ]

null

# 公募私募人员数据获取 import math import sys sys.path.append('..') from configure.settings import DBSelector from common.BaseService import BaseService import requests import warnings import datetime warnings.filterwarnings("ignore") class FundMembers(BaseService): def __init__(self, kind, date, first_use=False): super(FundMembers, self).__init__(first_use) self.lof_url = 'http://query.sse.com.cn/commonQuery.do?=&jsonCallBack=jsonpCallback1681&sqlId=COMMON_SSE_FUND_LOF_SCALE_CX_S&pageHelp.pageSize=10000&FILEDATE={}&_=161146986468' self.etf_url = 'http://query.sse.com.cn/commonQuery.do?jsonCallBack=jsonpCallback28550&isPagination=true&pageHelp.pageSize=25&pageHelp.pageNo={}&pageHelp.cacheSize=1&sqlId=COMMON_SSE_ZQPZ_ETFZL_XXPL_ETFGM_SEARCH_L&STAT_DATE={}&pageHelp.beginPage={}&pageHelp.endPage=30&_=1611473902414' self.db = DBSelector() # self.today ='2021-01-22' # ETF self.today_='' # TODO failed self.ETF_COUNT_PER_PAGE = 25 self.url_option_dict = { 'ETF': {'url': self.etf_url, 'date': self.today}, 'LOF': {'url': self.lof_url, 'date': self.today_} } self.kind = kind.lower() self.session = requests.Session() self.logger.info('start...sh fund') self.LAST_TEXT = '' if first_use: self.create_table() self.db_session = self.get_session() self.sess = self.db_session() def crawl_lof(self): options = self.url_option_dict['LOF'] date = options.get('date') url = options.get('url') content = self.get(url.format(date), js=False) js_data = self.jsonp2json(content) self.process_lof(js_data) @property def headers(self): return { "Host": "query.sse.com.cn", "User-Agent": "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:84.0) Gecko/20100101 Firefox/84.0", "Accept": "*/*", "Accept-Language": "en-US,en;q=0.5", "Accept-Encoding": "gzip, deflate", "Connection": "keep-alive", "Referer": "http://www.sse.com.cn/market/funddata/volumn/lofvolumn/", } def process_lof(self, js_data): result = js_data.get('result') for item in result: code = item['FUND_CODE'] name = item['FUND_ABBR'] date = item['TRADE_DATE'] try: share = float(item['INTERNAL_VOL'].replace(',', '')) except Exception as e: print(e) share = None self.process_model(code, name, date, share, 'LOF') def post(self, url, post_data, _josn=False, binary=False, retry=5): pass def crawl_etf(self): options = self.url_option_dict['ETF'] date = options.get('date') url = options.get('url') current_page = 1 while True: content = self.get(url.format(current_page, date, current_page), _json=False) js_data = self.jsonp2json(content) total_count = js_data.get('pageHelp').get('total') print(f'page : {current_page}') self.process_etf(js_data) max_page = math.ceil(total_count / self.ETF_COUNT_PER_PAGE) # 每页 10个 if current_page > max_page: break current_page += 1 def process_etf(self, js_data): result = js_data.get('result') for item in result: code = item['SEC_CODE'] name = item['SEC_NAME'] date = item['STAT_DATE'] share = item['TOT_VOL'] try: share = float(share) except Exception as e: print(e) self.process_model(code, name, date, share, 'ETF') def run(self): 'LOF 与 ETF' # for type_, options in self.url_option_dict.items(): if self.kind == 'etf': self.logger.info('crawling etf .....') self.crawl_etf() if self.kind == 'lof': self.logger.info('crawling lof .....') self.crawl_lof() def process_model(self, code, name, date, share, type_): obj = self.sess.query(FundBaseInfoModel).filter_by(code=code).first() if not obj: obj = FundBaseInfoModel( code=code, name=name, category=type_, invest_type=None, manager_name=None, issue_date=None, ) try: self.sess.add(obj) except Exception as e: print(e) else: self.sess.commit() print(f'插入一条记录{code}，{date}') if not self.sess.query(ShareModel).filter_by(code=code, date=date).first(): share_info = ShareModel( code=code, date=date, share=share, crawltime=datetime.datetime.now(), ) try: self.sess.add(share_info) except Exception as e: print(e) else: print(f'插入一条记录{code}，{date}') self.sess.commit()

32.811321

293

0.554533

a8140e97d075051e2c1f3cf4fe6cf3e427341d1c

72

py

Python

openml_tensorflow/config.py

prabhant/openml-tf

a3bc69c2aa404cb54bea1c492edb8a6941c1175b

[ "BSD-3-Clause" ]

null

openml_tensorflow/config.py

prabhant/openml-tf

a3bc69c2aa404cb54bea1c492edb8a6941c1175b

[ "BSD-3-Clause" ]

12

2020-01-28T23:09:20.000Z

2022-02-10T00:36:46.000Z

openml_tensorflow/config.py

prabhant/openml-tensorflow

a3bc69c2aa404cb54bea1c492edb8a6941c1175b

[ "BSD-3-Clause" ]

3

2020-10-08T13:05:59.000Z

2021-05-10T05:34:11.000Z

# Config file to define all hyperparameters epoch = 10 batch_size = 32

14.4

43

0.763889

4ac29a581d8deb5f5596fddaebf794a7edf89e12

4,489

py

Python

opsplugins/notification_subscription.py

OpenSwitchNOS/openswitch-ops-restd

baf2f631d53966b7084c7c99e88639d857a041c5

[ "Apache-2.0" ]

null

opsplugins/notification_subscription.py

OpenSwitchNOS/openswitch-ops-restd

baf2f631d53966b7084c7c99e88639d857a041c5

[ "Apache-2.0" ]

null

opsplugins/notification_subscription.py

OpenSwitchNOS/openswitch-ops-restd

baf2f631d53966b7084c7c99e88639d857a041c5

[ "Apache-2.0" ]

1

2021-09-10T08:17:24.000Z

# Copyright (C) 2016 Hewlett Packard Enterprise Development LP # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. from tornado.log import app_log from opsvalidator.base import BaseValidator from opsvalidator import error from opsvalidator.error import ValidationError from opsrest.utils.utils import get_column_data_from_row from opsrest.notifications.constants import ( SUBSCRIBER_NAME, SUBSCRIPTION_TABLE_LOWER, SUBSCRIPTION_URI ) from opsrest.constants import REQUEST_TYPE_READ from opsrest.parse import parse_url_path class NotificationSubscriptionValidator(BaseValidator): resource = SUBSCRIPTION_TABLE_LOWER def validate_modification(self, validation_args): if validation_args.is_new: schema = validation_args.schema idl = validation_args.idl subscriber_row = validation_args.p_resource_row subscription_row = validation_args.resource_row subscription_schema = validation_args.resource_schema subscriber_name = get_column_data_from_row(subscriber_row, SUBSCRIBER_NAME) resource_uri = get_column_data_from_row(subscription_row, SUBSCRIPTION_URI) app_log.debug("Verifying if subscription can be added for " "subscriber %s" % subscriber_name) self._verify_duplicate_subscription(subscriber_name, subscriber_row, subscription_row, subscription_schema, resource_uri) self._verify_valid_resource_uri(subscriber_name, subscription_row, resource_uri, schema, idl) def _verify_valid_resource_uri(self, subscriber_name, subscription_row, resource_uri, schema, idl): app_log.debug("Verifying a valid resource URI") resource_path = parse_url_path(resource_uri, schema, idl, REQUEST_TYPE_READ) if resource_path is None: app_log.debug("Invalid resource URI detected") details = "Subscriber: %s. " % subscriber_name details += "Invalid URI %s" % resource_uri raise ValidationError(error.VERIFICATION_FAILED, details) def _verify_duplicate_subscription(self, subscriber_name, subscriber_row, subscription_row, subscription_schema, resource_uri): app_log.debug("Verifying if the subscription is a duplicate") subscriber_subscriptions = \ get_column_data_from_row(subscriber_row, subscription_schema.plural_name) # Length == 1 indicates this is the only subscription if not subscriber_subscriptions or \ len(subscriber_subscriptions) == 1: app_log.debug("No duplicate resource subscriptions detected.") return # Compare the resource URI of the new subscription to parent's # subscription resource URIs for sub_name, sub_row in subscriber_subscriptions.iteritems(): # Skip if the subscription row is the current one that is # being validated if sub_row == subscription_row: continue curr_resource_uri = get_column_data_from_row(sub_row, SUBSCRIPTION_URI) if curr_resource_uri == resource_uri: app_log.debug("Duplicate resource URI detected") details = "Subscriber: %s. " % subscriber_name details += "URI %s already exists" % resource_uri raise ValidationError(error.DUPLICATE_RESOURCE, details)

45.343434

78

0.625306

bea118fc1b4eff811557b7db735f8f76d25c1912

1,325

py

Python

calvin/actorstore/systemactors/time/Timestamp.py

gabrielcercel/calvin-base

c0315f100643230d65aed1745e1c22df3e7a7c2c

[ "Apache-2.0" ]

334

2015-06-04T15:14:28.000Z

2022-02-09T11:14:17.000Z

calvin/actorstore/systemactors/time/Timestamp.py

gabrielcercel/calvin-base

c0315f100643230d65aed1745e1c22df3e7a7c2c

[ "Apache-2.0" ]

89

2015-06-13T19:15:35.000Z

2019-12-03T19:23:20.000Z

calvin/actorstore/systemactors/time/Timestamp.py

gabrielcercel/calvin-base

c0315f100643230d65aed1745e1c22df3e7a7c2c

[ "Apache-2.0" ]

112

2015-06-06T19:16:54.000Z

2020-10-19T01:27:55.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2017 Ericsson AB # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from calvin.actor.actor import Actor, manage, condition, calvinlib class Timestamp(Actor): """ Return the (UTC) time in seconds since Jan 1st 1970 Detailed information Input: trigger : any token Output: timestamp : floating point number """ @manage([]) def init(self): self.setup() def did_migrate(self): self.setup() def setup(self): self.time = calvinlib.use('time') @condition(['trigger'], ['timestamp']) def action(self, consume_trigger): return (self.time.timestamp(),) action_priority = (action,) requires = ['time'] test_set = [ { 'inports': {'trigger': [True]} } ]

24.090909

74

0.649811

7fd8ce19682ebd5a9e6868984b019f164e25a65f

1,440

py

Python

test/__init__.py

EmbeddedML-EDAGroup/PIT

02897f6977b481d3072e9aa915aec0fe43faeb02

[ "Apache-2.0" ]

2

2021-12-18T21:04:29.000Z

2022-01-04T14:14:27.000Z

test/__init__.py

EmbeddedML-EDAGroup/PIT

02897f6977b481d3072e9aa915aec0fe43faeb02

[ "Apache-2.0" ]

null

test/__init__.py

EmbeddedML-EDAGroup/PIT

02897f6977b481d3072e9aa915aec0fe43faeb02

[ "Apache-2.0" ]

null

#*----------------------------------------------------------------------------* #* Copyright (C) 2021 Politecnico di Torino, Italy * #* SPDX-License-Identifier: Apache-2.0 * #* * #* Licensed under the Apache License, Version 2.0 (the "License"); * #* you may not use this file except in compliance with the License. * #* You may obtain a copy of the License at * #* * #* http://www.apache.org/licenses/LICENSE-2.0 * #* * #* Unless required by applicable law or agreed to in writing, software * #* distributed under the License is distributed on an "AS IS" BASIS, * #* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * #* See the License for the specific language governing permissions and * #* limitations under the License. * #* * #* Author: Matteo Risso <matteo.risso@polito.it> * #*----------------------------------------------------------------------------*

75.789474

79

0.360417

cac243b4540637be3fe1c1839a3202227fe026c0

3,133

py

Python

books_spider/books_spider/settings.py

behappycc/price-tracker

3ef8c451dc45f2775d6dbde19bb179d78d5fea97

[ "MIT" ]

null

books_spider/books_spider/settings.py

behappycc/price-tracker

3ef8c451dc45f2775d6dbde19bb179d78d5fea97

[ "MIT" ]

null

books_spider/books_spider/settings.py

behappycc/price-tracker

3ef8c451dc45f2775d6dbde19bb179d78d5fea97

[ "MIT" ]

null

# -*- coding: utf-8 -*- # Scrapy settings for books_spider project # # For simplicity, this file contains only settings considered important or # commonly used. You can find more settings consulting the documentation: # # https://doc.scrapy.org/en/latest/topics/settings.html # https://doc.scrapy.org/en/latest/topics/downloader-middleware.html # https://doc.scrapy.org/en/latest/topics/spider-middleware.html BOT_NAME = 'books_spider' SPIDER_MODULES = ['books_spider.spiders'] NEWSPIDER_MODULE = 'books_spider.spiders' # Crawl responsibly by identifying yourself (and your website) on the user-agent #USER_AGENT = 'books_spider (+http://www.yourdomain.com)' # Obey robots.txt rules ROBOTSTXT_OBEY = False # Configure maximum concurrent requests performed by Scrapy (default: 16) #CONCURRENT_REQUESTS = 32 # Configure a delay for requests for the same website (default: 0) # See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay # See also autothrottle settings and docs DOWNLOAD_DELAY = 0.5 # The download delay setting will honor only one of: #CONCURRENT_REQUESTS_PER_DOMAIN = 16 #CONCURRENT_REQUESTS_PER_IP = 16 # Disable cookies (enabled by default) #COOKIES_ENABLED = False # Disable Telnet Console (enabled by default) #TELNETCONSOLE_ENABLED = False # Override the default request headers: #DEFAULT_REQUEST_HEADERS = { # 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8', # 'Accept-Language': 'en', #} # Enable or disable spider middlewares # See https://doc.scrapy.org/en/latest/topics/spider-middleware.html #SPIDER_MIDDLEWARES = { # 'books_spider.middlewares.BooksSpiderSpiderMiddleware': 543, #} # Enable or disable downloader middlewares # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html #DOWNLOADER_MIDDLEWARES = { # 'books_spider.middlewares.BooksSpiderDownloaderMiddleware': 543, #} # Enable or disable extensions # See https://doc.scrapy.org/en/latest/topics/extensions.html #EXTENSIONS = { # 'scrapy.extensions.telnet.TelnetConsole': None, #} # Configure item pipelines # See https://doc.scrapy.org/en/latest/topics/item-pipeline.html ITEM_PIPELINES = { 'books_spider.pipelines.BooksSpiderPipeline': 300, } # Enable and configure the AutoThrottle extension (disabled by default) # See https://doc.scrapy.org/en/latest/topics/autothrottle.html #AUTOTHROTTLE_ENABLED = True # The initial download delay #AUTOTHROTTLE_START_DELAY = 5 # The maximum download delay to be set in case of high latencies #AUTOTHROTTLE_MAX_DELAY = 60 # The average number of requests Scrapy should be sending in parallel to # each remote server #AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0 # Enable showing throttling stats for every response received: #AUTOTHROTTLE_DEBUG = False # Enable and configure HTTP caching (disabled by default) # See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings #HTTPCACHE_ENABLED = True #HTTPCACHE_EXPIRATION_SECS = 0 #HTTPCACHE_DIR = 'httpcache' #HTTPCACHE_IGNORE_HTTP_CODES = [] #HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'

34.428571

102

0.778168

e9bc14f1e5ca9ff72e7653bb55aa3fc7c094bcfd

16,060

py

Python

rest-service/manager_rest/test/endpoints/test_blueprints.py

yeshess/cloudify-manager

04dd199ce7df54355b87e9594f9db9fb1582924b

[ "Apache-2.0" ]

null

rest-service/manager_rest/test/endpoints/test_blueprints.py

yeshess/cloudify-manager

04dd199ce7df54355b87e9594f9db9fb1582924b

[ "Apache-2.0" ]

null

rest-service/manager_rest/test/endpoints/test_blueprints.py

yeshess/cloudify-manager

04dd199ce7df54355b87e9594f9db9fb1582924b

[ "Apache-2.0" ]

null

######### # Copyright (c) 2013 GigaSpaces Technologies Ltd. All rights reserved # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # * See the License for the specific language governing permissions and # * limitations under the License. import os import tempfile import shutil from nose.plugins.attrib import attr from manager_rest import archiving from manager_rest.storage import FileServer from manager_rest.test import base_test from cloudify_rest_client.exceptions import CloudifyClientError from .test_utils import generate_progress_func @attr(client_min_version=1, client_max_version=base_test.LATEST_API_VERSION) class BlueprintsTestCase(base_test.BaseServerTestCase): def test_get_empty(self): result = self.client.blueprints.list() self.assertEquals(0, len(result)) def test_get_nonexistent_blueprint(self): try: self.client.blueprints.get('15') except CloudifyClientError, e: self.assertEqual(404, e.status_code) def test_server_traceback_on_error(self): try: self.client.blueprints.get('15') except CloudifyClientError, e: self.assertIsNotNone(e.server_traceback) def test_post_and_then_search(self): post_blueprints_response = self.put_file( *self.put_blueprint_args(blueprint_id='hello_world')).json self.assertEquals('hello_world', post_blueprints_response['id']) get_blueprints_response = self.client.blueprints.list() self.assertEquals(1, len(get_blueprints_response)) self.assertEquals(post_blueprints_response, get_blueprints_response[0]) def test_post_blueprint_already_exists(self): self.put_file(*self.put_blueprint_args()) post_blueprints_response = self.put_file(*self.put_blueprint_args()) self.assertTrue('already exists' in post_blueprints_response.json['message']) self.assertEqual(409, post_blueprints_response.status_code) def test_put_blueprint_archive(self): self._test_put_blueprint_archive(archiving.make_targzfile, 'tar.gz') def test_post_without_application_file_form_data(self): post_blueprints_response = self.put_file( *self.put_blueprint_args('blueprint_with_workflows.yaml', blueprint_id='hello_world')).json self.assertEquals('hello_world', post_blueprints_response['id']) @attr(client_min_version=2, client_max_version=base_test.LATEST_API_VERSION) def test_blueprint_description(self): post_blueprints_response = self.put_file( *self.put_blueprint_args('blueprint.yaml', blueprint_id='blueprint')).json self.assertEquals('blueprint', post_blueprints_response['id']) self.assertEquals("this is my blueprint's description", post_blueprints_response['description']) def test_get_blueprint_by_id(self): post_blueprints_response = self.put_file( *self.put_blueprint_args()).json get_blueprint_by_id_response = self.get( '/blueprints/{0}'.format(post_blueprints_response['id'])).json # setting 'source' field to be None as expected self.assertEquals(post_blueprints_response, get_blueprint_by_id_response) def test_delete_blueprint(self): post_blueprints_response = self.put_file( *self.put_blueprint_args()).json # testing if resources are on fileserver self.assertTrue( self.check_if_resource_on_fileserver( post_blueprints_response['id'], 'blueprint.yaml')) # deleting the blueprint that was just uploaded delete_blueprint_response = self.delete( '/blueprints/{0}'.format(post_blueprints_response['id'])).json self.assertEquals(post_blueprints_response['id'], delete_blueprint_response['id']) # verifying deletion of blueprint resp = self.get('/blueprints/{0}'.format(post_blueprints_response[ 'id'])) self.assertEquals(404, resp.status_code) # verifying deletion of fileserver resources self.assertFalse( self.check_if_resource_on_fileserver( post_blueprints_response['id'], 'blueprint.yaml')) # trying to delete a nonexistent blueprint resp = self.delete('/blueprints/nonexistent-blueprint') self.assertEquals(404, resp.status_code) def test_zipped_plugin(self): self.put_file(*self.put_blueprint_args()) self.check_if_resource_on_fileserver('hello_world', 'plugins/stub-installer.zip') def test_put_blueprint_archive_from_url(self): port = 53230 blueprint_id = 'new_blueprint_id' archive_path = self.archive_mock_blueprint( archive_func=archiving.make_tarbz2file) archive_filename = os.path.basename(archive_path) archive_dir = os.path.dirname(archive_path) archive_url = 'http://localhost:{0}/{1}'.format( port, archive_filename) fs = FileServer(archive_dir, False, port) fs.start() try: self.wait_for_url(archive_url) blueprint_id = self.client.blueprints.publish_archive( archive_url, blueprint_id).id # verifying blueprint exists result = self.client.blueprints.get(blueprint_id) self.assertEqual(blueprint_id, result.id) finally: fs.stop() def test_put_blueprint_archive_from_unavailable_url(self): blueprint_id = 'new_blueprint_id' resource_path = '/blueprints/{0}'.format(blueprint_id) response = self.put( resource_path, None, {'blueprint_archive_url': 'http://www.fake.url/does/not/exist'}) self.assertTrue("not found - can't download blueprint archive" in response.json['message']) self.assertEqual(400, response.status_code) def test_put_blueprint_archive_from_malformed_url(self): blueprint_id = 'new_blueprint_id' resource_path = '/blueprints/{0}'.format(blueprint_id) response = self.put( resource_path, None, {'blueprint_archive_url': 'malformed/url_is.bad'}) self.assertIn("is malformed - can't download blueprint archive", response.json['message']) self.assertEqual(400, response.status_code) def test_put_blueprint_archive_from_url_and_data(self): blueprint_id = 'new_blueprint_id' resource_path = '/blueprints/{0}'.format(blueprint_id) response = self.put( resource_path, 'data pretending to be the actual blueprint archive data', {'blueprint_archive_url': 'malformed/url_is.bad'}) self.assertIn("Can't pass both", response.json['message']) self.assertEqual(400, response.status_code) def test_put_zip_archive(self): self._test_put_blueprint_archive(archiving.make_zipfile, 'zip') def test_put_tar_archive(self): self._test_put_blueprint_archive(archiving.make_tarfile, 'tar') def test_put_bz2_archive(self): self._test_put_blueprint_archive(archiving.make_tarbz2file, 'tar.bz2') def test_put_unsupported_archive_blueprint(self): archive_path = tempfile.mkstemp()[1] with open(archive_path, 'w') as f: f.write('this is not a valid archive obviously') response = self.put_file( '/blueprints/unsupported_archive_bp', archive_path) self.assertIn("Blueprint archive is of an unrecognized format.", response.json['message']) self.assertEqual(400, response.status_code) def test_put_blueprint_non_existing_filename(self): blueprint_id = 'new_blueprint_id' put_blueprints_response = self.put_file( *self.put_blueprint_args(blueprint_id=blueprint_id, blueprint_file_name='non-existing')) self.assertEqual( put_blueprints_response.json['message'], 'non-existing does not exist in the application directory') self.assertEqual(put_blueprints_response.status_code, 400) def test_put_blueprint_no_default_yaml(self): blueprint_id = 'new_blueprint_id' put_blueprints_response = self.put_file( *self.put_blueprint_args( blueprint_id=blueprint_id, blueprint_dir='mock_blueprint_no_default')) self.assertEqual( put_blueprints_response.json['message'], 'application directory is missing blueprint.yaml and ' 'application_file_name query parameter was not passed' ) self.assertEqual(put_blueprints_response.status_code, 400) @attr(client_min_version=2, client_max_version=base_test.LATEST_API_VERSION) def test_blueprint_main_file_name(self): blueprint_id = 'blueprint_main_file_name' blueprint_file = 'blueprint_with_inputs.yaml' blueprint_path = os.path.join( self.get_blueprint_path('mock_blueprint'), blueprint_file) response = self.client.blueprints.upload(blueprint_path, blueprint_id) self.assertEqual(blueprint_file, response.main_file_name) blueprint = self.client.blueprints.get(blueprint_id) self.assertEqual(blueprint_file, blueprint.main_file_name) blueprint = self.client.blueprints.list()[0] self.assertEqual(blueprint_file, blueprint.main_file_name) @attr(client_min_version=3, client_max_version=base_test.LATEST_API_VERSION) def test_sort_list(self): blueprint_file = 'blueprint_with_inputs.yaml' blueprint_path = os.path.join( self.get_blueprint_path('mock_blueprint'), blueprint_file) self.client.blueprints.upload(blueprint_path, '0') self.client.blueprints.upload(blueprint_path, '1') blueprints = self.client.blueprints.list(sort='created_at') self.assertEqual(2, len(blueprints)) self.assertEqual('0', blueprints[0].id) self.assertEqual('1', blueprints[1].id) blueprints = self.client.blueprints.list( sort='created_at', is_descending=True) self.assertEqual(2, len(blueprints)) self.assertEqual('1', blueprints[0].id) self.assertEqual('0', blueprints[1].id) @attr(client_min_version=3, client_max_version=base_test.LATEST_API_VERSION) def test_blueprint_upload_progress(self): tmp_dir = '/tmp/tmp_upload_blueprint' blueprint_path = self._create_big_blueprint('empty_blueprint.yaml', tmp_dir) size = self.client.blueprints.calc_size(blueprint_path) progress_func = generate_progress_func( total_size=size, assert_equal=self.assertEqual, assert_almost_equal=self.assertAlmostEqual) try: self.client.blueprints.upload(blueprint_path, '0', progress_callback=progress_func) finally: self.quiet_delete_directory(tmp_dir) @attr(client_min_version=3, client_max_version=base_test.LATEST_API_VERSION) def test_blueprint_download_progress(self): tmp_dir = '/tmp/tmp_upload_blueprint' tmp_local_path = '/tmp/blueprint.bl' blueprint_path = self._create_big_blueprint('empty_blueprint.yaml', tmp_dir) size = self.client.blueprints.calc_size(blueprint_path) try: self.client.blueprints.upload(blueprint_path, '0') progress_func = generate_progress_func( total_size=size, assert_equal=self.assertEqual, assert_almost_equal=self.assertAlmostEqual) self.client.blueprints.download('0', tmp_local_path, progress_func) finally: self.quiet_delete_directory(tmp_dir) self.quiet_delete(tmp_local_path) def _create_big_blueprint(self, blueprint, tmp_dir): """ Create a large file, and put it in a folder with some blueprint. This is used in order to create a sizable blueprint archive, for checking upload/download :param blueprint: The name of the mock_blueprint file :param tmp_dir: The folder that will be used to store the blueprint and the new file :return: The local path of the mock blueprint """ self.quiet_delete_directory(tmp_dir) os.mkdir(tmp_dir) blueprint_file = blueprint blueprint_path = os.path.join( self.get_blueprint_path('mock_blueprint'), blueprint_file) shutil.copy(blueprint_path, tmp_dir) blueprint_path = os.path.join(tmp_dir, blueprint_file) tmpfile_path = os.path.join(tmp_dir, 'tmp_file') with open(tmpfile_path, 'wb') as big_file: big_file.seek(32 * 1024 * 1024 - 1) big_file.write('\0') return blueprint_path @attr(client_min_version=2, client_max_version=base_test.LATEST_API_VERSION) def test_blueprint_default_main_file_name(self): blueprint_id = 'blueprint_default_main_file_name' blueprint_file = 'blueprint.yaml' response = self.put_file( *self.put_blueprint_args(blueprint_id=blueprint_id)).json self.assertEquals(blueprint_file, response['main_file_name']) @attr(client_min_version=2, client_max_version=base_test.LATEST_API_VERSION) def test_publish_archive_blueprint_main_file_name(self): port = 53230 blueprint_id = 'publish_archive_blueprint_main_file_name' main_file_name = 'blueprint_with_workflows.yaml' archive_path = self.archive_mock_blueprint() archive_filename = os.path.basename(archive_path) archive_dir = os.path.dirname(archive_path) fs = FileServer(archive_dir, False, port) fs.start() try: archive_url = 'http://localhost:{0}/{1}'.format( port, archive_filename) self.wait_for_url(archive_url) response = self.client.blueprints.publish_archive(archive_url, blueprint_id, main_file_name) finally: fs.stop() self.assertEqual(blueprint_id, response.id) self.assertEqual(main_file_name, response.main_file_name) def _test_put_blueprint_archive(self, archive_func, archive_type): blueprint_id = 'new_blueprint_id' put_blueprints_response = self.put_file( *self.put_blueprint_args(blueprint_id=blueprint_id, archive_func=archive_func)).json self.assertEqual(blueprint_id, put_blueprints_response['id']) url = self._version_url( '/blueprints/{0}/archive'.format(blueprint_id)) response = self.app.get(url) archive_filename = '{0}.{1}'.format(blueprint_id, archive_type) self.assertTrue(archive_filename in response.headers['Content-Disposition']) self.assertTrue(archive_filename in response.headers['X-Accel-Redirect'])

41.932115

79

0.657472

f880b7fcfa1b367483b71686308d1f4aebb9ff40

1,154

py

Python

quickstartproject/production.py

vmagelo/msdocs-python-django-webapp-quickstart

10ea918c191ec040000e1558d30a89e0a7ad9b28

[ "MIT" ]

1

2022-03-21T13:34:34.000Z

quickstartproject/production.py

vmagelo/msdocs-python-django-webapp-quickstart

10ea918c191ec040000e1558d30a89e0a7ad9b28

[ "MIT" ]

3

2022-03-07T23:40:42.000Z

2022-03-08T22:38:37.000Z

quickstartproject/production.py

vmagelo/msdocs-python-django-webapp-quickstart

10ea918c191ec040000e1558d30a89e0a7ad9b28

[ "MIT" ]

24

2022-01-18T22:15:19.000Z

2022-03-29T21:20:52.000Z

from .settings import * import os # Configure the domain name using the environment variable # that Azure automatically creates for us. ALLOWED_HOSTS = [os.environ['WEBSITE_HOSTNAME']] if 'WEBSITE_HOSTNAME' in os.environ else [] # WhiteNoise configuration MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', # Add whitenoise middleware after the security middleware 'whitenoise.middleware.WhiteNoiseMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] STATICFILES_STORAGE = 'whitenoise.storage.CompressedManifestStaticFilesStorage' STATIC_ROOT = os.path.join(BASE_DIR, 'static')

50.173913

92

0.62825

2cdd66db1d2add1a41dbe9e77aa2807eeacd2af8

101

py

Python

package_tool/demo/hello.py

gottaegbert/penter

8cbb6be3c4bf67c7c69fa70e597bfbc3be4f0a2d

[ "MIT" ]

13

2020-01-04T07:37:38.000Z

2021-08-31T05:19:58.000Z

package_tool/demo/hello.py

gottaegbert/penter

8cbb6be3c4bf67c7c69fa70e597bfbc3be4f0a2d

[ "MIT" ]

3

2020-06-05T22:42:53.000Z

2020-08-24T07:18:54.000Z

package_tool/demo/hello.py

gottaegbert/penter

8cbb6be3c4bf67c7c69fa70e597bfbc3be4f0a2d

[ "MIT" ]

9

2020-10-19T04:53:06.000Z

2021-08-31T05:20:01.000Z

def hello(): f = open('testdd.txt', 'r') content = f.read() f.close() print(content)

16.833333

31

0.524752

96bd973cfd337315c13c9609bb9f013d032de298

4,070

py

Python

batracker/tests/localisation/test_spiesberger_wahlberg.py

thejasvibr/batracker

def2ae9a0f18df0b9b95d67a203d2afd8be0f2ce

[ "MIT" ]

null

batracker/tests/localisation/test_spiesberger_wahlberg.py

thejasvibr/batracker

def2ae9a0f18df0b9b95d67a203d2afd8be0f2ce

[ "MIT" ]

null

batracker/tests/localisation/test_spiesberger_wahlberg.py

thejasvibr/batracker

def2ae9a0f18df0b9b95d67a203d2afd8be0f2ce

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Tests for Spiesberger & Wahlberg 2002 """ import unittest import numpy as np np.random.seed(82319) import scipy.spatial as spatial from batracker.localisation import spiesberger_wahlberg_2002 as sw02 class SimpleTest(unittest.TestCase): '''With tristar and one position ''' def setUp(self): # a tristar config self.source_position = np.array([10,8,-2]) def make_tristar_geom(self): R = 1.2 theta = np.pi/3 random_noise = np.random.normal(0,10**-6,4) array_geom = np.array([[0,0.0,0], [-R*np.sin(theta),0.0,-R*np.cos(theta)], [ R*np.sin(theta),0.0,-R*np.cos(theta)], [0,0.0,R]]) array_geom[:,1] = random_noise return array_geom def calculate_d_matrix(self, array_geom, source_position): # create source positions D_matrix = np.apply_along_axis(spatial.distance.euclidean, 1,array_geom, source_position) # %% Range difference , ref. Range of mic 4 (obtained from TDOAs) d_matrix = D_matrix - D_matrix[0] return d_matrix[1:].reshape(-1,1) def test_simple(self): #self.array_geom = self.array_geom - self.array_geom[0,:] self.array_geom = self.make_tristar_geom() self.d = self.calculate_d_matrix(self.array_geom, self.source_position) output_positions = sw02.spiesberger_wahlberg_solution(self.array_geom, self.d) num_matches = [] for each in output_positions: num_matches.append(np.allclose(self.source_position, each, atol=10**-2)) self.assertEqual(sum(num_matches), 1) def test_multiple_points(self): self.array_geom = self.make_tristar_geom() x_rand = np.random.choice(np.linspace(-10,10,50), 30) y_rand = np.random.choice(np.linspace(0.2,20,50), 30) z_rand = np.random.choice(np.linspace(-2,10,50), 30) multiple_points = np.column_stack((x_rand, y_rand, z_rand)) all_calc_positions = [] for i, position in enumerate(multiple_points): d = self.calculate_d_matrix(self.array_geom, position) output_positions = sw02.spiesberger_wahlberg_solution(self.array_geom, d) valid_position = list(filter(lambda X: X[1]>0, output_positions))[0] all_calc_positions.append(valid_position) all_true = np.allclose(np.array(all_calc_positions).reshape(-1,3), multiple_points, atol=1e-2) self.assertTrue(all_true) def test_withgreaterthan4mics(self): ''' A 15 microphone array placed everywhere ''' n_mics = 15 num_sources = 20 self.array_geom = np.random.normal(0,1,n_mics*3).reshape(n_mics,-1) self.array_geom *= 5 x_rand = np.linspace(-10,13,num_sources) y_rand = np.linspace(0.2,20,num_sources) z_rand = np.linspace(-2,10,num_sources) for each in [x_rand, y_rand, z_rand]: np.random.shuffle(each) multiple_points = np.column_stack((x_rand, y_rand, z_rand)) all_calc_positions = [] for i in range(multiple_points.shape[0]): position = multiple_points[i,:] d = self.calculate_d_matrix(self.array_geom, position) output_positions = sw02.spiesberger_wahlberg_solution(self.array_geom, d) all_calc_positions.append(output_positions) match = [] for i,(candidate1, candidate2) in enumerate(all_calc_positions): match_result = np.logical_or(np.allclose(candidate1, multiple_points[i,:], atol=1e-2), np.allclose(candidate2, multiple_points[i,:], atol=1e-2)) match.append(match_result) self.assertTrue(np.all(match)) if __name__ == '__main__': unittest.main()

37.33945

98

0.59828

7aa5cc5bf947184c4d1da0e6e7c30f8e4ae94f6a

482

py

Python

trick_source/er7_utils/sims/SIM_ball_L1/RUN_test/long_er7_velocity_verlet.py

gilbertguoze/trick

f0537efb0fa3cb5c0c84e36b60f055c1d1c60d21

[ "NASA-1.3" ]

null

trick_source/er7_utils/sims/SIM_ball_L1/RUN_test/long_er7_velocity_verlet.py

gilbertguoze/trick

f0537efb0fa3cb5c0c84e36b60f055c1d1c60d21

[ "NASA-1.3" ]

null

trick_source/er7_utils/sims/SIM_ball_L1/RUN_test/long_er7_velocity_verlet.py

gilbertguoze/trick

f0537efb0fa3cb5c0c84e36b60f055c1d1c60d21

[ "NASA-1.3" ]

null

# This one of several ways to include files from the input file # execfile("Modified_data/auto_test.dr") # execfile("Modified_data/data_record.dr") ball.state.input.print_off = 1 # Set the freeze frame rate # trick.exec_set_freeze_frame(0.10) # Configure integration ball.trick_technique = trick.User_Defined ball.er7_technique = trick.Integration.VelocityVerlet ball.use_trick_integ = False end_time = 600000.0 / 2 # Set the stop time trick.exec_set_terminate_time(end_time)

25.368421

63

0.794606

37544c9ea99ab0b54570180f27d6e2583fa511ff

372

py

Python

Day-4/day-4-2-exercise/banker-roulette2.py

MihirMore/100daysofcode-Python

947d91842639c04ee7d23cc82bf04053d3982a85

[ "MIT" ]

4

2021-04-09T20:01:22.000Z

2022-03-18T20:49:58.000Z

Day-4/day-4-2-exercise/banker-roulette2.py

MihirMore/100daysofcode-Python

947d91842639c04ee7d23cc82bf04053d3982a85

[ "MIT" ]

null

Day-4/day-4-2-exercise/banker-roulette2.py

MihirMore/100daysofcode-Python

947d91842639c04ee7d23cc82bf04053d3982a85

[ "MIT" ]

null

# Split string method names_string = input("Give me everybody's names, separated by a comma. ") names = names_string.split(", ") # 🚨 Don't change the code above 👆 #Write your code below this line 👇 import random random_choice = random.randint(0, len(names)-1) person_who_will_pay = names[random_choice] print(f"{person_who_will_pay} is going to buy the meal today!")

