xszheng2020/the_stack_dedup_python_hits_0 · Datasets at Hugging Face

44d81f4a562fc325b6fdaf33b5effc0dd60d28d3

26,298

py

Python

hgapp/powers/createPowerFormUtilities.py

shadytradesman/The-Contract-Website

d8b353064f91c53ebab951dec784a0a36caba260

[ "Apache-2.0" ]

6

2020-10-03T12:15:05.000Z

2021-10-15T04:43:36.000Z

hgapp/powers/createPowerFormUtilities.py

shadytradesman/The-Contract-Website

d8b353064f91c53ebab951dec784a0a36caba260

[ "Apache-2.0" ]

99

2020-06-04T17:43:56.000Z

2022-03-12T01:07:20.000Z

hgapp/powers/createPowerFormUtilities.py

shadytradesman/The-Contract-Website

d8b353064f91c53ebab951dec784a0a36caba260

[ "Apache-2.0" ]

9

2020-06-06T16:39:09.000Z

2020-10-02T16:24:17.000Z

from django.forms import formset_factory from django.utils import timezone from django.shortcuts import get_object_or_404 import bleach import json from .forms import CreatePowerForm, make_enhancement_form, make_drawback_form, make_parameter_form, \ SystemFieldRollForm, SystemFieldTextForm, MIND_, BODY_, PARRY_ from .models import Enhancement_Instance, Drawback_Instance, Power, DICE_SYSTEM, Enhancement, Drawback, \ Power_Param, SystemFieldText, SystemFieldRoll, SystemFieldTextInstance, SystemFieldRollInstance, \ Parameter_Value, Base_Power_System, Power_Full, CREATION_REASON, PowerTutorial from characters.models import Roll, Attribute, Ability, NO_PARRY_INFO, REACTION, THROWN def get_create_power_context_from_base(base_power, character=None): system = base_power.get_system() primary_form = CreatePowerForm(base_power, initial={'system': system.system_text}) enhancement_forms = [] for enhancement in Enhancement.objects.filter(pk__in=base_power.enhancements.all()): enhancement_forms.append(formset_factory(make_enhancement_form(enhancement), extra = 1)()) drawback_forms = [] for drawback in Drawback.objects.filter(pk__in=base_power.drawbacks.all()): drawback_forms.append(formset_factory(make_drawback_form(drawback), extra = 1)()) parameter_forms = [] for parameter in Power_Param.objects.filter(relevant_base_power=base_power).all(): parameter_forms.append(formset_factory(make_parameter_form(parameter))()) roll_fields_formset = _get_system_roll_field_formset(system) text_fields_formset = _get_system_text_field_formset(system) system = Base_Power_System.objects.filter(dice_system=DICE_SYSTEM[1][0]).get(base_power=base_power.slug) requirements = _get_modifier_requirements(Enhancement.objects.filter(pk__in=base_power.enhancements.all()), Drawback.objects.filter(pk__in=base_power.drawbacks.all())) context = { 'base_power': base_power, 'power_system': system, 'form': primary_form, 'parameters': parameter_forms, 'enhancements': enhancement_forms, 'drawbacks': drawback_forms, 'requirements_json': json.dumps(requirements), 'character': character, 'roll_fields': roll_fields_formset, 'text_fields': text_fields_formset, } if character: unspent_rewards = [] for reward in character.unspent_rewards().all(): unspent_rewards.append("{} from {}".format(reward.type_text(), reward.reason_text())) context["unspent_rewards_json"] = json.dumps(unspent_rewards) spent_rewards = [] context["spent_rewards_json"] = json.dumps(spent_rewards) context = _add_tutorial_to_context(context) return context def get_create_power_context_from_power(power, new=True): initial = {'system': power.get_system(), 'description': power.description, 'flavor': power.flavor_text, 'activation_style': power.activation_style, 'power_name': power.name} if power.parent_power: initial['tags'] = power.parent_power.tags.all() initial['example_description'] = power.parent_power.example_description system = Base_Power_System.objects.filter(dice_system=DICE_SYSTEM[1][0]).get(base_power=power.base.slug) text_fields_formset = _get_text_field_formsets_for_edit(power, system) roll_fields_formset = _get_roll_field_formsets_for_edit(power, system) primary_form = CreatePowerForm(power.base, initial=initial) enhancement_forms = _get_enhancement_formsets_from_power(power) drawback_forms = _get_drawback_formsets_from_power(power) parameter_forms = [] for parameter_value in Parameter_Value.objects.filter(relevant_power=power).all(): init = [{'level_picker': parameter_value.value}] parameter_forms.append(formset_factory(make_parameter_form(parameter_value.relevant_power_param), extra = 0)(initial = init)) requirements = _get_modifier_requirements(Enhancement.objects.filter(pk__in=power.base.enhancements.all()), Drawback.objects.filter(pk__in=power.base.drawbacks.all())) context = { 'base_power': power.base, 'power_system': system, 'form': primary_form, 'parameters': parameter_forms, 'enhancements': enhancement_forms, 'drawbacks': drawback_forms, 'requirements_json': json.dumps(requirements), 'roll_fields': roll_fields_formset, 'text_fields': text_fields_formset, } if power.parent_power is not None: if power.parent_power.character is not None and new: context["character"] = power.parent_power.character unspent_rewards = [] for reward in power.parent_power.character.unspent_rewards().all(): unspent_rewards.append("{} from {}".format(reward.type_text(), reward.reason_text())) context["unspent_rewards_json"] = json.dumps(unspent_rewards) spent_rewards = [] for reward in power.parent_power.reward_list(): spent_rewards.append("{} from {}".format(reward.type_text(), reward.reason_text())) context["spent_rewards_json"] = json.dumps(spent_rewards) context = _add_tutorial_to_context(context) return context def _get_text_field_formsets_for_edit(power, system): TextFieldsFormset = formset_factory(SystemFieldTextForm, extra=0) text_system_fields = system.systemfieldtext_set.order_by("id").all() instances = power.systemfieldtextinstance_set.all() value_by_field_id = {n.relevant_field.id: n.value for n in instances} return TextFieldsFormset( initial=[{'system_field_id': x.id, 'system_field': x, 'field_text': value_by_field_id[x.id] if x.id in value_by_field_id else "" } for x in text_system_fields], prefix="system_text_fields") def _get_roll_field_formsets_for_edit(power, system): RollFieldsFormset = formset_factory(SystemFieldRollForm, extra=0) roll_system_fields = system.systemfieldroll_set.order_by("id").all() instances = power.systemfieldrollinstance_set.all() value_by_field_id = {n.relevant_field.id: n.roll for n in instances} return RollFieldsFormset( initial=[{'system_field_id': x.id, 'system_field': x, 'ability_roll': _get_roll_initial_ability(value_by_field_id[x.id]) if x.id in value_by_field_id else None, 'attribute_roll': _get_roll_initial_attribute(value_by_field_id[x.id]) if x.id in value_by_field_id else None, } for x in roll_system_fields], prefix="system_roll_fields") def _get_roll_initial_ability(roll): if roll.ability: return roll.ability.id else: return None def _get_roll_initial_attribute(roll): if roll.attribute: return roll.attribute.id elif roll.is_mind: return MIND_ elif roll.is_body: return BODY_ elif roll.parry_type != NO_PARRY_INFO: return PARRY_ else: raise ValueError("Unknown roll attribute") def get_edit_power_context_from_power(og_power): context = get_create_power_context_from_power(og_power) if og_power.parent_power is not None and og_power.parent_power.owner is not None: context["owner"] = og_power.parent_power.owner context["og_power"] = og_power return context def create_power_for_new_edit(base_power, request, power_full): power_form = CreatePowerForm(base_power, request.POST) if power_form.is_valid(): old_power = power_full.latest_revision() if request.user.is_superuser: power_full.tags.set(power_form.cleaned_data["tags"]) power_full.example_description = power_form.cleaned_data["example_description"] power_full.save() new_power = _create_power_from_post_and_base(base_power, request, power_full) new_power.creation_reason = _get_power_creation_reason(new_power, old_power) new_power.creation_reason_expanded_text = _get_power_creation_reason_expanded_text(new_power, old_power) new_power.save() if hasattr(power_full, "character") and power_full.character: power_full.character.reset_attribute_bonuses() return new_power def create_new_power_and_parent(base_power, request, character=None): form = CreatePowerForm(base_power, request.POST) if form.is_valid(): power_full = _create_new_full_power(power_form=form, base=base_power) if request.user.id: power_full.owner = request.user if character: power_full.character = character power_full.save() if request.user.is_superuser: power_full.tags.set(form.cleaned_data["tags"]) power_full.example_description = form.cleaned_data["example_description"] power_full.save() new_power = _create_power_from_post_and_base(base_power, request, power_full) new_power.creation_reason = CREATION_REASON[0][0] new_power.creation_reason_expanded_text = "Initial power creation" new_power.save() if character: character.reset_attribute_bonuses() return new_power else: print(form.errors) return None def refund_or_assign_rewards(new_power, old_power=None): og_point_value = 0 if old_power: og_point_value=old_power.get_point_value() delta = new_power.get_point_value() - og_point_value if delta == 0: return if delta > 0: if new_power.parent_power.character is not None: unspent_gifts = new_power.parent_power.character.unspent_rewards() for a in range(delta): if a == len(unspent_gifts): break unspent_gifts[a].assign_to_power(new_power) if delta < 0: if new_power.parent_power.character is not None and old_power: spent_gifts = old_power.parent_power.reward_list() for a in range(delta*-1): if a == len(spent_gifts): break spent_gifts[a].refund_keeping_character_assignment() def _get_enhancement_formsets_from_power(power): enhancement_forms = [] enhancement_instances = Enhancement_Instance.objects.filter(relevant_power=power).all() for base_enhancement in Enhancement.objects.filter(pk__in=power.base.enhancements.all()): instances_of_this_enhancement = set( x for x in enhancement_instances if (x.relevant_enhancement == base_enhancement)) init = [] num_extra = 0 for enhancement_instance in instances_of_this_enhancement: init.append({ 'is_selected': True, 'detail_text': enhancement_instance.detail, }) if base_enhancement.multiplicity_allowed or not instances_of_this_enhancement: num_extra = 1 new_form = formset_factory(make_enhancement_form(base_enhancement), extra=num_extra, max_num=4)(initial=init) enhancement_forms.append(new_form) return enhancement_forms def _get_drawback_formsets_from_power(power): drawback_forms = [] drawback_instances = Drawback_Instance.objects.filter(relevant_power=power).all() for base_drawback in Drawback.objects.filter(pk__in=power.base.drawbacks.all()): instances_of_this_drawback = set( x for x in drawback_instances if (x.relevant_drawback == base_drawback)) init = [] num_extra = 0 for drawback_instance in instances_of_this_drawback: init.append({ 'is_selected': True, 'detail_text': drawback_instance.detail, }) if base_drawback.multiplicity_allowed or not instances_of_this_drawback: num_extra = 1 new_form = formset_factory(make_drawback_form(base_drawback), extra=num_extra, max_num=4)(initial=init) drawback_forms.append(new_form) return drawback_forms def _add_tutorial_to_context(context): tutorial = get_object_or_404(PowerTutorial) context['modal_header'] = tutorial.modal_edit_header context['modal_text'] = tutorial.modal_edit context['modal_art'] = 'overrides/art/ocean-walking-copy.jpg' return context def _get_modifier_requirements(enhancements, drawbacks): requirements = {} for enhancement in enhancements: if enhancement.required_Enhancements: required = [] for req_enhancement in enhancement.required_Enhancements.all(): required.append( req_enhancement.form_name() ) requirements[enhancement.form_name()] = required for drawback in drawbacks: if drawback.required_drawbacks: required = [] for req_drawback in drawback.required_drawbacks.all(): required.append(req_drawback.form_name()) requirements[drawback.form_name()] = required return requirements def _get_enhancement_instances(post_data, enhancements, new_power): instances = [] for enhancement in enhancements: if enhancement.slug + "-e-is_selected" in post_data: detail_texts = [] if enhancement.slug + "-e-detail_text" in post_data: detail_texts = post_data.getlist(enhancement.slug + "-e-detail_text") for on in post_data.getlist(enhancement.slug + "-e-is_selected"): if detail_texts: new_detail_text = bleach.clean(detail_texts.pop(0)) else: new_detail_text = "" instances.append(Enhancement_Instance(relevant_enhancement=enhancement, relevant_power=new_power, detail=new_detail_text)) return instances def _get_drawback_instances(post_data, drawbacks, new_power): instances = [] for drawback in drawbacks: if drawback.slug + "-d-is_selected" in post_data: detail_texts = [] if drawback.slug + "-d-detail_text" in post_data: detail_texts = post_data.getlist(drawback.slug + "-d-detail_text") for on in post_data.getlist(drawback.slug + "-d-is_selected"): if detail_texts: new_detail_text = bleach.clean(detail_texts.pop(0)) else: new_detail_text = "" instances.append(Drawback_Instance(relevant_drawback=drawback, relevant_power=new_power, detail=new_detail_text)) return instances def _create_new_full_power(power_form, base): return Power_Full(name=power_form.cleaned_data['power_name'], dice_system=DICE_SYSTEM[1][0], base=base, pub_date=timezone.now()) def _get_power_from_form(power_form, base): return Power(name=power_form.cleaned_data['power_name'], flavor_text=power_form.cleaned_data['flavor'], description=power_form.cleaned_data['description'], system=power_form.cleaned_data['system'], activation_style=power_form.cleaned_data['activation_style'], base=base, dice_system=DICE_SYSTEM[1][0], pub_date=timezone.now()) def _get_roll_from_form_and_system(form, system_field): attr = form.cleaned_data["attribute_roll"] difficulty = 6 if system_field.difficulty: difficulty = system_field.difficulty if attr == BODY_[0] or attr == MIND_[0] or attr == PARRY_[0]: if attr == BODY_[0]: return Roll.get_body_roll(difficulty=difficulty) elif attr == MIND_[0]: return Roll.get_mind_roll(difficulty=difficulty) elif attr == PARRY_[0]: return Roll.get_roll(difficulty=difficulty, parry_type=system_field.parry_type, speed=REACTION) else: raise ValueError("Unexpected attr") else: attribute = get_object_or_404(Attribute, id=attr) ability = get_object_or_404(Ability, id=form.cleaned_data["ability_roll"]) return Roll.get_roll(attribute = attribute, ability = ability, difficulty = difficulty, speed=system_field.speed) def _create_power_from_post_and_base(base_power, request, power_full): form = CreatePowerForm(base_power, request.POST) if form.is_valid(): system = Base_Power_System.objects.filter(dice_system=DICE_SYSTEM[1][0]).get(base_power=base_power.slug) power = _get_power_from_form(power_form=form, base=base_power) if request.user.id: power.created_by = request.user power.parent_power = power_full power.save() enhancement_instances = _get_enhancement_instances(post_data=request.POST, enhancements=Enhancement.objects.filter( pk__in=base_power.enhancements.all()), new_power=power) for enhancement_instance in enhancement_instances: enhancement_instance.save() drawback_instances = _get_drawback_instances(post_data=request.POST, drawbacks=Drawback.objects.filter( pk__in=base_power.drawbacks.all()), new_power=power) for drawback_instance in drawback_instances: drawback_instance.save() for power_param in Power_Param.objects.filter(relevant_base_power=base_power): param_val = Parameter_Value(relevant_power=power, relevant_power_param=power_param, value=request.POST[power_param.relevant_parameter.slug]) param_val.save() text_field_formset = _get_system_text_field_formset(system, request.POST) if text_field_formset.is_valid(): for form in text_field_formset: system_field = get_object_or_404(SystemFieldText, id=form.cleaned_data["system_field_id"]) field_instance = SystemFieldTextInstance(relevant_power=power, relevant_field=system_field, value=form.cleaned_data["field_text"]) field_instance.save() else: raise ValueError("Invalid text field formset") roll_field_formset = _get_system_roll_field_formset(system, request.POST) if roll_field_formset.is_valid(): for form in roll_field_formset: system_field = get_object_or_404(SystemFieldRoll, id=form.cleaned_data["system_field_id"]) roll = _get_roll_from_form_and_system(form, system_field) field_instance = SystemFieldRollInstance(relevant_power=power, relevant_field=system_field, roll=roll) field_instance.save() else: raise ValueError("Invalid roll field formset") return power else: raise ValueError("Invalid Power Form") def _get_system_text_field_formset(system, POST = None): TextFieldsFormset = formset_factory(SystemFieldTextForm, extra=0) text_system_fields = system.systemfieldtext_set.order_by("id").all() text_fields_formset = TextFieldsFormset( POST, initial=[{'system_field_id': x.id, 'system_field': x} for x in text_system_fields], prefix="system_text_fields") return text_fields_formset def _get_system_roll_field_formset(system, POST=None): RollFieldsFormset = formset_factory(SystemFieldRollForm, extra=0) roll_system_fields = system.systemfieldroll_set.order_by("id").all() roll_fields_formset = RollFieldsFormset( POST, initial=[{'system_field_id': x.id, 'system_field': x} for x in roll_system_fields], prefix="system_roll_fields") return roll_fields_formset def _get_power_creation_reason(new_power, old_power): if old_power is None: # new return CREATION_REASON[0][0] new_points = new_power.get_point_value() old_points = old_power.get_point_value() if new_points > old_points: # improvement return CREATION_REASON[1][0] if new_points < old_points\ or _get_param_difference_text(new_power, old_power)\ or _get_added_enhancements(new_power, old_power)\ or _get_removed_enhancements(new_power, old_power)\ or _get_added_drawbacks(new_power, old_power)\ or _get_removed_drawbacks(new_power, old_power): # revision return CREATION_REASON[2][0] # adjustment return CREATION_REASON[3][0] def _get_power_creation_reason_expanded_text(new_power, old_power): edit_text = "" if new_power.creation_reason == CREATION_REASON[3][0]: edit_text = "Text field change" if new_power.creation_reason == CREATION_REASON[1][0] or new_power.creation_reason == CREATION_REASON[2][0]: # improvement or revision added_enhancements = _get_added_enhancements(new_power, old_power) if len(added_enhancements) > 0: edit_text = edit_text + "Added Enhancement" if len(added_enhancements) > 1: edit_text = edit_text + "s" edit_text = edit_text + ": " for enhancement in added_enhancements: edit_text = edit_text + enhancement.relevant_enhancement.name + ", " removed_enhancements = _get_removed_enhancements(new_power, old_power) if len(removed_enhancements) > 0: edit_text = edit_text + "Removed Enhancement" if len(removed_enhancements) > 1: edit_text = edit_text + "s" edit_text = edit_text + ": " for enhancement in removed_enhancements: edit_text = edit_text + enhancement.relevant_enhancement.name + ", " added_drawbacks = _get_added_drawbacks(new_power, old_power) if len(added_drawbacks) > 0: edit_text = edit_text + "Added Drawback" if len(added_drawbacks) > 1: edit_text = edit_text + "s" edit_text = edit_text + ": " for drawback in added_drawbacks: edit_text = edit_text + drawback.relevant_drawback.name + ", " removed_drawbacks = _get_removed_drawbacks(new_power, old_power) if len(removed_drawbacks) > 0: edit_text = edit_text + "Removed Drawback" if len(removed_drawbacks) > 1: edit_text = edit_text + "s" edit_text = edit_text + ": " for drawback in removed_drawbacks: edit_text = edit_text + drawback.relevant_drawback.name + ", " edit_text = edit_text + _get_param_difference_text(new_power, old_power) #stopgap bugfix measure until we fix the _get_added_enhancements method by properly using form fields. if len(edit_text) < 3: edit_text = "Power Adjustment" if edit_text[-2] == ',': edit_text = edit_text[:-2] return edit_text[:1500] def _get_added_enhancements(new_power, old_power): added_enhancements = [] for new_enhancement in new_power.enhancement_instance_set.all(): in_old = False for old_enhancement in old_power.enhancement_instance_set.all(): if old_enhancement.relevant_enhancement.slug == new_enhancement.relevant_enhancement.slug: in_old = True if not in_old: added_enhancements.append(new_enhancement) return added_enhancements def _get_removed_enhancements(new_power, old_power): removed_enhancements = [] for old_enhancement in old_power.enhancement_instance_set.all(): in_new = False for new_enhancement in new_power.enhancement_instance_set.all(): if old_enhancement.relevant_enhancement.slug == new_enhancement.relevant_enhancement.slug: in_new = True if not in_new: removed_enhancements.append(old_enhancement) return removed_enhancements def _get_added_drawbacks(new_power, old_power): added_drawbacks = [] for new_drawback in new_power.drawback_instance_set.all(): in_old = False for old_drawback in old_power.drawback_instance_set.all(): if old_drawback.relevant_drawback.slug == new_drawback.relevant_drawback.slug: in_old = True if not in_old: added_drawbacks.append(new_drawback) return added_drawbacks def _get_removed_drawbacks(new_power, old_power): removed_drawbacks = [] for old_drawback in old_power.drawback_instance_set.all(): in_new = False for new_drawback in new_power.drawback_instance_set.all(): if old_drawback.relevant_drawback.slug == new_drawback.relevant_drawback.slug: in_new = True if not in_new: removed_drawbacks.append(old_drawback) return removed_drawbacks def _get_param_difference_text(new_power, old_power): param_text = "" param_counter = 0 for new_param_value in new_power.parameter_value_set.order_by('relevant_power_param_id').all(): try: old_param_value = old_power.parameter_value_set.order_by('relevant_power_param_id').all()[param_counter] if old_param_value.value != new_param_value.value: param_text = param_text + "Parameter {} changed from {} to {}. " param_text = param_text.format(new_param_value.relevant_power_param.relevant_parameter.name, old_param_value.value, new_param_value.value) except: return "Base Parameters Changed. " param_counter = param_counter + 1 return param_text

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py

Python

cargan/loss/pitch.py

mdc202002/cargan

5bfb44a1d8c2de8126e8053bed6078ad2e20819c

[ "MIT" ]

72

2021-10-20T01:17:54.000Z

2022-02-22T07:40:35.000Z

cargan/loss/pitch.py

mdc202002/cargan

5bfb44a1d8c2de8126e8053bed6078ad2e20819c

[ "MIT" ]

7

2021-10-21T21:44:00.000Z

2022-03-17T18:24:42.000Z

cargan/loss/pitch.py

mdc202002/cargan

5bfb44a1d8c2de8126e8053bed6078ad2e20819c

[ "MIT" ]

16

2021-10-20T02:07:46.000Z

2022-03-16T08:18:37.000Z

import torch import torchcrepe ############################################################################### # CREPE perceptual loss ############################################################################### class CREPEPerceptualLoss(torch.nn.Module): def __init__(self): super().__init__() # Register model self.add_module('model', torchcrepe.Crepe()) # Don't update model weights self.requires_grad_(False) def forward(self, x, y): # Get feature maps x_maps = self.activations(x) y_maps = self.activations(y) # Compute distance loss = 0. for x_map, y_map in zip(x_maps, y_maps): loss += torch.nn.functional.l1_loss(x_map, y_map) return loss def activations(self, x): activations = [] # shape=(batch, 1, 1024, 1) x = x[:, None, :, None] # Forward pass through model and save activations x = self.model.layer(x, self.model.conv1, self.model.conv1_BN, (0, 0, 254, 254)) activations.append(x) x = self.model.layer(x, self.model.conv2, self.model.conv2_BN) activations.append(x) x = self.model.layer(x, self.model.conv3, self.model.conv3_BN) activations.append(x) x = self.model.layer(x, self.model.conv4, self.model.conv4_BN) activations.append(x) x = self.model.layer(x, self.model.conv5, self.model.conv5_BN) activations.append(x) x = self.model.layer(x, self.model.conv6, self.model.conv6_BN) activations.append(x) # shape=(batch, self.in_features) x = x.permute(0, 2, 1, 3).reshape(-1, self.model.in_features) # Compute unnormalized probability distribution x = self.model.classifier(x) activations.append(x) return activations

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null

0

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44dbb055e9de0b9ac2e1f37ae940e8d05cde499b

6,230

py

Python

XML_to_Semark/xml_semark.py

prashankkadam/Maer_1

e201866429a1231df7f439797ef100f9e4e6da37

[ "MIT" ]

null

XML_to_Semark/xml_semark.py

prashankkadam/Maer_1

e201866429a1231df7f439797ef100f9e4e6da37

[ "MIT" ]

null

XML_to_Semark/xml_semark.py

prashankkadam/Maer_1

e201866429a1231df7f439797ef100f9e4e6da37

[ "MIT" ]

null

# -*- coding:/ utf-8 -*- """ Created on Tue Jul 23 12:07:20 2019 This piece of software is bound by The MIT License (MIT) Copyright (c) 2019 Prashank Kadam Code written by : Prashank Kadam User name - ADM-PKA187 Email ID : prashank.kadam@maersktankers.com Created on - Tue Jul 30 10:00:14 2019 version : 1.0 """ # Importing the required libraries import pandas as pd import xml.etree.ElementTree as et # Importing the standard semark light format for sea and port reports inorder to ger the column # names of all the standard columns into runtime df_init_sea = pd.read_excel('semark_light.xlsx', 'Sea') df_init_port = pd.read_excel('semark_light.xlsx', 'Port') # Importing the xml converted data into a dataframe df_target = pd.read_excel('test_1.xlsx') # Taking a subset of only the required columns from the dataframe df_target = df_target[['ImoNumber', 'VesselName', 'ReportTime', 'Longitude', 'Port', 'Location', 'Latitude', 'VoyageNo', 'ObservedDistance', 'FWDDraft', 'LOG_DISTANCE', 'WindForce', 'SeaDir', 'SwellHeight', 'CurrentDirection', 'WindDirection', 'SeaHeight', 'SwellDir', 'SeaState', 'Swell', 'Current', 'FuelType', 'AuxEngineConsumption', 'BoilerEngineConsumption', 'Units', 'Received', 'Consumption', 'SeaTemp', 'VesselCondition']] # Initializing the rows list in which we will append the mapped data rows = [] # Looping over the dataframe to map each row to the corresponding columns in the other dataframe for index, row in df_target.iterrows(): # Kindly note that the time complexity of the below code higher than the optimum as the same data values are # repeated for 4 rows in a succession but since the time is not an issue in our particular use case we can # refrain from adding further validations to complicate the code s_imo = row['ImoNumber'] s_vesselname = row['VesselName'] s_time = row['ReportTime'] s_longitutde = row['Longitude'] s_port = row['Port'] s_latitude = row['Latitude'] s_voyage = row['VoyageNo'] s_obvdis = row['ObservedDistance'] s_draught = row['FWDDraft'] s_dist = row['LOG_DISTANCE'] s_wind = row['WindForce'] s_seadir = row['SeaDir'] s_swellhgt = row['SeaDir'] s_curdir = row['CurrentDirection'] s_windir = row['WindDirection'] s_seahgt = row['SeaHeight'] s_swelldir = row['SwellDir'] s_seastate = row['SeaState'] s_swell = row['Swell'] s_curr = row['Current'] s_units = row['Units'] s_seatemp = row['SeaTemp'] s_vesscon = row['VesselCondition'] # Filling in the corresponding fields for the repective fuel types: if row['FuelType'] == 'IFO': s_hshfo_ae = row['AuxEngineConsumption'] s_hshfo_blr = row['BoilerEngineConsumption'] s_hshfo_me = row['Consumption'] elif row['FuelType'] == 'LSF': s_lshfo_ae = row['AuxEngineConsumption'] s_lshfo_blr = row['BoilerEngineConsumption'] s_lshfo_me = row['Consumption'] elif row['FuelType'] == 'LSG': s_lsmdo_ae = row['AuxEngineConsumption'] s_lsmdo_blr = row['BoilerEngineConsumption'] s_lsmdo_me = row['Consumption'] elif row['FuelType'] == 'MGO': s_hsmdo_ae = row['AuxEngineConsumption'] s_hsmdo_blr = row['BoilerEngineConsumption'] s_hsmdo_me = row['Consumption'] # Since MGO is the last fuel type for a particular report, we append the mapped values to the # other dataframe rows.append({'Vessel_Name': s_vesselname, 'Report_Date': s_time, 'IMO_NO': s_imo, 'Main Engine Fuel Consumption (H.S.HFO)': s_hshfo_me, 'Main Engine Fuel Consumption (L.S.HFO)': s_lshfo_me, 'Main Engine Fuel Consumption (H.S.MDO)': s_hsmdo_me, 'Main Engine Fuel Consumption (L.S.MDO)': s_lsmdo_me, 'Boiler Consumption (H.S.HFO)': s_hshfo_blr, 'Boiler Consumption (L.S.HFO)': s_lshfo_blr, 'Boiler Consumption (H.S.MDO)': s_hsmdo_blr, 'Boiler Consumption (L.S.MDO)': s_lsmdo_blr, 'Auxiliary Engine (Diesel Generator ) (H.S.HFO)': s_hshfo_ae, 'Auxiliary Engine (Diesel Generator ) (L.S.HFO)': s_lshfo_ae, 'Auxiliary Engine (Diesel Generator ) (H.S.MDO)': s_hsmdo_ae, 'Auxiliary Engine (Diesel Generator ) (L.S.MDO)': s_lsmdo_ae, 'Vessel State( Loaded\Ballast)': s_vesscon, 'True Wind Direction ': s_windir, 'True Wave Direction': s_seadir, 'True Swell Direction': s_swelldir}) # Creating the final dataframe with the mapped data and using the column values mapped from the semark sheet df_final = pd.DataFrame(rows, columns=df_init_sea.columns) # Exporting the data to excel sheet df_final.to_excel('final.xlsx', index=False) ###################################################################################################### # The below piece of code is for xml to pandas dataframe conversion. # Kindly note that all the fields have not yet been added to the dictionary # xtree = et.parse("vess_test.xml") # xroot = xtree.getroot() # # df_cols = ["VesselName", "ReportTime", "Longitude", "Port", "IMO_NUMBER"] # rows = [] # # for node in xroot: # # s_name = node.attrib.get("name") # s_vess_name = node.find("VesselName").text if node is not None else None # s_report_time = node.find("ReportTime").text if node is not None else None # s_longitude = node.find("Longitude").text if node is not None else None # s_port = node.find("Port").text if node is not None else None # s_imo = node.find("IMO_NUMBER").text if node is not None else None # # s_location = node.find("Location").text if node is not None else None # # rows.append({"VesselName": s_vess_name, "ReportTime": s_report_time, "Longitude":s_longitude, # "Port": s_port, "IMO_NUMBER": s_imo}) # # out_df = pd.DataFrame(rows, columns=df_cols) # # print(out_df.head(10))

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0

null

0

null

0

1

0

44dc0bec2aec12c7dc578a3fe8630954d0075baf

3,506

py

Python

bot.py

justletterh/cutiecafe

148f4708ab0b852552f6b91a25e084ac0011b2f0

[ "WTFPL" ]

null

bot.py

justletterh/cutiecafe

148f4708ab0b852552f6b91a25e084ac0011b2f0

[ "WTFPL" ]

null

bot.py

justletterh/cutiecafe

148f4708ab0b852552f6b91a25e084ac0011b2f0

[ "WTFPL" ]

null

import discord, jishaku from discord.ext import commands from time import sleep hid=666317117154525185 did=676454199742955530 lid=701254727534510129 owners=[hid,did,lid] status="— ୨୧ 𝐬𝐧𝐮𝐠𝐠𝐥𝐢𝐧’ 𝐭𝐡𝐞 𝐜𝐮𝐭𝐢𝐞 𝐩𝐢𝐞𝐬! ₓ˚. ୭ ˚○◦" join="""\U00002601 . . . ⇢ ˗ˏˋ <@&689140834200846374> ࿐ྂ **welcome sweetheart!! please verify to gain access to the rest of the server!** <:b_powheart:727644834265038918> <:b_teddy:727644836819107860> <:b_powheart:727644834265038918> <:b_wingies2:727644834806104124> **get some roles in** <a:b_arrow:727644833597882459> <#650563103699763240> <:b_wingies2:727644834806104124> **make an intro in** <a:b_arrow:727644833597882459> <#650562789546655790> <:b_wingies2:727644834806104124> **read and react to the triggers and rules list** <a:b_arrow:727644833597882459> <#662158949239226388> + <#668220102482722821> <:b_wingies2:727644834806104124> **ping staff in** <a:b_arrow:727644833597882459> <#694558376029454386> <a:b_butterflies:727644835023945778> — **and have loads of fun, $USER!**""" leave= """<a:B4562AEA046F4DB6B1892479B9ADA72D:727644835023945778> — **oh no!! an angel named $USER left us :c god speed little angel. god speed.** <:5CD871E9E3E34685A9E579DA3BC0D982:727644834265038918>""" welcomechan=650560380271067148 color=0xf8dfea def isown(usr): if usr.id in owners: return True else: return False bot = commands.Bot(command_prefix='~',owner_ids=owners) bot.remove_command('help') @bot.event async def on_ready(): await bot.change_presence(activity=discord.Game(name=status), status=discord.Status('online')) @bot.event async def on_member_join(member): with open('./app/join.gif', 'rb') as fp: await bot.get_channel(welcomechan).send(content=join.replace("$USER",member.mention),file=discord.File(fp,"join.gif")) @bot.event async def on_member_remove(member): with open('./app/leave.gif', 'rb') as fp: await bot.get_channel(welcomechan).send(content=leave.replace("$USER",f"@{member.name}#{member.discriminator}"),file=discord.File(fp,"leave.gif")) @bot.event async def on_message(message): if ("h " in message.content.lower() or "hh" in message.content.lower() or message.content.lower()=="h") and message.author.id==hid: await message.channel.send(content="h") await bot.process_commands(message) @bot.command(name='join') @commands.is_owner() async def _join(ctx): with open('./app/join.gif', 'rb') as fp: await bot.get_channel(welcomechan).send(content=join.replace("$USER",ctx.author.mention),file=discord.File(fp,"join.gif")) await ctx.send(content="Done!") @bot.command(name='leave') @commands.is_owner() async def _leave(ctx): with open('./app/leave.gif', 'rb') as fp: await bot.get_channel(welcomechan).send(content=leave.replace("$USER",f"@{ctx.author.name}#{ctx.author.discriminator}"),file=discord.File(fp,"leave.gif")) await ctx.send(content="Done!") @bot.command(name='say') @commands.is_owner() async def _say(ctx, *, arg): await ctx.send(content=arg) await ctx.message.delete() @bot.command() @commands.is_owner() async def tst(ctx): await ctx.send(content=join) @bot.command(name='fetchmsg') @commands.is_owner() async def _msg(ctx, arg): arg=int(arg) m=await ctx.channel.fetch_message(arg) await ctx.send(content=f"\U00000060\U00000060\U00000060{m.content}\U00000060\U00000060\U00000060") bot.load_extension('jishaku') bot.load_extension("utils") bot.load_extension("misc") bot.load_extension("voice") bot.run('BOT_TOKEN_HERE')

40.767442

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5.206967

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0.029516

0.039355

0.355765

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0.210153

0.158205

0.126722

0

0.170755

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3,506

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false

0

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0

0.082192

0

null

0

null

0

1

0

44dd2af0e04f2bcde0f9679219132a6850d1e347

1,611

py

Python

src/05_concurrent/concurrent_queue.py

edgardeng/python-advance-interview

59fd7bee8e871acdc7fdfecf2a110db840c47ebb

[ "Apache-2.0" ]

1

2022-03-06T13:03:56.000Z

src/05_concurrent/concurrent_queue.py

edgardeng/python-advance-interview

59fd7bee8e871acdc7fdfecf2a110db840c47ebb

[ "Apache-2.0" ]

null

src/05_concurrent/concurrent_queue.py

edgardeng/python-advance-interview

59fd7bee8e871acdc7fdfecf2a110db840c47ebb

[ "Apache-2.0" ]

null

from multiprocessing import Queue, Process, Pool, Manager, Pipe from time import sleep def basic_usage(): q = Queue(3) # 指定队列大小，如果不写默认无限 q.put('消息1') q.put('消息2') q.put('消息3') # q.put('消息4') # 一直等待直到进入 if not q.full(): q.put('消息5', block=True, timeout=1) # 等待1s，如果还没有put成功，直接抛异常 print('判断队列是否已满: %s' % q.full()) print(q.get()) # 获取并删除 print(q.get()) print(q.get()) # print(q.get()) # 一直等待获取 if not q.empty(): print(q.get(block=True,timeout=1)) # 等待获取，超时1s，则抛异常 print('判断队列是否为空: %s' % q.empty()) # print('队列大小 %d' % q.qsize()) # qsize error in mac osx ''' ' 队列中通信 ' 如果使用Pool创建进程，需要使用 Manager中的Queue来完成进程间的通信 ' 如果使用Process,则使用multiprocessing.Queue ''' def write(q:Queue): a = ['a', 'b', 'c', 'd'] for i in a: print('is writing %s' % i ) q.put(i) sleep(1) def read(q:Queue): for i in range(4): print('is redding %s' % q.get()) sleep(1) def queue_usage(): # 进程的通信 q = Queue() pw = Process(target=write, args=(q,)) pr = Process(target=read, args=(q,)) pw.start() pr.start() pw.join() pr.join() # 进程池 q = Manager().Queue() pool = Pool(3) pool.apply(write, (q,)) pool.apply(read, (q,)) pool.close() ''' ' pip u管道的使用 ''' def func_pipe(conn): conn.send('send by child') print('child recv:', conn.recv()) conn.close() def pipe_usage(): parent_conn, child_conn = Pipe() # 获得 Pipe 连接的两端 p = Process(target=func_pipe, args=(child_conn, )) p.start() print('parent recv:', parent_conn.recv()) parent_conn.send('send by parent') p.join() if __name__ == '__main__': # basic_usage() pipe_usage()

19.888889

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0

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1,611

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false

0

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0.153846

0.192308

0

null

0

null

0

1

0

44e34cab338c76b526dd9c77da1242eee658adad

458

py

Python

cfgov/paying_for_college/migrations/0010_program_median_monthly_debt.py

flacoman91/consumerfinance.gov

64e3d68d1c023ae944baf66a99e54236e5976097

[ "CC0-1.0" ]

37

2020-08-18T19:52:39.000Z

2022-03-23T08:08:41.000Z

cfgov/paying_for_college/migrations/0010_program_median_monthly_debt.py

flacoman91/consumerfinance.gov

64e3d68d1c023ae944baf66a99e54236e5976097

[ "CC0-1.0" ]

338

2020-08-14T20:46:36.000Z

2022-03-31T20:49:32.000Z

cfgov/paying_for_college/migrations/0010_program_median_monthly_debt.py

raft-tech/cfgov-refresh

7c63c31fd6bb95ed4f7d368f1e1252175f0c71ca

[ "CC0-1.0" ]

14

2020-10-21T15:27:03.000Z

2022-03-17T03:16:36.000Z

# -*- coding: utf-8 -*- from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('paying_for_college', '0009_expandable_group_help_text'), ] operations = [ migrations.AddField( model_name='program', name='median_monthly_debt', field=models.IntegerField(blank=True, help_text='MEDIAN MONTHLY PAYMENT FOR A 10-YEAR LOAN', null=True), ), ]

25.444444

116

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49

458

5.693878

0.77551

0.057348

0

0.020408

0.251092

458

17

117

26.941176

0.793003

0.045852

0

0.266667

0.071264

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false

0

0.083333

0

0.333333

0

null

0

null

0

1

0

44e680a8fac14428a219624044650be58b2e4334

374

py

Python

Tools/AtlasMaker/Assets/listdir.py

fakhirsh/cyclicshift

d255a3cc82703decdbbd477df3fa14791cd528d5

[ "MIT" ]

1

2019-11-12T17:47:23.000Z

Tools/AtlasMaker/Assets/listdir.py

fakhirsh/cyclicshift

d255a3cc82703decdbbd477df3fa14791cd528d5

[ "MIT" ]

31

2019-10-25T11:28:21.000Z

2019-12-10T16:57:30.000Z

Tools/AtlasMaker/Assets/listdir.py

fakhirsh/cyclicshift

d255a3cc82703decdbbd477df3fa14791cd528d5

[ "MIT" ]

null

import os import sys if(len(sys.argv) != 2): print("Error: usage --> python3 lstdir.py [DIRNAME]") exit(0) path = sys.argv[1] files = [] # r=root, d=directories, f = files for r, d, f in os.walk(path): for file in f: if '.png' in file: files.append(os.path.join(r, file)) #files.append(file) for f in files: print(f)

17.809524

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374

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false

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0.153846

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null

0

null

0

1

0

44e6bc135f28e05f2002c2b95899b3825d3fcdcd

1,022

py

Python

preprocess-scripts/build_movies.py

rohanrb302/End-to-End-Movie-Recommendation--Service

eabbf843e599cbfa4ae17f9e7c7eb0e73fd852d4

[ "Apache-2.0" ]

null

preprocess-scripts/build_movies.py

rohanrb302/End-to-End-Movie-Recommendation--Service

eabbf843e599cbfa4ae17f9e7c7eb0e73fd852d4

[ "Apache-2.0" ]

null

preprocess-scripts/build_movies.py

rohanrb302/End-to-End-Movie-Recommendation--Service

eabbf843e599cbfa4ae17f9e7c7eb0e73fd852d4

[ "Apache-2.0" ]

null

import pandas as pd import requests ratings = pd.read_csv("processed_ratings.csv") # fetch movie details of all the unique movieids from the movie API movie_api_url = "http://128.2.204.215:8080/movie/" movies = [requests.get(movie_api_url + movie).json() for movie in ratings['movieid'].unique()] # filter out records for which movie details does not exist movies = list(filter(lambda x: x.get('message','None') == 'None', movies)) # convert JSON data to dataframe movies = list(map(pd.io.json.json_normalize, movies)) movies_data = pd.concat(movies).reset_index(drop=True) movies_data = movies_data.drop_duplicates(subset=['id']) # keep only important columns movies_data = movies_data[['id','imdb_id','title','adult','budget','genres','original_language','release_date','vote_count','vote_average','popularity','overview']] # preprocess the genres column to make it readable movies_data['genres'] = movies_data['genres'].apply(lambda x: ",".join([y['name'] for y in x])) movies_data.to_csv("movies.csv", index=False)

48.666667

164

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1,022

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0

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165

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0.166667

0

null

0

null

0

1

0

44ea1e24de27283dea1dea7e3321f3745fc9d01f

400

py

Python

docs/en/conf.py

sdaityari/e-cidadania

2fc7f312145e7cd674033f3d765ff9ff8d4fb23c

[ "Apache-2.0" ]

40

2015-03-26T20:46:16.000Z

2022-02-28T09:15:30.000Z

docs/en/conf.py

zixtor/e-cidadania

2fc7f312145e7cd674033f3d765ff9ff8d4fb23c

[ "Apache-2.0" ]

1

2017-07-29T09:44:12.000Z

2017-08-08T16:27:22.000Z

docs/en/conf.py

zixtor/e-cidadania

2fc7f312145e7cd674033f3d765ff9ff8d4fb23c

[ "Apache-2.0" ]

19

2015-01-13T20:40:49.000Z

2021-11-02T03:53:39.000Z

import sys import os cwd = os.path.dirname(os.path.realpath(__file__)) main_dir = os.path.normpath(cwd + '/../') sys.path.append(main_dir) #print sys.path from config.all import * language = 'en' #html_logo = '../images/logos/logo-en.png' latex_logo = '../images/logos/logo-en.png' latex_documents = [ ('index', 'e-cidadania.tex', u'Documentation', u'Cidadania S. Coop. Galega', 'manual'), ]

22.222222

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0

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0.25

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null

0

null

0

1

0

44f1e748bd22b2cafab30bc7205e2a0aa86a626c

2,191

py

Python

tests/data_source/test_ec.py

KarrLab/Kinetic-Datanator

8aff047fd117033b98eca8ee3b21a8f07c430dec

[ "CC-BY-3.0", "CC0-1.0", "CC-BY-4.0", "MIT" ]

10

2018-11-20T17:04:09.000Z

2021-08-24T18:29:06.000Z

tests/data_source/test_ec.py

KarrLab/Kinetic-Datanator

8aff047fd117033b98eca8ee3b21a8f07c430dec

[ "CC-BY-3.0", "CC0-1.0", "CC-BY-4.0", "MIT" ]

59

2018-11-23T20:42:11.000Z

2020-11-08T19:51:36.000Z

tests/data_source/test_ec.py

KarrLab/Kinetic-Datanator

8aff047fd117033b98eca8ee3b21a8f07c430dec

[ "CC-BY-3.0", "CC0-1.0", "CC-BY-4.0", "MIT" ]

3

2018-12-15T00:53:54.000Z

2021-08-24T18:29:08.000Z

import unittest from datanator.data_source import ec import datanator.config.core import shutil import tempfile from pathlib import Path class TestEC(unittest.TestCase): @classmethod def setUpClass(cls): cls.cache_dir = tempfile.mkdtemp() db = 'test' username = datanator.config.core.get_config()['datanator']['mongodb']['user'] password = datanator.config.core.get_config()['datanator']['mongodb']['password'] MongoDB = datanator.config.core.get_config()['datanator']['mongodb']['server'] cls.src = ec.EC(server=MongoDB, db=db, username=username, password=password, authSource='admin', readPreference='nearest', max_entries=20, cache_dir=cls.cache_dir) @classmethod def tearDownClass(cls): shutil.rmtree(cls.cache_dir) cls.src.db.drop_collection(cls.src.collection_str) cls.src.client.close() @unittest.skip('IP') def test_establish_ftp(self): ftp = self.src.establish_ftp() self.assertTrue('enzyme.dat' in ftp.nlst()) @unittest.skip('IP') def test_retrieve_content(self): p = Path(self.cache_dir+'/enzyme.dat') self.src.retrieve_content() self.assertTrue(p.exists()) @unittest.skip('circle directory error.') def test_parse_content(self): location = str(Path('~/karr_lab/datanator/docs/enzyme.dat').expanduser()) self.src.parse_content(location) def test_make_doc(self): lines = ["ID 1.1.1.1", "DE Alcohol dehydrogenase.", "AN Aldehyde reductase.", "CA (1) A primary alcohol + NAD(+) = an aldehyde + NADH.", "CA (2) A secondary alcohol + NAD(+) = a ketone + NADH.", "CF Zn(2+) or Fe cation."] result = self.src.make_doc(lines) self.assertEqual(result, {'ec_number': '1.1.1.1', 'ec_name': 'Alcohol dehydrogenase', 'ec_synonyms': ['Aldehyde reductase'], 'catalytic_activity': ['(1) A primary alcohol + NAD(+) = an aldehyde + NADH', '(2) A secondary alcohol + NAD(+) = a ketone + NADH'], 'cofactor': 'Zn(2+) or Fe cation'})

42.960784

167

0.614331

264

2,191

4.988636

0.382576

0.009112

0.057707

0.050114

0.250569

0.198937

0.098709

0

0.009615

0.240529

2,191

51

168

42.960784

0.781851

0

0.093023

0

0.267336

0.016423

0

0.069767

1

0.139535

false

0.046512

0.139535

0

0.302326

0

null

0

null

0

1

0

44f8c313abc197a6cbaf4a7a0e766b03137619a3

3,922

py

Python

faps/make_offspring.py

ellisztamas/faps

fdf5ba990eaf85bfcf05b5eb757285ef40e8f918

[ "MIT" ]

null

faps/make_offspring.py

ellisztamas/faps

fdf5ba990eaf85bfcf05b5eb757285ef40e8f918

[ "MIT" ]

9

2018-02-15T11:19:04.000Z

2020-05-22T17:54:07.000Z

faps/make_offspring.py

ellisztamas/faps

fdf5ba990eaf85bfcf05b5eb757285ef40e8f918

[ "MIT" ]

null

import numpy as np from faps.genotypeArray import genotypeArray from faps.calculate_geno_probs import calculate_geno_probs def make_offspring(parents, noffs=None, dam_list=None, sire_list=None, mu=1e-12, family_name='offs'): """ Mate individuals in a base population to create simulated offspring. Lists of specific sires and dams can be provided with the options dam_list and sire_list. If only the number of offspring are specified parents are mated at random from the base population. Parameters ---------- parents: genotypeArray Genotype information on the parents to be mated. noffs: int Number of offspring to be produced. If specific dams and sires are specified, this is ignored. dam_list, sire_list: lists Integer lists of positions of sires and dams to be mated. Pairs are mated in order (i.e. the first dam with the first sire, and so forth). If used these two lists must be of the same length. If no arguments are given for either list, parents are mated at random with replacement, and the possibility of self-fertilisation. mu: float or 1-d array between 0 and 1 Per locus genotype error rate; the probability that the called genotype is incorrect. Alternatively, supply a vector of error rates for each locus. Defaults to 1e-12. family_name: str, optional String denoting the name for this family. Returns ------- A genotypeArray object. """ if dam_list is None and sire_list is None and noffs is None: raise ValueError("Either noffs needs to be a positive integer, or else lists of dams and sires should be given.") # If parents haven't been specified, choose these at random. if dam_list is None and sire_list is None: if noffs < 1 or not isinstance(noffs, int): raise ValueError("noffs should be a positive integer.") nparents = parents.geno.shape[0] dam_list = np.random.choice(range(nparents), noffs, replace=True).tolist() sire_list = np.random.choice(range(nparents), noffs, replace=True).tolist() # if parents have been specified, set noffs to the length of sires and dams. if dam_list is not None or sire_list is not None: noffs = len(dam_list) if len(dam_list) != len(sire_list): raise ValueError("List of sires must be the same length as the list of dams.") nloci = parents.geno.shape[1] # pull out the number of loci offs_genotypes= np.zeros([noffs, nloci, 2]) # empty array to store offspring genotypes. # pull out arrays of genotype data for the dams and sires. dam_genotypes = parents.subset(dam_list).geno sire_genotypes = parents.subset(sire_list).geno # draw an array of indices for whether the first or second allele should be drawn. dam_alleles = np.random.binomial(1, 0.5, nloci*noffs).reshape([noffs, nloci]) sire_alleles = np.random.binomial(1, 0.5, nloci*noffs).reshape([noffs, nloci]) # loop over every mating pair and send the selected alleles to offs_genotypes. for o in range(noffs): offs_genotypes[o,:,0] = np.array([dam_genotypes [o,l][dam_alleles [o,l]] for l in range(nloci)]) offs_genotypes[o,:,1] = np.array([sire_genotypes[o,l][sire_alleles[o,l]] for l in range(nloci)]) offs_genotypes = offs_genotypes.astype(float) # extra information on names. offspring_names = np.array([family_name+'_'+str(a) for a in np.arange(noffs)]) maternal_names = parents.subset(dam_list).names paternal_names = parents.subset(sire_list).names geno_probs = calculate_geno_probs(offs_genotypes, mu) return genotypeArray( geno = offs_genotypes, geno_probs = geno_probs, names = offspring_names, mothers = maternal_names, fathers = paternal_names, markers = np.arange(nloci) )

46.141176

121

0.694544

589

3,922

4.528014

0.302207

0.028871

0.014998

0.012373

0.149231

0.131984

0

0.006931

0.227435

3,922

84

122

46.690476

0.873267

0.413819

0

0.087776

0

1

0.026316

false

0

0.078947

0

0.131579

0

null

0

null

0

1

0

44f956a849a6c4d35aee25d62b6bcf916bf88c50

7,576

py

Python

iprir/tests.py

account-login/iprir

6b268bfff3f5af68f1cbf812f01104d4db238e68

[ "MIT" ]

2

2017-03-01T09:27:18.000Z

2019-10-03T06:36:18.000Z

iprir/tests.py

account-login/iprir

6b268bfff3f5af68f1cbf812f01104d4db238e68

[ "MIT" ]

null

iprir/tests.py

account-login/iprir

6b268bfff3f5af68f1cbf812f01104d4db238e68

[ "MIT" ]

null

from ipaddress import IPv4Address, IPv6Address, IPv6Network from contextlib import contextmanager import tempfile import os import random import unittest import requests import iprir from iprir.record import RIRRecord, ip_to_int from iprir.parser import parse_file, parse_string from iprir.database import DB from iprir.ipset import IpSet import iprir.updater SAMPLE_TEXT_DB_CONTENT = ''' # 2|apnic|20170120|50186|19830613|20170119|+1000 apnic|*|asn|*|7517|summary apnic|*|ipv4|*|36581|summary apnic|*|ipv6|*|6088|summary apnic|NZ|asn|681|1|20020801|allocated apnic|AU|ipv4|1.0.0.0|256|20110811|assigned apnic|CN|ipv4|1.0.1.0|256|20110414|allocated apnic|CN|ipv6|2001:250::|35|20000426|allocated apnic|CN|ipv6|2001:250:2000::|35|20020726|allocated ''' REAL_RECORDS = None # noinspection PyPep8Naming def setUpModule(): global REAL_RECORDS iprir.updater.initialize() REAL_RECORDS = sum(map(parse_file, iprir.TEXT_DB_PATH.values()), []) @contextmanager def patch(obj, key, value): origin = getattr(obj, key) setattr(obj, key, value) try: yield finally: setattr(obj, key, origin) @contextmanager def patch_db_path(): fd, text_db_path = tempfile.mkstemp(prefix='iprir_test_', suffix='.txt') os.close(fd) fd, sql_db_path = tempfile.mkstemp(prefix='iprir_test_', suffix='.sqlite') os.close(fd) print('text_db_path', text_db_path) print('sql_db_path', sql_db_path) with patch(iprir, 'TEXT_DB_PATH', dict(test=text_db_path)): with patch(iprir, 'TEXT_DB_URLS', dict(test='https://dummy/')): with patch(iprir, 'SQL_DB_PATH', sql_db_path): try: yield text_db_path, sql_db_path except Exception: raise else: os.remove(text_db_path) os.remove(sql_db_path) def write_string_to_file(filename: str, string: str): with open(filename, 'wt') as fp: fp.write(string) def test_record_ipv4(): r = RIRRecord('CN', 'ipv4', '1.0.1.0', '256', 'assigned') assert r.length == 256 assert r.ipv4.exploded == '1.0.1.0' assert r.ipv4_network.network_address == r.ipv4 assert r.ipv4_network.prefixlen == 24 assert r.ipv4 == IPv4Address(r.as_int) def test_record_ipv6(): r = RIRRecord('CN', 'ipv6', '2001:250::', '35', 'allocated') assert r.length == 2 ** (128 - 35) assert r.ipv6.compressed == '2001:250::' assert r.ipv6_network.network_address == r.ipv6 assert r.ipv6_network.prefixlen == 35 assert r.ipv6 == IPv6Address(r.as_int) def test_parse(): records = parse_string(SAMPLE_TEXT_DB_CONTENT) assert len(records) == 5 r = records[-1] assert (r.country, r.ipv6, r.ipv6_network, r.status) == ( 'CN', IPv6Address('2001:250:2000::'), IPv6Network('2001:250:2000::/35'), 'allocated' ) def test_ip_overlap(): def verify(lst): lst.sort(key=lambda x: x[0]) for i in range(1, len(lst)): prev_start, prev_len = lst[i - 1] assert prev_start + prev_len <= lst[i][0] lst4 = [] lst6 = [] for r in REAL_RECORDS: if r.country == 'AP': # asia/pacific # XXX: conflicts # apnic|AP|ipv4|159.117.192.0|2048|19920409|allocated|A928972C # ripencc|NL|ipv4|159.117.192.0|2048|19920409|assigned| continue if not DB.filter_record(r): continue if r.type == 'ipv4': lst4.append((r.as_int, r.length)) elif r.type == 'ipv6': lst6.append((r.as_int, r.length)) verify(lst4) verify(lst6) def test_db(): with patch_db_path() as pathes: text_db_path, sql_db_path = pathes write_string_to_file(text_db_path, SAMPLE_TEXT_DB_CONTENT) records = parse_file(text_db_path) db = DB() try: ret = db.reset_table() assert ret ret = db.add_records(records) assert ret cn4 = db.by_country('ipv4', 'CN') assert len(cn4) == 1 assert cn4[0] == records[2] cn6 = db.by_country('ipv6', 'CN') assert len(cn6) == 2 assert cn6 == records[3:5] r = db.by_ip(IPv4Address('1.0.1.0')) assert r == records[2] r = db.by_ip(IPv4Address('1.0.1.255')) assert r == records[2] r = db.by_ip(IPv4Address('1.0.2.0')) assert r is None r = db.by_ip(IPv6Address('2001:250::')) assert r == records[3] net = records[3].ipv6_network r = db.by_ip(net.network_address + net.num_addresses) assert r == records[4] net = records[4].ipv6_network r = db.by_ip(net.network_address + net.num_addresses) assert r is None finally: db.close() def test_update(): def fake_get(*args, **kwargs): class Obj: pass o = Obj() o.text = SAMPLE_TEXT_DB_CONTENT return o with patch(requests, 'get', fake_get): with patch_db_path(): iprir.updater.update() db = DB() try: records = parse_string(SAMPLE_TEXT_DB_CONTENT) records = list(filter(lambda r: r.type in ('ipv4', 'ipv6'), records)) assert db.all() == records finally: db.close() def test_ipset(): def to_int(ips): return [ip_to_int(IPv4Address(ip)) for ip in ips] text = ''' 2|apnic|20170120|50186|19830613|20170119|+1000 apnic|*|ipv6|*|6088|summary apnic|AU|ipv4|1.0.0.0|256|20110811|assigned apnic|CN|ipv4|1.0.1.0|256|20110414|allocated apnic|CN|ipv4|1.0.5.0|256|20110414|allocated ''' records = parse_string(text) random.shuffle(records) ipset = IpSet(records) assert ipset.lo == to_int(['1.0.0.0', '1.0.5.0']) assert ipset.hi == to_int(['1.0.2.0', '1.0.6.0']) assert IPv4Address('0.255.255.255') not in ipset assert IPv4Address('1.0.0.0') in ipset assert IPv4Address('1.0.1.0') in ipset assert IPv4Address('1.0.1.255') in ipset assert IPv4Address('1.0.2.0') not in ipset assert IPv4Address('1.0.4.255') not in ipset assert IPv4Address('1.0.5.0') in ipset assert IPv4Address('1.0.5.255') in ipset assert IPv4Address('1.0.6.0') not in ipset # test IpSet.by_country() with patch_db_path() as pathes: text_db_path, sql_db_path = pathes write_string_to_file(text_db_path, text) iprir.updater.update_sql_db() ipset = IpSet.by_country('ipv4', 'CN') assert ipset.lo == to_int(['1.0.1.0', '1.0.5.0']) assert ipset.hi == to_int(['1.0.2.0', '1.0.6.0']) class TestIpSetOnRealData(unittest.TestCase): by_country = staticmethod(IpSet.by_country) def test_by_country(self): # test on real data cn4 = self.by_country('ipv4', 'CN') assert IPv4Address('1.2.4.8') in cn4 assert IPv4Address('111.13.101.208') in cn4 assert IPv4Address('112.124.47.27') in cn4 assert IPv4Address('74.125.68.105') not in cn4 class TestRealDataWithApi(TestIpSetOnRealData): by_country = staticmethod(iprir.by_country) def test_by_ip(self): assert iprir.by_ip(IPv4Address('8.8.8.8')) == RIRRecord( country='US', type='ipv4', start='8.0.0.0', value='16777216', status='allocated', ) # noinspection PyPep8Naming def tearDownModule(): iprir.get_db().close()

29.59375

93

0.606389

1,084

7,576

4.089483

0.197417

0.014888

0.029326

0.007219

0.372208

0.312655

0.272727

0.194451

0.123618

0

0.102573

0.256204

7,576

255

94

29.709804

0.684117

0.031151

0

0.21608

0

0.150866

0.075297

0

0.221106

1

0.085427

false

0.005025

0.065327

0.005025

0.18593

0.01005

0

null

0

null

0

1

0

44fa7ab080f682a67685d77fc4c1632e1036494b

820

py

Python

Aron/Day10/answer.py

coolafabbe/AdventOfCode2021

97a2e4c7d887ef6f1ae477becb25cc1d97114781

[ "MIT" ]

null

Aron/Day10/answer.py

coolafabbe/AdventOfCode2021

97a2e4c7d887ef6f1ae477becb25cc1d97114781

[ "MIT" ]

null

Aron/Day10/answer.py

coolafabbe/AdventOfCode2021

97a2e4c7d887ef6f1ae477becb25cc1d97114781

[ "MIT" ]

null

import sys with open(sys.argv[1], "r") as file: entries = file.read().splitlines() open_chars = ['(', '[', '{', '<'] close_chars = [')', ']', '}', '>'] char_map = {o:c for o, c in zip(open_chars, close_chars)} points = {')': 3, ']':57, '}':1197, '>': 25137} syntax_score = 0 acp_scores = [] for line in entries: levels = [] for c in line: if c in open_chars: levels.append(c) elif c == char_map[levels[-1]]: levels.pop() else: syntax_score += points[c] break else: score = 0 for l in reversed(levels): score = score * 5 + 1 + open_chars.index(l) acp_scores.append(score) acp_score = sorted(acp_scores)[len(acp_scores)//2] print('Answer 1:', syntax_score) print('Answer 2:', acp_score)

21.578947

57

0.536585

111

820

3.810811

0.432432

0.085106

0.066194

0.089835

0

0.035775

0.284146

820

37

58

22.162162

0.684838

0

0.074074

0

0.037897

0

1

0

false

0

0.037037

0

0.037037

0.074074

0

null

0

null

0

1

0

44fc2f7950308d857850a4bf2b09c5e329cf743e

1,207

py

Python

bin/compute_stats.py

krayzpipes/ACE-1

138bf2aecad949f0b72b66519c32893df033de39

[ "Apache-2.0" ]

28

2018-08-08T11:57:31.000Z

2022-01-12T23:06:18.000Z

bin/compute_stats.py

krayzpipes/ACE-1

138bf2aecad949f0b72b66519c32893df033de39

[ "Apache-2.0" ]

108

2018-08-08T12:35:06.000Z

2019-07-19T22:57:19.000Z

bin/compute_stats.py

krayzpipes/ACE-1

138bf2aecad949f0b72b66519c32893df033de39

[ "Apache-2.0" ]

16

2018-08-03T18:48:00.000Z

2021-11-09T00:35:35.000Z

#!/usr/bin/env python3 import sys import argparse import os import os.path import re regex = re.compile(r'^(\d+):(\d\d):(\d\d)\.(\d+)$') alt_regex = re.compile(r'^(\d+):(\d\d):(\d\d)$') count = 0 total = 0.0 _max = 0.0 _min = 100000 minimum_considered = 1.0 excluded = 0 for line in sys.stdin: if count == 1000000: break m = regex.match(line.strip()) if m: hour, minute, second, frac = m.groups() else: m = alt_regex.match(line.strip()) if m: hour, minute, second = m.groups() frac = "000000" else: sys.stderr.write("ERROR: line {} in {} failed regex\n".format(line.strip(), stats_file)) continue total_seconds = float('0.{}'.format(frac)) + float(second) + (float(minute) * 60.0) + (float(hour) * 60.0 * 60.0) if total_seconds < minimum_considered: excluded += 1 continue total += total_seconds count += 1 if total_seconds > _max: _max = total_seconds if total_seconds < _min: _min = total_seconds if count: print("total {} averge {:.2f} max {:.2f} min {:.2f} (excluded {})".format(count, total / float(count), _max, _min, excluded))

24.632653

129

0.574979

169

1,207

3.988166

0.337278

0.026706

0.031157

0.029674

0.173591

0.172107

0

0.048098

0.259321

1,207

48

130

25.145833

0.705817

0.017399

0

0.153846

0

0.12827

0.04135

0

1

0

false

0

0.128205

0

0.128205

0.025641

0

null

0

null

0

1

0

44fcd428acfed83c3b1b8317f6fe3c4d1cb8bb2f

1,752

py

Python

endtoend_tests/forseti/notifier/inventory_summary_export_test.py

VGerris/forseti-security

59dc7607b14709e7da4db2751889b4fc757816b6

[ "Apache-2.0" ]

921

2017-03-09T01:01:24.000Z

2019-04-16T11:38:25.000Z

endtoend_tests/forseti/notifier/inventory_summary_export_test.py

VGerris/forseti-security

59dc7607b14709e7da4db2751889b4fc757816b6

[ "Apache-2.0" ]

1,996

2017-03-03T22:07:50.000Z

2019-04-17T00:02:28.000Z

endtoend_tests/forseti/notifier/inventory_summary_export_test.py

VGerris/forseti-security

59dc7607b14709e7da4db2751889b4fc757816b6

[ "Apache-2.0" ]

