Reset23/the-stack-v2-blamed2 · Datasets at Hugging Face

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8dbde2f77c1c213a3477d21979d1cbec9aba8df7

/baselines/her_pddl/ddpg.py

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permissive

fabawi/goal_conditioned_RL_baselines

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refs/heads/master

2023-01-08T12:11:57.130496

2020-08-14T13:28:52

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from collections import OrderedDict import numpy as np import tensorflow as tf from tensorflow.contrib.staging import StagingArea from baselines import logger from baselines.util import ( import_function, store_args, flatten_grads, transitions_in_episode_batch, prob_dist2discrete) from baselines.her_pddl.normalizer import Normalizer from baselines.her_pddl.replay_buffer import ReplayBuffer from baselines.common.mpi_adam import MpiAdam from baselines.template.policy import Policy from baselines.her_pddl.obs2preds import Obs2PredsModel, Obs2PredsBuffer, Obs2PredsAttnModel def dims_to_shapes(input_dims): return {key: tuple([val]) if val > 0 else tuple() for key, val in input_dims.items()} class DDPG_PDDL(Policy): @store_args def __init__(self, input_dims, buffer_size, hidden, layers, network_class, polyak, batch_size, Q_lr, pi_lr, norm_eps, norm_clip, max_u, action_l2, clip_obs, scope, T, rollout_batch_size, subtract_goals, relative_goals, clip_pos_returns, clip_return, sample_transitions, gamma, n_preds, reuse=False, **kwargs): """Implementation of DDPG that is used in combination with Hindsight Experience Replay (HER). Args: input_dims (dict of ints): dimensions for the observation (o), the goal (g), and the actions (u) buffer_size (int): number of transitions that are stored in the replay buffer hidden (int): number of units in the hidden layers layers (int): number of hidden layers network_class (str): the network class that should be used (e.g. 'baselines.her.ActorCritic') polyak (float): coefficient for Polyak-averaging of the target network batch_size (int): batch size for training Q_lr (float): learning rate for the Q (critic) network pi_lr (float): learning rate for the pi (actor) network norm_eps (float): a small value used in the normalizer to avoid numerical instabilities norm_clip (float): normalized inputs are clipped to be in [-norm_clip, norm_clip] max_u (float): maximum action magnitude, i.e. actions are in [-max_u, max_u] action_l2 (float): coefficient for L2 penalty on the actions clip_obs (float): clip observations before normalization to be in [-clip_obs, clip_obs] scope (str): the scope used for the TensorFlow graph T (int): the time horizon for rollouts rollout_batch_size (int): number of parallel rollouts per DDPG agent subtract_goals (function): function that subtracts goals from each other relative_goals (boolean): whether or not relative goals should be fed into the network clip_pos_returns (boolean): whether or not positive returns should be clipped clip_return (float): clip returns to be in [-clip_return, clip_return] sample_transitions (function) function that samples from the replay buffer gamma (float): gamma used for Q learning updates reuse (boolean): whether or not the networks should be reused """ Policy.__init__(self, input_dims, T, rollout_batch_size, **kwargs) self.hidden = hidden self.layers = layers self.max_u = max_u self.network_class = network_class self.sample_transitions = sample_transitions self.scope = scope self.subtract_goals = subtract_goals self.relative_goals = relative_goals self.clip_obs = clip_obs self.Q_lr = Q_lr self.pi_lr = pi_lr self.batch_size = batch_size self.buffer_size = buffer_size self.clip_pos_returns = clip_pos_returns self.gamma = gamma self.polyak = polyak self.clip_return = clip_return self.norm_eps = norm_eps self.norm_clip = norm_clip self.action_l2 = action_l2 self.n_preds = n_preds self.rep_lr = Q_lr if self.clip_return is None: self.clip_return = np.inf self.create_actor_critic = import_function(self.network_class) self.rep_network = import_function(kwargs['rep_network_class']) # Create network. with tf.variable_scope(self.scope): self.staging_tf = StagingArea( dtypes=[tf.float32 for _ in self.stage_shapes.keys()], shapes=list(self.stage_shapes.values())) self.buffer_ph_tf = [ tf.placeholder(tf.float32, shape=shape) for shape in self.stage_shapes.values()] self.stage_op = self.staging_tf.put(self.buffer_ph_tf) self._create_network(reuse=reuse) # Configure the replay buffer. buffer_shapes = {key: (self.T if key != 'o' else self.T+1, *self.input_shapes[key]) for key, val in self.input_shapes.items()} buffer_shapes['g'] = (buffer_shapes['g'][0], self.dimg) buffer_shapes['ag'] = (self.T+1, self.dimg) buffer_size = (self.buffer_size // self.rollout_batch_size) * self.rollout_batch_size self.buffer = ReplayBuffer(buffer_shapes, buffer_size, self.T, self.sample_transitions) # Creat rep. network with tf.variable_scope(self.scope): self._create_rep_network(reuse=reuse) self.obs2preds_buffer = Obs2PredsBuffer(buffer_len=2000) def _random_action(self, n): return np.random.uniform(low=-self.max_u, high=self.max_u, size=(n, self.dimu)) def _preprocess_og(self, o, ag, g): if self.relative_goals: g_shape = g.shape g = g.reshape(-1, self.dimg) ag = ag.reshape(-1, self.dimg) g = self.subtract_goals(g, ag) g = g.reshape(*g_shape) o = np.clip(o, -self.clip_obs, self.clip_obs) g = np.clip(g, -self.clip_obs, self.clip_obs) return o, g def get_actions(self, o, ag, g, noise_eps=0., random_eps=0., use_target_net=False, compute_Q=False, exploit=True): noise_eps = noise_eps if not exploit else 0. random_eps = random_eps if not exploit else 0. o, g = self._preprocess_og(o, ag, g) policy = self.target if use_target_net else self.main # values to compute vals = [policy.pi_tf] if compute_Q: vals += [policy.Q_pi_tf] # feed feed = { policy.o_tf: o.reshape(-1, self.dimo), policy.g_tf: g.reshape(-1, self.dimg), policy.u_tf: np.zeros((o.size // self.dimo, self.dimu), dtype=np.float32) } ret = self.sess.run(vals, feed_dict=feed) # action postprocessing u = ret[0] noise = noise_eps * self.max_u * np.random.randn(*u.shape) # gaussian noise u += noise u = np.clip(u, -self.max_u, self.max_u) u += np.random.binomial(1, random_eps, u.shape[0]).reshape(-1, 1) * (self._random_action(u.shape[0]) - u) # eps-greedy if u.shape[0] == 1: u = u[0] u = u.copy() ret[0] = u if len(ret) == 1: return ret[0] else: return ret def store_episode(self, episode_batch, update_stats=True): """ episode_batch: array of batch_size x (T or T+1) x dim_key 'o' is of size T+1, others are of size T """ self.buffer.store_episode(episode_batch) if update_stats: # add transitions to normalizer episode_batch['o_2'] = episode_batch['o'][:, 1:, :] episode_batch['ag_2'] = episode_batch['ag'][:, 1:, :] num_normalizing_transitions = transitions_in_episode_batch(episode_batch) transitions = self.sample_transitions(episode_batch, num_normalizing_transitions) o, o_2, g, ag = transitions['o'], transitions['o_2'], transitions['g'], transitions['ag'] transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g) # No need to preprocess the o_2 and g_2 since this is only used for stats self.o_stats.update(transitions['o']) self.g_stats.update(transitions['g']) self.o_stats.recompute_stats() self.g_stats.recompute_stats() def get_current_buffer_size(self): return self.buffer.get_current_size() def _sync_optimizers(self): self.Q_adam.sync() self.pi_adam.sync() def _sync_rep_optimizers(self): self.rep_adam.sync() # self.pi_adam.sync() def _grads(self): # Avoid feed_dict here for performance! critic_loss, actor_loss, Q_grad, pi_grad = self.sess.run([ self.Q_loss_tf, self.main.Q_pi_tf, self.Q_grad_tf, self.pi_grad_tf ]) return critic_loss, actor_loss, Q_grad, pi_grad def _update(self, Q_grad, pi_grad): import os # print("PID: {}. Updating AC.".format(os.getpid())) self.Q_adam.update(Q_grad, self.Q_lr) self.pi_adam.update(pi_grad, self.pi_lr) def sample_batch(self): transitions = self.buffer.sample(self.batch_size) o, o_2, g = transitions['o'], transitions['o_2'], transitions['g'] ag, ag_2 = transitions['ag'], transitions['ag_2'] transitions['o'], transitions['g'] = self._preprocess_og(o, ag, g) transitions['o_2'], transitions['g_2'] = self._preprocess_og(o_2, ag_2, g) transitions_batch = [transitions[key] for key in self.stage_shapes.keys()] return transitions_batch def stage_batch(self, batch=None): if batch is None: batch = self.sample_batch() assert len(self.buffer_ph_tf) == len(batch) self.sess.run(self.stage_op, feed_dict=dict(zip(self.buffer_ph_tf, batch))) def train(self, stage=True): if stage: self.stage_batch() critic_loss, actor_loss, Q_grad, pi_grad = self._grads() self._update(Q_grad, pi_grad) return critic_loss, actor_loss def train_representation(self): rep_batch_size = 64 batch = self.obs2preds_buffer.sample_batch(rep_batch_size) indexes = batch['indexes'] feed_dict = {self.obs2preds_model.inputs_o: batch['obs'], self.obs2preds_model.inputs_g: batch['goals'], self.obs2preds_model.preds: batch['preds']} rep_grad = self.sess.run([self.rep_grad_tf], feed_dict=feed_dict)[0] self.rep_adam.update(rep_grad, self.rep_lr) # opti_res, celoss, celosses = self.sess.run([self.obs2preds_model.optimizer, # self.obs2preds_model.celoss, # self.obs2preds_model.celosses], # feed_dict=feed_dict) # # celosses = np.mean(celosses, axis=-1) _, celosses_after = self.predict_representation(batch) celoss = np.mean(celosses_after) return celoss, celosses_after, indexes def predict_representation(self, batch): feed_dict = {self.obs2preds_model.inputs_o: batch['obs'], self.obs2preds_model.inputs_g: batch['goals']} pred_dist = self.sess.run([self.obs2preds_model.prob_out], feed_dict=feed_dict) losses = None if 'preds' in batch: preds = batch['preds'] if len(preds.shape) != 3: preds_probdist = np.zeros(shape=[preds.shape[0], preds.shape[1], 2]) for j,p in enumerate(preds): for i, v in enumerate(p): preds_probdist[j][i][int(v)] = 1 preds = preds_probdist feed_dict.update({self.obs2preds_model.preds: preds}) pred_dist, loss = self.sess.run([self.obs2preds_model.prob_out, self.obs2preds_model.celosses], feed_dict=feed_dict) loss = np.mean(loss, axis=-1) losses = np.reshape(loss,newshape=(preds.shape[0])) preds = prob_dist2discrete(pred_dist) return preds, losses def _init_target_net(self): self.sess.run(self.init_target_net_op) def update_target_net(self): self.sess.run(self.update_target_net_op) def clear_buffer(self): self.buffer.clear_buffer() def _vars(self, scope): res = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.scope + '/' + scope) assert len(res) > 0 return res def _global_vars(self, scope): res = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=self.scope + '/' + scope) return res def _create_rep_network(self, reuse=False): self.obs2preds_model = self.rep_network(self.n_preds, self.dimo, self.dimg) self.rep_loss_tf = tf.reduce_mean(self.obs2preds_model.celoss) rep_grads_tf = tf.gradients(self.rep_loss_tf, self._vars('obs2preds')) self.rep_grad_tf = flatten_grads(grads=rep_grads_tf, var_list=self._vars('obs2preds')) self.rep_adam = MpiAdam(self._vars('obs2preds'), scale_grad_by_procs=False) self._sync_rep_optimizers() def _create_network(self, reuse=False): logger.info("Creating a DDPG agent with action space %d x %s..." % (self.dimu, self.max_u)) self.sess = tf.get_default_session() if self.sess is None: self.sess = tf.InteractiveSession() # running averages with tf.variable_scope('o_stats') as vs: if reuse: vs.reuse_variables() self.o_stats = Normalizer(self.dimo, self.norm_eps, self.norm_clip, sess=self.sess) with tf.variable_scope('g_stats') as vs: if reuse: vs.reuse_variables() self.g_stats = Normalizer(self.dimg, self.norm_eps, self.norm_clip, sess=self.sess) # mini-batch sampling. batch = self.staging_tf.get() batch_tf = OrderedDict([(key, batch[i]) for i, key in enumerate(self.stage_shapes.keys())]) batch_tf['r'] = tf.reshape(batch_tf['r'], [-1, 1]) # networks with tf.variable_scope('main') as vs: if reuse: vs.reuse_variables() self.main = self.create_actor_critic(batch_tf, net_type='main', **self.__dict__) vs.reuse_variables() with tf.variable_scope('target') as vs: if reuse: vs.reuse_variables() target_batch_tf = batch_tf.copy() target_batch_tf['o'] = batch_tf['o_2'] target_batch_tf['g'] = batch_tf['g_2'] self.target = self.create_actor_critic( target_batch_tf, net_type='target', **self.__dict__) vs.reuse_variables() assert len(self._vars("main")) == len(self._vars("target")) # loss functions target_Q_pi_tf = self.target.Q_pi_tf clip_range = (-self.clip_return, 0. if self.clip_pos_returns else np.inf) target_tf = tf.clip_by_value(batch_tf['r'] + self.gamma * target_Q_pi_tf, *clip_range) self.Q_loss_tf = tf.reduce_mean(tf.square(tf.stop_gradient(target_tf) - self.main.Q_tf)) self.pi_loss_tf = -tf.reduce_mean(self.main.Q_pi_tf) self.pi_loss_tf += self.action_l2 * tf.reduce_mean(tf.square(self.main.pi_tf / self.max_u)) Q_grads_tf = tf.gradients(self.Q_loss_tf, self._vars('main/Q')) pi_grads_tf = tf.gradients(self.pi_loss_tf, self._vars('main/pi')) assert len(self._vars('main/Q')) == len(Q_grads_tf) assert len(self._vars('main/pi')) == len(pi_grads_tf) self.Q_grads_vars_tf = zip(Q_grads_tf, self._vars('main/Q')) self.pi_grads_vars_tf = zip(pi_grads_tf, self._vars('main/pi')) self.Q_grad_tf = flatten_grads(grads=Q_grads_tf, var_list=self._vars('main/Q')) self.pi_grad_tf = flatten_grads(grads=pi_grads_tf, var_list=self._vars('main/pi')) # optimizers self.Q_adam = MpiAdam(self._vars('main/Q'), scale_grad_by_procs=False) self.pi_adam = MpiAdam(self._vars('main/pi'), scale_grad_by_procs=False) # polyak averaging self.main_vars = self._vars('main/Q') + self._vars('main/pi') self.target_vars = self._vars('target/Q') + self._vars('target/pi') self.stats_vars = self._global_vars('o_stats') + self._global_vars('g_stats') self.init_target_net_op = list( map(lambda v: v[0].assign(v[1]), zip(self.target_vars, self.main_vars))) self.update_target_net_op = list( map(lambda v: v[0].assign(self.polyak * v[0] + (1. - self.polyak) * v[1]), zip(self.target_vars, self.main_vars))) # initialize all variables tf.variables_initializer(self._global_vars('')).run() self._sync_optimizers() self._init_target_net() def logs(self, prefix=''): logs = [] logs += [('stats_o/mean', np.mean(self.sess.run([self.o_stats.mean])))] logs += [('stats_o/std', np.mean(self.sess.run([self.o_stats.std])))] logs += [('stats_g/mean', np.mean(self.sess.run([self.g_stats.mean])))] logs += [('stats_g/std', np.mean(self.sess.run([self.g_stats.std])))] if prefix is not '' and not prefix.endswith('/'): return [(prefix + '/' + key, val) for key, val in logs] else: return logs def __getstate__(self): """Our policies can be loaded from pkl, but after unpickling you cannot continue training. """ # [print(key, ": ", item) for key,item in self.__dict__.items()] excluded_subnames = ['_tf', '_op', '_vars', '_adam', 'buffer', 'sess', '_stats', 'main', 'target', 'lock', 'env', 'sample_transitions', 'stage_shapes', 'create_actor_critic', 'obs2preds_buffer', 'obs2preds_model'] state = {k: v for k, v in self.__dict__.items() if all([not subname in k for subname in excluded_subnames])} state['buffer_size'] = self.buffer_size state['tf'] = self.sess.run([x for x in self._global_vars('') if 'buffer' not in x.name and 'obs2preds_buffer' not in x.name]) return state def __setstate__(self, state): if 'sample_transitions' not in state: # We don't need this for playing the policy. state['sample_transitions'] = None self.__init__(**state) # set up stats (they are overwritten in __init__) for k, v in state.items(): if k[-6:] == '_stats': self.__dict__[k] = v # load TF variables vars = [x for x in self._global_vars('') if 'buffer' not in x.name and 'obs2preds_buffer' not in x.name] assert(len(vars) == len(state["tf"])) node = [tf.assign(var, val) for var, val in zip(vars, state["tf"])] self.sess.run(node)

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/exercises/networking_selfpaced/networking-workshop/collections/ansible_collections/community/general/plugins/modules/kubevirt_vm.py

ddcab20e9863af4105ed6489bcd7c4e7021b0e5a

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2020-03-25T17:38:41

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#!/usr/bin/python # -*- coding: utf-8 -*- # Copyright: (c) 2019, Ansible Project # GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt) from __future__ import absolute_import, division, print_function __metaclass__ = type ANSIBLE_METADATA = {'metadata_version': '1.1', 'status': ['preview'], 'supported_by': 'community'} DOCUMENTATION = ''' --- module: kubevirt_vm short_description: Manage KubeVirt virtual machine description: - Use Openshift Python SDK to manage the state of KubeVirt virtual machines. author: KubeVirt Team (@kubevirt) options: state: description: - Set the virtual machine to either I(present), I(absent), I(running) or I(stopped). - "I(present) - Create or update a virtual machine. (And run it if it's ephemeral.)" - "I(absent) - Remove a virtual machine." - "I(running) - Create or update a virtual machine and run it." - "I(stopped) - Stop a virtual machine. (This deletes ephemeral VMs.)" default: "present" choices: - present - absent - running - stopped type: str name: description: - Name of the virtual machine. required: true type: str namespace: description: - Namespace where the virtual machine exists. required: true type: str ephemeral: description: - If (true) ephemeral virtual machine will be created. When destroyed it won't be accessible again. - Works only with C(state) I(present) and I(absent). type: bool default: false datavolumes: description: - "DataVolumes are a way to automate importing virtual machine disks onto pvcs during the virtual machine's launch flow. Without using a DataVolume, users have to prepare a pvc with a disk image before assigning it to a VM or VMI manifest. With a DataVolume, both the pvc creation and import is automated on behalf of the user." type: list template: description: - "Name of Template to be used in creation of a virtual machine." type: str template_parameters: description: - "New values of parameters from Template." type: dict extends_documentation_fragment: - community.kubernetes.k8s_auth_options - community.general.kubevirt_vm_options - community.general.kubevirt_common_options requirements: - python >= 2.7 - openshift >= 0.8.2 ''' EXAMPLES = ''' - name: Start virtual machine 'myvm' kubevirt_vm: state: running name: myvm namespace: vms - name: Create virtual machine 'myvm' and start it kubevirt_vm: state: running name: myvm namespace: vms memory: 64Mi cpu_cores: 1 bootloader: efi smbios_uuid: 5d307ca9-b3ef-428c-8861-06e72d69f223 cpu_model: Conroe headless: true hugepage_size: 2Mi tablets: - bus: virtio name: tablet1 cpu_limit: 3 cpu_shares: 2 disks: - name: containerdisk volume: containerDisk: image: kubevirt/cirros-container-disk-demo:latest path: /custom-disk/cirros.img disk: bus: virtio - name: Create virtual machine 'myvm' with multus network interface kubevirt_vm: name: myvm namespace: vms memory: 512M interfaces: - name: default bridge: {} network: pod: {} - name: mynet bridge: {} network: multus: networkName: mynetconf - name: Combine inline definition with Ansible parameters kubevirt_vm: # Kubernetes specification: definition: metadata: labels: app: galaxy service: web origin: vmware # Ansible parameters: state: running name: myvm namespace: vms memory: 64M disks: - name: containerdisk volume: containerDisk: image: kubevirt/cirros-container-disk-demo:latest path: /custom-disk/cirros.img disk: bus: virtio - name: Start ephemeral virtual machine 'myvm' and wait to be running kubevirt_vm: ephemeral: true state: running wait: true wait_timeout: 180 name: myvm namespace: vms memory: 64M labels: kubevirt.io/vm: myvm disks: - name: containerdisk volume: containerDisk: image: kubevirt/cirros-container-disk-demo:latest path: /custom-disk/cirros.img disk: bus: virtio - name: Start fedora vm with cloud init kubevirt_vm: state: running wait: true name: myvm namespace: vms memory: 1024M cloud_init_nocloud: userData: |- #cloud-config password: fedora chpasswd: { expire: False } disks: - name: containerdisk volume: containerDisk: image: kubevirt/fedora-cloud-container-disk-demo:latest path: /disk/fedora.qcow2 disk: bus: virtio node_affinity: soft: - weight: 1 term: match_expressions: - key: security operator: In values: - S2 - name: Create virtual machine with datavolume and specify node affinity kubevirt_vm: name: myvm namespace: default memory: 1024Mi datavolumes: - name: mydv source: http: url: https://url/disk.qcow2 pvc: accessModes: - ReadWriteOnce storage: 5Gi node_affinity: hard: - term: match_expressions: - key: security operator: In values: - S1 - name: Remove virtual machine 'myvm' kubevirt_vm: state: absent name: myvm namespace: vms ''' RETURN = ''' kubevirt_vm: description: - The virtual machine dictionary specification returned by the API. - "This dictionary contains all values returned by the KubeVirt API all options are described here U(https://kubevirt.io/api-reference/master/definitions.html#_v1_virtualmachine)" returned: success type: complex contains: {} ''' import copy import traceback from ansible_collections.community.kubernetes.plugins.module_utils.k8s.common import AUTH_ARG_SPEC from ansible_collections.community.general.plugins.module_utils.kubevirt import ( virtdict, KubeVirtRawModule, VM_COMMON_ARG_SPEC, VM_SPEC_DEF_ARG_SPEC ) VM_ARG_SPEC = { 'ephemeral': {'type': 'bool', 'default': False}, 'state': { 'type': 'str', 'choices': [ 'present', 'absent', 'running', 'stopped' ], 'default': 'present' }, 'datavolumes': {'type': 'list'}, 'template': {'type': 'str'}, 'template_parameters': {'type': 'dict'}, } # Which params (can) modify 'spec:' contents of a VM: VM_SPEC_PARAMS = list(VM_SPEC_DEF_ARG_SPEC.keys()) + ['datavolumes', 'template', 'template_parameters'] class KubeVirtVM(KubeVirtRawModule): @property def argspec(self): """ argspec property builder """ argument_spec = copy.deepcopy(AUTH_ARG_SPEC) argument_spec.update(VM_COMMON_ARG_SPEC) argument_spec.update(VM_ARG_SPEC) return argument_spec @staticmethod def fix_serialization(obj): if obj and hasattr(obj, 'to_dict'): return obj.to_dict() return obj def _wait_for_vmi_running(self): for event in self._kind_resource.watch(namespace=self.namespace, timeout=self.params.get('wait_timeout')): entity = event['object'] if entity.metadata.name != self.name: continue status = entity.get('status', {}) phase = status.get('phase', None) if phase == 'Running': return entity self.fail("Timeout occurred while waiting for virtual machine to start. Maybe try a higher wait_timeout value?") def _wait_for_vm_state(self, new_state): if new_state == 'running': want_created = want_ready = True else: want_created = want_ready = False for event in self._kind_resource.watch(namespace=self.namespace, timeout=self.params.get('wait_timeout')): entity = event['object'] if entity.metadata.name != self.name: continue status = entity.get('status', {}) created = status.get('created', False) ready = status.get('ready', False) if (created, ready) == (want_created, want_ready): return entity self.fail("Timeout occurred while waiting for virtual machine to achieve '{0}' state. " "Maybe try a higher wait_timeout value?".format(new_state)) def manage_vm_state(self, new_state, already_changed): new_running = True if new_state == 'running' else False changed = False k8s_obj = {} if not already_changed: k8s_obj = self.get_resource(self._kind_resource) if not k8s_obj: self.fail("VirtualMachine object disappeared during module operation, aborting.") if k8s_obj.spec.get('running', False) == new_running: return False, k8s_obj newdef = dict(metadata=dict(name=self.name, namespace=self.namespace), spec=dict(running=new_running)) k8s_obj, err = self.patch_resource(self._kind_resource, newdef, k8s_obj, self.name, self.namespace, merge_type='merge') if err: self.fail_json(**err) else: changed = True if self.params.get('wait'): k8s_obj = self._wait_for_vm_state(new_state) return changed, k8s_obj def _process_template_defaults(self, proccess_template, processedtemplate, defaults): def set_template_default(default_name, default_name_index, definition_spec): default_value = proccess_template['metadata']['annotations'][default_name] if default_value: values = definition_spec[default_name_index] default_values = [d for d in values if d.get('name') == default_value] defaults[default_name_index] = default_values if definition_spec[default_name_index] is None: definition_spec[default_name_index] = [] definition_spec[default_name_index].extend([d for d in values if d.get('name') != default_value]) devices = processedtemplate['spec']['template']['spec']['domain']['devices'] spec = processedtemplate['spec']['template']['spec'] set_template_default('defaults.template.cnv.io/disk', 'disks', devices) set_template_default('defaults.template.cnv.io/volume', 'volumes', spec) set_template_default('defaults.template.cnv.io/nic', 'interfaces', devices) set_template_default('defaults.template.cnv.io/network', 'networks', spec) def construct_definition(self, kind, our_state, ephemeral): definition = virtdict() processedtemplate = {} # Construct the API object definition: defaults = {'disks': [], 'volumes': [], 'interfaces': [], 'networks': []} vm_template = self.params.get('template') if vm_template: # Find the template the VM should be created from: template_resource = self.client.resources.get(api_version='template.openshift.io/v1', kind='Template', name='templates') proccess_template = template_resource.get(name=vm_template, namespace=self.params.get('namespace')) # Set proper template values taken from module option 'template_parameters': for k, v in self.params.get('template_parameters', {}).items(): for parameter in proccess_template.parameters: if parameter.name == k: parameter.value = v # Proccess the template: processedtemplates_res = self.client.resources.get(api_version='template.openshift.io/v1', kind='Template', name='processedtemplates') processedtemplate = processedtemplates_res.create(proccess_template.to_dict()).to_dict()['objects'][0] # Process defaults of the template: self._process_template_defaults(proccess_template, processedtemplate, defaults) if not ephemeral: definition['spec']['running'] = our_state == 'running' template = definition if ephemeral else definition['spec']['template'] template['metadata']['labels']['vm.cnv.io/name'] = self.params.get('name') dummy, definition = self.construct_vm_definition(kind, definition, template, defaults) return self.merge_dicts(definition, processedtemplate) def execute_module(self): # Parse parameters specific to this module: ephemeral = self.params.get('ephemeral') k8s_state = our_state = self.params.get('state') kind = 'VirtualMachineInstance' if ephemeral else 'VirtualMachine' _used_params = [name for name in self.params if self.params[name] is not None] # Is 'spec:' getting changed? vm_spec_change = True if set(VM_SPEC_PARAMS).intersection(_used_params) else False changed = False crud_executed = False method = '' # Underlying module_utils/k8s/* code knows only of state == present/absent; let's make sure not to confuse it if ephemeral: # Ephemerals don't actually support running/stopped; we treat those as aliases for present/absent instead if our_state == 'running': self.params['state'] = k8s_state = 'present' elif our_state == 'stopped': self.params['state'] = k8s_state = 'absent' else: if our_state != 'absent': self.params['state'] = k8s_state = 'present' # Start with fetching the current object to make sure it exists # If it does, but we end up not performing any operations on it, at least we'll be able to return # its current contents as part of the final json self.client = self.get_api_client() self._kind_resource = self.find_supported_resource(kind) k8s_obj = self.get_resource(self._kind_resource) if not self.check_mode and not vm_spec_change and k8s_state != 'absent' and not k8s_obj: self.fail("It's impossible to create an empty VM or change state of a non-existent VM.") # If there are (potential) changes to `spec:` or we want to delete the object, that warrants a full CRUD # Also check_mode always warrants a CRUD, as that'll produce a sane result if vm_spec_change or k8s_state == 'absent' or self.check_mode: definition = self.construct_definition(kind, our_state, ephemeral) result = self.execute_crud(kind, definition) changed = result['changed'] k8s_obj = result['result'] method = result['method'] crud_executed = True if ephemeral and self.params.get('wait') and k8s_state == 'present' and not self.check_mode: # Waiting for k8s_state==absent is handled inside execute_crud() k8s_obj = self._wait_for_vmi_running() if not ephemeral and our_state in ['running', 'stopped'] and not self.check_mode: # State==present/absent doesn't involve any additional VMI state management and is fully # handled inside execute_crud() (including wait logic) patched, k8s_obj = self.manage_vm_state(our_state, crud_executed) changed = changed or patched if changed: method = method or 'patch' # Return from the module: self.exit_json(**{ 'changed': changed, 'kubevirt_vm': self.fix_serialization(k8s_obj), 'method': method }) def main(): module = KubeVirtVM() try: module.execute_module() except Exception as e: module.fail_json(msg=str(e), exception=traceback.format_exc()) if __name__ == '__main__': main()

[ "joshuamadison+gh@gmail.com" ]

joshuamadison+gh@gmail.com

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/02_generate.py

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koharukoharu/AI_carcof

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refs/heads/master

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# -*- coding: utf-8 -*- from GenerateText import GenerateText def generate_tweet(): generator = GenerateText() print(generator.generate()) if __name__ == '__main__': generate_tweet()

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masayoshi_sakino@waku-2.com

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/Travelling Salesman Problem.py

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permissive

anshuljain21120/Genetic-Algorithms

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2020-09-12T02:51:12.559464

2019-12-12T05:25:49

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import random import pandas as pd import numpy as np import matplotlib.pyplot as plt def fitness(chrm,distances): dis = 0 for i in range(len(chrm)-1): dis += distances[chrm[i]][chrm[i+1]] dis += distances[chrm[0]][chrm[len(chrm)-1]] return dis def randinit(n,lenchrm,dis): df = pd.DataFrame(np.zeros((n,2)),columns=["Chromosome","Fitness"], dtype=object) i = 0 arr = list(range(lenchrm)) while i < n: random.shuffle(arr) if arr not in list(df["Chromosome"]): df.at[i,"Chromosome"] = arr.copy() df.at[i,"Fitness"] = fitness(arr,dis) i = i+1 df["Fitness"] = df["Fitness"].astype('int64') return df def distances(No_Of_Cities): distance = np.zeros((No_Of_Cities,No_Of_Cities)) for city in range(No_Of_Cities): cities = [i for i in range(No_Of_Cities) if not i==city] for to_city in cities: if(distance[city][to_city]==0 and distance[to_city][city]==0): distance[city][to_city] = distance[to_city][city] = random.randint(1000,100000) return distance def crossover(chrm1,chrm2): n = len(chrm1) chrm3 = chrm1[:int(n/2)] for i in range(int(n/2), int(n/2)+n): if chrm2[i%n] not in chrm3: chrm3.append(chrm2[i%n]) n = len(chrm2) chrm4 = chrm2[:int(n/2)] for i in range(int(n/2), int(n/2)+n): if chrm1[i%n] not in chrm4: chrm4.append(chrm1[i%n]) return chrm3, chrm4 def mutation(chrm): if random.random() < 1/len(chrm): n = len(chrm) a = random.randint(0, n-1) b = random.randint(0, n-1) while b==a: b = random.randint(0, n-1) chrm[a], chrm[b] = chrm[b], chrm[a] return chrm def giveParent(pop): n = pop.shape[0] parents = pd.DataFrame(np.zeros((5,2)),columns=['Parents',"Fitness"], dtype=object) i = 0 while len(parents) <= 5: r = random.randint(0, n-1) parent = pop.iloc[r, 0] fitness = pop.iloc[r,1] if parent not in list(parents["Parents"]): parents.at[i,"Parents"] = parent parents.at[i,"Fitness"] = fitness i = i+1 parents = parents.sort_values(by=['Fitness']) return parents.iloc[0, 0], parents.iloc[1, 0] def replaceWorst(p, dis, v=False): pop = p.copy() p1, p2 = giveParent(pop) c1, c2 = crossover(p1, p2) c1 = mutation(c1) c2 = mutation(c2) if v: print('Child 1:{0}\tFitness: {1}\nChild 2:{2}\tFitness: {3}'.format(c1, fitness(c1,dis), c2, fitness(c2,dis))) pop = pop.sort_values(by=['Fitness']) pop = pop.reset_index(drop=True) if c1 not in list(pop["Chromosome"]): pop.loc[p.shape[0]-2] = [c1, fitness(c1,dis)] if c2 not in list(pop["Chromosome"]): pop.loc[p.shape[0]-1] = [c2, fitness(c2,dis)] return pop def bestFitness(pop): return min(list(pop['Fitness'])) def bestChromosome(pop): p = pop.sort_values(by=['Fitness']) return p.iloc[0, 0] N = int(input('Value of N: ')) pop_size = int(input('Population Size: ')) show_progress = False NUMBER_OF_CITIES = N; dis = distances(N) generationInfo = pd.DataFrame(np.zeros((10000, 2)), columns=['Chromosome', 'Fitness'], dtype=object) for generation in range(10000): if generation == 0: pop = randinit(pop_size, N, dis) generationInfo.loc[generation] = [bestChromosome(pop), bestFitness(pop)] if show_progress: print("\nGeneration {}".format(generation)) pop = replaceWorst(pop,dis,show_progress) Optimal_Solution = [bestChromosome(pop), bestFitness(pop)] print(Optimal_Solution) import matplotlib.pyplot as plt plt.plot(range(generationInfo.shape[0]), list(generationInfo['Fitness']))

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noreply@github.com

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/cmake-build-debug/catkin_generated/pkg.develspace.context.pc.py

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fbjelonic/xbox_controller

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# generated from catkin/cmake/template/pkg.context.pc.in CATKIN_PACKAGE_PREFIX = "" PROJECT_PKG_CONFIG_INCLUDE_DIRS = "/home/filip/banana_ws/src/xbox_controller/cmake-build-debug/devel/include;/home/filip/banana_ws/src/xbox_controller/include".split(';') if "/home/filip/banana_ws/src/xbox_controller/cmake-build-debug/devel/include;/home/filip/banana_ws/src/xbox_controller/include" != "" else [] PROJECT_CATKIN_DEPENDS = "roscpp;std_msgs;geometry_msgs;message_runtime".replace(';', ' ') PKG_CONFIG_LIBRARIES_WITH_PREFIX = "".split(';') if "" != "" else [] PROJECT_NAME = "xbox_controller" PROJECT_SPACE_DIR = "/home/filip/banana_ws/src/xbox_controller/cmake-build-debug/devel" PROJECT_VERSION = "0.0.0"

[ "filip.bjelonic@gmail.com" ]

filip.bjelonic@gmail.com

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/Code/python_quote.py

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franklin-phan/CS-2-Tweet-Generator

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2021-07-14T14:37:13.404088

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import random quotes = ("It's just a flesh wound.", "He's not the Messiah. He's a very naughty boy!", "THIS IS AN EX-PARROT!!") def random_python_quote(): rand_index = random.randint(0, len(quotes) - 1) return quotes[rand_index] if __name__ == '__main__': quote = random_python_quote() print

[ "franklin.phan123@gmail.com" ]

franklin.phan123@gmail.com

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/guvi3.py

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unknownboyy/GUVI

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refs/heads/master

2020-03-27T00:07:12.449280

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def check(n): count = 0 for i in str(n): count+=int(i) if str(count)[0]=='1': return count else: return False n = int(input()) l = [8] c = 0 diff = 2 curr = 800 while curr+diff<=n: curr+=diff w = check(curr) if w!=False: l.append(w) diff+=2 c+=1 print(*l) print(c)

[ "ankitagrawal11b@gmail.com" ]

ankitagrawal11b@gmail.com

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/chapter-4/7_build_order.py

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shanminlin/Cracking_the_Coding_Interview

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refs/heads/master

2022-09-12T21:28:08.926935

2020-06-03T07:49:49

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""" Chapter 4 - Problem 4.7 - Build Order Problem: You are given a list of projects and a list of dependencies (which is a list of pairs of projects, where the second project is dependent on the first project). All of a project's dependencis must be built before th project is. Find a build order that will allow the projects to be built. If there is no valid build order, return an error """

[ "shanminlin@gmail.com" ]

shanminlin@gmail.com

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/Python - University of Michigan/02 Python Data Structures - University of Michigan/170813_coursera_pds.py

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rohannanaware/Python

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refs/heads/master

2021-09-12T08:46:35.391638

2018-04-15T16:54:00

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#Author : Rohan M. Nanaware #Date C.: 13th Aug 2017 #Date M.: 13th Aug 2017 #Purpose: Python data structures code documentation #6.1 Strings fruit = 'banana' print(fruit[0]) print(len(fruit)) for i in range(len(fruit)): print(i, fruit[i]) for letter in fruit: print(letter) #Slicing strings - Upto but not including string = 'Python is great!' print(string[1:4]) print(string[7:]) #6.2 Manipulating strings fruit = 'banana' if 'nana' in fruit: print('Present') if 'Rohan' < 'rohan': print('Capital first') greet = 'Hello World!' greet.lower() print('Hello World!'.lower()) string = 'Hello World' type(string) dir(string)#Methods applicable on string string.replace('Hello','Hi') string = ' Yahallo! ' string.lstrip() string.rstrip() string.strip() line = 'The quick brown fox!' line.startswith('T') line.startswith('t') #String slicing string = 'Koreva teme#$! Makhinayo ' start = string.find('!')+2 end = string.find(' ',start) print(string[start:end]) #7.1 Files # Use words.txt as the file name fname = input("Enter file name: ") fh = open(fname) for line in fh: line_strip = line.rstrip().upper() #line_strip_upper = line_strip.upper() print(line_strip) # Use the file name mbox-short.txt as the file name #sample data - "X-DSPAM-Confidence: 0.8475" fname = input("Enter file name: ") fh = open(fname) confidence = 0 count = 0 for line in fh: if not line.startswith("X-DSPAM-Confidence:") : continue #print(line) confidence = float(line[line.find(":")+1:len(line)]) + confidence count = count + 1 avg_confidence = confidence/count print("Average spam confidence:",avg_confidence) #8.4 fname = input("Enter file name: ") fh = open(fname) lst = list() #for line in fh: # for word in line.splitsplit(): # if word in lst: # print(word) # continue # lst.append(word) #print(lst.sort()) for line in fh: line.rstrip() print(line.rstrip()) #9.1 Dictionaries purse = dict() purse['money'] = 10 purse['wallet'] = 1 print(purse['wallet']) for w in purse: print(w) print(purse[w]) purse[1] = 15 dict_ = dict() names = ['A','B','C','D','E','F','G','A','B','C'] for name in names: if name in dict_: dict_[name] += 1 else: dict_[name] = 1 print(dict_) dict_ = dict() names = ['A','B','C','D','E','F','G','A','B','C'] for name in names: dict_[name] = dict_.get(name, 0) + 1 name = input("Enter file:") if len(name) < 1 : name = "mbox-short.txt" handle = open(name) word_dict = dict() word_list = list() for line in handle: if 'From:' not in line: continue else: words = line.split() word_list.append(words[1]) for word in word_list: word_dict[word] = word_dict.get(word, 0) + 1 max_count = max(word_dict.values()) #print(max_count) for sender, count_ in word_dict.items(): #print(sender) if count_ == max_count: print(sender, count_)

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noreply@github.com

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/extras/[Day03.x]CrossedWires-Rendering.py

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no_license

cgdilley/AdventOfCode2019

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refs/heads/master

2020-09-22T12:55:16.869258

2019-12-13T10:10:11

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import pygame from shared.day3 import load_wires, Wire import time import sys from typing import Tuple Coord = Tuple[int, int] def main(): wire1, wire2 = load_wires() render_wires(wire1, wire2, progressive=True, speed=30) # # def render_wires(*wires: Wire, progressive: bool, speed: int): bounds = (( min([c[0] for w in wires for c in w.coords]), min([c[1] for w in wires for c in w.coords]) ), ( max([c[0] for w in wires for c in w.coords]), max([c[1] for w in wires for c in w.coords]) )) # Calculate window size based on bounds, and determine scale factor pygame.init() max_width, max_height = 1920, 1080 width, height = bounds[1][0] - bounds[0][0], bounds[1][1] - bounds[0][1] scale_x = 1 if width < max_width else max_width / width scale_y = 1 if height < max_height else max_height / height if scale_x < scale_y: scale = scale_x else: scale = scale_y width = int(width * scale) height = int(height * scale) screen = pygame.display.set_mode((width, height), pygame.RESIZABLE) # Keep track of coords traversed seen = set() seen_by_index = {i: set() for i in range(len(wires))} red_render_coords = set() max_steps = max([w.total_length for w in wires]) for step in range(max_steps): for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit() # Render each wire for i, w in enumerate(wires): if step >= len(w.raw): continue c = w.raw[step] render_c = _offset(c, bounds=bounds, scale=scale) if c in seen and c not in seen_by_index[i]: color = (255, 0, 0) size = 5 for x in range(render_c[0] - int(size/2), render_c[0] + int(size/2)): for y in range(render_c[1] - int(size/2), render_c[1] + int(size/2)): red_render_coords.add((x, y)) elif c in seen: color = (255, 0, 0) size = 1 red_render_coords.add(render_c) elif render_c in red_render_coords: color = (255, 0, 0) size = 1 else: color = _wire_color(i) size = 1 pygame.draw.rect(screen, color, pygame.Rect(render_c[0] - int(size/2), render_c[1] - int(size/2), size, size)) seen.add(c) seen_by_index[i].add(c) if progressive and step % speed == 0: pygame.display.flip() # time.sleep(0.0001) pygame.display.flip() while True: for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit() # def _offset(coord: Coord, bounds: Tuple[Coord, Coord], scale: float) -> Coord: return (int((coord[0] - bounds[0][0]) * scale), int((coord[1] - bounds[0][1]) * scale)) def _wire_color(index: int) -> Tuple[int, int, int]: if index == 0: return 150, 150, 230 elif index == 1: return 150, 230, 150 else: return 255, 255, 255 # if __name__ == "__main__": main()