24.8

73

0.741935

011895383120f527c9cb92ad4e4f2c565141b215

18,098

py

Python

tests/test_transforms.py

IRDonch/datumaro

d029e67549b7359c887bd15039997bd8bbae7c0c

[ "MIT" ]

null

tests/test_transforms.py

IRDonch/datumaro

d029e67549b7359c887bd15039997bd8bbae7c0c

[ "MIT" ]

null

tests/test_transforms.py

IRDonch/datumaro

d029e67549b7359c887bd15039997bd8bbae7c0c

[ "MIT" ]

null

from unittest import TestCase import logging as log import numpy as np from datumaro.components.annotation import ( AnnotationType, Bbox, Label, LabelCategories, Mask, MaskCategories, Points, PointsCategories, Polygon, PolyLine, ) from datumaro.components.extractor import DatasetItem from datumaro.components.project import Dataset from datumaro.util.test_utils import compare_datasets import datumaro.plugins.transforms as transforms import datumaro.util.mask_tools as mask_tools from .requirements import Requirements, mark_requirement class TransformsTest(TestCase): @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_reindex(self): source = Dataset.from_iterable([ DatasetItem(id=10), DatasetItem(id=10, subset='train'), DatasetItem(id='a', subset='val'), ]) expected = Dataset.from_iterable([ DatasetItem(id=5), DatasetItem(id=6, subset='train'), DatasetItem(id=7, subset='val'), ]) actual = transforms.Reindex(source, start=5) compare_datasets(self, expected, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_mask_to_polygons(self): source = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 10, 3)), annotations=[ Mask(np.array([ [0, 1, 1, 1, 0, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ]), ), ]), ]) expected = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 10, 3)), annotations=[ Polygon([1, 0, 3, 2, 3, 0, 1, 0]), Polygon([5, 0, 5, 3, 8, 0, 5, 0]), ]), ]) actual = transforms.MasksToPolygons(source) compare_datasets(self, expected, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_mask_to_polygons_small_polygons_message(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 10, 3)), annotations=[ Mask(np.array([ [0, 0, 0], [0, 1, 0], [0, 0, 0], ]), ), ]), ]) target_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 10, 3))), ]) with self.assertLogs(level=log.DEBUG) as logs: actual = transforms.MasksToPolygons(source_dataset) compare_datasets(self, target_dataset, actual) self.assertRegex('\n'.join(logs.output), 'too small polygons') @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_polygons_to_masks(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 10, 3)), annotations=[ Polygon([0, 0, 4, 0, 4, 4]), Polygon([5, 0, 9, 0, 5, 5]), ]), ]) target_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 10, 3)), annotations=[ Mask(np.array([ [0, 0, 0, 0, 0, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ]), ), Mask(np.array([ [0, 1, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 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], ]), ), ]), ]) actual = transforms.PolygonsToMasks(source_dataset) compare_datasets(self, target_dataset, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_crop_covered_segments(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 5, 3)), annotations=[ # The mask is partially covered by the polygon Mask(np.array([ [0, 0, 1, 1, 1], [0, 0, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0]], ), z_order=0), Polygon([1, 1, 4, 1, 4, 4, 1, 4], z_order=1), ]), ]) target_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 5, 3)), annotations=[ Mask(np.array([ [0, 0, 1, 1, 1], [0, 0, 0, 0, 1], [1, 0, 0, 0, 1], [1, 0, 0, 0, 0], [1, 1, 1, 0, 0]], ), z_order=0), Polygon([1, 1, 4, 1, 4, 4, 1, 4], z_order=1), ]), ]) actual = transforms.CropCoveredSegments(source_dataset) compare_datasets(self, target_dataset, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_merge_instance_segments(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 5, 3)), annotations=[ Mask(np.array([ [0, 0, 1, 1, 1], [0, 0, 0, 0, 1], [1, 0, 0, 0, 1], [1, 0, 0, 0, 0], [1, 1, 1, 0, 0]], ), z_order=0, group=1), Polygon([1, 1, 4, 1, 4, 4, 1, 4], z_order=1, group=1), Polygon([0, 0, 0, 2, 2, 2, 2, 0], z_order=1), ] ), ]) target_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 5, 3)), annotations=[ Mask(np.array([ [0, 0, 1, 1, 1], [0, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 0], [1, 1, 1, 0, 0]], ), z_order=0, group=1), Mask(np.array([ [1, 1, 0, 0, 0], [1, 1, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ), z_order=1), ] ), ]) actual = transforms.MergeInstanceSegments(source_dataset, include_polygons=True) compare_datasets(self, target_dataset, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_map_subsets(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, subset='a'), DatasetItem(id=2, subset='b'), DatasetItem(id=3, subset='c'), ]) target_dataset = Dataset.from_iterable([ DatasetItem(id=1, subset=''), DatasetItem(id=2, subset='a'), DatasetItem(id=3, subset='c'), ]) actual = transforms.MapSubsets(source_dataset, { 'a': '', 'b': 'a' }) compare_datasets(self, target_dataset, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_shapes_to_boxes(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 5, 3)), annotations=[ Mask(np.array([ [0, 0, 1, 1, 1], [0, 0, 0, 0, 1], [1, 0, 0, 0, 1], [1, 0, 0, 0, 0], [1, 1, 1, 0, 0]], ), id=1), Polygon([1, 1, 4, 1, 4, 4, 1, 4], id=2), PolyLine([1, 1, 2, 1, 2, 2, 1, 2], id=3), Points([2, 2, 4, 2, 4, 4, 2, 4], id=4), ] ), ]) target_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 5, 3)), annotations=[ Bbox(0, 0, 4, 4, id=1), Bbox(1, 1, 3, 3, id=2), Bbox(1, 1, 1, 1, id=3), Bbox(2, 2, 2, 2, id=4), ] ), ]) actual = transforms.ShapesToBoxes(source_dataset) compare_datasets(self, target_dataset, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_id_from_image(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, image='path.jpg'), DatasetItem(id=2), ]) target_dataset = Dataset.from_iterable([ DatasetItem(id='path', image='path.jpg'), DatasetItem(id=2), ]) actual = transforms.IdFromImageName(source_dataset) compare_datasets(self, target_dataset, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_boxes_to_masks(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 5, 3)), annotations=[ Bbox(0, 0, 3, 3, z_order=1), Bbox(0, 0, 3, 1, z_order=2), Bbox(0, 2, 3, 1, z_order=3), ] ), ]) target_dataset = Dataset.from_iterable([ DatasetItem(id=1, image=np.zeros((5, 5, 3)), annotations=[ Mask(np.array([ [1, 1, 1, 0, 0], [1, 1, 1, 0, 0], [1, 1, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ), z_order=1), Mask(np.array([ [1, 1, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ), z_order=2), Mask(np.array([ [0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [1, 1, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], ), z_order=3), ] ), ]) actual = transforms.BoxesToMasks(source_dataset) compare_datasets(self, target_dataset, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_random_split(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, subset="a"), DatasetItem(id=2, subset="a"), DatasetItem(id=3, subset="b"), DatasetItem(id=4, subset="b"), DatasetItem(id=5, subset="b"), DatasetItem(id=6, subset=""), DatasetItem(id=7, subset=""), ]) actual = transforms.RandomSplit(source_dataset, splits=[ ('train', 4.0 / 7.0), ('test', 3.0 / 7.0), ]) self.assertEqual(4, len(actual.get_subset('train'))) self.assertEqual(3, len(actual.get_subset('test'))) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_random_split_gives_error_on_wrong_ratios(self): source_dataset = Dataset.from_iterable([DatasetItem(id=1)]) with self.assertRaises(Exception): transforms.RandomSplit(source_dataset, splits=[ ('train', 0.5), ('test', 0.7), ]) with self.assertRaises(Exception): transforms.RandomSplit(source_dataset, splits=[]) with self.assertRaises(Exception): transforms.RandomSplit(source_dataset, splits=[ ('train', -0.5), ('test', 1.5), ]) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_remap_labels(self): src_dataset = Dataset.from_iterable([ DatasetItem(id=1, annotations=[ # Should be remapped Label(1), Bbox(1, 2, 3, 4, label=2), Mask(image=np.array([1]), label=3), # Should be deleted Polygon([1, 1, 2, 2, 3, 4], label=4), # Should be kept PolyLine([1, 3, 4, 2, 5, 6]), Bbox(4, 3, 2, 1, label=5), ]) ], categories={ AnnotationType.label: LabelCategories.from_iterable( 'label%s' % i for i in range(6)), AnnotationType.mask: MaskCategories( colormap=mask_tools.generate_colormap(6)), }) dst_dataset = Dataset.from_iterable([ DatasetItem(id=1, annotations=[ Label(1), Bbox(1, 2, 3, 4, label=0), Mask(image=np.array([1]), label=1), PolyLine([1, 3, 4, 2, 5, 6], label=None), Bbox(4, 3, 2, 1, label=2), ]), ], categories={ AnnotationType.label: LabelCategories.from_iterable( ['label0', 'label9', 'label5']), AnnotationType.mask: MaskCategories(colormap={ k: v for k, v in mask_tools.generate_colormap(6).items() if k in { 0, 1, 3, 5 } }) }) actual = transforms.RemapLabels(src_dataset, mapping={ 'label1': 'label9', # rename & join with new label9 (from label3) 'label2': 'label0', # rename & join with existing label0 'label3': 'label9', # rename & join with new label9 (form label1) 'label4': '', # delete the label and associated annotations # 'label5' - unchanged }, default='keep') compare_datasets(self, dst_dataset, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_remap_labels_delete_unspecified(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, annotations=[ Label(0, id=0), # will be removed Label(1, id=1), Bbox(1, 2, 3, 4, label=None), ]) ], categories=['label0', 'label1']) target_dataset = Dataset.from_iterable([ DatasetItem(id=1, annotations=[ Label(0, id=1), ]), ], categories=['label1']) actual = transforms.RemapLabels(source_dataset, mapping={ 'label1': 'label1' }, default='delete') compare_datasets(self, target_dataset, actual) @mark_requirement(Requirements.DATUM_BUG_314) def test_remap_labels_ignore_missing_labels_in_secondary_categories(self): source_dataset = Dataset.from_iterable([ DatasetItem(id=1, annotations=[ Label(0), ]) ], categories={ AnnotationType.label: LabelCategories.from_iterable(['a', 'b', 'c']), AnnotationType.points: PointsCategories.from_iterable([]), # all missing AnnotationType.mask: MaskCategories.generate(2) # no c color }) target_dataset = Dataset.from_iterable([ DatasetItem(id=1, annotations=[ Label(0), ]), ], categories={ AnnotationType.label: LabelCategories.from_iterable(['d', 'e', 'f']), AnnotationType.points: PointsCategories.from_iterable([]), AnnotationType.mask: MaskCategories.generate(2) }) actual = transforms.RemapLabels(source_dataset, mapping={ 'a': 'd', 'b': 'e', 'c': 'f' }, default='delete') compare_datasets(self, target_dataset, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_transform_labels(self): src_dataset = Dataset.from_iterable([ DatasetItem(id=1, annotations=[ Label(1), Bbox(1, 2, 3, 4, label=2), Bbox(1, 3, 3, 3), Mask(image=np.array([1]), label=3), Polygon([1, 1, 2, 2, 3, 4], label=4), PolyLine([1, 3, 4, 2, 5, 6], label=5) ]) ], categories=['label%s' % i for i in range(6)]) dst_dataset = Dataset.from_iterable([ DatasetItem(id=1, annotations=[ Label(1), Label(2), Label(3), Label(4), Label(5) ]), ], categories=['label%s' % i for i in range(6)]) actual = transforms.AnnsToLabels(src_dataset) compare_datasets(self, dst_dataset, actual) @mark_requirement(Requirements.DATUM_GENERAL_REQ) def test_bboxes_values_decrement_transform(self): src_dataset = Dataset.from_iterable([ DatasetItem(id=1, annotations=[ Label(1), Bbox(2, 3, 3, 4, label=2), Bbox(1.3, 3.5, 3.33, 3.12) ]) ], categories=['label%s' % i for i in range(6)]) dst_dataset = Dataset.from_iterable([ DatasetItem(id=1, annotations=[ Label(1), Bbox(1, 2, 3, 4, label=2), Bbox(0.3, 2.5, 3.33, 3.12) ]), ], categories=['label%s' % i for i in range(6)]) actual = transforms.BboxValuesDecrement(src_dataset) compare_datasets(self, dst_dataset, actual)

36.635628

84

0.460382

115c383542c6f798ff71a9010e2c654939e70707

17,028

py

Python

docs/autogen.py

braingineer/keras

86dfaacb7296a4e3bb46f88ea08c24552c824ea1

[ "MIT" ]

2

2017-09-13T21:17:54.000Z

2017-09-26T02:38:00.000Z

docs/autogen.py

braingineer/keras

86dfaacb7296a4e3bb46f88ea08c24552c824ea1

[ "MIT" ]

null

docs/autogen.py

braingineer/keras

86dfaacb7296a4e3bb46f88ea08c24552c824ea1

[ "MIT" ]

1

2019-11-11T10:51:10.000Z

# -*- coding: utf-8 -*- ''' General documentation architecture: Home Index - Getting started Getting started with the sequential model Getting started with the functional api Examples FAQ Installation guide - Models About Keras models explain when one should use Sequential or functional API explain compilation step explain weight saving, weight loading explain serialization, deserialization Sequential Model (functional API) - Layers About Keras layers explain common layer functions: get_weights, set_weights, get_config explain input_shape explain usage on non-Keras tensors Core layers Convolutional Recurrent Embeddings Normalization Advanced activations Noise - Preprocessing Image preprocessing Text preprocessing Sequence preprocessing Objectives Optimizers Activations Callbacks Datasets Backend Initializations Regularizers Constraints Visualization Scikit-learn API ''' from __future__ import print_function import re import inspect import os import shutil from keras.layers import convolutional from keras.layers import recurrent from keras.layers import core from keras.layers import noise from keras.layers import normalization from keras.layers import advanced_activations from keras.layers import embeddings from keras.layers import wrappers from keras import optimizers from keras import callbacks from keras import models from keras.engine import topology from keras import objectives from keras import backend from keras import constraints from keras import activations from keras import regularizers EXCLUDE = { 'Optimizer', 'Wrapper', 'get_session', 'set_session', } PAGES = [ { 'page': 'models/sequential.md', 'functions': [ models.Sequential.compile, models.Sequential.fit, models.Sequential.evaluate, models.Sequential.predict, models.Sequential.predict_classes, models.Sequential.predict_proba, models.Sequential.train_on_batch, models.Sequential.test_on_batch, models.Sequential.predict_on_batch, models.Sequential.fit_generator, models.Sequential.evaluate_generator, ], }, { 'page': 'models/model.md', 'functions': [ models.Model.compile, models.Model.fit, models.Model.evaluate, models.Model.predict, models.Model.train_on_batch, models.Model.test_on_batch, models.Model.predict_on_batch, models.Model.fit_generator, models.Model.evaluate_generator, models.Model.get_layer, ] }, { 'page': 'layers/core.md', 'classes': [ core.Dense, core.Activation, core.Dropout, core.Flatten, core.Reshape, core.Permute, core.RepeatVector, topology.Merge, core.Lambda, core.ActivityRegularization, core.Masking, core.Highway, core.MaxoutDense, core.TimeDistributedDense, ], }, { 'page': 'layers/convolutional.md', 'classes': [ convolutional.Convolution1D, convolutional.Convolution2D, convolutional.Convolution3D, convolutional.MaxPooling1D, convolutional.MaxPooling2D, convolutional.MaxPooling3D, convolutional.AveragePooling1D, convolutional.AveragePooling2D, convolutional.AveragePooling3D, convolutional.UpSampling1D, convolutional.UpSampling2D, convolutional.UpSampling3D, convolutional.ZeroPadding1D, convolutional.ZeroPadding2D, convolutional.ZeroPadding3D, ], }, { 'page': 'layers/recurrent.md', 'classes': [ recurrent.Recurrent, recurrent.SimpleRNN, recurrent.GRU, recurrent.LSTM, ], }, { 'page': 'layers/embeddings.md', 'classes': [ embeddings.Embedding, ], }, { 'page': 'layers/normalization.md', 'classes': [ normalization.BatchNormalization, ], }, { 'page': 'layers/advanced-activations.md', 'all_module_classes': [advanced_activations], }, { 'page': 'layers/noise.md', 'all_module_classes': [noise], }, { 'page': 'layers/wrappers.md', 'all_module_classes': [wrappers], }, { 'page': 'optimizers.md', 'all_module_classes': [optimizers], }, { 'page': 'callbacks.md', 'all_module_classes': [callbacks], }, { 'page': 'backend.md', 'all_module_functions': [backend], }, ] ROOT = 'http://keras.io/' def get_earliest_class_that_defined_member(member, cls): ancestors = get_classes_ancestors([cls]) result = None for ancestor in ancestors: if member in dir(ancestor): result = ancestor if not result: return cls return result def get_classes_ancestors(classes): ancestors = [] for cls in classes: ancestors += cls.__bases__ filtered_ancestors = [] for ancestor in ancestors: if ancestor.__name__ in ['object']: continue filtered_ancestors.append(ancestor) if filtered_ancestors: return filtered_ancestors + get_classes_ancestors(filtered_ancestors) else: return filtered_ancestors def get_function_signature(function, method=True): signature = inspect.getargspec(function) defaults = signature.defaults if method: args = signature.args[1:] else: args = signature.args if defaults: kwargs = zip(args[-len(defaults):], defaults) args = args[:-len(defaults)] else: kwargs = [] st = '%s.%s(' % (function.__module__, function.__name__) for a in args: st += str(a) + ', ' for a, v in kwargs: if type(v) == str: v = '\'' + v + '\'' elif type(v) == unicode: v = 'u\'' + v + '\'' st += str(a) + '=' + str(v) + ', ' if kwargs or args: return st[:-2] + ')' else: return st + ')' def get_class_signature(cls): try: class_signature = get_function_signature(cls.__init__) class_signature = class_signature.replace('__init__', cls.__name__) except: # in case the class inherits from object and does not # define __init__ class_signature = cls.__module__ + '.' + cls.__name__ + '()' return class_signature def class_to_docs_link(cls): module_name = cls.__module__ assert module_name[:6] == 'keras.' module_name = module_name[6:] link = ROOT + module_name.replace('.', '/') + '#' + cls.__name__.lower() return link def class_to_source_link(cls): module_name = cls.__module__ assert module_name[:6] == 'keras.' path = module_name.replace('.', '/') path += '.py' line = inspect.getsourcelines(cls)[-1] link = 'https://github.com/fchollet/keras/blob/master/' + path + '#L' + str(line) return '[[source]](' + link + ')' def code_snippet(snippet): result = '```python\n' result += snippet + '\n' result += '```\n' return result def process_class_docstring(docstring): docstring = re.sub(r'\n # (.*)\n', r'\n __\1__\n\n', docstring) docstring = re.sub(r' ([^\s\\]+):(.*)\n', r' - __\1__:\2\n', docstring) docstring = docstring.replace(' ' * 5, '\t\t') docstring = docstring.replace(' ' * 3, '\t') docstring = docstring.replace(' ', '') return docstring def process_function_docstring(docstring): docstring = re.sub(r'\n # (.*)\n', r'\n __\1__\n\n', docstring) docstring = re.sub(r'\n # (.*)\n', r'\n __\1__\n\n', docstring) docstring = re.sub(r' ([^\s\\]+):(.*)\n', r' - __\1__:\2\n', docstring) docstring = docstring.replace(' ' * 6, '\t\t') docstring = docstring.replace(' ' * 4, '\t') docstring = docstring.replace(' ', '') return docstring print('Cleaning up existing sources directory.') if os.path.exists('sources'): shutil.rmtree('sources') print('Populating sources directory with templates.') for subdir, dirs, fnames in os.walk('templates'): for fname in fnames: new_subdir = subdir.replace('templates', 'sources') if not os.path.exists(new_subdir): os.makedirs(new_subdir) if fname[-3:] == '.md': fpath = os.path.join(subdir, fname) new_fpath = fpath.replace('templates', 'sources') shutil.copy(fpath, new_fpath) print('Starting autogeneration.') for page_data in PAGES: blocks = [] classes = page_data.get('classes', []) for module in page_data.get('all_module_classes', []): module_classes = [] for name in dir(module): if name[0] == '_' or name in EXCLUDE: continue module_member = getattr(module, name) if inspect.isclass(module_member): cls = module_member if cls.__module__ == module.__name__: if cls not in module_classes: module_classes.append(cls) module_classes.sort(key=lambda x: id(x)) classes += module_classes for cls in classes: subblocks = [] signature = get_class_signature(cls) subblocks.append('<span style="float:right;">' + class_to_source_link(cls) + '</span>') subblocks.append('### ' + cls.__name__ + '\n') subblocks.append(code_snippet(signature)) docstring = cls.__doc__ if docstring: subblocks.append(process_class_docstring(docstring)) blocks.append('\n'.join(subblocks)) functions = page_data.get('functions', []) for module in page_data.get('all_module_functions', []): module_functions = [] for name in dir(module): if name[0] == '_' or name in EXCLUDE: continue module_member = getattr(module, name) if inspect.isfunction(module_member): function = module_member if module.__name__ in function.__module__: if function not in module_functions: module_functions.append(function) module_functions.sort(key=lambda x: id(x)) functions += module_functions for function in functions: subblocks = [] signature = get_function_signature(function, method=False) signature = signature.replace(function.__module__ + '.', '') subblocks.append('### ' + function.__name__ + '\n') subblocks.append(code_snippet(signature)) docstring = function.__doc__ if docstring: subblocks.append(process_function_docstring(docstring)) blocks.append('\n\n'.join(subblocks)) mkdown = '\n----\n\n'.join(blocks) # save module page. # Either insert content into existing page, # or create page otherwise page_name = page_data['page'] path = os.path.join('sources', page_name) if os.path.exists(path): template = open(path).read() assert '{{autogenerated}}' in template, ('Template found for ' + path + ' but missing {{autogenerated}} tag.') mkdown = template.replace('{{autogenerated}}', mkdown) print('...inserting autogenerated content into template:', path) else: print('...creating new page with autogenerated content:', path) subdir = os.path.dirname(path) if not os.path.exists(subdir): os.makedirs(subdir) open(path, 'w').write(mkdown) # covered_so_far = set() # for module, module_name in MODULES: # class_pages = [] # for name in dir(module): # if name in SKIP: # continue # if name[0] == '_': # continue # module_member = getattr(module, name) # if module_member in covered_so_far: # continue # if inspect.isclass(module_member): # cls = module_member # if cls.__module__ == module_name: # try: # class_signature = get_function_signature(cls.__init__) # class_signature = class_signature.replace('__init__', cls.__name__) # except: # # in case the class inherits from object and does not # # define __init__ # class_signature = module_name + '.' + cls.__name__ + '()' # functions = [] # functions_not_defined_here = [] # for name in dir(cls): # if name in SKIP: # continue # if name[0] == '_': # continue # cls_member = getattr(cls, name) # if inspect.isfunction(cls_member): # function = cls_member # signature = inspect.getargspec(function) # defaults = signature.defaults # args = signature.args[1:] # if defaults: # kwargs = zip(args[-len(defaults):], defaults) # args = args[:-len(defaults)] # else: # kwargs = [] # defined_by = get_earliest_class_that_defined_member(function.__name__, cls) # if cls == defined_by: # functions.append(function) # else: # functions_not_defined_here.append((function, defined_by)) # blocks = [] # blocks.append('<span style="float:right;">' + class_to_source_link(cls) + '</span>') # blocks.append('# ' + cls.__name__ + '\n') # blocks.append(code_snippet(class_signature)) # docstring = cls.__doc__ # if docstring: # blocks.append(process_class_docstring(docstring)) # if cls.__name__ in INCLUDE_functionS_FOR: # if functions or functions_not_defined_here: # blocks.append('### functions\n') # for function in functions: # signature = get_function_signature(function) # signature = signature.replace(module_name + '.', '') # blocks.append(code_snippet(signature)) # docstring = function.__doc__ # if docstring: # blocks.append(process_function_docstring(docstring)) # for function, defined_by in functions_not_defined_here: # signature = get_function_signature(function) # function_module_name = function.__module__ # signature = signature.replace(function_module_name + '.', '') # link = '[' + defined_by.__name__ + '](' + class_to_docs_link(defined_by) + ')' # blocks.append(code_snippet(signature)) # blocks.append('Defined by ' + link + '.\n') # mkdown = '\n'.join(blocks) # class_pages.append((id(cls), mkdown)) # covered_so_far.add(module_member) # class_pages.sort(key=lambda x: x[0]) # class_pages = [x[1] for x in class_pages] # module_page = '\n----\n\n'.join(class_pages) # # save module page. # # Either insert content into existing page, # # or create page otherwise # path = 'sources/' + module_name.replace('.', '/')[6:] + '.md' # if os.path.exists(path): # template = open(path).read() # assert '{{autogenerated}}' in template, ('Template found for ' + path + # ' but missing {{autogenerated}} tag.') # module_page = template.replace('{{autogenerated}}', module_page) # print('...inserting autogenerated content into template:', path) # else: # print('...creating new page with autogenerated content:', path) # subdir = os.path.dirname(path) # if not os.path.exists(subdir): # os.makedirs(subdir) # open(path, 'w').write(module_page)

32.93617

108

0.558551

fe1ca719f2ce402b30e53ab99808b6115e75b405

4,891

py

Python

RNNG/scripts/evaluation.py

Psarpei/Recognition-of-logical-document-structures

5ab3cc44f43409191b2b2903491f8e1ac4a34026

[ "MIT" ]

4

2020-12-13T20:19:51.000Z

2021-09-05T01:44:26.000Z

RNNG/scripts/evaluation.py

Psarpei/Recognition-of-logical-document-structures

5ab3cc44f43409191b2b2903491f8e1ac4a34026

[ "MIT" ]

1

2021-02-12T23:52:47.000Z

RNNG/scripts/evaluation.py

Psarpei/Recognition-of-logical-document-structures

5ab3cc44f43409191b2b2903491f8e1ac4a34026

[ "MIT" ]

null

from copy import deepcopy file_ground_truth = open("C:/Users/pasca/Dropbox/PaktikumTextimaging/rnng-master/Thomas_Mann_Der_kleine_Herr_Friedemann_Ground_Truth.txt", "r") #Franz_Kafka_Auf_der_Galerie_Quelle_DigBib_Ground_Truth.txt","r") #Franz_Kafka_Das_Urteil_Ground_Truth.txt","r") file_pred = open("C:/Users/pasca/Dropbox/PaktikumTextimaging/rnng-master/Thomas_Mann_Der_kleine_Herr_Friedemann_predict.txt", "r") #Franz_Kafka_Auf_der_Galerie_Quelle_DigBib_predict.txt","r") #Franz_Kafka_Das_Urteil_predict.txt", "r") chars = ["(t", "(s", "(seg", "))", "T1","T2","T3","T4","T5","T6"] def eval_char(list_gt, list_pred, char): chars_gt = 0 chars_pred = 0 matches = 0 char_list = [] t_list = ["))",")))","))))",")))))","))))))",")))))))"] for elem in list_gt: if(char[0] == "T"): if((t_list[int(char[1])-1] in elem[0]) and (t_list[int(char[1])] not in elem[0])): chars_gt += 1 elif(char == elem[0] or (char in elem[0] and char == "))")): chars_gt += elem[2] for elem in list_pred: if(char[0] == "T"): if((t_list[int(char[1])-1] in elem[0]) and (t_list[int(char[1])] not in elem[0])): chars_pred += 1 char_list.append(elem) elif(char == elem[0] or (char in elem[0] and char == "))")): chars_pred += elem[2] char_list.append(elem) gt_copy = deepcopy(list_gt) for elem in char_list: for i in range(len(gt_copy)): if(elem[0] == gt_copy[i][0] and elem[1] == gt_copy[i][1]): if(char[0] == "T"): matches += 1 else: matches += elem[2] if (elem[2] <= gt_copy[i][2]) else gt_copy[i][2] gt_copy.pop(i) break if(char == "))"): char = ")" prec = 0 if chars_gt == 0 else (matches/chars_gt)*100 rec = 0 if chars_pred == 0 else (matches/chars_pred)*100 print("{:6s}|{:16d}|{:15d}|{:13.3f}|{:9.3f}".format(char, chars_gt, chars_pred, prec, rec)) gt = [] lines = file_ground_truth .read().splitlines() for line in lines: gt += line.split(" ") help1= [] #only test help2= [] # -_____- """ for i in range(len(gt)): if("(w" in gt[i]): help1.append(gt[i] + gt[i+1]) elif("(c" in gt[i]): if("»" in gt[i+1] or "«" in gt[i+1] or "€" in gt[i+1]): help1.append(gt[i] + gt[i+1]) else: for char in gt[i+1]: if(char != ")"): help1.append(gt[i] + char) """ list_gt = [] counter = 0 for i in range(len(gt)): if("(c" in gt[i]): if("»" in gt[i+1] or "«" in gt[i+1] or "€" in gt[i+1]): counter += 1 else: for char in gt[i+1]: counter += 1 if(char != ")") else 0 #for position from token elif("(w" in gt[i]): counter += 1 elif("(t" in gt[i] or "(s" in gt[i] or "(seg" in gt[i]): list_gt.append((gt[i],counter,1)) elif("))" in gt[i]): number = -1 #for # ")" ,-1 because 1 is for closed token w or c for char in gt[i]: number += 1 if(char == ")") else 0 list_gt.append((gt[i],counter,number)) else: pass print("#Ground Truth: ", len(list_gt)) pred = [] lines = file_pred.read().splitlines() for line in lines: split = line.split(" ") help_ = 0 #for finding "(t ... " for elem in split: if("(t" == elem): break help_ += 1 pred += split[help_:] """ for i in range(len(pred)-1): if("(XX" in pred[i]): help2.append(pred[i] + pred[i+1]) for i in range(len(help1)): print(help1[i],"_____",help2[i])######################################################## """ list_pred = [] counter = 0 for elem in pred: if("(XX" in elem): counter += 1 #for position from token elif("(t" in elem or "(s" in elem or "(seg" in elem): list_pred.append((elem,counter,1)) elif("))" in elem): number = -1 #for # ")" ,-1 because 1 is for closed token w or c for char in elem: if(char == ")"): number += 1 list_pred.append((elem,counter,number)) else: pass """ print("#Prediction: ", len(list_pred)) min_ = len(list_pred) if(len(list_pred) <= len(list_gt)) else len(list_gt) for i in range(min_): if("(t" in list_gt[i][0] or "(t" in list_pred[i]): print(list_gt[i],"_____",list_pred[i]) """ print("#Wörter und Zeichen Ground Truth:", list_gt[-1][1]) print("#Wörter und Zeichen Preciction:", list_pred[-1][1]) print("File: Franz_Kafka_Auf_der_Galerie_Quelle_DigBib") print("Token | # Ground Truth | # predictions | % precision | % recall") print("---------------------------------------------------------------") for char in chars: eval_char(list_gt, list_pred, char)

33.5

256

0.521775

450de7417df3efd12c9ee751ce86f94607ba5932

3,058

py

Python

LeetCode/279-perfect-squares.py

leaving-voider/LeetCode.cn-Record

2922cbdab85556bc0625adc9e6ce44849232e4f4

[ "MIT" ]

null

LeetCode/279-perfect-squares.py

leaving-voider/LeetCode.cn-Record

2922cbdab85556bc0625adc9e6ce44849232e4f4

[ "MIT" ]

null

LeetCode/279-perfect-squares.py

leaving-voider/LeetCode.cn-Record

2922cbdab85556bc0625adc9e6ce44849232e4f4

[ "MIT" ]

null

############################################################################################### # 同样用动规的思想去解，居然超时了没想到 ########### # 时间复杂度：O(n*sqrt(n)*total)，total为总数/平方和 # 空间复杂度：O(n*sqrt(n))，动规数组开销 ############################################################################################### class Solution: def numSquares(self, n: int) -> int: sqrt_ = int(sqrt(n)) # 定义dp[i][j]：任意使用≤i的自然数的完全平方，组成和为j，使用的完全平方数的最少数量 dp = [[0]*(n+1) for _ in range(sqrt_+1)] for i in range(1, sqrt_+1): dp[i][1] = 1 for i in range(1, n+1): dp[1][i] = i for i in range(2, sqrt_+1): square = i**2 for j in range(2, n+1): if square > j: # 没法使用这个square dp[i][j] = dp[i-1][j] else: dp[i][j] = min([dp[i-1][j-per*square]+per for per in range(j//square+1)]) return dp[sqrt_][n] ## 使用滚动数组，稍微化简后如下，但还是超时 class Solution: def numSquares(self, n: int) -> int: sqrt_ = int(sqrt(n)) # 定义dp[i][j]：任意使用≤i的自然数的完全平方，组成和为j，使用的完全平方数的最少数量 dp = [0]*(n+1) # 直接从第1层开始 for i in range(1, n+1): dp[i] = i for i in range(2, sqrt_+1): square = i**2 for j in range(n, square-1, -1): dp[j] = min([dp[j-per*square]+per for per in range(j//square+1)]) return dp[n] ############################################################################################### # 继续化简，去掉第三重循环，只需要从小到大遍历每一个dp[j]的时候，和前面的dp[j-square]取min即可 # 至此，成功没看官方解法，解出此题，看来我对动规算法已经稍稍入门了 ########### # 时间复杂度：O(n*sqrt(n)) # 空间复杂度：O(n) ############################################################################################### class Solution: def numSquares(self, n: int) -> int: # 定义dp[i][j]：任意使用≤i的自然数的完全平方，组成和为j，使用的完全平方数的最少数量 dp = list(range(n+1)) # 直接从第1层开始 for i in range(2, int(sqrt(n))+1): square = i**2 for j in range(square, n+1): dp[j] = min(dp[j], dp[j-square]+1) return dp[n] ############################################################################################### # 官方竟然给出了一个基于数学的定理【四平方和定理】，直接代替本题的动规法；仅判断四种情况即可 ########### # 时间复杂度：O(sqrt(n)), 判断能否被 4 整除的while循环的时间复杂度为 O(logn)，忽略 # 空间复杂度：O(1) ############################################################################################### class Solution: # 判断是否为完全平方数 def isPerfectSquare(self, x): y = int(sqrt(x)) return y * y == x # 判断是否能表示为 4^k*(8m+7) def checkAnswer4(self, x): while (x % 4 == 0): x /= 4 return x % 8 == 7 def numSquares(self, n: int) -> int: if self.isPerfectSquare(n): # 判断是否为完全平方数 return 1 if self.checkAnswer4(n): # 判断是否能表示为 4^k*(8m+7) return 4 for i in range(1, int(sqrt(n))+1): if (self.isPerfectSquare(n - i**2)): return 2 return 3