241

2017-03-09T01:00:04.000Z

2019-04-15T18:53:35.000Z

# Copyright 2020 The Forseti Security Authors. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Notifier inventory summary export tests""" import pytest import re import subprocess class TestNotifierInventorySummaryExport: """Tests for the notifier inventory summary export feature.""" @pytest.mark.e2e @pytest.mark.notifier @pytest.mark.server def test_inventory_summary_export_gcs( self, forseti_notifier_readonly: subprocess.CompletedProcess, forseti_server_bucket_name: str): """Test that the inventory summary is exported to GCS. Args: forseti_notifier_readonly (subprocess.CompletedProcess): Notifier run process result. forseti_server_bucket_name (str): Forseti server bucket name. """ match = re.search( fr'gs://{forseti_server_bucket_name}/inventory_summary/(.*).csv', str(forseti_notifier_readonly.stdout)) assert match gcs_path = match.group(0) cmd = ['sudo', 'gsutil', 'ls', gcs_path] result = subprocess.run(cmd, stderr=subprocess.PIPE, stdout=subprocess.PIPE) assert result.returncode == 0

35.04

77

0.687215

214

1,752

5.523364

0.528037

0.050761

0.064298

0.077834

0.126904

0

0.008197

0.234018

1,752

49

78

35.755102

0.872578

0.515411

0

0.092308

0.076923

0

0.1

1

0.05

false

0

0.15

0

0.25

0

null

0

null

0

1

0

44fcf97e01a85045813ffdd909c930123aef9a7d

2,084

py

Python

telemetry/roles/slurm_telemetry/files/monster/process.py

Lakshmi-Patneedi/omnia

40a5dd9496af16ab6fd18f2d807a4d8dea11bbf3

[ "Apache-2.0" ]

1

2021-10-13T21:48:15.000Z

telemetry/roles/slurm_telemetry/files/monster/process.py

Lakshmi-Patneedi/omnia

40a5dd9496af16ab6fd18f2d807a4d8dea11bbf3

[ "Apache-2.0" ]

null

telemetry/roles/slurm_telemetry/files/monster/process.py

Lakshmi-Patneedi/omnia

40a5dd9496af16ab6fd18f2d807a4d8dea11bbf3

[ "Apache-2.0" ]

null

""" MIT License Copyright (c) 2022 Texas Tech University Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ """ This file is part of MonSter. Author: Jie Li, jie.li@ttu.edu """ import logger import time import multiprocessing log = logger.get_logger(__name__) def partition(arr:list, cores: int): """partition Partition a list Partition urls/nodes into several groups based on # of cores Args: arr (list): A list to be partitioned cores (int): Number of cores of the compute running MonSter Returns: list: partitioned list """ groups = [] try: arr_len = len(arr) arr_per_core = arr_len // cores arr_surplus = arr_len % cores increment = 1 for i in range(cores): if(arr_surplus != 0 and i == (cores-1)): groups.append(arr[i * arr_per_core:]) else: groups.append(arr[i * arr_per_core : increment * arr_per_core]) increment += 1 except Exception as err: log.error(f"Cannot Partition the list: {err}") return groups

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304

2,084

4.753289

0.5

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0.027682

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2,084

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0.25

0

null

0

null

0

1

0

44fef0a3b086e649cd8bbdf5e4629c04e482ccb9

2,594

py

Python

data/main.py

danielxiangzl/hotstuff

e701f5556102aae99dd1e3a654c15b2cda15579f

[ "Apache-2.0" ]

null

data/main.py

danielxiangzl/hotstuff

e701f5556102aae99dd1e3a654c15b2cda15579f

[ "Apache-2.0" ]

null

data/main.py

danielxiangzl/hotstuff

e701f5556102aae99dd1e3a654c15b2cda15579f

[ "Apache-2.0" ]

1

2021-08-08T05:08:49.000Z

from glob import glob from os.path import join import os from matplotlib.pyplot import hexbin from parse import LogAggregator from plot import Ploter if __name__ == '__main__': max_latencies = [2_000, 5_000] # For TPS graphs. # Parse the results. for system in ['3-chain', '2-chain', 'ditto-async', 'ditto-sync', 'vaba']: [os.remove(x) for x in glob(f'{system}.*.txt')] files = glob(join(system, 'results', '*.txt')) LogAggregator(system, files, max_latencies).print() LogAggregator(system, files, max_latencies, end_to_end=False).print() # Plot 'Happy path' graph. ploter = Ploter(width=12.8) for system in ['3-chain', '2-chain', 'ditto-sync', 'vaba']: ploter.plot_latency(system, [10, 20, 50], [0], 512) ploter.finalize('happy-path', legend_cols=4) # Plot 'Happy path TPS' graph. ploter = Ploter() for system in ['3-chain', '2-chain', 'ditto-sync', 'vaba']: ploter.plot_tps(system, [0], max_latencies, 512) ploter.finalize('happy-path-tps', legend_cols=2) # Plot 'Happy path commit latency' graph. ploter = Ploter() for system in ['3-chain', '2-chain']: ploter.plot_commit_lantecy( system, [0], [20000], 512, graph_type='commit_latency' ) ploter.finalize('happy-path-commit', legend_cols=2, top_lim=1_500) # Plot 'Leader under DoS' graph. ploter = Ploter() for i, system in enumerate(['3-chain', '2-chain']): name = Ploter.legend_name(system) ploter.plot_free( [i*500], [0], [f'{name}, {x} nodes' for x in [10, 20, 50]] ) for system in ['ditto-async', 'vaba']: ploter.plot_latency(system, [10, 20, 50], [0], 512) ploter.finalize('leader-under-dos', legend_cols=2) # Plot 'Dead nodes' graph. ploter = Ploter(width=12.8) for system in ['3-chain', '2-chain', 'ditto-sync', 'vaba']: ploter.plot_latency(system, [20], [0, 1, 3], 512) ploter.finalize('dead-nodes', legend_cols=4) # Plot 'Dead nodes and DoS' graph. ploter = Ploter() for i, system in enumerate(['3-chain', '2-chain']): name = Ploter.legend_name(system) ploter.plot_free( [i*500], [0], [ f'{name}, 20 nodes', f'{name}, 20 nodes (1 faulty)', f'{name}, 20 nodes (3 faulty)' ] ) for system in ['ditto-async', 'vaba']: ploter.plot_latency(system, [20], [0, 1, 3], 512) ploter.finalize('dead-nodes-and-dos', legend_cols=2)

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0.581727

353

2,594

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0.215297

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0.05231

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null

0

null

0

1

0

7803a8c8d00022860aa2c1e297b0776b9bac5e6f

4,437

py

Python

LGTV/__init__.py

Maccraft123/LGWebOSRemote

52c481c83e78d06457b58cc68a87fefbfb80c7ef

[ "MIT" ]

null

LGTV/__init__.py

Maccraft123/LGWebOSRemote

52c481c83e78d06457b58cc68a87fefbfb80c7ef

[ "MIT" ]

null

LGTV/__init__.py

Maccraft123/LGWebOSRemote

52c481c83e78d06457b58cc68a87fefbfb80c7ef

[ "MIT" ]

null

# -*- coding: utf-8 -*- from __future__ import print_function from inspect import getargspec import json import os import sys from time import sleep import logging from .scan import LGTVScan from .remote import LGTVRemote from .auth import LGTVAuth search_config = [ "/etc/lgtv/config.json", "~/.lgtv/config.json", "/opt/venvs/lgtv/config/config.json" ] def usage(error=None): if error: print ("Error: " + error) print ("LGTV Controller") print ("Author: Karl Lattimer <karl@qdh.org.uk>") print ("Usage: lgtv <command> [parameter]\n") print ("Available Commands:") print (" -i interactive mode") print (" scan") print (" auth <host> <tv_name>") commands = LGTVRemote.getCommands() for c in commands: args = getargspec(LGTVRemote.__dict__[c]) if len(args.args) > 1: a = ' <' + '> <'.join(args.args[1:-1]) + '>' print (' <tv_name> ' + c + a) else: print (' <tv_name> ' + c) def parseargs(command, argv): args = getargspec(LGTVRemote.__dict__[command]) args = args.args[1:-1] #if len(args) != len(argv): # raise Exception("Argument lengths do not match") output = {} for (i, a) in enumerate(args): if argv[i].lower() == "true": argv[i] = True elif argv[i].lower() == "false": argv[i] = False try: f = int(argv[i]) argv[i] = f except: try: f = float(argv[i]) argv[i] = f except: pass output[a] = argv[i] return output def find_config(): w = None for f in search_config: f = os.path.expanduser(f) f = os.path.abspath(f) d = os.path.dirname(f) if os.path.exists(d): if os.access(d, os.W_OK): w = f if os.path.exists(f): if os.access(f, os.W_OK): return f elif os.access(os.path.dirname(d), os.W_OK): os.makedirs(d) w = f if w is None: print ("Cannot find suitable config path to write, create one in %s" % ' or '.join(search_config)) raise Exception("No config file") return w def main(): if len(sys.argv) < 2: usage("Too few arguments") sys.exit(1) logging.basicConfig(level=logging.DEBUG) command = None filename = None config = {} filename = find_config() if filename is not None: try: with open(filename) as f: config = json.loads(f.read()) except: pass if sys.argv[1] == "scan": results = LGTVScan() if len(results) > 0: print (json.dumps({ "result": "ok", "count": len(results), "list": results })) sys.exit(0) else: print (json.dumps({ "result": "failed", "count": len(results) })) sys.exit(1) if sys.argv[1] == "-i": pass elif sys.argv[1] == "auth": if len(sys.argv) < 3: usage("Hostname or IP is required for auth") sys.exit(1) if len(sys.argv) < 4: usage("TV name is required for auth") sys.exit(1) name = sys.argv[3] host = sys.argv[2] ws = LGTVAuth(name, host) ws.connect() ws.run_forever() sleep(1) config[name] = ws.serialise() if filename is not None: with open(filename, 'w') as f: f.write(json.dumps(config)) print ("Wrote config file: " + filename) sys.exit(0) elif len(sys.argv) >= 2 and sys.argv[2] == "on": name = sys.argv[1] ws = LGTVRemote(name, **config[name]) ws.on() sleep(1) sys.exit(0) else: try: args = parseargs(sys.argv[2], sys.argv[3:]) name = sys.argv[1] command = sys.argv[2] except Exception as e: usage(str(e)) sys.exit(1) try: ws = LGTVRemote(name, **config[name]) ws.connect() if command is not None: ws.execute(command, args) ws.run_forever() except KeyboardInterrupt: ws.close() if __name__ == '__main__': main()

25.5

106

0.497634

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4,437

3.988991

0.26789

0.048298

0.022079

0.016559

0.095676

0.064397

0.022999

0

0.011412

0.368041

4,437

173

107

25.647399

0.763909

0.022312

0

0.285714

0

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0.01269

0.006803

0

1

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false

0.020408

0.068027

0

0.115646

0.102041

0

null

0

null

0

1

0

7804b1932916d494a0caef5d17c396d3ce3feb1f

5,809

py

Python

data/master_cycle_gan_dataset.py

RegentLee/master_research

ee8e45abc890c7103c1c9917954c5958b48782f6

[ "BSD-3-Clause" ]

null

data/master_cycle_gan_dataset.py

RegentLee/master_research

ee8e45abc890c7103c1c9917954c5958b48782f6

[ "BSD-3-Clause" ]

null

data/master_cycle_gan_dataset.py

RegentLee/master_research

ee8e45abc890c7103c1c9917954c5958b48782f6

[ "BSD-3-Clause" ]

null

"""Dataset class template This module provides a template for users to implement custom datasets. You can specify '--dataset_mode template' to use this dataset. The class name should be consistent with both the filename and its dataset_mode option. The filename should be <dataset_mode>_dataset.py The class name should be <Dataset_mode>Dataset.py You need to implement the following functions: -- <modify_commandline_options>:　Add dataset-specific options and rewrite default values for existing options. -- <__init__>: Initialize this dataset class. -- <__getitem__>: Return a data point and its metadata information. -- <__len__>: Return the number of images. """ from data.base_dataset import BaseDataset, get_transform # from data.image_folder import make_dataset # from PIL import Image import torch import torchvision.transforms as transforms import random from data.MyFunction import my_data_creator from data.MyFunction import my_transforms from util import my_util class MasterCycleGANDataset(BaseDataset): """A template dataset class for you to implement custom datasets.""" @staticmethod def modify_commandline_options(parser, is_train): """Add new dataset-specific options, and rewrite default values for existing options. Parameters: parser -- original option parser is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options. Returns: the modified parser. """ # parser.add_argument('--new_dataset_option', type=float, default=1.0, help='new dataset option') # parser.set_defaults(max_dataset_size=10, new_dataset_option=2.0) # specify dataset-specific default values parser.add_argument('--matrix', type=str, default='Cb', help='input matrix') parser.add_argument('--LOOid', type=int, default=-1, help='Leave-one-out cross-validation id') parser.add_argument('--diff', type=bool, default=False) parser.set_defaults(input_nc=1, output_nc=1) # specify dataset-specific default values return parser def __init__(self, opt): """Initialize this dataset class. Parameters: opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions A few things can be done here. - save the options (have been done in BaseDataset) - get image paths and meta information of the dataset. - define the image transformation. """ # save the option and dataset root BaseDataset.__init__(self, opt) # get the image paths of your dataset; # self.image_paths = [] # You can call sorted(make_dataset(self.root, opt.max_dataset_size)) to get all the image paths under the directory self.root # define the default transform function. You can use <base_dataset.get_transform>; You can also define your custom transform function # self.transform = get_transform(opt) data = my_data_creator.MyDataCreator(opt) matrix_size = [len(i) for i in data.data_A] input_n = max(matrix_size) for i in range(4): if input_n%4 == 0: break input_n += 1 transform = transforms.Compose([ my_transforms.preprocess(input_n), transforms.ToTensor() ]) data_A = data.data_A if opt.diff: data_B = [data.data_B[i//3] - data_A[i] for i in range(len(data_A))] else: data_B = data.data_B if opt.LOOid < 0: val_A = [data_A[i] for i in range(3)] if opt.diff: val_B = [data_B[i] for i in range(3)] else: val_B = [data_B[0]] else: val_A = [data_A[i] for i in range(opt.LOOid*3, opt.LOOid*3 + 3)] data_A = data_A[:opt.LOOid*3] + data_A[opt.LOOid*3 + 3:] if opt.diff: val_B = data_B[opt.LOOid*3:opt.LOOid*3 + 3] data_B = data_B[:opt.LOOid*3] + data_B[opt.LOOid*3 + 3:] else: val_B = [data_B[opt.LOOid]] data_B = data_B[:opt.LOOid] + data_B[opt.LOOid + 1:] if not my_util.val: self.data_A = [transform(i) for i in data_A] self.data_B = [transform(i) for i in data_B] else: self.data_A = [transform(i) for i in val_A] self.data_B = [transform(i) for i in val_B] def __getitem__(self, index): """Return a data point and its metadata information. Parameters: index -- a random integer for data indexing Returns: a dictionary of data with their names. It usually contains the data itself and its metadata information. Step 1: get a random image path: e.g., path = self.image_paths[index] Step 2: load your data from the disk: e.g., image = Image.open(path).convert('RGB'). Step 3: convert your data to a PyTorch tensor. You can use helpder functions such as self.transform. e.g., data = self.transform(image) Step 4: return a data point as a dictionary. """ path = 'temp' # needs to be a string # data_A = torch.Tensor(self.data.data_A) # needs to be a tensor # data_B = torch.Tensor(self.data.data_B) # needs to be a tensor A = self.data_A[index % len(self.data_A)] index_B = random.randint(0, len(self.data_B) - 1) B = self.data_B[index_B] return {'A': A, 'B': B, 'A_paths': path, 'B_paths': path} def __len__(self): """Return the total number of images.""" return max(len(self.data_A), len(self.data_B))

41.791367

158

0.632123

828

5,809

4.27657

0.229469

0.031065

0.016944

0.017792

0.260378

0.171985

0.138379

0.123129

0.062129

0.035583

0

0.008789

0.275263

5,809

138

159

42.094203

0.832304

0.484076

0

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0

0.031769

0

1

0.064516

false

0

0.112903

0

0.241935

0

null

0

null

0

1

0

780aa1d44fdf013fa8158b9597ec531da2bacc1c

882

py

Python

zero_to_one_hundred/processors/refresh_map_processor.py

fossabot/0to100

37faa1340b2ec8b87e5d4c268c8caf521ea164cb

[ "Apache-2.0" ]

null

zero_to_one_hundred/processors/refresh_map_processor.py

fossabot/0to100

37faa1340b2ec8b87e5d4c268c8caf521ea164cb

[ "Apache-2.0" ]

null

zero_to_one_hundred/processors/refresh_map_processor.py

fossabot/0to100

37faa1340b2ec8b87e5d4c268c8caf521ea164cb

[ "Apache-2.0" ]

null

"""RefreshMapProcessor: refresh sections in map """ # pylint: disable=C0116,R0903,E0401,W0703,W1201,redefined-outer-name,missing-function-docstring,E0401,C0114,W0511,W1203,C0200,C0103,W1203 from configs.config import ConfigMap from models.map import Map class RefreshMapProcessor: """RefreshMapProcessor""" def __init__(self, config_map: ConfigMap, persist_fs): """init""" self.config_map = config_map self.persist_fs = persist_fs def process(self): """Scan the repo and for each new_section add it to the map, save the map file.""" sections = Map.build_from_dirs( self.config_map, self.persist_fs, self.persist_fs.list_dirs(self.config_map.get_repo_path), ) map_: Map = Map(self.config_map, self.persist_fs, sections) map_.write(self.config_map.get_repo_sorted)

32.666667

137

0.689342

117

882

4.965812

0.478632

0.108434

0.134251

0.10327

0.196213

0.089501

0

0.06867

0.207483

882

26

138

33.923077

0.762518

0.323129

0

1

0.142857

false

0

0.142857

0

0.357143

0

null

0

null

0

1

0

780b6383b9441cbbb52f13931df26519c2613bf3

14,047

py

Python

sharkdata_core/dataset_utils.py

sharkdata/sharkdata

67793fd1771c9c2e599e62d57fcef432be5a8340

[ "MIT" ]

2

2016-07-20T07:09:51.000Z

2016-08-12T12:20:20.000Z

sharkdata_core/dataset_utils.py

sharkdata/sharkdata

67793fd1771c9c2e599e62d57fcef432be5a8340

[ "MIT" ]

1

2016-01-21T12:18:17.000Z

2016-01-21T12:20:50.000Z

sharkdata_core/dataset_utils.py

sharkdata/sharkdata

67793fd1771c9c2e599e62d57fcef432be5a8340

[ "MIT" ]

2

2016-07-20T07:13:35.000Z

2016-08-12T11:40:15.000Z

#!/usr/bin/env python # -*- coding:utf-8 -*- # # Copyright (c) 2013-present SMHI, Swedish Meteorological and Hydrological Institute # License: MIT License (see LICENSE.txt or http://opensource.org/licenses/mit). import pathlib from django.conf import settings import app_datasets.models as datasets_models import app_ctdprofiles.models as ctdprofiles_models import sharkdata_core @sharkdata_core.singleton class DatasetUtils(object): """ Singleton class. """ def __init__(self): """ """ self._data_header = None self._translations = None self._data_in_datasets = settings.SHARKDATA_DATA_IN_DATASETS self._data_datasets = pathlib.Path(settings.SHARKDATA_DATA, "datasets") self._metadata_update_thread = None self._generate_archives_thread = None def translateDataHeaders( self, data_header, resource_name="translate_headers", language="darwin_core" ): # language = 'english'): """ """ return sharkdata_core.ResourcesUtils().translateHeaders( data_header, resource_name, language ) def getDatasetListHeaders(self): """ """ if not self._data_header: self._data_header = [ "dataset_name", "datatype", "version", "dataset_file_name", ] # return self._data_header def translateDatasetListHeaders(self, data_header, language=None): """ """ # if not language: # return data_header # translated = [] # if not self._translations: self._translations = { "dataset_name": "Dataset name", "datatype": "Datatype", "version": "Version", "dataset_file_name": "File name", } # for item in data_header: if item in self._translations: translated.append(self._translations[item]) else: translated.append(item) # return translated def getDataAsText(self, dataset_name): """ Data is not stored in database, get from zip file.""" db_dataset = datasets_models.Datasets.objects.get(dataset_name=dataset_name) # # Extract data part. data_content = "" zipreader = sharkdata_core.SharkArchiveFileReader( db_dataset.dataset_file_name, self._data_in_datasets ) try: zipreader.open() data_content = zipreader.getDataAsText().decode( "cp1252" ) # Default encoding in archive data. finally: zipreader.close() # print(data_content) # return data_content def getDataColumnsAsText(self, dataset_name): """ Data is not stored in database, get from zip file.""" db_dataset = datasets_models.Datasets.objects.get(dataset_name=dataset_name) # # Extract data part. data_content = "" zipreader = sharkdata_core.SharkArchiveFileReader( db_dataset.dataset_file_name, self._data_in_datasets ) try: zipreader.open() data_content = zipreader.getDataColumnsAsText().decode( "cp1252" ) # Default encoding in archive data. finally: zipreader.close() # print(data_content) # return data_content def getMetadataAsText(self, dataset_name): """ """ db_dataset = datasets_models.Datasets.objects.get(dataset_name=dataset_name) # Fix line breaks for windows. Remove rows with no key-value-pairs. metadata_list = [] concat_metadata = ( db_dataset.content_metadata + "\n" + db_dataset.content_metadata_auto ) for row in concat_metadata.split("\n"): if ":" in row: parts = row.split(":", 1) # Split on first occurence. key = parts[0].strip() value = parts[1].strip() metadata_list.append(key + ": " + value) # return "\r\n".join(metadata_list) def writeLatestDatasetsInfoToDb(self, logfile_name=None, user=""): """Updates the database from datasets stored in the FTP area. I multiple versions of a dataset are in the FTP area only the latest will be loaded. """ # Check dataset in 'data_in/datasets'. Create a list of dataset names. dataset_names = [] for dataset_path in self._data_in_datasets.glob("SHARK_*.zip"): print(dataset_path.name) parts = dataset_path.name.split("_version") if len(parts) >= 1: dataset_names.append(parts[0]) # Remove all datasets from 'data/datasets' not included in 'dataset_names'. for dataset_path in self._data_datasets.glob("SHARK_*.zip"): print(dataset_path.name) parts = dataset_path.name.split("_version") if len(parts) >= 1: if parts[0] not in dataset_names: # Delete the file. dataset_path.unlink() # Removes file. # Remove from database. datasets_models.Datasets.objects.get( dataset_name=dataset_path.name ).delete() error_counter = 0 # Remove all db rows. datasets_models.Datasets.objects.all().delete() # CTD profiles. ctdprofiles_models.CtdProfiles.objects.all().delete() # Get latest datasets from FTP archive. archive = sharkdata_core.SharkArchive(self._data_in_datasets) for file_name in sorted(archive.getLatestSharkArchiveFilenames()): if logfile_name: sharkdata_core.SharkdataAdminUtils().log_write( logfile_name, log_row="Loading file: " + file_name + "..." ) try: error_string = self.writeFileInfoToDb(file_name, logfile_name, user) if error_string: error_counter += 1 sharkdata_core.SharkdataAdminUtils().log_write( logfile_name, log_row="ERROR: Failed to load: " + file_name + ". Error: " + error_string, ) except Exception as e: error_counter += 1 sharkdata_core.SharkdataAdminUtils().log_write( logfile_name, log_row="ERROR: Failed to load: " + file_name + ". Error: " + str(e), ) # return error_counter def writeFileInfoToDb(self, file_name, logfile_name=None, user=""): """ Extracts info from the dataset filename and from the zip file content and adds to database. """ try: # ftp_file_path = pathlib.Path(self._data_in_datasets, file_name) # Extract info from file name. dataset_name, datatype, version = self.splitFilename(file_name) # Extract metadata parts. metadata = "" metadata_auto = "" columndata_available = False # zipreader = sharkdata_core.SharkArchiveFileReader( file_name, self._data_in_datasets ) try: zipreader.open() # try: metadata = zipreader.getMetadataAsText() encoding = "cp1252" metadata = str(metadata, encoding, "strict") except Exception as e: sharkdata_core.SharkdataAdminUtils().log_write( logfile_name, log_row="WARNING: " + str(e) ) # try: metadata_auto = zipreader.getMetadataAutoAsText() encoding = "cp1252" metadata_auto = str(metadata_auto, encoding, "strict") except Exception as e: sharkdata_core.SharkdataAdminUtils().log_write( logfile_name, log_row="WARNING: " + str(e) ) # columndata_available = zipreader.isDataColumnsAvailable() # CTD profiles. ctd_profiles_table = None # if datatype == 'CTDprofile': if datatype == "Profile": ctd_profiles_table = zipreader.getDataAsText() finally: zipreader.close() # Remove from database. try: db_dataset = datasets_models.Datasets.objects.get( dataset_name=dataset_name ) db_dataset.delete() except datasets_models.Datasets.DoesNotExist: pass # Not found. # Save to db. dataset = datasets_models.Datasets( dataset_name=dataset_name, datatype=datatype, version=version, dataset_file_name=file_name, ftp_file_path=ftp_file_path, content_data="NOT USED", content_metadata=metadata, content_metadata_auto=metadata_auto, # column_data_available=columndata_available, ) dataset.save() if ctd_profiles_table: data_header = [] ctd_profiles_table = ctd_profiles_table.decode("cp1252") for index, row in enumerate(ctd_profiles_table.split("\n")): rowitems = row.strip().split("\t") if index == 0: data_header = rowitems else: if len(rowitems) > 1: row_dict = dict(zip(data_header, rowitems)) water_depth_m = 0.0 try: water_depth_m = float( row_dict.get("water_depth_m", -99) ) except: pass db_profiles = ctdprofiles_models.CtdProfiles( visit_year=row_dict.get("visit_year", ""), # '2002', platform_code=row_dict.get( "platform_code", "" ), # 'Svea', expedition_id=row_dict.get( "expedition_id", "" ), # 'aa-bb-11', visit_id=row_dict.get("visit_id", ""), # '123456', station_name=row_dict.get( "station_name", "" ), # 'Station1A', latitude=float( row_dict.get("sample_latitude_dd", -99) ), # 70.00, longitude=float( row_dict.get("sample_longitude_dd", -99) ), # 10.00, water_depth_m=water_depth_m, # '80.0', sampler_type_code=row_dict.get( "sampler_type_code", "" ), # 'CTD', sample_date=row_dict.get( "visit_date", "" ), # '2000-01-01', sample_project_code=row_dict.get( "sample_project_code", "" ), # 'Proj', # sample_project_code = row_dict.get('sample_project_name_sv', ''), # 'Proj', sample_orderer_code=row_dict.get( "sample_orderer_code", "" ), # 'Orderer', # sample_orderer_code = row_dict.get('sample_orderer_name_sv', ''), # 'Orderer', sampling_laboratory_code=row_dict.get( "sampling_laboratory_code", "" ), # 'Slabo', # sampling_laboratory_code = row_dict.get('sampling_laboratory_name_sv', ''), # 'Slabo', revision_date=row_dict.get( "revision_date", "" ), # '2010-10-10', ctd_profile_name=row_dict.get( "profile_file_name_db", "" ), # 'ctd.profile', dataset_file_name=file_name, ftp_file_path=ftp_file_path, ) db_profiles.save() # return None # No error message. # except Exception as e: return str(e) def splitFilename(self, file_name): """ """ filename = pathlib.Path(file_name).stem parts = filename.split("version") name = parts[0].strip("_").strip() version = parts[1].strip("_").strip() if len(parts) > 0 else "" # parts = filename.split("_") datatype = parts[1].strip("_").strip() # return name, datatype, version

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py

Python

RayTracing/gui/gui.py

TEM-Gemini-Centre/RayTracing

fa4b6057c9a7307f75e52f0bd7a4a13751f832ec

[ "MIT" ]

null

RayTracing/gui/gui.py

TEM-Gemini-Centre/RayTracing

fa4b6057c9a7307f75e52f0bd7a4a13751f832ec

[ "MIT" ]

null

RayTracing/gui/gui.py

TEM-Gemini-Centre/RayTracing

fa4b6057c9a7307f75e52f0bd7a4a13751f832ec

[ "MIT" ]

null

from PyQt5 import QtWidgets, QtGui, QtCore, Qt, uic from PyQt5.QtCore import pyqtSignal, pyqtSlot from RayTracing.gui.mplwidget import * from RayTracing.RayTracing import * from tabulate import tabulate from pathlib import Path from matplotlib.lines import lineStyles from matplotlib.colors import to_hex, to_rgb import sys import time import argparse class Error(Exception): pass class OperatorModelError(Error): pass class SourceModelError(Error): pass class ScreenModelError(Error): pass class OpticalOperatorModel(QtCore.QObject): """ Model for controlling an OpticalOperator The model should ensure that proper signals are sent whenever the data of the OpticalOperator has been changed. The model emits the following signals: :param valueChanged: Signal ([], [float]) emitted whenever the value of the OpticalOperator has changed. :param zChanged: Signal ([], [float]) emitted whenever the z-value of the OpticalOperator has changed. :param offsetChanged: Signal ([], [float]) emitted whenever the offset-value of the OpticalOperator has changed. :param labelChanged: Signal([], [float]) emitted whenever the label of the OpticalOperator has changed. :param operatorChanged: Signal wmitted whenever any change has been made to the OpticalOperator, inculding the above. """ valueChanged = pyqtSignal([], [float], name='valueChanged') zChanged = pyqtSignal([], [float], name='zChanged') offsetChanged = pyqtSignal([], [float], name='offsetChanged') labelChanged = pyqtSignal([], [str], name='labelChanged') operatorChanged = pyqtSignal(name='operatorChanged') styleChanged = pyqtSignal([dict], name='styleChanged') @property def z(self): return self._operator.z @z.setter def z(self, value): if isinstance(value, float): self._operator.z = value self.zChanged.emit() self.zChanged[float].emit(value) self.operatorChanged.emit() else: raise OperatorModelError( f'Cannot set Z-value of {self.__class__.__name__} of {self._operator!r}.') from TypeError( f'Value {value!r} must be `float`') @property def offset(self): return self._operator.offset @offset.setter def offset(self, value): if isinstance(value, float): self._operator.offset = value self.offsetChanged.emit() self.offsetChanged[float].emit(value) self.operatorChanged.emit() else: raise OperatorModelError( f'Cannot set offset-value of {self.__class__.__name__} of {self._operator!r}.') from TypeError( f'Value {value!r} must be `float`') @property def value(self): return self._operator.value @value.setter def value(self, value): if isinstance(value, float): self._operator.value = value self.valueChanged.emit() self.valueChanged[float].emit(value) self.operatorChanged.emit() else: raise OperatorModelError( f'Cannot set operator-value of {self.__class__.__name__} of {self._operator!r}.') from TypeError( f'Value {value!r} must be `float`') @property def label(self): return self._operator.label @label.setter def label(self, value): if isinstance(value, str): self._operator.label = value self.labelChanged.emit() self.labelChanged[str].emit(value) self.operatorChanged.emit() else: raise OperatorModelError( f'Cannot set label-value of {self.__class__.__name__} of {self._operator!r}.') from TypeError( f'Value {value!r} must be `str`') @property def silent(self): return self._silent @silent.setter def silent(self, value): self._silent = bool(value) self.blockSignals(self._silent) @property def operator_type(self): return type(self._operator) @property def operator_classname(self): return self._operator.__class__.__name__ @property def is_deflector(self): return isinstance(self._operator, Deflector) @property def is_lens(self): return isinstance(self._operator, Lens) @property def is_propagator(self): return isinstance(self._operator, Propagator) @property def style(self): return dict(self._style) @property def focal_style(self): if self.is_lens: return dict(self._focal_style) else: return dict() # raise AttributeError(f'Cannot get focal_style for {self}. Operator {self._operator!r} is not a lens') def __init__(self, operator, *args, **kwargs): """ Create a model for an OpticalOperator :param operator: The OpticalOperator to model :param args: Optional positional arguments passed to QtCore.QObject constructor :param kwargs: Optional keyword arguments passed to QtCore.QObject constructor :type operator: OpticalOperator """ super(OpticalOperatorModel, self).__init__(*args, **kwargs) if not isinstance(operator, OpticalOperator): raise TypeError( f'Cannot create {self.__class__.__name__} for {operator!r}. Invalid type {type(operator)}. Accepted types are OpticalOperator and subclasses.') self._operator = operator self._silent = False self._style = dict([['ls', '-'], ['alpha', 1.], ['color', 'k'], ['lw', 1.]]) self._focal_style = dict([['ls', '--'], ['alpha', 0.5], ['color', 'k'], ['lw', 0.5]]) def __repr__(self): return f'{self.__class__.__name__}({self._operator!r}, {self.parent()})' def __str__(self): return f'{self._operator}' def show(self, *args, **kwargs): """ Shows the operator :param args: Optional positional arguments passed to OpticalOperator.show() :param kwargs: Optional keyword arguments passed to OpticalOperator.show() :return: """ kwargs.update(self.style) print(kwargs) if self.is_lens: return self._operator.show(*args, focal_plane_kwargs=self._focal_style, **kwargs) else: return self._operator.show(*args, **kwargs) def set_style(self, key, value, focal=False): f""" Sets one of the style fields to the given value :param focal: Whether to set the style for focal planes or not. Only applicable if the optical operator is a Lens. :param key: The key to set. Should be one of {list(self._style.keys())} :param value: The value to set the field to. :type key: str :type value: Union[float, int, str] :return: """ if focal: if key in self.focal_style: self._focal_style[key] = value else: raise ValueError(f'Cannot set focal style {key} to {value} for {self}: Key {key!r} not recognized') else: if key in self.style: self._style[key] = value else: raise ValueError(f'Cannot set style {key} to {value} for {self}: Key {key!r} not recognized') self.styleChanged.emit() class OpticalOperatorController(QtCore.QObject): """ Controller for controlling an OpticalOperatorModel The controller has a series of preset values that can be used to store certain values in a dictionary with integer keys. """ presetsChanged = pyqtSignal([], name='presetsChanged') @property def value_presets(self): return self._value_presets @property def model_name(self): return str(self._model.label) @property def model(self): return self._model def __init__(self, model, *args, **kwargs): """ Create a controller for an OpticalOperatorModel :param model: The model to control :param args: Optional positional arguments passed to QtCore.QObject constructor :param kwargs: Optional keyword arguments passed to QtCore.QObject constructor :type model: OpticalOperatorModel """ super(OpticalOperatorController, self).__init__(*args, **kwargs) if not isinstance(model, OpticalOperatorModel): raise TypeError( f'Cannot create {self.__class__.__name__} for {model!r}. Invalid type {type(model)}. Accepted types are `OpticalOperatorModel` and subclasses') self._model = model self._value_presets = dict() @pyqtSlot(int, float, name='setValuePreset') def setValuePreset(self, preset, value): """ Sets/adds a preset value :param preset: Preset-key :param value: Preset-value :type preset: int :type value: float """ self._value_presets[preset] = value self.presetsChanged.emit() @pyqtSlot(int, name='setSilent') @pyqtSlot(bool, name='setSilent') @pyqtSlot(float, name='setSilent') def setSilent(self, value): """ Disable signals from the model :param value: whether to disable or enable signals :param value: Union[int, float, bool] :return: """ self._model.silent = value @pyqtSlot(float, name='setZ') def setZ(self, value): """ Set the z-position of the model :param value: z-value :type value: float """ self._model.z = value @pyqtSlot(float, name='setOffset') def setOffset(self, value): """ Set the offset-value of the model :param value: offset-value :type value: float :return: """ self._model.offset = value @pyqtSlot(float, name='setValue') def setFloatValue(self, value): """ Set the value of the model :param value: the value :type value: float :return: """ self._model.value = value @pyqtSlot(int, name='setValue') def setIntValue(self, value): """ Set the value of the model based on preset values :param value: The preset-key to use :type value: int :return: """ operator_value = self._value_presets.get(value, float(value)) self._model.value = operator_value @pyqtSlot(str, float) def setParameter(self, parameter, value): """ Sets a given parameter to a given value :param parameter: The parameter to set. SHould be either "z", "offset", "value-float" or "value-int" :param value: The value to set :type parameter: str :type value: float :return: """ if parameter.lower() == 'z': self.setZ(value) elif parameter.lower() == 'offset': self.setOffset(value) elif parameter.lower() == 'value-float': self.setFloatValue(value) elif parameter.lower() == 'value-int': self.setIntValue(int(value)) else: raise ValueError(f'Could not set parameter {parameter} to {value} for {self!r}: Parameter not recognized.') @pyqtSlot(str, float, bool) @pyqtSlot(str, int, bool) @pyqtSlot(str, str, bool) def setStyle(self, field, value, focal): if self._model.is_lens: self._model.set_style(field, value, focal) else: self._model.set_style(field, value, False) @pyqtSlot(dict, bool) def setStyleDict(self, styles, focal): blocked = self._model.signalsBlocked() if not blocked: self._model.blockSignals(True) for key in styles: self._model.set_style(key, styles[key], focal) if not blocked: self._model.blockSignals(False) self._model.styleChanged[dict].emit(styles) class StyleWidget(QtWidgets.QWidget): styleChanged = pyqtSignal([dict]) @property def styleDict(self): return dict(self._styledict) @property def widgets(self): return {'style': self._linestyleCombobox, 'width': self._linewidthSpinbox, 'alpha': self._aSpinbox, 'color': self._colorWidget} def __init__(self, *args, **kwargs): super(StyleWidget, self).__init__(*args, **kwargs) self._styledict = dict() self._linewidthSpinbox = QtWidgets.QDoubleSpinBox(self) self._linestyleCombobox = QtWidgets.QComboBox(self) self._colorWidget = QtWidgets.QWidget(self) self._rSpinbox = QtWidgets.QDoubleSpinBox(self._colorWidget) self._gSpinbox = QtWidgets.QDoubleSpinBox(self._colorWidget) self._bSpinbox = QtWidgets.QDoubleSpinBox(self._colorWidget) self._aSpinbox = QtWidgets.QDoubleSpinBox(self._colorWidget) self._linewidthSpinbox.setMinimum(0) self._linewidthSpinbox.setMaximum(10) self._linewidthSpinbox.setDecimals(2) self._linewidthSpinbox.setSingleStep(0.1) self._linewidthSpinbox.blockSignals(True) self._linewidthSpinbox.setValue(1) self._linewidthSpinbox.blockSignals(False) self._linestyleCombobox.addItems(lineStyles.keys()) self._linestyleCombobox.blockSignals(True) self._linestyleCombobox.setCurrentText('-') self._linestyleCombobox.blockSignals(False) self._rSpinbox.setMinimum(0) self._rSpinbox.setMaximum(1) self._rSpinbox.setDecimals(2) self._rSpinbox.setSingleStep(0.1) self._rSpinbox.blockSignals(True) self._rSpinbox.setValue(1) self._rSpinbox.blockSignals(False) self._gSpinbox.setMinimum(0) self._gSpinbox.setMaximum(1) self._gSpinbox.setDecimals(2) self._gSpinbox.setSingleStep(0.1) self._gSpinbox.blockSignals(True) self._gSpinbox.setValue(1) self._gSpinbox.blockSignals(False) self._bSpinbox.setMinimum(0) self._bSpinbox.setMaximum(1) self._gSpinbox.setDecimals(2) self._bSpinbox.setSingleStep(0.1) self._bSpinbox.blockSignals(True) self._bSpinbox.setValue(1) self._bSpinbox.blockSignals(False) self._aSpinbox.setMinimum(0) self._aSpinbox.setMaximum(1) self._aSpinbox.setDecimals(2) self._aSpinbox.setSingleStep(0.1) self._aSpinbox.blockSignals(True) self._aSpinbox.setValue(1.) self._aSpinbox.blockSignals(False) gridlayout = QtWidgets.QGridLayout() gridlayout.addWidget(QtWidgets.QLabel('R'), 0, 0) gridlayout.addWidget(QtWidgets.QLabel('G'), 0, 1) gridlayout.addWidget(QtWidgets.QLabel('B'), 0, 2) gridlayout.addWidget(QtWidgets.QLabel('A'), 0, 3) gridlayout.addWidget(self._rSpinbox, 1, 0) gridlayout.addWidget(self._gSpinbox, 1, 1) gridlayout.addWidget(self._bSpinbox, 1, 2) gridlayout.addWidget(self._aSpinbox, 1, 3) self._colorWidget.setLayout(gridlayout) self._styledict['lw'] = self._linewidthSpinbox.value() self._styledict['ls'] = self._linestyleCombobox.currentText() self._styledict['color'] = to_hex([self._rSpinbox.value(), self._gSpinbox.value(), self._bSpinbox.value()]) self._styledict['alpha'] = self._aSpinbox.value() @pyqtSlot(float, float, float) def setColorRGB(self, r, g, b): blocked = self.signalsBlocked() if not blocked: self.blockSignals(True) self.setRValue(r) self.setGValue(g) self.setBValue(b) if not blocked: self.blockSignals(False) self.styleChanged[dict].emit(self.styleDict) @pyqtSlot(str) def setColorHex(self, hex): try: color = to_rgb(hex) except ValueError as e: raise ValueError(f'Cannot set color for {self!r} for hex-string {hex!r}') from e else: self.setColorRGB(*color) @pyqtSlot(float) def setRValue(self, value): self._rSpinbox.blockSignals(True) self._rSpinbox.setValue(value) self._rSpinbox.blockSignals(False) self._styledict['color'] = to_hex([self._rSpinbox.value(), self._gSpinbox.value(), self._bSpinbox.value()]) self.styleChanged[dict].emit(self.styleDict) @pyqtSlot(float) def setGValue(self, value): self._gSpinbox.blockSignals(True) self._gSpinbox.setValue(value) self._gSpinbox.blockSignals(False) self._styledict['color'] = to_hex([self._rSpinbox.value(), self._gSpinbox.value(), self._bSpinbox.value()]) self.styleChanged[dict].emit(self.styleDict) @pyqtSlot(float) def setBValue(self, value): self._bSpinbox.blockSignals(True) self._bSpinbox.setValue(value) self._bSpinbox.blockSignals(False) self._styledict['color'] = to_hex([self._rSpinbox.value(), self._gSpinbox.value(), self._bSpinbox.value()]) self.styleChanged[dict].emit(self.styleDict) @pyqtSlot(float) def setAValue(self, value): self._aSpinbox.blockSignals(True) self._aSpinbox.setValue(value) self._aSpinbox.blockSignals(False) self._styledict['alpha'] = self._aSpinbox.value() self.styleChanged[dict].emit(self.styleDict) @pyqtSlot(float) def setLinewidth(self, value): self._linewidthSpinbox.blockSignals(True) self._linewidthSpinbox.setValue(value) self._linewidthSpinbox.blockSignals(False) self._styledict['lw'] = self._linewidthSpinbox.value() self.styleChanged[dict].emit(self.styleDict) @pyqtSlot(str) def setLinestyle(self, value): self._linestyleCombobox.blockSignals(True) self._linestyleCombobox.setCurrentText(value) self._linestyleCombobox.blockSignals(False) self._styledict['ls'] = self._linestyleCombobox.currentText() self.styleChanged[dict].emit(self._styledict) @pyqtSlot(dict) def setStyles(self, styles): blocked = self.signalsBlocked() if not blocked: self.blockSignals(True) self.setLinestyle(styles['ls']) self.setLinewidth(styles['lw']) self.setAValue(styles['alpha']) self.setColorHex(styles['color']) if not blocked: self.blockSignals(False) self.styleChanged[dict].emit(self.styleDict) class OpticalOperatorView(QtWidgets.QWidget): """ Create a view for an OpticalOperator. This object provides a series of widgets and setup-tools for the widgets. The widgets are connected to a controller that controls the model, and changes in the model are reflected in the view - as long as the underlying data object (i.e. the OpticalOperator) is changed directly (not through the corresponding OpticalOperatorModel) """ value_min = -999 value_max = 999 value_step = 0.1 value_decimals = 2 z_min = -999 z_max = 999 z_step = 0.5 z_decimals = 2 offset_min = -999 offset_max = 999 offset_step = 0.05 offset_decimals = 2 plotUpdated = pyqtSignal(name='plotUpdated') @property def model(self): return self._model def __init__(self, controller, *args, plot_widget=None, **kwargs): """ Create a view for a controller. The following widgets will be created: -typeLabel: A QLabel to show the type of the operator -nameLabel: A QLabel to show the name of the operator -zSpinbox: A QDoubleSpinBox to control/show the z-position of the operator -offsetSpinbox: A QDoubleSpinBox to control/show the offset of the operator -valueSpinbox: A QDoubleSpinBox to control/show the value of the operator -valueDial: A QDial to control/show the value of the operator through preset values -valueIndicator: A QLabel to show the current value of the operator below the valueDial. -zStepSpinbox: A QDoubleSpinBox to control/show the singleStep of the zSpinbox. -offsetStepSpinbox: A QDoubleSpinBox to control/show the singleStep of the offsetSpinbox. -valueStepSpinbox: A QDoubleSpinBox to control/show the singleStep of the valueSpinbox. -plotWidget: A MplWidget to show the operator graphically in a plot area. :param controller: The controller to connect to. The model will be extracted from this controller. :param args: Optional positional arguments passed to QtWidgets.QWidget :param plot_widget: The plot-widget to use to show the optical operator on :param kwargs: Optional keyword arguments passed to QtWidgets.QWidget :type controller: OpticalOperatorController :type plot_widget: MplWidget """ super(OpticalOperatorView, self).__init__(*args, **kwargs) if not isinstance(controller, OpticalOperatorController): raise TypeError() self._controller = controller self._model = self._controller.model self.typeLabel = QtWidgets.QLabel(self._model.operator_classname, self) self.nameLabel = QtWidgets.QLabel(self._model.label, self) self.zSpinbox = QtWidgets.QDoubleSpinBox(self) self.offsetSpinbox = QtWidgets.QDoubleSpinBox(self) self.valueSpinbox = QtWidgets.QDoubleSpinBox(self) self.valueDial = QtWidgets.QDial(self) self.valueIndicator = QtWidgets.QLabel(self) self.styleWidget = StyleWidget(self) if self._model.is_lens: self.focalStyleWidget = StyleWidget(self) else: self.focalStyleWidget = None # self.zStepSpinbox = QtWidgets.QDoubleSpinBox(self) # self.offsetStepSpinbox = QtWidgets.QDoubleSpinBox(self) # self.valueStepSpinbox = QtWidgets.QDoubleSpinBox(self) # self.zDecimalsSpinbox = QtWidgets.QSpinBox(self) # self.offsetDecimalsSpinbox = QtWidgets.QSpinBox(self) # self.valueDecimalsSpinbox = QtWidgets.QSpinBox(self) # self.zMinimumLineEdit = QtWidgets.QLineEdit(self) # self.offsetMinimumLineEdit = QtWidgets.QLineEdit(self) # self.valueMinimumLineEdit = QtWidgets.QLineEdit(self) # self.zMaximumLineEdit = QtWidgets.QLineEdit(self) # self.offsetMaximumLineEdit = QtWidgets.QLineEdit(self) # self.valueMaximumLineEdit = QtWidgets.QLineEdit(self) if plot_widget is None: self.plotWidget = MplWidget(self) else: if isinstance(plot_widget, MplWidget): self.plotWidget = plot_widget else: raise TypeError( f'Cannot create {self.__class__.__name__} for controller {self._controller!r} with model {self._model!r}. Provided plotWidget is not a MplWidget but a {type(plot_widget)}') self._plot_data = None self.setupZSpinbox() self.setupValueDial() self.setupValueSpinbox() self.setupOffsetSpinbox() self.setupValueIndicator() self.styleWidget.setStyles(self._model.style) # Simple setup for the stylewidgets # Listeners self._model.valueChanged[float].connect(self.on_value_changed) self._model.zChanged[float].connect(self.on_z_changed) self._model.offsetChanged[float].connect(self.on_offset_changed) self._model.labelChanged[str].connect(self.on_label_changed) self._model.operatorChanged.connect(lambda: self.on_model_changed()) self._model.styleChanged[dict].connect(self.on_style_changed) # Signals self.zSpinbox.valueChanged[float].connect(self._controller.setZ) self.offsetSpinbox.valueChanged[float].connect(self._controller.setOffset) self.valueSpinbox.valueChanged[float].connect(self._controller.setFloatValue) self.valueDial.valueChanged[int].connect(self._controller.setIntValue) self.styleWidget.styleChanged[dict].connect(lambda x: self._controller.setStyleDict(x, False)) def setupValueSpinbox(self): """ Sets up the value spinbox :return: """ self.valueSpinbox.setMinimum(self.value_min) self.valueSpinbox.setMaximum(self.value_max) self.valueSpinbox.setDecimals(self.value_decimals) self.valueSpinbox.setSingleStep(self.value_step) self.valueSpinbox.blockSignals(True) self.valueSpinbox.setValue(self._model.value) self.valueSpinbox.blockSignals(False) def setupZSpinbox(self): self.zSpinbox.setMinimum(self.z_min) self.zSpinbox.setMaximum(self.z_max) self.zSpinbox.setDecimals(self.z_decimals) self.zSpinbox.setSingleStep(self.z_step) self.zSpinbox.blockSignals(True) self.zSpinbox.setValue(self._model.z) self.zSpinbox.blockSignals(False) def setupOffsetSpinbox(self): if self._model.is_deflector or self._model.is_propagator: self.offsetSpinbox.setEnabled(False) else: self.offsetSpinbox.setMinimum(self.offset_min) self.offsetSpinbox.setMaximum(self.offset_max) self.offsetSpinbox.setDecimals(self.offset_decimals) self.offsetSpinbox.setSingleStep(self.offset_step) self.offsetSpinbox.blockSignals(True) self.offsetSpinbox.setValue(self._model.offset) self.offsetSpinbox.blockSignals(False) def setupValueDial(self): if len(self._controller.value_presets) < 2: self.valueDial.setEnabled(False) dial_value = None else: self.valueDial.setMinimum(min(self._controller.value_presets.keys())) self.valueDial.setMaximum(max(self._controller.value_presets.keys())) preset_matches = [key for key in self._controller.value_presets if self._controller.value_presets[key] == self._model.value] if len(preset_matches) > 0: dial_value = min(preset_matches) else: dial_value = None self.valueDial.setTracking(True) self.valueDial.setNotchesVisible(True) if dial_value is None: if self.valueDial.isEnabled(): self.valueDial.setStyleSheet('background-color : lightblue') else: pass else: self.valueDial.setStyleSheet('background-color : lightgreen') self.valueDial.blockSignals(True) self.valueDial.setValue(dial_value) self.valueDial.blockSignals(False) def setupValueIndicator(self): self.valueIndicator.setText(f'{self._model.value}') @pyqtSlot(float) def on_z_changed(self, value): if self.zSpinbox.minimum() > value: self.zSpinbox.setMinimum(value) if self.zSpinbox.maximum() < value: self.zSpinbox.setMaximum(value) self.zSpinbox.blockSignals(True) self.zSpinbox.setValue(value) self.zSpinbox.blockSignals(False) @pyqtSlot(float) def on_offset_changed(self, value): if self.offsetSpinbox.minimum() > value: self.offsetSpinbox.setMinimum(value) if self.offsetSpinbox.maximum() < value: self.offsetSpinbox.setMaximum(value) self.offsetSpinbox.blockSignals(True) self.offsetSpinbox.setValue(value) self.offsetSpinbox.blockSignals(False) @pyqtSlot(float) def on_value_changed(self, value): if self.valueSpinbox.minimum() > value: self.valueSpinbox.setMinimum(value) if self.valueSpinbox.maximum() < value: self.valueSpinbox.setMaximum(value) self.valueSpinbox.blockSignals(True) self.valueSpinbox.setValue(value) self.valueSpinbox.blockSignals(False) preset_values = [key for key in self._controller.value_presets if self._controller.value_presets[key] == value] if len(preset_values) == 0: self.valueDial.setStyleSheet('background-color : lightblue') else: self.valueDial.setStyleSheet('background-color : lightgreen') self.valueDial.blockSignals(True) self.valueDial.setValue(preset_values[0]) self.valueDial.blockSignals(False) self.valueIndicator.setText(f'{value}') @pyqtSlot(str) def on_label_changed(self, value): self.nameLabel.setText(value) def on_model_changed(self, *args, **kwargs): kwargs.update({'ax': self.plotWidget.canvas.ax}) if self._plot_data is None: _, _, self._plot_data = self._model.show(*args, **kwargs) else: if self._model.is_deflector: self._plot_data[0].set_ydata([self._model.z, self._model.z]) elif self._model.is_lens: [line.set_ydata([z, z]) for z, line in zip([self._model.z, self._model.z + self._model.value, self._model.z - self._model.value], self._plot_data)] self.plotUpdated.emit() @pyqtSlot(dict) def on_style_changed(self, style): self.styleWidget.blockSignals(True) self.styleWidget.setStyles(style) self.styleWidget.blockSignals(False) self.on_model_changed() class SourceModel(QtCore.QObject): """ Model for controlling a Source The model should ensure that proper signals are sent whenever the data of the Source has been changed. The model emits the following signals: :param zChanged: Signal ([], [float]) emitted whenever the z-value of the Source has changed. :param offsetChanged: Signal ([], [float]) emitted whenever the offset-value of the Source has changed. :param sizeChanged: Signal ([], [float]) emitted whenever the size-value of the Source has changed. :param anglesChanged: Signal ([]) emitted whenever the angles of the Source has changed. :param pointsChanged: Signal ([], [int]) emitted whenever the points-value of the Source has changed. :param sourceChanged: Signal wmitted whenever any change has been made to the Source, inculding the above. """ zChanged = pyqtSignal([], [float], name='zChanged') offsetChanged = pyqtSignal([], [float], name='offsetChanged') sizeChanged = pyqtSignal([], [float], name='sizeChanged') anglesChanged = pyqtSignal([], [np.ndarray], name='anglesChanged') pointsChanged = pyqtSignal([], [int], name='pointsChanged') sourceChanged = pyqtSignal(name='operatorChanged') @property def z(self): return self._source.z @z.setter def z(self, value): if isinstance(value, float): self._source.z = value self.zChanged.emit() self.zChanged[float].emit(value) self.sourceChanged.emit() else: raise SourceModelError( f'Cannot set Z-value of {self.__class__.__name__} of {self._source!r}.') from TypeError( f'Value {value!r} must be `float`') @property def offset(self): return self._source.offset @offset.setter def offset(self, value): if isinstance(value, float): self._source.offset = value self.offsetChanged.emit() self.offsetChanged[float].emit(value) self.sourceChanged.emit() else: raise SourceModelError( f'Cannot set offset-value of {self.__class__.__name__} of {self._source!r}.') from TypeError( f'Value {value!r} must be `float`') @property def angles(self): return self._source.angles @angles.setter def angles(self, value): if isinstance(value, (list, tuple, np.ndarray)): if len(np.shape(value)) == 1: self._source.angles = np.array(value) self.anglesChanged.emit() self.angelesChanged[np.ndarray].emit(np.array(value)) self.operatorChanged.emit() else: raise SourceModelError( f'Cannot set angles of {self.__class__.__name__} of {self._source!r}.') from ValueError( f'Argument {value!r} has invalid shape {np.shape(value)} != (1,).') else: raise SourceModelError( f'Cannot set angles of {self.__class__.__name__} of {self._source!r}.') from TypeError( f'Value {value!r} must be `tuple`, `list`, or `np.ndarray`.') @property def size(self): return self._source.size @size.setter def size(self, value): if isinstance(value, float): self._source.size = value self.sizeChanged.emit() self.sizeChanged[float].emit(value) self.sourceChanged.emit() else: raise SourceModelError( f'Cannot set size-value of {self.__class__.__name__} of {self._source!r}.') from TypeError( f'Value {value!r} must be `float`') @property def points(self): return self._source.points @points.setter def points(self, value): if isinstance(value, int): self._source.points = value self.pointsChanged.emit() self.pointsChanged[int].emit(value) self.sourceChanged.emit() else: raise SourceModelError( f'Cannot set points-value of {self.__class__.__name__} of {self._source!r}.') from TypeError( f'Value {value!r} must be `int`') @property def silent(self): return self._silent @silent.setter def silent(self, value): self._silent = bool(value) self.blockSignals(self._silent) def __init__(self, source, *args, **kwargs): """ Create a model for a Source :param source: The Source to model :param args: Optional positional arguments passed to QtCore.QObject constructor :param kwargs: Optional keyword arguments passed to QtCore.QObject constructor :type source: Source """ super(SourceModel, self).__init__(*args, **kwargs) if not isinstance(source, Source): raise TypeError( f'Cannot create {self.__class__.__name__} for {source!r}. Invalid type {type(source)}. Accepted types are OpticalOperator and subclasses.') self._source = source self._silent = False def __repr__(self): return f'{self.__class__.__name__}({self._source!r}, {self.parent()})' def __str__(self): return f'{self._source}' class SourceController(QtCore.QObject): """ Controller for controlling a SourceModel """ @property def model(self): return self._model def __init__(self, model, *args, **kwargs): """ Create a controller for a SourceModel :param model: The model to control :param args: Optional positional arguments passed to QtCore.QObject constructor :param kwargs: Optional keyword arguments passed to QtCore.QObject constructor :type model: SourceModel """ super(SourceController, self).__init__(*args, **kwargs) if not isinstance(model, SourceModel): raise TypeError( f'Cannot create {self.__class__.__name__} for {model!r}. Invalid type {type(model)}. Accepted type is `SourceModel`') self._model = model @pyqtSlot(int, name='setSilent') @pyqtSlot(bool, name='setSilent') @pyqtSlot(float, name='setSilent') def setSilent(self, value): """ Disable signals from the model :param value: whether to disable or enable signals :param value: Union[int, float, bool] :return: """ self._model.silent = value @pyqtSlot(float, name='setZ') def setZ(self, value): """ Set the z-position of the model :param value: z-value :type value: float """ self._model.z = value @pyqtSlot(float, name='setOffset') def setOffset(self, value): """ Set the offset-value of the model :param value: offset-value :type value: float :return: """ self._model.offset = value @pyqtSlot(float, name='setAngleMin') def setAngleMin(self, value): """ Set the minimum angle of the source model :param value: minimum angle :type value: float :return: """ self._model.angles = np.linspace(value, np.max(self._model.angles), num=len(self._model.angles)) @pyqtSlot(float, name='setAngleMax') def setAngleMax(self, value): """ Set the maximum angle of the source model :param value: maximum angle :type value: float :return: """ self._model.angles = np.linspace(np.min(self._model.angles), value, num=len(self._model.angles)) @pyqtSlot(int, name='setAngleNumber') def setAngleNumber(self, value): """ Set the number of angles of the source model :param value: the number of angles :type value: int :return: """ self._model.angles = np.linspace(np.min(self._model.angles), np.max(self._model.angles), num=value) @pyqtSlot(list, name='setAngles') @pyqtSlot(tuple, name='setAngles') @pyqtSlot(np.ndarray, name='setAngles') def setAngles(self, value): """ Set the angles of the model :param value: the angles :type value: Union[list, tuple, np.ndarray] :return: """ self._model.angles = np.array(value) @pyqtSlot(float, name='addAngle') def addAngle(self, value): """ Add an angle to the source :param value: The angle to add :type value: float :return: """ self._model.angles = np.array(list(self._model.angles) + [value]) @pyqtSlot(float, name='setSize') def setSize(self, value): """ Set the size-value of the model :param value: size-value :type value: float :return: """ self._model.size = value @pyqtSlot(int, name='setPoints') def setPoints(self, value): """ Set the number of points to emit rays from :param value: The number of points :type value: int :return: """ self._model.points = value @pyqtSlot(str, float) def setParameter(self, parameter, value): """ Sets a given parameter to a given value :param parameter: The parameter to set. Should be either "z", "offset", "size", or "angle" :param value: The value to set :type parameter: str :type value: float :return: """ if parameter.lower() == 'z': self.setZ(value) elif parameter.lower() == 'offset': self.setOffset(value) elif parameter.lower() == 'size': self.setSize(value) elif parameter.lower() == 'angle': self.addAngle(value) else: raise ValueError(f'Could not set parameter {parameter} to {value} for {self!r}: Parameter not recognized.') class SourceView(QtWidgets.QWidget): """ Create a view for a SourceModel. This object provides a series of widgets and setup-tools for the widgets. The widgets are connected to a controller that controls the model, and changes in the model are reflected in the view - as long as the underlying data object (i.e. the Source) is changed directly (not through the corresponding SourceModel) """ size_min = -999 size_max = 999 size_step = 0.01 value_decimals = 2 size_points_min = 1 size_points_max = 50 size_points_step = 1 z_min = -999 z_max = 999 z_step = 0.5 z_decimals = 2 offset_min = -999 offset_max = 999 offset_step = 0.05 offset_decimals = 2 angles_min = -90 angles_max = 90 angles_step = 0.01 angles_decimals = 2 angles_points_min = 1 angles_points_max = 50 angles_points_step = 1 @property def model(self): return self._model def __init__(self, controller, *args, **kwargs): """ Create a view for a controller. The following widgets will be created: -zSpinbox: A QDoubleSpinBox to control/show the z-position of the source -offsetSpinbox: A QDoubleSpinBox to control/show the offset of the source -sizeSpinBox: A QDoubleSpinBox to control/show the size of the source -pointsSpinBox: A QSpinBox to control/show the number of points to emit rays from the source for -anglesMinSpinBox: A QDoubleSpinBox to control/show the minimum angle to emit -anglesMaxSpinBox: A QDoubleSpinBox to control/show the maximum angle to emit -anglesNumberSpinBox: A QSpinBox to control/show the number of angles to emit from each point. :param controller: The controller to connect to. The model will be extracted from this controller. :param args: Optional positional arguments passed to QtWidgets.QWidget :param kwargs: Optional keyword arguments passed to QtWidgets.QWidget :type controller: SourceController """ super(SourceView, self).__init__(*args, **kwargs) if not isinstance(controller, SourceController): raise TypeError() self._controller = controller self._model = self._controller.model self.zSpinbox = QtWidgets.QDoubleSpinBox(self) self.offsetSpinbox = QtWidgets.QDoubleSpinBox(self) self.sizeSpinbox = QtWidgets.QDoubleSpinBox(self) self.pointsSpinBox = QtWidgets.QSpinBox(self) self.anglesMinSpinBox = QtWidgets.QDoubleSpinBox(self) self.anglesMaxSpinBox = QtWidgets.QDoubleSpinBox(self) self.anglesNumberSpinBox = QtWidgets.QSpinBox(self) self.setupZSpinbox() self.setupOffsetSpinbox() self.setupSizeSpinbox() self.setupAnglesSpinBox() # Listeners self._model.zChanged[float].connect(self.on_z_changed) self._model.offsetChanged[float].connect(self.on_offset_changed) self._model.sizeChanged[float].connect(self.on_size_changed) self._model.anglesChanged[np.ndarra].connect(self.on_angles_changed) self._model.pointsChanged[int].connect(self.on_points_changed) # Signals self.zSpinbox.valueChanged[float].connect(self._controller.setZ) self.offsetSpinbox.valueChanged[float].connect(self._controller.setOffset) self.sizeSpinbox.valueChanged[float].connect(self._controller.setSize) self.pointsSpinBox.valueChanged[int].connect(self._controller.setPoints) self.anglesMinSpinBox.valueChanged[float].connect(self._controller.setAngleMin) self.anglesMaxSpinBox.valueChanged[float].connect(self._controller.setAngleMax) self.anglesNumberSpinBox.valueChanged[int].connect(self._controller.setAngleNumber) def setupZSpinbox(self): self.zSpinbox.setMinimum(self.z_min) self.zSpinbox.setMaximum(self.z_max) self.zSpinbox.setDecimals(self.z_decimals) self.zSpinbox.setSingleStep(self.z_step) self.zSpinbox.blockSignals(True) self.zSpinbox.setValue(self._model.z) self.zSpinbox.blockSignals(False) def setupOffsetSpinbox(self): self.offsetSpinbox.setMinimum(self.offset_min) self.offsetSpinbox.setMaximum(self.offset_max) self.offsetSpinbox.setDecimals(self.offset_decimals) self.offsetSpinbox.setSingleStep(self.offset_step) self.offsetSpinbox.blockSignals(True) self.offsetSpinbox.setValue(self._model.offset) self.offsetSpinbox.blockSignals(False) def setupSizeSpinbox(self): self.sizeSpinbox.setMinimum(self.size_min) self.sizeSpinbox.setMaximum(self.size_max) self.sizeSpinbox.setDecimals(self.size_decimals) self.sizeSpinbox.setSingleStep(self.size_step) self.sizeSpinbox.blockSignals(True) self.sizeSpinbox.setValue(self._model.size) self.sizeSpinbox.blockSignals(False) self.pointsSpinbox.setMinimum(self.size_points_min) self.pointsSpinbox.setMaximum(self.size_points_max) self.pointsSpinbox.setSingleStep(self.size_points_step) self.pointsSpinbox.blockSignals(True) self.pointsSpinbox.setValue(self._model.points) self.pointsSpinbox.blockSignals(False) def setupAnglesSpinbox(self): self.anglesMinSpinbox.setMinimum(self.anglesMin_min) self.anglesMinSpinbox.setMaximum(self.anglesMin_max) self.anglesMinSpinbox.setDecimals(self.anglesMin_decimals) self.anglesMinSpinbox.setSingleStep(self.anglesMin_step) self.anglesMinSpinbox.blockSignals(True) self.anglesMinSpinbox.setValue(self._model.anglesMin) self.anglesMinSpinbox.blockSignals(False) self.anglesMaxSpinbox.setMinimum(self.anglesMax_min) self.anglesMaxSpinbox.setMaximum(self.anglesMax_max) self.anglesMaxSpinbox.setDecimals(self.anglesMax_decimals) self.anglesMaxSpinbox.setSingleStep(self.anglesMax_step) self.anglesMaxSpinbox.blockSignals(True) self.anglesMaxSpinbox.setValue(self._model.anglesMax) self.anglesMaxSpinbox.blockSignals(False) self.anglesNumberSpinbox.setMinimum(self.angles_points_min) self.anglesNumberSpinbox.setMaximum(self.angles_points_max) self.anglesNumberSpinbox.setSingleStep(self.angles_points_step) self.anglesNumberSpinbox.blockSignals(True) self.anglesNumberSpinbox.setValue(len(self._model.angles)) self.anglesNumberSpinbox.blockSignals(False) @pyqtSlot(float) def on_z_changed(self, value): if self.zSpinbox.minimum() > value: self.zSpinbox.setMinimum(value) if self.zSpinbox.maximum() < value: self.zSpinbox.setMaximum(value) self.zSpinbox.blockSignals(True) self.zSpinbox.setValue(value) self.zSpinbox.blockSignals(False) @pyqtSlot(float) def on_offset_changed(self, value): if self.offsetSpinbox.minimum() > value: self.offsetSpinbox.setMinimum(value) if self.offsetSpinbox.maximum() < value: self.offsetSpinbox.setMaximum(value) self.offsetSpinbox.blockSignals(True) self.offsetSpinbox.setValue(value) self.offsetSpinbox.blockSignals(False) @pyqtSlot(float) def on_size_changed(self, value): if self.sizeSpinbox.minimum() > value: self.sizeSpinbox.setMinimum(value) if self.sizeSpinbox.maximum() < value: self.sizeSpinbox.setMaximum(value) self.sizeSpinbox.blockSignals(True) self.sizeSpinbox.setValue(value) self.sizeSpinbox.blockSignals(False) @pyqtSlot(float) def on_points_changed(self, value): if self.pointsSpinbox.minimum() > value: self.pointsSpinbox.setMinimum(value) if self.pointsSpinbox.maximum() < value: self.pointsSpinbox.setMaximum(value) self.pointsSpinbox.blockSignals(True) self.pointsSpinbox.setValue(value) self.pointsSpinbox.blockSignals(False) @pyqtSlot(np.ndarray) def on_angles_changed(self, value): minimum = np.min(value) maximum = np.maximum(value) n = len(value) if self.anglesMinSpinBox.minimum() > minimum: self.anglesMinSpinbox.setMinimum(minimum) if self.anglesMinSpinbox.maximum() < minimum: self.anglesMinSpinbox.setMaximum(minimum) if self.anglesMaxSpinBox.maximum() > maximum: self.anglesMaxSpinbox.setMinimum(maximum) if self.anglesMaxSpinbox.maximum() < maximum: self.anglesMaxSpinbox.setMaximum(maximum) if self.anglesNumberSpinBox.minimum() > n: self.anglesNumberSpinbox.setMinimum(n) if self.anglesNumberSpinbox.maximum() < n: self.anglesNumberSpinbox.setMaximum(n) self.anglesMinSpinbox.blockSignals(True) self.anglesMaxSpinbox.blockSignals(True) self.anglesNumberSpinbox.blockSignals(True) self.anglesMinSpinBox.setValue(minimum) self.anglesMinSpinBox.setValue(maximum) self.anglesNumberSpinBox.setValue(n) self.anglesMinSpinbox.blockSignals(False) self.anglesMaxSpinbox.blockSignals(False) self.anglesNumberSpinbox.blockSignals(False) class ScreenModel(QtCore.QObject): """ Model for controlling a Screen The model should ensure that proper signals are sent whenever the data of the Screen has been changed. The model emits the following signals: :param zChanged: Signal ([], [float]) emitted whenever the z-value of the Screen has changed. :param ScreenChanged: Signal emitted whenever any change has been made to the Screen, inculding the above. """ zChanged = pyqtSignal([], [float], name='zChanged') screenChanged = pyqtSignal(name='operatorChanged') @property def z(self): return self._screen.z @z.setter def z(self, value): if isinstance(value, float): self.screen.z = value self.zChanged.emit() self.zChanged[float].emit(value) self.screenChanged.emit() else: raise ScreenModelError( f'Cannot set Z-value of {self.__class__.__name__} of {self._screen!r}.') from TypeError( f'Value {value!r} must be `float`') @property def silent(self): return self._silent @silent.setter def silent(self, value): self._silent = bool(value) self.blockSignals(self._silent) def __init__(self, screen, *args, **kwargs): """ Create a model for a Screen :param screen: The Screen to model :param args: Optional positional arguments passed to QtCore.QObject constructor :param kwargs: Optional keyword arguments passed to QtCore.QObject constructor :type screen: Screen """ super(ScreenModel, self).__init__(*args, **kwargs) if not isinstance(screen, Screen): raise TypeError( f'Cannot create {self.__class__.__name__} for {screen!r}. Invalid type {type(screen)}. Accepted type is Screen.') self._screen = screen self._silent = False def __repr__(self): return f'{self.__class__.__name__}({self._screen!r}, {self.parent()})' def __str__(self): return f'{self._screen}' class ScreenController(QtCore.QObject): """ Controller for controlling a ScreenModel """ @property def model(self): return self._model def __init__(self, model, *args, **kwargs): """ Create a controller for a ScreenModel :param model: The model to control :param args: Optional positional arguments passed to QtCore.QObject constructor :param kwargs: Optional keyword arguments passed to QtCore.QObject constructor :type model: ScreenModel """ super(ScreenController, self).__init__(*args, **kwargs) if not isinstance(model, ScreenModel): raise TypeError( f'Cannot create {self.__class__.__name__} for {model!r}. Invalid type {type(model)}. Accepted type is `ScreenModel`') self._model = model @pyqtSlot(int, name='setSilent') @pyqtSlot(bool, name='setSilent') @pyqtSlot(float, name='setSilent') def setSilent(self, value): """ Disable signals from the model :param value: whether to disable or enable signals :param value: Union[int, float, bool] :return: """ self._model.silent = value @pyqtSlot(float, name='setZ') def setZ(self, value): """ Set the z-position of the model :param value: z-value :type value: float """ self._model.z = value @pyqtSlot(str, float) def setParameter(self, parameter, value): """ Sets a given parameter to a given value :param parameter: The parameter to set. Should be "z" :param value: The value to set :type parameter: str :type value: float :return: """ if parameter.lower() == 'z': self.setZ(value) else: raise ValueError(f'Could not set parameter {parameter} to {value} for {self!r}: Parameter not recognized.') #WIP: Make ScreenView class MicroscopeModel(QtCore.QObject): modelChanged = pyqtSignal([], name='modelChanged') systemFilled = pyqtSignal([], name='systemFilled') systemTraced = pyqtSignal([list], name='systemTraced') @property def operatorModels(self): return [model for model in self._operatorModels] @property def sourceModel(self): return self._sourceModel @property def screenModel(self): return self._screenModel def __init__(self, optical_system, *args, **kwargs): super(MicroscopeModel, self).__init__(*args, **kwargs) if not isinstance(optical_system, OpticalSystem): raise TypeError( f'Cannot create {self.__class__.__name__} for source: {optical_system!r}. Expected type OpticalSystem not {type(optical_system)}') self._optical_system = optical_system self._sourceModel = SourceModel(optical_system.source) self._screenModel = ScreenModel(optical_system.screen) self._operatorModels = [OpticalOperatorModel(operator, self.parent()) for operator in self._optical_system] def __iter__(self): for obj in [self.sourceModel] + self.operatorModels + [self.screenModel]: yield obj @pyqtSlot() def fillSystem(self): self._optical_system.fill() self.systemFilled.emit() @pyqtSlot(name='trace', result=list) def trace(self): self.blockSignals(True) self.fillSystem() self.blockSignals(False) traces = self._optical_system.trace self.systemTraced[list].emit(traces) return traces @pyqtSlot(name='printSystem') def printSystem(self): print(self._optical_system) @pyqtSlot(name='printTraces') def printTraces(self): traces = self._optical_system.trace for trace in traces: print(f'Trace {trace.label}:') t = tabulate([[i, ray.x, ray.angle_deg, ray.z] for i, ray in enumerate(trace)], headers=['#', 'X', 'Angle [deg]', 'Z']) print(t) class MicroscopeController(QtCore.QObject): @property def model(self): return self._model @property def sourceController(self): return self._sourceController @property def screenController(self): return self._screenController @property def operatorControllers(self): return [controller for controller in self._operatorControllers] def __init__(self, model, *args, **kwargs): super(MicroscopeController, self).__init__(*args, **kwargs) if not isinstance(model, MicroscopeModel): raise TypeError( f'Cannot create {self.__class__.__name__} for model: {model!r}. Expected type MicroscopeModel not {type(model)}') self._model = model self._sourceController = None self._screenController = None self._operatorControllers = [OpticalOperatorController(model) for model in self._model.operatorModels if (model.is_lens or model.is_deflector)] def __iter__(self): for obj in [self.sourceController] + self.operatorControllers + [self.screenController]: yield obj @pyqtSlot(str, str, float) def setOperatorParameterByName(self, name, parameter, value): print(f'Setting {name} {parameter}={value}') changes = len([controller.setParameter(parameter, value) for controller in self._operatorControllers if controller.model_name == name]) if changes > 0: self._model.modelChanged.emit() @pyqtSlot(name='trace', result=list) def trace(self): return self._model.trace() class MicroscopeView(QtWidgets.QMainWindow): # colors = plt.rcParams['axes.prop_cycle'].by_key()['color'] # colors = plt.get_cmap('inferno', 10) colors = plt.get_cmap('tab20', 10) @property def screenView(self): return self._screenView @property def sourceView(self): return self._sourceView @property def operatorViews(self): return [view for view in self._operatorViews] def __init__(self, controller, *args, **kwargs): super(MicroscopeView, self).__init__(*args, **kwargs) if not isinstance(controller, MicroscopeController): raise TypeError() self._controller = controller self._model = self._controller.model self.plot_widget = MplWidget(self) self.lens_widgets = QtWidgets.QWidget(self) self.lens_widgets.setLayout(QtWidgets.QGridLayout()) self.plot_button = QtWidgets.QPushButton('Plot') self.print_system_button = QtWidgets.QPushButton('Print system') self.print_traces_button = QtWidgets.QPushButton('Print rays') self._screenView = None self._sourceView = None self._operatorViews = [OpticalOperatorView(controller, self, plot_widget=self.plot_widget) for controller in self._controller.operatorControllers] self._trace_lines = None self.setCentralWidget(QtWidgets.QWidget(self)) self.centralWidget().setLayout(QtWidgets.QGridLayout()) self.centralWidget().layout().addWidget(self.plot_widget, 0, 0) self.centralWidget().layout().addWidget(self.lens_widgets, 0, 1) self.centralWidget().layout().addWidget(self.plot_button, 1, 0) self.centralWidget().layout().addWidget(self.print_system_button, 2, 0) self.centralWidget().layout().addWidget(self.print_traces_button, 3, 0) self.lensStyleWindow = QtWidgets.QMainWindow() self.lensStyleWindow.setCentralWidget(QtWidgets.QWidget()) self.lensStyleWindow.centralWidget().setLayout(QtWidgets.QGridLayout()) self.lensStyleWindow.centralWidget().layout().addWidget(QtWidgets.QLabel('Name'), 0, 0) self.lensStyleWindow.centralWidget().layout().addWidget(QtWidgets.QLabel('Style'), 0, 1) self.lensStyleWindow.centralWidget().layout().addWidget(QtWidgets.QLabel('Width'), 0, 2) self.lensStyleWindow.centralWidget().layout().addWidget(QtWidgets.QLabel('Color'), 0, 4) [self.lensStyleWindow.centralWidget().layout().addWidget(QtWidgets.QLabel(f'{view.nameLabel.text()}'), i + 1, 0) for i, view in enumerate(self.operatorViews) if view.model.is_lens] [self.lensStyleWindow.centralWidget().layout().addWidget(view.styleWidget.widgets['style'], i + 1, 1) for i, view in enumerate(self.operatorViews) if view.model.is_lens] [self.lensStyleWindow.centralWidget().layout().addWidget(view.styleWidget.widgets['width'], i + 1, 2) for i, view in enumerate(self.operatorViews) if view.model.is_lens] [self.lensStyleWindow.centralWidget().layout().addWidget(view.styleWidget.widgets['color'], i + 1, 4) for i, view in enumerate(self.operatorViews) if view.model.is_lens] # [v for view in self.operatorViews] menubar = self.menuBar() self.controlMenu = menubar.addMenu('Controls') self.operatorAction = QtWidgets.QAction('&Operators', self) self.sourceAction = QtWidgets.QAction('&Source', self) self.screenAction = QtWidgets.QAction('&Screen', self) self.controlMenu.addAction(self.operatorAction) self.controlMenu.addAction(self.sourceAction) self.controlMenu.addAction(self.screenAction) self.styleMenu = menubar.addMenu('Styles') self.lensStyleAction = QtWidgets.QAction('&Lenses', self) self.deflectorStyleAction = QtWidgets.QAction('&Deflectors', self) self.rayStyleAction = QtWidgets.QAction('&Rays', self) self.styleMenu.addAction(self.lensStyleAction) self.styleMenu.addAction(self.deflectorStyleAction) self.styleMenu.addAction(self.rayStyleAction) self.lensStyleAction.triggered.connect(self.openLensStyle) # Source control self.sourceControlWindow = QtWidgets.QMainWindow() self.sourceControlWindow.setCentralWidget(QtWidgets.QWidget()) self.sourceControlWindow.centralWidget().setLayout(QtWidgets.QGridLayout()) self.sourceAngleMinimumSpinBox = QtWidgets.QDoubleSpinBox() self.sourceAngleMinimumSpinBox.setMinimum(-90) self.sourceAngleMinimumSpinBox.setMaximum(0) self.sourceAngleMinimumSpinBox.setDecimals(2) self.sourceAngleMinimumSpinBox.setSingleStep(0.01) self.sourceAngleMinimumSpinBox.setValue(-0.10) self.sourceAngleMaximumSpinBox = QtWidgets.QDoubleSpinBox() self.sourceAngleMaximumSpinBox.setMinimum(0) self.sourceAngleMaximumSpinBox.setMaximum(90) self.sourceAngleMaximumSpinBox.setDecimals(2) self.sourceAngleMaximumSpinBox.setSingleStep(0.01) self.sourceAngleMaximumSpinBox.setValue(0.10) self.sourceAngles = QtWidgets.QSpinBox() self.sourceAngles.setMinimum(1) self.sourceAngles.setMaximum(500) self.sourceAngles.setSingleStep(1) self.sourceAngles.setValue(3) self.sourceControlWindow.centralWidget().layout().addWidget(QtWidgets.QLabel('Angular range from')) self.sourceControlWindow.centralWidget().layout().addWidget(self.sourceAngleMinimumSpinBox) self.sourceControlWindow.centralWidget().layout().addWidget(QtWidgets.QLabel('to')) self.sourceControlWindow.centralWidget().layout().addWidget(self.sourceAngleMaximumSpinBox) self.sourceControlWindow.centralWidget().layout().addWidget(QtWidgets.QLabel('in')) self.sourceControlWindow.centralWidget().layout().addWidget(self.sourceAngles) self.sourceControlWindow.centralWidget().layout().addWidget(QtWidgets.QLabel('steps')) self.sourceAction.triggered.connect(self.openSourceControl) self.operatorAction.triggered.connect(self.openOperatorControl) self.screenAction.triggered.connect(self.openScreenControl) # Signals self.plot_button.clicked.connect(self.on_model_changed) self.print_system_button.clicked.connect(self._model.printSystem) [view.plotUpdated.connect(self._model.modelChanged) for view in self._operatorViews] self.print_traces_button.clicked.connect(self._model.printTraces) # Listeners self._model.modelChanged.connect(self.on_model_changed) self._model.systemTraced[list].connect(self.on_retraced) self.setup_lens_widgets() # show lenses [operator_view.on_model_changed(annotate=False) for operator_view in self._operatorViews] # Run raytracing and update the plot fo an initial inspection self.on_model_changed() def setup_lens_widgets(self): self.lens_widgets.layout().addWidget(QtWidgets.QLabel('Type', self.lens_widgets), 0, 0) self.lens_widgets.layout().addWidget(QtWidgets.QLabel('Name', self.lens_widgets), 0, 1) self.lens_widgets.layout().addWidget(QtWidgets.QLabel('Z', self.lens_widgets), 0, 2) self.lens_widgets.layout().addWidget(QtWidgets.QLabel('Offset', self.lens_widgets), 0, 3) self.lens_widgets.layout().addWidget(QtWidgets.QLabel('Value', self.lens_widgets), 0, 4) for i, view in enumerate(self.operatorViews): self.lens_widgets.layout().addWidget(view.typeLabel, i + 1, 0) self.lens_widgets.layout().addWidget(view.nameLabel, i + 1, 1) self.lens_widgets.layout().addWidget(view.zSpinbox, i + 1, 2) self.lens_widgets.layout().addWidget(view.offsetSpinbox, i + 1, 3) self.lens_widgets.layout().addWidget(view.valueSpinbox, i + 1, 4) @pyqtSlot(list, name='on_retraced') def on_retraced(self, traces): if len(traces) > self.colors.N: self.colors = plt.get_cmap(self.colors.name, len(traces)) if self._trace_lines is not None: [line[0].remove() for line in self._trace_lines] self.plot_widget.canvas.ax.set_prop_cycle(None) colors = {} for trace in traces: if trace[0].x in colors: pass else: # colors[trace[0].x] = self.colors[len(colors)] colors[trace[0].x] = self.colors(len(colors) / len(traces)) self._trace_lines = [trace.show(ax=self.plot_widget.canvas.ax, annotate=False, color=colors[trace[0].x])[2] for i, trace in enumerate(traces)] xs = [[ray.x for ray in raytrace] for raytrace in traces] minimum_x = min([min(x) for x in xs]) maximum_x = max([max(x) for x in xs]) ys = [[ray.z for ray in raytrace] for raytrace in traces] minimum_y = min([min(y) for y in ys]) maximum_y = max([max(y) for y in ys]) ticks = [(operator.z, operator.label) for operator in self._model.operatorModels if (operator.is_deflector or operator.is_lens)] additional_ticks = [(operator.z + operator.value, f'{operator.label} FFP') for operator in self._model.operatorModels if operator.is_lens] additional_ticks.extend( [(operator.z - operator.value, f'{operator.label} BFP') for operator in self._model.operatorModels if operator.is_lens]) ticks.extend(additional_ticks) self.plot_widget.canvas.ax.set_yticks([tick[0] for tick in ticks]) self.plot_widget.canvas.ax.set_yticklabels([tick[1] for tick in ticks]) self.plot_widget.canvas.ax.set_xlim(minimum_x, maximum_x) self.plot_widget.canvas.ax.set_ylim(minimum_y, maximum_y) print('Plot updated') self.plot_widget.canvas.draw() @pyqtSlot() def on_model_changed(self): self._model.trace() @pyqtSlot() def openLensStyle(self): self.lensStyleWindow.show() @pyqtSlot() def openSourceControl(self): self.sourceControlWindow.show() @pyqtSlot() def openScreenControl(self): self.screenView.show() @pyqtSlot() def openOperatorControl(self): pass # self.operatorViews.show() def full_column(angles=(-1, 0, 1), size=0, n_points=1): mygui = QtWidgets.QApplication(sys.argv) source = Source(150, angles, size=size, points=n_points) screen = Screen(-100) GUN1 = Deflector(0, label='GUN1', z=95) GUN2 = Deflector(0, label='GUN2', z=85) CL1 = Lens(10, label='CL1', z=80) CL2 = Lens(10, label='CL2', z=70) CL3 = Lens(10, label='CL3', z=60) CLA1 = Deflector(0, label='CLA1', z=50) CLA2 = Deflector(0, label='CLA2', z=40) CM = Lens(10, label='CM', z=30) OLPre = Lens(10, label='OLPre', z=5) OLPost = Lens(10, label='OLPost', z=-5) OM = Lens(10, label='OM', z=-15) ILA1 = Deflector(0, label='ILA1', z=-25) ILA2 = Deflector(0, label='ILA2', z=-30) IL1 = Lens(10, label='IL1', z=-40) IL2 = Lens(10, label='IL2', z=-50) IL3 = Lens(10, label='IL3', z=-60) PLA = Deflector(0, label='PLA', z=-70) PL = Lens(10, label='PLA', z=-80) optical_system = OpticalSystem(source, [GUN1, GUN2, CL1, CL2, CL3, CLA1, CLA2, CM, OLPre, OLPost, OM, ILA1, ILA2, IL1, IL2, IL3, PLA, PL], screen) microscope_model = MicroscopeModel(optical_system) microscope_controller = MicroscopeController(microscope_model) microscope_view = MicroscopeView(microscope_controller) microscope_view.show() sys.exit(mygui.exec_()) def condenser_system(angles=(-1, 0, 1), size=0, n_points=1): mygui = QtWidgets.QApplication(sys.argv) source = Source(100, angles, size=size, points=n_points) screen = Screen(0) CL1 = Lens(6.3, label='CL1', z=82) CL3 = Lens(8, label='CL3', z=60) CLA1 = Deflector(0, label='CLA1', z=49) CLA2 = Deflector(0, label='CLA2', z=42.5) CM = Lens(10, label='CM', z=27) OLPre = Lens(8.5, label='OLPre', z=8.5) optical_system = OpticalSystem(source, [CL1, CL3, CLA1, CLA2, CM, OLPre], screen) microscope_model = MicroscopeModel(optical_system) microscope_controller = MicroscopeController(microscope_model) microscope_view = MicroscopeView(microscope_controller) microscope_view.show() sys.exit(mygui.exec_()) if __name__ == '__main__': parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('--system', type=str, default='full', choices=['full', 'condenser', 'imaging'], help='The system to show, i.e. the condenser, imaging, or full system.') parser.add_argument('--min_angle', dest='min_angle', type=float, default=-1, help='The minimum angle to emit from the source') parser.add_argument('--max_angle', dest='max_angle', type=float, default=1, help='The maximum angle to emit from the source') parser.add_argument('--n_angles', dest='n_angles', type=int, default=3, help='The number of angles to emit from the source') parser.add_argument('--source_size', dest='source_size', type=float, default=0.0, help='The size of the source') parser.add_argument('--source_points', dest='source_points', type=int, default=1, help='The number of points to emit beams from the source') arguments = parser.parse_args() angles = np.linspace(arguments.min_angle, arguments.max_angle, num=arguments.n_angles) if arguments.system == 'full': full_column(angles, size=arguments.source_size, n_points=arguments.source_points) elif arguments.system == 'condenser': condenser_system(angles, size=arguments.source_size, n_points=arguments.source_points) elif arguments.system == 'imaging': raise NotImplementedError(f'System {arguments.system} is not supported yet.') else: raise ValueError(f'System {arguments.system} not recognized')