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import os import subprocess SPJ_WA = 1 SPJ_AC = 0 SPJ_ERROR = -1 PY_SPJ_HOME = "/code/ProblemCenter/PythonSPJ/" def py_spj_run(spj_src_path, test_in_file_path, user_out_file_path): problem_id = get_problem_id(spj_src_path) return _py_spj_run(problem_id, test_in_file_path, user_out_file_path) def _py_spj_run(problem_id, test_in_file_path, user_out_file_path): cmd_list = ["python3", PY_SPJ_HOME + str(problem_id)+".py", test_in_file_path, user_out_file_path] """ with open("/code/py_spj_log", "a") as f: print("in_file",test_in_file_path,file=f) print("user_output",user_out_file_path,file=f) print("problem_id",problem_id,file=f) print("cmd"," ".join(cmd_list),file=f) """ try: spj_program_path = PY_SPJ_HOME+str(problem_id)+".py" p_handler = subprocess.Popen(["python3",spj_program_path, test_in_file_path, user_out_file_path]) p_handler.wait() if p_handler.returncode in [SPJ_WA, SPJ_AC, SPJ_ERROR]: return p_handler.returncode else: return SPJ_ERROR except Exception as e: print(e) return SPJ_ERROR def get_problem_id(spj_src_path): with open(spj_src_path, "r") as f: line = f.readline() line = line.replace("/", "") line = line.replace("\n", "") return line if __name__ == "__main__": _py_spj_run(1001, "/code/ProblemCenter/PythonSPJ/test.in", "/code/ProblemCenter/PythonSPJ/cmd.txt")

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/src_detection/HOG_SVM/pedestran_detect_me_without_train_16.py

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xuelanglv/KCF_IOU_Tracker

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bccaa0c145b6f58dec75d5843de4995c5aeba136

refs/heads/master

2020-05-25T19:36:57.125575

2019-04-27T03:09:01

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# *_*coding:utf-8 *_* # 参考资料：https://blog.csdn.net/qq_33662995/article/details/79356939 # # author: 许鸿斌 import os import sys import cv2 import time import logging import numpy as np import pandas as pd def logger_init(): ''' 自定义python的日志信息打印配置 :return logger: 日志信息打印模块 ''' # 获取logger实例，如果参数为空则返回root logger logger = logging.getLogger("PedestranDetect") # 指定logger输出格式 formatter = logging.Formatter('%(asctime)s %(levelname)-8s: %(message)s') # 文件日志 # file_handler = logging.FileHandler("test.log") # file_handler.setFormatter(formatter) # 可以通过setFormatter指定输出格式 # 控制台日志 console_handler = logging.StreamHandler(sys.stdout) console_handler.formatter = formatter # 也可以直接给formatter赋值 # 为logger添加的日志处理器 # logger.addHandler(file_handler) logger.addHandler(console_handler) # 指定日志的最低输出级别，默认为WARN级别 logger.setLevel(logging.INFO) return logger def test_hog_detect(logger): ''' 导入测试集，测试结果 :param test: 测试数据集 :param svm_detector: 用于HOGDescriptor的SVM检测器 :param logger: 日志信息打印模块 :return: 无 ''' hog = cv2.HOGDescriptor() # opencv自带的训练好了的分类器 hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector()) pwd = os.getcwd() DATA_PATH = 'F:/mot/obj_det/mAP_16/input/detection-results/' brenchmark = ['MOT16-02', 'MOT16-04', 'MOT16-05', 'MOT16-09', 'MOT16-10', 'MOT16-11', 'MOT16-13'] # cv2.namedWindow('Detect') for seq_name in brenchmark: test_dir = 'F:/MOT16/train/%s/img1/' % seq_name save_path = DATA_PATH + '%s-%06d.txt' idx = 1 test = os.listdir(test_dir) for f in test: file_path = os.path.join(test_dir, f) logger.info('Processing {}'.format(file_path)) img = cv2.imread(file_path) s_time = time.time() rects, scores = hog.detectMultiScale(img, winStride=(8,8), padding=(8,8), scale=1.2) print("run time = %f"%(time.time() - s_time)) dets = [] for (x,y,w,h), s in zip(rects, scores): s = 100 - float(s[0]) # if s > 1: # s = 2 - s # if s < 0.6: # continue dets.append(['Person', s, x, y, x+w, y+h]) dets = pd.DataFrame(dets) dets.to_csv(save_path % (seq_name, idx), sep=' ', index=False, header=False) # for (x,y,w,h), (s) in zip(rects, scores): # cv2.putText(img, '#%.03f' % s, (x,y), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 3) # cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 3) # cv2.putText(img, '#%03d' % idx, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.2, (255, 0, 0), 3) # cv2.imshow('Detect', cv2.resize(img, (int(img.shape[:2][1]*0.5), int(img.shape[:2][0]*0.5)))) # c = cv2.waitKey(1) & 0xff # if c == 27: # break idx = idx + 1 # if True: # cv2.imwrite('imgs/TUD-Stadtmitte/'+ f, img); # cv2.destroyAllWindows() if __name__ == '__main__': logger = logger_init() test_hog_detect(logger)

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chinaylssly/fluent-python

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# -*- coding: utf-8 -*- import random,sys from time import sleep,time from concurrent import futures MAX_WORKERS= 20 tl=[i*0.01 for i in range(20)] def do_one(t=0.2): # print (t) sleep(t) return t def do_many(tl=tl): workers=min(len(tl),MAX_WORKERS) with futures.ThreadPoolExecutor(workers) as executor: ''' executor.__exit__()方法会调用executor.shutdown(wait=True)方法,它会在所有的线程都执行完毕前阻塞线程 ''' res=executor.map(do_one,tl) return len(list(res)) ##返回获取结果的数量，如果有线程抛出异常，异常会在这里抛出，这与隐式调用next()函数从迭代器中回去相应的返回值一样 def main(do_many=do_many): t0=time() count=do_many() t=time()-t0 msg='execute {:2d} task cost {:.2f} s' print (msg.format(count,t)) if __name__ =='__main__': main()

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chinaylssly@qq.com

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import enum import torch from torch.autograd import Function import torch.distributed as dist # must be consistent with Aluminum ReductionOperator: https://github.com/BaguaSys/Aluminum/blob/master/include/aluminum/base.hpp class ReduceOp(enum.IntEnum): """An enum-like class for available reduction operations: ``SUM``, ``PRODUCT``, ``MIN``, ``MAX``, ``BAND``, ``BOR``, ``BXOR`` and ``AVG``.""" SUM = 0 PRODUCT = 1 MIN = 2 MAX = 3 BOR = 7 BAND = 8 BXOR = 9 AVG = 10 def torch_reduce_op_to_bagua(op): if op is torch.distributed.ReduceOp.SUM: return ReduceOp.SUM elif op is torch.distributed.ReduceOp.MAX: return ReduceOp.MAX else: raise Exception("Unexpect input={}".format(op)) def all_reduce(tensor, op=dist.ReduceOp.SUM, group=dist.group.WORLD): """ Reduces the tensor data across all machines in such a way that all get the final result. After the call the returned tensor is going to be bitwise identical in all processes. Arguments: tensor (Tensor): Input of the collective. op (optional): One of the values from ``torch.distributed.ReduceOp`` enum. Specifies an operation used for element-wise reductions. group (ProcessGroup, optional): The process group to work on. Returns: Tensor: Output of the collective """ if group is None: group = dist.group.WORLD return _AllReduce.apply(op, group, tensor) class _AllReduce(Function): @staticmethod def forward(ctx, op, group, tensor): ctx.group = group ctx.op = op tensor = tensor.clone() comm = group.bagua_patch().bagua_get_global_communicator() event = torch.cuda.current_stream().record_event() comm.cuda_stream.wait_event(event) with torch.cuda.stream(comm.cuda_stream): comm.allreduce_inplace( tensor.to_bagua_tensor().bagua_backend_tensor(), int(torch_reduce_op_to_bagua(op)), ) comm.cuda_stream.synchronize() return tensor @staticmethod def backward(ctx, grad_output): return (None, None) + (_AllReduce.apply(ctx.op, ctx.group, grad_output),)

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noreply@github.com

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pwanner/ISM-4402

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#!/usr/bin/env python # coding: utf-8 # In[1]: path_to_zip_file = "datasets.zip" directory_to_extract_to = "" import zipfile zip_ref = zipfile.ZipFile(path_to_zip_file, 'r') zip_ref.extractall(directory_to_extract_to) zip_ref.close() # In[2]: import pandas as pd Location = "datasets/smallgradesh.csv" df = pd.read_csv(Location, header=None) # In[3]: df.head() # In[4]: import pandas as pd Location = "datasets/gradedata.csv" df = pd.read_csv(Location) # In[5]: df.head() # In[6]: import pandas as pd Location = "datasets/smallgrades.csv" # To add headers as we load the data... df = pd.read_csv(Location, names=['Names','Grades']) # To add headers to a dataframe df.columns = ['Names','Grades'] # In[7]: df.head() # In[8]: import pandas as pd Location = "all_040_in_08.P1.csv" censusdf = pd.read_csv(Location) # In[9]: censusdf.head() # In[10]: import pandas as pd Location = "all_040_in_08.P1.csv" censusdf = pd.read_csv(Location) # In[11]: censusdf.head() # In[14]: import pandas as pd names = ['Bob','Jessica','Mary','John','Mel'] grades = [76,95,77,78,99] GradeList = zip(names,grades) df = pd.DataFrame(data = GradeList, columns=['Names','Grades']) df.to_csv('studentgrades.csv',index=False,header=False) # In[15]: df.head() # In[20]: import pandas as pd names = ['Bob','Jessica','Mary','John','Mel'] grades = [76,95,77,78,99] bsdegrees = [1,1,0,0,1] msdegrees = [2,1,0,0,0] phddegrees = [0,1,0,0,0] Degrees = zip(names,grades,bsdegrees,msdegrees,phddegrees) columns = ['Names','Grades','BS','MS','PhD'] df = pd.DataFrame(data = Degrees, columns=column) df # In[21]: import pandas as pd Location = "datasets/gradedata.xlsx" df = pd.read_excel(Location) # In[22]: df.head() # In[23]: df.columns = ['first','last','sex','age','exer','hrs','grd','addr'] df.head() # In[24]: path_to_zip_file = "EDU.zip" directory_to_extract_to = "" import zipfile zip_ref = zipfile.ZipFile(path_to_zip_file, 'r') zip_ref.extractall(directory_to_extract_to) zip_ref.close() # In[ ]:

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noreply@github.com

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choicoding1026/ML

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refs/heads/master

2023-01-01T07:35:09.655664

2020-10-15T07:41:50

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''' 서울시 코로나19 데이터 수집 및 분석 26. 여행력 ''' import pandas as pd import numpy as np file_name = "seoul_corona_10_11_.csv" df = pd.read_csv(file_name, encoding="utf-8") # 한글처리 # 1. '연번' 기준으로 오름차순 정렬 df = df.sort_values(by="연번", ascending=False) print("1. '연번' 기준으로 오름차순 정렬:\n", df.head()) # 2. 확진일의 빈도수 ==> 어느 날짜에 가장 많이 확진이 되었는지 확인 가능 # value_counts() 자동으로 내림차순 정렬해서 반환 print("2. 확진일의 빈도수: \n", df["확진일"].value_counts()) # 3. '확진일자' 컬럼 추가 => 2020_10_11 날짜형식 # 기존의 '확진일' 컬럼값은 문자이기 때문에 날짜로 변경해야 된다. ''' 1) 10.11 --> 10-11 변경 2) 10-11 --> 2020-10-11 로 변경 3) 2020-10-11 문자열 ---- > 2020-10-11 날짜로 변경 (pd.to_datetime 함수 ) 4) df["확진일자"] = 날짜 ''' df["확진일자"] = pd.to_datetime("2020-"+df["확진일"].str.replace(".", "-")) print("3. '확진일자' 컬럼 추가: \n", df.head()) # 4. '확진일자' 날짜 데이터 컬럼 이용하여 '월' 컬럼 추가 df["월"] = df["확진일자"].dt.month print("4. '월' 컬럼 추가: \n", df.head()) # 5. '확진일자' 날짜 데이터 컬럼 이용하여 '주(week)' 컬럼 추가 # 해당년도의 몇번째 주(week)인지 반환 df["주"] = df["확진일자"].dt.isocalendar().week print("5. '주' 컬럼 추가: \n", df.head()) # 6. '확진일자' 날짜 데이터 컬럼 이용하여 '월-일' 컬럼 추가 # m = df["확진일자"].dt.month # d = df["확진일자"].dt.day # df["월-일"] = m.astype(str) + "-" + d.astype(str) df["월-일"] = df["확진일자"].astype(str).map(lambda x:x[-5:]) # map함수는 데이터가공시 사용 print("6. '월-일' 컬럼 추가: \n", df.head()) print("6. '월-일' 컬럼 추가: \n", df.tail()) ######################################################################## # 26. 여행력 print(df["여행력"]) print(df["여행력"].unique()) print(df["여행력"].value_counts()) ''' 1. '-' ==> NaN 처리 ==> "-"을 np.nan 으로 변경 처리 2. 공통명으로 변경 '아랍에미리트', 'UAE' ===> 아랍에미리트 '중국 청도','우한교민','우한 교민', '중국 우한시', '중국' ==> 중국 '프랑스, 스페인','스페인, 프랑스' ==> 프랑스, 스페인 체코,헝가리,오스트리아,이탈리아,프랑스,모로코,독일,스페인,영국,폴란드,터키,아일랜드 ==>유럽 브라질,아르헨티아,칠레,볼리비아, 멕시코, 페루 => 남미 ''' ## 공통명으로 변경하고 시각화 df["해외"]=df["여행력"] print(df["해외"].str.contains('아랍에미리트|UAE')) df.loc[df["해외"].str.contains('아랍에미리트|UAE'), "해외"] = "아랍에미리트" df.loc[df["해외"].str.contains('우한|중국'), "해외"] = "중국" df.loc[df["해외"]. str.contains('체코|헝가리|오스트리아|이탈리아|프랑스|모로코|독일,스페인|영국\폴란드|터키|아일랜드'), "해외"] = "유럽" df.loc[df["해외"].str.contains('브라질|아르헨티아|칠레|볼리비아|멕시코|페루'), "해외"] = "남미" ## "-"을 np.nan 으로 변경 처리 df["해외"]=df["해외"].replace("-", np.nan) print(df["해외"].unique()) print(df["해외"].value_counts()) # 상위 15개만 시각화 import matplotlib.pyplot as plt plt.rc("font", family="Malgun Gothic") # 한글 처리 # plt.rc("figure", titlesize=4) # title 크기 plt.rc("ytick", labelsize=8) # y축 라벨 크기 plt.rc("xtick", labelsize=8) # x축 라벨 크기 plt.style.use("fivethirtyeight") g = df["해외"].value_counts().head(15).sort_values().plot.barh(title="xxxx", figsize=(16,4)) plt.show()

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justin6130@gmail.com

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# 8.5 Open the file mbox-short.txt and read it line by line. When you find a line that starts with 'From ' like the following line: ##### From stephen.marquard@uct.ac.za Sat Jan 5 09:14:16 2008 # You will parse the From line using split() and print out the second word in the line (i.e. the entire address of the person who sent the message). # Then print out a count at the end. # Hint: make sure not to include the lines that start with 'From:'. # You can download the sample data at http://www.pythonlearn.com/code/mbox-short.txt fname = "Data/mbox-short.txt" if len(fname) < 1: fname = "Data/mbox-short.txt" # raw_input("Enter file name: ") fh = open(fname) count = 0 for line in fh: if line.startswith("From:"): continue if line.startswith("From"): print line.rstrip().split()[1] count+=1 print "There were", count, "lines in the file with From as the first word"

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# -*- coding: utf-8 -*- # Copyright (C) 2017 NetworkX Developers # Aric Hagberg <hagberg@lanl.gov> # Dan Schult <dschult@colgate.edu> # Pieter Swart <swart@lanl.gov> # Loïc Séguin-C. <loicseguin@gmail.com> # All rights reserved. # BSD license. """ Algorithms for calculating min/max spanning trees/forests. """ from heapq import heappop, heappush from operator import itemgetter from itertools import count from math import isnan import networkx as nx from networkx.utils import UnionFind, not_implemented_for __all__ = [ 'minimum_spanning_edges', 'maximum_spanning_edges', 'minimum_spanning_tree', 'maximum_spanning_tree', ] @not_implemented_for('multigraph') def boruvka_mst_edges(G, minimum=True, weight='weight', keys=False, data=True, ignore_nan=False): """Iterate over edges of a Borůvka's algorithm min/max spanning tree. Parameters ---------- G : NetworkX Graph The edges of `G` must have distinct weights, otherwise the edges may not form a tree. minimum : bool (default: True) Find the minimum (True) or maximum (False) spanning tree. weight : string (default: 'weight') The name of the edge attribute holding the edge weights. keys : bool (default: True) This argument is ignored since this function is not implemented for multigraphs; it exists only for consistency with the other minimum spanning tree functions. data : bool (default: True) Flag for whether to yield edge attribute dicts. If True, yield edges `(u, v, d)`, where `d` is the attribute dict. If False, yield edges `(u, v)`. ignore_nan : bool (default: False) If a NaN is found as an edge weight normally an exception is raised. If `ignore_nan is True` then that edge is ignored instead. """ # Initialize a forest, assuming initially that it is the discrete # partition of the nodes of the graph. forest = UnionFind(G) def best_edge(component): """Returns the optimum (minimum or maximum) edge on the edge boundary of the given set of nodes. A return value of ``None`` indicates an empty boundary. """ sign = 1 if minimum else -1 minwt = float('inf') boundary = None for e in nx.edge_boundary(G, component, data=True): wt = e[-1].get(weight, 1) * sign if isnan(wt): if ignore_nan: continue msg = "NaN found as an edge weight. Edge %s" raise ValueError(msg % (e,)) if wt < minwt: minwt = wt boundary = e return boundary # Determine the optimum edge in the edge boundary of each component # in the forest. best_edges = (best_edge(component) for component in forest.to_sets()) best_edges = [edge for edge in best_edges if edge is not None] # If each entry was ``None``, that means the graph was disconnected, # so we are done generating the forest. while best_edges: # Determine the optimum edge in the edge boundary of each # component in the forest. # # This must be a sequence, not an iterator. In this list, the # same edge may appear twice, in different orientations (but # that's okay, since a union operation will be called on the # endpoints the first time it is seen, but not the second time). # # Any ``None`` indicates that the edge boundary for that # component was empty, so that part of the forest has been # completed. # # TODO This can be parallelized, both in the outer loop over # each component in the forest and in the computation of the # minimum. (Same goes for the identical lines outside the loop.) best_edges = (best_edge(component) for component in forest.to_sets()) best_edges = [edge for edge in best_edges if edge is not None] # Join trees in the forest using the best edges, and yield that # edge, since it is part of the spanning tree. # # TODO This loop can be parallelized, to an extent (the union # operation must be atomic). for u, v, d in best_edges: if forest[u] != forest[v]: if data: yield u, v, d else: yield u, v forest.union(u, v) def kruskal_mst_edges(G, minimum, weight='weight', keys=True, data=True, ignore_nan=False): """Iterate over edges of a Kruskal's algorithm min/max spanning tree. Parameters ---------- G : NetworkX Graph The graph holding the tree of interest. minimum : bool (default: True) Find the minimum (True) or maximum (False) spanning tree. weight : string (default: 'weight') The name of the edge attribute holding the edge weights. keys : bool (default: True) If `G` is a multigraph, `keys` controls whether edge keys ar yielded. Otherwise `keys` is ignored. data : bool (default: True) Flag for whether to yield edge attribute dicts. If True, yield edges `(u, v, d)`, where `d` is the attribute dict. If False, yield edges `(u, v)`. ignore_nan : bool (default: False) If a NaN is found as an edge weight normally an exception is raised. If `ignore_nan is True` then that edge is ignored instead. """ subtrees = UnionFind() if G.is_multigraph(): edges = G.edges(keys=True, data=True) def filter_nan_edges(edges=edges, weight=weight): sign = 1 if minimum else -1 for u, v, k, d in edges: wt = d.get(weight, 1) * sign if isnan(wt): if ignore_nan: continue msg = "NaN found as an edge weight. Edge %s" raise ValueError(msg % ((u, v, f, k, d),)) yield wt, u, v, k, d else: edges = G.edges(data=True) def filter_nan_edges(edges=edges, weight=weight): sign = 1 if minimum else -1 for u, v, d in edges: wt = d.get(weight, 1) * sign if isnan(wt): if ignore_nan: continue msg = "NaN found as an edge weight. Edge %s" raise ValueError(msg % ((u, v, d),)) yield wt, u, v, d edges = sorted(filter_nan_edges(), key=itemgetter(0)) # Multigraphs need to handle edge keys in addition to edge data. if G.is_multigraph(): for wt, u, v, k, d in edges: if subtrees[u] != subtrees[v]: if keys: if data: yield u, v, k, d else: yield u, v, k else: if data: yield u, v, d else: yield u, v subtrees.union(u, v) else: for wt, u, v, d in edges: if subtrees[u] != subtrees[v]: if data: yield (u, v, d) else: yield (u, v) subtrees.union(u, v) def prim_mst_edges(G, minimum, weight='weight', keys=True, data=True, ignore_nan=False): """Iterate over edges of Prim's algorithm min/max spanning tree. Parameters ---------- G : NetworkX Graph The graph holding the tree of interest. minimum : bool (default: True) Find the minimum (True) or maximum (False) spanning tree. weight : string (default: 'weight') The name of the edge attribute holding the edge weights. keys : bool (default: True) If `G` is a multigraph, `keys` controls whether edge keys ar yielded. Otherwise `keys` is ignored. data : bool (default: True) Flag for whether to yield edge attribute dicts. If True, yield edges `(u, v, d)`, where `d` is the attribute dict. If False, yield edges `(u, v)`. ignore_nan : bool (default: False) If a NaN is found as an edge weight normally an exception is raised. If `ignore_nan is True` then that edge is ignored instead. """ is_multigraph = G.is_multigraph() push = heappush pop = heappop nodes = list(G) c = count() sign = 1 if minimum else -1 while nodes: u = nodes.pop(0) frontier = [] visited = [u] if is_multigraph: for v, keydict in G.adj[u].items(): for k, d in keydict.items(): wt = d.get(weight, 1) * sign if isnan(wt): if ignore_nan: continue msg = "NaN found as an edge weight. Edge %s" raise ValueError(msg % ((u, v, k, d),)) push(frontier, (wt, next(c), u, v, k, d)) else: for v, d in G.adj[u].items(): wt = d.get(weight, 1) * sign if isnan(wt): if ignore_nan: continue msg = "NaN found as an edge weight. Edge %s" raise ValueError(msg % ((u, v, d),)) push(frontier, (wt, next(c), u, v, d)) while frontier: if is_multigraph: W, _, u, v, k, d = pop(frontier) else: W, _, u, v, d = pop(frontier) if v in visited: continue # Multigraphs need to handle edge keys in addition to edge data. if is_multigraph and keys: if data: yield u, v, k, d else: yield u, v, k else: if data: yield u, v, d else: yield u, v # update frontier visited.append(v) nodes.remove(v) if is_multigraph: for w, keydict in G.adj[v].items(): if w in visited: continue for k2, d2 in keydict.items(): new_weight = d2.get(weight, 1) * sign push(frontier, (new_weight, next(c), v, w, k2, d2)) else: for w, d2 in G.adj[v].items(): if w in visited: continue new_weight = d2.get(weight, 1) * sign push(frontier, (new_weight, next(c), v, w, d2)) ALGORITHMS = { 'boruvka': boruvka_mst_edges, u'borůvka': boruvka_mst_edges, 'kruskal': kruskal_mst_edges, 'prim': prim_mst_edges } @not_implemented_for('directed') def minimum_spanning_edges(G, algorithm='kruskal', weight='weight', keys=True, data=True, ignore_nan=False): """Generate edges in a minimum spanning forest of an undirected weighted graph. A minimum spanning tree is a subgraph of the graph (a tree) with the minimum sum of edge weights. A spanning forest is a union of the spanning trees for each connected component of the graph. Parameters ---------- G : undirected Graph An undirected graph. If `G` is connected, then the algorithm finds a spanning tree. Otherwise, a spanning forest is found. algorithm : string The algorithm to use when finding a minimum spanning tree. Valid choices are 'kruskal', 'prim', or 'boruvka'. The default is 'kruskal'. weight : string Edge data key to use for weight (default 'weight'). keys : bool Whether to yield edge key in multigraphs in addition to the edge. If `G` is not a multigraph, this is ignored. data : bool, optional If True yield the edge data along with the edge. ignore_nan : bool (default: False) If a NaN is found as an edge weight normally an exception is raised. If `ignore_nan is True` then that edge is ignored instead. Returns ------- edges : iterator An iterator over edges in a maximum spanning tree of `G`. Edges connecting nodes `u` and `v` are represented as tuples: `(u, v, k, d)` or `(u, v, k)` or `(u, v, d)` or `(u, v)` If `G` is a multigraph, `keys` indicates whether the edge key `k` will be reported in the third position in the edge tuple. `data` indicates whether the edge datadict `d` will appear at the end of the edge tuple. If `G` is not a multigraph, the tuples are `(u, v, d)` if `data` is True or `(u, v)` if `data` is False. Examples -------- >>> from networkx.algorithms import tree Find minimum spanning edges by Kruskal's algorithm >>> G = nx.cycle_graph(4) >>> G.add_edge(0, 3, weight=2) >>> mst = tree.minimum_spanning_edges(G, algorithm='kruskal', data=False) >>> edgelist = list(mst) >>> sorted(edgelist) [(0, 1), (1, 2), (2, 3)] Find minimum spanning edges by Prim's algorithm >>> G = nx.cycle_graph(4) >>> G.add_edge(0, 3, weight=2) >>> mst = tree.minimum_spanning_edges(G, algorithm='prim', data=False) >>> edgelist = list(mst) >>> sorted(edgelist) [(0, 1), (1, 2), (2, 3)] Notes ----- For Borůvka's algorithm, each edge must have a weight attribute, and each edge weight must be distinct. For the other algorithms, if the graph edges do not have a weight attribute a default weight of 1 will be used. Modified code from David Eppstein, April 2006 http://www.ics.uci.edu/~eppstein/PADS/ """ try: algo = ALGORITHMS[algorithm] except KeyError: msg = '{} is not a valid choice for an algorithm.'.format(algorithm) raise ValueError(msg) return algo(G, minimum=True, weight=weight, keys=keys, data=data, ignore_nan=ignore_nan) @not_implemented_for('directed') def maximum_spanning_edges(G, algorithm='kruskal', weight='weight', keys=True, data=True, ignore_nan=False): """Generate edges in a maximum spanning forest of an undirected weighted graph. A maximum spanning tree is a subgraph of the graph (a tree) with the maximum possible sum of edge weights. A spanning forest is a union of the spanning trees for each connected component of the graph. Parameters ---------- G : undirected Graph An undirected graph. If `G` is connected, then the algorithm finds a spanning tree. Otherwise, a spanning forest is found. algorithm : string The algorithm to use when finding a maximum spanning tree. Valid choices are 'kruskal', 'prim', or 'boruvka'. The default is 'kruskal'. weight : string Edge data key to use for weight (default 'weight'). keys : bool Whether to yield edge key in multigraphs in addition to the edge. If `G` is not a multigraph, this is ignored. data : bool, optional If True yield the edge data along with the edge. ignore_nan : bool (default: False) If a NaN is found as an edge weight normally an exception is raised. If `ignore_nan is True` then that edge is ignored instead. Returns ------- edges : iterator An iterator over edges in a maximum spanning tree of `G`. Edges connecting nodes `u` and `v` are represented as tuples: `(u, v, k, d)` or `(u, v, k)` or `(u, v, d)` or `(u, v)` If `G` is a multigraph, `keys` indicates whether the edge key `k` will be reported in the third position in the edge tuple. `data` indicates whether the edge datadict `d` will appear at the end of the edge tuple. If `G` is not a multigraph, the tuples are `(u, v, d)` if `data` is True or `(u, v)` if `data` is False. Examples -------- >>> from networkx.algorithms import tree Find maximum spanning edges by Kruskal's algorithm >>> G = nx.cycle_graph(4) >>> G.add_edge(0, 3, weight=2) >>> mst = tree.maximum_spanning_edges(G, algorithm='kruskal', data=False) >>> edgelist = list(mst) >>> sorted(edgelist) [(0, 1), (0, 3), (1, 2)] Find maximum spanning edges by Prim's algorithm >>> G = nx.cycle_graph(4) >>> G.add_edge(0, 3, weight=2) # assign weight 2 to edge 0-3 >>> mst = tree.maximum_spanning_edges(G, algorithm='prim', data=False) >>> edgelist = list(mst) >>> sorted(edgelist) [(0, 1), (0, 3), (3, 2)] Notes ----- For Borůvka's algorithm, each edge must have a weight attribute, and each edge weight must be distinct. For the other algorithms, if the graph edges do not have a weight attribute a default weight of 1 will be used. Modified code from David Eppstein, April 2006 http://www.ics.uci.edu/~eppstein/PADS/ """ try: algo = ALGORITHMS[algorithm] except KeyError: msg = '{} is not a valid choice for an algorithm.'.format(algorithm) raise ValueError(msg) return algo(G, minimum=False, weight=weight, keys=keys, data=data, ignore_nan=ignore_nan) def minimum_spanning_tree(G, weight='weight', algorithm='kruskal', ignore_nan=False): """Returns a minimum spanning tree or forest on an undirected graph `G`. Parameters ---------- G : undirected graph An undirected graph. If `G` is connected, then the algorithm finds a spanning tree. Otherwise, a spanning forest is found. weight : str Data key to use for edge weights. algorithm : string The algorithm to use when finding a minimum spanning tree. Valid choices are 'kruskal', 'prim', or 'boruvka'. The default is 'kruskal'. ignore_nan : bool (default: False) If a NaN is found as an edge weight normally an exception is raised. If `ignore_nan is True` then that edge is ignored instead. Returns ------- G : NetworkX Graph A minimum spanning tree or forest. Examples -------- >>> G = nx.cycle_graph(4) >>> G.add_edge(0, 3, weight=2) >>> T = nx.minimum_spanning_tree(G) >>> sorted(T.edges(data=True)) [(0, 1, {}), (1, 2, {}), (2, 3, {})] Notes ----- For Borůvka's algorithm, each edge must have a weight attribute, and each edge weight must be distinct. For the other algorithms, if the graph edges do not have a weight attribute a default weight of 1 will be used. There may be more than one tree with the same minimum or maximum weight. See :mod:`networkx.tree.recognition` for more detailed definitions. Isolated nodes with self-loops are in the tree as edgeless isolated nodes. """ edges = minimum_spanning_edges(G, algorithm, weight, keys=True, data=True, ignore_nan=ignore_nan) T = G.fresh_copy() # Same graph class as G T.graph.update(G.graph) T.add_nodes_from(G.nodes.items()) T.add_edges_from(edges) return T def maximum_spanning_tree(G, weight='weight', algorithm='kruskal', ignore_nan=False): """Returns a maximum spanning tree or forest on an undirected graph `G`. Parameters ---------- G : undirected graph An undirected graph. If `G` is connected, then the algorithm finds a spanning tree. Otherwise, a spanning forest is found. weight : str Data key to use for edge weights. algorithm : string The algorithm to use when finding a minimum spanning tree. Valid choices are 'kruskal', 'prim', or 'boruvka'. The default is 'kruskal'. ignore_nan : bool (default: False) If a NaN is found as an edge weight normally an exception is raised. If `ignore_nan is True` then that edge is ignored instead. Returns ------- G : NetworkX Graph A minimum spanning tree or forest. Examples -------- >>> G = nx.cycle_graph(4) >>> G.add_edge(0, 3, weight=2) >>> T = nx.maximum_spanning_tree(G) >>> sorted(T.edges(data=True)) [(0, 1, {}), (0, 3, {'weight': 2}), (1, 2, {})] Notes ----- For Borůvka's algorithm, each edge must have a weight attribute, and each edge weight must be distinct. For the other algorithms, if the graph edges do not have a weight attribute a default weight of 1 will be used. There may be more than one tree with the same minimum or maximum weight. See :mod:`networkx.tree.recognition` for more detailed definitions. Isolated nodes with self-loops are in the tree as edgeless isolated nodes. """ edges = maximum_spanning_edges(G, algorithm, weight, keys=True, data=True, ignore_nan=ignore_nan) edges = list(edges) T = G.fresh_copy() # Same graph class as G T.graph.update(G.graph) T.add_nodes_from(G.nodes.items()) T.add_edges_from(edges) return T

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import numpy as np import scipy.io as sio import dictionary import matplotlib.pyplot as plt import matplotlib.image as mpimg from multiOGD import * from kernels import * import sys import argparse import utility sys.stdout = utility.Logger() print 'Starting run of MNIST.py' parser = argparse.ArgumentParser(description=\ 'MNIST: Encode sparse dictionary and fit model') parser.add_argument('dict_fit',\ help="model for fitting dictionary (linreg, lasso, lars)") parser.add_argument('dict_init',\ help='initialization of dictionary') parser.add_argument('dict_atoms',\ help='nr of atoms in dictionary') parser.add_argument('dict_reg',\ help='regularization in sparse encoding') parser.add_argument('mod_reg', \ help='regularization svm fit') params = parser.parse_args(sys.argv[1:]) DICT_FIT = params.dict_fit DICT_INIT = params.dict_init DICT_ATOMS = int(params.dict_atoms) DICT_REG = float(params.dict_reg) MOD_REG = float(params.mod_reg) print params def showimage(x): img = np.reshape(x, (28, 28), order = 'F') imgplot = plt.imshow(img) plt.show() mnist_train = sio.loadmat('./data/mnist/MNIST_train.mat') mnist_test = sio.loadmat('./data/mnist/MNIST_test.mat') X_train = mnist_train['X'][0][0][2].transpose() y_train = mnist_train['y'] X_test = mnist_test['Xtest'].transpose() y_test = mnist_test['ytest'] dim = X_train.shape[1] ## Dictionary lasso_d = dictionary.Dictionary(dim, DICT_ATOMS, DICT_FIT, DICT_REG, \ DICT_INIT) lasso_d.batchtrain(X_train # Save dictionary atoms as images #lasso_d.dimagesave((28, 28), 'mnist') # Find reconstructions alphas_train = lasso_d.batchreconstruction(X_train, \ 'mnist_train_s') alphas_test = lasso_d.batchreconstruction(X_test, \ 'mnist_test_s') ## Classification ogd_m = multiOGD(10, DICT_ATOMS, MOD_REG) ogd_m.train(alphas_train, y_train) ogd_m.predict(alphas_test, y_test) print 'Run of MNIST.py is complete!' ''' Atoms: 200 Reg: 0.05 too much '''

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#Import semua prosedur dari file Datahandler.py import Datahandler as dh import ann2 as ann import numpy as np #BAGIAN INI DIKHUSUSKAN UNTUK TRAINING DATA #CHANGEABLE VARIABLE : File DATA TRAINING fileTrain = 'DataTrainSMA.xlsx' data, target = dh.generateToSeries(fileTrain, 3) #CHANGEABLE VARIABLE : HIDDEN NODE num_hidden = 5 # print data[1] #MERUBAH DATA MENJADI MATRIX data = np.array(data) target = np.array(target) #Modul TRAINING #CHANGEABLE VARIABLE : EPOCH dan LEARNING RATE mape, model = ann.train(data, num_hidden, target, epoch=1000, lr=0.1) y = ann.test(data,model) print y mape = np.mean(np.abs(target-y)) print mape, (1-mape)*100 print 'test' #BAGIAN INI DIKHUSUSKAN UNTUK TESTING DATA #CHANGEABLE VARIABLE : File DATA TESTING fileTest = 'DataTestSMA.xlsx' data, target = dh.generateToSeries(fileTest, 3) data = np.array(data) target = np.array(target) y = ann.test(data,model) mape = np.mean(np.abs(target-y)) print mape, (1-mape)*100

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# Generated by Django 2.1 on 2018-09-10 05:16 from django.conf import settings from django.db import migrations, models import django.db.models.deletion class Migration(migrations.Migration): dependencies = [ migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('pets', '0006_auto_20180910_0011'), ] operations = [ migrations.RemoveField( model_name='mascota', name='dueno', ), migrations.AddField( model_name='mascota', name='dueno', field=models.ForeignKey(default=3, on_delete=django.db.models.deletion.PROTECT, related_name='get_pets', to=settings.AUTH_USER_MODEL, verbose_name='Dueños'), preserve_default=False, ), ]

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# coding=utf-8 # from __future__ import print_function """Împăratul a primit serie de mesaje importante pe care este important să le descifreze cât mai repede. Din păcate mesagerul nu a apucat să îi spună împăratul care au fost cheile alese pentru fiecare mesaj și tu ai fost ales să descifrezi misterul. Informații: În criptografie, cifrul lui Caesar este o metodă simplă de a cripta un mesaj prin înlocuirea fiecărei litere cu litera de pe poziția aflată la un n pași de ea în alfabet (unde este n este un număr întreg cunoscut """ def afla_pasul(mesaj): """ Afla pasul encodarii """ first_letter = 'a' my_letter = mesaj[0] return ord(my_letter) - ord(first_letter) def real_letter(character, key): """ Afla caracterul """ if character.isalpha(): character = ord(character)-key if character < ord('a'): character = ord('z') - abs(ord('a') - character) + 1 return chr(character) else: return character def decripteaza_mesajul(mesaj, fisier): """ Decriptarea mesajului """ key = afla_pasul(mesaj) puncte = 0. for index in mesaj: if index == ".": if puncte == 1: print ".\n" fisier.write("\n") else: puncte = puncte + 1 print ".", fisier.write(".") else: print real_letter(index, key), fisier.write(real_letter(index, key)) def main(): """ Main function docstring """ try: fisier = open("../../../date_intrare/mesaje.secret", "r") towrite = open("../../../date_iesire/mesaje.decodat", "w") mesaje = fisier.read() fisier.close() except IOError: print "Nu am putut obtine mesajele." return for mesaj in mesaje.splitlines(): decripteaza_mesajul(mesaj, towrite) if __name__ == "__main__": main()