35.976471

96

0.397973

816f021232314eb03f88bec3efda00487bb916c2

1,635

py

Python

alipay/aop/api/response/AlipayFinanceFundFundnetvaluesBatchqueryResponse.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

[ "Apache-2.0" ]

213

2018-08-27T16:49:32.000Z

2021-12-29T04:34:12.000Z

alipay/aop/api/response/AlipayFinanceFundFundnetvaluesBatchqueryResponse.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

[ "Apache-2.0" ]

29

2018-09-29T06:43:00.000Z

2021-09-02T03:27:32.000Z

alipay/aop/api/response/AlipayFinanceFundFundnetvaluesBatchqueryResponse.py

snowxmas/alipay-sdk-python-all

96870ced60facd96c5bce18d19371720cbda3317

[ "Apache-2.0" ]

59

2018-08-27T16:59:26.000Z

2022-03-25T10:08:15.000Z

#!/usr/bin/env python # -*- coding: utf-8 -*- import json from alipay.aop.api.response.AlipayResponse import AlipayResponse from alipay.aop.api.domain.NetValueVO import NetValueVO class AlipayFinanceFundFundnetvaluesBatchqueryResponse(AlipayResponse): def __init__(self): super(AlipayFinanceFundFundnetvaluesBatchqueryResponse, self).__init__() self._fund_code = None self._fund_type = None self._net_values = None @property def fund_code(self): return self._fund_code @fund_code.setter def fund_code(self, value): self._fund_code = value @property def fund_type(self): return self._fund_type @fund_type.setter def fund_type(self, value): self._fund_type = value @property def net_values(self): return self._net_values @net_values.setter def net_values(self, value): if isinstance(value, list): self._net_values = list() for i in value: if isinstance(i, NetValueVO): self._net_values.append(i) else: self._net_values.append(NetValueVO.from_alipay_dict(i)) def parse_response_content(self, response_content): response = super(AlipayFinanceFundFundnetvaluesBatchqueryResponse, self).parse_response_content(response_content) if 'fund_code' in response: self.fund_code = response['fund_code'] if 'fund_type' in response: self.fund_type = response['fund_type'] if 'net_values' in response: self.net_values = response['net_values']

30.849057

121

0.658716

225a9440ef4e863d0895ea7e69c825e3dda25ca1

33,512

py

Python

client/verta/verta/repository/_commit.py

TMFRook/modeldb

7096b8fd7e98dc97c3c87cf0ce0e3862ff042f6c

[ "Apache-2.0" ]

null

client/verta/verta/repository/_commit.py

TMFRook/modeldb

7096b8fd7e98dc97c3c87cf0ce0e3862ff042f6c

[ "Apache-2.0" ]

null

client/verta/verta/repository/_commit.py

TMFRook/modeldb

7096b8fd7e98dc97c3c87cf0ce0e3862ff042f6c

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from __future__ import print_function import collections from datetime import datetime import heapq import time import requests from .._protos.public.modeldb.versioning import VersioningService_pb2 as _VersioningService from ..external import six from .._internal_utils import ( _artifact_utils, _utils, ) from .. import code from .. import configuration from .. import dataset from .. import environment from . import _blob from . import _diff class Commit(object): """ Commit within a ModelDB Repository. There should not be a need to instantiate this class directly; please use :meth:`Repository.get_commit() <verta.repository.Repository.get_commit>`. Attributes ---------- id : str or None ID of the Commit, or ``None`` if the Commit has not yet been saved. """ def __init__(self, conn, repo, commit_msg, branch_name=None): self._conn = conn self._commit_json = _utils.proto_to_json(commit_msg) # dict representation of Commit protobuf self._repo = repo self._parent_ids = list(collections.OrderedDict.fromkeys(commit_msg.parent_shas or [])) # remove duplicates while maintaining order self.branch_name = branch_name # TODO: find a way to clear if branch is moved self._blobs = dict() # will be loaded when needed self._loaded_from_remote = False @property def id(self): return self._commit_json['commit_sha'] or None @property def parent(self): return self._repo.get_commit(id=self._parent_ids[0]) if self._parent_ids else None def _lazy_load_blobs(self): if self._loaded_from_remote: return # until Commits can be created from blob diffs, load in blobs if self.id is not None: self._update_blobs_from_commit(self.id) else: for parent_id in self._parent_ids: # parents will be read in first-to-last, possibly overwriting previous blobs self._update_blobs_from_commit(parent_id) self._loaded_from_remote = True def describe(self): self._lazy_load_blobs() contents = '\n'.join(( "{} ({}.{})".format(path, blob.__class__.__module__.split('.')[1], blob.__class__.__name__) for path, blob in sorted(six.viewitems(self._blobs)) )) if not contents: contents = "<no contents>" components = [self.__repr__(), 'Contents:', contents] return '\n'.join(components) def __repr__(self): branch_and_tag = ' '.join(( "Branch: {}".format(self.branch_name) if self.branch_name is not None else '', # TODO: put tag here )) if self.id is None: header = "unsaved Commit" if branch_and_tag: header = header + " (was {})".format(branch_and_tag) else: header = "Commit {}".format(self.id) if branch_and_tag: header = header + " ({})".format(branch_and_tag) # TODO: add author # TODO: make data more similar to git date_created = int(self._commit_json['date_created']) # protobuf uint64 is str, so cast to int date = 'Date: ' + datetime.fromtimestamp(date_created/1000.).strftime('%Y-%m-%d %H:%M:%S') message = '\n'.join(' ' + c for c in self._commit_json['message'].split('\n')) components = [header, date, '', message, ''] return '\n'.join(components) @classmethod def _from_id(cls, conn, repo, id_, **kwargs): endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/commits/{}".format( conn.scheme, conn.socket, repo.id, id_, ) response = _utils.make_request("GET", endpoint, conn) _utils.raise_for_http_error(response) response_msg = _utils.json_to_proto(_utils.body_to_json(response), _VersioningService.GetCommitRequest.Response) commit_msg = response_msg.commit return cls(conn, repo, commit_msg, **kwargs) @staticmethod def _raise_lookup_error(path): e = LookupError("Commit does not contain path \"{}\"".format(path)) six.raise_from(e, None) # TODO: consolidate this with similar method in `_ModelDBEntity` def _get_url_for_artifact(self, blob_path, dataset_component_path, method, part_num=0): """ Obtains a URL to use for accessing stored artifacts. Parameters ---------- blob_path : str Path to blob within repo. dataset_component_path : str Filepath in dataset component blob. method : {'GET', 'PUT'} HTTP method to request for the generated URL. part_num : int, optional If using Multipart Upload, number of part to be uploaded. Returns ------- response_msg : `_VersioningService.GetUrlForBlobVersioned.Response` Backend response. """ if method.upper() not in ("GET", "PUT"): raise ValueError("`method` must be one of {'GET', 'PUT'}") Message = _VersioningService.GetUrlForBlobVersioned msg = Message( location=path_to_location(blob_path), path_dataset_component_blob_path=dataset_component_path, method=method, part_number=part_num, ) data = _utils.proto_to_json(msg) endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/commits/{}/getUrlForBlobVersioned".format( self._conn.scheme, self._conn.socket, self._repo.id, self.id, ) response = _utils.make_request("POST", endpoint, self._conn, json=data) _utils.raise_for_http_error(response) response_msg = _utils.json_to_proto(response.json(), Message.Response) url = response_msg.url # accommodate port-forwarded NFS store if 'https://localhost' in url[:20]: url = 'http' + url[5:] if 'localhost%3a' in url[:20]: url = url.replace('localhost%3a', 'localhost:') if 'localhost%3A' in url[:20]: url = url.replace('localhost%3A', 'localhost:') response_msg.url = url return response_msg # TODO: consolidate this with similar method in `ExperimentRun` def _upload_artifact(self, blob_path, dataset_component_path, file_handle, part_size=_artifact_utils._64MB): """ Uploads `file_handle` to ModelDB artifact store. Parameters ---------- blob_path : str Path to blob within repo. dataset_component_path : str Filepath in dataset component blob. file_handle : file-like Artifact to be uploaded. part_size : int, default 64 MB If using multipart upload, number of bytes to upload per part. """ file_handle.seek(0) # check if multipart upload ok url_for_artifact = self._get_url_for_artifact(blob_path, dataset_component_path, "PUT", part_num=1) print("uploading {} to ModelDB".format(dataset_component_path)) if url_for_artifact.multipart_upload_ok: # TODO: parallelize this file_parts = iter(lambda: file_handle.read(part_size), b'') for part_num, file_part in enumerate(file_parts, start=1): print("uploading part {}".format(part_num), end='\r') # get presigned URL url = self._get_url_for_artifact(blob_path, dataset_component_path, "PUT", part_num=part_num).url # wrap file part into bytestream to avoid OverflowError # Passing a bytestring >2 GB (num bytes > max val of int32) directly to # ``requests`` will overwhelm CPython's SSL lib when it tries to sign the # payload. But passing a buffered bytestream instead of the raw bytestring # indicates to ``requests`` that it should perform a streaming upload via # HTTP/1.1 chunked transfer encoding and avoid this issue. # https://github.com/psf/requests/issues/2717 part_stream = six.BytesIO(file_part) # upload part response = _utils.make_request("PUT", url, self._conn, data=part_stream) _utils.raise_for_http_error(response) # commit part url = "{}://{}/api/v1/modeldb/versioning/commitVersionedBlobArtifactPart".format( self._conn.scheme, self._conn.socket, ) msg = _VersioningService.CommitVersionedBlobArtifactPart( commit_sha=self.id, location=path_to_location(blob_path), path_dataset_component_blob_path=dataset_component_path, ) msg.repository_id.repo_id = self._repo.id msg.artifact_part.part_number = part_num msg.artifact_part.etag = response.headers['ETag'] data = _utils.proto_to_json(msg) response = _utils.make_request("POST", url, self._conn, json=data) _utils.raise_for_http_error(response) print() # complete upload url = "{}://{}/api/v1/modeldb/versioning/commitMultipartVersionedBlobArtifact".format( self._conn.scheme, self._conn.socket, ) msg = _VersioningService.CommitMultipartVersionedBlobArtifact( commit_sha=self.id, location=path_to_location(blob_path), path_dataset_component_blob_path=dataset_component_path, ) msg.repository_id.repo_id = self._repo.id data = _utils.proto_to_json(msg) response = _utils.make_request("POST", url, self._conn, json=data) _utils.raise_for_http_error(response) else: # upload full artifact if url_for_artifact.fields: # if fields were returned by backend, make a POST request and supply them as form fields response = _utils.make_request( "POST", url_for_artifact.url, self._conn, # requests uses the `files` parameter for sending multipart/form-data POSTs. # https://stackoverflow.com/a/12385661/8651995 # the file contents must be the final form field # https://docs.aws.amazon.com/AmazonS3/latest/dev/HTTPPOSTForms.html#HTTPPOSTFormFields files=list(url_for_artifact.fields.items()) + [('file', file_handle)], ) else: response = _utils.make_request("PUT", url_for_artifact.url, self._conn, data=file_handle) _utils.raise_for_http_error(response) print("upload complete") def _update_blobs_from_commit(self, id_): """Fetches commit `id_`'s blobs and stores them as objects in `self._blobs`.""" endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/commits/{}/blobs".format( self._conn.scheme, self._conn.socket, self._repo.id, id_, ) response = _utils.make_request("GET", endpoint, self._conn) _utils.raise_for_http_error(response) response_msg = _utils.json_to_proto(_utils.body_to_json(response), _VersioningService.ListCommitBlobsRequest.Response) self._blobs.update({ '/'.join(blob_msg.location): blob_msg_to_object(blob_msg.blob) for blob_msg in response_msg.blobs }) def _become_child(self): """ This method is for when `self` had been saved and is then modified, meaning that this commit object has become a child of the commit that had been saved. """ self._lazy_load_blobs() self._parent_ids = [self.id] self._commit_json['commit_sha'] = "" def _become_saved_child(self, child_id): """ This method is for when a child commit is created in the back end from `self`, and `self` and its branch need to be updated to become that newly-created commit. """ if self.branch_name is not None: # update branch to child commit set_branch(self._conn, self._repo.id, child_id, self.branch_name) new_commit = self._repo.get_commit(branch=self.branch_name) else: new_commit = self._repo.get_commit(id=child_id) self.__dict__ = new_commit.__dict__ def _to_create_msg(self, commit_message): self._lazy_load_blobs() msg = _VersioningService.CreateCommitRequest() msg.repository_id.repo_id = self._repo.id # pylint: disable=no-member msg.commit.parent_shas.extend(self._parent_ids) # pylint: disable=no-member msg.commit.message = commit_message for path, blob in six.viewitems(self._blobs): blob_msg = _VersioningService.BlobExpanded() blob_msg.location.extend(path_to_location(path)) # pylint: disable=no-member blob_msg.blob.CopyFrom(blob._as_proto()) msg.blobs.append(blob_msg) # pylint: disable=no-member return msg def walk(self): """ Generates folder names and blob names in this commit by walking through its folder tree. Similar to the Python standard library's ``os.walk()``, the yielded `folder_names` can be modified in-place to remove subfolders from upcoming iterations or alter the order in which they are to be visited. Note that, also similar to ``os.walk()``, `folder_names` and `blob_names` are simply the *names* of those entities, and *not* their full paths. Yields ------ folder_path : str Path to current folder. folder_names : list of str Names of subfolders in `folder_path`. blob_names : list of str Names of blobs in `folder_path`. """ if self.id is None: raise RuntimeError("Commit must be saved before it can be walked") endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/commits/{}/path".format( self._conn.scheme, self._conn.socket, self._repo.id, self.id, ) locations = [()] while locations: location = locations.pop() msg = _VersioningService.GetCommitComponentRequest() msg.location.extend(location) # pylint: disable=no-member data = _utils.proto_to_json(msg) response = _utils.make_request("GET", endpoint, self._conn, params=data) _utils.raise_for_http_error(response) response_msg = _utils.json_to_proto(_utils.body_to_json(response), msg.Response) folder_msg = response_msg.folder folder_path = '/'.join(location) folder_names = list(sorted(element.element_name for element in folder_msg.sub_folders)) blob_names = list(sorted(element.element_name for element in folder_msg.blobs)) yield (folder_path, folder_names, blob_names) locations.extend( location + (folder_name,) for folder_name in reversed(folder_names) # maintains order, because locations are popped from end ) def update(self, path, blob): """ Adds `blob` to this Commit at `path`. If `path` is already in this Commit, it will be updated to the new `blob`. Parameters ---------- path : str Location to add `blob` to. blob : :mod:`~verta._repository.blob` ModelDB versioning blob. """ if not isinstance(blob, _blob.Blob): raise TypeError("unsupported type {}".format(type(blob))) self._lazy_load_blobs() if self.id is not None: self._become_child() self._blobs[path] = blob def get(self, path): """ Retrieves the blob at `path` from this Commit. Parameters ---------- path : str Location of a blob. Returns ------- blob : :mod:`~verta._repository.blob` ModelDB versioning blob. Raises ------ LookupError If `path` is not in this Commit. """ self._lazy_load_blobs() try: blob = self._blobs[path] except KeyError: self._raise_lookup_error(path) if isinstance(blob, dataset._Dataset): # for _Dataset.download() blob._set_commit_and_blob_path(self, path) return blob def remove(self, path): """ Deletes the blob at `path` from this Commit. Parameters ---------- path : str Location of a blob. Raises ------ LookupError If `path` is not in this Commit. """ self._lazy_load_blobs() if self.id is not None: self._become_child() try: del self._blobs[path] except KeyError: self._raise_lookup_error(path) def save(self, message): """ Saves this commit to ModelDB. .. note:: If this commit contains new S3 datasets to be versioned by ModelDB, a very large temporary download may occur before uploading them to ModelDB. Parameters ---------- message : str Description of this Commit. """ # prepare ModelDB-versioned blobs, and track for upload after commit save mdb_versioned_blobs = dict() for blob_path, blob in self._blobs.items(): if isinstance(blob, dataset._Dataset) and blob._mdb_versioned: blob._prepare_components_to_upload() mdb_versioned_blobs[blob_path] = blob msg = self._to_create_msg(commit_message=message) self._save(msg) # upload ModelDB-versioned blobs for blob_path, blob in mdb_versioned_blobs.items(): for component in blob._components_map.values(): if component._internal_versioned_path: with open(component._local_path, 'rb') as f: self._upload_artifact(blob_path, component.path, f) blob._clean_up_uploaded_components() def _save(self, proto_message): data = _utils.proto_to_json(proto_message) endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/commits".format( self._conn.scheme, self._conn.socket, self._repo.id, ) response = _utils.make_request("POST", endpoint, self._conn, json=data) _utils.raise_for_http_error(response) response_msg = _utils.json_to_proto(_utils.body_to_json(response), proto_message.Response) self._become_saved_child(response_msg.commit.commit_sha) # TODO: Add ways to retrieve and delete tag def tag(self, tag): """ Assigns a tag to this Commit. Parameters ---------- tag : str Tag. Raises ------ RuntimeError If this Commit has not yet been saved. """ if self.id is None: raise RuntimeError("Commit must be saved before it can be tagged") data = self.id endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/tags/{}".format( self._conn.scheme, self._conn.socket, self._repo.id, tag, ) response = _utils.make_request("PUT", endpoint, self._conn, json=data) _utils.raise_for_http_error(response) def log(self): """ Yields ancestors, starting from this Commit until the root of the Repository. Analogous to ``git log``. Yields ------ commit : :class:`Commit` Ancestor commit. """ if self.id is None: # unsaved commit # use parent commit_id = self._parent_ids[0] else: commit_id = self.id endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/commits/{}/log".format( self._conn.scheme, self._conn.socket, self._repo.id, commit_id, ) response = _utils.make_request("GET", endpoint, self._conn) _utils.raise_for_http_error(response) response_msg = _utils.json_to_proto(_utils.body_to_json(response), _VersioningService.ListCommitsLogRequest.Response) commits = response_msg.commits for c in commits: yield Commit(self._conn, self._repo, c, self.branch_name if c.commit_sha == commit_id else None) def new_branch(self, branch): """ Creates a branch at this Commit and returns the checked-out branch. If `branch` already exists, it will be moved to this Commit. Parameters ---------- branch : str Branch name. Returns ------- commit : :class:`Commit` This Commit as the head of `branch`. Raises ------ RuntimeError If this Commit has not yet been saved. Examples -------- .. code-block:: python master = repo.get_commit(branch="master") dev = master.new_branch("development") """ if self.id is None: raise RuntimeError("Commit must be saved before it can be attached to a branch") set_branch(self._conn, self._repo.id, self.id, branch) return self._repo.get_commit(branch=branch) def diff_from(self, reference=None): """ Returns the diff from `reference` to `self`. Parameters ---------- reference : :class:`Commit`, optional Commit to be compared to. Returns ------- :class:`~verta.repository.Diff` Commit diff. Raises ------ RuntimeError If this Commit or `reference` has not yet been saved, or if they do not belong to the same Repository. """ if self.id is None: raise RuntimeError("Commit must be saved before a diff can be calculated") if reference is None: reference_id = self._parent_ids[0] elif not isinstance(reference, Commit) or reference.id is None: raise TypeError("`reference` must be a saved Commit") elif self._repo.id != reference._repo.id: raise ValueError("Commit and `reference` must belong to the same Repository") else: reference_id = reference.id endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/diff?commit_a={}&commit_b={}".format( self._conn.scheme, self._conn.socket, self._repo.id, reference_id, self.id, ) response = _utils.make_request("GET", endpoint, self._conn) _utils.raise_for_http_error(response) response_msg = _utils.json_to_proto(_utils.body_to_json(response), _VersioningService.ComputeRepositoryDiffRequest.Response) return _diff.Diff(response_msg.diffs) def apply_diff(self, diff, message, other_parents=[]): """ Applies a diff to this Commit. This method creates a new Commit in ModelDB, and assigns a new ID to this object. Parameters ---------- diff : :class:`~verta._repository.diff.Diff` Commit diff. message : str Description of the diff. Raises ------ RuntimeError If this Commit has not yet been saved. """ if self.id is None: raise RuntimeError("Commit must be saved before a diff can be applied") msg = _VersioningService.CreateCommitRequest() msg.repository_id.repo_id = self._repo.id msg.commit.parent_shas.append(self.id) msg.commit.parent_shas.extend(other_parents) msg.commit.message = message msg.commit_base = self.id msg.diffs.extend(diff._diffs) self._save(msg) def get_revert_diff(self): return self.parent.diff_from(self) def revert(self, other=None, message=None): """ Reverts `other`. This method creates a new Commit in ModelDB, and assigns a new ID to this object. Parameters ---------- other : :class:`Commit`, optional Commit to be reverted. If not provided, this Commit will be reverted. message : str, optional Description of the revert. If not provided, a default message will be used. Raises ------ RuntimeError If this Commit or `other` has not yet been saved, or if they do not belong to the same Repository. """ if self.id is None: raise RuntimeError("Commit must be saved before a revert can be performed") if other is None: other = self elif not isinstance(other, Commit) or other.id is None: raise TypeError("`other` must be a saved Commit") elif self._repo.id != other._repo.id: raise ValueError("Commit and `other` must belong to the same Repository") msg = _VersioningService.RevertRepositoryCommitsRequest() msg.base_commit_sha = self.id msg.commit_to_revert_sha = other.id if message is not None: msg.content.message = message data = _utils.proto_to_json(msg) endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/commits/{}/revert".format( self._conn.scheme, self._conn.socket, self._repo.id, msg.commit_to_revert_sha, ) response = _utils.make_request("POST", endpoint, self._conn, json=data) _utils.raise_for_http_error(response) response_msg = _utils.json_to_proto(_utils.body_to_json(response), msg.Response) self._become_saved_child(response_msg.commit.commit_sha) def _to_heap_element(self): date_created = int(self._commit_json['date_created']) # protobuf uint64 is str, so cast to int # Most recent has higher priority return (-date_created, self.id, self) def get_common_parent(self, other): if self.id is None: raise RuntimeError("Commit must be saved before a common parent can be calculated") if not isinstance(other, Commit) or other.id is None: raise TypeError("`other` must be a saved Commit") elif self._repo.id != other._repo.id: raise ValueError("Commit and `other` must belong to the same Repository") # Keep a set of all parents we see for each side. This doesn't have to be *all* but facilitates implementation left_ids = set([self.id]) right_ids = set([other.id]) # Keep a heap of all candidate commits to be the common parent, ordered by the date so that we fetch the most recent first heap = [] heapq.heappush(heap, self._to_heap_element()) heapq.heappush(heap, other._to_heap_element()) while heap: # Get the most recent commit _, _, commit = heapq.heappop(heap) # If it's in the list for both sides, then it's a parent of both and return if commit.id in left_ids and commit.id in right_ids: return commit # Update the heap with all the current parents parent_ids = commit._parent_ids for parent_id in parent_ids: parent_commit = self._repo.get_commit(id=parent_id) heap_element = parent_commit._to_heap_element() try: heapq.heappush(heap, heap_element) except TypeError: # already in heap, because comparison between Commits failed pass # Update the parent sets based on which side the commit came from # We know the commit came from the left if its ID is in the left set. If it was on the right too, then it would be the parent and we would have returned early if commit.id in left_ids: left_ids.update(parent_ids) if commit.id in right_ids: right_ids.update(parent_ids) # Should never happen, since we have the initial commit return None def merge(self, other, message=None): """ Merges a branch headed by `other` into this Commit. This method creates a new Commit in ModelDB, and assigns a new ID to this object. Parameters ---------- other : :class:`Commit` Commit to be merged. message : str, optional Description of the merge. If not provided, a default message will be used. Raises ------ RuntimeError If this Commit or `other` has not yet been saved, or if they do not belong to the same Repository. """ if self.id is None: raise RuntimeError("Commit must be saved before a merge can be performed") if not isinstance(other, Commit) or other.id is None: raise TypeError("`other` must be a saved Commit") elif self._repo.id != other._repo.id: raise ValueError("Commit and `other` must belong to the same Repository") msg = _VersioningService.MergeRepositoryCommitsRequest() if self.branch_name is not None: msg.branch_b = self.branch_name else: msg.commit_sha_b = self.id if other.branch_name is not None: msg.branch_a = other.branch_name else: msg.commit_sha_a = other.id if message is not None: msg.content.message = message data = _utils.proto_to_json(msg) endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/merge".format( self._conn.scheme, self._conn.socket, self._repo.id, ) response = _utils.make_request("POST", endpoint, self._conn, json=data) _utils.raise_for_http_error(response) response_msg = _utils.json_to_proto(_utils.body_to_json(response), msg.Response) # raise for conflict if response_msg.conflicts: raise RuntimeError('\n '.join([ "merge conflict", "resolution is not currently supported through the Client", "please create a new Commit with the updated blobs", "see https://docs.verta.ai/verta/experiment-management/guides/merge for instructions", ])) self._become_saved_child(response_msg.commit.commit_sha) def blob_msg_to_object(blob_msg): # TODO: make this more concise content_type = blob_msg.WhichOneof('content') content_subtype = None blob_cls = None if content_type == 'code': content_subtype = blob_msg.code.WhichOneof('content') if content_subtype == 'git': blob_cls = code.Git elif content_subtype == 'notebook': blob_cls = code.Notebook elif content_type == 'config': blob_cls = configuration.Hyperparameters elif content_type == 'dataset': content_subtype = blob_msg.dataset.WhichOneof('content') if content_subtype == 's3': blob_cls = dataset.S3 elif content_subtype == 'path': blob_cls = dataset.Path elif content_type == 'environment': content_subtype = blob_msg.environment.WhichOneof('content') if content_subtype == 'python': blob_cls = environment.Python elif content_subtype == 'docker': raise NotImplementedError if blob_cls is None: if content_subtype is None: raise NotImplementedError("found unexpected content type {};" " please notify the Verta development team".format(content_type)) else: raise NotImplementedError("found unexpected {} type {};" " please notify the Verta development team".format(content_type, content_subtype)) return blob_cls._from_proto(blob_msg) def path_to_location(path): """Messages take a `repeated string` of path components.""" if path.startswith('/'): # `path` is already meant to be relative to repo root path = path[1:] return path.split('/') def location_to_path(location): return '/'.join(location) def set_branch(conn, repo_id, commit_id, branch): """Sets `branch` to Commit `commit_id`.""" data = commit_id endpoint = "{}://{}/api/v1/modeldb/versioning/repositories/{}/branches/{}".format( conn.scheme, conn.socket, repo_id, branch, ) response = _utils.make_request("PUT", endpoint, conn, json=data) _utils.raise_for_http_error(response)

35.995704

170

0.597577

ee3c7021cce70e4c1828a60d3c720bbe5f81820d

2,609

py

Python

var/spack/repos/builtin/packages/c-blosc2/package.py

padamson/spack

d3f67a48552691b4846ccc4a10f76740b154090c

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

2

2021-03-05T10:54:32.000Z

2021-03-05T14:14:52.000Z

var/spack/repos/builtin/packages/c-blosc2/package.py

padamson/spack

d3f67a48552691b4846ccc4a10f76740b154090c

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

16

2021-05-12T06:03:24.000Z

2022-03-16T15:26:46.000Z

var/spack/repos/builtin/packages/c-blosc2/package.py

padamson/spack

d3f67a48552691b4846ccc4a10f76740b154090c

[ "ECL-2.0", "Apache-2.0", "MIT-0", "MIT" ]

null

# Copyright 2013-2021 Lawrence Livermore National Security, LLC and other # Spack Project Developers. See the top-level COPYRIGHT file for details. # # SPDX-License-Identifier: (Apache-2.0 OR MIT) from spack import * class CBlosc2(CMakePackage): """Next generation c-blosc with a new API, a new container and other bells and whistles""" homepage = "http://www.blosc.org" url = "https://github.com/Blosc/c-blosc2/archive/refs/tags/v2.0.1.tar.gz" git = "https://github.com/Blosc/c-blosc2.git" maintainers = ['ax3l', 'robert-mijakovic'] version('develop', branch='master') version('2.0.2', sha256='fba51ba601610441eea6046e384284b2d8d7884922060cf15369d01d713b9b77') version('2.0.1', sha256='35b93dfed479b1dfd9372d41d7843b60254ed1d71792577b95e489c28705874f') variant('avx2', default=True, description='Enable AVX2 support') variant('lizard', default=True, description='support for LIZARD (LZ5)') variant('lz4', default=True, description='support for LZ4') variant('snappy', default=True, description='support for SNAPPY') variant('zlib', default=True, description='support for ZLIB') variant('zstd', default=True, description='support for ZSTD') depends_on('cmake@2.8.10:', type='build') depends_on('lizard', when='+lizard') depends_on('lz4', when='+lz4') depends_on('snappy', when='+snappy') depends_on('zlib', when='+zlib') depends_on('zstd', when='+zstd') def cmake_args(self): spec = self.spec args = [ '-DDEACTIVATE_LZ4={0}'.format( 'ON' if '~lz4' in spec else 'OFF'), '-DDEACTIVATE_LIZARD={0}'.format( 'ON' if '~lizard' in spec else 'OFF'), '-DDEACTIVATE_SNAPPY={0}'.format( 'ON' if '~snappy' in spec else 'OFF'), '-DDEACTIVATE_ZLIB={0}'.format( 'ON' if '~zlib' in spec else 'OFF'), '-DDEACTIVATE_ZSTD={0}'.format( 'ON' if '~zstd' in spec else 'OFF'), '-DPREFER_EXTERNAL_LIZARD=ON', '-DPREFER_EXTERNAL_LZ4=ON', # snappy is supported via external install only '-DPREFER_EXTERNAL_ZLIB=ON', '-DPREFER_EXTERNAL_ZSTD=ON', '-DDEACTIVATE_AVX2={0}'.format( 'ON' if '~avx2' in spec else 'OFF'), self.define('BUILD_TESTS', self.run_tests), self.define('BUILD_BENCHMARKS', self.run_tests), self.define('BUILD_EXAMPLES', self.run_tests) ] return args

37.271429

95

0.604829

0060798351f2adc51d90410368cc8b3ab076963b

1,036

py

Python

problem_042.py

arboreus/project_euler

796173e8e72fcbfc15bbb3def7a36349639fafcf

[ "MIT" ]

null

problem_042.py

arboreus/project_euler

796173e8e72fcbfc15bbb3def7a36349639fafcf

[ "MIT" ]

null

problem_042.py

arboreus/project_euler

796173e8e72fcbfc15bbb3def7a36349639fafcf

[ "MIT" ]

null

#42) Coded triangle numbers #The nth term of the sequence of triangle numbers is given by, tn = (1/2)*n*(n+1); so the first ten triangle numbers are: #1, 3, 6, 10, 15, 21, 28, 36, 45, 55, ... #By converting each letter in a word to a number corresponding to its alphabetical position and adding these values we form a word value. For example, the word value for SKY is 19 + 11 + 25 = 55 = t10. If the word value is a triangle number then we shall call the word a triangle word. #Using words.txt (right click and 'Save Link/Target As...'), a 16K text file containing nearly two-thousand common English words, how many are triangle words? #%% Solution def triangle_nums(x): n = 1 while int(1/2*n*(n+1)) <= x: yield int(1/2*n*(n+1)) n += 1 with open("p042_words.txt", mode='r') as doc: list_words = doc.read().replace('"', '').split(',') list_values = [sum([ord(x)-64 for x in word]) for word in list_words] list_triangle = [x for x in list_values if x in triangle_nums(max(list_values))] len(list_triangle)