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0

null

0

null

0

1

0

7810ed6a751d4492d7c42bf9fe88d5e9684d0272

532

py

Python

app.py

lilbillybiscuit/tensorflow_chessbot

7e8c49ea173c8f7ba05faf036c10b1b2ddf67f45

[ "MIT" ]

null

app.py

lilbillybiscuit/tensorflow_chessbot

7e8c49ea173c8f7ba05faf036c10b1b2ddf67f45

[ "MIT" ]

null

app.py

lilbillybiscuit/tensorflow_chessbot

7e8c49ea173c8f7ba05faf036c10b1b2ddf67f45

[ "MIT" ]

null

import json import base64 import os def lambda_handler(event, context): #os.system("./tensorflow_chessbot.py") #return "Hi" text=base64.b64decode(event['body']) image = open("/tmp/image.png", "wb") image.write(text) image.close() os.system("./tensorflow_chessbot.py --filepath /tmp/image.png") fen=open("/tmp/fen.txt", "r") str1=fen.readline() fen.close() print("Final FEN" + str1) return { 'statusCode': 200, 'body': str(str1), "headers": { "Access-Control-Allow-Origin" : "*", } }

22.166667

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0.25

0.05

0

null

0

null

0

1

0

7811235be67bd4477bed0dde9c6d06a0d0935165

976

py

Python

plugins/grafana/icon_grafana/actions/do_proxied_datasource_call/action.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

46

2019-06-05T20:47:58.000Z

2022-03-29T10:18:01.000Z

plugins/grafana/icon_grafana/actions/do_proxied_datasource_call/action.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

386

2019-06-07T20:20:39.000Z

2022-03-30T17:35:01.000Z

plugins/grafana/icon_grafana/actions/do_proxied_datasource_call/action.py

lukaszlaszuk/insightconnect-plugins

8c6ce323bfbb12c55f8b5a9c08975d25eb9f8892

[ "MIT" ]

43

2019-07-09T14:13:58.000Z

2022-03-28T12:04:46.000Z

import komand from .schema import DoProxiedDatasourceCallInput, DoProxiedDatasourceCallOutput # Custom imports below class DoProxiedDatasourceCall(komand.Action): def __init__(self): super(self.__class__, self).__init__( name="do_proxied_datasource_call", description="Proxies all calls to the actual datasource", input=DoProxiedDatasourceCallInput(), output=DoProxiedDatasourceCallOutput(), ) def run(self, params={}): urlparts = ["datasources", "proxy", params.get("datasource_id")] + params.get("path").strip("/").split("/") response = self.connection.request("GET", urlparts, params=params.get("parameters")) if response.ok: return {"response": response.json()} else: self.logger.error("Grafana API: " + response.json().get("message", "")) response.raise_for_status() def test(self): return self.connection.test()

34.857143

115

0.646516

93

976

6.591398

0.623656

0.044046

0

0.223361

976

27

116

36.148148

0.808707

0.020492

0

0.150943

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false

0

0.1

0.05

0.4

0

null

0

null

0

1

0

7812b8f4ff19f85ceff7a725ced6fd5ca666d547

2,748

py

Python

3/deck_handler.py

diblaze/TDP002

41c9c2155e2ad8cc4047ea912edd463042d95362

[ "MIT" ]

null

3/deck_handler.py

diblaze/TDP002

41c9c2155e2ad8cc4047ea912edd463042d95362

[ "MIT" ]

null

3/deck_handler.py

diblaze/TDP002

41c9c2155e2ad8cc4047ea912edd463042d95362

[ "MIT" ]

null

#! /usr/bin/env python3 import random # acc. to assignment we only need two suits (half of deck) #spades = 1..13 #hearts = 1..13 * 2 suits = {"spades": 1, "hearts": 2} values = {"one": 1, "two": 2, "three": 3, "four": 4, "five": 5, "six": 6, "seven": 7, "eight": 8, "nine": 9, "ten": 10, "elseven": 11, "twelve": 12, "thirteen": 13} joker_a = ["joker_a", 27] joker_b = ["joker_b", 27] def create_deck(): """Creates a deck of 26 cards (-2 jokers)""" # list to hold the deck _deck = [] for i in range(1, 3): for j in range(1, 14): _deck.append([i, j]) return _deck def shuffle_deck(deck_to_shuffle): """Shuffles a deck. The shuffle occurs IN PLACE, but for others to better understand this function I will return the same deck but shuffeled.""" #random seed is set to 10 to ensure same passkey. random.seed(10) random.shuffle(deck_to_shuffle) return deck_to_shuffle def pick_card(deck_to_pick_from): """Returns a random card from the deck""" return random.choice(deck_to_pick_from) def insert_jokers(deck_to_insert_into): """Inserts joker_a and joker_b into deck""" deck_to_insert_into.append(joker_a) deck_to_insert_into.append(joker_b) def insert_card_by_name(card_in_text, deck_to_insert_into): """Adds a new card to the last postion of the deck Use by inputting card either by text or by [i,j]. """ splitted_string = card_in_text.split() value = splitted_string[0] value = values[value] suit = splitted_string[2] if suit == "spades" or suit == "Spades": suit = 0 elif suit == "hearts" or suit == "Hearts": suit = 1 card_to_add = [suit, value] deck_to_insert_into.append(card_to_add) def insert_card_by_dict(card, deck_to_insert_into): """Adds a new card to the last postion of the deck Use by inputting card by [i,j]. """ deck_to_insert_into.append(card) # print(card) def get_value_of_card(position_of_card, deck): """Returns the value of the card that has the specific position in the deck""" # print(deck[position_of_card]) value_int = deck[position_of_card][1] return value_int def get_suit_of_card(position_of_card, deck): """Returns the suit of the card that has the specific position in the deck""" suit_int = deck[position_of_card][0] if suit_int == 0: return "Spades" elif suit_int == 1: return "Hearts" def display_card(position_of_card, deck): """Displays the card in the specific position in the deck.""" suit = get_suit_of_card(position_of_card, deck) value = str(get_value_of_card(position_of_card, deck)) text_printed = value + " of " + suit return text_printed

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0.664483

443

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3.893905

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0.041667

0

null

0

null

0

1

0

78150c3f14d3b4c2aff18129bfca862c94bb1aec

2,106

py

Python

django_db_views/tests/tests.py

Skylude/django-db-views

048a1e3ab7465d3e19481de82b3737f780b175c0

[ "MIT" ]

null

django_db_views/tests/tests.py

Skylude/django-db-views

048a1e3ab7465d3e19481de82b3737f780b175c0

[ "MIT" ]

null

django_db_views/tests/tests.py

Skylude/django-db-views

048a1e3ab7465d3e19481de82b3737f780b175c0

[ "MIT" ]

null

import django import os from unittest.mock import patch from django.apps import apps from django.conf import settings from django.db.migrations.loader import MigrationLoader from django.db.migrations.state import ProjectState from django.db.migrations.recorder import MigrationRecorder from django.db import connections from django.core.management import call_command from django.test import TransactionTestCase, override_settings os.environ['DJANGO_SETTINGS_MODULE'] = 'test_settings' django.setup() class MigrationTests(TransactionTestCase): def tearDown(self): for db in self.databases: recorder = MigrationRecorder(connections[db]) recorder.migration_qs.filter(app='migrations').delete() available_apps = ['migrations'] def assertTableNotExists(self, table, using='default'): with connections[using].cursor() as cursor: self.assertNotIn(table, connections[using].introspection.table_names(cursor)) def assertViewExists(self, view, using='default'): with connections[using].cursor() as cursor: tables = [ table.name for table in connections[using].introspection.get_table_list(cursor) if table.type == 'v' ] self.assertIn(view, tables) def assertViewNotExists(self, view, using='default'): with connections[using].cursor() as cursor: tables = [ table.name for table in connections[using].introspection.get_table_list(cursor) if table.type == 'v' ] self.assertNotIn(view, tables) @override_settings(MIGRATION_MODULES={'migrations': 'migrations.test_basic_view_creation'}) def test_migrate_successfully_creates_view(self): call_command('migrate') self.assertViewExists('question_stat') @override_settings(MIGRATION_MODULES={'migrations': 'migrations.test_basic_view_creation'}) def test_roll_back_successfully_removes_view(self): call_command('migrate') call_command('migrate', 'migrations', 'zero') self.assertViewNotExists('question_stat')

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116

0.716049

234

2,106

6.290598

0.324786

0.054348

0.032609

0.044837

0.368207

0.33288

0.30163

0

0.187085

2,106

58

117

36.310345

0.859813

0

0.255814

0

0.108944

0.043768

0

0.186047

1

0.139535

false

0

0.255814

0

0.44186

0

null

0

null

0

1

0

781955390893d95f18fcf3689df89dc587380e35

4,236

py

Python

iotsim/controls.py

mmamaev/iotsim

d47587dea106f312ff4ee407a3d693a96fc46799

[ "BSD-3-Clause" ]

null

iotsim/controls.py

mmamaev/iotsim

d47587dea106f312ff4ee407a3d693a96fc46799

[ "BSD-3-Clause" ]

null

iotsim/controls.py

mmamaev/iotsim

d47587dea106f312ff4ee407a3d693a96fc46799

[ "BSD-3-Clause" ]

null

from .core import Control, AssemblyContext, Trigger from .utils import to_name import numpy as np from typing import List, Callable class ContextRetriever: def __call__(self, assembly_context: AssemblyContext): return None class CopyFromParameter(ContextRetriever): def __init__(self, src_component, src_parameter, apply: Callable = None): self._src_component = to_name(src_component) self._src_parameter = src_parameter if not apply is None: assert callable(apply) self._apply = apply def __call__(self, assembly_context: AssemblyContext): x = assembly_context.get_parameter(self._src_component, self._src_parameter) if not self._apply is None: x = self._apply(x) return x class CopyFromHistory(ContextRetriever): def __init__(self, src_component, lag, apply: Callable = None): self._src_component = to_name(src_component) self._lag = lag if not apply is None: assert callable(apply) self._apply = apply def __call__(self, assembly_context: AssemblyContext): x = assembly_context.query(self._src_component, self._lag) if not self._apply is None: x = self._apply(x) return x class CopyFromCounter(ContextRetriever): def __init__(self, src_component, src_counter, apply: Callable = None): self._src_component = to_name(src_component) self._src_counter = src_counter if not apply is None: assert callable(apply) self._apply = apply def __call__(self, assembly_context: AssemblyContext): x = assembly_context.read_counter(self._src_component, self._src_counter) if not self._apply is None: x = self._apply(x) return x class UpdateParametersControl(Control): def __init__(self, name, behavior, when, trigger: Trigger, update_choices: List, p: List = None, priority=0 ): def choose_and_update(assembly_context: AssemblyContext, update_choices: List, p=None): choice_idx = np.random.choice(np.arange(len(update_choices)), p=p) choice = update_choices[choice_idx] if choice is not None: for param_tuple in choice: component, parameter, value = param_tuple if isinstance(value, ContextRetriever): value = value(assembly_context) assembly_context.set_parameter(component, parameter, value) super().__init__(name, behavior, when, trigger, action=choose_and_update, action_parameters=dict(update_choices=update_choices, p=p), priority=priority) class ResetCounterControl(Control): def __init__(self, name, behavior, when, trigger: Trigger, component, counter, priority=0 ): def reset_counter(assembly_context: AssemblyContext, component, counter): assembly_context.reset_counter(component, counter) super().__init__(name, behavior, when, trigger, action=reset_counter, action_parameters=dict(component=component, counter=counter), priority=priority) class IncrementCounterControl(Control): def __init__(self, name, behavior, when, trigger: Trigger, component, counter, increment=1, priority=0 ): def increment_counter(assembly_context: AssemblyContext, component, counter, increment): assembly_context.increment_counter(component, counter, increment) super().__init__(name, behavior, when, trigger, action=increment_counter, action_parameters=dict(component=component, counter=counter, increment=increment), priority=priority)

36.205128

84

0.598206

419

4,236

5.720764

0.174224

0.08761

0.060075

0.057572

0.573217

0.540676

0.463079

0.380476

0.331247

0.311222

0

0.00141

0.3305

4,236

116

85

36.517241

0.843794

0

0.47191

0

0.033708

1

0.146067

false

0

0.044944

0.011236

0.314607

0

null

0

null

0

1

0

7820dee9a037495f3b5046586b45f8279d2ed452

3,147

py

Python

fpn/test.py

jjjump-tutu/depository

2667e2217c4e0ee1dcdbcf2f94630487d3c14c70

[ "MIT" ]

null

fpn/test.py

jjjump-tutu/depository

2667e2217c4e0ee1dcdbcf2f94630487d3c14c70

[ "MIT" ]

1

2020-12-01T07:11:08.000Z

2020-12-01T09:28:55.000Z

fpn/test.py

jjjump-tutu/depository

2667e2217c4e0ee1dcdbcf2f94630487d3c14c70

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sun Mar 17 23:2 7:28 2019 @author: Winham 网络测试 """ import os import numpy as np from keras.models import load_model from keras.utils import to_categorical from data_preprocess import * import mit_utils as utils import time import matplotlib.pyplot as plt import tensorflow_addons as tfa target_class = ['W', 'N1', 'N2', 'N3', 'REM'] target_sig_length = 3072 tic = time.time() trainX, trainY, TestX, TestY = dataload('channel0.npz') toc = time.time() markov_matrix = [[66927., 3996., 179., 6., 86.], [2252., 17891., 4269., 9., 753.], [1271., 2262., 80861., 3546., 1043.], [179., 113., 3247., 15892., 23.], [565., 912., 427., 1., 32279.]] markov_matrix = np.array(markov_matrix) # markov_matrix_copy = markov_matrix.copy() # for i in range(5): # markov_matrix_copy[i] /= markov_matrix_copy[i].sum() # print(markov_matrix_copy) markov_matrix = np.log2(markov_matrix) ** 3 for i in range(5): max = np.max(markov_matrix[i]) markov_matrix[i] /= max # print(markov_matrix) # assert False print('Time for data processing--- '+str(toc-tic)+' seconds---') model_name = 'myNet.h5' model = load_model(model_name) # model.summary() pred_vt = model.predict(TestX, batch_size=256, verbose=1) pred_v = np.argmax(pred_vt, axis=1) true_v = np.argmax(TestY, axis=1) def weight_decay(order): weights = [] for i in range(order): weights.append(4 ** (-i)) return weights order = 6 weight = weight_decay(order) for i in range(1,len(pred_vt)-order): factor = 1 if pred_v[i-1] != pred_v[i]: for j in range(1,order+1): if pred_v[i+j] == pred_v[i-1]: factor += weight[j-1]*2.1 elif pred_v[i+j] == pred_v[i]: factor -= 0.55 * weight[j-1] if factor < 0.1: factor = 0.1 vector = markov_matrix[pred_v[i - 1]].copy() vector[pred_v[i-1]] *= factor re_pred = pred_vt[i] * vector # print(re_pred) pred_v[i] = np.argmax(re_pred) # f1 = 3.1 # f2 = 0.45 # for i in range(1,len(pred_vt)-1): # if pred_v[i-1] != pred_v[i]: # if pred_v[i-1] == pred_v[i+1]: # factor = f1 # elif pred_v[i] == pred_v[i+1]: # factor = f2 # else: # factor = 1 # # print(pred_vt[i]) # vector = markov_matrix[pred_v[i - 1]].copy() # vector[pred_v[i-1]] *= factor # re_pred = pred_vt[i] * vector # # print(re_pred) # pred_v[i] = np.argmax(re_pred) utils.plot_confusion_matrix(true_v, pred_v, np.array(target_class)) utils.print_results(true_v, pred_v, target_class) plt.savefig('cm.png') # pred_v = pred_v[:10000] # pred_v.resize((100,100)) # plt.subplot(121) # plt.matshow(pred_v, cmap = plt.cm.Blues) # plt.savefig('cm_pred.png') # # true_v = true_v[:10000] # true_v.resize((100,100)) # plt.subplot(122) # plt.matshow(true_v, cmap = plt.cm.Blues) # plt.savefig('cm_true.png')

27.605263

68

0.575151

480

3,147

3.59375

0.314583

0.072464

0.062609

0.04058

0.302029

0.238261

0.211594

0.196522

0.132174

0.113623

0

0.079792

0.267239

3,147

113

69

27.849558

0.668257

0.327614

0

0.038304

0

1

0.018519

false

0

0.166667

0

0.203704

0.037037

0

null

0

null

0

1

0

7822ce08c98bbae4ad9b61ae8abb7997cf1d7c6e

902

py

Python

solutions/codeforces/158B.py

forxhunter/ComputingIntro

50fa2ac030748626c694ec5c884c5ac32f0b42a8

[ "Apache-2.0" ]

1

2021-01-02T04:31:34.000Z

solutions/codeforces/158B.py

forxhunter/ComputingIntro

50fa2ac030748626c694ec5c884c5ac32f0b42a8

[ "Apache-2.0" ]

null

solutions/codeforces/158B.py

forxhunter/ComputingIntro

50fa2ac030748626c694ec5c884c5ac32f0b42a8

[ "Apache-2.0" ]

null

''' check ''' groups = [0, 0, 0, 0] n = int(input()) data = input() carsnum = 0 for i in range(4): groups[i] = data.count(str(i+1)) # deal with 4 people group carsnum += groups[3] groups[3] = 0 # deal with 2 people group carsnum += groups[1] // 2 groups[1] %= 2 # deal with 1 and 3 people group if groups[0] <= groups[2]: carsnum += groups[0] groups[2] -= groups[0] groups[0] = 0 # deal with the 3 people group left carsnum += groups[2] if groups[1] != 0: carsnum += 1 else: carsnum += groups[2] groups[0] -= groups[2] groups[2] = 0 # deal with the 1 people group left carsnum += groups[0] // 4 groups[0] %= 4 if groups[1] == 0: if groups[0] != 0: carsnum += 1 else: # 2 people group has 1 group if groups[0] == 3: carsnum += 2 else: carsnum += 1 print(carsnum)

19.608696

39

0.531042

139

902

3.446043

0.208633

0.146138

0.10856

0.087683

0.229645

0

0.082927

0.318182

902

46

40

19.608696

0.695935

0.201774

0

0.25

0

1

0

false

0

0.03125

0

null

0

null

0

1

0

78242ab3f8e265a2bb01183be9d47df6398aa178

4,113

py

Python

mppsolar/devices/device.py

BarkinSpider/mpp-solar

071ca0cd9feea458b1e36dc020aa704b2000e431

[ "MIT" ]

1

2021-03-02T22:44:04.000Z

mppsolar/devices/device.py

BarkinSpider/mpp-solar

071ca0cd9feea458b1e36dc020aa704b2000e431

[ "MIT" ]

null

mppsolar/devices/device.py

BarkinSpider/mpp-solar

071ca0cd9feea458b1e36dc020aa704b2000e431

[ "MIT" ]

null

import abc import importlib import logging log = logging.getLogger("MPP-Solar") SERIAL_TYPE_TEST = 1 SERIAL_TYPE_USB = 2 SERIAL_TYPE_ESP32 = 4 SERIAL_TYPE_SERIAL = 8 class AbstractDevice(metaclass=abc.ABCMeta): """ Abstract device class """ def __init__(self, *args, **kwargs): self._protocol = None self._protocol_class = None self._port = None def is_test_device(self, serial_device): return "test" in serial_device.lower() def is_directusb_device(self, serial_device): """ Determine if this instance is using direct USB connection (instead of a serial connection) """ if not serial_device: return False if "hidraw" in serial_device: log.debug("Device matches hidraw") return True if "mppsolar" in serial_device: log.debug("Device matches mppsolar") return True return False def is_ESP32_device(self, serial_device): return "esp" in serial_device.lower() def get_port_type(self, port): if self.is_test_device(port): return SERIAL_TYPE_TEST elif self.is_directusb_device(port): return SERIAL_TYPE_USB elif self.is_ESP32_device(port): return SERIAL_TYPE_ESP32 else: return SERIAL_TYPE_SERIAL def set_protocol(self, protocol=None): """ Set the protocol for this device """ log.debug(f"device.set_protocol with protocol {protocol}") if protocol is None: self._protocol = None self._protocol_class = None return protocol_id = protocol.lower() # Try to import the protocol module with the supplied name (may not exist) try: proto_module = importlib.import_module( "mppsolar.protocols." + protocol_id, "." ) except ModuleNotFoundError: log.error(f"No module found for protocol {protocol_id}") self._protocol = None self._protocol_class = None return # Find the protocol class - classname must be the same as the protocol_id try: self._protocol_class = getattr(proto_module, protocol_id) except AttributeError: log.error(f"Module {proto_module} has no attribute {protocol_id}") self._protocol = None self._protocol_class = None return # Instantiate the class # TODO: fix protocol instantiate self._protocol = self._protocol_class( "init_var", proto_keyword="value", second_keyword=123 ) def set_port(self, port=None): port_type = self.get_port_type(port) if port_type == SERIAL_TYPE_TEST: log.info("Using testio for communications") from mppsolar.io.testio import TestIO self._port = TestIO() elif port_type == SERIAL_TYPE_USB: log.info("Using hidrawio for communications") from mppsolar.io.hidrawio import HIDRawIO self._port = HIDRawIO(device_path=port) elif port_type == SERIAL_TYPE_ESP32: log.info("Using esp32io for communications") from mppsolar.io.esp32io import ESP32IO self._port = ESP32IO(device_path=port) elif port_type == SERIAL_TYPE_SERIAL: log.info("Using serialio for communications") from mppsolar.io.serialio import SerialIO self._port = SerialIO(serial_port=port, serial_baud=2400) else: self._port = None @abc.abstractmethod def run_command(self, command=None, show_raw=False): raise NotImplementedError @abc.abstractmethod def get_status(self, show_raw): raise NotImplementedError @abc.abstractmethod def get_settings(self, show_raw): raise NotImplementedError def run_default_command(self, show_raw): return self.run_command( command=self._protocol.DEFAULT_COMMAND, show_raw=show_raw )

31.883721

82

0.624605

479

4,113

5.125261

0.242171

0.063544

0.041548

0.032587

0.322607

0.171894

0.133605

0.09002

0.043177

0

0.010105

0.302213

4,113

128

83

32.132813

0.845296

0.083637

0

0.284211

0

0.10119

0

0.007813

0

1

0.115789

false

0

0.084211

0.031579

0.357895

0

null

0

null

0

1

0

7824f64ce3bb3cc3961afdec7e3c6fa3721e5452

2,539

py

Python

autism/views.py

jenith-hue/Lung_Cancer

69171d26ab1a2eccf4ae7243e8bdd2d9f1ccbfb5

[ "MIT" ]

null

autism/views.py

jenith-hue/Lung_Cancer

69171d26ab1a2eccf4ae7243e8bdd2d9f1ccbfb5

[ "MIT" ]

null

autism/views.py

jenith-hue/Lung_Cancer

69171d26ab1a2eccf4ae7243e8bdd2d9f1ccbfb5

[ "MIT" ]

null

from django.shortcuts import render, redirect, get_object_or_404 from django.http import HttpResponseRedirect from .forms import Predict from .ML_ALGORITHM import you import numpy def index(request): return render(request, 'autism/home.html') def predict(request): return render(request, 'autism/predict.html') def predicted(request): if request.method == "POST": form = Predict(request.POST) type1 = int(request.POST['type1']) type2 = int(request.POST['type2']) type3 = int(request.POST['type3']) type4 = int(request.POST['type4']) type5 = int(request.POST['type5']) type6 = float(request.POST['type6']) type7 = float(request.POST['type7']) type8 = int(request.POST['type8']) x= [] new_list = [] x.append(type1) x.append(type2) x.append(type3) x.append(type4) x.append(type5) x.append(type6) x.append(type7) x.append(type8) list = you.getPrediction(x) yes = list[0] no = 100-list[0] new_list.append(yes) new_list.append(no) label = ['yes','no'] zipped_list = zip(list) context = { 'zipped_list': zipped_list, 'list': new_list, 'label': label, } print(list) return render(request, 'autism/predicted.html',context) else: form = Predict() return render(request,'autism/predicted.html',{'form':form}) def restapi(request): type1 = request.GET.get('value1', -1) type2 = request.GET.get('value2', -1) type3 = request.GET.get('value3', -1) type4 = request.GET.get('value4', -1) type5 = request.GET.get('value5', -1) type6 = request.GET.get('value6', -1) type7 = request.GET.get('value7', -1) type8 = request.GET.get('value8', -1) x= [] new_list = [] x.append(type1) x.append(type2) x.append(type3) x.append(type4) x.append(type5) x.append(type6) x.append(type7) x.append(type8) list = you.getPrediction(x) yes = list[0] no = 100-list[0] new_list.append(yes) new_list.append(no) label = ['yes','no'] zipped_list = zip(list) context = { 'zipped_list': zipped_list, 'list': new_list, 'label': label, } print(list) return render(request, 'autism/predicted.html',context)

29.523256

64

0.554943

301

2,539

4.621262

0.215947

0.080518

0.074766

0.089863

0.508986

0.462976

0.435658

0

0.038983

0.302875

2,539

86

65

29.523256

0.746893

0

0.575

0

0.096063

0.024803

0

1

0.05

false

0

0.0625

0.025

0.175

0.025

0

null

0

null

0

1

0

78267247f1ad73db4ca30cdb83a52c4d8988159a

1,708

py

Python

Program.py

evanxia1018/CSE539_Project_LLE

881ea2278c39c16716e5de83dd8abbd267806a35

[ "MIT" ]

2

2019-11-10T02:04:52.000Z

2020-04-19T03:51:51.000Z

Program.py

SarahLynnePu/CSE539_Project_LLE

881ea2278c39c16716e5de83dd8abbd267806a35

[ "MIT" ]

null

Program.py

SarahLynnePu/CSE539_Project_LLE

881ea2278c39c16716e5de83dd8abbd267806a35

[ "MIT" ]

3

2017-12-28T14:09:24.000Z

2020-04-19T04:25:03.000Z

import time import Generation_Stage import Evaluate_lle import Evaluate_pca import Evaluation_Stage print("**********************************************************************") print("Hello. This is CSE569 Project Demo, produced by Haisi Yi and Zheng Xia") print("**********************************************************************\n\n") while True: option = input("\nPlease specify the task to perform:\n" "1: Generate five artificial dataset and read MNIST_images dataset\n" "2: Perform PCA to all artificial dataset and MNIST_images dataset\n" "3: Perform LLE 11 * 6 times, using parameter k = 5, 6, ..., 15, to all artificial dataset and MNIST_images dataset\n" "4: Do task 1, task 2 and task 3. This task will take about 20 min\n" "5: Evaluate the data produced by PCA. This task will take about 40 min.\n" "6: Evaluate the data produced by LLE. This task will take about 40 min.\n" "7: Run everything. This will take about 8 hours.\n" "0: Exit this Demo\n") option = int(option) if option == 1: Generation_Stage.generate_original_datasets() elif option == 2: Generation_Stage.perform_pca_to_original_datasets() elif option == 3: Generation_Stage.perform_lle_to_orginal_datasets() elif option == 4: Generation_Stage.run() elif option == 5: Evaluate_pca.run() elif option == 6: Evaluate_lle.run() elif option == 7: Evaluation_Stage.run() break elif option == 0: break else: print("Invalid option, try again")

32.846154

137

0.566745

215

1,708

4.390698

0.367442

0.074153

0.055085

0.060381

0.225636

0.150424

0.09322

0

0.029103

0.275761

1,708

51

138

33.490196

0.734034

0

0.052632

0

0.052632

0.475631

0.084557

0

1

0

false

0

0.131579

0

0.131579

0.105263

0

null

0

null

0

1

0

7828bc8cfbb067ddc1a2bb084a724e2b6671e88f

3,868

py

Python

medical_peek_api/controller/exception_handler_controller.py

WillCallahan/medical_peek

e27e547ea7c8bc1deea8668090ff582020d7d6b2

[ "MIT" ]

null

medical_peek_api/controller/exception_handler_controller.py

WillCallahan/medical_peek

e27e547ea7c8bc1deea8668090ff582020d7d6b2

[ "MIT" ]

12

2021-04-06T18:25:47.000Z

2022-03-12T00:52:42.000Z

medical_peek_api/controller/exception_handler_controller.py

WillCallahan/medical_peek

e27e547ea7c8bc1deea8668090ff582020d7d6b2

[ "MIT" ]

null

import logging from django.core.handlers.wsgi import WSGIRequest from django.http import JsonResponse from django.views.defaults import server_error, page_not_found from rest_framework import status from medical_peek_core.model.j_send import JSend, JSendSerializer from medical_peek_core.utility.exception_utility import ExceptionUtility logger = logging.getLogger(__name__) def rest_exception_handler(exception, context): """ Exception handler utilized by the Django Rest Framework The exception handler will override the default implementation of the Django Rest Framework Exception handler if the "Accept" header of the request in the current context has "application/json" in its value. If this is true, a JSonResponse View will be returned to the user containing a JSend object that represents the exception. :param exception: Exception that occurred :type exception: object :param context: Context of the exception (i.e. request) :type context: dict :return: JSonResponse View with JSend error if the Accept header of the request has a value of "application/json" :rtype: JsonResponse """ if context.get('request', None) is not None \ and 'application/json' in context.get('request').META.get('HTTP_ACCEPT', ''): logger.error("Unhandled exception!") logger.exception(exception) j_send = ExceptionUtility.get_jsend_from_exception(exception) j_send_serializer = JSendSerializer(data = j_send.__dict__) j_send_serializer.is_valid(True) return JsonResponse(j_send_serializer.data, status = j_send.code) def handler500(request, template_name = '500.html'): """ Overrides the default Django implementation of a 500 error so that a JSon response will be provided if the accept header of the request has a value of "application/json". Otherwise the default server error implementation is called. To enable this handler, the DEBUG setting in the Django settings must be set to False :param request: Current Request :type request: WSGIRequest :param template_name: Template of the error page :type template_name: str :return: Response :rtype: object """ if request is not None and 'application/json' in request.META.get('HTTP_ACCEPT', ''): logger.error("Unhandled exception!") j_send = JSend() j_send.status = JSend.Status.error j_send.code = status.HTTP_500_INTERNAL_SERVER_ERROR j_send.message = 'Unexpected API Server Error' j_send_serializer = JSendSerializer(data = j_send.__dict__) j_send_serializer.is_valid(True) return JsonResponse(j_send_serializer.data, status = status.HTTP_500_INTERNAL_SERVER_ERROR) return server_error(request = request, template_name = template_name) def handler404(request, template_name = '404.html'): """ Overrides the default Django implementation of a 404 error so that a JSon response will be provided if the accept header of the request has a value of "application/json". Otherwise the default server error implementation is called. To enable this handler, the DEBUG setting in the Django settings must be set to False :param request: Current Request :type request: WSGIRequest :param template_name: Template of the error page :type template_name: str :return: Response :rtype: object """ if 'application/json' in request.META.get('HTTP_ACCEPT', ''): j_send = JSend() j_send.status = JSend.Status.error j_send.code = status.HTTP_404_NOT_FOUND j_send.message = 'Not found' j_send_serializer = JSendSerializer(data = j_send.__dict__) j_send_serializer.is_valid(True) return JsonResponse(j_send_serializer.data, status = j_send.code) return page_not_found(request, template_name)

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0.735264

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0.394737

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null

0

null

0

1

0

782abecf863cb582e8ab49f243f14f653cba734a

1,188

py

Python

00 UNICEF/03 Data New/aedes-main/best_aedes_model.py

Cirrolytix/aedes_unicef_2022

23a26d57d5316ba44d573b4c1dcefcad4e10b157

[ "MIT" ]

null

00 UNICEF/03 Data New/aedes-main/best_aedes_model.py

Cirrolytix/aedes_unicef_2022

23a26d57d5316ba44d573b4c1dcefcad4e10b157

[ "MIT" ]

null

00 UNICEF/03 Data New/aedes-main/best_aedes_model.py

Cirrolytix/aedes_unicef_2022

23a26d57d5316ba44d573b4c1dcefcad4e10b157

[ "MIT" ]

null

import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from xgboost import XGBClassifier from sklearn.impute import SimpleImputer # NOTE: Make sure that the outcome column is labeled 'target' in the data file tpot_data = pd.read_csv('PATH/TO/DATA/FILE', sep='COLUMN_SEPARATOR', dtype=np.float64) features = tpot_data.drop('target', axis=1) training_features, testing_features, training_target, testing_target = \ train_test_split(features, tpot_data['target'], random_state=42) imputer = SimpleImputer(strategy="median") imputer.fit(training_features) training_features = imputer.transform(training_features) testing_features = imputer.transform(testing_features) # Average CV score on the training set was: 0.889950753668092 exported_pipeline = XGBClassifier(learning_rate=1.0, max_depth=1, min_child_weight=10, n_estimators=100, n_jobs=1, subsample=0.6000000000000001, verbosity=0) # Fix random state in exported estimator if hasattr(exported_pipeline, 'random_state'): setattr(exported_pipeline, 'random_state', 42) exported_pipeline.fit(training_features, training_target) results = exported_pipeline.predict(testing_features)

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null

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null

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1

0

782c3b76e7c1eaa2f8d3ae8a4ab9167a73ffb9ea

3,379

py

Python

espressodb/base/tests/views/urls.py

remram44/espressodb

5aad7222ab81c0f1694b51171e5d197dbcc8a65f

[ "BSD-3-Clause" ]

8

2019-12-10T04:30:01.000Z

2020-10-30T09:40:22.000Z

espressodb/base/tests/views/urls.py

remram44/espressodb

5aad7222ab81c0f1694b51171e5d197dbcc8a65f

[ "BSD-3-Clause" ]

41

2019-10-23T00:26:25.000Z

2021-10-21T07:55:57.000Z

espressodb/base/tests/views/urls.py

remram44/espressodb

5aad7222ab81c0f1694b51171e5d197dbcc8a65f

[ "BSD-3-Clause" ]

3

2020-01-09T21:29:09.000Z

2021-03-14T22:20:52.000Z

"""Unittest for all present urls """ from django.test import TestCase from django.contrib.auth.models import User from espressodb.base.utilities.apps import get_apps_slug_map import espressodb.base.utilities.blackmagicsorcery as re URLS = ["/", "/populate/", "/populate-result/"] LOGGED_IN_URLS = [ "/notifications/", "/notifications/debug/", "/notifications/info/", "/notifications/warning/", "/notifications/error/", "/admin/", "/admin/auth/group/", "/admin/auth/user/", "/admin/notifications/notification/", ] class URLViewTest(TestCase): """Tests if all urls are present """ exclude_urls = [] @classmethod def url_excluded(cls, url: str) -> bool: """Checks if the url is in the exclude_urls pattern list Arguments: url: Regex pattern to match. """ return any([re.match(pattern, url) is not None for pattern in cls.exclude_urls]) def setUp(self): """Create a user for the test """ self.username = "test user" self.password = "admin1234" user = User.objects.create(username=self.username) user.set_password(self.password) user.save() def test_open_urls(self): """Tests the HTTP status of the client. """ for url in URLS: if self.url_excluded(url): continue with self.subTest(url=url): response = self.client.get(url) self.assertEqual(response.status_code, 200) def test_logged_in_urls_as_logged_out(self): """Tests wether login required URLS are present but require login. """ for url in LOGGED_IN_URLS: if self.url_excluded(url): continue with self.subTest(url=url): with self.subTest(follow=False): response = self.client.get(url, follow=False) self.assertEqual(response.status_code, 302) with self.subTest(follow=True): response = self.client.get(url, follow=True) self.assertEqual(response.status_code, 200) self.assertEqual( response.redirect_chain[-1][0], ("/admin" if "admin" in url else "") + f"/login/?next={url}", ) def test_logged_in_urls_as_logged_in(self): """Tests wether login required URLS are present and viewable by logged in user. """ login = self.client.login(username=self.username, password=self.password) self.assertTrue(login) for url in LOGGED_IN_URLS: if self.url_excluded(url): continue with self.subTest(url=url): response = self.client.get(url) self.assertEqual(response.status_code, 302 if "admin" in url else 200) def test_documentation_pages(self): """Tests wether documentation pages are present for each project app with models. """ for app_slug, app in get_apps_slug_map().items(): if not app.get_models(): continue url = f"/documentation/{app_slug}/" with self.subTest(app=app, url=url): response = self.client.get(url) self.assertEqual(response.status_code, 200)

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0.19697

0

null

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null

0

1

0

782dc6f49c603eca2fa962e30fbfc31a6e32254b

1,723

py

Python

python/datagraph/graphviz/dmo/digraph_text_cleanser.py

jiportilla/ontology

8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40

[ "MIT" ]

null

python/datagraph/graphviz/dmo/digraph_text_cleanser.py

jiportilla/ontology

8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40

[ "MIT" ]

null

python/datagraph/graphviz/dmo/digraph_text_cleanser.py

jiportilla/ontology

8a66bb7f76f805c64fc76cfc40ab7dfbc1146f40

[ "MIT" ]

null

# !/usr/bin/env python # -*- coding: UTF-8 -*- from base import BaseObject class DigraphTextCleanser(BaseObject): """ Purpose: Edge Generation for a graphviz.Digraph object Traceability: https://github.ibm.com/GTS-CDO/unstructured-analytics/issues/1426#issuecomment-16165027 """ def __init__(self, graph_style: dict, is_debug: bool = True): """ Created: 21-Nov-2019 craig.trim@ibm.com * https://github.ibm.com/GTS-CDO/unstructured-analytics/issues/1426#issuecomment-16165027 :param graph_style: a graph style defined in a graph stylesheet e.g.: - resources/config/graph/graphviz_nlp_graph.yml - resources/config/graph/graphviz_big_graph.yml :param is_debug: True increase log output at DEBUG level """ BaseObject.__init__(self, __name__) self._is_debug = is_debug self._graph_style = graph_style def process(self, some_text: str) -> str: """ Purpose: determine whether to split the text for readability :param some_text: input text :return: (optionally) processed text """ if "graph" not in self._graph_style: return some_text if "split_text" not in self._graph_style["graph"]: return some_text if not self._graph_style["graph"]["split_text"]: return some_text if " " not in some_text: return some_text tokens = some_text.split(" ") return "{}\\n{}".format(tokens[0], " ".join(tokens[1:]))

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103

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4.871134

0.458763

0.084656

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0.060317

0.237037

0.156614

0

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0.327916

1,723

58

104

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0

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0.45

0

null

0

null

0

1

0

782ed811b859ad654cfa2961ac4c649ce8d9f83b

7,550

py

Python

sqlglot/optimizer/qualify_columns.py

RobinL/sqlglot

7ec1022ac4c1fbaeb44e47d5f187a78e5c14735a

[ "MIT" ]

null

sqlglot/optimizer/qualify_columns.py

RobinL/sqlglot

7ec1022ac4c1fbaeb44e47d5f187a78e5c14735a

[ "MIT" ]

null

sqlglot/optimizer/qualify_columns.py

RobinL/sqlglot

7ec1022ac4c1fbaeb44e47d5f187a78e5c14735a

[ "MIT" ]

null

import itertools import sqlglot.expressions as exp from sqlglot.errors import OptimizeError from sqlglot.optimizer.schema import ensure_schema from sqlglot.optimizer.scope import traverse_scope def qualify_columns(expression, schema): """ Rewrite sqlglot AST to have fully qualified columns. Example: >>> import sqlglot >>> schema = {"tbl": {"col": "INT"}} >>> expression = sqlglot.parse_one("SELECT col FROM tbl") >>> qualify_columns(expression, schema).sql() 'SELECT tbl.col AS col FROM tbl' Args: expression (sqlglot.Expression): expression to qualify schema (dict|sqlglot.optimizer.Schema): Database schema Returns: sqlglot.Expression: qualified expression """ schema = ensure_schema(schema) # We'll use this when generating alias names sequence = itertools.count() for scope in traverse_scope(expression): _check_union_outputs(scope) _qualify_derived_tables(scope.ctes, scope, sequence) _qualify_derived_tables(scope.derived_tables, scope, sequence) _qualify_columns(scope, schema) _expand_stars(scope, schema) _qualify_outputs(scope) _check_unknown_tables(scope) return expression def _check_union_outputs(scope): """Assert that the outputs of both sides of a UNION are the same""" if not isinstance(scope.expression, exp.Union): return left, right = scope.union if left.outputs != right.outputs: raise OptimizeError( f"UNION outputs not equal: {left.outputs} vs. {left.outputs}" ) def _qualify_derived_tables(derived_tables, scope, sequence): """Ensure all derived tables have aliases""" for derived_table in derived_tables: table_alias = derived_table.args.get("alias") if not table_alias: table_alias = exp.TableAlias() derived_table.set("alias", table_alias) alias = table_alias.args.get("this") if not alias: alias = exp.to_identifier(f"_q_{next(sequence)}") scope.rename_selectable(None, alias.name) table_alias.set("this", alias) # Remove any alias column list # (e.g. SELECT ... FROM (SELECT ...) AS foo(col1, col2) table_alias.args.pop("columns", None) def _qualify_columns(scope, schema): """Disambiguate columns, ensuring each column reference specifies a selectable""" unambiguous_columns = None # lazily loaded for column in scope.references: column_table = column.text("table") column_name = column.text("this") if ( column_table and column_table in scope.selectables and column_name not in _get_selectable_columns(column_table, scope.selectables, schema) ): raise OptimizeError(f"Unknown column: {column_name}") if not column_table: if unambiguous_columns is None: selectable_columns = { k: _get_selectable_columns(k, scope.selectables, schema) for k in scope.referenced_selectables } unambiguous_columns = _get_unambiguous_columns(selectable_columns) column_table = unambiguous_columns.get(column_name) if not column_table and not scope.is_subquery: raise OptimizeError(f"Ambiguous column: {column_name}") column.set("table", exp.to_identifier(column_table)) def _expand_stars(scope, schema): """Expand stars to lists of column selections""" all_new_columns = [] for expression in scope.selects: if isinstance(expression, exp.Star): tables = list(scope.referenced_selectables) elif isinstance(expression, exp.Column) and isinstance( expression.this, exp.Star ): tables = [expression.text("table")] else: continue new_columns = [] for table in tables: if table not in scope.selectables: raise OptimizeError(f"Unknown table: {table}") columns = _get_selectable_columns(table, scope.selectables, schema) for column in columns: new_columns.append( exp.Column( this=exp.to_identifier(column), table=exp.to_identifier(table) ) ) expression.replace(*new_columns) all_new_columns.extend(new_columns) scope.columns.extend(all_new_columns) def _qualify_outputs(scope): """Ensure all output columns are aliased""" for i, (selection, aliased_column) in enumerate( itertools.zip_longest(scope.selects, scope.outer_column_list) ): if isinstance(selection, exp.Column): selection_name = selection.text("this") new_selection = exp.alias_(selection.copy(), selection_name) selection.replace(new_selection) selection = new_selection elif not isinstance(selection, exp.Alias): selection_name = f"_col_{i}" new_selection = exp.alias_(selection.copy(), selection_name) selection.replace(new_selection) selection = new_selection if aliased_column: selection.set("alias", exp.to_identifier(aliased_column)) def _check_unknown_tables(scope): if scope.external_references and not scope.is_correlated_subquery: raise OptimizeError( f"Unknown table: {scope.external_references[0].text('table')}" ) def _get_unambiguous_columns(selectable_columns): """ Find all the unambiguous columns in selectables. Args: selectable_columns (dict): Mapping of names to selectable columns Returns: dict: Mapping of column name to selectable name """ if not selectable_columns: return {} selectable_columns = list(selectable_columns.items()) first_table, first_columns = selectable_columns[0] unambiguous_columns = { col: first_table for col in _find_unique_columns(first_columns) } for table, columns in selectable_columns[1:]: unique = _find_unique_columns(columns) ambiguous = set(unambiguous_columns).intersection(unique) for column in ambiguous: unambiguous_columns.pop(column) for column in unique.difference(ambiguous): unambiguous_columns[column] = table return unambiguous_columns def _find_unique_columns(columns): """ Find the unique columns in a list of columns. Example: >>> sorted(_find_unique_columns(["a", "b", "b", "c"])) ['a', 'c'] This is necessary because duplicate column names are ambiguous. """ counts = {} for column in columns: counts[column] = counts.get(column, 0) + 1 return {column for column, count in counts.items() if count == 1} def _get_selectable_columns(name, selectables, schema): """Resolve the selectable columns for a given selectable `name`""" if name not in selectables: raise OptimizeError(f"Unknown table: {name}") selectable = selectables[name] # If referencing a table, return the columns from the schema if isinstance(selectable, exp.Table): try: return schema.column_names(selectable) except Exception as e: raise OptimizeError(str(e)) from e # Otherwise, if referencing another scope, return that scope's outputs return selectable.outputs