[ "mmicu@cloudbasesolutions.com" ]

mmicu@cloudbasesolutions.com

5f3bf0a00a97af0ee8dafb8ced4351ea6a96ee71

6373ae8a308d5d8a7a100a36933b2e7de22638cf

/ldpc_jossy/py/ldpc.py

7841745ac8da20912be0cd1097fb01eb0a6de236

[]

no_license

appleginny/Test_LDPC

5c0b79eeb0b61180cc0a7d833f5c1b95529fb46c

a39a3f92dc836eaff6cc29459e753e1c23ae52e1

refs/heads/main

2023-05-08T03:23:30.124992

2021-05-26T08:46:05

370,964,996

0

null

UTF-8

Python

false

35,147

py

from types import DynamicClassAttribute import numpy as nphttps://github.com/appleginny/Test_LDPC/blob/main/ldpc_jossy/py/ldpc.py import ctypes as ct class code: def __init__(self, standard = '802.11n', rate = '1/2', z=27, ptype='A'): self.standard = standard self.rate = rate self.z = z self.ptype = ptype self.proto = self.assign_proto() vdeg, cdeg, intrlv = self.prepare_decoder() self.vdeg = vdeg self.cdeg = cdeg self.intrlv = intrlv self.Nv = len(vdeg) self.Nc = len(cdeg) self.Nmsg = len(intrlv) self.N = self.Nv self.K = self.Nv - self.Nc return def assign_proto(self): """ Generates arrays to enable the construction of IEEE standard-compliant LDPC codes Parameters ---------- standard: string Specifies the IEEE standard used, 802.11n or 802.16 rate: string Specifies the code rate, 1/2, 2/3, 3/4 or 5/6 z: int Optional parameter (not needed for for 802.16, required for 802.11n) Specifies the protograph expansion factor, freely chooseable >= 3 for IEEE 802.16, restricted to (27, 54, 81) for IEEE 802.11n ptype: character Optional parameter. Either A or B for 802.16 rates 2/3 and 3/4 where two options are specified in the standard. Parameter unused for all other codes. Returns ------- np.ndarray Protograph for an LDPC parity-check matrix """ standard = self.standard rate = self.rate z = self.z ptype = self.ptype if standard == "802.16": # N = z*24 if rate == '1/2': proto = np.array([ [-1, 94, 73, -1, -1, -1, -1, -1, 55, 83, -1, -1, 7, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [-1, 27, -1, -1, -1, 22, 79, 9, -1, -1, -1, 12, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, 24, 22, 81, -1, 33, -1, -1, -1, 0, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1], [61, -1, 47, -1, -1, -1, -1, -1, 65, 25, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1], [-1, -1, 39, -1, -1, -1, 84, -1, -1, 41, 72, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, 46, 40, -1, 82, -1, -1, -1, 79, 0, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1], [-1, -1, 95, 53, -1, -1, -1, -1, -1, 14, 18, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1], [-1, 11, 73, -1, -1, -1, 2, -1, -1, 47, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1], [12, -1, -1, -1, 83, 24, -1, 43, -1, -1, -1, 51, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1], [-1, -1, -1, -1, -1, 94, -1, 59, -1, -1, 70, 72, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1], [-1, -1, 7, 65, -1, -1, -1, -1, 39, 49, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0], [43, -1, -1, -1, -1, 66, -1, 41, -1, -1, -1, 26, 7, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0] ]) elif rate == '2/3': if ptype == 'A': proto = np.array([ [3, 0, -1, -1, 2, 0, -1, 3, 7, -1, 1, 1, -1, -1, -1, -1, 1, 0, -1, -1, -1, -1, -1, -1], [-1, -1, 1, -1, 36, -1, -1, 34, 10, -1, -1, 18, 2, -1, 3, 0, -1, 0, 0, -1, -1, -1, -1, -1], [-1, -1, 12, 2, -1, 15, -1, 40, -1, 3, -1, 15, -1, 2, 13, -1, -1, -1, 0, 0, -1, -1, -1, -1], [-1, -1, 19, 24, -1, 3, 0, -1, 6, -1, 17, -1, -1, -1, 8, 39, -1, -1, -1, 0, 0, -1, -1, -1], [20, -1, 6, -1, -1, 10, 29, -1, -1, 28, -1, 14, -1, 38, -1, -1, 0, -1, -1, -1, 0, 0, -1, -1], [-1, -1, 10, -1, 28, 20, -1, -1, 8, -1, 36, -1, 9, -1, 21, 45, -1, -1, -1, -1, -1, 0, 0, -1], [35, 25, -1, 37, -1, 21, -1, -1, 5, -1, -1, 0, -1, 4, 20, -1, -1, -1, -1, -1, -1, -1, 0, 0], [-1, 6, 6, -1, -1, -1, 4, -1, 14, 30, -1, 3, 36, -1, 14, -1, 1, -1, -1, -1, -1, -1, -1, 0] ]) elif ptype == 'B': proto = np.array([ [2, -1, 19, -1, 47, -1, 48, -1, 36, -1, 82, -1, 47, -1, 15, -1, 95, 0, -1, -1, -1, -1, -1, -1], [-1, 69, -1, 88, -1, 33, -1, 3, -1, 16, -1, 37, -1, 40, -1, 48, -1, 0, 0, -1, -1, -1, -1, -1], [10, -1, 86, -1, 62, -1, 28, -1, 85, -1, 16, -1, 34, -1, 73, -1, -1, -1, 0, 0, -1, -1, -1, -1], [-1, 28, -1, 32, -1, 81, -1, 27, -1, 88, -1, 5, -1, 56, -1, 37, -1, -1, -1, 0, 0, -1, -1, -1], [23, -1, 29, -1, 15, -1, 30, -1, 66, -1, 24, -1, 50, -1, 62, -1, -1, -1, -1, -1, 0, 0, -1, -1], [-1, 30, -1, 65, -1, 54, -1, 14, -1, 0, -1, 30, -1, 74, -1, 0, -1, -1, -1, -1, -1, 0, 0, -1], [32, -1, 0, -1, 15, -1, 56, -1, 85, -1, 5, -1, 6, -1, 52, -1, 0, -1, -1, -1, -1, -1, 0, 0], [-1, 0, -1, 47, -1, 13, -1, 61, -1, 84, -1, 55, -1, 78, -1, 41, 95, -1, -1, -1, -1, -1, -1, 0] ]) else: raise NameError('802.16 type must be either A or B') elif rate == '3/4': if ptype == 'A': proto = np.array([ [6, 38, 3, 93, -1, -1, -1, 30, 70, -1, 86, -1, 37, 38, 4, 11, -1, 46, 48, 0, -1, -1, -1, -1], [62, 94, 19, 84, -1, 92, 78, -1, 15, -1, -1, 92, -1, 45, 24, 32, 30, -1, -1, 0, 0, -1, -1, -1], [71, -1, 55, -1, 12, 66, 45, 79, -1, 78, -1, -1, 10, -1, 22, 55, 70, 82, -1, -1, 0, 0, -1, -1], [38, 61, -1, 66, 9, 73, 47, 64, -1, 39, 61, 43, -1, -1, -1, -1, 95, 32, 0, -1, -1, 0, 0, -1], [-1, -1, -1, -1, 32, 52, 55, 80, 95, 22, 6, 51, 24, 90, 44, 20, -1, -1, -1, -1, -1, -1, 0, 0], [-1, 63, 31, 88, 20, -1, -1, -1, 6, 40, 56, 16, 71, 53, -1, -1, 27, 26, 48, -1, -1, -1, -1, 0] ]) elif ptype == 'B': proto = np.array([ [-1, 81, -1, 28, -1, -1, 14, 25, 17, -1, -1, 85, 29, 52, 78, 95, 22, 92, 0, 0, -1, -1, -1, -1], [42, -1, 14, 68, 32, -1, -1, -1, -1, 70, 43, 11, 36, 40, 33, 57, 38, 24, -1, 0, 0, -1, -1, -1], [-1, -1, 20, -1, -1, 63, 39, -1, 70, 67, -1, 38, 4, 72, 47, 29, 60, 5, 80, -1, 0, 0, -1, -1], [64, 2, -1, -1, 63, -1, -1, 3, 51, -1, 81, 15, 94, 9, 85, 36, 14, 19, -1, -1, -1, 0, 0, -1], [-1, 53, 60, 80, -1, 26, 75, -1, -1, -1, -1, 86, 77, 1, 3, 72, 60, 25, -1, -1, -1, -1, 0, 0], [77, -1, -1, -1, 15, 28, -1, 35, -1, 72, 30, 68, 85, 84, 26, 64, 11, 89, 0, -1, -1, -1, -1, 0] ]) else: raise NameError('802.16 type must be either A or B') elif rate == '5/6': proto = np.array([ [1, 25, 55, -1, 47, 4, -1, 91, 84, 8, 86, 52, 82, 33, 5, 0, 36, 20, 4, 77, 80, 0, -1, -1], [-1, 6, -1, 36, 40, 47, 12, 79, 47, -1, 41, 21, 12, 71, 14, 72, 0, 44, 49, 0, 0, 0, 0, -1], [51, 81, 83, 4, 67, -1, 21, -1, 31, 24, 91, 61, 81, 9, 86, 78, 60, 88, 67, 15, -1, -1, 0, 0], [50, -1, 50, 15, -1, 36, 13, 10, 11, 20, 53, 90, 29, 92, 57, 30, 84, 92, 11, 66, 80, -1, -1, 0] ]) else: raise NameError('802.16 invalid rate') elif standard == "802.11n": if z == 27: # N = 648 if rate == '1/2': proto = np.array([ [0, -1, -1, -1, 0, 0, -1, -1, 0, -1, -1, 0, 1, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [22, 0, -1, -1, 17, -1, 0, 0, 12, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1], [6, -1, 0, -1, 10, -1, -1, -1, 24, -1, 0, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1], [2, -1, -1, 0, 20, -1, -1, -1, 25, 0, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1], [23, -1, -1, -1, 3, -1, -1, -1, 0, -1, 9, 11, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1], [24, -1, 23, 1, 17, -1, 3, -1, 10, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1], [25, -1, -1, -1, 8, -1, -1, -1, 7, 18, -1, -1, 0, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1], [13, 24, -1, -1, 0, -1, 8, -1, 6, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1], [7, 20, -1, 16, 22, 10, -1, -1, 23, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1], [11, -1, -1, -1, 19, -1, -1, -1, 13, -1, 3, 17, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1], [25, -1, 8, -1, 23, 18, -1, 14, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0], [3, -1, -1, -1, 16, -1, -1, 2, 25, 5, -1, -1, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0] ]) elif rate == '2/3': proto = np.array([ [25, 26, 14, -1, 20, -1, 2, -1, 4, -1, -1, 8, -1, 16, -1, 18, 1, 0, -1, -1, -1, -1, -1, -1], [10, 9, 15, 11, -1, 0, -1, 1, -1, -1, 18, -1, 8, -1, 10, -1, -1, 0, 0, -1, -1, -1, -1, -1], [16, 2, 20, 26, 21, -1, 6, -1, 1, 26, -1, 7, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1], [10, 13, 5, 0, -1, 3, -1, 7, -1, -1, 26, -1, -1, 13, -1, 16, -1, -1, -1, 0, 0, -1, -1, -1], [23, 14, 24, -1, 12, -1, 19, -1, 17, -1, -1, -1, 20, -1, 21, -1, 0, -1, -1, -1, 0, 0, -1, -1], [6, 22, 9, 20, -1, 25, -1, 17, -1, 8, -1, 14, -1, 18, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1], [14, 23, 21, 11, 20, -1, 24, -1, 18, -1, 19, -1, -1, -1, -1, 22, -1, -1, -1, -1, -1, -1, 0, 0], [17, 11, 11, 20, -1, 21, -1, 26, -1, 3, -1, -1, 18, -1, 26, -1, 1, -1, -1, -1, -1, -1, -1, 0] ]) elif rate == '3/4': proto = np.array([ [16, 17, 22, 24, 9, 3, 14, -1, 4, 2, 7, -1, 26, -1, 2, -1, 21, -1, 1, 0, -1, -1, -1, -1], [25, 12, 12, 3, 3, 26, 6, 21, -1, 15, 22, -1, 15, -1, 4, -1, -1, 16, -1, 0, 0, -1, -1, -1], [25, 18, 26, 16, 22, 23, 9, -1, 0, -1, 4, -1, 4, -1, 8, 23, 11, -1, -1, -1, 0, 0, -1, -1], [9, 7, 0, 1, 17, -1, -1, 7, 3, -1, 3, 23, -1, 16, -1, -1, 21, -1, 0, -1, -1, 0, 0, -1], [24, 5, 26, 7, 1, -1, -1, 15, 24, 15, -1, 8, -1, 13, -1, 13, -1, 11, -1, -1, -1, -1, 0, 0], [2, 2, 19, 14, 24, 1, 15, 19, -1, 21, -1, 2, -1, 24, -1, 3, -1, 2, 1, -1, -1, -1, -1, 0] ]) elif rate == '5/6': proto = np.array([ [17, 13, 8, 21, 9, 3, 18, 12, 10, 0, 4, 15, 19, 2, 5, 10, 26, 19, 13, 13, 1, 0, -1, -1], [3, 12, 11, 14, 11, 25, 5, 18, 0, 9, 2, 26, 26, 10, 24, 7, 14, 20, 4, 2, -1, 0, 0, -1], [22, 16, 4, 3, 10, 21, 12, 5, 21, 14, 19, 5, -1, 8, 5, 18, 11, 5, 5, 15, 0, -1, 0, 0], [7, 7, 14, 14, 4, 16, 16, 24, 24, 10, 1, 7, 15, 6, 10, 26, 8, 18, 21, 14, 1, -1, -1, 0] ]) else: raise NameError('802.11n invalid rate') elif z == 54: # N = 1296 if rate == '1/2': proto = np.array([ [40, -1, -1, -1, 22, -1, 49, 23, 43, -1, -1, -1, 1, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [50, 1, -1, -1, 48, 35, -1, -1, 13, -1, 30, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1], [39, 50, -1, -1, 4, -1, 2, -1, -1, -1, -1, 49, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1], [33, -1, -1, 38, 37, -1, -1, 4, 1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1], [45, -1, -1, -1, 0, 22, -1, -1, 20, 42, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1], [51, -1, -1, 48, 35, -1, -1, -1, 44, -1, 18, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1], [47, 11, -1, -1, -1, 17, -1, -1, 51, -1, -1, -1, 0, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1], [5, -1, 25, -1, 6, -1, 45, -1, 13, 40, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1], [33, -1, -1, 34, 24, -1, -1, -1, 23, -1, -1, 46, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1], [1, -1, 27, -1, 1, -1, -1, -1, 38, -1, 44, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1], [-1, 18, -1, -1, 23, -1, -1, 8, 0, 35, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0], [49, -1, 17, -1, 30, -1, -1, -1, 34, -1, -1, 19, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0] ]) elif rate == '2/3': proto = np.array([ [39, 31, 22, 43, -1, 40, 4, -1, 11, -1, -1, 50, -1, -1, -1, 6, 1, 0, -1, -1, -1, -1, -1, -1], [25, 52, 41, 2, 6, -1, 14, -1, 34, -1, -1, -1, 24, -1, 37, -1, -1, 0, 0, -1, -1, -1, -1, -1], [43, 31, 29, 0, 21, -1, 28, -1, -1, 2, -1, -1, 7, -1, 17, -1, -1, -1, 0, 0, -1, -1, -1, -1], [20, 33, 48, -1, 4, 13, -1, 26, -1, -1, 22, -1, -1, 46, 42, -1, -1, -1, -1, 0, 0, -1, -1, -1], [45, 7, 18, 51, 12, 25, -1, -1, -1, 50, -1, -1, 5, -1, -1, -1, 0, -1, -1, -1, 0, 0, -1, -1], [35, 40, 32, 16, 5, -1, -1, 18, -1, -1, 43, 51, -1, 32, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1], [9, 24, 13, 22, 28, -1, -1, 37, -1, -1, 25, -1, -1, 52, -1, 13, -1, -1, -1, -1, -1, -1, 0, 0], [32, 22, 4, 21, 16, -1, -1, -1, 27, 28, -1, 38, -1, -1, -1, 8, 1, -1, -1, -1, -1, -1, -1, 0] ]) elif rate == '3/4': proto = np.array([ [39, 40, 51, 41, 3, 29, 8, 36, -1, 14, -1, 6, -1, 33, -1, 11, -1, 4, 1, 0, -1, -1, -1, -1], [48, 21, 47, 9, 48, 35, 51, -1, 38, -1, 28, -1, 34, -1, 50, -1, 50, -1, -1, 0, 0, -1, -1, -1], [30, 39, 28, 42, 50, 39, 5, 17, -1, 6, -1, 18, -1, 20, -1, 15, -1, 40, -1, -1, 0, 0, -1, -1], [29, 0, 1, 43, 36, 30, 47, -1, 49, -1, 47, -1, 3, -1, 35, -1, 34, -1, 0, -1, -1, 0, 0, -1], [1, 32, 11, 23, 10, 44, 12, 7, -1, 48, -1, 4, -1, 9, -1, 17, -1, 16, -1, -1, -1, -1, 0, 0], [13, 7, 15, 47, 23, 16, 47, -1, 43, -1, 29, -1, 52, -1, 2, -1, 53, -1, 1, -1, -1, -1, -1, 0] ]) elif rate == '5/6': proto = np.array([ [48, 29, 37, 52, 2, 16, 6, 14, 53, 31, 34, 5, 18, 42, 53, 31, 45, -1, 46, 52, 1, 0, -1, -1], [17, 4, 30, 7, 43, 11, 24, 6, 14, 21, 6, 39, 17, 40, 47, 7, 15, 41, 19, -1, -1, 0, 0, -1], [7, 2, 51, 31, 46, 23, 16, 11, 53, 40, 10, 7, 46, 53, 33, 35, -1, 25, 35, 38, 0, -1, 0, 0], [19, 48, 41, 1, 10, 7, 36, 47, 5, 29, 52, 52, 31, 10, 26, 6, 3, 2, -1, 51, 1, -1, -1, 0] ]) else: raise NameError('802.11n invalid rate') elif z == 81: # N = 1944 if rate == '1/2': proto = np.array([ [57, -1, -1, -1, 50, -1, 11, -1, 50, -1, 79, -1, 1, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1], [3, -1, 28, -1, 0, -1, -1, -1, 55, 7, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1, -1], [30, -1, -1, -1, 24, 37, -1, -1, 56, 14, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1, -1], [62, 53, -1, -1, 53, -1, -1, 3, 35, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1, -1], [40, -1, -1, 20, 66, -1, -1, 22, 28, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1, -1], [0, -1, -1, -1, 8, -1, 42, -1, 50, -1, -1, 8, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1], [69, 79, 79, -1, -1, -1, 56, -1, 52, -1, -1, -1, 0, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1], [65, -1, -1, -1, 38, 57, -1, -1, 72, -1, 27, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1], [64, -1, -1, -1, 14, 52, -1, -1, 30, -1, -1, 32, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1, -1], [-1, 45, -1, 70, 0, -1, -1, -1, 77, 9, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0, -1], [2, 56, -1, 57, 35, -1, -1, -1, -1, -1, 12, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 0], [24, -1, 61, -1, 60, -1, -1, 27, 51, -1, -1, 16, 1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0] ]) elif rate == '2/3': proto = np.array([ [61, 75, 4, 63, 56, -1, -1, -1, -1, -1, -1, 8, -1, 2, 17, 25, 1, 0, -1, -1, -1, -1, -1, -1], [56, 74, 77, 20, -1, -1, -1, 64, 24, 4, 67, -1, 7, -1, -1, -1, -1, 0, 0, -1, -1, -1, -1, -1], [28, 21, 68, 10, 7, 14, 65, -1, -1, -1, 23, -1, -1, -1, 75, -1, -1, -1, 0, 0, -1, -1, -1, -1], [48, 38, 43, 78, 76, -1, -1, -1, -1, 5, 36, -1, 15, 72, -1, -1, -1, -1, -1, 0, 0, -1, -1, -1], [40, 2, 53, 25, -1, 52, 62, -1, 20, -1, -1, 44, -1, -1, -1, -1, 0, -1, -1, -1, 0, 0, -1, -1], [69, 23, 64, 10, 22, -1, 21, -1, -1, -1, -1, -1, 68, 23, 29, -1, -1, -1, -1, -1, -1, 0, 0, -1], [12, 0, 68, 20, 55, 61, -1, 40, -1, -1, -1, 52, -1, -1, -1, 44, -1, -1, -1, -1, -1, -1, 0, 0], [58, 8, 34, 64, 78, -1, -1, 11, 78, 24, -1, -1, -1, -1, -1, 58, 1, -1, -1, -1, -1, -1, -1, 0] ]) elif rate == '3/4': proto = np.array([ [48, 29, 28, 39, 9, 61, -1, -1, -1, 63, 45, 80, -1, -1, -1, 37, 32, 22, 1, 0, -1, -1, -1, -1], [4, 49, 42, 48, 11, 30, -1, -1, -1, 49, 17, 41, 37, 15, -1, 54, -1, -1, -1, 0, 0, -1, -1, -1], [35, 76, 78, 51, 37, 35, 21, -1, 17, 64, -1, -1, -1, 59, 7, -1, -1, 32, -1, -1, 0, 0, -1, -1], [9, 65, 44, 9, 54, 56, 73, 34, 42, -1, -1, -1, 35, -1, -1, -1, 46, 39, 0, -1, -1, 0, 0, -1], [3, 62, 7, 80, 68, 26, -1, 80, 55, -1, 36, -1, 26, -1, 9, -1, 72, -1, -1, -1, -1, -1, 0, 0], [26, 75, 33, 21, 69, 59, 3, 38, -1, -1, -1, 35, -1, 62, 36, 26, -1, -1, 1, -1, -1, -1, -1, 0] ]) elif rate == '5/6': proto = np.array([ [13, 48, 80, 66, 4, 74, 7, 30, 76, 52, 37, 60, -1, 49, 73, 31, 74, 73, 23, -1, 1, 0, -1, -1], [69, 63, 74, 56, 64, 77, 57, 65, 6, 16, 51, -1, 64, -1, 68, 9, 48, 62, 54, 27, -1, 0, 0, -1], [51, 15, 0, 80, 24, 25, 42, 54, 44, 71, 71, 9, 67, 35, -1, 58, -1, 29, -1, 53, 0, -1, 0, 0], [16, 29, 36, 41, 44, 56, 59, 37, 50, 24, -1, 65, 4, 65, 52, -1, 4, -1, 73, 52, 1, -1, -1, 0] ]) else: raise NameError('802.11n invalid rate') else: raise NameError('802.11n invalid z (must be 27,54 or 81)') else: raise NameError('IEEE standard unknown') return proto def pcmat(self): """ Converts from a protograph to an LDPC parity-check matrix. This function is not used in the live system but is made available e.g. if one wants to visualise the actual parity-check matrix. Returns ------- np.ndarray Parity-check matrix for the LDPC code """ # traverses protograph row/column-wise and assigns all-zero submatrices # where the protograph entry is -1, or suitably cyclic-shifted zxz identity # matrices where the entry is not -1. Note that use of "np.roll" which # operates a cyclic shift of the columns by proto[row,col]%z, and note # that the mod z at the end is merely cosmetic since np.roll will # natively cyclic shift modulo z if asked to roll a matrix by a shift # that exceeds the matrix dimensions. proto = self.proto z = self.z pcmat = np.zeros((z*len(proto),z*len(proto[0])),dtype=int) (row,col) = np.nonzero(proto != -1) for j in range(len(row)): pcmat[row[j]*z:row[j]*z+z,col[j]*z:col[j]*z+z] = np.roll(np.eye(z),proto[row[j],col[j]]%z,1) return pcmat def prepare_decoder(self): """ Generates the elements required for the LDPC decoder from the protograph. Parameters ---------- proto: array Specifies the protograph for the code. z: int Specifies the expansion factor for the protograph Returns ------- np.array vdeg vector of variable node degrees np.array cdeg vector of constraint node degrees np.array intrlv vector specifies the interleaver between variable node messages and constraint node messages (from a variable node perspective) The messages are assumed ordered as required for constraint node processing (which is the "harder" processing) and must be addressed through this interleaver when processing variable nodes (which is the "easier" processing) """ proto = self.proto z = self.z # This method operates by assigning interleaver entries and "flagging" them # as it traverses the parity-check matrix, so that later visits to the same # variable (or constraint) node know to move on to the next available message # connection ("port") in the node. # Variable node degrees and constraint node degrees are expanded from the # "degrees" in the protograph by a factor of z. Note that each column in # the protograph results in z columns of the same degree in the actual code, # and the same for rows. cdeg = np.repeat(np.sum(proto != -1, 1), z) vdeg = np.repeat(np.sum(proto != -1, 0), z) # Cumulative degrees with a 0 inserted at the start because we need the # cumulation "up to and NOT including" the degree of the current node, # whereas numpy's cumsum gives us the degree "up to and including" # Note that cumvdeg and cumcdeg will be one element too long than # we need (we will never use the last entry) cumcdeg = np.insert(np.cumsum(cdeg),0,0) cumvdeg = np.insert(np.cumsum(vdeg),0,0) # Initialise the interleaver and a vector of flags telling us which "ports" # have been used for the constraint nodes, i.e., which messages in each # constraint nodes have already been assigned. We also need such flags for # the variable node side, but the interleaver doubles up as a flag since # we initialised it as -1s, we know that any message that still has a -1 # is an unuseed port for a variable node. intrlv = -np.ones(np.sum(cdeg),dtype=int) vflag = np.zeros(np.sum(cdeg),dtype=bool) # We will traverse the protograph stopping at each sub-graph that doesn't have # a -1 in the protograph (the -1s in the protograph correspond to an all-zero # submatrix in the parity-check matrix. (xp,yp) = np.nonzero(proto != -1) for j in range(xp.size): # offset specifies the exponent of the permutation matrix that is inserted # at this position in the protograph. An offset of 0 means that the matrix # is an identity matrix, whereas an offset of +1 means that the matrix is a # "shift one to the right" permutation matrix, etc. Offsets larger than z # result in shifts modulo z. offset = proto[xp[j],yp[j]] for k in range(z): # Determine the variable node and constraint node index from the index # of the protograph position and the index k of the row/column within # the zxz submatrix at this position in the protograph cind = xp[j]*z+k vind = yp[j]*z+(k+offset)%z # Find an unused "port" for the message in the constraint node for xi in range(cumcdeg[cind],cumcdeg[cind+1]): if intrlv[xi] == -1: break # Error handling if no unused port found, should never occur if intrlv[xi] != -1: raise NameError('No unused port found in constraint node') # Find an unused "port" for the message in the variable node for yi in range(cumvdeg[vind],cumvdeg[vind+1]): if vflag[yi] == 0: break # Error handling if no unused port found, should never occur if vflag[yi] != 0: raise NameError('No unused port found in variable node') # now assign the interleaver entry and flag the constraint node "port" vflag[yi] = 1 intrlv[xi] = yi intrlv = np.argsort(intrlv) return vdeg, cdeg, intrlv def encode(self, info): z = self.z proto = self.proto # check dimensions before starting Np = len(proto[0]) N = Np*z # length of codeword Mp = len(proto) Kp = Np - Mp K = Kp*z # length of information if len(info) != K: raise NameError('information word length not compatible with proto and z') # x is the codeword, composed of K bits information and N-K bits parity x = np.zeros(N, dtype=int) x[0:K] = info # pre-fill the first K bits with the information # for the encoding, we will address x z bits at a time, so we reshape it to # be Np x z and the rows of x are our new "super-symbols" x = np.reshape(x,(Np,z)) # the following p will contain sum_k x_k H_jk for each row of the prototype parity # check matrix, where the sum is only over the systematic (information) part p = np.zeros((Mp,z), dtype=int) for j in range(Mp): ind = np.nonzero(proto[j,0:Kp] != -1)[0] for k in ind.tolist(): p[j] = np.add(p[j],np.roll(x[k],-proto[j,k])) p = np.mod(p,2) tp = np.mod(np.sum(p,0),2) # tp is the sum of the p's # The sum of all the super parity-check (vector) equations gives an equation that # has only information symbols and the first parity symbol. Most protographs were # designed so that the coefficient of the parity symbol in that equation is the # identity matrix, but there are a few exceptions where the coefficient is not # an identity. The following few lines compute that coefficient and compute its # inverse. toff = np.zeros(z, dtype = int) ind = np.nonzero(proto[:,Kp] != -1)[0] for j in ind.tolist(): toff[proto[j,Kp]%z] += 1 toff = np.mod(toff, 2) tnz = np.nonzero(toff)[0] # the coefficients in proto in column Kp come in pairs except one coefficient, # resulting in a single coefficient for the first parity symbol. If this is # not the case, call an error. if len(tnz) != 1: raise NameError('The offsets in colum Kp+1 of proto do not add to a single offset') toff = tnz[0] # now compute the first parity symbol as tp times the inverse coefficient # (which will be an offset by 0 in most cases, when the resulting coefficient is # an identity matrix) x[Kp] = np.roll(tp, toff) # the remaining parity symbols are computed using one parity equation at a time for j in range(Mp-1): myk = Kp+j+1 # parity symbol to be computed x[myk] = p[j] # initialise with value of acumulated systematic part ind = np.nonzero(proto[j,Kp:myk]!=-1)[0] # search for remaining coefficients for k in ind.tolist(): x[myk] = np.add(x[myk], np.roll(x[Kp+k], -proto[j,Kp+k])) x = np.mod(x,2) return(np.reshape(x,-1)) def decode(self, ch, dectype='sumprod2', corr_factor=0.7): vdeg = self.vdeg cdeg = self.cdeg intrlv = self.intrlv c_ldpc = ct.CDLL('./bin/c_ldpc.so') # preliminary consistency checks if len(ch) != len(vdeg): raise NameError('Channel inputs not consistent with variable degrees') # prepare arguments and outputs Nv = self.Nv Nc = self.Nc Nmsg = self.Nmsg app = np.zeros(Nv, dtype=np.double) app_p = app.ctypes.data_as(ct.POINTER(ct.c_double)) ch_p = ch.ctypes.data_as(ct.POINTER(ct.c_double)) vdeg_p = self.vdeg.ctypes.data_as(ct.POINTER(ct.c_long)) cdeg_p = self.cdeg.ctypes.data_as(ct.POINTER(ct.c_long)) intrlv_p = self.intrlv.ctypes.data_as(ct.POINTER(ct.c_long)) # call C function for the sum product algorithm if dectype == 'sumprod': it = c_ldpc.sumprod(ch_p, vdeg_p, cdeg_p, intrlv_p, Nv, Nc, Nmsg, app_p) elif dectype == 'sumprod2': it = c_ldpc.sumprod2(ch_p, vdeg_p, cdeg_p, intrlv_p, Nv, Nc, Nmsg, app_p) elif dectype == 'minsum': it = c_ldpc.minsum(ch_p, vdeg_p, cdeg_p, intrlv_p, Nv, Nc, Nmsg, app_p, ct.c_double(corr_factor)) else: raise NameError('Decoder type unknonwn') return app, it def Lxor(self, L1, L2, corrflag=1): c_ldpc = ct.CDLL('./bin/c_ldpc.so') c_ldpc.Lxor.restype = ct.c_double return c_ldpc.Lxor(ct.c_double(L1), ct.c_double(L2), corrflag) # min rule, first multiply the signs, both -1 or both 1 senarios give 1 # if L1 * L2 >0: # L = 1 # else: # L = -1 # L *= min(abs(L1), abs(L2)) # if corrflag: # L += np.log(1 + np.exp(-abs(L1 + L2))) # L -= np.log(1 + np.exp(-abs(L1 - L2))) # return L def Lxfb(self, L, corrflag=1): c_ldpc = ct.CDLL('./bin/c_ldpc.so') dc = len(L) L = np.array(L, dtype=float) L_p = L.ctypes.data_as(ct.POINTER(ct.c_double)) c_ldpc.Lxfb.restype = ct.c_double return c_ldpc.Lxfb(L_p, dc, corrflag), L # dc = len(L) # f = [] # b = [] # f[0] = L[0] # b[dc-1] = L[dc-1] # for k in range(dc): # f[k] = self.Lxor(f[k-1], L[k], corrflag) # b[dc-k-1] = self.Lxor(b[dc-k-1], L[dc-k-1], corrflag) # L[0] = b[1] # L[dc-1] = f[dc-2] # for k_ in range(dc-1): # L[k] = self.Lxor(f[k-1], b[k+1], corrflag) # return b[0] # def sumprod(self, ch, vdeg, cdeg, intrlv, Nv, Nc, Nmsg, app): # msg = np.zeros(Nmsg) # # main loop, will iterate until stopping criterion is fulfilled # for itcount in range(self.MAX_ITCOUNT): # # variable node rule ('sum') # imsg = 0 # for j in range(Nv): # aggr = ch[j] # for k in range(vdeg[j]):# # aggr += msg[intrlv[imsg]] # imsg += 1 # imsg -= vdeg[j] # for k in range(vdeg[j]): # msg[intrlv[imsg]] = aggr - msg[intrlv[imsg]] # imsg += 1 # app[j] = aggr # stopflag = 0 # # constraint node rule ('product') # imsg = 0 # for j in range(Nc): # aggr = 1 # for k in range(cdeg[j]): # msg[imsg] = np.tanh(msg[imsg]/2) # aggr *= msg[imsg] # imsg += 1 # if (stopflag == 0 and 2*np.arctanh(aggr) <= 0): # stopflag = 1 # imsg -= cdeg[j] # for k in range(cdeg[j]): # msg[imsg] = 2 *np.arctanh(aggr/msg[imsg]) # imsg += 1 # if stopflag == False: # break # return itcount # def sumprod2(self, ch, vdeg, cdeg, intrlv, Nv, Nc, Nmsg, app): # msg = np.zeros(Nmsg) # for itcount in range(self.MAX_ITCOUNT): # # Vairable node # imsg = 0 # for j in range(Nv): # aggr = ch[j] # for k in range(vdeg[j]): # aggr += msg[intrlv[imsg]] # imsg += 1 # imsg -= vdeg[j] # for k in range(vdeg[j]): # msg[intrlv[imsg]] = aggr - msg[intrlv[imsg]] # imsg += 1 # app[j] = aggr # stopflag = 0 # # Constraint mode # imsg = 0 # for j in range(Nc): # aggr = self.Lxfb(np.asarray[msg[imsg]], cdeg[j], 1) # if (stopflag == 0 and aggr <=0): # stopflag = 1 # imsg += cdeg[j] # if stopflag == False: # break # return itcount # def minsum(self, ch, vdeg, cdeg, intrlv, Nv, Nc, Nmsg, app, corr_factor): # msg = np.zeros(Nmsg) # for itcount in range(self.MAX_ITCOUNT): # # var node # imsg = 0 # for j in range(Nv): # aggr = ch[j] # for k in range(vdeg[j]): # msg[intrlv[imsg]] = aggr - msg[intrlv[imsg]] # imsg += 1 # app[j] = aggr # stopflag = 0 # # constraint node # imsg = 0 # for j in range(Nc): # imsg += cdeg[j] # aggr = self.Lxfb(msg[imsg], cdeg[j], 0) # if (stopflag == 0 and aggr <=0): # stopflag = 1 # for k in range(cdeg[j]): # msg[imsg + k] *= corr_factor # if stopflag == False: # break # return itcount

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theblackcat102/who-wants-to-join

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from dataset.aminer import Aminer from dataset.meetup import Meetup,locations_id import numpy as np import pickle from sklearn.metrics import f1_score from src.utils import dict2table, np_confusion, str2bool def similarity_evaluation(dataset, embeddings, user_node_id, top_k=20, user_size = 399212): name2id = embeddings['name2id'] embedding = embeddings['embedding'] precisions, recalls = [], [] max_node_idx= 0 for data in dataset: # extract user node with known before known_user_node_id = (data.x[:, 2] == user_node_id) & (data.x[:, 1] == 1) known_nodes = [ '{}_{}'.format(int(node[-1]), int(node[0])) for node in data.x[known_user_node_id, :] ] # known_paper_node_id = (data.x[:, 2] == 1) # known_nodes += [ '{}_{}'.format(int(node[-1]), int(node[0])) for node in data.x[known_user_node_id, :] ] # print(known_nodes) user_embeddings = np.array([ embedding[name2id[nameid]] for nameid in known_nodes if nameid in name2id ]) candidate_user_node_id = (data.x[:, 2] == user_node_id) & (data.x[:, 1] != 1) target_node_id = [ int(node[0]) for node in data.x[data.y == 1, :]] max_node_idx = max([ max_node_idx ] + target_node_id) candidate_node_id = [ int(node[0]) for node in data.x[candidate_user_node_id, :]] max_node_idx = max([ max_node_idx ] + candidate_node_id) candidate_nodes = [ '{}_{}'.format(int(node[-1]), int(node[0])) for node in data.x[candidate_user_node_id, :] ] candidate_nodes = [ '{}_{}'.format(int(node[-1]), int(node[0])) for node in data.x[candidate_user_node_id, :] ] candidate_user_embeddings = np.array([ embedding[name2id[nameid]] for nameid in candidate_nodes if nameid in name2id ]) if len(user_embeddings) > 0 and len(candidate_user_embeddings) > 0: norm_embeddings = user_embeddings.sum(0)/len(user_embeddings) norm_embeddings = np.linalg.norm(norm_embeddings) candidate_user_embeddings = np.linalg.norm(candidate_user_embeddings, axis=1) dot_prod = candidate_user_embeddings.dot(norm_embeddings) rank = [ (candidate_node_id[idx], weight) for idx, weight in enumerate(dot_prod) ] rank.sort(key=lambda x: x[1]) pred_nodes = [ ] if len(rank) < top_k: pred_nodes = [ pair[0] for pair in rank ] else: pred_nodes = [ rank[i][0] for i in range(top_k)] # print(pred_nodes, target_node_id) y_pred, y_target = np.zeros(user_size), np.zeros(user_size) y_pred[pred_nodes] = 1.0 y_target[target_node_id] = 1.0 TP, FP, TN, FN = np_confusion(y_pred, y_target) recall = 0 if (TP+FN) < 1e-5 else TP/(TP+FN) precision = 0 if (TP+FP) < 1e-5 else TP/(TP+FP) precisions.append(precision) recalls.append(recall) avg_recalls = np.mean(recalls) avg_precisions = np.mean(precisions) f1 = 2*(avg_recalls*avg_precisions)/(avg_recalls+avg_precisions) print(max_node_idx) print(f1, avg_recalls, avg_precisions) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser( description='CF rank method for group expansion') parser.add_argument('--top-k', type=int, default=5) parser.add_argument('--city', type=str, default='SF', choices=['NY', 'SF']) parser.add_argument('--dataset', type=str, default='aminer', choices=['meetup', 'aminer']) parser.add_argument('--user-node', type=int, default=0, help='integer which user node id is represented in') parser.add_argument('--user-size', type=int, default=399212, help='maximum user node id') parser.add_argument('--embeddings', type=str, help='graphvite embedding pickle') args = parser.parse_args() if args.dataset == 'aminer': dataset = Aminer() else: dataset = Meetup(city_id=locations_id[args.city]) data_size = len(dataset) train_split, val, test = int(data_size*0.7), int(data_size*0.1), int(data_size*0.2) indexes = np.array(list(range(data_size)), dtype=np.long)[train_split+val:] print(indexes[:10]) val_dataset = dataset[list(indexes)] with open(args.embeddings, 'rb') as f: embeddings = pickle.load(f) similarity_evaluation(val_dataset, embeddings, user_node_id=args.user_node, user_size=args.user_size, top_k=args.top_k)

[ "zhirui09400@gmail.com" ]

zhirui09400@gmail.com

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/network/tf_net.py

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joshualley/VoiceScope

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import time import numpy as np import tensorflow as tf import os from configuration.config import PATH from utils.utils import read_fontnames, load_words os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' def read_from_npy(batch_size, num=1): path = PATH.DATASET_DIR+'train_one.npy' features = np.load(path).astype(np.float32) labels = np.array(list(range(3557))*num) dataset = tf.data.Dataset.from_tensor_slices((features, labels)) dataset = dataset.shuffle(1000).repeat().batch(batch_size) iterator = dataset.make_one_shot_iterator() return iterator def read_record(path, epochs, batch_size): def paser(record): features = tf.parse_single_example( record, features={ 'label': tf.FixedLenFeature([], tf.int64), 'img_raw': tf.FixedLenFeature([], tf.string), } ) label = tf.cast(features['label'], tf.float32) image = tf.decode_raw(features['img_raw'], tf.float32) return image, label dataset = tf.data.TFRecordDataset(path) dataset = dataset.map(paser) dataset = dataset.shuffle(1000).repeat(epochs).batch(batch_size) iterator = dataset.make_one_shot_iterator() return iterator def W(shape): init = tf.truncated_normal(shape, mean=0.0, stddev=0.01) return tf.Variable(init) def B(shape): init = tf.constant(0.1) return tf.Variable(init) def fc_layer(x, ws, bs): w = W(ws) b = B(bs) net = tf.matmul(x, w) + b return tf.nn.relu(net) def conv2d_layer(x, ws, bs): w = W(ws) b = B(bs) net = tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME') + b net = tf.nn.relu(net) net = tf.nn.max_pool(net, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME') return net def batch_norm_layer(x): fc_mean, fc_var = tf.nn.moments(x, axes=[0]) scale = tf.Variable(tf.ones(x.shape[-1:])) shift = tf.Variable(tf.zeros(x.shape[-1:])) x = tf.nn.batch_normalization(x, fc_mean, fc_var, shift, scale, variance_epsilon=0.001) return x def built_net(x): f_size = 32 k_size = 5 x = tf.reshape(x, (-1,48,48,1)) net = conv2d_layer(x, ws=[k_size,k_size,1,f_size], bs=[f_size]) net = tf.nn.dropout(net, 0.2) net = conv2d_layer(net, ws=[k_size,k_size,f_size,f_size], bs=[f_size]) net = tf.nn.dropout(net, 0.6) net = tf.reshape(net,shape=[-1, 12*12*f_size]) net = fc_layer(net, ws=[12*12*f_size, 1024], bs=[1024]) net = batch_norm_layer(net) net = tf.nn.dropout(net, 0.6) out = tf.nn.softmax(fc_layer(net, ws=[1024, 3557], bs=[3557])) return out def optimizer(y_in, y_out): y_out = tf.reshape(y_out, (-1, 3557)) loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_in, logits=y_out)) train_op = tf.train.AdamOptimizer(1e-3).minimize(loss) correct_prediction = tf.equal(tf.argmax(y_in, 1), tf.argmax(y_out, 1)) acc = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) return train_op, loss, acc import cv2 import matplotlib.pyplot as plt def main(): words = load_words() sess = tf.InteractiveSession() path = PATH.DATASET_DIR+'test7x1x2.record' #iterator = read_record(path, epochs=10, batch_size=1024) iterator = read_from_npy(batch_size=1024) x, y = iterator.get_next() y = tf.cast(y, tf.int64) x = tf.reshape(x, (-1, 48,48,1)) y_in = tf.one_hot(y, 3557, dtype=tf.int64) y_out = built_net(x) train_op, loss, acc = optimizer(y_in, y_out) tf.global_variables_initializer().run() t0 = time.time() for epoch in range(1, 100): for i in range(235): sess.run(train_op) if i%10 == 0: x_,y_ = sess.run([y,y_out]) print('in: %s\nout: %s' %(x_[1:10], np.argmax(y_, 1)[1:10])) losses, accurary = sess.run([loss, acc]) print('[==>] Epoch: %d \tStep: %d \tLoss: %s \tAcc: %s \tTime: %ss' %(epoch, i, losses, accurary, round(time.time()-t0, 2))) t0 = time.time() """ for k in range(len(x_)): cv2.imshow('i', np.asarray(x_[k])) cv2.waitKey(0) print('key:',words[y_[k]]) sums = [] for m in range(48): s = 0 for n in range(48): s += x_[k][m,n] sums.append(s) plt.plot(sums) plt.show() """ if __name__ == "__main__": main()

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import sys map = [y for y in sys.stdin]

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''' very simple shortcuts for making plots ''' import matplotlib.pyplot as plt def new_column(nrow, xlim): ''' a single column with multiple rows ''' f, ax = plt.subplots(nrow, 1, figsize = (6, 1+3*nrow)) plt.subplots_adjust(hspace = 0) for i in range(nrow): ax[i].set_xlim(xlim) ax[i].tick_params(labelsize = 18) if i != nrow - 1: ax[i].set_xticklabels([]) return f, ax

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# __init__.py it is used to set up db and our application from flask import Flask, render_template, url_for, redirect from flask_sqlalchemy import SQLAlchemy from flask_migrate import Migrate import os # setting our flask application app = Flask(__name__) app.config['SECRET_KEY'] = 'mysecretkey' basedir = os.path.abspath(os.path.dirname(__file__)) app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///' + os.path.join(basedir, 'data.sqlite') app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False db = SQLAlchemy(app) Migrate(app, db) # register bluprint is done once after db is being found by our application. from myproject.puppies.views import puppies_blueprint from myproject.owners.views import owners_blueprints app.register_blueprint(owners_blueprints, url_prefix='/owners') # url_prefix is used in URL bar of browser. app.register_blueprint(puppies_blueprint, url_prefix='/puppies')

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import pyaf.Bench.TS_datasets as tsds import tests.artificial.process_artificial_dataset as art art.process_dataset(N = 128 , FREQ = 'D', seed = 0, trendtype = "MovingMedian", cycle_length = 7, transform = "Difference", sigma = 0.0, exog_count = 20, ar_order = 0);