51.8

285

0.686293

d5dc6e847822a644e2e049d7625a0c74a209cb56

45,530

py

Python

python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py

littletomatodonkey/Paddle

3ec289a6a33c5392b914cc256736dcb00b2cecce

[ "Apache-2.0" ]

null

python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py

littletomatodonkey/Paddle

3ec289a6a33c5392b914cc256736dcb00b2cecce

[ "Apache-2.0" ]

null

python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py

littletomatodonkey/Paddle

3ec289a6a33c5392b914cc256736dcb00b2cecce

[ "Apache-2.0" ]

null

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import print_function import unittest import paddle.fluid as fluid from paddle.fluid import Embedding, LayerNorm, Linear, Layer from paddle.fluid.dygraph import to_variable, guard from paddle.fluid.dygraph import TracedLayer from test_imperative_base import new_program_scope from paddle.fluid import core import numpy as np import six np.set_printoptions(suppress=True) from utils import DyGraphProgramDescTracerTestHelper, is_equal_program # Copy from models class TrainTaskConfig(object): # support both CPU and GPU now. use_gpu = True # the epoch number to train. pass_num = 30 # the number of sequences contained in a mini-batch. # deprecated, set batch_size in args. batch_size = 32 # the hyper parameters for Adam optimizer. # This static learning_rate will be multiplied to the LearningRateScheduler # derived learning rate the to get the final learning rate. learning_rate = 2.0 beta1 = 0.9 beta2 = 0.997 eps = 1e-9 # the parameters for learning rate scheduling. warmup_steps = 8000 # the weight used to mix up the ground-truth distribution and the fixed # uniform distribution in label smoothing when training. # Set this as zero if label smoothing is not wanted. label_smooth_eps = 0.1 # the directory for saving trained models. model_dir = "trained_models" # the directory for saving checkpoints. ckpt_dir = "trained_ckpts" # the directory for loading checkpoint. # If provided, continue training from the checkpoint. ckpt_path = None # the parameter to initialize the learning rate scheduler. # It should be provided if use checkpoints, since the checkpoint doesn't # include the training step counter currently. start_step = 0 # the frequency to save trained models. save_freq = 10000 class InferTaskConfig(object): use_gpu = True # the number of examples in one run for sequence generation. batch_size = 10 # the parameters for beam search. beam_size = 5 max_out_len = 256 # the number of decoded sentences to output. n_best = 1 # the flags indicating whether to output the special tokens. output_bos = False output_eos = False output_unk = True # the directory for loading the trained model. model_path = "trained_models/pass_1.infer.model" class ModelHyperParams(object): # These following five vocabularies related configurations will be set # automatically according to the passed vocabulary path and special tokens. # size of source word dictionary. src_vocab_size = 10000 # size of target word dictionay trg_vocab_size = 10000 # index for <bos> token bos_idx = 0 # index for <eos> token eos_idx = 1 # index for <unk> token unk_idx = 2 # max length of sequences deciding the size of position encoding table. max_length = 4 # the dimension for word embeddings, which is also the last dimension of # the input and output of multi-head attention, position-wise feed-forward # networks, encoder and decoder. d_model = 512 # size of the hidden layer in position-wise feed-forward networks. d_inner_hid = 2048 # the dimension that keys are projected to for dot-product attention. d_key = 64 # the dimension that values are projected to for dot-product attention. d_value = 64 # number of head used in multi-head attention. n_head = 8 # number of sub-layers to be stacked in the encoder and decoder. n_layer = 6 # dropout rates of different modules. prepostprocess_dropout = 0.1 attention_dropout = 0.1 relu_dropout = 0.1 # to process before each sub-layer preprocess_cmd = "n" # layer normalization # to process after each sub-layer postprocess_cmd = "da" # dropout + residual connection # random seed used in dropout for CE. dropout_seed = None # the flag indicating whether to share embedding and softmax weights. # vocabularies in source and target should be same for weight sharing. weight_sharing = True def merge_cfg_from_list(cfg_list, g_cfgs): """ Set the above global configurations using the cfg_list. """ assert len(cfg_list) % 2 == 0 for key, value in zip(cfg_list[0::2], cfg_list[1::2]): for g_cfg in g_cfgs: if hasattr(g_cfg, key): try: value = eval(value) except Exception: # for file path pass setattr(g_cfg, key, value) break def position_encoding_init(n_position, d_pos_vec): """ Generate the initial values for the sinusoid position encoding table. """ channels = d_pos_vec position = np.arange(n_position) num_timescales = channels // 2 log_timescale_increment = (np.log(float(1e4) / float(1)) / (num_timescales - 1)) inv_timescales = np.exp(np.arange( num_timescales)) * -log_timescale_increment scaled_time = np.expand_dims(position, 1) * np.expand_dims(inv_timescales, 0) signal = np.concatenate([np.sin(scaled_time), np.cos(scaled_time)], axis=1) signal = np.pad(signal, [[0, 0], [0, np.mod(channels, 2)]], 'constant') position_enc = signal return position_enc.astype("float32") def create_data(is_static=False): if is_static: return [ src_word_np, src_pos_np, src_slf_attn_bias_np, trg_word_np, trg_pos_np, trg_slf_attn_bias_np, trg_src_attn_bias_np, lbl_word_np, lbl_weight_np ] else: enc_inputs = [ to_variable( src_word_np, name='src_word'), to_variable( src_pos_np, name='src_pos'), to_variable( src_slf_attn_bias_np, name='src_slf_attn_bias') ] dec_inputs = [ to_variable( trg_word_np, name='trg_word'), to_variable( trg_pos_np, name='trg_pos'), to_variable( trg_slf_attn_bias_np, name='trg_slf_attn_bias'), to_variable( trg_src_attn_bias_np, name='trg_src_attn_bias') ] label = to_variable(lbl_word_np, name='lbl_word') weight = to_variable(lbl_weight_np, name='lbl_weight') return enc_inputs, dec_inputs, label, weight def create_feed_dict_list(data, init=False): if init: data_input_names = encoder_data_input_fields + \ decoder_data_input_fields[:-1] + label_data_input_fields + pos_enc_param_names else: data_input_names = encoder_data_input_fields + \ decoder_data_input_fields[:-1] + label_data_input_fields feed_dict_list = dict() for i in range(len(data_input_names)): feed_dict_list[data_input_names[i]] = data[i] return feed_dict_list def make_all_inputs(input_fields): """ Define the input data layers for the transformer model. """ inputs = [] for input_field in input_fields: input_var = fluid.layers.data( name=input_field, shape=input_descs[input_field][0], dtype=input_descs[input_field][1], lod_level=input_descs[input_field][2] if len(input_descs[input_field]) == 3 else 0, append_batch_size=False) inputs.append(input_var) return inputs # The placeholder for batch_size in compile time. Must be -1 currently to be # consistent with some ops' infer-shape output in compile time, such as the # sequence_expand op used in beamsearch decoder. batch_size = -1 # The placeholder for squence length in compile time. seq_len = ModelHyperParams.max_length # Here list the data shapes and data types of all inputs. # The shapes here act as placeholder and are set to pass the infer-shape in # compile time. input_descs = { # The actual data shape of src_word is: # [batch_size, max_src_len_in_batch] "src_word": [(batch_size, seq_len), "int64", 2], # The actual data shape of src_pos is: # [batch_size, max_src_len_in_batch] "src_pos": [(batch_size, seq_len), "int64"], # This input is used to remove attention weights on paddings in the # encoder. # The actual data shape of src_slf_attn_bias is: # [batch_size, n_head, max_src_len_in_batch, max_src_len_in_batch] "src_slf_attn_bias": [(batch_size, ModelHyperParams.n_head, seq_len, seq_len), "float32"], # The actual data shape of trg_word is: # [batch_size, max_trg_len_in_batch] "trg_word": [(batch_size, seq_len), "int64", 2], # lod_level is only used in fast decoder. # The actual data shape of trg_pos is: # [batch_size, max_trg_len_in_batch] "trg_pos": [(batch_size, seq_len), "int64"], # This input is used to remove attention weights on paddings and # subsequent words in the decoder. # The actual data shape of trg_slf_attn_bias is: # [batch_size, n_head, max_trg_len_in_batch, max_trg_len_in_batch] "trg_slf_attn_bias": [(batch_size, ModelHyperParams.n_head, seq_len, seq_len), "float32"], # This input is used to remove attention weights on paddings of the source # input in the encoder-decoder attention. # The actual data shape of trg_src_attn_bias is: # [batch_size, n_head, max_trg_len_in_batch, max_src_len_in_batch] "trg_src_attn_bias": [(batch_size, ModelHyperParams.n_head, seq_len, seq_len), "float32"], # This input is used in independent decoder program for inference. # The actual data shape of enc_output is: # [batch_size, max_src_len_in_batch, d_model] "enc_output": [(batch_size, seq_len, ModelHyperParams.d_model), "float32"], # The actual data shape of label_word is: # [batch_size * max_trg_len_in_batch, 1] "lbl_word": [(batch_size * seq_len, 1), "int64"], # This input is used to mask out the loss of paddding tokens. # The actual data shape of label_weight is: # [batch_size * max_trg_len_in_batch, 1] "lbl_weight": [(batch_size * seq_len, 1), "float32"], # This input is used in beam-search decoder. "init_score": [(batch_size, 1), "float32", 2], # This input is used in beam-search decoder for the first gather # (cell states updation) "init_idx": [(batch_size, ), "int32"], } # Names of word embedding table which might be reused for weight sharing. word_emb_param_names = ( "src_word_emb_table", "trg_word_emb_table", ) # Names of position encoding table which will be initialized externally. pos_enc_param_names = ( "src_pos_enc_table", "trg_pos_enc_table", ) # separated inputs for different usages. encoder_data_input_fields = ( "src_word", "src_pos", "src_slf_attn_bias", ) decoder_data_input_fields = ( "trg_word", "trg_pos", "trg_slf_attn_bias", "trg_src_attn_bias", "enc_output", ) label_data_input_fields = ( "lbl_word", "lbl_weight", ) # In fast decoder, trg_pos (only containing the current time step) is generated # by ops and trg_slf_attn_bias is not needed. fast_decoder_data_input_fields = ( "trg_word", "init_score", "init_idx", "trg_src_attn_bias", ) # if we use py_reader use_py_reader = False # if we run sync mode sync = False # how many batches we use batch_num = 5 np.random.seed = 90 src_word_np = np.arange(1, TrainTaskConfig.batch_size * seq_len + 1).reshape( [TrainTaskConfig.batch_size, seq_len]).astype('int64') src_pos_np = np.random.randint( 1, seq_len, size=(TrainTaskConfig.batch_size, seq_len), dtype='int64') src_slf_attn_bias_np = np.random.randn(TrainTaskConfig.batch_size, ModelHyperParams.n_head, seq_len, seq_len).astype('float32') trg_word_np = np.arange(1, TrainTaskConfig.batch_size * seq_len + 1).reshape( [TrainTaskConfig.batch_size, seq_len]).astype('int64') trg_pos_np = np.random.randint( 1, seq_len, size=(TrainTaskConfig.batch_size, seq_len), dtype='int64') trg_slf_attn_bias_np = np.random.randn(TrainTaskConfig.batch_size, ModelHyperParams.n_head, seq_len, seq_len).astype('float32') trg_src_attn_bias_np = np.random.randn(TrainTaskConfig.batch_size, ModelHyperParams.n_head, seq_len, seq_len).astype('float32') lbl_word_np = np.random.randint( 1, ModelHyperParams.src_vocab_size - 1, size=(TrainTaskConfig.batch_size * seq_len, 1), dtype='int64') lbl_weight_np = np.random.randn(TrainTaskConfig.batch_size * seq_len, 1).astype('float32') pos_inp1 = position_encoding_init(ModelHyperParams.max_length, ModelHyperParams.d_model) pos_inp2 = position_encoding_init(ModelHyperParams.max_length, ModelHyperParams.d_model) class PrePostProcessLayer(Layer): def __init__(self, d_model, process_cmd, shape_len=None): super(PrePostProcessLayer, self).__init__() for cmd in process_cmd: if cmd == "n": self._layer_norm = LayerNorm( normalized_shape=d_model, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(1.)), bias_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(0.))) def forward(self, prev_out, out, process_cmd, dropout_rate=0.): for cmd in process_cmd: if cmd == "a": # add residual connection out = out + prev_out if prev_out else out elif cmd == "n": # add layer normalization out = self._layer_norm(out) elif cmd == "d": # add dropout if dropout_rate: out = fluid.layers.dropout( out, dropout_prob=dropout_rate, seed=ModelHyperParams.dropout_seed, is_test=False) return out class PositionwiseFeedForwardLayer(Layer): def __init__(self, d_inner_hid, d_hid, dropout_rate): super(PositionwiseFeedForwardLayer, self).__init__() self._i2h = Linear(d_hid, d_inner_hid, act="relu") self._h2o = Linear(d_inner_hid, d_hid) self._dropout_rate = dropout_rate def forward(self, x): hidden = self._i2h(x) if self._dropout_rate: hidden = fluid.layers.dropout( hidden, dropout_prob=self._dropout_rate, seed=ModelHyperParams.dropout_seed, is_test=False) out = self._h2o(hidden) return out class MultiHeadAttentionLayer(Layer): def __init__(self, d_key, d_value, d_model, n_head=1, dropout_rate=0., cache=None, gather_idx=None, static_kv=False): super(MultiHeadAttentionLayer, self).__init__() self._n_head = n_head self._d_key = d_key self._d_value = d_value self._d_model = d_model self._dropout_rate = dropout_rate self._q_fc = Linear(self._d_model, d_key * n_head, bias_attr=False) self._k_fc = Linear(self._d_model, d_key * n_head, bias_attr=False) self._v_fc = Linear(self._d_model, d_value * n_head, bias_attr=False) self._proj_fc = Linear(d_value * n_head, self._d_model, bias_attr=False) def forward(self, queries, keys, values, attn_bias): # compute q ,k ,v keys = queries if keys is None else keys values = keys if values is None else values q = self._q_fc(queries) k = self._k_fc(keys) v = self._v_fc(values) # split head reshaped_q = fluid.layers.reshape( x=q, shape=[0, 0, self._n_head, self._d_key], inplace=False) transpose_q = fluid.layers.transpose(x=reshaped_q, perm=[0, 2, 1, 3]) reshaped_k = fluid.layers.reshape( x=k, shape=[0, 0, self._n_head, self._d_key], inplace=False) transpose_k = fluid.layers.transpose(x=reshaped_k, perm=[0, 2, 1, 3]) reshaped_v = fluid.layers.reshape( x=v, shape=[0, 0, self._n_head, self._d_value], inplace=False) transpose_v = fluid.layers.transpose(x=reshaped_v, perm=[0, 2, 1, 3]) # scale dot product attention product = fluid.layers.matmul( x=transpose_q, y=transpose_k, transpose_y=True, alpha=self._d_model**-0.5) if attn_bias: product += attn_bias weights = fluid.layers.softmax(product) if self._dropout_rate: weights_droped = fluid.layers.dropout( weights, dropout_prob=self._dropout_rate, seed=ModelHyperParams.dropout_seed, is_test=False) out = fluid.layers.matmul(weights_droped, transpose_v) else: out = fluid.layers.matmul(weights, transpose_v) # combine heads if len(out.shape) != 4: raise ValueError("Input(x) should be a 4-D Tensor.") trans_x = fluid.layers.transpose(out, perm=[0, 2, 1, 3]) final_out = fluid.layers.reshape( x=trans_x, shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]], inplace=False) # fc to output proj_out = self._proj_fc(final_out) return proj_out class EncoderSubLayer(Layer): def __init__(self, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd="n", postprocess_cmd="da"): super(EncoderSubLayer, self).__init__() self._preprocess_cmd = preprocess_cmd self._postprocess_cmd = postprocess_cmd self._prepostprocess_dropout = prepostprocess_dropout self._preprocess_layer = PrePostProcessLayer(d_model, self._preprocess_cmd, 3) self._multihead_attention_layer = MultiHeadAttentionLayer( d_key, d_value, d_model, n_head, attention_dropout) self._postprocess_layer = PrePostProcessLayer( d_model, self._postprocess_cmd, None) self._preprocess_layer2 = PrePostProcessLayer(d_model, self._preprocess_cmd, 3) self._positionwise_feed_forward = PositionwiseFeedForwardLayer( d_inner_hid, d_model, relu_dropout) self._postprocess_layer2 = PrePostProcessLayer( d_model, self._postprocess_cmd, None) def forward(self, enc_input, attn_bias): pre_process_multihead = self._preprocess_layer( None, enc_input, self._preprocess_cmd, self._prepostprocess_dropout) attn_output = self._multihead_attention_layer(pre_process_multihead, None, None, attn_bias) attn_output = self._postprocess_layer(enc_input, attn_output, self._postprocess_cmd, self._prepostprocess_dropout) pre_process2_output = self._preprocess_layer2( None, attn_output, self._preprocess_cmd, self._prepostprocess_dropout) ffd_output = self._positionwise_feed_forward(pre_process2_output) return self._postprocess_layer2(attn_output, ffd_output, self._postprocess_cmd, self._prepostprocess_dropout) class EncoderLayer(Layer): def __init__(self, n_layer, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd="n", postprocess_cmd="da"): super(EncoderLayer, self).__init__() self._preprocess_cmd = preprocess_cmd self._encoder_sublayers = list() self._prepostprocess_dropout = prepostprocess_dropout self._n_layer = n_layer self._preprocess_layer = PrePostProcessLayer(d_model, self._preprocess_cmd, 3) for i in range(n_layer): self._encoder_sublayers.append( self.add_sublayer( 'esl_%d' % i, EncoderSubLayer(n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd))) def forward(self, enc_input, attn_bias): for i in range(self._n_layer): enc_output = self._encoder_sublayers[i](enc_input, attn_bias) enc_input = enc_output return self._preprocess_layer(None, enc_output, self._preprocess_cmd, self._prepostprocess_dropout) class PrepareEncoderDecoderLayer(Layer): def __init__(self, src_vocab_size, src_emb_dim, src_max_len, dropout_rate, is_sparse=False, word_emb_param_name=None, pos_enc_param_name=None): super(PrepareEncoderDecoderLayer, self).__init__() self._src_max_len = src_max_len self._src_emb_dim = src_emb_dim self._src_vocab_size = src_vocab_size self._dropout_rate = dropout_rate self._input_emb = Embedding( size=[src_vocab_size, src_emb_dim], is_sparse=is_sparse, padding_idx=0, param_attr=fluid.ParamAttr( name=word_emb_param_name, initializer=fluid.initializer.Normal(0., src_emb_dim**-0.5))) if pos_enc_param_name is pos_enc_param_names[0]: pos_inp = pos_inp1 else: pos_inp = pos_inp2 self._pos_emb = Embedding( size=[self._src_max_len, src_emb_dim], is_sparse=is_sparse, param_attr=fluid.ParamAttr( name=pos_enc_param_name, initializer=fluid.initializer.NumpyArrayInitializer(pos_inp), trainable=False)) # use in dygraph_mode to fit different length batch # self._pos_emb._w = to_variable( # position_encoding_init(self._src_max_len, self._src_emb_dim)) def forward(self, src_word, src_pos): src_word_emb = self._input_emb(src_word) src_word_emb = fluid.layers.scale( x=src_word_emb, scale=self._src_emb_dim**0.5) # # TODO change this to fit dynamic length input src_pos_emb = self._pos_emb(src_pos) src_pos_emb.stop_gradient = True enc_input = src_word_emb + src_pos_emb return fluid.layers.dropout( enc_input, dropout_prob=self._dropout_rate, seed=ModelHyperParams.dropout_seed, is_test=False) if self._dropout_rate else enc_input class WrapEncoderLayer(Layer): def __init__(self, src_vocab_size, max_length, n_layer, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, weight_sharing, is_sparse=False): """ The wrapper assembles together all needed layers for the encoder. """ super(WrapEncoderLayer, self).__init__() self._prepare_encoder_layer = PrepareEncoderDecoderLayer( src_vocab_size, d_model, max_length, prepostprocess_dropout, is_sparse=is_sparse, word_emb_param_name=word_emb_param_names[0], pos_enc_param_name=pos_enc_param_names[0]) self._encoder = EncoderLayer(n_layer, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd) def forward(self, enc_inputs): src_word, src_pos, src_slf_attn_bias = enc_inputs enc_input = self._prepare_encoder_layer(src_word, src_pos) enc_output = self._encoder(enc_input, src_slf_attn_bias) return enc_output class DecoderSubLayer(Layer): def __init__(self, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, cache=None, gather_idx=None): super(DecoderSubLayer, self).__init__() self._postprocess_cmd = postprocess_cmd self._preprocess_cmd = preprocess_cmd self._prepostprcess_dropout = prepostprocess_dropout self._pre_process_layer = PrePostProcessLayer(d_model, preprocess_cmd, 3) self._multihead_attention_layer = MultiHeadAttentionLayer( d_key, d_value, d_model, n_head, attention_dropout, cache=cache, gather_idx=gather_idx) self._post_process_layer = PrePostProcessLayer(d_model, postprocess_cmd, None) self._pre_process_layer2 = PrePostProcessLayer(d_model, preprocess_cmd, 3) self._multihead_attention_layer2 = MultiHeadAttentionLayer( d_key, d_value, d_model, n_head, attention_dropout, cache=cache, gather_idx=gather_idx, static_kv=True) self._post_process_layer2 = PrePostProcessLayer(d_model, postprocess_cmd, None) self._pre_process_layer3 = PrePostProcessLayer(d_model, preprocess_cmd, 3) self._positionwise_feed_forward_layer = PositionwiseFeedForwardLayer( d_inner_hid, d_model, relu_dropout) self._post_process_layer3 = PrePostProcessLayer(d_model, postprocess_cmd, None) def forward(self, dec_input, enc_output, slf_attn_bias, dec_enc_attn_bias): pre_process_rlt = self._pre_process_layer( None, dec_input, self._preprocess_cmd, self._prepostprcess_dropout) slf_attn_output = self._multihead_attention_layer(pre_process_rlt, None, None, slf_attn_bias) slf_attn_output_pp = self._post_process_layer( dec_input, slf_attn_output, self._postprocess_cmd, self._prepostprcess_dropout) pre_process_rlt2 = self._pre_process_layer2(None, slf_attn_output_pp, self._preprocess_cmd, self._prepostprcess_dropout) enc_attn_output_pp = self._multihead_attention_layer2( pre_process_rlt2, enc_output, enc_output, dec_enc_attn_bias) enc_attn_output = self._post_process_layer2( slf_attn_output_pp, enc_attn_output_pp, self._postprocess_cmd, self._prepostprcess_dropout) pre_process_rlt3 = self._pre_process_layer3(None, enc_attn_output, self._preprocess_cmd, self._prepostprcess_dropout) ffd_output = self._positionwise_feed_forward_layer(pre_process_rlt3) dec_output = self._post_process_layer3(enc_attn_output, ffd_output, self._postprocess_cmd, self._prepostprcess_dropout) return dec_output class DecoderLayer(Layer): def __init__(self, n_layer, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, caches=None, gather_idx=None): super(DecoderLayer, self).__init__() self._pre_process_layer = PrePostProcessLayer(d_model, preprocess_cmd, 3) self._decoder_sub_layers = list() self._n_layer = n_layer self._preprocess_cmd = preprocess_cmd self._prepostprocess_dropout = prepostprocess_dropout for i in range(n_layer): self._decoder_sub_layers.append( self.add_sublayer( 'dsl_%d' % i, DecoderSubLayer( n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, cache=None if caches is None else caches[i], gather_idx=gather_idx))) def forward(self, dec_input, enc_output, dec_slf_attn_bias, dec_enc_attn_bias): for i in range(self._n_layer): tmp_dec_output = self._decoder_sub_layers[i]( dec_input, enc_output, dec_slf_attn_bias, dec_enc_attn_bias) dec_input = tmp_dec_output dec_output = self._pre_process_layer(None, tmp_dec_output, self._preprocess_cmd, self._prepostprocess_dropout) return dec_output class WrapDecoderLayer(Layer): def __init__(self, trg_vocab_size, max_length, n_layer, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, weight_sharing, caches=None, gather_idx=None, is_sparse=False): """ The wrapper assembles together all needed layers for the encoder. """ super(WrapDecoderLayer, self).__init__() self._prepare_decoder_layer = PrepareEncoderDecoderLayer( trg_vocab_size, d_model, max_length, prepostprocess_dropout, is_sparse=is_sparse, word_emb_param_name=word_emb_param_names[1], pos_enc_param_name=pos_enc_param_names[1]) self._decoder_layer = DecoderLayer( n_layer, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, caches=caches, gather_idx=gather_idx) self._weight_sharing = weight_sharing if not weight_sharing: self._fc = Linear(d_model, trg_vocab_size, bias_attr=False) def forward(self, dec_inputs=None, enc_output=None): trg_word, trg_pos, trg_slf_attn_bias, trg_src_attn_bias = dec_inputs dec_input = self._prepare_decoder_layer(trg_word, trg_pos) dec_output = self._decoder_layer(dec_input, enc_output, trg_slf_attn_bias, trg_src_attn_bias) dec_output_reshape = fluid.layers.reshape( dec_output, shape=[-1, dec_output.shape[-1]], inplace=False) if self._weight_sharing: predict = fluid.layers.matmul( x=dec_output_reshape, y=self._prepare_decoder_layer._input_emb._w, transpose_y=True) else: predict = self._fc(dec_output_reshape) if dec_inputs is None: # Return probs for independent decoder program. predict_out = fluid.layers.softmax(predict) return predict_out return predict class TransFormer(Layer): def __init__(self, src_vocab_size, trg_vocab_size, max_length, n_layer, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, weight_sharing, label_smooth_eps, use_py_reader=False, is_test=False, is_sparse=False): super(TransFormer, self).__init__() self._label_smooth_eps = label_smooth_eps self._trg_vocab_size = trg_vocab_size if weight_sharing: assert src_vocab_size == trg_vocab_size, ( "Vocabularies in source and target should be same for weight sharing." ) self._wrap_encoder_layer = WrapEncoderLayer( src_vocab_size, max_length, n_layer, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, weight_sharing, is_sparse=is_sparse) self._wrap_decoder_layer = WrapDecoderLayer( trg_vocab_size, max_length, n_layer, n_head, d_key, d_value, d_model, d_inner_hid, prepostprocess_dropout, attention_dropout, relu_dropout, preprocess_cmd, postprocess_cmd, weight_sharing, is_sparse=is_sparse) if weight_sharing: self._wrap_decoder_layer._prepare_decoder_layer._input_emb._w = self._wrap_encoder_layer._prepare_encoder_layer._input_emb._w def forward(self, enc_inputs, dec_inputs, label, weights): enc_output = self._wrap_encoder_layer(enc_inputs) predict = self._wrap_decoder_layer(dec_inputs, enc_output) if self._label_smooth_eps: label_out = fluid.layers.label_smooth( label=fluid.layers.one_hot( input=label, depth=self._trg_vocab_size), epsilon=self._label_smooth_eps) cost = fluid.layers.softmax_with_cross_entropy( logits=predict, label=label_out, soft_label=True if self._label_smooth_eps else False) weighted_cost = cost * weights sum_cost = fluid.layers.reduce_sum(weighted_cost) token_num = fluid.layers.reduce_sum(weights) token_num.stop_gradient = True avg_cost = sum_cost / token_num return sum_cost, avg_cost, predict, token_num class TestDygraphTransformerSortGradient(unittest.TestCase): def test_transformer_sort_gradient(self): for is_sparse in [True, False]: self.transformer_sort_gradient_float32(is_sparse) def transformer_sort_gradient_float32(self, is_sparse): seed = 90 with guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed backward_strategy = fluid.dygraph.BackwardStrategy() backward_strategy.sort_sum_gradient = True transformer = TransFormer( ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1, ModelHyperParams.n_layer, ModelHyperParams.n_head, ModelHyperParams.d_key, ModelHyperParams.d_value, ModelHyperParams.d_model, ModelHyperParams.d_inner_hid, ModelHyperParams.prepostprocess_dropout, ModelHyperParams.attention_dropout, ModelHyperParams.relu_dropout, ModelHyperParams.preprocess_cmd, ModelHyperParams.postprocess_cmd, ModelHyperParams.weight_sharing, TrainTaskConfig.label_smooth_eps, use_py_reader=use_py_reader, is_test=False, is_sparse=is_sparse) if sync: lr_decay = fluid.layers.learning_rate_scheduler.noam_decay( ModelHyperParams.d_model, TrainTaskConfig.warmup_steps) with fluid.default_main_program()._lr_schedule_guard(): learning_rate = lr_decay * TrainTaskConfig.learning_rate optimizer = fluid.optimizer.Adam( learning_rate=learning_rate, beta1=TrainTaskConfig.beta1, beta2=TrainTaskConfig.beta2, epsilon=TrainTaskConfig.eps, parameter_list=transformer.parameters()) else: optimizer = fluid.optimizer.SGD( learning_rate=0.003, parameter_list=transformer.parameters()) dy_param_init = dict() dy_param_updated = dict() helper = DyGraphProgramDescTracerTestHelper(self) program = None for i in range(batch_num): enc_inputs, dec_inputs, label, weights = create_data() if i % 2 == 0: outs, traced_layer = TracedLayer.trace( transformer, [enc_inputs, dec_inputs, label, weights]) ins_static = enc_inputs + dec_inputs + [label, weights] outs_static = traced_layer(ins_static) helper.assertEachVar(outs, outs_static) if program is not None: self.assertTrue( is_equal_program(program, traced_layer.program)) program = traced_layer.program traced_layer.save_inference_model( './infer_imperative_transformer', feed=range(len(ins_static)), fetch=range(len(outs_static))) else: outs = transformer(enc_inputs, dec_inputs, label, weights) dy_sum_cost, dy_avg_cost, dy_predict, dy_token_num = outs if i == 0: for param in transformer.parameters(): dy_param_init[param.name] = param.numpy() dy_avg_cost.backward(backward_strategy) optimizer.minimize(dy_avg_cost) transformer.clear_gradients() if i == batch_num - 1: for param in transformer.parameters(): dy_param_updated[param.name] = param.numpy() dy_avg_cost_value = dy_avg_cost.numpy() dy_sum_cost_value = dy_sum_cost.numpy() dy_predict_value = dy_predict.numpy() dy_token_num_value = dy_token_num.numpy() with new_program_scope(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed transformer = TransFormer( ModelHyperParams.src_vocab_size, ModelHyperParams.trg_vocab_size, ModelHyperParams.max_length + 1, ModelHyperParams.n_layer, ModelHyperParams.n_head, ModelHyperParams.d_key, ModelHyperParams.d_value, ModelHyperParams.d_model, ModelHyperParams.d_inner_hid, ModelHyperParams.prepostprocess_dropout, ModelHyperParams.attention_dropout, ModelHyperParams.relu_dropout, ModelHyperParams.preprocess_cmd, ModelHyperParams.postprocess_cmd, ModelHyperParams.weight_sharing, TrainTaskConfig.label_smooth_eps, use_py_reader=use_py_reader, is_test=False, is_sparse=is_sparse) exe = fluid.Executor(fluid.CPUPlace( ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0)) optimizer = fluid.optimizer.SGD(learning_rate=0.003) data_input_names = encoder_data_input_fields + decoder_data_input_fields[: -1] + label_data_input_fields all_inputs = make_all_inputs(data_input_names) enc_inputs_len = len(encoder_data_input_fields) dec_inputs_len = len(decoder_data_input_fields[:-1]) enc_inputs = all_inputs[0:enc_inputs_len] dec_inputs = all_inputs[enc_inputs_len:enc_inputs_len + dec_inputs_len] label = all_inputs[-2] weights = all_inputs[-1] static_param_updated = dict() static_param_init = dict() static_param_name_list = list() static_sum_cost, static_avg_cost, static_predict, static_token_num = transformer( enc_inputs, dec_inputs, label, weights) optimizer.minimize(static_avg_cost) for param in transformer.parameters(): static_param_name_list.append(param.name) out = exe.run(fluid.default_startup_program(), fetch_list=static_param_name_list) for i in range(len(static_param_name_list)): static_param_init[static_param_name_list[i]] = out[i] static_sum_cost_value = None static_avg_cost_value = None static_predict_value = None static_token_num_value = None for i in range(batch_num): feed_dict = create_feed_dict_list(create_data(True)) fetch_list = [ static_sum_cost, static_avg_cost, static_predict, static_token_num ] fetch_list.extend(static_param_name_list) out = exe.run(fluid.default_main_program(), feed=feed_dict, fetch_list=fetch_list) static_sum_cost_value = out[0] static_avg_cost_value = out[1] static_predict_value = out[2] static_token_num_value = out[3] if i == batch_num - 1: for k in range(4, len(out)): static_param_updated[static_param_name_list[k - 4]] = out[k] self.assertTrue( np.array_equal(static_avg_cost_value, dy_avg_cost_value)) self.assertTrue( np.array_equal(static_sum_cost_value, dy_sum_cost_value)) self.assertTrue(np.array_equal(static_predict_value, dy_predict_value)) self.assertTrue( np.array_equal(static_token_num_value, dy_token_num_value)) for key, value in six.iteritems(static_param_init): self.assertTrue(np.array_equal(value, dy_param_init[key])) for key, value in six.iteritems(static_param_updated): self.assertTrue(np.array_equal(value, dy_param_updated[key])) if __name__ == '__main__': unittest.main()

40.435169

137

0.594619

b30fcd8d7451e463fcd7b5db0d7ef6cb16f0786e

2,198

py

Python

tests/test_api.py

cclauss/personfinder

62417192e79c9711d0c6c7cfc042f6d6b0dc2dc2

[ "Apache-2.0" ]

1

2021-11-18T20:09:09.000Z

tests/test_api.py

cclauss/personfinder

62417192e79c9711d0c6c7cfc042f6d6b0dc2dc2

[ "Apache-2.0" ]

8

2021-03-11T01:06:03.000Z

2022-02-27T10:46:12.000Z

tests/test_api.py

cclauss/personfinder

62417192e79c9711d0c6c7cfc042f6d6b0dc2dc2

[ "Apache-2.0" ]

1

2022-01-05T07:06:43.000Z

#!/usr/bin/python2.7 # Copyright 2010 Google Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Unittest for api.py module.""" __author__ = 'ichikawa@google.com (Hiroshi Ichikawa)' import datetime import unittest import api import model import test_handler from google.appengine.ext import testbed class APITests(unittest.TestCase): def setUp(self): self.testbed = testbed.Testbed() self.testbed.activate() self.testbed.init_user_stub() def tearDown(self): self.testbed.deactivate() def test_sms_render_person(self): handler = test_handler.initialize_handler( api.HandleSMS, 'api/handle_sms') person = model.Person.create_original( 'haiti', full_name='John Smith', latest_status='believed_alive', sex='male', age='30', home_city='Los Angeles', home_state='California', entry_date=datetime.datetime(2010, 1, 1)) assert (handler.render_person(person) == 'John Smith / ' 'Someone has received information that this person is alive / ' 'male / 30 / From: Los Angeles California') person = model.Person.create_original( 'haiti', full_name='John Smith', entry_date=datetime.datetime(2010, 1, 1)) assert handler.render_person(person) == 'John Smith' person = model.Person.create_original( 'haiti', full_name='John Smith', home_state='California', entry_date=datetime.datetime(2010, 1, 1)) assert handler.render_person(person) == 'John Smith / From: California'

31.855072

79

0.650591

d19660c7bb95fbbedcd40ac7bb2336b47b29560f

9,057

py

Python

vn.trader/ctaAlgo/strategyAtrRsi.py

Zollern233/vnpy

c813ec970b241c7761a7b020bf2b70c1010b508f

[ "MIT" ]

2

2016-11-04T01:35:47.000Z

2016-11-04T01:35:50.000Z

vn.trader/ctaAlgo/strategyAtrRsi.py

samson-huang/vnpy

c813ec970b241c7761a7b020bf2b70c1010b508f

[ "MIT" ]

null

vn.trader/ctaAlgo/strategyAtrRsi.py

samson-huang/vnpy

c813ec970b241c7761a7b020bf2b70c1010b508f

[ "MIT" ]