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0

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7,550

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0

0.160584

0

null

0

null

0

1

0

782f027acd854915903862b95967abf5208d8465

5,314

py

Python

model_zoo/cifar10_subclass/cifar10_subclass.py

sorrycc/elasticdl

01439e0bf7bba6ebfffe265916fd41370a59c29d

[ "MIT" ]

2

2021-07-07T16:31:50.000Z

2021-11-08T09:23:01.000Z

model_zoo/cifar10_subclass/cifar10_subclass.py

sorrycc/elasticdl

01439e0bf7bba6ebfffe265916fd41370a59c29d

[ "MIT" ]

null

model_zoo/cifar10_subclass/cifar10_subclass.py

sorrycc/elasticdl

01439e0bf7bba6ebfffe265916fd41370a59c29d

[ "MIT" ]

1

2021-08-18T18:14:38.000Z

import tensorflow as tf from elasticdl.python.common.constants import Mode class CustomModel(tf.keras.Model): def __init__(self, channel_last=True): super(CustomModel, self).__init__(name="cifar10_model") use_bias = True self._conv_1 = tf.keras.layers.Conv2D( 32, kernel_size=(3, 3), padding="same", use_bias=use_bias, activation=None, ) self._bn_1 = tf.keras.layers.BatchNormalization( epsilon=1e-06, axis=-1, momentum=0.9 ) self._relu_1 = tf.keras.layers.Activation(tf.nn.relu) self._conv_2 = tf.keras.layers.Conv2D( 32, kernel_size=(3, 3), padding="same", use_bias=use_bias, activation=None, ) self._bn_2 = tf.keras.layers.BatchNormalization( epsilon=1e-06, axis=-1, momentum=0.9 ) self._relu_2 = tf.keras.layers.Activation(tf.nn.relu) self._max_pool_1 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2)) self._dropout_1 = tf.keras.layers.Dropout(0.2) self._conv_3 = tf.keras.layers.Conv2D( 64, kernel_size=(3, 3), padding="same", use_bias=use_bias, activation=None, ) self._bn_3 = tf.keras.layers.BatchNormalization( epsilon=1e-06, axis=-1, momentum=0.9 ) self._relu_3 = tf.keras.layers.Activation(tf.nn.relu) self._conv_4 = tf.keras.layers.Conv2D( 64, kernel_size=(3, 3), padding="same", use_bias=use_bias, activation=None, ) self._bn_4 = tf.keras.layers.BatchNormalization( epsilon=1e-06, axis=-1, momentum=0.9 ) self._relu_4 = tf.keras.layers.Activation(tf.nn.relu) self._max_pool_2 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2)) self._dropout_2 = tf.keras.layers.Dropout(0.3) self._conv_5 = tf.keras.layers.Conv2D( 128, kernel_size=(3, 3), padding="same", use_bias=use_bias, activation=None, ) self._bn_5 = tf.keras.layers.BatchNormalization( epsilon=1e-06, axis=-1, momentum=0.9 ) self._relu_5 = tf.keras.layers.Activation(tf.nn.relu) self._conv_6 = tf.keras.layers.Conv2D( 128, kernel_size=(3, 3), padding="same", use_bias=use_bias, activation=None, ) self._bn_6 = tf.keras.layers.BatchNormalization( epsilon=1e-06, axis=-1, momentum=0.9 ) self._relu_6 = tf.keras.layers.Activation(tf.nn.relu) self._max_pool_3 = tf.keras.layers.MaxPooling2D(pool_size=(2, 2)) self._dropout_3 = tf.keras.layers.Dropout(0.4) self._flatten_1 = tf.keras.layers.Flatten() self._dense_1 = tf.keras.layers.Dense(10, name="output") def call(self, inputs, training=False): x = self._conv_1(inputs["image"]) x = self._bn_1(x) x = self._relu_1(x) x = self._conv_2(x) x = self._bn_2(x) x = self._relu_2(x) x = self._max_pool_1(x) x = self._dropout_1(x) x = self._conv_3(x) x = self._bn_3(x) x = self._relu_3(x) x = self._conv_4(x) x = self._bn_4(x) x = self._relu_4(x) x = self._max_pool_2(x) x = self._dropout_2(x) x = self._conv_5(x) x = self._bn_5(x) x = self._relu_5(x) x = self._conv_6(x) x = self._bn_6(x) x = self._relu_6(x) x = self._max_pool_3(x) x = self._dropout_3(x) x = self._flatten_1(x) return self._dense_1(x) def loss(output, labels): labels = tf.reshape(labels, [-1]) return tf.reduce_mean( input_tensor=tf.nn.sparse_softmax_cross_entropy_with_logits( logits=output, labels=labels ) ) def optimizer(lr=0.1): return tf.optimizers.SGD(lr) def dataset_fn(dataset, mode): def _parse_data(record): if mode == Mode.PREDICTION: feature_description = { "image": tf.io.FixedLenFeature([32, 32, 3], tf.float32) } else: feature_description = { "image": tf.io.FixedLenFeature([32, 32, 3], tf.float32), "label": tf.io.FixedLenFeature([1], tf.int64), } r = tf.io.parse_single_example(record, feature_description) features = { "image": tf.math.divide(tf.cast(r["image"], tf.float32), 255.0) } if mode == Mode.PREDICTION: return features else: return features, tf.cast(r["label"], tf.int32) dataset = dataset.map(_parse_data) if mode != Mode.PREDICTION: dataset = dataset.shuffle(buffer_size=1024) return dataset def eval_metrics_fn(predictions, labels): labels = tf.reshape(labels, [-1]) return { "accuracy": tf.reduce_mean( input_tensor=tf.cast( tf.equal( tf.argmax(predictions, 1, output_type=tf.dtypes.int32), labels, ), tf.float32, ) ) }

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75

0.547234

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false

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0.111842

0

null

0

null

0

1

0

78323ad51e27c4fa0767acc4613f077ad4236011

2,686

py

Python

XNATSlicer/XnatSlicerLib/ui/custom-qt-widgets/HoverButton.py

QwaddleMan/XNATSlicer

5aa06e4f2a578898d34cf0ea703963b9556f2da3

[ "BSD-3-Clause" ]

4

2016-03-03T08:56:52.000Z

2021-12-10T21:14:58.000Z

XNATSlicer/XnatSlicerLib/ui/custom-qt-widgets/HoverButton.py

keithcallenberg/XNATSlicer

4a8462b2e81984cc114d25fb2b1c981457a11878

[ "BSD-3-Clause" ]

null

XNATSlicer/XnatSlicerLib/ui/custom-qt-widgets/HoverButton.py

keithcallenberg/XNATSlicer

4a8462b2e81984cc114d25fb2b1c981457a11878

[ "BSD-3-Clause" ]

5

2015-04-22T01:53:40.000Z

2021-03-29T12:14:32.000Z

__author__ = "Sunil Kumar (kumar.sunil.p@gmail.com)" __copyright__ = "Copyright 2014, Washington University in St. Louis" __credits__ = ["Sunil Kumar", "Steve Pieper", "Dan Marcus"] __license__ = "XNAT Software License Agreement " + \ "(see: http://xnat.org/about/license.php)" __version__ = "2.1.1" __maintainer__ = "Rick Herrick" __email__ = "herrickr@mir.wustl.edu" __status__ = "Production" from __main__ import qt comment = """ HoverButton is a customized QWidget where the user can set the style of the button upon hovering. TODO: """ class HoverButton (qt.QPushButton): """ Descriptor above. """ def __init__(self, parent = None): """ Init function. """ #-------------------- # Call parent init. #-------------------- if parent: super(HoverButton, self).__init__(parent) else: super(HoverButton, self).__init__(self) #-------------------- # Install the event filter to # interpret the hovers. #-------------------- self.installEventFilter(self) #-------------------- # Track the stylesheets for # the hover/not-hovered states. #-------------------- self.defaultStyleSheet = None self.hoverStyleSheet = None def setDefaultStyleSheet(self, styleSheet): """ Set stylesheet for when the mouse is not hovering over the button. """ self.defaultStyleSheet = styleSheet self.setStyleSheet(styleSheet) def setHoverStyleSheet(self, styleSheet): """ Set stylesheet for when the mouse is hovering over the button. """ self.hoverStyleSheet = styleSheet def eventFilter(self, widget, event): """ Event filter function inherited from QObject. Specifically targets the 'Enter' and 'Leave' events for hovering purposes. """ if event.type() == qt.QEvent.Enter: self.onHoverEnter() elif event.type() == qt.QEvent.Leave: self.onHoverLeave() def onHoverEnter(self): """ Callback when the mouse begins hovering over the button: applies the 'hoverStyleSheet'. """ self.setStyleSheet(self.hoverStyleSheet) def onHoverLeave(self): """ Callback when the mouse leaves hovering over the button: applies the 'defaultStyleSheet'. """ self.setStyleSheet(self.defaultStyleSheet)

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0.546165

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0.059197

0.174771

0.105708

0.062016

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0.003853

0.323529

2,686

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0.026316

0

0.210526

0

null

0

null

0

1

0

7833d1ecd63dcbafc3405efa977d29556dcffc33

363

py

Python

abrirMochila.py

KozlowskiJ2/avengers

304e677ac45becbd182db71b0fd148be90fa7050

[ "MIT" ]

null

abrirMochila.py

KozlowskiJ2/avengers

304e677ac45becbd182db71b0fd148be90fa7050

[ "MIT" ]

null

abrirMochila.py

KozlowskiJ2/avengers

304e677ac45becbd182db71b0fd148be90fa7050

[ "MIT" ]

null

def abreMochila(mochila) : if len(mochila) == 0: print('Bolso Vazio!') return False if len(mochila)!= 0: print("Itens no bolso:\nFale o numero correspondente dele para escolher") for item in mochila: print(mochila.index(item)+1,"-",item) i=escutar() escolha=mochila[int(i)-1] return(escolha)

33

81

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0.630435

0.046729

0.11215

0.121495

0.168224

0

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363

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1

0.090909

false

0

0.181818

0.272727

0

null

0

null

0

1

0

7835dd4b0a54930acba1f3445b9513092882a896

24,101

py

Python

fst2/processers.py

superjcd/fst2

da4bd97bc9e028af55e1099940bd0b1e5bb34ded

[ "MIT" ]

2

2020-03-15T07:44:46.000Z

2021-05-17T04:32:46.000Z

fst2/processers.py

superjcd/fst2

da4bd97bc9e028af55e1099940bd0b1e5bb34ded

[ "MIT" ]

null

fst2/processers.py

superjcd/fst2

da4bd97bc9e028af55e1099940bd0b1e5bb34ded

[ "MIT" ]

1

2020-05-13T08:56:25.000Z

import copy import os import csv import json import torch import logging from transformers.file_utils import is_tf_available, is_torch_available from functools import wraps from .utils import CACHE_PARAMS logger = logging.getLogger(__name__) class InputExample(object): """ A single training/test example for simple sequence classification. Args: guid: Unique id for the example. text_a: string. The untokenized text of the first sequence. For single sequence tasks, only this sequence must be specified. text_b: (Optional) string. The untokenized text of the second sequence. Only must be specified for sequence pair tasks. label: (Optional) string. The label of the example. This should be specified for train and dev examples, but not for test examples. """ def __init__(self, guid, text_a, text_b=None, label=None): self.guid = guid self.text_a = text_a self.text_b = text_b self.label = label def __repr__(self): return str(self.to_json_string()) def to_dict(self): """Serializes this instance to a Python dictionary.""" output = copy.deepcopy(self.__dict__) return output def to_json_string(self): """Serializes this instance to a JSON string.""" return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" class InputFeatures(object): """ A single set of features of data. Args: input_ids: Indices of input sequence tokens in the vocabulary. attention_mask: Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: Usually ``1`` for tokens that are NOT MASKED, ``0`` for MASKED (padded) tokens. token_type_ids: Segment token indices to indicate first and second portions of the inputs. label: Label corresponding to the input """ def __init__(self, input_ids, attention_mask=None, token_type_ids=None, label=None): self.input_ids = input_ids self.attention_mask = attention_mask self.token_type_ids = token_type_ids self.label = label def __repr__(self): return str(self.to_json_string()) def to_dict(self): """Serializes this instance to a Python dictionary.""" output = copy.deepcopy(self.__dict__) return output def to_json_string(self): """Serializes this instance to a JSON string.""" return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n" class DataProcessor(object): """Base class for data converters for sequence classification data sets.""" def get_example_from_tensor_dict(self, tensor_dict): """Gets an example from a dict with tensorflow tensors Args: tensor_dict: Keys and values should match the corresponding Glue tensorflow_dataset examples. """ raise NotImplementedError() def get_train_examples(self, data_dir): """Gets a collection of `InputExample`s for the train set.""" raise NotImplementedError() def get_dev_examples(self, data_dir): """Gets a collection of `InputExample`s for the dev set.""" raise NotImplementedError() def get_labels(self): """Gets the list of labels for this data set.""" raise NotImplementedError() def tfds_map(self, example): """Some tensorflow_datasets datasets are not formatted the same way the GLUE datasets are. This method converts examples to the correct format.""" if len(self.get_labels()) > 1: example.label = self.get_labels()[int(example.label)] return example @classmethod def _read_csv(cls, input_file, delimiter="\t", quotechar=None): """Reads a tab separated csv/tsv file.""" with open(input_file, "r", encoding="utf-8-sig") as f: return list(csv.reader(f, delimiter=delimiter, quotechar=quotechar)) class SingleSentenceClassificationProcessor(DataProcessor): """ Generic processor for a single sentence classification data set.""" def __init__(self, labels=None, examples=None, mode="classification", verbose=False): self.labels = [] if labels is None else labels self.examples = [] if examples is None else examples self.mode = mode self.verbose = verbose def __len__(self): return len(self.examples) def __getitem__(self, idx): if isinstance(idx, slice): return SingleSentenceClassificationProcessor(labels=self.labels, examples=self.examples[idx]) return self.examples[idx] @classmethod def create_from_csv( cls, file_name, delimiter, split_name="", column_label=0, column_text=1, column_id=None, skip_first_row=False, **kwargs ): processor = cls(**kwargs) processor.add_examples_from_csv( file_name, delimiter, split_name=split_name, column_label=column_label, column_text=column_text, column_id=column_id, skip_first_row=skip_first_row, overwrite_labels=True, overwrite_examples=True, ) return processor @classmethod def create_from_examples(cls, texts_or_text_and_labels, labels=None, **kwargs): processor = cls(**kwargs) processor.add_examples(texts_or_text_and_labels, labels=labels) return processor def add_examples_from_csv( self, file_name, delimiter, split_name="", column_label=0, column_text=1, column_id=None, skip_first_row=False, overwrite_labels=False, overwrite_examples=False, ): lines = self._read_csv(file_name, delimiter=delimiter) if skip_first_row: lines = lines[1:] texts = [] labels = [] ids = [] for (i, line) in enumerate(lines): texts.append(line[column_text]) labels.append(line[column_label]) if column_id is not None: ids.append(line[column_id]) else: guid = "%s-%s" % (split_name, i) if split_name else "%s" % i ids.append(guid) return self.add_examples( texts, labels, ids, overwrite_labels=overwrite_labels, overwrite_examples=overwrite_examples ) def add_examples( self, texts_or_text_and_labels, labels=None, ids=None, overwrite_labels=False, overwrite_examples=False ): assert labels is None or len(texts_or_text_and_labels) == len(labels) assert ids is None or len(texts_or_text_and_labels) == len(ids) if ids is None: ids = [None] * len(texts_or_text_and_labels) if labels is None: labels = [None] * len(texts_or_text_and_labels) examples = [] added_labels = set() for (text_or_text_and_label, label, guid) in zip(texts_or_text_and_labels, labels, ids): if isinstance(text_or_text_and_label, (tuple, list)) and label is None: text, label = text_or_text_and_label else: text = text_or_text_and_label added_labels.add(label) examples.append(InputExample(guid=guid, text_a=text, text_b=None, label=label)) # Update examples if overwrite_examples: self.examples = examples else: self.examples.extend(examples) # Update labels if overwrite_labels: self.labels = list(added_labels) else: self.labels = list(set(self.labels).union(added_labels)) return self.examples def get_features( self, tokenizer, max_length=None, pad_on_left=False, pad_token=0, mask_padding_with_zero=True, return_tensors="pt", ): """ Convert examples in a list of ``InputFeatures`` Args: tokenizer: Instance of a tokenizer that will tokenize the examples max_length: Maximum example length task: GLUE task label_list: List of labels. Can be obtained from the processor using the ``processor.get_labels()`` method output_mode: String indicating the output mode. Either ``regression`` or ``classification`` pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default) pad_token: Padding token mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for actual values) Returns: If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset`` containing the task-specific features. If the input is a list of ``InputExamples``, will return a list of task-specific ``InputFeatures`` which can be fed to the model. """ if max_length is None: max_length = tokenizer.max_len label_map = {label: i for i, label in enumerate(self.labels)} all_input_ids = [] for (ex_index, example) in enumerate(self.examples): if ex_index % 10000 == 0: logger.info("Tokenizing example %d", ex_index) input_ids = tokenizer.encode( example.text_a, add_special_tokens=True, max_length=min(max_length, tokenizer.max_len), ) all_input_ids.append(input_ids) batch_length = max(len(input_ids) for input_ids in all_input_ids) features = [] for (ex_index, (input_ids, example)) in enumerate(zip(all_input_ids, self.examples)): if ex_index % 10000 == 0: logger.info("Writing example %d/%d" % (ex_index, len(self.examples))) # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids) # Zero-pad up to the sequence length. padding_length = batch_length - len(input_ids) if pad_on_left: input_ids = ([pad_token] * padding_length) + input_ids attention_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask else: input_ids = input_ids + ([pad_token] * padding_length) attention_mask = attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length) assert len(input_ids) == batch_length, "Error with input length {} vs {}".format( len(input_ids), batch_length ) assert len(attention_mask) == batch_length, "Error with input length {} vs {}".format( len(attention_mask), batch_length ) if self.mode == "classification": label = label_map[example.label] elif self.mode == "regression": label = float(example.label) else: raise ValueError(self.mode) if ex_index < 5 and self.verbose: logger.info("*** Example ***") logger.info("guid: %s" % (example.guid)) logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids])) logger.info("attention_mask: %s" % " ".join([str(x) for x in attention_mask])) logger.info("label: %s (id = %d)" % (example.label, label)) features.append(InputFeatures(input_ids=input_ids, attention_mask=attention_mask, label=label)) if return_tensors is None: return features elif return_tensors == "tf": if not is_tf_available(): raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported") import tensorflow as tf def gen(): for ex in features: yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label) dataset = tf.data.Dataset.from_generator( gen, ({"input_ids": tf.int32, "attention_mask": tf.int32}, tf.int64), ({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([])), ) return dataset elif return_tensors == "pt": if not is_torch_available(): raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported") import torch from torch.utils.data import TensorDataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long) if self.mode == "classification": all_labels = torch.tensor([f.label for f in features], dtype=torch.long) elif self.mode == "regression": all_labels = torch.tensor([f.label for f in features], dtype=torch.float) dataset = TensorDataset(all_input_ids, all_attention_mask, all_labels) return dataset else: raise ValueError("return_tensors should be one of 'tf' or 'pt'") class SequnceTokenClassificationProcessor(DataProcessor): def __init__(self, labels=None, examples=None, mode="classification", verbose=False): self.labels = [] if labels is None else labels self.examples = [] if examples is None else examples self.mode = mode self.verbose = verbose @classmethod def create_from_txt(cls, file_name, delimiter, **kwargs): processor = cls(**kwargs) processor.read_examples_from_txt(file_name, delimiter) return processor def read_examples_from_txt(self, file_name, delimiter): examples = [] guid_index = 1 with open(file_name, encoding="utf-8") as f: words = [] labels = [] for line in f: if line.startswith("-DOCSTART-") or line == "" or line == "\n": if words: examples.append(InputExample(guid=guid_index, text_a=words, label=labels)) words = [] labels = [] guid_index +=1 else: splits = line.split(delimiter) if len(splits) > 1: words.append(splits[0]) labels.append(splits[-1].replace("\n", "")) else: labels.append("O") if words: examples.append(InputExample(guid=guid_index, text_a=words, label=labels)) self.examples = examples def get_features( self, max_seq_length, tokenizer, return_tensors, cls_token_at_end=False, cls_token="[CLS]", cls_token_segment_id=0, sep_token="[SEP]", sep_token_extra=False, pad_on_left=False, pad_token=0, pad_token_segment_id=0, pad_token_label_id=-100, sequence_a_segment_id=0, mask_padding_with_zero=True, ): """ Loads a data file into a list of `InputBatch`s `cls_token_at_end` define the location of the CLS token: - False (Default, BERT/XLM pattern): [CLS] + A + [SEP] + B + [SEP] - True (XLNet/GPT pattern): A + [SEP] + B + [SEP] + [CLS] `cls_token_segment_id` define the segment id associated to the CLS token (0 for BERT, 2 for XLNet) """ label_map = {label: i for i, label in enumerate(self.labels)} features = [] # [[inout_ids, input_mask, segment_ids, label_id]] for (ex_index, example) in enumerate(self.examples): if ex_index % 10000 == 0: logger.info("Writing example %d of %d", ex_index, len(self.examples)) tokens = [] label_ids = [] for word, label in zip(example.text_a, example.label): word_tokens = tokenizer.tokenize(word) tokens.extend(word_tokens) # Use the real label id for the first token of the word, and padding ids for the remaining tokens label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(word_tokens) - 1)) # some language like german one word will be tokenized to several subwords # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa. special_tokens_count = 3 if sep_token_extra else 2 if len(tokens) > max_seq_length - special_tokens_count: tokens = tokens[: (max_seq_length - special_tokens_count)] label_ids = label_ids[: (max_seq_length - special_tokens_count)] tokens += [sep_token] label_ids += [pad_token_label_id] if sep_token_extra: # roberta uses an extra separator b/w pairs of sentences tokens += [sep_token] label_ids += [pad_token_label_id] segment_ids = [pad_token_segment_id] * len(tokens) if cls_token_at_end: tokens += [cls_token] label_ids += [pad_token_label_id] segment_ids += [cls_token_segment_id] else: tokens = [cls_token] + tokens label_ids = [pad_token_label_id] + label_ids segment_ids = [cls_token_segment_id] + segment_ids input_ids = tokenizer.convert_tokens_to_ids(tokens) # 输入的是列表，返回的也是列表 # The mask has 1 for real tokens and 0 for padding tokens. Only real # tokens are attended to. input_mask = [1 if mask_padding_with_zero else 0] * len(input_ids) # Zero-pad up to the sequence length. padding_length = max_seq_length - len(input_ids) if pad_on_left: input_ids = ([pad_token] * padding_length) + input_ids input_mask = ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask segment_ids = ([pad_token_segment_id] * padding_length) + segment_ids label_ids = ([pad_token_label_id] * padding_length) + label_ids else: input_ids += [pad_token] * padding_length input_mask += [0 if mask_padding_with_zero else 1] * padding_length segment_ids += [pad_token_segment_id] * padding_length label_ids += [pad_token_label_id] * padding_length # 保证我的input_id, input_mask, segment_ids, 和label_ids都是一致的 assert len(input_ids) == max_seq_length assert len(input_mask) == max_seq_length assert len(segment_ids) == max_seq_length assert len(label_ids) == max_seq_length if ex_index < 5: logger.info("*** Example ***") logger.info("guid: %s", example.guid) logger.info("tokens: %s", " ".join([str(x) for x in tokens])) logger.info("input_ids: %s", " ".join([str(x) for x in input_ids])) logger.info("input_mask: %s", " ".join([str(x) for x in input_mask])) logger.info("segment_ids: %s", " ".join([str(x) for x in segment_ids])) logger.info("label_ids: %s", " ".join([str(x) for x in label_ids])) features.append( InputFeatures(input_ids=input_ids, attention_mask=input_mask, token_type_ids=segment_ids, label=label_ids) ) # note segement_ids has 1 in the front? but we don't use it right now # fetures to dataset if return_tensors is None: return features elif return_tensors == "tf": if not is_tf_available(): raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported") import tensorflow as tf def gen(): for ex in features: yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label) dataset = tf.data.Dataset.from_generator( gen, ({"input_ids": tf.int32, "attention_mask": tf.int32}, tf.int64), ({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([])), ) return dataset elif return_tensors == "pt": if not is_torch_available(): raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported") import torch from torch.utils.data import TensorDataset all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long) all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long) all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long) if self.mode == "classification": all_labels = torch.tensor([f.label for f in features], dtype=torch.long) elif self.mode == "regression": all_labels = torch.tensor([f.label for f in features], dtype=torch.float) dataset = TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels) return dataset else: raise ValueError("return_tensors should be one of 'tf' or 'pt'") ############## help functions ###### def load_and_cache_dataset(func): @wraps(func) def inner(*args, **kwargs): logger = logging.getLogger("Load-Cache_Dataset") cached_features_file = os.path.join( CACHE_PARAMS["data_dir"], "cached_{}_{}_{}".format( CACHE_PARAMS["mode"], list(filter(None, CACHE_PARAMS["model_name_or_path"].split("/"))).pop(), str(CACHE_PARAMS["max_seq_length"]) ),) if os.path.exists(cached_features_file): dataset = torch.load(cached_features_file) logger.info("Load dataset from {}".format(cached_features_file)) return dataset else: logger.info("Read data and preparing dataset") dataset = func(*args, **kwargs) torch.save(dataset, cached_features_file) logger.info("Cached dataset at {}".format(cached_features_file)) return dataset return inner # if __name__ == "__main__": # from transformers import BertTokenizer # processer = SingleSentenceClassificationProcessor.create_from_csv(file_name="test_data.tsv", delimiter=",") # tokenizer = BertTokenizer.from_pretrained("/Users/jiangchaodi/Code/NLP/transformer_examples/models/chinese_L-12_H-768_A-12") # dataset = processer.get_features(tokenizer=tokenizer, max_length=128) # print(dataset) # test torch.load(dataset) # import torch # torch.save(dataset, "test_cached") # dataset2 = torch.load("test_cached") # print(dataset2) # if __name__ == "__main__": # from transformers import BertTokenizer # from pipelines import get_labels # processer = SequnceTokenClassificationProcessor.create_from_txt(file_name="/Users/jiangchaodi/Code/NLP/fasttransformer/fst2/data/train.txt", delimiter="\t", labels=get_labels("/Users/jiangchaodi/Code/NLP/fasttransformer/fst2/data/labels.txt")) # tokenizer = BertTokenizer.from_pretrained("/Users/jiangchaodi/Code/NLP/transformer_examples/models/chinese_L-12_H-768_A-12") # dataset = processer.get_features(tokenizer=tokenizer, max_seq_length=128, return_tensors="pt") # print(dataset)

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null

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null

0

1

0

783cc0ae7ce9094203cd5255e4827fee654d5210

530

py

Python

jupyter_cache/cli/utils.py

ExecutableBookProject/sandbox

e72c2121b460c8558f9e6257b3b53353b9e7f35c

[ "MIT" ]

2

2020-03-11T23:14:00.000Z

2020-04-07T14:58:51.000Z

jupyter_cache/cli/utils.py

ExecutableBookProject/sandbox

e72c2121b460c8558f9e6257b3b53353b9e7f35c

[ "MIT" ]

41

2020-02-19T20:18:56.000Z

2020-04-20T01:25:55.000Z

jupyter_cache/cli/utils.py

ExecutableBookProject/sandbox

e72c2121b460c8558f9e6257b3b53353b9e7f35c

[ "MIT" ]

1

2020-03-15T05:45:15.000Z

import logging import click class ClickLogHandler(logging.Handler): _use_stderr = True def emit(self, record): try: msg = self.format(record) click.echo(msg, err=self._use_stderr) except Exception: self.handleError(record) def setup_logger(logger: logging.Logger) -> None: """Add handler to log to click.""" try: import click_log except ImportError: logger.addHandler(ClickLogHandler()) else: click_log.basic_config(logger)

21.2

49

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530

5.45

0.533333

0.067278

0

0.275472

530

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783e84924972e61c147a0b1b3dc18448354ffac3

4,401

py

Python

Samples/codes/matopt_review/add_optimizer.py

wilsongis/3DP_Experiments

da9bd3b4ba1d82bac7dcfa27d86634add59db087

[ "MIT", "Unlicense" ]

null

Samples/codes/matopt_review/add_optimizer.py

wilsongis/3DP_Experiments

da9bd3b4ba1d82bac7dcfa27d86634add59db087

[ "MIT", "Unlicense" ]

null

Samples/codes/matopt_review/add_optimizer.py

wilsongis/3DP_Experiments

da9bd3b4ba1d82bac7dcfa27d86634add59db087

[ "MIT", "Unlicense" ]

null

# -*- coding: utf-8 -*- """ Copyright (c) 2021 Showa Denko Materials co., Ltd. All rights reserved. This software is for non-profit use only. THIS SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THIS SOFTWARE OR THE USE OR OTHER DEALINGS IN THIS SOFTWARE. """ import GPyOpt from GPyOpt.optimization.optimizer import OptLbfgs, OptDirect, OptCma, apply_optimizer from GPyOpt.optimization.anchor_points_generator import ObjectiveAnchorPointsGenerator, ThompsonSamplingAnchorPointsGenerator max_objective_anchor_points_logic = "max_objective" thompson_sampling_anchor_points_logic = "thompsom_sampling" sobol_design_type = "sobol" random_design_type = "random" class InvalidArgumentError(Exception): pass class AcquisitionOptimizer(GPyOpt.optimization.AcquisitionOptimizer): """ AcquisitionOptimizer of GPyOpt was modified to control some parameters including, max_AcOpt_iter and num_anchor_points. Note that the default paramaters of GPyOpt were used in the study of the goal-oriented Bayesian optimization. :param space: design space class from GPyOpt. :param optimizer: optimizer to use. Can be selected among: - 'lbfgs': L-BFGS. - 'DIRECT': Dividing Rectangles. - 'CMA': covariance matrix adaptation. :param max_AcOpt_iter: maximun number of optimization steps. :param num_anchor_points: number of initial search points. """ def __init__(self, space, optimizer='lbfgs', max_AcOpt_iter=1000, num_anchor_points=1000, **kwargs): super(AcquisitionOptimizer, self).__init__(space, optimizer, **kwargs) self.max_AcOpt_iter = max_AcOpt_iter self.num_anchor_points = num_anchor_points def optimize(self, f=None, df=None, f_df=None, duplicate_manager=None): """ Optimizes the input function. :param f: function to optimize. :param df: gradient of the function to optimize. :param f_df: returns both the function to optimize and its gradient. """ self.f = f self.df = df self.f_df = f_df ## --- Update the optimizer, in case context has beee passed. self.optimizer = self.choose_optimizermod(self.optimizer_name, self.context_manager.noncontext_bounds) ## --- Selecting the anchor points and removing duplicates if self.type_anchor_points_logic == max_objective_anchor_points_logic: anchor_points_generator = ObjectiveAnchorPointsGenerator(self.space, random_design_type, f, num_samples=self.num_anchor_points) elif self.type_anchor_points_logic == thompson_sampling_anchor_points_logic: anchor_points_generator = ThompsonSamplingAnchorPointsGenerator(self.space, sobol_design_type, self.model) ## -- Select the anchor points (with context) anchor_points = anchor_points_generator.get(num_anchor=5, duplicate_manager=duplicate_manager, context_manager=self.context_manager) ## --- Applying local optimizers at the anchor points and update bounds of the optimizer (according to the context) optimized_points = [apply_optimizer(self.optimizer, a, f=f, df=df, f_df=f_df, duplicate_manager=duplicate_manager, context_manager=self.context_manager, space = self.space) for a in anchor_points] x_min, fx_min = min(optimized_points, key=lambda t:t[1]) return x_min, fx_min def choose_optimizermod(self, optimizer_name, bounds): """ Selects the type of local optimizer """ if optimizer_name == 'lbfgs': optimizer = OptLbfgs(bounds, self.max_AcOpt_iter) elif optimizer_name == 'DIRECT': optimizer = OptDirect(bounds, self.max_AcOpt_iter) elif optimizer_name == 'CMA': optimizer = OptCma(bounds, self.max_AcOpt_iter) else: print(optimizer_name) raise InvalidArgumentError('Invalid optimizer selected.') return optimizer

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4,401

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0.026316

0

null

0

null

0

1

0

783f73ab72792697849c2074b2a97ea34ea37f7c

1,354

py

Python

eos_db/test/test_credits.py

cedadev/eos-db

b97b1b7c469779e370aab8ad68cf7e8d2e6ff8e6

[ "BSD-3-Clause" ]

null

eos_db/test/test_credits.py

cedadev/eos-db

b97b1b7c469779e370aab8ad68cf7e8d2e6ff8e6

[ "BSD-3-Clause" ]

null

eos_db/test/test_credits.py

cedadev/eos-db

b97b1b7c469779e370aab8ad68cf7e8d2e6ff8e6

[ "BSD-3-Clause" ]

null

"""Tests for credit addition, subtraction and querying. See also tests in test_user_api """ import unittest import requests from eos_db.server import choose_engine, create_user, touch_to_add_credit from eos_db.server import check_credit, check_actor_id class TestCreditFunctions(unittest.TestCase): """Tests credit functions in server module.""" def setUp(self): choose_engine('SQLite') def test_create_user(self): """ Add a user. """ user = create_user('user','testuser','testuser','testuser') self.assertEqual(check_actor_id(user), user) def test_add(self): """ Behaviour: Calling the API to add credit should result credit being added to the database. """ user = create_user('user','testuser2','testuser2','testuser2') touch_to_add_credit(user,1000) credit = check_credit(user) self.assertEqual(credit, 1000) def test_subtract(self): """ Behaviour: Calling the API to add credit should result credit being subtracted from the database. """ user = create_user('user', 'testuser3', 'testuser3', 'testuser3') touch_to_add_credit(user,-500) credit = check_credit(user) self.assertEqual(credit, -500) if __name__ == '__main__': unittest.main()

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null

0

null

0

1

0

783fc3228d1bb98e46b23e9c61a33784429ce728

2,733

py

Python

visualization/data_plot.py

Jingyu6/forl_2021

8679b41ece66551d14cfb31fa42a467eb4c1fb0b

[ "MIT" ]

null

visualization/data_plot.py

Jingyu6/forl_2021

8679b41ece66551d14cfb31fa42a467eb4c1fb0b

[ "MIT" ]

null

visualization/data_plot.py

Jingyu6/forl_2021

8679b41ece66551d14cfb31fa42a467eb4c1fb0b

[ "MIT" ]

null

import gym # type: ignore import matplotlib # type: ignore import matplotlib.pyplot as plt # type: ignore import numpy as np from pathlib import Path # type: ignore from typing import List, Union, Literal, Dict, Any from visualization.data_parser import Records, D3rlpyCSVDataParser def plot_records_list( axes: matplotlib.axes.Axes, records_list: List[Records], env_name: str, value_description: str = 'loss', horizon_name: Union[Literal['epochs', 'steps']] = 'epochs', **kwargs: Any # arguments to the plot function ) -> None: """ Plot the graph of different algorithms, each algorithm contains multiple experiments, all experiments are from the same environment """ assert len(records_list) > 0, "Can not pass in empty records." # group them together algo_to_records: Dict[str, List[Records]] = {} for records in records_list: algo_name = records.algo_name if algo_name not in algo_to_records: algo_to_records[algo_name] = [] algo_to_records[algo_name].append(records) # make sure all algorithms have the same number of experiments experiment_counts = set([len(data) for data in algo_to_records.values()]) assert len(experiment_counts) == 1, \ "All algorithms should have the same number of experiments" # truncate horizon (assuming monotonic increasing) min_horizon = min([len(records.get_data()[horizon_name]) for records in records_list]) for algo_name in sorted(algo_to_records.keys()): print(algo_name) algo_records_list = algo_to_records[algo_name] horizon = algo_records_list[0].get_data(min_horizon)[horizon_name] values = np.array([records.get_data(min_horizon)['values'] for records in algo_records_list]) value_mean = np.mean(values, axis=0) value_std = np.std(values, axis=0) axes.plot(horizon, value_mean, **kwargs) axes.fill_between(horizon, value_mean - value_std, value_mean + value_std, alpha=0.2, interpolate=True) axes.set_title('{}: {} plots of {} over {} trials'.format( env_name, value_description, horizon_name, next(iter(experiment_counts)))) axes.set_ylabel(value_description) axes.set_xlabel(horizon_name) axes.legend(sorted(list(algo_to_records.keys()))) def plot_records_in_dir( log_dir: str, env_name: str, value_description: str = 'loss', horizon_name: Union[Literal['epochs', 'steps']] = 'epochs', **kwargs: Any ) -> None: log_dir_path = Path(log_dir) assert log_dir_path.is_dir(), "Invalid log dir." parser = D3rlpyCSVDataParser() records_list: List[Records] = [] for sub_dir in log_dir_path.iterdir(): records_list.append(parser.parse(str(sub_dir), value_description=value_description)) plot_records_list(plt.gca(), records_list, env_name, value_description, horizon_name, **kwargs) plt.show()

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0

null

0

null

0

1

0

7840eb695f4d59312dcc4726ecededb6274d25a1

2,947

py

Python

game_matrix.py

GeorgiaLu/game-2048

f256974ad65d869943630564d9eac697c4e8dc04

[ "MIT" ]

1

2019-01-08T04:10:22.000Z

game_matrix.py

GeorgiaLu/game-2048

f256974ad65d869943630564d9eac697c4e8dc04

[ "MIT" ]

null

game_matrix.py

GeorgiaLu/game-2048

f256974ad65d869943630564d9eac697c4e8dc04

[ "MIT" ]

null

import numpy import random from enum import Enum class Direction(Enum): kUp, kRight, kDown, kLeft = range(4) class GameMatrix(object): def __init__(self, dim): self.dim = dim self.matrix = numpy.zeros((dim, dim)) self.init_matrix() self.tube = [] def init_matrix(self): indices = [i for i in range(self.dim * self.dim)] random.shuffle(indices) pick_number = random.choices([2, 4], k=2) self.matrix[indices[0] // self.dim][indices[0] % self.dim] = pick_number[0] self.matrix[indices[1] // self.dim][indices[1] % self.dim] = pick_number[1] def random_pick_empty(self): empty_spots = [] for i in range(self.dim): for j in range(self.dim): if self.matrix[i][j] == 0: empty_spots.append([i, j]) return random.choices(empty_spots)[0] def random_add_one(self): my_pick = self.random_pick_empty() self.matrix[my_pick[0]][my_pick[1]] = random.choices([2, 4], weights=[.5, .5])[0] def tube_append(self, elem): if elem != 0: if len(self.tube) != 0 and self.tube[-1] == elem: self.tube[-1] *= 2 else: self.tube.append(elem) def move(self, direction): if direction == Direction.kDown: for j in range(self.dim): for i in range(self.dim): elem = self.matrix[self.dim - 1 - i][j] self.tube_append(elem) self.matrix[self.dim - 1 - i][j] = 0 for i in range(len(self.tube)): self.matrix[self.dim - 1 - i][j] = self.tube[i] self.tube.clear() elif direction == Direction.kUp: for j in range(self.dim): for i in range(self.dim): elem = self.matrix[i][j] self.tube_append(elem) self.matrix[i][j] = 0 for i in range(len(self.tube)): self.matrix[i][j] = self.tube[i] self.tube.clear() elif direction == Direction.kLeft: for i in range(self.dim): for j in range(self.dim): elem = self.matrix[i][j] self.tube_append(elem) self.matrix[i][j] = 0 for j in range(len(self.tube)): self.matrix[i][j] = self.tube[j] self.tube.clear() elif direction == Direction.kRight: for i in range(self.dim): for j in range(self.dim): elem = self.matrix[i][self.dim - 1 - j] self.tube_append(elem) self.matrix[i][self.dim - 1 - j] = 0 for j in range(len(self.tube)): self.matrix[i][self.dim - 1 - j] = self.tube[j] self.tube.clear()

33.488636

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389

2,947

3.647815

0.133676

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0.085271

0.108527

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0.48203

0.462297

0.422128

0.379845

0

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0.376994

2,947

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false

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0

0.171429

0

null

0

null

0

1

0

78442b33bc9640a1fb48adf69170375475735e0f

5,581

py

Python

cognite/model_hosting/schedules/schedules.py

cognitedata/cognite-model-hosting

89f58e25f0e3c3a37006e60f52246da0b00a0066

[ "Apache-2.0" ]

4

2019-05-27T12:51:45.000Z

2020-02-26T08:16:30.000Z

cognite/model_hosting/schedules/schedules.py

cognitedata/cognite-model-hosting

89f58e25f0e3c3a37006e60f52246da0b00a0066

[ "Apache-2.0" ]

26

2019-03-18T15:10:20.000Z

2021-06-21T05:47:24.000Z

cognite/model_hosting/schedules/schedules.py

cognitedata/cognite-model-hosting

89f58e25f0e3c3a37006e60f52246da0b00a0066

[ "Apache-2.0" ]

null

import json from collections import defaultdict from typing import Dict, List, Union import numpy as np import pandas as pd from marshmallow import EXCLUDE, Schema, ValidationError, fields, validate from cognite.model_hosting._cognite_model_hosting_common.utils import timestamp_to_ms from cognite.model_hosting.schedules.exceptions import DuplicateAliasInScheduledOutput, InvalidScheduleOutputFormat def to_output(dataframe: Union[pd.DataFrame, List[pd.DataFrame]]) -> Dict: """Converts your data to a json serializable output format complying with the schedules feature. Args: dataframe (Union[List[pd.DataFrame, pd.DataFrame]]: A dataframe or list of dataframes. Returns: Dict: The data on a json serializable and schedules compliant output format. Examples: The correct output format looks like this:: { "timeSeries": { "my-alias-1": [(t0, p0), (t1, p1), ...], "my-alias-2": [(t0, p0), (t1, p1), ...], } } """ output = defaultdict(lambda: {}) if isinstance(dataframe, pd.DataFrame): output["timeSeries"] = _convert_df_to_output_format(dataframe) elif isinstance(dataframe, List): for df in dataframe: if set(df.columns) - set(output["timeSeries"].keys()) != set(df.columns): raise DuplicateAliasInScheduledOutput("An alias has been provided multiple times") output["timeSeries"].update(_convert_df_to_output_format(df)) else: raise TypeError("dataframe should be a pandas DataFrame or list of pandas DataFrames") return output def _convert_df_to_output_format(df: pd.DataFrame): return {name: list(zip([timestamp_to_ms(ts) for ts in df.index], df[name])) for name in df.columns} class _ScheduleOutputSchema(Schema): class Meta: unknown = EXCLUDE timeSeries = fields.Dict( keys=fields.Str(), values=fields.List(fields.List(fields.Float(), validate=validate.Length(equal=2))) ) _schedule_output_schema = _ScheduleOutputSchema(unknown=EXCLUDE) class ScheduleOutput: """Helper class for parsing and converting output from scheduled predictions. Args: output(Dict): The output returned from the scheduled prediction. """ def __init__(self, output: Dict): self._output = self._load(output) def __str__(self): return json.dumps(self._output, indent=4, sort_keys=True) @staticmethod def _load(output): try: return _schedule_output_schema.load(output) except ValidationError as e: raise InvalidScheduleOutputFormat(e.messages) from e def _validate_alias(self, type: str, alias: str): assert self._output.get(type, {}).get(alias) is not None, "{} is not a valid alias".format(alias) def _validate_aligned(self, aliases: List[str]): timestamps = set() for alias in aliases: self._validate_alias("timeSeries", alias) timestamps.add(tuple(point[0] for point in self._output["timeSeries"][alias])) assert 1 == len(timestamps), "Timestamps for aliases {} are not aligned".format(aliases) def _get_dataframe_single_alias(self, alias) -> pd.DataFrame: self._validate_alias("timeSeries", alias) data = self._output["timeSeries"][alias] timestamps = [int(point[0]) for point in data] datapoints = [point[1] for point in data] return pd.DataFrame({alias: datapoints}, index=np.array(timestamps, dtype="datetime64[ms]")) def _get_dataframe_multiple_aliases(self, aliases: List[str]) -> pd.DataFrame: self._validate_aligned(aliases) data = {} timestamps = [int(p[0]) for p in self._output["timeSeries"][aliases[0]]] for a in aliases: data[a] = [p[1] for p in self._output["timeSeries"][a]] return pd.DataFrame(data, index=np.array(timestamps, dtype="datetime64[ms]")) def get_dataframe(self, alias: Union[str, List[str]]) -> pd.DataFrame: """Returns a time-aligned dataframe of the specified alias(es). Assumes that all aliases specify output time series with matching timestamps. Args: alias(Union[str, List[str]]): alias or list of aliases Returns: pd.DataFrame: The dataframe containing the time series for the specified alias(es). """ if isinstance(alias, str): return self._get_dataframe_single_alias(alias) elif isinstance(alias, List): return self._get_dataframe_multiple_aliases(alias) raise TypeError("alias must be a string or list of strings") def get_datapoints(self, alias: Union[str, List[str]]) -> Union[pd.DataFrame, Dict[str, pd.DataFrame]]: """Returns the dataframes for the specified alias(es). Args: alias (Union[str, List[str]]): alias or list of aliases. Returns: Union[pd.DataFrame, Dict[str, pd.DataFrame]: A single dataframe if a single alias has been specified. Or a dictionary mapping alias to dataframe if a list of aliases has been provided. """ if isinstance(alias, str): return self._get_dataframe_single_alias(alias) elif isinstance(alias, List): dataframes = {} for a in alias: dataframes[a] = self._get_dataframe_single_alias(a) return dataframes raise TypeError("alias must be a string or list of strings")

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0.434211

0

null

0

null

0

1

0

78480e99cab78715400c1a2ba08f71032a31b6fe

578

py

Python

src/matplot.py

AutuanLiu/PyCon

ba0e2005d1e0301d77bb8111ff67b663dc234784

[ "MIT" ]

1

2018-03-18T11:07:15.000Z

src/matplot.py

AutuanLiu/PyCon

ba0e2005d1e0301d77bb8111ff67b663dc234784

[ "MIT" ]

null

src/matplot.py

AutuanLiu/PyCon

ba0e2005d1e0301d77bb8111ff67b663dc234784

[ "MIT" ]

null

# -*- coding: utf-8 -*- """ Created on Sun Jun 4 14:56:00 2017 @author: AutuanLiu """ import numpy as np import matplotlib.pyplot as plt # test so so plt.plot([3, 1, 4, -5, 6]) plt.ylabel("grade") plt.savefig("test", dpi = 600) plt.show() def f(x): return np.exp(-x) * np.cos(2 * np.pi * x) a = np.arange(0, 5, .02) plt.subplot(2, 1, 1) plt.plot(a, f(a)) plt.subplot(2, 1, 2) plt.plot(a, np.cos(2 * np.pi * a), 'r--') plt.show() # plot multi image plt.plot(a, np.sin(a), a, np.sinh(a), a, np.exp(a), a, a ** 3) plt.xlabel('x axis') plt.ylabel('y axis') plt.show()

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false

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0.055556

0.222222

0

null

0

null

0

1

0

7848a09465a19c587425eb6807accc3751bd0e0a

1,439

py

Python

lib.py

Zenahr/ALUB

cc9161d6e30a7f5278761954333fcdcee9598259

[ "MIT" ]

null

lib.py

Zenahr/ALUB

cc9161d6e30a7f5278761954333fcdcee9598259

[ "MIT" ]

null

lib.py

Zenahr/ALUB

cc9161d6e30a7f5278761954333fcdcee9598259

[ "MIT" ]

null

import random import autopy import pyautogui import time import json from threading import Timer import cv2 import pytesseract def click_readyup_button(): print('CHECKING...') if should_click(): try: autopy.mouse.move(*(230, 928)) time.sleep(.2) autopy.mouse.click() except TypeError: print('INTERNAL ERROR OCCURED. CONTACT DEVELOPER.') def get_position(): print( pyautogui.position() ) def should_click(): box = ((140, 950), (170, 42)) screenshot = autopy.bitmap.capture_screen(box) screenshot.save('screenshot.png') img = cv2.imread('screenshot.png') threshold = 90 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) thresh = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY_INV)[1] # cv2.imshow('thresh', thresh) # cv2.waitKey(0) scanned_text = pytesseract.image_to_string(img, lang='eng', config='--psm 6') try: print(scanned_text) if 'READY' in scanned_text: print('read READY in screenshot') return True elif 'CANCEL' in scanned_text: print('read CANCEL in screenshot') return True else: print('read nothing in screenshot') return False except AttributeError: raise Exception(f'OCR MODULE: COULD NOT RETRIEVE TEXT') if __name__ == '__main__': print( should_click() )

27.150943

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false

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0.297872

0.170213

0

null

0

null

0

1

0

784be104b596367c04897f8c45fd427758c904d4

10,993

py

Python

games/bocce/frame.py

OddballSports-tv/obies-eyes

2dd4fc9686f852b9adf89edd3246ad642063ac8b

[ "Apache-2.0" ]

null

games/bocce/frame.py

OddballSports-tv/obies-eyes

2dd4fc9686f852b9adf89edd3246ad642063ac8b

[ "Apache-2.0" ]

1

2022-02-19T20:40:44.000Z

games/bocce/frame.py

OddballSports-tv/obies-eyes

2dd4fc9686f852b9adf89edd3246ad642063ac8b

[ "Apache-2.0" ]

null

# imports from .ball import Pallino from .throw import Throw from .cv.ballfinder import BallFinder from scipy.spatial import distance as dist # for now, these are "pixels" (not "inches" or "cm") TOO_CLOSE_MARGIN = 5 class Frame: def __init__(self, frameNumber, throwingEnd, pallinoThrowingTeam, teamHome, teamAway, cam): self.frameNumer = frameNumber self.throwingEnd = throwingEnd self.frameWinner = None self.pallinoThrowingTeam = pallinoThrowingTeam self.teamHome = teamHome self.teamAway = teamAway # todo self.cam = cam self.pallinoInPlay = False self.ballMotion = False self.whoseIn = None self.inPoints = 0 self.framePoints = 0 #### throws #### self.throw = None self.throws = [] self.first_bocce_thrown = False self.second_bocce_thrown = False self.numThrowsTeamHome = 0 self.numThrowsTeamAway = 0 self.throw_trigger = False self.num_total_team_balls = None def initialize_balls(self, playersPerTeam): self.pallino = Pallino("yellow") if playersPerTeam == 1: self.num_total_team_balls = 2 self.teamHome.add_balls(self.num_total_team_balls) self.teamAway.add_balls(self.num_total_team_balls) elif playersPerTeam == 2 or playersPerTeam == 4: self.num_total_team_balls = 4 self.teamHome.add_balls(self.num_total_team_balls) self.teamAway.add_balls(self.num_total_team_balls) else: self.num_total_team_balls = None raise ValueError("valid playersPerTeam must be 1, 2, or 4") def start(self): print("Frame {} is started".format(str(self.frameNumer))) def throw_pallino(self, team): # throw the pallino # todo: determine throwing player; currently gets RANDOM player self.pallino.set_thrower(team.get_random_player()) self.throw = Throw(self.pallino.thrownBy, self.pallino) self.throw.throw() valid = self.throw.valid # debug print("{} threw the pallino. Throw is {}.".format( self.pallino.thrownBy, "valid" if valid else "invalid")) if valid: self.pallino.isThrown = True self.pallinoInPlay = True return valid def increment_team_throw_count(self, team): if team == self.teamHome: self.numThrowsTeamHome += 1 elif team == self.teamAway: self.numThrowsTeamAway += 1 def throw_bocce(self, team, followPallino=False): thrower = None # whichever team threw the pallino throws again if followPallino: print("Following the pallino") team = self.pallinoThrowingTeam # otherwise, it is the furthest team's throw else: # if the furthest team has no more balls, then switch teams if self.get_num_remaining_team_balls(team) <= 0: # switch team team = self.get_other_team(team) # grab a bocce ball from the team ball = self.get_a_team_ball(team.balls) # grab a player from the team # todo: determine the throwing player; currently gets a random player with ball thrower = team.get_random_player_with_balls() # throw the bocce ball ball.set_thrower(thrower) self.throw = Throw(thrower, ball) self.throw.throw() self.increment_team_throw_count(team) valid = self.throw.valid # update who is in if followPallino: self.whoseIn = self.get_other_team(self.pallinoThrowingTeam) else: self.whoseIn = self.determine_whose_in(self.cam.last_frame) # debug print("{}({}) threw a bocce. Throw is {}. {} is in with points={}. {} remaining balls={}".format( str(thrower), team.teamBallColor, "valid" if valid else "invalid", self.whoseIn, self.inPoints, str(team), self.get_num_remaining_team_balls(team))) return valid def get_a_team_ball(self, balls): for ball in balls: # go to the next ball if this one is already thrown if ball.isThrown: continue else: # determined that this team has more balls to throw return ball # by default, the team doesn't have any more balls to throw return None def either_team_has_balls(self): if self.get_num_remaining_team_balls(self.teamHome) > 0 \ or self.get_num_remaining_team_balls(self.teamAway) > 0: return True return False def get_num_remaining_team_balls(self, team): numBalls = 4 for ball in team.balls: # go to the next ball if this one is already thrown if ball.isThrown: numBalls -= 1 return numBalls def handle_throw(self): if not self.pallino.isThrown: # throw the pallino self.throw_pallino(self.pallinoThrowingTeam) # check to see if the pallino is in play if not self.pallinoInPlay: # swap pallino throwing team if self.pallinoThrowingTeam == self.teamHome: self.pallinoThrowingTeam = self.teamAway elif self.pallinoThrowingTeam == self.teamAway: self.pallinoThrowingTeam = self.teamHome # indicate that the pallino hasn't been thrown self.pallino.isThrown = False return return # the pallino thrower NEEDS to throw their first ball if not self.first_bocce_thrown: self.throw_bocce(self.pallinoThrowingTeam, followPallino=True) self.first_bocce_thrown = True self.update_in_points(1) # force to one point self.first_bocce_thrown = True return # the other team ALWAYS throws their first ball next if not self.second_bocce_thrown: print("The other team ALWAYS throws their first ball next") self.throw_bocce(self.get_other_team(self.pallinoThrowingTeam), followPallino=False) self.second_bocce_thrown = True # todo we need to determine who is in but kmeans fails if all dff ball nums = 1 self.update_in_points(1) return else: if self.either_team_has_balls(): # throw all remaining balls self.inPoints, self.whoseIn = self.determine_whose_in(self.cam) # the other team (furthest team) throws valid = self.throw_bocce(self.get_other_team(self.whoseIn), followPallino=False) else: print("Please score the frame") # if we reach this, then the frame is done, so cleanup self.set_frame_points(self.whoseIn, self.inPoints) def get_other_team(self, team): if team == self.teamHome: return self.teamAway return self.teamHome """Finds closest ball with computer vision""" def determine_whose_in(self, court): bf = BallFinder() bf.pipeline(court, self.numThrowsTeamHome, self.numThrowsTeamAway) self.pallino = bf.pallino self.teamHome.balls = bf.homeBalls self.teamAway.balls = bf.awayBalls points, frameLeader = self.get_frame_points_and_frame_leader(self.pallino, self.teamHome.balls, self.teamAway.balls) return points, frameLeader def get_frame_points_and_frame_leader(self, pallino, homeBalls, awayBalls): def get_frame_points(ballDistancesA, ballDistancesB): framePoints = 0 for (i, dB) in enumerate(ballDistancesB): for (j, dA) in enumerate(ballDistancesA): if dA < dB: framePoints += 1 else: break break return framePoints if pallino is None: print("not annotating; couldn't find pallino") # calculate Euclidean distance for each ball to the pallino homeBallsDistances = [] awayBallsDistances = [] for ball in homeBalls: D = dist.euclidean(pallino.coordinates, ball.coordinates) homeBallsDistances.append(D) for ball in awayBalls: D = dist.euclidean(pallino.coordinates, ball.coordinates) awayBallsDistances.append(D) # sort balls and distances homeBallsDistances, homeBalls = zip(*sorted(zip(homeBallsDistances, homeBalls))) awayBallsDistances, awayBalls = zip(*sorted(zip(awayBallsDistances, awayBalls))) # grab each min distance (the 0th element in the sorted list) homeBallsMinDistance = homeBallsDistances[0] awayBallsMinDistance = awayBallsDistances[0] # who is closer? homeIsCloser = homeBallsMinDistance < awayBallsMinDistance awayIsCloser = awayBallsMinDistance < homeBallsMinDistance equidistant = homeBallsMinDistance == awayBallsMinDistance # check if it is "too close to call" tooCloseToCall = abs(homeBallsMinDistance - awayBallsMinDistance) <= TOO_CLOSE_MARGIN # determine framePoints and frameWinner framePoints = None frameLeader = None if homeIsCloser: framePoints = get_frame_points(homeBallsDistances, awayBallsDistances) frameLeader = self.teamHome elif awayIsCloser: framePoints = get_frame_points(awayBallsDistances, homeBallsDistances) frameLeader = self.teamAway elif equidistant or tooCloseToCall: # todo how do we handle when both teams' closest ball is equidistant framePoints = None return framePoints, frameLeader """Determine's who is in and accounts for their points""" def update_in_points(self, points=None): # determine who is in if points is not None: self.inPoints = points return # check for balls closest to pallino ballsThrown = 1 + self.numThrowsTeamHome + self.numThrowsTeamAway # if at least two bocce balls are thrown if ballsThrown >= 2: self.inPoints, self.whoseIn = self.determine_whose_in(self.cam.last_frame) else: self.inPoints = 0 def set_frame_points(self, inTeam, inPoints): self.framePoints = inPoints self.frameWinner = inTeam def end(self): print("[INFO] frame winner is {} with points={}".format( self.frameWinner, self.framePoints)) self.teamAway.balls = [] self.teamHome.balls = [] return self.frameWinner, self.framePoints

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105

0.615665

1,220

10,993

5.42377

0.189344

0.020402

0.014508

0.019344

0.211878

0.163216

0.134804

0.10065

0.078585

0.065891

0

0.00411

0.313927

10,993

314

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35.009554

0.873243

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0

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0.040116

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false

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0.191176

0.039216

0

null

0

null

0

1

0

784d88dfa8270065fc4ec2b2539f03dba716b534

2,738

py

Python

timer.py

mrozowski/TaskTimer

66b069c14a7117502bb657738869f3ae33870c04

[ "MIT" ]

1

2021-07-10T17:51:01.000Z

timer.py

mrozowski/TaskTimer

66b069c14a7117502bb657738869f3ae33870c04

[ "MIT" ]

null

timer.py

mrozowski/TaskTimer

66b069c14a7117502bb657738869f3ae33870c04

[ "MIT" ]

null

import threading, time, signal from datetime import timedelta from PyQt5 import QtWidgets from playsound import playsound import win10toast from view import Dial isActive = False # global variable that says if timer is set class MyTimer(threading.Thread): """This class count down timer and move dial back to the default position""" def __init__(self, hours: QtWidgets.QLabel, minutes: QtWidgets.QLabel, seconds: QtWidgets.QLabel, dial: QtWidgets.QDial, set_default): threading.Thread.__init__(self) self.counter = 0 self.dial_controller = True self.fun = set_default self.dial = dial self.hours_label = hours self.minutes_label = minutes self.seconds_label = seconds self.seconds_label.show() self.hours = int(hours.text()) self.min = int(minutes.text()) self.sec = 59 self.daemon = True # True: if the main thread is killed this thread will be killed too self.stopped = threading.Event() self.interval = timedelta(seconds=1) self.execute = self.count_down def count_down(self): self.sec -= 1 self.seconds_label.setText(str(self.sec)) if self.sec == 0: self.sec = 59 if self.counter == 60: self.min -= 1 if self.min == -1: if self.hours > 0: self.hours -= 1 self.min = 59 self.hours_label.setText(str(self.hours)) else: """show message time left""" self.times_up() self.stop() self.dial.setValue(self.hours * 60 + self.min) self.minutes_label.setText(str(self.min)) self.counter = 0 self.counter += 1 def times_up(self): self.back_to_default() self.fun() playsound("Sounds/alarm2.mp3", False) toaster = win10toast.ToastNotifier() toaster.show_toast("Timer", "Times's up!", icon_path="Graphic/timer_icon.ico", duration=5) def back_to_default(self): global isActive isActive = False self.seconds_label.setText("59") self.dial.setDisabled(False) self.seconds_label.hide() def stop(self): self.back_to_default() self.stopped.set() self.join() def run(self): while not self.stopped.wait(self.interval.total_seconds()): try: self.execute() except RuntimeError: """This exception is rised when progrem is closed""" self.stopped.set()

31.471264

139

0.565741

318

2,738

4.764151

0.333333

0.047525

0.052805

0.037624

0.048845

0.033003

0

0.016584

0.339299

2,738

86

140

31.837209

0.820896

0.065376

0

0.151515

0

0.023909

0.009228

0

1

0.090909

false

0

0.090909

0

0.19697

0

null

0

null

0

1

0

7853fc867b9268e27df1fa9c7f6378f0521175e4

7,310

py

Python

untitled.py

czyczyyzc/MyMaskRCNN

e5a451fd05c593ae05d6e596813fc63aad7af2de

[ "MIT" ]

