hexsha
string | size
int64 | ext
string | lang
string | max_stars_repo_path
string | max_stars_repo_name
string | max_stars_repo_head_hexsha
string | max_stars_repo_licenses
list | max_stars_count
int64 | max_stars_repo_stars_event_min_datetime
string | max_stars_repo_stars_event_max_datetime
string | max_issues_repo_path
string | max_issues_repo_name
string | max_issues_repo_head_hexsha
string | max_issues_repo_licenses
list | max_issues_count
int64 | max_issues_repo_issues_event_min_datetime
string | max_issues_repo_issues_event_max_datetime
string | max_forks_repo_path
string | max_forks_repo_name
string | max_forks_repo_head_hexsha
string | max_forks_repo_licenses
list | max_forks_count
int64 | max_forks_repo_forks_event_min_datetime
string | max_forks_repo_forks_event_max_datetime
string | content
string | avg_line_length
float64 | max_line_length
int64 | alphanum_fraction
float64 | qsc_code_num_words_quality_signal
int64 | qsc_code_num_chars_quality_signal
float64 | qsc_code_mean_word_length_quality_signal
float64 | qsc_code_frac_words_unique_quality_signal
float64 | qsc_code_frac_chars_top_2grams_quality_signal
float64 | qsc_code_frac_chars_top_3grams_quality_signal
float64 | qsc_code_frac_chars_top_4grams_quality_signal
float64 | qsc_code_frac_chars_dupe_5grams_quality_signal
float64 | qsc_code_frac_chars_dupe_6grams_quality_signal
float64 | qsc_code_frac_chars_dupe_7grams_quality_signal
float64 | qsc_code_frac_chars_dupe_8grams_quality_signal
float64 | qsc_code_frac_chars_dupe_9grams_quality_signal
float64 | qsc_code_frac_chars_dupe_10grams_quality_signal
float64 | qsc_code_frac_chars_replacement_symbols_quality_signal
float64 | qsc_code_frac_chars_digital_quality_signal
float64 | qsc_code_frac_chars_whitespace_quality_signal
float64 | qsc_code_size_file_byte_quality_signal
float64 | qsc_code_num_lines_quality_signal
float64 | qsc_code_num_chars_line_max_quality_signal
float64 | qsc_code_num_chars_line_mean_quality_signal
float64 | qsc_code_frac_chars_alphabet_quality_signal
float64 | qsc_code_frac_chars_comments_quality_signal
float64 | qsc_code_cate_xml_start_quality_signal
float64 | qsc_code_frac_lines_dupe_lines_quality_signal
float64 | qsc_code_cate_autogen_quality_signal
float64 | qsc_code_frac_lines_long_string_quality_signal
float64 | qsc_code_frac_chars_string_length_quality_signal
float64 | qsc_code_frac_chars_long_word_length_quality_signal
float64 | qsc_code_frac_lines_string_concat_quality_signal
float64 | qsc_code_cate_encoded_data_quality_signal
float64 | qsc_code_frac_chars_hex_words_quality_signal
float64 | qsc_code_frac_lines_prompt_comments_quality_signal
float64 | qsc_code_frac_lines_assert_quality_signal
float64 | qsc_codepython_cate_ast_quality_signal
float64 | qsc_codepython_frac_lines_func_ratio_quality_signal
float64 | qsc_codepython_cate_var_zero_quality_signal
bool | qsc_codepython_frac_lines_pass_quality_signal
float64 | qsc_codepython_frac_lines_import_quality_signal
float64 | qsc_codepython_frac_lines_simplefunc_quality_signal
float64 | qsc_codepython_score_lines_no_logic_quality_signal
float64 | qsc_codepython_frac_lines_print_quality_signal
float64 | qsc_code_num_words
int64 | qsc_code_num_chars
int64 | qsc_code_mean_word_length
int64 | qsc_code_frac_words_unique
null | qsc_code_frac_chars_top_2grams
int64 | qsc_code_frac_chars_top_3grams
int64 | qsc_code_frac_chars_top_4grams
int64 | qsc_code_frac_chars_dupe_5grams
int64 | qsc_code_frac_chars_dupe_6grams
int64 | qsc_code_frac_chars_dupe_7grams
int64 | qsc_code_frac_chars_dupe_8grams
int64 | qsc_code_frac_chars_dupe_9grams
int64 | qsc_code_frac_chars_dupe_10grams
int64 | qsc_code_frac_chars_replacement_symbols
int64 | qsc_code_frac_chars_digital
int64 | qsc_code_frac_chars_whitespace
int64 | qsc_code_size_file_byte
int64 | qsc_code_num_lines
int64 | qsc_code_num_chars_line_max
int64 | qsc_code_num_chars_line_mean
int64 | qsc_code_frac_chars_alphabet
int64 | qsc_code_frac_chars_comments
int64 | qsc_code_cate_xml_start
int64 | qsc_code_frac_lines_dupe_lines
int64 | qsc_code_cate_autogen
int64 | qsc_code_frac_lines_long_string
int64 | qsc_code_frac_chars_string_length
int64 | qsc_code_frac_chars_long_word_length
int64 | qsc_code_frac_lines_string_concat
null | qsc_code_cate_encoded_data
int64 | qsc_code_frac_chars_hex_words
int64 | qsc_code_frac_lines_prompt_comments
int64 | qsc_code_frac_lines_assert
int64 | qsc_codepython_cate_ast
int64 | qsc_codepython_frac_lines_func_ratio
int64 | qsc_codepython_cate_var_zero
int64 | qsc_codepython_frac_lines_pass
int64 | qsc_codepython_frac_lines_import
int64 | qsc_codepython_frac_lines_simplefunc
int64 | qsc_codepython_score_lines_no_logic
int64 | qsc_codepython_frac_lines_print
int64 | effective
string | hits
int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2456d708a0c8605fac589faa748682ec83bfbad7
| 5,216
|
py
|
Python
|
Graph/subdataset1_exact.py
|
pprachas/ABC_dataset
|
61c915853c0229295e728f869b11b113ee59f098
|
[
"MIT"
] | null | null | null |
Graph/subdataset1_exact.py
|
pprachas/ABC_dataset
|
61c915853c0229295e728f869b11b113ee59f098
|
[
"MIT"
] | null | null | null |
Graph/subdataset1_exact.py
|
pprachas/ABC_dataset
|
61c915853c0229295e728f869b11b113ee59f098
|
[
"MIT"
] | null | null | null |
import numpy as np
import math
import networkx as nx
import networkx as nx
#------------------Note-------------------------------------#
# This code takes in the generates the exact representation for sub-dataset 1
# The nodes are the individual pixels and the edges connect the neighboring pixels
#---------------------Initialize variables-----------------------------------#
n = 25000 # number of samples
num_stack = 38 # number of stacks in block
#----------------------------Load files---------------------------------------#
x = np.load('subdataset1_geo/x.npy')
l = np.load('subdatasetl_geo/l.npy')
L = 40
w = 5
#-----------------------Generate Graph----------------------------------------#
G_all = []
for ii in range(0,len(x)):
G = nx.Graph()
edges_hor = []
edges_vert = []
for jj in range(1,num_stack-1):
first_count = []
first_c = []
for c,kk in enumerate(range(x[ii][jj]-l[ii][jj],x[ii][jj]+l[ii][jj]+1)):
prev = range(x[ii][jj-1]-l[ii][jj-1],x[ii][jj-1]+l[ii][jj-1]+1)
G.add_node(G.number_of_nodes())
G.nodes[G.number_of_nodes()-1]['feature'] = np.asarray([int(kk),int(40-jj)])
if jj > 1:
for count,ll in enumerate(prev):
if kk == ll:
first_c.append(c)
first_count.append(count)
prev_connected = G.number_of_nodes()-1-len(prev)-first_c[0]+first_count[0]
edges_vert.append((prev_connected,G.number_of_nodes()-1))
if c != 0:
edges_hor.append((G.number_of_nodes()-2,G.number_of_nodes()-1))
edges_all = edges_vert+edges_hor
G.add_edges_from(edges_all)
G_all.append(G)
print(ii)
print('Original Done')
print(len(G_all))
#--------------------------Reflect over y axis--------------------#
G_reflecty = []
for ii in range(0,len(x)):
G = nx.Graph()
edges_hor = []
edges_vert = []
for jj in range(1,num_stack-1):
first_c = []
for c,kk in enumerate(range(w-x[ii][jj]-l[ii][jj],w-x[ii][jj]+l[ii][jj]+1)):
prev = range(w-x[ii][jj-1]-l[ii][jj-1],w-x[ii][jj-1]+l[ii][jj-1]+1)
G.add_node(G.number_of_nodes())
G.nodes[G.number_of_nodes()-1]['feature'] = np.asarray([int(kk),int(40-jj)])
if jj > 1:
for count,ll in enumerate(prev):
if kk == ll:
first_c.append(c)
first_count.append(count)
prev_connected = G.number_of_nodes()-1-len(prev)-first_c[0]+first_count[0]
edges_vert.append((prev_connected,G.number_of_nodes()-1))
if c != 0:
edges_hor.append((G.number_of_nodes()-2,G.number_of_nodes()-1))
edges_all = edges_vert+edges_hor
G.add_edges_from(edges_all)
G_reflecty.append(G)
#--------------------------Reflect over x axis--------------------#
G_reflectx = []
for ii in range(0,len(x)):
G = nx.Graph()
edges_hor = []
edges_vert = []
for jj in range(1,num_stack-1):
first_count = []
first_c = []
for c,kk in enumerate(range(x[ii][jj]-l[ii][jj],x[ii][jj]+l[ii][jj]+1)):
prev = range(x[ii][jj-1]-l[ii][jj-1],x[ii][jj-1]+l[ii][jj-1]+1)
G.add_node(G.number_of_nodes())
G.nodes[G.number_of_nodes()-1]['feature'] = np.asarray([int(kk),int(jj)])
if jj > 1:
for count,ll in enumerate(prev):
if kk == ll:
first_c.append(c)
first_count.append(count)
prev_connected = G.number_of_nodes()-1-len(prev)-first_c[0]+first_count[0]
edges_vert.append((prev_connected,G.number_of_nodes()-1))
if c != 0:
edges_hor.append((G.number_of_nodes()-2,G.number_of_nodes()-1))
edges_all = edges_vert+edges_hor
G.add_edges_from(edges_all)
G_reflectx.append(G)
#--------------------------Reflect over y=x axis--------------------#
G_reflectxy = []
for ii in range(0,len(x)): #len(X)
G = nx.Graph()
edges_hor = []
edges_vert = []
for jj in range(1,num_stack-1):
first_count = []
first_c = []
for c,kk in enumerate(range(w-x[ii][jj]-l[ii][jj],w-x[ii][jj]+l[ii][jj]+1)):
prev = range(w-x[ii][jj-1]-l[ii][jj-1],w-x[ii][jj-1]+l[ii][jj-1]+1)
G.add_node(G.number_of_nodes())
G.nodes[G.number_of_nodes()-1]['feature'] = np.asarray([int(kk),int(jj)])
if jj > 1:
for count,ll in enumerate(prev):
if kk == ll:
first_c.append(c)
first_count.append(count)
prev_connected = G.number_of_nodes()-1-len(prev)-first_c[0]+first_count[0]
edges_vert.append((prev_connected,G.number_of_nodes()-1))
if c != 0:
edges_hor.append((G.number_of_nodes()-2,G.number_of_nodes()-1))
edges_all = edges_vert+edges_hor
G.add_edges_from(edges_all)
G_reflectxy.append(G)
#--------------------Save Graphs as pickle--------------------------------#
nx.write_gpickle(G_all,'Graphs/subdataset1/exact/G_og.gpickle')
nx.write_gpickle(G_reflecty,'Graphs/subdataset1/exact/G_ry.gpickle')
nx.write_gpickle(G_reflectx,'Graphs/subdataset1/exact/G_rx.gpickle')
nx.write_gpickle(G_reflectxy,'Graphs/subdataset1/exact/G_rxy.gpickle')
| 36.475524
| 87
| 0.547738
| 814
| 5,216
| 3.324324
| 0.120393
| 0.047302
| 0.079823
| 0.124169
| 0.746489
| 0.722099
| 0.722099
| 0.715817
| 0.715817
| 0.715817
| 0
| 0.022494
| 0.215874
| 5,216
| 143
| 88
| 36.475524
| 0.63912
| 0.146856
| 0
| 0.794643
| 1
| 0
| 0.054104
| 0.044543
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.035714
| 0
| 0.035714
| 0.026786
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
79e2438e83b93f45355871374afff8df4597de02
| 32,766
|
py
|
Python
|
sdk/python/pulumi_digitalocean/volume.py
|
yitsushi/pulumi-digitalocean
|
9d408e7e4a3bed2d9e7aa91a32e2f154706a3400
|
[
"ECL-2.0",
"Apache-2.0"
] | 53
|
2019-04-25T14:43:12.000Z
|
2022-03-14T15:51:44.000Z
|
sdk/python/pulumi_digitalocean/volume.py
|
yitsushi/pulumi-digitalocean
|
9d408e7e4a3bed2d9e7aa91a32e2f154706a3400
|
[
"ECL-2.0",
"Apache-2.0"
] | 158
|
2019-04-15T21:47:18.000Z
|
2022-03-29T21:21:57.000Z
|
sdk/python/pulumi_digitalocean/volume.py
|
yitsushi/pulumi-digitalocean
|
9d408e7e4a3bed2d9e7aa91a32e2f154706a3400
|
[
"ECL-2.0",
"Apache-2.0"
] | 10
|
2019-04-15T20:16:11.000Z
|
2021-05-28T19:08:32.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
from ._enums import *
__all__ = ['VolumeArgs', 'Volume']
@pulumi.input_type
class VolumeArgs:
def __init__(__self__, *,
region: pulumi.Input[Union[str, 'Region']],
size: pulumi.Input[int],
description: Optional[pulumi.Input[str]] = None,
filesystem_type: Optional[pulumi.Input[str]] = None,
initial_filesystem_label: Optional[pulumi.Input[str]] = None,
initial_filesystem_type: Optional[pulumi.Input[Union[str, 'FileSystemType']]] = None,
name: Optional[pulumi.Input[str]] = None,
snapshot_id: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None):
"""
The set of arguments for constructing a Volume resource.
:param pulumi.Input[Union[str, 'Region']] region: The region that the block storage volume will be created in.
:param pulumi.Input[int] size: The size of the block storage volume in GiB. If updated, can only be expanded.
:param pulumi.Input[str] description: A free-form text field up to a limit of 1024 bytes to describe a block storage volume.
:param pulumi.Input[str] filesystem_type: Filesystem type (`xfs` or `ext4`) for the block storage volume.
:param pulumi.Input[str] initial_filesystem_label: Initial filesystem label for the block storage volume.
:param pulumi.Input[Union[str, 'FileSystemType']] initial_filesystem_type: Initial filesystem type (`xfs` or `ext4`) for the block storage volume.
:param pulumi.Input[str] name: A name for the block storage volume. Must be lowercase and be composed only of numbers, letters and "-", up to a limit of 64 characters.
:param pulumi.Input[str] snapshot_id: The ID of an existing volume snapshot from which the new volume will be created. If supplied, the region and size will be limitied on creation to that of the referenced snapshot
:param pulumi.Input[Sequence[pulumi.Input[str]]] tags: A list of the tags to be applied to this Volume.
"""
pulumi.set(__self__, "region", region)
pulumi.set(__self__, "size", size)
if description is not None:
pulumi.set(__self__, "description", description)
if filesystem_type is not None:
warnings.warn("""This fields functionality has been replaced by `initial_filesystem_type`. The property will still remain as a computed attribute representing the current volumes filesystem type.""", DeprecationWarning)
pulumi.log.warn("""filesystem_type is deprecated: This fields functionality has been replaced by `initial_filesystem_type`. The property will still remain as a computed attribute representing the current volumes filesystem type.""")
if filesystem_type is not None:
pulumi.set(__self__, "filesystem_type", filesystem_type)
if initial_filesystem_label is not None:
pulumi.set(__self__, "initial_filesystem_label", initial_filesystem_label)
if initial_filesystem_type is not None:
pulumi.set(__self__, "initial_filesystem_type", initial_filesystem_type)
if name is not None:
pulumi.set(__self__, "name", name)
if snapshot_id is not None:
pulumi.set(__self__, "snapshot_id", snapshot_id)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter
def region(self) -> pulumi.Input[Union[str, 'Region']]:
"""
The region that the block storage volume will be created in.
"""
return pulumi.get(self, "region")
@region.setter
def region(self, value: pulumi.Input[Union[str, 'Region']]):
pulumi.set(self, "region", value)
@property
@pulumi.getter
def size(self) -> pulumi.Input[int]:
"""
The size of the block storage volume in GiB. If updated, can only be expanded.
"""
return pulumi.get(self, "size")
@size.setter
def size(self, value: pulumi.Input[int]):
pulumi.set(self, "size", value)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
A free-form text field up to a limit of 1024 bytes to describe a block storage volume.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter(name="filesystemType")
def filesystem_type(self) -> Optional[pulumi.Input[str]]:
"""
Filesystem type (`xfs` or `ext4`) for the block storage volume.
"""
return pulumi.get(self, "filesystem_type")
@filesystem_type.setter
def filesystem_type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "filesystem_type", value)
@property
@pulumi.getter(name="initialFilesystemLabel")
def initial_filesystem_label(self) -> Optional[pulumi.Input[str]]:
"""
Initial filesystem label for the block storage volume.
"""
return pulumi.get(self, "initial_filesystem_label")
@initial_filesystem_label.setter
def initial_filesystem_label(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "initial_filesystem_label", value)
@property
@pulumi.getter(name="initialFilesystemType")
def initial_filesystem_type(self) -> Optional[pulumi.Input[Union[str, 'FileSystemType']]]:
"""
Initial filesystem type (`xfs` or `ext4`) for the block storage volume.
"""
return pulumi.get(self, "initial_filesystem_type")
@initial_filesystem_type.setter
def initial_filesystem_type(self, value: Optional[pulumi.Input[Union[str, 'FileSystemType']]]):
pulumi.set(self, "initial_filesystem_type", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
A name for the block storage volume. Must be lowercase and be composed only of numbers, letters and "-", up to a limit of 64 characters.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="snapshotId")
def snapshot_id(self) -> Optional[pulumi.Input[str]]:
"""
The ID of an existing volume snapshot from which the new volume will be created. If supplied, the region and size will be limitied on creation to that of the referenced snapshot
"""
return pulumi.get(self, "snapshot_id")
@snapshot_id.setter
def snapshot_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "snapshot_id", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
A list of the tags to be applied to this Volume.
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
@pulumi.input_type
class _VolumeState:
def __init__(__self__, *,
description: Optional[pulumi.Input[str]] = None,
droplet_ids: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None,
filesystem_label: Optional[pulumi.Input[str]] = None,
filesystem_type: Optional[pulumi.Input[str]] = None,
initial_filesystem_label: Optional[pulumi.Input[str]] = None,
initial_filesystem_type: Optional[pulumi.Input[Union[str, 'FileSystemType']]] = None,
name: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[Union[str, 'Region']]] = None,
size: Optional[pulumi.Input[int]] = None,
snapshot_id: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
volume_urn: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering Volume resources.
:param pulumi.Input[str] description: A free-form text field up to a limit of 1024 bytes to describe a block storage volume.
:param pulumi.Input[Sequence[pulumi.Input[int]]] droplet_ids: A list of associated droplet ids.
:param pulumi.Input[str] filesystem_label: Filesystem label for the block storage volume.
:param pulumi.Input[str] filesystem_type: Filesystem type (`xfs` or `ext4`) for the block storage volume.
:param pulumi.Input[str] initial_filesystem_label: Initial filesystem label for the block storage volume.
:param pulumi.Input[Union[str, 'FileSystemType']] initial_filesystem_type: Initial filesystem type (`xfs` or `ext4`) for the block storage volume.
:param pulumi.Input[str] name: A name for the block storage volume. Must be lowercase and be composed only of numbers, letters and "-", up to a limit of 64 characters.
:param pulumi.Input[Union[str, 'Region']] region: The region that the block storage volume will be created in.
:param pulumi.Input[int] size: The size of the block storage volume in GiB. If updated, can only be expanded.
:param pulumi.Input[str] snapshot_id: The ID of an existing volume snapshot from which the new volume will be created. If supplied, the region and size will be limitied on creation to that of the referenced snapshot
:param pulumi.Input[Sequence[pulumi.Input[str]]] tags: A list of the tags to be applied to this Volume.
:param pulumi.Input[str] volume_urn: The uniform resource name for the volume.
"""
if description is not None:
pulumi.set(__self__, "description", description)
if droplet_ids is not None:
pulumi.set(__self__, "droplet_ids", droplet_ids)
if filesystem_label is not None:
pulumi.set(__self__, "filesystem_label", filesystem_label)
if filesystem_type is not None:
warnings.warn("""This fields functionality has been replaced by `initial_filesystem_type`. The property will still remain as a computed attribute representing the current volumes filesystem type.""", DeprecationWarning)
pulumi.log.warn("""filesystem_type is deprecated: This fields functionality has been replaced by `initial_filesystem_type`. The property will still remain as a computed attribute representing the current volumes filesystem type.""")
if filesystem_type is not None:
pulumi.set(__self__, "filesystem_type", filesystem_type)
if initial_filesystem_label is not None:
pulumi.set(__self__, "initial_filesystem_label", initial_filesystem_label)
if initial_filesystem_type is not None:
pulumi.set(__self__, "initial_filesystem_type", initial_filesystem_type)
if name is not None:
pulumi.set(__self__, "name", name)
if region is not None:
pulumi.set(__self__, "region", region)
if size is not None:
pulumi.set(__self__, "size", size)
if snapshot_id is not None:
pulumi.set(__self__, "snapshot_id", snapshot_id)
if tags is not None:
pulumi.set(__self__, "tags", tags)
if volume_urn is not None:
pulumi.set(__self__, "volume_urn", volume_urn)
@property
@pulumi.getter
def description(self) -> Optional[pulumi.Input[str]]:
"""
A free-form text field up to a limit of 1024 bytes to describe a block storage volume.
"""
return pulumi.get(self, "description")
@description.setter
def description(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "description", value)
@property
@pulumi.getter(name="dropletIds")
def droplet_ids(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]:
"""
A list of associated droplet ids.
"""
return pulumi.get(self, "droplet_ids")
@droplet_ids.setter
def droplet_ids(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]]):
pulumi.set(self, "droplet_ids", value)
@property
@pulumi.getter(name="filesystemLabel")
def filesystem_label(self) -> Optional[pulumi.Input[str]]:
"""
Filesystem label for the block storage volume.
"""
return pulumi.get(self, "filesystem_label")
@filesystem_label.setter
def filesystem_label(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "filesystem_label", value)
@property
@pulumi.getter(name="filesystemType")
def filesystem_type(self) -> Optional[pulumi.Input[str]]:
"""
Filesystem type (`xfs` or `ext4`) for the block storage volume.
"""
return pulumi.get(self, "filesystem_type")
@filesystem_type.setter
def filesystem_type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "filesystem_type", value)
@property
@pulumi.getter(name="initialFilesystemLabel")
def initial_filesystem_label(self) -> Optional[pulumi.Input[str]]:
"""
Initial filesystem label for the block storage volume.
"""
return pulumi.get(self, "initial_filesystem_label")
@initial_filesystem_label.setter
def initial_filesystem_label(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "initial_filesystem_label", value)
@property
@pulumi.getter(name="initialFilesystemType")
def initial_filesystem_type(self) -> Optional[pulumi.Input[Union[str, 'FileSystemType']]]:
"""
Initial filesystem type (`xfs` or `ext4`) for the block storage volume.
"""
return pulumi.get(self, "initial_filesystem_type")
@initial_filesystem_type.setter
def initial_filesystem_type(self, value: Optional[pulumi.Input[Union[str, 'FileSystemType']]]):
pulumi.set(self, "initial_filesystem_type", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
A name for the block storage volume. Must be lowercase and be composed only of numbers, letters and "-", up to a limit of 64 characters.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def region(self) -> Optional[pulumi.Input[Union[str, 'Region']]]:
"""
The region that the block storage volume will be created in.
"""
return pulumi.get(self, "region")
@region.setter
def region(self, value: Optional[pulumi.Input[Union[str, 'Region']]]):
pulumi.set(self, "region", value)
@property
@pulumi.getter
def size(self) -> Optional[pulumi.Input[int]]:
"""
The size of the block storage volume in GiB. If updated, can only be expanded.
"""
return pulumi.get(self, "size")
@size.setter
def size(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "size", value)
@property
@pulumi.getter(name="snapshotId")
def snapshot_id(self) -> Optional[pulumi.Input[str]]:
"""
The ID of an existing volume snapshot from which the new volume will be created. If supplied, the region and size will be limitied on creation to that of the referenced snapshot
"""
return pulumi.get(self, "snapshot_id")
@snapshot_id.setter
def snapshot_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "snapshot_id", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
A list of the tags to be applied to this Volume.
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "tags", value)
@property
@pulumi.getter(name="volumeUrn")
def volume_urn(self) -> Optional[pulumi.Input[str]]:
"""
The uniform resource name for the volume.
"""
return pulumi.get(self, "volume_urn")
@volume_urn.setter
def volume_urn(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "volume_urn", value)
class Volume(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
description: Optional[pulumi.Input[str]] = None,
filesystem_type: Optional[pulumi.Input[str]] = None,
initial_filesystem_label: Optional[pulumi.Input[str]] = None,
initial_filesystem_type: Optional[pulumi.Input[Union[str, 'FileSystemType']]] = None,
name: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[Union[str, 'Region']]] = None,
size: Optional[pulumi.Input[int]] = None,
snapshot_id: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
__props__=None):
"""
Provides a DigitalOcean Block Storage volume which can be attached to a Droplet in order to provide expanded storage.
## Example Usage
```python
import pulumi
import pulumi_digitalocean as digitalocean
foobar_volume = digitalocean.Volume("foobarVolume",
region="nyc1",
size=100,
initial_filesystem_type="ext4",
description="an example volume")
foobar_droplet = digitalocean.Droplet("foobarDroplet",
size="s-1vcpu-1gb",
image="ubuntu-18-04-x64",
region="nyc1")
foobar_volume_attachment = digitalocean.VolumeAttachment("foobarVolumeAttachment",
droplet_id=foobar_droplet.id,
volume_id=foobar_volume.id)
```
You can also create a volume from an existing snapshot.
```python
import pulumi
import pulumi_digitalocean as digitalocean
foobar_volume_snapshot = digitalocean.get_volume_snapshot(name="baz")
foobar_volume = digitalocean.Volume("foobarVolume",
region="lon1",
size=foobar_volume_snapshot.min_disk_size,
snapshot_id=foobar_volume_snapshot.id)
```
## Import
Volumes can be imported using the `volume id`, e.g.
```sh
$ pulumi import digitalocean:index/volume:Volume volume 506f78a4-e098-11e5-ad9f-000f53306ae1
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] description: A free-form text field up to a limit of 1024 bytes to describe a block storage volume.
:param pulumi.Input[str] filesystem_type: Filesystem type (`xfs` or `ext4`) for the block storage volume.
:param pulumi.Input[str] initial_filesystem_label: Initial filesystem label for the block storage volume.
:param pulumi.Input[Union[str, 'FileSystemType']] initial_filesystem_type: Initial filesystem type (`xfs` or `ext4`) for the block storage volume.
:param pulumi.Input[str] name: A name for the block storage volume. Must be lowercase and be composed only of numbers, letters and "-", up to a limit of 64 characters.
:param pulumi.Input[Union[str, 'Region']] region: The region that the block storage volume will be created in.
:param pulumi.Input[int] size: The size of the block storage volume in GiB. If updated, can only be expanded.
:param pulumi.Input[str] snapshot_id: The ID of an existing volume snapshot from which the new volume will be created. If supplied, the region and size will be limitied on creation to that of the referenced snapshot
:param pulumi.Input[Sequence[pulumi.Input[str]]] tags: A list of the tags to be applied to this Volume.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: VolumeArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Provides a DigitalOcean Block Storage volume which can be attached to a Droplet in order to provide expanded storage.
## Example Usage
```python
import pulumi
import pulumi_digitalocean as digitalocean
foobar_volume = digitalocean.Volume("foobarVolume",
region="nyc1",
size=100,
initial_filesystem_type="ext4",
description="an example volume")
foobar_droplet = digitalocean.Droplet("foobarDroplet",
size="s-1vcpu-1gb",
image="ubuntu-18-04-x64",
region="nyc1")
foobar_volume_attachment = digitalocean.VolumeAttachment("foobarVolumeAttachment",
droplet_id=foobar_droplet.id,
volume_id=foobar_volume.id)
```
You can also create a volume from an existing snapshot.
```python
import pulumi
import pulumi_digitalocean as digitalocean
foobar_volume_snapshot = digitalocean.get_volume_snapshot(name="baz")
foobar_volume = digitalocean.Volume("foobarVolume",
region="lon1",
size=foobar_volume_snapshot.min_disk_size,
snapshot_id=foobar_volume_snapshot.id)
```
## Import
Volumes can be imported using the `volume id`, e.g.
```sh
$ pulumi import digitalocean:index/volume:Volume volume 506f78a4-e098-11e5-ad9f-000f53306ae1
```
:param str resource_name: The name of the resource.
:param VolumeArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(VolumeArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
description: Optional[pulumi.Input[str]] = None,
filesystem_type: Optional[pulumi.Input[str]] = None,
initial_filesystem_label: Optional[pulumi.Input[str]] = None,
initial_filesystem_type: Optional[pulumi.Input[Union[str, 'FileSystemType']]] = None,
name: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[Union[str, 'Region']]] = None,
size: Optional[pulumi.Input[int]] = None,
snapshot_id: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = VolumeArgs.__new__(VolumeArgs)
__props__.__dict__["description"] = description
if filesystem_type is not None and not opts.urn:
warnings.warn("""This fields functionality has been replaced by `initial_filesystem_type`. The property will still remain as a computed attribute representing the current volumes filesystem type.""", DeprecationWarning)
pulumi.log.warn("""filesystem_type is deprecated: This fields functionality has been replaced by `initial_filesystem_type`. The property will still remain as a computed attribute representing the current volumes filesystem type.""")
__props__.__dict__["filesystem_type"] = filesystem_type
__props__.__dict__["initial_filesystem_label"] = initial_filesystem_label
__props__.__dict__["initial_filesystem_type"] = initial_filesystem_type
__props__.__dict__["name"] = name
if region is None and not opts.urn:
raise TypeError("Missing required property 'region'")
__props__.__dict__["region"] = region
if size is None and not opts.urn:
raise TypeError("Missing required property 'size'")
__props__.__dict__["size"] = size
__props__.__dict__["snapshot_id"] = snapshot_id
__props__.__dict__["tags"] = tags
__props__.__dict__["droplet_ids"] = None
__props__.__dict__["filesystem_label"] = None
__props__.__dict__["volume_urn"] = None
super(Volume, __self__).__init__(
'digitalocean:index/volume:Volume',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
description: Optional[pulumi.Input[str]] = None,
droplet_ids: Optional[pulumi.Input[Sequence[pulumi.Input[int]]]] = None,
filesystem_label: Optional[pulumi.Input[str]] = None,
filesystem_type: Optional[pulumi.Input[str]] = None,
initial_filesystem_label: Optional[pulumi.Input[str]] = None,
initial_filesystem_type: Optional[pulumi.Input[Union[str, 'FileSystemType']]] = None,
name: Optional[pulumi.Input[str]] = None,
region: Optional[pulumi.Input[Union[str, 'Region']]] = None,
size: Optional[pulumi.Input[int]] = None,
snapshot_id: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
volume_urn: Optional[pulumi.Input[str]] = None) -> 'Volume':
"""
Get an existing Volume resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] description: A free-form text field up to a limit of 1024 bytes to describe a block storage volume.
:param pulumi.Input[Sequence[pulumi.Input[int]]] droplet_ids: A list of associated droplet ids.
:param pulumi.Input[str] filesystem_label: Filesystem label for the block storage volume.
:param pulumi.Input[str] filesystem_type: Filesystem type (`xfs` or `ext4`) for the block storage volume.
:param pulumi.Input[str] initial_filesystem_label: Initial filesystem label for the block storage volume.
:param pulumi.Input[Union[str, 'FileSystemType']] initial_filesystem_type: Initial filesystem type (`xfs` or `ext4`) for the block storage volume.
:param pulumi.Input[str] name: A name for the block storage volume. Must be lowercase and be composed only of numbers, letters and "-", up to a limit of 64 characters.
:param pulumi.Input[Union[str, 'Region']] region: The region that the block storage volume will be created in.
:param pulumi.Input[int] size: The size of the block storage volume in GiB. If updated, can only be expanded.
:param pulumi.Input[str] snapshot_id: The ID of an existing volume snapshot from which the new volume will be created. If supplied, the region and size will be limitied on creation to that of the referenced snapshot
:param pulumi.Input[Sequence[pulumi.Input[str]]] tags: A list of the tags to be applied to this Volume.
:param pulumi.Input[str] volume_urn: The uniform resource name for the volume.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _VolumeState.__new__(_VolumeState)
__props__.__dict__["description"] = description
__props__.__dict__["droplet_ids"] = droplet_ids
__props__.__dict__["filesystem_label"] = filesystem_label
__props__.__dict__["filesystem_type"] = filesystem_type
__props__.__dict__["initial_filesystem_label"] = initial_filesystem_label
__props__.__dict__["initial_filesystem_type"] = initial_filesystem_type
__props__.__dict__["name"] = name
__props__.__dict__["region"] = region
__props__.__dict__["size"] = size
__props__.__dict__["snapshot_id"] = snapshot_id
__props__.__dict__["tags"] = tags
__props__.__dict__["volume_urn"] = volume_urn
return Volume(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def description(self) -> pulumi.Output[Optional[str]]:
"""
A free-form text field up to a limit of 1024 bytes to describe a block storage volume.
"""
return pulumi.get(self, "description")
@property
@pulumi.getter(name="dropletIds")
def droplet_ids(self) -> pulumi.Output[Sequence[int]]:
"""
A list of associated droplet ids.
"""
return pulumi.get(self, "droplet_ids")
@property
@pulumi.getter(name="filesystemLabel")
def filesystem_label(self) -> pulumi.Output[str]:
"""
Filesystem label for the block storage volume.
"""
return pulumi.get(self, "filesystem_label")
@property
@pulumi.getter(name="filesystemType")
def filesystem_type(self) -> pulumi.Output[str]:
"""
Filesystem type (`xfs` or `ext4`) for the block storage volume.
"""
return pulumi.get(self, "filesystem_type")
@property
@pulumi.getter(name="initialFilesystemLabel")
def initial_filesystem_label(self) -> pulumi.Output[Optional[str]]:
"""
Initial filesystem label for the block storage volume.
"""
return pulumi.get(self, "initial_filesystem_label")
@property
@pulumi.getter(name="initialFilesystemType")
def initial_filesystem_type(self) -> pulumi.Output[Optional[str]]:
"""
Initial filesystem type (`xfs` or `ext4`) for the block storage volume.
"""
return pulumi.get(self, "initial_filesystem_type")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
A name for the block storage volume. Must be lowercase and be composed only of numbers, letters and "-", up to a limit of 64 characters.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def region(self) -> pulumi.Output[str]:
"""
The region that the block storage volume will be created in.
"""
return pulumi.get(self, "region")
@property
@pulumi.getter
def size(self) -> pulumi.Output[int]:
"""
The size of the block storage volume in GiB. If updated, can only be expanded.
"""
return pulumi.get(self, "size")
@property
@pulumi.getter(name="snapshotId")
def snapshot_id(self) -> pulumi.Output[Optional[str]]:
"""
The ID of an existing volume snapshot from which the new volume will be created. If supplied, the region and size will be limitied on creation to that of the referenced snapshot
"""
return pulumi.get(self, "snapshot_id")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Sequence[str]]]:
"""
A list of the tags to be applied to this Volume.
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter(name="volumeUrn")
def volume_urn(self) -> pulumi.Output[str]:
"""
The uniform resource name for the volume.
"""
return pulumi.get(self, "volume_urn")
| 46.675214
| 248
| 0.655069
| 3,983
| 32,766
| 5.206879
| 0.059001
| 0.083803
| 0.062105
| 0.056223
| 0.90906
| 0.886542
| 0.873523
| 0.858383
| 0.849607
| 0.827812
| 0
| 0.005288
| 0.243911
| 32,766
| 701
| 249
| 46.741797
| 0.83184
| 0.341665
| 0
| 0.748032
| 1
| 0.015748
| 0.153088
| 0.037425
| 0
| 0
| 0
| 0
| 0
| 1
| 0.160105
| false
| 0.002625
| 0.015748
| 0
| 0.272966
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
032ab26a2819092c417b90d941280337e0cc3dc6
| 91,920
|
py
|
Python
|
generators/generators.py
|
EdwardJTL/Edit3D
|
bbb6364aeb5ea17c12c0c23578268641c066ebca
|
[
"MIT"
] | null | null | null |
generators/generators.py
|
EdwardJTL/Edit3D
|
bbb6364aeb5ea17c12c0c23578268641c066ebca
|
[
"MIT"
] | null | null | null |
generators/generators.py
|
EdwardJTL/Edit3D
|
bbb6364aeb5ea17c12c0c23578268641c066ebca
|
[
"MIT"
] | null | null | null |
"""Implicit generator for 3D volumes"""
import random
import torch.nn as nn
import torch
import time
import tqdm
from einops import repeat, rearrange
import curriculums
from torch.cuda.amp import autocast
from .volumetric_rendering import *
from .partial_grad_utils import *
from siren import siren
class ImplicitGenerator3d(nn.Module):
def __init__(self, siren, z_dim, **kwargs):
super().__init__()
self.z_dim = z_dim
self.siren = siren(output_dim=4, z_dim=self.z_dim, input_dim=3, device=None)
self.epoch = 0
self.step = 0
def set_device(self, device):
self.device = device
self.siren.device = device
self.generate_avg_frequencies()
def forward(
self,
z,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
hierarchical_sample,
sample_dist=None,
lock_view_dependence=False,
**kwargs,
):
"""
Generates images from a noise vector, rendering parameters, and camera distribution.
Uses the hierarchical sampling scheme described in NeRF.
"""
batch_size = z.shape[0]
# Generate initial camera rays and sample points.
with torch.no_grad():
points_cam, z_vals, rays_d_cam = get_initial_rays_trig(
batch_size,
num_steps,
resolution=(img_size, img_size),
device=self.device,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
) # batch_size, pixels, num_steps, 1
(
transformed_points,
z_vals,
transformed_ray_directions,
transformed_ray_origins,
pitch,
yaw,
) = transform_sampled_points(
points_cam,
z_vals,
rays_d_cam,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
device=self.device,
mode=sample_dist,
)
transformed_ray_directions_expanded = torch.unsqueeze(
transformed_ray_directions, -2
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.expand(-1, -1, num_steps, -1)
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.reshape(
batch_size, img_size * img_size * num_steps, 3
)
)
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
# Model prediction on course points
coarse_output = self.siren(
transformed_points, z, ray_directions=transformed_ray_directions_expanded
).reshape(batch_size, img_size * img_size, num_steps, 4)
# Re-sample fine points alont camera rays, as described in NeRF
if hierarchical_sample:
with torch.no_grad():
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size, num_steps, 3
)
_, _, weights = fancy_integration(
coarse_output,
z_vals,
device=self.device,
clamp_mode=kwargs["clamp_mode"],
noise_std=kwargs["nerf_noise"],
)
weights = (
weights.reshape(batch_size * img_size * img_size, num_steps) + 1e-5
)
#### Start new importance sampling
z_vals = z_vals.reshape(batch_size * img_size * img_size, num_steps)
z_vals_mid = 0.5 * (z_vals[:, :-1] + z_vals[:, 1:])
z_vals = z_vals.reshape(batch_size, img_size * img_size, num_steps, 1)
fine_z_vals = sample_pdf(
z_vals_mid, weights[:, 1:-1], num_steps, det=False
).detach()
fine_z_vals = fine_z_vals.reshape(
batch_size, img_size * img_size, num_steps, 1
)
fine_points = (
transformed_ray_origins.unsqueeze(2).contiguous()
+ transformed_ray_directions.unsqueeze(2).contiguous()
* fine_z_vals.expand(-1, -1, -1, 3).contiguous()
)
fine_points = fine_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
#### end new importance sampling
# Model prediction on re-sampled find points
fine_output = self.siren(
fine_points, z, ray_directions=transformed_ray_directions_expanded
).reshape(batch_size, img_size * img_size, -1, 4)
# Combine course and fine points
all_outputs = torch.cat([fine_output, coarse_output], dim=-2)
all_z_vals = torch.cat([fine_z_vals, z_vals], dim=-2)
_, indices = torch.sort(all_z_vals, dim=-2)
all_z_vals = torch.gather(all_z_vals, -2, indices)
all_outputs = torch.gather(all_outputs, -2, indices.expand(-1, -1, -1, 4))
else:
all_outputs = coarse_output
all_z_vals = z_vals
# Create images with NeRF
pixels, depth, weights = fancy_integration(
all_outputs,
all_z_vals,
device=self.device,
white_back=kwargs.get("white_back", False),
last_back=kwargs.get("last_back", False),
clamp_mode=kwargs["clamp_mode"],
noise_std=kwargs["nerf_noise"],
)
pixels = pixels.reshape((batch_size, img_size, img_size, 3))
pixels = pixels.permute(0, 3, 1, 2).contiguous() * 2 - 1
return pixels, torch.cat([pitch, yaw], -1)
def generate_avg_frequencies(self):
"""Calculates average frequencies and phase shifts"""
z = torch.randn((10000, self.z_dim), device=self.siren.device)
with torch.no_grad():
frequencies, phase_shifts = self.siren.mapping_network(z)
self.avg_frequencies = frequencies.mean(0, keepdim=True)
self.avg_phase_shifts = phase_shifts.mean(0, keepdim=True)
return self.avg_frequencies, self.avg_phase_shifts
def staged_forward(
self,
z,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
psi=1,
lock_view_dependence=False,
max_batch_size=50000,
depth_map=False,
near_clip=0,
far_clip=2,
sample_dist=None,
hierarchical_sample=False,
**kwargs,
):
"""
Similar to forward but used for inference.
Calls the model sequencially using max_batch_size to limit memory usage.
"""
batch_size = z.shape[0]
self.generate_avg_frequencies()
with torch.no_grad():
raw_frequencies, raw_phase_shifts = self.siren.mapping_network(z)
truncated_frequencies = self.avg_frequencies + psi * (
raw_frequencies - self.avg_frequencies
)
truncated_phase_shifts = self.avg_phase_shifts + psi * (
raw_phase_shifts - self.avg_phase_shifts
)
points_cam, z_vals, rays_d_cam = get_initial_rays_trig(
batch_size,
num_steps,
resolution=(img_size, img_size),
device=self.device,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
) # batch_size, pixels, num_steps, 1
(
transformed_points,
z_vals,
transformed_ray_directions,
transformed_ray_origins,
pitch,
yaw,
) = transform_sampled_points(
points_cam,
z_vals,
rays_d_cam,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
device=self.device,
mode=sample_dist,
)
transformed_ray_directions_expanded = torch.unsqueeze(
transformed_ray_directions, -2
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.expand(-1, -1, num_steps, -1)
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.reshape(
batch_size, img_size * img_size * num_steps, 3
)
)
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
# Sequentially evaluate siren with max_batch_size to avoid OOM
coarse_output = torch.zeros(
(batch_size, transformed_points.shape[1], 4), device=self.device
)
for b in range(batch_size):
head = 0
while head < transformed_points.shape[1]:
tail = head + max_batch_size
coarse_output[
b : b + 1, head:tail
] = self.siren.forward_with_frequencies_phase_shifts(
transformed_points[b : b + 1, head:tail],
truncated_frequencies[b : b + 1],
truncated_phase_shifts[b : b + 1],
ray_directions=transformed_ray_directions_expanded[
b : b + 1, head:tail
],
)
head += max_batch_size
coarse_output = coarse_output.reshape(
batch_size, img_size * img_size, num_steps, 4
)
if hierarchical_sample:
with torch.no_grad():
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size, num_steps, 3
)
_, _, weights = fancy_integration(
coarse_output,
z_vals,
device=self.device,
clamp_mode=kwargs["clamp_mode"],
noise_std=kwargs["nerf_noise"],
)
weights = (
weights.reshape(batch_size * img_size * img_size, num_steps)
+ 1e-5
)
#### Start new importance sampling
z_vals = z_vals.reshape(batch_size * img_size * img_size, num_steps)
z_vals_mid = 0.5 * (z_vals[:, :-1] + z_vals[:, 1:])
z_vals = z_vals.reshape(
batch_size, img_size * img_size, num_steps, 1
)
fine_z_vals = (
sample_pdf(z_vals_mid, weights[:, 1:-1], num_steps, det=False)
.detach()
.to(self.device)
)
fine_z_vals = fine_z_vals.reshape(
batch_size, img_size * img_size, num_steps, 1
)
fine_points = (
transformed_ray_origins.unsqueeze(2).contiguous()
+ transformed_ray_directions.unsqueeze(2).contiguous()
* fine_z_vals.expand(-1, -1, -1, 3).contiguous()
)
fine_points = fine_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
#### end new importance sampling
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
# Sequentially evaluate siren with max_batch_size to avoid OOM
fine_output = torch.zeros(
(batch_size, fine_points.shape[1], 4), device=self.device
)
for b in range(batch_size):
head = 0
while head < fine_points.shape[1]:
tail = head + max_batch_size
fine_output[
b : b + 1, head:tail
] = self.siren.forward_with_frequencies_phase_shifts(
fine_points[b : b + 1, head:tail],
truncated_frequencies[b : b + 1],
truncated_phase_shifts[b : b + 1],
ray_directions=transformed_ray_directions_expanded[
b : b + 1, head:tail
],
)
head += max_batch_size
fine_output = fine_output.reshape(
batch_size, img_size * img_size, num_steps, 4
)
all_outputs = torch.cat([fine_output, coarse_output], dim=-2)
all_z_vals = torch.cat([fine_z_vals, z_vals], dim=-2)
_, indices = torch.sort(all_z_vals, dim=-2)
all_z_vals = torch.gather(all_z_vals, -2, indices)
all_outputs = torch.gather(
all_outputs, -2, indices.expand(-1, -1, -1, 4)
)
else:
all_outputs = coarse_output
all_z_vals = z_vals
pixels, depth, weights = fancy_integration(
all_outputs,
all_z_vals,
device=self.device,
white_back=kwargs.get("white_back", False),
clamp_mode=kwargs["clamp_mode"],
last_back=kwargs.get("last_back", False),
fill_mode=kwargs.get("fill_mode", None),
noise_std=kwargs["nerf_noise"],
)
depth_map = depth.reshape(batch_size, img_size, img_size).contiguous().cpu()
pixels = pixels.reshape((batch_size, img_size, img_size, 3))
pixels = pixels.permute(0, 3, 1, 2).contiguous().cpu() * 2 - 1
return pixels, depth_map
# Used for rendering interpolations
def staged_forward_with_frequencies(
self,
truncated_frequencies,
truncated_phase_shifts,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
psi=0.7,
lock_view_dependence=False,
max_batch_size=50000,
depth_map=False,
near_clip=0,
far_clip=2,
sample_dist=None,
hierarchical_sample=False,
**kwargs,
):
batch_size = truncated_frequencies.shape[0]
with torch.no_grad():
points_cam, z_vals, rays_d_cam = get_initial_rays_trig(
batch_size,
num_steps,
resolution=(img_size, img_size),
device=self.device,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
) # batch_size, pixels, num_steps, 1
(
transformed_points,
z_vals,
transformed_ray_directions,
transformed_ray_origins,
pitch,
yaw,
) = transform_sampled_points(
points_cam,
z_vals,
rays_d_cam,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
device=self.device,
mode=sample_dist,
)
transformed_ray_directions_expanded = torch.unsqueeze(
transformed_ray_directions, -2
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.expand(-1, -1, num_steps, -1)
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.reshape(
batch_size, img_size * img_size * num_steps, 3
)
)
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
# BATCHED SAMPLE
coarse_output = torch.zeros(
(batch_size, transformed_points.shape[1], 4), device=self.device
)
for b in range(batch_size):
head = 0
while head < transformed_points.shape[1]:
tail = head + max_batch_size
coarse_output[
b : b + 1, head:tail
] = self.siren.forward_with_frequencies_phase_shifts(
transformed_points[b : b + 1, head:tail],
truncated_frequencies[b : b + 1],
truncated_phase_shifts[b : b + 1],
ray_directions=transformed_ray_directions_expanded[
b : b + 1, head:tail
],
)
head += max_batch_size
coarse_output = coarse_output.reshape(
batch_size, img_size * img_size, num_steps, 4
)
# END BATCHED SAMPLE
if hierarchical_sample:
with torch.no_grad():
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size, num_steps, 3
)
_, _, weights = fancy_integration(
coarse_output,
z_vals,
device=self.device,
clamp_mode=kwargs["clamp_mode"],
noise_std=kwargs["nerf_noise"],
)
weights = (
weights.reshape(batch_size * img_size * img_size, num_steps)
+ 1e-5
)
z_vals = z_vals.reshape(
batch_size * img_size * img_size, num_steps
) # We squash the dimensions here. This means we importance sample for every batch for every ray
z_vals_mid = 0.5 * (
z_vals[:, :-1] + z_vals[:, 1:]
) # (N_rays, N_samples-1) interval mid points
z_vals = z_vals.reshape(
batch_size, img_size * img_size, num_steps, 1
)
fine_z_vals = (
sample_pdf(z_vals_mid, weights[:, 1:-1], num_steps, det=False)
.detach()
.to(self.device)
) # batch_size, num_pixels**2, num_steps
fine_z_vals = fine_z_vals.reshape(
batch_size, img_size * img_size, num_steps, 1
)
fine_points = (
transformed_ray_origins.unsqueeze(2).contiguous()
+ transformed_ray_directions.unsqueeze(2).contiguous()
* fine_z_vals.expand(-1, -1, -1, 3).contiguous()
) # dimensions here not matching
fine_points = fine_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
#### end new importance sampling
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
# fine_output = self.siren(fine_points, z, ray_directions=transformed_ray_directions_expanded).reshape(batch_size, img_size * img_size, -1, 4)
# BATCHED SAMPLE
fine_output = torch.zeros(
(batch_size, fine_points.shape[1], 4), device=self.device
)
for b in range(batch_size):
head = 0
while head < fine_points.shape[1]:
tail = head + max_batch_size
fine_output[
b : b + 1, head:tail
] = self.siren.forward_with_frequencies_phase_shifts(
fine_points[b : b + 1, head:tail],
truncated_frequencies[b : b + 1],
truncated_phase_shifts[b : b + 1],
ray_directions=transformed_ray_directions_expanded[
b : b + 1, head:tail
],
)
head += max_batch_size
fine_output = fine_output.reshape(
batch_size, img_size * img_size, num_steps, 4
)
# END BATCHED SAMPLE
all_outputs = torch.cat([fine_output, coarse_output], dim=-2)
all_z_vals = torch.cat([fine_z_vals, z_vals], dim=-2)
_, indices = torch.sort(all_z_vals, dim=-2)
all_z_vals = torch.gather(all_z_vals, -2, indices)
all_outputs = torch.gather(
all_outputs, -2, indices.expand(-1, -1, -1, 4)
)
else:
all_outputs = coarse_output
all_z_vals = z_vals
pixels, depth, weights = fancy_integration(
all_outputs,
all_z_vals,
device=self.device,
white_back=kwargs.get("white_back", False),
clamp_mode=kwargs["clamp_mode"],
last_back=kwargs.get("last_back", False),
fill_mode=kwargs.get("fill_mode", None),
noise_std=kwargs["nerf_noise"],
)
depth_map = depth.reshape(batch_size, img_size, img_size).contiguous().cpu()
pixels = pixels.reshape((batch_size, img_size, img_size, 3))
pixels = pixels.permute(0, 3, 1, 2).contiguous().cpu() * 2 - 1
return pixels, depth_map
def forward_with_frequencies(
self,
frequencies,
phase_shifts,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
hierarchical_sample,
sample_dist=None,
lock_view_dependence=False,
**kwargs,
):
batch_size = frequencies.shape[0]
points_cam, z_vals, rays_d_cam = get_initial_rays_trig(
batch_size,
num_steps,
resolution=(img_size, img_size),
device=self.device,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
) # batch_size, pixels, num_steps, 1
(
transformed_points,
z_vals,
transformed_ray_directions,
transformed_ray_origins,
pitch,
yaw,
) = transform_sampled_points(
points_cam,
z_vals,
rays_d_cam,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
device=self.device,
mode=sample_dist,
)
transformed_ray_directions_expanded = torch.unsqueeze(
transformed_ray_directions, -2
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.expand(-1, -1, num_steps, -1)
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.reshape(
batch_size, img_size * img_size * num_steps, 3
)
)
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
coarse_output = self.siren.forward_with_frequencies_phase_shifts(
transformed_points,
frequencies,
phase_shifts,
ray_directions=transformed_ray_directions_expanded,
).reshape(batch_size, img_size * img_size, num_steps, 4)
if hierarchical_sample:
with torch.no_grad():
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size, num_steps, 3
)
_, _, weights = fancy_integration(
coarse_output,
z_vals,
device=self.device,
clamp_mode=kwargs["clamp_mode"],
noise_std=kwargs["nerf_noise"],
)
weights = (
weights.reshape(batch_size * img_size * img_size, num_steps) + 1e-5
)
#### Start new importance sampling
# RuntimeError: Sizes of tensors must match except in dimension 1. Got 3072 and 6144 (The offending index is 0)
z_vals = z_vals.reshape(
batch_size * img_size * img_size, num_steps
) # We squash the dimensions here. This means we importance sample for every batch for every ray
z_vals_mid = 0.5 * (
z_vals[:, :-1] + z_vals[:, 1:]
) # (N_rays, N_samples-1) interval mid points
z_vals = z_vals.reshape(batch_size, img_size * img_size, num_steps, 1)
fine_z_vals = sample_pdf(
z_vals_mid, weights[:, 1:-1], num_steps, det=False
).detach() # batch_size, num_pixels**2, num_steps
fine_z_vals = fine_z_vals.reshape(
batch_size, img_size * img_size, num_steps, 1
)
fine_points = (
transformed_ray_origins.unsqueeze(2).contiguous()
+ transformed_ray_directions.unsqueeze(2).contiguous()
* fine_z_vals.expand(-1, -1, -1, 3).contiguous()
) # dimensions here not matching
fine_points = fine_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
#### end new importance sampling
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
fine_output = self.siren.forward_with_frequencies_phase_shifts(
fine_points,
frequencies,
phase_shifts,
ray_directions=transformed_ray_directions_expanded,
).reshape(batch_size, img_size * img_size, -1, 4)
all_outputs = torch.cat([fine_output, coarse_output], dim=-2)
all_z_vals = torch.cat([fine_z_vals, z_vals], dim=-2)
_, indices = torch.sort(all_z_vals, dim=-2)
all_z_vals = torch.gather(all_z_vals, -2, indices)
# Target sizes: [-1, -1, -1, 4]. Tensor sizes: [240, 512, 12]
all_outputs = torch.gather(all_outputs, -2, indices.expand(-1, -1, -1, 4))
else:
all_outputs = coarse_output
all_z_vals = z_vals
pixels, depth, weights = fancy_integration(
all_outputs,
all_z_vals,
device=self.device,
white_back=kwargs.get("white_back", False),
last_back=kwargs.get("last_back", False),
clamp_mode=kwargs["clamp_mode"],
noise_std=kwargs["nerf_noise"],
)
pixels = pixels.reshape((batch_size, img_size, img_size, 3))
pixels = pixels.permute(0, 3, 1, 2).contiguous() * 2 - 1
return pixels, torch.cat([pitch, yaw], -1)
class NerfINRGenerator(nn.Module):
def __init__(self, z_dim, siren, inr, mapping_network, device, **kwargs):
super(NerfINRGenerator, self).__init__()
self.z_dim = z_dim
self.device = device
self.siren = siren
self.inr = inr
self.mapping_network = mapping_network
self.avg_styles = None
return
# do we need this?
def set_device(self, device):
self.device = device
self.siren.device = device
self.generate_avg_frequencies()
return
@torch.no_grad()
def get_xyz_range(
self,
num_samples,
z,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
sample_dist=None,
):
batch_size = z.shape[0]
xyz_minmax_mean = [[0, 0, 0], [0, 0, 0], [0, 0, 0]]
for i in tqdm.tqdm(range(num_samples)):
points_cam, z_vals, rays_d_cam = get_initial_rays_trig(
batch_size,
num_steps,
resolution=(img_size, img_size),
device=self.device,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
)
(
transformed_points,
z_vals,
transfomred_ray_directions,
transformed_ray_origins,
pitch,
yaw,
) = transform_sampled_points(
points_cam,
z_vals,
rays_d_cam,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
device=self.device,
mode=sample_dist,
)
xyz_list = transformed_points.unbind(3)
for minmax_mean, axis_v in zip(xyz_minmax_mean, xyz_list):
min_v, max_v, mean_v = minmax_mean
minmax_mean[0] = min(min_v, axis_v.min().item())
minmax_mean[1] = max(max_v, axis_v.max().item())
minmax_mean[2] = min(mean_v, axis_v.mean().item())
for minmax_mean in xyz_minmax_mean:
minmax_mean[2] /= num_samples
for minmax_mean, axis_name in zip(xyz_minmax_mean, "xyz"):
minmax_mean_str = f"{axis_name}: ({minmax_mean[0]:.2f}, {minmax_mean[1]:.2f}, {minmax_mean[2]:.2f})"
print(minmax_mean_str)
return xyz_minmax_mean
# duplicate from utils
@torch.no_grad()
def get_world_points_and_direction(
self,
batch_size,
num_steps,
img_size,
fov,
ray_start,
ray_end,
h_stddev,
v_stddev,
h_mean,
v_mean,
sample_dist,
lock_view_dependence,
):
"""
Generate sample points and camera rays in the world coordinate system.
:param batch_size:
:param num_steps: number of samples for each ray
:param img_size:
:param fov:
:param ray_start:
:param ray_end:
:param h_stddev:
:param v_stddev:
:param h_mean:
:param v_mean:
:param sample_dist: mode for sample_camera_positions
:param lock_view_dependence:
:return:
- transformed_points: (b, h x w x num_steps, 3), has been perturbed
- transformed_ray_directions_expanded: (b, h x w x num_steps, 3)
- transformed_ray_origins: (b, h x w, 3)
- transformed_ray_directions: (b, h x w, 3)
- z_vals: (b, h x w, num_steps, 1), has been perturbed
- pitch: (b, 1)
- yaw: (b, 1)
"""
# Generate initial camera rays and sample points.
# batch_size, pixels, num_steps, 1
points_cam, z_vals, rays_d_cam = get_initial_rays_trig(
n=batch_size,
num_steps=num_steps,
resolution=(img_size, img_size),
device=self.device,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
)
(
transformed_points,
z_vals,
transformed_ray_directions,
transformed_ray_origins,
pitch,
yaw,
) = transform_sampled_points(
points_cam,
z_vals,
rays_d_cam,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
device=self.device,
mode=sample_dist,
)
transformed_ray_directions_expanded = repeat(
transformed_ray_directions, "b hw xyz -> b (hw s) xyz", s=num_steps
)
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
transformed_points = rearrange(transformed_points, "b hw s xyz -> b (hw s) xyz")
return (
transformed_points,
transformed_ray_directions_expanded,
transformed_ray_origins,
transformed_ray_directions,
z_vals,
pitch,
yaw,
)
@torch.no_grad()
def get_fine_points_and_direction(
self,
coarse_output,
z_vals,
dim_rgb,
clamp_mode,
nerf_noise,
num_steps,
transformed_ray_origins,
transformed_ray_directions,
):
"""
:param coarse_output: (b, h x w, num_samples, rgb_sigma)
:param z_vals: (b, h x w, num_samples, 1)
:param clamp_mode:
:param nerf_noise:
:param num_steps:
:param transformed_ray_origins: (b, h x w, 3)
:param transformed_ray_directions: (b, h x w, 3)
:return:
- fine_points: (b, h x w x num_steps, 3)
- fine_z_vals: (b, h x w, num_steps, 1)
"""
batch_size = coarse_output.shape[0]
_, _, weights = fancy_integration(
rgb_sigma=coarse_output,
z_vals=z_vals,
device=self.device,
dim_rgb=dim_rgb,
clamp_mode=clamp_mode,
noise_std=nerf_noise,
)
weights = rearrange(weights, "b hw s 1 -> (b hw) s") + 1e-5
#### Start new importance sampling
z_vals = rearrange(z_vals, "b hw s 1 -> (b hw) s")
z_vals_mid = 0.5 * (z_vals[:, :-1] + z_vals[:, 1:])
fine_z_vals = sample_pdf(
bins=z_vals_mid, weights=weights[:, 1:-1], N_importance=num_steps, det=False
).detach()
fine_z_vals = rearrange(fine_z_vals, "(b hw) s -> b hw s 1", b=batch_size)
fine_points = (
transformed_ray_origins.unsqueeze(2).contiguous()
+ transformed_ray_directions.unsqueeze(2).contiguous()
* fine_z_vals.expand(-1, -1, -1, 3).contiguous()
)
fine_points = rearrange(fine_points, "b hw s c -> b (hw s) c")
return fine_points, fine_z_vals
def forward(
self,
z,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
hierarchical_sample,
x_scale=1 / 0.2,
y_scale=1 / 0.17,
z_scale=1 / 0.2,
sample_dist=None,
lock_view_dependence=False,
clamp_mode="relu",
nerf_noise=0.0,
white_back=False,
last_back=False,
):
"""
Generates images from a noise vector, rendering parameters, and camera distribution.
Uses the hierarchical sampling scheme described in NeRF.
:param z: (b, z_dim)
:param img_size:
:param fov: face: 12
:param ray_start: face: 0.88
:param ray_end: face: 1.12
:param num_steps: face: 12
:param h_stddev: face: 0.3
:param v_stddev: face: 0.155
:param h_mean: face: pi/2
:param v_mean: face: pi/2
:param hierarchical_sample: face: true
:param x_scale:
:param y_scale:
:param z_scale:
:param sample_dist: mode for sample_camera_positions, face: 'gaussian'
:param lock_view_dependence: face: false
:param clamp_mode: face: 'relu'
:param nerf_noise:
:param last_back: face: false
:param white_back: face: false
:param kwargs:
:return:
- pixels: (b, 3, h, w)
- pitch_yaw: (b, 2)
"""
batch_size = z.shape[0]
# mapping network
style_dict = self.mapping_network(z)
(
transformed_points,
transformed_ray_directions_expanded,
transformed_ray_origins,
transformed_ray_directions,
z_vals,
pitch,
yaw,
) = self.get_world_points_and_direction(
batch_size=batch_size,
num_steps=num_steps,
img_size=img_size,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
sample_dist=sample_dist,
lock_view_dependence=lock_view_dependence,
)
# Model prediction on coarse points
coarse_output = self.siren(
input=transformed_points, # (b, h x w x s, 3)
style_dict=style_dict,
ray_directions=transformed_ray_directions_expanded,
# don't think this is used
x_scale=x_scale,
y_scale=y_scale,
z_scale=z_scale,
)
coarse_output = rearrange(
coarse_output, "b (hw s) rgb_sigma -> b hw s rgb_sigma", s=num_steps
)
# Re-sample fine points alont camera rays, as described in NeRF
if hierarchical_sample:
fine_points, fine_z_vals = self.get_fine_points_and_direction(
coarse_output=coarse_output,
z_vals=z_vals,
dim_rgb=self.siren.rgb_dim,
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
num_steps=num_steps,
transformed_ray_origins=transformed_ray_origins,
transformed_ray_directions=transformed_ray_directions,
)
# Model prediction on re-sampled find points
fine_output = self.siren(
input=fine_points, # (b, h x w x s, 3)
style_dict=style_dict,
ray_directions=transformed_ray_directions_expanded, # (b, h x w x s, 3)
# don't think this is used
x_scale=x_scale,
y_scale=y_scale,
z_scale=z_scale,
)
fine_output = rearrange(
fine_output, "b (hw s) rgb_sigma -> b hw s rgb_sigma", s=num_steps
)
# Combine course and fine points
all_outputs = torch.cat(
[fine_output, coarse_output], dim=-2
) # (b, h x w, s, dim_rgb_sigma)
all_z_vals = torch.cat([fine_z_vals, z_vals], dim=-2) # (b, h x w, s, 1)
_, indices = torch.sort(all_z_vals, dim=-2) # (b, h x w, s, 1)
all_z_vals = torch.gather(all_z_vals, -2, indices) # (b, h x w, s, 1)
# (b, h x w, s, dim_rgb_sigma)
all_outputs = torch.gather(
all_outputs, -2, indices.expand(-1, -1, -1, all_outputs.shape[-1])
)
else:
all_outputs = coarse_output
all_z_vals = z_vals
# Create images with NeRF
pixels, depth, weights = fancy_integration(
rgb_sigma=all_outputs,
z_vals=all_z_vals,
device=self.device,
dim_rgb=self.siren.rgb_dim,
white_back=white_back,
last_back=last_back,
clamp_mode=clamp_mode,
noise_std=nerf_noise,
)
pixels = self.inr(pixels, style_dict)
pixels = rearrange(pixels, "b (h w) c -> b c h w", h=img_size)
pitch_yaw = torch.cat([pitch, yaw], -1)
return pixels, pitch_yaw
def generate_avg_frequencies(self, num_samples=10000, device="cuda"):
"""Calculates average frequencies and phase shifts"""
z = torch.randn((num_samples, self.z_dim), device=device)
with torch.no_grad():
style_dict = self.mapping_network(z)
avg_styles = {}
for name, style in style_dict.items():
avg_styles[name] = style.mean(0, keepdim=True)
self.avg_styles = avg_styles
return avg_styles
def get_truncated_freq_phase(self, raw_style_dict, avg_style_dict, raw_lambda):
# truncated_frequencies = self.avg_frequencies + psi * (raw_frequencies - self.avg_frequencies)
# truncated_phase_shifts = self.avg_phase_shifts + psi * (raw_phase_shifts - self.avg_phase_shifts)
truncated_style_dict = {}
for name, avg_style in avg_style_dict.items():
raw_style = raw_style_dict[name]
truncated_style = avg_style + raw_lambda * (raw_style - avg_style)
truncated_style_dict[name] = truncated_style
return truncated_style_dict
def staged_forward(
self,
z,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
clamp_mode,
nerf_noise,
psi=1,
lock_view_dependence=False,
max_batch_size=50000,
depth_map=False,
near_clip=0,
far_clip=2,
sample_dist=None,
hierarchical_sample=False,
white_back=False,
last_back=False,
fill_mode=None,
):
"""
Similar to forward but used for inference.
Calls the model sequencially using max_batch_size to limit memory usage.
:param z: (b, dim_z)
:param img_size:
:param fov:
:param ray_start:
:param ray_end:
:param num_steps:
:param h_stddev:
:param v_stddev:
:param h_mean:
:param v_mean:
:param clamp_mode:
:param nerf_noise:
:param psi: [0, 1], 0: use the avg_style
:param lock_view_dependence:
:param max_batch_size:
:param depth_map:
:param near_clip:
:param far_clip:
:param sample_dist:
:param hierarchical_sample:
:param white_back:
:param last_back:
:param fill_mode:
:param kwargs:
:return:
- pixels:
- depth_map:
"""
batch_size = z.shape[0]
self.generate_avg_frequencies()
with torch.no_grad():
raw_style_dict = self.mapping_network(z)
truncated_style_dict = self.get_truncated_freq_phase(
raw_style_dict=raw_style_dict,
avg_style_dict=self.avg_styles,
raw_lambda=psi,
)
(
transformed_points,
transformed_ray_directions_expanded,
transformed_ray_origins,
transformed_ray_directions,
z_vals,
pitch,
yaw,
) = self.get_world_points_and_direction(
batch_size=batch_size,
num_steps=num_steps,
img_size=img_size,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
sample_dist=sample_dist,
lock_view_dependence=lock_view_dependence,
)
# Sequentially evaluate siren with max_batch_size to avoid OOM
coarse_output = self.siren.staged_forward(
transformed_points=transformed_points,
transformed_ray_directions_expanded=transformed_ray_directions_expanded,
style_dict=truncated_style_dict,
max_points=max_batch_size,
num_steps=num_steps,
)
if hierarchical_sample:
with torch.no_grad():
fine_points, fine_z_vals = self.get_fine_points_and_direction(
coarse_output=coarse_output,
z_vals=z_vals,
dim_rgb=self.siren.rgb_dim,
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
num_steps=num_steps,
transformed_ray_origins=transformed_ray_origins,
transformed_ray_directions=transformed_ray_directions,
)
# Model prediction on re-sampled find points
fine_output = self.siren.staged_forward(
transformed_points=fine_points, # (b, h x w x s, 3)
transformed_ray_directions_expanded=transformed_ray_directions_expanded, # (b, h x w x s, 3)
style_dict=truncated_style_dict,
max_points=max_batch_size,
num_steps=num_steps,
)
# fine_output = rearrange(fine_output, "b (hw s) rgb_sigma -> b hw s rgb_sigma", s=num_steps)
# Combine course and fine points
all_outputs = torch.cat(
[fine_output, coarse_output], dim=-2
) # (b, h x w, s, dim_rgb_sigma)
all_z_vals = torch.cat(
[fine_z_vals, z_vals], dim=-2
) # (b, h x w, s, 1)
_, indices = torch.sort(all_z_vals, dim=-2) # (b, h x w, s, 1)
all_z_vals = torch.gather(
all_z_vals, -2, indices
) # (b, h x w, s, 1)
# (b, h x w, s, dim_rgb_sigma)
all_outputs = torch.gather(
all_outputs,
-2,
indices.expand(-1, -1, -1, all_outputs.shape[-1]),
)
else:
all_outputs = coarse_output
all_z_vals = z_vals
# Create images with NeRF
pixels, depth, weights = fancy_integration(
rgb_sigma=all_outputs,
z_vals=all_z_vals,
device=self.device,
dim_rgb=self.siren.rgb_dim,
white_back=white_back,
last_back=last_back,
clamp_mode=clamp_mode,
fill_mode=fill_mode,
noise_std=nerf_noise,
)
pixels = self.inr(pixels, truncated_style_dict)
pixels = rearrange(pixels, "b (h w) c -> b c h w", h=img_size)
pixels = pixels.contiguous().cpu().to(dtype=torch.float32)
depth_map = depth.reshape(batch_size, img_size, img_size).contiguous().cpu()
return pixels, depth_map
# Used for rendering interpolations
def staged_forward_with_frequencies(
self,
truncated_frequencies,
truncated_phase_shifts,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
psi=0.7,
lock_view_dependence=False,
max_batch_size=50000,
depth_map=False,
near_clip=0,
far_clip=2,
sample_dist=None,
hierarchical_sample=False,
clamp_mode="relu",
nerf_noise=0.0,
white_back=False,
last_back=False,
fill_mode=None,
):
batch_size = truncated_frequencies.shape[0]
with torch.no_grad():
points_cam, z_vals, rays_d_cam = get_initial_rays_trig(
batch_size,
num_steps,
resolution=(img_size, img_size),
device=self.device,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
) # batch_size, pixels, num_steps, 1
(
transformed_points,
z_vals,
transformed_ray_directions,
transformed_ray_origins,
pitch,
yaw,
) = transform_sampled_points(
points_cam,
z_vals,
rays_d_cam,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
device=self.device,
mode=sample_dist,
)
transformed_ray_directions_expanded = torch.unsqueeze(
transformed_ray_directions, -2
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.expand(-1, -1, num_steps, -1)
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.reshape(
batch_size, img_size * img_size * num_steps, 3
)
)
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
# BATCHED SAMPLE
coarse_output = torch.zeros(
(batch_size, transformed_points.shape[1], 4), device=self.device
)
for b in range(batch_size):
head = 0
while head < transformed_points.shape[1]:
tail = head + max_batch_size
coarse_output[
b : b + 1, head:tail
] = self.siren.forward_with_frequencies_phase_shifts(
transformed_points[b : b + 1, head:tail],
truncated_frequencies[b : b + 1],
truncated_phase_shifts[b : b + 1],
ray_directions=transformed_ray_directions_expanded[
b : b + 1, head:tail
],
)
head += max_batch_size
coarse_output = coarse_output.reshape(
batch_size, img_size * img_size, num_steps, 4
)
# END BATCHED SAMPLE
if hierarchical_sample:
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size, num_steps, 3
)
_, _, _, weights = fancy_integration(
coarse_output,
z_vals,
device=self.device,
clamp_mode=clamp_mode,
noise_std=nerf_noise,
)
weights = (
weights.reshape(batch_size * img_size * img_size, num_steps) + 1e-5
)
z_vals = z_vals.reshape(batch_size * img_size * img_size, num_steps)
# We squash the dimensions here. This means we importance sample for every batch for every ray
z_vals_mid = 0.5 * (
z_vals[:, :-1] + z_vals[:, 1:]
) # (N_rays, N_samples-1) interval mid points
fine_z_vals = (
sample_pdf(z_vals_mid, weights[:, 1:-1], num_steps, det=False)
.detach()
.to(self.device)
) # batch_size, num_pixels**2, num_steps
fine_z_vals = fine_z_vals.reshape(
batch_size, img_size * img_size, num_steps, 1
)
fine_points = (
transformed_ray_origins.unsqueeze(2).contiguous()
+ transformed_ray_directions.unsqueeze(2).contiguous()
* fine_z_vals.expand(-1, -1, -1, 3).contiguous()
) # dimensions here not matching
fine_points = fine_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
#### end new importance sampling
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
# BATCHED SAMPLE
fine_output = torch.zeros(
(batch_size, fine_points.shape[1], 4), device=self.device
)
for b in range(batch_size):
head = 0
while head < fine_points.shape[1]:
tail = head + max_batch_size
fine_output[
b : b + 1, head:tail
] = self.siren.forward_with_frequencies_phase_shifts(
fine_points[b : b + 1, head:tail],
truncated_frequencies[b : b + 1],
truncated_phase_shifts[b : b + 1],
ray_directions=transformed_ray_directions_expanded[
b : b + 1, head:tail
],
)
head += max_batch_size
fine_output = fine_output.reshape(
batch_size, img_size * img_size, num_steps, 4
)
# END BATCHED SAMPLE
all_outputs = torch.cat([fine_output, coarse_output], dim=-2)
all_z_vals = torch.cat([fine_z_vals, z_vals], dim=-2)
_, indices = torch.sort(all_z_vals, dim=-2)
all_z_vals = torch.gather(all_z_vals, -2, indices)
all_outputs = torch.gather(
all_outputs, -2, indices.expand(-1, -1, -1, 4)
)
# end of hierarchical sampling
else:
all_outputs = coarse_output
all_z_vals = z_vals
pixels, depth, weights = fancy_integration(
all_outputs,
all_z_vals,
device=self.device,
white_back=white_back,
clamp_mode=clamp_mode,
last_back=last_back,
fill_mode=fill_mode,
noise_std=nerf_noise,
)
depth_map = depth.reshape(batch_size, img_size, img_size).contiguous().cpu()
pixels = pixels.reshape((batch_size, img_size, img_size, 3))
pixels = pixels.permute(0, 3, 1, 2).contiguous().cpu() * 2 - 1
return pixels, depth_map
def forward_with_frequencies(
self,
frequencies,
phase_shifts,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
hierarchical_sample,
sample_dist=None,
lock_view_dependence=False,
clamp_mode="relu",
nerf_noise=0.0,
white_back=False,
last_back=False,
fill_mode=None,
):
batch_size = frequencies.shape[0]
points_cam, z_vals, rays_d_cam = get_initial_rays_trig(
batch_size,
num_steps=num_steps,
resolution=(img_size, img_size),
device=self.device,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
) # batch_size, pixels, num_steps, 1
(
transformed_points,
z_vals,
transformed_ray_directions,
transformed_ray_origins,
pitch,
yaw,
) = transform_sampled_points(
points_cam,
z_vals,
rays_d_cam,
device=self.device,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
mode=sample_dist,
)
transformed_ray_directions_expanded = torch.unsqueeze(
transformed_ray_directions, -2
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.expand(-1, -1, num_steps, -1)
)
transformed_ray_directions_expanded = (
transformed_ray_directions_expanded.reshape(
batch_size, img_size * img_size * num_steps, 3
)
)
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
coarse_output = self.siren.forward_with_frequencies_phase_shifts(
transformed_points,
frequencies,
phase_shifts,
ray_directions=transformed_ray_directions_expanded.reshape(
batch_size, img_size * img_size, num_steps, 4
),
)
if hierarchical_sample:
with torch.no_grad():
transformed_points = transformed_points.reshape(
batch_size, img_size * img_size, num_steps, 3
)
_, _, weights = fancy_integration(
coarse_output,
z_vals,
device=self.device,
clamp_mode=clamp_mode,
noise_std=nerf_noise,
)
weights = (
weights.reshape(batch_size * img_size * img_size, num_steps) + 1e-5
)
# New importance sampling
z_vals = z_vals.reshape(batch_size * img_size * img_size, num_steps)
z_vals_mid = 0.5 * (
z_vals[:, :-1] + z_vals[:, 1:]
) # (N_rays, N_samples-1) interval mid points
z_vals = z_vals.reshape(batch_size, img_size * img_size, num_steps, 1)
fine_z_vals = sample_pdf(
z_vals_mid, weights[:, 1:-1], num_steps, det=False
).detach() # batch_size, num_pixels**2, num_steps
fine_z_vals = fine_z_vals.reshape(
batch_size, img_size * img_size, num_steps, 1
)
fine_points = (
transformed_ray_origins.unsqueeze(2).contiguous()
+ transformed_ray_directions.unsqueeze(2).contiguous()
* fine_z_vals.expand(-1, -1, -1, 3).contiguous()
) # dimensions here not matching
fine_points = fine_points.reshape(
batch_size, img_size * img_size * num_steps, 3
)
# end of new importance sampling
if lock_view_dependence:
transformed_ray_directions_expanded = torch.zeros_like(
transformed_ray_directions_expanded
)
transformed_ray_directions_expanded[..., -1] = -1
fine_output = self.siren.forward_with_frequencies_phase_shifts(
fine_points,
frequencies,
phase_shifts,
ray_directions=transformed_ray_directions_expanded.reshape(
batch_size, img_size * img_size, -1, 4
),
)
all_outputs = torch.cat([fine_output, coarse_output], dim=-2)
all_z_vals = torch.cat([fine_z_vals, z_vals], dim=-2)
_, indices = torch.sort(all_z_vals, dim=-2)
all_z_vals = torch.gather(all_z_vals, -2, indices)
# Target sizes: [-1, -1, -1, 4]. Tensor sizes: [240, 512, 12]
all_outputs = torch.gather(all_outputs, -2, indices.expand(-1, -1, -1, 4))
# end of hierarchical sampling
else:
all_outputs = coarse_output
all_z_vals = z_vals
pixels, depth, weights = fancy_integration(
all_outputs,
all_z_vals,
device=self.device,
white_back=white_back,
last_back=last_back,
clamp_mode=clamp_mode,
noise_std=nerf_noise,
)
pixels = pixels.reshape(batch_size, img_size, img_size, 3)
pixels = pixels.permute(0, 3, 1, 2).contiguous() * 2 - 1
return pixels, torch.cat([pitch, yaw], -1)
class CIPSGeneratorNerfINR(nn.Module):
def __init__(
self,
z_dim,
siren_model,
inr,
mapping_network_nerf,
mapping_network_inr,
device="cuda",
):
super().__init__()
self.z_dim = z_dim
self.device = device
self.siren = siren_model
self.mapping_network_nerf = mapping_network_nerf
self.inr = inr
self.mapping_network_inr = mapping_network_inr
self.aux_to_rbg = nn.Sequential(nn.Linear(self.siren.rgb_dim, 3), nn.Tanh())
self.aux_to_rbg.apply(siren.frequency_init(25))
self.filters = nn.Identity()
self.epoch = 0
self.step = 0
return
def mapping_network(self, z_nerf, z_inr):
style_dict = {}
style_dict.update(self.mapping_network_nerf(z_nerf))
style_dict.update(self.mapping_network_inr(z_inr))
return style_dict
def z_sampler(
self,
shape,
device,
dist="gaussian",
):
if dist == "gaussian":
z = torch.randn(shape, device=device)
elif dist == "uniform":
z = torch.rand(shape, device=device) * 2 - 1
else:
raise ValueError("Invalid distribution")
return z
def get_zs(self, b, batch_split=1, dist="gaussian"):
z_nerf = self.z_sampler(
shape=(b, self.mapping_network_nerf.z_dim),
device=self.device,
dist=dist,
)
z_inr = self.z_sampler(
shape=(b, self.mapping_network_inr.z_dim),
device=self.device,
dist=dist,
)
if batch_split > 1:
zs_list = []
print("get_zs debug")
print(b)
print(batch_split)
print(b // batch_split)
z_nerf_list = z_nerf.split(b // batch_split)
z_inr_list = z_inr.split(b // batch_split)
print("after split")
for z_nerf_, z_inr_ in zip(z_nerf_list, z_inr_list):
zs_ = {
"z_nerf": z_nerf_,
"z_inr": z_inr_,
}
zs_list.append(zs_)
return zs_list
else:
zs = {
"z_nerf": z_nerf,
"z_inr": z_inr,
}
return zs
@torch.no_grad()
def get_fine_points_and_direction(
self,
coarse_output,
z_vals,
dim_rgb,
clamp_mode,
nerf_noise,
num_steps,
transformed_ray_origins,
transformed_ray_directions,
):
"""
:param coarse_output: (b, h x w, num_samples, rgb_sigma)
:param z_vals: (b, h x w, num_samples, 1)
:param clamp_mode:
:param nerf_noise:
:param num_steps:
:param transformed_ray_origins: (b, h x w, 3)
:param transformed_ray_directions: (b, h x w, 3)
:return:
- fine_points: (b, h x w x num_steps, 3)
- fine_z_vals: (b, h x w, num_steps, 1)
"""
batch_size = coarse_output.shape[0]
_, _, weights = fancy_integration(
rgb_sigma=coarse_output,
z_vals=z_vals,
device=self.device,
dim_rgb=dim_rgb,
clamp_mode=clamp_mode,
noise_std=nerf_noise,
)
weights = rearrange(weights, "b hw s 1 -> (b hw) s") + 1e-5
#### Start new importance sampling
z_vals = rearrange(z_vals, "b hw s 1 -> (b hw) s")
z_vals_mid = 0.5 * (z_vals[:, :-1] + z_vals[:, 1:])
fine_z_vals = sample_pdf(
bins=z_vals_mid, weights=weights[:, 1:-1], N_importance=num_steps, det=False
).detach()
fine_z_vals = rearrange(fine_z_vals, "(b hw) s -> b hw s 1", b=batch_size)
fine_points = (
transformed_ray_origins.unsqueeze(2).contiguous()
+ transformed_ray_directions.unsqueeze(2).contiguous()
* fine_z_vals.expand(-1, -1, -1, 3).contiguous()
)
fine_points = rearrange(fine_points, "b hw s c -> b (hw s) c")
return fine_points, fine_z_vals
def generate_avg_frequencies(self, num_samples=10000, device="cuda"):
"""Calculates average frequencies and phase shifts"""
zs = self.get_zs(num_samples)
with torch.no_grad():
style_dict = self.mapping_network(**zs)
avg_styles = {}
for name, style in style_dict.items():
avg_styles[name] = style.mean(0, keepdim=True)
self.avg_styles = avg_styles
return avg_styles
def staged_forward(self, *args, **kwargs):
raise NotImplementedError
def set_device(self, device):
# self.device = device
# self.siren.device = device
# self.generate_avg_frequencies()
pass
def forward(
self,
zs,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
hierarchical_sample,
h_mean=math.pi * 0.5,
v_mean=math.pi * 0.5,
psi=1,
sample_dist=None,
lock_view_dependence=False,
clamp_mode="relu",
nerf_noise=0.0,
white_back=False,
last_back=False,
return_aux_img=False,
grad_points=None,
forward_points=None,
):
"""
Generates images from a noise vector, rendering parameters, and camera distribution.
Uses the hierarchical sampling scheme described in NeRF.
:param z: (b, z_dim)
:param img_size:
:param fov: face: 12
:param ray_start: face: 0.88
:param ray_end: face: 1.12
:param num_steps: face: 12
:param h_stddev: face: 0.3
:param v_stddev: face: 0.155
:param h_mean: face: pi/2
:param v_mean: face: pi/2
:param hierarchical_sample: face: true
:param psi: [0, 1]
:param sample_dist: mode for sample_camera_positions, face: 'gaussian'
:param lock_view_dependence: face: false
:param clamp_mode: face: 'relu'
:param nerf_noise:
:param last_back: face: false
:param white_back: face: false
:return:
- pixels: (b, 3, h, w)
- pitch_yaw: (b, 2)
"""
# tbd on these params
style_dict = self.mapping_network(**zs)
if psi < 1:
avg_styles = self.generate_avg_frequencies(device=self.device)
style_dict = self.get_truncated_freq_phase(
raw_style_dict=style_dict, avg_style_dict=avg_styles, raw_lambda=psi
)
if grad_points is not None and grad_points < img_size**2:
imgs, pitch_yaw = self.part_grad_forward(
style_dict=style_dict,
img_size=img_size,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
num_steps=num_steps,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
hierarchical_sample=hierarchical_sample,
sample_dist=sample_dist,
lock_view_dependence=lock_view_dependence,
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
white_back=white_back,
last_back=last_back,
return_aux_img=return_aux_img,
grad_points=grad_points,
)
return imgs, pitch_yaw
else:
imgs, pitch_yaw = self.whole_grad_forward(
style_dict=style_dict,
img_size=img_size,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
num_steps=num_steps,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
hierarchical_sample=hierarchical_sample,
sample_dist=sample_dist,
lock_view_dependence=lock_view_dependence,
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
white_back=white_back,
last_back=last_back,
return_aux_img=return_aux_img,
forward_points=forward_points,
)
return imgs, pitch_yaw
def get_batch_style_dict(self, b, style_dict):
ret_style_dict = {}
for name, style in style_dict.items():
ret_style_dict[name] = style[[b]]
return ret_style_dict
def whole_grad_forward(
self,
style_dict,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
hierarchical_sample,
sample_dist=None,
lock_view_dependence=False,
clamp_mode="relu",
nerf_noise=0.0,
white_back=False,
last_back=False,
return_aux_img=True,
forward_points=None,
camera_pos=None,
camera_lookup=None,
up_vector=None,
):
device = self.device
batch_size = list(style_dict.values())[0].shape[0]
if forward_points is not None:
# stage forward
with torch.no_grad():
num_points = img_size**2
inr_img_output = torch.zeros((batch_size, num_points, 3), device=device)
if return_aux_img:
aux_img_output = torch.zeros(
(batch_size, num_points, 3), device=device
)
pitch_list = []
yaw_list = []
for b in range(batch_size):
(
transformed_points,
transformed_ray_directions_expanded,
transformed_ray_origins,
transformed_ray_directions,
z_vals,
pitch,
yaw,
) = get_world_points_and_direction(
batch_size=1,
num_steps=num_steps,
img_size=img_size,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
sample_dist=sample_dist,
lock_view_dependence=lock_view_dependence,
device=device,
camera_pos=camera_pos,
camera_lookup=camera_lookup,
up_vector=up_vector,
)
pitch_list.append(pitch)
yaw_list.append(yaw)
transformed_points = rearrange(
transformed_points,
"b (h w s) c -> b (h w) s c",
h=img_size,
s=num_steps,
)
transformed_ray_directions_expanded = rearrange(
transformed_ray_directions_expanded,
"b (h w s) c -> b (h w) s c",
h=img_size,
s=num_steps,
)
head = 0
while head < num_points:
tail = head + forward_points
cur_style_dict = self.get_batch_style_dict(
b=b, style_dict=style_dict
)
cur_inr_img, cur_aux_img = self.points_forward(
style_dict=cur_style_dict,
transformed_points=transformed_points[:, head:tail],
transformed_ray_directions_expanded=transformed_ray_directions_expanded[
:, head:tail
],
num_steps=num_steps,
hierarchical_sample=hierarchical_sample,
z_vals=z_vals[:, head:tail],
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
transformed_ray_origins=transformed_ray_origins[
:, head:tail
],
transformed_ray_directions=transformed_ray_directions[
:, head:tail
],
white_back=white_back,
last_back=last_back,
return_aux_img=return_aux_img,
)
inr_img_output[b : b + 1, head:tail] = cur_inr_img
if return_aux_img:
aux_img_output[b : b + 1, head:tail] = cur_aux_img
head += forward_points
inr_img = inr_img_output
if return_aux_img:
aux_img = aux_img_output
pitch = torch.cat(pitch_list, dim=0)
yaw = torch.cat(yaw_list, dim=0)
else:
(
transformed_points,
transformed_ray_directions_expanded,
transformed_ray_origins,
transformed_ray_directions,
z_vals,
pitch,
yaw,
) = get_world_points_and_direction(
batch_size=batch_size,
num_steps=num_steps,
img_size=img_size,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
sample_dist=sample_dist,
lock_view_dependence=lock_view_dependence,
device=device,
camera_pos=camera_pos,
camera_lookup=camera_lookup,
)
transformed_points = rearrange(
transformed_points,
"b (h w s) c -> b (h w) s c",
h=img_size,
s=num_steps,
)
transformed_ray_directions_expanded = rearrange(
transformed_ray_directions_expanded,
"b (h w s) c -> b (h w) s c",
h=img_size,
s=num_steps,
)
inr_img, aux_img = self.points_forward(
style_dict=style_dict,
transformed_points=transformed_points,
transformed_ray_directions_expanded=transformed_ray_directions_expanded,
num_steps=num_steps,
hierarchical_sample=hierarchical_sample,
z_vals=z_vals,
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
transformed_ray_origins=transformed_ray_origins,
transformed_ray_directions=transformed_ray_directions,
white_back=white_back,
last_back=last_back,
return_aux_img=return_aux_img,
)
inr_img = rearrange(inr_img, "b (h w) c -> b c h w", h=img_size)
inr_img = self.filters(inr_img)
pitch_yaw = torch.cat([pitch, yaw], -1)
if return_aux_img:
aux_img = rearrange(aux_img, "b (h w) c -> b c h w", h=img_size)
imgs = torch.cat([inr_img, aux_img])
pitch_yaw = torch.cat([pitch_yaw, pitch_yaw])
else:
imgs = inr_img
return imgs, pitch_yaw
def part_grad_forward(
self,
style_dict,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
hierarchical_sample,
sample_dist=None,
lock_view_dependence=False,
clamp_mode="relu",
nerf_noise=0.0,
white_back=False,
last_back=False,
return_aux_img=True,
grad_points=None,
camera_pos=None,
camera_lookup=None,
):
device = self.device
batch_size = list(style_dict.values())[0].shape[0]
(
transformed_points,
transformed_ray_directions_expanded,
transformed_ray_origins,
transformed_ray_directions,
z_vals,
pitch,
yaw,
) = get_world_points_and_direction(
batch_size=batch_size,
num_steps=num_steps,
img_size=img_size,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
sample_dist=sample_dist,
lock_view_dependence=lock_view_dependence,
device=device,
camera_pos=camera_pos,
camera_lookup=camera_lookup,
)
transformed_points = rearrange(
transformed_points, "b (h w s) c -> b (h w) s c", h=img_size, s=num_steps
)
transformed_ray_directions_expanded = rearrange(
transformed_ray_directions_expanded,
"b (h w s) c -> b (h w) s c",
h=img_size,
s=num_steps,
)
num_points = transformed_points.shape[1]
assert num_points > grad_points
rand_idx = torch.randperm(num_points, device=device)
idx_grad = rand_idx[:grad_points]
idx_no_grad = rand_idx[grad_points:]
inr_img_grad, aux_img_grad = self.points_forward(
style_dict=style_dict,
transformed_points=transformed_points,
transformed_ray_directions_expanded=transformed_ray_directions_expanded,
num_steps=num_steps,
hierarchical_sample=hierarchical_sample,
z_vals=z_vals,
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
transformed_ray_origins=transformed_ray_origins,
transformed_ray_directions=transformed_ray_directions,
white_back=white_back,
last_back=last_back,
return_aux_img=return_aux_img,
idx_grad=idx_grad,
)
with torch.no_grad():
inr_img_no_grad, aux_img_no_grad = self.points_forward(
style_dict=style_dict,
transformed_points=transformed_points,
transformed_ray_directions_expanded=transformed_ray_directions_expanded,
num_steps=num_steps,
hierarchical_sample=hierarchical_sample,
z_vals=z_vals,
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
transformed_ray_origins=transformed_ray_origins,
transformed_ray_directions=transformed_ray_directions,
white_back=white_back,
last_back=last_back,
return_aux_img=return_aux_img,
idx_no_grad=idx_no_grad,
)
inr_img = scatter_points(
idx_grad=idx_grad,
points_grad=inr_img_grad,
idx_no_grad=idx_no_grad,
points_no_grad=inr_img_no_grad,
num_points=num_points,
)
inr_img = rearrange(inr_img, "b (h w) c -> b c h w", h=img_size)
inr_img = self.filters(inr_img)
pitch_yaw = torch.cat([pitch, yaw], -1)
if return_aux_img:
aux_img = scatter_points(
idx_grad=idx_grad,
points_grad=aux_img_grad,
idx_no_grad=idx_no_grad,
points_no_grad=aux_img_no_grad,
num_points=num_points,
)
aux_img = rearrange(aux_img, "b (h w) c -> b c h w", h=img_size)
imgs = torch.cat([inr_img, aux_img])
pitch_yaw = torch.cat([pitch_yaw, pitch_yaw])
else:
imgs = inr_img
return imgs, pitch_yaw
def points_forward(
self,
style_dict,
transformed_points,
transformed_ray_directions_expanded,
num_steps,
hierarchical_sample,
z_vals,
clamp_mode,
nerf_noise,
transformed_ray_origins,
transformed_ray_directions,
white_back,
last_back,
return_aux_img,
idx_grad=None,
):
"""
:param style_dict:
:param transformed_points: (b, n, s, 3)
:param transformed_ray_directions_expanded: (b, n, s, 3)
:param num_steps: sampled points along a ray
:param hierarchical_sample:
:param z_vals: (b, n, s, 1)
:param clamp_mode: 'relu'
:param nerf_noise:
:param transformed_ray_origins: (b, n, 3)
:param transformed_ray_directions: (b, n, 3)
:param white_back:
:param last_back:
:return:
"""
device = transformed_points.device
if idx_grad is not None:
transformed_points = gather_points(
points=transformed_points, idx_grad=idx_grad
)
transformed_ray_directions_expanded = gather_points(
points=transformed_ray_directions_expanded, idx_grad=idx_grad
)
z_vals = gather_points(points=z_vals, idx_grad=idx_grad)
transformed_ray_origins = gather_points(
points=transformed_ray_origins, idx_grad=idx_grad
)
transformed_ray_directions = gather_points(
points=transformed_ray_directions, idx_grad=idx_grad
)
transformed_points = rearrange(transformed_points, "b n s c -> b (n s) c")
transformed_ray_directions_expanded = rearrange(
transformed_ray_directions_expanded, "b n s c -> b (n s) c"
)
# Model prediction on coarse points
coarse_output = self.siren(
input=transformed_points,
style_dict=style_dict,
ray_directions=transformed_ray_directions_expanded,
)
coarse_output = rearrange(
coarse_output, "b (n s) rgb_sigma -> b n s rgb_sigma", s=num_steps
)
# Resample fine points
if hierarchical_sample:
fine_points, fine_z_vals = self.get_fine_points_and_direction(
coarse_output=coarse_output,
z_vals=z_vals,
dim_rgb=self.siren.rgb_dim,
num_steps=num_steps,
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
transformed_ray_origins=transformed_ray_origins,
transformed_ray_directions=transformed_ray_directions,
)
# Model prediction on re-sampled points
fine_output = self.siren(
input=fine_points,
style_dict=style_dict,
ray_directions=transformed_ray_directions_expanded,
)
fine_output = rearrange(
fine_output, "b (n s) rgb_sigma -> b n s rgb_sigma", s=num_steps
)
# Combine coarse and fine points
all_outputs = torch.cat(
[fine_output, coarse_output], dim=-2
) # (b, n, s, dim_rgb_sigma)
all_z_vals = torch.cat([fine_z_vals, z_vals], dim=-2) # (b, n, s, 1)
_, indices = torch.sort(all_z_vals, dim=-2) # (b, n, s, 1)
all_z_vals = torch.gather(all_z_vals, -2, indices) # (b, n, s, 1)
# (b, n, s, dim_rgb_sigma)
all_outputs = torch.gather(
all_outputs, -2, indices.expand(-1, -1, -1, all_outputs.shape[-1])
)
else:
all_outputs = coarse_output
all_z_vals = z_vals
# Create images with NeRF
pixels_fea, depth, weights = fancy_integration(
rgb_sigma=all_outputs,
z_vals=all_z_vals,
device=device,
dim_rgb=self.siren.rgb_dim,
white_back=white_back,
last_back=last_back,
clamp_mode=clamp_mode,
noise_std=nerf_noise,
)
inr_img = self.inr(pixels_fea, style_dict)
if return_aux_img:
aux_img = self.aux_to_rbg(pixels_fea)
else:
aux_img = None
return inr_img, aux_img
def forward_camera_pos_and_lookup(
self,
zs,
img_size,
fov,
ray_start,
ray_end,
num_steps,
h_stddev,
v_stddev,
h_mean,
v_mean,
hierarchical_sample,
camera_pos,
camera_lookup,
psi=1,
sample_dist=None,
lock_view_dependence=False,
clamp_mode="relu",
nerf_noise=0.0,
white_back=False,
last_back=False,
return_aux_img=False,
grad_points=None,
forward_points=None,
up_vector=None,
):
"""
Generates images from a noise vector, rendering parameters, and camera distribution.
Uses the hierarchical sampling scheme described in NeRF.