1

2020-05-26T12:50:30.000Z

# encoding: UTF-8 """ 一个ATR-RSI指标结合的交易策略，适合用在股指的1分钟和5分钟线上。注意事项： 1. 作者不对交易盈利做任何保证，策略代码仅供参考 2. 本策略需要用到talib，没有安装的用户请先参考www.vnpy.org上的教程安装 3. 将IF0000_1min.csv用ctaHistoryData.py导入MongoDB后，直接运行本文件即可回测策略 """ from ctaBase import * from ctaTemplate import CtaTemplate import talib import numpy as np ######################################################################## class AtrRsiStrategy(CtaTemplate): """结合ATR和RSI指标的一个分钟线交易策略""" className = 'AtrRsiStrategy' author = u'用Python的交易员' # 策略参数 atrLength = 22 # 计算ATR指标的窗口数 atrMaLength = 10 # 计算ATR均线的窗口数 rsiLength = 5 # 计算RSI的窗口数 rsiEntry = 16 # RSI的开仓信号 trailingPercent = 0.8 # 百分比移动止损 initDays = 10 # 初始化数据所用的天数 # 策略变量 bar = None # K线对象 barMinute = EMPTY_STRING # K线当前的分钟 bufferSize = 100 # 需要缓存的数据的大小 bufferCount = 0 # 目前已经缓存了的数据的计数 highArray = np.zeros(bufferSize) # K线最高价的数组 lowArray = np.zeros(bufferSize) # K线最低价的数组 closeArray = np.zeros(bufferSize) # K线收盘价的数组 atrCount = 0 # 目前已经缓存了的ATR的计数 atrArray = np.zeros(bufferSize) # ATR指标的数组 atrValue = 0 # 最新的ATR指标数值 atrMa = 0 # ATR移动平均的数值 rsiValue = 0 # RSI指标的数值 rsiBuy = 0 # RSI买开阈值 rsiSell = 0 # RSI卖开阈值 intraTradeHigh = 0 # 移动止损用的持仓期内最高价 intraTradeLow = 0 # 移动止损用的持仓期内最低价 orderList = [] # 保存委托代码的列表 # 参数列表，保存了参数的名称 paramList = ['name', 'className', 'author', 'vtSymbol', 'atrLength', 'atrMaLength', 'rsiLength', 'rsiEntry', 'trailingPercent'] # 变量列表，保存了变量的名称 varList = ['inited', 'trading', 'pos', 'atrValue', 'atrMa', 'rsiValue', 'rsiBuy', 'rsiSell'] #---------------------------------------------------------------------- def __init__(self, ctaEngine, setting): """Constructor""" super(AtrRsiStrategy, self).__init__(ctaEngine, setting) # 注意策略类中的可变对象属性（通常是list和dict等），在策略初始化时需要重新创建， # 否则会出现多个策略实例之间数据共享的情况，有可能导致潜在的策略逻辑错误风险， # 策略类中的这些可变对象属性可以选择不写，全都放在__init__下面，写主要是为了阅读 # 策略时方便（更多是个编程习惯的选择） #---------------------------------------------------------------------- def onInit(self): """初始化策略（必须由用户继承实现）""" self.writeCtaLog(u'%s策略初始化' %self.name) # 初始化RSI入场阈值 self.rsiBuy = 50 + self.rsiEntry self.rsiSell = 50 - self.rsiEntry # 载入历史数据，并采用回放计算的方式初始化策略数值 initData = self.loadBar(self.initDays) for bar in initData: self.onBar(bar) self.putEvent() #---------------------------------------------------------------------- def onStart(self): """启动策略（必须由用户继承实现）""" self.writeCtaLog(u'%s策略启动' %self.name) self.putEvent() #---------------------------------------------------------------------- def onStop(self): """停止策略（必须由用户继承实现）""" self.writeCtaLog(u'%s策略停止' %self.name) self.putEvent() #---------------------------------------------------------------------- def onTick(self, tick): """收到行情TICK推送（必须由用户继承实现）""" # 计算K线 tickMinute = tick.datetime.minute if tickMinute != self.barMinute: if self.bar: self.onBar(self.bar) bar = CtaBarData() bar.vtSymbol = tick.vtSymbol bar.symbol = tick.symbol bar.exchange = tick.exchange bar.open = tick.lastPrice bar.high = tick.lastPrice bar.low = tick.lastPrice bar.close = tick.lastPrice bar.date = tick.date bar.time = tick.time bar.datetime = tick.datetime # K线的时间设为第一个Tick的时间 self.bar = bar # 这种写法为了减少一层访问，加快速度 self.barMinute = tickMinute # 更新当前的分钟 else: # 否则继续累加新的K线 bar = self.bar # 写法同样为了加快速度 bar.high = max(bar.high, tick.lastPrice) bar.low = min(bar.low, tick.lastPrice) bar.close = tick.lastPrice #---------------------------------------------------------------------- def onBar(self, bar): """收到Bar推送（必须由用户继承实现）""" # 撤销之前发出的尚未成交的委托（包括限价单和停止单） for orderID in self.orderList: self.cancelOrder(orderID) self.orderList = [] # 保存K线数据 self.closeArray[0:self.bufferSize-1] = self.closeArray[1:self.bufferSize] self.highArray[0:self.bufferSize-1] = self.highArray[1:self.bufferSize] self.lowArray[0:self.bufferSize-1] = self.lowArray[1:self.bufferSize] self.closeArray[-1] = bar.close self.highArray[-1] = bar.high self.lowArray[-1] = bar.low self.bufferCount += 1 if self.bufferCount < self.bufferSize: return # 计算指标数值 self.atrValue = talib.ATR(self.highArray, self.lowArray, self.closeArray, self.atrLength)[-1] self.atrArray[0:self.bufferSize-1] = self.atrArray[1:self.bufferSize] self.atrArray[-1] = self.atrValue self.atrCount += 1 if self.atrCount < self.bufferSize: return self.atrMa = talib.MA(self.atrArray, self.atrMaLength)[-1] self.rsiValue = talib.RSI(self.closeArray, self.rsiLength)[-1] # 判断是否要进行交易 # 当前无仓位 if self.pos == 0: self.intraTradeHigh = bar.high self.intraTradeLow = bar.low # ATR数值上穿其移动平均线，说明行情短期内波动加大 # 即处于趋势的概率较大，适合CTA开仓 if self.atrValue > self.atrMa: # 使用RSI指标的趋势行情时，会在超买超卖区钝化特征，作为开仓信号 if self.rsiValue > self.rsiBuy: # 这里为了保证成交，选择超价5个整指数点下单 self.buy(bar.close+5, 1) elif self.rsiValue < self.rsiSell: self.short(bar.close-5, 1) # 持有多头仓位 elif self.pos > 0: # 计算多头持有期内的最高价，以及重置最低价 self.intraTradeHigh = max(self.intraTradeHigh, bar.high) self.intraTradeLow = bar.low # 计算多头移动止损 longStop = self.intraTradeHigh * (1-self.trailingPercent/100) # 发出本地止损委托，并且把委托号记录下来，用于后续撤单 orderID = self.sell(longStop, 1, stop=True) self.orderList.append(orderID) # 持有空头仓位 elif self.pos < 0: self.intraTradeLow = min(self.intraTradeLow, bar.low) self.intraTradeHigh = bar.high shortStop = self.intraTradeLow * (1+self.trailingPercent/100) orderID = self.cover(shortStop, 1, stop=True) self.orderList.append(orderID) # 发出状态更新事件 self.putEvent() #---------------------------------------------------------------------- def onOrder(self, order): """收到委托变化推送（必须由用户继承实现）""" pass #---------------------------------------------------------------------- def onTrade(self, trade): pass if __name__ == '__main__': # 提供直接双击回测的功能 # 导入PyQt4的包是为了保证matplotlib使用PyQt4而不是PySide，防止初始化出错 from ctaBacktesting import * from PyQt4 import QtCore, QtGui # 创建回测引擎 engine = BacktestingEngine() # 设置引擎的回测模式为K线 engine.setBacktestingMode(engine.BAR_MODE) # 设置回测用的数据起始日期 engine.setStartDate('20120101') # 设置产品相关参数 engine.setSlippage(0.2) # 股指1跳 engine.setRate(0.3/10000) # 万0.3 engine.setSize(300) # 股指合约大小 # 设置使用的历史数据库 engine.setDatabase(MINUTE_DB_NAME, 'IF0000') ## 在引擎中创建策略对象 #d = {'atrLength': 11} #engine.initStrategy(AtrRsiStrategy, d) ## 开始跑回测 ##engine.runBacktesting() ## 显示回测结果 ##engine.showBacktestingResult() # 跑优化 setting = OptimizationSetting() # 新建一个优化任务设置对象 setting.setOptimizeTarget('capital') # 设置优化排序的目标是策略净盈利 setting.addParameter('atrLength', 11, 20, 1) # 增加第一个优化参数atrLength，起始11，结束12，步进1 setting.addParameter('atrMa', 20, 30, 5) # 增加第二个优化参数atrMa，起始20，结束30，步进1 # 性能测试环境：I7-3770，主频3.4G, 8核心，内存16G，Windows 7 专业版 # 测试时还跑着一堆其他的程序，性能仅供参考 import time start = time.time() # 运行单进程优化函数，自动输出结果，耗时：359秒 #engine.runOptimization(AtrRsiStrategy, setting) # 多进程优化，耗时：89秒 engine.runParallelOptimization(AtrRsiStrategy, setting) print u'耗时：%s' %(time.time()-start)

31.3391

86

0.499503

5bbc27691ef60710be730a34518a081349fca24c

6,412

py

Python

satcfe/rede.py

base4sistemas/satcfe

530c4cda3ba0ee0114c73d78cdfd47ba79427cbf

[ "Apache-2.0" ]

38

2015-05-25T02:57:16.000Z

2022-01-18T21:01:49.000Z

satcfe/rede.py

base4sistemas/satcfe

530c4cda3ba0ee0114c73d78cdfd47ba79427cbf

[ "Apache-2.0" ]

15

2015-08-19T13:30:46.000Z

2022-01-19T22:34:17.000Z

satcfe/rede.py

base4sistemas/satcfe

530c4cda3ba0ee0114c73d78cdfd47ba79427cbf

[ "Apache-2.0" ]

13

2015-05-07T01:10:12.000Z

2022-02-04T14:30:01.000Z

# -*- coding: utf-8 -*- # # satcfe/rede.py # # Copyright 2015 Base4 Sistemas Ltda ME # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import absolute_import from __future__ import print_function from __future__ import unicode_literals import xml.etree.ElementTree as ET from satcomum.constantes import REDE_TIPOINTER_OPCOES from satcomum.constantes import REDE_SEG_OPCOES from satcomum.constantes import REDE_TIPOLAN_OPCOES from satcomum.constantes import REDE_PROXY_OPCOES from .entidades import Entidade class ConfiguracaoRede(Entidade): """Uma entidade que contém os parâmetros de configurações da interface de rede do equipamento SAT. Uma instância desta classe é usada como argumento para :meth:`~satcfe.base._FuncoesSAT.configurar_interface_de_rede`. :param str tipoInter: Tipo de interface de rede que o equipamento SAT deverá utilizar. As opções de tipos de rede estão disponíveis na constante :attr:`~satcomum.constantes.REDE_TIPOINTER_OPCOES`. :param str SSID: *Opcional* Nome da rede sem fio, se for o caso, contendo até 32 caracteres. :param str seg: *Opcional* Tipo de segurança da rede sem fio. As opções estão na constante :attr:`~satcomum.constantes.REDE_SEG_OPCOES`. :param str codigo: *Opcional* Senha de acesso à rede sem fio, contendo até 64 caracteres. :param str tipoLan: Tipo da rede LAN. As opções estão disponíveis na constante :attr:`~satcomum.constantes.REDE_TIPOLAN_OPCOES`. :param str lanIP: *Opcional* Endereço IP do equipamento SAT. :param str lanMask: *Opcional* Máscara de sub-rede. :param str lanGW: *Opcional* Endereço IP do gateway padrão. :param str lanDNS1: *Opcional* Endereço IP do DNS primário. :param str lanDNS2: *Opcional* Endereço IP do DNS secundário. :param str usuario: *Opcional* Nome do usuário para obtenção do endereço IP, se necessário, contendo até 64 caracteres. :param str senha: *Opcional* Senha do usuário para obtenção do endereço IP, relacionado ao parâmetro ``usuario``, se necessário, contendo até 32 caracteres. :param str proxy: *Opcional* Indica a configuração de proxy da rede. As opções estão disponíveis na constante :attr:`~satcomum.constantes.REDE_PROXY_OPCOES`. :param str proxy_ip: *Opcional* Endereço IP do servidor proxy. :param int proxy_porta: *Opcional* Número da porta por onde o servidor de proxy responde. :param str proxy_user: *Opcional* Nome do usuário para acesso ao proxy, se necessário, contendo até 64 caracteres. :param str proxy_senha: *Opcional* Senha do usuário para acesso ao proxy, relacionado ao parâmetro ``proxy_user``, se necessário, contendo até 64 caracteres. """ def __init__(self, **kwargs): super(ConfiguracaoRede, self).__init__(schema={ 'tipoInter': { 'type': 'string', 'required': True, 'allowed': [v for v, s in REDE_TIPOINTER_OPCOES]}, 'SSID': { 'type': 'string', 'required': False, 'minlength': 1, 'maxlength': 32}, 'seg': { 'type': 'string', 'required': False, 'allowed': [v for v, s in REDE_SEG_OPCOES]}, 'codigo': { 'type': 'string', 'required': False, 'minlength': 1, 'maxlength': 64}, 'tipoLan': { 'type': 'string', 'required': True, 'allowed': [v for v, s in REDE_TIPOLAN_OPCOES]}, 'lanIP': { 'type': 'ipv4', 'required': False}, 'lanMask': { 'type': 'ipv4', 'required': False}, 'lanGW': { 'type': 'ipv4', 'required': False}, 'lanDNS1': { 'type': 'ipv4', 'required': False}, 'lanDNS2': { 'type': 'ipv4', 'required': False}, 'usuario': { 'type': 'string', 'required': False, 'minlength': 1, 'maxlength': 64}, 'senha': { 'type': 'string', 'required': False, 'minlength': 1, 'maxlength': 64}, 'proxy': { 'type': 'string', 'required': False, 'allowed': [v for v, s in REDE_PROXY_OPCOES]}, 'proxy_ip': { 'type': 'ipv4', 'required': False}, 'proxy_porta': { 'type': 'integer', 'required': False, 'min': 0, 'max': 65535}, 'proxy_user': { 'type': 'string', 'required': False, 'minlength': 1, 'maxlength': 64}, 'proxy_senha': { 'type': 'string', 'required': False, 'minlength': 1, 'maxlength': 64}, }, **kwargs) def _construir_elemento_xml(self, *args, **kwargs): config = ET.Element('config') for elemento in self._schema.keys(): valor = getattr(self, elemento, None) if valor: if isinstance(valor, int): valor = str(valor) ET.SubElement(config, elemento).text = valor return config

39.097561

79

0.541953

d2c966ae8aeacec3a3bf1c4e605562c09d0d6f86

3,541

py

Python

web-frameworks/django/djangoWebPush/djangoWebPush/settings.py

suroegin-learning/learn-python

be5bda86add0dcd6f2fd3db737bb7d0d3ec5f853

[ "MIT" ]

null

web-frameworks/django/djangoWebPush/djangoWebPush/settings.py

suroegin-learning/learn-python

be5bda86add0dcd6f2fd3db737bb7d0d3ec5f853

[ "MIT" ]

null

web-frameworks/django/djangoWebPush/djangoWebPush/settings.py

suroegin-learning/learn-python

be5bda86add0dcd6f2fd3db737bb7d0d3ec5f853

[ "MIT" ]

null

""" Django settings for djangoWebPush project. Generated by 'django-admin startproject' using Django 2.1.3. For more information on this file, see https://docs.djangoproject.com/en/2.1/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.1/ref/settings/ """ import os # Build paths inside the project like this: os.path.join(BASE_DIR, ...) BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/2.1/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = '&vc$u)geq9lfckqif27j06_y^^0v=6dw7vy0eorp-sc+8t1cyd' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] # Application definition INSTALLED_APPS = [ 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'webpush', ] MIDDLEWARE = [ 'django.middleware.security.SecurityMiddleware', 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', ] ROOT_URLCONF = 'djangoWebPush.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', ], }, }, { "BACKEND": "django_jinja.backend.Jinja2", "OPTIONS": { 'extensions': ['webpush.jinja2.WebPushExtension'], } }, ] WSGI_APPLICATION = 'djangoWebPush.wsgi.application' # Database # https://docs.djangoproject.com/en/2.1/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } # Password validation # https://docs.djangoproject.com/en/2.1/ref/settings/#auth-password-validators AUTH_PASSWORD_VALIDATORS = [ { 'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator', }, { 'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator', }, { 'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator', }, { 'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator', }, ] # Internationalization # https://docs.djangoproject.com/en/2.1/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/2.1/howto/static-files/ STATIC_URL = '/static/' WEBPUSH_SETTINGS = { "VAPID_PUBLIC_KEY": "BFRTDHKk1A9xsTby9aYNP5tt4avC0azbsM9pmtBBhSyj0LSoM6FP65m2ABP4zmU2Jivxs0lGAHIIFgdHD9-bpzY", "VAPID_PRIVATE_KEY":"rvuSmW7nYAlqNmbiUx9VReefT8djJDpdRkb8-XkhvBM", "VAPID_ADMIN_EMAIL": "ivan.suroegin@gmail.com" }

26.22963

114

0.69839

d139b9f480cc324820e9654116833eac007452b5

38,095

py

Python

nova/volume/cinder.py

gyliu513/nova

14e974a5f77c72a9bb44c6801746abb2eda8e91d

[ "Apache-2.0" ]

1

2019-11-07T03:11:37.000Z

nova/volume/cinder.py

gyliu513/nova

14e974a5f77c72a9bb44c6801746abb2eda8e91d

[ "Apache-2.0" ]

1

2021-03-31T19:35:21.000Z

nova/volume/cinder.py

Mattlk13/nova

5b13eb59540aaf535a53920e783964d106de2620

[ "Apache-2.0" ]

1

2020-07-22T22:14:40.000Z

# Copyright 2010 United States Government as represented by the # Administrator of the National Aeronautics and Space Administration. # All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. """ Handles all requests relating to volumes + cinder. """ import collections import copy import functools import sys from cinderclient import api_versions as cinder_api_versions from cinderclient import client as cinder_client from cinderclient import exceptions as cinder_exception from keystoneauth1 import exceptions as keystone_exception from keystoneauth1 import loading as ks_loading from oslo_log import log as logging from oslo_serialization import jsonutils from oslo_utils import encodeutils from oslo_utils import excutils from oslo_utils import strutils import six from six.moves import urllib from nova import availability_zones as az import nova.conf from nova import exception from nova.i18n import _ from nova.i18n import _LE from nova.i18n import _LW from nova import service_auth CONF = nova.conf.CONF LOG = logging.getLogger(__name__) _ADMIN_AUTH = None _SESSION = None def reset_globals(): """Testing method to reset globals. """ global _ADMIN_AUTH global _SESSION _ADMIN_AUTH = None _SESSION = None def _load_auth_plugin(conf): auth_plugin = ks_loading.load_auth_from_conf_options(conf, nova.conf.cinder.cinder_group.name) if auth_plugin: return auth_plugin if conf.cinder.auth_type is None: LOG.error('The [cinder] section of your nova configuration file ' 'must be configured for authentication with the ' 'block-storage service endpoint.') err_msg = _('Unknown auth type: %s') % conf.cinder.auth_type raise cinder_exception.Unauthorized(401, message=err_msg) def _load_session(): global _SESSION if not _SESSION: _SESSION = ks_loading.load_session_from_conf_options( CONF, nova.conf.cinder.cinder_group.name) def _get_auth(context): global _ADMIN_AUTH # NOTE(lixipeng): Auth token is none when call # cinder API from compute periodic tasks, context # from them generated from 'context.get_admin_context' # which only set is_admin=True but is without token. # So add load_auth_plugin when this condition appear. if context.is_admin and not context.auth_token: if not _ADMIN_AUTH: _ADMIN_AUTH = _load_auth_plugin(CONF) return _ADMIN_AUTH else: return service_auth.get_auth_plugin(context) # NOTE(efried): Bug #1752152 # This method is copied/adapted from cinderclient.client.get_server_version so # we can use _SESSION.get rather than a raw requests.get to retrieve the # version document. This enables HTTPS by gleaning cert info from the session # config. def _get_server_version(context, url): """Queries the server via the naked endpoint and gets version info. :param context: The nova request context for auth. :param url: url of the cinder endpoint :returns: APIVersion object for min and max version supported by the server """ min_version = "2.0" current_version = "2.0" _load_session() auth = _get_auth(context) try: u = urllib.parse.urlparse(url) version_url = None # NOTE(andreykurilin): endpoint URL has at least 2 formats: # 1. The classic (legacy) endpoint: # http://{host}:{optional_port}/v{2 or 3}/{project-id} # http://{host}:{optional_port}/v{2 or 3} # 3. Under wsgi: # http://{host}:{optional_port}/volume/v{2 or 3} for ver in ['v2', 'v3']: if u.path.endswith(ver) or "/{0}/".format(ver) in u.path: path = u.path[:u.path.rfind(ver)] version_url = '%s://%s%s' % (u.scheme, u.netloc, path) break if not version_url: # NOTE(andreykurilin): probably, it is one of the next cases: # * https://volume.example.com/ # * https://example.com/volume # leave as is without cropping. version_url = url response = _SESSION.get(version_url, auth=auth) data = jsonutils.loads(response.text) versions = data['versions'] for version in versions: if '3.' in version['version']: min_version = version['min_version'] current_version = version['version'] break except cinder_exception.ClientException as e: LOG.warning("Error in server version query:%s\n" "Returning APIVersion 2.0", six.text_type(e.message)) return (cinder_api_versions.APIVersion(min_version), cinder_api_versions.APIVersion(current_version)) # NOTE(efried): Bug #1752152 # This method is copied/adapted from # cinderclient.client.get_highest_client_server_version. See note on # _get_server_version. def _get_highest_client_server_version(context, url): """Returns highest APIVersion supported version by client and server.""" min_server, max_server = _get_server_version(context, url) max_client = cinder_api_versions.APIVersion( cinder_api_versions.MAX_VERSION) return min(max_server, max_client) def _check_microversion(context, url, microversion): """Checks to see if the requested microversion is supported by the current version of python-cinderclient and the volume API endpoint. :param context: The nova request context for auth. :param url: Cinder API endpoint URL. :param microversion: Requested microversion. If not available at the given API endpoint URL, a CinderAPIVersionNotAvailable exception is raised. :returns: The microversion if it is available. This can be used to construct the cinder v3 client object. :raises: CinderAPIVersionNotAvailable if the microversion is not available. """ max_api_version = _get_highest_client_server_version(context, url) # Check if the max_api_version matches the requested minimum microversion. if max_api_version.matches(microversion): # The requested microversion is supported by the client and the server. return microversion raise exception.CinderAPIVersionNotAvailable(version=microversion) def _get_cinderclient_parameters(context): _load_session() auth = _get_auth(context) url = None service_type, service_name, interface = CONF.cinder.catalog_info.split(':') service_parameters = {'service_type': service_type, 'interface': interface, 'region_name': CONF.cinder.os_region_name} # Only include the service_name if it's provided. if service_name: service_parameters['service_name'] = service_name if CONF.cinder.endpoint_template: url = CONF.cinder.endpoint_template % context.to_dict() else: url = _SESSION.get_endpoint(auth, **service_parameters) return auth, service_parameters, url def is_microversion_supported(context, microversion): # NOTE(efried): Work around bug #1752152. Call the cinderclient() builder # in a way that just does a microversion check. cinderclient(context, microversion=microversion, check_only=True) def cinderclient(context, microversion=None, skip_version_check=False, check_only=False): """Constructs a cinder client object for making API requests. :param context: The nova request context for auth. :param microversion: Optional microversion to check against the client. This implies that Cinder v3 is required for any calls that require a microversion. If the microversion is not available, this method will raise an CinderAPIVersionNotAvailable exception. :param skip_version_check: If True and a specific microversion is requested, the version discovery check is skipped and the microversion is used directly. This should only be used if a previous check for the same microversion was successful. :param check_only: If True, don't build the actual client; just do the setup and version checking. :raises: UnsupportedCinderAPIVersion if a major version other than 3 is requested. :raises: CinderAPIVersionNotAvailable if microversion checking is requested and the specified microversion is higher than what the service can handle. :returns: A cinderclient.client.Client wrapper, unless check_only is False. """ endpoint_override = None auth, service_parameters, url = _get_cinderclient_parameters(context) if CONF.cinder.endpoint_template: endpoint_override = url # TODO(jamielennox): This should be using proper version discovery from # the cinder service rather than just inspecting the URL for certain string # values. version = cinder_client.get_volume_api_from_url(url) if version != '3': raise exception.UnsupportedCinderAPIVersion(version=version) version = '3.0' # Check to see a specific microversion is requested and if so, can it # be handled by the backing server. if microversion is not None: if skip_version_check: version = microversion else: version = _check_microversion(context, url, microversion) if check_only: return return cinder_client.Client(version, session=_SESSION, auth=auth, endpoint_override=endpoint_override, connect_retries=CONF.cinder.http_retries, global_request_id=context.global_id, **service_parameters) def _untranslate_volume_summary_view(context, vol): """Maps keys for volumes summary view.""" d = {} d['id'] = vol.id d['status'] = vol.status d['size'] = vol.size d['availability_zone'] = vol.availability_zone d['created_at'] = vol.created_at # TODO(jdg): The calling code expects attach_time and # mountpoint to be set. When the calling # code is more defensive this can be # removed. d['attach_time'] = "" d['mountpoint'] = "" d['multiattach'] = getattr(vol, 'multiattach', False) if vol.attachments: d['attachments'] = collections.OrderedDict() for attachment in vol.attachments: a = {attachment['server_id']: {'attachment_id': attachment.get('attachment_id'), 'mountpoint': attachment.get('device')} } d['attachments'].update(a.items()) d['attach_status'] = 'attached' else: d['attach_status'] = 'detached' d['display_name'] = vol.name d['display_description'] = vol.description # TODO(jdg): Information may be lost in this translation d['volume_type_id'] = vol.volume_type d['snapshot_id'] = vol.snapshot_id d['bootable'] = strutils.bool_from_string(vol.bootable) d['volume_metadata'] = {} for key, value in vol.metadata.items(): d['volume_metadata'][key] = value if hasattr(vol, 'volume_image_metadata'): d['volume_image_metadata'] = copy.deepcopy(vol.volume_image_metadata) # The 3.48 microversion exposes a shared_targets boolean and service_uuid # string parameter which can be used with locks during volume attach # and detach. if hasattr(vol, 'shared_targets'): d['shared_targets'] = vol.shared_targets d['service_uuid'] = vol.service_uuid if hasattr(vol, 'migration_status'): d['migration_status'] = vol.migration_status return d def _untranslate_volume_type_view(volume_type): """Maps keys for volume type view.""" v = {} v['id'] = volume_type.id v['name'] = volume_type.name return v def _untranslate_snapshot_summary_view(context, snapshot): """Maps keys for snapshots summary view.""" d = {} d['id'] = snapshot.id d['status'] = snapshot.status d['progress'] = snapshot.progress d['size'] = snapshot.size d['created_at'] = snapshot.created_at d['display_name'] = snapshot.name d['display_description'] = snapshot.description d['volume_id'] = snapshot.volume_id d['project_id'] = snapshot.project_id d['volume_size'] = snapshot.size return d def _translate_attachment_ref(attachment_ref): """Building old style connection_info by adding the 'data' key back.""" translated_con_info = {} connection_info_data = attachment_ref.pop('connection_info', None) if connection_info_data: connection_info_data.pop('attachment_id', None) translated_con_info['driver_volume_type'] = \ connection_info_data.pop('driver_volume_type', None) translated_con_info['data'] = connection_info_data translated_con_info['status'] = attachment_ref.pop('status', None) translated_con_info['instance'] = attachment_ref.pop('instance', None) translated_con_info['attached_at'] = attachment_ref.pop('attached_at', None) translated_con_info['detached_at'] = attachment_ref.pop('detached_at', None) # Now the catch all... for k, v in attachment_ref.items(): # Keep these as top-level fields on the attachment record. if k not in ("id", "attach_mode"): translated_con_info[k] = v attachment_ref['connection_info'] = translated_con_info return attachment_ref def translate_cinder_exception(method): """Transforms a cinder exception but keeps its traceback intact.""" @functools.wraps(method) def wrapper(self, ctx, *args, **kwargs): try: res = method(self, ctx, *args, **kwargs) except (cinder_exception.ConnectionError, keystone_exception.ConnectionError) as exc: err_msg = encodeutils.exception_to_unicode(exc) _reraise(exception.CinderConnectionFailed(reason=err_msg)) except (keystone_exception.BadRequest, cinder_exception.BadRequest) as exc: err_msg = encodeutils.exception_to_unicode(exc) _reraise(exception.InvalidInput(reason=err_msg)) except (keystone_exception.Forbidden, cinder_exception.Forbidden) as exc: err_msg = encodeutils.exception_to_unicode(exc) _reraise(exception.Forbidden(err_msg)) return res return wrapper def translate_create_exception(method): """Transforms the exception for create but keeps its traceback intact. """ def wrapper(self, ctx, size, *args, **kwargs): try: res = method(self, ctx, size, *args, **kwargs) except (keystone_exception.NotFound, cinder_exception.NotFound) as e: _reraise(exception.NotFound(message=e.message)) except cinder_exception.OverLimit as e: _reraise(exception.OverQuota(message=e.message)) return res return translate_cinder_exception(wrapper) def translate_volume_exception(method): """Transforms the exception for the volume but keeps its traceback intact. """ def wrapper(self, ctx, volume_id, *args, **kwargs): try: res = method(self, ctx, volume_id, *args, **kwargs) except (keystone_exception.NotFound, cinder_exception.NotFound): _reraise(exception.VolumeNotFound(volume_id=volume_id)) except cinder_exception.OverLimit as e: _reraise(exception.OverQuota(message=e.message)) return res return translate_cinder_exception(wrapper) def translate_attachment_exception(method): """Transforms the exception for the attachment but keeps its traceback intact. """ def wrapper(self, ctx, attachment_id, *args, **kwargs): try: res = method(self, ctx, attachment_id, *args, **kwargs) except (keystone_exception.NotFound, cinder_exception.NotFound): _reraise(exception.VolumeAttachmentNotFound( attachment_id=attachment_id)) return res return translate_cinder_exception(wrapper) def translate_snapshot_exception(method): """Transforms the exception for the snapshot but keeps its traceback intact. """ def wrapper(self, ctx, snapshot_id, *args, **kwargs): try: res = method(self, ctx, snapshot_id, *args, **kwargs) except (keystone_exception.NotFound, cinder_exception.NotFound): _reraise(exception.SnapshotNotFound(snapshot_id=snapshot_id)) return res return translate_cinder_exception(wrapper) def translate_mixed_exceptions(method): """Transforms exceptions that can come from both volumes and snapshots.""" def wrapper(self, ctx, res_id, *args, **kwargs): try: res = method(self, ctx, res_id, *args, **kwargs) except (keystone_exception.NotFound, cinder_exception.NotFound): _reraise(exception.VolumeNotFound(volume_id=res_id)) except cinder_exception.OverLimit: _reraise(exception.OverQuota(overs='snapshots')) return res return translate_cinder_exception(wrapper) def _reraise(desired_exc): six.reraise(type(desired_exc), desired_exc, sys.exc_info()[2]) class API(object): """API for interacting with the volume manager.""" @translate_volume_exception def get(self, context, volume_id, microversion=None): """Get the details about a volume given it's ID. :param context: the nova request context :param volume_id: the id of the volume to get :param microversion: optional string microversion value :raises: CinderAPIVersionNotAvailable if the specified microversion is not available. """ item = cinderclient( context, microversion=microversion).volumes.get(volume_id) return _untranslate_volume_summary_view(context, item) @translate_cinder_exception def get_all(self, context, search_opts=None): search_opts = search_opts or {} items = cinderclient(context).volumes.list(detailed=True, search_opts=search_opts) rval = [] for item in items: rval.append(_untranslate_volume_summary_view(context, item)) return rval def check_attached(self, context, volume): if volume['status'] != "in-use": msg = _("volume '%(vol)s' status must be 'in-use'. Currently in " "'%(status)s' status") % {"vol": volume['id'], "status": volume['status']} raise exception.InvalidVolume(reason=msg) def check_availability_zone(self, context, volume, instance=None): """Ensure that the availability zone is the same. :param context: the nova request context :param volume: the volume attached to the instance :param instance: nova.objects.instance.Instance object :raises: InvalidVolume if the instance availability zone does not equal the volume's availability zone """ # TODO(walter-boring): move this check to Cinder as part of # the reserve call. if instance and not CONF.cinder.cross_az_attach: instance_az = az.get_instance_availability_zone(context, instance) if instance_az != volume['availability_zone']: msg = _("Instance %(instance)s and volume %(vol)s are not in " "the same availability_zone. Instance is in " "%(ins_zone)s. Volume is in %(vol_zone)s") % { "instance": instance.uuid, "vol": volume['id'], 'ins_zone': instance_az, 'vol_zone': volume['availability_zone']} raise exception.InvalidVolume(reason=msg) @translate_volume_exception def reserve_volume(self, context, volume_id): cinderclient(context).volumes.reserve(volume_id) @translate_volume_exception def unreserve_volume(self, context, volume_id): cinderclient(context).volumes.unreserve(volume_id) @translate_volume_exception def begin_detaching(self, context, volume_id): cinderclient(context).volumes.begin_detaching(volume_id) @translate_volume_exception def roll_detaching(self, context, volume_id): cinderclient(context).volumes.roll_detaching(volume_id) @translate_volume_exception def attach(self, context, volume_id, instance_uuid, mountpoint, mode='rw'): cinderclient(context).volumes.attach(volume_id, instance_uuid, mountpoint, mode=mode) @translate_volume_exception def detach(self, context, volume_id, instance_uuid=None, attachment_id=None): client = cinderclient(context) if attachment_id is None: volume = self.get(context, volume_id) if volume['multiattach']: attachments = volume.get('attachments', {}) if instance_uuid: attachment_id = attachments.get(instance_uuid, {}).\ get('attachment_id') if not attachment_id: LOG.warning(_LW("attachment_id couldn't be retrieved " "for volume %(volume_id)s with " "instance_uuid %(instance_id)s. The " "volume has the 'multiattach' flag " "enabled, without the attachment_id " "Cinder most probably cannot perform " "the detach."), {'volume_id': volume_id, 'instance_id': instance_uuid}) else: LOG.warning(_LW("attachment_id couldn't be retrieved for " "volume %(volume_id)s. The volume has the " "'multiattach' flag enabled, without the " "attachment_id Cinder most probably " "cannot perform the detach."), {'volume_id': volume_id}) client.volumes.detach(volume_id, attachment_id) @translate_volume_exception def initialize_connection(self, context, volume_id, connector): try: connection_info = cinderclient( context).volumes.initialize_connection(volume_id, connector) connection_info['connector'] = connector return connection_info except cinder_exception.ClientException as ex: with excutils.save_and_reraise_exception(): LOG.error(_LE('Initialize connection failed for volume ' '%(vol)s on host %(host)s. Error: %(msg)s ' 'Code: %(code)s. Attempting to terminate ' 'connection.'), {'vol': volume_id, 'host': connector.get('host'), 'msg': six.text_type(ex), 'code': ex.code}) try: self.terminate_connection(context, volume_id, connector) except Exception as exc: LOG.error(_LE('Connection between volume %(vol)s and host ' '%(host)s might have succeeded, but attempt ' 'to terminate connection has failed. ' 'Validate the connection and determine if ' 'manual cleanup is needed. Error: %(msg)s ' 'Code: %(code)s.'), {'vol': volume_id, 'host': connector.get('host'), 'msg': six.text_type(exc), 'code': ( exc.code if hasattr(exc, 'code') else None)}) @translate_volume_exception def terminate_connection(self, context, volume_id, connector): return cinderclient(context).volumes.terminate_connection(volume_id, connector) @translate_cinder_exception def migrate_volume_completion(self, context, old_volume_id, new_volume_id, error=False): return cinderclient(context).volumes.migrate_volume_completion( old_volume_id, new_volume_id, error) @translate_create_exception def create(self, context, size, name, description, snapshot=None, image_id=None, volume_type=None, metadata=None, availability_zone=None): client = cinderclient(context) if snapshot is not None: snapshot_id = snapshot['id'] else: snapshot_id = None kwargs = dict(snapshot_id=snapshot_id, volume_type=volume_type, availability_zone=availability_zone, metadata=metadata, imageRef=image_id, name=name, description=description) item = client.volumes.create(size, **kwargs) return _untranslate_volume_summary_view(context, item) @translate_volume_exception def delete(self, context, volume_id): cinderclient(context).volumes.delete(volume_id) @translate_volume_exception def update(self, context, volume_id, fields): raise NotImplementedError() @translate_cinder_exception def get_absolute_limits(self, context): """Returns quota limit and usage information for the given tenant See the <volumev3>/v3/{project_id}/limits API reference for details. :param context: The nova RequestContext for the user request. Note that the limit information returned from Cinder is specific to the project_id within this context. :returns: dict of absolute limits """ # cinderclient returns a generator of AbsoluteLimit objects, so iterate # over the generator and return a dictionary which is easier for the # nova client-side code to handle. limits = cinderclient(context).limits.get().absolute return {limit.name: limit.value for limit in limits} @translate_snapshot_exception def get_snapshot(self, context, snapshot_id): item = cinderclient(context).volume_snapshots.get(snapshot_id) return _untranslate_snapshot_summary_view(context, item) @translate_cinder_exception def get_all_snapshots(self, context): items = cinderclient(context).volume_snapshots.list(detailed=True) rvals = [] for item in items: rvals.append(_untranslate_snapshot_summary_view(context, item)) return rvals @translate_mixed_exceptions def create_snapshot(self, context, volume_id, name, description): item = cinderclient(context).volume_snapshots.create(volume_id, False, name, description) return _untranslate_snapshot_summary_view(context, item) @translate_mixed_exceptions def create_snapshot_force(self, context, volume_id, name, description): item = cinderclient(context).volume_snapshots.create(volume_id, True, name, description) return _untranslate_snapshot_summary_view(context, item) @translate_snapshot_exception def delete_snapshot(self, context, snapshot_id): cinderclient(context).volume_snapshots.delete(snapshot_id) @translate_cinder_exception def get_all_volume_types(self, context): items = cinderclient(context).volume_types.list() rvals = [] for item in items: rvals.append(_untranslate_volume_type_view(item)) return rvals @translate_cinder_exception def get_volume_encryption_metadata(self, context, volume_id): return cinderclient(context).volumes.get_encryption_metadata(volume_id) @translate_snapshot_exception def update_snapshot_status(self, context, snapshot_id, status): vs = cinderclient(context).volume_snapshots # '90%' here is used to tell Cinder that Nova is done # with its portion of the 'creating' state. This can # be removed when we are able to split the Cinder states # into 'creating' and a separate state of # 'creating_in_nova'. (Same for 'deleting' state.) vs.update_snapshot_status( snapshot_id, {'status': status, 'progress': '90%'} ) @translate_volume_exception def attachment_create(self, context, volume_id, instance_id, connector=None, mountpoint=None): """Create a volume attachment. This requires microversion >= 3.44. The attachment_create call was introduced in microversion 3.27. We need 3.44 as minmum here as we need attachment_complete to finish the attaching process and it which was introduced in version 3.44. :param context: The nova request context. :param volume_id: UUID of the volume on which to create the attachment. :param instance_id: UUID of the instance to which the volume will be attached. :param connector: host connector dict; if None, the attachment will be 'reserved' but not yet attached. :param mountpoint: Optional mount device name for the attachment, e.g. "/dev/vdb". This is only used if a connector is provided. :returns: a dict created from the cinderclient.v3.attachments.VolumeAttachment object with a backward compatible connection_info dict """ # NOTE(mriedem): Due to a limitation in the POST /attachments/ # API in Cinder, we have to pass the mountpoint in via the # host connector rather than pass it in as a top-level parameter # like in the os-attach volume action API. Hopefully this will be # fixed some day with a new Cinder microversion but until then we # work around it client-side. _connector = connector if _connector and mountpoint and 'mountpoint' not in _connector: # Make a copy of the connector so we don't modify it by # reference. _connector = copy.deepcopy(connector) _connector['mountpoint'] = mountpoint try: attachment_ref = cinderclient(context, '3.44').attachments.create( volume_id, _connector, instance_id) return _translate_attachment_ref(attachment_ref) except cinder_exception.ClientException as ex: with excutils.save_and_reraise_exception(): # NOTE: It is unnecessary to output BadRequest(400) error log, # because operators don't need to debug such cases. if getattr(ex, 'code', None) != 400: LOG.error(('Create attachment failed for volume ' '%(volume_id)s. Error: %(msg)s Code: %(code)s'), {'volume_id': volume_id, 'msg': six.text_type(ex), 'code': getattr(ex, 'code', None)}, instance_uuid=instance_id) @translate_attachment_exception def attachment_get(self, context, attachment_id): """Gets a volume attachment. :param context: The nova request context. :param attachment_id: UUID of the volume attachment to get. :returns: a dict created from the cinderclient.v3.attachments.VolumeAttachment object with a backward compatible connection_info dict """ try: attachment_ref = cinderclient( context, '3.44', skip_version_check=True).attachments.show( attachment_id) translated_attach_ref = _translate_attachment_ref( attachment_ref.to_dict()) return translated_attach_ref except cinder_exception.ClientException as ex: with excutils.save_and_reraise_exception(): LOG.error(('Show attachment failed for attachment ' '%(id)s. Error: %(msg)s Code: %(code)s'), {'id': attachment_id, 'msg': six.text_type(ex), 'code': getattr(ex, 'code', None)}) @translate_attachment_exception def attachment_update(self, context, attachment_id, connector, mountpoint=None): """Updates the connector on the volume attachment. An attachment without a connector is considered reserved but not fully attached. :param context: The nova request context. :param attachment_id: UUID of the volume attachment to update. :param connector: host connector dict. This is required when updating a volume attachment. To terminate a connection, the volume attachment for that connection must be deleted. :param mountpoint: Optional mount device name for the attachment, e.g. "/dev/vdb". Theoretically this is optional per volume backend, but in practice it's normally required so it's best to always provide a value. :returns: a dict created from the cinderclient.v3.attachments.VolumeAttachment object with a backward compatible connection_info dict """ # NOTE(mriedem): Due to a limitation in the PUT /attachments/{id} # API in Cinder, we have to pass the mountpoint in via the # host connector rather than pass it in as a top-level parameter # like in the os-attach volume action API. Hopefully this will be # fixed some day with a new Cinder microversion but until then we # work around it client-side. _connector = connector if mountpoint and 'mountpoint' not in connector: # Make a copy of the connector so we don't modify it by # reference. _connector = copy.deepcopy(connector) _connector['mountpoint'] = mountpoint try: attachment_ref = cinderclient( context, '3.44', skip_version_check=True).attachments.update( attachment_id, _connector) translated_attach_ref = _translate_attachment_ref( attachment_ref.to_dict()) return translated_attach_ref except cinder_exception.ClientException as ex: with excutils.save_and_reraise_exception(): LOG.error(('Update attachment failed for attachment ' '%(id)s. Error: %(msg)s Code: %(code)s'), {'id': attachment_id, 'msg': six.text_type(ex), 'code': getattr(ex, 'code', None)}) @translate_attachment_exception def attachment_delete(self, context, attachment_id): try: cinderclient( context, '3.44', skip_version_check=True).attachments.delete( attachment_id) except cinder_exception.ClientException as ex: with excutils.save_and_reraise_exception(): LOG.error(('Delete attachment failed for attachment ' '%(id)s. Error: %(msg)s Code: %(code)s'), {'id': attachment_id, 'msg': six.text_type(ex), 'code': getattr(ex, 'code', None)}) @translate_attachment_exception def attachment_complete(self, context, attachment_id): """Marks a volume attachment complete. This call should be used to inform Cinder that a volume attachment is fully connected on the compute host so Cinder can apply the necessary state changes to the volume info in its database. :param context: The nova request context. :param attachment_id: UUID of the volume attachment to update. """ try: cinderclient( context, '3.44', skip_version_check=True).attachments.complete( attachment_id) except cinder_exception.ClientException as ex: with excutils.save_and_reraise_exception(): LOG.error(('Complete attachment failed for attachment ' '%(id)s. Error: %(msg)s Code: %(code)s'), {'id': attachment_id, 'msg': six.text_type(ex), 'code': getattr(ex, 'code', None)})