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2022-12-04T19:05:16.509630

2020-08-25T10:28:17

286,216,431

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null

UTF-8

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false

150

py

# Исправь программу # Сделай так, чтобы программа напечатала "How do you do?" print(How do you do?)

[ "mustafo.xon@gmail.com" ]

mustafo.xon@gmail.com

e39687a83d7901840b63d3e947089e5c408f944d

11137bde91389c04a95df6f6fdaf64f7f49f5f80

/secondTest/introduction_MIT2/5_1.py

47272dfbccb9fef4086d3fd1feaa61aff6aa3068

[]

no_license

starschen/learning

cf3c5a76c867567bce73e9cacb2cf0979ba053d9

34decb8f9990117a5f40b8db6dba076a7f115671

refs/heads/master

2020-04-06T07:02:56.444233

2016-08-24T08:11:49

39,417,895

0

null

UTF-8

Python

false

718

py

#encoding:utf8 def findDivisors(n1,n2): divisors=() for i in range(1,min(n1,n2)+1): if n1%i==0 and n2%i==0: divisors=divisors+(i,) return divisors divisors=findDivisors(20,200) # print divisors total=0 for d in divisors: total+=d # print total def findExtremeDivisors(n1,n2): divisors=() minVal,maxVal=None,None for i in range(2,min(n1,n2)+1): if n1%i==0 and n2%i==0: if minVal==None or i<minVal: minVal=i if maxVal==None or i >maxVal: maxVal=i return (minVal,maxVal) # minVal,maxVal=findExtremeDivisors(100,200) # print 'minVal=',minVal # print 'maxVal=',maxVal print findExtremeDivisors(100,200)

[ "stars_chenjiao@163.com" ]

stars_chenjiao@163.com

c3a82f8ae4512e4c66cb1f0c074facd96d2a4bf3

e89a3a203bd4b433c0f1acc05b2e3c89a7020a9f

/src/robots/descriptions/cheetah_simu/cheetah_sim/ros_package/cheetah_core/src/leg_control/__init__.py

88699440d88fb79604aa080b53b86fcbe3df9d3b

[]

no_license

JJHbrams/QuadrupedMotionPlanning

03e5dfdd8410710a26553441aa557e9585b5f10f

cf83de83776b8215b2e94dbc2afa5f59039e6d4d

refs/heads/master

2023-01-12T11:30:38.202023

2020-11-11T10:52:20

311,865,277

0

null

UTF-8

Python

false

26

py

__all__=["legController"]

[ "dpswpfrhdid@naver.com" ]

dpswpfrhdid@naver.com

2f56ea2d6cedf1b7304b6de0a323b6d4b0d96f6b

382d0c69a9c4f7da95ef057e9babd657fd7963e0

/dog_classes.py

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[]

no_license

mewen38/mikes_repo

9503523481218486f52365f05d7aa9fb3d0263fe

64e086317ed6663bf470708f3cee9d03777dbb7e

refs/heads/master

2022-12-04T18:51:46.215587

2020-07-29T04:49:04

2020-07-29T05:04:05

202,954,078

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null

UTF-8

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1,964

py

# I remember feeling very uncomfortable with classes, so I'm forcing myself to mess around after reading an article class animal: def __init__ (self, name, color, type): self.animal_name = name self.animal_color = color self.animal_type = type def speak(self): print('happy thoughts, happy thoughts, boy im getting mighty sick of this') def dance(self): print('{} does an animal class wiggle'.format(self.animal_name)) class dog(animal): def __init__ (self, name, color, type, special_dance): super().__init__ (name, color, type) self.dog_special_dance = special_dance def speak(self): print('woof woof, bow-wow') def dance(self): print('{}, as a dog, knows a special overriding animal subclass (dog) {} wiggle'.format(self.animal_name, self.dog_special_dance)) class cat(animal): def speak(self): print("the toppings contain potassium benzoate .................. that's bad") class princess(animal): def __init__ (self, name, color, type, area_of_expertise): super().__init__ (name, color, type) self.princess_area_of_expertise = area_of_expertise def speak(self): print("i wrote this program, im a princess, and i love kibble") bailey = animal('bailey','red', 'red mage dog') weezer = dog('weezer','grey', 'wizard dog', 'spicy bachata') zelda = princess('zelda', 'red', 'princess', 'java, python, react, go, npm, pyenv, aws, airflow, sql, redshift, s3, s3 glacier, spark, kibble') pepper = cat('pepper', 'black', 'cat') print(bailey,bailey.animal_name, bailey.animal_type) bailey.speak() bailey.dance() print(weezer, weezer.animal_name, weezer.animal_type) weezer.speak() weezer.dance() print(zelda, zelda.animal_name, zelda.animal_color, zelda.animal_type, zelda.princess_area_of_expertise) zelda.speak() print(pepper, pepper.animal_name, pepper.animal_color, pepper.animal_type) pepper.speak()

[ "michael.ewen@compass.com" ]

michael.ewen@compass.com

49ba3fc51d417b778a7e6f918187d160af935600

a737f2f6b731afa8c041e8f06322730069193132

/start_game.py

692513f10d6eff56ce7e09c65eda2be91eb78e75

[]

no_license

qinjiang03/PokerAgent

514c01566d4dfaa1667e3a0f097ed3b9502c8428

1d7d50362393d4fd29972a2357fc056fb97524e8

refs/heads/master

2020-04-29T02:50:33.850260

2019-04-08T11:09:04

175,785,512

0

null

2019-04-08T08:07:09

2019-03-15T09:05:53

Python

UTF-8

Python

false

1,611

py

from pypokerengine.api.game import setup_config, start_poker from randomplayer import RandomPlayer from raise_player import RaisedPlayer from custom.minimax_player import MiniMaxPlayer from custom.honest_player import HonestPlayer from custom.honest_player2 import HonestPlayer2 from custom.call_player import CallPlayer import pprint from custom.logging_functions import startLogging, stopLogging import os, datetime, logging import numpy as np import itertools #TODO:config the config as our wish # logFile = os.path.join('log', "log_{}.log".format(datetime.datetime.now().strftime("%Y%m%d_%H%M%S"))) # logger = startLogging(logFile) # w1s = np.arange(0.3,0.5,0.05) # w2s = np.arange(0.7,0.85,0.05) # w3s = np.arange(0.2,0.5,0.05) # # w2s = [0.75] # # w3s = [0.3] # w_list = list(itertools.product(w1s, w2s, w3s)) # results = [] # for w in w_list: config = setup_config(max_round=100, initial_stack=10000, small_blind_amount=10) config.register_player(name="RaisePlayer", algorithm=RaisedPlayer()) # config.register_player(name="CallPlayer", algorithm=CallPlayer()) # config.register_player(name="HonestPlayer1", algorithm=HonestPlayer()) config.register_player(name="HonestPlayer2", algorithm=HonestPlayer()) # config.register_player(name="MiniMaxPlayer", algorithm=MiniMaxPlayer()) # config.register_player(name="HonestPlayer2", algorithm=HonestPlayer2(w[0], w[1], w[2])) game_result = start_poker(config, verbose=0) print(game_result) # result = list(w) + [player["stack"] for player in game_result["players"]] # logging.info(result) # results.append(result) # stopLogging(logger)

[ "qinjiang03@gmail.com" ]

qinjiang03@gmail.com

a1b7503c84df5db765cd1c0cf077084232aec8f4

88386ed170ab6d496b87ae3e8501130b92b82cf4

/gui-framework/app.py

681e45e0ed55088ee3d88f1942ab3b811ce99ac6

[]

no_license

LiuYuancheng/tls_attack

f6186dc74704928ee883f1c1a9142d42954dc7ae

3abe611d23a87aad9c35ad253e15bc06ff4bea35

refs/heads/master

2022-02-22T10:42:41.478189

2019-09-26T08:42:25

221,579,890

2

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null

2019-11-14T00:48:49

2019-11-14T00:48:48

null

UTF-8

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import os import sys import argparse from template import Ui_MainWindow from PyQt5 import QtCore, QtGui, QtWidgets parser = argparse.ArgumentParser() # parser.add_argument('-r', '--rootdir', help='Input the root directory path containing the data in json format for all trained model. Typically, foo/bar/rnn-model/trained-rnn/', required=True) parser.add_argument('-p', '--pcapdir', help='Input all directories to where pcap files are located', required=True) parser.add_argument('-m', '--modeldir', help='Input the root directory of trained rnn models. Typically, foo/bar/rnn-model/trained-rnn', required=True) parser.add_argument('-f', '--featuredir', help='Input the root directory of the feature cvs files with other supporting files. Typically, foo/bar/feature-extraction/extracted-features', required=True) args = parser.parse_args() # Search iteratively for all data.json files in the root directory # json_dirs = [] # for root, dirs, files in os.walk(args.rootdir): # for f in files: # if f == "data.json": # json_dirs.append(os.path.join(root, f)) pcap_dirs = args.pcapdir model_dirs = args.modeldir feature_dirs = args.featuredir app = QtWidgets.QApplication(sys.argv) # app.resize(1838, 963) MainWindow = QtWidgets.QMainWindow() ui = Ui_MainWindow(pcap_dirs, model_dirs, feature_dirs) ui.setupUi(MainWindow) MainWindow.show() sys.exit(app.exec_())

[ "yilong_tan@mymail.sutd.edu.sg" ]

yilong_tan@mymail.sutd.edu.sg

6c42121e14c982c244c5e02c8719f1cf0456c50b

00829e1ff78f73dab073a201d68139960c1d1922

/tools/toolset/tool/rigging/beam/core/maths/color.py

dc40bd6d1cd95c9443bd68690d5e0cbba7ef7e09

[]

no_license

liangyongg/Beam_Tools

a021ceb4187107508536c46726da5b9629ffd1cf

21b5d06e660f058434e589ae4f672f96296b7540

refs/heads/master

2018-11-04T04:43:02.523654

2018-08-26T12:33:09

115,005,481

0

null

UTF-8

Python

false

9,953

py

"""Kraken - maths.color module. Classes: Color -- Color object. """ import random import math from rigging.beam.core.beam_system import bs from math_object import MathObject class Color(MathObject): """Vector 4 object.""" def __init__(self, r=0.0, g=0.0, b=0.0, a=1.0): """Initializes r, g b and a values for Color object.""" super(Color, self).__init__() #if bs.getRTValTypeName(r) == 'Color': # self._rtval = r #else: # self._rtval = bs.rtVal('Color') # if isinstance(r, Color): # self.set(r=r.r, g=r.g, b=r.b, a=r.b) # else: # self.set(r=r, g=g, b=b, a=a) def __str__(self): """String representation of the Color object. Returns: str: String representation of the Color object.""" stringRep = "Color(" stringRep += str(self.r) + "," stringRep += str(self.g) + "," stringRep += str(self.b) + "," stringRep += str(self.a) + ")" return stringRep @property def r(self): """Gets red channel of this color. Returns: float: red channel of this color. """ return self._rtval.r.getSimpleType() @r.setter def r(self, value): """Sets red channel from the input channel. Args: channel (float): Value to set the red channel to. Returns: bool: True if successful. """ self._rtval.r = bs.rtVal('Scalar', value) return True @property def g(self): """Gets green channel of this color. Returns: float: green channel of this color. """ return self._rtval.g.getSimpleType() @g.setter def g(self, value): """Sets green channel from the input channel. Args: channel (float): Value to set the green property as. Returns: bool: True if successful. """ self._rtval.g = bs.rtVal('Scalar', value) return True @property def b(self): """Gets blue channel of this color. Returns: float: blue channel of this color. """ return self._rtval.b.getSimpleType() @b.setter def b(self, value): """Sets blue channel from the input channel. Args: channel (float): Value to set the blue property as. Returns: bool: True if successful. """ self._rtval.b = bs.rtVal('Scalar', value) return True @property def a(self): """Gets alpha channel of this color. Returns: float: alpha channel of this color. """ return self._rtval.a.getSimpleType() @a.setter def a(self, value): """Sets a channel from the input channel. Args: channel (float): Value to set the a property as. Returns: bool: True if successful. """ self._rtval.a = bs.rtVal('Scalar', value) def __eq__(self, other): return self.equal(other) def __ne__(self, other): return not self.equal(other) def __add__(self, other): return self.add(other) def __sub__(self, other): return self.subtract(other) def __mul__(self, other): return self.multiply(other) def __div__(self, other): return self.divide(other) def clone(self): """Returns a clone of the Color. Returns: Color: The cloned Color """ color = Color() color.r = self.r color.g = self.g color.b = self.b return color def set(self, r, g, b, a): """Sets the r, g, b, and a value from the input values. Args: r (float): Value to set the r channel to. g (float): Value to set the g channel to. b (float): Value to set the b channel to. a (float): Value to set the a channel to. Returns: bool: True if successful. """ self._rtval.set('', bs.rtVal('Scalar', r), bs.rtVal('Scalar', g), bs.rtVal('Scalar', b), bs.rtVal('Scalar', a)) return True def equal(self, other): """Checks equality of this color with another. Args: other (Color): other color to check equality with. Returns: bool: True if equal. """ return self._rtval.equal('Boolean', other._rtval).getSimpleType() def almostEqual(self, other, precision): """Checks almost equality of this Color with another. Args: other (Color): other value to check equality with. precision (float): Precision value. Returns: bool: True if almost equal. """ return self._rtval.almostEqual('Boolean', other._rtval, bs.rtVal('Scalar', precision)).getSimpleType() def component(self, i ): """Gets the component of this Color by index. Args: i (int): index of the component to return. Returns: float: component of this Color. """ return self._rtval.component('Scalar', bs.rtVal('Size', i)).getSimpleType() def setComponent(self, i, v ): """Sets the component of this Color by index. Args: i (int): index of the component to set. v (float): Value to set component as. Returns: bool: True if successful. """ return self._rtval.setComponent('', bs.rtVal('Size', i), bs.rtVal('Scalar', v)) def add(self, other): """Overload method for the add operator. Args: other (Color): other color to add to this one. Returns: Color: New Color of the sum of the two Color's. """ return Color(self._rtval.add('Color', other._rtval)) def subtract(self, other): """Overload method for the subtract operator. Args: other (Color): other color to subtract from this one. Returns: Color: New Color of the difference of the two Color's. """ return Color(self._rtval.subtract('Color', other._rtval)) def multiply(self, other): """Overload method for the multiply operator. Args: other (Color): other color to multiply from this one. Returns: Color: New Color of the product of the two Color's. """ return Color(self._rtval.multiply('Color', other._rtval)) def divide(self, other): """Divides this color and an other. Args: other (Color): other color to divide by. Returns: Color: Quotient of the division of this color by the other. """ return Color(self._rtval.divide('Color', other._rtval)) def multiplyScalar(self, other): """Product of this color and a scalar. Args: other (float): Scalar value to multiply this color by. Returns: Color: Product of the multiplication of the scalar and this color. """ return Color(self._rtval.multiplyScalar('Color', bs.rtVal('Scalar', other))) def divideScalar(self, other): """Divides this color and a scalar. Args: other (float): Value to divide this color by. Returns: Color: Quotient of the division of the color by the scalar. """ return Color(self._rtval.divideScalar('Color', bs.rtVal('Scalar', other))) def linearInterpolate(self, other, t): """Linearly interpolates this color with another one based on a scalar blend value (0.0 to 1.0). Args: other (Color): color to blend to. t (float): Blend value. Returns: Color: New color blended between this and the input color. """ return Color(self._rtval.linearInterpolate('Color', bs.rtVal('Color', other), bs.rtVal('Scalar', t))) @classmethod def randomColor(cls, gammaAdjustment): """ Generates a random color based on a seed and offset with gamma adjustment. Example: # Generate a regular random color color = randomColor(seed) # Generate a light random color color = randomColor(seed, 0.5) # Generate a dark random color color = randomColor(seed, -0.5) Args: gammaAdjustment (float): A gamma adjustment to offset the range of the generated color. Returns: Color: New random color. """ def lerp( val1, val2, t): return val1 + ((val2 - val1) * t) if(gammaAdjustment > 0.0001): # Generate a light color with values between gammaAdjustment and 1.0 return Color( lerp(gammaAdjustment, 1.0, random.random()), lerp(gammaAdjustment, 1.0, random.random()), lerp(gammaAdjustment, 1.0, random.random()) ) elif(gammaAdjustment < -0.0001): # Generate a dark color with values between 0.0 and 1.0-gammaAdjustment return Color( lerp(0.0, 1.0+gammaAdjustment, random.random()), lerp(0.0, 1.0+gammaAdjustment, random.random()), lerp(0.0, 1.0+gammaAdjustment, random.random()) ) else: # We add an arbitrary offset to the provided offset so that each color # generated based on the seed and offset is unique. return Color( random.random(), random.random(), random.random() )

[ "hhhh" ]

hhhh

9a987096b65f6e71be52e794d735cf712074ae9b

28938a381a60249062d18685c36ddaa5bd4069e2

/Fibanocci.py

264239845db697eb77bf0f5181b20e56fb8e4b66

[]

no_license

AmitH2000/Mycap

ab108024da938c4616ce271e3ec2ae2b0db7b528

881e8603474be3552464ec9f6d5a362b0fa7e73a

refs/heads/master

2021-05-26T03:51:24.033224

2020-04-12T10:57:34

254,040,542

0

null

UTF-8

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false

112

py

num=int(input("Enter the limit :")) a,b=0,1 while a<num: print(a,end=",") a,b=b,a+b print()

[ "noreply@github.com" ]

noreply@github.com

a090dee8f11f5d2e00a19ab4bc1ac21126f49a4a

1ea0e2b4f064ba0de45a73c527ee89a36771e8fc

/tests/sentry/api/endpoints/test_project_create_sample.py

89581e77fde5be59d789372f824b173ee52fe4f8

[ "BSD-2-Clause" ]

permissive

atlassian/sentry

6775e59c317f20f96982e91c2b3c88c02ecbb56b

b937615079d7b24dc225a83b99b1b65da932fc66

refs/heads/master

2023-08-27T15:45:47.699173

2017-09-18T22:14:55

103,999,066

1

5

BSD-3-Clause

2023-04-01T07:49:37

2017-09-18T22:38:18

Python

UTF-8

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false

4,940

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from __future__ import absolute_import from django.core.urlresolvers import reverse import json from sentry.testutils import APITestCase class ProjectCreateSampleTest(APITestCase): def setUp(self): self.login_as(user=self.user) self.team = self.create_team() def test_simple(self): project = self.create_project(team=self.team, name='foo') url = reverse( 'sentry-api-0-project-create-sample', kwargs={ 'organization_slug': project.organization.slug, 'project_slug': project.slug, } ) response = self.client.post(url, format='json') assert response.status_code == 200, response.content assert 'groupID' in json.loads(response.content) def test_project_platform(self): project = self.create_project(team=self.team, name='foo', platform='javascript-react') url = reverse( 'sentry-api-0-project-create-sample', kwargs={ 'organization_slug': project.organization.slug, 'project_slug': project.slug, } ) response = self.client.post(url, format='json') assert response.status_code == 200, response.content assert 'groupID' in json.loads(response.content) def test_cocoa(self): project = self.create_project(team=self.team, name='foo', platform='cocoa') url = reverse( 'sentry-api-0-project-create-sample', kwargs={ 'organization_slug': project.organization.slug, 'project_slug': project.slug, } ) response = self.client.post(url, format='json') assert response.status_code == 200, response.content assert 'groupID' in json.loads(response.content) def test_java(self): project = self.create_project(team=self.team, name='foo', platform='java') url = reverse( 'sentry-api-0-project-create-sample', kwargs={ 'organization_slug': project.organization.slug, 'project_slug': project.slug, } ) response = self.client.post(url, format='json') assert response.status_code == 200, response.content assert 'groupID' in json.loads(response.content) def test_javascript(self): project = self.create_project(team=self.team, name='foo', platform='javascript') url = reverse( 'sentry-api-0-project-create-sample', kwargs={ 'organization_slug': project.organization.slug, 'project_slug': project.slug, } ) response = self.client.post(url, format='json') assert response.status_code == 200, response.content assert 'groupID' in json.loads(response.content) def test_php(self): project = self.create_project(team=self.team, name='foo', platform='php') url = reverse( 'sentry-api-0-project-create-sample', kwargs={ 'organization_slug': project.organization.slug, 'project_slug': project.slug, } ) response = self.client.post(url, format='json') assert response.status_code == 200, response.content assert 'groupID' in json.loads(response.content) def test_python(self): project = self.create_project(team=self.team, name='foo', platform='python') url = reverse( 'sentry-api-0-project-create-sample', kwargs={ 'organization_slug': project.organization.slug, 'project_slug': project.slug, } ) response = self.client.post(url, format='json') assert response.status_code == 200, response.content assert 'groupID' in json.loads(response.content) def test_reactnative(self): project = self.create_project(team=self.team, name='foo', platform='react-native') url = reverse( 'sentry-api-0-project-create-sample', kwargs={ 'organization_slug': project.organization.slug, 'project_slug': project.slug, } ) response = self.client.post(url, format='json') assert response.status_code == 200, response.content assert 'groupID' in json.loads(response.content) def test_ruby(self): project = self.create_project(team=self.team, name='foo', platform='ruby') url = reverse( 'sentry-api-0-project-create-sample', kwargs={ 'organization_slug': project.organization.slug, 'project_slug': project.slug, } ) response = self.client.post(url, format='json') assert response.status_code == 200, response.content assert 'groupID' in json.loads(response.content)

[ "noreply@github.com" ]

noreply@github.com

f72ea5adb6bb93fb22ed43dc90bdc32c3d350e5e

e9c9e38ed91969df78bbd7f9ca2a0fdb264d8ddb

/lib/python3.8/site-packages/ansible_collections/fortinet/fortimanager/plugins/modules/fmgr_pkg_firewall_policy6.py

4e8e6249ef88271ad42c8c22653a3b534363cbf7

[]

no_license

Arceusir/PRELIM_SKILLS_EXAM

882fcf2868926f0bbfe1fb18d50e5fe165936c02

b685c5b28d058f59de2875c7579739c545df2e0c

refs/heads/master

2023-08-15T07:30:42.303283

2021-10-09T01:27:19

415,167,192

0

null

UTF-8

Python

false

80,550

py

#!/usr/bin/python from __future__ import absolute_import, division, print_function # Copyright 2019-2021 Fortinet, Inc. # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # 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 # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program. If not, see <https://www.gnu.org/licenses/>. __metaclass__ = type ANSIBLE_METADATA = {'status': ['preview'], 'supported_by': 'community', 'metadata_version': '1.1'} DOCUMENTATION = ''' --- module: fmgr_pkg_firewall_policy6 short_description: Configure IPv6 policies. description: - This module is able to configure a FortiManager device. - Examples include all parameters and values which need to be adjusted to data sources before usage. version_added: "2.10" author: - Link Zheng (@chillancezen) - Jie Xue (@JieX19) - Frank Shen (@fshen01) - Hongbin Lu (@fgtdev-hblu) notes: - Running in workspace locking mode is supported in this FortiManager module, the top level parameters workspace_locking_adom and workspace_locking_timeout help do the work. - To create or update an object, use state present directive. - To delete an object, use state absent directive. - Normally, running one module can fail when a non-zero rc is returned. you can also override the conditions to fail or succeed with parameters rc_failed and rc_succeeded options: enable_log: description: Enable/Disable logging for task required: false type: bool default: false proposed_method: description: The overridden method for the underlying Json RPC request required: false type: str choices: - update - set - add bypass_validation: description: only set to True when module schema diffs with FortiManager API structure, module continues to execute without validating parameters required: false type: bool default: false workspace_locking_adom: description: the adom to lock for FortiManager running in workspace mode, the value can be global and others including root required: false type: str workspace_locking_timeout: description: the maximum time in seconds to wait for other user to release the workspace lock required: false type: int default: 300 state: description: the directive to create, update or delete an object type: str required: true choices: - present - absent rc_succeeded: description: the rc codes list with which the conditions to succeed will be overriden type: list required: false rc_failed: description: the rc codes list with which the conditions to fail will be overriden type: list required: false adom: description: the parameter (adom) in requested url type: str required: true pkg: description: the parameter (pkg) in requested url type: str required: true pkg_firewall_policy6: description: the top level parameters set required: false type: dict suboptions: action: type: str description: 'Policy action (allow/deny/ipsec).' choices: - 'deny' - 'accept' - 'ipsec' - 'ssl-vpn' app-category: type: str description: 'Application category ID list.' application: description: no description type: int application-list: type: str description: 'Name of an existing Application list.' auto-asic-offload: type: str description: 'Enable/disable policy traffic ASIC offloading.' choices: - 'disable' - 'enable' av-profile: type: str description: 'Name of an existing Antivirus profile.' comments: type: str description: 'Comment.' custom-log-fields: type: str description: 'Log field index numbers to append custom log fields to log messages for this policy.' devices: type: str description: 'Names of devices or device groups that can be matched by the policy.' diffserv-forward: type: str description: 'Enable to change packets DiffServ values to the specified diffservcode-forward value.' choices: - 'disable' - 'enable' diffserv-reverse: type: str description: 'Enable to change packets reverse (reply) DiffServ values to the specified diffservcode-rev value.' choices: - 'disable' - 'enable' diffservcode-forward: type: str description: 'Change packets DiffServ to this value.' diffservcode-rev: type: str description: 'Change packets reverse (reply) DiffServ to this value.' dlp-sensor: type: str description: 'Name of an existing DLP sensor.' dscp-match: type: str description: 'Enable DSCP check.' choices: - 'disable' - 'enable' dscp-negate: type: str description: 'Enable negated DSCP match.' choices: - 'disable' - 'enable' dscp-value: type: str description: 'DSCP value.' dsri: type: str description: 'Enable DSRI to ignore HTTP server responses.' choices: - 'disable' - 'enable' dstaddr: type: str description: 'Destination address and address group names.' dstaddr-negate: type: str description: 'When enabled dstaddr specifies what the destination address must NOT be.' choices: - 'disable' - 'enable' dstintf: type: str description: 'Outgoing (egress) interface.' firewall-session-dirty: type: str description: 'How to handle sessions if the configuration of this firewall policy changes.' choices: - 'check-all' - 'check-new' fixedport: type: str description: 'Enable to prevent source NAT from changing a sessions source port.' choices: - 'disable' - 'enable' global-label: type: str description: 'Label for the policy that appears when the GUI is in Global View mode.' groups: type: str description: 'Names of user groups that can authenticate with this policy.' icap-profile: type: str description: 'Name of an existing ICAP profile.' inbound: type: str description: 'Policy-based IPsec VPN: only traffic from the remote network can initiate a VPN.' choices: - 'disable' - 'enable' ippool: type: str description: 'Enable to use IP Pools for source NAT.' choices: - 'disable' - 'enable' ips-sensor: type: str description: 'Name of an existing IPS sensor.' label: type: str description: 'Label for the policy that appears when the GUI is in Section View mode.' logtraffic: type: str description: 'Enable or disable logging. Log all sessions or security profile sessions.' choices: - 'disable' - 'enable' - 'all' - 'utm' logtraffic-start: type: str description: 'Record logs when a session starts and ends.' choices: - 'disable' - 'enable' mms-profile: type: str description: 'Name of an existing MMS profile.' name: type: str description: 'Policy name.' nat: type: str description: 'Enable/disable source NAT.' choices: - 'disable' - 'enable' natinbound: type: str description: 'Policy-based IPsec VPN: apply destination NAT to inbound traffic.' choices: - 'disable' - 'enable' natoutbound: type: str description: 'Policy-based IPsec VPN: apply source NAT to outbound traffic.' choices: - 'disable' - 'enable' np-accelation: type: str description: 'Enable/disable UTM Network Processor acceleration.' choices: - 'disable' - 'enable' outbound: type: str description: 'Policy-based IPsec VPN: only traffic from the internal network can initiate a VPN.' choices: - 'disable' - 'enable' per-ip-shaper: type: str description: 'Per-IP traffic shaper.' policyid: type: int description: 'Policy ID.' poolname: type: str description: 'IP Pool names.' profile-group: type: str description: 'Name of profile group.' profile-protocol-options: type: str description: 'Name of an existing Protocol options profile.' profile-type: type: str description: 'Determine whether the firewall policy allows security profile groups or single profiles only.' choices: - 'single' - 'group' replacemsg-override-group: type: str description: 'Override the default replacement message group for this policy.' rsso: type: str description: 'Enable/disable RADIUS single sign-on (RSSO).' choices: - 'disable' - 'enable' schedule: type: str description: 'Schedule name.' send-deny-packet: type: str description: 'Enable/disable return of deny-packet.' choices: - 'disable' - 'enable' service: type: str description: 'Service and service group names.' service-negate: type: str description: 'When enabled service specifies what the service must NOT be.' choices: - 'disable' - 'enable' session-ttl: type: int description: 'Session TTL in seconds for sessions accepted by this policy. 0 means use the system default session TTL.' spamfilter-profile: type: str description: 'Name of an existing Spam filter profile.' srcaddr: type: str description: 'Source address and address group names.' srcaddr-negate: type: str description: 'When enabled srcaddr specifies what the source address must NOT be.' choices: - 'disable' - 'enable' srcintf: type: str description: 'Incoming (ingress) interface.' ssl-mirror: type: str description: 'Enable to copy decrypted SSL traffic to a FortiGate interface (called SSL mirroring).' choices: - 'disable' - 'enable' ssl-mirror-intf: type: str description: 'SSL mirror interface name.' ssl-ssh-profile: type: str description: 'Name of an existing SSL SSH profile.' status: type: str description: 'Enable or disable this policy.' choices: - 'disable' - 'enable' tags: type: str description: 'Names of object-tags applied to this policy.' tcp-mss-receiver: type: int description: 'Receiver TCP maximum segment size (MSS).' tcp-mss-sender: type: int description: 'Sender TCP maximum segment size (MSS).' tcp-session-without-syn: type: str description: 'Enable/disable creation of TCP session without SYN flag.' choices: - 'all' - 'data-only' - 'disable' timeout-send-rst: type: str description: 'Enable/disable sending RST packets when TCP sessions expire.' choices: - 'disable' - 'enable' traffic-shaper: type: str description: 'Reverse traffic shaper.' traffic-shaper-reverse: type: str description: 'Reverse traffic shaper.' url-category: type: str description: 'URL category ID list.' users: type: str description: 'Names of individual users that can authenticate with this policy.' utm-status: type: str description: 'Enable AV/web/ips protection profile.' choices: - 'disable' - 'enable' uuid: type: str description: 'Universally Unique Identifier (UUID; automatically assigned but can be manually reset).' vlan-cos-fwd: type: int description: 'VLAN forward direction user priority: 255 passthrough, 0 lowest, 7 highest' vlan-cos-rev: type: int description: 'VLAN reverse direction user priority: 255 passthrough, 0 lowest, 7 highest' voip-profile: type: str description: 'Name of an existing VoIP profile.' vpntunnel: type: str description: 'Policy-based IPsec VPN: name of the IPsec VPN Phase 1.' webfilter-profile: type: str description: 'Name of an existing Web filter profile.' anti-replay: type: str description: 'Enable/disable anti-replay check.' choices: - 'disable' - 'enable' app-group: type: str description: 'Application group names.' cifs-profile: type: str description: 'Name of an existing CIFS profile.' dnsfilter-profile: type: str description: 'Name of an existing DNS filter profile.' emailfilter-profile: type: str description: 'Name of an existing email filter profile.' http-policy-redirect: type: str description: 'Redirect HTTP(S) traffic to matching transparent web proxy policy.' choices: - 'disable' - 'enable' inspection-mode: type: str description: 'Policy inspection mode (Flow/proxy). Default is Flow mode.' choices: - 'proxy' - 'flow' np-acceleration: type: str description: 'Enable/disable UTM Network Processor acceleration.' choices: - 'disable' - 'enable' ssh-filter-profile: type: str description: 'Name of an existing SSH filter profile.' ssh-policy-redirect: type: str description: 'Redirect SSH traffic to matching transparent proxy policy.' choices: - 'disable' - 'enable' tos: type: str description: 'ToS (Type of Service) value used for comparison.' tos-mask: type: str description: 'Non-zero bit positions are used for comparison while zero bit positions are ignored.' tos-negate: type: str description: 'Enable negated TOS match.' choices: - 'disable' - 'enable' vlan-filter: type: str description: 'Set VLAN filters.' waf-profile: type: str description: 'Name of an existing Web application firewall profile.' webcache: type: str description: 'Enable/disable web cache.' choices: - 'disable' - 'enable' webcache-https: type: str description: 'Enable/disable web cache for HTTPS.' choices: - 'disable' - 'enable' webproxy-forward-server: type: str description: 'Web proxy forward server name.' webproxy-profile: type: str description: 'Webproxy profile name.' fsso-groups: type: str description: 'Names of FSSO groups.' decrypted-traffic-mirror: type: str description: 'Decrypted traffic mirror.' ''' EXAMPLES = ''' - hosts: fortimanager-inventory collections: - fortinet.fortimanager connection: httpapi vars: ansible_httpapi_use_ssl: True ansible_httpapi_validate_certs: False ansible_httpapi_port: 443 tasks: - name: Configure IPv6 policies. fmgr_pkg_firewall_policy6: bypass_validation: False workspace_locking_adom: <value in [global, custom adom including root]> workspace_locking_timeout: 300 rc_succeeded: [0, -2, -3, ...] rc_failed: [-2, -3, ...] adom: <your own value> pkg: <your own value> state: <value in [present, absent]> pkg_firewall_policy6: action: <value in [deny, accept, ipsec, ...]> app-category: <value of string> application: <value of integer> application-list: <value of string> auto-asic-offload: <value in [disable, enable]> av-profile: <value of string> comments: <value of string> custom-log-fields: <value of string> devices: <value of string> diffserv-forward: <value in [disable, enable]> diffserv-reverse: <value in [disable, enable]> diffservcode-forward: <value of string> diffservcode-rev: <value of string> dlp-sensor: <value of string> dscp-match: <value in [disable, enable]> dscp-negate: <value in [disable, enable]> dscp-value: <value of string> dsri: <value in [disable, enable]> dstaddr: <value of string> dstaddr-negate: <value in [disable, enable]> dstintf: <value of string> firewall-session-dirty: <value in [check-all, check-new]> fixedport: <value in [disable, enable]> global-label: <value of string> groups: <value of string> icap-profile: <value of string> inbound: <value in [disable, enable]> ippool: <value in [disable, enable]> ips-sensor: <value of string> label: <value of string> logtraffic: <value in [disable, enable, all, ...]> logtraffic-start: <value in [disable, enable]> mms-profile: <value of string> name: <value of string> nat: <value in [disable, enable]> natinbound: <value in [disable, enable]> natoutbound: <value in [disable, enable]> np-accelation: <value in [disable, enable]> outbound: <value in [disable, enable]> per-ip-shaper: <value of string> policyid: <value of integer> poolname: <value of string> profile-group: <value of string> profile-protocol-options: <value of string> profile-type: <value in [single, group]> replacemsg-override-group: <value of string> rsso: <value in [disable, enable]> schedule: <value of string> send-deny-packet: <value in [disable, enable]> service: <value of string> service-negate: <value in [disable, enable]> session-ttl: <value of integer> spamfilter-profile: <value of string> srcaddr: <value of string> srcaddr-negate: <value in [disable, enable]> srcintf: <value of string> ssl-mirror: <value in [disable, enable]> ssl-mirror-intf: <value of string> ssl-ssh-profile: <value of string> status: <value in [disable, enable]> tags: <value of string> tcp-mss-receiver: <value of integer> tcp-mss-sender: <value of integer> tcp-session-without-syn: <value in [all, data-only, disable]> timeout-send-rst: <value in [disable, enable]> traffic-shaper: <value of string> traffic-shaper-reverse: <value of string> url-category: <value of string> users: <value of string> utm-status: <value in [disable, enable]> uuid: <value of string> vlan-cos-fwd: <value of integer> vlan-cos-rev: <value of integer> voip-profile: <value of string> vpntunnel: <value of string> webfilter-profile: <value of string> anti-replay: <value in [disable, enable]> app-group: <value of string> cifs-profile: <value of string> dnsfilter-profile: <value of string> emailfilter-profile: <value of string> http-policy-redirect: <value in [disable, enable]> inspection-mode: <value in [proxy, flow]> np-acceleration: <value in [disable, enable]> ssh-filter-profile: <value of string> ssh-policy-redirect: <value in [disable, enable]> tos: <value of string> tos-mask: <value of string> tos-negate: <value in [disable, enable]> vlan-filter: <value of string> waf-profile: <value of string> webcache: <value in [disable, enable]> webcache-https: <value in [disable, enable]> webproxy-forward-server: <value of string> webproxy-profile: <value of string> fsso-groups: <value of string> decrypted-traffic-mirror: <value of string> ''' RETURN = ''' request_url: description: The full url requested returned: always type: str sample: /sys/login/user response_code: description: The status of api request returned: always type: int sample: 0 response_message: description: The descriptive message of the api response type: str returned: always sample: OK. ''' from ansible.module_utils.basic import AnsibleModule from ansible.module_utils.connection import Connection from ansible_collections.fortinet.fortimanager.plugins.module_utils.napi import NAPIManager from ansible_collections.fortinet.fortimanager.plugins.module_utils.napi import check_galaxy_version from ansible_collections.fortinet.fortimanager.plugins.module_utils.napi import check_parameter_bypass def main(): jrpc_urls = [ '/pm/config/adom/{adom}/pkg/{pkg}/firewall/policy6' ] perobject_jrpc_urls = [ '/pm/config/adom/{adom}/pkg/{pkg}/firewall/policy6/{policy6}' ] url_params = ['adom', 'pkg'] module_primary_key = 'policyid' module_arg_spec = { 'enable_log': { 'type': 'bool', 'required': False, 'default': False }, 'proposed_method': { 'type': 'str', 'required': False, 'choices': [ 'set', 'update', 'add' ] }, 'bypass_validation': { 'type': 'bool', 'required': False, 'default': False }, 'workspace_locking_adom': { 'type': 'str', 'required': False }, 'workspace_locking_timeout': { 'type': 'int', 'required': False, 'default': 300 }, 'rc_succeeded': { 'required': False, 'type': 'list' }, 'rc_failed': { 'required': False, 'type': 'list' }, 'state': { 'type': 'str', 'required': True, 'choices': [ 'present', 'absent' ] }, 'adom': { 'required': True, 'type': 'str' }, 'pkg': { 'required': True, 'type': 'str' }, 'pkg_firewall_policy6': { 'required': False, 'type': 'dict', 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True }, 'options': { 'action': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'deny', 'accept', 'ipsec', 'ssl-vpn' ], 'type': 'str' }, 'app-category': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'application': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'int' }, 'application-list': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'auto-asic-offload': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'av-profile': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'comments': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'custom-log-fields': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'devices': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': False, '6.2.3': False, '6.2.5': False, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'diffserv-forward': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'diffserv-reverse': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'diffservcode-forward': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'diffservcode-rev': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'dlp-sensor': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'dscp-match': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': False, '6.2.3': False, '6.2.5': False, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'dscp-negate': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': False, '6.2.3': False, '6.2.5': False, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'dscp-value': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': False, '6.2.3': False, '6.2.5': False, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'dsri': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'dstaddr': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'dstaddr-negate': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'dstintf': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'firewall-session-dirty': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'check-all', 'check-new' ], 'type': 'str' }, 'fixedport': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'global-label': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'groups': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'icap-profile': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'inbound': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'ippool': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'ips-sensor': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'label': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'logtraffic': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable', 'all', 'utm' ], 'type': 'str' }, 'logtraffic-start': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'mms-profile': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'name': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'nat': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'natinbound': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'natoutbound': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'np-accelation': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': False, '6.2.3': False, '6.2.5': False, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'outbound': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'per-ip-shaper': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'policyid': { 'required': True, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'int' }, 'poolname': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'profile-group': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'profile-protocol-options': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'profile-type': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'single', 'group' ], 'type': 'str' }, 'replacemsg-override-group': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'rsso': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'schedule': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'send-deny-packet': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'service': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'service-negate': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'session-ttl': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'int' }, 'spamfilter-profile': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': False, '6.2.3': False, '6.2.5': False, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'srcaddr': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'srcaddr-negate': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'srcintf': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'ssl-mirror': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'ssl-mirror-intf': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'ssl-ssh-profile': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'status': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'tags': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': False, '6.2.3': False, '6.2.5': False, '6.4.0': False, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'tcp-mss-receiver': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'int' }, 'tcp-mss-sender': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'int' }, 'tcp-session-without-syn': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'all', 'data-only', 'disable' ], 'type': 'str' }, 'timeout-send-rst': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'traffic-shaper': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'traffic-shaper-reverse': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'url-category': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'users': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'utm-status': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'uuid': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'vlan-cos-fwd': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'int' }, 'vlan-cos-rev': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'int' }, 'voip-profile': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'vpntunnel': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'webfilter-profile': { 'required': False, 'revision': { '6.0.0': True, '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'anti-replay': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'app-group': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'cifs-profile': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'dnsfilter-profile': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'emailfilter-profile': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'http-policy-redirect': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'inspection-mode': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'proxy', 'flow' ], 'type': 'str' }, 'np-acceleration': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'ssh-filter-profile': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'ssh-policy-redirect': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'tos': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'tos-mask': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'tos-negate': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'vlan-filter': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'waf-profile': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'webcache': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'webcache-https': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'choices': [ 'disable', 'enable' ], 'type': 'str' }, 'webproxy-forward-server': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'webproxy-profile': { 'required': False, 'revision': { '6.2.1': True, '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'fsso-groups': { 'required': False, 'revision': { '6.2.3': True, '6.2.5': True, '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' }, 'decrypted-traffic-mirror': { 'required': False, 'revision': { '6.4.0': True, '6.4.2': False, '6.4.5': False, '7.0.0': False }, 'type': 'str' } } } } params_validation_blob = [] check_galaxy_version(module_arg_spec) module = AnsibleModule(argument_spec=check_parameter_bypass(module_arg_spec, 'pkg_firewall_policy6'), supports_check_mode=False) fmgr = None if module._socket_path: connection = Connection(module._socket_path) connection.set_option('enable_log', module.params['enable_log'] if 'enable_log' in module.params else False) fmgr = NAPIManager(jrpc_urls, perobject_jrpc_urls, module_primary_key, url_params, module, connection, top_level_schema_name='data') fmgr.validate_parameters(params_validation_blob) fmgr.process_curd(argument_specs=module_arg_spec) else: module.fail_json(msg='MUST RUN IN HTTPAPI MODE') module.exit_json(meta=module.params) if __name__ == '__main__': main()