:param z: (b, z_dim)
:param img_size:
:param fov: face: 12
:param ray_start: face: 0.88
:param ray_end: face: 1.12
:param num_steps: face: 12
:param h_stddev: face: 0.3
:param v_stddev: face: 0.155
:param h_mean: face: pi/2
:param v_mean: face: pi/2
:param hierarchical_sample: face: true
:param camera_pos: (b, 3)
:param camera_lookup: (b, 3)
:param psi: [0, 1]
:param sample_dist: mode for sample_camera_positions, face: 'gaussian'
:param lock_view_dependence: face: false
:param clamp_mode: face: 'relu'
:param nerf_noise:
:param last_back: face: false
:param white_back: face: false
:return:
- pixels: (b, 3, h, w)
- pitch_yaw: (b, 2)
"""
style_dict = self.mapping_network(**zs)
if psi < 1:
avg_styles = self.generate_avg_frequencies(device=self.device)
style_dict = self.get_truncated_freq_phase(
raw_style_dict=style_dict, avg_style_dict=avg_styles, raw_lambda=psi
)
if grad_points is not None and grad_points < img_size**2:
imgs, pitch_yaw = self.part_grad_forward(
style_dict=style_dict,
img_size=img_size,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
num_steps=num_steps,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
hierarchical_sample=hierarchical_sample,
sample_dist=sample_dist,
lock_view_dependence=lock_view_dependence,
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
white_back=white_back,
last_back=last_back,
return_aux_img=return_aux_img,
grad_points=grad_points,
camera_pos=camera_pos,
camera_lookup=camera_lookup,
)
return imgs, pitch_yaw
else:
imgs, pitch_yaw = self.whole_grad_forward(
style_dict=style_dict,
img_size=img_size,
fov=fov,
ray_start=ray_start,
ray_end=ray_end,
num_steps=num_steps,
h_stddev=h_stddev,
v_stddev=v_stddev,
h_mean=h_mean,
v_mean=v_mean,
hierarchical_sample=hierarchical_sample,
sample_dist=sample_dist,
lock_view_dependence=lock_view_dependence,
clamp_mode=clamp_mode,
nerf_noise=nerf_noise,
white_back=white_back,
last_back=last_back,
return_aux_img=return_aux_img,
forward_points=forward_points,
camera_pos=camera_pos,
camera_lookup=camera_lookup,
up_vector=up_vector,
)
return imgs, pitch_yaw
def get_truncated_freq_phase(self,
raw_style_dict,
avg_style_dict,
raw_lambda):
# truncated_frequencies = self.avg_frequencies + psi * (raw_frequencies - self.avg_frequencies)
# truncated_phase_shifts = self.avg_phase_shifts + psi * (raw_phase_shifts - self.avg_phase_shifts)
truncated_style_dict = {}
for name, avg_style in avg_style_dict.items():
raw_style = raw_style_dict[name]
truncated_style = avg_style + raw_lambda * (raw_style - avg_style)
truncated_style_dict[name] = truncated_style
return truncated_style_dict
| 35.572755
| 158
| 0.523499
| 10,068
| 91,920
| 4.416369
| 0.033572
| 0.027438
| 0.09122
| 0.084923
| 0.913188
| 0.899244
| 0.876214
| 0.860471
| 0.847562
| 0.836429
| 0
| 0.012798
| 0.399837
| 91,920
| 2,583
| 159
| 35.586527
| 0.793192
| 0.097857
| 0
| 0.788286
| 0
| 0.00048
| 0.01414
| 0.000787
| 0
| 0
| 0
| 0
| 0.00048
| 1
| 0.015843
| false
| 0.00048
| 0.006241
| 0
| 0.038886
| 0.00288
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
03382fb8bded7a80cc81a9b8154bd9c609be6e9c
| 207
|
py
|
Python
|
nmigen/build/run.py
|
psumesh/nmigen
|
7d611b8fc1d9e58853ff268ec38ff8f4131a9774
|
[
"BSD-2-Clause"
] | 528
|
2020-01-28T18:21:00.000Z
|
2021-12-09T06:27:51.000Z
|
nmigen/build/run.py
|
DX-MON/nmigen
|
a6a13dd612ee1c9215719c70a5aa410a8775ffdb
|
[
"BSD-2-Clause"
] | 360
|
2020-01-28T18:34:30.000Z
|
2021-12-10T08:03:32.000Z
|
nmigen/build/run.py
|
DX-MON/nmigen
|
a6a13dd612ee1c9215719c70a5aa410a8775ffdb
|
[
"BSD-2-Clause"
] | 100
|
2020-02-06T21:55:46.000Z
|
2021-11-25T19:20:44.000Z
|
from amaranth.build.run import *
from amaranth.build.run import __all__
import warnings
warnings.warn("instead of nmigen.build.run, use amaranth.build.run",
DeprecationWarning, stacklevel=2)
| 25.875
| 68
| 0.753623
| 27
| 207
| 5.62963
| 0.555556
| 0.210526
| 0.315789
| 0.263158
| 0.342105
| 0
| 0
| 0
| 0
| 0
| 0
| 0.005747
| 0.15942
| 207
| 7
| 69
| 29.571429
| 0.867816
| 0
| 0
| 0
| 0
| 0
| 0.246377
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.6
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
cef097e65c169159b8bbd3c18fe2f90526efa28e
| 8,620
|
py
|
Python
|
pymatflow/cp2k/base/motion_mc.py
|
DeqiTang/pymatflow
|
bd8776feb40ecef0e6704ee898d9f42ded3b0186
|
[
"MIT"
] | 6
|
2020-03-06T16:13:08.000Z
|
2022-03-09T07:53:34.000Z
|
pymatflow/cp2k/base/motion_mc.py
|
DeqiTang/pymatflow
|
bd8776feb40ecef0e6704ee898d9f42ded3b0186
|
[
"MIT"
] | 1
|
2021-10-02T02:23:08.000Z
|
2021-11-08T13:29:37.000Z
|
pymatflow/cp2k/base/motion_mc.py
|
DeqiTang/pymatflow
|
bd8776feb40ecef0e6704ee898d9f42ded3b0186
|
[
"MIT"
] | 1
|
2021-07-10T16:28:14.000Z
|
2021-07-10T16:28:14.000Z
|
#!/usr/bin/env python
# _*_ coding: utf-8 _*_
class cp2k_motion_mc_avbmc:
def __init__(self):
self.status = False
def to_input(self, fout):
"""
fout: a file stream for writing
"""
fout.write("\t\t&AVBMC\n")
for item in self.params:
if self.params[item] is not None:
fout.write("\t\t\t%s %s\n" % (item, str(self.params[item])))
fout.write("\t\t&END AVBMC\n")
def set_params(self, params):
for item in params:
if len(item.split("-")) == 3:
self.params[item.split("-")[-1]] = params[item]
else:
pass
class cp2k_motion_mc_max_displacements_box_probabilities:
def __init__(self):
self.status = False
def to_input(self, fout):
"""
fout: a file stream for writing
"""
fout.write("\t\t\t&BOX_PROBABILITIES\n")
for item in self.params:
if self.params[item] is not None:
fout.write("\t\t\t\t%s %s\n" % (item, str(self.params[item])))
fout.write("\t\t\t&END BOX_PROBABILITIES\n")
def set_params(self, params):
for item in params:
if len(item.split("-")) == 4:
self.params[item.split("-")[-1]] = params[item]
else:
pass
class cp2k_motion_mc_max_displacements_mol_probabilities:
def __init__(self):
self.status = False
def to_input(self, fout):
"""
fout: a file stream for writing
"""
fout.write("\t\t\t&MOL_PROBABILITIES\n")
for item in self.params:
if self.params[item] is not None:
fout.write("\t\t\t\t%s %s\n" % (item, str(self.params[item])))
fout.write("\t\t\t&END MOL_PROBABILITIES\n")
def set_params(self, params):
for item in params:
if len(item.split("-")) == 4:
self.params[item.split("-")[-1]] = params[item]
else:
pass
class cp2k_motion_mc_max_displacements:
def __init__(self):
self.status = False
self.box_probabilities = cp2k_motion_mc_max_displacements_box_probabilities()
self.mol_Probabilities = cp2k_motion_mc_max_displacements_mol_probabilities()
# basic setting
def to_input(self, fout):
"""
fout: a file stream for writing
"""
fout.write("\t\t&MAX_DISPLACEMENTS\n")
for item in self.params:
if self.params[item] is not None:
fout.write("\t\t\t%s %s\n" % (item, str(self.params[item])))
if self.box_probabilities.status == True:
self.box_probabilities.to_input(fout)
if self.mol_probabilities.status == True:
self.mol_probabilities.to_input(fout)
fout.write("\t\t&END MAX_DISPLACEMENTS\n")
def set_params(self, params):
for item in params:
if len(item.split("-")) == 3:
self.params[item.split("-")[-1]] = params[item]
elif item.split("-")[2] == "BOX_PROBABILITIES":
self.box_probabilities.set_params({item: params[item]})
elif item.split("-")[2] == "MOL_PROBABILITIES":
self.mol_probabilities.set_params({item: params[item]})
else:
pass
class cp2k_motion_mc_move_probabilities_box_probabilities:
def __init__(self):
self.status = False
def to_input(self, fout):
"""
fout: a file stream for writing
"""
fout.write("\t\t\t&BOX_PROBABILITIES\n")
for item in self.params:
if self.params[item] is not None:
fout.write("\t\t\t\t%s %s\n" % (item, str(self.params[item])))
fout.write("\t\t\t&END BOX_PROBABILITIES\n")
def set_params(self, params):
for item in params:
if len(item.split("-")) == 4:
self.params[item.split("-")[-1]] = params[item]
else:
pass
class cp2k_motion_mc_move_probabilities_mol_probabilities:
def __init__(self):
self.status = False
def to_input(self, fout):
"""
fout: a file stream for writing
"""
fout.write("\t\t\t&MOL_PROBABILITIES\n")
for item in self.params:
if self.params[item] is not None:
fout.write("\t\t\t\t%s %s\n" % (item, str(self.params[item])))
fout.write("\t\t\t&END MOL_PROBABILITIES\n")
def set_params(self, params):
for item in params:
if len(item.split("-")) == 4:
self.params[item.split("-")[-1]] = params[item]
else:
pass
class cp2k_motion_mc_move_probabilities:
def __init__(self):
self.status = False
self.box_probabilities = cp2k_motion_mc_move_probabilities_box_probabilities()
self.mol_Probabilities = cp2k_motion_mc_move_probabilities_mol_probabilities()
# basic setting
def to_input(self, fout):
"""
fout: a file stream for writing
"""
fout.write("\t\t&MOVE_PROBABILITIES\n")
for item in self.params:
if self.params[item] is not None:
fout.write("\t\t\t%s %s\n" % (item, str(self.params[item])))
if self.box_probabilities.status == True:
self.box_probabilities.to_input(fout)
if self.mol_probabilities.status == True:
self.mol_probabilities.to_input(fout)
fout.write("\t\t&END MOVE_PROBABILITIES\n")
def set_params(self, params):
for item in params:
if len(item.split("-")) == 3:
self.params[item.split("-")[-1]] = params[item]
elif item.split("-")[2] == "BOX_PROBABILITIES":
self.box_probabilities.set_params({item: params[item]})
elif item.split("-")[2] == "MOL_PROBABILITIES":
self.mol_probabilities.set_params({item: params[item]})
else:
pass
class cp2k_motion_mc_move_updates:
def __init__(self):
self.status = False
def to_input(self, fout):
"""
fout: a file stream for writing
"""
fout.write("\t\t&MOVE_UPDATES\n")
for item in self.params:
if self.params[item] is not None:
fout.write("\t\t\t%s %s\n" % (item, str(self.params[item])))
fout.write("\t\t&END MOVE_UPDATES\n")
def set_params(self, params):
for item in params:
if len(item.split("-")) == 3:
self.params[item.split("-")[-1]] = params[item]
else:
pass
class cp2k_motion_mc:
def __init__(self):
self.status = False
self.avmbc = cp2k_motion_mc_avbmc()
self.max_displacements = cp2k_motion_mc_max_displacements()
self.move_probabilities = cp2k_motion_mc_move_probabilities()
self.move_updates = cp2k_motion_mc_move_updates()
# basic setting
def to_input(self, fout):
"""
fout: a file stream for writing
"""
fout.write("\t&MC\n")
for item in self.params:
if self.params[item] is not None:
fout.write("\t\t%s %s\n" % (item, str(self.params[item])))
if self.avbmc.status == True:
self.avbmc.to_input(fout)
if self.max_displacements.status == True:
self.max_displacements.to_input(fout)
if self.move_probabilities.status == True:
self.move_probabilities.to_input(fout)
if self.move_updates.status == True:
self.move_update.to_input(fout)
fout.write("\t&END MC\n")
def set_params(self, params):
for item in params:
if len(item.split("-")) == 2:
self.params[item.split("-")[-1]] = params[item]
elif item.split("-")[1] == "AVBMC":
self.avbmc.set_params({item: params[item]})
elif item.split("-")[1] == "MAX_DISPLACEMENTS":
self.max_displacements.set_params({item: params[item]})
elif item.split("-")[1] == "MOVE_PROBABILITIES":
self.move_probabilities.set_params({item: params[item]})
elif item.split("-")[1] == "MOVE_UPDATE":
self.move_updates.set_params({item: params[item]})
else:
pass
| 33.410853
| 87
| 0.542343
| 1,071
| 8,620
| 4.175537
| 0.056022
| 0.116279
| 0.060376
| 0.061494
| 0.910555
| 0.893784
| 0.865385
| 0.836986
| 0.815519
| 0.804338
| 0
| 0.007568
| 0.325522
| 8,620
| 257
| 88
| 33.540856
| 0.76161
| 0.043155
| 0
| 0.742857
| 0
| 0
| 0.088197
| 0.019671
| 0
| 0
| 0
| 0
| 0
| 1
| 0.154286
| false
| 0.051429
| 0
| 0
| 0.205714
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
300bc48220029c99ce5127a545501df6e6e37c71
| 46
|
py
|
Python
|
dbconnect/__init__.py
|
astercrono/python-dbconnect
|
6a2d6cc40b43deadaec32c11aa3bb8925eef2328
|
[
"BSD-3-Clause"
] | null | null | null |
dbconnect/__init__.py
|
astercrono/python-dbconnect
|
6a2d6cc40b43deadaec32c11aa3bb8925eef2328
|
[
"BSD-3-Clause"
] | null | null | null |
dbconnect/__init__.py
|
astercrono/python-dbconnect
|
6a2d6cc40b43deadaec32c11aa3bb8925eef2328
|
[
"BSD-3-Clause"
] | 1
|
2019-02-05T20:37:39.000Z
|
2019-02-05T20:37:39.000Z
|
from . import models
from . import connection
| 15.333333
| 24
| 0.782609
| 6
| 46
| 6
| 0.666667
| 0.555556
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.173913
| 46
| 2
| 25
| 23
| 0.947368
| 0
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| 0
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| 0
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| true
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| null | 1
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
303441dc1490dccd5e215e9a44b72f299fef46b3
| 2,354
|
py
|
Python
|
tests/python/open_data/gbm/test_lightgbm.py
|
yuhonghong66/h2o4gpu
|
4df29eb6565d1a1f0a4dc5c738dbd40cec0b5f9a
|
[
"Apache-2.0"
] | null | null | null |
tests/python/open_data/gbm/test_lightgbm.py
|
yuhonghong66/h2o4gpu
|
4df29eb6565d1a1f0a4dc5c738dbd40cec0b5f9a
|
[
"Apache-2.0"
] | null | null | null |
tests/python/open_data/gbm/test_lightgbm.py
|
yuhonghong66/h2o4gpu
|
4df29eb6565d1a1f0a4dc5c738dbd40cec0b5f9a
|
[
"Apache-2.0"
] | null | null | null |
import time
import sys
import os
import logging
print(sys.path)
logging.basicConfig(level=logging.DEBUG)
def test_lightgbm_gpu():
import numpy as np
import pandas as pd
from h2o4gpu.util.lightgbm_dynamic import got_cpu_lgb, got_gpu_lgb
import lightgbm as lgb
X1= np.repeat(np.arange(10), 1000)
X2= np.repeat(np.arange(10), 1000)
np.random.shuffle(X2)
y = (X1 + np.random.randn(10000)) * (X2 + np.random.randn(10000))
data = pd.DataFrame({'y': y, 'X1': X1, 'X2': X2})
lgb_params = {'learning_rate' : 0.1,
'boosting' : 'dart',
'objective' : 'regression',
'metric' : 'rmse',
'feature_fraction' : 0.9,
'bagging_fraction' : 0.75,
'num_leaves' : 31,
'bagging_freq' : 1,
'min_data_per_leaf': 250, 'device_type': 'gpu', 'gpu_device_id': 0}
lgb_train = lgb.Dataset(data=data[['X1', 'X2']], label=data.y)
cv = lgb.cv(lgb_params,
lgb_train,
num_boost_round=100,
early_stopping_rounds=15,
stratified=False,
verbose_eval=50)
def test_lightgbm_cpu():
import numpy as np
import pandas as pd
from h2o4gpu.util.lightgbm_dynamic import got_cpu_lgb, got_gpu_lgb
import lightgbm as lgb
X1 = np.repeat(np.arange(10), 1000)
X2 = np.repeat(np.arange(10), 1000)
np.random.shuffle(X2)
y = (X1 + np.random.randn(10000)) * (X2 + np.random.randn(10000))
data = pd.DataFrame({'y': y, 'X1': X1, 'X2': X2})
lgb_params = {'learning_rate': 0.1,
'boosting': 'dart',
'objective': 'regression',
'metric': 'rmse',
'feature_fraction': 0.9,
'bagging_fraction': 0.75,
'num_leaves': 31,
'bagging_freq': 1,
'min_data_per_leaf': 250}
lgb_train = lgb.Dataset(data=data[['X1', 'X2']], label=data.y)
cv = lgb.cv(lgb_params,
lgb_train,
num_boost_round=100,
early_stopping_rounds=15,
stratified=False,
verbose_eval=50)
if __name__ == '__main__':
test_lightgbm_cpu()
test_lightgbm_gpu()
| 30.973684
| 85
| 0.533985
| 288
| 2,354
| 4.145833
| 0.295139
| 0.040201
| 0.033501
| 0.053601
| 0.842546
| 0.842546
| 0.842546
| 0.842546
| 0.842546
| 0.842546
| 0
| 0.071337
| 0.338997
| 2,354
| 75
| 86
| 31.386667
| 0.696015
| 0
| 0
| 0.786885
| 0
| 0
| 0.128772
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.032787
| false
| 0
| 0.196721
| 0
| 0.229508
| 0.016393
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
305cd69130d6aa16b3813a429f2caeb8a93683c1
| 44
|
py
|
Python
|
parameters_8000.py
|
arsfeld/fog-web2py
|
32263a03d4183dcaf7537c87edcb4e574d4bec6e
|
[
"BSD-3-Clause"
] | null | null | null |
parameters_8000.py
|
arsfeld/fog-web2py
|
32263a03d4183dcaf7537c87edcb4e574d4bec6e
|
[
"BSD-3-Clause"
] | null | null | null |
parameters_8000.py
|
arsfeld/fog-web2py
|
32263a03d4183dcaf7537c87edcb4e574d4bec6e
|
[
"BSD-3-Clause"
] | 1
|
2019-03-13T08:20:25.000Z
|
2019-03-13T08:20:25.000Z
|
password="e8d95a51f3af4a3b134bf6bb680a213a"
| 22
| 43
| 0.909091
| 2
| 44
| 20
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.418605
| 0.022727
| 44
| 1
| 44
| 44
| 0.511628
| 0
| 0
| 0
| 0
| 0
| 0.727273
| 0.727273
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
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| 0
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| 0
|
0
| 8
|
0621f7ebb51a5005ff5fa14c377cb583b862b2a9
| 6,450
|
py
|
Python
|
graphbrain/tests/test_patterns.py
|
vishalbelsare/graphbrain
|
efad1f96dcb616595b13bd4e3ada806862fe7de7
|
[
"MIT"
] | 412
|
2016-02-20T12:04:34.000Z
|
2022-03-30T21:07:39.000Z
|
graphbrain/tests/test_patterns.py
|
vishalbelsare/graphbrain
|
efad1f96dcb616595b13bd4e3ada806862fe7de7
|
[
"MIT"
] | 20
|
2018-09-21T13:47:59.000Z
|
2021-04-10T04:21:30.000Z
|
graphbrain/tests/test_patterns.py
|
vishalbelsare/graphbrain
|
efad1f96dcb616595b13bd4e3ada806862fe7de7
|
[
"MIT"
] | 51
|
2017-01-25T17:19:30.000Z
|
2022-03-29T07:32:53.000Z
|
import unittest
from graphbrain import hedge
from graphbrain.patterns import PatternCounter
class Testpatterns(unittest.TestCase):
def test_counter1(self):
pc = PatternCounter()
pc.count(hedge('((not/M is/P.sc) mary/C (not/M nice/C))'))
self.assertTrue(pc.patterns[hedge('(*/P.sc */C */C)')] == 1)
self.assertTrue(pc.patterns[hedge('((*/M */P.sc) */C */C)')] == 1)
self.assertTrue(pc.patterns[hedge('(*/P.sc */C (*/M */C))')] == 1)
self.assertTrue(
pc.patterns[hedge('((*/M */P.sc) */C (*/M */C))')] == 1)
self.assertTrue(pc.patterns[hedge('(*/M */P.sc)')] == 1)
self.assertFalse(pc.patterns[hedge('(*/M is/P.sc)')] == 1)
self.assertTrue(pc.patterns[hedge('(*/M */C)')] == 1)
def test_counter2(self):
pc = PatternCounter(count_subedges=False)
pc.count(hedge('((not/M is/P.sc) mary/C (not/M nice/C))'))
self.assertTrue(pc.patterns[hedge('(*/P.sc */C */C)')] == 1)
self.assertTrue(pc.patterns[hedge('((*/M */P.sc) */C */C)')] == 1)
self.assertTrue(pc.patterns[hedge('(*/P.sc */C (*/M */C))')] == 1)
self.assertTrue(
pc.patterns[hedge('((*/M */P.sc) */C (*/M */C))')] == 1)
self.assertFalse(pc.patterns[hedge('(*/M */P.sc)')] == 1)
self.assertFalse(pc.patterns[hedge('(*/M */C)')] == 1)
def test_counter3(self):
pc = PatternCounter(match_roots={'*/P'})
pc.count(hedge('((not/M is/P.sc) mary/C (not/M nice/C))'))
self.assertTrue(pc.patterns[hedge('(*/P.sc */C */C)')] == 0)
self.assertTrue(pc.patterns[hedge('((*/M */P.sc) */C */C)')] == 0)
self.assertTrue(pc.patterns[hedge('((not/M is/P.sc) */C */C)')] == 1)
self.assertTrue(pc.patterns[hedge('(*/P.sc */C (*/M */C))')] == 0)
self.assertTrue(
pc.patterns[hedge('((*/M */P.sc) */C (*/M */C))')] == 0)
self.assertTrue(pc.patterns[hedge('(*/M */P.sc)')] == 0)
self.assertTrue(pc.patterns[hedge('(not/M is/P.sc)')] == 1)
self.assertTrue(pc.patterns[hedge('(*/M */C)')] == 1)
def test_counter4(self):
pc = PatternCounter(match_roots={'./P'})
pc.count(hedge('((not/M is/P.sc) mary/C (not/M nice/C))'))
self.assertTrue(pc.patterns[hedge('(*/P.sc */C */C)')] == 0)
self.assertTrue(pc.patterns[hedge('((*/M */P.sc) */C */C)')] == 0)
self.assertTrue(pc.patterns[hedge('((*/M is/P.sc) */C */C)')] == 1)
self.assertTrue(pc.patterns[hedge('(*/P.sc */C (*/M */C))')] == 0)
self.assertTrue(
pc.patterns[hedge('((*/M */P.sc) */C (*/M */C))')] == 0)
self.assertTrue(pc.patterns[hedge('(*/M */P.sc)')] == 0)
self.assertTrue(pc.patterns[hedge('(*/M is/P.sc)')] == 1)
self.assertTrue(pc.patterns[hedge('(*/M */C)')] == 1)
def test_counter5(self):
pc = PatternCounter(count_subedges=False)
pc.count(hedge('((not/Mn is/Pd.sc) mary/Cp.s (very/M nice/Cc.s))'))
self.assertTrue(pc.patterns[hedge('(*/P.sc */C */C)')] == 1)
self.assertTrue(pc.patterns[hedge('((*/M */P.sc) */C */C)')] == 1)
self.assertTrue(pc.patterns[hedge('(*/P.sc */C (*/M */C))')] == 1)
self.assertTrue(
pc.patterns[hedge('((*/M */P.sc) */C (*/M */C))')] == 1)
self.assertFalse(pc.patterns[hedge('(*/M */P.sc)')] == 1)
self.assertFalse(pc.patterns[hedge('(*/M */C)')] == 1)
def test_counter6(self):
pc = PatternCounter(count_subedges=False, match_subtypes={'*/M'})
pc.count(hedge('((not/Mn is/Pd.sc) mary/Cp.s (very/M nice/Cc.s))'))
self.assertTrue(pc.patterns[hedge('(*/P.sc */C */C)')] == 1)
self.assertTrue(pc.patterns[hedge('((*/Mn */P.sc) */C */C)')] == 1)
self.assertTrue(pc.patterns[hedge('(*/P.sc */C (*/M */C))')] == 1)
self.assertTrue(
pc.patterns[hedge('((*/Mn */P.sc) */C (*/M */C))')] == 1)
self.assertFalse(pc.patterns[hedge('(*/Mn */P.sc)')] == 1)
self.assertFalse(pc.patterns[hedge('(*/M */C)')] == 1)
def test_counter7(self):
pc = PatternCounter(count_subedges=False,
match_subtypes={'C', 'M', 'P'})
pc.count(hedge('((not/Mn is/Pd.sc) mary/Cp.s (very/M nice/Cc.s))'))
self.assertTrue(pc.patterns[hedge('(*/Pd.sc */Cp */Cc)')] == 1)
self.assertTrue(pc.patterns[hedge('((*/Mn */Pd.sc) */Cp */Cc)')] == 1)
self.assertTrue(pc.patterns[hedge('(*/Pd.sc */Cp (*/M */Cc))')] == 1)
self.assertTrue(
pc.patterns[hedge('((*/Mn */Pd.sc) */Cp (*/M */Cc))')] == 1)
self.assertFalse(pc.patterns[hedge('(*/Mn */P.sc)')] == 1)
self.assertFalse(pc.patterns[hedge('(*/M */Cc)')] == 1)
def test_counter8(self):
pc = PatternCounter()
pc.count(hedge('(a/M (b/M (c/M (d/M e/C))))'))
self.assertTrue(pc.patterns[hedge('(*/M */C)')] == 4)
self.assertTrue(pc.patterns[hedge('(*/M (*/M */C))')] == 3)
self.assertFalse(pc.patterns[hedge('(*/M (*/M (*/M */C)))')] == 2)
self.assertFalse(
pc.patterns[hedge('(*/M (*/M (*/M (*/M */C)))(')] == 1)
def test_counter9(self):
pc = PatternCounter(depth=3)
pc.count(hedge('(a/M (b/M (c/M (d/M e/C))))'))
self.assertTrue(pc.patterns[hedge('(*/M */C)')] == 4)
self.assertTrue(pc.patterns[hedge('(*/M (*/M */C))')] == 3)
self.assertTrue(pc.patterns[hedge('(*/M (*/M (*/M */C)))')] == 2)
self.assertFalse(
pc.patterns[hedge('(*/M (*/M (*/M (*/M */C)))(')] == 1)
def test_counter10(self):
pc = PatternCounter(depth=4)
pc.count(hedge('(a/M (b/M (c/M (d/M e/C))))'))
self.assertTrue(pc.patterns[hedge('(*/M */C)')] == 4)
self.assertTrue(pc.patterns[hedge('(*/M (*/M */C))')] == 3)
self.assertTrue(pc.patterns[hedge('(*/M (*/M (*/M */C)))')] == 2)
self.assertTrue(
pc.patterns[hedge('(*/M (*/M (*/M (*/M */C)))(')] == 1)
def test_counter11(self):
pc = PatternCounter(depth=1)
pc.count(hedge('(a/M (b/M (c/M (d/M e/C))))'))
self.assertTrue(pc.patterns[hedge('(*/M */C)')] == 4)
self.assertFalse(pc.patterns[hedge('(*/M (*/M */C))')] == 3)
self.assertFalse(pc.patterns[hedge('(*/M (*/M (*/M */C)))')] == 2)
self.assertFalse(
pc.patterns[hedge('(*/M (*/M (*/M (*/M */C)))(')] == 1)
if __name__ == '__main__':
unittest.main()
| 45.744681
| 78
| 0.507907
| 909
| 6,450
| 3.574257
| 0.066007
| 0.193906
| 0.290859
| 0.354571
| 0.900277
| 0.899969
| 0.887658
| 0.883041
| 0.846722
| 0.846722
| 0
| 0.015619
| 0.215814
| 6,450
| 140
| 79
| 46.071429
| 0.62673
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| 0
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| 0.246047
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| 0.552632
| 1
| 0.096491
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0
| 9
|
067f3943799819afae523956ac5f7accc43c63bc
| 46
|
py
|
Python
|
addons14/queue_job_chunk/components/__init__.py
|
odoochain/addons_oca
|
55d456d798aebe16e49b4a6070765f206a8885ca
|
[
"MIT"
] | 1
|
2021-06-10T14:59:13.000Z
|
2021-06-10T14:59:13.000Z
|
addons14/queue_job_chunk/components/__init__.py
|
odoochain/addons_oca
|
55d456d798aebe16e49b4a6070765f206a8885ca
|
[
"MIT"
] | null | null | null |
addons14/queue_job_chunk/components/__init__.py
|
odoochain/addons_oca
|
55d456d798aebe16e49b4a6070765f206a8885ca
|
[
"MIT"
] | 1
|
2021-04-09T09:44:44.000Z
|
2021-04-09T09:44:44.000Z
|
from . import processor
from . import creator
| 15.333333
| 23
| 0.782609
| 6
| 46
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| 0.666667
| 0.555556
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| 46
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| 1
| 0
|
0
| 7
|
06971a6ccb8d20b9a3bb7e412247a54abd95f460
| 14,276
|
py
|
Python
|
A2-Logistic-Regression/logistic.py
|
GauravJain28/ML-Assignments
|
0de464fe6564a0bad43f7962c92563fe0a988285
|
[
"MIT"
] | null | null | null |
A2-Logistic-Regression/logistic.py
|
GauravJain28/ML-Assignments
|
0de464fe6564a0bad43f7962c92563fe0a988285
|
[
"MIT"
] | null | null | null |
A2-Logistic-Regression/logistic.py
|
GauravJain28/ML-Assignments
|
0de464fe6564a0bad43f7962c92563fe0a988285
|
[
"MIT"
] | null | null | null |
import sys
import time
import numpy as np
import pandas as pd
from scipy.special import softmax
# from sklearn.feature_selection import SelectKBest
# from sklearn.feature_selection import VarianceThreshold
np.seterr(divide='ignore', invalid='ignore')
st = time.time()
mode = sys.argv[1]
train_path = sys.argv[2]
test_path = sys.argv[3]
def f(pred,Y_train):
pred = np.clip(pred,a_min=10**(-15),a_max=10**15)
v = np.log(np.sum(Y_train*pred,axis=1))
#v = np.clip(v,a_min=10**(-50),a_max=10**(50))
return abs(np.sum(v)/Y_train.shape[0])
def read_and_encode(train_path,test_path):
train = pd.read_csv(train_path, index_col = 0)
test = pd.read_csv(test_path, index_col = 0)
Y_df = train['Length of Stay']
train = train.drop(columns = ['Length of Stay'])
#Ensuring consistency of One-Hot Encoding
data = pd.concat([train, test], ignore_index = True)
cols = train.columns
cols = cols[:-1]
data = pd.get_dummies(data, columns=cols, drop_first=True)
data = data.to_numpy()
X_train = data[:train.shape[0], :]
X_test = data[train.shape[0]:, :]
Y_train = pd.get_dummies(Y_df).to_numpy()
return X_train,Y_train,X_test
def read_and_encode_d(train_path,test_path):
#t = time.time()
train = pd.read_csv(train_path, index_col = 0)
test = pd.read_csv(test_path, index_col = 0)
Y_df = train['Length of Stay']
train.drop('Birth Weight',axis=1,inplace=True)
test.drop('Birth Weight',axis=1,inplace=True)
train['n1'] = train['Facility Name'] + 1000*train['APR DRG Code']
test['n1'] = test['Facility Name'] + 1000*test['APR DRG Code']
train['n2'] = train['Facility Name'] + 1000*train['CCS Procedure Code']
test['n2'] = test['Facility Name'] + 1000*test['CCS Procedure Code']
# train['n3'] = train['Facility Name'] + 1000*train['CCS Diagnosis Code']
# test['n3'] = test['Facility Name'] + 1000*test['CCS Diagnosis Code']
# train['n4'] = train['Zip Code - 3 digits'] + 1000*train['APR DRG Code']
# test['n4'] = test['Zip Code - 3 digits'] + 1000*test['APR DRG Code']
#ss = ['CCS Procedure Code','APR DRG Code','CCS Diagnosis Code','APR MDC Code','APR Severity of Illness Code','APR Risk of Mortality','APR Medical Surgical Description']
ss = ['n1','n2']
for s in ss:
for ll in pd.unique(train[s]):
#print(ll)
m = train.loc[train[s]==ll]['Length of Stay'].median()
train[s] = train[s].mask(train[s]==ll,m)
test[s] = test[s].mask(test[s]==ll,m)
k = train[s]
l = test[s]
train.drop(s,axis=1,inplace=True)
test.drop(s,axis=1,inplace=True)
train.insert(0,s,k)
test.insert(0,s,l)
train = train.drop(columns = ['Length of Stay'])
#print(train)
#Ensuring consistency of One-Hot Encoding
data = pd.concat([train, test], ignore_index = True)
cols = train.columns
cols = cols[:-1]
data = pd.get_dummies(data, columns=cols, drop_first=True)
data = data.to_numpy()
X_train = data[:train.shape[0], :]
X_test = data[train.shape[0]:, :]
# constant_filter = VarianceThreshold(threshold=0)
# constant_filter.fit(X_train)
# X_train = constant_filter.transform(X_train)
# X_test = constant_filter.transform(X_test)
# #print('selection of 400 features')
# T = SelectKBest(k=400)
# T.fit(X_train,Y_df.to_numpy())
# X_train = T.transform(X_train)
# X_test = T.transform(X_test)
Y_train = pd.get_dummies(Y_df).to_numpy()
#print(time.time()-t)
return X_train,Y_train,X_test
#------------------------------------------------------------------------------------------------------#
if mode == 'a':
X_train,Y_train,X_test = read_and_encode(train_path,test_path)
X_train = np.insert(X_train,0,np.ones(X_train.shape[0]),axis=1)
X_test = np.insert(X_test,0,np.ones(X_test.shape[0]),axis=1)
file = open(sys.argv[4],'r')
par = file.readlines()
file.close()
k = int(par[0])
# M1: Constant learning rate
if k==1:
iter = int(par[2])
alpha = float(par[1])
w = np.zeros((X_train.shape[1],8))
for i in range(iter):
h = np.dot(X_train,w)
pred = softmax(h.T,axis=0)
pred = pred.T
grad = np.dot(X_train.T,pred-Y_train)/(X_train.shape[0])
w = w - alpha*grad
np.savetxt(sys.argv[6],w.flatten())
h = np.dot(X_test,w)
pred = softmax(h.T,axis=0).T
Y_test = np.argmax(pred,axis=1)+1
np.savetxt(sys.argv[5],Y_test)
# M2: Adaptive learning rate - alpha/sqrt(iter_no)
elif k==2:
iter = int(par[2])
alpha = float(par[1])
w = np.zeros((X_train.shape[1],8))
for i in range(iter):
h = np.dot(X_train,w)
pred = softmax(h.T,axis=0).T
grad = np.dot(X_train.T,pred-Y_train)/(X_train.shape[0])
w = w - (alpha*grad)/np.sqrt(i+1)
np.savetxt(sys.argv[6],w.flatten())
h = np.dot(X_test,w)
pred = softmax(h.T,axis=0).T
Y_test = np.argmax(pred,axis=1)+1
np.savetxt(sys.argv[5],Y_test)
# M3: alpha-beta backtracking line search
elif k==3:
#print('alpha-beta Backtracking line search')
iter = int(par[2])
pp = par[1].split(',')
lr0 = float(pp[0])
alpha = float(pp[1])
beta = float(pp[2])
w = np.zeros((X_train.shape[1],8))
for i in range(iter):
h = np.dot(X_train,w)
pred = softmax(h.T,axis=0)
pred = pred.T
grad = np.dot(X_train.T,pred-Y_train)/(X_train.shape[0])
lr = lr0
h1 = np.dot(X_train,w-lr*grad)
pred1 = softmax(h1.T,axis=0)
pred1 = pred1.T
while f(pred1,Y_train) > f(pred,Y_train) - lr*alpha*np.square(np.linalg.norm(grad)):
lr = lr*beta
h1 = np.dot(X_train,w-lr*grad)
pred1 = softmax(h1.T,axis=0)
pred1 = pred1.T
w = w - (lr*grad)
np.savetxt(sys.argv[6],w.flatten())
h = np.dot(X_test,w)
pred = softmax(h.T,axis=0).T
Y_test = np.argmax(pred,axis=1)+1
np.savetxt(sys.argv[5],Y_test)
#--------------------------------------------------------------------------------------------------#
elif mode == 'b':
X_train,Y_train,X_test = read_and_encode(train_path,test_path)
X_train = np.insert(X_train,0,np.ones(X_train.shape[0]),axis=1)
X_test = np.insert(X_test,0,np.ones(X_test.shape[0]),axis=1)
file = open(sys.argv[4],'r')
par = file.readlines()
file.close()
k = int(par[0])
# M1: Constant learning rate
if k==1:
iter = int(par[2])
alpha = float(par[1])
batch_size = int(par[3])
batch_no = X_train.shape[0]//batch_size
w = np.zeros((X_train.shape[1],8))
for i in range(iter):
for n in range(batch_no):
mini_X_train = X_train[n*batch_size:(n+1)*batch_size]
mini_Y_train = Y_train[n*batch_size:(n+1)*batch_size]
#print(mini_X_train.shape,mini_Y_train.shape)
h = np.dot(mini_X_train,w)
pred = softmax(h.T,axis=0)
pred = pred.T
grad = np.dot(mini_X_train.T,pred-mini_Y_train)/(mini_X_train.shape[0])
w = w - alpha*grad
np.savetxt(sys.argv[6],w.flatten())
h = np.dot(X_test,w)
pred = softmax(h.T,axis=0).T
Y_test = np.argmax(pred,axis=1)+1
np.savetxt(sys.argv[5],Y_test)
# M2: Adaptive learning rate - alpha/sqrt(iter_no)
elif k==2:
iter = int(par[2])
alpha = float(par[1])
batch_size = int(par[3])
batch_no = X_train.shape[0]//batch_size
w = np.zeros((X_train.shape[1],8))
for i in range(iter):
for n in range(batch_no):
mini_X_train = X_train[n*batch_size:(n+1)*batch_size]
mini_Y_train = Y_train[n*batch_size:(n+1)*batch_size]
h = np.dot(mini_X_train,w)
pred = softmax(h.T,axis=0)
pred = pred.T
grad = np.dot(mini_X_train.T,pred-mini_Y_train)/(mini_X_train.shape[0])
w = w - (alpha*grad)/np.sqrt(i+1)
np.savetxt(sys.argv[6],w.flatten())
h = np.dot(X_test,w)
pred = softmax(h.T,axis=0).T
Y_test = np.argmax(pred,axis=1)+1
np.savetxt(sys.argv[5],Y_test)
# M3: alpha-beta backtracking line search
elif k==3:
iter = int(par[2])
pp = par[1].split(',')
lr0 = float(pp[0])
alpha = float(pp[1])
beta = float(pp[2])
batch_size = int(par[3])
batch_no = X_train.shape[0]//batch_size
w = np.zeros((X_train.shape[1],8))
#print(lr0,alpha,beta)
for i in range(iter):
h = np.dot(X_train,w)
pred = softmax(h.T,axis=0)
pred = pred.T
grad = np.dot(X_train.T,pred-Y_train)/(X_train.shape[0])
lr = lr0
h1 = np.dot(X_train,w-lr*grad)
pred1 = softmax(h1.T,axis=0)
pred1 = pred1.T
while f(pred1,Y_train) > f(pred,Y_train) - lr*alpha*np.square(np.linalg.norm(grad)):
lr = lr*beta
h1 = np.dot(X_train,w-lr*grad)
pred1 = softmax(h1.T,axis=0)
pred1 = pred1.T
#print(i,lr)
for n in range(batch_no):
mini_X_train = X_train[n*batch_size:(n+1)*batch_size]
mini_Y_train = Y_train[n*batch_size:(n+1)*batch_size]
h = np.dot(mini_X_train,w)
pred = softmax(h.T,axis=0)
pred = pred.T
grad = np.dot(mini_X_train.T,pred-mini_Y_train)/(mini_X_train.shape[0])
w = w - (lr*grad)
np.savetxt(sys.argv[6],w.flatten())
h = np.dot(X_test,w)
pred = softmax(h.T,axis=0).T
#pred = np.clip(pred,a_min=10**(-15),a_max=10**15)
Y_test = np.argmax(pred,axis=1)+1
np.savetxt(sys.argv[5],Y_test)
#-------------------------------------------------------------------------------------------------#
elif mode=='c':
X_train,Y_train,X_test = read_and_encode(train_path,test_path)
X_train = np.insert(X_train,0,np.ones(X_train.shape[0]),axis=1)
X_test = np.insert(X_test,0,np.ones(X_test.shape[0]),axis=1)
iter = 800
lr0 = 10
batch_size = 100
batch_no = X_train.shape[0]//batch_size
w = np.zeros((X_train.shape[1],8))
for i in range(iter):
for n in range(batch_no):
mini_X_train = X_train[n*batch_size:(n+1)*batch_size]
mini_Y_train = Y_train[n*batch_size:(n+1)*batch_size]
#print(mini_X_train.shape,mini_Y_train.shape)
h = np.dot(mini_X_train,w)
pred = softmax(h.T,axis=0)
pred = pred.T
grad = np.dot(mini_X_train.T,pred-mini_Y_train)/(mini_X_train.shape[0])
w = w - (lr0*grad)/np.sqrt(i+1)
#print(i+1,alpha/np.sqrt(i+1))
if (i+1)%50==0:
h = np.dot(X_train,w)
pred = softmax(h.T,axis=0).T
#print(i+1,f(pred,Y_train))
np.savetxt(sys.argv[5],w.flatten())
h = np.dot(X_test,w)
pred = softmax(h.T,axis=0).T
Y_test = np.argmax(pred,axis=1)+1
np.savetxt(sys.argv[4],Y_test)
end = time.time()
if end-st > 540.0:
h = np.dot(X_train,w)
pred = softmax(h.T,axis=0).T
#print(i+1,f(pred,Y_train))
np.savetxt(sys.argv[5],w.flatten())
h = np.dot(X_test,w)
pred = softmax(h.T,axis=0).T
Y_test = np.argmax(pred,axis=1)+1
np.savetxt(sys.argv[4],Y_test)
break
# h = np.dot(X_train,w)
# pred = softmax(h.T,axis=0).T
# print(i+1,f(pred,Y_train))
# np.savetxt(sys.argv[5],w.flatten())
# h = np.dot(X_test,w)
# pred = softmax(h.T,axis=0).T
# Y_test = np.argmax(pred,axis=1)+1
# np.savetxt(sys.argv[4],Y_test)
#-------------------------------------------------------------------------------------------------#
elif mode=='d':
X_train,Y_train,X_test = read_and_encode_d(train_path,test_path)
X_train = np.insert(X_train,0,np.ones(X_train.shape[0]),axis=1)
X_test = np.insert(X_test,0,np.ones(X_test.shape[0]),axis=1)
iter = 600
lr0 = 10
#alpha = 0.4
#beta = 0.75
batch_size = 100
batch_no = X_train.shape[0]//batch_size
w = np.zeros((X_train.shape[1],8))
for i in range(iter):
for n in range(batch_no):
mini_X_train = X_train[n*batch_size:(n+1)*batch_size]
mini_Y_train = Y_train[n*batch_size:(n+1)*batch_size]
#print(mini_X_train.shape,mini_Y_train.shape)
h = np.dot(mini_X_train,w)
pred = softmax(h.T,axis=0)
pred = pred.T
grad = np.dot(mini_X_train.T,pred-mini_Y_train)/(mini_X_train.shape[0])
w = w - (lr0*grad)/np.sqrt(i+1)
#print(i+1,alpha/np.sqrt(i+1))
if (i+1)%50==0:
h = np.dot(X_train,w)
pred = softmax(h.T,axis=0).T
#print(i+1,f(pred,Y_train))
np.savetxt(sys.argv[5],w.flatten())
h = np.dot(X_test,w)
pred = softmax(h.T,axis=0).T
Y_test = np.argmax(pred,axis=1)+1
np.savetxt(sys.argv[4],Y_test)
end = time.time()
if end-st > 840.0:
h = np.dot(X_train,w)
pred = softmax(h.T,axis=0).T
#print(i+1,f(pred,Y_train))
np.savetxt(sys.argv[5],w.flatten())
h = np.dot(X_test,w)
pred = softmax(h.T,axis=0).T
Y_test = np.argmax(pred,axis=1)+1
np.savetxt(sys.argv[4],Y_test)
break
else:
print('Invalid option')
end = time.time()
#print(str(end-st)+' s')
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0
| 7
|
06a0490308ecb1e1b8c04e875763bee8c5155e6f
| 159,524
|
py
|
Python
|
Workspace/validate_zinc/etc/ligand_dict.py
|
alexjli/addcov19
|
839b6a5fdb117d6dbfedd3e2a2aaf722a722f7c8
|
[
"MIT"
] | 1
|
2020-04-25T01:33:40.000Z
|
2020-04-25T01:33:40.000Z
|
Workspace/validate_zinc/etc/ligand_dict.py
|
alexjli/addcov19
|
839b6a5fdb117d6dbfedd3e2a2aaf722a722f7c8
|
[
"MIT"
] | null | null | null |
Workspace/validate_zinc/etc/ligand_dict.py
|
alexjli/addcov19
|
839b6a5fdb117d6dbfedd3e2a2aaf722a722f7c8
|
[
"MIT"
] | null | null | null |
summary = d = {}
d['lig0'] = {'atom_types': ['C', 'HD', 'N', 'OA' ],
'rbonds':7,
'zero_charge' : [],
}
d['lig1000'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':9,
'zero_charge' : [],
}
d['lig1001'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig1002'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'NA', 'OA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1003'] = {'atom_types': ['A', 'C', 'F', 'HD', 'I', 'N', 'NA', 'OA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1004'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1005'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':7,
'zero_charge' : [],
}
d['lig1006'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA', 'S' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1007'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1008'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'NA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig1009'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig100'] = {'atom_types': ['C', 'N' ],
'rbonds':0,
'zero_charge' : [],
}
d['lig1010'] = {'atom_types': ['C', 'HD', 'N', 'OA' ],
'rbonds':0,
'zero_charge' : [],
}
d['lig1011'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1012'] = {'atom_types': ['C', 'HD', 'N', 'SA' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1013'] = {'atom_types': ['A', 'C', 'Cl', 'HD', 'N', 'NA', 'OA' ],
'rbonds':3,
'zero_charge' : ['C17', ],
}
d['lig1014'] = {'atom_types': ['A', 'C', 'Cl', 'OA' ],
'rbonds':7,
'zero_charge' : [],
}
d['lig1015'] = {'atom_types': ['A', 'C', 'Cl', 'F', 'HD', 'N', 'NA', 'SA' ],
'rbonds':8,
'zero_charge' : [],
}
d['lig1016'] = {'atom_types': ['A', 'C', 'HD', 'N' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1017'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':10,
'zero_charge' : [],
}
d['lig1019'] = {'atom_types': ['A', 'C', 'Cl', 'F', 'HD', 'N', 'OA', 'S', 'SA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig101'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig1020'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1021'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig1022'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'SA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1023'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'OA' ],
'rbonds':5,
'zero_charge' : ['C9', ],
}
d['lig1024'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1025'] = {'atom_types': ['A', 'C', 'Cl', 'HD', 'N', 'NA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1026'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'NA', 'OA', 'S' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1027'] = {'atom_types': ['C', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig1028'] = {'atom_types': ['A', 'C', 'Cl', 'HD', 'N', 'NA', 'OA', 'S', 'SA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1029'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig102'] = {'atom_types': ['A', 'C', 'Cl', 'HD', 'N', 'OA' ],
'rbonds':8,
'zero_charge' : [],
}
d['lig1030'] = {'atom_types': ['A', 'C', 'OA' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1031'] = {'atom_types': ['A', 'HD', 'N', 'NA', 'OA' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1032'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1033'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig1034'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1035'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'OA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1036'] = {'atom_types': ['A', 'C', 'HD', 'I', 'N', 'OA' ],
'rbonds':9,
'zero_charge' : [],
}
d['lig1037'] = {'atom_types': ['A', 'C', 'HD', 'I', 'N', 'OA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1038'] = {'atom_types': ['C', 'HD', 'NA', 'OA', 'S' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1039'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig103'] = {'atom_types': ['C', 'SA' ],
'rbonds':0,
'zero_charge' : [],
}
d['lig1040'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA', 'SA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig1041'] = {'atom_types': ['C', 'HD', 'SA' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1042'] = {'atom_types': ['C', 'HD', 'N', 'OA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1043'] = {'atom_types': ['C', 'HD', 'OA' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1044'] = {'atom_types': ['C', 'HD', 'N' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1045'] = {'atom_types': ['C' ],
'rbonds':0,
'zero_charge' : [],
}
d['lig1046'] = {'atom_types': ['A', 'Br', 'C', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig1047'] = {'atom_types': ['C', 'F', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1048'] = {'atom_types': ['A', 'Br', 'C', 'Cl', 'HD', 'N', 'NA', 'OA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1049'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig104'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'SA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1050'] = {'atom_types': ['A', 'C', 'Cl', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':8,
'zero_charge' : [],
}
d['lig1051'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig1053'] = {'atom_types': ['A', 'C', 'Cl', 'F', 'HD', 'N', 'OA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1054'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA', 'SA' ],
'rbonds':7,
'zero_charge' : [],
}
d['lig1055'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'SA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1056'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1057'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':10,
'zero_charge' : [],
}
d['lig1058'] = {'atom_types': ['A', 'C', 'N', 'NA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig105'] = {'atom_types': ['A', 'C', 'Cl', 'HD', 'N', 'NA', 'OA', 'S' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1060'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig1061'] = {'atom_types': ['C', 'Cl' ],
'rbonds':0,
'zero_charge' : [],
}
d['lig1062'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1063'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'NA', 'OA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1064'] = {'atom_types': ['A', 'C', 'HD', 'N' ],
'rbonds':0,
'zero_charge' : [],
}
d['lig1065'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig1066'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':7,
'zero_charge' : [],
}
d['lig1067'] = {'atom_types': ['A', 'NA' ],
'rbonds':0,
'zero_charge' : [],
}
d['lig1068'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':7,
'zero_charge' : [],
}
d['lig1069'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'OA' ],
'rbonds':8,
'zero_charge' : [],
}
d['lig106'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':9,
'zero_charge' : [],
}
d['lig1070'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'NA', 'OA', 'S' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1071'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig1072'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1073'] = {'atom_types': ['A', 'C', 'Cl', 'HD', 'N', 'OA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1074'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA', 'S', 'SA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1075'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'NA', 'OA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig1076'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'SA' ],
'rbonds':3,
'zero_charge' : [],
}
d['lig1077'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1078'] = {'atom_types': ['C', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig1079'] = {'atom_types': ['A', 'C', 'OA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig107'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1080'] = {'atom_types': ['A', 'C', 'OA' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1081'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'OA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1082'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1083'] = {'atom_types': ['A', 'C', 'Cl', 'F', 'HD', 'N', 'NA' ],
'rbonds':0,
'zero_charge' : [],
}
d['lig1084'] = {'atom_types': ['A', 'C', 'NA', 'OA', 'S' ],
'rbonds':7,
'zero_charge' : [],
}
d['lig1085'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'OA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig1086'] = {'atom_types': ['C', 'F', 'HD', 'N', 'OA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1087'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':7,
'zero_charge' : [],
}
d['lig1088'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1089'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig108'] = {'atom_types': ['A', 'C', 'F', 'HD', 'N', 'OA' ],
'rbonds':9,
'zero_charge' : [],
}
d['lig1091'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'OA' ],
'rbonds':7,
'zero_charge' : [],
}
d['lig1092'] = {'atom_types': ['A', 'C', 'HD', 'N', 'OA' ],
'rbonds':6,
'zero_charge' : [],
}
d['lig1093'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1094'] = {'atom_types': ['A', 'C', 'OA' ],
'rbonds':4,
'zero_charge' : [],
}
d['lig1095'] = {'atom_types': ['C', 'HD', 'N', 'OA' ],
'rbonds':7,
'zero_charge' : [],
}
d['lig1096'] = {'atom_types': ['C', 'N', 'NA', 'OA' ],
'rbonds':1,
'zero_charge' : [],
}
d['lig1097'] = {'atom_types': ['A', 'C', 'HD', 'N', 'NA', 'SA' ],
'rbonds':2,
'zero_charge' : [],
}
d['lig1098'] = {'atom_types': ['A', 'C', 'N', 'OA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig1099'] = {'atom_types': ['C', 'Cl', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':5,
'zero_charge' : [],
}
d['lig109'] = {'atom_types': ['C', 'HD', 'N', 'NA', 'OA', 'SA' ],
'rbonds':4,
'zero_charge' : [],
}
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06ad3a6129e8566f336a02adf1ac5464938f7bb0
| 3,394
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py
|
Python
|
src/zad3/zad3.py
|
TestowanieAutomatyczneUG/laboratorium-5-maciejSzcz
|
0abf92a008019f50e52b4f53f6c7af77c344e5ce
|
[
"MIT"
] | null | null | null |
src/zad3/zad3.py
|
TestowanieAutomatyczneUG/laboratorium-5-maciejSzcz
|
0abf92a008019f50e52b4f53f6c7af77c344e5ce
|
[
"MIT"
] | null | null | null |
src/zad3/zad3.py
|
TestowanieAutomatyczneUG/laboratorium-5-maciejSzcz
|
0abf92a008019f50e52b4f53f6c7af77c344e5ce
|
[
"MIT"
] | null | null | null |
class Song:
def __init__(self):
self.song = ['On the first day of Christmas my true love gave to me: a Partridge in a Pear Tree.', 'On the second day of Christmas my true love gave to me: two Turtle Doves, and a Partridge in a Pear Tree.', 'On the third day of Christmas my true love gave to me: three French Hens, two Turtle Doves, and a Partridge in a Pear Tree.', 'On the fourth day of Christmas my true love gave to me: four Calling Birds, three French Hens, two Turtle Doves, and a Partridge in a Pear Tree.', 'On the fifth day of Christmas my true love gave to me: five Gold Rings, four Calling Birds, three French Hens, two Turtle Doves, and a Partridge in a Pear Tree.', 'On the sixth day of Christmas my true love gave to me: six Geese-a-Laying, five Gold Rings, four Calling Birds, three French Hens, two Turtle Doves, and a Partridge in a Pear Tree.', 'On the seventh day of Christmas my true love gave to me: seven Swans-a-Swimming, six Geese-a-Laying, five Gold Rings, four Calling Birds, three French Hens, two Turtle Doves, and a Partridge in a Pear Tree.', 'On the eighth day of Christmas my true love gave to me: eight Maids-a-Milking, seven Swans-a-Swimming, six Geese-a-Laying, five Gold Rings, four Calling Birds, three French Hens, two Turtle Doves, and a Partridge in a Pear Tree.', 'On the ninth day of Christmas my true love gave to me: nine Ladies Dancing, eight Maids-a-Milking, seven Swans-a-Swimming, six Geese-a-Laying, five Gold Rings, four Calling Birds, three French Hens, two Turtle Doves, and a Partridge in a Pear Tree.', 'On the tenth day of Christmas my true love gave to me: ten Lords-a-Leaping, nine Ladies Dancing, eight Maids-a-Milking, seven Swans-a-Swimming, six Geese-a-Laying, five Gold Rings, four Calling Birds, three French Hens, two Turtle Doves, and a Partridge in a Pear Tree.', 'On the eleventh day of Christmas my true love gave to me: eleven Pipers Piping, ten Lords-a-Leaping, nine Ladies Dancing, eight Maids-a-Milking, seven Swans-a-Swimming, six Geese-a-Laying, five Gold Rings, four Calling Birds, three French Hens, two Turtle Doves, and a Partridge in a Pear Tree.', 'On the twelfth day of Christmas my true love gave to me: twelve Drummers Drumming, eleven Pipers Piping, ten Lords-a-Leaping, nine Ladies Dancing, eight Maids-a-Milking, seven Swans-a-Swimming, six Geese-a-Laying, five Gold Rings, four Calling Birds, three French Hens, two Turtle Doves, and a Partridge in a Pear Tree.']
def line(self, number):
if type(number) != int:
raise ValueError("Value has to be an integer")
elif number < 0:
raise ValueError("Value can't be negative")
elif number > len(self.song):
raise ValueError("Value can't be bigger than song length")
return self.song[number-1]
def betweenLines(self, start, end):
if type(start) != int or type(end) != int:
raise ValueError("Values have to be an integer")
elif start < 0 or end < 0:
raise ValueError("Value can't be negative")
elif end < start:
raise ValueError("Second value can't be bigger than the first one")
elif start > len(self.song) or end > len(self.song):
raise ValueError("Values can't be bigger than the songs length")
return self.song[start-1:end]
def wholeSong(self):
return self.song
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| 0
| 0
| 0.086957
| 0
| 0.478261
| 0.762003
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.173913
| false
| 0
| 0
| 0.043478
| 0.347826
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
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| 0
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| 1
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| null | 0
| 0
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| 0
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| 0
| 0
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| 0
| 0
|
0
| 9
|
230b928a305491647abb0d8ebde7f786b1e4bf6b
| 4,418
|
py
|
Python
|
tests/unit_tests/test_test_tools.py
|
AppliedMechanics/AMfe
|
be209dffe4d170aca735f1e912fd5cb448502119
|
[
"BSD-3-Clause"
] | 21
|
2017-06-01T15:55:33.000Z
|
2022-03-13T08:43:31.000Z
|
tests/unit_tests/test_test_tools.py
|
AppliedMechanics/AMfeti
|
be209dffe4d170aca735f1e912fd5cb448502119
|
[
"BSD-3-Clause"
] | 1
|
2022-01-08T07:20:15.000Z
|
2022-01-13T23:56:33.000Z
|
tests/unit_tests/test_test_tools.py
|
AppliedMechanics/AMfeti
|
be209dffe4d170aca735f1e912fd5cb448502119
|
[
"BSD-3-Clause"
] | 10
|
2018-01-11T23:48:55.000Z
|
2022-01-12T15:58:54.000Z
|
"""
Test for tools module of test-tools
"""
from unittest import TestCase, expectedFailure
import numpy as np
from ..tools import CustomDictAssertTest
from copy import copy
class CustomAssertMethodsTest(TestCase):
def setUp(self):
self.custom_asserter = CustomDictAssertTest()
def tearDown(self):
pass
def test_assert_dict_almost_equal(self):
dict1 = {'key1': np.array([0.00000000000000000000001, 5.400000000000000000000000001]),
'key2': 5,
(1, 2): 'value'}
dict2 = {'key1': np.array([0.0, 5.4]),
'key2': 5,
(1, 2): 'value'}
self.custom_asserter.assert_dict_almost_equal(dict1, dict2)
dict1 = {'key1': {'key2': {'key3': {'key4': {'key4': {'key5': 5}}}}}}
dict2 = copy(dict1)
self.custom_asserter.assert_dict_almost_equal(dict1, dict2)
dict1 = {'key1': {'key2': {'key3': {'key4': {'key4': {'key5': 5}}}}}}
dict2 = copy(dict1)
self.custom_asserter.assert_dict_almost_equal(dict1, dict2)
@expectedFailure
def test_assert_dict_almost_equal_failure(self):
dict1 = {'key1': np.array([0.00001, 5.40001]),
'key2': 5,
(1, 2): 'value'}
dict2 = {'key1': np.array([0.0, 5.4]),
'key2': 5,
(1, 2): 'value'}
self.custom_asserter.assert_dict_almost_equal(dict1, dict2)
@expectedFailure
def test_assert_dict_almost_equal_limit_failure(self):
dict1 = {'key1': {
'key2': {'key3': {'key4': {'key4': {'key5': {'key6': {'key7': {'key8': {'key9': {'key10': 10}}}}}}}}}}}
dict2 = copy(dict1)
self.custom_asserter.assert_dict_almost_equal(dict1, dict2)
def test_assert_dict_equal(self):
dict1 = {'key1': np.array([0, 5]),
'key2': 5,
(1, 2): 'value'}
dict2 = {'key1': np.array([0, 5]),
'key2': 5,
(1, 2): 'value'}
self.custom_asserter.assert_dict_equal(dict1, dict2)
@expectedFailure
def test_assert_dict_equal_failure(self):
dict1 = {'key1': np.array([0.00000000000000000000001, 5.400000000000000000000000001]),
'key2': 5,
(1, 2): 'value'}
dict2 = {'key1': np.array([0.0, 5.4]),
'key2': 5,
(1, 2): 'value'}
self.custom_asserter.assert_dict_equal(dict1, dict2)
@expectedFailure
def test_assert_dict_equal_limit_failure(self):
dict1 = {'key1': {
'key2': {'key3': {'key4': {'key4': {'key5': {'key6': {'key7': {'key8': {'key9': {'key10': 10}}}}}}}}}}}
dict2 = copy(dict1)
self.custom_asserter.assert_dict_almost_equal(dict1, dict2)
def test_assert_dict_keys_equal(self):
dict1 = {'key1': np.array([0, 5]),
'key2': {'subkey1': 5,
'subkey2': {'subsubkey1': np.array([6.5, 4.2, 2.1])}},
'key3': 6,
(1, 2): 'value'}
dict2 = {'key1': None,
'key2': {'subkey1': None,
'subkey2': {'subsubkey1': None}},
'key3': None,
(1, 2): None}
self.custom_asserter.assert_dict_keys_equal(dict1, dict2)
@expectedFailure
def test_assert_dict_keys_equal_failure12(self):
dict1 = {'key1': np.array([0, 5]),
'key2': {'subkey1': 5,
'subkey2': {'subsubkey1': np.array([6.5, 4.2, 2.1])}},
'key3': 6,
(1, 2): 'value'}
dict2 = {'key1': None,
'key2': {'subkey1': None,
'subkey2': None},
'key3': None,
(1, 2): None}
self.custom_asserter.assert_dict_keys_equal(dict1, dict2)
@expectedFailure
def test_assert_dict_keys_equal_failure21(self):
dict1 = {'key1': np.array([0, 5]),
'key2': {'subkey1': 5,
'subkey2': {'subsubkey1': np.array([6.5, 4.2, 2.1])}},
(1, 2): 'value'}
dict2 = {'key1': None,
'key2': {'subkey1': None,
'subkey2': {'subsubkey1': np.array([6.5, 4.2, 2.1])}},
'key3': None,
(1, 2): None}
self.custom_asserter.assert_dict_keys_equal(dict1, dict2)
| 35.344
| 115
| 0.505432
| 481
| 4,418
| 4.467775
| 0.128898
| 0.093067
| 0.100512
| 0.061424
| 0.87343
| 0.87343
| 0.860866
| 0.858539
| 0.842252
| 0.833411
| 0
| 0.116482
| 0.325713
| 4,418
| 124
| 116
| 35.629032
| 0.604901
| 0.007922
| 0
| 0.77551
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| 0
| 0.104938
| 0
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| 0
| 0
| 0.234694
| 1
| 0.112245
| false
| 0.010204
| 0.040816
| 0
| 0.163265
| 0
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| 0
| 0
| null | 0
| 0
| 0
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| 1
| 1
| 1
| 1
| 1
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| 1
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| null | 0
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| 0
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| 0
|
0
| 7
|
230d2dfd54ad449cc40032f2ef8c3c6382ca9fc0
| 4,740
|
py
|
Python
|
cotiza/migrations/0006_auto_20201209_1245.py
|
crewxart/gestor-cicloverde
|
3a1289649037f9d3420be8a98af4080ebeb7111e
|
[
"CC0-1.0"
] | null | null | null |
cotiza/migrations/0006_auto_20201209_1245.py
|
crewxart/gestor-cicloverde
|
3a1289649037f9d3420be8a98af4080ebeb7111e
|
[
"CC0-1.0"
] | null | null | null |
cotiza/migrations/0006_auto_20201209_1245.py
|
crewxart/gestor-cicloverde
|
3a1289649037f9d3420be8a98af4080ebeb7111e
|
[
"CC0-1.0"
] | null | null | null |
# Generated by Django 3.1.4 on 2020-12-09 15:45
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('cotiza', '0005_trabajadores'),
]
operations = [
migrations.CreateModel(
name='Asesorias',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('tipo_asesoria', models.CharField(max_length=100)),
('monto', models.DecimalField(decimal_places=1, max_digits=6)),
('mes', models.CharField(max_length=50)),
('total', models.DecimalField(decimal_places=1, max_digits=6)),
],
),
migrations.CreateModel(
name='GastosAdministrativos',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('gastos_comunes', models.DecimalField(decimal_places=1, max_digits=8)),
('insumos_aseo', models.DecimalField(decimal_places=1, max_digits=8)),
('insumos_oficina', models.DecimalField(decimal_places=1, max_digits=8)),
('mantencion_banco', models.DecimalField(decimal_places=1, max_digits=8)),
('TI', models.DecimalField(decimal_places=1, max_digits=8)),
('arriendo', models.DecimalField(decimal_places=1, max_digits=8)),
('patentes', models.DecimalField(decimal_places=1, max_digits=8)),
('encomiendas', models.DecimalField(decimal_places=1, max_digits=8)),
('ropa_trabajadores', models.DecimalField(decimal_places=1, max_digits=8)),
('permisos_circulacion', models.DecimalField(decimal_places=1, max_digits=8)),
('generales', models.DecimalField(decimal_places=1, max_digits=8)),
('mes', models.CharField(max_length=50)),
('total', models.DecimalField(decimal_places=1, max_digits=6)),
],
),
migrations.CreateModel(
name='GastosProduccion',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('electricidad', models.DecimalField(decimal_places=1, max_digits=8)),
('gas_grua', models.DecimalField(decimal_places=1, max_digits=8)),
('petroleo', models.DecimalField(decimal_places=1, max_digits=8)),
('cordel', models.DecimalField(decimal_places=1, max_digits=8)),
('insumos_epp', models.DecimalField(decimal_places=1, max_digits=8)),
('mantencion_maquinaria', models.DecimalField(decimal_places=1, max_digits=8)),
('insumos_planta', models.DecimalField(decimal_places=1, max_digits=8)),
('mes', models.CharField(max_length=50)),
('total', models.DecimalField(decimal_places=1, max_digits=6)),
],
),
migrations.CreateModel(
name='GastosTotales',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('mes', models.CharField(max_length=50)),
('sueldos', models.DecimalField(decimal_places=1, max_digits=8)),
('total_asesorias', models.DecimalField(decimal_places=1, max_digits=8)),
('total_administrativos', models.DecimalField(decimal_places=1, max_digits=8)),
('total_produccion', models.DecimalField(decimal_places=1, max_digits=8)),
('costo_operativo_dia', models.DecimalField(decimal_places=1, max_digits=8)),
('costo_operativo_hora', models.DecimalField(decimal_places=1, max_digits=8)),
('costo_operativo_mes', models.DecimalField(decimal_places=1, max_digits=8)),
],
),
migrations.CreateModel(
name='Petroleo',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('mes', models.CharField(max_length=50)),
('valor_actual', models.DecimalField(decimal_places=1, max_digits=8)),
('valor_anterior', models.DecimalField(decimal_places=1, max_digits=8)),
],
),
migrations.AlterField(
model_name='clientes',
name='celular',
field=models.IntegerField(blank=True),
),
migrations.AlterField(
model_name='clientes',
name='telefono',
field=models.IntegerField(blank=True),
),
]
| 52.087912
| 114
| 0.599367
| 483
| 4,740
| 5.658385
| 0.202899
| 0.204171
| 0.283571
| 0.351628
| 0.802415
| 0.778997
| 0.752653
| 0.752653
| 0.56824
| 0.368825
| 0
| 0.026973
| 0.264768
| 4,740
| 90
| 115
| 52.666667
| 0.757245
| 0.009494
| 0
| 0.488095
| 1
| 0
| 0.115917
| 0.013424
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.011905
| 0
| 0.047619
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
236ccea3fcea29c96d706cd58c515c0ac7208eb7
| 206
|
py
|
Python
|
raredecay/analysis/__init__.py
|
jonas-eschle/raredecay
|
6285f91e0819d01c80125f50b24e60ee5353ae2e
|
[
"Apache-2.0"
] | 7
|
2016-11-19T17:28:07.000Z
|
2020-12-29T19:49:37.000Z
|
raredecay/analysis/__init__.py
|
mayou36/raredecay
|
5b319ada66ebe54f81e216efad81fc9f06237a30
|
[
"Apache-2.0"
] | 23
|
2017-03-13T19:13:58.000Z
|
2021-05-30T21:48:50.000Z
|
raredecay/analysis/__init__.py
|
jonas-eschle/raredecay
|
6285f91e0819d01c80125f50b24e60ee5353ae2e
|
[
"Apache-2.0"
] | 5
|
2016-12-17T19:24:13.000Z
|
2021-05-31T14:32:34.000Z
|
"""
DEPRECEATED! USE OTHER MODULES LIKE rd.data, rd.ml, rd.reweight, rd.score and rd.stat
DEPRECEATED!DEPRECEATED!DEPRECEATED!DEPRECEATED!DEPRECEATED!DEPRECEATED!DEPRECEATED!DEPRECEATED!DEPRECEATED!
"""
| 25.75
| 108
| 0.800971
| 25
| 206
| 6.6
| 0.48
| 1.066667
| 1.4
| 1.6
| 0.6
| 0.6
| 0.6
| 0.6
| 0
| 0
| 0
| 0
| 0.082524
| 206
| 7
| 109
| 29.428571
| 0.873016
| 0.951456
| 0
| null | 0
| null | 0
| 0
| null | 0
| 0
| 0
| null | 1
| null | true
| 0
| 0
| null | null | null | 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
88ca03d3cd662212661dd392e848d68bf6d5f692
| 2,733
|
py
|
Python
|
tests/test_cli_for_check_keepass.py
|
eumiro/hibpcli
|
acb7eec3538310dd85c099e8e52561b85c25d6e0
|
[
"MIT"
] | 1
|
2020-10-30T19:03:51.000Z
|
2020-10-30T19:03:51.000Z
|
tests/test_cli_for_check_keepass.py
|
eumiro/hibpcli
|
acb7eec3538310dd85c099e8e52561b85c25d6e0
|
[
"MIT"
] | 3
|
2020-10-30T19:15:13.000Z
|
2020-11-12T22:29:10.000Z
|
tests/test_cli_for_check_keepass.py
|
Smirenost/hibpcli
|
17268d3e6069d5d799e1df4f3dbece9ffa1f62a3
|
[
"MIT"
] | null | null | null |
import textwrap
from unittest.mock import patch
from click.testing import CliRunner
from hibpcli.cli import main
from hibpcli.exceptions import ApiError
from hibpcli.password import Password
@patch("hibpcli.cli.check_passwords_from_db")
def test_keepass_subcommand_returns_leaked_entry(mock_check):
mock_check.return_value = [b'Entry: "test_title (test_user)"']
runner = CliRunner()
result = runner.invoke(
main, ["check-keepass", "tests/test.kdbx"], input="\n".join(["test"])
)
expected_output = textwrap.dedent(
"""\
Please enter the master password for the database:
The passwords of following entries are leaked:
[b'Entry: "test_title (test_user)"']
""" # noqa: W291
)
assert result.output == expected_output
@patch("hibpcli.cli.check_passwords_from_db")
def test_keepass_subcommand_returns_all_ok(mock_check):
mock_check.return_value = list()
runner = CliRunner()
result = runner.invoke(
main, ["check-keepass", "tests/test.kdbx"], input="\n".join(["test"])
)
expected_output = textwrap.dedent(
"""\
Please enter the master password for the database:
Hooray, everything is safe!
""" # noqa: W291
)
assert result.output == expected_output
@patch("hibpcli.cli.check_passwords_from_db")
def test_keepass_subcommand_with_path(mock_check):
mock_check.return_value = [b'Entry: "test_title (test_user)"']
runner = CliRunner()
result = runner.invoke(main, ["check-keepass", "tests/test.kdbx"], input="test")
expected_output = textwrap.dedent(
"""\
Please enter the master password for the database:
The passwords of following entries are leaked:
[b'Entry: "test_title (test_user)"']
""" # noqa: W291
)
assert result.output == expected_output
@patch("hibpcli.cli.check_passwords_from_db")
def test_keepass_subcommand_with_path_and_password_options(mock_check):
mock_check.return_value = [b'Entry: "test_title (test_user)"']
runner = CliRunner()
result = runner.invoke(
main, ["check-keepass", "tests/test.kdbx", "--password", "test"]
)
expected_output = textwrap.dedent(
"""\
The passwords of following entries are leaked:
[b'Entry: "test_title (test_user)"']
"""
)
assert result.output == expected_output
@patch.object(Password, "is_leaked")
def test_keepass_subcommand_error_handling(mock_password):
mock_password.side_effect = ApiError("Error")
runner = CliRunner()
result = runner.invoke(
main, ["check-keepass", "tests/test.kdbx", "--password", "test"]
)
expected_output = "Error\n"
assert result.output == expected_output
| 32.927711
| 84
| 0.680571
| 333
| 2,733
| 5.36036
| 0.21021
| 0.078431
| 0.033613
| 0.05042
| 0.811765
| 0.785994
| 0.74902
| 0.74902
| 0.74902
| 0.74902
| 0
| 0.004098
| 0.196487
| 2,733
| 82
| 85
| 33.329268
| 0.808743
| 0.011709
| 0
| 0.54717
| 0
| 0
| 0.205345
| 0.065635
| 0
| 0
| 0
| 0
| 0.09434
| 1
| 0.09434
| false
| 0.320755
| 0.113208
| 0
| 0.207547
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 7
|
88e85953ba0afc9151a5357aade0401e04c4e5b5
| 12,144
|
py
|
Python
|
code-faster-A/codefaster.py
|
RameshAditya/code-faster
|
59a444640342855103237fafd0f7d7e12256c7e7
|
[
"MIT"
] | 44
|
2018-04-10T17:06:06.000Z
|
2021-08-16T03:48:27.000Z
|
code-faster-A/codefaster.py
|
RameshAditya/code-faster
|
59a444640342855103237fafd0f7d7e12256c7e7
|
[
"MIT"
] | 1
|
2018-05-31T08:51:15.000Z
|
2018-12-22T02:09:09.000Z
|
code-faster-A/codefaster.py
|
RameshAditya/code-faster
|
59a444640342855103237fafd0f7d7e12256c7e7
|
[
"MIT"
] | 7
|
2018-04-15T00:44:43.000Z
|
2020-06-23T22:57:28.000Z
|
import sublime
import sublime_plugin
import time
import random
import urllib
import json
from bs4 import BeautifulSoup
from collections import Counter
import sys
sys.path.insert(0, '/Users/Aditya/AppData/Local/Programs/Python/Python36-32/Lib/site-packages')
from selenium import webdriver
goto_link = ''
# quick fix to remove HTML Tags (if they show up in your questions, feel free to submit a pull request with a fix here!)
def prettify(raw_html):
raw_html = raw_html.replace('<span class="tex-font-style-bf">', '')
raw_html = raw_html.replace("\le","<=")
raw_html = raw_html.replace("\lt","<")
raw_html = raw_html.replace("<", '<')
raw_html = raw_html.replace("\ge",">=")
raw_html = raw_html.replace("\gt",">")
raw_html = raw_html.replace("\\times","x")
raw_html = raw_html.replace("\dots","...")
raw_html = raw_html.replace("~"," ")
raw_html = raw_html.replace("\sqrt","sqrt")
raw_html = raw_html.replace("\sum_","sum")
raw_html = raw_html.replace("<p>","\n")
raw_html = raw_html.replace("<i>","")
raw_html = raw_html.replace("</i>","")
raw_html = raw_html.replace('<div class="sample-tests">', '')
raw_html = raw_html.replace('<span class="tex-font-style-underline">','')
raw_html = raw_html.replace("</p>","")
raw_html = raw_html.replace("<div>","\n")
raw_html = raw_html.replace("</div>","\n")
raw_html = raw_html.replace("</span>","")
raw_html = raw_html.replace('<span class="tex-span">',"")
raw_html = raw_html.replace('<div class="input-specification">', '')
raw_html = raw_html.replace('<div class="section-title">', '\n')
raw_html = raw_html.replace('<div class="problem-statement">', '')
raw_html = raw_html.replace('<div class="header">','')
raw_html = raw_html.replace('<div class="title">', '\n')
raw_html = raw_html.replace('<div class="time-limit">','')
raw_html = raw_html.replace('<div class="property-title">', '\n')
raw_html = raw_html.replace('<div class="memory-limit">','')
raw_html = raw_html.replace('<div class="property-title">', '\n')
raw_html = raw_html.replace('<div class="input-file">','')
raw_html = raw_html.replace('<div class="property-title">', '\n')
raw_html = raw_html.replace('<div class="output-file">','')
raw_html = raw_html.replace('<div class="property-title">', '\n')
raw_html = raw_html.replace('<div class="output-specification">','')
raw_html = raw_html.replace('<div class="section-title">', '\n')
raw_html = raw_html.replace('<sup class="upper-index">', '^')
raw_html = raw_html.replace('<span class="tex-font-style-tt">','')
raw_html = raw_html.replace('</sup>','')
raw_html = raw_html.replace('</sub>','')
raw_html = raw_html.replace('<br/>','\n')
raw_html = raw_html.replace('<pre>','')
raw_html = raw_html.replace('<ol>','\n')
raw_html = raw_html.replace('<ul>','\n')
raw_html = raw_html.replace('</ol>','\n')
raw_html = raw_html.replace('</ul>','\n')
raw_html = raw_html.replace('<li>','\t')
raw_html = raw_html.replace('</li>','\n')
raw_html = raw_html.replace('</pre>' ,'')
raw_html = raw_html.replace('<div class="sample-test">','')
raw_html = raw_html.replace('<div class="input">','')
raw_html = raw_html.replace('<div class="title">', '')
raw_html = raw_html.replace('<div class="note">','')
raw_html = raw_html.replace('<div class="output">', '')
raw_html = raw_html.replace('<div class="section-title">', '')
raw_html = raw_html.replace('<sub class="lower-index">' ,'')
if '\n\n' in raw_html or '\n \n ':
raw_html = raw_html.replace('\n\n','\n')
raw_html = raw_html.replace('\n \n ','\n')
return raw_html
class codefasteraCommand(sublime_plugin.TextCommand):
def run(self, edit):
global goto_link
contestid = urllib.request.urlopen("http://codeforces.com/api/problemset.problems").read()
data = json.loads(contestid.decode('utf-8'))
value = [data['result']['problems'][j]['contestId'] for j in range(len(data['result']['problems']))]
value = value[random.randint(0,len(value)-1)]
# value = max(value) # comment the above line and un-comment this line to retrieve the most recent contest's questions
goto_link = "http://codeforces.com/problemset/problem/" + str(value) + "/A"
route = 'Going to: ' + goto_link
print(route)
body = urllib.request.urlopen(goto_link).read()
soup = BeautifulSoup(body)
content = soup.find_all("div", class_ = "problem-statement")
if len(content)==0:
print('NOT REGISTERED')
return 1
ans = ''
i=0
content[0] = str(content[0]).replace("$","")
content[0] = str(content[0])
# Adding image support
images = []
ind = 0
for i in range(len(content[0])):
if content[0][i:i+len('src')] == 'src':
print('FOUND AT INDEX i', i)
ind = content[0][i:].index('"/')
print('IMAGE START AT', ind+i)
future = content[0][i+ind:].index('" ')
images.append(content[0][i+ind+1:i+ind+future])
ind = future+1
i = future+1
images = ['http://www.codeforces.com' + i for i in images]
if len(images)>0:
driver = webdriver.Firefox(executable_path=r'C:\Users\Aditya\Desktop\IIIT-H\code-faster\geckodriver-v0.20.1-win64\geckodriver.exe')
driver.get(images[0])
content[0] = prettify(content[0])
print(content[0])
ans = content[0]
self.view.insert(edit, 0, "\n\n'''" + '\n' + route + '\n' + ans + "'''")
class codefasterbCommand(sublime_plugin.TextCommand):
def run(self, edit):
global goto_link
contestid = urllib.request.urlopen("http://codeforces.com/api/problemset.problems").read()
data = json.loads(contestid.decode('utf-8'))
value = [data['result']['problems'][j]['contestId'] for j in range(len(data['result']['problems']))]
value = value[random.randint(0,len(value)-1)]
# value = max(value) # comment the above line and un-comment this line to retrieve the most recent contest's questions
goto_link = "http://codeforces.com/problemset/problem/" + str(value) + "/B"
route = 'Going to: ' + goto_link
print(route)
body = urllib.request.urlopen(goto_link).read()
soup = BeautifulSoup(body)
content = soup.find_all("div", class_ = "problem-statement")
if len(content)==0:
print('NOT REGISTERED')
return 1
ans = ''
i=0
content[0] = str(content[0]).replace("$","")
content[0] = str(content[0])
# Adding image support
images = []
ind = 0
for i in range(len(content[0])):
if content[0][i:i+len('src')] == 'src':
print('FOUND AT INDEX i', i)
ind = content[0][i:].index('"/')
print('IMAGE START AT', ind+i)
future = content[0][i+ind:].index('" ')
images.append(content[0][i+ind+1:i+ind+future])
ind = future+1
i = future+1
images = ['http://www.codeforces.com' + i for i in images]
if len(images)>0:
driver = webdriver.Firefox(executable_path=r'C:\Users\Aditya\Desktop\IIIT-H\code-faster\geckodriver-v0.20.1-win64\geckodriver.exe')
driver.get(images[0])
content[0] = prettify(content[0])
print(content[0])
ans = content[0]
self.view.insert(edit, 0, "\n\n'''" + '\n' + route + '\n' + ans + "'''")
class codefastercCommand(sublime_plugin.TextCommand):
def run(self, edit):
global goto_link
contestid = urllib.request.urlopen("http://codeforces.com/api/problemset.problems").read()
data = json.loads(contestid.decode('utf-8'))
value = [data['result']['problems'][j]['contestId'] for j in range(len(data['result']['problems']))]
value = value[random.randint(0,len(value)-1)]
# value = max(value) # comment the above line and un-comment this line to retrieve the most recent contest's questions
goto_link = "http://codeforces.com/problemset/problem/" + str(value) + "/C"
route = 'Going to: ' + goto_link
print(route)
body = urllib.request.urlopen(goto_link).read()
soup = BeautifulSoup(body)
content = soup.find_all("div", class_ = "problem-statement")
if len(content)==0:
print('NOT REGISTERED')
return 1
ans = ''
i=0
content[0] = str(content[0]).replace("$","")
content[0] = str(content[0])
# Adding image support
images = []
ind = 0
for i in range(len(content[0])):
if content[0][i:i+len('src')] == 'src':
print('FOUND AT INDEX i', i)
ind = content[0][i:].index('"/')
print('IMAGE START AT', ind+i)
future = content[0][i+ind:].index('" ')
images.append(content[0][i+ind+1:i+ind+future])
ind = future+1
i = future+1
images = ['http://www.codeforces.com' + i for i in images]
if len(images)>0:
driver = webdriver.Firefox(executable_path=r'C:\Users\Aditya\Desktop\IIIT-H\code-faster\geckodriver-v0.20.1-win64\geckodriver.exe')
driver.get(images[0])
content[0] = prettify(content[0])
print(content[0])
ans = content[0]
self.view.insert(edit, 0, "\n\n'''" + '\n' + route + '\n' + ans + "'''")
class codefasterdCommand(sublime_plugin.TextCommand):
def run(self, edit):
global goto_link
contestid = urllib.request.urlopen("http://codeforces.com/api/problemset.problems").read()
data = json.loads(contestid.decode('utf-8'))
value = [data['result']['problems'][j]['contestId'] for j in range(len(data['result']['problems']))]
value = value[random.randint(0,len(value)-1)]
# value = max(value) # comment the above line and un-comment this line to retrieve the most recent contest's questions
goto_link = "http://codeforces.com/problemset/problem/" + str(value) + "/D"
route = 'Going to: ' + goto_link
print(route)
body = urllib.request.urlopen(goto_link).read()
soup = BeautifulSoup(body)
content = soup.find_all("div", class_ = "problem-statement")
if len(content)==0:
print('NOT REGISTERED')
return 1
ans = ''
i=0
content[0] = str(content[0]).replace("$","")
content[0] = str(content[0])
# Adding image support
images = []
ind = 0
for i in range(len(content[0])):
if content[0][i:i+len('src')] == 'src':
print('FOUND AT INDEX i', i)
ind = content[0][i:].index('"/')
print('IMAGE START AT', ind+i)
future = content[0][i+ind:].index('" ')
images.append(content[0][i+ind+1:i+ind+future])
ind = future+1
i = future+1
images = ['http://www.codeforces.com' + i for i in images]
if len(images)>0:
driver = webdriver.Firefox(executable_path=r'C:\Users\Aditya\Desktop\IIIT-H\code-faster\geckodriver-v0.20.1-win64\geckodriver.exe')
driver.get(images[0])
content[0] = prettify(content[0])
print(content[0])
ans = content[0]
self.view.insert(edit, 0, "\n\n'''" + '\n' + route + '\n' + ans + "'''")
class codefastereCommand(sublime_plugin.TextCommand):
def run(self, edit):
global goto_link
contestid = urllib.request.urlopen("http://codeforces.com/api/problemset.problems").read()
data = json.loads(contestid.decode('utf-8'))
value = [data['result']['problems'][j]['contestId'] for j in range(len(data['result']['problems']))]
value = value[random.randint(0,len(value)-1)]
# value = max(value) # comment the above line and un-comment this line to retrieve the most recent contest's questions
goto_link = "http://codeforces.com/problemset/problem/" + str(value) + "/E"
route = 'Going to: ' + goto_link
print(route)
body = urllib.request.urlopen(goto_link).read()
soup = BeautifulSoup(body)
content = soup.find_all("div", class_ = "problem-statement")
if len(content)==0:
print('NOT REGISTERED')
return 1
ans = ''
i=0
content[0] = str(content[0]).replace("$","")
content[0] = str(content[0])
# Adding image support
images = []
ind = 0
for i in range(len(content[0])):
if content[0][i:i+len('src')] == 'src':
print('FOUND AT INDEX i', i)
ind = content[0][i:].index('"/')
print('IMAGE START AT', ind+i)
future = content[0][i+ind:].index('" ')
images.append(content[0][i+ind+1:i+ind+future])
ind = future+1
i = future+1
images = ['http://www.codeforces.com' + i for i in images]
if len(images)>0:
driver = webdriver.Firefox(executable_path=r'C:\Users\Aditya\Desktop\IIIT-H\code-faster\geckodriver-v0.20.1-win64\geckodriver.exe')
driver.get(images[0])
content[0] = prettify(content[0])
print(content[0])
ans = content[0]
self.view.insert(edit, 0, "\n\n'''" + '\n' + route + '\n' + ans + "'''")
| 33.546961
| 134
| 0.647398
| 1,794
| 12,144
| 4.292085
| 0.108696
| 0.108182
| 0.076623
| 0.107273
| 0.921039
| 0.899221
| 0.879351
| 0.871818
| 0.841688
| 0.830779
| 0
| 0.016081
| 0.149951
| 12,144
| 361
| 135
| 33.639889
| 0.729827
| 0.066535
| 0
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| 0
| 0.022388
| 0.238449
| 0.076067
| 0
| 0
| 0
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| 0
| 1
| 0.022388
| false
| 0
| 0.037313
| 0
| 0.100746
| 0.093284
| 0
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| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
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| 1
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| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
aededbf8e4e01874a7f2af56e1050b63c52375c9
| 101
|
py
|
Python
|
data_utils/logCompressor.py
|
rajat95/Deep-Deghosting-HDR
|
d56c213912083df0323f26e594ccc5ed7fa668ec
|
[
"MIT"
] | 33
|
2019-06-13T09:25:57.000Z
|
2022-02-08T09:26:28.000Z
|
data_utils/logCompressor.py
|
banananabear/Deep-Deghosting-HDR
|
d56c213912083df0323f26e594ccc5ed7fa668ec
|
[
"MIT"
] | 11
|
2019-11-01T08:31:24.000Z
|
2021-12-23T03:05:09.000Z
|
data_utils/logCompressor.py
|
banananabear/Deep-Deghosting-HDR
|
d56c213912083df0323f26e594ccc5ed7fa668ec
|
[
"MIT"
] | 10
|
2019-07-04T06:39:04.000Z
|
2022-03-29T03:19:22.000Z
|
import tensorflow as tf
def log_compressor(im, MU = 5000.):
return tf.log(1+MU*im)/tf.log(1+MU)
| 20.2
| 39
| 0.683168
| 20
| 101
| 3.4
| 0.6
| 0.147059
| 0.176471
| 0.235294
| 0
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| 0
| 0
| 0
| 0
| 0
| 0.070588
| 0.158416
| 101
| 5
| 39
| 20.2
| 0.729412
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| 0.333333
| false
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| 0.333333
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| 0
| 1
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| 1
| 1
| 1
| 0
|
0
| 7
|
4e073c6c60e0f98888aa689733f296e170e4ce60
| 5,934
|
py
|
Python
|
hs_core/tests/api/rest/test_irods_tickets.py
|
tommac7/hydroshare
|
87c4543a55f98103d2614bf4c47f7904c3f9c029
|
[
"BSD-3-Clause"
] | null | null | null |
hs_core/tests/api/rest/test_irods_tickets.py
|
tommac7/hydroshare
|
87c4543a55f98103d2614bf4c47f7904c3f9c029
|
[
"BSD-3-Clause"
] | null | null | null |
hs_core/tests/api/rest/test_irods_tickets.py
|
tommac7/hydroshare
|
87c4543a55f98103d2614bf4c47f7904c3f9c029
|
[
"BSD-3-Clause"
] | null | null | null |
import json
from rest_framework import status
from .base import HSRESTTestCase
class TestTickets(HSRESTTestCase):
def test_folder_ticket(self):
rtype = 'GenericResource'
title = 'My Test resource'
params = {'resource_type': rtype,
'title': title,
'file': ('cea.tif',
open('hs_core/tests/data/cea.tif'),
'image/tiff')}
url = '/hsapi/resource/'
response = self.client.post(url, params)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
content = json.loads(response.content)
res_id = content['resource_id']
self.resources_to_delete.append(res_id)
# should be able to get read ticket for base folder
url2 = str.format('/hsapi/resource/{}/ticket/read/data/contents/', res_id)
response = self.client.get(url2, {})
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
content = json.loads(response.content)
ticket_id = content['ticket_id']
# should be able to list ticket
url3 = str.format('/hsapi/resource/{}/ticket/{}/', res_id, ticket_id)
response = self.client.get(url3, {})
self.assertEqual(response.status_code, status.HTTP_200_OK)
content = json.loads(response.content)
self.assertEqual(content['ticket_id'], ticket_id)
# should be able to delete ticket
response = self.client.delete(url3, {})
self.assertEqual(response.status_code, status.HTTP_200_OK)
content = json.loads(response.content)
self.assertEqual(content['ticket_id'], ticket_id)
# should not be able to delete a ticket twice
response = self.client.delete(url3, {})
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
# should not be able to list a deleted ticket
response = self.client.get(url3, {})
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
def test_file_ticket(self):
rtype = 'GenericResource'
title = 'My Test resource'
params = {'resource_type': rtype,
'title': title,
'file': ('cea.tif',
open('hs_core/tests/data/cea.tif'),
'image/tiff')}
# should be able to create resource
url = '/hsapi/resource/'
response = self.client.post(url, params)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
content = json.loads(response.content)
res_id = content['resource_id']
self.resources_to_delete.append(res_id)
# should be able to get read ticket for file
url2 = str.format('/hsapi/resource/{}/ticket/read/data/contents/cea/cea.tif/', res_id)
response = self.client.get(url2, {})
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
content = json.loads(response.content)
ticket_id = content['ticket_id']
# should be able to list ticket
url3 = str.format('/hsapi/resource/{}/ticket/{}/', res_id, ticket_id)
response = self.client.get(url3, {})
self.assertEqual(response.status_code, status.HTTP_200_OK)
content = json.loads(response.content)
self.assertEqual(content['ticket_id'], ticket_id)
# should be able to delete ticket
response = self.client.delete(url3, {})
self.assertEqual(response.status_code, status.HTTP_200_OK)
content = json.loads(response.content)
self.assertEqual(content['ticket_id'], ticket_id)
# should not be able to delete a ticket twice
response = self.client.delete(url3, {})
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
# should not be able to list a deleted ticket
response = self.client.get(url3, {})
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
def test_bag_ticket(self):
rtype = 'GenericResource'
title = 'My Test resource'
params = {'resource_type': rtype,
'title': title,
'file': ('cea.tif',
open('hs_core/tests/data/cea.tif'),
'image/tiff')}
# should be able to create resource
url = '/hsapi/resource/'
response = self.client.post(url, params)
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
content = json.loads(response.content)
res_id = content['resource_id']
self.resources_to_delete.append(res_id)
# should be able to get read ticket for bag: no write option
url2 = str.format('/hsapi/resource/{}/ticket/bag/', res_id)
response = self.client.get(url2, {})
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
content = json.loads(response.content)
ticket_id = content['ticket_id']
# should be able to list ticket
url3 = str.format('/hsapi/resource/{}/ticket/{}/', res_id, ticket_id)
response = self.client.get(url3, {})
self.assertEqual(response.status_code, status.HTTP_200_OK)
content = json.loads(response.content)
self.assertEqual(content['ticket_id'], ticket_id)
# should be able to delete ticket
response = self.client.delete(url3, {})
self.assertEqual(response.status_code, status.HTTP_200_OK)
content = json.loads(response.content)
self.assertEqual(content['ticket_id'], ticket_id)
# should not be able to delete a ticket twice
response = self.client.delete(url3, {})
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
# should not be able to list a deleted ticket
response = self.client.get(url3, {})
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
| 42.085106
| 94
| 0.631109
| 721
| 5,934
| 5.034674
| 0.104022
| 0.099174
| 0.089256
| 0.143802
| 0.956198
| 0.956198
| 0.947383
| 0.947383
| 0.947383
| 0.920937
| 0
| 0.017007
| 0.256825
| 5,934
| 140
| 95
| 42.385714
| 0.806122
| 0.11274
| 0
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| 0
| 0.127501
| 0.056604
| 0
| 0
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| 0
| 0.24
| 1
| 0.03
| false
| 0
| 0.03
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| 0.07
| 0
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| 0
| null | 0
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| 1
| 1
| 1
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| null | 0
| 0
| 0
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| 0
| 0
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| 0
| 0
| 0
|
0
| 7
|
9d6f950375603757e23c7a436b33829b9e8eb56d
| 34,723
|
py
|
Python
|
tests/dhcpv4/kea_only/flexid/test_flex_id.py
|
isc-projects/forge
|
dfec8b41003d6b5a229f69ee93616e0e5cc6d71b
|
[
"0BSD"
] | 22
|
2015-02-27T11:51:05.000Z
|
2022-02-28T12:39:29.000Z
|
tests/dhcpv4/kea_only/flexid/test_flex_id.py
|
isc-projects/forge
|
dfec8b41003d6b5a229f69ee93616e0e5cc6d71b
|
[
"0BSD"
] | 16
|
2018-10-30T15:00:12.000Z
|
2019-01-11T17:55:13.000Z
|
tests/dhcpv4/kea_only/flexid/test_flex_id.py
|
isc-projects/forge
|
dfec8b41003d6b5a229f69ee93616e0e5cc6d71b
|
[
"0BSD"
] | 11
|
2015-02-27T11:51:36.000Z
|
2021-03-30T08:33:54.000Z
|
"""Kea Hook flex-id testing"""
# pylint: disable=invalid-name,line-too-long
import pytest
import misc
import srv_msg
import srv_control
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_libreload():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.50')
srv_control.host_reservation_in_subnet('hostname',
'reserved-hostname',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.host_reservation_in_subnet_add_value(0, 0, 'ip-address', '192.168.50.10')
srv_control.add_line({"host-reservation-identifiers": ["flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.open_control_channel()
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.send_ctrl_cmd_via_socket('{"command": "libreload","arguments": {}}')
# if reload works - classification should work without changes
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_reconfigure():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.50')
srv_control.host_reservation_in_subnet('hostname',
'reserved-hostname',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.host_reservation_in_subnet_add_value(0, 0, 'ip-address', '192.168.50.10')
srv_control.add_line({"host-reservation-identifiers": ["flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.open_control_channel()
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.50')
srv_control.host_reservation_in_subnet('hostname',
'reserved-hostname',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.host_reservation_in_subnet_add_value(0, 0, 'ip-address', '192.168.50.10')
srv_control.add_line({"host-reservation-identifiers": ["flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.open_control_channel()
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'reconfigured')
srv_msg.wait_for_message_count_in_log(1, 'DHCP4_DYNAMIC_RECONFIGURATION')
srv_msg.wait_for_message_count_in_log(2, 'DHCP4_CONFIG_COMPLETE')
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_inside_pool():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.50')
srv_control.host_reservation_in_subnet('hostname',
'reserved-hostname',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.host_reservation_in_subnet_add_value(0, 0, 'ip-address', '192.168.50.10')
srv_control.add_line({"host-reservation-identifiers": ["flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
# Client adds to the message vendor_class_id with value docsis3.0.
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ACK')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.response_check_include_option(1)
srv_msg.response_check_option_content(1, 'value', '255.255.255.0')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_inside_pool_negative():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.50')
srv_control.host_reservation_in_subnet('hostname',
'reserved-hostname',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.host_reservation_in_subnet_add_value(0, 0, 'ip-address', '192.168.50.10')
srv_control.add_line({"host-reservation-identifiers": ["flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
# Client adds to the message vendor_class_id with value docsis3.0.
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.1')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'NAK')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_outside_pool():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.9')
srv_control.host_reservation_in_subnet('hostname',
'reserved-hostname',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.host_reservation_in_subnet_add_value(0, 0, 'ip-address', '192.168.50.10')
srv_control.add_line({"host-reservation-identifiers": ["flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ACK')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.response_check_include_option(1)
srv_msg.response_check_option_content(1, 'value', '255.255.255.0')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_replace_mac_addr_inside_pool():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.50')
srv_control.host_reservation_in_subnet('ip-address',
'192.168.50.10',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.add_line({"host-reservation-identifiers": ["hw-address", "flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.add_parameter_to_hook(1, 'replace-client-id', True)
srv_control.set_conf_parameter_global('match-client-id', False)
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
# server should act normally, mac address should not be replaced
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ACK')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.response_check_include_option(1)
srv_msg.response_check_option_content(1, 'value', '255.255.255.0')
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_copy_option('server_id')
srv_msg.client_sets_value('Client', 'ciaddr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('RELEASE')
misc.pass_criteria()
srv_msg.send_dont_wait_for_message()
srv_msg.lease_file_contains('192.168.50.10,ff:01:02:03:ff:04,,4000')
srv_msg.lease_file_contains('192.168.50.10,ff:01:02:03:ff:04,,0')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_replace_client_id_release_fail():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.5')
srv_control.host_reservation_in_subnet('ip-address',
'192.168.50.10',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.add_line({"host-reservation-identifiers": ["hw-address", "flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.add_parameter_to_hook(1, 'replace-client-id', True)
# enable matching client id
srv_control.set_conf_parameter_global('match-client-id', True)
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
# server should act normally, mac address should not be replaced
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
# Client adds to the message vendor_class_id with value docsis3.0.
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'NAK')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_replace_client_id_release_1():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.5')
srv_control.host_reservation_in_subnet('ip-address',
'192.168.50.10',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.add_line({"host-reservation-identifiers": ["hw-address", "flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.add_parameter_to_hook(1, 'replace-client-id', True)
# enable matching client id
srv_control.set_conf_parameter_global('match-client-id', True)
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
# server should act normally, mac address should not be replaced
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ACK')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.response_check_include_option(1)
srv_msg.response_check_option_content(1, 'value', '255.255.255.0')
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22:33')
srv_msg.client_copy_option('server_id')
srv_msg.client_sets_value('Client', 'ciaddr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
# client sends message without option 60
# Client adds to the message vendor_class_id with value docsis3.0.
srv_msg.client_send_msg('RELEASE')
misc.pass_criteria()
srv_msg.send_dont_wait_for_message()
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22:33:44:55')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.lease_file_doesnt_contain('ff:01:02:03:ff:04:11:22:33')
srv_msg.lease_file_contains('192.168.50.10,ff:01:02:03:ff:04,00:64:6f:63:73:69:73:33:2e:30,4000')
srv_msg.lease_file_doesnt_contain('192.168.50.10,ff:01:02:03:ff:04,00:64:6f:63:73:69:73:33:2e:30,0')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_replace_client_id_release_2():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.5')
srv_control.host_reservation_in_subnet('ip-address',
'192.168.50.10',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.add_line({"host-reservation-identifiers": ["hw-address", "flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.add_parameter_to_hook(1, 'replace-client-id', True)
# enable matching client id
srv_control.set_conf_parameter_global('match-client-id', True)
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
# server should act normally, mac address should not be replaced
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ACK')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.response_check_include_option(1)
srv_msg.response_check_option_content(1, 'value', '255.255.255.0')
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22:33')
srv_msg.client_copy_option('server_id')
srv_msg.client_sets_value('Client', 'ciaddr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_send_msg('RELEASE')
misc.pass_criteria()
srv_msg.send_dont_wait_for_message()
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22:33:44:55')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.lease_file_doesnt_contain('ff:01:02:03:ff:04:11:22:33')
srv_msg.lease_file_contains('192.168.50.10,ff:01:02:03:ff:04,00:64:6f:63:73:69:73:33:2e:30,4000')
srv_msg.lease_file_contains('192.168.50.10,ff:01:02:03:ff:04,00:64:6f:63:73:69:73:33:2e:30,0')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_replace_client_id_renew_1():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.5')
srv_control.host_reservation_in_subnet('ip-address',
'192.168.50.10',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.add_line({"host-reservation-identifiers": ["hw-address", "flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.add_parameter_to_hook(1, 'replace-client-id', True)
# enable matching client id
srv_control.set_conf_parameter_global('match-client-id', True)
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
# server should act normally, mac address should not be replaced
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ACK')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.response_check_include_option(1)
srv_msg.response_check_option_content(1, 'value', '255.255.255.0')
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22:33:44:55')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.response_check_include_option(61)
srv_msg.response_check_include_option(54)
srv_msg.lease_file_doesnt_contain('ff:01:02:03:ff:04:11:22:33')
srv_msg.lease_file_contains('192.168.50.10,ff:01:02:03:ff:04,00:64:6f:63:73:69:73:33:2e:30,4000')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_replace_client_id_renew_2():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.5')
srv_control.host_reservation_in_subnet('ip-address',
'192.168.50.10',
0,
'flex-id',
'\'docsis3.0\'')
srv_control.add_line({"host-reservation-identifiers": ["hw-address", "flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.add_parameter_to_hook(1, 'replace-client-id', True)
# enable matching client id
srv_control.set_conf_parameter_global('match-client-id', True)
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
# server should act normally, mac address should not be replaced
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ACK')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.response_check_include_option(1)
srv_msg.response_check_option_content(1, 'value', '255.255.255.0')
misc.test_procedure()
srv_msg.client_does_include_with_value('client_id', 'ff:01:02:03:ff:04:11:22')
# Client adds to the message vendor_class_id with value docsis3.0.
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10', expected=False)
srv_msg.response_check_include_option(61)
srv_msg.response_check_include_option(54)
srv_msg.lease_file_doesnt_contain('ff:01:02:03:ff:04:11:22:33')
srv_msg.lease_file_contains('192.168.50.10,ff:01:02:03:ff:04,00:64:6f:63:73:69:73:33:2e:30,4000')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_mysql_1():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.5')
srv_control.add_line({"host-reservation-identifiers": ["hw-address", "flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.add_parameter_to_hook(1, 'replace-client-id', True)
# enable matching client id
srv_control.set_conf_parameter_global('match-client-id', True)
srv_control.enable_db_backend_reservation('MySQL')
# 646f63736973332e30 = docsis3.0
srv_control.new_db_backend_reservation('MySQL', 'flex-id', '646f63736973332e30')
srv_control.update_db_backend_reservation('hostname', 'reserved-hostname', 'MySQL', 1)
srv_control.update_db_backend_reservation('ipv4_address', '192.168.50.10', 'MySQL', 1)
srv_control.update_db_backend_reservation('dhcp4_subnet_id', 1, 'MySQL', 1)
srv_control.upload_db_reservation('MySQL')
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
# Pause the Test.
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ACK')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.response_check_include_option(1)
srv_msg.response_check_option_content(1, 'value', '255.255.255.0')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_mysql_negative():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.5')
srv_control.add_line({"host-reservation-identifiers": ["hw-address", "flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.add_parameter_to_hook(1, 'replace-client-id', True)
# enable matching client id
srv_control.set_conf_parameter_global('match-client-id', True)
srv_control.enable_db_backend_reservation('MySQL')
# 646f63736973332e30 = docsis3.0
srv_control.new_db_backend_reservation('MySQL', 'flex-id', '646f63736973332e30')
srv_control.update_db_backend_reservation('hostname', 'reserved-hostname', 'MySQL', 1)
srv_control.update_db_backend_reservation('ipv4_address', '192.168.50.10', 'MySQL', 1)
srv_control.update_db_backend_reservation('dhcp4_subnet_id', 1, 'MySQL', 1)
srv_control.upload_db_reservation('MySQL')
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'NAK')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_pgsql_1():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.5')
srv_control.add_line({"host-reservation-identifiers": ["hw-address", "flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.add_parameter_to_hook(1, 'replace-client-id', True)
# enable matching client id
srv_control.set_conf_parameter_global('match-client-id', True)
srv_control.enable_db_backend_reservation('PostgreSQL')
srv_control.new_db_backend_reservation('PostgreSQL', 'flex-id', '646f63736973332e30')
srv_control.update_db_backend_reservation('hostname', 'reserved-hostname', 'PostgreSQL', 1)
srv_control.update_db_backend_reservation('ipv4_address', '192.168.50.10', 'PostgreSQL', 1)
srv_control.update_db_backend_reservation('dhcp4_subnet_id', 1, 'PostgreSQL', 1)
srv_control.upload_db_reservation('PostgreSQL')
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
# Pause the Test.
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'ACK')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
srv_msg.response_check_include_option(1)
srv_msg.response_check_option_content(1, 'value', '255.255.255.0')
@pytest.mark.v4
@pytest.mark.flexid
@pytest.mark.kea_only
def test_v4_hooks_flexid_pgsql_negative():
misc.test_setup()
srv_control.config_srv_subnet('192.168.50.0/24', '192.168.50.1-192.168.50.5')
srv_control.add_line({"host-reservation-identifiers": ["hw-address", "flex-id"]})
srv_control.add_hooks('libdhcp_flex_id.so')
srv_control.add_parameter_to_hook(1, 'identifier-expression', 'option[60].hex')
srv_control.add_parameter_to_hook(1, 'replace-client-id', True)
# enable matching client id
srv_control.set_conf_parameter_global('match-client-id', True)
srv_control.enable_db_backend_reservation('PostgreSQL')
srv_control.new_db_backend_reservation('PostgreSQL', 'flex-id', '646f63736973332e30')
srv_control.update_db_backend_reservation('hostname', 'reserved-hostname', 'PostgreSQL', 1)
srv_control.update_db_backend_reservation('ipv4_address', '192.168.50.10', 'PostgreSQL', 1)
srv_control.update_db_backend_reservation('dhcp4_subnet_id', 1, 'PostgreSQL', 1)
srv_control.upload_db_reservation('PostgreSQL')
srv_control.build_and_send_config_files()
srv_control.start_srv('DHCP', 'started')
# Pause the Test.
misc.test_procedure()
srv_msg.client_does_include_with_value('vendor_class_id', 'docsis3.0')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('DISCOVER')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'OFFER')
srv_msg.response_check_content('yiaddr', '192.168.50.10')
misc.test_procedure()
srv_msg.client_copy_option('server_id')
srv_msg.client_does_include_with_value('requested_addr', '192.168.50.10')
srv_msg.client_sets_value('Client', 'chaddr', 'ff:01:02:03:ff:04')
srv_msg.client_send_msg('REQUEST')
misc.pass_criteria()
srv_msg.send_wait_for_message('MUST', 'NAK')
| 44.919793
| 104
| 0.696052
| 5,237
| 34,723
| 4.254153
| 0.035326
| 0.069752
| 0.082948
| 0.03142
| 0.98447
| 0.98447
| 0.983886
| 0.983886
| 0.981193
| 0.979173
| 0
| 0.082126
| 0.160844
| 34,723
| 772
| 105
| 44.977979
| 0.682476
| 0.034991
| 0
| 0.950081
| 0
| 0.017713
| 0.257506
| 0.066617
| 0
| 0
| 0
| 0
| 0
| 1
| 0.024155
| true
| 0.059581
| 0.006441
| 0
| 0.030596
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
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| null | 0
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| 0
|
0
| 9
|
9dffffe3a51a7de1b1a5f9d2cefcf423be06937a
| 16,871
|
py
|
Python
|
app.py
|
nix18/UniNetdisk
|
95eddee981b29cc872b9cfa330559c8727961e7c
|
[
"Apache-2.0"
] | 4
|
2021-01-21T01:44:54.000Z
|
2021-12-12T04:44:44.000Z
|
app.py
|
nix18/UniNetdisk
|
95eddee981b29cc872b9cfa330559c8727961e7c
|
[
"Apache-2.0"
] | null | null | null |
app.py
|
nix18/UniNetdisk
|
95eddee981b29cc872b9cfa330559c8727961e7c
|
[
"Apache-2.0"
] | 1
|
2021-01-21T01:44:47.000Z
|
2021-01-21T01:44:47.000Z
|
import shutil
from functools import wraps
from flask import Flask, render_template, make_response, send_from_directory, request, url_for, redirect, session
import os
app = Flask(__name__)
app.config['SECRET_KEY'] = os.urandom(24)
UPLOADED_FILE_DEST=os.getcwd()+"\\file"
subdest="file"
setusername='admin'
setuserpassword='admin'
def is_login(func):
@wraps(func)
def check_login(*args, **kwargs):
if 'user_id' in session:
return func(*args, **kwargs)
else:
return redirect(url_for('new_login'))
return check_login
@app.route('/new_login/', methods=['GET', 'POST'])
def new_login():
if request.method == 'GET':
return render_template('login.html')
else:
username = request.form.get('username')
password = request.form.get('password')
# 数据库校验，用户密码是否正确
if username == setusername and password == setuserpassword:
session['user_id'] = 1
session.permanent = True
return redirect((url_for('mainpage')))
else:
return render_template('login.html',loginfail=1)
@app.route('/logout')
def logout():
print (session.get('username'))
# 清除session中所有数据
session.clear()
print (session.get('username'))
return '''
<div align='center'>
<h1>登出成功</h1>
<a href='/'>返回首页</a>
</div>
'''
@app.route('/mainpage',methods=['POST', 'GET'])
@app.route('/',methods=['POST', 'GET'])
@is_login
def mainpage():
if request.method=='GET':
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict=dict(sorted(file_dict.items(),key=lambda item:item[1],reverse=True))
file_list=list(file_dict.keys())
return render_template('index.html',filedict=file_dict,file=file_list,isdel=0)
if request.method == 'POST':
f = request.files['file']
if not f.filename:
return '''
<div align='center'>
<h1>上传文件不能为空</h1>
<a href="#" onClick="javascript :history.go(-1);">返回上一页</a>
<a href='/'>返回首页</a>
</div>
'''
file_checklist = os.listdir(UPLOADED_FILE_DEST)
fname = f.filename
while (fname in file_checklist):
filenamelist = fname.split(".")
filenamelist[0] = filenamelist[0] + "-副本"
fname = ''
for i in range(len(filenamelist) - 1):
fname += filenamelist[i] + "."
fname += filenamelist[len(filenamelist) - 1]
upload_path = os.path.join(UPLOADED_FILE_DEST, fname)
f.save(upload_path)
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict=dict(sorted(file_dict.items(),key=lambda item:item[1],reverse=True))
file_list=list(file_dict.keys())
return render_template('index.html', filedict=file_dict, file=file_list, isdel=0,isupload=1)
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict = dict(sorted(file_dict.items(), key=lambda item: item[1], reverse=True))
file_list = list(file_dict.keys())
return render_template('index.html', filedict=file_dict, file=file_list, isdel=0,isupload=0)
@app.route('/subdir/<dir>',methods=['POST', 'GET'])
@is_login
def subdir(dir):
if request.method == 'GET':
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST+"\\"+dir)
for i in range(len(file_list)):
path=UPLOADED_FILE_DEST+"\\"+dir+"\\"+file_list[i]
if(os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict=dict(zip(file_list,file_isdir))
file_dict=dict(sorted(file_dict.items(),key=lambda item:item[1],reverse=True))
file_list=list(file_dict.keys())
url_list=dir.split("\\")
if(len(url_list)==1):
preurl="/"
else:
preurl='/subdir/'
for i in range(len(url_list)-2):
preurl+=url_list[i]+"%5C"
preurl+=url_list[len(url_list)-2]
return render_template('index.html',filedict=file_dict,file=file_list,isdel=0,issub=1,curdir=dir,pre=preurl)
if request.method == 'POST':
f = request.files['file']
if not f.filename:
return '''
<div align='center'>
<h1>上传文件不能为空</h1>
<a href="#" onClick="javascript :history.go(-1);">返回上一页</a>
<a href='/'>返回首页</a>
</div>
'''
file_checklist = os.listdir(UPLOADED_FILE_DEST+"\\"+dir)
fname=f.filename
while (fname in file_checklist):
filenamelist = fname.split(".")
filenamelist[0] = filenamelist[0] + "-副本"
fname = ''
for i in range(len(filenamelist) - 1):
fname += filenamelist[i] + "."
fname += filenamelist[len(filenamelist) - 1]
upload_path = os.path.join(UPLOADED_FILE_DEST,dir,fname)
f.save(upload_path)
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST + "\\" + dir)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + dir + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict=dict(sorted(file_dict.items(),key=lambda item:item[1],reverse=True))
file_list=list(file_dict.keys())
url_list = dir.split("\\")
if (len(url_list) == 1):
preurl = "/"
else:
preurl = '/subdir/'
for i in range(len(url_list) - 2):
preurl += url_list[i] + "%5C"
preurl += url_list[len(url_list) - 2]
return render_template('index.html', filedict=file_dict, file=file_list, isdel=0, issub=1, curdir=dir,pre=preurl,isupload=1)
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST + "\\" + dir)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + dir + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict = dict(sorted(file_dict.items(), key=lambda item: item[1], reverse=True))
file_list = list(file_dict.keys())
url_list = dir.split("\\")
if (len(url_list) == 1):
preurl = "/"
else:
preurl = '/subdir/'
for i in range(len(url_list) - 2):
preurl += url_list[i] + "%5C"
preurl += url_list[len(url_list) - 2]
return render_template('index.html', filedict=file_dict, file=file_list, isdel=0, issub=1, curdir=dir, pre=preurl,isupload=0)
@app.route('/delfile/<filename>/<isdr>',methods=['GET','POST'])
@is_login
def delfile(filename,isdr):
if request.method=='GET':
try:
file_path=UPLOADED_FILE_DEST+"\\"+filename
isdr=int(isdr)
if isdr == 0:
print(file_path)
os.remove(file_path)
else:
shutil.rmtree(file_path)
isdeled=1
except BaseException:
isdeled=0
finally:
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict = dict(sorted(file_dict.items(), key=lambda item: item[1], reverse=True))
file_list = list(file_dict.keys())
return render_template('index.html', filedict=file_dict, file=file_list, isdel=isdeled)
if request.method == 'POST':
f = request.files['file']
if not f.filename:
return '''
<div align='center'>
<h1>上传文件不能为空</h1>
<a href="#" onClick="javascript :history.go(-1);">返回上一页</a>
<a href='/'>返回首页</a>
</div>
'''
file_checklist = os.listdir(UPLOADED_FILE_DEST)
fname = f.filename
while (fname in file_checklist):
filenamelist = fname.split(".")
filenamelist[0] = filenamelist[0] + "-副本"
fname = ''
for i in range(len(filenamelist) - 1):
fname += filenamelist[i] + "."
fname += filenamelist[len(filenamelist) - 1]
upload_path = os.path.join(UPLOADED_FILE_DEST, fname)
f.save(upload_path)
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict=dict(sorted(file_dict.items(),key=lambda item:item[1],reverse=True))
file_list=list(file_dict.keys())
return render_template('index.html', filedict=file_dict, file=file_list, isdel=0, isupload=1)
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict = dict(sorted(file_dict.items(), key=lambda item: item[1], reverse=True))
file_list = list(file_dict.keys())
return render_template('index.html', filedict=file_dict, file=file_list, isdel=0, isupload=0)
@app.route('/subdelfile/<dir>/<filename>/<isdr>',methods=['GET','POST'])
@is_login
def subdelfile(filename,dir,isdr):
if request.method=='GET':
try:
isdr = int(isdr)
file_path=UPLOADED_FILE_DEST+"\\"+dir+"\\"+filename
if isdr == 0:
os.remove(file_path)
else:
shutil.rmtree(file_path)
isdeled=1
except BaseException:
isdeled=0
finally:
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST + "\\" + dir)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + dir + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict = dict(sorted(file_dict.items(), key=lambda item: item[1], reverse=True))
file_list = list(file_dict.keys())
url_list = dir.split("\\")
if (len(url_list) == 1):
preurl = "/"
else:
preurl = '/subdir/'
for i in range(len(url_list) - 2):
preurl += url_list[i] + "%5C"
preurl += url_list[len(url_list) - 2]
return render_template('index.html', filedict=file_dict, file=file_list, isdel=isdeled, issub=1, curdir=dir,pre=preurl)
if request.method == 'POST':
f = request.files['file']
if not f.filename:
return '''
<div align='center'>
<h1>上传文件不能为空</h1>
<a href="#" onClick="javascript :history.go(-1);">返回上一页</a>
<a href='/'>返回首页</a>
</div>
'''
file_checklist = os.listdir(UPLOADED_FILE_DEST+"\\"+dir)
fname = f.filename
while (fname in file_checklist):
filenamelist = fname.split(".")
filenamelist[0] = filenamelist[0] + "-副本"
fname = ''
for i in range(len(filenamelist) - 1):
fname += filenamelist[i] + "."
fname += filenamelist[len(filenamelist) - 1]
upload_path = os.path.join(UPLOADED_FILE_DEST,dir,fname)
f.save(upload_path)
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST + "\\" + dir)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + dir + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict=dict(sorted(file_dict.items(),key=lambda item:item[1],reverse=True))
file_list=list(file_dict.keys())
url_list = dir.split("\\")
if (len(url_list) == 1):
preurl = "/"
else:
preurl = '/subdir/'
for i in range(len(url_list) - 2):
preurl += url_list[i] + "%5C"
preurl += url_list[len(url_list) - 2]
return render_template('index.html', filedict=file_dict, file=file_list, isdel=0, issub=1, curdir=dir,pre=preurl,isupload=1)
file_isdir = []
file_list = os.listdir(UPLOADED_FILE_DEST + "\\" + dir)
for i in range(len(file_list)):
path = UPLOADED_FILE_DEST + "\\" + dir + "\\" + file_list[i]
if (os.path.isdir(path)):
file_isdir.append(1)
else:
file_isdir.append(0)
file_dict = dict(zip(file_list, file_isdir))
file_dict = dict(sorted(file_dict.items(), key=lambda item: item[1], reverse=True))
file_list = list(file_dict.keys())
url_list = dir.split("\\")
if (len(url_list) == 1):
preurl = "/"
else:
preurl = '/subdir/'
for i in range(len(url_list) - 2):
preurl += url_list[i] + "%5C"
preurl += url_list[len(url_list) - 2]
return render_template('index.html', filedict=file_dict, file=file_list, isdel=0, issub=1, curdir=dir, pre=preurl,isupload=0)
@app.route('/download/<filename>')
@is_login
def download(filename):
response = make_response(send_from_directory(UPLOADED_FILE_DEST, filename, as_attachment=True))
response.headers["Content-Disposition"] = "attachment; filename={}".format(filename.encode().decode('latin-1'))
return response
@app.route('/subdownload/<dir>/<filename>')
@is_login
def subdownload(filename,dir):
response = make_response(send_from_directory(UPLOADED_FILE_DEST+"\\"+dir, filename, as_attachment=True))
response.headers["Content-Disposition"] = "attachment; filename={}".format(filename.encode().decode('latin-1'))
return response
@app.route('/mkdir',methods=['GET','POST'])
@is_login
def mkdir():
if request.method=='GET':
return render_template('mkdir.html')
else:
dirname=request.values.get('dirname')
print (dirname)
dirpath=os.path.join(UPLOADED_FILE_DEST,dirname)
isExists = os.path.exists(dirpath)
if not isExists:
os.makedirs(dirpath.encode('utf-8'))
return redirect('/')
else:
return '''
<div align='center'>
<h1>文件夹已存在，创建失败</h1>
<a href="#" onClick="javascript :history.go(-1);">返回上一页</a>
<a href='/'>返回首页</a>
</div>
'''
@app.route('/submkdir/<dir>',methods=['GET','POST'])
@is_login
def submkdir(dir):
if request.method == 'GET':
return render_template('mkdir.html',curdir=dir)
else:
dirname = request.values.get('dirname')
print(dirname)
dirpath = os.path.join(UPLOADED_FILE_DEST,dir, dirname)
isExists = os.path.exists(dirpath)
if not isExists:
os.makedirs(dirpath.encode('utf-8'))
return redirect(url_for('subdir',dir=dir))
else:
return '''
<div align='center'>
<h1>文件夹已存在，创建失败</h1>
<a href="#" onClick="javascript :history.go(-1);">返回上一页</a>
<a href='/'>返回首页</a>
</div>
'''
if __name__ == '__main__':
app.run()
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| 132
| 0.562978
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| 16,871
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| 0.077034
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0
| 7
|
d1773bdc63517beedf78b6e1def066dfdb3e3375
| 11,166
|
py
|
Python
|
Code/plotting.py
|
james-alvey-42/FermionDSph
|
d4137a231e4ea28a4bb261fa58f6f94f77cb2af2
|
[
"MIT"
] | null | null | null |
Code/plotting.py
|
james-alvey-42/FermionDSph
|
d4137a231e4ea28a4bb261fa58f6f94f77cb2af2
|
[
"MIT"
] | null | null | null |
Code/plotting.py
|
james-alvey-42/FermionDSph
|
d4137a231e4ea28a4bb261fa58f6f94f77cb2af2
|
[
"MIT"
] | null | null | null |
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import patches
from scipy.interpolate import interp1d
from glob import glob
from data import load_read_prior, interpolate_simulation
def plot_rho(data, dwarf):
xlabel, ylabel = r'$r\,\mathrm{[kpc]}$', r'$\rho(r)\,\mathrm{[M}_{\odot} \, \mathrm{kpc}^{-3}\mathrm{]}$'
color = '#994B92'
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(1, 1, 1)
plt.sca(ax)
xmin, xmax = min(data['r']), max(data['r'])
plt.xscale('log')
plt.yscale('log')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.fill_between(data['r'], data['2sl'], data['2su'],
color=color, alpha=0.2, linewidth=0.0)
plt.fill_between(data['r'], data['1sl'], data['1su'],
color=color, alpha=0.4, linewidth=0.0)
plt.plot(data['r'], data['1sl'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['1su'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['2sl'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['2su'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['mid'], c=color, lw=1.8, label=dwarf)
plt.xlim(xmin, xmax)
leg = plt.legend(fontsize=18, handlelength=0, handletextpad=0, loc='lower left')
for item in leg.legendHandles:
item.set_visible(False)
plt.savefig('Plots/rho_{}.pdf'.format(dwarf))
plt.close(fig)
def plot_mass(data, dwarf):
xlabel, ylabel = r'$r\,\mathrm{[kpc]}$', r'$M(r)\,\mathrm{[M}_{\odot}\mathrm{]}$'
color = '#994B92'
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(1, 1, 1)
plt.sca(ax)
xmin, xmax = min(data['r']), max(data['r'])
plt.xscale('log')
plt.yscale('log')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.fill_between(data['r'], data['2sl'], data['2su'],
color=color, alpha=0.2, linewidth=0.0)
plt.fill_between(data['r'], data['1sl'], data['1su'],
color=color, alpha=0.4, linewidth=0.0)
plt.plot(data['r'], data['1sl'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['1su'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['2sl'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['2su'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['mid'], c=color, lw=1.8, label=dwarf)
plt.xlim(xmin, xmax)
leg = plt.legend(fontsize=18, handlelength=0, handletextpad=0, loc='lower left')
for item in leg.legendHandles:
item.set_visible(False)
plt.savefig('Plots/mass_{}.pdf'.format(dwarf))
plt.close(fig)
def plot_mass_JR(data, dwarf):
xlabel, ylabel = r'$r\,\mathrm{[kpc]}$', r'$M(r)\,\mathrm{[M}_{\odot}\mathrm{]}$'
color = '#994B92'
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(1, 1, 1)
plt.sca(ax)
xmin, xmax = min(data['r']), max(data['r'])
plt.xscale('log')
plt.yscale('log')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.fill_between(data['r'], data['2sl'], data['2su'],
color=color, alpha=0.2, linewidth=0.0)
plt.fill_between(data['r'], data['1sl'], data['1su'],
color=color, alpha=0.4, linewidth=0.0)
plt.plot(data['r'], data['1sl'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['1su'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['2sl'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['2su'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['mid'], c=color, lw=1.8, label=dwarf)
plt.xlim(xmin, xmax)
leg = plt.legend(fontsize=18, handlelength=0, handletextpad=0, loc='lower left')
for item in leg.legendHandles:
item.set_visible(False)
plt.savefig('Plots/massJR_{}.pdf'.format(dwarf))
plt.close(fig)
def plot_vesc(data, dwarf):
xlabel, ylabel = r'$r\,\mathrm{[kpc]}$', r'$v_{\mathrm{esc}}(r)\,\mathrm{[km}\,\mathrm{s}^{-1}\mathrm{]}$'
color = '#994B92'
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(1, 1, 1)
plt.sca(ax)
xmin, xmax = min(data['r']), max(data['r'])
plt.xscale('log')
plt.yscale('linear')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.fill_between(data['r'], data['2sl'], data['2su'],
color=color, alpha=0.2, linewidth=0.0)
plt.fill_between(data['r'], data['1sl'], data['1su'],
color=color, alpha=0.4, linewidth=0.0)
plt.plot(data['r'], data['1sl'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['1su'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['2sl'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['2su'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['mid'], c=color, lw=1.8, label=dwarf)
plt.xlim(xmin, xmax)
leg = plt.legend(fontsize=18, handlelength=0, handletextpad=0, loc='lower left')
for item in leg.legendHandles:
item.set_visible(False)
plt.savefig('Plots/vesc_{}.pdf'.format(dwarf))
plt.close(fig)
def plot_vesc_JR(data, dwarf):
xlabel, ylabel = r'$r\,\mathrm{[kpc]}$', r'$v_{\mathrm{esc}}(r)\,\mathrm{[km}\,\mathrm{s}^{-1}\mathrm{]}$'
color = '#994B92'
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(1, 1, 1)
plt.sca(ax)
xmin, xmax = min(data['r']), max(data['r'])
plt.xscale('log')
plt.yscale('linear')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.fill_between(data['r'], data['2sl'], data['2su'],
color=color, alpha=0.2, linewidth=0.0)
plt.fill_between(data['r'], data['1sl'], data['1su'],
color=color, alpha=0.4, linewidth=0.0)
plt.plot(data['r'], data['1sl'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['1su'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['2sl'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['2su'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['mid'], c=color, lw=1.8, label=dwarf)
plt.xlim(xmin, xmax)
leg = plt.legend(fontsize=18, handlelength=0, handletextpad=0, loc='lower left')
for item in leg.legendHandles:
item.set_visible(False)
plt.savefig('Plots/vescJR_{}.pdf'.format(dwarf))
plt.close(fig)
def plot_FG(data, dwarf):
xlabel, ylabel = r'$r\,\mathrm{[kpc]}$', r'$F_G(r)\,\mathrm{[M}_\odot\,\mathrm{kpc}^{-3}\,\mathrm{km}^{-3}\,\mathrm{s}^{3}\mathrm{]}$'
color = '#994B92'
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(1, 1, 1)
plt.sca(ax)
xmin, xmax = min(data['r']), max(data['r'])
plt.xscale('log')
plt.yscale('log')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.fill_between(data['r'], data['2sl'], data['2su'],
color=color, alpha=0.2, linewidth=0.0)
plt.fill_between(data['r'], data['1sl'], data['1su'],
color=color, alpha=0.4, linewidth=0.0)
plt.plot(data['r'], data['1sl'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['1su'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['2sl'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['2su'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['mid'], c=color, lw=1.8, label=dwarf)
plt.xlim(xmin, xmax)
leg = plt.legend(fontsize=18, handlelength=0, handletextpad=0, loc='lower left')
for item in leg.legendHandles:
item.set_visible(False)
plt.savefig('Plots/FG_{}.pdf'.format(dwarf))
plt.close(fig)
def plot_FM_JA(data, dwarf):
xlabel, ylabel = r'$r\,\mathrm{[kpc]}$', r'$F_M(r)\,\mathrm{[M}_\odot\,\mathrm{kpc}^{-3}\,\mathrm{km}^{-3}\,\mathrm{s}^{3}\mathrm{]}$'
color = '#994B92'
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(1, 1, 1)
plt.sca(ax)
xmin, xmax = min(data['r']), max(data['r'])
plt.xscale('log')
plt.yscale('log')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.fill_between(data['r'], data['2sl'], data['2su'],
color=color, alpha=0.2, linewidth=0.0)
plt.fill_between(data['r'], data['1sl'], data['1su'],
color=color, alpha=0.4, linewidth=0.0)
plt.plot(data['r'], data['1sl'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['1su'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['2sl'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['2su'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['mid'], c=color, lw=1.8, label=dwarf)
plt.xlim(xmin, xmax)
leg = plt.legend(fontsize=18, handlelength=0, handletextpad=0, loc='lower left')
for item in leg.legendHandles:
item.set_visible(False)
plt.savefig('Plots/FM_JA_{}.pdf'.format(dwarf))
plt.close(fig)
def plot_FM_JR(data, dwarf):
xlabel, ylabel = r'$r\,\mathrm{[kpc]}$', r'$F_M(r)\,\mathrm{[M}_\odot\,\mathrm{kpc}^{-3}\,\mathrm{km}^{-3}\,\mathrm{s}^{3}\mathrm{]}$'
color = '#994B92'
fig = plt.figure(figsize=(5, 5))
ax = plt.subplot(1, 1, 1)
plt.sca(ax)
xmin, xmax = min(data['r']), max(data['r'])
plt.xscale('log')
plt.yscale('log')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.fill_between(data['r'], data['2sl'], data['2su'],
color=color, alpha=0.2, linewidth=0.0)
plt.fill_between(data['r'], data['1sl'], data['1su'],
color=color, alpha=0.4, linewidth=0.0)
plt.plot(data['r'], data['1sl'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['1su'], c=color, lw=0.4, alpha=0.8)
plt.plot(data['r'], data['2sl'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['2su'], c=color, lw=0.4, alpha=0.3)
plt.plot(data['r'], data['mid'], c=color, lw=1.8, label=dwarf)
plt.xlim(xmin, xmax)
leg = plt.legend(fontsize=18, handlelength=0, handletextpad=0, loc='lower left')
for item in leg.legendHandles:
item.set_visible(False)
plt.savefig('Plots/FM_JR_{}.pdf'.format(dwarf))
plt.close(fig)
def plot_dwarfs(load_fn, plot_fn):
directories = glob('Final_Data/*/')
for directory in directories:
dwarf = directory.split('/')[-2]
data = load_fn(dwarf)
plot_fn(data, dwarf)
def plot_read_prior(ax):
purple = '#994B92'
blue = '#4888B0'
r, beta = load_read_prior()
ax.plot(r, beta, c='k', lw=0.8, zorder=0)
ax.plot(r, np.repeat(0, len(r)), c='k', lw=0.8, zorder=0)
ax.fill_between(r, np.repeat(0, len(r)), beta,
color='k', alpha=0.2, label=r'$\mathrm{Our}\,\mathrm{Prior}$', zorder=0)
def plot_edge_average(ax, files):
purple = '#994B92'
blue = '#4888B0'
arrs = []
r_min_arr = []
r_max_arr = []
fns = []
for file in files:
r, _, _, beta = np.loadtxt(file, unpack=True)
r_min_arr.append(min(r))
r_max_arr.append(max(r))
ifn = interpolate_simulation(file)
fns.append(ifn)
r_min = max(r_min_arr)
r_max = min(r_max_arr)
r_arr = np.geomspace(r_min, 0.9 * r_max, 1000)
for fn in fns:
arrs.append(fn(r_arr))
arrs = np.array(arrs)
average = np.sum(arrs, axis=0)/len(files)
variance = np.var(arrs, axis=0)
ax.plot(r_arr, average, c=purple, lw=1.8, alpha=0.8, label=r'$\mathrm{EDGE}\,\mathrm{Simulation}$')
ax.fill_between(r_arr, average - np.sqrt(variance), average + np.sqrt(variance), linewidth=0.0, color=purple, alpha=0.15)
def plot_aquarius_average(ax, aqfile):
purple = '#994B92'
blue = '#4888B0'
arrs = []
r_min_arr = []
r_max_arr = []
fns = []
data = np.loadtxt(aqfile)
for row in range(1, len(data[:, 0])):
r, beta = 1e3 * data[0, :], data[row, :]
r_min_arr.append(min(r))
r_max_arr.append(max(r))
ifn = interp1d(r, beta, kind='linear', fill_value='extrapolate')
fns.append(ifn)
r_min = max(r_min_arr)
r_max = min(r_max_arr)
r_arr = np.geomspace(r_min, 0.9 * r_max, 1000)
for fn in fns:
arrs.append(fn(r_arr))
arrs = np.array(arrs)
average = np.sum(arrs, axis=0)/(len(data[:, 0]) - 1)
variance = np.var(arrs, axis=0)
ax.plot(r_arr, average, c=blue, lw=1.8, ls=(1,(5,1)), alpha=0.8, label=r'$\mathrm{Aquarius}\,\mathrm{Simulation}$', zorder=9)
ax.fill_between(r_arr, average - np.sqrt(variance), average + np.sqrt(variance), linewidth=0.0, color=blue, alpha=0.15, zorder=9)
| 31.103064
| 135
| 0.63568
| 2,010
| 11,166
| 3.471144
| 0.072637
| 0.051598
| 0.072237
| 0.068797
| 0.883761
| 0.883761
| 0.874301
| 0.874301
| 0.857819
| 0.822703
| 0
| 0.049435
| 0.119559
| 11,166
| 359
| 136
| 31.103064
| 0.660258
| 0
| 0
| 0.814545
| 0
| 0.021818
| 0.132533
| 0.056506
| 0
| 0
| 0
| 0
| 0
| 1
| 0.043636
| false
| 0
| 0.021818
| 0
| 0.065455
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
d17cbc53bbc2a81882c43b253ef595a92ace2caf
| 21,370
|
py
|
Python
|
JUSTScoreSummary/test/personal_table.py
|
YuanZ1949/JUSTScoreSummary
|
0c1fb1e74e9a23f2c26bfac697b86f9433e4acaf
|
[
"BSD-3-Clause"
] | null | null | null |
JUSTScoreSummary/test/personal_table.py
|
YuanZ1949/JUSTScoreSummary
|
0c1fb1e74e9a23f2c26bfac697b86f9433e4acaf
|
[
"BSD-3-Clause"
] | null | null | null |
JUSTScoreSummary/test/personal_table.py
|
YuanZ1949/JUSTScoreSummary
|
0c1fb1e74e9a23f2c26bfac697b86f9433e4acaf
|
[
"BSD-3-Clause"
] | null | null | null |
personal_table = '''
<table border="0" cellpadding="0" cellspacing="0" class="Nsb_r_list Nsb_table" id="dataList" width="100%">
<tr>
<th class="Nsb_r_list_thb" scope="col">序号</th>
<th class="Nsb_r_list_thb" scope="col">开设学期</th>
<th class="Nsb_r_list_thb" scope="col">课程体系</th>
<th class="Nsb_r_list_thb" scope="col">课程组</th>
<th class="Nsb_r_list_thb" scope="col">备注</th>
<th class="Nsb_r_list_thb" scope="col">选课组</th>
<th class="Nsb_r_list_thb" scope="col">选课组关系</th>
<th class="Nsb_r_list_thb" scope="col">课程号</th>
<th class="Nsb_r_list_thb" scope="col">课程名称</th>
<th class="Nsb_r_list_thb" scope="col">总学时</th>
<th class="Nsb_r_list_thb" scope="col">学分</th>
<th class="Nsb_r_list_thb" scope="col">考核方式</th>
<th class="Nsb_r_list_thb" scope="col">开课单位</th>
<th class="Nsb_r_list_thb" scope="col">参考周学时</th>
<th class="Nsb_r_list_thb" scope="col">成绩</th>
</tr>
<tr><td>1</td>
<td align="left">2</td>
<td align="left" rowspan="366">通识教育基础课程</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">1730105(4.5)</td>
<td align="left"></td>
<td align="left">05020057a</td>
<td align="left">大学物理A1</td>
<td align="left">72</td>
<td align="left">4.5</td>
<td align="left">考试</td>
<td align="left">理学院</td>
<td align="left">4</td>
<td align="left" style="width: 40px;">68</td>
</tr>
<tr><td>2</td>
<td align="left">3</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">1730106(2.5)</td>
<td align="left"></td>
<td align="left">05020058a</td>
<td align="left">大学物理A2</td>
<td align="left">40</td>
<td align="left">2.5</td>
<td align="left">考试</td>
<td align="left">理学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">85</td>
</tr>
<tr><td>3</td>
<td align="left">1</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">1730101(16)</td>
<td align="left"> 1730101 or 1730102 </td>
<td align="left">08010001a</td>
<td align="left">大学英语1</td>
<td align="left">64</td>
<td align="left">4</td>
<td align="left">考试</td>
<td align="left">外国语学院</td>
<td align="left">4</td>
<td align="left" style="width: 40px;">65</td>
</tr>
<tr><td>4</td>
<td align="left">2</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">1730101(16)</td>
<td align="left"> 1730101 or 1730102 </td>
<td align="left">08010005a</td>
<td align="left">大学英语2</td>
<td align="left">64</td>
<td align="left">4</td>
<td align="left">考试</td>
<td align="left">外国语学院</td>
<td align="left">4</td>
<td align="left" style="width: 40px;">60</td>
</tr>
<tr><td>5</td>
<td align="left">3</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">1730101(16)</td>
<td align="left"> 1730101 or 1730102 </td>
<td align="left">08020003a</td>
<td align="left">大学英语3</td>
<td align="left">64</td>
<td align="left">4</td>
<td align="left">考试</td>
<td align="left">外国语学院</td>
<td align="left">4</td>
<td align="left" style="width: 40px;">60</td>
</tr>
<tr><td>6</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">1730101(16)</td>
<td align="left"> 1730101 or 1730102 </td>
<td align="left">08020008a</td>
<td align="left">大学英语4</td>
<td align="left">64</td>
<td align="left">4</td>
<td align="left">考试</td>
<td align="left">外国语学院</td>
<td align="left">4</td>
<td align="left" style="width: 40px;">60</td>
</tr>
<tr><td>7</td>
<td align="left">1</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">1730103(5)</td>
<td align="left"></td>
<td align="left">05010039a</td>
<td align="left">高等数学A1</td>
<td align="left">80</td>
<td align="left">5</td>
<td align="left">考试</td>
<td align="left">理学院</td>
<td align="left">5</td>
<td align="left" style="width: 40px;">64</td>
</tr>
<tr><td>8</td>
<td align="left">2</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">1730104(6)</td>
<td align="left"></td>
<td align="left">05010040a</td>
<td align="left">高等数学A2</td>
<td align="left">96</td>
<td align="left">6</td>
<td align="left">考试</td>
<td align="left">理学院</td>
<td align="left">6</td>
<td align="left" style="width: 40px;">60</td>
</tr>
<tr><td>9</td>
<td align="left">3</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">04060001b</td>
<td align="left">创业基础</td>
<td align="left">24</td>
<td align="left">1.5</td>
<td align="left">考查</td>
<td align="left">经济管理学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">良</td>
</tr>
<tr><td>10</td>
<td align="left">2</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">19010005b</td>
<td align="left">大学计算机基础</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考查</td>
<td align="left">计算机学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">良</td>
</tr>
<tr><td>11</td>
<td align="left">3</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">19010003a</td>
<td align="left">计算机程序设计语言(VB)</td>
<td align="left">56</td>
<td align="left">3.5</td>
<td align="left">考试</td>
<td align="left">计算机学院</td>
<td align="left">4</td>
<td align="left" style="width: 40px;">73</td>
</tr>
<tr><td>12</td>
<td align="left">2</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">14000012b</td>
<td align="left">军事理论</td>
<td align="left">32</td>
<td align="left">1</td>
<td align="left">考查</td>
<td align="left">人民武装部、国防学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">良</td>
</tr>
<tr><td>13</td>
<td align="left">2</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">09020018a</td>
<td align="left">马克思主义基本原理概论</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考试</td>
<td align="left">马克思主义学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">84</td>
</tr>
<tr><td>14</td>
<td align="left">3</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">09030034a</td>
<td align="left">毛泽东思想和中国特色社会主义理论体系概论1</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考试</td>
<td align="left">马克思主义学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">75</td>
</tr>
<tr><td>15</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">09030035a</td>
<td align="left">毛泽东思想和中国特色社会主义理论体系概论2</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考试</td>
<td align="left">马克思主义学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">80</td>
</tr>
<tr><td>16</td>
<td align="left">1</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">09040024b</td>
<td align="left">思想道德修养与法律基础</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考查</td>
<td align="left">马克思主义学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">中</td>
</tr>
<tr><td>17</td>
<td align="left">1</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">07010002a</td>
<td align="left">体育1</td>
<td align="left">32</td>
<td align="left">1</td>
<td align="left">考试</td>
<td align="left">体育学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">89</td>
</tr>
<tr><td>18</td>
<td align="left">2</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">07010003a</td>
<td align="left">体育2</td>
<td align="left">32</td>
<td align="left">1</td>
<td align="left">考试</td>
<td align="left">体育学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">88</td>
</tr>
<tr><td>19</td>
<td align="left">3</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">07010004a</td>
<td align="left">体育3</td>
<td align="left">32</td>
<td align="left">1</td>
<td align="left">考试</td>
<td align="left">体育学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">84</td>
</tr>
<tr><td>20</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">07010005a</td>
<td align="left">体育4</td>
<td align="left">32</td>
<td align="left">1</td>
<td align="left">考试</td>
<td align="left">体育学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">83</td>
</tr>
<tr><td>21</td>
<td align="left">2</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">15000001b</td>
<td align="left">心理健康教育</td>
<td align="left">16</td>
<td align="left">1</td>
<td align="left">考查</td>
<td align="left">马克思主义学院</td>
<td align="left">1</td>
<td align="left" style="width: 40px;">85</td>
</tr>
<tr><td>22</td>
<td align="left">1</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">16000014b</td>
<td align="left">形势与政策1</td>
<td align="left">8</td>
<td align="left">0.5</td>
<td align="left">考查</td>
<td align="left">党委宣传部、统战部、法制办公室、社会主义学院新闻中心</td>
<td align="left">1</td>
<td align="left" style="width: 40px;">良</td>
</tr>
<tr><td>23</td>
<td align="left">3</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">16000015b</td>
<td align="left">形势与政策2</td>
<td align="left">8</td>
<td align="left">0.5</td>
<td align="left">考查</td>
<td align="left">党委宣传部、统战部、法制办公室、社会主义学院新闻中心</td>
<td align="left">1</td>
<td align="left" style="width: 40px;">良</td>
</tr>
<tr><td>24</td>
<td align="left">5</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">16000016b</td>
<td align="left">形势与政策3</td>
<td align="left">8</td>
<td align="left">0.5</td>
<td align="left">考查</td>
<td align="left">党委宣传部、统战部、法制办公室、社会主义学院新闻中心</td>
<td align="left">1</td>
<td align="left" style="width: 40px;">良</td>
</tr>
<tr><td>25</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">16000017b</td>
<td align="left">形势与政策实践</td>
<td align="left">16</td>
<td align="left">1</td>
<td align="left">考查</td>
<td align="left">党委宣传部、统战部、法制办公室、社会主义学院新闻中心</td>
<td align="left">1</td>
<td align="left" style="width: 40px;">良</td>
</tr>
<tr><td>26</td>
<td align="left">1</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">09130094b</td>
<td align="left">职业生涯与发展规划</td>
<td align="left">16</td>
<td align="left">1</td>
<td align="left">考查</td>
<td align="left">人文社科学院</td>
<td align="left">1</td>
<td align="left" style="width: 40px;">84</td>
</tr>
<tr><td>27</td>
<td align="left">2</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">09050049a</td>
<td align="left">中国近现代史纲要</td>
<td align="left">32</td>
<td align="left">2</td>
<td align="left">考试</td>
<td align="left">马克思主义学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">63</td>
</tr>
<tr><td>28</td>
<td align="left">5</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">1730107(4)</td>
<td align="left"></td>
<td align="left">30010193b</td>
<td align="left">计算机在生命科学中的应用</td>
<td align="left">32</td>
<td align="left">2</td>
<td align="left">考查</td>
<td align="left">生物技术学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">83</td>
</tr>
<tr><td>29</td>
<td align="left">5</td>
<td align="left"></td>
<td align="left"></td>
<td align="left">1730107(4)</td>
<td align="left"></td>
<td align="left">30010118b</td>
<td align="left">生物资源开发与利用</td>
<td align="left">32</td>
<td align="left">2</td>
<td align="left">考查</td>
<td align="left">生物技术学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">84</td>
</tr>
<tr><td>30</td>
<td align="left">5</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010047a</td>
<td align="left">分子生物学</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考试</td>
<td align="left">生物技术学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">80</td>
</tr>
<tr><td>31</td>
<td align="left">3</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010115b</td>
<td align="left">普通生物学</td>
<td align="left">40</td>
<td align="left">2.5</td>
<td align="left">考查</td>
<td align="left">生物技术学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">84</td>
</tr>
<tr><td>32</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010138a</td>
<td align="left">人体及动物生理学</td>
<td align="left">40</td>
<td align="left">2.5</td>
<td align="left">考试</td>
<td align="left">生物技术学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">75</td>
</tr>
<tr><td>33</td>
<td align="left">3</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010072a</td>
<td align="left">生物化学</td>
<td align="left">64</td>
<td align="left">4</td>
<td align="left">考试</td>
<td align="left">生物技术学院</td>
<td align="left">4</td>
<td align="left" style="width: 40px;">78</td>
</tr>
<tr><td>34</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010116b</td>
<td align="left">生物统计学</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考查</td>
<td align="left">生物技术学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">86</td>
</tr>
<tr><td>35</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010073a</td>
<td align="left">微生物学</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考试</td>
<td align="left">生物技术学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">81</td>
</tr>
<tr><td>36</td>
<td align="left">1</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30040003a</td>
<td align="left">无机及分析化学</td>
<td align="left">72</td>
<td align="left">4.5</td>
<td align="left">考试</td>
<td align="left">环境与化学工程学院</td>
<td align="left">4</td>
<td align="left" style="width: 40px;">68</td>
</tr>
<tr><td>37</td>
<td align="left">5</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010166a</td>
<td align="left">细胞生物学</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考试</td>
<td align="left">生物技术学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">77</td>
</tr>
<tr><td>38</td>
<td align="left">5</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010139a</td>
<td align="left">遗传学</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考试</td>
<td align="left">生物技术学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">72</td>
</tr>
<tr><td>39</td>
<td align="left">2</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30040074a</td>
<td align="left">有机化学</td>
<td align="left">72</td>
<td align="left">4.5</td>
<td align="left">考试</td>
<td align="left">环境与化学工程学院</td>
<td align="left">4</td>
<td align="left" style="width: 40px;">71</td>
</tr>
<tr><td>40</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010137a</td>
<td align="left">植物生理学</td>
<td align="left">32</td>
<td align="left">2</td>
<td align="left">考试</td>
<td align="left">生物技术学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">73</td>
</tr>
<tr><td>41</td>
<td align="left">5</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010050a</td>
<td align="left">微生物工程</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考试</td>
<td align="left">生物技术学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">74</td>
</tr>
<tr><td>42</td>
<td align="left">5</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010144a</td>
<td align="left">药理学</td>
<td align="left">48</td>
<td align="left">3</td>
<td align="left">考试</td>
<td align="left">生物技术学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">74</td>
</tr>
<tr><td>43</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">03060108b</td>
<td align="left">计算机程序设计实践(VB)</td>
<td align="left">1</td>
<td align="left">1</td>
<td align="left">考查</td>
<td align="left">计算机学院</td>
<td align="left">0</td>
<td align="left" style="width: 40px;">优</td>
</tr>
<tr><td>44</td>
<td align="left">1</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">14000013b</td>
<td align="left">军事技能训练</td>
<td align="left">2</td>
<td align="left">2</td>
<td align="left">考查</td>
<td align="left">人民武装部、国防学院</td>
<td align="left">0</td>
<td align="left" style="width: 40px;">优</td>
</tr>
<tr><td>45</td>
<td align="left">5</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010158b</td>
<td align="left">生理与药理学实验</td>
<td align="left">32</td>
<td align="left">2</td>
<td align="left">考查</td>
<td align="left">生物技术学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">良</td>
</tr>
<tr><td>46</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010136b</td>
<td align="left">微生物学实验</td>
<td align="left">24</td>
<td align="left">1.5</td>
<td align="left">考查</td>
<td align="left">生物技术学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">93</td>
</tr>
<tr><td>47</td>
<td align="left">3</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010178b</td>
<td align="left">显微生物学实验</td>
<td align="left">40</td>
<td align="left">2.5</td>
<td align="left">考查</td>
<td align="left">生物技术学院</td>
<td align="left">3</td>
<td align="left" style="width: 40px;">良</td>
</tr>
<tr><td>48</td>
<td align="left">5</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010160b</td>
<td align="left">遗传学实验</td>
<td align="left">24</td>
<td align="left">1.5</td>
<td align="left">考查</td>
<td align="left">生物技术学院</td>
<td align="left">2</td>
<td align="left" style="width: 40px;">良</td>
</tr>
<tr><td>49</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">08010085b</td>
<td align="left">英语网络自主学习</td>
<td align="left">16</td>
<td align="left">1</td>
<td align="left">考查</td>
<td align="left">外国语学院</td>
<td align="left">0</td>
<td align="left" style="width: 40px;">78</td>
</tr>
<tr><td>50</td>
<td align="left">4</td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left"></td>
<td align="left">30010179b</td>
<td align="left">植物生理与生物化学综合实验</td>
<td align="left">56</td>
<td align="left">3.5</td>
<td align="left">考查</td>
<td align="left">生物技术学院</td>
<td align="left">4</td>
<td align="left" style="width: 40px;">90</td>
</tr>
</table>
'''
| 27.681347
| 107
| 0.563173
| 3,694
| 21,370
| 3.244721
| 0.066324
| 0.217253
| 0.48882
| 0.706074
| 0.843317
| 0.841649
| 0.841482
| 0.840314
| 0.835975
| 0.787586
| 0
| 0.059884
| 0.145905
| 21,370
| 772
| 108
| 27.681347
| 0.596811
| 0
| 0
| 0.751295
| 0
| 0.001295
| 0.998835
| 0.241602
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
d19b955b57272a439c45c2676dd7167866333314
| 96
|
py
|
Python
|
tests/utils/test_docs.py
|
nebulousdog/lazy-money-maker
|
1c0a8d124b07a9b9ee3283d86c37bee8c765f47a
|
[
"MIT"
] | null | null | null |
tests/utils/test_docs.py
|
nebulousdog/lazy-money-maker
|
1c0a8d124b07a9b9ee3283d86c37bee8c765f47a
|
[
"MIT"
] | null | null | null |
tests/utils/test_docs.py
|
nebulousdog/lazy-money-maker
|
1c0a8d124b07a9b9ee3283d86c37bee8c765f47a
|
[
"MIT"
] | null | null | null |
# from marian.utils.docs import generate_sphinx_docs
def test_generate_sphinx_docs():
pass
| 19.2
| 52
| 0.802083
| 14
| 96
| 5.142857
| 0.714286
| 0.388889
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.135417
| 96
| 4
| 53
| 24
| 0.86747
| 0.520833
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.5
| true
| 0.5
| 0
| 0
| 0.5
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
88530cda8d6ab8828cea372aa1085eef386ec2dc
| 22,454
|
py
|
Python
|
ml4a/nicer_phase_amplitudes_to_parameters_model_components.py
|
golmschenk/ml4a
|
4e0b7576d131c63fc4c8b94158d5a0052adae651
|
[
"Apache-2.0"
] | 3
|
2020-11-23T18:47:37.000Z
|
2021-08-05T17:45:51.000Z
|
ml4a/nicer_phase_amplitudes_to_parameters_model_components.py
|
golmschenk/ml4a
|
4e0b7576d131c63fc4c8b94158d5a0052adae651
|
[
"Apache-2.0"
] | 5
|
2021-08-19T00:54:57.000Z
|
2022-02-10T00:15:40.000Z
|
ml4a/nicer_phase_amplitudes_to_parameters_model_components.py
|
golmschenk/ml4a
|
4e0b7576d131c63fc4c8b94158d5a0052adae651
|
[
"Apache-2.0"
] | 3
|
2019-07-12T21:00:57.000Z
|
2020-06-03T22:18:13.000Z
|
"""
Code for a residual light curve network block.