41.725082

79

0.627379

244e0faaa1e59445a8baccb93512dc5be88873ac

20,916

py

Python

astropy/time/tests/test_methods.py

b1quint/astropy

a170a74739e4356c169429a42e554f9777b53f4d

[ "BSD-3-Clause" ]

8

2019-04-27T01:19:45.000Z

2020-09-21T03:31:01.000Z

astropy/time/tests/test_methods.py

b1quint/astropy

a170a74739e4356c169429a42e554f9777b53f4d

[ "BSD-3-Clause" ]

null

astropy/time/tests/test_methods.py

b1quint/astropy

a170a74739e4356c169429a42e554f9777b53f4d

[ "BSD-3-Clause" ]

5

2019-04-27T01:19:47.000Z

2020-09-20T15:15:19.000Z

# Licensed under a 3-clause BSD style license - see LICENSE.rst import itertools import copy import pytest import numpy as np from astropy.time import Time @pytest.fixture(scope="module", params=[True, False]) def masked(request): # Could not figure out a better way to parametrize the setup method global use_masked_data use_masked_data = request.param yield use_masked_data class TestManipulation(): """Manipulation of Time objects, ensuring attributes are done correctly.""" def setup(self): mjd = np.arange(50000, 50010) frac = np.arange(0., 0.999, 0.2) if use_masked_data: frac = np.ma.array(frac) frac[1] = np.ma.masked self.t0 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc') self.t1 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc', location=('45d', '50d')) self.t2 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc', location=(np.arange(len(frac)), np.arange(len(frac)))) # Note: location is along last axis only. self.t2 = Time(mjd[:, np.newaxis] + frac, format='mjd', scale='utc', location=(np.arange(len(frac)), np.arange(len(frac)))) def test_ravel(self, masked): t0_ravel = self.t0.ravel() assert t0_ravel.shape == (self.t0.size,) assert np.all(t0_ravel.jd1 == self.t0.jd1.ravel()) assert np.may_share_memory(t0_ravel.jd1, self.t0.jd1) assert t0_ravel.location is None t1_ravel = self.t1.ravel() assert t1_ravel.shape == (self.t1.size,) assert np.all(t1_ravel.jd1 == self.t1.jd1.ravel()) assert np.may_share_memory(t1_ravel.jd1, self.t1.jd1) assert t1_ravel.location is self.t1.location t2_ravel = self.t2.ravel() assert t2_ravel.shape == (self.t2.size,) assert np.all(t2_ravel.jd1 == self.t2.jd1.ravel()) assert np.may_share_memory(t2_ravel.jd1, self.t2.jd1) assert t2_ravel.location.shape == t2_ravel.shape # Broadcasting and ravelling cannot be done without a copy. assert not np.may_share_memory(t2_ravel.location, self.t2.location) def test_flatten(self, masked): t0_flatten = self.t0.flatten() assert t0_flatten.shape == (self.t0.size,) assert t0_flatten.location is None # Flatten always makes a copy. assert not np.may_share_memory(t0_flatten.jd1, self.t0.jd1) t1_flatten = self.t1.flatten() assert t1_flatten.shape == (self.t1.size,) assert not np.may_share_memory(t1_flatten.jd1, self.t1.jd1) assert t1_flatten.location is not self.t1.location assert t1_flatten.location == self.t1.location t2_flatten = self.t2.flatten() assert t2_flatten.shape == (self.t2.size,) assert not np.may_share_memory(t2_flatten.jd1, self.t2.jd1) assert t2_flatten.location.shape == t2_flatten.shape assert not np.may_share_memory(t2_flatten.location, self.t2.location) def test_transpose(self, masked): t0_transpose = self.t0.transpose() assert t0_transpose.shape == (5, 10) assert np.all(t0_transpose.jd1 == self.t0.jd1.transpose()) assert np.may_share_memory(t0_transpose.jd1, self.t0.jd1) assert t0_transpose.location is None t1_transpose = self.t1.transpose() assert t1_transpose.shape == (5, 10) assert np.all(t1_transpose.jd1 == self.t1.jd1.transpose()) assert np.may_share_memory(t1_transpose.jd1, self.t1.jd1) assert t1_transpose.location is self.t1.location t2_transpose = self.t2.transpose() assert t2_transpose.shape == (5, 10) assert np.all(t2_transpose.jd1 == self.t2.jd1.transpose()) assert np.may_share_memory(t2_transpose.jd1, self.t2.jd1) assert t2_transpose.location.shape == t2_transpose.shape assert np.may_share_memory(t2_transpose.location, self.t2.location) # Only one check on T, since it just calls transpose anyway. t2_T = self.t2.T assert t2_T.shape == (5, 10) assert np.all(t2_T.jd1 == self.t2.jd1.T) assert np.may_share_memory(t2_T.jd1, self.t2.jd1) assert t2_T.location.shape == t2_T.location.shape assert np.may_share_memory(t2_T.location, self.t2.location) def test_diagonal(self, masked): t0_diagonal = self.t0.diagonal() assert t0_diagonal.shape == (5,) assert np.all(t0_diagonal.jd1 == self.t0.jd1.diagonal()) assert t0_diagonal.location is None assert np.may_share_memory(t0_diagonal.jd1, self.t0.jd1) t1_diagonal = self.t1.diagonal() assert t1_diagonal.shape == (5,) assert np.all(t1_diagonal.jd1 == self.t1.jd1.diagonal()) assert t1_diagonal.location is self.t1.location assert np.may_share_memory(t1_diagonal.jd1, self.t1.jd1) t2_diagonal = self.t2.diagonal() assert t2_diagonal.shape == (5,) assert np.all(t2_diagonal.jd1 == self.t2.jd1.diagonal()) assert t2_diagonal.location.shape == t2_diagonal.shape assert np.may_share_memory(t2_diagonal.jd1, self.t2.jd1) assert np.may_share_memory(t2_diagonal.location, self.t2.location) def test_swapaxes(self, masked): t0_swapaxes = self.t0.swapaxes(0, 1) assert t0_swapaxes.shape == (5, 10) assert np.all(t0_swapaxes.jd1 == self.t0.jd1.swapaxes(0, 1)) assert np.may_share_memory(t0_swapaxes.jd1, self.t0.jd1) assert t0_swapaxes.location is None t1_swapaxes = self.t1.swapaxes(0, 1) assert t1_swapaxes.shape == (5, 10) assert np.all(t1_swapaxes.jd1 == self.t1.jd1.swapaxes(0, 1)) assert np.may_share_memory(t1_swapaxes.jd1, self.t1.jd1) assert t1_swapaxes.location is self.t1.location t2_swapaxes = self.t2.swapaxes(0, 1) assert t2_swapaxes.shape == (5, 10) assert np.all(t2_swapaxes.jd1 == self.t2.jd1.swapaxes(0, 1)) assert np.may_share_memory(t2_swapaxes.jd1, self.t2.jd1) assert t2_swapaxes.location.shape == t2_swapaxes.shape assert np.may_share_memory(t2_swapaxes.location, self.t2.location) def test_reshape(self, masked): t0_reshape = self.t0.reshape(5, 2, 5) assert t0_reshape.shape == (5, 2, 5) assert np.all(t0_reshape.jd1 == self.t0._time.jd1.reshape(5, 2, 5)) assert np.all(t0_reshape.jd2 == self.t0._time.jd2.reshape(5, 2, 5)) assert np.may_share_memory(t0_reshape.jd1, self.t0.jd1) assert np.may_share_memory(t0_reshape.jd2, self.t0.jd2) assert t0_reshape.location is None t1_reshape = self.t1.reshape(2, 5, 5) assert t1_reshape.shape == (2, 5, 5) assert np.all(t1_reshape.jd1 == self.t1.jd1.reshape(2, 5, 5)) assert np.may_share_memory(t1_reshape.jd1, self.t1.jd1) assert t1_reshape.location is self.t1.location # For reshape(5, 2, 5), the location array can remain the same. t2_reshape = self.t2.reshape(5, 2, 5) assert t2_reshape.shape == (5, 2, 5) assert np.all(t2_reshape.jd1 == self.t2.jd1.reshape(5, 2, 5)) assert np.may_share_memory(t2_reshape.jd1, self.t2.jd1) assert t2_reshape.location.shape == t2_reshape.shape assert np.may_share_memory(t2_reshape.location, self.t2.location) # But for reshape(5, 5, 2), location has to be broadcast and copied. t2_reshape2 = self.t2.reshape(5, 5, 2) assert t2_reshape2.shape == (5, 5, 2) assert np.all(t2_reshape2.jd1 == self.t2.jd1.reshape(5, 5, 2)) assert np.may_share_memory(t2_reshape2.jd1, self.t2.jd1) assert t2_reshape2.location.shape == t2_reshape2.shape assert not np.may_share_memory(t2_reshape2.location, self.t2.location) t2_reshape_t = self.t2.reshape(10, 5).T assert t2_reshape_t.shape == (5, 10) assert np.may_share_memory(t2_reshape_t.jd1, self.t2.jd1) assert t2_reshape_t.location.shape == t2_reshape_t.shape assert np.may_share_memory(t2_reshape_t.location, self.t2.location) # Finally, reshape in a way that cannot be a view. t2_reshape_t_reshape = t2_reshape_t.reshape(10, 5) assert t2_reshape_t_reshape.shape == (10, 5) assert not np.may_share_memory(t2_reshape_t_reshape.jd1, self.t2.jd1) assert (t2_reshape_t_reshape.location.shape == t2_reshape_t_reshape.shape) assert not np.may_share_memory(t2_reshape_t_reshape.location, t2_reshape_t.location) def test_shape_setting(self, masked): t0_reshape = self.t0.copy() mjd = t0_reshape.mjd # Creates a cache of the mjd attribute t0_reshape.shape = (5, 2, 5) assert t0_reshape.shape == (5, 2, 5) assert mjd.shape != t0_reshape.mjd.shape # Cache got cleared assert np.all(t0_reshape.jd1 == self.t0._time.jd1.reshape(5, 2, 5)) assert np.all(t0_reshape.jd2 == self.t0._time.jd2.reshape(5, 2, 5)) assert t0_reshape.location is None # But if the shape doesn't work, one should get an error. t0_reshape_t = t0_reshape.T with pytest.raises(AttributeError): t0_reshape_t.shape = (10, 5) # check no shape was changed. assert t0_reshape_t.shape == t0_reshape.T.shape assert t0_reshape_t.jd1.shape == t0_reshape.T.shape assert t0_reshape_t.jd2.shape == t0_reshape.T.shape t1_reshape = self.t1.copy() t1_reshape.shape = (2, 5, 5) assert t1_reshape.shape == (2, 5, 5) assert np.all(t1_reshape.jd1 == self.t1.jd1.reshape(2, 5, 5)) # location is a single element, so its shape should not change. assert t1_reshape.location.shape == () # For reshape(5, 2, 5), the location array can remain the same. # Note that we need to work directly on self.t2 here, since any # copy would cause location to have the full shape. self.t2.shape = (5, 2, 5) assert self.t2.shape == (5, 2, 5) assert self.t2.jd1.shape == (5, 2, 5) assert self.t2.jd2.shape == (5, 2, 5) assert self.t2.location.shape == (5, 2, 5) assert self.t2.location.strides == (0, 0, 24) # But for reshape(50), location would need to be copied, so this # should fail. oldshape = self.t2.shape with pytest.raises(AttributeError): self.t2.shape = (50,) # check no shape was changed. assert self.t2.jd1.shape == oldshape assert self.t2.jd2.shape == oldshape assert self.t2.location.shape == oldshape # reset t2 to its original. self.setup() def test_squeeze(self, masked): t0_squeeze = self.t0.reshape(5, 1, 2, 1, 5).squeeze() assert t0_squeeze.shape == (5, 2, 5) assert np.all(t0_squeeze.jd1 == self.t0.jd1.reshape(5, 2, 5)) assert np.may_share_memory(t0_squeeze.jd1, self.t0.jd1) assert t0_squeeze.location is None t1_squeeze = self.t1.reshape(1, 5, 1, 2, 5).squeeze() assert t1_squeeze.shape == (5, 2, 5) assert np.all(t1_squeeze.jd1 == self.t1.jd1.reshape(5, 2, 5)) assert np.may_share_memory(t1_squeeze.jd1, self.t1.jd1) assert t1_squeeze.location is self.t1.location t2_squeeze = self.t2.reshape(1, 1, 5, 2, 5, 1, 1).squeeze() assert t2_squeeze.shape == (5, 2, 5) assert np.all(t2_squeeze.jd1 == self.t2.jd1.reshape(5, 2, 5)) assert np.may_share_memory(t2_squeeze.jd1, self.t2.jd1) assert t2_squeeze.location.shape == t2_squeeze.shape assert np.may_share_memory(t2_squeeze.location, self.t2.location) def test_add_dimension(self, masked): t0_adddim = self.t0[:, np.newaxis, :] assert t0_adddim.shape == (10, 1, 5) assert np.all(t0_adddim.jd1 == self.t0.jd1[:, np.newaxis, :]) assert np.may_share_memory(t0_adddim.jd1, self.t0.jd1) assert t0_adddim.location is None t1_adddim = self.t1[:, :, np.newaxis] assert t1_adddim.shape == (10, 5, 1) assert np.all(t1_adddim.jd1 == self.t1.jd1[:, :, np.newaxis]) assert np.may_share_memory(t1_adddim.jd1, self.t1.jd1) assert t1_adddim.location is self.t1.location t2_adddim = self.t2[:, :, np.newaxis] assert t2_adddim.shape == (10, 5, 1) assert np.all(t2_adddim.jd1 == self.t2.jd1[:, :, np.newaxis]) assert np.may_share_memory(t2_adddim.jd1, self.t2.jd1) assert t2_adddim.location.shape == t2_adddim.shape assert np.may_share_memory(t2_adddim.location, self.t2.location) def test_take(self, masked): t0_take = self.t0.take((5, 2)) assert t0_take.shape == (2,) assert np.all(t0_take.jd1 == self.t0._time.jd1.take((5, 2))) assert t0_take.location is None t1_take = self.t1.take((2, 4), axis=1) assert t1_take.shape == (10, 2) assert np.all(t1_take.jd1 == self.t1.jd1.take((2, 4), axis=1)) assert t1_take.location is self.t1.location t2_take = self.t2.take((1, 3, 7), axis=0) assert t2_take.shape == (3, 5) assert np.all(t2_take.jd1 == self.t2.jd1.take((1, 3, 7), axis=0)) assert t2_take.location.shape == t2_take.shape t2_take2 = self.t2.take((5, 15)) assert t2_take2.shape == (2,) assert np.all(t2_take2.jd1 == self.t2.jd1.take((5, 15))) assert t2_take2.location.shape == t2_take2.shape def test_broadcast(self, masked): """Test using a callable method.""" t0_broadcast = self.t0._apply(np.broadcast_to, shape=(3, 10, 5)) assert t0_broadcast.shape == (3, 10, 5) assert np.all(t0_broadcast.jd1 == self.t0.jd1) assert np.may_share_memory(t0_broadcast.jd1, self.t0.jd1) assert t0_broadcast.location is None t1_broadcast = self.t1._apply(np.broadcast_to, shape=(3, 10, 5)) assert t1_broadcast.shape == (3, 10, 5) assert np.all(t1_broadcast.jd1 == self.t1.jd1) assert np.may_share_memory(t1_broadcast.jd1, self.t1.jd1) assert t1_broadcast.location is self.t1.location t2_broadcast = self.t2._apply(np.broadcast_to, shape=(3, 10, 5)) assert t2_broadcast.shape == (3, 10, 5) assert np.all(t2_broadcast.jd1 == self.t2.jd1) assert np.may_share_memory(t2_broadcast.jd1, self.t2.jd1) assert t2_broadcast.location.shape == t2_broadcast.shape assert np.may_share_memory(t2_broadcast.location, self.t2.location) class TestArithmetic(): """Arithmetic on Time objects, using both doubles.""" kwargs = ({}, {'axis': None}, {'axis': 0}, {'axis': 1}, {'axis': 2}) functions = ('min', 'max', 'sort') def setup(self): mjd = np.arange(50000, 50100, 10).reshape(2, 5, 1) frac = np.array([0.1, 0.1+1.e-15, 0.1-1.e-15, 0.9+2.e-16, 0.9]) if use_masked_data: frac = np.ma.array(frac) frac[1] = np.ma.masked self.t0 = Time(mjd, frac, format='mjd', scale='utc') # Define arrays with same ordinal properties frac = np.array([1, 2, 0, 4, 3]) if use_masked_data: frac = np.ma.array(frac) frac[1] = np.ma.masked self.t1 = Time(mjd + frac, format='mjd', scale='utc') self.jd = mjd + frac @pytest.mark.parametrize('kw, func', itertools.product(kwargs, functions)) def test_argfuncs(self, kw, func, masked): """ Test that np.argfunc(jd, **kw) is the same as t0.argfunc(**kw) where jd is a similarly shaped array with the same ordinal properties but all integer values. Also test the same for t1 which has the same integral values as jd. """ t0v = getattr(self.t0, 'arg' + func)(**kw) t1v = getattr(self.t1, 'arg' + func)(**kw) jdv = getattr(np, 'arg' + func)(self.jd, **kw) if self.t0.masked and kw == {'axis': None} and func == 'sort': t0v = np.ma.array(t0v, mask=self.t0.mask.reshape(t0v.shape)[t0v]) t1v = np.ma.array(t1v, mask=self.t1.mask.reshape(t1v.shape)[t1v]) jdv = np.ma.array(jdv, mask=self.jd.mask.reshape(jdv.shape)[jdv]) assert np.all(t0v == jdv) assert np.all(t1v == jdv) assert t0v.shape == jdv.shape assert t1v.shape == jdv.shape @pytest.mark.parametrize('kw, func', itertools.product(kwargs, functions)) def test_funcs(self, kw, func, masked): """ Test that np.func(jd, **kw) is the same as t1.func(**kw) where jd is a similarly shaped array and the same integral values. """ t1v = getattr(self.t1, func)(**kw) jdv = getattr(np, func)(self.jd, **kw) assert np.all(t1v.value == jdv) assert t1v.shape == jdv.shape def test_argmin(self, masked): assert self.t0.argmin() == 2 assert np.all(self.t0.argmin(axis=0) == 0) assert np.all(self.t0.argmin(axis=1) == 0) assert np.all(self.t0.argmin(axis=2) == 2) def test_argmax(self, masked): assert self.t0.argmax() == self.t0.size - 2 if masked: # The 0 is where all entries are masked in that axis assert np.all(self.t0.argmax(axis=0) == [1, 0, 1, 1, 1]) assert np.all(self.t0.argmax(axis=1) == [4, 0, 4, 4, 4]) else: assert np.all(self.t0.argmax(axis=0) == 1) assert np.all(self.t0.argmax(axis=1) == 4) assert np.all(self.t0.argmax(axis=2) == 3) def test_argsort(self, masked): order = [2, 0, 4, 3, 1] if masked else [2, 0, 1, 4, 3] assert np.all(self.t0.argsort() == np.array(order)) assert np.all(self.t0.argsort(axis=0) == np.arange(2).reshape(2, 1, 1)) assert np.all(self.t0.argsort(axis=1) == np.arange(5).reshape(5, 1)) assert np.all(self.t0.argsort(axis=2) == np.array(order)) ravel = np.arange(50).reshape(-1, 5)[:, order].ravel() if masked: t0v = self.t0.argsort(axis=None) # Manually remove elements in ravel that correspond to masked # entries in self.t0. This removes the 10 entries that are masked # which show up at the end of the list. mask = self.t0.mask.ravel()[ravel] ravel = ravel[~mask] assert np.all(t0v[:-10] == ravel) else: assert np.all(self.t0.argsort(axis=None) == ravel) def test_min(self, masked): assert self.t0.min() == self.t0[0, 0, 2] assert np.all(self.t0.min(0) == self.t0[0]) assert np.all(self.t0.min(1) == self.t0[:, 0]) assert np.all(self.t0.min(2) == self.t0[:, :, 2]) assert self.t0.min(0).shape == (5, 5) assert self.t0.min(0, keepdims=True).shape == (1, 5, 5) assert self.t0.min(1).shape == (2, 5) assert self.t0.min(1, keepdims=True).shape == (2, 1, 5) assert self.t0.min(2).shape == (2, 5) assert self.t0.min(2, keepdims=True).shape == (2, 5, 1) def test_max(self, masked): assert self.t0.max() == self.t0[-1, -1, -2] assert np.all(self.t0.max(0) == self.t0[1]) assert np.all(self.t0.max(1) == self.t0[:, 4]) assert np.all(self.t0.max(2) == self.t0[:, :, 3]) assert self.t0.max(0).shape == (5, 5) assert self.t0.max(0, keepdims=True).shape == (1, 5, 5) def test_ptp(self, masked): assert self.t0.ptp() == self.t0.max() - self.t0.min() assert np.all(self.t0.ptp(0) == self.t0.max(0) - self.t0.min(0)) assert self.t0.ptp(0).shape == (5, 5) assert self.t0.ptp(0, keepdims=True).shape == (1, 5, 5) def test_sort(self, masked): order = [2, 0, 4, 3, 1] if masked else [2, 0, 1, 4, 3] assert np.all(self.t0.sort() == self.t0[:, :, order]) assert np.all(self.t0.sort(0) == self.t0) assert np.all(self.t0.sort(1) == self.t0) assert np.all(self.t0.sort(2) == self.t0[:, :, order]) if not masked: assert np.all(self.t0.sort(None) == self.t0[:, :, order].ravel()) # Bit superfluous, but good to check. assert np.all(self.t0.sort(-1)[:, :, 0] == self.t0.min(-1)) assert np.all(self.t0.sort(-1)[:, :, -1] == self.t0.max(-1)) def test_regression(): # For #5225, where a time with a single-element delta_ut1_utc could not # be copied, flattened, or ravelled. (For copy, it is in test_basic.) t = Time(49580.0, scale='tai', format='mjd') t_ut1 = t.ut1 t_ut1_copy = copy.deepcopy(t_ut1) assert type(t_ut1_copy.delta_ut1_utc) is np.ndarray t_ut1_flatten = t_ut1.flatten() assert type(t_ut1_flatten.delta_ut1_utc) is np.ndarray t_ut1_ravel = t_ut1.ravel() assert type(t_ut1_ravel.delta_ut1_utc) is np.ndarray assert t_ut1_copy.delta_ut1_utc == t_ut1.delta_ut1_utc

48.082759

79

0.619143

565a590fde0f19a720809e9f9d74acd1017a1702

153

py

Python

vectors2d/__init__.py

MitryP/vectors

5ff73419c194a8228a4b03b83dcb2319685885e7

[ "MIT" ]

2

2021-01-21T21:23:18.000Z

2021-01-24T14:34:11.000Z

vectors2d/__init__.py

MitryP/vectors

5ff73419c194a8228a4b03b83dcb2319685885e7

[ "MIT" ]

null

vectors2d/__init__.py

MitryP/vectors

5ff73419c194a8228a4b03b83dcb2319685885e7

[ "MIT" ]

null

from vectors2d.vectors2d import Vector2D, absolute_vector, sum_vectors, sub_vectors, mult_vector, scalar_mult_vectors, \ get_angle, vector_from_dots

51

120

0.836601

219dc969e7050db61674426166dbf32f37150d6c

2,701

py

Python

train.py

RajputJay41/Using-bert-and-pytorch-for-IMDB

74100f7a9d1121c49d28e047c736da06fee48e5c

[ "MIT" ]

2

2020-10-01T14:36:27.000Z

2020-10-19T08:57:54.000Z

train.py

RajputJay41/Using-bert-and-pytorch-for-IMDB

74100f7a9d1121c49d28e047c736da06fee48e5c

[ "MIT" ]

null

train.py

RajputJay41/Using-bert-and-pytorch-for-IMDB

74100f7a9d1121c49d28e047c736da06fee48e5c

[ "MIT" ]

1

2021-07-05T06:56:30.000Z

import config import pandas as pd from sklearn import model_selection import dataset import torch from model import BERTBaseUncased from transformers import AdamW from transformers import get_linear_schedule_with_warmup import engine from sklearn import metrics import numpy as np import torch.nn as nn def run(): dfx = pd.read_csv(config.TRAINING_FILE).fillna("none") dfx.sentiment = dfx.sentiment.apply( lambda x: 1 if x == "positve" else 0 ) df_train, df_valid = model_selection.train_test_split( dfx, test_size = 0.1, random_state = 42, stratify = dfx.sentiment.values ) df_train = df_train.reset_index(drop=True) df_valid = df_valid.reset_index(drop=True) train_dataset = dataset.BERTDataset( review = df_train.review.values, target = df_train.sentiment.values ) train_data_loader = torch.utils.data.DataLoader( train_dataset, batch_size=config.TRAIN_BATCH_SIZE, num_workers=4 ) valid_dataset = dataset.BERTDataset( review = df_valid.review.values, target = df_valid.sentiment.values ) valid_data_loader = torch.utils.data.DataLoader( valid_dataset, batch_size=config.VALID_BATCH_SIZE, num_workers=1 ) device = torch.device(config.DEVICE) model = BERTBaseUncased() model.to(device) param_optimizer = list(model.named_parameters()) no_decay = ["bias", "LayerNorm.bais", "LayerNorm.weight"] optimizer_parameters = [ { "params": [ p for n, p in param_optimizer if not any(nd in n for nd in no_decay) ], "weight_decay": 0.001, }, { "params": [ p for n, p in param_optimizer if any(nd in n for nd in no_decay) ], "weight_decay": 0.0, }, ] num_train_steps = int(len(df_train) / config.TRAIN_BATCH_SIZE * config.EPOCHS) optimizer = AdamW(optimizer_parameters, lr=3e-5) scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=0, num_training_steps=num_train_steps) best_accuracy = 0 for epoch in range(config.EPOCHS): engine.train_fn(train_data_loader, model, optimizer, device, scheduler) outputs, targets = engine.eval_fn(valid_data_loader, model, device) outputs = np.array(outputs) >= 0.5 accuracy = metrics.accuracy_score(targets, outputs) print(f"Accuracy Score = {accuracy}") if accuracy > best_accuracy: torch.save(model.state_dict(), config.MODEL_PATH) best_accuracy = accuracy if __name__ == "__main__": run()

27.01

114

0.65124

0795099f6bad0a4a94eecf02a511058619d1f2db

613

py

Python

dj_twitter_clone_app/blog/urls.py

ivanprytula/dj_demo_app

49ca506b22d3d99608e192b28787e185b39d3c24

[ "MIT" ]

null

dj_twitter_clone_app/blog/urls.py

ivanprytula/dj_demo_app

49ca506b22d3d99608e192b28787e185b39d3c24

[ "MIT" ]

null

dj_twitter_clone_app/blog/urls.py

ivanprytula/dj_demo_app

49ca506b22d3d99608e192b28787e185b39d3c24

[ "MIT" ]

null

from django.urls import path from .views import ( BlogListView, PostCreateView, PostDetailView, PostUpdateView, PostDeleteView, BlogCategory, ) app_name = 'blog' urlpatterns = [ path('', BlogListView.as_view(), name='blog_list'), path('new/', PostCreateView.as_view(), name='post_new'), path('<int:pk>/', PostDetailView.as_view(), name='post_detail'), path('<int:pk>/update', PostUpdateView.as_view(), name='post_update'), path('<int:pk>/delete', PostDeleteView.as_view(), name='post_delete'), path('<category>/', BlogCategory.as_view(), name='post_category'), ]

29.190476

74

0.673736

6582135770973670789e3ad716e2d1136ee7c2a5

1,438

py

Python

pages/__init__.py

djaodjin/djaodjin-pages

e225783b30f82c79fd947e584b1fb61df7dff423

[ "BSD-2-Clause" ]

11

2015-04-26T20:13:12.000Z

2021-10-01T06:43:38.000Z

pages/__init__.py

djaodjin/djaodjin-pages

e225783b30f82c79fd947e584b1fb61df7dff423

[ "BSD-2-Clause" ]

32

2015-03-17T22:05:07.000Z

2022-03-30T16:37:03.000Z

pages/__init__.py

djaodjin/djaodjin-pages

e225783b30f82c79fd947e584b1fb61df7dff423

[ "BSD-2-Clause" ]

10

2015-04-26T20:12:25.000Z

2021-02-02T01:42:38.000Z

# Copyright (c) 2021, Djaodjin Inc. # All rights reserved. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, # THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR # PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR # CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, # EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, # PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; # OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, # WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR # OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF # ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. """ PEP 386-compliant version number for the pages django app. """ __version__ = '0.4.3-dev'

47.933333

78

0.778164

47f0ac4de0d8cda7a0b2d3010dc257ed50c86d62

1,425

py

Python

filtering/Frequency_Filtering.py

NANOGravDataManagement/bridge

a03cf262767b210d1fbb62bb934f87cf411f1885

[ "Apache-2.0" ]

null

filtering/Frequency_Filtering.py

NANOGravDataManagement/bridge

a03cf262767b210d1fbb62bb934f87cf411f1885

[ "Apache-2.0" ]

null

filtering/Frequency_Filtering.py

NANOGravDataManagement/bridge

a03cf262767b210d1fbb62bb934f87cf411f1885

[ "Apache-2.0" ]

null

# Frequency_Filtering.py # A script that takes in a .tim file, frequency range, and an output directory # as a result, it creates a new file with frequencies in the frequency range, stored in output directory # sample input: # python Frequency_Filtering.py /Users/fkeri/Desktop/B1855+09_NANOGrav_9yv0.tim 1300.0 1600.0 /Users/fkeri/Desktop/ # we can see that it takes in 4 line arguments: [INPUT FILE], [FREQ START], [FREQ END], [OUTPUT DIRECTORY] # the output file will have the same name as the input file, with "FreqRange_" as a prefix: "FreqRange_B1855+09_NANOGrav_9yv0.tim" # it is possible to name the output file differently by putting the file name in [OUTPUT DIRECTORY]: /Users/fkeri/Desktop/filename.tim import sys import os.path inFile = open( sys.argv[1], "r" ) inFile.readline() #omit first line ALLlines = inFile.readlines() start = float( sys.argv[2] ) end = float( sys.argv[3] ) save_path = sys.argv[4] if save_path[-4] != '.': nameFile = os.path.join( save_path, "FreqRange_"+sys.argv[1].split("/")[-1] ) else: nameFile = save_path outFile = open( nameFile, "w" ) L = [] for i in range( 0, len( ALLlines ) ): L.append( ALLlines[i].split(' ') ) #L.sort(key = lamda row: row[2] ) for i in range( 0, len( ALLlines )): Frequency = float( L[i][1] ) if(Frequency >= start and Frequency <= end ): X = ' '.join( L[i] ) outFile.write( X ) inFile.close() outFile.close()