[ "aaronchristopher.dalmacio@gmail.com" ]

aaronchristopher.dalmacio@gmail.com

fec7c69adfe85662ef8afe4fad53eb0ed76d2067

e35cd19277b6440371bb5dd1b92c0abfd90c1e49

/get_background.py

17cd858a39421c138234ef7006b073fd1618aeab

[]

no_license

sbrems/background_sources

eb1272f37b0e3b5bf7134ee69f3caa5a46bbc7f0

127ec0452afd01eda93dd980e6db5f592940f4f0

refs/heads/master

2021-01-25T05:02:12.436972

2017-06-14T11:46:03

93,503,241

0

null

UTF-8

Python

false

10,468

py

import numpy as np import matplotlib.pyplot as plt from matplotlib import gridspec from astropy.coordinates import Angle,SkyCoord import astropy.units as u from astropy.table import Table from starclass import Star from parameters import * import pickle import os import ipdb star = Star(srcname) star.coordinates = 'auto' def do(): '''Does everything except downloading the data. Use down_catalogues for that.''' if not os.path.exists(outdir): os.makedirs(outdir) twomasscat = read_cat(vvv=False) references = [] results = []#in order catalogue,coords,raudius,wavelength,[histvalues,],[histbins,] if grid == 'auto': print('Auto-making the grid') cencoords = make_comp_grid() elif grid == 'manual': print('Using manually center points') cencoords = load_comp_grid() else: raise ValueError('Unknown keyword {} for grid. Select auto or manual'.format(grid)) print('Processing the 2Mass referencepoint') for radius in comp_radii: for wave in twomassbands: references.append(['2mass',star.coordinates,radius,wave] + [x[0] for x in list(make_cumhists(twomasscat,star.coordinates, radius,[wave,]))]) print('Processing the 2Masssurvey') for ii,center in enumerate(cencoords.icrs): print('Processing center {} ({} of {})'.format(center,ii,len(cencoords)-1)) for radius in comp_radii: for wave in twomassbands: results.append(['2mass',center,radius,wave] + [x[0] for x in list(make_cumhists(twomasscat,SkyCoord([center,]), radius,[wave,]))]) pn_results = os.path.join(outdir,'results.py') pn_references = os.path.join(outdir,'references.py') pickle.dump(results,open(pn_results,'wb')) pickle.dump(references,open(pn_references,'wb')) print('Done with cumhists. Saved them to {} , {}'.format(pn_results,pn_references)) if len(cencoords) > 100: plot = False else: plot=True t_maxdiff = plot_bins(results,references,cencoords,plot=plot) #find the best result in a min of max diff sense t_diff_grouped = t_maxdiff.group_by(['cen_lon','cen_lat','radius']) t_diff_max = t_diff_grouped.groups.aggregate(np.max) t_diff_max.sort(['radius','maxdiff']) #mindiffglob = np.min(t_diff_max['maxdiff']) #t_bestcens = t_diff_max.sort([np.where(t_diff_max['maxdiff'] == mindiffglob)] #save the results pn_tgrouped = os.path.join(outdir,'centerprops_grouped.csv') pn_tall = os.path.join(outdir,'centerprops_all.csv') #pn_tbest= os.path.join('centerprops_best.csv') i_largest_radius = len(cencoords)*(len(comp_radii)-1) print('The 10 best centers seem {}.\n Saved them to {}'.format(\ t_diff_max[i_largest_radius:i_largest_radius+10],pn_tgrouped)) t_diff_grouped.write(pn_tall,format='ascii.csv',overwrite=True) t_diff_max.write(pn_tgrouped,format='ascii.csv',overwrite=True) #t_bestcens.write(pn_tbest,format='ascii.csv') print('DONE with get_background.do') def plot_bins(results,references,cencoords,plot=True): '''make a grid plot in one file. Returns a table with simple statistics.''' import seaborn as sn wb2col = {'Jmag':'blue', 'Hmag':'green', 'Kmag':'red'} #store stuff in a table. Fill and delete dummy values for right column properties t_maxdiff = Table([[np.nan],[np.nan],[np.nan,],['Dummy_waveband',],[-999]], names=['cen_lon','cen_lat','radius','waveband','maxdiff']) t_maxdiff = t_maxdiff[:0] bins = references[-1][-1][1:] cummax = np.max([x[-2] for x in results]) if plot: fig = plt.figure(figsize=(5*len(comp_radii), 5*len(cencoords))) outergrid = gridspec.GridSpec(len(cencoords), len(comp_radii), wspace=0.12,hspace=0.12) for icen,center in enumerate(cencoords.icrs): if plot: print('Plotting and getting statistics for Position {} of {}'.format(icen, len(cencoords.icrs)-1)) else: print('Not plotting. Getting statistics for Position {} of {}'.format(icen, len(cencoords.icrs)-1)) for irad,radius in enumerate(comp_radii): if plot: innergrid = gridspec.GridSpecFromSubplotSpec(3, 1, subplot_spec=outergrid[icen,irad], wspace=0.1, hspace=0.1) ax1 = plt.Subplot(fig,innergrid[:-1,0]) ax2 = plt.Subplot(fig,innergrid[-1,0]) for wave in twomassbands: refvals = [x[-2] for x in references if ((x[2] == radius) and (x[3] == wave))][0] scivals = [x[-2] for x in results if ((x[2] == radius) and (x[1].separation(center) < 0.01*u.arcsec) and (x[3] == wave))][0] if plot: ax1.plot(bins,refvals,ls='--',c=wb2col[wave],label=wave) ax1.plot(bins,scivals,ls='-' ,c=wb2col[wave]) ax1.set_ylim(0,cummax) ax2.set_ylim(-cummax/8,cummax/8) ax2.plot(bins,refvals-scivals,c=wb2col[wave]) t_maxdiff.add_row([center.galactic.l,center.galactic.b, radius,wave,int(np.max(np.abs(refvals-scivals)))]) if plot: ax1.legend(loc='upper left') #ax1.annotate('icen,irad: {},{}'.format(icen,irad),xy=(10,20)) #ax1.set_title('2MASS, r={}, cen={}'.format(radius,center.galactic)) #make the labels where needed if irad == 0: ax1.set_ylabel('lon={0:.4f}, lat={1:4f} \n\nCumulative nr of sources'.format(center.galactic.l.value,center.galactic.b.value)) ax2.set_ylabel('Residuals') #plt.subplot(outergrid[icen,irad]).set_ylabel('lon={10.6f}, lat={8.6f}\n\n\n'.format(center.galactic.l.value,center.galactic.b.value)) #plt.subplot(outergrid[icen,irad]).set_yticks([]) #plt.subplot(outergrid[icen,irad]).set_xticks([]) else: ax1.set_yticklabels([]) ax1.set_ylabel('') ax2.set_ylabel('') ax2.set_yticklabels([]) if icen == len(cencoords)-1: ax2.set_xlabel('mag') ax1.set_xticklabels([]) elif icen == 0: ax1.set_title('search radius = {}'.format(radius)) ax1.set_xticklabels([]) #plt.subplot(outergrid[icen,irad]).set_ylabel('search radius = {}'.format(radius)) #plt.subplot(outergrid[icen,irad]).set_yticks([]) #plt.subplot(outergrid[icen,irad]).set_xticks([]) else: ax2.set_xlabel('') ax1.set_xticklabels([]) ax2.set_xticklabels([]) if plot: fig.add_subplot(ax1) fig.add_subplot(ax2,sharex=ax2) if plot: fp_plot = os.path.join(outdir,'sourcedensity_overview.pdf') try: fig.savefig(fp_plot) except: print('Somehow could not save the figure.') print('Saved plot to {}'.format(fp_plot)) plt.close('all') return t_maxdiff def make_cumhists(cat,center,radius,wavebands,magrange=[5,14.5]): '''Make a magnitude histogram around the center with a given radius for each waveband''' nbins = 200 idz1,idz2,sep2d,sep3d = cat['skycoord'].search_around_sky(center,radius) small_cat = cat[idz2] hists = [] for wave in wavebands: values, bins = np.histogram(small_cat[wave],bins=nbins,range=magrange) hists.append(np.cumsum(values)) return hists,[bins,] def read_cat(vvv=True,twomass=True): '''Read the catalogues. Mainly convert coordinates to the ones given in column skycoord.''' if vvv: print('Reading vvvcat') vvvcat = Table.read(os.path.join(maindir,'vvv_catalogue.csv'),format='ascii.csv') vvvcat['skycoord'] = SkyCoord(vvvcat['RAJ2000'],vvvcat['DEJ2000'],frame='icrs',unit=u.deg) if twomass: print('Reading 2masscat') twomasscat = Table.read(os.path.join(maindir,'2mass_catalogue.csv'),format='ascii.csv') twomasscat['skycoord']= SkyCoord(twomasscat['RAJ2000'],twomasscat['DEJ2000'], frame='icrs',unit=u.deg) if vvv and twomass: return vvvcat,twomasscat elif vvv: return vvvcat else: return towmasscat def make_comp_grid(): '''Makes the center points of the comparison. Returns a SkyCoord object with the centrag points''' max_rad = np.max(comp_radii) #make the longitude and latitude vectors in galactic coords veclon = np.linspace(vvv_topright.l + max_rad, vvv_leftbot.l - max_rad, nlon) veclat = np.linspace(vvv_leftbot.b + max_rad, vvv_topright.b - max_rad, nlat) vlon = np.meshgrid(veclon,veclat)[0].flatten() vlat = np.meshgrid(veclon,veclat)[1].flatten() cencoords = SkyCoord(vlon,vlat,unit=u.deg,frame='galactic') return cencoords def load_comp_grid(): '''Uses manually entered grid. Counterpart to make_comp_grid''' return manual_center_points def down_catalogues(): '''Download and save the catalogs''' from astroquery.vizier import Vizier Vizier.ROW_LIMIT = -1 for cat in query_cats.keys(): res = Vizier.query_region(query_center,catalog=query_cats[cat], radius=query_radius) cat_pname = os.path.join(maindir,cat+'_catalogue.csv') res[0].write(cat_pname, format='ascii.csv', delimiter=',',overwrite=True) print('Saved cat {} ({} rows) to {}'.format(cat,len(res[0]),cat_pname)) del res import ipdb;ipdb.set_trace()

[ "sbrems@lsw.uni-heidelberg.de" ]

sbrems@lsw.uni-heidelberg.de

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856ada709d85f7c2c385277bbfdf07fabb24b762

/mysite/cocktails/views.py

e67443d0717c2a5edd839bf45b05f8e1c6921ffc

[]

no_license

laurence-liu/DearLiquor.Django

91ca130ac088ef2815355d88e940d5fb69da5350

395f9edda908ffedef7b9e41bc67e6cca6d5e71c

refs/heads/master

2020-04-28T20:18:15.994826

2019-03-29T08:30:11

175,540,088

0

null

UTF-8

Python

false

240

py

from django.shortcuts import render from .models import Cocktail # Create your views here. def cocktail(request): cocktails = Cocktail.objects.all() return render(request, 'cocktail.html', { 'cocktails': cocktails, })

[ "laurence.liuuu@gmail.com" ]

laurence.liuuu@gmail.com

fc68572641d5a74b599133888502a7f9c82b6d3d

cd050f1f91d516101f3a8c05f36e9ac506560b5d

/crypto_api.py

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[]

no_license

jacinthd/progyny-assessment

684dbe2b9062e106340549c0ad69cd8b8df1c967

a2a4ca5a2de09fe2a13ffc18d8e79a6a6829f4a0

refs/heads/main

2023-05-14T11:56:05.373384

2021-06-09T17:23:24

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0

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null

2021-06-11T02:38:08

2021-06-09T17:36:53

Python

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Python

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py

"""Crypto API.""" from typing import Dict, List import requests # API Documentation - https://www.coingecko.com/en/api#explore-api def get_coins() -> List[Dict]: """This function will get the top 10 coins at the current time, sorted by market cap in desc order.""" response = requests.get('https://api.coingecko.com/api/v3/coins/markets?vs_currency=usd&order=market_cap_desc&per_page=10&page=1&sparkline=false') # Important keys # - id # - symbol # - name # - current_price return response.json() def get_coin_price_history(coin_id: str) -> List[Dict]: response = requests.get(f"https://api.coingecko.com/api/v3/coins/{coin_id}/market_chart?vs_currency=usd&days=9&interval=daily") # Returns a list of tuples # Item 0 -> Unix Timestamp # Item 1 -> price return response.json()['prices'] # utilize this function when submitting an order def submit_order(coin_id: str, quantity: int, bid: float): """ Mock function to submit an order to an exchange. Assume order went through successfully and the return value is the price the order was filled at. """ return bid

[ "jacinthdavid@gmail.com" ]

jacinthdavid@gmail.com

b24a4d5318b0f26c286c6480a6924b655e13f8e5

a2fc37a497c73629c88fededb4c3c925ad2d75c6

/proyectoWeb/servicios/urls.py

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[]

no_license

atziripe/GestionPedidos

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7522ce7ed8407c6e70938f7cd24a0ae92ea5fbfc

refs/heads/main

2023-06-17T13:31:25.227177

2021-07-09T00:53:33

348,610,009

0

null

UTF-8

Python

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117

py

from django.urls import path from . import views urlpatterns = [ path('', views.servicios, name="Servicios"), ]

[ "atziripg.99@gmail.com" ]

atziripg.99@gmail.com

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/_commands/_CreateDirectory.py

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no_license

pallavigusain92/FTP

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68483b49cb8d70d1a7797438164c7cd89d6b4829

refs/heads/master

2020-04-26T00:08:03.095976

2019-02-28T19:15:33

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0

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2019-02-28T18:55:33

2019-02-28T18:39:29

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class Command: def do_createdirectory(self, input_directory): """ Command to create directory with name of new directory Args: input_directory (str): Name of directory you want to create """ print(input_directory) response = self._perform_ftp_command('listdir') print(response)

[ "noreply@github.com" ]

noreply@github.com

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/실습과제2/In[22].py

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[]

no_license

kimhyeyun/2020-DataMining

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a0c7c7d70158a828072169e13f6d1662aa2e0324

refs/heads/master

2021-04-07T13:24:35.317906

2020-06-25T01:07:23

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from pandas import Series s3 = Series([1.2,2.5,-2.2,3.1,-0.8,-3.2], index = ['Jan 1','Jan 2','Jan 3','Jan 4','Jan 5','Jan 6',]) print(s3 + 4) #applying scalar operation on a numeric Series print(s3 / 4)

[ "cqqwer@gmail.com" ]

cqqwer@gmail.com

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4842612668bd2378dbc2b38f922616688892ace0

/Django/env/FTM/views.py

7793b68bd4379d46ebe531941083e100cdbef630

[]

no_license

CoupDgrace/FamilyProjectTracker

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1a3621fe459c9c93ebfcba57eaafb08d29435a87

refs/heads/main

2023-07-13T02:12:49.369827

2021-08-30T04:34:28

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2021-08-30T04:34:29

2021-04-23T06:47:50

TSQL

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Python

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from django.shortcuts import render from django.http import HttpResponse from .models import * from django.db.models import Q # Create your views here. # Home Page def home(request): nTicks = Tasks.objects.filter(assignedMember = 1) kTicks = Tasks.objects.filter(assignedMember = 2) gTicks = Tasks.objects.filter(assignedMember = 3) eTicks = Tasks.objects.filter(assignedMember = 4) tTicks = Tasks.objects.filter(assignedMember = 5) mTicks = Tasks.objects.filter(assignedMember = 6) backTicks = Tasks.objects.filter(Q(taskStatus='backlog') | Q(taskStatus='Backlog')) doneTicks = Tasks.objects.filter(Q(taskStatus='Complete') | Q(taskStatus='complete')) waitTicks = Tasks.objects.filter(taskStatus='waiting') return render ( request, 'FTM/HTML/BanksBoardIndex.html', {'nTicks': nTicks, 'backTicks':backTicks, 'kTicks':kTicks, 'gTicks':gTicks, 'eTicks':eTicks, 'tTicks':tTicks, 'mTicks':mTicks, 'doneTicks':doneTicks, 'waitTicks':waitTicks}, ) '''def admin(request): return render ( request, 'admin/', )''' # # # A few forms # # # # Members Form ''' def Members(request): memForm = MembersForm() return render(request,'FTM/HTML/membersForm.html',{'form':form}) '''

[ "nathan.banksd@gmail.com" ]

nathan.banksd@gmail.com

634e1c3d02aa18881bd9b4af1f650f111c07e4c7

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/qa/pull-tester/rpc-tests.py

ec86971a09eedebd51c8dc16e6d568722ede2eb9

[ "MIT" ]

permissive

mtx-coin/Matrix-Blockchain

bd0495038146cd639db655787edfa5f406806ef9

cb8c2c4c7ec5ee911c4f43a718785241cd4ceafe

refs/heads/master

2020-06-02T15:19:01.450287

2019-06-10T17:13:12

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#!/usr/bin/env python3 # Copyright (c) 2014-2016 The Bitcoin Core developers # Distributed under the MIT software license, see the accompanying # file COPYING or http://www.opensource.org/licenses/mit-license.php. """ Run Regression Test Suite This module calls down into individual test cases via subprocess. It will forward all unrecognized arguments onto the individual test scripts, other than: - `-extended`: run the "extended" test suite in addition to the basic one. - `-win`: signal that this is running in a Windows environment, and we should run the tests. - `--coverage`: this generates a basic coverage report for the RPC interface. For a description of arguments recognized by test scripts, see `qa/pull-tester/test_framework/test_framework.py:BitcoinTestFramework.main`. """ import os import time import shutil import sys import subprocess import tempfile import re sys.path.append("qa/pull-tester/") from tests_config import * BOLD = ("","") if os.name == 'posix': # primitive formatting on supported # terminal via ANSI escape sequences: BOLD = ('\033[0m', '\033[1m') RPC_TESTS_DIR = SRCDIR + '/qa/rpc-tests/' #If imported values are not defined then set to zero (or disabled) if 'ENABLE_WALLET' not in vars(): ENABLE_WALLET=0 if 'ENABLE_BITCOIND' not in vars(): ENABLE_BITCOIND=0 if 'ENABLE_UTILS' not in vars(): ENABLE_UTILS=0 if 'ENABLE_ZMQ' not in vars(): ENABLE_ZMQ=0 # python-zmq may not be installed. Handle this gracefully and with some helpful info if ENABLE_ZMQ: try: import zmq except ImportError: print("WARNING: \"import zmq\" failed. Setting ENABLE_ZMQ=0. " \ "To run zmq tests, see dependency info in /qa/README.md.") ENABLE_ZMQ=0 ENABLE_COVERAGE=0 #Create a set to store arguments and create the passon string opts = set() passon_args = [] PASSON_REGEX = re.compile("^--") PARALLEL_REGEX = re.compile('^-parallel=') print_help = False run_parallel = 4 for arg in sys.argv[1:]: if arg == "--help" or arg == "-h" or arg == "-?": print_help = True break if arg == '--coverage': ENABLE_COVERAGE = 1 elif PASSON_REGEX.match(arg): passon_args.append(arg) elif PARALLEL_REGEX.match(arg): run_parallel = int(arg.split(sep='=', maxsplit=1)[1]) else: opts.add(arg) #Set env vars if "MTXD" not in os.environ: os.environ["MTXD"] = BUILDDIR + '/src/matrixd' + EXEEXT if EXEEXT == ".exe" and "-win" not in opts: # https://github.com/bitcoin/bitcoin/commit/d52802551752140cf41f0d9a225a43e84404d3e9 # https://github.com/bitcoin/bitcoin/pull/5677#issuecomment-136646964 print("Win tests currently disabled by default. Use -win option to enable") sys.exit(0) if not (ENABLE_WALLET == 1 and ENABLE_UTILS == 1 and ENABLE_BITCOIND == 1): print("No rpc tests to run. Wallet, utils, and bitcoind must all be enabled") sys.exit(0) # python3-zmq may not be installed. Handle this gracefully and with some helpful info if ENABLE_ZMQ: try: import zmq except ImportError: print("ERROR: \"import zmq\" failed. Set ENABLE_ZMQ=0 or " "to run zmq tests, see dependency info in /qa/README.md.") # ENABLE_ZMQ=0 raise testScripts = [ # longest test should go first, to favor running tests in parallel 'wallet-hd.py', 'walletbackup.py', # vv Tests less than 5m vv 'p2p-fullblocktest.py', # NOTE: needs matrix_hash to pass 'fundrawtransaction.py', 'fundrawtransaction-hd.py', # vv Tests less than 2m vv 'wallet.py', 'wallet-accounts.py', 'wallet-dump.py', 'listtransactions.py', # vv Tests less than 60s vv 'sendheaders.py', # NOTE: needs matrix_hash to pass 'zapwallettxes.py', 'importmulti.py', 'mempool_limit.py', 'merkle_blocks.py', 'receivedby.py', 'abandonconflict.py', 'bip68-112-113-p2p.py', 'rawtransactions.py', 'reindex.py', # vv Tests less than 30s vv 'mempool_resurrect_test.py', 'txn_doublespend.py --mineblock', 'txn_clone.py', 'getchaintips.py', 'rest.py', 'mempool_spendcoinbase.py', 'mempool_reorg.py', 'httpbasics.py', 'multi_rpc.py', 'proxy_test.py', 'signrawtransactions.py', 'nodehandling.py', 'addressindex.py', 'timestampindex.py', 'spentindex.py', 'decodescript.py', 'blockchain.py', 'disablewallet.py', 'keypool.py', 'keypool-hd.py', 'p2p-mempool.py', 'prioritise_transaction.py', 'invalidblockrequest.py', # NOTE: needs matrix_hash to pass 'invalidtxrequest.py', # NOTE: needs matrix_hash to pass 'p2p-versionbits-warning.py', 'preciousblock.py', 'importprunedfunds.py', 'signmessages.py', 'nulldummy.py', 'import-rescan.py', 'rpcnamedargs.py', 'listsinceblock.py', 'p2p-leaktests.py', 'p2p-compactblocks.py', ] if ENABLE_ZMQ: testScripts.append('zmq_test.py') testScriptsExt = [ # 'pruning.py', # Prune mode is incompatible with -txindex. # vv Tests less than 20m vv 'smartfees.py', # vv Tests less than 5m vv 'maxuploadtarget.py', 'mempool_packages.py', # vv Tests less than 2m vv 'bip68-sequence.py', 'getblocktemplate_longpoll.py', # FIXME: "socket.error: [Errno 54] Connection reset by peer" on my Mac, same as https://github.com/bitcoin/bitcoin/issues/6651 'p2p-timeouts.py', # vv Tests less than 60s vv 'bip9-softforks.py', 'rpcbind_test.py', # vv Tests less than 30s vv 'bip65-cltv.py', 'bip65-cltv-p2p.py', # NOTE: needs matrix_hash to pass 'bipdersig-p2p.py', # NOTE: needs matrix_hash to pass 'bipdersig.py', 'getblocktemplate_proposals.py', 'txn_doublespend.py', 'txn_clone.py --mineblock', 'forknotify.py', 'invalidateblock.py', 'maxblocksinflight.py', 'p2p-acceptblock.py', # NOTE: needs matrix_hash to pass # 'replace-by-fee.py', # RBF is disabled in MATRIX Core ] def runtests(): test_list = [] if '-extended' in opts: test_list = testScripts + testScriptsExt elif len(opts) == 0 or (len(opts) == 1 and "-win" in opts): test_list = testScripts else: for t in testScripts + testScriptsExt: if t in opts or re.sub(".py$", "", t) in opts: test_list.append(t) if print_help: # Only print help of the first script and exit subprocess.check_call((RPC_TESTS_DIR + test_list[0]).split() + ['-h']) sys.exit(0) coverage = None if ENABLE_COVERAGE: coverage = RPCCoverage() print("Initializing coverage directory at %s\n" % coverage.dir) flags = ["--srcdir=%s/src" % BUILDDIR] + passon_args flags.append("--cachedir=%s/qa/cache" % BUILDDIR) if coverage: flags.append(coverage.flag) if len(test_list) > 1 and run_parallel > 1: # Populate cache subprocess.check_output([RPC_TESTS_DIR + 'create_cache.py'] + flags) #Run Tests max_len_name = len(max(test_list, key=len)) time_sum = 0 time0 = time.time() job_queue = RPCTestHandler(run_parallel, test_list, flags) results = BOLD[1] + "%s | %s | %s\n\n" % ("TEST".ljust(max_len_name), "PASSED", "DURATION") + BOLD[0] all_passed = True for _ in range(len(test_list)): (name, stdout, stderr, passed, duration) = job_queue.get_next() all_passed = all_passed and passed time_sum += duration print('\n' + BOLD[1] + name + BOLD[0] + ":") print('' if passed else stdout + '\n', end='') print('' if stderr == '' else 'stderr:\n' + stderr + '\n', end='') results += "%s | %s | %s s\n" % (name.ljust(max_len_name), str(passed).ljust(6), duration) print("Pass: %s%s%s, Duration: %s s\n" % (BOLD[1], passed, BOLD[0], duration)) results += BOLD[1] + "\n%s | %s | %s s (accumulated)" % ("ALL".ljust(max_len_name), str(all_passed).ljust(6), time_sum) + BOLD[0] print(results) print("\nRuntime: %s s" % (int(time.time() - time0))) if coverage: coverage.report_rpc_coverage() print("Cleaning up coverage data") coverage.cleanup() sys.exit(not all_passed) class RPCTestHandler: """ Trigger the testscrips passed in via the list. """ def __init__(self, num_tests_parallel, test_list=None, flags=None): assert(num_tests_parallel >= 1) self.num_jobs = num_tests_parallel self.test_list = test_list self.flags = flags self.num_running = 0 # In case there is a graveyard of zombie bitcoinds, we can apply a # pseudorandom offset to hopefully jump over them. # (625 is PORT_RANGE/MAX_NODES) self.portseed_offset = int(time.time() * 1000) % 625 self.jobs = [] def get_next(self): while self.num_running < self.num_jobs and self.test_list: # Add tests self.num_running += 1 t = self.test_list.pop(0) port_seed = ["--portseed={}".format(len(self.test_list) + self.portseed_offset)] log_stdout = tempfile.SpooledTemporaryFile(max_size=2**16) log_stderr = tempfile.SpooledTemporaryFile(max_size=2**16) self.jobs.append((t, time.time(), subprocess.Popen((RPC_TESTS_DIR + t).split() + self.flags + port_seed, universal_newlines=True, stdout=log_stdout, stderr=log_stderr), log_stdout, log_stderr)) if not self.jobs: raise IndexError('pop from empty list') while True: # Return first proc that finishes time.sleep(.5) for j in self.jobs: (name, time0, proc, log_out, log_err) = j if proc.poll() is not None: log_out.seek(0), log_err.seek(0) [stdout, stderr] = [l.read().decode('utf-8') for l in (log_out, log_err)] log_out.close(), log_err.close() passed = stderr == "" and proc.returncode == 0 self.num_running -= 1 self.jobs.remove(j) return name, stdout, stderr, passed, int(time.time() - time0) print('.', end='', flush=True) class RPCCoverage(object): """ Coverage reporting utilities for pull-tester. Coverage calculation works by having each test script subprocess write coverage files into a particular directory. These files contain the RPC commands invoked during testing, as well as a complete listing of RPC commands per `bitcoin-cli help` (`rpc_interface.txt`). After all tests complete, the commands run are combined and diff'd against the complete list to calculate uncovered RPC commands. See also: qa/rpc-tests/test_framework/coverage.py """ def __init__(self): self.dir = tempfile.mkdtemp(prefix="coverage") self.flag = '--coveragedir=%s' % self.dir def report_rpc_coverage(self): """ Print out RPC commands that were unexercised by tests. """ uncovered = self._get_uncovered_rpc_commands() if uncovered: print("Uncovered RPC commands:") print("".join((" - %s\n" % i) for i in sorted(uncovered))) else: print("All RPC commands covered.") def cleanup(self): return shutil.rmtree(self.dir) def _get_uncovered_rpc_commands(self): """ Return a set of currently untested RPC commands. """ # This is shared from `qa/rpc-tests/test-framework/coverage.py` REFERENCE_FILENAME = 'rpc_interface.txt' COVERAGE_FILE_PREFIX = 'coverage.' coverage_ref_filename = os.path.join(self.dir, REFERENCE_FILENAME) coverage_filenames = set() all_cmds = set() covered_cmds = set() if not os.path.isfile(coverage_ref_filename): raise RuntimeError("No coverage reference found") with open(coverage_ref_filename, 'r') as f: all_cmds.update([i.strip() for i in f.readlines()]) for root, dirs, files in os.walk(self.dir): for filename in files: if filename.startswith(COVERAGE_FILE_PREFIX): coverage_filenames.add(os.path.join(root, filename)) for filename in coverage_filenames: with open(filename, 'r') as f: covered_cmds.update([i.strip() for i in f.readlines()]) return all_cmds - covered_cmds if __name__ == '__main__': runtests()

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class Ball: color="" speed=0 def setSpeed(self,value): self.speed=value ball01=Ball() ball01.color="Red" ball01.setSpeed(10) ball02=Ball() ball02.color="Blue" ball02.setSpeed(20) print("Ball01 color:%s" %ball01.color) print("Ball01 speed:%s" %ball01.speed) print("Ball02 color:%s" %ball02.color) print("Ball02 speed:%s" %ball02.speed)

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AI-Candy-Yang/algorithm017

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#-*- encoding:utf-8 -*- #作业 #1.删除排序数组中的重复项,返回去重后的数组长度 def removeDuplicates(nums): # 双指针快指针指向不重复的元素 low = 0 for fast in range(len(nums)): if (nums[fast] != nums[low]): low += 1 nums[low] = nums[fast] return low + 1 #2.旋转数组 def rotate(nums, k): """ Do not return anything, modify nums in-place instead. """ #解法一：将数组切分成两部分，前面的n-k和后面的k个元素 #首先将两个子数组进行反转[4,3,2,1,7,6,5] #然后将整体进行反转 [5,6,7,1,2,3,4] # def reverse(l,r): # while l < r: # nums[l],nums[r] = nums[r],nums[l] # l += 1 # r -= 1 # k = k % len(nums) # reverse(0,len(nums)-k-1) # reverse(len(nums)-k,len(nums)-1) # reverse(0,len(nums)-1) #解法二：使用额外的数组 n = len(nums) a = [0] * n for i in range(n): a[(i+k)%n] = nums[i] for i in range(n): nums[i] = a[i] #3.合并两个有序链表 class ListNode: def __init__(self, x): self.val = x self.next = None def mergeTwoLists(self, l1: ListNode, l2: ListNode) -> ListNode: #递归 # if not l1: # return l2 # if not l2: # return l1 # if l1.val < l2.val: # l1.next = mergeTwoLists(l1.next,l2) # return l1 # else: # l2.next = mergeTwoLists(l1,l2.next) # return l2 #迭代 prehead = ListNode(-1) pre = prehead while l1 and l2: if l1.val < l2.val: pre.next = l1 l1 = l1.next else: pre.next = l2 l2 = l2.next pre = pre.next #将未被合并的直接添加到末尾 pre.next = l1 if l1 is not None else l2 return prehead.next #4.合并两个有序列表 def merge(nums1, m, nums2, n): #解法一，从前往后开始比较，申请一个新的数组空间，时间复杂度为O(n+m) 空间复杂度为O(m) #获取nums1有效的元素 nums1_copy = nums1[:m] #将nums1数组赋值为空数组 nums1[:] = [] while (len(nums1_copy) > 0) and (len(nums2) > 0): if (nums1_copy[0] < nums2[0]): nums1.append(nums1_copy.pop(0)) else: nums1.append(nums2.pop(0)) #将两个数组未比较完的元素添加进来 nums1.extend(nums1_copy) nums1.extend(nums2) #解法二：从后往前开始比较，不需要申请额外的内存空间时间复杂度为O(m+n) 空间复杂度为O(1) p1 = m - 1 p2 = n - 1 p = m + n - 1 while (p1 > 0) and (p2 > 0): if nums1[p1] < nums2[p2]: nums1[p] = nums2[p2] p2 -= 1 else: nums1[p] = nums1[p1] p1 -= 1 p -= 1 #将最后多的元素加到前面，只可能有一个数组不为空，如果是第一个不为空的话，num1不需要改变 nums1[:p2+1] = nums2[:p2+1] #5.两数之和 def twoSum(nums, target): if len(nums) < 2: return [] n = len(nums) # 排序加双指针 nums.sort() l = 0 r = n - 1 while l < r: sum1 = nums[l] + nums[r] if sum1 < target: l += 1 elif sum1 > target: r -= 1 else: return [l,r] #6.移动零 def moveZeroes(nums): #解法一：快慢指针解法，快指针用于查找非零元素，慢指针用于记录非零元素存放的位置 # low = 0 # for fast in range(len(nums)): # if (nums[fast] != 0): # nums[low] = nums[fast] # if (fast != low): # nums[fast] = 0 # low += 1 #解法二:当快指针指向非零元素的时候直接蒋欢快慢指针的位置，然后同步前进 low = 0 for fast in range(len(nums)): if (nums[fast] != 0): nums[low],nums[fast] = nums[fast],nums[low] low += 1 print(nums) #7.加一 def plusOne(digits): n = len(digits) for i in range(n-1,-1,-1): digits[i] += 1 digits[i] %= 10 #如果不等于0，则说明这个位置原来不为9，直接返回即可，如果等于0，则直接移到前一位加1，继续判断 if digits[i] != 0: return digits #如果都没有返回，表示所有的位置加1后都变成了0，则直接在首尾加1 digits.insert(0, 1) return digits #8.设计循环双端队列 #9.接雨水 def trap(height): #和柱状图中最大的矩形比较像遍历柱子的高度，找出左右边界 #解法一：对每个元素，找出下雨后谁能达到的最大高度（左右两边最大高度的最小值减去当前高度值），最后将所有的高度相加 #时间复杂度为O(n^2) 空间复杂度为O(1) # res = 0 # for i in range(len(height)): # max_left = 0 # max_right = 0 # #分别从当前元素向左和向右查找 # for j in range(i,-1,-1): # max_left = max(max_left,height[j]) # # #向右遍历 # for j in range(i,len(height)): # max_right = max(max_right,height[j]) # # #加上每个元素可以接雨水的高度 # res += min(max_left,max_right) - height[i] # return res #解法二：提前存储每个位置可以看到的左边和右边的最大值 # if len(height) <= 1: # return 0 # # res = 0 # n = len(height) # left_max = [0]*n # right_max = [0]*n # # # #从左往右遍历记录每个位置的最大值 # left_max[0] = height[0] # for i in range(n): # left_max[i] = max(height[i],left_max[i-1]) # # #从右往左记录每个位置的最大值 # right_max[n-1] = height[n-1] # for i in range(n-2,-1,-1): # right_max[i] = max(height[i],right_max[i+1]) # # #结合每个位置的左边的最大值和右边的最大值计算 # for i in range(n): # res += min(left_max[i],right_max[i]) - height[i] # # return res #解法三：按照柱状图中最大的矩形面积使用单调栈来找出每个位置的左右边界，栈底到栈顶由大变小，维持一个单调递减的栈 #当前元素比栈顶元素大栈顶元素出栈 #当前元素比栈顶元素小则继续入栈 n = len(height) if n < 3: return 0 res,idx = 0,0 stack = [] while idx < n: while len(stack) > 0 and height[idx] > height[stack[-1]]: top = stack.pop() if len(stack) == 0: break #高度为左边界高度和右边界高度最小值-当前元素高度 h = min(height[stack[-1]],height[idx])-height[top] #间距 dist = idx - stack[-1] - 1 res += (dist * h) stack.append(idx) idx += 1 return res #练习 #2.盛水的最大容器面积 #双指针左右移动找出最大的面积，最大面积纸盒左右两个柱子的高度有关 def maxArea(heights): l,r = 0,len(heights)-1 area = 0 while (l < r): if heights[l] < heights[r]: area = max(area,(r-l)*heights(l)) l += 1 else: area = max(area,(r-l)*heights[r]) r -= 1 return area #3.爬楼梯 def climbStairs(n): if n <= 3: return n x,y,z = 1,2,3 for i in range(4,n+1): x,y,z = y,z,y+z return z #4.三数之和 #从头到尾遍历表示其中一个数 def threeSum(nums): #排序+遍历+双指针 if len(nums) < 3: return [] res = [] for i in range(len(nums)-2): if (i > 0) and (nums[i] == nums[i-1]): continue l = i + 1 r = len(nums) - 1 while (l < r): sum1 = nums[i] + nums[l] + nums[r] if sum1 == 0: res.append([nums[i],nums[l],nums[r]]) while (l < r) and (nums[l] == nums[l+1]): l += 1 while (l < r) and (nums[r] == nums[r-1]): r -= 1 l += 1 r -= 1 elif sum1 > 0: r -= 1 else: l -= 1 return res #5.反转链表 def reverseList(self, head: ListNode) -> ListNode: #初始化前一个节点 pre = None #初始化当前节点 cur = head while cur: #初始化临时变量保存当前节点的下一节点 tmp = cur.next #将当前节点指向前一个节点 cur.next = pre #向后移动前一个节点和当前节点 pre = cur cur = tmp #返回头节点 pre return pre #6.两两交换链表中的节点 class Solution: def swapPairs(self, head: ListNode) -> ListNode: if (not head) or (not head.next): return head #初始化下个节点 next = head.next #将当前节点指向下下个节点进行交换后的子链表 head.next = self.swapPairs(next.next) #下个节点指向当前节点 next.next = head return next #7.环形链表 def hasCycle(self, head: ListNode) -> bool: #设置快慢指针快指针每次走两步，慢指针每次走一步，如果快慢指针相遇，则表示有环 if (not head) or (not head.next): return False low = fast = head while fast and fast.next: low = low.next fast = fast.next.next if low is fast: return True else: return False #8.K个一组翻转链表 #9.有效的括号 def isValid(s): #使用栈，左括号入栈，判断和栈顶元素能否抵消 if len(s) % 2 != 0: return False pairs = {"]":"[",")":"(","}":"{"} stack = [] for ch in s: #如果当前括号是右括号 if ch in pairs.keys(): #栈顶元素和当前括号不匹配 if not stack or stack[-1] != pairs[ch]: return False #匹配的情况，直接将栈顶元素出栈 stack.pop() else: #当前括号是左括号，则入栈 stack.append(ch) return not stack #10.柱状图中最大的矩形面积 def largestRectangleArea(heights): #解法一遍历高度，分别找出每根柱子的左右边界 # res = 0 # n = len(heights) # for i in range(n): # left_i = i # right_i = i # while left_i > 0 and heights[i] <= heights[left_i]: # left_i -= 1 # while right_i < n and heights[i] <= heights[right_i]: # right_i += 1 # res = max(res,(right_i-left_i-i)*heights[i]) # return res #解法二：使用递增的栈来获取每个元素的左右边界 heights = [0] + heights + [0] res = 0 stack = [] for i in range(len(heights)): #栈里面存放的是每个元素的索引 while stack and heights[stack[-1]] > heights[i]: #记录栈顶元素的索引 tmp = stack.pop() #根据左右边界计算对应的面积 res = max(res,(i-stack[-1]-1)*tmp) #如果大于栈顶元素则直接入栈 stack.append(i) return res import collections #11.滑动窗口最大值 def maxSlidingWindow(nums,k): #采用双端队列 if len(nums) < 2: return nums queue = collections.deque() res = [] for i in range(len(nums)): #将元素加入双端队列，保证从大到小排序，新加入的如果比队尾元素大，则删除队尾元素，加入新得元素 while queue and nums[queue[-1]] < nums[i]: queue.pop() #当队列为空，或加入的元素比队尾元素小，则直接加入队列 queue.append(i) #判断队首是否在窗口内部 if queue[0] <= i - k: queue.popleft() #当窗口长度为k时加入队首元素到结果列表 if i + 1 >= k: res.append(nums[queue[0]]) return res if __name__ =='__main__': # moveZeroes([0,1,0,3,12]) print(twoSum([2, 7, 11, 15],9))

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from jinja2 import Template def welcomeHTML(): with open('welcome.html') as f: s = f.read() return s def main(): user1 = { 'name': 'Alice', 'likes': 123} user2 = { 'name': 'Jimmy', 'likes': 1234} tmpl = Template(welcomeHTML()) print(tmpl.render({ 'user': user1})) print(tmpl.render({ 'user': user2})) main()

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438043@ibsh.tw

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/tensorflow/test/misc/graph.py

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[]

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muryliang/python_prac

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2021-01-21T11:03:48.397178

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import tensorflow as tf g1 = tf.Graph() with g1.as_default(): v = tf.get_variable( "v", initializer=tf.zeros_initializer(shape=[1])) g2 = tf.Graph() with g2.as_default(): v= tf.get_variable( "v", initializer=tf.ones_initializer(shape=[1])) with tf.Session(graph=g1) as sess: tf.initialize_all_variables().run() with tf.variable_scope("", reuse=True): print sess.run(tf.get_variable("v")