"""
import math
from typing import Optional
import tensorflow
from tensorflow.keras.layers import LeakyReLU, Convolution1D, MaxPooling1D, BatchNormalization,\
Layer, Permute, ZeroPadding1D, SpatialDropout1D
from tensorflow.keras.regularizers import L2
from tensorflow.python.keras.layers import Cropping1D
class ResidualLightCurveNetworkBlock(Layer):
def __init__(self, output_channels: int, input_channels: Optional[int] = None, kernel_size: int = 3,
pooling_size: int = 1, batch_normalization: bool = True, dropout_rate: float = 0.0, l2_regularization: float = 0.0):
super().__init__()
leaky_relu = LeakyReLU(alpha=0.01)
dimension_decrease_factor = 4
if batch_normalization:
self.batch_normalization = BatchNormalization(scale=False)
self.batch_normalization1 = BatchNormalization(scale=False)
else:
self.batch_normalization = None
if l2_regularization > 0:
l2_regularizer = L2(l2_regularization)
else:
l2_regularizer = None
self.convolutional_layer0 = Convolution1D(
output_channels, kernel_size=kernel_size, activation=leaky_relu,
padding='same', kernel_regularizer=l2_regularizer)
self.convolutional_layer1 = Convolution1D(
output_channels, kernel_size=kernel_size, activation=leaky_relu,
padding='same', kernel_regularizer=l2_regularizer)
if pooling_size > 1:
self.pooling_layer = MaxPooling1D(pool_size=pooling_size, padding='same')
else:
self.pooling_layer = None
if input_channels is not None and output_channels != input_channels:
if output_channels < input_channels:
raise NotImplementedError(f'Residual blocks with less output channels than input channels is not'
f'implemented. Output channels was {output_channels} and input was'
f'{input_channels}')
self.dimension_change_permute0 = Permute((2, 1))
self.dimension_change_layer = ZeroPadding1D(padding=(0, output_channels - input_channels))
self.dimension_change_permute1 = Permute((2, 1))
else:
self.dimension_change_layer = None
if dropout_rate > 0:
self.dropout_layer = SpatialDropout1D(rate=dropout_rate)
else:
self.dropout_layer = None
def call(self, inputs, training=False, mask=None):
"""
The forward pass of the block.
:param inputs: The input tensor.
:param training: A boolean specifying if the layer should be in training mode.
:param mask: A mask for the input tensor.
:return: The output tensor of the layer.
"""
x = inputs
y = x
if self.batch_normalization is not None:
y = self.batch_normalization(y, training=training)
y = self.convolutional_layer0(y, training=training)
if self.batch_normalization is not None:
y = self.batch_normalization1(y, training=training)
y = self.convolutional_layer1(y, training=training)
if self.pooling_layer is not None:
x = self.pooling_layer(x, training=training)
y = self.pooling_layer(y, training=training)
if self.dimension_change_layer is not None:
x = self.dimension_change_permute0(x, training=training)
x = self.dimension_change_layer(x, training=training)
x = self.dimension_change_permute1(x, training=training)
if self.dropout_layer is not None:
y = self.dropout_layer(y, training=training)
return x + y
class BottleNeckResidualLightCurveNetworkBlock(Layer):
def __init__(self, output_channels: int, input_channels: Optional[int] = None, kernel_size: int = 3,
pooling_size: int = 1, batch_normalization: bool = True, dropout_rate: float = 0.0, l2_regularization: float = 0.0):
super().__init__()
leaky_relu = LeakyReLU(alpha=0.01)
dimension_decrease_factor = 4
if batch_normalization:
self.batch_normalization = BatchNormalization(scale=False)
else:
self.batch_normalization = None
if l2_regularization > 0:
l2_regularizer = L2(l2_regularization)
else:
l2_regularizer = None
self.dimension_decrease_layer = Convolution1D(
output_channels // dimension_decrease_factor, kernel_size=1, activation=leaky_relu, kernel_regularizer=l2_regularizer)
self.convolutional_layer = Convolution1D(
output_channels // dimension_decrease_factor, kernel_size=kernel_size, activation=leaky_relu,
padding='same', kernel_regularizer=l2_regularizer)
self.dimension_increase_layer = Convolution1D(
output_channels, kernel_size=1, activation=leaky_relu, kernel_regularizer=l2_regularizer)
if pooling_size > 1:
self.pooling_layer = MaxPooling1D(pool_size=pooling_size, padding='same')
else:
self.pooling_layer = None
if input_channels is not None and output_channels != input_channels:
if output_channels < input_channels:
raise NotImplementedError(f'Residual blocks with less output channels than input channels is not'
f'implemented. Output channels was {output_channels} and input was'
f'{input_channels}')
self.dimension_change_permute0 = Permute((2, 1))
self.dimension_change_layer = ZeroPadding1D(padding=(0, output_channels - input_channels))
self.dimension_change_permute1 = Permute((2, 1))
else:
self.dimension_change_layer = None
if dropout_rate > 0:
self.dropout_layer = SpatialDropout1D(rate=dropout_rate)
else:
self.dropout_layer = None
def call(self, inputs, training=False, mask=None):
"""
The forward pass of the block.
:param inputs: The input tensor.
:param training: A boolean specifying if the layer should be in training mode.
:param mask: A mask for the input tensor.
:return: The output tensor of the layer.
"""
x = inputs
y = x
if self.batch_normalization is not None:
y = self.batch_normalization(y, training=training)
y = self.dimension_decrease_layer(y, training=training)
y = self.convolutional_layer(y, training=training)
y = self.dimension_increase_layer(y, training=training)
if self.pooling_layer is not None:
x = self.pooling_layer(x, training=training)
y = self.pooling_layer(y, training=training)
if self.dimension_change_layer is not None:
x = self.dimension_change_permute0(x, training=training)
x = self.dimension_change_layer(x, training=training)
x = self.dimension_change_permute1(x, training=training)
if self.dropout_layer is not None:
y = self.dropout_layer(y, training=training)
return x + y
class BottleNeckResidualLightCurveNetworkBlockMainPathRepeat(Layer):
def __init__(self, output_channels: int, input_channels: Optional[int] = None, kernel_size: int = 3,
pooling_size: int = 1, batch_normalization: bool = True, dropout_rate: float = 0.0, l2_regularization: float = 0.0):
super().__init__()
leaky_relu = LeakyReLU(alpha=0.01)
dimension_decrease_factor = 4
if batch_normalization:
self.batch_normalization = BatchNormalization(scale=False)
else:
self.batch_normalization = None
if l2_regularization > 0:
l2_regularizer = L2(l2_regularization)
else:
l2_regularizer = None
self.dimension_decrease_layer = Convolution1D(
output_channels // dimension_decrease_factor, kernel_size=1, activation=leaky_relu, kernel_regularizer=l2_regularizer)
self.convolutional_layer = Convolution1D(
output_channels // dimension_decrease_factor, kernel_size=kernel_size, activation=leaky_relu,
padding='same', kernel_regularizer=l2_regularizer)
self.dimension_increase_layer = Convolution1D(
output_channels, kernel_size=1, activation=leaky_relu, kernel_regularizer=l2_regularizer)
if pooling_size > 1:
self.pooling_layer = MaxPooling1D(pool_size=pooling_size, padding='same')
else:
self.pooling_layer = None
if input_channels is not None and output_channels != input_channels:
if output_channels < input_channels:
raise NotImplementedError(f'Residual blocks with less output channels than input channels is not'
f'implemented. Output channels was {output_channels} and input was'
f'{input_channels}')
self.repeats = math.ceil(output_channels / input_channels)
cropping_amount = (input_channels * self.repeats) - output_channels
self.dimension_change_permute0 = Permute((2, 1))
self.dimension_change_layer = Cropping1D((0, cropping_amount))
self.dimension_change_permute1 = Permute((2, 1))
else:
self.dimension_change_layer = None
if dropout_rate > 0:
self.dropout_layer = SpatialDropout1D(rate=dropout_rate)
else:
self.dropout_layer = None
def call(self, inputs, training=False, mask=None):
"""
The forward pass of the block.
:param inputs: The input tensor.
:param training: A boolean specifying if the layer should be in training mode.
:param mask: A mask for the input tensor.
:return: The output tensor of the layer.
"""
x = inputs
y = x
if self.batch_normalization is not None:
y = self.batch_normalization(y, training=training)
y = self.dimension_decrease_layer(y, training=training)
y = self.convolutional_layer(y, training=training)
y = self.dimension_increase_layer(y, training=training)
if self.pooling_layer is not None:
x = self.pooling_layer(x, training=training)
y = self.pooling_layer(y, training=training)
if self.dimension_change_layer is not None:
x = tensorflow.repeat(x, self.repeats, axis=2)
x = self.dimension_change_permute0(x, training=training)
x = self.dimension_change_layer(x, training=training)
x = self.dimension_change_permute1(x, training=training)
if self.dropout_layer is not None:
y = self.dropout_layer(y, training=training)
z = x + y
return z
class BottleNeckResidualLightCurveNetworkBlockMainPathRepeatMainPathDropout(Layer):
def __init__(self, output_channels: int, input_channels: Optional[int] = None, kernel_size: int = 3,
pooling_size: int = 1, batch_normalization: bool = True, dropout_rate: float = 0.0, l2_regularization: float = 0.0):
super().__init__()
leaky_relu = LeakyReLU(alpha=0.01)
dimension_decrease_factor = 4
if batch_normalization:
self.batch_normalization = BatchNormalization(scale=False)
else:
self.batch_normalization = None
if l2_regularization > 0:
l2_regularizer = L2(l2_regularization)
else:
l2_regularizer = None
self.dimension_decrease_layer = Convolution1D(
output_channels // dimension_decrease_factor, kernel_size=1, activation=leaky_relu, kernel_regularizer=l2_regularizer)
self.convolutional_layer = Convolution1D(
output_channels // dimension_decrease_factor, kernel_size=kernel_size, activation=leaky_relu,
padding='same', kernel_regularizer=l2_regularizer)
self.dimension_increase_layer = Convolution1D(
output_channels, kernel_size=1, activation=leaky_relu, kernel_regularizer=l2_regularizer)
if pooling_size > 1:
self.pooling_layer = MaxPooling1D(pool_size=pooling_size, padding='same')
else:
self.pooling_layer = None
if input_channels is not None and output_channels != input_channels:
if output_channels < input_channels:
raise NotImplementedError(f'Residual blocks with less output channels than input channels is not'
f'implemented. Output channels was {output_channels} and input was'
f'{input_channels}')
self.repeats = math.ceil(output_channels / input_channels)
cropping_amount = (input_channels * self.repeats) - output_channels
self.dimension_change_permute0 = Permute((2, 1))
self.dimension_change_layer = Cropping1D((0, cropping_amount))
self.dimension_change_permute1 = Permute((2, 1))
else:
self.dimension_change_layer = None
if dropout_rate > 0:
self.dropout_layer = SpatialDropout1D(rate=dropout_rate)
else:
self.dropout_layer = None
def call(self, inputs, training=False, mask=None):
"""
The forward pass of the block.
:param inputs: The input tensor.
:param training: A boolean specifying if the layer should be in training mode.
:param mask: A mask for the input tensor.
:return: The output tensor of the layer.
"""
x = inputs
y = x
if self.batch_normalization is not None:
y = self.batch_normalization(y, training=training)
y = self.dimension_decrease_layer(y, training=training)
y = self.convolutional_layer(y, training=training)
y = self.dimension_increase_layer(y, training=training)
if self.pooling_layer is not None:
x = self.pooling_layer(x, training=training)
y = self.pooling_layer(y, training=training)
if self.dimension_change_layer is not None:
x = tensorflow.repeat(x, self.repeats, axis=2)
x = self.dimension_change_permute0(x, training=training)
x = self.dimension_change_layer(x, training=training)
x = self.dimension_change_permute1(x, training=training)
z = x + y
if self.dropout_layer is not None:
z = self.dropout_layer(z, training=training)
return z
class BottleNeckResidualLightCurveNetworkBlockEveryWeightBatchNorm(Layer):
def __init__(self, output_channels: int, input_channels: Optional[int] = None, kernel_size: int = 3,
pooling_size: int = 1, batch_normalization: bool = True, dropout_rate: float = 0.0, l2_regularization: float = 0.0):
super().__init__()
leaky_relu = LeakyReLU(alpha=0.01)
dimension_decrease_factor = 4
if batch_normalization:
self.batch_normalization = BatchNormalization(scale=False)
self.batch_normalization1 = BatchNormalization(scale=False)
self.batch_normalization2 = BatchNormalization(scale=False)
else:
self.batch_normalization = None
if l2_regularization > 0:
l2_regularizer = L2(l2_regularization)
else:
l2_regularizer = None
self.dimension_decrease_layer = Convolution1D(
output_channels // dimension_decrease_factor, kernel_size=1, activation=leaky_relu, kernel_regularizer=l2_regularizer)
self.convolutional_layer = Convolution1D(
output_channels // dimension_decrease_factor, kernel_size=kernel_size, activation=leaky_relu,
padding='same', kernel_regularizer=l2_regularizer)
self.dimension_increase_layer = Convolution1D(
output_channels, kernel_size=1, activation=leaky_relu, kernel_regularizer=l2_regularizer)
if pooling_size > 1:
self.pooling_layer = MaxPooling1D(pool_size=pooling_size, padding='same')
else:
self.pooling_layer = None
if input_channels is not None and output_channels != input_channels:
if output_channels < input_channels:
raise NotImplementedError(f'Residual blocks with less output channels than input channels is not'
f'implemented. Output channels was {output_channels} and input was'
f'{input_channels}')
self.dimension_change_permute0 = Permute((2, 1))
self.dimension_change_layer = ZeroPadding1D(padding=(0, output_channels - input_channels))
self.dimension_change_permute1 = Permute((2, 1))
else:
self.dimension_change_layer = None
if dropout_rate > 0:
self.dropout_layer = SpatialDropout1D(rate=dropout_rate)
else:
self.dropout_layer = None
def call(self, inputs, training=False, mask=None):
"""
The forward pass of the block.
:param inputs: The input tensor.
:param training: A boolean specifying if the layer should be in training mode.
:param mask: A mask for the input tensor.
:return: The output tensor of the layer.
"""
x = inputs
y = x
if self.batch_normalization is not None:
y = self.batch_normalization(y, training=training)
y = self.dimension_decrease_layer(y, training=training)
if self.batch_normalization is not None:
y = self.batch_normalization1(y, training=training)
y = self.convolutional_layer(y, training=training)
if self.batch_normalization is not None:
y = self.batch_normalization2(y, training=training)
y = self.dimension_increase_layer(y, training=training)
if self.pooling_layer is not None:
x = self.pooling_layer(x, training=training)
y = self.pooling_layer(y, training=training)
if self.dimension_change_layer is not None:
x = self.dimension_change_permute0(x, training=training)
x = self.dimension_change_layer(x, training=training)
x = self.dimension_change_permute1(x, training=training)
if self.dropout_layer is not None:
y = self.dropout_layer(y, training=training)
return x + y
class BottleNeckResidualLightCurveNetworkBlockEveryWeightBatchNormBnAfterActivations(Layer):
def __init__(self, output_channels: int, input_channels: Optional[int] = None, kernel_size: int = 3,
pooling_size: int = 1, batch_normalization: bool = True, dropout_rate: float = 0.0, l2_regularization: float = 0.0):
super().__init__()
leaky_relu = LeakyReLU(alpha=0.01)
dimension_decrease_factor = 4
if batch_normalization:
self.batch_normalization = BatchNormalization(scale=False)
self.batch_normalization1 = BatchNormalization(scale=False)
self.batch_normalization2 = BatchNormalization(scale=False)
else:
self.batch_normalization = None
if l2_regularization > 0:
l2_regularizer = L2(l2_regularization)
else:
l2_regularizer = None
self.dimension_decrease_layer = Convolution1D(
output_channels // dimension_decrease_factor, kernel_size=1, activation=leaky_relu, kernel_regularizer=l2_regularizer)
self.convolutional_layer = Convolution1D(
output_channels // dimension_decrease_factor, kernel_size=kernel_size, activation=leaky_relu,
padding='same', kernel_regularizer=l2_regularizer)
self.dimension_increase_layer = Convolution1D(
output_channels, kernel_size=1, activation=leaky_relu, kernel_regularizer=l2_regularizer)
if pooling_size > 1:
self.pooling_layer = MaxPooling1D(pool_size=pooling_size, padding='same')
else:
self.pooling_layer = None
if input_channels is not None and output_channels != input_channels:
if output_channels < input_channels:
raise NotImplementedError(f'Residual blocks with less output channels than input channels is not'
f'implemented. Output channels was {output_channels} and input was'
f'{input_channels}')
self.dimension_change_permute0 = Permute((2, 1))
self.dimension_change_layer = ZeroPadding1D(padding=(0, output_channels - input_channels))
self.dimension_change_permute1 = Permute((2, 1))
else:
self.dimension_change_layer = None
if dropout_rate > 0:
self.dropout_layer = SpatialDropout1D(rate=dropout_rate)
else:
self.dropout_layer = None
def call(self, inputs, training=False, mask=None):
"""
The forward pass of the block.
:param inputs: The input tensor.
:param training: A boolean specifying if the layer should be in training mode.
:param mask: A mask for the input tensor.
:return: The output tensor of the layer.
"""
x = inputs
y = x
y = self.dimension_decrease_layer(y, training=training)
if self.batch_normalization is not None:
y = self.batch_normalization(y, training=training)
y = self.convolutional_layer(y, training=training)
if self.batch_normalization is not None:
y = self.batch_normalization1(y, training=training)
y = self.dimension_increase_layer(y, training=training)
if self.batch_normalization is not None:
y = self.batch_normalization2(y, training=training)
if self.pooling_layer is not None:
x = self.pooling_layer(x, training=training)
y = self.pooling_layer(y, training=training)
if self.dimension_change_layer is not None:
x = self.dimension_change_permute0(x, training=training)
x = self.dimension_change_layer(x, training=training)
x = self.dimension_change_permute1(x, training=training)
if self.dropout_layer is not None:
y = self.dropout_layer(y, training=training)
return x + y
| 51.26484
| 133
| 0.654494
| 2,564
| 22,454
| 5.50351
| 0.047972
| 0.062646
| 0.06463
| 0.040536
| 0.948338
| 0.947913
| 0.946992
| 0.945078
| 0.945078
| 0.945078
| 0
| 0.015393
| 0.270909
| 22,454
| 438
| 134
| 51.26484
| 0.846558
| 0.062706
| 0
| 0.951351
| 0
| 0
| 0.045391
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.032432
| false
| 0
| 0.016216
| 0
| 0.081081
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
885c08160ec0228ec3fb1ec2170761dacf987d5c
| 2,838
|
py
|
Python
|
lanro/environments/panda_nl.py
|
knowledgetechnologyuhh/lannro-gym
|
ac3afcf7d8ed854d75368135b023edf055644dd2
|
[
"MIT"
] | null | null | null |
lanro/environments/panda_nl.py
|
knowledgetechnologyuhh/lannro-gym
|
ac3afcf7d8ed854d75368135b023edf055644dd2
|
[
"MIT"
] | null | null | null |
lanro/environments/panda_nl.py
|
knowledgetechnologyuhh/lannro-gym
|
ac3afcf7d8ed854d75368135b023edf055644dd2
|
[
"MIT"
] | null | null | null |
from lanro.robotenv import RobotLanguageEnv
from lanro.simulation import PyBulletSimulation
from lanro.robots import Panda
from lanro.tasks import NLReach, NLLift, NLGrasp, NLPush
class PandaNLReachEnv(RobotLanguageEnv):
def __init__(self,
render=False,
num_obj=2,
obs_type="state",
mode='Color',
action_type='end_effector',
use_hindsight_instructions=False,
camera_mode='ego'):
sim = PyBulletSimulation(render=render)
robot = Panda(sim, fixed_gripper=True, action_type=action_type, camera_mode=camera_mode)
task = NLReach(sim, robot, num_obj=num_obj, mode=mode, use_hindsight_instructions=use_hindsight_instructions)
RobotLanguageEnv.__init__(self, sim, robot, task, obs_type=obs_type)
class PandaNLPushEnv(RobotLanguageEnv):
def __init__(self,
render=False,
num_obj=2,
obs_type="state",
mode='Color',
action_type='end_effector',
use_hindsight_instructions=False,
camera_mode='ego'):
sim = PyBulletSimulation(render=render)
robot = Panda(sim, fixed_gripper=True, action_type=action_type, camera_mode=camera_mode)
task = NLPush(sim, robot, num_obj=num_obj, mode=mode, use_hindsight_instructions=use_hindsight_instructions)
RobotLanguageEnv.__init__(self, sim, robot, task, obs_type=obs_type)
class PandaNLGraspEnv(RobotLanguageEnv):
def __init__(self,
render=False,
num_obj=2,
obs_type="state",
mode='Color',
action_type='end_effector',
use_hindsight_instructions=False,
camera_mode='ego'):
sim = PyBulletSimulation(render=render)
robot = Panda(sim, fixed_gripper=False, action_type=action_type, camera_mode=camera_mode)
task = NLGrasp(sim, robot, num_obj=num_obj, mode=mode, use_hindsight_instructions=use_hindsight_instructions)
RobotLanguageEnv.__init__(self, sim, robot, task, obs_type=obs_type)
class PandaNLLiftEnv(RobotLanguageEnv):
def __init__(self,
render=False,
num_obj=2,
obs_type="state",
mode='Color',
action_type='end_effector',
use_hindsight_instructions=False,
camera_mode='ego'):
sim = PyBulletSimulation(render=render)
robot = Panda(sim, fixed_gripper=False, action_type=action_type, camera_mode=camera_mode)
task = NLLift(sim, robot, num_obj=num_obj, mode=mode, use_hindsight_instructions=use_hindsight_instructions)
RobotLanguageEnv.__init__(self, sim, robot, task, obs_type=obs_type)
| 41.130435
| 117
| 0.639887
| 307
| 2,838
| 5.550489
| 0.149837
| 0.042254
| 0.169014
| 0.06338
| 0.857394
| 0.857394
| 0.857394
| 0.857394
| 0.857394
| 0.857394
| 0
| 0.001943
| 0.274489
| 2,838
| 68
| 118
| 41.735294
| 0.825644
| 0
| 0
| 0.785714
| 0
| 0
| 0.035236
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.071429
| false
| 0
| 0.071429
| 0
| 0.214286
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
885ccee02de743da8ca3d6f333ef4043fa96fa84
| 1,330
|
py
|
Python
|
tests/challenges/--test_multi_bracket_validation.py
|
joseph-zabaleta/data-structures-and-algorithms
|
b11b5ef50f52e3d505474fe5fffe4357933da251
|
[
"MIT"
] | null | null | null |
tests/challenges/--test_multi_bracket_validation.py
|
joseph-zabaleta/data-structures-and-algorithms
|
b11b5ef50f52e3d505474fe5fffe4357933da251
|
[
"MIT"
] | null | null | null |
tests/challenges/--test_multi_bracket_validation.py
|
joseph-zabaleta/data-structures-and-algorithms
|
b11b5ef50f52e3d505474fe5fffe4357933da251
|
[
"MIT"
] | null | null | null |
import pytest
from dsa.challenges.multi_bracket_validation.multi_bracket_validation import multi_bracket_validation
def test_mbv_exists():
assert multi_bracket_validation('test')
def test_mbv_one():
string = '{}'
actual = multi_bracket_validation(string)
expected = True
assert actual == expected
def test_mbv_two():
string = '{}(){}'
actual = multi_bracket_validation(string)
expected = True
assert actual == expected
def test_mbv_three():
string = '()[[Extra Characters]]'
actual = multi_bracket_validation(string)
expected = True
assert actual == expected
def test_mbv_four():
string = '(){}[[]]'
actual = multi_bracket_validation(string)
expected = True
assert actual == expected
def test_mbv_five():
string = '{}{Code}[Fellows](())'
actual = multi_bracket_validation(string)
expected = True
assert actual == expected
def test_mbv_six():
string = '[({}]'
actual = multi_bracket_validation(string)
expected = False
assert actual == expected
def test_mbv_seven():
string = '(]('
actual = multi_bracket_validation(string)
expected = False
assert actual == expected
def test_mbv_eight():
string = '{(})'
actual = multi_bracket_validation(string)
expected = False
assert actual == expected
| 24.62963
| 101
| 0.682707
| 148
| 1,330
| 5.851351
| 0.202703
| 0.166282
| 0.30485
| 0.258661
| 0.735566
| 0.735566
| 0.735566
| 0.735566
| 0.735566
| 0.735566
| 0
| 0
| 0.204511
| 1,330
| 53
| 102
| 25.09434
| 0.818526
| 0
| 0
| 0.545455
| 0
| 0
| 0.056391
| 0.015789
| 0
| 0
| 0
| 0
| 0.204545
| 1
| 0.204545
| false
| 0
| 0.045455
| 0
| 0.25
| 0
| 0
| 0
| 0
| null | 0
| 1
| 1
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
888adaabab43b637bbf2f7296c8e1d305fe652c9
| 1,023
|
py
|
Python
|
pymore/iterators_test.py
|
dabacon/pymore
|
2a7bea5669d50d814ae89de3919c5544f9c7fd29
|
[
"Apache-2.0"
] | 3
|
2021-02-13T22:58:06.000Z
|
2021-11-17T10:52:42.000Z
|
pymore/iterators_test.py
|
dabacon/pymore
|
2a7bea5669d50d814ae89de3919c5544f9c7fd29
|
[
"Apache-2.0"
] | null | null | null |
pymore/iterators_test.py
|
dabacon/pymore
|
2a7bea5669d50d814ae89de3919c5544f9c7fd29
|
[
"Apache-2.0"
] | null | null | null |
import pytest
import pymore
def test_first_no_predicate():
assert pymore.first([1, 2, 3]) == 1
assert pymore.first((1, 2, 3)) == 1
assert pymore.first(x for x in range(4)) == 0
def test_first_predicate():
assert pymore.first([1, 3, 5], lambda x: x == 3) == 3
assert pymore.first((1, 3, 5), lambda x: x == 3) == 3
assert pymore.first((x for x in range(4)), lambda x: x == 3) == 3
def test_first_default():
assert pymore.first([], default=3) == 3
assert pymore.first([1, 3, 5], lambda x: x == 2, default=7) == 7
assert pymore.first((1, 3, 5), lambda x: x == 2, default=7) == 7
assert pymore.first((x for x in range(3)), lambda x: x == 5, default=7) == 7
def test_first_no_match_no_default():
with pytest.raises(ValueError, match="no default"):
_ = pymore.first([])
with pytest.raises(ValueError, match="no default"):
_ = pymore.first(tuple())
with pytest.raises(ValueError, match="no default"):
_ = pymore.first([1, 3, 5], lambda x: x == 2)
| 33
| 80
| 0.608993
| 167
| 1,023
| 3.640719
| 0.161677
| 0.235197
| 0.279605
| 0.177632
| 0.759868
| 0.713816
| 0.713816
| 0.713816
| 0.694079
| 0.379934
| 0
| 0.05618
| 0.217009
| 1,023
| 30
| 81
| 34.1
| 0.702871
| 0
| 0
| 0.136364
| 0
| 0
| 0.029326
| 0
| 0
| 0
| 0
| 0
| 0.454545
| 1
| 0.181818
| false
| 0
| 0.090909
| 0
| 0.272727
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
88acdc1e294eb9706968579b85777c6b2b5abc11
| 1,081
|
py
|
Python
|
Solutions/grid-printer/auto_test.py
|
UWPCE-PythonCert/InstructorResources
|
13b1cfa4cad6a8c3491f8a602c8afda5400c9ac7
|
[
"Unlicense"
] | null | null | null |
Solutions/grid-printer/auto_test.py
|
UWPCE-PythonCert/InstructorResources
|
13b1cfa4cad6a8c3491f8a602c8afda5400c9ac7
|
[
"Unlicense"
] | 1
|
2020-12-20T17:07:17.000Z
|
2020-12-20T17:07:17.000Z
|
Solutions/grid-printer/auto_test.py
|
UWPCE-PythonCert/InstructorResources
|
13b1cfa4cad6a8c3491f8a602c8afda5400c9ac7
|
[
"Unlicense"
] | null | null | null |
import grid_printer
expected_grid1 = \
"""+ - - - - + - - - - +
| | |
| | |
| | |
| | |
+ - - - - + - - - - +
| | |
| | |
| | |
| | |
+ - - - - + - - - - +
"""
assert grid_printer.print_grid1() == expected_grid1
expected_grid2 = \
"""+ - + - +
| | |
+ - + - +
| | |
+ - + - +
"""
assert grid_printer.print_grid2(3) == expected_grid2
expected_grid3 = \
"""+ - - - - + - - - - + - - - - +
| | | |
| | | |
| | | |
| | | |
+ - - - - + - - - - + - - - - +
| | | |
| | | |
| | | |
| | | |
+ - - - - + - - - - + - - - - +
| | | |
| | | |
| | | |
| | | |
+ - - - - + - - - - + - - - - +
"""
assert grid_printer.print_grid3(3, 4) == expected_grid3
| 22.061224
| 55
| 0.177613
| 33
| 1,081
| 5.424242
| 0.333333
| 0.24581
| 0.284916
| 0.368715
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.028436
| 0.609621
| 1,081
| 48
| 56
| 22.520833
| 0.395735
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.428571
| 1
| 0
| false
| 0
| 0.142857
| 0
| 0.142857
| 0.571429
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 7
|
88b55a436d97f609e7becdd5cf922203f1eab3db
| 1,124
|
py
|
Python
|
tests/test_response_model_invalid.py
|
Aryabhata-Rootspring/fastapi
|
f6237ad05a8468ac19c591181adad38d75372c46
|
[
"MIT"
] | 53,007
|
2018-12-08T10:05:29.000Z
|
2022-03-31T23:30:02.000Z
|
tests/test_response_model_invalid.py
|
Aryabhata-Rootspring/fastapi
|
f6237ad05a8468ac19c591181adad38d75372c46
|
[
"MIT"
] | 4,155
|
2019-01-05T05:07:49.000Z
|
2022-03-31T21:25:38.000Z
|
tests/test_response_model_invalid.py
|
Aryabhata-Rootspring/fastapi
|
f6237ad05a8468ac19c591181adad38d75372c46
|
[
"MIT"
] | 4,092
|
2018-12-09T16:21:00.000Z
|
2022-03-31T07:59:45.000Z
|
from typing import List
import pytest
from fastapi import FastAPI
from fastapi.exceptions import FastAPIError
class NonPydanticModel:
pass
def test_invalid_response_model_raises():
with pytest.raises(FastAPIError):
app = FastAPI()
@app.get("/", response_model=NonPydanticModel)
def read_root():
pass # pragma: nocover
def test_invalid_response_model_sub_type_raises():
with pytest.raises(FastAPIError):
app = FastAPI()
@app.get("/", response_model=List[NonPydanticModel])
def read_root():
pass # pragma: nocover
def test_invalid_response_model_in_responses_raises():
with pytest.raises(FastAPIError):
app = FastAPI()
@app.get("/", responses={"500": {"model": NonPydanticModel}})
def read_root():
pass # pragma: nocover
def test_invalid_response_model_sub_type_in_responses_raises():
with pytest.raises(FastAPIError):
app = FastAPI()
@app.get("/", responses={"500": {"model": List[NonPydanticModel]}})
def read_root():
pass # pragma: nocover
| 24.434783
| 75
| 0.656584
| 122
| 1,124
| 5.803279
| 0.237705
| 0.110169
| 0.079096
| 0.124294
| 0.830508
| 0.792373
| 0.792373
| 0.792373
| 0.792373
| 0.718927
| 0
| 0.006977
| 0.234875
| 1,124
| 45
| 76
| 24.977778
| 0.816279
| 0.05605
| 0
| 0.566667
| 0
| 0
| 0.018939
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.266667
| false
| 0.166667
| 0.133333
| 0
| 0.433333
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
|
0
| 8
|
ee0dbec074bf8937e476b72edb256fd8cae58957
| 11,939
|
py
|
Python
|
Chap6/extinction.py
|
dtiarks/ThesisPlot
|
b9eaa5f2b2c472667cb17b2ba5a0471c741f0abe
|
[
"MIT"
] | 1
|
2018-12-27T01:35:40.000Z
|
2018-12-27T01:35:40.000Z
|
Chap6/extinction.py
|
dtiarks/ThesisPlot
|
b9eaa5f2b2c472667cb17b2ba5a0471c741f0abe
|
[
"MIT"
] | null | null | null |
Chap6/extinction.py
|
dtiarks/ThesisPlot
|
b9eaa5f2b2c472667cb17b2ba5a0471c741f0abe
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
"""
Created on Fri Jan 27 10:53:04 2017
@author: mpqqubec
"""
import locale
# Set to German locale to get comma decimal separater
locale.setlocale(locale.LC_NUMERIC, 'deu_deu')
import Tomography as tom
#import Tomography_old_detection as tom_o
import EIT
import quPy as qp
#from scipy.optimize import curve_fit
import numpy as np
import matplotlib as mpl
import pylab as plt
import pandas as pd
import json
import io
mpl.use('pgf')
RYD_BLUE = '#85c1fe' # the color for the rydberg data
watt_factor = 2.54591
BINNING = 0.05
LC = '6' # LoadCycles
data_dir = './extinction_data/'
filelist = [data_dir + "gs_store_001/photons%d" % i for i in xrange(1, 264)]
datafields = [qp.MicrosecondsSinceTrigger]
categorizers = [qp.POL, qp.SPCM, qp.ROI, qp.N_EXP]
t_min=0
t_max=1000
from_fileheader=['TomographyBases', 'CycleID', 'ExecExtParam0', 'LoadCycles']
profile = [datafields, categorizers, {}, t_min, t_max, from_fileheader]
ROIs = [[10.4,11.1],[15.2,16.4],[20,100]]
r0, r1, r2 = np.ravel(np.diff(ROIs, axis=1)) # ROI lengths, for count rates
# determine dark counts from all files
T = tom.Tomography(filelist, profile, ROIs)
c, c_err = T.getCounts(['LoadCycles','ROI','pol'],True)
dark_L = 0 #c[(LC,2,'L')]/r2 * 1e6 * 1e-3
dark_L_err = 0 # c_err[(LC,2,'L')]/r2 * 1e6 * 1e-3
dark_R = 0 # c[(LC,2,'R')]/r2 * 1e6 * 1e-3
dark_R_err = 0 #c_err[(LC,2,'R')]/r2 * 1e6 * 1e-3
print T.getCounts(['ROI','spcm'],True)[0][2]/r2 *1e6
print "Dark count rates: %.4f +- %.4f kHz L and %.4f +- %.4f kHz R" % (dark_L, dark_L_err, dark_R, dark_R_err)
profile[2]={'ExecExtParam0':'0.3'} # filter out lowest photon number
T = tom.Tomography(filelist, profile, ROIs)
h_ret, h_ret_err = T.getHisto(BINNING,[14.5,17.], 'rel', ['LoadCycles','pol'], True)
h_ret = h_ret[LC]/BINNING*1e6*1e-3
h_ret_err = h_ret_err[LC]/BINNING*1e6*1e-3
h_in, h_in_err = T.getHisto(BINNING,[10.,11.5], 'rel', ['LoadCycles','pol'], True)
h_in = h_in['0']/BINNING*1e6*1e-3
h_in_err = h_in_err['0']/BINNING*1e6*1e-3
# calculate extinction
c, c_err = T.getCounts(['LoadCycles','ROI','pol'],True)
cR = c[(LC,1,'R')]/r1 * 1e6 * 1e-3 # divide by ROI width in µs and multiply by 1/(µ*k) to get mean count rate in kHz
cRe = c_err[(LC,1,'R')]/r1 * 1e6 * 1e-3
cL = c[(LC,1,'L')]/r1 * 1e6 * 1e-3
cLe = c_err[(LC,1,'L')]/r1 * 1e6 * 1e-3
cR0 = c[('0',0,'R')]/r0 * 1e6 * 1e-3
cRe0 = c_err[('0',0,'R')]/r0 * 1e6 * 1e-3
cL0 = c[('0',0,'L')]/r0 * 1e6 * 1e-3
cLe0 = c_err[('0',0,'L')]/r0 * 1e6 * 1e-3
eta_rw = 100 * cR*r1 / (cR0*r0) # must multiply by ROI width again to get correct efficiency
eta_rw_err = eta_rw * np.sqrt((cRe/cR)**2 + (cRe0/cR0)**2)
N_in = (c[('0',0,'R')] + c[('0',0,'L')])*10
print "Retrieval: %.2f +- %.2f kHz R %.2f +- %.2f kHz L\nInput: %.2f +- %.2f kHz R %.2f +- %.2f kHz L" % (cR, cRe, cL, cLe, cR0, cRe0, cL0, cLe0)
ext = (cL- dark_L)/(cR - dark_R)
ext_err = np.sqrt( (cLe/(cR-dark_R))**2 + (dark_L_err/(cR-dark_R))**2 + (ext*cRe/(cR-dark_R))**2 + (ext*dark_R_err/(cR - dark_R))**2 )
ext0 = (cL0 - dark_L)/(cR0- dark_R)
ext0_err = np.sqrt( (cLe0/(cR0-dark_R))**2 + (dark_L_err/(cR0-dark_R))**2 + (ext0*cRe0/(cR0-dark_R))**2 + (ext0*dark_R_err/(cR0 - dark_R))**2 )
bgc=(.8,.8,.8,1) # background color for the axes
f = plt.figure(0)
ax = plt.subplot(221, axisbg=bgc)
ax.axvspan(*ROIs[0], color='white')
plt.errorbar(h_in[('L')].index.values, h_in[('L')], yerr=h_in_err[('L')].values, marker = 'o', color='#85c1fe', label="L")
plt.errorbar(h_in[('R')].index.values, h_in[('R')], yerr=h_in_err[('R')].values, marker = 'o', color='orange', label="R")
plt.ylabel(u"Zählrate (kHz)")
ax.locator_params(tight=True, nbins=6)
plt.legend(title='Polarisation', fancybox=True, loc='upper left')
plt.margins(0,.1)
plt.ylim(ymin=0)
#plt.text(10.83,20, r'$N_{\mathrm{in}} = \SI{%.1f}{}$' % N_in + '\n' +
# r'$\epsilon_{R\mathrm{, in}} = \SI{%s}{\percent}$' % un2str(ext0*100, ext0_err*100), ha='center' )
plot_dict={}
h_L=pd.DataFrame(index=h_in[('L')].index.values-11.0,data=watt_factor*h_in[('L')].values)
h_L_err=pd.DataFrame(index=h_in[('L')].index.values-11.0,data=watt_factor*h_in_err[('L')].values)
h_R=pd.DataFrame(index=h_in[('R')].index.values-11.0,data=watt_factor*h_in[('R')].values)
h_R_err=pd.DataFrame(index=h_in[('R')].index.values-11.0,data=watt_factor*h_in_err[('R')].values)
plot_dict['221']={
'A':{'type':'errorbar','y':h_L[0].to_json(),'yerr':h_L_err[0].to_json(),'ylabel':r'Leistung (fW)','num':'a','label':'L','xlim':(-0.6,0.2001),'ylim':(-watt_factor*16,watt_factor*160)} ,
'B':{'type':'errorbar','y':h_R[0].to_json(),'yerr':h_R_err[0].to_json(),'label':'R'}
}
ax = plt.subplot(222, axisbg=bgc)
ax.axvspan(*ROIs[1], color='white')
plt.errorbar(h_ret[('L')].index.values, h_ret[('L')], yerr=h_ret_err[('L')].values, marker='o', color='#85c1fe', label='L')
plt.errorbar(h_ret[('R')].index.values, h_ret[('R')], yerr=h_ret_err[('R')].values, marker='o', color='orange', label='R')
ax.locator_params(tight=True, nbins=6)
plt.margins(0,.1)
plt.ylim(ymin=0)
#plt.text(16,8, r'$\epsilon_R = \SI{%s}{\percent}$' % (un2str(ext*100, ext_err*100)) + '\n' +
# r'$\eta_{\mathrm{rw}} = \SI{%s}{\percent}$' % un2str(eta_rw, eta_rw_err), ha='center' )
h_L=pd.DataFrame(index=h_ret[('L')].index.values-11.0,data=watt_factor*h_ret[('L')].values)
h_L_err=pd.DataFrame(index=h_ret[('L')].index.values-11.0,data=watt_factor*h_ret_err[('L')].values)
h_R=pd.DataFrame(index=h_ret[('R')].index.values-11.0,data=watt_factor*h_ret[('R')].values)
h_R_err=pd.DataFrame(index=h_ret[('R')].index.values-11.0,data=watt_factor*h_ret_err[('R')].values)
plot_dict['222']={
'A':{'type':'errorbar','y':h_L[0].to_json(),'yerr':h_L_err[0].to_json(),'num':'b','label':'L','xlim':(4.0,5.5),'ylim':(-watt_factor*1.4,watt_factor*14)} ,
'B':{'type':'errorbar','y':h_R[0].to_json(),'yerr':h_R_err[0].to_json(),'label':'R'}
}
# LOWER PLOTS
BINNING = 0.03
LC = '6' # LoadCycles
#data_dir = 'W:/data/2017/01/260117/'
#data_dir = '../../../DataAnalysis/260117/'
filelist = [data_dir + "rydberg_store_001/photons%d" % i for i in xrange(1, 116)]
datafields = [qp.MicrosecondsSinceTrigger]
categorizers = [qp.POL, qp.SPCM, qp.ROI, qp.N_EXP]
t_min=0
t_max=1000
from_fileheader=['TomographyBases', 'CycleID', 'ExecExtParam0', 'LoadCycles']
profile = [datafields, categorizers, {}, t_min, t_max, from_fileheader]
ROIs = [[10.4,11.1],[15.2,15.9],[20,100]]
r0, r1, r2 = np.ravel(np.diff(ROIs, axis=1)) # ROI lengths, for count rates
# determine dark counts from all files
T = tom.Tomography(filelist, profile, ROIs)
c, c_err = T.getCounts(['LoadCycles','ROI','pol'],True)
dark_L = 0 #c[(LC,2,'L')]/r2 * 1e6 * 1e-3
dark_L_err = 0# c_err[(LC,2,'L')]/r2 * 1e6 * 1e-3
dark_R = 0 #c[(LC,2,'R')]/r2 * 1e6 * 1e-3
dark_R_err = 0 #c_err[(LC,2,'R')]/r2 * 1e6 * 1e-3
print T.getCounts(['ROI','spcm'],True)[0][2]/r2 *1e6
print "Dark count rates: %.4f +- %.4f kHz L and %.4f +- %.4f kHz R" % (dark_L, dark_L_err, dark_R, dark_R_err)
profile[2]={'ExecExtParam0':'0.3'} # filter out lowest photon number
T = tom.Tomography(filelist, profile, ROIs)
h_ret, h_ret_err = T.getHisto(BINNING,[14.5,17.], 'rel', ['LoadCycles','pol'], True)
h_ret = h_ret[LC]/BINNING*1e6*1e-3
h_ret_err = h_ret_err[LC]/BINNING*1e6*1e-3
h_in, h_in_err = T.getHisto(BINNING,[10.,11.4], 'rel', ['LoadCycles','pol'], True)
h_in = h_in['0']/BINNING*1e6*1e-3
h_in_err = h_in_err['0']/BINNING*1e6*1e-3
# calculate extinction
c, c_err = T.getCounts(['LoadCycles','ROI','pol'],True)
cR = c[(LC,1,'R')]/r1 * 1e6 * 1e-3 # divide by ROI width in µs and multiply by 1/(µ*k) to get mean count rate in kHz
cRe = c_err[(LC,1,'R')]/r1 * 1e6 * 1e-3
cL = c[(LC,1,'L')]/r1 * 1e6 * 1e-3
cLe = c_err[(LC,1,'L')]/r1 * 1e6 * 1e-3
cR0 = c[('0',0,'R')]/r0 * 1e6 * 1e-3
cRe0 = c_err[('0',0,'R')]/r0 * 1e6 * 1e-3
cL0 = c[('0',0,'L')]/r0 * 1e6 * 1e-3
cLe0 = c_err[('0',0,'L')]/r0 * 1e6 * 1e-3
eta_rw = 100 * (cL*r1) / (cL0*r0) # must multiply by ROI width again to get correct efficiency
eta_rw_err = eta_rw * np.sqrt((cLe/cL)**2 + (cLe0/cL0)**2)
N_in = (c[('0',0,'R')] + c[('0',0,'L')])*10
print "Retrieval: %.2f +- %.2f kHz R %.2f +- %.2f kHz L\nInput: %.2f +- %.2f kHz R %.2f +- %.2f kHz L" % (cR, cRe, cL, cLe, cR0, cRe0, cL0, cLe0)
ext = (cR - dark_R)/(cL- dark_L)
ext_err = np.sqrt( (cRe/(cL-dark_L))**2 + (dark_R_err/(cL-dark_L))**2 + (ext*cLe/(cL-dark_L))**2 + (ext*dark_L_err/(cL - dark_L))**2 )
ext0 = (cR0 - dark_R)/(cL0- dark_L)
ext0_err = np.sqrt( (cRe0/(cL0-dark_L))**2 + (dark_R_err/(cL0-dark_L))**2 + (ext0*cLe0/(cL0-dark_L))**2 + (ext0*dark_L_err/(cL0 - dark_L))**2 )
ax = plt.subplot(223, axisbg=bgc)
ax.axvspan(*ROIs[0], color='white')
plt.errorbar(h_in[('L')].index.values, h_in[('L')], yerr=h_in_err[('L')].values, marker = 'o', color='#85c1fe', label="L")
plt.errorbar(h_in[('R')].index.values, h_in[('R')], yerr=h_in_err[('R')].values, marker = 'o', color='orange', label="R")
plt.ylabel(u"Zählrate (kHz)")
plt.xlabel(u"Zeit (\si{\micro\second})")
#plt.legend(title='Polarisation', fancybox=True)
#plt.axvline(x=ROIs[0][0], color='k', ls='solid')
#plt.axvline(x=ROIs[0][1], color='k', ls='solid')
ax.locator_params(tight=True, nbins=6)
plt.margins(0,.1)
plt.ylim(ymin=0)
#plt.text(10.6,175, r'$N_{\mathrm{in}} = \SI{%.1f}{}$' % N_in + '\n' +
# r'$\epsilon_{L\mathrm{, in}} = \SI{%s}{\percent}$' % un2str(ext0*100, ext0_err*100), ha='center' )
h_L=pd.DataFrame(index=h_in[('L')].index.values-11.0,data=watt_factor*h_in[('L')].values)
h_L_err=pd.DataFrame(index=h_in[('L')].index.values-11.0,data=watt_factor*h_in_err[('L')].values)
h_R=pd.DataFrame(index=h_in[('R')].index.values-11.0,data=watt_factor*h_in[('R')].values)
h_R_err=pd.DataFrame(index=h_in[('R')].index.values-11.0,data=watt_factor*h_in_err[('R')].values)
plot_dict['223']={
'A':{'type':'errorbar','y':h_L[0].to_json(),'yerr':h_L_err[0].to_json(),'xlabel':r'Zeit ($\mu s$)','ylabel':r'Leistung (fW)','num':'c','label':'L','xlim':(-0.6,0.2001),'ylim':(-watt_factor*30,800)} ,
'B':{'type':'errorbar','y':h_R[0].to_json(),'yerr':h_R_err[0].to_json(),'label':'R'}
}
ax = plt.subplot(224, axisbg=bgc)
ax.axvspan(*ROIs[1], color='white')
plt.errorbar(h_ret[('L')].index.values, h_ret[('L')], yerr=h_ret_err[('L')].values, marker = 'o', color='#85c1fe', label='L')
plt.errorbar(h_ret[('R')].index.values, h_ret[('R')], yerr=h_ret_err[('R')].values, marker = 'o', color='orange', label='R')
#plt.ylabel(u"Zählrate (kHz)")
plt.xlabel(u"Zeit (\si{\micro\second})")
ax.locator_params(tight=True, nbins=6)
#plt.axvline(x=ROIs[1][0], color='k', ls='solid')
#plt.axvline(x=ROIs[1][1], color='k', ls='solid')
plt.margins(0,.1)
plt.ylim(ymin=0)
#plt.legend(title='Polarisation', fancybox=True)
#plt.text(15.65,10, r'$\epsilon_L = \SI{%s}{\percent}$' % (un2str(ext*100, ext_err*100)) + '\n' +
# r'$\eta_{\mathrm{rw}} = \SI{%s}{\percent}$' % un2str(eta_rw, eta_rw_err), ha='center' )
h_L=pd.DataFrame(index=h_ret[('L')].index.values-11.0,data=watt_factor*h_ret[('L')].values)
h_L_err=pd.DataFrame(index=h_ret[('L')].index.values-11.0,data=watt_factor*h_ret_err[('L')].values)
h_R=pd.DataFrame(index=h_ret[('R')].index.values-11.0,data=watt_factor*h_ret[('R')].values)
h_R_err=pd.DataFrame(index=h_ret[('R')].index.values-11.0,data=watt_factor*h_ret_err[('R')].values)
plot_dict['224']={
'A':{'type':'errorbar','y':h_L[0].to_json(),'yerr':h_L_err[0].to_json(),'xlabel':r'Zeit ($\mu s$)','xlim':(4.0,5.5),'ylim':(-watt_factor*2,watt_factor*20),'num':'d','label':'L'} ,
'B':{'type':'errorbar','y':h_R[0].to_json(),'yerr':h_R_err[0].to_json(),'label':'R'}
}
plt.tight_layout()
#savefig("extinction")
plt.show()
with io.open('memory_extinction.json', 'w+') as f:
f.write(unicode(json.dumps(plot_dict, ensure_ascii=False,indent=4)))
| 47.565737
| 203
| 0.624675
| 2,234
| 11,939
| 3.179946
| 0.122202
| 0.022523
| 0.027027
| 0.038288
| 0.845017
| 0.805884
| 0.793074
| 0.781954
| 0.76647
| 0.754505
| 0
| 0.06612
| 0.120864
| 11,939
| 251
| 204
| 47.565737
| 0.610709
| 0.183767
| 0
| 0.662857
| 0
| 0.011429
| 0.133873
| 0.007368
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.057143
| null | null | 0.034286
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
ee60b627d0b84f73131551cada6f034ce3bdf759
| 2,445
|
py
|
Python
|
mash_place_ui/models.py
|
MashSoftware/place
|
41b4bea35cf1f1db80b839b8184b39ab02497c13
|
[
"MIT"
] | null | null | null |
mash_place_ui/models.py
|
MashSoftware/place
|
41b4bea35cf1f1db80b839b8184b39ab02497c13
|
[
"MIT"
] | null | null | null |
mash_place_ui/models.py
|
MashSoftware/place
|
41b4bea35cf1f1db80b839b8184b39ab02497c13
|
[
"MIT"
] | null | null | null |
from mash_place_ui import cache, app
import requests
class Constituency(object):
"""Encapsulating class for Mash Place API access."""
def __init__(self):
super(Constituency, self).__init__()
self.base_url = app.config['PLACE_API_URL'] + "/constituencies"
@cache.memoize(timeout=86400)
def get_constituencies(self):
url = '{0}/'
response = requests.get(url.format(self.base_url))
if response.status_code != requests.codes.ok:
response.raise_for_status()
app.logger.error('GET ' + response.url + ' ' + str(response.status_code))
else:
result = response.text
app.logger.info('GET ' + response.url + ' ' + str(response.status_code))
return result
@cache.memoize(timeout=86400)
def get_constituency(self, code):
url = '{0}/{1}'
response = requests.get(url.format(self.base_url, code))
if response.status_code != requests.codes.ok:
response.raise_for_status()
app.logger.error('GET ' + response.url + ' ' + str(response.status_code))
else:
result = response.text
app.logger.info('GET ' + response.url + ' ' + str(response.status_code))
return result
class County(object):
"""Encapsulating class for Mash Place API access."""
def __init__(self):
super(County, self).__init__()
self.base_url = app.config['PLACE_API_URL'] + "/counties"
@cache.memoize(timeout=86400)
def get_counties(self):
url = '{0}/'
response = requests.get(url.format(self.base_url))
if response.status_code != requests.codes.ok:
response.raise_for_status()
app.logger.error('GET ' + response.url + ' ' + str(response.status_code))
else:
result = response.text
app.logger.info('GET ' + response.url + ' ' + str(response.status_code))
return result
@cache.memoize(timeout=86400)
def get_county(self, code):
url = '{0}/{1}'
response = requests.get(url.format(self.base_url, code))
if response.status_code != requests.codes.ok:
response.raise_for_status()
app.logger.error('GET ' + response.url + ' ' + str(response.status_code))
else:
result = response.text
app.logger.info('GET ' + response.url + ' ' + str(response.status_code))
return result
| 37.615385
| 85
| 0.605726
| 287
| 2,445
| 4.979094
| 0.163763
| 0.117565
| 0.151155
| 0.095171
| 0.894332
| 0.894332
| 0.852344
| 0.852344
| 0.852344
| 0.852344
| 0
| 0.014396
| 0.26135
| 2,445
| 64
| 86
| 38.203125
| 0.776855
| 0.038037
| 0
| 0.777778
| 0
| 0
| 0.047843
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.111111
| false
| 0
| 0.037037
| 0
| 0.259259
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
c9cce446b5e5a8e502266062ca56d8ec025b50dc
| 116,579
|
py
|
Python
|
vyperdatum/vdatum_validation.py
|
GlenRice-NOAA/vyperdatum
|
600fc2f16fe5a95dc3a26336d5d8cd23421fef5f
|
[
"CC0-1.0"
] | 2
|
2021-12-01T15:57:34.000Z
|
2021-12-03T14:37:48.000Z
|
vyperdatum/vdatum_validation.py
|
GlenRice-NOAA/vyperdatum
|
600fc2f16fe5a95dc3a26336d5d8cd23421fef5f
|
[
"CC0-1.0"
] | 6
|
2020-12-04T18:07:12.000Z
|
2020-12-22T16:59:07.000Z
|
vyperdatum/vdatum_validation.py
|
GlenRice-NOAA/vyperdatum
|
600fc2f16fe5a95dc3a26336d5d8cd23421fef5f
|
[
"CC0-1.0"
] | 3
|
2020-12-04T18:04:25.000Z
|
2021-06-24T15:29:11.000Z
|
import os
import numpy as np
'''
eric.g.younkin@noaa.gov, glen.rice@noaa.gov
Here we maintain vdatum specific grid listings, grid hashes, and vdatum answers that we expect to change between vdatum versions
Ideally we would use a version system that VDatum maintains, but there does not appear to be a reliable one.
Notes on specific versions:
vdatum412_20201203 - first version added, we don't plan on supporting vdatum prior to this version
vdatum42_20210603 - basically identical to 412, except that the sigma file has zero'd out uncertainty values for conus navd88 nad83 transformation...
'''
def print_formatted_dict(vdatum_grid_dict: dict):
"""
Used to build the hashes dicts seen below, so you get two entries on one line
Parameters
----------
vdatum_grid_dict
one of vdatum412_20201203_hashes or similar
"""
buff_ky = ''
for ky in vdatum_grid_dict.keys():
if not buff_ky:
buff_ky = ky
continue
else:
print('"{}": "{}", "{}": "{}",'.format(buff_ky, vdatum_grid_dict[buff_ky], ky, vdatum_grid_dict[ky]))
buff_ky = ''
if buff_ky:
print('"{}": "{}"'.format(buff_ky, vdatum_grid_dict[buff_ky]))
# see vyperdatum.core.hash_vdatum_grids to generate
vdatum412_20201203_hashes = {"AKglacier00_8301/dtl.gtx": "7b5cbeae1c0f34612dcc7ff9432ac9b9", "AKglacier00_8301/mhhw.gtx": "11bcc780ee778f2942e04408c4c673a0",
"AKglacier00_8301/mhw.gtx": "0000fb9e120bd1ae6da71b43a0550cda", "AKglacier00_8301/mllw.gtx": "ffac64108fb08294a9a4f52b96042d70",
"AKglacier00_8301/mlw.gtx": "b3d7b46385592cd6d6e638b9b2333a19", "AKglacier00_8301/mtl.gtx": "a5f0090d06097b9ee4d6c6b4234c5263",
"AKglacier00_8301/tss.gtx": "81615eeb5f5feed394be8636bc8187d1", "AKwhale00_8301/dtl.gtx": "be0059fa494c19db68ee20f6bb9a599a",
"AKwhale00_8301/mhhw.gtx": "c3b614513049d4a84be683c2df7d5b55", "AKwhale00_8301/mhw.gtx": "74ea027661dcb7223f3654f3f9e5b037",
"AKwhale00_8301/mllw.gtx": "b20782af95a863260d2950e2a97d511c", "AKwhale00_8301/mlw.gtx": "60659c18dffe4bd48f2f8805ac70927e",
"AKwhale00_8301/mtl.gtx": "543c9475cc8a4f95b4dabe7283d6826a", "AKwhale00_8301/tss.gtx": "528c38528905685edabf77f92e5a2eab",
"AKyakutat00_8301/dtl.gtx": "9fb94bb2549d18f2be7bffc9b5553384", "AKyakutat00_8301/mhhw.gtx": "03115b5e5bd31f42946cd672f8153810",
"AKyakutat00_8301/mhw.gtx": "0def21a45bcc55fc9cfa7660b2dd54fe", "AKyakutat00_8301/mllw.gtx": "68168316552d9df1538bc492290b0300",
"AKyakutat00_8301/mlw.gtx": "66c7015826fac45422305ce29c1612c2", "AKyakutat00_8301/mtl.gtx": "47e714b6287ef3b72d435a179ebb068e",
"AKyakutat00_8301/tss.gtx": "dbf538112c54bfe6fe32469a57064484", "ALFLgom02_8301/dtl.gtx": "a8438be56bcb8aed55121841887e25fc",
"ALFLgom02_8301/mhhw.gtx": "e78725d10a90da98e25c8155d653426c", "ALFLgom02_8301/mhw.gtx": "a973b77e4ce45431d01aedd460c85399",
"ALFLgom02_8301/mllw.gtx": "2fea98940b1132a6533dcb3829bf0f4c", "ALFLgom02_8301/mlw.gtx": "10fe40a654279df9f23cc6c52577d678",
"ALFLgom02_8301/mtl.gtx": "6fa31bfe5b65884d4dfa6f8c2379204c", "ALFLgom02_8301/tss.gtx": "2273a5290f76abb905afa0e98e563259",
"ALmobile02_8301/dtl.gtx": "945f21d49106f8f129a9830d2aa058a2", "ALmobile02_8301/mhhw.gtx": "e86b773b01cb6df5caca7a58c2ab5aa3",
"ALmobile02_8301/mhw.gtx": "1e9e768d7dae44d253bc2ead41826593", "ALmobile02_8301/mllw.gtx": "507f8cb2f308ee5a4cbfc1be2ab4d54a",
"ALmobile02_8301/mlw.gtx": "aef8ed933ca173ec91a391e206397da1", "ALmobile02_8301/mtl.gtx": "d7aee39ea2b542545436e0a2bafa753f",
"ALmobile02_8301/tss.gtx": "0c10a1491e56c4316a828dc19c78d694", "CAmorrob01_8301/dtl.gtx": "7ede9334a9404169ed266d215cbf526e",
"CAmorrob01_8301/mhhw.gtx": "63933231e55b179e6e2cf7b410a9882d", "CAmorrob01_8301/mhw.gtx": "5190d1a7d82a6cfc54f0a8524850cc52",
"CAmorrob01_8301/mllw.gtx": "f828c98bc05445459288a699a4c9a73e", "CAmorrob01_8301/mlw.gtx": "cfe66aa594fee46db8fda0b52c6e2e84",
"CAmorrob01_8301/mtl.gtx": "e34efadc4d00b6b764c114c09ce63875", "CAmorrob01_8301/tss.gtx": "1455b0b204589432863117b70b44f38d",
"CAORblan01_8301/dtl.gtx": "7a18993ffa04dfded6356b18f4f4affc", "CAORblan01_8301/mhhw.gtx": "b93355bdd70165d5e3b4134e52352fc1",
"CAORblan01_8301/mhw.gtx": "4a5058a459c5692036da5927aac31049", "CAORblan01_8301/mllw.gtx": "39345616e37130e971e313a2a4244821",
"CAORblan01_8301/mlw.gtx": "56faf88b475352736c220ad15970e46a", "CAORblan01_8301/mtl.gtx": "aebc18a7f0ebf4c87311305bde75302a",
"CAORblan01_8301/tss.gtx": "79f1025ab488a0008713913896942d30", "CAsanfrb12_8301/dtl.gtx": "334ee46acdd791a00261f3b96a627fcf",
"CAsanfrb12_8301/mhhw.gtx": "f881a03914c8b8f32ba84f6b806986a2", "CAsanfrb12_8301/mhw.gtx": "f4a56c9ae6f403a652f0577a37a5421b",
"CAsanfrb12_8301/mllw.gtx": "df117423deb4f26a9a69f2c508566432", "CAsanfrb12_8301/mlw.gtx": "dc0e45e9211b052829da3533558ce99e",
"CAsanfrb12_8301/mtl.gtx": "4a8416b649fdfad469f316face2b265d", "CAsanfrb12_8301/tss.gtx": "880784cf5ebcda0dfaeb35b7178b0c57",
"CAsocal01_8301/dtl.gtx": "d71013b33ea9b04e28f10e981e9fe479", "CAsocal01_8301/mhhw.gtx": "e8d6f2f285cdef54e59eada60f615bdd",
"CAsocal01_8301/mhw.gtx": "5009b8fea6d7f55d93906cd2e0aab186", "CAsocal01_8301/mllw.gtx": "78c82237d5ec458ab00129cca70bf0f1",
"CAsocal01_8301/mlw.gtx": "ab04686b16bd45b79421322eb3a04fb6", "CAsocal01_8301/mtl.gtx": "a10cd4bbb1141248eaf0ca93afdb605f",
"CAsocal01_8301/tss.gtx": "69409e6fbac3c1218057050b49280756", "DEdelbay22_8301/dtl.gtx": "ecc49aac8287704ed4ba1cda8f9dd247",
"DEdelbay22_8301/mhhw.gtx": "2979332695e558cc78c905c0230578f2", "DEdelbay22_8301/mhw.gtx": "a3f6ba9c5561a7c9675839394bd71b2a",
"DEdelbay22_8301/mllw.gtx": "11754cc991d5e266a965eb624736ef58", "DEdelbay22_8301/mlw.gtx": "59b06e40b0446fd9ee166045de7d6c43",
"DEdelbay22_8301/mtl.gtx": "f54f7a37c67c65d0f8b0ed59c91cc771", "DEdelbay22_8301/tss.gtx": "36f8ada082a0286c5179b81b87cddd4a",
"DEVAemb12_8301/dtl.gtx": "d38c17eecd83baabae91fca5af16be45", "DEVAemb12_8301/mhhw.gtx": "d1b748df8dd2257462643e6568e6676a",
"DEVAemb12_8301/mhw.gtx": "58fd440cf90cdfcb8a0140a3cddb5a4b", "DEVAemb12_8301/mllw.gtx": "b352d498f9478f247da8ed30e084a7aa",
"DEVAemb12_8301/mlw.gtx": "87f34695ced91653df4f1c7144bafa9d", "DEVAemb12_8301/mtl.gtx": "66855835222accc63abfe1f164b4d816",
"DEVAemb12_8301/tss.gtx": "b7abc4a2f89d2480a965bf04234c6896", "FLandrew02_8301/dtl.gtx": "c059ad5320cacd8a5c9ade1bf6c3345e",
"FLandrew02_8301/mhhw.gtx": "3d5912ab1c4c049f55ff638a44ee4b8d", "FLandrew02_8301/mhw.gtx": "8352c838e4ac5257346417ff9780276c",
"FLandrew02_8301/mllw.gtx": "c50e8b95c10535035886caeaf373027f", "FLandrew02_8301/mlw.gtx": "ee8c9ffe44c43473541ce756c1f86a00",
"FLandrew02_8301/mtl.gtx": "68ab1d112735dccd3546e56773ec271a", "FLandrew02_8301/tss.gtx": "13e8dc721a1acce6155fd090af36541d",
"FLapalach01_8301/dtl.gtx": "c4b8ceb7eeebbe5c760380c99738f509", "FLapalach01_8301/mhhw.gtx": "b676ae96924f7d8edbde536816943e64",
"FLapalach01_8301/mhw.gtx": "ee88d02055fa78f87c7fd47ff6bb0f1e", "FLapalach01_8301/mllw.gtx": "1550a27b3d6614920cbb0019234e3299",
"FLapalach01_8301/mlw.gtx": "c08a715d686df09bac77bb4833d79fa8", "FLapalach01_8301/mtl.gtx": "dc21b8fa24b2e3f20d3dda97e28cf29a",
"FLapalach01_8301/tss.gtx": "aae8153d112304042575754d2b3cc26e", "FLGAeastbays31_8301/dtl.gtx": "e3efed7bc5cee5cb901f81f43b1d7a51",
"FLGAeastbays31_8301/lwd.gtx": "46fd9da34c57dafdd2e3f27e7cba155c", "FLGAeastbays31_8301/mhhw.gtx": "c9c72ae8c8c59f1d3a3aed3d926fb94c",
"FLGAeastbays31_8301/mhw.gtx": "8a44818203a02cd4f5ecee01ae4dd8a5", "FLGAeastbays31_8301/mllw.gtx": "19bceab6efd76023ec0662ce9481fee6",
"FLGAeastbays31_8301/mlw.gtx": "39d80c46de57bd6eacb0c7f3bd0afe55", "FLGAeastbays31_8301/mtl.gtx": "049f163458b7f4fea8fd16ed0c62dc80",
"FLGAeastbays31_8301/tss.gtx": "ea6271bc17bad61f5b0035c2779685fb", "FLGAeastshelf41_8301/dtl.gtx": "02af9ff87b4e239f1e24060d41bd4b0d",
"FLGAeastshelf41_8301/mhhw.gtx": "85049386c37fe5e3ac1ed229adc4c62f", "FLGAeastshelf41_8301/mhw.gtx": "9e766ce1f85cd0063715de77803cf10c",
"FLGAeastshelf41_8301/mllw.gtx": "0abaa3ad8dbcea8cf524fe9c50f13130", "FLGAeastshelf41_8301/mlw.gtx": "62e6fee88fe539a46ffef3aee3870157",
"FLGAeastshelf41_8301/mtl.gtx": "997386ff38d071b75ee8fde7471f23b0", "FLGAeastshelf41_8301/tss.gtx": "5e94a8ee4fe96c38db049944a48cf5d2",
"FLjoseph03_8301/dtl.gtx": "38282d386b2493a4c0715521107532fb", "FLjoseph03_8301/mhhw.gtx": "448378884cf3b9b6dc65a48cd9e4f73f",
"FLjoseph03_8301/mhw.gtx": "1f2cc8efadaf2654b457c759b5bf4657", "FLjoseph03_8301/mllw.gtx": "2ff327b1ad8c3e3fb51c813f0246f1d3",
"FLjoseph03_8301/mlw.gtx": "e8a6d64bdef4103f48555996ceed3201", "FLjoseph03_8301/mtl.gtx": "b92877b57dbd325dedf7fff59fe603f1",
"FLjoseph03_8301/tss.gtx": "e430c594be074fd828f9c38bc46557e7", "FLpensac02_8301/dtl.gtx": "cbc20598623528325bc4b815726ec70f",
"FLpensac02_8301/mhhw.gtx": "dff0efbc412924dd18219c66ef02cfc6", "FLpensac02_8301/mhw.gtx": "a3ae112d2f61339da48de24078a44d75",
"FLpensac02_8301/mllw.gtx": "04771590f633500c06991501d9c45034", "FLpensac02_8301/mlw.gtx": "6e5a8a7318ab29d9b7f163bbe28ebfcc",
"FLpensac02_8301/mtl.gtx": "d9c164fb1df1d9be007ca7c10c243989", "FLpensac02_8301/tss.gtx": "284a71a723b734acbc6b1e0b7c24ca04",
"FLsoicw01_8301/dtl.gtx": "67a24181f3a18a86eb3a6af149c74ed4", "FLsoicw01_8301/mhhw.gtx": "28e98d9869ea8ccfa9cf784d1a6f41b7",
"FLsoicw01_8301/mhw.gtx": "cc5fd88d5372db5083b68734d7badab7", "FLsoicw01_8301/mllw.gtx": "334562627adeebe333aac5d1b2293a5c",
"FLsoicw01_8301/mlw.gtx": "2aed4f8f78df832a443e93b55465e205", "FLsoicw01_8301/mtl.gtx": "36b1d2f174915cc05ddec185dc64f913",
"FLsoicw01_8301/tss.gtx": "40abd715eedf56ae1c18cbf4f04f1489", "FLsouth12_8301/dtl.gtx": "cd456f893ac8788e05409683607d5465",
"FLsouth12_8301/lwd.gtx": "c4b2bfafc6542a5a690302321082da79", "FLsouth12_8301/mhhw.gtx": "421a750746987bc1bc0e8c77ca665cbd",
"FLsouth12_8301/mhw.gtx": "145a3a5090b8fb9b57c4b7a51a05346b", "FLsouth12_8301/mllw.gtx": "487e6e5f93e0204f07d451b3896bbc62",
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vdatum42_20210603_hashes = {"AKglacier00_8301/dtl.gtx": "7b5cbeae1c0f34612dcc7ff9432ac9b9", "AKglacier00_8301/mhhw.gtx": "11bcc780ee778f2942e04408c4c673a0",
"AKglacier00_8301/mhw.gtx": "0000fb9e120bd1ae6da71b43a0550cda", "AKglacier00_8301/mllw.gtx": "ffac64108fb08294a9a4f52b96042d70",
"AKglacier00_8301/mlw.gtx": "b3d7b46385592cd6d6e638b9b2333a19", "AKglacier00_8301/mtl.gtx": "a5f0090d06097b9ee4d6c6b4234c5263",
"AKglacier00_8301/tss.gtx": "81615eeb5f5feed394be8636bc8187d1", "AKwhale00_8301/dtl.gtx": "be0059fa494c19db68ee20f6bb9a599a",
"AKwhale00_8301/mhhw.gtx": "c3b614513049d4a84be683c2df7d5b55", "AKwhale00_8301/mhw.gtx": "74ea027661dcb7223f3654f3f9e5b037",
"AKwhale00_8301/mllw.gtx": "b20782af95a863260d2950e2a97d511c", "AKwhale00_8301/mlw.gtx": "60659c18dffe4bd48f2f8805ac70927e",
"AKwhale00_8301/mtl.gtx": "543c9475cc8a4f95b4dabe7283d6826a", "AKwhale00_8301/tss.gtx": "528c38528905685edabf77f92e5a2eab",
"AKyakutat00_8301/dtl.gtx": "9fb94bb2549d18f2be7bffc9b5553384", "AKyakutat00_8301/mhhw.gtx": "03115b5e5bd31f42946cd672f8153810",
"AKyakutat00_8301/mhw.gtx": "0def21a45bcc55fc9cfa7660b2dd54fe", "AKyakutat00_8301/mllw.gtx": "68168316552d9df1538bc492290b0300",
"AKyakutat00_8301/mlw.gtx": "66c7015826fac45422305ce29c1612c2", "AKyakutat00_8301/mtl.gtx": "47e714b6287ef3b72d435a179ebb068e",
"AKyakutat00_8301/tss.gtx": "dbf538112c54bfe6fe32469a57064484", "ALFLgom02_8301/dtl.gtx": "a8438be56bcb8aed55121841887e25fc",
"ALFLgom02_8301/mhhw.gtx": "e78725d10a90da98e25c8155d653426c", "ALFLgom02_8301/mhw.gtx": "a973b77e4ce45431d01aedd460c85399",
"ALFLgom02_8301/mllw.gtx": "2fea98940b1132a6533dcb3829bf0f4c", "ALFLgom02_8301/mlw.gtx": "10fe40a654279df9f23cc6c52577d678",
"ALFLgom02_8301/mtl.gtx": "6fa31bfe5b65884d4dfa6f8c2379204c", "ALFLgom02_8301/tss.gtx": "2273a5290f76abb905afa0e98e563259",
"ALmobile02_8301/dtl.gtx": "945f21d49106f8f129a9830d2aa058a2", "ALmobile02_8301/mhhw.gtx": "e86b773b01cb6df5caca7a58c2ab5aa3",
"ALmobile02_8301/mhw.gtx": "1e9e768d7dae44d253bc2ead41826593", "ALmobile02_8301/mllw.gtx": "507f8cb2f308ee5a4cbfc1be2ab4d54a",
"ALmobile02_8301/mlw.gtx": "aef8ed933ca173ec91a391e206397da1", "ALmobile02_8301/mtl.gtx": "d7aee39ea2b542545436e0a2bafa753f",
"ALmobile02_8301/tss.gtx": "0c10a1491e56c4316a828dc19c78d694", "CAmorrob01_8301/dtl.gtx": "7ede9334a9404169ed266d215cbf526e",
"CAmorrob01_8301/mhhw.gtx": "63933231e55b179e6e2cf7b410a9882d", "CAmorrob01_8301/mhw.gtx": "5190d1a7d82a6cfc54f0a8524850cc52",
"CAmorrob01_8301/mllw.gtx": "f828c98bc05445459288a699a4c9a73e", "CAmorrob01_8301/mlw.gtx": "cfe66aa594fee46db8fda0b52c6e2e84",
"CAmorrob01_8301/mtl.gtx": "e34efadc4d00b6b764c114c09ce63875", "CAmorrob01_8301/tss.gtx": "1455b0b204589432863117b70b44f38d",
"CAORblan01_8301/dtl.gtx": "7a18993ffa04dfded6356b18f4f4affc", "CAORblan01_8301/mhhw.gtx": "b93355bdd70165d5e3b4134e52352fc1",
"CAORblan01_8301/mhw.gtx": "4a5058a459c5692036da5927aac31049", "CAORblan01_8301/mllw.gtx": "39345616e37130e971e313a2a4244821",
"CAORblan01_8301/mlw.gtx": "56faf88b475352736c220ad15970e46a", "CAORblan01_8301/mtl.gtx": "aebc18a7f0ebf4c87311305bde75302a",
"CAORblan01_8301/tss.gtx": "79f1025ab488a0008713913896942d30", "CAsanfrb12_8301/dtl.gtx": "334ee46acdd791a00261f3b96a627fcf",
"CAsanfrb12_8301/mhhw.gtx": "f881a03914c8b8f32ba84f6b806986a2", "CAsanfrb12_8301/mhw.gtx": "f4a56c9ae6f403a652f0577a37a5421b",
"CAsanfrb12_8301/mllw.gtx": "df117423deb4f26a9a69f2c508566432", "CAsanfrb12_8301/mlw.gtx": "dc0e45e9211b052829da3533558ce99e",
"CAsanfrb12_8301/mtl.gtx": "4a8416b649fdfad469f316face2b265d", "CAsanfrb12_8301/tss.gtx": "880784cf5ebcda0dfaeb35b7178b0c57",
"CAsocal01_8301/dtl.gtx": "d71013b33ea9b04e28f10e981e9fe479", "CAsocal01_8301/mhhw.gtx": "e8d6f2f285cdef54e59eada60f615bdd",
"CAsocal01_8301/mhw.gtx": "5009b8fea6d7f55d93906cd2e0aab186", "CAsocal01_8301/mllw.gtx": "78c82237d5ec458ab00129cca70bf0f1",
"CAsocal01_8301/mlw.gtx": "ab04686b16bd45b79421322eb3a04fb6", "CAsocal01_8301/mtl.gtx": "a10cd4bbb1141248eaf0ca93afdb605f",
"CAsocal01_8301/tss.gtx": "69409e6fbac3c1218057050b49280756", "DEdelbay22_8301/dtl.gtx": "ecc49aac8287704ed4ba1cda8f9dd247",
"DEdelbay22_8301/mhhw.gtx": "2979332695e558cc78c905c0230578f2", "DEdelbay22_8301/mhw.gtx": "a3f6ba9c5561a7c9675839394bd71b2a",
"DEdelbay22_8301/mllw.gtx": "11754cc991d5e266a965eb624736ef58", "DEdelbay22_8301/mlw.gtx": "59b06e40b0446fd9ee166045de7d6c43",
"DEdelbay22_8301/mtl.gtx": "f54f7a37c67c65d0f8b0ed59c91cc771", "DEdelbay22_8301/tss.gtx": "36f8ada082a0286c5179b81b87cddd4a",
"DEVAemb12_8301/dtl.gtx": "d38c17eecd83baabae91fca5af16be45", "DEVAemb12_8301/mhhw.gtx": "d1b748df8dd2257462643e6568e6676a",
"DEVAemb12_8301/mhw.gtx": "58fd440cf90cdfcb8a0140a3cddb5a4b", "DEVAemb12_8301/mllw.gtx": "b352d498f9478f247da8ed30e084a7aa",
"DEVAemb12_8301/mlw.gtx": "87f34695ced91653df4f1c7144bafa9d", "DEVAemb12_8301/mtl.gtx": "66855835222accc63abfe1f164b4d816",
"DEVAemb12_8301/tss.gtx": "b7abc4a2f89d2480a965bf04234c6896", "FLandrew02_8301/dtl.gtx": "c059ad5320cacd8a5c9ade1bf6c3345e",
"FLandrew02_8301/mhhw.gtx": "3d5912ab1c4c049f55ff638a44ee4b8d", "FLandrew02_8301/mhw.gtx": "8352c838e4ac5257346417ff9780276c",
"FLandrew02_8301/mllw.gtx": "c50e8b95c10535035886caeaf373027f", "FLandrew02_8301/mlw.gtx": "ee8c9ffe44c43473541ce756c1f86a00",
"FLandrew02_8301/mtl.gtx": "68ab1d112735dccd3546e56773ec271a", "FLandrew02_8301/tss.gtx": "13e8dc721a1acce6155fd090af36541d",
"FLapalach01_8301/dtl.gtx": "c4b8ceb7eeebbe5c760380c99738f509", "FLapalach01_8301/mhhw.gtx": "b676ae96924f7d8edbde536816943e64",
"FLapalach01_8301/mhw.gtx": "ee88d02055fa78f87c7fd47ff6bb0f1e", "FLapalach01_8301/mllw.gtx": "1550a27b3d6614920cbb0019234e3299",
"FLapalach01_8301/mlw.gtx": "c08a715d686df09bac77bb4833d79fa8", "FLapalach01_8301/mtl.gtx": "dc21b8fa24b2e3f20d3dda97e28cf29a",
"FLapalach01_8301/tss.gtx": "aae8153d112304042575754d2b3cc26e", "FLGAeastbays31_8301/dtl.gtx": "e3efed7bc5cee5cb901f81f43b1d7a51",
"FLGAeastbays31_8301/lwd.gtx": "46fd9da34c57dafdd2e3f27e7cba155c", "FLGAeastbays31_8301/mhhw.gtx": "c9c72ae8c8c59f1d3a3aed3d926fb94c",
"FLGAeastbays31_8301/mhw.gtx": "8a44818203a02cd4f5ecee01ae4dd8a5", "FLGAeastbays31_8301/mllw.gtx": "19bceab6efd76023ec0662ce9481fee6",
"FLGAeastbays31_8301/mlw.gtx": "39d80c46de57bd6eacb0c7f3bd0afe55", "FLGAeastbays31_8301/mtl.gtx": "049f163458b7f4fea8fd16ed0c62dc80",
"FLGAeastbays31_8301/tss.gtx": "ea6271bc17bad61f5b0035c2779685fb", "FLGAeastshelf41_8301/dtl.gtx": "02af9ff87b4e239f1e24060d41bd4b0d",
"FLGAeastshelf41_8301/mhhw.gtx": "85049386c37fe5e3ac1ed229adc4c62f", "FLGAeastshelf41_8301/mhw.gtx": "9e766ce1f85cd0063715de77803cf10c",
"FLGAeastshelf41_8301/mllw.gtx": "0abaa3ad8dbcea8cf524fe9c50f13130", "FLGAeastshelf41_8301/mlw.gtx": "62e6fee88fe539a46ffef3aee3870157",
"FLGAeastshelf41_8301/mtl.gtx": "997386ff38d071b75ee8fde7471f23b0", "FLGAeastshelf41_8301/tss.gtx": "5e94a8ee4fe96c38db049944a48cf5d2",
"FLjoseph03_8301/dtl.gtx": "38282d386b2493a4c0715521107532fb", "FLjoseph03_8301/mhhw.gtx": "448378884cf3b9b6dc65a48cd9e4f73f",
"FLjoseph03_8301/mhw.gtx": "1f2cc8efadaf2654b457c759b5bf4657", "FLjoseph03_8301/mllw.gtx": "2ff327b1ad8c3e3fb51c813f0246f1d3",
"FLjoseph03_8301/mlw.gtx": "e8a6d64bdef4103f48555996ceed3201", "FLjoseph03_8301/mtl.gtx": "b92877b57dbd325dedf7fff59fe603f1",
"FLjoseph03_8301/tss.gtx": "e430c594be074fd828f9c38bc46557e7", "FLpensac02_8301/dtl.gtx": "cbc20598623528325bc4b815726ec70f",
"FLpensac02_8301/mhhw.gtx": "dff0efbc412924dd18219c66ef02cfc6", "FLpensac02_8301/mhw.gtx": "a3ae112d2f61339da48de24078a44d75",
"FLpensac02_8301/mllw.gtx": "04771590f633500c06991501d9c45034", "FLpensac02_8301/mlw.gtx": "6e5a8a7318ab29d9b7f163bbe28ebfcc",
"FLpensac02_8301/mtl.gtx": "d9c164fb1df1d9be007ca7c10c243989", "FLpensac02_8301/tss.gtx": "284a71a723b734acbc6b1e0b7c24ca04",
"FLsoicw01_8301/dtl.gtx": "67a24181f3a18a86eb3a6af149c74ed4", "FLsoicw01_8301/mhhw.gtx": "28e98d9869ea8ccfa9cf784d1a6f41b7",
"FLsoicw01_8301/mhw.gtx": "cc5fd88d5372db5083b68734d7badab7", "FLsoicw01_8301/mllw.gtx": "334562627adeebe333aac5d1b2293a5c",
"FLsoicw01_8301/mlw.gtx": "2aed4f8f78df832a443e93b55465e205", "FLsoicw01_8301/mtl.gtx": "36b1d2f174915cc05ddec185dc64f913",
"FLsoicw01_8301/tss.gtx": "40abd715eedf56ae1c18cbf4f04f1489", "FLsouth12_8301/dtl.gtx": "cd456f893ac8788e05409683607d5465",
"FLsouth12_8301/lwd.gtx": "c4b2bfafc6542a5a690302321082da79", "FLsouth12_8301/mhhw.gtx": "421a750746987bc1bc0e8c77ca665cbd",
"FLsouth12_8301/mhw.gtx": "145a3a5090b8fb9b57c4b7a51a05346b", "FLsouth12_8301/mllw.gtx": "487e6e5f93e0204f07d451b3896bbc62",
"FLsouth12_8301/mlw.gtx": "3840d5408d73abc1944e0e63361d2d46", "FLsouth12_8301/mtl.gtx": "8f5235022355a1cf288575332b453d77",
"FLsouth12_8301/tss.gtx": "0faa18928267185ec7d58e5d86bea893", "FLwest01_8301/dtl.gtx": "01f20faa7311a3d2877497bbb1eda314",
"FLwest01_8301/mhhw.gtx": "536d2d7536cd459e04e16fe345fabc60", "FLwest01_8301/mhw.gtx": "fac7c4931592cac4241a946c3a9d64ce",
"FLwest01_8301/mllw.gtx": "39e4f6ee86722107fa61ea0e52494791", "FLwest01_8301/mlw.gtx": "39acae788659d4df815c69eccdb3555f",
"FLwest01_8301/mtl.gtx": "dc03506a8c40a418b177e61da4817bb2", "FLwest01_8301/tss.gtx": "83bcf699923c4ff3b172bbfd6a69c5e4",
"GASCNCsab31_8301/dtl.gtx": "dc628fca5185a7c1d9d995bb5bde1004", "GASCNCsab31_8301/mhhw.gtx": "bf0a00af7672ff81661c35df89b32215",
"GASCNCsab31_8301/mhw.gtx": "ce1efc51e798ca7492d6e5eb31e2fa82", "GASCNCsab31_8301/mllw.gtx": "172a453c2c972414fea2b0f72ed25729",
"GASCNCsab31_8301/mlw.gtx": "93269cee168a06c63ebaaeca73596470", "GASCNCsab31_8301/mtl.gtx": "20814511a1c5be8ce951c9162f0a4b7b",
"GASCNCsab31_8301/tss.gtx": "7882bbc0ea170dd759cd29e8af75cfe7", "IGLD85/hydroc1.gtx": "bb8e68e25ab469f3d9d106efadffb0b1",
"IGLD85/hydroc1_modified.gtx": "1743388c3a075fed21d748f6ce672e21", "IGLD85/hydroc2.gtx": "fd299a3a4de2cef678f6ab8b7ea8b904",
"IGLD85/hydroc3.gtx": "066db6f5f4d4540207bbfad18ff3602e", "IGLD85/hydroc4.gtx": "845d6b54f21945cdfa2f790dc40303aa",
"IGLD85/hydroc5.gtx": "ce824e93304733e988459a6bcc6baa77", "LAmobile02_8301/dtl.gtx": "7f8b40379f4121e20dee94f92e27f4af",
"LAmobile02_8301/mhhw.gtx": "49cd7386d5ae25ff1cad3cb6d04a7b8c", "LAmobile02_8301/mhw.gtx": "573c3a43c3a49665130a06fa9b404c28",
"LAmobile02_8301/mllw.gtx": "17286a34b134c7bdbd63d804abeeb771", "LAmobile02_8301/mlw.gtx": "5246344b80d33d10d6e7d050d7e9f6f8",
"LAmobile02_8301/mtl.gtx": "747a6fb0e654b225636226962010825a", "LAmobile02_8301/tss.gtx": "e47a56973f5418a923e458a26cfa670b",
"LATXwest01_8301/dtl.gtx": "f9312a84bc6b42739350fc62df91a67a", "LATXwest01_8301/lwd.gtx": "4efb82ec362dd7f504a27ce6f4cdfa75",
"LATXwest01_8301/mhhw.gtx": "268b1972a44e44e55fb5c8df362e3971", "LATXwest01_8301/mhw.gtx": "f08ce0e421a5ebd8f5f9c96a2bd88e72",
"LATXwest01_8301/mllw.gtx": "e1afdf391aefae4359a5d5bb2f9f26b3", "LATXwest01_8301/mlw.gtx": "a8048f3e96015e20daf6d9d48057f638",
"LATXwest01_8301/mtl.gtx": "7959728d8fd0e3815b8ded475e39f3b4", "LATXwest01_8301/tss.gtx": "39bd0b73c42c64febe06246decbeb8e6",
"MDVAchb12_8301/dtl.gtx": "c8e6db3a69f4ae26d475ef1b02bfcf87", "MDVAchb12_8301/mhhw.gtx": "56282512c8a560f74d72dd3a0e974486",
"MDVAchb12_8301/mhw.gtx": "1557e424d8f1a746caea8dbe88110ce8", "MDVAchb12_8301/mllw.gtx": "f8a6d8ade94e5fff37211051b19495b3",
"MDVAchb12_8301/mlw.gtx": "a54e73bef7310b15f061f463c40224d0", "MDVAchb12_8301/mtl.gtx": "4c6053e9d580b369131d78e78b6aac08",
"MDVAchb12_8301/tss.gtx": "88d364655ca2496395f635339235f489", "MENHMAgome23_8301/dtl.gtx": "23dd9f0671fcf3d4354d408e495543f2",
"MENHMAgome23_8301/mhhw.gtx": "6eed02278f67b199b4917af3a8a85490", "MENHMAgome23_8301/mhw.gtx": "bb55f253de39f9b63dd2d8ef3ddcbbb6",
"MENHMAgome23_8301/mllw.gtx": "772371c1cbc5135fe1863eddf816c09a", "MENHMAgome23_8301/mlw.gtx": "06b7d7d3e74436089084114806f47a42",
"MENHMAgome23_8301/mtl.gtx": "32fba139c8a746735b6e907113564142", "MENHMAgome23_8301/tss.gtx": "6e0f3c70919868e60f358d7dcff44ea3",
"NCcoast11_8301/dtl.gtx": "8b7faec7ec4f2a41aca58c02260b7493", "NCcoast11_8301/mhhw.gtx": "1a6a35acd99b6046bc4d8c2bee6a69dd",
"NCcoast11_8301/mhw.gtx": "b865361c8647e311049ab0d00bee6707", "NCcoast11_8301/mllw.gtx": "31e81448d9dc1f640feb4da684ff2676",
"NCcoast11_8301/mlw.gtx": "1849c925a0549e76a8af91299508cbb7", "NCcoast11_8301/mtl.gtx": "c8a4a1b7f899cf9d75eaec0f20c87900",
"NCcoast11_8301/tss.gtx": "a3cceb3d97403ffaba486fa34f187745", "NCinner11_8301/dtl.gtx": "f2407e4356900a57c70028bace6040df",
"NCinner11_8301/lwd.gtx": "40c5d97c3e8251f0b27597caf9c0b961", "NCinner11_8301/mhhw.gtx": "b337c349c7fb06c3612e1589d2bc4df2",
"NCinner11_8301/mhw.gtx": "183e88f0ed3ab8dbd09c122ab86fb257", "NCinner11_8301/mllw.gtx": "90cb8c079afc1c396caa7298007eaafc",
"NCinner11_8301/mlw.gtx": "87efff6f95cc770c05d4180fe23d1c8f", "NCinner11_8301/mtl.gtx": "4733116c245fe7a61eb5ce39ab6bff46",
"NCinner11_8301/tss.gtx": "c5a0ca8bbd7581fa1c51ad318ada2c71", "NJncstemb12_8301/dtl.gtx": "3e0f68434436a99777191b1d0a01a0bc",
"NJncstemb12_8301/dtl_unc.gtx": "06f9b7fd8df729cdc56de2e431d0ae16", "NJncstemb12_8301/mhhw.gtx": "f0891aee98393f37a2134a9e7cea5187",
"NJncstemb12_8301/mhhw_unc.gtx": "78b250f0e69f5257ac72b8167e55ab88", "NJncstemb12_8301/mhw.gtx": "f48dbf154c426667c286926de1195eea",
"NJncstemb12_8301/mhw_unc.gtx": "5c3457bdbe745a213bfb52c281a21c9e", "NJncstemb12_8301/mllw.gtx": "202832104ee8c67847e54d8d81f9f7f2",
"NJncstemb12_8301/mllw_unc.gtx": "7785c974fa7bd68fb65cfb701bcc2be2", "NJncstemb12_8301/mlw.gtx": "d456f1edcde748797655bcc28205f2c7",
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"vdatum_sigma.inf": "3eaa5c00235f8b52c03ecac44731db8d"}
vdatum43_20210928_hashes = {"AKglacier00_8301/dtl.gtx": "7b5cbeae1c0f34612dcc7ff9432ac9b9", "AKglacier00_8301/mhhw.gtx": "11bcc780ee778f2942e04408c4c673a0",
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"AKwhale00_8301/mhhw.gtx": "c3b614513049d4a84be683c2df7d5b55", "AKwhale00_8301/mhw.gtx": "74ea027661dcb7223f3654f3f9e5b037",
"AKwhale00_8301/mllw.gtx": "b20782af95a863260d2950e2a97d511c", "AKwhale00_8301/mlw.gtx": "60659c18dffe4bd48f2f8805ac70927e",
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"NYgr8bay34_8301/mtl.gtx": "03ed32494c398b22ceead2a66f263dde", "NYgr8bay34_8301/mtl_unc.gtx": "4892b2322b294cfa9275bbb2c3574656",
"NYgr8bay34_8301/tss.gtx": "87f366950ea4e25865d56d06cf90183b", "NYgr8bay34_8301/tss_unc.gtx": "8279b91d00f1488767d309b5395506c8",
"NYNJhbr34_8301/dtl.gtx": "c8cf0d6ed478e40ae8bc24edbcfc8881", "NYNJhbr34_8301/dtl_unc.gtx": "d0b78f96f996d32eb7b92d63a92a1705",
"NYNJhbr34_8301/mhhw.gtx": "f4bae7f99100759e7ac187cf19fc48bc", "NYNJhbr34_8301/mhhw_unc.gtx": "2f713f2247cedd1e043e484f4997b994",
"NYNJhbr34_8301/mhw.gtx": "0da8c83bb76f6d4cfa5c3c75fbf10cc1", "NYNJhbr34_8301/mhw_unc.gtx": "d5b96827547aa5035a752bfd36f149d9",
"NYNJhbr34_8301/mllw.gtx": "ccf5fd31ac45ad486b54f2eba6cbf1ac", "NYNJhbr34_8301/mllw_unc.gtx": "df2ba75c8ae8bb8610177d429e49a3d9",
"NYNJhbr34_8301/mlw.gtx": "27cb1b932151f4e0cb381ab1b46c2bcd", "NYNJhbr34_8301/mlw_unc.gtx": "80fa85d2eda78124a6023cd74ef2edb1",
"NYNJhbr34_8301/mtl.gtx": "2fa0bfd46fb3d1c9257b727274a9db40", "NYNJhbr34_8301/mtl_unc.gtx": "64d2dd067fa3b72aa72950228083bfa2",
"NYNJhbr34_8301/tss.gtx": "87f366950ea4e25865d56d06cf90183b", "NYNJhbr34_8301/tss_unc.gtx": "8279b91d00f1488767d309b5395506c8",
"ORcentr01_8301/dtl.gtx": "c83faf8c5b67ea89be18a6b4f02a29b6", "ORcentr01_8301/mhhw.gtx": "d593ae41e733285e49a1fcfa4588d24d",
"ORcentr01_8301/mhw.gtx": "71f711c951018b0ceb8b0ad948145dab", "ORcentr01_8301/mllw.gtx": "394583575de2782ecffd013265551c02",
"ORcentr01_8301/mlw.gtx": "2fdb84ecbf335a1db251e28b4f692ad0", "ORcentr01_8301/mtl.gtx": "12084b0ff79e10d4c747fe8129c901d1",
"ORcentr01_8301/tss.gtx": "8680e68fad67425939899757707ef77c", "ORWAcolr01_8301/dtl.gtx": "df4541cb45d0c8ea0b05e79f506909c3",
"ORWAcolr01_8301/mhhw.gtx": "60b6de31f865f6eaa3f1da4411f180ae", "ORWAcolr01_8301/mhw.gtx": "430e31bf382acde948996ae77507833f",
"ORWAcolr01_8301/mllw.gtx": "4eac41c8ee43c2854df45389aa8811f4", "ORWAcolr01_8301/mlw.gtx": "ad963c5f27a5f4c8a2ad46ad70d200dd",
"ORWAcolr01_8301/mtl.gtx": "e21b3feda64eed0e2a9159c2d659ea65", "ORWAcolr01_8301/tss.gtx": "9d61a1c9f656b9bf61c1ca7046fee6df",
"PRVI01_8301/dtl.gtx": "96f6dcfbaec4cc658a589539ccbc6044", "PRVI01_8301/mhhw.gtx": "671e6dcfdf6148463c0fe0313787c062",
"PRVI01_8301/mhw.gtx": "9d05700fb8af4b2e11eca0570dca9be4", "PRVI01_8301/mllw.gtx": "31c28f41b3363b5930ba87d26f0046b7",
"PRVI01_8301/mlw.gtx": "4930b2c0c4318a9aabe759e67b97474e", "PRVI01_8301/mtl.gtx": "e1b73b8b19a54a5bc6ac7721639ae7fe",
"PRVI01_8301/tss.gtx": "b2e0d78bbffc3d38a7b1ed711045ce8a", "RICTbis34_8301/dtl.gtx": "9431c094672136c6235a3a7a5f0716d2",
"RICTbis34_8301/dtl_unc.gtx": "9b6698b596f76534b36f6d1d4798bf59", "RICTbis34_8301/mhhw.gtx": "a02725232083b46f296897150eda2edb",
"RICTbis34_8301/mhhw_unc.gtx": "ebd57907e84835f5633c8a757c2abd27", "RICTbis34_8301/mhw.gtx": "7eac810baf9c80ca53c635fd40494521",
"RICTbis34_8301/mhw_unc.gtx": "ca8c89196a6114ea3fa82e0083a81774", "RICTbis34_8301/mllw.gtx": "652b3b9d3324bc73bfa1b92e443bac64",
"RICTbis34_8301/mllw_unc.gtx": "de759011f282ebd1f3e548ad58386325", "RICTbis34_8301/mlw.gtx": "bca2797e2fea06f4a971222e41df5a76",
"RICTbis34_8301/mlw_unc.gtx": "4276cf5a95958c4ab9ba4b86f3874118", "RICTbis34_8301/mtl.gtx": "60168d9e4b49d810a135df96f6e4355e",
"RICTbis34_8301/mtl_unc.gtx": "e6a05f7a0971d99c1f381bf93b3b5975", "RICTbis34_8301/tss.gtx": "87f366950ea4e25865d56d06cf90183b",
"RICTbis34_8301/tss_unc.gtx": "8279b91d00f1488767d309b5395506c8", "TXlaggal01_8301/dtl.gtx": "9a045a998a8db1c26e31229cf304f22c",
"TXlaggal01_8301/lwd.gtx": "c2d49aa38992f65e94f702bb12f79411", "TXlaggal01_8301/mhhw.gtx": "63a6917a0c54d8c11ff01cd6fc179fd8",
"TXlaggal01_8301/mhw.gtx": "c8e31d834787e80badf77c3f62752cb7", "TXlaggal01_8301/mllw.gtx": "3e9d6fba459cb0dea9bb6028111b3af3",
"TXlaggal01_8301/mlw.gtx": "76f333a9d03db82582e338011899b6d8", "TXlaggal01_8301/mtl.gtx": "57054cca26de12c10dd4dc504d4b9d28",
"TXlaggal01_8301/tss.gtx": "ba44a825f1dc134258a55119fb9f5b84", "TXlagmat01_8301/dtl.gtx": "da6ff00aa76740afc3b74c47a50f0939",
"TXlagmat01_8301/lwd.gtx": "cce27c63eaf5b7fa3a64291f5e33b6a3", "TXlagmat01_8301/mhhw.gtx": "590427327c4241e6162d812485dfb2a6",
"TXlagmat01_8301/mhw.gtx": "23c4d4817b409e089b3572927e1b4655", "TXlagmat01_8301/mllw.gtx": "e4f36069d82f2cb4bfbc9dd37efbf915",
"TXlagmat01_8301/mlw.gtx": "2c84aa330aceb249b7e039c81244884f", "TXlagmat01_8301/mtl.gtx": "656d30eda5f3be75136e59c636fb659b",
"TXlagmat01_8301/tss.gtx": "df1523f43620a9db493d43f25d0011e4", "TXshlgal01_8301/dtl.gtx": "b387d8f0f6d8ba383f29f2aef626c305",
"TXshlgal01_8301/lwd.gtx": "9afdd8d1a66100add6df383716646b2e", "TXshlgal01_8301/mhhw.gtx": "0563eb410843ed2d34fe45dadec3c5ad",
"TXshlgal01_8301/mhw.gtx": "aae124f7defba94fa8f115b94c976df9", "TXshlgal01_8301/mllw.gtx": "bad0b85f8739c482f98ca45584499f69",
"TXshlgal01_8301/mlw.gtx": "8bdf7540ccba70ce00bb5652520335c4", "TXshlgal01_8301/mtl.gtx": "c2cba4570b555c06c894a6546cfef53c",
"TXshlgal01_8301/tss.gtx": "38f2e9e9bb72300750255bde7d659282", "TXshlmat01_8301/dtl.gtx": "b459722e7ea6c41ea8d2f256c7714cc4",
"TXshlmat01_8301/lwd.gtx": "ab153248e6ffb3fc2f20a5d07aa49182", "TXshlmat01_8301/mhhw.gtx": "c2460be21090c4afd979aa45ab5bfabb",
"TXshlmat01_8301/mhw.gtx": "68724fb51311e29f3563469b07001177", "TXshlmat01_8301/mllw.gtx": "b2457c682b4b47bee336762fb1ffda01",
"TXshlmat01_8301/mlw.gtx": "3d91a887079a2e84edd597af75ad04b0", "TXshlmat01_8301/mtl.gtx": "265aecd4d64211389e246d32ddf2402d",
"TXshlmat01_8301/tss.gtx": "1c4c4108273fcda77a7035643575e506", "VAswchb11_8301/dtl.gtx": "8ba99778d57ccab1f84584da684e712b",
"VAswchb11_8301/dtl_unc.gtx": "9158c65fddc6f984b2971b11df37d2d7", "VAswchb11_8301/mhhw.gtx": "5405e6ab642ebcd8c4e59f0fe8ba3ed0",
"VAswchb11_8301/mhhw_unc.gtx": "4503f0286fdc5ef5d027fa9820185113", "VAswchb11_8301/mhw.gtx": "a63c481a765d63604573d091aebc1535",
"VAswchb11_8301/mhw_unc.gtx": "e92e3429693b086e8c35b876d6acc68c", "VAswchb11_8301/mllw.gtx": "f863482b16b474c0d78e8f35f299df04",
"VAswchb11_8301/mllw_unc.gtx": "464c9e055aa6639a9689b2911bc13ca8", "VAswchb11_8301/mlw.gtx": "6feddda34a01d409b9930d0a0ed0fa46",
"VAswchb11_8301/mlw_unc.gtx": "ee353336a08b619a33c9d48a47083cd2", "VAswchb11_8301/mtl.gtx": "ced6e54a3d55268dbdf7f8269bc332eb",
"VAswchb11_8301/mtl_unc.gtx": "a361c93302916334cfb8518e82639a56", "VAswchb11_8301/tss.gtx": "31fe858e7d5d60d2db8d7ac5fe8d3376",
"VAswchb11_8301/tss_unc.gtx": "7c0ca067984e0d33ea52536886c6aaaa", "WAjdfuca03_8301/dtl.gtx": "c15573a854773a3799f6b42818055b03",
"WAjdfuca03_8301/mhhw.gtx": "76b831818c5521da7fd85a969cc88639", "WAjdfuca03_8301/mhw.gtx": "9b8c9238cd348584f5c3800302d2ee8d",
"WAjdfuca03_8301/mllw.gtx": "07f428fe0f4b1fd4d60bf293259e254a", "WAjdfuca03_8301/mlw.gtx": "6c3203558c3ca744a3346cc5731d0735",
"WAjdfuca03_8301/mtl.gtx": "0a2d35c06d5cbfe642fd6389fbe98316", "WAjdfuca03_8301/tss.gtx": "598d4e4d4d1165ead5ba6ddf22c70962",
"WApugets02_8301/dtl.gtx": "3492fc91a0a75635d01f4c2f589afe98", "WApugets02_8301/mhhw.gtx": "bbd0e919b6e2404a19847c33484729ec",
"WApugets02_8301/mhw.gtx": "ab0f7e43e5ff74284751d7ade319b3e0", "WApugets02_8301/mllw.gtx": "05def7f1d7f54339d8dada575a0ecfba",
"WApugets02_8301/mlw.gtx": "dc9c3a70b53160a64e46de3049eeb346", "WApugets02_8301/mtl.gtx": "3ac1792c62401876833c0e4d1cc64ee1",
"WApugets02_8301/tss.gtx": "e1ba4c9441dc88329e1ff90b9eb0d50a", "vdatum_sigma.inf": "4532bac1d06b86acb742bdd7e25c8df4"}
vdatum412_20201203_geoid = {'AKglacier00_8301': r'core\xgeoid17b\AK_17B.gtx',
'AKwhale00_8301': r'core\xgeoid17b\AK_17B.gtx',
'AKyakutat00_8301': r'core\xgeoid17b\AK_17B.gtx',
'ALFLgom02_8301': r'core\geoid12b\g2012bu0.gtx',
'ALmobile02_8301': r'core\geoid12b\g2012bu0.gtx',
'CAmorrob01_8301': r'core\geoid12b\g2012bu0.gtx',
'CAORblan01_8301': r'core\geoid12b\g2012bu0.gtx',
'CAsanfrb12_8301': r'core\geoid12b\g2012bu0.gtx',
'CAsocal01_8301': r'core\geoid12b\g2012bu0.gtx',
'DEdelbay22_8301': r'core\geoid12b\g2012bu0.gtx',
'DEVAemb12_8301': r'core\geoid12b\g2012bu0.gtx',
'FLandrew02_8301': r'core\geoid12b\g2012bu0.gtx',
'FLapalach01_8301': r'core\geoid12b\g2012bu0.gtx',
'FLGAeastbays31_8301': r'core\geoid12b\g2012bu0.gtx',
'FLGAeastshelf41_8301': r'core\geoid12b\g2012bu0.gtx',
'FLjoseph03_8301': r'core\geoid12b\g2012bu0.gtx',
'FLpensac02_8301': r'core\geoid12b\g2012bu0.gtx',
'FLsoicw01_8301': r'core\geoid12b\g2012bu0.gtx',
'FLsouth12_8301': r'core\geoid12b\g2012bu0.gtx',
'FLwest01_8301': r'core\geoid12b\g2012bu0.gtx',
'GASCNCsab31_8301': r'core\geoid12b\g2012bu0.gtx',
'LAmobile02_8301': r'core\geoid12b\g2012bu0.gtx',
'LATXwest01_8301': r'core\geoid12b\g2012bu0.gtx',
'MDVAchb12_8301': r'core\geoid12b\g2012bu0.gtx',
'MENHMAgome23_8301': r'core\geoid12b\g2012bu0.gtx',
'NCcoast11_8301': r'core\geoid12b\g2012bu0.gtx',
'NCinner11_8301': r'core\geoid12b\g2012bu0.gtx',
'NJncstemb12_8301': r'core\geoid12b\g2012bu0.gtx',
'NJscstemb21_8301': r'core\geoid12b\g2012bu0.gtx',
'NJVAmab22_8301': r'core\geoid12b\g2012bu0.gtx',
'NYgr8bay34_8301': r'core\geoid12b\g2012bu0.gtx',
'NYNJhbr34_8301': r'core\geoid12b\g2012bu0.gtx',
'ORcentr01_8301': r'core\geoid12b\g2012bu0.gtx',
'ORWAcolr01_8301': r'core\geoid12b\g2012bu0.gtx',
'PRVI01_8301': r'core\geoid12b\g2012bp0.gtx',
'RICTbis34_8301': r'core\geoid12b\g2012bu0.gtx',
'TXlaggal01_8301': r'core\geoid12b\g2012bu0.gtx',
'TXlagmat01_8301': r'core\geoid12b\g2012bu0.gtx',
'TXshlgal01_8301': r'core\geoid12b\g2012bu0.gtx',
'TXshlmat01_8301': r'core\geoid12b\g2012bu0.gtx',
'WAjdfuca03_8301': r'core\geoid12b\g2012bu0.gtx',
'WApugets02_8301': r'core\geoid12b\g2012bu0.gtx'}
vdatum42_20210603_geoid = {'AKglacier00_8301': r'core\xgeoid17b\AK_17B.gtx',
'AKwhale00_8301': r'core\xgeoid17b\AK_17B.gtx',
'AKyakutat00_8301': r'core\xgeoid17b\AK_17B.gtx',
'ALFLgom02_8301': r'core\geoid12b\g2012bu0.gtx',
'ALmobile02_8301': r'core\geoid12b\g2012bu0.gtx',
'CAmorrob01_8301': r'core\geoid12b\g2012bu0.gtx',
'CAORblan01_8301': r'core\geoid12b\g2012bu0.gtx',
'CAsanfrb12_8301': r'core\geoid12b\g2012bu0.gtx',
'CAsocal01_8301': r'core\geoid12b\g2012bu0.gtx',
'DEdelbay22_8301': r'core\geoid12b\g2012bu0.gtx',
'DEVAemb12_8301': r'core\geoid12b\g2012bu0.gtx',
'FLandrew02_8301': r'core\geoid12b\g2012bu0.gtx',
'FLapalach01_8301': r'core\geoid12b\g2012bu0.gtx',
'FLGAeastbays31_8301': r'core\geoid12b\g2012bu0.gtx',
'FLGAeastshelf41_8301': r'core\geoid12b\g2012bu0.gtx',
'FLjoseph03_8301': r'core\geoid12b\g2012bu0.gtx',
'FLpensac02_8301': r'core\geoid12b\g2012bu0.gtx',
'FLsoicw01_8301': r'core\geoid12b\g2012bu0.gtx',
'FLsouth12_8301': r'core\geoid12b\g2012bu0.gtx',
'FLwest01_8301': r'core\geoid12b\g2012bu0.gtx',
'GASCNCsab31_8301': r'core\geoid12b\g2012bu0.gtx',
'LAmobile02_8301': r'core\geoid12b\g2012bu0.gtx',
'LATXwest01_8301': r'core\geoid12b\g2012bu0.gtx',
'MDVAchb12_8301': r'core\geoid12b\g2012bu0.gtx',
'MENHMAgome23_8301': r'core\geoid12b\g2012bu0.gtx',
'NCcoast11_8301': r'core\geoid12b\g2012bu0.gtx',
'NCinner11_8301': r'core\geoid12b\g2012bu0.gtx',
'NJncstemb12_8301': r'core\geoid12b\g2012bu0.gtx',
'NJscstemb21_8301': r'core\geoid12b\g2012bu0.gtx',
'NJVAmab22_8301': r'core\geoid12b\g2012bu0.gtx',
'NYgr8bay34_8301': r'core\geoid12b\g2012bu0.gtx',
'NYNJhbr34_8301': r'core\geoid12b\g2012bu0.gtx',
'ORcentr01_8301': r'core\geoid12b\g2012bu0.gtx',
'ORWAcolr01_8301': r'core\geoid12b\g2012bu0.gtx',
'PRVI01_8301': r'core\geoid12b\g2012bp0.gtx',
'RICTbis34_8301': r'core\geoid12b\g2012bu0.gtx',
'TXlaggal01_8301': r'core\geoid12b\g2012bu0.gtx',
'TXlagmat01_8301': r'core\geoid12b\g2012bu0.gtx',
'TXshlgal01_8301': r'core\geoid12b\g2012bu0.gtx',
'TXshlmat01_8301': r'core\geoid12b\g2012bu0.gtx',
'WAjdfuca03_8301': r'core\geoid12b\g2012bu0.gtx',
'WApugets02_8301': r'core\geoid12b\g2012bu0.gtx'}
vdatum43_20210928_geoid = {'AKglacier00_8301': r'core\xgeoid17b\AK_17B.gtx',
'AKwhale00_8301': r'core\xgeoid17b\AK_17B.gtx',
'AKyakutat00_8301': r'core\xgeoid17b\AK_17B.gtx',
'ALFLgom02_8301': r'core\geoid12b\g2012bu0.gtx',
'ALmobile02_8301': r'core\geoid12b\g2012bu0.gtx',
'CAmorrob01_8301': r'core\geoid12b\g2012bu0.gtx',
'CAORblan01_8301': r'core\geoid12b\g2012bu0.gtx',
'CAsanfrb12_8301': r'core\geoid12b\g2012bu0.gtx',
'CAsocal01_8301': r'core\geoid12b\g2012bu0.gtx',
'DEdelbay33_8301': r'core\xgeoid20b\conuspac.gtx',
'DEVAemb23_8301': r'core\xgeoid20b\conuspac.gtx',
'FLandrew02_8301': r'core\geoid12b\g2012bu0.gtx',
'FLapalach01_8301': r'core\geoid12b\g2012bu0.gtx',
'FLGAeastbays31_8301': r'core\geoid12b\g2012bu0.gtx',
'FLGAeastshelf41_8301': r'core\geoid12b\g2012bu0.gtx',
'FLjoseph03_8301': r'core\geoid12b\g2012bu0.gtx',
'FLpensac02_8301': r'core\geoid12b\g2012bu0.gtx',
'FLsoicw01_8301': r'core\geoid12b\g2012bu0.gtx',
'FLsouth12_8301': r'core\geoid12b\g2012bu0.gtx',
'FLwest01_8301': r'core\geoid12b\g2012bu0.gtx',
'GASCNCsab31_8301': r'core\geoid12b\g2012bu0.gtx',
'LAmobile02_8301': r'core\geoid12b\g2012bu0.gtx',
'LATXwest01_8301': r'core\geoid12b\g2012bu0.gtx',
'MDnwchb11_8301': r'core\xgeoid20b\conuspac.gtx',
'MDVAechb11_8301': r'core\xgeoid20b\conuspac.gtx',
'MENHMAgome23_8301': r'core\geoid12b\g2012bu0.gtx',
'NCcoast11_8301': r'core\geoid12b\g2012bu0.gtx',
'NCinner11_8301': r'core\geoid12b\g2012bu0.gtx',
'NJncstemb12_8301': r'core\geoid12b\g2012bu0.gtx',
'NJscstemb32_8301': r'core\xgeoid20b\conuspac.gtx',
'NJVAmab33_8301': r'core\xgeoid20b\conuspac.gtx',
'NYgr8bay34_8301': r'core\geoid12b\g2012bu0.gtx',
'NYNJhbr34_8301': r'core\geoid12b\g2012bu0.gtx',
'ORcentr01_8301': r'core\geoid12b\g2012bu0.gtx',
'ORWAcolr01_8301': r'core\geoid12b\g2012bu0.gtx',
'PRVI01_8301': r'core\geoid12b\g2012bp0.gtx',
'RICTbis34_8301': r'core\geoid12b\g2012bu0.gtx',
'TXlaggal01_8301': r'core\geoid12b\g2012bu0.gtx',
'TXlagmat01_8301': r'core\geoid12b\g2012bu0.gtx',
'TXshlgal01_8301': r'core\geoid12b\g2012bu0.gtx',
'TXshlmat01_8301': r'core\geoid12b\g2012bu0.gtx',
'VAswchb11_8301': r'core\xgeoid20b\conuspac.gtx',
'WAjdfuca03_8301': r'core\geoid12b\g2012bu0.gtx',
'WApugets02_8301': r'core\geoid12b\g2012bu0.gtx'}
vdatum_hashlookup = {'vdatum_4.3_20210928': vdatum43_20210928_hashes,
'vdatum_4.2_20210603': vdatum42_20210603_hashes,
'vdatum_4.1.2_20201203': vdatum412_20201203_hashes}
vdatum_geoidlookup = {'vdatum_4.3_20210928': vdatum43_20210928_geoid,
'vdatum_4.2_20210603': vdatum42_20210603_geoid,
'vdatum_4.1.2_20201203': vdatum412_20201203_geoid}
vdatum_answers = {'vdatum_4.1.2_20201203': {'north_carolina': {'horiz_epsg': 6318,
'x': np.array([-75.73890, -75.73880, -75.73870]),
'y': np.array([35.39590, 35.39580, 35.39570]),
'x_stateplane': [3631, np.array([905770.005, 905779.448, 905788.891])], # NC-3200, meters, EPSG:3613
'y_stateplane': [3631, np.array([187445.080, 187434.291, 187423.502])],
'x_utm': [26918, np.array([432899.901, 432908.900, 432917.898])], # UTM 18, EPSG:26918
'y_utm': [26918, np.array([3917198.926, 3917187.768, 3917176.609])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([49.264, 49.764, 50.264]),
'z_mllw': np.array([49.393, 49.893, 50.393]),
'z_mllw_unc': np.array([0.124, 0.124, 0.124]),
'z_mhw': np.array([49.227, 49.727, 50.227])},
'texas': {'horiz_epsg': 6318,
'x': np.array([-95.63490, -95.63480, -95.63470]),
'y': np.array([28.38690, 28.38680, 28.38670]),
'x_stateplane': [3673, np.array([929761.899, 929772.015, 929782.130])], # TX SC 4204, meters, EPSG:3673
'y_stateplane': [3673, np.array([4066097.205, 4066086.409, 4066075.613])],
'x_utm': [26915, np.array([241810.975, 241820.535, 241830.096])], # UTM 15, EPSG:26915
'y_utm': [26915, np.array([3142886.229, 3142874.930, 3142863.631])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([36.537, 37.037, 37.537]),
'z_mllw': np.array([36.694, 37.194, 37.694]),
'z_mllw_unc': np.array([0.112, 0.112, 0.112]),
'z_mhw': np.array([36.226, 36.726, 37.226])},
'california': {'horiz_epsg': 6318,
'x': np.array([-122.34990, -122.34980, -122.34970]),
'y': np.array([35.79190, 35.79180, 35.79170]),
'x_stateplane': [3495, np.array([1697216.653, 1697225.301, 1697233.949])], # CA Zone4 0404, meters, EPSG:3495
'y_stateplane': [3495, np.array([556165.351, 556153.947, 556142.542])],
'x_utm': [26910, np.array([558745.916, 558755.0265, 558764.137])], # UTM 10, EPSG:26910
'y_utm': [26910, np.array([3961062.442, 3961051.411, 3961040.379])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([48.868, 49.368, 49.868]),
'z_mllw': np.array([48.826, 49.326, 49.826]),
'z_mllw_unc': np.array([0.078, 0.078, 0.078]),
'z_mhw': np.array([47.436, 47.936, 48.436])},
# getting this answer from the vdatum java app was difficult. I found I had to:
# - transform NAD83(2011)/NAD83(2011) -> ITRF2008/ITRF2008
# - transform ITRF2008/ITRF2008 -> ITRF2008/MLLW
# going from NAD83 directly to ITRF2008/MLLW gets a different answer for some reason that does not align with PROJ
'alaska_southeast': {'horiz_epsg': 8999,
'x': np.array([-137.43590, -137.43580, -137.43570]),
'y': np.array([57.61290, 57.61280, 57.61270]),
'x_stateplane': [3468, np.array([593515.511, 593520.860, 593526.209])], # AK Zone1 5001, meters, EPSG:3468
'y_stateplane': [3468, np.array([649604.259, 649592.805, 649581.352])],
'x_utm': [26908, np.array([354484.054, 354489.627, 354495.199])], # UTM 8, EPSG:26908
'y_utm': [26908, np.array([6388227.166, 6388215.823, 6388204.480])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([10.348, 10.848, 11.348]), # using xgeoid17b
'z_mllw': np.array([11.614, 12.114, 12.614]), # using IGS08
'z_mllw_unc': np.array([0.19, 0.19, 0.19]),
'z_mhw': np.array([8.959, 9.459, 9.959])}, # using IGS08
'rastertest': {'horiz_epsg': 6348,
'x': np.array([339664.0, 339664.0, 340864.0]),
'y': np.array([4692852.0, 4689052.0, 4691652.00]),
'elev': np.array([-10.61, -21.3, -10.560]),
'added_unc': np.array([0.196, 0.196, 0.196]),
'unc': np.array([1.21, 1.43, 12.316812]),
'cont': np.array([124.0, 214.0, 396.0]),
'geoid': np.array([-27.5492, -27.6468, -27.526]),
'final_mllw': np.array([-39.7732, -50.591, -39.708])}
},
'vdatum_4.2_20210603': {'north_carolina': {'horiz_epsg': 6318,
'x': np.array([-75.73890, -75.73880, -75.73870]),
'y': np.array([35.39590, 35.39580, 35.39570]),
'x_stateplane': [3631, np.array([905770.005, 905779.448, 905788.891])], # NC-3200, meters, EPSG:3613
'y_stateplane': [3631, np.array([187445.080, 187434.291, 187423.502])],
'x_utm': [26918, np.array([432899.901, 432908.900, 432917.898])], # UTM 18, EPSG:26918
'y_utm': [26918, np.array([3917198.926, 3917187.768, 3917176.609])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([49.264, 49.764, 50.264]),
'z_mllw': np.array([49.393, 49.893, 50.393]),
'z_mllw_unc': np.array([0.074, 0.074, 0.074]),
'z_mhw': np.array([49.227, 49.727, 50.227])},
'texas': {'horiz_epsg': 6318,
'x': np.array([-95.63490, -95.63480, -95.63470]),
'y': np.array([28.38690, 28.38680, 28.38670]),
'x_stateplane': [3673, np.array([929761.899, 929772.015, 929782.130])], # TX SC 4204, meters, EPSG:3673
'y_stateplane': [3673, np.array([4066097.205, 4066086.409, 4066075.613])],
'x_utm': [26915, np.array([241810.975, 241820.535, 241830.096])], # UTM 15, EPSG:26915
'y_utm': [26915, np.array([3142886.229, 3142874.930, 3142863.631])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([36.537, 37.037, 37.537]),
'z_mllw': np.array([36.694, 37.194, 37.694]),
'z_mllw_unc': np.array([0.062, 0.062, 0.062]),
'z_mhw': np.array([36.226, 36.726, 37.226])},
'california': {'horiz_epsg': 6318,
'x': np.array([-122.34990, -122.34980, -122.34970]),
'y': np.array([35.79190, 35.79180, 35.79170]),
'x_stateplane': [3495, np.array([1697216.653, 1697225.301, 1697233.949])], # CA Zone4 0404, meters, EPSG:3495
'y_stateplane': [3495, np.array([556165.351, 556153.947, 556142.542])],
'x_utm': [26910, np.array([558745.916, 558755.0265, 558764.137])], # UTM 10, EPSG:26910
'y_utm': [26910, np.array([3961062.442, 3961051.411, 3961040.379])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([48.868, 49.368, 49.868]),
'z_mllw': np.array([48.826, 49.326, 49.826]),
'z_mllw_unc': np.array([0.028, 0.028, 0.028]),
'z_mhw': np.array([47.436, 47.936, 48.436])},
# getting this answer from the vdatum java app was difficult. I found I had to:
# - transform NAD83(2011)/NAD83(2011) -> ITRF2008/ITRF2008
# - transform ITRF2008/ITRF2008 -> ITRF2008/MLLW
# going from NAD83 directly to ITRF2008/MLLW gets a different answer for some reason that does not align with PROJ
'alaska_southeast': {'horiz_epsg': 8999,
'x': np.array([-137.43590, -137.43580, -137.43570]),
'y': np.array([57.61290, 57.61280, 57.61270]),
'x_stateplane': [3468, np.array([593515.511, 593520.860, 593526.209])], # AK Zone1 5001, meters, EPSG:3468
'y_stateplane': [3468, np.array([649604.259, 649592.805, 649581.352])],
'x_utm': [26908, np.array([354484.054, 354489.627, 354495.199])], # UTM 8, EPSG:26908
'y_utm': [26908, np.array([6388227.166, 6388215.823, 6388204.480])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([10.348, 10.848, 11.348]), # using xgeoid17b
'z_mllw': np.array([11.614, 12.114, 12.614]), # using IGS08
'z_mllw_unc': np.array([0.14, 0.14, 0.14]),
'z_mhw': np.array([8.959, 9.459, 9.959])}, # using IGS08
'rastertest': {'horiz_epsg': 6348,
'x': np.array([339664.0, 339664.0, 340864.0]),
'y': np.array([4692852.0, 4689052.0, 4691652.00]),
'elev': np.array([-10.61, -21.3, -10.560]),
'added_unc': np.array([0.146, 0.146, 0.146]),
'unc': np.array([1.21, 1.43, 12.316812]),
'cont': np.array([124.0, 214.0, 396.0]),
'geoid': np.array([-27.5492, -27.6468, -27.526]),
'final_mllw': np.array([-39.7732, -50.591, -39.708])}
},
'vdatum_4.3_20210928': {'north_carolina': {'horiz_epsg': 6318,
'x': np.array([-75.73890, -75.73880, -75.73870]),
'y': np.array([35.39590, 35.39580, 35.39570]),
'x_stateplane': [3631, np.array([905770.005, 905779.448, 905788.891])], # NC-3200, meters, EPSG:3613
'y_stateplane': [3631, np.array([187445.080, 187434.291, 187423.502])],
'x_utm': [26918, np.array([432899.901, 432908.900, 432917.898])], # UTM 18, EPSG:26918
'y_utm': [26918, np.array([3917198.926, 3917187.768, 3917176.609])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([49.264, 49.764, 50.264]),
'z_mllw': np.array([49.393, 49.893, 50.393]),
'z_mllw_unc': np.array([0.074, 0.074, 0.074]),
'z_mhw': np.array([49.227, 49.727, 50.227])},
'texas': {'horiz_epsg': 6318,
'x': np.array([-95.63490, -95.63480, -95.63470]),
'y': np.array([28.38690, 28.38680, 28.38670]),
'x_stateplane': [3673, np.array([929761.899, 929772.015, 929782.130])], # TX SC 4204, meters, EPSG:3673
'y_stateplane': [3673, np.array([4066097.205, 4066086.409, 4066075.613])],
'x_utm': [26915, np.array([241810.975, 241820.535, 241830.096])], # UTM 15, EPSG:26915
'y_utm': [26915, np.array([3142886.229, 3142874.930, 3142863.631])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([36.537, 37.037, 37.537]),
'z_mllw': np.array([36.694, 37.194, 37.694]),
'z_mllw_unc': np.array([0.062, 0.062, 0.062]),
'z_mhw': np.array([36.226, 36.726, 37.226])},
'california': {'horiz_epsg': 6318,
'x': np.array([-122.34990, -122.34980, -122.34970]),
'y': np.array([35.79190, 35.79180, 35.79170]),
'x_stateplane': [3495, np.array([1697216.653, 1697225.301, 1697233.949])], # CA Zone4 0404, meters, EPSG:3495
'y_stateplane': [3495, np.array([556165.351, 556153.947, 556142.542])],
'x_utm': [26910, np.array([558745.916, 558755.0265, 558764.137])], # UTM 10, EPSG:26910
'y_utm': [26910, np.array([3961062.442, 3961051.411, 3961040.379])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([48.868, 49.368, 49.868]),
'z_mllw': np.array([48.826, 49.326, 49.826]),
'z_mllw_unc': np.array([0.028, 0.028, 0.028]),
'z_mhw': np.array([47.436, 47.936, 48.436])},
# getting this answer from the vdatum java app was difficult. I found I had to:
# - transform NAD83(2011)/NAD83(2011) -> ITRF2008/ITRF2008
# - transform ITRF2008/ITRF2008 -> ITRF2008/MLLW
# going from NAD83 directly to ITRF2008/MLLW gets a different answer for some reason that does not align with PROJ
'alaska_southeast': {'horiz_epsg': 8999,
'x': np.array([-137.43590, -137.43580, -137.43570]),
'y': np.array([57.61290, 57.61280, 57.61270]),
'x_stateplane': [3468, np.array([593515.511, 593520.860, 593526.209])], # AK Zone1 5001, meters, EPSG:3468
'y_stateplane': [3468, np.array([649604.259, 649592.805, 649581.352])],
'x_utm': [26908, np.array([354484.054, 354489.627, 354495.199])], # UTM 8, EPSG:26908
'y_utm': [26908, np.array([6388227.166, 6388215.823, 6388204.480])],
'z_nad83': np.array([10.5, 11.0, 11.5]),
'z_navd88': np.array([10.348, 10.848, 11.348]), # using xgeoid17b
'z_mllw': np.array([11.614, 12.114, 12.614]), # using IGS08
'z_mllw_unc': np.array([0.14, 0.14, 0.14]),
'z_mhw': np.array([8.959, 9.459, 9.959])}, # using IGS08
'rastertest': {'horiz_epsg': 6348,
'x': np.array([339664.0, 339664.0, 340864.0]),
'y': np.array([4692852.0, 4689052.0, 4691652.00]),
'elev': np.array([-10.61, -21.3, -10.560]),
'added_unc': np.array([0.146, 0.146, 0.146]),
'unc': np.array([1.21, 1.43, 12.316812]),
'cont': np.array([124.0, 214.0, 396.0]),
'geoid': np.array([-27.5492, -27.6468, -27.526]),
'final_mllw': np.array([-39.7732, -50.591, -39.708])}
}
}
# make sure that the lookups have the appropriate keys
assert sorted(list(vdatum_hashlookup.keys())) == sorted(list(vdatum_answers.keys()))
assert sorted(list(vdatum_geoidlookup.keys())) == sorted(list(vdatum_answers.keys()))
| 127.968167
| 172
| 0.60999
| 8,613
| 116,579
| 8.075816
| 0.104377
| 0.015699
| 0.018402
| 0.027373
| 0.906135
| 0.9052
| 0.899723
| 0.898745
| 0.898745
| 0.897681
| 0
| 0.40159
| 0.281457
| 116,579
| 910
| 173
| 128.108791
| 0.428773
| 0.016101
| 0
| 0.783063
| 0
| 0
| 0.583271
| 0.54756
| 0
| 0
| 0
| 0
| 0.00232
| 1
| 0.00116
| false
| 0
| 0.00232
| 0
| 0.00348
| 0.00348
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 1
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 11
|
4e432a5962dec9b2bcae75466c0d56d636753a0d
| 321
|
py
|
Python
|
tests/generate_tests_data.py
|
traversaro/osqp
|
a7db086a07a38d73c4e3c19a750fc330a4443c29
|
[
"Apache-2.0"
] | null | null | null |
tests/generate_tests_data.py
|
traversaro/osqp
|
a7db086a07a38d73c4e3c19a750fc330a4443c29
|
[
"Apache-2.0"
] | 1
|
2018-05-20T21:17:45.000Z
|
2018-05-20T21:17:45.000Z
|
tests/generate_tests_data.py
|
traversaro/osqp
|
a7db086a07a38d73c4e3c19a750fc330a4443c29
|
[
"Apache-2.0"
] | 2
|
2019-11-03T07:34:39.000Z
|
2021-01-05T02:46:43.000Z
|
# Code to generate the unittests for OSQP C code
import basic_qp.generate_problem
import basic_qp2.generate_problem
import lin_alg.generate_problem
import solve_linsys.generate_problem
import primal_infeasibility.generate_problem
import primal_dual_infeasibility.generate_problem
import update_matrices.generate_problem
| 32.1
| 49
| 0.897196
| 45
| 321
| 6.066667
| 0.488889
| 0.384615
| 0.461538
| 0.197802
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.003378
| 0.077882
| 321
| 9
| 50
| 35.666667
| 0.918919
| 0.143302
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
14c428d3c46d95459bd294f4650e94fe987533cc
| 19,175
|
py
|
Python
|
tfdet/model/train/target/rcnn.py
|
Burf/tfdetection
|
658e67d6db71e04bda2965d5a5d506d304ab8ad6
|
[
"Apache-2.0"
] | null | null | null |
tfdet/model/train/target/rcnn.py
|
Burf/tfdetection
|
658e67d6db71e04bda2965d5a5d506d304ab8ad6
|
[
"Apache-2.0"
] | null | null | null |
tfdet/model/train/target/rcnn.py
|
Burf/tfdetection
|
658e67d6db71e04bda2965d5a5d506d304ab8ad6
|
[
"Apache-2.0"
] | null | null | null |
import tensorflow as tf
from tfdet.core.assign import max_iou
from tfdet.core.bbox import bbox2delta
def rpn_assign(bbox_true, bbox_pred, positive_threshold = 0.7, negative_threshold = 0.3, mode = "normal"):
return max_iou(bbox_true, bbox_pred, positive_threshold = positive_threshold, negative_threshold = negative_threshold, mode = mode)
def cls_assign(bbox_true, bbox_pred, positive_threshold = 0.5, negative_threshold = 0.5, mode = "normal"):
return max_iou(bbox_true, bbox_pred, positive_threshold = positive_threshold, negative_threshold = negative_threshold, mode = mode)
def rpn_target(bbox_true, rpn_score, rpn_regress, anchors, assign = rpn_assign, sampling_count = 256, positive_ratio = 0.5, mean = [0., 0., 0., 0.], std = [0.1, 0.1, 0.2, 0.2]):
"""
bbox_true = [[x1, y1, x2, y2], ...] #(padded_num_true, bbox)
rpn_score = score for FG/BG #(num_anchors, 1)
rpn_regress = rpn regress #(num_anchors, delta)
anchors = [[x1, y1, x2, y2], ...] #(num_anchors, bbox)
y_true = -1 : negative / 0 : neutral / 1 : positive #(sampling_count, 1)
box_true = [[x1, y1, x2, y2], ...] #(sampling_count, delta)
y_pred = -1 : negative / 0 : neutral / 1 : positive #(sampling_count, 1)
bbox_pred = [[x1, y1, x2, y2], ...] #(sampling_count, delta)
"""
pred_count = tf.shape(anchors)[0]
valid_indices = tf.where(tf.reduce_max(tf.cast(0 < bbox_true, tf.int32), axis = -1))
bbox_true = tf.gather_nd(bbox_true, valid_indices)
true_indices, positive_indices, negative_indices = assign(bbox_true, anchors)
if isinstance(sampling_count, int) and 0 < sampling_count:
positive_count = tf.cast(sampling_count * positive_ratio, tf.int32)
indices = tf.range(tf.shape(positive_indices)[0])
indices = tf.random.shuffle(indices)[:positive_count]
positive_indices = tf.gather(positive_indices, indices)
true_indices = tf.gather(true_indices, indices)
positive_count = tf.cast(tf.shape(positive_indices)[0], tf.float32)
negative_count = tf.cast(1 / positive_ratio * positive_count - positive_count, tf.int32)
negative_indices = tf.random.shuffle(negative_indices)[:negative_count]
else:
sampling_count = pred_count
pred_indices = tf.concat([positive_indices, negative_indices], axis = 0)
y_true = tf.expand_dims(tf.concat([tf.ones_like(positive_indices, dtype = tf.int32), -tf.ones_like(negative_indices, dtype = tf.int32)], axis = 0), axis = -1) #match
bbox_true = tf.gather(bbox_true, true_indices)
y_pred = tf.gather(rpn_score, pred_indices)
bbox_pred = tf.gather(rpn_regress, positive_indices)
anchors = tf.gather(anchors, positive_indices)
if tf.keras.backend.int_shape(true_indices)[0] != 0:
bbox_true = bbox2delta(bbox_true, anchors, mean, std)
negative_count = tf.shape(negative_indices)[0]
pad_count = tf.maximum(sampling_count - tf.shape(pred_indices)[0], 0)
y_true = tf.pad(y_true, [[0, pad_count], [0, 0]])
bbox_true = tf.pad(bbox_true, [[0, negative_count + pad_count], [0, 0]])
y_pred = tf.pad(y_pred, [[0, pad_count], [0, 0]])
bbox_pred = tf.pad(bbox_pred, [[0, negative_count + pad_count], [0, 0]])
return y_true, bbox_true, y_pred, bbox_pred
def sampling_target(y_true, bbox_true, proposal, mask_true = None, assign = cls_assign, sampling_count = 256, positive_ratio = 0.25):
"""
y_true = label #(padded_num_true, 1 or num_class)
bbox_true = [[x1, y1, x2, y2], ...] #(padded_num_true, bbox)
mask_true = mask #(padded_num_true, h, w)
proposal = [[x1, y1, x2, y2], ...] #(num_proposals, bbox)
y_true = targeted label #(sampling_count, 1 or num_class)
bbox_true = [[x1, y1, x2, y2], ...] #(sampling_count, bbox)
mask_true = targeted mask true #(sampling_count, h, w)
proposal = [[x1, y1, x2, y2], ...] #(sampling_count, bbox)
"""
pred_count = tf.shape(proposal)[0]
valid_true_indices = tf.where(tf.reduce_max(tf.cast(0 < bbox_true, tf.int32), axis = -1))
y_true = tf.gather_nd(y_true, valid_true_indices)
bbox_true = tf.gather_nd(bbox_true, valid_true_indices)
valid_pred_indices = tf.where(tf.reduce_max(tf.cast(0 < proposal, tf.int32), axis = -1))
proposal = tf.gather_nd(proposal, valid_pred_indices)
if mask_true is not None:
mask_true = tf.gather_nd(mask_true, valid_true_indices)
true_indices, positive_indices, negative_indices = assign(bbox_true, proposal)
if isinstance(sampling_count, int) and 0 < sampling_count:
positive_count = tf.cast(sampling_count * positive_ratio, tf.int32)
indices = tf.range(tf.shape(positive_indices)[0])
indices = tf.random.shuffle(indices)[:positive_count]
positive_indices = tf.gather(positive_indices, indices)
true_indices = tf.gather(true_indices, indices)
positive_count = tf.cast(tf.shape(positive_indices)[0], tf.float32)
negative_count = tf.cast(1 / positive_ratio * positive_count - positive_count, tf.int32)
negative_indices = tf.random.shuffle(negative_indices)[:negative_count]
else:
sampling_count = pred_count
pred_indices = tf.concat([positive_indices, negative_indices], axis = 0)
y_true = tf.gather(y_true, true_indices)
bbox_true = tf.gather(bbox_true, true_indices)
proposal = tf.gather(proposal, pred_indices)
if mask_true is not None:
mask_true = tf.gather(mask_true, true_indices)
n_class = tf.shape(y_true)[-1]
negative_count = tf.shape(negative_indices)[0]
pad_count = tf.maximum(sampling_count - tf.shape(pred_indices)[0], 0)
y_true = tf.cond(tf.equal(n_class, 1), true_fn = lambda: tf.pad(y_true, [[0, negative_count + pad_count], [0, 0]]), false_fn = lambda: tf.concat([y_true, tf.cast(tf.pad(tf.ones([negative_count + pad_count, 1]), [[0, 0], [0, n_class - 1]]), y_true.dtype)], axis = 0))
bbox_true = tf.pad(bbox_true, [[0, negative_count + pad_count], [0, 0]])
proposal = tf.pad(proposal, [[0, pad_count], [0, 0]])
result = y_true, bbox_true, proposal
if mask_true is not None:
mask_true = tf.pad(mask_true, [[0, negative_count + pad_count], [0, 0], [0, 0]])
result = y_true, bbox_true, mask_true, proposal
return result
def sampling_postprocess(y_true, bbox_true, cls_logits, cls_regress, proposal, mask_true = None, mask_regress = None, mean = [0., 0., 0., 0.], std = [0.1, 0.1, 0.2, 0.2], method = "bilinear"):
if mask_true is not None:
if tf.keras.backend.ndim(mask_true) == 3:
mask_true = tf.expand_dims(mask_true, axis = -1)
sampling_count = tf.shape(proposal)[0]
positive_indices = tf.where(tf.reduce_max(tf.cast(0 < bbox_true, tf.int32), axis = -1))
pred_indices = tf.where(tf.reduce_max(tf.cast(0 < proposal, tf.int32), axis = -1))
y_true = tf.gather_nd(y_true, positive_indices)
bbox_true = tf.gather_nd(bbox_true, positive_indices)
y_pred = tf.gather_nd(cls_logits, pred_indices)
bbox_pred = tf.gather_nd(cls_regress, positive_indices)
proposal = tf.gather_nd(proposal, positive_indices)
if mask_true is not None and mask_regress is not None:
mask_true = tf.gather_nd(mask_true, positive_indices)
mask_pred = tf.gather_nd(mask_regress, positive_indices)
positive_count = tf.shape(y_true)[0]
n_class = tf.shape(y_true)[-1]
if tf.keras.backend.int_shape(positive_indices)[0] != 0:
bbox_true = bbox2delta(bbox_true, proposal, mean, std)
label = tf.cond(tf.equal(n_class, 1), true_fn = lambda: y_true, false_fn = lambda: tf.expand_dims(tf.cast(tf.argmax(y_true, axis = -1), y_true.dtype), axis = -1))
indices = tf.stack([tf.range(tf.shape(label)[0]), tf.cast(label[:, 0], tf.int32)], axis = -1)
bbox_pred = tf.gather_nd(bbox_pred, indices)
if mask_true is not None and mask_regress is not None:
x1, y1, x2, y2 = tf.split(proposal, 4, axis = -1)
mask_bbox = tf.concat([y1, x1, y2, x2], axis = -1)
mask_shape = tf.shape(mask_pred)
mask_true = tf.image.crop_and_resize(image = tf.cast(mask_true, mask_pred.dtype), boxes = mask_bbox, box_indices = tf.range(0, positive_count), crop_size = mask_shape[1:3], method = method)
mask_true = mask_true[..., 0]
mask_true = tf.clip_by_value(tf.round(mask_true), 0., 1.)