34.756098

134

0.687018

074124152a1b7afa6a29332bace989e302a0c03c

298

py

Python

forms.py

yichao-l/amr-eager-multilingual

6d96445a23ff493ceedea02712fbcceffe08b879

[ "BSD-2-Clause" ]

15

2018-02-26T08:02:31.000Z

2021-11-05T05:49:43.000Z

forms.py

yichao-l/amr-eager-multilingual

6d96445a23ff493ceedea02712fbcceffe08b879

[ "BSD-2-Clause" ]

1

2021-01-06T09:27:40.000Z

2021-01-06T12:45:08.000Z

forms.py

yichao-l/amr-eager-multilingual

6d96445a23ff493ceedea02712fbcceffe08b879

[ "BSD-2-Clause" ]

7

2018-07-24T21:21:03.000Z

2022-03-07T06:19:58.000Z

from flask_wtf import FlaskForm from wtforms import StringField, TextAreaField from wtforms.validators import DataRequired class InputForm(FlaskForm): #input_sent = StringField('input_sent', validators=[DataRequired()]) input_sent = TextAreaField('input_sent', validators=[DataRequired()])

42.571429

73

0.802013

7703cf9e0cb59214b34b7c7d8683840d2d78ea6e

28,943

py

Python

features/eolearn/features/interpolation.py

chorng/eo-learn

a1a3c6fa5568d398f5e43f5ad5aecdfeb05e8d3c

[ "MIT" ]

null

features/eolearn/features/interpolation.py

chorng/eo-learn

a1a3c6fa5568d398f5e43f5ad5aecdfeb05e8d3c

[ "MIT" ]

null

features/eolearn/features/interpolation.py

chorng/eo-learn

a1a3c6fa5568d398f5e43f5ad5aecdfeb05e8d3c

[ "MIT" ]

null

""" Module for interpolating, smoothing and re-sampling features in EOPatch Credits: Copyright (c) 2017-2022 Matej Aleksandrov, Matej Batič, Grega Milčinski, Domagoj Korais, Matic Lubej (Sinergise) Copyright (c) 2017-2022 Žiga Lukšič, Devis Peressutti, Nejc Vesel, Jovan Višnjić, Anže Zupanc (Sinergise) Copyright (c) 2017-2019 Blaž Sovdat, Andrej Burja (Sinergise) Copyright (c) 2018-2019 Filip Koprivec (Jožef Stefan Institute) Copyright (c) 2018-2019 William Ouellette This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree. """ import warnings import datetime as dt import inspect from functools import partial import dateutil import scipy.interpolate import numpy as np from sklearn.gaussian_process import GaussianProcessRegressor from eolearn.core import EOTask, EOPatch, FeatureType, FeatureTypeSet from eolearn.core.exceptions import EOUserWarning try: import numba except ImportError as exception: warnings.warn( f"Failed to import numba with exception: '{exception}'. Some interpolation tasks won't work", EOUserWarning ) def base_interpolation_function(data, times, resampled_times): """Interpolates data feature :param data: Array in a shape of t x (h x w x n) :type data: numpy.ndarray :param times: Array of reference times relative to the first timestamp :type times: :param resampled_times: Array of reference times relative to the first timestamp in initial timestamp array. :type resampled_times: numpy.array :return: Array of interpolated values :rtype: numpy.ndarray """ _, height_width_depth = data.shape new_bands = np.empty((len(resampled_times), height_width_depth)) for n_feat in numba.prange(height_width_depth): mask1d = ~np.isnan(data[:, n_feat]) if not mask1d.any(): new_data = np.empty(len(resampled_times)) new_data[:] = np.nan else: new_data = np.interp( resampled_times.astype(np.float64), times[mask1d].astype(np.float64), data[:, n_feat][mask1d].astype(np.float64), ) true_index = np.where(mask1d) index_first, index_last = true_index[0][0], true_index[0][-1] min_time, max_time = times[index_first], times[index_last] first = np.where(resampled_times < min_time)[0] if first.size: new_data[: first[-1] + 1] = np.nan last = np.where(max_time < resampled_times)[0] if last.size: new_data[last[0] :] = np.nan new_bands[:, n_feat] = new_data.astype(data.dtype) return new_bands try: # pylint: disable=invalid-name interpolation_function = numba.njit(base_interpolation_function) interpolation_function_parallel = numba.njit(base_interpolation_function, parallel=True) except NameError: pass class InterpolationTask(EOTask): """Main EOTask class for interpolation and resampling of time-series. The task takes from EOPatch the specified data feature and timestamps. For each pixel in the spatial grid it creates an interpolation model using values that are not NaN or masked with `eopatch.mask['VALID_DATA']`. Then it replaces invalid values using interpolation model. If ``resample_range`` parameter is used the values in time series will be resampled to new timestamps. In the process the interpolated feature is overwritten and so are the timestamps. After the execution of the task the feature will contain interpolated and resampled values and corresponding new timestamps. :param feature: A feature to be interpolated with optional new feature name :type feature: (FeatureType, str) or (FeatureType, str, str) :param interpolation_object: Interpolation class which is initialized with :type interpolation_object: object :param resample_range: If None the data will be only interpolated over existing timestamps and NaN values will be replaced with interpolated values (if possible) in the existing EOPatch. Otherwise ``resample_range`` can be set to tuple in a form of (start_date, end_date, step_days), e.g. ('2018-01-01', '2018-06-01', 16). This will create a new EOPatch with resampled values for times start_date, start_date + step_days, start_date + 2 * step_days, ... . End date is excluded from timestamps. Additionally, ``resample_range`` can be a list of dates or date-strings where the interpolation will be evaluated. :type resample_range: (str, str, int) or list(str) or list(datetime.datetime) or None :param result_interval: Maximum and minimum of returned data :type result_interval: (float, float) :param mask_feature: A mask feature which will be used to mask certain features :type mask_feature: (FeatureType, str) :param copy_features: List of tuples of type (FeatureType, str) or (FeatureType, str, str) that are copied over into the new EOPatch. The first string is the feature name, and the second one (optional) is a new name to be used for the feature :type copy_features: list((FeatureType, str) or (FeatureType, str, str)) :param unknown_value: Value which will be used for timestamps where interpolation cannot be calculated :type unknown_value: float or numpy.nan :param filling_factor: Multiplication factor used to create temporal gap between consecutive observations. Value has to be greater than 1. Default is `10` :type filling_factor: int :param scale_time: Factor used to scale the time difference in seconds between acquisitions. If `scale_time=60`, returned time is in minutes, if `scale_time=3600` in hours. Default is `3600` :type scale_time: int :param interpolate_pixel_wise: Flag to indicate pixel wise interpolation or fast interpolation that creates a single interpolation object for the whole image :type interpolate_pixel_wise: bool :param interpolation_parameters: Parameters which will be propagated to ``interpolation_object`` """ def __init__( self, feature, interpolation_object, *, resample_range=None, result_interval=None, mask_feature=None, copy_features=None, unknown_value=np.nan, filling_factor=10, scale_time=3600, interpolate_pixel_wise=False, **interpolation_parameters, ): self.renamed_feature = self.parse_renamed_feature(feature, allowed_feature_types=FeatureTypeSet.RASTER_TYPES_4D) self.interpolation_object = interpolation_object self.resample_range = resample_range self.result_interval = result_interval self.mask_feature_parser = ( None if mask_feature is None else self.get_feature_parser( mask_feature, allowed_feature_types={FeatureType.MASK, FeatureType.MASK_TIMELESS, FeatureType.LABEL} ) ) if resample_range is None and copy_features is not None: self.copy_features = None warnings.warn( 'Argument "copy_features" will be ignored if "resample_range" is None. Nothing to copy.', EOUserWarning ) else: self.copy_features_parser = None if copy_features is None else self.get_feature_parser(copy_features) self.unknown_value = unknown_value self.interpolation_parameters = interpolation_parameters self.scale_time = scale_time self.filling_factor = filling_factor self.interpolate_pixel_wise = interpolate_pixel_wise self._resampled_times = None @staticmethod def _mask_feature_data(feature_data, mask, mask_type): """Masks values of data feature with a given mask of given mask type. The masking is done by assigning `numpy.nan` value. :param feature_data: Data array which will be masked :type feature_data: numpy.ndarray :param mask: Mask array :type mask: numpy.ndarray :param mask_type: Feature type of mask :type mask_type: FeatureType :return: Masked data array :rtype: numpy.ndarray """ if mask_type.is_spatial() and feature_data.shape[1:3] != mask.shape[-3:-1]: raise ValueError( f"Spatial dimensions of interpolation and mask feature do not match: {feature_data.shape} {mask.shape}" ) if mask_type.is_temporal() and feature_data.shape[0] != mask.shape[0]: raise ValueError( f"Time dimension of interpolation and mask feature do not match: {feature_data.shape} {mask.shape}" ) # This allows masking each channel differently but causes some complications while masking with label if mask.shape[-1] != feature_data.shape[-1]: mask = mask[..., 0] if mask_type is FeatureType.MASK: feature_data[mask, ...] = np.nan elif mask_type is FeatureType.MASK_TIMELESS: feature_data[:, mask, ...] = np.nan elif mask_type is FeatureType.LABEL: np.swapaxes(feature_data, 1, 3) feature_data[mask, ..., :, :] = np.nan np.swapaxes(feature_data, 1, 3) return feature_data @staticmethod def _get_start_end_nans(data): """Find NaN values in data that either start or end the time-series Function to return a binary array of same size as data where `True` values correspond to NaN values present at beginning or end of time-series. NaNs internal to the time-series are not included in the binary mask. :param data: Array of observations of size TxNOBS :type data: numpy.array :return: Binary array of shape TxNOBS. `True` values indicate NaNs present at beginning or end of time-series :rtype: numpy.array """ # find NaNs that start a time-series start_nan = np.isnan(data) for idx, row in enumerate(start_nan[:-1]): start_nan[idx + 1] = np.logical_and(row, start_nan[idx + 1]) # find NaNs that end a time-series end_nan = np.isnan(data) for idx, row in enumerate(end_nan[-2::-1]): end_nan[-idx - 2] = np.logical_and(row, end_nan[-idx - 1]) return np.logical_or(start_nan, end_nan) @staticmethod def _get_unique_times(data, times): """Replace duplicate acquisitions which have same values on the chosen time scale with their average. The average is calculated with numpy.nanmean, meaning that NaN values are ignored when calculating the average. :param data: Array in a shape of t x nobs, where nobs = h x w x n :type data: numpy.ndarray :param times: Array of reference times relative to the first timestamp :type times: numpy.array :return: cleaned versions of data input :rtype: numpy.ndarray :return: cleaned versions of times input :rtype: numpy.array """ seen = set() duplicated_indices = np.array( [idx for idx, item in enumerate(times) if item in seen or seen.add(item)], dtype=int ) duplicated_times = np.unique(times[duplicated_indices]) for time in duplicated_times: indices = np.where(times == time)[0] nan_mask = np.all(np.isnan(data[indices]), axis=0) data[indices[0], ~nan_mask] = np.nanmean(data[indices][:, ~nan_mask], axis=0) times = np.delete(times, duplicated_indices, axis=0) data = np.delete(data, duplicated_indices, axis=0) return data, times def _copy_old_features(self, new_eopatch, old_eopatch): """Copy features from old EOPatch :param new_eopatch: New EOPatch container where the old features will be copied to :type new_eopatch: EOPatch :param old_eopatch: Old EOPatch container where the old features are located :type old_eopatch: EOPatch """ if self.copy_features_parser is not None: existing_features = set(new_eopatch.get_feature_list()) renamed_features = self.copy_features_parser.get_renamed_features(old_eopatch) for copy_feature_type, copy_feature_name, copy_new_feature_name in renamed_features: new_feature = copy_feature_type, copy_new_feature_name if new_feature in existing_features: raise ValueError( f"Feature {copy_new_feature_name} of {copy_feature_type} already exists in the " "new EOPatch! Use a different name!" ) existing_features.add(new_feature) new_eopatch[copy_feature_type][copy_new_feature_name] = old_eopatch[copy_feature_type][ copy_feature_name ] return new_eopatch def interpolate_data(self, data, times, resampled_times): """Interpolates data feature :param data: Array in a shape of t x nobs, where nobs = h x w x n :type data: numpy.ndarray :param times: Array of reference times relative to the first timestamp :type times: numpy.array :param resampled_times: Array of reference times relative to the first timestamp in initial timestamp array. :type resampled_times: numpy.array :return: Array of interpolated values :rtype: numpy.ndarray """ # pylint: disable=too-many-locals # get size of 2d array t x nobs nobs = data.shape[-1] if self.interpolate_pixel_wise: # initialise array of interpolated values new_data = ( data if self.resample_range is None else np.full((len(resampled_times), nobs), np.nan, dtype=data.dtype) ) # Interpolate for each pixel, could be easily parallelized for obs in range(nobs): valid = ~np.isnan(data[:, obs]) obs_interpolating_func = self.get_interpolation_function(times[valid], data[valid, obs]) new_data[:, obs] = obs_interpolating_func(resampled_times[:, np.newaxis]) # return interpolated values return new_data # mask representing overlap between reference and resampled times time_mask = (resampled_times >= np.min(times)) & (resampled_times <= np.max(times)) # define time values as linear monotonically increasing over the observations const = int(self.filling_factor * (np.max(times) - np.min(times))) temp_values = times[:, np.newaxis] + const * np.arange(nobs)[np.newaxis, :].astype(np.float64) res_temp_values = resampled_times[:, np.newaxis] + const * np.arange(nobs)[np.newaxis, :].astype(np.float64) # initialise array of interpolated values new_data = np.full((len(resampled_times), nobs), np.nan, dtype=data.dtype) # array defining index correspondence between reference times and resampled times ori2res = np.array( [ np.abs(resampled_times - o).argmin() if np.min(resampled_times) <= o <= np.max(resampled_times) else None for o in times ] ) # find NaNs that start or end a time-series row_nans, col_nans = np.where(self._get_start_end_nans(data)) nan_row_res_indices = np.array([index for index in ori2res[row_nans] if index is not None], dtype=np.int32) nan_col_res_indices = np.array([index is not None for index in ori2res[row_nans]], dtype=bool) if nan_row_res_indices.size: # mask out from output values the starting/ending NaNs res_temp_values[nan_row_res_indices, col_nans[nan_col_res_indices]] = np.nan # if temporal values outside the reference dates are required (extrapolation) masked them to NaN res_temp_values[~time_mask, :] = np.nan # build 1d array for interpolation. Spline functions require monotonically increasing values of x, # so .T is used input_x = temp_values.T[~np.isnan(data).T] input_y = data.T[~np.isnan(data).T] # build interpolation function if len(input_x) > 1: interp_func = self.get_interpolation_function(input_x, input_y) # interpolate non-NaN values in resampled time values new_data[~np.isnan(res_temp_values)] = interp_func(res_temp_values[~np.isnan(res_temp_values)]) # return interpolated values return new_data def get_interpolation_function(self, times, series): """Initializes interpolation model :param times: Array of reference times in second relative to the first timestamp :type times: numpy.array :param series: One dimensional array of time series :type series: numpy.array :return: Initialized interpolation model class """ if str(inspect.getmodule(self.interpolation_object))[9:14] == "numpy": return partial(self.interpolation_object, xp=times, fp=series, left=np.nan, right=np.nan) return self.interpolation_object(times, series, **self.interpolation_parameters) def get_resampled_timestamp(self, timestamp): """Takes a list of timestamps and generates new list of timestamps according to ``resample_range`` :param timestamp: list of timestamps :type timestamp: list(datetime.datetime) :return: new list of timestamps :rtype: list(datetime.datetime) """ if self.resample_range is None: return timestamp if not isinstance(self.resample_range, (tuple, list)): raise ValueError(f"Invalid resample_range {self.resample_range}, expected tuple") if tuple(map(type, self.resample_range)) == (str, str, int): start_date = dateutil.parser.parse(self.resample_range[0]) end_date = dateutil.parser.parse(self.resample_range[1]) step = dt.timedelta(days=self.resample_range[2]) days = [start_date] while days[-1] + step < end_date: days.append(days[-1] + step) elif self.resample_range and np.all([isinstance(date, str) for date in self.resample_range]): days = [dateutil.parser.parse(date) for date in self.resample_range] elif self.resample_range and np.all([isinstance(date, dt.datetime) for date in self.resample_range]): days = list(self.resample_range) else: raise ValueError("Invalid format in {self.resample_range}, expected strings or datetimes") return days def execute(self, eopatch): """Execute method that processes EOPatch and returns EOPatch""" # pylint: disable=too-many-locals feature_type, feature_name, new_feature_name = self.renamed_feature # Make a copy not to change original numpy array feature_data = eopatch[feature_type][feature_name].copy() time_num, height, width, band_num = feature_data.shape if time_num <= 1: raise ValueError( f"Feature {(feature_type, feature_name)} has time dimension of size {time_num}, " "required at least size 2" ) # Apply a mask on data if self.mask_feature_parser is not None: for mask_type, mask_name in self.mask_feature_parser.get_features(eopatch): negated_mask = ~eopatch[mask_type][mask_name].astype(bool) feature_data = self._mask_feature_data(feature_data, negated_mask, mask_type) # Flatten array feature_data = np.reshape(feature_data, (time_num, height * width * band_num)) # If resampling create new EOPatch new_eopatch = EOPatch() if self.resample_range else eopatch # Resample times times = eopatch.get_time_series(scale_time=self.scale_time) new_eopatch.timestamp = self.get_resampled_timestamp(eopatch.timestamp) total_diff = int((new_eopatch.timestamp[0].date() - eopatch.timestamp[0].date()).total_seconds()) resampled_times = new_eopatch.get_time_series(scale_time=self.scale_time) + total_diff // self.scale_time # Add BBox to eopatch if it was created anew if new_eopatch.bbox is None: new_eopatch.bbox = eopatch.bbox # Replace duplicate acquisitions which have same values on the chosen time scale with their average feature_data, times = self._get_unique_times(feature_data, times) # Interpolate feature_data = self.interpolate_data(feature_data, times, resampled_times) # Normalize if self.result_interval: min_val, max_val = self.result_interval valid_mask = ~np.isnan(feature_data) feature_data[valid_mask] = np.maximum(np.minimum(feature_data[valid_mask], max_val), min_val) # Replace unknown value if not np.isnan(self.unknown_value): feature_data[np.isnan(feature_data)] = self.unknown_value # Reshape back new_eopatch[feature_type][new_feature_name] = np.reshape( feature_data, (feature_data.shape[0], height, width, band_num) ) # append features from old patch new_eopatch = self._copy_old_features(new_eopatch, eopatch) return new_eopatch class LinearInterpolationTask(InterpolationTask): """Implements `eolearn.features.InterpolationTask` by using `numpy.interp` and `@numba.jit(nopython=True)` :param parallel: interpolation is calculated in parallel using as many CPUs as detected by the multiprocessing module. :type parallel: bool :param kwargs: parameters of InterpolationTask(EOTask) """ def __init__(self, feature, parallel=False, **kwargs): self.parallel = parallel super().__init__(feature, np.interp, **kwargs) def interpolate_data(self, data, times, resampled_times): """Interpolates data feature :param data: Array in a shape of t x nobs, where nobs = h x w x n :type data: numpy.ndarray :param times: Array of reference times in second relative to the first timestamp :type times: numpy.array :param resampled_times: Array of reference times in second relative to the first timestamp in initial timestamp array. :type resampled_times: numpy.array :return: Array of interpolated values :rtype: numpy.ndarray """ if self.parallel: return interpolation_function_parallel(data, times, resampled_times) return interpolation_function(data, times, resampled_times) class CubicInterpolationTask(InterpolationTask): """ Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.interp1d(kind='cubic')` """ def __init__(self, feature, **kwargs): super().__init__(feature, scipy.interpolate.interp1d, kind="cubic", **kwargs) class SplineInterpolationTask(InterpolationTask): """ Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.UnivariateSpline` """ def __init__(self, feature, *, spline_degree=3, smoothing_factor=0, **kwargs): super().__init__(feature, scipy.interpolate.UnivariateSpline, k=spline_degree, s=smoothing_factor, **kwargs) class BSplineInterpolationTask(InterpolationTask): """ Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.BSpline` """ def __init__(self, feature, *, spline_degree=3, **kwargs): super().__init__(feature, scipy.interpolate.make_interp_spline, k=spline_degree, **kwargs) class AkimaInterpolationTask(InterpolationTask): """ Implements `eolearn.features.InterpolationTask` by using `scipy.interpolate.Akima1DInterpolator` """ def __init__(self, feature, **kwargs): super().__init__(feature, scipy.interpolate.Akima1DInterpolator, **kwargs) class KrigingObject: """ Interpolation function like object for Kriging """ def __init__(self, times, series, **kwargs): self.regressor = GaussianProcessRegressor(**kwargs) # Since most of data is close to zero (relatively to time points), first get time data in [0,1] range # to ensure nonzero results # Should normalize by max in resample time to be totally consistent, # but this works fine (0.03% error in testing) self.normalizing_factor = max(times) - min(times) self.regressor.fit(times.reshape(-1, 1) / self.normalizing_factor, series) self.call_args = kwargs.get("call_args", {}) def __call__(self, new_times, **kwargs): call_args = self.call_args.copy() call_args.update(kwargs) return self.regressor.predict(new_times.reshape(-1, 1) / self.normalizing_factor, **call_args) class KrigingInterpolationTask(InterpolationTask): """ Implements `eolearn.features.InterpolationTask` by using `sklearn.gaussian_process.GaussianProcessRegressor` Gaussian processes (superset of kriging) are especially used in geological missing data estimation. Compared to spline interpolation, gaussian processes produce much more smoothed results (which may or may not be desirable). """ def __init__(self, feature, **kwargs): super().__init__(feature, KrigingObject, interpolate_pixel_wise=True, **kwargs) class ResamplingTask(InterpolationTask): """ A subclass of InterpolationTask task that works only with data with no missing, masked or invalid values. It always resamples timeseries to different timestamps. """ def __init__( self, feature, interpolation_object, resample_range, *, result_interval=None, unknown_value=np.nan, **interpolation_parameters, ): if resample_range is None: raise ValueError("resample_range parameter must be in form ('start_date', 'end_date', step_days)") super().__init__( feature, interpolation_object, resample_range=resample_range, result_interval=result_interval, unknown_value=unknown_value, **interpolation_parameters, ) def interpolate_data(self, data, times, resampled_times): """Interpolates data feature :param data: Array in a shape of t x nobs, where nobs = h x w x n :type data: numpy.ndarray :param times: Array of reference times in second relative to the first timestamp :type times: numpy.array :param resampled_times: Array of reference times in second relative to the first timestamp in initial timestamp array. :type resampled_times: numpy.array :return: Array of interpolated values :rtype: numpy.ndarray """ if True in np.unique(np.isnan(data)): raise ValueError("Data must not contain any masked/invalid pixels or NaN values") interp_func = self.get_interpolation_function(times, data) time_mask = (resampled_times >= np.min(times)) & (resampled_times <= np.max(times)) new_data = np.full((resampled_times.size,) + data.shape[1:], np.nan, dtype=data.dtype) new_data[time_mask] = interp_func(resampled_times[time_mask]) return new_data def get_interpolation_function(self, times, series): """Initializes interpolation model :param times: Array of reference times in second relative to the first timestamp :type times: numpy.array :param series: One dimensional array of time series :type series: numpy.array :return: Initialized interpolation model class """ return self.interpolation_object(times, series, axis=0, **self.interpolation_parameters) class NearestResamplingTask(ResamplingTask): """ Implements `eolearn.features.ResamplingTask` by using `scipy.interpolate.interp1d(kind='nearest')` """ def __init__(self, feature, resample_range, **kwargs): super().__init__(feature, scipy.interpolate.interp1d, resample_range, kind="nearest", **kwargs) class LinearResamplingTask(ResamplingTask): """ Implements `eolearn.features.ResamplingTask` by using `scipy.interpolate.interp1d(kind='linear')` """ def __init__(self, feature, resample_range, **kwargs): super().__init__(feature, scipy.interpolate.interp1d, resample_range, kind="linear", **kwargs) class CubicResamplingTask(ResamplingTask): """ Implements `eolearn.features.ResamplingTask` by using `scipy.interpolate.interp1d(kind='cubic')` """ def __init__(self, feature, resample_range, **kwargs): super().__init__(feature, scipy.interpolate.interp1d, resample_range, kind="cubic", **kwargs)

42.815089

120

0.675569

f1d12e90d1036a74c57f1b2fc14a287e73f72f3d

304

py

Python

bdd/entry_scenarios.py

nataliasviattseva/python_training

04572706948c90bba9714cb082e7ee1e2fcb52f1

[ "Apache-2.0" ]

null

bdd/entry_scenarios.py

nataliasviattseva/python_training

04572706948c90bba9714cb082e7ee1e2fcb52f1

[ "Apache-2.0" ]

null

bdd/entry_scenarios.py

nataliasviattseva/python_training

04572706948c90bba9714cb082e7ee1e2fcb52f1

[ "Apache-2.0" ]

null

from pytest_bdd import scenario from .entry_steps import * @scenario('entries.feature', 'Add new entry') def test_add_new_entry(): pass @scenario('entries.feature', 'Edit the entry') def test_edit_entry(): pass @scenario('entries.feature', 'Delete the entry') def test_delete_entry(): pass

21.714286

48

0.733553

49f6ec66b2f72a52234ec5421420dc35c4beb5aa

5,383

py

Python

quidel/delphi_quidel/run.py

qx-teo/covidcast-indicators

6eabe62748a206b5e6d65f9e11c65ef1c76cdb0a

[ "MIT" ]

null

quidel/delphi_quidel/run.py

qx-teo/covidcast-indicators

6eabe62748a206b5e6d65f9e11c65ef1c76cdb0a

[ "MIT" ]

5

2021-08-18T17:33:13.000Z

2021-08-19T15:09:22.000Z

quidel/delphi_quidel/run.py

qx-teo/covidcast-indicators

6eabe62748a206b5e6d65f9e11c65ef1c76cdb0a

[ "MIT" ]

null

# -*- coding: utf-8 -*- """Functions to call when running the function. This module should contain a function called `run_module`, that is executed when the module is run with `python -m MODULE_NAME`. """ import time from os.path import join from typing import Dict, Any import pandas as pd from delphi_utils import ( add_prefix, create_export_csv, get_structured_logger ) from .constants import (END_FROM_TODAY_MINUS, EXPORT_DAY_RANGE, GEO_RESOLUTIONS, SENSORS) from .generate_sensor import (generate_sensor_for_states, generate_sensor_for_other_geores) from .geo_maps import geo_map from .pull import (pull_quidel_data, check_export_start_date, check_export_end_date, update_cache_file) def run_module(params: Dict[str, Any]): """Run Quidel flu test module. The `params` argument is expected to have the following structure: - "common": - "export_dir": str, directory to write output - "log_exceptions" (optional): bool, whether to log exceptions to file - "log_filename" (optional): str, name of file to write logs - indicator": - "static_file_dir": str, directory name with population information - "input_cache_dir": str, directory in which to cache input data - "export_start_date": str, YYYY-MM-DD format of earliest date to create output - "export_end_date": str, YYYY-MM-DD format of latest date to create output or "" to create through the present - "pull_start_date": str, YYYY-MM-DD format of earliest date to pull input - "pull_end_date": str, YYYY-MM-DD format of latest date to create output or "" to create through the present - "aws_credentials": Dict[str, str], authentication parameters for AWS S3; see S3 documentation - "bucket_name": str, name of AWS bucket in which to find data - "wip_signal": List[str], list of signal names that are works in progress - "test_mode": bool, whether we are running in test mode """ start_time = time.time() logger = get_structured_logger( __name__, filename=params["common"].get("log_filename"), log_exceptions=params["common"].get("log_exceptions", True)) cache_dir = params["indicator"]["input_cache_dir"] export_dir = params["common"]["export_dir"] static_file_dir = params["indicator"]["static_file_dir"] export_start_dates = params["indicator"]["export_start_date"] export_end_dates = params["indicator"]["export_end_date"] map_df = pd.read_csv( join(static_file_dir, "fips_prop_pop.csv"), dtype={"fips": int} ) # Pull data and update export date dfs, _end_date = pull_quidel_data(params["indicator"]) if _end_date is None: print("The data is up-to-date. Currently, no new data to be ingested.") return export_end_dates = check_export_end_date(export_end_dates, _end_date, END_FROM_TODAY_MINUS) export_start_dates = check_export_start_date(export_start_dates, export_end_dates, EXPORT_DAY_RANGE) # Add prefix, if required sensors = add_prefix(list(SENSORS.keys()), wip_signal=params["indicator"]["wip_signal"], prefix="wip_") for sensor in sensors: # Check either covid_ag or flu_ag test_type = "covid_ag" if "covid_ag" in sensor else "flu_ag" print("state", sensor) data = dfs[test_type].copy() state_groups = geo_map("state", data, map_df).groupby("state_id") first_date, last_date = data["timestamp"].min(), data["timestamp"].max() # For State Level state_df = generate_sensor_for_states( state_groups, smooth=SENSORS[sensor][1], device=SENSORS[sensor][0], first_date=first_date, last_date=last_date) create_export_csv(state_df, geo_res="state", sensor=sensor, export_dir=export_dir, start_date=export_start_dates[test_type], end_date=export_end_dates[test_type]) # County/HRR/MSA level for geo_res in GEO_RESOLUTIONS: print(geo_res, sensor) data = dfs[test_type].copy() data, res_key = geo_map(geo_res, data, map_df) res_df = generate_sensor_for_other_geores( state_groups, data, res_key, smooth=SENSORS[sensor][1], device=SENSORS[sensor][0], first_date=first_date, last_date=last_date) create_export_csv(res_df, geo_res=geo_res, sensor=sensor, export_dir=export_dir, start_date=export_start_dates[test_type], end_date=export_end_dates[test_type], remove_null_samples=True) # Export the cache file if the pipeline runs successfully. # Otherwise, don't update the cache file update_cache_file(dfs, _end_date, cache_dir) elapsed_time_in_seconds = round(time.time() - start_time, 2) logger.info("Completed indicator run", elapsed_time_in_seconds = elapsed_time_in_seconds)

45.235294

99

0.634033

ec34ada8d083b66e5a2d3e2434f20cfd304f5fc7

10,999

py

Python

kubernetes/client/models/v1_priority_class.py

mariusgheorghies/python

68ac7e168963d8b5a81dc493b1973d29e903a15b

[ "Apache-2.0" ]

null

kubernetes/client/models/v1_priority_class.py

mariusgheorghies/python

68ac7e168963d8b5a81dc493b1973d29e903a15b

[ "Apache-2.0" ]

null

kubernetes/client/models/v1_priority_class.py

mariusgheorghies/python

68ac7e168963d8b5a81dc493b1973d29e903a15b

[ "Apache-2.0" ]

null

# coding: utf-8 """ Kubernetes No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 The version of the OpenAPI document: v1.20.7 Generated by: https://openapi-generator.tech """ import pprint import re # noqa: F401 import six from kubernetes.client.configuration import Configuration class V1PriorityClass(object): """NOTE: This class is auto generated by OpenAPI Generator. Ref: https://openapi-generator.tech Do not edit the class manually. """ """ Attributes: openapi_types (dict): The key is attribute name and the value is attribute type. attribute_map (dict): The key is attribute name and the value is json key in definition. """ openapi_types = { 'api_version': 'str', 'description': 'str', 'global_default': 'bool', 'kind': 'str', 'metadata': 'V1ObjectMeta', 'preemption_policy': 'str', 'value': 'int' } attribute_map = { 'api_version': 'apiVersion', 'description': 'description', 'global_default': 'globalDefault', 'kind': 'kind', 'metadata': 'metadata', 'preemption_policy': 'preemptionPolicy', 'value': 'value' } def __init__(self, api_version=None, description=None, global_default=None, kind=None, metadata=None, preemption_policy=None, value=None, local_vars_configuration=None): # noqa: E501 """V1PriorityClass - a model defined in OpenAPI""" # noqa: E501 if local_vars_configuration is None: local_vars_configuration = Configuration() self.local_vars_configuration = local_vars_configuration self._api_version = None self._description = None self._global_default = None self._kind = None self._metadata = None self._preemption_policy = None self._value = None self.discriminator = None if api_version is not None: self.api_version = api_version if description is not None: self.description = description if global_default is not None: self.global_default = global_default if kind is not None: self.kind = kind if metadata is not None: self.metadata = metadata if preemption_policy is not None: self.preemption_policy = preemption_policy self.value = value @property def api_version(self): """Gets the api_version of this V1PriorityClass. # noqa: E501 APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources # noqa: E501 :return: The api_version of this V1PriorityClass. # noqa: E501 :rtype: str """ return self._api_version @api_version.setter def api_version(self, api_version): """Sets the api_version of this V1PriorityClass. APIVersion defines the versioned schema of this representation of an object. Servers should convert recognized schemas to the latest internal value, and may reject unrecognized values. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#resources # noqa: E501 :param api_version: The api_version of this V1PriorityClass. # noqa: E501 :type: str """ self._api_version = api_version @property def description(self): """Gets the description of this V1PriorityClass. # noqa: E501 description is an arbitrary string that usually provides guidelines on when this priority class should be used. # noqa: E501 :return: The description of this V1PriorityClass. # noqa: E501 :rtype: str """ return self._description @description.setter def description(self, description): """Sets the description of this V1PriorityClass. description is an arbitrary string that usually provides guidelines on when this priority class should be used. # noqa: E501 :param description: The description of this V1PriorityClass. # noqa: E501 :type: str """ self._description = description @property def global_default(self): """Gets the global_default of this V1PriorityClass. # noqa: E501 globalDefault specifies whether this PriorityClass should be considered as the default priority for pods that do not have any priority class. Only one PriorityClass can be marked as `globalDefault`. However, if more than one PriorityClasses exists with their `globalDefault` field set to true, the smallest value of such global default PriorityClasses will be used as the default priority. # noqa: E501 :return: The global_default of this V1PriorityClass. # noqa: E501 :rtype: bool """ return self._global_default @global_default.setter def global_default(self, global_default): """Sets the global_default of this V1PriorityClass. globalDefault specifies whether this PriorityClass should be considered as the default priority for pods that do not have any priority class. Only one PriorityClass can be marked as `globalDefault`. However, if more than one PriorityClasses exists with their `globalDefault` field set to true, the smallest value of such global default PriorityClasses will be used as the default priority. # noqa: E501 :param global_default: The global_default of this V1PriorityClass. # noqa: E501 :type: bool """ self._global_default = global_default @property def kind(self): """Gets the kind of this V1PriorityClass. # noqa: E501 Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds # noqa: E501 :return: The kind of this V1PriorityClass. # noqa: E501 :rtype: str """ return self._kind @kind.setter def kind(self, kind): """Sets the kind of this V1PriorityClass. Kind is a string value representing the REST resource this object represents. Servers may infer this from the endpoint the client submits requests to. Cannot be updated. In CamelCase. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#types-kinds # noqa: E501 :param kind: The kind of this V1PriorityClass. # noqa: E501 :type: str """ self._kind = kind @property def metadata(self): """Gets the metadata of this V1PriorityClass. # noqa: E501 :return: The metadata of this V1PriorityClass. # noqa: E501 :rtype: V1ObjectMeta """ return self._metadata @metadata.setter def metadata(self, metadata): """Sets the metadata of this V1PriorityClass. :param metadata: The metadata of this V1PriorityClass. # noqa: E501 :type: V1ObjectMeta """ self._metadata = metadata @property def preemption_policy(self): """Gets the preemption_policy of this V1PriorityClass. # noqa: E501 PreemptionPolicy is the Policy for preempting pods with lower priority. One of Never, PreemptLowerPriority. Defaults to PreemptLowerPriority if unset. This field is beta-level, gated by the NonPreemptingPriority feature-gate. # noqa: E501 :return: The preemption_policy of this V1PriorityClass. # noqa: E501 :rtype: str """ return self._preemption_policy @preemption_policy.setter def preemption_policy(self, preemption_policy): """Sets the preemption_policy of this V1PriorityClass. PreemptionPolicy is the Policy for preempting pods with lower priority. One of Never, PreemptLowerPriority. Defaults to PreemptLowerPriority if unset. This field is beta-level, gated by the NonPreemptingPriority feature-gate. # noqa: E501 :param preemption_policy: The preemption_policy of this V1PriorityClass. # noqa: E501 :type: str """ self._preemption_policy = preemption_policy @property def value(self): """Gets the value of this V1PriorityClass. # noqa: E501 The value of this priority class. This is the actual priority that pods receive when they have the name of this class in their pod spec. # noqa: E501 :return: The value of this V1PriorityClass. # noqa: E501 :rtype: int """ return self._value @value.setter def value(self, value): """Sets the value of this V1PriorityClass. The value of this priority class. This is the actual priority that pods receive when they have the name of this class in their pod spec. # noqa: E501 :param value: The value of this V1PriorityClass. # noqa: E501 :type: int """ if self.local_vars_configuration.client_side_validation and value is None: # noqa: E501 raise ValueError("Invalid value for `value`, must not be `None`") # noqa: E501 self._value = value def to_dict(self): """Returns the model properties as a dict""" result = {} for attr, _ in six.iteritems(self.openapi_types): value = getattr(self, attr) if isinstance(value, list): result[attr] = list(map( lambda x: x.to_dict() if hasattr(x, "to_dict") else x, value )) elif hasattr(value, "to_dict"): result[attr] = value.to_dict() elif isinstance(value, dict): result[attr] = dict(map( lambda item: (item[0], item[1].to_dict()) if hasattr(item[1], "to_dict") else item, value.items() )) else: result[attr] = value return result def to_str(self): """Returns the string representation of the model""" return pprint.pformat(self.to_dict()) def __repr__(self): """For `print` and `pprint`""" return self.to_str() def __eq__(self, other): """Returns true if both objects are equal""" if not isinstance(other, V1PriorityClass): return False return self.to_dict() == other.to_dict() def __ne__(self, other): """Returns true if both objects are not equal""" if not isinstance(other, V1PriorityClass): return True return self.to_dict() != other.to_dict()