1

2020-10-16T08:10:12.000Z

untitled.py

czyczyyzc/MyMaskRCNN

e5a451fd05c593ae05d6e596813fc63aad7af2de

[ "MIT" ]

null

untitled.py

czyczyyzc/MyMaskRCNN

e5a451fd05c593ae05d6e596813fc63aad7af2de

[ "MIT" ]

null

BACKBONE = "resnet101" BACKBONE_STRIDES = [4, 8, 16, 32, 64] POST_NMS_ROIS_TRAINING = 2000 POST_NMS_ROIS_INFERENCE = 1000 RPN_NMS_THRESHOLD = 0.7 POOL_SIZE = 7 MASK_POOL_SIZE = 14 TRAIN_BN = False # Defaulting to False since batch size is often small FPN_CLASSIF_FC_LAYERS_SIZE = 1024 TRAIN_ROIS_PER_IMAGE = 200 ROI_POSITIVE_RATIO = 0.33 MASK_SHAPE = [28, 28] RPN_BBOX_STD_DEV = np.array([0.1, 0.1, 0.2, 0.2]) BBOX_STD_DEV = np.array([0.1, 0.1, 0.2, 0.2]) DETECTION_MAX_INSTANCES = 100 DETECTION_MIN_CONFIDENCE = 0.7 DETECTION_NMS_THRESHOLD = 0.3 RPN_ANCHOR_SCALES = (32, 64, 128, 256, 512) RPN_ANCHOR_RATIOS = [0.5, 1, 2] RPN_ANCHOR_STRIDE = 1 RPN_NMS_THRESHOLD = 0.7 BACKBONE_STRIDES = [4, 8, 16, 32, 64] FPN_CLASSIF_FC_LAYERS_SIZE = 1024 TOP_DOWN_PYRAMID_SIZE = 256 import numpy as np import tensorflow as tf from .bbox import * class BBoxesLayer(object): def __init__(self, img_shp=None, img_num=None): self.img_shp = img_shp self.img_num = img_num self.box_siz_min = 5 self.box_prb_min = 0.5 self.box_nms_pre = None self.box_nms_pst = 100 #200 self.box_nms_max = 0.3 #0.2 self.box_msk_min = 0.5 self.box_msk_siz = [28, 28] def generate_boxs(self, rois=None, roi_prbs_pst=None, roi_prds_pst=None, roi_imxs=None): #取出最佳类的预测值 box_clss = tf.argmax(roi_prbs_pst, axis=1) box_clss = tf.cast(box_clss, tf.int32) box_prbs = tf.reduce_max(roi_prbs_pst, axis=1) #设置一个box索引，避免大量的gather操作(prds、msks)，节省内存，提升速度 box_idxs = tf.range(tf.shape(rois)[0]) #剔除背景box idxs = tf.where(box_clss>0) box_clss = tf.gather_nd(box_clss, idxs) box_prbs = tf.gather_nd(box_prbs, idxs) box_idxs = tf.gather_nd(box_idxs, idxs) #剔除得分较低的box if self.box_prb_min is not None: idxs = tf.where(box_prbs>=self.box_prb_min) box_clss = tf.gather_nd(box_clss, idxs) box_prbs = tf.gather_nd(box_prbs, idxs) box_idxs = tf.gather_nd(box_idxs, idxs) #根据box_idxs进行剩余的gather操作 rois = tf.gather(rois, box_idxs) box_imxs = tf.gather(roi_imxs, box_idxs) box_idxs = tf.stack([box_idxs, box_clss], axis=-1) #如果box的预测是定类的话要加上这句 roi_prds_pst = tf.gather(roi_prds_pst, box_idxs) #还原出box以进行后续的滤除 boxs = bbox_transform_inv(rois, roi_prds_pst) boxs = bbox_clip(boxs, [0.0, 0.0, self.img_shp[0]-1.0, self.img_shp[1]-1.0]) #剔除过小的box idxs = bbox_filter(boxs, self.box_siz_min) boxs = tf.gather_nd(boxs, idxs) box_clss = tf.gather_nd(box_clss, idxs) box_prbs = tf.gather_nd(box_prbs, idxs) box_imxs = tf.gather_nd(box_imxs, idxs) #做逐img逐cls的nms #设置一个box索引，避免大量的concat操作(boxs、clss、prbs、imxs)，节省内存，提升速度 box_idxs = tf.zeros(shape=[0], dtype=tf.int32) def cond0(i, boxs, box_clss, box_prbs, box_imxs, box_idxs): c = tf.less(i, self.img_num) return c def body0(i, boxs, box_clss, box_prbs, box_imxs, box_idxs): box_idxs_img = tf.where(tf.equal(box_imxs, i)) boxs_img = tf.gather_nd(boxs, box_idxs_img) #和box_idxs_img对应 box_clss_img = tf.gather_nd(box_clss, box_idxs_img) box_prbs_img = tf.gather_nd(box_prbs, box_idxs_img) #进一步剔除过多的roi if self.box_nms_pre is not None: box_nms_pre = tf.minimum(self.box_nms_pre, tf.shape(boxs_img)[0]) box_prbs_img, idxs = tf.nn.top_k(box_prbs_img, k=box_nms_pre, sorted=True) boxs_img = tf.gather(boxs_img, idxs) box_clss_img = tf.gather(box_clss_img, idxs) box_idxs_img = tf.gather(box_idxs_img, idxs) ##################################### box_idxs_kep = tf.zeros(shape=[0], dtype=tf.int32) box_clss_unq, idxs = tf.unique(box_clss_img) def cond1(j, boxs_img, box_clss_img, box_prbs_img, box_clss_unq, box_idxs_kep): box_cls_num = tf.shape(box_clss_unq)[0] c = tf.less(j, box_cls_num) return c def body1(j, boxs_img, box_clss_img, box_prbs_img, box_clss_unq, box_idxs_kep): #选出对应类的rois box_cls = box_clss_unq[j] box_idxs_cls = tf.where(tf.equal(box_clss_img, box_cls)) boxs_cls = tf.gather_nd(boxs_img, box_idxs_cls) box_prbs_cls = tf.gather_nd(box_prbs_img, box_idxs_cls) #进行非极大值抑制操作 idxs = tf.image.non_max_suppression(boxs_cls, box_prbs_cls, self.box_nms_pst, self.box_nms_max) box_idxs_cls = tf.gather(box_idxs_cls, idxs) # 保存结果 box_idxs_kep = tf.concat([box_idxs_kep, box_idxs_cls], axis=0) return [j+1, boxs_img, box_clss_img, box_prbs_img, box_clss_unq, box_idxs_kep] j = tf.constant(0) [j, boxs_img, box_clss_img, box_prbs_img, box_clss_unq, box_idxs_kep] = \ tf.while_loop(cond1, body1, loop_vars=[j, boxs_img, box_clss_img, box_prbs_img, box_clss_unq, box_idxs_kep], \ shape_invariants=[j.get_shape(), boxs_img.get_shape(), box_clss_img.get_shape(), \ box_prbs_img.get_shape(), box_clss_unq.get_shape(), tf.TensorShape([None])], \ parallel_iterations=10, back_prop=False, swap_memory=True) box_prbs_img = tf.gather(box_prbs_img, box_idxs_kep) box_idxs_img = tf.gather(box_idxs_img, box_idxs_kep) box_num_img = tf.minimum(self.box_nms_pst, tf.shape(box_idxs_img)[0]) box_prbs_img, idxs = tf.nn.top_k(box_prbs_img, k=box_num_img, sorted=True) box_idxs_img = tf.gather(box_idxs_img, idxs) # 保存结果 box_idxs = tf.concat([box_idxs, box_idxs_img], axis=0) return [i+1, boxs, box_clss, box_prbs, box_imxs, box_idxs] i = tf.constant(0) [i, boxs, box_clss, box_prbs, box_imxs, box_idxs] = \ tf.while_loop(cond, body, loop_vars=[i, boxs, box_clss, box_prbs, box_imxs, box_idxs], \ shape_invariants=[i.get_shape(), boxs.get_shape(), box_clss.get_shape(), \ box_prbs.get_shape(), box_imxs.get_shape(), tf.TensorShape([None])], \ parallel_iterations=10, back_prop=False, swap_memory=True) boxs = tf.gather_nd(boxs, box_idxs) box_clss = tf.gather_nd(box_clss, box_idxs) box_prbs = tf.gather_nd(box_prbs, box_idxs) box_imxs = tf.gather_nd(box_imxs, box_idxs) return boxs, box_clss, box_prbs, box_imxs def generate_msks(self, boxs=None, box_clss=None, box_msks_pst=None): return

41.067416

126

0.583174

1,073

7,310

3.603914

0.16589

0.090509

0.049134

0.050427

0.471683

0.359969

0.331006

0.285751

0.250323

0.225498

0

0.031344

0.314774

7,310

178

127

41.067416

0.740667

0.042818

0

0.168067

0

0.001297

0

1

0.058824

false

0

0.02521

0.008403

0.142857

0

null

0

null

0

1

0

7854206db388f69d88e15202d41b4806273c060f

6,198

py

Python

skm_tea/engine/trainer.py

StanfordMIMI/skm-tea

5678bfcebad4fdc30de62b319d96ec1775e1671c

[ "MIT" ]

26

2021-08-28T06:57:50.000Z

2022-02-17T06:33:41.000Z

skm_tea/engine/trainer.py

StanfordMIMI/skm-tea

5678bfcebad4fdc30de62b319d96ec1775e1671c

[ "MIT" ]

6

2021-10-20T16:04:12.000Z

2022-03-15T20:16:52.000Z

skm_tea/engine/trainer.py

StanfordMIMI/skm-tea

5678bfcebad4fdc30de62b319d96ec1775e1671c

[ "MIT" ]

4

2021-11-15T08:32:41.000Z

2022-02-23T18:54:30.000Z

import logging import os import pytorch_lightning as pl from meddlr.config.config import CfgNode from meddlr.engine.trainer import convert_cfg_time_to_iter as _convert_cfg_time_to_iter from meddlr.engine.trainer import format_as_iter from meddlr.utils import env from meddlr.utils.env import supports_wandb from pytorch_lightning.callbacks import EarlyStopping from pytorch_lightning.loggers import CSVLogger from pytorch_lightning.profiler import SimpleProfiler from pytorch_lightning.utilities.distributed import rank_zero_only from skm_tea.callbacks import PLPeriodicCheckpointer from skm_tea.utils.pl_utils import LoggerCollection, TensorBoardLogger, WandbLogger __all__ = ["PLDefaultTrainer"] def convert_cfg_time_to_iter(cfg: CfgNode, iters_per_epoch: int): """Convert all config time-related parameters to iterations. Note: When adding to this list, be careful not to convert config parameters multiple times. """ time_scale = cfg.TIME_SCALE cfg = _convert_cfg_time_to_iter(cfg.clone(), iters_per_epoch, ignore_missing=True).defrost() cfg.SOLVER.EARLY_STOPPING.PATIENCE = format_as_iter( cfg.SOLVER.EARLY_STOPPING.PATIENCE, iters_per_epoch, time_scale ) cfg.TIME_SCALE = "iter" cfg.freeze() return cfg class PLDefaultTrainer(pl.Trainer): def __init__( self, cfg, iters_per_epoch: int, log_gpu_memory=None, replace_sampler_ddp=False, num_gpus=0, resume=False, eval_only=False, **kwargs, ): logger = logging.getLogger("skm_tea") self.eval_only = eval_only if "limit_train_batches" in kwargs: iters_per_epoch = kwargs["limit_train_batches"] cfg = convert_cfg_time_to_iter(cfg, iters_per_epoch) self.cfg = cfg callbacks = self.build_callbacks() # includes user-specified callbacks kwargs["callbacks"] = callbacks if resume: assert not kwargs.get( "resume_from_checkpoint", None ), "Cannot specify resume=True and resume_from_checkpoint" resume_from_checkpoint = self.configure_resume(callbacks) logger.info(f"Resuming from checkpoint {resume_from_checkpoint}") kwargs["resume_from_checkpoint"] = resume_from_checkpoint early_stopping_callback = self.build_early_stopping(iters_per_epoch) if early_stopping_callback: callbacks.append(early_stopping_callback) # Hacky way to get around the definition of "step" as optimizer.step in pt-lightning. # Without this the training time would be scaled by a factor of SOLVER.GRAD_ACCUM_ITERS. max_steps = cfg.SOLVER.MAX_ITER // cfg.SOLVER.GRAD_ACCUM_ITERS # Default arguments based on Trainer. Any keyword args provided will overwrite these. args = dict( logger=self.build_logger() if not self.eval_only else False, default_root_dir=cfg.OUTPUT_DIR, max_steps=max_steps, # TODO Issue #4406: https://github.com/PyTorchLightning/pytorch-lightning/issues/4406 val_check_interval=min( cfg.TEST.EVAL_PERIOD, kwargs.get("limit_train_batches", float("inf")) ), accumulate_grad_batches=cfg.SOLVER.GRAD_ACCUM_ITERS, log_gpu_memory=log_gpu_memory, checkpoint_callback=False, sync_batchnorm=False, profiler=SimpleProfiler(dirpath=cfg.OUTPUT_DIR, filename="profile.txt"), log_every_n_steps=5, replace_sampler_ddp=replace_sampler_ddp, deterministic=env.is_repro(), ) if num_gpus > 0: args.update({"gpus": num_gpus, "auto_select_gpus": True}) args.update(kwargs) super().__init__(**args) def build_early_stopping(self, iters_per_epoch): monitor = self.cfg.SOLVER.EARLY_STOPPING.MONITOR patience = self.cfg.SOLVER.EARLY_STOPPING.PATIENCE min_delta = self.cfg.SOLVER.EARLY_STOPPING.MIN_DELTA if patience == 0: return False patience = patience / iters_per_epoch assert ( self.cfg.TIME_SCALE == "iter" and patience > 0 and int(patience) == patience ), f"Got time scale '{self.cfg.TIME_SCALE}' and patience '{patience}'" return EarlyStopping(monitor=monitor, min_delta=min_delta, patience=patience, verbose=True) @rank_zero_only def build_logger(self): cfg = self.cfg version = "" loggers = [ CSVLogger(cfg.OUTPUT_DIR, name="", version=version), TensorBoardLogger(cfg.OUTPUT_DIR, name="", version=version, log_graph=False), ] if supports_wandb(): import wandb loggers.append(WandbLogger(experiment=wandb.run)) return LoggerCollection(loggers) def build_callbacks(self, **kwargs): """Append default callbacks to list of user-defined callbacks.""" cfg = self.cfg callbacks = list(kwargs.get("callbacks", [])) if "checkpoint_callback" not in kwargs and not any( isinstance(x, PLPeriodicCheckpointer) for x in callbacks ): callbacks.append( PLPeriodicCheckpointer( frequency=cfg.SOLVER.CHECKPOINT_PERIOD, filepath=os.path.join(cfg.OUTPUT_DIR, "{global_step:07d}-{epoch:03d}"), save_after_val=True, ) ) return callbacks def configure_resume(self, callbacks): """Configure setup for resume. Currently finds the latest epoch and resumes from there. """ # cfg = self.cfg checkpointer = [x for x in callbacks if isinstance(x, PLPeriodicCheckpointer)] if len(checkpointer) == 0: raise ValueError("Resuming training only works with PLPeriodicCheckpointer") elif len(checkpointer) > 1 and any( cp.dirpath != checkpointer[0].dirpath for cp in checkpointer ): raise ValueError("Found more than one checkpointer with different save directories") return checkpointer[0].get_latest()

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0.303935

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0.231405

0

null

0

null

0

1

0

78542c8e023300ee416e5d670aa78aae94c4eae7

1,189

py

Python

uw_canvas/tests/test_quizzes.py

uw-it-aca/uw-restclients-canvas

2c54d7676649ec18129817992890878ace1ec6c6

[ "Apache-2.0" ]

1

2019-11-26T21:38:50.000Z

uw_canvas/tests/test_quizzes.py

uw-it-aca/uw-restclients-canvas

2c54d7676649ec18129817992890878ace1ec6c6

[ "Apache-2.0" ]

135

2017-04-04T23:11:26.000Z

2021-05-28T17:00:20.000Z

uw_canvas/tests/test_quizzes.py

uw-it-aca/uw-restclients-canvas

2c54d7676649ec18129817992890878ace1ec6c6

[ "Apache-2.0" ]

2

2020-05-20T20:36:55.000Z

2022-03-05T00:23:44.000Z

# Copyright 2021 UW-IT, University of Washington # SPDX-License-Identifier: Apache-2.0 from unittest import TestCase from uw_canvas.utilities import fdao_canvas_override from uw_canvas.quizzes import Quizzes from uw_canvas.models import Quiz @fdao_canvas_override class CanvasTestQuizzes(TestCase): def test_quizzes_by_course_id(self): canvas = Quizzes() submissions = canvas.get_quizzes("862539") sub = submissions[0] self.assertEquals(sub.quiz_id, 762037, "Has correct quiz id") self.assertEquals(sub.published, True, "Is published") self.assertEquals(sub.due_at.day, 1, "due at datetime") def test_quizzes_by_sis_id(self): canvas = Quizzes() submissions = canvas.get_quizzes_by_sis_id("2013-autumn-PHYS-248-A") self.assertEquals(len(submissions), 1, "Submission Count") def test_quiz_without_due_date(self): quiz = Quiz(data={ "id": "1", "title": "title", "html_url": "http://...", "published": False, "points_possible": 0, }) self.assertEquals(quiz.title, "title") self.assertEquals(quiz.due_at, None)

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5.108108

0.459459

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0.047619

0.042328

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1,189

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0

0.296296

0

null

0

null

0

1

0

7859d6c0896c5f80478ef39ac42a3fbaba78584d

4,554

py

Python

diofant/solvers/utils.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

null

diofant/solvers/utils.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

null

diofant/solvers/utils.py

rajkk1/diofant

6b361334569e4ec2e8c7d30dc324387a4ad417c2

[ "BSD-3-Clause" ]

null

"""General utility functions for solvers.""" import warnings from ..core import (expand_mul, expand_multinomial, nan, oo, preorder_traversal, zoo) from ..core.sympify import sympify from ..simplify.simplify import posify, simplify __all__ = 'checksol', def checksol(f, sol, **flags): r"""Checks whether sol is a solution of equations f. Examples ======== >>> checksol(x**4 - 1, {x: 1}) True >>> checksol(x**4 - 1, {x: 0}) False >>> checksol(x**2 + y**2 - 5**2, {x: 3, y: 4}) True Returns ======= bool or None Return True, if solution satisfy all equations in ``f``. Return False, if a solution doesn't satisfy any equation. Else (i.e. one or more checks are inconclusive), return None. Parameters ========== f : Expr or iterable of Expr's Equations to substitute solutions in. sol : dict of Expr's Mapping of symbols to values. \*\*flags : dict A dictionary of following parameters: minimal : bool, optional Do a very fast, minimal testing. Default is False. warn : bool, optional Show a warning if it could not conclude. Default is False. simplify : bool, optional Simplify solution before substituting into function and simplify the function before trying specific simplifications. Default is True. force : bool, optional Make positive all symbols without assumptions regarding sign. Default is False. """ minimal = flags.get('minimal', False) if not isinstance(sol, dict): raise ValueError(f'Expecting dictionary but got {sol}') if sol and not f.has(*list(sol)): # if f(y) == 0, x=3 does not set f(y) to zero...nor does it not if f.is_Number: return f.is_zero else: return illegal = {nan, zoo, oo, -oo} if any(sympify(v).atoms() & illegal for k, v in sol.items()): return False was = f attempt = -1 while 1: attempt += 1 if attempt == 0: val = f.subs(sol) if val.atoms() & illegal: return False elif attempt == 1: assert val.free_symbols if not val.is_constant(*list(sol), simplify=not minimal): return False # there are free symbols -- simple expansion might work _, val = val.as_content_primitive() val = expand_mul(expand_multinomial(val)) elif attempt == 2: if minimal: return if flags.get('simplify', True): for k in sol: sol[k] = simplify(sol[k]) # start over without the failed expanded form, possibly # with a simplified solution val = simplify(f.subs(sol)) if flags.get('force', True): val, reps = posify(val) # expansion may work now, so try again and check exval = expand_mul(expand_multinomial(val)) if exval.is_number or not exval.free_symbols: # we can decide now val = exval else: # if there are no radicals and no functions then this can't be # zero anymore -- can it? pot = preorder_traversal(expand_mul(val)) seen = set() saw_pow_func = False for p in pot: if p in seen: continue seen.add(p) if p.is_Pow and not p.exp.is_Integer: saw_pow_func = True elif p.is_Function: saw_pow_func = True if saw_pow_func: break if saw_pow_func is False: return False if flags.get('force', True): # don't do a zero check with the positive assumptions in place val = val.subs(reps) val # XXX "peephole" optimization, http://bugs.python.org/issue2506 break if val == was: continue elif val.is_Rational: return val == 0 elif val.is_nonzero: return False if not val.free_symbols: return bool(abs(val.evalf(18, strict=False).evalf(12, chop=True)) < 1e-9) was = val if flags.get('warn', False): warnings.warn(f'\n\tWarning: could not verify solution {sol}.')

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0

0.197368

0

null

0

null

0

1

0

78607ee463c1efbeda346568d754d388788b0a46

16,798

py

Python

client/admin.py

AhmedElmawary/erp

c998787c62194e26e10e3cbc61e35935e901e56d

[ "MIT" ]

null

client/admin.py

AhmedElmawary/erp

c998787c62194e26e10e3cbc61e35935e901e56d

[ "MIT" ]

null

client/admin.py

AhmedElmawary/erp

c998787c62194e26e10e3cbc61e35935e901e56d

[ "MIT" ]

null

import os from django.template.response import TemplateResponse from _helpers.common import make_list_of_lists from app_user.models import ClosingPeriod from datetime import datetime from django.utils import timezone from django.core.paginator import Paginator from django.db.models.query import QuerySet from django.template.loader import render_to_string from num2words import num2words from xhtml2pdf import pisa from _helpers.models import areas_ar_en, find_in, get_currency, get_payment_type, modified_num2words from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union from django.http.request import HttpHeaders, HttpRequest from django.http.response import HttpResponse, HttpResponseRedirect from django.urls.base import reverse from django.urls.conf import path from django.urls.resolvers import URLPattern from django.utils.html import format_html from payment.models import ClientPaymentTransaction, PaymentTransactionType from django.contrib import admin from .models import Client from django.utils.translation import gettext, ugettext as _, ugettext_lazy import xlsxwriter from _helpers.admin import Amount, ClientHelper, CommonMethods, ConsumerTransaction,ConsumerTransactionDownloder, admin_client_download_transaction_pdf, admin_supplier_download_transaction_pdf, make_xls_data, make_xls_headers, str_to_date from zipfile import ZipFile class ClientAdmin(admin.ModelAdmin): class Media: js = ( 'client_transactions.js', 'client_account_statment.js' ) css = { "all":('client_admin.css',) } model = Client search_fields = ['name', 'phone', 'email'] extra = 1 list_display_links = [ 'name' ] list_display = [ 'id', 'name', 'phone', 'is_active', 'taxes', # 'make_transaction', 'parsed_get_debit', 'parsed_get_credit', 'parsed_get_net', 'account_statment_from', 'account_statment_to', 'account_statment_btn', ] add_fieldsets = ( (ugettext_lazy('Main info'), { 'classes': ("wide",), "fields": ( _('id'), _('name'), _('email'), _('phone'), _('gender'), _('img'), ), }), (ugettext_lazy("Location"), { 'classes': ('collapse', 'wide'), 'fields': ( _('country'), _('area'), _('city'), _('address'), ) }) ,(ugettext_lazy('Cash'), { 'classes': ('collapse', 'wide'), "fields" : ( _('debit'), _('credit'), _('get_cash'), _('cash') ) }),(ugettext_lazy('Taxes'), { "classes": ('collapse', 'wide'), 'fields' : ( _('taxes'), _('taxes_rate'), ) }), (ugettext_lazy('Status'), {'classes': ('collapse',),"fields": ('is_active',)}) ) fieldsets = ( (ugettext_lazy('Main info'), { 'classes': ("wide",), "fields": ( _('id'), _('name'), _('email'), _('phone'), _('gender'), _('img'), ), }), (ugettext_lazy("Location"), { 'classes': ('collapse', 'wide'), 'fields': ( _('country'), _('area'), _('city'), _('address'), ) }) ,(ugettext_lazy('Cash'), { 'classes': ('collapse', 'wide'), "fields" : ( _('debit'), _('credit'), _('get_cash'), _('period_close'), ) }),(ugettext_lazy('Taxes'), { "classes": ('collapse', 'wide'), 'fields' : ( _('taxes'), _('taxes_rate'), ) }), (ugettext_lazy('Status'), {'classes': ('collapse',),"fields": ('is_active',)}) ) search_fields = ['name', 'phone', 'email','area__name', 'city'] list_filter = ('is_active', 'taxes') list_per_page = 20 actions = [ _('activate'), _('deactivate'), _('export_as_xls'), _('export_invoices_for'), ] change_form_template = 'admin/client/client/custom_change_form.html' change_list_template = 'admin/client/client/custom_change_list.html' def activate(self, request, queryset): client_no = queryset.update(is_active=True) supplier_string = 'clients have' if client_no < 1 else 'client has' self.message_user(request, f'{client_no} {supplier_string} activated successfully') activate.short_description = ugettext_lazy('Activate selected clients') def deactivate(self, request, queryset): client_no = queryset.update(is_active=False) supplier_string = 'clients have' if client_no < 1 else 'client has' self.message_user(request, f'{client_no} {supplier_string} activated successfully') deactivate.short_description = ugettext_lazy('Deactivate selected clinets') def export_invoices_for(self, request, queryset): response = HttpResponse(content_type='application/vnd.ms-excel') xls_sheet = xlsxwriter.Workbook(response) headers_format = xls_sheet.add_format() headers_format.set_font_shadow() headers_format.set_bg_color('gray') headers_format.set_border(1) headers_format.set_font_color('white') headers_format.set_bold() headers_format.set_align('center') headers_format.set_locked(True) headers_format.set_size(15) data_format = xls_sheet.add_format() data_format.set_align('center') data_format.set_bg_color('#8d8894') data_format.set_font_color('#e7e7e7') data_format.set_font_size(14) data_format.set_bold() data_format.set_border() data_format.set_border_color('black') clients = queryset.all() for supplier in clients: qs_values = supplier.transactions.order_by('-id').all() work_sheet = xls_sheet.add_worksheet() make_xls_headers(work_sheet, [ _('issued_at'), _('type_tranasction'), _('amount'), _('description'), _('id'), _('client'), ], headers_format) data_fields_names = [ 'client', 'id', 'description', 'amount', 'type_tranasction', 'issued_at', ] make_xls_data(work_sheet, qs_values, data_fields_names, data_format) xls_sheet.close() return response export_invoices_for.short_description = ugettext_lazy('Export invoices for') def get_search_results(self, request: HttpRequest, queryset: QuerySet, search_term: str) -> Tuple[QuerySet, bool]: area = find_in(areas_ar_en(), search_term) if area: search_term = area[search_term] return super().get_search_results(request, queryset, search_term) def get_readonly_fields(self, request: HttpRequest, obj: Optional["Client"]) -> Union[List[str], Tuple]: readonly_fields = [ 'id', 'make_transaction', 'debit', 'credit', 'period_close', 'get_cash', 'get_net', ] if obj: return readonly_fields+ ['cash'] return readonly_fields def add_view(self, *args, **kwargs): self.fieldsets = self.add_fieldsets return super().add_view(*args, **kwargs) def changeform_view(self, request: HttpRequest, object_id: Optional[str], form_url: str, extra_context: Optional[Dict[str, bool]]) -> Any: transact_to_value = request.COOKIES.get('to_value') transact_from_value = request.COOKIES.get('from_value') today_full = datetime.today().date() extra_context = { "filter_label": _('Filter Transactions'), 'from' :format_html('<label>{}</label>: <input type=date value={} id=transactions_from>', _('from'), today_full), 'to' : format_html(' <label>{}</label>: <input type=date value={} id=transactions_to>', _('to'), today_full), 'trs': [ _('id of transaction'), _('ISSUED AT'), _('TRANSACTION TYPE'), _('Description'), _('debit'), _('credit'), _('balance'), _('VIEW'), _('CSV'), _('PDF'), ], "page": _('Page'), 'of': _('of'), 'next': _('next'), 'previous':_('previous'), 'last': _('last page'), 'first': _('first') } client = self.get_object(request=request,object_id=object_id) if not client: return super().changeform_view(request, object_id=object_id, form_url=form_url, extra_context=extra_context) transactions = client.transactions.all() if transact_to_value: to_date=str_to_date(transact_to_value) from_date=str_to_date(transact_from_value) transactions = transactions.filter(issued_at__gte=from_date).filter(issued_at__lte=to_date).all() extra_context.update(ConsumerTransaction.prepare_tarnsactions_table(request, transactions, 'admin:payment_clientpaymenttransaction_change', consumer= 'client')) return super().changeform_view(request, object_id=object_id, form_url=form_url, extra_context=extra_context) def get_urls(self) -> List[URLPattern]: urls = super().get_urls() urls += [ path('<int:client_id>/make_a_transaction', self.process_make_transaction, ), path('<int:client_id>/change/<int:id>/download/csv', self.download_transaction_csv ,), path('<int:client_id>/change/<int:id>/download/pdf', self.download_transaction_pdf , name='client_transaction_download_pdf'), path('<int:client_id>/change/period_close', self.period_close_controller , name='client_period_close'), path('account-statment/<str:date_from>/<str:date_to>/<str:client_id>', self.account_statment_handler , name='client_account_statment'), ] return urls def account_statment_handler(self, request, date_from, date_to, client_id): client = self.get_object(request, client_id) return CommonMethods.account_statment_pdf( date_from=date_from, date_to=date_to, consumer_obj=client, request=request ) def period_close_controller(self, request, client_id): client = self.get_object(request, client_id) transactions = client.transactions return CommonMethods.make_peroid_close(transactions, client) def changelist_view(self, request: HttpRequest, extra_context: Optional[Dict[str, str]]=None) -> TemplateResponse: extra_context = { 'to': _('to'), 'from': _('from'), 'export': _('export'), "account_stament_label": _('account statment') } response = super().changelist_view(request, extra_context=extra_context) if request.COOKIES.get('client_id'): response.delete_cookie('client_id') return response def process_make_transaction(self, request, **kwargs): url = reverse("admin:payment_clientpaymenttransaction_add") response = HttpResponseRedirect(url) response.set_cookie('client_id', kwargs.get('client_id')) return response def get_queryset(self, request: HttpRequest) -> QuerySet: client_id = request.COOKIES.get('client_id') if not client_id: return super().get_queryset(request) client_list_path = reverse('admin:client_client_changelist') queryset = super().get_queryset(request) if not request.path == client_list_path: queryset = queryset.filter(id=client_id) return queryset def download_transaction_pdf(self, request, **kwargs): transaction = ClientPaymentTransaction.objects.get(id=kwargs['id']) return admin_client_download_transaction_pdf(transaction, request) def download_transaction_csv(self, *args_, **kwargs): url = reverse('admin:client_transaction_download_csv', args=[kwargs['id']]) return HttpResponseRedirect(url) def has_delete_permission(self, request, obj=None): return False def activate_clients(self, request, queryset): clinets_no = queryset.update(is_active=True) client_string = 'clients have' if clinets_no < 1 else 'client has' self.message_user(request, f'{clinets_no} {client_string} activated successfully') def dactivate_clients(self, request, queryset): clinets_no = queryset.update(is_active=False) client_string = 'clients have' if clinets_no < 1 else 'client has' self.message_user(request, f'{clinets_no} {client_string} deactivated successfully') def get_action(self, action: Union[Callable, str]) -> Tuple[Callable, str, str]: return super().get_action(action) def save_model(self, request: Any, client: "Client", form: Any, change: Any) -> None: super().save_model(request, client, form, change) if (not client.cash == 0) and (change == False): if not ClientPaymentTransaction.objects.filter(client_id=client.pk).exists(): if client.cash < 0: try: type_tranasction = PaymentTransactionType.objects.get(name=_('Opening account')) except PaymentTransactionType.DoesNotExist: type_tranasction = PaymentTransactionType.objects.create( name=_('Opening account'), transaction_for=2, ) ClientPaymentTransaction.objects.create( amount=abs(client.cash), client=client, type_tranasction=type_tranasction, payment_type=2, issued_by = request.user ) return try: type_tranasction = PaymentTransactionType.objects.get(name=_('Opening account')) except PaymentTransactionType.DoesNotExist: type_tranasction = PaymentTransactionType.objects.create( name=_('Opening account'), transaction_for=1, ) ClientPaymentTransaction.objects.create( amount=client.cash, client=client, type_tranasction=type_tranasction, payment_type=1, issued_by = request.user ) def export_as_xls(self, request, queryset): response = HttpResponse(content_type='application/vnd.ms-excel') xls = xlsxwriter.Workbook(response) work_sheet = xls.add_worksheet() headers_format = xls.add_format() headers_format.set_font_shadow() headers_format.set_bg_color('gray') headers_format.set_border(1) headers_format.set_font_color('white') headers_format.set_bold() headers_format.set_align('center') headers_format.set_locked(True) headers_format.set_size(15) data_format = xls.add_format() data_format.set_align('center') data_format.set_bg_color('#8d8894') data_format.set_font_color('#e7e7e7') data_format.set_font_size(14) data_format.set_bold() data_format.set_border() data_format.set_border_color('black') sheet_headers = [ _('city'), _('address'), _('phone'), _('email'), _('id'), _('name'), ] qs_values = queryset.order_by('-id').all() make_xls_headers(work_sheet, sheet_headers, headers_format) data_fields_names = [ 'name', 'id', 'email', 'phone', 'address', 'city', ] make_xls_data(work_sheet, qs_values, data_fields_names, data_format) xls.close() return response export_as_xls.short_description = ugettext_lazy("Export Selected as xls") admin.site.register(Client, ClientAdmin)

35.816631

238

0.589177

1,692

16,798

5.529551

0.160165

0.028858

0.027362

0.016032

0.416097

0.354853

0.345019

0.337751

0.318298

0

0.003626

0.294023

16,798

468

239

35.893162

0.785311

0.001131

0

0.379397

0

0.154318

0.040055

0

1

0.052764

false

0

0.065327

0.005025

0.20603

0

null

0

null

0

1

0

786303dad3d68e212f04a894e451f0bed72ebf96

949

py

Python

mountainproject/util/util.py

calebwang/mountainproject

6a986d33a1e44710308e66eea77b66167a0ef2a7

[ "MIT" ]

null

mountainproject/util/util.py

calebwang/mountainproject

6a986d33a1e44710308e66eea77b66167a0ef2a7

[ "MIT" ]

null

mountainproject/util/util.py

calebwang/mountainproject

6a986d33a1e44710308e66eea77b66167a0ef2a7

[ "MIT" ]

null

import itertools def chunk(iterable, n): it = iter(iterable) cls = list chunk = cls(itertools.islice(it, n)) while chunk: yield chunk chunk = cls(itertools.islice(it, n)) def map_chunk(iterable, n, f_chunk): """ Map over iterable in chunks of size n, applying f_chunk to each chunk, and then flattening the result back into the original shape of iterable """ cls = iterable.__class__ it_result = itertools.chain.from_iterable( f_chunk(c) for c in chunk(iterable, n) ) return cls(it_result) def paginator(page_getter, page_limit, n): start_pos = 0 num_results = 0 while True: page = page_getter(start_pos) yield page page_size = len(page) num_results += page_size start_pos += page_size if num_results >= n or page_size < page_limit: return def paginate(page_getter, page_limit, n): return list(itertools.chain.from_iterable(paginator(page_getter, page_limit, n)))

24.333333

83

0.700738

145

949

4.37931

0.358621

0.062992

0.066142

0.089764

0.204724

0.173228

0

0.00266

0.207587

949

38

84

24.973684

0.841755

0.150685

0

0.074074

0

1

0.148148

false

0

0.037037

0.296296

0

null

0

null

0

1

0

78652027358f752a4fa3b5d361b4256d3dc48763

5,372

py

Python

src/psypose/MEVA/scripts/eval_vae.py

scraplab/psypose

81753e29b78023b8a7c48356ec54c67b7182c183

[ "MIT" ]

null

src/psypose/MEVA/scripts/eval_vae.py

scraplab/psypose

81753e29b78023b8a7c48356ec54c67b7182c183

[ "MIT" ]

1

2021-10-13T16:27:34.000Z

src/psypose/MEVA/scripts/eval_vae.py

scraplab/psypose

81753e29b78023b8a7c48356ec54c67b7182c183

[ "MIT" ]

null

import glob import os import sys import pdb import os.path as osp sys.path.append(os.getcwd()) import math import pickle as pk import argparse import time from torch import optim from torch.utils.tensorboard import SummaryWriter from tqdm import tqdm import joblib from khrylib.utils import * from meva.utils.config import Config from meva.lib.model import * from meva.utils.transform_utils import * from meva.utils.image_utils import * from meva.lib.smpl import SMPL, SMPL_MODEL_DIR, H36M_TO_J14, SMPL_MEAN_PARAMS from meva.utils.video_config import MEVA_DATA_DIR from meva.utils.eval_utils import ( compute_accel, compute_error_accel, compute_error_verts, batch_compute_similarity_transform_torch, smpl_to_joints, compute_metric_on_seqs ) from copycat.smpllib.smpl_mujoco import SMPL_M_Renderer if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--gpu_index", type=int, default=0) parser.add_argument("--cfg", default=None) parser.add_argument("--image_size", action="store_true", default=400) parser.add_argument("--render", action="store_true", default=False) parser.add_argument("--iter", type=int, default=-2) args = parser.parse_args() dtype = torch.float32 torch.set_default_dtype(dtype) cfg_name = args.cfg cfg = Config(args.cfg) gpu_index = args.gpu_index device = torch.device('cuda', index=gpu_index) image_size = args.image_size has_smpl_root = cfg.data_specs['has_smpl_root'] model, _, run_batch = get_models(cfg, iter = args.iter) model.to(device) model.eval() smpl = SMPL( SMPL_MODEL_DIR, batch_size=50, create_transl=False, dtype = dtype ).to(device) J_regressor = torch.from_numpy(np.load(osp.join(MEVA_DATA_DIR, 'J_regressor_h36m.npy'))).float() output_base = "/hdd/zen/data/ActmixGenenerator/output/3dpw" output_path = osp.join(output_base, cfg_name) if not osp.isdir(output_path): os.makedirs(output_path) dataset_3dpw = joblib.load("/hdd/zen/data/ActBound/AMASS/3dpw_train_res.pkl") # dataset_3dpw = joblib.load("/hdd/zen/data/ActBound/AMASS/3dpw_val_res.pkl") # dataset_3dpw = joblib.load("/hdd/zen/data/ActBound/AMASS/3dpw_test_res.pkl") image_size = 400 total = cfg.data_specs['t_total'] if args.render: # renderer = SMPL_Renderer(device = device, image_size = 400, camera_mode="look_at") renderer = SMPL_M_Renderer(render_size = (image_size, image_size)) eval_recs =[] # eval_vibe =[] idx = 0 for k, v in tqdm(dataset_3dpw.items()): curr_name = v mocap_thetas = v['target_traj'] vibe_thetas = v['traj'] vis_feats = v['feat'] mocap_betas = v['target_beta'] vibe_betas = v['traj_beta'] with torch.no_grad(): vibe_pose = torch.tensor(vibe_thetas).squeeze().to(device) mocap_pose = torch.tensor(mocap_thetas).squeeze().to(device) vis_feats = torch.tensor(vis_feats).squeeze().to(device) vibe_betas = torch.tensor(vibe_betas).squeeze().to(device) mocap_betas = torch.tensor(mocap_betas).squeeze().to(device) mocap_pose_6d = convert_aa_to_orth6d(mocap_pose).reshape(-1, 144) mocap_pose_6d = mocap_pose_6d[None, :].permute(1, 0, 2) vibe_pose_6d = convert_aa_to_orth6d(vibe_pose).reshape(-1, 144) vibe_pose_6d = vibe_pose_6d[None, :].permute(1, 0, 2) vis_feats = vis_feats[None, :].permute(1, 0, 2) mocap_pose_6d_chunks = torch.split(mocap_pose_6d, total, dim=0) vibe_pose_6d_chunks = torch.split(vibe_pose_6d, total, dim=0) vis_feats_chunks = torch.split(vis_feats, total, dim=0) X_r_acc = [] for i in range(len(mocap_pose_6d_chunks)): mocap_pose_chunk = mocap_pose_6d_chunks[i] vibe_pose_chunk = vibe_pose_6d_chunks[i] vis_feats_chunk = vis_feats_chunks[i] label_rl = torch.tensor([[1,0]]).to(device).float() X_r, mu, logvar = model(mocap_pose_chunk) X_r_acc.append(X_r[:mocap_pose_chunk.shape[0]]) X_r = torch.cat(X_r_acc) X_r = X_r.permute(1,0,2) ref_pose_curr_rl = convert_orth_6d_to_aa(X_r.squeeze()) ######## Rendering...... ######## if args.render: mocap_pose = vertizalize_smpl_root(mocap_pose).cpu().numpy() ref_pose_curr_rl = vertizalize_smpl_root(ref_pose_curr_rl).cpu().numpy() tgt_images = renderer.render_smpl(mocap_pose) ref_images = renderer.render_smpl(ref_pose_curr_rl) grid_size = [1,2] videos = [tgt_images, ref_images] descriptions = ["Mocap", "VAE"] output_name = "{}/output_vae{:02d}.mp4".format(output_path, idx) assemble_videos(videos, grid_size, descriptions, output_name) print(output_name) idx += 1 else: eval_acc = compute_metric_on_seqs(ref_pose_curr_rl, mocap_betas, mocap_pose, mocap_betas, smpl, J_regressor=J_regressor) eval_recs.append(eval_acc) print(np.mean(eval_recs, axis = 0))

36.794521

136

0.648734

748

5,372

4.332888

0.256684

0.044431

0.023758

0.020056

0.139463

0.074668

0.060475

0.048133

0

0.019311

0.238459

5,372

145

137

37.048276

0.772916

0.04933

0

0.017544

0

0.053884

0.022222

0

1

0

false

0

0.192982

0

0.192982

0.017544

0

null

0

null

0

1

0

786642d4839cecf971d110875afa5237e48ae23f

475

py

Python

exercise1_6.py

ccie8030/pynet

84be459c6cb50a025a801e3d4b9bd237c698776a

[ "Apache-2.0" ]

1

2016-01-30T03:36:15.000Z

exercise1_6.py

ccie8030/pynet

84be459c6cb50a025a801e3d4b9bd237c698776a

[ "Apache-2.0" ]

null

exercise1_6.py

ccie8030/pynet

84be459c6cb50a025a801e3d4b9bd237c698776a

[ "Apache-2.0" ]

null

import yaml import json def main(): yaml_file = 'my_yaml.yml' json_file = 'my_json.json' net_dict = {'ip_addr' : '192.168.1.214', 'model' : 'wlc', 'manufacturer' : 'Cisco', 'model': '2504'} net_list = ['test_strings','1','2','3', net_dict, 'python', 'neteng'] with open(yaml_file, "w") as f: f.write(yaml.dump(net_list, default_flow_style=False)) with open(json_file, "w") as f: json.dump(net_list, f) main()

18.269231

104

0.581053

71

475

3.676056

0.549296

0.08046

0.05364

0.061303

0

0.046832

0.235789

475

25

105

19

0.672176

0

0.225053

0

1

0.083333

false

0

0.166667

0

0.25

0

null

0

null

0

1

0

786662f7fd60f019ba19ea1741f61dafe91fb64a

1,978

py

Python

Structral Similarity/stsim_2.py

Michelle0903/Performance-Comparison-of-Structural-Similarity-Metrics

c2c409eefe335e4946ca895ad1d22b4930263819

[ "MIT" ]

null

Structral Similarity/stsim_2.py

Michelle0903/Performance-Comparison-of-Structural-Similarity-Metrics

c2c409eefe335e4946ca895ad1d22b4930263819

[ "MIT" ]

null

Structral Similarity/stsim_2.py

Michelle0903/Performance-Comparison-of-Structural-Similarity-Metrics

c2c409eefe335e4946ca895ad1d22b4930263819

[ "MIT" ]

null

from perceptual.metric_copy import Metric import cv2 import os import glob import heapq import torch import time from torch.utils.data import Dataset, DataLoader data_dir = "/Users/yuxiao/Desktop/data/Corbis128BigExperiment_gray/" data = glob.glob(data_dir + "*.tiff") class ImgData(Dataset): def __init__(self, k, data): self.data = data self.img1 = cv2.imread(data[k], cv2.IMREAD_GRAYSCALE) def __len__(self): return len(self.data) def __getitem__(self, idx): img2_path = self.data[idx] img2 = cv2.imread(img2_path, cv2.IMREAD_GRAYSCALE) score = m.STSIM2(self.img1, img2) sample = score return sample def del_path(s): (_, temp) = os.path.split(s) return temp def takesecond(elem): return elem[1] m = Metric() res = [] knum = 10 for k in range(len(data)): tmp = [] score = [] img1name = del_path(data[k]) tmp.append(img1name) dataset = ImgData(k, data) #print(len(dataset)) dataloader = DataLoader(dataset, batch_size = 16, shuffle = False, num_workers = 16, pin_memory = True) score_list = [] for idx, batch_data in enumerate(dataloader): score_list.extend(batch_data.numpy().tolist()) max_num_index_list = list(map(score_list.index, heapq.nlargest(knum, score_list))) for ind in max_num_index_list: tmp.append(del_path(data[ind])) score.append(score_list[ind]) tmp.extend(score) res.append(tmp) if k%256 == 0: print("%d images done"%(k+1)) #----------------------------------------- outputfile = "./stsim_2_result.txt" with open(outputfile, 'a') as f: for i in range(len(res)): line = '' for name in res[i]: line = line + str(name) + ',' line = line[:-1] + '\n' f.write(line) f.close()

22.224719

86

0.570779

252

1,978

4.313492

0.400794

0.041398

0.033119

0.027599

0

0.022048

0.289181

1,978

88

87

22.477273

0.751067

0.030334

0

0.051724

0.028736

0

1

0.081967

false

0

0.131148

0.032787

0.295082

0.016393

0

null

0

null

0

1

0

7867596a29dbeb64fbd242bc6c8c7720e3a87739

509

py

Python

src/codebase/controllers/default.py

ooclab/ga.service

894b4703628b2ce93790db31939009783e8e7b09

[ "MIT" ]

1

2019-09-20T04:32:52.000Z

src/codebase/controllers/default.py

ooclab/ga.service

894b4703628b2ce93790db31939009783e8e7b09

[ "MIT" ]

1

2019-02-01T04:57:27.000Z

src/codebase/controllers/default.py

ooclab/ga.service

894b4703628b2ce93790db31939009783e8e7b09

[ "MIT" ]

1

2019-01-14T06:51:17.000Z

# pylint: disable=W0221,W0223 import os from codebase.web import APIRequestHandler class HealthHandler(APIRequestHandler): def get(self): self.write("ok") class SpecHandler(APIRequestHandler): """ 提供 SwaggerUI YAML 文档 """ def get(self): path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir)) abspath = os.path.join(path, "schema.yml") self.set_header("Content-Type", "text/plain") self.write(open(abspath, "rb").read())

20.36

82

0.650295

63

509

5.174603

0.619048

0.07362

0.06135

0.104294

0

0.019802

0.206287

509

24

83

21.208333

0.787129

0.096267

0

0.181818

0

0.081081

0

1

0.181818

false

0

0.181818

0

0.545455

0

null

0

null

0

1

0

787089ab58a2c573c193c13a1c54f7ae8051fe13

1,692

py

Python

graphql/backend/tests/test_compileddocument.py

ThanksBoomerang/graphql-core-legacy

6e2fbccdec655ce9122b84d3808c14242c4e6b96

[ "MIT" ]

8

2020-03-23T21:34:02.000Z

2021-11-12T11:27:45.000Z

graphql/backend/tests/test_compileddocument.py

ThanksBoomerang/graphql-core-legacy

6e2fbccdec655ce9122b84d3808c14242c4e6b96

[ "MIT" ]

17

2020-03-14T22:22:29.000Z

2022-03-16T19:26:37.000Z

graphql/backend/tests/test_compileddocument.py

ThanksBoomerang/graphql-core-legacy

6e2fbccdec655ce9122b84d3808c14242c4e6b96

[ "MIT" ]

17

2020-03-23T12:06:23.000Z

2022-02-13T05:33:32.000Z

from ...language.base import parse from ...utils.ast_to_code import ast_to_code from ..compiled import GraphQLCompiledDocument from .schema import schema def test_compileddocument_from_module_dict(): # type: () -> None document_string = "{ hello }" document_ast = parse(document_string) document = GraphQLCompiledDocument.from_module_dict( schema, { "document_string": document_string, "document_ast": document_ast, "execute": lambda *_: True, }, ) assert document.operations_map == {None: "query"} assert document.document_string == document_string assert document.document_ast == document_ast assert document.schema == schema assert document.execute() def test_compileddocument_from_code(): # type: () -> None document_string = "{ hello }" document_ast = parse(document_string) code = ''' # -*- coding: utf-8 -*- from __future__ import unicode_literals from graphql.language import ast from graphql.language.parser import Loc from graphql.language.source import Source schema = None document_string = """{document_string}""" source = Source(document_string) def loc(start, end): return Loc(start, end, source) document_ast = {document_ast} def execute(*_): return True '''.format( document_string=document_string, document_ast=ast_to_code(document_ast) ) document = GraphQLCompiledDocument.from_code(schema, code) assert document.operations_map == {None: "query"} assert document.document_string == document_string assert document.document_ast == document_ast assert document.schema == schema assert document.execute()

28.2

79

0.708038

191

1,692

6

0.225131

0.183246

0.153578

0.122164

0.448517

0.380454

0

0.000732

0.19208

1,692

59

80

28.677966

0.837601

0.019504

0

0.304348

0

0.27657

0.055556

0

0.217391

1

0.043478

false

0

0.173913

0

0.26087

0

null

0

null

0

1

0

78715707268ed203ef8de172b23e49fc52b02ddc

9,568

py

Python

pid/eco/views.py

PlanetaryResources/pid

ecb146cc26c6ade2863bcdc6d271ead3cbcbbe40

[ "Apache-2.0" ]

3

2019-06-14T18:05:22.000Z

2020-01-22T17:38:17.000Z

pid/eco/views.py

PlanetaryResources/pid

ecb146cc26c6ade2863bcdc6d271ead3cbcbbe40

[ "Apache-2.0" ]

null

pid/eco/views.py

PlanetaryResources/pid

ecb146cc26c6ade2863bcdc6d271ead3cbcbbe40

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- """Design views.""" from flask import Blueprint, request, jsonify, render_template, make_response from flask_login import login_required, current_user from .forms import CreateECOForm from .models import ECO from pid.common.models import Project, Approver from pid.mail import send_email from pid.user.models import User from pid.design.models import Design blueprint = Blueprint('eco', __name__, url_prefix='/eco', static_folder='../static') @blueprint.route('/create', methods=['POST']) @login_required def create_eco(): """Create new ECO.""" form = CreateECOForm(request.form) validated = form.validate_on_submit() design_ids = form.designs.data.split(',') designs = [] for design_id in design_ids: design = Design.get_by_id(design_id) if design != None: designs.append(design) if validated: variables = { 'name': form.name.data, 'owner': form.owner.data, 'project': designs[0].project } eco = ECO.create(**variables) for design in designs: eco.designs.append(design) eco.save() jsonData = { 'success': True, 'ecoId': eco.id, 'url': eco.get_url() } return jsonify(jsonData), 200, {'ContentType': 'application/json'} else: return make_response(render_template('eco/create_eco.html', form=form, designs=designs), 500) @blueprint.route('/update', methods=['POST']) @login_required def update_eco(): id = request.form['pk'] # UID for field will be ala [fieldname]-[classname]-[id]-editable, field name will be first section always field = request.form['name'].split('-')[0] field_value = request.form['value'] eco = ECO.get_by_id(id) original_value = None if field == 'name': original_value = eco.name eco.update(name=field_value) if field == 'summary': original_value = eco.summary eco.update(summary=field_value) if field == 'analysis': original_value = eco.analysis eco.update(analysis=field_value) if field == 'corrective_action': original_value = eco.corrective_action eco.update(corrective_action=field_value) elif field == 'project': if eco.project: original_value = eco.project.name project = Project.get_by_id(field_value) eco.update(project=project) field_value = project.name if project else None elif field == 'owner': if eco.owner: original_value = eco.owner.get_name() if eco.owner.padawan: for approver in eco.approvers: if approver.approver == eco.owner.supervisor and approver.capacity == 'Supervisor': eco.approvers.remove(approver) approver.delete() owner = User.get_by_id(field_value) if owner.padawan: approver = Approver.create(approver_id=owner.supervisor_id, capacity='Supervisor') eco.approvers.append(approver) eco.update(owner=owner) field_value = owner.get_name() if owner else None if field == 'thumbnail_id': thumbnail_id = None if field_value == 'default' else field_value eco.update(thumbnail_id=thumbnail_id) return render_template('shared/thumbnail_return.html', record=eco) eco.add_change_log_entry(action='Edit', field=field.title().replace('_', ' '), original_value=original_value, new_value=field_value) return jsonify({'success': True}), 200, {'ContentType': 'application/json'} @blueprint.route('/update_state', methods=['POST']) @login_required def update_eco_state(): # TODO: verify that current_user is owner of record and can edit it design_id = request.values['parent_id'] state = request.form['state'] transition = request.form['transition'] comment = request.values['comment'] eco = ECO.get_by_id(design_id) eco.update(state=state) eco.add_workflow_log_entry(capacity='Owner', action=transition, comment=comment) if state == eco.workflow.get_approval_state(): for approver in eco.approvers: if not approver.approved_at: variables = { 'record': eco, 'approver': approver, 'comment': comment } send_email(subject='Approval Required for {0}: {1}'.format(eco.descriptor, eco.get_name()), recipients=[approver.approver.email], text_body=render_template('mail/approvals/new_approver.txt', **variables), html_body=render_template('mail/approvals/new_approver.html', **variables)) elif state == eco.workflow.released_state: # Only self-approval will trigger this eco.add_workflow_log_entry(capacity='PLAIDmin', action='Approved') return jsonify({'success': True}), 200, {'ContentType': 'application/json'} @blueprint.route('/<string:key>', methods=['GET']) @login_required def view_eco(key): """View existing eco.""" eco = ECO.get_by_key(key) users = User.query.all() projects = Project.query.all() variables = { 'eco': eco, 'users': users, 'projects': projects } return render_template('eco/view_eco.html', **variables) @blueprint.route('/typeahead_search', methods=['GET']) @login_required def typeahead_search(): query = request.args.get('query') ecos = ECO.typeahead_search(query) results = [] for eco in ecos: eco_dict = {} eco_dict['class'] = eco.get_class_name() eco_dict['icon'] = '<i class="pri-typeahead-icon pri-icons-record-eco" aria-hidden="true"></i>' eco_dict['id'] = eco.id eco_dict['name'] = eco.name eco_dict['number'] = eco.key eco_dict['object_type'] = 'ECO' eco_dict['state'] = eco.state eco_dict['thumb_url'] = eco.get_thumbnail_url() eco_dict['url'] = eco.get_url() results.append(eco_dict) return jsonify({'success': True, 'data': results}), 200, {'ContentType': 'application/json'} @blueprint.route('/get_create_modal', methods=['POST']) @login_required def get_eco_modal(): form = CreateECOForm(request.form) variables = { 'form': form } design_id = request.form.get('design_id', None) if design_id: variables['designs'] = [Design.get_by_id(design_id)] return render_template('eco/create_eco.html', **variables) @blueprint.route('/advanced_search', methods=['GET']) @login_required def advanced_search_ecos(): params = request.args.to_dict() ecos = ECO.advanced_search(params) results = [] for eco in ecos: eco_dict = { 'eco_number': eco.key, 'name': eco.name, 'state': eco.state, 'project': eco.project.name, 'summary': eco.summary, 'owner': eco.owner.get_name(), 'created_by': eco.created_by.get_name(), 'created_at': eco.created_at, 'url': eco.get_url() } results.append(eco_dict) return jsonify({'success': True, 'data': results}), 200, {'ContentType': 'application/json'} @blueprint.route('/get_add_design_typeahead_modal', methods=['POST']) @login_required def get_add_design_typeahead_modal(): eco_id = request.values['eco_id'] eco = ECO.get_by_id(eco_id) designs = [] for design in eco.designs: designs.extend([rev_design.id for rev_design in design.find_all_revisions()]) variables = { 'eco': eco, 'designs': designs } return render_template('eco/add_design_typeahead_modal.html', **variables) @blueprint.route('/update_design', methods=['POST']) @login_required def update_design(): eco_id = request.values['eco_id'] old_design_id = request.values['old_design_id'] new_design_id = request.values['new_design_id'] eco = ECO.get_by_id(eco_id) old_design = Design.get_by_id(old_design_id) new_design = Design.get_by_id(new_design_id) eco.designs.remove(old_design) eco.designs.append(new_design) eco.add_change_log_entry(action='Edit', field='Design', original_value=old_design.get_descriptive_url(), new_value=new_design.get_descriptive_url()) eco.save() variables = { 'eco': eco, 'design': new_design } return render_template('eco/eco_design_row.html', **variables) @blueprint.route('/add_design', methods=['POST']) @login_required def add_design(): eco_id = request.values['eco_id'] design_id = request.values['design_id'] eco = ECO.get_by_id(eco_id) design = Design.get_by_id(design_id) eco.designs.append(design) eco.add_change_log_entry(action='Add', field='Design', new_value=design.get_descriptive_url()) eco.save() variables = { 'eco': eco, 'design': design } return render_template('eco/eco_design_row.html', **variables) @blueprint.route('/remove_design', methods=['POST']) @login_required def remove_design(): eco_id = request.values['eco_id'] eco = ECO.get_by_id(eco_id) design_id = request.values['design_id'] design = Design.get_by_id(design_id) eco.designs.remove(design) eco.add_change_log_entry(action='Remove', field='Design', original_value=design.get_descriptive_url()) eco.save() return jsonify({'success': True}), 200, {'ContentType': 'application/json'}

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null

0

null

0

1

0

7872f59905b7d2b01c5c5e396418732a9653183b

3,485

py

Python

psono/restapi/serializers/create_membership.py

dirigeant/psono-server

a18c5b3c4d8bbbe4ecf1615b210d99fb77752205

[ "Apache-2.0", "CC0-1.0" ]

48

2018-04-19T15:50:58.000Z

2022-01-23T15:58:11.000Z

psono/restapi/serializers/create_membership.py

dirigeant/psono-server

a18c5b3c4d8bbbe4ecf1615b210d99fb77752205

[ "Apache-2.0", "CC0-1.0" ]

9

2018-09-13T14:56:18.000Z

2020-01-17T16:44:33.000Z

psono/restapi/serializers/create_membership.py

dirigeant/psono-server

a18c5b3c4d8bbbe4ecf1615b210d99fb77752205

[ "Apache-2.0", "CC0-1.0" ]

11

2019-09-20T11:53:47.000Z

2021-07-18T22:41:31.000Z

from django.utils.translation import ugettext_lazy as _ from rest_framework import serializers, exceptions from ..fields import UUIDField, BooleanField from ..models import User, User_Group_Membership import re class CreateMembershipSerializer(serializers.Serializer): user_id = UUIDField(required=True) group_id = UUIDField(required=True) secret_key = serializers.CharField(required=True) secret_key_nonce = serializers.CharField(max_length=64, required=True) secret_key_type = serializers.CharField(default='asymmetric') private_key = serializers.CharField(required=True) private_key_nonce = serializers.CharField(max_length=64, required=True) private_key_type = serializers.CharField(default='asymmetric') group_admin = BooleanField(default=False) share_admin = BooleanField(default=True) def validate_secret_key(self, value): value = value.strip() if not re.match('^[0-9a-f]*$', value, re.IGNORECASE): msg = _('secret_key must be in hex representation') raise exceptions.ValidationError(msg) return value def validate_secret_key_nonce(self, value): value = value.strip() if not re.match('^[0-9a-f]*$', value, re.IGNORECASE): msg = _('secret_key_nonce must be in hex representation') raise exceptions.ValidationError(msg) return value def validate_secret_key_type(self, value): value = value.strip() if value not in ('symmetric', 'asymmetric'): msg = _('Unknown secret key type') raise exceptions.ValidationError(msg) return value def validate_private_key(self, value): value = value.strip() if not re.match('^[0-9a-f]*$', value, re.IGNORECASE): msg = _('private_key must be in hex representation') raise exceptions.ValidationError(msg) return value def validate_private_key_nonce(self, value): value = value.strip() if not re.match('^[0-9a-f]*$', value, re.IGNORECASE): msg = _('private_key_nonce must be in hex representation') raise exceptions.ValidationError(msg) return value def validate_private_key_type(self, value): value = value.strip() if value not in ('symmetric', 'asymmetric'): msg = _('Unknown private key type') raise exceptions.ValidationError(msg) return value def validate_user_id(self, value): try: User.objects.get(pk=value) except User.DoesNotExist: msg = _('Target user does not exist.') raise exceptions.ValidationError(msg) return value def validate_group_id(self, value): # This line also ensures that the desired group exists and that the user firing the request has admin rights if not User_Group_Membership.objects.filter(group_id=value, user=self.context['request'].user, group_admin=True, accepted=True).exists(): msg = "NO_PERMISSION_OR_NOT_EXIST" raise exceptions.ValidationError(msg) return value def validate(self, attrs: dict) -> dict: user_id = attrs.get('user_id') group_id = attrs.get('group_id') if User_Group_Membership.objects.filter(group_id=group_id, user_id=user_id).count() > 0: msg = _("User is already part of the group.") raise exceptions.ValidationError(msg) return attrs

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0.663415

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3,485

5.31829

0.232779

0.053595

0.12059

0.132649

0.640464

0.591782

0.552479

0.517642

0.472086

0

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0.240172

3,485

104

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33.509615

0.840634

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0.126761

false

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0.070423

0

0.478873

0

null

0

null

0

1

0

78736c505d7315485f6b5015659e5139ab914041

7,158

py

Python

stanza_wrapper/stanza_wrapper.py

Filter-Bubble/stanza_wrapper

04388869cbbe419132628422663e4c7c987cf1d0

[ "Apache-2.0" ]

null

stanza_wrapper/stanza_wrapper.py

Filter-Bubble/stanza_wrapper

04388869cbbe419132628422663e4c7c987cf1d0

[ "Apache-2.0" ]

null

stanza_wrapper/stanza_wrapper.py

Filter-Bubble/stanza_wrapper

04388869cbbe419132628422663e4c7c987cf1d0

[ "Apache-2.0" ]

null

# -*- coding: utf-8 -*- from . import __version__ import logging import stanza from KafNafParserPy import * from lxml.etree import XMLSyntaxError from io import BytesIO import sys from itertools import groupby from operator import itemgetter from xml.sax.saxutils import escape logger = logging.getLogger(__name__) this_name = 'Morphosyntactic parser based on stanza' default_treebank = 'alpino' def get_naf(input_file): input = input_file.read() try: naf = KafNafParser(BytesIO(input)) except XMLSyntaxError: input = input.decode("utf-8") if "<NAF" in input and "</NAF>" in input: # I'm guessing this should be a NAF file but something is wrong logging.exception("Error parsing NAF file") raise naf = KafNafParser(type="NAF") naf.set_version("3.0") naf.set_language("nl") naf.lang = "nl" naf.raw = input naf.set_raw(naf.raw) return naf def create_text_layer(st_doc, knaf_obj): id_to_tokenid = {} wcount = 1 offsets = {} txt = knaf_obj.get_raw() for sid, sentence in enumerate(st_doc.sentences): id_to_tokenid[sid+1] = {} for token in sentence.tokens: token_obj = Cwf(type=knaf_obj.get_type()) token_id = 'w{}'.format(wcount) token_length = len(token.text) offsets[wcount] = txt.find(token.text, offsets.get(wcount-1, 0)) token_obj.set_id(token_id) token_obj.set_length(str(token_length)) # token_obj.set_offset(str(offset)) # Is this correct???? token_obj.set_para('1') token_obj.set_sent(str(sid+1)) token_obj.set_text(token.text) token_obj.set_offset(str(offsets[wcount])) wcount += 1 id_to_tokenid[sid+1][token.id[0]] = token_id knaf_obj.add_wf(token_obj) return id_to_tokenid def get_term_type(pos): if pos in ['det', 'pron', 'prep', 'vg', 'conj']: return 'close' else: return 'open' def create_term_layer(st_doc, knaf_obj, id_to_tokenid): tcount = 0 term_id_mapping = {} # Mapping from stanford word index -> NAF term id for sid, sentence in enumerate(st_doc.sentences): for term in sentence.words: new_term_id = 't_'+str(tcount) term_id_mapping[(sid, term.id)] = new_term_id term_obj = Cterm(type=knaf_obj.get_type()) term_obj.set_id(new_term_id) new_span = Cspan() new_span.create_from_ids([id_to_tokenid[sid+1] [term.parent.id[0]]]) term_obj.set_span(new_span) # lemma: copy from stanza term_obj.set_lemma(term.lemma) # pos: take the UD UPOS value term_obj.set_pos(term.upos.lower()) # external reference: the UD FEATS value if term.feats: ext_ref = CexternalReference() ext_ref.set_resource('Stanza') ext_ref.set_reftype('FEATS') ext_ref.set_reference(term.feats) term_obj.add_external_reference(ext_ref) # morphofeat: reformatted UD XPOS value if term.xpos: feats = term.xpos.split('|') feat = feats[0] + '(' + ','.join(feats[1:]) + ')' term_obj.set_morphofeat(feat) termtype = get_term_type(term.upos.lower()) term_obj.set_type(termtype) knaf_obj.add_term(term_obj) tcount += 1 return term_id_mapping def create_dependency_layer(st_doc, knaf_obj, term_id_mapping): for s_id, sent in enumerate(st_doc.sentences): for source, rel, target in sent.dependencies: # Do not include root if rel != 'root': # Creating comment str_comment = ' '+rel+'('+str(target.lemma)+','+str(source.lemma)+') ' str_comment = escape(str_comment, {"--": "&ndash"}) my_dep = Cdependency() my_dep.set_from(term_id_mapping.get((s_id, source.id))) my_dep.set_to(term_id_mapping.get((s_id, target.id))) my_dep.set_function(rel) my_dep.set_comment(str_comment) knaf_obj.add_dependency(my_dep) def add_linguistic_processors(in_obj, added_text_layer, treebank): name = this_name + ' using {} treebank'.format(treebank) if added_text_layer: my_lp = Clp() my_lp.set_name(name) my_lp.set_version(__version__) my_lp.set_timestamp() in_obj.add_linguistic_processor('text', my_lp) my_lp = Clp() my_lp.set_name(name) my_lp.set_version(__version__) my_lp.set_timestamp() in_obj.add_linguistic_processor('terms', my_lp) my_lp = Clp() my_lp.set_name(name) my_lp.set_version(__version__) my_lp.set_timestamp() in_obj.add_linguistic_processor('deps', my_lp) return in_obj def parse(input_file, treebank=None): treebank = treebank if treebank is not None else default_treebank if isinstance(input_file, KafNafParser): in_obj = input_file else: in_obj = get_naf(input_file) lang = in_obj.get_language() if lang != 'nl': logging.warning('ERROR! Language is {} and must be nl (Dutch)' .format(lang)) sys.exit(-1) if in_obj.text_layer is None: added_text_layer = True nlp = stanza.Pipeline(lang='nl', processors='tokenize,pos,lemma,depparse', package=treebank) text = in_obj.get_raw() in_obj.remove_text_layer() doc = nlp(text) id_to_tokenid = create_text_layer(doc, in_obj) else: # Use existing tokenization added_text_layer = False nlp = stanza.Pipeline(lang='nl', tokenize_pretokenized=True, processors='tokenize,pos,lemma,depparse', package=treebank) sent_tokens_ixa = [(token.get_sent(), token.get_text()) for token in in_obj.get_tokens()] text = [[t for s2, t in toks] for s, toks in groupby(sent_tokens_ixa, itemgetter(0))] # TODO: is this correct??? can we make it more elegant? id_to_tokenid = {int(k): {i+1: t.get_id() for i, t in enumerate(g)} for k, g in groupby(in_obj.get_tokens(), lambda t: t.get_sent())} doc = nlp(text) # Check that we don't get mutli-word get_tokens if any([len(sent.tokens) != len(sent.words) for sent in doc.sentences]): raise Exception('stanza returns MutliWordTokens. ' 'This is not allowed for Dutch.') term_id_mapping = create_term_layer(doc, in_obj, id_to_tokenid) create_dependency_layer(doc, in_obj, term_id_mapping) in_obj = add_linguistic_processors(in_obj, added_text_layer, treebank) return in_obj