[ "muryliang@gmail.com" ]

muryliang@gmail.com

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/prologlib/ui.py

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gpiancastelli/prologlib

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import errno import sys from .core import Engine, PrologError from .parser import PrologParser, InvalidTermException __version__ = 'UNRELEASED' __date__ = '2010-05-14' header = """ prologlib {} ({}) Type 'help.' for help, 'halt.' to exit. """.lstrip().format(__version__, __date__) HELP_TEXT = """ Welcome to prologlib, an ISO Prolog processor written in Python 3. Type a goal at the '?-' prompt, press Return to issue it to the Prolog processor. If the goal fails, you will get 'no.' as an answer. If the goal succeeds, you will get a valid substitution in resposnse (or a blank line if the substitution is empty), followed by a '?' prompt that you can use to ask for further solutions to the goal. Type ';' (a semicolon) followed by Return if you want another solution, just press Return if you are satisfied with what you already got. A solution is followed by 'yes.' as an answer. Ask help for any ISO builtin predicate by using help/1, e.g. help(findall/3). Please note that if the predicate functor is also an operator, you have to enclose it in parentheses, e.g. help((is)/2). To exit the interactive toplevel, type 'halt.' and press Return. """.strip() class Console: def __init__(self, stdin=sys.stdin, stdout=sys.stdout): self.stdin = stdin self.stdout = stdout self.engine = Engine() def solveloop(self): self.write(header) stop = None while not stop: self.stdout.write('?- ') self.stdout.flush() line = self.stdin.readline().strip() if not line: continue try: goal = self.read_term(line) if hasattr(self, 'do_' + goal.name): cmd = getattr(self, 'do_' + goal.name) stop = cmd(goal) if not stop: self.write('yes.') else: # TODO Resolve the line/goal ambiguity self.solve(line) # yeah, should be goal except InvalidTermException as ite: self.write('SyntaxError: {}'.format(ite)) except IOError as ioe: if ioe.errno == errno.ENOENT: message = str(ioe) name = message[message.rfind(':')+2:] self.write('Error: the file {} cannot be found'.format(name)) else: raise def read_term(self, line): parser = PrologParser(line) return parser.read_term() def solve(self, goal): try: result = self.engine.solve(goal) if result: self.write('') subst = self.engine.currsubst() for variable in sorted(subst): if not variable.startswith('_'): self.write(variable + ' = ' + subst[variable]._touiform()) #if self.engine.haschoicepoint(): if len(self.engine._s) > 1: self.solvenextloop() else: self.write('yes.') else: self.write('no.') except PrologError as e: self.write('Error: {0}'.format(e.error_term())) def solvenextloop(self): #while self.engine.haschoicepoint() while len(self.engine._s) > 1: self.stdout.write(' ? ') self.stdout.flush() line = self.stdin.readline().strip() if not line: self.write('yes.') break elif line == ';': result = self.engine.solve_next() if result: self.write('') subst = self.engine.currsubst() for variable in sorted(subst): if not variable.startswith('_'): self.write(variable + ' = ' + subst[variable]._touiform()) else: self.write('no.') else: self.write('Type ; and press Return to ask for another solution,') self.write('or just press Return to accept the current solution.') def write(self, message): message = '{0}\n'.format(message) self.stdout.write(message) def do_clear(self, goal): self.engine._clear() def do_consult(self, goal): f = goal.value[1].value with open(f) as theory: self.engine._consult(theory) def do_listing(self, goal): for procedure in self.engine._kb: self.write(str(procedure)) def do_help(self, goal): if goal.arity == 0: self.write(HELP_TEXT) else: self.help(goal.value[1]) def help(self, indicator): from .builtin import search_builtin term = mock_term(*indicator.value[1:]) procedure = search_builtin(term) if procedure: self.write(procedure.__doc__) else: pi = '{}/{}'.format(*indicator.value[1:]) self.write('No built-in predicate known with indicator: ' + pi) def mock_term(name, arity): '''Create a fake term to use as a key for searching in the set of available builtin predicates, so as to retrieve the documentation''' from .parser import Compound, Variable t = tuple(Variable('_') for i in range(arity.value)) term = Compound(name.name, *t) return term

[ "giulio.piancastelli@gmail.com" ]

giulio.piancastelli@gmail.com

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/SplitGuys/wsgi.py

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Rozhiin/SplitGuys

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""" WSGI config for SplitGuys project. It exposes the WSGI callable as a module-level variable named ``application``. For more information on this file, see https://docs.djangoproject.com/en/2.2/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'SplitGuys.settings') application = get_wsgi_application()

[ "salarkiama@gmail.com" ]

salarkiama@gmail.com

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/detection/configs/anti-uav/faster_rcnn_r50_fpn_2x_anti-uav-full.py

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[]

no_license

maloo135/UAVDetectionTrackingBenchmark

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_base_ = [ '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' ] model = dict( roi_head=dict( bbox_head=dict( num_classes=1,))) checkpoint_config = dict(interval=3) classes = ('drone',) data = dict( samples_per_gpu=4, workers_per_gpu=2, train=dict( img_prefix='data/anti-uav/images/', classes=classes, ann_file='data/anti-uav/train-full.json'), val=dict( img_prefix='data/anti-uav/images/', classes=classes, ann_file='data/anti-uav/val-full.json'), test=dict( img_prefix='data/anti-uav/images/', classes=classes, ann_file='data/anti-uav/val-full.json')) load_from = 'checkpoints/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth' optimizer = dict(lr=0.001)

[ "brian.k.isaac-medina@durham.ac.uk" ]

brian.k.isaac-medina@durham.ac.uk

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/langs/8/u19.py

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[]

no_license

G4te-Keep3r/HowdyHackers

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refs/heads/master

2020-08-01T12:08:10.782018

2016-11-13T20:45:50

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import sys def printFunction(lineRemaining): if lineRemaining[0] == '"' and lineRemaining[-1] == '"': if len(lineRemaining) > 2: #data to print lineRemaining = lineRemaining[1:-1] print ' '.join(lineRemaining) else: print def main(fileName): with open(fileName) as f: for line in f: data = line.split() if data[0] == 'u19': printFunction(data[1:]) else: print 'ERROR' return if __name__ == '__main__': main(sys.argv[1])

[ "juliettaylorswift@gmail.com" ]

juliettaylorswift@gmail.com

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/yt_dlp/extractor/mirrativ.py

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[ "Unlicense", "GPL-2.0-or-later", "MPL-2.0", "BSD-3-Clause", "GPL-3.0-or-later", "LGPL-2.1-only", "BSD-2-Clause", "MIT" ]

permissive

yt-dlp/yt-dlp

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2023-09-05T21:15:21.050538

2023-09-05T20:35:23

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2023-09-14T05:22:08

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from .common import InfoExtractor from ..utils import ( ExtractorError, dict_get, traverse_obj, try_get, ) class MirrativBaseIE(InfoExtractor): def assert_error(self, response): error_message = traverse_obj(response, ('status', 'error')) if error_message: raise ExtractorError('Mirrativ says: %s' % error_message, expected=True) class MirrativIE(MirrativBaseIE): IE_NAME = 'mirrativ' _VALID_URL = r'https?://(?:www\.)?mirrativ\.com/live/(?P<id>[^/?#&]+)' TESTS = [{ 'url': 'https://mirrativ.com/live/UQomuS7EMgHoxRHjEhNiHw', 'info_dict': { 'id': 'UQomuS7EMgHoxRHjEhNiHw', 'title': 'ねむいぃ、。『参加型』🔰jcが初めてやるCOD✨初見さん大歓迎💗', 'is_live': True, 'description': 'md5:bfcd8f77f2fab24c3c672e5620f3f16e', 'thumbnail': r're:https?://.+', 'uploader': '# あちゅ。💡', 'uploader_id': '118572165', 'duration': None, 'view_count': 1241, 'release_timestamp': 1646229192, 'timestamp': 1646229167, 'was_live': False, }, 'skip': 'livestream', }, { 'url': 'https://mirrativ.com/live/POxyuG1KmW2982lqlDTuPw', 'only_matching': True, }] def _real_extract(self, url): video_id = self._match_id(url) webpage = self._download_webpage('https://www.mirrativ.com/live/%s' % video_id, video_id) live_response = self._download_json(f'https://www.mirrativ.com/api/live/live?live_id={video_id}', video_id) self.assert_error(live_response) hls_url = dict_get(live_response, ('archive_url_hls', 'streaming_url_hls')) is_live = bool(live_response.get('is_live')) if not hls_url: raise ExtractorError('Neither archive nor live is available.', expected=True) formats = self._extract_m3u8_formats( hls_url, video_id, ext='mp4', entry_protocol='m3u8_native', m3u8_id='hls', live=is_live) return { 'id': video_id, 'title': self._og_search_title(webpage, default=None) or self._search_regex( r'<title>\s*(.+?) - Mirrativ\s*</title>', webpage) or live_response.get('title'), 'is_live': is_live, 'description': live_response.get('description'), 'formats': formats, 'thumbnail': live_response.get('image_url'), 'uploader': traverse_obj(live_response, ('owner', 'name')), 'uploader_id': traverse_obj(live_response, ('owner', 'user_id')), 'duration': try_get(live_response, lambda x: x['ended_at'] - x['started_at']) if not is_live else None, 'view_count': live_response.get('total_viewer_num'), 'release_timestamp': live_response.get('started_at'), 'timestamp': live_response.get('created_at'), 'was_live': bool(live_response.get('is_archive')), } class MirrativUserIE(MirrativBaseIE): IE_NAME = 'mirrativ:user' _VALID_URL = r'https?://(?:www\.)?mirrativ\.com/user/(?P<id>\d+)' _TESTS = [{ # Live archive is available up to 3 days # see: https://helpfeel.com/mirrativ/%E9%8C%B2%E7%94%BB-5e26d3ad7b59ef0017fb49ac (Japanese) 'url': 'https://www.mirrativ.com/user/110943130', 'note': 'multiple archives available', 'only_matching': True, }] def _entries(self, user_id): page = 1 while page is not None: api_response = self._download_json( f'https://www.mirrativ.com/api/live/live_history?user_id={user_id}&page={page}', user_id, note=f'Downloading page {page}') self.assert_error(api_response) lives = api_response.get('lives') if not lives: break for live in lives: if not live.get('is_archive') and not live.get('is_live'): # neither archive nor live is available, so skip it # or the service will ban your IP address for a while continue live_id = live.get('live_id') url = 'https://www.mirrativ.com/live/%s' % live_id yield self.url_result(url, video_id=live_id, video_title=live.get('title')) page = api_response.get('next_page') def _real_extract(self, url): user_id = self._match_id(url) user_info = self._download_json( f'https://www.mirrativ.com/api/user/profile?user_id={user_id}', user_id, note='Downloading user info', fatal=False) self.assert_error(user_info) return self.playlist_result( self._entries(user_id), user_id, user_info.get('name'), user_info.get('description'))

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jinxu06/pixel-cnn

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2021-09-06T13:50:08.190072

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""" Trains a Pixel-CNN++ generative model on CIFAR-10 or Tiny ImageNet data. Uses multiple GPUs, indicated by the flag --nr-gpu Example usage: CUDA_VISIBLE_DEVICES=0,1,2,3 python train_double_cnn.py --nr_gpu 4 """ import os import sys import time import json import argparse import numpy as np import tensorflow as tf import pixel_cnn_pp.nn as nn import pixel_cnn_pp.mask as mk import pixel_cnn_pp.plotting as plotting from pixel_cnn_pp.model_hr import model_spec import data.cifar10_data as cifar10_data import data.imagenet_data as imagenet_data import data.celeba_data_hr as celeba_data #!!!!!! import data.svhn_data as svhn_data from utils import parse_args from configs import configs # ----------------------------------------------------------------------------- parser = argparse.ArgumentParser() # data I/O parser.add_argument('-i', '--data_dir', type=str, default='/tmp/pxpp/data', help='Location for the dataset') parser.add_argument('-o', '--save_dir', type=str, default='/tmp/pxpp/save', help='Location for parameter checkpoints and samples') parser.add_argument('-d', '--data_set', type=str, default='cifar', help='Can be either cifar|imagenet') parser.add_argument('-t', '--save_interval', type=int, default=20, help='Every how many epochs to write checkpoint/samples?') parser.add_argument('-r', '--load_params', dest='load_params', action='store_true', help='Restore training from previous model checkpoint?') # model parser.add_argument('-q', '--nr_resnet', type=int, default=5, help='Number of residual blocks per stage of the model') parser.add_argument('-n', '--nr_filters', type=int, default=160, help='Number of filters to use across the model. Higher = larger model.') parser.add_argument('-m', '--nr_logistic_mix', type=int, default=10, help='Number of logistic components in the mixture. Higher = more flexible model') parser.add_argument('-z', '--resnet_nonlinearity', type=str, default='concat_elu', help='Which nonlinearity to use in the ResNet layers. One of "concat_elu", "elu", "relu" ') parser.add_argument('-c', '--class_conditional', dest='class_conditional', action='store_true', help='Condition generative model on labels?') # optimization parser.add_argument('-l', '--learning_rate', type=float, default=0.001, help='Base learning rate') parser.add_argument('-e', '--lr_decay', type=float, default=0.999995, help='Learning rate decay, applied every step of the optimization') parser.add_argument('-b', '--batch_size', type=int, default=12, help='Batch size during training per GPU') parser.add_argument('-a', '--init_batch_size', type=int, default=100, help='How much data to use for data-dependent initialization.') parser.add_argument('-p', '--dropout_p', type=float, default=0.5, help='Dropout strength (i.e. 1 - keep_prob). 0 = No dropout, higher = more dropout.') parser.add_argument('-x', '--max_epochs', type=int, default=5000, help='How many epochs to run in total?') parser.add_argument('-g', '--nr_gpu', type=int, default=8, help='How many GPUs to distribute the training across?') # evaluation parser.add_argument('--polyak_decay', type=float, default=0.9995, help='Exponential decay rate of the sum of previous model iterates during Polyak averaging') # reproducibility parser.add_argument('-s', '--seed', type=int, default=1, help='Random seed to use') parser.add_argument('-k', '--masked', dest='masked', action='store_true', help='Randomly mask input images?') parser.add_argument('-j', '--rot180', dest='rot180', action='store_true', help='Rot180 the images?') args = parser.parse_args() parse_args(args, **configs['celeba-hr-forward']) args.save_dir = "/data/ziz/jxu/save64-backward-new-20-missing" args.nr_logistic_mix = 20 args.learning_rate = 0.0005 args.masked = True args.load_params = True print('input args:\n', json.dumps(vars(args), indent=4, separators=(',', ':'))) # pretty print args # ----------------------------------------------------------------------------- # fix random seed for reproducibility rng = np.random.RandomState(args.seed) tf.set_random_seed(args.seed) # initialize data loaders for train/test splits if args.data_set == 'imagenet' and args.class_conditional: raise("We currently don't have labels for the small imagenet data set") DataLoader = {'cifar': cifar10_data.DataLoader, 'imagenet': imagenet_data.DataLoader, 'celeba': celeba_data.DataLoader, 'svhn': svhn_data.DataLoader}[args.data_set] train_data = DataLoader(args.data_dir, 'train', args.batch_size * args.nr_gpu, rng=rng, shuffle=True, return_labels=args.class_conditional) test_data = DataLoader(args.data_dir, 'valid', args.batch_size * args.nr_gpu, shuffle=False, return_labels=args.class_conditional) obs_shape = train_data.get_observation_size() # e.g. a tuple (32,32,3) assert len(obs_shape) == 3, 'assumed right now' # data place holders x_init = tf.placeholder(tf.float32, shape=(args.init_batch_size,) + obs_shape) xs = [tf.placeholder(tf.float32, shape=(args.batch_size, ) + obs_shape) for i in range(args.nr_gpu)] # if the model is class-conditional we'll set up label placeholders + # one-hot encodings 'h' to condition on if args.class_conditional: num_labels = train_data.get_num_labels() y_init = tf.placeholder(tf.int32, shape=(args.init_batch_size,)) h_init = tf.one_hot(y_init, num_labels) y_sample = np.split( np.mod(np.arange(args.batch_size * args.nr_gpu), num_labels), args.nr_gpu) h_sample = [tf.one_hot(tf.Variable( y_sample[i], trainable=False), num_labels) for i in range(args.nr_gpu)] ys = [tf.placeholder(tf.int32, shape=(args.batch_size,)) for i in range(args.nr_gpu)] hs = [tf.one_hot(ys[i], num_labels) for i in range(args.nr_gpu)] else: h_init = None h_sample = [None] * args.nr_gpu hs = h_sample if args.masked: masks = tf.placeholder(tf.float32, shape=(args.batch_size,) + obs_shape[:-1]) else: masks = None # create the model model_opt = {'nr_resnet': args.nr_resnet, 'nr_filters': args.nr_filters, 'nr_logistic_mix': args.nr_logistic_mix, 'resnet_nonlinearity': args.resnet_nonlinearity} model = tf.make_template('model', model_spec) # run once for data dependent initialization of parameters gen_par = model(x_init, None, h_init, init=True, dropout_p=args.dropout_p, **model_opt) # keep track of moving average all_params = tf.trainable_variables() ema = tf.train.ExponentialMovingAverage(decay=args.polyak_decay) maintain_averages_op = tf.group(ema.apply(all_params)) # get loss gradients over multiple GPUs grads = [] loss_gen = [] loss_gen_test = [] for i in range(args.nr_gpu): with tf.device('/gpu:%d' % i): # train gen_par = model(xs[i], masks, hs[i], ema=None, dropout_p=args.dropout_p, **model_opt) loss_gen.append(nn.discretized_mix_logistic_loss(xs[i], gen_par, masks=masks)) # gradients grads.append(tf.gradients(loss_gen[i], all_params)) # test gen_par = model(xs[i], masks, hs[i], ema=ema, dropout_p=0., **model_opt) loss_gen_test.append(nn.discretized_mix_logistic_loss(xs[i], gen_par, masks=masks)) # add losses and gradients together and get training updates tf_lr = tf.placeholder(tf.float32, shape=[]) with tf.device('/gpu:0'): for i in range(1, args.nr_gpu): loss_gen[0] += loss_gen[i] loss_gen_test[0] += loss_gen_test[i] for j in range(len(grads[0])): grads[0][j] += grads[i][j] # training op optimizer = tf.group(nn.adam_updates( all_params, grads[0], lr=tf_lr, mom1=0.95, mom2=0.9995), maintain_averages_op) # convert loss to bits/dim bits_per_dim = loss_gen[ 0] / (args.nr_gpu * np.log(2.) * np.prod(obs_shape) * args.batch_size) bits_per_dim_test = loss_gen_test[ 0] / (args.nr_gpu * np.log(2.) * np.prod(obs_shape) * args.batch_size) # sample from the model new_x_gen = [] for i in range(args.nr_gpu): with tf.device('/gpu:%d' % i): gen_par = model(xs[i], None, h_sample[i], ema=ema, dropout_p=0, **model_opt) new_x_gen.append(nn.sample_from_discretized_mix_logistic( gen_par, args.nr_logistic_mix)) def sample_from_model(sess): x_gen = [np.zeros((args.batch_size,) + obs_shape, dtype=np.float32) for i in range(args.nr_gpu)] for yi in range(obs_shape[0]): for xi in range(obs_shape[1]): new_x_gen_np = sess.run( new_x_gen, {xs[i]: x_gen[i] for i in range(args.nr_gpu)}) for i in range(args.nr_gpu): x_gen[i][:, yi, xi, :] = new_x_gen_np[i][:, yi, xi, :] return np.concatenate(x_gen, axis=0) # init & save initializer = tf.global_variables_initializer() saver = tf.train.Saver() # turn numpy inputs into feed_dict for use with tensorflow #mgen = mk.RecMaskGenerator(obs_shape[0], obs_shape[1]) mgen = mk.RectangleInProgressMaskGenerator(obs_shape[0], obs_shape[1]) agen = mk.AllOnesMaskGenerator(obs_shape[0], obs_shape[1]) def make_feed_dict(data, init=False, masks=None, is_test=False): if type(data) is tuple: x, y = data else: x = data y = None if args.rot180: x = np.rot90(x, 2, (1,2)) #### ROT # input to pixelCNN is scaled from uint8 [0,255] to float in range [-1,1] x = np.cast[np.float32]((x - 127.5) / 127.5) if init: feed_dict = {x_init: x} if y is not None: feed_dict.update({y_init: y}) else: x = np.split(x, args.nr_gpu) feed_dict = {xs[i]: x[i] for i in range(args.nr_gpu)} if masks is not None: if is_test: feed_dict[masks] = agen.gen(args.batch_size) else: feed_dict[masks] = mgen.gen(args.batch_size) if y is not None: y = np.split(y, args.nr_gpu) feed_dict.update({ys[i]: y[i] for i in range(args.nr_gpu)}) return feed_dict # //////////// perform training ////////////// if not os.path.exists(args.save_dir): os.makedirs(args.save_dir) print('starting training') test_bpd = [] lr = args.learning_rate with tf.Session() as sess: for epoch in range(args.max_epochs): begin = time.time() # init if epoch == 0: # manually retrieve exactly init_batch_size examples feed_dict = make_feed_dict( train_data.next(args.init_batch_size), init=True) train_data.reset() # rewind the iterator back to 0 to do one full epoch sess.run(initializer, feed_dict) print('initializing the model...') if args.load_params: ckpt_file = args.save_dir + '/params_' + args.data_set + '.ckpt' print('restoring parameters from', ckpt_file) saver.restore(sess, ckpt_file) # train for one epoch train_losses = [] for d in train_data: feed_dict = make_feed_dict(d, masks=masks) # forward/backward/update model on each gpu lr *= args.lr_decay feed_dict.update({tf_lr: lr}) l, _ = sess.run([bits_per_dim, optimizer], feed_dict) train_losses.append(l) train_loss_gen = np.mean(train_losses) # compute likelihood over test data test_losses = [] for d in test_data: feed_dict = make_feed_dict(d, masks=masks, is_test=True) l = sess.run(bits_per_dim_test, feed_dict) test_losses.append(l) test_loss_gen = np.mean(test_losses) test_bpd.append(test_loss_gen) # log progress to console print("Iteration %d, time = %ds, train bits_per_dim = %.4f, test bits_per_dim = %.4f" % ( epoch, time.time() - begin, train_loss_gen, test_loss_gen)) sys.stdout.flush() if epoch % args.save_interval == 0: # generate samples from the model sample_x = sample_from_model(sess) img_tile = plotting.img_tile(sample_x[:int(np.floor(np.sqrt( args.batch_size * args.nr_gpu))**2)], aspect_ratio=1.0, border_color=1.0, stretch=True) img = plotting.plot_img(img_tile, title=args.data_set + ' samples') plotting.plt.savefig(os.path.join( args.save_dir, '%s_sample%d.png' % (args.data_set, epoch))) plotting.plt.close('all') # save params saver.save(sess, args.save_dir + '/params_' + args.data_set + '.ckpt') np.savez(args.save_dir + '/test_bpd_' + args.data_set + '.npz', test_bpd=np.array(test_bpd))

[ "s1673820@sms.ed.ac.uk" ]

s1673820@sms.ed.ac.uk

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/constant.py

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fazli1702/tic-tac-toe

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import pygame WIDTH, HEIGHT = 600, 600 SQUARE_SIZE = WIDTH // 3 or HEIGHT // 3 WHITE = (255, 255, 255) BLACK = (0, 0, 0) RED = (255, 0, 0) BLUE = (0, 0, 255) def get_row_col(x, y): row = y // SQUARE_SIZE col = x // SQUARE_SIZE return row, col

[ "fazliyazid1702@gmail.com" ]

fazliyazid1702@gmail.com

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/notes/Items = Judge Amandeep.py

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Judge61/CSE

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class Item(object): def __init__(self, name): self.name = name class Weapon(Item): def __init__(self, name, damage): super(Weapon, self).__init__(name) self.damage = damage print("Nice attack") class Armor(Item): def __init__(self, name, armor_amt): super(Armor, self). __init__(name) self.armor_amt = armor_amt class Character(object): def __init__(self, name, health: int, weapon, armor): self.name = name self.health = health self.weapon = weapon self.armor = armor def take_damage(self, damage: int): if self.armor.armor_amt > damage: print("No damage is done because of some AMAZING armor!") else: self.health -= damage - self.armor.armor_amt self.health -= damage - self.armor.armor_amt print("%s has %d health left" % (self.name, self.health)) def attack(self, target): print("%s attacks %s for %d damage" % (self.name, target.name, self.weapon.damage)) target.take_damage(self.weapon.damage) sword = Weapon("Sword", 10) canoe = Weapon("Canoe", 42) troll_armor = Armor("Armor of the gods", 10000000) lich = Character("Lich", 100, sword, Armor("Generic Armor", 2)) troll = Character("Troll", 10000, canoe, troll_armor) lich.attack(troll) troll.attack(lich)

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42385606+Judge61@users.noreply.github.com

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/repcal/__init__.py

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from .RepublicanDate import RepublicanDate from .DecimalTime import DecimalTime from .RepublicanFormatter import RepublicanFormatter

[ "tomas.thelander0@gmail.com" ]

tomas.thelander0@gmail.com

c9ba661b2eaf706150acc8d70fa5c78430f74e48

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/intermediate tasks/task3.1/myModels.py

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[]

no_license

nirajmahajan/Diabetic-Retinopathy-Image-Generation

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refs/heads/main

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import numpy as np import torch import torchvision import matplotlib.pyplot as plt from torchvision import transforms, models from torchvision.models import vgg16, vgg16_bn, alexnet from torch import nn, optim from torch.nn import functional as F import pickle import argparse import sys import os import PIL device = torch.device("cuda" if torch.cuda.is_available() else "cpu") class VAE(nn.Module): def __init__(self, n_channels = 1, latent_space = 64): super(VAE, self).__init__() self.enc = Encoder(n_channels, latent_space) self.dec = Decoder(n_channels, latent_space) self.latent_space = latent_space def forward(self, x): mean,log_var = self.enc(x) reconstruction = self.dec(self.reparameterize(mean,log_var)) return mean, log_var, reconstruction def reparameterize(self, mean, log_var): std = torch.exp(0.5*log_var) eps = torch.randn_like(std) return mean + eps * log_var def generate_new(self, n): data = torch.randn(n,self.latent_space).to(device) return self.dec(data).detach().cpu().numpy().reshape(-1,256,256) class Encoder(nn.Module): def __init__(self, n_channels, latent_space): super(Encoder, self).__init__() self.model = nn.Sequential( nn.Conv2d(n_channels,64,3,stride = 2,padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2), nn.Conv2d(64,64,3,padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2), nn.Conv2d(64,128,3,stride = 2,padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2), nn.Conv2d(128,128,3,padding=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.2), nn.Conv2d(128,256,3,stride = 2,padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2), nn.Conv2d(256,256,3,padding=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.2), nn.Conv2d(256,512,3,stride = 2,padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Conv2d(512,512,3,padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Conv2d(512,512,3,stride=2,padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Conv2d(512,512,3,padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Conv2d(512,512,3,stride=2,padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Conv2d(512,512,3,padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Conv2d(512,512,3,stride=2,padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Conv2d(512,512,3,padding=1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Conv2d(512,512,2), ) self.mean_layer = nn.Sequential( nn.Linear(512, latent_space), nn.LeakyReLU(), nn.Dropout(0.2) ) self.log_var_layer = nn.Sequential( nn.Linear(512, latent_space), nn.LeakyReLU(), nn.Dropout(0.2) ) def forward(self, x): a = self.model(x).reshape(-1,512) return self.mean_layer(a), self.log_var_layer(a) class Decoder(nn.Module): def __init__(self, n_channels, latent_space): super(Decoder, self).__init__() self.model = nn.Sequential( nn.Upsample(scale_factor=2), nn.Conv2d(512,512,3,padding = 1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Upsample(scale_factor=2), nn.Conv2d(512,512,3,padding = 1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Upsample(scale_factor=2), nn.Conv2d(512,512,3,padding = 1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Upsample(scale_factor=2), nn.Conv2d(512,512,3,padding = 1), nn.BatchNorm2d(512), nn.LeakyReLU(0.1), nn.Upsample(scale_factor=2), nn.Conv2d(512,256,3,padding = 1), nn.BatchNorm2d(256), nn.LeakyReLU(0.1), nn.Conv2d(256,256,3,padding = 1), nn.BatchNorm2d(256), nn.LeakyReLU(0.1), nn.Upsample(scale_factor=2), nn.Conv2d(256,128,3,padding = 1), nn.BatchNorm2d(128), nn.LeakyReLU(0.1), nn.Conv2d(128,128,3,padding = 1), nn.BatchNorm2d(128), nn.LeakyReLU(0.1), nn.Upsample(scale_factor=2), nn.Conv2d(128,64,3,padding = 1), nn.BatchNorm2d(64), nn.LeakyReLU(0.1), nn.Conv2d(64,64,3,padding = 1), nn.BatchNorm2d(64), nn.LeakyReLU(0.1), nn.Upsample(scale_factor=2), nn.Conv2d(64,64,3,padding = 1), nn.BatchNorm2d(64), nn.LeakyReLU(0.1), nn.Conv2d(64,64,3,padding = 1), nn.BatchNorm2d(64), nn.LeakyReLU(0.1), nn.Conv2d(64,n_channels,3,padding = 1), nn.Sigmoid(), ) self.decoder_linear = nn.Sequential( nn.Linear(latent_space, 512), nn.LeakyReLU(), nn.Dropout(0.2) ) def forward(self, x): return self.model(self.decoder_linear(x).view(-1,512,1,1))

[ "nirajmahajan007@gmail.com" ]

nirajmahajan007@gmail.com

462fdb171366888fdd4b6940b64acb815518deda

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/comment_spider/comment_spider/spiders/commentspider.py

bd87f5509af3e7661af55f82cad58599c3b1d1d7

[ "Apache-2.0" ]

permissive

moxi255/jd-spider

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8f92f7e7a9a27c2f83f6432f17e1f0bb920b4213

refs/heads/master

2020-05-23T20:58:22.578965

2019-05-16T03:35:20

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# -*- coding: utf-8 -*- import scrapy import json, re from tools.sql_tools import * from tools.jieba_content import get_jieba_comment from ..items import CommentSpiderItem from ..settings import EXISTS_CONMENTS class CommentspiderSpider(scrapy.Spider): name = 'commentspider' allowed_domains = ['jd.com'] start_urls = get_start_urls() def parse(self, response): if response.text: json_obj = json.loads(response.text) if json_obj: tag_data = json_obj['hotCommentTagStatistics'] tags = '|'.join([tag['name'] for tag in tag_data]) count = '|'.join([str(tag['count']) for tag in tag_data]) url = response._url page_num = int(url.split('&')[4].split('=')[1]) computer_id = int(url.split('&')[1].split('=')[1]) comments = json_obj['comments'] # 保存数据 if page_num == 1: save_tags(tags, count, computer_id) if 0 < len(comments) < 10: for comment in comments: comment_id = str(computer_id) + str(comment['id']) content = re.sub(r"…|\.| |~|'", '', comment['content']) print(content) jieba_content = get_jieba_comment(content) print(jieba_content) create_time = comment['creationTime'] score = comment['score'] print(comment_id, content, jieba_content, score, create_time, computer_id) if comment_id in EXISTS_CONMENTS: print(f'{comment_id} 评论已存在') else: save_comment(comment_id, content, jieba_content, score, create_time, computer_id) # 该商品评论爬取完成更新if_spider字段 update_if_spider(computer_id) elif len(comments) == 10: for comment in comments: comment_id = str(computer_id) + str(comment['id']) content = comment['content'].replace(' ', '') jieba_content = get_jieba_comment(content) create_time = comment['creationTime'] score = comment['score'] print(comment_id, content, jieba_content, score, create_time, computer_id) if comment_id in EXISTS_CONMENTS: print(f'{comment_id} 评论已存在') else: save_comment(comment_id, content, jieba_content, score, create_time, computer_id) page_num += 1 if page_num == 101: # 该商品评论爬取完成更新if_spider字段 update_if_spider(computer_id) # 找下一页 if page_num < 101: next_url = f'https://club.jd.com/comment/skuProductPageComments.action?&productId={computer_id}&score=0&sortType=5&page={page_num}&pageSize=10&isShadowSku=0&rid=0&fold=1%27' yield scrapy.Request(url=next_url, callback=self.parse) else: update_if_spider(computer_id) # 进行下一个商品评论收集 yield CommentspiderSpider()

[ "614303219@qq.com" ]

614303219@qq.com

951eff5775832bdb7d6d2882d73b8b743dbe75e8

60f93042cd806b56362232105dad1a5ba8e10d51

/energy_budget/dependencies/useful_functions.py

a2357ccf5d32d2752b3b38e08eca0312ce22ec18

[]

no_license

robertladwig/LakeGeneva

264d5dac287891ccc1f264056e6b5ff4b1b34f14

25370cf570ad8d112ea6a7f3990eeb5fbd4e2e1a

refs/heads/main

2023-07-13T03:45:56.772603

2021-08-28T19:46:26

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## USEFUL FUNCTIONS #B. Fernandez Castro 2020.03.03 import numpy as np import sys import matplotlib.pyplot as plt def moving_average(x,n, window = "flat"): if n%2 == 0: n+=1 N = x.size cx = np.full(x.size, np.nan) for i in range(N): ii = np.arange(i-n//2, i+n//2+1,1) if window == "flat": ww = np.ones(ii.size) elif window == "gauss": xx = ii - i ww = np.exp(- xx**2/(float(n)/4)**2 ) elif window == "hanning": ww = np.hanning(ii.size) ww = ww[ (ii>=0) & (ii<N)] ii = ii[ (ii>=0) & (ii<N)] kk = np.isfinite(x[ii]) if np.sum(kk)<0.25*ii.size: continue cx[i] = np.sum(x[ii[kk]]*ww[kk])/np.sum(ww[kk]) return cx def linfit_modelII(X,Y): # lsqfitma.m by: Edward T Peltzer, MBARI # revised: 2016 Mar 17. # # M-file to calculate a "MODEL-2" least squares fit. # # The line is fit by MINIMIZING the NORMAL deviates. # # The equation of the line is: y = mx + b. # # This line is called the MAJOR AXIS. All points are given EQUAL # weight. The units and range for X and Y must be the same. # Equations are from York (1966) Canad. J. Phys. 44: 1079-1086; # re-written from Kermack & Haldane (1950) Biometrika 37: 30-41; # after a derivation by Pearson (1901) Phil. Mag. V2(6): 559-572. # # Data are input and output as follows: # # [m,b,r,sm,sb] = lsqfitma(X,Y) # # X = x data (vector) # Y = y data (vector) # # m = slope # b = y-intercept # r = correlation coefficient # sm = standard deviation of the slope # sb = standard deviation of the y-intercept # # Note that the equation passes through the centroid: (x-mean, y-mean) iif = np.isfinite(X+Y) X = X[iif] Y = Y[iif] n = float(np.sum(iif)) Sx = np.sum(X) Sy = np.sum(Y) xbar = Sx/n ybar = Sy/n U = X - xbar V = Y - ybar Suv = np.sum(U*V) Suu = np.sum(U*U) Svv = np.sum(V*V) sigx = np.sqrt(Suu/(n-1)) sigy = np.sqrt(Svv/(n-1)) m = (Svv - Suu + np.sqrt(((Svv-Suu)**2) + (4*Suv**2)))/(2*Suv) b = ybar - m*xbar r = Suv/np.sqrt( Suu * Svv ) sm = (m/r)*np.sqrt((1 - r**2)/n) sb1 = (sigy - sigx*m)**2 sb2 = (2*sigx*sigy) + ((xbar**2*m*(1+r))/r**2) sb = np.sqrt( (sb1 + ((1-r)*m*sb2))/n) return np.array([m, b]), np.array([sm, sb]), r def FCD_2d(x, y, axis = 0): if x.ndim != 2 or y.ndim !=2: sys.exit("Invalid dimensions") if axis != 0 and axis != 1: sys.exit("Invalid axis") if axis == 1: x = x.T y = y.T dy = np.full(y.shape,np.nan) for i in range(x.shape[1]): dy[:,i] = first_centered_differences(x[:,i], y[:,i]) if axis == 1: dy = dy.T return dy def first_centered_differences(x, y, fill = False): if x.size != y.size: print "first-centered differences: vectors do not have the same size" dy = np.full( x.size, np.nan ) iif = np.where( (np.isfinite(x)) & (np.isfinite(y))) [0] if iif.size == 0: return dy x0 = x[iif] y0 = y[iif] dy0 = np.full( x0.size, np.nan ) #calculates differences dy0[0] = (y0[1] - y0[0])/(x0[1]-x0[0]) dy0[-1] = (y0[-1] - y0[-2])/(x0[-1]-x0[-2]) dy0[1:-1] = (y0[2:] - y0[0:-2])/(x0[2:]- x0[0:-2]) dy[iif] = dy0 if fill: dy[0:iif[0]] = dy[iif[0]] dy[iif[-1]+1:] = dy[iif[-1]] return dy def centered_differences(x, y, fill = False): if x.size != y.size: print "first-centered differences: vectors do not have the same size" dy = np.full( x.size, np.nan ) iif = np.where( (np.isfinite(x)) & (np.isfinite(y))) [0] if iif.size == 0: return dy x0 = x[iif] y0 = y[iif] dy0 = np.full( x0.size, np.nan ) #calculates differences dy0[1:-1] = (y0[2:] - y0[0:-2])/(x0[2:]- x0[0:-2]) dy[iif] = dy0 if fill: dy[0:iif[0]] = dy[iif[0]] dy[iif[-1]+1:] = dy[iif[-1]] return dy def mixed_layer_depth(z0, den0, Dd = 0.05, crit = "diff"): #Mixed layer calculation if crit != "diff" and crit != "grad": crit = "diff" print "Incorrect criterion, set to diff" c,f = den0.shape MLD = np.full(f, np.nan) for i in range(f): if z0.ndim ==1: z = np.copy(z0) else: z = z0[:,i] den = np.sort(den0[:,i]) iif = np.isfinite(den+z) if np.sum(iif)<=1: continue den = den[iif] z = z[iif] if crit == "diff": sden = den[0] denp = den-sden imld = np.where( denp>=Dd )[0] if imld.size == 0: MLD[i] = np.max(z) elif imld[0]>0: imld = imld[0] z2 = z[imld] z1 = z[imld-1] denp2 = denp[imld] denp1 = denp[imld-1] MLD[i] = (z2-z1)/(denp2-denp1)*(Dd - denp1) + z1 else: MLD[i] = np.max(z) #MLD[i] = z0[0,i] elif crit == "grad": grden = np.abs(first_centered_differences(z, den)) imld = np.where(grden>=Dd)[0] if imld.size == 0: MLD[i] = np.max(z) elif imld[0]>0: imld = imld[0] z2 = z[imld] z1 = z[imld-1] grd2 = grden[imld] grd1 = grden[imld-1] MLD[i] = (z2-z1)/(grd2-grd1)*(Dd - grd1) + z1 else: MLD[i] = z[0] return MLD

[ "noreply@github.com" ]

noreply@github.com

af93385675fcf4822c571cccec3632e16d5f819f

af275f01dfdf5636e61ecb8977b207109031f79a

/deneme/models.py

0dd77a30e2db97a36f48461963433abb8b7a0164

[]

no_license

malialtinel/Newspaper

871e0990bc4d34b3998587d5f2c1fb4c4b9990e5

b594c1a9afc145e1535f60e76357f059b7d9a4a2

refs/heads/master

2020-03-22T14:11:50.539698

2018-07-08T11:24:40

140,160,237

1

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py

from django.db import models from django.contrib.auth.models import User from ckeditor.fields import RichTextField class Halk_User(models.Model): vatandas=models.OneToOneField( User, on_delete=models.CASCADE, primary_key=True, ) def __str__(self): return self.vatandas.last_name + ", " + self.vatandas.first_name class Deneme(models.Model): f = models.TextField() class Postumuz(models.Model): user=models.ForeignKey('auth.User',verbose_name='Yazar',related_name='posts') kat=models.ForeignKey('Kategori',verbose_name='Kategori',related_name='posts') baslik=models.CharField(max_length=120,verbose_name="Başlık") content=RichTextField(verbose_name="İçerik") tarihi=models.DateTimeField(verbose_name="Yayımlanma Tarihi",auto_now_add=True) image=models.FileField(null=True,blank=True) def __str__(self): return self.baslik # Create your models here. class Comment(models.Model): post=models.ForeignKey('Postumuz',on_delete=models.CASCADE,related_name='comments') name=models.CharField(max_length=400,verbose_name='isim') content=models.TextField(verbose_name='Yorum') created_date=models.DateTimeField(auto_now_add=True) class Kategori(models.Model): katadi=models.CharField(max_length=30,verbose_name='kategoriadi') def __str__(self): return self.katadi

[ "malialtinel@gmail.com" ]

malialtinel@gmail.com

deeae75fb55700a9bf1ce2d3c2f15007fbc511d0

c9af78f03563391def0e1cb808afda4d88a21066

/ex7.py

8ae5bbca0fd2aeffb24f76cb66d2f833a6d6269d

[]

no_license

Psyconne/python-examples

b873b30db237524d8bff9123e334db096d0239c5

35a4bb668c49aa51d9645d68a7bdf39daa0b58a4

refs/heads/master

2021-01-02T09:14:23.549963

2015-07-20T15:00:39

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py

print "I had a little lamb" print "its fleece was white as %s." % 'snow' print "." * 10 #printing it 10 times l1 = 'M' l2 = 'a' l3 = 'r' l4 = 'o' l5 = 'u' l6 = 'a' l7 = 'n' end = 'E' print l1 + l2 + l3 + l4 + l5 + l6 + l7 + end print l1 + l2 + l3 + l4, #a space print l5 + l6 + l7, print end

[ "iimen.elidrissi@gmail.com" ]

iimen.elidrissi@gmail.com

38910cfa0d829421a6d14748e1081970a606abe0

2734b77a68f6d7e22e8b823418ad1c59fe1a34af

/opengever/dossier/upgrades/20170307184059_reindex_searchable_text_for_dossier_templates/upgrade.py

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[]

no_license

4teamwork/opengever.core

5963660f5f131bc12fd0a5898f1d7c8f24a5e2b1

a01bec6c00d203c21a1b0449f8d489d0033c02b7

refs/heads/master

2023-08-30T23:11:27.914905

2023-08-25T14:27:15

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2023-09-14T13:28:56

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Python

UTF-8

Python

false

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py

from ftw.upgrade import UpgradeStep class ReindexSearchableTextForDossierTemplates(UpgradeStep): """Reindex SearchableText for dossier templates. """ def __call__(self): self.install_upgrade_profile() self.catalog_reindex_objects( {'portal_type': 'opengever.dossier.dossiertemplate'}, idxs=['SearchableText'])