mask_pred = tf.transpose(mask_pred, [0, 3, 1, 2])
mask_pred = tf.gather_nd(mask_pred, indices)
else:
bbox_pred = bbox_pred[:, 0]
if mask_true is not None and mask_regress is not None:
mask_pred = mask_pred[..., 0]
mask_true = tf.zeros_like(mask_pred, dtype = mask_pred.dtype)
pred_count = tf.shape(pred_indices)[0]
pad_count = sampling_count - pred_count
negative_count = tf.maximum(pred_count - positive_count , 0)
y_true = tf.cond(tf.equal(n_class, 1), true_fn = lambda: tf.pad(y_true, [[0, negative_count + pad_count], [0, 0]]), false_fn = lambda: tf.concat([y_true, tf.cast(tf.pad(tf.ones([negative_count + pad_count, 1]), [[0, 0], [0, n_class - 1]]), y_true.dtype)], axis = 0))
y_pred = tf.pad(y_pred, [[0, pad_count], [0, 0]])
bbox_true = tf.pad(bbox_true, [[0, negative_count + pad_count], [0, 0]])
bbox_pred = tf.pad(bbox_pred, [[0, negative_count + pad_count], [0, 0]])
result = y_true, bbox_true, y_pred, bbox_pred
if mask_true is not None and mask_regress is not None:
mask_true = tf.pad(mask_true, [[0, negative_count + pad_count], [0, 0], [0, 0]])
mask_pred = tf.pad(mask_pred, [[0, negative_count + pad_count], [0, 0], [0, 0]])
result = y_true, bbox_true, mask_true, y_pred, bbox_pred, mask_pred
return result
def cls_target(y_true, bbox_true, cls_logit, cls_regress, proposal, mask_true = None, mask_regress = None, assign = cls_assign, sampling_count = 256, positive_ratio = 0.25, mean = [0., 0., 0., 0.], std = [0.1, 0.1, 0.2, 0.2], method = "bilinear"):
"""
y_true = label #(padded_num_true, 1 or num_class)
bbox_true = [[x1, y1, x2, y2], ...] #(padded_num_true, bbox)
mask_true = mask #(padded_num_true, h, w)
cls_logit = classifier logit #(num_proposals, num_class)
cls_regress = classifier regress #(num_proposals, num_class, delta)
mask_regress = mask regress #(num_proposals, h, w, num_class)
proposal = [[x1, y1, x2, y2], ...] #(num_proposals, bbox)
y_true = targeted label #(sampling_count, 1 or num_class)
bbox_true = [[x1, y1, x2, y2], ...] #(sampling_count, delta)
mask_true = targeted mask true #(sampling_count, h, w)
y_pred = targeted logit #(sampling_count, num_class)
bbox_pred = [[x1, y1, x2, y2], ...] #(sampling_count, delta)
mask_pred = targeted mask regress #(sampling_count, h, w)
"""
if mask_true is not None and mask_regress is not None:
if tf.keras.backend.ndim(mask_true) == 3:
mask_true = tf.expand_dims(mask_true, axis = -1)
pred_count = tf.shape(proposal)[0]
valid_true_indices = tf.where(tf.reduce_max(tf.cast(0 < bbox_true, tf.int32), axis = -1))
y_true = tf.gather_nd(y_true, valid_true_indices)
bbox_true = tf.gather_nd(bbox_true, valid_true_indices)
valid_pred_indices = tf.where(tf.reduce_max(tf.cast(0 < proposal, tf.int32), axis = -1))
cls_logit = tf.gather_nd(cls_logit, valid_pred_indices)
cls_regress = tf.gather_nd(cls_regress, valid_pred_indices)
proposal = tf.gather_nd(proposal, valid_pred_indices)
if mask_true is not None and mask_regress is not None:
mask_true = tf.gather_nd(mask_true, valid_true_indices)
mask_regress = tf.gather_nd(mask_regress, valid_pred_indices)
true_indices, positive_indices, negative_indices = assign(bbox_true, proposal)
if isinstance(sampling_count, int) and 0 < sampling_count:
positive_count = tf.cast(sampling_count * positive_ratio, tf.int32)
indices = tf.range(tf.shape(positive_indices)[0])
indices = tf.random.shuffle(indices)[:positive_count]
positive_indices = tf.gather(positive_indices, indices)
true_indices = tf.gather(true_indices, indices)
positive_count = tf.cast(tf.shape(positive_indices)[0], tf.float32)
negative_count = tf.cast(1 / positive_ratio * positive_count - positive_count, tf.int32)
negative_indices = tf.random.shuffle(negative_indices)[:negative_count]
else:
sampling_count = pred_count
pred_indices = tf.concat([positive_indices, negative_indices], axis = 0)
y_true = tf.gather(y_true, true_indices)
bbox_true = tf.gather(bbox_true, true_indices)
y_pred = tf.gather(cls_logit, pred_indices)
bbox_pred = tf.gather(cls_regress, positive_indices)
proposal = tf.gather(proposal, positive_indices)
if mask_true is not None and mask_regress is not None:
mask_true = tf.gather(mask_true, true_indices)
mask_pred = tf.gather(mask_regress, positive_indices)
n_class = tf.shape(y_true)[-1]
if tf.keras.backend.int_shape(true_indices)[0] != 0:
bbox_true = bbox2delta(bbox_true, proposal, mean, std)
label = tf.cond(tf.equal(n_class, 1), true_fn = lambda: y_true, false_fn = lambda: tf.expand_dims(tf.cast(tf.argmax(y_true, axis = -1), y_true.dtype), axis = -1))
indices = tf.stack([tf.range(tf.shape(label)[0]), tf.cast(label[:, 0], tf.int32)], axis = -1)
bbox_pred = tf.gather_nd(bbox_pred, indices)
if mask_true is not None and mask_regress is not None:
x1, y1, x2, y2 = tf.split(proposal, 4, axis = -1)
mask_bbox = tf.concat([y1, x1, y2, x2], axis = -1)
mask_shape = tf.shape(mask_pred)
mask_true = tf.image.crop_and_resize(image = tf.cast(mask_true, mask_pred.dtype), boxes = mask_bbox, box_indices = tf.range(0, tf.cast(positive_count, tf.int32)), crop_size = mask_shape[1:3], method = method)
mask_true = mask_true[..., 0]
mask_true = tf.clip_by_value(tf.round(mask_true), 0., 1.)
mask_pred = tf.transpose(mask_pred, [0, 3, 1, 2])
mask_pred = tf.gather_nd(mask_pred, indices)
else:
bbox_pred = bbox_pred[:, 0]
if mask_true is not None and mask_regress is not None:
mask_pred = mask_pred[..., 0]
mask_true = tf.zeros_like(mask_pred, dtype = mask_pred.dtype)
negative_count = tf.shape(negative_indices)[0]
pad_count = tf.maximum(sampling_count - tf.shape(pred_indices)[0], 0)
y_true = tf.cond(tf.equal(n_class, 1), true_fn = lambda: tf.pad(y_true, [[0, negative_count + pad_count], [0, 0]]), false_fn = lambda: tf.concat([y_true, tf.cast(tf.pad(tf.ones([negative_count + pad_count, 1]), [[0, 0], [0, n_class - 1]]), y_true.dtype)], axis = 0))
bbox_true = tf.pad(bbox_true, [[0, negative_count + pad_count], [0, 0]])
y_pred = tf.pad(y_pred, [[0, pad_count], [0, 0]])
bbox_pred = tf.pad(bbox_pred, [[0, negative_count + pad_count], [0, 0]])
result = y_true, bbox_true, y_pred, bbox_pred
if mask_true is not None and mask_regress is not None:
mask_true = tf.pad(mask_true, [[0, negative_count + pad_count], [0, 0], [0, 0]])
mask_pred = tf.pad(mask_pred, [[0, negative_count + pad_count], [0, 0], [0, 0]])
result = y_true, bbox_true, mask_true, y_pred, bbox_pred, mask_pred
return result
def mask_target(y_true, bbox_true, mask_true, mask_regress, proposal, assign = cls_assign, sampling_count = 256, positive_ratio = 0.25, mean = [0., 0., 0., 0.], std = [0.1, 0.1, 0.2, 0.2], method = "bilinear"):
"""
y_true = label #(padded_num_true, 1 or num_class)
bbox_true = [[x1, y1, x2, y2], ...] #(padded_num_true, bbox)
mask_true = mask #(padded_num_true, h, w)
mask_regress = mask regress #(num_proposals, h, w, num_class)
proposal = [[x1, y1, x2, y2], ...] #(num_proposals, bbox)
mask_true = targeted mask true #(sampling_count, h, w)
mask_pred = targeted mask regress #(sampling_count, h, w)
"""
if tf.keras.backend.ndim(mask_true) == 3:
mask_true = tf.expand_dims(mask_true, axis = -1)
pred_count = tf.shape(proposal)[0]
valid_true_indices = tf.where(tf.reduce_max(tf.cast(0 < bbox_true, tf.int32), axis = -1))
y_true = tf.gather_nd(y_true, valid_true_indices)
bbox_true = tf.gather_nd(bbox_true, valid_true_indices)
valid_pred_indices = tf.where(tf.reduce_max(tf.cast(0 < proposal, tf.int32), axis = -1))
proposal = tf.gather_nd(proposal, valid_pred_indices)
mask_true = tf.gather_nd(mask_true, valid_true_indices)
mask_regress = tf.gather_nd(mask_regress, valid_pred_indices)
true_indices, positive_indices, negative_indices = assign(bbox_true, proposal)
if isinstance(sampling_count, int) and 0 < sampling_count:
positive_count = tf.cast(sampling_count * positive_ratio, tf.int32)
indices = tf.range(tf.shape(positive_indices)[0])
indices = tf.random.shuffle(indices)[:positive_count]
positive_indices = tf.gather(positive_indices, indices)
true_indices = tf.gather(true_indices, indices)
positive_count = tf.cast(tf.shape(positive_indices)[0], tf.float32)
negative_count = tf.cast(1 / positive_ratio * positive_count - positive_count, tf.int32)
negative_indices = tf.random.shuffle(negative_indices)[:negative_count]
else:
sampling_count = pred_count
pred_indices = tf.concat([positive_indices, negative_indices], axis = 0)
y_true = tf.gather(y_true, true_indices)
proposal = tf.gather(proposal, positive_indices)
mask_true = tf.gather(mask_true, true_indices)
mask_pred = tf.gather(mask_regress, positive_indices)
n_class = tf.shape(y_true)[-1]
if tf.keras.backend.int_shape(true_indices)[0] != 0:
label = tf.cond(tf.equal(n_class, 1), true_fn = lambda: y_true, false_fn = lambda: tf.expand_dims(tf.cast(tf.argmax(y_true, axis = -1), y_true.dtype), axis = -1))
indices = tf.stack([tf.range(tf.shape(label)[0]), tf.cast(label[:, 0], tf.int32)], axis = -1)
if mask_true is not None and mask_regress is not None:
x1, y1, x2, y2 = tf.split(proposal, 4, axis = -1)
mask_bbox = tf.concat([y1, x1, y2, x2], axis = -1)
mask_shape = tf.shape(mask_pred)
mask_true = tf.image.crop_and_resize(image = tf.cast(mask_true, mask_pred.dtype), boxes = mask_bbox, box_indices = tf.range(0, tf.cast(positive_count, tf.int32)), crop_size = mask_shape[1:3], method = method)
mask_true = mask_true[..., 0]
mask_true = tf.clip_by_value(tf.round(mask_true), 0., 1.)
mask_pred = tf.transpose(mask_pred, [0, 3, 1, 2])
mask_pred = tf.gather_nd(mask_pred, indices)
else:
mask_pred = mask_pred[..., 0]
mask_true = tf.zeros_like(mask_pred, dtype = mask_pred.dtype)
negative_count = tf.shape(negative_indices)[0]
pad_count = tf.maximum(sampling_count - tf.shape(pred_indices)[0], 0)
mask_true = tf.pad(mask_true, [[0, negative_count + pad_count], [0, 0], [0, 0]])
mask_pred = tf.pad(mask_pred, [[0, negative_count + pad_count], [0, 0], [0, 0]])
return mask_true, mask_pred
| 59.735202
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0
| 7
|
09439a3b08c6faa40b5e1696ce47a19aa3617e2a
| 46,290
|
py
|
Python
|
sdk/python/pulumi_alicloud/alikafka/instance.py
|
pulumi/pulumi-alicloud
|
9c34d84b4588a7c885c6bec1f03b5016e5a41683
|
[
"ECL-2.0",
"Apache-2.0"
] | 42
|
2019-03-18T06:34:37.000Z
|
2022-03-24T07:08:57.000Z
|
sdk/python/pulumi_alicloud/alikafka/instance.py
|
pulumi/pulumi-alicloud
|
9c34d84b4588a7c885c6bec1f03b5016e5a41683
|
[
"ECL-2.0",
"Apache-2.0"
] | 152
|
2019-04-15T21:03:44.000Z
|
2022-03-29T18:00:57.000Z
|
sdk/python/pulumi_alicloud/alikafka/instance.py
|
pulumi/pulumi-alicloud
|
9c34d84b4588a7c885c6bec1f03b5016e5a41683
|
[
"ECL-2.0",
"Apache-2.0"
] | 3
|
2020-08-26T17:30:07.000Z
|
2021-07-05T01:37:45.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['InstanceArgs', 'Instance']
@pulumi.input_type
class InstanceArgs:
def __init__(__self__, *,
deploy_type: pulumi.Input[int],
disk_size: pulumi.Input[int],
disk_type: pulumi.Input[int],
io_max: pulumi.Input[int],
topic_quota: pulumi.Input[int],
vswitch_id: pulumi.Input[str],
config: Optional[pulumi.Input[str]] = None,
eip_max: Optional[pulumi.Input[int]] = None,
name: Optional[pulumi.Input[str]] = None,
paid_type: Optional[pulumi.Input[str]] = None,
security_group: Optional[pulumi.Input[str]] = None,
service_version: Optional[pulumi.Input[str]] = None,
spec_type: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, Any]]] = None):
"""
The set of arguments for constructing a Instance resource.
:param pulumi.Input[int] deploy_type: The deploy type of the instance. Currently only support two deploy type, 4: eip/vpc instance, 5: vpc instance.
:param pulumi.Input[int] disk_size: The disk size of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[int] disk_type: The disk type of the instance. 0: efficient cloud disk , 1: SSD.
:param pulumi.Input[int] io_max: The max value of io of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[int] topic_quota: The max num of topic can be create of the instance. When modify this value, it only adjust to a greater value.
:param pulumi.Input[str] vswitch_id: The ID of attaching vswitch to instance.
:param pulumi.Input[str] config: （Optional, Available in v1.112.0+） The basic config for this instance. The input should be json type, only the following key allowed: enable.acl, enable.vpc_sasl_ssl, kafka.log.retention.hours, kafka.message.max.bytes.
:param pulumi.Input[int] eip_max: The max bandwidth of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[str] name: Name of your Kafka instance. The length should between 3 and 64 characters. If not set, will use instance id as instance name.
:param pulumi.Input[str] paid_type: The paid type of the instance. Support two type, "PrePaid": pre paid type instance, "PostPaid": post paid type instance. Default is PostPaid. When modify this value, it only support adjust from post pay to pre pay.
:param pulumi.Input[str] security_group: （Optional, ForceNew, Available in v1.93.0+） The ID of security group for this instance. If the security group is empty, system will create a default one.
:param pulumi.Input[str] service_version: （Optional, Available in v1.112.0+） The kafka openSource version for this instance. Only 0.10.2 or 2.2.0 is allowed, default is 0.10.2.
:param pulumi.Input[str] spec_type: The spec type of the instance. Support two type, "normal": normal version instance, "professional": professional version instance. Default is normal. When modify this value, it only support adjust from normal to professional. Note only pre paid type instance support professional specific type.
:param pulumi.Input[Mapping[str, Any]] tags: A mapping of tags to assign to the resource.
"""
pulumi.set(__self__, "deploy_type", deploy_type)
pulumi.set(__self__, "disk_size", disk_size)
pulumi.set(__self__, "disk_type", disk_type)
pulumi.set(__self__, "io_max", io_max)
pulumi.set(__self__, "topic_quota", topic_quota)
pulumi.set(__self__, "vswitch_id", vswitch_id)
if config is not None:
pulumi.set(__self__, "config", config)
if eip_max is not None:
pulumi.set(__self__, "eip_max", eip_max)
if name is not None:
pulumi.set(__self__, "name", name)
if paid_type is not None:
pulumi.set(__self__, "paid_type", paid_type)
if security_group is not None:
pulumi.set(__self__, "security_group", security_group)
if service_version is not None:
pulumi.set(__self__, "service_version", service_version)
if spec_type is not None:
pulumi.set(__self__, "spec_type", spec_type)
if tags is not None:
pulumi.set(__self__, "tags", tags)
@property
@pulumi.getter(name="deployType")
def deploy_type(self) -> pulumi.Input[int]:
"""
The deploy type of the instance. Currently only support two deploy type, 4: eip/vpc instance, 5: vpc instance.
"""
return pulumi.get(self, "deploy_type")
@deploy_type.setter
def deploy_type(self, value: pulumi.Input[int]):
pulumi.set(self, "deploy_type", value)
@property
@pulumi.getter(name="diskSize")
def disk_size(self) -> pulumi.Input[int]:
"""
The disk size of the instance. When modify this value, it only support adjust to a greater value.
"""
return pulumi.get(self, "disk_size")
@disk_size.setter
def disk_size(self, value: pulumi.Input[int]):
pulumi.set(self, "disk_size", value)
@property
@pulumi.getter(name="diskType")
def disk_type(self) -> pulumi.Input[int]:
"""
The disk type of the instance. 0: efficient cloud disk , 1: SSD.
"""
return pulumi.get(self, "disk_type")
@disk_type.setter
def disk_type(self, value: pulumi.Input[int]):
pulumi.set(self, "disk_type", value)
@property
@pulumi.getter(name="ioMax")
def io_max(self) -> pulumi.Input[int]:
"""
The max value of io of the instance. When modify this value, it only support adjust to a greater value.
"""
return pulumi.get(self, "io_max")
@io_max.setter
def io_max(self, value: pulumi.Input[int]):
pulumi.set(self, "io_max", value)
@property
@pulumi.getter(name="topicQuota")
def topic_quota(self) -> pulumi.Input[int]:
"""
The max num of topic can be create of the instance. When modify this value, it only adjust to a greater value.
"""
return pulumi.get(self, "topic_quota")
@topic_quota.setter
def topic_quota(self, value: pulumi.Input[int]):
pulumi.set(self, "topic_quota", value)
@property
@pulumi.getter(name="vswitchId")
def vswitch_id(self) -> pulumi.Input[str]:
"""
The ID of attaching vswitch to instance.
"""
return pulumi.get(self, "vswitch_id")
@vswitch_id.setter
def vswitch_id(self, value: pulumi.Input[str]):
pulumi.set(self, "vswitch_id", value)
@property
@pulumi.getter
def config(self) -> Optional[pulumi.Input[str]]:
"""
（Optional, Available in v1.112.0+） The basic config for this instance. The input should be json type, only the following key allowed: enable.acl, enable.vpc_sasl_ssl, kafka.log.retention.hours, kafka.message.max.bytes.
"""
return pulumi.get(self, "config")
@config.setter
def config(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "config", value)
@property
@pulumi.getter(name="eipMax")
def eip_max(self) -> Optional[pulumi.Input[int]]:
"""
The max bandwidth of the instance. When modify this value, it only support adjust to a greater value.
"""
return pulumi.get(self, "eip_max")
@eip_max.setter
def eip_max(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "eip_max", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Name of your Kafka instance. The length should between 3 and 64 characters. If not set, will use instance id as instance name.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="paidType")
def paid_type(self) -> Optional[pulumi.Input[str]]:
"""
The paid type of the instance. Support two type, "PrePaid": pre paid type instance, "PostPaid": post paid type instance. Default is PostPaid. When modify this value, it only support adjust from post pay to pre pay.
"""
return pulumi.get(self, "paid_type")
@paid_type.setter
def paid_type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "paid_type", value)
@property
@pulumi.getter(name="securityGroup")
def security_group(self) -> Optional[pulumi.Input[str]]:
"""
（Optional, ForceNew, Available in v1.93.0+） The ID of security group for this instance. If the security group is empty, system will create a default one.
"""
return pulumi.get(self, "security_group")
@security_group.setter
def security_group(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "security_group", value)
@property
@pulumi.getter(name="serviceVersion")
def service_version(self) -> Optional[pulumi.Input[str]]:
"""
（Optional, Available in v1.112.0+） The kafka openSource version for this instance. Only 0.10.2 or 2.2.0 is allowed, default is 0.10.2.
"""
return pulumi.get(self, "service_version")
@service_version.setter
def service_version(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "service_version", value)
@property
@pulumi.getter(name="specType")
def spec_type(self) -> Optional[pulumi.Input[str]]:
"""
The spec type of the instance. Support two type, "normal": normal version instance, "professional": professional version instance. Default is normal. When modify this value, it only support adjust from normal to professional. Note only pre paid type instance support professional specific type.
"""
return pulumi.get(self, "spec_type")
@spec_type.setter
def spec_type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "spec_type", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:
"""
A mapping of tags to assign to the resource.
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Mapping[str, Any]]]):
pulumi.set(self, "tags", value)
@pulumi.input_type
class _InstanceState:
def __init__(__self__, *,
config: Optional[pulumi.Input[str]] = None,
deploy_type: Optional[pulumi.Input[int]] = None,
disk_size: Optional[pulumi.Input[int]] = None,
disk_type: Optional[pulumi.Input[int]] = None,
eip_max: Optional[pulumi.Input[int]] = None,
end_point: Optional[pulumi.Input[str]] = None,
io_max: Optional[pulumi.Input[int]] = None,
name: Optional[pulumi.Input[str]] = None,
paid_type: Optional[pulumi.Input[str]] = None,
security_group: Optional[pulumi.Input[str]] = None,
service_version: Optional[pulumi.Input[str]] = None,
spec_type: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
topic_quota: Optional[pulumi.Input[int]] = None,
vpc_id: Optional[pulumi.Input[str]] = None,
vswitch_id: Optional[pulumi.Input[str]] = None,
zone_id: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering Instance resources.
:param pulumi.Input[str] config: （Optional, Available in v1.112.0+） The basic config for this instance. The input should be json type, only the following key allowed: enable.acl, enable.vpc_sasl_ssl, kafka.log.retention.hours, kafka.message.max.bytes.
:param pulumi.Input[int] deploy_type: The deploy type of the instance. Currently only support two deploy type, 4: eip/vpc instance, 5: vpc instance.
:param pulumi.Input[int] disk_size: The disk size of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[int] disk_type: The disk type of the instance. 0: efficient cloud disk , 1: SSD.
:param pulumi.Input[int] eip_max: The max bandwidth of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[str] end_point: The EndPoint to access the kafka instance.
:param pulumi.Input[int] io_max: The max value of io of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[str] name: Name of your Kafka instance. The length should between 3 and 64 characters. If not set, will use instance id as instance name.
:param pulumi.Input[str] paid_type: The paid type of the instance. Support two type, "PrePaid": pre paid type instance, "PostPaid": post paid type instance. Default is PostPaid. When modify this value, it only support adjust from post pay to pre pay.
:param pulumi.Input[str] security_group: （Optional, ForceNew, Available in v1.93.0+） The ID of security group for this instance. If the security group is empty, system will create a default one.
:param pulumi.Input[str] service_version: （Optional, Available in v1.112.0+） The kafka openSource version for this instance. Only 0.10.2 or 2.2.0 is allowed, default is 0.10.2.
:param pulumi.Input[str] spec_type: The spec type of the instance. Support two type, "normal": normal version instance, "professional": professional version instance. Default is normal. When modify this value, it only support adjust from normal to professional. Note only pre paid type instance support professional specific type.
:param pulumi.Input[Mapping[str, Any]] tags: A mapping of tags to assign to the resource.
:param pulumi.Input[int] topic_quota: The max num of topic can be create of the instance. When modify this value, it only adjust to a greater value.
:param pulumi.Input[str] vpc_id: The ID of attaching VPC to instance.
:param pulumi.Input[str] vswitch_id: The ID of attaching vswitch to instance.
:param pulumi.Input[str] zone_id: The Zone to launch the kafka instance.
"""
if config is not None:
pulumi.set(__self__, "config", config)
if deploy_type is not None:
pulumi.set(__self__, "deploy_type", deploy_type)
if disk_size is not None:
pulumi.set(__self__, "disk_size", disk_size)
if disk_type is not None:
pulumi.set(__self__, "disk_type", disk_type)
if eip_max is not None:
pulumi.set(__self__, "eip_max", eip_max)
if end_point is not None:
pulumi.set(__self__, "end_point", end_point)
if io_max is not None:
pulumi.set(__self__, "io_max", io_max)
if name is not None:
pulumi.set(__self__, "name", name)
if paid_type is not None:
pulumi.set(__self__, "paid_type", paid_type)
if security_group is not None:
pulumi.set(__self__, "security_group", security_group)
if service_version is not None:
pulumi.set(__self__, "service_version", service_version)
if spec_type is not None:
pulumi.set(__self__, "spec_type", spec_type)
if tags is not None:
pulumi.set(__self__, "tags", tags)
if topic_quota is not None:
pulumi.set(__self__, "topic_quota", topic_quota)
if vpc_id is not None:
pulumi.set(__self__, "vpc_id", vpc_id)
if vswitch_id is not None:
pulumi.set(__self__, "vswitch_id", vswitch_id)
if zone_id is not None:
pulumi.set(__self__, "zone_id", zone_id)
@property
@pulumi.getter
def config(self) -> Optional[pulumi.Input[str]]:
"""
（Optional, Available in v1.112.0+） The basic config for this instance. The input should be json type, only the following key allowed: enable.acl, enable.vpc_sasl_ssl, kafka.log.retention.hours, kafka.message.max.bytes.
"""
return pulumi.get(self, "config")
@config.setter
def config(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "config", value)
@property
@pulumi.getter(name="deployType")
def deploy_type(self) -> Optional[pulumi.Input[int]]:
"""
The deploy type of the instance. Currently only support two deploy type, 4: eip/vpc instance, 5: vpc instance.
"""
return pulumi.get(self, "deploy_type")
@deploy_type.setter
def deploy_type(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "deploy_type", value)
@property
@pulumi.getter(name="diskSize")
def disk_size(self) -> Optional[pulumi.Input[int]]:
"""
The disk size of the instance. When modify this value, it only support adjust to a greater value.
"""
return pulumi.get(self, "disk_size")
@disk_size.setter
def disk_size(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "disk_size", value)
@property
@pulumi.getter(name="diskType")
def disk_type(self) -> Optional[pulumi.Input[int]]:
"""
The disk type of the instance. 0: efficient cloud disk , 1: SSD.
"""
return pulumi.get(self, "disk_type")
@disk_type.setter
def disk_type(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "disk_type", value)
@property
@pulumi.getter(name="eipMax")
def eip_max(self) -> Optional[pulumi.Input[int]]:
"""
The max bandwidth of the instance. When modify this value, it only support adjust to a greater value.
"""
return pulumi.get(self, "eip_max")
@eip_max.setter
def eip_max(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "eip_max", value)
@property
@pulumi.getter(name="endPoint")
def end_point(self) -> Optional[pulumi.Input[str]]:
"""
The EndPoint to access the kafka instance.
"""
return pulumi.get(self, "end_point")
@end_point.setter
def end_point(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "end_point", value)
@property
@pulumi.getter(name="ioMax")
def io_max(self) -> Optional[pulumi.Input[int]]:
"""
The max value of io of the instance. When modify this value, it only support adjust to a greater value.
"""
return pulumi.get(self, "io_max")
@io_max.setter
def io_max(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "io_max", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Name of your Kafka instance. The length should between 3 and 64 characters. If not set, will use instance id as instance name.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter(name="paidType")
def paid_type(self) -> Optional[pulumi.Input[str]]:
"""
The paid type of the instance. Support two type, "PrePaid": pre paid type instance, "PostPaid": post paid type instance. Default is PostPaid. When modify this value, it only support adjust from post pay to pre pay.
"""
return pulumi.get(self, "paid_type")
@paid_type.setter
def paid_type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "paid_type", value)
@property
@pulumi.getter(name="securityGroup")
def security_group(self) -> Optional[pulumi.Input[str]]:
"""
（Optional, ForceNew, Available in v1.93.0+） The ID of security group for this instance. If the security group is empty, system will create a default one.
"""
return pulumi.get(self, "security_group")
@security_group.setter
def security_group(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "security_group", value)
@property
@pulumi.getter(name="serviceVersion")
def service_version(self) -> Optional[pulumi.Input[str]]:
"""
（Optional, Available in v1.112.0+） The kafka openSource version for this instance. Only 0.10.2 or 2.2.0 is allowed, default is 0.10.2.
"""
return pulumi.get(self, "service_version")
@service_version.setter
def service_version(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "service_version", value)
@property
@pulumi.getter(name="specType")
def spec_type(self) -> Optional[pulumi.Input[str]]:
"""
The spec type of the instance. Support two type, "normal": normal version instance, "professional": professional version instance. Default is normal. When modify this value, it only support adjust from normal to professional. Note only pre paid type instance support professional specific type.
"""
return pulumi.get(self, "spec_type")
@spec_type.setter
def spec_type(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "spec_type", value)
@property
@pulumi.getter
def tags(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:
"""
A mapping of tags to assign to the resource.
"""
return pulumi.get(self, "tags")
@tags.setter
def tags(self, value: Optional[pulumi.Input[Mapping[str, Any]]]):
pulumi.set(self, "tags", value)
@property
@pulumi.getter(name="topicQuota")
def topic_quota(self) -> Optional[pulumi.Input[int]]:
"""
The max num of topic can be create of the instance. When modify this value, it only adjust to a greater value.
"""
return pulumi.get(self, "topic_quota")
@topic_quota.setter
def topic_quota(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "topic_quota", value)
@property
@pulumi.getter(name="vpcId")
def vpc_id(self) -> Optional[pulumi.Input[str]]:
"""
The ID of attaching VPC to instance.
"""
return pulumi.get(self, "vpc_id")
@vpc_id.setter
def vpc_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "vpc_id", value)
@property
@pulumi.getter(name="vswitchId")
def vswitch_id(self) -> Optional[pulumi.Input[str]]:
"""
The ID of attaching vswitch to instance.
"""
return pulumi.get(self, "vswitch_id")
@vswitch_id.setter
def vswitch_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "vswitch_id", value)
@property
@pulumi.getter(name="zoneId")
def zone_id(self) -> Optional[pulumi.Input[str]]:
"""
The Zone to launch the kafka instance.
"""
return pulumi.get(self, "zone_id")
@zone_id.setter
def zone_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "zone_id", value)
class Instance(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
config: Optional[pulumi.Input[str]] = None,
deploy_type: Optional[pulumi.Input[int]] = None,
disk_size: Optional[pulumi.Input[int]] = None,
disk_type: Optional[pulumi.Input[int]] = None,
eip_max: Optional[pulumi.Input[int]] = None,
io_max: Optional[pulumi.Input[int]] = None,
name: Optional[pulumi.Input[str]] = None,
paid_type: Optional[pulumi.Input[str]] = None,
security_group: Optional[pulumi.Input[str]] = None,
service_version: Optional[pulumi.Input[str]] = None,
spec_type: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
topic_quota: Optional[pulumi.Input[int]] = None,
vswitch_id: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
Provides an ALIKAFKA instance resource.
> **NOTE:** Available in 1.59.0+
> **NOTE:** Creation or modification may took about 10-40 minutes.
> **NOTE:** Only the following regions support create alikafka pre paid instance.
[`cn-hangzhou`,`cn-beijing`,`cn-shenzhen`,`cn-shanghai`,`cn-qingdao`,`cn-hongkong`,`cn-huhehaote`,`cn-zhangjiakou`,`cn-chengdu`,`cn-heyuan`,`ap-southeast-1`,`ap-southeast-3`,`ap-southeast-5`,`ap-south-1`,`ap-northeast-1`,`eu-central-1`,`eu-west-1`,`us-west-1`,`us-east-1`]
> **NOTE:** Only the following regions support create alikafka post paid instance.
[`cn-hangzhou`,`cn-beijing`,`cn-shenzhen`,`cn-shanghai`,`cn-qingdao`,`cn-hongkong`,`cn-huhehaote`,`cn-zhangjiakou`,`cn-chengdu`,`cn-heyuan`,`ap-southeast-1`,`ap-southeast-3`,`ap-southeast-5`,`ap-south-1`,`ap-northeast-1`,`eu-central-1`,`eu-west-1`,`us-west-1`,`us-east-1`]
## Example Usage
Basic Usage
```python
import pulumi
import pulumi_alicloud as alicloud
config = pulumi.Config()
instance_name = config.get("instanceName")
if instance_name is None:
instance_name = "alikafkaInstanceName"
default_zones = alicloud.get_zones(available_resource_creation="VSwitch")
default_network = alicloud.vpc.Network("defaultNetwork", cidr_block="172.16.0.0/12")
default_switch = alicloud.vpc.Switch("defaultSwitch",
vpc_id=default_network.id,
cidr_block="172.16.0.0/24",
zone_id=default_zones.zones[0].id)
default_security_group = alicloud.ecs.SecurityGroup("defaultSecurityGroup", vpc_id=default_network.id)
default_instance = alicloud.alikafka.Instance("defaultInstance",
topic_quota=50,
disk_type=1,
disk_size=500,
deploy_type=4,
io_max=20,
vswitch_id=default_switch.id,
security_group=default_security_group.id)
```
## Import
ALIKAFKA TOPIC can be imported using the id, e.g.
```sh
$ pulumi import alicloud:alikafka/instance:Instance instance alikafka_post-cn-123455abc
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] config: （Optional, Available in v1.112.0+） The basic config for this instance. The input should be json type, only the following key allowed: enable.acl, enable.vpc_sasl_ssl, kafka.log.retention.hours, kafka.message.max.bytes.
:param pulumi.Input[int] deploy_type: The deploy type of the instance. Currently only support two deploy type, 4: eip/vpc instance, 5: vpc instance.
:param pulumi.Input[int] disk_size: The disk size of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[int] disk_type: The disk type of the instance. 0: efficient cloud disk , 1: SSD.
:param pulumi.Input[int] eip_max: The max bandwidth of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[int] io_max: The max value of io of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[str] name: Name of your Kafka instance. The length should between 3 and 64 characters. If not set, will use instance id as instance name.
:param pulumi.Input[str] paid_type: The paid type of the instance. Support two type, "PrePaid": pre paid type instance, "PostPaid": post paid type instance. Default is PostPaid. When modify this value, it only support adjust from post pay to pre pay.
:param pulumi.Input[str] security_group: （Optional, ForceNew, Available in v1.93.0+） The ID of security group for this instance. If the security group is empty, system will create a default one.
:param pulumi.Input[str] service_version: （Optional, Available in v1.112.0+） The kafka openSource version for this instance. Only 0.10.2 or 2.2.0 is allowed, default is 0.10.2.
:param pulumi.Input[str] spec_type: The spec type of the instance. Support two type, "normal": normal version instance, "professional": professional version instance. Default is normal. When modify this value, it only support adjust from normal to professional. Note only pre paid type instance support professional specific type.
:param pulumi.Input[Mapping[str, Any]] tags: A mapping of tags to assign to the resource.
:param pulumi.Input[int] topic_quota: The max num of topic can be create of the instance. When modify this value, it only adjust to a greater value.
:param pulumi.Input[str] vswitch_id: The ID of attaching vswitch to instance.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: InstanceArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
Provides an ALIKAFKA instance resource.
> **NOTE:** Available in 1.59.0+
> **NOTE:** Creation or modification may took about 10-40 minutes.
> **NOTE:** Only the following regions support create alikafka pre paid instance.
[`cn-hangzhou`,`cn-beijing`,`cn-shenzhen`,`cn-shanghai`,`cn-qingdao`,`cn-hongkong`,`cn-huhehaote`,`cn-zhangjiakou`,`cn-chengdu`,`cn-heyuan`,`ap-southeast-1`,`ap-southeast-3`,`ap-southeast-5`,`ap-south-1`,`ap-northeast-1`,`eu-central-1`,`eu-west-1`,`us-west-1`,`us-east-1`]
> **NOTE:** Only the following regions support create alikafka post paid instance.
[`cn-hangzhou`,`cn-beijing`,`cn-shenzhen`,`cn-shanghai`,`cn-qingdao`,`cn-hongkong`,`cn-huhehaote`,`cn-zhangjiakou`,`cn-chengdu`,`cn-heyuan`,`ap-southeast-1`,`ap-southeast-3`,`ap-southeast-5`,`ap-south-1`,`ap-northeast-1`,`eu-central-1`,`eu-west-1`,`us-west-1`,`us-east-1`]
## Example Usage
Basic Usage
```python
import pulumi
import pulumi_alicloud as alicloud
config = pulumi.Config()
instance_name = config.get("instanceName")
if instance_name is None:
instance_name = "alikafkaInstanceName"
default_zones = alicloud.get_zones(available_resource_creation="VSwitch")
default_network = alicloud.vpc.Network("defaultNetwork", cidr_block="172.16.0.0/12")
default_switch = alicloud.vpc.Switch("defaultSwitch",
vpc_id=default_network.id,
cidr_block="172.16.0.0/24",
zone_id=default_zones.zones[0].id)
default_security_group = alicloud.ecs.SecurityGroup("defaultSecurityGroup", vpc_id=default_network.id)
default_instance = alicloud.alikafka.Instance("defaultInstance",
topic_quota=50,
disk_type=1,
disk_size=500,
deploy_type=4,
io_max=20,
vswitch_id=default_switch.id,
security_group=default_security_group.id)
```
## Import
ALIKAFKA TOPIC can be imported using the id, e.g.
```sh
$ pulumi import alicloud:alikafka/instance:Instance instance alikafka_post-cn-123455abc
```
:param str resource_name: The name of the resource.
:param InstanceArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(InstanceArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
config: Optional[pulumi.Input[str]] = None,
deploy_type: Optional[pulumi.Input[int]] = None,
disk_size: Optional[pulumi.Input[int]] = None,
disk_type: Optional[pulumi.Input[int]] = None,
eip_max: Optional[pulumi.Input[int]] = None,
io_max: Optional[pulumi.Input[int]] = None,
name: Optional[pulumi.Input[str]] = None,
paid_type: Optional[pulumi.Input[str]] = None,
security_group: Optional[pulumi.Input[str]] = None,
service_version: Optional[pulumi.Input[str]] = None,
spec_type: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
topic_quota: Optional[pulumi.Input[int]] = None,
vswitch_id: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = InstanceArgs.__new__(InstanceArgs)
__props__.__dict__["config"] = config
if deploy_type is None and not opts.urn:
raise TypeError("Missing required property 'deploy_type'")
__props__.__dict__["deploy_type"] = deploy_type
if disk_size is None and not opts.urn:
raise TypeError("Missing required property 'disk_size'")
__props__.__dict__["disk_size"] = disk_size
if disk_type is None and not opts.urn:
raise TypeError("Missing required property 'disk_type'")
__props__.__dict__["disk_type"] = disk_type
__props__.__dict__["eip_max"] = eip_max
if io_max is None and not opts.urn:
raise TypeError("Missing required property 'io_max'")
__props__.__dict__["io_max"] = io_max
__props__.__dict__["name"] = name
__props__.__dict__["paid_type"] = paid_type
__props__.__dict__["security_group"] = security_group
__props__.__dict__["service_version"] = service_version
__props__.__dict__["spec_type"] = spec_type
__props__.__dict__["tags"] = tags
if topic_quota is None and not opts.urn:
raise TypeError("Missing required property 'topic_quota'")
__props__.__dict__["topic_quota"] = topic_quota
if vswitch_id is None and not opts.urn:
raise TypeError("Missing required property 'vswitch_id'")
__props__.__dict__["vswitch_id"] = vswitch_id
__props__.__dict__["end_point"] = None
__props__.__dict__["vpc_id"] = None
__props__.__dict__["zone_id"] = None
super(Instance, __self__).__init__(
'alicloud:alikafka/instance:Instance',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
config: Optional[pulumi.Input[str]] = None,
deploy_type: Optional[pulumi.Input[int]] = None,
disk_size: Optional[pulumi.Input[int]] = None,
disk_type: Optional[pulumi.Input[int]] = None,
eip_max: Optional[pulumi.Input[int]] = None,
end_point: Optional[pulumi.Input[str]] = None,
io_max: Optional[pulumi.Input[int]] = None,
name: Optional[pulumi.Input[str]] = None,
paid_type: Optional[pulumi.Input[str]] = None,
security_group: Optional[pulumi.Input[str]] = None,
service_version: Optional[pulumi.Input[str]] = None,
spec_type: Optional[pulumi.Input[str]] = None,
tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
topic_quota: Optional[pulumi.Input[int]] = None,
vpc_id: Optional[pulumi.Input[str]] = None,
vswitch_id: Optional[pulumi.Input[str]] = None,
zone_id: Optional[pulumi.Input[str]] = None) -> 'Instance':
"""
Get an existing Instance resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] config: （Optional, Available in v1.112.0+） The basic config for this instance. The input should be json type, only the following key allowed: enable.acl, enable.vpc_sasl_ssl, kafka.log.retention.hours, kafka.message.max.bytes.
:param pulumi.Input[int] deploy_type: The deploy type of the instance. Currently only support two deploy type, 4: eip/vpc instance, 5: vpc instance.
:param pulumi.Input[int] disk_size: The disk size of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[int] disk_type: The disk type of the instance. 0: efficient cloud disk , 1: SSD.
:param pulumi.Input[int] eip_max: The max bandwidth of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[str] end_point: The EndPoint to access the kafka instance.
:param pulumi.Input[int] io_max: The max value of io of the instance. When modify this value, it only support adjust to a greater value.
:param pulumi.Input[str] name: Name of your Kafka instance. The length should between 3 and 64 characters. If not set, will use instance id as instance name.
:param pulumi.Input[str] paid_type: The paid type of the instance. Support two type, "PrePaid": pre paid type instance, "PostPaid": post paid type instance. Default is PostPaid. When modify this value, it only support adjust from post pay to pre pay.
:param pulumi.Input[str] security_group: （Optional, ForceNew, Available in v1.93.0+） The ID of security group for this instance. If the security group is empty, system will create a default one.
:param pulumi.Input[str] service_version: （Optional, Available in v1.112.0+） The kafka openSource version for this instance. Only 0.10.2 or 2.2.0 is allowed, default is 0.10.2.
:param pulumi.Input[str] spec_type: The spec type of the instance. Support two type, "normal": normal version instance, "professional": professional version instance. Default is normal. When modify this value, it only support adjust from normal to professional. Note only pre paid type instance support professional specific type.
:param pulumi.Input[Mapping[str, Any]] tags: A mapping of tags to assign to the resource.
:param pulumi.Input[int] topic_quota: The max num of topic can be create of the instance. When modify this value, it only adjust to a greater value.
:param pulumi.Input[str] vpc_id: The ID of attaching VPC to instance.
:param pulumi.Input[str] vswitch_id: The ID of attaching vswitch to instance.
:param pulumi.Input[str] zone_id: The Zone to launch the kafka instance.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _InstanceState.__new__(_InstanceState)
__props__.__dict__["config"] = config
__props__.__dict__["deploy_type"] = deploy_type
__props__.__dict__["disk_size"] = disk_size
__props__.__dict__["disk_type"] = disk_type
__props__.__dict__["eip_max"] = eip_max
__props__.__dict__["end_point"] = end_point
__props__.__dict__["io_max"] = io_max
__props__.__dict__["name"] = name
__props__.__dict__["paid_type"] = paid_type
__props__.__dict__["security_group"] = security_group
__props__.__dict__["service_version"] = service_version
__props__.__dict__["spec_type"] = spec_type
__props__.__dict__["tags"] = tags
__props__.__dict__["topic_quota"] = topic_quota
__props__.__dict__["vpc_id"] = vpc_id
__props__.__dict__["vswitch_id"] = vswitch_id
__props__.__dict__["zone_id"] = zone_id
return Instance(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter
def config(self) -> pulumi.Output[str]:
"""
（Optional, Available in v1.112.0+） The basic config for this instance. The input should be json type, only the following key allowed: enable.acl, enable.vpc_sasl_ssl, kafka.log.retention.hours, kafka.message.max.bytes.
"""
return pulumi.get(self, "config")
@property
@pulumi.getter(name="deployType")
def deploy_type(self) -> pulumi.Output[int]:
"""
The deploy type of the instance. Currently only support two deploy type, 4: eip/vpc instance, 5: vpc instance.
"""
return pulumi.get(self, "deploy_type")
@property
@pulumi.getter(name="diskSize")
def disk_size(self) -> pulumi.Output[int]:
"""
The disk size of the instance. When modify this value, it only support adjust to a greater value.
"""
return pulumi.get(self, "disk_size")
@property
@pulumi.getter(name="diskType")
def disk_type(self) -> pulumi.Output[int]:
"""
The disk type of the instance. 0: efficient cloud disk , 1: SSD.
"""
return pulumi.get(self, "disk_type")
@property
@pulumi.getter(name="eipMax")
def eip_max(self) -> pulumi.Output[int]:
"""
The max bandwidth of the instance. When modify this value, it only support adjust to a greater value.
"""
return pulumi.get(self, "eip_max")
@property
@pulumi.getter(name="endPoint")
def end_point(self) -> pulumi.Output[str]:
"""
The EndPoint to access the kafka instance.
"""
return pulumi.get(self, "end_point")
@property
@pulumi.getter(name="ioMax")
def io_max(self) -> pulumi.Output[int]:
"""
The max value of io of the instance. When modify this value, it only support adjust to a greater value.
"""
return pulumi.get(self, "io_max")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
Name of your Kafka instance. The length should between 3 and 64 characters. If not set, will use instance id as instance name.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="paidType")
def paid_type(self) -> pulumi.Output[Optional[str]]:
"""
The paid type of the instance. Support two type, "PrePaid": pre paid type instance, "PostPaid": post paid type instance. Default is PostPaid. When modify this value, it only support adjust from post pay to pre pay.
"""
return pulumi.get(self, "paid_type")
@property
@pulumi.getter(name="securityGroup")
def security_group(self) -> pulumi.Output[str]:
"""
（Optional, ForceNew, Available in v1.93.0+） The ID of security group for this instance. If the security group is empty, system will create a default one.
"""
return pulumi.get(self, "security_group")
@property
@pulumi.getter(name="serviceVersion")
def service_version(self) -> pulumi.Output[str]:
"""
（Optional, Available in v1.112.0+） The kafka openSource version for this instance. Only 0.10.2 or 2.2.0 is allowed, default is 0.10.2.
"""
return pulumi.get(self, "service_version")
@property
@pulumi.getter(name="specType")
def spec_type(self) -> pulumi.Output[Optional[str]]:
"""
The spec type of the instance. Support two type, "normal": normal version instance, "professional": professional version instance. Default is normal. When modify this value, it only support adjust from normal to professional. Note only pre paid type instance support professional specific type.
"""
return pulumi.get(self, "spec_type")
@property
@pulumi.getter
def tags(self) -> pulumi.Output[Optional[Mapping[str, Any]]]:
"""
A mapping of tags to assign to the resource.
"""
return pulumi.get(self, "tags")
@property
@pulumi.getter(name="topicQuota")
def topic_quota(self) -> pulumi.Output[int]:
"""
The max num of topic can be create of the instance. When modify this value, it only adjust to a greater value.
"""
return pulumi.get(self, "topic_quota")
@property
@pulumi.getter(name="vpcId")
def vpc_id(self) -> pulumi.Output[str]:
"""
The ID of attaching VPC to instance.
"""
return pulumi.get(self, "vpc_id")
@property
@pulumi.getter(name="vswitchId")
def vswitch_id(self) -> pulumi.Output[str]:
"""
The ID of attaching vswitch to instance.
"""
return pulumi.get(self, "vswitch_id")
@property
@pulumi.getter(name="zoneId")
def zone_id(self) -> pulumi.Output[str]:
"""
The Zone to launch the kafka instance.
"""
return pulumi.get(self, "zone_id")
| 49.192349
| 338
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|
0
| 8
|
117d1a37956146f8664bf8c46d0afb08a95e2faf
| 15,707
|
py
|
Python
|
clinica/utils/atlas.py
|
yogeshmj/clinica
|
1588d708eb401731c62ca23c2db26c29e2ad92e6
|
[
"MIT"
] | 1
|
2020-12-14T06:07:14.000Z
|
2020-12-14T06:07:14.000Z
|
clinica/utils/atlas.py
|
yogeshmj/clinica
|
1588d708eb401731c62ca23c2db26c29e2ad92e6
|
[
"MIT"
] | null | null | null |
clinica/utils/atlas.py
|
yogeshmj/clinica
|
1588d708eb401731c62ca23c2db26c29e2ad92e6
|
[
"MIT"
] | null | null | null |
# coding: utf8
"""
This module contains utilities to handle atlases in Clinica.
An atlas is currently defined by its name, a set of labels in a template space and
the map of this template space (e.g. T1w, FA map derived from DWI).
This current implementation has some drawbacks:
- Atlas is misleading: it is only a set of labels in a template space
- This implementation can not handle case where there are several maps (e.g. both T1w and T2w) in template space
Either a refactoring of this module or the use of an external API
(e.g. TemplateFlow - https://www.templateflow.org/) needs to be considered.
"""
import abc
class AtlasAbstract:
"""
Abstract class for Atlas handling.
Naming convention for children classes of AtlasAbstract:
<name_atlas>[<resolution>][<map>]
"""
__metaclass__ = abc.ABCMeta
@abc.abstractmethod
def get_name_atlas(self):
"""
Returns the name of the atlas (as defined in BIDS/CAPS specifications).
"""
pass
def get_spatial_resolution(self):
"""
Returns the spatial resolution of the atlas (in format "XxXxX" e.g.
1x1x1 or 1.5x1.5x1.5).
"""
import nibabel as nib
img_map = nib.load(self.get_atlas_map())
img_labels = nib.load(self.get_atlas_labels())
voxels_map = img_map.header.get_zooms()
voxels_labels = img_labels.header.get_zooms()
if (voxels_map[0] != voxels_labels[0]) or \
(voxels_map[1] != voxels_labels[1]) or \
(voxels_map[2] != voxels_labels[2]):
# if voxels_map != voxels_labels:
print("Spatial resolution of labels and map image from %s atlas mismatch" % (self.get_name_atlas()))
# raise Exception(
# "Spatial resolution of labels and map image from %s atlas mismatch" % (self.get_name_atlas()))
# else:
# Will display integers without decimals
if int(voxels_map[0]) == voxels_map[0]:
s_x = str(int(voxels_map[0]))
else:
s_x = str(voxels_map[0])
if int(voxels_map[1]) == voxels_map[1]:
s_y = str(int(voxels_map[1]))
else:
s_y = str(voxels_map[1])
if int(voxels_map[2]) == voxels_map[2]:
s_z = str(int(voxels_map[2]))
else:
s_z = str(voxels_map[2])
return s_x + "x" + s_y + "x" + s_z
@abc.abstractmethod
def get_atlas_labels(self):
"""
Returns the image with the different labels/ROIs.
"""
pass
@abc.abstractmethod
def get_atlas_map(self):
"""
Returns the map associated to the atlas (e.g. T1, FA map from DTI).
"""
pass
@abc.abstractmethod
def get_tsv_roi(self):
"""
Returns the TSV file containing the ROI (regions of interest) of
the atlas.
"""
pass
def get_index(self):
import nibabel as nib
import numpy as np
img_labels = nib.load(self.get_atlas_labels())
img_labels = img_labels.get_data()
labels = list(set(img_labels.ravel()))
index_vector = np.zeros(len(labels))
for index, n in enumerate(labels):
index_vector[index] = index
return index_vector
class JHUDTI812mm(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "JHUDTI81"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-labels-2mm.nii.gz')
@staticmethod
def get_atlas_map():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-FA-2mm.nii.gz')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases', 'JHUDTI81_FS_LUT_newformat.txt')
class JHUDTI811mm(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "JHUDTI81"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-labels-1mm.nii.gz')
@staticmethod
def get_atlas_map():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-FA-1mm.nii.gz')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases', 'JHUDTI81_FS_LUT_newformat.txt')
class JHUTracts01mm(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "JHUTracts0"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-tracts-maxprob-thr0-1mm.nii.gz')
@staticmethod
def get_atlas_map():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-FA-1mm.nii.gz')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases', 'JHUTract_FS_LUT_newformat.txt')
class JHUTracts02mm(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "JHUTracts0"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-tracts-maxprob-thr0-2mm.nii.gz')
@staticmethod
def get_atlas_map():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-FA-2mm.nii.gz')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases', 'JHUTract_FS_LUT_newformat.txt')
class JHUTracts251mm(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "JHUTracts25"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-tracts-maxprob-thr25-1mm.nii.gz')
@staticmethod
def get_atlas_map():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-FA-1mm.nii.gz')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases', 'JHUTract_FS_LUT_newformat.txt')
class JHUTracts252mm(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "JHUTracts25"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-tracts-maxprob-thr25-2mm.nii.gz')
@staticmethod
def get_atlas_map():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-FA-2mm.nii.gz')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases', 'JHUTract_FS_LUT_newformat.txt')
class JHUTracts501mm(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "JHUTracts50"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-tracts-maxprob-thr50-1mm.nii.gz')
@staticmethod
def get_atlas_map():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-FA-1mm.nii.gz')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases', 'JHUTract_FS_LUT_newformat.txt')
class JHUTracts502mm(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "JHUTracts50"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-tracts-maxprob-thr50-2mm.nii.gz')
@staticmethod
def get_atlas_map():
import os
from .check_dependency import check_environment_variable
fsl_dir = check_environment_variable('FSLDIR', 'FSL')
return os.path.join(fsl_dir, 'data', 'atlases', 'JHU', 'JHU-ICBM-FA-2mm.nii.gz')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases', 'JHUTract_FS_LUT_newformat.txt')
class AAL2(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "AAL2"
@staticmethod
def get_atlas_labels():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'AAL2.nii')
@staticmethod
def get_atlas_map():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'Template_MNI152.nii')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'lut_AAL2_newformat.txt')
class Hammers(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "Hammers"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_cat12
cat12 = check_cat12()
return os.path.join(cat12, 'templates_1.50mm', 'hammers.nii')
@staticmethod
def get_atlas_map():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'Template_MNI152.nii')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'lut_Hammers_newformat.txt')
class LPBA40(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "LPBA40"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_cat12
cat12 = check_cat12()
return os.path.join(cat12, 'templates_1.50mm', 'lpba40.nii')
@staticmethod
def get_atlas_map():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'Template_MNI152.nii')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'lut_LPBA40_newformat.txt')
class AICHA(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "AICHA"
@staticmethod
def get_atlas_labels():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'AICHA.nii')
@staticmethod
def get_atlas_map():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'Template_MNI152.nii')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'lut_AICHA_newformat.txt')
class Neuromorphometrics(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "Neuromorphometrics"
@staticmethod
def get_atlas_labels():
import os
from .check_dependency import check_cat12
cat12 = check_cat12()
return os.path.join(cat12, 'templates_1.50mm', 'neuromorphometrics.nii')
@staticmethod
def get_atlas_map():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'Template_MNI152.nii')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'lut_Neuromorphometrics_newformat.txt')
class MCALT_ADIR122(AtlasAbstract):
def __init__(self):
AtlasAbstract.__init__(self)
@staticmethod
def get_name_atlas(): return "MCALT_ADIR122"
@staticmethod
def get_atlas_labels():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'MCALT_ADIR122.nii')
@staticmethod
def get_atlas_map():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'Template_MNI152.nii')
@staticmethod
def get_tsv_roi():
from os.path import join, split, realpath
return join(split(realpath(__file__))[0], '../resources/atlases_spm', 'MCALT_ADIR122_ROI.tsv')
class AtlasLoader:
def __init__(self, atlases=None):
self.atlas = {}
if atlases:
for atlas in atlases:
self.add_atlas(atlas)
def add_atlas(self, atlas):
if not isinstance(atlas, AtlasAbstract):
raise Exception("Atlas element must be an AtlasAbstract type")
def get_atlases(self): return self.atlas
| 32.928721
| 117
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0
| 7
|
11fac94437ca88a83a388f7dfb5c156e666fd99a
| 25,709
|
py
|
Python
|
imblearn/ensemble/tests/test_balance_cascade.py
|
christophe-rannou/imbalanced-learn
|
c3f3b0fd9815e206ea63f3f11728f097608bf580
|
[
"MIT"
] | null | null | null |
imblearn/ensemble/tests/test_balance_cascade.py
|
christophe-rannou/imbalanced-learn
|
c3f3b0fd9815e206ea63f3f11728f097608bf580
|
[
"MIT"
] | null | null | null |
imblearn/ensemble/tests/test_balance_cascade.py
|
christophe-rannou/imbalanced-learn
|
c3f3b0fd9815e206ea63f3f11728f097608bf580
|
[
"MIT"
] | null | null | null |
"""Test the module balance cascade."""
from __future__ import print_function
import numpy as np
from numpy.testing import (assert_array_equal, assert_equal,
assert_raises, assert_raises_regex)
from sklearn.ensemble import RandomForestClassifier
from imblearn.ensemble import BalanceCascade
# Generate a global dataset to use
RND_SEED = 0
X = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141],
[1.25192108, -0.22367336], [0.53366841, -0.30312976],
[1.52091956, -0.49283504], [-0.28162401, -2.10400981],
[0.83680821, 1.72827342], [0.3084254, 0.33299982],
[0.70472253, -0.73309052], [0.28893132, -0.38761769],
[1.15514042, 0.0129463], [0.88407872, 0.35454207],
[1.31301027, -0.92648734], [-1.11515198, -0.93689695],
[-0.18410027, -0.45194484], [0.9281014, 0.53085498],
[-0.14374509, 0.27370049], [-0.41635887, -0.38299653],
[0.08711622, 0.93259929], [1.70580611, -0.11219234]])
Y = np.array([0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0])
def test_bc_init():
# Define a ratio
ratio = 1.
bc = BalanceCascade(ratio=ratio, random_state=RND_SEED)
assert_equal(bc.ratio, ratio)
assert_equal(bc.bootstrap, True)
assert_equal(bc.n_max_subset, None)
assert_equal(bc.random_state, RND_SEED)
def test_fit_sample_auto():
# Define the ratio parameter
ratio = 'auto'
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
bootstrap=False)
# Get the different subset
X_resampled, y_resampled, idx_under = bc.fit_sample(X, Y)
X_gt = np.array(
[
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695]]),
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.70472253, -0.73309052],
[-0.18410027, -0.45194484], [0.77481731, 0.60935141],
[0.3084254, 0.33299982], [0.28893132, -0.38761769],
[0.9281014, 0.53085498]])
],
dtype=object)
y_gt = np.array(
[
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
],
dtype=object)
idx_gt = np.array(
[
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 18, 8, 16, 6, 14, 5,
13]),
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 8, 14, 1, 7, 9, 15])
],
dtype=object)
# Check each array
for idx in range(X_gt.size):
assert_array_equal(X_resampled[idx], X_gt[idx])
assert_array_equal(y_resampled[idx], y_gt[idx])
assert_array_equal(idx_under[idx], idx_gt[idx])
def test_fit_sample_half():
# Define the ratio parameter
ratio = 0.8
# Create the sampling object
bc = BalanceCascade(ratio=ratio, random_state=RND_SEED, bootstrap=False)
# Get the different subset
X_resampled, y_resampled = bc.fit_sample(X, Y)
X_gt = np.array(
[
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695],
[0.9281014, 0.53085498], [0.3084254, 0.33299982]]),
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.70472253, -0.73309052],
[-0.18410027, -0.45194484], [0.77481731, 0.60935141],
[0.28893132, -0.38761769]])
],
dtype=object)
y_gt = np.array(
[
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
],
dtype=object)
# Check each array
for idx in range(X_gt.size):
assert_array_equal(X_resampled[idx], X_gt[idx])
assert_array_equal(y_resampled[idx], y_gt[idx])
def test_fit_sample_auto_decision_tree():
# Define the ratio parameter
ratio = 'auto'
classifier = 'decision-tree'
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
classifier=classifier)
# Get the different subset
X_resampled, y_resampled, idx_under = bc.fit_sample(X, Y)
X_gt = np.array(
[
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695]]),
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[-1.11515198, -0.93689695], [0.77481731, 0.60935141],
[0.3084254, 0.33299982], [0.28893132, -0.38761769],
[0.9281014, 0.53085498]])
],
dtype=object)
y_gt = np.array(
[
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
],
dtype=object)
idx_gt = np.array(
[
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 18, 8, 16, 6, 14, 5,
13]),
np.array([0, 2, 3, 4, 11, 12, 17, 19, 13, 1, 7, 9, 15])
],
dtype=object)
# Check each array
for idx in range(X_gt.size):
assert_array_equal(X_resampled[idx], X_gt[idx])
assert_array_equal(y_resampled[idx], y_gt[idx])
assert_array_equal(idx_under[idx], idx_gt[idx])
def test_fit_sample_auto_random_forest():
# Define the ratio parameter
ratio = 'auto'
classifier = 'random-forest'
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
classifier=classifier)
# Get the different subset
X_resampled, y_resampled, idx_under = bc.fit_sample(X, Y)
X_gt = np.array(
[
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695]]),
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [-0.14374509, 0.27370049],
[-1.11515198, -0.93689695], [0.77481731, 0.60935141],
[0.3084254, 0.33299982], [0.28893132, -0.38761769],
[0.9281014, 0.53085498]])
],
dtype=object)
y_gt = np.array(
[
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
],
dtype=object)
idx_gt = np.array(
[
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 18, 8, 16, 6, 14, 5,
13]),
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 16, 13, 1, 7, 9, 15])
],
dtype=object)
# Check each array
for idx in range(X_gt.size):
assert_array_equal(X_resampled[idx], X_gt[idx])
assert_array_equal(y_resampled[idx], y_gt[idx])
assert_array_equal(idx_under[idx], idx_gt[idx])
def test_fit_sample_auto_adaboost():
# Define the ratio parameter
ratio = 'auto'
classifier = 'adaboost'
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
classifier=classifier)
# Get the different subset
X_resampled, y_resampled, idx_under = bc.fit_sample(X, Y)
X_gt = np.array(
[
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695]]),
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[-0.14374509, 0.27370049], [-1.11515198, -0.93689695],
[0.77481731, 0.60935141], [0.3084254, 0.33299982],
[0.28893132, -0.38761769], [0.9281014, 0.53085498]])
],
dtype=object)
y_gt = np.array(
[
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
],
dtype=object)
idx_gt = np.array(
[
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 18, 8, 16, 6, 14, 5,
13]),
np.array([0, 2, 3, 4, 11, 12, 17, 19, 16, 13, 1, 7, 9, 15])
],
dtype=object)
# Check each array
for idx in range(X_gt.size):
assert_array_equal(X_resampled[idx], X_gt[idx])
assert_array_equal(y_resampled[idx], y_gt[idx])
assert_array_equal(idx_under[idx], idx_gt[idx])
def test_fit_sample_auto_gradient_boosting():
# Define the ratio parameter
ratio = 'auto'
classifier = 'gradient-boosting'
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
classifier=classifier)
# Get the different subset
X_resampled, y_resampled, idx_under = bc.fit_sample(X, Y)
X_gt = np.array(
[
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695]]),
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[-0.14374509, 0.27370049], [-1.11515198, -0.93689695],
[0.77481731, 0.60935141], [0.3084254, 0.33299982],
[0.28893132, -0.38761769], [0.9281014, 0.53085498]])
],
dtype=object)
y_gt = np.array(
[
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
],
dtype=object)
idx_gt = np.array(
[
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 18, 8, 16, 6, 14, 5,
13]),
np.array([0, 2, 3, 4, 11, 12, 17, 19, 16, 13, 1, 7, 9, 15])
],
dtype=object)
# Check each array
for idx in range(X_gt.size):
assert_array_equal(X_resampled[idx], X_gt[idx])
assert_array_equal(y_resampled[idx], y_gt[idx])
assert_array_equal(idx_under[idx], idx_gt[idx])
def test_fit_sample_auto_linear_svm():
# Define the ratio parameter
ratio = 'auto'
classifier = 'linear-svm'
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
classifier=classifier)
# Get the different subset
X_resampled, y_resampled, idx_under = bc.fit_sample(X, Y)
X_gt = np.array(
[
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695]]),
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.70472253, -0.73309052],
[0.77481731, 0.60935141], [0.3084254, 0.33299982],
[0.28893132, -0.38761769], [0.9281014, 0.53085498]])
],
dtype=object)
y_gt = np.array(
[
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
],
dtype=object)
idx_gt = np.array(
[
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 18, 8, 16, 6, 14, 5,
13]),
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 8, 1, 7, 9, 15])
],
dtype=object)
# Check each array
for idx in range(X_gt.size):
assert_array_equal(X_resampled[idx], X_gt[idx])
assert_array_equal(y_resampled[idx], y_gt[idx])
assert_array_equal(idx_under[idx], idx_gt[idx])
def test_init_wrong_classifier():
# Define the ratio parameter
classifier = 'rnd'
bc = BalanceCascade(classifier=classifier)
assert_raises(NotImplementedError, bc.fit_sample, X, Y)
def test_fit_sample_auto_early_stop():
# Define the ratio parameter
ratio = 'auto'
n_subset = 1
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
n_max_subset=n_subset)
# Get the different subset
X_resampled, y_resampled, idx_under = bc.fit_sample(X, Y)
X_gt = np.array([[[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695]]])
y_gt = np.array([[0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]])
idx_gt = np.array(
[[0, 2, 3, 4, 11, 12, 17, 19, 10, 18, 8, 16, 6, 14, 5, 13]])
# Check each array
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)
assert_array_equal(idx_under, idx_gt)
def test_fit_sample_auto_early_stop_2():
# Define the ratio parameter
ratio = 'auto'
n_subset = 2
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
n_max_subset=n_subset,
bootstrap=False)
# Get the different subset
X_resampled, y_resampled, idx_under = bc.fit_sample(X, Y)
X_gt = np.array(
[
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695]]),
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.70472253, -0.73309052],
[-0.18410027, -0.45194484], [0.77481731, 0.60935141],
[0.3084254, 0.33299982], [0.28893132, -0.38761769],
[0.9281014, 0.53085498]])
],
dtype=object)
y_gt = np.array(
[
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
],
dtype=object)
idx_gt = np.array(
[
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 18, 8, 16, 6, 14, 5,
13]),
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 8, 14, 1, 7, 9, 15])
],
dtype=object)
# Check each array
for idx in range(X_gt.size):
assert_array_equal(X_resampled[idx], X_gt[idx])
assert_array_equal(y_resampled[idx], y_gt[idx])
assert_array_equal(idx_under[idx], idx_gt[idx])
def test_give_classifier_obj():
# Define the ratio parameter
ratio = 'auto'
classifier = RandomForestClassifier(random_state=RND_SEED)
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
estimator=classifier)
# Get the different subset
X_resampled, y_resampled, idx_under = bc.fit_sample(X, Y)
X_gt = np.array(
[
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695]]),
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [-0.14374509, 0.27370049],
[-1.11515198, -0.93689695], [0.77481731, 0.60935141],
[0.3084254, 0.33299982], [0.28893132, -0.38761769],
[0.9281014, 0.53085498]])
],
dtype=object)
y_gt = np.array(
[
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1])
],
dtype=object)
idx_gt = np.array(
[
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 18, 8, 16, 6, 14, 5,
13]),
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 16, 13, 1, 7, 9, 15])
],
dtype=object)
# Check each array
for idx in range(X_gt.size):
assert_array_equal(X_resampled[idx], X_gt[idx])
assert_array_equal(y_resampled[idx], y_gt[idx])
assert_array_equal(idx_under[idx], idx_gt[idx])
def test_give_classifier_wrong_obj():
# Define the ratio parameter
ratio = 'auto'
classifier = 2
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
estimator=classifier)
# Get the different subset
assert_raises_regex(ValueError, "Invalid parameter `estimator`",
bc.fit_sample, X, Y)
def test_rf_wth_bootstrap():
# Define the ratio parameter
ratio = 'auto'
classifier = RandomForestClassifier(random_state=RND_SEED)
# Create the sampling object
bc = BalanceCascade(
ratio=ratio,
random_state=RND_SEED,
return_indices=True,
estimator=classifier,
bootstrap=False)
# Get the different subset
X_resampled, y_resampled, idx_under = bc.fit_sample(X, Y)
X_gt = np.array(
[
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.08711622, 0.93259929],
[0.70472253, -0.73309052], [-0.14374509, 0.27370049],
[0.83680821, 1.72827342], [-0.18410027, -0.45194484],
[-0.28162401, -2.10400981], [-1.11515198, -0.93689695]]),
np.array([[0.11622591, -0.0317206], [1.25192108, -0.22367336],
[0.53366841, -0.30312976], [1.52091956, -0.49283504],
[0.88407872, 0.35454207], [1.31301027, -0.92648734],
[-0.41635887, -0.38299653], [1.70580611, -0.11219234],
[1.15514042, 0.0129463], [0.77481731, 0.60935141],
[0.3084254, 0.33299982], [0.28893132, -0.38761769],
[0.9281014, 0.53085498]])
],
dtype=object)
y_gt = np.array(
[
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]),
np.array([0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1])
],
dtype=object)
idx_gt = np.array(
[
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 18, 8, 16, 6, 14, 5,
13]),
np.array([0, 2, 3, 4, 11, 12, 17, 19, 10, 1, 7, 9, 15])
],
dtype=object)
# Check each array
for idx in range(X_gt.size):
assert_array_equal(X_resampled[idx], X_gt[idx])
assert_array_equal(y_resampled[idx], y_gt[idx])
assert_array_equal(idx_under[idx], idx_gt[idx])
| 40.743265
| 79
| 0.514878
| 3,359
| 25,709
| 3.838642
| 0.044954
| 0.023267
| 0.029549
| 0.032573
| 0.929812
| 0.909493
| 0.90259
| 0.877695
| 0.861719
| 0.861021
| 0
| 0.392299
| 0.323194
| 25,709
| 630
| 80
| 40.807937
| 0.348736
| 0.048349
| 0
| 0.8
| 0
| 0
| 0.005612
| 0
| 0
| 0
| 0
| 0
| 0.079208
| 1
| 0.027723
| false
| 0
| 0.009901
| 0
| 0.037624
| 0.00198
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
11fffd6e7ef371d0abc610aa5cbb84b23d48984a
| 24,044
|
py
|
Python
|
packages/gtmapi/lmsrvlabbook/tests/snapshots/snap_test_labbook_mutations.py
|
jjwatts/gigantum-client
|
88ce0475fb6880322bdd06d987c494e29064f278
|
[
"MIT"
] | null | null | null |
packages/gtmapi/lmsrvlabbook/tests/snapshots/snap_test_labbook_mutations.py
|
jjwatts/gigantum-client
|
88ce0475fb6880322bdd06d987c494e29064f278
|
[
"MIT"
] | null | null | null |
packages/gtmapi/lmsrvlabbook/tests/snapshots/snap_test_labbook_mutations.py
|
jjwatts/gigantum-client
|
88ce0475fb6880322bdd06d987c494e29064f278
|
[
"MIT"
] | null | null | null |
# -*- coding: utf-8 -*-
# snapshottest: v1 - https://goo.gl/zC4yUc
from __future__ import unicode_literals
from snapshottest import Snapshot
snapshots = Snapshot()
snapshots['TestLabBookServiceMutations.test_makedir 1'] = {
'data': {
'makeLabbookDirectory': {
'newLabbookFileEdge': {
'node': {
'isDir': True,
'key': 'new_folder/',
'size': '0'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_move_file_many 1'] = {
'data': {
'moveLabbookFile': {
'newLabbookFileEdge': {
'node': {
'isDir': False,
'key': 'subdir/sillyfile',
'size': '7'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_move_file_many 2'] = {
'data': {
'moveLabbookFile': {
'newLabbookFileEdge': {
'node': {
'isDir': False,
'key': 'sillyfile',
'size': '7'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_move_file_many 3'] = {
'data': {
'moveLabbookFile': {
'newLabbookFileEdge': {
'node': {
'isDir': False,
'key': 'subdir/sillyfile',
'size': '7'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_move_file_many 4'] = {
'data': {
'moveLabbookFile': {
'newLabbookFileEdge': {
'node': {
'isDir': False,
'key': 'sillyfile',
'size': '7'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_move_file_many 5'] = {
'data': {
'moveLabbookFile': {
'newLabbookFileEdge': {
'node': {
'isDir': False,
'key': 'subdir/sillyfile',
'size': '7'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_move_file_many 6'] = {
'data': {
'moveLabbookFile': {
'newLabbookFileEdge': {
'node': {
'isDir': False,
'key': 'sillyfile',
'size': '7'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_add_favorite 1'] = {
'data': {
'labbook': {
'code': {
'favorites': {
'edges': [
]
}
},
'name': 'labbook1'
}
}
}
snapshots['TestLabBookServiceMutations.test_add_favorite 2'] = {
'data': {
'addFavorite': {
'newFavoriteEdge': {
'node': {
'description': 'my test favorite',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0LnR4dA==',
'index': 0,
'isDir': False,
'key': 'test.txt'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_add_favorite 3'] = {
'data': {
'labbook': {
'code': {
'favorites': {
'edges': [
{
'node': {
'description': 'my test favorite',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0LnR4dA==',
'index': 0,
'isDir': False,
'key': 'test.txt'
}
}
]
}
},
'name': 'labbook1'
}
}
}
snapshots['TestLabBookServiceMutations.test_add_favorite_dir 1'] = {
'data': {
'labbook': {
'input': {
'favorites': {
'edges': [
]
}
},
'name': 'labbook1'
}
}
}
snapshots['TestLabBookServiceMutations.test_add_favorite_dir 2'] = {
'data': {
'addFavorite': {
'newFavoriteEdge': {
'node': {
'description': 'my data dir',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmaW5wdXQmc2FtcGxlMS8=',
'index': 0,
'isDir': True,
'key': 'sample1/'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_add_favorite_dir 3'] = {
'data': {
'labbook': {
'input': {
'favorites': {
'edges': [
{
'node': {
'description': 'my data dir',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmaW5wdXQmc2FtcGxlMS8=',
'index': 0,
'isDir': True,
'key': 'sample1/'
}
}
]
}
},
'name': 'labbook1'
}
}
}
snapshots['TestLabBookServiceMutations.test_add_favorite_dir 4'] = {
'data': {
'addFavorite': {
'newFavoriteEdge': {
'node': {
'description': 'my data dir 2',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmaW5wdXQmc2FtcGxlMi8=',
'index': 1,
'isDir': True,
'key': 'sample2/'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_add_favorite_dir 5'] = {
'data': {
'labbook': {
'input': {
'favorites': {
'edges': [
{
'node': {
'description': 'my data dir',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmaW5wdXQmc2FtcGxlMS8=',
'index': 0,
'isDir': True,
'key': 'sample1/'
}
},
{
'node': {
'description': 'my data dir 2',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmaW5wdXQmc2FtcGxlMi8=',
'index': 1,
'isDir': True,
'key': 'sample2/'
}
}
]
}
},
'name': 'labbook1'
}
}
}
snapshots['TestLabBookServiceMutations.test_delete_favorite 1'] = {
'data': {
'addFavorite': {
'newFavoriteEdge': {
'node': {
'description': 'my test favorite',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0LnR4dA==',
'index': 0,
'isDir': False,
'key': 'test.txt'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_delete_favorite 2'] = {
'data': {
'labbook': {
'code': {
'favorites': {
'edges': [
{
'node': {
'description': 'my test favorite',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0LnR4dA==',
'index': 0,
'isDir': False,
'key': 'test.txt'
}
}
]
}
},
'name': 'labbook1'
}
}
}
snapshots['TestLabBookServiceMutations.test_delete_favorite 3'] = {
'data': {
'removeFavorite': {
'removedNodeId': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0LnR4dA==',
'success': True
}
}
}
snapshots['TestLabBookServiceMutations.test_delete_favorite 4'] = {
'data': {
'labbook': {
'code': {
'favorites': {
'edges': [
]
}
},
'name': 'labbook1'
}
}
}
snapshots['TestLabBookServiceMutations.test_move_file 1'] = {
'data': {
'moveLabbookFile': {
'newLabbookFileEdge': {
'node': {
'isDir': False,
'key': 'subdir/sillyfile',
'size': '7'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_move_file 2'] = {
'data': {
'moveLabbookFile': {
'newLabbookFileEdge': {
'node': {
'isDir': True,
'key': 'subdir2/',
'size': '0'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_create_labbook 1'] = {
'data': {
'createLabbook': {
'labbook': {
'description': 'my test description',
'id': 'TGFiYm9vazpkZWZhdWx0JnRlc3QtbGFiLWJvb2sx',
'name': 'test-lab-book1'
}
}
}
}
snapshots['TestLabBookServiceMutations.test_create_labbook_already_exists 1'] = {
'data': {
'createLabbook': {
'labbook': {
'description': 'my test description',
'id': 'TGFiYm9vazpkZWZhdWx0JnRlc3QtbGFiLWR1cGxpY2F0ZQ==',
'name': 'test-lab-duplicate'
}
}
}
}
snapshots['TestLabBookServiceMutations.test_create_labbook_already_exists 2'] = {
'data': {
'labbook': {
'description': 'my test description',
'name': 'test-lab-duplicate'
}
}
}
snapshots['TestLabBookServiceMutations.test_create_labbook_already_exists 3'] = {
'data': {
'createLabbook': None
},
'errors': [
{
'locations': [
{
'column': 11,
'line': 4
}
],
'message': 'LabBook `test-lab-duplicate` already exists locally. Choose a new LabBook name',
'path': [
'createLabbook'
]
}
]
}
snapshots['TestLabBookServiceMutations.test_update_favorite 1'] = {
'data': {
'labbook': {
'code': {
'favorites': {
'edges': [
]
}
},
'name': 'labbook1'
}
}
}
snapshots['TestLabBookServiceMutations.test_update_favorite 2'] = {
'data': {
'addFavorite': {
'newFavoriteEdge': {
'node': {
'description': 'my test favorite',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0LnR4dA==',
'index': 0,
'isDir': False,
'key': 'test.txt'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_update_favorite 3'] = {
'data': {
'addFavorite': {
'newFavoriteEdge': {
'node': {
'description': 'my test favorite 2',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0Mi50eHQ=',
'index': 1,
'isDir': False,
'key': 'test2.txt'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_update_favorite 4'] = {
'data': {
'labbook': {
'code': {
'favorites': {
'edges': [
{
'node': {
'description': 'my test favorite',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0LnR4dA==',
'index': 0,
'isDir': False,
'key': 'test.txt'
}
},
{
'node': {
'description': 'my test favorite 2',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0Mi50eHQ=',
'index': 1,
'isDir': False,
'key': 'test2.txt'
}
}
]
}
},
'name': 'labbook1'
}
}
}
snapshots['TestLabBookServiceMutations.test_update_favorite 5'] = {
'data': {
'updateFavorite': {
'updatedFavoriteEdge': {
'node': {
'description': 'UPDATED',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0Mi50eHQ=',
'index': 0,
'isDir': False,
'key': 'test2.txt'
}
}
}
}
}
snapshots['TestLabBookServiceMutations.test_update_favorite 6'] = {
'data': {
'labbook': {
'code': {
'favorites': {
'edges': [
{
'node': {
'description': 'UPDATED',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0Mi50eHQ=',
'index': 0,
'isDir': False,
'key': 'test2.txt'
}
},
{
'node': {
'description': 'my test favorite',
'id': 'TGFiYm9va0Zhdm9yaXRlOmRlZmF1bHQmbGFiYm9vazEmY29kZSZ0ZXN0LnR4dA==',
'index': 1,
'isDir': False,
'key': 'test.txt'
}
}
]
}
},
'name': 'labbook1'
}
}
}
snapshots['TestLabBookServiceMutations.test_create_labbook 2'] = {
'data': {
'labbook': {
'activityRecords': {
'edges': [
{
'node': {
'detailObjects': [
{
'data': [
[
'text/plain',
'''Added base quickstart-jupyterlab
Data Science Quickstart using Jupyterlab, numpy, and Matplotlib. A great base for any analysis.