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0.58955

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0.023996

0.025006

0.018186

0.214953

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7,158

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false

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0

0.15

0

null

0

null

0

1

0

78747f79066b20c85ec7068912df47ccb366ae61

2,749

py

Python

micro_center_price_monitor/price_checker.py

Nintendude64/micro-center-price-monitor

5aee275ef2e6a65d1fd69aa07956225bad7f30ac

[ "MIT" ]

null

micro_center_price_monitor/price_checker.py

Nintendude64/micro-center-price-monitor

5aee275ef2e6a65d1fd69aa07956225bad7f30ac

[ "MIT" ]

null

micro_center_price_monitor/price_checker.py

Nintendude64/micro-center-price-monitor

5aee275ef2e6a65d1fd69aa07956225bad7f30ac

[ "MIT" ]

null

from micro_center_price_monitor.scraper import MicroCenterScraper from micro_center_price_monitor.mail import Email import datetime, time class PriceChecker: """ PriceChecker: Manages execution flow for: -> Retrieving search results list -> Selected wanted product -> Monitoring price -> Sending product email """ def search(self): try: # Prompt to enter a product name search_for = input('Enter a product:\n') # Init scraper obj, passing user input for search term scraper = MicroCenterScraper(search_term=search_for) # GET request to retrieve first page results scraper.search_for_products() # Print search results scraper.get_products() # Prompt to search for one of list items product_selection = int(input('Select a product:\n')) # Selects product from list scraper.select_product(product_selection) # Prompt to enter expected price at discount expected_price = float(input('Enter your expected price\n')) while True: # update pricing info scraper.check_product_price() # Get float value of price attribute price = float(str(scraper.key_product.price).replace(',','')) # currency symbol for output (e.g., "$"" for USD) currency_symbol = scraper.key_product.currency # Print current time and price print('Price at: %s -> %s%s' % (datetime.datetime.now(), currency_symbol, str(price))) # Email if the price is beneath expected threshold. Otherwise, continue to loop. if price <= expected_price: print('Price at or below %s%.2f at %s%.2f' % (currency_symbol, expected_price, currency_symbol, price)) print('Sending email now...') email = Email(scraper.key_product.name, currency_symbol + scraper.key_product.price, scraper.data.product_url) email.send_email() break # sleep for n seconds time.sleep(scraper.data.REFRESH_SECS) except ValueError: print('Invalid product selection value provided. Please try again later.') except IndexError: print('Unable to find any search results. Please try again.') except Exception as e: print('Unexpected error has occured. %s' % e)

39.84058

123

0.559112

285

2,749

5.280702

0.414035

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0.045183

0.026578

0.077076

0

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0.368498

2,749

68

124

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0.865783

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0

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0

1

0.028571

false

0

0.085714

0

0.142857

0.171429

0

null

0

null

0

1

0

78756479e1ce298be3ad0fcb9bbe4e75724a401d

3,613

py

Python

yakbak/diff.py

shiroyuki/2019-cfp

90c20ad01c19ddf17b0bfd1f96b264c715456c01

[ "BSD-3-Clause" ]

null

yakbak/diff.py

shiroyuki/2019-cfp

90c20ad01c19ddf17b0bfd1f96b264c715456c01

[ "BSD-3-Clause" ]

6

2019-04-27T16:48:33.000Z

2019-08-06T20:28:23.000Z

yakbak/diff.py

shiroyuki/2019-cfp

90c20ad01c19ddf17b0bfd1f96b264c715456c01

[ "BSD-3-Clause" ]

2

2019-08-06T15:23:57.000Z

2019-08-21T23:16:01.000Z

# Per Google's recommendation [1], this is copied from [2], with # the line ending match adjusted to find spans of whitespace. # # The original [2] is used under the Apache License, Version 2.0: # # Diff Match and Patch # Copyright 2018 The diff-match-patch Authors. # https://github.com/google/diff-match-patch # # 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. # # [1] https://github.com/google/diff-match-patch/wiki/Line-or-Word-Diffs#word-mode # [2] https://github.com/google/diff-match-patch/blob/858b3812cc02e7d48da4beebb21d4d80dc1d3062/python3/diff_match_patch.py from typing import Dict, Tuple import re def diff_wordsToChars(text1: str, text2: str) -> Tuple[str, str, object]: """Split two texts into an array of strings. Reduce the texts to a string of hashes where each Unicode character represents one line. Args: text1: First string. text2: Second string. Returns: Three element tuple, containing the encoded text1, the encoded text2 and the array of unique strings. The zeroth element of the array of unique strings is intentionally blank. """ lineArray = [] # e.g. lineArray[4] == "Hello\n" lineHash: Dict[str, int] = {} # e.g. lineHash["Hello\n"] == 4 # "\x00" is a valid character, but various debuggers don't like it. # So we'll insert a junk entry to avoid generating a null character. lineArray.append('') def next_word_end(text: str, start: int) -> int: """Find the next word end (any whitespace) after `start`. """ pattern = re.compile(r"([^ \t\n]+)[ \t\n]") match = pattern.search(text, start) if not match: return -1 return start + len(match.group(1)) def diff_linesToCharsMunge(text: str) -> str: """Split a text into an array of strings. Reduce the texts to a string of hashes where each Unicode character represents one line. Modifies linearray and linehash through being a closure. Args: text: String to encode. Returns: Encoded string. """ chars = [] # Walk the text, pulling out a substring for each line. # text.split('\n') would would temporarily double our memory footprint. # Modifying text would create many large strings to garbage collect. lineStart = 0 lineEnd = -1 while lineEnd < len(text) - 1: lineEnd = next_word_end(text, lineStart) if lineEnd == -1: lineEnd = len(text) - 1 line = text[lineStart:lineEnd + 1] if line in lineHash: chars.append(chr(lineHash[line])) else: if len(lineArray) == maxLines: # Bail out at 1114111 because chr(1114112) throws. line = text[lineStart:] lineEnd = len(text) lineArray.append(line) lineHash[line] = len(lineArray) - 1 chars.append(chr(len(lineArray) - 1)) lineStart = lineEnd + 1 return "".join(chars) # Allocate 2/3rds of the space for text1, the rest for text2. maxLines = 666666 chars1 = diff_linesToCharsMunge(text1) maxLines = 1114111 chars2 = diff_linesToCharsMunge(text2) return (chars1, chars2, lineArray) # flake8: noqa

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null

0

null

0

1

0

787af07ddabb27f12cabf735e06b4adc1ae9725b

4,498

py

Python

tukey/api/nova.py

Li-Ko/tukey_portal

8dc395ef1a1ebaa806d23c88ce51460e6c202921

[ "Apache-2.0" ]

null

tukey/api/nova.py

Li-Ko/tukey_portal

8dc395ef1a1ebaa806d23c88ce51460e6c202921

[ "Apache-2.0" ]

null

tukey/api/nova.py

Li-Ko/tukey_portal

8dc395ef1a1ebaa806d23c88ce51460e6c202921

[ "Apache-2.0" ]

null

from collections import Sequence from django.conf import settings from openstack_dashboard.api import nova from openstack_dashboard.api.base import Quota from openstack_dashboard.api.nova import flavor_list from openstack_dashboard.api.nova import novaclient from openstack_dashboard.api.nova import server_list from openstack_dashboard.api.nova import tenant_floating_ip_list from openstack_dashboard.api.nova import tenant_quota_get from horizon.utils.memoized import memoized from tukey.cloud_attribute import get_cloud from collections import OrderedDict class NovaUsage(nova.NovaUsage): _attrs = ['start', 'server_usages', 'stop', 'tenant_id', 'total_local_gb_usage', 'total_memory_mb_usage', 'total_vcpus_usage', 'total_hours', 'cloud_cores', 'cloud_du', 'cloud_ram', 'hadoop_jobs', 'hadoop_hdfsdu'] + settings.USAGE_ATTRIBUTES.values() def get_summary(self): #TODO: find some way to make this ordered oh well it is not # going to happen :( return OrderedDict([('instances', self.total_active_instances), ('memory_mb', self.memory_mb), ('vcpus', getattr(self, "total_vcpus_usage", 0)), ('vcpu_hours', self.vcpu_hours), ('local_gb', self.local_gb), ('disk_gb_hours', self.disk_gb_hours), ('cloud_cores', getattr(self, "cloud_cores", -1)), ('cloud_du', getattr(self, "cloud_du", -1)), ('hadoop_hdfsdu', getattr(self, "hadoop_hdfsdu", -1)), ('hadoop_jobs', getattr(self, "hadoop_jobs", -1)), ('Cloud Core Hours', getattr(self, "cloud_cores", -1)), ('Cloud Disk Usage (GB)', getattr(self, "cloud_du", -1)), ('Cloud RAM Hours (GB Hours)', getattr(self, "cloud_ram", -1)), ('Hadoop Disk Usage (GB)', getattr(self, "hadoop_hdfsdu", -1)), ('Hadoop Job Hours', getattr(self, "hadoop_jobs", -1))] + [(key, getattr(self, value, -1)) for key, value in settings.USAGE_ATTRIBUTES.items()]) class QuotaSet2(Sequence): """ Wrapper for client QuotaSet objects which turns the individual quotas into Quota objects for easier handling/iteration. `QuotaSet` objects support a mix of `list` and `dict` methods; you can use the bracket notiation (`qs["my_quota"] = 0`) to add new quota values, and use the `get` method to retrieve a specific quota, but otherwise it behaves much like a list or tuple, particularly in supporting iteration. """ def __init__(self, apiresource=None): self.items = [] if apiresource: for k, v in apiresource.items(): #for k, v in apiresource._info.items(): if k == 'id': continue self[k] = v def __setitem__(self, k, v): v = int(v) if v is not None else v q = Quota(k, v) self.items.append(q) def __getitem__(self, index): return self.items[index] def __len__(self): return len(self.items) def __repr__(self): return repr(self.items) def get(self, key, default=None): match = [quota for quota in self.items if quota.name == key] return match.pop() if len(match) else Quota(key, default) def default_quota_get(request, tenant_id): return cloud_quota(request, novaclient(request).quotas.defaults(tenant_id)) def tenant_quota_get(request, tenant_id): return cloud_quota(request, novaclient(request).quotas.get(tenant_id)) def cloud_quota(request, quotas): cloud = None if 'cloud' in request.GET: cloud = request.GET['cloud'] elif 'cloud' in request.POST: cloud = request.POST['cloud'] if cloud is not None: quotas = quotas._info[cloud] del(quotas['cloud']) else: # "sum" the quotas! # The attributes not to sum ignore = ['cloud', 'id'] if hasattr(quotas, '_info'): clouds = quotas._info.keys() if 'cloud' in quotas._info[clouds[0]]: keys = [] for cloud in clouds: keys += quotas._info[cloud].keys() quotas = {key: reduce( lambda s, c: s + quotas._info[c][key] if key in quotas._info[c] else 0, [0] + clouds) for key in keys if key not in ignore} return QuotaSet2(quotas)

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null

0

null

0

1

0

787c3c6f3e40d9b934038220ef7e2d375b00740c

1,172

py

Python

uptimer/uptimer.py

sourcepirate/uptimer

07ec5586cc1f57676073a8b3098f705ca9c843ec

[ "MIT" ]

1

2021-10-10T16:17:00.000Z

uptimer/uptimer.py

sourcepirate/uptimer

07ec5586cc1f57676073a8b3098f705ca9c843ec

[ "MIT" ]

null

uptimer/uptimer.py

sourcepirate/uptimer

07ec5586cc1f57676073a8b3098f705ca9c843ec

[ "MIT" ]

null

"""Main module.""" import logging from datetime import datetime from urllib.parse import urlparse from typing import Any, Dict, List import requests def check_domain(domain_url: str) -> Dict[str, Any]: try: current_time = datetime.now() session = requests.Session() response = session.get(domain_url) return { "healthy": response.ok, "latency": response.elapsed.microseconds // 1000, "content_type": response.headers.get("Content-Type"), "current_time": int(current_time.timestamp()), "domain_url": domain_url, "domain": urlparse(domain_url).hostname, } except Exception: return { "healthy": False, "latency": 0, "current_time": int(current_time.timestamp()), "domain_url": domain_url, "domain": urlparse(domain_url).hostname, } def access_domains(domains: List[str]) -> Dict[str, Any]: responses = [] for domain_url in domains: try: responses.append(check_domain(domain_url)) except Exception: pass return responses

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0.088365

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0.294118

0

null

0

null

0

1

0

787c830352c3b6240940e7f3f6a544c89fc5bdf9

1,071

py

Python

tests/python_frontend/arithmetic_conversions_test.py

jnice-81/dace

5211794a2d17b7189037ac485ab0b292fb02aa0d

[ "BSD-3-Clause" ]

227

2019-03-15T23:39:06.000Z

2022-03-30T07:49:08.000Z

tests/python_frontend/arithmetic_conversions_test.py

jnice-81/dace

5211794a2d17b7189037ac485ab0b292fb02aa0d

[ "BSD-3-Clause" ]

834

2019-07-31T22:49:31.000Z

2022-03-28T14:01:32.000Z

tests/python_frontend/arithmetic_conversions_test.py

jnice-81/dace

5211794a2d17b7189037ac485ab0b292fb02aa0d

[ "BSD-3-Clause" ]

64

2019-03-19T05:40:37.000Z

2022-03-11T15:02:42.000Z

# Copyright 2019-2021 ETH Zurich and the DaCe authors. All rights reserved. import dace import numpy as np @dace.program def add(A: dace.complex64[5, 5], B: dace.float64[5, 5]): return A + B def test_add(): A = np.random.randint(0, high=10, size=(5, 5), dtype=np.uint64).astype(np.complex64) B = np.random.randint(-10, high=0, size=(5, 5), dtype=np.int32).astype(np.float64) C = add(A, B) assert(np.linalg.norm(C - A - B) / np.linalg.norm(A + B) < 1e-12) @dace.program def complex_conversion(a: dace.complex128[1], b: dace.int32): return a[0] + b def test_complex_conversion(): a = np.zeros((1,), dtype=np.complex128) a[0] = 5 + 6j b = 7 c = complex_conversion(a=a, b=b) assert(c[0] == 12 + 6j) @dace.program def float_conversion(a: dace.float32, b: dace.int64): return a + b def test_float_conversion(): a = np.float32(5.2) b = np.int64(7) c = float_conversion(a=a, b=b) assert(c[0] == a + b) if __name__ == "__main__": test_add() test_complex_conversion() test_float_conversion()

23.282609

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183

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3.584699

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0.064024

0.033537

0.152439

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0.079625

0.202614

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null

0

null

0

1

0

7880e8c114adeeed696cf6d28a33365f19d2d6f6

7,612

py

Python

Seeder/settings/base.py

WebarchivCZ/Seeder

1958c5d3f6bdcbbdb2c81dcb6abc7f689125b6a8

[ "MIT" ]

8

2017-08-16T19:18:57.000Z

2022-01-24T10:08:19.000Z

Seeder/settings/base.py

WebarchivCZ/Seeder

1958c5d3f6bdcbbdb2c81dcb6abc7f689125b6a8

[ "MIT" ]

242

2017-02-03T19:15:52.000Z

2022-03-25T08:02:52.000Z

Seeder/settings/base.py

WebarchivCZ/Seeder

1958c5d3f6bdcbbdb2c81dcb6abc7f689125b6a8

[ "MIT" ]

2

2019-03-06T12:36:29.000Z

2019-07-08T12:52:20.000Z

""" Django settings for Seeder project. For more information on this file, see https://docs.djangoproject.com/en/1.7/topics/settings/ For the full list of settings and their values, see https://docs.djangoproject.com/en/1.7/ref/settings/ """ # Build paths inside the project like this: os.path.join(BASE_DIR, ...) import os import re from django.utils.translation import ugettext_lazy as _ # Import version to be displayed further from .version import VERSION, VERSION_DATETIME # that double dirname is necessary since setting is in folder... BASE_DIR = os.path.dirname(os.path.dirname(__file__)) # Quick-start development settings - unsuitable for production # See https://docs.djangoproject.com/en/1.7/howto/deployment/checklist/ # SECURITY WARNING: don't run with debug turned on in production! DEBUG = True ALLOWED_HOSTS = [] DATABASES = { 'default': { 'ENGINE': 'django.db.backends.postgresql_psycopg2', 'NAME': 'postgres', 'USER': 'postgres', 'PASSWORD': 'postgres', 'HOST': 'postgres', }, 'legacy_seeder': { 'ENGINE': 'django.db.backends.mysql', 'NAME': 'legacy_seeder', 'USER': 'root', 'PASSWORD': 'legacy' } } ADMINS = ( ('Visgean Skeloru', 'visgean@gmail.com'), ('Petr Manas', 'peter@petermanas.com'), ) IGNORABLE_404_URLS = ( re.compile(r'\.(php|cgi)$'), re.compile(r'^/phpmyadmin/'), ) # Application definition INSTALLED_APPS = ( 'raven.contrib.django.raven_compat', 'dal', 'dal_select2', 'modeltranslation', 'django.contrib.admin', 'django.contrib.auth', 'django.contrib.contenttypes', 'django.contrib.sessions', 'django.contrib.messages', 'django.contrib.staticfiles', 'django.contrib.humanize', 'django.contrib.postgres', # 'djangobower', # everything is on cdn 'django_extensions', 'django_tables2', 'django_filters', 'bootstrap3', 'mptt', 'formtools', 'reversion', 'ckeditor', 'ckeditor_uploader', 'debug_toolbar', 'django_crontab', 'sorl.thumbnail', 'rest_framework', 'rest_framework.authtoken', 'captcha', 'ordered_model', # 'haystack', # 'elasticstack', 'core', 'publishers', 'source', 'voting', 'comments', 'contracts', 'legacy_db', 'harvests', 'blacklists', 'qa', 'www', 'search_blob', ) 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', 'debug_toolbar.middleware.DebugToolbarMiddleware', 'reversion.middleware.RevisionMiddleware', 'django.middleware.locale.LocaleMiddleware', ) SESSION_COOKIE_NAME = 'seeder_sessionid' # In seconds, 14400 = 4 * 60 * 60 (4 hours) try: SESSION_COOKIE_AGE = int(os.environ.get("SESSION_COOKIE_AGE", "14400")) except: SESSION_COOKIE_AGE = 14400 ROOT_URLCONF = 'urls' WSGI_APPLICATION = 'wsgi.application' STATICFILES_FINDERS = ( "django.contrib.staticfiles.finders.FileSystemFinder", "django.contrib.staticfiles.finders.AppDirectoriesFinder", # 'djangobower.finders.BowerFinder', ) STATICFILES_DIRS = ( os.path.join(BASE_DIR, 'static'), ) TEMPLATES = [ { 'BACKEND': 'django.template.backends.django.DjangoTemplates', 'DIRS': [os.path.join(BASE_DIR, 'templates')], 'APP_DIRS': True, 'OPTIONS': { 'context_processors': ( "django.contrib.auth.context_processors.auth", "django.template.context_processors.debug", "django.template.context_processors.i18n", "django.template.context_processors.media", "django.template.context_processors.static", "django.template.context_processors.tz", "django.contrib.messages.context_processors.messages", 'django.template.context_processors.request', 'core.context_processors.core_processor', ) }, }, ] # APP_DIRS = True # # TEMPLATES = { # 'BACKEND': 'django.template.backends.django.DjangoTemplates', # 'DIRS': TEMPLATE_DIRS, # 'APP_DIRS': True, # 'OPTIONS': { # 'context_processors': TEMPLATE_CONTEXT_PROCESSORS # } # } LANGUAGES = ( ('cs', _('Czech')), ('en', _('English')), ) CALENDAR_LANGUAGES = { 'cs': 'cs-CZ', 'en': 'en-US' } MODELTRANSLATION_DEFAULT_LANGUAGE = 'cs' LOCALE_PATHS = ( os.path.join(BASE_DIR, 'locale'), ) BOWER_COMPONENTS_ROOT = BASE_DIR BOWER_INSTALLED_APPS = () # everything is on CDN now LOGIN_URL = '/seeder/auth/login/' LOGOUT_URL = '/seeder/auth/logout/' LOGIN_REDIRECT_URL = '/' TIME_ZONE = 'UTC' USE_I18N = True USE_L10N = True USE_TZ = True # Static files (CSS, JavaScript, Images) # https://docs.djangoproject.com/en/1.7/howto/static-files/ STATIC_URL = '/static/' STATIC_ROOT = os.path.join(BASE_DIR, 'static_root') MEDIA_ROOT = os.path.join(BASE_DIR, 'media') MEDIA_URL = '/media/' MESSAGE_STORAGE = 'django.contrib.messages.storage.session.SessionStorage' CKEDITOR_UPLOAD_PATH = "uploads/" CKEDITOR_IMAGE_BACKEND = 'pillow' CKEDITOR_CONFIGS = { 'default': { 'toolbar': 'Custom', 'toolbar_Custom': [ ['Bold', 'Italic', 'Underline'], ['NumberedList', 'BulletedList', 'Link'], ], }, 'mini': { 'toolbar': 'Custom', 'toolbar_Custom': [ ['Bold', 'Italic', 'Underline'], ['NumberedList', 'BulletedList', 'Link'], ], 'width': 800, 'height': 100, }, } DEBUG_TOOLBAR_CONFIG = { 'SHOW_TOOLBAR_CALLBACK': 'core.utils.show_toolbar', } CRONJOBS = [ ('1 * * * *', 'source.screenshots.take_screenshots'), ('10 * * * *', 'voting.cron.revive_postponed_rounds'), ('20 * * * *', 'contracts.cron.expire_contracts'), ('30 * * * *', 'contracts.cron.send_emails'), ] # * * * * * command to be executed # - - - - - # | | | | | # | | | | +----- day of week (0 - 6) (Sunday=0) # | | | +------- month (1 - 12) # | | +--------- day of month (1 - 31) # | +----------- hour (0 - 23) # +------------- min (0 - 59) REST_FRAMEWORK = { 'DEFAULT_AUTHENTICATION_CLASSES': [ # 'rest_framework.authentication.BasicAuthentication', 'rest_framework.authentication.SessionAuthentication', 'rest_framework.authentication.TokenAuthentication', ], 'DEFAULT_PERMISSION_CLASSES': [ 'rest_framework.permissions.IsAuthenticated', ] } if DEBUG: REST_FRAMEWORK['DEFAULT_PERMISSION_CLASSES'] = [ 'rest_framework.permissions.AllowAny' ] WAKAT_URL = 'http://forpsi.kitakitsune.org:8080/?url_id={id}' WAYBACK_URL = "http://wayback.webarchiv.cz/wayback/query?type=urlquery&url={url}" SEEDS_EXPORT_DIR = 'seeds' MANET_URL = '127.0.0.1:8891' QA_EVERY_N_MONTHS = 24 LEGACY_URL = 'http://intranet.webarchiv.cz/wadmin/tables/resources/view/{pk}' LEGACY_SCREENSHOT_URL = 'http://www.webarchiv.cz/images/resource/thumb/small_{id}_{date}.jpg' LEGACY_SCREENSHOT_URL_PNG = 'http://www.webarchiv.cz/images/resource/thumb/small_{id}_{date}.png' WEBARCHIV_EMAIL = 'webarchiv@nkp.cz' # RECAPTCHA_PUBLIC_KEY = '' # RECAPTCHA_PRIVATE_KEY = '' NOCAPTCHA = True

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null

0

null

0

1

0

78848f7ee34a7d0fca0d77ac1f78fbe0189d64c4

2,437

py

Python

tools/internal/tizenrt_testresult_collector.py

JoshWorld/RT-OCF

fd41fc4ccd0b3a56e6a2a1bee3e164a559a0fd45

[ "Apache-2.0" ]

15

2018-03-07T12:53:30.000Z

2021-07-26T07:08:13.000Z

tools/internal/tizenrt_testresult_collector.py

JoshWorld/RT-OCF

fd41fc4ccd0b3a56e6a2a1bee3e164a559a0fd45

[ "Apache-2.0" ]

2

2018-01-19T06:38:20.000Z

2018-04-09T06:34:28.000Z

tools/internal/tizenrt_testresult_collector.py

JoshWorld/RT-OCF

fd41fc4ccd0b3a56e6a2a1bee3e164a559a0fd45

[ "Apache-2.0" ]

4

2018-01-18T09:53:00.000Z

2020-08-30T13:09:14.000Z

#!/usr/bin/env python import glob import serial import sys from internal.common import Result import time WIFI_SSID = 'ZEROROOT' WIFI_PASSWORD = 'zeroroot' class TestResultCollector: def __init__(self, usb_device=None): if usb_device is None: usb_device = self.get_usb_tty_number() self.serial = self.create_serial(usb_device) def get_usb_tty_number(self): ttyUSBs = glob.glob('/sys/class/tty/ttyUSB*') if len(ttyUSBs) == 0: print('TizenRT is not connected') exit(1) return '/dev/{}'.format(ttyUSBs[0].split('/')[-1]) def create_serial(self, usb_device): return serial.Serial(usb_device, 115200, timeout=70) def collect(self, options=''): time.sleep(2) self.write_connecting_wifi_command() command = 'iot_rt_unittest ' + options + '\n' self.serial.write(command) return self.read_serial_output() def write_connecting_wifi_command(self): self.serial.write('wifi startsta\n') time.sleep(2) self.serial.write('wifi join {} {} wpa2_aes\n'.format(WIFI_SSID, WIFI_PASSWORD)) time.sleep(2) self.serial.write('ifconfig wl1 dhcp\n') time.sleep(2) def read_serial_output(self): while True: line = self.serial.readline() if line == '': print('Timeout') return Result(exitcode=1, message='timeout: Core Dump may occur') sys.stdout.write(line) if self.is_test_result(line): return Result( exitcode=self.get_test_exitcode(line), message=line) if self.is_core_dump(line): return Result(exitcode=1, message=line) def get_test_exitcode(self, line): arr = line.split(' ') if arr[2] == '0': return 0 return 1 def is_test_result(self, line): return 'Tests' in line and 'Failure' in line and 'Ignored' in line def is_core_dump(self, line): return '(core dumped)' in line def test_get_usb_tty_number(): assert '/dev/ttyUSB1' == TestResultCollector().get_usb_tty_number() def test_create_serial(): assert None != TestResultCollector().create_serial('/dev/ttyUSB1') def test_is_core_dump(): assert True == TestResultCollector().is_core_dump('Aborted (core dumped)')

30.08642

88

0.608535

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0.025334

0.042224

0.101337

0.035186

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null

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0

7888949bd7f932e7b291514995f398653ef1a039

9,561

py

Python

tethys_apps/cli/services_commands.py

quyendong/tethys

99bcb524d5b2021b88d5fa15b7ed6b8acb460997

[ "BSD-2-Clause" ]

1

2020-10-08T20:38:33.000Z

tethys_apps/cli/services_commands.py

quyendong/tethys

99bcb524d5b2021b88d5fa15b7ed6b8acb460997

[ "BSD-2-Clause" ]

1

2018-04-14T19:40:54.000Z

tethys_apps/cli/services_commands.py

quyendong/tethys

99bcb524d5b2021b88d5fa15b7ed6b8acb460997

[ "BSD-2-Clause" ]

1

2021-09-07T14:47:11.000Z

from __future__ import print_function from django.core.exceptions import ObjectDoesNotExist from django.db.utils import IntegrityError from django.forms.models import model_to_dict from .cli_colors import BOLD, pretty_output, FG_RED, FG_GREEN from .cli_helpers import add_geoserver_rest_to_endpoint from builtins import input SERVICES_CREATE = 'create' SERVICES_CREATE_PERSISTENT = 'persistent' SERVICES_CREATE_SPATIAL = 'spatial' SERVICES_LINK = 'link' SERVICES_LIST = 'list' class FormatError(Exception): def __init__(self): Exception.__init__(self) def services_create_persistent_command(args): """ Interact with Tethys Services (Spatial/Persistent Stores) to create them and/or link them to existing apps """ from tethys_services.models import PersistentStoreService name = None try: name = args.name connection = args.connection parts = connection.split('@') cred_parts = parts[0].split(':') store_username = cred_parts[0] store_password = cred_parts[1] url_parts = parts[1].split(':') host = url_parts[0] port = url_parts[1] new_persistent_service = PersistentStoreService(name=name, host=host, port=port, username=store_username, password=store_password) new_persistent_service.save() with pretty_output(FG_GREEN) as p: p.write('Successfully created new Persistent Store Service!') except IndexError: with pretty_output(FG_RED) as p: p.write('The connection argument (-c) must be of the form "<username>:<password>@<host>:<port>".') except IntegrityError: with pretty_output(FG_RED) as p: p.write('Persistent Store Service with name "{0}" already exists. Command aborted.'.format(name)) def services_remove_persistent_command(args): from tethys_services.models import PersistentStoreService persistent_service_id = None try: persistent_service_id = args.service_uid force = args.force try: persistent_service_id = int(persistent_service_id) service = PersistentStoreService.objects.get(pk=persistent_service_id) except ValueError: service = PersistentStoreService.objects.get(name=persistent_service_id) if force: service.delete() with pretty_output(FG_GREEN) as p: p.write('Successfully removed Persistent Store Service {0}!'.format(persistent_service_id)) exit(0) else: proceed = input('Are you sure you want to delete this Persistent Store Service? [y/n]: ') while proceed not in ['y', 'n', 'Y', 'N']: proceed = input('Please enter either "y" or "n": ') if proceed in ['y', 'Y']: service.delete() with pretty_output(FG_GREEN) as p: p.write('Successfully removed Persistent Store Service {0}!'.format(persistent_service_id)) exit(0) else: with pretty_output(FG_RED) as p: p.write('Aborted. Persistent Store Service not removed.') exit(0) except ObjectDoesNotExist: with pretty_output(FG_RED) as p: p.write('A Persistent Store Service with ID/Name "{0}" does not exist.'.format(persistent_service_id)) exit(0) def services_create_spatial_command(args): """ Interact with Tethys Services (Spatial/Persistent Stores) to create them and/or link them to existing apps """ from tethys_services.models import SpatialDatasetService name = None try: name = args.name connection = args.connection parts = connection.split('@') cred_parts = parts[0].split(':') service_username = cred_parts[0] service_password = cred_parts[1] endpoint = parts[1] public_endpoint = args.public_endpoint or '' apikey = args.apikey or '' if 'http' not in endpoint or '://' not in endpoint: raise IndexError() if public_endpoint and 'http' not in public_endpoint or '://' not in public_endpoint: raise FormatError() endpoint = add_geoserver_rest_to_endpoint(endpoint) if public_endpoint: public_endpoint = add_geoserver_rest_to_endpoint(public_endpoint) new_persistent_service = SpatialDatasetService(name=name, endpoint=endpoint, public_endpoint=public_endpoint, apikey=apikey, username=service_username, password=service_password) new_persistent_service.save() with pretty_output(FG_GREEN) as p: p.write('Successfully created new Spatial Dataset Service!') except IndexError: with pretty_output(FG_RED) as p: p.write('The connection argument (-c) must be of the form ' '"<username>:<password>@<protocol>//<host>:<port>".') except FormatError: with pretty_output(FG_RED) as p: p.write('The public_endpoint argument (-p) must be of the form ' '"<protocol>//<host>:<port>".') except IntegrityError: with pretty_output(FG_RED) as p: p.write('Spatial Dataset Service with name "{0}" already exists. Command aborted.'.format(name)) def services_remove_spatial_command(args): from tethys_services.models import SpatialDatasetService spatial_service_id = None try: spatial_service_id = args.service_uid force = args.force try: spatial_service_id = int(spatial_service_id) service = SpatialDatasetService.objects.get(pk=spatial_service_id) except ValueError: service = SpatialDatasetService.objects.get(name=spatial_service_id) if force: service.delete() with pretty_output(FG_GREEN) as p: p.write('Successfully removed Spatial Dataset Service {0}!'.format(spatial_service_id)) exit(0) else: proceed = input('Are you sure you want to delete this Persistent Store Service? [y/n]: ') while proceed not in ['y', 'n', 'Y', 'N']: proceed = input('Please enter either "y" or "n": ') if proceed in ['y', 'Y']: service.delete() with pretty_output(FG_GREEN) as p: p.write('Successfully removed Spatial Dataset Service {0}!'.format(spatial_service_id)) exit(0) else: with pretty_output(FG_RED) as p: p.write('Aborted. Spatial Dataset Service not removed.') exit(0) except ObjectDoesNotExist: with pretty_output(FG_RED) as p: p.write('A Spatial Dataset Service with ID/Name "{0}" does not exist.'.format(spatial_service_id)) exit(0) def services_list_command(args): """ Interact with Tethys Services (Spatial/Persistent Stores) to create them and/or link them to existing apps """ from tethys_services.models import SpatialDatasetService, PersistentStoreService list_persistent = False list_spatial = False if not args.spatial and not args.persistent: list_persistent = True list_spatial = True elif args.spatial: list_spatial = True elif args.persistent: list_persistent = True if list_persistent: persistent_entries = PersistentStoreService.objects.order_by('id').all() if len(persistent_entries) > 0: with pretty_output(BOLD) as p: p.write('\nPersistent Store Services:') is_first_entry = True for entry in persistent_entries: model_dict = model_to_dict(entry) if is_first_entry: with pretty_output(BOLD) as p: p.write('{0: <3}{1: <50}{2: <25}{3: <6}'.format('ID', 'Name', 'Host', 'Port')) is_first_entry = False print('{0: <3}{1: <50}{2: <25}{3: <6}'.format(model_dict['id'], model_dict['name'], model_dict['host'], model_dict['port'])) if list_spatial: spatial_entries = SpatialDatasetService.objects.order_by('id').all() if len(spatial_entries) > 0: with pretty_output(BOLD) as p: p.write('\nSpatial Dataset Services:') is_first_entry = True for entry in spatial_entries: model_dict = model_to_dict(entry) if is_first_entry: with pretty_output(BOLD) as p: p.write('{0: <3}{1: <50}{2: <50}{3: <50}{4: <30}'.format('ID', 'Name', 'Endpoint', 'Public Endpoint', 'API Key')) is_first_entry = False print('{0: <3}{1: <50}{2: <50}{3: <50}{4: <30}'.format(model_dict['id'], model_dict['name'], model_dict['endpoint'], model_dict['public_endpoint'], model_dict['apikey'] if model_dict['apikey'] else "None"))

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9,561

5.021257

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null

0

null

0

1

0

788a11df4d7eb86501d6c98b081b63c1da73fda6

6,078

py

Python

graphAttack/gaUtilities/neuralNetwork.py

jgolebiowski/graphAttack

ec8488444b44d0bd54498bf917ee42d821643ee8

[ "MIT" ]

51

2017-08-16T13:04:43.000Z

2022-03-30T09:10:30.000Z

graphAttack/gaUtilities/neuralNetwork.py

jgolebiowski/graphAttack

ec8488444b44d0bd54498bf917ee42d821643ee8

[ "MIT" ]

null

graphAttack/gaUtilities/neuralNetwork.py

jgolebiowski/graphAttack

ec8488444b44d0bd54498bf917ee42d821643ee8

[ "MIT" ]

12

2017-09-27T01:10:02.000Z

2021-05-05T09:44:56.000Z

"""Neural networks utilities""" import numpy as np from ..coreDataContainers import Variable from ..operations.activationOperations import * from ..operations.costOperations import * from ..operations.twoInputOperations import * from ..operations.singleInputOperations import * from ..operations.convolutionOperation import * from ..operations.transformationOperations import * from ..operations.multipleInputOperations import * from .misc import generateRandomVariable, generateZeroVariable def addDenseLayer(mainGraph, nOutputNodes, inputOperation=None, activation=ReLUActivation, dropoutRate=0, batchNormalisation=False): """Append a dense layer to the graph Parameters ---------- mainGraph : ga.Graph computation graph to which append the dense layer nOutputNodes : int Number of output nodes inputOperation : ga.Operation operation feeding the data to the layer activation : ga.SingleInputOperation activatin operation of choice dropoutRate : float dropout rate at the end of this layer batchNormalisation: bool Whether to use Batch normalisation w : np.array weigthts in shape (nOutputNodes, nFeatures) if None randomly initialized b : np.array biases, in shape (nOutputNodes, ) if None, randomly initialized Returns ------- ga.Operation Last operation of the dense layer """ N, D = inputOperation.shape if (inputOperation is None): inputOperation = mainGraph.operations[-1] w = generateRandomVariable(shape=(nOutputNodes, D), transpose=True, nInputs=D) b = generateRandomVariable(shape=nOutputNodes, transpose=False, nInputs=1) wo = mainGraph.addOperation(w, doGradient=True) bo = mainGraph.addOperation(b, doGradient=True) mmo = mainGraph.addOperation(MatMatmulOperation(inputOperation, wo), doGradient=False, finalOperation=False) addo = mainGraph.addOperation(AddOperation(mmo, bo), doGradient=False, finalOperation=False) if (dropoutRate > 0): dpo = mainGraph.addOperation(DropoutOperation(addo, dropoutRate), doGradient=False, finalOperation=False) else: dpo = addo if (batchNormalisation): beta = mainGraph.addOperation(generateRandomVariable((1, nOutputNodes)), doGradient=True) gamma = mainGraph.addOperation(generateRandomVariable((1, nOutputNodes)), doGradient=True) bnorm = mainGraph.addOperation(BatchNormalisationOperation(dpo, beta, gamma)) else: bnorm = dpo acto = mainGraph.addOperation(activation(bnorm), doGradient=False, finalOperation=False) return acto def addConv2dLayer(mainGraph, inputOperation=None, nFilters=1, filterHeigth=2, filterWidth=2, padding="SAME", convStride=1, activation=ReLUActivation, batchNormalisation=False, pooling=MaxPoolOperation, poolHeight=2, poolWidth=2, poolStride=2): """Append a convolution2D layer with pooling Parameters ---------- mainGraph : ga.Graph computation graph to which append the dense layer inputOperation : ga.Operation operation feeding the data to the layer nFilters : int number of filter to be applied for the convolution filterHeigth : int convolution filter heigth filterWidth : int convolution filter width padding: "SAME" or "VALID" padding method for the convolution convStride : int stride for the convolution filter activation : ga.SingleInputOperation activatin operation of choice batchNormalisation: bool Whether to use Batch normalisation pooling : ga.SingleInputOperation pooling operation of choice poolHeight : int heigth of the pooling filter poolWidth : int width of the pooling filter poolStride : int stride of the pooling operation Returns ------- ga.Operation Last operation of the dense layer """ N, C, H, W = inputOperation.shape w = generateRandomVariable(shape=(nFilters, C, filterHeigth, filterWidth), transpose=False, nInputs=(filterHeigth * filterWidth * C)) b = generateRandomVariable(shape=(1, nFilters, 1, 1), transpose=False, nInputs=1) filterWop = mainGraph.addOperation(w, doGradient=True, feederOperation=False) opConv2d = mainGraph.addOperation(Conv2dOperation( inputOperation, filterWop, stride=convStride, paddingMethod=padding)) filterBop = mainGraph.addOperation(b, doGradient=True, feederOperation=False) addConv2d = mainGraph.addOperation(AddOperation(opConv2d, filterBop)) if (batchNormalisation): beta = mainGraph.addOperation(generateRandomVariable((1, *addConv2d.shape[1:])), doGradient=True) gamma = mainGraph.addOperation(generateRandomVariable((1, *addConv2d.shape[1:])), doGradient=True) bnorm = mainGraph.addOperation(BatchNormalisationOperation(addConv2d, beta, gamma)) else: bnorm = addConv2d actop = mainGraph.addOperation(activation(bnorm), doGradient=False, finalOperation=False) poolOP = mainGraph.addOperation(pooling(inputA=actop, poolHeight=poolHeight, poolWidth=poolWidth, stride=poolStride)) return poolOP

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0.170732

0

null

0

null

0

1

0

788c4b05fc916695f80ef1ea25828672d59aa6d7

28,835

py

Python

unity/MMutils.py

kreimanlab/WhenPigsFlyContext

4d03bb29f3be3e96c2b9d1945dc08c381abae513

[ "MIT" ]

13

2021-04-07T15:39:24.000Z

2022-03-08T19:01:20.000Z

unity/MMutils.py

kreimanlab/WhenPigsFlyContext

4d03bb29f3be3e96c2b9d1945dc08c381abae513

[ "MIT" ]

1

2021-11-13T17:18:03.000Z

2021-12-03T02:05:33.000Z

unity/MMutils.py

kreimanlab/WhenPigsFlyContext

4d03bb29f3be3e96c2b9d1945dc08c381abae513

[ "MIT" ]

1

2021-04-18T18:14:51.000Z

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Nov 1 17:14:58 2020 @author: mengmi """ import IPython.display # cd into virtualhome repo import sys sys.path.append('../simulation/') from unity_simulator.comm_unity import UnityCommunication import PIL import numpy as np from collections import defaultdict import cv2 import os import math import pickle import random def display_grid_img(images_old, nrows=1): images = [x for x in images_old] h, w, _ = images[0].shape ncols = int((len(images)+nrows-1)/nrows) missing = ncols - (len(images)%ncols) for m in range(missing): images.append(np.zeros((h, w, 3)).astype(np.uint8)) img_final = [] for it_r in range(nrows): init_ind = it_r * ncols end_ind = init_ind + ncols images_take = [images[it] for it in range(init_ind, end_ind)] img_final.append(np.concatenate(images_take, 1)) img_final = np.concatenate(img_final, 0) img_final = PIL.Image.fromarray(img_final[:,:,::-1]) return img_final def display_scene_modalities(img_height, img_width, comm, ids, modalities=['normal', 'seg_class', 'seg_inst', 'depth'], nrows=1): # Check the number of cameras _, ncameras = comm.camera_count() #print(ncameras) cameras_select = list(range(ncameras)) cameras_select = [cameras_select[x] for x in ids] imgs_modality = [] for mode_name in modalities: (ok_img, imgs) = comm.camera_image(cameras_select, mode=mode_name, image_width=img_height, image_height=img_width) #print(imgs) if mode_name == 'depth': #imgs = [((x/np.max(x))*255.).astype(np.uint8) for x in imgs] imgs = [(x*255.).astype(np.uint8) for x in imgs] imgs_modality += imgs img_final = display_grid_img(imgs_modality, nrows=nrows) return img_final def find_nodes(graph, **kwargs): if len(kwargs) == 0: return None else: k, v = next(iter(kwargs.items())) return [n for n in graph['nodes'] if n[k] == v] def find_nodes_byclassname(graph, classname): return [n for n in graph['nodes'] if n['class_name'] == classname] def find_nodes_byid(graph, idnum): return [n for n in graph['nodes'] if n['id'] == idnum] def find_edges(graph, **kwargs): if len(kwargs) == 0: return None else: k, v = next(iter(kwargs.items())) return [n for n in graph['edges'] if n[k] == v] def find_allRooms(graph): return [n for n in graph['nodes'] if n['category'] == 'Rooms'] def find_rooms(graph, fromnode): roomnodes = find_allRooms(graph) if fromnode['category'] != 'Rooms': for node in roomnodes: bboxroom = node['bounding_box'] bboxobj = fromnode['bounding_box'] status = checkTwo3DBboxOverlap(bboxobj, bboxroom) if status: return node['class_name'] return fromnode['class_name'] def find_rooms_graphedges(graph, fromnode): while fromnode['category'] != 'Rooms': objedge = find_edges(graph, from_id = fromnode['id'])[0] fromnode_id = objedge['to_id'] fromnode = find_nodes_byid(graph, fromnode_id)[0] return fromnode def displayAllBbox(img_height, img_width, JasonData, img): #convert to cv2 image and ready to draw img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) for infor in JasonData.items(): left = infor[1]['bbox'][2] top = infor[1]['bbox'][0] right = infor[1]['bbox'][3] bottom = infor[1]['bbox'][1] color = (0, 0, 255) thick = 3 label = infor[1]['class_name'] +', ' + infor[1]['roomtype'] cv2.rectangle(img,(left, top), (right, bottom), color, thick) cv2.putText(img, label, (left, top - 12), 0, 1e-3 * img_width, color, thick//3) status = True return status, img def displayTargetBbox(img_height, img_width, JasonData, img, targetid, textflag, boxflag): #convert to cv2 image and ready to draw img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) status = False for infor in JasonData.items(): if infor[1]['prefab_id'] == targetid: left = infor[1]['bbox'][2] top = infor[1]['bbox'][0] right = infor[1]['bbox'][3] bottom = infor[1]['bbox'][1] targetbbox = [left, top, right, bottom] color = (0, 0, 255) thick = 3 label = infor[1]['class_name'] +', ' + infor[1]['roomtype'] if boxflag: cv2.rectangle(img,(left, top), (right, bottom), color, thick) if textflag: cv2.putText(img, label, (left, top - 12), 0, 1e-3 * img_width, color, thick//3) status = True targetarea = infor[1]['area']#(bottom - top)*(right - left) return status, targetarea, targetbbox, img return status, 0, 0, img def extractColorInstanceTable(graph, message_color): ColorInstLookUpTab = {} for prefab_id in message_color: prefab_id = int(prefab_id) #print(type(prefab_id)) objcolor_sm = message_color.get(str(prefab_id)) #color range from [0,1] #print(objcolor_sm) objcolor = np.round(np.array(objcolor_sm['Item1'], dtype=np.float32)*255.0).astype(np.uint8) #color range from [0,255] objcolor = tuple(objcolor) objnode = find_nodes_byid(graph, prefab_id)[0] infor = {} infor['prefab_id'] = prefab_id infor['prefab_name'] = objnode['prefab_name'] infor['class_name'] = objnode['class_name'] infor['category'] = objnode['category'] roomname = find_rooms(graph, objnode) infor['roomtype'] = roomname ColorInstLookUpTab[objcolor] = infor return ColorInstLookUpTab def extractJasonInstanceTable(img_inst_pil, img_inst_np, ColorInstLookUpTab): img_inst_color_tab = defaultdict(int) for pixel in img_inst_pil.getdata(): img_inst_color_tab[pixel] +=1 [imgw, imgh, imgc] = img_inst_np.shape #consolidate all objects infor on image and output jasondata for this image JasonData = {} for pixel in img_inst_color_tab: if pixel in ColorInstLookUpTab.keys(): X,Y = np.where(np.all(img_inst_np==np.asarray(pixel),axis=2)) bbox = [min(X), max(X), min(Y), max(Y)] instinfor = ColorInstLookUpTab.get(pixel) infor = {} infor['prefab_id'] = instinfor['prefab_id'] infor['prefab_name'] = instinfor['prefab_name'] infor['class_name'] = instinfor['class_name'] infor['roomtype'] = instinfor['roomtype'] infor['category'] = instinfor['category'] infor['bbox'] = bbox infor['color'] = pixel infor['area'] = img_inst_color_tab.get(pixel)*1.0/(imgw*imgh) #ratio of isntance area on the entire image JasonData[pixel] = infor return JasonData def convertPILImageToNumpyImage(img_all_pil, img_height, img_width): #img contains modalities=['normal', 'seg_class', 'seg_inst'], nrows=3 #split into three images (normal, seg_class, seg_instance) img_ori_pil = img_all_pil.crop((0, img_width*0, img_height, img_width*1)) img_class_pil = img_all_pil.crop((0, img_width*1, img_height, img_width*2)) img_inst_pil = img_all_pil.crop((0, img_width*2, img_height, img_width*3)) #convert to numpy array img_ori_np = np.array(img_ori_pil) img_class_np = np.array(img_class_pil) img_inst_np = np.array(img_inst_pil) return img_ori_pil, img_class_pil, img_inst_pil, img_ori_np, img_class_np, img_inst_np def IsHighContrast(img_height, img_width, ThresContrast, RatioCroppedContrast, JasonData, img, targetid): #convert to cv2 image and ready to draw img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) imgY = cv2.cvtColor(img, cv2.COLOR_BGR2YUV)[:,:,0] status = False for infor in JasonData.items(): if infor[1]['prefab_id'] == targetid: left = infor[1]['bbox'][2] top = infor[1]['bbox'][0] right = infor[1]['bbox'][3] bottom = infor[1]['bbox'][1] #print(infor[1]['bbox']) width = bottom - top height = right - left if int(left-RatioCroppedContrast*height) <0: left = 0 else: left = int(left-RatioCroppedContrast*height) if int(right+RatioCroppedContrast*height) > (img_height-1): right = img_height - 1 else: right = int(right+RatioCroppedContrast*height) if int(top-RatioCroppedContrast*width) <0: top = 0 else: top = int(top-RatioCroppedContrast*width) if int(bottom+RatioCroppedContrast*width) > (img_width-1): bottom = img_width-1 else: bottom = int(bottom+RatioCroppedContrast*width) cropped_imgY = imgY[top:bottom, left:right] # compute min and max of Y #print(cropped_imgY.shape) if cropped_imgY.shape[0] == 0 or cropped_imgY.shape[1] == 0: return False Ymin = np.min(cropped_imgY) Ymax = np.max(cropped_imgY) #print(Ymin) #print(Ymax) # compute contrast contrast = (Ymax-Ymin)/(Ymax+Ymin) #print(contrast) if contrast > ThresContrast: status = True return status def checkCameraImageFitness(JasonData, targetprefabid, ThresRoomArea): #two criterias for a good pic: #1. the target object is on the pic #2. the camera is mostly looking at one room (not crossing two rooms); ThresRoomArea statusTarget = False #cond1 flag statusRoom = False #cond2 flag #keep track of total areas for each room type roomarea = defaultdict(float) for infor in JasonData.items(): roomarea[infor[1]['roomtype']] += infor[1]['area'] if infor[1]['prefab_id'] == targetprefabid: statusTarget = True targetroom = infor[1]['roomtype'] if not statusTarget: #print('Target not in pic') return False else: return True # otherarea = 0.0 # for roomtype in roomarea: # if roomtype != targetroom: # otherarea += roomarea.get(roomtype) # if otherarea <= ThresRoomArea: # statusRoom = True # # if not statusRoom: # print('contain too many rooms!') # return statusTarget & statusRoom def checkCameraImageBlackSky(img_ori_np, ThresBlackSkyArea): [imgw, imgh, imgc] = img_ori_np.shape X,Y = np.where(np.all(img_ori_np==np.asarray([0,0,0]),axis=2)) area = len(X)*1.0/(imgw*imgh) if area >= ThresBlackSkyArea: return False else: return True def IsTargetCollision(JasonData, graph, target_id): targetnode = find_nodes_byid(graph, target_id)[0] targetbbox = targetnode['bounding_box'] for infor in JasonData.items(): if infor[1]['prefab_id'] == target_id: continue elif infor[1]['category'] == 'Rooms': continue else: objbbox = find_nodes_byid(graph, infor[1]['prefab_id'])[0]['bounding_box'] status = checkTwo3DBboxOverlap(targetbbox, objbbox) or checkTwo3DBboxOverlap(objbbox, targetbbox) if status: print("collided with: " + infor[1]['prefab_name'] + "; from: " + infor[1]['category']) return True #collision is happening return False def checkTwo3DBboxOverlap(bbox1, bbox2): #get 8 vertex of bbox1 vertexlist = [] for i in [-1,1]: for j in [-1,1]: for k in [-1,1]: point = np.array([bbox1['center'][0]+i*bbox1['size'][0]/2, bbox1['center'][1]+j*bbox1['size'][1]/2, bbox1['center'][2]+k*bbox1['size'][2]/2]) vertexlist.append(point) #check wehtehr each point is within bbox2 for i in range(8): status = isPointInsideBox(vertexlist[i], bbox2) if status: return True return False def checkCamCollision(cam_pos, graph): status = False for node in graph['nodes']: if node['category'] == 'Rooms' or node['category'] == 'Walls': continue else: bbox = node['bounding_box'] statusInside = isPointInsideBox(cam_pos,bbox) if statusInside: status = True print(node['prefab_name']) return status return status def isPointInsideBox(point, bbox): #get bbox2 boundaries minX = bbox['center'][0] - bbox['size'][0]/2 maxX = bbox['center'][0] + bbox['size'][0]/2 minY = bbox['center'][1] - bbox['size'][1]/2 maxY = bbox['center'][1] + bbox['size'][1]/2 minZ = bbox['center'][2] - bbox['size'][2]/2 maxZ = bbox['center'][2] + bbox['size'][2]/2 return (point[0] >= minX and point[0] <= maxX) and (point[1] >= minY and point[1] <= maxY) and (point[2] >= minZ and point[2] <= maxZ) def FindOptimalCamTargetConfig_original(targetSz, targetYpos, NumRes): if targetSz <0.5: Radius = np.sqrt(2) elif targetSz <1: Radius = 1.5*np.sqrt(2) elif targetSz <2: Radius = 2.5*np.sqrt(2) else: Radius = 4*np.sqrt(2) if targetYpos > 1.4: camYStepSz = 0 targetYStepSz = -0.25 elif targetYpos>0.7: targetYStepSz = 0.25 camYStepSz = 0.5 else: targetYStepSz = 0.25 camYStepSz = 1 circ = CircleTrajectory(Radius, NumRes) return circ, camYStepSz, targetYStepSz def FindOptimalCamTargetConfig_size(targetSz, sizeMult, targetYpos, NumRes): if targetSz <0.5: Radius = 2*np.sqrt(2) elif targetSz <1: Radius = 1.5*2*np.sqrt(2) elif targetSz <2: Radius = 2.5*1.5*np.sqrt(2) else: Radius = 4*np.sqrt(2) if targetYpos > 1.4: camYStepSz = 0 targetYStepSz = -0.25-0.2 elif targetYpos>0.7: targetYStepSz = 0.25 camYStepSz = 0.5+0.3 else: targetYStepSz = 0.25 camYStepSz = 1+0.5 circ = CircleTrajectory(Radius, NumRes) return circ, camYStepSz, targetYStepSz #objects in their original place def FindOptimalCamTargetConfig_gravity(targetSz, targetYpos, NumRes): if targetSz <0.5: Radius = np.sqrt(2) elif targetSz <1: Radius = 1.5*np.sqrt(2) elif targetSz <2: Radius = 2.5*np.sqrt(2) else: Radius = 4*np.sqrt(2) if targetYpos > 1.4: camYStepSz = 0 targetYStepSz = -0.25 elif targetYpos>0.7: targetYStepSz = 0.25 camYStepSz = 0.5 else: targetYStepSz = 0.25 camYStepSz = 1 circ = CircleTrajectory(Radius, NumRes) return circ, camYStepSz, targetYStepSz def FindOptimalCamTargetConfig_trained(targetSz, targetYpos, NumRes): if targetSz <0.5: Radius = 1*np.sqrt(2) elif targetSz <1: Radius = 1.5*np.sqrt(2) elif targetSz <2: Radius = 2*np.sqrt(2) else: Radius = 2.5*np.sqrt(2) if targetYpos > 1.4: pitch = [np.pi/2 + np.pi/9, 7*np.pi/18] #pitch angle in radians [-20, 20] elif targetYpos>0.7: pitch = [7*np.pi/18, np.pi/6] #pitch angle in radians [20, 60] else: pitch = [np.pi/3, np.pi/9] #pitch angle in radians [30, 70] circ = SphereTrajectory(Radius, pitch, NumRes) return circ def SphereTrajectory(radius, pitch, Res): #takes in radius and how many uniformly sampled points on the circle #generate list of tuple (x,y) coordinates on the circle equally spaced circ = list() for p in pitch: for j in range(Res): circ.append( ( radius* np.sin(p) * np.cos(j* 2 * np.pi / Res), radius*np.cos(p), radius* np.sin(p) * np.sin(j* 2 * np.pi / Res) )) return circ def FindOptimalCamTargetConfig_trained2(targetSz, targetYpos, NumRes): Resolution = 2.0 # 1 deg angle resolution radius = [] pitch = [] yaw = [] for i in range(NumRes): RandSzTimes = random.randrange(2,7) #random int from [2,10] inclusive radius.append(1.0*RandSzTimes*targetSz) yaw.append( random.randrange(0, int(360/Resolution), Resolution)*Resolution/360 * math.pi*2) if targetYpos > 1.4: pitch.append( random.randrange(-int(35/Resolution), int(55/Resolution), Resolution)*Resolution/90 * math.pi/2) else: pitch.append( random.randrange(int(10/Resolution), int(90/Resolution), Resolution)*Resolution/90 * math.pi/2) # print(radius) # print(pitch) # print(yaw) circ = SphereTrajectory2(radius, pitch, yaw) return circ, radius, pitch, yaw def FindOptimalCamTargetConfig_trained3(targetSz, targetYpos, NumRes): Resolution = 2.0 # 1 deg angle resolution radius = [] pitch = [] yaw = [] for i in range(NumRes): RandSzTimes = random.randrange(1,10) #random int from [2,10] inclusive radius.append(1.0*RandSzTimes*0.5) yaw.append( random.randrange(0, int(360/Resolution), Resolution)*Resolution/360 * math.pi*2) if targetYpos > 1.4: pitch.append( random.randrange(-int(35/Resolution), int(55/Resolution), Resolution)*Resolution/90 * math.pi/2) else: pitch.append( random.randrange(int(10/Resolution), int(90/Resolution), Resolution)*Resolution/90 * math.pi/2) # print(radius) # print(pitch) # print(yaw) circ = SphereTrajectory2(radius, pitch, yaw) return circ, radius, pitch, yaw def SphereTrajectory2(radius, pitch, yaw): #takes in radius and how many uniformly sampled points on the circle #generate list of tuple (x,y) coordinates on the circle equally spaced circ = list() for i, R in enumerate(radius): p = pitch[i] y = yaw[i] circ.append( ( R* np.sin(p) * np.cos(y), R*np.cos(p), R* np.sin(p) * np.sin(y) )) return circ def CircleTrajectory(radius, Res): #takes in radius and how many uniformly sampled points on the circle #generate list of tuple (x,y) coordinates on the circle equally spaced circ = list() for j in range(Res): circ.append( ( radius* np.cos(j* 2 * np.pi / Res), radius* np.sin(j* 2 * np.pi / Res) )) return circ def saveImgList(writedir, writedirjason, imageprefix, imgformat, sort_index, CamMImg, CamMID, TargetInfor, propFirstN, saveJasonflag): N = int(propFirstN * len(sort_index)) for index in sort_index[:N]: count_camview = CamMID[index] img_inst_target_cv2 = CamMImg[index] print(writedir + imageprefix + str(count_camview) + imgformat) cv2.imwrite(writedir + imageprefix + str(count_camview) + imgformat, img_inst_target_cv2) if saveJasonflag: storeinfor = TargetInfor[index] #storeinfor_json = json.dumps(storeinfor) f = open(writedirjason + imageprefix + str(count_camview) + ".pkl","wb") pickle.dump(storeinfor,f) f.close() def saveImgList_train(writedir, writedirjason, imageprefix, imgformat, sort_index, CamMImg, CamMID, TargetInfor, propFirstN, saveJasonflag): N = int(propFirstN * len(sort_index)) for index in sort_index[:N]: count_camview = CamMID[index] img_inst_target_cv2 = CamMImg[index] print(writedir + imageprefix + str(count_camview) + imgformat) img_inst_target_cv2 = cv2.resize(img_inst_target_cv2, (640, 512)) cv2.imwrite(writedir + imageprefix + str(count_camview) + imgformat, img_inst_target_cv2) if saveJasonflag: storeinfor = TargetInfor[index] #storeinfor_json = json.dumps(storeinfor) f = open(writedirjason + imageprefix + str(count_camview) + ".pkl","wb") pickle.dump(storeinfor,f) f.close() def findAllPossibleDestNodes(targetclass, wantedClass, ItemToRoom, SurfaceToRoom, RoomList, SurfaceList, graph): destnodesIDs = [] destPrefabs = [] destTargetRooms = [] destSurfaceList=[] destRooms = [] for i in np.where(ItemToRoom[wantedClass.index(targetclass)] == 1)[0]: destRooms.append(RoomList[i]) destSurface = [] for dstR in destRooms: for i in np.where( SurfaceToRoom[:, RoomList.index(dstR)] == 1)[0]: destSurface.append(SurfaceList[i]) destSurface = set(destSurface) destSurface = list(destSurface) for node in graph['nodes']: if node['class_name'] not in destSurface: continue roomIn = find_rooms(graph, node) if roomIn not in destRooms: #print("warning! " + roomIn + " doesnt belong to any rooms!") continue destnodesIDs.append(node['id']) destPrefabs.append(node['prefab_name']) destTargetRooms.append(roomIn) destSurfaceList.append(node['class_name']) return destnodesIDs, destPrefabs, destTargetRooms, destSurfaceList def findAllPossibleDestNodes_anomaly(targetclass, wantedClass, ItemToRoom, RoomList, SurfaceList, graph): destnodesIDs = [] destPrefabs = [] destTargetRooms = [] destSurfaceList=[] destRoomNode = [] destWallNode = [] destSurface = [] for i in np.where(ItemToRoom[wantedClass.index(targetclass)] == 1)[0]: surfacename = SurfaceList[i] if 'floor_' in surfacename: surfacename = surfacename[6:] destSurface.append(surfacename) else: destSurface.append(surfacename) destSurface = set(destSurface) destSurface = list(destSurface) #find all wall surfaces and their corresponding room # wallnodes=[] # wallroom = [] # for node in graph['nodes']: # if node['class_name'] == 'wall': # sz = node['bounding_box']['size'] # if all(x > 2 for x in sz): # continue; # else: # roomIn = find_rooms_graphedges(graph, node) # wallroom.append(roomIn) # wallnodes.append(node) for node in graph['nodes']: if node['class_name'] != 'wall': if node['class_name'] not in destSurface: continue if node['class_name'] in RoomList: roomIn = node['class_name'] else: roomIn = find_rooms(graph, node) destnodesIDs.append(node['id']) destPrefabs.append(node['prefab_name']) destTargetRooms.append(roomIn) destSurfaceList.append(node['class_name']) destRoomNode.append(float("nan")) destWallNode.append(float("nan")) else: sz = node['bounding_box']['size'] if all(x > 2 for x in sz): continue; else: roomNode = find_rooms_graphedges(graph, node) roomIn = roomNode['class_name'] destsurf = 'wall_' + roomIn if destsurf in destSurface: destnodesIDs.append(node['id']) destPrefabs.append(node['prefab_name']) destTargetRooms.append(roomIn) destSurfaceList.append(node['class_name']) destRoomNode.append(roomNode) destWallNode.append(node) return destnodesIDs, destPrefabs, destTargetRooms, destSurfaceList, destRoomNode, destWallNode def add_node(graph, n): graph['nodes'].append(n) def add_edge(graph, fr_id, rel, to_id): graph['edges'].append({'from_id': fr_id, 'relation_type': rel, 'to_id': to_id}) def deleteGraphByClassname(graph, target_classname): #print(graph) ToDeleteList = find_nodes_byclassname(graph, target_classname) #print(ToDeleteList) ToDeleteIDList = [] for i, mc in enumerate(ToDeleteList): ToDeleteIDList.append(mc['id']) #del mc['obj_transform'] #del mc['bounding_box'] flagAll = True while flagAll: for i, node in enumerate(graph['nodes']): if node['class_name'] == target_classname: del graph['nodes'][i] flagAll = True break else: flagAll = False #print(ToDeleteIDList) #for idDelete in ToDeleteIDList: graph['edges'] = [edge for edge in graph['edges'] if (edge['from_id'] not in ToDeleteIDList) and (edge['to_id'] not in ToDeleteIDList)] return graph def computeMoveNodeOffset_anomaly(destwallnode, destroomnode, targetnode): wallcenter = destwallnode['bounding_box']['center'] roomcenter = destroomnode['bounding_box']['center'] if destwallnode['bounding_box']['size'][0]<2: alongaxis = 0 else: alongaxis = 2 if wallcenter[alongaxis] - roomcenter[alongaxis] > 0: axisorient = -1 else: axisorient = 1 desiredpos = wallcenter.copy() desiredpos[alongaxis] = wallcenter[alongaxis] + axisorient*targetnode['bounding_box']['size'][alongaxis]/2 movenode_offset = desiredpos.copy() for dim in range(3): movenode_offset[dim] = desiredpos[dim] - targetnode['bounding_box']['center'][dim] return movenode_offset def find_destsurfnode_byclassname(graph, targetnode, destsurf): targetid = targetnode['id'] destsurflist = find_nodes_byclassname(graph, destsurf) destsurfidlist = [node['id'] for node in destsurflist] targetsurfidlist = [edge['to_id'] for edge in graph['edges'] if edge['from_id'] == targetid and edge['relation_type'] == 'ON'] surfnode = [] if len(destsurfidlist)>0 and len(targetsurfidlist)>0 : counter = 0 for did in destsurfidlist: if did in targetsurfidlist: surfnode.append(destsurflist[counter]) break counter = counter + 1 return surfnode def computePossibleLocationsOnSurf(targetnode, surfnode, scaleStepSz): targetSzX = targetnode['bounding_box']['size'][0] targetSzZ = targetnode['bounding_box']['size'][2] leftBoundSurfX = surfnode['bounding_box']['center'][0] - surfnode['bounding_box']['size'][0]/2 + targetSzX/2 rightBoundSurfX = surfnode['bounding_box']['center'][0] + surfnode['bounding_box']['size'][0]/2 - targetSzX/2 leftBoundSurfZ = surfnode['bounding_box']['center'][2] - surfnode['bounding_box']['size'][2]/2 + targetSzZ/2 rightBoundSurfZ = surfnode['bounding_box']['center'][2] + surfnode['bounding_box']['size'][2]/2 - targetSzZ/2 x = np.arange(leftBoundSurfX,rightBoundSurfX,scaleStepSz*targetSzX) z = np.arange(leftBoundSurfZ,rightBoundSurfZ,scaleStepSz*targetSzZ) # x = np.arange(leftBoundSurfX,rightBoundSurfX,0.1) # z = np.arange(leftBoundSurfZ,rightBoundSurfZ,0.1) xpos, zpos = np.meshgrid(x,z) xpos = xpos.flatten() zpos = zpos.flatten() xoffset = xpos - targetnode['bounding_box']['center'][0] zoffset = zpos - targetnode['bounding_box']['center'][2] return xoffset, zoffset def segmentTargetBbox(img_height, img_width, JasonData, img, targetid): #convert to cv2 image and ready to draw #img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) seg = np.zeros((img_width, img_height)).astype('uint8') status = False for infor in JasonData.items(): if infor[1]['prefab_id'] == targetid: pixel = infor[1]['color'] X,Y = np.where(np.all(img==np.asarray(pixel),axis=2)) left = infor[1]['bbox'][2] top = infor[1]['bbox'][0] right = infor[1]['bbox'][3] bottom = infor[1]['bbox'][1] targetbbox = [left, top, right, bottom] seg[X,Y] = 255 status = True targetarea = infor[1]['area']#(bottom - top)*(right - left) seg = cv2.cvtColor(seg, cv2.COLOR_GRAY2BGR) return status, targetarea, targetbbox, seg return status, 0,0, img