[ "david.erni@4teamwork.ch" ]

david.erni@4teamwork.ch

0c9d8876bb93f2c786e18e37dc1213e7ef6c6c2d

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/xadmintest/settings.py

ff250650fd023fd3bc87381d949c7e0e1b6833b0

[]

no_license

loveguan/xadmintest

4472c13bd68f4b4ae47479449f4319e6f50df4fc

f20093afe25216154861fd8f6f061bcfee7269f2

refs/heads/master

2020-09-04T23:24:06.094513

2019-11-06T05:57:04

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""" Django settings for xadmintest project. Generated by 'django-admin startproject' using Django 2.0.7. For more information on this file, see https://docs.djangoproject.com/en/2.0/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/2.0/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/2.0/howto/deployment/checklist/ # SECURITY WARNING: keep the secret key used in production secret! SECRET_KEY = 'goero%l-vd)wg%)1*5rt29kv8#=qo40=94_vvp(!+o(g#^^n%c' # 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', 'Xadmin.apps.XadminConfig', 'app01.apps.App01Config', 'app02.apps.App02Config', ] 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 = 'xadmintest.urls' TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, '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', ], }, }, ] WSGI_APPLICATION = 'xadmintest.wsgi.application' # Database # https://docs.djangoproject.com/en/2.0/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/2.0/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/2.0/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/2.0/howto/static-files/ STATIC_URL = '/static/' STATICFILES_DIRS=[os.path.join(BASE_DIR,"static")]

[ "zhouguanjie@qq.com" ]

zhouguanjie@qq.com

6fbbd263a969c5cf0e893a592532066c44019962

9d425b64bd8d66993fc2253613095d82ff667a6f

/day_7/parse_input.py

44682ccb370fad08ced644dbbfc4c61dd285d510

[]

no_license

evanptang/advent-of-code-2020

bcf4766ff2c9bd7a9d09ed4b4ad1faf47c4920ff

e4a6871e8495142e7e4644d06e133fa421eb440c

refs/heads/master

2023-02-02T09:50:10.432697

2020-12-13T20:04:16

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py

import json file_name = 'sample.txt' # file_name = 'input.txt' final_object = dict() with open(f'parsed_{file_name}', 'w+') as f: with open(file_name) as g: for line in g: line = line.replace('\n', '') line = line.replace('bags', 'bag') line = line.replace('.', '') line = line.split('contain') line[0] = line[0][:-1] line[1] = line[1].split(',') temp = dict() try: for item in line[1]: item = item[1:] temp[item[2:]] = int(item[0]) except: temp = {} line[1] = temp final_object[line[0]] = line[1] json.dump(final_object, f)

[ "evantang2019@u.northwestern.edu" ]

evantang2019@u.northwestern.edu

49f3e7d823cd9ee17a9c42cca9f6a42c42a6c33e

6fa7f99d3d3d9b177ef01ebf9a9da4982813b7d4

/ENJTPoWCyEGgnXYjM_18.py

35efe9177850a3a6a90e03c5f007d2d7ec0fb8cc

[]

no_license

daniel-reich/ubiquitous-fiesta

26e80f0082f8589e51d359ce7953117a3da7d38c

9af2700dbe59284f5697e612491499841a6c126f

refs/heads/master

2023-04-05T06:40:37.328213

2021-04-06T20:17:44

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null

UTF-8

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false

125

py

def percent_filled(box): return str(round((''.join(box).count('o') / ((len(box[0]) - 2) * (len(box) - 2))) * 100)) + '%'

[ "daniel.reich@danielreichs-MacBook-Pro.local" ]

daniel.reich@danielreichs-MacBook-Pro.local

d25e02789b4302272e941ce1371e76e9d26a1faf

898fc24e399b203bf8072cec03b9bfd22d3160e6

/Neural Networks/Assignment1/venv/Scripts/pip-script.py

d2c4c69759d833d8db79036eb12596186b31282b

[]

no_license

ismael-martinez/AQM

94505fe8acf44e459b731e6563e304789ec9db47

a9c08ca6f9be2b07e7892773c0bad165f695899a

refs/heads/master

2022-12-23T12:19:40.591906

2018-02-02T08:08:04

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0

1

null

2022-12-09T23:40:38

2018-02-01T22:18:18

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UTF-8

Python

false

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py

#!"C:\Users\Ismael Martinez\Desktop\AQM\Neural Networks\Assignment1\venv\Scripts\python.exe" # EASY-INSTALL-ENTRY-SCRIPT: 'pip==9.0.1','console_scripts','pip' __requires__ = 'pip==9.0.1' import re import sys from pkg_resources import load_entry_point if __name__ == '__main__': sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) sys.exit( load_entry_point('pip==9.0.1', 'console_scripts', 'pip')() )

[ "ismael.martinez@hotmail.ca" ]

ismael.martinez@hotmail.ca

19cf498c8d5f4bfad7938928cf5d71d9ab7e19e4

d8c589e279b59a0ad6ac79ef42e073e0e86340e5

/caluma/core/tests/test_pagination.py

82a9cfc701678272b3d073c0e262679a53303ee8

[ "AGPL-3.0-only", "MIT" ]

permissive

Yelinz/caluma

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b5d4f1ed3fa3e144d8516e7823dd57c3b68617be

refs/heads/master

2023-08-16T14:00:11.207559

2019-10-18T12:40:03

181,823,650

0

MIT

2019-04-17T05:34:28

2019-04-17T05:34:27

null

UTF-8

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py

import pytest @pytest.mark.parametrize( "first,last,before,after,has_next,has_previous", [ (1, None, None, None, True, False), (None, 1, None, None, False, True), (None, None, None, None, False, False), (None, None, None, "YXJyYXljb25uZWN0aW9uOjI=", False, True), (None, None, "YXJyYXljb25uZWN0aW9uOjI=", None, True, False), ], ) def test_has_next_previous( db, first, last, before, after, has_next, has_previous, schema_executor, document_factory, ): document_factory.create_batch(5) query = """ query AllDocumentsQuery ($first: Int, $last: Int, $before: String, $after: String) { allDocuments(first: $first, last: $last, before: $before, after: $after) { pageInfo { hasNextPage hasPreviousPage } edges { node { id } } } } """ inp = {"first": first, "last": last, "before": before, "after": after} result = schema_executor(query, variables=inp) assert not result.errors assert result.data["allDocuments"]["pageInfo"]["hasNextPage"] == has_next assert result.data["allDocuments"]["pageInfo"]["hasPreviousPage"] == has_previous

[ "fabio.raemi@adfinis-sygroup.ch" ]

fabio.raemi@adfinis-sygroup.ch

7d50401e7f1cf6286d23132c3ea577467c6a556e

84caee3f7b0e9811f91da65f59c93b08f76453f3

/later/task.py

a201c2d1487a33d00572d896156b06435581c5c9

[ "Apache-2.0" ]

permissive

thatch/later

541492ca2ebd77d4b5859c00ff46247847a1d1a5

29c614c8a14eb290555bd1708fafea6542365e60

refs/heads/master

2021-01-09T16:10:39.247170

2020-02-19T01:33:45

2020-02-19T01:36:01

242,367,315

0

null

2020-02-22T15:46:40

2020-02-22T15:46:39

null

UTF-8

Python

false

11,199

py

# Copyright (c) Facebook, Inc. and its affiliates. 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 annotations import asyncio import contextvars import logging from contextlib import suppress from functools import partial, wraps from inspect import isawaitable from types import TracebackType from typing import ( Any, Awaitable, Callable, Dict, List, Optional, Sequence, Type, TypeVar, Union, cast, ) from unittest.mock import Mock from .event import BiDirectionalEvent FixerType = Callable[[asyncio.Task], Union[asyncio.Task, Awaitable[asyncio.Task]]] logger = logging.getLogger(__name__) F = TypeVar("F", bound=Callable[..., Awaitable[Any]]) __all__: Sequence[str] = ["Watcher", "START_TASK", "TaskSentinel", "cancel", "as_task"] class TaskSentinel(asyncio.Task): """ When you need a done task for typing """ def __init__(self): fake = Mock() asyncio.Future.__init__(self, loop=fake) # typing: ignore, don't create a loop asyncio.Future.set_result(self, None) async def cancel(fut: asyncio.Future) -> None: """ Cancel a future/task and await for it to cancel. This method suppresses the CancelledError """ fut.cancel() await asyncio.sleep(0) # let loop cycle with suppress(asyncio.CancelledError): await fut def as_task(func: F) -> F: """ Decorate a function, So that when called it is wrapped in a task on the running loop. """ @wraps(func) def create_task(*args, **kws): loop = asyncio.get_running_loop() return loop.create_task(func(*args, **kws)) return cast(F, create_task) # Sentinel Task START_TASK: asyncio.Task = TaskSentinel() # ContextVar for Finding an existing Task Watcher WATCHER_CONTEXT: contextvars.ContextVar[Watcher] = contextvars.ContextVar( "WATCHER_CONTEXT" ) class WatcherError(RuntimeError): pass class Watcher: _tasks: Dict[asyncio.Future, Optional[FixerType]] _scheduled: List[FixerType] _tasks_changed: BiDirectionalEvent _cancelled: asyncio.Event _cancel_timeout: float _preexit_callbacks: List[Callable[[], None]] _shielded_tasks: Dict[asyncio.Task, asyncio.Future] loop: asyncio.AbstractEventLoop running: bool @staticmethod def get() -> Watcher: return WATCHER_CONTEXT.get() def __init__(self, *, cancel_timeout: float = 300, context: bool = False) -> None: """ cancel_timeout is the time in seconds we will wait after cancelling all the tasks watched by this watcher. context is wether to expose this Watcher via contextvars now or at __aenter__ """ if context: WATCHER_CONTEXT.set(self) self._cancel_timeout = cancel_timeout self._tasks = {} self._scheduled = [] self._tasks_changed = BiDirectionalEvent() self._cancelled = asyncio.Event() self._preexit_callbacks = [] self._shielded_tasks = {} self.running = False async def _run_scheduled(self) -> None: scheduled = self._scheduled while scheduled: fixer = scheduled.pop() task = fixer(START_TASK) if not isinstance(task, asyncio.Task) and isawaitable(task): task = await task if isinstance(task, asyncio.Task): self._tasks[task] = fixer else: raise TypeError(f"{fixer}(START_TASK) failed to return a task.") async def unwatch( self, task: asyncio.Task = START_TASK, fixer: Optional[FixerType] = None, *, shield: bool = False, ) -> bool: """ The ability to unwatch a task, by task or fixer This is a coroutine to insure the watcher has re-watched the tasks list If the task was shielded then you need to specify here so we can find the shield and remove it from the watch list. When unwatching a fixer, if the returned task is not the same as the one passed in we will cancel it, and await it. """ async def tasks_changed(): if self.running: await self._tasks_changed.set() if shield: if task in self._shielded_tasks: del self._tasks[self._shielded_tasks[task]] del self._shielded_tasks[task] await tasks_changed() return True elif fixer is not None: for t, fix in tuple(self._tasks.items()): if fix is fixer: del self._tasks[t] await tasks_changed() if t is not task: await cancel(t) return True elif task is not START_TASK: if task in self._tasks: del self._tasks[task] await tasks_changed() return True return False def watch( self, task: asyncio.Task = START_TASK, fixer: Optional[FixerType] = None, *, shield: bool = False, ) -> None: """ Add a task to be watched by the watcher You can also attach a fixer co-routine or function to be used to fix a task that has died. The fixer will be passed the failed task, and is expected to return a working task, or raise if that is impossible. You can also just pass in the fixer and we will use it to create the task to be watched. The fixer will be passed a dummy task singleton: `later.task.START_TASK` shield argument lets you watch a task, but not cancel it in this watcher. Useful for triggering on task failures, but not managing said task. """ # Watching a coro, leads to a confusing error deep in watcher # so use runtime checks not just static types. if not isinstance(task, asyncio.Task): raise TypeError("only asyncio.Task objects can be watched.") if task is START_TASK: if not fixer: raise ValueError("fixer must be specified when using START_TASK.") self._scheduled.append(fixer) elif shield: if fixer: raise ValueError("`fixer` can not be used with shield=True") self._shielded_tasks[task] = asyncio.shield(task) self._tasks[self._shielded_tasks[task]] = None else: self._tasks[task] = fixer self._tasks_changed.set_nowait() def cancel(self) -> None: """ Stop the watcher and cause it to cancel all the tasks in its care. """ self._cancelled.set() def add_preexit_callback(self, callback: Callable[..., None], *args, **kws) -> None: self._preexit_callbacks.append(partial(callback, *args, **kws)) def _run_preexit_callbacks(self) -> None: for callback in self._preexit_callbacks: try: callback() except Exception as e: logger.exception( f"ignoring exception from pre-exit callback {callback}: {e}" ) async def __aenter__(self) -> "Watcher": WATCHER_CONTEXT.set(self) self.loop = asyncio.get_running_loop() return self async def __aexit__( self, exc_type: Optional[Type[BaseException]], exc: Optional[BaseException], tb: Optional[TracebackType], ) -> bool: cancel_task: asyncio.Task = self.loop.create_task(self._cancelled.wait()) changed_task: asyncio.Task = START_TASK try: self.running = True while not self._cancelled.is_set(): if self._scheduled: await self._run_scheduled() if changed_task is START_TASK or changed_task.done(): changed_task = self.loop.create_task(self._tasks_changed.wait()) try: if not self._tasks: return False # There are no tasks just exit. done, pending = await asyncio.wait( [cancel_task, changed_task, *self._tasks.keys()], return_when=asyncio.FIRST_COMPLETED, ) if cancel_task in done: break # Don't bother doing fixes just break out for task in done: task = cast(asyncio.Task, task) if task is changed_task: continue else: await self._fix_task(task) except asyncio.CancelledError: self.cancel() finally: self.running = False self._run_preexit_callbacks() await self._event_task_cleanup(cancel_task, changed_task) await self._handle_cancel() self._tasks.clear() self._shielded_tasks.clear() return False async def _event_task_cleanup(self, *tasks): for task in tasks: if task is not START_TASK: await cancel(task) async def _fix_task(self, task: asyncio.Task) -> None: # Insure we "retrieve" the result of failed tasks exc = task.exception() if exc is None: task.result() fixer = self._tasks[task] if fixer is None: raise RuntimeError(f"{task} finished and there is no fixer!") from exc new_task = fixer(task) if not isinstance(new_task, asyncio.Task) and isawaitable(new_task): new_task = await new_task if isinstance(new_task, asyncio.Task): del self._tasks[task] self._tasks[new_task] = fixer else: raise TypeError( f"{fixer}(task) failed to return a task, returned:" f"{new_task}!" ) from exc async def _handle_cancel(self): tasks = [task for task in self._tasks if not task.done()] if not tasks: return for task in tasks: task.cancel() done, pending = await asyncio.wait(tasks, timeout=self._cancel_timeout) bad_tasks: List[asyncio.Task] = [] for task in done: if task.cancelled(): continue if task.exception() is not None: bad_tasks.append(task) bad_tasks.extend(pending) if bad_tasks: raise WatcherError( "The following tasks didn't cancel cleanly or at all!", bad_tasks )

[ "facebook-github-bot@users.noreply.github.com" ]

facebook-github-bot@users.noreply.github.com

9e44aadf38af013a19b2183bcb83b8f98eec8804

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/pichr/fixtures/parse_pichr_json.py

f53a730d9eda7b51ce9dc70de06d150d5b6fe23a

[]

no_license

emilymye/pichr

3a5cb3a2c8f54bf07c832c45fe4225882dac87b5

7441b54a2450358fb3b6a85f4be67e2bf695e9b8

refs/heads/master

2021-01-13T01:54:12.737860

2015-03-12T22:18:46

31,866,993

0

null

UTF-8

Python

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1,609

py

import json import csv if __name__ == "__main__": with open("data.csv") as data_f: reader = csv.DictReader(data_f) print reader.fieldnames players = {} recoveries = [] for row in reader: player = { "name": row['Name'].strip(), "pid": int(row['PlayerId']), "team": row['Team'].strip() } players[player["pid"]] = player date = row["On Date"].split('/') if len(date[0]) == 1: date[0] = "0%s" % date[0] if len(date[1]) == 1: date[1] = "0%s" % date[1] print date recovery = { "date": "%s-%s-%s" % (date[2], date[0], date[1]), "duration" : row['Days'], "sct_injury": row['InjurySCTID'], "player": player["pid"], "preERA": float(row['preERA']), "postERA": float(row['postERA']), "preFastball": float(row['preFB']), "postFastball": float(row['postFB']) } if "yes" in row['reinjury'].lower(): recovery['reinjury'] = True if "yes" in row["offseason"].lower(): recovery['offseason'] = True if "procedure" in row: recovery["ProcedureSCTID"] = row["ProcedureSCTID"] recoveries.append(recovery) players = [{ "model": "pichr.Player", "fields": players[pid] } for pid in players] recoveries = [{ "model": "pichr.Recovery", "pk": i, "fields": rec } for i, rec in enumerate(recoveries)] enc = json.JSONEncoder() with open("instances.json", "w+") as outF: outF.write(json.dumps(players + recoveries, sort_keys=False, indent=4, separators=(',', ': ')))

[ "emily3ye@gmail.com" ]

emily3ye@gmail.com

a08c27bb4b36f95989b207b71198e8f3254b8485

827cc3633a99f90eed504da343ecc86684556c24

/24.py

fc9b8c38d637f320fcafc3b4a42faa0f723416f0

[]

no_license

fs-source/fs-source

7f6e5c0504decc8111c50d0e3bba2e48e0f27658

5a39697e5b7fe1e895e92f0fedab4889dc8812ad

refs/heads/main

2023-07-14T23:13:54.307676

2021-08-26T20:37:22

328,153,163

0

null

UTF-8

Python

false

18

py

print(292 + 774)

[ "tlxt5650508@163.com" ]

tlxt5650508@163.com

218c12ac6119307e120b6b12b5bfe4a790c1aa59

33fab1fae9b4a099e705dcc10443b7721da8f430

/1.py

4963f2a6e3bdf4eb5409bc2735c4474c71f9afa8

[]

no_license

cm12348/tryGithub

4d797e40ae28b87a20aaad543997e2531d279ba5

5300bdd4a008d597595aea08a831361f85fc95e9

refs/heads/master

2020-04-16T06:24:27.109767

2019-01-12T04:36:38

165,345,404

0

null

UTF-8

Python

false

48

py

""" I did some changes. """ print("Helloworld")

[ "noreply@github.com" ]

noreply@github.com

c8bca5286d0a9ad049f59155f5a9114d8f06dd8c

b92eee41d665314bc42043d1ff46c608af5ffdfd

/sesion_3/prog.4.py

eda17bf266e753571861d3d45fc42db362032da6

[]

no_license

badillosoft/python-economics

40efe8326558a8fb93f84fdbd2137428844ee5f3

82af43c7a47297ce186dc0e23e30620d46e6693a

refs/heads/master

2021-01-11T18:55:15.762752

2017-05-09T01:15:59

79,656,798

0

null

UTF-8

Python

false

278

py

from openpyxl import load_workbook from geg import * wb = load_workbook("puntos.xlsx") ws = wb.active puntos = automatic_load_data(ws, "A2") def f(x, y): return x**2 + y**2 for p in puntos: x = p["X"] y = p["Y"] z = f(x, y) print "%f, %f, %f" %(x, y, z)

[ "kmmx@hsoft.local" ]

kmmx@hsoft.local

533e43f778d33b82ee7fcc0f71873a2f54f2a562

1abb00f7a02635918eba197cb31a16f9cff8550f

/Zadanie5.py

7e8db8044cd0327daedda99eae58f101384011f7

[]

no_license

Exoticisignis/Sem5SL

5535513408905778d9cfb3920c44663cd0d60fb8

a823f1956c07aeceede75571fb83bc5738796461

refs/heads/master

2023-03-28T12:17:32.243911

2021-04-05T18:20:41

354,929,965

0

null

UTF-8

Python

false

666

py

list = [7, 'x', 'y', 6, "uaua", 9, 10, 99] def process(list): new_list = [i for i in list if isinstance(i, int)] summ = sum(new_list) length = len(new_list) average = float(summ / length) minn = min(new_list) maxx = max(new_list) variance = calc_variance(new_list, length, average) result = ("Długość: "+str(length),"Średnia wartość: "+str(average),"Wariancja: "+str(variance),"Min: "+str(minn),"Max: "+ str(maxx)) return result def calc_variance(calc_list, length, ave): var = 0 for i in calc_list: var += float((i - ave) ** 2) return var / length if __name__ == '__main__': print(process(list))

[ "debysh99@gmail.com" ]

debysh99@gmail.com

4403759cc3a6535b10eb3e09928d293cb9555aad

bb151500b0fc5bb9ef1b1a9e5bba98e485b4b34d

/problemSet/591C_Median_Smoothing.py

9436f6108c5e3ab88ea40e68a7cd92378f7749a0

[]

no_license

yamaton/codeforces

47b98b23da0a3a8237d9021b0122eaa498d98628

e0675fd010df852c94eadffdf8b801eeea7ad81b

refs/heads/master

2021-01-10T01:22:02.338425

2018-11-28T02:45:04

2018-11-28T03:21:45

45,873,825

0

null

UTF-8

Python

false

2,697

py

""" Codeforces Round #327 (Div. 2) Problem 591 C. Median Smoothing @author yamaton @date 2015-11-06 """ def reduce_consec(iterable): """ [1, 2, 3, 6, 7, 9, 10, 11, 12, 13, 20] --> [(1, 3), (6, 2), (9, 5), (20, 1)] Detect consecutive part and (starting_value, length) pair :param xs: List of int :return: List of pair of int """ stack = [] for x in iterable: if stack: # check if consective if stack[-1] + 1 == x: stack.append(x) # if not consecutive, flush stack and start with new element else: yield (stack[0], len(stack)) stack = [x] else: # starting element stack.append(x) if stack: yield (stack[0], len(stack)) def alternating_indices(xs): for i, x in enumerate(xs): if i == 0 or i == len(xs) - 1: continue if xs[i-1] != x and xs[i+1] != x: yield i def alternating_position_and_length(xs): for x in xs: pass def solve(xs, n): # zigzag = [] # alternating part # for i, x in enumerate(xs): # if i == 0 or i == n - 1: # continue # if xs[i-1] != x and xs[i+1] != x: # zigzag.append(i) zigzag = alternating_indices(xs) zigzag_start_length_pairs = reduce_consec(zigzag) count = 0 result = xs[:] for (i, n) in zigzag_start_length_pairs: n_half = n // 2 count = max(count, (n + 1) // 2) if n % 2 == 0: for j in range(i, i + n_half): result[j] = xs[i-1] for j in range(i + n_half, i + n): result[j] = 1 - xs[i-1] else: for j in range(i, i + n): result[j] = xs[i-1] return count, result def solve_bruteforce(xs, n): def transform(ps): result = [] for i in range(n): if i == 0 or i == n-1: result.append(ps[i]) else: median = int(sum(ps[i-1:i+2]) >= 2) result.append(median) return tuple(result) xs = tuple(xs) seen = set() seen.add(xs) ys = transform(xs) count = 0 while ys != xs: # Actually, this system always ends up to a fixed point. No cycle exists. if ys in seen: return -1, xs xs = ys seen.add(xs) count += 1 ys = transform(xs) return count, xs def main(): n = int(input()) xs = [int(i) for i in input().strip().split()] count, seq = solve(xs, n) print(count) print(' '.join(str(n) for n in seq)) if __name__ == '__main__': main()

[ "yamaton@gmail.com" ]

yamaton@gmail.com

cba35a947a9ee82ae669d9f1be77bc9ee3889334

3d5a99baa64e8c1bb0946f2b3e2ea29bf1d12b98

/ch02/r28.py

5a8a9bf99f9bb9cb1f5f7a49e0f0da4b65a56418

[]

no_license

lakchaud89/python_data_structures

a9809d3a6b7d1db48d24e53ed376224673565ce2

97198952d4d275775da21224a3754b7a6b37fc03

refs/heads/master

2021-01-21T06:48:58.710228

2017-09-08T21:33:54

101,947,272

0

null

2017-09-02T03:19:33

2017-08-31T01:53:16

null

UTF-8

Python

false

2,262

py

# Modify the declaration of the first for loop in the CreditCard tests, from Code Fragment 2.3, # so that it will eventually cause exactly one of the three credit cards to go over its credit limit. # Which credit card is it? class CreditCard: def __init__(self,customer,bank,account,limit,balance = None): self._customer = customer self._bank = bank self._account = account self._limit = limit if balance == None: self._balance = 0 else: self._balance = balance def get_customer(self): return self._customer def get_bank(self): return self._bank def get_account(self): return self._account def get_limit(self): return self._limit def get_balance(self): return self._balance def charge(self,price): if not isinstance(price, (int, float)): raise TypeError( "price must be numeric" ) elif price < 0: raise ValueError( "price cannot be negative" ) if (price + self._balance > self._limit): return False else: self._balance +=price return True def make_payment(self,amount): if not isinstance(amount, (int, float)): raise TypeError("amount must be numeric" ) elif amount < 0: raise ValueError( "amount cannot be negative" ) self._balance-=amount #if name == __main__ : wallet = [ ] wallet.append(CreditCard( 'John Bowman' , 'California Savings' ,'5391 0375 9387 5309' , 2500) ) wallet.append(CreditCard( 'John Bowman', 'California Federal' ,'3485 0399 3395 1954' , 3500) ) wallet.append(CreditCard( 'John Bowman' , 'California Finance' ,'5391 0375 9387 5309' , 5000) ) for val in range(1, 17): wallet[0].charge(val*500) wallet[1].charge(2*val) wallet[2].charge(3*val) for c in range(3): print( 'Customer = ', wallet[c].get_customer()) print( 'Bank = ', wallet[c].get_bank()) print( 'Account = ', wallet[c].get_account()) print( 'Limit = ', wallet[c].get_limit()) print( 'Balance = ', wallet[c].get_balance()) while wallet[c].get_balance() > 100: wallet[c].make_payment(100) print( "New balance = ", wallet[c].get_balance()) print( )

[ "lakshmichaudhari@lakshmis-mbp.gateway.pace.com" ]

lakshmichaudhari@lakshmis-mbp.gateway.pace.com

a5255900b8026034fbd41ea3c3b5ab5f8ea68614

1c61d9e996f24e87ae686c2b6a2f01ef052425d1

/accountapp/views.py

b383b2612cc5d41cb64536203155784c87823fbb

[]

no_license

joft-ware/django_web

ba3be679299fdacba7fc1c49cb5317209e895a59

06f750fd4a2bdced2b0d67a198ebafd5d3407637

refs/heads/master

2023-02-26T19:52:49.247705

2021-02-02T14:12:53

325,967,749

0

null

UTF-8

Python

false

1,896

py

from django.contrib.auth.decorators import login_required from django.contrib.auth.forms import UserCreationForm from django.contrib.auth.models import User from django.http import HttpResponse, HttpResponseRedirect from django.shortcuts import render # Create your views here. from django.urls import reverse, reverse_lazy from django.utils.decorators import method_decorator from django.views.generic import CreateView, DetailView, UpdateView, DeleteView from django.views.generic.list import MultipleObjectMixin from accountapp.decorators import account_ownership_required from accountapp.forms import AccountUpdateForm from accountapp.models import hw from articleapp.models import Article has_ownership = [account_ownership_required, login_required] class AccountCreateView(CreateView): model = User form_class = UserCreationForm success_url = reverse_lazy('accountapp:login') template_name = 'accountapp/create.html' class AccountDetailView(DetailView, MultipleObjectMixin): model = User context_object_name='target_user' template_name = 'accountapp/detail.html' paginate_by = 25 def get_context_data(self, **kwargs): object_list=Article.objects.filter(writer=self.get_object()) return super(AccountDetailView, self).get_context_data(object_list=object_list, **kwargs) @method_decorator(has_ownership, 'get') @method_decorator(has_ownership, 'post') class AccountUpdateView(UpdateView): model = User form_class = AccountUpdateForm context_object_name='target_user' success_url = reverse_lazy('home') template_name = 'accountapp/update.html' @method_decorator(has_ownership, 'get') @method_decorator(has_ownership, 'post') class AccountDeleteView(DeleteView): model = User context_object_name='target_user' success_url = reverse_lazy('accountapp:login') template_name = 'accountapp/delete.html'

[ "34998542+sky033116@users.noreply.github.com" ]

34998542+sky033116@users.noreply.github.com

c122747c667ba2b5cd4b66de7acfc231a1576ac7

9634a7dd26605fbf6260f2338bfb1f061111c8e6

/wishes/urls.py

d4ac2c932e9d1ccfb6786700bf76f6175716a944

[]

no_license

pkc035/21-1st-MealKatMarket-backend

985b18e28e55758e08455c597fcfc48822c92c65

1d43eea1cf8f30bcd893fb74b30e2645dd0f5561

refs/heads/main

2023-06-03T06:49:52.052308

2021-06-20T11:22:01

378,775,535

1

0

null

2021-06-21T01:35:25

null

UTF-8

Python

false

155

py

from django.urls import path from .views import WishView urlpatterns = [ path('',WishView.as_view()), path('/<int:food_id>', WishView.as_view()) ]

[ "pkc0305@gmail.com" ]

pkc0305@gmail.com

d40f02e6db1890c62e20ceca6eb08cad3e7ac968

674a882b1f958766e3ac2db0fc445a7a7fd7ee16

/weather/views.py

324005a5103ce55e011138cfe0ddb753ad0b2766

[]

no_license

harinder8407/weather-app

2463bd9155e887d51bca83223de5828a570ab694

e30adedfb351d5e2da5d1be42686323293d86ae9

refs/heads/master

2023-03-09T00:25:10.219507

2021-03-01T09:14:33

343,339,049

0

null

UTF-8

Python

false

1,919

py

import requests from django.shortcuts import render, redirect from .models import City from .forms import CityForm def index(request): url = 'http://api.openweathermap.org/data/2.5/weather?q={}&units=imperial&appid=14c01bd544e820b6681e78683c1103a7' err_msg = '' message = '' message_class = '' if request.method == 'POST': form = CityForm(request.POST) if form.is_valid(): new_city = form.cleaned_data['name'] existing_city_count = City.objects.filter(name=new_city).count() if existing_city_count == 0: r = requests.get(url.format(new_city)).json() if r['cod'] == 200: form.save() else: err_msg = 'City does not exist in the world!' else: err_msg = 'City already exists in the database!' if err_msg: message = err_msg message_class = 'is-danger' else: message = 'City added successfully!' message_class = 'is-success' form = CityForm() cities = City.objects.all() weather_data = [] for city in cities: r = requests.get(url.format(city)).json() city_weather = { 'city' : city.name, 'temperature' : r['main']['temp'], 'description' : r['weather'][0]['description'], 'icon' : r['weather'][0]['icon'], } weather_data.append(city_weather) context = { 'weather_data' : weather_data, 'form' : form, 'message' : message, 'message_class' : message_class } return render(request, 'weather/weather.html', context) def delete_city(request, city_name): City.objects.get(name=city_name).delete() return redirect('index')

[ "harinderss2899@gmail.com" ]

harinderss2899@gmail.com

a5961849eacd9651fe5f3146a8524c53eda4f663

4db107059e4f77740bea34705fc459bb6a2fe981

/MicroAOD/python/flashggTriLeptons_cfi.py

2a7348b193e44455a9fd1bb1c4dd5f03b8c01bfe

[]

no_license

GiuliaNegro/dafne

6e4188e233113387b67096bf5b0eed362771f1e3

628076faec651c6ebaedd068b1b1626c8b3d9a1d

refs/heads/master

2020-09-26T23:59:11.712519

2019-01-31T19:50:45

67,401,535

0

1

null

UTF-8

Python

false

595

py

import FWCore.ParameterSet.Config as cms flashggTriLeptons = cms.EDProducer('FlashggTriLeptonsProducer', ElectronTag=cms.InputTag('flashggElectrons'), #'slimmedElectrons'), MuonTag=cms.InputTag('flashggMuons'), #'slimmedMuons'), VertexTag=cms.InputTag('offlineSlimmedPrimaryVertices'), ##Parameters minElectronPt=cms.double(5.), maxElectronEta=cms.double(2.4), minMuonPt=cms.double(5.), maxMuonEta=cms.double(2.4) )

[ "giulia.negro@cern.ch" ]

giulia.negro@cern.ch

2a5cef818c311103bcb5f7606a771da35e81fd54

2c9750d1f89b0c3d2936f826e7af750d850e9cce

/RHMDashboard/wsgi.py

1ba08fb3f4211fbb2e844033d12d8210abd9cf63

[]

no_license

AGhosh-SPC/RegulatorHealthMonitor

738a97ed9f243489e833bc3390c792ff4137b86f

fa3404126112d9b013a1bba3bd54132bee8d188b

refs/heads/main

2023-04-06T23:29:53.735996

2021-04-14T20:07:34

358,028,166

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""" WSGI config for RHMDashboard 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. For more information, visit https://docs.djangoproject.com/en/2.1/howto/deployment/wsgi/ """ import os from django.core.wsgi import get_wsgi_application os.environ.setdefault( 'DJANGO_SETTINGS_MODULE', 'RHMDashboard.settings') # 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()

[ "Ghosh.Aindrila@spartancontrols.com" ]

Ghosh.Aindrila@spartancontrols.com

2ac3a4e371b349d7e8e7c8a07168edbe9ea3a41a

04d3f37b043364cd0233348863036436dfabedd6

/manage.py

7a4745372d94347843ee1bf9eb1a5ae4839100bb

[]

no_license

anonim0zero/NASA-challenges-covid-19

b281d0f8faad453bd5c1ddd81510f94b8acc6625

1eed3aa9b8ae871539605084a22da3c037ea8843

refs/heads/main

2023-08-16T20:46:44.454581

2021-10-03T16:50:45

413,138,339

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#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): """Run administrative tasks.""" os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'NASA.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "igorteplov2001@gmail.com" ]

igorteplov2001@gmail.com

53c51e60a3111190c14795ad4a983b2bcccfe5af

b397e24cdb458fa7cb9ba1838e8236b425eed66e

/Locators.py

9c4a3dba58c4c8b2e1710c1ad47a318c638d4b96

[]

no_license

Mrkabu/PageObjectPatern

29b177a2598f85c87aa9947cdce61883857b043b

7e4a208adecd36cbb449c68f1e3c7baf6b34e9c9

refs/heads/master

2020-03-16T03:58:19.924212

2018-05-17T20:33:47

132,499,689

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class Locators(object): logo = '//*[@id="top"]/div/div/div/div/nav/ul/li[2]/a' terazwtv = '//*[@id="top"]/div/div/div/div/div/nav/a[1]' login = '//*[@id="Email"]' password = '//*[@id="Password"]' buttonzaloguj = '//*[@id="login-form"]/fieldset/div/div[1]/div[3]/button' logowyszukiwarka = '//*[@id="top"]/div/div/div/div/nav/ul/li[1]/a/img' wyszukiwarka = '//*[@id="search"]' rozumiem = '//*[@id="Account_Login"]/div[2]/div/div/div/a[2]' kruk = '//*[@id="Search_Search"]/main/div/div[2]/ul/li[1]/div/div[2]/h2/a' #programtv = (By.XPATH, '/html/body/header/div/div/div/div/div/nav/a[2]') #zaloguj = (By.XPATH, '/html/body/header/div/div/div/div/nav/ul/li[3]/a')

[ "sagan07@gmail.com" ]

sagan07@gmail.com

0a2a4d1068f3c40fd8c94b02d7e99a8d7d07b26f

47718952e24df62386dc5e858a889ba37323c6c0

/anal-lexico.py

ea41df822875166bb2859730fe681f8daf898fb7

[]

no_license

rv0lt/PDL

4ae5f9acdf5b30885c81670d9ac8fef043c26b37

4a016bc8becbb8d742dd7b9ce56418e449c46b5d

refs/heads/master

2022-04-28T01:16:12.451534

2020-03-18T17:02:34

211,706,759

0

null

UTF-8

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14,778

py

import ply.lex as lex from ply.lex import TOKEN import sys import re from TablaSimbolos import TablaSimbolos from TablaSimbolos import Fun from gramatica import parser import gramatica lexema="" tipo=None comentario=False tabla_simbolos = TablaSimbolos() estoy_en_fun=False #sirve para indicar si voy a leer una funcion flagFor = False contador=0 contReturn=0 flagIf = False metaFlagIf = False flagExpresion=False anotherFlag = False opEsp=False listaExpresion=[] listaTokens=[] #Para pasarle al sintactico funcion = Fun() funcionAux = Fun() tipos=( #palabras clave que hacen referencia a los tipos de datos 'int', 'boolean', 'string' ) palabras_clave = tipos +( #Definir las palabras clave 'var', 'for', 'print', 'return', 'function', 'if', 'input' ) tokens = palabras_clave + ( 'id', 'cte_entera', 'asignacion', # = 'coma', # , 'cadena', #"hola" 'opArt', #+ 'opRel', # > 'opLog', # ! 'opEsp', # b1|=b2 -> b1 = b1 || b2 'puntoComa', # ; 'parAb', # ( 'parCerr', # ) 'corchAb', # { 'corchCerr' # } ) def evaluar_expresion(): check=False tipoRetorno=None simboloMayorQue = False while len(listaExpresion)>0: elem= listaExpresion.pop() if not check: if elem == ">" or elem == "!" or elem == "+": return "error" else: if tipoRetorno is not None: if tipoRetorno == "+" and elem != "int": return "error" elif tipoRetorno == ">" and elem != "int": return "error" tipoRetorno=elem check=True else: if elem == "!" and tipoRetorno != "boolean": return "error" elif elem == ">": if tipoRetorno != "int" or simboloMayorQue: return "error" simboloMayorQue=True elif elem == "+" and tipoRetorno != "int": return "error" check=False tipoRetorno = elem if tipoRetorno == "!" or simboloMayorQue: tipoRetorno = "boolean" return tipoRetorno # Reglas de expresiones regulares para los tokens def t_opEsp(t): r'\|=' global lexema global flagExpresion global opEsp if(not comentario): listaTokens.append(t.value) flagExpresion=True opEsp=True t.value=" " return t def t_opRel(t): r'>' if(not comentario): listaTokens.append(t.value) if flagExpresion: listaExpresion.append(t.value) t.value=" " return t def t_opLog(t): r'!' global flagExpresion if(not comentario): listaTokens.append(t.value) if flagExpresion: listaExpresion.append(t.value) t.value=" " return t def t_asignacion(t): r'=' global flagExpresion if(not comentario): flagExpresion=True listaTokens.append(t.value) t.value=" " return t def t_coma(t): r',' if(not comentario): listaTokens.append(t.value) t.value=" " return t def t_puntoComa(t): r';' global listaExpresion global flagExpresion global lexema global opEsp global flagFor,contador global metaFlagIf if(not comentario): listaTokens.append(t.value) if metaFlagIf: metaFlagIf=False if flagFor: contador -=1 if contador == 0: #flagExpresion=False exp=evaluar_expresion() if exp!='boolean': raise Exception ("Error en la condicion del for") if flagExpresion: flagExpresion = False exp = evaluar_expresion() if exp == "error": raise Exception ("La expresion escrita no tiene los tipos correctos") elif lexema == "return": if exp!=funcionAux.retorno: raise Exception("Tipo no valido para el return") elif lexema=="print": if exp == 'boolean': raise Exception ("Tipo logico en print o input") elif opEsp: opEsp=False if ( (tabla_simbolos.buscarTipo(lexema) != 'boolean') or (exp!='boolean') ): raise Exception ("Tipo no logico en asignacion con or logico") elif tabla_simbolos.buscarTipo(lexema) != exp: if flagFor and exp!=None: raise Exception("Asignacion de tipos distintos en la iniciacion del for") elif not flagFor: raise Exception("Tipo incorrecto en asignacion") if contador ==1 and flagFor: flagExpresion=True t.value=" " return t def t_parAb(t): r'$' if(not comentario): listaTokens.append(t.value) t.value=" " return t def t_parCerr(t): r'$' global anotherFlag,flagFor, opEsp if(not comentario): listaTokens.append(t.value) if flagFor: flagFor=False exp=evaluar_expresion() if exp == "error" or tabla_simbolos.buscarTipo(lexema) !=exp: if exp !=None: raise Exception("Asignacion de tipos distintos en la actualizacion del for") elif opEsp: opEsp=False if ( (tabla_simbolos.buscarTipo(lexema) != 'boolean') or (exp!='boolean') ): raise Exception ("Tipo incorrecto en actualizacion del for") if anotherFlag and flagExpresion: anotherFlag=False if funcionAux.nParam > 1: if funcionAux.nParam-1 != tabla_simbolos.buscarnParamFuncion(funcion.nombre): raise Exception ("Numero de parametros equivocado") funcionAux.tipoParam.reverse() funcionAux.tipoParam.pop() funcionAux.tipoParam.reverse() if funcionAux.tipoParam != tabla_simbolos.buscarTipoParamFuncion(funcionAux.nombre): raise Exception ("Tipos de los atributos equivocado") else: if tabla_simbolos.buscarnParamFuncion(funcionAux.nombre) > 0: raise Exception ("Numero de argumentos erroneo") funcionAux.reinicio t.value=" " return t def t_corchAb(t): r'\{' global estoy_en_fun if(not comentario): if estoy_en_fun: #si estoy leyendo una funcion y veo las llaves abiertas signigica que ya he dejado de declararla estoy_en_fun=False tabla_simbolos.crearFuncion(funcion) listaTokens.append(t.value) t.value=" " return t def t_corchCerr(t): r'\}' global flagFor,contReturn if(not comentario): listaTokens.append(t.value) t.value=" " if not flagFor: if contReturn <=0 and funcionAux.retorno is not None: raise Exception("Error en el cuerpo de la funcion") tabla_simbolos.destuirTSL() funcion.reinicio() funcionAux.reinicio() else: flagFor=False return t def t_opArt(t): r'\+' global flagExpresion if(not comentario): if flagExpresion: listaExpresion.append(t.value) listaTokens.append(t.value) t.value = " " return t def t_id(t): r'[a-zA-z_]\w*' if(not comentario): global tipo global estoy_en_fun global flagFor global flagIf,metaFlagIf global flagExpresion global anotherFlag global lexema global contador, contReturn if t.value in palabras_clave: listaTokens.append(t.value) t.type = t.value t.value = " " if t.type == 'var': #cada vez que leo var entro en zona de declaraciom tabla_simbolos.declaracion=True elif t.type in tipos and (tabla_simbolos.declaracion or estoy_en_fun): #si estoy en zona de declaracion o acabo de leer un function me quiero guardar el tipo tipo= t.type elif t.type == 'function': #voy a leer una funcion estoy_en_fun=True contReturn=0 elif t.type == 'for': flagFor=True contador=2 elif t.type == 'if': flagIf=True metaFlagIf=True elif t.type == 'print' or t.type == 'input': flagExpresion=True lexema="print" elif t.type == 'return': flagExpresion=True lexema="return" contReturn+=1 if metaFlagIf: contReturn-=1 else: t.type = "id" if not flagExpresion: lexema=t.value if flagExpresion and not anotherFlag: if tabla_simbolos.buscarTipo(t.value) == "funcion": listaExpresion.append(tabla_simbolos.buscarTipoRetorno(t.value)) funcionAux.nombre=t.value anotherFlag = True else: listaExpresion.append(tabla_simbolos.buscarTipo(t.value)) listaTokens.append(t.type) if flagIf: flagIf=False if tabla_simbolos.buscarTipo(t.value) != "boolean": raise Exception("error en el if") if anotherFlag: if flagExpresion: funcionAux.nParam+=1 funcionAux.tipoParam.append(tabla_simbolos.buscarTipo(t.value)) if estoy_en_fun: if not funcion.flag: funcion.retorno=tipo funcion.nombre=t.value funcionAux.retorno=tipo funcion.flag=True q=tabla_simbolos.insertarTS(t.value, "funcion") else: funcion.nParam+=1 funcion.tipoParam.append(tipo) funcion.nombreParam.append(t.value) q = tabla_simbolos.insertarTSL() elif tabla_simbolos.declaracion: #zona declaracion q=tabla_simbolos.buscarTS(t.value) if q is not None: raise Exception("id ya declarada") else: q= tabla_simbolos.insertarTS(t.value,tipo) #una vez que he leido el ID paso a zona de uso tabla_simbolos.declaracion=False else: #zona de uso q= tabla_simbolos.buscarTS(t.value) if q is None: raise Exception("id no declarado") t.value=q tipo=None return t def t_cte_entera(t): r'\d+\.?(\d+)?' global flagExpresion global anotherFlag if(not comentario): if eval(t.value) > 32767 or '.' in t.value: raise Exception ("Lexical: illegal character '%s' in line '%d' position" % (t.value, t.lineno)) t.lexer.skip(1) else: t.value = eval(t.value) if flagExpresion: if anotherFlag: funcionAux.nParam+=1 funcionAux.tipoParam.append("int") else: listaExpresion.append("int") listaTokens.append(t.type) return t def t_cadena(t) : r'"([^"\\]|(\\.))*"' global flagExpresion if(not comentario): if len(t.value)-2>64 : raise Exception("Cadena demasiado larga") if flagExpresion: if anotherFlag: funcionAux.nParam+=1 funcionAux.tipoParam.append("string") else: listaExpresion.append("string") listaTokens.append(t.type) return t def t_newline(t): r'\n' t.lexer.lineno+=1 #t_ignore_COMMENT = r'/\*(.|\n)*?\*/' #@TOKEN(regex) def t_commentab(t): r'/\*' global comentario comentario = True def t_commentcer(t): r'\*/' global comentario comentario = False def t_error(t): if(not comentario): print("") t.lexer.skip(1) t_ignore = ' \t' #Contiene espacios y tabuladores if __name__ == '__main__': if len(sys.argv) != 2: print ("ERROR parametros incorrectos") sys.exit(1) lexer=lex.lex(reflags=re.DOTALL) tabla_simbolos =TablaSimbolos() data = open(sys.argv[1], 'r') linea = data.readline() output = open("tokens.txt", 'w') lex.lex(reflags=re.DOTALL) while linea != "": lexer.input(linea) linea = data.readline() while True: tok = lexer.token() if not tok: break tokens = ("<" + tok.type + "," + str(tok.value) +">" ) output.write(tokens+"\n") tokens+= " token number "+ str(tok.lexpos +1) + " in line " + str(tok.lineno) +"\n" tabla_simbolos.volcar() #Ahora he generado un fichero con los tokens y he creado la tabla de Simbolos #El siguiente paso es pasar la lista de Tokens identificados al parser #print(listaTokens) if parser.match(listaTokens): print("ACEPT") else: print("REJEC") parser.verbose_match(listaTokens, False) #gramatica.res # print(gramatica.res) data.close() output.close() #lex.input("a+b") #for tok in iter(lex.token, None): # print repr(tok.type), repr(tok.value) #lex.runmain(lexer)