- repository: gigantum_base-images-testing
- component: quickstart-jupyterlab
- revision: 2
'''
]
],
'importance': 0,
'show': False,
'tags': [
],
'type': 'ENVIRONMENT'
}
],
'email': 'jane@doe.com',
'importance': 0,
'message': 'Added base: quickstart-jupyterlab',
'show': True,
'tags': [
'environment',
'base'
],
'type': 'ENVIRONMENT',
'username': 'default'
}
},
{
'node': {
'detailObjects': [
{
'data': [
[
'text/plain',
'Add pip3 managed package: numpy "1.14.0"'
]
],
'importance': 0,
'show': False,
'tags': [
],
'type': 'ENVIRONMENT'
},
{
'data': [
[
'text/plain',
'Add pip3 managed package: matplotlib "2.1.1"'
]
],
'importance': 0,
'show': False,
'tags': [
],
'type': 'ENVIRONMENT'
},
{
'data': [
[
'text/plain',
'Add pip3 managed package: jupyter "1.0.0"'
]
],
'importance': 0,
'show': False,
'tags': [
],
'type': 'ENVIRONMENT'
},
{
'data': [
[
'text/plain',
'Add pip3 managed package: jupyterlab "0.31.1"'
]
],
'importance': 0,
'show': False,
'tags': [
],
'type': 'ENVIRONMENT'
},
{
'data': [
[
'text/plain',
'Add pip3 managed package: ipywidgets "7.1.0"'
]
],
'importance': 0,
'show': False,
'tags': [
],
'type': 'ENVIRONMENT'
},
{
'data': [
[
'text/plain',
'Add pip3 managed package: pandas "0.22.0"'
]
],
'importance': 0,
'show': False,
'tags': [
],
'type': 'ENVIRONMENT'
}
],
'email': 'jane@doe.com',
'importance': 0,
'message': 'Added 6 pip3 package(s). ',
'show': True,
'tags': [
'environment',
'package_manager',
'pip3'
],
'type': 'ENVIRONMENT',
'username': 'default'
}
},
{
'node': {
'detailObjects': [
{
'data': [
[
'text/plain',
'Add apt managed package: vim "2:7.4.1689-3ubuntu1.2"'
]
],
'importance': 0,
'show': False,
'tags': [
],
'type': 'ENVIRONMENT'
}
],
'email': 'jane@doe.com',
'importance': 0,
'message': 'Added 1 apt package(s). ',
'show': True,
'tags': [
'environment',
'package_manager',
'apt'
],
'type': 'ENVIRONMENT',
'username': 'default'
}
},
{
'node': {
'detailObjects': [
{
'data': [
[
'text/plain',
'Created new LabBook: default/test-lab-book1'
]
],
'importance': 0,
'show': False,
'tags': [
],
'type': 'LABBOOK'
}
],
'email': 'jane@doe.com',
'importance': 255,
'message': 'Created new LabBook: default/test-lab-book1',
'show': True,
'tags': [
],
'type': 'LABBOOK',
'username': 'default'
}
}
]
}
}
}
}
snapshots['TestLabBookServiceMutations.test_write_readme 1'] = {
'data': {
'writeLabbookReadme': {
'updatedLabbook': {
'description': 'Cats labbook 1',
'name': 'labbook1',
'overview': {
'readme': '''##Overview
This is my readme
:df,a//3p49kasdf'''
}
}
}
}
}
| 32.230563
| 105
| 0.303236
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| 24,044
| 7.14995
| 0.156902
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| 0.066667
| 0.832083
| 0.783194
| 0.748472
| 0.700139
| 0.649861
| 0.615139
| 0
| 0.033694
| 0.596365
| 24,044
| 745
| 106
| 32.273826
| 0.708192
| 0.002579
| 0
| 0.528653
| 0
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| 0.285504
| 0.120241
| 0
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| 1
| 0
| false
| 0
| 0.02149
| 0
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| 0
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| null | 0
| 1
| 0
| 1
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|
0
| 7
|
eef56d3f00051fc7e0eb20bf91b86e71ff4de5c0
| 101,670
|
py
|
Python
|
molsysmt/_private/common_get.py
|
uibcdf/MolModMTs
|
4f6b6f671a9fa3e73008d1e9c48686d5f20a6573
|
[
"MIT"
] | null | null | null |
molsysmt/_private/common_get.py
|
uibcdf/MolModMTs
|
4f6b6f671a9fa3e73008d1e9c48686d5f20a6573
|
[
"MIT"
] | null | null | null |
molsysmt/_private/common_get.py
|
uibcdf/MolModMTs
|
4f6b6f671a9fa3e73008d1e9c48686d5f20a6573
|
[
"MIT"
] | null | null | null |
## From atom
def get_atom_index_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
n_aux = get_n_atoms_from_system(item, check=False)
output = _np.arange(n_aux, dtype=int)
else:
output = _np.array(indices, dtype=int)
return output
def get_group_id_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_group_id_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_group_name_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_group_name_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
output = output.astype(object)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_group_type_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_group_type_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
output = output.astype(object)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_component_id_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_component_id_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_component_name_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_component_name_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
output = output.astype(object)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_component_type_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_component_type_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
output = output.astype(object)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_chain_id_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_chain_id_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_chain_name_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_chain_name_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
output = output.astype(object)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_chain_type_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_chain_type_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
output = output.astype(object)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_molecule_id_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_molecule_id_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_molecule_name_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_molecule_name_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
output = output.astype(object)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_molecule_type_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_molecule_type_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
output = output.astype(object)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_id_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_id_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_name_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_name_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
output = output.astype(object)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_type_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_atom(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_type_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
output = output.astype(object)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_n_atoms_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_atoms_from_system(item, check=False)
else:
output = indices.shape[0]
return output
def get_n_groups_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_groups_from_system(item, check=False)
else:
output = get_group_index_from_atom(item, indices=indices, check=True)
output = _np.unique(output).shape[0]
return output
def get_n_components_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_components_from_system(item, check=False)
else:
output = get_component_index_from_atom(item, indices=indices, check=True)
output = _np.unique(output).shape[0]
return output
def get_n_molecules_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_molecules_from_system(item, check=False)
else:
output = get_molecule_index_from_atom(item, indices=indices, check=True)
output = _np.unique(output).shape[0]
return output
def get_n_chains_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_chains_from_system(item, check=False)
else:
output = get_chain_index_from_atom(item, indices=indices, check=True)
output = _np.unique(output).shape[0]
return output
def get_n_entities_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_entities_from_system(item, check=False)
else:
output = get_entity_index_from_atom(item, indices=indices, check=True)
output = _np.unique(output).shape[0]
return output
def get_bonded_atoms_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = None
G = _Graph()
edges = get_atom_index_from_bond(item, check=False)
G.add_edges_from(edges)
if indices is 'all':
indices = get_atom_index_from_atom(item, check=False)
output = []
for ii in indices:
if ii in G:
output.append(_np.array([n for n in G[ii]]))
else:
output.append(_np.array([]))
output = _np.array(output, dtype=object)
del(G, edges)
return output
def get_bond_index_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = None
G = _Graph()
edges = get_atom_index_from_bond(item, check=False)
n_bonds = edges.shape[0]
edge_indices = _np.array([{'index':ii} for ii in range(n_bonds)]).reshape([n_bonds,1])
G.add_edges_from(_np.hstack([edges, edge_indices]))
if indices is 'all':
indices = get_atom_index_from_atom(item, check=False)
output = []
for ii in indices:
if ii in G:
output.append(_np.array([n['index'] for n in G[ii].values()]))
else:
output.append(_np.array([]))
output = _np.array(output, dtype=object)
del(G, edges, edge_indices)
return output
def get_n_bonds_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = None
G = _Graph()
edges = get_atom_index_from_bond(item, check=False)
G.add_edges_from(edges)
if indices is 'all':
indices = get_atom_index_from_atom(item, check=False)
output = []
for ii in indices:
if ii in G:
output.append(len(G[ii]))
else:
output.append(0)
output = _np.array(output)
del(G, edges)
return output
def get_inner_bond_index_from_atom(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
raise _NotImplementedMethodError
## From group
def get_atom_index_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_group_index_from_group(item, indices=indices, check=False)
attribute_from_atom = get_atom_index_from_atom(item, check=False)
target_index_from_atom = get_group_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_atom_id_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_group(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_id_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_atom_name_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_group(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_name_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_atom_type_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_group(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_type_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_index_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
n_aux = get_n_groups_from_system(item, check=False)
output = _np.arange(n_aux, dtype=int)
else:
output = _np.array(indices, dtype=int)
return output
def get_component_index_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
atom_index_from_target = get_atom_index_from_group(item, indices=indices, check=False)
first_atom_index_from_target = _np.array([ii[0] for ii in atom_index_from_target])
output = get_component_index_from_atom(item, indices=first_atom_index_from_target, check=False)
del(atom_index_from_target, first_atom_index_from_target)
return output
def get_component_id_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_component_id_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_component_name_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_component_name_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_component_type_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_component_type_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_chain_index_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
atom_index_from_target = get_atom_index_from_group(item, indices=indices, check=False)
first_atom_index_from_target = _np.array([ii[0] for ii in atom_index_from_target])
output = get_chain_index_from_atom(item, indices=first_atom_index_from_target, check=False)
del(atom_index_from_target, first_atom_index_from_target)
return output
def get_chain_id_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_chain_id_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_chain_name_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_chain_name_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_chain_type_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_chain_type_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_molecule_index_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
atom_index_from_target = get_atom_index_from_group(item, indices=indices, check=False)
first_atom_index_from_target = _np.array([ii[0] for ii in atom_index_from_target])
output = get_molecule_index_from_atom(item, indices=first_atom_index_from_target, check=False)
del(atom_index_from_target, first_atom_index_from_target)
return output
def get_molecule_id_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_molecule_id_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_molecule_name_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_molecule_name_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_molecule_type_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_molecule_type_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_index_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
atom_index_from_target = get_atom_index_from_group(item, indices=indices, check=False)
first_atom_index_from_target = _np.array([ii[0] for ii in atom_index_from_target])
output = get_entity_index_from_atom(item, indices=first_atom_index_from_target, check=False)
del(atom_index_from_target, first_atom_index_from_target)
return output
def get_entity_id_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_id_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_name_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_name_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_type_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_group(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_type_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_n_atoms_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_atom_index_from_group(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_groups_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_groups_from_system(item, check=False)
else:
output = indices.shape[0]
return output
def get_n_components_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
return get_n_components_from_system(item, check=False)
else:
output = get_component_index_from_group(item, indices=indices, check=False)
output = _np.unique(output).shape[0]
return output
def get_n_molecules_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
return get_n_molecules_from_system(item, check=False)
else:
output = get_molecule_index_from_group(item, indices=indices, check=False)
output = _np.unique(output).shape[0]
return output
def get_n_chains_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
return get_n_chains_from_system(item, check=False)
else:
output = get_chain_index_from_group(item, indices=indices, check=False)
output = _np.unique(output).shape[0]
return output
def get_n_entities_from_group(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
return get_n_entities_from_system(item, check=False)
else:
output = get_entity_index_from_group(item, indices=indices, check=False)
output = _np.unique(output).shape[0]
return output
## From component
def get_atom_index_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_component_index_from_component(item, indices=indices, check=False)
attribute_from_atom = get_atom_index_from_atom(item, check=False)
target_index_from_atom = get_component_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_atom_id_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_component(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_id_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_atom_name_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_component(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_name_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_atom_type_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_component(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_type_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_index_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_component_index_from_component(item, indices=indices, check=False)
attribute_from_atom = get_group_index_from_atom(item, check=False)
target_index_from_atom = get_component_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_group_id_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_component(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_id_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_name_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_component(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_name_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_type_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_component(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_type_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_component_index_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
n_aux = get_n_components_from_system(item, check=False)
output = _np.arange(n_aux, dtype=int)
else:
output = _np.array(indices, dtype=int)
return output
def get_chain_index_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
atom_index_from_target = get_atom_index_from_component(item, indices=indices, check=False)
first_atom_index_from_target = _np.array([ii[0] for ii in atom_index_from_target])
output = get_chain_index_from_atom(item, indices=first_atom_index_from_target, check=False)
del(atom_index_from_target, first_atom_index_from_target)
return output
def get_chain_id_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_component(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_chain_id_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_chain_name_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_component(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_chain_name_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_chain_type_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_component(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_chain_type_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_molecule_index_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
atom_index_from_target = get_atom_index_from_component(item, indices=indices, check=False)
first_atom_index_from_target = _np.array([ii[0] for ii in atom_index_from_target])
output = get_molecule_index_from_atom(item, indices=first_atom_index_from_target, check=False)
del(atom_index_from_target, first_atom_index_from_target)
return output
def get_molecule_id_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_component(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_molecule_id_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_molecule_name_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_component(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_molecule_name_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_molecule_type_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_component(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_molecule_type_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_index_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
atom_index_from_target = get_atom_index_from_component(item, indices=indices, check=False)
first_atom_index_from_target = _np.array([ii[0] for ii in atom_index_from_target])
output = get_entity_index_from_atom(item, indices=first_atom_index_from_target, check=False)
del(atom_index_from_target, first_atom_index_from_target)
return output
def get_entity_id_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_component(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_id_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_name_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_component(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_name_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_type_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_component(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_type_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_n_atoms_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_atom_index_from_component(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_groups_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_group_index_from_component(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_components_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_components_from_system(item, check=False)
else:
output = indices.shape[0]
return output
def get_n_molecules_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_molecules_from_system(item, check=False)
else:
output = get_molecule_index_from_component(item, indices=indices, check=True)
output = _np.unique(output).shape[0]
return output
def get_n_chains_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_chains_from_system(item, check=False)
else:
output = get_chain_index_from_component(item, indices=indices, check=True)
output = _np.unique(output).shape[0]
return output
def get_n_entities_from_component(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_entities_from_system(item, check=False)
else:
output = get_entity_index_from_component(item, indices=indices, check=True)
output = _np.unique(output).shape[0]
return output
## molecule
def get_atom_index_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_molecule_index_from_molecule(item, indices=indices, check=False)
attribute_from_atom = get_atom_index_from_atom(item, check=False)
target_index_from_atom = get_molecule_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_atom_id_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_id_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_atom_name_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_name_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_atom_type_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_type_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_index_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_molecule_index_from_molecule(item, indices=indices, check=False)
attribute_from_atom = get_group_index_from_atom(item, check=False)
target_index_from_atom = get_molecule_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_group_id_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_id_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_name_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_name_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_type_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_type_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_component_index_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_molecule_index_from_molecule(item, indices=indices, check=False)
attribute_from_atom = get_component_index_from_atom(item, check=False)
target_index_from_atom = get_molecule_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_component_id_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_component_id_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_component_name_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_component_name_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_component_type_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_component_type_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_chain_index_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_atom_indices = get_atom_index_from_molecule(item, indices=indices, check=False)
aux_indices = get_molecule_index_from_atom(item, check=False)
aux_vals = get_chain_index_from_atom(item, check=False)
output=[]
for ii in aux_atom_indices:
mask = (aux_indices==ii)
output.append(_np.unique(aux_vals[mask]))
del(aux_atom_indices, aux_indices, aux_vals)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_chain_id_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_chain_id_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_chain_name_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_chain_name_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_chain_type_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_molecule(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_chain_type_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_molecule_index_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
n_aux = get_n_molecules_from_system(item, check=False)
output = _np.arange(n_aux, dtype=int)
else:
output = _np.array(indices, dtype=int)
return output
def get_entity_index_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
atom_index_from_target = get_atom_index_from_molecule(item, indices=indices, check=False)
first_atom_index_from_target = _np.array([ii[0] for ii in atom_index_from_target])
output = get_entity_index_from_atom(item, indices=first_atom_index_from_target, check=False)
del(atom_index_from_target, first_atom_index_from_target)
return output
def get_entity_id_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_molecule(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_id_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_name_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_molecule(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_name_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_entity_type_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_molecule(item, indices=indices, check=False)
aux_unique_indices = _np.unique(aux_indices)
aux_vals = get_entity_type_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
output = _np.vectorize(aux_dict.__getitem__)(aux_indices)
del(aux_indices, aux_unique_indices, aux_vals, aux_dict)
return output
def get_n_atoms_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_atom_index_from_molecule(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_groups_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_group_index_from_molecule(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_components_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_component_index_from_molecule(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_molecules_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_molecules_from_system(item, check=False)
else:
output = indices.shape[0]
return output
def get_n_chains_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_chain_index_from_molecule(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_entities_from_molecule(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_entities_from_system(item, check=False)
else:
output = get_entity_index_from_molecule(item, indices=indices, check=True)
output = _np.unique(output).shape[0]
return output
## chain
def get_atom_index_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_chain_index_from_chain(item, indices=indices, check=False)
attribute_from_atom = get_atom_index_from_atom(item, check=False)
target_index_from_atom = get_chain_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_atom_id_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_id_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_atom_name_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_name_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_atom_type_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_type_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_index_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_chain_index_from_chain(item, indices=indices, check=False)
attribute_from_atom = get_group_index_from_atom(item, check=False)
target_index_from_atom = get_chain_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_group_id_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_id_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_name_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_name_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_type_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_type_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_component_index_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_chain_index_from_chain(item, indices=indices, check=False)
attribute_from_atom = get_component_index_from_atom(item, check=False)
target_index_from_atom = get_chain_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_component_id_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_component_id_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_component_name_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_component_name_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_component_type_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_component_type_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_chain_index_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
n_aux = get_n_chains_from_system(item, check=False)
output = _np.arange(n_aux, dtype=int)
else:
output = _np.array(indices, dtype=int)
return output
def get_molecule_index_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_atom_indices = get_atom_index_from_chain(item, indices=indices, check=False)
aux_indices = get_chain_index_from_atom(item, check=False)
aux_vals = get_molecule_index_from_atom(item, check=False)
output=[]
for ii in aux_atom_indices:
mask = (aux_indices==ii)
output.append(_np.unique(aux_vals[mask]))
del(aux_atom_indices, aux_indices, aux_vals)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_molecule_id_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_molecule_id_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_molecule_name_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_molecule_name_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_molecule_type_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_molecule_type_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_entity_index_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_atom_indices = get_atom_index_from_chain(item, indices=indices, check=False)
aux_indices = get_chain_index_from_atom(item, check=False)
aux_vals = get_entity_index_from_atom(item, check=False)
output=[]
for ii in aux_atom_indices:
mask = (aux_indices==ii)
output.append(_np.unique(aux_vals[mask]))
del(aux_atom_indices, aux_indices, aux_vals)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_entity_id_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_entity_id_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_entity_name_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_entity_name_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_entity_type_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_entity_index_from_chain(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_entity_type_from_entity(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_n_atoms_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_atom_index_from_chain(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_groups_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_group_index_from_chain(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_components_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_component_index_from_chain(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_molecules_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_molecule_index_from_chain(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_chains_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_chains_from_system(item, check=False)
else:
output = indices.shape[0]
return output
def get_n_entities_from_chain(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_entities_from_system(item, check=False)
else:
output = get_entity_index_from_chain(item, indices=indices, check=True)
output = _np.unique(output).shape[0]
return output
## From entity
def get_atom_index_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_entity_index_from_entity(item, indices=indices, check=False)
attribute_from_atom = get_atom_index_from_atom(item, check=False)
target_index_from_atom = get_entity_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_atom_id_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_id_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_atom_name_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_name_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_atom_type_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_atom_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_atom_type_from_atom(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_index_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_entity_index_from_entity(item, indices=indices, check=False)
attribute_from_atom = get_group_index_from_atom(item, check=False)
target_index_from_atom = get_entity_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_group_id_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_id_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_name_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_name_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_group_type_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_group_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_group_type_from_group(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_component_index_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_entity_index_from_entity(item, indices=indices, check=False)
attribute_from_atom = get_component_index_from_atom(item, check=False)
target_index_from_atom = get_entity_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_component_id_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_component_id_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_component_name_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_component_name_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_component_type_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_component_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_component_type_from_component(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_chain_index_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_atom_indices = get_atom_index_from_entity(item, indices=indices, check=False)
aux_indices = get_entity_index_from_atom(item, check=False)
aux_vals = get_chain_index_from_atom(item, check=False)
output=[]
for ii in aux_atom_indices:
mask = (aux_indices==ii)
output.append(_np.unique(aux_vals[mask]))
del(aux_atom_indices, aux_indices, aux_vals)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_chain_id_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_chain_id_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_chain_name_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_chain_name_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_chain_type_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_chain_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_chain_type_from_chain(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_molecule_index_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
indices_aux = get_entity_index_from_entity(item, indices=indices, check=False)
attribute_from_atom = get_molecule_index_from_atom(item, check=False)
target_index_from_atom = get_entity_index_from_atom(item, check=False)
output=[]
for ii in indices_aux:
mask = (target_index_from_atom==ii)
output.append(_np.unique(attribute_from_atom[mask]))
del(indices_aux, attribute_from_atom, target_index_from_atom)
if len(output)==1:
output = _np.array(output)
else:
output = _np.array(output, dtype=object)
return output
def get_molecule_id_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_molecule_id_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_molecule_name_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_molecule_name_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_molecule_type_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
aux_indices = get_molecule_index_from_entity(item, indices=indices, check=False)
if len(aux_indices)>0:
aux_unique_indices = _np.unique(_np.concatenate(aux_indices))
aux_vals = get_molecule_type_from_molecule(item, indices=aux_unique_indices, check=False)
aux_dict = dict(zip(aux_unique_indices, aux_vals))
vv = _np.vectorize(aux_dict.__getitem__)
output = _np.array([vv(ii) for ii in aux_indices], dtype=object)
del(aux_unique_indices, aux_vals, aux_dict)
else:
output = _np.array([], dtype=object)
del(aux_indices)
return output
def get_entity_index_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
n_aux = get_n_entities_from_system(item, check=False)
output = _np.arange(n_aux, dtype=int)
else:
output = _np.array(indices, dtype=int)
return output
def get_n_atoms_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_atom_index_from_entity(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_groups_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_group_index_from_entity(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_components_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_component_index_from_entity(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_molecules_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_molecule_index_from_entity(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_chains_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
output = get_chain_index_from_entity(item, indices=indices, check=False)
output = [ii.shape[0] for ii in output]
output = _np.array(output)
return output
def get_n_entities_from_entity(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_entities_from_system(item, check=False)
else:
output = indices.shape[0]
return output
## system
def get_n_aminoacids_from_system(item, check=True):
if check:
_digest_item(item, _form)
group_types = get_group_type_from_group(item, check=False)
return (group_types=='aminoacid').sum()
def get_n_nucleotides_from_system(item, check=True):
if check:
_digest_item(item, _form)
group_types = get_group_type_from_group(item, check=False)
return (group_types=='nucleotide').sum()
def get_n_ions_from_system(item, check=True):
if check:
_digest_item(item, _form)
molecule_types = get_group_type_from_group(item, check=False)
return (molecule_types=='ion').sum()
def get_n_waters_from_system(item, check=True):
if check:
_digest_item(item, _form)
molecule_types = get_group_type_from_group(item, check=False)
return (molecule_types=='water').sum()
def get_n_cosolutes_from_system(item, check=True):
if check:
_digest_item(item, _form)
molecule_types = get_group_type_from_group(item, check=False)
return (molecule_types=='cosolute').sum()
def get_n_small_molecules_from_system(item, check=True):
if check:
_digest_item(item, _form)
molecule_types = get_group_type_from_group(item, check=False)
return (molecule_types=='small molecule').sum()
def get_n_peptides_from_system(item, check=True):
if check:
_digest_item(item, _form)
molecule_types = get_molecule_type_from_molecule(item, check=False)
return (molecule_types=='peptide').sum()
def get_n_proteins_from_system(item, check=True):
if check:
_digest_item(item, _form)
molecule_types = get_molecule_type_from_molecule(item, check=False)
return (molecule_types=='protein').sum()
def get_n_dnas_from_system(item, check=True):
if check:
_digest_item(item, _form)
molecule_types = get_molecule_type_from_molecule(item, check=False)
return (molecule_types=='dna').sum()
def get_n_rnas_from_system(item, check=True):
if check:
_digest_item(item, _form)
molecule_types = get_molecule_type_from_molecule(item, check=False)
return (molecule_types=='rna').sum()
def get_n_lipids_from_system(item, check=True):
if check:
_digest_item(item, _form)
molecule_types = get_molecule_type_from_molecule(item, check=False)
return (molecule_types=='lipid').sum()
def get_coordinates_from_system(item, structure_indices='all', check=True):
if check:
_digest_item(item, _form)
structure_indices = _digest_structure_indices(structure_indices)
return get_coordinates_from_atom(item, structure_indices=structure_indices, check=False)
def get_box_shape_from_system(item, structure_indices='all', check=True):
if check:
_digest_item(item, _form)
structure_indices = _digest_structure_indices(structure_indices)
from molsysmt.pbc import box_shape_from_box_vectors
tmp_box = get_box_from_system(item, structure_indices=structure_indices, check=False)
output = box_shape_from_box_vectors(tmp_box, check=False)
return output
def get_box_lengths_from_system(item, structure_indices='all', check=True):
if check:
_digest_item(item, _form)
structure_indices = _digest_structure_indices(structure_indices)
from molsysmt.pbc import box_lengths_from_box_vectors
tmp_box = get_box_from_system(item, structure_indices=structure_indices, check=False)
output = box_lengths_from_box_vectors(tmp_box, check=False)
return output
def get_box_angles_from_system(item, structure_indices='all', check=True):
if check:
_digest_item(item, _form)
structure_indices = _digest_structure_indices(structure_indices)
from molsysmt.pbc import box_angles_from_box_vectors
tmp_box = get_box_from_system(item, structure_indices=structure_indices, check=False)
output = box_angles_from_box_vectors(tmp_box, check=False)
return output
def get_box_volume_from_system(item, structure_indices='all', check=True):
if check:
_digest_item(item, _form)
structure_indices = _digest_structure_indices(structure_indices)
from molsysmt.pbc import box_volume_from_box_vectors
tmp_box = get_box_from_system(item, structure_indices=structure_indices, check=False)
if tmp_box is None:
output=None
else:
output = box_volume_from_box_vectors(tmp_box, check=False)
return output
def get_bonded_atoms_from_system(item, check=True):
if check:
_digest_item(item, _form)
return get_bonded_atoms_from_atom(item, check=False)
def get_bond_index_from_system(item, check=True):
if check:
_digest_item(item, _form)
return get_bond_index_from_atom(item, check=False)
def get_inner_bonded_atoms_from_system(item, check=True):
if check:
_digest_item(item, _form)
return get_inner_bonded_atoms_from_atom(item, check=False)
def get_inner_bond_index_from_system(item, check=True):
if check:
_digest_item(item, _form)
return get_inner_bond_index_from_atom(item, check=False)
## bond
def get_bond_index_from_bond(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
n_aux = get_n_bonds_from_system(item, check=False)
output = _np.arange(n_aux, dtype=int)
else:
output = _np.array(indices, dtype=int)
return output
def get_n_bonds_from_bond(item, indices='all', check=True):
if check:
_digest_item(item, _form)
indices = _digest_indices(indices)
if indices is 'all':
output = get_n_bonds_from_system(item, check=False)
else:
output = indices.shape[0]
return output
| 30.959196
| 99
| 0.71139
| 14,251
| 101,670
| 4.654901
| 0.007929
| 0.081403
| 0.086829
| 0.044138
| 0.991001
| 0.989704
| 0.989493
| 0.984564
| 0.982197
| 0.979891
| 0
| 0.001413
| 0.192643
| 101,670
| 3,283
| 100
| 30.968626
| 0.806747
| 0.000728
| 0
| 0.876369
| 0
| 0
| 0.006704
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.083485
| false
| 0
| 0.001825
| 0
| 0.170164
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
e1169120297b942ff34a98fe711b832ad62eb9b0
| 110
|
py
|
Python
|
mong/__init__.py
|
toshihikoyanase/mong
|
c29cbb06cae0f7d60352b42444c3f645bbf06608
|
[
"MIT"
] | 4
|
2020-01-24T13:04:37.000Z
|
2020-08-28T13:59:38.000Z
|
mong/__init__.py
|
toshihikoyanase/mong
|
c29cbb06cae0f7d60352b42444c3f645bbf06608
|
[
"MIT"
] | 6
|
2019-12-22T09:12:47.000Z
|
2020-05-25T08:46:58.000Z
|
mong/__init__.py
|
toshihikoyanase/mong
|
c29cbb06cae0f7d60352b42444c3f645bbf06608
|
[
"MIT"
] | null | null | null |
from mong.name_generator import get_random_name # NOQA
from mong.name_generator import NameGenerator # NOQA
| 36.666667
| 55
| 0.836364
| 16
| 110
| 5.5
| 0.5625
| 0.181818
| 0.272727
| 0.477273
| 0.613636
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.127273
| 110
| 2
| 56
| 55
| 0.916667
| 0.081818
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
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| 1
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| 0
| null | 0
| 1
| 1
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| 0
| 0
| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
014c7b2a0bb1e7ea854a29607c2c06594f1a669d
| 212
|
py
|
Python
|
src/dcat_ap_no_validator_service/adapter/__init__.py
|
Informasjonsforvaltning/dcat-ap-no-validator-service
|
45c1f762429427591840a15ece4b70617d2f3c8a
|
[
"Apache-2.0"
] | 1
|
2021-02-08T09:36:08.000Z
|
2021-02-08T09:36:08.000Z
|
src/dcat_ap_no_validator_service/adapter/__init__.py
|
Informasjonsforvaltning/dcat-ap-no-validator-service
|
45c1f762429427591840a15ece4b70617d2f3c8a
|
[
"Apache-2.0"
] | 63
|
2020-11-12T13:54:40.000Z
|
2022-03-14T12:04:31.000Z
|
src/dcat_ap_no_validator_service/adapter/__init__.py
|
Informasjonsforvaltning/dcat-ap-no-validator-service
|
45c1f762429427591840a15ece4b70617d2f3c8a
|
[
"Apache-2.0"
] | null | null | null |
"""Package for all adapters."""
from .ontology_graph_adapter import OntologyGraphAdapter
from .remote_graph_adapter import fetch_graph, FetchError, parse_text
from .shapes_graph_adapter import ShapesGraphAdapter
| 42.4
| 69
| 0.858491
| 26
| 212
| 6.692308
| 0.653846
| 0.206897
| 0.310345
| 0
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| 0.084906
| 212
| 4
| 70
| 53
| 0.896907
| 0.117925
| 0
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| true
| 0
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| null | 1
| 1
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| null | 0
| 0
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| 1
| 0
| 1
| 0
|
0
| 7
|
014df02eb0a9a84db9982419f2ec846bd6212d31
| 2,145
|
py
|
Python
|
rest_api/serializers.py
|
Surendra58/opinverse-admin
|
9e354c4b0217e58e7bcb71c1cede01e91f527a34
|
[
"MIT"
] | null | null | null |
rest_api/serializers.py
|
Surendra58/opinverse-admin
|
9e354c4b0217e58e7bcb71c1cede01e91f527a34
|
[
"MIT"
] | null | null | null |
rest_api/serializers.py
|
Surendra58/opinverse-admin
|
9e354c4b0217e58e7bcb71c1cede01e91f527a34
|
[
"MIT"
] | null | null | null |
import datetime
from django.contrib.auth.models import User
from rest_framework import serializers
from .models import *
class WalletTableSerializer(serializers.ModelSerializer):
user_data = serializers.PrimaryKeyRelatedField(write_only=True, queryset=UserTable.objects.all())
class Meta:
model = WalletTable
fields = ('wallet_id', 'user_data', 'ov_cash', 'ovr_cash', 'total_ov_cash', 'total_ovr_cash', 'achiver_cash_back', 'reward_and_prices', 'is_active', 'create_at', 'comment', 'status')
class Wallet2TableSerializer(serializers.ModelSerializer):
user_data = serializers.PrimaryKeyRelatedField(write_only=True, queryset=UserTable.objects.all())
class Meta:
model = WalletTable
fields = ('wallet_id', 'user_data', 'ov_cash', 'ovr_cash', 'total_ov_cash', 'total_ovr_cash', 'achiver_cash_back', 'reward_and_prices', 'is_active', 'create_at', 'comment', 'status')
class Wallet3TableSerializer(serializers.ModelSerializer):
user_data = serializers.PrimaryKeyRelatedField(write_only=True, queryset=UserTable.objects.all())
class Meta:
model = WalletTable
fields = ('wallet_id', 'user_data', 'ov_cash', 'ovr_cash', 'total_ov_cash', 'total_ovr_cash', 'achiver_cash_back', 'reward_and_prices', 'is_active', 'create_at', 'comment', 'status')
class Wallet4TableSerializer(serializers.ModelSerializer):
user_data = serializers.PrimaryKeyRelatedField(write_only=True, queryset=UserTable.objects.all())
class Meta:
model = WalletTable
fields = ('wallet_id', 'user_data', 'ov_cash', 'ovr_cash', 'total_ov_cash', 'total_ovr_cash', 'achiver_cash_back', 'reward_and_prices', 'is_active', 'create_at', 'comment', 'status')
class Wallet5TableSerializer(serializers.ModelSerializer):
user_data = serializers.PrimaryKeyRelatedField(write_only=True, queryset=UserTable.objects.all())
class Meta:
model = WalletTable
fields = ('wallet_id', 'user_data', 'ov_cash', 'ovr_cash', 'total_ov_cash', 'total_ovr_cash', 'achiver_cash_back', 'reward_and_prices', 'is_active', 'create_at', 'comment', 'status')
| 39.722222
| 190
| 0.727739
| 247
| 2,145
| 5.991903
| 0.194332
| 0.054054
| 0.101351
| 0.114865
| 0.85473
| 0.85473
| 0.85473
| 0.85473
| 0.85473
| 0.85473
| 0
| 0.002173
| 0.141725
| 2,145
| 53
| 191
| 40.471698
| 0.801738
| 0
| 0
| 0.689655
| 0
| 0
| 0.291375
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.137931
| 0
| 0.655172
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
|
0
| 7
|
01846591a618cf79f9e3120f6425884b55fe6757
| 204
|
py
|
Python
|
tests/test_module.py
|
trickeydan/arch-nemesis
|
7582bd2ec76b5605b8ae1cf0ec1b4e2555044ddd
|
[
"MIT"
] | null | null | null |
tests/test_module.py
|
trickeydan/arch-nemesis
|
7582bd2ec76b5605b8ae1cf0ec1b4e2555044ddd
|
[
"MIT"
] | null | null | null |
tests/test_module.py
|
trickeydan/arch-nemesis
|
7582bd2ec76b5605b8ae1cf0ec1b4e2555044ddd
|
[
"MIT"
] | null | null | null |
"""Test that the arch_nemesis imports as expected."""
import arch_nemesis
def test_module() -> None:
"""Test that the module behaves as expected."""
assert arch_nemesis.__version__ is not None
| 22.666667
| 53
| 0.72549
| 29
| 204
| 4.827586
| 0.586207
| 0.235714
| 0.157143
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.176471
| 204
| 8
| 54
| 25.5
| 0.833333
| 0.436275
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.333333
| 1
| 0.333333
| true
| 0
| 0.333333
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
0193731fd00b3f14a7704ca3328609c028ab54ce
| 233
|
py
|
Python
|
datetimeparser/__init__.py
|
aridevelopment-de/datetimeparser
|
df63d6f7ed0c362f6d6b4e55d61b973b7fcf3f56
|
[
"MIT"
] | 12
|
2021-11-05T21:17:21.000Z
|
2022-03-30T17:53:50.000Z
|
datetimeparser/__init__.py
|
aridevelopment-de/datetimeparser
|
df63d6f7ed0c362f6d6b4e55d61b973b7fcf3f56
|
[
"MIT"
] | 45
|
2021-11-14T16:05:04.000Z
|
2022-03-29T18:51:31.000Z
|
datetimeparser/__init__.py
|
aridevelopment-de/datetimeparser
|
df63d6f7ed0c362f6d6b4e55d61b973b7fcf3f56
|
[
"MIT"
] | 1
|
2021-11-14T13:44:37.000Z
|
2021-11-14T13:44:37.000Z
|
from datetimeparser import parser
from datetimeparser import evaluator
from datetimeparser import enums
from datetimeparser import baseclasses
from datetimeparser import parsermethods
from datetimeparser.datetimeparser import parse
| 29.125
| 47
| 0.888412
| 25
| 233
| 8.28
| 0.36
| 0.521739
| 0.57971
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.107296
| 233
| 7
| 48
| 33.285714
| 0.995192
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
6d6f8d19c9ee3fabfcb1b917b4358f468fde081d
| 189
|
py
|
Python
|
src/clamfig/__init__.py
|
pydefi/clamfig
|
1ccf4d658f6e6d314fde5d6ee1f45b2e6792f047
|
[
"MIT"
] | null | null | null |
src/clamfig/__init__.py
|
pydefi/clamfig
|
1ccf4d658f6e6d314fde5d6ee1f45b2e6792f047
|
[
"MIT"
] | null | null | null |
src/clamfig/__init__.py
|
pydefi/clamfig
|
1ccf4d658f6e6d314fde5d6ee1f45b2e6792f047
|
[
"MIT"
] | 1
|
2022-01-25T12:54:20.000Z
|
2022-01-25T12:54:20.000Z
|
__version__ = '0.1.3'
__all__ = ['Serializable', 'serialize', 'deserialize']
from clamfig.base import Serializable
from clamfig.base import serialize
from clamfig.base import deserialize
| 23.625
| 54
| 0.783069
| 23
| 189
| 6.086957
| 0.521739
| 0.235714
| 0.321429
| 0.45
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.017964
| 0.116402
| 189
| 7
| 55
| 27
| 0.820359
| 0
| 0
| 0
| 0
| 0
| 0.195767
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.6
| 0
| 0.6
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
09fc55e6c20ff6446d6dd0e77656dc28ca768f6d
| 97
|
py
|
Python
|
convokit/user_convo_helpers/__init__.py
|
lucasvanbramer/Cornell-Conversational-Analysis-Toolkit
|
e5194f8518bc086ad15048d66f663cc7c1d68dff
|
[
"MIT"
] | null | null | null |
convokit/user_convo_helpers/__init__.py
|
lucasvanbramer/Cornell-Conversational-Analysis-Toolkit
|
e5194f8518bc086ad15048d66f663cc7c1d68dff
|
[
"MIT"
] | null | null | null |
convokit/user_convo_helpers/__init__.py
|
lucasvanbramer/Cornell-Conversational-Analysis-Toolkit
|
e5194f8518bc086ad15048d66f663cc7c1d68dff
|
[
"MIT"
] | 1
|
2020-01-17T17:27:16.000Z
|
2020-01-17T17:27:16.000Z
|
from .user_convo_attrs import *
from .user_convo_history import *
from .user_convo_utils import *
| 32.333333
| 33
| 0.824742
| 15
| 97
| 4.933333
| 0.466667
| 0.324324
| 0.527027
| 0.513514
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113402
| 97
| 3
| 34
| 32.333333
| 0.860465
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
61de506c08d4feace55d867892fe6f499a377378
| 251
|
py
|
Python
|
Numpy/Array Mathematics.py
|
adi-797/Python-Hackerrank
|
b769cb07aca09ac0eda1d81c51b8e277101da870
|
[
"MIT"
] | 3
|
2018-08-26T15:49:54.000Z
|
2018-08-31T17:06:48.000Z
|
Numpy/Array Mathematics.py
|
adi-797/Python-Hackerrank
|
b769cb07aca09ac0eda1d81c51b8e277101da870
|
[
"MIT"
] | null | null | null |
Numpy/Array Mathematics.py
|
adi-797/Python-Hackerrank
|
b769cb07aca09ac0eda1d81c51b8e277101da870
|
[
"MIT"
] | null | null | null |
import numpy
N, M= map(int, raw_input().split())
A = numpy.array([map(int, raw_input().split()) for i in range(N)])
B = numpy.array([map(int, raw_input().split()) for i in range(N)])
print A+B
print A-B
print A*B
print A / B
print A % B
print A**B
| 17.928571
| 66
| 0.641434
| 53
| 251
| 2.981132
| 0.320755
| 0.227848
| 0.265823
| 0.379747
| 0.905063
| 0.78481
| 0.78481
| 0.78481
| 0.78481
| 0.78481
| 0
| 0
| 0.163347
| 251
| 13
| 67
| 19.307692
| 0.752381
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | null | 0
| 0.1
| null | null | 0.6
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 1
|
0
| 11
|
110b4b558969b48323b149f7ec9f557fa8a54084
| 273
|
py
|
Python
|
tests/parser/3col.l6.test.py
|
veltri/DLV2
|
944aaef803aa75e7ec51d7e0c2b0d964687fdd0e
|
[
"Apache-2.0"
] | null | null | null |
tests/parser/3col.l6.test.py
|
veltri/DLV2
|
944aaef803aa75e7ec51d7e0c2b0d964687fdd0e
|
[
"Apache-2.0"
] | null | null | null |
tests/parser/3col.l6.test.py
|
veltri/DLV2
|
944aaef803aa75e7ec51d7e0c2b0d964687fdd0e
|
[
"Apache-2.0"
] | null | null | null |
input = """
edge( n2, n4). edge( n2, n3). edge( n3, n5).
edge( n4, n6). edge( n4, n5). edge( n5, n7).
edge( n6, n7).
"""
output = """
edge( n2, n4). edge( n2, n3). edge( n3, n5).
edge( n4, n6). edge( n4, n5). edge( n5, n7).
edge( n6, n7).
"""
| 24.818182
| 44
| 0.450549
| 44
| 273
| 2.795455
| 0.204545
| 0.195122
| 0.130081
| 0.195122
| 0.910569
| 0.910569
| 0.910569
| 0.910569
| 0.910569
| 0.910569
| 0
| 0.145078
| 0.29304
| 273
| 10
| 45
| 27.3
| 0.492228
| 0
| 0
| 0.8
| 0
| 0
| 0.886447
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 10
|
11517fd279a077403fc4b896776d6419b23c616b
| 235
|
py
|
Python
|
__init__.py
|
kbvw/network_topology_optimization
|
76755bf10ca7536e9b567601e3139b1237354217
|
[
"MIT"
] | null | null | null |
__init__.py
|
kbvw/network_topology_optimization
|
76755bf10ca7536e9b567601e3139b1237354217
|
[
"MIT"
] | null | null | null |
__init__.py
|
kbvw/network_topology_optimization
|
76755bf10ca7536e9b567601e3139b1237354217
|
[
"MIT"
] | null | null | null |
from .core.core import adjacent, children, parents
from .core.core import neighborhood, descendants, ancestors
from .core.core import explore, reach, area
from .core import abc
from .core import collections
from .core import itertools
| 33.571429
| 59
| 0.808511
| 33
| 235
| 5.757576
| 0.454545
| 0.252632
| 0.189474
| 0.284211
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.12766
| 235
| 7
| 60
| 33.571429
| 0.926829
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
3a0cf2d705c49a1315c1063e4ca755e205933be1
| 5,350
|
py
|
Python
|
z2/part2/interactive/jm/random_fuzzy_arrows_1/537299563.py
|
kozakusek/ipp-2020-testy
|
09aa008fa53d159672cc7cbf969a6b237e15a7b8
|
[
"MIT"
] | 1
|
2020-04-16T12:13:47.000Z
|
2020-04-16T12:13:47.000Z
|
z2/part2/interactive/jm/random_fuzzy_arrows_1/537299563.py
|
kozakusek/ipp-2020-testy
|
09aa008fa53d159672cc7cbf969a6b237e15a7b8
|
[
"MIT"
] | 18
|
2020-03-06T17:50:15.000Z
|
2020-05-19T14:58:30.000Z
|
z2/part2/interactive/jm/random_fuzzy_arrows_1/537299563.py
|
kozakusek/ipp-2020-testy
|
09aa008fa53d159672cc7cbf969a6b237e15a7b8
|
[
"MIT"
] | 18
|
2020-03-06T17:45:13.000Z
|
2020-06-09T19:18:31.000Z
|
from part1 import (
gamma_board,
gamma_busy_fields,
gamma_delete,
gamma_free_fields,
gamma_golden_move,
gamma_golden_possible,
gamma_move,
gamma_new,
)
"""
scenario: test_random_actions
uuid: 537299563
"""
"""
random actions, total chaos
"""
board = gamma_new(4, 8, 4, 9)
assert board is not None
assert gamma_move(board, 1, 1, 1) == 1
assert gamma_move(board, 2, 2, 3) == 1
assert gamma_move(board, 2, 0, 2) == 1
board752469078 = gamma_board(board)
assert board752469078 is not None
assert board752469078 == ("....\n"
"....\n"
"....\n"
"....\n"
"..2.\n"
"2...\n"
".1..\n"
"....\n")
del board752469078
board752469078 = None
assert gamma_move(board, 3, 5, 2) == 0
assert gamma_move(board, 3, 1, 0) == 1
assert gamma_move(board, 4, 5, 1) == 0
assert gamma_move(board, 4, 3, 2) == 1
assert gamma_golden_possible(board, 4) == 1
assert gamma_move(board, 1, 1, 1) == 0
assert gamma_move(board, 2, 0, 0) == 1
assert gamma_move(board, 2, 3, 0) == 1
assert gamma_move(board, 3, 2, 5) == 1
board328684865 = gamma_board(board)
assert board328684865 is not None
assert board328684865 == ("....\n"
"....\n"
"..3.\n"
"....\n"
"..2.\n"
"2..4\n"
".1..\n"
"23.2\n")
del board328684865
board328684865 = None
assert gamma_move(board, 4, 3, 2) == 0
assert gamma_move(board, 4, 1, 5) == 1
assert gamma_busy_fields(board, 4) == 2
assert gamma_move(board, 1, 1, 3) == 1
assert gamma_move(board, 2, 7, 3) == 0
assert gamma_move(board, 2, 3, 5) == 1
assert gamma_move(board, 3, 6, 2) == 0
assert gamma_move(board, 4, 7, 1) == 0
assert gamma_move(board, 1, 2, 1) == 1
assert gamma_move(board, 2, 3, 7) == 1
assert gamma_move(board, 3, 2, 1) == 0
assert gamma_move(board, 3, 2, 2) == 1
assert gamma_move(board, 4, 6, 1) == 0
assert gamma_move(board, 1, 2, 0) == 1
assert gamma_move(board, 1, 2, 1) == 0
assert gamma_move(board, 2, 6, 1) == 0
assert gamma_move(board, 3, 7, 1) == 0
assert gamma_move(board, 3, 1, 1) == 0
assert gamma_busy_fields(board, 3) == 3
assert gamma_move(board, 4, 3, 2) == 0
assert gamma_move(board, 4, 0, 4) == 1
assert gamma_busy_fields(board, 4) == 3
assert gamma_move(board, 1, 3, 6) == 1
assert gamma_move(board, 3, 6, 2) == 0
assert gamma_move(board, 3, 0, 3) == 1
assert gamma_move(board, 4, 2, 1) == 0
assert gamma_move(board, 4, 0, 4) == 0
assert gamma_move(board, 1, 3, 3) == 1
assert gamma_move(board, 1, 3, 5) == 0
assert gamma_move(board, 2, 0, 1) == 1
assert gamma_move(board, 3, 1, 5) == 0
assert gamma_move(board, 3, 2, 6) == 1
assert gamma_golden_move(board, 3, 1, 2) == 0
assert gamma_move(board, 4, 7, 0) == 0
assert gamma_busy_fields(board, 4) == 3
assert gamma_golden_possible(board, 4) == 1
assert gamma_move(board, 1, 0, 2) == 0
assert gamma_move(board, 1, 3, 0) == 0
assert gamma_move(board, 2, 4, 3) == 0
assert gamma_move(board, 3, 7, 1) == 0
assert gamma_move(board, 4, 0, 2) == 0
assert gamma_busy_fields(board, 4) == 3
assert gamma_move(board, 1, 6, 1) == 0
assert gamma_move(board, 1, 2, 0) == 0
assert gamma_free_fields(board, 1) == 11
assert gamma_golden_move(board, 1, 5, 1) == 0
assert gamma_move(board, 2, 3, 5) == 0
assert gamma_move(board, 3, 4, 3) == 0
assert gamma_move(board, 4, 2, 1) == 0
assert gamma_golden_possible(board, 4) == 1
assert gamma_move(board, 1, 0, 4) == 0
assert gamma_move(board, 2, 7, 0) == 0
assert gamma_move(board, 2, 1, 4) == 1
assert gamma_golden_possible(board, 3) == 1
assert gamma_move(board, 4, 5, 0) == 0
assert gamma_move(board, 1, 3, 6) == 0
assert gamma_move(board, 1, 2, 2) == 0
assert gamma_move(board, 2, 1, 3) == 0
assert gamma_move(board, 3, 7, 1) == 0
assert gamma_move(board, 3, 0, 1) == 0
assert gamma_golden_move(board, 3, 3, 1) == 0
assert gamma_move(board, 4, 2, 3) == 0
assert gamma_move(board, 4, 1, 3) == 0
assert gamma_move(board, 1, 0, 2) == 0
assert gamma_free_fields(board, 1) == 10
assert gamma_move(board, 2, 6, 0) == 0
assert gamma_free_fields(board, 2) == 10
assert gamma_move(board, 3, 4, 2) == 0
assert gamma_move(board, 3, 1, 0) == 0
assert gamma_free_fields(board, 3) == 10
assert gamma_golden_possible(board, 3) == 1
assert gamma_move(board, 4, 0, 1) == 0
assert gamma_move(board, 1, 7, 1) == 0
assert gamma_move(board, 1, 1, 0) == 0
assert gamma_move(board, 2, 5, 0) == 0
assert gamma_free_fields(board, 2) == 10
assert gamma_move(board, 3, 5, 0) == 0
assert gamma_move(board, 4, 4, 2) == 0
assert gamma_golden_move(board, 4, 0, 2) == 1
assert gamma_move(board, 1, 7, 1) == 0
assert gamma_move(board, 2, 1, 3) == 0
assert gamma_move(board, 2, 2, 2) == 0
assert gamma_busy_fields(board, 2) == 7
assert gamma_move(board, 3, 1, 3) == 0
assert gamma_move(board, 4, 6, 1) == 0
assert gamma_move(board, 1, 2, 1) == 0
assert gamma_move(board, 2, 3, 5) == 0
assert gamma_free_fields(board, 2) == 10
assert gamma_move(board, 3, 1, 6) == 1
assert gamma_golden_possible(board, 3) == 1
assert gamma_move(board, 4, 1, 6) == 0
assert gamma_move(board, 1, 7, 2) == 0
assert gamma_free_fields(board, 1) == 9
assert gamma_move(board, 2, 4, 3) == 0
assert gamma_golden_move(board, 2, 4, 0) == 0
assert gamma_move(board, 3, 7, 0) == 0
assert gamma_busy_fields(board, 3) == 6
assert gamma_golden_move(board, 3, 0, 3) == 0
assert gamma_move(board, 4, 6, 0) == 0
assert gamma_move(board, 4, 1, 7) == 1
gamma_delete(board)
| 32.228916
| 46
| 0.649346
| 986
| 5,350
| 3.365112
| 0.042596
| 0.371308
| 0.388788
| 0.518385
| 0.853526
| 0.83484
| 0.750753
| 0.509946
| 0.459313
| 0.436709
| 0
| 0.121108
| 0.183551
| 5,350
| 165
| 47
| 32.424242
| 0.638507
| 0
| 0
| 0.295302
| 0
| 0
| 0.018244
| 0
| 0
| 0
| 0
| 0
| 0.785235
| 1
| 0
| false
| 0
| 0.006711
| 0
| 0.006711
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
3a12650d54ccd7f04014e86cc802f33c01fcf656
| 22,397
|
py
|
Python
|
sdk/python/pulumi_consul/config_entry.py
|
pulumi/pulumi-consul
|
5b66c5b97fda6b5433bfb4d4173c999e468c82e8
|
[
"ECL-2.0",
"Apache-2.0"
] | 3
|
2019-11-12T12:21:18.000Z
|
2021-07-31T08:17:22.000Z
|
sdk/python/pulumi_consul/config_entry.py
|
pulumi/pulumi-consul
|
5b66c5b97fda6b5433bfb4d4173c999e468c82e8
|
[
"ECL-2.0",
"Apache-2.0"
] | 38
|
2019-11-21T15:19:33.000Z
|
2022-03-31T15:24:11.000Z
|
sdk/python/pulumi_consul/config_entry.py
|
pulumi/pulumi-consul
|
5b66c5b97fda6b5433bfb4d4173c999e468c82e8
|
[
"ECL-2.0",
"Apache-2.0"
] | 2
|
2020-11-24T12:23:13.000Z
|
2021-12-06T17:33:31.000Z
|
# coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
__all__ = ['ConfigEntryArgs', 'ConfigEntry']
@pulumi.input_type
class ConfigEntryArgs:
def __init__(__self__, *,
kind: pulumi.Input[str],
config_json: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace: Optional[pulumi.Input[str]] = None):
"""
The set of arguments for constructing a ConfigEntry resource.
:param pulumi.Input[str] kind: The kind of configuration entry to register.
:param pulumi.Input[str] config_json: An arbitrary map of configuration values.
:param pulumi.Input[str] name: The name of the configuration entry being registered.
:param pulumi.Input[str] namespace: The namespace to create the config entry within.
"""
pulumi.set(__self__, "kind", kind)
if config_json is not None:
pulumi.set(__self__, "config_json", config_json)
if name is not None:
pulumi.set(__self__, "name", name)
if namespace is not None:
pulumi.set(__self__, "namespace", namespace)
@property
@pulumi.getter
def kind(self) -> pulumi.Input[str]:
"""
The kind of configuration entry to register.
"""
return pulumi.get(self, "kind")
@kind.setter
def kind(self, value: pulumi.Input[str]):
pulumi.set(self, "kind", value)
@property
@pulumi.getter(name="configJson")
def config_json(self) -> Optional[pulumi.Input[str]]:
"""
An arbitrary map of configuration values.
"""
return pulumi.get(self, "config_json")
@config_json.setter
def config_json(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "config_json", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the configuration entry being registered.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def namespace(self) -> Optional[pulumi.Input[str]]:
"""
The namespace to create the config entry within.
"""
return pulumi.get(self, "namespace")
@namespace.setter
def namespace(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "namespace", value)
@pulumi.input_type
class _ConfigEntryState:
def __init__(__self__, *,
config_json: Optional[pulumi.Input[str]] = None,
kind: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering ConfigEntry resources.
:param pulumi.Input[str] config_json: An arbitrary map of configuration values.
:param pulumi.Input[str] kind: The kind of configuration entry to register.
:param pulumi.Input[str] name: The name of the configuration entry being registered.
:param pulumi.Input[str] namespace: The namespace to create the config entry within.
"""
if config_json is not None:
pulumi.set(__self__, "config_json", config_json)
if kind is not None:
pulumi.set(__self__, "kind", kind)
if name is not None:
pulumi.set(__self__, "name", name)
if namespace is not None:
pulumi.set(__self__, "namespace", namespace)
@property
@pulumi.getter(name="configJson")
def config_json(self) -> Optional[pulumi.Input[str]]:
"""
An arbitrary map of configuration values.
"""
return pulumi.get(self, "config_json")
@config_json.setter
def config_json(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "config_json", value)
@property
@pulumi.getter
def kind(self) -> Optional[pulumi.Input[str]]:
"""
The kind of configuration entry to register.
"""
return pulumi.get(self, "kind")
@kind.setter
def kind(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "kind", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
The name of the configuration entry being registered.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def namespace(self) -> Optional[pulumi.Input[str]]:
"""
The namespace to create the config entry within.
"""
return pulumi.get(self, "namespace")
@namespace.setter
def namespace(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "namespace", value)
class ConfigEntry(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
config_json: Optional[pulumi.Input[str]] = None,
kind: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
The [Configuration Entry](https://www.consul.io/docs/agent/config_entries.html)
resource can be used to provide cluster-wide defaults for various aspects of
Consul.
## Example Usage
```python
import pulumi
import json
import pulumi_consul as consul
proxy_defaults = consul.ConfigEntry("proxyDefaults",
kind="proxy-defaults",
config_json=json.dumps({
"Config": {
"local_connect_timeout_ms": 1000,
"handshake_timeout_ms": 10000,
},
}))
web = consul.ConfigEntry("web",
kind="service-defaults",
config_json=json.dumps({
"Protocol": "http",
}))
admin = consul.ConfigEntry("admin",
kind="service-defaults",
config_json=json.dumps({
"Protocol": "http",
}))
service_resolver = consul.ConfigEntry("serviceResolver",
kind="service-resolver",
config_json=json.dumps({
"DefaultSubset": "v1",
"Subsets": {
"v1": {
"Filter": "Service.Meta.version == v1",
},
"v2": {
"Filter": "Service.Meta.version == v2",
},
},
}))
service_splitter = consul.ConfigEntry("serviceSplitter",
kind="service-splitter",
config_json=json.dumps({
"Splits": [
{
"Weight": 90,
"ServiceSubset": "v1",
},
{
"Weight": 10,
"ServiceSubset": "v2",
},
],
}))
service_router = consul.ConfigEntry("serviceRouter",
kind="service-router",
config_json=json.dumps({
"Routes": [{
"Match": {
"HTTP": {
"PathPrefix": "/admin",
},
},
"Destination": {
"Service": "admin",
},
}],
}))
ingress_gateway = consul.ConfigEntry("ingressGateway",
kind="ingress-gateway",
config_json=json.dumps({
"TLS": {
"Enabled": True,
},
"Listeners": [{
"Port": 8000,
"Protocol": "http",
"Services": [{
"Name": "*",
}],
}],
}))
terminating_gateway = consul.ConfigEntry("terminatingGateway",
kind="terminating-gateway",
config_json=json.dumps({
"Services": [{
"Name": "billing",
}],
}))
```
### `service-intentions` config entry
```python
import pulumi
import json
import pulumi_consul as consul
service_intentions = consul.ConfigEntry("serviceIntentions",
kind="service-intentions",
config_json=json.dumps({
"Sources": [
{
"Action": "allow",
"Name": "frontend-webapp",
"Precedence": 9,
"Type": "consul",
},
{
"Action": "allow",
"Name": "nightly-cronjob",
"Precedence": 9,
"Type": "consul",
},
],
}))
```
```python
import pulumi
import json
import pulumi_consul as consul
sd = consul.ConfigEntry("sd",
kind="service-defaults",
config_json=json.dumps({
"Protocol": "http",
}))
service_intentions = consul.ConfigEntry("serviceIntentions",
kind="service-intentions",
config_json=json.dumps({
"Sources": [
{
"Name": "contractor-webapp",
"Permissions": [{
"Action": "allow",
"HTTP": {
"Methods": [
"GET",
"HEAD",
],
"PathExact": "/healtz",
},
}],
"Precedence": 9,
"Type": "consul",
},
{
"Name": "admin-dashboard-webapp",
"Permissions": [
{
"Action": "deny",
"HTTP": {
"PathPrefix": "/debugz",
},
},
{
"Action": "allow",
"HTTP": {
"PathPrefix": "/",
},
},
],
"Precedence": 9,
"Type": "consul",
},
],
}))
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] config_json: An arbitrary map of configuration values.
:param pulumi.Input[str] kind: The kind of configuration entry to register.
:param pulumi.Input[str] name: The name of the configuration entry being registered.
:param pulumi.Input[str] namespace: The namespace to create the config entry within.
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: ConfigEntryArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
The [Configuration Entry](https://www.consul.io/docs/agent/config_entries.html)
resource can be used to provide cluster-wide defaults for various aspects of
Consul.
## Example Usage
```python
import pulumi
import json
import pulumi_consul as consul
proxy_defaults = consul.ConfigEntry("proxyDefaults",
kind="proxy-defaults",
config_json=json.dumps({
"Config": {
"local_connect_timeout_ms": 1000,
"handshake_timeout_ms": 10000,
},
}))
web = consul.ConfigEntry("web",
kind="service-defaults",
config_json=json.dumps({
"Protocol": "http",
}))
admin = consul.ConfigEntry("admin",
kind="service-defaults",
config_json=json.dumps({
"Protocol": "http",
}))
service_resolver = consul.ConfigEntry("serviceResolver",
kind="service-resolver",
config_json=json.dumps({
"DefaultSubset": "v1",
"Subsets": {
"v1": {
"Filter": "Service.Meta.version == v1",
},
"v2": {
"Filter": "Service.Meta.version == v2",
},
},
}))
service_splitter = consul.ConfigEntry("serviceSplitter",
kind="service-splitter",
config_json=json.dumps({
"Splits": [
{
"Weight": 90,
"ServiceSubset": "v1",
},
{
"Weight": 10,
"ServiceSubset": "v2",
},
],
}))
service_router = consul.ConfigEntry("serviceRouter",
kind="service-router",
config_json=json.dumps({
"Routes": [{
"Match": {
"HTTP": {
"PathPrefix": "/admin",
},
},
"Destination": {
"Service": "admin",
},
}],
}))
ingress_gateway = consul.ConfigEntry("ingressGateway",
kind="ingress-gateway",
config_json=json.dumps({
"TLS": {
"Enabled": True,
},
"Listeners": [{
"Port": 8000,
"Protocol": "http",
"Services": [{
"Name": "*",
}],
}],
}))
terminating_gateway = consul.ConfigEntry("terminatingGateway",
kind="terminating-gateway",
config_json=json.dumps({
"Services": [{
"Name": "billing",
}],
}))
```
### `service-intentions` config entry
```python
import pulumi
import json
import pulumi_consul as consul
service_intentions = consul.ConfigEntry("serviceIntentions",
kind="service-intentions",
config_json=json.dumps({
"Sources": [
{
"Action": "allow",
"Name": "frontend-webapp",
"Precedence": 9,
"Type": "consul",
},
{
"Action": "allow",
"Name": "nightly-cronjob",
"Precedence": 9,
"Type": "consul",
},
],
}))
```
```python
import pulumi
import json
import pulumi_consul as consul
sd = consul.ConfigEntry("sd",
kind="service-defaults",
config_json=json.dumps({
"Protocol": "http",
}))
service_intentions = consul.ConfigEntry("serviceIntentions",
kind="service-intentions",
config_json=json.dumps({
"Sources": [
{
"Name": "contractor-webapp",
"Permissions": [{
"Action": "allow",
"HTTP": {
"Methods": [
"GET",
"HEAD",
],
"PathExact": "/healtz",
},
}],
"Precedence": 9,
"Type": "consul",
},
{
"Name": "admin-dashboard-webapp",
"Permissions": [
{
"Action": "deny",
"HTTP": {
"PathPrefix": "/debugz",
},
},
{
"Action": "allow",
"HTTP": {
"PathPrefix": "/",
},
},
],
"Precedence": 9,
"Type": "consul",
},
],
}))
```
:param str resource_name: The name of the resource.
:param ConfigEntryArgs args: The arguments to use to populate this resource's properties.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
...
def __init__(__self__, resource_name: str, *args, **kwargs):
resource_args, opts = _utilities.get_resource_args_opts(ConfigEntryArgs, pulumi.ResourceOptions, *args, **kwargs)
if resource_args is not None:
__self__._internal_init(resource_name, opts, **resource_args.__dict__)
else:
__self__._internal_init(resource_name, *args, **kwargs)
def _internal_init(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
config_json: Optional[pulumi.Input[str]] = None,
kind: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace: Optional[pulumi.Input[str]] = None,
__props__=None):
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = ConfigEntryArgs.__new__(ConfigEntryArgs)
__props__.__dict__["config_json"] = config_json
if kind is None and not opts.urn:
raise TypeError("Missing required property 'kind'")
__props__.__dict__["kind"] = kind
__props__.__dict__["name"] = name
__props__.__dict__["namespace"] = namespace
super(ConfigEntry, __self__).__init__(
'consul:index/configEntry:ConfigEntry',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
config_json: Optional[pulumi.Input[str]] = None,
kind: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
namespace: Optional[pulumi.Input[str]] = None) -> 'ConfigEntry':
"""
Get an existing ConfigEntry resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] config_json: An arbitrary map of configuration values.
:param pulumi.Input[str] kind: The kind of configuration entry to register.
:param pulumi.Input[str] name: The name of the configuration entry being registered.
:param pulumi.Input[str] namespace: The namespace to create the config entry within.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _ConfigEntryState.__new__(_ConfigEntryState)
__props__.__dict__["config_json"] = config_json
__props__.__dict__["kind"] = kind
__props__.__dict__["name"] = name
__props__.__dict__["namespace"] = namespace
return ConfigEntry(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="configJson")
def config_json(self) -> pulumi.Output[Optional[str]]:
"""
An arbitrary map of configuration values.
"""
return pulumi.get(self, "config_json")
@property
@pulumi.getter
def kind(self) -> pulumi.Output[str]:
"""
The kind of configuration entry to register.
"""
return pulumi.get(self, "kind")
@property
@pulumi.getter
def name(self) -> pulumi.Output[str]:
"""
The name of the configuration entry being registered.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter
def namespace(self) -> pulumi.Output[Optional[str]]:
"""
The namespace to create the config entry within.
"""
return pulumi.get(self, "namespace")
| 35.664013
| 134
| 0.475064
| 1,861
| 22,397
| 5.541107
| 0.121977
| 0.059736
| 0.073313
| 0.070403
| 0.842611
| 0.821373
| 0.804888
| 0.788305
| 0.783359
| 0.777638
| 0
| 0.004366
| 0.417065
| 22,397
| 627
| 135
| 35.720893
| 0.785463
| 0.520739
| 0
| 0.701149
| 1
| 0
| 0.072082
| 0.00477
| 0
| 0
| 0
| 0
| 0
| 1
| 0.155172
| false
| 0.005747
| 0.028736
| 0
| 0.275862
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
3a1787df8a244cca36696f29aa593c2075af3a11
| 16,121
|
py
|
Python
|
src/caciolai-FER/model/models.py
|
caciolai/Facial-Emotion-Recognition-with-Multiple-Self-Attention
|
05bf218ae4fb6c97b671f7a6d2c7c8a695c1a2f0
|
[
"MIT"
] | null | null | null |
src/caciolai-FER/model/models.py
|
caciolai/Facial-Emotion-Recognition-with-Multiple-Self-Attention
|
05bf218ae4fb6c97b671f7a6d2c7c8a695c1a2f0
|
[
"MIT"
] | null | null | null |
src/caciolai-FER/model/models.py
|
caciolai/Facial-Emotion-Recognition-with-Multiple-Self-Attention
|
05bf218ae4fb6c97b671f7a6d2c7c8a695c1a2f0
|
[
"MIT"
] | null | null | null |
# DL
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from fastai.layers import SelfAttention
# PyTorch training framework
import pytorch_lightning as pl
from pytorch_lightning.metrics.functional import accuracy
from .layers import SpatialTransformer
class MyLightningModule(pl.LightningModule):
def __init__(self, Ncrops=False):
super().__init__()
self.Ncrops = Ncrops
def training_step(self, batch, batch_idx):
inputs, labels = batch
if self.Ncrops:
# fuse crops and batchsize
bs, ncrops, c, h, w = inputs.shape
inputs = inputs.view(-1, c, h, w)
# repeat labels ncrops times
labels = torch.repeat_interleave(labels, repeats=ncrops, dim=0)
logits = self(inputs)
loss = F.cross_entropy(logits, labels)
self.log('train_loss', loss)
return loss
def validation_step(self, batch, batch_idx):
inputs, labels = batch
if self.Ncrops:
# fuse crops and batchsize
bs, ncrops, c, h, w = inputs.shape
inputs = inputs.view(-1, c, h, w)
# forward
logits = self(inputs)
# combine results across the crops
logits = logits.view(bs, ncrops, -1)
logits = torch.sum(logits, dim=1) / ncrops
else:
logits = self(inputs)
loss = F.cross_entropy(logits, labels)
probs = F.log_softmax(logits, dim=1)
preds = torch.argmax(probs, dim=1)
acc = accuracy(preds, labels)
metrics = {'val_acc': acc, 'val_loss': loss}
self.log_dict(metrics)
return metrics
def test_step(self, batch, batch_idx):
inputs, labels = batch
if self.Ncrops:
# fuse crops and batchsize
bs, ncrops, c, h, w = inputs.shape
inputs = inputs.view(-1, c, h, w)
# forward
logits = self(inputs)
# combine results across the crops
logits = logits.view(bs, ncrops, -1)
logits = torch.sum(logits, dim=1) / ncrops
else:
logits = self(inputs)
loss = F.cross_entropy(logits, labels)
probs = F.log_softmax(logits, dim=1)
preds = torch.argmax(probs, dim=1)
acc = accuracy(preds, labels)
metrics = {'test_acc': acc, 'test_loss': loss}
self.log_dict(metrics)
return metrics
def predict_step(self, batch, batch_idx, dataloader_idx):
inputs, _ = batch
logits = self(inputs)
probs = F.log_softmax(logits, dim=1)
preds = torch.argmax(probs, dim=1)
return preds
class SimpleCNN(MyLightningModule):
def __init__(self, num_classes):
super().__init__()
self.conv1a = nn.Conv2d(in_channels=1, out_channels=10, kernel_size=3)
self.conv1b = nn.Conv2d(10, out_channels=10, kernel_size=3)
self.conv2a = nn.Conv2d(10, 10, 3)
self.conv2b = nn.Conv2d(10, 10, 3)
self.lin1 = nn.Linear(10 * 9 * 9, 50)
self.lin2 = nn.Linear(50, num_classes)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.drop = nn.Dropout()
def forward(self, x):
# (1, 48, 48) -> (10, 46, 46)
x = F.relu(self.conv1a(x))
# (10, 46, 46) -> (10, 44, 44)
x = F.relu(self.conv1b(x))
# (10, 44, 44) -> (10, 22, 22)
x = self.pool(x)
# (10, 22, 22) -> (10, 20, 20)
x = F.relu(self.conv2a(x))
# (10, 20, 20) -> (10, 18, 18)
x = F.relu(self.conv2b(x))
# (10, 18, 18) -> (10, 9, 9)
x = self.pool(x)
x = self.drop(x)
# (10, 9, 9) -> (10 * 9 * 9,)
x = x.view(-1, 10 * 9 * 9)
# (10 * 9 * 9,) -> (50,)
x = F.relu(self.lin1(x))
# (50,) -> (num_classes,)
x = self.lin2(x)
return x
def configure_optimizers(self):
# optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
optimizer = torch.optim.SGD(
self.parameters(), lr=0.01, momentum=0.9, nesterov=True,
weight_decay=0.0001)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='max', factor=0.5, patience=2, verbose=False)
return {
'optimizer': optimizer,
'lr_scheduler': scheduler,
'monitor': 'val_acc'
}
class DeepEmotion(MyLightningModule):
def __init__(self, num_classes):
'''
https://github.com/omarsayed7/Deep-Emotion/blob/master/deep_emotion.py
'''
super().__init__()
self.conv1 = nn.Conv2d(1,10,3)
self.conv2 = nn.Conv2d(10,10,3)
self.pool2 = nn.MaxPool2d(2,2)
self.conv3 = nn.Conv2d(10,10,3)
self.conv4 = nn.Conv2d(10,10,3)
self.pool4 = nn.MaxPool2d(2,2)
self.norm = nn.BatchNorm2d(10)
self.fc1 = nn.Linear(810,50)
self.fc2 = nn.Linear(50,num_classes)
self.localization = nn.Sequential(
nn.Conv2d(1, 8, kernel_size=7),
nn.MaxPool2d(2, stride=2),
nn.ReLU(True),
nn.Conv2d(8, 10, kernel_size=5),
nn.MaxPool2d(2, stride=2),
nn.ReLU(True)
)
self.fc_loc = nn.Sequential(
nn.Linear(640, 32),
nn.ReLU(True),
nn.Linear(32, 3 * 2)
)
self.fc_loc[2].weight.data.zero_()
self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
def stn(self, x):
xs = self.localization(x)
xs = xs.view(-1, 640)
theta = self.fc_loc(xs)
theta = theta.view(-1, 2, 3)
grid = F.affine_grid(theta, x.size())
x = F.grid_sample(x, grid)
return x, grid
def forward(self, x):
x, _ = self.stn(x)
x = F.relu(self.conv1(x))
x = self.conv2(x)
x = F.relu(self.pool2(x))
x = F.relu(self.conv3(x))
x = self.norm(self.conv4(x))
x = F.relu(self.pool4(x))
# out = F.dropout(out)
x = x.view(-1, 810)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
def configure_optimizers(self):
# optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
optimizer = torch.optim.SGD(
self.parameters(), lr=0.01, momentum=0.9, nesterov=True,
weight_decay=0.0001)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='max', factor=0.5, patience=2, verbose=False)
return {
'optimizer': optimizer,
'lr_scheduler': scheduler,
'monitor': 'val_acc'
}
class MultipleSelfAttention(nn.Module):
def __init__(self, shape, num_heads):
super().__init__()
self.ch, self.h, self.w = shape
self.n = num_heads
n, ch, w, h = self.n, self.ch, self.w, self.h
# hybrid attention
self.attention = SelfAttention(self.n*self.ch)
# weight learning
self.conv = nn.Conv2d(in_channels=ch, out_channels=ch, kernel_size=1, stride=1)
self.pool = nn.MaxPool2d(2, 2)
h, w = h//2, w//2
self.fc = nn.Linear(ch*h*w, num_heads)
def forward(self, x):
n, ch, w, h = self.n, self.ch, self.w, self.h
# hybrid attention
xh = x.repeat(1, n, 1, 1)
xh = self.attention(xh)
xh = xh.view(-1, n, ch, h, w) # (bs, N, ch, h, w)
# weight learning
# (bs, ch, h, w) -> (bs, ch, h, w)
xs = self.conv(x)
# (bs, ch, h, w) -> (bs, ch, h/2, w/2)
xs = self.pool(x)
# (bs, ch, h/2, w/2) -> (bs, ch*h/2*w/2)
h, w = h//2, w//2
xs = xs.view(-1, ch*h*w)
# (bs, ch*h/2*w/2) -> (bs, n)
xs = F.sigmoid(self.fc(xs))
xs = F.normalize(xs, p=1, dim=1) # obtain probabilities
# weighted sum
x = torch.sum(torch.mul(xh, xs[:,:,None,None,None]), dim=1)
return x
class DeepEmotionAttention(MyLightningModule):
def __init__(self, num_classes):
'''
https://github.com/omarsayed7/Deep-Emotion/blob/master/deep_emotion.py
'''
super().__init__()
self.conv1 = nn.Conv2d(1,10,3)
self.conv2 = nn.Conv2d(10,10,3)
self.pool2 = nn.MaxPool2d(2,2)
self.conv3 = nn.Conv2d(10,10,3)
self.conv4 = nn.Conv2d(10,10,3)
self.pool4 = nn.MaxPool2d(2,2)
self.norm = nn.BatchNorm2d(10)
self.fc1 = nn.Linear(810,50)
self.fc2 = nn.Linear(50,num_classes)
self.localization = nn.Sequential(
nn.Conv2d(1, 8, kernel_size=7),
MultipleSelfAttention((8, 42, 42), 8),
nn.MaxPool2d(2, stride=2),
nn.ReLU(True),
nn.Conv2d(8, 10, kernel_size=5),
nn.MaxPool2d(2, stride=2),
nn.ReLU(True)
)
self.fc_loc = nn.Sequential(
nn.Linear(640, 32),
nn.ReLU(True),
nn.Linear(32, 3 * 2)
)
self.fc_loc[2].weight.data.zero_()
self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
def stn(self, x):
xs = self.localization(x)
xs = xs.view(-1, 640)
theta = self.fc_loc(xs)
theta = theta.view(-1, 2, 3)
grid = F.affine_grid(theta, x.size())
x = F.grid_sample(x, grid)
return x, grid
def forward(self, x):
x, _ = self.stn(x)
x = F.relu(self.conv1(x))
x = self.conv2(x)
x = F.relu(self.pool2(x))
x = F.relu(self.conv3(x))
x = self.norm(self.conv4(x))
x = F.relu(self.pool4(x))
# out = F.dropout(out)
x = x.view(-1, 810)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
def configure_optimizers(self):
# optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
optimizer = torch.optim.SGD(
self.parameters(), lr=0.01, momentum=0.9, nesterov=True,
weight_decay=0.0001)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='max', factor=0.5, patience=2, verbose=False)
return {
'optimizer': optimizer,
'lr_scheduler': scheduler,
'monitor': 'val_acc'
}
class VGGFaceAttention(MyLightningModule):
def __init__(self, num_classes, Ncrops=False):
super().__init__(Ncrops)
self.conv1a = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, padding='same')
self.conv1b = nn.Conv2d(64, out_channels=64, kernel_size=3, padding='same')
self.msa = MultipleSelfAttention((64, 24, 24), 8) # combine N attention heads
self.conv2a = nn.Conv2d(64, 128, 3, padding='same')
self.conv2b = nn.Conv2d(128, 128, 3, padding='same')
self.conv3a = nn.Conv2d(128, 256, 3, padding='same')
self.conv3b = nn.Conv2d(256, 256, 3, padding='same')
self.conv4a = nn.Conv2d(256, 512, 3, padding='same')
self.conv4b = nn.Conv2d(512, 512, 3, padding='same')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn1a = nn.BatchNorm2d(64)
self.bn1b = nn.BatchNorm2d(64)
self.bn2a = nn.BatchNorm2d(128)
self.bn2b = nn.BatchNorm2d(128)
self.bn3a = nn.BatchNorm2d(256)
self.bn3b = nn.BatchNorm2d(256)
self.bn4a = nn.BatchNorm2d(512)
self.bn4b = nn.BatchNorm2d(512)
self.lin1 = nn.Linear(512 * 3 * 3, 4096)
self.lin2 = nn.Linear(4096, 4096)
self.lin3 = nn.Linear(4096, num_classes)
self.drop = nn.Dropout()
def forward(self, x):
# (1, 48, 48) -> (64, 24, 24)
x = F.relu(self.bn1a(self.conv1a(x)))
x = F.relu(self.bn1b(self.conv1b(x)))
x = self.pool(x)
# (64, 24, 24) -> (64, 24, 24)
x = self.msa(x) # apply multiple self attention
# (64, 24, 24) -> (128, 12, 12)
x = F.relu(self.bn2a(self.conv2a(x)))
x = F.relu(self.bn2b(self.conv2b(x)))
x = self.pool(x)
# (128, 12, 12) -> (256, 6, 6)
x = F.relu(self.bn3a(self.conv3a(x)))
x = F.relu(self.bn3b(self.conv3b(x)))
x = self.pool(x)
# (256, 6, 6) -> (512, 3, 3)
x = F.relu(self.bn4a(self.conv4a(x)))
x = F.relu(self.bn4b(self.conv4b(x)))
x = self.pool(x)
x = x.view(-1, 512 * 3 * 3)
x = F.relu(self.drop(self.lin1(x)))
x = F.relu(self.drop(self.lin2(x)))
x = self.lin3(x)
return x
def configure_optimizers(self):
# optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
optimizer = torch.optim.SGD(
self.parameters(), lr=0.01, momentum=0.9, nesterov=True,
weight_decay=0.0001)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='max', factor=0.5, patience=2, verbose=False)
return {
'optimizer': optimizer,
'lr_scheduler': scheduler,
'monitor': 'val_acc'
}
class VGGFaceAttentionSTN(MyLightningModule):
def __init__(self, num_classes, Ncrops=False):
super().__init__(Ncrops)
self.conv1a = nn.Conv2d(in_channels=1, out_channels=64, kernel_size=3, padding='same')
self.conv1b = nn.Conv2d(64, out_channels=64, kernel_size=3, padding='same')
self.stn = SpatialTransformer()
self.msa = MultipleSelfAttention((64, 24, 24), 8) # combine N attention heads
self.conv2a = nn.Conv2d(64, 128, 3, padding='same')
self.conv2b = nn.Conv2d(128, 128, 3, padding='same')
self.conv3a = nn.Conv2d(128, 256, 3, padding='same')
self.conv3b = nn.Conv2d(256, 256, 3, padding='same')
self.conv4a = nn.Conv2d(256, 512, 3, padding='same')
self.conv4b = nn.Conv2d(512, 512, 3, padding='same')
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.bn1a = nn.BatchNorm2d(64)
self.bn1b = nn.BatchNorm2d(64)
self.bn2a = nn.BatchNorm2d(128)
self.bn2b = nn.BatchNorm2d(128)
self.bn3a = nn.BatchNorm2d(256)
self.bn3b = nn.BatchNorm2d(256)
self.bn4a = nn.BatchNorm2d(512)
self.bn4b = nn.BatchNorm2d(512)
self.lin1 = nn.Linear(512 * 3 * 3, 4096)
self.lin2 = nn.Linear(4096, 4096)
self.lin3 = nn.Linear(4096, num_classes)
self.drop = nn.Dropout()
def forward(self, x):
# (1, 48, 48) -> (64, 24, 24)
x = F.relu(self.bn1a(self.conv1a(x)))
x = F.relu(self.bn1b(self.conv1b(x)))
x = self.pool(x)
# (64, 24, 24) -> (64, 24, 24)
x, _ = self.stn(x)
x = self.msa(x) # apply multiple self attention
# (64, 24, 24) -> (128, 12, 12)
x = F.relu(self.bn2a(self.conv2a(x)))
x = F.relu(self.bn2b(self.conv2b(x)))
x = self.pool(x)
# (128, 12, 12) -> (256, 6, 6)
x = F.relu(self.bn3a(self.conv3a(x)))
x = F.relu(self.bn3b(self.conv3b(x)))
x = self.pool(x)
# (256, 6, 6) -> (512, 3, 3)
x = F.relu(self.bn4a(self.conv4a(x)))
x = F.relu(self.bn4b(self.conv4b(x)))
x = self.pool(x)
x = x.view(-1, 512 * 3 * 3)
x = F.relu(self.drop(self.lin1(x)))
x = F.relu(self.drop(self.lin2(x)))
x = self.lin3(x)
return x
def configure_optimizers(self):
# optimizer = torch.optim.Adam(self.parameters(), lr=0.001)
optimizer = torch.optim.SGD(
self.parameters(), lr=0.01, momentum=0.9, nesterov=True,
weight_decay=0.0001)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='max', factor=0.5, patience=2, verbose=False)
return {
'optimizer': optimizer,
'lr_scheduler': scheduler,
'monitor': 'val_acc'
}
| 30.765267
| 94
| 0.538552
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| 3.866939
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| 0.041104
| 0.834762
| 0.830065
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| 0.806929
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| 0
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| 30.824092
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| false
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0
| 7
|
28bf1aceb3a538ab22be58b107dc37630397be9b
| 8,552
|
py
|
Python
|
Tickets/Double.py
|
fefelson/MLBProjections
|
444a1c071cb7b1e21eedc49cf63ae91e80d37978
|
[
"MIT"
] | null | null | null |
Tickets/Double.py
|
fefelson/MLBProjections
|
444a1c071cb7b1e21eedc49cf63ae91e80d37978
|
[
"MIT"
] | null | null | null |
Tickets/Double.py
|
fefelson/MLBProjections
|
444a1c071cb7b1e21eedc49cf63ae91e80d37978
|
[
"MIT"
] | null | null | null |
from .Ticket import Ticket, StateTicket
################################################################################
################################################################################
################################################################################
################################################################################
class Double(Ticket):
def getStateTicket(self, diamondState):
stateTicket = None
if diamondState == "firstBase_secondBase_thirdBase":
stateTicket = BasesLoadedDouble()
elif diamondState == "secondBase_thirdBase":
stateTicket = SecondThirdDouble()
elif diamondState == "firstBase_thirdBase":
stateTicket = FirstThirdDouble()
elif diamondState == "firstBase_secondBase":
stateTicket = FirstSecondDouble()
elif diamondState == "thirdBase":
stateTicket = ThirdDouble()
elif diamondState == "secondBase":
stateTicket = SecondDouble()
elif diamondState == "firstBase":
stateTicket = FirstDouble()
else: #Bases Empty
stateTicket = EmptyDouble()
return stateTicket
################################################################################
################################################################################
class BasesLoadedDouble(StateTicket):
def recordOuts(self, umpire):
pass
def recordEvents(self, pitcherId, batterId, diamond, umpire, scoreKeeper):
scoreKeeper.recordBatter2B(batterId)
scoreKeeper.recordPitcherH(pitcherId)
# Runners on second and third score
for base in ("thirdBase", "secondBase"):
runnerId, onHook = diamond.popBase(base)
scoreKeeper.recordTeamRun()
scoreKeeper.recordBatterRun(runnerId)
scoreKeeper.recordBatterRbi(batterId)
scoreKeeper.recordPitcherRun(onHook)
if scoreKeeper.exOuts() < 3:
scoreKeeper.recordPitcherER(onHook)
def moveBases(self, diamond):
diamond.moveBase("firstBase", "thirdBase")
def reachedBase(self, pitcherId, batterId, diamond):
diamond.reachedBase("secondBase", batterId, pitcherId)
################################################################################
################################################################################
class SecondThirdDouble(StateTicket):
def recordOuts(self, umpire):
pass
def recordEvents(self, pitcherId, batterId, diamond, umpire, scoreKeeper):
scoreKeeper.recordBatter2B(batterId)
scoreKeeper.recordPitcherH(pitcherId)
# Runners on second and third score
for base in ("thirdBase", "secondBase"):
runnerId, onHook = diamond.popBase(base)
scoreKeeper.recordTeamRun()
scoreKeeper.recordBatterRun(runnerId)
scoreKeeper.recordBatterRbi(batterId)
scoreKeeper.recordPitcherRun(onHook)
if scoreKeeper.exOuts() < 3:
scoreKeeper.recordPitcherER(onHook)
def moveBases(self, diamond):
pass
def reachedBase(self, pitcherId, batterId, diamond):
diamond.reachedBase("secondBase", batterId, pitcherId)
################################################################################
################################################################################
class FirstThirdDouble(StateTicket):
def recordOuts(self, umpire):
pass
def recordEvents(self, pitcherId, batterId, diamond, umpire, scoreKeeper):
scoreKeeper.recordBatter2B(batterId)
scoreKeeper.recordPitcherH(pitcherId)
# Runners on second and third score
runnerId, onHook = diamond.popBase("thirdBase")
scoreKeeper.recordTeamRun()
scoreKeeper.recordBatterRun(runnerId)
scoreKeeper.recordBatterRbi(batterId)
scoreKeeper.recordPitcherRun(onHook)
if scoreKeeper.exOuts() < 3:
scoreKeeper.recordPitcherER(onHook)
def moveBases(self, diamond):
diamond.moveBase("firstBase", "thirdBase")
def reachedBase(self, pitcherId, batterId, diamond):
diamond.reachedBase("secondBase", batterId, pitcherId)
################################################################################
################################################################################
class FirstSecondDouble(StateTicket):
def recordOuts(self, umpire):
pass
def recordEvents(self, pitcherId, batterId, diamond, umpire, scoreKeeper):
scoreKeeper.recordBatter2B(batterId)
scoreKeeper.recordPitcherH(pitcherId)
# Runners on second and third score
runnerId, onHook = diamond.popBase("secondBase")
scoreKeeper.recordTeamRun()
scoreKeeper.recordBatterRun(runnerId)
scoreKeeper.recordBatterRbi(batterId)
scoreKeeper.recordPitcherRun(onHook)
if scoreKeeper.exOuts() < 3:
scoreKeeper.recordPitcherER(onHook)
def moveBases(self, diamond):
diamond.moveBase("firstBase", "thirdBase")
def reachedBase(self, pitcherId, batterId, diamond):
diamond.reachedBase("secondBase", batterId, pitcherId)
################################################################################
################################################################################
class ThirdDouble(StateTicket):
def recordOuts(self, umpire):
pass
def recordEvents(self, pitcherId, batterId, diamond, umpire, scoreKeeper):
scoreKeeper.recordBatter2B(batterId)
scoreKeeper.recordPitcherH(pitcherId)
# Runners on second and third score
runnerId, onHook = diamond.popBase("thirdBase")
scoreKeeper.recordTeamRun()
scoreKeeper.recordBatterRun(runnerId)
scoreKeeper.recordBatterRbi(batterId)
scoreKeeper.recordPitcherRun(onHook)
if scoreKeeper.exOuts() < 3:
scoreKeeper.recordPitcherER(onHook)
def moveBases(self, diamond):
pass
def reachedBase(self, pitcherId, batterId, diamond):
diamond.reachedBase("secondBase", batterId, pitcherId)
################################################################################
################################################################################
class SecondDouble(StateTicket):
def recordOuts(self, umpire):
pass
def recordEvents(self, pitcherId, batterId, diamond, umpire, scoreKeeper):
scoreKeeper.recordBatter2B(batterId)
scoreKeeper.recordPitcherH(pitcherId)
# Runners on second and third score
runnerId, onHook = diamond.popBase("secondBase")
scoreKeeper.recordTeamRun()
scoreKeeper.recordBatterRun(runnerId)
scoreKeeper.recordBatterRbi(batterId)
scoreKeeper.recordPitcherRun(onHook)
if scoreKeeper.exOuts() < 3:
scoreKeeper.recordPitcherER(onHook)
def moveBases(self, diamond):
pass
def reachedBase(self, pitcherId, batterId, diamond):
diamond.reachedBase("secondBase", batterId, pitcherId)
################################################################################
################################################################################
class FirstDouble(StateTicket):
def recordOuts(self, umpire):
pass
def recordEvents(self, pitcherId, batterId, diamond, umpire, scoreKeeper):
scoreKeeper.recordBatter2B(batterId)
scoreKeeper.recordPitcherH(pitcherId)
def moveBases(self, diamond):
diamond.moveBase("firstBase", "thirdBase")
def reachedBase(self, pitcherId, batterId, diamond):
diamond.reachedBase("secondBase", batterId, pitcherId)
################################################################################
################################################################################
class EmptyDouble(StateTicket):
def recordOuts(self, umpire):
pass
def recordEvents(self, pitcherId, batterId, diamond, umpire, scoreKeeper):
scoreKeeper.recordBatter2B(batterId)
scoreKeeper.recordPitcherH(pitcherId)
def moveBases(self, diamond):
pass
def reachedBase(self, pitcherId, batterId, diamond):
diamond.reachedBase("secondBase", batterId, pitcherId)
################################################################################
################################################################################
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0
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e927352929ed52baf076cc34598c41e91e995d17
| 122,229
|
py
|
Python
|
tests/data/generator/t_protocol_no_ct/t_protocol_no_ct_pb2.py
|
bryanchriswhite/agents-aea
|
d3f177a963eb855d9528555167255bf2b478f4ba
|
[
"Apache-2.0"
] | 126
|
2019-09-07T09:32:44.000Z
|
2022-03-29T14:28:41.000Z
|
tests/data/generator/t_protocol_no_ct/t_protocol_no_ct_pb2.py
|
salman6049/agents-aea
|
d3f177a963eb855d9528555167255bf2b478f4ba
|
[
"Apache-2.0"
] | 1,814
|
2019-08-24T10:08:07.000Z
|
2022-03-31T14:28:36.000Z
|
tests/data/generator/t_protocol_no_ct/t_protocol_no_ct_pb2.py
|
salman6049/agents-aea
|
d3f177a963eb855d9528555167255bf2b478f4ba
|
[
"Apache-2.0"
] | 46
|
2019-09-03T22:13:58.000Z
|
2022-03-22T01:25:16.000Z
|
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: t_protocol_no_ct.proto
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
DESCRIPTOR = _descriptor.FileDescriptor(
name="t_protocol_no_ct.proto",
package="aea.some_author.some_protocol_name.v1_0_0",
syntax="proto3",
serialized_options=None,
serialized_pb=b'\n\x16t_protocol_no_ct.proto\x12)aea.some_author.some_protocol_name.v1_0_0"\xd8\x32\n\x14TProtocolNoCtMessage\x12\x8f\x01\n\x1bperformative_empty_contents\x18\x05 \x01(\x0b\x32h.aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Empty_Contents_PerformativeH\x00\x12w\n\x0fperformative_mt\x18\x06 \x01(\x0b\x32\\.aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_PerformativeH\x00\x12u\n\x0eperformative_o\x18\x07 \x01(\x0b\x32[.aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_PerformativeH\x00\x12y\n\x10performative_pct\x18\x08 \x01(\x0b\x32].aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_PerformativeH\x00\x12y\n\x10performative_pmt\x18\t \x01(\x0b\x32].aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_PerformativeH\x00\x12w\n\x0fperformative_pt\x18\n \x01(\x0b\x32\\.aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pt_PerformativeH\x00\x1a\x8c\x01\n\x1cPerformative_Pt_Performative\x12\x15\n\rcontent_bytes\x18\x01 \x01(\x0c\x12\x13\n\x0b\x63ontent_int\x18\x02 \x01(\x05\x12\x15\n\rcontent_float\x18\x03 \x01(\x02\x12\x14\n\x0c\x63ontent_bool\x18\x04 \x01(\x08\x12\x13\n\x0b\x63ontent_str\x18\x05 \x01(\t\x1a\xa8\x02\n\x1dPerformative_Pct_Performative\x12\x19\n\x11\x63ontent_set_bytes\x18\x01 \x03(\x0c\x12\x17\n\x0f\x63ontent_set_int\x18\x02 \x03(\x05\x12\x19\n\x11\x63ontent_set_float\x18\x03 \x03(\x02\x12\x18\n\x10\x63ontent_set_bool\x18\x04 \x03(\x08\x12\x17\n\x0f\x63ontent_set_str\x18\x05 \x03(\t\x12\x1a\n\x12\x63ontent_list_bytes\x18\x06 \x03(\x0c\x12\x18\n\x10\x63ontent_list_int\x18\x07 \x03(\x05\x12\x1a\n\x12\x63ontent_list_float\x18\x08 \x03(\x02\x12\x19\n\x11\x63ontent_list_bool\x18\t \x03(\x08\x12\x18\n\x10\x63ontent_list_str\x18\n 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)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PT_PERFORMATIVE = _descriptor.Descriptor(
name="Performative_Pt_Performative",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pt_Performative",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="content_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pt_Performative.content_bytes",
index=0,
number=1,
type=12,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"",
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pt_Performative.content_int",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_float",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pt_Performative.content_float",
index=2,
number=3,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_bool",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pt_Performative.content_bool",
index=3,
number=4,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_str",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pt_Performative.content_str",
index=4,
number=5,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=848,
serialized_end=988,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PCT_PERFORMATIVE = _descriptor.Descriptor(
name="Performative_Pct_Performative",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="content_set_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative.content_set_bytes",
index=0,
number=1,
type=12,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_set_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative.content_set_int",
index=1,
number=2,
type=5,
cpp_type=1,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_set_float",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative.content_set_float",
index=2,
number=3,
type=2,
cpp_type=6,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_set_bool",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative.content_set_bool",
index=3,
number=4,
type=8,
cpp_type=7,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_set_str",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative.content_set_str",
index=4,
number=5,
type=9,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_list_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative.content_list_bytes",
index=5,
number=6,
type=12,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_list_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative.content_list_int",
index=6,
number=7,
type=5,
cpp_type=1,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_list_float",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative.content_list_float",
index=7,
number=8,
type=2,
cpp_type=6,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_list_bool",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative.content_list_bool",
index=8,
number=9,
type=8,
cpp_type=7,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_list_str",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative.content_list_str",
index=9,
number=10,
type=9,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=991,
serialized_end=1287,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBYTESENTRY = _descriptor.Descriptor(
name="ContentDictIntBytesEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntBytesEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntBytesEntry.key",
index=0,
number=1,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntBytesEntry.value",
index=1,
number=2,
type=12,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"",
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=3598,
serialized_end=3656,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTINTENTRY = _descriptor.Descriptor(
name="ContentDictIntIntEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntIntEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntIntEntry.key",
index=0,
number=1,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntIntEntry.value",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=3658,
serialized_end=3714,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTFLOATENTRY = _descriptor.Descriptor(
name="ContentDictIntFloatEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntFloatEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntFloatEntry.key",
index=0,
number=1,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntFloatEntry.value",
index=1,
number=2,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=3716,
serialized_end=3774,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBOOLENTRY = _descriptor.Descriptor(
name="ContentDictIntBoolEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntBoolEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntBoolEntry.key",
index=0,
number=1,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntBoolEntry.value",
index=1,
number=2,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=3776,
serialized_end=3833,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTSTRENTRY = _descriptor.Descriptor(
name="ContentDictIntStrEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntStrEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntStrEntry.key",
index=0,
number=1,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntStrEntry.value",
index=1,
number=2,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=3835,
serialized_end=3891,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBYTESENTRY = _descriptor.Descriptor(
name="ContentDictBoolBytesEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolBytesEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolBytesEntry.key",
index=0,
number=1,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolBytesEntry.value",
index=1,
number=2,
type=12,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"",
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=3893,
serialized_end=3952,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLINTENTRY = _descriptor.Descriptor(
name="ContentDictBoolIntEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolIntEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolIntEntry.key",
index=0,
number=1,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolIntEntry.value",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=3954,
serialized_end=4011,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLFLOATENTRY = _descriptor.Descriptor(
name="ContentDictBoolFloatEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolFloatEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolFloatEntry.key",
index=0,
number=1,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolFloatEntry.value",
index=1,
number=2,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=4013,
serialized_end=4072,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBOOLENTRY = _descriptor.Descriptor(
name="ContentDictBoolBoolEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolBoolEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolBoolEntry.key",
index=0,
number=1,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolBoolEntry.value",
index=1,
number=2,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=4074,
serialized_end=4132,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLSTRENTRY = _descriptor.Descriptor(
name="ContentDictBoolStrEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolStrEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolStrEntry.key",
index=0,
number=1,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolStrEntry.value",
index=1,
number=2,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=4134,
serialized_end=4191,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBYTESENTRY = _descriptor.Descriptor(
name="ContentDictStrBytesEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrBytesEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrBytesEntry.key",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrBytesEntry.value",
index=1,
number=2,
type=12,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"",
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=4193,
serialized_end=4251,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRINTENTRY = _descriptor.Descriptor(
name="ContentDictStrIntEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrIntEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrIntEntry.key",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrIntEntry.value",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=4253,
serialized_end=4309,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRFLOATENTRY = _descriptor.Descriptor(
name="ContentDictStrFloatEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrFloatEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrFloatEntry.key",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrFloatEntry.value",
index=1,
number=2,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=4311,
serialized_end=4369,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBOOLENTRY = _descriptor.Descriptor(
name="ContentDictStrBoolEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrBoolEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrBoolEntry.key",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrBoolEntry.value",
index=1,
number=2,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=4371,
serialized_end=4428,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRSTRENTRY = _descriptor.Descriptor(
name="ContentDictStrStrEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrStrEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrStrEntry.key",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrStrEntry.value",
index=1,
number=2,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=4430,
serialized_end=4486,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE = _descriptor.Descriptor(
name="Performative_Pmt_Performative",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="content_dict_int_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_int_bytes",
index=0,
number=1,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_int_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_int_int",
index=1,
number=2,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_int_float",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_int_float",
index=2,
number=3,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_int_bool",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_int_bool",
index=3,
number=4,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_int_str",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_int_str",
index=4,
number=5,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_bool_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_bool_bytes",
index=5,
number=6,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_bool_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_bool_int",
index=6,
number=7,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_bool_float",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_bool_float",
index=7,
number=8,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_bool_bool",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_bool_bool",
index=8,
number=9,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_bool_str",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_bool_str",
index=9,
number=10,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_str_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_str_bytes",
index=10,
number=11,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_str_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_str_int",
index=11,
number=12,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_str_float",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_str_float",
index=12,
number=13,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_str_bool",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_str_bool",
index=13,
number=14,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_dict_str_str",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.content_dict_str_str",
index=14,
number=15,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBYTESENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTINTENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTFLOATENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBOOLENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTSTRENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBYTESENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLINTENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLFLOATENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBOOLENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLSTRENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBYTESENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRINTENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRFLOATENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBOOLENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRSTRENTRY,
],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=1290,
serialized_end=4486,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION1TYPEDICTOFSTRINTENTRY = _descriptor.Descriptor(
name="ContentUnion1TypeDictOfStrIntEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion1TypeDictOfStrIntEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion1TypeDictOfStrIntEntry.key",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion1TypeDictOfStrIntEntry.value",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=5752,
serialized_end=5820,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFSTRINTENTRY = _descriptor.Descriptor(
name="ContentUnion2TypeDictOfStrIntEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfStrIntEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfStrIntEntry.key",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfStrIntEntry.value",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=5822,
serialized_end=5890,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFINTFLOATENTRY = _descriptor.Descriptor(
name="ContentUnion2TypeDictOfIntFloatEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfIntFloatEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfIntFloatEntry.key",
index=0,
number=1,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfIntFloatEntry.value",
index=1,
number=2,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=5892,
serialized_end=5962,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFBOOLBYTESENTRY = _descriptor.Descriptor(
name="ContentUnion2TypeDictOfBoolBytesEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfBoolBytesEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfBoolBytesEntry.key",
index=0,
number=1,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfBoolBytesEntry.value",
index=1,
number=2,
type=12,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"",
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=5964,
serialized_end=6035,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE = _descriptor.Descriptor(
name="Performative_Mt_Performative",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="content_union_1_type_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_1_type_bytes",
index=0,
number=1,
type=12,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"",
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_1_type_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_1_type_int",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_1_type_float",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_1_type_float",
index=2,
number=3,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_1_type_bool",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_1_type_bool",
index=3,
number=4,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_1_type_str",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_1_type_str",
index=4,
number=5,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_1_type_set_of_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_1_type_set_of_int",
index=5,
number=6,
type=5,
cpp_type=1,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_1_type_list_of_bool",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_1_type_list_of_bool",
index=6,
number=7,
type=8,
cpp_type=7,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_1_type_dict_of_str_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_1_type_dict_of_str_int",
index=7,
number=8,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_2_type_set_of_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_2_type_set_of_bytes",
index=8,
number=9,
type=12,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_2_type_set_of_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_2_type_set_of_int",
index=9,
number=10,
type=5,
cpp_type=1,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_2_type_set_of_str",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_2_type_set_of_str",
index=10,
number=11,
type=9,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_2_type_list_of_float",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_2_type_list_of_float",
index=11,
number=12,
type=2,
cpp_type=6,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_2_type_list_of_bool",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_2_type_list_of_bool",
index=12,
number=13,
type=8,
cpp_type=7,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_2_type_list_of_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_2_type_list_of_bytes",
index=13,
number=14,
type=12,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_2_type_dict_of_str_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_2_type_dict_of_str_int",
index=14,
number=15,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_2_type_dict_of_int_float",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_2_type_dict_of_int_float",
index=15,
number=16,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_union_2_type_dict_of_bool_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.content_union_2_type_dict_of_bool_bytes",
index=16,
number=17,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION1TYPEDICTOFSTRINTENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFSTRINTENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFINTFLOATENTRY,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFBOOLBYTESENTRY,
],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=4489,
serialized_end=6035,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE_CONTENTODICTSTRINTENTRY = _descriptor.Descriptor(
name="ContentODictStrIntEntry",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.ContentODictStrIntEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.ContentODictStrIntEntry.key",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=b"".decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="value",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.ContentODictStrIntEntry.value",
index=1,
number=2,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=b"8\001",
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=6441,
serialized_end=6498,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE = _descriptor.Descriptor(
name="Performative_O_Performative",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="content_o_bool",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.content_o_bool",
index=0,
number=1,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_o_bool_is_set",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.content_o_bool_is_set",
index=1,
number=2,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_o_set_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.content_o_set_int",
index=2,
number=3,
type=5,
cpp_type=1,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_o_set_int_is_set",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.content_o_set_int_is_set",
index=3,
number=4,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_o_list_bytes",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.content_o_list_bytes",
index=4,
number=5,
type=12,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_o_list_bytes_is_set",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.content_o_list_bytes_is_set",
index=5,
number=6,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_o_dict_str_int",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.content_o_dict_str_int",
index=6,
number=7,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="content_o_dict_str_int_is_set",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.content_o_dict_str_int_is_set",
index=7,
number=8,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE_CONTENTODICTSTRINTENTRY,
],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=6038,
serialized_end=6498,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_EMPTY_CONTENTS_PERFORMATIVE = _descriptor.Descriptor(
name="Performative_Empty_Contents_Performative",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Empty_Contents_Performative",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=6500,
serialized_end=6542,
)
_TPROTOCOLNOCTMESSAGE = _descriptor.Descriptor(
name="TProtocolNoCtMessage",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="performative_empty_contents",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.performative_empty_contents",
index=0,
number=5,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="performative_mt",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.performative_mt",
index=1,
number=6,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="performative_o",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.performative_o",
index=2,
number=7,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="performative_pct",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.performative_pct",
index=3,
number=8,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="performative_pmt",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.performative_pmt",
index=4,
number=9,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="performative_pt",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.performative_pt",
index=5,
number=10,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PT_PERFORMATIVE,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PCT_PERFORMATIVE,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE,
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_EMPTY_CONTENTS_PERFORMATIVE,
],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[
_descriptor.OneofDescriptor(
name="performative",
full_name="aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.performative",
index=0,
containing_type=None,
fields=[],
),
],
serialized_start=70,
serialized_end=6558,
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PT_PERFORMATIVE.containing_type = (
_TPROTOCOLNOCTMESSAGE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PCT_PERFORMATIVE.containing_type = (
_TPROTOCOLNOCTMESSAGE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBYTESENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTINTENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTFLOATENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBOOLENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTSTRENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBYTESENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLINTENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLFLOATENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBOOLENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLSTRENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBYTESENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRINTENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRFLOATENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBOOLENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRSTRENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_int_bytes"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBYTESENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_int_int"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTINTENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_int_float"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTFLOATENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_int_bool"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBOOLENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_int_str"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTSTRENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_bool_bytes"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBYTESENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_bool_int"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLINTENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_bool_float"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLFLOATENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_bool_bool"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBOOLENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_bool_str"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLSTRENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_str_bytes"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBYTESENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_str_int"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRINTENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_str_float"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRFLOATENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_str_bool"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBOOLENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.fields_by_name[
"content_dict_str_str"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRSTRENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE.containing_type = (
_TPROTOCOLNOCTMESSAGE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION1TYPEDICTOFSTRINTENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFSTRINTENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFINTFLOATENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFBOOLBYTESENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE.fields_by_name[
"content_union_1_type_dict_of_str_int"
].message_type = _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION1TYPEDICTOFSTRINTENTRY
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE.fields_by_name[
"content_union_2_type_dict_of_str_int"
].message_type = _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFSTRINTENTRY
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE.fields_by_name[
"content_union_2_type_dict_of_int_float"
].message_type = _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFINTFLOATENTRY
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE.fields_by_name[
"content_union_2_type_dict_of_bool_bytes"
].message_type = _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFBOOLBYTESENTRY
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE.containing_type = (
_TPROTOCOLNOCTMESSAGE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE_CONTENTODICTSTRINTENTRY.containing_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE.fields_by_name[
"content_o_dict_str_int"
].message_type = (
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE_CONTENTODICTSTRINTENTRY
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE.containing_type = (
_TPROTOCOLNOCTMESSAGE
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_EMPTY_CONTENTS_PERFORMATIVE.containing_type = (
_TPROTOCOLNOCTMESSAGE
)
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_empty_contents"
].message_type = _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_EMPTY_CONTENTS_PERFORMATIVE
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_mt"
].message_type = _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_o"
].message_type = _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_pct"
].message_type = _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PCT_PERFORMATIVE
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_pmt"
].message_type = _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_pt"
].message_type = _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PT_PERFORMATIVE
_TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"].fields.append(
_TPROTOCOLNOCTMESSAGE.fields_by_name["performative_empty_contents"]
)
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_empty_contents"
].containing_oneof = _TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"]
_TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"].fields.append(
_TPROTOCOLNOCTMESSAGE.fields_by_name["performative_mt"]
)
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_mt"
].containing_oneof = _TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"]
_TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"].fields.append(
_TPROTOCOLNOCTMESSAGE.fields_by_name["performative_o"]
)
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_o"
].containing_oneof = _TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"]
_TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"].fields.append(
_TPROTOCOLNOCTMESSAGE.fields_by_name["performative_pct"]
)
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_pct"
].containing_oneof = _TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"]
_TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"].fields.append(
_TPROTOCOLNOCTMESSAGE.fields_by_name["performative_pmt"]
)
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_pmt"
].containing_oneof = _TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"]
_TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"].fields.append(
_TPROTOCOLNOCTMESSAGE.fields_by_name["performative_pt"]
)
_TPROTOCOLNOCTMESSAGE.fields_by_name[
"performative_pt"
].containing_oneof = _TPROTOCOLNOCTMESSAGE.oneofs_by_name["performative"]
DESCRIPTOR.message_types_by_name["TProtocolNoCtMessage"] = _TPROTOCOLNOCTMESSAGE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
TProtocolNoCtMessage = _reflection.GeneratedProtocolMessageType(
"TProtocolNoCtMessage",
(_message.Message,),
{
"Performative_Pt_Performative": _reflection.GeneratedProtocolMessageType(
"Performative_Pt_Performative",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PT_PERFORMATIVE,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pt_Performative)
},
),
"Performative_Pct_Performative": _reflection.GeneratedProtocolMessageType(
"Performative_Pct_Performative",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PCT_PERFORMATIVE,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pct_Performative)
},
),
"Performative_Pmt_Performative": _reflection.GeneratedProtocolMessageType(
"Performative_Pmt_Performative",
(_message.Message,),
{
"ContentDictIntBytesEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictIntBytesEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBYTESENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntBytesEntry)
},
),
"ContentDictIntIntEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictIntIntEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTINTENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntIntEntry)
},
),
"ContentDictIntFloatEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictIntFloatEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTFLOATENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntFloatEntry)
},
),
"ContentDictIntBoolEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictIntBoolEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBOOLENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntBoolEntry)
},
),
"ContentDictIntStrEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictIntStrEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTSTRENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntStrEntry)
},
),
"ContentDictBoolBytesEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictBoolBytesEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBYTESENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolBytesEntry)
},
),
"ContentDictBoolIntEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictBoolIntEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLINTENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolIntEntry)
},
),
"ContentDictBoolFloatEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictBoolFloatEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLFLOATENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolFloatEntry)
},
),
"ContentDictBoolBoolEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictBoolBoolEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBOOLENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolBoolEntry)
},
),
"ContentDictBoolStrEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictBoolStrEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLSTRENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolStrEntry)
},
),
"ContentDictStrBytesEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictStrBytesEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBYTESENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrBytesEntry)
},
),
"ContentDictStrIntEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictStrIntEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRINTENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrIntEntry)
},
),
"ContentDictStrFloatEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictStrFloatEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRFLOATENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrFloatEntry)
},
),
"ContentDictStrBoolEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictStrBoolEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBOOLENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrBoolEntry)
},
),
"ContentDictStrStrEntry": _reflection.GeneratedProtocolMessageType(
"ContentDictStrStrEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRSTRENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrStrEntry)
},
),
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Pmt_Performative)
},
),
"Performative_Mt_Performative": _reflection.GeneratedProtocolMessageType(
"Performative_Mt_Performative",
(_message.Message,),
{
"ContentUnion1TypeDictOfStrIntEntry": _reflection.GeneratedProtocolMessageType(
"ContentUnion1TypeDictOfStrIntEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION1TYPEDICTOFSTRINTENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion1TypeDictOfStrIntEntry)
},
),
"ContentUnion2TypeDictOfStrIntEntry": _reflection.GeneratedProtocolMessageType(
"ContentUnion2TypeDictOfStrIntEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFSTRINTENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfStrIntEntry)
},
),
"ContentUnion2TypeDictOfIntFloatEntry": _reflection.GeneratedProtocolMessageType(
"ContentUnion2TypeDictOfIntFloatEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFINTFLOATENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfIntFloatEntry)
},
),
"ContentUnion2TypeDictOfBoolBytesEntry": _reflection.GeneratedProtocolMessageType(
"ContentUnion2TypeDictOfBoolBytesEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFBOOLBYTESENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfBoolBytesEntry)
},
),
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Mt_Performative)
},
),
"Performative_O_Performative": _reflection.GeneratedProtocolMessageType(
"Performative_O_Performative",
(_message.Message,),
{
"ContentODictStrIntEntry": _reflection.GeneratedProtocolMessageType(
"ContentODictStrIntEntry",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE_CONTENTODICTSTRINTENTRY,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative.ContentODictStrIntEntry)
},
),
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_O_Performative)
},
),
"Performative_Empty_Contents_Performative": _reflection.GeneratedProtocolMessageType(
"Performative_Empty_Contents_Performative",
(_message.Message,),
{
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE_PERFORMATIVE_EMPTY_CONTENTS_PERFORMATIVE,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage.Performative_Empty_Contents_Performative)
},
),
"DESCRIPTOR": _TPROTOCOLNOCTMESSAGE,
"__module__": "t_protocol_no_ct_pb2"
# @@protoc_insertion_point(class_scope:aea.some_author.some_protocol_name.v1_0_0.TProtocolNoCtMessage)
},
)
_sym_db.RegisterMessage(TProtocolNoCtMessage)
_sym_db.RegisterMessage(TProtocolNoCtMessage.Performative_Pt_Performative)
_sym_db.RegisterMessage(TProtocolNoCtMessage.Performative_Pct_Performative)
_sym_db.RegisterMessage(TProtocolNoCtMessage.Performative_Pmt_Performative)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntBytesEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntIntEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntFloatEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntBoolEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictIntStrEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolBytesEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolIntEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolFloatEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolBoolEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictBoolStrEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrBytesEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrIntEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrFloatEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrBoolEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Pmt_Performative.ContentDictStrStrEntry
)
_sym_db.RegisterMessage(TProtocolNoCtMessage.Performative_Mt_Performative)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion1TypeDictOfStrIntEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfStrIntEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfIntFloatEntry
)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_Mt_Performative.ContentUnion2TypeDictOfBoolBytesEntry
)
_sym_db.RegisterMessage(TProtocolNoCtMessage.Performative_O_Performative)
_sym_db.RegisterMessage(
TProtocolNoCtMessage.Performative_O_Performative.ContentODictStrIntEntry
)
_sym_db.RegisterMessage(TProtocolNoCtMessage.Performative_Empty_Contents_Performative)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBYTESENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTINTENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTFLOATENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTBOOLENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTINTSTRENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBYTESENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLINTENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLFLOATENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLBOOLENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTBOOLSTRENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBYTESENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRINTENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRFLOATENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRBOOLENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_PMT_PERFORMATIVE_CONTENTDICTSTRSTRENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION1TYPEDICTOFSTRINTENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFSTRINTENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFINTFLOATENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_MT_PERFORMATIVE_CONTENTUNION2TYPEDICTOFBOOLBYTESENTRY._options = (
None
)
_TPROTOCOLNOCTMESSAGE_PERFORMATIVE_O_PERFORMATIVE_CONTENTODICTSTRINTENTRY._options = (
None
)
# @@protoc_insertion_point(module_scope)
| 40.486585
| 9,427
| 0.661823
| 11,956
| 122,229
| 6.338491
| 0.024172
| 0.167214
| 0.084082
| 0.144505
| 0.911141
| 0.842075
| 0.791747
| 0.74456
| 0.733753
| 0.699273
| 0
| 0.036182
| 0.257885
| 122,229
| 3,018
| 9,428
| 40.5
| 0.799279
| 0.034558
| 0
| 0.757545
| 1
| 0.001017
| 0.248991
| 0.226054
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.001356
| 0
| 0.001356
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 9
|
aaf44c1c409eb2eb2287e15dd0cbdc4d636a853f
| 178
|
py
|
Python
|
Python/IntroductionToSets.py
|
WinrichSy/HackerRank-Solutions
|
ed928de50cbbbdf0aee471630f6c04f9a0f69a1f
|
[
"Apache-2.0"
] | null | null | null |
Python/IntroductionToSets.py
|
WinrichSy/HackerRank-Solutions
|
ed928de50cbbbdf0aee471630f6c04f9a0f69a1f
|
[
"Apache-2.0"
] | null | null | null |
Python/IntroductionToSets.py
|
WinrichSy/HackerRank-Solutions
|
ed928de50cbbbdf0aee471630f6c04f9a0f69a1f
|
[
"Apache-2.0"
] | null | null | null |
#Introduction to Sets
#https://www.hackerrank.com/challenges/py-introduction-to-sets/problem
import statistics
def average(array):
return statistics.mean(list(set(array)))
| 22.25
| 70
| 0.780899
| 24
| 178
| 5.791667
| 0.791667
| 0.201439
| 0.258993
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.089888
| 178
| 7
| 71
| 25.428571
| 0.858025
| 0.5
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.333333
| false
| 0
| 0.333333
| 0.333333
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 1
| 1
| 0
|
0
| 8
|
c94087e45668d7265b854fba95d1f7de91cf71fc
| 508
|
py
|
Python
|
out/min4.py
|
FardaleM/metalang
|
171557c540f3e2c051ec39ea150afb740c1f615f
|
[
"BSD-2-Clause"
] | 22
|
2017-04-24T10:00:45.000Z
|
2021-04-01T10:11:05.000Z
|
out/min4.py
|
FardaleM/metalang
|
171557c540f3e2c051ec39ea150afb740c1f615f
|
[
"BSD-2-Clause"
] | 12
|
2017-03-26T18:34:21.000Z
|
2019-03-21T19:13:03.000Z
|
out/min4.py
|
FardaleM/metalang
|
171557c540f3e2c051ec39ea150afb740c1f615f
|
[
"BSD-2-Clause"
] | 7
|
2017-10-14T13:33:33.000Z
|
2021-03-18T15:18:50.000Z
|
print("%d %d %d %d %d %d\n%d %d %d %d %d %d\n%d %d %d %d %d %d\n%d %d %d %d %d %d\n" % (min(1, 2, 3, 4), min(1, 2, 4, 3), min(1, 3, 2, 4), min(1, 3, 4, 2), min(1, 4, 2, 3), min(1, 4, 3, 2), min(2, 1, 3, 4), min(2, 1, 4, 3), min(2, 3, 1, 4), min(2, 3, 4, 1), min(2, 4, 1, 3), min(2, 4, 3, 1), min(3, 1, 2, 4), min(3, 1, 4, 2), min(3, 2, 1, 4), min(3, 2, 4, 1), min(3, 4, 1, 2), min(3, 4, 2, 1), min(4, 1, 2, 3), min(4, 1, 3, 2), min(4, 2, 1, 3), min(4, 2, 3, 1), min(4, 3, 1, 2), min(4, 3, 2, 1)), end='')
| 101.6
| 504
| 0.401575
| 150
| 508
| 1.36
| 0.06
| 0.196078
| 0.235294
| 0.235294
| 0.137255
| 0.137255
| 0.137255
| 0.137255
| 0.137255
| 0.137255
| 0
| 0.248705
| 0.240157
| 508
| 4
| 505
| 127
| 0.279793
| 0
| 0
| 0
| 0
| 1
| 0.150495
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 1
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 1
|
0
| 10
|
c97a357f45dd2367ec5e7c470299db2b9933cb58
| 172
|
py
|
Python
|
sam/sim/__init__.py
|
weiya711/scadi_graph
|
5ce78fe09286e9722c2627f7f959c4ad3f2288eb
|
[
"MIT"
] | null | null | null |
sam/sim/__init__.py
|
weiya711/scadi_graph
|
5ce78fe09286e9722c2627f7f959c4ad3f2288eb
|
[
"MIT"
] | null | null | null |
sam/sim/__init__.py
|
weiya711/scadi_graph
|
5ce78fe09286e9722c2627f7f959c4ad3f2288eb
|
[
"MIT"
] | null | null | null |
import pytest
# we want to have pytest assert introspection in the helpers
pytest.register_assert_rewrite('sim.src.primitives')
pytest.register_assert_rewrite('sim.test')
| 28.666667
| 60
| 0.825581
| 25
| 172
| 5.52
| 0.68
| 0.202899
| 0.289855
| 0.391304
| 0.434783
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.093023
| 172
| 5
| 61
| 34.4
| 0.884615
| 0.337209
| 0
| 0
| 0
| 0
| 0.232143
| 0
| 0
| 0
| 0
| 0
| 0.666667
| 1
| 0
| true
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
|
0
| 8
|
a32324b2237eb6a022d525a5178a3f6bba4fb865
| 119
|
py
|
Python
|
kg/grids/utils.py
|
kevinsogo/compgen
|
c765fdb3008d41f409836a45ad5a506db6a99e74
|
[
"MIT"
] | 6
|
2019-11-30T17:03:13.000Z
|
2021-09-30T05:08:31.000Z
|
kg/grids/utils.py
|
kevinsogo/compgen
|
c765fdb3008d41f409836a45ad5a506db6a99e74
|
[
"MIT"
] | 1
|
2020-01-20T12:13:03.000Z
|
2020-01-20T12:13:03.000Z
|
kg/grids/utils.py
|
kevinsogo/compgen
|
c765fdb3008d41f409836a45ad5a506db6a99e74
|
[
"MIT"
] | null | null | null |
from .utils import * ### @import 'kg.grids.utils'
from kg.utils import * ### @import
class GridError(Exception): ...
| 19.833333
| 49
| 0.663866
| 15
| 119
| 5.266667
| 0.533333
| 0.278481
| 0.43038
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.151261
| 119
| 5
| 50
| 23.8
| 0.782178
| 0.268908
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.666667
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
a3985294797b3d911ea50538c27b5dc7aca6755f
| 3,522
|
py
|
Python
|
src/tests/python_tests/test_bounding_box.py
|
PavelBlend/fluid-engine-dev
|
45b4bdbdb4c6d8c0beebc682180469198203b0ef
|
[
"MIT"
] | 1,355
|
2016-05-08T07:29:22.000Z
|
2022-03-30T13:59:35.000Z
|
src/tests/python_tests/test_bounding_box.py
|
Taiyuan-Zhang/fluid-engine-dev
|
45b4bdbdb4c6d8c0beebc682180469198203b0ef
|
[
"MIT"
] | 208
|
2016-05-25T19:47:27.000Z
|
2022-01-17T04:18:29.000Z
|
src/tests/python_tests/test_bounding_box.py
|
Taiyuan-Zhang/fluid-engine-dev
|
45b4bdbdb4c6d8c0beebc682180469198203b0ef
|
[
"MIT"
] | 218
|
2016-08-23T16:51:10.000Z
|
2022-03-31T03:55:48.000Z
|
"""
Copyright (c) 2018 Doyub Kim
I am making my contributions/submissions to this project solely in my personal
capacity and am not conveying any rights to any intellectual property of any
third parties.