37.69281

157

0.594902

3,435

28,835

4.893741

0.1377

0.012849

0.010113

0.009102

0.466865

0.41749

0.392683

0.365021

0.350446

0.325164

0

0.026204

0.283995

28,835

764

158

37.742147

0.787998

0.11167

0

0.455185

0

0.053735

0

1

0.072056

false

0

0.019332

0.005272

0.175747

0.00703

0

null

0

null

0

1

0

788eb0842dabcf5bdcfbc33b4a3c93db411e4720

316

py

Python

test/testConnectDB.py

dantegg/pythonWeather

ceda06e0fb2fe68695b56f8bf0d206099d8779d9

[ "MIT" ]

null

test/testConnectDB.py

dantegg/pythonWeather

ceda06e0fb2fe68695b56f8bf0d206099d8779d9

[ "MIT" ]

null

test/testConnectDB.py

dantegg/pythonWeather

ceda06e0fb2fe68695b56f8bf0d206099d8779d9

[ "MIT" ]

null

#coding:utf-8 import sys sys.path.append("..") from connectDB import connectDB testDB = connectDB testWeatherRecord = { "collectTime": '2016-10-16', "ctemp":'22' } testconnection = testDB.connectMongo() testDB.saveWeather(testWeatherRecord,testconnection) testDB.printWeather(testconnection)

18.588235

52

0.727848

31

316

7.419355

0.677419

0.173913

0

0.040892

0.148734

316

17

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18.588235

0.814126

0.037975

0

0.098684

0

1

0

false

0

0.181818

0

0.181818

0.090909

0

null

0

null

0

1

0

7890861cf35effbd978b1f446fe1133e0f69c410

12,683

py

Python

analysis/analysis/seg_stats.py

asaran/sawyer-demos_human-audio

b9f1d1df152234569a95b525441e2afba43f54bf

[ "MIT" ]

null

analysis/analysis/seg_stats.py

asaran/sawyer-demos_human-audio

b9f1d1df152234569a95b525441e2afba43f54bf

[ "MIT" ]

null

analysis/analysis/seg_stats.py

asaran/sawyer-demos_human-audio

b9f1d1df152234569a95b525441e2afba43f54bf

[ "MIT" ]

null

# Analyze the ground truth hand annotated audio features with error presence/segment presence. (Level II) # [What kind of mistake types there are? What are the types of audio that are labeled there?] # TODO: bar plots indicating how frequent errors/segment types are # TODO: What is the distribution of human audio types for the different error/segment types # frequency count of different speech types during precision, non-precision, no-seg chunks # frequency count of different speech types during seg, no-seg chunks import librosa import os import pickle as pkl import argparse import csv class SegmentAnalysis(): def __init__(self, args): self.seg_ann = args.segmentation_annotator self.utt_ann = args.utterances_annotator self.demo_dir = '../../' self.audio_dir = '../../data/demo_audio' # self.seg_dir = os.path.join(self.demo_dir, 'annotations/A4') self.tasks = ['box', 'cutting'] self.demo_types = ['video', 'kt'] self.users = ['user2', 'user3', 'user4', 'user5', 'user6', 'user7', 'user8', 'user9', 'user10',\ 'user11', 'user12','user14', 'user15', 'user16', 'user17', 'user18', 'user19', 'user20'] with open('../../data/seg_'+self.seg_ann+'.pkl', 'rb') as fp: self.seg_annot = pkl.load(fp) with open('../../data/audio_'+self.utt_ann+'.pkl', 'rb') as fp: self.utt_annot = pkl.load(fp) self.box_time, self.cutting_time = 0, 0 self.box_pr_time, self.box_non_pr_time = 0, 0 self.cutting_pr_time, self.cutting_non_pr_time = 0, 0 self.box_utt_time, self.cutting_utt_time = 0, 0 self.box_pr_utt_time, self.box_non_pr_utt_time, self.box_non_seg_utt_time = 0, 0, 0 self.cutting_pr_utt_time, self.cutting_non_pr_utt_time, self.cutting_non_seg_utt_time = 0, 0, 0 def get_precision_labels(self): # segment list for precise, no-precise subtasks with open('../../data/box_precise.pkl', 'rb') as fp: self.box_precise = pkl.load(fp) with open('../../data/box_not-precise.pkl', 'rb') as fp: self.box_not_precise = pkl.load(fp) with open('../../data/cutting_precise.pkl', 'rb') as fp: self.cutting_precise = pkl.load(fp) with open('../../data/cutting_not-precise.pkl', 'rb') as fp: self.cutting_not_precise = pkl.load(fp) def get_demo_time(self,demo_id): user_id, task, demo_type = demo_id.split('_') # total demo time (not just speech time) audio_path = os.path.join(self.audio_dir,user_id,task,demo_type,'env.wav') audio, sr = librosa.load(audio_path) demo_len = (audio.shape[0])/sr return demo_len def get_stats(self): for demo_id in self.seg_annot: # What % of a demonstration is not a segment or an error? # get total demo time from wav file, get err/seg duration from annotated json demo_time = self.get_demo_time(demo_id) user_id, task, demo_type = demo_id.split('_') # utt start and stop times for each annotation utt_start = self.utt_annot[demo_id]['start'] utt_stop = self.utt_annot[demo_id]['stop'] utt_duration = self.utt_annot[demo_id]['duration'] if task=='box': self.box_time+=demo_time if task=='cutting': self.cutting_time+=demo_time for k,l,d in zip(utt_start,utt_stop,utt_duration): assert(d==0 or d==l-k) if task=='box': self.box_utt_time+=d if task=='cutting': self.cutting_utt_time+=d # start and stop times for each annotated segment seg_start = self.seg_annot[demo_id]['start'] seg_stop = self.seg_annot[demo_id]['stop'] segments = self.seg_annot[demo_id]['seg_label'] self.get_precision_labels() for i,j,s in zip(seg_start,seg_stop,segments): dur = j-i if dur>0: if task=='box': if s in self.box_precise: # precision_label = 'precision' self.box_pr_time+=dur # During such parts of a demonstration, what % of time are people talking? # would require to find overlap of both seg annot and utt annot # how much people talk in precision, non-precision, and no-seg chunks? for k,l,d in zip(utt_start,utt_stop,utt_duration): if d>0: # utt completely inside seg if k>=i and k<=j and l>=i and l<=j: self.box_pr_utt_time+=d # utt stop inside seg elif k<i and k<j and l>=i and l<=j: assert(l-i>=0) self.box_pr_utt_time+=(l-i) # utt start inside seg elif k>=i and k<=j and l>i and l>j: assert(j-k>=0) self.box_pr_utt_time+=(j-k) # seg completely inside utt elif k>=i and k<=j and l>=i and l<=j: self.box_pr_utt_time+=dur elif s in self.box_not_precise: # precision_label = 'non-precision' self.box_non_pr_time+=dur # During such parts of a demonstration, what % of time are people talking? # would require to find overlap of both seg annot and utt annot # how much people talk in precision, non-precision, and no-seg chunks? for k,l,d in zip(utt_start,utt_stop,utt_duration): if d>0: # utt completely inside seg if k>=i and k<=j and l>=i and l<=j: self.box_non_pr_utt_time+=d # utt stop inside seg elif k<i and k<j and l>=i and l<=j: assert(l-i>=0) self.box_non_pr_utt_time+=(l-i) # utt start inside seg elif k>=i and k<=j and l>i and l>j: assert(j-k>=0) self.box_non_pr_utt_time+=(j-k) # seg completely inside utt elif k>=i and k<=j and l>=i and l<=j: self.box_non_pr_utt_time+=dur elif task=='cutting': if s in self.cutting_precise: # precision_label = 'precision' self.cutting_pr_time+=dur # During such parts of a demonstration, what % of time are people talking? # would require to find overlap of both seg annot and utt annot # how much people talk in precision, non-precision, and no-seg chunks? for k,l,d in zip(utt_start,utt_stop,utt_duration): if d>0: # utt completely inside seg if k>=i and k<=j and l>=i and l<=j: self.cutting_pr_utt_time+=d # utt stop inside seg elif k<i and k<j and l>=i and l<=j: assert(l-i>=0) self.cutting_pr_utt_time+=(l-i) # utt start inside seg elif k>=i and k<=j and l>i and l>j: assert(j-k>=0) self.cutting_pr_utt_time+=(j-k) # seg completely inside utt elif k>=i and k<=j and l>=i and l<=j: self.cutting_pr_utt_time+=dur elif s in self.cutting_not_precise: # precision_label = 'non-precision' self.cutting_non_pr_time+=dur # During such parts of a demonstration, what % of time are people talking? # would require to find overlap of both seg annot and utt annot # how much people talk in precision, non-precision, and no-seg chunks? for k,l,d in zip(utt_start,utt_stop,utt_duration): if d>0: # utt completely inside seg if k>=i and k<=j and l>=i and l<=j: self.cutting_non_pr_utt_time+=d # utt stop inside seg elif k<i and k<j and l>=i and l<=j: assert(l-i>=0) self.cutting_non_pr_utt_time+=(l-i) # utt start inside seg elif k>=i and k<=j and l>i and l>j: assert(j-k>=0) self.cutting_non_pr_utt_time+=(j-k) # seg completely inside utt elif k>=i and k<=j and l>=i and l<=j: self.cutting_non_pr_utt_time+=dur # utt during non-seg parts = total utt_time in demo - utt_time during seg self.box_non_seg_utt_time = self.box_utt_time - self.box_pr_utt_time - self.box_non_pr_utt_time # utt during non-seg parts = total utt_time in demo - utt_time during seg self.cutting_non_seg_utt_time = self.cutting_utt_time - self.cutting_pr_utt_time - self.cutting_non_pr_utt_time def write_csv(self): self.get_stats() exp_file = open('seg_stats.csv', mode='w') writer = csv.writer(exp_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL) column_labels = ['Box Precision Seg Time', 'Box Non-Precision Seg Time', 'Box Total Demo Time','',\ 'Cutting Precision Seg Time', 'Cutting Non-Precision Seg Time', 'Cutting Total Demo Time'] writer.writerow(column_labels) column_values = [self.box_pr_time, self.box_non_pr_time, self.box_time, '',\ self.cutting_pr_time, self.cutting_non_pr_time, self.cutting_time] writer.writerow(column_values) writer.writerow([]) column_labels = ['Box Total Utterance Time', 'Box Precision Utterance Time', 'Box Non-Precision Utt Time',\ 'Box Total Seg Utt Time', 'Box Total Non-Seg Utt Time', '', 'Cutting Total Utterance Time',\ 'Cutting Precision Utt Time', 'Cutting Non-Precision Utt Time', 'Cutting Total Seg Utt Time',\ 'Cutting Total Non-Seg Utt Time'] writer.writerow(column_labels) column_values = [self.box_utt_time, self.box_pr_utt_time, self.box_non_pr_utt_time,\ self.box_pr_utt_time+self.box_non_pr_utt_time, self.box_non_seg_utt_time, '',\ self.cutting_utt_time, self.cutting_pr_utt_time, self.cutting_non_pr_utt_time,\ self.cutting_pr_utt_time+self.cutting_non_pr_utt_time, self.cutting_non_seg_utt_time] writer.writerow(column_values) exp_file.close() def main(): parser = argparse.ArgumentParser() parser.add_argument('-s', '--segmentation-annotator',type=str,default='A4') parser.add_argument('-u', '--utterances-annotator',type=str,default='A2') args = parser.parse_args() analysis = SegmentAnalysis(args) analysis.write_csv() if __name__ == '__main__': main()

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3.775189

0.120907

0.066555

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0.032027

0.640867

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0.083871

0

null

0

null

0

1

0

7890d4f24ed4a643bfc95b0ced319e559f2a1e26

4,786

py

Python

AVSD_Baseline/Feature_Extraction/extract_i3d_rgb_features.py

hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge

6a5ee8542132ad6634ee02896d7c935b8c447d78

[ "MIT" ]

51

2018-06-04T11:34:58.000Z

2022-03-09T09:18:08.000Z

AVSD_Baseline/Feature_Extraction/extract_i3d_rgb_features.py

TwentyBN/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge

61ea13cd680fc4743ad20e010c6d3047e03b993c

[ "MIT" ]

4

2018-08-17T12:40:34.000Z

2020-01-09T19:00:56.000Z

AVSD_Baseline/Feature_Extraction/extract_i3d_rgb_features.py

hudaAlamri/DSTC7-Audio-Visual-Scene-Aware-Dialog-AVSD-Challenge

6a5ee8542132ad6634ee02896d7c935b8c447d78

[ "MIT" ]

13

2018-06-01T19:50:44.000Z

2020-12-04T03:37:48.000Z

"""I3D feature extration using a tensorflow model. Copyright 2018 Mitsubishi Electric Research Labs """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import h5py import numpy as np import tensorflow as tf import time import os import scipy.io as sio import skimage.io from skimage.transform import rescale, resize, downscale_local_mean from random import randint import cv2 import i3d from i3d import Unit3D import sonnet as snt import skvideo.io import pickle import argparse parser = argparse.ArgumentParser() parser.add_argument('--input', default='data/Charades_v1_rgb', type=str, help='Directory that includes image files') parser.add_argument('--net_output', default='Mixed_5c', type=str, help="layer used as output features") parser.add_argument('--feature_dim', '-f', default=2048, type=int, help='output feature dimension') parser.add_argument('--model_path', default='data/i3d_model/data/checkpoints/rgb_imagenet', type=str, help='model path') parser.add_argument('--stride', default=4, type=int, help='stride of frame features') parser.add_argument('--output',default='data/Charades/i3d_rgb', type=str, help='output pickle file of feature vectors') parser.add_argument('--seq_length', default=16, type=int, help='window size of frame features') args = parser.parse_args() _IMAGE_SIZE = 224 _NUM_CLASSES = 400 def train(): print (args.model_path) model_path = args.model_path pose_net_path = os.path.join(model_path, 'model.ckpt') tf.reset_default_graph() with tf.variable_scope('RGB'): rgb_input = tf.placeholder(tf.float32, [None, args.seq_length, _IMAGE_SIZE, _IMAGE_SIZE, 3]) rgb_y = tf.placeholder(tf.float32, [None, _NUM_CLASSES]) lr = tf.placeholder("float") drop_out_prob = tf.placeholder("float") i3d_model = i3d.InceptionI3d(num_classes=_NUM_CLASSES, final_endpoint='Mixed_5c') net, end_points = i3d_model(rgb_input, is_training=False, dropout_keep_prob=drop_out_prob) rgb_variable_map = {} for variable in tf.global_variables(): if variable.name.split('/')[0] == 'RGB': rgb_variable_map[variable.name.replace(':0', '')] = variable tf_config = tf.ConfigProto() restorer = tf.train.Saver(var_list=rgb_variable_map, reshape=True) with tf.Session(config=tf_config) as sess: restorer.restore(sess, pose_net_path) lr_s = 0.0001 drop_out = 1 save_folder = args.output root_folder = args.input num_seq = len(os.listdir(root_folder)) for f1 in os.listdir(root_folder): seq = os.listdir(os.path.join(root_folder, f1)) f_exit = os.listdir(save_folder) if f1 not in f_exit: os.mkdir(os.path.join(save_folder, f1)) else: if os.listdir(os.path.join(save_folder, f1)) !=[]: continue num_frame = len(seq) if num_frame < args.seq_length: print("There should be at least",args.seq_length," frames") num_sample = num_frame//args.stride features = np.zeros(shape=[num_sample, args.feature_dim]) for i in range(0, num_sample): Start_f = i*args.stride + 1 input = np.zeros(shape=[1, args.seq_length, _IMAGE_SIZE, _IMAGE_SIZE, 3]) gth_label = np.zeros(shape=[1, _NUM_CLASSES]) for j in range(0, args.seq_length): pick_f = Start_f + j if pick_f > num_frame: pick_f = Start_f im = cv2.imread(os.path.join(root_folder, f1, (f1 + '-' + ("%06d" % pick_f) + '.jpg'))) im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB) im = cv2.resize(im, (_IMAGE_SIZE, _IMAGE_SIZE)) im = (im - 128)/128 input[:, j, :, :, :] = im gth_label[0] = 1 feed_dict = { rgb_input: input, rgb_y: gth_label, lr: lr_s, drop_out_prob: drop_out } logits, net_feature = sess.run([net, end_points], feed_dict) Mix5c = net_feature[args.net_output] feature = Mix5c.mean(axis=(2,3)) feature = feature.reshape((1, 2048)) features[i, :] = feature pickle.dump(features, open(os.path.join(save_folder, f1) + '/feature.pkl', 'wb'), 2) def main(argv=None): train() if __name__ == '__main__': tf.app.run()

39.553719

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621

4,786

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0.320451

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0.043574

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0.023435

0

0.024569

0.285625

4,786

120

121

39.883333

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1

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false

0

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0

0.205882

0.029412

0

null

0

null

0

1

0

7891d89406deccb9af158b35a76ea6e08c700edb

1,037

py

Python

code/add_country_lat_lon.py

ParhamP/Global-Trade-Network

106d3e55fba04e72feda2844d092745ce170e55d

[ "BSD-3-Clause" ]

2

2021-08-22T10:02:08.000Z

2021-11-09T11:30:31.000Z

code/add_country_lat_lon.py

ParhamP/Global-Trade-Network

106d3e55fba04e72feda2844d092745ce170e55d

[ "BSD-3-Clause" ]

null

code/add_country_lat_lon.py

ParhamP/Global-Trade-Network

106d3e55fba04e72feda2844d092745ce170e55d

[ "BSD-3-Clause" ]

null

import csv import collections # iterator and counter libraries with open("../MIT_WT_datafiles/country_names.csv", 'r') as cntry, open("../MIT_WT_datafiles/country_lat_lon_from_google.csv", 'r') as ll, open("../MIT_WT_datafiles/cntry_lat_lon_combined.csv", 'w') as output: reader = csv.reader(cntry) #,delimiter='\t') #... was a tsv file llread = csv.reader(ll) writer = csv.writer(output) next(reader) next(llread) writer.writerow(["id", "id_3char","name","latitude","longitude"]) count = 0 latlon = dict() for row in llread: print(row) latlon[row[3].casefold()]=(row[1],row[2]) # make a dictionary with country name as key - row[3]. # casefold makes all letters lowercase. for row in reader: if row[2].casefold() in set(latlon.keys()): #country_count[row[4]] += 1 writer.writerow([ row[0].casefold(), row[1].casefold(), row[2].casefold(), latlon[row[2].casefold()][0], latlon[row[2].casefold()][1]] ) else: writer.writerow([ row[0].casefold(), row[1].casefold(), row[2].casefold() ])

35.758621

208

0.66731

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1,037

4.305732

0.414013

0.035503

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0.079882

0.221893

0.147929

0

0.021324

0.140791

1,037

28

209

37.035714

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0

0.198113

0.158019

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1

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false

0

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0.105263

0.052632

0

null

0

null

0

1

0

789ccecbc53bbdded880ac33de58a7bbeecb50e7

1,062

py

Python

pinnwand/cli.py

aether-space/pinnwand

427c8fe68486f2afa0832abbe584595e51848c03

[ "BSD-3-Clause" ]

null

pinnwand/cli.py

aether-space/pinnwand

427c8fe68486f2afa0832abbe584595e51848c03

[ "BSD-3-Clause" ]

null

pinnwand/cli.py

aether-space/pinnwand

427c8fe68486f2afa0832abbe584595e51848c03

[ "BSD-3-Clause" ]

null

#!/usr/bin/env python import sys from datetime import datetime, timedelta from pinnwand.models import Base, engine, session, Paste def main(): args = sys.argv[1:] if args: if args[0] == "init_db": Base.metadata.create_all(engine) if args[0] == "add": paste = Paste("<html>hi</html>", lexer="html", expiry=timedelta(seconds=5)) session.add(paste) session.commit() if args[0] == "remove": paste = session.query(Paste).filter(Paste.id == int(args[1])).first() session.delete(paste) session.commit() if args[0] == "list": for paste in session.query(Paste).all(): print(paste) if args[0] == "reap": pastes = session.query(Paste).filter(Paste.exp_date < datetime.utcnow()).all() for paste in pastes: session.delete(paste) session.commit() print("Reaped {} expired pastes".format(len(pastes))) if __name__ == "__main__": main()

25.285714

90

0.548964

125

1,062

4.576

0.448

0.062937

0.061189

0.06993

0.262238

0.087413

0

0.01084

0.305085

1,062

41

91

25.902439

0.764228

0.018832

0

0.185185

0

0.072046

0

1

0.037037

false

0

0.111111

0

0.148148

0.074074

0

null

0

null

0

1

0

78a3b061984d25f892de9e2d172a18884e735177

1,781

py

Python

fastquotes/fund/history.py

YangzhenZhao/fastquotes

1faba9f7fc7801a11359001e08cefa9cfbc41d64

[ "MIT" ]

4

2020-11-18T11:25:00.000Z

2021-04-08T01:02:49.000Z

fastquotes/fund/history.py

YangzhenZhao/fastquotes

1faba9f7fc7801a11359001e08cefa9cfbc41d64

[ "MIT" ]

null

fastquotes/fund/history.py

YangzhenZhao/fastquotes

1faba9f7fc7801a11359001e08cefa9cfbc41d64

[ "MIT" ]

1

2020-11-18T11:25:01.000Z

import json from datetime import datetime from typing import Optional import requests from ..const import CUSTOM_HEADER def get_dividend(msg: str) -> Optional[float]: if not msg: return None left, right = 0, len(msg) - 1 while not msg[left].isdigit() or not msg[right].isdigit(): if not msg[left].isdigit(): left += 1 if not msg[right].isdigit(): right -= 1 return float(msg[left : right + 1]) def fund_history_data(fund_code: str) -> list: url = f"http://fund.eastmoney.com/pingzhongdata/{fund_code}.js" text = requests.get(url, headers=CUSTOM_HEADER).text text = text[ text.find("Data_netWorthTrend") + 21 : text.find("Data_ACWorthTrend") - 15 ] res_list = [] dividend_sum = 0.0 growth_rate_factor = 1.0 for item in json.loads(text): dividend = get_dividend(item["unitMoney"]) unit_nv = item["y"] if dividend is not None: dividend_sum += dividend growth_rate_factor *= (unit_nv + dividend) / unit_nv res_list.append( { "日期": datetime.fromtimestamp(item["x"] // 1000).strftime("%Y%m%d"), "单位净值": unit_nv, "累计净值": unit_nv + dividend_sum, "复权净值": unit_nv * growth_rate_factor, "日涨幅": item["equityReturn"], "分红送配": dividend, } ) return res_list def fund_history_profit_dict(fund_code: str) -> dict: fund_history_list = fund_history_data(fund_code) res_dic = {} for i in range(1, len(fund_history_list)): item = fund_history_list[i] last_item = fund_history_list[i - 1] res_dic[item["日期"]] = item["复权净值"] / last_item["复权净值"] - 1 return res_dic

30.706897

83

0.590679

233

1,781

4.309013

0.347639

0.076693

0.059761

0.033865

0.085657

0

0.015748

0.286917

1,781

57

84

31.245614

0.774803

0

0.083661

0

1

0.061224

false

0

0.102041

0

0.244898

0

null

0

null

0

1

0

78a897b7f40cac7c3cf59971f4394a4765aa060b

599

py

Python

dacy/tests/test_download.py

MalteHB/DaCy

1c3d348b14368c772d13344d35dc076b01d5bf07

[ "Apache-2.0" ]

1

2021-07-24T19:14:34.000Z

dacy/tests/test_download.py

MalteHB/DaCy

1c3d348b14368c772d13344d35dc076b01d5bf07

[ "Apache-2.0" ]

null

dacy/tests/test_download.py

MalteHB/DaCy

1c3d348b14368c772d13344d35dc076b01d5bf07

[ "Apache-2.0" ]

null

import urllib import os from dacy.download import models_url from dacy.load import load def test_urls(): for m, url in models_url.items(): print(m) req = urllib.request.Request(url, method="HEAD") f = urllib.request.urlopen(req) assert f.status == 200 print("\t Status:", f.status) size = int(f.headers["Content-Length"]) / 1e6 assert size > 20 print("\t File Size:", round(size), "mb") def test_load(): models = ["da_dacy_medium_tft-0.0.0"] for m in models: nlp = load(m) nlp("Dette er en test tekst")

24.958333

56

0.599332

89

599

3.955056

0.516854

0.045455

0

0.022831

0.268781

599

23

57

26.043478

0.780822

0

0.148581

0.040067

0

0.105263

1

0.105263

false

0

0.210526

0

0.315789

0.157895

0

null

0

null

0

1

0

78ab84fbfc0799cfbe7822b26a69af440075f9ad

19,503

py

Python

flowsa/USDA_CoA_Cropland.py

ericmbell1/flowsa

d251301864289a4de42dda118c9c6da41bcf4cf0

[ "CC0-1.0" ]

null

flowsa/USDA_CoA_Cropland.py

ericmbell1/flowsa

d251301864289a4de42dda118c9c6da41bcf4cf0

[ "CC0-1.0" ]

null

flowsa/USDA_CoA_Cropland.py

ericmbell1/flowsa

d251301864289a4de42dda118c9c6da41bcf4cf0

[ "CC0-1.0" ]

null

# USDA_CoA_Cropland.py (flowsa) # !/usr/bin/env python3 # coding=utf-8 import json import numpy as np import pandas as pd from flowsa.common import * from flowsa.flowbyfunctions import assign_fips_location_system, sector_disaggregation def CoA_Cropland_URL_helper(build_url, config, args): """This helper function uses the "build_url" input from flowbyactivity.py, which is a base url for coa cropland data that requires parts of the url text string to be replaced with info specific to the usda nass quickstats API. This function does not parse the data, only modifies the urls from which data is obtained. """ # initiate url list for coa cropland data urls = [] # call on state acronyms from common.py (and remove entry for DC) state_abbrevs = abbrev_us_state state_abbrevs = {k: v for (k, v) in state_abbrevs.items() if k != "DC"} # replace "__aggLevel__" in build_url to create three urls for x in config['agg_levels']: for y in config['sector_levels']: # at national level, remove the text string calling for state acronyms if x == 'NATIONAL': url = build_url url = url.replace("__aggLevel__", x) url = url.replace("__secLevel__", y) url = url.replace("&state_alpha=__stateAlpha__", "") if y == "ECONOMICS": url = url.replace( "AREA HARVESTED&statisticcat_desc=AREA IN PRODUCTION&statisticcat_desc=TOTAL&statisticcat_desc=AREA BEARING %26 NON-BEARING", "AREA&statisticcat_desc=AREA OPERATED") else: url = url.replace("&commodity_desc=AG LAND&commodity_desc=FARM OPERATIONS", "") url = url.replace(" ", "%20") urls.append(url) else: # substitute in state acronyms for state and county url calls for z in state_abbrevs: url = build_url url = url.replace("__aggLevel__", x) url = url.replace("__secLevel__", y) url = url.replace("__stateAlpha__", z) if y == "ECONOMICS": url = url.replace( "AREA HARVESTED&statisticcat_desc=AREA IN PRODUCTION&statisticcat_desc=TOTAL&statisticcat_desc=AREA BEARING %26 NON-BEARING", "AREA&statisticcat_desc=AREA OPERATED") else: url = url.replace("&commodity_desc=AG LAND&commodity_desc=FARM OPERATIONS", "") url = url.replace(" ", "%20") urls.append(url) return urls def coa_cropland_call(url, coa_response, args): cropland_json = json.loads(coa_response.text) df_cropland = pd.DataFrame(data=cropland_json["data"]) return df_cropland def coa_cropland_parse(dataframe_list, args): """Modify the imported data so it meets the flowbyactivity criteria and only includes data on harvested acreage (irrigated and total). """ df = pd.concat(dataframe_list, sort=False) # specify desired data based on domain_desc df = df[~df['domain_desc'].isin(['ECONOMIC CLASS', 'FARM SALES', 'IRRIGATION STATUS', 'CONCENTRATION', 'ORGANIC STATUS', 'NAICS CLASSIFICATION', 'PRODUCERS'])] df = df[df['statisticcat_desc'].isin(['AREA HARVESTED', 'AREA IN PRODUCTION', 'AREA BEARING & NON-BEARING', 'AREA', 'AREA OPERATED'])] # drop rows that subset data into farm sizes (ex. 'area harvested: (1,000 to 1,999 acres) df = df[~df['domaincat_desc'].str.contains(' ACRES')].reset_index(drop=True) # drop Descriptions that contain certain phrases, as these data are included in other categories df = df[~df['short_desc'].str.contains('FRESH MARKET|PROCESSING|ENTIRE CROP|NONE OF CROP|PART OF CROP')] # drop Descriptions that contain certain phrases - only occur in AG LAND data df = df[~df['short_desc'].str.contains('INSURANCE|OWNED|RENTED|FAILED|FALLOW|IDLE')].reset_index(drop=True) # Many crops are listed as their own commodities as well as grouped within a broader category (for example, orange # trees are also part of orchards). As this dta is not needed, takes up space, and can lead to double counting if # included, want to drop these unused columns # subset dataframe into the 5 crop types and land in farms and drop rows # crop totals: drop all data # field crops: don't want certain commodities and don't want detailed types of wheat, cotton, or sunflower df_fc = df[df['group_desc'] == 'FIELD CROPS'] df_fc = df_fc[~df_fc['commodity_desc'].isin(['GRASSES', 'GRASSES & LEGUMES, OTHER', 'LEGUMES', 'HAY', 'HAYLAGE'])] df_fc = df_fc[~df_fc['class_desc'].str.contains('SPRING|WINTER|TRADITIONAL|OIL|PIMA|UPLAND', regex=True)] # fruit and tree nuts: only want a few commodities df_ftn = df[df['group_desc'] == 'FRUIT & TREE NUTS'] df_ftn = df_ftn[df_ftn['commodity_desc'].isin(['BERRY TOTALS', 'ORCHARDS'])] df_ftn = df_ftn[df_ftn['class_desc'].isin(['ALL CLASSES'])] # horticulture: only want a few commodities df_h = df[df['group_desc'] == 'HORTICULTURE'] df_h = df_h[df_h['commodity_desc'].isin(['CUT CHRISTMAS TREES', 'SHORT TERM WOODY CROPS'])] # vegetables: only want a few commodities df_v = df[df['group_desc'] == 'VEGETABLES'] df_v = df_v[df_v['commodity_desc'].isin(['VEGETABLE TOTALS'])] # only want ag land and farm operations in farms & land & assets df_fla = df[df['group_desc'] == 'FARMS & LAND & ASSETS'] df_fla = df_fla[df_fla['short_desc'].str.contains("AG LAND|FARM OPERATIONS")] # drop the irrigated acreage in farms (want the irrigated harvested acres) df_fla = df_fla[((df_fla['domaincat_desc'] == 'AREA CROPLAND, HARVESTED:(ANY)') & (df_fla['domain_desc'] == 'AREA CROPLAND, HARVESTED ') & (df_fla['short_desc'] == 'AG LAND, IRRIGATED - ACRES'))] # concat data frames df = pd.concat([df_fc, df_ftn, df_h, df_v, df_fla], sort=False).reset_index(drop=True) # drop unused columns df = df.drop(columns=['agg_level_desc', 'location_desc', 'state_alpha', 'sector_desc', 'country_code', 'begin_code', 'watershed_code', 'reference_period_desc', 'asd_desc', 'county_name', 'source_desc', 'congr_district_code', 'asd_code', 'week_ending', 'freq_desc', 'load_time', 'zip_5', 'watershed_desc', 'region_desc', 'state_ansi', 'state_name', 'country_name', 'county_ansi', 'end_code', 'group_desc']) # create FIPS column by combining existing columns df.loc[df['county_code'] == '', 'county_code'] = '000' # add county fips when missing df['Location'] = df['state_fips_code'] + df['county_code'] df.loc[df['Location'] == '99000', 'Location'] = US_FIPS # modify national level fips # address non-NAICS classification data # use info from other columns to determine flow name df.loc[:, 'FlowName'] = df['statisticcat_desc'] + ', ' + df['prodn_practice_desc'] df.loc[:, 'FlowName'] = df['FlowName'].str.replace(", ALL PRODUCTION PRACTICES", "", regex=True) df.loc[:, 'FlowName'] = df['FlowName'].str.replace(", IN THE OPEN", "", regex=True) # combine column information to create activity information, and create two new columns for activities df['Activity'] = df['commodity_desc'] + ', ' + df['class_desc'] + ', ' + df['util_practice_desc'] # drop this column later df['Activity'] = df['Activity'].str.replace(", ALL CLASSES", "", regex=True) # not interested in all data from class_desc df['Activity'] = df['Activity'].str.replace(", ALL UTILIZATION PRACTICES", "", regex=True) # not interested in all data from class_desc df['ActivityProducedBy'] = np.where(df["unit_desc"] == 'OPERATIONS', df["Activity"], None) df['ActivityConsumedBy'] = np.where(df["unit_desc"] == 'ACRES', df["Activity"], None) # rename columns to match flowbyactivity format df = df.rename(columns={"Value": "FlowAmount", "unit_desc": "Unit", "year": "Year", "CV (%)": "Spread", "short_desc": "Description"}) # drop remaining unused columns df = df.drop(columns=['Activity', 'class_desc', 'commodity_desc', 'domain_desc', 'state_fips_code', 'county_code', 'statisticcat_desc', 'prodn_practice_desc', 'domaincat_desc', 'util_practice_desc']) # modify contents of units column df.loc[df['Unit'] == 'OPERATIONS', 'Unit'] = 'p' # modify contents of flowamount column, "D" is supressed data, "z" means less than half the unit is shown df['FlowAmount'] = df['FlowAmount'].str.strip() # trim whitespace df.loc[df['FlowAmount'] == "(D)", 'FlowAmount'] = withdrawn_keyword df.loc[df['FlowAmount'] == "(Z)", 'FlowAmount'] = withdrawn_keyword df['FlowAmount'] = df['FlowAmount'].str.replace(",", "", regex=True) # USDA CoA 2017 states that (H) means CV >= 99.95, therefore replacing with 99.95 so can convert column to int # (L) is a CV of <= 0.05 df['Spread'] = df['Spread'].str.strip() # trim whitespace df.loc[df['Spread'] == "(H)", 'Spread'] = 99.95 df.loc[df['Spread'] == "(L)", 'Spread'] = 0.05 df.loc[df['Spread'] == "", 'Spread'] = None # for instances where data is missing df.loc[df['Spread'] == "(D)", 'Spread'] = withdrawn_keyword # add location system based on year of data df = assign_fips_location_system(df, args['year']) # Add hardcoded data df['Class'] = np.where(df["Unit"] == 'ACRES', "Land", "Other") df['SourceName'] = "USDA_CoA_Cropland" df['MeasureofSpread'] = "RSD" df['DataReliability'] = None df['DataCollection'] = 2 return df def coa_irrigated_cropland_fba_cleanup(fba): """ When using irrigated cropland, aggregate sectors to cropland and total ag land. Doing this because published values for irrigated harvested cropland do not include the water use for vegetables, woody crops, berries. :param fba: :return: """ fba = fba[~fba['ActivityConsumedBy'].isin(['AG LAND', 'AG LAND, CROPLAND, HARVESTED'])] return fba def disaggregate_coa_cropland_to_6_digit_naics(fba_w_sector, attr): """ Disaggregate usda coa cropland to naics 6 :param fba_w_sector: :param attr: :return: """ # use ratios of usda 'land in farms' to determine animal use of pasturelands at 6 digit naics fba_w_sector = disaggregate_pastureland(fba_w_sector, attr) # use ratios of usda 'harvested cropland' to determine missing 6 digit naics fba_w_sector = disaggregate_cropland(fba_w_sector, attr) return fba_w_sector def disaggregate_pastureland(fba_w_sector, attr): """ The USDA CoA Cropland irrigated pastureland data only links to the 3 digit NAICS '112'. This function uses state level CoA 'Land in Farms' to allocate the county level acreage data to 6 digit NAICS. :param fba_w_sector: The CoA Cropland dataframe after linked to sectors :return: The CoA cropland dataframe with disaggregated pastureland data """ import flowsa from flowsa.flowbyfunctions import allocate_by_sector, clean_df, flow_by_activity_fields, \ fba_fill_na_dict # subset the coa data so only pastureland p = fba_w_sector.loc[fba_w_sector['Sector'] == '112'] # add temp loc column for state fips p.loc[:, 'Location_tmp'] = p['Location'].apply(lambda x: str(x[0:2])) # load usda coa cropland naics df_f = flowsa.getFlowByActivity(flowclass=['Land'], years=[attr['allocation_source_year']], datasource='USDA_CoA_Cropland_NAICS') df_f = clean_df(df_f, flow_by_activity_fields, fba_fill_na_dict) # subset to land in farms data df_f = df_f[df_f['FlowName'] == 'FARM OPERATIONS'] # subset to rows related to pastureland df_f = df_f.loc[df_f['ActivityConsumedBy'].apply(lambda x: str(x[0:3])) == '112'] # drop rows with "&' df_f = df_f[~df_f['ActivityConsumedBy'].str.contains('&')] # create sector column df_f.loc[:, 'Sector'] = df_f['ActivityConsumedBy'] # create proportional ratios df_f = allocate_by_sector(df_f, 'proportional') # drop naics = '11 df_f = df_f[df_f['Sector'] != '11'] # drop 000 in location df_f.loc[:, 'Location'] = df_f['Location'].apply(lambda x: str(x[0:2])) # merge the coa pastureland data with land in farm data df = p.merge(df_f[['Sector', 'Location', 'FlowAmountRatio']], how='left', left_on="Location_tmp", right_on="Location") # multiply the flowamount by the flowratio df.loc[:, 'FlowAmount'] = df['FlowAmount'] * df['FlowAmountRatio'] # drop columns and rename df = df.drop(columns=['Location_tmp', 'Sector_x', 'Location_y', 'FlowAmountRatio']) df = df.rename(columns={"Sector_y": "Sector", "Location_x": 'Location'}) # drop rows where sector = 112 and then concat with original fba_w_sector fba_w_sector = fba_w_sector[fba_w_sector['Sector'].apply(lambda x: str(x[0:3])) != '112'].reset_index(drop=True) fba_w_sector = pd.concat([fba_w_sector, df], sort=False).reset_index(drop=True) return fba_w_sector def disaggregate_cropland(fba_w_sector, attr): """ In the event there are 4 (or 5) digit naics for cropland at the county level, use state level harvested cropland to create ratios :param fba_w_sector: :param attr: :return: """ import flowsa from flowsa.flowbyfunctions import generalize_activity_field_names, sector_aggregation,\ fbs_default_grouping_fields, clean_df, fba_fill_na_dict, add_missing_flow_by_fields from flowsa.mapping import add_sectors_to_flowbyactivity # drop pastureland data crop = fba_w_sector.loc[fba_w_sector['Sector'].apply(lambda x: str(x[0:3])) != '112'].reset_index(drop=True) # drop sectors < 4 digits crop = crop[crop['Sector'].apply(lambda x: len(x) > 3)].reset_index(drop=True) # create tmp location crop.loc[:, 'Location_tmp'] = crop['Location'].apply(lambda x: str(x[0:2])) # load the relevant state level harvested cropland by naics naics_load = flowsa.getFlowByActivity(flowclass=['Land'], years=[attr['allocation_source_year']], datasource="USDA_CoA_Cropland_NAICS").reset_index(drop=True) # clean df naics = clean_df(naics_load, flow_by_activity_fields, fba_fill_na_dict) # subset the harvested cropland by naics naics = naics[naics['FlowName'] == 'AG LAND, CROPLAND, HARVESTED'].reset_index(drop=True) # add sectors naics = add_sectors_to_flowbyactivity(naics, sectorsourcename='NAICS_2012_Code', levelofSectoragg='agg') # add missing fbs fields naics = add_missing_flow_by_fields(naics, flow_by_sector_fields) # aggregate sectors to create any missing naics levels naics = sector_aggregation(naics, fbs_default_grouping_fields) # add missing naics5/6 when only one naics5/6 associated with a naics4 naics = sector_disaggregation(naics) # drop rows where sector consumed by is none and FlowAmount 0 naics = naics[naics['SectorConsumedBy'].notnull()] naics = naics.loc[naics['FlowAmount'] != 0] # create ratios naics = sector_ratios(naics) # drop sectors < 4 digits #naics = naics[naics['SectorConsumedBy'].apply(lambda x: len(x) > 3)].reset_index(drop=True) # create temporary sector column to match the two dfs on naics.loc[:, 'Location_tmp'] = naics['Location'].apply(lambda x: str(x[0:2])) # for loop through naics lengths to determine naics 4 and 5 digits to disaggregate for i in range(4, 6): # subset df to sectors with length = i and length = i + 1 crop_subset = crop.loc[crop['Sector'].apply(lambda x: i+1 >= len(x) >= i)] crop_subset.loc[:, 'Sector_tmp'] = crop_subset['Sector'].apply(lambda x: x[0:i]) # if duplicates drop all rows df = crop_subset.drop_duplicates(subset=['Location', 'Sector_tmp'], keep=False).reset_index(drop=True) # drop sector temp column df = df.drop(columns=["Sector_tmp"]) # subset df to keep the sectors of length i df_subset = df.loc[df['Sector'].apply(lambda x: len(x) == i)] # subset the naics df where naics length is i + 1 naics_subset = naics.loc[naics['SectorConsumedBy'].apply(lambda x: len(x) == i+1)].reset_index(drop=True) naics_subset.loc[:, 'Sector_tmp'] = naics_subset['SectorConsumedBy'].apply(lambda x: x[0:i]) # merge the two df based on locations df_subset = pd.merge(df_subset, naics_subset[['SectorConsumedBy', 'FlowAmountRatio', 'Sector_tmp', 'Location_tmp']], how='left', left_on=['Sector', 'Location_tmp'], right_on=['Sector_tmp', 'Location_tmp']) # create flow amounts for the new NAICS based on the flow ratio df_subset.loc[:, 'FlowAmount'] = df_subset['FlowAmount'] * df_subset['FlowAmountRatio'] # drop rows of 0 and na df_subset = df_subset[df_subset['FlowAmount'] != 0] df_subset = df_subset[~df_subset['FlowAmount'].isna()].reset_index(drop=True) # drop columns df_subset = df_subset.drop(columns=['Sector', 'FlowAmountRatio', 'Sector_tmp']) # rename columns df_subset = df_subset.rename(columns={"SectorConsumedBy": "Sector"}) # add new rows of data to crop df crop = pd.concat([crop, df_subset], sort=True).reset_index(drop=True) # clean up df crop = crop.drop(columns=['Location_tmp']) # pasture data pasture = fba_w_sector.loc[fba_w_sector['Sector'].apply(lambda x: str(x[0:3])) == '112'].reset_index(drop=True) # concat crop and pasture fba_w_sector = pd.concat([pasture, crop], sort=True).reset_index(drop=True) return fba_w_sector def sector_ratios(df): # find the longest length sector length = max(df['SectorConsumedBy'].apply(lambda x: len(x)).unique()) # for loop in reverse order longest length naics minus 1 to 2 # appends missing naics levels to df sector_ratios = [] for i in range(length, 3, -1): # subset df to sectors with length = i and length = i + 1 df_subset = df.loc[df['SectorConsumedBy'].apply(lambda x: len(x) == i)] # create column for sector grouping df_subset.loc[:, 'Sector_group'] = df_subset['SectorConsumedBy'].apply(lambda x: x[0:i-1]) # subset df to create denominator df_denom = df_subset[['FlowAmount', 'Location', 'Sector_group']] df_denom = df_denom.groupby(['Location', 'Sector_group'], as_index=False)[["FlowAmount"]].agg("sum") df_denom = df_denom.rename(columns={"FlowAmount": "Denominator"}) # merge the denominator column with fba_w_sector df ratio_df = df_subset.merge(df_denom, how='left') # calculate ratio ratio_df.loc[:, 'FlowAmountRatio'] = ratio_df['FlowAmount'] / ratio_df['Denominator'] ratio_df = ratio_df.drop(columns=['Denominator', 'Sector_group']).reset_index() sector_ratios.append(ratio_df) # concat list of dataframes (info on each page) df_w_ratios = pd.concat(sector_ratios, sort=True).reset_index(drop=True) return df_w_ratios

53.57967

153

0.654463

2,651

19,503

4.64089

0.179555

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0.021946

0.024872

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0.252621

0.190198

0.141266

0.126961

0.110461

0

0.009008

0.220171

19,503

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0.799921

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0.153846

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0.276331

0.038541

0

1

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false

0

0.051282

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0.133333

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null

0

null

0

1

0

78b114541d2883d76b5e6615c0019a1dbcd48b43

1,129

py

Python

src/experiments/models/index_emb_classifier.py

clemens33/thesis

c94e066c2fe22881a7465eb9c3859bd02138748e

[ "MIT" ]

null

src/experiments/models/index_emb_classifier.py

clemens33/thesis

c94e066c2fe22881a7465eb9c3859bd02138748e

[ "MIT" ]

null

src/experiments/models/index_emb_classifier.py

clemens33/thesis

c94e066c2fe22881a7465eb9c3859bd02138748e

[ "MIT" ]

null

import torch from torch import nn from tabnet_lightning import TabNetClassifier class IndexEmbTabNetClassifier(TabNetClassifier): """test model implementation using index based embeddings""" def __init__(self, **kwargs): super(IndexEmbTabNetClassifier, self).__init__(**kwargs) self.index_embeddings = nn.Embedding(num_embeddings=kwargs["input_size"], embedding_dim=1) def embeddings(self, inputs: torch.Tensor) -> torch.Tensor: indices = torch.nonzero(inputs, as_tuple=True) # gets the indices which are active values = self.index_embeddings(indices[-1]).squeeze() output = torch.index_put_(inputs, indices, values) return output # # # test # if __name__ == "__main__": # inputs = torch.Tensor([ # [0, 0, 1, 0, 0, 0, 0, 1], # [1, 0, 0, 0, 0, 0, 0, 0], # [0, 0, 0, 0, 0, 0, 0, 1], # ]) # e = nn.Embedding(num_embeddings=8, embedding_dim=1) # # indices = torch.nonzero(inputs, as_tuple=True) # # emb = e(indices[-1]).squeeze() # # # indices[..., -1] = emb # # inputs = torch.index_put_(inputs, indices, emb)

28.225

98

0.635075

142

1,129

4.838028

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0.049491

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0.212518

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0.034091

0.220549

1,129

39

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0.583333

0

null

0

null

0

1

0

78b1ddaf10061b10db5d28ce24732f514b36c95b

616

py

Python

MOD/game_stats.py

divineflatus/MOD

988299e0e75d8f8fa7893c22ab0db707f02a8f1d

[ "MIT" ]

null

MOD/game_stats.py

divineflatus/MOD

988299e0e75d8f8fa7893c22ab0db707f02a8f1d

[ "MIT" ]

null

MOD/game_stats.py

divineflatus/MOD

988299e0e75d8f8fa7893c22ab0db707f02a8f1d

[ "MIT" ]

null

import pygame #Class to store game statistics class GameStats(): def __init__(self, mod_settings): #Initialize MOD settings self.mod_settings = mod_settings #Number of lives available self.ninjas_left = self.mod_settings.ninja_limit #Starts inactive until 'Play' is clicked self.game_active = False #Resets statistics self.reset_stats() self.high_score = 0 #Resets statistics to appropriate values def reset_stats(self): self.ninjas_left = self.mod_settings.ninja_limit self.score = 0

24.64

57

0.641234

73

616

5.178082

0.520548

0.174603

0.15873

0.095238

0.206349

0

0.00463

0.298701

616

24

58

25.666667

0.87037

0.280844

0

0.181818

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1

0.181818

false

0

0.090909

0

0.363636

0

null

0

null

0

1

0

78b6dc301c1b043d7640e21cef75902cf0f201a3

1,373

py

Python

tests/grammar/test_sql_file.py

Daniihh/sqlpyparser

aad1d613c02d4f8fa6b833c060a683cf7e194b1c

[ "MIT" ]

28

2016-02-13T10:20:21.000Z

2022-03-10T02:41:58.000Z

tests/grammar/test_sql_file.py

Daniihh/sqlpyparser

aad1d613c02d4f8fa6b833c060a683cf7e194b1c

[ "MIT" ]

22

2016-02-15T15:55:09.000Z

2017-09-12T13:49:17.000Z

tests/grammar/test_sql_file.py

Daniihh/sqlpyparser

aad1d613c02d4f8fa6b833c060a683cf7e194b1c

[ "MIT" ]

16

2016-02-15T16:41:23.000Z

2021-05-18T04:51:52.000Z

# -*- encoding:utf-8 -*- from __future__ import absolute_import, division, print_function, unicode_literals import unittest from mysqlparse.grammar.sql_file import sql_file_syntax class SqlFileSyntaxTest(unittest.TestCase): def test_multiple_statements(self): sql_file = sql_file_syntax.parseString(""" CREATE TABLE test_table1 ( test_column1 INT(11) PRIMARY KEY AUTO_INCREMENT NOT NULL, test_column2 INT(11) NOT NULL ); ALTER TABLE test_table2 ADD col_no0 BIT(8) NOT NULL DEFAULT 0 FIRST, ADD col_no1 LONGTEXT NOT NULL, ADD col_no2 VARCHAR(200) NULL, ADD col_no3 BIT(8) AFTER col0; CREATE TABLE test_table3 ( test_column INT(11) PRIMARY KEY AUTO_INCREMENT NOT NULL ); ALTER TABLE test_table4 ADD col_no0 BIT(8) NOT NULL DEFAULT 0 FIRST, ADD col_no1 LONGTEXT NOT NULL, ADD col_no2 VARCHAR(200) NULL, ADD col_no3 BIT(8) AFTER col0; """) self.assertEqual(len(sql_file.statements), 4) self.assertEqual(sql_file.statements[0].table_name, 'test_table1') self.assertEqual(sql_file.statements[1].table_name, 'test_table2') self.assertEqual(sql_file.statements[2].table_name, 'test_table3') self.assertEqual(sql_file.statements[3].table_name, 'test_table4')

35.205128

82

0.674436

186

1,373

4.736559

0.360215

0.07151

0.096481

0.099886

0.496027

0.31101

0.231555

0

0.04243

0.24472

1,373

38

83

36.131579

0.807136

0.016023

0

0.296296

0

0.547072

0

0.185185

1

0.037037

false

0

0.111111

0

0.185185

0.037037

0

null

0

null

0

1

0

78b76294cbc37248ab04281f09991a89a57a24b6

2,061

py

Python

barchart2.py

ahealy19/F-IDE-2016

82fd4664fc105174cbe2f1a57e2a099fbf3c81d8

[ "Apache-2.0" ]

2

2017-10-13T09:16:01.000Z

2018-01-23T04:03:19.000Z

barchart2.py

ahealy19/F-IDE-2016

82fd4664fc105174cbe2f1a57e2a099fbf3c81d8

[ "Apache-2.0" ]

null

barchart2.py

ahealy19/F-IDE-2016

82fd4664fc105174cbe2f1a57e2a099fbf3c81d8

[ "Apache-2.0" ]

null

import numpy as np from pandas import DataFrame import matplotlib.pyplot as plt import os """ plots the results for each solver and strategy on the test set as a stacked barchart Andrew Healy, Aug. 2016 """ fig = plt.figure(figsize=(10,5)) ax = fig.add_subplot(1,1,1) df = DataFrame.from_csv('data_for_second_barchart.csv') provers = ['Alt-Ergo-0.95.2', 'Alt-Ergo-1.01', 'CVC3', 'CVC4', 'veriT', 'Yices', 'Z3-4.3.2', 'Z3-4.4.1', 'Best','Random','Worst','Where4'] df = df.reindex(columns=provers) N = len(provers) valids = list(df.ix['Valid']) unknown = list(df.ix['Unknown']) timeout = list(df.ix['Timeout']) failure = list(df.ix['Failure']) ind = np.arange(N) # the x locations for the groups offset = lambda x: 1 if x > 7 else 0 for i,_ in enumerate(ind): ind[i] += offset(i) # x offset for strategies and Where4 width = 0.35 # the width of the bars p1 = ax.bar(ind, valids, width, color='1.0') p2 = ax.bar(ind, unknown, width, color='0.55', bottom=valids) bottom = [unknown[i]+valids[i] for i in xrange(N)] p3 = ax.bar(ind, timeout, width, bottom=bottom, color='0.8') bottom = [bottom[i]+timeout[i] for i in xrange(N)] p4 = ax.bar(ind, failure, width, bottom=bottom, color='0.3') ax.set_ylabel('Number of proof obligations') ax.set_xticks(ind) ax.set_xticklabels(provers, rotation = 30) ax.set_yticks(np.arange(0, 263, 50)) ax.legend((p1[0], p2[0], p3[0], p4[0]), ('Valid', 'Unknown', 'Timeout', 'Failure'), loc='upper center', ncol=4, bbox_to_anchor=(0.5, 1.05)) ind = np.arange(N) for i,v in enumerate(valids): plt.annotate(str(v), xy=(ind[i]+width+0.05+offset(i),v/2.-0.5)) for i,u in enumerate(unknown): plt.annotate(str(u), xy=(ind[i]+width+0.05+offset(i),valids[i]+u/2.-0.5)) for i,t in enumerate(timeout): plt.annotate(str(t), xy=(ind[i]+width+0.05+offset(i),valids[i]+unknown[i]+t/2.-0.5)) for i,f in enumerate(failure): plt.annotate(str(f), xy=(ind[i]+width+0.05+offset(i),valids[i]+unknown[i]+timeout[i]+f/2.-0.5)) plt.savefig(os.path.join('paper','barcharts2.pdf'), bbox_inches='tight')

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0.020664

0.023616

0.032472

0.15941

0.109963

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0.073801

0

0.053132

0.13246

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0.086957

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null

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null

0

1

0

78b8980c397285a3ee6cf5a9943a92060add2e64

727

py

Python

Procedural Paradigm/exercises/week-7-basic/HitungJarak.py

morenzoe/IF1210_Dasar_Pemrograman

6bfd5300c18bfb9c6ba80f6108e2206aa9cbf015

[ "BSD-3-Clause" ]

null

Procedural Paradigm/exercises/week-7-basic/HitungJarak.py

morenzoe/IF1210_Dasar_Pemrograman

6bfd5300c18bfb9c6ba80f6108e2206aa9cbf015

[ "BSD-3-Clause" ]

null

Procedural Paradigm/exercises/week-7-basic/HitungJarak.py

morenzoe/IF1210_Dasar_Pemrograman

6bfd5300c18bfb9c6ba80f6108e2206aa9cbf015

[ "BSD-3-Clause" ]

1

2022-02-21T05:03:26.000Z

# Program HitungJarak # Menghitung jarak (s) berdasarkan kecepatan (v) dan waktu tempuh (t), yaitu: s = v * t # KAMUS # s : float # v : float # t : float # ALGORITMA v = float(input()) # menerima input kecepatan dalam m/s t = float (input()) # menerima input waktu dalam s s = v * t # menghitung jarak dalam m print(s) # menampilkan hasil perhitungan # NOTASI ALGORITMIK ''' Program HitungJarak {Menghitung jarak (s) berdasarkan kecepatan (v) dan waktu tempuh (t), yaitu: s = v * t} Kamus s : real v : real t : real ALGORITMA input(v) {menerima input kecepatan dalam m/s} input(t) {menerima input waktu dalam s} s <- v * t {menghitung jarak dalam m} output(s) {menampilkan hasil perhitungan} '''

21.382353

88

0.671252

106

727

4.603774

0.264151

0.122951

0.02459

0.135246

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0.220083

727

34

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false

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0.25

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null

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null

0

1

0

78b92735f679218e246415e9c15eb48e474ed578

4,414

py

Python

os_xml_automation/text_manipulation/_text_manipulation_mapper.py

osfunapps/os-xml-automation-py

2e339642fcfa11a9b71c231c652e6e3aa3849354

[ "MIT" ]

1

2020-10-25T10:30:40.000Z

os_xml_automation/text_manipulation/_text_manipulation_mapper.py

osfunapps/os-xml-automation-py

2e339642fcfa11a9b71c231c652e6e3aa3849354

[ "MIT" ]

null

os_xml_automation/text_manipulation/_text_manipulation_mapper.py

osfunapps/os-xml-automation-py

2e339642fcfa11a9b71c231c652e6e3aa3849354

[ "MIT" ]

null

import os_xml_handler.xml_handler as xh from os_xml_automation import shared_res as shared_res from os_xml_automation import shared_tools as shared_tools from os_xml_automation.text_manipulation import _res as res # manipulate the files by the text mapper def manipulate(xml_path, xml, place_holder_map): file_nodes = xh.get_all_direct_child_nodes(xh.get_root_node(xml)) # run on all of the root's direction children for file_node in file_nodes: # get the <file_src> and <file_dst> nodes paths src_file_path = shared_tools.get_file_node_path(xml_path, place_holder_map, file_node, shared_res.NODE_FILE_SRC) dst_file_path = shared_tools.get_file_node_path(xml_path, place_holder_map, file_node, shared_res.NODE_FILE_DST, src_file_path) texts_node = xh.get_child_nodes(file_node, res.NODE_TEXTS)[0] text_nodes = xh.get_child_nodes(texts_node, res.NODE_TEXT) for text_node in text_nodes: init_text_node_cycle(text_node, place_holder_map, src_file_path, dst_file_path) # will do a specific text node def init_text_node_cycle(text_node, place_holder_map, src_file_path, dst_file_path): # get the current action and text action = str(xh.get_node_att(text_node, shared_res.ACTION)) original_text = xh.get_text_from_child_node(text_node, shared_res.NODE_ORIGINAL_TEXT) cancel_if_already_present = False new_text = '' # delete range and set in range are special. They will need a special way to be dealt with if action == res.NODE_TEXT_ATT_ACTION_VAL_DELETE_RANGE or action == res.NODE_TEXT_ATT_ACTION_VAL_REPLACE_IN_RANGE: handle_delete_range(text_node, place_holder_map, src_file_path, dst_file_path) if action == res.NODE_TEXT_ATT_ACTION_VAL_DELETE_RANGE: return else: # set in range will change the action to above line and set the required text above the bottom boundary action = res.NODE_TEXT_ATT_ACTION_VAL_ABOVE original_text = xh.get_text_from_child_node(text_node, res.NODE_TO_TEXT) original_text = shared_tools.fill_place_holders(original_text, place_holder_map) if action != res.NODE_TEXT_ATT_ACTION_VAL_DELETE_LINE: new_text_node = xh.get_child_nodes(text_node, shared_res.NODE_NEW_TEXT)[0] new_text = xh.get_text_from_node(new_text_node) cancel_if_already_present = xh.get_node_att(new_text_node, res.NODE_TEXT_ATT_IF_ALREADY_PRESENT) == res.NODE_TEXT_ATT_IF_ALREADY_PRESENT_VAL_CANCEL # replace place holders for key, value in place_holder_map.items(): if key in original_text: original_text = original_text.replace(key, value) if new_text and key in new_text: new_text = new_text.replace(key, value) from os_file_stream_handler import file_stream_handler as fsh if action == res.NODE_TEXT_ATT_ACTION_VAL_DELETE_LINE: fsh.delete_line_in_file(src_file_path, dst_file_path, original_text) elif action == res.NODE_TEXT_ATT_ACTION_VAL_REPLACE or action == res.NODE_TEXT_ATT_ACTION_VAL_REPLACE_LINE: fsh.replace_text_in_file(src_file_path, dst_file_path, original_text, new_text if new_text else '', action == res.NODE_TEXT_ATT_ACTION_VAL_REPLACE_LINE, cancel_if_already_present) elif action == res.NODE_TEXT_ATT_ACTION_VAL_ABOVE: fsh.append_text_above_line_in_file(src_file_path, dst_file_path, original_text, new_text, cancel_if_already_present) elif action == res.NODE_TEXT_ATT_ACTION_VAL_BELOW: fsh.append_text_below_line_in_file(src_file_path, dst_file_path, original_text, new_text, cancel_if_already_present) # will delete a text in range def handle_delete_range(text_node, place_holder_map, src_file_path, dst_file_path): from_text = xh.get_text_from_child_node(text_node, res.NODE_FROM_TEXT) to_text = xh.get_text_from_child_node(text_node, res.NODE_TO_TEXT) from_text = shared_tools.fill_place_holders(from_text, place_holder_map) to_text = shared_tools.fill_place_holders(to_text, place_holder_map) include_boundaries = xh.get_node_att(text_node, res.NODE_TEXT_ATT_INCLUDE_BOUNDARIES) include_boundaries = not include_boundaries or include_boundaries == 'false' from os_file_stream_handler import file_stream_handler as fsh fsh.delete_text_range_in_file(src_file_path, dst_file_path, from_text, to_text, include_bundaries=include_boundaries)