[ "arevuelta@Rv0lt" ]

arevuelta@Rv0lt

13c9307b9b5ccdc95f35925e7cd437499b488c9b

5cd67aa3b3c43937e623bd7f4deede198956061d

/migrations/versions/f1318346a431_.py

a723d1b76520dd99b9160eaeb5d082d57fec78aa

[]

no_license

GDG-SSU/gdg-homepage-project

596d6379c8f93d2bbdc70c67703c80a42b07ec16

883bfdb9fa0e6f07d84a3b671daefbd8c69647c5

refs/heads/backup

2023-01-10T07:36:41.575334

2016-03-17T04:33:04

49,634,424

6

2

null

2022-12-26T20:00:24

2016-01-14T08:45:02

JavaScript

UTF-8

Python

false

954

py

"""empty message Revision ID: f1318346a431 Revises: None Create Date: 2016-01-30 16:42:51.170407 """ # revision identifiers, used by Alembic. revision = 'f1318346a431' down_revision = None from alembic import op import sqlalchemy as sa def upgrade(): ### commands auto generated by Alembic - please adjust! ### op.create_table('t_gdg_help_desk', sa.Column('id', sa.Integer(), nullable=False), sa.Column('date_created', sa.DateTime(), nullable=True), sa.Column('date_modified', sa.DateTime(), nullable=True), sa.Column('help_title', sa.String(length=200), nullable=True), sa.Column('help_content', sa.Text(), nullable=True), sa.Column('author_address', sa.String(length=15), nullable=True), sa.PrimaryKeyConstraint('id') ) ### end Alembic commands ### def downgrade(): ### commands auto generated by Alembic - please adjust! ### op.drop_table('t_gdg_help_desk') ### end Alembic commands ###

[ "Genus@Genusui-MacBook-Pro.local" ]

Genus@Genusui-MacBook-Pro.local

ee3e2068130cbb427286d11a91b9b4c4a9af71c5

db6c003ab9d407979386fa8fd48eb88b791ace7a

/aws_scripts/zero_all_asgs.py

4f9224b49fe2e6e98d512679ea18ba17a46b6a52

[]

no_license

aria-jpl/ops_scripts2

319e3450358748531dce5d2e3e8fbe0ac8ef0ade

332b1ecd2de3978724800136162aead998391ee6

refs/heads/master

2021-08-08T08:13:43.492801

2021-07-14T18:24:00

188,817,794

0

null

2021-07-14T18:24:00

2019-05-27T09:56:04

Python

UTF-8

Python

false

6,178

py

''' This script will set the max size, min size, and desired capacity values for all auto-scaling groups to zero then wait until all instances have been terminated after which these initial values will be restored. A list of asg's with active instances is printed to the console until no active instances are detected. There is also an option to read in an asg config file that will assign the max, min, and desired capacity values. The format for this file is: Flags: -f: input asg config file -p <int>: specifies interval over which to print ASG's with active instances. If not specified, default is '2'. Example usage: python zero_all_asgs.py -p 3 ''' import boto3 import json import time import argparse # AWS ASG parameters MAX_SIZE = "MaxSize" MIN_SIZE = "MinSize" DESIRED_CAPACITY = "DesiredCapacity" client = boto3.client('autoscaling') ''' Stores maxsize, minsize, and desired capacity values to a config file then sets these ASG parameters to zero. ''' def set_asgs_to_zero(): f = open("asg_config.json", "w") data = {} response = client.describe_auto_scaling_groups() asgs = response['AutoScalingGroups'] for asg in asgs: # Get asg name asg_name = asg['AutoScalingGroupName'] print("\n" + asg_name) data[asg_name] = [] # Collect initial asg values then, if desired capacity, max, or min are non-zero, set to zero if (asg['MinSize'] is not 0): data[asg_name].append({'MinSize':asg['MinSize']}) client.update_auto_scaling_group(AutoScalingGroupName=asg_name, MinSize=0) print("...setting MinSize to 0") if (asg['MaxSize'] is not 0): data[asg_name].append({'MaxSize':asg['MaxSize']}) client.update_auto_scaling_group(AutoScalingGroupName=asg_name, MaxSize=0) print("...setting MaxSize to 0") if (asg['DesiredCapacity'] is not 0): #data[asg_name].append({'DesiredCapacity':asg['DesiredCapacity']}) client.update_auto_scaling_group(AutoScalingGroupName=asg_name, DesiredCapacity=0) print("...setting DesiredCapacity to 0") # Write initial asg values to config file json.dump(data, f) f.close() ''' Print list of ASG's with active instances until all instances have been terminated. ''' def wait_for_asgs_to_zero(period): while(True): response = client.describe_auto_scaling_instances() asg_set = set() if (len(response['AutoScalingInstances']) <= 0): print("\n***********************************************") print("\nAll asg's zero'd. No active instances detected.") print("\n***********************************************") break; print("\nWaiting for active instances from the following ASG's to shutdown:") for i in response['AutoScalingInstances']: asg_set.add(i['AutoScalingGroupName']) print('\n'.join(asg_set)) print("Total number of active instances: " + str(len(response['AutoScalingInstances']))) time.sleep(int(period)) ''' Sets maxsize, minsize, and desired capacity back to initial values once all instances have been terminated. ''' def set_asgs_to_defaults(): # Open file containing default asg values f = open("asg_config.json", "r") data = json.load(f) for asg in data.items(): param = tuple(asg)[1] asg_name = tuple(asg)[0] print("\n" + asg_name) # Set asg default values if (len(param) > 0): for obj in param: if ("MaxSize" in obj): maxSize = obj["MaxSize"] client.update_auto_scaling_group(AutoScalingGroupName=asg_name, MaxSize=maxSize) print("Set MaxSize to", maxSize) ''' if (DESIRED_CAPACITY in obj): desiredCapacity = obj[DESIRED_CAPACITY] client.update_auto_scaling_group(AutoScalingGroupName=asg_name, DesiredCapacity=desiredCapacity) print("Set DesiredCapacity to", desiredCapacity) ''' if (MIN_SIZE in obj): minSize = obj[MIN_SIZE] client.update_auto_scaling_group(AutoScalingGroupName=asg_name, MinSize=minSize) print("Set MinSize to", minSize) ''' Sets asg min, max, and/or desired capacity values from config ''' def set_asg_from_config(config): print("Setting config values...") f = open(config, "r") data = json.load(f) for asg in data.items(): param = tuple(asg)[1] asg_name = tuple(asg)[0] print("\n" + asg_name) # Set asg default values if (len(param) > 0): for obj in param: if ("MaxSize" in obj): maxSize = obj["MaxSize"] client.update_auto_scaling_group(AutoScalingGroupName=asg_name, MaxSize=maxSize) print("Set MaxSize to", maxSize) if (DESIRED_CAPACITY in obj): desiredCapacity = obj[DESIRED_CAPACITY] client.update_auto_scaling_group(AutoScalingGroupName=asg_name, DesiredCapacity=desiredCapacity) print("Set DesiredCapacity to", desiredCapacity) if (MIN_SIZE in obj): minSize = obj[MIN_SIZE] client.update_auto_scaling_group(AutoScalingGroupName=asg_name, MinSize=minSize) print("Set MinSize to", minSize) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("-p", "--period", default=2, help="Interval in seconds to print list of ASG's with active instances.") parser.add_argument("-f", "--f", help="Input asg config file containing desired values.") args = parser.parse_args() if (args.f is not None): # Set asg values to those specified in config set_asg_from_config(args.f) else: set_asgs_to_zero() wait_for_asgs_to_zero(args.period) set_asgs_to_defaults()

[ "marjorie.j.lucas@jpl.nasa.gov" ]

marjorie.j.lucas@jpl.nasa.gov

d3d86e14f08837fc614c41663d93f3d0699382aa

79c89b881f488310bff3354dc71171371cf33987

/fast_text_embeddings.py

46cd6246a771888cc32d0927a9941020a944634e

[]

no_license

sreeja-g/NER-code-mix

4f32aa3de1cb2aff4ce98b97557fe9974357e3e1

f84d1b43c1ffdd01b6db61dd379d670f396eb06c

refs/heads/master

2022-11-29T07:16:41.941878

2020-07-30T22:58:38

281,765,112

0

null

UTF-8

Python

false

709

py

import pandas as pd import csv import nltk nltk.download('stopwords') from nltk.corpus import stopwords stop_words = set(stopwords.words('english')) from gensim.models import FastText data = pd.read_csv("processed_data/annotatedVec.tsv",sep='\t', quoting=csv.QUOTE_NONE, header=None) data.columns=['Sent', 'words', 'lang', 'ner-tag'] data = data.fillna(method="ffill") for index, each in data.iterrows(): if each['words'] in stop_words: data.drop(data[data['words'] == each['words']].index, inplace = True) corpus=[] for i in data['words'].values: corpus.append(str(i).split(" ")) corpus[:1] model = FastText(corpus, size=100, workers=4,window=5) model.save('saved_models/fasttext.model')

[ "37956427+sreeja-g@users.noreply.github.com" ]

37956427+sreeja-g@users.noreply.github.com

930560ddc5302324fc791a268bd09fb10d2e8b48

42a91364a9d25096f7b23791f666f25e02520ddf

/FETCH_CODE/quilt_flatten/distance.py

47ef94afaf28004972fe594793db81d8bec1ec9e

[]

no_license

Jie-Tree/FETCH_CODE

0d9d2ab04a085beefa3baf6cf15c2c671aa65944

37b0286843619fd231ff7b8277e6a0a8dff40ac7

refs/heads/master

2020-04-11T13:10:07.739588

2018-12-14T16:12:41

161,806,079

2

1

null

UTF-8

Python

false

4,933

py

from fetch_core import robot_interface, camera import time import cv2 import numpy as np import requests import rospy import socket from moveit_python import MoveGroupInterface, PlanningSceneInterface import os from matplotlib import pyplot as plt from cal_position import CalPosition from manage_fetch_robot import FetchPose, GripperClient from geometry_msgs.msg import PoseStamped, Pose, Point, Quaternion from moveit_msgs.msg import MoveItErrorCodes class Detect: def __init__(self): self.detect_ip = '172.31.76.30' self.detect_port = 7777 self.cam = camera.RGBD() self.cal_position = CalPosition() self.fetch_pose = FetchPose() def get_detect_info(self): print "get_detect_info" file_size = 0 while file_size == 0: time.sleep(0.05) img = self.cam.read_color_data() cv2.imwrite("image/fetch.png", img) path = "image/fetch.png" # path = "../fetch_core/image/26.png" file_size = os.stat(path).st_size time.sleep(0.2) print 'get img' def move_corner(self, x, y): position = self.cal_position.get_base_position_from_pix(x, y) position[0] = position[0] - 0.20 move_group = MoveGroupInterface("arm_with_torso", "base_link") planning_scene = PlanningSceneInterface("base_link") planning_scene.removeCollisionObject("my_front_ground") planning_scene.removeCollisionObject("my_back_ground") planning_scene.removeCollisionObject("my_right_ground") planning_scene.removeCollisionObject("my_left_ground") planning_scene.addCube("my_front_ground", 2, 1.1, 0.0, -1.0) planning_scene.addCube("my_back_ground", 2, -1.2, 0.0, -1.0) planning_scene.addCube("my_left_ground", 2, 0.0, 1.2, -1.0) planning_scene.addCube("my_right_ground", 2, 0.0, -1.2, -1.0) # This is the wrist link not the gripper itself gripper_frame = 'wrist_roll_link' pose = Pose(Point(position[0], position[1], position[2]), Quaternion(0, 0, 0, 1)) # Construct a "pose_stamped" message as required by moveToPose gripper_pose_stamped = PoseStamped() gripper_pose_stamped.header.frame_id = 'base_link' # Finish building the Pose_stamped message # If the message stamp is not current it could be ignored gripper_pose_stamped.header.stamp = rospy.Time.now() # Set the message pose gripper_pose_stamped.pose = pose # Move gripper frame to the pose specified move_group.moveToPose(gripper_pose_stamped, gripper_frame) result = move_group.get_move_action().get_result() if result: # Checking the MoveItErrorCode if result.error_code.val == MoveItErrorCodes.SUCCESS: rospy.loginfo("Hello there!") else: # If you get to this point please search for: # moveit_msgs/MoveItErrorCodes.msg rospy.logerr("Arm goal in state: %s", move_group.get_move_action().get_state()) else: rospy.logerr("MoveIt! failure no result returned.") time.sleep(1) joint_names = ["torso_lift_joint", "shoulder_pan_joint", "shoulder_lift_joint", "upperarm_roll_joint", "elbow_flex_joint", "forearm_roll_joint", "wrist_flex_joint", "wrist_roll_joint"] joints_value = [0.3, 1.32, 0.7, 0.0, -2.0, 0.0, -0.57, 0.0] move_group.moveToJointPosition(joint_names, joints_value, wait=False) # Since we passed in wait=False above we need to wait here move_group.get_move_action().wait_for_result() result = move_group.get_move_action().get_result() if result: # Checking the MoveItErrorCode if result.error_code.val == MoveItErrorCodes.SUCCESS: rospy.loginfo("pose Success!") else: # If you get to this point please search for: # moveit_msgs/MoveItErrorCodes.msg rospy.logerr("Arm goal in state: %s", self.move_group.get_move_action().get_state()) else: rospy.logerr("MoveIt! failure no result returned.") def on_press(event): if event.inaxes is None: print 'None' return print event.xdata, event.ydata detect.move_corner(int(event.ydata), int(event.xdata)) if __name__ == '__main__': rospy.init_node("test_connect") detect = Detect() while True: detect.get_detect_info() plt.imshow(plt.imread('image/fetch.png')) plt.connect("button_press_event", on_press) plt.show()

[ "904615562@qq.com" ]

904615562@qq.com

85d502c61f2fa92729891532280aef50f4e2f0f3

4c5c0a3883a9416568930f7e434e1b4cfb333831

/form_test.py

2e436dd367465b7cf1987029b3698ce8036b0b2a

[]

no_license

SugimuraMichael/pfscm_change_tracker

8bc9dec4ad180da55f0f765babd431152cbcd9e5

467d3500c7e16ea5d29160c2f843fc54e8cbf146

refs/heads/master

2021-01-01T16:32:22.036952

2017-07-20T15:57:00

97,853,919

0

null

UTF-8

Python

false

9,404

py

''' current version of this code takes a google sheet, copy of original which is static... next thing would be to implement a copy in google drive... I can go do that now... total time is --- 149.125 seconds --- ''' import gspread import pandas as pd from oauth2client.service_account import ServiceAccountCredentials import time from spreadsheets.url_keeper import url_form_test start_timez = time.time() import json from pprint import pprint #with open('C:/Users/585000/Desktop/Python Projects/PPM USAID/spreadsheets/client_secret_2.json') as data_file: # data = json.load(data_file) #pprint(data) scope = ['https://spreadsheets.google.com/feeds'] credentials = ServiceAccountCredentials.from_json_keyfile_name('C:/Users/585000/Desktop/Python Projects/PPM USAID/spreadsheets/client_secret_2.json', scope) gc = gspread.authorize(credentials) wks = gc.open_by_url(url_form_test()).get_worksheet(0) internal_wks = gc.open_by_url(url_form_test()).get_worksheet(1) external_wks = gc.open_by_url(url_form_test()).get_worksheet(2) change_sheet = pd.DataFrame() internal_sheet = pd.DataFrame() external_sheet = pd.DataFrame() for i in range(22): n = i+1 print n column = pd.DataFrame([sub for sub in wks.col_values(n)]) column.columns = column.iloc[0] column = column.ix[1:] change_sheet = pd.concat([change_sheet, column], axis=1) del column column = pd.DataFrame([sub for sub in internal_wks.col_values(n)]) column.columns = column.iloc[0] column = column.ix[1:] internal_sheet = pd.concat([internal_sheet, column], axis=1) del column column = pd.DataFrame([sub for sub in external_wks.col_values(n)]) column.columns = column.iloc[0] column = column.ix[1:] external_sheet = pd.concat([external_sheet, column], axis=1) del column def checkEqual1(iterator): iterator = iter(iterator) try: first = next(iterator) except StopIteration: return True return all(first == rest for rest in iterator) # need to deal with duplicate coluimns Orion Field Name col_list_change_sheet = ['Timestamp', 'Email Address', "Requester's Name", 'Reason for change', 'Program', 'Do you need bulk changes?', 'Please upload file with bulk changes', 'PE No', 'PQ No', 'Order No', 'Shipment No', 'What type of change is needed?', 'Orion field name', 'Current date', 'Requested new date', 'Orion field name_2', 'Current data', 'Requested new data', 'Correction ID', 'Date approved by PMU', 'Global Fund or Internal tab?', 'Status of correction by ORION'] change_sheet.columns = col_list_change_sheet drop_list = [] for indexz, row in change_sheet.iterrows(): if checkEqual1(row.tolist()) == True: drop_list.append(indexz-1) change_sheet=change_sheet.drop(change_sheet.index[drop_list]) drop_list = [] for indexz, row in internal_sheet.iterrows(): if checkEqual1(row.tolist()) == True: drop_list.append(indexz-1) internal_sheet = internal_sheet.drop(internal_sheet.index[drop_list]) drop_list = [] for indexz, row in external_sheet.iterrows(): if checkEqual1(row.tolist()) == True: drop_list.append(indexz-1) change_sheet = change_sheet.rename(index={15:'Orion field name_2'}) external_sheet = external_sheet.drop(external_sheet.index[drop_list]) external_sheet = external_sheet.drop('',axis=1) change_sheet_c = change_sheet.copy() additional_blank_cols = ['AD/UD Code', 'Number of days (+/-)', 'AD/UD Comments', 'Current AD', 'Current UD', 'Actual Delivery Date', 'COTD impact % (+/-)','Date change approved by TGF?', 'Shared with Client?' ] for col in additional_blank_cols: change_sheet_c[col]= '' done_sheet = change_sheet_c[change_sheet_c['Status of correction by ORION'].str.lower()=='done'] internal_edits =done_sheet[done_sheet['Global Fund or Internal tab?'].str.lower().str.contains('internal only')==True] external_edits =done_sheet[done_sheet['Global Fund or Internal tab?'].str.lower().str.contains('internal only')!=True] external_edits = external_edits.rename(index = str, columns={'Timestamp':'Date of request', 'Date approved by PMU':'Date Done', #seems not like a 1:1 ratio 'Reason for change':'Reason for change', #same but for good measure 'Program':'Program', #'' 'PE No':'PE No', 'PQ No':'PQ No', 'Order No':'Order No', 'Shipment No':'Shipment No', 'Orion field name':'Field Name associated with correction', 'Current date':'Existing Data (Orion)', 'Requested new date':'New Data (Revised)', 'Status of correction by ORION':'Done' } ) external_edits = external_edits[['Date of request', 'Date Done', 'Reason for change', 'Program', 'PE No', 'PQ No', 'Order No', 'Shipment No', 'Field Name associated with correction', 'Existing Data (Orion)', 'New Data (Revised)', 'AD/UD Code', 'Number of days (+/-)', 'AD/UD Comments', 'Current AD', 'Current UD', 'Actual Delivery Date', 'COTD impact % (+/-)', 'Done', 'Date change approved by TGF?', 'Shared with Client?']] external_result = pd.concat([external_sheet,external_edits]) internal_edits = internal_edits.rename(index = str, columns={'Timestamp':'Date of request', 'Date approved by PMU':'Date Done', #seems not like a 1:1 ratio 'Reason for change':'Reason for Correction', #same but for good measure 'Program':'Program', #'' 'PE No':'PE No', 'PQ No':'PQ No', 'Order No':'Order No', 'Shipment No':'Shipment No', 'Orion field name':'Field Name associated with correction', 'Current date':'Existing Data (Orion)', 'Requested new date':'New Data (Revised)', 'Status of correction by ORION':'Status' } ) internal_edits['Reason for change'] = '' internal_col_list =['Date of request', 'Date Done', 'Reason for Correction', 'Program', 'PE No', 'PQ No', 'Order No', 'Shipment No', 'Field Name associated with correction', 'Existing Data (Orion)', 'New Data (Revised)', 'AD/UD Code', 'Number of days (+/-)', 'AD/UD Comments', 'Current AD', 'Current UD', 'Actual Delivery Date', 'COTD impact % (+/-)', 'Reason for change', 'Status', 'Date change approved by TGF?', 'Shared with Client?'] #df a dataframe and ws a google api worksheet object def to_googlesheet(df,ws): def numberToLetters(q): q = q - 1 result = '' while q >= 0: remain = q % 26 result = chr(remain+65) + result; q = q//26 - 1 return result # columns names columns = df.columns.values.tolist() # selection of the range that will be updated cell_list = ws.range('A1:'+numberToLetters(len(columns))+'1') # modifying the values in the range for cell in cell_list: val = columns[cell.col-1] if type(val) is str: val = val.decode('utf-8') cell.value = val # update in batch ws.update_cells(cell_list) # number of lines and columns num_lines, num_columns = df.shape # selection of the range that will be updated cell_list = ws.range('A2:' + numberToLetters(num_columns) + str(num_lines + 1)) # modifying the values in the range for cell in cell_list: val = df.iloc[cell.row - 2, cell.col - 1] if type(val) is str: val = val.decode('utf-8') elif isinstance(val, (int, long, float, complex)): # note that we round all numbers val = int(round(val)) cell.value = val # update in batch ws.update_cells(cell_list) internal_edits = internal_edits[internal_col_list] internal_result = pd.concat([internal_sheet,internal_edits]) internal_save_loc = gc.open_by_url(url_form_test()).get_worksheet(3) external_save_loc = gc.open_by_url(url_form_test()).get_worksheet(4) internal_save_loc.clear() external_save_loc.clear() to_googlesheet(internal_result,internal_save_loc) to_googlesheet(external_result,external_save_loc) #external_result.to_csv('C:/Users/585000/Desktop/PCFSM/2017 KPIs/external_change_test_v1.csv',index = False) #internal_result.to_csv('C:/Users/585000/Desktop/PCFSM/2017 KPIs/internal_change_test_v1.csv',index = False) print("total time --- %s seconds ---" % (time.time() - start_timez))

[ "sugimura_michael@bah.com" ]

sugimura_michael@bah.com

713e56b0dfc1b28ab55d67e75f8720cff692e593

ac5e52a3fc52dde58d208746cddabef2e378119e

/exps-mrsp.0/mrsp_ut=3.5_rd=0.5_rw=0.06_rn=4_u=0.075-0.325_p=harmonic-2/sched=RUN_trial=49/params.py

fcbfbdfe45d4c06dbfe8c250d00b2d4aa9ae3364

[]

no_license

ricardobtxr/experiment-scripts

1e2abfcd94fb0ef5a56c5d7dffddfe814752eef1

7bcebff7ac2f2822423f211f1162cd017a18babb

refs/heads/master

2023-04-09T02:37:41.466794

2021-04-25T03:27:16

358,926,457

0

null

UTF-8

Python

false

250

py

{'cpus': 4, 'duration': 30, 'final_util': '3.557024', 'max_util': '3.5', 'periods': 'harmonic-2', 'release_master': False, 'res_distr': '0.5', 'res_nmb': '4', 'res_weight': '0.06', 'scheduler': 'RUN', 'trial': 49, 'utils': 'uni-medium-3'}

[ "ricardo.btxr@gmail.com" ]

ricardo.btxr@gmail.com

2c15394b0845b5b8e849bf6d0b52a048d9f8be8d

016cab497a506bb50ac5b89e3e4622c244fe1339

/src/TrainModel/study1_rawdata_plot.py

ac605a5599dd121b62cc445cc9f248d7e0aa7597

[]

no_license

SYilei/LimiTouch

917595cb87cf012542c3da3375342fa492d5a892

f757a7838572617239a4039a0daa31a826238830

refs/heads/master

2023-07-20T22:57:59.315684

2019-09-19T22:35:29

195,982,686

0

null

2023-07-06T21:41:32

2019-07-09T10:00:32

Python

UTF-8

Python

false

1,916

py

import torch import numpy as np import os import sys import pandas as pd import random from study1_modelclass import LeNet import matplotlib.pyplot as plt import plotly.graph_objects as go touch_data = pd.read_csv('../../data/Data_Study1/chamod_touch_free_circle.csv')[['ax','ay','az','gx','gy','gz']].values nontouch_data = pd.read_csv('../../data/Data_Study1/chamod_nontouch_free_circle.csv')[['ax','ay','az','gx','gy','gz']].values # touch_data = pd.read_csv('../../data/Data_Study1_Difference/chamod_touch_free_circle.csv')[['0','1','2','3','4','5']].values # nontouch_data = pd.read_csv('../../data/Data_Study1_Difference/chamod_nontouch_free_circle.csv')[['0','1','2','3','4','5']].values n = 2000 random_x = [i for i in range(n)] # random_y0 = touch_data[:n,0] # random_y1 = touch_data[:n,1] # random_y2 = touch_data[:n,2] # random_y3 = touch_data[:n,3] # random_y4 = touch_data[:n,4] # random_y5 = touch_data[:n,5] random_y0 = nontouch_data[:n,0] random_y1 = nontouch_data[:n,1] random_y2 = nontouch_data[:n,2] random_y3 = nontouch_data[:n,3] random_y4 = nontouch_data[:n,4] random_y5 = nontouch_data[:n,5] # print(random_y0) # quit(0) # Create traces fig = go.Figure() fig.add_trace(go.Scatter(x=random_x, y=random_y0, mode='lines', name='acc_x')) fig.add_trace(go.Scatter(x=random_x, y=random_y1, mode='lines', name='acc_y')) fig.add_trace(go.Scatter(x=random_x, y=random_y2, mode='lines', name='acc_z')) fig.add_trace(go.Scatter(x=random_x, y=random_y3, mode='lines', name='gyro_x')) fig.add_trace(go.Scatter(x=random_x, y=random_y4, mode='lines', name='gyro_y')) fig.add_trace(go.Scatter(x=random_x, y=random_y5, mode='lines', name='gyro_z')) fig.show()

[ "1000839@wifi-staff-172-24-20-243.net.auckland.ac.nz" ]

1000839@wifi-staff-172-24-20-243.net.auckland.ac.nz

c28398d854bb76f93aa2b6c24c128a40872bddbc

cc2a0a075a2f069e5b65bb749c7af0ac488ce1af

/manage.py

0e98d8243e2c62f9a996d0df5086d14f2899816c

[]

no_license

PaoAguilar/sistema-de-compras

ff88a39beceb42088a1b7b4662b2e2eff819c369

ae66deb9ca92fe7c168901ac526cb18b34ad2b8c

refs/heads/main

2023-02-10T07:56:34.657052

2020-11-29T23:48:48

316,593,342

0

1

null

2020-11-30T06:06:30

2020-11-27T20:24:07

JavaScript

UTF-8

Python

false

634

py

#!/usr/bin/env python """Django's command-line utility for administrative tasks.""" import os import sys def main(): os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'sistemaCompras.settings') try: from django.core.management import execute_from_command_line except ImportError as exc: raise ImportError( "Couldn't import Django. Are you sure it's installed and " "available on your PYTHONPATH environment variable? Did you " "forget to activate a virtual environment?" ) from exc execute_from_command_line(sys.argv) if __name__ == '__main__': main()

[ "pao.aquev29@gmail.com" ]

pao.aquev29@gmail.com

8b37a3439da396e141ef81dfd7a5c6ecb6a958c9

2763387c80e808e63bd9aa190e64a2582454f4f9

/aoc18/day05/day05.py

655f65e24f0534d2b87cf80ec34035f34501882c

[ "MIT" ]

permissive

dds/advent18

ca68bb26a3d8d890eeb0cdc49b15f6de7e6a7d7f

51c6f32cd90f50657d33527e1c9b3a0768f28538

refs/heads/master

2020-04-09T06:19:04.378438

2019-03-22T15:17:50

160,107,033

1

0

null

UTF-8

Python

false

1,614

py

import util from string import lowercase test_input = """ aA abBA aBAB aabAAB dabAcCaCBAcCcaDA """ def reaction(left, right): if left != right and (ord(left) % 32) == (ord(right) % 32): return True return False def reactPolymer(polymer): index = 1 while True: if len(polymer) == 0 or index >= len(polymer): break if reaction(polymer[index-1], polymer[index]): polymer = polymer[0:index-1] + polymer[index+1:] index -= 1 else: index += 1 return polymer polymers = [] for line in util.get_data(5).read().split('\n'): # for line in test_input.split('\n'): polymer = line if not polymer: continue polymers.append(polymer) def removeUnit(polymer, unit): index = 0 while True: if len(polymer) == 0 or index >= len(polymer): break if ord(polymer[index]) % 32 == ord(unit) % 32: polymer = polymer[:index] + polymer[index+1:] else: index += 1 return polymer for polymer in polymers: reactedPolymer = reactPolymer(polymer) print 'A: %d' % len(reactedPolymer) reducedPolymers = [removeUnit(polymer, c) for c in lowercase] print 'got reducedPolymers' reactedReducedPolymers = [] for i, p in enumerate(reducedPolymers): reactedReducedPolymers.append(reactPolymer(p)) print 'got %s reduced polymers' % chr(ord('a') + i) index, bestReducedPolymer = util.best([len(i) for i in reactedReducedPolymers], True) print 'B: %d (worst unit: %s)' % (bestReducedPolymer, chr(ord('a') + index))

[ "davidsmith@acm.org" ]

davidsmith@acm.org

541a41450f5c3cc864969e5d8d374347713e4ae8

92150eee1ef8c331b3a417cc9d5917cbf8274d37

/src/01_06_image_kernel.py

05ebe6a4136adf939742b67abc7f61baa872972c

[]

no_license

deepdeepdot/nano-deep-learning

87606acd7a0a9c0b1d4108f7dd5749e106eed652

20da799fac155e7e59e4fa56d3e949a81b895a89

refs/heads/master

2020-04-26T06:36:44.944351

2019-04-21T18:32:13

173,370,424

2

1

null

UTF-8

Python

false

572

py

import matplotlib.pyplot as plt import numpy as np img = plt.imread("panda-corner.jpg") nrows, ncols = img.shape[0], img.shape[1] nchannels = img.shape[2] emboss = [ [-2, -1, 0], [-1, 1, 1], [0, 1, 2] ] buffer = np.zeros((nrows, ncols, 3)) for i in range(1, nrows-1): for j in range(1, ncols-1): for c in range(nchannels): source = img[i-1:i+2, j-1:j+2, c] buffer[i][j][c] = np.sum(np.multiply(source, emboss)) buffer = np.clip(buffer, 0, 255).astype(np.int8) plt.imsave(f"out/panda-emboss.png", buffer) print("Done!")

[ "ph@educreational.com" ]

ph@educreational.com

9a4567c7387b96986d9b3a7c804bb75fdfa5cae6

e29d2cd7dd16f961a964dbf90a7b3e011ecf7c4c

/LambdaAWSFunction/message.py

bcc9a6323e1350a0f2352d92bd579524d14d686c

[]

no_license

shrirangbagdi/LogParser

8c0af19a7f01ec2682eed0818bbb3eb7201ddf7f

a5a2dabbfab828e103f5062bb65d2988a72323e4

refs/heads/master

2022-12-01T06:28:05.091601

2020-08-16T22:17:05

274,524,768

1

0

null

UTF-8

Python

false

4,754

py

import boto3 from PatternFour import PatternFour from PatternOne import PatternOne from PatternThree import PatternThree class message: def __init__(self, caseID, event): self.caseID = caseID self.event = event def generate_messages(self): s3 = boto3.client('s3') list_of_messages = [] warning = {} event = self.event file_name = str(event['Records'][0]['s3']['object']['key']) bucket = str(event['Records'][0]['s3']['bucket']['name']) obj = s3.get_object(Bucket=bucket, Key=file_name) previous_type = "" first_iteration = True for line in obj["Body"].read().decode(encoding="utf-8").splitlines(): pattern_one = PatternOne(line) pattern_three = PatternThree(line) pattern_four = PatternFour(line) if pattern_one.IsPatternOne(): if (not first_iteration) and (previous_type == "WARN" or previous_type == "ERROR"): list_of_messages.append(warning) else: first_iteration = False warning = {} current_type = pattern_one.GetCurrentType() if current_type == "WARN": timestamp = pattern_one.GetTimeStamp() message = pattern_one.GetMessage() warning = {'File Name': file_name, 'Case ID': self.caseID, 'Timestamp': pattern_one.ConvertTimestamp(timestamp), 'Type': current_type, 'Message': message.strip()} if current_type == "ERROR": timestamp = pattern_one.GetTimeStamp() message = pattern_one.GetMessage() warning = {'File Name': file_name, 'Case ID': self.caseID, 'Timestamp': pattern_one.ConvertTimestamp(timestamp), 'Type': current_type, 'Message': message.strip()} previous_type = current_type elif pattern_three.IsPatternThree(): if (not first_iteration) and (previous_type == "WARN" or previous_type == "ERROR"): list_of_messages.append(warning) else: first_iteration = False warning = {} current_type = pattern_three.GetCurrentType() if current_type == "WARN": timestamp = pattern_three.GetTimeStamp() message = pattern_three.GetMessage() warning = {'File Name': file_name, 'Case ID': self.caseID, 'Timestamp': pattern_three.ConvertTimestamp(timestamp), 'Type': current_type, 'Message': message.strip()} if current_type == "ERROR": timestamp = pattern_three.GetTimeStamp() message = pattern_three.GetMessage() warning = {'File Name': file_name, 'Case ID': self.caseID, 'Timestamp': pattern_three.ConvertTimestamp(timestamp), 'Type': current_type, 'Message': message.strip()} previous_type = current_type elif pattern_four.IsPatternFour(): if (not first_iteration) and (previous_type == "WARN" or previous_type == "ERROR"): list_of_messages.append(warning) else: first_iteration = False warning = {} current_type = pattern_four.GetCurrentType() if current_type == "WARN": timestamp = pattern_four.GetTimeStamp() message = pattern_four.GetMessage() warning = {'File Name': file_name, 'Case ID': self.caseID, 'Timestamp': pattern_four.ConvertTimestamp(timestamp), 'Type': current_type, 'Message': message.strip()} if current_type == "ERROR": timestamp = pattern_four.GetTimeStamp() message = pattern_four.GetMessage() warning = {'File Name': file_name, 'Case ID': self.caseID, 'Timestamp': pattern_four.ConvertTimestamp(timestamp), 'Type': current_type, 'Message': message.strip()} previous_type = current_type elif previous_type == "WARN" or previous_type == "ERROR": warning["Message"] += line if previous_type == "ERROR" or previous_type == "WARN": list_of_messages.append(warning) return list_of_messages

[ "sbagdi2@illinois.edu" ]

sbagdi2@illinois.edu

9ac194b9a29467ac4a1e58124ee6a21762943045

e2b0f6994076bf5183108bc8ee016ea09648cc5e

/apps/organization/migrations/0003_auto_20171119_2204.py

e78d20a4db95a404a9b0a0fcf71932ddb795d212

[]

no_license

tongguoweizpp/MxOnline

aaa8bf7e12be133e94cd6a10f76bdfab049c023b

8ad57dd0eba5c297de8da09d3908cd238d0c23b7

refs/heads/master

2021-09-04T09:09:34.663455

2018-01-17T15:13:49

117,853,001

1

0

null

UTF-8

Python

false

668

py

# -*- coding: utf-8 -*- # Generated by Django 1.9 on 2017-11-19 22:04 from __future__ import unicode_literals from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('organization', '0002_auto_20171119_1113'), ] operations = [ migrations.AddField( model_name='courseorg', name='course_nums', field=models.IntegerField(default=0, verbose_name='课程数'), ), migrations.AddField( model_name='courseorg', name='students', field=models.IntegerField(default=0, verbose_name='学习人数'), ), ]

[ "tongguoweizpp@gmail.com" ]

tongguoweizpp@gmail.com

d0b17bbfb828844b9aade0e41ba7dacd041135e1

b83bebc43ba07d299c5e8a3d954f710024d27bcc

/owners_api/serializers.py

3ed8fe27b47c19714055045d0ff8595764dc12a8

[]

no_license

Faiza1987/Salon-API

eaf601b44abed4fcd2501f1076aa4f043e14caa7

8bfab422accb2a5cae253a08f9cdab05a7469e6d

refs/heads/master

2022-11-27T14:29:56.986608

2019-07-22T20:49:14

196,279,240

0

null

2022-11-22T04:07:37

2019-07-10T21:48:18

Python

UTF-8

Python

false

2,117

py

from rest_framework import serializers from owners_api.models import User, UserProfile class UserProfileSerializer(serializers.ModelSerializer): class Meta: model = UserProfile fields = ('salon_name', 'salon_address', 'salon_city', 'salon_state', 'salon_zip', 'salon_phone_number', 'salon_description') class UserSerializer(serializers.HyperlinkedModelSerializer): profile = UserProfileSerializer(required=True) jobs = serializers.HyperlinkedRelatedField( many=True, read_only=True, view_name='job-detail' ) class Meta: model = User fields = ('url', 'email', 'first_name', 'last_name', 'password', 'profile', 'jobs') extra_kwargs = {'password': {'write_only': True}} def create(self, validated_data): profile_data = validated_data.pop('profile') password = validated_data.pop('password') user = User(**validated_data) user.set_password(password) user.save() UserProfile.objects.create(user=user, **profile_data) return user def update(self, instance, validated_data): profile_data = validated_data.pop('profile') profile = instance.profile instance.email = validated_data.get('email', instance.email) instance.save() profile.salon_name = profile_data.get( 'salon_name', profile.salon_name) profile.salon_address = profile_data.get( 'salon_address', profile.salon_address) profile.salon_city = profile_data.get( 'salon_city', profile.salon_city) profile.salon_state = profile_data.get( 'salon_state', profile.salon_state) profile.salon_zip = profile_data.get( 'salon_zip', profile.salon_zip) profile.salon_phone_number = profile_data.get( 'salon_phone_number', profile.salon_phone_number) profile.salon_description = profile_data.get( 'salon_description', profile.salon_description) profile.save() return instance

[ "faiza.ahsan1222@gmail.com" ]

faiza.ahsan1222@gmail.com