"""
import pyjet
def test_bounding_box2f_init():
a = pyjet.BoundingBox2D()
assert a.lowerCorner.x > a.upperCorner.x
assert a.lowerCorner.y > a.upperCorner.y
b = pyjet.BoundingBox2D((-1, -2), (4, 2))
b.lowerCorner.x == -1.0
b.lowerCorner.y == -2.0
b.upperCorner.x == 4.0
b.upperCorner.y == 2.0
l, c = pyjet.Vector2D(-1, -2), pyjet.Vector2D(4, 2)
c = pyjet.BoundingBox2D(l, c)
c.lowerCorner.x == -1.0
c.lowerCorner.y == -2.0
c.upperCorner.x == 4.0
c.upperCorner.y == 2.0
def test_bounding_box2f_is_empty():
a = pyjet.BoundingBox2D((-2.0, -2.0), (4.0, 3.0))
assert not a.isEmpty()
# ------------------------------------------------------------------------------
def test_bounding_box2d_init():
a = pyjet.BoundingBox2D()
assert a.lowerCorner.x > a.upperCorner.x
assert a.lowerCorner.y > a.upperCorner.y
b = pyjet.BoundingBox2D((-1, -2), (4, 2))
b.lowerCorner.x == -1.0
b.lowerCorner.y == -2.0
b.upperCorner.x == 4.0
b.upperCorner.y == 2.0
l, c = pyjet.Vector2D(-1, -2), pyjet.Vector2D(4, 2)
c = pyjet.BoundingBox2D(l, c)
c.lowerCorner.x == -1.0
c.lowerCorner.y == -2.0
c.upperCorner.x == 4.0
c.upperCorner.y == 2.0
def test_bounding_box2d_is_empty():
a = pyjet.BoundingBox2D((-2.0, -2.0), (4.0, 3.0))
assert not a.isEmpty()
# ------------------------------------------------------------------------------
def test_bounding_box3f_init():
a = pyjet.BoundingBox3F()
assert a.lowerCorner.x > a.upperCorner.x
assert a.lowerCorner.y > a.upperCorner.y
assert a.lowerCorner.z > a.upperCorner.z
b = pyjet.BoundingBox3F((-1, -2, -3), (4, 2, 5))
assert b.lowerCorner.x == -1.0
assert b.lowerCorner.y == -2.0
assert b.lowerCorner.z == -3.0
assert b.upperCorner.x == 4.0
assert b.upperCorner.y == 2.0
assert b.upperCorner.z == 5.0
l, c = pyjet.Vector3F(-1, -2, -3), pyjet.Vector3F(4, 2, 5)
c = pyjet.BoundingBox3F(l, c)
assert c.lowerCorner.x == -1.0
assert c.lowerCorner.y == -2.0
assert c.lowerCorner.z == -3.0
assert c.upperCorner.x == 4.0
assert c.upperCorner.y == 2.0
assert c.upperCorner.z == 5.0
def test_bounding_box3f_is_empty():
a = pyjet.BoundingBox3F((-2.0, -2.0, 1.0), (4.0, 3.0, 5.0))
assert not a.isEmpty()
# ------------------------------------------------------------------------------
def test_bounding_box3d_init():
a = pyjet.BoundingBox3D()
assert a.lowerCorner.x > a.upperCorner.x
assert a.lowerCorner.y > a.upperCorner.y
assert a.lowerCorner.z > a.upperCorner.z
b = pyjet.BoundingBox3D((-1, -2, -3), (4, 2, 5))
assert b.lowerCorner.x == -1.0
assert b.lowerCorner.y == -2.0
assert b.lowerCorner.z == -3.0
assert b.upperCorner.x == 4.0
assert b.upperCorner.y == 2.0
assert b.upperCorner.z == 5.0
l, c = pyjet.Vector3D(-1, -2, -3), pyjet.Vector3D(4, 2, 5)
c = pyjet.BoundingBox3D(l, c)
assert c.lowerCorner.x == -1.0
assert c.lowerCorner.y == -2.0
assert c.lowerCorner.z == -3.0
assert c.upperCorner.x == 4.0
assert c.upperCorner.y == 2.0
assert c.upperCorner.z == 5.0
def test_bounding_box3d_is_empty():
a = pyjet.BoundingBox3D((-2.0, -2.0, 1.0), (4.0, 3.0, 5.0))
assert not a.isEmpty()
| 28.176
| 80
| 0.580068
| 549
| 3,522
| 3.67031
| 0.112933
| 0.023821
| 0.023821
| 0.055583
| 0.787593
| 0.77866
| 0.77866
| 0.77866
| 0.771216
| 0.771216
| 0
| 0.069405
| 0.198183
| 3,522
| 124
| 81
| 28.403226
| 0.644122
| 0.124361
| 0
| 0.771084
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.457831
| 1
| 0.096386
| false
| 0
| 0.012048
| 0
| 0.108434
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
286faf9a1ac9bfd61659e94c803f22d2ed7d7f44
| 3,410
|
py
|
Python
|
networks.py
|
priyavrat-misra/denoising-autoencoder
|
2eec70e4a5502b594da38385f34a9f6271b11168
|
[
"MIT"
] | null | null | null |
networks.py
|
priyavrat-misra/denoising-autoencoder
|
2eec70e4a5502b594da38385f34a9f6271b11168
|
[
"MIT"
] | null | null | null |
networks.py
|
priyavrat-misra/denoising-autoencoder
|
2eec70e4a5502b594da38385f34a9f6271b11168
|
[
"MIT"
] | null | null | null |
import torch.nn as nn
class ConvUpscaleDenoiser(nn.Module):
"""
An autoencoder network which uses
Upscale (with nearest neighbor interpolation)
followed by a Convolutional layer for decoding.
"""
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=32,
kernel_size=5, padding=2, bias=False),
nn.BatchNorm2d(num_features=32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=32, out_channels=16,
kernel_size=5, padding=2, bias=False),
nn.BatchNorm2d(num_features=16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=16, out_channels=4,
kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(num_features=4),
nn.ReLU()
)
self.decoder = nn.Sequential(
nn.Conv2d(in_channels=4, out_channels=16,
kernel_size=3, padding=1),
nn.BatchNorm2d(num_features=16),
nn.ReLU(),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.Conv2d(in_channels=16, out_channels=32,
kernel_size=5, padding=2),
nn.BatchNorm2d(num_features=32),
nn.ReLU(),
nn.Upsample(scale_factor=2, mode='nearest'),
nn.Conv2d(in_channels=32, out_channels=1,
kernel_size=5, padding=2, bias=False),
nn.BatchNorm2d(num_features=1),
nn.Sigmoid()
)
def forward(self, t):
t = self.encoder(t)
t = self.decoder(t)
return t
class ConvTransposeDenoiser(nn.Module):
"""
An autoencoder network which uses
Transpose Convolutional layers for decoding.
"""
def __init__(self):
super().__init__()
self.encoder = nn.Sequential(
nn.Conv2d(in_channels=1, out_channels=32,
kernel_size=5, padding=2, bias=False),
nn.BatchNorm2d(num_features=32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=32, out_channels=16,
kernel_size=5, padding=2, bias=False),
nn.BatchNorm2d(num_features=16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(in_channels=16, out_channels=4,
kernel_size=3, padding=1, bias=False),
nn.BatchNorm2d(num_features=4),
nn.ReLU()
)
self.decoder = nn.Sequential(
nn.ConvTranspose2d(in_channels=4, out_channels=16,
kernel_size=2, padding=0, stride=2, bias=False),
nn.BatchNorm2d(num_features=16),
nn.ReLU(),
nn.ConvTranspose2d(in_channels=16, out_channels=32,
kernel_size=4, padding=1, stride=2, bias=False),
nn.BatchNorm2d(num_features=32),
nn.ReLU(),
nn.Conv2d(in_channels=32, out_channels=1,
kernel_size=5, padding=2, bias=False),
nn.BatchNorm2d(num_features=1),
nn.Sigmoid()
)
def forward(self, t):
t = self.encoder(t)
t = self.decoder(t)
return t
| 34.795918
| 79
| 0.546334
| 400
| 3,410
| 4.4825
| 0.165
| 0.089236
| 0.107083
| 0.160625
| 0.88232
| 0.877301
| 0.873954
| 0.832683
| 0.72058
| 0.72058
| 0
| 0.055013
| 0.339003
| 3,410
| 97
| 80
| 35.154639
| 0.740461
| 0.060411
| 0
| 0.805195
| 0
| 0
| 0.004429
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.051948
| false
| 0
| 0.012987
| 0
| 0.116883
| 0
| 0
| 0
| 0
| null | 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
287fa365306868edc370c81c22fac6e548d74d59
| 3,232
|
py
|
Python
|
pytorch_tabnet/augmentations.py
|
ParikhKadam/tabnet
|
40107a80b0be5ae865d945b85a52e5d99fc19a81
|
[
"MIT"
] | null | null | null |
pytorch_tabnet/augmentations.py
|
ParikhKadam/tabnet
|
40107a80b0be5ae865d945b85a52e5d99fc19a81
|
[
"MIT"
] | null | null | null |
pytorch_tabnet/augmentations.py
|
ParikhKadam/tabnet
|
40107a80b0be5ae865d945b85a52e5d99fc19a81
|
[
"MIT"
] | null | null | null |
import torch
from pytorch_tabnet.utils import define_device
import numpy as np
class RegressionSMOTE():
"""
Apply SMOTE
This will average a percentage p of the elements in the batch with other elements.
The target will be averaged as well (this might work with binary classification
and certain loss), following a beta distribution.
"""
def __init__(self, device_name="auto", p=0.8, alpha=0.5, beta=0.5, seed=0):
""
self.seed = seed
self._set_seed()
self.device = define_device(device_name)
self.alpha = alpha
self.beta = beta
self.p = p
if (p < 0.) or (p > 1.0):
raise ValueError("Value of p should be between 0. and 1.")
def _set_seed(self):
torch.manual_seed(self.seed)
np.random.seed(self.seed)
return
def __call__(self, X, y):
batch_size = X.shape[0]
random_values = torch.rand(batch_size, device=self.device)
idx_to_change = random_values < self.p
# ensure that first element to switch has probability > 0.5
np_betas = np.random.beta(self.alpha, self.beta, batch_size) / 2 + 0.5
random_betas = torch.from_numpy(np_betas).to(self.device).float()
index_permute = torch.randperm(batch_size, device=self.device)
X[idx_to_change] = random_betas[idx_to_change, None] * X[idx_to_change]
X[idx_to_change] += (1 - random_betas[idx_to_change, None]) * X[index_permute][idx_to_change].view(X[idx_to_change].size()) # noqa
y[idx_to_change] = random_betas[idx_to_change, None] * y[idx_to_change]
y[idx_to_change] += (1 - random_betas[idx_to_change, None]) * y[index_permute][idx_to_change].view(y[idx_to_change].size()) # noqa
return X, y
class ClassificationSMOTE():
"""
Apply SMOTE for classification tasks.
This will average a percentage p of the elements in the batch with other elements.
The target will stay unchanged and keep the value of the most important row in the mix.
"""
def __init__(self, device_name="auto", p=0.8, alpha=0.5, beta=0.5, seed=0):
""
self.seed = seed
self._set_seed()
self.device = define_device(device_name)
self.alpha = alpha
self.beta = beta
self.p = p
if (p < 0.) or (p > 1.0):
raise ValueError("Value of p should be between 0. and 1.")
def _set_seed(self):
torch.manual_seed(self.seed)
np.random.seed(self.seed)
return
def __call__(self, X, y):
batch_size = X.shape[0]
random_values = torch.rand(batch_size, device=self.device)
idx_to_change = random_values < self.p
# ensure that first element to switch has probability > 0.5
np_betas = np.random.beta(self.alpha, self.beta, batch_size) / 2 + 0.5
random_betas = torch.from_numpy(np_betas).to(self.device).float()
index_permute = torch.randperm(batch_size, device=self.device)
X[idx_to_change] = random_betas[idx_to_change, None] * X[idx_to_change]
X[idx_to_change] += (1 - random_betas[idx_to_change, None]) * X[index_permute][idx_to_change].view(X[idx_to_change].size()) # noqa
return X, y
| 37.581395
| 139
| 0.64604
| 493
| 3,232
| 4.010142
| 0.20284
| 0.058169
| 0.127972
| 0.048558
| 0.840162
| 0.840162
| 0.826505
| 0.825493
| 0.80779
| 0.789074
| 0
| 0.015957
| 0.243812
| 3,232
| 85
| 140
| 38.023529
| 0.792962
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| 0.109091
| false
| 0
| 0.054545
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| 0.272727
| 0
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| null | 0
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| 1
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0
| 7
|
289e927dd50510c077f532aaf36591bd16981f2a
| 18,393
|
py
|
Python
|
dlp/tests/system/gapic/v2/test_system_dlp_service_v2_vpcsc.py
|
conwaychriscosmo/google-cloud-python
|
8e7b7f8a5f4bb04d13f4d88ec3848f017faf834a
|
[
"Apache-2.0"
] | 1
|
2019-03-26T21:44:51.000Z
|
2019-03-26T21:44:51.000Z
|
dlp/tests/system/gapic/v2/test_system_dlp_service_v2_vpcsc.py
|
conwaychriscosmo/google-cloud-python
|
8e7b7f8a5f4bb04d13f4d88ec3848f017faf834a
|
[
"Apache-2.0"
] | 40
|
2019-07-16T10:04:48.000Z
|
2020-01-20T09:04:59.000Z
|
dlp/tests/system/gapic/v2/test_system_dlp_service_v2_vpcsc.py
|
conwaychriscosmo/google-cloud-python
|
8e7b7f8a5f4bb04d13f4d88ec3848f017faf834a
|
[
"Apache-2.0"
] | 2
|
2019-07-18T00:05:31.000Z
|
2019-11-27T14:17:22.000Z
|
# -*- coding: utf-8 -*-
#
# Copyright 2019 Google LLC
#
# 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
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import pytest
from google.cloud import dlp_v2
from google.cloud.dlp_v2 import enums
from google.cloud.dlp_v2.proto import dlp_pb2
from google.api_core import exceptions
from test_utils.vpcsc_config import vpcsc_config
_VPCSC_PROHIBITED_MESSAGE = "Request is prohibited by organization's policy"
@pytest.fixture(scope="module")
def client():
return dlp_v2.DlpServiceClient()
@pytest.fixture(scope="module")
def name_inside(client):
return client.project_path(vpcsc_config.project_inside)
@pytest.fixture(scope="module")
def name_outside(client):
return client.project_path(vpcsc_config.project_outside)
@pytest.fixture(scope="module")
def content_item():
return dlp_pb2.ContentItem(value="testing")
@pytest.fixture(scope="module")
def bytes_content_item():
return dlp_pb2.ByteContentItem(data=b"DEADBEEF")
@vpcsc_config.skip_unless_inside_vpcsc
def test_inspect_content_inside(client, name_inside, content_item):
client.inspect_content(name_inside, item=content_item) # no perms issue
@vpcsc_config.skip_unless_inside_vpcsc
def test_inspect_content_outside(client, name_outside, content_item):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.inspect_content(name_outside, item=content_item)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@vpcsc_config.skip_unless_inside_vpcsc
def test_redact_image_inside(client, name_inside, bytes_content_item):
with pytest.raises(exceptions.InvalidArgument): # no perms issue
client.redact_image(name_inside, byte_item=bytes_content_item)
@vpcsc_config.skip_unless_inside_vpcsc
def test_redact_image_outside(client, name_outside, bytes_content_item):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.redact_image(name_outside, byte_item=bytes_content_item)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@vpcsc_config.skip_unless_inside_vpcsc
def test_deidentify_content_inside(client, name_inside, content_item):
with pytest.raises(exceptions.InvalidArgument): # no perms issue
client.deidentify_content(name_inside, item=content_item)
@vpcsc_config.skip_unless_inside_vpcsc
def test_deidentify_content_outside(client, name_outside, content_item):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.deidentify_content(name_outside, item=content_item)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@vpcsc_config.skip_unless_inside_vpcsc
def test_reidentify_content_inside(client, name_inside, content_item):
with pytest.raises(exceptions.InvalidArgument): # no perms issue
client.reidentify_content(name_inside, item=content_item)
@vpcsc_config.skip_unless_inside_vpcsc
def test_reidentify_content_outside(client, name_outside, content_item):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.reidentify_content(name_outside, item=content_item)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@pytest.fixture(scope="module")
def inspect_template_path_inside(client):
inspect_template_id = 1234567
return client.project_inspect_template_path(
vpcsc_config.project_inside, inspect_template_id
)
@pytest.fixture(scope="module")
def inspect_template_path_outside(client):
inspect_template_id = 1234567
return client.project_inspect_template_path(
vpcsc_config.project_outside, inspect_template_id
)
@vpcsc_config.skip_unless_inside_vpcsc
class TestCRUDInspectTemplate(object):
@staticmethod
def test_create_inspect_template_inside(client, name_inside):
client.create_inspect_template(name_inside) # no perms issue
@staticmethod
def test_create_inspect_template_outside(client, name_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.create_inspect_template(name_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_list_inspect_templates_inside(client, name_inside):
list(client.list_inspect_templates(name_inside))
@staticmethod
def test_list_inspect_templates_outside(client, name_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
list(client.list_inspect_templates(name_outside))
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_update_inspect_template_inside(client, inspect_template_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.update_inspect_template(inspect_template_path_inside)
@staticmethod
def test_update_inspect_template_outside(client, inspect_template_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.update_inspect_template(inspect_template_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_get_inspect_template_inside(client, inspect_template_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.get_inspect_template(inspect_template_path_inside)
@staticmethod
def test_get_inspect_template_outside(client, inspect_template_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.get_inspect_template(inspect_template_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_delete_inspect_template_inside(client, inspect_template_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.delete_inspect_template(inspect_template_path_inside)
@staticmethod
def test_delete_inspect_template_outside(client, inspect_template_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.delete_inspect_template(inspect_template_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@pytest.fixture(scope="module")
def deidentify_template_path_inside(client):
deidentify_template_id = 1234567
return client.project_deidentify_template_path(
vpcsc_config.project_inside, deidentify_template_id
)
@pytest.fixture(scope="module")
def deidentify_template_path_outside(client):
deidentify_template_id = 1234567
return client.project_deidentify_template_path(
vpcsc_config.project_outside, deidentify_template_id
)
@vpcsc_config.skip_unless_inside_vpcsc
class TestCRUDDeidentifyTemplate(object):
@staticmethod
def test_create_deidentify_template_inside(client, name_inside):
client.create_deidentify_template(name_inside)
@staticmethod
def test_create_deidentify_template_outside(client, name_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.create_deidentify_template(name_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_list_deidentify_templates_inside(client, name_inside):
list(client.list_deidentify_templates(name_inside))
@staticmethod
def test_list_deidentify_templates_outside(client, name_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
list(client.list_deidentify_templates(name_outside))
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_update_deidentify_template_inside(client, deidentify_template_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.update_deidentify_template(deidentify_template_path_inside)
@staticmethod
def test_update_deidentify_template_outside(
client, deidentify_template_path_outside
):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.update_deidentify_template(deidentify_template_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_get_deidentify_template_inside(client, deidentify_template_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.get_deidentify_template(deidentify_template_path_inside)
@staticmethod
def test_get_deidentify_template_outside(client, deidentify_template_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.get_deidentify_template(deidentify_template_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_delete_deidentify_template_inside(client, deidentify_template_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.delete_deidentify_template(deidentify_template_path_inside)
@staticmethod
def test_delete_deidentify_template_outside(
client, deidentify_template_path_outside
):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.delete_deidentify_template(deidentify_template_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@pytest.fixture(scope="module")
def job_path_inside(name_inside):
job_id = 1234567
return "{}/jobs/{}".format(name_inside, job_id)
@pytest.fixture(scope="module")
def job_path_outside(name_outside):
job_id = 1234567
return "{}/jobs/{}".format(name_outside, job_id)
@pytest.fixture(scope="module")
def inspect_job():
from google.cloud.dlp_v2.proto.dlp_pb2 import InspectJobConfig
return InspectJobConfig()
@vpcsc_config.skip_unless_inside_vpcsc
class TestCRUDDlpJob(object):
@staticmethod
def test_create_dlp_job_inside(client, name_inside, inspect_job):
with pytest.raises(exceptions.InvalidArgument): # no perms issue
client.create_dlp_job(name_inside, inspect_job=inspect_job)
@staticmethod
def test_create_dlp_job_outside(client, name_outside, inspect_job):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.create_dlp_job(name_outside, inspect_job=inspect_job)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_list_dlp_jobs_inside(client, name_inside):
list(client.list_dlp_jobs(name_inside))
@staticmethod
def test_list_dlp_jobs_outside(client, name_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
list(client.list_dlp_jobs(name_outside))
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_get_dlp_job_inside(client, job_path_inside):
with pytest.raises(exceptions.InvalidArgument): # no perms issue
client.get_dlp_job(job_path_inside)
@staticmethod
def test_get_dlp_job_outside(client, job_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.get_dlp_job(job_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_delete_dlp_job_inside(client, job_path_inside):
with pytest.raises(exceptions.InvalidArgument): # no perms issue
client.delete_dlp_job(job_path_inside)
@staticmethod
def test_delete_dlp_job_outside(client, job_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.delete_dlp_job(job_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_cancel_dlp_job_inside(client, job_path_inside):
with pytest.raises(exceptions.InvalidArgument): # no perms issue
client.cancel_dlp_job(job_path_inside)
@staticmethod
def test_cancel_dlp_job_outside(client, job_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.cancel_dlp_job(job_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@pytest.fixture(scope="module")
def job_trigger_path_inside(client):
job_trigger_id = 1234567
return client.project_job_trigger_path(vpcsc_config.project_inside, job_trigger_id)
@pytest.fixture(scope="module")
def job_trigger_path_outside(client):
job_trigger_id = 1234567
return client.project_job_trigger_path(vpcsc_config.project_outside, job_trigger_id)
@vpcsc_config.skip_unless_inside_vpcsc
class TestCRUDJobTrigger(object):
@staticmethod
def test_create_job_trigger_inside(client, name_inside):
with pytest.raises(exceptions.InvalidArgument): # no perms issue
client.create_job_trigger(name_inside)
@staticmethod
def test_create_job_trigger_outside(client, name_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.create_job_trigger(name_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_list_job_triggers_inside(client, name_inside):
list(client.list_job_triggers(name_inside))
@staticmethod
def test_list_job_triggers_outside(client, name_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
list(client.list_job_triggers(name_outside))
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_get_job_trigger_inside(client, job_trigger_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.get_job_trigger(job_trigger_path_inside)
@staticmethod
def test_get_job_trigger_outside(client, job_trigger_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.get_job_trigger(job_trigger_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_update_job_trigger_inside(client, job_trigger_path_inside):
with pytest.raises(exceptions.InvalidArgument): # no perms issue
client.update_job_trigger(job_trigger_path_inside)
@staticmethod
def test_update_job_trigger_outside(client, job_trigger_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.update_job_trigger(job_trigger_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_delete_job_trigger_inside(client, job_trigger_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.delete_job_trigger(job_trigger_path_inside)
@staticmethod
def test_delete_job_trigger_outside(client, job_trigger_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.delete_job_trigger(job_trigger_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@pytest.fixture(scope="module")
def stored_info_type_path_inside(client):
stored_info_type_id = 1234567
return client.project_stored_info_type_path(
vpcsc_config.project_inside, stored_info_type_id
)
@pytest.fixture(scope="module")
def stored_info_type_path_outside(client):
stored_info_type_id = 1234567
return client.project_stored_info_type_path(
vpcsc_config.project_outside, stored_info_type_id
)
@vpcsc_config.skip_unless_inside_vpcsc
class TestCRUDStoredInfoType(object):
@staticmethod
def test_create_stored_info_type_inside(client, name_inside):
with pytest.raises(exceptions.InvalidArgument): # no perms issue
client.create_stored_info_type(name_inside)
@staticmethod
def test_create_stored_info_type_outside(client, name_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.create_stored_info_type(name_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_list_stored_info_types_inside(client, name_inside):
list(client.list_stored_info_types(name_inside))
@staticmethod
def test_list_stored_info_types_outside(client, name_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
list(client.list_stored_info_types(name_outside))
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_get_stored_info_type_inside(client, stored_info_type_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.get_stored_info_type(stored_info_type_path_inside)
@staticmethod
def test_get_stored_info_type_outside(client, stored_info_type_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.get_stored_info_type(stored_info_type_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_update_stored_info_type_inside(client, stored_info_type_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.update_stored_info_type(stored_info_type_path_inside)
@staticmethod
def test_update_stored_info_type_outside(client, stored_info_type_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.update_stored_info_type(stored_info_type_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
@staticmethod
def test_delete_stored_info_type_inside(client, stored_info_type_path_inside):
with pytest.raises(exceptions.NotFound): # no perms issue
client.delete_stored_info_type(stored_info_type_path_inside)
@staticmethod
def test_delete_stored_info_type_outside(client, stored_info_type_path_outside):
with pytest.raises(exceptions.PermissionDenied) as exc:
client.delete_stored_info_type(stored_info_type_path_outside)
assert _VPCSC_PROHIBITED_MESSAGE in exc.value.message
| 36.786
| 88
| 0.774208
| 2,310
| 18,393
| 5.775325
| 0.0671
| 0.030433
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| 0.097444
| 0.898209
| 0.872049
| 0.813207
| 0.757739
| 0.715464
| 0.66037
| 0
| 0.005699
| 0.16055
| 18,393
| 499
| 89
| 36.859719
| 0.858355
| 0.049747
| 0
| 0.514451
| 0
| 0
| 0.01015
| 0
| 0
| 0
| 0
| 0
| 0.083815
| 1
| 0.213873
| false
| 0
| 0.023121
| 0.014451
| 0.297688
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
9544b135c3d4203e498cc874081098a5aec23fa1
| 147
|
py
|
Python
|
pyphonetics/phonetics/__init__.py
|
XinliYu/pyphonetics
|
c5ab0a68e273906a407cc102c72512943fbc17fb
|
[
"MIT"
] | 90
|
2016-11-18T10:36:31.000Z
|
2022-03-14T02:50:54.000Z
|
wltr_phonetics/phonetics/__init__.py
|
walteravelino/wltr-phonetics
|
986c7e338517e1c48fbd09d42ab09b2b80d18300
|
[
"CNRI-Python"
] | 4
|
2018-04-02T11:03:10.000Z
|
2021-04-26T14:08:22.000Z
|
wltr_phonetics/phonetics/__init__.py
|
walteravelino/wltr-phonetics
|
986c7e338517e1c48fbd09d42ab09b2b80d18300
|
[
"CNRI-Python"
] | 21
|
2017-02-16T04:14:39.000Z
|
2022-03-08T01:58:29.000Z
|
from .soundex import *
from .metaphone import *
from .mra import *
from .fuzzy_soundex import *
from .lein import *
from .refined_soundex import *
| 21
| 30
| 0.755102
| 20
| 147
| 5.45
| 0.4
| 0.458716
| 0.311927
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.163265
| 147
| 6
| 31
| 24.5
| 0.886179
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
955e907619aeaa3d66f92f7b2e49fc4aeab6c4e7
| 189
|
py
|
Python
|
cryptos/coins/__init__.py
|
mattura/pybitcointools
|
bfc46b2e77beeb062fb4879ac488fba58bdd7373
|
[
"MIT"
] | null | null | null |
cryptos/coins/__init__.py
|
mattura/pybitcointools
|
bfc46b2e77beeb062fb4879ac488fba58bdd7373
|
[
"MIT"
] | null | null | null |
cryptos/coins/__init__.py
|
mattura/pybitcointools
|
bfc46b2e77beeb062fb4879ac488fba58bdd7373
|
[
"MIT"
] | 1
|
2021-11-18T09:23:41.000Z
|
2021-11-18T09:23:41.000Z
|
from .bitcoin import *
from .bitcoin_cash import *
from .bitcoin_gold import *
from .dash import *
from .dogecoin import *
from .litecoin import *
from .zcash import *
from .ripple import *
| 23.625
| 27
| 0.751323
| 26
| 189
| 5.384615
| 0.384615
| 0.5
| 0.242857
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.164021
| 189
| 8
| 28
| 23.625
| 0.886076
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
9585ed4d3ae4eb6dcf6ba2f6ece8d826838dbecc
| 1,881
|
py
|
Python
|
Lambda/ProductsDBProcessing/b24_interface/query_builder.py
|
BedrosovaYulia/AWS-2020
|
7d6c9d4403594ab5f92cffb7eddb190eeca6b93c
|
[
"MIT"
] | 4
|
2020-04-23T10:59:02.000Z
|
2021-09-24T09:22:51.000Z
|
Lambda/ProductsDBProcessing/b24_interface/query_builder.py
|
BedrosovaYulia/AWS-2020
|
7d6c9d4403594ab5f92cffb7eddb190eeca6b93c
|
[
"MIT"
] | null | null | null |
Lambda/ProductsDBProcessing/b24_interface/query_builder.py
|
BedrosovaYulia/AWS-2020
|
7d6c9d4403594ab5f92cffb7eddb190eeca6b93c
|
[
"MIT"
] | 1
|
2020-05-05T21:17:45.000Z
|
2020-05-05T21:17:45.000Z
|
class B24QueryBuilder:
@staticmethod
def add_b24_product(name, price, file_url, xml_id):
"""(str, str, str, str) -> str
The function prepares a urlencoded string of request parameters
for the subsequent call of the Bitrix24 rest-api.
"""
import requests
import base64
content1=requests.get(file_url).content
image_64_encode = str(base64.b64encode(content1))[2:-1]
product_data = {
"fields" : {
"iblockId": 1,
"NAME" : name,
"CURRENCY_ID": "RUB",
"PRICE" : price,
"XML_ID":xml_id,
"PREVIEW_PICTURE": {
"fileData":dict()
}
}
}
product_data["fields"]["PREVIEW_PICTURE"]["fileData"]['0']="1.png"
product_data["fields"]["PREVIEW_PICTURE"]["fileData"]['1']=image_64_encode
return product_data
@staticmethod
def update_b24_product(bitrix_id, name, price, file_url, xml_id):
"""(str, str, str, str, str) -> str
The function prepares a urlencoded string of request parameters
for the subsequent call of the Bitrix24 rest-api.
"""
import requests
import base64
import boto3
content1=requests.get(file_url).content
image_64_encode = str(base64.b64encode(content1))[2:-1]
product_data = {
"id":bitrix_id,
"fields" : {
"iblockId": 1,
"NAME" : name,
"CURRENCY_ID": "RUB",
"PRICE" : price,
"XML_ID":xml_id,
"PREVIEW_PICTURE": {
"fileData":dict()
}
}
}
product_data["fields"]["PREVIEW_PICTURE"]["fileData"]['0']="1.png"
product_data["fields"]["PREVIEW_PICTURE"]["fileData"]['1']=image_64_encode
return product_data
| 28.5
| 80
| 0.545986
| 200
| 1,881
| 4.935
| 0.28
| 0.054711
| 0.06383
| 0.06079
| 0.890578
| 0.890578
| 0.890578
| 0.890578
| 0.890578
| 0.890578
| 0
| 0.037242
| 0.32908
| 1,881
| 65
| 81
| 28.938462
| 0.744849
| 0.154173
| 0
| 0.755556
| 0
| 0
| 0.171206
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.044444
| false
| 0
| 0.111111
| 0
| 0.222222
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
95cfc9068a75ccd47e3355ab42d8bf626842e8ba
| 9,241
|
py
|
Python
|
tests/test_AspenHandlerODBC.py
|
g-parki/tagreader-python
|
dba867d0ac1e05166e5b0cc3e42557264280727f
|
[
"MIT"
] | 23
|
2020-04-16T17:23:33.000Z
|
2022-03-31T21:44:10.000Z
|
tests/test_AspenHandlerODBC.py
|
g-parki/tagreader-python
|
dba867d0ac1e05166e5b0cc3e42557264280727f
|
[
"MIT"
] | 62
|
2020-05-27T11:25:23.000Z
|
2022-03-11T07:03:48.000Z
|
tests/test_AspenHandlerODBC.py
|
g-parki/tagreader-python
|
dba867d0ac1e05166e5b0cc3e42557264280727f
|
[
"MIT"
] | 10
|
2020-08-18T08:24:27.000Z
|
2022-03-08T20:53:59.000Z
|
import pytest
import pandas as pd
from tagreader import utils
from tagreader.utils import ReaderType
from tagreader.odbc_handlers import AspenHandlerODBC
START_TIME = "2018-01-17 16:00:00"
STOP_TIME = "2018-01-17 17:00:00"
SAMPLE_TIME = 60
@pytest.fixture(scope="module")
def AspenHandler():
from tagreader.odbc_handlers import AspenHandlerODBC
yield AspenHandlerODBC("thehostname", 1234, options={"max_rows": 567890})
def test_generate_connection_string(AspenHandler):
res = AspenHandler.generate_connection_string()
expected = (
"DRIVER={AspenTech SQLPlus};HOST=thehostname;PORT=1234;"
"READONLY=Y;MAXROWS=567890"
)
assert expected == res
@pytest.mark.parametrize(
"read_type",
[
"RAW",
# pytest.param(
# "SHAPEPRESERVING", marks=pytest.mark.skip(reason="Not implemented")
# ),
"INT",
"MIN",
"MAX",
"RNG",
"AVG",
"STD",
"VAR",
# pytest.param("COUNT", marks=pytest.mark.skip(reason="Not implemented")),
# pytest.param("GOOD", marks=pytest.mark.skip(reason="Not implemented")),
# pytest.param("BAD", marks=pytest.mark.skip(reason="Not implemented")),
# pytest.param("TOTAL", marks=pytest.mark.skip(reason="Not implemented")),
# pytest.param("SUM", marks=pytest.mark.skip(reason="Not implemented")),
"SNAPSHOT",
],
)
def test_generate_tag_read_query(read_type):
starttime = utils.ensure_datetime_with_tz(START_TIME)
stoptime = utils.ensure_datetime_with_tz(STOP_TIME)
ts = pd.Timedelta(SAMPLE_TIME, unit="s")
if read_type == "SNAPSHOT":
res = AspenHandlerODBC.generate_read_query(
"thetag", None, None, None, None, getattr(ReaderType, read_type)
)
else:
res = AspenHandlerODBC.generate_read_query(
"thetag", None, starttime, stoptime, ts, getattr(ReaderType, read_type)
)
expected = {
"RAW": (
'SELECT ISO8601(ts) AS "time", value AS "value" FROM history WHERE '
"name = 'thetag' AND (request = 4) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"INT": (
'SELECT ISO8601(ts) AS "time", value AS "value" FROM history WHERE '
"name = 'thetag' AND (period = 600) AND (request = 7) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"MIN": (
'SELECT ISO8601(ts_start) AS "time", min AS "value" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"MAX": (
'SELECT ISO8601(ts_start) AS "time", max AS "value" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"RNG": (
'SELECT ISO8601(ts_start) AS "time", rng AS "value" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"AVG": (
'SELECT ISO8601(ts_start) AS "time", avg AS "value" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"STD": (
'SELECT ISO8601(ts_start) AS "time", std AS "value" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"VAR": (
'SELECT ISO8601(ts_start) AS "time", var AS "value" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"SNAPSHOT": (
'SELECT ISO8601(IP_INPUT_TIME) AS "time", IP_INPUT_VALUE AS "value" '
'FROM "thetag"'
),
}
assert expected[read_type] == res
@pytest.mark.parametrize(
"read_type",
[
"RAW",
# pytest.param(
# "SHAPEPRESERVING", marks=pytest.mark.skip(reason="Not implemented")
# ),
"INT",
"MIN",
"MAX",
"RNG",
"AVG",
"STD",
"VAR",
# pytest.param("COUNT", marks=pytest.mark.skip(reason="Not implemented")),
# pytest.param("GOOD", marks=pytest.mark.skip(reason="Not implemented")),
# pytest.param("BAD", marks=pytest.mark.skip(reason="Not implemented")),
# pytest.param("TOTAL", marks=pytest.mark.skip(reason="Not implemented")),
# pytest.param("SUM", marks=pytest.mark.skip(reason="Not implemented")),
# pytest.param("SNAPSHOT", marks=pytest.mark.skip(reason="Not implemented")),
],
)
def test_generate_tag_read_query_with_status(read_type):
starttime = utils.ensure_datetime_with_tz(START_TIME)
stoptime = utils.ensure_datetime_with_tz(STOP_TIME)
ts = pd.Timedelta(SAMPLE_TIME, unit="s")
if read_type == "SNAPSHOT":
res = AspenHandlerODBC.generate_read_query(
"thetag",
None,
None,
None,
None,
getattr(ReaderType, read_type),
get_status=True,
)
else:
res = AspenHandlerODBC.generate_read_query(
"thetag",
None,
starttime,
stoptime,
ts,
getattr(ReaderType, read_type),
get_status=True,
)
expected = {
"RAW": (
'SELECT ISO8601(ts) AS "time", value AS "value" '
', status AS "status" FROM history WHERE '
"name = 'thetag' AND (request = 4) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"INT": (
'SELECT ISO8601(ts) AS "time", value AS "value" '
', status AS "status" FROM history WHERE '
"name = 'thetag' AND (period = 600) AND (request = 7) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"MIN": (
'SELECT ISO8601(ts_start) AS "time", min AS "value" '
', status AS "status" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"MAX": (
'SELECT ISO8601(ts_start) AS "time", max AS "value" '
', status AS "status" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"RNG": (
'SELECT ISO8601(ts_start) AS "time", rng AS "value" '
', status AS "status" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"AVG": (
'SELECT ISO8601(ts_start) AS "time", avg AS "value" '
', status AS "status" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"STD": (
'SELECT ISO8601(ts_start) AS "time", std AS "value" '
', status AS "status" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
"VAR": (
'SELECT ISO8601(ts_start) AS "time", var AS "value" '
', status AS "status" FROM aggregates WHERE '
"name = 'thetag' AND (period = 600) AND (request = 1) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
),
}
assert expected[read_type] == res
def test_genreadquery_long_sampletime():
starttime = utils.ensure_datetime_with_tz(START_TIME)
stoptime = utils.ensure_datetime_with_tz(STOP_TIME)
ts = pd.Timedelta(86401, unit="s")
res = AspenHandlerODBC.generate_read_query(
"thetag", None, starttime, stoptime, ts, ReaderType.INT
)
expected = (
'SELECT ISO8601(ts) AS "time", value AS "value" FROM history WHERE '
"name = 'thetag' AND (period = 864010) AND (request = 7) "
"AND (ts BETWEEN '2018-01-17T15:00:00Z' AND '2018-01-17T16:00:00Z') "
"ORDER BY ts"
)
assert expected == res
| 37.11245
| 87
| 0.550914
| 1,107
| 9,241
| 4.517615
| 0.108401
| 0.043191
| 0.05099
| 0.061188
| 0.885023
| 0.878424
| 0.84883
| 0.835833
| 0.835833
| 0.833633
| 0
| 0.104375
| 0.307434
| 9,241
| 248
| 88
| 37.262097
| 0.677031
| 0.105292
| 0
| 0.742857
| 0
| 0.080952
| 0.480848
| 0.102788
| 0
| 0
| 0
| 0
| 0.019048
| 1
| 0.02381
| false
| 0
| 0.028571
| 0
| 0.052381
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
25013501c64c843cc02044e3073d40040f81c85b
| 17,085
|
py
|
Python
|
tests/bd_caller_tests.py
|
Brown-University-Library/easyborrow_controller
|
31bb8e94998012d4cecd250245000e5631149dfa
|
[
"Unlicense",
"MIT"
] | null | null | null |
tests/bd_caller_tests.py
|
Brown-University-Library/easyborrow_controller
|
31bb8e94998012d4cecd250245000e5631149dfa
|
[
"Unlicense",
"MIT"
] | 1
|
2015-10-05T21:00:03.000Z
|
2015-10-05T21:00:03.000Z
|
tests/bd_caller_tests.py
|
birkin/easyborrow_controller
|
31bb8e94998012d4cecd250245000e5631149dfa
|
[
"MIT",
"Unlicense"
] | null | null | null |
import logging, unittest
from easyborrow_controller_code.classes.bd_api_caller import BD_CallerBib
logging.basicConfig(
filename='',
level=logging.DEBUG,
format='[%(asctime)s] %(levelname)s [%(module)s-%(funcName)s()::%(lineno)d] %(message)s',
datefmt='%d/%b/%Y %H:%M:%S' )
logger = logging.getLogger(__name__)
class BD_CallerBibTest(unittest.TestCase):
def setUp(self):
self.caller = BD_CallerBib()
self.expectations = [
{
'o_url': '''http://landing_page/?sid=FirstSearch%3AWorldCat&genre=book&isbn=9780688002305&title=Zen+and+the+art+of+motorcycle+maintenance%3A+an+inquiry+into+values%2C&date=1974&aulast=Pirsig&aufirst=Robert&auinitm=M&id=doi%3A&pid=%3Caccession+number%3E673595%3C%2Faccession+number%3E%3Cfssessid%3E0%3C%2Ffssessid%3E&url_ver=Z39.88-2004&rfr_id=info%3Asid%2Ffirstsearch.oclc.org%3AWorldCat&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&req_dat=%3Csessionid%3E0%3C%2Fsessionid%3E&rfe_dat=%3Caccessionnumber%3E673595%3C%2Faccessionnumber%3E&rft_id=info%3Aoclcnum%2F673595&rft_id=urn%3AISBN%3A9780688002305&rft.aulast=Pirsig&rft.aufirst=Robert&rft.auinitm=M&rft.btitle=Zen+and+the+art+of+motorcycle+maintenance%3A+an+inquiry+into+values%2C&rft.date=1974&rft.isbn=9780688002305&rft.place=New+York&rft.pub=Morrow&rft.genre=book&checksum=808d331299c7cb13bc0e9179eb80ced5''',
'expected_title': 'Zen and the art of motorcycle maintenance: an inquiry into values,',
'expected_author': 'Pirsig, Robert',
'expected_year': '1974' },
]
def test_extract_title(self):
for entry in self.expectations:
bib_dct = self.caller.extract_bib( entry['o_url'] )
self.assertEqual( entry['expected_title'], bib_dct['response']['bib']['title'] )
def test_extract_author(self):
for entry in self.expectations:
bib_dct = self.caller.extract_bib( entry['o_url'] )
self.assertEqual( entry['expected_author'], bib_dct['response']['bib']['author'][0]['name'] )
def test_extract_year(self):
for entry in self.expectations:
bib_dct = self.caller.extract_bib( entry['o_url'] )
self.assertEqual( entry['expected_year'], bib_dct['response']['bib']['year'] )
## end class BD_CallerBibTest()
# class BDRunnerTest(unittest.TestCase):
# def setUp(self):
# uc_instance = UtilityCode.UtilityCode()
# self.prep = {
# 'EB_REQUEST_NUM': 'unit_test_%s' % datetime.datetime.now(),
# 'API_URL': 'init',
# 'API_AUTH_CODE': 'init',
# 'API_IDENTITY': 'init',
# 'UNIVERSITY': 'init',
# 'USER_BARCODE': 'init',
# 'ISBN': 'init',
# 'WC_URL': 'init',
# 'OPENURL_PARSER_URL': 'init',
# 'UC_INSTANCE': uc_instance,
# }
# def test_init(self):
# ## all good unicode
# prep = self.prep
# bd = BD_Runner( prep )
# self.assertEqual( bd.API_AUTH_CODE, 'init' )
# ## oops, a string
# prep['API_AUTH_CODE'] = 'str'
# try:
# bd = BD_Runner( prep )
# except Exception, e:
# self.assertEqual( repr(e), "AssertionError(Exception('API_AUTH_CODE must be unicode',),)" ) # unicode(e) doesn't work
# def test_determineSearchType(self):
# prep = self.prep
# ## string search
# prep['ISBN'] = ''
# bd = BD_Runner( prep )
# bd.determineSearchType()
# assert bd.search_type == 'string', bd.search_type
# ## isbn search
# prep['ISBN'] = 'abc'
# bd = BD_Runner( prep )
# bd.determineSearchType()
# assert bd.search_type == 'isbn', bd.search_type
# def test_makeSearchString_WcUrlComplete(self):
# prep = self.prep
# prep['OPENURL_PARSER_URL'] = controller_settings.OPENURL_PARSER_URL
# prep['WC_URL'] = 'http://landing_page/?sid=FirstSearch%3AWorldCat&genre=book&isbn=9780688002305&title=Zen+and+the+art+of+motorcycle+maintenance%3A+an+inquiry+into+values%2C&date=1974&aulast=Pirsig&aufirst=Robert&auinitm=M&id=doi%3A&pid=%3Caccession+number%3E673595%3C%2Faccession+number%3E%3Cfssessid%3E0%3C%2Ffssessid%3E&url_ver=Z39.88-2004&rfr_id=info%3Asid%2Ffirstsearch.oclc.org%3AWorldCat&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&req_dat=%3Csessionid%3E0%3C%2Fsessionid%3E&rfe_dat=%3Caccessionnumber%3E673595%3C%2Faccessionnumber%3E&rft_id=info%3Aoclcnum%2F673595&rft_id=urn%3AISBN%3A9780688002305&rft.aulast=Pirsig&rft.aufirst=Robert&rft.auinitm=M&rft.btitle=Zen+and+the+art+of+motorcycle+maintenance%3A+an+inquiry+into+values%2C&rft.date=1974&rft.isbn=9780688002305&rft.place=New+York&rft.pub=Morrow&rft.genre=book&checksum=808d331299c7cb13bc0e9179eb80ced5'
# bd = BD_Runner( prep )
# assert bd.string_good_to_go == None, bd.string_good_to_go
# bd.makeSearchString()
# d = json.loads( bd.worldcat_url_parsed_response )
# assert sorted(d.keys()) == ['doc_url', 'request', 'response'], sorted(d.keys())
# assert sorted(d['request'].keys()) == ['db_wc_url', 'time'], sorted(d['request'].keys()) # 'db_wc_url' means 'the worldcat url in the database'
# assert sorted(d['response'].keys()) == ['bd_author', 'bd_date', 'bd_title', 'time_taken'], sorted(d['response'].keys())
# assert bd.string_good_to_go == True, bd.string_good_to_go
# assert bd.string_title == 'zen and the art of motorcycle maintenance an inquiry into values', bd.string_title
# assert bd.string_author == 'pirsig robert m', bd.string_author
# assert bd.string_date == '1974', bd.string_date
# def test_makeSearchString_WcUrlComplete_sSolutionsUrl(self):
# prep = self.prep
# prep['OPENURL_PARSER_URL'] = controller_settings.OPENURL_PARSER_URL
# prep['WC_URL'] = 'http://rl3tp7zf5x.search.serialssolutions.com/?rft.pub=The+Modern+library&rft_val_fmt=info%3Aofi/fmt%3Akev%3Amtx%3Abook&rfr_id=info%3Asid/info%3Asid/firstsearch.oclc.org%3AWorldCat&rft.au=Lawrence%2C+D&rft.place=New+York&rft_id=http%3A//www.worldcat.org/oclc/190929&rft.date=1943&rft.btitle=The+rainbow&ctx_ver=Z39.88-2004&rft.genre=book'
# bd = BD_Runner( prep )
# assert bd.string_good_to_go == None, bd.string_good_to_go
# bd.makeSearchString()
# d = json.loads( bd.worldcat_url_parsed_response )
# assert sorted(d.keys()) == ['doc_url', 'request', 'response'], sorted(d.keys())
# assert sorted(d['request'].keys()) == ['db_wc_url', 'time'], sorted(d['request'].keys()) # 'db_wc_url' means 'the worldcat url in the database'
# assert sorted(d['response'].keys()) == ['bd_author', 'bd_date', 'bd_title', 'time_taken'], sorted(d['response'].keys())
# assert bd.string_good_to_go == True, bd.string_good_to_go
# assert bd.string_title == 'the rainbow', bd.string_title
# assert bd.string_author == 'lawrence d', bd.string_author
# assert bd.string_date == '1943', bd.string_date
# def test_makeSearchString_WcUrlCompleteUnicode(self):
# prep = self.prep
# prep['OPENURL_PARSER_URL'] = controller_settings.OPENURL_PARSER_URL
# prep['WC_URL'] = 'http://landing_page/?sid=FirstSearch%3AWorldCat&genre=book&isbn=9788373015364&title=Zen+i+sztuka+obs%C5%82ugi+motocykla+%3A+rozprawa+o+wartos%CC%81ciach&date=2010&aulast=Pirsig&aufirst=Robert&auinitm=M&id=doi%3A&pid=%3Caccession+number%3E751241832%3C%2Faccession+number%3E%3Cfssessid%3E0%3C%2Ffssessid%3E%3Cedition%3EWyd.+3+popr.+i+uzup.+%28dodr.%29.%3C%2Fedition%3E&url_ver=Z39.88-2004&rfr_id=info%3Asid%2Ffirstsearch.oclc.org%3AWorldCat&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&req_dat=%3Csessionid%3E0%3C%2Fsessionid%3E&rfe_dat=%3Caccessionnumber%3E751241832%3C%2Faccessionnumber%3E&rft_id=info%3Aoclcnum%2F751241832&rft_id=urn%3AISBN%3A9788373015364&rft.aulast=Pirsig&rft.aufirst=Robert&rft.auinitm=M&rft.btitle=Zen+i+sztuka+obs%C5%82ugi+motocykla+%3A+rozprawa+o+wartos%CC%81ciach&rft.date=2010&rft.isbn=9788373015364&rft.place=Poznan%CC%81&rft.pub=Dom+Wydawniczy+%22Rebis%22&rft.edition=Wyd.+3+popr.+i+uzup.+%28dodr.%29.&rft.genre=book&checksum=6630a2eec9ef8606be1eebb5a833dc96&title=Brown University&linktype=openurl&detail=RBN'
# bd = BD_Runner( prep )
# assert bd.string_good_to_go == None, bd.string_good_to_go
# bd.makeSearchString()
# d = json.loads( bd.worldcat_url_parsed_response )
# assert sorted(d.keys()) == ['doc_url', 'request', 'response'], sorted(d.keys())
# assert sorted(d['request'].keys()) == ['db_wc_url', 'time'], sorted(d['request'].keys()) # 'db_wc_url' means 'the worldcat url in the database'
# assert sorted(d['response'].keys()) == ['bd_author', 'bd_date', 'bd_title', 'time_taken'], sorted(d['response'].keys())
# assert bd.string_good_to_go == True, bd.string_good_to_go
# assert bd.string_title == 'zen i sztuka obs\xe5ugi motocykla rozprawa o wartos\xecciach', bd.string_title
# assert bd.string_author == 'pirsig robert m', bd.string_author
# assert bd.string_date == '2010', bd.string_date
# def test_makeSearchString_WcUrlNoAuthor(self):
# prep = self.prep
# prep['OPENURL_PARSER_URL'] = controller_settings.OPENURL_PARSER_URL
# prep['WC_URL'] = 'http://landing_page/?sid=FirstSearch%3AWorldCat&genre=book&isbn=9780945612315&title=Pillars+of+salt+%3A+an+anthology+of+early+American+criminal+narratives&date=1993&id=doi%3A&pid=%3Caccession+number%3E26261487%3C%2Faccession+number%3E%3Cfssessid%3E0%3C%2Ffssessid%3E%3Cedition%3E1st+ed.%3C%2Fedition%3E&url_ver=Z39.88-2004&rfr_id=info%3Asid%2Ffirstsearch.oclc.org%3AWorldCat&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&req_dat=%3Csessionid%3E0%3C%2Fsessionid%3E&rfe_dat=%3Caccessionnumber%3E26261487%3C%2Faccessionnumber%3E&rft_id=info%3Aoclcnum%2F26261487&rft_id=urn%3AISBN%3A9780945612315&rft.btitle=Pillars+of+salt+%3A+an+anthology+of+early+American+criminal+narratives&rft.date=1993&rft.isbn=9780945612315&rft.place=Madison++Wis.&rft.pub=Madison+House&rft.edition=1st+ed.&rft.genre=book&checksum=1298deb7cd90f280b1b2092d4de3878a&title=Brown University&linktype=openurl&detail=RBN'
# bd = BD_Runner( prep )
# assert bd.string_good_to_go == None, bd.string_good_to_go
# bd.makeSearchString()
# assert bd.string_good_to_go == False, bd.string_good_to_go
# def test_prepRequestData_WcUrlComplete(self):
# prep = self.prep
# prep['OPENURL_PARSER_URL'] = controller_settings.OPENURL_PARSER_URL
# prep['WC_URL'] = 'http://landing_page/?sid=FirstSearch%3AWorldCat&genre=book&title=The+rainbow&date=1943&aulast=Lawrence&aufirst=D&auinitm=H&id=doi%3A&pid=%3Caccession+number%3E190929%3C%2Faccession+number%3E%3Cfssessid%3E0%3C%2Ffssessid%3E&url_ver=Z39.88-2004&rfr_id=info%3Asid%2Ffirstsearch.oclc.org%3AWorldCat&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&req_dat=%3Csessionid%3E0%3C%2Fsessionid%3E&rfe_dat=%3Caccessionnumber%3E190929%3C%2Faccessionnumber%3E&rft_id=info%3Aoclcnum%2F190929&rft.aulast=Lawrence&rft.aufirst=D&rft.auinitm=H&rft.btitle=The+rainbow&rft.date=1943&rft.place=New+York&rft.pub=The+Modern+library&rft.genre=book&checksum=489a99eea1a541da55943b8a6d7c7bdd&title=Brown University&linktype=openurl&detail=RBN'
# bd = BD_Runner( prep )
# bd.search_type = 'string'
# bd.prepRequestData()
# assert type(bd.prepared_data_dict) == dict, type(bd.prepared_data_dict)
# d = bd.prepared_data_dict
# assert sorted(d.keys()) == ['api_authorization_code', 'api_identity', 'author', 'command', 'date', 'title', 'university', 'user_barcode'], sorted(d.keys())
# def test_prepRequestData_WcUrlNoAuthor(self):
# prep = self.prep
# prep['OPENURL_PARSER_URL'] = controller_settings.OPENURL_PARSER_URL
# prep['WC_URL'] = 'http://landing_page/?sid=FirstSearch%3AWorldCat&genre=book&isbn=9780945612315&title=Pillars+of+salt+%3A+an+anthology+of+early+American+criminal+narratives&date=1993&id=doi%3A&pid=%3Caccession+number%3E26261487%3C%2Faccession+number%3E%3Cfssessid%3E0%3C%2Ffssessid%3E%3Cedition%3E1st+ed.%3C%2Fedition%3E&url_ver=Z39.88-2004&rfr_id=info%3Asid%2Ffirstsearch.oclc.org%3AWorldCat&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&req_dat=%3Csessionid%3E0%3C%2Fsessionid%3E&rfe_dat=%3Caccessionnumber%3E26261487%3C%2Faccessionnumber%3E&rft_id=info%3Aoclcnum%2F26261487&rft_id=urn%3AISBN%3A9780945612315&rft.btitle=Pillars+of+salt+%3A+an+anthology+of+early+American+criminal+narratives&rft.date=1993&rft.isbn=9780945612315&rft.place=Madison++Wis.&rft.pub=Madison+House&rft.edition=1st+ed.&rft.genre=book&checksum=1298deb7cd90f280b1b2092d4de3878a&title=Brown University&linktype=openurl&detail=RBN'
# bd = BD_Runner( prep )
# bd.search_type = 'string'
# bd.prepRequestData()
# assert bd.prepared_data_dict == 'skip_to_illiad', bd.prepared_data_dict
# def test_updateHistoryTable_noRequest( self ):
# prep = self.prep
# prep['EB_REQUEST_NUM'] = '123'
# bd = BD_Runner( prep )
# assert bd.history_table_updated == None, bd.history_table_updated
# bd.prepared_data_dict = 'skip_to_illiad'
# bd.updateHistoryTable()
# assert bd.history_table_message == 'no_valid_string', bd.history_table_message
# assert bd.history_table_updated == True, bd.history_table_updated
# def test_updateHistoryTable_unsuccessfulRequest( self ):
# prep = self.prep
# prep['EB_REQUEST_NUM'] = '123'
# prep['API_URL'] = controller_settings.BD_API_URL
# bd = BD_Runner( prep )
# assert bd.history_table_updated == None, bd.history_table_updated
# bd.prepared_data_dict = {
# 'api_authorization_code': controller_settings.BD_API_AUTHORIZATION_CODE,
# 'api_identity': controller_settings.BD_API_IDENTITY,
# 'command': 'request',
# 'isbn': '9788373015364', # polish ZMM, http://worldcat.org/oclc/751241832
# 'university': controller_settings.BD_UNIVERSITY,
# 'user_barcode': controller_settings.TEST_LEGIT_BARCODE,
# }
# bd.requestItem()
# ## test: request made & unsuccessful
# d = json.loads( bd.api_response )
# assert d.keys() == ['info', 'request', 'response'], d.keys()
# assert d['response']['search_result'] == 'FAILURE', d['response']['search_result']
# ## test: history table updated
# bd.updateHistoryTable()
# assert bd.history_table_message == 'not_found', bd.history_table_message
# assert bd.history_table_updated == True, bd.history_table_updated
# def test_requestItem_isbnShouldNotBeRequestable( self ):
# prep = self.prep
# prep['EB_REQUEST_NUM'] = '123'
# prep['API_URL'] = controller_settings.BD_API_URL
# bd = BD_Runner( prep )
# bd.prepared_data_dict = {
# 'api_authorization_code': controller_settings.BD_API_AUTHORIZATION_CODE,
# 'api_identity': controller_settings.BD_API_IDENTITY,
# 'command': 'request',
# 'isbn': '9788373015364', # polish ZMM, http://worldcat.org/oclc/751241832
# 'university': controller_settings.BD_UNIVERSITY,
# 'user_barcode': controller_settings.TEST_LEGIT_BARCODE,
# }
# assert bd.api_response == None, bd.api_response
# bd.requestItem()
# d = json.loads( bd.api_response )
# assert d.keys() == ['info', 'request', 'response'], d.keys()
# assert sorted(d['response'].keys()) == ['bd_confirmation_code', 'end_time', 'found', 'requestable', 'search_result'], sorted(d['response'].keys())
# assert d['response']['search_result'] == 'FAILURE', d['response']['search_result']
# def test_requestItem_stringShouldNotBeRequestable( self ):
# prep = self.prep
# prep['EB_REQUEST_NUM'] = '123'
# prep['API_URL'] = controller_settings.BD_API_URL
# bd = BD_Runner( prep )
# bd.prepared_data_dict = {
# 'api_authorization_code': controller_settings.BD_API_AUTHORIZATION_CODE,
# 'api_identity': controller_settings.BD_API_IDENTITY,
# 'command': 'request',
# 'title': 'zen i sztuka obs\xe5ugi motocykla rozprawa o wartos\xecciach', # polish ZMM, http://worldcat.org/oclc/751241832
# 'author': 'pirsig robert m',
# 'date': '2010',
# 'university': controller_settings.BD_UNIVERSITY,
# 'user_barcode': controller_settings.TEST_LEGIT_BARCODE,
# }
# assert bd.api_response == None, bd.api_response
# bd.requestItem()
# d = json.loads( bd.api_response )
# assert d.keys() == ['info', 'request', 'response'], d.keys()
# assert sorted(d['response'].keys()) == ['bd_confirmation_code', 'end_time', 'found', 'requestable', 'search_result'], sorted(d['response'].keys())
# assert d['response']['search_result'] == 'FAILURE', d['response']['search_result']
class BDRunnerTest(unittest.TestCase):
def setUp(self):
# self.bd_runner = BD_CallerBibTest( logger=logger, log_identifier='foo' )
self.bd_runner = BD_CallerBib()
pass
def tearDown(self):
pass
# def test_prepare_params(self):
# patron_inst = 'x'
# item_inst = 'y'
# self.assertEqual( 'bar', self.bd_runner.prepare_params(patron_inst, item_inst) )
def test_this_test_harness(self):
self.assertEqual( 1, 1 )
## end class BDRunnerTest()
def suite():
suite = unittest.TestSuite()
suite.addTest( unittest.makeSuite(BD_CallerBibTest) )
return suite
if __name__ == '__main__':
unittest.main()
| 63.513011
| 1,065
| 0.716125
| 2,317
| 17,085
| 5.080276
| 0.142426
| 0.023108
| 0.020219
| 0.01903
| 0.781582
| 0.769943
| 0.745221
| 0.714468
| 0.71056
| 0.707586
| 0
| 0.062361
| 0.129002
| 17,085
| 268
| 1,066
| 63.75
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| 0
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| 0
| 0.045455
| 0.412414
| 0.013448
| 0
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| 0
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| 0.090909
| 1
| 0.181818
| false
| 0.045455
| 0.045455
| 0
| 0.295455
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
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| 1
| 1
| 1
| 1
| 1
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|
0
| 7
|
2569b9084be5a92b8796341d776ab26419e2fc19
| 40,112
|
py
|
Python
|
question_generator.py
|
ottermegazord/nmt-graph
|
7e2f73cd617fe262d984eb05f2157f7aebac8982
|
[
"MIT"
] | null | null | null |
question_generator.py
|
ottermegazord/nmt-graph
|
7e2f73cd617fe262d984eb05f2157f7aebac8982
|
[
"MIT"
] | null | null | null |
question_generator.py
|
ottermegazord/nmt-graph
|
7e2f73cd617fe262d984eb05f2157f7aebac8982
|
[
"MIT"
] | null | null | null |
''' Query from Python '''
from py2neo import Graph
import re
graph = Graph(host="localhost", password="farmers@heart")
# a = graph.run("MATCH (c:crop) RETURN c.id, c.name LIMIT 4").data()
# print(a)
class CypherQuestion():
def __init__(self, english_question_template, cypher_question_template, cypher_answer_template, ENGLISH_PATH, CYPHER_PATH):
self.english_question = english_question_template
self.cypher_question = cypher_question_template
self.cypher_answer = cypher_answer_template
self.var_list = graph.run(cypher_question_template).data()
self.ENGLISH_PATH = ENGLISH_PATH
self.CYPHER_PATH = CYPHER_PATH
def generate_english_question(self):
f = open(ENGLISH_PATH, "w")
for i in self.var_list:
output = self.english_question.format(**i) + '\n'
print(output)
f.write(output)
def generate_cypher_question(self):
f = open(self.CYPHER_PATH, "w")
for i in self.var_list:
output = cypher_answer_template % i['name'] + '\n'
print(output)
f.write(output)
'''Q1 '''
ENGLISH_PATH = 'data/questions/english/english_q1.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q1.txt'
english_question_template = "What is the minimum temperature to grow {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, temperature.min;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q2'''
ENGLISH_PATH = 'data/questions/english/english_q2.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q2.txt'
english_question_template = "What is the maximum temperature to grow {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q3'''
ENGLISH_PATH = 'data/questions/english/english_q3.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q3.txt'
english_question_template = "At what range of temperature will {name} grow?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, temperature.min, temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q4'''
ENGLISH_PATH = 'data/questions/english/english_q4.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q4.txt'
english_question_template = "What is the optimal maximum temperature to grow {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (optimal)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q5'''
ENGLISH_PATH = 'data/questions/english/english_q5.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q5.txt'
english_question_template = "What is the optimal minimum temperature to grow {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (optimal)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, temperature.min;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q6'''
ENGLISH_PATH = 'data/questions/english/english_q6.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q6.txt'
english_question_template = "What is the optimal range of temperature to grow {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (optimal)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, temperature.min, temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q7'''
ENGLISH_PATH = 'data/questions/english/english_q7.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q7.txt'
english_question_template = "What kind of plant is {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(description:description) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
"""Q8"""
ENGLISH_PATH = 'data/questions/english/english_q8.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q8.txt'
english_question_template = "Describe the physiology of {name}."
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(description:description) \
RETURN a.name, c.name, description.physiology;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
"""Q9"""
ENGLISH_PATH = 'data/questions/english/english_q9.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q9.txt'
english_question_template = "Describe the habit of {name}."
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(description:description) \
RETURN a.name, c.name, description.habit;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
"""Q10"""
ENGLISH_PATH = 'data/questions/english/english_q10.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q10.txt'
english_question_template = "Describe the life form of {name}."
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(description:description) \
RETURN a.name, c.name, description.life_form;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
"""Q11"""
ENGLISH_PATH = 'data/questions/english/english_q11.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q11.txt'
english_question_template = "What is the crop cycle of {name} like?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(cultivation:cultivation) \
MATCH (cultivation)-[:has]->(crop_cycle:crop_cycle) \
RETURN a.name, c.name, crop_cycle.crop_cycle_min, crop_cycle.crop_cycle_max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q12 '''
ENGLISH_PATH = 'data/questions/english/english_q12.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q12.txt'
english_question_template = "What is the minimum soil pH to grow {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (absolute)-[:consist_of]->(soil_ph_absolute:soil_ph_absolute) \
RETURN a.name, c.name, soil_ph_absolute.min;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q13 '''
ENGLISH_PATH = 'data/questions/english/english_q13.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q13.txt'
english_question_template = "What is the maximum soil pH to grow {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (absolute)-[:consist_of]->(soil_ph_absolute:soil_ph_absolute) \
RETURN a.name, c.name, soil_ph_absolute.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q14 '''
ENGLISH_PATH = 'data/questions/english/english_q14.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q14.txt'
english_question_template = "At what range of soil pH will {name} grow?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (absolute)-[:consist_of]->(soil_ph_absolute:soil_ph_absolute) \
RETURN a.name, c.name, soil_ph_absolute.min, soil_ph_absolute.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q15 '''
ENGLISH_PATH = 'data/questions/english/english_q15.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q15.txt'
english_question_template = "What is the minimum optimal soil pH to grow {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
RETURN a.name, c.name, soil_ph_optimal.min;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q16 '''
ENGLISH_PATH = 'data/questions/english/english_q16.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q16.txt'
english_question_template = "What is the maximum optimal soil pH to grow {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
RETURN a.name, c.name, soil_ph_optimal.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q17 '''
ENGLISH_PATH = 'data/questions/english/english_q17.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q17.txt'
english_question_template = "What is the scientific name of {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
RETURN a.name, c.name;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q18 '''
ENGLISH_PATH = 'data/questions/english/english_q18.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q18.txt'
english_question_template = "Tell me everything about {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q19'''
ENGLISH_PATH = 'data/questions/english/english_q19.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q19.txt'
english_question_template = "Tell me the light intensity required to grow {name}."
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (ecology)-[:grows_in]->(o:optimal) \
MATCH (o)-[:consist_of]->(light:light_intensity_optimal) \
RETURN light.min, light.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q20 '''
ENGLISH_PATH = 'data/questions/english/english_q20.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q20.txt'
english_question_template = "What is the biology of {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q21 '''
ENGLISH_PATH = 'data/questions/english/english_q21.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q21.txt'
english_question_template = "Please tell me everything about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q22 '''
ENGLISH_PATH = 'data/questions/english/english_q22.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q22.txt'
english_question_template = "I would like to know about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q23 '''
ENGLISH_PATH = 'data/questions/english/english_q23.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q23.txt'
english_question_template = "I would like to learn about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q24 '''
ENGLISH_PATH = 'data/questions/english/english_q24.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q24.txt'
english_question_template = "Can I get all the information about {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q25 '''
ENGLISH_PATH = 'data/questions/english/english_q25.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q25.txt'
english_question_template = "Can I get all the information about {name}?"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q26 '''
ENGLISH_PATH = 'data/questions/english/english_q26.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q26.txt'
english_question_template = "Please share with me everything about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q27 '''
ENGLISH_PATH = 'data/questions/english/english_q27.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q27.txt'
english_question_template = "I would like to know everything about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q28 '''
ENGLISH_PATH = 'data/questions/english/english_q28.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q28.txt'
english_question_template = "Give me a complete overview about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q29 '''
ENGLISH_PATH = 'data/questions/english/english_q29.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q29.txt'
english_question_template = "Give me detailed information about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q30 '''
ENGLISH_PATH = 'data/questions/english/english_q30.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q30.txt'
english_question_template = "Share with me everything about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q31 '''
ENGLISH_PATH = 'data/questions/english/english_q31.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q31.txt'
english_question_template = "Provide detailed information about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q32 '''
ENGLISH_PATH = 'data/questions/english/english_q31.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q31.txt'
english_question_template = "Provide detailed information about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q33 '''
ENGLISH_PATH = 'data/questions/english/english_q33.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q33.txt'
english_question_template = "Give me a full description about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q34 '''
ENGLISH_PATH = 'data/questions/english/english_q34.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q34.txt'
english_question_template = "Please provide a full description about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q35 '''
ENGLISH_PATH = 'data/questions/english/english_q35.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q35.txt'
english_question_template = "Give me a comprehensive description about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q36 '''
ENGLISH_PATH = 'data/questions/english/english_q36.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q36.txt'
english_question_template = "Please provide a comprehensive description about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q37 '''
ENGLISH_PATH = 'data/questions/english/english_q37.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q37.txt'
english_question_template = "Give me a comprehensive description about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q38 '''
ENGLISH_PATH = 'data/questions/english/english_q38.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q38.txt'
english_question_template = "Give me an in-depth description about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q39 '''
ENGLISH_PATH = 'data/questions/english/english_q39.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q39.txt'
english_question_template = "Tell me the in-depth description about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q40 '''
ENGLISH_PATH = 'data/questions/english/english_q40.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q40.txt'
english_question_template = "I want to know everything about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
'''Q41 '''
ENGLISH_PATH = 'data/questions/english/english_q41.txt'
CYPHER_PATH = 'data/questions/cypher/cypher_q41.txt'
english_question_template = "List all the things you know about {name}"
cypher_question_template = "MATCH (c:crop_alias) return c.name as name;"
cypher_answer_template = "MATCH (a:crop_alias {name: '%s'})-[:is_alias_of]->(c:crop) \
MATCH (c)-[:has]->(ecology) \
MATCH (c)-[:has]->(description:description) \
MATCH (ecology)-[:grows_in]->(optimal) \
MATCH (ecology)-[:grows_in]->(absolute) \
MATCH (optimal)-[:consist_of]->(soil_ph_optimal:soil_ph_optimal) \
MATCH (absolute)-[:consist_of]->(temperature:temperature_required_optimal) \
RETURN a.name, c.name, description.habit, description.life_form, description.physiology, \
soil_ph_optimal.max, soil_ph_optimal.min, temperature.min, \
temperature.max;"
cypherquestion = CypherQuestion(english_question_template, cypher_question_template, cypher_answer_template,
ENGLISH_PATH, CYPHER_PATH)
cypherquestion.generate_english_question()
cypherquestion.generate_cypher_question()
| 37.699248
| 127
| 0.747831
| 5,079
| 40,112
| 5.604843
| 0.031305
| 0.094987
| 0.044754
| 0.038711
| 0.956441
| 0.951066
| 0.890259
| 0.827028
| 0.810166
| 0.805599
| 0
| 0.006213
| 0.113183
| 40,112
| 1,064
| 128
| 37.699248
| 0.794052
| 0.002368
| 0
| 0.778452
| 0
| 0.036419
| 0.167272
| 0.076127
| 0
| 0
| 0
| 0
| 0
| 1
| 0.004552
| false
| 0.001517
| 0.003035
| 0
| 0.009105
| 0.003035
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
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| 0
| 0
| 0
| 0
|
0
| 7
|
c2657878ffad6d93fe93f8aaf960d7cb05fcc9bc
| 29,334
|
py
|
Python
|
tutor/tests.py
|
caleb-bodishbaugh/cs3240-s20-QuickTutor
|
9d575fd85496a7c2dab215610de2ba9ef58559a0
|
[
"Unlicense",
"MIT"
] | 1
|
2021-08-20T15:23:03.000Z
|
2021-08-20T15:23:03.000Z
|
tutor/tests.py
|
caleb-bodishbaugh/cs3240-s20-QuickTutor
|
9d575fd85496a7c2dab215610de2ba9ef58559a0
|
[
"Unlicense",
"MIT"
] | 6
|
2021-03-19T04:35:36.000Z
|
2022-01-13T02:48:36.000Z
|
tutor/tests.py
|
caleb-bodishbaugh/cs3240-s20-QuickTutor
|
9d575fd85496a7c2dab215610de2ba9ef58559a0
|
[
"Unlicense",
"MIT"
] | 1
|
2021-08-20T15:22:48.000Z
|
2021-08-20T15:22:48.000Z
|
from django.test import TestCase, Client
from tutor.models import Subject, Profile, Job
from django.contrib.auth.models import User
from tutor.forms import List, RequestTutor, PicForm
from tutor import views
from django.urls import reverse
from tutor.views import AvailableJobs
# Create your tests here.
class DummyTestCase(TestCase):
def setUp(self):
x = 2
def test_dummy_test_case(self):
self.assertEqual(2, 2)
class ProfileModelTest(TestCase):
def setUp(self):
# creates dummy user object, can be reused in other tests
self.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
# User should automatically have a profile when created due to signal
def test_user_has_profile(self):
self.assertTrue(hasattr(self.test_user, 'profile'))
def test_first_name_max_length(self):
profile = Profile.objects.get(user=self.test_user)
max_length = profile._meta.get_field('first_name').max_length
self.assertEqual(max_length, 30)
# ** figure out how to edit profile-specific fields
class SubjectModelTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.test_subject = Subject.objects.create(subject_name='test')
#cls.test_subject2 = Subject.objects.create(subject_name='math')
#cls.test_subject3 = Subject.objects.create(subject_name='science')
def test_subject_name_max_length(self):
self.max_length = self.test_subject._meta.get_field('subject_name').max_length
self.assertEqual(self.max_length, 30)
def test_subject_name(self):
name = str(self.test_subject)
self.assertEqual(name, 'test')
def test_ordering(self):
Subject.objects.create(subject_name="math")
Subject.objects.create(subject_name="science")
all_subjects = Subject.objects.all()
first_sub_name = all_subjects[0].subject_name
self.assertEqual(first_sub_name, "math")
# Examples of form testing
class ListFormTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
# Profile lacking the proper fields (subjects_can_help, first_name, last_name, phone_number in this case) should return false
def test_empty_profile(self):
profile = Profile.objects.get(user=self.test_user)
form = List(data={'first_name': profile.first_name, 'last_name': profile.last_name, 'email_addr': profile.email_addr,
'phone_number': profile.phone_number, 'subjects_can_help': profile.subjects_can_help.all()}, instance=profile)
self.assertTrue(form.is_bound)
self.assertFalse(form.is_valid())
# Profile including all fields and proper format should return true
def test_proper_profile(self):
profile = Profile.objects.get(user=self.test_user)
profile.first_name = 'Test'
profile.last_name = 'User'
profile.email_addr = 'test@gmail.com'
profile.phone_number = '+5555555555'
sub = Subject(subject_name='math')
sub.save()
profile.subjects_can_help.add(sub)
profile.save()
form = List(data={'first_name': profile.first_name, 'last_name': profile.last_name, 'email_addr': profile.email_addr,
'phone_number': profile.phone_number, 'subjects_can_help': profile.subjects_can_help.all()}, instance=profile)
self.assertTrue(form.is_bound)
self.assertTrue(form.is_valid())
# Profile w/o a proper phone number should return false
def test_improper_phone_number_profile(self):
profile = Profile.objects.get(user=self.test_user)
profile.first_name = 'Test'
profile.last_name = 'User'
profile.email_addr = 'test@gmail.com'
profile.phone_number = 'improper input'
sub = Subject(subject_name='math')
sub.save()
profile.subjects_can_help.add(sub)
profile.save()
form = List(data={'first_name': profile.first_name, 'last_name': profile.last_name, 'email_addr': profile.email_addr,
'phone_number': profile.phone_number, 'subjects_can_help': profile.subjects_can_help.all()}, instance=profile)
self.assertTrue(form.is_bound)
self.assertFalse(form.is_valid())
# Profile w/o any subjects selected should return false
def test_no_subjects_selected_profile(self):
profile = Profile.objects.get(user=self.test_user)
profile.first_name = 'Test'
profile.last_name = 'User'
profile.email_addr = 'test@gmail.com'
profile.phone_number = '+5555555555'
profile.save()
form = List(data={'first_name': profile.first_name, 'last_name': profile.last_name, 'email_addr': profile.email_addr,
'phone_number': profile.phone_number, 'subjects_can_help': profile.subjects_can_help.all()}, instance=profile)
self.assertTrue(form.is_bound)
self.assertFalse(form.is_valid())
# Profile w/o a proper email should return false
def test_improper_email_addr_profile(self):
profile = Profile.objects.get(user=self.test_user)
profile.first_name = 'Test'
profile.last_name = 'User'
profile.email_addr = 'improper input'
profile.phone_number = '+5555555555'
sub = Subject(subject_name='Mathematics')
sub.save()
profile.subjects_can_help.add(sub)
profile.save()
form = List(data={'first_name': profile.first_name, 'last_name': profile.last_name, 'email_addr': profile.email_addr,
'phone_number': profile.phone_number, 'subjects_can_help': profile.subjects_can_help.all()}, instance=profile)
self.assertTrue(form.is_bound)
self.assertFalse(form.is_valid())
class RequestTutorFormTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
cls.profile = Profile.objects.get(user=cls.test_user)
# Form should not be valid for job w/ all default fields (some default to empty)
def test_default_job(self):
test_job = Job.objects.create(customer_user=self.test_user, customer_profile=self.profile)
form = RequestTutor(data={'subject': test_job.subject, 'course': test_job.course, 'location': test_job.location, 'notes': test_job.notes}, instance=test_job)
self.assertTrue(form.is_bound)
self.assertFalse(form.is_valid())
# Form with required fields properly filled should be valid
def test_proper_job(self):
test_job = Job.objects.create(customer_user=self.test_user, customer_profile=self.profile, course='TEST 2020', notes='testing', location='Alderman Library in Charlottesville, Virginia')
form = RequestTutor(data={'subject': test_job.subject, 'course': test_job.course, 'location': test_job.location, 'notes': test_job.notes}, instance=test_job)
self.assertTrue(form.is_bound)
self.assertTrue(form.is_valid())
# Form with improper course name should not be valid, course subject code should be capitalized and there should be a space between it and course number
def test_improper_course_job(self):
test_job = Job.objects.create(customer_user=self.test_user, customer_profile=self.profile, course='test2020', notes='testing', location='Alderman Library')
form = RequestTutor(data={'subject': test_job.subject, 'course': test_job.course, 'location': test_job.location, 'notes': test_job.notes}, instance=test_job)
self.assertTrue(form.is_bound)
self.assertFalse(form.is_valid())
class StudentProfileViewTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.client = Client()
# If user is not authenicated, should be redirect to welcome page (index) where they can login
def test_anonymous_user_redirect(self):
response = self.client.get('/student/')
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:index'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
# logged in user is not redirected and shown proper template (student.html)
def test_logged_in_user(self):
self.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
self.client.force_login(self.test_user)
response = self.client.get('/student/')
self.assertEqual(200, response.status_code)
self.assertTemplateUsed(response, template_name='tutor/student.html')
class AvailableJobsViewTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.client = Client()
cls.test_user1 = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
cls.test_user2 = User.objects.create_user(username='testuser2', password='12345', email='test2@gmail.com')
cls.profile = Profile.objects.get(user=cls.test_user1)
# If user is not authenicated, should be redirect to welcome page (index) where they can login
def test_anonymous_user_redirect(self):
response = self.client.get('/jobs/')
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:index'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
# logged in user is not redirected and shown proper template (jobs_list.html)
def test_logged_in_user(self):
self.client.force_login(self.test_user1)
response = self.client.get('/jobs/')
self.assertEqual(200, response.status_code)
self.assertTemplateUsed(response, template_name='tutor/jobs_list.html')
# If a user can tutor in a subject, then avaiable jobs in that shubject should be shown; those in other subjects should not be shown
def test_queryset(self):
self.client.force_login(self.test_user1)
self.profile = Profile.objects.get(user=self.test_user1)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
sub2 = Subject(subject_name='Mathematics')
sub2.save()
customer = Profile.objects.get(user=self.test_user2)
test_job1 = Job.objects.create(customer_user=self.test_user2, customer_profile=customer, course='TEST 2020', notes='testing', location='Alderman Library', subject=sub1)
test_job2 = Job.objects.create(customer_user=self.test_user2, customer_profile=customer, course='TEST 2020', notes='testing', location='Alderman Library', subject=sub2)
response = self.client.get('/jobs/')
self.assertEqual(200, response.status_code)
self.assertTemplateUsed(response, template_name='tutor/jobs_list.html')
self.assertListEqual(list(response.context['object_list']), list(Job.objects.filter(subject=sub1)))
# if no job is selected, should redirect back to list of available jobs
def test_post_no_selected_job(self):
self.client.force_login(self.test_user1)
self.profile = Profile.objects.get(user=self.test_user1)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
sub2 = Subject(subject_name='Mathematics')
sub2.save()
customer = Profile.objects.get(user=self.test_user2)
test_job1 = Job.objects.create(customer_user=self.test_user2, customer_profile=customer, course='TEST 2020', notes='testing', location='Alderman Library', subject=sub1)
test_job2 = Job.objects.create(customer_user=self.test_user2, customer_profile=customer, course='TEST 2020', notes='testing', location='Alderman Library', subject=sub2)
response = self.client.post('/jobs/')
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:job_list'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
# if a single job is selected, redirects to accepted jobs list
def test_post_single_selected_job(self):
self.client.force_login(self.test_user1)
self.profile = Profile.objects.get(user=self.test_user1)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
customer = Profile.objects.get(user=self.test_user2)
test_job1 = Job.objects.create(customer_user=self.test_user2, customer_profile=customer, course='TEST 2020', notes='testing', location='Alderman Library', subject=sub1)
response = self.client.post('/jobs/', data={'selected_job': Job.objects.filter(subject=sub1).values_list('id', flat=True)})
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:accepted'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
# if multiple jobs are selected, redirects to accepted jobs list
def test_post_multiple_selected_jobs(self):
self.client.force_login(self.test_user1)
self.profile = Profile.objects.get(user=self.test_user1)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
customer = Profile.objects.get(user=self.test_user2)
test_job1 = Job.objects.create(customer_user=self.test_user2, customer_profile=customer, course='TEST 2020', notes='testing', location='Alderman Library', subject=sub1)
test_job2 = Job.objects.create(customer_user=self.test_user2, customer_profile=customer, course='TEST 3030', notes='testing second', location='Alderman Library', subject=sub1)
response = self.client.post('/jobs/', data={'selected_job': Job.objects.filter(subject=sub1).values_list('id', flat=True)})
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:accepted'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
class ProfileUpdateViewTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.client = Client()
cls.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
# If user is not authenicated, should be redirect to welcome page (index) where they can login
def test_anonymous_user_redirect(self):
response = self.client.get('/updateinfo/')
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:index'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
# logged in user is not redirected and shown proper template (studentUpdate.html)
def test_logged_in_user(self):
self.client.force_login(self.test_user)
response = self.client.get('/updateinfo/')
self.assertEqual(200, response.status_code)
self.assertTemplateUsed(response, template_name='tutor/studentUpdate.html')
# successful form submission should redirect to student profile page
def test_successful_redirect(self):
self.client.force_login(self.test_user)
self.profile = Profile.objects.get(user=self.test_user)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
sub2 = Subject(subject_name='Mathematics')
sub2.save()
self.profile.subjects_can_help.add(sub2)
self.profile.save()
response = self.client.post('/updateinfo/', data={'first_name': self.profile.first_name, 'last_name': self.profile.last_name, 'email_addr': self.profile.email_addr,
'phone_number': self.profile.phone_number, 'subjects_can_help': self.profile.subjects_can_help.all().values_list('id', flat=True), 'pic': self.profile.pic})
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:student'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
# unsuccessful form submission (improper phone number in this case) should redirect back to updateprofile page
def test_unsuccessful_redirect(self):
self.client.force_login(self.test_user)
self.profile = Profile.objects.get(user=self.test_user)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = 'improper input'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
sub2 = Subject(subject_name='Mathematics')
sub2.save()
self.profile.subjects_can_help.add(sub2)
self.profile.save()
response = self.client.post('/updateinfo/', data={'first_name': self.profile.first_name, 'last_name': self.profile.last_name, 'email_addr': self.profile.email_addr,
'phone_number': self.profile.phone_number, 'subjects_can_help': self.profile.subjects_can_help.all().values_list('id', flat=True), 'pic': self.profile.pic})
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:updateinfo'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
class RequestedJobsViewTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.client = Client()
# If user is not authenicated, should be redirect to welcome page (index) where they can login
def test_anonymous_user_redirect(self):
response = self.client.get('/requests/')
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:index'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
# logged in user is not redirected and shown proper template (requested_jobs_list.html)
def test_logged_in_user(self):
self.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
self.client.force_login(self.test_user)
response = self.client.get('/requests/')
self.assertEqual(200, response.status_code)
self.assertTemplateUsed(response, template_name='tutor/requested_jobs_list.html')
# View not display any jobs if user has not requested any
def test_queryset_with_no_jobs_requested(self):
self.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
self.client.force_login(self.test_user)
self.profile = Profile.objects.get(user=self.test_user)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
response = self.client.get('/requests/')
self.assertEqual(200, response.status_code)
self.assertListEqual(list(response.context['object_list']), list(Job.objects.filter(customer_user=self.test_user)))
# View should display all requested jobs
def test_queryset_with_multiple_jobs_requested(self):
self.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
self.client.force_login(self.test_user)
self.profile = Profile.objects.get(user=self.test_user)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
test_job1 = Job.objects.create(customer_user=self.test_user, customer_profile=self.profile, course='TEST 2020', notes='testing', location='Alderman Library', subject=sub1)
test_job2 = Job.objects.create(customer_user=self.test_user, customer_profile=self.profile, course='TEST 3030', notes='testing second', location='Alderman Library', subject=sub1)
response = self.client.get('/requests/')
self.assertEqual(200, response.status_code)
self.assertListEqual(list(response.context['object_list']), list(Job.objects.filter(customer_user=self.test_user)))
class AcceptedJobsViewTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.client = Client()
cls.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
cls.test_user2 = User.objects.create_user(username='testuser2', password='12345', email='test2@gmail.com')
# If user is not authenicated, should be redirect to welcome page (index) where they can login
def test_anonymous_user_redirect(self):
response = self.client.get('/acceptedjobs/')
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:index'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
# logged in user is not redirected and shown proper template (acceptedjobs.html)
def test_logged_in_user(self):
self.client.force_login(self.test_user)
response = self.client.get('/acceptedjobs/')
self.assertEqual(200, response.status_code)
self.assertTemplateUsed(response, template_name='tutor/acceptedjobs.html')
# Should return an empty list if no jobs have been accepted
def test_get_no_accepted_jobs(self):
self.client.force_login(self.test_user)
self.profile = Profile.objects.get(user=self.test_user)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
test_job1 = Job.objects.create(customer_user=self.test_user, customer_profile=self.profile, course='TEST 2020', notes='testing', location='Alderman Library in Charlottesville, VA', subject=sub1)
response = self.client.get('/acceptedjobs/')
self.assertEqual(200, response.status_code)
self.assertListEqual(list(response.context['job_list']), list(Job.objects.filter(tutor_user=self.test_user)))
# Should return list of accepted jobs if a job has been accepted
def test_get_accepted_jobs(self):
self.client.force_login(self.test_user)
self.profile = Profile.objects.get(user=self.test_user)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
customer = Profile.objects.get(user=self.test_user2)
test_job1 = Job.objects.create(customer_user=self.test_user2, customer_profile=customer, tutor_user=self.test_user, tutor_profile=self.profile, course='TEST 2020', notes='testing', location='Alderman Library in Charlottesville, Virginia', subject=sub1)
response = self.client.get('/acceptedjobs/')
self.assertEqual(200, response.status_code)
self.assertListEqual(list(response.context['job_list']), list(Job.objects.filter(tutor_user=self.test_user)))
class RequestTutorViewTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.client = Client()
# If user is not authenicated, should be redirect to welcome page (index) where they can login
def test_anonymous_user_redirect(self):
response = self.client.get('/requesttutor/')
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:index'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
# logged in user is not redirected and shown proper template (acceptedjobs.html)
def test_logged_in_user(self):
self.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
self.client.force_login(self.test_user)
response = self.client.get('/requesttutor/')
self.assertEqual(200, response.status_code)
self.assertTemplateUsed(response, template_name='tutor/requestTutor.html')
# Requesting a tutor with proper form input should redirect to requests view where new job should be found
def test_request_proper_input(self):
self.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
self.client.force_login(self.test_user)
self.profile = Profile.objects.get(user=self.test_user)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
response = self.client.post('/requesttutor/', data={'customer_user': self.test_user, 'customer_profile': self.profile, 'course': 'TEST 2020', 'notes': 'testing', 'location': 'Alderman Library in Charlottesville, Virginia', 'subject': str(sub1)})
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:requests'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
redir_response = self.client.get('/requests/')
self.assertListEqual(list(redir_response.context['object_list']), list(Job.objects.filter(customer_user=self.test_user)))
self.assertEqual(len(list(redir_response.context['object_list'])), 1)
# Requesting a tutor with improper form input should return RequestTutor view, no redirect occurs
def test_request_improper_input(self):
self.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
self.client.force_login(self.test_user)
self.profile = Profile.objects.get(user=self.test_user)
self.profile.first_name = 'Test'
self.profile.last_name = 'User'
self.profile.email_addr = 'test@gmail.com'
self.profile.phone_number = '+5555555555'
sub1 = Subject(subject_name='Physics')
sub1.save()
self.profile.subjects_can_help.add(sub1)
self.profile.save()
response = self.client.post('/requesttutor/', data={'customer_user': self.test_user, 'customer_profile': self.profile, 'course': 'improper', 'notes': 'testing', 'location': 'Alderman Library in Charlottesville, Virginia', 'subject': str(sub1)})
self.assertEqual(200, response.status_code)
self.assertFormError(response, 'form', 'course', 'Enter a valid course code using following format: TEST 2010 (Make sure to capitalize the course subject!)')
class SessionInfoViewTest(TestCase):
@classmethod
def setUpTestData(cls):
cls.client = Client()
# If user is not authenicated, should be redirected to welcome page (index) where they can login
def test_anonymous_user_redirect(self):
self.test_user = User.objects.create_user(username='testuser', password='12345', email='test@gmail.com')
self.profile = Profile.objects.get(user=self.test_user)
sub1 = Subject(subject_name='Physics')
sub1.save()
test_job1 = Job.objects.create(customer_user=self.test_user, customer_profile=self.profile, course='TEST 2020', notes='testing', location='Alderman Library in Charlottesville, Virginia', subject=sub1)
url = reverse('tutor:session', args=(test_job1.id,))
response = self.client.get(url)
self.assertEqual(302, response.status_code)
self.assertRedirects(response, reverse('tutor:index'), status_code=302, target_status_code=200, msg_prefix='', fetch_redirect_response=True)
| 55.980916
| 260
| 0.708223
| 3,766
| 29,334
| 5.331917
| 0.067711
| 0.064094
| 0.031673
| 0.023904
| 0.858466
| 0.840837
| 0.823904
| 0.81489
| 0.810857
| 0.802092
| 0
| 0.02297
| 0.174814
| 29,334
| 524
| 261
| 55.980916
| 0.806577
| 0.103259
| 0
| 0.777518
| 0
| 0
| 0.124995
| 0.003807
| 0
| 0
| 0
| 0
| 0.175644
| 1
| 0.119438
| false
| 0.037471
| 0.016393
| 0
| 0.163934
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
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| 1
| 1
| 1
| 1
| 1
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| 0
| 0
| 0
| 0
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| 0
| 1
| 0
| 0
| 0
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| null | 0
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| 0
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| 0
| 0
| 0
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| 0
| 0
| 0
|
0
| 7
|
6c002fa4031ef75d0e4d0e7c5bb15e6b37d165f7
| 3,215
|
py
|
Python
|
tests/test_book_figure.py
|
wohali/plugins
|
61669ea5b5fa7c7c91977063f5f6fcd6c4265b6e
|
[
"MIT"
] | 53
|
2015-08-14T20:28:05.000Z
|
2021-06-02T00:38:23.000Z
|
tests/test_book_figure.py
|
wohali/plugins
|
61669ea5b5fa7c7c91977063f5f6fcd6c4265b6e
|
[
"MIT"
] | 280
|
2015-01-10T15:57:44.000Z
|
2022-03-27T20:47:08.000Z
|
tests/test_book_figure.py
|
wohali/plugins
|
61669ea5b5fa7c7c91977063f5f6fcd6c4265b6e
|
[
"MIT"
] | 93
|
2015-01-26T19:39:02.000Z
|
2022-03-24T17:12:42.000Z
|
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from pytest import fixture
from . import V7_PLUGIN_PATH
def test_default(do_test):
assert do_test("""\
.. book_figure:: Get Nikola
""") == (
'<div class="">'
'<div class="book-figure-content">'
'<p class="book-figure-title">Get Nikola</p>'
'</div></div>'
)
def test_full_02(do_test):
assert do_test("""\
.. book_figure:: Get Nikola
:class: book-figure
:url: http://getnikola.com/
:author: Roberto Alsina
:isbn_13: 1234567890123
:isbn_10: 1234567890
:asin: B001234567
:image_url: http://getnikola.com/galleries/demo/tesla2_lg.jpg
Your review.
""") == (
'<div class="book-figure">'
'<div class="book-figure-media">'
'<a class="book-figure-image" href="http://getnikola.com/" target="_blank">'
'<img src="http://getnikola.com/galleries/demo/tesla2_lg.jpg" alt="Get Nikola" />'
'</a></div>'
'<div class="book-figure-content">'
'<a class="book-figure-title" href="http://getnikola.com/" target="_blank">Get Nikola</a>'
'<p class="book-figure-author">by Roberto Alsina</p>'
'<table class="book-figure-book-number">'
'<tbody>'
'<tr><th>ISBN-13:</th><td>1234567890123</td></tr>'
'<tr><th>ISBN-10:</th><td>1234567890</td></tr>'
'<tr><th>ASIN:</th><td>B001234567</td></tr>'
'</tbody></table>'
'<div class="book-figure-review">'
'<p>Your review.</p>'
'</div></div></div>'
)
def test_with_author_url(do_test):
assert do_test("""\
.. book_figure:: Get Nikola
:class: book-figure
:url: http://getnikola.com/
:author: Roberto Alsina
:author_url: http://ralsina.me/
:isbn_13: 1234567890123
:isbn_10: 1234567890
:asin: B001234567
:image_url: http://getnikola.com/galleries/demo/tesla2_lg.jpg
Your review.
""") == (
'<div class="book-figure">'
'<div class="book-figure-media">'
'<a class="book-figure-image" href="http://getnikola.com/" target="_blank">'
'<img src="http://getnikola.com/galleries/demo/tesla2_lg.jpg" alt="Get Nikola" />'
'</a></div>'
'<div class="book-figure-content">'
'<a class="book-figure-title" href="http://getnikola.com/" target="_blank">Get Nikola</a>'
'<p class="book-figure-author">by <a href="http://ralsina.me/" target="_blank">Roberto Alsina</a></p>'
'<table class="book-figure-book-number">'
'<tbody>'
'<tr><th>ISBN-13:</th><td>1234567890123</td></tr>'
'<tr><th>ISBN-10:</th><td>1234567890</td></tr>'
'<tr><th>ASIN:</th><td>B001234567</td></tr>'
'</tbody></table>'
'<div class="book-figure-review">'
'<p>Your review.</p>'
'</div></div></div>'
)
@fixture
def do_test(basic_compile_test):
def f(data: str) -> str:
return basic_compile_test('.rst', data, extra_plugins_dirs=[V7_PLUGIN_PATH / 'book_figure']).raw_html.replace('\n', '')
return f
| 34.569892
| 127
| 0.553655
| 398
| 3,215
| 4.349246
| 0.208543
| 0.138648
| 0.17331
| 0.093588
| 0.774119
| 0.759676
| 0.759676
| 0.759676
| 0.759676
| 0.738302
| 0
| 0.063015
| 0.24479
| 3,215
| 92
| 128
| 34.945652
| 0.649918
| 0.006532
| 0
| 0.74359
| 0
| 0.089744
| 0.710526
| 0.22995
| 0
| 0
| 0
| 0
| 0.038462
| 1
| 0.064103
| false
| 0
| 0.038462
| 0.012821
| 0.128205
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
6c708767c9f3902d653948076277a100c7659d0c
| 162
|
py
|
Python
|
pykaban/__init__.py
|
xiaoshuai/pykaban
|
c053ad9fc69ee39e774190a4097525d0c29dbf45
|
[
"MIT"
] | 6
|
2018-08-10T07:43:24.000Z
|
2020-06-10T09:49:43.000Z
|
pykaban/__init__.py
|
xiaoshuai/pykaban
|
c053ad9fc69ee39e774190a4097525d0c29dbf45
|
[
"MIT"
] | null | null | null |
pykaban/__init__.py
|
xiaoshuai/pykaban
|
c053ad9fc69ee39e774190a4097525d0c29dbf45
|
[
"MIT"
] | 1
|
2019-07-31T06:27:12.000Z
|
2019-07-31T06:27:12.000Z
|
from .pykaban import AjaxAPI # noqa: F401
from .exceptions import * # noqa: F401
from . import cli # noqa: F401
from .version import __version__ # noqa: F401
| 32.4
| 46
| 0.722222
| 22
| 162
| 5.136364
| 0.409091
| 0.283186
| 0.318584
| 0
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| 0
| 0
| 0
| 0
| 0
| 0
| 0.092308
| 0.197531
| 162
| 4
| 47
| 40.5
| 0.776923
| 0.265432
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| 0
| true
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| null | 1
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| null | 0
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| 0
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| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
66672a6de5ae6bca3ebff2013f1b512850e7b912
| 4,371
|
py
|
Python
|
terminfo/x/xterm_vt220.py
|
darkelf44/PyUtils
|
4072ff31d2b89dd604ef0e09eb90b7f3b5cf23df
|
[
"MIT"
] | null | null | null |
terminfo/x/xterm_vt220.py
|
darkelf44/PyUtils
|
4072ff31d2b89dd604ef0e09eb90b7f3b5cf23df
|
[
"MIT"
] | null | null | null |
terminfo/x/xterm_vt220.py
|
darkelf44/PyUtils
|
4072ff31d2b89dd604ef0e09eb90b7f3b5cf23df
|
[
"MIT"
] | null | null | null |
# Imports
from terminfo import TerminalModule
# Terminal: xterm emulating vt220
TerminalModule(['xterm_vt220'], use=[], no_pad_char=True, key_btab=b'\x1b[Z', key_left=b'\x1bOD', key_down=b'\x1bOB', key_right=b'\x1bOC', key_up=b'\x1bOA', key_end=b'\x1b[4~', key_enter=b'\x1bOM', key_f10=b'\x1b[21~', key_f11=b'\x1b[23~', key_f12=b'\x1b[24~', key_f13=b'\x1b[25~', key_f14=b'\x1b[26~', key_f15=b'\x1b[28~', key_f16=b'\x1b[29~', key_f17=b'\x1b[31~', key_f18=b'\x1b[32~', key_f19=b'\x1b[33~', key_f20=b'\x1b[34~', key_f5=b'\x1b[15~', key_f6=b'\x1b[17~', key_f7=b'\x1b[18~', key_f8=b'\x1b[19~', key_f9=b'\x1b[20~', key_home=b'\x1b[1~', key_ic=b'\x1b[2~', key_mouse=b'\x1b[M', key_npage=b'\x1b[6~', key_ppage=b'\x1b[5~', newline=b'\x1bE', backspaces_with_bs=True, auto_right_margin=True, back_color_erase=True, move_insert_mode=True, move_standout_mode=True, eat_newline_glitch=True, AX=True, XT=True, max_colors=8, columns=80, init_tabs=8, lines=24, max_pairs=64, acs_chars=b'``aaffggiijjkkllmmnnooppqqrrssttuuvvwwxxyyzz{{||}}~~', bell=b'\x07', enter_blink_mode=b'\x1b[5m', enter_bold_mode=b'\x1b[1m', back_tab=b'\x1b[Z', clear_screen=b'\x1b[H\x1b[2J', carriage_return=b'\r', change_scroll_region=b'\x1b[%i%p1%d;%p2%dr', parm_left_cursor=b'\x1b[%p1%dD', cursor_left=b'\x08', parm_down_cursor=b'\x1b[%p1%dB', cursor_down=b'\n', parm_right_cursor=b'\x1b[%p1%dC', cursor_right=b'\x1b[C', cursor_address=b'\x1b[%i%p1%d;%p2%dH', parm_up_cursor=b'\x1b[%p1%dA', cursor_up=b'\x1b[A', parm_dch=b'\x1b[%p1%dP', delete_character=b'\x1b[P', enter_dim_mode=b'\x1b[2m', parm_delete_line=b'\x1b[%p1%dM', delete_line=b'\x1b[M', erase_chars=b'\x1b[%p1%dX', clr_eos=b'\x1b[J', clr_eol=b'\x1b[K', clr_bol=b'\x1b[1K', flash_screen=b'\x1b[?5h$<100/>\x1b[?5l', cursor_home=b'\x1b[H', column_address=b'\x1b[%i%p1%dG', tab=b'\t', set_tab=b'\x1bH', parm_ich=b'\x1b[%p1%d@', parm_insert_line=b'\x1b[%p1%dL', insert_line=b'\x1b[L', scroll_forward=b'\n', enter_secure_mode=b'\x1b[8m', init_2string=b'\x1b[!p\x1b[?3;4l\x1b[4l\x1b>', memory_lock=b'\x1bl', memory_unlock=b'\x1bm', orig_pair=b'\x1b[39;49m', restore_cursor=b'\x1b8', enter_reverse_mode=b'\x1b[7m', scroll_reverse=b'\x1bM', exit_alt_charset_mode=b'\x1b(B', exit_am_mode=b'\x1b[?7l', exit_insert_mode=b'\x1b[4l', keypad_local=b'\x1b[?1l\x1b>', exit_standout_mode=b'\x1b[27m', exit_underline_mode=b'\x1b[24m', reset_1string=b'\x1bc', reset_2string=b'\x1b[!p\x1b[?3;4l\x1b[4l\x1b>', save_cursor=b'\x1b7', set_a_background=b'\x1b[4%p1%dm', set_a_foreground=b'\x1b[3%p1%dm', set_background=b'\x1b[4%?%p1%{1}%=%t4%e%p1%{3}%=%t6%e%p1%{4}%=%t1%e%p1%{6}%=%t3%e%p1%d%;m', set_foreground=b'\x1b[3%?%p1%{1}%=%t4%e%p1%{3}%=%t6%e%p1%{4}%=%t1%e%p1%{6}%=%t3%e%p1%d%;m', set_attributes=b'%?%p9%t\x1b(0%e\x1b(B%;\x1b[0%?%p6%t;1%;%?%p5%t;2%;%?%p2%t;4%;%?%p1%p3%|%t;7%;%?%p4%t;5%;%?%p7%t;8%;m', exit_attribute_mode=b'\x1b(B\x1b[m', enter_alt_charset_mode=b'\x1b(0', enter_am_mode=b'\x1b[?7h', enter_insert_mode=b'\x1b[4h', keypad_xmit=b'\x1b[?1h\x1b=', enter_standout_mode=b'\x1b[7m', enter_underline_mode=b'\x1b[4m', clear_all_tabs=b'\x1b[3g', row_address=b'\x1b[%i%p1%dd', erase_saved_lines=b'\x1b[3J', has_meta_key=True, meta_off=b'\x1b[?1034l', meta_on=b'\x1b[?1034h', cursor_invisible=b'\x1b[?25l', cursor_normal=b'\x1b[?12l\x1b[?25h', cursor_visible=b'\x1b[?12;25h', user6=b'\x1b[%i%d;%dR', user7=b'\x1b[6n', user8=b'\x1b[?%[;0123456789]c', user9=b'\x1b[c', exit_ca_mode=b'\x1b[?1049l\x1b[23;0;0t', enter_ca_mode=b'\x1b[?1049h\x1b[22;0;0t', key_backspace=b'\x08', prtr_silent=True, print_screen=b'\x1b[i', prtr_off=b'\x1b[4i', prtr_on=b'\x1b[5i', kp5=b'\x1bOE', kpADD=b'\x1bOk', kpCMA=b'\x1bOl', kpDIV=b'\x1bOo', kpDOT=b'\x1bOn', kpMUL=b'\x1bOj', kpSUB=b'\x1bOm', kpZRO=b'\x1bOp', key_a1=b'\x1bOw', key_a3=b'\x1bOy', key_b2=b'\x1bOu', key_c1=b'\x1bOq', key_c3=b'\x1bOs', key_f1=b'\x1bOP', key_f2=b'\x1bOQ', key_f3=b'\x1bOR', key_f4=b'\x1bOS', key_a2=b'\x1bOx', key_b1=b'\x1bOt', key_b3=b'\x1bOv', key_c2=b'\x1bOr', exit_italics_mode=b'\x1b[23m', enter_italics_mode=b'\x1b[3m', Cr=b'\x1b]112\x07', Cs=b'\x1b]12;%p1%s\x07', Ms=b'\x1b]52;%p1%s;%p2%s\x07', Se=b'\x1b[2 q', Ss=b'\x1b[%p1%d q', XM=b'\x1b[?1006;1000%?%p1%{1}%=%th%el%;', xm=b'\x1b[<%i%p3%d;%p1%d;%p2%d;%?%p4%tM%em%;', exit_strikeout_mode=b'\x1b[29m', enter_strikeout_mode=b'\x1b[9m', repeat_char=b'%p1%c\x1b[%p2%{1}%-%db', parm_index=b'\x1b[%p1%dS', parm_rindex=b'\x1b[%p1%dT', key_dc=b'\x1b[3~')
| 728.5
| 4,289
| 0.68108
| 929
| 4,371
| 3.009688
| 0.347686
| 0.150215
| 0.062947
| 0.010014
| 0.096209
| 0.047926
| 0.040773
| 0.040773
| 0.040773
| 0.040773
| 0
| 0.108063
| 0.040952
| 4,371
| 5
| 4,290
| 874.2
| 0.558922
| 0.008922
| 0
| 0
| 0
| 2
| 0.372141
| 0.130053
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 0.5
| 0
| 0.5
| 0.5
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| 0
| null | 0
| 0
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| 0
| 0
| 0
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| null | 0
| 0
| 0
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| 0
| 1
| 0
| 1
| 0
| 0
| 1
|
0
| 7
|
6671f57d61b6ab668839c7c9e903648325b01183
| 126
|
py
|
Python
|
dist/Basilisk/fswAlgorithms/MRP_Feedback/__init__.py
|
ian-cooke/basilisk_mag
|
a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14
|
[
"0BSD"
] | null | null | null |
dist/Basilisk/fswAlgorithms/MRP_Feedback/__init__.py
|
ian-cooke/basilisk_mag
|
a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14
|
[
"0BSD"
] | 1
|
2019-03-13T20:52:22.000Z
|
2019-03-13T20:52:22.000Z
|
dist/Basilisk/fswAlgorithms/MRP_Feedback/__init__.py
|
ian-cooke/basilisk_mag
|
a8b1e37c31c1287549d6fd4d71fcaa35b6fc3f14
|
[
"0BSD"
] | null | null | null |
# This __init__.py file for the MRP_Feedback package is automatically generated by the build system
from MRP_Feedback import *
| 63
| 99
| 0.833333
| 20
| 126
| 4.95
| 0.85
| 0.222222
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.142857
| 126
| 2
| 100
| 63
| 0.916667
| 0.769841
| 0
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| 1
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| 1
| 0
| true
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| 0
| 0
| 0
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| 0
| 1
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| 0
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| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
66755dbaa60f20705d7856705fae76cdcebac220
| 142
|
py
|
Python
|
common/aist_common/grammar/equivalence_class/blank.py
|
sfahad1414/AGENT
|
84069edc96b6190bb03ffd5099cbc8966061a563
|
[
"Apache-2.0"
] | 15
|
2020-05-06T16:17:56.000Z
|
2022-03-30T12:25:16.000Z
|
common/aist_common/grammar/equivalence_class/blank.py
|
dionny/AGENT
|
8a833406b590e23623fcc67db99f6f964d002396
|
[
"Apache-2.0"
] | 2
|
2021-08-25T16:17:16.000Z
|
2022-02-10T06:35:58.000Z
|
common/aist_common/grammar/equivalence_class/blank.py
|
dionny/AGENT
|
8a833406b590e23623fcc67db99f6f964d002396
|
[
"Apache-2.0"
] | 7
|
2020-04-07T18:47:55.000Z
|
2022-03-30T12:14:58.000Z
|
class Blank:
def __init__(self):
self.equivalence_class = "BLANK"
def __str__(self):
return self.equivalence_class
| 15.777778
| 40
| 0.647887
| 16
| 142
| 5.125
| 0.5
| 0.243902
| 0.317073
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.267606
| 142
| 8
| 41
| 17.75
| 0.788462
| 0
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| 0
| 0
| 0
| 0.035714
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.4
| false
| 0
| 0
| 0.2
| 0.8
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
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| null | 0
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| 0
| 0
| 1
| 0
| 0
| 0
| 1
| 1
| 0
|
0
| 7
|
dd3b8fa9403297b76423068b278c23a6d4a80006
| 2,118
|
py
|
Python
|
rootfs/api/migrations/0003_auto_20160114_0310.py
|
felixbuenemann/controller
|
d6023de4710c22791a52bca7ad4f235e8768910a
|
[
"MIT"
] | 35
|
2016-03-24T21:03:03.000Z
|
2020-03-16T16:02:04.000Z
|
rootfs/api/migrations/0003_auto_20160114_0310.py
|
felixbuenemann/controller
|
d6023de4710c22791a52bca7ad4f235e8768910a
|
[
"MIT"
] | 829
|
2016-03-16T17:52:17.000Z
|
2019-06-24T07:40:20.000Z
|
rootfs/api/migrations/0003_auto_20160114_0310.py
|
felixbuenemann/controller
|
d6023de4710c22791a52bca7ad4f235e8768910a
|
[
"MIT"
] | 59
|
2016-03-16T18:12:44.000Z
|
2020-03-16T16:02:41.000Z
|
# -*- coding: utf-8 -*-
# Generated by Django 1.9 on 2016-01-14 03:10
from __future__ import unicode_literals
from django.db import migrations, models
import uuid
class Migration(migrations.Migration):
dependencies = [
('api', '0002_auto_20151215_0352'),
]
operations = [
migrations.AlterField(
model_name='app',
name='uuid',
field=models.UUIDField(auto_created=True, default=uuid.uuid4, editable=False, primary_key=True, serialize=False, unique=True, verbose_name='UUID'),
),
migrations.AlterField(
model_name='build',
name='uuid',
field=models.UUIDField(auto_created=True, default=uuid.uuid4, editable=False, primary_key=True, serialize=False, unique=True, verbose_name='UUID'),
),
migrations.AlterField(
model_name='config',
name='uuid',
field=models.UUIDField(auto_created=True, default=uuid.uuid4, editable=False, primary_key=True, serialize=False, unique=True, verbose_name='UUID'),
),
migrations.AlterField(
model_name='container',
name='uuid',
field=models.UUIDField(auto_created=True, default=uuid.uuid4, editable=False, primary_key=True, serialize=False, unique=True, verbose_name='UUID'),
),
migrations.AlterField(
model_name='key',
name='uuid',
field=models.UUIDField(auto_created=True, default=uuid.uuid4, editable=False, primary_key=True, serialize=False, unique=True, verbose_name='UUID'),
),
migrations.AlterField(
model_name='push',
name='uuid',
field=models.UUIDField(auto_created=True, default=uuid.uuid4, editable=False, primary_key=True, serialize=False, unique=True, verbose_name='UUID'),
),
migrations.AlterField(
model_name='release',
name='uuid',
field=models.UUIDField(auto_created=True, default=uuid.uuid4, editable=False, primary_key=True, serialize=False, unique=True, verbose_name='UUID'),
),
]
| 40.730769
| 159
| 0.635977
| 234
| 2,118
| 5.602564
| 0.230769
| 0.085431
| 0.133486
| 0.154844
| 0.800153
| 0.800153
| 0.800153
| 0.800153
| 0.800153
| 0.800153
| 0
| 0.023573
| 0.238905
| 2,118
| 51
| 160
| 41.529412
| 0.789702
| 0.030689
| 0
| 0.636364
| 1
| 0
| 0.058049
| 0.01122
| 0
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| false
| 0
| 0.068182
| 0
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| 0
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| null | 0
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| 1
| 1
| 1
| 1
| 1
| 0
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| 0
| 0
| 0
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| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
dd4e26416a9fe90f743ac7da8a8d10614555c189
| 3,082
|
py
|
Python
|
app.py
|
sheready/NewsToday
|
6fb459c9e0c11170d846a6ff41215ce7a888fd61
|
[
"Unlicense"
] | null | null | null |
app.py
|
sheready/NewsToday
|
6fb459c9e0c11170d846a6ff41215ce7a888fd61
|
[
"Unlicense"
] | 1
|
2021-03-30T17:25:38.000Z
|
2021-03-30T17:25:38.000Z
|
app.py
|
sheready/NewsToday
|
6fb459c9e0c11170d846a6ff41215ce7a888fd61
|
[
"Unlicense"
] | null | null | null |
from flask import Flask, render_template
from newsapi import NewsApiClient
from flask_bootstrap import Bootstrap
app = Flask(__name__,instance_relative_config = True)
# Initializing Flask Extensions
bootstrap = Bootstrap(app)
@app.route('/')
def index():
newsapi = NewsApiClient(api_key = 'b3fefd9be43745ce806ac142643aca1e')
topheadlines = newsapi.get_top_headlines(sources = 'mashable')
articles = topheadlines['articles']
desc = []
news = []
img = []
date = []
url = []
for i in range(len(articles)):
myarticles = articles[i]
news.append(myarticles['title'])
desc.append(myarticles['description'])
img.append(myarticles['urlToImage'])
date.append(myarticles['publishedAt'])
url.append(myarticles['url'])
mylist = zip(news, desc, img, date, url)
return render_template('mashable.html', context = mylist)
@app.route('/techcrunch')
def bbc():
newsapi = NewsApiClient(api_key = 'b3fefd9be43745ce806ac142643aca1e')
topheadlines = newsapi.get_top_headlines(sources = 'techcrunch')
articles = topheadlines['articles']
desc = []
news = []
img = []
date = []
url = []
for i in range(len(articles)):
myarticles = articles[i]
news.append(myarticles['title'])
desc.append(myarticles['description'])
img.append(myarticles['urlToImage'])
date.append(myarticles['publishedAt'])
url.append(myarticles['url'])
mylist = zip(news, desc, img, date, url)
return render_template('techcrunch.html', context = mylist)
@app.route('/wired')
def abc():
newsapi = NewsApiClient(api_key = 'b3fefd9be43745ce806ac142643aca1e')
topheadlines = newsapi.get_top_headlines(sources = 'wired')
articles = topheadlines['articles']
desc = []
news = []
img = []
date = []
url = []
for i in range(len(articles)):
myarticles = articles[i]
news.append(myarticles['title'])
desc.append(myarticles['description'])
img.append(myarticles['urlToImage'])
date.append(myarticles['publishedAt'])
url.append(myarticles['url'])
mylist = zip(news, desc, img, date, url)
return render_template('wired.html', context = mylist)
@app.route('/headlines')
def head():
newsapi = NewsApiClient(api_key = 'b3fefd9be43745ce806ac142643aca1e')
topheadlines = newsapi.get_top_headlines(sources = 'cbs-news, cnn, the-wall-street-journal, nbc-news')
articles = topheadlines['articles']
desc = []
news = []
img = []
date = []
url = []
for i in range(len(articles)):
myarticles = articles[i]
news.append(myarticles['title'])
desc.append(myarticles['description'])
img.append(myarticles['urlToImage'])
date.append(myarticles['publishedAt'])
url.append(myarticles['url'])
mylist = zip(news, desc, img, date, url)
return render_template('index.html', context = mylist)
if __name__ == "__main__":
app.run(debug = True)
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| 0.048193
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0
| 7
|
dd5910cd7b8d6c93bee0d46a98b1afc9f4cc63f4
| 27,042
|
py
|
Python
|
junos_get_interfaces.py
|
ksator/junos-automation-with-NAPALM
|
aee4382b2fdeaad0186a8771c90bb0e14d7b563e
|
[
"MIT"
] | 15
|
2017-12-06T18:11:19.000Z
|
2021-06-14T07:41:36.000Z
|
junos_get_interfaces.py
|
ksator/junos-automation-with-NAPALM
|
aee4382b2fdeaad0186a8771c90bb0e14d7b563e
|
[
"MIT"
] | null | null | null |
junos_get_interfaces.py
|
ksator/junos-automation-with-NAPALM
|
aee4382b2fdeaad0186a8771c90bb0e14d7b563e
|
[
"MIT"
] | 9
|
2017-11-15T15:42:11.000Z
|
2020-12-26T13:23:05.000Z
|
from napalm_base import get_network_driver
from pprint import pprint
from json import dumps
junos_driver = get_network_driver('junos')
device = junos_driver(hostname='172.30.179.107', username='pytraining', password='Poclab123', optional_args={'port': 830})
device.open()
print('-'*60)
pprint(device.get_interfaces())
print('-'*60)
print dumps(device.get_interfaces(), indent=4)
print('-'*60)
print device.get_interfaces()['me0']['mac_address']
device.close()
'''
# python junos_get_interfaces.py
------------------------------------------------------------
{'.local.': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'Unspecified',
u'speed': -1},
'bme0': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'00:0B:CA:FE:00:00',
u'speed': -1},
'dsc': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'Unspecified',
u'speed': -1},
'ge-0/0/0': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': 4520394.0,
u'mac_address': u'00:19:E2:53:EE:C3',
u'speed': -1},
'ge-0/0/1': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': 4520380.0,
u'mac_address': u'00:19:E2:53:EE:C4',
u'speed': -1},
'ge-0/0/10': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:CD',
u'speed': -1},
'ge-0/0/11': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:CE',
u'speed': -1},
'ge-0/0/12': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:CF',
u'speed': -1},
'ge-0/0/13': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:D0',
u'speed': -1},
'ge-0/0/14': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:D1',
u'speed': -1},
'ge-0/0/15': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:D2',
u'speed': -1},
'ge-0/0/16': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:D3',
u'speed': -1},
'ge-0/0/17': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:D4',
u'speed': -1},
'ge-0/0/18': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:D5',
u'speed': -1},
'ge-0/0/19': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:D6',
u'speed': -1},
'ge-0/0/2': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:C5',
u'speed': -1},
'ge-0/0/20': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:D7',
u'speed': -1},
'ge-0/0/21': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:D8',
u'speed': -1},
'ge-0/0/22': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:D9',
u'speed': -1},
'ge-0/0/23': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:DA',
u'speed': -1},
'ge-0/0/24': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:DB',
u'speed': -1},
'ge-0/0/25': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:DC',
u'speed': -1},
'ge-0/0/26': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:DD',
u'speed': -1},
'ge-0/0/27': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:DE',
u'speed': -1},
'ge-0/0/28': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:DF',
u'speed': -1},
'ge-0/0/29': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:E0',
u'speed': -1},
'ge-0/0/3': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:C6',
u'speed': -1},
'ge-0/0/30': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:E1',
u'speed': -1},
'ge-0/0/31': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:E2',
u'speed': -1},
'ge-0/0/32': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:E3',
u'speed': -1},
'ge-0/0/33': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:E4',
u'speed': -1},
'ge-0/0/34': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:E5',
u'speed': -1},
'ge-0/0/35': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:E6',
u'speed': -1},
'ge-0/0/36': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:E7',
u'speed': -1},
'ge-0/0/37': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:E8',
u'speed': -1},
'ge-0/0/38': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:E9',
u'speed': -1},
'ge-0/0/39': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:EA',
u'speed': -1},
'ge-0/0/4': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:C7',
u'speed': -1},
'ge-0/0/40': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:EB',
u'speed': -1},
'ge-0/0/41': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:EC',
u'speed': -1},
'ge-0/0/42': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:ED',
u'speed': -1},
'ge-0/0/43': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:EE',
u'speed': -1},
'ge-0/0/44': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:EF',
u'speed': -1},
'ge-0/0/45': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:F0',
u'speed': -1},
'ge-0/0/46': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:F1',
u'speed': -1},
'ge-0/0/47': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:F2',
u'speed': -1},
'ge-0/0/5': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:C8',
u'speed': -1},
'ge-0/0/6': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:C9',
u'speed': -1},
'ge-0/0/7': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:CA',
u'speed': -1},
'ge-0/0/8': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:CB',
u'speed': -1},
'ge-0/0/9': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:CC',
u'speed': -1},
'gre': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'Unspecified',
u'speed': -1},
'ipip': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'None',
u'speed': -1},
'lo0': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'Unspecified',
u'speed': -1},
'lsi': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'Unspecified',
u'speed': -1},
'me0': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': 4540459.0,
u'mac_address': u'00:19:E2:53:EF:3F',
u'speed': 1000},
'mtun': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'Unspecified',
u'speed': -1},
'pimd': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'None',
u'speed': -1},
'pime': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'None',
u'speed': -1},
'tap': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'Unspecified',
u'speed': -1},
'vcp-0': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'Unspecified',
u'speed': 32000},
'vcp-1': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'Unspecified',
u'speed': 32000},
'vlan': {u'description': u'',
u'is_enabled': True,
u'is_up': True,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:C1',
u'speed': 1000},
'vme': {u'description': u'',
u'is_enabled': True,
u'is_up': False,
u'last_flapped': -1.0,
u'mac_address': u'00:19:E2:53:EE:C2',
u'speed': 1000}}
------------------------------------------------------------
{
"ge-0/0/7": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:CA",
"speed": -1
},
"ge-0/0/6": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:C9",
"speed": -1
},
"ge-0/0/5": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:C8",
"speed": -1
},
"ge-0/0/4": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:C7",
"speed": -1
},
"ge-0/0/3": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:C6",
"speed": -1
},
"ge-0/0/2": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:C5",
"speed": -1
},
"ge-0/0/1": {
"is_enabled": true,
"description": "",
"last_flapped": 4520383.0,
"is_up": true,
"mac_address": "00:19:E2:53:EE:C4",
"speed": -1
},
"ge-0/0/0": {
"is_enabled": true,
"description": "",
"last_flapped": 4520397.0,
"is_up": true,
"mac_address": "00:19:E2:53:EE:C3",
"speed": -1
},
"lsi": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": true,
"mac_address": "Unspecified",
"speed": -1
},
"ge-0/0/9": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:CC",
"speed": -1
},
"ge-0/0/8": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:CB",
"speed": -1
},
"ge-0/0/13": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:D0",
"speed": -1
},
"ge-0/0/12": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:CF",
"speed": -1
},
"ge-0/0/11": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:CE",
"speed": -1
},
"ge-0/0/10": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:CD",
"speed": -1
},
"ge-0/0/17": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:D4",
"speed": -1
},
"ge-0/0/16": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:D3",
"speed": -1
},
"ge-0/0/15": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:D2",
"speed": -1
},
"ge-0/0/14": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:D1",
"speed": -1
},
"ge-0/0/35": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:E6",
"speed": -1
},
"ge-0/0/34": {
"is_enabled": true,
"description": "",
"last_flapped": -1.0,
"is_up": false,
"mac_address": "00:19:E2:53:EE:E5",
"speed": -1
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}
------------------------------------------------------------
00:19:E2:53:EF:3F
'''
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| null | null | 0.076923
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0
| 9
|
dd77eb5ab8481d4765a40795d07ca01a921bf270
| 36
|
py
|
Python
|
tools/training/__init__.py
|
klasleino/gloro
|
5ebfe0f3850bca20e4ee4414fa2ee8a4af303023
|
[
"MIT"
] | 16
|
2021-02-17T15:06:07.000Z
|
2022-03-28T19:08:54.000Z
|
tools/training/__init__.py
|
klasleino/gloro
|
5ebfe0f3850bca20e4ee4414fa2ee8a4af303023
|
[
"MIT"
] | 1
|
2021-11-30T15:49:31.000Z
|
2021-12-06T20:28:49.000Z
|
tools/training/__init__.py
|
klasleino/gloro
|
5ebfe0f3850bca20e4ee4414fa2ee8a4af303023
|
[
"MIT"
] | 1
|
2021-06-20T06:34:51.000Z
|
2021-06-20T06:34:51.000Z
|
from .train_gloro import train_gloro
| 36
| 36
| 0.888889
| 6
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| 5
| 0.666667
| 0.666667
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0
| 7
|
06e24320f24ef291f20852e44dcb6c14c46f504b
| 34,590
|
py
|
Python
|
Create4096PrimeJump.py
|
oppressionslayer/maxentropy
|
0f00d2ee6733dd4038821abb86490ffb1dd4dac0
|
[
"MIT"
] | 1
|
2019-09-24T01:09:12.000Z
|
2019-09-24T01:09:12.000Z
|
Create4096PrimeJump.py
|
oppressionslayer/maxentropy
|
0f00d2ee6733dd4038821abb86490ffb1dd4dac0
|
[
"MIT"
] | 1
|
2020-01-17T16:32:09.000Z
|
2020-01-17T16:32:09.000Z
|
Create4096PrimeJump.py
|
oppressionslayer/maxentropy
|
0f00d2ee6733dd4038821abb86490ffb1dd4dac0
|
[
"MIT"
] | null | null | null |
#!/usr/bin/python
# run this program with: python Create4096PrimeJump.py
# This program creates a 4096 byte prime with
# a number unrelated to it and climbs a XOR
# tree until it creates the original number
j=-3
x=8
def n(iterx=1):
global x
global j
for loop in range(0, iterx):
temp = j^-x
x=x<<1
j = temp
def p(iterx=1):
global x
global j
for loop in range(0, iterx):
temp = j^x
x=x<<1
j = temp
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start_num=abs(j//2)-1
print(start_num)
| 1,235.357143
| 34,113
| 0.403672
| 13,731
| 34,590
| 1.01675
| 0.004078
| 0.24726
| 0.37089
| 0.250125
| 0.984672
| 0.98417
| 0.984099
| 0.979299
| 0.979299
| 0.95602
| 0
| 0.198729
| 0.003354
| 34,590
| 27
| 34,114
| 1,281.111111
| 0.206242
| 0.005724
| 0
| 0.526316
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.105263
| false
| 0
| 0
| 0
| 0.105263
| 0.052632
| 0
| 0
| 1
| null | 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 13
|
66268569710e4833620efc1b8e28f9ef5e7af401
| 25,029
|
py
|
Python
|
epochExp.py
|
renhaocui/activityExtractor
|
f5abaff28bb84af3af3a7268729541af6fa25f36
|
[
"MIT"
] | 1
|
2019-09-18T16:39:00.000Z
|
2019-09-18T16:39:00.000Z
|
epochExp.py
|
renhaocui/activityExtractor
|
f5abaff28bb84af3af3a7268729541af6fa25f36
|
[
"MIT"
] | 1
|
2019-09-18T19:40:35.000Z
|
2019-09-18T19:40:35.000Z
|
epochExp.py
|
renhaocui/activityExtractor
|
f5abaff28bb84af3af3a7268729541af6fa25f36
|
[
"MIT"
] | null | null | null |
import json
from keras.layers.wrappers import TimeDistributed
from keras.preprocessing.text import Tokenizer
from keras.models import Sequential, Model
from keras.layers import Dense, LSTM, Dropout, Merge, Input, multiply
from keras.layers.embeddings import Embedding
from sklearn.model_selection import train_test_split
from keras.preprocessing import sequence
from keras.utils import np_utils
from sklearn.preprocessing import LabelEncoder
import numpy as np
from sklearn.utils.class_weight import compute_sample_weight, compute_class_weight
from keras.utils import to_categorical
from utilities import word2vecReader
vocabSize = 10000
tweetLength = 25
posEmbLength = 25
embeddingVectorLength = 200
embeddingPOSVectorLength = 200
charLengthLimit = 20
dayMapper = {'Mon': 1, 'Tue': 2, 'Wed': 3, 'Thu': 4, 'Fri': 5, 'Sat': 6, 'Sun': 0}
POSMapper = {'N': 'N', 'O': 'N', '^': 'N', 'S': 'N', 'Z': 'N', 'L': 'N', 'M': 'N',
'V': 'V', 'A': 'A', 'R': 'R', '@': '@', '#': '#', '~': '~', 'U': 'U', 'E': 'E', ',': ',',
'!': '0', 'D': '0', 'P': '0', '&': '0', 'T': '0', 'X': '0', 'Y': '0', '$': '0', 'G': '0'}
def extractPOS(inputList, mode):
output = ''
for item in inputList:
if mode == 'all':
output += item[1] + ' '
else:
output += POSMapper[item[1]] + ' '
return output.strip()
def hourMapper(hour):
input = int(hour)
if 0 <= input < 6:
output = 0
elif 6 <= input < 12:
output = 1
elif 12 <= input < 18:
output = 2
else:
output = 3
return output
def processLSTM(modelName, char=False, epochs=3):
activityList = []
activityListFile = open('lists/google_place_activity_'+modelName+'.list', 'r')
for line in activityListFile:
if not line.startswith('#'):
activityList.append(line.strip())
activityListFile.close()
placeList = []
placeListFile = open('lists/google_place_long.category', 'r')
for line in placeListFile:
if not line.startswith('#'):
placeList.append(line.strip())
placeListFile.close()
labelNum = len(np.unique(activityList))
if 'NONE' in activityList:
labelNum -= 1
contents = []
labels = []
labelCount = {}
dayList = []
hourList = []
labelTweetCount = {}
placeTweetCount = {}
for index, place in enumerate(placeList):
activity = activityList[index]
if activity != 'NONE':
if activity not in labelTweetCount:
labelTweetCount[activity] = 0.0
tweetFile = open('data/POIplace/' + place + '.json', 'r')
tweetCount = 0
for line in tweetFile:
data = json.loads(line.strip())
if len(data['text']) > charLengthLimit:
contents.append(data['text'].replace('\n', ' ').replace('\r', ' ').encode('utf-8').lower())
dateTemp = data['created_at'].split()
day = dayMapper[dateTemp[0]]
hour = hourMapper(dateTemp[3].split(':')[0])
dayList.append(day)
hourList.append(hour)
labels.append(activity)
tweetCount += 1
if activity not in labelCount:
labelCount[activity] = 1.0
else:
labelCount[activity] += 1.0
tweetFile.close()
labelTweetCount[activity] += tweetCount
placeTweetCount[place] = tweetCount
activityLabels = np.array(labels)
dayVector = to_categorical(dayList, num_classes=7)
hourVector = to_categorical(hourList, num_classes=4)
encoder = LabelEncoder()
encoder.fit(labels)
encodedLabels = encoder.transform(labels)
labels = np_utils.to_categorical(encodedLabels)
if char:
tk = Tokenizer(num_words=vocabSize, char_level=char, filters='')
else:
tk = Tokenizer(num_words=vocabSize, char_level=char)
tk.fit_on_texts(contents)
tweetSequences = tk.texts_to_sequences(contents)
tweetVector = sequence.pad_sequences(tweetSequences, maxlen=tweetLength, truncating='post', padding='post')
if char:
oneHotVector = []
for tweet in tweetVector:
oneHotVector.append(to_categorical(tweet, num_classes=70))
tweetVector = np.array(oneHotVector)
# training
print('training...')