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187

0.783643

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4,414

4.250685

0.136986

0.051885

0.053174

0.063165

0.584273

0.547212

0.47728

0.457944

0.419594

0.380922

0

0.000538

0.157227

4,414

78

188

56.589744

0.833602

0.097644

0

0.037736

0

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0

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false

0

0.113208

0

0.188679

0

null

0

null

0

1

0

78b964ea1f80a7b69e03614379bba228f287598a

2,304

py

Python

src/lib/mine/task/task_manager.py

rdw20170120/workstation

ed19aa930a83885c2a8cb58eb0bb5afe58f95df3

[ "MIT" ]

null

src/lib/mine/task/task_manager.py

rdw20170120/workstation

ed19aa930a83885c2a8cb58eb0bb5afe58f95df3

[ "MIT" ]

2

2021-04-06T18:07:32.000Z

2021-06-02T01:50:40.000Z

src/lib/mine/task/task_manager.py

rdw20170120/workstation

ed19aa930a83885c2a8cb58eb0bb5afe58f95df3

[ "MIT" ]

null

#!/usr/bin/env false """Manage tasks.""" # Internal packages (absolute references, distributed with Python) from logging import getLogger # External packages (absolute references, NOT distributed with Python) # Library modules (absolute references, NOT packaged, in project) from task.exception import Abort from task.exception import Skip from task.queue import TaskQueue from utility.my_logging import log_exception # Project modules (relative references, NOT packaged, in project) class TaskManager(object): def __init__(self, config, mapping): self._log = getLogger(self.__class__.__name__) self._config = config self._mapping = mapping self._q = TaskQueue() super().__init__() def _add(self, task): self._q.put(task) def _execute_task(self, the_task): try: the_task.execute() except Abort as e: self._log.debug("From %s _execute_task() except Abort", __name__) self._log.info(repr(e)) except KeyboardInterrupt as e: self._log.debug( "From %s _execute_task() except KeyboardInterrupt", __name__ ) self._log.fatal(repr(e)) raise except NotImplementedError as e: self._log.debug( "From %s _execute_task() except NotImplementedError", __name__ ) self._log.debug(repr(e)) except Skip as e: self._log.debug("From %s _execute_task() except Skip", __name__) self._log.info(repr(e)) except BaseException as e: self._log.debug( "From %s _execute_task() except BaseException", __name__ ) if self._config.should_abort_upon_task_failure: log_exception(self._log, e) raise else: log_exception(self._log, e, with_traceback=True) @property def config(self): return self._config @property def mapping(self): return self._mapping def run(self): self._log.info("Running task manager...") while not self._q.empty(): self._execute_task(self._q.get()) self._log.debug("Queue contains %d tasks", self._q.length) """DisabledContent """

31.135135

78

0.613715

265

2,304

5.011321

0.30566

0.073795

0.063253

0.037651

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0

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2,304

73

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31.561644

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0

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0

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false

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0.037037

0.259259

0

null

0

null

0

1

0

78bc6d363f695604891dee0584bdb38942f09a51

2,519

py

Python

robosuite/scripts/Final_Copy/utils.py

spatric5/robosuite

9e6b9691eb949fbf33a23fbe8a8c6faea61c50b6

[ "MIT" ]

null

robosuite/scripts/Final_Copy/utils.py

spatric5/robosuite

9e6b9691eb949fbf33a23fbe8a8c6faea61c50b6

[ "MIT" ]

null

robosuite/scripts/Final_Copy/utils.py

spatric5/robosuite

9e6b9691eb949fbf33a23fbe8a8c6faea61c50b6

[ "MIT" ]

null

from mpi4py import MPI import numpy as np import torch # sync_networks across the different cores def sync_networks(network): """ netowrk is the network you want to sync """ comm = MPI.COMM_WORLD flat_params, params_shape = _get_flat_params(network) comm.Bcast(flat_params, root=0) # set the flat params back to the network _set_flat_params(network, params_shape, flat_params) # get the flat params from the network def _get_flat_params(network): param_shape = {} flat_params = None for key_name, value in network.named_parameters(): param_shape[key_name] = value.detach().numpy().shape if flat_params is None: flat_params = value.detach().numpy().flatten() else: flat_params = np.append(flat_params, value.detach().numpy().flatten()) return flat_params, param_shape # set the params from the network def _set_flat_params(network, params_shape, params): pointer = 0 for key_name, values in network.named_parameters(): # get the length of the parameters len_param = np.prod(params_shape[key_name]) copy_params = params[pointer:pointer + len_param].reshape(params_shape[key_name]) copy_params = torch.tensor(copy_params) # copy the params values.data.copy_(copy_params.data) # update the pointer pointer += len_param # sync the networks def sync_grads(network): flat_grads, grads_shape = _get_flat_grads(network) comm = MPI.COMM_WORLD global_grads = np.zeros_like(flat_grads) comm.Allreduce(flat_grads, global_grads, op=MPI.SUM) _set_flat_grads(network, grads_shape, global_grads) def _set_flat_grads(network, grads_shape, flat_grads): pointer = 0 for key_name, value in network.named_parameters(): len_grads = np.prod(grads_shape[key_name]) copy_grads = flat_grads[pointer:pointer + len_grads].reshape(grads_shape[key_name]) copy_grads = torch.tensor(copy_grads) # copy the grads value.grad.data.copy_(copy_grads.data) pointer += len_grads def _get_flat_grads(network): grads_shape = {} flat_grads = None for key_name, value in network.named_parameters(): grads_shape[key_name] = value.grad.data.cpu().numpy().shape if flat_grads is None: flat_grads = value.grad.data.cpu().numpy().flatten() else: flat_grads = np.append(flat_grads, value.grad.data.cpu().numpy().flatten()) return flat_grads, grads_shape

35.985714

91

0.692735

354

2,519

4.649718

0.189266

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0.043742

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0.382746

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0.052248

0

0.002017

0.212783

2,519

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false

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0.22

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null

0

null

0

1

0

78bd42c19113c497e1993e92b673e02423c0f0b9

9,143

py

Python

src/apd/aggregation/cli.py

MatthewWilkes/apd.aggregation

427fa908f45332d623295f92e1ccfdaf545d6997

[ "BSD-3-Clause" ]

null

src/apd/aggregation/cli.py

MatthewWilkes/apd.aggregation

427fa908f45332d623295f92e1ccfdaf545d6997

[ "BSD-3-Clause" ]

11

2020-11-23T21:36:48.000Z

2022-03-12T00:48:58.000Z

src/apd/aggregation/cli.py

MatthewWilkes/apd.aggregation

427fa908f45332d623295f92e1ccfdaf545d6997

[ "BSD-3-Clause" ]

1

2020-08-09T01:47:59.000Z

import asyncio import functools import importlib.util import logging import signal import sys import typing as t import uuid import aiohttp import click from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker from . import collect from .actions.runner import DataProcessor from .actions.source import get_data_ongoing, refeed_queue_var from .database import Deployment, deployment_table from .query import with_database logger = logging.getLogger(__name__) @click.command() @click.argument("server", nargs=-1) @click.option( "--db", metavar="<CONNECTION_STRING>", default="postgresql+psycopg2://localhost/apd", help="The connection string to a PostgreSQL database", envvar="APD_DB_URI", ) @click.option("--api-key", metavar="<KEY>", envvar="APD_API_KEY") @click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") def collect_sensor_data( db: str, server: t.Tuple[str], api_key: str, verbose: bool ) -> None: """This loads data from one or more sensors into the specified database. Only PostgreSQL databases are supported, as the column definitions use multiple pg specific features. The database must already exist and be populated with the required tables. The --api-key option is used to specify the access token for the sensors being queried. You may specify any number of servers, the variable should be the full URL to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple URLs should be separated with a space. """ success = True try: collect.standalone(db, server, api_key, echo=verbose) except ValueError as e: click.secho(str(e), err=True, fg="red") success = False if not success: sys.exit(1) def load_handler_config(path: str) -> t.List[DataProcessor]: # Create a module called user_config backed by the file specified, and load it # This uses Python's import internals to fake a module in a known location # Based on an SO answer by Sebastian Rittau and sample code from Brett Cannon module_spec = importlib.util.spec_from_file_location("user_config", path) module = importlib.util.module_from_spec(module_spec) loader = module_spec.loader if isinstance(loader, importlib.abc.Loader): loader.exec_module(module) try: return module.handlers # type: ignore except AttributeError as err: raise ValueError(f"Could not load config file from {path}") from err else: # No valid loader could be found raise ValueError(f"Could not load config file from {path}") def actually_exit(sig, frame): click.secho("Exiting...", bold=True) sys.exit(1) def stats_signal_handler(sig, frame, handlers=None): for handler in handlers: click.echo( click.style(handler.name, bold=True, fg="red") + " " + handler.stats() ) if sig == signal.SIGINT: click.secho("Press Ctrl+C again to end the process", bold=True) handler = signal.getsignal(signal.SIGINT) signal.signal(signal.SIGINT, actually_exit) asyncio.get_running_loop().call_later(5, install_ctrl_c_signal_handler, handler) return def install_ctrl_c_signal_handler(signal_handler): click.secho("Press Ctrl+C to view statistics", bold=True) signal.signal(signal.SIGINT, signal_handler) @click.command() @click.argument("config", nargs=1) @click.option( "--db", metavar="<CONNECTION_STRING>", default="postgresql+psycopg2://localhost/apd", help="The connection string to a PostgreSQL database", envvar="APD_DB_URI", ) @click.option( "--historical", is_flag=True, help="Also trigger actions for data points that were already present in the database", ) @click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") def run_actions(config: str, db: str, verbose: bool, historical: bool): """This runs the long-running action processors defined in a config file. The configuration file specified should be a Python file that defines a list of DataProcessor objects called processors.n """ logging.basicConfig( format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", level=logging.DEBUG if verbose else logging.WARN, ) async def main_loop(): with with_database(db): logger.info("Loading configuration") handlers = load_handler_config(config) # Set up the refeed queue before starting the handlers # or source, so they all have access to it refeed_queue_var.set(asyncio.Queue()) logger.info(f"Configured {len(handlers)} handlers") starters = [handler.start() for handler in handlers] await asyncio.gather(*starters) logger.info("Ingesting data") data = get_data_ongoing(historical=historical) signal_handler = functools.partial( stats_signal_handler, handlers=handlers, ) for signal_name in "SIGINFO", "SIGUSR1", "SIGINT": try: signal.signal(signal.Signals[signal_name], signal_handler) except KeyError: pass async for datapoint in data: for handler in handlers: await handler.push(datapoint) asyncio.run(main_loop()) @click.group() def deployments(): pass @deployments.command() @click.argument("uri") @click.argument("name") @click.option( "--db", metavar="<CONNECTION_STRING>", default="postgresql+psycopg2://localhost/apd", help="The connection string to a PostgreSQL database", envvar="APD_DB_URI", ) @click.option("--api-key", metavar="<KEY>", envvar="APD_API_KEY") @click.option("--colour") def add( db: str, uri: str, name: str, api_key: t.Optional[str], colour: t.Optional[str], ) -> None: """This creates a record of a new deployment in the database.""" deployment = Deployment(id=None, uri=uri, name=name, api_key=api_key, colour=colour) async def http_get_deployment_id(): async with aiohttp.ClientSession() as http: collect.http_session_var.set(http) return await collect.get_deployment_id(uri) deployment.id = asyncio.run(http_get_deployment_id()) insert = deployment_table.insert().values(**deployment._asdict()) engine = create_engine(db) sm = sessionmaker(engine) Session = sm() Session.execute(insert) Session.commit() @deployments.command() @click.option( "--db", metavar="<CONNECTION_STRING>", default="postgresql+psycopg2://localhost/apd", help="The connection string to a PostgreSQL database", envvar="APD_DB_URI", ) def list(db: str) -> None: """This creates a record of a new deployment in the database.""" engine = create_engine(db) sm = sessionmaker(engine) Session = sm() deployments = Session.query(deployment_table).all() for deployment in deployments: click.secho(deployment.name, bold=True) click.echo(click.style("ID ", bold=True) + deployment.id.hex) click.echo(click.style("URI ", bold=True) + deployment.uri) click.echo(click.style("API key ", bold=True) + deployment.api_key) click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) click.echo() Session.rollback() @deployments.command() @click.argument("id") @click.option("--uri") @click.option("--name") @click.option( "--db", metavar="<CONNECTION_STRING>", default="postgresql+psycopg2://localhost/apd", help="The connection string to a PostgreSQL database", envvar="APD_DB_URI", ) @click.option("--api-key", metavar="<KEY>", envvar="APD_API_KEY") @click.option("--colour") def edit( db: str, id, uri: t.Optional[str], name: t.Optional[str], api_key: t.Optional[str], colour: t.Optional[str], ) -> None: """This creates a record of a new deployment in the database.""" update = {} if uri is not None: update["uri"] = uri if name is not None: update["name"] = name if api_key is not None: update["api_key"] = api_key if colour is not None: update["colour"] = colour deployment_id = uuid.UUID(id) update_stmt = ( deployment_table.update() .where(deployment_table.c.id == deployment_id) .values(**update) ) engine = create_engine(db) sm = sessionmaker(engine) Session = sm() Session.execute(update_stmt) deployments = Session.query(deployment_table).filter( deployment_table.c.id == deployment_id ) Session.commit() for deployment in deployments: click.secho(deployment.name, bold=True) click.echo(click.style("ID ", bold=True) + deployment.id.hex) click.echo(click.style("URI ", bold=True) + deployment.uri) click.echo(click.style("API key ", bold=True) + deployment.api_key) click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) click.echo()

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78c41a1434c06c1642f44349fdd1eea2106f0e22

17,447

py

Python

backend/integration/tests.py

Tim6FTN/UKS

3cf19f014cdc7845bf0b808b97c4e05dc49b062e

[ "MIT" ]

1

2021-01-10T12:34:59.000Z

backend/integration/tests.py

Tim6FTN/UKS

3cf19f014cdc7845bf0b808b97c4e05dc49b062e

[ "MIT" ]

37

2021-01-07T22:31:25.000Z

2021-02-20T10:59:46.000Z

backend/integration/tests.py

Tim6FTN/UKS

3cf19f014cdc7845bf0b808b97c4e05dc49b062e

[ "MIT" ]

null

from unittest.mock import MagicMock, Mock import six from django.contrib.auth.models import User from django.core.exceptions import SuspiciousOperation from django.test import SimpleTestCase, Client, TransactionTestCase from django.urls import reverse, resolve from branch.models import Branch from integration.views import receive_webhook_request from integration.webhook_handler import WebhookHandler, _format_event from project.models import Project from repository.models import Repository class TestWebhookHandler(SimpleTestCase): def test_if_secret_not_initialized(self): webhook_handler = WebhookHandler() self.assertIsNone(webhook_handler.secret) def test_if_secret_properly_initialized(self): webhook_handler = WebhookHandler(secret="test-secret") self.assertIsNotNone(webhook_handler.secret) self.assertIsInstance(webhook_handler.secret, bytes) self.assertEqual(webhook_handler.secret, "test-secret".encode("utf-8")) def test_format_event_if_key_is_present(self): data = {'pusher': {'name': 'test_name'}, 'ref': 'test_ref', 'repository': {'full_name': 'test_repository_full_name'}} push_event_description = _format_event("push", data) self.assertEqual(push_event_description, "test_name pushed test_ref in test_repository_full_name") def test_format_event_if_key_is_not_present(self): push_event_description = _format_event("non-existing-key", {}) self.assertEqual(push_event_description, "non-existing-key") def test__get_header_if_key_is_present(self): request = Mock() request.headers = {WebhookHandler.X_GITHUB_DELIVERY: 'some-guid'} header_value = WebhookHandler._get_header(WebhookHandler.X_GITHUB_DELIVERY, request) self.assertEqual(header_value, 'some-guid') def test__get_header_if_key_is_not_present(self): with self.assertRaisesMessage(SuspiciousOperation, f'Missing header: {WebhookHandler.X_GITHUB_DELIVERY}'): request = Mock() request.headers = {} WebhookHandler._get_header(WebhookHandler.X_GITHUB_DELIVERY, request) def test__get_digest_if_secret_is_present(self): request = Mock() request.body = '{"key": "value"}'.encode('utf-8') webhook_handler = WebhookHandler(secret="test-secret") digest = webhook_handler._get_digest(request) self.assertIsNotNone(digest) self.assertIsInstance(digest, six.text_type) def test__get_digest_if_secret_is_not_present(self): request = Mock() request.body = {} webhook_handler = WebhookHandler() digest = webhook_handler._get_digest(request) self.assertIsNone(digest) def test_handle_if_no_signature(self): request = Mock() request.headers = {WebhookHandler.X_HUB_SIGNATURE_256: 'incorrect-digest'} webhook_handler = WebhookHandler() webhook_handler._get_digest = MagicMock(return_value="sha256-digest") with self.assertRaisesMessage(SuspiciousOperation, "Signature required."): webhook_handler.handle(request) def test_handle_if_signature_invalid(self): request = Mock() request.headers = {WebhookHandler.X_HUB_SIGNATURE_256: 'sha256=incorrect-digest'} webhook_handler = WebhookHandler() webhook_handler._get_digest = MagicMock(return_value="sha256-digest") with self.assertRaisesMessage(SuspiciousOperation, "Invalid signature."): webhook_handler.handle(request) def test_handle_if_event_type_missing(self): request = Mock() request.headers = {} webhook_handler = WebhookHandler() webhook_handler._get_digest = MagicMock(return_value=None) with self.assertRaisesMessage(SuspiciousOperation, f'Missing header: {WebhookHandler.X_GITHUB_EVENT}'): webhook_handler.handle(request) def test_handle_when_content_type_form(self): webhook_handler = WebhookHandler() webhook_handler._get_digest = MagicMock(return_value=None) request = Mock() request.headers = {'content-type': 'application/x-www-form-urlencoded', WebhookHandler.X_GITHUB_EVENT: 'push'} with self.assertRaisesMessage(SuspiciousOperation, "Unsupported operation."): webhook_handler.handle(request) def test_handle_when_content_type_json_and_data_invalid(self): webhook_handler = WebhookHandler() webhook_handler._get_digest = MagicMock(return_value=None) request = Mock() request.headers = { 'content-type': 'application/json', 'X-Github-Delivery': 'some-guid', WebhookHandler.X_GITHUB_EVENT: 'push' } request.body = ''.encode('utf-8') with self.assertRaisesMessage(SuspiciousOperation, "Request body must contain valid JSON data."): webhook_handler.handle(request) def test_handle_when_content_type_json_and_data_valid(self): webhook_handler = WebhookHandler() webhook_handler._get_digest = MagicMock(return_value=None) request = Mock() request.headers = { 'content-type': 'application/json', 'X-Github-Delivery': 'some-guid', WebhookHandler.X_GITHUB_EVENT: 'push' } request.body = '{"key": "value"}'.encode('utf-8') webhook_handler.handle(request) def test_if_webhook_handler_handle_called(self): webhook_handler = WebhookHandler() webhook_handler.handle = MagicMock(return_value=None) webhook_handler.handle(request=Mock()) webhook_handler.handle.assert_called_once() def test_if_webhook_handler_called_all_registered_hook_handlers(self): webhook_handler = WebhookHandler() webhook_handler._get_digest = MagicMock(return_value=None) request = Mock() request.headers = { 'content-type': 'application/json', 'X-Github-Delivery': 'some-guid', WebhookHandler.X_GITHUB_EVENT: 'push' } request.body = '{"key": "value"}'.encode('utf-8') @webhook_handler.hook(event_type="push") @MagicMock def first_decorated_func(): pass @webhook_handler.hook(event_type="push") @MagicMock def second_decorated_func(): pass @webhook_handler.hook(event_type="ping") @MagicMock def third_decorated_func(): pass webhook_handler.handle(request) first_decorated_func.assert_called_once() second_decorated_func.assert_called_once() third_decorated_func.assert_not_called() class TestIntegrationURLs(SimpleTestCase): def test_notify_url(self): notify_url = reverse('notify') self.assertEquals(resolve(notify_url).func, receive_webhook_request) class TestIntegrationViews(TransactionTestCase): def setUp(self): self.client = Client() self.notify_url = reverse('notify') self.user = User.objects.create_user('test_username', 'test@email.com', 'test_password') self.repository = Repository.objects.create( url="https://github.com/fivkovic/uks-demo", name="uks-demo", description="uks-demo repository description", is_public=True) self.project = Project.objects.create( name="UKS DEMO PROJECT", description="UKS demo project description", is_public=True, wiki_content="Wiki", repository=self.repository, owner=self.user) self.branch = Branch.objects.create(name="main", repository=self.repository) self.task = None def test_receive_webhook_request_view(self): headers = { 'HTTP_' + WebhookHandler.X_GITHUB_EVENT: 'push', 'HTTP_' + WebhookHandler.X_GITHUB_DELIVERY: 'some-guid' } response = self.client.post( self.notify_url, INTEGRATION_TEST_REQUEST_BODY, content_type='application/json', **headers) self.assertEquals(response.status_code, 204) INTEGRATION_TEST_REQUEST_BODY = { "ref": "refs/heads/main", "before": "2f781a5371291ce8ba3f3a8acdf8bd673889dcaf", "after": "9549a348a9c4e175cf8a27e45bab93407d178767", "repository": { "id": 339193534, "node_id": "MDEwOlJlcG9zaXRvcnkzMzkxOTM1MzQ=", "name": "uks-demo", "full_name": "fivkovic/uks-demo", "private": False, "owner": { "name": "fivkovic", "email": "f.ivkovic16@gmail.com", "login": "fivkovic", "id": 17569172, "node_id": "MDQ6VXNlcjE3NTY5MTcy", "avatar_url": "https://avatars.githubusercontent.com/u/17569172?v=4", "gravatar_id": "", "url": "https://api.github.com/users/fivkovic", "html_url": "https://github.com/fivkovic", "followers_url": "https://api.github.com/users/fivkovic/followers", "following_url": "https://api.github.com/users/fivkovic/following{/other_user}", "gists_url": "https://api.github.com/users/fivkovic/gists{/gist_id}", "starred_url": "https://api.github.com/users/fivkovic/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/fivkovic/subscriptions", "organizations_url": "https://api.github.com/users/fivkovic/orgs", "repos_url": "https://api.github.com/users/fivkovic/repos", "events_url": "https://api.github.com/users/fivkovic/events{/privacy}", "received_events_url": "https://api.github.com/users/fivkovic/received_events", "type": "User", "site_admin": False }, "html_url": "https://github.com/fivkovic/uks-demo", "description": "Demo repository for testing UKS project", "fork": False, "url": "https://github.com/fivkovic/uks-demo", "forks_url": "https://api.github.com/repos/fivkovic/uks-demo/forks", "keys_url": "https://api.github.com/repos/fivkovic/uks-demo/keys{/key_id}", "collaborators_url": "https://api.github.com/repos/fivkovic/uks-demo/collaborators{/collaborator}", "teams_url": "https://api.github.com/repos/fivkovic/uks-demo/teams", "hooks_url": "https://api.github.com/repos/fivkovic/uks-demo/hooks", "issue_events_url": "https://api.github.com/repos/fivkovic/uks-demo/issues/events{/number}", "events_url": "https://api.github.com/repos/fivkovic/uks-demo/events", "assignees_url": "https://api.github.com/repos/fivkovic/uks-demo/assignees{/user}", "branches_url": "https://api.github.com/repos/fivkovic/uks-demo/branches{/branch}", "tags_url": "https://api.github.com/repos/fivkovic/uks-demo/tags", "blobs_url": "https://api.github.com/repos/fivkovic/uks-demo/git/blobs{/sha}", "git_tags_url": "https://api.github.com/repos/fivkovic/uks-demo/git/tags{/sha}", "git_refs_url": "https://api.github.com/repos/fivkovic/uks-demo/git/refs{/sha}", "trees_url": "https://api.github.com/repos/fivkovic/uks-demo/git/trees{/sha}", "statuses_url": "https://api.github.com/repos/fivkovic/uks-demo/statuses/{sha}", "languages_url": "https://api.github.com/repos/fivkovic/uks-demo/languages", "stargazers_url": "https://api.github.com/repos/fivkovic/uks-demo/stargazers", "contributors_url": "https://api.github.com/repos/fivkovic/uks-demo/contributors", "subscribers_url": "https://api.github.com/repos/fivkovic/uks-demo/subscribers", "subscription_url": "https://api.github.com/repos/fivkovic/uks-demo/subscription", "commits_url": "https://api.github.com/repos/fivkovic/uks-demo/commits{/sha}", "git_commits_url": "https://api.github.com/repos/fivkovic/uks-demo/git/commits{/sha}", "comments_url": "https://api.github.com/repos/fivkovic/uks-demo/comments{/number}", "issue_comment_url": "https://api.github.com/repos/fivkovic/uks-demo/issues/comments{/number}", "contents_url": "https://api.github.com/repos/fivkovic/uks-demo/contents/{+path}", "compare_url": "https://api.github.com/repos/fivkovic/uks-demo/compare/{base}...{head}", "merges_url": "https://api.github.com/repos/fivkovic/uks-demo/merges", "archive_url": "https://api.github.com/repos/fivkovic/uks-demo/{archive_format}{/ref}", "downloads_url": "https://api.github.com/repos/fivkovic/uks-demo/downloads", "issues_url": "https://api.github.com/repos/fivkovic/uks-demo/issues{/number}", "pulls_url": "https://api.github.com/repos/fivkovic/uks-demo/pulls{/number}", "milestones_url": "https://api.github.com/repos/fivkovic/uks-demo/milestones{/number}", "notifications_url": "https://api.github.com/repos/fivkovic/uks-demo/notifications{?since,all,participating}", "labels_url": "https://api.github.com/repos/fivkovic/uks-demo/labels{/name}", "releases_url": "https://api.github.com/repos/fivkovic/uks-demo/releases{/id}", "deployments_url": "https://api.github.com/repos/fivkovic/uks-demo/deployments", "created_at": 1613419653, "updated_at": "2021-02-15T20:07:41Z", "pushed_at": 1613420915, "git_url": "git://github.com/fivkovic/uks-demo.git", "ssh_url": "git@github.com:fivkovic/uks-demo.git", "clone_url": "https://github.com/fivkovic/uks-demo.git", "svn_url": "https://github.com/fivkovic/uks-demo", "homepage": None, "size": 0, "stargazers_count": 0, "watchers_count": 0, "language": None, "has_issues": True, "has_projects": True, "has_downloads": True, "has_wiki": True, "has_pages": False, "forks_count": 0, "mirror_url": None, "archived": False, "disabled": False, "open_issues_count": 0, "license": { "key": "mit", "name": "MIT License", "spdx_id": "MIT", "url": "https://api.github.com/licenses/mit", "node_id": "MDc6TGljZW5zZTEz" }, "forks": 0, "open_issues": 0, "watchers": 0, "default_branch": "main", "stargazers": 0, "master_branch": "main" }, "pusher": { "name": "fivkovic", "email": "f.ivkovic16@gmail.com" }, "sender": { "login": "fivkovic", "id": 17569172, "node_id": "MDQ6VXNlcjE3NTY5MTcy", "avatar_url": "https://avatars.githubusercontent.com/u/17569172?v=4", "gravatar_id": "", "url": "https://api.github.com/users/fivkovic", "html_url": "https://github.com/fivkovic", "followers_url": "https://api.github.com/users/fivkovic/followers", "following_url": "https://api.github.com/users/fivkovic/following{/other_user}", "gists_url": "https://api.github.com/users/fivkovic/gists{/gist_id}", "starred_url": "https://api.github.com/users/fivkovic/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/fivkovic/subscriptions", "organizations_url": "https://api.github.com/users/fivkovic/orgs", "repos_url": "https://api.github.com/users/fivkovic/repos", "events_url": "https://api.github.com/users/fivkovic/events{/privacy}", "received_events_url": "https://api.github.com/users/fivkovic/received_events", "type": "User", "site_admin": False }, "created": False, "deleted": False, "forced": False, "base_ref": None, "compare": "https://github.com/fivkovic/uks-demo/compare/2f781a537129...9549a348a9c4", "commits": [ { "id": "9549a348a9c4e175cf8a27e45bab93407d178767", "tree_id": "20f7ae1a25f3c039e7d6442440672bd012c3a78d", "distinct": True, "message": "First test commit closes #1 #2", "timestamp": "2021-02-15T21:12:35+01:00", "url": "https://github.com/fivkovic/uks-demo/commit/9549a348a9c4e175cf8a27e45bab93407d178767", "author": { "name": "Filip Ivkovic", "email": "fivkovic@uns.ac.rs", "username": "fivkovic" }, "committer": { "name": "Filip Ivkovic", "email": "fivkovic@uns.ac.rs", "username": "fivkovic" }, "added": [ "F1.txt", "F2.txt" ], "removed": [], "modified": [] } ], "head_commit": { "id": "9549a348a9c4e175cf8a27e45bab93407d178767", "tree_id": "20f7ae1a25f3c039e7d6442440672bd012c3a78d", "distinct": True, "message": "First test commit closes #1 #2", "timestamp": "2021-02-15T21:12:35+01:00", "url": "https://github.com/fivkovic/uks-demo/commit/9549a348a9c4e175cf8a27e45bab93407d178767", "author": { "name": "Filip Ivkovic", "email": "fivkovic@uns.ac.rs", "username": "fivkovic" }, "committer": { "name": "Filip Ivkovic", "email": "fivkovic@uns.ac.rs", "username": "fivkovic" }, "added": [ "F1.txt", "F2.txt" ], "removed": [], "modified": [] } }

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78c876141f5329c52ad1e038a8ee03869db886d7

6,705

py

Python

ketu/characterization/prepare.py

dfm/turnstile

13a9a3b489b458396a6ad1e8a2d1e89a0dd6312d

[ "MIT" ]

10

2015-02-19T09:13:24.000Z

2020-04-25T10:50:38.000Z

ketu/characterization/prepare.py

dfm/turnstile

13a9a3b489b458396a6ad1e8a2d1e89a0dd6312d

[ "MIT" ]

1

2015-07-10T19:50:31.000Z

2015-07-11T03:51:15.000Z

ketu/characterization/prepare.py

dfm/turnstile

13a9a3b489b458396a6ad1e8a2d1e89a0dd6312d

[ "MIT" ]

7

2015-04-20T06:42:28.000Z

2019-02-25T03:04:45.000Z

# -*- coding: utf-8 -*- from __future__ import division, print_function __all__ = ["prepare_characterization"] import kplr import transit import numpy as np from scipy.stats import beta import matplotlib.pyplot as pl import george from george import kernels from ..prepare import Prepare from ..download import Download from ..discontinuity import Discontinuity def prepare_characterization(kicid, periods, time0s, rors, impacts, es=None, data_window_hw=3.0, min_data_window_hw=0.5): # Download and process the light curves. pipe = Download() pipe = Prepare(pipe) pipe = Discontinuity(pipe) r = pipe.query(kicid=kicid) # Find the data chunks that hit a transit. lcs = [] for lc in r.light_curves: # Build the mask of times that hit transits. m = np.zeros_like(lc.time, dtype=bool) mmin = np.zeros_like(lc.time, dtype=bool) for p, t0 in zip(periods, time0s): hp = 0.5 * p t0 = t0 % p dt = np.abs((lc.time - t0 + hp) % p - hp) m += dt < data_window_hw mmin += dt < min_data_window_hw # Trim the dataset and set up the Gaussian Process model. if np.any(mmin) and np.sum(m) > 10: # Re-normalize the trimmed light curve. mu = np.median(lc.flux[m]) lc.time = np.ascontiguousarray(lc.time[m]) lc.flux = np.ascontiguousarray(lc.flux[m] / mu) lc.ferr = np.ascontiguousarray(lc.ferr[m] / mu) # Make sure that the light curve knows its integration time. lc.texp = kplr.EXPOSURE_TIMES[1] / 86400.0 # Heuristically guess the Gaussian Process parameters. lc.factor = 1000.0 amp = np.median((lc.factor * (lc.flux-1.0))**2) kernel = amp*kernels.Matern32Kernel(4.0) lc.gp = george.GP(kernel) # Run an initial computation of the GP. lc.gp.compute(lc.time, lc.ferr * lc.factor) # Save this light curve. lcs.append(lc) # Set up the initial system model. spars = r.star.huber star = transit.Central(mass=spars.M, radius=spars.R) s = transit.System(star) for i in range(len(periods)): planet = transit.Body(r=rors[i] * star.radius, period=periods[i], t0=time0s[i] % periods[i], b=impacts[i], e=0.0 if es is None else es[i]) s.add_body(planet) # Approximate the stellar mass and radius measurements as log-normal. q = np.array(spars[["R", "E_R", "e_R"]], dtype=float) lnsr = (np.log(q[0]), 1.0 / np.mean([np.log(q[0] + q[1]) - np.log(q[0]), np.log(q[0]) - np.log(q[0] - q[2])]) ** 2) q = np.array(spars[["M", "E_M", "e_M"]], dtype=float) lnsm = (np.log(q[0]), 1.0 / np.mean([np.log(q[0] + q[1]) - np.log(q[0]), np.log(q[0]) - np.log(q[0] - q[2])]) ** 2) return ProbabilisticModel(lcs, s, lnsr, lnsm) class ProbabilisticModel(object): def __init__(self, lcs, system, lnsr, lnsm): self.lcs = lcs self.system = system self.lnsr = lnsr self.lnsm = lnsm self.fit_star = False def pack(self): star = self.system.central planets = self.system.bodies vec = list(self.lcs[0].gp.kernel.vector) if self.fit_star: vec += [np.log(star.radius), np.log(star.mass)] vec += [ star.q1, star.q2, ] vec += [v for p in planets for v in ( np.log(p.r), np.log(p.period), p.t0, p.b, np.sqrt(p.e) * np.sin(p.pomega), np.sqrt(p.e) * np.cos(p.pomega) )] return np.array(vec) def unpack(self, pars): # Update the kernel. i = len(self.lcs[0].gp.kernel) for lc in self.lcs: lc.gp.kernel[:] = pars[:i] # Update the star. star = self.system.central if self.fit_star: star.radius, star.mass = np.exp(pars[i:i+2]) i += 2 star.q1, star.q2 = pars[i:i+2] i += 2 # Update the planets. for p in self.system.bodies: p.r, p.period = np.exp(pars[i:i+2]) i += 2 p.t0, p.b = pars[i:i+2] i += 2 sqesn, sqecs = pars[i:i+2] p.e = sqesn**2 + sqecs**2 p.pomega = np.arctan2(sqesn, sqecs) i += 2 def lnprior(self): lnp = 0.0 # Apply the stellar parameter constraints. star = self.system.central if not (0 < star.q1 < 1 and 0 < star.q2 < 1): return -np.inf lnsr = np.log(star.radius) lnp -= 0.5 * self.lnsr[1] * (self.lnsr[0] - lnsr) ** 2 lnsm = np.log(star.mass) lnp -= 0.5 * self.lnsm[1] * (self.lnsm[0] - lnsm) ** 2 # And the planet parameters. for p in self.system.bodies: if p.b < 0.0 or not (-2 * np.pi < p.pomega < 2 * np.pi): return -np.inf # Kipping (2013) lnp += beta(1.12, 3.09).logpdf(p.e) return lnp def lnlike(self): ll = 0.0 for lc in self.lcs: try: mu = self.system.light_curve(lc.time, texp=lc.texp) except RuntimeError: return -np.inf r = (lc.flux - mu) * lc.factor ll += lc.gp.lnlikelihood(r, quiet=True) if not np.isfinite(ll): return -np.inf return ll def lnprob(self, p): try: self.unpack(p) except ValueError: return -np.inf lp = self.lnprior() if not np.isfinite(lp): return -np.inf ll = self.lnlike() if not np.isfinite(ll): return -np.inf return lp + ll def plot(self, dy=1e-2): fig = pl.figure() ax = fig.add_subplot(111) period = self.system.bodies[0].period t0 = self.system.bodies[0].t0 for i, lc in enumerate(self.lcs): t = (lc.time - t0 + 0.5 * period) % period - 0.5 * period ax.plot(t, lc.flux + i*dy, ".k", alpha=0.5) mu = self.system.light_curve(lc.time, texp=lc.texp) r = lc.factor * (lc.flux - mu) pred = lc.gp.predict(r, lc.time, mean_only=True) / lc.factor ax.plot(t, pred + 1.0 + i*dy, "r", alpha=0.5) ax.plot(t, pred + mu + i*dy, "b", alpha=0.5) ax.axvline(0.0, color="k", alpha=0.3, lw=3) return fig

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null

0

1

0

78c9b51f8e253459950c6aba31616db59d9ecbca

1,242

py

Python

docs/python/f_1st_partial_ex4.py

Voldemort373/Notes-and-Reference

796885e315e9c349ff1cb37760abc56327547140

[ "CC-BY-4.0", "CC0-1.0" ]

30

2018-11-12T09:03:45.000Z

2021-12-09T02:20:08.000Z

docs/python/f_1st_partial_ex4.py

Voldemort373/Notes-and-Reference

796885e315e9c349ff1cb37760abc56327547140

[ "CC-BY-4.0", "CC0-1.0" ]

36

2018-11-11T21:32:31.000Z

2019-02-02T16:18:11.000Z

docs/python/f_1st_partial_ex4.py

Voldemort373/Notes-and-Reference

796885e315e9c349ff1cb37760abc56327547140

[ "CC-BY-4.0", "CC0-1.0" ]

8

2018-11-14T17:09:21.000Z

2020-05-28T16:18:12.000Z

# -*- coding: utf-8 -*- # Copyright (c) 2018, Silvio Peroni <essepuntato@gmail.com> # # Permission to use, copy, modify, and/or distribute this software for any purpose # with or without fee is hereby granted, provided that the above copyright notice # and this permission notice appear in all copies. # # THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH # REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND # FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, # OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, # DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS # ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS # SOFTWARE. from re import findall def f(cur_digit): l = list() l.append("a") l.append("b") l.extend(l) l.extend(l) l.append("c") for i in range(int(cur_digit)): if l[i] != "a" and "a" in l: l.remove("a") else: l.insert(i, "c") return l rightmost_digit = "".join(findall("\d", input("Please provide your matriculation number: ")))[-1] print("Result:", f(rightmost_digit))

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0

78cb4d79d62529d58ce3023b98597dd72ee5c35a

1,916

py

Python

DataBase/Mongo/MongoTest.py

InverseLina/python-practice

496d2020916d8096a32131cdedd25a4da7b7735e

[ "Apache-2.0" ]

null

DataBase/Mongo/MongoTest.py

InverseLina/python-practice

496d2020916d8096a32131cdedd25a4da7b7735e

[ "Apache-2.0" ]

null

DataBase/Mongo/MongoTest.py

InverseLina/python-practice

496d2020916d8096a32131cdedd25a4da7b7735e

[ "Apache-2.0" ]

null

import pymongo from bson.son import SON from pymongo import MongoClient # encoding=utf-8 __author__ = 'Hinsteny' print(pymongo.get_version_string()) class SingleClient(object): ''' Single Client hold the client object ''' client = MongoClient('127.0.0.1', 27017) client.the_database.authenticate('hinsteny', 'welcome', source='admin', mechanism='SCRAM-SHA-1') def __new__(cls, *args, **kw): if not hasattr(cls, '_instance'): orig = super(SingleClient, cls) cls._instance = orig.__new__(cls, *args, **kw) return cls._instance def getClient(): client = MongoClient('127.0.0.1', 27017) client.the_database.authenticate('hinsteny', 'welcome', source='admin', mechanism='SCRAM-SHA-1') return client def test_connection(): client = getClient() db = client.cube_test query = {} cursor = db.user.find(query) print(cursor.count()) print(cursor[0]) def test_addUser(): client = getClient() db = client.admin query = {} cursor = db.system.users.find(query) if cursor.count() == 0 : db.runCommand({createUser})({"user":"admin","pwd":"welcome","roles":["root"]}) else: print(cursor[0]) def create_test_data(db): db.things.drop() result = db.things.insert_many([{"x": 1, "tags": ["dog", "cat"]},{"x": 2, "tags": ["cat"]},{"x": 2, "tags": ["mouse", "cat", "dog"]},{"x": 3, "tags": ["eat","pear"]}]) print(result.inserted_ids) def doAggregation(collection, pipeline): print(list(collection.aggregate(pipeline))) # Do test if __name__ == "__main__": test_connection() # test_addUser() db = getClient().aggregation_example create_test_data(db) pipeline = [ {"$unwind": "$tags"}, {"$group": {"_id": "", "count": {"$sum": "$x"}}}, {"$sort": SON([("count", -1), ("_id", -1)])} ] doAggregation(db.things, pipeline)

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null

0

null

0

1

0

78cd90912da668d8ba7cfecb75291ed8ec65c67c

933

py

Python

python/std_scripts/numerical_operations.py

IamPhytan/Cookbook

a903f9098b0d2ddccdf343f740858731242bde97

[ "MIT" ]

null

python/std_scripts/numerical_operations.py

IamPhytan/Cookbook

a903f9098b0d2ddccdf343f740858731242bde97

[ "MIT" ]

null

python/std_scripts/numerical_operations.py

IamPhytan/Cookbook

a903f9098b0d2ddccdf343f740858731242bde97

[ "MIT" ]

null

# # Get divisor and modulo # Often forgotten, often useful # a = 5 b = 3 n, m = divmod(a, b) print(n) # 1 print(m) # 2 # # Next multiple of a number n # Used a lot in CodinGame Clash of Code # n = 3 idx = [*range(10)] res = [a + (n - (a % n)) % n for a in idx] print(idx) # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] print(res) # [0, 3, 3, 3, 6, 6, 6, 9, 9, 9] # # Show a multiplication # Used in CodinGame Clash of Code # # Numbers to multiply a = 500 b = 1300 # Second number => String b_s = str(b) # Small multiplications mults = list(reversed([a * int(b_s[i]) * 10 ** (len(b_s) - i - 1) for i in range(len(b_s))])) mults = [m for m in mults if m != 0] # Strings to list s = [str(a), b_s, "-", *map(str, mults), "-"] s.append(str(sum(list(mults)))) # Add mult sign s[1] = "x " + b_s # Adjust right align n = len(max(s, key=len)) s = [w.rjust(n, " ") for w in s] # Horizontal bars s[2] = s[-2] = n * "-" print("\n".join(s))

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null

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null

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1

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78d0276dde967a30b789dae789ec250967368d4b

8,842

py

Python

src/kgmk/nlp/tpn/src/classification.py

kagemeka/python

486ce39d97360b61029527bacf00a87fdbcf552c

[ "MIT" ]

null

src/kgmk/nlp/tpn/src/classification.py

kagemeka/python

486ce39d97360b61029527bacf00a87fdbcf552c

[ "MIT" ]

null

src/kgmk/nlp/tpn/src/classification.py

kagemeka/python

486ce39d97360b61029527bacf00a87fdbcf552c

[ "MIT" ]

null

from _base import * config = config['classification'] tp = TP() tokenizer_path = f'{project_root}/{config["tokenizer_path"]}' tokenizer_gen_path = f'{project_root}/{config["tokenizer_gen_path"]}' def load_category_master(): try: dba = clsDbAccessor() category_master = dba.execQuery("SELECT category_id, category_name FROM mst_categories;") dba.close() return category_master except Exception as e: print('failed: SELECT mst_categories') raise e def load_keyword_master(): try: dba = clsDbAccessor() keyword_master = dba.execQuery("SELECT category_id, keyword FROM mst_keywords;") dba.close() return keyword_master except Exception as e: print('failed: SELECT mst_keywords') raise e def load_gen_category_master(): try: dba = clsDbAccessor() gen_category_master = dba.execQuery("SELECT category_id, category_name FROM mst_gen_categories;") dba.close() return gen_category_master except Exception as e: print('failed: SELECT mst_categories') raise e # return pd.read_csv(f'{project_root}/data/mst_gen_categories.csv') def load_gen_keyword_master(): # localで学習時に使用 return pd.read_csv(f'{project_root}/data/mst_gen_keywords.csv') category_master = load_category_master() categories = list(category_master['category_name'].values) gen_category_master = load_gen_category_master() gen_categories = list(gen_category_master['category_name'].values) import tensorflow as tf from tensorflow.keras import Sequential, layers, losses, optimizers, callbacks def create_model(emb_dim=10): model = Sequential([ layers.Embedding(input_dim=10**6, output_dim=emb_dim), layers.Conv1D(256, 3, activation='relu'), layers.GlobalMaxPooling1D(), layers.Dense(128, activation='relu'), layers.Dense(64, activation='relu'), layers.Dense(1, activation='sigmoid') ]) model.compile( loss=losses.BinaryCrossentropy(), optimizer=optimizers.Adam(), metrics=['accuracy'] ) return model def regex_and(s): return ''.join([f'(?=.*{w})' for w in s.split()]) def create_dataset(texts, keywords): # bl = texts.str.contains('|'.join(category_keywords[category]), regex=True) bl = texts.str.contains(r'{}'.format('|'.join(map(regex_and, keywords))), regex=True) true_datas = texts[bl] n = len(true_datas) false_datas = texts.drop(true_datas.index).sample(n=n, random_state=10) x = pd.concat([true_datas, false_datas]).map(tp.norm_wakati) y = np.array([1]*n + [0]*n) return x, y from tensorflow.keras.preprocessing.text import Tokenizer, tokenizer_from_json from tensorflow.keras.preprocessing.sequence import pad_sequences def pad(x, maxlen): return pad_sequences(x, maxlen=maxlen, padding='post', truncating='post') def train(df): # texts = df['text'].drop_duplicates() # if not os.path.exists(tokenizer_path): # tokenizer = Tokenizer() # tokenizer.fit_on_texts(texts.map(tp.norm_wakati)) # with open(tokenizer_path, 'w', encoding='utf-8') as f: # f.write(json.dumps(tokenizer.to_json(), ensure_ascii=False)) # with open(tokenizer_path) as f: # tokenizer = tokenizer_from_json(json.load(f)) # keyword_master = load_keyword_master() # id2category = dict(zip(category_master['category_id'], category_master['category_name'])) # category_keywords = dict() # for category_id, df in keyword_master.groupby(['category_id']): # keywords = df['keyword'].values # category_keywords[id2category[category_id]] = list(keywords) # categories = list(category_keywords.keys()) # for category in categories: # x, y = create_dataset(texts, category_keywords[category]) # x = pad(tokenizer.texts_to_sequences(x), maxlen=100) # model = create_model(emb_dim=2) # weights_save_path = f'{project_root}/model/classification_model/{category}.ckpt' # model.fit( # x, y, # epochs=config['epochs'], batch_size=config['batch_size'], # callbacks=[ # callbacks.EarlyStopping(patience=config['patience']), # callbacks.ModelCheckpoint(weights_save_path, save_best_only=True, save_weights_only=True) # ], # validation_split=0.1 # ) # print(f'{category} end') texts = df['text'].drop_duplicates() if not os.path.exists(tokenizer_gen_path): tokenizer = Tokenizer() tokenizer.fit_on_texts(texts.map(tp.norm_wakati)) with open(tokenizer_gen_path, 'w', encoding='utf-8') as f: f.write(json.dumps(tokenizer.to_json(), ensure_ascii=False)) with open(tokenizer_gen_path) as f: tokenizer = tokenizer_from_json(json.load(f)) keyword_master = load_gen_keyword_master() id2category = dict(zip(gen_category_master['category_id'], gen_category_master['category_name'])) category_keywords = dict() for category_id, df in keyword_master.groupby(['category_id']): keywords = df['keyword'].values category_keywords[id2category[category_id]] = list(keywords) gen_categories = list(category_keywords.keys()) for category in gen_categories: x, y = create_dataset(texts, category_keywords[category]) x = pad(tokenizer.texts_to_sequences(x), maxlen=40) model = create_model(emb_dim=10) weights_save_path = f'{project_root}/model/gen_classification_model/{category}.ckpt' model.fit( x, y, epochs=config['epochs'], batch_size=config['batch_size'], callbacks=[ callbacks.EarlyStopping(patience=config['patience']), callbacks.ModelCheckpoint(weights_save_path, save_best_only=True, save_weights_only=True) ], validation_split=0.1 ) print(f'{category} end') def infer(df, categories=categories, gen_categories=gen_categories): toppan = (df['tw_id'] >= 9*10**18).map(int).values texts = df['wakati_text'] with open(tokenizer_path) as f: tokenizer = tokenizer_from_json(json.load(f)) x = pad(tokenizer.texts_to_sequences(texts), maxlen=100) print('data prepared!') category2id = dict(zip(category_master['category_name'], category_master['category_id'])) res = [] n_categories = [] for category in categories: model = create_model(emb_dim=2) try: model.load_weights(f'{project_root}/model/classification_model/{category}.ckpt') n_categories.append(category) except: continue predicted = np.around(model.predict(x).ravel(), 4)# probability res.append(predicted) print(f'{category} end') del model categories = n_categories category_score = [json.dumps(dict([(str(category2id[categories[j]]), {"val": str(res[j][i])}) for j in range(len(categories))] + [('8', {'val': str(toppan[i])})]), ensure_ascii=False) for i in range(len(res[0]))] df['category'] = category_score ''' 記事カテゴリ ''' with open(tokenizer_gen_path) as f: tokenizer = tokenizer_from_json(json.load(f)) x = pad(tokenizer.texts_to_sequences(texts), maxlen=100) print('data prepared!') category2id = dict(zip(gen_category_master['category_name'], gen_category_master['category_id'])) res = [] n_categories = [] for category in gen_categories: model = create_model(emb_dim=10) try: model.load_weights(f'{project_root}/model/gen_classification_model/{category}.ckpt') n_categories.append(category) except: continue predicted = np.around(model.predict(x).ravel(), 4)# probability res.append(predicted) print(f'{category} end') del model gen_categories = n_categories category_score = [json.dumps(dict([(str(category2id[gen_categories[j]]), {"val": str(res[j][i])}) for j in range(len(gen_categories))]), ensure_ascii=False) for i in range(len(res[0]))] df['gen_category'] = category_score # from main import update_local_df def update_past_all(): dba = clsDbAccessor() df = dba.execQuery("SELECT `tw_id` FROM `tbl_twitters` WHERE proc_flag=1 AND deleted_at IS NULL;") dba.close() print(df) local_df = load_local_df().set_index('tw_id').reset_index() print(local_df.head()) df = df.merge(local_df, how='left', on='tw_id').set_index('tw_id', drop=False).dropna() print(df) infer(df) print(df.head()) update_tbl_twitter(df, ['category', 'gen_category']) pass if __name__ == '__main__': # df = pd.read_csv(f'{project_root}/data/tbl_twitter.csv', names=['text']) # train(df) update_past_all() pass

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null

0

null

0

1

0

78d1bbfc4644ff6a02d35970a20eee982aa45909

7,281

py

Python

projects/advanced_lane_lines/lane_detector/utils/camera_utils.py

stoicio/RoboCar

65591e8c217e61d0571df39fe9d9993e5984d8fe

[ "MIT" ]

null

projects/advanced_lane_lines/lane_detector/utils/camera_utils.py

stoicio/RoboCar

65591e8c217e61d0571df39fe9d9993e5984d8fe

[ "MIT" ]

null

projects/advanced_lane_lines/lane_detector/utils/camera_utils.py

stoicio/RoboCar

65591e8c217e61d0571df39fe9d9993e5984d8fe

[ "MIT" ]

null

import json import logging import os import cv2 import numpy as np from tqdm import tqdm logger = logging.getLogger('CameraUtils') class CameraCalibration(object): @staticmethod def get_image_paths(chessboard_img_dir): allowed_extensions = ['.jpg', '.png', '.jpeg'] full_image_paths = [] if not os.path.exists(chessboard_img_dir) or os.path.isfile(chessboard_img_dir): raise ValueError("Chessboard images directory not found") files_in_dir = os.listdir(chessboard_img_dir) for _file in files_in_dir: if os.path.splitext(_file)[-1] in allowed_extensions: full_image_paths.append(os.path.join(chessboard_img_dir, _file)) else: logger.info("Skipping {name} - Not an image file".format(name=_file)) if not full_image_paths: raise RuntimeError("No chessboard images found") return full_image_paths def __init__(self, n_cols=None, n_rows=None, chessboard_img_dir=None, params_load_path=None, store_output_images=False,): ''' Args: n_cols (int) : Number of corners along horizontal axis n_rows (int) : Number of corners along vertical axis chessboard_img_dir (str) : directory where the chessboard images are stored ''' if params_load_path: if os.path.exists(params_load_path): self.load_params_from_file(params_load_path) logger.info('Camera params loaded and ready to use') else: logger.error('Cannot load params from file. Please recalibrate') raise ValueError('Cannot load params from file. Please recalibrate') return if not all([n_cols, n_rows, chessboard_img_dir]): raise ValueError('Pass in chess board params and location to images') self.images_dir = chessboard_img_dir self.image_paths = self.get_image_paths(chessboard_img_dir) self.pattern_size = (n_cols, n_rows) self.mtx = None self.dist = None self.output_images_path = [] self.failed_images = [] self.__is_calibrated = False self.__store_output_images = store_output_images self.__calibrate_camera() def load_params_from_file(self, json_file_path): expected_keys = ['mtx', 'dist'] with open(json_file_path, 'r') as fp: data = json.load(fp) if not all([k in data.keys() for k in expected_keys]): raise ValueError('Cannot load camera params. Use a different file or recalibrate') self.mtx = np.array(data['mtx']) self.dist = np.array(data['dist']) self.__is_calibrated = True def save_params_to_file(self, file_path): data = { 'mtx': self.mtx.tolist(), 'dist': self.dist.tolist() } with open(file_path, 'w') as fp: json.dump(data, fp) def __calibrate_camera(self): # Termination criteria to choose accurate corners. terminate sub-pixel detection # after 30 iterations or if improvement is less than 0.001 termination_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001) # Arrays to store collection of 3d and 2d chessboard corners chessboard_corners_3d = [] image_points_2d = [] corner_points_3d = np.zeros((self.pattern_size[0] * self.pattern_size[1], 3), np.float32) # Fill with 3D Coordinates representing the corners in chess board corner_points_3d[:, :2] = np.mgrid[0: self.pattern_size[0], 0:self.pattern_size[1]].T.reshape(-1, 2) # flake8: noqa # if we have to store output images of detected chess boards, create a target folder output_imgs_dir = os.path.join(self.images_dir, 'output') if self.__store_output_images and not os.path.exists(output_imgs_dir): os.makedirs(output_imgs_dir) for image in tqdm(self.image_paths, desc='Finding chessboard corners'): img = cv2.imread(image) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Find corners - return early if no corners are detectable found_corners, corners = cv2.findChessboardCorners(gray, self.pattern_size, None, cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK) if found_corners: chessboard_corners_3d.append(corner_points_3d) accurate_corners = cv2.cornerSubPix(gray, corners, (11, 11), (-1, -1), termination_criteria) image_points_2d.append(accurate_corners) if self.__store_output_images: new_img_path = os.path.join(output_imgs_dir, os.path.basename(image)) cv2.drawChessboardCorners(img, self.pattern_size, accurate_corners, found_corners) cv2.imwrite(new_img_path, img) self.output_images_path.append(new_img_path) else: logger.debug("Failed to find chessboard in {name}".format(name=image)) self.failed_images.append(image) (success, self.mtx, self.dist, _, _) = cv2.calibrateCamera(chessboard_corners_3d, image_points_2d, gray.shape[::-1], None, None) if not success: raise RuntimeError("Calibration failed ! Retry with better chessboard images") # Set Calibration Result to Trues logger.info(('Successfully calculated Camera Matrix.' 'Skipped processing {count} images').format(count=len(self.failed_images))) self.__is_calibrated = True def get_camera_params(self, redo_calibration=False): if not self.__is_calibrated or redo_calibration: self.__calibrate_camera() return (self.mtx, self.dist) def get_processed_images(self): '''Returns a list of chessboard images with corners drawn and a list of images in which corner detection failed Returns data (dict): data['output_images'] : list of paths with corners drawn data['failed_images'] : list of path in which corner detection failed ''' if not self.__store_output_images: logger.warn(('Output images are not stored. To write output images,' 'set "store_ store_output_images=True" during init')) return { 'output_images': self.output_images_path, 'failed_images': self.failed_images } def undistort_image(self, image): '''Takes an numpy array representing an image or a string pointing to a image path and undistorts with the calibrated camera matrix and distortion coffiecients''' if not self.__is_calibrated: self.__calibrate_camera() img_data = cv2.imread(image) if isinstance(image, str) else image return cv2.undistort(img_data, self.mtx, self.dist, None, self.mtx)

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78d4eb146a133aea0467f8cf0a76b0671ef0d2b6

5,375

py

Python

spirou/sandbox/ccf_tools/analyse_TOI736.py

njcuk9999/apero-utils

f77de4c9123874e5bb6ed6bd03a7de3b27057402

[ "MIT" ]

2

2020-10-08T17:03:45.000Z

2021-03-09T17:49:44.000Z

spirou/sandbox/ccf_tools/analyse_TOI736.py

njcuk9999/apero-utils

f77de4c9123874e5bb6ed6bd03a7de3b27057402

[ "MIT" ]

17

2020-09-24T17:35:38.000Z

2020-12-11T16:10:13.000Z

spirou/sandbox/ccf_tools/analyse_TOI736.py

njcuk9999/apero-utils

f77de4c9123874e5bb6ed6bd03a7de3b27057402

[ "MIT" ]

5

2020-04-10T06:41:00.000Z

2020-12-16T21:09:14.000Z

import numpy as np import matplotlib.pyplot as plt from astropy.table import Table from bisector import * from astropy.time import Time from ccf2rv import * from per_epoch_table import per_epoch_table def sinusoidal(phase,dphase,amp,zp): return np.sin( (phase+dphase))*amp+zp # do not *formally* exclude an order, but this is done later with the bandpass keyword exclude_orders = [28,47,48] object = 'TOI-736' mask = 'gl699_neg' method = 'all' sanitize = True # number of median-absolute deviations within an epoch to consider a point discrepant tbl,dico = get_object_rv(object,mask =mask, method = method,force = True, exclude_orders = exclude_orders, snr_min = 20.0, velocity_window = 20, sanitize = sanitize, dvmax_per_order = 500.0, bandpass = 'H', doplot = True, do_blacklist = True, detailed_output = True, sed_match = False) rv = np.array(tbl['RV']) rv -= np.mean(rv) ccf = np.array(dico['MEAN_CCF']) ccf2 = np.array(ccf) for i in range(34): ccf2[:,i] = np.roll(ccf2[:,i],int(-rv[i]*10)) moy = np.mean(ccf2,axis=1) for i in range(34): ccf2[:,i] -= moy for i in range(34): ccf2[:,i] = np.roll(ccf2[:,i],int(rv[i]*10)) damps = np.arange(10,55,0.1) all_ccs = np.zeros([ccf2.shape[0],len(damps)]) for ite in range(len(damps)): print(ite) ccf3 = np.zeros_like(ccf2) for i in range(34): ccf3[:,i] = np.roll(ccf2[:,i],int(damps[ite]*rv[i]*10)) all_ccs[:,ite] = np.nanmean(ccf3,axis=1) plt.plot(dico['ccf_RV'],moy) plt.show() plt.plot(damps,all_ccs[np.argmin(moy),:]) plt.show() plt.imshow(all_ccs/np.std(all_ccs),aspect = 'auto',extent = [np.min(damps),np.max(damps),np.min(dico['ccf_RV']),np.max(dico['ccf_RV'])]) plt.show() # period for the sinusoidal currve period = 14.4 # create the table with bis per epoch tbl_bin = per_epoch_table(tbl,nMAD_cut = 5) # get time stamps friendly for plotting t2 = Time(tbl_bin['MJDATE_MEAN'], format = 'mjd') t3 = Time(tbl['MJDATE'], format = 'mjd') # get phase for sine fitting phase_bin = 2*np.pi*tbl_bin['MJDATE_MEAN']/period phase = 2*np.pi*tbl['MJDATE']/period # fit sinusoid fit, pcov = curve_fit(sinusoidal, phase_bin, tbl_bin['RV']) # some plotting fiddling dt = np.max(tbl_bin['MJDATE_MEAN']) - np.min(tbl_bin['MJDATE_MEAN']) time_plot = np.arange(np.min(tbl_bin['MJDATE_MEAN'])-dt/10,np.max(tbl_bin['MJDATE_MEAN'])+dt/10,dt/1000) phase_plot = 2*np.pi*time_plot/period model_bin = sinusoidal(phase_bin,*fit) model= sinusoidal(phase,*fit) model_plot = sinusoidal(phase_plot,*fit) print('Amplitude of the sinusoidal at {0} days: {1:.2f} m/s'.format(period, 1000*fit[1])) print('Mean velocity: {1:.2f} m/s'.format(period, 1000*fit[2])) print('Mean/Median per-epoch STDDEV {0}/{1} km/s'.format(np.mean(tbl_bin["ERROR_RV"]) ,np.median(tbl_bin["ERROR_RV"]))) fig, ax = plt.subplots(nrows = 2, ncols = 1,sharex = True, figsize = (14,8)) for i in range(len(t2)): ax[0].plot_date(t2.plot_date,tbl_bin['RV'],'g.') ax[0].plot_date([t2[i].plot_date,t2[i].plot_date],[tbl_bin['RV'][i]-tbl_bin['ERROR_RV'][i], tbl_bin['RV'][i]+tbl_bin['ERROR_RV'][i]],'g') ax[0].plot_date(t3.plot_date,tbl['RV'],'r.',alpha = 0.5) ax[1].errorbar(t3.plot_date,tbl['RV'] - model,yerr=tbl['ERROR_RV'], linestyle="None", fmt='o',color = 'green', alpha = 0.2, label = 'Individual measurements') ax[0].plot(Time(time_plot, format = 'mjd').plot_date,model_plot,'r:') ax[0].set(ylabel = 'Velocity [km/s]',title = object) ax[1].errorbar(t2.plot_date, tbl_bin['RV'] - model_bin, yerr=tbl_bin['ERROR_RV'], linestyle="None", fmt='o', alpha = 0.5, capsize = 2, color = 'black',label = 'Epoch mean') ax[1].legend() ax[1].plot(Time(time_plot, format = 'mjd').plot_date,np.zeros(len(time_plot)),'r:') ax[1].set(xlabel = 'Date', ylabel = 'Residuals [km/s]',ylim = [-.15,0.15], xlim = [np.min(Time(time_plot, format = 'mjd').plot_date), np.max(Time(time_plot, format = 'mjd').plot_date)] ) for label in ax[1].get_xticklabels(): label.set_rotation(25) label.set_ha('right') plt.tight_layout() plt.savefig(object+'.pdf') plt.show() sigma = np.std((tbl_bin['RV'] - model_bin)) mean_error = np.mean(tbl_bin['ERROR_RV']) median_error = np.nanmedian(tbl_bin['ERROR_RV']) reduced_chi2 = np.std((tbl_bin['RV'] - model_bin)/tbl_bin['ERROR_RV']) print('\n--- values for the per-night weighted-mean points ---\n') print(' mean ERROR_RV {0:.2f} m/s, median ERROR_RV {1:.2f} m/s, ' 'reduced chi2 {2:.2f} '.format(mean_error*1e3, median_error*1e3, reduced_chi2)) mean_error = np.mean(tbl['ERROR_RV']) median_error = np.nanmedian(tbl['ERROR_RV']) print('\n--- values for the individual points ---\n') print(' mean ERROR_RV {0:.2f} m/s, median ERROR_RV {1:.2f} m/s'.format( mean_error*1e3,median_error*1e3)) f = open('TOI1278_obslog.tex','w') # create an observation log in tex format # Nice when you want to write a paper in the end, hey, that's the point of all these observations! for i in range(len(tbl)): f.write('{0:.4f} & ${1:.3f} \pm {2:.3f}$ & {3:.3f} \\\\ \n'.format(tbl['MJDATE'][i],tbl['RV'][i], tbl['ERROR_RV'][i],tbl['D2_RESIDUAL_CCF'][i])) f.close()

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