37.927586

411

0.654878

7ff96a1ee6fed093503a23fbf6ba09f35240435e

6,540

py

Python

scripts-dev/build_debian_packages.py

buffless-matt/synapse

dda9b7fc4d2e6ca84a1a994a7ff1943b590e71df

[ "Apache-2.0" ]

null

scripts-dev/build_debian_packages.py

buffless-matt/synapse

dda9b7fc4d2e6ca84a1a994a7ff1943b590e71df

[ "Apache-2.0" ]

1

2022-03-23T08:03:58.000Z

scripts-dev/build_debian_packages.py

buffless-matt/synapse

dda9b7fc4d2e6ca84a1a994a7ff1943b590e71df

[ "Apache-2.0" ]

1

2022-03-31T09:03:27.000Z

#!/usr/bin/env python3 # Build the Debian packages using Docker images. # # This script builds the Docker images and then executes them sequentially, each # one building a Debian package for the targeted operating system. It is # designed to be a "single command" to produce all the images. # # By default, builds for all known distributions, but a list of distributions # can be passed on the commandline for debugging. import argparse import json import os import signal import subprocess import sys import threading from concurrent.futures import ThreadPoolExecutor from typing import Optional, Sequence DISTS = ( "debian:buster", # oldstable: EOL 2022-08 "debian:bullseye", "debian:bookworm", "debian:sid", "ubuntu:focal", # 20.04 LTS (our EOL forced by Py38 on 2024-10-14) "ubuntu:impish", # 21.10 (EOL 2022-07) ) DESC = """\ Builds .debs for synapse, using a Docker image for the build environment. By default, builds for all known distributions, but a list of distributions can be passed on the commandline for debugging. """ projdir = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) class Builder(object): def __init__( self, redirect_stdout=False, docker_build_args: Optional[Sequence[str]] = None ): self.redirect_stdout = redirect_stdout self._docker_build_args = tuple(docker_build_args or ()) self.active_containers = set() self._lock = threading.Lock() self._failed = False def run_build(self, dist, skip_tests=False): """Build deb for a single distribution""" if self._failed: print("not building %s due to earlier failure" % (dist,)) raise Exception("failed") try: self._inner_build(dist, skip_tests) except Exception as e: print("build of %s failed: %s" % (dist, e), file=sys.stderr) self._failed = True raise def _inner_build(self, dist, skip_tests=False): tag = dist.split(":", 1)[1] # Make the dir where the debs will live. # # Note that we deliberately put this outside the source tree, otherwise # we tend to get source packages which are full of debs. (We could hack # around that with more magic in the build_debian.sh script, but that # doesn't solve the problem for natively-run dpkg-buildpakage). debsdir = os.path.join(projdir, "../debs") os.makedirs(debsdir, exist_ok=True) if self.redirect_stdout: logfile = os.path.join(debsdir, "%s.buildlog" % (tag,)) print("building %s: directing output to %s" % (dist, logfile)) stdout = open(logfile, "w") else: stdout = None # first build a docker image for the build environment build_args = ( ( "docker", "build", "--tag", "dh-venv-builder:" + tag, "--build-arg", "distro=" + dist, "-f", "docker/Dockerfile-dhvirtualenv", ) + self._docker_build_args + ("docker",) ) subprocess.check_call( build_args, stdout=stdout, stderr=subprocess.STDOUT, cwd=projdir, ) container_name = "synapse_build_" + tag with self._lock: self.active_containers.add(container_name) # then run the build itself subprocess.check_call( [ "docker", "run", "--rm", "--name", container_name, "--volume=" + projdir + ":/synapse/source:ro", "--volume=" + debsdir + ":/debs", "-e", "TARGET_USERID=%i" % (os.getuid(),), "-e", "TARGET_GROUPID=%i" % (os.getgid(),), "-e", "DEB_BUILD_OPTIONS=%s" % ("nocheck" if skip_tests else ""), "dh-venv-builder:" + tag, ], stdout=stdout, stderr=subprocess.STDOUT, ) with self._lock: self.active_containers.remove(container_name) if stdout is not None: stdout.close() print("Completed build of %s" % (dist,)) def kill_containers(self): with self._lock: active = list(self.active_containers) for c in active: print("killing container %s" % (c,)) subprocess.run( [ "docker", "kill", c, ], stdout=subprocess.DEVNULL, ) with self._lock: self.active_containers.remove(c) def run_builds(builder, dists, jobs=1, skip_tests=False): def sig(signum, _frame): print("Caught SIGINT") builder.kill_containers() signal.signal(signal.SIGINT, sig) with ThreadPoolExecutor(max_workers=jobs) as e: res = e.map(lambda dist: builder.run_build(dist, skip_tests), dists) # make sure we consume the iterable so that exceptions are raised. for _ in res: pass if __name__ == "__main__": parser = argparse.ArgumentParser( description=DESC, ) parser.add_argument( "-j", "--jobs", type=int, default=1, help="specify the number of builds to run in parallel", ) parser.add_argument( "--no-check", action="store_true", help="skip running tests after building", ) parser.add_argument( "--docker-build-arg", action="append", help="specify an argument to pass to docker build", ) parser.add_argument( "--show-dists-json", action="store_true", help="instead of building the packages, just list the dists to build for, as a json array", ) parser.add_argument( "dist", nargs="*", default=DISTS, help="a list of distributions to build for. Default: %(default)s", ) args = parser.parse_args() if args.show_dists_json: print(json.dumps(DISTS)) else: builder = Builder( redirect_stdout=(args.jobs > 1), docker_build_args=args.docker_build_arg ) run_builds( builder, dists=args.dist, jobs=args.jobs, skip_tests=args.no_check, )

30

99

0.562844

d3f9540a9f6278c870263b97438527bddb09fa9a

10,483

py

Python

case-design/a3c.py

transys-project/metis

3a3cdf5ba852b543c2398762364bbfd663b4ebf2

[ "MIT" ]

50

2020-06-07T11:50:30.000Z

2022-03-30T07:51:10.000Z

case-design/a3c.py

transys-project/Metis

3a3cdf5ba852b543c2398762364bbfd663b4ebf2

[ "MIT" ]

2

2020-08-27T08:52:33.000Z

2021-09-02T06:25:28.000Z

case-design/a3c.py

transys-project/Metis

3a3cdf5ba852b543c2398762364bbfd663b4ebf2

[ "MIT" ]

16

2020-05-08T12:53:27.000Z

2022-02-28T10:34:07.000Z

import numpy as np import tensorflow as tf import tflearn GAMMA = 0.99 A_DIM = 6 ENTROPY_WEIGHT = 0.5 ENTROPY_EPS = 1e-6 S_INFO = 4 class ActorNetwork(object): """ Input to the network is the state, output is the distribution of all actions. """ def __init__(self, sess, state_dim, action_dim, learning_rate): self.sess = sess self.s_dim = state_dim self.a_dim = action_dim self.lr_rate = learning_rate # Create the actor network self.inputs, self.out = self.create_actor_network() # Get all network parameters self.network_params = \ tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='actor') # Set all network parameters self.input_network_params = [] for param in self.network_params: self.input_network_params.append( tf.placeholder(tf.float32, shape=param.get_shape())) self.set_network_params_op = [] for idx, param in enumerate(self.input_network_params): self.set_network_params_op.append(self.network_params[idx].assign(param)) # Selected action, 0-1 vector self.acts = tf.placeholder(tf.float32, [None, self.a_dim]) # This gradient will be provided by the critic network self.act_grad_weights = tf.placeholder(tf.float32, [None, 1]) # Compute the objective (log action_vector and entropy) self.obj = tf.reduce_sum(tf.multiply( tf.log(tf.reduce_sum(tf.multiply(self.out, self.acts), reduction_indices=1, keep_dims=True)), -self.act_grad_weights)) \ + ENTROPY_WEIGHT * tf.reduce_sum(tf.multiply(self.out, tf.log(self.out + ENTROPY_EPS))) # Combine the gradients here self.actor_gradients = tf.gradients(self.obj, self.network_params) # Optimization Op self.optimize = tf.train.RMSPropOptimizer(self.lr_rate).\ apply_gradients(zip(self.actor_gradients, self.network_params)) def create_actor_network(self): with tf.variable_scope('actor'): inputs = tflearn.input_data(shape=[None, self.s_dim[0], self.s_dim[1]]) split_0 = tflearn.fully_connected(inputs[:, 0:1, -1], 128, activation='relu') split_1 = tflearn.fully_connected(inputs[:, 1:2, -1], 128, activation='relu') split_2 = tflearn.conv_1d(inputs[:, 2:3, :], 128, 4, activation='relu') split_3 = tflearn.conv_1d(inputs[:, 3:4, :], 128, 4, activation='relu') split_4 = tflearn.conv_1d(inputs[:, 4:5, :A_DIM], 128, 4, activation='relu') split_5 = tflearn.fully_connected(inputs[:, 4:5, -1], 128, activation='relu') split_2_flat = tflearn.flatten(split_2) split_3_flat = tflearn.flatten(split_3) split_4_flat = tflearn.flatten(split_4) merge_net = tflearn.merge([split_1, split_2_flat, split_3_flat, split_4_flat, split_5], 'concat') dense_net_0 = tflearn.fully_connected(merge_net, 128, activation='relu') merge_net_1 = tflearn.merge([split_0, dense_net_0], 'concat') out = tflearn.fully_connected(merge_net_1, self.a_dim, activation='softmax') return inputs, out def train(self, inputs, acts, act_grad_weights): self.sess.run(self.optimize, feed_dict={ self.inputs: inputs, self.acts: acts, self.act_grad_weights: act_grad_weights }) def predict(self, inputs): return self.sess.run(self.out, feed_dict={ self.inputs: inputs }) def get_gradients(self, inputs, acts, act_grad_weights): return self.sess.run(self.actor_gradients, feed_dict={ self.inputs: inputs, self.acts: acts, self.act_grad_weights: act_grad_weights }) def apply_gradients(self, actor_gradients): return self.sess.run(self.optimize, feed_dict={ i: d for i, d in zip(self.actor_gradients, actor_gradients) }) def get_network_params(self): return self.sess.run(self.network_params) def set_network_params(self, input_network_params): self.sess.run(self.set_network_params_op, feed_dict={ i: d for i, d in zip(self.input_network_params, input_network_params) }) class CriticNetwork(object): """ Input to the network is the state and action, output is V(s). On policy: the action must be obtained from the output of the Actor network. """ def __init__(self, sess, state_dim, learning_rate): self.sess = sess self.s_dim = state_dim self.lr_rate = learning_rate # Create the critic network self.inputs, self.out = self.create_critic_network() # Get all network parameters self.network_params = \ tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='critic') # Set all network parameters self.input_network_params = [] for param in self.network_params: self.input_network_params.append( tf.placeholder(tf.float32, shape=param.get_shape())) self.set_network_params_op = [] for idx, param in enumerate(self.input_network_params): self.set_network_params_op.append(self.network_params[idx].assign(param)) # Network target V(s) self.td_target = tf.placeholder(tf.float32, [None, 1]) # Temporal Difference, will also be weights for actor_gradients self.td = tf.subtract(self.td_target, self.out) # Mean square error self.loss = tflearn.mean_square(self.td_target, self.out) # Compute critic gradient self.critic_gradients = tf.gradients(self.loss, self.network_params) # Optimization Op self.optimize = tf.train.RMSPropOptimizer(self.lr_rate).\ apply_gradients(zip(self.critic_gradients, self.network_params)) def create_critic_network(self): with tf.variable_scope('critic'): inputs = tflearn.input_data(shape=[None, self.s_dim[0], self.s_dim[1]]) split_0 = tflearn.fully_connected(inputs[:, 0:1, -1], 128, activation='relu') split_1 = tflearn.fully_connected(inputs[:, 1:2, -1], 128, activation='relu') split_2 = tflearn.conv_1d(inputs[:, 2:3, :], 128, 4, activation='relu') split_3 = tflearn.conv_1d(inputs[:, 3:4, :], 128, 4, activation='relu') split_4 = tflearn.conv_1d(inputs[:, 4:5, :A_DIM], 128, 4, activation='relu') split_5 = tflearn.fully_connected(inputs[:, 4:5, -1], 128, activation='relu') split_2_flat = tflearn.flatten(split_2) split_3_flat = tflearn.flatten(split_3) split_4_flat = tflearn.flatten(split_4) merge_net = tflearn.merge([split_1, split_2_flat, split_3_flat, split_4_flat, split_5], 'concat') dense_net_0 = tflearn.fully_connected(merge_net, 128, activation='relu') merge_net_1 = tflearn.merge([split_0, dense_net_0], 'concat') out = tflearn.fully_connected(merge_net_1, 1, activation='linear') return inputs, out def train(self, inputs, td_target): return self.sess.run([self.loss, self.optimize], feed_dict={ self.inputs: inputs, self.td_target: td_target }) def predict(self, inputs): return self.sess.run(self.out, feed_dict={ self.inputs: inputs }) def get_td(self, inputs, td_target): return self.sess.run(self.td, feed_dict={ self.inputs: inputs, self.td_target: td_target }) def get_gradients(self, inputs, td_target): return self.sess.run(self.critic_gradients, feed_dict={ self.inputs: inputs, self.td_target: td_target }) def apply_gradients(self, critic_gradients): return self.sess.run(self.optimize, feed_dict={ i: d for i, d in zip(self.critic_gradients, critic_gradients) }) def get_network_params(self): return self.sess.run(self.network_params) def set_network_params(self, input_network_params): self.sess.run(self.set_network_params_op, feed_dict={ i: d for i, d in zip(self.input_network_params, input_network_params) }) def compute_gradients(s_batch, a_batch, r_batch, terminal, actor, critic): """ batch of s, a, r is from samples in a sequence the format is in np.array([batch_size, s/a/r_dim]) terminal is True when sequence ends as a terminal state """ assert s_batch.shape[0] == a_batch.shape[0] assert s_batch.shape[0] == r_batch.shape[0] ba_size = s_batch.shape[0] v_batch = critic.predict(s_batch) R_batch = np.zeros(r_batch.shape) if terminal: R_batch[-1, 0] = 0 # terminal state else: R_batch[-1, 0] = v_batch[-1, 0] # boot strap from last state for t in reversed(xrange(ba_size - 1)): R_batch[t, 0] = r_batch[t] + GAMMA * R_batch[t + 1, 0] td_batch = R_batch - v_batch actor_gradients = actor.get_gradients(s_batch, a_batch, td_batch) critic_gradients = critic.get_gradients(s_batch, R_batch) return actor_gradients, critic_gradients, td_batch def discount(x, gamma): """ Given vector x, computes a vector y such that y[i] = x[i] + gamma * x[i+1] + gamma^2 x[i+2] + ... """ out = np.zeros(len(x)) out[-1] = x[-1] for i in reversed(xrange(len(x)-1)): out[i] = x[i] + gamma*out[i+1] assert x.ndim >= 1 # More efficient version: # scipy.signal.lfilter([1],[1,-gamma],x[::-1], axis=0)[::-1] return out def compute_entropy(x): """ Given vector x, computes the entropy H(x) = - sum( p * log(p)) """ H = 0.0 for i in xrange(len(x)): if 0 < x[i] < 1: H -= x[i] * np.log(x[i]) return H def build_summaries(): td_loss = tf.Variable(0.) tf.summary.scalar("TD_loss", td_loss) eps_total_reward = tf.Variable(0.) tf.summary.scalar("Eps_total_reward", eps_total_reward) avg_entropy = tf.Variable(0.) tf.summary.scalar("Avg_entropy", avg_entropy) summary_vars = [td_loss, eps_total_reward, avg_entropy] summary_ops = tf.summary.merge_all() return summary_ops, summary_vars

36.273356

109

0.627969

a18a69a94bb3b07f88de8062d373a3b2d27c4521

6,194

py

Python

awx/main/tests/functional/test_rbac_team.py

bhyunki/awx

ce588a6af5a5c7f71a5b176ffe53eda5ebc3492c

[ "Apache-2.0" ]

11,396

2017-09-07T04:56:02.000Z

2022-03-31T13:56:17.000Z

awx/main/tests/functional/test_rbac_team.py

bhyunki/awx

ce588a6af5a5c7f71a5b176ffe53eda5ebc3492c

[ "Apache-2.0" ]

11,046

2017-09-07T09:30:46.000Z

2022-03-31T20:28:01.000Z

awx/main/tests/functional/test_rbac_team.py

bhyunki/awx

ce588a6af5a5c7f71a5b176ffe53eda5ebc3492c

[ "Apache-2.0" ]

3,592

2017-09-07T04:14:31.000Z

2022-03-31T23:53:09.000Z

import pytest from unittest import mock from awx.main.access import TeamAccess from awx.main.models import Project, Organization, Team @pytest.mark.django_db def test_team_attach_unattach(team, user): u = user('member', False) access = TeamAccess(u) team.member_role.members.add(u) assert not access.can_attach(team, team.member_role, 'member_role.children', None) assert not access.can_unattach(team, team.member_role, 'member_role.children') team.admin_role.members.add(u) assert access.can_attach(team, team.member_role, 'member_role.children', None) assert access.can_unattach(team, team.member_role, 'member_role.children') u2 = user('non-member', False) access = TeamAccess(u2) assert not access.can_attach(team, team.member_role, 'member_role.children', None) assert not access.can_unattach(team, team.member_role, 'member_role.children') @pytest.mark.django_db @pytest.mark.parametrize('ext_auth', [True, False]) def test_team_org_resource_role(ext_auth, team, user, rando): with mock.patch('awx.main.access.settings') as settings_mock: settings_mock.MANAGE_ORGANIZATION_AUTH = ext_auth u = user('member', False) team.organization.admin_role.members.add(u) access = TeamAccess(u) assert access.can_attach(team, rando, 'member_role.members') == ext_auth team.member_role.members.add(rando) assert access.can_unattach(team, rando, 'member_role.members') == ext_auth @pytest.mark.django_db def test_team_access_superuser(team, user): team.member_role.members.add(user('member', False)) access = TeamAccess(user('admin', True)) assert access.can_add(None) assert access.can_change(team, None) assert access.can_delete(team) t = access.get_queryset()[0] assert len(t.member_role.members.all()) == 1 assert len(t.organization.admin_role.members.all()) == 0 @pytest.mark.django_db def test_team_access_org_admin(organization, team, user): a = user('admin', False) organization.admin_role.members.add(a) team.organization = organization team.save() access = TeamAccess(a) assert access.can_add({'organization': organization.pk}) assert access.can_change(team, None) assert access.can_delete(team) t = access.get_queryset()[0] assert len(t.member_role.members.all()) == 0 assert len(t.organization.admin_role.members.all()) == 1 @pytest.mark.django_db def test_team_access_member(organization, team, user): u = user('member', False) team.member_role.members.add(u) team.organization = organization team.save() access = TeamAccess(u) assert not access.can_add({'organization': organization.pk}) assert not access.can_change(team, None) assert not access.can_delete(team) t = access.get_queryset()[0] assert len(t.member_role.members.all()) == 1 assert len(t.organization.admin_role.members.all()) == 0 @pytest.mark.django_db def test_team_accessible_by(team, user, project): u = user('team_member', False) team.member_role.children.add(project.use_role) assert team in project.read_role assert u not in project.read_role team.member_role.members.add(u) assert u in project.read_role @pytest.mark.django_db def test_team_accessible_objects(team, user, project): u = user('team_member', False) team.member_role.children.add(project.use_role) assert len(Project.accessible_objects(team, 'read_role')) == 1 assert not Project.accessible_objects(u, 'read_role') team.member_role.members.add(u) assert len(Project.accessible_objects(u, 'read_role')) == 1 @pytest.mark.django_db def test_team_admin_member_access(team, user, project): u = user('team_admin', False) team.member_role.children.add(project.use_role) team.admin_role.members.add(u) assert len(Project.accessible_objects(u, 'use_role')) == 1 @pytest.mark.django_db def test_team_member_org_role_access_project(team, rando, project, organization): team.member_role.members.add(rando) assert rando not in project.read_role team.member_role.children.add(organization.project_admin_role) assert rando in project.admin_role @pytest.mark.django_db def test_team_member_org_role_access_workflow(team, rando, workflow_job_template, organization): team.member_role.members.add(rando) assert rando not in workflow_job_template.read_role team.member_role.children.add(organization.workflow_admin_role) assert rando in workflow_job_template.admin_role @pytest.mark.django_db def test_team_member_org_role_access_inventory(team, rando, inventory, organization): team.member_role.members.add(rando) assert rando not in inventory.read_role team.member_role.children.add(organization.inventory_admin_role) assert rando in inventory.admin_role @pytest.mark.django_db def test_org_admin_team_access(organization, team, user, project): u = user('team_admin', False) organization.admin_role.members.add(u) team.organization = organization team.save() team.member_role.children.add(project.use_role) assert len(Project.accessible_objects(u, 'use_role')) == 1 @pytest.mark.django_db @pytest.mark.parametrize('enabled', [True, False]) def test_org_admin_view_all_teams(org_admin, enabled): access = TeamAccess(org_admin) other_org = Organization.objects.create(name='other-org') other_team = Team.objects.create(name='other-team', organization=other_org) with mock.patch('awx.main.access.settings') as settings_mock: settings_mock.ORG_ADMINS_CAN_SEE_ALL_USERS = enabled assert access.can_read(other_team) is enabled @pytest.mark.django_db def test_team_member_read(rando, organization, team): assert team.organization == organization organization.member_role.members.add(rando) assert TeamAccess(rando).can_read(team) assert team in TeamAccess(rando).get_queryset() @pytest.mark.django_db def test_team_list_no_duplicate_entries(rando, organization, team): organization.member_role.members.add(rando) team.read_role.members.add(rando) assert list(TeamAccess(rando).get_queryset()) == [team]

33.663043

96

0.746529

c858f0bba4b8e32a4ea7ee4d3b1fdb4401774b39

777

py

Python

corpus/term_to_id_make.py

kwashio/filling_missing_path

b5ab85c9d1e42ee47857299df3fcd79b6127e1fe

[ "Apache-2.0" ]

null

corpus/term_to_id_make.py

kwashio/filling_missing_path

b5ab85c9d1e42ee47857299df3fcd79b6127e1fe

[ "Apache-2.0" ]

null

corpus/term_to_id_make.py

kwashio/filling_missing_path

b5ab85c9d1e42ee47857299df3fcd79b6127e1fe

[ "Apache-2.0" ]

null

# coding: utf-8 import pickle import argparse parser = argparse.ArgumentParser() parser.add_argument('input_prefix', help='the Wikipedia dump file') args = parser.parse_args() alp = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't'] terms = set() for i in alp: with open(args.input_prefix+'_a{}_parsed'.format(i), 'r') as f: for line in f: w1, w2, path = line.strip().split('\t') terms.add(w1) terms.add(w2) term_to_id = {w:i for (i, w) in enumerate(terms)} id_to_term = {i:w for (i, w) in enumerate(terms)} with open('term_to_id.dump', 'wb') as f: pickle.dump(term_to_id,f) with open('id_to_term.dump', 'wb') as f: pickle.dump(id_to_term,f)

24.28125

67

0.56242

92dfedc52e8def9094240023055dd249c3968435

5,364

py

Python

sweet_cms/applications/eventy/utils.py

SunilPragroot/sweet

d048126007fd892cb8b6f80348920c8c5152a85c

[ "MIT" ]

1

2021-04-16T14:25:36.000Z

sweet_cms/applications/eventy/utils.py

SunilPragroot/sweet

d048126007fd892cb8b6f80348920c8c5152a85c

[ "MIT" ]

94

2020-12-31T06:37:49.000Z

2022-03-10T14:07:47.000Z

sweet_cms/applications/eventy/utils.py

SunilPragroot/sweet

d048126007fd892cb8b6f80348920c8c5152a85c

[ "MIT" ]

null

import json import os import base64 import errno import re from sweet_cms.extensions import db def ensure_dir_exists(dir_path): if not os.path.exists(dir_path): try: os.makedirs(dir_path) except OSError as exc: # Guard against race condition if exc.errno != errno.EEXIST: raise def save_base64file(file_path, file_name, data): ensure_dir_exists(file_path) file_full_path = os.path.join(file_path, file_name) data = ','.join(data.split(',')[1:]) data = bytes(data, 'utf-8') with open(file_full_path, "wb") as fh: fh.write(base64.decodebytes(data)) def save_base64video(video_upload_path, file_name, data): ensure_dir_exists(video_upload_path) file_full_path = os.path.join(video_upload_path, file_name) with open(file_full_path, 'wb+') as destination: for chunk in data.chunks(): destination.write(chunk) def m3u8_fix(file_path, prefix): if os.path.exists(file_path): file_dir = os.path.dirname(file_path) reader = open(file_path, 'r') original_text = reader.read() new_text = "" reader.close() for line in original_text.splitlines(): if '.m3u8' in line: new_line = os.path.join(prefix, line) new_play_list_path = os.path.join(file_dir, line) m3u8_fix(new_play_list_path, prefix) elif '.ts' in line: new_line = os.path.join(prefix, line) else: new_line = line new_text = new_text + new_line + "\n" writer = open(file_path, 'w') writer.write(new_text) writer.close() def process_video(file_id, **kwargs): from sweet_cms.models import ProgrammeFile from libs.sweet_apps import get_sweet_app from autoapp import app import ffmpeg_streaming from ffmpeg_streaming import Formats import ffmpeg from shutil import copy from flask import url_for with app.app_context(): eventy_app = get_sweet_app('Eventy') programme_file = ProgrammeFile.query.filter_by(id=file_id).first() if not programme_file: print("File Was deleted , stopping now") return file_path = programme_file.file_url app.config['SERVER_NAME'] = 'localhost' file_url = '/admin/eventy/uploads/programmes/{}/videos/'.format(programme_file.programme.id) file_path = os.path.join(eventy_app.app_path, app.config['EVENTY_UPLOADS_DIR'], file_path) print(file_id, programme_file, file_path, file_url) if os.path.exists(file_path): programme_file.file_status = 'processing' db.session.add(programme_file) db.session.commit() file_dir = os.path.dirname(file_path) file_name = os.path.basename(file_path) bare_file_name = '.'.join(file_name.split('.')[:-1]) new_path = os.path.join(file_dir, bare_file_name+'.mp4') # data = ffmpeg.probe(file_path) input_video = ffmpeg.input(file_path) output_path = os.path.join('/tmp', bare_file_name+'.mp4') ( ffmpeg .output(input_video, output_path, vcodec='libx264', acodec='aac', movflags='faststart') .overwrite_output() .run() ) copy(output_path, file_dir) # os.remove(output_path) if new_path != file_path: os.remove(file_path) # if "bit_rate" not in str(data): # ( # ffmpeg # .output(input_video, output_path, vcodec='copy', acodec='copy') # .overwrite_output() # .run() # ) # copy(output_path, file_path) # os.remove(output_path) # video = ffmpeg_streaming.input(file_path) # hls = video.hls(Formats.h264()) # hls.auto_generate_representations() # hls.output(playlist_path) # m3u8_fix(playlist_path, file_url) data = ffmpeg.probe(new_path) programme_file.file_details = json.dumps(data) programme_file.file_status = 'ready' programme_file.file_name = bare_file_name+'.mp4' db.session.add(programme_file) db.session.commit() print("Done on : {}".format(str(file_path))) def fire_programme_start(programme_id, **kwargs): import datetime from sweet_cms.models import Programme from libs.sweet_apps import get_sweet_app from autoapp import app import socketio with app.app_context(): programme = Programme.query.filter_by(id=programme_id).first() if not programme: print("Programme Was deleted , stopping now") return if not app.config['DEBUG']: ws_url = "https://www.mediville.com" path = 'sockets/socket.io' else: ws_url = "http://127.0.0.1:5501" path = "socket.io" sio = socketio.Client() sio.connect(ws_url, socketio_path=path) sio.emit("programme_started", {"event_id": programme.event.id, "programme_id": programme.id}) print("HI", datetime.datetime.now())

36.739726

107

0.596943

b8a7c05039de9fdeed7bb8bbdd3674f0556eeffb

3,046

py

Python

tests/settings.py

dwoods/django-material

3b159d3c42984f15bf8398fcb24fb83ebc8eeacb

[ "BSD-3-Clause" ]

null

tests/settings.py

dwoods/django-material

3b159d3c42984f15bf8398fcb24fb83ebc8eeacb

[ "BSD-3-Clause" ]

null

tests/settings.py

dwoods/django-material

3b159d3c42984f15bf8398fcb24fb83ebc8eeacb

[ "BSD-3-Clause" ]

null

import django import os BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'fn4_(9z4f8w+3!&(j2x88^ca0m0=s+aj$jp^^cf^3h740xhr3=' # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] INSTALLED_APPS = ( # material apps 'material', 'material.frontend', # standard django apps 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'debug_toolbar', # test apps 'tests', 'demo', ) if django.VERSION < (3, 0): INSTALLED_APPS += ( 'template_debug', ) MIDDLEWARE = ( 'django.contrib.sessions.middleware.SessionMiddleware', 'django.middleware.locale.LocaleMiddleware', 'django.middleware.common.CommonMiddleware', 'django.middleware.csrf.CsrfViewMiddleware', 'django.contrib.auth.middleware.AuthenticationMiddleware', 'django.contrib.messages.middleware.MessageMiddleware', 'django.middleware.clickjacking.XFrameOptionsMiddleware', 'debug_toolbar.middleware.DebugToolbarMiddleware', ) SITE_ID = 1 ROOT_URLCONF = 'tests.urls' WSGI_APPLICATION = 'tests.wsgi.application' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [ os.path.join(BASE_DIR, 'tests', 'templates') ], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': [ 'django.template.context_processors.debug', 'django.template.context_processors.request', 'django.contrib.auth.context_processors.auth', 'django.contrib.messages.context_processors.messages', 'django.template.context_processors.request', ], 'builtins': [ 'material.templatetags.material_form', ], 'debug': True, }, }, ] if django.VERSION < (3, 0): TEMPLATES[0]['OPTIONS']['builtins'] += [ 'template_debug.templatetags.debug_tags' ] # Database # https://docs.djangoproject.com/en/1.7/ref/settings/#databases DATABASES = { 'default': { 'ENGINE': 'django.db.backends.sqlite3', 'NAME': os.path.join(BASE_DIR, 'db.sqlite3'), } } MIGRATION_MODULES = [] # Internationalization # https://docs.djangoproject.com/en/1.7/topics/i18n/ LANGUAGE_CODE = 'en-us' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.7/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS = ( os.path.join(BASE_DIR, "tests/static"), ) SESSION_ENGINE = "django.contrib.sessions.backends.signed_cookies" STATIC_ROOT = os.path.join(BASE_DIR, 'deploy/static')

23.796875

71

0.66021

26efa9fbf914e80a611ad7bb38bad037091f7baf

2,496

py

Python

src/commercetools/testing/discount_codes.py

BramKaashoek/commercetools-python-sdk

4a4191d7816c921401b782d8ae37626cb32791a1

[ "MIT" ]

null

src/commercetools/testing/discount_codes.py

BramKaashoek/commercetools-python-sdk

4a4191d7816c921401b782d8ae37626cb32791a1

[ "MIT" ]

null

src/commercetools/testing/discount_codes.py

BramKaashoek/commercetools-python-sdk

4a4191d7816c921401b782d8ae37626cb32791a1

[ "MIT" ]

null

import datetime import typing import uuid from commercetools import schemas, types from commercetools.testing import utils from commercetools.testing.abstract import BaseModel, ServiceBackend from commercetools.testing.utils import update_attribute class DiscountCodesModel(BaseModel): _resource_schema = schemas.DiscountCodeSchema _primary_type_name = "discount-code" def _create_from_draft( self, draft: types.DiscountCodeDraft, id: typing.Optional[str] = None ) -> types.DiscountCode: object_id = str(uuid.UUID(id) if id is not None else uuid.uuid4()) return types.DiscountCode( id=str(object_id), version=1, name=draft.name, description=draft.description, code=draft.code, cart_discounts=draft.cart_discounts, cart_predicate=draft.cart_predicate, is_active=draft.is_active or False, max_applications=draft.max_applications, max_applications_per_customer=draft.max_applications_per_customer, groups=draft.groups or [], references=[], valid_from=draft.valid_from, valid_until=draft.valid_until, created_at=datetime.datetime.now(datetime.timezone.utc), last_modified_at=datetime.datetime.now(datetime.timezone.utc), custom=utils.create_from_draft(draft.custom), ) class DiscountCodesBackend(ServiceBackend): service_path = "discount-codes" model_class = DiscountCodesModel _schema_draft = schemas.DiscountCodeDraftSchema _schema_update = schemas.DiscountCodeUpdateSchema _schema_query_response = schemas.DiscountCodePagedQueryResponseSchema def urls(self): return [ ("^$", "GET", self.query), ("^$", "POST", self.create), ("^(?P<id>[^/]+)$", "GET", self.get_by_id), ("^(?P<id>[^/]+)$", "POST", self.update_by_id), ] _actions = { "changeIsActive": update_attribute("isActive", "is_active"), "setName": update_attribute("name", "name"), "setDescription": update_attribute("description", "description"), "setCartPredicate": update_attribute("cartPredicate", "cart_predicate"), "setMaxApplications": update_attribute("maxApplications", "max_applications"), "setMaxApplicationsPerCustomer": update_attribute( "maxApplicationsPerCustomer", "max_applications_per_customer" ), }

38.4

86

0.669071