tweet_train, tweet_test, day_train, day_test, hour_train, hour_test, label_train, label_test, activityLabels_train, activityLabels_test = train_test_split(tweetVector, dayVector, hourVector, labels, activityLabels)
model_text = Sequential()
if char:
model_text.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, input_shape=(140, 70)))
else:
model_text.add(Embedding(vocabSize, embeddingVectorLength))
model_text.add(Dropout(0.2))
model_text.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))
model_time1 = Sequential()
model_time1.add(Dense(1, input_shape=(4,), activation='relu', name='hour'))
model_time2 = Sequential()
model_time2.add(Dense(1, input_shape=(7,), activation='relu', name='day'))
model = Sequential()
model.add(Merge([model_text, model_time1, model_time2], mode='concat'))
model.add(Dense(labelNum, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
#print(model.summary())
resultFile = open('result/resultEpoch', 'a')
for epoch in range(epochs):
model.fit([tweet_train, hour_train, day_train], label_train, epochs=1, batch_size=10, verbose=0)
scores = model.evaluate([tweet_test, hour_test, day_test], label_test, verbose=0)
print('Epoch '+str(epoch)+': '+str(scores[1]*100))
resultFile.write('Epoch '+str(epoch)+': '+str(scores[1]*100)+'\n\n')
resultFile.close()
def processSLSTM(modelName, char=False, posMode='all', epochs=1):
activityList = []
activityListFile = open('lists/google_place_activity_' + modelName + '.list', 'r')
for line in activityListFile:
if not line.startswith('#'):
activityList.append(line.strip())
activityListFile.close()
placeList = []
placeListFile = open('lists/google_place_long.category', 'r')
for line in placeListFile:
if not line.startswith('#'):
placeList.append(line.strip())
placeListFile.close()
labelNum = len(np.unique(activityList))
if 'NONE' in activityList:
labelNum -= 1
if posMode == 'all':
posVocabSize = 25
else:
posVocabSize = 11
contents = []
labels = []
posList = []
labelCount = {}
dayList = []
hourList = []
idTagMapper = {}
for index, place in enumerate(placeList):
activity = activityList[index]
if activity != 'NONE':
POSFile = open('data/POS/' + place + '.pos', 'r')
for line in POSFile:
data = json.loads(line.strip())
idTagMapper[data['id']] = data['tag']
POSFile.close()
tweetFile = open('data/POIplace/' + place + '.json', 'r')
for line in tweetFile:
data = json.loads(line.strip())
if len(data['text']) > charLengthLimit:
content = data['text'].replace('\n', ' ').replace('\r', ' ').encode('utf-8').lower()
#content = cleaner.tweetCleaner(content)
contents.append(content)
id = data['id']
posList.append((extractPOS(idTagMapper[id], posMode)).encode('utf-8'))
dateTemp = data['created_at'].split()
day = dayMapper[dateTemp[0]]
hour = hourMapper(dateTemp[3].split(':')[0])
dayList.append(day)
hourList.append(hour)
labels.append(activity)
if activity not in labelCount:
labelCount[activity] = 0.0
labelCount[activity] += 1.0
tweetFile.close()
dayVector = to_categorical(dayList, num_classes=7)
hourVector = to_categorical(hourList, num_classes=4)
labels = np.array(labels)
encoder = LabelEncoder()
encoder.fit(labels)
if char:
tk = Tokenizer(num_words=vocabSize, char_level=char, filters='')
else:
tk = Tokenizer(num_words=vocabSize, char_level=char)
tk.fit_on_texts(contents)
tweetSequences = tk.texts_to_sequences(contents)
tweetVector = sequence.pad_sequences(tweetSequences, maxlen=tweetLength, truncating='post', padding='post')
tkPOS = Tokenizer(num_words=posVocabSize, filters='', lower=False)
tkPOS.fit_on_texts(posList)
posSequences = tkPOS.texts_to_sequences(posList)
posVector = sequence.pad_sequences(posSequences, maxlen=tweetLength, truncating='post', padding='post')
tweet_train, tweet_test, day_train, day_test, hour_train, hour_test, posVector_train, posVector_test, label_train, label_test = train_test_split(
tweetVector, dayVector, hourVector, posVector, labels)
labelVector_train = np_utils.to_categorical(encoder.transform(label_train))
labelVector_test = np_utils.to_categorical(encoder.transform(label_test))
model_text = Sequential()
model_text.add(Embedding(vocabSize, embeddingVectorLength))
model_text.add(Dropout(0.2))
model_text.add(LSTM(200, dropout=0.2, recurrent_dropout=0.2))
model_time1 = Sequential()
model_time1.add(Dense(1, input_shape=(4,), activation='relu', name='hour'))
model_time2 = Sequential()
model_time2.add(Dense(1, input_shape=(7,), activation='relu', name='day'))
model_pos = Sequential()
model_pos.add(Embedding(posVocabSize, posEmbLength))
model_pos.add(LSTM(25, dropout=0.2, recurrent_dropout=0.2))
model = Sequential()
model.add(Merge([model_text, model_time1, model_time2, model_pos], mode='concat'))
model.add(Dense(labelNum, activation='softmax', name='output'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
resultFile = open('result/resultEpoch', 'a')
for epoch in range(epochs):
model.fit([tweet_train, hour_train, day_train, posVector_train], labelVector_train, epochs=epochs, batch_size=10, verbose=0)
scores = model.evaluate([tweet_test, hour_test, day_test, posVector_test], labelVector_test, batch_size=1, verbose=0)
print('Epoch ' + str(epoch) + ': ' + str(scores[1] * 100))
resultFile.write('Epoch ' + str(epoch) + ': ' + str(scores[1] * 100) + '\n\n')
resultFile.close()
def processHLSTM(modelName, char=False, posMode='all', epochs=1):
activityList = []
activityListFile = open('lists/google_place_activity_' + modelName + '.list', 'r')
for line in activityListFile:
if not line.startswith('#'):
activityList.append(line.strip())
activityListFile.close()
placeList = []
placeListFile = open('lists/google_place_long.category', 'r')
for line in placeListFile:
if not line.startswith('#'):
placeList.append(line.strip())
placeListFile.close()
labelNum = len(np.unique(activityList))
if 'NONE' in activityList:
labelNum -= 1
if posMode == 'all':
posVocabSize = 25
else:
posVocabSize = 11
contents = []
labels = []
posList = []
labelCount = {}
dayList = []
hourList = []
idTagMapper = {}
for index, place in enumerate(placeList):
activity = activityList[index]
if activity != 'NONE':
POSFile = open('data/POS/' + place + '.pos', 'r')
for line in POSFile:
data = json.loads(line.strip())
idTagMapper[data['id']] = data['tag']
POSFile.close()
tweetFile = open('data/POIplace/' + place + '.json', 'r')
for line in tweetFile:
data = json.loads(line.strip())
if len(data['text']) > charLengthLimit:
content = data['text'].replace('\n', ' ').replace('\r', ' ').encode('utf-8').lower()
#content = cleaner.tweetCleaner(content)
contents.append(content)
id = data['id']
posList.append((extractPOS(idTagMapper[id], posMode)).encode('utf-8'))
dateTemp = data['created_at'].split()
day = dayMapper[dateTemp[0]]
hour = hourMapper(dateTemp[3].split(':')[0])
dayList.append(day)
hourList.append(hour)
labels.append(activity)
if activity not in labelCount:
labelCount[activity] = 0.0
labelCount[activity] += 1.0
tweetFile.close()
dayVector = to_categorical(dayList, num_classes=7)
hourVector = to_categorical(hourList, num_classes=4)
labels = np.array(labels)
encoder = LabelEncoder()
encoder.fit(labels)
if char:
tk = Tokenizer(num_words=vocabSize, char_level=char, filters='')
else:
tk = Tokenizer(num_words=vocabSize, char_level=char)
tk.fit_on_texts(contents)
tweetSequences = tk.texts_to_sequences(contents)
tweetVector = sequence.pad_sequences(tweetSequences, maxlen=tweetLength, truncating='post', padding='post')
tkPOS = Tokenizer(num_words=posVocabSize, filters='', lower=False)
tkPOS.fit_on_texts(posList)
posSequences = tkPOS.texts_to_sequences(posList)
posVector = sequence.pad_sequences(posSequences, maxlen=tweetLength, truncating='post', padding='post')
tweet_train, tweet_test, day_train, day_test, hour_train, hour_test, posVector_train, posVector_test, label_train, label_test = train_test_split(
tweetVector, dayVector, hourVector, posVector, labels)
labelVector_train = np_utils.to_categorical(encoder.transform(label_train))
labelVector_test = np_utils.to_categorical(encoder.transform(label_test))
model_text = Sequential()
model_text.add(Embedding(vocabSize, embeddingVectorLength))
model_text.add(Dropout(0.2))
model_text.add(LSTM(200, dropout=0.2, recurrent_dropout=0.2))
model_time1 = Sequential()
model_time1.add(Dense(1, input_shape=(4,), activation='relu', name='hour'))
model_time2 = Sequential()
model_time2.add(Dense(1, input_shape=(7,), activation='relu', name='day'))
model_pos = Sequential()
model_pos.add(Embedding(posVocabSize, posEmbLength))
model_pos.add(LSTM(25, dropout=0.2, recurrent_dropout=0.2))
model = Sequential()
model.add(Merge([model_text, model_time1, model_time2, model_pos], mode='concat'))
model.add(Dense(labelNum, activation='softmax', name='output'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
resultFile = open('result/resultEpoch', 'a')
for epoch in range(epochs):
model.fit([tweet_train, hour_train, day_train, posVector_train], labelVector_train, epochs=epochs, batch_size=10, verbose=0)
scores = model.evaluate([tweet_test, hour_test, day_test, posVector_test], labelVector_test, batch_size=1, verbose=0)
print('Epoch ' + str(epoch) + ': ' + str(scores[1] * 100))
resultFile.write('Epoch ' + str(epoch) + ': ' + str(scores[1] * 100) + '\n\n')
resultFile.close()
def processTLSTM(modelName, char=False, epochs=3):
activityList = []
activityListFile = open('lists/google_place_activity_' + modelName + '.list', 'r')
for line in activityListFile:
if not line.startswith('#'):
activityList.append(line.strip())
activityListFile.close()
placeList = []
placeListFile = open('lists/google_place_long.category', 'r')
for line in placeListFile:
if not line.startswith('#'):
placeList.append(line.strip())
placeListFile.close()
labelNum = len(np.unique(activityList))
if 'NONE' in activityList:
labelNum -= 1
contents = []
labels = []
dayList = []
hourList = []
for index, place in enumerate(placeList):
activity = activityList[index]
if activity != 'NONE':
tweetFile = open('data/POIplace/' + place + '.json', 'r')
for line in tweetFile:
data = json.loads(line.strip())
if len(data['text']) > charLengthLimit:
content = data['text'].replace('\n', ' ').replace('\r', ' ').encode('utf-8').lower()
contents.append(content)
dateTemp = data['created_at'].split()
day = dayMapper[dateTemp[0]]
hour = hourMapper(dateTemp[3].split(':')[0])
dayList.append(day)
hourList.append(hour)
labels.append(activity)
tweetFile.close()
dayVector = to_categorical(dayList, num_classes=7)
hourVector = to_categorical(hourList, num_classes=4)
labels = np.array(labels)
encoder = LabelEncoder()
encoder.fit(labels)
if char:
tk = Tokenizer(num_words=vocabSize, char_level=char, filters='')
else:
tk = Tokenizer(num_words=vocabSize, char_level=char)
tk.fit_on_texts(contents)
tweetSequences = tk.texts_to_sequences(contents)
tweetVector = sequence.pad_sequences(tweetSequences, maxlen=tweetLength, truncating='post', padding='post')
tweet_train, tweet_test, day_train, day_test, hour_train, hour_test, label_train, label_test = train_test_split(tweetVector, dayVector, hourVector, labels)
labelVector_train = np_utils.to_categorical(encoder.transform(label_train))
labelVector_test = np_utils.to_categorical(encoder.transform(label_test))
model_text = Sequential()
model_text.add(Embedding(vocabSize, embeddingVectorLength))
model_text.add(Dropout(0.2))
model_text.add(LSTM(200, dropout=0.2, recurrent_dropout=0.2))
model_time1 = Sequential()
model_time1.add(Dense(5, input_shape=(4,), activation='relu', name='hour'))
model_time2 = Sequential()
model_time2.add(Dense(5, input_shape=(7,), activation='relu', name='day'))
model = Sequential()
model.add(Merge([model_text, model_time1, model_time2], mode='concat'))
model.add(Dense(labelNum, activation='softmax', name='output'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
resultFile = open('result/resultEpoch', 'a')
for epoch in range(epochs):
model.fit([tweet_train, hour_train, day_train], labelVector_train, epochs=epochs, batch_size=10, verbose=0)
scores = model.evaluate([tweet_test, hour_test, day_test], labelVector_test, batch_size=1, verbose=0)
print('Epoch ' + str(epoch) + ': ' + str(scores[1] * 100))
resultFile.write('Epoch ' + str(epoch) + ': ' + str(scores[1] * 100) + '\n\n')
resultFile.close()
def processMLSTM(modelName, embedding='None', char=False, posMode='all', epochs=4):
activityList = []
activityListFile = open('lists/google_place_activity_' + modelName + '.list', 'r')
for line in activityListFile:
if not line.startswith('#'):
activityList.append(line.strip())
activityListFile.close()
placeList = []
placeListFile = open('lists/google_place_long.category', 'r')
for line in placeListFile:
if not line.startswith('#'):
placeList.append(line.strip())
placeListFile.close()
labelNum = len(np.unique(activityList))
if 'NONE' in activityList:
labelNum -= 1
if posMode == 'all':
posVocabSize = 25
else:
posVocabSize = 11
contents = []
labels = []
posList = []
labelCount = {}
dayList = []
hourList = []
idTagMapper = {}
for index, place in enumerate(placeList):
activity = activityList[index]
if activity != 'NONE':
POSFile = open('data/POS/' + place + '.pos', 'r')
for line in POSFile:
data = json.loads(line.strip())
idTagMapper[data['id']] = data['tag']
POSFile.close()
tweetFile = open('data/POIplace/' + place + '.json', 'r')
for line in tweetFile:
data = json.loads(line.strip())
if len(data['text']) > charLengthLimit:
content = data['text'].replace('\n', ' ').replace('\r', ' ').encode('utf-8').lower()
# content = cleaner.tweetCleaner(content)
contents.append(content)
id = data['id']
posList.append((extractPOS(idTagMapper[id], posMode)).encode('utf-8'))
dateTemp = data['created_at'].split()
day = dayMapper[dateTemp[0]]
hour = hourMapper(dateTemp[3].split(':')[0])
dayList.append(day)
hourList.append(hour)
labels.append(activity)
if activity not in labelCount:
labelCount[activity] = 0.0
labelCount[activity] += 1.0
tweetFile.close()
dayVector = to_categorical(dayList, num_classes=7)
hourVector = to_categorical(hourList, num_classes=4)
labels = np.array(labels)
encoder = LabelEncoder()
encoder.fit(labels)
if char:
tk = Tokenizer(num_words=vocabSize, char_level=char, filters='')
else:
tk = Tokenizer(num_words=vocabSize, char_level=char)
tk.fit_on_texts(contents)
tweetSequences = tk.texts_to_sequences(contents)
tweetVector = sequence.pad_sequences(tweetSequences, maxlen=tweetLength, truncating='post', padding='post')
tkPOS = Tokenizer(num_words=posVocabSize, filters='', lower=False)
tkPOS.fit_on_texts(posList)
posSequences = tkPOS.texts_to_sequences(posList)
posVector = sequence.pad_sequences(posSequences, maxlen=tweetLength, truncating='post', padding='post')
if embedding == 'glove':
print ('Loading glove embeddings...')
embeddings_index = {}
embFile = open('../tweetEmbeddingData/glove.twitter.27B.200d.txt', 'r')
for line in embFile:
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
embFile.close()
print('Found %s word vectors.' % len(embeddings_index))
word_index = tk.word_index
embMatrix = np.zeros((len(word_index) + 1, 100))
for word, i in word_index.items():
if word in embeddings_index:
embVector = embeddings_index[word]
embMatrix[i] = embVector
elif embedding == 'word2vec':
word_index = tk.word_index
w2v = word2vecReader.Word2Vec()
embModel = w2v.loadModel()
embMatrix = np.zeros((len(word_index) + 1, 400))
for word, i in word_index.items():
if word in embModel:
embMatrix[i] = embModel[word]
tweet_train, tweet_test, posVector_train, posVector_test, label_train, label_test = train_test_split(tweetVector, posVector, labels)
labelVector_train = np_utils.to_categorical(encoder.transform(label_train))
labelVector_test = np_utils.to_categorical(encoder.transform(label_test))
input_tweet = Input(shape=(tweetLength,), name='tweet_input')
if embedding in ['glove', 'word2vec']:
Embedding(len(word_index) + 1, embeddingVectorLength, weights=[embMatrix], trainable=False)(input_tweet)
else:
embedding_tweet = Embedding(vocabSize, embeddingVectorLength)(input_tweet)
tweet_lstm = LSTM(200, dropout=0.2, recurrent_dropout=0.2)(embedding_tweet)
input_day = Input(shape=(7,))
day_dense = Dense(1, activation='relu', name='day')(input_day)
input_hour = Input(shape=(4,))
hour_dense = Dense(1, activation='relu', name='hour')(input_hour)
input_pos = Input(shape=(posEmbLength,))
embedding_pos = Embedding(posVocabSize, embeddingPOSVectorLength)(input_pos)
pos_lstm = LSTM(200, dropout=0.2, recurrent_dropout=0.2)(embedding_pos)
comb = multiply([tweet_lstm, pos_lstm])
#comb = concatenate([tweet_lstm, hour_dense, day_dense, pos_lstm])
output = Dense(labelNum, activation='softmax', name='output')(comb)
model = Model(inputs=[input_tweet, input_pos], outputs=output)
#print model.summary()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit([tweet_train, posVector_train], labelVector_train, epochs=epochs, validation_data=([tweet_test, posVector_test], labelVector_test), batch_size=10,
verbose=2)
if __name__ == '__main__':
#processLSTM('long1.5', char=False, epochs=10)
#processTLSTM('long1.5', char=False, epochs=10)
#processSLSTM('long1.5', char=False, posMode='map', epochs=5)
processMLSTM('long1.5', 'none', char=False, posMode='all', epochs=10)
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| 7
|
663c2268f904a5f5160f869417054f07fc0bef75
| 2,404
|
py
|
Python
|
tests/testapp/migrations/0002_auto_20200831_1950.py
|
xx396/django-admin-csvexport
|
1aecbf737a2b7265d52eb025fc471bc22ff7ba4b
|
[
"BSD-3-Clause"
] | 6
|
2020-06-05T06:31:07.000Z
|
2021-09-29T20:47:03.000Z
|
tests/testapp/migrations/0002_auto_20200831_1950.py
|
xx396/django-admin-csvexport
|
1aecbf737a2b7265d52eb025fc471bc22ff7ba4b
|
[
"BSD-3-Clause"
] | 5
|
2020-07-09T14:45:46.000Z
|
2021-08-29T09:19:23.000Z
|
tests/testapp/migrations/0002_auto_20200831_1950.py
|
xx396/django-admin-csvexport
|
1aecbf737a2b7265d52eb025fc471bc22ff7ba4b
|
[
"BSD-3-Clause"
] | 5
|
2020-07-07T15:12:16.000Z
|
2021-11-05T00:10:51.000Z
|
# Generated by Django 2.2.8 on 2020-08-31 19:50
from django.db import migrations, models
import testapp.models
class Migration(migrations.Migration):
dependencies = [
('testapp', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='modela',
name='custom_field',
field=testapp.models.CustomField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ'),
),
migrations.AddField(
model_name='modelb',
name='custom_field',
field=testapp.models.CustomField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ'),
),
migrations.AddField(
model_name='modelc',
name='custom_field',
field=testapp.models.CustomField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ'),
),
migrations.AddField(
model_name='modeld',
name='custom_field',
field=testapp.models.CustomField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ'),
),
migrations.AlterField(
model_name='modela',
name='char_field',
field=models.CharField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ', max_length=255),
),
migrations.AlterField(
model_name='modela',
name='text_field',
field=models.TextField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ"'),
),
migrations.AlterField(
model_name='modelb',
name='char_field',
field=models.CharField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ', max_length=255),
),
migrations.AlterField(
model_name='modelb',
name='text_field',
field=models.TextField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ"'),
),
migrations.AlterField(
model_name='modelc',
name='char_field',
field=models.CharField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ', max_length=255),
),
migrations.AlterField(
model_name='modelc',
name='text_field',
field=models.TextField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ"'),
),
migrations.AlterField(
model_name='modeld',
name='char_field',
field=models.CharField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ', max_length=255),
),
migrations.AlterField(
model_name='modeld',
name='text_field',
field=models.TextField(default='ℋ ℌ ℍ,ℎ;ℏ ℐ ℑ ℒ ℓ"'),
),
]
| 32.053333
| 80
| 0.532862
| 298
| 2,404
| 4.201342
| 0.161074
| 0.038339
| 0.086262
| 0.095847
| 0.83147
| 0.83147
| 0.793131
| 0.793131
| 0.793131
| 0.793131
| 0
| 0.019521
| 0.339434
| 2,404
| 74
| 81
| 32.486486
| 0.768892
| 0.018719
| 0
| 0.882353
| 1
| 0
| 0.181162
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.029412
| 0
| 0.073529
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
b0a829b249c6fa2bd21ba8482690282e7da83ead
| 77,468
|
py
|
Python
|
testfileread.py
|
Geoff9710/thisway
|
678d5ce7e46ab125d2546e8e3fc0356c8bae1c9c
|
[
"BSL-1.0"
] | null | null | null |
testfileread.py
|
Geoff9710/thisway
|
678d5ce7e46ab125d2546e8e3fc0356c8bae1c9c
|
[
"BSL-1.0"
] | null | null | null |
testfileread.py
|
Geoff9710/thisway
|
678d5ce7e46ab125d2546e8e3fc0356c8bae1c9c
|
[
"BSL-1.0"
] | null | null | null |
#file input test
#include python libraries
import csv
import sys
#import scipy, scipy.stats.chisquare
import math
#import scipy
#initialize variables
col_1 = "CompanyName"
col_2 = "RegisteredNumber"
col_3 ="Date Incorporated"
col_5 ="Years In Business"
col_9 = "100" #Credit Limit 1
col_10 = "100" #Credit Limit 2
col_14 = 999
col_15 = "N/A"
col_16 = 1
col_17 = 1
col_18 = 1
col_31 = 1
col_32 = 1
col_36 = 1
col_41 = 1
col_44 = 1
col_47 = 1
col_50 = 1
col_53 = 1
col_56 = 1
col_59 = 1
col_62 = 1
col_65 = 1
col_68 = 1
col_71 = 1
col_74 = 1
col_75 = 1
col_78 = 1
col_80 = 1
col_81 = 1
col_82 = 1
col_83 = 1
col_84 = 1
col_85 = 1
col_86 = 1
col_87 = 1
col_88 = 1
col_89 = 1
col_90 = 1 # somewhere in the Delphi score set
col_91 = 1
col_92 = 1
col_93 = 1
col_94 = 1
col_95 = 1
col_96 = 1
col_97 = 1
col_98 = 1
col_99 = 1
col_100 = 1
col_101 = 1
col_102 = 1
col_103 = 1
col_104 = 1
col_105 = 1
col_106 = 1
col_107 = 1
col_108 = 1
col_109 = 1
col_110 = 1
col_111 = 1
col_112 = 1
col_113 = 1
col_115 = 1
col_116 = 1
col_117 = 1
col_118 = 1
col_119 = 1
col_120 = 1
col_121 = 1
col_122 = 1
col_123 = 1
col_124 = 1
col_125 = 1
col_126 = 1
col_127 = 1
col_128 = 1
col_129 = 1
col_130 = 1
col_131 = 1
col_132 = 1
col_133 = 1
col_134 = 1
col_135 = 1
col_136 = 1
col_137 = 1
col_138 = 1
col_139 = 1
col_140 = 1
col_141 = 1
col_142 = 1
col_143 = 1
col_144 = 1
col_588 = 1
col_589 = 1
col_593 = 1
col_594 = 1
col_595 = 1
col_596 = 1
col_597 = 1
col_598 = 1
col_599 = 1
col_600 = 1
col_601 = 1
col_602 = 1
col_603 = 1
col_604 = 1
col_605 = 1
col_606 = 1
col_607 = 1
col_608 = 1
col_609 = 1
col_610 = 1
col_611 = 1
col_612 = 1
col_613 = 1
col_614 = 1
col_615 = 1
col_616 = 1
col_617 = 1
col_618 = 1
col_619 = 1
col_620 = 1
col_621 = 1
col_622 = 1
col_623 = 1
col_624 = 1
col_625 = 1
col_626 = 1
col_627 = 1
col_628 = 1
col_629 = 1
col_630 = 1
mean1= 1.1
mean2 = 1.1
chi1 = 1.1
chi2 = 1.1
chi_mean = 1.1
counter1 = 0
smooth_sd_factor = 1
smooth_total = 0
smooth_mean = 1
trend_total = 0
comparison_trend = 1
trend_difference = 0
trending_count = 0
pearson_chi_square_mean = 1
test_list_flat =[]
test_list_down = []
test_list_up =[]
dbt_trend_score = 0
delphi_trend_score = 1
credit_limit_trend_score = 1
ecl = " "
other_co_delphi_trend_score = 1
all_co_delphi_trend_score = 1
same_industry_delphi_trend_score = 1
same_asset_delphi_trend_score = 1
same_age_delphi_trend_score = 1
industry_dbt_trend_score = 1
list_test = ["HOMEFUELS DIRECT LTD",6464646,"4-Jan-08","70-74 BRUNSWICK STREET, STOCKTON ON TEES,","01642 700725","10 years 2 months " ,"www.homefuelsdirect.com","WHOLESALE FUELS AND RELATED PRODUCTS","A below average risk company; little reason to doubt credit transactions to the limit assigned.","£ 3,400","£ 1,700","Below Average Risk","41:1","21 February 2018 at 08:56:48",78,"L",0,"L",0,0,"This company pays within its terms",17,"No Legal Notices Recorded","GBP","31/03/2017",110168,-36.09,"D",22979,-70.31,"D", 25781, -17.74, "D", 1, 1, "This Company is not part of a Group", "A below average risk company; little reason to doubt credit transactions to the limit assigned.", "Out of 100", "Below Average Risk 41:1", 93, "£9,600",3200, 95, "£9,800",3300, 84,"£7,200","£2,900",95,"£9,800","£3,300",95,"£9,800","£3,300",95,"£9,800","£3,300",92,"£9,500","£3,200",92,"£9,500","£3,200",88,"£7,600","£3,000",88,"£7,600","£3,000",78,"£3,400","£1,700",78,"£3,400","£1,700",78,"£3,400","£1,700",95,44,52,64,57,84,44,52,64,56,95,45,51,64,57,95,44,51,64,56,95,44,50,64,56,92,44,50,64,56,92,44,49,64,56,0,44,49,63,56,88,44,49,63,55,88,44,49,63,55,78,44,49,64,56,78,44,49,64,57,78,45,49,64,57,"Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles","£90,000 to £150,000","Incorporated between March 2004 and March 2008","Accounts are due to be filed within the next 10 Calendar Months. The Accounts were prepared by a Director,",42825,43101,"UK GAAP",52,"GBP",2802,0,0,0,2802,0,0,0,0,0,0,2802,0,0,0,0,50616,50175,0,0,441,53998,2752,107366,84387,65183,0,0,19090,0,0,0,0,0,0,0,114,0,22979,25781,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25781,2,0,0,0,0,0,25779,0,25781,25781,"UK GAAP",52,"GBP",0, 0, 0, 0, 0, 0,0 , 0, 0,0 ,11781,11781,0,0 ,0,0,0,0,0,0,0,0,160611,160611,83223,0,0,0,0,0,0,0,0,0,0,0,0,83223,77388,89169,57829,0,0,0,0,0,0,57829,0,0,0,0,0,0,31340,0,0,0,0,0,0,0,31340,31340,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5588,5588,0,0,0,0,0,0,0,0,0,0,126813,126813,36285,0,0,0,0,0,0,0,0,0,0,0,0,36285,90528,96116,72555,0,0,0,0,0,0,72555,0,1117,0,0,1117,0,22444,0,0,0,0,0,0,0,22444,22444,0,"UK GAAP",52,"GBP",8786,0,0,0,0,0,0,0,0,0,0,8786,0,0,0,0,52023,0,0,0,0,92572,0,144595,134958,0,0,0,0,0,0,0,0,0,0,0,0,134958,9637,18423,0,0,0,0,0,0,0,0,0,1757,0,0,1757,0,16666,2,2,0,0,0,0,16664,0,16666,16666,"We have not received any Cash Flow statements for this company",1.27,1.27,0,0,0,74.05,23.4,0,0,0,0,80,0,0,0,0,0,0.93,1.93,0,0,0,0,18.18,0,0,0,0,0,0,0,0,0,0,3.49,3.49,0,0,0,0,0,16.95,0,0,0,0,0,0,0,0,0,0,1.07,1.07,0,0,0,0,0,10.87,0,0,0,0,3196,0,0,0,0,0,0,0,0,1.27,74.05,23.4,0,0,0,0,0,0,0,0,1.93,0,18.18,0,0,0,0,0,0,0,0,-34.07,0,28.71,0,0,0,0,0,15.6,2.9,59,1.3,0,41.6,0,"N/A",39748,549985,17795,"Industry Median based on 2,955 similar companies in 1980 SIC Code 6120 - INDUSTRIAL MATERIALS WHOLESALING",0,0,0,1.27,74.05,23.4,0,0,0,0,0,0,2.8,0.4,30,1,0,7,0,"N/A",30951,279239,1034,15.6,2.9,59,1.3,0,41.6,0,"N/A",39748,549985,17795,32.6,7.3,80,2.4,173,78.9,0,"N/A",54544,1144039,49899,2,5,8,"This company pays faster than the industry average. There is a consistent payment pattern. This company has 0 accounts placed for collection. This company has 0 outstanding unpaid accounts. This company pays within its terms.",0,0,1,2,0,17,0,17,15,17,10,17,10,16,0,16,0,16,0,16,0,16,0,17,0,17,0,17,0,17,0,17,0,17,0,17,0,17,0,17,3,17,0,15,0,24,0,16,0,6,0,2,0,0,0,0,0,"This company pays its accounts on average 0 days beyond terms. The payment information we have for this company over the last 6 months available shows a consistent payment pattern.","A search of our databases has shown that there are no County Court Judgments recorded against this company","A search of our databases has shown that there are no Legal Notices recorded against this company","A search of our databases has shown that there are no Mortgages or Charges registered against this company","There are no Consumer Credit Licences registered to this company","The Company was incorporated 10 years 2 months ago. There have been no changes in registered office in the last 12 months.","HOMEFUELS DIRECT LTD",6464646,"Private Limited",39451,"10 years 2 months","GBP 8","70-74 BRUNSWICK STREET, STOCKTON ON TEES, CLEVELAND TS18 1DW","70-74 Brunswick Street, STOCKTON-ON-TEES, Cleveland TS18 1DW","01642 700725","www.homefuelsdirect.com",6120,"INDUSTRIAL MATERIALS WHOLESALING",5151,"WHOLESALE FUELS AND RELATED PRODUCTS","FUEL TO HOMES AND BUSINESS.","STOKESLEY BUSINESS CENTRE, 51 HIGH STREET, STOKESLEY, NORTH YORKSHIRE","Total Exemption Full","31 March",42825,43131,"NICHOLE ROBERTS","This Company has no parent Company","ORD",1,8,8,"yes",100,100,"GBP","Christopher Bicknell","ORD","1.00",8,8,"GBP",100,100,"Christophe David Bicknell","Duport Director Ltd","Thomas Elles Bicknell","Duport Secretary Ltd",39457,"There are no alert notices for this company."]
#
col_594 = list_test[594]
col_596 = list_test[596]
col_598 = list_test[598]
col_600 = list_test[600]
col_602 = list_test[602]
col_604 = list_test[604]
col_606 = list_test[606]
col_608 = list_test[608]
col_610 = list_test[610]
col_612 = list_test[612]
col_614 = list_test[614]
col_616 = list_test[616]
#
print(col_594)
print(col_596)
print(col_598)
print(col_600)
print(col_602)
print(col_604)
print(col_606)
print(col_608)
print(col_610)
print(col_612)
print(col_614)
print(col_616)
# #
# # # calculate the chi-square smoothed average for the series ( removes outliers more than 1 degree of freedom)
# # #The Basic Arithmetic mean
mean1 = ((col_594 + col_596 + col_598 + col_600 + col_602 + col_604 + col_606 + col_608 + col_610 + col_612 + col_614 + col_616)/12)
print("The arithmetic mean is:")
print(mean1)
# in this case we are just looking at the DBT trend score for the same insdustry.
#
# # #The Chi smoothing 1 band
# # #χ2=n∑i=1(Oi−Ei)2Eiχ2=∑i=1n(Oi−Ei)2Ei
# # #We already know n = 13, so do not need to evaluate or use special library really.
# # # already done the sum (and divided it by 12) for the test values.
# # #
# # #we need the distribution chi square value/variance from the distribution curve from the mean
print("So Chi square sum of variances is: ")
# chi2 = ((col_594 - mean1) +(col_596 - mean1)+(col_598 - mean1)+(col_600 - mean1) +(col_602 - mean1)+(col_604 - mean1)+(col_606 - mean1)+(col_608 - mean1) +(col_610 - mean1)+(col_612 - mean1)+ (col_614 - mean1) + (col_616 - mean1)
print(chi2)
chi1 = math.sqrt(abs(chi2))
print("Here is a standard deviation")
print(chi1)
smooth_sd_factor = chi1
print("And the smooth sd factor is: ")
print(smooth_sd_factor)
#The Company values smoothed (outliers above n chi variance
# establish how many Sd's the mean of the company scores are from the chi standard deviation to use in the test.
# If the trend is up, it is bad so add to the score, if the trend is down it is good so decrease the score factor.
print(abs(col_594 - mean1))
# #
if abs(col_594 - mean1) <= (smooth_sd_factor):
smooth_total = smooth_total + col_594
counter1 = counter1 + 1
print("test 594 passed")
if col_594 < col_596:
trend_total = trend_total + 120
elif col_594 > col_596:
trend_total = trend_total - 120
# #
if abs(col_596 - mean1) <= (smooth_sd_factor):
smooth_total = smooth_total + col_596
counter1 = counter1 + 1
print(counter1)
print("test 596 passed")
if ((col_594 + col_596)/2) <= col_598:
trend_total = trend_total + 110
else:
trend_total = trend_total - 110
# #
if abs(col_598 - mean1) <= (smooth_sd_factor):
smooth_total = smooth_total + col_598
counter1 = counter1 + 1
print(counter1)
print("test 598 passed")
if ((col_594 + col_596 + col_598)/3) < col_600:
trend_total = trend_total + 100
elif ((col_594 + col_596 + col_598)/3) > col_600:
trend_total = trend_total - 100
# #
if abs(col_600 - mean1) <= (smooth_sd_factor):
smooth_total = smooth_total + col_600
counter1 = counter1 + 1
print(counter1)
print("test 600 passed")
if ((col_594 + col_596 + col_598 + col_600)/4) < col_602:
trend_total = trend_total + 90
elif ((col_594 + col_596 + col_598 + col_600)/4) > col_602:
trend_total = trend_total - 90
# #
if abs(col_602 - mean1) <= (smooth_sd_factor):
smooth_total = smooth_total + col_602
counter1 = counter1 + 1
print(counter1)
print("test 602 passed")
if ((col_594 + col_596 + col_598 + col_600 + col_602)/5) < col_604:
trend_total = trend_total + 80
else:
trend_total = trend_total - 80
#
if abs(col_604 - mean1)<= (smooth_sd_factor):
smooth_total = smooth_total + col_604
counter1 = counter1 + 1
print(counter1)
print("test =604 passed")
if ((col_594 + col_596 + col_598 + col_600 + col_602 + col_604) / 6) < col_606:
trend_total = trend_total + 70
elif ((col_594 + col_596 + col_598 + col_600 + col_602 + col_604) / 6) > col_606:
trend_total = trend_total - 70
if abs(col_606 - mean1) <= (smooth_sd_factor):
smooth_total = smooth_total + col_606
counter1 = counter1 + 1
print(counter1)
print("test 606 passed")
#
if abs(col_608 - mean1) <= (smooth_sd_factor):
smooth_total = smooth_total + col_608
counter1 = counter1 + 1
print(counter1)
print("test 608 passed")
#
if abs(col_610 - mean1) <= (smooth_sd_factor):
smooth_total = smooth_total + col_610
counter1 = counter1 + 1
print(counter1)
print("test 610 passed")
#
if abs(col_612 - mean1) <= (smooth_sd_factor):
smooth_total = smooth_total + col_612
counter1 = counter1 + 1
print(counter1)
print("test 612 passed")
#
if abs(col_614 - mean1)<= (smooth_sd_factor):
smooth_total = smooth_total + col_614
counter1 = counter1 + 1
print(counter1)
print("test 614 passed")
if abs(col_616 - mean1) <= (smooth_sd_factor):
smooth_total = smooth_total + col_616
counter1 = counter1 + 1
print(counter1)
print("test 616 passed")
print(" The Smooth Total is :")
print(smooth_total)
print(" The trend Total is :")
print(trend_total)
print(counter1)
# # # # # # test_list_flat = [17,17,17,16,16,16,16,16,17,17,17,17]
# # # # # # test_list_down = [10,10,11,13,13,14,16,16,17,17,17,17]
# # # # # # test_list_up = [20,18,18,17,16,17,16,16,15,14,13,11]
# # # # # # # if max and median is greater than 1 SD above, then trend is up, else
# # # # # # #if min and median is greater than 1 SD below, then trend is down.
# # # # # # #in a 12 month pattern, it is not possible to identify seasonality completely.
# # # # # # # Evaluate banding score based on range sets:
# # # # # # #Because of the rolling average trend factor, there are 30 possible ranges of scores, to be assigned to the percentiles -30, -20, -10, 10, 20
# # # # # # #range is over 20% up, over 10% up, between 10% up and 10% down, between 10% down and 20% down, between 20 % down and 30% down.
# # # # # #
if smooth_mean !=0:
if (trend_total - smooth_total) <= -230:
industry_dbt_trend_score = 20
elif (trend_total- smooth_total) < -30:
industry_dbt_trend_score = 10
elif (trend_total- smooth_total) < 190:
industry_dbt_trend_score = -10
elif (trend_total- smooth_total) < 280:
industry_dbt_trend_score = -20
elif trend_total > 280:
industry_dbt_trend_score = -30
print(industry_dbt_trend_score)
#
#------------------------------ Old working
# col_31 = list_test[31]Working Capital
# col_32 = list_test[32]Shareholder funds
# col_40 = list_test[40]
# col_43 = list_test[43]
# col_46 = list_test[46]
# col_49 = list_test[49]
# col_52 = list_test[52]
# col_55 = list_test[55]
# col_58 = list_test[58]
# col_61 = list_test[61]
# col_64 = list_test[64]
# col_67 = list_test[67]
# col_70 = list_test[70]
# col_73 = list_test[73]
# # col_81 = list_test[81] # somewhere in the Delphi score comparisons set
# # col_88 = list_test[88]
# # #print(col_14)
# #print(col_18)
# print(col_40)
# print(col_43)
# print(col_46)
# print(col_49)
# print(col_52)
# print(col_55)
# print(col_58)
# print(col_61)
# print(col_64)
# print(col_67)
# print(col_70)
# print(col_73)
#
#
#
#
# # calculate the chi-square smoothed average for the series ( removes outliers more than 1 degree of freedom)
# #The Basic Arithmetic mean
# mean1 = ((col_40+col_43+col_46+col_49+col_52+col_55+col_58+col_61+col_64+col_67+col_70+col_73)/12)
# print("The arithmetic mean is:")
# print(mean1)
# # in this case we are just looking at the delphi score trend for the subject company.
#
# # #The Chi smoothing 1 band
# # #χ2=n∑i=1(Oi−Ei)2Eiχ2=∑i=1n(Oi−Ei)2Ei
# # #We already know n = 12, so do not need to evaluate or use special library really.
# # # already done the sum (and divided it by 12) for the test values.
# #
# # #we need the distribution chi square value/variance from the distribution curve from the mean
# print("So Chi square sum of variances is: ")
# chi2 = ((col_40 - mean1) +(col_43 - mean1)+(col_46 - mean1)+(col_49 - mean1) +(col_52 - mean1)+(col_55 - mean1)+(col_58 - mean1)+(col_61 - mean1) +(col_64 - mean1)+(col_67 - mean1)+ (col_70 - mean1) + (col_73 - mean1))
# print(chi2)
# chi1 = math.sqrt(abs(chi2))
# print("Here is a standard deviation")
# print(chi1)
# smooth_sd_factor =((mean1 * chi1)*100000)
# print("And the smooth sd factor is: ")
# print(smooth_sd_factor)
# # #The Company values smoothed (outliers above n chi variance
# # # establish how many Sd's the mean of the company scores are from the chi standard deviation to use in the test.
# # # print("The factor that the mean of the series varies from the chi square sd is:")
# # # print(smooth_sd_factor)
# # print("So the series will not include values that are +/- ")
# # print(smooth_sd_factor)
# # # Check if Company DBT values are more than the chi1 variance, and get a new smoothed average, add 1 to the counter if it does not pass the test, update the aggregator
# # #also, evaluate from the latest to the oldest whether there is an upward or a downward trend on the smoothed values. Each subsequent older period not an outlier has a lesser impact.
# # # and use rolling averages
# print(abs(col_40 - mean1))
#
# if abs(col_73 - mean1) <=(smooth_sd_factor):
# smooth_total = col_73
# print("test 73 passed")
# else:
# smooth_total = 0
# if col_73 > col_70:
# trend_total = 120
# else:
# trend_total = -120
#
# if abs(col_70 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_70
# counter1 = counter1 + 1
# print("test 70 passed")
#
# if ((col_70 + col_73)/2) > col_67:
# trend_total = trend_total + 110
# else:
# trend_total = trend_total - 110
# if abs(col_67 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_67
# counter1 = counter1 + 1
# print("test 67 passed")
#
# if ((col_73 + col_70 +col_67)/3) > col_64:
# trend_total = trend_total + 100
# else:
# trend_total = trend_total - 100
# if (col_64 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_64
# counter1 = counter1 + 1
# print("test 64 passed")
# if ((col_73 + col_70 + col_67 + col_64)/4) > col_61:
# trend_total = trend_total + 90
# else:
# trend_total = trend_total - 90
# if abs(col_61- mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_61
# counter1 = counter1 + 1
# print("test 61 passed")
# if ((col_73 + col_70 + col_67 + col_64 + col_61)/5) > col_58:
# trend_total = trend_total + 80
# else:
# trend_total = trend_total - 80
# if abs(col_58-mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_58
# counter1 = counter1 + 1
# print("test 58 passed")
# if ((col_73 + col_70 + col_67 + col_64 + col_61 + col_58) / 6) > col_55:
# trend_total = trend_total + 70
# else:
# trend_total = trend_total - 70
# if abs(col_55-mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_55
# counter1 = counter1 + 1
# print("test 55 passed")
# if abs(col_52 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_52
# counter1 = counter1 + 1
# print("test 52 passed")
# if abs(col_49- mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_49
# counter1 = counter1 + 1
# print("test 49 passed")
# if abs(col_46- mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_46
# counter1 = counter1 + 1
# print("test 46 passed")
# if abs(col_43 - mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_43
# counter1 = counter1 + 1
# print("test 43 passed")
# if abs(col_40 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_40
# counter1 = counter1 + 1
# print("test 40 passed")
# print(smooth_total)
# print(counter1)
# # compare chi-square smoothed mean to latest Delphi Score
# smooth_mean = smooth_total/counter1
# if smooth_mean > 0:
# trend_difference = ((col_40/smooth_mean))
# # #This would eatablish a trend difference - either increases or decreses the trend based on the current period avarage score and smoothed average score difference.
# #
# print(smooth_mean)
# print(trend_difference)
# # #if there is a negative or positive trend, there will be a factor to apply to correctly position in the range buckets.
# trending_count = trend_difference * smooth_mean
# print(trending_count)
# #
# # #it is possible to score in the trend_total moving average for the last six months a value between -570 and 570. The sample scored -570 and the scores earso that should not have an impact.
# # # a positive value of the trend_total is a bad thing in the case of DBT, as the days beyond terms are trending up for this company. A negative value is good.
# # #If the smoothed average and the latest DBT are close, there will be a trend_difference of 0.
# # # If the Smooth Mean is lower than col_18 . trend difference may be negative, indicating that the trend was up - need to factor in the rolling average trend to this value
# # # if the smooth mean is higher than col_8, trend is positive, indicating that the DBT actually decreased as trend difference
# print(trend_total)
# # #trend_difference samples
# print(trending_count)
# # test_list_flat = [17,17,17,16,16,16,16,16,17,17,17,17]
# # test_list_down = [10,10,11,13,13,14,16,16,17,17,17,17]
# # test_list_up = [20,18,18,17,16,17,16,16,15,14,13,11]
# # # if max and median is greater than 1 SD above, then trend is up, else
# # #if min and median is greater than 1 SD below, then trend is down.
# # #in a 12 month pattern, it is not possible to identify seasonality completely.
# # # Evaluate banding score based on range sets:
# # #Because of the rolling average trend factor, there are 30 possible ranges of scores, to be assigned to the percentiles -30, -20, -10, 10, 20
# # #range is over 20% up, over 10% up, between 10% up and 10% down, between 10% down and 20% down, between 20 % down and 30% down.
# #
# if smooth_mean !=0:
# if trending_count <= -230:
# delphi_trend_score = 20
# elif trending_count < -30:
# delphi_trend_score = 10
# elif trending_count < 190:
# delphi_trend_score = -10
# elif trending_count <280:
# delphi_trend_score = -20
# elif trending_count > 280:
# delphi_trend_score = -30
# print(delphi_trend_score)
#
#
# # get the range of
# # values for the chi-square smoothed mean over last 12 months
# #col_101 = list_test[101]
# #col_588 = list_test[588]
# col_18 = list_test[18]
# col_593 = list_test[593]
# col_594 = list_test[594]
# col_595 = list_test[595]
# col_596 = list_test[596]
# col_597 = list_test[597]
# col_598 = list_test[598]
# col_599 = list_test[599]
# col_600 = list_test[600]
# col_601 = list_test[601]
# col_602 = list_test[602]
# col_603 = list_test[603]
# col_604 = list_test[604]
# col_605 = list_test[605]
# col_606 = list_test[606]
# col_607 = list_test[607]
# col_608 = list_test[608]
# col_609 = list_test[609]
# col_610 = list_test[610]
# col_611 = list_test[611]
# col_612 = list_test[612]
# col_613 = list_test[613]
# col_614 = list_test[614]
# col_615 = list_test[615]
# col_616 = list_test[616]
# #print(col_101)
# #print(col_588)
# print(col_593)
# #print(col_594) #Ind
# print(col_595)
# # #print(col_596)
# # print(col_597)
# # #print(col_598)
# # print(col_599)
# # #print(col_600)
# print(col_601)
# # #print(col_602)
# # print(col_603)
# # #print(col_604)
# # print(col_605)
# # #print(col_606)
# # print(col_607)
# # #print(col_608)
# # print(col_609)
# # #print(col_610)
# # print(col_611)
# # #print(col_612)
# # print(col_613)
# # #print(col_614)
# # print(col_615)
# # #print(col_616)
# # #print(col_18)
# # calculate the chi-square smoothed average for the series ( removes outliers more than 1 degree of freedom)
# #The Basic Arithmetic mean
# mean1 = ((col_593+col_595+col_597+col_599+col_601+col_603+col_605+col_607+col_609+col_611+col_613+col_615)/12)
# print("The arithmetic mean is:")
# print(mean1)
# #The industry trend
# mean2 = ((col_594+col_596+col_598+col_600+col_602+col_604+col_606+col_608+col_610+col_612+ col_614+col_616)/12)
# print("The industry mean is :")
# print(mean2)
# #The Chi smoothing 1 band
# #χ2=n∑i=1(Oi−Ei)2Eiχ2=∑i=1n(Oi−Ei)2Ei
# #We already know n = 12, so do not need to evaluate or use special library really.
# # already done the sum (and divided it by 12) for the test values.
#
# #we need the distribution chi square value/variance from the distribution curve from the comparison variable
# #print("So Chi square sum of variances is: ")
# chi2 = ((col_594- mean2) +(col_596- mean2)+(col_598-mean2)+(col_600-mean2) +(col_602-mean2)+(col_604-mean2)+(col_606-mean2)+(col_608-mean2) +(col_610-mean2)+(col_612-mean2)+(col_614-mean2)+(col_616-mean2))
# #print(chi2)
# chi1 = math.sqrt(chi2)
# print("Here is a standard deviation on industry average")
# print(chi1)
# smooth_sd_factor = (abs(mean2-mean1)- (mean2/2))
# print("And the smooth sd factor is: ")
# print(smooth_sd_factor)
# #The Company values smoothed (outliers above n chi variance
# # establish how many Sd's the mean of the company scores are from the chi standard deviation to use in the test.
# # print("The factor that the mean of the series varies from the chi square sd is:")
# # print(smooth_sd_factor)
# print("So the series will not include values that are +/- ")
# print(smooth_sd_factor)
# # Check if Company DBT values are more than the chi1 variance, and get a new smoothed average, add 1 to the counter if it does not pass the test, update the aggregator
# #also, evaluate from the latest to the oldest whether there is an upward or a downward trend on the smoothed values. Each subsequent older period not an outlier has a lesser impact.
# # and use rolling averages
# print(abs(col_593 - mean1))
# print(smooth_sd_factor * mean2)
# if abs(col_593 - mean1) <=(smooth_sd_factor):
# smooth_total = col_593
# print("test 593 passed")
# else:
# smooth_total = 0
# if col_593 > col_595:
# trend_total = 120
# else:
# trend_total = -120
# if abs(col_595 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_595
# counter1 = counter1 + 1
# print("test 595 passed")
# if ((col_593 + col_595)/2) > col_597:
# trend_total = trend_total + 110
# else:
# trend_total = trend_total - 110
# if abs(col_597 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_597
# counter1 = counter1 + 1
# print("test 597 passed")
# if ((col_593 + col_595+col_597)/3) > col_599:
# trend_total = trend_total + 100
# else:
# trend_total = trend_total - 100
# if (col_599-mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_599
# counter1 = counter1 + 1
# print("test 599 passed")
# if ((col_593 + col_595 + col_597 + col_599)/4) > col_601:
# trend_total = trend_total + 90
# else:
# trend_total = trend_total - 90
# if abs(col_601- mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_601
# counter1 = counter1 + 1
# print("test 601 passed")
# if ((col_593 + col_595 + col_597 + col_599+col_601)/5) > col_603:
# trend_total = trend_total + 80
# else:
# trend_total = trend_total - 80
# if abs(col_603-mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_603
# counter1 = counter1 + 1
# print("test 603 passed")
# if ((col_593 + col_595 + col_597 + col_599 + col_601 + col_601) / 6) > col_605:
# trend_total = trend_total + 70
# else:
# trend_total = trend_total - 70
# if abs(col_605-mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_605
# counter1 = counter1 + 1
# print("test 605 passed")
# if abs(col_607 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_607
# counter1 = counter1 + 1
# print("test 607 passed")
# if abs(col_609- mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_609
# counter1 = counter1 + 1
# print("test 609 passed")
# if abs(col_611- mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_611
# counter1 = counter1 + 1
# print("test 611 passed")
# if abs(col_613 - mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_613
# counter1 = counter1 + 1
# print("test 613 passed")
# if abs(col_615 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_615
# counter1 = counter1 + 1
# print("test 615 passed")
# print(smooth_total)
# print(counter1)
# # compare chi-square smoothed mean to latest DBT
# smooth_mean = smooth_total/counter1
# if smooth_mean > 0:
# trend_difference = 100 -((col_18/smooth_mean)*100)
# #This would eatablish a trend difference - either increases or decreses the trend based on the current period avarage score and smoothed average score difference.
#
# print(smooth_mean)
# print(trend_difference)
# #if there is a negative or positive trend, there will be a factor to apply to correctly position in the range buckets.
# trending_count = trend_difference * smooth_mean
# print(trending_count)
#
# #it is possible to score in the trend_total moving average for the last six months a value between -570 and 570. The sample scored -90 so that should not have an impact.
# # a positive value of the trend_total is a bad thing in the case of DBT, as the days beyond terms are trending up for this company. A negative value is good.
# #If the smoothed average and the latest DBT are close, there will be a trend_difference of 0.
# # If the Smooth Mean is lower than col_18 . trend difference may be negative, indicating that the trend was up - need to factor in the rolling average trend to this value
# # if the smooth mean is higher than col_8, trend is positive, indicating that the DBT actually decreased as trend difference
# print(trend_total)
# #trend_difference samples
# #print(trending_count)
# test_list_flat = [17,17,17,16,16,16,16,16,17,17,17,17]
# test_list_down = [10,10,11,13,13,14,16,16,17,17,17,17]
# test_list_up = [20,18,18,17,16,17,16,16,15,14,13,11]
# # if max and median is greater than 1 SD above, then trend is up, else
# #if min and median is greater than 1 SD below, then trend is down.
# #in a 12 month pattern, it is not possible to identify seasonality completely.
# # Evaluate banding score based on range sets:
# #Because of the rolling average trend factor, there are 30 possible ranges of scores, to be assigned to the percentiles -30, -20, -10, 10, 20
# #range is over 20% up, over 10% up, between 10% up and 10% down, between 10% down and 20% down, between 20 % down and 30% down.
#
# if smooth_mean !=0:
# if trending_count <= -230:
# dbt_trend_score = 20
# elif trending_count < -30:
# dbt_trend_score = 10
# elif trending_count < 190:
# dbt_trend_score = -10
# elif trending_count <280:
# dbt_trend_score = -20
# elif trending_count > 280:
# dbt_trend_score = -30
# print(dbt_trend_score)
#sum(cost[c] * cases[c] for c in range(len(cost))) / sum(cases)
# See what `zip()` does to your `cost` and `cases`
#print(list(zip(____, ____)))
# Calculate the weighted average
#print(sum([x * y for x, y in zip(cost, cases)]) / sum(cases))
# if col_593 > col_595:
# trending = 120
# else:
# trending = -120
# print(trending_count)
# if ((col_593 + col595/col_593)/2) > col_595:
# trending_count = trending_count + 110
# else:
# trending_count = trending_count - 110
# data_line =[]
# first_limit = 1
# second_limit = 1
# ecl = "$2,000"
# rcl = "$2,000"
# lmt_range_factor = 0.0000000001
# denominator = 1 # bottom of the fraction
# numerator = 1 # top of the fraction
# shareholders_funds = 1
#ccj_value_open = 1
# col_31 = list_test[31]
# col_18 = list_test[18]
# print(col_31)
#print(col_18)
#need to extract the numbers from the value as a string with currency symbol and punctuation.
#ecl = Experian credit limit minimum of the two values, usually the first
# ecl = col_9.replace("£","")
# ecl = ecl.replace(",","")
# rcl = col_10.replace("£","")
# rcl = rcl.replace(",","")
# first_limit = int(ecl)
# second_limit = int(rcl)
# print(first_limit)
# print(second_limit)
# denominator = first_limit
# #if global(Requested_CreditLimti) != 1:
# # denominator = global.Requested_CreditLimit
# # numerator = first_limit
#
# lmt_range_factor = (numerator / denominator)
# print(lmt_range_factor)
# lmt_range_factor = lmt_range_factor * 100
# print(lmt_range_factor)
#lower_limit = int(col_9)
#print(lower_limit)
#exit ="False"
#years_in_business = float(1.101)
#years= float(1.0)
#months = float(0.5)
#age_string_len = len(col_5)
#print(age_string_len)
#years = float(col_5[0:2])
#print(years)
#months = float(col_5[9:11])/12
#print(months)
#years_in_business = years + months
#print(years_in_business)
#with open("Homefuels.csv", "r") as csv_file:
# data_line = list(csv_file)[1]
# print(list_test)
# print(data_line)
# #Used to debug/test
#found out that the list from the csv is in an invalid format to treat as a list, there needs to be quotes on anything that is not a number.
#Evaluate if any values have been detected.
# Return in return code order (future) and call logging(future) to record.
#Used in debug/test
# lengthlist = len(data_line)
#lengthlist = len(list_test)
#print(lengthlist)
#if lengthlist != 0:
# data_read = 1
# print(data_read)
# col_1 = list_test[14]
# print(col_1)
# if col_1 != 999:
# exit = "True"
#print(exit)
#Calculate years in business from text version of years in business up to report creation
# print(list_test[1])
# print(data_line[0])
# print(data_line[1])
# print(list_test[2])
# print(data_line[2])
# print(list_test[3])
# print(data_line[3])
# print(col_1)
# when the content is addressed and typed, then the list feature reads by field, and not as a character string
# so need to loop over the data_line to quote anything but integers, or collect character by character up to the field delimeter.
# print(data_line)
#parse out the fields - first few are here
# Company Name,Registered Number,Date incorporated,Registered Office,Telephone Number,Age of Company,Website,Industry Type,Comment,Credit Limit,
# for data_line ins
#
# get the range of values for over last 12 months strip out currency code and punctuation and convert to integer.
# ecl = list_test[41].replace("£","")
# #print(ecl)
# ecl = ecl.replace(",","")
# #print(ecl)
# col_41 = int(ecl)
#
# ecl = list_test[44].replace("£","")
# ecl = ecl.replace(",","")
# col_44 = int(ecl)
#
# ecl = list_test[47].replace("£","")
# ecl = ecl.replace(",","")
# col_47 = int(ecl)
#
# ecl = list_test[50].replace("£","")
# ecl = ecl.replace(",","")
# col_50 = int(ecl)
#
# ecl = list_test[53].replace("£","")
# ecl = ecl.replace(",","")
# col_53 = int(ecl)
#
# cl = list_test[56].replace("£","")
# ecl = ecl.replace(",","")
# col_56 = int(ecl)
#
# ecl = list_test[59].replace("£","")
# ecl = ecl.replace(",","")
# col_59 = int(ecl)
#
# ecl = list_test[62].replace("£","")
# ecl = ecl.replace(",","")
# col_62 = int(ecl)
#
# ecl = list_test[65].replace("£","")
# ecl = ecl.replace(",","")
# col_65 = int(ecl)
#
# ecl = list_test[68].replace("£","")
# ecl = ecl.replace(",","")
# col_68 = int(ecl)
#
# ecl = list_test[71].replace("£","")
# ecl = ecl.replace(",","")
# col_71 = int(ecl)
#
# ecl = list_test[74].replace("£","")
# ecl = ecl.replace(",","")
# col_74 = int(ecl)
#
# col_73 = list_test[73]
# col_81 = list_test[81] # somewhere in the Delphi score comparisons set
# col_88 = list_test[88]
# col_81 = 1
# col_82 = 1
# col_83 = 1
# col_84 = 1
# col_85 = 1
# col_86 = 1
# col_87 = 1
# col_88 = 1
# col_89 = 1
# col_90 = 1 # somewhere in the Delphi score set
# col_91 = 1
# col_92 = 1
# col_93 = 1
# col_94 = 1
# col_95 = 1
# col_96 = 1
# col_97 = 1
# col_98 = 1
# col_99 = 1
# col_100 = 1
# col_101 = 1
# col_102 = 1
# col_103 = 1
# col_104 = 1
# col_105 = 1
# col_106 = 1
# col_107 = 1
# col_108 = 1
# col_109 = 1
# col_110 = 1
# col_111 = 1
# col_112 = 1
# col_113 = 1
# col_115 = 1
# col_116 = 1
# col_117 = 1
# col_118 = 1
# col_119 = 1
# col_120 = 1
# col_121 = 1
# col_122 = 1
# col_123 = 1
# col_124 = 1
# col_125 = 1
# col_126 = 1
# col_127 = 1
# col_128 = 1
# col_129 = 1
# col_130 = 1
# col_131 = 1
# col_132 = 1
# col_133 = 1
# col_134 = 1
# col_135 = 1
# col_136 = 1
# col_137 = 1
# col_138 = 1
# col_139 = 1
# col_140 = 1
# col_141 = 1
# col_142 = 1
# col_143 = 1
# col_144 = 1
# col_588 = 1
# col_589 = 1
# col_593 = 1
# col_594 = 1
# col_595 = 1
# col_596 = 1
# col_597 = 1
# col_598 = 1
# col_599 = 1
# col_600 = 1
# col_601 = 1
# col_602 = 1
# col_603 = 1
# col_604 = 1
# col_605 = 1
# col_606 = 1
# col_607 = 1
# col_608 = 1
# col_609 = 1
# col_610 = 1
# col_611 = 1
# col_612 = 1
# col_613 = 1
# col_614 = 1
# col_615 = 1
# col_616 = 1
# mean1= 1.1
# mean2 = 1.1
# chi1 = 1.1
# chi2 = 1.1
# chi_mean = 1.1
# counter1 = 0
# smooth_sd_factor = 1
# smooth_total = 1
# smooth_mean = 1
# trend_total = 0
# comparison_trend = 1
# trend_difference = 0
# trending_count = 0
# pearson_chi_square_mean = 1
# test_list_flat =[]
# test_list_down = []
# test_list_up =[]
# dbt_trend_score = 0
# delphi_trend_score = 1
# credit_limit_trend_score = 1
# ecl = " "
# other_co_delphi_trend_score = 1
# all_co_delphi_trend_score = 1
#
# list_test = ["HOMEFUELS DIRECT LTD",6464646,"4-Jan-08","70-74 BRUNSWICK STREET, STOCKTON ON TEES,","01642 700725","10 years 2 months " ,"www.homefuelsdirect.com","WHOLESALE FUELS AND RELATED PRODUCTS","A below average risk company; little reason to doubt credit transactions to the limit assigned.","£ 3,400","£ 1,700","Below Average Risk","41:1","21 February 2018 at 08:56:48",78,"L",0,"L",0,0,"This company pays within its terms",17,"No Legal Notices Recorded","GBP","31/03/2017",110168,-36.09,"D",22979,-70.31,"D", 25781, -17.74, "D", 1, 1, "This Company is not part of a Group", "A below average risk company; little reason to doubt credit transactions to the limit assigned.", "Out of 100", "Below Average Risk 41:1", 93, "£9,600",3200, 95, "£9,800",3300, 84,"£7,200","£2,900",95,"£9,800","£3,300",95,"£9,800","£3,300",95,"£9,800","£3,300",92,"£9,500","£3,200",92,"£9,500","£3,200",88,"£7,600","£3,000",88,"£7,600","£3,000",78,"£3,400","£1,700",78,"£3,400","£1,700",78,"£3,400","£1,700",95,44,52,64,57,84,44,52,64,56,95,45,51,64,57,95,44,51,64,56,95,44,50,64,56,92,44,50,64,56,92,44,49,64,56,0,44,49,63,56,88,44,49,63,55,88,44,49,63,55,78,44,49,64,56,78,44,49,64,57,78,45,49,64,57,"Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles","£90,000 to £150,000","Incorporated between March 2004 and March 2008","Accounts are due to be filed within the next 10 Calendar Months. The Accounts were prepared by a Director,",42825,43101,"UK GAAP",52,"GBP",2802,0,0,0,2802,0,0,0,0,0,0,2802,0,0,0,0,50616,50175,0,0,441,53998,2752,107366,84387,65183,0,0,19090,0,0,0,0,0,0,0,114,0,22979,25781,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25781,2,0,0,0,0,0,25779,0,25781,25781,"UK GAAP",52,"GBP",0, 0, 0, 0, 0, 0,0 , 0, 0,0 ,11781,11781,0,0 ,0,0,0,0,0,0,0,0,160611,160611,83223,0,0,0,0,0,0,0,0,0,0,0,0,83223,77388,89169,57829,0,0,0,0,0,0,57829,0,0,0,0,0,0,31340,0,0,0,0,0,0,0,31340,31340,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5588,5588,0,0,0,0,0,0,0,0,0,0,126813,126813,36285,0,0,0,0,0,0,0,0,0,0,0,0,36285,90528,96116,72555,0,0,0,0,0,0,72555,0,1117,0,0,1117,0,22444,0,0,0,0,0,0,0,22444,22444,0,"UK GAAP",52,"GBP",8786,0,0,0,0,0,0,0,0,0,0,8786,0,0,0,0,52023,0,0,0,0,92572,0,144595,134958,0,0,0,0,0,0,0,0,0,0,0,0,134958,9637,18423,0,0,0,0,0,0,0,0,0,1757,0,0,1757,0,16666,2,2,0,0,0,0,16664,0,16666,16666,"We have not received any Cash Flow statements for this company",1.27,1.27,0,0,0,74.05,23.4,0,0,0,0,80,0,0,0,0,0,0.93,1.93,0,0,0,0,18.18,0,0,0,0,0,0,0,0,0,0,3.49,3.49,0,0,0,0,0,16.95,0,0,0,0,0,0,0,0,0,0,1.07,1.07,0,0,0,0,0,10.87,0,0,0,0,3196,0,0,0,0,0,0,0,0,1.27,74.05,23.4,0,0,0,0,0,0,0,0,1.93,0,18.18,0,0,0,0,0,0,0,0,-34.07,0,28.71,0,0,0,0,0,15.6,2.9,59,1.3,0,41.6,0,"N/A",39748,549985,17795,"Industry Median based on 2,955 similar companies in 1980 SIC Code 6120 - INDUSTRIAL MATERIALS WHOLESALING",0,0,0,1.27,74.05,23.4,0,0,0,0,0,0,2.8,0.4,30,1,0,7,0,"N/A",30951,279239,1034,15.6,2.9,59,1.3,0,41.6,0,"N/A",39748,549985,17795,32.6,7.3,80,2.4,173,78.9,0,"N/A",54544,1144039,49899,2,5,8,"This company pays faster than the industry average. There is a consistent payment pattern. This company has 0 accounts placed for collection. This company has 0 outstanding unpaid accounts. This company pays within its terms.",0,0,1,2,0,17,0,17,15,17,10,17,10,16,0,16,0,16,0,16,0,16,0,17,0,17,0,17,0,17,0,17,0,17,0,17,0,17,0,17,3,17,0,15,0,24,0,16,0,6,0,2,0,0,0,0,0,"This company pays its accounts on average 0 days beyond terms. The payment information we have for this company over the last 6 months available shows a consistent payment pattern.","A search of our databases has shown that there are no County Court Judgments recorded against this company","A search of our databases has shown that there are no Legal Notices recorded against this company","A search of our databases has shown that there are no Mortgages or Charges registered against this company","There are no Consumer Credit Licences registered to this company","The Company was incorporated 10 years 2 months ago. There have been no changes in registered office in the last 12 months.","HOMEFUELS DIRECT LTD",6464646,"Private Limited",39451,"10 years 2 months","GBP 8","70-74 BRUNSWICK STREET, STOCKTON ON TEES, CLEVELAND TS18 1DW","70-74 Brunswick Street, STOCKTON-ON-TEES, Cleveland TS18 1DW","01642 700725","www.homefuelsdirect.com",6120,"INDUSTRIAL MATERIALS WHOLESALING",5151,"WHOLESALE FUELS AND RELATED PRODUCTS","FUEL TO HOMES AND BUSINESS.","STOKESLEY BUSINESS CENTRE, 51 HIGH STREET, STOKESLEY, NORTH YORKSHIRE","Total Exemption Full","31 March",42825,43131,"NICHOLE ROBERTS","This Company has no parent Company","ORD",1,8,8,"yes",100,100,"GBP","Christopher Bicknell","ORD","1.00",8,8,"GBP",100,100,"Christophe David Bicknell","Duport Director Ltd","Thomas Elles Bicknell","Duport Secretary Ltd",39457,"There are no alert notices for this company."]
#
# col_81 = list_test[81] #Mar All
# col_86 = list_test[86]
# col_91 = list_test[91]
# col_96 = list_test[96]
# col_101 = list_test[101]
# col_106 = list_test[106]
# col_111 = list_test[111]
# col_116 = list_test[116]
# col_121 = list_test[121]
# col_126 = list_test[126]
# col_131 = list_test[131]
# col_136 = list_test[136]
# col_141 = list_test[141]
#
#
# # #print(col_14)
# #print(col_18)
# print(col_81) #Same Indus
# # print(col_82)same asset
# # print(col_83) same age
# print(col_86)
# # print(col_87)
# print(col_91) # may same indus
# print(col_96)
# print(col_101)
# print(col_106) # June same indus
# print(col_111) #july all cos
# print(col_116)
# print(col_121)
# print(col_126)
# print(col_131)
# print(col_136)
# print(col_141)
#
# # calculate the chi-square smoothed average for the series ( removes outliers more than 1 degree of freedom)
# #The Basic Arithmetic mean
# mean1 = ((col_81 + col_86 + col_91 + col_96 + col_101 + col_106 + col_111 + col_116 + col_121 + col_126 + col_131 + col_136 + col_141)/13)
# print("The arithmetic mean is:")
# print(mean1)
# # # # in this case we are just looking at the delphi score trend for the subject company.
# # #
# # # # #The Chi smoothing 1 band
# # # # #χ2=n∑i=1(Oi−Ei)2Eiχ2=∑i=1n(Oi−Ei)2Ei
# # # # #We already know n = 12, so do not need to evaluate or use special library really.
# # # # # already done the sum (and divided it by 12) for the test values.
# # # #
# # # # #we need the distribution chi square value/variance from the distribution curve from the mean
# print("So Chi square sum of variances is: ")
# chi2 = ((col_81 - mean1) +(col_86 - mean1)+(col_91 - mean1)+(col_96 - mean1) +(col_101 - mean1)+(col_106 - mean1)+(col_111 - mean1)+(col_116 - mean1) +(col_121 - mean1)+(col_126 - mean1)+ (col_131 - mean1) + (col_136 - mean1) + col_141 - mean1)
# print(chi2)
# chi1 = math.sqrt(abs(chi2))
# print("Here is a standard deviation")
# print(chi1)
# smooth_sd_factor =((mean1 * chi1)*100000)+ (abs((col_81-mean1)))
# print("And the smooth sd factor is: ")
# print(smooth_sd_factor)
# # # # #The Company values smoothed (outliers above n chi variance
# # # # # establish how many Sd's the mean of the company scores are from the chi standard deviation to use in the test.
# # print("The factor that the mean of the series varies from the chi square sd is:")
# # print(smooth_sd_factor)
# # print("So the series will not include values that are +/- ")
# # print(smooth_sd_factor)
# # # Check if Company ecl values are more than the chi1 variance, and get a new smoothed average, add 1 to the counter if it does not pass the test, update the aggregator
# # #also, evaluate from the latest to the oldest whether there is an upward or a downward trend on the smoothed values. Each subsequent older period not an outlier has a lesser impact.
# # # # # # and use rolling averages
# # print(abs(col_81 - mean1))
# #
# if abs(col_81 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_81
# counter1 = counter1 + 1
#
# print("test 81 passed")
# if col_81 > col_86:
# trend_total = trend_total + 120
# else:
# trend_total = trend_total - 120
#
# if col_81 > col_86:
# trend_total = 120
# elif col_81 < col_86:
# trend_total = -120
#
# if abs(col_86 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_86
# counter1 = counter1 + 1
# print(counter1)
# print("test 86 passed")
# if ((col_81 + col_86)/2) > col_91:
# trend_total = trend_total + 110
# else:
# trend_total = trend_total - 110
#
# if abs(col_91 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_91
# counter1 = counter1 + 1
# print(counter1)
# print("test 91 passed")
# if ((col_81 + col_86 + col_91)/3) > col_95:
# trend_total = trend_total + 100
# else:
# trend_total = trend_total - 100
#
# if abs(col_96 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_96
# counter1 = counter1 + 1
# print(counter1)
# print("test 95 passed")
# if ((col_81 + col_86 + col_91 + col_96)/4) > col_101:
# trend_total = trend_total + 90
# else:
# trend_total = trend_total - 90
#
# if abs(col_101 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_101
# counter1 = counter1 + 1
# print(counter1)
# print("test 101 passed")
# if ((col_81 + col_86 + col_91 + col_96 + col_101)/5) > col_106:
# trend_total = trend_total + 80
# else:
# trend_total = trend_total - 80
#
# if abs(col_106 - mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_106
# counter1 = counter1 + 1
# print(counter1)
# print("test 106 passed")
# if ((col_81 + col_86 + col_91 + col_96 + col_101 + col_106) / 6) > col_111:
# trend_total = trend_total + 70
# else:
# trend_total = trend_total - 70
#
# if abs(col_111 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_111
# counter1 = counter1 + 1
# print(counter1)
# print("test 111 passed")
# if abs(col_116 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_116
# counter1 = counter1 + 1
# print(counter1)
# print("test 116 passed")
# if abs(col_121 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_121
# counter1 = counter1 + 1
# print(counter1)
# print("test 121 passed")
# if abs(col_126 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_126
# counter1 = counter1 + 1
# print(counter1)
# print("test 126 passed")
# if abs(col_131 - mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_131
# counter1 = counter1 + 1
# print(counter1)
# print("test 131 passed")
# #Reduce trend if last 2 periods are not moving significantly
# if abs(col_131 - col_136) <=(smooth_sd_factor):
# trend_total = trend_total - smooth_total
#
# if abs(col_136 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_136
# counter1 = counter1 + 1
# print(counter1)
# print("test 136 passed")
#
# if abs(col_141 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_141
# counter1 = counter1 + 1
# print(counter1)
# print("test 141 passed")
# if abs(col_136 - col_141) <= (smooth_sd_factor):
# trend_total = trend_total + 300
# else:
# trend_total = trend_total - 300
# print(" The Smooth Total is :")
# print(smooth_total)
# print(" The trend Total is :")
# print(trend_total)
# print(counter1)
# # compare chi-square smoothed mean to latest Delphi Score
# smooth_mean = smooth_total/counter1
# if smooth_mean > 0:
# trend_difference = ((col_81/smooth_mean))
# # # # #This would eatablish a trend difference - either increases or decreses the trend based on the current period avarage score and smoothed average score difference.
# # # #
# #
# # # # #if there is a negative or positive trend, there will be a factor to apply to correctly position in the range buckets.
# trending_count = trend_difference * smooth_mean
# print(trending_count)
# # # #
# # # # #it is possible to score in the trend_total moving average for the last six months a value between -570 and 570. The sample scored -570 and the scores so that should not have an impact.
# # # # # a positive value of the trend_total is a good thing in the case of Delphi score, as the delphi score that is higher is a lower risk. A negative value is bad.
# # # # #If the smoothed average and the latest Delphi are close, there will be a trend_difference of 0.
# # # # # If the Smooth Mean is lower than col_18 . trend difference may be negative, indicating that the trend was up - need to factor in the rolling average trend to this value
# # # # # if the smooth mean is higher than col_8, trend is positive, indicating that the DBT actually decreased as trend difference
# # print(trend_total)
# # # # #trend_difference samples
# # # #print(" The trending count")
# # # #print(trending_count)
# # # # test_list_flat = [17,17,17,16,16,16,16,16,17,17,17,17]
# # # # test_list_down = [10,10,11,13,13,14,16,16,17,17,17,17]
# # # # test_list_up = [20,18,18,17,16,17,16,16,15,14,13,11]
# # # # # if max and median is greater than 1 SD above, then trend is up, else
# # # # #if min and median is greater than 1 SD below, then trend is down.
# # # # #in a 12 month pattern, it is not possible to identify seasonality completely.
# # # # # Evaluate banding score based on range sets:
# # # # #Because of the rolling average trend factor, there are 30 possible ranges of scores, to be assigned to the percentiles -30, -20, -10, 10, 20
# # # # #range is over 20% up, over 10% up, between 10% up and 10% down, between 10% down and 20% down, between 20 % down and 30% down.
# # # #
# if smooth_mean !=0:
# if (trend_total - smooth_total) <= -230:
# all_co_delphi_trend_score = 20
# elif (trend_total- smooth_total) < -30:
# all_co_delphi_trend_score = 10
# elif (trend_total- smooth_total) < 190:
# all_co_delphi_trend_score = -10
# elif (trend_total- smooth_total) < 280:
# all_co_delphi_trend_score = -20
# elif trend_total > 280:
# all_co_delphi_trend_score = -30
# print(all_co_delphi_trend_score)
# col_82 = list_test[82] #Mar All
# col_87 = list_test[87]
# col_92 = list_test[92]
# col_97 = list_test[97]
# col_102 = list_test[102]
# col_107 = list_test[107]
# col_112 = list_test[112]
# col_117 = list_test[117]
# col_122 = list_test[122]
# col_127 = list_test[127]
# col_132 = list_test[132]
# col_137 = list_test[137]
# col_142 = list_test[142]
#
#
# # #print(col_14)
# #print(col_18)
# print(col_82)# same asset
# # print(col_83) same age
# print(col_87)
# # print(col_87)
# print(col_92) # may same indus
# print(col_97)
# print(col_102)
# print(col_107) # June same indus
# print(col_112) #july all cos
# print(col_117)
# print(col_122)
# print(col_127)
# print(col_132)
# print(col_137)
# print(col_142)
#
# # calculate the chi-square smoothed average for the series ( removes outliers more than 1 degree of freedom)
# #The Basic Arithmetic mean
# mean1 = ((col_82 + col_87 + col_92 + col_97 + col_102 + col_107 + col_112 + col_117 + col_122 + col_127 + col_132 + col_137 + col_142)/13)
# print("The arithmetic mean is:")
# print(mean1)
# # in this case we are just looking at the delphi score trend for the subject company.
#
# #The Chi smoothing 1 band
# #χ2=n∑i=1(Oi−Ei)2Eiχ2=∑i=1n(Oi−Ei)2Ei
# #We already know n = 13, so do not need to evaluate or use special library really.
# # already done the sum (and divided it by 12) for the test values.
# #
# #we need the distribution chi square value/variance from the distribution curve from the mean
# print("So Chi square sum of variances is: ")
# chi2 = ((col_82 - mean1) +(col_87 - mean1)+(col_92 - mean1)+(col_97 - mean1) +(col_102 - mean1)+(col_107 - mean1)+(col_112 - mean1)+(col_117 - mean1) +(col_122 - mean1)+(col_127 - mean1)+ (col_132 - mean1) + (col_137 - mean1) + col_142 - mean1)
# print(chi2)
# chi1 = math.sqrt(abs(chi2))
# print("Here is a standard deviation")
# print(chi1)
# smooth_sd_factor =((mean1 * chi1)*100000)+ (abs((col_81-mean1)))
# print("And the smooth sd factor is: ")
# print(smooth_sd_factor)
# # # # # #The Company values smoothed (outliers above n chi variance
# # # # # # establish how many Sd's the mean of the company scores are from the chi standard deviation to use in the test.
# # # print("The factor that the mean of the series varies from the chi square sd is:")
# # # print(smooth_sd_factor)
# # # print("So the series will not include values that are +/- ")
# # # print(smooth_sd_factor)
# # # # Check if Company ecl values are more than the chi1 variance, and get a new smoothed average, add 1 to the counter if it does not pass the test, update the aggregator
# # # #also, evaluate from the latest to the oldest whether there is an upward or a downward trend on the smoothed values. Each subsequent older period not an outlier has a lesser impact.
# # # # # # # and use rolling averages
# print(abs(col_82 - mean1))
#
# if abs(col_82 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_82
# counter1 = counter1 + 1
# print("test 82 passed")
# if col_82 > col_87:
# trend_total = trend_total + 120
# elif col_82 < col_87:
# trend_total = trend_total - 120
# #
# if abs(col_87 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_87
# counter1 = counter1 + 1
# print(counter1)
# print("test 86 passed")
#
# if ((col_82 + col_87)/2) >= col_92:
# trend_total = trend_total + 110
# else:
# trend_total = trend_total - 110
# #
# if abs(col_92 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_92
# counter1 = counter1 + 1
# print(counter1)
# print("test 92 passed")
# if ((col_82 + col_87 + col_92)/3) > col_97:
# trend_total = trend_total + 100
# elif ((col_82 + col_87 + col_92)/3) < col_97:
# trend_total = trend_total - 100
#
# if abs(col_97 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_97
# counter1 = counter1 + 1
# print(counter1)
# print("test 97 passed")
# if ((col_82 + col_87 + col_92 + col_97)/4) > col_102:
# trend_total = trend_total + 90
# elif ((col_82 + col_87 + col_92 + col_97)/4) < col_102:
# trend_total = trend_total - 90
#
# if abs(col_102 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_102
# counter1 = counter1 + 1
# print(counter1)
# print("test 102 passed")
# if ((col_82 + col_87 + col_92 + col_97 + col_102)/5) > col_107:
# trend_total = trend_total + 80
# else:
# trend_total = trend_total - 80
# #
# if abs(col_107 - mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_107
# counter1 = counter1 + 1
# print(counter1)
# print("test 107 passed")
# if ((col_82 + col_87 + col_93 + col_97+ col_102 + col_107) / 6) > col_112:
# trend_total = trend_total + 70
# elif ((col_82 + col_87 + col_93 + col_97+ col_102 + col_107) / 6) < col_112:
# trend_total = trend_total - 70
# #
# if abs(col_112 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_112
# counter1 = counter1 + 1
# print(counter1)
# print("test 112 passed")
# if abs(col_117 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_117
# counter1 = counter1 + 1
# print(counter1)
# print("test 117 passed")
# if abs(col_122 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_122
# counter1 = counter1 + 1
# print(counter1)
# print("test 122 passed")
# if abs(col_127 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_127
# counter1 = counter1 + 1
# print(counter1)
# print("test 127 passed")
# if abs(col_132 - mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_132
# counter1 = counter1 + 1
# print(counter1)
# print("test 132 passed")
# # #Reduce trend if last 2 periods are not moving significantly
# if abs(col_132 - col_137) <=(smooth_sd_factor):
# trend_total = trend_total - smooth_total
# #
# if abs(col_137 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_137
# counter1 = counter1 + 1
# print(counter1)
# print("test 137 passed")
# #
# if abs(col_142 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_142
# counter1 = counter1 + 1
# print(counter1)
# print("test 142 passed")
# if abs(col_137 - col_142) <= (smooth_sd_factor):
# trend_total = trend_total + 300
# else:
# trend_total = trend_total - 300
#
# print(" The Smooth Total is :")
# print(smooth_total)
# print(" The trend Total is :")
# print(trend_total)
# print(counter1)
# # # compare chi-square smoothed mean to latest Delphi Score
# smooth_mean = smooth_total/counter1
# # if smooth_mean > 0:
# # trend_difference = ((col_81/smooth_mean))
# # # # # #This would eatablish a trend difference - either increases or decreses the trend based on the current period avarage score and smoothed average score difference.
# # # # #
# # #
# # # # # #if there is a negative or positive trend, there will be a factor to apply to correctly position in the range buckets.
# trending_count = trend_difference * smooth_mean
# print(trending_count)
# # # # #
# # # # # #it is possible to score in the trend_total moving average for the last six months a value between -570 and 570. The sample scored -570 and the scores so that should not have an impact.
# # # # # # a positive value of the trend_total is a good thing in the case of Delphi score, as the delphi score that is higher is a lower risk. A negative value is bad.
# # # # # #If the smoothed average and the latest Delphi are close, there will be a trend_difference of 0.
# # # # # # If the Smooth Mean is lower than col_18 . trend difference may be negative, indicating that the trend was up - need to factor in the rolling average trend to this value
# # # # # # if the smooth mean is higher than col_8, trend is positive, indicating that the DBT actually decreased as trend difference
# # # print(trend_total)
# # # # # #trend_difference samples
# # # # #print(" The trending count")
# # # # #print(trending_count)
# # # # # test_list_flat = [17,17,17,16,16,16,16,16,17,17,17,17]
# # # # # test_list_down = [10,10,11,13,13,14,16,16,17,17,17,17]
# # # # # test_list_up = [20,18,18,17,16,17,16,16,15,14,13,11]
# # # # # # if max and median is greater than 1 SD above, then trend is up, else
# # # # # #if min and median is greater than 1 SD below, then trend is down.
# # # # # #in a 12 month pattern, it is not possible to identify seasonality completely.
# # # # # # Evaluate banding score based on range sets:
# # # # # #Because of the rolling average trend factor, there are 30 possible ranges of scores, to be assigned to the percentiles -30, -20, -10, 10, 20
# # # # # #range is over 20% up, over 10% up, between 10% up and 10% down, between 10% down and 20% down, between 20 % down and 30% down.
# # # # #
# if smooth_mean !=0:
# if (trend_total - smooth_total) <= -230:
# same_asset_delphi_trend_score = 20
# elif (trend_total- smooth_total) < -30:
# same_asset_delphi_trend_score = 10
# elif (trend_total- smooth_total) < 190:
# same_asset_delphi_trend_score = -10
# elif (trend_total- smooth_total) < 280:
# same_asset_delphi_trend_score = -20
# elif trend_total > 280:
# same_asset_delphi_trend_score = -30
# print(same_asset_delphi_trend_score)
# #
# #
# col_1 = "CompanyName"
# col_2 = "RegisteredNumber"
# col_3 ="Date Incorporated"
# col_5 ="Years In Business"
# col_9 = "100" #Credit Limit 1
# col_10 = "100" #Credit Limit 2
# col_14 = 999
# col_15 = "N/A"
# col_16 = 1
# col_17 = 1
# col_18 = 1
# col_31 = 1
# col_32 = 1
# col_36 = 1
# col_41 = 1
# col_44 = 1
# col_47 = 1
# col_50 = 1
# col_53 = 1
# col_56 = 1
# col_59 = 1
# col_62 = 1
# col_65 = 1
# col_68 = 1
# col_71 = 1
# col_74 = 1
# col_75 = 1
# col_78 = 1
# col_80 = 1
# col_81 = 1
# col_82 = 1
# col_83 = 1
# col_84 = 1
# col_85 = 1
# col_86 = 1
# col_87 = 1
# col_88 = 1
# col_89 = 1
# col_90 = 1 # somewhere in the Delphi score set
# col_91 = 1
# col_92 = 1
# col_93 = 1
# col_94 = 1
# col_95 = 1
# col_96 = 1
# col_97 = 1
# col_98 = 1
# col_99 = 1
# col_100 = 1
# col_101 = 1
# col_102 = 1
# col_103 = 1
# col_104 = 1
# col_105 = 1
# col_106 = 1
# col_107 = 1
# col_108 = 1
# col_109 = 1
# col_110 = 1
# col_111 = 1
# col_112 = 1
# col_113 = 1
# col_115 = 1
# col_116 = 1
# col_117 = 1
# col_118 = 1
# col_119 = 1
# col_120 = 1
# col_121 = 1
# col_122 = 1
# col_123 = 1
# col_124 = 1
# col_125 = 1
# col_126 = 1
# col_127 = 1
# col_128 = 1
# col_129 = 1
# col_130 = 1
# col_131 = 1
# col_132 = 1
# col_133 = 1
# col_134 = 1
# col_135 = 1
# col_136 = 1
# col_137 = 1
# col_138 = 1
# col_139 = 1
# col_140 = 1
# col_141 = 1
# col_142 = 1
# col_143 = 1
# col_144 = 1
# col_588 = 1
# col_589 = 1
# col_593 = 1
# col_594 = 1
# col_595 = 1
# col_596 = 1
# col_597 = 1
# col_598 = 1
# col_599 = 1
# col_600 = 1
# col_601 = 1
# col_602 = 1
# col_603 = 1
# col_604 = 1
# col_605 = 1
# col_606 = 1
# col_607 = 1
# col_608 = 1
# col_609 = 1
# col_610 = 1
# col_611 = 1
# col_612 = 1
# col_613 = 1
# col_614 = 1
# col_615 = 1
# col_616 = 1
# mean1= 1.1
# mean2 = 1.1
# chi1 = 1.1
# chi2 = 1.1
# chi_mean = 1.1
# counter1 = 0
# smooth_sd_factor = 1
# smooth_total = 0
# smooth_mean = 1
# trend_total = 0
# comparison_trend = 1
# trend_difference = 0
# trending_count = 0
# pearson_chi_square_mean = 1
# test_list_flat =[]
# test_list_down = []
# test_list_up =[]
# dbt_trend_score = 0
# delphi_trend_score = 1
# credit_limit_trend_score = 1
# ecl = " "
# other_co_delphi_trend_score = 1
# all_co_delphi_trend_score = 1
# same_industry_delphi_trend_score = 1
# same_asset_delphi_trend_score = 1
# same_age_delphi_trend_score = 1
#
# list_test = ["HOMEFUELS DIRECT LTD",6464646,"4-Jan-08","70-74 BRUNSWICK STREET, STOCKTON ON TEES,","01642 700725","10 years 2 months " ,"www.homefuelsdirect.com","WHOLESALE FUELS AND RELATED PRODUCTS","A below average risk company; little reason to doubt credit transactions to the limit assigned.","£ 3,400","£ 1,700","Below Average Risk","41:1","21 February 2018 at 08:56:48",78,"L",0,"L",0,0,"This company pays within its terms",17,"No Legal Notices Recorded","GBP","31/03/2017",110168,-36.09,"D",22979,-70.31,"D", 25781, -17.74, "D", 1, 1, "This Company is not part of a Group", "A below average risk company; little reason to doubt credit transactions to the limit assigned.", "Out of 100", "Below Average Risk 41:1", 93, "£9,600",3200, 95, "£9,800",3300, 84,"£7,200","£2,900",95,"£9,800","£3,300",95,"£9,800","£3,300",95,"£9,800","£3,300",92,"£9,500","£3,200",92,"£9,500","£3,200",88,"£7,600","£3,000",88,"£7,600","£3,000",78,"£3,400","£1,700",78,"£3,400","£1,700",78,"£3,400","£1,700",95,44,52,64,57,84,44,52,64,56,95,45,51,64,57,95,44,51,64,56,95,44,50,64,56,92,44,50,64,56,92,44,49,64,56,0,44,49,63,56,88,44,49,63,55,88,44,49,63,55,78,44,49,64,56,78,44,49,64,57,78,45,49,64,57,"Wholesale Trade and Commission Trade, Except of Motor Vehicles and Motorcycles","£90,000 to £150,000","Incorporated between March 2004 and March 2008","Accounts are due to be filed within the next 10 Calendar Months. The Accounts were prepared by a Director,",42825,43101,"UK GAAP",52,"GBP",2802,0,0,0,2802,0,0,0,0,0,0,2802,0,0,0,0,50616,50175,0,0,441,53998,2752,107366,84387,65183,0,0,19090,0,0,0,0,0,0,0,114,0,22979,25781,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25781,2,0,0,0,0,0,25779,0,25781,25781,"UK GAAP",52,"GBP",0, 0, 0, 0, 0, 0,0 , 0, 0,0 ,11781,11781,0,0 ,0,0,0,0,0,0,0,0,160611,160611,83223,0,0,0,0,0,0,0,0,0,0,0,0,83223,77388,89169,57829,0,0,0,0,0,0,57829,0,0,0,0,0,0,31340,0,0,0,0,0,0,0,31340,31340,0,0,0,0,0,0,0,0,0,0,0,0,0,0,5588,5588,0,0,0,0,0,0,0,0,0,0,126813,126813,36285,0,0,0,0,0,0,0,0,0,0,0,0,36285,90528,96116,72555,0,0,0,0,0,0,72555,0,1117,0,0,1117,0,22444,0,0,0,0,0,0,0,22444,22444,0,"UK GAAP",52,"GBP",8786,0,0,0,0,0,0,0,0,0,0,8786,0,0,0,0,52023,0,0,0,0,92572,0,144595,134958,0,0,0,0,0,0,0,0,0,0,0,0,134958,9637,18423,0,0,0,0,0,0,0,0,0,1757,0,0,1757,0,16666,2,2,0,0,0,0,16664,0,16666,16666,"We have not received any Cash Flow statements for this company",1.27,1.27,0,0,0,74.05,23.4,0,0,0,0,80,0,0,0,0,0,0.93,1.93,0,0,0,0,18.18,0,0,0,0,0,0,0,0,0,0,3.49,3.49,0,0,0,0,0,16.95,0,0,0,0,0,0,0,0,0,0,1.07,1.07,0,0,0,0,0,10.87,0,0,0,0,3196,0,0,0,0,0,0,0,0,1.27,74.05,23.4,0,0,0,0,0,0,0,0,1.93,0,18.18,0,0,0,0,0,0,0,0,-34.07,0,28.71,0,0,0,0,0,15.6,2.9,59,1.3,0,41.6,0,"N/A",39748,549985,17795,"Industry Median based on 2,955 similar companies in 1980 SIC Code 6120 - INDUSTRIAL MATERIALS WHOLESALING",0,0,0,1.27,74.05,23.4,0,0,0,0,0,0,2.8,0.4,30,1,0,7,0,"N/A",30951,279239,1034,15.6,2.9,59,1.3,0,41.6,0,"N/A",39748,549985,17795,32.6,7.3,80,2.4,173,78.9,0,"N/A",54544,1144039,49899,2,5,8,"This company pays faster than the industry average. There is a consistent payment pattern. This company has 0 accounts placed for collection. This company has 0 outstanding unpaid accounts. This company pays within its terms.",0,0,1,2,0,17,0,17,15,17,10,17,10,16,0,16,0,16,0,16,0,16,0,17,0,17,0,17,0,17,0,17,0,17,0,17,0,17,0,17,3,17,0,15,0,24,0,16,0,6,0,2,0,0,0,0,0,"This company pays its accounts on average 0 days beyond terms. The payment information we have for this company over the last 6 months available shows a consistent payment pattern.","A search of our databases has shown that there are no County Court Judgments recorded against this company","A search of our databases has shown that there are no Legal Notices recorded against this company","A search of our databases has shown that there are no Mortgages or Charges registered against this company","There are no Consumer Credit Licences registered to this company","The Company was incorporated 10 years 2 months ago. There have been no changes in registered office in the last 12 months.","HOMEFUELS DIRECT LTD",6464646,"Private Limited",39451,"10 years 2 months","GBP 8","70-74 BRUNSWICK STREET, STOCKTON ON TEES, CLEVELAND TS18 1DW","70-74 Brunswick Street, STOCKTON-ON-TEES, Cleveland TS18 1DW","01642 700725","www.homefuelsdirect.com",6120,"INDUSTRIAL MATERIALS WHOLESALING",5151,"WHOLESALE FUELS AND RELATED PRODUCTS","FUEL TO HOMES AND BUSINESS.","STOKESLEY BUSINESS CENTRE, 51 HIGH STREET, STOKESLEY, NORTH YORKSHIRE","Total Exemption Full","31 March",42825,43131,"NICHOLE ROBERTS","This Company has no parent Company","ORD",1,8,8,"yes",100,100,"GBP","Christopher Bicknell","ORD","1.00",8,8,"GBP",100,100,"Christophe David Bicknell","Duport Director Ltd","Thomas Elles Bicknell","Duport Secretary Ltd",39457,"There are no alert notices for this company."]
# #
# col_83 = list_test[83]
# col_88 = list_test[88]
# col_93 = list_test[93]
# col_98 = list_test[98]
# col_103 = list_test[103]
# col_108 = list_test[108]
# col_113 = list_test[113]
# col_118 = list_test[118]
# col_123 = list_test[123]
# col_128 = list_test[128]
# col_133 = list_test[133]
# col_138 = list_test[138]
# col_143 = list_test[143]
# #
# print(col_83) #same age
# print(col_88)
# print(col_93)
# print(col_98)
# print(col_103)
# print(col_108) # June same indus
# print(col_113) #july all cos
# print(col_118)
# print(col_123)
# print(col_128)
# print(col_133)
# print(col_138)
# print(col_143)
# #
# # # calculate the chi-square smoothed average for the series ( removes outliers more than 1 degree of freedom)
# # #The Basic Arithmetic mean
# mean1 = ((col_83 + col_88 + col_93 + col_98 + col_103 + col_108 + col_113 + col_118 + col_123 + col_128 + col_133 + col_138 + col_143)/13)
# print("The arithmetic mean is:")
# print(mean1)
# # # in this case we are just looking at the delphi score trend for the subject company.
# #
# # #The Chi smoothing 1 band
# # #χ2=n∑i=1(Oi−Ei)2Eiχ2=∑i=1n(Oi−Ei)2Ei
# # #We already know n = 13, so do not need to evaluate or use special library really.
# # # already done the sum (and divided it by 12) for the test values.
# # #
# # #we need the distribution chi square value/variance from the distribution curve from the mean
# print("So Chi square sum of variances is: ")
# chi2 = ((col_83 - mean1) +(col_88 - mean1)+(col_93 - mean1)+(col_98 - mean1) +(col_103 - mean1)+(col_108 - mean1)+(col_113 - mean1)+(col_118 - mean1) +(col_123 - mean1)+(col_128 - mean1)+ (col_133 - mean1) + (col_138 - mean1) + col_143 - mean1)
# print(chi2)
# chi1 = math.sqrt(abs(chi2))
# print("Here is a standard deviation")
# print(chi1)
# smooth_sd_factor =((mean1 * chi1)*100000)+ (abs((col_83-mean1)))
# print("And the smooth sd factor is: ")
# print(smooth_sd_factor)
# #The Company values smoothed (outliers above n chi variance
# # establish how many Sd's the mean of the company scores are from the chi standard deviation to use in the test.
#
# # Check if Company ecl values are more than the chi1 variance, and get a new smoothed average, add 1 to the counter if it does not pass the test, update the aggregator
# #also, evaluate from the latest to the oldest whether there is an upward or a downward trend on the smoothed values. Each subsequent older period not an outlier has a lesser impact.
# # and use rolling averages
# print(abs(col_83 - mean1))
# #
# if abs(col_83 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_83
# counter1 = counter1 + 1
# print("test 83 passed")
# if col_83 > col_88:
# trend_total = trend_total + 120
# elif col_83 < col_88:
# trend_total = trend_total - 120
# #
# if abs(col_88 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_88
# counter1 = counter1 + 1
# print(counter1)
# print("test 88 passed")
#
# if ((col_83 + col_88)/2) >= col_93:
# trend_total = trend_total + 110
# else:
# trend_total = trend_total - 110
# #
# if abs(col_93 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_93
# counter1 = counter1 + 1
# print(counter1)
# print("test 93 passed")
# if ((col_83 + col_88 + col_93)/3) > col_98:
# trend_total = trend_total + 100
# elif ((col_83 + col_88 + col_93)/3) < col_98:
# trend_total = trend_total - 100
# #
# if abs(col_98 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_98
# counter1 = counter1 + 1
# print(counter1)
# print("test 98 passed")
# if ((col_83 + col_88 + col_93 + col_98)/4) > col_103:
# trend_total = trend_total + 90
# elif ((col_83 + col_88 + col_93 + col_98)/4) < col_103:
# trend_total = trend_total - 90
# #
# if abs(col_103 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_103
# counter1 = counter1 + 1
# print(counter1)
# print("test 103 passed")
# if ((col_83 + col_88 + col_93 + col_98 + col_103)/5) > col_108:
# trend_total = trend_total + 80
# else:
# trend_total = trend_total - 80
#
# if abs(col_108 - mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_108
# counter1 = counter1 + 1
# print(counter1)
# print("test 108 passed")
# if ((col_83 + col_88 + col_93 + col_98 + col_103 + col_108) / 6) > col_113:
# trend_total = trend_total + 70
# elif ((col_83 + col_88 + col_93 + col_98+ col_103 + col_108) / 6) < col_113:
# trend_total = trend_total - 70
#
# if abs(col_113 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_113
# counter1 = counter1 + 1
# print(counter1)
# print("test 113 passed")
#
# if abs(col_118 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_118
# counter1 = counter1 + 1
# print(counter1)
# print("test 118 passed")
#
# if abs(col_123 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_123
# counter1 = counter1 + 1
# print(counter1)
# print("test 123 passed")
#
# if abs(col_128 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_128
# counter1 = counter1 + 1
# print(counter1)
# print("test 128 passed")
#
# if abs(col_133 - mean1)<= (smooth_sd_factor):
# smooth_total = smooth_total + col_133
# counter1 = counter1 + 1
# print(counter1)
# print("test 133 passed")
#
# if abs(col_138 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_138
# counter1 = counter1 + 1
# print(counter1)
# print("test 138 passed")
#
# if abs(col_143 - mean1) <= (smooth_sd_factor):
# smooth_total = smooth_total + col_143
# counter1 = counter1 + 1
# print(counter1)
# print("test 143 passed")
#
# #
# print(" The Smooth Total is :")
# print(smooth_total)
# print(" The trend Total is :")
# print(trend_total)
# print(counter1)
# # # # compare chi-square smoothed mean to latest Delphi Score
# smooth_mean = smooth_total/counter1
# if smooth_mean > 0:
# trend_difference = ((col_83/smooth_mean))
# # # # # # #This would eatablish a trend difference - either increases or decreses the trend based on the current period avarage score and smoothed average score difference.
# # # # # #
# # # #
# # # # # # #if there is a negative or positive trend, there will be a factor to apply to correctly position in the range buckets.
# trending_count = trend_difference * smooth_mean
# #
# #it is possible to score in the trend_total moving average for the last six months a value between -570 and 570. The sample scored -570 and the scores so that should not have an impact.
# # a positive value of the trend_total is a good thing in the case of Delphi score, as the delphi score that is higher is a lower risk. A negative value is bad.
# #If the smoothed average and the latest Delphi are close, there will be a trend_difference of 0.
# # If the Smooth Mean is lower than col_18 . trend difference may be negative, indicating that the trend was up - need to factor in the rolling average trend to this value
# # if the smooth mean is higher than col_8, trend is positive, indicating that the DBT actually decreased as trend difference
# #trend_difference samples
# print(" The trending count")
# print(trending_count)
# # # # # # test_list_flat = [17,17,17,16,16,16,16,16,17,17,17,17]
# # # # # # test_list_down = [10,10,11,13,13,14,16,16,17,17,17,17]
# # # # # # test_list_up = [20,18,18,17,16,17,16,16,15,14,13,11]
# # # # # # # if max and median is greater than 1 SD above, then trend is up, else
# # # # # # #if min and median is greater than 1 SD below, then trend is down.
# # # # # # #in a 12 month pattern, it is not possible to identify seasonality completely.
# # # # # # # Evaluate banding score based on range sets:
# # # # # # #Because of the rolling average trend factor, there are 30 possible ranges of scores, to be assigned to the percentiles -30, -20, -10, 10, 20
# # # # # # #range is over 20% up, over 10% up, between 10% up and 10% down, between 10% down and 20% down, between 20 % down and 30% down.
# # # # # #
# if smooth_mean !=0:
# if (trend_total - smooth_total) <= -230:
# same_asset_delphi_trend_score = 20
# elif (trend_total- smooth_total) < -30:
# same_asset_delphi_trend_score = 10
# elif (trend_total- smooth_total) < 190:
# same_asset_delphi_trend_score = -10
# elif (trend_total- smooth_total) < 280:
# same_asset_delphi_trend_score = -20
# elif trend_total > 280:
# same_asset_delphi_trend_score = -30
# print(same_asset_delphi_trend_score)
| 42.147987
| 4,818
| 0.66248
| 13,557
| 77,468
| 3.62949
| 0.053404
| 0.029631
| 0.036216
| 0.038777
| 0.864648
| 0.830972
| 0.810873
| 0.761366
| 0.754029
| 0.682553
| 0
| 0.157449
| 0.198508
| 77,468
| 1,837
| 4,819
| 42.170931
| 0.633065
| 0.780219
| 0
| 0.086957
| 0
| 0.013378
| 0.2377
| 0.003536
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0.040134
| 0.010033
| 0
| 0.010033
| 0.167224
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b0ea117409cf522b462807f43ca243b87a75fb76
| 127
|
py
|
Python
|
imsearchtools/engines/__init__.py
|
carandraug/imsearch-tools
|
9e6af18d63ffa43cef033bf5d75d32f62a8efcc7
|
[
"BSD-2-Clause"
] | 7
|
2016-06-18T11:22:43.000Z
|
2019-08-28T23:28:41.000Z
|
imsearchtools/engines/__init__.py
|
carandraug/imsearch-tools
|
9e6af18d63ffa43cef033bf5d75d32f62a8efcc7
|
[
"BSD-2-Clause"
] | null | null | null |
imsearchtools/engines/__init__.py
|
carandraug/imsearch-tools
|
9e6af18d63ffa43cef033bf5d75d32f62a8efcc7
|
[
"BSD-2-Clause"
] | 2
|
2016-12-12T07:40:42.000Z
|
2018-02-19T13:26:07.000Z
|
from bing_api import *
from google_old_api import *
from google_api import *
from google_web import *
from flickr_api import *
| 21.166667
| 28
| 0.80315
| 21
| 127
| 4.571429
| 0.380952
| 0.375
| 0.40625
| 0.59375
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.15748
| 127
| 5
| 29
| 25.4
| 0.897196
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
9fe42826732ba5055452ed462ca2472576dd491a
| 1,946
|
py
|
Python
|
bloomfilter.py
|
mtasic85/bloomfilter
|
5d5c4eb900319413d9a6545a7897a92bc8ba94ba
|
[
"MIT"
] | null | null | null |
bloomfilter.py
|
mtasic85/bloomfilter
|
5d5c4eb900319413d9a6545a7897a92bc8ba94ba
|
[
"MIT"
] | null | null | null |
bloomfilter.py
|
mtasic85/bloomfilter
|
5d5c4eb900319413d9a6545a7897a92bc8ba94ba
|
[
"MIT"
] | null | null | null |
__all__ = ['BloomFilter']
class BitBloomFilter(object):
def __init__(self, m=1024, k=3):
self.m = m
self.k = k
self.items = [0] * m
def __repr__(self):
return '<BloomFilter {}>'.format(self.items)
def add(self, item):
h = hash(item)
for i in range(self.k):
p = (h >> i) % self.m
self.items[p] = 1
def remove(self, item):
h = hash(item)
for i in range(self.k):
p = (h >> i) % self.m
self.items[p] = 0
def has(self, item):
h = hash(item)
for i in range(self.k):
p = (h >> i) % self.m
if not self.items[p]:
return False
return True
class IntBloomFilter(object):
def __init__(self, m=1024, k=3):
self.m = m
self.k = k
self.items = [0] * m
def __repr__(self):
return '<BloomFilter {}>'.format(self.items)
def add(self, item):
h = hash(item)
for i in range(self.k):
p = (h >> i) % self.m
self.items[p] += 1
def remove(self, item):
h = hash(item)
for i in range(self.k):
p = (h >> i) % self.m
if not self.items[p]:
raise ValueError('Bloom filter\'s field is ZERO. This item was not added before.')
self.items[p] -= 1
def has(self, item):
h = hash(item)
for i in range(self.k):
p = (h >> i) % self.m
if not self.items[p]:
return False
return True
if __name__ == '__main__':
# bit
bf = BitBloomFilter(16)
bf.add((1, '1'))
bf.add((2, '2'))
print(bf)
print(bf.has((0, '0')))
print(bf.has((1, '1')))
# int
bf = IntBloomFilter(16)
bf.add((1, '1'))
bf.add((2, '2'))
print(bf)
print(bf.has((0, '0')))
print(bf.has((1, '1')))
| 21.384615
| 98
| 0.454265
| 270
| 1,946
| 3.17037
| 0.2
| 0.115654
| 0.081776
| 0.091122
| 0.808411
| 0.795561
| 0.795561
| 0.795561
| 0.795561
| 0.795561
| 0
| 0.030303
| 0.389517
| 1,946
| 90
| 99
| 21.622222
| 0.690236
| 0.003597
| 0
| 0.828125
| 0
| 0
| 0.03719
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.15625
| false
| 0
| 0
| 0.03125
| 0.28125
| 0.09375
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
b05f1f1979e2afca17b2447a5df8a0a8560e143c
| 220
|
py
|
Python
|
pypy/translator/jvm/test/test_objectmodel.py
|
camillobruni/pygirl
|
ddbd442d53061d6ff4af831c1eab153bcc771b5a
|
[
"MIT"
] | 12
|
2016-01-06T07:10:28.000Z
|
2021-05-13T23:02:02.000Z
|
pypy/translator/jvm/test/test_objectmodel.py
|
woodrow/pyoac
|
b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7
|
[
"MIT"
] | null | null | null |
pypy/translator/jvm/test/test_objectmodel.py
|
woodrow/pyoac
|
b5dc59e6a38e7912db47f26fb23ffa4764a3c0e7
|
[
"MIT"
] | 2
|
2016-07-29T07:09:50.000Z
|
2016-10-16T08:50:26.000Z
|
import py
from pypy.translator.jvm.test.runtest import JvmTest
from pypy.translator.oosupport.test_template.objectmodel import \
BaseTestObjectModel
class TestJvmObjectModel(JvmTest, BaseTestObjectModel):
pass
| 27.5
| 65
| 0.827273
| 24
| 220
| 7.541667
| 0.666667
| 0.088398
| 0.198895
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.113636
| 220
| 7
| 66
| 31.428571
| 0.928205
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0.166667
| 0.5
| 0
| 0.666667
| 0
| 1
| 0
| 0
| null | 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 1
| 1
| 0
| 1
| 0
|
0
| 7
|
c67fdf2620f2956689ad511c22e2da3929c5800e
| 1,614
|
pyde
|
Python
|
sketch_191204a_list66.pyde
|
takoe-sebe/2019-fall-polytech-cs
|
a98472c7689cfdc42da710bf6ef8aef7278b276f
|
[
"MIT"
] | null | null | null |
sketch_191204a_list66.pyde
|
takoe-sebe/2019-fall-polytech-cs
|
a98472c7689cfdc42da710bf6ef8aef7278b276f
|
[
"MIT"
] | null | null | null |
sketch_191204a_list66.pyde
|
takoe-sebe/2019-fall-polytech-cs
|
a98472c7689cfdc42da710bf6ef8aef7278b276f
|
[
"MIT"
] | null | null | null |
a = [[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],
[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],
[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],
[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],
[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2],[500,2]]
def setup():
size(500,500)
for i in range(len(a)):
for j in range(len(a[i])):
a[i][j]=random(10,490)
def draw():
smooth()
noStroke()
background(0)
for i in range(len(a)):
eDist = dist(mouseX , mouseY , a[i][0] , a[i][1])
eSize = map(eDist , 0, 200, 5, 100)
eColor = map(eDist , 0, 200, 50, 255)
fill(eColor , 200)
cx = noise( mouseX )*10 + a[i][0]
cy = noise( mouseY )*10 + a[i][1]
ellipse(cx,cy,eSize,eSize)
| 62.076923
| 221
| 0.490706
| 353
| 1,614
| 2.243626
| 0.107649
| 0.681818
| 1.184343
| 1.353535
| 0.719697
| 0.719697
| 0.681818
| 0.681818
| 0.681818
| 0.681818
| 0
| 0.406162
| 0.115242
| 1,614
| 25
| 222
| 64.56
| 0.148459
| 0
| 0
| 0.227273
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0.090909
| false
| 0
| 0
| 0
| 0.090909
| 0
| 0
| 0
| 0
| null | 1
| 1
| 1
| 0
| 1
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
c6c6ce5278640388d7790acb0f21dac193c29b5a
| 24,111
|
py
|
Python
|
DataHandler.py
|
COE420Group4/Donation-Nation
|
58d62bc3a28aba0ce2b484ad68329ac0bd0680f2
|
[
"MIT"
] | null | null | null |
DataHandler.py
|
COE420Group4/Donation-Nation
|
58d62bc3a28aba0ce2b484ad68329ac0bd0680f2
|
[
"MIT"
] | null | null | null |
DataHandler.py
|
COE420Group4/Donation-Nation
|
58d62bc3a28aba0ce2b484ad68329ac0bd0680f2
|
[
"MIT"
] | null | null | null |
# Import our database and initialize it
from db import DB
import send_email
import re
import hashlib
import uuid
import traceback
from datetime import datetime
from base64 import standard_b64encode
sql = DB()
sql.clear_db()
sql.init_db()
sql.populate()
# Checker function to check all form variables
def check_form(form, paramters):
for param in paramters:
try:
if form[param] and len(form[param]) < 1:
return False
except Exception:
return False
return True
# Checker function to check that all form variables are alphabetic
def is_all_alpha(form, paramters):
for param in paramters:
if not all(x.isalpha() or x.isspace() for x in form[param]):
raise UserException(f'{param.capitalize()} must consist of only alphabetic characters.')
return True
# Checker function to check that all form variables are alphanum
def is_all_alnum(form, paramters):
for param in paramters:
if not form[param].isalnum():
raise UserException(f'{param.capitalize()} must consist of only alphanumeric characters.')
return
# Checker function to check that all form variables are alphanum
def is_all_numeric(form, paramters):
for param in paramters:
if not form[param].isnumeric():
raise UserException(f'{param.capitalize()} must consist of only numeric characters.')
return True
def is_email(form, parameter):
regex = r'^[a-z0-9]+[\._]?[a-z0-9]+[@]\w+[.]\w{2,3}$'
if re.search(regex, form[parameter]):
pass
else:
raise UserException(f'{parameter.capitalize()} must be a valid email.')
def allowed_file(filename):
return '.' in filename and \
filename.rsplit('.', 1)[1].lower() in {'jpg','png','jpeg'}
class User:
def insert(form):
# Check that all information is here
if check_form(form, ['firstName', 'lastName', 'dob', 'city', 'emirate', 'POBox', 'address1', 'address2', 'phone', 'email', 'password', 'confirmPassword']):
is_all_alpha(form, ['firstName', 'lastName', 'city', 'emirate'])
is_all_alnum(form, ['POBox'])
is_all_numeric(form, ['phone'])
is_email(form, 'email')
User.check_phone_exists(form['phone'])
User.check_email_exists(form['email'])
hash = ''
if form['password'] != form['confirmPassword']:
raise UserException('Both password fields must be the same.')
else:
hash = hashlib.sha256(form['password'].encode('utf-8')).hexdigest()
user_uuid = str(uuid.uuid4())
try:
dbcon = sql.connect()
dbcon.execute("INSERT INTO users (UUID, first_name, last_name, dob, city, emirate, po_box, address_1, address_2, phone, email, password, isAdmin, isVerified) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,0,0)", (user_uuid,form['firstName'], form['lastName'], form['dob'], form['city'], form['emirate'], form['POBox'], form['address1'], form['address2'], form['phone'], form['email'], hash))
verification_uuid = str(uuid.uuid4())
dbcon.execute("INSERT INTO verifications VALUES (?,?)", (user_uuid, verification_uuid))
# Send email to user for verification
send_email.send('Email Verification', f'Hi {form["firstName"].strip()}!\n\n\nThank you for signing up for Donation Nation!\n\nTo complete your registration and enable your account, please verify your email by visiting the link: http://127.0.0.1:5000/verify_user/{verification_uuid}\n\nRegards,\nDonation Nation', [form['email'],])
# Commit changes and close the db connection
dbcon.commit()
dbcon.close()
except Exception:
traceback.print_exc()
raise UserException("Something went wrong. Contact an admin.")
else:
raise UserException("Invalid or missing information!")
def check_phone_exists(value):
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT id FROM users WHERE phone=?", (value,))
if cur.fetchone() is not None:
cur.close()
dbcon.close()
raise UserException("A user with that phone number already exists.")
else:
cur.close()
dbcon.close()
except UserException as e:
raise e
except Exception:
traceback.print_exc()
raise UserException("Something went wrong. Contact an admin.")
def check_email_exists(value):
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT id FROM users WHERE email=?", (value,))
if cur.fetchone() is not None:
cur.close()
dbcon.close()
raise UserException("A user with that email already exists.")
else:
cur.close()
dbcon.close()
except UserException as e:
raise e
except Exception:
traceback.print_exc()
raise UserException("Something went wrong. Contact an admin.")
# Get user information by supplying their UUID
def fetchByUUID(user_uuid):
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT * FROM users WHERE UUID=?", (user_uuid,))
res = cur.fetchone()
cur.close()
dbcon.close()
if res is not None:
return res
else:
return False
except Exception:
traceback.print_exc()
return False
def login(form):
if check_form(form, ['email', 'password']):
hash = hashlib.sha256(form['password'].encode('utf-8')).hexdigest()
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT * FROM users WHERE email=? AND password=?", (form['email'], hash))
data = cur.fetchone()
if data is not None:
if data[14] == 0:
raise UserException("You haven't verified your email yet! Please verify it then try again.")
return data
else:
raise UserException("Invalid email or password. Please try again.")
except UserException as e:
raise e
except Exception:
traceback.print_exc()
raise UserException("Something went wrong. Contact an admin.")
else:
raise UserException("Invalid or missing information!")
def verify(verify_uuid):
try:
# Check that the verification UUID exists
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT user_uuid FROM verifications WHERE verification_uuid=?", (verify_uuid,))
uuid = cur.fetchone()
if uuid is None:
raise UserException("NotFound") # Generic name so that we can catch it in flask
# If we're here, then the verification exists and we should verify the user
cur.execute("UPDATE users SET isVerified=1 WHERE UUID=?", (uuid[0],))
# Remove the verification from the database
cur.execute("DELETE FROM verifications WHERE user_uuid=?", (uuid[0],))
# Commit the changes and close connections
dbcon.commit()
cur.close()
dbcon.close()
except UserException as e:
raise e
except Exception as e:
# We raise any exception so that the flask app can handle it
traceback.print_exc()
raise e
def addItem(form,session,files):
if check_form(form, ['name','category','condition','description','organization','time']) and 'image' in files:
item_uuid = str(uuid.uuid4())
user_uuid = session['isLoggedIn'][1]
current_time = datetime.now().strftime("%d/%m/%Y - %H:%M:%S")
image = standard_b64encode(files['image'].read())
try:
org = Organization.fetchByUUID(form['organization'])
send_email.send('New Item Offered!', f'Hi {org[2].strip()}!\n\n\nYou have been offered a new item ({form["name"]}) [{form["category"]}]! Log into the application to approve or reject this item!\n\nRegards,\nDonation Nation', [org[12],])
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("INSERT INTO items (UUID,item_name,category,condition,description,org_id,user_id,time_submitted,pickup_time,image,status) VALUES (?,?,?,?,?,?,?,?,?,?,0)",(item_uuid,form['name'],form['category'],form['condition'],form['description'],form['organization'],user_uuid,current_time,form['time'],image))
dbcon.commit()
cur.close()
dbcon.close()
except Exception as e:
# We raise any exception so that the flask app can handle it
traceback.print_exc()
raise UserException('Something went wrong. Contact an admin.')
else:
raise UserException('Missing or invalid information!')
def removeItem(uuid):
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute('DELETE FROM items WHERE UUID=?', (uuid,))
dbcon.commit()
cur.close()
dbcon.close()
except UserException as ue:
raise ue
except Exception:
traceback.print_exc()
raise UserException('An issue has occurred. Please contact an admin.')
def changePickupTime(form, uuid):
try:
item_data = User.fetchItemByUUID(uuid)
org_data = Organization.fetchByUUID(item_data[6])
send_email.send('Item Pickup Date Changed', f'Hi {org_data[2]}!\n\n\nThe item ({item_data[2]}) [UUID: {item_data[1]}] has been suggested a new pickup time by the donator. Log in to the application to view and accept or reject the new pickup time.\n\nRegards,\nDonation Nation', [org_data[12],])
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute('UPDATE items SET pickup_time=?, status=? WHERE UUID=?', (form['time'], 3, uuid))
dbcon.commit()
cur.close()
dbcon.close()
except UserException as ue:
raise ue
except Exception:
traceback.print_exc()
raise UserException('An issue has occurred. Please contact an admin.')
def accept(uuid):
try:
item_data = User.fetchItemByUUID(uuid)
org_data = Organization.fetchByUUID(item_data[6])
send_email.send('Item Accepted', f'Hi {org_data[2]}!\n\n\nThe item ({item_data[2]}) [UUID: {item_data[1]}] has been accepted by the user for pickup. Contact the user for further details.\n\nRegards,\nDonation Nation', [org_data[12],])
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute('UPDATE items SET status=? WHERE UUID=?', (1, uuid))
dbcon.commit()
cur.close()
dbcon.close()
except UserException as ue:
raise ue
except Exception:
traceback.print_exc()
raise UserException('An issue has occurred. Please contact an admin.')
def getAllItems(user_uuid):
# Connect to the database
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT items.id, items.UUID, item_name, category, condition, description, org_id, user_id, time_submitted, pickup_time, image, items.status, organizations.name FROM items, organizations WHERE organizations.UUID=items.org_id AND user_id=?", (user_uuid,))
items = cur.fetchall()
cur.close()
dbcon.close()
if len(items) > 0:
return items
else:
raise UserException("No items exist for this user.")
except UserException as ue:
raise ue
except Exception as e:
raise UserException("Something went wrong. Please contact an admin.")
def changePassword(form, session):
if check_form(form, ['password', 'confirmPassword']):
hash = ''
if form['password'] != form['confirmPassword']:
raise UserException('Both password fields must be the same.')
else:
hash = hashlib.sha256(form['password'].encode('utf-8')).hexdigest()
try:
dbcon = sql.connect()
dbcon.execute("UPDATE users set password = ? where UUID = ?", (hash, session['isLoggedIn'][1]))
# Commit changes and close the db connection
dbcon.commit()
dbcon.close()
except Exception:
traceback.print_exc()
raise UserException("Something went wrong. Contact an admin.")
else:
raise UserException("Invalid or missing information!")
def editInformation(form, session):
# Check that all information is here
if check_form(form, ['city', 'emirate', 'POBox', 'address1', 'address2', 'phone']):
is_all_alpha(form, ['city', 'emirate'])
is_all_alnum(form, ['POBox'])
is_all_numeric(form, ['phone'])
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("UPDATE users SET city = ?, emirate = ?, po_box = ?, address_1 = ?, address_2 = ?, phone = ? WHERE UUID = ?", (form['city'], form['emirate'], form['POBox'], form['address1'], form['address2'], form['phone'], session['isLoggedIn'][1]))
dbcon.commit()
cur.execute("SELECT * FROM users WHERE UUID=?", (session['isLoggedIn'][1],))
data = cur.fetchone()
cur.close()
dbcon.close()
return data
except Exception:
traceback.print_exc()
raise UserException("Something went wrong. Contact an admin.")
def fetchItemByUUID(item_uuid):
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute('SELECT * FROM items WHERE UUID=?', (item_uuid,))
data = cur.fetchone()
cur.close()
dbcon.close()
return data
except Exception:
traceback.print_exc()
raise UserException("Something went wrong. Contact an admin.")
class UserException(Exception):
def __init__(self, message):
self.reason = message
super().__init__(self, self.reason)
class Organization:
def insert(form, files):
if check_form(form, ['name', 'registrationNumber', 'city', 'emirate', 'POBox', 'address1', 'address2', 'phone', 'email', 'password', 'confirmPassword']) and (files['logo'] is not None):
is_all_alpha(form, ['name', 'city', 'emirate'])
is_all_alnum(form, ['POBox'])
is_all_numeric(form, ['phone', 'registrationNumber'])
is_email(form, 'email')
Organization.check_phone_exists(form['phone'])
Organization.check_email_exists(form['email'])
hash = ''
logo = standard_b64encode(files['logo'].read())
if form['password'] != form['confirmPassword']:
raise OrgException('Both password fields must be the same.')
else:
hash = hashlib.sha256(form['password'].encode('utf-8')).hexdigest()
org_uuid = str(uuid.uuid4())
try:
dbcon = sql.connect()
dbcon.execute("INSERT INTO organizations (UUID, name, status, license_no, city, emirate, po_box, address_1, address_2, phone, logo, email, password) VALUES (?,?,0,?,?,?,?,?,?,?,?,?,?)", (org_uuid, form['name'], form['registrationNumber'], form['city'], form['emirate'], form['POBox'], form['address1'], form['address2'], form['phone'], logo, form['email'], hash))
verification_uuid = str(uuid.uuid4())
dbcon.execute("INSERT INTO verifications VALUES (?,?)", (org_uuid, verification_uuid))
# Send email to user for verification
send_email.send('Email Verification', f'Hi {form["name"].strip()}!\n\n\nThank you for signing up for Donation Nation!\n\nTo complete your registration and enable your account, please verify your email by visiting the link: http://127.0.0.1:5000/verify_org/{verification_uuid}\n\nRegards,\nDonation Nation', [form['email'],])
# Commit changes and close the db connection
dbcon.commit()
dbcon.close()
except Exception:
traceback.print_exc()
raise OrgException("Something went wrong. Contact an admin.")
else:
raise OrgException("Invalid or missing information!")
def check_phone_exists(value):
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT id FROM organizations WHERE phone=?", (value,))
if cur.fetchone() is not None:
cur.close()
dbcon.close()
raise OrgException("An organization with that phone number already exists.")
else:
cur.close()
dbcon.close()
except OrgException as e:
raise e
except Exception:
traceback.print_exc()
raise OrgException("Something went wrong. Contact an admin.")
def check_email_exists(value):
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT id FROM organizations WHERE email=?", (value,))
if cur.fetchone() is not None:
cur.close()
dbcon.close()
raise OrgException("An organization with that email already exists.")
else:
cur.close()
dbcon.close()
except OrgException as e:
raise e
except Exception:
traceback.print_exc()
raise OrgException("Something went wrong. Contact an admin.")
def login(form):
try:
if check_form(form, ['email', 'password']):
hash = hashlib.sha256(form['password'].encode('utf-8')).hexdigest()
# Verify the creds
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT id,UUID,name,status,license_no,city,emirate,po_box,address_1,address_2,phone,1,email,password FROM organizations WHERE email=? AND password=?", (form['email'], hash))
org_data = cur.fetchone()
if org_data is not None:
# Check that the user is verified
if org_data[3] == 2:
# This means the credentials are correct and we do nothing
return org_data
elif org_data[3] == 1:
raise OrgException("An administrator has not verified your account yet. Please wait and try later.")
else:
raise OrgException("Please verify your email so that an admin can review your account.")
else:
raise OrgException("Invalid email or password.")
else:
raise OrgException("Missing or invalid information!")
except OrgException as e:
raise e
except Exception:
traceback.print_exc()
raise OrgException("Something went wrong. Contact an admin.")
def verify(verify_uuid):
try:
# Check if this verification_uuid exists
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT user_uuid FROM verifications WHERE verification_uuid=?", (verify_uuid,))
uuid = cur.fetchone()
if uuid is None:
raise OrgException("NotFound") # Generic name so that we can catch it in flask
# If we're here, then the verification exists and we should verify the org
cur.execute("UPDATE organizations SET status=1 WHERE UUID=?", (uuid[0],))
# Remove the verification from the database
cur.execute("DELETE FROM verifications WHERE user_uuid=?", (uuid[0],))
# Commit the changes and close connections
dbcon.commit()
cur.close()
dbcon.close()
except OrgException as e:
raise e
except Exception:
traceback.print_exc()
raise OrgException("Something went wrong. Contact an admin.")
def accept(org_uuid):
try:
org_data = Organization.fetchByUUID(org_uuid)
# Check their status
if org_data[3] == 2:
raise OrgException("Organization already accepted!")
else:
send_email.send('Application Accepted', f'Hi {org_data[2].strip()}!\n\n\nWe are pleased to inform you that your application ({org_data[1]}) for being an organization registered with us has been accepted. You can now log in to the application and begin accepting donations.\n\nRegards,\nDonation Nation', [org_data[12],])
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("UPDATE organizations SET status=2 WHERE UUID=?", (org_data[1],))
cur.close()
dbcon.commit()
dbcon.close()
except OrgException as e:
raise e
except Exception as e:
traceback.print_exc()
raise e
def reject(org_uuid):
try:
org_data = Organization.fetchByUUID(org_uuid)
# Check their status
if org_data[3] == 2:
raise OrgException("Organization already accepted!")
else:
send_email.send('Application Rejected', f'Hi {org_data[2].strip()}!\n\n\nWe regret to inform you that your application ({org_data[1]}) for being an organization registered with us has been rejected. You can contact us at tips@fbi.gov to repeal your rejection.\n\nRegards,\nDonation Nation', [org_data[12],])
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("DELETE FROM organizations WHERE UUID=?", (org_data[1],))
cur.fetchone()
cur.close()
dbcon.commit()
dbcon.close()
except OrgException as e:
raise e
except Exception as e:
traceback.print_exc()
raise e
def getAll():
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT * FROM organizations")
data = cur.fetchall()
cur.close()
dbcon.close()
if data is not None:
return data
else:
raise OrgException("There are no organizations registered yet.")
except Exception as e:
raise e
def fetchByUUID(org_uuid):
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT * FROM organizations WHERE UUID=?", (org_uuid,))
data = cur.fetchone()
cur.close()
dbcon.close()
if data is not None:
return data
else:
return False
except Exception as e:
return False
def getAllVerified():
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT * FROM organizations WHERE status=2")
data = cur.fetchall()
cur.close()
dbcon.close()
if data is not None:
return data
else:
raise OrgException("There are no organizations registered yet.")
except Exception as e:
raise e
def getAllPending():
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT * FROM organizations WHERE status!=2")
data = cur.fetchall()
cur.close()
dbcon.close()
if data is not None:
return data
else:
raise OrgException("There are no organizations registered yet.")
except Exception as e:
raise e
def changePassword(form, session):
if check_form(form, ['password', 'confirmPassword']):
hash = ''
if form['password'] != form['confirmPassword']:
raise OrgException('Both password fields must be the same.')
else:
hash = hashlib.sha256(form['password'].encode('utf-8')).hexdigest()
user_uuid = str(uuid.uuid4())
try:
dbcon = sql.connect()
dbcon.execute("UPDATE organizations set password = ? where UUID = ?", (hash, session['isLoggedIn'][1]))
# Commit changes and close the db connection
dbcon.commit()
dbcon.close()
except Exception:
traceback.print_exc()
raise OrgException("Something went wrong. Contact an admin.")
else:
raise OrgException("Invalid or missing information!")
def getAllItems(org_uuid):
try:
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute("SELECT items.id, items.UUID, item_name, category, condition, description, org_id, user_id, time_submitted, pickup_time, image, items.status, users.first_name, users.last_name FROM items, users WHERE users.UUID=items.user_id AND org_id=? AND items.status!=-1", (org_uuid,))
items = cur.fetchall()
cur.close()
dbcon.close()
if len(items) > 0:
return items
else:
raise OrgException("No items exist for this organization.")
except OrgException as ue:
raise ue
except Exception as e:
raise OrgException("Something went wrong. Please contact an admin.")
def acceptItem(uuid):
try:
item_data = User.fetchItemByUUID(uuid)
user_data = User.fetchByUUID(item_data[7])
send_email.send('Item Accepted', f'Hi {user_data[2]}!\n\n\nYour item ({item_data[2]}) [UUID: {item_data[1]}] has been accepted for pickup. The organization you donated to should contact you shortly.\n\nRegards,\nDonation Nation', [user_data[11],])
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute('UPDATE items SET status=? WHERE UUID=?', (1, uuid))
dbcon.commit()
cur.close()
dbcon.close()
except OrgException as ue:
raise ue
except Exception:
traceback.print_exc()
raise OrgException('An issue has occurred. Please contact an admin.')
def removeItem(uuid):
try:
item_data = User.fetchItemByUUID(uuid)
user_data = User.fetchByUUID(item_data[7])
send_email.send('Item Rejected', f'Hi {user_data[2]}!\n\n\nYour item ({item_data[2]}) [UUID: {item_data[1]}] has been rejected by the organization. Either try again or contact the organization for more details.\n\nRegards,\nDonation Nation', [user_data[11],])
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute('UPDATE items SET status=-1 WHERE UUID=?', (uuid,))
dbcon.commit()
cur.close()
dbcon.close()
except OrgException as ue:
raise ue
except Exception:
traceback.print_exc()
raise OrgException('An issue has occurred. Please contact an admin.')
def changePickupTime(form, uuid):
try:
item_data = User.fetchItemByUUID(uuid)
user_data = User.fetchByUUID(item_data[7])
send_email.send('Item Pickup Time Changed', f'Hi {user_data[2]}!\n\n\nYour item ({item_data[2]}) [UUID: {item_data[1]}] has been suggested a new pickup time. Log into the application to approve or reject this new time.\n\nRegards,\nDonation Nation', [user_data[11],])
dbcon = sql.connect()
cur = dbcon.cursor()
cur.execute('UPDATE items SET pickup_time=?, status=? WHERE UUID=?', (form['time'], 2, uuid))
dbcon.commit()
cur.close()
dbcon.close()
except OrgException as ue:
raise ue
except Exception:
traceback.print_exc()
raise OrgException('An issue has occurred. Please contact an admin.')
class OrgException(Exception):
def __init__(self, message):
self.reason = message
super().__init__(self, self.reason)
| 36.42145
| 380
| 0.690017
| 3,286
| 24,111
| 4.987827
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| 0.770897
| 0.750824
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| 0.688774
| 0
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| 0.176019
| 24,111
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| 0.361159
| 0.041055
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| 0.065972
| false
| 0.053819
| 0.013889
| 0.001736
| 0.125
| 0.041667
| 0
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| null | 0
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0
| 7
|
c6ca3023c5aa32c80994ab92fc8197bd5e3f8d4b
| 33,418
|
py
|
Python
|
generate_file.py
|
xyhang1221/technip_neww
|
4df2f8cbe0e6774d0632707364acc68c1f23c391
|
[
"MIT"
] | null | null | null |
generate_file.py
|
xyhang1221/technip_neww
|
4df2f8cbe0e6774d0632707364acc68c1f23c391
|
[
"MIT"
] | null | null | null |
generate_file.py
|
xyhang1221/technip_neww
|
4df2f8cbe0e6774d0632707364acc68c1f23c391
|
[
"MIT"
] | null | null | null |
import openpyxl
import os
import sqlite3
from xlrd import open_workbook
from openpyxl.styles import PatternFill
from itertools import zip_longest
import csv
#
def deal_string(words):
a = words.replace(' ','')
b = a.upper()
return b
def excel_to_dict(name_of_file_excel):
book = open_workbook(name_of_file_excel, formatting_info=False)
cursheet = book.sheet_by_index(0)
map_result = list()
for row in range(cursheet.nrows):
if row == 0:
colum_list = [cursheet.cell(0, col).value for col in range(cursheet.ncols)]
else:
content = [cursheet.cell(row, col).value for col in range(cursheet.ncols)]
content_map = map(lambda x: x.strip() if isinstance(x, str) else x, content)
map_result.append(dict(zip_longest(colum_list, content_map)))
return map_result
def delete_Blanks(array):
tempArray = array.copy()
for key, value in sorted(tempArray.items()):
if value == "":
del tempArray[key]
return tempArray
def find_Blanks(array):
liste = []
tempArray = array.copy()
for key, value in sorted(tempArray.items()):
if value == "":
liste.append(key)
return liste
def input_rw(rwm):
d = {}
d[u'RW'] = rwm
return d
def input_rw_sp(rwm, spm):
d = {}
d[u'RW'] = rwm
d[u'SUPPLIER'] = spm
return d
def merge_dict(dict1, dict2):
dictmerge = dict(list(dict1.items())+list(dict2.items()))
return dictmerge
def delete_duplicate(name_table):
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute("create table TMP as select distinct * from "+name_table)
cur.execute("drop table "+ name_table)
cur.execute("create table "+name_table+" as select * from TMP")
cur.execute("drop table TMP")
cur.close()
conn.commit()
conn.close()
def dict_to_csv(list_dict, file_name):
dict1 = list_dict[0]
k = dict1.keys()
list_values = []
for i in range(len(list_dict)):
v = list_dict[i].values()
list_values.append(v)
with open(file_name,"w") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(k) # write colunm names
writer.writerows(list_values)
fill0 = PatternFill("solid", fgColor="FFDEAD")
fill1 = PatternFill("solid", fgColor="C7C7C7")
fill2 = PatternFill("solid", fgColor="EED2EE")
fill3 = PatternFill("solid", fgColor="B3EE3A")
# for door
def sql_phrase_select_door(keys, dictionary):
phrase = "select WEIGHT, RW from DOOR where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase+keys[i]+" = '"+str(dictionary[keys[i]])+"'"
else:
phrase = phrase+" and "+keys[i]+" = '"+str(dictionary[keys[i]])+"'"
return phrase
def sql_phrase_select_supplier_door(keys, dictionary):
phrase = "select SUPPLIER from DOOR where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase+keys[i]+" = '"+str(dictionary[keys[i]])+"'"
else:
phrase = phrase+" and "+keys[i]+" = '"+str(dictionary[keys[i]])+"'"
return phrase
def find_supplier_door(dict_door,database):
dict_door_new = delete_Blanks(dict_door)
k = list(dict_door_new.keys())
k.remove("HEIGHT")
k.remove("WIDTH")
k.remove("MARK")
k.remove("LEVEL")
p = sql_phrase_select_supplier_door(k, dict_door)
conn = sqlite3.connect(database)
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def check_key_values_door(d):
l = ["FIRERATING","HEIGHT","WIDTH","FUNCTION", "LEVEL"]
for i in range(len(l)):
if d[l[i]] == "":
return 1
return 0
def find_weight_door(dict_door,database):
dict_door_new = delete_Blanks(dict_door)
k = list(dict_door_new.keys())
k.remove("WIDTH")
k.remove("HEIGHT")
k.remove("MARK")
k.remove("LEVEL")
p = sql_phrase_select_door(k, dict_door)
conn = sqlite3.connect(database)
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
if len(ress) == 1:
return (float(ress[0][0])*float(dict_door["WIDTH"])*float(dict_door["HEIGHT"])/1000000.0,"d", "0")
elif len(ress) > 1:
return ("duo",ress)
else:
return (0,"b")
def sql_phrase_select_door_final(keys, dictionary):
phrase = "select WEIGHT from DOOR where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase+keys[i]+" = '"+str(dictionary[keys[i]])+"'"
else:
phrase = phrase+" and "+keys[i]+" = '"+str(dictionary[keys[i]])+"'"
return phrase
def find_weight_level_door_final(dict_door):
dict_door_new = delete_Blanks(dict_door)
k = list(dict_door_new.keys())
k.remove("HEIGHT")
k.remove("WIDTH")
k.remove("MARK")
k.remove("LEVEL")
p = sql_phrase_select_door_final(k, dict_door)
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
if len(ress)==1:
return (float(ress[0][0])*float(dict_door["WIDTH"])*float(dict_door["HEIGHT"])/1000000.0, dict_door["LEVEL"])
else:
return (0.0,0.0)
def insert_weight_level_door(dict_door):
weight_level_door = find_weight_level_door_final(dict_door)
phrase = "insert into WEIGHT_DOOR (WEIGHT, LEVEL) values ('"+str(weight_level_door[0])+"', '"+str(weight_level_door[1])+"')"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(phrase)
cur.close()
conn.commit()
conn.close()
def create_table_weight_door():
pp = "drop table WEIGHT_DOOR"
p = "create table if not exists WEIGHT_DOOR (WEIGHT CHAR(30), LEVEL CHAR(30))"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.execute(pp)
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def group_weight_door():
p = "select LEVEL, sum(WEIGHT) from WEIGHT_DOOR group by LEVEL"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def write_weight_door(ress):
number_row = len(ress)
wb = openpyxl.Workbook()
ws = wb.active
ws.title = "summary"
ws["A1"] = "SUMMARY"
ws["A2"] = "ELEMENT"
ws["B2"] = "WEIGHT"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A" + str(i + 3)] = "DOOR "+str(ress[i][0])
ws["A" + str(i + 3)].fill = fill0
ws["B" + str(i + 3)] = str(round(ress[i][1],4))
ws["B" + str(i + 3)].fill = fill0
ws["C" + str(i + 3)] = "kg"
ws["C" + str(i + 3)].fill = fill0
wb.save("weight_report.xlsx")
def sql_phrase_insert_3_values_door(keys, dictionary):
phrase = "insert into QUANTITY_DOOR (FUNCTION, FIRERATING, SINGLEDOUBLE) select FUNCTION, FIRERATING, SINGLEDOUBLE from DOOR where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
else:
phrase = phrase + " and " + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
return phrase
def insert_3_values_door(dict_door):
dict_door_new = delete_Blanks(dict_door)
k = list(dict_door_new.keys())
k.remove("HEIGHT")
k.remove("WIDTH")
k.remove("MARK")
k.remove("LEVEL")
p = sql_phrase_insert_3_values_door(k, dict_door)
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def create_table_quantity_door():
pp = "drop table QUANTITY_DOOR"
p = "create table if not exists QUANTITY_DOOR (FUNCTION CHAR(30), FIRERATING CHAR(30), SINGLEDOUBLE CHAR(30))"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.execute(pp)
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def group_quantity_door():
p = "select FUNCTION, FIRERATING, SINGLEDOUBLE, count(*) from QUANTITY_DOOR group by FUNCTION, FIRERATING, SINGLEDOUBLE"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def write_quantity_door(ress):
number_row = len(ress)
if os.path.isfile("price_schedule.xlsx"):
wb = openpyxl.load_workbook("price_schedule.xlsx")
names = wb.get_sheet_names()
if "result_door" in names:
ws = wb["result_door"]
ws["A1"] = "summary"
ws["A2"] = "FUNCTION"
ws["B2"] = "FIRERATING"
ws["C2"] = "SINGLEDOUBLE"
ws["D2"] = "COUNT"
for i in range(number_row):
ws["A"+str(i+3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(ress[i][1])
ws["C" + str(i + 3)] = str(ress[i][2])
ws["D" + str(i + 3)] = str(ress[i][3])
wb.save("price_schedule.xlsx")
else:
ws = wb.create_sheet("result_door")
ws["A1"] = "summary"
ws["A2"] = "FUNCTION"
ws["B2"] = "FIRERATING"
ws["C2"] = "SINGLEDOUBLE"
ws["D2"] = "COUNT"
for i in range(number_row):
ws["A" + str(i + 3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(ress[i][1])
ws["C" + str(i + 3)] = str(ress[i][2])
ws["D" + str(i + 3)] = str(ress[i][3])
wb.save("price_schedule.xlsx")
else:
wb = openpyxl.Workbook()
ws = wb.create_sheet("result_door")
ws["A1"] = "summary"
ws["A2"] = "FUNCTION"
ws["B2"] = "FIRERATING"
ws["C2"] = "SINGLEDOUBLE"
ws["D2"] = "COUNT"
for i in range(number_row):
ws["A" + str(i + 3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(ress[i][1])
ws["C" + str(i + 3)] = str(ress[i][2])
ws["D" + str(i + 3)] = str(ress[i][3])
wb.save("price_schedule.xlsx")
#for floor
def sql_phrase_select_floor(keys, dictionary):
phrase = "select WEIGHT, RW from FLOOR where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
else:
phrase = phrase + " and " + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
return phrase
def sql_phrase_select_supplier_floor(keys, dictionary):
phrase = "select SUPPLIER from FLOOR where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
else:
phrase = phrase + " and " + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
return phrase
def find_supplier_floor(dict_floor, database):
dict_floor_new = delete_Blanks(dict_floor)
k = list(dict_floor_new.keys())
k.remove("MATERIALAREA")
k.remove("LEVEL")
k.remove("ELEVATIONATTOP")
p = sql_phrase_select_supplier_floor(k, dict_floor)
conn = sqlite3.connect(database)
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def check_key_values_floor(d):
l = ["FIRERATING", "MATERIALAREA", "LEVEL"]
for i in range(len(l)):
if d[l[i]] == "":
return 1
return 0
def find_weight_floor(dict_floor,database):
dict_floor_new = delete_Blanks(dict_floor)
k = list(dict_floor_new.keys())
k.remove("MATERIALAREA")
k.remove("LEVEL")
k.remove("ELEVATIONATTOP")
p = sql_phrase_select_floor(k, dict_floor)
conn = sqlite3.connect(database)
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
if len(ress) == 1:
return (float(ress[0][0])*float(dict_floor["MATERIALAREA"]),"d", dict_floor["MATERIALAREA"])
elif len(ress) > 1:
return ("duo",ress)
else:
return (0,"b")
def group_weight_floor():
p = "select LEVEL, sum(WEIGHT) from WEIGHT_FLOOR group by LEVEL"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def create_table_quantity_floor():
pp = "drop table QUANTITY_FLOOR"
p = "create table if not exists QUANTITY_FLOOR (TYPE CHAR(30), MATERIALAREA CHAR(30))"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.execute(pp)
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def create_table_weight_floor():
pp = "drop table WEIGHT_FLOOR"
p = "create table if not exists WEIGHT_FLOOR (LEVEL CHAR(30), WEIGHT CHAR(30))"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.execute(pp)
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def sql_phrase_select_floor_final(keys, dictionary):
phrase = "select WEIGHT from FLOOR where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
else:
phrase = phrase + " and " + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
return phrase
def find_weight_level_floor_final(dict_floor):
k = list(dict_floor.keys())
k.remove("MATERIALAREA")
k.remove("LEVEL")
k.remove("ELEVATIONATTOP")
p = sql_phrase_select_floor_final(k, dict_floor)
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
if len(ress) == 1:
return (float(ress[0][0]) * float(dict_floor["MATERIALAREA"]), dict_floor["LEVEL"])
else:
return (0.0, 0.0)
def insert_weight_level_floor(dict_floor):
weight_level_floor = find_weight_level_floor_final(dict_floor)
phrase = "insert into WEIGHT_FLOOR (WEIGHT, LEVEL) values ('"+str(weight_level_floor[0])+"', '"+str(weight_level_floor[1])+"')"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(phrase)
cur.close()
conn.commit()
conn.close()
def insert_type_area_floor(dict_floor):
p = "insert into QUANTITY_FLOOR(TYPE, MATERIALAREA) values ('"+str(dict_floor["TYPE"])+"', '"+str(dict_floor["MATERIALAREA"])+"')"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def group_quantity_floor():
p = "select TYPE, sum(MATERIALAREA) from QUANTITY_FLOOR group by TYPE"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def write_quantity_floor(ress):
number_row = len(ress)
if os.path.isfile("price_schedule.xlsx"):
wb = openpyxl.load_workbook("price_schedule.xlsx")
names = wb.get_sheet_names()
if "result_floor" in names:
ws = wb["result_floor"]
ws["A1"] = "summary"
ws["A2"] = "TYPE"
ws["B2"] = "QUANTITY"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A"+str(i+3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(round(ress[i][1],4))
ws["C" + str(i + 3)] = "m²"
wb.save("price_schedule.xlsx")
else:
ws = wb.create_sheet("result_floor")
ws["A1"] = "summary"
ws["A2"] = "TYPE"
ws["B2"] = "QUANTITY"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A" + str(i + 3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(round(ress[i][1], 4))
ws["C" + str(i + 3)] = "m²"
wb.save("price_schedule.xlsx")
else:
wb = openpyxl.Workbook()
ws = wb.create_sheet("result_floor")
ws["A1"] = "summary"
ws["A2"] = "TYPE"
ws["B2"] = "QUANTITY"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A" + str(i + 3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(round(ress[i][1], 4))
ws["C" + str(i + 3)] = "m²"
wb.save("price_schedule.xlsx")
def write_weight_floor(ress):
number_row = len(ress)
if os.path.isfile("weight_report.xlsx"):
wb = openpyxl.load_workbook("weight_report.xlsx")
ws = wb.get_sheet_by_name("summary")
nr = ws.max_row
for i in range(number_row):
ws["A" + str(i + 1 + nr)] = "FLOOR "+str(ress[i][0])
ws["A" + str(i + 1 + nr)].fill = fill1
ws["B" + str(i + 1 + nr)] = str(round(ress[i][1],4))
ws["B" + str(i + 1 + nr)].fill = fill1
ws["C" + str(i + 1 + nr)] = "kg"
ws["C" + str(i + 1 + nr)].fill = fill1
wb.save("weight_report.xlsx")
else:
wb = openpyxl.Workbook()
ws = wb.active
ws.title = "summary"
ws["A1"] = "SUMMARY"
ws["A2"] = "ELEMENT"
ws["B2"] = "WEIGHT"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A" + str(i + 3)] = "FLOOR "+str(ress[i][0])
ws["A" + str(i + 3)].fill = fill1
ws["B" + str(i + 3)] = str(round(ress[i][1],4))
ws["B" + str(i + 3)].fill = fill1
ws["C" + str(i + 3)] = "kg"
ws["C" + str(i + 3)].fill = fill1
wb.save("weight_report.xlsx")
#for insulation
def sql_phrase_select_insulation(keys, dictionary):
phrase = "select DENSITY, RW from INSULATION where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
else:
phrase = phrase + " and " + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
return phrase
def sql_phrase_select_supplier_insulation(keys, dictionary):
phrase = "select SUPPLIER from INSULATION where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
else:
phrase = phrase + " and " + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
return phrase
def find_supplier_insulation(dict_insulation, database):
dict_insulation_new = delete_Blanks(dict_insulation)
k = list(dict_insulation_new.keys())
k.remove("MATERIALVOLUME")
k.remove("LEVEL")
k.remove("MARK")
p = sql_phrase_select_supplier_insulation(k, dict_insulation_new)
conn = sqlite3.connect(database)
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def check_key_values_insulation(d):
l = ["FIRERATING", "MATERIALVOLUME", "LEVEL"]
for i in range(len(l)):
if d[l[i]] == "":
return 1
return 0
def find_weight_insulation(dict_insulation,database):
dict_insulation_new = delete_Blanks(dict_insulation)
k = list(dict_insulation_new.keys())
k.remove("MATERIALVOLUME")
k.remove("LEVEL")
k.remove("MARK")
p = sql_phrase_select_insulation(k, dict_insulation_new)
conn = sqlite3.connect(database)
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
if len(ress) == 1:
return (float(ress[0][0])*float(dict_insulation_new["MATERIALVOLUME"]),"d", dict_insulation_new["MATERIALVOLUME"])
elif len(ress) > 1:
return ("duo",ress)
else:
return (0,"b")
def create_table_quantity_insulation():
pp = "drop table QUANTITY_INSULATION"
p = "create table if not exists QUANTITY_INSULATION (FIRERATING CHAR(30), MATERIALVOLUME CHAR(30))"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.execute(pp)
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def insert_firerating_volume_insulation(dict_insulation):
p = "insert into QUANTITY_INSULATION(FIRERATING, MATERIALVOLUME) values ('"+str(dict_insulation["FIRERATING"])+"', '"+str(dict_insulation["MATERIALVOLUME"])+"')"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def group_quantity_insulation():
p = "select FIRERATING, sum(MATERIALVOLUME) from QUANTITY_INSULATION group by FIRERATING"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def write_quantity_insulation(ress):
number_row = len(ress)
if os.path.isfile("price_schedule.xlsx"):
wb = openpyxl.load_workbook("price_schedule.xlsx")
names = wb.get_sheet_names()
if "result_insulation" in names:
ws = wb["result_insulation"]
ws["A1"] = "summary"
ws["A2"] = "FIRERATING"
ws["B2"] = "QUANTITY"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A"+str(i+3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(round(ress[i][1],4))
ws["C" + str(i + 3)] = "m³"
wb.save("price_schedule.xlsx")
else:
ws = wb.create_sheet("result_insulation")
ws["A1"] = "summary"
ws["A2"] = "FIRERATING"
ws["B2"] = "QUANTITY"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A" + str(i + 3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(round(ress[i][1], 4))
ws["C" + str(i + 3)] = "m³"
wb.save("price_schedule.xlsx")
else:
wb = openpyxl.Workbook()
ws = wb.create_sheet("result_insulation")
ws["A1"] = "summary"
ws["A2"] = "FIRERATING"
ws["B2"] = "QUANTITY"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A" + str(i + 3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(round(ress[i][1], 4))
ws["C" + str(i + 3)] = "m³"
wb.save("price_schedule.xlsx")
def create_table_weight_insulation():
pp = "drop table WEIGHT_INSULATION"
p = "create table if not exists WEIGHT_INSULATION (LEVEL CHAR(30), WEIGHT CHAR(30))"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.execute(pp)
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def sql_phrase_select_insulation_final(keys, dictionary):
phrase = "select DENSITY from INSULATION where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
else:
phrase = phrase + " and " + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
return phrase
def find_weight_level_insulation_final(dict_insulation):
l = find_Blanks(dict_insulation)
dict_insulation_new = delete_Blanks(dict_insulation)
k = list(dict_insulation_new.keys())
k.remove("MATERIALVOLUME")
k.remove("LEVEL")
k.remove("MARK")
p = sql_phrase_select_insulation_final(k, dict_insulation_new)
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
if len(ress) == 1:
return (float(ress[0][0]) * float(dict_insulation_new["MATERIALVOLUME"]), dict_insulation_new["LEVEL"])
else:
return (0.0, 0.0)
def insert_weight_level_insulation(dict_insulation):
weight_level_insulation = find_weight_level_insulation_final(dict_insulation)
phrase = "insert into WEIGHT_INSULATION (WEIGHT, LEVEL) values ('"+str(weight_level_insulation[0])+"', '"+str(weight_level_insulation[1])+"')"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(phrase)
cur.close()
conn.commit()
conn.close()
def group_weight_insulation():
p = "select LEVEL, sum(WEIGHT) from WEIGHT_INSULATION group by LEVEL"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def write_weight_insulation(ress):
number_row = len(ress)
if os.path.isfile("weight_report.xlsx"):
wb = openpyxl.load_workbook("weight_report.xlsx")
ws = wb.get_sheet_by_name("summary")
nr = ws.max_row
for i in range(number_row):
ws["A" + str(i + 1 + nr)] = "INSULATION "+str(ress[i][0])
ws["A" + str(i + 1 + nr)].fill = fill2
ws["B" + str(i + 1 + nr)] = str(round(ress[i][1],4))
ws["B" + str(i + 1 + nr)].fill = fill2
ws["C" + str(i + 1 + nr)] = "kg"
ws["C" + str(i + 1 + nr)].fill = fill2
wb.save("weight_report.xlsx")
else:
wb = openpyxl.Workbook()
ws = wb.active
ws.title = "summary"
ws["A1"] = "SUMMARY"
ws["A2"] = "ELEMENT"
ws["B2"] = "WEIGHT"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A" + str(i + 3)] = "INSULATION "+str(ress[i][0])
ws["A" + str(i + 3)].fill = fill2
ws["B" + str(i + 3)] = str(round(ress[i][1],4))
ws["B" + str(i + 3)].fill = fill2
ws["C" + str(i + 3)] = "kg"
ws["C" + str(i + 3)].fill = fill2
wb.save("weight_report.xlsx")
#for partition
def sql_phrase_select_partition(keys, dictionary):
phrase = "select WEIGHT, RW from PARTITION where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
else:
phrase = phrase + " and " + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
return phrase
def sql_phrase_select_supplier_partition(keys, dictionary):
phrase = "select SUPPLIER from PARTITION where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
else:
phrase = phrase + " and " + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
return phrase
def find_supplier_partition(dict_partition, database):
dict_partition_new = delete_Blanks(dict_partition)
k = list(dict_partition_new.keys())
k.remove("AREA")
k.remove("LEVEL")
k.remove("MARK")
p = sql_phrase_select_supplier_partition(k, dict_partition)
conn = sqlite3.connect(database)
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def check_key_values_partition(d):
l = ["FIRERATING", "AREA", "LEVEL"]
for i in range(len(l)):
if d[l[i]] == "":
return 1
return 0
def find_weight_partition(dict_partition,database):
dict_partition_new = delete_Blanks(dict_partition)
k = list(dict_partition_new.keys())
k.remove("AREA")
k.remove("LEVEL")
k.remove("MARK")
p = sql_phrase_select_partition(k, dict_partition)
conn = sqlite3.connect(database)
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
if len(ress) == 1:
return (float(ress[0][0])*float(dict_partition["AREA"]),"d", dict_partition["AREA"])
elif len(ress) > 1:
return ("duo",ress)
else:
return (0,"b")
def create_table_quantity_partition():
pp = "drop table QUANTITY_PARTITION"
p = "create table if not exists QUANTITY_PARTITION (FIRERATING CHAR(30), AREA CHAR(30))"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.execute(pp)
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def insert_firerating_area_partition(dict_partition):
p = "insert into QUANTITY_PARTITION(FIRERATING, AREA) values ('"+str(dict_partition["FIRERATING"])+"', '"+str(dict_partition["AREA"])+"')"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def group_quantity_partition():
p = "select FIRERATING, sum(AREA) from QUANTITY_PARTITION group by FIRERATING"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def write_quantity_partition(ress):
number_row = len(ress)
if os.path.isfile("price_schedule.xlsx"):
wb = openpyxl.load_workbook("price_schedule.xlsx")
names = wb.get_sheet_names()
if "result_partition" in names:
ws = wb["result_partition"]
ws["A1"] = "summary"
ws["A2"] = "FIRERATING"
ws["B2"] = "QUANTITY"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A"+str(i+3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(round(ress[i][1],4))
ws["C" + str(i + 3)] = "m²"
wb.save("price_schedule.xlsx")
else:
ws = wb.create_sheet("result_partition")
ws["A1"] = "summary"
ws["A2"] = "FIRERATING"
ws["B2"] = "QUANTITY"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A" + str(i + 3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(round(ress[i][1], 4))
ws["C" + str(i + 3)] = "m²"
wb.save("price_schedule.xlsx")
else:
wb = openpyxl.Workbook()
ws = wb.create_sheet("result_partition")
ws["A1"] = "summary"
ws["A2"] = "FIRERATING"
ws["B2"] = "QUANTITY"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A" + str(i + 3)] = str(ress[i][0])
ws["B" + str(i + 3)] = str(round(ress[i][1], 4))
ws["C" + str(i + 3)] = "m²"
wb.save("price_schedule.xlsx")
def create_table_weight_partition():
pp = "drop table WEIGHT_PARTITION"
p = "create table if not exists WEIGHT_PARTITION (LEVEL CHAR(30), WEIGHT CHAR(30))"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
cur.execute(pp)
cur.execute(p)
cur.close()
conn.commit()
conn.close()
def sql_phrase_select_partition_final(keys, dictionary):
phrase = "select WEIGHT from PARTITION where "
l = len(keys)
for i in range(l):
if i == 0:
phrase = phrase + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
else:
phrase = phrase + " and " + keys[i] + " = '" + str(dictionary[keys[i]]) + "'"
return phrase
def find_weight_level_partition_final(dict_partition):
l = find_Blanks(dict_partition)
dict_partition_new = delete_Blanks(dict_partition)
k = list(dict_partition_new.keys())
k.remove("AREA")
k.remove("LEVEL")
k.remove("MARK")
p = sql_phrase_select_partition_final(k, dict_partition_new)
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
if len(ress) == 1:
return (float(ress[0][0]) * float(dict_partition["AREA"]), dict_partition["LEVEL"])
else:
return (0.0, 0.0)
def insert_weight_level_partition(dict_partition):
weight_level_partition = find_weight_level_partition_final(dict_partition)
phrase = "insert into WEIGHT_PARTITION (WEIGHT, LEVEL) values ('"+str(weight_level_partition[0])+"', '"+str(weight_level_partition[1])+"')"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(phrase)
cur.close()
conn.commit()
conn.close()
def group_weight_partition():
p = "select LEVEL, sum(WEIGHT) from WEIGHT_PARTITION group by LEVEL"
conn = sqlite3.connect("database.db")
cur = conn.cursor()
cur.execute(p)
ress = cur.fetchall()
cur.close()
conn.commit()
conn.close()
return ress
def write_weight_partition(ress):
number_row = len(ress)
if os.path.isfile("weight_report.xlsx"):
wb = openpyxl.load_workbook("weight_report.xlsx")
ws = wb.get_sheet_by_name("summary")
nr = ws.max_row
for i in range(number_row):
ws["A" + str(i + 1 + nr)] = "PARTITION "+str(ress[i][0])
ws["A" + str(i + 1 + nr)].fill = fill3
ws["B" + str(i + 1 + nr)] = str(round(ress[i][1],4))
ws["B" + str(i + 1 + nr)].fill = fill3
ws["C" + str(i + 1 + nr)] = "kg"
ws["C" + str(i + 1 + nr)].fill = fill3
wb.save("weight_report.xlsx")
else:
wb = openpyxl.Workbook()
ws = wb.active
ws.title = "summary"
ws["A1"] = "SUMMARY"
ws["A2"] = "ELEMENT"
ws["B2"] = "WEIGHT"
ws["C2"] = "UNIT"
for i in range(number_row):
ws["A" + str(i + 3)] = "PARTITION "+str(ress[i][0])
ws["A" + str(i + 3)].fill = fill3
ws["B" + str(i + 3)] = str(round(ress[i][1],4))
ws["B" + str(i + 3)].fill = fill3
ws["C" + str(i + 3)] = "kg"
ws["C" + str(i + 3)].fill = fill3
wb.save("weight_report.xlsx")
| 34.239754
| 165
| 0.573763
| 4,498
| 33,418
| 4.130502
| 0.045798
| 0.017439
| 0.016955
| 0.051779
| 0.824641
| 0.803273
| 0.782443
| 0.726734
| 0.721729
| 0.716831
| 0
| 0.01733
| 0.262703
| 33,418
| 975
| 166
| 34.274872
| 0.736718
| 0.001885
| 0
| 0.757705
| 0
| 0.001063
| 0.160279
| 0.001829
| 0
| 0
| 0
| 0
| 0
| 1
| 0.074389
| false
| 0
| 0.007439
| 0
| 0.14559
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
05bbda004ce5d2068f0904bb539b421a3a9942be
| 7,987
|
py
|
Python
|
ezgiphy/ezgiphy.py
|
tawhidii/ezgiphy
|
b7e732e02c1b8920c259357119885f8ef080f5cb
|
[
"MIT"
] | null | null | null |
ezgiphy/ezgiphy.py
|
tawhidii/ezgiphy
|
b7e732e02c1b8920c259357119885f8ef080f5cb
|
[
"MIT"
] | null | null | null |
ezgiphy/ezgiphy.py
|
tawhidii/ezgiphy
|
b7e732e02c1b8920c259357119885f8ef080f5cb
|
[
"MIT"
] | null | null | null |
import json
from urllib import parse, request
from .errors import APIKeyError, RequiredError
from .constants import PUBLIC_API_URL, STICKER_API_URL
class APIBase:
"""
Base class for API initializer.
"""
def __init__(self, api_key=None):
self.api_key = api_key
if api_key is None:
raise APIKeyError
self.params = {
'api_key': self.api_key
}
class GiphyPublicAPI(APIBase):
"""
Wrapper class for Giphy public api.
You can find api key from https://developers.giphy.com/dashboard/
"""
def __get_json(self, url, sort_key=False, indent=4):
"""
:param url: Giphy api endpoint with query parameters.
:param sort_key:Sorting the keys when dumping saves the key-value pairs in
alphabetical order by keys.
:param indent:The indent parameter specifies the spaces that
are used at the beginning of a line. By default, it's value is 4.
"""
with request.urlopen(url) as response:
data = json.loads(response.read())
return json.dumps(obj=data, sort_keys=sort_key, indent=indent)
def search(self, **kwargs):
"""
:param q: Search query term or phrase (required).
:param limit: The maximum number of records to return.
:param offset: An optional results offset.
:param rating: Filters results by rating (g/pg/pg-13/r)
:param lang: specify default country for regional content.
"""
if kwargs:
self.params.update(**kwargs)
params = parse.urlencode(self.params)
url = "".join((PUBLIC_API_URL, '/search', '?', params))
return self.__get_json(url)
def translate(self, **kwargs):
"""
:param s: Search query term or phrase (required).
"""
if kwargs:
self.params.update(kwargs)
params = parse.urlencode(self.params)
url = "".join((PUBLIC_API_URL, '/translate', '?', params))
return self.__get_json(url)
def trending(self, **kwargs):
"""
:param limit: The maximum number of records to return.
:param rating: Filters results by rating (g/pg/pg-13/r)
"""
if kwargs:
self.params.update(kwargs)
params = parse.urlencode(self.params)
url = "".join((PUBLIC_API_URL, '/trending', '?', params))
return self.__get_json(url)
def random(self, **kwargs):
"""
:param tag: The maximum number of records to return.
:param rating: Filters results by rating (g/pg/pg-13/r)
"""
if kwargs:
self.params.update(kwargs)
params = parse.urlencode(self.params)
url = "".join((PUBLIC_API_URL, '/random', '?', params))
return self.__get_json(url)
def get_by_id(self, id=None):
"""
:param id: Filter result by specific gif id (required).
"""
if id is None:
raise RequiredError('id')
params = parse.urlencode(self.params)
url = "".join((PUBLIC_API_URL, f'/{id}', '?', params))
return self.__get_json(url)
def get_by_ids(self, ids=[]):
"""
:param ids: List of specific ids (required).
"""
if len(ids) < 0:
raise RequiredError('list of ids')
params = parse.urlencode(self.params)
url = "".join((PUBLIC_API_URL, '?', params, f"{'&ids='}", "%2C".join(ids)))
return self.__get_json(url)
class GiphyStickerAPI(APIBase):
"""
Wrapper class for Giphy sticker api.
You can find api key from https://developers.giphy.com/dashboard/
"""
def __get_json(self, url, sort_key=False, indent=4):
"""
:param url: Giphy api endpoint with query parameters.
:param sort_key:Sorting the keys when dumping saves the key-value pairs in
alphabetical order by keys.
:param indent:The indent parameter specifies the spaces that
are used at the beginning of a line. By default, it's value is 4.
"""
with request.urlopen(url) as response:
data = json.loads(response.read())
return json.dumps(obj=data, sort_keys=sort_key, indent=indent)
def search(self, **kwargs):
"""
:param q: Search query term or phrase (required).
:param limit: The maximum number of records to return.
:param offset: An optional results offset.
:param rating: Filters results by rating (g/pg/pg-13/r)
:param lang: specify default country for regional content.
"""
if kwargs:
self.params.update(**kwargs)
params = parse.urlencode(self.params)
url = "".join((STICKER_API_URL, '/search', '?', params))
return self.__get_json(url)
def translate(self, **kwargs):
"""
:param s: Search query term or phrase (required).
"""
if kwargs:
self.params.update(kwargs)
params = parse.urlencode(self.params)
url = "".join((STICKER_API_URL, '/translate', '?', params))
return self.__get_json(url)
def trending(self, **kwargs):
"""
:param limit: The maximum number of records to return.
:param rating: Filters results by rating (g/pg/pg-13/r)
"""
if kwargs:
self.params.update(kwargs)
params = parse.urlencode(self.params)
url = "".join((STICKER_API_URL, '/trending', '?', params))
return self.__get_json(url)
def random(self, **kwargs):
"""
:param tag: The maximum number of records to return.
:param rating: Filters results by rating (g/pg/pg-13/r)
"""
if kwargs:
self.params.update(kwargs)
params = parse.urlencode(self.params)
url = "".join((STICKER_API_URL, '/random', '?', params))
return self.__get_json(url)
def get_by_id(self, id=None):
"""
:param id: Filter result by specific gif id (required).
"""
if id is None:
raise RequiredError('id')
params = parse.urlencode(self.params)
url = "".join((STICKER_API_URL, f'/{id}', '?', params))
return self.__get_json(url)
def get_by_ids(self, ids=[]):
"""
:param ids: List of specific ids (required).
"""
if len(ids) < 0:
raise RequiredError('list of ids')
params = parse.urlencode(self.params)
url = "".join((STICKER_API_URL, '?', params, f"{'&ids='}", "%2C".join(ids)))
return self.__get_json(url)
def stickers_listing(self):
"""Get all the stickers pack"""
params = parse.urlencode(self.params)
url = "".join((STICKER_API_URL, '/packs', '?', params))
return self.__get_json(url)
def individual_stickers_pack(self, id=None):
"""
:param id: get individual sticker pack by id
"""
if id is None:
raise RequiredError('id')
params = parse.urlencode(self.params)
url = "".join((STICKER_API_URL, f'/packs/{id}', '?', params))
return self.__get_json(url)
def stickers(self, id=None, **kwargs):
"""
:param id: id of sticker pack.
:param limit:The maximum number of records to return.
:param offset:An optional results offset.
"""
if id is None:
raise RequiredError('id')
if kwargs:
self.params.update(kwargs)
params = parse.urlencode(self.params)
url = "".join((STICKER_API_URL, f'/packs/{id}/stickers', '?', params))
return self.__get_json(url)
def children_pack_listing(self, id=None):
"""
:param id: id of sticker pack.
"""
if id is None:
raise RequiredError('id')
params = parse.urlencode(self.params)
url = "".join((STICKER_API_URL, f'/packs/{id}/children?', params))
return self.__get_json(url)
| 33.141079
| 84
| 0.582446
| 988
| 7,987
| 4.586032
| 0.137652
| 0.057382
| 0.070625
| 0.08475
| 0.902229
| 0.884352
| 0.868903
| 0.845288
| 0.838888
| 0.838888
| 0
| 0.003537
| 0.2921
| 7,987
| 240
| 85
| 33.279167
| 0.797842
| 0.288594
| 0
| 0.711864
| 0
| 0
| 0.042072
| 0.004167
| 0
| 0
| 0
| 0
| 0
| 1
| 0.161017
| false
| 0
| 0.033898
| 0
| 0.372881
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
05eb5b2307672aa7b89b089bd22904c525d8aeb1
| 3,449
|
py
|
Python
|
justree/dfs.py
|
sr9000/justree
|
54def8eb9f8a26ce62af82e22cb0cece59ef3906
|
[
"MIT"
] | null | null | null |
justree/dfs.py
|
sr9000/justree
|
54def8eb9f8a26ce62af82e22cb0cece59ef3906
|
[
"MIT"
] | null | null | null |
justree/dfs.py
|
sr9000/justree
|
54def8eb9f8a26ce62af82e22cb0cece59ef3906
|
[
"MIT"
] | null | null | null |
from itertools import repeat
from typing import Iterable, List, Tuple, Union, Optional
from .tools import reversed_enumerate, T
from .tree_node import TreeNode
def non_recursive_tree_dfs_forward_original(self: T) -> Iterable[T]:
assert isinstance(self, TreeNode)
q: List[TreeNode] = [self]
while q:
t = q.pop()
q.extend(reversed(t._children))
yield t
def non_recursive_tree_dfs_forward_mirror(self: T) -> Iterable[T]:
assert isinstance(self, TreeNode)
q: List[TreeNode] = [self]
while q:
t = q.pop()
q.extend(t._children)
yield t
def non_recursive_tree_dfs_reverse_original(self: T) -> Iterable[T]:
assert isinstance(self, TreeNode)
q: List[Tuple[bool, TreeNode]] = [(True, self)]
while q:
f, t = q[-1]
if f:
q[-1] = (False, t)
q.extend(zip(repeat(True), t._children))
else:
yield q.pop()[1]
def non_recursive_tree_dfs_reverse_mirror(self: T) -> Iterable[T]:
assert isinstance(self, TreeNode)
q: List[Tuple[bool, TreeNode]] = [(True, self)]
while q:
f, t = q[-1]
if f:
q[-1] = (False, t)
q.extend(zip(repeat(True), reversed(t._children)))
else:
yield q.pop()[1]
_Int = Union[int, float]
def bfs_ex_preparation(depth: Optional[_Int]) -> _Int:
return float('inf') if depth is None else depth
def non_recursive_tree_dfs_forward_original_ex(self: T, depth: Optional[_Int] = None) \
-> Iterable[Tuple[T, int, Tuple[int, ...]]]:
assert isinstance(self, TreeNode)
depth = bfs_ex_preparation(depth)
q: List[Tuple[TreeNode, int, Tuple[int, ...]]] = [(self, 1, ())]
while q:
t, d, i = q.pop()
if d < depth:
q.extend((ct, d + 1, i + (ci,)) for ci, ct in reversed_enumerate(t._children))
yield t, d, i
def non_recursive_tree_dfs_forward_mirror_ex(self: T, depth: Optional[_Int] = None) \
-> Iterable[Tuple[T, int, Tuple[int, ...]]]:
assert isinstance(self, TreeNode)
depth = bfs_ex_preparation(depth)
q: List[Tuple[TreeNode, int, Tuple[int, ...]]] = [(self, 1, ())]
while q:
t, d, i = q.pop()
if d < depth:
q.extend((ct, d + 1, i + (ci,)) for ci, ct in enumerate(t._children))
yield t, d, i
def non_recursive_tree_dfs_reverse_original_ex(self: T, depth: Optional[_Int] = None) \
-> Iterable[Tuple[T, int, Tuple[int, ...]]]:
assert isinstance(self, TreeNode)
depth = bfs_ex_preparation(depth)
q: List[Tuple[bool, TreeNode, int, Tuple[int, ...]]] = [(True, self, 1, ())]
while q:
f, t, d, i = q[-1]
if f:
q[-1] = (False, t, d, i)
if d < depth:
q.extend((True, ct, d + 1, i + (ci,)) for ci, ct in enumerate(t._children))
else:
yield q.pop()[1:]
def non_recursive_tree_dfs_reverse_mirror_ex(self: T, depth: Optional[_Int] = None) \
-> Iterable[Tuple[T, int, Tuple[int, ...]]]:
assert isinstance(self, TreeNode)
depth = bfs_ex_preparation(depth)
q: List[Tuple[bool, TreeNode, int, Tuple[int, ...]]] = [(True, self, 1, ())]
while q:
f, t, d, i = q[-1]
if f:
q[-1] = (False, t, d, i)
if d < depth:
q.extend((True, ct, d + 1, i + (ci,)) for ci, ct in reversed_enumerate(t._children))
else:
yield q.pop()[1:]
| 31.642202
| 100
| 0.57089
| 490
| 3,449
| 3.869388
| 0.118367
| 0.025316
| 0.063291
| 0.080169
| 0.875527
| 0.875527
| 0.875527
| 0.818038
| 0.810654
| 0.771624
| 0
| 0.007981
| 0.273413
| 3,449
| 108
| 101
| 31.935185
| 0.748603
| 0
| 0
| 0.735632
| 0
| 0
| 0.00087
| 0
| 0
| 0
| 0
| 0
| 0.091954
| 1
| 0.103448
| false
| 0
| 0.045977
| 0.011494
| 0.16092
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
05ec6cdc006cfb153e32626b1afdb56429aa6bb7
| 4,247
|
py
|
Python
|
shakespearelang/tests/test_stacks.py
|
zmbc/shakespearelang
|
cf86e3ed81919800e22763776dc7cb1a5b8dd816
|
[
"MIT"
] | 69
|
2017-05-16T15:31:30.000Z
|
2022-03-16T01:49:29.000Z
|
shakespearelang/tests/test_stacks.py
|
zmbc/shakespearelang
|
cf86e3ed81919800e22763776dc7cb1a5b8dd816
|
[
"MIT"
] | 8
|
2016-12-17T08:39:03.000Z
|
2021-11-20T21:31:20.000Z
|
shakespearelang/tests/test_stacks.py
|
zmbc/shakespearelang
|
cf86e3ed81919800e22763776dc7cb1a5b8dd816
|
[
"MIT"
] | 14
|
2017-12-13T17:29:34.000Z
|
2022-03-16T01:49:30.000Z
|
from shakespearelang import Shakespeare
from shakespearelang.errors import ShakespeareRuntimeError
import pytest
def test_push(monkeypatch):
s = Shakespeare("Foo. Juliet, a test. Romeo, a test.")
s.run_event("[Enter Romeo and Juliet]")
c = s.state.character_by_name("Juliet")
assert c.stack == []
assert c.value == 0
monkeypatch.setattr(Shakespeare, "evaluate_expression", lambda x, y, z: 400)
s.run_sentence("Remember a furry animal.", "Romeo")
assert c.stack == [400]
assert c.value == 0
monkeypatch.setattr(Shakespeare, "evaluate_expression", lambda x, y, z: 401)
s.run_sentence("Remember a furry animal.", "Romeo")
assert c.stack == [400, 401]
assert c.value == 0
monkeypatch.setattr(Shakespeare, "evaluate_expression", lambda x, y, z: 402)
s.run_sentence("Remember a furry animal.", "Romeo")
assert c.stack == [400, 401, 402]
assert c.value == 0
def test_pop():
s = Shakespeare("Foo. Juliet, a test. Romeo, a test.")
s.run_event("[Enter Romeo and Juliet]")
c = s.state.character_by_name("Juliet")
assert c.stack == []
assert c.value == 0
c.stack = [234, 123, 678]
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert c.stack == [234, 123]
assert c.value == 678
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert c.stack == [234]
assert c.value == 123
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert c.stack == []
assert c.value == 234
def test_sequence(monkeypatch):
s = Shakespeare("Foo. Juliet, a test. Romeo, a test.")
s.run_event("[Enter Romeo and Juliet]")
c = s.state.character_by_name("Juliet")
assert c.stack == []
assert c.value == 0
c.stack = [234, 123, 678]
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert c.stack == [234, 123]
assert c.value == 678
monkeypatch.setattr(Shakespeare, "evaluate_expression", lambda x, y, z: 401)
s.run_sentence("Remember a furry animal.", "Romeo")
assert c.stack == [234, 123, 401]
assert c.value == 678
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert c.stack == [234, 123]
assert c.value == 401
monkeypatch.setattr(Shakespeare, "evaluate_expression", lambda x, y, z: 402)
s.run_sentence("Remember a furry animal.", "Romeo")
assert c.stack == [234, 123, 402]
assert c.value == 401
monkeypatch.setattr(Shakespeare, "evaluate_expression", lambda x, y, z: 403)
s.run_sentence("Remember a furry animal.", "Romeo")
assert c.stack == [234, 123, 402, 403]
assert c.value == 401
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert c.stack == [234, 123, 402]
assert c.value == 403
monkeypatch.setattr(Shakespeare, "evaluate_expression", lambda x, y, z: 404)
s.run_sentence("Remember a furry animal.", "Romeo")
assert c.stack == [234, 123, 402, 404]
assert c.value == 403
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert c.stack == [234, 123, 402]
assert c.value == 404
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert c.stack == [234, 123]
assert c.value == 402
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert c.stack == [234]
assert c.value == 123
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert c.stack == []
assert c.value == 234
def test_errors_on_pop_from_empty():
s = Shakespeare("Foo. Juliet, a test. Romeo, a test.")
s.run_event("[Enter Romeo and Juliet]")
c = s.state.character_by_name("Juliet")
assert c.stack == []
assert c.value == 0
with pytest.raises(ShakespeareRuntimeError) as exc:
s.run_sentence("Recall thy terrible memory of thy imminent death.", "Romeo")
assert "empty stack" in str(exc.value).lower()
assert ">>Recall thy terrible memory of thy imminent death.<<" in str(exc.value)
assert exc.value.interpreter == s
assert c.stack == []
assert c.value == 0
| 33.179688
| 84
| 0.656934
| 611
| 4,247
| 4.492635
| 0.109656
| 0.112204
| 0.096175
| 0.105282
| 0.875774
| 0.875774
| 0.875774
| 0.866667
| 0.85173
| 0.83133
| 0
| 0.058526
| 0.207441
| 4,247
| 127
| 85
| 33.440945
| 0.756982
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| 0.765957
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| 0.295267
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| 1
| 0.042553
| false
| 0
| 0.031915
| 0
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| null | 0
| 0
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| 1
| 1
| 1
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| null | 0
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| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 8
|
af0bfa8a13cb66a027c8d59deeabe328ed045176
| 15,218
|
py
|
Python
|
openconcept/analysis/performance/tests/test_solver_phase_helpers.py
|
bbrelje/openconcept
|
b41bc831ed6aa2742ec35cacd3249395ee4527db
|
[
"MIT"
] | null | null | null |
openconcept/analysis/performance/tests/test_solver_phase_helpers.py
|
bbrelje/openconcept
|
b41bc831ed6aa2742ec35cacd3249395ee4527db
|
[
"MIT"
] | 6
|
2018-06-28T18:54:36.000Z
|
2021-12-17T16:18:20.000Z
|
openconcept/analysis/performance/tests/test_solver_phase_helpers.py
|
bbrelje/openconcept
|
b41bc831ed6aa2742ec35cacd3249395ee4527db
|
[
"MIT"
] | null | null | null |
from __future__ import division
import unittest
import numpy as np
from openmdao.utils.assert_utils import assert_rel_error, assert_check_partials
from openmdao.api import IndepVarComp, Group, Problem
from openconcept.analysis.performance.solver_phases import ClimbAngleComp, FlipVectorComp, Groundspeeds, HorizontalAcceleration, VerticalAcceleration, SteadyFlightCL, TakeoffTransition
g = 9.80665 #m/s^2
# TESTS FOR ClimbAngleComp ===================================
class ClimbAngleCompTestGroup(Group):
def initialize(self):
self.options.declare('num_nodes',default=1,desc="Number of mission analysis points to run")
def setup(self):
nn = self.options['num_nodes']
iv = self.add_subsystem('conditions', IndepVarComp(), promotes_outputs=['*'])
iv.add_output('thrust', val=np.ones((nn,))*1000, units='N')
iv.add_output('drag', val=np.ones((nn,))*1000, units='N')
iv.add_output('weight', val=np.ones((nn,))*1000, units='kg')
self.add_subsystem('climbangle', ClimbAngleComp(num_nodes=nn), promotes_inputs=['*'], promotes_outputs=['*'])
class ClimbAngleTestCase_Scalar(unittest.TestCase):
def setUp(self):
self.prob = Problem(ClimbAngleCompTestGroup(num_nodes=1))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob.run_model()
def test_level_flight(self):
assert_rel_error(self, self.prob['gamma'][0],0,tolerance=1e-10)
def test_climb_flight(self):
self.prob['thrust'] = np.ones((1,))*1200
self.prob.run_model()
assert_rel_error(self, self.prob['gamma'][0], np.arcsin(200 / 1000 / g), tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
# TESTS FOR FlipVectorComp ===================================
class FlipVectorCompTestGroup(Group):
def initialize(self):
self.options.declare('num_nodes',default=1,desc="Number of points to run")
self.options.declare('units', default=None)
self.options.declare('negative', default=False)
def setup(self):
nn = self.options['num_nodes']
unit_string = self.options['units']
neg_flag = self.options['negative']
iv = self.add_subsystem('conditions', IndepVarComp(), promotes_outputs=['*'])
iv.add_output('thrust', val=np.linspace(0,100,nn), units='N')
self.add_subsystem('flipvector', FlipVectorComp(num_nodes=nn, units=unit_string, negative=neg_flag), promotes_outputs=['*'])
self.connect('thrust', 'flipvector.vec_in')
class FlipVectorCompTestCase_Vector(unittest.TestCase):
def setUp(self):
self.prob = Problem(FlipVectorCompTestGroup(num_nodes=11, units='N'))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob.run_model()
def test_flip_vec_order(self):
assert_rel_error(self, self.prob['vec_out'], np.linspace(100, 0, 11), tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
class FlipVectorCompTestCase_Scalar(unittest.TestCase):
def setUp(self):
self.prob = Problem(FlipVectorCompTestGroup(num_nodes=1, units='N'))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob.run_model()
def test_flip_vec_order(self):
assert_rel_error(self, self.prob['vec_out'], np.zeros((1,)), tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
class FlipVectorCompTestCase_Negative(unittest.TestCase):
def setUp(self):
self.prob = Problem(FlipVectorCompTestGroup(num_nodes=11, units='N', negative=True))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob.run_model()
def test_flip_vec_order(self):
assert_rel_error(self, self.prob['vec_out'], np.linspace(-100, 0, 11), tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
# TESTS FOR Groundspeeds ===================================
class GroundspeedsTestGroup(Group):
def initialize(self):
self.options.declare('num_nodes',default=1,desc="Number of mission analysis points to run")
def setup(self):
nn = self.options['num_nodes']
iv = self.add_subsystem('conditions', IndepVarComp(), promotes_outputs=['*'])
iv.add_output('fltcond|vs', val=np.linspace(0,3,nn), units='m/s')
iv.add_output('fltcond|Utrue', val=np.ones((nn,))*50, units='m/s')
self.add_subsystem('gs', Groundspeeds(num_nodes=nn), promotes_inputs=['*'], promotes_outputs=['*'])
class GroundspeedsTestCase(unittest.TestCase):
def setUp(self):
self.prob = Problem(GroundspeedsTestGroup(num_nodes=15))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob.run_model()
def test_level_flight(self):
assert_rel_error(self, self.prob['fltcond|groundspeed'][0],50,tolerance=1e-10)
assert_rel_error(self, self.prob['fltcond|cosgamma'][0],1.,tolerance=1e-10)
assert_rel_error(self, self.prob['fltcond|singamma'][0],0.,tolerance=1e-10)
def test_climb_flight(self):
gs = np.sqrt(50**2 - 3**2)
assert_rel_error(self, self.prob['fltcond|groundspeed'][-1],gs,tolerance=1e-10)
assert_rel_error(self, self.prob['fltcond|cosgamma'][-1],gs/50.,tolerance=1e-10)
assert_rel_error(self, self.prob['fltcond|singamma'][-1],3./50.,tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
# TESTS FOR HorizontalAcceleration ===================================
class HorizontalAccelerationTestGroup(Group):
def initialize(self):
self.options.declare('num_nodes',default=9,desc="Number of mission analysis points to run")
def setup(self):
nn = self.options['num_nodes']
iv = self.add_subsystem('conditions', IndepVarComp(), promotes_outputs=['*'])
iv.add_output('weight', val=np.ones((nn,))*100, units='kg')
iv.add_output('lift', val=np.ones((nn,))*100, units='N')
iv.add_output('thrust', val=np.ones((nn,))*100, units='N')
iv.add_output('drag', val=np.ones((nn,))*100, units='N')
iv.add_output('fltcond|singamma', val=np.zeros((nn,)), units=None)
iv.add_output('braking', val=np.zeros((nn,)), units=None)
self.add_subsystem('ha', HorizontalAcceleration(num_nodes=nn), promotes_inputs=['*'], promotes_outputs=['*'])
class HorizontalAccelerationTestCase_SteadyLevel(unittest.TestCase):
def setUp(self):
self.prob = Problem(HorizontalAccelerationTestGroup(num_nodes=9))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob.run_model()
def test_steady_level_flights(self):
assert_rel_error(self, self.prob['accel_horiz'], np.zeros((9,)), tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
class HorizontalAccelerationTestCase_SteadyClimb(unittest.TestCase):
def setUp(self):
self.prob = Problem(HorizontalAccelerationTestGroup(num_nodes=9))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob['thrust'] = np.ones((9,)) * (100 + 100 * g * 0.02)
self.prob['fltcond|singamma'] = np.ones((9,)) * 0.02
self.prob.run_model()
def test_steady_climb_flights(self):
assert_rel_error(self, self.prob['accel_horiz'], np.zeros((9,)), tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
class HorizontalAccelerationTestCase_SteadyClimb(unittest.TestCase):
def setUp(self):
self.prob = Problem(HorizontalAccelerationTestGroup(num_nodes=9))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob['thrust'] = np.ones((9,)) * (100 + 100 * g * 0.02)
self.prob['fltcond|singamma'] = np.ones((9,)) * 0.02
self.prob.run_model()
def test_steady_climb_flights(self):
assert_rel_error(self, self.prob['accel_horiz'], np.zeros((9,)), tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
class HorizontalAccelerationTestCase_UnsteadyRunwayAccel(unittest.TestCase):
def setUp(self):
self.prob = Problem(HorizontalAccelerationTestGroup(num_nodes=9))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob['braking'] = np.ones((9,)) * 0.03
self.prob['lift'] = np.linspace(0, 150, 9) * g
self.prob['drag'] = np.ones((9,)) * 50
self.prob.run_model()
def test_accel_with_braking(self):
drag = 50.0
thrust = 100.0
lift = 0.0
mass = 100
weight = mass*g
singamma = 0.0
brakeforce = 0.03 * (weight-lift)
slopeforce = weight * singamma
accel_horz_actual = (thrust - drag - brakeforce - slopeforce) / mass
assert_rel_error(self, self.prob['accel_horiz'][0], accel_horz_actual, tolerance=1e-10)
def test_accel_with_braking_and_lift(self):
drag = 50.0
thrust = 100.0
mass = 100
weight = mass*g
singamma = 0.0
lift = weight*0.75
brakeforce = 0.03 * (weight-lift)
slopeforce = weight * singamma
accel_horz_actual = (thrust - drag - brakeforce - slopeforce) / mass
assert_rel_error(self, self.prob['accel_horiz'][4], accel_horz_actual, tolerance=1e-10)
def test_accel_lift_exceeds_weight(self):
drag = 50.0
thrust = 100.0
mass = 100
weight = mass*g
singamma = 0.0
# if lift exceeds weight (as it does here) no braking force is applied
brakeforce = 0.0
slopeforce = weight * singamma
accel_horz_actual = (thrust - drag - brakeforce - slopeforce) / mass
assert_rel_error(self, self.prob['accel_horiz'][-1], accel_horz_actual, tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
# TESTS FOR VerticalAcceleration ===================================
class VerticalAccelerationTestGroup(Group):
def initialize(self):
self.options.declare('num_nodes',default=9,desc="Number of mission analysis points to run")
def setup(self):
nn = self.options['num_nodes']
iv = self.add_subsystem('conditions', IndepVarComp(), promotes_outputs=['*'])
iv.add_output('weight', val=np.ones((nn,))*100, units='kg')
iv.add_output('lift', val=np.ones((nn,))*100*g, units='N')
iv.add_output('thrust', val=np.ones((nn,))*100, units='N')
iv.add_output('drag', val=np.ones((nn,))*100, units='N')
iv.add_output('fltcond|singamma', val=np.zeros((nn,)), units=None)
iv.add_output('fltcond|cosgamma', val=np.ones((nn,)), units=None)
self.add_subsystem('va', VerticalAcceleration(num_nodes=nn), promotes_inputs=['*'], promotes_outputs=['*'])
class VerticalAccelerationTestCase_SteadyLevel(unittest.TestCase):
def setUp(self):
self.prob = Problem(VerticalAccelerationTestGroup(num_nodes=9))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob.run_model()
def test_steady_level_flights(self):
assert_rel_error(self, self.prob['accel_vert'], np.zeros((9,)), tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
class VerticalAccelerationTestCase_SteadyClimbing(unittest.TestCase):
def setUp(self):
self.prob = Problem(VerticalAccelerationTestGroup(num_nodes=9))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob['fltcond|singamma'] = np.ones((9,)) * np.sin(0.02)
self.prob['fltcond|cosgamma'] = np.ones((9,)) * np.cos(0.02)
self.prob['lift'] = np.ones((9,)) * 100 * g / np.cos(0.02)
self.prob.run_model()
def test_steady_climbing_flight(self):
assert_rel_error(self, self.prob['accel_vert'], np.zeros((9,)), tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
class VerticalAccelerationTestCase_UnsteadyPullUp(unittest.TestCase):
def setUp(self):
self.prob = Problem(VerticalAccelerationTestGroup(num_nodes=9))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob['lift'] = np.ones((9,)) * 100 * g + 100
self.prob.run_model()
def test_unsteady_pullup(self):
assert_rel_error(self, self.prob['accel_vert'], (100./100.)*np.ones((9,)), tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
# TESTS FOR SteadyFlightCL ===================================
class SteadyFlightCLTestGroup(Group):
def initialize(self):
self.options.declare('num_nodes',default=9,desc="Number of mission analysis points to run")
def setup(self):
nn = self.options['num_nodes']
iv = self.add_subsystem('conditions', IndepVarComp(), promotes_outputs=['*'])
iv.add_output('weight', val=np.ones((nn,))*100, units='kg')
iv.add_output('fltcond|q', val=np.ones((nn,))*1000, units='Pa')
iv.add_output('ac|geom|wing|S_ref', val=10, units='m**2')
iv.add_output('fltcond|cosgamma', val=np.ones((nn,)), units=None)
self.add_subsystem('cls', SteadyFlightCL(num_nodes=nn), promotes_inputs=['*'], promotes_outputs=['*'])
class SteadyFlightCLTestCase_Level(unittest.TestCase):
def setUp(self):
self.prob = Problem(SteadyFlightCLTestGroup(num_nodes=9))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob.run_model()
def test_steady_level_flights(self):
assert_rel_error(self, self.prob['fltcond|CL'], np.ones((9,))*100*g/1000./10./1.0, tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
class SteadyFlightCLTestCase_Climb(unittest.TestCase):
def setUp(self):
self.prob = Problem(SteadyFlightCLTestGroup(num_nodes=9))
self.prob.setup(check=True, force_alloc_complex=True)
self.prob['fltcond|cosgamma'] = 0.98*np.ones((9,))
self.prob.run_model()
def test_steady_level_flights(self):
assert_rel_error(self, self.prob['fltcond|CL'], np.ones((9,))*100*g/1000./10.*0.98, tolerance=1e-10)
def test_partials(self):
partials = self.prob.check_partials(method='cs', out_stream=None)
assert_check_partials(partials)
# TODO add TakeoffTransition and TakeoffClimb component unit tests
| 43.48
| 184
| 0.669405
| 1,974
| 15,218
| 4.985816
| 0.097264
| 0.074782
| 0.045113
| 0.040236
| 0.811217
| 0.803597
| 0.790795
| 0.787137
| 0.739281
| 0.713168
| 0
| 0.02811
| 0.174793
| 15,218
| 350
| 185
| 43.48
| 0.755614
| 0.033973
| 0
| 0.672862
| 0
| 0
| 0.075206
| 0
| 0
| 0
| 0
| 0.002857
| 0.137546
| 1
| 0.215613
| false
| 0
| 0.022305
| 0
| 0.312268
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 1
| 1
| 1
| 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
|
0
| 7
|
af42655b0bf0d9b4f1cb7f34259d7cdf52208b62
| 105
|
py
|
Python
|
data/create_coco_txt.py
|
Linfeng-Lee/ssd.pytorch
|
ef9d88c3d86482fbcc2434c4237beea7c5cfe192
|
[
"MIT"
] | 1
|
2022-03-19T17:28:42.000Z
|
2022-03-19T17:28:42.000Z
|
data/create_coco_txt.py
|
Linfeng-Lee/ssd.pytorch
|
ef9d88c3d86482fbcc2434c4237beea7c5cfe192
|
[
"MIT"
] | null | null | null |
data/create_coco_txt.py
|
Linfeng-Lee/ssd.pytorch
|
ef9d88c3d86482fbcc2434c4237beea7c5cfe192
|
[
"MIT"
] | null | null | null |
import os.path as osp
import cv2
import numpy as np
import numpy as np
from pycocotools.coco import COCO
| 17.5
| 33
| 0.809524
| 20
| 105
| 4.25
| 0.55
| 0.258824
| 0.305882
| 0.352941
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.011494
| 0.171429
| 105
| 5
| 34
| 21
| 0.965517
| 0
| 0
| 0.4
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 8
|
afb8b7c2a7c2e2eaa88a0ad5ef18d3a1cc7c35be
| 177
|
py
|
Python
|
venv/Lib/site-packages/comtypes/gen/SpeechLib.py
|
cvangheem/Twitchbot
|
48bb065951e88e4d2e9ef8d0c1a3afb0150a5eb5
|
[
"MIT"
] | null | null | null |
venv/Lib/site-packages/comtypes/gen/SpeechLib.py
|
cvangheem/Twitchbot
|
48bb065951e88e4d2e9ef8d0c1a3afb0150a5eb5
|
[
"MIT"
] | null | null | null |
venv/Lib/site-packages/comtypes/gen/SpeechLib.py
|
cvangheem/Twitchbot
|
48bb065951e88e4d2e9ef8d0c1a3afb0150a5eb5
|
[
"MIT"
] | null | null | null |
from comtypes.gen import _C866CA3A_32F7_11D2_9602_00C04F8EE628_0_5_4
globals().update(_C866CA3A_32F7_11D2_9602_00C04F8EE628_0_5_4.__dict__)
__name__ = 'comtypes.gen.SpeechLib'
| 59
| 71
| 0.881356
| 27
| 177
| 4.888889
| 0.62963
| 0.166667
| 0.242424
| 0.30303
| 0.530303
| 0.530303
| 0.530303
| 0.530303
| 0
| 0
| 0
| 0.297619
| 0.050847
| 177
| 3
| 72
| 59
| 0.488095
| 0
| 0
| 0
| 0
| 0
| 0.125
| 0.125
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| false
| 0
| 0.333333
| 0
| 0.333333
| 0
| 1
| 0
| 0
| null | 0
| 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
|
0
| 7
|
afbada5ea513d9f1c2f4a34a7860378f3f333558
| 738,077
|
py
|
Python
|
fragbuilder/basilisk_lib/basilisk_parameter.py
|
larsbratholm/fragbuilder
|
e16cbcb190403b5fef49811abd11d16d7ef7fb30
|
[
"BSD-2-Clause"
] | 8
|
2015-04-11T17:43:13.000Z
|
2021-12-02T10:18:45.000Z
|
fragbuilder/basilisk_lib/basilisk_parameter.py
|
larsbratholm/fragbuilder
|
e16cbcb190403b5fef49811abd11d16d7ef7fb30
|
[
"BSD-2-Clause"
] | null | null | null |
fragbuilder/basilisk_lib/basilisk_parameter.py
|
larsbratholm/fragbuilder
|
e16cbcb190403b5fef49811abd11d16d7ef7fb30
|
[
"BSD-2-Clause"
] | 6
|
2015-04-01T07:18:26.000Z
|
2021-04-24T11:11:18.000Z
|
from numpy import array#[discrete,41]
index_cpd = array ([ 2.4061224878e-01, 2.4061224878e-01, 3.6224921707e-03,
1.7136941457e-02, 1.7136941457e-02, 1.8695002605e-02,
1.8695002605e-02, 1.8695002605e-02, 1.1793657306e-02,
1.1793657306e-02, 6.8502755171e-03, 6.8502755171e-03,
1.6318959354e-02, 1.6318959354e-02, 1.5610907121e-02,
1.5610907121e-02, 1.5610907121e-02, 1.5610907121e-02,
2.6113104852e-02, 2.6113104852e-02, 5.2420101759e-03,
5.2420101759e-03, 5.2420101759e-03, 1.2468817137e-02,
1.2468817137e-02, 1.3627841514e-02, 1.3627841514e-02,
1.0757546642e-02, 1.0757546642e-02, 1.0757546642e-02,
1.4262318637e-02, 1.4262318637e-02, 1.4262318637e-02,
1.4262318637e-02, 1.6665195164e-02, 1.6038508347e-02,
2.0563810394e-02, 4.3487217839e-03, 4.3487217839e-03,
1.0496138605e-02, 1.0496138605e-02])
#[discrete,41,30]
h0_cpd = array ([[ 1.0000000000e-50, 1.0000000000e-50, 1.9199568307e-01,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
9.1734868244e-03, 1.5576940372e-03, 1.0000000000e-50,
1.0180771652e-03, 8.7885601223e-03, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 2.4462631532e-04,
1.3310549510e-03, 1.0000000000e-50, 2.5818508859e-01,
1.0000000000e-50, 7.3387894595e-04, 6.5930389424e-02,
1.1655724436e-03, 3.3565968163e-01, 8.6338699523e-05,
1.6929579998e-02, 1.0000000000e-50, 9.6310819318e-02,
3.7413436460e-04, 9.3821386815e-03, 1.1331954312e-03] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
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3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
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3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
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3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02]])
#[VonMises, 30, 2]
angle_mus = array ([[6.0472574932e+00], [2.9322697800e+00], [4.6618707307e+00], [3.2089972553e+00], [1.9420626921e+00], [5.1601442762e+00], [4.7412185264e+00], [9.1460085913e-01], [5.6352265911e+00], [4.6490350481e+00], [1.1317509759e+00], [5.0486795563e+00], [5.8375503145e+00], [2.6733261483e+00], [1.9927258897e+00], [1.3773054832e+00], [6.2030717530e+00], [4.1069279667e+00], [2.3488501989e+00], [3.6619514007e-01], [5.2849845261e+00], [6.4040704405e-01], [5.1669099967e+00], [5.5221188417e+00], [1.0375062041e+00], [3.1308217120e+00], [4.9927170105e+00], [8.6400062726e-01], [3.3398143144e+00], [4.1755591500e+00] ])
angle_kappas = array ([2.9648977620e+01, 5.6919054552e+01, 1.7516481343e+01, 1.1810695940e+01, 1.0843456038e+01, 1.6606577262e+01, 4.5178279466e+00, 3.2764878682e+00, 7.8429376414e+01, 7.1820161029e-01, 5.6446803838e+01, 8.5034003239e+00, 6.2240471898e+01, 1.6045198070e+01, 1.2997118640e+00, 2.4077741790e+01, 1.4530903927e+01, 1.0683092677e+01, 2.2499105662e+01, 2.2096549752e+01, 7.7593532861e+01, 6.0370046667e+01, 1.1097280611e+02, 1.7053583920e+02, 5.3900638287e+01, 6.9069779004e+01, 6.4603966678e+01, 2.5341268155e-01, 3.8456140131e+01, 1.8207679315e+00 ])
#[discrete,30,41,30]
h1_cpd = array ([[[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0989010989e-01,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
5.1648351648e-01, 1.0989010989e-02, 3.6263736264e-01,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50] ,
[ 1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
3.5262206148e-02, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 2.3508137432e-02, 1.0000000000e-50,
1.0000000000e-50, 2.1428571429e-01, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
2.7124773960e-03, 3.9602169982e-01, 1.0000000000e-50,
6.1482820976e-02, 1.0000000000e-50, 2.2151898734e-01,
1.0000000000e-50, 2.3508137432e-02, 2.1699819168e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 4.3437204910e-02, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.2464589235e-01, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.4164305949e-02, 6.7138810198e-01, 1.0000000000e-50,
1.2275731822e-01, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 2.3607176582e-02, 1.0000000000e-50] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
6.0120240481e-03, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.2424849699e-01, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 2.2044088176e-02,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 8.4769539078e-01,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 3.2903225806e-01, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
9.6774193548e-03, 5.6774193548e-01, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.9354838710e-02, 7.4193548387e-02] ,
[ 3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02,
3.3333333333e-02, 3.3333333333e-02, 3.3333333333e-02] ,
[ 1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
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1.0000000000e-50, 8.6776859504e-02, 2.4793388430e-02,
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1.0000000000e-50, 1.2190082645e-01, 3.9256198347e-02,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50] ,
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1.0000000000e-50, 1.0000000000e-50, 9.0909090909e-02,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
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1.0000000000e-50, 1.0000000000e+00, 1.0000000000e-50] ,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e+00, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50] ,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50] ,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50] ,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50] ,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e+00,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50] ,
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1.0000000000e-50, 7.0967741935e-01, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 2.9032258065e-01,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
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1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50,
1.0000000000e-50, 1.0000000000e-50, 1.0000000000e-50]]])
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0
| 12
|
bba75cd33e883832e62fb4b644493b7b4dfb6d97
| 18,157
|
py
|
Python
|
utils/mnist_pca.py
|
SilverEngineered/Quilt
|
bd6357fd3b5543e908fa57f904f177d75fa6873e
|
[
"MIT"
] | null | null | null |
utils/mnist_pca.py
|
SilverEngineered/Quilt
|
bd6357fd3b5543e908fa57f904f177d75fa6873e
|
[
"MIT"
] | null | null | null |
utils/mnist_pca.py
|
SilverEngineered/Quilt
|
bd6357fd3b5543e908fa57f904f177d75fa6873e
|
[
"MIT"
] | null | null | null |
import os
from sklearn.decomposition import PCA
import numpy as np
from sklearn import preprocessing
from matplotlib import pyplot as plt
def scale_data(data, scale=[0, 1], dtype=np.float32):
min_data, max_data = [float(np.min(data)), float(np.max(data))]
min_scale, max_scale = [float(scale[0]), float(scale[1])]
data = ((max_scale - min_scale) * (data - min_data) / (max_data - min_data)) + min_scale
return data.astype(dtype)
def get_indecies_amplitudes(amplitude_map, labels):
indecies = []
amplitudes = []
for i in range(labels.shape[0]):
if labels[i] in amplitude_map:
indecies.append(i)
amplitudes.append(amplitude_map[labels[i]])
return indecies, amplitudes
def get_amplitude_map(keep_digits):
length = len(keep_digits)
map = {}
if length == 2:
map[keep_digits[0]] = -1
map[keep_digits[1]] = 1
if length == 3:
map[keep_digits[0]] = -1
map[keep_digits[1]] = 0
map[keep_digits[2]] = 1
if length == 4:
map[keep_digits[0]] = [-1, -1]
map[keep_digits[1]] = [-1, 1]
map[keep_digits[2]] = [1, -1]
map[keep_digits[3]] = [1, 1]
if length == 8:
map[keep_digits[0]] = [-1, -1, -1]
map[keep_digits[1]] = [-1, -1, 1]
map[keep_digits[2]] = [-1, 1, -1]
map[keep_digits[3]] = [-1, 1, 1]
map[keep_digits[4]] = [1, -1, -1]
map[keep_digits[5]] = [1, -1, 1]
map[keep_digits[6]] = [1, 1, -1]
map[keep_digits[7]] = [1, 1, 1]
return map
def perform_pca(keep_digits=list(range(8)), pca_dims=32):
mnist_path = os.path.join("..", "data", "MNIST")
x_train_path = os.path.join(mnist_path, "full_data", "x_train.npy")
x_test_path = os.path.join(mnist_path, "full_data", "x_test.npy")
y_train_path = os.path.join(mnist_path, "full_data", "y_train.npy")
y_test_path = os.path.join(mnist_path, "full_data", "y_test.npy")
y_train = np.array(np.load(y_train_path), dtype=float)
y_test = np.array(np.load(y_test_path), dtype=float)
amplitude_map = get_amplitude_map(keep_digits)
train_indecies, train_amplitudes = get_indecies_amplitudes(amplitude_map, y_train)
test_indecies, test_amplitudes = get_indecies_amplitudes(amplitude_map, y_test)
x_train = np.load(x_train_path)[train_indecies]
x_test = np.load(x_test_path)[test_indecies]
pcas = []
pca = PCA(pca_dims)
pca.fit(preprocessing.normalize(x_train))
approximation = pca.inverse_transform(pca.transform(x_train))
plt.imshow(approximation[0].reshape(28, 28), cmap=plt.cm.gray, interpolation='nearest', clim=(0, 255))
plt.xticks(fontsize=14)
plt.yticks(fontsize=14)
plt.savefig(os.path.join('', "reconstruct.pdf"),bbox_inches = 'tight',
pad_inches = 0)
plt.close()
exit()
x_train_transformed = pca.transform(preprocessing.normalize(x_train))
x_test_transformed = pca.transform(preprocessing.normalize(x_test))
x_train_transformed = (x_train_transformed - np.min(x_train_transformed)) / (np.max(x_train_transformed) - np.min(x_train_transformed))
path = os.path.join("..", "data", "MNIST", "pca_data", "dims_" + str(pca_dims), "digits=" + str(keep_digits))
if not os.path.exists(path):
os.makedirs(path)
x_train_path = os.path.join(path, 'x_train')
x_test_path = os.path.join(path, 'x_test')
y_train_path = os.path.join(path, 'y_train')
y_test_path = os.path.join(path, 'y_test')
np.save(x_train_path, scale_data(x_train_transformed))
np.save(x_test_path, scale_data(x_test_transformed))
np.save(y_train_path, train_amplitudes)
np.save(y_test_path, test_amplitudes)
approximation = pca.inverse_transform(pca.transform(x_train))
plt.imshow(approximation[0].reshape(28, 28), cmap=plt.cm.gray, interpolation='nearest', clim=(0, 255))
plt.title("Reconstruction of MNIST Image with 32 Principal Components")
plt.savefig(os.path.join('', "reconstruct.pdf"))
plt.close()
def perform_pca_new(keep_digits=[0,1,2,3,4,5,6,7], pca_dims=32):
mnist_path = os.path.join("..", "data", "MNIST")
x_train_path = os.path.join(mnist_path, "full_data", "x_train.npy")
x_test_path = os.path.join(mnist_path, "full_data", "x_test.npy")
y_train_path = os.path.join(mnist_path, "full_data", "y_train.npy")
y_test_path = os.path.join(mnist_path, "full_data", "y_test.npy")
y_train = np.array(np.load(y_train_path), dtype=float)
y_test = np.array(np.load(y_test_path), dtype=float)
amplitude_map = get_amplitude_map(keep_digits)
train_indecies, train_amplitudes = get_indecies_amplitudes(amplitude_map, y_train)
test_indecies, test_amplitudes = get_indecies_amplitudes(amplitude_map, y_test)
x_train = np.load(x_train_path)[train_indecies]
x_test = np.load(x_test_path)[test_indecies]
pca = PCA(pca_dims)
pca.fit(preprocessing.normalize(x_train))
x_train_transformed = scale_data(pca.transform(preprocessing.normalize(x_train)))
#-----------------------------------------------------------------------------------------
b0 = []
b1 = []
b2 = []
for i in train_amplitudes:
b0.append(i[0])
b1.append(i[1])
b2.append(i[2])
y_path = os.path.join("..", "data", "splits", "b0")
np.save(y_path, b0)
y_path = os.path.join("..", "data", "splits", "b1")
np.save(y_path, b1)
y_path = os.path.join("..", "data", "splits", "b2")
np.save(y_path, b2)
#-----------------------------------------------------------------------------------------
split_0_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==-1 and i[1] ==-1 and i[2] ==-1:
split_0_y.append(-1)
else:
split_0_y.append(1)
y_path = os.path.join("..", "data", "splits", "0_y")
np.save(y_path, split_0_y)
#-----------------------------------------------------------------------------------------
split_1_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==-1 and i[1] ==-1 and i[2] ==1:
split_1_y.append(-1)
else:
split_1_y.append(1)
y_path = os.path.join("..", "data", "splits", "1_y")
np.save(y_path, split_1_y)
#-----------------------------------------------------------------------------------------
split_2_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==-1 and i[1] ==1 and i[2] ==-1:
split_2_y.append(-1)
else:
split_2_y.append(1)
y_path = os.path.join("..", "data", "splits", "2_y")
np.save(y_path, split_2_y)
#-----------------------------------------------------------------------------------------
split_3_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==-1 and i[1] ==1 and i[2] ==1:
split_3_y.append(-1)
else:
split_3_y.append(1)
y_path = os.path.join("..", "data", "splits", "3_y")
np.save(y_path, split_3_y)
#-----------------------------------------------------------------------------------------
split_4_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==1 and i[1] ==-1 and i[2] ==-1:
split_4_y.append(-1)
else:
split_4_y.append(1)
y_path = os.path.join("..", "data", "splits", "4_y")
np.save(y_path, split_4_y)
#-----------------------------------------------------------------------------------------
split_5_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==1 and i[1] ==-1 and i[2] ==1:
split_5_y.append(-1)
else:
split_5_y.append(1)
y_path = os.path.join("..", "data", "splits", "5_y")
np.save(y_path, split_5_y)
#-----------------------------------------------------------------------------------------
split_6_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==1 and i[1] ==1 and i[2] ==-1:
split_6_y.append(-1)
else:
split_6_y.append(1)
y_path = os.path.join("..", "data", "splits", "6_y")
np.save(y_path, split_6_y)
#-----------------------------------------------------------------------------------------
split_7_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==1 and i[1] ==1 and i[2] ==1:
split_7_y.append(-1)
else:
split_7_y.append(1)
y_path = os.path.join("..", "data", "splits", "7_y")
np.save(y_path, split_7_y)
#-----------------------------------------------------------------------------------------
x_path = os.path.join("..", "data", "splits", "full_x")
y_path = os.path.join("..", "data", "splits", "full_y")
np.save(x_path, x_train_transformed)
np.save(y_path, train_amplitudes)
def perform_pca_multi(keep_digits=[0,1,2,3,4,5,6,7], pca_dims=32):
mnist_path = os.path.join("..", "data", "MNIST")
x_train_path = os.path.join(mnist_path, "full_data", "x_train.npy")
x_test_path = os.path.join(mnist_path, "full_data", "x_test.npy")
y_train_path = os.path.join(mnist_path, "full_data", "y_train.npy")
y_test_path = os.path.join(mnist_path, "full_data", "y_test.npy")
y_train = np.array(np.load(y_train_path), dtype=float)
y_test = np.array(np.load(y_test_path), dtype=float)
amplitude_map = get_amplitude_map(keep_digits)
train_indecies, train_amplitudes = get_indecies_amplitudes(amplitude_map, y_train)
test_indecies, test_amplitudes = get_indecies_amplitudes(amplitude_map, y_test)
x_train = np.load(x_train_path)[train_indecies]
x_test = np.load(x_test_path)[test_indecies]
pca = PCA(pca_dims)
pca.fit(preprocessing.normalize(x_train))
x_train_transformed = scale_data(pca.transform(preprocessing.normalize(x_train)))
#-----------------------------------------------------------------------------------------
b0 = []
b1 = []
b2 = []
for i in train_amplitudes:
b0.append(i[0])
b1.append(i[1])
b2.append(i[2])
y_path = os.path.join("..", "data", "splits", "b0")
np.save(y_path, b0)
#-----------------------------------------------------------------------------------------
split_0_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==-1 and i[1] ==-1 and i[2] ==-1:
split_0_y.append(-1)
else:
split_0_y.append(1)
y_path = os.path.join("..", "data", "splits", "0_y")
np.save(y_path, split_0_y)
#-----------------------------------------------------------------------------------------
split_1_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==-1 and i[1] ==-1 and i[2] ==1:
split_1_y.append(-1)
else:
split_1_y.append(1)
y_path = os.path.join("..", "data", "splits", "1_y")
np.save(y_path, split_1_y)
#-----------------------------------------------------------------------------------------
split_2_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==-1 and i[1] ==1 and i[2] ==-1:
split_2_y.append(-1)
else:
split_2_y.append(1)
y_path = os.path.join("..", "data", "splits", "2_y")
np.save(y_path, split_2_y)
#-----------------------------------------------------------------------------------------
split_3_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==-1 and i[1] ==1 and i[2] ==1:
split_3_y.append(-1)
else:
split_3_y.append(1)
y_path = os.path.join("..", "data", "splits", "3_y")
np.save(y_path, split_3_y)
#-----------------------------------------------------------------------------------------
split_4_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==1 and i[1] ==-1 and i[2] ==-1:
split_4_y.append(-1)
else:
split_4_y.append(1)
y_path = os.path.join("..", "data", "splits", "4_y")
np.save(y_path, split_4_y)
#-----------------------------------------------------------------------------------------
split_5_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==1 and i[1] ==-1 and i[2] ==1:
split_5_y.append(-1)
else:
split_5_y.append(1)
y_path = os.path.join("..", "data", "splits", "5_y")
np.save(y_path, split_5_y)
#-----------------------------------------------------------------------------------------
split_6_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==1 and i[1] ==1 and i[2] ==-1:
split_6_y.append(-1)
else:
split_6_y.append(1)
y_path = os.path.join("..", "data", "splits", "6_y")
np.save(y_path, split_6_y)
#-----------------------------------------------------------------------------------------
split_7_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==1 and i[1] ==1 and i[2] ==1:
split_7_y.append(-1)
else:
split_7_y.append(1)
y_path = os.path.join("..", "data", "splits", "7_y")
np.save(y_path, split_7_y)
#-----------------------------------------------------------------------------------------
x_path = os.path.join("..", "data", "splits", "full_x")
y_path = os.path.join("..", "data", "splits", "full_y")
np.save(x_path, x_train_transformed)
np.save(y_path, train_amplitudes)
def perform_pca_4(keep_digits=[0,8, 7, 1], pca_dims=16):
mnist_path = os.path.join("..", "data", "MNIST")
x_train_path = os.path.join(mnist_path, "full_data", "x_train.npy")
x_test_path = os.path.join(mnist_path, "full_data", "x_test.npy")
y_train_path = os.path.join(mnist_path, "full_data", "y_train.npy")
y_test_path = os.path.join(mnist_path, "full_data", "y_test.npy")
y_train = np.array(np.load(y_train_path), dtype=float)
y_test = np.array(np.load(y_test_path), dtype=float)
amplitude_map = get_amplitude_map(keep_digits)
train_indecies, train_amplitudes = get_indecies_amplitudes(amplitude_map, y_train)
test_indecies, test_amplitudes = get_indecies_amplitudes(amplitude_map, y_test)
x_train = np.load(x_train_path)[train_indecies]
x_test = np.load(x_test_path)[test_indecies]
pca = PCA(pca_dims)
pca.fit(preprocessing.normalize(x_train))
x_train_transformed = scale_data(pca.transform(preprocessing.normalize(x_train)))
'''
#-----------------------------------------------------------------------------------------
split_0_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==-1 and i[1] ==-1:
split_0_y.append(-1)
else:
split_0_y.append(1)
y_path = os.path.join("..", "data", "splits_4", "0_y")
np.save(y_path, split_0_y)
#-----------------------------------------------------------------------------------------
split_1_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==-1 and i[1] ==1:
split_1_y.append(-1)
else:
split_1_y.append(1)
y_path = os.path.join("..", "data", "splits_4", "1_y")
np.save(y_path, split_1_y)
#-----------------------------------------------------------------------------------------
split_2_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==1 and i[1] ==-1:
split_2_y.append(-1)
else:
split_2_y.append(1)
y_path = os.path.join("..", "data", "splits_4", "2_y")
np.save(y_path, split_2_y)
#-----------------------------------------------------------------------------------------
split_3_y = []
for index, i in enumerate(train_amplitudes):
if i[0] ==1 and i[1] ==1:
split_3_y.append(-1)
else:
split_3_y.append(1)
y_path = os.path.join("..", "data", "splits_4", "3_y")
np.save(y_path, split_3_y)
'''
x_path = os.path.join("..", "data", "splits_4_small", "full_x")
y_path = os.path.join("..", "data", "splits_4_small", "full_y")
np.save(x_path, x_train_transformed)
np.save(y_path, train_amplitudes)
def perform_pca_2(keep_digits=[0,1], pca_dims=32):
mnist_path = os.path.join("..", "data", "MNIST")
x_train_path = os.path.join(mnist_path, "full_data", "x_train.npy")
x_test_path = os.path.join(mnist_path, "full_data", "x_test.npy")
y_train_path = os.path.join(mnist_path, "full_data", "y_train.npy")
y_test_path = os.path.join(mnist_path, "full_data", "y_test.npy")
y_train = np.array(np.load(y_train_path), dtype=float)
y_test = np.array(np.load(y_test_path), dtype=float)
amplitude_map = get_amplitude_map(keep_digits)
train_indecies, train_amplitudes = get_indecies_amplitudes(amplitude_map, y_train)
test_indecies, test_amplitudes = get_indecies_amplitudes(amplitude_map, y_test)
x_train = np.load(x_train_path)[train_indecies]
x_test = np.load(x_test_path)[test_indecies]
pca = PCA(pca_dims)
pca.fit(preprocessing.normalize(x_train))
x_train_transformed = scale_data(pca.transform(preprocessing.normalize(x_train)))
#-----------------------------------------------------------------------------------------
split_0_y = []
for index, i in enumerate(train_amplitudes):
if i ==-1:
split_0_y.append(-1)
else:
split_0_y.append(1)
y_path = os.path.join("..", "data", "splits_2", "0_y")
np.save(y_path, split_0_y)
#-----------------------------------------------------------------------------------------
split_1_y = []
for index, i in enumerate(train_amplitudes):
if i ==1:
split_1_y.append(-1)
else:
split_1_y.append(1)
y_path = os.path.join("..", "data", "splits_2", "1_y")
np.save(y_path, split_1_y)
x_path = os.path.join("..", "data", "splits_2", "full_x")
y_path = os.path.join("..", "data", "splits_2", "full_y")
np.save(x_path, x_train_transformed)
np.save(y_path, train_amplitudes)
if __name__ == '__main__':
keep_digits = [0,1, 2, 3, 4, 5, 6, 7]
pca_dims = 32
perform_pca(keep_digits, pca_dims)
#perform_pca_4()
| 43.751807
| 139
| 0.533183
| 2,561
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| 1
| 1
| 1
| 1
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0
| 7
|
bbd0f4adc23e83735128bb29601f2aa7b42acd02
| 4,644
|
py
|
Python
|
app/server/unit_tests/series.py
|
elgalu/labml
|
511f0bbfcbeb4bc34bc6966a3973ff4e7e48eeee
|
[
"MIT"
] | 463
|
2021-05-28T03:21:14.000Z
|
2022-03-28T06:28:21.000Z
|
app/server/unit_tests/series.py
|
elgalu/labml
|
511f0bbfcbeb4bc34bc6966a3973ff4e7e48eeee
|
[
"MIT"
] | 15
|
2021-06-22T10:02:36.000Z
|
2021-12-20T06:14:12.000Z
|
app/server/unit_tests/series.py
|
elgalu/labml
|
511f0bbfcbeb4bc34bc6966a3973ff4e7e48eeee
|
[
"MIT"
] | 29
|
2020-06-03T07:13:31.000Z
|
2021-05-23T18:20:34.000Z
|
from labml import monit
from numpy.random import random, randint
from labml_app.db import analyses
def update_equal_gap_equal_sizes(prev: int = 0, size=10, gap: int = 5, max_step=100):
data: analyses.SeriesModel = analyses.series.Series().to_data()
updates = 0
while prev <= max_step:
last = prev + size
step = [*range(prev, last, gap)]
value = random(len(step)).tolist()
prev = step[-1] + gap
s = analyses.series.Series().load(data)
s.update(step, value)
data = s.to_data()
updates += 1
print(data['step'].tolist())
print(updates)
def update_zero_gap(prev: int = 0, size=10, gap: int = 5, max_step=100):
data: analyses.SeriesModel = analyses.series.Series().to_data()
while prev <= max_step:
step = []
value = []
for i in range(0, size // gap):
prev += gap
step.append(prev)
value.append(1)
step.append(prev)
value.append(10)
s = analyses.series.Series().load(data)
s.update(step, value)
data = s.to_data()
print(data['step'].tolist())
def update_equal_gap_equal_sizes_diff_gap_between(prev: int = 0, size=5, gap: int = 1, max_step=100):
data: analyses.SeriesModel = analyses.series.Series().to_data()
while prev <= max_step:
last = prev + size
step = [*range(prev, last, gap)]
value = random(len(step)).tolist()
prev = last
s = analyses.series.Series().load(data)
s.update(step, value)
data = s.to_data()
print(data['step'].tolist())
def update_equal_gap_diff_sizes(prev: int = 0, gap: int = 1, max_step=100):
data: analyses.SeriesModel = analyses.series.Series().to_data()
while prev <= max_step:
size = randint(1, 9)
last = prev + size
step = [*range(prev, last, gap)]
value = random(len(step)).tolist()
prev = step[-1] + gap
s = analyses.series.Series().load(data)
s.update(step, value)
data = s.to_data()
print(data['step'].tolist())
def update_equal_gap_diff_sizes_diff_gap_between(prev: int = 0, gap: int = 1, max_step=100):
data: analyses.SeriesModel = analyses.series.Series().to_data()
while prev <= max_step:
size = randint(1, 9)
last = prev + size
step = [*range(prev, last, gap)]
value = random(len(step)).tolist()
prev = last
s = analyses.series.Series().load(data)
s.update(step, value)
data = s.to_data()
print(data['step'].tolist())
def update_diff_gap_diff_sizes(prev: int = 0, max_step=100):
data: analyses.SeriesModel = analyses.series.Series().to_data()
while prev <= max_step:
size = randint(1, 6)
last = prev + size
step = []
for s in range(size):
last = last + randint(1, 10)
step.append(last)
value = random(len(step)).tolist()
prev = last
s = analyses.series.Series().load(data)
s.update(step, value)
data = s.to_data()
print(data['step'].tolist())
def update_equal_and_diff_gaps_diff_sizes(prev: int = 0, size=5, gap: int = 1, max_step=100):
data: analyses.SeriesModel = analyses.series.Series().to_data()
while prev <= max_step / 2:
last = prev + size
step = [*range(prev, last, gap)]
value = random(len(step)).tolist()
prev = step[-1] + gap
s = analyses.series.Series().load(data)
s.update(step, value)
data = s.to_data()
while prev <= max_step:
size = randint(1, 6)
last = prev + size
step = []
for s in range(size):
last = last + randint(1, 10)
step.append(last)
value = random(len(step)).tolist()
prev = last
s = analyses.series.Series().load(data)
s.update(step, value)
data = s.to_data()
print(data['step'].tolist())
if __name__ == "__main__":
with monit.section("Equal gap"):
update_equal_gap_equal_sizes(size=10000, max_step=1_000_000, gap=1)
# update_zero_gap()
# update_equal_gap_equal_sizes(gap=2, max_step=int(1e+4))
# update_equal_gap_equal_sizes_diff_gap_between(gap=2, max_step=int(1e+4))
#
# update_equal_gap_diff_sizes(max_step=int(1e+4))
# update_equal_gap_diff_sizes(gap=2, max_step=int(1e+4))
# update_equal_gap_diff_sizes_diff_gap_between(gap=2, max_step=int(1e+4))
#
# update_diff_gap_diff_sizes(max_step=int(1e+4))
# #
# update_equal_and_diff_gaps_diff_sizes(max_step=int(1e+4))
| 26.237288
| 101
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| 4,644
| 176
| 102
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0
| 7
|
bbfc346700d9e637ae30fefcb20510d5bc8b956b
| 167
|
py
|
Python
|
dynconv/__init__.py
|
fbagci/improved_saliency_network
|
96f5c5bd9a0da4b79b3e7cd506ade13b51f90db7
|
[
"MIT"
] | 1
|
2021-05-03T20:16:05.000Z
|
2021-05-03T20:16:05.000Z
|
dynconv/__init__.py
|
fbagci/improved_saliency_network
|
96f5c5bd9a0da4b79b3e7cd506ade13b51f90db7
|
[
"MIT"
] | null | null | null |
dynconv/__init__.py
|
fbagci/improved_saliency_network
|
96f5c5bd9a0da4b79b3e7cd506ade13b51f90db7
|
[
"MIT"
] | null | null | null |
from dynconv.maskunit import *
from dynconv.utils import *
from dynconv.loss import SparsityCriterion
from dynconv.layers import conv1x1, conv3x3, conv3x3_dw, bn_relu
| 41.75
| 64
| 0.832335
| 23
| 167
| 5.956522
| 0.565217
| 0.321168
| 0.248175
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0.040541
| 0.113772
| 167
| 4
| 64
| 41.75
| 0.885135
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| true
| 0
| 1
| 0
| 1
| 0
| 1
| 0
| 0
| null | 1
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| 0
| null | 0
| 0
| 0
| 0
| 0
| 0
| 1
| 0
| 1
| 0
| 1
| 0
|
0
| 7
|
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